From 65ef9ff7deadc63a873328b7ca4b827053f5f6d6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 9 May 2014 19:51:26 -0700
Subject: [PATCH 0001/2053] specify NetParameters directly in the
 SolverParameter

---
 src/caffe/proto/caffe.proto | 12 ++++++++++--
 src/caffe/solver.cpp        | 26 ++++++++++++++++++++++----
 2 files changed, 32 insertions(+), 6 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index ab3c2fecc5c..eb086a15443 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -57,8 +57,16 @@ message NetParameter {
 }
 
 message SolverParameter {
-  optional string train_net = 1; // The proto file for the training net.
-  optional string test_net = 2; // The proto file for the testing net.
+  // {train,test}_net specify a path to a file containing the {train,test} net
+  // parameters; {train,test}_net_param specify the net parameters directly
+  // inside the SolverParameter.
+  //
+  // If {train,test}_net is specified, {train,test}_net_param should not be,
+  // and vice versa.
+  optional string train_net = 1; // The proto filename for the train net.
+  optional string test_net = 2; // The proto filename for the test net.
+  optional NetParameter train_net_param = 21; // Full params for the train net.
+  optional NetParameter test_net_param = 22; // Full params for the test net.
   // The number of iterations for each testing phase.
   optional int32 test_iter = 3 [default = 0];
   // The number of iterations between two testing phases.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 4932968d0b6..77782ac69fb 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -38,11 +38,29 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
     Caffe::set_random_seed(param_.random_seed());
   }
   // Scaffolding code
-  LOG(INFO) << "Creating training net.";
-  net_.reset(new Net<Dtype>(param_.train_net()));
-  if (param_.has_test_net()) {
-    LOG(INFO) << "Creating testing net.";
+  if (param_.has_train_net_param()) {
+    CHECK(!param_.has_train_net()) << "Either train_net_param or train_net may "
+                                   << "be specified, but not both.";
+    LOG(INFO) << "Creating training net specified in SolverParameter.";
+    net_.reset(new Net<Dtype>(param_.train_net_param()));
+  } else {
+    LOG(INFO) << "Creating training net from file: " << param_.train_net();
+    net_.reset(new Net<Dtype>(param_.train_net()));
+  }
+  bool has_test_net = false;
+  NetParameter test_net_param;
+  if (param_.has_test_net_param()) {
+    CHECK(!param_.has_test_net()) << "Either test_net_param or test_net may be "
+                                  << "specified, but not both.";
+    LOG(INFO) << "Creating testing net specified in SolverParameter.";
+    test_net_.reset(new Net<Dtype>(param_.test_net_param()));
+    has_test_net = true;
+  } else if (param_.has_test_net()) {
+    LOG(INFO) << "Creating testing net from file: " << param_.test_net();
     test_net_.reset(new Net<Dtype>(param_.test_net()));
+    has_test_net = true;
+  }
+  if (has_test_net) {
     CHECK_GT(param_.test_iter(), 0);
     CHECK_GT(param_.test_interval(), 0);
   }

From bf66ac27539c63377d432006589bc43ea6203933 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 9 May 2014 19:51:59 -0700
Subject: [PATCH 0002/2053] log {Net,Solver}Parameters on Init

---
 src/caffe/net.cpp    | 2 ++
 src/caffe/solver.cpp | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 6f57d8d09ad..8eeff57efc0 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -33,6 +33,8 @@ Net<Dtype>::Net(const string& param_file) {
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  LOG(INFO) << "Initializing net from parameters: " << std::endl
+            << in_param.DebugString();
   // Create a copy of in_param with splits added where necessary.
   NetParameter param;
   InsertSplits(in_param, &param);
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 77782ac69fb..9420ca3c9e1 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -33,6 +33,8 @@ Solver<Dtype>::Solver(const string& param_file)
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
+  LOG(INFO) << "Initializing solver from parameters: " << std::endl
+            << param.DebugString();
   param_ = param;
   if (param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());

From c97fff670299c8600ddd8b0ba54bbd7c325ee2f5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 9 May 2014 20:28:55 -0700
Subject: [PATCH 0003/2053] allow multiple test nets

---
 include/caffe/solver.hpp    |  5 ++--
 src/caffe/proto/caffe.proto |  4 +--
 src/caffe/solver.cpp        | 55 ++++++++++++++++++++++---------------
 3 files changed, 38 insertions(+), 26 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index aef9b22c44d..3112c59e0fc 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -33,7 +33,8 @@ class Solver {
   // written to disk together with the learned net.
   void Snapshot();
   // The test routine
-  void Test();
+  void TestAll();
+  void Test(const int test_net_id = 0);
   virtual void SnapshotSolverState(SolverState* state) = 0;
   // The Restore function implements how one should restore the solver to a
   // previously snapshotted state. You should implement the RestoreSolverState()
@@ -44,7 +45,7 @@ class Solver {
   SolverParameter param_;
   int iter_;
   shared_ptr<Net<Dtype> > net_;
-  shared_ptr<Net<Dtype> > test_net_;
+  vector<shared_ptr<Net<Dtype> > > test_nets_;
 
   DISABLE_COPY_AND_ASSIGN(Solver);
 };
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index eb086a15443..cf3a9b76c54 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -64,9 +64,9 @@ message SolverParameter {
   // If {train,test}_net is specified, {train,test}_net_param should not be,
   // and vice versa.
   optional string train_net = 1; // The proto filename for the train net.
-  optional string test_net = 2; // The proto filename for the test net.
+  repeated string test_net = 2; // The proto filenames for the test nets.
   optional NetParameter train_net_param = 21; // Full params for the train net.
-  optional NetParameter test_net_param = 22; // Full params for the test net.
+  repeated NetParameter test_net_param = 22; // Full params for the test nets.
   // The number of iterations for each testing phase.
   optional int32 test_iter = 3 [default = 0];
   // The number of iterations between two testing phases.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 9420ca3c9e1..e3dc70546a0 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -19,13 +19,13 @@ namespace caffe {
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const SolverParameter& param)
-    : net_(), test_net_() {
+    : net_() {
   Init(param);
 }
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const string& param_file)
-    : net_(), test_net_() {
+    : net_() {
   SolverParameter param;
   ReadProtoFromTextFile(param_file, &param);
   Init(param);
@@ -49,23 +49,25 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
     LOG(INFO) << "Creating training net from file: " << param_.train_net();
     net_.reset(new Net<Dtype>(param_.train_net()));
   }
-  bool has_test_net = false;
-  NetParameter test_net_param;
-  if (param_.has_test_net_param()) {
-    CHECK(!param_.has_test_net()) << "Either test_net_param or test_net may be "
-                                  << "specified, but not both.";
-    LOG(INFO) << "Creating testing net specified in SolverParameter.";
-    test_net_.reset(new Net<Dtype>(param_.test_net_param()));
-    has_test_net = true;
-  } else if (param_.has_test_net()) {
-    LOG(INFO) << "Creating testing net from file: " << param_.test_net();
-    test_net_.reset(new Net<Dtype>(param_.test_net()));
-    has_test_net = true;
-  }
-  if (has_test_net) {
+  const int num_test_net_params = param_.test_net_param_size();
+  const int num_test_net_files = param_.test_net_size();
+  const int num_test_nets = num_test_net_params + num_test_net_files;
+  if (num_test_nets) {
     CHECK_GT(param_.test_iter(), 0);
     CHECK_GT(param_.test_interval(), 0);
   }
+  test_nets_.resize(num_test_nets);
+  for (int i = 0; i < num_test_net_params; ++i) {
+      LOG(INFO) << "Creating testing net (#" << i
+                << ") specified in SolverParameter.";
+      test_nets_[i].reset(new Net<Dtype>(param_.test_net_param(i)));
+  }
+  for (int i = 0, test_net_id = num_test_net_params;
+       i < num_test_net_files; ++i, ++test_net_id) {
+      LOG(INFO) << "Creating testing net (#" << test_net_id
+                << ") from file: " << param.test_net(i);
+      test_nets_[test_net_id].reset(new Net<Dtype>(param_.test_net(i)));
+  }
   LOG(INFO) << "Solver scaffolding done.";
 }
 
@@ -92,7 +94,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // there's not enough memory to run the test net and crash, etc.; and to gauge
   // the effect of the first training iterations.
   if (param_.test_interval()) {
-    Test();
+    TestAll();
   }
 
   // For a network that is trained by the solver, no bottom or top vecs
@@ -107,7 +109,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
       LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
     }
     if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
-      Test();
+      TestAll();
     }
     // Check if we need to do snapshot
     if (param_.snapshot() && iter_ % param_.snapshot() == 0) {
@@ -122,18 +124,27 @@ void Solver<Dtype>::Solve(const char* resume_file) {
 
 
 template <typename Dtype>
-void Solver<Dtype>::Test() {
-  LOG(INFO) << "Iteration " << iter_ << ", Testing net";
+void Solver<Dtype>::TestAll() {
+  for (int test_net_id = 0; test_net_id < test_nets_.size(); ++test_net_id) {
+    Test(test_net_id);
+  }
+}
+
+
+template <typename Dtype>
+void Solver<Dtype>::Test(const int test_net_id) {
+  LOG(INFO) << "Iteration " << iter_ << ", Testing net (#" << test_net_id << ")";
   // We need to set phase to test before running.
   Caffe::set_phase(Caffe::TEST);
-  CHECK_NOTNULL(test_net_.get())->ShareTrainedLayersWith(net_.get());
+  CHECK_NOTNULL(test_nets_[test_net_id].get())->
+      ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
   vector<Blob<Dtype>*> bottom_vec;
   Dtype loss = 0;
   for (int i = 0; i < param_.test_iter(); ++i) {
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
-        test_net_->Forward(bottom_vec, &iter_loss);
+        test_nets_[test_net_id]->Forward(bottom_vec, &iter_loss);
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }

From b64c597357c85ef1cd882831006a5d3227c613c9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 9 May 2014 20:47:06 -0700
Subject: [PATCH 0004/2053] add a lenet example of specifying train/test net
 directly in solver; multiple test nets

---
 .../mnist/lenet_consolidated_solver.prototxt  | 396 ++++++++++++++++++
 1 file changed, 396 insertions(+)
 create mode 100644 examples/mnist/lenet_consolidated_solver.prototxt

diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
new file mode 100644
index 00000000000..14a048a3664
--- /dev/null
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -0,0 +1,396 @@
+# lenet_consolidated_solver.prototxt consolidates the lenet_solver, lenet_train,
+# and lenet_test prototxts into a single file.  It also adds an additional test
+# net which runs on the training set, e.g., for the purpose of comparing
+# train/test accuracy (accuracy is computed only on the test set in the included
+# LeNet example.  This is mainly included as an example of using these features
+# (specify NetParameters directly in the solver, specify multiple test nets)
+# if desired.
+# 
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# Set a random_seed for repeatable results.
+# (For results that vary due to random initialization, comment out the below
+# line, or set to a negative integer -- e.g. "random_seed: -1")
+random_seed: 1701
+# solver mode: CPU or GPU
+solver_mode: GPU
+# The training protocol buffer definition
+train_net_param {
+  name: "LeNet"
+  layers {
+    name: "mnist"
+    type: DATA
+    top: "data"
+    top: "label"
+    data_param {
+      source: "mnist-train-leveldb"
+      scale: 0.00390625
+      batch_size: 64
+    }
+  }
+  layers {
+    name: "conv1"
+    type: CONVOLUTION
+    bottom: "data"
+    top: "conv1"
+    blobs_lr: 1
+    blobs_lr: 2
+    convolution_param {
+      num_output: 20
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool1"
+    type: POOLING
+    bottom: "conv1"
+    top: "pool1"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "conv2"
+    type: CONVOLUTION
+    bottom: "pool1"
+    top: "conv2"
+    blobs_lr: 1
+    blobs_lr: 2
+    convolution_param {
+      num_output: 50
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool2"
+    type: POOLING
+    bottom: "conv2"
+    top: "pool2"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "ip1"
+    type: INNER_PRODUCT
+    bottom: "pool2"
+    top: "ip1"
+    blobs_lr: 1
+    blobs_lr: 2
+    inner_product_param {
+      num_output: 500
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "relu1"
+    type: RELU
+    bottom: "ip1"
+    top: "ip1"
+  }
+  layers {
+    name: "ip2"
+    type: INNER_PRODUCT
+    bottom: "ip1"
+    top: "ip2"
+    blobs_lr: 1
+    blobs_lr: 2
+    inner_product_param {
+      num_output: 10
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "loss"
+    type: SOFTMAX_LOSS
+    bottom: "ip2"
+    bottom: "label"
+  }
+}
+# The testing protocol buffer definition
+test_net_param {
+  name: "LeNet-test"
+  layers {
+    name: "mnist"
+    type: DATA
+    top: "data"
+    top: "label"
+    data_param {
+      source: "mnist-test-leveldb"
+      scale: 0.00390625
+      batch_size: 100
+    }
+  }
+  layers {
+    name: "conv1"
+    type: CONVOLUTION
+    bottom: "data"
+    top: "conv1"
+    convolution_param {
+      num_output: 20
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool1"
+    type: POOLING
+    bottom: "conv1"
+    top: "pool1"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "conv2"
+    type: CONVOLUTION
+    bottom: "pool1"
+    top: "conv2"
+    convolution_param {
+      num_output: 50
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool2"
+    type: POOLING
+    bottom: "conv2"
+    top: "pool2"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "ip1"
+    type: INNER_PRODUCT
+    bottom: "pool2"
+    top: "ip1"
+    inner_product_param {
+      num_output: 500
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "relu1"
+    type: RELU
+    bottom: "ip1"
+    top: "ip1"
+  }
+  layers {
+    name: "ip2"
+    type: INNER_PRODUCT
+    bottom: "ip1"
+    top: "ip2"
+    inner_product_param {
+      num_output: 10
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "prob"
+    type: SOFTMAX
+    bottom: "ip2"
+    top: "prob"
+  }
+  layers {
+    name: "accuracy"
+    type: ACCURACY
+    bottom: "prob"
+    bottom: "label"
+    top: "accuracy"
+  }
+}
+# The protocol buffer definition to test on the train set
+test_net_param {
+  name: "LeNet-test-on-train"
+  layers {
+    name: "mnist"
+    type: DATA
+    top: "data"
+    top: "label"
+    data_param {
+      source: "mnist-train-leveldb"
+      scale: 0.00390625
+      batch_size: 100
+    }
+  }
+  layers {
+    name: "conv1"
+    type: CONVOLUTION
+    bottom: "data"
+    top: "conv1"
+    convolution_param {
+      num_output: 20
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool1"
+    type: POOLING
+    bottom: "conv1"
+    top: "pool1"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "conv2"
+    type: CONVOLUTION
+    bottom: "pool1"
+    top: "conv2"
+    convolution_param {
+      num_output: 50
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool2"
+    type: POOLING
+    bottom: "conv2"
+    top: "pool2"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "ip1"
+    type: INNER_PRODUCT
+    bottom: "pool2"
+    top: "ip1"
+    inner_product_param {
+      num_output: 500
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "relu1"
+    type: RELU
+    bottom: "ip1"
+    top: "ip1"
+  }
+  layers {
+    name: "ip2"
+    type: INNER_PRODUCT
+    bottom: "ip1"
+    top: "ip2"
+    inner_product_param {
+      num_output: 10
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "prob"
+    type: SOFTMAX
+    bottom: "ip2"
+    top: "prob"
+  }
+  layers {
+    name: "accuracy"
+    type: ACCURACY
+    bottom: "prob"
+    bottom: "label"
+    top: "accuracy"
+  }
+}

From 2cd46db70d7625968cbf232d70723825d74d5040 Mon Sep 17 00:00:00 2001
From: Tobias Domhan <tdomhan@gmail.com>
Date: Sat, 10 May 2014 18:41:05 +0200
Subject: [PATCH 0005/2053] multiple test_iter

---
 src/caffe/proto/caffe.proto | 2 +-
 src/caffe/solver.cpp        | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index cf3a9b76c54..12c4dc6a8fb 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -68,7 +68,7 @@ message SolverParameter {
   optional NetParameter train_net_param = 21; // Full params for the train net.
   repeated NetParameter test_net_param = 22; // Full params for the test nets.
   // The number of iterations for each testing phase.
-  optional int32 test_iter = 3 [default = 0];
+  repeated int32 test_iter = 3;
   // The number of iterations between two testing phases.
   optional int32 test_interval = 4 [default = 0];
   optional bool test_compute_loss = 19 [default = false];
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index e3dc70546a0..e68f7194a90 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -53,7 +53,7 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   const int num_test_net_files = param_.test_net_size();
   const int num_test_nets = num_test_net_params + num_test_net_files;
   if (num_test_nets) {
-    CHECK_GT(param_.test_iter(), 0);
+    CHECK_EQ(param_.test_iter().size(), num_test_nets) << "you need to specify test_iter for each test network.";
     CHECK_GT(param_.test_interval(), 0);
   }
   test_nets_.resize(num_test_nets);
@@ -141,7 +141,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
   vector<Dtype> test_score;
   vector<Blob<Dtype>*> bottom_vec;
   Dtype loss = 0;
-  for (int i = 0; i < param_.test_iter(); ++i) {
+  for (int i = 0; i < param_.test_iter().Get(test_net_id); ++i) {
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
         test_nets_[test_net_id]->Forward(bottom_vec, &iter_loss);
@@ -166,12 +166,12 @@ void Solver<Dtype>::Test(const int test_net_id) {
     }
   }
   if (param_.test_compute_loss()) {
-    loss /= param_.test_iter();
+    loss /= param_.test_iter().Get(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
   }
   for (int i = 0; i < test_score.size(); ++i) {
     LOG(INFO) << "Test score #" << i << ": "
-        << test_score[i] / param_.test_iter();
+        << test_score[i] / param_.test_iter().Get(test_net_id);
   }
   Caffe::set_phase(Caffe::TRAIN);
 }

From 0ec86f22d7689a7db98f799a910a7d1b03dab890 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 10 May 2014 11:46:19 -0700
Subject: [PATCH 0006/2053] lint and two test_iters in
 lenet_consolidated_solver

---
 examples/mnist/lenet_consolidated_solver.prototxt | 13 +++++++++----
 src/caffe/solver.cpp                              | 12 +++++++-----
 2 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 14a048a3664..76ccbfcccb9 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -6,10 +6,6 @@
 # (specify NetParameters directly in the solver, specify multiple test nets)
 # if desired.
 # 
-# test_iter specifies how many forward passes the test should carry out.
-# In the case of MNIST, we have test batch size 100 and 100 test iterations,
-# covering the full 10,000 testing images.
-test_iter: 100
 # Carry out testing every 500 training iterations.
 test_interval: 500
 # The base learning rate, momentum and the weight decay of the network.
@@ -33,6 +29,7 @@ snapshot_prefix: "lenet"
 random_seed: 1701
 # solver mode: CPU or GPU
 solver_mode: GPU
+
 # The training protocol buffer definition
 train_net_param {
   name: "LeNet"
@@ -154,6 +151,11 @@ train_net_param {
     bottom: "label"
   }
 }
+
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
 # The testing protocol buffer definition
 test_net_param {
   name: "LeNet-test"
@@ -274,6 +276,9 @@ test_net_param {
     top: "accuracy"
   }
 }
+
+# The train set has 60K images, so we run 600 test iters (600 * 100 = 60K).
+test_iter: 600
 # The protocol buffer definition to test on the train set
 test_net_param {
   name: "LeNet-test-on-train"
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index e68f7194a90..48434f4bc32 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -53,7 +53,8 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   const int num_test_net_files = param_.test_net_size();
   const int num_test_nets = num_test_net_params + num_test_net_files;
   if (num_test_nets) {
-    CHECK_EQ(param_.test_iter().size(), num_test_nets) << "you need to specify test_iter for each test network.";
+    CHECK_EQ(param_.test_iter_size(), num_test_nets)
+        << "test_iter must be specified for each test network.";
     CHECK_GT(param_.test_interval(), 0);
   }
   test_nets_.resize(num_test_nets);
@@ -133,7 +134,8 @@ void Solver<Dtype>::TestAll() {
 
 template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
-  LOG(INFO) << "Iteration " << iter_ << ", Testing net (#" << test_net_id << ")";
+  LOG(INFO) << "Iteration " << iter_
+            << ", Testing net (#" << test_net_id << ")";
   // We need to set phase to test before running.
   Caffe::set_phase(Caffe::TEST);
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
@@ -141,7 +143,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
   vector<Dtype> test_score;
   vector<Blob<Dtype>*> bottom_vec;
   Dtype loss = 0;
-  for (int i = 0; i < param_.test_iter().Get(test_net_id); ++i) {
+  for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
         test_nets_[test_net_id]->Forward(bottom_vec, &iter_loss);
@@ -166,12 +168,12 @@ void Solver<Dtype>::Test(const int test_net_id) {
     }
   }
   if (param_.test_compute_loss()) {
-    loss /= param_.test_iter().Get(test_net_id);
+    loss /= param_.test_iter(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
   }
   for (int i = 0; i < test_score.size(); ++i) {
     LOG(INFO) << "Test score #" << i << ": "
-        << test_score[i] / param_.test_iter().Get(test_net_id);
+        << test_score[i] / param_.test_iter(test_net_id);
   }
   Caffe::set_phase(Caffe::TRAIN);
 }

From 511665931f9b31995987a67a1503a2fff81d5750 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 10 May 2014 12:12:28 -0700
Subject: [PATCH 0007/2053] add script to run lenet_consolidated_solver and add
 comment with results for first/last 500 iterations

---
 .../mnist/lenet_consolidated_solver.prototxt  | 55 ++++++++++++++++++-
 examples/mnist/train_lenet_consolidated.sh    |  5 ++
 2 files changed, 58 insertions(+), 2 deletions(-)
 create mode 100755 examples/mnist/train_lenet_consolidated.sh

diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 76ccbfcccb9..07cbc211414 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -2,10 +2,10 @@
 # and lenet_test prototxts into a single file.  It also adds an additional test
 # net which runs on the training set, e.g., for the purpose of comparing
 # train/test accuracy (accuracy is computed only on the test set in the included
-# LeNet example.  This is mainly included as an example of using these features
+# LeNet example).  This is mainly included as an example of using these features
 # (specify NetParameters directly in the solver, specify multiple test nets)
 # if desired.
-# 
+#
 # Carry out testing every 500 training iterations.
 test_interval: 500
 # The base learning rate, momentum and the weight decay of the network.
@@ -399,3 +399,54 @@ test_net_param {
     top: "accuracy"
   }
 }
+
+# Expected results for first and last 500 iterations:
+# (with portions of log omitted for brevity)
+#
+# Iteration 0, Testing net (#0)
+# Test score #0: 0.067
+# Test score #1: 2.30256
+# Iteration 0, Testing net (#1)
+# Test score #0: 0.0670334
+# Test score #1: 2.30258
+# Iteration 100, lr = 0.00992565
+# Iteration 100, loss = 0.280585
+# Iteration 200, lr = 0.00985258
+# Iteration 200, loss = 0.345601
+# Iteration 300, lr = 0.00978075
+# Iteration 300, loss = 0.172217
+# Iteration 400, lr = 0.00971013
+# Iteration 400, loss = 0.261836
+# Iteration 500, lr = 0.00964069
+# Iteration 500, loss = 0.157803
+# Iteration 500, Testing net (#0)
+# Test score #0: 0.968
+# Test score #1: 0.0993772
+# Iteration 500, Testing net (#1)
+# Test score #0: 0.965883
+# Test score #1: 0.109374
+#
+# [...]
+#
+# Iteration 9500, Testing net (#0)
+# Test score #0: 0.9899
+# Test score #1: 0.0308299
+# Iteration 9500, Testing net (#1)
+# Test score #0: 0.996816
+# Test score #1: 0.0118238
+# Iteration 9600, lr = 0.00603682
+# Iteration 9600, loss = 0.0126215
+# Iteration 9700, lr = 0.00601382
+# Iteration 9700, loss = 0.00579304
+# Iteration 9800, lr = 0.00599102
+# Iteration 9800, loss = 0.00500633
+# Iteration 9900, lr = 0.00596843
+# Iteration 9900, loss = 0.00796607
+# Iteration 10000, lr = 0.00594604
+# Iteration 10000, loss = 0.00271736
+# Iteration 10000, Testing net (#0)
+# Test score #0: 0.9914
+# Test score #1: 0.0276671
+# Iteration 10000, Testing net (#1)
+# Test score #0: 0.997782
+# Test score #1: 0.00908085
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
new file mode 100755
index 00000000000..ff6897e263d
--- /dev/null
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env sh
+
+TOOLS=../../build/tools
+
+GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_consolidated_solver.prototxt

From 41da421e613f161174cd046205583f148998dfa5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 10 May 2014 13:57:52 -0700
Subject: [PATCH 0008/2053] fix proto comment for multiple test nets

---
 src/caffe/proto/caffe.proto | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 12c4dc6a8fb..ce2f25b8db5 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -61,8 +61,11 @@ message SolverParameter {
   // parameters; {train,test}_net_param specify the net parameters directly
   // inside the SolverParameter.
   //
-  // If {train,test}_net is specified, {train,test}_net_param should not be,
-  // and vice versa.
+  // Only either train_net or train_net_param (not both) should be specified.
+  // You may specify 0 or more test_net and/or test_net_param.  All
+  // nets specified using test_net_param will be tested first, followed by all
+  // nets specified using test_net (each processed in the order specified in
+  // the prototxt).
   optional string train_net = 1; // The proto filename for the train net.
   repeated string test_net = 2; // The proto filenames for the test nets.
   optional NetParameter train_net_param = 21; // Full params for the train net.

From ba2875ba9416a5789cc8e6d18758ce87177495a8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 10 May 2014 14:09:31 -0700
Subject: [PATCH 0009/2053] require either train_net or train_net_param to be
 specified

---
 src/caffe/solver.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 48434f4bc32..6a8f18f1331 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -46,6 +46,8 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
     LOG(INFO) << "Creating training net specified in SolverParameter.";
     net_.reset(new Net<Dtype>(param_.train_net_param()));
   } else {
+    CHECK(param_.has_train_net())
+        << "Neither train_net nor train_net_param were specified.";
     LOG(INFO) << "Creating training net from file: " << param_.train_net();
     net_.reset(new Net<Dtype>(param_.train_net()));
   }

From 20b447fe3a5e0f80aa598b712fd94d5097ef9e89 Mon Sep 17 00:00:00 2001
From: James Thewlis <jamt9000@gmail.com>
Date: Mon, 19 May 2014 19:33:42 +0100
Subject: [PATCH 0010/2053] Don't modify index in im2col kernel loop

---
 src/caffe/util/im2col.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 6aecb0e5722..8f5c6b04820 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -17,9 +17,9 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
     int w_out = index % width_col;
-    index /= width_col;
-    int h_out = index % height_col;
-    int channel_in = index / height_col;
+    int h_index = index / width_col;
+    int h_out = h_index % height_col;
+    int channel_in = h_index / height_col;
     int channel_out = channel_in * ksize * ksize;
     int h_in = h_out * stride - pad;
     int w_in = w_out * stride - pad;

From b6da40a57cbfa82829d5f520092257935291c0b3 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 20 May 2014 14:42:37 -0700
Subject: [PATCH 0011/2053] fix detection notebook link

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index bc1969f6329..2a23af04033 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -39,7 +39,7 @@ Even in CPU mode, computing predictions on an image takes only 20 ms when images
 * [Feature extraction with C++](/feature_extraction.html): feature extraction using pre-trained model.
 
 [imagenet_classification]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_classification.ipynb
-[detection]:   http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection_search_demo.ipynb
+[detection]:   http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb
 [visualizing_filters]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb
 
 ## Citing Caffe

From 5c982ee9c8bd6bedb70994fd267ed44c03a0aa2a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 20 May 2014 14:44:47 -0700
Subject: [PATCH 0012/2053] link canonical bvlc site

---
 docs/_layouts/default.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index bec2d83c1ce..29c7a081046 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -34,7 +34,7 @@ <h1 class="header"><a href="index.html">Caffe</a></h1>
           <li class="download"><a class="buttons" href="https://github.com/BVLC/caffe/tarball/master">Download TAR</a></li>-->
           <li><a class="buttons github" href="https://github.com/BVLC/caffe">View On GitHub</a></li>
         </ul>
-        <p class="header">Maintained by<br><a class="header name" href="http://ucbvlc.org/">BVLC</a></p>
+        <p class="header">Maintained by<br><a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a></p>
         <p class="header">Created by<br><a class="header name" href="http://daggerfs.com/">Yangqing Jia</a></p>
 
       </header>

From 84788c61d79964b5508fba6022ee6f6a5926ccec Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 17:38:03 -0700
Subject: [PATCH 0013/2053] Added ArgMax Layer

Conflicts:

	src/caffe/proto/caffe.proto
---
 include/caffe/vision_layers.hpp   | 19 ++++++++++
 src/caffe/layer_factory.cpp       |  2 ++
 src/caffe/layers/argmax_layer.cpp | 59 +++++++++++++++++++++++++++++++
 src/caffe/proto/caffe.proto       |  7 ++++
 4 files changed, 87 insertions(+)
 create mode 100644 src/caffe/layers/argmax_layer.cpp

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index de99bc3033f..21e3fd0126c 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -22,6 +22,25 @@ ConcatLayer
   Takes at least two blobs and concatenates them along either num or
   channel dim, outputting the result.
 */
+template <typename Dtype>
+class ArgMaxLayer : public Layer<Dtype> {
+ public:
+  explicit ArgMaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  // For now ArgMax layer should not be used to compute backward operations.
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+  bool out_max_val_;
+};
+
 template <typename Dtype>
 class ConcatLayer : public Layer<Dtype> {
  public:
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 2991c81f559..ae15ba5bb44 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -24,6 +24,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   switch (type) {
   case LayerParameter_LayerType_ACCURACY:
     return new AccuracyLayer<Dtype>(param);
+  case LayerParameter_LayerType_ARGMAX:
+    return new ArgMaxLayer<Dtype>(param);
   case LayerParameter_LayerType_BNLL:
     return new BNLLLayer<Dtype>(param);
   case LayerParameter_LayerType_CONCAT:
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
new file mode 100644
index 00000000000..b09fd7883e2
--- /dev/null
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -0,0 +1,59 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+using std::max;
+
+namespace caffe {
+
+template <typename Dtype>
+void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 1) << "ArgMaxLayer Layer takes 1 input.";
+  CHECK_EQ(top->size(), 1) << "ArgMaxLayer Layer takes 1 output.";
+  out_max_val_ = this->layer_param_.argmax_param().out_max_val();
+  // Produces max_ind and max_val
+  if (out_max_val_) { 
+    (*top)[0]->Reshape(bottom[0]->num(), 2, 1, 1);
+  } // Produces only max_ind
+  else {
+    (*top)[0]->Reshape(bottom[0]->num(), 1, 1, 1);
+  }
+}
+
+template <typename Dtype>
+Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  for (int i = 0; i < num; ++i) {
+    // Accuracy
+    Dtype max_val = -FLT_MAX;
+    int max_ind = 0;
+    for (int j = 0; j < dim; ++j) {
+      if (bottom_data[i * dim + j] > max_val) {
+        max_val = bottom_data[i * dim + j];
+        max_ind = j;
+      }
+    }
+    if (out_max_val_) {
+      top_data[i * 2] = max_ind;
+      top_data[i * 2 + 1] = max_val;
+    }
+    else {
+      top_data[i] = max_ind;
+    }
+  }
+  return Dtype(0);
+}
+
+INSTANTIATE_CLASS(ArgMaxLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index ce2f25b8db5..8d8b2144e96 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -160,6 +160,7 @@ message LayerParameter {
     SPLIT = 22;
     TANH = 23;
     WINDOW_DATA = 24;
+    ARGMAX = 30;
   }
   optional LayerType type = 5; // the layer type from the enum above
 
@@ -186,6 +187,7 @@ message LayerParameter {
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
   optional WindowDataParameter window_data_param = 20;
+  optional ArgMaxLayer argmax_param = 23;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -193,6 +195,11 @@ message LayerParameter {
   optional V0LayerParameter layer = 1;
 }
 
+// Message that stores parameters used by ArgMaxLayer
+message ArgMaxLayer {
+  // If true produce pairs (argmax, maxval) 
+  optional bool out_max_val = 1 [default = false]; 
+
 // Message that stores parameters used by ConcatLayer
 message ConcatParameter {
   // Concat Layer needs to specify the dimension along the concat will happen,

From cdebe7a617c83501ebeffc42239f8d89c0d71d7e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 15 May 2014 09:49:36 -0700
Subject: [PATCH 0014/2053] Added Test for ArgMax Layer

---
 src/caffe/test/test_argmax_layer.cpp | 113 +++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 src/caffe/test/test_argmax_layer.cpp

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
new file mode 100644
index 00000000000..c99dc23021f
--- /dev/null
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -0,0 +1,113 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "cuda_runtime.h"
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class ArgMaxLayerTest : public ::testing::Test {
+ protected:
+  ArgMaxLayerTest()
+      : blob_bottom_(new Blob<Dtype>(20, 10, 1, 1)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ArgMaxLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(ArgMaxLayerTest, Dtypes);
+
+
+TYPED_TEST(ArgMaxLayerTest, TestSetup) {
+  LayerParameter layer_param;
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_out_max_val(true)
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
+  EXPECT_EQ(this->blob_top_->channels(), 2);
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPU) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(top_data[i], 0);
+    EXPECT_LE(top_data[i], dim);
+    max_ind = top_data[i];
+    max_val = bottom_data[i * dim + max_ind];
+    for (int j = 0; j < dim; ++j) {
+      EXPECT_LE(bottom_data[i * dim + j], max_val);
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_out_max_val(true)
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(top_data[i], 0);
+    EXPECT_LE(top_data[i], dim);
+    max_ind = top_data[i * 2];
+    max_val = top_data[i * 2 + 1];
+    EXPECT_EQ(bottom_data[i * dim + max_ind],max_val);
+    for (int j = 0; j < dim; ++j) {
+      EXPECT_LE(bottom_data[i * dim + j], max_val);
+    }
+  }
+}
+
+}  // namespace caffe

From 69dbbc2c045dcec996157875ea644c200b9d98c4 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 17:39:52 -0700
Subject: [PATCH 0015/2053] Fixed numbers in proto and name of ArgMaxParameter

Conflicts:

	src/caffe/proto/caffe.proto
---
 src/caffe/proto/caffe.proto | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8d8b2144e96..b5bc26bb744 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -187,7 +187,8 @@ message LayerParameter {
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
   optional WindowDataParameter window_data_param = 20;
-  optional ArgMaxLayer argmax_param = 23;
+  optional ArgMaxParameter argmax_param = 23;
+
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -196,9 +197,10 @@ message LayerParameter {
 }
 
 // Message that stores parameters used by ArgMaxLayer
-message ArgMaxLayer {
+message ArgMaxParameter {
   // If true produce pairs (argmax, maxval) 
   optional bool out_max_val = 1 [default = false]; 
+}
 
 // Message that stores parameters used by ConcatLayer
 message ConcatParameter {

From bdcd75e48746be0aab0d4c8b606eeb2aac6c9ae8 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 17:42:38 -0700
Subject: [PATCH 0016/2053] Fix types of ArgMax Layers params

Conflicts:

	include/caffe/vision_layers.hpp
	src/caffe/proto/caffe.proto
---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b5bc26bb744..d73357cb944 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -189,7 +189,6 @@ message LayerParameter {
   optional WindowDataParameter window_data_param = 20;
   optional ArgMaxParameter argmax_param = 23;
 
-
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
   // LayerParameter specification.
@@ -197,6 +196,7 @@ message LayerParameter {
 }
 
 // Message that stores parameters used by ArgMaxLayer
+
 message ArgMaxParameter {
   // If true produce pairs (argmax, maxval) 
   optional bool out_max_val = 1 [default = false]; 

From d19c18060134b7b409291cf7c9ffe815b1a9b0df Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 16:09:07 -0700
Subject: [PATCH 0017/2053] Added FLT_MAX to argmax layer

---
 src/caffe/layers/argmax_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index b09fd7883e2..33ec1d34089 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -1,12 +1,11 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <algorithm>
 #include <vector>
+#include <cfloat>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
 
 namespace caffe {
 

From aa57dfb84c763d20d46f449290b3d00a7870dc9d Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 16:43:01 -0700
Subject: [PATCH 0018/2053] Added missing ;

---
 src/caffe/test/test_argmax_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index c99dc23021f..bc3a8ecd1e0 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -52,7 +52,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetup) {
 TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   LayerParameter layer_param;
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
-  argmax_param->set_out_max_val(true)
+  argmax_param->set_out_max_val(true);
   ThresholdLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
@@ -87,7 +87,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
-  argmax_param->set_out_max_val(true)
+  argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));

From d6748cb994e6e2fa082569cc10299f77e29aa60c Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 16:54:21 -0700
Subject: [PATCH 0019/2053] Fixed name of ArgMaxLayerParameter

---
 src/caffe/test/test_argmax_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index bc3a8ecd1e0..b30e7edbf66 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -51,7 +51,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetup) {
 
 TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   LayerParameter layer_param;
-  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  ArgMaxLayerParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ThresholdLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -86,7 +86,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPU) {
 TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
-  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  ArgMaxLayerParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));

From 91ab1f684c8ea03cb1bd0ffe223937dc9e5381cd Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 16:55:45 -0700
Subject: [PATCH 0020/2053] Fixed name of blob_bottom_

---
 src/caffe/test/test_argmax_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index b30e7edbf66..0174e8988e1 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -45,7 +45,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetup) {
   LayerParameter layer_param;
   ThresholdLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 1);
 }
 
@@ -55,7 +55,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   argmax_param->set_out_max_val(true);
   ThresholdLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 2);
 }
 

From 766dd362db857171de45299fa75ecb8799f98289 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 18:01:04 -0700
Subject: [PATCH 0021/2053] Change ThresholdLayer to ArgMaxLayer in test_argmax

---
 src/caffe/test/test_argmax_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 0174e8988e1..f0754f0c1d2 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -43,7 +43,7 @@ TYPED_TEST_CASE(ArgMaxLayerTest, Dtypes);
 
 TYPED_TEST(ArgMaxLayerTest, TestSetup) {
   LayerParameter layer_param;
-  ThresholdLayer<TypeParam> layer(layer_param);
+  ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -53,7 +53,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   LayerParameter layer_param;
   ArgMaxLayerParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
-  ThresholdLayer<TypeParam> layer(layer_param);
+  ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 2);

From f6c2d933c7c281379fd5bab5131a65856a54dec0 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 18:02:08 -0700
Subject: [PATCH 0022/2053] Change ArgMaxLayerParam to ArgMaxParam for
 consitency

---
 src/caffe/test/test_argmax_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index f0754f0c1d2..627dd575904 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -51,7 +51,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetup) {
 
 TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   LayerParameter layer_param;
-  ArgMaxLayerParameter* argmax_param = layer_param.mutable_argmax_param();
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -86,7 +86,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPU) {
 TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
-  ArgMaxLayerParameter* argmax_param = layer_param.mutable_argmax_param();
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));

From a3fbe2d6669756c727a17bb082d3319d00b451f5 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 20 May 2014 21:24:03 -0700
Subject: [PATCH 0023/2053] corrected the caffe.proto ids

---
 src/caffe/proto/caffe.proto | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d73357cb944..2d9a1aa8519 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -112,26 +112,29 @@ message SolverState {
   repeated BlobProto history = 3; // The history for sgd solvers
 }
 
+// NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 23 (last added: memory_data_param)
+// LayerParameter next available ID: 24 (last added: argmax_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
   optional string name = 4; // the layer name
 
+  // NOTE
   // Add new LayerTypes to the enum below in lexicographical order (other than
   // starting with NONE), starting with the next available ID in the comment
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 30 (last added: MEMORY_DATA)
+  // LayerType next available ID: 31 (last added: ARGMAX)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
     // the type is unspecified).
     NONE = 0;
     ACCURACY = 1;
+    ARGMAX = 30;
     BNLL = 2;
     CONCAT = 3;
     CONVOLUTION = 4;
@@ -160,7 +163,6 @@ message LayerParameter {
     SPLIT = 22;
     TANH = 23;
     WINDOW_DATA = 24;
-    ARGMAX = 30;
   }
   optional LayerType type = 5; // the layer type from the enum above
 
@@ -173,6 +175,7 @@ message LayerParameter {
   repeated float weight_decay = 8;
 
   // Parameters for particular layer types.
+  optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
   optional ConvolutionParameter convolution_param = 10;
   optional DataParameter data_param = 11;
@@ -187,7 +190,6 @@ message LayerParameter {
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
   optional WindowDataParameter window_data_param = 20;
-  optional ArgMaxParameter argmax_param = 23;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -196,10 +198,9 @@ message LayerParameter {
 }
 
 // Message that stores parameters used by ArgMaxLayer
-
 message ArgMaxParameter {
-  // If true produce pairs (argmax, maxval) 
-  optional bool out_max_val = 1 [default = false]; 
+  // If true produce pairs (argmax, maxval)
+  optional bool out_max_val = 1 [default = false];
 }
 
 // Message that stores parameters used by ConcatLayer

From a31dc6537e79f51ff6ad6f9d863af0d8ece8ee7a Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 20 May 2014 21:32:07 -0700
Subject: [PATCH 0024/2053] Documented ArgMax layer in vision_layers.hpp

---
 include/caffe/vision_layers.hpp | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 21e3fd0126c..29daf09f656 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -17,10 +17,14 @@
 
 namespace caffe {
 
-/*
-ConcatLayer
-  Takes at least two blobs and concatenates them along either num or
-  channel dim, outputting the result.
+/* ArgmaxLayer
+  Compute the index of the max value across all (channels x height x width).
+  [In the future, can take specific dimension.]
+  Intended for use after a classification layer to produce prediction.
+  If parameter out_max_val is set to true, then output is a vector of pairs
+  (max_ind, max_val) for each image.
+
+  NOTE: does not implement Backwards operation.
 */
 template <typename Dtype>
 class ArgMaxLayer : public Layer<Dtype> {
@@ -33,7 +37,6 @@ class ArgMaxLayer : public Layer<Dtype> {
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  // For now ArgMax layer should not be used to compute backward operations.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
@@ -41,6 +44,10 @@ class ArgMaxLayer : public Layer<Dtype> {
   bool out_max_val_;
 };
 
+/* ConcatLayer
+  Takes at least two blobs and concatenates them along either num or
+  channel dim, outputting the result.
+*/
 template <typename Dtype>
 class ConcatLayer : public Layer<Dtype> {
  public:
@@ -126,6 +133,8 @@ class EltwiseProductLayer : public Layer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
+/* FlattenLayer
+*/
 template <typename Dtype>
 class FlattenLayer : public Layer<Dtype> {
  public:
@@ -308,6 +317,8 @@ class MemoryDataLayer : public Layer<Dtype> {
   int pos_;
 };
 
+/* PoolingLayer
+*/
 template <typename Dtype>
 class PoolingLayer : public Layer<Dtype> {
  public:

From 0033f9c607f7f5079f38e569b7949e3073d2fc01 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 20 May 2014 21:32:19 -0700
Subject: [PATCH 0025/2053] Fixed lint errors due to ArgmaxLayer

---
 src/caffe/layers/argmax_layer.cpp    | 11 +++++------
 src/caffe/test/test_argmax_layer.cpp |  2 +-
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 33ec1d34089..e04588d4baa 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -15,11 +15,11 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_EQ(bottom.size(), 1) << "ArgMaxLayer Layer takes 1 input.";
   CHECK_EQ(top->size(), 1) << "ArgMaxLayer Layer takes 1 output.";
   out_max_val_ = this->layer_param_.argmax_param().out_max_val();
-  // Produces max_ind and max_val
-  if (out_max_val_) { 
+  if (out_max_val_) {
+    // Produces max_ind and max_val
     (*top)[0]->Reshape(bottom[0]->num(), 2, 1, 1);
-  } // Produces only max_ind
-  else {
+  } else {
+    // Produces only max_ind
     (*top)[0]->Reshape(bottom[0]->num(), 1, 1, 1);
   }
 }
@@ -44,8 +44,7 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     if (out_max_val_) {
       top_data[i * 2] = max_ind;
       top_data[i * 2 + 1] = max_val;
-    }
-    else {
+    } else {
       top_data[i] = max_ind;
     }
   }
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 627dd575904..c4150e5abe0 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -103,7 +103,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
     EXPECT_LE(top_data[i], dim);
     max_ind = top_data[i * 2];
     max_val = top_data[i * 2 + 1];
-    EXPECT_EQ(bottom_data[i * dim + max_ind],max_val);
+    EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
     for (int j = 0; j < dim; ++j) {
       EXPECT_LE(bottom_data[i * dim + j], max_val);
     }

From 4d52ca7df5283bea4ee0abf95039538387d007c0 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 20 May 2014 21:48:23 -0700
Subject: [PATCH 0026/2053] setting canonical random seed

---
 src/caffe/test/test_argmax_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index c4150e5abe0..ab2d75b322c 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -22,7 +22,7 @@ class ArgMaxLayerTest : public ::testing::Test {
   ArgMaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(20, 10, 1, 1)),
         blob_top_(new Blob<Dtype>()) {
-    Caffe::set_random_seed(1701);
+    Caffe::set_random_seed(this->seed_);
     // fill the values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);

From a13e7ee43a4efaac058aaecd40d93d5e2b018a90 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 20 May 2014 22:33:29 -0700
Subject: [PATCH 0027/2053] Revert "setting canonical random seed"

1701 is the canonical random seed, and as this test makes only one call
for seeding there's no need for a member var.
---
 src/caffe/test/test_argmax_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index ab2d75b322c..c4150e5abe0 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -22,7 +22,7 @@ class ArgMaxLayerTest : public ::testing::Test {
   ArgMaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(20, 10, 1, 1)),
         blob_top_(new Blob<Dtype>()) {
-    Caffe::set_random_seed(this->seed_);
+    Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);

From ffedfa6bd92ed982cfbfbb7335c79c3eee4bcd3f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 21 May 2014 18:38:19 -0700
Subject: [PATCH 0028/2053] EltwiseProductLayer -> EltwiseLayer for generality

Reproduce elementwise product layer in more generality.
Add elementwise operation parameter.
Prepare for elementwise sum operation choice.
---
 include/caffe/vision_layers.hpp               | 10 +++--
 src/caffe/layer_factory.cpp                   |  4 +-
 ...se_product_layer.cpp => eltwise_layer.cpp} | 31 +++++++++----
 ...wise_product_layer.cu => eltwise_layer.cu} | 28 ++++++++----
 src/caffe/layers/lrn_layer.cpp                |  4 +-
 src/caffe/proto/caffe.proto                   | 13 +++++-
 ...oduct_layer.cpp => test_eltwise_layer.cpp} | 44 ++++++++++++-------
 7 files changed, 91 insertions(+), 43 deletions(-)
 rename src/caffe/layers/{eltwise_product_layer.cpp => eltwise_layer.cpp} (63%)
 rename src/caffe/layers/{eltwise_product_layer.cu => eltwise_layer.cu} (52%)
 rename src/caffe/test/{test_eltwise_product_layer.cpp => test_eltwise_layer.cpp} (69%)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 29daf09f656..1b5da11eea1 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -112,12 +112,12 @@ class ConvolutionLayer : public Layer<Dtype> {
   int N_;
 };
 
-/* EltwiseProductLayer
+/* EltwiseLayer
 */
 template <typename Dtype>
-class EltwiseProductLayer : public Layer<Dtype> {
+class EltwiseLayer : public Layer<Dtype> {
  public:
-  explicit EltwiseProductLayer(const LayerParameter& param)
+  explicit EltwiseLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -131,6 +131,8 @@ class EltwiseProductLayer : public Layer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  EltwiseParameter_EltwiseOp op_;
 };
 
 /* FlattenLayer
@@ -276,7 +278,7 @@ class LRNLayer : public Layer<Dtype> {
   shared_ptr<PowerLayer<Dtype> > power_layer_;
   Blob<Dtype> power_output_;
   vector<Blob<Dtype>*> power_top_vec_;
-  shared_ptr<EltwiseProductLayer<Dtype> > product_layer_;
+  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
   Blob<Dtype> product_data_input_;
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index ae15ba5bb44..58c20b1f430 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -38,8 +38,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new DropoutLayer<Dtype>(param);
   case LayerParameter_LayerType_EUCLIDEAN_LOSS:
     return new EuclideanLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_ELTWISE_PRODUCT:
-    return new EltwiseProductLayer<Dtype>(param);
+  case LayerParameter_LayerType_ELTWISE:
+    return new EltwiseLayer<Dtype>(param);
   case LayerParameter_LayerType_FLATTEN:
     return new FlattenLayer<Dtype>(param);
   case LayerParameter_LayerType_HDF5_DATA:
diff --git a/src/caffe/layers/eltwise_product_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
similarity index 63%
rename from src/caffe/layers/eltwise_product_layer.cpp
rename to src/caffe/layers/eltwise_layer.cpp
index b394450d6ae..dd466182877 100644
--- a/src/caffe/layers/eltwise_product_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -9,7 +9,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void EltwiseProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK_GE(bottom.size(), 2) <<
       "Eltwise Product Layer takes at least 2 blobs as input.";
@@ -26,22 +26,29 @@ void EltwiseProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(width, bottom[i]->width());
   }
   (*top)[0]->Reshape(num, channels, height, width);
+  op_ = this->layer_param_.eltwise_param().operation();
 }
 
 template <typename Dtype>
-Dtype EltwiseProductLayer<Dtype>::Forward_cpu(
+Dtype EltwiseLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   const int count = (*top)[0]->count();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
-  for (int i = 2; i < bottom.size(); ++i) {
-    caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data);
+  switch (op_) {
+  case EltwiseParameter_EltwiseOp_PROD:
+    caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
+    for (int i = 2; i < bottom.size(); ++i) {
+      caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data);
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown elementwise operation.";
   }
   return Dtype(0.);
 }
 
 template <typename Dtype>
-void EltwiseProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
   if (propagate_down) {
     const int count = top[0]->count();
@@ -50,13 +57,19 @@ void EltwiseProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     for (int i = 0; i < bottom->size(); ++i) {
       const Dtype* bottom_data = (*bottom)[i]->cpu_data();
       Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
-      caffe_div(count, top_data, bottom_data, bottom_diff);
-      caffe_mul(count, bottom_diff, top_diff, bottom_diff);
+      switch (op_) {
+      case EltwiseParameter_EltwiseOp_PROD:
+        caffe_div(count, top_data, bottom_data, bottom_diff);
+        caffe_mul(count, bottom_diff, top_diff, bottom_diff);
+        break;
+      default:
+        LOG(FATAL) << "Unknown elementwise operation.";
+      }
     }
   }
 }
 
-INSTANTIATE_CLASS(EltwiseProductLayer);
+INSTANTIATE_CLASS(EltwiseLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/eltwise_product_layer.cu b/src/caffe/layers/eltwise_layer.cu
similarity index 52%
rename from src/caffe/layers/eltwise_product_layer.cu
rename to src/caffe/layers/eltwise_layer.cu
index 9c66033c20f..9072f884b30 100644
--- a/src/caffe/layers/eltwise_product_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -9,19 +9,25 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype EltwiseProductLayer<Dtype>::Forward_gpu(
+Dtype EltwiseLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   const int count = (*top)[0]->count();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data);
-  for (int i = 2; i < bottom.size(); ++i) {
-    caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
+  switch (op_) {
+  case EltwiseParameter_EltwiseOp_PROD:
+    caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data);
+    for (int i = 2; i < bottom.size(); ++i) {
+      caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown elementwise operation.";
   }
   return Dtype(0.);
 }
 
 template <typename Dtype>
-void EltwiseProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
   if (propagate_down) {
     const int count = top[0]->count();
@@ -30,13 +36,19 @@ void EltwiseProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     for (int i = 0; i < bottom->size(); ++i) {
       const Dtype* bottom_data = (*bottom)[i]->gpu_data();
       Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-      caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
-      caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
+      switch (op_) {
+      case EltwiseParameter_EltwiseOp_PROD:
+        caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+        caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
+        break;
+      default:
+        LOG(FATAL) << "Unknown elementwise operation.";
+      }
     }
   }
 }
 
-INSTANTIATE_CLASS(EltwiseProductLayer);
+INSTANTIATE_CLASS(EltwiseLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index cfcc59c9feb..6f7af75957a 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -85,7 +85,9 @@ void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       product_bottom_vec_.push_back(bottom[0]);
       product_bottom_vec_.push_back(&power_output_);
       LayerParameter product_param;
-      product_layer_.reset(new EltwiseProductLayer<Dtype>(product_param));
+      EltwiseParameter* eltwise_param = product_param.mutable_eltwise_param();
+      eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+      product_layer_.reset(new EltwiseLayer<Dtype>(product_param));
       product_layer_->SetUp(product_bottom_vec_, top);
       CHECK_EQ((*top)[0]->num(), num_);
       CHECK_EQ((*top)[0]->channels(), channels_);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 2d9a1aa8519..a0a855e7e14 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -115,7 +115,7 @@ message SolverState {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 24 (last added: argmax_param)
+// LayerParameter next available ID: 25 (last added: eltwise_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -141,7 +141,7 @@ message LayerParameter {
     DATA = 5;
     DROPOUT = 6;
     EUCLIDEAN_LOSS = 7;
-    ELTWISE_PRODUCT = 25;
+    ELTWISE = 25;
     FLATTEN = 8;
     HDF5_DATA = 9;
     HDF5_OUTPUT = 10;
@@ -180,6 +180,7 @@ message LayerParameter {
   optional ConvolutionParameter convolution_param = 10;
   optional DataParameter data_param = 11;
   optional DropoutParameter dropout_param = 12;
+  optional EltwiseParameter eltwise_param = 24;
   optional HDF5DataParameter hdf5_data_param = 13;
   optional HDF5OutputParameter hdf5_output_param = 14;
   optional ImageDataParameter image_data_param = 15;
@@ -250,6 +251,14 @@ message DropoutParameter {
   optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
 }
 
+// Message that stores parameters used by EltwiseLayer
+message EltwiseParameter {
+  enum EltwiseOp {
+    PROD = 0;
+  }
+  optional EltwiseOp operation = 1; // element-wise operation to compute
+}
+
 // Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
diff --git a/src/caffe/test/test_eltwise_product_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
similarity index 69%
rename from src/caffe/test/test_eltwise_product_layer.cpp
rename to src/caffe/test/test_eltwise_layer.cpp
index 86d6fdc5334..86e47516ca7 100644
--- a/src/caffe/test/test_eltwise_product_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -17,9 +17,9 @@ namespace caffe {
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
 template <typename Dtype>
-class EltwiseProductLayerTest : public ::testing::Test {
+class EltwiseLayerTest : public ::testing::Test {
  protected:
-  EltwiseProductLayerTest()
+  EltwiseLayerTest()
       : blob_bottom_a_(new Blob<Dtype>(2, 3, 4, 5)),
         blob_bottom_b_(new Blob<Dtype>(2, 3, 4, 5)),
         blob_bottom_c_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -35,7 +35,7 @@ class EltwiseProductLayerTest : public ::testing::Test {
     blob_bottom_vec_.push_back(blob_bottom_c_);
     blob_top_vec_.push_back(blob_top_);
   }
-  virtual ~EltwiseProductLayerTest() {
+  virtual ~EltwiseLayerTest() {
     delete blob_bottom_a_;
     delete blob_bottom_b_;
     delete blob_bottom_c_;
@@ -50,12 +50,14 @@ class EltwiseProductLayerTest : public ::testing::Test {
 };
 
 typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(EltwiseProductLayerTest, Dtypes);
+TYPED_TEST_CASE(EltwiseLayerTest, Dtypes);
 
-TYPED_TEST(EltwiseProductLayerTest, TestSetUp) {
+TYPED_TEST(EltwiseLayerTest, TestSetUp) {
   LayerParameter layer_param;
-  shared_ptr<EltwiseProductLayer<TypeParam> > layer(
-      new EltwiseProductLayer<TypeParam>(layer_param));
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
@@ -63,11 +65,13 @@ TYPED_TEST(EltwiseProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->width(), 5);
 }
 
-TYPED_TEST(EltwiseProductLayerTest, TestCPU) {
+TYPED_TEST(EltwiseLayerTest, TestCPU) {
   Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
-  shared_ptr<EltwiseProductLayer<TypeParam> > layer(
-      new EltwiseProductLayer<TypeParam>(layer_param));
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   const TypeParam* data = this->blob_top_->cpu_data();
@@ -80,11 +84,13 @@ TYPED_TEST(EltwiseProductLayerTest, TestCPU) {
   }
 }
 
-TYPED_TEST(EltwiseProductLayerTest, TestGPU) {
+TYPED_TEST(EltwiseLayerTest, TestGPU) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
-  shared_ptr<EltwiseProductLayer<TypeParam> > layer(
-      new EltwiseProductLayer<TypeParam>(layer_param));
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   const TypeParam* data = this->blob_top_->cpu_data();
@@ -97,19 +103,23 @@ TYPED_TEST(EltwiseProductLayerTest, TestGPU) {
   }
 }
 
-TYPED_TEST(EltwiseProductLayerTest, TestCPUGradient) {
+TYPED_TEST(EltwiseLayerTest, TestCPUGradient) {
   Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
-  EltwiseProductLayer<TypeParam> layer(layer_param);
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  EltwiseLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(EltwiseProductLayerTest, TestGPUGradient) {
+TYPED_TEST(EltwiseLayerTest, TestGPUGradient) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
-  EltwiseProductLayer<TypeParam> layer(layer_param);
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  EltwiseLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-2);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));

From b23b2ee70cea10f5b0386cb3ea6be55b5effc95c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 21 May 2014 18:57:33 -0700
Subject: [PATCH 0029/2053] add caffe_gpu_add() and caffe_gpu_sub()

---
 include/caffe/util/math_functions.hpp |  6 ++++
 src/caffe/util/math_functions.cu      | 48 +++++++++++++++++++++++++++
 2 files changed, 54 insertions(+)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index d9c78355b6f..25ed4705961 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -86,9 +86,15 @@ void caffe_sqr(const int N, const Dtype* a, Dtype* y);
 template <typename Dtype>
 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
 template <typename Dtype>
 void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
 template <typename Dtype>
 void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 184613c0a84..63c8fac69c5 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -63,6 +63,54 @@ void caffe_gpu_add_scalar(const int N, const double alpha, double* Y) {
       N, alpha, Y);
 }
 
+template <typename Dtype>
+__global__ void add_kernel(const int n, const Dtype* a,
+    const Dtype* b, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = a[index] + b[index];
+  }
+}
+
+template <>
+void caffe_gpu_add<float>(const int N, const float* a, const float* b,
+    float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  add_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
+template <>
+void caffe_gpu_add<double>(const int N, const double* a, const double* b,
+    double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  add_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
+template <typename Dtype>
+__global__ void sub_kernel(const int n, const Dtype* a,
+    const Dtype* b, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = a[index] - b[index];
+  }
+}
+
+template <>
+void caffe_gpu_sub<float>(const int N, const float* a, const float* b,
+    float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sub_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
+template <>
+void caffe_gpu_sub<double>(const int N, const double* a, const double* b,
+    double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sub_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
 template <typename Dtype>
 __global__ void mul_kernel(const int n, const Dtype* a,
     const Dtype* b, Dtype* y) {

From 9c539dcf1d31f5d2e4a06698377b5a8618591b97 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 21 May 2014 19:31:47 -0700
Subject: [PATCH 0030/2053] Elementwise layer learns summation

---
 src/caffe/layers/eltwise_layer.cpp    |  9 ++++
 src/caffe/layers/eltwise_layer.cu     |  9 ++++
 src/caffe/proto/caffe.proto           |  1 +
 src/caffe/test/test_eltwise_layer.cpp | 59 ++++++++++++++++++++++++---
 4 files changed, 73 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index dd466182877..769d0157d44 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -41,6 +41,12 @@ Dtype EltwiseLayer<Dtype>::Forward_cpu(
       caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data);
     }
     break;
+  case EltwiseParameter_EltwiseOp_SUM:
+    caffe_add(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
+    for (int i = 2; i < bottom.size(); ++i) {
+      caffe_add(count, top_data, bottom[i]->cpu_data(), top_data);
+    }
+    break;
   default:
     LOG(FATAL) << "Unknown elementwise operation.";
   }
@@ -62,6 +68,9 @@ void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         caffe_div(count, top_data, bottom_data, bottom_diff);
         caffe_mul(count, bottom_diff, top_diff, bottom_diff);
         break;
+      case EltwiseParameter_EltwiseOp_SUM:
+        caffe_copy(count, top_diff, bottom_diff);
+        break;
       default:
         LOG(FATAL) << "Unknown elementwise operation.";
       }
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 9072f884b30..8bdb6a3ca37 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -20,6 +20,12 @@ Dtype EltwiseLayer<Dtype>::Forward_gpu(
       caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
     }
     break;
+  case EltwiseParameter_EltwiseOp_SUM:
+    caffe_gpu_add(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data);
+    for (int i = 2; i < bottom.size(); ++i) {
+      caffe_gpu_add(count, top_data, bottom[i]->gpu_data(), top_data);
+    }
+    break;
   default:
     LOG(FATAL) << "Unknown elementwise operation.";
   }
@@ -41,6 +47,9 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
         caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
         break;
+      case EltwiseParameter_EltwiseOp_SUM:
+        caffe_gpu_copy(count, top_diff, bottom_diff);
+        break;
       default:
         LOG(FATAL) << "Unknown elementwise operation.";
       }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a0a855e7e14..a7fb37bb76b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -255,6 +255,7 @@ message DropoutParameter {
 message EltwiseParameter {
   enum EltwiseOp {
     PROD = 0;
+    SUM = 1;
   }
   optional EltwiseOp operation = 1; // element-wise operation to compute
 }
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 86e47516ca7..dbb77fcee2c 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -65,7 +65,7 @@ TYPED_TEST(EltwiseLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->width(), 5);
 }
 
-TYPED_TEST(EltwiseLayerTest, TestCPU) {
+TYPED_TEST(EltwiseLayerTest, TestProdCPU) {
   Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
@@ -84,7 +84,26 @@ TYPED_TEST(EltwiseLayerTest, TestCPU) {
   }
 }
 
-TYPED_TEST(EltwiseLayerTest, TestGPU) {
+TYPED_TEST(EltwiseLayerTest, TestSumCPU) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const TypeParam* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
+  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
+  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
+  }
+}
+
+TYPED_TEST(EltwiseLayerTest, TestProdGPU) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
@@ -103,7 +122,26 @@ TYPED_TEST(EltwiseLayerTest, TestGPU) {
   }
 }
 
-TYPED_TEST(EltwiseLayerTest, TestCPUGradient) {
+TYPED_TEST(EltwiseLayerTest, TestSumGPU) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const TypeParam* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
+  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
+  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
+  }
+}
+
+TYPED_TEST(EltwiseLayerTest, TestProdCPUGradient) {
   Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
@@ -114,11 +152,22 @@ TYPED_TEST(EltwiseLayerTest, TestCPUGradient) {
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(EltwiseLayerTest, TestGPUGradient) {
+TYPED_TEST(EltwiseLayerTest, TestSumCPUGradient) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  EltwiseLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(EltwiseLayerTest, TestSumGPUGradient) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
-  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   EltwiseLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-2);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),

From a26094e356f2d089f8f402fbd71dedaee6b953b4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 21 May 2014 19:47:15 -0700
Subject: [PATCH 0031/2053] add EltwiseLayer docstring

---
 include/caffe/vision_layers.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 1b5da11eea1..3acb8df6c27 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -113,6 +113,7 @@ class ConvolutionLayer : public Layer<Dtype> {
 };
 
 /* EltwiseLayer
+  Compute elementwise operations like product or sum.
 */
 template <typename Dtype>
 class EltwiseLayer : public Layer<Dtype> {

From 2b0087194a46ce2f34626bdbfc781d289f24e9be Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 May 2014 00:30:12 -0700
Subject: [PATCH 0032/2053] reduce example image size

---
 examples/images/cat.jpg                  | Bin 642416 -> 140391 bytes
 src/caffe/test/test_image_data_layer.cpp |   8 ++++----
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/images/cat.jpg b/examples/images/cat.jpg
index 5303803b1c12213f986fb30e0b91e9d0b82fce85..b4efc6c98b7e7bed698b10f4b7f06031267d52dd 100644
GIT binary patch
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diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 42a1d0358db..fea5793d4be 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -70,8 +70,8 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
-  EXPECT_EQ(this->blob_top_data_->height(), 1200);
-  EXPECT_EQ(this->blob_top_data_->width(), 1600);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
   EXPECT_EQ(this->blob_top_label_->num(), 5);
   EXPECT_EQ(this->blob_top_label_->channels(), 1);
   EXPECT_EQ(this->blob_top_label_->height(), 1);
@@ -122,8 +122,8 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
-  EXPECT_EQ(this->blob_top_data_->height(), 1200);
-  EXPECT_EQ(this->blob_top_data_->width(), 1600);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
   EXPECT_EQ(this->blob_top_label_->num(), 5);
   EXPECT_EQ(this->blob_top_label_->channels(), 1);
   EXPECT_EQ(this->blob_top_label_->height(), 1);

From 334b92474ccc1853fa92ba280bfff81312428ea5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 May 2014 02:13:04 -0700
Subject: [PATCH 0033/2053] fix layer name in logging

---
 src/caffe/layers/eltwise_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 769d0157d44..13cd34d0602 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -12,9 +12,9 @@ template <typename Dtype>
 void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK_GE(bottom.size(), 2) <<
-      "Eltwise Product Layer takes at least 2 blobs as input.";
+      "Eltwise Layer takes at least 2 blobs as input.";
   CHECK_EQ(top->size(), 1) <<
-      "Eltwise Product Layer takes a single blob as output.";
+      "Eltwise Layer takes a single blob as output.";
   const int num = bottom[0]->num();
   const int channels = bottom[0]->channels();
   const int height = bottom[0]->height();

From 385fc6c5a4be831f22675f0d84242ed57ee36010 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 May 2014 17:41:24 -0700
Subject: [PATCH 0034/2053] make sum the default eltwise operation

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a7fb37bb76b..da07af08920 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -257,7 +257,7 @@ message EltwiseParameter {
     PROD = 0;
     SUM = 1;
   }
-  optional EltwiseOp operation = 1; // element-wise operation to compute
+  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
 }
 
 // Message that stores parameters used by HDF5DataLayer

From 2dd9b43a954a8b5be2a48f7b449e301800bcf728 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 May 2014 20:12:11 -0700
Subject: [PATCH 0035/2053] weight elementwise sum with per-blob coefficients

---
 include/caffe/vision_layers.hpp       |  1 +
 src/caffe/layers/eltwise_layer.cpp    | 29 +++++++++--
 src/caffe/layers/eltwise_layer.cu     | 13 +++--
 src/caffe/proto/caffe.proto           |  1 +
 src/caffe/test/test_eltwise_layer.cpp | 74 +++++++++++++++++++++++++++
 5 files changed, 110 insertions(+), 8 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 3acb8df6c27..07ab5779b66 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -134,6 +134,7 @@ class EltwiseLayer : public Layer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
   EltwiseParameter_EltwiseOp op_;
+  vector<Dtype> coeffs_;
 };
 
 /* FlattenLayer
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 13cd34d0602..77eae3042c3 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -6,6 +6,8 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
+using std::fill;
+
 namespace caffe {
 
 template <typename Dtype>
@@ -15,6 +17,13 @@ void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       "Eltwise Layer takes at least 2 blobs as input.";
   CHECK_EQ(top->size(), 1) <<
       "Eltwise Layer takes a single blob as output.";
+  CHECK(this->layer_param().eltwise_param().coeff_size() == 0
+      || this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<
+      "Eltwise Layer takes one coefficient per bottom blob.";
+  CHECK(!(this->layer_param().eltwise_param().operation()
+      == EltwiseParameter_EltwiseOp_PROD
+      && this->layer_param().eltwise_param().coeff_size())) <<
+      "Eltwise layer only takes coefficients for summation.";
   const int num = bottom[0]->num();
   const int channels = bottom[0]->channels();
   const int height = bottom[0]->height();
@@ -27,6 +36,13 @@ void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }
   (*top)[0]->Reshape(num, channels, height, width);
   op_ = this->layer_param_.eltwise_param().operation();
+  // Blob-wise coefficients for the elementwise operation.
+  coeffs_ = vector<Dtype>(bottom.size(), 1);
+  if (this->layer_param().eltwise_param().coeff_size()) {
+    for (int i = 0; i < bottom.size(); ++i) {
+      coeffs_[i] = this->layer_param().eltwise_param().coeff(i);
+    }
+  }
 }
 
 template <typename Dtype>
@@ -42,9 +58,10 @@ Dtype EltwiseLayer<Dtype>::Forward_cpu(
     }
     break;
   case EltwiseParameter_EltwiseOp_SUM:
-    caffe_add(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
-    for (int i = 2; i < bottom.size(); ++i) {
-      caffe_add(count, top_data, bottom[i]->cpu_data(), top_data);
+    caffe_set(count, Dtype(0), top_data);
+    // TODO(shelhamer) does BLAS optimize to sum for coeff = 1?
+    for (int i = 0; i < bottom.size(); ++i) {
+      caffe_axpy(count, coeffs_[i], bottom[i]->cpu_data(), top_data);
     }
     break;
   default:
@@ -69,7 +86,11 @@ void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         caffe_mul(count, bottom_diff, top_diff, bottom_diff);
         break;
       case EltwiseParameter_EltwiseOp_SUM:
-        caffe_copy(count, top_diff, bottom_diff);
+        if (coeffs_[i] == Dtype(1)) {
+          caffe_copy(count, top_diff, bottom_diff);
+        } else {
+          caffe_cpu_scale(count, coeffs_[i], top_diff, bottom_diff);
+        }
         break;
       default:
         LOG(FATAL) << "Unknown elementwise operation.";
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 8bdb6a3ca37..e1b63b11177 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -21,9 +21,10 @@ Dtype EltwiseLayer<Dtype>::Forward_gpu(
     }
     break;
   case EltwiseParameter_EltwiseOp_SUM:
-    caffe_gpu_add(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data);
-    for (int i = 2; i < bottom.size(); ++i) {
-      caffe_gpu_add(count, top_data, bottom[i]->gpu_data(), top_data);
+    caffe_gpu_set(count, Dtype(0.), top_data);
+    // TODO(shelhamer) does cuBLAS optimize to sum for coeff = 1?
+    for (int i = 0; i < bottom.size(); ++i) {
+      caffe_gpu_axpy(count, coeffs_[i], bottom[i]->gpu_data(), top_data);
     }
     break;
   default:
@@ -48,7 +49,11 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
         break;
       case EltwiseParameter_EltwiseOp_SUM:
-        caffe_gpu_copy(count, top_diff, bottom_diff);
+        if (coeffs_[i] == Dtype(1.)) {
+          caffe_gpu_copy(count, top_diff, bottom_diff);
+        } else {
+          caffe_gpu_scale(count, coeffs_[i], top_diff, bottom_diff);
+        }
         break;
       default:
         LOG(FATAL) << "Unknown elementwise operation.";
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index da07af08920..b792972fc62 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -258,6 +258,7 @@ message EltwiseParameter {
     SUM = 1;
   }
   optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
+  repeated float coeff = 2; // blob-wise coefficient
 }
 
 // Message that stores parameters used by HDF5DataLayer
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index dbb77fcee2c..5f72f625da4 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -103,6 +103,29 @@ TYPED_TEST(EltwiseLayerTest, TestSumCPU) {
   }
 }
 
+TYPED_TEST(EltwiseLayerTest, TestSumCoeffCPU) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  eltwise_param->add_coeff(1);
+  eltwise_param->add_coeff(-0.5);
+  eltwise_param->add_coeff(2);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const TypeParam* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
+  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
+  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] - 0.5*in_data_b[i] + 2*in_data_c[i],
+        1e-4);
+  }
+}
+
 TYPED_TEST(EltwiseLayerTest, TestProdGPU) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
@@ -141,6 +164,29 @@ TYPED_TEST(EltwiseLayerTest, TestSumGPU) {
   }
 }
 
+TYPED_TEST(EltwiseLayerTest, TestSumCoeffGPU) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  eltwise_param->add_coeff(1);
+  eltwise_param->add_coeff(-0.5);
+  eltwise_param->add_coeff(2);
+  shared_ptr<EltwiseLayer<TypeParam> > layer(
+      new EltwiseLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const TypeParam* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
+  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
+  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] - 0.5*in_data_b[i] + 2*in_data_c[i],
+        1e-4);
+  }
+}
+
 TYPED_TEST(EltwiseLayerTest, TestProdCPUGradient) {
   Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
@@ -163,6 +209,20 @@ TYPED_TEST(EltwiseLayerTest, TestSumCPUGradient) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(EltwiseLayerTest, TestSumCoeffCPUGradient) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  eltwise_param->add_coeff(1);
+  eltwise_param->add_coeff(-0.5);
+  eltwise_param->add_coeff(2);
+  EltwiseLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 TYPED_TEST(EltwiseLayerTest, TestSumGPUGradient) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
@@ -174,4 +234,18 @@ TYPED_TEST(EltwiseLayerTest, TestSumGPUGradient) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(EltwiseLayerTest, TestSumCoeffGPUGradient) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  eltwise_param->add_coeff(1);
+  eltwise_param->add_coeff(-0.5);
+  eltwise_param->add_coeff(2);
+  EltwiseLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 }  // namespace caffe

From b57d8f2eb740a8ceada56742c8125fd86476cf2d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 May 2014 21:24:16 -0700
Subject: [PATCH 0036/2053] commment, lint

---
 src/caffe/layers/eltwise_layer.cpp | 2 --
 src/caffe/layers/eltwise_layer.cu  | 3 ++-
 src/caffe/proto/caffe.proto        | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 77eae3042c3..5d05124c4c8 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -6,8 +6,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::fill;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index e1b63b11177..75827badb80 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -15,7 +15,8 @@ Dtype EltwiseLayer<Dtype>::Forward_gpu(
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   switch (op_) {
   case EltwiseParameter_EltwiseOp_PROD:
-    caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data);
+    caffe_gpu_mul(count, bottom[0]->gpu_data(),
+        bottom[1]->gpu_data(), top_data);
     for (int i = 2; i < bottom.size(); ++i) {
       caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
     }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b792972fc62..5e230a11482 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -258,7 +258,7 @@ message EltwiseParameter {
     SUM = 1;
   }
   optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
-  repeated float coeff = 2; // blob-wise coefficient
+  repeated float coeff = 2; // blob-wise coefficient for SUM operation
 }
 
 // Message that stores parameters used by HDF5DataLayer

From f1a85a3a495b41f0c8ce9ee7e96cfb61cf44c908 Mon Sep 17 00:00:00 2001
From: James Thewlis <jamt9000@gmail.com>
Date: Fri, 23 May 2014 15:59:52 +0100
Subject: [PATCH 0037/2053] Correctly invert the swapping of colour channels

In the 'deprocess' method, get back the image with the original channel order
by inverting the original transform, rather than reversing the tuple which is
incorrect.
---
 python/caffe/pycaffe.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 537e737b5c0..4827300708c 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -284,7 +284,8 @@ def _Net_deprocess(self, input_name, input_):
         decaf_in += mean
     decaf_in = decaf_in.transpose((1,2,0))
     if channel_order:
-        decaf_in = decaf_in[:, :, channel_order[::-1]]
+        channel_order_inverse = tuple([channel_order.index(i) for i in (0,1,2)])
+        decaf_in = decaf_in[:, :, channel_order_inverse]
     if input_scale:
         decaf_in /= input_scale
     return decaf_in

From f56f86a38bc990119f511a460f1c87bc39146f4c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 May 2014 09:13:44 -0700
Subject: [PATCH 0038/2053] follow-up on #443 to invert k channels (instead of
 3)

---
 python/caffe/pycaffe.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 4827300708c..8bed7046cc8 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -284,7 +284,8 @@ def _Net_deprocess(self, input_name, input_):
         decaf_in += mean
     decaf_in = decaf_in.transpose((1,2,0))
     if channel_order:
-        channel_order_inverse = tuple([channel_order.index(i) for i in (0,1,2)])
+        channel_order_inverse = [channel_order.index(i)
+                                 for i in range(decaf_in.shape[2])]
         decaf_in = decaf_in[:, :, channel_order_inverse]
     if input_scale:
         decaf_in /= input_scale

From 2e292c44a7a21d301210bcb724cd91e2108d649e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 23 May 2014 14:57:02 -0700
Subject: [PATCH 0039/2053] add convert_imageset option to resize images; use
 in convert_imageset.cpp and document

---
 docs/imagenet_training.md            |  4 +--
 examples/imagenet/create_imagenet.sh | 42 ++++++++++++++++++++++++----
 tools/convert_imageset.cpp           | 19 +++++++++----
 3 files changed, 53 insertions(+), 12 deletions(-)

diff --git a/docs/imagenet_training.md b/docs/imagenet_training.md
index 9e0076cf65f..f628f795652 100644
--- a/docs/imagenet_training.md
+++ b/docs/imagenet_training.md
@@ -30,7 +30,7 @@ You will first need to prepare some auxiliary data for training. This data can b
 
 The training and validation input are described in `train.txt` and `val.txt` as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See `synset_words.txt` for the synset/name mapping.
 
-You will also need to resize the images to 256x256: we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightedweighted [mincepie](https://github.com/Yangqing/mincepie) package to do mapreduce on the Berkeley cluster. If you would things to be rather simple and straightforward, you can also use shell commands, something like:
+You may want to resize the images to 256x256 in advance. By default, we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightedweighted [mincepie](https://github.com/Yangqing/mincepie) package to do mapreduce on the Berkeley cluster. If you would things to be rather simple and straightforward, you can also use shell commands, something like:
 
     for name in /path/to/imagenet/val/*.JPEG; do
         convert -resize 256x256\! $name $name
@@ -38,7 +38,7 @@ You will also need to resize the images to 256x256: we do not explicitly do this
 
 Go to `$CAFFE_ROOT/examples/imagenet/` for the rest of this guide.
 
-Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `imagenet_train_leveldb` and `imagenet_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
+Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set "RESIZE=true" to resize all images to 256x256 if you haven't resized the images in advance. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `imagenet_train_leveldb` and `imagenet_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
 
 Compute Image Mean
 ------------------
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index a1bcb7bfc5a..8767f120a83 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -5,16 +5,48 @@
 TOOLS=../../build/tools
 DATA=../../data/ilsvrc12
 
-echo "Creating leveldb..."
+TRAIN_DATA_ROOT=/path/to/imagenet/train/
+VAL_DATA_ROOT=/path/to/imagenet/val/
+
+# Set RESIZE=true to resize the images to 256x256. Leave as false if images have
+# already been resized using another tool.
+RESIZE=false
+if $RESIZE; then
+  RESIZE_HEIGHT=256
+  RESIZE_WIDTH=256
+else
+  RESIZE_HEIGHT=0
+  RESIZE_WIDTH=0
+fi
+
+if [ ! -d "$TRAIN_DATA_ROOT" ]; then
+  echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"
+  echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \
+       "where the ImageNet training data is stored."
+  exit 1
+fi
+
+if [ ! -d "$VAL_DATA_ROOT" ]; then
+  echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT"
+  echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \
+       "where the ImageNet validation data is stored."
+  exit 1
+fi
+
+echo "Creating train leveldb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
-    /path/to/imagenet/train/ \
+    $TRAIN_DATA_ROOT \
     $DATA/train.txt \
-    imagenet_train_leveldb 1
+    imagenet_train_leveldb 1 \
+    $RESIZE_HEIGHT $RESIZE_WIDTH
+
+echo "Creating val leveldb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
-    /path/to/imagenet/val/ \
+    $VAL_DATA_ROOT \
     $DATA/val.txt \
-    imagenet_val_leveldb 1
+    imagenet_val_leveldb 1 \
+    $RESIZE_HEIGHT $RESIZE_WIDTH
 
 echo "Done."
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 2d4d4c77b52..900183a649b 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -2,7 +2,8 @@
 // This program converts a set of images to a leveldb by storing them as Datum
 // proto buffers.
 // Usage:
-//    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME [0/1]
+//    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME [0/1] \
+//                     [resize_height] [resize_width]
 // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
 // should be a list of files as well as their labels, in the format as
 //   subfolder1/file1.JPEG 7
@@ -29,12 +30,12 @@ using std::string;
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 4 || argc > 5) {
+  if (argc < 4 || argc > 7) {
     printf("Convert a set of images to the leveldb format used\n"
         "as input for Caffe.\n"
         "Usage:\n"
         "    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME"
-        " RANDOM_SHUFFLE_DATA[0 or 1]\n"
+        " RANDOM_SHUFFLE_DATA[0 or 1] [resize_height] [resize_width]\n"
         "The ImageNet dataset for the training demo is at\n"
         "    http://www.image-net.org/download-images\n");
     return 1;
@@ -46,12 +47,20 @@ int main(int argc, char** argv) {
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
   }
-  if (argc == 5 && argv[4][0] == '1') {
+  if (argc >= 5 && argv[4][0] == '1') {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";
     std::random_shuffle(lines.begin(), lines.end());
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
+  int resize_height = 0;
+  int resize_width = 0;
+  if (argc >= 6) {
+    resize_height = atoi(argv[5]);
+  }
+  if (argc >= 7) {
+    resize_width = atoi(argv[6]);
+  }
 
   leveldb::DB* db;
   leveldb::Options options;
@@ -73,7 +82,7 @@ int main(int argc, char** argv) {
   bool data_size_initialized = false;
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     if (!ReadImageToDatum(root_folder + lines[line_id].first,
-                          lines[line_id].second, &datum)) {
+        lines[line_id].second, resize_height, resize_width, &datum)) {
       continue;
     }
     if (!data_size_initialized) {

From 75aa8df452894de1107e9321cbd292e00ac2ce89 Mon Sep 17 00:00:00 2001
From: James Thewlis <jamt9000@gmail.com>
Date: Sat, 24 May 2014 17:53:13 +0100
Subject: [PATCH 0040/2053] Don't modify data pointers in im2col loop

---
 src/caffe/util/im2col.cu | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 8f5c6b04820..ec4465eff66 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -23,15 +23,17 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     int channel_out = channel_in * ksize * ksize;
     int h_in = h_out * stride - pad;
     int w_in = w_out * stride - pad;
-    data_col += (channel_out * height_col + h_out) * width_col + w_out;
-    data_im += (channel_in * height + h_in) * width + w_in;
+    Dtype* data_col_ptr = data_col;
+    data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+    const Dtype* data_im_ptr = data_im;
+    data_im_ptr += (channel_in * height + h_in) * width + w_in;
     for (int i = 0; i < ksize; ++i) {
       for (int j = 0; j < ksize; ++j) {
         int h = h_in + i;
         int w = w_in + j;
-        *data_col = (h >= 0 && w >= 0 && h < height && w < width) ?
-            data_im[i * width + j] : 0;
-        data_col += height_col * width_col;
+        *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
+            data_im_ptr[i * width + j] : 0;
+        data_col_ptr += height_col * width_col;
       }
     }
   }

From 6b91c0aeab041cc84817ec37019bdad12b4e0bcf Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 19:49:02 -0700
Subject: [PATCH 0041/2053] Added max_idx to Pooling layer CPU

---
 include/caffe/vision_layers.hpp       |  1 +
 src/caffe/layers/pooling_layer.cpp    | 35 +++++++++++++++------------
 src/caffe/test/test_pooling_layer.cpp | 27 +++++++++++++++++++++
 3 files changed, 47 insertions(+), 16 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 07ab5779b66..c0bbd370fce 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -350,6 +350,7 @@ class PoolingLayer : public Layer<Dtype> {
   int pooled_height_;
   int pooled_width_;
   Blob<Dtype> rand_idx_;
+  shared_ptr<SyncedMemory> max_idx_;
 };
 
 /* SoftmaxLayer
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 7e880a27b69..63ea12d9abc 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -4,8 +4,10 @@
 #include <cfloat>
 #include <vector>
 
+#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
+#include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 
 using std::max;
@@ -35,6 +37,10 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       width_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
+  // If max pooling, we will initialize the vector index part.
+  if (this->layer_param_.pool() == LayerParameter_PoolMethod_MAX) {
+    max_idx_.reset(new SyncedMemory((*top)[0]->count() * sizeof(int)));
+  }
   // If stochastic pooling, we will initialize the random index part.
   if (this->layer_param_.pooling_param().pool() ==
       PoolingParameter_PoolMethod_STOCHASTIC) {
@@ -53,11 +59,14 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
   int top_count = (*top)[0]->count();
+  int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    // Initialize
+  // Initialize
+    mask = (int*)max_idx_->mutable_cpu_data();
     for (int i = 0; i < top_count; ++i) {
       top_data[i] = -FLT_MAX;
+      mask[i] = 0;
     }
     // The main loop
     for (int n = 0; n < bottom[0]->num(); ++n) {
@@ -70,9 +79,10 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
             int wend = min(wstart + kernel_size_, width_);
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {
-                top_data[ph * pooled_width_ + pw] =
-                  max(top_data[ph * pooled_width_ + pw],
-                      bottom_data[h * width_ + w]);
+                if (bottom_data[h * width_ + w] > top_data[ph * pooled_width_ + pw]) {
+                  top_data[ph * pooled_width_ + pw] = bottom_data[h * width_ + w];
+                  mask[ph * pooled_width_ + pw] = h * width_ + w;
+                }
               }
             }
           }
@@ -80,6 +90,7 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
         top_data += (*top)[0]->offset(0, 1);
+        mask += (*top)[0]->offset(0, 1);
       }
     }
     break;
@@ -138,25 +149,16 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
   memset(bottom_diff, 0, (*bottom)[0]->count() * sizeof(Dtype));
+  int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // The main loop
+    mask = (int*)max_idx_->cpu_data();
     for (int n = 0; n < top[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_;
-            int wstart = pw * stride_;
-            int hend = min(hstart + kernel_size_, height_);
-            int wend = min(wstart + kernel_size_, width_);
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                bottom_diff[h * width_ + w] +=
-                    top_diff[ph * pooled_width_ + pw] *
-                    (bottom_data[h * width_ + w] ==
-                        top_data[ph * pooled_width_ + pw]);
-              }
-            }
+            bottom_diff[mask[ph * pooled_width_ + pw]]+=top_diff[ph * pooled_width_ + pw];
           }
         }
         // offset
@@ -164,6 +166,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         top_data += top[0]->offset(0, 1);
         bottom_diff += (*bottom)[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
+        mask += top[0]->offset(0, 1);
       }
     }
     break;
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 41d4841f444..11bbd14a5c4 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -99,6 +99,33 @@ TYPED_TEST(PoolingLayerTest, PrintGPUBackward) {
 }
 */
 
+/*
+TYPED_TEST(PoolingLayerTest, PrintCPUBackward) {
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  layer_param.set_pool(LayerParameter_PoolMethod_MAX);
+  Caffe::set_mode(Caffe::CPU);
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
+  }
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    cout << "top data " << i << " " << this->blob_top_->cpu_data()[i] << endl;
+  }
+
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = i;
+  }
+  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
+  }
+}
+*/
+
 TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();

From 134922c54f817ebe92e8499c91650b2671fafdad Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Mon, 24 Feb 2014 17:36:17 -0800
Subject: [PATCH 0042/2053] Default mask idx is -1

---
 src/caffe/layers/pooling_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 63ea12d9abc..014f47bbd41 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -66,7 +66,7 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     mask = (int*)max_idx_->mutable_cpu_data();
     for (int i = 0; i < top_count; ++i) {
       top_data[i] = -FLT_MAX;
-      mask[i] = 0;
+      mask[i] = -1;
     }
     // The main loop
     for (int n = 0; n < bottom[0]->num(); ++n) {

From 22639018dcd23dc3d85297dd0dc1d2d3c0d486f8 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 19:58:05 -0700
Subject: [PATCH 0043/2053] Added max_idx to Pooling layer GPU

---
 src/caffe/layers/pooling_layer.cu | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 95bfaefc951..ff46109210c 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,7 +17,7 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* top_data) {
+    const int kernel_size, const int stride, Dtype* top_data, int* mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
@@ -28,13 +28,19 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     int wstart = pw * stride;
     int wend = min(wstart + kernel_size, width);
     Dtype maxval = -FLT_MAX;
+    int maxidx = -1;
     bottom_data += (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        maxval = max(maxval, bottom_data[h * width + w]);
+        if (bottom_data[h * width + w] > maxval) {
+          maxidx = h * width + w;
+          maxval = bottom_data[maxidx];
+        }
+        
       }
     }
     top_data[index] = maxval;
+    mask[index] = maxidx;
   }
 }
 
@@ -145,13 +151,15 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   int count = (*top)[0]->count();
+  int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // NOLINT_NEXT_LINE(whitespace/operators)
+    mask = (int*)max_idx_->mutable_gpu_data();
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        top_data);
+        top_data, mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
@@ -192,7 +200,7 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
     const Dtype* top_data, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* bottom_diff) {
+    const int kernel_size, const int stride, Dtype* bottom_diff, int* mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -209,6 +217,7 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
         bottom_data[((n * channels + c) * height + h) * width + w];
     top_data += (n * channels + c) * pooled_height * pooled_width;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
+    //bottom_diff[index] += top_diff[mask[index]];
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         gradient += top_diff[ph * pooled_width + pw] *
@@ -295,13 +304,14 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int count = (*bottom)[0]->count();
+  int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, (*bottom)[0]->gpu_data(), top[0]->gpu_data(), top_diff,
         top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_size_, stride_, bottom_diff);
+        pooled_width_, kernel_size_, stride_, bottom_diff, mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)

From 75e11f5d2ef73d1e83ab93f4858dd314db51251f Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:02:34 -0700
Subject: [PATCH 0044/2053] Added test for  Pooling layer GPU

---
 src/caffe/test/test_pooling_layer.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 11bbd14a5c4..d21fc8bc5e0 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -25,7 +25,7 @@ class PoolingLayerTest : public ::testing::Test {
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1701);
-    blob_bottom_->Reshape(2, 3, 6, 5);
+    blob_bottom_->Reshape(1, 1, 6, 5);
     // fill the values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
@@ -82,6 +82,7 @@ TYPED_TEST(PoolingLayerTest, PrintGPUBackward) {
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
   }

From 842af5e8659e64c8cd1c1a7210bfda81fc1c6c09 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:06:38 -0700
Subject: [PATCH 0045/2053] Use mask_idx to compute backward Max Pooling

---
 src/caffe/layers/pooling_layer.cpp |  7 ----
 src/caffe/layers/pooling_layer.cu  | 65 ++++++++++++++++++------------
 2 files changed, 40 insertions(+), 32 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 014f47bbd41..e052f78f8ca 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -143,8 +143,6 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     return;
   }
   const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* top_data = top[0]->cpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
@@ -161,9 +159,6 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
             bottom_diff[mask[ph * pooled_width_ + pw]]+=top_diff[ph * pooled_width_ + pw];
           }
         }
-        // offset
-        bottom_data += (*bottom)[0]->offset(0, 1);
-        top_data += top[0]->offset(0, 1);
         bottom_diff += (*bottom)[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
         mask += top[0]->offset(0, 1);
@@ -194,8 +189,6 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           }
         }
         // offset
-        bottom_data += (*bottom)[0]->offset(0, 1);
-        top_data += top[0]->offset(0, 1);
         bottom_diff += (*bottom)[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
       }
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index ff46109210c..b88b09721f1 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -195,40 +195,55 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+// template <typename Dtype>
+// __global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
+//     const Dtype* top_data, const Dtype* top_diff,
+//     const int num, const int channels, const int height,
+//     const int width, const int pooled_height, const int pooled_width,
+//     const int ksize, const int stride, Dtype* bottom_diff, int* mask) {
+//   int index = threadIdx.x + blockIdx.x * blockDim.x;
+//   if (index < nthreads) {
+//     // find out the local index
+//     // find out the local offset
+//     int w = index % width;
+//     int h = (index / width) % height;
+//     int c = (index / width / height) % channels;
+//     int n = index / width / height / channels;
+//     int phstart = (h < ksize) ? 0 : (h - ksize) / stride + 1;
+//     int phend = min(h / stride + 1, pooled_height);
+//     int pwstart = (w < ksize) ? 0 : (w - ksize) / stride + 1;
+//     int pwend = min(w / stride + 1, pooled_width);
+//     Dtype gradient = 0;
+//     Dtype bottom_datum =
+//         bottom_data[((n * channels + c) * height + h) * width + w];
+//     top_data += (n * channels + c) * pooled_height * pooled_width;
+//     top_diff += (n * channels + c) * pooled_height * pooled_width;
+//     //bottom_diff[index] += top_diff[mask[index]];
+//     for (int ph = phstart; ph < phend; ++ph) {
+//       for (int pw = pwstart; pw < pwend; ++pw) {
+//         gradient += top_diff[ph * pooled_width + pw] *
+//             (bottom_datum == top_data[ph * pooled_width + pw]);
+//       }
+//     }
+//     bottom_diff[index] = gradient;
+//   }  // (if index < nthreads)
+// }
+
 template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
-    const Dtype* top_data, const Dtype* top_diff,
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_size, const int stride, Dtype* bottom_diff, int* mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
-    Dtype gradient = 0;
-    Dtype bottom_datum =
-        bottom_data[((n * channels + c) * height + h) * width + w];
-    top_data += (n * channels + c) * pooled_height * pooled_width;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
-    //bottom_diff[index] += top_diff[mask[index]];
-    for (int ph = phstart; ph < phend; ++ph) {
-      for (int pw = pwstart; pw < pwend; ++pw) {
-        gradient += top_diff[ph * pooled_width + pw] *
-            (bottom_datum == top_data[ph * pooled_width + pw]);
-      }
-    }
-    bottom_diff[index] = gradient;
-  }
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+    bottom_diff += (n * channels + c) * height * width;
+    bottom_diff[mask[index]] += top_diff[index];
+  }  
 }
 
-
 template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,

From c77737735ebced62f7d4d0d23996e14613d70ee1 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:10:10 -0700
Subject: [PATCH 0046/2053] Remove top_data from backward Max Pooling

---
 src/caffe/layers/pooling_layer.cu | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index b88b09721f1..95a754f4a17 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -320,13 +320,17 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int count = (*bottom)[0]->count();
   int* mask;
+  
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
+    mask = (int*)max_idx_->gpu_data();
+    // Since we have the mask we only need count top_diff
+    count = top[0]->count(); 
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, (*bottom)[0]->gpu_data(), top[0]->gpu_data(), top_diff,
-        top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_size_, stride_, bottom_diff, mask);
+        count, top_diff, top[0]->num(), channels_,
+        height_, width_, pooled_height_, pooled_width_,
+        kernel_size_, stride_, bottom_diff, mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)

From a2b1785ac6df619ae7b6e47e31b988853729baa3 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:11:49 -0700
Subject: [PATCH 0047/2053] Set bottom_diff to 0 and remove Async memcopy

---
 src/caffe/layers/pooling_layer.cu | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 95a754f4a17..77126e6b120 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -327,6 +327,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     // Since we have the mask we only need count top_diff
     count = top[0]->count(); 
     // NOLINT_NEXT_LINE(whitespace/operators)
+    caffe_gpu_memset(count,Dtype(0.),bottom_diff);
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,

From 46b79e1cd71a3ce55ac58ef75ecd52421342a08e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:13:05 -0700
Subject: [PATCH 0048/2053] Cleaned prints from test_pooling_layer.cpp

---
 src/caffe/test/test_pooling_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index d21fc8bc5e0..a26078ee5a2 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -25,7 +25,7 @@ class PoolingLayerTest : public ::testing::Test {
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1701);
-    blob_bottom_->Reshape(1, 1, 6, 5);
+    blob_bottom_->Reshape(2, 3, 6, 5);
     // fill the values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);

From e08651da2c73be3a3803e86b812f545c17105b52 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:14:43 -0700
Subject: [PATCH 0049/2053] Fixed parameter order

---
 src/caffe/layers/pooling_layer.cu | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 77126e6b120..8be15211e54 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -319,6 +319,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int count = (*bottom)[0]->count();
+  CUDA_CHECK(cudaMemset(bottom_diff, 0, sizeof(Dtype) * count));
   int* mask;
   
   switch (this->layer_param_.pooling_param().pool()) {

From 8f5448be12ae4df1a9bf4250183a4756733abfa9 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:21:15 -0700
Subject: [PATCH 0050/2053] Use loops in GPU again to avoid over-writting of
 bottom_diff

---
 src/caffe/layers/pooling_layer.cu | 74 +++++++++++++++----------------
 1 file changed, 35 insertions(+), 39 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 8be15211e54..e3d7546feb5 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -12,7 +12,7 @@ using std::max;
 using std::min;
 
 namespace caffe {
-
+ 
 template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
@@ -195,40 +195,6 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
-// template <typename Dtype>
-// __global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
-//     const Dtype* top_data, const Dtype* top_diff,
-//     const int num, const int channels, const int height,
-//     const int width, const int pooled_height, const int pooled_width,
-//     const int ksize, const int stride, Dtype* bottom_diff, int* mask) {
-//   int index = threadIdx.x + blockIdx.x * blockDim.x;
-//   if (index < nthreads) {
-//     // find out the local index
-//     // find out the local offset
-//     int w = index % width;
-//     int h = (index / width) % height;
-//     int c = (index / width / height) % channels;
-//     int n = index / width / height / channels;
-//     int phstart = (h < ksize) ? 0 : (h - ksize) / stride + 1;
-//     int phend = min(h / stride + 1, pooled_height);
-//     int pwstart = (w < ksize) ? 0 : (w - ksize) / stride + 1;
-//     int pwend = min(w / stride + 1, pooled_width);
-//     Dtype gradient = 0;
-//     Dtype bottom_datum =
-//         bottom_data[((n * channels + c) * height + h) * width + w];
-//     top_data += (n * channels + c) * pooled_height * pooled_width;
-//     top_diff += (n * channels + c) * pooled_height * pooled_width;
-//     //bottom_diff[index] += top_diff[mask[index]];
-//     for (int ph = phstart; ph < phend; ++ph) {
-//       for (int pw = pwstart; pw < pwend; ++pw) {
-//         gradient += top_diff[ph * pooled_width + pw] *
-//             (bottom_datum == top_data[ph * pooled_width + pw]);
-//       }
-//     }
-//     bottom_diff[index] = gradient;
-//   }  // (if index < nthreads)
-// }
-
 template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
@@ -237,13 +203,43 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    bottom_diff += (n * channels + c) * height * width;
-    bottom_diff[mask[index]] += top_diff[index];
+    int w = index % width;
+    int h = (index / width) % height;
+    int c = (index / width / height) % channels;
+    int n = index / width / height / channels;
+    int phstart = (h < ksize) ? 0 : (h - ksize) / stride + 1;
+    int phend = min(h / stride + 1, pooled_height);
+    int pwstart = (w < ksize) ? 0 : (w - ksize) / stride + 1;
+    int pwend = min(w / stride + 1, pooled_width);
+    Dtype gradient = 0;
+    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    mask += (n * channels + c) * pooled_height * pooled_width;
+    //bottom_diff[index] += top_diff[mask[index]];
+    for (int ph = phstart; ph < phend; ++ph) {
+      for (int pw = pwstart; pw < pwend; ++pw) {
+        if (mask[ph * pooled_width + pw] == h * width + w)
+          gradient += top_diff[ph * pooled_width + pw];
+      }
+    }
+    bottom_diff[index] = gradient;
   }  
 }
 
+// template <typename Dtype>
+// __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
+//     const int num, const int channels, const int height,
+//     const int width, const int pooled_height, const int pooled_width,
+//     const int ksize, const int stride, Dtype* bottom_diff, int* mask) {
+//   CUDA_KERNEL_LOOP(index, nthreads) {
+//     // find out the local index
+//     // find out the local offset
+//     int c = (index / pooled_width / pooled_height) % channels;
+//     int n = index / pooled_width / pooled_height / channels;
+//     bottom_diff += (n * channels + c) * height * width;
+//     bottom_diff[mask[index]] += top_diff[index];
+//   } 
+// }
+
 template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,

From 868677bcd7b3c73d1e061058f203d88b6507ffa4 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 25 Feb 2014 17:29:43 -0800
Subject: [PATCH 0051/2053] Added test for maxpool layer followed by dropout

---
 .../test/test_maxpool_dropout_layers.cpp      | 181 ++++++++++++++++++
 1 file changed, 181 insertions(+)
 create mode 100644 src/caffe/test/test_maxpool_dropout_layers.cpp

diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
new file mode 100644
index 00000000000..7468d8e40ba
--- /dev/null
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -0,0 +1,181 @@
+// Copyright 2014 Sergio Guadarrama
+
+#include <cstring>
+#include <cuda_runtime.h>
+
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class MaxPoolingDropoutTest : public ::testing::Test {
+ protected:
+  MaxPoolingDropoutTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {};
+  virtual void SetUp() {
+    Caffe::set_random_seed(1703);
+    blob_bottom_->Reshape(2, 3, 6, 5);
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    ConstantFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  };
+  virtual ~MaxPoolingDropoutTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(MaxPoolingDropoutTest, Dtypes);
+
+TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 2);
+}
+
+
+TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  Caffe::set_mode(Caffe::CPU);
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  TypeParam sum = 0.;
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+  	sum += top_data[i];
+  }
+  EXPECT_EQ(sum, this->blob_top_->count());
+  // Dropout in-place
+  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  sum = 0.;
+  TypeParam scale = 1. / (1. - layer_param.dropout_ratio());
+  top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+  	sum += top_data[i];
+  }
+  EXPECT_GE(sum, 0);
+  EXPECT_LE(sum, this->blob_top_->count()*scale);
+}
+
+TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  Caffe::set_mode(Caffe::GPU);
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  TypeParam sum = 0.;
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+  	sum += top_data[i];
+  }
+  EXPECT_EQ(sum, this->blob_top_->count());
+
+  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  sum = 0.;
+  TypeParam scale = 1. / (1. - layer_param.dropout_ratio());
+  top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+  	sum += top_data[i];
+  }
+  EXPECT_GE(sum, 0);
+  EXPECT_LE(sum, this->blob_top_->count()*scale);
+}
+
+TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  Caffe::set_mode(Caffe::CPU);
+  Caffe::set_phase(Caffe::TRAIN);
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }  
+  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
+  TypeParam sum = 0.;
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+  	sum += bottom_diff[i];
+  }
+  EXPECT_EQ(sum, this->blob_top_->count());
+  // Dropout in-place
+  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Backward(this->blob_top_vec_, true, &(this->blob_top_vec_));
+  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  TypeParam sum_with_dropout = 0.;
+  bottom_diff = this->blob_bottom_->cpu_diff();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+  	sum_with_dropout += bottom_diff[i];
+  }
+  EXPECT_GE(sum_with_dropout, sum);
+}
+
+TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  Caffe::set_mode(Caffe::GPU);
+  Caffe::set_phase(Caffe::TRAIN);
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }  
+  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
+  TypeParam sum = 0.;
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+  	sum += bottom_diff[i];
+  }
+  EXPECT_EQ(sum, this->blob_top_->count());
+  // Dropout in-place
+  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Backward(this->blob_top_vec_, true, &(this->blob_top_vec_));
+  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  TypeParam sum_with_dropout = 0.;
+  bottom_diff = this->blob_bottom_->cpu_diff();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+  	sum_with_dropout += bottom_diff[i];
+  }
+  EXPECT_GE(sum_with_dropout, sum);
+}
+
+}
\ No newline at end of file

From 418e459a263f49b77df22b885ec6c6056382fed0 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:27:16 -0700
Subject: [PATCH 0052/2053] Attempt to use AtomicAdd but it seems slower

---
 src/caffe/layers/pooling_layer.cu | 64 ++++++++++++++++++++++++-------
 1 file changed, 50 insertions(+), 14 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index e3d7546feb5..cfcee5281a9 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -195,6 +195,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+/*
 template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
@@ -224,21 +225,56 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     bottom_diff[index] = gradient;
   }  
 }
+*/
 
-// template <typename Dtype>
-// __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-//     const int num, const int channels, const int height,
-//     const int width, const int pooled_height, const int pooled_width,
-//     const int ksize, const int stride, Dtype* bottom_diff, int* mask) {
-//   CUDA_KERNEL_LOOP(index, nthreads) {
-//     // find out the local index
-//     // find out the local offset
-//     int c = (index / pooled_width / pooled_height) % channels;
-//     int n = index / pooled_width / pooled_height / channels;
-//     bottom_diff += (n * channels + c) * height * width;
-//     bottom_diff[mask[index]] += top_diff[index];
-//   } 
-// }
+__device__ double atomicAdd(double* address, double val)
+{
+    unsigned long long int* address_as_ull =
+                              (unsigned long long int*)address;
+    unsigned long long int old = *address_as_ull, assumed;
+    do {
+        assumed = old;
+        old = atomicCAS(address_as_ull, assumed,
+                        __double_as_longlong(val +
+                               __longlong_as_double(assumed)));
+    } while (assumed != old);
+    return __longlong_as_double(old);
+}
+
+__device__ float atomicAdd(float* address, float val)
+{
+    unsigned int* address_as_ull = (unsigned int*)address;
+    unsigned int old = *address_as_ull, assumed;
+    do {
+        assumed = old;
+        old = atomicCAS(address_as_ull, assumed,
+                        __float_as_int(val +
+                               __int_as_float(assumed)));
+    } while (assumed != old);
+    return __int_as_float(old);
+}
+
+template <typename Dtype>
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
+    const int num, const int channels, const int height,
+    const int width, const int pooled_height, const int pooled_width,
+    const int ksize, const int stride, Dtype* bottom_diff, int* mask) {
+  // __shared__ Dtype cache[CAFFE_CUDA_NUM_THREADS];
+  // cache[threadIdx.x] = Dtype(0.);
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // find out the local index
+    // find out the local offset
+    int c = (index / pooled_width / pooled_height) % channels;
+    int n = index / pooled_width / pooled_height / channels;
+    bottom_diff += (n * channels + c) * height * width;
+    // read from global bottom_diff into cache
+    // atomicAdd(cache + mask[index],bottom_diff[mask[index]]);
+    atomicAdd(bottom_diff + mask[index],top_diff[index]);
+    // __syncthreads();
+    // write from cache to global bottom_diff
+    // bottom_diff[mask[index]] = cache[mask[index]];
+  }  // (if index < nthreads)
+}
 
 template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,

From 8764b296ab5bd5dec293a9dba0f3f3081b572f5a Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 20:29:26 -0700
Subject: [PATCH 0053/2053] Commented Atomic Add, back to loop in GPU
 MaxPoolBackward

---
 src/caffe/layers/pooling_layer.cu | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index cfcee5281a9..3b5b8ee31d8 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -195,7 +195,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
-/*
+
 template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
@@ -225,8 +225,8 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     bottom_diff[index] = gradient;
   }  
 }
-*/
 
+/*
 __device__ double atomicAdd(double* address, double val)
 {
     unsigned long long int* address_as_ull =
@@ -275,6 +275,7 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     // bottom_diff[mask[index]] = cache[mask[index]];
   }  // (if index < nthreads)
 }
+*/
 
 template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,

From 4ef555eb5133d196651893bcb5d46c671d8d4562 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 13 Apr 2014 21:03:06 -0700
Subject: [PATCH 0054/2053] Adapted to V1 proto definition, test don't pass

---
 src/caffe/layers/pooling_layer.cpp            |  2 +-
 src/caffe/layers/pooling_layer.cu             |  6 +--
 .../test/test_maxpool_dropout_layers.cpp      | 41 +++++++++++--------
 3 files changed, 27 insertions(+), 22 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index e052f78f8ca..97506cc6eb7 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -38,7 +38,7 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
   // If max pooling, we will initialize the vector index part.
-  if (this->layer_param_.pool() == LayerParameter_PoolMethod_MAX) {
+  if (this->layer_param_.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) {
     max_idx_.reset(new SyncedMemory((*top)[0]->count() * sizeof(int)));
   }
   // If stochastic pooling, we will initialize the random index part.
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 3b5b8ee31d8..3bdb562867f 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -208,9 +208,9 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     int h = (index / width) % height;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < ksize) ? 0 : (h - ksize) / stride + 1;
+    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
     int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < ksize) ? 0 : (w - ksize) / stride + 1;
+    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
     int pwend = min(w / stride + 1, pooled_width);
     Dtype gradient = 0;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
@@ -361,7 +361,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     // Since we have the mask we only need count top_diff
     count = top[0]->count(); 
     // NOLINT_NEXT_LINE(whitespace/operators)
-    caffe_gpu_memset(count,Dtype(0.),bottom_diff);
+    caffe_gpu_set(count,Dtype(0.),bottom_diff);
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 7468d8e40ba..9d302633fef 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -43,10 +43,11 @@ TYPED_TEST_CASE(MaxPoolingDropoutTest, Dtypes);
 
 TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
   LayerParameter layer_param;
-  layer_param.set_kernelsize(3);
-  layer_param.set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  PoolingLayer<TypeParam> max_layer(layer_param);
+  max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   DropoutLayer<TypeParam> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
@@ -57,10 +58,11 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
 
 
 TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
-  LayerParameter layer_param;
-  layer_param.set_kernelsize(3);
-  layer_param.set_stride(2);
   Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -75,7 +77,7 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
   sum = 0.;
-  TypeParam scale = 1. / (1. - layer_param.dropout_ratio());
+  TypeParam scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
   	sum += top_data[i];
@@ -85,10 +87,11 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
 }
 
 TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
-  LayerParameter layer_param;
-  layer_param.set_kernelsize(3);
-  layer_param.set_stride(2);
   Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -103,7 +106,7 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
   sum = 0.;
-  TypeParam scale = 1. / (1. - layer_param.dropout_ratio());
+  TypeParam scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
   	sum += top_data[i];
@@ -113,11 +116,12 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
 }
 
 TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
-  LayerParameter layer_param;
-  layer_param.set_kernelsize(3);
-  layer_param.set_stride(2);
   Caffe::set_mode(Caffe::CPU);
   Caffe::set_phase(Caffe::TRAIN);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -146,11 +150,12 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
 }
 
 TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
-  LayerParameter layer_param;
-  layer_param.set_kernelsize(3);
-  layer_param.set_stride(2);
   Caffe::set_mode(Caffe::GPU);
   Caffe::set_phase(Caffe::TRAIN);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));

From a1d97e0d6de696108762e46eb3719d2f69f688d5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 15:29:35 -0700
Subject: [PATCH 0055/2053] lint and make compilable (using static_cast's found
 a couple bugs at compile time)

---
 src/caffe/layers/pooling_layer.cpp            | 20 +++++++-----
 src/caffe/layers/pooling_layer.cu             | 15 ++++-----
 .../test/test_maxpool_dropout_layers.cpp      | 32 ++++++++++---------
 3 files changed, 35 insertions(+), 32 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 97506cc6eb7..5c740de4752 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -38,7 +38,8 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
   // If max pooling, we will initialize the vector index part.
-  if (this->layer_param_.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) {
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_MAX) {
     max_idx_.reset(new SyncedMemory((*top)[0]->count() * sizeof(int)));
   }
   // If stochastic pooling, we will initialize the random index part.
@@ -63,7 +64,7 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
   // Initialize
-    mask = (int*)max_idx_->mutable_cpu_data();
+    mask = static_cast<int*>(max_idx_->mutable_cpu_data());
     for (int i = 0; i < top_count; ++i) {
       top_data[i] = -FLT_MAX;
       mask[i] = -1;
@@ -77,11 +78,13 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
             int wstart = pw * stride_;
             int hend = min(hstart + kernel_size_, height_);
             int wend = min(wstart + kernel_size_, width_);
+            const int pool_index = ph * pooled_width_ + pw;
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {
-                if (bottom_data[h * width_ + w] > top_data[ph * pooled_width_ + pw]) {
-                  top_data[ph * pooled_width_ + pw] = bottom_data[h * width_ + w];
-                  mask[ph * pooled_width_ + pw] = h * width_ + w;
+                const int index = h * width_ + w;
+                if (bottom_data[index] > top_data[pool_index]) {
+                  top_data[pool_index] = bottom_data[index];
+                  mask[pool_index] = index;
                 }
               }
             }
@@ -147,16 +150,17 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
   memset(bottom_diff, 0, (*bottom)[0]->count() * sizeof(Dtype));
-  int* mask;
+  const int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // The main loop
-    mask = (int*)max_idx_->cpu_data();
+    mask = static_cast<const int*>(max_idx_->cpu_data());
     for (int n = 0; n < top[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            bottom_diff[mask[ph * pooled_width_ + pw]]+=top_diff[ph * pooled_width_ + pw];
+            const int index = ph * pooled_width_ + pw;
+            bottom_diff[mask[index]] += top_diff[index];
           }
         }
         bottom_diff += (*bottom)[0]->offset(0, 1);
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 3bdb562867f..60c52e32b00 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -12,7 +12,7 @@ using std::max;
 using std::min;
 
 namespace caffe {
- 
+
 template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
@@ -36,7 +36,6 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
           maxidx = h * width + w;
           maxval = bottom_data[maxidx];
         }
-        
       }
     }
     top_data[index] = maxval;
@@ -154,8 +153,8 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
+    mask = static_cast<int*>(max_idx_->mutable_gpu_data());
     // NOLINT_NEXT_LINE(whitespace/operators)
-    mask = (int*)max_idx_->mutable_gpu_data();
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
@@ -215,7 +214,6 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     Dtype gradient = 0;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
     mask += (n * channels + c) * pooled_height * pooled_width;
-    //bottom_diff[index] += top_diff[mask[index]];
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         if (mask[ph * pooled_width + pw] == h * width + w)
@@ -223,7 +221,7 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
       }
     }
     bottom_diff[index] = gradient;
-  }  
+  }
 }
 
 /*
@@ -354,14 +352,13 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   int count = (*bottom)[0]->count();
   CUDA_CHECK(cudaMemset(bottom_diff, 0, sizeof(Dtype) * count));
   int* mask;
-  
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    mask = (int*)max_idx_->gpu_data();
+    mask = static_cast<int*>(max_idx_->mutable_gpu_data());
     // Since we have the mask we only need count top_diff
-    count = top[0]->count(); 
+    count = top[0]->count();
+    caffe_gpu_set(count, Dtype(0.), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
-    caffe_gpu_set(count,Dtype(0.),bottom_diff);
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 9d302633fef..3862e12697b 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -1,7 +1,9 @@
-// Copyright 2014 Sergio Guadarrama
+// Copyright 2014 BVLC and contributors.
 
 #include <cstring>
-#include <cuda_runtime.h>
+#include <vector>
+
+#include "cuda_runtime.h"
 
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
@@ -19,7 +21,7 @@ class MaxPoolingDropoutTest : public ::testing::Test {
  protected:
   MaxPoolingDropoutTest()
       : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()) {};
+        blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1703);
     blob_bottom_->Reshape(2, 3, 6, 5);
@@ -30,7 +32,7 @@ class MaxPoolingDropoutTest : public ::testing::Test {
     filler.Fill(this->blob_bottom_);
     blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
-  };
+  }
   virtual ~MaxPoolingDropoutTest() { delete blob_bottom_; delete blob_top_; }
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
@@ -69,7 +71,7 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
   const TypeParam* top_data = this->blob_top_->cpu_data();
   TypeParam sum = 0.;
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-  	sum += top_data[i];
+    sum += top_data[i];
   }
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
@@ -80,7 +82,7 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
   TypeParam scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-  	sum += top_data[i];
+    sum += top_data[i];
   }
   EXPECT_GE(sum, 0);
   EXPECT_LE(sum, this->blob_top_->count()*scale);
@@ -98,7 +100,7 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
   const TypeParam* top_data = this->blob_top_->cpu_data();
   TypeParam sum = 0.;
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-  	sum += top_data[i];
+    sum += top_data[i];
   }
   EXPECT_EQ(sum, this->blob_top_->count());
 
@@ -109,7 +111,7 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
   TypeParam scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-  	sum += top_data[i];
+    sum += top_data[i];
   }
   EXPECT_GE(sum, 0);
   EXPECT_LE(sum, this->blob_top_->count()*scale);
@@ -127,12 +129,12 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }  
+  }
   layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
   const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
   TypeParam sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-  	sum += bottom_diff[i];
+    sum += bottom_diff[i];
   }
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
@@ -144,7 +146,7 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
   TypeParam sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-  	sum_with_dropout += bottom_diff[i];
+    sum_with_dropout += bottom_diff[i];
   }
   EXPECT_GE(sum_with_dropout, sum);
 }
@@ -161,12 +163,12 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }  
+  }
   layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
   const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
   TypeParam sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-  	sum += bottom_diff[i];
+    sum += bottom_diff[i];
   }
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
@@ -178,9 +180,9 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
   TypeParam sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-  	sum_with_dropout += bottom_diff[i];
+    sum_with_dropout += bottom_diff[i];
   }
   EXPECT_GE(sum_with_dropout, sum);
 }
 
-}
\ No newline at end of file
+}  // namespace caffe

From 67f6034a8d87d5dad9925221ed71c49e0a03b597 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 16:10:23 -0700
Subject: [PATCH 0056/2053] remove commented out code

---
 src/caffe/layers/pooling_layer.cu | 51 -------------------------------
 1 file changed, 51 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 60c52e32b00..00c78466357 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -224,57 +224,6 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
   }
 }
 
-/*
-__device__ double atomicAdd(double* address, double val)
-{
-    unsigned long long int* address_as_ull =
-                              (unsigned long long int*)address;
-    unsigned long long int old = *address_as_ull, assumed;
-    do {
-        assumed = old;
-        old = atomicCAS(address_as_ull, assumed,
-                        __double_as_longlong(val +
-                               __longlong_as_double(assumed)));
-    } while (assumed != old);
-    return __longlong_as_double(old);
-}
-
-__device__ float atomicAdd(float* address, float val)
-{
-    unsigned int* address_as_ull = (unsigned int*)address;
-    unsigned int old = *address_as_ull, assumed;
-    do {
-        assumed = old;
-        old = atomicCAS(address_as_ull, assumed,
-                        __float_as_int(val +
-                               __int_as_float(assumed)));
-    } while (assumed != old);
-    return __int_as_float(old);
-}
-
-template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int ksize, const int stride, Dtype* bottom_diff, int* mask) {
-  // __shared__ Dtype cache[CAFFE_CUDA_NUM_THREADS];
-  // cache[threadIdx.x] = Dtype(0.);
-  CUDA_KERNEL_LOOP(index, nthreads) {
-    // find out the local index
-    // find out the local offset
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    bottom_diff += (n * channels + c) * height * width;
-    // read from global bottom_diff into cache
-    // atomicAdd(cache + mask[index],bottom_diff[mask[index]]);
-    atomicAdd(bottom_diff + mask[index],top_diff[index]);
-    // __syncthreads();
-    // write from cache to global bottom_diff
-    // bottom_diff[mask[index]] = cache[mask[index]];
-  }  // (if index < nthreads)
-}
-*/
-
 template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,

From c85f9d2ed32be3c576f22a12961d8f648623dd54 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 16:14:04 -0700
Subject: [PATCH 0057/2053] mask should be const in backward pass

---
 src/caffe/layers/pooling_layer.cu | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 00c78466357..a91b1378cde 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -197,9 +197,9 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-    const int num, const int channels, const int height,
+    const int* mask, const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* bottom_diff, int* mask) {
+    const int kernel_size, const int stride, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -300,18 +300,18 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int count = (*bottom)[0]->count();
   CUDA_CHECK(cudaMemset(bottom_diff, 0, sizeof(Dtype) * count));
-  int* mask;
+  const int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    mask = static_cast<int*>(max_idx_->mutable_gpu_data());
+    mask = static_cast<const int*>(max_idx_->gpu_data());
     // Since we have the mask we only need count top_diff
     count = top[0]->count();
     caffe_gpu_set(count, Dtype(0.), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, top[0]->num(), channels_,
+        count, top_diff, mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
-        kernel_size_, stride_, bottom_diff, mask);
+        kernel_size_, stride_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)

From cda3c3192646c8153bf4de8d2d712751d5fa98c7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 16:14:44 -0700
Subject: [PATCH 0058/2053] bugfix: setting count to the top count in backward
 doesn't process all of the bottom (assuming the bottom is larger, which
 happens for nontrivial poolsize>1)

---
 src/caffe/layers/pooling_layer.cu | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index a91b1378cde..cb3bfabe4d4 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -304,8 +304,6 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     mask = static_cast<const int*>(max_idx_->gpu_data());
-    // Since we have the mask we only need count top_diff
-    count = top[0]->count();
     caffe_gpu_set(count, Dtype(0.), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(

From 96d47af12882b3a43f9268f32fe3242634cf7d3a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 16:27:38 -0700
Subject: [PATCH 0059/2053] use a Blob<int> instead of a SyncedMemory to store
 max_idx_

---
 include/caffe/vision_layers.hpp    | 2 +-
 src/caffe/blob.cpp                 | 9 +++++++++
 src/caffe/layers/pooling_layer.cpp | 7 ++++---
 src/caffe/layers/pooling_layer.cu  | 4 ++--
 4 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index c0bbd370fce..a43f7f98c09 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -350,7 +350,7 @@ class PoolingLayer : public Layer<Dtype> {
   int pooled_height_;
   int pooled_width_;
   Blob<Dtype> rand_idx_;
-  shared_ptr<SyncedMemory> max_idx_;
+  shared_ptr<Blob<int> > max_idx_;
 };
 
 /* SoftmaxLayer
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 444e9cf4009..597e848c685 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -108,6 +108,14 @@ void Blob<Dtype>::ShareDiff(const Blob& other) {
   diff_ = other.diff();
 }
 
+template <>
+void Blob<int>::Update() {
+  // The "update" method is used for parameter blobs in a Net, which are stored
+  // as Blob<float>s or Blob<double>s -- hence we do not define it for
+  // Blob<int>s.
+  NOT_IMPLEMENTED;
+}
+
 template <typename Dtype>
 void Blob<Dtype>::Update() {
   // We will perform update based on where the data is located.
@@ -201,6 +209,7 @@ void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
 }
 
 INSTANTIATE_CLASS(Blob);
+template class Blob<int>;
 
 }  // namespace caffe
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 5c740de4752..3d216de6ec5 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -40,7 +40,8 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // If max pooling, we will initialize the vector index part.
   if (this->layer_param_.pooling_param().pool() ==
       PoolingParameter_PoolMethod_MAX) {
-    max_idx_.reset(new SyncedMemory((*top)[0]->count() * sizeof(int)));
+    max_idx_.reset(new Blob<int>(bottom[0]->num(), channels_,
+                                 pooled_height_, pooled_width_));
   }
   // If stochastic pooling, we will initialize the random index part.
   if (this->layer_param_.pooling_param().pool() ==
@@ -64,7 +65,7 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
   // Initialize
-    mask = static_cast<int*>(max_idx_->mutable_cpu_data());
+    mask = max_idx_->mutable_cpu_data();
     for (int i = 0; i < top_count; ++i) {
       top_data[i] = -FLT_MAX;
       mask[i] = -1;
@@ -154,7 +155,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // The main loop
-    mask = static_cast<const int*>(max_idx_->cpu_data());
+    mask = max_idx_->cpu_data();
     for (int n = 0; n < top[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index cb3bfabe4d4..49e5d7780ab 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -153,7 +153,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    mask = static_cast<int*>(max_idx_->mutable_gpu_data());
+    mask = max_idx_->mutable_gpu_data();
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
@@ -303,7 +303,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const int* mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    mask = static_cast<const int*>(max_idx_->gpu_data());
+    mask = max_idx_->gpu_data();
     caffe_gpu_set(count, Dtype(0.), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(

From dcd5743b4b3b8133d54228418a47a9bc72a4031f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 16:59:15 -0700
Subject: [PATCH 0060/2053] make a Blob<unsigned int> and use in dropout layer

---
 include/caffe/neuron_layers.hpp       |  2 +-
 include/caffe/util/math_functions.hpp |  3 +++
 src/caffe/blob.cpp                    | 13 ++++++-------
 src/caffe/layers/dropout_layer.cpp    |  7 ++++---
 src/caffe/util/math_functions.cpp     | 20 ++++++++++++++++++++
 5 files changed, 34 insertions(+), 11 deletions(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index fb2347da436..4e67a29ace3 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -87,7 +87,7 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  shared_ptr<SyncedMemory> rand_vec_;
+  shared_ptr<Blob<unsigned int> > rand_vec_;
   Dtype threshold_;
   Dtype scale_;
   unsigned int uint_thres_;
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 25ed4705961..99519974243 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -144,6 +144,9 @@ void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
 template <typename Dtype>
 void caffe_rng_bernoulli(const int n, const Dtype p, int* r);
 
+template <typename Dtype>
+void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
+
 template <typename Dtype>
 void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
 
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 597e848c685..e603712fd82 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -108,13 +108,11 @@ void Blob<Dtype>::ShareDiff(const Blob& other) {
   diff_ = other.diff();
 }
 
-template <>
-void Blob<int>::Update() {
-  // The "update" method is used for parameter blobs in a Net, which are stored
-  // as Blob<float>s or Blob<double>s -- hence we do not define it for
-  // Blob<int>s.
-  NOT_IMPLEMENTED;
-}
+// The "update" method is used for parameter blobs in a Net, which are stored
+// as Blob<float> or Blob<double> -- hence we do not define it for
+// Blob<int> or Blob<unsigned int>.
+template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
+template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }
 
 template <typename Dtype>
 void Blob<Dtype>::Update() {
@@ -210,6 +208,7 @@ void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
 
 INSTANTIATE_CLASS(Blob);
 template class Blob<int>;
+template class Blob<unsigned int>;
 
 }  // namespace caffe
 
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index e1b69f363b4..f1e54112986 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -17,7 +17,8 @@ void DropoutLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   NeuronLayer<Dtype>::SetUp(bottom, top);
   // Set up the cache for random number generation
-  rand_vec_.reset(new SyncedMemory(bottom[0]->count() * sizeof(int)));
+  rand_vec_.reset(new Blob<unsigned int>(bottom[0]->num(),
+      bottom[0]->channels(), bottom[0]->height(), bottom[0]->width()));
   threshold_ = this->layer_param_.dropout_param().dropout_ratio();
   DCHECK(threshold_ > 0.);
   DCHECK(threshold_ < 1.);
@@ -30,7 +31,7 @@ Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());
+  unsigned int* mask = rand_vec_->mutable_cpu_data();
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
     // Create random numbers
@@ -52,7 +53,7 @@ void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down) {
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int* mask = reinterpret_cast<const int*>(rand_vec_->cpu_data());
+    const unsigned int* mask = rand_vec_->cpu_data();
     const int count = (*bottom)[0]->count();
     for (int i = 0; i < count; ++i) {
       bottom_diff[i] = top_diff[i] * mask[i] * scale_;
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index ba2492aa635..2196b44bf24 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -381,6 +381,26 @@ void caffe_rng_bernoulli<double>(const int n, const double p, int* r);
 template
 void caffe_rng_bernoulli<float>(const int n, const float p, int* r);
 
+template <typename Dtype>
+void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r) {
+  CHECK_GE(n, 0);
+  CHECK(r);
+  CHECK_GE(p, 0);
+  CHECK_LE(p, 1);
+  boost::bernoulli_distribution<Dtype> random_distribution(p);
+  boost::variate_generator<caffe::rng_t*, boost::bernoulli_distribution<Dtype> >
+      variate_generator(caffe_rng(), random_distribution);
+  for (int i = 0; i < n; ++i) {
+    r[i] = static_cast<unsigned int>(variate_generator());
+  }
+}
+
+template
+void caffe_rng_bernoulli<double>(const int n, const double p, unsigned int* r);
+
+template
+void caffe_rng_bernoulli<float>(const int n, const float p, unsigned int* r);
+
 template <>
 float caffe_cpu_dot<float>(const int n, const float* x, const float* y) {
   return cblas_sdot(n, x, 1, y, 1);

From 42a2a3fc09a8268cc9e5fac43472b38b7aaf1336 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 18:09:05 -0700
Subject: [PATCH 0061/2053] optionally output the mask to a top blob instead of
 storing internally

---
 src/caffe/layers/pooling_layer.cpp | 66 +++++++++++++++++++++++------
 src/caffe/layers/pooling_layer.cu  | 67 ++++++++++++++++++++++--------
 2 files changed, 103 insertions(+), 30 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 3d216de6ec5..928c8c7f42f 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -19,7 +19,15 @@ template <typename Dtype>
 void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK_EQ(bottom.size(), 1) << "PoolingLayer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "PoolingLayer takes a single blob as output.";
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_MAX) {
+    CHECK_GE(top->size(), 1)
+        << "MaxPoolingLayer takes at least one blob as output.";
+    CHECK_LE(top->size(), 2)
+        << "MaxPoolingLayer takes at most two blobs as output.";
+  } else {
+    CHECK_EQ(top->size(), 1) << "PoolingLayer takes a single blob as output.";
+  }
   kernel_size_ = this->layer_param_.pooling_param().kernel_size();
   stride_ = this->layer_param_.pooling_param().stride();
   pad_ = this->layer_param_.pooling_param().pad();
@@ -37,9 +45,12 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       width_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
+  if (top->size() > 1) {
+    (*top)[1]->ReshapeLike(*(*top)[0]);
+  }
   // If max pooling, we will initialize the vector index part.
   if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_MAX) {
+      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
     max_idx_.reset(new Blob<int>(bottom[0]->num(), channels_,
                                  pooled_height_, pooled_width_));
   }
@@ -61,15 +72,23 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
   int top_count = (*top)[0]->count();
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top->size() > 1;
   int* mask;
+  Dtype* top_mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-  // Initialize
-    mask = max_idx_->mutable_cpu_data();
-    for (int i = 0; i < top_count; ++i) {
-      top_data[i] = -FLT_MAX;
-      mask[i] = -1;
+    // Initialize
+    if (use_top_mask) {
+      top_mask = (*top)[1]->mutable_cpu_data();
+      caffe_set(top_count, Dtype(-1), top_mask);
+    } else {
+      mask = max_idx_->mutable_cpu_data();
+      for (int i = 0; i < top_count; ++i) {
+        mask[i] = -1;
+      }
     }
+    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
     // The main loop
     for (int n = 0; n < bottom[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
@@ -85,7 +104,11 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
                 const int index = h * width_ + w;
                 if (bottom_data[index] > top_data[pool_index]) {
                   top_data[pool_index] = bottom_data[index];
-                  mask[pool_index] = index;
+                  if (use_top_mask) {
+                    top_mask[pool_index] = static_cast<Dtype>(index);
+                  } else {
+                    mask[pool_index] = index;
+                  }
                 }
               }
             }
@@ -94,7 +117,11 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
         top_data += (*top)[0]->offset(0, 1);
-        mask += (*top)[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += (*top)[0]->offset(0, 1);
+        } else {
+          mask += (*top)[0]->offset(0, 1);
+        }
       }
     }
     break;
@@ -150,23 +177,36 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
-  memset(bottom_diff, 0, (*bottom)[0]->count() * sizeof(Dtype));
+  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top.size() > 1;
   const int* mask;
+  const Dtype* top_mask;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // The main loop
-    mask = max_idx_->cpu_data();
+    if (use_top_mask) {
+      top_mask = top[1]->cpu_data();
+    } else {
+      mask = max_idx_->cpu_data();
+    }
     for (int n = 0; n < top[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
             const int index = ph * pooled_width_ + pw;
-            bottom_diff[mask[index]] += top_diff[index];
+            const int bottom_index =
+                use_top_mask ? top_mask[index] : mask[index];
+            bottom_diff[bottom_index] += top_diff[index];
           }
         }
         bottom_diff += (*bottom)[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
-        mask += top[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += top[0]->offset(0, 1);
+        } else {
+          mask += top[0]->offset(0, 1);
+        }
       }
     }
     break;
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 49e5d7780ab..31be47efc0c 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,7 +17,8 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* top_data, int* mask) {
+    const int kernel_size, const int stride, Dtype* top_data,
+    int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
@@ -39,7 +40,11 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
       }
     }
     top_data[index] = maxval;
-    mask[index] = maxidx;
+    if (mask) {
+      mask[index] = maxidx;
+    } else {
+      top_mask[index] = maxidx;
+    }
   }
 }
 
@@ -150,15 +155,22 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   int count = (*top)[0]->count();
-  int* mask;
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top->size() > 1;
+  int* mask = NULL;
+  Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    mask = max_idx_->mutable_gpu_data();
+    if (use_top_mask) {
+      top_mask = (*top)[1]->mutable_gpu_data();
+    } else {
+      mask = max_idx_->mutable_gpu_data();
+    }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        top_data, mask);
+        top_data, mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
@@ -197,9 +209,10 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-    const int* mask, const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* bottom_diff) {
+    const int* mask, const Dtype* top_mask, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_size, const int stride,
+    Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -212,12 +225,25 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
     int pwend = min(w / stride + 1, pooled_width);
     Dtype gradient = 0;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
-    mask += (n * channels + c) * pooled_height * pooled_width;
-    for (int ph = phstart; ph < phend; ++ph) {
-      for (int pw = pwstart; pw < pwend; ++pw) {
-        if (mask[ph * pooled_width + pw] == h * width + w)
-          gradient += top_diff[ph * pooled_width + pw];
+    int offset = (n * channels + c) * pooled_height * pooled_width;
+    top_diff += offset;
+    if (mask) {
+      mask += offset;
+      for (int ph = phstart; ph < phend; ++ph) {
+        for (int pw = pwstart; pw < pwend; ++pw) {
+          if (mask[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff[ph * pooled_width + pw];
+          }
+        }
+      }
+    } else {
+      top_mask += offset;
+      for (int ph = phstart; ph < phend; ++ph) {
+        for (int pw = pwstart; pw < pwend; ++pw) {
+          if (top_mask[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff[ph * pooled_width + pw];
+          }
+        }
       }
     }
     bottom_diff[index] = gradient;
@@ -300,14 +326,21 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int count = (*bottom)[0]->count();
   CUDA_CHECK(cudaMemset(bottom_diff, 0, sizeof(Dtype) * count));
-  const int* mask;
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top.size() > 1;
+  const int* mask = NULL;
+  const Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
-    mask = max_idx_->gpu_data();
+    if (use_top_mask) {
+      top_mask = top[1]->gpu_data();
+    } else {
+      mask = max_idx_->gpu_data();
+    }
     caffe_gpu_set(count, Dtype(0.), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, mask, top[0]->num(), channels_,
+        count, top_diff, mask, top_mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
         kernel_size_, stride_, bottom_diff);
     break;

From 48534870710ae828ed5388827f277ed5882f764b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 18:40:03 -0700
Subject: [PATCH 0062/2053] add tests for maxpooling layer forward, and for
 maxpooling with top mask

---
 src/caffe/test/test_pooling_layer.cpp | 132 +++++++++++++++++++++++++-
 1 file changed, 130 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index a26078ee5a2..6628fc5dfc6 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -22,7 +22,8 @@ class PoolingLayerTest : public ::testing::Test {
  protected:
   PoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()) {}
+        blob_top_(new Blob<Dtype>()),
+        blob_top_mask_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1701);
     blob_bottom_->Reshape(2, 3, 6, 5);
@@ -33,11 +34,88 @@ class PoolingLayerTest : public ::testing::Test {
     blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
   }
-  virtual ~PoolingLayerTest() { delete blob_bottom_; delete blob_top_; }
+  virtual ~PoolingLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+    delete blob_top_mask_;
+  }
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_mask_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+
+  void TestForward() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_size(2);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 3, 5);
+    // Input: 2x 2 channels of:
+    //     [1 2 5 2 3]
+    //     [9 4 1 4 8]
+    //     [1 2 5 2 3]
+    for (int i = 0; i < 15 * num * channels; i += 15) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 2;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 5;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 2;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 9;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 4;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 4;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 1;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 3;
+    }
+    PoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 2);
+    EXPECT_EQ(blob_top_->width(), 4);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 2);
+      EXPECT_EQ(blob_top_mask_->width(), 4);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    //     [9 5 5 8]
+    //     [9 5 5 8]
+    for (int i = 0; i < 8 * num * channels; i += 8) {
+      EXPECT_EQ(blob_top_->cpu_data()[i + 0], 9);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 1], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 2], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 3], 8);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 4], 9);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 5], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 6], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 7], 8);
+    }
+    if (blob_top_vec_.size() > 1) {
+      // Expected mask output: 2x 2 channels of:
+      //     [5  2  2 9]
+      //     [5 12 12 9]
+      for (int i = 0; i < 8 * num * channels; i += 8) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0],  5);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1],  2);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2],  2);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3],  9);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4],  5);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7],  9);
+      }
+    }
+  }
 };
 
 typedef ::testing::Types<float, double> Dtypes;
@@ -127,6 +205,28 @@ TYPED_TEST(PoolingLayerTest, PrintCPUBackward) {
 }
 */
 
+TYPED_TEST(PoolingLayerTest, TestCPUForwardMax) {
+  Caffe::set_mode(Caffe::CPU);
+  this->TestForward();
+}
+
+TYPED_TEST(PoolingLayerTest, TestGPUForwardMax) {
+  Caffe::set_mode(Caffe::GPU);
+  this->TestForward();
+}
+
+TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxTopMask) {
+  Caffe::set_mode(Caffe::CPU);
+  this->blob_top_vec_.push_back(this->blob_top_mask_);
+  this->TestForward();
+}
+
+TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxTopMask) {
+  Caffe::set_mode(Caffe::GPU);
+  this->blob_top_vec_.push_back(this->blob_top_mask_);
+  this->TestForward();
+}
+
 TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -153,6 +253,34 @@ TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(PoolingLayerTest, TestCPUGradientMaxTopMask) {
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+  this->blob_top_vec_.push_back(this->blob_top_mask_);
+  Caffe::set_mode(Caffe::CPU);
+  PoolingLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-4, 1e-2);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(PoolingLayerTest, TestGPUGradientMaxTopMask) {
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+  this->blob_top_vec_.push_back(this->blob_top_mask_);
+  Caffe::set_mode(Caffe::GPU);
+  PoolingLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-4, 1e-2);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 
 TYPED_TEST(PoolingLayerTest, TestCPUForwardAve) {
   LayerParameter layer_param;

From 0a0f9be2fe60a66472c36838f38940e05827d259 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 24 May 2014 19:02:51 -0700
Subject: [PATCH 0063/2053] merge caffe_set definitions; define for int as well

---
 src/caffe/layers/pooling_layer.cpp |  4 +---
 src/caffe/util/math_functions.cpp  | 19 ++++++-------------
 2 files changed, 7 insertions(+), 16 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 928c8c7f42f..5d6921f1da8 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -84,9 +84,7 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       caffe_set(top_count, Dtype(-1), top_mask);
     } else {
       mask = max_idx_->mutable_cpu_data();
-      for (int i = 0; i < top_count; ++i) {
-        mask[i] = -1;
-      }
+      caffe_set(top_count, -1, mask);
     }
     caffe_set(top_count, Dtype(-FLT_MAX), top_data);
     // The main loop
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 2196b44bf24..67274ef73cc 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -120,10 +120,10 @@ void caffe_gpu_axpy<double>(const int N, const double alpha, const double* X,
   CUBLAS_CHECK(cublasDaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
 }
 
-template <>
-void caffe_set(const int N, const float alpha, float* Y) {
+template <typename Dtype>
+void caffe_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
-    memset(Y, 0, sizeof(float) * N);
+    memset(Y, 0, sizeof(Dtype) * N);
     return;
   }
   for (int i = 0; i < N; ++i) {
@@ -131,16 +131,9 @@ void caffe_set(const int N, const float alpha, float* Y) {
   }
 }
 
-template <>
-void caffe_set(const int N, const double alpha, double* Y) {
-  if (alpha == 0) {
-    memset(Y, 0, sizeof(double) * N);
-    return;
-  }
-  for (int i = 0; i < N; ++i) {
-    Y[i] = alpha;
-  }
-}
+template void caffe_set<int>(const int N, const int alpha, int* Y);
+template void caffe_set<float>(const int N, const float alpha, float* Y);
+template void caffe_set<double>(const int N, const double alpha, double* Y);
 
 template <>
 void caffe_add_scalar(const int N, const float alpha, float* Y) {

From 07ba3fd9cfabf39dcbae460ade9aee7c2874d9e8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 25 May 2014 16:19:14 -0700
Subject: [PATCH 0064/2053] 10.9 install doc formatting

---
 docs/installation.md | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 5ad6c6cb8cb..1210bfc615e 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -124,16 +124,14 @@ The prerequisite homebrew formulae are
 
 For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
 
-```
-    def install
-        # ADD THE FOLLOWING:
-        ENV.append "CXXFLAGS", "-stdlib=libstdc++"
-        ENV.append "CFLAGS", "-stdlib=libstdc++"
-        ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
-        # The following is necessary because libtool likes to strip LDFLAGS:
-        ENV["CXX"] = "/usr/bin/clang -stdlib=libstdc++"
-        ...
-```
+      def install
+          # ADD THE FOLLOWING:
+          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
+          ENV.append "CFLAGS", "-stdlib=libstdc++"
+          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
+          # The following is necessary because libtool likes to strip LDFLAGS:
+          ENV["CXX"] = "/usr/bin/clang -stdlib=libstdc++"
+          ...
 
 To edit the formulae in turn, run
 

From 608abe267ff8f5628ef3e097526fec8e3db30faa Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 26 May 2014 01:34:32 -0700
Subject: [PATCH 0065/2053] don't pass LDFLAGS when only compiling

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 3863fbfdcfb..a092ac890fc 100644
--- a/Makefile
+++ b/Makefile
@@ -341,12 +341,12 @@ $(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
 	@ echo
 
 $(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ $(LDFLAGS)
+	$(CXX) $< $(CXXFLAGS) -c -o $@
 	@ echo
 
 $(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
 		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ $(LDFLAGS)
+	$(CXX) $< $(CXXFLAGS) -c -o $@
 	@ echo
 
 $(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)

From a53978d2b045dbab198b28fe38fd75e1ce5ddec0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 26 May 2014 10:50:00 -0700
Subject: [PATCH 0066/2053] convert imageset comment fixup

---
 tools/convert_imageset.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 900183a649b..2420f9538fb 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -2,13 +2,13 @@
 // This program converts a set of images to a leveldb by storing them as Datum
 // proto buffers.
 // Usage:
-//    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME [0/1] \
+//    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1] \
 //                     [resize_height] [resize_width]
 // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
 // should be a list of files as well as their labels, in the format as
 //   subfolder1/file1.JPEG 7
 //   ....
-// if the last argument is 1, a random shuffle will be carried out before we
+// if RANDOM_SHUFFLE is 1, a random shuffle will be carried out before we
 // process the file lines.
 
 #include <glog/logging.h>

From 8c609da73666bba762a50ba39e23ca82724fbad4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 26 May 2014 21:50:39 -0700
Subject: [PATCH 0067/2053] caffe.Net preprocessing members belong to object,
 not class

---
 python/caffe/pycaffe.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 8bed7046cc8..5c1512cd8b9 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -14,11 +14,6 @@
 # inheritance) so that nets created by caffe (e.g., by SGDSolver) will
 # automatically have the improved interface.
 
-# Input preprocessing
-Net.mean = {}       # input mean (ndarray, input dimensional or broadcastable)
-Net.input_scale = {}    # for a model that expects data = input * input_scale
-Net.channel_swap = {}  # for RGB -> BGR and the like
-
 
 @property
 def _Net_blobs(self):
@@ -187,10 +182,12 @@ def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
 
     Take
     input_: which input to assign this mean.
-    mean_f: path to mean .npy
+    mean_f: path to mean .npy with ndarray (input dimensional or broadcastable)
     mode: elementwise = use the whole mean (and check dimensions)
           channel = channel constant (e.g. mean pixel instead of mean image)
     """
+    if not hasattr(self, 'mean'):
+        self.mean = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     in_shape = self.blobs[input_].data.shape
@@ -218,6 +215,8 @@ def _Net_set_input_scale(self, input_, scale):
     input_: which input to assign this scale factor
     scale: scale coefficient
     """
+    if not hasattr(self, 'input_scale'):
+        self.input_scale = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     self.input_scale[input_] = scale
@@ -233,6 +232,8 @@ def _Net_set_channel_swap(self, input_, order):
     order: the order to take the channels.
            (2,1,0) maps RGB to BGR for example.
     """
+    if not hasattr(self, 'channel_swap'):
+        self.channel_swap = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     self.channel_swap[input_] = order

From 2fb868cbd32b542f4f77912e92bf105421fb9c0c Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 15 May 2014 09:30:07 -0700
Subject: [PATCH 0068/2053] Test for Threshold layer

---
 src/caffe/test/test_threshold_layer.cpp | 71 +++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 src/caffe/test/test_threshold_layer.cpp

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
new file mode 100644
index 00000000000..c475f669445
--- /dev/null
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -0,0 +1,71 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "cuda_runtime.h"
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class ThresholdLayerTest : public ::testing::Test {
+ protected:
+  ThresholdLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ThresholdLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(ThresholdLayerTest, Dtypes);
+
+
+TYPED_TEST(ThresholdLayerTest, TestSetup) {
+  LayerParameter layer_param;
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->bottom_top_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->bottom_top_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->bottom_top_->width());
+}
+TYPED_TEST(ThresholdLayerTest, TestCPU) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam threshold_ = layer.theshold_;
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    EXPECT_TRUE(top_data[i] == 0 && bottom_data[i] <= threshold_);
+    EXPECT_TRUE(top_data[i] == 1 && bottom_data[i] > threshold_);
+  }
+}
+
+}  // namespace caffe

From 07c764488e6ec2dd1cc56fd8545f6cf2baa79597 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 10:32:11 -0700
Subject: [PATCH 0069/2053] Fixed ThresholdParam

Conflicts:

	src/caffe/proto/caffe.proto

Conflicts:

	src/caffe/proto/caffe.proto

Conflicts:
	src/caffe/proto/caffe.proto
---
 src/caffe/proto/caffe.proto | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5e230a11482..7d44085b118 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -127,7 +127,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 31 (last added: ARGMAX)
+  // LayerType next available ID: 32 (last added: THRESHOLD)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -163,6 +163,7 @@ message LayerParameter {
     SPLIT = 22;
     TANH = 23;
     WINDOW_DATA = 24;
+    THRESHOLD = 31;
   }
   optional LayerType type = 5; // the layer type from the enum above
 
@@ -175,6 +176,7 @@ message LayerParameter {
   repeated float weight_decay = 8;
 
   // Parameters for particular layer types.
+  // Parameters next available ID: 26 (last added: ThresholdParameter)
   optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
   optional ConvolutionParameter convolution_param = 10;
@@ -191,6 +193,7 @@ message LayerParameter {
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
   optional WindowDataParameter window_data_param = 20;
+  optional ThresholdParameter threshold_param = 25;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -261,6 +264,11 @@ message EltwiseParameter {
   repeated float coeff = 2; // blob-wise coefficient for SUM operation
 }
 
+// Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+  optional float threshold = 1 [default = 0]; // Strictly Positive values
+}
+
 // Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.

From 59096b9471ab8e4eb59103449d98b2abc150a115 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 16:57:50 -0700
Subject: [PATCH 0070/2053] Fixed name of threshold_ var

---
 src/caffe/test/test_threshold_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index c475f669445..454b3cb7550 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -59,7 +59,7 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer.theshold_;
+  const TypeParam threshold_ = layer.threshold_;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);

From 3e13081b7b622a0da7666359c84539f55703379b Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 16:58:27 -0700
Subject: [PATCH 0071/2053] Fixed name of blob_bottom_

---
 src/caffe/test/test_threshold_layer.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 454b3cb7550..628500eb986 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -45,10 +45,10 @@ TYPED_TEST(ThresholdLayerTest, TestSetup) {
   LayerParameter layer_param;
   ThresholdLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), this->bottom_top_->num());
-  EXPECT_EQ(this->blob_top_->channels(), this->bottom_top_->channels());
-  EXPECT_EQ(this->blob_top_->height(), this->bottom_top_->height());
-  EXPECT_EQ(this->blob_top_->width(), this->bottom_top_->width());
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_->width());
 }
 TYPED_TEST(ThresholdLayerTest, TestCPU) {
   LayerParameter layer_param;

From 37d5113d9f3cff1f9bf34da750a49fdfcdeaea5c Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 17:01:50 -0700
Subject: [PATCH 0072/2053] Added threshold setting test

---
 src/caffe/test/test_threshold_layer.cpp | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 628500eb986..7191bb29513 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -50,6 +50,7 @@ TYPED_TEST(ThresholdLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_->height());
   EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_->width());
 }
+
 TYPED_TEST(ThresholdLayerTest, TestCPU) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
@@ -59,7 +60,7 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer.threshold_;
+  const TypeParam threshold_ = layer_param.threshold_param().threshold();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
@@ -68,4 +69,24 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
   }
 }
 
+TYPED_TEST(ThresholdLayerTest, TestCPU2) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  ThresholdLayerParameter* threshold_param =
+    layer_param.mutable_threshold_param();
+  threshold_param->set_threshold(0.5);
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    EXPECT_TRUE(top_data[i] == 0 && bottom_data[i] <= threshold_);
+    EXPECT_TRUE(top_data[i] == 1 && bottom_data[i] > threshold_);
+  }
+}
 }  // namespace caffe

From 0c8f32e51d8e89afe4d09c6ce1001bcc8639b33f Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 17:14:00 -0700
Subject: [PATCH 0073/2053] Added NeuronLayer<Dtype>::SetUp(bottom, top) to
 ThresholdLayer

---
 src/caffe/test/test_threshold_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 7191bb29513..6232d0c2c6d 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -89,4 +89,5 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
     EXPECT_TRUE(top_data[i] == 1 && bottom_data[i] > threshold_);
   }
 }
+
 }  // namespace caffe

From 876766bce609acaa4a668c6d4eab2bfba7d03a5f Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 18:42:50 -0700
Subject: [PATCH 0074/2053] Added the code for threshold_layer to the repo

---
 src/caffe/layers/threshold_layer.cpp | 32 ++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 src/caffe/layers/threshold_layer.cpp

diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
new file mode 100644
index 00000000000..bec89cde63a
--- /dev/null
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -0,0 +1,32 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+void ThresholdLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  NeuronLayer<Dtype>::SetUp(bottom, top);
+  threshold_ = this->layer_param_.threshold_param().threshold();
+}
+
+template <typename Dtype>
+Dtype ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  for (int i = 0; i < count; ++i) {
+    top_data[i] = ( bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
+  }
+  return Dtype(0);
+}
+
+INSTANTIATE_CLASS(ThresholdLayer);
+
+}  // namespace caffe

From 01d582b6d11f7965cd54fe3c0d32f7b12ac7c56f Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 18:47:57 -0700
Subject: [PATCH 0075/2053] Fix typo in test_threshold ThresholdParameter

---
 src/caffe/test/test_threshold_layer.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 6232d0c2c6d..25e9f0222cb 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -72,7 +72,7 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
 TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
-  ThresholdLayerParameter* threshold_param =
+  ThresholdParameter* threshold_param =
     layer_param.mutable_threshold_param();
   threshold_param->set_threshold(0.5);
   ThresholdLayer<TypeParam> layer(layer_param);
@@ -82,6 +82,7 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
   const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  EXPECT_FLOAT_EQ(threshold_,0.5);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);

From c5c41884323dab271547457dcefd36caa18b523f Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 15 May 2014 18:58:04 -0700
Subject: [PATCH 0076/2053] Corrected conditions in test_threshold

---
 src/caffe/test/test_threshold_layer.cpp | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 25e9f0222cb..4f87d0d03dd 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -64,8 +64,12 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
-    EXPECT_TRUE(top_data[i] == 0 && bottom_data[i] <= threshold_);
-    EXPECT_TRUE(top_data[i] == 1 && bottom_data[i] > threshold_);
+    if (top_data[i] == 0) {
+	EXPECT_LE(bottom_data[i],threshold_);
+    }
+    if (top_data[i] == 1) {
+	EXPECT_GT(bottom_data[i],threshold_);
+    }
   }
 }
 
@@ -86,8 +90,12 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
-    EXPECT_TRUE(top_data[i] == 0 && bottom_data[i] <= threshold_);
-    EXPECT_TRUE(top_data[i] == 1 && bottom_data[i] > threshold_);
+    if (top_data[i] == 0) {
+	EXPECT_LE(bottom_data[i],threshold_);
+    }
+    if (top_data[i] == 1) {
+	EXPECT_TRUE(bottom_data[i] > threshold_); 
+    }
   }
 }
 

From 27d53249d1479c62548ba95d0ed3929ed0cfb57e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 10:54:21 -0700
Subject: [PATCH 0077/2053] Added Threshold layer to neuron_layers.hpp

---
 include/caffe/neuron_layers.hpp | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 4e67a29ace3..85d5a1607fd 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -202,6 +202,34 @@ class TanHLayer : public NeuronLayer<Dtype> {
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
+/* ThresholdLayer
+  Outputs 1 if value in input is above threshold, 0 otherwise.
+  The defult threshold = 0, which means positive values would become 1 and 
+  negative or 0, would become 0
+
+  y = 1 if x > threshold
+  y = 0 if x <= threshold
+  y' = don't have
+*/
+template <typename Dtype>
+class ThresholdLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit DropoutLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+  
+  Dtype threshold_;
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_NEURON_LAYERS_HPP_

From 868dd61449a85dfed2f2807b9178584098473166 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 10:57:26 -0700
Subject: [PATCH 0078/2053] Added ForwardGPU to ThresholdLayer and to the tests

---
 include/caffe/neuron_layers.hpp         |  7 ++--
 src/caffe/layers/threshold_layer.cu     | 38 ++++++++++++++++++++
 src/caffe/test/test_threshold_layer.cpp | 48 +++++++++++++++++++++++++
 3 files changed, 91 insertions(+), 2 deletions(-)
 create mode 100644 src/caffe/layers/threshold_layer.cu

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 85d5a1607fd..180ce7a2b97 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -209,7 +209,8 @@ class TanHLayer : public NeuronLayer<Dtype> {
 
   y = 1 if x > threshold
   y = 0 if x <= threshold
-  y' = don't have
+  
+  y' = don't differenciable
 */
 template <typename Dtype>
 class ThresholdLayer : public NeuronLayer<Dtype> {
@@ -222,11 +223,13 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
   }
-  
+
   Dtype threshold_;
 };
 
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
new file mode 100644
index 00000000000..0497507655b
--- /dev/null
+++ b/src/caffe/layers/threshold_layer.cu
@@ -0,0 +1,38 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+using std::max;
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ThresholdForward(const int n, const, threshold, const Dtype* in, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out[index] = in[index] > threshold ? 1 : 0;
+  }
+}
+
+template <typename Dtype>
+Dtype ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ReLUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, threshold_, bottom_data, top_data);
+  CUDA_POST_KERNEL_CHECK;
+
+  return Dtype(0);
+}
+
+
+INSTANTIATE_CLASS(ReLULayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 4f87d0d03dd..b8f9d9697ec 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -99,4 +99,52 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   }
 }
 
+TYPED_TEST(ThresholdLayerTest, TestGPU) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::GPU);
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    if (top_data[i] == 0) {
+  EXPECT_LE(bottom_data[i],threshold_);
+    }
+    if (top_data[i] == 1) {
+  EXPECT_GT(bottom_data[i],threshold_);
+    }
+  }
+}
+
+TYPED_TEST(ThresholdLayerTest, TestGPU2) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::GPU);
+  ThresholdParameter* threshold_param =
+    layer_param.mutable_threshold_param();
+  threshold_param->set_threshold(0.5);
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  EXPECT_FLOAT_EQ(threshold_,0.5);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    if (top_data[i] == 0) {
+  EXPECT_LE(bottom_data[i],threshold_);
+    }
+    if (top_data[i] == 1) {
+  EXPECT_TRUE(bottom_data[i] > threshold_); 
+    }
+  }
+}
+
 }  // namespace caffe

From 1e87c20e2e7ca22f8848ea27f23959f6f6dc28a3 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 10:59:36 -0700
Subject: [PATCH 0079/2053] Fixed typo in Threshold Layer definition

---
 include/caffe/neuron_layers.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 180ce7a2b97..5118e8a5225 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -215,7 +215,7 @@ class TanHLayer : public NeuronLayer<Dtype> {
 template <typename Dtype>
 class ThresholdLayer : public NeuronLayer<Dtype> {
  public:
-  explicit DropoutLayer(const LayerParameter& param)
+  explicit ThresholdLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);

From 6f9de3e7c5a0979c7d26db2056fff5741c2b374d Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 11:01:19 -0700
Subject: [PATCH 0080/2053] Fixed type in ThresholdLayer.cu

---
 src/caffe/layers/threshold_layer.cu | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 0497507655b..624a8221486 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -11,7 +11,8 @@ using std::max;
 namespace caffe {
 
 template <typename Dtype>
-__global__ void ThresholdForward(const int n, const, threshold, const Dtype* in, Dtype* out) {
+__global__ void ThresholdForward(const int n, const Dtype threshold,
+    const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
     out[index] = in[index] > threshold ? 1 : 0;
   }

From 8cb4e45fb7301b02e36df40827f8fc56eaf8e2f6 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 11:02:20 -0700
Subject: [PATCH 0081/2053] Fixed call to ThresholdForward in ThresholdLayer.cu

---
 src/caffe/layers/threshold_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 624a8221486..61b1eca81d3 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -25,7 +25,7 @@ Dtype ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
-  ReLUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+  ThresholdForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
       count, threshold_, bottom_data, top_data);
   CUDA_POST_KERNEL_CHECK;
 

From 8c405388f4f028f5f802c3622fd5b6bdfd0591b7 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 11:06:56 -0700
Subject: [PATCH 0082/2053] Make lint happy

---
 src/caffe/layers/threshold_layer.cpp    |  2 +-
 src/caffe/test/test_threshold_layer.cpp | 20 ++++++++++----------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index bec89cde63a..e6ed8a6b40e 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -22,7 +22,7 @@ Dtype ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
-    top_data[i] = ( bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
+    top_data[i] = (bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
   }
   return Dtype(0);
 }
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index b8f9d9697ec..8303e442007 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -65,10 +65,10 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
     if (top_data[i] == 0) {
-	EXPECT_LE(bottom_data[i],threshold_);
+      EXPECT_LE(bottom_data[i], threshold_);
     }
     if (top_data[i] == 1) {
-	EXPECT_GT(bottom_data[i],threshold_);
+      EXPECT_GT(bottom_data[i], threshold_);
     }
   }
 }
@@ -86,15 +86,15 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
   const TypeParam threshold_ = layer_param.threshold_param().threshold();
-  EXPECT_FLOAT_EQ(threshold_,0.5);
+  EXPECT_FLOAT_EQ(threshold_, 0.5);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
     if (top_data[i] == 0) {
-	EXPECT_LE(bottom_data[i],threshold_);
+      EXPECT_LE(bottom_data[i], threshold_);
     }
     if (top_data[i] == 1) {
-	EXPECT_TRUE(bottom_data[i] > threshold_); 
+      EXPECT_GT(bottom_data[i], threshold_);
     }
   }
 }
@@ -113,10 +113,10 @@ TYPED_TEST(ThresholdLayerTest, TestGPU) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
     if (top_data[i] == 0) {
-  EXPECT_LE(bottom_data[i],threshold_);
+      EXPECT_LE(bottom_data[i], threshold_);
     }
     if (top_data[i] == 1) {
-  EXPECT_GT(bottom_data[i],threshold_);
+      EXPECT_GT(bottom_data[i], threshold_);
     }
   }
 }
@@ -134,15 +134,15 @@ TYPED_TEST(ThresholdLayerTest, TestGPU2) {
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
   const TypeParam threshold_ = layer_param.threshold_param().threshold();
-  EXPECT_FLOAT_EQ(threshold_,0.5);
+  EXPECT_FLOAT_EQ(threshold_, 0.5);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
     if (top_data[i] == 0) {
-  EXPECT_LE(bottom_data[i],threshold_);
+      EXPECT_LE(bottom_data[i], threshold_);
     }
     if (top_data[i] == 1) {
-  EXPECT_TRUE(bottom_data[i] > threshold_); 
+      EXPECT_GT(bottom_data[i], threshold_);
     }
   }
 }

From c4e4ca82e47c37ed1ef09cf94a43153bd5a0c374 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 11:44:17 -0700
Subject: [PATCH 0083/2053] Comment Test GPUs cases

---
 src/caffe/test/test_threshold_layer.cpp | 92 ++++++++++++-------------
 1 file changed, 46 insertions(+), 46 deletions(-)

diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 8303e442007..a34972ef575 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -99,52 +99,52 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   }
 }
 
-TYPED_TEST(ThresholdLayerTest, TestGPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ThresholdLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer_param.threshold_param().threshold();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_LE(top_data[i], 1.);
-    if (top_data[i] == 0) {
-      EXPECT_LE(bottom_data[i], threshold_);
-    }
-    if (top_data[i] == 1) {
-      EXPECT_GT(bottom_data[i], threshold_);
-    }
-  }
-}
+// TYPED_TEST(ThresholdLayerTest, TestGPU) {
+//   LayerParameter layer_param;
+//   Caffe::set_mode(Caffe::GPU);
+//   ThresholdLayer<TypeParam> layer(layer_param);
+//   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+//   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+//   // Now, check values
+//   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+//   const TypeParam* top_data = this->blob_top_->cpu_data();
+//   const TypeParam threshold_ = layer_param.threshold_param().threshold();
+//   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+//     EXPECT_GE(top_data[i], 0.);
+//     EXPECT_LE(top_data[i], 1.);
+//     if (top_data[i] == 0) {
+//       EXPECT_LE(bottom_data[i], threshold_);
+//     }
+//     if (top_data[i] == 1) {
+//       EXPECT_GT(bottom_data[i], threshold_);
+//     }
+//   }
+// }
 
-TYPED_TEST(ThresholdLayerTest, TestGPU2) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ThresholdParameter* threshold_param =
-    layer_param.mutable_threshold_param();
-  threshold_param->set_threshold(0.5);
-  ThresholdLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer_param.threshold_param().threshold();
-  EXPECT_FLOAT_EQ(threshold_, 0.5);
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_LE(top_data[i], 1.);
-    if (top_data[i] == 0) {
-      EXPECT_LE(bottom_data[i], threshold_);
-    }
-    if (top_data[i] == 1) {
-      EXPECT_GT(bottom_data[i], threshold_);
-    }
-  }
-}
+// TYPED_TEST(ThresholdLayerTest, TestGPU2) {
+//   LayerParameter layer_param;
+//   Caffe::set_mode(Caffe::GPU);
+//   ThresholdParameter* threshold_param =
+//     layer_param.mutable_threshold_param();
+//   threshold_param->set_threshold(0.5);
+//   ThresholdLayer<TypeParam> layer(layer_param);
+//   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+//   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+//   // Now, check values
+//   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+//   const TypeParam* top_data = this->blob_top_->cpu_data();
+//   const TypeParam threshold_ = layer_param.threshold_param().threshold();
+//   EXPECT_FLOAT_EQ(threshold_, 0.5);
+//   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+//     EXPECT_GE(top_data[i], 0.);
+//     EXPECT_LE(top_data[i], 1.);
+//     if (top_data[i] == 0) {
+//       EXPECT_LE(bottom_data[i], threshold_);
+//     }
+//     if (top_data[i] == 1) {
+//       EXPECT_GT(bottom_data[i], threshold_);
+//     }
+//   }
+// }
 
 }  // namespace caffe

From 5a4c9bcdfc4f08faa34bda22ce6a364b7095821a Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 27 May 2014 11:54:53 -0700
Subject: [PATCH 0084/2053] Un comment Test GPUs cases, fixed ThresholdLayer.cu

---
 src/caffe/layers/threshold_layer.cu     |  2 +-
 src/caffe/test/test_threshold_layer.cpp | 92 ++++++++++++-------------
 2 files changed, 47 insertions(+), 47 deletions(-)

diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 61b1eca81d3..03eb3f32d49 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -33,7 +33,7 @@ Dtype ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 
-INSTANTIATE_CLASS(ReLULayer);
+INSTANTIATE_CLASS(ThresholdLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index a34972ef575..8303e442007 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -99,52 +99,52 @@ TYPED_TEST(ThresholdLayerTest, TestCPU2) {
   }
 }
 
-// TYPED_TEST(ThresholdLayerTest, TestGPU) {
-//   LayerParameter layer_param;
-//   Caffe::set_mode(Caffe::GPU);
-//   ThresholdLayer<TypeParam> layer(layer_param);
-//   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-//   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-//   // Now, check values
-//   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-//   const TypeParam* top_data = this->blob_top_->cpu_data();
-//   const TypeParam threshold_ = layer_param.threshold_param().threshold();
-//   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-//     EXPECT_GE(top_data[i], 0.);
-//     EXPECT_LE(top_data[i], 1.);
-//     if (top_data[i] == 0) {
-//       EXPECT_LE(bottom_data[i], threshold_);
-//     }
-//     if (top_data[i] == 1) {
-//       EXPECT_GT(bottom_data[i], threshold_);
-//     }
-//   }
-// }
+TYPED_TEST(ThresholdLayerTest, TestGPU) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::GPU);
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    if (top_data[i] == 0) {
+      EXPECT_LE(bottom_data[i], threshold_);
+    }
+    if (top_data[i] == 1) {
+      EXPECT_GT(bottom_data[i], threshold_);
+    }
+  }
+}
 
-// TYPED_TEST(ThresholdLayerTest, TestGPU2) {
-//   LayerParameter layer_param;
-//   Caffe::set_mode(Caffe::GPU);
-//   ThresholdParameter* threshold_param =
-//     layer_param.mutable_threshold_param();
-//   threshold_param->set_threshold(0.5);
-//   ThresholdLayer<TypeParam> layer(layer_param);
-//   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-//   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-//   // Now, check values
-//   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-//   const TypeParam* top_data = this->blob_top_->cpu_data();
-//   const TypeParam threshold_ = layer_param.threshold_param().threshold();
-//   EXPECT_FLOAT_EQ(threshold_, 0.5);
-//   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-//     EXPECT_GE(top_data[i], 0.);
-//     EXPECT_LE(top_data[i], 1.);
-//     if (top_data[i] == 0) {
-//       EXPECT_LE(bottom_data[i], threshold_);
-//     }
-//     if (top_data[i] == 1) {
-//       EXPECT_GT(bottom_data[i], threshold_);
-//     }
-//   }
-// }
+TYPED_TEST(ThresholdLayerTest, TestGPU2) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::GPU);
+  ThresholdParameter* threshold_param =
+    layer_param.mutable_threshold_param();
+  threshold_param->set_threshold(0.5);
+  ThresholdLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  EXPECT_FLOAT_EQ(threshold_, 0.5);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    if (top_data[i] == 0) {
+      EXPECT_LE(bottom_data[i], threshold_);
+    }
+    if (top_data[i] == 1) {
+      EXPECT_GT(bottom_data[i], threshold_);
+    }
+  }
+}
 
 }  // namespace caffe

From ec84febd9c69b5c1f6cda58cba73d48c1064e89b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 28 May 2014 17:18:22 -0700
Subject: [PATCH 0085/2053] fix OSX 10.9 homebrew CXX doc

clang++ and not clang as accidentally committed in 2dcbcd9.
---
 docs/installation.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 1210bfc615e..a6f9f137027 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -111,7 +111,7 @@ If you're not using Anaconda, include `hdf5` in the list above.
 
 #### 10.9-specific Instructions
 
-In OS X 10.9, clang is the default compiler and uses `libc++` as the standard library.
+In OS X 10.9, clang++ is the default C++ compiler and uses `libc++` as the standard library.
 However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
 This makes it necessary to change the compilation settings for each of the dependencies.
 
@@ -130,7 +130,7 @@ For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as
           ENV.append "CFLAGS", "-stdlib=libstdc++"
           ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
           # The following is necessary because libtool likes to strip LDFLAGS:
-          ENV["CXX"] = "/usr/bin/clang -stdlib=libstdc++"
+          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
           ...
 
 To edit the formulae in turn, run

From cc30340f0150f5c58c410b981a4fbd7eed0bd932 Mon Sep 17 00:00:00 2001
From: Rob Currie <rcurrie@uaudio.com>
Date: Thu, 29 May 2014 14:46:29 -0700
Subject: [PATCH 0086/2053] Update docs on building boost on OSX for the python
 wrappers

---
 docs/installation.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/installation.md b/docs/installation.md
index a6f9f137027..b84ae3b83e4 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -144,6 +144,10 @@ After this, run
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
 
+**Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
+
+    brew install --build-from-source --with-python --fresh -vd boost
+
 #### Windows
 
 There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng).

From d21da02474a0f5be28ce62517de017be761ac8e0 Mon Sep 17 00:00:00 2001
From: Jerod Weinman <jerod@acm.org>
Date: Mon, 2 Jun 2014 15:54:25 -0500
Subject: [PATCH 0087/2053] Added an iscolor flag to io.cpp method
 ReadImageToDatum to handle grayscale images and a corresponding commandline
 flag [-g] to convert_imageset.cpp.

---
 include/caffe/util/io.hpp  |  8 +++++++-
 src/caffe/util/io.cpp      | 25 ++++++++++++++++++-------
 tools/convert_imageset.cpp | 35 +++++++++++++++++++++--------------
 3 files changed, 46 insertions(+), 22 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 056b573db4c..829293ecaef 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -61,13 +61,19 @@ inline void WriteProtoToBinaryFile(
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, Datum* datum);
+    const int height, const int width, const bool iscolor, Datum* datum);
+
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const int height, const int width, Datum* datum) {
+  return ReadImageToDatum(filename, label, height, width, true, datum);
+}
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     Datum* datum) {
   return ReadImageToDatum(filename, label, 0, 0, datum);
 }
 
+
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
   hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 44858f48a36..f2650e9a22a 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -72,32 +72,43 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, Datum* datum) {
+    const int height, const int width, const bool iscolor, Datum* datum) {
   cv::Mat cv_img;
+  int cv_read_flag = (iscolor ? CV_LOAD_IMAGE_COLOR : CV_LOAD_IMAGE_GRAYSCALE);
   if (height > 0 && width > 0) {
-    cv::Mat cv_img_origin = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
+    cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
     cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
   } else {
-    cv_img = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
+    cv_img = cv::imread(filename, cv_read_flag);
   }
   if (!cv_img.data) {
     LOG(ERROR) << "Could not open or find file " << filename;
     return false;
   }
-  datum->set_channels(3);
+  int num_channels = (iscolor ? 3 : 1);
+  datum->set_channels(num_channels);
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
   datum->set_label(label);
   datum->clear_data();
   datum->clear_float_data();
   string* datum_string = datum->mutable_data();
-  for (int c = 0; c < 3; ++c) {
+  if (iscolor) {
+    for (int c = 0; c < num_channels; ++c) {
+      for (int h = 0; h < cv_img.rows; ++h) {
+        for (int w = 0; w < cv_img.cols; ++w) {
+          datum_string->push_back(
+            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+        }
+      }
+    }
+  } else {  // Faster than repeatedly testing iscolor for each pixel w/i loop
     for (int h = 0; h < cv_img.rows; ++h) {
       for (int w = 0; w < cv_img.cols; ++w) {
         datum_string->push_back(
-            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+          static_cast<char>(cv_img.at<uchar>(h, w)));
+        }
       }
-    }
   }
   return true;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 2420f9538fb..bbf848adc8a 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -2,7 +2,7 @@
 // This program converts a set of images to a leveldb by storing them as Datum
 // proto buffers.
 // Usage:
-//    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1] \
+//   convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1] \
 //                     [resize_height] [resize_width]
 // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
 // should be a list of files as well as their labels, in the format as
@@ -10,6 +10,9 @@
 //   ....
 // if RANDOM_SHUFFLE is 1, a random shuffle will be carried out before we
 // process the file lines.
+// Optional flag -g indicates the images should be read as
+// single-channel grayscale. If omitted, grayscale images will be
+// converted to color.
 
 #include <glog/logging.h>
 #include <leveldb/db.h>
@@ -30,24 +33,28 @@ using std::string;
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 4 || argc > 7) {
+  if (argc < 4 || argc > 8) {
     printf("Convert a set of images to the leveldb format used\n"
         "as input for Caffe.\n"
         "Usage:\n"
-        "    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME"
+        "    convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME"
         " RANDOM_SHUFFLE_DATA[0 or 1] [resize_height] [resize_width]\n"
         "The ImageNet dataset for the training demo is at\n"
         "    http://www.image-net.org/download-images\n");
     return 1;
   }
-  std::ifstream infile(argv[2]);
+
+  // Test whether argv[1] == "-g"
+  bool iscolor= !(string("-g") == string(argv[1]));
+  int  arg_offset = (iscolor ? 0 : 1);
+  std::ifstream infile(argv[arg_offset+2]);
   std::vector<std::pair<string, int> > lines;
   string filename;
   int label;
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
   }
-  if (argc >= 5 && argv[4][0] == '1') {
+  if (argc >= (arg_offset+5) && argv[arg_offset+4][0] == '1') {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";
     std::random_shuffle(lines.begin(), lines.end());
@@ -55,11 +62,11 @@ int main(int argc, char** argv) {
   LOG(INFO) << "A total of " << lines.size() << " images.";
   int resize_height = 0;
   int resize_width = 0;
-  if (argc >= 6) {
-    resize_height = atoi(argv[5]);
+  if (argc >= (arg_offset+6)) {
+    resize_height = atoi(argv[arg_offset+5]);
   }
-  if (argc >= 7) {
-    resize_width = atoi(argv[6]);
+  if (argc >= (arg_offset+7)) {
+    resize_width = atoi(argv[arg_offset+6]);
   }
 
   leveldb::DB* db;
@@ -67,12 +74,12 @@ int main(int argc, char** argv) {
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  LOG(INFO) << "Opening leveldb " << argv[3];
+  LOG(INFO) << "Opening leveldb " << argv[arg_offset+3];
   leveldb::Status status = leveldb::DB::Open(
-      options, argv[3], &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << argv[3];
+      options, argv[arg_offset+3], &db);
+  CHECK(status.ok()) << "Failed to open leveldb " << argv[arg_offset+3];
 
-  string root_folder(argv[1]);
+  string root_folder(argv[arg_offset+1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;
@@ -82,7 +89,7 @@ int main(int argc, char** argv) {
   bool data_size_initialized = false;
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     if (!ReadImageToDatum(root_folder + lines[line_id].first,
-        lines[line_id].second, resize_height, resize_width, &datum)) {
+         lines[line_id].second, resize_height, resize_width, iscolor, &datum)) {
       continue;
     }
     if (!data_size_initialized) {

From a8dfed49e36d2d90a9be8633514a350936e2f5ee Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 2 Jun 2014 23:47:09 -0700
Subject: [PATCH 0088/2053] compile debug/release into separate directories so
 you don't have to rebuild the whole thing to switch back and forth

---
 Makefile | 32 +++++++++++++++++++++++++++++---
 1 file changed, 29 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index a092ac890fc..2c42516cebf 100644
--- a/Makefile
+++ b/Makefile
@@ -4,6 +4,19 @@ PROJECT := caffe
 CONFIG_FILE := Makefile.config
 include $(CONFIG_FILE)
 
+BUILD_DIR_LINK := $(BUILD_DIR)
+RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
+DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+
+DEBUG ?= 0
+ifeq ($(DEBUG), 1)
+	BUILD_DIR := $(DEBUG_BUILD_DIR)
+	OTHER_BUILD_DIR := $(RELEASE_BUILD_DIR)
+else
+	BUILD_DIR := $(RELEASE_BUILD_DIR)
+	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
+endif
+
 # The target static library and shared library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
 NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
@@ -167,7 +180,6 @@ ifeq ($(OSX), 1)
 endif
 
 # Debugging
-DEBUG ?= 0
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS := -DDEBUG -g -O0
 else
@@ -277,7 +289,19 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 runtest: $(TEST_ALL_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle
 
-$(ALL_BUILD_DIRS):
+$(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
+
+# Create a target ".linked" in this BUILD_DIR to tell Make that the "build" link
+# is currently correct, then delete the one in the OTHER_BUILD_DIR in case it
+# exists and $(DEBUG) is toggled later.
+$(BUILD_DIR)/.linked:
+	@ mkdir -p $(BUILD_DIR)
+	@ $(RM) $(OTHER_BUILD_DIR)/.linked
+	@ $(RM) $(BUILD_DIR_LINK)
+	@ ln -s $(BUILD_DIR) $(BUILD_DIR_LINK)
+	@ touch $@
+
+$(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
 $(NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
@@ -357,7 +381,7 @@ proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
 $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
 		$(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
-	protoc --proto_path=src --cpp_out=build/src $<
+	protoc --proto_path=src --cpp_out=$(BUILD_DIR)/src $<
 	@ echo
 
 $(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
@@ -370,6 +394,8 @@ $(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
 
 clean:
 	@- $(RM) -rf $(ALL_BUILD_DIRS)
+	@- $(RM) -rf $(OTHER_BUILD_DIR)
+	@- $(RM) -rf $(BUILD_DIR_LINK)
 	@- $(RM) -rf $(DISTRIBUTE_DIR)
 	@- $(RM) $(PY$(PROJECT)_SO)
 	@- $(RM) $(MAT$(PROJECT)_SO)

From add82f97ce3eec3b8fec55457eea8f4a6dd7daad Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 2 Jun 2014 23:57:37 -0700
Subject: [PATCH 0089/2053] update .gitignore appropriately for separate
 debug/release build dirs

---
 .gitignore | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index fee9a6baac1..292716666f1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,7 +27,9 @@
 *.mex*
 
 # build, distribute, and bins
-build/*
+build
+.build_debug/*
+.build_release/*
 distribute/*
 *.testbin
 *.bin

From 713c873a0a7651a4c7ac0e91438757ab90bddc72 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 5 Jun 2014 17:20:30 -0700
Subject: [PATCH 0090/2053] Make TanH cleaner, more efficient, and possible to
 use in-place

---
 src/caffe/layers/tanh_layer.cpp | 11 +++++------
 src/caffe/layers/tanh_layer.cu  | 20 +++++++-------------
 2 files changed, 12 insertions(+), 19 deletions(-)

diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 46d11d0a17a..66f530f82d5 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -18,8 +18,8 @@ Dtype TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype exp2x;
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
-    exp2x = exp(2*bottom_data[i]);
-    top_data[i] = (exp2x - Dtype(1))/(exp2x + Dtype(1));
+    exp2x = exp(2 * bottom_data[i]);
+    top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
   return Dtype(0);
 }
@@ -29,16 +29,15 @@ void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const bool propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
     Dtype exp2x;
     Dtype tanhx;
     for (int i = 0; i < count; ++i) {
-      exp2x = exp(2*bottom_data[i]);
-      tanhx = (exp2x - Dtype(1))/(exp2x + Dtype(1));
-      bottom_diff[i] = top_diff[i] * (1 - tanhx*tanhx);
+      tanhx = top_data[i];
+      bottom_diff[i] = top_diff[i] * (1 - tanhx * tanhx);
     }
   }
 }
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index 13bb001ffc0..aa822d84f38 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -13,8 +13,8 @@ namespace caffe {
 template <typename Dtype>
 __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
-    Dtype exp2x = exp(2*in[index]);
-    out[index] = (exp2x - Dtype(1))/(exp2x + Dtype(1));
+    Dtype exp2x = exp(2 * in[index]);
+    out[index] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
 }
 
@@ -28,21 +28,15 @@ Dtype TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   TanHForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
       count, bottom_data, top_data);
   CUDA_POST_KERNEL_CHECK;
-  // << " count: " << count << " bottom_data: "
-  //     << (unsigned long)bottom_data
-  //     << " top_data: " << (unsigned long)top_data
-  //     << " blocks: " << CAFFE_GET_BLOCKS(count)
-  //     << " threads: " << CAFFE_CUDA_NUM_THREADS;
   return Dtype(0);
 }
 
 template <typename Dtype>
 __global__ void TanHBackward(const int n, const Dtype* in_diff,
-    const Dtype* in_data, Dtype* out_diff) {
+    const Dtype* out_data, Dtype* out_diff) {
   CUDA_KERNEL_LOOP(index, n) {
-    Dtype exp2x = exp(2*in_data[index]);
-    Dtype tanhx = (exp2x - Dtype(1))/(exp2x + Dtype(1));
-    out_diff[index] = in_diff[index] * (1 - tanhx*tanhx);
+    Dtype tanhx = out_data[index];
+    out_diff[index] = in_diff[index] * (1 - tanhx * tanhx);
   }
 }
 
@@ -51,13 +45,13 @@ void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const bool propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const int count = (*bottom)[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     TanHBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, bottom_data, bottom_diff);
+        count, top_diff, top_data, bottom_diff);
     CUDA_POST_KERNEL_CHECK;
   }
 }

From 0d257e431404ad11df48816b85a94898bc86bf13 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 5 Jun 2014 10:53:42 -0700
Subject: [PATCH 0091/2053] padding for max pooling

Max pooling pads by -inf if the padding parameter is set.

Padding for pooling, like padding for convolution, can preserve the
dimensions of the bottom at the top. By setting the padding to
floor(kernel_size / 2) the top output is the "same" instead of the
"valid" part of the bottom input.
---
 src/caffe/layers/pooling_layer.cpp | 14 +++++++++-----
 src/caffe/layers/pooling_layer.cu  | 24 ++++++++++++++----------
 src/caffe/proto/caffe.proto        |  3 ++-
 3 files changed, 25 insertions(+), 16 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 5d6921f1da8..80ce4df6d29 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -32,9 +32,11 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   stride_ = this->layer_param_.pooling_param().stride();
   pad_ = this->layer_param_.pooling_param().pad();
   if (pad_ != 0) {
-    CHECK_EQ(this->layer_param_.pooling_param().pool(),
-             PoolingParameter_PoolMethod_AVE)
-        << "Padding implemented only for average pooling.";
+    CHECK(this->layer_param_.pooling_param().pool()
+        == PoolingParameter_PoolMethod_AVE
+        || this->layer_param_.pooling_param().pool()
+        == PoolingParameter_PoolMethod_MAX)
+        << "Padding implemented only for average and max pooling.";
   }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -92,10 +94,12 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_;
-            int wstart = pw * stride_;
+            int hstart = ph * stride_ - pad_;
+            int wstart = pw * stride_ - pad_;
             int hend = min(hstart + kernel_size_, height_);
             int wend = min(wstart + kernel_size_, width_);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
             const int pool_index = ph * pooled_width_ + pw;
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 31be47efc0c..7bb397bba47 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,17 +17,19 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* top_data,
+    const int kernel_size, const int stride, const int pad, Dtype* top_data,
     int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride;
+    int hstart = ph * stride - pad;
+    int wstart = pw * stride - pad;
     int hend = min(hstart + kernel_size, height);
-    int wstart = pw * stride;
     int wend = min(wstart + kernel_size, width);
+    hstart = max(hstart, 0);
+    wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
     int maxidx = -1;
     bottom_data += (n * channels + c) * height * width;
@@ -170,7 +172,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        top_data, mask, top_mask);
+        pad_, top_data, mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
@@ -212,7 +214,7 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int* mask, const Dtype* top_mask, const int num, const int channels,
     const int height, const int width, const int pooled_height,
     const int pooled_width, const int kernel_size, const int stride,
-    Dtype* bottom_diff) {
+    const int pad, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -220,10 +222,12 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     int h = (index / width) % height;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
+    int phstart =
+        (h + pad < kernel_size) ? 0 : (h + pad - kernel_size) / stride + 1;
+    int phend = min((h + pad) / stride + 1, pooled_height);
+    int pwstart =
+        (w + pad < kernel_size) ? 0 : (w + pad - kernel_size) / stride + 1;
+    int pwend = min((w + pad) / stride + 1, pooled_width);
     Dtype gradient = 0;
     int offset = (n * channels + c) * pooled_height * pooled_width;
     top_diff += offset;
@@ -342,7 +346,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, mask, top_mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
-        kernel_size_, stride_, bottom_diff);
+        kernel_size_, stride_, pad_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 7d44085b118..e540a95a921 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -353,7 +353,8 @@ message PoolingParameter {
   optional PoolMethod pool = 1 [default = MAX]; // The pooling method
   optional uint32 kernel_size = 2; // The kernel size
   optional uint32 stride = 3 [default = 1]; // The stride
-  // The padding size -- currently implemented only for average pooling.
+  // The padding size -- currently implemented only for average and max pooling.
+  // average pooling zero pads. max pooling -inf pads.
   optional uint32 pad = 4 [default = 0];
 }
 

From 223918825e12e3e1147cc196d8527147fad8c675 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 6 Jun 2014 13:59:19 -0700
Subject: [PATCH 0092/2053] check the last pooling in padding and add padded
 max pooling test

[Adapted from a commit by @jeffdonahue by @shelhamer.]
---
 src/caffe/layers/pooling_layer.cpp    | 19 +++++-
 src/caffe/layers/pooling_layer.cu     |  5 +-
 src/caffe/test/test_pooling_layer.cpp | 94 +++++++++++++++++++++++++++
 3 files changed, 112 insertions(+), 6 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 80ce4df6d29..929d7dfaa02 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -37,6 +37,7 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
         || this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_MAX)
         << "Padding implemented only for average and max pooling.";
+    CHECK_LT(pad_, kernel_size_);
   }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -45,6 +46,18 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       height_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
   pooled_width_ = static_cast<int>(ceil(static_cast<float>(
       width_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
+  if (pad_) {
+    // If we have padding, ensure that the last pooling starts strictly
+    // inside the image (instead of at the padding); otherwise clip the last.
+    if ((pooled_height_ - 1) * stride_ >= height_ + pad_) {
+      --pooled_height_;
+    }
+    if ((pooled_width_ - 1) * stride_ >= width_ + pad_) {
+      --pooled_width_;
+    }
+    CHECK_LT((pooled_height_ - 1) * stride_, height_ + pad_);
+    CHECK_LT((pooled_width_ - 1) * stride_, width_ + pad_);
+  }
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
   if (top->size() > 1) {
@@ -71,13 +84,13 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  // Different pooling methods. We explicitly do the switch outside the for
-  // loop to save time, although this results in more codes.
-  int top_count = (*top)[0]->count();
+  const int top_count = (*top)[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top->size() > 1;
   int* mask;
   Dtype* top_mask;
+  // Different pooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more code.
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // Initialize
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 7bb397bba47..abba6252cc6 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -328,8 +328,8 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  int count = (*bottom)[0]->count();
-  CUDA_CHECK(cudaMemset(bottom_diff, 0, sizeof(Dtype) * count));
+  const int count = (*bottom)[0]->count();
+  caffe_gpu_set(count, Dtype(0.), bottom_diff);
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
   const int* mask = NULL;
@@ -341,7 +341,6 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     } else {
       mask = max_idx_->gpu_data();
     }
-    caffe_gpu_set(count, Dtype(0.), bottom_diff);
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, mask, top_mask, top[0]->num(), channels_,
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 6628fc5dfc6..b13d11f6c6d 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -232,6 +232,7 @@ TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
+  pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
   Caffe::set_mode(Caffe::CPU);
   PoolingLayer<TypeParam> layer(layer_param);
@@ -245,6 +246,7 @@ TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
+  pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
   Caffe::set_mode(Caffe::GPU);
   PoolingLayer<TypeParam> layer(layer_param);
@@ -253,6 +255,98 @@ TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxPadded) {
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  pooling_param->set_pad(2);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+  Caffe::set_mode(Caffe::CPU);
+  this->blob_bottom_->Reshape(1, 1, 3, 3);
+  // Input:
+  //     [ 1 2 4 ]
+  //     [ 2 3 2 ]
+  //     [ 4 2 1 ]
+  this->blob_bottom_->mutable_cpu_data()[0] = 1;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 4;
+  this->blob_bottom_->mutable_cpu_data()[3] = 2;
+  this->blob_bottom_->mutable_cpu_data()[4] = 3;
+  this->blob_bottom_->mutable_cpu_data()[5] = 2;
+  this->blob_bottom_->mutable_cpu_data()[6] = 4;
+  this->blob_bottom_->mutable_cpu_data()[7] = 2;
+  this->blob_bottom_->mutable_cpu_data()[8] = 1;
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  TypeParam epsilon = 1e-8;
+  // Output:
+  //     [ 1 4 4 ]
+  //     [ 4 4 4 ]
+  //     [ 4 4 1 ]
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 1, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
+}
+
+
+TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxPadded) {
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  pooling_param->set_pad(2);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+  Caffe::set_mode(Caffe::GPU);
+  this->blob_bottom_->Reshape(1, 1, 3, 3);
+  // Input:
+  //     [ 1 2 4 ]
+  //     [ 2 3 2 ]
+  //     [ 4 2 1 ]
+  this->blob_bottom_->mutable_cpu_data()[0] = 1;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 4;
+  this->blob_bottom_->mutable_cpu_data()[3] = 2;
+  this->blob_bottom_->mutable_cpu_data()[4] = 3;
+  this->blob_bottom_->mutable_cpu_data()[5] = 2;
+  this->blob_bottom_->mutable_cpu_data()[6] = 4;
+  this->blob_bottom_->mutable_cpu_data()[7] = 2;
+  this->blob_bottom_->mutable_cpu_data()[8] = 1;
+  PoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  TypeParam epsilon = 1e-8;
+  // Output:
+  //     [ 1 4 4 ]
+  //     [ 4 4 4 ]
+  //     [ 4 4 1 ]
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 1, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
+}
+
+
 TYPED_TEST(PoolingLayerTest, TestCPUGradientMaxTopMask) {
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();

From 781761eb74744bb00f6b9579eecbe868e180c570 Mon Sep 17 00:00:00 2001
From: Jerod Weinman <jerod@acm.org>
Date: Sun, 8 Jun 2014 15:46:23 -0500
Subject: [PATCH 0093/2053] Changed variable name: iscolor to is_color.

---
 include/caffe/util/io.hpp  |  2 +-
 src/caffe/util/io.cpp      | 11 ++++++-----
 tools/convert_imageset.cpp |  6 +++---
 3 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 829293ecaef..4458096ef78 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -61,7 +61,7 @@ inline void WriteProtoToBinaryFile(
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool iscolor, Datum* datum);
+    const int height, const int width, const bool is_color, Datum* datum);
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, Datum* datum) {
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index f2650e9a22a..65f82547071 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -72,9 +72,10 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool iscolor, Datum* datum) {
+    const int height, const int width, const bool is_color, Datum* datum) {
   cv::Mat cv_img;
-  int cv_read_flag = (iscolor ? CV_LOAD_IMAGE_COLOR : CV_LOAD_IMAGE_GRAYSCALE);
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
   if (height > 0 && width > 0) {
     cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
     cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
@@ -85,7 +86,7 @@ bool ReadImageToDatum(const string& filename, const int label,
     LOG(ERROR) << "Could not open or find file " << filename;
     return false;
   }
-  int num_channels = (iscolor ? 3 : 1);
+  int num_channels = (is_color ? 3 : 1);
   datum->set_channels(num_channels);
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
@@ -93,7 +94,7 @@ bool ReadImageToDatum(const string& filename, const int label,
   datum->clear_data();
   datum->clear_float_data();
   string* datum_string = datum->mutable_data();
-  if (iscolor) {
+  if (is_color) {
     for (int c = 0; c < num_channels; ++c) {
       for (int h = 0; h < cv_img.rows; ++h) {
         for (int w = 0; w < cv_img.cols; ++w) {
@@ -102,7 +103,7 @@ bool ReadImageToDatum(const string& filename, const int label,
         }
       }
     }
-  } else {  // Faster than repeatedly testing iscolor for each pixel w/i loop
+  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
     for (int h = 0; h < cv_img.rows; ++h) {
       for (int w = 0; w < cv_img.cols; ++w) {
         datum_string->push_back(
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index bbf848adc8a..524d1950cb0 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -45,8 +45,8 @@ int main(int argc, char** argv) {
   }
 
   // Test whether argv[1] == "-g"
-  bool iscolor= !(string("-g") == string(argv[1]));
-  int  arg_offset = (iscolor ? 0 : 1);
+  bool is_color= !(string("-g") == string(argv[1]));
+  int  arg_offset = (is_color ? 0 : 1);
   std::ifstream infile(argv[arg_offset+2]);
   std::vector<std::pair<string, int> > lines;
   string filename;
@@ -89,7 +89,7 @@ int main(int argc, char** argv) {
   bool data_size_initialized = false;
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     if (!ReadImageToDatum(root_folder + lines[line_id].first,
-         lines[line_id].second, resize_height, resize_width, iscolor, &datum)) {
+        lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
       continue;
     }
     if (!data_size_initialized) {

From 25aa12922c87a8a89e5b9c92d8f3d3aa261c8167 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Jun 2014 18:08:24 -0700
Subject: [PATCH 0094/2053] fix Makefile build dir link upgrade bug reported by
 @jamt9000

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 2c42516cebf..c43b30ee33a 100644
--- a/Makefile
+++ b/Makefile
@@ -297,7 +297,7 @@ $(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
 $(BUILD_DIR)/.linked:
 	@ mkdir -p $(BUILD_DIR)
 	@ $(RM) $(OTHER_BUILD_DIR)/.linked
-	@ $(RM) $(BUILD_DIR_LINK)
+	@ $(RM) -r $(BUILD_DIR_LINK)
 	@ ln -s $(BUILD_DIR) $(BUILD_DIR_LINK)
 	@ touch $@
 

From 69af0c8955c9be0c37e37d9fab702e1167c31b08 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 6 Jun 2014 15:40:26 -0700
Subject: [PATCH 0095/2053] layers declare their names and number of
 input/output blobs, and don't check top/bottom blob counts explicitly in
 SetUp; instead call base Layer::SetUp.

---
 include/caffe/data_layers.hpp            | 35 +++++++++-
 include/caffe/layer.hpp                  | 72 +++++++++++++++++++-
 include/caffe/loss_layers.hpp            | 27 ++++++++
 include/caffe/neuron_layers.hpp          | 34 ++++++++++
 include/caffe/vision_layers.hpp          | 83 +++++++++++++++++++++++-
 src/caffe/layers/accuracy_layer.cpp      |  3 +-
 src/caffe/layers/argmax_layer.cpp        |  3 +-
 src/caffe/layers/concat_layer.cpp        |  6 +-
 src/caffe/layers/conv_layer.cpp          |  3 +-
 src/caffe/layers/data_layer.cpp          |  4 +-
 src/caffe/layers/eltwise_layer.cpp       |  5 +-
 src/caffe/layers/flatten_layer.cpp       |  3 +-
 src/caffe/layers/hdf5_data_layer.cpp     |  4 +-
 src/caffe/layers/hdf5_output_layer.cpp   |  8 ---
 src/caffe/layers/im2col_layer.cpp        |  3 +-
 src/caffe/layers/image_data_layer.cpp    |  3 +-
 src/caffe/layers/inner_product_layer.cpp |  3 +-
 src/caffe/layers/loss_layer.cpp          |  3 +-
 src/caffe/layers/lrn_layer.cpp           |  5 +-
 src/caffe/layers/memory_data_layer.cpp   |  3 +-
 src/caffe/layers/neuron_layer.cpp        |  3 +-
 src/caffe/layers/pooling_layer.cpp       | 12 ++--
 src/caffe/layers/softmax_layer.cpp       |  3 +-
 src/caffe/layers/softmax_loss_layer.cpp  |  3 +-
 src/caffe/layers/split_layer.cpp         |  3 +-
 src/caffe/layers/window_data_layer.cpp   |  4 +-
 26 files changed, 272 insertions(+), 66 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index d9865ce485b..4678b327994 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -28,7 +28,15 @@ class HDF5OutputLayer : public Layer<Dtype> {
   explicit HDF5OutputLayer(const LayerParameter& param);
   virtual ~HDF5OutputLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HDF5_OUTPUT;
+  }
+  // TODO: no limit on the number of blobs
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
   inline std::string file_name() const { return file_name_; }
 
  protected:
@@ -58,6 +66,12 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HDF5_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -96,6 +110,13 @@ class DataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -141,6 +162,12 @@ class ImageDataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_IMAGE_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -188,6 +215,12 @@ class WindowDataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_WINDOW_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 14bba63594b..af3d5441b34 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -3,11 +3,14 @@
 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_
 
+#include <string>
 #include <vector>
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+using std::string;
 using std::vector;
 
 namespace caffe {
@@ -30,9 +33,12 @@ class Layer {
       }
     }
   virtual ~Layer() {}
-  // SetUp: your function should implement this.
+  // SetUp: your function should implement this, and call Layer::SetUp for
+  // common SetUp functionality.
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top) {
+    CheckBlobCounts(bottom, *top);
+  }
 
   // Forward and backward wrappers. You should implement the cpu and
   // gpu specific implementations instead, and should not change these
@@ -53,6 +59,31 @@ class Layer {
   // Writes the layer parameter to a protocol buffer
   virtual void ToProto(LayerParameter* param, bool write_diff = false);
 
+  // Returns the layer type as an enum value.
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_NONE;
+  }
+
+  // Returns the layer type name.
+  virtual inline const string& type_name() const {
+    return LayerParameter_LayerType_Name(type());
+  }
+
+  // These methods can be overwritten to declare that this layer type expects
+  // a certain number of blobs as input and output.
+  //
+  // ExactNum{Bottom,Top}Blobs return a non-negative number to require an exact
+  // number of bottom/top blobs; the Min/Max versions return a non-negative
+  // number to require a minimum and/or maximum number of blobs.
+  // If Exact is specified, neither Min nor Max should be specified, and vice
+  // versa.  These methods may not rely on SetUp having been called.
+  virtual inline int ExactNumBottomBlobs() const { return -1; }
+  virtual inline int MinBottomBlobs() const { return -1; }
+  virtual inline int MaxBottomBlobs() const { return -1; }
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return -1; }
+  virtual inline int MaxTopBlobs() const { return -1; }
+
  protected:
   // The protobuf that stores the layer parameters
   LayerParameter layer_param_;
@@ -82,6 +113,43 @@ class Layer {
     Backward_cpu(top, propagate_down, bottom);
   }
 
+  // CheckBlobCounts: called by the parent Layer's SetUp to check that the
+  // number of bottom and top Blobs provided as input match the expected
+  // numbers specified by the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
+  virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
+                               const vector<Blob<Dtype>*>& top) {
+    if (ExactNumBottomBlobs() >= 0) {
+      CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
+          << type_name() << " Layer takes " << ExactNumBottomBlobs()
+          << " bottom blob(s) as input.";
+    }
+    if (MinBottomBlobs() >= 0) {
+      CHECK_LE(MinBottomBlobs(), bottom.size())
+          << type_name() << " Layer takes at least " << MinBottomBlobs()
+          << " bottom blob(s) as input.";
+    }
+    if (MaxBottomBlobs() >= 0) {
+      CHECK_GE(MaxBottomBlobs(), bottom.size())
+          << type_name() << " Layer takes at most " << MaxBottomBlobs()
+          << " bottom blob(s) as input.";
+    }
+    if (ExactNumTopBlobs() >= 0) {
+      CHECK_EQ(ExactNumTopBlobs(), top.size())
+          << type_name() << " Layer produces " << ExactNumTopBlobs()
+          << " top blob(s) as output.";
+    }
+    if (MinTopBlobs() >= 0) {
+      CHECK_LE(MinTopBlobs(), top.size())
+          << type_name() << " Layer produces at least " << MinTopBlobs()
+          << " top blob(s) as output.";
+    }
+    if (MaxTopBlobs() >= 0) {
+      CHECK_GE(MaxTopBlobs(), top.size())
+          << type_name() << " Layer produces at most " << MaxTopBlobs()
+          << " top blob(s) as output.";
+    }
+  }
+
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index cc798c499d3..381bf0f4f8f 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -35,6 +35,9 @@ class LossLayer : public Layer<Dtype> {
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
   virtual void FurtherSetUp(
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {}
+
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
 };
 
 /* SigmoidCrossEntropyLossLayer
@@ -49,6 +52,10 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -81,6 +88,10 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -100,6 +111,10 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_INFOGAIN_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -117,6 +132,10 @@ class HingeLossLayer : public LossLayer<Dtype> {
   explicit HingeLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HINGE_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -134,6 +153,10 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -153,6 +176,10 @@ class AccuracyLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ACCURACY;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 5118e8a5225..ed664df7570 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -33,6 +33,12 @@ class NeuronLayer : public Layer<Dtype> {
      : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_NONE;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
 };
 
 /* BNLLLayer
@@ -48,6 +54,10 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   explicit BNLLLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_BNLL;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -77,6 +87,10 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DROPOUT;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -107,6 +121,10 @@ class PowerLayer : public NeuronLayer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_POWER;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -138,6 +156,10 @@ class ReLULayer : public NeuronLayer<Dtype> {
   explicit ReLULayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_RELU;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -167,6 +189,10 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   explicit SigmoidLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SIGMOID;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -191,6 +217,10 @@ class TanHLayer : public NeuronLayer<Dtype> {
   explicit TanHLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_TANH;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -220,6 +250,10 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_THRESHOLD;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index a43f7f98c09..39779800bee 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -34,6 +34,12 @@ class ArgMaxLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ARGMAX;
+  }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -56,6 +62,12 @@ class ConcatLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONCAT;
+  }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -85,6 +97,12 @@ class ConvolutionLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONVOLUTION;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -123,6 +141,12 @@ class EltwiseLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ELTWISE;
+  }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -147,6 +171,12 @@ class FlattenLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_FLATTEN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -170,6 +200,12 @@ class Im2colLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_IM2COL;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -198,6 +234,12 @@ class InnerProductLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_INNER_PRODUCT;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -230,6 +272,12 @@ class LRNLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_LRN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -285,7 +333,7 @@ class LRNLayer : public Layer<Dtype> {
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
-/* PoolingLayer
+/* MemoryDataLayer
 */
 template <typename Dtype>
 class MemoryDataLayer : public Layer<Dtype> {
@@ -294,6 +342,13 @@ class MemoryDataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MEMORY_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() { return 0; }
+  virtual inline int ExactNumTopBlobs() { return 2; }
+
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
@@ -331,6 +386,13 @@ class PoolingLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_POOLING;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return max_top_blobs_; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -341,6 +403,7 @@ class PoolingLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const bool propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  int max_top_blobs_;
   int kernel_size_;
   int stride_;
   int pad_;
@@ -363,6 +426,12 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -395,6 +464,12 @@ class SoftmaxWithLossLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX_LOSS;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -423,6 +498,12 @@ class SplitLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SPLIT;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 3e671704465..fbc943eaf2d 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -17,8 +17,7 @@ namespace caffe {
 template <typename Dtype>
 void AccuracyLayer<Dtype>::SetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Accuracy Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 1) << "Accuracy Layer takes 1 output.";
+  Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   CHECK_EQ(bottom[1]->channels(), 1);
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index e04588d4baa..70ccfff5a36 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -12,8 +12,7 @@ namespace caffe {
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "ArgMaxLayer Layer takes 1 input.";
-  CHECK_EQ(top->size(), 1) << "ArgMaxLayer Layer takes 1 output.";
+  Layer<Dtype>::SetUp(bottom, top);
   out_max_val_ = this->layer_param_.argmax_param().out_max_val();
   if (out_max_val_) {
     // Produces max_ind and max_val
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 8036bdab675..4541ee74215 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -11,11 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void ConcatLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_GT(bottom.size(), 1) <<
-    "ConcatLayer takes at least two blobs as input.";
-  CHECK_EQ(top->size(), 1) <<
-    "ConcatLayer takes a single blob as output.";
-
+  Layer<Dtype>::SetUp(bottom, top);
   concat_dim_ = this->layer_param_.concat_param().concat_dim();
   CHECK_GE(concat_dim_, 0) <<
     "concat_dim should be >= 0";
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 55966b54bde..880b83afccb 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -13,8 +13,7 @@ namespace caffe {
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Conv Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Conv Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   kernel_size_ = this->layer_param_.convolution_param().kernel_size();
   stride_ = this->layer_param_.convolution_param().stride();
   group_ = this->layer_param_.convolution_param().group();
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 6d7392a6b2a..348753e5db9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -129,9 +129,7 @@ DataLayer<Dtype>::~DataLayer<Dtype>() {
 template <typename Dtype>
 void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "Data Layer takes no input blobs.";
-  CHECK_GE(top->size(), 1) << "Data Layer takes at least one blob as output.";
-  CHECK_LE(top->size(), 2) << "Data Layer takes at most two blobs as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   if (top->size() == 1) {
     output_labels_ = false;
   } else {
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 5d05124c4c8..5e5d760c993 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -11,10 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_GE(bottom.size(), 2) <<
-      "Eltwise Layer takes at least 2 blobs as input.";
-  CHECK_EQ(top->size(), 1) <<
-      "Eltwise Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   CHECK(this->layer_param().eltwise_param().coeff_size() == 0
       || this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<
       "Eltwise Layer takes one coefficient per bottom blob.";
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index e954030d260..95f4859f8d9 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -11,8 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void FlattenLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Flatten Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Flatten Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   int channels_out = bottom[0]->channels() * bottom[0]->height()
       * bottom[0]->width();
   (*top)[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index cff4f7c7318..d5c64f05696 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -52,9 +52,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "HDF5DataLayer takes no input blobs.";
-  CHECK_EQ(top->size(), 2) << "HDF5DataLayer takes two blobs as output.";
-
+  Layer<Dtype>::SetUp(bottom, top);
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
   LOG(INFO) << "Loading filename from " << source;
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index e491697e17c..0961b9b73a3 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -41,14 +41,6 @@ void HDF5OutputLayer<Dtype>::SaveBlobs() {
   LOG(INFO) << "Successfully saved " << data_blob_.num() << " rows";
 }
 
-template <typename Dtype>
-void HDF5OutputLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // TODO: no limit on the number of blobs
-  CHECK_EQ(bottom.size(), 2) << "HDF5OutputLayer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "HDF5OutputLayer takes no output blobs.";
-}
-
 template <typename Dtype>
 Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 749ea3c2d6a..f0c26c9a1cb 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -12,8 +12,7 @@ namespace caffe {
 template <typename Dtype>
 void Im2colLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Im2col Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Im2col Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   kernel_size_ = this->layer_param_.convolution_param().kernel_size();
   stride_ = this->layer_param_.convolution_param().stride();
   pad_ = this->layer_param_.convolution_param().pad();
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index ed064d0608d..9a79e72fedc 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -140,8 +140,7 @@ ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
 template <typename Dtype>
 void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "Input Layer takes no input blobs.";
-  CHECK_EQ(top->size(), 2) << "Input Layer takes two blobs as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   const int new_height  = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_height();
   CHECK((new_height == 0 && new_width == 0) ||
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index c60261e9486..971254c9c45 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -14,8 +14,7 @@ namespace caffe {
 template <typename Dtype>
 void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "IP Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "IP Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
   // Figure out the dimensions
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 1efb6235f98..14ea975ad0d 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -17,8 +17,7 @@ namespace caffe {
 template <typename Dtype>
 void LossLayer<Dtype>::SetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "Loss Layer takes no output.";
+  Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   FurtherSetUp(bottom, top);
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 6f7af75957a..071e7198544 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -11,10 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) <<
-      "Local Response Normalization Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) <<
-      "Local Response Normalization Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 60bce27b8c9..15eedb317e3 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -10,8 +10,7 @@ namespace caffe {
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "Memory Data Layer takes no blobs as input.";
-  CHECK_EQ(top->size(), 2) << "Memory Data Layer takes two blobs as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
   datum_channels_ = this->layer_param_.memory_data_param().channels();
   datum_height_ = this->layer_param_.memory_data_param().height();
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index e9dbd0eb75c..1b8fcecd482 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -10,8 +10,7 @@ namespace caffe {
 template <typename Dtype>
 void NeuronLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Neuron Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Neuron Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   // NeuronLayer allows in-place computations. If the computation is not
   // in-place, we will need to initialize the top blob.
   if ((*top)[0] != bottom[0]) {
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 929d7dfaa02..8f5f82d6ff7 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -18,16 +18,16 @@ namespace caffe {
 template <typename Dtype>
 void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "PoolingLayer takes a single blob as input.";
+  // Set the max number of top blobs before calling base Layer::SetUp.
+  // If doing MAX pooling, we can optionally output an extra top Blob
+  // for the mask.  Otherwise, we only have one top Blob.
   if (this->layer_param_.pooling_param().pool() ==
       PoolingParameter_PoolMethod_MAX) {
-    CHECK_GE(top->size(), 1)
-        << "MaxPoolingLayer takes at least one blob as output.";
-    CHECK_LE(top->size(), 2)
-        << "MaxPoolingLayer takes at most two blobs as output.";
+    max_top_blobs_ = 2;
   } else {
-    CHECK_EQ(top->size(), 1) << "PoolingLayer takes a single blob as output.";
+    max_top_blobs_ = 1;
   }
+  Layer<Dtype>::SetUp(bottom, top);
   kernel_size_ = this->layer_param_.pooling_param().kernel_size();
   stride_ = this->layer_param_.pooling_param().stride();
   pad_ = this->layer_param_.pooling_param().pad();
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index e9983608e94..dbe16da2343 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -14,8 +14,7 @@ namespace caffe {
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Softmax Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Softmax Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   sum_multiplier_.Reshape(1, bottom[0]->channels(),
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index fecd7a520df..ef6eebabadd 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -15,8 +15,7 @@ namespace caffe {
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "SoftmaxLoss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "SoftmaxLoss Layer takes no blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.push_back(&prob_);
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index aa2b6f6a308..2f99ca1842c 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -11,8 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void SplitLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Split Layer takes a single blob as input.";
-  CHECK_GE(top->size(), 1) << "Split Layer takes at least one blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   count_ = bottom[0]->count();
   for (int i = 0; i < top->size(); ++i) {
     // Allow the 0th top blob to be 'in-place', but no others.
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 862c0347082..e08bed7d56f 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -258,14 +258,12 @@ WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
 template <typename Dtype>
 void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
   // SetUp runs through the window_file and creates two structures
   // that hold windows: one for foreground (object) windows and one
   // for background (non-object) windows. We use an overlap threshold
   // to decide which is which.
 
-  CHECK_EQ(bottom.size(), 0) << "Window data Layer takes no input blobs.";
-  CHECK_EQ(top->size(), 2) << "Window data Layer prodcues two blobs as output.";
-
   // window_file format
   // repeated:
   //    # image_index

From fe969919bf2283ebaf6946dbe2e102b88a452240 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Jun 2014 12:45:17 -0700
Subject: [PATCH 0096/2053] move MemoryDataLayer decl. from vision_layers.hpp
 to data_layers.hpp

---
 include/caffe/data_layers.hpp   | 42 ++++++++++++++++++++++++++++++++
 include/caffe/vision_layers.hpp | 43 ---------------------------------
 2 files changed, 42 insertions(+), 43 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 4678b327994..87be0f8e742 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -198,6 +198,48 @@ class ImageDataLayer : public Layer<Dtype> {
   Caffe::Phase phase_;
 };
 
+/* MemoryDataLayer
+*/
+template <typename Dtype>
+class MemoryDataLayer : public Layer<Dtype> {
+ public:
+  explicit MemoryDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MEMORY_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() { return 0; }
+  virtual inline int ExactNumTopBlobs() { return 2; }
+
+  // Reset should accept const pointers, but can't, because the memory
+  //  will be given to Blob, which is mutable
+  void Reset(Dtype* data, Dtype* label, int n);
+  int datum_channels() { return datum_channels_; }
+  int datum_height() { return datum_height_; }
+  int datum_width() { return datum_width_; }
+  int batch_size() { return batch_size_; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+
+  Dtype* data_;
+  Dtype* labels_;
+  int datum_channels_;
+  int datum_height_;
+  int datum_width_;
+  int datum_size_;
+  int batch_size_;
+  int n_;
+  int pos_;
+};
 
 // This function is used to create a pthread that prefetches the window data.
 template <typename Dtype>
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 39779800bee..eb067bb624e 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -333,49 +333,6 @@ class LRNLayer : public Layer<Dtype> {
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
-/* MemoryDataLayer
-*/
-template <typename Dtype>
-class MemoryDataLayer : public Layer<Dtype> {
- public:
-  explicit MemoryDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MEMORY_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() { return 0; }
-  virtual inline int ExactNumTopBlobs() { return 2; }
-
-  // Reset should accept const pointers, but can't, because the memory
-  //  will be given to Blob, which is mutable
-  void Reset(Dtype* data, Dtype* label, int n);
-  int datum_channels() { return datum_channels_; }
-  int datum_height() { return datum_height_; }
-  int datum_width() { return datum_width_; }
-  int batch_size() { return batch_size_; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
-
-  Dtype* data_;
-  Dtype* labels_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  int batch_size_;
-  int n_;
-  int pos_;
-};
-
 /* PoolingLayer
 */
 template <typename Dtype>

From 62b3537117eb0ec87dac9c403e558c72d6ab56fc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 8 Jun 2014 20:44:36 -0700
Subject: [PATCH 0097/2053] add fish bike example image

---
 examples/images/fish-bike.jpg | Bin 0 -> 47638 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 examples/images/fish-bike.jpg

diff --git a/examples/images/fish-bike.jpg b/examples/images/fish-bike.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..39d9bd432e202c3b34d2585c946aa1192cee226d
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HcmV?d00001


From e17b3fea57b6166a4cd65e604bf5e5e702c08c21 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Jun 2014 22:44:32 -0700
Subject: [PATCH 0098/2053] fix ArgMaxLayer bug in num bottom blobs decl.
 pointed out by @sguada

---
 include/caffe/vision_layers.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index eb067bb624e..fc3dbbe1938 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -37,7 +37,7 @@ class ArgMaxLayer : public Layer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ARGMAX;
   }
-  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:

From 6442be3994222b07192150a44367a211165dd42f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Jun 2014 14:58:53 -0700
Subject: [PATCH 0099/2053] add DummyDataLayer

---
 include/caffe/data_layers.hpp         |  27 +++++++
 src/caffe/layer_factory.cpp           |   2 +
 src/caffe/layers/dummy_data_layer.cpp | 100 ++++++++++++++++++++++++++
 src/caffe/proto/caffe.proto           |  26 +++++--
 4 files changed, 151 insertions(+), 4 deletions(-)
 create mode 100644 src/caffe/layers/dummy_data_layer.cpp

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 87be0f8e742..3d3ce94bc88 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -14,6 +14,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
@@ -146,6 +147,32 @@ class DataLayer : public Layer<Dtype> {
   Caffe::Phase phase_;
 };
 
+template <typename Dtype>
+class DummyDataLayer : public Layer<Dtype> {
+ public:
+  explicit DummyDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DUMMY_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+
+  vector<shared_ptr<Filler<Dtype> > > fillers_;
+  vector<bool> refill_;
+};
+
 // This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
 void* ImageDataLayerPrefetch(void* layer_pointer);
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 58c20b1f430..d6e506dfb9f 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -36,6 +36,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new DataLayer<Dtype>(param);
   case LayerParameter_LayerType_DROPOUT:
     return new DropoutLayer<Dtype>(param);
+  case LayerParameter_LayerType_DUMMY_DATA:
+    return new DummyDataLayer<Dtype>(param);
   case LayerParameter_LayerType_EUCLIDEAN_LOSS:
     return new EuclideanLossLayer<Dtype>(param);
   case LayerParameter_LayerType_ELTWISE:
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
new file mode 100644
index 00000000000..38568580485
--- /dev/null
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -0,0 +1,100 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const int num_top = top->size();
+  const DummyDataParameter& param = this->layer_param_.dummy_data_param();
+  const int num_data_filler = param.data_filler_size();
+  CHECK(num_data_filler == 0 || num_data_filler == 1 ||
+        num_data_filler == num_top)
+      << "Number of data fillers must be 0, 1 or equal to the number of tops: "
+      << num_top << "; you specified " << num_data_filler << " data fillers.";
+  CHECK(param.num_size() == 1 || param.num_size() == num_top)
+      << "Must specify either a single (1) 'num' or one for each top blob ("
+      << num_top << "); you specified " << param.num_size() << ".";
+  CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
+      << "Must specify either a single (1) 'channels' or one for each top blob ("
+      << num_top << "); you specified " << param.channels_size() << ".";
+  CHECK(param.height_size() == 1 || param.height_size() == num_top)
+      << "Must specify either a single (1) 'height' or one for each top blob ("
+      << num_top << "); you specified " << param.height_size() << ".";
+  CHECK(param.width_size() == 1 || param.width_size() == num_top)
+      << "Must specify either a single (1) 'width' or one for each top blob ("
+      << num_top << "); you specified " << param.width_size() << ".";
+  // refill_[i] tells Forward i whether or not to actually refill top Blob i.
+  // If refill_[i] is false, Forward does nothing for Blob i. We use this to
+  // avoid wastefully refilling "constant" Blobs in every forward pass.
+  // We first fill refill_ in with the INVERSE of its final values.
+  // The first time we run Forward from the SetUp method, we'll fill only the
+  // Blobs for which refill_ is normally false.  These Blobs will never be
+  // filled again.
+  refill_.clear();
+  fillers_.clear();
+  if (num_data_filler <= 1) {
+    FillerParameter filler_param;
+    if (num_data_filler == 0) {
+      filler_param.set_type("constant");
+      filler_param.set_value(0);
+    } else {
+      filler_param.CopyFrom(param.data_filler(0));
+    }
+    // Refill on each iteration iff not using a constant filler,
+    // but use the inverse of this rule for the first run.
+    refill_.resize(1);
+    refill_[0] = (strcmp(filler_param.type().c_str(), "constant") == 0);
+    fillers_.resize(1);
+    fillers_[0].reset(GetFiller<Dtype>(filler_param));
+  } else {
+    refill_.resize(num_top);
+    fillers_.resize(num_top);
+    for (int i = 0; i < num_top; ++i) {
+      fillers_[i].reset(GetFiller<Dtype>(param.data_filler(i)));
+      // Refill on each iteration iff not using a constant filler,
+      // but use the inverse of this rule for the first run.
+      refill_[i] =
+          (strcmp(param.data_filler(i).type().c_str(), "constant") == 0);
+    }
+  }
+  for (int i = 0; i < num_top; ++i) {
+    const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
+    const int channels =
+        (param.channels_size() == 1) ? param.channels(0) : param.channels(i);
+    const int height =
+        (param.height_size() == 1) ? param.height(0) : param.height(i);
+    const int width =
+        (param.width_size() == 1) ? param.width(0) : param.width(i);
+    (*top)[i]->Reshape(num, channels, height, width);
+  }
+  // Run Forward once, with refill_ inverted, to fill the constant Blobs.
+  Forward(bottom, top);
+  // Invert the inverted refill_ values to refill the desired (non-constant)
+  // Blobs in every usual forward pass.
+  for (int i = 0; i < refill_.size(); ++i) {
+    refill_[i] = !refill_[i];
+  }
+}
+
+template <typename Dtype>
+Dtype DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  for (int i = 0; i < top->size(); ++i) {
+    const int filler_id = (fillers_.size() > 1) ? i : 0;
+    if (refill_[filler_id]) {
+      fillers_[filler_id]->Fill((*top)[i]);
+    }
+  }
+  return Dtype(0.);
+}
+
+INSTANTIATE_CLASS(DummyDataLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e540a95a921..60c7daa9031 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -115,7 +115,7 @@ message SolverState {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 25 (last added: eltwise_param)
+// LayerParameter next available ID: 27 (last added: dummy_data_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -127,7 +127,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 32 (last added: THRESHOLD)
+  // LayerType next available ID: 33 (last added: DUMMY_DATA)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -140,6 +140,7 @@ message LayerParameter {
     CONVOLUTION = 4;
     DATA = 5;
     DROPOUT = 6;
+    DUMMY_DATA = 32;
     EUCLIDEAN_LOSS = 7;
     ELTWISE = 25;
     FLATTEN = 8;
@@ -175,13 +176,12 @@ message LayerParameter {
   // The weight decay that is multiplied on the global weight decay.
   repeated float weight_decay = 8;
 
-  // Parameters for particular layer types.
-  // Parameters next available ID: 26 (last added: ThresholdParameter)
   optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
   optional ConvolutionParameter convolution_param = 10;
   optional DataParameter data_param = 11;
   optional DropoutParameter dropout_param = 12;
+  optional DummyDataParameter dummy_data_param = 26;
   optional EltwiseParameter eltwise_param = 24;
   optional HDF5DataParameter hdf5_data_param = 13;
   optional HDF5OutputParameter hdf5_output_param = 14;
@@ -254,6 +254,24 @@ message DropoutParameter {
   optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
 }
 
+// Message that stores parameters used by DummyDataLayer.
+// DummyDataLayer fills any number of arbitrarily shaped blobs with random
+// (or constant) data generated by "Fillers" (see "message FillerParameter").
+message DummyDataParameter {
+  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
+  // num, N channels, N height, and N width fields, and must specify 0, 1 or N
+  // data_fillers.
+  //
+  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
+  // specified, the ith is applied to the ith top blob.
+  repeated FillerParameter data_filler = 1;
+  repeated uint32 num = 2;
+  repeated uint32 channels = 3;
+  repeated uint32 height = 4;
+  repeated uint32 width = 5;
+}
+
 // Message that stores parameters used by EltwiseLayer
 message EltwiseParameter {
   enum EltwiseOp {

From a8ab6277742ee414413c562855360eca5463c064 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Jun 2014 16:04:41 -0700
Subject: [PATCH 0100/2053] add DummyDataLayer tests

---
 src/caffe/test/test_dummy_data_layer.cpp | 202 +++++++++++++++++++++++
 1 file changed, 202 insertions(+)
 create mode 100644 src/caffe/test/test_dummy_data_layer.cpp

diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
new file mode 100644
index 00000000000..7d9287e86ab
--- /dev/null
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -0,0 +1,202 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <string>
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::string;
+using std::stringstream;
+
+namespace caffe {
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class DummyDataLayerTest : public ::testing::Test {
+ protected:
+  DummyDataLayerTest()
+      : blob_top_a_(new Blob<Dtype>()),
+        blob_top_b_(new Blob<Dtype>()),
+        blob_top_c_(new Blob<Dtype>()) {}
+
+  virtual void SetUp() {
+    blob_bottom_vec_.clear();
+    blob_top_vec_.clear();
+    blob_top_vec_.push_back(blob_top_a_);
+    blob_top_vec_.push_back(blob_top_b_);
+    blob_top_vec_.push_back(blob_top_c_);
+  }
+
+  virtual ~DummyDataLayerTest() {
+    delete blob_top_a_;
+    delete blob_top_b_;
+    delete blob_top_c_;
+  }
+
+  Blob<Dtype>* const blob_top_a_;
+  Blob<Dtype>* const blob_top_b_;
+  Blob<Dtype>* const blob_top_c_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(DummyDataLayerTest, Dtypes);
+
+TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
+  dummy_data_param->add_num(5);
+  dummy_data_param->add_channels(3);
+  dummy_data_param->add_height(2);
+  dummy_data_param->add_width(4);
+  this->blob_top_vec_.resize(1);
+  DummyDataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_a_->num(), 5);
+  EXPECT_EQ(this->blob_top_a_->channels(), 3);
+  EXPECT_EQ(this->blob_top_a_->height(), 2);
+  EXPECT_EQ(this->blob_top_a_->width(), 4);
+  EXPECT_EQ(this->blob_top_b_->count(), 0);
+  EXPECT_EQ(this->blob_top_c_->count(), 0);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+}
+
+TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
+  dummy_data_param->add_num(5);
+  dummy_data_param->add_channels(3);
+  dummy_data_param->add_height(2);
+  dummy_data_param->add_width(4);
+  dummy_data_param->add_num(5);
+  // Don't explicitly set number of channels or height for 2nd top blob; should
+  // default to first channels and height (as we check later).
+  dummy_data_param->add_height(1);
+  FillerParameter* data_filler_param = dummy_data_param->add_data_filler();
+  data_filler_param->set_value(7);
+  this->blob_top_vec_.resize(2);
+  DummyDataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_a_->num(), 5);
+  EXPECT_EQ(this->blob_top_a_->channels(), 3);
+  EXPECT_EQ(this->blob_top_a_->height(), 2);
+  EXPECT_EQ(this->blob_top_a_->width(), 4);
+  EXPECT_EQ(this->blob_top_b_->num(), 5);
+  EXPECT_EQ(this->blob_top_b_->channels(), 3);
+  EXPECT_EQ(this->blob_top_b_->height(), 1);
+  EXPECT_EQ(this->blob_top_b_->width(), 4);
+  EXPECT_EQ(this->blob_top_c_->count(), 0);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+}
+
+TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
+  dummy_data_param->add_num(5);
+  dummy_data_param->add_channels(3);
+  dummy_data_param->add_height(2);
+  dummy_data_param->add_width(4);
+  FillerParameter* data_filler_param_a = dummy_data_param->add_data_filler();
+  data_filler_param_a->set_value(7);
+  FillerParameter* data_filler_param_b = dummy_data_param->add_data_filler();
+  data_filler_param_b->set_type("gaussian");
+  TypeParam gaussian_mean = 3.0;
+  TypeParam gaussian_std = 0.01;
+  data_filler_param_b->set_mean(gaussian_mean);
+  data_filler_param_b->set_std(gaussian_std);
+  FillerParameter* data_filler_param_c = dummy_data_param->add_data_filler();
+  data_filler_param_c->set_value(9);
+  DummyDataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_a_->num(), 5);
+  EXPECT_EQ(this->blob_top_a_->channels(), 3);
+  EXPECT_EQ(this->blob_top_a_->height(), 2);
+  EXPECT_EQ(this->blob_top_a_->width(), 4);
+  EXPECT_EQ(this->blob_top_b_->num(), 5);
+  EXPECT_EQ(this->blob_top_b_->channels(), 3);
+  EXPECT_EQ(this->blob_top_b_->height(), 2);
+  EXPECT_EQ(this->blob_top_b_->width(), 4);
+  EXPECT_EQ(this->blob_top_c_->num(), 5);
+  EXPECT_EQ(this->blob_top_c_->channels(), 3);
+  EXPECT_EQ(this->blob_top_c_->height(), 2);
+  EXPECT_EQ(this->blob_top_c_->width(), 4);
+  for (int i = 0; i < this->blob_top_a_->count(); ++i) {
+    EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
+  }
+  // Blob b uses a Gaussian filler, so SetUp should not have initialized it.
+  // Blob b's data should therefore be the default Blob data value: 0.
+  for (int i = 0; i < this->blob_top_b_->count(); ++i) {
+    EXPECT_EQ(0, this->blob_top_b_->cpu_data()[i]);
+  }
+  for (int i = 0; i < this->blob_top_c_->count(); ++i) {
+    EXPECT_EQ(9, this->blob_top_c_->cpu_data()[i]);
+  }
+
+  // Do a Forward pass to fill in Blob b with Gaussian data.
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_a_->count(); ++i) {
+    EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
+  }
+  // Check that the Gaussian's data has been filled in with values within
+  // 10 standard deviations of the mean. Record the first and last sample.
+  // to check that they're different after the next Forward pass.
+  for (int i = 0; i < this->blob_top_b_->count(); ++i) {
+    EXPECT_NEAR(gaussian_mean, this->blob_top_b_->cpu_data()[i],
+                gaussian_std * 10);
+  }
+  const TypeParam first_gaussian_sample = this->blob_top_b_->cpu_data()[0];
+  const TypeParam last_gaussian_sample =
+      this->blob_top_b_->cpu_data()[this->blob_top_b_->count() - 1];
+  for (int i = 0; i < this->blob_top_c_->count(); ++i) {
+    EXPECT_EQ(9, this->blob_top_c_->cpu_data()[i]);
+  }
+
+  // Do another Forward pass to fill in Blob b with Gaussian data again,
+  // checking that we get different values.
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_a_->count(); ++i) {
+    EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
+  }
+  for (int i = 0; i < this->blob_top_b_->count(); ++i) {
+    EXPECT_NEAR(gaussian_mean, this->blob_top_b_->cpu_data()[i],
+                gaussian_std * 10);
+  }
+  EXPECT_NE(first_gaussian_sample, this->blob_top_b_->cpu_data()[0]);
+  EXPECT_NE(last_gaussian_sample,
+      this->blob_top_b_->cpu_data()[this->blob_top_b_->count() - 1]);
+  for (int i = 0; i < this->blob_top_c_->count(); ++i) {
+    EXPECT_EQ(9, this->blob_top_c_->cpu_data()[i]);
+  }
+}
+
+}  // namespace caffe

From bbb8e40acbe0613370bcdf8549db82ca3a5b5adf Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 9 Jun 2014 11:52:06 -0700
Subject: [PATCH 0101/2053] lint dummy data layer

---
 src/caffe/layers/dummy_data_layer.cpp | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 38568580485..8925772f3a5 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -19,17 +19,17 @@ void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       << "Number of data fillers must be 0, 1 or equal to the number of tops: "
       << num_top << "; you specified " << num_data_filler << " data fillers.";
   CHECK(param.num_size() == 1 || param.num_size() == num_top)
-      << "Must specify either a single (1) 'num' or one for each top blob ("
-      << num_top << "); you specified " << param.num_size() << ".";
+      << "Must specify either a single (1) 'num' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.num_size() << ".";
   CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
-      << "Must specify either a single (1) 'channels' or one for each top blob ("
-      << num_top << "); you specified " << param.channels_size() << ".";
+      << "Must specify either a single (1) 'channels' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.channels_size() << ".";
   CHECK(param.height_size() == 1 || param.height_size() == num_top)
-      << "Must specify either a single (1) 'height' or one for each top blob ("
-      << num_top << "); you specified " << param.height_size() << ".";
+      << "Must specify either a single (1) 'height' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.height_size() << ".";
   CHECK(param.width_size() == 1 || param.width_size() == num_top)
-      << "Must specify either a single (1) 'width' or one for each top blob ("
-      << num_top << "); you specified " << param.width_size() << ".";
+      << "Must specify either a single (1) 'width' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.width_size() << ".";
   // refill_[i] tells Forward i whether or not to actually refill top Blob i.
   // If refill_[i] is false, Forward does nothing for Blob i. We use this to
   // avoid wastefully refilling "constant" Blobs in every forward pass.

From a57b8d5b4093b7bdba9f02b278b9823c290a0b7d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 9 Jun 2014 18:12:16 -0700
Subject: [PATCH 0102/2053] fix old detect.py default

---
 python/detect.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/detect.py b/python/detect.py
index 15418bba5c2..05b524495d2 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -57,9 +57,9 @@ def main(argv):
     )
     parser.add_argument(
         "--crop_mode",
-        default="center_only",
+        default="selective_search",
         choices=CROP_MODES,
-        help="Image crop mode"
+        help="How to generate windows for detection."
     )
     parser.add_argument(
         "--gpu",

From 4992abecec7214bce3c07497438c2e1ff963e657 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 8 Jun 2014 20:31:35 -0700
Subject: [PATCH 0103/2053] pycaffe Detector crops with surrounding context

- caffe.Detector learned how to crop windows with context in the R-CNN
  style s.t. the bordero of the network input is a given amount of
  context.
- add --context_pad arg to detect.py for amount of context. Default is
  16, as in R-CNN.
---
 python/caffe/detector.py | 91 ++++++++++++++++++++++++++++++++++++----
 python/detect.py         |  9 +++-
 2 files changed, 92 insertions(+), 8 deletions(-)

diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 5a30dab92f5..b4e96029e98 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -12,10 +12,6 @@
 The selective_search_ijcv_with_python code required for the selective search
 proposal mode is available at
     https://github.com/sergeyk/selective_search_ijcv_with_python
-
-TODO
-- R-CNN crop mode / crop with context.
-- Bundle with R-CNN model for example.
 """
 import numpy as np
 import os
@@ -29,11 +25,14 @@ class Detector(caffe.Net):
     selective search proposals.
     """
     def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
-                 input_scale=None, channel_swap=None):
+                 input_scale=None, channel_swap=None, context_pad=None):
         """
         Take
         gpu, mean_file, input_scale, channel_swap: convenience params for
             setting mode, mean, input scale, and channel order.
+        context_pad: amount of surrounding context to take s.t. a `context_pad`
+            sized border of pixels in the network input image is context, as in
+            R-CNN feature extraction.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
         self.set_phase_test()
@@ -50,6 +49,8 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
         if channel_swap:
             self.set_channel_swap(self.inputs[0], channel_swap)
 
+        self.configure_crop(context_pad)
+
 
     def detect_windows(self, images_windows):
         """
@@ -58,6 +59,7 @@ def detect_windows(self, images_windows):
 
         Take
         images_windows: (image filename, window list) iterable.
+        context_crop: size of context border to crop in pixels.
 
         Give
         detections: list of {filename: image filename, window: crop coordinates,
@@ -68,8 +70,7 @@ def detect_windows(self, images_windows):
         for image_fname, windows in images_windows:
             image = caffe.io.load_image(image_fname).astype(np.float32)
             for window in windows:
-                window_inputs.append(image[window[0]:window[2],
-                                           window[1]:window[3]])
+                window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
         caffe_in = np.asarray([self.preprocess(self.inputs[0], window_in)
@@ -109,3 +110,79 @@ def detect_selective_search(self, image_fnames):
         windows_list = selective_search.get_windows(image_fnames)
         # Run windowed detection on the selective search list.
         return self.detect_windows(zip(image_fnames, windows_list))
+
+
+    def crop(self, im, window):
+        """
+        Crop a window from the image for detection. Include surrounding context
+        according to the `context_pad` configuration.
+
+        Take
+        im: H x W x K image ndarray to crop.
+        window: bounding box coordinates as ymin, xmin, ymax, xmax.
+
+        Give
+        crop: cropped window.
+        """
+        # Crop window from the image.
+        crop = im[window[0]:window[2], window[1]:window[3]]
+
+        if self.context_pad:
+            box = window.copy()
+            crop_size = self.blobs[self.inputs[0]].width  # assumes square
+            scale = crop_size / (1. * crop_size - self.context_pad * 2)
+            # Crop a box + surrounding context.
+            half_h = (box[2] - box[0] + 1) / 2.
+            half_w = (box[3] - box[1] + 1) / 2.
+            center = (box[0] + half_h, box[1] + half_w)
+            scaled_dims = scale * np.array((-half_h, -half_w, half_h, half_w))
+            box = np.round(np.tile(center, 2) + scaled_dims)
+            full_h = box[2] - box[0] + 1
+            full_w = box[3] - box[1] + 1
+            scale_h = crop_size / full_h
+            scale_w = crop_size / full_w
+            pad_y = round(max(0, -box[0]) * scale_h)  # amount out-of-bounds
+            pad_x = round(max(0, -box[1]) * scale_w)
+
+            # Clip box to image dimensions.
+            im_h, im_w = im.shape[:2]
+            box = np.clip(box, 0., [im_h, im_w, im_h, im_w])
+            clip_h = box[2] - box[0] + 1
+            clip_w = box[3] - box[1] + 1
+            assert(clip_h > 0 and clip_w > 0)
+            crop_h = round(clip_h * scale_h)
+            crop_w = round(clip_w * scale_w)
+            if pad_y + crop_h > crop_size:
+                crop_h = crop_size - pad_y
+            if pad_x + crop_w > crop_size:
+                crop_w = crop_size - pad_x
+
+            # collect with context padding and place in input
+            # with mean padding
+            context_crop = im[box[0]:box[2], box[1]:box[3]]
+            context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
+            crop = self.crop_mean.copy()
+            crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
+
+        return crop
+
+
+    def configure_crop(self, context_pad):
+        """
+        Configure amount of context for cropping.
+        If context is included, make the special input mean for context padding.
+
+        Take
+        context_pad: amount of context for cropping.
+        """
+        self.context_pad = context_pad
+        if self.context_pad:
+            input_scale = self.input_scale.get(self.inputs[0])
+            channel_order = self.channel_swap.get(self.inputs[0])
+            # Padding context crops needs the mean in unprocessed input space.
+            self.crop_mean = self.mean[self.inputs[0]].copy()
+            self.crop_mean = self.crop_mean.transpose((1,2,0))
+            channel_order_inverse = [channel_order.index(i)
+                                     for i in range(self.crop_mean.shape[2])]
+            self.crop_mean = self.crop_mean[:,:, channel_order_inverse]
+            self.crop_mean /= input_scale
diff --git a/python/detect.py b/python/detect.py
index 05b524495d2..a3bee5c5cf8 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -86,6 +86,12 @@ def main(argv):
              "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
 
     )
+    parser.add_argument(
+        "--context_pad",
+        type=int,
+        default='16',
+        help="Amount of surrounding context to collect in input window."
+    )
     args = parser.parse_args()
 
     channel_swap = [int(s) for s in args.channel_swap.split(',')]
@@ -93,7 +99,8 @@ def main(argv):
     # Make detector.
     detector = caffe.Detector(args.model_def, args.pretrained_model,
             gpu=args.gpu, mean_file=args.mean_file,
-            input_scale=args.input_scale, channel_swap=channel_swap)
+            input_scale=args.input_scale, channel_swap=channel_swap,
+            context_pad=args.context_pad)
 
     if args.gpu:
         print 'GPU mode'

From d443d4611e1a6740960a758e600de02f72e29dbc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 8 Jun 2014 16:53:06 -0700
Subject: [PATCH 0104/2053] make R-CNN the Caffe detection example

---
 docs/getting_pretrained_models.md             |   2 +
 examples/detection.ipynb                      | 671 +++++++++++-------
 .../imagenet/get_caffe_rcnn_imagenet_model.sh |  27 +
 .../imagenet/rcnn_imagenet_deploy.prototxt    | 207 ++++++
 4 files changed, 664 insertions(+), 243 deletions(-)
 create mode 100755 examples/imagenet/get_caffe_rcnn_imagenet_model.sh
 create mode 100644 examples/imagenet/rcnn_imagenet_deploy.prototxt

diff --git a/docs/getting_pretrained_models.md b/docs/getting_pretrained_models.md
index bd342f70ec3..a7b5e875c0d 100644
--- a/docs/getting_pretrained_models.md
+++ b/docs/getting_pretrained_models.md
@@ -24,4 +24,6 @@ This page will be updated as more models become available.
 - The best validation performance during training was iteration 358,000 with
   validation accuracy 57.258% and loss 1.83948.
 
+**R-CNN (ILSVRC13)**: The pure Caffe instantiation of the [R-CNN](https://github.com/rbgirshick/rcnn) model for ILSVRC13 detection. Download the model (230.8MB) by running `examples/imagenet/get_caffe_rcnn_imagenet_model.sh` from the Caffe root directory. This model was made by transplanting the R-CNN SVM classifiers into a `fc-rcnn` classification layer, provided here as an off-the-shelf Caffe detector. Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb) to see it in action. For the full details, refer to the R-CNN site. *N.B. For research purposes, make use of the official R-CNN package and not this example.*
+
 Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index feb3e36fe8e..5fdfc098679 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -11,13 +11,17 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "This approach follows ideas described in Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. *Rich feature hierarchies for accurate object detection and semantic segmentation*. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
+      "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
       "\n",
-      "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to run the Matlab Selective Search code.\n",
+      "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
       "\n",
-      "Let's run detection on an image of a couple of cats frolicking (one of the ImageNet detection challenge pictures), which we will download from the web.\n",
+      "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
       "\n",
-      "Before you get started with this notebook, make sure to follow [instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) for getting the pretrained ImageNet model."
+      "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
+      "\n",
+      "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to make the region proposals by running the Selective Search MATLAB code. If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.\n",
+      "\n",
+      "Before you get started with this notebook, make sure to follow [instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) for getting the Caffe R-CNN ImageNet model."
      ]
     },
     {
@@ -25,9 +29,8 @@
      "collapsed": false,
      "input": [
       "!mkdir _temp\n",
-      "!curl http://farm1.static.flickr.com/220/512450093_7717fb8ce8.jpg > _temp/cat.jpg\n",
-      "!echo `pwd`/_temp/cat.jpg > _temp/cat.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_reference_imagenet_model --model_def=imagenet/imagenet_deploy.prototxt _temp/cat.txt _temp/cat.h5"
+      "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
@@ -36,18 +39,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
-        "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
-        "\r",
-        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\r",
-        "100  212k  100  212k    0     0   852k      0 --:--:-- --:--:-- --:--:--  858k\r\n"
+        "mkdir: cannot create directory `_temp': File exists\r\n"
        ]
       },
       {
@@ -55,158 +47,348 @@
        "stream": "stdout",
        "text": [
         "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0520 12:14:46.505362  2522 net.cpp:75] Creating Layer conv1\r\n",
-        "I0520 12:14:46.505406  2522 net.cpp:85] conv1 <- data\r\n",
-        "I0520 12:14:46.505462  2522 net.cpp:111] conv1 -> conv1\r\n",
-        "I0520 12:14:46.505530  2522 net.cpp:126] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0520 12:14:46.505542  2522 net.cpp:152] conv1 needs backward computation.\r\n",
-        "I0520 12:14:46.505550  2522 net.cpp:75] Creating Layer relu1\r\n",
-        "I0520 12:14:46.505556  2522 net.cpp:85] relu1 <- conv1\r\n",
-        "I0520 12:14:46.505563  2522 net.cpp:99] relu1 -> conv1 (in-place)\r\n",
-        "I0520 12:14:46.505570  2522 net.cpp:126] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0520 12:14:46.505578  2522 net.cpp:152] relu1 needs backward computation.\r\n",
-        "I0520 12:14:46.505584  2522 net.cpp:75] Creating Layer pool1\r\n",
-        "I0520 12:14:46.505590  2522 net.cpp:85] pool1 <- conv1\r\n",
-        "I0520 12:14:46.505596  2522 net.cpp:111] pool1 -> pool1\r\n",
-        "I0520 12:14:46.505606  2522 net.cpp:126] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0520 12:14:46.505612  2522 net.cpp:152] pool1 needs backward computation.\r\n",
-        "I0520 12:14:46.505620  2522 net.cpp:75] Creating Layer norm1\r\n",
-        "I0520 12:14:46.505626  2522 net.cpp:85] norm1 <- pool1\r\n",
-        "I0520 12:14:46.505632  2522 net.cpp:111] norm1 -> norm1\r\n",
-        "I0520 12:14:46.505640  2522 net.cpp:126] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0520 12:14:46.505646  2522 net.cpp:152] norm1 needs backward computation.\r\n",
-        "I0520 12:14:46.505656  2522 net.cpp:75] Creating Layer conv2\r\n",
-        "I0520 12:14:46.505661  2522 net.cpp:85] conv2 <- norm1\r\n",
-        "I0520 12:14:46.505668  2522 net.cpp:111] conv2 -> conv2\r\n",
-        "I0520 12:14:46.506363  2522 net.cpp:126] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0520 12:14:46.506383  2522 net.cpp:152] conv2 needs backward computation.\r\n",
-        "I0520 12:14:46.506392  2522 net.cpp:75] Creating Layer relu2\r\n",
-        "I0520 12:14:46.506398  2522 net.cpp:85] relu2 <- conv2\r\n",
-        "I0520 12:14:46.506409  2522 net.cpp:99] relu2 -> conv2 (in-place)\r\n",
-        "I0520 12:14:46.506417  2522 net.cpp:126] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0520 12:14:46.506422  2522 net.cpp:152] relu2 needs backward computation.\r\n",
-        "I0520 12:14:46.506429  2522 net.cpp:75] Creating Layer pool2\r\n",
-        "I0520 12:14:46.506435  2522 net.cpp:85] pool2 <- conv2\r\n",
-        "I0520 12:14:46.506441  2522 net.cpp:111] pool2 -> pool2\r\n",
-        "I0520 12:14:46.506448  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.506454  2522 net.cpp:152] pool2 needs backward computation.\r\n",
-        "I0520 12:14:46.506463  2522 net.cpp:75] Creating Layer norm2\r\n",
-        "I0520 12:14:46.506469  2522 net.cpp:85] norm2 <- pool2\r\n",
-        "I0520 12:14:46.506475  2522 net.cpp:111] norm2 -> norm2\r\n",
-        "I0520 12:14:46.506482  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.506489  2522 net.cpp:152] norm2 needs backward computation.\r\n",
-        "I0520 12:14:46.506496  2522 net.cpp:75] Creating Layer conv3\r\n",
-        "I0520 12:14:46.506502  2522 net.cpp:85] conv3 <- norm2\r\n",
-        "I0520 12:14:46.506508  2522 net.cpp:111] conv3 -> conv3\r\n",
-        "I0520 12:14:46.508342  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.508359  2522 net.cpp:152] conv3 needs backward computation.\r\n",
-        "I0520 12:14:46.508369  2522 net.cpp:75] Creating Layer relu3\r\n",
-        "I0520 12:14:46.508375  2522 net.cpp:85] relu3 <- conv3\r\n",
-        "I0520 12:14:46.508383  2522 net.cpp:99] relu3 -> conv3 (in-place)\r\n",
-        "I0520 12:14:46.508389  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.508395  2522 net.cpp:152] relu3 needs backward computation.\r\n",
-        "I0520 12:14:46.508402  2522 net.cpp:75] Creating Layer conv4\r\n",
-        "I0520 12:14:46.508409  2522 net.cpp:85] conv4 <- conv3\r\n",
-        "I0520 12:14:46.508415  2522 net.cpp:111] conv4 -> conv4\r\n",
-        "I0520 12:14:46.509848  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.509870  2522 net.cpp:152] conv4 needs backward computation.\r\n",
-        "I0520 12:14:46.509877  2522 net.cpp:75] Creating Layer relu4\r\n",
-        "I0520 12:14:46.509884  2522 net.cpp:85] relu4 <- conv4\r\n",
-        "I0520 12:14:46.509891  2522 net.cpp:99] relu4 -> conv4 (in-place)\r\n",
-        "I0520 12:14:46.509897  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.509903  2522 net.cpp:152] relu4 needs backward computation.\r\n",
-        "I0520 12:14:46.509912  2522 net.cpp:75] Creating Layer conv5\r\n",
-        "I0520 12:14:46.509917  2522 net.cpp:85] conv5 <- conv4\r\n",
-        "I0520 12:14:46.509923  2522 net.cpp:111] conv5 -> conv5\r\n",
-        "I0520 12:14:46.510815  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.510850  2522 net.cpp:152] conv5 needs backward computation.\r\n",
-        "I0520 12:14:46.510860  2522 net.cpp:75] Creating Layer relu5\r\n",
-        "I0520 12:14:46.510867  2522 net.cpp:85] relu5 <- conv5\r\n",
-        "I0520 12:14:46.510875  2522 net.cpp:99] relu5 -> conv5 (in-place)\r\n",
-        "I0520 12:14:46.510884  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.510890  2522 net.cpp:152] relu5 needs backward computation.\r\n",
-        "I0520 12:14:46.510897  2522 net.cpp:75] Creating Layer pool5\r\n",
-        "I0520 12:14:46.510903  2522 net.cpp:85] pool5 <- conv5\r\n",
-        "I0520 12:14:46.510910  2522 net.cpp:111] pool5 -> pool5\r\n",
-        "I0520 12:14:46.510920  2522 net.cpp:126] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0520 12:14:46.510926  2522 net.cpp:152] pool5 needs backward computation.\r\n",
-        "I0520 12:14:46.510936  2522 net.cpp:75] Creating Layer fc6\r\n",
-        "I0520 12:14:46.510942  2522 net.cpp:85] fc6 <- pool5\r\n",
-        "I0520 12:14:46.510949  2522 net.cpp:111] fc6 -> fc6\r\n"
+        "I0608 20:59:28.978440 28360 net.cpp:36] Initializing net from parameters: \r\n",
+        "name: \"R-CNN-ilsvrc13\"\r\n",
+        "layers {\r\n",
+        "  bottom: \"data\"\r\n",
+        "  top: \"conv1\"\r\n",
+        "  name: \"conv1\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 96\r\n",
+        "    kernel_size: 11\r\n",
+        "    stride: 4\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv1\"\r\n",
+        "  top: \"conv1\"\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv1\"\r\n",
+        "  top: \"pool1\"\r\n",
+        "  name: \"pool1\"\r\n",
+        "  type: POOLING\r\n",
+        "  pooling_param {\r\n",
+        "    pool: MAX\r\n",
+        "    kernel_size: 3\r\n",
+        "    stride: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"pool1\"\r\n",
+        "  top: \"norm1\"\r\n",
+        "  name: \"norm1\"\r\n",
+        "  type: LRN\r\n",
+        "  lrn_param {\r\n",
+        "    local_size: 5\r\n",
+        "    alpha: 0.0001\r\n",
+        "    beta: 0.75\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"norm1\"\r\n",
+        "  top: \"conv2\"\r\n",
+        "  name: \"conv2\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 256\r\n",
+        "    pad: 2\r\n",
+        "    kernel_size: 5\r\n",
+        "    group: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv2\"\r\n",
+        "  top: \"conv2\"\r\n",
+        "  name: \"relu2\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv2\"\r\n",
+        "  top: \"pool2\"\r\n",
+        "  name: \"pool2\"\r\n",
+        "  type: POOLING\r\n",
+        "  pooling_param {\r\n",
+        "    pool: MAX\r\n",
+        "    kernel_size: 3\r\n",
+        "    stride: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"pool2\"\r\n",
+        "  top: \"norm2\"\r\n",
+        "  name: \"norm2\"\r\n",
+        "  type: LRN\r\n",
+        "  lrn_param {\r\n",
+        "    local_size: 5\r\n",
+        "    alpha: 0.0001\r\n",
+        "    beta: 0.75\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"norm2\"\r\n",
+        "  top: \"conv3\"\r\n",
+        "  name: \"conv3\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 384\r\n",
+        "    pad: 1\r\n",
+        "    kernel_size: 3\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv3\"\r\n",
+        "  top: \"conv3\"\r\n",
+        "  name: \"relu3\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv3\"\r\n",
+        "  top: \"conv4\"\r\n",
+        "  name: \"conv4\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 384\r\n",
+        "    pad: 1\r\n",
+        "    kernel_size: 3\r\n",
+        "    group: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv4\"\r\n",
+        "  top: \"conv4\"\r\n",
+        "  name: \"relu4\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv4\"\r\n",
+        "  top: \"conv5\"\r\n",
+        "  name: \"conv5\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 256\r\n",
+        "    pad: 1\r\n",
+        "    kernel_size: 3\r\n",
+        "    group: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv5\"\r\n",
+        "  top: \"conv5\"\r\n",
+        "  name: \"relu5\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv5\"\r\n",
+        "  top: \"pool5\"\r\n",
+        "  name: \"pool5\"\r\n",
+        "  type: POOLING\r\n",
+        "  pooling_param {\r\n",
+        "    pool: MAX\r\n",
+        "    kernel_size: 3\r\n",
+        "    stride: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"pool5\"\r\n",
+        "  top: \"fc6\"\r\n",
+        "  name: \"fc6\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 4096\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc6\"\r\n",
+        "  top: \"fc6\"\r\n",
+        "  name: \"relu6\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc6\"\r\n",
+        "  top: \"fc6\"\r\n",
+        "  name: \"drop6\"\r\n",
+        "  type: DROPOUT\r\n",
+        "  dropout_param {\r\n",
+        "    dropout_ratio: 0.5\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc6\"\r\n",
+        "  top: \"fc7\"\r\n",
+        "  name: \"fc7\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 4096\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc7\"\r\n",
+        "  top: \"fc7\"\r\n",
+        "  name: \"relu7\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc7\"\r\n",
+        "  top: \"fc7\"\r\n",
+        "  name: \"drop7\"\r\n",
+        "  type: DROPOUT\r\n",
+        "  dropout_param {\r\n",
+        "    dropout_ratio: 0.5\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc7\"\r\n",
+        "  top: \"fc-rcnn\"\r\n",
+        "  name: \"fc-rcnn\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 200\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "input: \"data\"\r\n",
+        "input_dim: 10\r\n",
+        "input_dim: 3\r\n",
+        "input_dim: 227\r\n",
+        "input_dim: 227\r\n",
+        "I0608 20:59:28.979029 28360 net.cpp:77] Creating Layer conv1\r\n",
+        "I0608 20:59:28.979038 28360 net.cpp:87] conv1 <- data\r\n",
+        "I0608 20:59:28.979045 28360 net.cpp:113] conv1 -> conv1\r\n",
+        "I0608 20:59:28.979110 28360 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0608 20:59:28.979120 28360 net.cpp:154] conv1 needs backward computation.\r\n",
+        "I0608 20:59:28.979128 28360 net.cpp:77] Creating Layer relu1\r\n",
+        "I0608 20:59:28.979135 28360 net.cpp:87] relu1 <- conv1\r\n",
+        "I0608 20:59:28.979141 28360 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
+        "I0608 20:59:28.979148 28360 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0608 20:59:28.979154 28360 net.cpp:154] relu1 needs backward computation.\r\n",
+        "I0608 20:59:28.979161 28360 net.cpp:77] Creating Layer pool1\r\n",
+        "I0608 20:59:28.979167 28360 net.cpp:87] pool1 <- conv1\r\n",
+        "I0608 20:59:28.979173 28360 net.cpp:113] pool1 -> pool1\r\n",
+        "I0608 20:59:28.979183 28360 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0608 20:59:28.979190 28360 net.cpp:154] pool1 needs backward computation.\r\n",
+        "I0608 20:59:28.979198 28360 net.cpp:77] Creating Layer norm1\r\n",
+        "I0608 20:59:28.979204 28360 net.cpp:87] norm1 <- pool1\r\n",
+        "I0608 20:59:28.979210 28360 net.cpp:113] norm1 -> norm1\r\n",
+        "I0608 20:59:28.979218 28360 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0608 20:59:28.979224 28360 net.cpp:154] norm1 needs backward computation.\r\n",
+        "I0608 20:59:28.979233 28360 net.cpp:77] Creating Layer conv2\r\n",
+        "I0608 20:59:28.979241 28360 net.cpp:87] conv2 <- norm1\r\n",
+        "I0608 20:59:28.979248 28360 net.cpp:113] conv2 -> conv2\r\n",
+        "I0608 20:59:28.979744 28360 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0608 20:59:28.979755 28360 net.cpp:154] conv2 needs backward computation.\r\n",
+        "I0608 20:59:28.979764 28360 net.cpp:77] Creating Layer relu2\r\n",
+        "I0608 20:59:28.979771 28360 net.cpp:87] relu2 <- conv2\r\n",
+        "I0608 20:59:28.979779 28360 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
+        "I0608 20:59:28.979786 28360 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0608 20:59:28.979794 28360 net.cpp:154] relu2 needs backward computation.\r\n",
+        "I0608 20:59:28.979800 28360 net.cpp:77] Creating Layer pool2\r\n",
+        "I0608 20:59:28.979806 28360 net.cpp:87] pool2 <- conv2\r\n",
+        "I0608 20:59:28.979812 28360 net.cpp:113] pool2 -> pool2\r\n",
+        "I0608 20:59:28.979820 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0608 20:59:28.979825 28360 net.cpp:154] pool2 needs backward computation.\r\n",
+        "I0608 20:59:28.979832 28360 net.cpp:77] Creating Layer norm2\r\n",
+        "I0608 20:59:28.979837 28360 net.cpp:87] norm2 <- pool2\r\n",
+        "I0608 20:59:28.979843 28360 net.cpp:113] norm2 -> norm2\r\n",
+        "I0608 20:59:28.979851 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0608 20:59:28.979857 28360 net.cpp:154] norm2 needs backward computation.\r\n",
+        "I0608 20:59:28.979864 28360 net.cpp:77] Creating Layer conv3\r\n",
+        "I0608 20:59:28.979871 28360 net.cpp:87] conv3 <- norm2\r\n",
+        "I0608 20:59:28.979876 28360 net.cpp:113] conv3 -> conv3\r\n",
+        "I0608 20:59:28.981308 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0608 20:59:28.981322 28360 net.cpp:154] conv3 needs backward computation.\r\n",
+        "I0608 20:59:28.981328 28360 net.cpp:77] Creating Layer relu3\r\n",
+        "I0608 20:59:28.981334 28360 net.cpp:87] relu3 <- conv3\r\n",
+        "I0608 20:59:28.981341 28360 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
+        "I0608 20:59:28.981348 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0608 20:59:28.981356 28360 net.cpp:154] relu3 needs backward computation.\r\n",
+        "I0608 20:59:28.981364 28360 net.cpp:77] Creating Layer conv4\r\n",
+        "I0608 20:59:28.981369 28360 net.cpp:87] conv4 <- conv3\r\n",
+        "I0608 20:59:28.981375 28360 net.cpp:113] conv4 -> conv4\r\n",
+        "I0608 20:59:28.982429 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0608 20:59:28.982441 28360 net.cpp:154] conv4 needs backward computation.\r\n",
+        "I0608 20:59:28.982449 28360 net.cpp:77] Creating Layer relu4\r\n",
+        "I0608 20:59:28.982455 28360 net.cpp:87] relu4 <- conv4\r\n",
+        "I0608 20:59:28.982460 28360 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
+        "I0608 20:59:28.982467 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0608 20:59:28.982472 28360 net.cpp:154] relu4 needs backward computation.\r\n",
+        "I0608 20:59:28.982480 28360 net.cpp:77] Creating Layer conv5\r\n",
+        "I0608 20:59:28.982486 28360 net.cpp:87] conv5 <- conv4\r\n",
+        "I0608 20:59:28.982491 28360 net.cpp:113] conv5 -> conv5\r\n",
+        "I0608 20:59:28.983197 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0608 20:59:28.983207 28360 net.cpp:154] conv5 needs backward computation.\r\n",
+        "I0608 20:59:28.983214 28360 net.cpp:77] Creating Layer relu5\r\n",
+        "I0608 20:59:28.983221 28360 net.cpp:87] relu5 <- conv5\r\n",
+        "I0608 20:59:28.983227 28360 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
+        "I0608 20:59:28.983233 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0608 20:59:28.983239 28360 net.cpp:154] relu5 needs backward computation.\r\n",
+        "I0608 20:59:28.983247 28360 net.cpp:77] Creating Layer pool5\r\n",
+        "I0608 20:59:28.983253 28360 net.cpp:87] pool5 <- conv5\r\n",
+        "I0608 20:59:28.983258 28360 net.cpp:113] pool5 -> pool5\r\n",
+        "I0608 20:59:28.983265 28360 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
+        "I0608 20:59:28.983273 28360 net.cpp:154] pool5 needs backward computation.\r\n",
+        "I0608 20:59:28.983283 28360 net.cpp:77] Creating Layer fc6\r\n",
+        "I0608 20:59:28.983289 28360 net.cpp:87] fc6 <- pool5\r\n",
+        "I0608 20:59:28.983295 28360 net.cpp:113] fc6 -> fc6\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0520 12:14:46.566017  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.566061  2522 net.cpp:152] fc6 needs backward computation.\r\n",
-        "I0520 12:14:46.566076  2522 net.cpp:75] Creating Layer relu6\r\n",
-        "I0520 12:14:46.566084  2522 net.cpp:85] relu6 <- fc6\r\n",
-        "I0520 12:14:46.566092  2522 net.cpp:99] relu6 -> fc6 (in-place)\r\n",
-        "I0520 12:14:46.566100  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.566140  2522 net.cpp:152] relu6 needs backward computation.\r\n",
-        "I0520 12:14:46.566149  2522 net.cpp:75] Creating Layer drop6\r\n",
-        "I0520 12:14:46.566155  2522 net.cpp:85] drop6 <- fc6\r\n",
-        "I0520 12:14:46.566161  2522 net.cpp:99] drop6 -> fc6 (in-place)\r\n",
-        "I0520 12:14:46.566174  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.566179  2522 net.cpp:152] drop6 needs backward computation.\r\n",
-        "I0520 12:14:46.566187  2522 net.cpp:75] Creating Layer fc7\r\n",
-        "I0520 12:14:46.566193  2522 net.cpp:85] fc7 <- fc6\r\n",
-        "I0520 12:14:46.566200  2522 net.cpp:111] fc7 -> fc7\r\n"
+        "I0608 20:59:29.042397 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0608 20:59:29.042438 28360 net.cpp:154] fc6 needs backward computation.\r\n",
+        "I0608 20:59:29.042453 28360 net.cpp:77] Creating Layer relu6\r\n",
+        "I0608 20:59:29.042460 28360 net.cpp:87] relu6 <- fc6\r\n",
+        "I0608 20:59:29.042498 28360 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
+        "I0608 20:59:29.042506 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0608 20:59:29.042512 28360 net.cpp:154] relu6 needs backward computation.\r\n",
+        "I0608 20:59:29.042520 28360 net.cpp:77] Creating Layer drop6\r\n",
+        "I0608 20:59:29.042526 28360 net.cpp:87] drop6 <- fc6\r\n",
+        "I0608 20:59:29.042533 28360 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
+        "I0608 20:59:29.042546 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0608 20:59:29.042551 28360 net.cpp:154] drop6 needs backward computation.\r\n",
+        "I0608 20:59:29.042559 28360 net.cpp:77] Creating Layer fc7\r\n",
+        "I0608 20:59:29.042565 28360 net.cpp:87] fc7 <- fc6\r\n",
+        "I0608 20:59:29.042572 28360 net.cpp:113] fc7 -> fc7\r\n",
+        "I0608 20:59:29.068634 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0608 20:59:29.068672 28360 net.cpp:154] fc7 needs backward computation.\r\n",
+        "I0608 20:59:29.068685 28360 net.cpp:77] Creating Layer relu7\r\n",
+        "I0608 20:59:29.068692 28360 net.cpp:87] relu7 <- fc7\r\n",
+        "I0608 20:59:29.068729 28360 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
+        "I0608 20:59:29.068737 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0608 20:59:29.068743 28360 net.cpp:154] relu7 needs backward computation.\r\n",
+        "I0608 20:59:29.068752 28360 net.cpp:77] Creating Layer drop7\r\n",
+        "I0608 20:59:29.068758 28360 net.cpp:87] drop7 <- fc7\r\n",
+        "I0608 20:59:29.068766 28360 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
+        "I0608 20:59:29.068774 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0608 20:59:29.068780 28360 net.cpp:154] drop7 needs backward computation.\r\n",
+        "I0608 20:59:29.068789 28360 net.cpp:77] Creating Layer fc-rcnn\r\n",
+        "I0608 20:59:29.068794 28360 net.cpp:87] fc-rcnn <- fc7\r\n",
+        "I0608 20:59:29.068800 28360 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
+        "I0608 20:59:29.070180 28360 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
+        "I0608 20:59:29.070194 28360 net.cpp:154] fc-rcnn needs backward computation.\r\n",
+        "I0608 20:59:29.070199 28360 net.cpp:165] This network produces output fc-rcnn\r\n",
+        "I0608 20:59:29.070217 28360 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0608 20:59:29.070231 28360 net.cpp:176] Network initialization done.\r\n",
+        "I0608 20:59:29.070237 28360 net.cpp:177] Memory required for Data 41950840\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0520 12:14:46.600733  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.600765  2522 net.cpp:152] fc7 needs backward computation.\r\n",
-        "I0520 12:14:46.600777  2522 net.cpp:75] Creating Layer relu7\r\n",
-        "I0520 12:14:46.600785  2522 net.cpp:85] relu7 <- fc7\r\n",
-        "I0520 12:14:46.600793  2522 net.cpp:99] relu7 -> fc7 (in-place)\r\n",
-        "I0520 12:14:46.600802  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.600808  2522 net.cpp:152] relu7 needs backward computation.\r\n",
-        "I0520 12:14:46.600816  2522 net.cpp:75] Creating Layer drop7\r\n",
-        "I0520 12:14:46.600823  2522 net.cpp:85] drop7 <- fc7\r\n",
-        "I0520 12:14:46.600829  2522 net.cpp:99] drop7 -> fc7 (in-place)\r\n",
-        "I0520 12:14:46.600836  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.600843  2522 net.cpp:152] drop7 needs backward computation.\r\n",
-        "I0520 12:14:46.600850  2522 net.cpp:75] Creating Layer fc8\r\n",
-        "I0520 12:14:46.600857  2522 net.cpp:85] fc8 <- fc7\r\n",
-        "I0520 12:14:46.600864  2522 net.cpp:111] fc8 -> fc8\r\n",
-        "I0520 12:14:46.615557  2522 net.cpp:126] Top shape: 10 1000 1 1 (10000)\r\n",
-        "I0520 12:14:46.615602  2522 net.cpp:152] fc8 needs backward computation.\r\n",
-        "I0520 12:14:46.615614  2522 net.cpp:75] Creating Layer prob\r\n",
-        "I0520 12:14:46.615623  2522 net.cpp:85] prob <- fc8\r\n",
-        "I0520 12:14:46.615631  2522 net.cpp:111] prob -> prob\r\n",
-        "I0520 12:14:46.615649  2522 net.cpp:126] Top shape: 10 1000 1 1 (10000)\r\n",
-        "I0520 12:14:46.615656  2522 net.cpp:152] prob needs backward computation.\r\n",
-        "I0520 12:14:46.615664  2522 net.cpp:163] This network produces output prob\r\n",
-        "I0520 12:14:46.615682  2522 net.cpp:181] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0520 12:14:46.615696  2522 net.cpp:174] Network initialization done.\r\n",
-        "I0520 12:14:46.615702  2522 net.cpp:175] Memory required for Data 42022840\r\n"
+        "Loading input...\r\n",
+        "selective_search({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpG3fe4u.mat')\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Loading input...\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "selective_search({'/home/shelhamer/caffe/examples/_temp/cat.jpg'}, '/tmp/tmplkH92s.mat')\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Processed 223 windows in 16.525 s.\r\n"
+        "Processed 190 windows in 8.631 s.\r\n"
        ]
       },
       {
@@ -218,7 +400,7 @@
         "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
         "\r\n",
         "  warnings.warn(ws, PerformanceWarning)\r\n",
-        "Saved to _temp/cat.h5 in 0.353 s.\r\n"
+        "Saved to _temp/det_output.h5 in 0.057 s.\r\n"
        ]
       }
      ],
@@ -229,7 +411,9 @@
      "metadata": {},
      "source": [
       "Running this outputs a DataFrame with the filenames, selected windows, and their ImageNet scores to an HDF5 file.\n",
-      "(We only ran on one image, so the filenames will all be the same.)"
+      "(We only ran on one image, so the filenames will all be the same.)\n",
+      "\n",
+      "This run was in CPU mode. For GPU mode detection, call `detect.py` with the `--gpu` argument."
      ]
     },
     {
@@ -238,7 +422,7 @@
      "input": [
       "import pandas as pd\n",
       "\n",
-      "df = pd.read_hdf('_temp/cat.h5', 'df')\n",
+      "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
       "print(df.shape)\n",
       "print(df.iloc[0])"
      ],
@@ -249,13 +433,13 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "(223, 5)\n",
-        "prediction    [6.67012e-06, 1.26349e-06, 1.86075e-06, 1.0960...\n",
+        "(190, 5)\n",
+        "prediction    [-2.10395, -2.06288, -2.76946, -2.32455, -1.77...\n",
         "ymin                                                          0\n",
         "xmin                                                          0\n",
-        "ymax                                                        500\n",
-        "xmax                                                        496\n",
-        "Name: /home/shelhamer/caffe/examples/_temp/cat.jpg, dtype: object\n"
+        "ymax                                                        323\n",
+        "xmax                                                        481\n",
+        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
        ]
       }
      ],
@@ -268,16 +452,16 @@
       "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
       "Simply list an image per line in the `images_file`, and it will process all of them.\n",
       "\n",
-      "Although this guide gives an example of ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. Refer to `python detect.py --help` for the parameters to describe your data set. No need for hardcoding.\n",
+      "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
       "\n",
-      "Anyway, let's now load ImageNet class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+      "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "with open('../data/ilsvrc12/synset_words.txt') as f:\n",
+      "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
       "    labels_df = pd.DataFrame([\n",
       "        {\n",
       "            'synset_id': l.strip().split(' ')[0],\n",
@@ -297,38 +481,38 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "tench                0.000007\n",
-        "goldfish             0.000001\n",
-        "great white shark    0.000002\n",
-        "tiger shark          0.000001\n",
-        "hammerhead           0.000007\n",
-        "electric ray         0.000004\n",
-        "stingray             0.000007\n",
-        "cock                 0.000057\n",
-        "hen                  0.002985\n",
-        "ostrich              0.000010\n",
-        "brambling            0.000004\n",
-        "goldfinch            0.000001\n",
-        "house finch          0.000004\n",
-        "junco                0.000002\n",
-        "indigo bunting       0.000001\n",
+        "accordion      -2.103952\n",
+        "airplane       -2.062878\n",
+        "ant            -2.769462\n",
+        "antelope       -2.324549\n",
+        "apple          -1.777492\n",
+        "armadillo      -2.152244\n",
+        "artichoke      -2.547566\n",
+        "axe            -1.905112\n",
+        "baby bed       -0.836331\n",
+        "backpack       -1.134832\n",
+        "bagel          -2.133529\n",
+        "balance beam   -2.338049\n",
+        "banana         -1.584361\n",
+        "band aid       -1.468755\n",
+        "banjo          -1.903778\n",
         "...\n",
-        "daisy                    0.000002\n",
-        "yellow lady's slipper    0.000002\n",
-        "corn                     0.000019\n",
-        "acorn                    0.000011\n",
-        "hip                      0.000003\n",
-        "buckeye                  0.000010\n",
-        "coral fungus             0.000005\n",
-        "agaric                   0.000019\n",
-        "gyromitra                0.000039\n",
-        "stinkhorn                0.000002\n",
-        "earthstar                0.000025\n",
-        "hen-of-the-woods         0.000035\n",
-        "bolete                   0.000036\n",
-        "ear                      0.000008\n",
-        "toilet tissue            0.000019\n",
-        "Name: 0, Length: 1000, dtype: float32\n"
+        "trombone        -1.880981\n",
+        "trumpet         -2.182214\n",
+        "turtle          -2.147969\n",
+        "tv or monitor   -1.362767\n",
+        "unicycle        -1.660976\n",
+        "vacuum          -1.484495\n",
+        "violin          -2.158709\n",
+        "volleyball      -2.454174\n",
+        "waffle iron     -1.827833\n",
+        "washer          -2.017761\n",
+        "water bottle    -1.709274\n",
+        "watercraft      -1.742185\n",
+        "whale           -2.385357\n",
+        "wine bottle     -2.493835\n",
+        "zebra           -1.921937\n",
+        "Name: 0, Length: 200, dtype: float32\n"
        ]
       }
      ],
@@ -358,22 +542,22 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x4798650>"
+        "<matplotlib.text.Text at 0x47d5190>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x4668990>"
+        "<matplotlib.figure.Figure at 0x4684d50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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qByysJNMVSOElCKKlKRQKGB4eNkNMOQ1IS5YswbFjx6IWjyAIgiAIggiR1Htp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\ny9ksXpNGv8/Hp8dW0TRNaxg4nRqam9dIFYOO345s7dq1rhNeUaakmLP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Bra2tmThiJpNBPB5HOp2GKIocdzOZTNjd3UW/30csFsPVq1c5XphMJrG7uwuL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sGp1Ot1FZ9vNpuVZClpJAAIb9Hm5ia6urpU2BAmVslBzfwVe8TIH6S8VXmzX79+HW63W37n8PAQ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VakspScxRb2+vyiCg+VLF43EMDQ0pI+Nz2NrawgsvvCCsC8tPt9vdwkZnsF5fX1dDm1kVS41G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\nSrbXGViN9mq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        "text": [
-        "<matplotlib.figure.Figure at 0x4668ad0>"
+        "<matplotlib.figure.Figure at 0x4684d10>"
        ]
       }
      ],
@@ -402,28 +586,28 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "proboscis monkey       0.920136\n",
-        "tiger cat              0.916973\n",
-        "milk can               0.791307\n",
-        "American black bear    0.625850\n",
-        "broccoli               0.609467\n",
-        "dhole                  0.513998\n",
-        "platypus               0.507829\n",
-        "tiger                  0.497029\n",
-        "lion                   0.481180\n",
-        "dingo                  0.474689\n",
+        "bicycle         0.773379\n",
+        "person          0.545096\n",
+        "unicycle       -0.210110\n",
+        "motorcycle     -0.503959\n",
+        "table          -0.591293\n",
+        "cart           -0.628063\n",
+        "electric fan   -0.664937\n",
+        "purse          -0.674998\n",
+        "baby bed       -0.677444\n",
+        "plastic bag    -0.717950\n",
         "dtype: float32\n"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Okay, there are indeed cats in there (and some nonsense).\n",
-      "Picking good localizations is work in progress; manually, we see that the third and thirteenth top detections correspond to the two cats."
+      "The positive detections are in fact a bicycle and person.\n",
+      "Picking good localizations is work in progress; manually, we see that the top and second-best scoring detections correspond to the bicycle and person."
      ]
     },
     {
@@ -433,8 +617,8 @@
       "# Find, print, and display max detection.\n",
       "window_order = pd.Series(predictions_df.values.max(1)).order(ascending=False)\n",
       "\n",
-      "i = window_order.index[3]\n",
-      "j = window_order.index[13]\n",
+      "i = window_order.index[0]\n",
+      "j = window_order.index[1]\n",
       "\n",
       "# Show top predictions for top detection.\n",
       "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
@@ -442,13 +626,13 @@
       "print(f.order(ascending=False)[:5])\n",
       "print('')\n",
       "\n",
-      "# Show top predictions for 10th top detection.\n",
+      "# Show top predictions for second-best detection.\n",
       "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
-      "print('10th detection:')\n",
+      "print('Second-best detection:')\n",
       "print(f.order(ascending=False)[:5])\n",
       "\n",
-      "# Show top detection in red, 10th top detection in blue.\n",
-      "im = imread('_temp/cat.jpg')\n",
+      "# Show top detection in red, second-best top detection in blue.\n",
+      "im = imread('images/fish-bike.jpg')\n",
       "imshow(im)\n",
       "currentAxis = plt.gca()\n",
       "\n",
@@ -469,47 +653,47 @@
        "text": [
         "Top detection:\n",
         "name\n",
-        "tiger cat       0.882972\n",
-        "tiger           0.073158\n",
-        "tabby           0.025290\n",
-        "lynx            0.012881\n",
-        "Egyptian cat    0.004481\n",
+        "bicycle        0.773379\n",
+        "unicycle      -0.519379\n",
+        "scorpion      -0.902484\n",
+        "soccer ball   -1.148745\n",
+        "cart          -1.167148\n",
         "dtype: float32\n",
         "\n",
-        "10th detection:\n",
+        "Second-best detection:\n",
         "name\n",
-        "tiger cat           0.677493\n",
-        "Pembroke            0.064214\n",
-        "dingo               0.050635\n",
-        "golden retriever    0.028331\n",
-        "tabby               0.021945\n",
+        "person             0.545096\n",
+        "swimming trunks   -1.098802\n",
+        "plastic bag       -1.130418\n",
+        "rubber eraser     -1.185333\n",
+        "tie               -1.195762\n",
         "dtype: float32\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 7,
        "text": [
-        "<matplotlib.patches.Rectangle at 0x4ab3510>"
+        "<matplotlib.patches.Rectangle at 0x4b89dd0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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MEMYykidy0hvSuiwQznIenk8wDR9VNZFVgWAWYGg66DFiKmPkEnEQIEgioeciWCLtzRpy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dup0uk+mM3f3LLFdTPvqR55CFOq+8eoPV0kfRIxodm7qt4M5nSKnKeLRkq/WAq3td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X6wwQ/X1AikYcnLN+8J2wi5D29Xv0LQJd5efk1aBRRiznK3YLHb0h8NEI0GSRVA0yjqmqKt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\nvXXszy6G+l9bWFjgiSeeYOfOnezatYv33nuvoXNXKhXGx8cZHR1lZ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F0SsuRLYBflWBkSIdgvBhK+fvj6TOf7/n7GPEK9UnaE16Rl1Ss0s6gjgy0yU+\nRT6FZ4XOozPMKSJMNJ5GaENUPHfZqyi7o6RIXP1FewjJQRnV26XVe3re+Px04CmL/86P3+HDDz/E\nuUDT1PzZn30b7z1lOeQ7f/an/OLX/xZlWXJycsL6xlpKVgVCaHn46B737n3M1772dR49vs8HH77F\nq6++yosvvkhTVRKGtm0f0iT3J2GJWmaxQ/B9OORSQiL40GuKfVP3/FTTNHgrD8M5hzUKYkNwChUN\ntsxpowJl8ERskaNyy/HhPvPZnMFgwHA4pMhy5k3Fs4N9Dk6OUMDutT0Ozk54fLiPVprR8QGH01Os\n0nx4/x5HpycYFNPZnLZ1zGcLNjY2yfKC73zve7xx50tkZcnZfEbdtHzhzTt4H9jZu8bhySm4OR/c\nfQuTl7x25w73Pr5PVc0IixJ3tGC9gfWYsfPCbWaNo8hKYt1ST7bw0bGYLgjaMz+bM8yHwhkXA4Ix\nNO2CUlmshcK3bI4HmNphXUC71LZAJfWC9zjdpgWo0MYyKQtK4zGjEUd1SbRbxNkZ7cxQTSsmWxNy\nIvFsRm0Dk9GI6AIxOuF18aIrjpEsLyiUFKg41aKU6LSdd9Q+kOdZQn6RqhYZZl237B88JJodgiup\n60CmLSbX+DhHmSGZKWn2K/K1MToqsEoKjIJwojF4rJakmVQIi8IjBo0PEdehBpbJ16qq+OEPfsCg\nLKnmC/I88cqJfvDB47wjL4u0c1Sq9A3UVc3O3i7VbMbpfM7G2hjfenav7rBrM+4/fIwPkY8fPOLv\n/Ov/Bm/c+TLD0TgVtC110VmiA1SQRKFrHUotjbW1Vqpv1ZLG6gz/km5RCTkv0efyMytGyIdUIdpJ\nMUV/DSRlypIyKVLLitXhPyGtS3kQtaRhehotbXAFZEqjrE6qpeV99EdJKhtjzhtMjcKnkv5eC56e\nQ3ef3fG0zpd1ASvXsayDiH2VtjLLNhyrI0R17tq6Y3za+NwMeNfv5Jd+6d/COc9sPqNa1Pytr3yJ\n+WIOSJa+ahvu333Aw4cPefPNN2nblvfv3uVb3/pTvvyFN3j77Xd48/VXuH37JlVzyGx+xocfvc8g\nL9jb2yPLuiyuKFPEk+YyUXTc5zK00Ul50ieilMLGlpCSVyC5vO4zgshd/8Cq4PFzj/dQjAp0bvDK\nM1wfU4wHfPjhB1wrrmFKS9XWTDbXqeYLYgyczE7JtMEUOXu7u2Atw/UJxhjWJms8Pdhnsr7OoCg5\nPTtla3cPPnZ5AAAgAElEQVSXtnUE5zBFzu61K0Rr+OCDd9nY2uHd99+jzDLu37+HB17/0pfZXBvw\n8htf4Ft/9m3ybCgGfnOdkyfPeOHKVTJl8REmG5u00xoTFKPhBqf1KU+PnnH79lWytS1JvNmMafQ4\nFzG2QKPQIbA9mTAcFVhToIgE7/Cupk0IRaUEmdEanVmUijyeHVCYlirAadwkGwVC60VSOtok39mj\nVJqFb9iaDCnyDFc3BO9J1dDk1qKVYlCUFMYSneesOhPe0ZoeuDnfMK9mKG1wPqKsovYVs8UJPuTE\nPENi8IgZGI5PD9nceYmDgwXlaMB0sWCQG7RSolTxUrxljVT7oqSPhkn69Zh4dGWtoM8QaOoGkN40\n0+kpKEWIDh0VBmijo/WeqBR5maE1yeiK4fVKURQDPvjgI157+TbRlxijmc1m5FnJ9s4OW1t7fPDh\nx4yGa7z2yhv8zM/8LEZbvv/97zMeZ2gtih7npUinR+MpTwRioEPw4C4YpPT3kraAtu39U08xSG6k\nkx5Gyk7vn3TSwi4s+fD0y3Kc+En0KdXyS8MnSFoSlt0+79BxR3HELiqPAeWlhUDszoHovxVSvNQl\nu3uEHSVJL4lGsUnGLCtGI5JgDSHg6HIvkjCVJ7mcj3OKmhXDfy6i0baf505q+5PG56dCsZbgPePR\nCO89w0FJURT41ovmu64x1tIGz40bN9ja2mI8HhOio8zf5JVXXmJjY5Mv3LmDdxWnJwfs7e3x5MkT\n5vM548Gw1+/a1LtDJ3mhUl11YvdQzDJJhoBzo+1SpO8T6kmWPqRkhyRFk5Y1ofygu5L4pZzNBeHg\nnXPkhSQ0A7C5s83pyQluPOKdd96hLAra2HDl+hV865jVC3au7PHg/gMWVcVLL78ixSdAMSiJwNbu\nLlXbcjyf8+4H77Ozt8dgMmHn6g20Mnz5zhucHB3x9OAZTx/d4/DZM/auXaNtGtom4mPk/Uf30Ys5\na80CU82YbK/x9PiQ6dGUq5NNrNV85Utfhbcz1gaRQuW0jSKWA5rFgtxmZD5Sek+oFmDA5hnRFBLF\nOIW3EKOnaWqikhC0CY7YOnGWxYAqeMJojbuPpzTzfcrgKNuG8aAkes1GYfFrJU01RVeRzFhyK6Xb\nQQHaEDQ4G4nKE/BkmemLMtq2FZRlLVmRi/48yqbMleX2i69Suw3uPzkjBk9WZpycnbJZwrRpeevd\nuxSDF1BF2evPte6kbWnT9moIT+sbIhGjBc1mxkhRVJRWYMZYdnd3+Dt/5xe5e/c9mqamOj1F61SL\nELxUByaEL8Ah9GqaZjZjNB7z+MlTSWKOBqyvrXHj5k0Oj445PDrh1s0X+PHdu9y580WuXL3O9OyM\njY2NBFbopWyyF0QeZ6yot+qq7oHMYDBI1bJp/3TJ4ZTI7A16MsZGJ3WID+cM16xaSJ2BtaIy8R5U\n12JAqja1NbJPP2HAY68zR8UlEo+fTPYpwHfJ7hR5i8NR5xqRdX8rJUn4vodXBJSAD6O1JN2DTwZc\nSSK7M9Baesfo1Mek2/sXG1NFhKIBUCEQOI+2Y4qsViuP/9pSKFmeyyJvG5SCsshQSG8OosZqiKEl\nKwopILEGHxy5MYzHYzY3N3FNy+uvv8J8Phd+LpuwsbEhN53QSp7ny14WcSXEiks04f2y619Ht0gx\nX1dmuyz3RUk5e240zkVspnquMkYp5ug0oqBwwZMFw2Q4pPWejfV1jDUJtVnRbGc5t2/doigKDg8O\npLDEB9qmpWka6kq0wq++/ApPnjzhvbvvcuPmTaqqIhuUqMyyubPJ1ZvXuXL1GoPhgLPDAzA5/88f\n/TE/++UvsDZeo/KS9JuenrG1ucl01qCMZmdvl+8//DbXX32RrReu8YO3fszZvOb1N+5QR3jy9CFn\nbsrjR/fJr42Zt4q8mND6wLODA67euMWoLBl6D5lB5xJ2+kYcYzAZ3jdSpowmyzPQXcZf5JUnLlAO\n1zn1Od4r6qjwjce3gFJUT6e0WwPGWcFaaVjM58QQWLQtjWvReZaMuKYOXmRxrQPvE9KSTWpsvuRy\nURitcG2bGjNtcjbzBN+iVE2IniLLCXrEw6cLZo3G25Ysg4OjGQqNzTLyrMT5mtl8nlo3SCLLJmWO\nCoHgQs9tGyMyTBc8ddvw1a9+lbIsefjwIWFzmxA8dfB4pMWAyDcS0ovQhkDjPUVecPPaNSbjIdvb\nW+AdpyfHfP/ttzk9m7KxvsnTwwNee/1N8rKkrh2D4SgBC0GVKNFPl3mG64pTtCLisbk0gXKtI0aH\n1qysbcRYeU/Epz2wRMHCq3dFMKTEroJM03onHTeNRmT2YjXFCYBKfWVMMk/Cb8u/TOKoewY7nTJT\nF/ThcVkTQAJfyX73FZDdupD/+/Q9ldaK2AurOimqUH8hpsghdP04l3bCJMeqknomTRJd5W+nC49R\nDHkX9XfAMUah+YxeVhr/ReNzM+Ah+tRZdCmXMUZhraJp6v5BNSkTbaxMWpu8Wls3RDyz+VSqlmKL\nb5dtIXX6u1OiSEHJ0rOhn+/ZzvFiaXIdCnzXqY2OdpOeDWrZSU3qveSzWmupkLQiPwsxkmWGmBpK\nBUS+Ndrd6zXVSinGg2HP09dVjdaaa1evEoHxeMJ4bcLG1gaTyYSP793DWosLgS2lOD07weTSJ8TY\ngqPjh2T5gA8+vMfW9jYvvvQKuzdv872334Wo+ZmvfJk8K9g/fEIxLDmYnfLxvY8Zao0P8OTBfarQ\nEl3DvffeYrMs+N6jdxjlY/72L/yb/ODuB7RK8ezpE668+jqP332Xg8f3uXLjCvNmzgbrbG9vURYl\n+XDEYjFjsLHFZG2CtlJOf3R4zOl0yq63lKMx//y7P8adKCgMGZ7Xb77I1vYeP/7oHtMTx+6NTbJ6\nn0Xd8OjhI27dvCEhtQvEhOi0Lmhrj289Bs1wNGRjYyM1T5J19fHHHzGfzSFGyrLk9OiEJ4c1tbME\nvyArGmazE3auvIJfRB49qcjKHUkI6xnlYJj06JF5WxODRxlYNHOapiHLsz6JPspzovdkeY7WlkVV\nUbcN3gfKYiAy1KIQh2wyAiSZnvSj6crIQxBKBWNxwGR9jbPpKafTE4pBzqAsePDkkfS60YrR+hpP\nHj/j3/sP/iNmVY05m7K5sSYgA+GGNT41VvJovUSp4HqqAFJbZQVE0fELxWmIPWKMSePe0Y+qT9p1\nfLtWCm2V9PUxGkMmHSE7dUiEoBVEDwF8Eu6JlFNUJKGtEw0qe1ErhQ8Bq7IVTjlK8ZjWyZkuFSzS\nbmGVzkh7WZGoGeh5eEgUmBh5lSSwKkaMTa0lO68TIXoPStoLrJgJQFpyKa2xRqi3BpIuPSy18jKr\ncv409+EvMOKfmwFfWsP0VfT40HXpWyYMtOo8+/lucBClP/ZSYXQu9Fi9776kOfFbSqm+dLb7ve7f\nXeZ6eZyIMdm5h3HOyKcF3wdJwUNIrU9XPm9SEx+lVF9eLJy88OxS5KHpojLnDOPRWOSIm5uS6NGa\nnd0dXuM1QvC88torLBYVOlECP/zR2xSDIU3V8KC+z50vfoHZ8ZEUEVtDEyLrmxtsb28xW9ScHh1Q\nZjlbRUmoGsZFTh4jxnk2NjeotKKxhpdefJ23/viM6ZOnuKJmdnDKxx+8R6Y10bUc7z/lfm556/vf\nYWNU8tbbB9x/cp+/+3P/NuPQSvtRM6B2LWERmFXS92Nje4vhZI1iNGJoJuwfHvPe996mWb9BtpmT\nx5a90ZgXrr/It7/7LlNTs7kx4JXNDcys4os//4uUZcGTZ0+ZNg2zusaWQ/YPjhmWI6zKOJyeMd7Y\nYWfvJmdnZyileOHFF9jdvcrJ0QF3332X06NDjFJc3d1hNmux5YhoTxkOrjPMdtFhm8XiGKMz8myE\nNy3VXNamsZpoIq2vyQpLZgy6ET48MzlZptEBwEivm8JQt1J4ZaxQYWdnZ/z+H/4BANOTOSaXJlRK\nQa41unWsFwOaRUU+HjNXkWxtwpMnj1gbDIkx8OH777O1sylGTsFgNJQqyej44pe+yNNnR9ROcXh4\nSIzS7jXimEwmeO+o2xaTGpo5J+AqxBZF4nzTugzep26JstatVkIVxYgiSx0xbZLvBunw2O0pBdFq\nlO+098lRqa63SFeBK0U/LkixS1HkuEYcSGdQO8omKKHKhWJayoE7A9BHzr2hPm9HdNp3goCXvb+7\nX+lBWrJHfSTU8+qpJXCqc+gjjZW9v5qsVEoK5IxKfXuSvDUkGqjITA9qw0oi9NPG52bAe2OrSD3Y\nlw1hktVOiLbLACP4diXbLGWznTENfZi16hz63g2xS1XIiBd+3o14wbDL90J/Xq2UhHgsPezq57Wi\n5+46RlCuKPbH6qhDnzzvck6kAY9OfatDlN7Ei7amLMs+xIpRuroN7YDxZExVV0RleOON13j2bJ/d\nrU1Kq8mN5dmjUhJ7owHDjTVGkwm/sLuDiuI0mlnFez9+jyvrG7z54m1yNHY4Ic8HHC/mmEGGPzmi\nnZ4wKAzja9eoTxY8e/yI7eu32BmPcUoTXM3NG1cZDUr2Tw742su/QJtZ9ts5hbKcLRoigVIr2qbh\nbOY5cBVN02KyjK1BoI4BpyKz6oT1WFDmirtvf5vDk6l0WRzkPJ1N0aMB68WA2cEx1197lb03dpm3\nLQfTKT/48V18A6rIKQdDhspg8yFns4qqET7z9OSMB/c+5PWXX+Js8wA/mzIsc0IGcVEzGY6x5QSl\nMhazGYeHB3z44bt4ZylHE/JBS91IBd1kUrK+MWQ2P4bYkNuM0WjMxvoWo+GIk5OaxbxmOBxJr+mu\n74tSGGu59+A+v/97vy/USNMwXpvQuhYTM/AO0zZsZAV3br5Akee8e+8jTqen1KEiBkV1eESWW6bH\nmoNnjzHWMBqN8b7l448+oiwGHO4fSN1DgEXd0LhGWiS3jqPjp1JMkxWYkKGNZTAosLnp0bNrWkBj\nlKIN0g62SzAaY4gq6xOsAltEzSX0wVKxEgJEp3tNte721GrhS+KRxSDaRGmmVlMxoqIkCbvWsVmW\n0fW5lyiga61Ar7KJ6X+S+1L4tsP2oLzr4PdyL6eoGkSu3PHzss1T4lardP1aGnSlFgxL2931YIqs\nAkuhleRnXctem2Xo9DNhC1JBVaCfo08bnx8HvqIOkcCha7dJn1BUWqNSm9GOougMaegnpnu4y4ZH\nggZWOOyUUOmOK6d9fo+BDq2fQ/shAtKfWVyz6vkrkKXTe3FFCgvVOcNuLnjl7vtxxcOGGJcJsRDw\neKpWFC71dIqxRtBMDGivllrz1Nu4LArWJmP29/fZ29vilZdf5nD/kKZppXmVd6jMcOvaFaGEvMdo\ny2BtjcnmmJ/54hdo2ob1zU2OTk65geL4YJ+H9z4gH1h+9is/S7SG+3c/pFnU3Lqxx2Fds3X1Cvc+\n/Ig8l859Gztb7N68zmzaUDtpaHV8OkMRybOcGAKF87TpZRtFUXA6X3C4f4wvIne+9DLZsODgvbd4\n+eZtjg+e4l3k5q0X2dq1NKcPeXZwgAJGT56wtrkBRcF3f/gjPnj4lMFwnbk7ZTp9wsNn93n55ZdE\nummlKVk1PWNva4O2XvDq7RfJfMUX77zJ//a7/4SB3WJnsIXKNR99+IitzVscu4p6foix68yPNfc/\nfozXhr29TbLMw8mUk5MnWBOoqwWuDXz1K/8av/L3/h6ZKXny+JCnz/apqorpouJsNifPchZ1zb/4\no3/JyXQKUQp1FvVckL1WqNCwN5nw5Vsv4k9nbI+HjF97BX3vfT48PmA8mOBrT2ktg2GJ846drW0+\n+vgeW9u7NFVLrjPee/fH3H7tC3gUWW4JwUsXSwsZIg8sypJ57ajrBT56wrzty9BdI90vy7zsFvAK\nEIm9DHC1MrKTH3bgqdPHh6jREjgnPb6mqupl0yaVeocAVkU0gbZtsd3+T9RIluk+oRt7WWJXEYoA\nwtR7ZPXtRFZbfBCH1O1BAXEh6e2XaF2ib3Nuv3ZfC6VkekOulEL5riXysiEVK3r0pcSQpLdPksxU\n5KViVx36SXnhp43PrxvhClWhVDKwIT0ipXvP3ZEXkiXWveHtetCBZIBXs7daa/TK/a+GTR3/Jcc6\nj7RXP7uK3I1KTf87GifRNjEuP0OQCCAonZrnxHPGubvfzpHo7tViekWSFYTHlUhB4YG8LFauL0oy\nJUjpbdM4rDGpL4MBAltbm4xHA4pMc3D4TDaeLTGDnCZ46cRmItE7jImgPLsv3uDW9SuopiVamJvI\n2s1d/Kxib2uNq9sTXnz5GpWrubKxi6o9m2vrrO/tsldkqDxnZ20N3Qbm1YLD2RmDzTWubBWyWJGE\nbHAtKnZOx0ivbpNhsMxCy3h9zBe/+CZtrqmrY65vj9geZKyPdjmj4uDRU4pizJOTQ56cHDIZDHl4\n8IzDZkHMctRoCEXBUVMzm50xm84xKJ4+22c2PWMyHjM/m7K7PuFvf+2rXNt+mWePHvDn3/oTDp48\nZHZyn1/4+pc5Pq55eP8B9z98wMaX98DN0VQM8g3a2hJroIgMBiUER72oGA0s169vs7u9xcGzI6Kv\n2H/8gNFwna2tbdbW1tm7do1HT57ywx/9iHff/4B33nmXx48fS5I9qB5NWqMY5IbtnXVujNdQbcOt\nnW2ePH7C5s2rFCowKgzKe0pjWRsMyUup2sy1YXMy4ez4iMFgzGI+45/97j/lP/sHr9F6RVvNKcuc\ntppStTVE6R3kXAtJcdI0FZ0V65KWMUgNBClo7HTNEYUtZI065/qaColkU9Mr78gyecMTPgXYKKL3\n0h5XK3JdSFTqXTJwBmu1tLVQKr3FKr2qrH+RAklh5MgHeU9zOOeIemlIuzfsoKQoSl4BmPqpIGX0\nHaWpOjonSMQeEp3TSyh9p8RJr0709EbbpBecXIzquxwcLMFblxiVaGMJAuUzYUnhrNDMzxuf+Tsx\n/zJDKcXv/C//uP83LDufdd87Z3ShTxTCeZrjoo5y9fe78bxbXD3X876/emytltRLZ4D79pMXHszF\nB7gsWFiGied0n51D4Dxi/0nXvnr/xmi8X7Z07WSTXQjZoYX+fljJGdA10jGsvuCgT8oqRbbSWgAi\n1ojPb+pa5iRdf7eZutLwcjCQrH1Xztxl1VOI1UVUh0c5/93/8HN8+zu7tO1P7rx2Of76DGtbXnr5\nA/7DX/1t1rcXKCVvBBoORjR1Q/AxCRMMxUCahLXe4W1Ae43FYrRIKrWBqIL0CfegvEZHRcjFOEsn\nREte5GiT4aJUKdaNw2iTlGaniXaymGiJBjCeKswJCky01FXAkkOUZmTynlFpmmW1IHeNkp4pUajQ\nrBA9vLRNkci6X88hRf7JPviBSslOeX2bd77fh8YYoUaj2I5SOnSBSlSSSn1cgkQBURJlGGP4r/+r\nf/ipiPxzQ+Dd67hgaZD0iuHoy2A7w71ihC4mH1d/7+Jnus+tjose8nnj3O+opR9cLc89nww5z1U9\n75zPO7fQPec/u6xiUyte+ZOa0GXIev6PHGPpULr+Lhedzer5Lhy4N/o+NZhfHdbavnFT99aS7vvd\n9eZZhtf6E+fqjtsd8zf+m5/jT/50j8vx0zWcy3j3ndf5X3/7V/mP/5N/IklZo2jaikhAW+njH6NU\nWOtUJONdK4VD3qTXvoHyYsAlMyUvbNEo0PLWJ6UFiTvfSl967+W9llre2bqoWpyryJW8yESoyYiL\nLR4nyVOlKPOC3BR416a39iSu24Dt9oaXFgkgUb4Ly3fuguThhKs/n+CMUd6TG8JSjijvYbXSbsFL\nN8JuT3cvZNdKWtJ2DIIkOZdv9vlrm8Tsims6OU9nDLqvuzCslwCuIOaLxmy1cul5ipJVg9j97OL5\nV5Fyd57u589D+N3PLqL9i9ey7FXtPxFhrJ73eaj7k3TOJ6+j+95qVny1kfzqMVcr6VYdypKr/GQU\ncS6zf+FYXbj6vLnoOjpenLOLzubPv73N5fjpHe+//xIxRhaLOUppskx6gjjX9tGZVJguOxwqDcG3\nBO+wifrr6zLweGUJ0RJ9mxJ9MjJT0DRVnytyTS095q0lyy3RBZqmQiuLMYo21ngt7+2MviHXnjY0\noELqLgo29c0PMdDnKE3qNYOhTvLJrhLWB3lJjNY2JTslDxdjJIsKpaUjobTiiGjv5eXJiadXWt5K\nZDKbkHtn61LuTXfOYtmu4yeNz7WZVY/gLlAHFymGT6LL0BvET/t5jLF/PdGqIekMzWoEcNEIfxry\nvWgUVx1DZwCfZ6gunvuisbx47OcZ39XrWT3+xX4LqwnYv0yUsUzIXJRpnr+W8zkL1Zdar55ndT66\nZ3TxM6vHUkrRNJe0yU/zcC7VHaT2y3Utzb5Su/L0ujkpaAtOeodLsyxpLxC8vPszpFeFpHfzoHBo\nSC9zEQ57Uc2WhUZR8l7WaKwJzOYLrM7QShBvXTuC8mSFRALee6Jv8K4hLwsCQeovWgcOIKITHaKU\n6l8ZGJRU6mZZJgoW35XZJ/vRJ9sCubZp7Ue8lmiAhNalQVVHCYe+PW/rmj5fsNS1dcDKfCKyvzg+\ndwR+kSqA85t9FRFfROvdZ1eN2MVjrhqo1Z+tGptVJ9J9/6J+szvW6terv9ddy/OQNEDR9dB+Dg/+\nk8ZFLv0iiv4059Jd4/OOd9HgXlTdrDqG7jjdz1eN/cX5WXUi8Pxo5nlzczl+ukcMUSIua7BZRusd\nTdPKS7NXCua06qqhAbN8d6iPrYgLFP0fpaSra/QBF7w4Ag3WaFzrcL5OyFmzaCpsmRN9wIda9PqF\nofW+7xBpEvLNMy3V3kD3LtcuJyMCghR1Bmk8FbQiBCm/D0He0iQ9wIXfF+pIbJlIDnWqTk1l8+lN\nP0p3+0WSlDazaKNSJeny3ZqNawlRXgQiPZZ+8tx/joU858Pu7k0nnWHsbugiB/STDNdFI/a8r1cR\n5V/WsKzSEqsG9SIFsmrkP82wfpqjuni+5329ahy78byG751T+ouSu58WJTwvh3ARiV+8j1UHeDFC\n+rRzf9r44z/6P+QcStFUNcZkmP6NJQGvAzp2SScwxqLiUk3QVdlqa1A+piIu6S/StG3PMeaZvFrP\ndO8wtNLbppnXwufHSOvmzM9OUToSPOT5gBg0xbAhKk1LRsuQ00Xg8Kzl/2XuvYNsS+77vk/3STdO\nDm/m5d23ebG7wO4SgQABEliAFEmI8B9kSZRNVVkSLVKyLJdLdLnKYpVsSVCxrJJkyZatUAYlB5EU\nKSaJBEkAJLgIi+Vic3ppX5yZN/HmE7v9R59zp++Zc+ctTLmWvTU7807o0/Hbv/z7Z//85xkMQnSi\niUchKEXGAMd1CIcps61FugcDnn7yaXQakyYhkpD9/Tt0e7vM+oJZ38UXGY6OCVwnj93SIFGKYZjg\nex5hNMSRKfMzDXzhESgPT7i0ak2E5+HWfTJf8NR3f4heMkK4DlHm8vo7d+ipAOkFnF1f4uxqnai3\ny62NLV586yYD3cYXku966BTB/Cw39/fZ73ZZW5hnwQ/4yz/1Xx6Zr3Nn76XX63HQ7ZCkCVqbsBeO\n6+XKbeOQ43huHo9doh1jlSKExhcOIqfAMyHQwjWJl9MI3zfWJZkqXNk1jufkZnjkJoUuce4HooUi\nzSJUqkwAMQe0ckykxSzFc3PLKMdB5Z6UJr6KIo5T4ymiD+MmpdokInaFETNmucNOIX+PswShcjFi\nlI4z2ANkSo6TegspDx10MkDIHNRVnnaPsfGBdA7j64g/qXbgwBiowTS+LOqokr+WRQnFNZsCtO9X\ngUZZ7GI/UyU/tov9TvnZcvAam7OwM1rbdVVRydNEGVXX7QPuiHK0QhRT9Z27Af3d2jaOgQ0T82nP\nRXlejitC5IpdbUK/ki99iTnclSxSVnGo/lV6/K/CaUqpFJE5Y4eJJBlRcLVaa5IwNW7aInfJFhkC\nSb1RBzRSKfygycJcO6eIVJ4tXEIWkWnQXo1LNzb5nT/8Fq9feodhmNKsNen193BShS8c3LCB60ja\nQjHa26Xtujz/td9hcWkZ6TosLC/STxQzS6cYbF3FUTHNwKXue2SOYJjE4xRdqc7QqSKoBfjCRWpB\n4Ad42qXu1EFLFmfmac3NMX9ymYPdDpGj0I5DP4G3L1+hl/pEacbt6w3aH3+Spufx+utvs7kzQtcF\n8XDEpSBj5sQJOmlMtz/kgXvu4+lHH6mcr6/+wddYWVlheXUFLQQ7e/tcuXyJmZkZGo0G87NzJHFM\nnCgaNZ84yc1epYMUCpkp0BlhFCE8n0ykBPUGaRYSxVFOiSoC34RrDYIAL3CNojJN8D0XmaYIoU2W\nKc8zoSjSFOG66EyzsrDAbKNFd2+PdDjAcYSxYnGdPG6KxnMc49qeHsYzEkoZ71Jh4tuM0hFe7stg\n0rod5g91Am9swWWwKwMJaaaoebU8JpORc4fRgMJ8K8uz8RDlRlrCJLz+E63ELAPXtI1dNvezQdq+\nX1ZUVlHpNvgUygH7QCiUplXlOPnwtPbblPvYnd8S6RTZgqr6XEW5VgFuub1VB1e5z+V7VVzOtPmw\n21MWM9nPTCvvjgMBYSUCGNeXyyU1Mk/QkJt9idx5RORjnjtIKPQ4m9LEN8UkV5NphcqpfRBjWagU\noHVKpzuiVqvheQFI3wTxb59CZZrf/eLv8s1vvsDefod4v0cWxWyOjK11GIcsnzvLauMMN965Rph0\n8dsBvajD3MkZOsMOp07dQ2cU0lpcYTQKUZ6HdjQxGkdr4iQhSTIcBCYdQh6/RJt8i0JLojih3myY\n0dCSpaUV6u0mOoMkUQxUSCLAb8/x1JMfQASzDKOYmaY5HFOlGUaxEWloxb3nz/DQw/fw8ltvc2t7\nm1ES8c1BSHfjNvBdR+ar3xtx//2LHOz3eOOtN9k76LK2tsbJtTPs7+7z+itvMRqOqAU+e3t38GoN\nPvrJT3Lx8hV2d7ZoeR7nTq9zYmWFQRRyc3ubmXnBXLtOp9Oh0WgSDkfU8lSKQgj2DkzURsdxkJ5r\nZAhYybkAACAASURBVNmYGCuDYY80VbhBjeFgRDRKaNWanFlb5+ypk/zBc98ENLWab8hdIRj0Rziu\ni3aMA6GTU75Ovr7SNCVTGYHnkSVxbt7oHzoJeR6pMBYsIqfepWOsshzf2JNrBzKVmqTkjmuJfw/F\nwK7vEeXr0BAdf0KVmNPEG2UlXfk+lJxiprDl5mwzpQpgbCXcce2aRslPE79MKvvGdyauT1pnTH6/\noA6rwLH8b/t75UOrChw9r5juowBbls2/G/GOrSso1wN5tDurX+baIac09dC2Lk/ULSx9SWHjnkeb\nE+QmYYKxvFEIQFUc4EVfx6MhQEBg0kuAlrmsNgPhUPeaCOEwijOEyNg/GPD1b11i48ZtXv3mH5Ht\ndXFHCU+dPE273SDUESOZEroZXjugMX+SDzx2P1/9xu9ybesyzVmfg/4dak6Nq1evcO/Je6hpSTto\n0guabN55h/WVBZAOaRTjeh5Ka1xXmmiG0kM6Ru7s1+rGjtoYReA7HleuXeLE+ilko06wOIenPJNF\nyPNJOgd0dveQUrI6d5r9nR1Goz7LK8ukuotCsbd1g1fpsbi4QrNV52C/w8LcDIHvUVVmZ9usrKzw\nH37rt4gTY5u9eXsLV/hsb97BlZJTK+uoNMNzHbrDEVeuXmMYjtDaHJyX3nybC2fP8e2XXub2zg5n\n7wMV11lYWCCKImr1NqMoY2dnh1qtju8HxGnCwtw8ozBkJmjS7x+QZUnezows00jhcfv2dRbas3z9\n699kfXWJZrPO7u4eCEkYJXh+jUwptMhjvQDKUThC4siMKEpwHSMacT0Plcu/DV/o5sSEQDqMAV04\nEoRCU5gXHu5pPzCx7AtHPxPqyZgZpqmJcliYIDpONU4V5Y8F4OfOnWNmZmYszH/uuefY29vjx37s\nx7h27Rrnzp3jF37hF5ibmzvybhV1bLPiVWz83cDVVmzmfxwBueJ3oTWvMq0r13/EtZ7pwdan1WOL\nXCYVotXUahngplHFVSKc8vWiZJmuvF91MB4H4McdYLZSebKdk208TpQikbldsPVdKUCbcAWuzONF\naM04pKdmLPsuWZ7nh4ceH47jQ6DUBlnIPp08k7nOWVrXIU4yaq0Z/ubf/DucOXMvQznDa99+kaWg\nwf2Pn8cbhkQHHR47d45MZFy6fZ29UZc0DZlrabp3tvnoI4/ypb1bdPc7zLZnULFAZpK9jS2efvz9\nXL98mVMn1+n275Bm2gSXEg4qSpG+hyclUrt4jsluJKWR+QpHgJMhPY9Mxdy6cZv9Xgddr/Pdn/kM\nC6vLZFIjHMnJ9XUThnc0IoxC/LkmSbuO67qcXDtFNIjQjoBAghK4SrByco31U+tEaVg5X61Wg42N\nWwihybIEgSYMRwx6PcLhkOWFJWabs3Q6B4SjIXt7+zitJimaRr3GTOAz53usr67QOzhgYW6W2zdv\n4J48xeLCCmdOrzMYhQyGQxYWHW7eus36+kkEHqlyeN/jT3HP0iI72xtsbt3gzs4dAsfFb8zw2huX\n2N45oPHkDOfPnGJvZwukyfVZqzcQMiWKIvxaQK1Wp9frotIM6UCtVjPZiTBmhGEYEUVRrrg0oYID\nz0NriKKIwPNNzK/cuEEIo431g9rY5DaKojyWkkY4thNPvkck+L5DlmmSNMJY20wvfywAF0Lwla98\nhYWFhfG1z3/+8zzzzDP8jb/xN/h7f+/v8fnPf57Pf/7zU9+vEm/8f2mHEOKIk0ixUasOi4mNTLVs\nvYrqtgHsONCv6k/5gLEPrKo+2b/L9U777jSxhn2v3E9bll8+DI8r5f4XAF622KlqR7kNk/UW0eDy\nvogivnL+PRP/lDJUm01g60FAiiLI2bj2XIyVHB1TTD5DpXI7X2MUzHA4ojUzR6okf+m/+Cn+8Nnn\n+Nq/+XVOnVjjzMo8Sg1RNfjEZz9FNggJcPhTn/wMO5vb3Lp2g+HNEVoGDAdDHnry0zx38RVevHoZ\n3WjRjSO8IODff/NLfPjD38X+7ibraycZdfZwhYsb1In1KA9TrGjWAkyqNmWSPqBxfMkwGhJHQ+pO\njZOnV3n40Se4fmePRrsJjiRVKTqOkSolEJpmyydr+YRaI/2AEydO4EuHQAj2ex0OsoSW12DUHeIK\nl9Ggx35vv3K+pCPoD3qcO3+Wnd09+v0h9VqNWi1gptU2QdW27tDpHKDclMWleYTQNOp10uGAoFlj\npt0iCUPOnznFZrfLfH2ObrfPSy+9wqnTZwjjiOXlVVZPnGT/oM+3X3qFMDSROLv9kMbj76PZrLG8\nuMzFyxeJkox6W/Pqq6+DFly9+g519zxRFPLg/Y+QKU1/MKBeaxDFCQcHHXq9Po1GnfZM2zgLZQmO\nzq1JtGZxcTFXeirqtTppmpLEKXGSUKvViKNhngjdRHUUBQUgTdTGoFY3Cbcdl8RXgINS6Xj/ua5L\nkiUIaXLsKiUMF3hM+WOLUMqb8Nd+7df4/d//fQB+4id+gk984hNTAbwMolBBmedEqsj/sx40rIsw\nV7XSY2L2OGXeNKq+TPlPAsEkqNsUaxWlXNXPqj5W/bvKguU4irhcR/FsWcF6nHy/6nqVbN7+Vnkc\nqtpcJQKy2zMVwB0B2iTUNa+IQvydhzGw2iZBa5XHVj6MZ23kkMb5ogD+4sdErLPbYCigKOihEwc3\nDfAyDxcfJRS1mkeYZLz21hv8o//5n7K92+UHHv0Y995zL/V2gxtbGyytnWC4tERH7vG+cxf42qsv\n4+13aGWK+083aM7NsD8Y4OPxoNtEDVO+Otggm/VhFDGvfF58+XUkCU8/9BDxYESz3iAe7BPGA0Tg\nk2ioOS5kxsrHr9fxpAt4xCpj5fx59rpdPvaZH2DpxBqnpQuOazIV6VyppjVaFSEOTHRIpTSqpsjS\nDImg7czhRREoqC/MIAS0VI3l1aOcNMBTTzzMcDgiUxnnTy2zv7dvYo5LhyReIByFdLtdZOBwazPk\n1OoZFleWuXjpEr7nst/t4AU+L1x8G9Wosz47w8LiYh5bRSEcl5XGMmmm2djaYDgcmWiaeBDB1Teu\ncPut51lYWOT8hXuJlMv2XodwYx9UhhSavYM77HZmuH7tKk8//D5mtMvu/pD9UcilG7cZJglzi8vM\nNFNa9YzlxUVOrJ7FaXVYXVln4/omw25EMkqYm5nlkYce5tSpdTKt+KMXXuSlF19C6qGJ+e+6RtGJ\nQCvB4uIas3MrZKlk+06H+fklZJARRiMcR7B957bRmYR99KCL1BmuJ8GDJE6m7kP4j0CBf+pTn8Jx\nHH7yJ3+Sv/gX/yJbW1usrq4CsLq6ytbWVuW7jsVmFBu7ygKkikqvAgwwZmRVpYpqtgGqSpZbrrt8\nrYqar7pXfP+4DBvTuI/jqOnjSnGaH3cQlb9T/vs4DsAGXyHEOCfotP4XxRY7HcdxFT4CVW3UWuPK\ncq5BAc5k24rckQV4256x5foK0YorAxMEyZGITJGRoJWJ93xrY5N//A//CeFQ8fiDj3HqzGk63Q6j\nZEQWx9RrNW7dus3pU6dZP3cONwxxdneh1+NOf5dsNMJvNFiqN3ho7X18oql56Yu/SieM0KmL6wZ4\nSqCEZBTFtNptBoMBMk1ptZtoKUiFYDQa4QhB4HokqSJouEjHI4oi7n3gQT794EMM4phBmhgPyDw1\noDHlOySYTMIUp9pbNieVikBRhQVVklSDSavZoNVsjMf49Ml1kiSZmIswDImimNHA2Hv7gU+z7jMY\nDjnodmm1m2QqY2tzk8FgwPzCPKury6yunshjZcc0ggZRGHKwu81cq4E36+MIlzAMcWSDNJNsb3Xp\nd1OuXds2IWmpMRr02Lq1y1x7nnZrie3NLbY2N3n1lZcZKoGoN3Gky6g/YHlxiZnZWR586CHm2228\nxT4ic7iRZbzx1lvMNWbp7HdYWlzE8z1q9RqNRouV1TVGyT69Xo96u83c7BxJYqKJZr5HL4m5dWOL\nc+fu4+rVG9zYuM4HnvwA0XDE0to5hFC0VILnCdApW1u3mWk36fa6U/cJ/DEB/Nlnn2VtbY3t7W2e\neeYZHnzwwYn7VSKGonzhX//i+N4Tjz3CE48/UhlgSlpUdxkkbWWfobiq5UVlqlAIcVRezlH5rn3d\n/mb5dxnQqvpemNWVAaxKCfidlGkUv231crdDYFq8FbsP5bG2AbAsCjru7/IBUFVsi6JyO6r6bh/8\nZTFO1bhM46JUWMNBG/mymwEpSgjqtRaf/7s/h5N5PPPdHwXlMBj1GA373LmyyfLiEldefoWTZ8+x\n9c47/NJrbzAf+Ny7usy9959n5J3ioN8lSzPi5hzN1hw/9tjneGfzJr/73DcYOoqBjBGJg+cZ8ZHr\nuohMEAQ1NKlxfMljgjiua4KXIQjTjHpd4tcbzM7PIxyHWr1mFKCpprAjdnIF7+HhpnJRUjYxFnBo\nA12McWHbXK/XK+dreXkZpZSJV5JbOhWmfMV6D4LAmN3NAEIQxTEzF+5BOCYFmQbiNOHC+dMmhZvA\nuMU70ljnKM0oCkn6Q86sLeE6Lr1ul4X5WZ54zCT2CGp1Xn7ldbZu7xIORggEw9GQdrPNsDdi6/Y2\n9917D8trq/TjiPuHQ1LHozeKSBDc2d5je+MWd25dJ+p32NnZ5txDs8zNLLC71SFwHXzPZX52nm99\n6zmeffarnD5zhlqtwZUrV5hbm2dmaZXuYMCd67cYDkecPXuOUGd0e3voOtQWPJbVPH09wG/VEb7D\nr/yHf4/Wmo9/z/fw0ovfRqiUrdu3mJ2dZTQaTt0n8McE8LW1tfEEfu5zn+O5555jdXWVzc1NTpw4\nwcbGBisr1YGK/vM//2emUnlFEcJ4bt2NsivK3dxOqwBp8n51NMKCmp1Wx7sBXXvTFCBmR2Ast3Ea\nCFb1qepaWWxzXJk2rmUwL3tnlgGxav6K9n8nB1PVgWgf7seBcPlZm5ovx4spfytIZxEyRssRSoxI\ndEKmPdI45mf/5v/Ib//q71BXdRpewObeO2xt3KLheoQHO7Tb8zhhiJAOjzzyIJffeouBI3jh2mWS\nhuShB+/nfQ8/yutvvsHlzU3WDxr8xIc+zvNf/l3CekDi+yw22ty6fYuNmRqPXzhLb2tIksQmfrV2\nCNMU1wsQjo8GEjRJHOHoJn/lr/9XjOKEKE2Qrgta4DhGVyCRxq2xJGIsrzF7LDJrrIoShtVKzCRJ\nEMJYdtXr9QkCyV4zSiniYUimMhOxUCmiJM4VfIZjaNc8arMtvNyBJ8lSosjYa2tdtME4xahUGwuk\nbIDKYjZu3+KdK68Qhl1WFhu5YtFDqSFrJ2bpdTZot+5le3+PNy++xSiMmVtcIajXOH36HJt37nD+\n3nvo93s4juRgdY5Y3UEnMTP1OkMGPP/cN/jA4+9nZ3ubwbBPkiY8/MhjtNttWm6LL/7G7/CDP/RD\nzK7P0mg0DMfkSDZ6G8zUfP7oG1/hkUce5tzZVW7fvsLFty/yoQ9+gHq9iZQes3ML+H7A+QuP0my2\nEFLym7/yb6fulf/PAD4cDsmyjHbO6n3xi1/kZ3/2Z/nsZz/LF77wBX7mZ36GL3zhC/zIj/xI5ftV\nJ39R7I1fZLcu3ys/X8g+q0qV8q5KrFJ+dpr4wK63XI99r1zKruU2hWLXbR8YR/pY0beqUuVp+m5A\ndNo7NkiXnabK7TturO/Wn+L5MqdTeHbah8a0ftnfTyuiKdp9sdvh4iGEQjkSpEBKF6VreM4cb73x\nOnW3TbzXod+9zSDboenCbMMnGYbEgw4bt64hmy1efus1mq0Wd/bu0Kr5hP0em69d4Z2XLvKJ7/9e\ndDiCWx0anT5/+8//Vf7Sv/z7xG6doRdQbxhRzHzg0HYVs602o7CPUwuIBkO0I0nzlF71Ro1as05z\nfp79Xo+g0QSVmZCkGO/HcQYrDi127HG216Q9TmVPXq31VNNbmzBJkmTM/ZXnW0qJ64EnPFqt+lgk\nU0S3VNoGe41Spr7YizH5RzPa9Rqj0Qjf9wnDkGajSa/XJ073mZvz+OT3PU2WaXq9Pv3BgN3dXcLR\nkDAc4vkLzM26aBfO3nuOJE4ZDiO2t3f50m//JidOnmTjxhVSnXHvhXuoNeqcP3mBYT/iIO1z/sxp\nzq6dJkliBoMevX6H1ZUVk4Q4y7j59iXOrazz1ouvksQpc3PzeJ7L3v4O84uzzM61eOT8OVZnmjiq\nw/qFdR6//wz93pCZ2XkyBRdf7TE316bX3eXWO1f+/3Pk2dra4nOf+xxgHER+/Md/nE9/+tM89dRT\n/OiP/ij/4l/8C86dM2aEVcW2GJkmuoBqasw+4e13FNPBrKirKFWKu/K37I0+DfzKQFYs3PLA28BX\nppDLwF/JiUwZn2kTfDermnL95ftVh1fV+1UOWeX5svUd7+YQsUU/dr1lE8Rp4qty36oOoyruxHE0\n2tVooUi0INEe0mnx9a+9itZ1ArfO9v4V0t4OTpBS81xkGuOQESdDzqzdz1C4bL5zhQ8/cB8zjQan\nT5zg6//3r7J4z7380a9/kZef/yYf/MhTPDa/zguvv8pTH/owf+rpj/DrV15llEW0ZmfYvHGNURix\nvr6Myob8N//tz7CyfpIbGxtcfec6Vy9eMtYt0RCnXufxp55ibmmZg24H6bgmWYIuxEuZ8UiVzji5\nrz0HxjtwulVVMV5CiKmR8YIgyL91dM2P7aLzesNczFKvGdM613VJ43i8Hwr9hpB6TCAW7wdBkMvz\nM4ajPlLOEScJ9YZPphp0DnoIRxDHCc1aDbKUpNHgnjOnCQIf13XxA5dEu7RnZ0HDuufzwIV7Uepp\nuv0ubs0niiP2ux1m23VWFxYRCx7PXvs6ly5eY2lxhXvuuQfECWp1D0dAt3Ng9AA1lxOra8zOztPr\nj5idW2BnZ5fl3jKuBy+99AIXL71JnEY4acJwMCIIAmqNJp3ugHZ7Dsfzudk/oNsb8N3f/TGCIOD/\nqBz1fG70dyp0/Y9QhBD83n/4N5ULpvxvV3oTFBkcBcnxhpRHKe1CTHEckFWBtv1sFaVp13k3ECl/\no1xXGaiqxmTaIfJuxCtlDqLc3iows8US5QO2LK642+FW5mrs5x9/+vuOvPfic79X2Y/i21UWNtNK\nGbyLtlT2OXXIZELQcBmlCV5tnq/+4SvceGef5fYK119/mf3rbyLTLjOByWIT+C79aERreZmF02eo\nr5zArTURWrC+uMzVN9/iUXeGG2+9xaNPPsYr197ixvYNHjl3gfev38Pvf/FLZItz/OabL/N6b4f2\nzDw7tzY4szrPo/ee5sH7zvFDP/yDxCiUdBDCwZcuvuMQpREJCikFaWbs3Z08sUeRLQqMwCG3tTky\nXndbr/YzSik+9D0/eOTZbz37W3etg/z7We4li9ZjL1pjRpab3FlrS5GHkkbkzlnkylF1GKkvl+t7\nwh87dTmOR5qmxFFsFLBoEw0QE98+cTVZkhoX+iTLbb1NaNfhaEiGwvFcBqMBgSfwnACBSxobhfnM\nTIvz954HIdjdO+DVV9+gPwhJ3RG9wZC5mUVc10drh0a9RW/QJ4pGtGeaNFt1onBEPTaOQnsH++wf\nmBgyg1HEtRs3aM/O0uv3TeKHWoNf/sVfnrrG31NXepsVn8pS68lFZrNmVYGdyuZ6BRVQ/LvM/lex\njkeaMAU4bSCpcvYpPz+NurHban+vDMD2O3f7lv1+cc12XprmhFSuo+qgsr9/t9gx5Xqr/l0uZQWz\nTb0rpfC8SY9AW7RSVcrxYorAaeWSihDXceh0R0hZ49f+3W/gyDlc5fLOpUuk4YDFpVn2tnYZDiS1\nmscwjKi3miydWOHyrWs8ceY0mYCtjS3cFLa3tvmjg8usLi3y6rW3WT93ijOP3MtzLzzP09/3cf7s\n43+Nf/hz/4DTc4v0Asn2IGJt/RQqHbK8usanv/8HcHwPoTK0MNnLoyw1QaOERkuMyEQUYyYAE/hr\n3P8iQYA+HK+7lSpuadq8VVHmlabBCEz6xHxuRZ5OMP+fwW/jEYnWCNcEKTOHDwhlov0JYWKO6zy4\nlUaTFnnJBHhKAybIVREgitxsFN9ntlUzEQOVQqeKLDUBq5RWzGZt4jQmzVKaDZOaME0UjlMjS82+\nqddrdDp7KCAchSwuzNFuZ8R0OLV+Aik90lQTRQntdh2hMyJHkCWKVq1FI2jSFAHb29vMLq5z+p6H\nGEYjur0+kTA25G6zTa1R56DTOXae3lNX+jJ4VbFyOjte7j0BcvqoqMKm1G1ZXZXlhc0m2nVMe3aa\nS38V4Fct6LKs137/bhuouHecKKgKOIt+lUPo2vULcdTM0h6770QcYven3Ifjni//uyr2ermP0+ou\nLIAKvUv5ABgfUI4mTTKW50/wwrdepUmNetDktbffpH9wQNPXBA2PheUVNm/u40jj4n723Hn2+n1c\nJDtbd7jv4ce5s3GHerNBfWaG3d3bDDsRKydOMExjTrZX+As/9dNsHuxzZ/s2P/jn/gyyXuPH//pP\nQ3sev16j3zvgpZdf5a/89E/SG3SMd5+UJsaG0kgFSJFLtY8qvMtezWUO7ti5E4baFbnzlHW5stxN\nHHl4gJrAYTqP+Df275DiMDiZPIxzo/N2oPPwClLmSmkTQkFh7P8BHJKckNdkWUwxLEIIcIz5qjYX\nGHSTcQ5KRxoFr3QFrgbp1GkKE0HRcRwQiiwDrU2kysKePk5iMpWiVMrsTIM4TiEznpVJklFvNRnJ\nEEcr5pcW6HS7+H4dkUK/P0LNNfFrLQ46B+z3NtACwiRkYWkFx3XItMbzfU6eOsMvTp+p9w7AyxYZ\nMAm+VQGdbCrLjmRYvOu7fiU4umO28ugCLoNilRVKleigfP07Bajy4VAFTMcdCkc3R/V37DIt9Gy5\nzYUSyu6PfRB+J32tevbdvGc/Vz54y9YkVUqzohScR7m/VSIC4bi0vDZf+u0v8+ZLFyFxaDb2qGUD\n3Dok8YgsqSFFC3fR4WA05OELF+j2RmRJSlvWUP2Idq3O8sIiYZZSm51hh4RkNKR3tcfm9Rvs3rzD\nyupJ7n/4Yb609YecWJyj6df583/2P+X//PdfZDAcEdTqaA1fffZZHnn8YVKFET+IzGR0yRRaCfKA\n0nn/D+PPTM69Jo8WUylCKxel9WFoXg7XYjrFuWtaRE37W+N9K4pDQY9PBJ0nF9ZajwEZyEOwKsz/\ni8bJ/HDRSDTjWN6qMA82wD+uXgijAM0FSEJInCQPKqdMgCpDxYNWCpGYUA5ZnIIG5So8LyDLNK7r\n4bguaWaCWknpITS4To0kySCM8TyPKApNQubAhK4VjkvNnSXLNOEoIRACHXeZaUhqXpswinA8D+ku\n0OsPaLVbSNej1+uZ1HTHlPcUwGESQMqyVTikwMfOBxWUY1FPVaS7MhtepsrLAHOcKKO4djfqcxqQ\nvNtnj7PKsUU2UA3gx1HvVe2Y1udy28pU3XF9sMtxYFFVqsbbnvPjFMVVdRUHV9ncsDz3WkjeuXKD\nZ3//65xdPsP83CyXL11kMOyyuDSP6wpGo5ia38BfaLDUOEmYxogkpeEGxKki7Y+IuwNu3riJDHyG\nUUi3s4saDGj4DeJQM7q5x+Y7G/yFv/Xf892f+hQ3tzZ5+7U3efzRx/ji115gPxogwoyz5+/loNPF\n8wLCZESmcxjWIPMkvBTrMQ+Zi4BMZ1Y/jZhFCokjjjrtVIIvmKBe7+LZaeNuvysLylmbYFH5jcP3\nHctSxj5ghCoiaBtzYgCVocBEnszfz4RGUCfJlIlFovVEhCElpfGKzJsfSBMdUEiJEuYgSJUy0QOR\nuEIgvACJYKQipHDGsUyyLEXphDQ1CZF1qpAiBC1xnYY55DyPzAHHD8i0CVHcnp0lS2FOuqSZRiY9\nI/cHiqBaozBiubVAFCf4NY85f45UHc/lvocp1QpTJRu4c9ZNHCbllfnEp1qRJYeZK8YsnqkARwoy\nEYwHpYinITAJTgsliBzH1CB/rgwqtmim2OBFtXniVWHXIZiswvbqzCkNive19YypN8sm0ykVz0l5\n2I4y4EzTA5Q5jGlANW7pGNQP+1i0xXzX/G1+mx0jy8MF5SAHh0VOerged4hMtssEClLqcLzyXqH1\nUZHP8cWFPPt4QbkZNtgopAyoSxCSmmjz7G/9Imszq9TqDWhIlOrSZICMJLIxw0GYMIfPmQceYHm2\nycXnv0Hbk+hoSDoM6e/ucOX552mEMfFBl4XAo5cJ5uptGo5He3WWpB8Sd3b53/7u3+E/++/+a5pB\nwHBrg61wyMraIvPpPLduXuLRJx7l/U++j0xl+DgmNkbeBzOu+XzllKsugEscjrHWoDONFppMVFPQ\n0zi98XVdOEdVj/e0EA1VFLhdQyHrHhNrTK718d92Wylk4kUwM22Se+Rmkkob8Yy0RDFm6ZpQr0pr\n4jzcpS7UugIcTa7qzchU/h0h8PJsO47njL8nRJDvF2UQVAgEkjhJxq2L4xRyeb6UJuSv1uZgFELg\nYUIaOLlIyPUdZuszaA1NCz/upq94TwE8yw7teYsFacrhhk8n0qGZW0rnJ5+UiPw9pSUqtyu12W0h\njIwNbQAhK9IhuS5CivGinGzHIWCAOUxsQC8GtsqbcxpYVkXhO/zm0fHJrAzW5qBTFBmvy84RVcBs\n/9jfmpyD4tAsxCWT9w4PLosz4ii3xNi+YbLYsH5cLJjqcnhAlq8JUX0AVB0MaoxZJk1VkfnF8Ryk\ndEBL0jTDcz1+4V//G7Y3dji9fo7+cMilq28yF7g4mUBHQ0StbtzYW03Oraxx+Y1X2Lt1C+Vk+Co2\n0R6VYwI0eT5pmhK4Pko69Do9Gu02ge/y+JOPIzoJ1w/2+Mov/Ts+/MlPUTvo4wSCduDRzULW11b5\n+X/1Bf70Z/8vdnbu4Hs+Wos8jpdG6UPxhhGQWKNUFhMdc15WOWKNRYkTe+H4eorvFmWaldbEHOUU\ngyieLXF/43esb1i7J/+/6b/rGG/TI6KjAgu0RkuTJm28aov6lT66JoWh0qUldgLjpWrk7LZObEJQ\n1AAAIABJREFUyMT5FhzG+fc9d9yHwpTS3i+pwkSczBMiJ0lyJAnKuynvqRLTPm3LMu1xGQMH48GQ\nUuYnl2HJdKYgUyYADMXUGkpLIpDisG4t9WHcaGvSyunRbJa97Oxgs/A2QBbXqgB92oRUUdNlSto4\noxw12TtO9l4+jKpEIuZ6ZbOmtsURR/syrW+2PPPdWKscfvvwAKkqVZ+b1j8j+JQY7smE7kRptNIm\njKhfJ4kybl6/xvatHRbXT9KJRngK3EHCMO0zP1cnTRLigy4feuKjNFZOcfPVV9h8+zWcQR+cFMdz\nqPkBSkEv7LKyeo69OwMWFmeYWV00azdO2b29yaVBxPvPPMjJ+gyp8Nh+/Q0W3IBf+X9+iYP5Oa73\nDrjvvjM89eT7CaMRzWadNM3FQMJBCxPnRQo5dnmfBpLla+XnpoVBqJqvafNcFivaYiv7vWlizuKe\n/XtaqRINFt8u6i9wwr5flslXrZcqjqG4Xs4FW4UdVaJdu72TfiDGc7W47+fZhsptuVt5zwC8rFg6\nBCox4fghpRyDdzGwRc7F8WDmMrRoHM7RQwpp0h5pE/je9dxxHF+TXdpM8HGy3/I34dBrLMsyarXa\nxKTDdIVO+e+q7xRBoWx5/+F9VRAsE2UamBZU6t1OdDPGRzdNlfhFCAFaHVnEUxeannRXL9c5rfxx\n79vFcSUGxfN5EeZPrWCm3aZz0GN1ZY3f+PXfot2c48x995MqzbO/+ps0owTfdTjodWnW6vhJRtw9\nIFhcote7Q5L0SdIhsVa0a02yLMF3POJBD09rwl6PcDQiqNdYPn2W8NYGm9sb6DDma7f38VsznFx9\niqc+873IfsQv/eIvQpKQxSG3b17nCz//z9i+cxvPN1YJhh8XuZDa5giPEh1Va66qlD1Vy+vFFldN\nU5jfDejL9ZfvF/UWe6cInnVcSOO79a9w+CmDdXG4lNfwIaerj6zXMUdS6sNR8Wb1XrPNXIu+Ztnh\n4WkTf3a73k1I5/dQhHJUrluZaV7psR3rxI+UOXGVxxPR4DqHmnal0jFIe54PiMPIaoBWtjy92qGm\naFfxu7xQCi8223KmClCnnfbFyW2b9oFZ0EmSTPWSKy+8ohiJgyUlLG2WqoVuONejAFxFwQNIDhfu\n3YB02qK/O5VlA8X0w+XdFHOQFT+ANnE0XNdlOIiYbc/z4vMvsXdnn8cf+SAhmr39fRaXFpE7eyg1\nIIkyBtmIbJRx8e23qI8GzC21COM59rIuSmWMVEqr3iSNNSLJiLoDatJjuNuj1pzl0qtv8NSZ8zz1\nkQ+h0LijDL/eZHu+xuu9HZJuj49/7rNc3Nrk9d/+DWo1RRyN8HyHOImQefwTrXMOohAlqmoALc9P\n1YEM1eu66tAXQlRGiSzuvVuurOqevQfv9n65rccdHlURT4vvlTkSO1ZRUW9h8WT7jpTHcXJdTzq/\nFc+WiUUhhJUd67Dd09IjHlfeUwC3wasoNiACONI17sC6yC2Zx3iQ0ighhCDDMMiuzihciB0h8X0/\nB+lcWSpyRlqIPNHt4TfthV1euIXLr53D0mZ57EVQBtmq07z4uxAb2ffG/c6pgWmLutzG4lArs3F3\nA0whGI/ZtFLFYdj9mmbCl1qUTrm+48tRPcRkm4+POmkXMw4WgOclihKa9TZZonnhWy+yNL/Mte0t\nZmZnufnOdWSWsbi8SBa5OLHD9u4OQrrUWz6tZg1PQKtRZzNKEDrDVeA4Kdrx2e91aScxtblFau0Z\n5s6u4Kaa7e0DxNYu9z98P3NekyhM0AttLjzwIKODLjtfe4OdzU2SOOSv/dWfYdA7wPEdpOMYl/OM\nfN3mQJAbU5fXSVn8Vda/HEe9TuO8qkDarqMMcNMOBrut9rovg+e7LeXv2e08bt+ViRtbLFq0sQqb\n7HVffr4gPMp4Mo71kksOiut2fJ9iH90t9km5vKcy8HIWenvgimeKZKJaFxRzrivOMmMXK0G6HnGa\ngpAoleH7PkmU4OrCmiVXluYAXnxbcJS9K7ex+Cl77xWDb7e3qpQVnVXAa5/+WusxpVNFPZQ3SnFo\n2NRC/uT4+SIfHxwNHWs4n+nhYItyuMGK+ovNVsikqzadyaRTWJMYyyBZORYTb8lDBVCVxYnx5nt3\njkRlhafWGoSDIyFJUp7/xgvs7uzy6EOPsR+49O/skXY6NDyXXhYyPzMLfcHqqQZDCc3lE+zs7jLr\nSU4sLbFdn2VwsE8vinG8Bm7gsXT6NKcefgDRnuP2wQHu0jLO4i7D3S7bnQ4bX/sGJ+eWefqRD1B3\nWsj9mOVglodP3su//fV/x6ef+SRPPfkU3f4uYIgFjcAVEqEdk9BCaGNOSDUYl8dYiKMhJcw6OtQp\nmPcLcD2qdH83410+4I/OR7W4o+qd7/TbZeq3CoDhMDKo/V7Zi1trI9IsQukWpdgHNnAXlL6NvYfj\nb4iFYmydPLdm0T673qqxuFu/3zMALzSux512xXMiEyidIUTuqCBAKdPZt69c5cL9D+LVG4SDA+pB\nDcf1EDhkcUyjVieOImN+JQROwQ5lmVFmwoTsvfz98nW7FDK24rdNQZQHvlgI0xawTRWUqe9ism3b\n72IB2WytvbDK41ochnYkv+LwqKK2yvMx3vxqkooq7lX1q7AlLo/L3cD3OFnrdFCvHltHOHmQMwNW\nQhhbai0kDpJvP/8COlF09g5oXzjJ9u42S406qU4ZKcVOr0OgBanrcu8T7+Pk+XO8/IffYrS3zUF/\nSHN+iSjRuCj8Zpu5xQUi12EUhjTnXUZxyiCMSV2HoVY4mSZDcbOzy+LGTZ48fZqFoEU/GjKzusL3\nPfMpnvjeD5IWabqyhCzLCGoNwylp8ozpGTIPq1oFXGXq04zNJItfHtvDMdRjMLLX0vQDdzL4WBUX\nWzWHRd12ZFJ7HqsIo+O4gPI6K/fVBvAqYrFqDdl12dFDq9o2qbOapO6Pa7td7L00LeSFXd5zR55p\nE12UNDXAXSQRlVIgHcfkrEPwzW99i//ln/9LPvY9H+eZ7/s4OsnItIRUUfNrRHFiBkXmMmpRmKK5\nYxGGDZ5VziFlQLOB1m63vYjsBVrup/3v4oCwF1uVxjuOTWAeRzoUFjQFKIvcgcMA09FgTUW/iuvl\nNpU9NMubYQy8gJvbxZZBvGpxSmfS2ciu6ziuxfOCyrErKMbyPJQ3o12Uym13x4yJAG0i27392kWG\n/QHrq6c52N3n8uYVOBiw6NdxfQfXbXAQHRBnmvbCMu3FE/SGCetnzrH0wSfYuH2btYfu59Xnv42M\nU9JhD89xEZki3NlleeUkQZhSSzWR79PzXHwpUL4mdgVv791i9tol5JVFFh48h7hvlTPdB5mfX0RK\nTZJCrd7A8z36AxOLW5iOAxohNVXipPJ6PKSsj8qZ7bE8vGf2WXl8p5VpoFe+VxYr2M9OzvHRvXK3\ndpTB1z50ykSNbf1xlCOZxKMycVbgQJVXcxUxZH9jGhbYxT7MqvCoXN5TEUpZblUeACgUEYAwhu9a\nmw2ZKoVXb+AFNR57/AncIOBffuFfc/LkOt//yWeYn2mT5YHmRQ7aWmsUxrvL4TDeseM4Y+q0igq3\n3bXhcGCLmBpVE1Q+3W3Kt8yCFb/t75UB3DjR5LbLWGDI5EYprhUgWbSjCGRlt8delEW7j+OKiraV\nqa1p+TNFSSs/zROyXJTK+XpRWJLnYyAKEcjdKZmiaKWgGHcMByFQqCzjy1/+Covzi+zt7LIwt8jg\n0nV8DQeOS71RR7sezXqTgRKsnL+HKDSprlr1Ok2dkbqSE2fPcvnSO+zfvImnFINuhyDwUaMBbddh\nbX6Guucj6jW6voThkP5oSMfVDDOXb355HxzB/W0POdsgc+HE2jr7nW0yYQItZSrB9wOjxBE6dyyX\nKKVR+ih4Ve2jKvGVfZCW574MrMfNVxXo2vfsUtRrA6O9PmxMuJu3drne8u9poD/tIKgiDMrttnGp\nvJarwPY4orCq2Jm7bK57WnnPALzI4lGAyzQW2MjuyDetseGO0oS5uTm6oxGtVptRcsDC0jKLS0tc\nvXKFv/+P/hE/9OnP8KEnn8KVLjpLQRsqrEomWgYY811x5Lo9EVLKcUB6e+GV2Sj7kKgCePs5myMo\nStmuvDhoig1gp7kaL14pcBw58Xxx4FRRBWVuoADl8qFlGnR0w03bVFplRzaUfXhMjR6YFdePyjP1\neB6PstpV60cbH7yxl57SGle4vPzKK9y+eYv7zt2H03Lpd7osZoKQFBxBb3sHjYSFRbz1k3gzM+hY\nkA4zFs6tsXf7Kq98+0VmnTqnVtcYbd3BUSm93gGtmVW8hkukQ/rpiN7tHlE0YGf7NvW9DioQhHVo\n06I1zIhfuYi65wzy7Cof/q4P8uKLL3Lq9JrJdl4P6A37eF6Q62KNV6kkA+ki5FEusIrq1bmeogoY\ny4BiONTJcTyOArfXfPFclR24DUyu6x7haot1WvxUzfE0QLP3a/m79top2maLI8vAbY/LtG9VHXBl\nfZc9zu/mICy34d2U9wzAPa/QzBoF5dHgVOa3KxSJ0mRZikgzhCtwfZ+408fzHBbOrHJtd4tlxyPW\nmg/c/xCP3/cAr772GhcvX+IHPvMZ5udmcKVEZBme66KzDJkmJta4UodxEBw3H7zcdrgE5rb8yxa9\n2MUOrWlT0PZklvNjliesSrxU/Lv4tk392z82uygdB8fJqXAhJ0QzBTgWm7roYzkaYxnodaZzz1bG\nmeDz3ub9s9t7lMqfRtnbpbhluAkT50PkUgCtFOjJdtntLY+bFCapQTEHrhMwGim+9Htf58SJs/Q7\nfebqdfb2NmjW6oTDDkInOG5KqgSd7janzp9i0D0g7I44c2oVEY/YffsywTDkhd//Cj/4gz/E7nyb\nbCBIXAjDlE64jQ7eohsnRN2QwPNwMol2fOquRxKP0DokQrC7s8VwZx8536DRmGPr5m1OnD+JTCXE\nKV49IBXgZ+AqJ4/tYSyrCo/dQqlcXk+iGLixDuDooVc1bnBUz3AcO19eq3cjZGwipLwWCkLCrtO2\n3qgqNvc6DQDL+6S4VvX94vdxgF4G8GnOb8W+tQ+wKiq/qj1/YkUoWZaWgGOSmi1+sjghdg07Hjgu\nSEOhuTEIx+Hk+TP8zh98hSBKqDfqHHS7ZI7D6okTtOfn+Gf/6uf5yIc/zPc/8ymGnS4eEtdxkDrD\nlZDqIk6byONJmG9plRkvT+tEdHJzroJ6KOy0oVoJUjWRBZAUYFrWRNuy6sKZxzglKNIknagLDCdT\njFXRLntj2xxHmiWkaZqDtMZxCmr/cLyLb9sUyuGCF+NvVAWV0rqwasgpEH3UhvbdmEllOeAe2vQf\nUmNKK6Se5JSKNlSJDrzAIRnFLC4u0OkMSOKMra19sswDGdBqBAz27hD19ki0pF6rgYoYqJh+HDO7\nukZdK25dvkivP6Tpw/bmbZKNO6zOzdCPQ1575QVarRqbuzv0D/pkyT6u51ELGswtzLIZjnB8n6DZ\nIk2hOddi1oU0HjLsR6R1Sb3ZJPPr+G4NN1FI6eBLD7II7UGCiULoKgeNIMstsgperdzv8ho0439U\nbFctjoCyvNzmLsulyj78KFV/KDqpKjbxUZQq7q5sulvutw2Y06j18mFQRR3bB45tNGB/r1y/vSbt\nPlel9SsTcXbbbFz8EytCsSnuspIBTEfSNEVmkhRwHSeXZSoylZEpQZZJFhcWSKOYURLRdudZXZ8l\naDRYzzIGUcj3fuJTvP7aq/zdP/o5/pMf/mHuv3CB4aBPzXGJ8kwcwpEkWWI0/K6JSCYcB0qsYdG2\nOE8BZS/owsyw6Fd5got8gjaYlQG+PJkT7CRiLP+2ix3b+nAcx7WOAbVYDPV63aKCCjHJJFWklMkw\nXpRDqpmxt2gBllWp7cp9KNdj962q2Cyo/S37XhWAF9ftw7Tb7dNo1Lh+/RaNepug1uSFb3+FR9/3\nOFvXbyHSmJsbt3EReAAKkjSj0ZxFNhQnT59nrz9k2BmwurjIzo0bXL98kWaWkqoEt1Hj1s2bLMzP\n0+t1QWXEoxApJf3OAUurSzjtGnKmRiOdp5fGaAGrq0sM9/cRmUvS6/PS177J+9d+CJVmbN+4xfUX\nXuXCo/czUgIv0QjPQbkmsp6jJTo3/RSl/pbHxv5dreQ9Ogdmbo66ok8rVXWUudbjRANF/QVhUAZA\ne87t9WO/W5TikCh+V32vKuZI1QFoEx0F6Fcla67mYA7vFd+zOYvyT/FslYf6ceU9VWLCpIy3LA/X\nWuPi4eV9EkZjgys8hNY4UuIjmZ+Z5frmbR5eOsPewQDZGzGKYxqtJp4T8PSTH0JrxS/88q/w4Q9+\nkI9/z8eIkxgZ+AitAYWUJhSlUuaAAAHSKA2zLEWKw3gtBWimaTqmyoHxaVuAU3HyFtS6raCxKd7j\nxqdYQCpTY2AuL7KysrK4bi9u0690ggqS0sFxJmOlF+/YVNWEKCiblN/bbSxvdpt6see0LFap6nsV\n1VNci6LoyMJ3XfeIWAkgqNfpdnssLK6QJpo//IOvs7O9S319hnPnz7F59QqtmQXC7j5ZNCRSKQpN\nrztgZnkFz2/gRiPmWpJAaTZu3cCPYxwp8KSkHgTEKjMu84GPyjSyXiMIfKJRaMR1SpFGIa4jiePI\nJCAWGrKY0d4BepRxK0nZ/L2Aj3z4o3z/pz7N3/4f/hY/96/+Vw7u9GkKByeFoQex0PiZMj4QaNBH\nD8/yXFbdK6+z0gxUWiu9G+6p/B17zUN1TPry83a7qsCx3IcC+Iu/bTFNeR/YbbDXls2R2D82QVZF\nNJSv2WtvgnMscaw24VPmAL6TcX7PAdzuOBxlZ6R0wMkXj8RkqdcCITWxynBSyUc/+GGuvPganX4P\nE5fXZaYR0Kg1SLVi/2CPOI342Ec/zgsvPs+trQ0++8N/mprvI1SKJ4yThIpDvDz5A8JFOC4ImQeh\nL8mCtR4rBQvqO8uyscemTaG6rjuWLduUyDQWztZAH3prYtI+qcPsQnYp/l1QyGX2FQ7juBTPFOBv\ns4vFIiu4jHIbpRVTumrR299Ns0kRSqH0vVuxZaM2yNuK4jLlVJY/FpsrTELqjSajYUQcZnzzG89z\n5swFVKaI4piNO3eot1oErqSe1Nnb32cYK0StSWvxBFt7fXqDIWfW15FxiIhCAp0hlGDY79GPh8jA\nQyvNqdUTDNQBKToPg5jRPzhAxhlpqo1ZZarQGWzevsMzn/kk1y5ehkwSNgKClUVudnY5e9/7uP+e\n+2jUGtQaDWSS4klJ4mhSYZTUXm466jAd4Mpc3nFUtF3Mc0c5nnImo+PqnWqZdAwFXnynCrzL67lc\nj71O7DVRRVlXcXPl8St+bPGHjVNl6x37Wll5WUVF2wRK1f27Ud5FeU/twG0xSplVKn6nOiPLA/Ta\n4SId10GiqCE5u36Kr3/lqzxzYo1Ot0vdN1mydZKQhCFNz6cR+IQq5IknnmBza5PP/0//gB/57Gd5\n+gOPM+zs4qiUVj0giSMDEm6RycNsGGHF5q1igQoqsGyIb3twVrFEtiih4D4KO9Ux9a1y6lsfXWxl\nJ4iqNoJZXMVi9Dxvom4bxIs2FfUVbRoDY6YmKJgqr9Fx//XkZrPZz+OojMJKwKZYbN1A1caYxp46\nvkOWKHynxsV3rjE3u8DSwiIqg+tXrjAcDhBSMNNs4aYw67swiqnPLeA0Z9i9vcFsu43WGVcvX0Sq\nhCBwieIUlWUc7HdJhMb3fZbnF2g0m/SihCiMcOsBg26P2vwyW70uC+snmDmxhtsPaTgOJ06e4+yZ\ne3Glz2s3b/DA+5/kxsZtYl/yvqefRAQNRmFErd0m7fURCpQPIZKaEggNWQUml6nY7xTEzdgdffZu\n1LNdijV2WN9RAu247x8nPiiv8/IaLt6dxn1UKRurTP1sQmuCC833dZUIuPi+/btcirVtU/BlLufd\niE/gPaXAHUxbbfHJpOy4ACcXmefSyzuqNbHWuIGPSDVri8vomovQMWQxaayRgO/5LJ1YpdPvIX2J\nFyzQCwesLC1x4aEn+M3f+HUO9nf55Mc+gi80o2HfeLkpk7XayRWbJti7IENNgEMQBHlvLEBNM3PY\n5JS3TQ0cBeVqrXYYhmN5OkxmoSlTpW6FXLygoLIsy1lsMK7XxnOvCA3iyJxb0FmliWFxwE4uUIXj\nehNadRvkJxayOuo5WQCt53lTRSh2xEe7PeUNWPxdcEGJZfc/bovS6FTgSbh68SonV9YI+wMWFhbo\ndfaQOmM0DPGFxnMyIiGpLyxw4dHHGcQp86nC04pO94A4HqHjGM938TyfURrnB2NMzfNIVAYZ+EGA\n67p0+z32dhVPf9eH6G7dZu3hh9C1Br3Lt1BRyksvv87ZkydZml3gkQsPsNxaZPmpk1y9dpUHPvI0\nV958Cz/w2NjfY9ZxkQloAbGbUUtNhh3tHlWk2RzW3cCkSjFpkokcigXfbbGfLSc6LotSymBbpoCr\nFNLFveMo1uIbVUrCKsrbrvM4Lqa85qq8LgvO8bhxKUpi5S6wf99NaVku7xmAw9FY2OVFJoRJguog\njDmYAKEFEkEqjJemLxxileG1Gmzf2aTdapNEEUGtwXA4oNPpENRrtLwWnYMO/WEfpCBxanzfxz/B\n22+9wT/+x/+UP/dnfpQTK0s4AkaDPkGtRhiFBmxcL5dBT56wVeZ+QghjZsck9ZkkyQQVab9nUxy2\n6GWalrt4pgDPKorZfqeoP1PZkXo1k2EubZCtmhN7Ixb3is1aRa3YgGq3uxiPqmIfCnC4McobxaaQ\n7PmwRVeJiqnX60jlcuXty5w/dx+j4ZDe3g5SxywvtNGhz6jXp68jYiSnT5+nMb9Ab3eP+x9+kLoj\neOlrX0XpDL8eEMUJdV+iEo1bC3BiTWtuhihL6XcHtLyAWhAw6zp4QYAUkvX1U0RA5gccDEOU0ly9\neYtOt8tTjz9BzZUkV69y6v2PkHouG719Hj5zD7t7d4iCusnKkypUkpIIyFJlUoExuUZsGWoVIJXn\nrjpPah6ygqMAU1UKavs4ita+Po0jKLftOxEtTIs/XqZky9+zDzlj7TWpvC33o+rAqQLdaW23gbpq\nbKf9Pa285wkdigm1nULsZ3AlKLOgtCNwlUBqkefBEwRKkrkOS6fW2dzcZOWxFaI4ZudgnyiMabZa\nZGi2d/cJo4hmq0WrVWd/NGRnv8MDF+7DlffzT/7p/85P/9Rfpua5rK2t0DvYZ3Z2jjgcGRdy60Q/\nbpAzpdDZUU9KG2SqgNoGy4KSLLNwxfiUqfqi3mms42GbRWXMcXsD2iBZ9pYrt6fof0FNV4GD3W97\nro+LdVwkobbl+fY4FYri8lqxlaZRFJnN5yhUpPnal7/M/Ow8r7/0CmdOn+Lty2/gORqnUWOhOUN7\nYZ4b/T1Wl05w4cGH2ekP6Q5HrCwv0t26SRKHLK8s0d/vEMYZ2SgEx2UYjWjMzbG4eoKDOzt4QUC9\n0WZuZhbP89g92GfzjcssP3iBnf0edccjCHycNCMlZUjMtZ3brNfPsLLQZGFhjrlBB6KE7eu3aLSb\n7PW7eE0f7UgcIQmkRLqQpUej+JVBpwoEptnMT66Zo9enUfFlatt+doJY0LpyH02jfqu+951wEkVd\nZdCuGp+iTTZxVei0qtplHwz2mi8/O+1aFRdwt/erynsK4FUiARsopJSkGoQ2XH+RPVpphXBdPCER\nI2MGd+qes7z6C7/HmXPniLOM5twc80Ed6bg4jkuWKuZzu2LpCE4uN1ldmGdnZ48oTfnAkx/kl3/1\nN3ny/U/QbLfxghpRFJLEITWvbnLvWTLZKgoQjJIVi6otmx8VlGUVpVzWmpcnvEzp2mM5qWAqFtak\nK78JzTuZ0d1kGp9cuAUol+eqPH9FKeKsl6/bm6Vo4zTzL7uUlZVFm4v+FwdGFXgV62Ysm3cz/l/m\n3jzYsuSu7/xknv2u796319ZVXVW9d3W3epHQCkYCmUUgPJYsOUAj7AFjmwjb4zHM2EGEPTNIzEx4\nHIQt2zEGI8AGBAJJtiSwWLQhuVtqSb1Wd9de9fb17mc/OX+cd97Ne96tluyYiXZGvHjvnXtunjyZ\nv/zl9/f9/fKXIrb4oz/8Qx594AnmZloMO/sYaYw/7GGGNqOdbepeldT1WFg6hutV2b2+Qq1eIQp8\nbl67ShaHmJaNV6kRRRmB30WZKcI0WDy2TJDEZIbEcR229/dJ4oTl5WUypeivbXDm/FlCx6RZmSFZ\nmCXY3SUjxY9HXL7xMs9d/BZ3rV3hR+bnqBs2mYDVW1dpV6o0ZxtEmWRIhBBghSlBQTFk3x79lZWY\njlanj0EeMz4NfEwrkxvE8joLf4tOhZTrKMZKV5y3sw6+XRum8drF98oLfRkoFt8vK+Uysp72jGn0\nS7no0SjldpXb9+0W33L5tgr8J3/yJ/n0pz/NwsICzz33HAB7e3u8973v5caNG5w+fZqPfexjzMzM\nAPChD32IX/3VX8UwDH75l3+Z7/u+75tab5FPu0BakxEXYwUupYGVKhKRkXDgPMggEUl+QGgKwpTc\nedd5Pn3z1+j7Pl6ljrRdjEoF07RZvbXOsD9ACkHNqzDTaNKs2QjLYPHee9jrjZhfOs7isZN8/kt/\nhldxOHfHCUhCXFsSJzn5qFsNWZYdxnaD7jgCcaBA9MEpPi8OatAVWfF52ZNeCLbOT+s8W7nvxgKi\nyP/M0PNfhGE4gZTTND8n1PXsIyhfz7ymT0z9OTo/eDukUz4PUI/cuT36y448s+ifAoHrwq+btXpI\npRCCKBmxtrZGvVLFMkxMz+PypRtImWCoBNu0icKQUS8mwkJKg9XVNTzXZX52jp3V6/T29iCOCLOM\nVmsWr9KkYxrs9Do0Z2YQRp4vvFGpUak1uHnpGsPBkGqjAULieJJUpBiOTUzCbneH4dY6hlRkmY+V\nJBgZvPDFL3Dlyio//Tf/Dotzs9TP3cXn/uBT/OCP/jAdM2FfhVQUGEHMQKYYlomZqqkYrcbRAAAg\nAElEQVSKRQcBZcvsOzHThZgEU7oMTxsvXU70estoWufni88cx5nq45lW7+1Cb2/nYC2+r79HOa2F\nfk/ZSpzGoRffK/u19HG4XV+XF6JyO/SQ5O+kfFsF/sEPfpCf/dmf5Sd+4icOr334wx/mHe94B//g\nH/wDfumXfokPf/jDfPjDH+bFF1/kd37nd3jxxRdZXV3l7W9/O6+88srUgdeVSdlZp5csS8kOIkBc\nw8Q0FWTkCAHIrPxA0WWnxswdS+z3u1i2w9Afsra+QcWqkEqDmYV5jCTFFZJOd5/dvR5CSuI0w7Bc\nKvU6KlO86U1v5av/+eskScJ9955HGQI1CiHLSCXEB7sLDdNAZSrfCSdyLlkZAlSGfvi3PjCFAirQ\nrT7I0xBuofx0h0dZOKYJhJTFJMg36xT32I59cIArCGlgWgKUyrOFlCaVbm3oSEwPBdSVrGDsyi1T\nLjq60q2MVxPSaZzm4bMO+dmDawqkyDca5dkqBWkak2UK267w4jMvEgcJtWaDZy4+hZVG2Iai1qhD\nFGFmMEoT6o6Tnyq/ts7xM6eJhza9vW0cKbG9OiLN8Ech0rRoHTuGbNZpzM/RC0Y4VoWKU2FzfQPL\nNVEk7HV2OHbiOFEc8cLqJUbbJhXDI9rdp5pKVJogpYWDASKjn4bs+H0++tFf455Tp/ieH/x+Ljz+\nCFaQUHFsLGWRkpFJMA3GWV7SFJWCYZgkCgzbJBN5NnYDMA6SXiVMWoy3Q4GFKOaX1CGdotR0JZkr\nxPLVQjEW8pDLZwF6dEvz1aKS9LaWLXW9yAPL9+Bb2jtMp2OmWbh5PZM7o/X79Pt1mR4/Ywzi0A4C\nHy8Y6vD37SwG/dp3osS/rcvzLW95C61Wa+Lapz71KT7wgQ8A8IEPfIBPfOITAHzyk5/kfe97H5Zl\ncfr0ac6dO8dTTz01/cEHq1eSJERRRBzn27z132EYIpUiEwe7ocKIOEmJsxQDMKXENxXCFLR7MW/9\nsXdwdeUKrUYdSUa7UmPOqWJlis29TYajLjYpS8ttzt73AKfuvJtzd93H/PwCg96A/e1ddnf2mF8+\nwdpen3/5Gx8j9JqYlk3FdbDcHLVbVRfLsfOBSnPHUhgGDH2fOD2KMKTME18Vys+yrPH5nGK8AadQ\nmnEco/OFurDrSFSPdNEVozzYgJRnoBOHXHeWpUhD5CGYMp+QaTbuc93JWq6/QOVFKdD/IR/NUSeN\n/m5FfLyeHuB2iE7fSVf86NRPnCUkKldKmRKoTACSNM4PuI6CgDDwkSIDVeGV569y97n72e928Qd9\nKkphRhFEYY5ghYlZqTFXqxL3O5jpiHiww8VnnuTit54mGvmYykSlkiiMGQ6G7KUxc2fPcuaeBzHN\nGjWvRTSMGXR7qCxGGCl+2KPb26G3tcXelWuwtYc7Cmg1asSWQWBaRJmBoRyczMZTFtgWD7/+MW6s\n3KCX+Sw8eJ71QY/hvk+DBn6kiGwLW0msOMYwwRC5Y9+2Xer1GZQwyZB58q40RSUxKpscO90nUVak\nSuVRYrn85Ju98g1f03VEHKckyeRPlimSJCWOk4P/Qc9H/l9SCoRbyIXe/rFPSZGmxU9GkmSk6eQ9\nMAkiiv91EKSDjvI8LlueRz+X2s/YainSb+SyLw4A1qR/rDwfvlMl/l/FgW9ubrK4uAjA4uIim5ub\nAKytrfGGN7zh8L4TJ06wuro6tQ6dN9PN8jIdUOxgLDqqUAZlHitJEh5/6CGe/PQfs93ZIonBERVU\ntcJCa57FqotnGnQ3Ngn6fda2tskOTiefnZ3jjpOnkYZEGJJBMMSPAnZ2dvjNX/tN3vdD72Cm6iER\nWCqFIEEYEtO1QeWdaKs8X3aUJlPNI908K66VY5qLdy7CE/W45rLwFKWoQ49B11d+vR7d2VQoRiHE\n4eHMuoO1zGvq71MUvU06gip4zWmlHFc+rZQ5cP0nyzIOVh8OkmNTcK6WbRNFAWmSUK1USdOM//jJ\nz7J05znMVBLudTCUIlAJrlCYSYoyDHAMTi4vM3vsBK9cvUyrNQtZxu76OoQhoepi12rY0iJMU/pB\niDIl9XaLXjDC8DzmF+e5/NxzJEmCqQQiVSSjkP2NHdqpzYJp4qb5uZbZXJ3twS5OJnHTDGHa1BKL\nc7U5bvRSXvj613jwzW+g1miB7dIxwY18gjiF2Qqj3V1O1GYIQ59YKUIDDNciVCmZ38dS4vDs0tSQ\nBEYeBmtyFDXq0VFlOup2Cqxcpn2ug4Fi3PK8PUctyUJ2y3WUUbEOIsqypyPuMWrP5aP8Hnp477T2\n61TLNGqkjMpvJ8u3m7MHn1LIrn5/2Rf1/4sCLzfydgNbfP5q14tBKedGKT7TFbuOFAoFoyseM5W4\nNZdYJMy35zGFw9CPGKx3MCwLJDRqVSp2haXjJ9nc3KbfH9Dt9tja2MZ2HWr1KtVaDQyb1z3wMHud\nfX7j93+Pn/rgB3BSsJC4psUoCglRKCkwEJhKYKUZjmmi7Mn0s/oKPy0PcvF+eoB/FEWHglYgUB2l\nF3UW/VAg22KnZVFn8dxiwVBKHSJ8XUnq7SiKLqS64I7vV2Mao9SmyYk0HsNpIW7lMi2iYLLPxMHm\nqrwNQgik4uCoMYFle4yClFs3V9jbGVKZneHkwjGev/JnnFo+Rn+4A34PFcb0RERWqXDMdNj3E2Jh\nsLC4xNbmKqP9faqGgSMyuttb1JstTMfBIaN1fBm7VuHGjRUqjSqGbWE4Jm6lQtzrwsGCHqcx2w0T\nz1C0fJ8F2rQqDZrNGH9tG0s6LNyxTJREzC+fQD17hQfe8jacN13gysYuZ4/XaS8vInc72LbB577y\nBd722Bu4cn2dxflZ/DQgMSVKRJjSwDLAjFMMJUiEIhGKQOZUn6mO9q+eC79syRVjp8faTyvlcS6P\n77iO8Xjpn+kye3Ssx4u+viiUlaNp3i4/91HZLs89/TtloFNG6NPQ8vR3ZmI+HNwx0Z5punHawvlq\n5b9KgS8uLrKxscHS0hLr6+ssLCwAcPz4cW7dunV438rKCsePH59ax7/99d8+bOTDF+7n4YcemDBf\n9GgNXZHozq9CwR0qqpHPufPnuHz9Ku4pB5SJ057j5MI8MhHgmgxDn0FvQG+wgh8E2LZDqzGD47hU\nq1W63Q6dTpdev4tpGcS+z+te/3p+83c/xv/wvh8nGoaEWYxj20Qyp3dSpTCy3GyMwmSCayyvqrqy\n1d9Ld/gJIQ5ReOEs1YVWFyrP8w6jQApzsai/+L7+bB0ZFxNHR7rlsEZdeU/jTgvKp6zgpy1gr2ZJ\n6KUY4zJaP5y8Wc4ZI4poiQIlSYRwCEYJzeYcz3zjc5w4fp7doMdXv/qfmTmwVNoLc8T7BkGvR5CE\neM05ZKXJ5c1NPKfC2voW27du0bBs6pZJGgQE/oggClGOw/zJOzh/190MRiOElFQbNXbW1onThFqj\njjBNVBgR+iEqShDzBpblEGcpMlHULZulU6fpKpfL168hLIuH3/AYtoIaJk9++Qucbrrcde8jkBm0\nz5zm4q0/obkbcqFv8me//1ke/YF3siYUjrBAJRhRnie83+9R8zwiBAkQZ5JMCkxpkHF085ht2xNz\nboxopx9LNq2Uzf3yvfq8NYxxlj3dAtfvLb5fjsiY1paxbIxpt7Gsjfn88iKjy3R54dEBzzRZvX0I\npE7LHKVovtPy9aef4WtPf+s7uve/SoG/613v4qMf/Sg/93M/x0c/+lF+9Ed/9PD6+9//fv7e3/t7\nrK6ucunSJZ544ompdfz1D/7Vicmp0yQwfvFpJzeXV+BCCKw049TJ0zz11W/w2PnX4YcpPX+EPwgx\nA0Uvi0gsSUMZuJ6J6zokSUoQBcRxyH5nF8e2mZ9t0W41SJOY4WhALwo4f/d9/Oqv/wYfeO/7CQZD\nPCFQaYo6OOFHocgMgW3aONpEKN5NTz2rhxcWbdc5Y8hRRxzHh/xZeVKVOUydLilKuS91Z1F5QSja\nVQ7Z00tB1eh1l79f1F120ujjrI/dtFK8U3khOKxXHTiySclEAuqAt8XCNDzqtSrf+sYLVKstlJQQ\nxvR39nCFwFQRIlNIr0aqbOqeS2PpBGujmG4Q4noee50dRv0hVRJQKYbKkI5NPw6IVUYjCuhs79Af\njjhz/AQoxXrgc+BbpVKvkzoRKflu43m7iUhSRmHApt9h30oBSf1YjUHXZqvfJXryaVR3xPHjc5gq\nY/viFWrVBfzugMVjs9i2RfziVV7vtdgSQ/7lb/0W9z54L9/zyCPMuw2MwRAzibEqVVKREYuMlPzc\nT0dJSCHO4iOKKY7jQ1qy6O//UqUzjTIsh3mOUXRy6D8pPivvI9B9QbpcFjJQPENvq17fWI4yzQFb\ndnpO7lsoK+hC1nRQpYOiaYtIcQzdQS2Hn5cpnfx5t3faPvH4Izzx+COH1/7V//PrU++F70CBv+99\n7+MLX/gCOzs7nDx5kn/yT/4JP//zP8973vMefuVXfoXTB2GEAPfddx/vec97uO+++zBNk4985CO3\nFYRiRdbD4HQEriMB27YPlVhRyomRlFK4tsvd5+5mbX2LkR/g1doI14VRQhgM2NzdwWhWyZRJ23Jw\nXId2s4VhmPR7fYaDPjtBQBrHVKou83OzHFtcZM5SrGys8cDDj/LP/+2v8rM//VNEfoBr2sgszQfF\nkoRpTJZEEI1Nw0LplU3AMkItb7vX83zrikz/XtEPhWCVN84Uwp6m463yegSMPjblmFu9nsJZmVNd\n4rC+Iq67HLo3NjPHucOL+nX663bF1LaH53JSZG/LiKIYmVookYHIFbgQCqREqAwhDCpOjf/4qc/w\n6CNP4Dgmyf4+LcvEH/XxTEHU8xG1OnuGy4mT93DP/Q/z5HMvcqbVIOx12FrfoIbANS1sUyAci54f\nkKKYabeo1mu89K1nMUyLY+151tbX8TtdXCEgjsEyELaN27KQlodbcVnbWsepeNza2eIN5+5k/tgy\nF69cxlxqsbPdwTig9oMk4B1vejN/8OzznLQF0V6X0foODz96N1/e/TL1eZcLp84xnF/kP33tKVYu\nX+Nn3v8+bASW5eBVPILEJ8syLCGRqUREKXGaksp0qhwWaQh057UQ48iRsSy8eqheMb7FvC42iely\nVCBTnTLUZVtPtlbImM6P63RPMe/z7+X3x3E8EapaOAv1re9xPFbKuvLWZVNKie+H2LZ9KLumOcnb\nj999PN8K4FP0VSH/xWfFSVlKTYa+Fm0rz9/bOfoPn62+E6Ll/+MihOBP//D3jqAynTrRV91y7HCZ\nCy4GIYxSVK3KJz79GYK9EfdeeIRQWHjKpCZcrEYdb7ZJ0h0SRrskaUwY5hEYhjTwXJeK6yKlIIkj\nAn9ImqRI00I6FqFIiVSMP+jzvW9+M2IwQsYRQigimRKJDAsDQx0951IXWN2ymJ6LQh6+q45Gsiyb\ncPhM4+509Frco9MnZUWro/OyBaSPV/G7QFBF3WXP/WTdxpG26kUpxb0PvfHI9YvPfFl7XjaxSGRZ\nBokBUqFEiiIFmU+Iqtug3/V56ivPcOml65jSoT5bp3/1Okm/By4QxMjM5Kbvw9Ixzt95P0ks6IqM\nh0/WufLiC2xfu4Lq76GGPWquSSYyIgWRYXLnXfcRI9je2qJWr2MaFttbW7iWgWuKPB94HJJJkyCB\nuy88xLHlBV64dJFYJXiJ4v47zpHZkr0s5IUXXqSRmniZSaxSjHTIY3c9wK31DkZ7kbvvvY+wu8fS\niTanTi7z3B99mcXmMrdMg0vVhJe3b9Hb3ea9f+lHaVc9bDKMLEOmKSpReW4fpRCmQZjFRxbOXElP\nykuBFHX5K9D6429655Hxeu7pL35bqqV4bppO5gAp5F2/T7fSdLksg5fJNufzSM/6mYfQTobE5j8c\n0ozlhaAc0ug4DlEUHW5MKmS9sFh0+daVdXmhKlC3lAWwm4zE0uedbv0ahsEjr3/Hbfv1NduJOU4E\ndTSKQTe7YJJzLV64WOH1aJRGrckoTfiBt38f//xf/GvuMcBFsLW+SejW6K7cpDk3h+u61JsGpmlQ\nb87iOi5BGNLr9dje6yCAasWh1ZqnWq2wt71PTJ6adLffZ36uzf/48z/Pv/qn/5Te5iZZHCFsA8M0\nJmLAi3cp2j+NW9M5/WlORz0/+rRE9dOyERb3wqTH/fbCP5nlr7iut0/nFPV26simGJfif8Owjpin\nOq/9X8oNHo6/IUAKVK6+EQIMw2LQHxGHGTeu3iCNYizbZLCzxvatq4gopDbXIIszUDa1VpvGqTsY\nhQG7m3ucefhBrrz0TdZXb1K3TYxGg1ES46uEMM0I0oyF44s02m1W1zaoVirYUrKztUl3c4vYMqkt\ntLFMSbVWY+GOMwwzyXe97W0szra5/7HX0Zpv82ef/A9srG9w18MP0OvvYtU80l6INCSGgo3A5+Of\n+wx/8eTr2P7mUyTHFqhfOE03DuhbBsfPnKX79BXOPfoQUS1meXmR1f1t/tH//iE+9KH/lZZTwd/r\nsFCtkWYBfuBTbTUIovDIIdy5/EyLsZ50/hVjVWzWK5derzcBwAzDwHGcQ9StRzTpgEYfV1156uke\ndNpUV+x62/I6cgd+gZjzOg2KU4gKKyPXG9YRnVOuu0zN6NZJUR9Myrtu8SqVTgAdyI+RNAx5uIjo\np1sVwBTyyDvP8yau3a68hkeqpRPKRik1YZYUglDepj1t8hd/+/4Qy7SYazawqzajYAjBgPnZJrX6\nDI2ZBt39LgMVMRxlB523hxICISTVSgWv2qLquQihGAUhvcEeaRCTZBmGY3L22GliFfND73o3/+xf\n/2v+yl96NxW3QhZHyExiSomhKTgYm1uFgBZRN2XlVrxL8b0iGqXMN+sKe9ohBsVnOn+n93eZPtGd\nkHqbpJQTC0B+PZsYs+K+4nu6c7act1tHOd9uJ+btFLwQAiXzEEIlZH56kjQRStBut/ncZ/+EOIo4\ntrRIGqXcvHIJw8xoeC5Bv0uUZkTSo76wyEKtxvbuPm94wwWG/pDrazcQSUgmBLZpYddq7Pc7jFKF\n6VWwag1urKyhkpRmu8ao32fY71JzbRyl6G1usrS0yB0nTjKzuIQxM0ut3uDZa1c5eXyZzuYuNgZJ\nGLF+7SbVmSpve/z1PPmFP2d7b4/uXgfbEsxUm8iKyX6wzerNS5y5s4VrV+gOfJitsVUTqK01ZOiy\nvx1iexZ/44M/xWc+8zmOLy3xpkcfZmvk46gMp+oRJxFRHGJbzhHkqy/Gen/rC3lRBoPB1PEq9okU\nii3LMobD4YRsjJXb0WiT8r4AHeyUI1But+iXZTGXP4vyQpS3QU0o4eKzwsotMwH6YqMHC+gyqlu6\nev8Wv/PvFT/ZQbx6/vyCviqYhiJ0etpRbOXymh/oANPPnSs6pNhyr5tz+sqpD6rlmmR+xGB/j3sv\n3MvK+i0eufM+dve6+HGIDFPuPX8PfZlgiipRlLCxscHO3h6GNBgOIizLYrbVIk2jnOd2LEwEaZKQ\nRTHhyCc2ElJg9sRx/o9/9S/40C/8AoxGqKE/sTOqzCUXpheMU6bqJlsxYPpJPjpPWNSpm5Q6P1hG\n4sX3Cg5QjzbRkXW5jqINZXSfj9VknHmZXyybvHq7pyn+aUWXA32CFGgnTVOUAKUkHIQTWqbDxvoW\nf/6lP+f8HXexvbnBXHsONeiTEuHHioZl4kvJIEuoVRxWXnqRzLUI/BmC9S1U7OMaJpHvYzkuwnHx\njFmS2KfaaBArwaDbpeFV6A37hOGIfBNlhi0lFiYiitm+tYKfKN5w74PYhgnSIOwHfPPPv8p8q8F3\nvfWtfOITv893fdfrCfc7CCkIooCKMjD6EbVGlee2r/H4e9/Ol7/yVe44f4aZk2fZX+uweP952t/v\n0OrFOJGis7uDlA79bsjDdz1IJxzykd/89/zE+9+DtCRGEmPFMVXHJc4m06Lq1JqugPK/J9FjIa/T\nShG+qjsaXdc9Ytnl83nsBNdlfxIkTMaq67JQjt4aLwxqYtPZGGFPi5CRU+vWaRHdKii3rRwAULQh\nCAKNRhnz38UJUrmTs/DrOYfzoRgDPSIsiqKJYw1vV14zBa6bDgXS0xVO8VLlmOLyyqd3cBz4VC2H\nKEu48847uH7ti8zPtXFdj0QYyDDl5ZcvEroW/jDFMm28SoUHH7gfIQ2yTLG/t0sQ+vR6fUhThKyR\nSkml4mFKCUIRpAGeW2X59AmEKfk3H/0o7/3hd9G0bUhS4ihBSUCAISTZwSHCGAZKAJkiifJVVxiT\nyKO8aOnKHMYmm86flxVlGRnoSrlw6lhWIezTHZjlFKFjBRwfmXj6GOhtLRw1t/Nt3K7k+Vv0I+R0\n5KNwDIuIfPdnGiUgJUEU8vGP/QGkFjt7PfqDAQYSf+BTm2uigj5pmhGlMfVWC9eW7KzvYjarvPCN\nrxFubeE5JrYtsDwXE5MoCJGWw+zCHCfuOM3myirKGGG4NQajbXZWVmk7HvWKh8xSKtIlCxMsZdDd\n3eflly5iNJrMLy5x/fkXaXoVXrr0MsfvO82PfeB9fO4zn2G23ca2DLIgpCUtbM9DJSkDYtb6uwS9\nPs9/5Slq75xj6cQd7O3sc+aRB1h/5iKDzX2qtkU/8Kl6Ht29PkqkPPrQo/zf/+Ij/NX3/mXOzs/h\nOlXikY/l2KRKIQyR88OZgiw7SAdh5LTUwTjLAyemnoyt2CpeLkVajLK5r4OF8Vw/unDf7v9yPp4s\nyw73MOjPyBW7hZSCLCunZj5K0eT3ygngMinj41z0jUaTLEtJ4tyRn6Tj+ZdlGagic6OiWq0CHDha\ni8RzeT6n/H2Sw1z/lhVN0KPF+xUWd2EN3O4UpKK8ZgpcVwxFmcbR6v/rJkV5EKWUWCIlkjGYkvla\nHSNJubW2gkwMTGEzO7eIeaKCcBziZMhoOCQIfK5ffR7HcWjUZ6i6Jq16g/nZOr1un+FoiJ+k+FmA\nYzvM1Bo0KlVAIfqKR88/wrPRN3j6pZd46HUP0UhSbNNipGIsN08baguJNAxCCaMkRgqBS35MWlkZ\n6gpYNyF1ISuHVZX5vKJPdMHI68vD1yb7UGAY5pH+1tujj1Mh+GWUrKP68f1jpFPmMF/NNCzSAJSf\nV6CVSmKjzJRUZRiJxHBr/KcvPMWVV3aY9ZrUmsus7nXYuXqVbCQIt4coGbFvZAihOF6vQejjmIoZ\ny2Rnb49edxdbGKjZFl6jCcMMJ7MYxgqj0sSYmcPrx1hWkxExo/Wb1ENBNQowaylzZ46jwoxoo4eJ\ny/GTp+n6A6J+h6Czw8rqTYIo4OrKNT545q8TxSHHTx9nb3OLdqNCp24yCCNakUGSpAjH5uWLlzkz\nd5L+1TXWdzcxHjuP8B3Cq7uYM0ukQYQdDnAqktQFO8loKBN/L+QdD76ZF5+5zFcGT/Oed72bdqVC\nEg0RtiTKIhAZpsgwUBhIUIIUgwQjP2szi1Eq55ZzWSlnvByX8uEJ5bEdK0t1SKHo1nSZ+ivKtANF\nyk78cWRWQppO+sly9D0ptwUC1wFi8VxdqR+2L03JVIY0JI7hYCv78CzS9CDSRGWKJC0OCJ/cSW4Y\nIGV6yDKYpn3Ax0cT1FGapoeIu7BodCvkduU1PdChvOGkvBKWzb2y0tcRvJQSS7qESYpTdZlpOJw6\ncYzt7Q0ef/gxNlY3eeXS81ieR6hS2s0ZHNth/tgylUqFke9jmSbdbo+1lRtkSlGtVJht1ak0ZnKU\nN/KJgoi97R36gz6t2SbuyOGNr38T//LffISZmRnuP3UHvu9Tq1QYjUZIQxBLgzgMEVJgc2AGHuzW\nlHBEIMucv24W6lRI2Qmj943eP4WiLSaPXq8QkuwgPljv3/I2/kLYdDNzvDAcjR3Or4sjil5fVG4n\nnDoCK1M9UkrCNMURBqlQ2K0GUSz488/9CaZTxVcR7Zk6VhRjODapYyNNRZhCEsXUWzOMwpRh0OHM\n6dOEwYidjRVcmZEoxWjQJ0oSGpUWuCZVz+HM6TvY2toiCnwu3H8fcRrTIcFcHqL8AXu9HeJ+wFxt\nhrBtklQsxGyN7sYmM1h8/ZnnOHXuDF/88y/ywb/+k/h+gDTgTW9+Mx//nY/hVqrcc999XHr5EiEx\n0pIEfgBSYBxbpGKa2ELQwCKt2Kxvdbjz/D24WchACK6vr2HZAmemiWdVSMKIlswwenvceeY0/8v/\n9HP8zN/4a9x57iRWFFGTkiyMMaUCU5IIgUKgsixH5YDSNqLoSmZa0aOExjzzUccl5IuA7pvRqYzy\n/J6M4BinTNDv0xWuTqnom9vK/HRhXep6RA+IKMJm9TmnMwQ6eDrMZ3QQDl3UV8i4YRh4nnd4fezc\njRHiKN9f0EA6rfxq5TXPB66/QNmUKj7TQ37KyFNXAlJYQEgcxiRCsby0yHPPPE+nu8vsfIP55Tam\n4xKmMaM9nyiMWL1xnWqthiEllm1hCkm7UcHzPEzTpNvtsrPhY9o2UhhU3QrtEycxDMFgNGQY9Nlc\n2+Inf+In+dJXvkyzUmGh2SD0I1qVGv1ghK8SpCmxs/ww2lRBKBRSgJFNps0sp6EtkG1RyjHUt4vf\nLvqoqAM4PGuzPA7lJEP6gqHzifmEyb9XVsBlmiR3mpq3NavHzz5aynlY9O8opQisjGqSIbOMzMr4\nnd/8bY41mmROlVQIrl2+yIxhILwaZtVBmIKeP6DaalBrzNDpDDCR9EYjtlauUbUkJCHCdEnjkDhO\n8IMYu9Hg9B1ncUwDooCHHnkQVxrs39jAs0wac7M0vWWcWzZpEtPd76Asl3sevp/r3V1sabD54mXu\nuPMEL196BbdS4U1vfhNBEpJJMCybd/7QD/GlP/08MYr548e4ce0aRprhOjaNRgPZrHH3hXNce/ky\n7TvuIGjPMFIwevZZ3nThAZ5fWWEGi/39Pn1LMrBGyDgjU4pmxWPQ7fOP/+Ev8Mk/+hS76ZDHzt2N\nbVcYdYfYVY8oy8gMRSrzkEOZHsRE32ZMppVpi3ihXMtjKMTRVAllC65QpvrBHRM+/qUAACAASURB\nVLouKAMAIQRhGB4+U9/UVj6QQf8p5ltRt76pqVDY46gRnbotFpKcY8+57ZQkyY48J4qiHMhpUSxS\nShzHnlgoLMs6jMwrz8NXK6+ZAtcHUV/ZyhNXP50FJtH3JN+q2O908GrVvEMMyT1338XF559jr7tD\nf2jR7/exbBfLc5lx5zh16tRh/cNhn16vR6e7j2EYhFFGtdbm/OIZotQgCCI6ex12tzbx/XyjxOxc\nm/ZsE2EZbK1v8+bXv5Evfu1LvOdHfgQ5ihgMRxi2RaRibMfCjjOMDFKVEacZjmVhaZE2umDpk0Vf\n3MoDWjZfdYWum4R6FMrtiv7cgq4qx8oaxlEKpGh3wU8WzykmRBmZ6wvEtFIoC/3gBr0oS0CSEQx9\nRoMh11+5zIXzD2IvLBCbkhtPfYuFRo3UUmzvbpGkArfZ4p5HHqc3DNjoXqZZrYGRR0vEwwEzFZPU\nMSBUZBls9TtYjmQpC6j4I2Y8l7nZJh4GV7++w9bmCr7n4c4v5NZWFHJldZWlM2f5xosvYHkO3ZVN\n7lta5Nr+Pt945pv84i99iFSAYZkkKsFyHRaPH+cNb30LX3/ya/hhjNGqofyAiuFiWyZbvT3OVAxU\nFDG8sYHXahJVPAzf5+IzzxIOfawgZtatYNYtAkOQDSOWZucYqJTFqkN3d5cfe9e7+bXf/y3WXrnO\nj7z97SwtLJGEQ7IkxlDk574KhTIOxiQdj1dRypvndDkrFHCZIpumNPUIrElwMM7Vk6bphLU3DY3r\nMuW67uEcKhSuDvZ0sFhspik+09tb5sClnGQE9EiR8vvWavUJGlEHpsXv4t19fzhh6ephvzrdM21H\ntF5e01PpywhbVzBjFCcnXlAXjLKJZllWHk+bZSRxgiklc3NzRFHM8WMnWV4+iZQmA98n9RUrq2v5\nM4TAq7iYpkV7dvZAoBRhGLG2vk4cZ1iWQ71eoV7zECqnHvxgxP7uHsIE0zLYXt/gztNn+dD/+X/x\n93/mbyIzhZXEmKYkDiKyJMOSkkxKDCnIkpQwSyaoiXKETfFbF6JCqRUKUhcSvX/LSGVqqNiYJjws\nuqBNKvw8RaiODKZZUuOJak4o6mnc+bRSIJEidresFBIjJVQZjUadP/nEf0AFIb1+B6ESrIpLlviY\nrgFhRGpIogxmWouEymGz12PpzN2YKuXKM19n6Ic0vQqCGCkMJPkmC9dzcBo1dre32L1+i4cfeQQ7\nTXn+W09zx2ybZKFJuL/PoN/H8RyGSUDrnrM4rVm6K1sQxNQdl6xq8/TT3+Rv/uzf5uz5c0RxlOfP\nyTKwBJlKabTbRKmi1p7l3uU5NldXEJ0hIhXEUciffeHzvPPRt+IlGb1ba1wKeswEJjVD8sYnHufG\nU8/ihxGdqEfgmjjKYPXWCrEJsSmRWR4C+ANveQf7gw4f++xn+Uvv/mGarosZCiyVIVVGojISkQJ5\nSuKyT+p2McnTwFd5nMcAJD0c2/I9hczpqSPCMDxUqMWu7UJP6M/TwUPZQalbbzk1MZ5fOl2jz69x\njHZ84JPhgKceI/3xZpw83lw/51WfE3oo9Lgd42CBYu6W53J5oZpWXnMFXjSwHG0CB+byQWiOPijF\nfcUqe2g62QIpMkzHxhECpOSx1z3BZz77R5h2FSkc6pU6jUYTb97DNHOnTJIk+KMRYRSSxgmu51Cr\n1XAch16vR9rv0OvtsbOzgSlNGo0mjUaT+fk2y8cWGfkDdvZ22d/fRbkWP/7j/z1/8NnP8u4f/AEs\n04YgQCqZH8Rs5890ZJ5BT4kxeoWxZVK8mx6ZAxxmKdSR9TTztein4rMcOUzmUBlzdhx+Txe8YlKN\nS767Mr9XN5Nz9CYPInmKUuYrCyHV2zetFJsghJBYVhGiVuSOjwELw/PodwKuv3iJmu3RHXTxsoS1\nK9v4/T6VmYy6MFGWQ6PepLVwjKsrm+x2R9x/30k6W+ukwsB0PcJRiOs5qDTn7SFHU9WZFmYGS4vL\nVDK4/vzzeCik7xNkEV7Fozvssz8IiKs291x4gBvXbhKFEdHQx7QkH/vjp/g7//PPcf9DD5FRTG6V\nJ0pLEsI0xXY82osL7O3scub4CWzL5ObFSwT7IzzTxKlY7O3vEKQ9up1tVnY3iWuzpHMtdrp7tBbm\nUd19GiYYjoFIFVXPRVkWyjYI/ZCaU2E48jk+v4xZ8fjf/tkv83f/9s8wazuAwDMsiH0MQEhBmk3O\nxXLWSr0UcqMj34IKLM/nPE93Ia/yIHIkQylxEJ9dbCE3D55ZIG6wrKMKbawwJx2chYzr2TrL0VHl\nuVKmZ4UQJEmMUtHhvUVe7zHSz3d85gpa314/aSHoCenyeZlOffbkO4kJCmhaeU0plKKTyqZ9mfMs\nfk9z7hUHIwAkaR6Co9IYECjDxLZtRn5Aq71AHCrWVrdYX9nDqlg5520YWLaNYUhM08BxPTBMOv0h\nYjAiSRJsx6I9e4yKVwNg0B/gj0b0eh2KDEa2ZXLy5ClGYciw20cZFi9du87DZ89TEQaGFEhLEBuQ\nxQlWlOUozJz0ehce6HLR+0rvn2lOQh3xTpqOk/Hhep3FxCtHkuh1l/nrslJWKk9UXxT99JXiO7eL\nVCi/68FfhwtCPj5O7oCSCj9JGfQGNKszOF6F9f4eye4Oo/UNIgNWwpC2sNmveDxy9/3EqSLyI9qt\nFqu3brF2/RJmEtGYaRGLlN5wQKpGuIZFGMcsnz6FV2uwdu063/PdjxH0+1y8+BLNagXHrtKaaTBI\nI7YSHz+JuGvhbq48/zLDUUStXmd3NOKVG5f52z/3d3ngkYeI0zQ/PydfH/Iic7noD4b0hz5Lx0+y\ntb5DpVbDbTZI+gFV2yM1BBcvvcxbHniM7s4u6coGw/kMu2IyCH1OzrUwqy42Mc/evEIqJYkMyYQk\nVQIlIBiMqNWqBN0B0XDIz/703+JPP/8F3vaW76LleSgJUgnqlkMUx3nIq0ZxvBoXq/PJOgIvj31B\nQejyU65D/06RAG5sDR6N2CorZX0+lCNdxpFMkzu8y++mt83zqkfeNf/J8+6M75UH8i8nQJhOx+jP\niuOcQy+3tdwn/007MYtSNLI8ufVB1RWU/qN7h23LQWSAhgKb9Rrt2TYvXHyRk8fPcPLkCdrNOYaJ\nT7fXYXd3l7SX4jgO1WoVhaDhOCRhRBCMiKKYLBmy3+kipUGlUsFxHOyKS31mBtu28X2f4XBE4Ec4\nlsMg8Hn4oYf57d/+d8y+9/2cmZvHtgziNCHlYOOJgFiQx+Vq76t76MtOoKIfivt06kW/p4ywdbRQ\nFpL83rH5W85qWKZAytd066E8ZrpTuly/Pt7losf/FkWndcxUYNZq/Nk3P88gSpiZb2L0O/jdHvO1\nOr6ZMQgjuv0B7lyL/b099js+C8dOUavXuX7tEpYKUWlECri1JonhMuh32R6OaC8sYLoVdjd3mKk2\nac202BuOcDPobWySWBb2jkfPgZEL7YVlLj13EQuLfhhx/+Ov4+rqDf7W3/+7PPzEQ4RxenDcHnnW\nyiz/HccxzeYMl16+wtD3aaYZOzt7yH3BXXfdw0qQ0VndYDAagoJnLj7LW1/3Xezt7PDy1cu4s3Wu\n37zBprHGhfP3cKa9xNbWBpvBkFhCs1onGAUkUmA5DkESYWYms1aVrUs3eOd3fx+/+fF/zw/84Pfj\nLC3hAcPeCMeyEOYkGHg1ZaIrwjKyLcbukP7Stp4Xv3VQMV0+Jzfb6Eq5XI8QYoJu0YFKGRXr8qlb\nvJPPn3SolxeI4vm5bI757PK8KfeNaRpk2dF9EmVl/2oLJ7zGceDlBpZRWSEYOidVXC+UgW7uq0yi\n0hTSPFc0KLAs7rnvHP/xs3/M448/yubNTbY2V1GmSaPZ5OzZM1QqFYIgxPd9+v0Bo9GI0WiI47g0\nGg0cs4EAgigkDDP2dnfwfT9X5LaNNCSe61Kt1rBti4qQ9Ht9fuHn/xF/+B8+xfzb3kpFCYQh8p1+\ngU8KGKZ5iOCLPin/XR7AsiVSToRf9KEeFjVeBBXFBplJJH10g0Z5gugLqd7nR2mW6WM8jTZ5NZO8\nfGqKziW6wsbfH/CtZ16AAGYzxd7aNrU0JSWCSOE4NqEHS7NtVBzgd3YZGgYbl19iOOxQ8ySGZxD4\nIxJhI+0abl0iGjMknksvjEljhV11uLW2ys7NG4goxkYwCAakQQh2nWajxcbl68wJj0FnwMLpEwjL\nJDPgsScepeN3kaabv6+ALM1IVRGeZjIa+dy8eZNGo0maKJZPnmL1xg3C/girWmEvHGIbINKMje11\nttfXuO/kKVZ7Ozz7zW8ws7zEyYcukJkS2Rkwqyz6hsHANIijCCNVCCGJkyinB2t1TGGi0pTN6yv8\n0F/4izzz9LeoPGEx41jMz9QZjnxkyedUyNTtxrk8N3Uwplt2OuCYJm/6olG2/HIKZpywrQzkdJ+J\nfhgKTJ5cVaxDOs2it0N/tmFMymFxcIT+bgXdVw4g0Ntf9m2laXwYzligdN3iNQwD27b/282FUubA\n1aFQG+gOtGJVLHfINGWfqdyUMaRACkjJSNOQs+dPk3xmRBD3WDrWRGYtusOYMI7ZWF/Fsm0c28G2\nHRbmZzENk+FoRBAEdDv7CGFhWTbVSoVq1aNSayKFxA98up0Oo/4A301IUxNh+dimRPoJX/njL9Jq\nt3n+6iUefvB+ZBgi/AhHGAhLEKuMfMvmGBHovF1xvXyEnD6oZTQy1VFJEZFSnEQ/mU5TR8w60ija\nVO77Iib4dqilKNPoHj1M8tshunIUwWGiJOHy5Oe/Qr3a5MSF81x6+hk8YeLZBmQRca9Hrz/AWpzD\nJKPX2aVuG5jRiHB3jWC4j1l3aDabuNUKQQRpamLXmiydOUFjcZ6VK1dJ+wGVSp2Xr1xm/+Yt5iwT\nfzQgdiW1EwvsD/v0Lt/A3B+Rypi3v/P7OfnEI1ROLPKnn/00xCmZmfs5JCJnwKXEFIXfJ+P5Z5/D\ncTxs08F1XAbBiDOnz/DcV59k+fRx7HaTcG8PM01p1mpcfOUFHrr3QZYXZjlz4iTveMc7SKs2W89f\n5qWvPsfJU6doVFy6qY8hbSxp4iuFa9lgSob+CNO0EEISdYckA5+3PfR6nvzKk9zz0D2IqotXd6iG\nt08dWy7TULEuP2XeuIyGy1FohdwWCr+QnXIOIV2R6/lEivt1PaEj2yLJmt7+sZU6ibCPREBNsUb1\nNutBBsViU86hP01/FXJflv3/ZimU8g6o26V3LJP4QuTbU/OXLPjag/whhosUEsMAA4UQCalQVF2X\nxx9/hKe//iR3nb6TJIhpNI8xU69RWahiGCbD0ZDRKGBjd/dAaVq0Wi2OLy5he3WGwxH9wYCdnV0G\nwwGObVNv1LnvvgdwbIdOp8P+/h4Dv08/CKhZFo40ufOuu/itT/0uXs3jkTPnsEVKGscIKcnSLD8U\nooRKHccZ0ymaOQiTu9PK5mUR4qQjlTxGddznBac8yblNj7cuC1PxdzFWr+bYmjZZ9R1mOldYLvmE\nzhPk55bVQdTGQTL8IAy5df0Gc602Xr1GEIY0TQeRhcQAwqBeq2HOzhP4PqQZy/OL3Lp6BeX3WWh6\nKGK2N2/Rnj+OZVdxhMP88jEaxxfpxiOOnzjO/Nl7uHX5MqsrqzAc4Fomo9EIpzZDXymQBnacseTW\n+e43fjdbieLmxjp31us0nQbD/QFxM8U0Dszqgks+6I9+f8De3h793oj2zCxhOKA+36Jzc4UHH3yY\nL339y9x13znWopCkO6A77JP4ip39XZxKhb/w9u9ldm6Oq3sbrG+s05AmchTSnm3RywTxKKZhN1BS\nkZBhGCZ4BgkCz/ZwDAvPMNh45Rr/3Q/9KJ/84h8S1W1OzM9TEflBJWLcXIQ8qnSK8cp/i0MH5Rhp\nF+h5fG8ZPY9BC4eHo4A6kka6zC/rIKeM3MfAo9gBqjv7x/NHd7qapnk0YiQb6xwO86pAlqoDeQTI\n55jeNr1fxu3isB909K6DpEM2oWQZ3K68pjsxi47WUZ8ezpMromji3jxKYDI8KO84gRAJiDyENQVU\nJpDCQvnw9jd8N7/26x+l/fgcSZrS28wY9od0TB/LyfNAGIbErVZIkhRTmgSjiH5nHdNex7Jyk+b0\nyTmUmkUpxXDQZ3fj2mEbPEvRrreI45ggTjCpcP3mOt/3th9g5dY1TswfZ7ZRQZoZWZZgynwjc/E+\nh2ghO4jBPjgKC6HIVIZSr36m5PizjGIrhmnaBxx1HkFiGEfpEF1pFz9l/l2/PnYGHY180dtWVuC6\neVimfyblIg/byrIsP4kmizHlmEK7GnYxFuZox5Lg0gp3zS4iREJn2KHvG+zGgmb7BCQNNoabuJbH\nK9dXMP0eC1VFlvTZCyJCDAJSjLhDNOizO+jA17/AYPsmWcVmaFhIPGYtlzuW59gPujDTwKnMYI1a\nNGfqGG7KUmRx5YWXuKYEK1cvsfobv8cZ9xi2UUfYHZI4wsJCkVNYWZor8s7OLrdurjK/cIwwkbhe\nlfWtHnPLp0i7He5eOk223uXEqVNcvXWd4a1dDDy+cusW7//Ff4hcWuLG1gY3r1wjGUS02m2sKMXZ\nHbJspmxlCftGyOYopt2egWhEnMUYBgyiIZ5h4AeKRsPjhWee5/zJe3j26StEZ1OM422ahoXlh8g0\nyU8/sqfnQomiYGJOj8MQx0qzAFwFoJhArUIiEAihEIyVfJKNQUBhheo+Il32pHQmnK3jaJRCvosz\nOSeBig4Q9YiVQ3mVB3OK4pkwzn/Cwbw6nIHFDJiYn/rcGLdhcp+GPj+K9utU1O3KaxqFUuZTixM4\n9JwLURQdDkySTIbAjZV6cW3yYAHDMFBCYNoW8TClUqlw9epVmjNN7r77dVTcKnGS0Ol16PQ6JGlM\nmibUalVajRaWYdLr9dnv7NLt5pSKYRjUajUqFZfWTItqpYJSCt/3GQwG7Hc6+a4q12N2pkmUxOx3\nO5xYPsGXvvRl/sqPvZtREiMQec5qSx5yYGmakKnsIClOlsMRIZEIIDs4vC0vxTsmpS28yUHiLNMc\nO0XK5m2ZYyyjomL1150rZVRRfB+K5D1HF99J/n1SkZfr0ksxUU3TZH19nePHj7O5uUm73UalGXbz\nFEbtFoaf0h12GOx2WFhoYgmJbQgeufAAljfDjZub1E5WCNc6DLdXmJ+12BntEw8VaWSRmZIgVFQr\nDZq1RYzzDW5+bRevMsdgtMfsksd2b4e4tszVzoBaq4XrZLz00pPY5hJeTWIbI0Zek6gv2UlN1ndH\nPHfxG7zpr/04fmWfUQSuMkizDEMKDC28zvVcarUqe/v7LC/XkVLS8irUDZNICa6+/BILtQqzssX5\nheNsxJIbF6/znh/+ce6qz9LtDPj4v/ttzi4c4+zySaJOl9XePgt+xsnFebY3VxEVj7vuupPtW6vM\ne1ViEjJLYFomlikRaYbjePhJSmIZnD1zmpcuPs+xmdeRKMGMY2MfnGIXh+Ft5nI+X3PEmFuvejTT\neLwN0oM4e4OxE3GsByA7mOeFjphGtZVlSd+kVt4PURTdUi1z9tOsxUJ+i1OndGoop4WOHl2oh9CW\nI8CmlTJtWli34341bmulFuU1U+DlLeOQd0QYhoQHgjLNJHq1rdnF/xOOPSFQgY9Xq3L3XXdz/eYN\n7r33XjbXrpGkKQqJ63nUKi6WbZOmGaPRiE5nF4EijiNqtQqLiwtIKej3+4RhQJIkrK+t5ZysY2M7\nNkE4wvUqKARhFBDvpQRBgGXb2KaJ51T4/Je+yOsevoBhWhjShExhmBJpCCxMlMqIovCQPpCGgTqg\nFHQ0M40nE6I4DLkIcxITvGFBPd0eHYyFd1ry/7KJV14MivvL3LU+Nnp7X80pViD0Y8eOEYYhCwuL\n+KMRhmliDDYx/T08WSGOBri1Crv9PsqUODNNTt9zjtWNHR5+4kH8ZJ+b68/TnlmkH+4xiFIarosh\nHazmDLWzp+l3+3iRYOvWy3T9faqOR5o1CXZjztaWWWgsk9ab3Oz0SfoGJ+0LDFlnf7tDo91my7aJ\nnIRRMKS/ucK9dx7j0bc8iqrZmKHEzAwMebD8qvH725ZNp9Ol3V7Acx3SNMZwTPajPv2ww9nHH+La\nt75J71Yfp1WDxQbnz76RO994gd72JpdfukQ7gmo3IKl0GYqQfTWkc3WD5e4eJ04scT32uXXzMg3L\nIw58YqGIY0V2gCTDIMBzXaRpEGQJZsXl0QsP8Xsf/yQ/+M7vx3ZtBnFExTIO902Uy2g4PJijBzn8\nSRACTMMCAVmaH4uHEJhGns4hzTLiJE+YZUgtZlyIgzitvLNul3GzkMGy0tblteyQ1Hnuyc06k8cP\n6pZiOaKkTG+8Gkgpg5iyjOttLOaLDn5uR0/q5TVT4LqS1Z1UMF5VkyTBdd2J1bWsKHSTo7hefHa4\nYpoGO5tbXLhwgd/9+O/x+OOP056tUKvUiZOM/f0ewWiIIQ0s02R+dhbXtYmTkG63y95ej36/T5bl\nKH5hYYFmvUGchAR+wMbmBoNhP1d6Eiqei+N4WKZDv9fLkXm/z1x7jv6oy8rmDidPHYcswpRFlsBi\nwBSWdeD0kLlAZ4e00aTDRx/gMeWU96tpSg2Rc0TQ9b4qR7IopQ7zphz1yk/G0MIkVTJtIhX36PW8\nWikQleM4DAYDpDRI4tFBGk6ba1//PEavx+Z+gFQjnGoNMslWd4f52Xn2hnskaojjRnQGHjVPsWSa\nrI1czLm7MWLwkhSzNUPr5BleDK5hOTbnFz0qsUW6F+FUTT744+/j6vPfxDQl3/uX383Lm7vcurqG\nuR/T9uD3PvkZ9mMF3hy3br2MmQ45PjPLP/7FD5E16/S6GTXHAsZ0kzz0Nwhcx6VarTLbbqGyBMf2\nyESGY9mMTIPWsSU2b8z8v+y9aZBlV3mm+6y1533GnIeaVKVSSSpJpdKA0MUSYCxE08aAgcYNjrYb\nPETYHTg6cFzfe922L0Tfxh3hoY0d0Ya2kdtuOowbzGRjsAEjwBYYoQGVJDTVXJWV85nPnte6P9bZ\nmSezUsbhcNv88PqT0859ztl77Xd96/3e7/0QvQFRq0djdhJvusHc/gVO/9lfs/nCea48f5rb7ns1\n6Jy8SEllTj/apB67uGvg1nwOHT1MazCgsF0KpUFIhBL4rkvNq6DyHDfwCFQGjmTY6/GGH3wz/+13\nPshP/fRPUgl94jzHfZFI0veMymacGhtGgx3crpQSIQVa5UhhYUuJsC2UECM+eZsf3qJe9Lb3STmn\nxneD43Nsr4KXMl+0uzgOdppdjePK7uYrZk3ZGYSYWoerzeOEuNoKe/cx5dit8irxKxnb5XxXA/ju\nJgDlwz9usWjbNlm63YtufEUrL/S4VE5aO1dCKQSWEMRpysTEBLnWvPQld/HYo49xYH4K1/HxvSqe\nV6VWqeB5AYNhTJ4N6XTaKJ3heQ4L83N4XoBSBXEU09pcZ211Gdd1CQKf2dkZAj8gSWPiLCdTBd21\nVYqsIPBCAtenWqkQZzFRmvD4k08xMTeLW+RkeYYlwRqpRMzkMIqFclupKZAYb/Hx6GCcQxuPLspk\ny/h1hr1NomDbna0cux+Y8Sii1HyPHz9Oh42P8Ydj/H6P36O954YJD5Uy7m2249IfDKg2mnz963/D\nmTMbeMLmzAsXqPsBRAmF1Ex4LieOXc9ae9MAhBQsX7mMpfp0dB+rUsXVVRxboaM2QeDhK485v8nR\n6RovdM/jFwELEzNce2Ceb3zlcwgnI5ic4uzaCmfX1zhxzw20L7zA3OUmv/qe/8zD55/n6Y0lNjtr\nNOxJ/vWb3khYmSQRAfNVj6i9jPatrftZ7qQY2QmnsWnlV6s38YMQlWYMen36620mDy5w9PhNfPGP\nPk7ddYn1OV56zXV8+vc/wt31A1jdiMC1WRps4skKV1aWaHXWiNIO8cqAO6duZQLN6vmzePMzrPVb\neE5A6FQo0gKJwNImr5QPYxzfIk9TLK1pt/q86jX/ks996UG+/7WvoR56yGhvCiXJTMENeUbZdUaj\nKYoMlZk+kI7jYAsboTRaajQSpQVKK7I0Qwsj2dui1fTVoLx7ju2Odsfn1PjudC/A36vgZjxI3I7I\n9/Z/Kc837r8y3ox7fI7vBcS7d75btNqoEUb5vr4TiP+TFvKMc6DjnFHJUW0rHcBEqOX/7lJRbBVI\njEUIyqTPNZowCEzlk4Q3/eAP8oEPfpCX332nUZZ0I3q9der1aSQ+1bAyEmUocpXS7bWJVIrnJbiO\ng+PaTE+bJGan06bdbiGUMXP3fZ+gEiAdh9D16XX6qDwjKzLiWGP5DpOT04SNGn/00Y/x4z/8Nqws\n2Zr0Qhj/YSkFAoneusc79dDlGOfOyhu97SlztVf4XlKuvSiU8Z//Nk5w/L2MR9jlsaZYwfCiJZ2j\n9U672BebG0mSIGSB43oUStFoTPDQQ1/nzz7zWW679mW0V5fxlYsdpcT9NZQlqM8vUAw1uaoQTs3Q\nVT7B5a+QRDnrokKhLCi6aDthWCT49QbnO5tsti9xcLJGMJREhWI9WmftyYtMVD2uO3CIpAVNq8k3\n//rP+O1f/3W+7+StvGr+OIQZdii4++jNfOFzH+N1P/BWbrj9DjIPsmIDJ/PwpUUiLcTIFAllqktV\nYcz7o2jIpYsXmZ3L8D2P1IKNqEtkwalLlyiyBP/oNayfuUR8donwm09w5023krk2rXhIn5xvLZ1F\nSBiurVMUCYmI6QceV/rrHJk8QpFmPPHEE2xKcJ2AIKhhWx71+gSBF6J0QaNeZdBrURQ5GYLa5AyJ\n0ATVJp/9/Bf4odf9AEnxIhz4OE8sBLbrYtsWaZZstRdEmMja0ubZTFPjcYIY2WQIC0sopGB0fQoy\nXUbIZSRt5rUpSCufi+3GDdvRukaI7Z24mZvjc3lnIn/867hu3LzmdjXxdiBjErHj1Iv5nquei3E8\nG6dexivRx19/fFH4ro7Ad37w7e/HwaH823jJ6fgW5cWiOKG3Y1CJ0e4UronK9QAAIABJREFUeY4f\nBtiuw+233cY3HnmExYUDTDSnmZwKQVu0Wi16vR4FpvVTvVHBdhwq1WBEKZiHLh1tv+r1GjOTk4SV\nEKUU7XabtbU1BlGE1JJGrcbUpOkXaDsuvWjAIEkIwwqe6/H0s89x/eFDWMLCD32iYR9ny0SodM6Q\nIEDsWuDKa+S67hg4loBeSp92Lojj13739YTt48ttcPm78f8pf7f7f/eOjNSOKGeb31M7HqLdI8sy\nNCY3AEYvbdk2H3rg92jUmzQ9QSJSXCsHVeBWQrpJRjdVPPLUc3RyxcyBnHavz0y0wsGwgacKUqm4\nohzORSGx47HYmCIUbbxrJskP3srSk5+l4goOHV5kdn6G7nqbc6cvM2O7rD38Zd73jjeT/ZvX8ev/\n6VdYa6xy+rFnEbN3MKttLFfw6h+8n/PrbWQAOhuQqYhCTFJoUCo3c1GZQEQgCP2Afr9Ha7PD8vIy\nT546hTs7Qb/fZ7JSQ1YCMqFZPHEzi1P76Fy4TG+pzcbEBh8//QgHDu1npjJDnxzhCLqrK6g4QjoF\nvbTgS6f+hlxojs0d4nhY59nWOlWvil8JoFLh/NoVLq6uUa1WyeKEiuNwZN9+pOVx5vR5lK2xsKgG\nDc6evcRCvbHn/RokRqXi2A7CdigkaCQZAqRNqc7IVYGPNLtJ2zZ69FGXdq0K0jgxhThSmpxSlI7s\nGEwuKs+3k/Gl5rvUmBu8KEYBw/Y8L8HfAOe2jLCcs7sLbMajZiMsyHbM8/Kc5fwdpybLY/fabe6V\nq9uL694rN/i3je+KlmrjvOn4z+bv4w53Ro+5G3TKiLO8F5JdWy0pcWwbiSCJYq4/doz/9bFvccdL\nXsaVS1fw3JRapcnERJ3ZuWniKCHJEgpV0O8NiKMhlUqIbdvEcUyaJuR5Qb/XxRpxtb7vE4YB+2tV\nNIIszej3+vQHfaQlIYmNukQKBknEXbfdyV9+8fMcO3KUXBUM4xQpbWzHpshztFKgjdE+CFShUGMl\n71tSqz1Kk02UuztquNqqoLz+4/Kp8ty7cwrjC8GLAffu+1gU29TYXjz5i1MoRj2UZRnStZGWpN8f\n8spXvopTT5yinbdYilt0fIfuUNCLc7qDnMWqYiLrkHcvkRdPU4lbnJF1dLRJvbPKRhbxaDHJqcEh\n6l5IrXiM6yZXuLye8sxZj2YlY3W1TW1dsrFyDiEHTM5PIsJZHt0ALqeI/vP8u5/4PoKFF1jrnuAv\nH6rz2CMb/Owv/DLn2ysUToAqJIIQZQkKv4LSA6QwUg6zYzSf+/nnn0doOHHiBEJaVCtVukmf2uFr\n2T+7wNTCAsFEg4oX0sDlKx/7NF/81KfJ2kMaU1Pc8Zr7mDiwAFKS6JTnn3+Gz/6vP8QvMoLQZUjG\nX5/6Jr2Ly9x87Q2cnJqhnSQknRaHrzvMyZfeRldotOWiC42daWqWjxA2Aw2Zyim06RRTDX0CuXcS\nU0vf8O+xceRL0giAWq2CF4aj4EIhHRtdKNRWEFZQ5IWREUqJ69u4vm+AuFD4vr/VYmzcQ2U8dzMe\nMY/Psx3U6lhisMSdcfzYa85v8/c757d5fWPItYUvW3N676K28aT+buuL3c/Cbinjdxr/pCoUYAf3\ntJsS0NqsvNvbpe1Ibpy3LY93HGNDaomd+soizfB8D6VNaD49OcWJ227noa99nZe85C7yNCdKBqwt\nn6VareP7Ps3mJPXmxGib2yPNUvr9HlmWEYYhjYZPvVodccKKfr/P6dOXSXOF53rUGw2azSae55mK\nzm6HdneTKIqwbBfH93jlK1/Fz//ie/jP7/uPuFKAyojjCMcyulhLSNOjEIESCq22qxHLiVIa2Y/f\ncDPB8h38n9kO7qxAK6/xePedcdlhObnHndzGee1x7nBvblvsuL/l8d+JQtEmRDX2nXlmdhTY3P2y\nl3H67HkeaWc8s5ay3ovJZUCufNwgJOpGHHcV109pmvEZXL3Cp5+b5xkxw4nrX0EiYzrrSyzaEjHs\n8PQLZwhvkBybcXGjb9AKcs7kPQbuPPvqU0wmCQcXZ/nk04qH2M+vfmSNu60r/Po9Lkn9MhPBUQIl\n8ex5vvQ3z/GTP/0mrrzwHE5ekFsVOlmM7SkcrdAjLbtlWQjLtDB79LFHsW2b8+fOMT07w9raCvuv\n3U9Qc4l1BHmOTjLaSYeNvOClr7+fi+tLdNOM//s9v0jqO2y22yzOLNDNhswdOYgtNZ//wz8k6Q9J\ndIZj2Ty7dA6lcg4dOcT1x67n4vo63/7aV9nXvpHG4cM4jSZxrrCkT4EFClzLNs0dtMQWClvbDJO9\nm+zabh1Xmnli26Ylm+NYbG6u8+xzF+h2u8zOzjIx2SC0ffIiRwqNlDau52NJ4/qX5hlaF9jCxnYF\naZoTjhaAsuHveJKxbIiwW8I6DsblceXPpm3Z1UHgbvDfrXobj9bL319NZ+7dpHk8yCnfc1mktDsg\n2/1630kHLvTfNVb/BxxCCP76S3+yo2R+HADGs9d5Hu/QI4+D/G7e3HECQ3BtZbPNBXAchyRLkZaF\ntCyElBS+ywMfeoBjR4+yMDeHN0qyJHFqqskQhGGVOMsJQx/X3da6aq3Rhelrp7WhMUpuWGCZji5R\nRJylOK6DtCx838fzPIpRxDwYDFnZbNOcmWX1ymVe9fLvQecJOomwhBjJCEfbNQwoq7GsfHm9dndK\nMdeT0Y5kt3vb3i5s4xOzjDJ229TunvC76ZzyXDt14nrLm2J8kRjfAdx8+yuumh+nHnkQJYwH+zBK\nqFbq9AYplUqND33oAT5xamB4UgSOGzKMUrLBEDvpMWX3OTapOTyRMeHEbExdz7llxcV+jVYKbtHi\noJtSsSyeWdmkORVyz9SQuzjHeRwiJohoovKcBU9RwWFYOcqZ8DCbUcRNxQpvCDrU71ij2pxgWOzj\n86drPLxc5+6Td/DShZymvUZUDVkVPtoJsHWK1BqJRGgxKlyRbG62eObbzzI/v0CeG7dCJRMykREN\nEkQMvgy41GuzWaRkWcqh5hSTfojlWRTKJnArFAKqzSp+aFP1JJ//6EfYeOE5Gq5HJxowlBpsTSP0\nuPHQtRw7cISoGxHl4E/P0jx4mOriQbRXoTtIkLaN69oUcYZU4LguCZrCsbjtuoNX3a+/efws0ir1\n1SV4KaQlEKPEvAC63Q5FHjMcRszPzRD4Lpcvnsd1bALfxZKgihw1mtOBvb3o7+VtMj7n96pzGAf7\nEiuMrNbZcezuYKU8zrzWTqrWnMckoMfxyszrvaF0/BndHcS8WAQ+/qzdcsf3viil8k+axHyx6G2c\n7E+S6CoQKbcisFNymGUGeG3LMvyYtMCyEVJQq1SJ08SAtypIs4x7XvFyPvunf8pb3vhG3BE9MjFZ\np1ppkOeKaJiw0b7CxYvncF2XMAypVCo06nXCSoDvTxDHMe12m83NTWzbpl5r4Hs+9VoNbZmmD5ut\nFhcurJgiJcumUasxOz1DY2qOtV6P9c0WS0tXmJtqEgYhRZYihXnQFaALRa6LLToJxmVOe2WzDeCP\nc9rlJC9HeR7LMj37tnc8xhjf9/0xHrDYES2Plzbv1uZvbzM1lrVLRjYWlZis/d7Tz3VdkjzBti2m\npqbIc8Xi4gLDYcKRI0dwHnsBVEatEkJRULddItemm3p09DSnuoonW0OqFYvGhS9gA3GrYGBfT9fb\nR9bPuefEYRa8BU49c4n+cx3q1zV5yWuvZ+O5FZ5/7AXyxjyDa49wafM0Uxf/iDfOw6SbcfjYLbR6\nNsnmNDP+Ck7lDNdffw/Zvvt46htXuPF4wuLiZdaHisVbXs6VzR6+G5JnObrQhkaRNrZlMzMzw9zs\nPFEU4boeSmmkGiClxrJd6OZU8MmqIWu+Jnct7EGCM4zpD3rEg5yzl1Y4vXyJT3zy46hkiOdqjl+7\nn1kFlcIC26fr51xON6nEPdrP9OitrjPv1JmsTjFdm2F4cQXtNakcmKSouHhhyPmzL3DtvoP0W118\n2yOOIzpRf8/7NRikOK6LpmzqC2ElRAhBlsWkaUGtVqPecMnyIUE1x/I8sC2uvf44oe+zdPk8ly6c\nRwrFzMwM1WpIPujvKOwr6cLxuVRiQSm/Gw8AsyzbUk2NB4GDwWDX83J1FFzmlixre6eZZdko4t9u\nbDK+kNj2zsbd43x9CfLj0fteu9aSYvm7JjK/YwT+zne+k8985jPMzs5y6tQpAN7znvfwu7/7u8zM\nzADwvve9j9e+9rUA/PIv/zIPPPAAlmXxm7/5m9x///1Xv6gQPPTgp7cedsdxtsxoxlce2Nszpbw4\n41WC5QUZN5LfTl5cLVsLlctAKH7nY3+IV6tyw8Ej1JVHxQ9waxV6aYpt2VRsD8u1sV1ni3NrtVpb\n4GnbNpVKBSklaZqS5+nWexi/aUEQjBoSpMRxbN63tugN+gS+y1/91Zd4+79+C7VKiC00RZbjeT5F\nZppaeJ4PRnCzHTmMQNp2zHtjtI1VeY5EYVg5PZKwgWObyEMCQpnkrtKQFxKtc4wjQYEQymh2tSbP\nQUgHy3LRhUJmPYR00bZLYTtkSlCoHNvSWDrFtVIoEiwUWrloMBW0WiMcF8f2yJUELKR0ueGW266a\nH//9gf/K8so6nV5CXEjOXrhIrdZASFMNeOqyRa4KHN8FKekM+kaKlmtEprCVoBHW6W62SYKIvFBg\nuTiOR5GnWMWQuZpksabRnYscmnSZqFgspS0Yptx+8Ci1zhC1uokKHFa9kGFtkqBS4ZpJj0NN2NfY\npDbXp+pY2P0Gwp9kqStxmabmwyOXVljzb+LY7fdh2QP8VGFZGb1wSCx8QkLIOwiZ4ykbraBtu0jl\nUJUuWilUUWCPStJLeajCFOFYloUjQhwpQfU4/cJz/N7vfJjzp5eZmmxyy4nDdLpLOK7HU0+dJZU+\nIvQ40Kwz69g48ZBG6HPzrSepzizyxOnL2I1Z8GocOHwNN5+8njTL6HQHTM7M02l3yYqCl95+01X3\n64sPPUYYVBFCkCYpQtijkniLvMioVo1CK80zVGy6XQVBQJpl5CrD80yn92q9itYFrVab9fU16tWC\nJIlRuqBaDZBSYKFBZdiAUDkohSUlKnUQUpLnGa7vEsURnueYyk492i1jJLO+cChKNZCQ5nttFlel\nCiwBcuRmqpE7sKVMmpZe4OOjbNIwTsmMR/jl193WsSWm7XYkLMdNt73iRYH8OwL4V7/6VarVKj/y\nIz+yBeDvfe97qdVqvPvd795x7NNPP83b3/52Hn74YS5fvsx9993Hc889t2OVgpJC+dToQ+urdMjj\nJL/rurtogJ18+XiiIs/zHVnc8hjH2Qbf8tiGVyMRmr5Q/Or7f4M3v+6N+EoS2D4Zmla/R5pkpku3\nJfF9U3RR0iWe5xFFEZ1OhyzL8H2fRqNBs9lACOh0OgwGAwaDwdZiU6lUmJqa2krADKMUy7JZWV1C\nCM0z336SH3rrWxAU1MOqSWYWijRJCSsVFJq8yA3XNuKJbMs2wD7O0Qmjf9di2xpdCyjyAgFYjCIF\njdHh6gTUtkG9FIIsM4kk27KxbVOhWiiF9BxqlYDWxhqB71JkKbbropBkwkJbHjgeYbVBHJkuPYXO\nSNOEJI3IiwwhFbkqGPQ6fP89Vy/w97/p7aSZxvUr2H4dYdnEUUy/16bfa4N3iFwpbM9HC8kgjlEq\nB10Q9TqErmTfzBQL87O4ls/axiYvnLlIqiRKSNAZgVUQWikya+PlA2YmKqykNfrdgiMzNe4/pqj1\nH6PT7tGTC8zt38+kvcyRekJdeNTDeZyDbQovgaSKP1VjJU2ZFAHVYY/Lg+M8vHwTjWtDjh47jrAU\nw3iNWt2jn8UI9mPnHrZ9jsTOKLIDuAVgDdACY3ylCuxdO5xCKwqdG44MhySKCX2Ja1k89a1v8yvv\n+zWKLOfotfuYm2+wsbHB+lqHrrYYoLn7+I1kGys0bEEt8Ljp1ls5dusdNPZdwy//5gf45qlnueGG\nG9m3MM3Jk7fzsnvuJUoyXNdnMIw4ft01V92vR049y+LCPlZWVlGFNk1RtKbsuBNFA2NGpsGyXBzH\nVByHYYW1zXVsS6K0wvWMKqXc/U01PSxLMoyGPP/8s6RpwuzUhDk+TciThEatQqfdoV6rE0cxeZHi\n+t4W721ZFhpBUUa7UmAXikJptBYoUQoFSuxRIwAHUAi57VRYRuEg8P1gC6NKXErTbbp3ZwegnW3S\ngKuwa/dOepwDP3Hnq/7+FMq9997LuXPnrvr9Xif81Kc+xdve9jYcx+Gaa67h6NGjfOMb3+Duu+++\n6thya1Ru2cuVbfz845nj8nfjq+FupcS44U2Z6ByPlMuLI4QgzQsKrbFQ3Pfyl/Pss9/m3rvvYX15\nlTzO2L9vH7broKVEKU2/36ff77O+vobjuLiuOwJ108G+TCieP38eKKPukOnpcCu5E8cxFy9eoihM\ndG07Lq4DszNzrG+sMTk9x3NnznPs6BG6UYzKUvxRx6H+sI8YVWgKKXGscfmU4fnLSWFJC12okRbe\nROFaaWzH2ZEjMDGGQsjcKAPQSGEjsAiDCllqNOqCAtcRYNm0lI3IMlzfxtEJgaMYDNustGLWY5tn\nlzqcXurQGeZEUX+bX2QUgWiFkMa5zpICuBrAa/tuI4pTcm2RaPO/hRfhihC7MNWZrhfgOh65sgh9\njziJSZM+1VqNPG0xvb9BrnrYcYaKB1DECG2jhEOSQYRk6AZYwmFi4hDRVJNjQZe1QcEL588zvepz\nz+w+DnotltYiVq5s0gkaRK0Wh50N8tok0okIqlCLE1AJXkOBs4Q10ac7OEFt3xv5xtMfZ27qaeza\nfsLKtVj9NSatnK4YYktJmNfwVUqsU7wiJxUFiTXajgsXlB4FJxmMKv2sMi8iFZVGaGhGJDfcfDPf\n84qX8cW/+DyDKAbmSBNJmuZEWYxTq9Pp9bhmYZHuyiWqroPfrPO1b32TJz7+xzz6zAu0koSnXnia\nK5erHDhwiG63S7XeRAjBwsLcnhhxeP8Cg36PxdlJsjRDSuPN3et1sYVDLXDwA980P8nyrcTi+sYS\nExMTdHsdmhMNhsMI1/FI04ip6Sm6nZ6REQqbG46dIC8yLpw/B0WKY1l4bp1+pAgqMwzTFq7vYOnA\nBG0jSkpro1xzSpVKrsAydtNKG6EAQkKpXNNiKxJHY3ZsI0B2HGckJzbnHN/5w84OVOO0o6GSsi3A\nLp/bErTLBuLjVAtcbXa11/h7c+C/9Vu/xR/8wR9w55138mu/9ms0m02WlpZ2gPX+/fu5fPnynv9f\n8llldFry2OUY30aMJzVLEC+TY+M8bJmsGL8Iu6P38hy26yJzRcVyuevWkzzwxB/w7NnnuPbgYUSS\nUcQRQsKVzTUmmpOEYUC9XkMp08m83+8zHPbp9435VBiGAFiWPVKuRMRxuvVZms0mQRAwNze/9VmH\n/QHCdhkMhkzPzOP4AX/+hS9QazZZmJ2lUasy7LTxXRuVWzCWoU7TFKU1crQ1y7ekh5pCWqiRhA+r\nLJQw+lstTKHD6Cqb66QkYkRrIC2UhiiLcGyJbQuUzhC2RtoOrvIZRn1mmg2eeuwRpBQcOXojdc/l\n4x/9HBuRTawr1CcXsYM1kxDWNhIbrSR5qtDqby8THqQuw1TguD6W65oGGEVOkUU4lksYxHT76xRF\nihdM4Fg+UV5gSweJ4siR6zh37jxS5lR0yNpmi0E6MLsDy6fSqFAoje24WI7FehyzsbyBtJfZt1Dn\n4A1TLJ25zDldZXJCc2Qx5pFOxEPtAyS9SV460WbfWowb5xyen+CYFZJlXdKsiVd1cPc5DHD58pPL\nXE5sqsPHKYYtTm0sc9uJm7Fzm1qoGOgWMQ0snYO1Tk+HCOkiREaRF6iRpWuhjEGZVtrUAyBQaOJk\ngOO52LZPkhQErs07fvLfMjFV57N/8he8bPEoa+s9KvWUuNsl6Q/JOj1E3TR1mJudRynodvt8+9ln\nqFaaOGGDPFXccMONzC8sUqvWkZbFxsYm5y9cAO686n6dPf0sa2vrHD9+E0FYwfc9er0+i4uzqEKN\nFhhNlkQ4novnhsRpwszMtbTabQ7uX2QYRdSrIQhB6PhkaUIlqDIYRFSqFbI0JS9g3+JhwiCgyFIs\nBJeXLtMbpoQ1jxzTMANpfFtkoSnhT46uGcKAs5Ejm2dAaVMeb3alFloXW+ovVLYF1Nv0idgy3BoH\n2DzPtrBm/G/jNRXls7tbgDHOOuwuzPvbxt8LwH/qp36KX/qlXwLgF3/xF/nZn/1ZPvShD+157G6e\nqBzGW2Rk6jPy3RgH43GeaFzuttsbAXbqmXevbmX0XR4jhGkw2ssTXCTpIMa2Hd74xjfwmx/8AG9+\n/RvJNnscnF/EEh5Hj11LZ9O811artZVQmZyc3JI4KWVkhK1WC9f1qFSqNJtNqtUqcRwzHA5JkoSN\njY0trn9hYQHXcUiTmJnpGS4tXaE6Ocn9r30dH/noH/MT73wH0bBHxbNJs9TQIblCCqNOsSywtBhZ\n0o6igdLESgu80ozKyMgBTTKMQJj+kmaLZyIQq6iMKn9AWJJcFyAgk4okjRBC4VoOeRSDEixdusK3\nnx1y8vaX8eiTz/Bf/uNvE6Vw+NrjpEnBRNNj/eJpgskatuVgSRdL2IAE37xOmsbEecxew5Y2Fd8x\n7ee0xrMk0nMQwmOYDfBck6hN0h7RIGFiYp56ICmURbsd8eg3Hmd+fpr+oI9TsciFh9cMEFKS5QWW\nDRaCoohJowLLslFKc6p/jOTCJq84EtDcV9BJ11lCMlvELFa7XCk2+UanyccvLVBNE6wNn+vO9njT\nYs51CxpBHTms0N54kuaE5qtf/zBH7zqMlyXY6kmK4ii/8kd93vbW1zOdPod2UlqOQlsaS9sUloWT\na5xyk1QoLNsiz3Is2zYcq9KMxER4roNtCeJhTqM2RRx1KHTGG//Vm+kPNH5tGmVXCJpw0+ICeS9m\nbqJB0e2zf2aeIs4Z9iLOn7mAJwIKZXHwwBHe+kM/zB0nb+fS0hWWllapNZoIYTM7u3cELoTmtttu\nNfcXTRRHI5pEkyTRSHmlmJqdod3rs7GxyuzsLGkSMTk5QRInNJumSUq318N3PUOp4OFPhcRxbBK8\nwsZxHIbDxOCE63Hd9TehlObxU3+J75kydFtK0lwjRgGNRIM2EbWUAmlbxkgOUztiMbKdVQohjcRT\n6BKL1I7d/rbXkLsDdM3vtrXoJeUyjoG7o+ndODUO3rsD0Bcbfy8An52d3fr+x3/8x/mBH/gBAPbt\n28fFixe3/nbp0iX27du35zk+8tFPb4HfbSdv5uSJm3YoHsazsqWYv/yge203ygtWVmeVzY7HL2h5\nI+I4RvgWtnCwCgNyi3NzvOGNb+DZZ5/lda+4j6TbJ8tSNpcu4VshnuMjtAalyfOMIsuJhxFBEOB5\nLo1anUatRpxkDAdDeklClpiV1paSxuQUs9MzdDodoigiS1LyJCHPc1qbmzQaDdqjKtAbbryJZ557\njhM33UCSJ7iBh8oKPMsxyS1lnvCtRUtKUOBYDshRkUOWmgKirUmgqYQBWZ6iioKiMP7iKImjA6Nw\nsQuEhSmnthTCttlopfT7EROTs4R+HStNWNx/Hd/6ytf5zH/9CPWZfSzcfC8SSTboY9GhIgbUZmw2\nkFvGXEIq8rygQGG5DtKViGzvxd2xjDc7crvoIc9TLJFTDR36qUXgWORJxHDQplUMENLBdQKqnqCx\nbz+vefVr+NKXHmRDu0iZkSYxlgZHgixA5RlSCxzLQ2egsdishZxOh9QuLnP3URfdyXnykuD41E3s\ntzZ5pbeErvb4mnMbT2WLuM4+is4Z1hp99vWGSNFimBU4NZvnnn2QRnUStdEgyersO7DBiabiyf71\n/MFXz/OON8xTT0/j6RZ5JqnaNoW6RJT6CG+aaq1BkiRoraAAjUBYpuBFa8Pa2lJRJBm+DEmjlCLX\nKCEohOQn3/UzfOmLX6edpORqSJ4NmPMCkmGXqme8Zabm52l1h2xuDvi+V72Wky+9h6A6AUJy5sxF\nojihUqsSxyYB+CJmhCwu7idJcvwwJFMK4/Oi6Q07eI4LlkUSZRQ5eK7PxIRNnpucy7A/NAn+kdVF\nrVLFsR2yPIdc41gCr1ZBVwKGcbJFkeZ5Tn84pD8cgoDDR25BCuj3e7Q3N1FFSrNWo8gTUMa2WakC\nKTRxprBtwyYWuUIr0+xCCNNkZZtGUQjr6u5QIHYEhCUWlZWYZbBZ5vZ2q0nGi3l2A7ZlWTz8zcd5\n+JHH/04qlL8XgF+5coWFhQUAPvGJT3DLLbcA8PrXv563v/3tvPvd7+by5cs8//zz3HXXXXue4yfe\n+cNbIvxxW8fdssISvMeTBWVknmUZruti2zbr6+vMzMwwGAwo5XAbGxtIKXFdl6Iotnhr27Y5d/o0\n0/UJpoI6QRiQ5AW3HL+RtSvLPPzoNzm0sI96o8HM5BSdzQHGIcLc2GpY2WpuPBgM6Ha6pGk6aoxc\noz43ZzTfRcFg0CeOEy5euECtVsVxXGZnZkz0nqVcWb5CkWcMkphCaYJahUpY4clTT7I4N8P+xTmG\naYxKUuq+TRIno8SumSRZlmG5FrZlb634JtFqqKnBYLCVCDYPvsR1HbSALM/xXA+RCbQFgyTGciy6\nyYBnXjiLEj6FChgO4OzlTfq9S9SciHaccXG1z/zBm8ENIc1JBx1cz8NKLFaXLzBVr9CYDFFk5LnG\nsUOUdEgLQZxotOUhqew5N2qNOoWCtDBUGdLBESFkGuFYNNwqi/MBjYk6QmRsttbY2Nyg3enR2uxD\n4fPM00+AKnC0IIpzfGlT5BlaFWSFSXarQpNnGsvxEBr8fJ3USTkV9XHOR9w2N0lYd7i00sUlZX/Q\n5c5QkTUvcTw6T3PqGEVrinOxxfKShxqm3H7UZ7pX48KZs0yzQaN/AEtOYA/Xud6NeP3hOT6z8VLe\n/8cv8NNvuYbZaIUZO0DpIZGs88WHvsmDX/461WqFEydu4fjx4xxpNxd0AAAgAElEQVQ8eAilCrI8\n37F7SvpDZqan2dyIAJPUrTaafOWv/ppPfuo3+P7vfwu5lGjL5+LyWbLAY7LeYJDaVCpVvvbkUxy+\n4SZ+9Kd/hrvu/V7OXlghzQAN7XaLW07czMrqBvV6nbxI2NjY3BsQpEVzaoJ2t0cQhihl/PFdP6Tf\n79Oo16l7Ia1WF68SEIY1Xnj+Ba677ghrK+vYwsb3fdMIJUpQTkFYqZCqIb4nSZKMOI6xhY3nuwS+\nyyCKTWTOqCIzk0jHYWqixqF9R7h48Rznz5+mWQupVF2UTk3wYEtsvK2dcBAEZGlqKEfMomL66Y50\n7SOKcrzXJugdlZglVgmxkyUYx63duvVS3lhy4uUioJTijttPcNvJm7ew7r996MMvisXfUYXytre9\njS9/+cusr68zNzfHe9/7Xh588EEef/xxhBAcPnyYD37wg8zNme3V+973Ph544AFs2+b9738/r3nN\na65+USH46hc/sWP1Gv+A46vR7t+XQFSC/9YcGq3MJRVT/t1xnK2ou5QBFUVBogvqQQU5ahKRS8ht\nwcraOn/+Z5/jFffcy0SjwWA4xHWqqIId9E0Z/Y83Jh0X6Zf6VM/zEEJsVWSWyQwA27JQWuF4Hpbt\nMExikiyj0+sgpeDJJ0/x6vteZWRYhjPAdV3S1HzNixzbdsiLHCHl1muVn911TTcex3FI01ESxZJb\njSEKrcjTnEEvIqgFxHlKJjSV5iTCDvnkJ/+CXg+SoU2aWLhegHa6JHnBvgOHWdvoUKnVSZIUoXNC\nF1TaJ416JFEPlQ1J4oxqtW4iZK+C7dbADshwiVPJh/7jW66aH2/9vx4gjlMsNyBXxscmT2OKZECR\nx9ixxzDqURQDlBqQ5V2SpIfnuawsr+J7VYSwsS2PNJWmmcJoe2uoIgHCJleghYV0PLJMESqbxFXY\ndsREtsFtswGHmjDhDqkNlpHtZbTrMZya57q0S5c5Loj9PN2r8tBSRlRJubY+4Ia8YJ/bQeVnmAjn\nedP33UowlTOoRgj3GP/zayf4kyvHYKLNZPItDlltAseiOXGczupprlx6CiEFU9PTZrenCpIkMSqn\nRpMw8AmDgGbVo1GtcvjwMf7m4Ue4cPkip556go12m9XVNidO3MbS0mWUTnFlTMVVxIOIyeYkhYLa\nxDTv/Kl/R1CfJKxNobEI/IDNtRZVPyQvFK7nkSQJ0pY0G1XmpqtX3a/BMGZ9s0290WCz3aFab5jn\nQGiiQYTvulgjIHRHQU8YBCiVkWXm+Qt8j8FgyPz8DCvL67TbG2T5kLASMjU5Y6S6QmBZDkmWIaVA\nC0maZUhpkcQFcRRhSUmeZdQbIaoo6LTX6HTXSJMBQeiQ5wkqN1WQrmMbya0QFCMFm9wq0imfkTE1\ny9ZuvrSr2K5INrm8qxUlu6mQEqfGaeDxgHV3ZC/E317I809YifmpqwC4jKx3C9/HNdzlBy7BCaBS\nqXDq1CluvfVW4jjeKrtVSpmGCiM6Zfz/4yxFCkmRmXJj27axfR/hu6y1NvmfH/4w7/zhH6G/2SYr\nDOhNThjjKjA35NKlS1srqIm+KzQaZvKurq7S6XRI05QkSajVaqNOPqGJllWx9f6zLDNbLt/B932Q\nkpXVVTr9Pi+cPs2b3/qv8AERR2ZRsm3SosD1XKQl6Q0GNJpNkhElYzsuaVqYqCZNcUZt1WzbRdoW\nly4v0e6ZDL/SMLN4AJAsb6yz0e7z9LefY6PVp9OOmJ87QJFAGFRRSFqqhxSSWlij4lcY9vsErkea\nphQ6Q0lFksZE8RCvdYn+oIdlKYbDPtISpHlGc2qWiZl53LDBf/mFf3/V/PjR//d/oAuN41dQ0iUf\nReJpGpOlKVk7B50hdMpwuEEStdA6YjDo4I6q92q1Ov1hhO530UJiOw694RAtLWbmFrG9gM12lzjJ\niOOMQoN2lFGtDBMsKXCsgn1Bxu1TihsmUlzRYThM6XRy2s4kDb+B7dRpWwe4qOY4E/V47sK3qccW\n9x+yuY5HuHmfi7f/di4t3MTG1DH8zOLYgZP82kfPst64iTh5Bnv1G8ilS9w4XaXfPw9WQV4U1Bp1\n0JrecGi6RDWMmVS/2yNNEjxbIChIsoSsUFQqDdY3WsRxjO952LYgzzIsYZOrIV4oGPT6xFHCz//8\nf2BpdZ3Pf/nL/J//z39gZbXF0SPH0LnClZrAD7iyvEatVmN1fY1ms0E1rHFgceKq+9XrDRlECVoI\nXM8jKxT9/sBUH9sWvU6HwPOphAFpnhMEAcPhkPX1NQ7s3z9SKBV8/BN/zJ//+Z/TbDRobbbAylhe\nXqbIco4cuZbXv/4NvPrVr6ZSraKUYrPdodmcIM1zHGmkrloZEIyTGCk1jiMR0jQ3f+TRh1lYmMWz\nXXRREEVDGtUK3U4bxx5Rq6Mq2S09trXbZ99GStPCcRxPjIBib6zLsmyLaSiVcbstKUosHC+xL1/3\nuxLAv/6VP92RuCw/SFk2W0bR4zx3OcZlg+X5yhWy1GSD0Vr6vr8FbKWGO0kSyBXKkhRa4douKslM\nFO3ZDKTi81/4PIGS3LB4iKAxieVuNxouvQw8z9vaIZQ3Ic8zPM/HsuRIVmT4XFPkkzMYDLY+l5AW\ntuOY1lZoitwcgxBkQD9KubC0RLvb575X3Mu+ySa2Y5NluXFx05oCZfS2loUSRnvb7nRJE0WeZkxM\nTrK2sk4QVBBCUqnVaHf7SMfG8wOGSc7Sesr5C5fZbPfJC9BK4NgWybBLNGhRZH1mp+pESURWmcGW\nDhWvhmcF5ImmyBXSdhC2oD3o4QSOKZoYtEiTPmtrl7FECmRkWWyUjNKhMTnHh3/jP101P971//0+\nWZqRZJpU2+TaIi00WT6SZgnFsN9Hao2lc4o0QegMSYHnWjSaNeJ4QJLG2GlOlCQkWYYb+ITVGlpr\nBsMIhMR3XVRuLHw3ig51LQiQDAvNem/IhLRpDNvMhQW57oHUdDe7fLlf5ea5kJMzVVrtmCvrA1ba\nPfpugKw43FTTHLEHTDUDujOHeHQpZC2/ESU0jeoKr/y+7+ehx1OoBiw2Mpa/+TmayeMoLJRs0O31\njLEZJliJ4wjbspACLGlhjSqK+8MOw7jDkSPXEHgN0gjyNEMXAyqBxMKiSCRBrUI7avP+9/8GT37r\nCY7ffDOpKnArAb/zwAPceP2N3HzjTcxPzdDp9ehFA4IgJI4T/CAwBWqWzTX7F666X6319qiwyifX\nxpd/MDSePnmS41oWqjDPTKYUzWadTqeD5/nkWcrTTz/FU089hevYVMIQ0LieQ5RExMOI1dVVLl+4\nyObmJiCZmJjgdW94Pfe/5jVko2dRFcYyViuBZVtEkdmFDuMIz3fRwhQQXb58kWzYQeUZQeCh8wzf\nsRkMe8ZNUZjyf2MkZ2jGcXwqwVUpdkTL5ncG5McLc4AtLBtvSF5SOLtL6seZhPL470oAf/Trf3FV\ncmB3tna8gmlcDjhuIVmCYbmilUBedmUHtgyoisJsRS3LwtEWhSXIJfiOi5UWCASRUAxc2Gi3+Njv\n/Q/e9Mr7mZjfj1epbC0u7XabOI5JkgTHcbZK7MMwRGtFHEdsbGxsUS5ltWbZXcgkp0yFZBQleJ6N\nZxs7xaLISfOCtXYLy6uiLYezFy6zduUib3ndv6Db7TI5PTUqXQbpWPQGA+I05fyF87TaLVZW1ul1\nE6SQvOb+f8HMzBxFoanXGrQ6XYZRghMEnDl3jjMXlunl0xQFeG6VNFUmMaIifCdDZatE0RJCdYjj\nAZv5DNOTcwgdMDd5gDQRuF6F/jAmzhP8qk+UDul0u7iOy+zMBJYsCD1BGg3QqqBem6QoHDQBv/Dj\n/8c/8uz75/EPPbobLbywwjCOsRyXKDHJRjR4to3QkMYRvuehHZulpcssLMyT5wWPP/YIL7zwPNdc\nc4hatYIQgnrdqLcsx0EXim6nw9nTZ7h4/pIxhut1OXvuHPe8/F5+5Ed/lNm5WYRWoyplmyLXSMsC\nAXkBaaZwPcjygjjJqNgJly5fZGN1mUa1Aio3ctmiQAiTMNaY/pxK7bR4Nc+zQ5nI3OnNst0Ba7cM\ncFwiPS4T3M2T27a9RYGWC8PJu+777gPwshJzvLQUtm1mhRBb/PK41Kb8fhzQt4t37B0fdPycJUdd\n/uxjo4BCQi41uSrIsgTXcigKhVcJ+eNPfAKkzT13vRxdKPq9Np5r4djQqFcBjZaCKM6Ik4zBMEbl\nCYHvYdsOjusRRTGN5iRxktHt9bFt1yQ6bAdVFKgiR6NNtOg6ZCOje601aRYTegF+EHBlZYO/+NJX\n+DdvfzvdToc777wDlRWkWU6Wa+Ik4/NffJDG5DR33vlSqvVpfvt3PoAX2tx+8mZeeuJmVs9dZH56\nnn4qyStNHnzsaTYGEUWeoFSOQOG5NlmaYgtJkWmEkqSJxpYu3fYmRXqWrMiYXzyAHzbICheVBxSF\nxLYkUdzCshKybMAtR28ky3Pj86EVrXYXaTnkSpGmOcM44f0//7Z/jCn3z+N/41hdXqVarzGMEuI4\nxQ8rpGlq+pdueYlk1MIKnU6bMAiRQrB8ZYmvfvUr3HTzcYMDtm2cGpFYjgOp4aXLPE673ebMmTOs\nrKzQ7XZ57LHHeO1rX8uP/diP4freVrC3vWs3GtpxmgNgmGb4vstgMGBzfYVo2DM7BKkN7ZQaCs14\nuG8nJIdxTBhWKEZywy3wlUY+KbXaEVWP+xWVu/ftaHzbMGs8yan11dz47Xff/6IA/k9mZlUmF8uL\nWq5m42BueFt7C3zHk55lZDteBWWkSdsJhPGSe9ipSU/SFKREOMYhzpYWtucjtUBK0xHke7/3e/nd\n//773HnLHXi2Sxj4RNGALFVcubKM7dh4QUC12kBj4fsh6NDcIOnRag8IKlXa3ciU60qPVqdPnBiT\nnSTOsKULAsJKQBT3Udps5x3Lot+PiKNNmo0GJ26/jZd8z6v45Cc+TjIc4vkhgedz4sQJNJK1tQ3u\nvfderrv+GGvrLWzHwvN8htGALz34FS6fPksoJC//ngZ20ODyyjJZGmGhyApo1CdIkog0jakENZIk\nxXJACguFKat3Qg9XNghkwfLyFe64Y4HBMCeOhyRDhe+ESC2p1yZoNPaTpbGhnKRJ+qgiJQg84t4Q\nKSX7Fmex7YI8/7t5H//z+O4bnqeRts1wGDGMUgqlafoejLx0ev0evufhuh6DOCIIKihVoAV84Qtf\n4PhNx7eBUG33oNTK1DyMg16j0eDEiRMsLy+zsbGBUorHHnuMD3zgA/zMv38XWu9s+GAM3eSIm5Zb\nLc+EbZEXBYHncc2hQ8TxgNWVFQaDDkWaEXgBg34Px3WwLGOU5fkePtvvpxQjKD1WKV5kVzVK3q04\n2V5g1JZiBbblicmYZe84Fr7Y+CcD8PHVp/T5HY+Uy5+T0XZsnNQfB3vYplxc13DfuzO746532zJE\no9zQmK7ZpvxdYEubLDM0y/TsLKkqUKkx1LFtm1qthuO6zB88TJpmtNod1ja7lA5lth1QKM2ly+fx\nfB/YxPWMMb3tetiOgxQuUV7gh9NoZVHkBcNUkmsL17WRQjAYDqg39jM763L9saOstTfZd3iaYzec\n4OL58zz45YeYmpjkkUdP8Y53/FvW1zep1uusLq/ieiFFoTh58iSnnn4SUW9wfmkFNRgShk0OHDnK\npZV1hr0+fm2C0J9kY2MDz3eYnd5Hu72BEDaO61IUOXExNABsaxqNOfJigCLj1Le+zuFrDjPTbKKr\nhp913cmthdUOHYR0ieIE23EIQ5s8H1Kv+kjbpt1a5eB1K5z59uI/5tT75/EPOO5+aURrs0uSZdQb\nTebmJ1nf7GA7DkkcmboMx8b3PaJIsbnRYqLR5OlvP8nJkyeND3iajjxLQCq5VaiENoqqeqPBoN/f\n2o0fPHiQiVEh3TPPPMPq6irvete7+Lmf+zkOHDhAkiQmmDInGalKxlsySjzHppCm5iHwKxw8dIhT\nTzwxWgBSvLBizOeyDA0Mh5GhLUcLhO+NkpHFqOkMwIhqGad8yyCyrDgvy+a13pnrK/HMtp0d+PW/\nRQf+DzHGtzolX12CbXkRSqJ/fJQ8Eewsty+N0mGbVhnP5o4DupSm36QS2yJ7C7CUKYcNfZ9BluLY\nDjNz/z97b/pj23rfeX2eNY97qrnOqTPd0fa1EzvBnSidxIljkk7UWCIMUpQWaTG0aBDiBf8BEuEV\nvIBIiEbdDa10WtA0NCAgTQTE7fgm7vg2iXPtO557xhr3vObhWQ8vnrV31T2xnRcI3SCxpKtTZ1fd\nOlV7r/17fr/v7zvsk6Qrju+/zDJJUcqgqGCaLGiloqo7Uk3DRUrIqlxjbuEetuOQ5TnTacruZJe0\nqpB51UNDJmVd00lD+6RkGbdv38Z1baLAJ/A8XRAti+Fkh9qweXI6Y7EqGU4OOe4M2roiz1P+u3/4\nP/HDn/shQt/HcV3SrKBRknv37jFfp1zOrhhOLFI15Q//+Nt0jsd0tWZ/74CsruiUiSM6XNOiLkrq\nSofSNrKiKDK9BFIdKIMia1GdSRDE2GbF1flHFKsA34n41Gs/hFQWdSkxLAvDBtlJrarsOnzHxPUD\n0jQD1fHy/RP+2r/7Pn/rPzX54J1d2ub/78T/v3LZdseP/XjJv//rV3hhTGw7XE1nTFdrqqrC9z2O\nj4/IsoR0Nif3PfI852BHW8XatsN6ucC0tF/4JgpNKbVN1DJtBwxBkiYf8wMvqhLLtji+fYs0z3j4\n8CFy1fLWP/0WR0eH/ftL3qgd4mN/qq6vNabV148O2Sg++7nPkSRrzs/PWSzmeL6PZwHtNQ5u9HBJ\n3XfK2r5Cq5s7wcd2b99LRXldu6478pswyvcLOfl+1ydWwOHjy8qbf79J2blp2HRT1XSzSG+Wl/Dx\n7wd/OhFjexo69vZjIcDoOoRUuLZLnhcoy2K6mOOFIZeXlxRJhhvEGG7E1TKlUSZ5UWHYNiYC13FZ\nLddUjcn+wTHzxYJQmISDfU7uvc58tcTsOoqiIE1TzQ33TPzYZzgcYhiGDklOGpaeTZGk2Jbg5PiY\nR08/ZP/wmOHuIeusJl2vmIxHTJNzFDaz2YrHT59y59YtVssFh8cnxMOIi0dPqKoG1ZnklaTqDF56\n7XUuZlfs7h/y9PljgiBCUrA3HrNapbiOReTaNJ3C9VyKImMQx6ySJVVV43kRdV6S5g2u4WCqlmSx\nRPmSIl2glE0YjxHCohMKpMR2LJI0RzY1rTDwbIu26zh9/gTf9/k3/70/YLqcowTs792iayXzi0tO\nDnawDMlkNGC5XpIUOfFwRJkucGwXIQyOjo6J4wFvv/0dHMflg/c/xHFcxuMJo9GQ4c5wa5y2Ws9Z\nzBacnZ0CguFwRKe0eq8sKnBjLTqJIrI0QQgos4zLyysO949ompY0TSnyir1b2lVyMBjRSYmJyWQy\noaq1P/x0MeXOyW2enz7DNofITrvsJXmO57vUTa3tgA3wPA/HsXBcB6RgvVxx9vwpgo7jg33i2Gc8\nHjKdTWlly2R3l2fPn3Gwu4/nOgyjkNCzEUpSFikffvAhn/3cZzk7vyQajtk7OgQs3n33Qy1IQZEk\nCWWe8fnPf457d+7y+PFHuI5DJ1uEsCirrg9paDk9PeVv/5d/i7/+b/11mqblJ37iLzK9muGHEb7v\nc3Y2ZZWsObp1TL7K2T/Yo1Md7773Hm3b0NQ1O+MRL7/8gNnFjO9+97vEYUhiroANBU8zO1TXYeoE\nZNpaN3BhGNI0miW2sWW2bZskSdjf32e5XNK0Bd/85h8wGg35mS/9DEmaEIWDzbu+L9TXjoRStpim\nhWmYgAmWIEnXxIMxUTyiKEreffcdOlOHNLuuS5kXGJZFW9e926LG2IXQQRamZX8M1oWbE7/c7rY2\nWZsbVtzGC0oz666DY3TGQfMDa+gntsT8+v/+329pMpvRYvP3zS/7vTCgm9vZm74neumpFYqbF+om\n9PIij1z18ImSHaZQWH3YqmwVhuOQdy1GHPA3/qu/w77lEvsBlhuRlBLhhGR1R92CZbv4ro9pClQn\nEcKnaTT2tlguiKJo+3M6rtMbE4Hb83SFJbbcdddxicKA5WKO6lpsA24dHXBy+xaGHbBYV7z33nvE\ncYTneSznVxR5ilCSQRhwsDvhjTc+zTpNKaXgm9/6Y9Z5jTJsnQKjWibDEN93qKsCqVTvkWzw7Nk5\nUTRh/+A2j56eUbeKSnaEcQgm2l/Z82kaUG1JYCocVdPmK8yuosgS3vjMG3h+SKu0UdRG7FAUJaIP\na7YchywraGXLYDBivV5RlhWmp2g7QVk2WIbNOIoJbJNR5CJoyfMU1/fJ64oyX2MIk/F4zHy+IElS\nhoMRZVkTx7pgLxZLPM8jrxe4rkNRZIyGQ4IwpGlrlBKUZUWaZAReQF4UJKU2MNJ+ORa2ZfUjsiJP\nC/KsYDyZ4FguwqzJspRWdliGXliXZYXr6l1J3ZYURUbbNpjCJctKgijEtG3qVqKE2dPdKhzHpa5q\n6rrRghPfZWc8xnNsZKO/Z7eZLg1Bsk6xHFsLsdqaYeRT5gm2ANc1CXp3TMN2ePL8nHg05uJiTlE1\n7Iwn+IFHHEW4jkNTlfi+y2Q0vo4vVIKmAdu2SLOE3/7t/xWpJL/6q7/Kk6fPCCMNIyZJyng0xlCC\npm017VFJ6qZmNBoyGg2gUwgkZVEyn8/xHZ9Pvf4Kb7/9J+TZGsc26bp2W/iEcc2zNi0TKTveeee7\nZFmOlJIHDx7w4MF9LR7rbZQvLy/51lvf5OrqildffZVf+ZVfYW93T99/4uMTvEZT+lqwSbTvG0eB\n0L7ghu6Mq6pidvGY9XqN73o0Tak1GGWJZRjYpqF9xjvNNy+a9k81pRsMfFOjrqMkP258tbnq+lo4\ntKld/4/sZP/fujY/5KZQ39wSb37RzTLgpiXsJilj89iLtJ1Np775XhuRzTVfs8eleu9rSXOjSxc4\nnguGSWA7NKbN2dkpRy+9juH6NEogTC1+UQrG4zFKaJvYqpF0UmKbBkEQYZkGYXBI1IsO1qnO1Szy\nlMFgSLJeMp6MQUHgBWRZTicV56eXOI6Fa7kYQrG3e4BsIUkWXM5WjEdDhqMxeZ5j2j7Ht3aYTS+4\nuJiyWiz57Gc+S1lWJEWJKeAnf+InePz0jOl8ThSFZMmc86tzdsZjmrrEEB3YLbGn2Bt71Pmc0AHa\nljAMyPKcyd4unuUwm00JRgMG413W0znrtWQU7DC7eAbKAmHTdBLZtXS0yMYiSRKCICDL11rNapla\nGWlapKsVjuNgGiZSpdiWgWFrmffB7g5tnlKVJbPpKY5j06mOIA65ffQKs9mMuq77N4fB+cU5lqlx\nyf39Q27dOiYMQsoq1rJuw2Q2nXP6/GwbwBFHQ+6e3GW5WOG7AsMyWMz16LxezvF9H3qRieM4tI3B\n/PKcyWTC4eGIg50hYRRTly1SKR4/fkyerSjzNXWZEYQerik4ub3P1XTKYrkkTSTKMAijGNE5DCOf\ntmxZrtYcHx2D8DFNRVNnLJIS17JBujiWToQyTRvfCnFdH+FALXPWiwW+71OmCWm6ZqamdApmizX3\nXnqZt99+m1u37+E4Pq1saBqTq6spR4cHBH6AZRm8/fbb3Lt3Fyk7sjRHKYMg9Knrmm9/+4/5F//l\nf4nT01OiOMYwTJK1tu5N05y6rAmjgIvpJVVdcXJyC8uyePbsGbs7uzx/9oyjgwNeeukVnj15xkeP\nnnL3zm0eP35EkSf9crFvrDrtmWPbDsvViq9//etMp1OEEMznc976p2/x+uuv89WvfpW2bbXdbRyz\ns7NDVVX8/u//Pj/5F3+SKIrw3ACpbriR8vFiKeiteW88bKBTsAzDxPcDjo5PiOKEJ0+eYAiFaeim\nzaCjVR0KgeNqHvpNseCmYAshtolXm45aK7OdGzs92TeeYmvs96JV7fe7PrEO/Jtf/58Btlvd79VR\n32SowMdjkTbQyWYbvPEFuUnJ2fxbN8VAAu2nLVupKUsCEAJbmNqKtenwwpCsaagNwX/4H/9H/MJP\n/SyOZeM4PnnZMl2scL2YVgnCaIBC4PsBUilEJ0jXST8q9QrE3nPB9/1e3m9RlAWGsFgnGZ4bkOYF\ngR/oF61TdG3F3Tsn7O2MWa3m7O7v00hJ3Ui8IODZ2QVV3ZAmCaLTUnPXNmnKnMnOiAevvcR77z9i\nMDygbiArS4QBbVsxHsXM5jNMBIPIp0jPSdOC1Spjscq5dfs+QTxhvlzjuD4Ii1WSsrM7YV3NSVcZ\nA3dE5ITIquRgd4hjddhOR56vCWJfm261lqZcCcF6vWZ3d4/1eo1hmHqM7XRYhR6JayzHw/MjfUC2\nksizaaqCySiilZJaSh3ukGd9d9IRhhFNoy0FBAadlLSNJMsy6qZhEIa0ssX13O2bWat4W/K8RGCQ\n5wWWaWPYgla2TGdX7O3v8eTJY/b29ljM5+zvawXfZLJLVZSk6xmy0wHWSZ5hWTbj8bjHXzsc26Ku\nSm2utFxQViV7h4eMdnYpywbDdFgu1ygJ06s54+GY8WCE5UmE0WGZxjbJvm072rrTfOpakqUFRVER\njgOCgUvXtlimANUhmwbZNtSNNnvqOs24enZ6xk/+1JeYzWf4rsf+/h6mYVJkGZ7nMhoOWa9XPZxh\nYtsOoHjrrbd4++0/4ctf+TKDgT6wiqLEsnUHHkcRg3jI1XSKH/h0SNbrFb7v4tq62To5vsV8Pqdt\nWqRUXF6ecffkmPVqhm1B110TEqTUQQvCMPmDf/JNLi8vt51okiQIISiKgq985Su8/vrrW0ji2bPH\nPHz4IY8fP8F1PX79P/h1BoMNhLLpuHVxNV/oyjef+16X7Om16/Wa+WxKXZXaCE30oj3HputtZDXy\nc82CeZERt4l905/7+JLymnknP1bfDMPg0z/8U3/+OvCPm8Po68VffMMBvZnCDn/6Sbop7tlcNyGT\nzeYX2LJORKdxK4RWNLY6eAzT0eo3EHzrD7/F/s4+whBUZSoDgLoAACAASURBVIkA7t66xe3DPYQw\nmC9WZEVKUdVk9ZpOKXzHYxy7uI6jfUramCRZk6QpZZpub0jf8wijEXePD7Fsj/U6o6wq6roFoSia\ninff/mPSW0d4rk3mmlzNpihhMhjt0tQlaVZgmBbCMDGVwjQE0WSPh4/ep1YFYTjG6BqKdUbdtARx\nSIvi7OKCKIyQjeT09IrABt8f4PlDOi7puprZ7IzRWBcbz3exrBjL6IithqOTQ0Tn4lk+lhhQVwlN\n19AUDU1TU5caeqibUpt8eS6mZbBaL7bUUFNZvV+LhWObjKMQw3axXR/X98jShNnVOYMw4NnZc8Iw\nwnQChIAgiFktV6Rpgup0Zxb4PoZhEoUhnusS+hMMQ5BnDXWVcnp6yWRnTBTHOkFIVdw+OaAuW9I0\n5/GjRwSBSVGWvP7qq7z/wfv4nkuRJyjVMoxDsjTj4vw5cRhy6/iEoiypmppWdRRVyTpdkWcZtq0z\nL3dGEwI/Yp3kKEPy7Pk5Hzx8wmK1oq4bgiBkZ7zDOB7Rlmsax2C9TjAsgR8EeK67XXJNJntkaY5r\nGARBQFGUOL7NKl0AglJAmmTUdYXv+xzu7+EFNYYBH330kFtHh7RVTuS7CAFNVWIHAfEgwnVcsrzA\nNG0mkyGr1ZI8T2nbmvfe+y6DQcQgjvFcm6rMEQgc02R3PCbPC54+eYIX+iwWU+I4xLZMBFBVFTvj\nMY8+eqy9khzBfLEgjgdUtWZ6tV0NSmIoXby1bazN5cVFf5+GlFVFkWUc37rFk6dPMUyTd957j1de\nfVVP4VLieX6fYq93Vk3bUjebhK4/XShfvATfm/EhMJEdjEa7uK6H6louLy+oygLXsvTh47rUZYUp\nrhvGm3GPW6JEz7bTj10n8lzv4sQWLXixef1+1ycaarz5cwODbIr1i8Yv13aN19RDz/O2H2+cCTde\n3ZsnZXN9r4gj0UgwDKTQ2ZJ9RKRms1QVfjDgza//Hp//4l9gdzJB0JGt1jT5gjgIUVJyshvQSB/T\n88GwqWVLkVfUVUXbZghpYwGTgcv92/uYlkVd39suZharBZeXU/JU+3F4XsjR3kQ/D2LAzu6Ipioo\ny5yuWRM6BrYfIJWGYqJoQNv0eL8Q2JbBcrXE9wPmiynJKmV3fIxtWBiuhSkUw2HMgAHL5YpklXN0\ncAfXVDowQUlu34lo2pbXbt/i0aPHFFWG5Zgkac4gCoktsGXBcBThuTpjsgu1BLkqO/Ym++RZje+H\nTLMp8UDDPZ7nYZgGWSZRStLRIbuWpjfGX80U8WhElmbkVUk8CDg83CfP1ty9e5eyalmlJXXR4IkO\nyzAZDoYM4yG3jo5QXUddFTpMI1tvXSl3dg7Y2x+zdzQhyzOE1XF+8RzDNHj6/AlN1dDWkqPjY4ah\nj+XoEOwv/jM/Qlak1E3DsydPODjYY2GbjOIh0+mUxTpjNp+jVMd4d4QbBjiuxWR/V1s3lC1PT8/R\nuYsWSviE8ZB4YjKc7NO0Fc+fPcMwOwYDl5Oj++Rpih1MNLbdNDRSBzlYps06WVDXLW3TC9aArmo4\nOjjk+fkFvh+xWKZ86Wd/njfffBNMlyDQ8WWvvfQKp+en5GlC22rzJ9W2ZGmC63lEoeb9x3HMOklo\npcSyDepaYtkmn/uhNxBCK4x9L0AIgzxLMAy9Jzg8PKAoM1wnZp2sesWxLsht22JbNukqJc0ylCGI\n4wGoqsefxceskbtOIRFcnF/QNA15UWjo1NUU3M2k/fz5c8w+iByhPfIHgwHrdcp8vuSP/uiP+dJP\nfwmBQCltDwF6h8D3quFK/SmABfTPYhgWdd3i+xFSNhweHfP40UOSrMTzNHwiLAslrxeOLxbeDY3w\nuuG8jpG8LvLXUWt/1oGzuT5RHvjmBLqZZfmiv4Druh8LMn6RYeI4znYJejMHb1PYN6fZzS4cwFF6\nadkJpTvuPvjAtC2iOOad9z5kuVwSByFtW6HaFtuE2cUZwdEhlqEIvQG1kBTlCmnYYFiEvknoBZi9\n82GapGg1WEW6XhH4PkVWIKQOAH7tlQPSLEehi2RdLfFdlzRdUFUdpqGYzR+zuFrhOEPisXYKjMOQ\nVZ7jOAFZVmCZgjQr8PwQ05ass5TBcITqWoq8wA9jDKFI04TpYsW9ey8RhRLLsOg6g7LJNPXKtJFN\nzcOPPmTvYJ8gCjXH1wgxBRwMY5KkxGgriqYkHHhUVY7r2TS1HimLpKHMa2zbpKxyglDnidZ1heeN\n0f4w18ncQRDQLRNsz6UVAitdkecZebZkOIhJshTZmUSDIVWjIFtrFkfXUBUleapx1L3dHUwheltQ\niWVaLJbneIGPadsIA9bJimjokaQZXmBwcucOJgZ13bBerzEtk7IqcTyLIPSxLJOTk1ucnj5jEMec\nnj7n7p07FJWN6wUUVU5SJCglMWuBYzs0dcv+3iG25VMWFacXc+bLBNd38QIP0xbsHRxxdHzIzmhA\nMptxefUc2zCpOkmHgWlpLx1lsLU0tVypxWpK85ersubp06fYjs/rn/o0puPz5Pk5+4e3qOoaJWtE\nnpEsFriOw+XFGcPhiGgc4dgOrutpM6imZTQaURQFUnbUdYltQ1mVhGHAwYH2/xdCYDvai2cyHpGs\nU1TXcXV1wd7eHqtkie1YGGaP5Rpd70boYZomgygmKQvOzs54/bUHVMWG2w11U4PqPUakFrpt7Jo3\nLK2yLLc7paOjI4qi2BY6wzBZLFbbOvLtb3+bu3fvsbuzRxgGN/ZsCsO8URQV2j5W9Iutm4/TC9mU\nwrIt2lb29cXm3r0HPH32iOnsCte1MYWBdUN1uUEINv/upqZt0nhuki0+3mxaHyveL9KoX7w+sQIe\nhtoLeuPpvemQm6b52GJy418CuiPfBDZsRpKN5N4wDAydha4LvVIYlrNdhKoen9pspaUptFS3U0jD\noLT0KW01LUWe8Tu/9w2O7t1HlQ1Z55FmNbapKJIamHH7+ICr+Zx4NCAMfDAMiromzzO6TrBerWnq\nluFQCw4EsL+zSy0bTNtiOpsiOsXV2VwzUmyTncEAfy8ABPbtE5aLNUmSEbr7DO7uY5gdy1XCzjBg\nub5gYjsIo8GwalqpkAZ4vsPY22d3MmQ4HJJlGaZrsFhe4istca6Wc4p5hOP6TC9neuHj++R5zTIp\n8B2HOB7QpTUj1wEDWqOgqiqyOuDWvfsIQ6vviryhkw6rrGF2tcA5dPA8ge8bLHOfhgZDmWTrfHsA\nSykpy4o8L/RrISW3Dod0RYdSsLe3y8HuDrZta8dHG6ZX5ziOLjrCNfADF9cO6WSD1UFd5ZyeZUjV\nITAJBwOCIOTw7h3KskIqwdV0zuXVnCAQRMEQL3JIlkvKImUYRewe7WwtgIs8Z71esZitaOua1157\njeViQRh4fPjh+4CB5/vs7+1xx5vgOA7L5RLHdfnwo4csrs5YrlasViviOOLucdRPggZt3ZBdPEfK\nDrs95uDwWLM6Mu0V09QNRZGRZbpYCyEYDmKassYUgjAIMQ2Dyir58OyMye3bTJ9+QGS2dLJCdTVx\n5NI1iqoqURFIYfDZH/kCCIHnBdoDHRNhWoSBSVkU2LZLVaZYpkNRVExnC8LQx7F0jODuZI9ONqwX\nc4p0jefr3MnxKGI+u8CyLDzLpS5rbM+iUQ1NUxGGAWWtmVa2Y/Dqqy8zm8+oJJiYCMOkrhtsx6Rt\nS2TXYlgNk50Bs/kFdqFtZNP1kjiOCX2fO7dvo6SkrhodFNI3gefnV9huwHj3kLySnE/nhEVFHMU4\njolrbZrBG8y0btMwboqlLuhaH6JT6gGMHqE1LBtlWty7+yphMNYhNrZNJzQMa5sGqm0xlA5b6Vot\nFGzrBoGhpwFxbRuyqd+a0CG3DSn82R34J04j3EjlNQfy+vS5eWrBx2XxN0+mmza0Xcc2CUNsMPMb\ncMqmCzdNE1d2GJ7LIksI/QADg6KV4Dr87td+j7e+8Qd85ee+ghV4uFaEbFrqKidbz6mKlIO9Cbt7\nE53dF8d0StF0kjCM9YukoKklQmlcs+06LNvSKTmmQHYS13aRzUaaW5OmeuHZNi1+FDGface2/f1D\nmqbSA51h0rZ6mVnVkuU6wfUC8lIbdkVRzGq1xHdtfZOYBgqFaWpl5WKxwrQdhLBxHRcpFYZlkGYZ\nUTQgz4reP7wi8j0MWnzXRrYVbdNg+S5FUSGEgR9EGEI7LTqWRZWnDEKfNF2Rpiui8bHurNAOeoah\n8xwV3KBW6UNZqBIpu60QQnciEIZ6ZN/4qa9WKyzPQSmJ59ggJcNhiJKa9mWYpg4AqBvqpsYwFF7v\nxIhh8+Tpc27fPkEgsO3+yO8kSnXU7TXFUws9tIteVRQ4joMhRP8aCeqmRQiDLMu2X+95Xt+lOkzn\nc1arNePxGN/Xjzu2rYOmlW4myrLsXSTBD8IeArxhGYGmoNmWRV1VW1vStmmoyooyy/A9j5defYWL\nyytsVwcVSBSyqQFJ6Hso1TFdLNg/PsF1feqmxbQcDEM7c1qmhWxbqrLUPx96wXz6/BHzxQU/9he+\nQF1WBF6AECam5VDXFYiu/1pz+zOXZaVho0770fu+31s662ZKKslqseDWrWOatqHKU8qq0IZStkGR\nZQA8ffqEspUsl0vmizl1WW8L7Gc+81k+9alP9WZyDijBxcUZHz36iLfe+r/YOzjkX/m1v8rx8S3S\nNOXk5IS61OlGo8GAoN8D6Hus24pzbu7NNoy177XefFFXkmUZp6enrJILwsDX/kZtS9c2OLaFkhuo\nqK9hHQhD29JvsO9Np76BjG8+/pnP//T3XWJ+YgX8W2/+NgB1XW+XkDd/lJuhCE3TbAntm079xSRn\n0M5+N5eZN6mEGyhlU/BDw2TRFFjDGA8LVwpKFP/4j97iN/6T3+Df+Sv/OnEQwcAnWRVUWc7h0QEm\nHVHkc3V5SV1m3Ll7m6IoODjY0/mXZamxvg5MDG7dOsG2He2p0DSUdcVitSRJUxzLxnd8EBDFI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H7Zdxfn7Ot771FkoJbp/cYTQaMRyO+udJYpkmda1HyjzLUUpxNZ2CMHT6j6EPgaIot0HQUra9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Z7nE2VJ432EUrXVb7sRGOmJQyllUVCUeROvYO5ptLg3F3taRki12JQ0MWLRV8bAVIvc8Kqq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\nUZs48IlNbGITu0Zt4sAnNrGJTewatf8Hq9sL34bnPZ4AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4a20f50>"
+        "<matplotlib.figure.Figure at 0x4b4d390>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "That's cool. Both of these detections are tiger cats. Let's take all 'tiger cat' detections and NMS them to get rid of overlapping windows."
+      "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
      ]
     },
     {
@@ -579,13 +763,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "scores = predictions_df['tiger cat']\n",
+      "scores = predictions_df['bicycle']\n",
+      "scores += scores.min()\n",
       "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
       "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
       "nms_dets = nms_detections(dets)"
@@ -593,13 +778,13 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Show top 3 NMS'd detections for 'tiger cat' in the image."
+      "Show top 3 NMS'd detections for 'bicycle' in the image."
      ]
     },
     {
@@ -621,13 +806,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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YMakNXlvhtNmhaGjMrTYYTyw6zTahH7I0v0r/dMzGhRV2jh6xtrGEkNjYwx5L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SAAAg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8/f8WD0EZbSJZhn5KnA/dNPWF2m3N+/jy306bX2qDSJq8mMRhN4dXHOZHnL0yePWcQTFLvB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\nVcMl27kAAAAASUVORK5CYII=\n",
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YNiYbPD06ZLK5yaAoOZ+es7O3R9s6gnOYImfv+lWiNXzwwTts7Vzhnfffo8wy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WiUZqPFKM/4oXf+AH//H/wKd1vgI9hYXSf2G3SvX6W1sIDv+8y0LGzbYqY1j+/5RHFMr9djc6eD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zF5YbSEPdd4BFpHnyDwQGsC/41vuxncBnUrxQpfLWYsMM++7XdeRNGAOWRKVQKm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\nAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x38c5110>"
+        "<matplotlib.figure.Figure at 0x4684b10>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -646,7 +831,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 11
     }
    ],
    "metadata": {}
diff --git a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
new file mode 100755
index 00000000000..7cd47fa33a6
--- /dev/null
+++ b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env sh
+# This scripts downloads the Caffe R-CNN ImageNet
+# for ILSVRC13 detection.
+
+MODEL=caffe_rcnn_imagenet_model
+CHECKSUM=42c1556d2d47a9128c4a90e0a9c5341c
+
+if [ -f $MODEL ]; then
+  echo "Model already exists. Checking md5..."
+  os=`uname -s`
+  if [ "$os" = "Linux" ]; then
+    checksum=`md5sum $MODEL | awk '{ print $1 }'`
+  elif [ "$os" = "Darwin" ]; then
+    checksum=`cat $MODEL | md5`
+  fi
+  if [ "$checksum" = "$CHECKSUM" ]; then
+    echo "Model checksum is correct. No need to download."
+    exit 0
+  else
+    echo "Model checksum is incorrect. Need to download again."
+  fi
+fi
+
+echo "Downloading..."
+
+wget --no-check-certificate https://www.dropbox.com/s/0i3etlgmsmgf5ei/$MODEL
+echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/rcnn_imagenet_deploy.prototxt b/examples/imagenet/rcnn_imagenet_deploy.prototxt
new file mode 100644
index 00000000000..ef75a0a5e95
--- /dev/null
+++ b/examples/imagenet/rcnn_imagenet_deploy.prototxt
@@ -0,0 +1,207 @@
+name: "R-CNN-ilsvrc13"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+# R-CNN classification layer made from R-CNN ILSVRC13 SVMs.
+layers {
+  name: "fc-rcnn"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc-rcnn"
+  inner_product_param {
+    num_output: 200
+  }
+}

From 186f521348c4f5a2ee91fdf734b5660b1882da25 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 9 Jun 2014 19:46:07 -0700
Subject: [PATCH 0105/2053] edit detection example, include R-CNN NMS

---
 examples/detection.ipynb | 477 ++++++++++++++++++++-------------------
 1 file changed, 250 insertions(+), 227 deletions(-)

diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 5fdfc098679..e4eedfef38c 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -19,35 +19,32 @@
       "\n",
       "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
       "\n",
-      "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to make the region proposals by running the Selective Search MATLAB code. If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.\n",
+      "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
       "\n",
-      "Before you get started with this notebook, make sure to follow [instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) for getting the Caffe R-CNN ImageNet model."
+      "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
+      "\n",
+      "- Follow the [model instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) to get the Caffe R-CNN ImageNet model.\n",
+      "\n",
+      "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "!mkdir _temp\n",
+      "!mkdir -p _temp\n",
       "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
       "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "mkdir: cannot create directory `_temp': File exists\r\n"
-       ]
-      },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0608 20:59:28.978440 28360 net.cpp:36] Initializing net from parameters: \r\n",
+        "I0609 19:20:40.532173 1915982608 net.cpp:36] Initializing net from parameters: \r\n",
         "name: \"R-CNN-ilsvrc13\"\r\n",
         "layers {\r\n",
         "  bottom: \"data\"\r\n",
@@ -254,153 +251,165 @@
         "input_dim: 3\r\n",
         "input_dim: 227\r\n",
         "input_dim: 227\r\n",
-        "I0608 20:59:28.979029 28360 net.cpp:77] Creating Layer conv1\r\n",
-        "I0608 20:59:28.979038 28360 net.cpp:87] conv1 <- data\r\n",
-        "I0608 20:59:28.979045 28360 net.cpp:113] conv1 -> conv1\r\n",
-        "I0608 20:59:28.979110 28360 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0608 20:59:28.979120 28360 net.cpp:154] conv1 needs backward computation.\r\n",
-        "I0608 20:59:28.979128 28360 net.cpp:77] Creating Layer relu1\r\n",
-        "I0608 20:59:28.979135 28360 net.cpp:87] relu1 <- conv1\r\n",
-        "I0608 20:59:28.979141 28360 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
-        "I0608 20:59:28.979148 28360 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0608 20:59:28.979154 28360 net.cpp:154] relu1 needs backward computation.\r\n",
-        "I0608 20:59:28.979161 28360 net.cpp:77] Creating Layer pool1\r\n",
-        "I0608 20:59:28.979167 28360 net.cpp:87] pool1 <- conv1\r\n",
-        "I0608 20:59:28.979173 28360 net.cpp:113] pool1 -> pool1\r\n",
-        "I0608 20:59:28.979183 28360 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0608 20:59:28.979190 28360 net.cpp:154] pool1 needs backward computation.\r\n",
-        "I0608 20:59:28.979198 28360 net.cpp:77] Creating Layer norm1\r\n",
-        "I0608 20:59:28.979204 28360 net.cpp:87] norm1 <- pool1\r\n",
-        "I0608 20:59:28.979210 28360 net.cpp:113] norm1 -> norm1\r\n",
-        "I0608 20:59:28.979218 28360 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0608 20:59:28.979224 28360 net.cpp:154] norm1 needs backward computation.\r\n",
-        "I0608 20:59:28.979233 28360 net.cpp:77] Creating Layer conv2\r\n",
-        "I0608 20:59:28.979241 28360 net.cpp:87] conv2 <- norm1\r\n",
-        "I0608 20:59:28.979248 28360 net.cpp:113] conv2 -> conv2\r\n",
-        "I0608 20:59:28.979744 28360 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0608 20:59:28.979755 28360 net.cpp:154] conv2 needs backward computation.\r\n",
-        "I0608 20:59:28.979764 28360 net.cpp:77] Creating Layer relu2\r\n",
-        "I0608 20:59:28.979771 28360 net.cpp:87] relu2 <- conv2\r\n",
-        "I0608 20:59:28.979779 28360 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
-        "I0608 20:59:28.979786 28360 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0608 20:59:28.979794 28360 net.cpp:154] relu2 needs backward computation.\r\n",
-        "I0608 20:59:28.979800 28360 net.cpp:77] Creating Layer pool2\r\n",
-        "I0608 20:59:28.979806 28360 net.cpp:87] pool2 <- conv2\r\n",
-        "I0608 20:59:28.979812 28360 net.cpp:113] pool2 -> pool2\r\n",
-        "I0608 20:59:28.979820 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0608 20:59:28.979825 28360 net.cpp:154] pool2 needs backward computation.\r\n",
-        "I0608 20:59:28.979832 28360 net.cpp:77] Creating Layer norm2\r\n",
-        "I0608 20:59:28.979837 28360 net.cpp:87] norm2 <- pool2\r\n",
-        "I0608 20:59:28.979843 28360 net.cpp:113] norm2 -> norm2\r\n",
-        "I0608 20:59:28.979851 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0608 20:59:28.979857 28360 net.cpp:154] norm2 needs backward computation.\r\n",
-        "I0608 20:59:28.979864 28360 net.cpp:77] Creating Layer conv3\r\n",
-        "I0608 20:59:28.979871 28360 net.cpp:87] conv3 <- norm2\r\n",
-        "I0608 20:59:28.979876 28360 net.cpp:113] conv3 -> conv3\r\n",
-        "I0608 20:59:28.981308 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0608 20:59:28.981322 28360 net.cpp:154] conv3 needs backward computation.\r\n",
-        "I0608 20:59:28.981328 28360 net.cpp:77] Creating Layer relu3\r\n",
-        "I0608 20:59:28.981334 28360 net.cpp:87] relu3 <- conv3\r\n",
-        "I0608 20:59:28.981341 28360 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
-        "I0608 20:59:28.981348 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0608 20:59:28.981356 28360 net.cpp:154] relu3 needs backward computation.\r\n",
-        "I0608 20:59:28.981364 28360 net.cpp:77] Creating Layer conv4\r\n",
-        "I0608 20:59:28.981369 28360 net.cpp:87] conv4 <- conv3\r\n",
-        "I0608 20:59:28.981375 28360 net.cpp:113] conv4 -> conv4\r\n",
-        "I0608 20:59:28.982429 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0608 20:59:28.982441 28360 net.cpp:154] conv4 needs backward computation.\r\n",
-        "I0608 20:59:28.982449 28360 net.cpp:77] Creating Layer relu4\r\n",
-        "I0608 20:59:28.982455 28360 net.cpp:87] relu4 <- conv4\r\n",
-        "I0608 20:59:28.982460 28360 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
-        "I0608 20:59:28.982467 28360 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0608 20:59:28.982472 28360 net.cpp:154] relu4 needs backward computation.\r\n",
-        "I0608 20:59:28.982480 28360 net.cpp:77] Creating Layer conv5\r\n",
-        "I0608 20:59:28.982486 28360 net.cpp:87] conv5 <- conv4\r\n",
-        "I0608 20:59:28.982491 28360 net.cpp:113] conv5 -> conv5\r\n",
-        "I0608 20:59:28.983197 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0608 20:59:28.983207 28360 net.cpp:154] conv5 needs backward computation.\r\n",
-        "I0608 20:59:28.983214 28360 net.cpp:77] Creating Layer relu5\r\n",
-        "I0608 20:59:28.983221 28360 net.cpp:87] relu5 <- conv5\r\n",
-        "I0608 20:59:28.983227 28360 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
-        "I0608 20:59:28.983233 28360 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0608 20:59:28.983239 28360 net.cpp:154] relu5 needs backward computation.\r\n",
-        "I0608 20:59:28.983247 28360 net.cpp:77] Creating Layer pool5\r\n",
-        "I0608 20:59:28.983253 28360 net.cpp:87] pool5 <- conv5\r\n",
-        "I0608 20:59:28.983258 28360 net.cpp:113] pool5 -> pool5\r\n",
-        "I0608 20:59:28.983265 28360 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0608 20:59:28.983273 28360 net.cpp:154] pool5 needs backward computation.\r\n",
-        "I0608 20:59:28.983283 28360 net.cpp:77] Creating Layer fc6\r\n",
-        "I0608 20:59:28.983289 28360 net.cpp:87] fc6 <- pool5\r\n",
-        "I0608 20:59:28.983295 28360 net.cpp:113] fc6 -> fc6\r\n"
+        "I0609 19:20:40.533190 1915982608 net.cpp:77] Creating Layer conv1\r\n",
+        "I0609 19:20:40.533205 1915982608 net.cpp:87] conv1 <- data\r\n",
+        "I0609 19:20:40.533215 1915982608 net.cpp:113] conv1 -> conv1\r\n",
+        "I0609 19:20:40.533259 1915982608 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0609 19:20:40.533267 1915982608 net.cpp:154] conv1 needs backward computation.\r\n",
+        "I0609 19:20:40.533274 1915982608 net.cpp:77] Creating Layer relu1\r\n",
+        "I0609 19:20:40.533279 1915982608 net.cpp:87] relu1 <- conv1\r\n",
+        "I0609 19:20:40.533285 1915982608 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
+        "I0609 19:20:40.533293 1915982608 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0609 19:20:40.533299 1915982608 net.cpp:154] relu1 needs backward computation.\r\n",
+        "I0609 19:20:40.533306 1915982608 net.cpp:77] Creating Layer pool1\r\n",
+        "I0609 19:20:40.533311 1915982608 net.cpp:87] pool1 <- conv1\r\n",
+        "I0609 19:20:40.533316 1915982608 net.cpp:113] pool1 -> pool1\r\n",
+        "I0609 19:20:40.533330 1915982608 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0609 19:20:40.533336 1915982608 net.cpp:154] pool1 needs backward computation.\r\n",
+        "I0609 19:20:40.533344 1915982608 net.cpp:77] Creating Layer norm1\r\n",
+        "I0609 19:20:40.533349 1915982608 net.cpp:87] norm1 <- pool1\r\n",
+        "I0609 19:20:40.533399 1915982608 net.cpp:113] norm1 -> norm1\r\n",
+        "I0609 19:20:40.533423 1915982608 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0609 19:20:40.533432 1915982608 net.cpp:154] norm1 needs backward computation.\r\n",
+        "I0609 19:20:40.533442 1915982608 net.cpp:77] Creating Layer conv2\r\n",
+        "I0609 19:20:40.533447 1915982608 net.cpp:87] conv2 <- norm1\r\n",
+        "I0609 19:20:40.533452 1915982608 net.cpp:113] conv2 -> conv2\r\n",
+        "I0609 19:20:40.534168 1915982608 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0609 19:20:40.534180 1915982608 net.cpp:154] conv2 needs backward computation.\r\n",
+        "I0609 19:20:40.534188 1915982608 net.cpp:77] Creating Layer relu2\r\n",
+        "I0609 19:20:40.534193 1915982608 net.cpp:87] relu2 <- conv2\r\n",
+        "I0609 19:20:40.534199 1915982608 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
+        "I0609 19:20:40.534205 1915982608 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0609 19:20:40.534210 1915982608 net.cpp:154] relu2 needs backward computation.\r\n",
+        "I0609 19:20:40.534216 1915982608 net.cpp:77] Creating Layer pool2\r\n",
+        "I0609 19:20:40.534221 1915982608 net.cpp:87] pool2 <- conv2\r\n",
+        "I0609 19:20:40.534227 1915982608 net.cpp:113] pool2 -> pool2\r\n",
+        "I0609 19:20:40.534235 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0609 19:20:40.534240 1915982608 net.cpp:154] pool2 needs backward computation.\r\n",
+        "I0609 19:20:40.534246 1915982608 net.cpp:77] Creating Layer norm2\r\n",
+        "I0609 19:20:40.534251 1915982608 net.cpp:87] norm2 <- pool2\r\n",
+        "I0609 19:20:40.534261 1915982608 net.cpp:113] norm2 -> norm2\r\n",
+        "I0609 19:20:40.534270 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0609 19:20:40.534275 1915982608 net.cpp:154] norm2 needs backward computation.\r\n",
+        "I0609 19:20:40.534373 1915982608 net.cpp:77] Creating Layer conv3\r\n",
+        "I0609 19:20:40.534381 1915982608 net.cpp:87] conv3 <- norm2\r\n",
+        "I0609 19:20:40.534386 1915982608 net.cpp:113] conv3 -> conv3\r\n",
+        "I0609 19:20:40.536221 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0609 19:20:40.536238 1915982608 net.cpp:154] conv3 needs backward computation.\r\n",
+        "I0609 19:20:40.536247 1915982608 net.cpp:77] Creating Layer relu3\r\n",
+        "I0609 19:20:40.536252 1915982608 net.cpp:87] relu3 <- conv3\r\n",
+        "I0609 19:20:40.536258 1915982608 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
+        "I0609 19:20:40.536264 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0609 19:20:40.536269 1915982608 net.cpp:154] relu3 needs backward computation.\r\n",
+        "I0609 19:20:40.536277 1915982608 net.cpp:77] Creating Layer conv4\r\n",
+        "I0609 19:20:40.536281 1915982608 net.cpp:87] conv4 <- conv3\r\n",
+        "I0609 19:20:40.536286 1915982608 net.cpp:113] conv4 -> conv4\r\n",
+        "I0609 19:20:40.537760 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0609 19:20:40.537786 1915982608 net.cpp:154] conv4 needs backward computation.\r\n",
+        "I0609 19:20:40.537803 1915982608 net.cpp:77] Creating Layer relu4\r\n",
+        "I0609 19:20:40.537809 1915982608 net.cpp:87] relu4 <- conv4\r\n",
+        "I0609 19:20:40.537814 1915982608 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
+        "I0609 19:20:40.537821 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0609 19:20:40.537825 1915982608 net.cpp:154] relu4 needs backward computation.\r\n",
+        "I0609 19:20:40.537832 1915982608 net.cpp:77] Creating Layer conv5\r\n",
+        "I0609 19:20:40.537837 1915982608 net.cpp:87] conv5 <- conv4\r\n",
+        "I0609 19:20:40.537842 1915982608 net.cpp:113] conv5 -> conv5\r\n",
+        "I0609 19:20:40.538887 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0609 19:20:40.538899 1915982608 net.cpp:154] conv5 needs backward computation.\r\n",
+        "I0609 19:20:40.538908 1915982608 net.cpp:77] Creating Layer relu5\r\n",
+        "I0609 19:20:40.538913 1915982608 net.cpp:87] relu5 <- conv5\r\n",
+        "I0609 19:20:40.538918 1915982608 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
+        "I0609 19:20:40.538923 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0609 19:20:40.538928 1915982608 net.cpp:154] relu5 needs backward computation.\r\n",
+        "I0609 19:20:40.538934 1915982608 net.cpp:77] Creating Layer pool5\r\n",
+        "I0609 19:20:40.538939 1915982608 net.cpp:87] pool5 <- conv5\r\n",
+        "I0609 19:20:40.538945 1915982608 net.cpp:113] pool5 -> pool5\r\n",
+        "I0609 19:20:40.538954 1915982608 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
+        "I0609 19:20:40.538959 1915982608 net.cpp:154] pool5 needs backward computation.\r\n",
+        "I0609 19:20:40.538965 1915982608 net.cpp:77] Creating Layer fc6\r\n",
+        "I0609 19:20:40.538970 1915982608 net.cpp:87] fc6 <- pool5\r\n",
+        "I0609 19:20:40.538975 1915982608 net.cpp:113] fc6 -> fc6\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0609 19:20:40.628031 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0609 19:20:40.628060 1915982608 net.cpp:154] fc6 needs backward computation.\r\n",
+        "I0609 19:20:40.628075 1915982608 net.cpp:77] Creating Layer relu6\r\n",
+        "I0609 19:20:40.628082 1915982608 net.cpp:87] relu6 <- fc6\r\n",
+        "I0609 19:20:40.628089 1915982608 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
+        "I0609 19:20:40.628096 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0609 19:20:40.628101 1915982608 net.cpp:154] relu6 needs backward computation.\r\n",
+        "I0609 19:20:40.628109 1915982608 net.cpp:77] Creating Layer drop6\r\n",
+        "I0609 19:20:40.628114 1915982608 net.cpp:87] drop6 <- fc6\r\n",
+        "I0609 19:20:40.628119 1915982608 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
+        "I0609 19:20:40.628136 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0609 19:20:40.628142 1915982608 net.cpp:154] drop6 needs backward computation.\r\n",
+        "I0609 19:20:40.628149 1915982608 net.cpp:77] Creating Layer fc7\r\n",
+        "I0609 19:20:40.628154 1915982608 net.cpp:87] fc7 <- fc6\r\n",
+        "I0609 19:20:40.628160 1915982608 net.cpp:113] fc7 -> fc7\r\n",
+        "I0609 19:20:40.666213 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0609 19:20:40.666244 1915982608 net.cpp:154] fc7 needs backward computation.\r\n",
+        "I0609 19:20:40.666255 1915982608 net.cpp:77] Creating Layer relu7\r\n",
+        "I0609 19:20:40.666261 1915982608 net.cpp:87] relu7 <- fc7\r\n",
+        "I0609 19:20:40.666267 1915982608 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
+        "I0609 19:20:40.666273 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0609 19:20:40.666280 1915982608 net.cpp:154] relu7 needs backward computation.\r\n",
+        "I0609 19:20:40.666286 1915982608 net.cpp:77] Creating Layer drop7\r\n",
+        "I0609 19:20:40.666290 1915982608 net.cpp:87] drop7 <- fc7\r\n",
+        "I0609 19:20:40.666296 1915982608 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
+        "I0609 19:20:40.666302 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0609 19:20:40.666307 1915982608 net.cpp:154] drop7 needs backward computation.\r\n",
+        "I0609 19:20:40.666314 1915982608 net.cpp:77] Creating Layer fc-rcnn\r\n",
+        "I0609 19:20:40.666321 1915982608 net.cpp:87] fc-rcnn <- fc7\r\n",
+        "I0609 19:20:40.666326 1915982608 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
+        "I0609 19:20:40.668617 1915982608 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
+        "I0609 19:20:40.668654 1915982608 net.cpp:154] fc-rcnn needs backward computation.\r\n",
+        "I0609 19:20:40.668663 1915982608 net.cpp:165] This network produces output fc-rcnn\r\n",
+        "I0609 19:20:40.668689 1915982608 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0609 19:20:40.668720 1915982608 net.cpp:176] Network initialization done.\r\n",
+        "I0609 19:20:40.668737 1915982608 net.cpp:177] Memory required for Data 41950840\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0608 20:59:29.042397 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0608 20:59:29.042438 28360 net.cpp:154] fc6 needs backward computation.\r\n",
-        "I0608 20:59:29.042453 28360 net.cpp:77] Creating Layer relu6\r\n",
-        "I0608 20:59:29.042460 28360 net.cpp:87] relu6 <- fc6\r\n",
-        "I0608 20:59:29.042498 28360 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
-        "I0608 20:59:29.042506 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0608 20:59:29.042512 28360 net.cpp:154] relu6 needs backward computation.\r\n",
-        "I0608 20:59:29.042520 28360 net.cpp:77] Creating Layer drop6\r\n",
-        "I0608 20:59:29.042526 28360 net.cpp:87] drop6 <- fc6\r\n",
-        "I0608 20:59:29.042533 28360 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
-        "I0608 20:59:29.042546 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0608 20:59:29.042551 28360 net.cpp:154] drop6 needs backward computation.\r\n",
-        "I0608 20:59:29.042559 28360 net.cpp:77] Creating Layer fc7\r\n",
-        "I0608 20:59:29.042565 28360 net.cpp:87] fc7 <- fc6\r\n",
-        "I0608 20:59:29.042572 28360 net.cpp:113] fc7 -> fc7\r\n",
-        "I0608 20:59:29.068634 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0608 20:59:29.068672 28360 net.cpp:154] fc7 needs backward computation.\r\n",
-        "I0608 20:59:29.068685 28360 net.cpp:77] Creating Layer relu7\r\n",
-        "I0608 20:59:29.068692 28360 net.cpp:87] relu7 <- fc7\r\n",
-        "I0608 20:59:29.068729 28360 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
-        "I0608 20:59:29.068737 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0608 20:59:29.068743 28360 net.cpp:154] relu7 needs backward computation.\r\n",
-        "I0608 20:59:29.068752 28360 net.cpp:77] Creating Layer drop7\r\n",
-        "I0608 20:59:29.068758 28360 net.cpp:87] drop7 <- fc7\r\n",
-        "I0608 20:59:29.068766 28360 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
-        "I0608 20:59:29.068774 28360 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0608 20:59:29.068780 28360 net.cpp:154] drop7 needs backward computation.\r\n",
-        "I0608 20:59:29.068789 28360 net.cpp:77] Creating Layer fc-rcnn\r\n",
-        "I0608 20:59:29.068794 28360 net.cpp:87] fc-rcnn <- fc7\r\n",
-        "I0608 20:59:29.068800 28360 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
-        "I0608 20:59:29.070180 28360 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0608 20:59:29.070194 28360 net.cpp:154] fc-rcnn needs backward computation.\r\n",
-        "I0608 20:59:29.070199 28360 net.cpp:165] This network produces output fc-rcnn\r\n",
-        "I0608 20:59:29.070217 28360 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0608 20:59:29.070231 28360 net.cpp:176] Network initialization done.\r\n",
-        "I0608 20:59:29.070237 28360 net.cpp:177] Memory required for Data 41950840\r\n"
+        "Loading input...\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Loading input...\r\n",
-        "selective_search({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpG3fe4u.mat')\r\n"
+        "selective_search({'/Users/shelhamer/h/desk/caffe/caffe/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpCk4v4e.mat')\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Processed 190 windows in 8.631 s.\r\n"
+        "Processed 190 windows in 14.397 s.\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "/home/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
+        "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
         "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
         "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
         "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n",
-        "Saved to _temp/det_output.h5 in 0.057 s.\r\n"
+        "  warnings.warn(ws, PerformanceWarning)\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Saved to _temp/det_output.h5 in 0.068 s.\r\n"
        ]
       }
      ],
@@ -410,10 +419,10 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Running this outputs a DataFrame with the filenames, selected windows, and their ImageNet scores to an HDF5 file.\n",
-      "(We only ran on one image, so the filenames will all be the same.)\n",
+      "This run was in CPU mode. For GPU mode detection, call `detect.py` with the `--gpu` argument.\n",
       "\n",
-      "This run was in CPU mode. For GPU mode detection, call `detect.py` with the `--gpu` argument."
+      "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
+      "(We only ran on one image, so the filenames will all be the same.)"
      ]
     },
     {
@@ -434,12 +443,12 @@
        "stream": "stdout",
        "text": [
         "(190, 5)\n",
-        "prediction    [-2.10395, -2.06288, -2.76946, -2.32455, -1.77...\n",
+        "prediction    [-2.31462, -1.8712, -2.74564, -2.41909, -1.863...\n",
         "ymin                                                          0\n",
         "xmin                                                          0\n",
         "ymax                                                        323\n",
         "xmax                                                        481\n",
-        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
+        "Name: /Users/shelhamer/h/desk/caffe/caffe/examples/images/fish-bike.jpg, dtype: object\n"
        ]
       }
      ],
@@ -449,6 +458,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "190 regions were proposed with this light configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant. Note that the full R-CNN system's selective search configuration is more exhaustive and generates ~2,000 proposals per image.\n",
+      "\n",
       "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
       "Simply list an image per line in the `images_file`, and it will process all of them.\n",
       "\n",
@@ -481,37 +492,37 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "accordion      -2.103952\n",
-        "airplane       -2.062878\n",
-        "ant            -2.769462\n",
-        "antelope       -2.324549\n",
-        "apple          -1.777492\n",
-        "armadillo      -2.152244\n",
-        "artichoke      -2.547566\n",
-        "axe            -1.905112\n",
-        "baby bed       -0.836331\n",
-        "backpack       -1.134832\n",
-        "bagel          -2.133529\n",
-        "balance beam   -2.338049\n",
-        "banana         -1.584361\n",
-        "band aid       -1.468755\n",
-        "banjo          -1.903778\n",
+        "accordion      -2.314622\n",
+        "airplane       -1.871199\n",
+        "ant            -2.745639\n",
+        "antelope       -2.419094\n",
+        "apple          -1.863528\n",
+        "armadillo      -1.965794\n",
+        "artichoke      -2.634531\n",
+        "axe            -1.960974\n",
+        "baby bed       -0.765412\n",
+        "backpack       -1.186638\n",
+        "bagel          -2.278808\n",
+        "balance beam   -2.417408\n",
+        "banana         -1.467084\n",
+        "band aid       -1.598927\n",
+        "banjo          -2.113579\n",
         "...\n",
-        "trombone        -1.880981\n",
-        "trumpet         -2.182214\n",
-        "turtle          -2.147969\n",
-        "tv or monitor   -1.362767\n",
-        "unicycle        -1.660976\n",
-        "vacuum          -1.484495\n",
-        "violin          -2.158709\n",
-        "volleyball      -2.454174\n",
-        "waffle iron     -1.827833\n",
-        "washer          -2.017761\n",
-        "water bottle    -1.709274\n",
-        "watercraft      -1.742185\n",
-        "whale           -2.385357\n",
-        "wine bottle     -2.493835\n",
-        "zebra           -1.921937\n",
+        "trombone        -1.890996\n",
+        "trumpet         -2.309392\n",
+        "turtle          -1.685296\n",
+        "tv or monitor   -1.517442\n",
+        "unicycle        -1.536263\n",
+        "vacuum          -1.304291\n",
+        "violin          -2.140619\n",
+        "volleyball      -2.413140\n",
+        "waffle iron     -1.830395\n",
+        "washer          -1.806998\n",
+        "water bottle    -1.686736\n",
+        "watercraft      -1.740314\n",
+        "whale           -2.516271\n",
+        "wine bottle     -2.607729\n",
+        "zebra           -1.917919\n",
         "Name: 0, Length: 200, dtype: float32\n"
        ]
       }
@@ -542,22 +553,22 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x47d5190>"
+        "<matplotlib.text.Text at 0x10da7df50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x4684d50>"
+        "<matplotlib.figure.Figure at 0x10da5c910>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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Bra2tmThiJpNBPB5HOp2GKIocdzOZTNjd3UW/30csFsPVq1c5XphMJrG7uwuL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sGp1Ot1FZ9vNpuVZClpJAAIb9Hm5ia6urpU2BAmVslBzfwVe8TIH6S8VXmzX79+HW63W37n8PAQ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fGQwGXlcqkKQ1JpeXYm1erxfdbpetJQBsnTabTUmdmyiKyGazmJqaQiKR4EA5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\nT1c76LRXe7XXe7r+HxL5t5LMloyTAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4684d10>"
+        "<matplotlib.figure.Figure at 0x10da5c8d0>"
        ]
       }
      ],
@@ -586,28 +597,28 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "bicycle         0.773379\n",
-        "person          0.545096\n",
-        "unicycle       -0.210110\n",
-        "motorcycle     -0.503959\n",
-        "table          -0.591293\n",
-        "cart           -0.628063\n",
+        "bicycle         0.800025\n",
+        "person          0.545095\n",
+        "unicycle       -0.210109\n",
+        "motorcycle     -0.233815\n",
+        "lizard         -0.366079\n",
+        "cart           -0.590633\n",
         "electric fan   -0.664937\n",
-        "purse          -0.674998\n",
-        "baby bed       -0.677444\n",
-        "plastic bag    -0.717950\n",
+        "purse          -0.681369\n",
+        "baby bed       -0.693025\n",
+        "snowmobile     -0.787456\n",
         "dtype: float32\n"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The positive detections are in fact a bicycle and person.\n",
-      "Picking good localizations is work in progress; manually, we see that the top and second-best scoring detections correspond to the bicycle and person."
+      "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
      ]
     },
     {
@@ -618,7 +629,7 @@
       "window_order = pd.Series(predictions_df.values.max(1)).order(ascending=False)\n",
       "\n",
       "i = window_order.index[0]\n",
-      "j = window_order.index[1]\n",
+      "j = window_order.index[2]\n",
       "\n",
       "# Show top predictions for top detection.\n",
       "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
@@ -653,16 +664,29 @@
        "text": [
         "Top detection:\n",
         "name\n",
-        "bicycle        0.773379\n",
-        "unicycle      -0.519379\n",
-        "scorpion      -0.902484\n",
-        "soccer ball   -1.148745\n",
-        "cart          -1.167148\n",
-        "dtype: float32\n",
+        "bicycle        0.800025\n",
+        "unicycle      -0.473696\n",
+        "scorpion      -0.932076\n",
+        "soccer ball   -1.147575\n",
+        "cart          -1.187498\n",
+        "dtype: float32\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Second-best detection:"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
         "\n",
-        "Second-best detection:\n",
         "name\n",
-        "person             0.545096\n",
+        "person             0.545095\n",
         "swimming trunks   -1.098802\n",
         "plastic bag       -1.130418\n",
         "rubber eraser     -1.185333\n",
@@ -673,9 +697,9 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 6,
        "text": [
-        "<matplotlib.patches.Rectangle at 0x4b89dd0>"
+        "<matplotlib.patches.Rectangle at 0x10e0b0b90>"
        ]
       },
       {
@@ -683,11 +707,11 @@
        "output_type": "display_data",
        "png": 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F0SsuRLYBflWBkSIdgvBhK+fvj6TOf7/n7GPEK9UnaE16Rl1Ss0s6gjgy0yU+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H7Zdxfn7Ot771FkoJbp/cYTQaMRyO+udJYpkmda1HyjzLUUpxNZ2CMHT6j6EPgaIot0HQUra9\nqKjqw4gVk/GYncmE0WjEerlkMBiQZVnv2mYiu4Y0y+ikpCgKLs4vyfOCJMnwPJ/zywv29g/4/Be+\nQDQY4Hs+ZVlgbqaEtqWqqp6RoBklruuyWq10Jx5oTLSum971UOPZm2Wk02eSep5H4PiUdc1sNsOy\nbVzX42p6hRCCyWTEZDJisViAUvyj3/5f+OpXv0ontV97HA1YrdYgBHleUdYVhm3psGlD7yBk15Em\niQ79UB2ObRPFsabuVQWWbVNXa6qqpihL5vMlda27w6PDA0LfxbVMRoOIPEnAtjQeb5gkSQL9G7WV\nUu9hTINGSlzPpSpqLes3DBqpM1QvLy+J4xjHcTi+fQsp9RIx9FyauqRtexZN3WIaBn4QUBYNYRyz\nWid6KSlbqipjuVhSFAWTyYT9/UOKsqRt9V6maTvqRr++XdvyjW/8Y37pl34BxzKxHRcpIVlnFFWF\nMGEwjDAtg7bUVN+6rnEcb+saaNv2dieh/U1sbM/BcZwex5akabrt6Hd3d5nNZoRhyHw2R8iaTqH9\n8ouCwXBA22pHz+lsznffeYf9/QN+6Zd+EdlJhsMhaZZh2w5lWfHgwQOmV1NC3yXPUsbDoebX2x5J\nknD79q0tBJJlGcORvuctSy/fQbBerxiNRlvKaxxHWzuMDY697ci5gXH3DJbp9IKPHn7IMA6RbY1l\n6Hpk3oBmbhbym1355vpzmYn5e//HP9ymNF+zSPSTFPb5kzcDHW7CK5uufOMYtsmqpN9Mm/3XKKlf\nHM/3dAq771HWFWcX5/xnf/Nv8kOvvM5PffHHMVwbdzSgzSrMssXzAy7LBMfzGNoeeVljWCZpkjCb\nz3EdF9OyGI/GOtm9gzTL9ZtTSJRqqcqCTjbcv3sH09CLMcsyaWWL7NBLLtvBdzw838PoqYVt21CW\npd74L1YMRyN8P9hCQnVd4/s+aZqhE0eub5rT0+d88P4HFGXBz/3cV3RKuONpz4uiIQhDLFsvHBeL\nOb7vEceD7Q1jWxZ5mhKF4ZY14bqu5ierjg7FYjHHmOc/2AAAIABJREFUtm0cxyEKYk2XbBryvECq\nDtPWnahOM9GvmWXZW6Optm23lEB954l+2mpZrdasE81o2DAXkiThwf2XuLy6IooiZNty6+hY+84I\n/Ta4ms44Oj7Gtm1msyuyVPOpz8/PePPNr3H37l1+4Z/9+Z7R0NA2UgfqdhtaZosyBHlSEAQa8zZN\no+96a80eKkvqpsH3fAxDYNDw9OlTZoslYTRACEN37gYgGz792quMIh/bMpAClNBWSrbtYAoTJRVK\nCFbrBC/w+6WryWqV0CnF+++/rz1KRkP29nbpGbGadulYBIFP18qtqCfwfFzX3T5njuvRth1uEFDV\nDVdXU5bLOWEYcOfkBNF7WINACYHsNIQiO4VCsbi65IP33+WHfvgNAj9AKYFlBzRth1SKsinp0BRH\nz9LhCZv3Yp4XN6bHa8W1ZVksk7WGKYJAW8M2ukFYr9eA2AqKDMPAs0xGoxGPHj/h6bOnfPjhQy6v\npty7f5/bJyfcv/8Ay9JUSM+3ieOYOB7gOC5XV1c0TcPuZIciTzGFFkTdvXvC2dk5YRSyXK64d+8e\nT548JopCgiAkikLKsiDPc81UKfTrEgQeZaHhK8/3cWwbwzQwRO/dIvS9DAphit6fR08qi/kVDz98\nnyjwdJAMXV/AN9ay18E2Nwv3BpH4QW6En2gHDtfJPJvxYdOtbYr6hte9Kdibz20cvjZbf8uyMCxH\nwxOlhh+8/gaar5Z4YYDteyR5xm/+vd/izq0TfvJHvki1zmhQPJ9NcYSJhyZoTu7eBtMgvZpTNRLH\ndfE9j2EU0lQVeZ5zdnZO20HbKTw/Ynf/AKUkZZlzdvqU1WKG45jcvXNCkaWYpoHjuzi2y3A0YhiN\nKPqkkbqpcBwbwzJu2OpqutX52QV7u7vEscZbi0rTn1Qv8pBSMp/P+c53vsOXv/xlbNsky7I+rVtw\nOZ0xGk84P7tAmBZJmjAcD6lrHVpB1zGdTrXVqOv2Ewxa9OF72K7LxdUl8/mc+y+/pCciBEJBmqbb\nLiYeDHF9PZ2YtoXqoFNaBLKBHwS6y1gnCb7nIQyD1SphsVghhOD4+JjJZEcXScMkS/XB2HUdOzs7\nhGHI+dkpk8kOZ+cXeGHI02dnLFfrbdE9PjpGiI77d+8xX58yiGIuzs959eVXWC2XuK5LnhV09M2C\n6rAdh0E0ZLVac3U1Je8pbGEU4wcBw+GQpmkpy4qrqyuuLp8zGk2IBgM8P9A0yrpmdnmB71o4ouPk\naB8pa2qlO1zZtuRZ2R+Y2iXTMEzqpmO5WvPs+XM6BLdu38Z2LEajIaHv0SnZW+Lm7O3skBcZUmod\nQV3VyFbS1BV5pg/fKI41E6lTPD895/fefJOf/umfZmdnB8syQXV0TYNCL8zSNKOqa8JoQNNqR82H\n779DGPr4gcve3j5VJUmSAtPx8IIQ07FoZU3dVGTLYhtEEEURo9GIJEl6wVa9bRAc18W09TS96djr\nHpIYDAYYhqGl87ZNkqQElqWft67jwYP7PH76HN/3eX56jud5ZGUJKA4Pj2mqBN/XjUeSJHz6059m\nMhnRtYrT50/Zm0yQUov0iqrkzp07LBYLpJQkScKdO7cpy5L1es3e3h6j0Yirq0tsy9eZrr0IbTgc\nbskGG6w6jiOU6rAsA9m0mJbZW9Jqoy7DUKzWC95/7x18x0YIhW1ci9o2UPGGUfNirfxzWcC/8X/+\nD9uPgW0R3kisXwTyb/6/NyX3N6PYNhaxQilsy8Z1dN5k3baUbcNsveS//gf/LTt7u/zcF36csm0w\nPYfI8vANh9lqgRF4NG2DrfQyqLEFYCKbFtXUuI4FbYNtWvhBSCNhuljTYlJWDa5rI+homoo0WWIi\nEaJjtVpqRkjfBUdxRF3U5OuMvb0dmrbGMASNbLbYl+9ruAcl8D0Xx9Ey4fl8rrvRrqOqa54+fYZS\nildeeUXj3Y5FJ2s8PyDPKzwvxLJdVok+LCzX6TNEtTF+13QIoXBMS0dG9ZOLHwTUTU1WFDx5+oT9\ngwMMx9Y+2a3E69k9Ukpc16VTirysdPQUCtVdv756anK2N+yGA962LafPz4kHE+2LXVcEQdjz4WsG\ng4H2AGm11N33fYTqMG2HppWcX03xA+1gKIQWvziuXg4DOL7etTimXmb7nk+eZTRNQ900WjzUdZq1\nYHvbyc+ybIqipG5a2t6joyjK7TJ6b09TG6VSGIZJVTeorsVQnfYrlzWmahgOY9woQPbp5qbQUnSl\nQLYdeVnxwYcfsbO7RxwPkEIQD2IMITQn2gDbNEB02qjKc/odkIlslPYi7w2b2qbCENqtz7QdLqdT\nkrTgtU/pcOmyrLWIhQ7LELrY9PsI2XUI0yLNcjzH4u//vd/kL/3izwNKq3KVAcKmbCDNc7DADxya\ntsW3fNpGbqcx0CwzzSDRC+SqqnRkmmVucW0hBHXVbP1NwijCMPXklec5rqkDO87PL7FdRyfVWw6L\n5RLD0kKv0ViHLwxCnV85Go0pypKun8oNBHEUsF4sGI9HCBR+GDCdTjk5OWE2u9JTZpKwu7tLkiQM\nh0OqqtB+87M14/EOlmVQlvWN+iSQsttCfQCR72yZPaLfG2nKoP5vNrvko4cfEHgulvi4DfbNOnjT\nRvbPKuCfqB/4i8HGcG36Ar20VAmEYdChaHve7Hbkt22U/L/Ze9NgS87yzvOX+3L2c+65S20qlSRA\nAiRWgc1izGrc7bansWHwhM3MtMcRdvT4w3SEPRHzvdGXDgJHu3sm3M2EPeHxeGl6oMcGbPCMDcaY\nRSAQEpKqpNpu3XvrLmfNPd/M+fC8mVWoBHR0xwx8UEYAURJ1lzyZ7/u8z/P///6S3CET4xorV5i+\ny3EW0a8VdVSw7ljMuxb/9nf/gNdt3cNPvPbHyH0LQ1f/yzgmtlPsrofvu3hejyiKODo6olhlOE6H\n8WhCb3NDL6DHYsbJMzzPY3tnhB94zGYnLGYRx8dLHMflvrsfYB0nrFZrvPA0cbLixtEK3y/pKYu3\n/Mt/xejRb2L+gNSNF68Xr/+/r58A+N3f/2H/GN/zqlyX/Ze/km//j/8Towv3kKcJnuext3tdyIe6\n/XLx4kXqWnHXPfdQFAVPPvkkW9Mp9z/wAHGcoSqTGpvReMrVa9eZbk4xbYvj/TnLdcRwMOZkfsx8\nNuPcubvo9ztcuXKdfr/PYNBjtliCZdHtdNk7OGAwGEgxk5eauVJj2xZFoZhOd6gqkytXrhD4mkhY\nVbi2UB6LPNezHlBVhWn+4OX5+1bg165d45d/+ZdbBOqv/uqv8hu/8RucnJzwgQ98gCtXrnD+/Hn+\n+I//mOFwCMCHP/xhPvaxj2FZFr/927/Nu9/97ju/qa7Am75Ys+vcnqDTAmG0lrvW7iXMW3xv27La\nPqBlmuRpiuHaGAocy6I0TRILjpZz/vSP/4SzO2d43StfRX6yJLdMLNtmOBziakCPTMlLGZj0+9RV\nRRCGJHFGkqTtBN11XbrdUBgbqmz79ACe16FSUJYVWVayWEWYpoXruRRlDqZivV7yD3/nX3P3k0/9\nJz28L14vXi9ech0+/Ea+9si/oBd4bUHYtA/7/T51XXN0dMRoNOLo6IgzZ86QRhG9niAnzp49QxTF\nAuEyDJJUTlm+72p1m6fnQY628xuMx4LRNW0Ly3bIikJOx2WNZZktHsSyDILQb3nnSpXUdcXVq1dZ\nL/eo6wrTEGdo4HlUt9noi7LENO0faKX/vqgrx3H4yEc+wre//W2+9KUv8Tu/8zs8+eSTPPLII7zr\nXe/i6aef5h3veAePPPIIAE888QR/9Ed/xBNPPMGnP/1pfv3Xf/378mybo9PtAcfNYt4cI/JSsJoN\nkOr21ooqS1Spe+iqwg0C4iLHdhzqtGQerTmsUv7gD/+QHafPmx58LcONCePT20wmU2zb5dKl57h0\n6TJlKUfF4XDCaDihyCssy2M+k0SZ6XSDbreL4zikacrR0QmWZRMEHYbDMWHYpSwrjg4PmS9mFGWO\n5VhsbU3xA5/ZbMZyuSRNM4Ig5Owzl/4zHtsXrxevFy+A8de/xmQ6bQ1Ow6EotHzfZ29vj/39fe6/\n/2W6tehy8+ZNTm1vc3J0BFXNarHi1PYm+7v7zE8WjAZDzpw6w8GNQyzDwXI9VutY5LCbG2xsTNjf\nP8ALAjBMjk9OGA67ZHlOlmciB10uUZWEjxwdHwvvpq6lzWdanDpzhrzUFbZh0e+NUKrGdpxWPthQ\nFZ/fE3/+9X1r9O3tbba3twEZTtx///3s7u7yyU9+kr/+678G4EMf+hBve9vbeOSRR/jEJz7BBz/4\nQRzH4fz589x77718+ctf5o1vfOMdX7uJF2p637cPKeG2gGMl5oDaMKC4xT5AVdIH03AgVUOkSqzK\nZL2O8Qc9ilTxuU/+OaOwz1t/8ifJo5QwVMzimL7XYTIas7251boFv/PEUziOy2DQFxNHLDbk/f19\nXNfB8zy63Q6DwQDDMFgslrrHJ1X5cDhmNBa5QJ5LUECSKlzP5tz5s8J0KTPyPMX+AR/Mi9eL14vX\nD76souDGjV3Ob29RliWL2QwTWC+XTEYjTk5OePbiJZbLJadPn9Z8natsbGzQH/R4+qlnpLduWfT7\nfZ5+6hl6vT6TyQQDg4sXL3LvvRc4OZqxe2OPfr/P1s62zBlsh/F4zI29m4SdED9wWCwWIslUFely\nQb/fZ7GM8Dxhs1e1wvV87r//QQ7298iSmHWUCMKgzDAN7cTUgK0fhJP9/v/2tuvy5ct8/etf5w1v\neAMHBwfSZwK2trY4ODgABEBz5syZ9u+cOXOG3d3dF/x6jW0+DMM73JiNTM3zPGzXxdKp4o06wzRM\nTMsSl+VtskLPdhkEXYLxgEOz4P/8sz/DPI55++t/nNyAje1tfAS4s4oWLJcLlJIUmcGgz7333sto\nJJwEzxMrru+LNbiqKvb393n66ae4fPkyBwcHmKbFcDCi1+2RxCnXr93gxt4N9g/2yLKEre0pfiCB\nuuv1kqpW2kXX+Y+97S9eL14vXj/g2p7K8FHcy6I3j+P4lpKoI0XX/v6+2PtVwTpesX9wwEteei/d\nXo9CA8mmm1scn5xgmhZlWXHXXXdx9ep1egPpeV+9eg1TG7PSLENVFZONMasoIk5zwm6XOElAEyb3\nDvbBNFnHMWmWUxsW6yjGsgImky2yXOG4PkpV2LaEsjfwPilw1ff93f+jhpjr9Zr3ve99fPSjH9UC\n91vX80Xnz7++178Lw7BN0mm+RnN0aP6eZVmYt+XltbCr+hbX5HbQ1QCXVV2yNGu+/NVH2du9wc+/\n9d0Mgw6WF3C8WtAJQjqOh78ZimwpjinKglIJzrTX7dPpnMKypFWyWi3BLAmCUEh4nkeaiolkdnLC\n7vVdLMtmMtngzJmz5CpBVQVpmvPkd57Etl0GgyGO7aEqhe/3WK+WL3hP/s2/+VcURcLhwQGVKrhw\n/rz0+k2Ti88+QxD6bGxscHBwwFe/+lWGwyFnzpzSrImA9XrN9vY2Tz/1NN2gy8/+o38kZMS6apkS\nvu8LFS+NcV2XOI6ZLebs7u4SRQnn7zrPdDrV91VSwUUxIHru9XpNp9Ph8PAYgL29PQb9IYOB8Mfh\n1hzjZLmSzXK1IIrXGAbMZzNWyyWPPvp1fu3Xfp08LzANkyAcCLnPMFjHkqazXK6oqhrLtiVF3ffp\ndrss5jNs2+Hk5IQsz1mtIqbTKZZl0esNKMpS9ziXHJ6sqakxjFqQAIaBYVTMTo4xTcHZ7u7ewLYd\nBqHN0dExRZrxute9jvFYdP6GSatNXusk9k5/gO1IpFaepMRxzHK54vDwiLvvuY+8ELu75TiUeYQp\nFGGODg9ZRys2Nsa4tiwu67WEN3fDLmWZgyHKFsO2xLFnGriODwZcvHgRz/c5ffo0ZVnc1l+VMOBe\nr8fu9V3GkzG25WhL+YJuL8Q0KpowAduW9KokTXEch6KQQJQ4TjBMk09+4hO8733vEzOYJSHGYU/a\nhNSmDmAQLXleptR1Rb83lOcwiuXrFSVJklJVFf3BgOFwSKUKVquVuCW1gicMw1YS7AcBy+WSJEk5\nNRkRdjrEcUwFJGnGe//hT9/x3rgWGK6jnZKp/DP958Ggz2q11KRPhziO6HQDvCDg8nPPcTKfM+gP\nuOe+e7h06TmyomBzZ5tclUTrhK7Z0Uk/SyzgZS97KQcHh+R5zmgy4WR2ItiNQR+jrJjNZnS0UzTL\nck6fPsV6HQMG6yjGznKhOJoGruPheSFZGuH6PlSlLkxFTVdqkcX3u37gAl4UBe973/v4pV/6JX7u\n534OkKp7f3+f7e1t9vb22NyUxI7Tp08L3Fxf169f5/Tp0y+8WP2v/3s7qHz9a1/Fw69/dZuX6Hle\nG+IgWlmRCdIGMtR4nt8u/nVdUxk1Va4oOzZ/+/d/x+Nf+goPP/hqclccbV6UMRp2KXwHY5WS5ymV\ndkz1+iFZmlOWOeto2bZ0pG3SJS8y6lqxmK/EGuz6bE03SZKUydggimLR4QKWKz+PgHFslKq1/Vz4\nEFSKQmMEnn8NhgNWy0pMP5bHer3m8PAmJ0dH3HvfPZi22ZqWTp06xXA45CUveRmO47BarTg6OuKx\nxx5j0B/iej67e/tsTCb4nkeWp9oF6bBaLSnLgr29PeIkZrKxwdmzZ/WJQ1xqtuW0UXTNJhxFK4JA\neCSj0QDDENRpkqRQIzyUoMONGzckKcbviY5czwIWiwVnz5yn2+nwxje+RX7nvhQEJ7OFIGfrmrAT\nYhgGw6For/O8aA0qSZLge24r91KVMFZWq0g7PTMx6Win7mg0wTBM0jQms2xu3LiO57mYhkuv12Nr\nc4t77nkpYRBQJGsAjo8OsS2Ly5evEYYhL3/5A5jYOI5FR2uNZzNxgQbaJt4UNkEQas63TU3FarnE\nc01ZXMqS6dYmw3yAYdT4rouqSnrdLo4Oe6hpcBCgCkWlAVZJkmijW8XW1hTTBIMa13FaZG1dSUHT\n7XQwaqhUyXKxYDIeEUVCiawq4YhUVYWl37W6roUtUxR4muEdhmEbRJ2mKZ1uSJokeL6P7bg4riYg\nGgahJWydsixZrQR45XsOhu/R64kUtiwqsiSmqhW2ZWKH8jNXdU1RSPFkWTZxtML3XDphgGNBlafU\nKqemJnBfuGGQx0uCzlCCI9LkFrM9z7BtC9s2ieMIw5DoQMuzOVmcMJpuCFlyPWf59JqtrW2uXL6C\n43v4YYDX9VmtliKJ1CHTR0dHTKdjkiQVP0Wvz2K1JFqt2J5utclgYRiSZimHh8ftJj0ej8gyLZG0\nXYy6ZjTe4Knv7OkAjgxQPProY3ztG4+3zJfvd31fFUpd13zoQx9iMpnwkY98pP3nv/mbv8lkMuG3\nfuu3eOSRR5jP5zzyyCM88cQT/OIv/iJf/vKX2d3d5Z3vfCcXL168owo3DIPPf+7ffxfEpe15695w\nk0phOpr7qyE6lm6bNI4+yQ2U3niUlczTNb//e7/Pmx/+cXbOnMbphpSzFcXhgihP8aZDRuMxG6Oe\ngJuSlMV8TtgJ6feG7e+d57nmrEgFOhwO6XS6KFVxfHxMnt2KcNvePqXTQPapDEWSJZiGRdjpaKSo\nT5Zl7O7eaCPiPvDBX7rjfv/BH3yMm4d7BL7LYnbC8dEhm9MpZ3ZOsVgtyIuUMOy284I4jnn22WcJ\nww51XTEej7l69Sp3nTtPrSAvCs6fP49tifNtPB4Tx2vs29KPJpMRRVniBwF1XbXDoG63y5UrV+Q+\nu4F+2YTH4nkS6rBcLrl48VkuXryIZdocHx9z/vwFXvva1/Lggw9i2CE3bx5g2RbResVnP/dZfvzH\n3ojv+ShVc3h4yHA4YnNzE8uTRJvVaoXnuZycnGAYpjCuXRfLkjba/t5NkixuTQ/33HOPJrjZLaFu\nuVwKUuDwkDQXXkmpcvIsBaPmla94OTWgypLZbMZa89IHoZzKRuMhnusS+gGmKe7TbhhycnKC5zuc\nPn2aXJWs1mtWiyWZdo6enJwwnW4Kl91xtUFLkSYxeZ6QZWJ4UUXG9evXeOtb3iRa+CynLEv6/QFg\ntIN/47YsV8e1ODw85Pj4mOnmpiyyfqirabvVrzfDukwjZS3bxjItDGpUWVAbBqWS+ROG0bI3onXE\neDQBpIr9dx//U97y1rcRBAHdbhfbdjSxryMse+8WTe/5uOdGrfX8q/Ee3O4NEJe1TVEK/lgEDArH\ncem6JqqsyIsCQxdxb3v3nRX4F//6r4gK1fLZDw8PNa45Jc/lHTg8PKSqxFT2kgfu5/DwkDiKwDB5\n+HWv5xvffIy7777A0dERlX42x+MJoe+SJSlZlnHh7rvJsoyjo2O2d04RhiE3jw4JwhDX89i9do3N\nzS3CTsjVq1cZDYd0OoGcEDenHB0di2HJgDDwyFJJt/+bv/4sp7bGCH9fNOxVVYk237B4+M0//Z9m\n5PnCF77AW9/6VnkZ9U3/8Ic/zMMPP8z73/9+rl69eoeM8J//83/Oxz72MWzb5qMf/Sjvec977vym\nhiTyAO0H3chnbo9Wa8wcliUp14ZhYFv2LeeSY1Oosu2BR4OQP/nt3+VUf8TLXv0QlWPimhaWbdMP\nO5hRznq14nJ8gpGXjAZDAeVzi3rYUMgaU5FhGBSl8FniOMZ1PRzH1Q+aLHYGBoYpVnTHFUNLnuft\nA6lUJQYeW9gQSZzwc7/wwTvuy7/+n/8FhlFjUFGrksvPPYfrOIz6fWzXBlM2vNVqJcEptUGaJBwe\nHvPAy1/OfDYjimM5TutEbss06fX6dDshVaW458LdVJViuVxw17lzMiXXL35ZFm1KUpqmDIZ9Db0f\nEMcxnbCD5wVEUcSVK1cYjSZUVc1kssHejX08z+PoSLCcw+GQqrbYmApHvChykiRmMBxQ1QZ1Jcfn\nKEpYrJY4rs18PsdxHC5cuPtW8kyWUdeQRCmTyUS+fyZVVjNHadpCaOa754tD0TJNohRtDFkTdkKC\nwNOIVbv1ItS1PH9FlrfO4CxLMA2pYl3HYjweEa1XwrMuCmzfwwBsy8IybZIk4dLFi9x7330YhjDm\nDUsjIGrRAzu2TZJEeK7NajnHMk02RiMpSIqSuqooSiV2dvmhbpE4Han+Oh1hh5umSV2BaZjtItyg\nJ2zbpFQlQRAQxzFQSwBCbWBqdUNW5G2bUikliUZpigG4nsvn/u/P8fa3v535YkEYdlqYlqmZ8bbj\ntioy07RucxUqiThrApltYcGrSmGZBq7tUhQ5juvhB37b+pHWlolpmVoDbZIlSTv7auiMP/mOn7rj\nvfl/PvcXuIFY8/M8Z71e0ev1tFHNJQwDFoulZqR3cDy/Tb9q1prRaNRuLrW2yy+XS7Y2pywXKyzT\n1FwhlzAMmS8WQn5skn0MA892KDR2YTqdavRtTLcrp7O6rls2kGXbVKqAumR/7yqL2U08z6LIYqiE\nF24gz+ir3viuHz0n5mf//P9oj+jNQzSfz+l2u4zHY53YYVCrW/3bSlXC1NULWZKlGI48kKZj8799\n5s8orh3yT37hF0nReMuiYH9xQmFDr7bZ7o5wp0OKXHHz4ICbNwVfubm5ydbWFo4j/fbG6mvbtryI\nt7V2lsulQJVuG8DWdc18Pme5Suj1evieh2XKYrher5nNZmJddxyGwyHv/dlfuOO+/N7v/Utc1+Lo\n6CaPfeMbvPc97+END7+BeLXmU5/5FDePD+l0OhiGQRQl0os/Oqbb7SNhtDndTh+lgVOdTodON+Tk\n5JhBv8/21ha7u9e59OxF/umv/TqOI/xsiaZy2miw5mVUSuG4NlG01nFf8MS3n+DatV1t2RfS4d7e\nAUVesr29jefJoiEbvmBmszxF1VpOqtGgruuTl0KZ63S6rNdLzpw5w+XLl4njiFpXIQ8++CDj0Ril\n4OLFS1y69CxnLpxvTxS+K+zrLI2ZTqeSZTlftPfd8oTxfOrUDkq7XNfrdfu8FYXSG7ZLrz/E9z15\naZdLTGriZM3hwT51rdjZ2WE8FAXSKklZrVYsZjPSNKWvWdpnzp5htV6haundz+YzXMcnSVM6gQ/U\nDPodTO2cdG2HThiSZ5JE47ghStVUdUnFLXhbliekqfSTB8MhGBWuE6CKxrFXt8/jar3Qz8iKblfw\nC7UyyJKCoiwpK0VFrWcGXSzToMgLhv2+uGzrio9/4t/xtre9jSiKWUcxk8mEbrfLcrliNBoD4i+M\n4hjfC9vKWSlFVSscxybXOZgg5MqyLCmz8haZs6pQNcJucV0GgwGTyYReryeeDMtjNp+3J7PA9fhv\n/5tfueO9+cynP43nC5qgoTg2oDTbdggCn36/z/HxsTaT2Lpt1MNxHObzE8bjcVsMyHNvSehGKUVa\nT0uIbdumrgym0ylxmrCOIiYbE46Oj/EdCe+OtNN3a2tLZkSDQYsI8X05jWM7IqBI1tRVzqWLTxD4\nFkatdKfBwjIdwOD+V/8IJvI0FXjz52bhaKrf5seyDLMFUzUtF1VVZIXkH8Z6Ef/a1x/ly1/6Km9/\n73s4u71DkNcEhoXhu5QWFCYoE1Sa4y8zMtvH1v2+PM9I00RzO9bUdaWtwLI5VMhxplHHNNyW1Wql\nrbK3wDOm4VLXpp6Ey8InH7xUK+jF8Wc/8Mt33Jc//qPf5dKzT9PtdXnola+g3+tRZDmdIKAC5ss5\nTz75HfI8ZzZbaLiOWLsXiyWdsCv9zbrGD32p6PKcMPCp6oo8k1xLw4R+v8d0sgHUhMEtO3BThXie\n1y7ipeYbr9drhsMhr3/9G/SAKqPfH+D7AVma6zQev61qhoO+VGe1krT3PEdVwouO05w4TomimCRN\nWS5k0RV7fUFVypygKBS+5xMEXS5cuMCFC/dydW+PJInpD/osF0s818WyDJSOd6uqkueee47hYMCp\nc+ek520apGlCEARSQTuCgHUct834XEcS81XVCmqZRRjUBL6HUQseYblc0AlD3KCLbdn4nqcxvxXL\n5ZJur0upFI7nYDs2RVliW4JnME0oskyjFhLWpGnUAAAgAElEQVQcy6bIUgI/EDs8BlleUVWGZCZa\nt4JNmkBdQcwOKVWJgU2tbqW4ZFlClqX4gUeWye+yXq/Z2dkmjXNMHPTBTbM6hOznOi6e65InCaYh\nEXv/16c+yfvf/36yLJN2puaKG4YhTHad5uSHHZRCiw80BtWUobG0R2s5gWkWfxZlGKYWLFgOjusS\np4luiyr9e+TESULt9zBNkzAIKYuCwPd5z0++44735i/+8q8oiyVhGOL7knbUBMY0z7Rt222Q+vxw\n2drnu92OHvI33HqlTwiuwN2KQmphyybLMjZGY1mH9IkhzXLCboeamkJTNEPdilutVmxubpGmma6+\nBVFcFAVFVVOWGXG0wDJKDm/u0g1dbG1UNGoDapkzPPSGd/zoWekbjkmzSMCtfLmyLEmShDAMScjo\nB11soEpLbNelpKZ2HPBk6Ha4f8C1567zM+94D+PBBnZR4Xg+RVVRlSXRIsL1XFzfo+d3qUwPVUaU\nKmEdrwj8gNGkQ5qmhJ0R69WahpIXBD6mZePYDovlnKObh7iOI4CjXh/HsdqqXPglmstsW3S6Y1ar\niNn8uO0LjoZDnCZQ4XnXlWcv8fa3/iS9Xk+m/FWN64aUlXA+Bv0Jb3zDm1Gq5LHHvsnly5cpy5JX\nv/pBrly5Spqm7O7u4rg2VimLpeM5YCluHtyUB9F2MWuLvYMj4rTkrrvOcc/9L0NevhJqYXYnccz+\n7j6PPvoob/rxH+e+e17C7vVdBv0uHc/D7feZzWYk0YKD/esEYZcw7FBUOatFRJqmrNN1q/UPw4Aa\nQxbQXDGfr5hOt8jyAh8D0/JxbJsdP8D1XCbjDUHsOi4HBze5ePEis1XKVx97nI2tHbrDDrbjsrEp\n9Lrj42PSLObo6JDFYk6aJpidPsmlizz00EN0OqFwx4uCOE4pS9mofV96vGHgM93YJIljVFVTliXz\n+Yw4ipmxAgPG4yGT6WlsxyYr1li+Q0mJQjYoNwxIs5w4SlivIwb9IVmWYoYeRVnSDQV6FPodHCfA\nd21sM8cwJDSkriuC7gDbdclTGbJXlWz60VpyIx3Hpi49HFPCoVXzwmPQ7XjYprzoHb9DBcSJ4uLF\n65w9dw7H98iLnCLLMWsT23Hpe76ojUpFdxhgGiY1CgyL+XJBFK3ZnG7iej5GZeE4HoFbo+qCLE1I\n4oRCSVtQVDE1tm3ok7VJ4AfYji2bne/jmzJPsSwLDPm+/VCCSGq7FpiWGeBaAZXhSUuxBBuDWgsb\nnn8ZKFwvwDAd8qLC7wzkRKnbp5ZpEWcJpiOttpHtokrFeGfKar2S9CPToOMHXL++y/a5u3QqUoBj\ndTAtaZEmyZrZ7ITBoE8URximiZVKEZOmGZubG0LZVDWOY7GxMUYpCZfe2NggjRPh+Bc5uUooi5xu\nx+XK5V06fohjmkL9VJVspOYPrq1/qBW4o51HLyQlbBQTpV2h0pyuG2CqGjDJjRoj8MmU5CR+8W++\ngGfbvOblD7aVYzOIvJWwQgvQybIMzFKrHUzSNEWGRzW+L/9f23I0O1tpdCtA1R6z2mBlHcbQyB9z\n3S9PU6lSfN9vd98miVwpxT/+wH99x3352t//Rdt3vz1zr1EMGIaklDeyKzn+zXn22WeZTqccHh5y\n9epVVFWCKQkopmFysH+TPBeAUq2hT2WpcB2HycaE2fyQLE2558LdLeDKNm06YYftrS3yrKDf6+sw\nYmG+LBdzDGA0GjHemAhwSgm+1LJkg2t+b2pYR2s9IM31Ai6QIMty6Pb6KE1WzPOc5XKl+6hKu+vG\n9LpdqromTVKcTperV67qoIYTPYOAU6d2GI0G+L6HaZkURUY8P2oDQ5oqrBkkN6emhlOuSrk3TUul\nwR3XBvozjSlLrdGtUkk4smym0ynL5YrlfMHmdJON8QTLssnTDNMySVVOqRRZmmPbHlmSthJa2xSF\njGkaVLq1oaoKg5o8y4CK8WhIELgMez38wCPPJGmpxgHTxjQNyjxH6c/ONG3SvCCKk5bvfe3GLr1+\nj+l0E89x9DNQ6p5thmlaWGgHoAlBYFHV0kePojV1ZZIlJZbpUFU1jmvh+xIW4er72TzjdV1BfSsf\nsyiK9nsVRUmlKmzb0aHNNoY+XjiOq01xQv8rK1HvyHBdUVPxjnfd2QP/1J//OZZjCnLZsjAtG/Tn\nWHEL12EYNqVSuHbVPpuSZWphNhmpSYKBBG+PxmMcUyr57e0tLMfU0LlbA9i8EEhYUSpCzyKO4nYN\nai7btkV54vrSGgacwOLk+IjJZMiN3esMOiGG9omYxq2s36r+EU3keb55p/mFn08lTKKEUMcppXGK\nH4aYvkdZKQpV8sxTT3PtyhXe+673UFODaeBYAuWvspQokSN6v98XyLzr4GYuWZEgwagS8eW6Ilec\nz+as1xG+L/2sMBT9d5Ik7XGyGX5ZlmBb57O5TgoC15eJdL/fbxO1RV61Igx9ZrNjnQjzwlfbOtIL\n+e33Rk6+huzyeqjjex73XLggi1MYcmpnh3W0JstT9vf3JfNv5xSVqrWVX9Q13X6PMAxZzGeURU6l\nFM8++xxFUfDyB17OaDxhvVrz9MVnJQDYkCPhK1/xAGByz30vxbZMqkpxeCjZglvbO9imxWKxlAGu\nkk3I931MDGKdRn908zo726cBg6JQ7O1ex9agf9k0JqRZimXKi/fcc89x86Ck1+8zGg4p05TjwwOm\n0016586QZsKp8R1bpKZ1jVEpCcz1vDbRPo5jjvXQ1vO89sjdsKkNQxKe5rMleRG1w0TPDeh0AyxL\nwiMKVXB0WGAZHa5eucp3vvMcjmUyHA64efPbvPXNbwJVUVQprmUTOCalUbOxPSWKEnqdgDQtwLTI\n0hxsj7IsKPOc3evP4fuunkm4bEw2iVYLqrpmMV9y6tQOlSpRlShTgtCV5B1bwqxV3XC5awzTYv/g\nBoZpszGZ4ngSehytYi0ZFZztaDQWhUiWayicTZpF+j3wGA6HGFhYhkddGcJUVzlKycJccyuIxdVt\nScO45QG53RbeFmpI4HVZKipoM0wB4nhNWSo8L5DAC0Mkk9X3sJuYhgygHdclTRIRGrgeRZ7jeAG5\nlpcOhgMoFJUS2mRdSau0rmo8Vxbdfm/EcrkkDHqooibNVxhGzWI5J04izpw+RVlJWHhVVXhegO8H\nFHlEEgtitnnnmwI1TVM6YagDTuT32d+/QRLHuK5FqJG5jmngug621WBFfrDT8oca6FAURbtzw62E\n52bxtiwLwzKodS+rUmC6DiWAbXHl2lU+/Wef4qH7X0Fou5w+e0Yqm1KSbhzHwQ8CbMsiThIODg4k\nEMH36XRlcQ5DgcoL4rNuByFND0um+LTM4kYn3VQVkpTiae6vZFcWRcl8MacsS86cOaN/L8WNGzfa\n08DPf/DOYcw3v/K5tg/cDG6bB9+ybWzHRimpcAxdmed5TqfTIYkTTOtWQnbze8RxzOXLl3VluxRm\n9fGxgOl9X6DzlBiGxWw249y5c+zvHXJyMmv57EEQatOFiW0YvPrVr+bG7nWOjw4ZDsTcNJ1OGY7G\n8tKZpih1lMS+VbU8xKZhslgs6Xb7UIPnilY+7HRIipjlUhb+1XqN5/qYlkW/36Pf61PVFUmccenS\nJQaj7VZ2ahjiyO0PBhgGxHFEFC2J4xhVlVgac7C5uUmn02GxkISV2xOCmjaP5Tp4vkTYhaEkslQV\nROuY2WxGVuRte4bKZXt7i243xDQNovWSui7xfBvbBN93GI9H5EWKynLyLCfN5KhfVlDVJobtcnwy\nJ4pj1lHEdHOLYd/Fc0x8z2+VVwOdAlOWBdFqjaoUnuuxjsXAlOeZbqMIelhViuUq4urVa3T7A3r9\nAYPBSJ82EspCWh2qFEyFbUsiURo1C7tLqTL29na59767Wa1WmKaDUVlYlgRxW7aBBC7XGJbZ6smz\nTGL98iz5LsZRs7jfrjqzLVGyeL5HqW7hUytVoyoFdSNJLFF6QPrOn/qZO96bv/z0Z3BcGY4LctbG\n9TxUJSEoVS2ZpHVVYWpVUFmUdDud9h1qEMcGEK1lCCqpSakuqmo8T7wDURTheh6O6xJFMWki72CR\nx5obX7Q+kuOjwxahbFmisnEdF8yaaL0GKiyjxjIMXNskSxNs22pbTKZpft9U+h/aAv6Vv/1U+2He\nOuIY7YcOmoeiFFmeE3Q6ZEVOZRk4fsizVy7zja89yuZ4g2HYZWtjk7V+aJob1kgDgyAgCCTluhly\nFGmhlS7QJGkAmvlc0+/3W6ZxUYiRgNpojTQNBzjLRCve4G1dL0BVMgyVdPQCCQYu6PW63Dy8yTe+\n8XX+l3/7J3fclye/8TckSdJuJI0+vjmVPJ8TDPKAN8aB5u8qJS2XpmXkeSJfzPKcLM2IojWPP/4t\nah1qXBsVx8cndHt9Vss188WKvCjw/IC61hWUaTAcjojXaw4ODji9vcV4OKTXDXFdh/VqTZwkFEVB\nvz9ge+cUm9NJKweVnEkbCaUFA6nUO52eqIXsWrcRKsJAUtrzTAajk8mk3ZROZjMqw8exXU5mMzzX\nw3NlSDgajQBRQ3i+S1nkpFFMqVttG5MJvX5Ph+6KtK2u5NhdVxWpylvut2lYgIXr+iwXa5brVauJ\n9jwPo3K1A9YnL1LyNMZxLdbRnCRe47kWo9GQbjekUoo8y/GCDqtlRG2YrOOM5TLiyrXr3HPfS8Rl\n2utRlxGWUbWLnWWYWJZDXVfkWS4c+ELS5g3L0FphaaepIidNZVN65uKznD5zhiDsYloWnhcINwip\nWCsNiBOZrBjNXC3RhZqqLtjdvcpDr3oFeZ5jmjau5aMU7fsFlYRJ3yb/dRxHnlstx5Ow6Vvveavs\n0u96VdXUGPpZF+WHZcnp3Lrd5a3/541vuXOI+dlPfwaQAkwCw+UztUyR69V1o+Q0bjsVOIRhKIux\nphgaiKomCKRqV6UCI/+uTagGPM+n5dtXItk1TItuRxKRan1f0zRuW3W+7xBHsbgwbQvDtjk8PJC0\nLsemyIT1rlQB2sBT68i21/7Ye370WiiLxeK7ZGu3W/K/K+TBsAQobxnUlcLQsUxZlvGtb36T/+oD\nH2RzMKbI8tYoYJomvrZdu64YRBaLBc8++yx1XdPpdDi9c5Z+f4jruiyXc+JYFpjNzU1tVImYzWYS\nBquddmEoLruGs1BVkujS6/UBOboeHB7guB6dToDtWCwWc+I4xbYtLl26xNPPfIff+Kf/PbzAAl4U\nhe45Ri2RETQX/fagC2S7adosjXHDNAwc18W0LNI0x/GcViNvGQWuLcG6vufw3p/6KWlPXH6Oo5Mj\nJuMJeSHtpCiK2Tl1hmu71/E8D88wWM1XkoDj+QSdDrPFAtMyyfOMo8Obbaup3++ztbWF6zg8+eST\njMdjtrY28TxRSuRZTqVq+v0hOy95CavVmqqqiXIJHq6qirqqsU2Lyc52u1FGUcTu7q4YP4yKTmBz\n792vIsukZ960s46PD7XKRJg6Ozs7OI5Dtysp9uv1GqgpSkmtL4qidfuONkSjPplMMAyL/b2bHB8f\nkaVFu6GfOXNGhuyRVO5Hx3OKIqcTBoDF9tYp+oMu68UCqDk4uEnY62G7Icqw6Y6GHB2eSPweFa96\n8OWcPXuO5WpFJ+xQlBamKaed5XKJ6/vkWUGR53S7fXZ3b7TvS15mFGVGraRgsCyLC3ffTdjpMp6M\nOXf+vM7tlI0w12nsZV7oKs9sq8uN6YSqEJmb6zqUZY7juBwcHGIY4nyOVILj+DKM9D1xg8p/kWUp\ncRy1bllVSLukVAWWZbYnyuZU1+RierpSlkLBIgyDW2sCOqC8qqUqVy9MNu12AkTUeCtbt0KgcuJB\nEL9ArmPrAt8jSyMWczHWVFXNSmOfO0EAdY5pKmpL6ValPCPrdcS5c+d0de6zXK0Ak0IHrcdRpDci\nQyvZZBMOfI+6UoSBS5bGeJolfvnys0xGQ4mAdEWi6Hsye6tqiWr7fjRX+CEPMRuNa9NGaVoGzT+3\nbc329n3SqqSkoqRmd3eXj//Jn/Lut7+T7Y0pvp4qG+6tnMyWWoj0YJtqttFIy3C/YZAD1G0Aa7Ox\nNDJCQ/ed8zwnDEM2NjZaY0OzYbS5nZZFURbMZjNMy6AqpV/2zDNPc+Geu3nPO99JFK143Zv+wR33\n5Wtf/PR3DdeaHlqj2LF0JmbTG2+uxlDR/CyG5J9roHzze6JZEx7QtKkUWZ6BNrXM52tOZjPiJOPa\n9V3my4VYjRcLFss5YSjO0vl8zng0wKgrPNvmrnNn6ff6hEFAVuQcHByyWq4oNXskTRO63U77wr7p\nx95MHCeiBc9yXMfDdOxW+tUMepuTRaOfbU5XSS5DQVVWeJ5Pnhd0wo7+rMB2bJ3gs2C+EC10mwCj\nP8NeT/TvhmHQ74t23nSk+k6SlCwrieOU8XgKyLCrpsYwxTjjOnqIXTfOXZHJ5XmG63rEcUwYBERR\njOG7rKKI+ewE17ZJkjWnt0VT7rqODmzRieS2HPcl2UaeWct0SOJEjDu68lssFhRlRlYkuI6NaZgs\nlwuuX7vGYDjkoYdeRVbkOI5HjYFju9SV/lnTDMexybJUvysGtmOBqto/u65DFC/pDzr6BKdQRYVt\neYBJWRby3hhQ6VOaZVkMhwPKssA2rVbvbZgGWZqK9NG45XRWSuG5PpZe3G9vnQJablng+5L4AwY/\n8a47nZhf/bu/pSxEoVIjLUdVST5rY9GXLFhpc5pWTaUUgR/ofnVT/Ck8z4FaWnGO41DqU7zrygLu\nuK6cELSiqhmOSxu1wHEckXL6PtF6RZ4l+tRh4nouviepUzunz/CdJ59k0O+RpQmh71Hp+yPzBNpB\n7I9kIk+zSBaFwG0ajeTtTPC6rsEwmS3nBIM+RZ7jhQFf+Pznef1rXstLLtxDpd2OlmOxjmXQ2Gh9\nm+ozaxLkq4ogCBiNRlhIxSbWbRloCchdWizL5RLP89uFdHt7B8Mw2Nvb4/HHv02WZZw6dYqzZ89S\n1zXHx4dkWc58tcC05QGcTqd887HHePRrX+NX/rtf4Z67z7NerfA8/wXvSXP8lIcgaxfyxnwSBAEV\nNZ7rthuLUqJccDyXWn/4jmNRlZUoCyyLKFqjlKAHKlWJsUYPnSzLwrEcoihhOBzguB625XL+7gu4\nrssXvvh5DKMiLxLquqRQBkHoEUVrtjY3OLW1zf7+HteuX0WVNZZtsb29g+f7PPjQQ3zt0a/wkpe9\njLOnz7C5ucHx0YmEzW5sSGVkmJycnFDWZvtc9Ps9ul237UsuFgvm83lrThpPx20rRpUSbnzz5s3W\ndej70pM/tbPN3RcuyKB5PqcoCrIsYbVacHQkp4bNTTFv1bUiXi8lG9LvkqdLet0uRZ6wuXmKbqeL\nZVmsY/laN2dHokjyhBuyMRjiedJuieMUy3KYL9YkSUK8WBIlMVSK+WLBoBty7q4z2IaJUSmqskAV\nJRQJSVZi6BABy7LI01y/D/KM3Njd5/HHH+c1r3kNpmUShD5hEGq/gU2lRF3kea6w9MtC2nyloq7Q\nTHtZvB3HYTQaUZQZ6/Uaz/fbYqCuDcJQuC++7zIejzFqE9MQc4koSqRdk2Q5aZoym804OTkhzzMc\n28IyLY1g7uL7Qfs+3t6SME0TVSlynUp1u6S4OYmnSUaWF6jqhRextMjo+n7rgHb1+1HWFY7joiu0\ndmOoVYGBDEsty8L1xBVpUlOVla7+FUVe4to6mLmG6XQDy7Rb9ZfKMwpV6vhYgziO8D2PVLdOHcch\nDAOgxnEkMNy1bW4e3KTT7dLvdbFME9e2pHevJ5a+L9Jlpaof3Qr885/79wDtBwq3JtmNQkV2Un28\nCDziPOUzn/4Mzz71NP/FP/gZpuMJSpXcPDoi6HXpamZHI19qXFGNHAxoZU2S9dfD9wNWK5GtNT3t\npscteYw5IEyGZjDW7XYwTUMHqC5ae22WpWSqRNWi233sscfY2d7h59/3j8GoKfNCBimmyQOv/ok7\n7ss3v/xZbQ+3da/davvhq9WKUJsAGkdoc6JoTi2e5+mhTIHvinmnsfPLIgVKFbqf7Or+Z4nUOgaS\nAGJjWkLJ+8vPfZZPffrPGU2GTDfH7O3tUWEzGg4wkRSkXicQzfRszmSywWg0Jgg1j8LzmW5sEIYB\ns/kJWZLS7/XY3tqm1+3roGOF57jYvigX5CRRtL+XUCtLvehkpFlG2O3KHKMoydKCyWRD3IuVGDLK\nssD3PZarBVUFeV7Q1X9nHa2lmq2lml+t11DLO+56cPPmETs7p5hOt3BsDz/oUFXNhKQWO7Xn4vqi\nwMnzUoeL1Bq6VbB7fY8g7GAaJpPJlAwlcDPToK4KalUyGvQo0hQqyae0tZQuo8LWn6OpVSqe53H1\n6lWefvoiL3vZ/WxsTEniRKpZamzblDQXA46Ojuj3+4zGY1GHaEeoUrVOsNdBKKp53rN2ztLxA30P\npc2xu3uZsOuxtSUnzsALUaUkrgdBILLHSmFrBRfQ9rjrSlHqU+F3L0IGWZbKac6ToaPt2BimKdLI\nWu6zCBwk2Ftc2NIrf9NPvP2O9+bP/sPHQSmMBjtg22IgA8morG6d9GsMbFPCtStV4bqy8SmN5GgN\nSEBVlRg4+p0SIikgfXal0F0a/fcVpgnLpQzQTVPCjjudDkkS4+uNbL0Wb4RlS4uPusYyhFPj+57e\nPGv9vAk2++Wv+t5OzB9aBd5cTQhqMwQB2qmwbdvYnkttmZzMZhyeHPOlL/4d733Hu6jykjSKsR2H\nM2fPktYlfmWxXCxFl2pKQnQnCEmzjEopqQg0pGixnrN7Y5fFYsFkPKHb7dHrdYnjA05OTrTuN+X8\n+bvp90YMh2M6nS5HR4fkeUEQeIRhQBj6zBczru9ek+o39IjTlMe+/nX+yw98gNOnz7BaL/E9DxOD\nSqYTL3jZlo2nj9+2JSaWuq5xHZcgCL+rLdS0UZphV9N+kDQS4X24tk+eZ22lLj1yp7U9V3WN60qF\nYdg2eaaYTCacnMz5+Mc/zjMXn+L8XWdYR0tmhzfpBh7zKOXo+ICqrFBlzqseepA0jQnCANt1ODo5\nwYsTzp47i8pL9m/eJNMyqtFYQp2f/M5TZGnGm37sTTLfUDVlXcgR1zIZdHtQQ5qlJMmKJE306cQk\nDAPN2xAlRakKLl26pPurPpPJSLdqfLp1het4LTflsW98g5e+9KV0NR+60fK3dvM64d4L95BmOYZh\nMR4Npf+pNe1JmpAkMWkaYURGOxdpipAkznj66afZmA7pdgWnoKqKwHJxHBnqdfpd1oulKE2cDnkm\nJ8NC1ai8pKA5kZq4XkCaply78hx7B4e89a1vpigKut2AJImwHbt1KdfUdLtdMYrU8tlESdZCp5oq\n0fUcnE5AEifYdgeoUUoWTJUXLdHRsmx8P8B17VbpFPoBlTIwjCZ8JUdV0l5sTozS8vBxLBvbNlvP\nRNMekXmQqDcWi2UL1cKgtZvfDrnzPA/LcfWC/8In17DbwTHQYgKTUilMy8KoalQtYCwwqA2p7ANX\nrP+1VWOaUFeCH7ktOh3LsaGWeZNp2Zi+jePY+muBraWShcpRqiSKlywWM+oaBoOBlv8WROsVk8mk\nFVWsVivm8zlnz57Fc13KIqMsSipVEseqhZJZtgPUmkD5va8f2gLeHNWaH/j2D635j2VZpJWwFbZO\n7fCHf/xHPPzww7zi/geos4LZ0RGVCdFujtMN6eMyHA6/S54olu5B23NTpSJXOYPBgPF4pAFABUmS\ncHh0iGVZ3HXXXYzHgoxcryP29va5cuUqdV3T7XYYj0fapltxfHyEZZucPXua5XLJ1x77BlgGv/Ir\n/0QcjUmM63m6KnYxDBPHdr/vvWnA9M2CnCQJjudS1hWVAbkqyVVJoVnQjuOwipsBikleFhiaMdFs\nhEDbnmnaVHKEtajqiiJJ8NyQw8ND/v7vv8IzTz/NaDRktZ4T+i6lKrEdi05HhoqOZbG9tUWpFLbr\n0PVCDNOiKGPObd/N7o0b1MqgUgUPPPAApmlydPMmT+89y3g4ZHtrm6eeeoowlCrl1NktXM8jy3JW\nqwUgJ6kg9Oj1Q6HR6WouWqeMRmOqqhYjVtIcmy0x78QxzzzzDMLmEHpkmqa85jWvadt0ZVm2ksuG\nUVMpEzcMsEyLPCtYrRZYlku2mLWQr42xMK+zUlGUJYv5ilKVpKlIDXd2dphMxnohEjWEUdS4rkg6\n48WMLI0xjT7rtWjNTdPBNF0Mz6HrBgQqb5ENnuvw+Le+xQP330+aRriuz+7uNcEXZBlGVRMEPpbr\nspjNKApB706nU/xOKBjXIm/nKXG8lsGc3jgc3RtO04zNyYY+xYHv+cTJglV0QhiOOT4+5qQ+oa5N\nXCfQJ2U5jXiep3nbblt85ZlouhtXdeO49v0OpmnhOAaO49Lr2eIW1s/n7Yor03b0MHdNHCfMTl6Y\no59lCZbrUdcVpao0JsEmL0psw9TYaLOVFqNMfepQrSJGDD0mDZHTwMIwaqqywDTE6JNnRctNb8Bv\nqi4wjJooXuP7nn6uFFVVMx6O8DyRHQdBwHw+J88ymaiYhsYYWJiGgR+G7XNYlqVGXlj432PTaq4f\nYgUuU9rKkPNrQ/Wr8hLPcVFlQVLk5EGI73p88fNfIF1GTC4MJbuuP+Ts+XupTZNclcRZSpkkVIZF\nbcJinRDHJ2RZgeseMxj0GY/HOJZUDIfHc+m39nqUFXS6A4JwwHq9xrBsdvf2sW2H4WBAfzgiiiOK\nXHrpRZ1zcnKs2cYWWZnz2OPf4rHHHuOnf/qnefCVrwRA5QWubVEppRdvA99z21PH869SV0+WLRD/\nqpLetqtph5aWYRmGgemiqYj6eGhY7cOoMKnKCsfx9AvZ7Oy3DYgtYZRAjSqUfvgLvvzVr/KNb3+T\n0VQY0o7vQq1w6pqqUKh1zNbmlO3tHZ3AHTCabPCXn/0rLMfFCzp868nvUNc1vbDPdGPClWu7FLoK\nf+m9L8H1XC5ffo4rV66QZ5nWr0vlcnKMPmIAACAASURBVPbsac6dO6fVAXKsTdOUspBWgmmZ9Ac9\nkVthkiSxqHRsaZVYlkW3G7K9s8VqteLrX/4yd911js3plCzLcM1aq3ogjhN8x6YuM6JkjWkpDFNm\nI8PBENNyKDXhUFWl5BgWBoUqME1xErr9PnUNSeyRrhPMGqK5LDSmaeD5Pr7jkkYp/U6XxLJxLFNa\nKVWBUckCZFg2eV4S+j4GNVkskthcFQz6HUZD+T6GabGztU2eF2RppBdhg7xQOK4r4DXH5ujoiO3t\nTVCKVGV4vocbhKSWTaUqKq9qNy/LNgj6LsvljLqqRd+crAlDn6vXbnD3hXsZDmh7wUqJUiiOEyql\nKOu4nRU17UfbFhu7Y9uEnS41tfZo5ORFgWO51MhpW+WKVLefHNvFcVzyUjHqi0+g0/ExDAsGL7yS\n9DtjVJZgmxaeK1V8kaa4npxglXZxGnWFgQGWaNYNu6KiBFWhqgLDMIVVVCvqSoiZhilKGtMQF2ae\n52AYVErpxV7aMJ5tUCILse8HuLZLlpcURYzvB6iyYDE/Js8yzpzeoROKK1OkjzZVDaWqQEkL2bR0\n7q/6z8jE/P/ysm0bpV1K2rEgQyG3Q55mgEknDLECl93r1/kPn/wkP/mWt/Gy+14KqmZ3b488KzEs\ni26vS9jtim4ZgywvcD2P0XjS2lqXyyXPXb5CHK+ZTqds72zS2OqXyxVFXmLrhBQwdF9VsX9wQNgV\nCE5v0CMIAr7z1JOEYUCSxNQG/N2X/g7btvkf/tk/w3dclFaD2NpGbun+ZlP1NWEVz7+aPldd11S1\nDAQFHFS20iugtZjXlQzwGhZGlsrgS76fge8FFKUMmMS0JANaQa9WlHmO57qCGrVMHn/iO3zl0a8w\nnmyQFTmqrrANE1UoLMNguVxx/q7z7OzscO7cXdx330u4eu06g9GY9//C+/n6t77FweEhvh+wWCxw\nRi7HJzPyNMazHTzX5bnLlzWZruCuu+7i7rvPa5lfj/Vahn77+/8vc28eZNl113l+7n7v21++3Cuz\nVpWkkkqlkmxZsi0vwpa8DGKxaYwxYTOMgwgmeogJEzM9MwQEgQ10BG2M2bqZMTTtNjYMDGAMEosF\n8iJLlixr32rfMqtye5lvf3c7Z/4459x8pQ1iIjo811EhRy2ZL9+793d+v+/vu1zh0UcfpdlscP11\n15JlGYuLC4zHI8ajMYnQ74nj43seQkC/31MThWUxGI5YW1+j1+tx49Ejiu87HBL4Pv1BjzAMSdIE\nx7WJol0bBGllCAFRFGnKYY/heIzvqyW354cT01FaUEpFnrO6ssrevUtMN6cUfKc7Tsd2SNMMy7YZ\nxWM2NzexLPXQ1nTCTcliN/3cdsnSlDxX8NhgNGR5WcUUWhZ0dzqKKmo7yvPGVs9NlmZY+uAPAsVH\nbrfbTLWaOGMVxjHsD7X6URUFz1PqXYX/OpSiUHHxgVTkBL7PqVOnuP22N+okpoqGGSSlUrmANRIt\nk1f3akqv11X8br2DieNEd+Y5YeiDrRK5gjDEdSwsx2W6VgNpMRorLr4fOrTbmwXnXP1XcvurPDf9\nQZ+SF5AmsbamVc+DhaLzeY6L5RjDObAciaWUHUiRY9sS33GRUuH2Uqs+kzghCH3G4yFC5AXpwhx8\nBlcPggblUoT0FHnCHFCu62FbMB6PaG9u4Do2c8t7qJQjxnoqgd38311bXlGgEZNss1eto6/7p/+N\nrygMVSeol0NxrHma2jJVau+OUydOcfOx41TKVYIwZNTrs3//ftI0oz8YMI5j4nhcMBKSWAka1tbW\nCt8LRf9rMRpFSAQnTpwovDGazSZhGJHEaQFZDIfDYoOOpZRhGxsbejRWnYNipDzLe997D8eOHSvw\nSPOBGGtLM35PQiOvdhnGDFB4PE9K6ifplSaFxXW9gg4ZahaBhY5jElkhbU6SRIskHN2xuoR2iVwo\nz5QsTnn424/QaDSKJUwpjBj2OyAEaZ5y5PojtFrTHD9+nDCM6PcHzE7PcO7CRQ4dvo6j1x/h4sWL\nJMMhS4sLyv40FywtLVGvVBgNB/Q6MXv2LBbK1pWVVbBtylHE3Nyc9u4ec8stt1Cv1bAsBXecOnWa\nVmuKSqVCZLk4js3GRpsXTp7URVFNVmimxWg05OjRm3BDj+5QQUnxMKZSrjBOFY3Otm2kpTq0PMsR\nMsW2FZ3RfB6+p6K5hBC6QBjaqEOtVqJeK7OyskIQeNTrdbqdTkEvA83V10v5PFfhAqPxkMFwQD6U\nSNSuxXV9giBirL+HZVu4QC5yLct2sG2Haq2M74VKgi5TQJDnmbJwtRwc19POfwLbcjlz6hSNZoNS\nVFZhII5TBFggc7I8xrZs4mSoMma1SZbjOiBTFhdmOXf+LIcOHVTdvusjtcVAngvSbEAuUn3/eThO\nUBSiSVEdAFIyGqgFcpIkdIfbSJR7ZhSW8AMFzTi2h+86tFqt4tmY1Im88pIMBir/Mh7Fyn3SVXXA\nuBxalg6EcV3STCX1ICDPUhKRgRAKipfGy1/BkINhD0D7JanD3dhoTE4clmUr50nXVR7xjo0feMhc\nsLMzIEkSZmentTAwRcpdBbpp3CbFepMw8utd37MCrpgfGbZZxnmhMv4ZjghKEbkUCCTjoeBbDz3M\n+9/7XvbML9Dv9UjHMeOxwgnrjRozfoCQgpWVi4o7HFXYt2+uoJqtra2xsbGmKIalgD179rB//z52\ndna4fPkyJ0+exLLUUurQoUM4roUtHLrdLuvr6ywv71GQRp4roxkJ//B3f8/S0h4+9cufZKu9QaKX\nOI1aTTm6adEC7CaRmA/dFN+XX5Zl0Wg0ivxFI8k1S0gj2jGjlxGgmK7A8JyN4ZUJdzU3glHsKWVY\nqBdzY4Ig5Nc//WmmZ2cYjcc4jpoeut0dGtUqO1ubHNq/n1JYolZrMDe3oIQhSQaOzc03HeM73/0u\n199wAx/64Af5gz/6z5QCnyQZ0+12uXw54+x4jGc73HLzcdI0ZbvT5fTZc0xNTVEOI2zL4Zmnn6E/\n6HPs2DGazRbPP/sML774ItMzU7zlLW/RrIWYXr+jZfcJt9xyM6PRiEatTpqk9Po9Uo2/9no9/DBg\na7tdHFyhyBiOh7v+59LCwUEgKEcRWZYzGAx0dyQol0qUdCKNKUjKbG1Er6s8roPAZzDoMtNqsrW1\nXXxeBlZItH2tZdts7WwTBMriYThUS/jW9CxJomie8Xis1YTGKC0ki2PSCUtSREbou2C5xOlYf7Z2\nQc8T0iKKqog8ZXFxgeeee47vfvcJut0+1113HXv37lVUWsfRZmGNwuLB0uKXPE8Z9vocveF6/vZv\n7+Pg/n3KZ6SmQsaFsLAclLGTY5aaGSPt+20us98yC/haWSVKZUL5hBu/oOFoSK/XUSZwaU4uchVo\nrpufyWi/l1/NegnbUhhyY6pJksQMen2iKMBxTSwgGlvO8ANHH9iZWp7aFmkuiEdjskwFyIg8ZzgY\nYrvqmRqP1d7FWNIatgsopaxt27hS0Nd7p1qthmNbamLUOwnfV5myahkaFlRKuLqImwYQeE24tagZ\n3zMvlAf/ChtwbUcB9ji4gU+SZ+S2Bb6L7Tj81m/8LjOtaY4cOcJUrU4UBnj2rvNZHMcMxgoWaDTq\n5LlayIhckGohiOM4WuWW6A9jpOXX5s9tBlpF1e12C0aMKY5K8KHe7JdeegnP87j11ls4evSo7p4c\nxrHiKOdpWhTwSUdBg12D4jpfd+zOV7wvLz71DYDiYZgMizBLKNOJm269gFD0AkhKJTYxKUaT4igV\n7KucH4WU9IcDwijiiaee5rFHH6PebLC9rTxQsjTBtW221te4/vBhrr3mELe/6Xb6ccqlCxfZv38/\nuYm1SzPSXPGNy7UKa+vrvPjSS5y5dEGxfxyHzk6XRrXGnsU9+J5PngvNZ1cUtmG/j+e6zM3NkWYp\nL7zwPFEYsrS0h+lWi3Z7i52dHcIooFZvYewO8iwj0F40rn5QXUd5SO9sb3Nu4wpLS0tq+slyRnqy\n8nRRkBKEhrbAjNmW/rlSdUhZxiXTK6A+17VJE9V5pknK6uoqlXKVer2O5/nF5+L7PtIoC23lrCdE\nju3aysGw09VRajYSS+e+WloAoz6zLM0UbVNPksqpUWC7tqYRKodAldKjlJVC5Mg8Y219jRdeeI4b\nj97Evn0HtKgp0/eOrambmYYIbEMIIc9jyhWVxPPzP/8L/PKnPsmgPyIIInIBavq38FwP28kKponj\nuEXBNJRAE0sohECMJZYtNb0w07seAyU4+nNQfHFhOdjaLlcdTII77nyllP6hr92PFLsLVM9zCx8Z\nIXIsnbLl2CrXMpG7zI48yxiPR+Qix9M8dctSnTmAICcIQmXopqFJKdE/p6Ioepq26IReAdOkifK/\nkVIQBQHNZl0HpyjK4iSzctIWwzz/xevLc265/bUTeb6nLBTXtnEtG5kI/CBQIpUgIrWgMx7w7ccf\nY2Ntgw/84A9TKpVIhiO2t7cZdHtMTU3RbDap12v4gUcuBb1eV1OPIhqNRoFRbm+36XYVX3tuboZy\nOSJLJeOdDlcur1EqhwX1p16vMxgMWF9f18KREr6v8g7vu+8+PvCBD/DWt75FLdayTOOlaRFcGmqP\nDUNRU3xZUQiXTAf3aleeq+6vWq0WXG/zwRn83Bwu5gAzogXzy3zfwXCIr/1QpBD4nk+SqeSdXCrY\npFQuk6QpJ06eJCqX6PZ6+n0b4ToOm2trHL/pJiwJN990nPZWG+H6XHv9EV58/nluPX6cK1euUCqV\nQLqsbKzQaFSZmZpi37vfxdOnXuSJx7/L6dNnFQ2tUqE7GFCJJGARhiUNs+SKceC5rFy5TLvdZnZ2\njr17l7ly5QqPPPoo2+02b37zHSwtLbGyukqjoQ6baqWCH7hkqVLX2Ta0N9vs6MO3UorYaW8RxzF7\nFhcRWUK5WVfB0rnCzLNcFeIgLGFiycbjMbZtUyqFDIcjbNslHitVXblSIY0zpqaaCAGpa7OwMI9l\nKSZDkoyJY62MxSIValFfqZYoVypKSDVSqTX1eoNev0+9OYXIIcliXEf597ieTZomBS3v8uoqvZ7y\nUS+XKyRZgpCKL27bmoqL2rnkeUp/0OPMmdO86U1vYnFxUQmD4qFmWQgcW9+PDuSA4xh/EiUGy9OE\nIAyolEO2NzdptKbx3ADL9shzSa/XZzDok8S7hmC2bSt6o2UXE6fnK49827bxfA8std/JMc1IiMgV\nq2c8UiEf6nWFWmjj66711Z+bwHfp9kY4ro0lJEGglKJpkipPFilBCnLLRliQW5k+SFV0WbVcKlgp\nRgmJVFBlLrXvjKUwdVW7DJ3Q0hi6+jlH2ibYGI/lubLgqFbKxUSuYFS3gGRMs2aKtplIjH3Ga8NG\n6vqedeDf+tqXsYSEXGBJhb2NkhSvHJEg6Ccx//H//H3efPQ2FhYWiIIA17GZmWoV3a0KLY01v9XH\n8YJCXJAkiaKhRdFV3fRoNEIIiWP7xZ/lIi3oTkmSEIZB8XXTNOX82bPkecY999zD7Ows/X6/kGfv\nCmnyq7tgS0l6DR3MvAbz5zfe+s5XvC+nn3+kGD9fbrE7KaE3BVx9uMrDuFCfZRmOqyABqbtcKSRp\nkuC6iqWApeTatuvw0MOP8J3HH1eJ3qMRtm2RxjEiy2jWayzMzXHz0ZsQWc6+vfvoas+ZzvYOrm1R\nr9eL12y7FqdOn2JpaQkhJUOZK4P8+Xkef/wJkDAajtmzuKiMwTLlka08kCFNk8IHvlSO2NzcZGtz\nk/m5Webn5xkM+jqwNmT//n3Mzy8Ujm6dTgfPdXAdm/n5eba3d2g0avTHY2Uha1kEfsDMtDLddx2V\noel5mrJmWSRaFq9EX1qSnSsB187ODuPxmM5Oh36/R6YNoK677jparWl2djosL+1FSotqtcZoOMRx\nPCzHxnIcHcqbkqQJUUlJ7FWHn9Lt9mlv79Dt9hFZTqNR174gIcPBAN/3GA4HmBzPMCpR1TzzcsVY\nCKj7K0szkIpxNBj02Nra5NZbbyHT0+fkNGfUypMHvxrfdcHTTo5//TdfYX5ugcPXHdGCKbBsxZP2\nPF/pCyayKycpmoZyp6LWtMsgUgvKMmUTK4U+gFxdHBW27Fle0X2DeobedvcPveK5+Yev/DGJkFSr\nFaU4lQJXTyPmZ1NTRUaeZ7haqGTgIlPMheGPT9Qpwa5QzraMtYajf+n33Xiri6yYkNM0IQg8KqXy\nVXXP1IRJQ6+C++04ReNgpm3btjly89v+/9eBo7tAbNQyQUqiSplY84pfeOJJZloz3PaGW5FCRT9d\nvnyZ1UsrBL7P9PQ01WqFZrNVFN9MxAX5X0pFgu/1ekVG3tRUi1ZrhjzP2dnuajzKw3EVG0WNlWBM\n+++//35mZ2d533vfw/LSEr1eryjYJsfP3KimA/Y856rT09zMBrow8uFXu0zBVurPXW90U7jN15w8\nKEyEmTkkzIcupcTxNafUjPppShbnCsO0JOQ2//zgP3Pw4CHa7ba2sh0gs4ww8KnXatxx++3Mz80z\nHgx56umnufGWW7Ftm3q9xvmzZ5meni4mCsWYWGYwHHLo0CHWul263Ze4664baW91+I3f+A1mZ+eI\n45SFhUXSJFWCD9sFW2ID1TCiUi3T21E+LKVKme1ulysb6+R5zsGDB2k2agxGI+77u/tZXJinVIoK\nmEQC51dXyLOcje0tLZyStKamiKJI+7KXcCybTGaMhzo41/cJJpZItm2xurrCs88+R7/fI9QOdEeO\nHOHaw4eYmWmRak+WSEvZhczp9/oEga8i1BxbL8VSrdqzCHyVcg5SR+Ntc3l1jdnZOVpTU/S6fWq1\nepH05PkqxxLbYRgrmGr77MUCpphpTREEIbOzs6rLdl3CoEyvt0MUKd8eUwx8zymaEiwLz1UBwmmy\ne29anldQBcFiPE44fOhazp0/z42uQyJzyqUSO9sdSpUqyBxLekryr3F/QOkDHIfA8wk9lbgjRI5b\n9kiTWNH6LAUcYSnIR7kF2vpwyXAAKQzvRUXNvdoV+C4ijXHsHMfVoeSOo50awdM7AiFs8txBSmWF\nq54x5Z9iOY7K22U3HjHPc2zXQgqU82Cuum2z08pzgeuqMBFQ05zQ0FC1XNbTd4Zt7yaPOY5Dlgmy\nbFy85wYSnWzQTJP6WrXCXN+zAp6LDJEpvNbV2Nk4SRR9px/z0Ne/wfe96266nW0ybUm6ODerTY2U\nZPXcufPk2pSmVIqwvQDHMd12XjAwZmZmVM7ecMSlS6tIQeH3qzDuMViqsG5ubrC2toYQOR/60I9y\nww03gMhJk4SZ6ZbOtxPUqlWGmtVhWxautqRMk7h44yc7ccNCmbSEffllFhZG9m8+WCOZN5f5ukBx\nepsOKkkSbMfGdlXX4Tm77JcgCHCkKorDeMz9f//3NBpN2ltt8ixFCltxljVvff++fczNz9Pr9gg8\nnwOHDrG+foXZ2VlAsrxvLydPnmR5eZlcKlGIwGW0vcOLJ08xt2eZXrfPpz75Kzz1zDM0mi3iJOWJ\np56mUqkRhWWEtHBsizhLiwXtaLNNmsT4UYnm9DSbmxtsttuILOP8+QucP5/Rbm/TaDRI8owjBw9y\n9uxZur1usdyt1Wq0pmdoNuv0ez0unL9AHMfsXVoiiCJc18ZhNzgkThIG/b4yIRoMuHjxIltbWyws\nzLN3+TYajQblcrnwgh/0+2okdl18z6VZr5FLSRi2gJww8nBsiyzTqe1SYdNZpsyihr0eJ186Qa3e\n4Prrr2XQHzLoD7As2NhYK+A0oUf6JElIM23M5IekqaK/rW+1abd3EPkzHDxwgIMHDuA4FtvtTU6c\neJED+/dRq9cIAw/HpgiwNgtyWy/jzMSXJglhqAp0uVwmSWJmZuZ4+OFHWFtbY2FhD6Hv02o1QKqk\ndmUSqLBjc3+qe1xn2OYZMsvIsoT1lSukWUrgugShj5LNa/qrZZPlyqccoOQrNpAQAiHzwg73FbUk\nyymHIYFe0NsaylKvQP15livYBEvZ6e529upF25by+Ex1w2XbVoFxZ1lGuRxoCqbmzjsOjmNpmuRY\nF1r1tcrlsm7ujEhRYpwfHcfTz216FRRq0uwNlPJ6jd7k9T0r4CLLcfRIn0sYjkdU63XiOOWbD34d\nB4el+XnKrqMXjzFZrjpTw9Yol8sgIUsV11Qp8yRJkunuu4sQuZY8V3Acl0q5SprmpGlcJGekaUqv\n36HT6TA9PcXtt9/O8vJSweLwHRsp1IbZdV1kntPTmLolQQgVaCqEQKCM281oZIypzGhl1JWvdU0u\nJM2DOzlimeI/GWFlLuVPHWE5FtgSx3LJtbeCmRbSNKXb7THOEi5cvEgYRQy6fTzPR4qc8XhEo1ZF\nap+Ira0tKuUKUlpKkOHYnD59mhuP3KBCW+fnyLSizXaVqdTeffu4cPEiv/s7v8eV9XXAZmF+D+3t\nbWr1BjOtGa6srXP4msM4uuOybJtMCIXPK9s9xvGYjQsX6Pe6jJOY6dY0UalE6MNUq0m31+PsuXN0\n+wNGsdr2K2VoQKPe5+kXX6JS8pidnWVhfoEszWj3Omx1tpmdniHUHh7JOCaKQmZmZtje3i7e78OH\nD+sFlc1wOGQ8HlGv1UAI4tEImeV4vsfm5qbiNQcq+3J7Z5tGo4mQamHnuVrebSv73SzPOXPqBMvL\nSzSnWuxsd8nzlCj0GceKv91ut7Fch4uXLiGEpNGcYn1jCwlEpSr1eoOtjStYMi9cHl986QTnL1yg\nFEXceMP13PsDP4jIUy5cXOH6aw+RasaK8SuZ9NExFrW+7/PAA/9MqdQkCDzVEDkQjxNGgzFrV1ax\nbUXRK5cqCCHBdrAdu7gnjV+M8jBRRdMPFPRTqoZkIsW1jBlbiuvUieMEKSRRVEHkOf1ej2otRJ97\natn8GsZO5VIJicDWHtraXFRPBMZGQDl0SiQyFwipuu9MTw2T74Ft2yCVkE6FVYSMx6r2VCoV5YmU\nG49zBacIIcgzQbNZx3YUXCbE7nOsnlUQIsGdMKoy39Nc5nNRcXP2v8hC+Z4V8FJUIs0FmZQICVGl\nzCiOaW9t8/xzz3HXO+7CFrC1uYEfBFSrVS13ltowRtG1wiCiXK7QbPrEuRIGjUbKwGrfvv34vsvl\ny1e4dOkS8Tghiiq6w1Wn3okTJ9jZ2eHoTTfw9rffycGDBwFZOBgGocqH7OuQ5clCqjjbdlF0zQiU\n59mrjkfA69IIYbe7Npg27BL9DV1wEhuf/P+2raPePKeIvLKxyKVgZ2eHcrmqxj8p1JShBUOe52Mj\niFNlJl+KSrzxjbdw9IajbG5ucfrMaZaX9pGLnCgKOHbsKC889zyLi3vwPI/Tp09z8JpDjEcjpmfn\n+Po3H+ILX/witWqLSrWmFkNS8s673l2oQLc2NnnsscdoNRW0UWrU9Gv3la2mbVNyLJIkRkhJKjK6\nvS7VWpXhsIeQkjve/GYOHDxEEIZ85jd/E9tx2Wy3cT2PnV6XwXAIMuX0hYtUymUc26FSKjHVaDKO\nM2ZmZqhWKmTATn/IysoVPYVtsn//fjY3N5lqNtUS0XMYDgacPHFC6QNKZUpaPu55Pu2dbSV2qZZ1\nVzYqph4VWqBYFqVyyMaGop0u71lkOBwzPa0yNB9//HFAqSiPHj1KfarJBxYXkbajgpiFxPUCVlbX\ncFwPmY0pBS79fp/Lq5c5c+Y06xubBL7HxQvnefLJGW6++Sbm52Y5f+EC+5YXi/vJdJHGI304HLK2\ntsaZM2d4z3u+H8eukGax5jM7LM4v8NxzzzI3f4RKqazzUSOCMAQN4WVZridMFTKhbIt3vXssCywB\nvu2DJZDCmI/lVEslbMshHiU4lsXc9AzjbFA8D6aovtplMHOgwNqzTEFAlu1gWyCloXYKZAbY6pmR\nuaIrm+fKPEOm847KZYTIabWmCjjWWBAY1ot6DQ6B5ynhVpIgZIbjOWAJhc3ngjTNGY1iRvmYINiN\nlFROkt5VwTamOfuX3Ai/ZwV8PBiQAZbrgusyHA6Ik4ynn36K1tQUiwtzZGlKqVTBti362vg/iiKd\nV+kW6qxuv4NtWcS5RbVWxcuV3ePWVlvR/4CoFBGEAWCxvb3BudOnqVYrHD68n1uOH1f/znNJxiPl\nFOY6OJriFKdjqtUqsMv/NGwEIRX7Qam6VNea53bRKU9i8q8l4DGXgVfMh2ZYK0acY2lBidDLGctW\nSjLHcXFd9TD2ukPVDbkOMhdYEjzXo1ark+oFk+N7nDxxkrIf0h8OVM9iW8TjEVONGtVymeXFJfr9\nvgppqNfpD0YIKdlY32Q8HLOwuMBWe4ul5WWuu/F6rqxvYNkuf/THf8KJk6eIyg3cqE5zZp7WVIsg\n9EnimKgUgZDs27uf6669Vo/ggl7cYbu9xZW1deIkplavUYpKBJHP5uoFer0dLAT1Vpnvv+se5ubm\n8AzjR+S8+Y238tDDjzBdr9If9BnsbCvDqzxn1OnxW//lN7h4/jzDwYB/euCfePjpZ5CWRalSYX5h\nnlKlTMP1WdyzBO02Zy9cYO++fdiOw4XLq2RZTOQHzC3OKa/sJGa1u0EaZziWRa1SZXZmBte2GQ2G\ndC5vMugqL3K7HFFrNrCkJE1Stta3uO0NtzEcKLl8v79Nngua9RpX1td511130pyaYjiKIU1J0hGe\npVlbtsXBpXmwbSxLYCNIkjr7lvfw1rfcDkiuXLlCZ2eby5dX+dYjj3Dl8mUOHNjHtdcc4uabjzE/\nP0e7vanojI6F67usn1/Hdmze9/73aSiwr8QuZKQJhJFSsTbqDRW44flEpbLSLLhKWOW6DkLoJai+\nXw010TQztlZdm0MNlEVElgsgxwkUTp+IpIgIRCpxEdarQyhK7q4DgTWESm6BpTpuoamVoNWOrpLW\nY6l+3ZZg2Z4KQwZsRy2x8xwsHTC8s9NBShVDGMdxcQAaxkkQKAX2YDjAcWw1UWaqGI9GsW60LM2m\noZjSDVQyOQFMduQGTn2t63tWJNZbOwAAIABJREFUwFtTLeIsJbdt0lyClVFvTPHkE09yz913g9QJ\n5YM+zWZTc2WVIkqlmahOItJ4ZhRFEAteeOF5fN9THbuvMLFxPEJIBQ889NBDBEHAhz74QZaW9hSn\nXRKPkEIn0Y/y4g2VUmojm6sXi47jIJL4FTl/L6cHGY9vg4ObheOrXeaDmxTfFB8sisdqqITmawW+\n+tpI5RMeBgFYaEGOiy0kCHUTKyZERrVU5tlnnqVZrxO4Hpbn0O10qNVr9Lpdlm+7TfuGD3GcRPmb\n2yoIdnZmll6vx6kzpzh27Bgnz5xiaXkvqZD8/P/xv3Pd9TcSVupcd+0RLLdErVaj29shckNCx8O2\nbNzABQvSTJLrLX8Uhrizs+zdv59nn3uebq/H2sYG1VLIlfXL/NiP/ghHrjtMkoyZL0+rCWg4xPdd\n5Qi5uYlngUhjkqES4vieh3Rczp6/wPrKKtUgYqbW4MVnnwPbptKo0e336fYHbHR2+PaJk9i2Q73Z\n5OZbbuXE6dOsXrnMVLPB7MwMtVYTy3W4sHIRe5BSKZUJbYfZ+Xk6nS5jEbPT6/KNbzxEqVqh0+sq\nGG6nQzwaUalUeetb7yQMQ2ZmZ+j1ekpcFjbVPZMlTE/X2dq8QhIPWVxcZqfdpjU9p5bycUzcH+GH\nAZbrMBwOMD7vZsHtui6tVpNms86Bgwcole7m1KlTfO3Bf+ab33qEf/jqA+xdXuIjH/kxHNsmSxMe\ne+wxwjDkzXfcARIGg6EKNkCwC+lJ3vGOtzMcDfVzpywnDM5tONxB4BaL+8l72uDNlj1hWjexJ7K9\n3eJsSrvM1VLTLIBfi1InESrEWLNqhBDkenkoi++tcWVLwYtSyiJJ0cICqYK7VaOkBICO6+l/q5as\nSsA1Lhox47xYrzeu2m+ZeuC5u8++oRoaeNS81izLGA5V9JpJn5qkBP9LOPj3jEb4yAN/wTCJCUsl\nhnFCVKnyZ3/+5+xs7/B9d72L0Av0QsXXKdt5sQg045L5/W5XRay5blB4n0ip6F+rl1cZDHpIKdi3\nbx/XH7megwcOkOjgYIMJGrP5STK967pqrNEuhuZmBq2u1CZRZnFmUnEM3jz5/w2WZaCVo2+46xXv\ny/NPfA3YDXeeVGAKIYoGxGyqDUY+SStUMmx109vKUw2wyAFhWwgLuv0BX/j85ynpRBI7UFi5LQVH\nrj2sTKcOX6O6iFxx9Cv1uhJtSH3zOQ6XVlaJs5yTp8/y5b/+G8rVJo2pGeYXlgjCEjiaqiUEtm3h\ne57y28BC5EJFSdlqWkhFgqKXZYyTseZSD9hpb1KrlPnQv/kgg36HSrnMqN3B0y54vV4PL1Cy/M98\n9rOsra8VQqZev088VnjvLTcfZ++eJZCS7zz+uJrGbQfLU8vzZqvF0lyLF55/iTTLqFTrSAsWFxdp\ntVp6DM/pd3aUl05Yo1wqkQnBeruNQDJIxgyTmOb0NLg2YalEr98nynPWVlaZm5sjCEK2Nje55ZZb\n1XJPKuGQSQe6dHmF0WBIvT7F9PQsnuPjub4y1bJtJX20JLlU76kRqRhYxNy7k4ttcy9tb29z/txZ\nOp0dLl06x/59e2lNNZluTXPNwYNkmvbnOq6OI9u1ckjTDMuy2d7eZmpqCinVcrBWrxdBCkb3MKlL\nAK5qhgzX3PyeEEIX7Fc2NY61ywox/739HT/8ir/3nW/+NZa1KznfXfIrls1kw6Rw5bH+e+bvG3hF\ni4m0VYBl29i2LOrNJCvETNaTkKqBpK6yw56Adsy/MxDMpOpS0S2zYm9miAeWZb1uqPHrduAXL17k\nox/9KOvr61iWxU//9E/zsz/7s/zSL/0Sn/vc55iZmQHgV3/1V3nf+94HwK/92q/xh3/4hziOw2/9\n1m9xzz33vOrXHozHOL5HmgukpRZjTz75FPfcfQ+uq1JY1A+jfEpc12M8HtNu7zAYKDjFRKWp5V2J\nvt7iC5Fx9uxZLl26xN69y/zERz5CqaxGn0qlQhzH1OtKGWUK7CTTY/IUjeMYkeXFIrDY1qcptqs6\nYcMvh10PE/OBxnFcfJjmw3ktfwNTuI1UvviQXKMs2z2RjWDASOPNiS7VtgeBxHX94vsmcYLtueS2\nxcOPPKz9PiRhFJIhiLOMwHPZ2dnh7ne9CwupFjwohs1me5vzF85TjQKuvfZ6sGz2H7yGr/zt/Tzw\n4DewvQpLy4exvYjp2b2srK4yNVOl0+0wNzvHoNcjiKrEaYbIcq1I00ubFNygTprGjMZd6tUmZzde\nYna6yekXnmffws18458e5OabbmJ2zyzR7AL9QZ9nnn2WXOQ8+LWv4WjfmUTbCGBZNKaabG3sUK/V\nuHRplXvufg+OhHd9391E5RL33X8fjz/xBPFoTL/T4YHnnuItb30bp0+f5sUTL9FsNhnGCSdOniLw\nPRzbZmamxbmLT7Lv+FGs0TYzrSmysksljDg0dZCVcxdYaE6zsXIZq59SilPacYfW3DT1qTpZkjIz\nO83ltRUWF/YwHo31wat4/nNzs3zzoW9h2w4LCwuEvhKMWAI1QSF0NJmN77pI7UYpshwbq/i8FbY9\nKPQK3W6PRqNOdN11ZHnKdYcP8sjDD9GoVtm3d6/a5+SCRr3OZrtNVN7lL6t72UYICsGO67o6FER1\nmlJKHb6Rv2axMV9r8jJc7Fe7JiGFyebp5Zf5fdPwFM+xVk1OTsjq2Vb4+O5LMYegzrO0FTFByhzP\nC67iz5vn37JUpNrkpGHwcyPeM3XA2GiYZ7egKOqao/J1RVHQkyQpfv1L1+sWcM/z+MxnPsPx48fp\n9/u84Q1v4O6778ayLD7xiU/wiU984qq///zzz/Onf/qnPP/886ysrPDud7+bEydOvOoYUCpVsFyH\nTr9Pc3qahx/5NseOHUMiWb28ihSSWrlSjBdpmjIcjcizDM/zC/yp0+2qpeZgQDwcEEUhfhBw/bWH\n+ciHP0StVlNS2TynFITILMWzVUSZbSsRjFkkTNJ4DEziOA5OqFLLJ+ELKSVpnhUJGpNGVUYQMfmh\nmQ7fsqwCZnn5ZYqyyYE0N4Hrusq7YQIbN4eOucnN6wX1oEtLIjOh+efK6EraFp7vcv78eRxLKQ6D\nMCRJY5I0Yd/SHvqdHSxLZRgKIZGWwhdrtRrHjh3DypXb2sWVizz0yKN8/Zvfpj41zw1Hj9CcnsX3\ny2x3RszN7aefdClVGvSGMWFUpTdM8V0X2/XAcpCuJJNqd5D0Bbbj4TohaZoxNzPL5vol4tEA8pTt\njR3+7Et/yvZWm/3X7GUwGBBFEeVqhT179rCwuMgbHIe//puvMByOkLZaYFWqVcbjmBfPvsjq6iq3\nveGN+K6HJeHNt9/BmTNnGY/HTFVqOHv38vRTT3HTsWMkSUZ7exvHUaPxoDdA5DmdTpcj119Pv90l\nSVJeevo5RJYzPz3LysWL1CsVrjl4EMe2VGSatNh76ACOq8Jvu8M+aZKRp+rzrZSrOLZTdHRXNta5\n5pqDXLpwiVKpxNLiElkuqNebjOOx9oj2yfKceDTGQiUjWZaatjLNhrJti1IQ4ujnuDI/R3t7m1Kp\nxHDYV/uFIOS6a6/FElIrGEP6nR6Nap2hzjOdHOmjKKRcLhXQQRyrKLZavaFpiWqBb3jSu0VSFr+E\n7m6LX5oPPhmnUFwT7JDXY26p6XZXKDdJxTO5kpPPjecFE99id+JWIkAzHSi7AlN8Jz31q9VqsQcz\nXbaZdCYni5fDPpMajkmxTrlcLv6NORwmodfXu163gM/PzzM/Pw8o+syRI0dYWVm56gefvL785S/z\n4Q9/GM/z2L9/P9dccw2PPvood9xxxyu/uG0RJ4lyIfN9zp07x9GbbiLPhfISsSVDrYw0p5jrumR5\nzuUrVzhz5gx5njM9Pc3S0hI33HADSwvTBIH2InZcpBS021v4vodtK8BLCFlk/pmCPGkcZX7PcGWF\nECq1FYo31+DeeXJ1UTXdx+TYZJYQpnC/HgY+qdo0hwfoLtVWUVimcJuxzXzY5jOJ45hc5Mo/3FYA\nitA3pOV6RaScG4TKmS1JVOjscIzn+7zlLW8p1HWG3pcLQS6V/3icpkSVCpcur/GtRx5ndmEfy/sO\nU65N4XhV4kwSlhtsbncIqq76nklCjouU4AUVsiQlxyJPUlzXBunguj4ij4miEiLvkaVjLJnxif/5\n35LHMc1aAwcl+gnrYcHIyUTOzk6XK+trnD1/nuFwRJrlDIZDms0mtuMjrZRGY4rPf/4LvPXNd5Kn\nCU8//TTHjx/nIz/2YV548UUe+85jOFhM1Ws8/cQT3HDDjbwYJ/S3t9UCW6iF15VLl/n+976frD/m\nq488wJtuv50//pMv8qaP/w8MOj2eeeFZvvrQNzh4zUH2HthHnMSEp59DJimubTPVapElqiicOX2W\nd77znVTKEaPRmEFvCFi61Ek63R1mZ2Z0R5cAufbZUcrVMFA01tFoVEyIeZZSq9XI8txAvCSJ8lyv\nVCokyRjPsbECnyltYiXynCgIiccxnuMSj8c4vl7oTSzY4jimVFJiqHJZpfWYAuy6tmafwG7BRj93\nppBbVxU5LFFQBK++do2drir2r9HZ7xY6NSUUDDCUL47UVF8wXb1xARQT3b/Ur9Uk+whcFzwdXmG6\n70kHQQMXGfjLdNTFwnaiiStqia4LkyLASXh1su4U7JvXuf7VS8xz587xxBNPcMcdd/DQQw/x27/9\n23z+85/njW98I5/+9KdpNBqsrq5eVayXlpaKgv/yazgc4vgevu/hWjbtrS1kLsjTlEzEVCpV0iQl\nHseY7L5eT3k+T01N8UM/eC+tVoupqaldrC9VIgXbVvQz13OoVpTfRpxkV8EYWE5hsWpufkPdK3Lp\ndDEWmTr9jc2osWUtlpkTXM9JSbH5UK7CsScK7ssvc9qaD9/ciGmaFnLll/NGzag1KRCyXR3HlktE\nmml7UBdsm3igJg7X8xiOx3iuS3/Yp1qtsNNuc+Kll/Bdj/m5WcIwKPwfpFDmYNL1SIXFX/zVVzh0\n7VGiSpOw0iTDg1QiLRdbWNSmpknzPlkmCMIyWS6oVOra30XdeFGlQjoeIxFImSGl6kxHww7nzpzg\nv//YjxO6Fp0kpr25Qa/TJ88ETsmjVCoTRiWazSat1jRzC4vc8Za38tyzLxBVyqyvb9DpdIhKZbWU\nK0XsbG3zqV/5FQ7sO0gUekxPT/OlL32JT33qU+RZxv1fvQ+R57TqDS6dP8edd9zOE088QafTVUwf\nx2F5cZG11ct87duPsL29jXjmCd73Qz/An//VX/Kxj36UyysXGXV6/E8f+zg3HT0KUrK+tY6Qklql\nSqPRZNhXobdPP/EE337kOxzcf5CZmVk21rdp97a4vL6KzAWlSHnOGy8O13GxbFQCu+2QJGNsqXy9\npZAgc+UxnsYq/CNOcF3FXhn0+8qmWarotW5nh+uuvY4sTYnKAYPBgFJUUpCOYyO0OjOHojs05lyB\np9SklUoF9AFtWWrJjlSsLCbwc1NMi44eiW0p0yqp/6eej92gbWMfYZ6F11voKbqmr5+5XSsLV38d\nNf3apKlaJlqGjmg5mhFjnh1JHCtzOqPKNIrYLJNUKo0CDVDKzmxCYQ1xnF3lQPpyGNTUg0l4xVwG\nQjHF3FhnGMLGa13/qgLe7/f5kR/5ET772c9SqVT4mZ/5GX7xF38RgF/4hV/g537u5/iDP/iDV/23\nr4Vb1Wo1xmlClib83u/8LvFoxNNPPUmlopKawzBkdnaOxcUFLV9VhbFSqaobR0uRk0QVBCkFIovB\nBs9SNzqWct2zHQcHtfwTuiBatiqEJo0+yzLG43ERDGxed5IklKNS0YGYwj1ZMCdvVLVM1QEB+gAw\nnYTJB3ytDnyyOzGneXEwuB7S2r2ZXz4WmtcshCATOeM4VpiorR7gDHWQmG4h11zrXB8+YamMyAUf\n/ehH2VzfIE8T4tGYNBdstreYnpmlVK7guBG//Ml/j+tXqNanCSoNLCfAdkJsV3GehZT4jkMmLMql\nimISqAwJjEui0OHKaJWaSIdEkcew32dz8wo/9VMfw7MFrmMxNz+La7mMhjG+6yMcW3WLgY/juORI\n4jgDK6bRbJHmOdPTs1y4cImt9g5CCGqVKv3hkAsXL/H+7/8BLl+8wK//+n9gZeUSv/qrv8rb3vY2\n2hsbHDxwgPPnLzAz3eLC2dPcecftnD13ltOnzhD5AdISOAhuOn6Mr3/jG5y/eIFKRUXn7WxtcfOR\no1w8eZZPf+rfc+ymm/jwh36U6uw0aZ4jcli7so5t2YR+wO23v4UbjxyjXC5z/vx5Fm5ZJMlH/Ncv\n/lf2Li1RrVbZ2tqk1ZpCyryAc/JcOedFfqDyFLO8MEoy918yVruXVP/9cinCcW0yRyl0K5UyYeAz\nHPTxHA/f9VQIsOvhBz75xFJxkl0RJ8oGYHNrA9uxtK4hYjDoI6XHZDiwMYcqGCiWKqSG/ns1fmwh\nX8bznlwevh6ubl1VhAG0FD7b7Xh3n1MlLrJt8zwoAyrbQScJuXqSsIpibeLihFCWHuOxohWbztw0\nXkmyyzozcOjk824gFENBnFxsAgVrbfJnrtVqr/lzw7+igKdpygc/+EF+4id+gh/6IWUko6TU6vr4\nxz/OvffeC8CePXu4ePFi8WeXLl1iz549r/p1P/s7/5d6A3yfW289xk/+5Ec1o8ItsvUApGaUOI5b\ndMbodIzA84oRTUEKu1BCLiSjfp/BYEgUhVdBEzkWJgvQfAjlchnjRGeYHYamOOwPXkH/yfMcy7GL\nJaIxwqrX63pUTej1egWubvy5X28sMiewCSh2Xbcw3HJsR8W3aac8c2KbE9/cREEQ4Nk+qcy1URhY\njoPvOHQGfXb0cthgrmY/EJVKuI5V4HqerRLrXd9m7969JGnGCy+8yFMvnGUwyth/zRFqU7MIy8cJ\nynQ6PapBRK+3w/T0FJdXV7jm8D7aW9uEQUie5viu9lAWOY7lIvMY17FwPRvPd1lfW6G9eZkPfuBe\nWo0W3c4mSZLjBGr5KiwbXE8VrED5x49GMba287Qsh2azxYNf+zoAluUq9ovv4nkB1VqN02fP8J/+\n4+9y0403Um3UuW3PIs8995xawkUREkmajdm/f5lz587y3DPf5ebjx6lVS5w5c47NrS3OnDnB3j0H\nODK3h+dfeIGzzz7HB9/33/Hlv/wL3vGOd3D98Zs4cfIEX3viUR569rv8bz/3v3Dj9UdUgR0McTwP\nz3EZDoaEocpMXF5eViyOSovVlVUW5xc4ceIE+5b3MhwOqWi3S8tSLo4Si1zklMphsQjLhRq9h70+\n5XIZB2cChlDq58DzSTN1f/uez9hx6OjJ1nVVIWc0VNS8iYW7elZdSmGFwWDAzMwMOzs7zM3Nc/bs\nGfbv31+kPeV5VhRCA12YLtV0x6aAqWWehRBWcUgYGMLS+PVkI/Naz83kVeyILBvLsotiagqpbTlX\nTd6OaxW1zgSWF3CmtcvmMa9BBYaMrpqkTQNlOufJQjxZyE1dMaQHM8Wb73HixAlWr2zw0okzV73m\n17pel0YopeRjH/sYrVaLz3zmM8XvX758mYWFBQA+85nP8Nhjj/HFL36R559/nh//8R/n0UcfLZaY\np06dekUXblkW33jg/1HFzFL4kB8EhRRZ7R4UhiuyXBeasUpq1+yJwrDJUTdMkiR4vnELU4kpZqlo\nbhZTQM1Y1u12ixPWnLaGU2uKOUCeZldhYAZmidOkcBYzOH0cx4RhWMAtZhQcDoeFAlNKyfHb737F\n+/3Ck1/HyLYnX6uUUpMBX3ljTC48zJ+N4xhcZdVLLhBSIm2HTApOnj3D395/P+UwQqRKeDAaj5mf\nnaVRrfDxn/pJxoMhMs9IsxxpqYRuFRYb8Jv/6Ytc2Wxz081vIBUuXlghySReGBHHY0qhCnJoNhr0\ne13AwkYFygZaZek6NkLExPGQIHABwcnnvsvm5jppMubWm48ic+XmlqeZVvPZhWo01lxkCwvbVZmL\nYGE7Ls+/9CKdjlowrq2vE4Qenc4OS0tLdHc6LC8tcurUKTzXoVlvsDg/xw03HEEKyTe+9QCVaoXO\ndpuZmSm+/cjDHDp0iKmpKa655jCO63Lh0gqrq5dplmdo1BskaYq0YO+B/Tiuy1PPPMvWTptz586D\nrYqsk6S8/c47+fCHP0ytWsPzfKUr0N7byg5Ww3uRx1/+1V/S3tqgFEWUo4hbjh8vCq4KA7PIc7UA\nG42GV+1sDNXW0PpMM2BhkSY5tmuodMq4Te2fzjMYDLh0aQXX9XjX930fyIlFoL6M7S6ofzcaDen1\n+kzp+MGyTtIyUJ/pIl/etLwc0zbLQ1MbzPMp5a4rp2lY3njnD7ziuXn063911ZKz+NqWRZYZCMUt\n3h+RCdIsLRTTwFVwqCq26ueRWAU12RRwU3tMbTHNHexOzeY9MM3eJDxioFxz8ExSPc10D+pA8X2f\nN7z5Pa85gbxuB/7QQw/xhS98gWPHjnHLLbcAijL4pS99iSeffBLLsjhw4AC///u/D8ANN9zAj/6o\nMoByXZff+73fe00IJcsyxqMRLW3W33AajAwvtvhQJVEY0ukMKJVKpFnMYNi7ymM7jkcF37o3HO12\nx/rNMMZWQggyacYsi2pJObXB1RxNs0AwHW0URXgVt1j+mTGnXC5TtitF95NlGdWqysw0BwfsepsE\nQVAU2td6T3q9XsEBNRSlArZxVL7m5MMgpSz+TbfbVWG8jQZRuUSSK1c0tNE/lpbZF+9JSjwcARZo\nhVm/3+dzn/sce+YXKIUBjuNSb04RVUrUG00yYfHIo4/zgz/8b5COh+V4JJmgMT3LuXNnmZubJR4P\nKZVCVlYvUC/VKJfLDIcjvCBkOBwQeC6WqxRuUeSzdnmFx7/7GKGTsLG2jsgyHmxvsX5ltZjEwjDE\nspVfN7bF8ux0wYFPspxOt6uVfBZ+FNHvDfACM5mkINVh3B/0eenESbIsZThI6Ha7PPPs0/zZX/w5\n1VKZsObyzne+HUGVJE24461v5sK5c1RqS3z3qcc5fO217D24TKkWMehkXFi7xNR0i4WFBXpDlST0\n5FNPEY9jIm2KVSlXKFcsHvv2wzz+2Lf5d//rv+PYsZvZ3u5SrVb1ZymIk4GGIoYcPHiQ5559hqU9\ni/iuiv3L85xeT+0ASpWKgpMClzD0Nfd4jOu6DId9QGVOSqlShWwbujtd6rUmQmZ0uh2mppp0Ojts\nbGwQRCEPf/sRPDfg3nvvRUiJO6EtUIs21ayY+3E4VCrdPM/Z3Nxk37599Pt9oigqDLMmL1OAzPJ5\nVzK+292+nC5oQqqvWny+yiWEKCZRc3CoBbzigpsmynwfZeDlaHLDpLEVRQRis9lURdXe9TU3r99A\noyZFfmtri1arRZYJtre3sW2bPXuUSLDf79PpdJQidwLHn2zAzOFhJhAD5UwuOF/r+p4JeR6474v4\nvs9wOGR6erpIwjHbXdA/ZC4LBzGzncZSQhDb0V2JOdFtiyAMiu45z3IwS0SU3zD6v3mmIBrXUYsM\nCwvb2eVYgzLDcT2PNEmLRQmaV+oHvjIrQi0IRa79jicOA/PBKCqWXXQTtm1zwy3veMX78tzj/zTB\niFHwkDA/m9C/MEZWtu5UnGLkDMMIkIzjMbajOl/Pc8Gyle+MyDm/ssrf3ncfjqU7AiEY9LvMz80S\n+S4/+2//R7o7HRwLRcFyHPrDEQKbP/m//5wXL/Y4dPh6qvVZLK/EYJjiBT7j8QjPsymHqkOuVspk\nuXKFS5ME33VwHQvHksg8od1eZ+3KJc6dP8M4HtLevKIWtY7FqD+kWa/R7/eKhWsuhToERiO8dKQO\n+TzHdjwl5XY9sjwrPM8t22E0HhOPh0RhxGg8UP7orstoNKIUBTiuS1SOOHDwgO5SU1pTDS0Zr3Hp\n4kUcz2H/vn1kecbG1hb1RgPbdujvxCwvLfPoY49RqVR40x138JW//gp3v+c9/OM//mORj1guVwg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Z7nE2VJ432EUrXVb7sRGOmJQyllUVCUeROvYO5ptLg3F3taRki12JQ0MWLRV8bAVIvc8Kqq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\nUZs48IlNbGITu0Zt4sAnNrGJTewatf8Hq9sL34bnPZ4AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4b4d390>"
+        "<matplotlib.figure.Figure at 0x10e077650>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -700,7 +724,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def nms_detections(dets, overlap=0.5):\n",
+      "def nms_detections(dets, overlap=0.3):\n",
       "    \"\"\"\n",
       "    Non-maximum suppression: Greedily select high-scoring detections and\n",
       "    skip detections that are significantly covered by a previously\n",
@@ -714,63 +738,54 @@
       "    dets: ndarray\n",
       "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
       "    overlap: float\n",
-      "        minimum overlap ratio (0.5 default)\n",
+      "        minimum overlap ratio (0.3 default)\n",
       "\n",
       "    Output\n",
       "    ------\n",
       "    dets: ndarray\n",
       "        remaining after suppression.\n",
       "    \"\"\"\n",
-      "    if np.shape(dets)[0] < 1:\n",
-      "        return dets\n",
-      "\n",
       "    x1 = dets[:, 0]\n",
       "    y1 = dets[:, 1]\n",
       "    x2 = dets[:, 2]\n",
       "    y2 = dets[:, 3]\n",
+      "    ind = np.argsort(dets[:, 4])\n",
       "\n",
       "    w = x2 - x1\n",
       "    h = y2 - y1\n",
-      "    area = w * h\n",
-      "\n",
-      "    s = dets[:, 4]\n",
-      "    ind = np.argsort(s)\n",
+      "    area = (w * h).astype(float)\n",
       "\n",
       "    pick = []\n",
-      "    counter = 0\n",
       "    while len(ind) > 0:\n",
-      "        last = len(ind) - 1\n",
-      "        i = ind[last]\n",
+      "        i = ind[-1]\n",
       "        pick.append(i)\n",
-      "        counter += 1\n",
+      "        ind = ind[:-1]\n",
       "\n",
-      "        xx1 = np.maximum(x1[i], x1[ind[:last]])\n",
-      "        yy1 = np.maximum(y1[i], y1[ind[:last]])\n",
-      "        xx2 = np.minimum(x2[i], x2[ind[:last]])\n",
-      "        yy2 = np.minimum(y2[i], y2[ind[:last]])\n",
+      "        xx1 = np.maximum(x1[i], x1[ind])\n",
+      "        yy1 = np.maximum(y1[i], y1[ind])\n",
+      "        xx2 = np.minimum(x2[i], x2[ind])\n",
+      "        yy2 = np.minimum(y2[i], y2[ind])\n",
       "\n",
-      "        w = np.maximum(0., xx2 - xx1 + 1)\n",
-      "        h = np.maximum(0., yy2 - yy1 + 1)\n",
+      "        w = np.maximum(0., xx2 - xx1)\n",
+      "        h = np.maximum(0., yy2 - yy1)\n",
       "\n",
-      "        o = w * h / area[ind[:last]]\n",
+      "        wh = w * h\n",
+      "        o = wh / (area[i] + area[ind] - wh)\n",
       "\n",
-      "        to_delete = np.concatenate(\n",
-      "            (np.nonzero(o > overlap)[0], np.array([last])))\n",
-      "        ind = np.delete(ind, to_delete)\n",
+      "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
       "\n",
       "    return dets[pick, :]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 19
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "scores = predictions_df['bicycle']\n",
-      "scores += scores.min()\n",
       "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
       "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
       "nms_dets = nms_detections(dets)"
@@ -778,7 +793,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 9
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -806,32 +821,40 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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YNiYbPD06ZLK5yaAoOZ+es7O3R9s6gnOYImfv+lWiNXzwwTts7Vzhnfffo8wy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WiUZqPFKM/4oXf+AH//H/wKd1vgI9hYXSf2G3SvX6W1sIDv+8y0LGzbYqY1j+/5RHFMr9djc6eD\nAOo1j3Z7kXq9xs7mLillatLtfp/FhTn+m5//ef7R3/279NbXKdIE4VpYtjURA67fRdd/GrdmcvrT\nnI5mfvRpieqnZSPU58Kkx/3Wwj+Z5U8fN+tncopmPU1ko/tFf7cs54B5WnVWjoX422dh2+9/S4AU\nqFJ9IwRYlsOgPyKNC65cvEKepDiuzWDrJpvXLiKSmMZCkyItQLk02nM0j9/GKI7YXt/h5EMPcOHV\nr7F64yozro3VbDLKUkKVEecFUV6wdGSZ5twcN26uUa/VcKVka2Od7voGqWPTWJrDsSX1RoOl204y\nLCRvevvbWZ6f477HHqG9OMenP/r7rK2ucedD99Prb+M0AvJejLQkloK1KOQjn/o4f+rYI2x+7Tmy\nw0vMnDlBN43oOxZHTt5B9ysXOPXogySNlEOHlrmxu8nf/l9/iV/6pf+Ztlcj3OmwVG+QFxFhFFJv\nN4mS+MAm3KX8TIuxnnT+6b6anZ0lTVPStAQ9SZKQ5yVNYQIwy7LwPG8fdZvOTxPQmP1qKk8z3YNJ\nm5qKvSrHQpQOfI2Yy3ta6F2ItJVR6o3JtLi6PlXwYFIzpnWi71eVd9PiVSqfADpQbiNpWXJ/EjF3\nt9LAFMrIuyAIJo7dqryOW6rlE8pGKTVhlmhBqC7TNge/Lvr/MBzi2A4LrSZu3WUUDSEasDjfojEz\nS3O2SXe3y0AlDEfFXuPtoIRACEm9ViOot6kHPkIoRlFMb7BDHqVkRYHl2dxx+ASpSvmR97yPv/cr\nv8Kf+Yn3UfNrFGmCLCS2lFgVblC/lxZQHXUzTVnto8y983VebvN3U2FP28RA/2byd2Z7V5Gu6YQ0\n6ySlnJgAyuPFRJ/p8/R1pnO2mrfbRDnV+H/HObiH4jT6TQiBkmUIoRKy3D1J2gglmJub41Of+GPS\nJOHwyjJ5knP1wjksu6AZ+ET9LklekMiAmaVllhoNNrd3eeqpMwzDIZdvXkFkMYUQuLaD22iw2+8w\nyhV2UMNpNLly/SYqy2nNNRj1+wz7XRq+i6cUvfV1VlaWue3oMWaXV7Bm52nMNPnGpYscO3KIzvo2\nLhZZnLB66Sr12Tpvf/xJnn3mC2zu7NDd6eA6gtl6C1mz2Y02uXH1HCdvb+O7NbqDEOYbbDQEauMm\nMvbZ3YxxA4e/9DM/y8c//imOrKzw5kcfYmMU4qkCrx6QZglJGuM63gHka07GZnubE7kug8FgX1EH\nQWDIxWREWVEUDIfDCdkYK7eD0Sam41wfrzqyTcpjWqnKYil/DtWJqKzDGJyYv1UTrOnnmpONGSxg\nyug0mdXXjbl0/SnI8/HzNX2lmQYdOj1tK7Zqed03dIDp+87pBtFL7k1zzpw5zU51fJsiTBjs7nDP\nmXu4vnqNh2+/l+2dLmEaI+Oce07fTV9m2KJOkmSsra2xtbODJS2Gg3IBzXy7TZ4nJc/tOdgI8iyj\nSFLiUUhqZeTA/NEj/O//6B/wS7/wCzAaoYbhRG44s25aALUy10vHTZNNd5i5k4/JE+p7mkrO5Aer\nSFxfJ8Rk5EsVWVfvoetQRfdlX03Gmet7w+TAq5rUVSsCqqGT+YF3M99JU0Swh3AEKCVhL5zQsT3W\nVjf4wue+wOnb7mRzfY2FuQXUoE9OQpgqmo5NKCWDIqNR87j+6isUvkMUzhKtbqDSEN+yScIQx/MR\nnk9gzZOlIfVmk1QJBt0uzaBGb9gnjkeUiygLXClxsBFJyua164SZ4ql7HsC1bJAWcT/ia1/4Eovt\nJm9629v4vd/7Hd70pieJdzsIKYiSiJqysPoJjWadFzcv8fhPvpPPf/FL3Hb6JLPH7mD3Zofl+04z\n926Pdi/FSxSd7S2k9Oh3Yx668wE68ZAP//q/4s998P1IR2JlKU6aUvd80mIyLapJrZltXv4/iR6r\nqQ5MWkuHr5qORt/3D1h25XgeO8FN2Z8ECZOWmCkL1eit8cSgJtJXjBH2tAgZOfXeJi1iWgXVulUD\nAHQdoigyaJQx/613kCqdnNqv5+2PB90HZkRYkiQT2xreqrxuCtw0HTTSMxWOfqlqTHF15jMbOI1C\n6o5HUmTcfvttXL70WRYX5vD9gExYyDjntdfOEvsO4TDHsV2CWo0H7r8PIS2KQrG7s00Uh/R6fchz\nhGyQS0mtFmBLCUIR5RGBX+fQiaMIW/JPnn6an/zR99ByXchy0iRDSUCAJSTF3go2LAslgEKRJeWs\nK6xJ5FGdtExlDmOTzeTPq4qyigxMpaydOo6jhX26A9PMUGgeL5eoTw48sw/MumpHza18G/r8sh8p\n22h/B3VzCzkT+Sg8yyGhQAF5koGUREnMR37rdyF32Nrp0R8MsJCEg5DGQgsV9cnzgiRPmWm38V3J\n1uo2dqvOy199nnhjg8CzcV2BE/jY2CRRjHQ85pcWOHrbCdav30BZIyy/wWC0ydb1G8x5ATO1AFnk\n1KRPEWc4yqK7vctrr57FarZYXF7h8kuv0ApqvHruNY7ce4If/+kP8KmPf5z5uTlcx6KIYtrSwQ0C\nVJYzIOVmf5uo1+elLz5H4wcXWDl6Gztbu5x8+H5WXzjLYH2XuuvQj0LqQUB3p48SOY8++Cj/5z/4\nMH/2J/80dywu4Ht10lGI47nkSiEsUU6YhYKi2EsHYZW01F4/yz0nppmMTYhyoi0Vm1byCqWm+3NM\nsDAe6wfTr97qezUfT1EUEzy8Kd+W5SCl2Kdzxsr3IEVTnjsed6bsaqWq37vZbFEUOVlaOvKzfDz+\niqIApR25inq9vj9Gi0InntOrWkvrQ+f6d5xkgh7V76ctbm0NTCapO1heNwVuKgZdpnG05nfTpKh2\nopQSR+QkMgVbstiYwcpyrt28jswsbOEyv7CMfbSG8DzSbMhoOCSKQi5ffAnP82jOzFL3bdozTRbn\nZ+h1+wxHQ8IsJywiPNdjttGkWasDCtFXPHr6Yb6RfJWvvPoqDz7yIM0sx7UdRirF8cu0oa6QSMsi\nljDKUqQQ+JT5I6rK0FTApglpClk1rKrK5+k2MQWjvF8ZvjbZhgLLsg+0t1kfs5/GiPlg5sSDyajG\nSKfKYZr9Xg5AHWWkuXZ54HkardQyF2Xn5KrAyiSW3+DfPfMcF765xXzQotE6xI2dDlsXL1KMBPHm\nECUTdq0CIRRHZhoQh3i2Ytax2drZodfdxhUWar5N0GzBsMArHIapwqq1sGYXCPopjtNiRMpo9Soz\nsaCeRNiNnIWTR1BxQbLWw8bnyLETdMMBSb9D1Nni+o2rREnExeuX+JmTf4EkjTly4gg76xvMNWt0\nZmwGcUI7sciyHOG5vHb2PCcXjtG/eJPV7XWsx04jQo/44jb27Ap5lODGA7yaJPfBzQqayibciXnX\nA2/hlRfO88XBV3j/e97HXK1GlgwRriQpEhAFtiiwUFhIUIIciwyr3GuzSFGq5JZLWSkndR0FYobU\nWdb0hWu6jJWl2qdQTGu6Sv3pMm1DkaoTfxyZlZHnk36yMSAY10EjcBMg6ueaSn2/fnlOoQqkJfEs\nD1e5+3uR5nuRJqpQZLneIHxyJbllgZT5Pstg2+4eH59MUEd5nu8jbm3RmFbIrcrruqFDdcFJdSas\nmntVpW8ieCkljvSJsxyv7jPb9Dh+9DCbm2s8/tBjrN1Y55vnXsIJAmKVM9eaxXM9Fg8folarMQpD\nHNum2+1x8/oVCqWo12rMt2eoNWdLlDcKSaKEnc0t+oM+7fkW/sjje558M//wn3yY2dlZ7jt+G2EY\n0qjVGI1GSEuQSos0jhFS4LJnBu6t1pRwQCCrnL9pFppUSNUJY7aN2T5a0erBY963RFXpgfatLuPX\nwmaameOJ4WDscHlcHFD05qRi0iVV7hwO5iTX/RznOZ6wyIXCbTdJUsEXPvXH2F6dUCXMzc7gJCmW\n55J7LtJWxDlkScpMe5ZRnDOMOpw8cYI4GrG1dh1fFmRKMRr0SbKMZq0Nvk098Dh54jY2NjZIopAz\n991Lmqd0yLAPDVHhgJ3eFmk/YqExSzxnk9UcxHyD7to6szh8+YUXOX7qJJ/9wmf5mb/wXxKGEdKC\nN7/lLXzkX/8Wfq3O3ffey7nXzhGTIh1JFEYgBdbhZWq2jSsETRzymsvqRofbT9+NX8QMhODy6k0c\nV+DNtgicGlmc0JYFVm+H20+e4L//b3+OD/2lP8/tp47hJAkNKSniFFsqsCWZECgEqihKVA4oYyGK\nqWT0MVP2TDplzDMfdFxCOQmYvhmTyqiO78kIjnHKBPM8U+GalIq5uK3KT2vr0tQjZkCEDps1ZdNk\nCEzwtB+JsxcObeoyfd8gCPaPj527KUIc5Ps1DWTSyt+qvG5hhF/49O8fmIWrXKxWEmbYYBV5mtV3\npUOYxhS2RSoUZ199jRdfeIk3PfYk9aCGtG1szyfOU0Y7IUmcMBgMqTcaWFLiuA5SSMJwRBAE2LZN\nt9slyiW26yKFRd2v4bs+liUYjIYMoz6NVp3ZhSaf++LnedNTj7DUahLkgmbg049GpLJAKnCLcjPa\nXEpiW5TZ4orJ5EJ6oExydpNtdyurpTrp6TJGwdPvp5FydQBV27e8//i55m9VmqR0mk7mnJj2PtXj\n07hv87iUkoiCeiagKJD1Ov/y6d9k6/ouhVcnF4IgLcg7XYQrsPMUYQt64YB6u0mjOUunU9IrrVrA\nxvVLhN1NyEKk7VMUUGCB4+M2m5y4535mlhe5duMG99x9D760uHnlKs7uJk3bphW4XL92hTxLiQch\nyvG5+61v4nJ3GzFK6b9ykfrth3nt2iXWtzf5+x/+v4myGGFBkWb0Oh0+9yefoUgzwsGIK5cuYeUF\nPhbNZpPFpRVO33aKS5ev8cgPfD/R3CwjBX4kePOZ+3npuWfp7XTZTSP6jkQ4DjItE6zlNZdBGnHX\niVN89N9+jFMP381jp+5ixa8x2tykUQ9IREFmKXIpsZRE5nscN5MyabZ/FW1PQ96m09yUpSniecCC\n0+PdDN3Tz5y2mEYrdl0/Mw2sWaexPE9y4FUlOinTcl+R6ms0Xz0GNtq3M/mu+r56rYuuW/lX7E8U\nWuGbQM5sl7d833tvOW5eNwRebagxZzU5ePUCHnMzA2DqS+92OgSNOkopHEty9113cvalF9npbtEf\nOvT7fRzXxwl8Zv0Fjh8/vn//4bBPr9ej093FsizipKDemOP08kmS3CKKEjo7HbY31gnDcqHE/MIc\nc/MthGOxsbrJW578Hj77/Od4/4/9GHKUMBiOsFyHRKW4noObFlgF5KogzQs8x8ExIm1MpFAVvGrn\n6lLd+8/ks02TsDqgphXzuaaAmv1hWQcpEF1vM3OiEGJ/kVEVmZuDUn+0kJtWmLlxg1mUIyAriIYh\no8GQy988z5nTD+AuLZHakivPfZ2lZoPcUWxub5DlAr/V5u6HH6c3jFjrnqdVb4BVRkukwwGzNZvc\nsyBWFAVs9Ds4nmSliKiFI2YDn4X5FgEWF7+8xcb6dcIgwF9cKq2tJObCjRusnLyDr77yMk7g0b2+\nzr0ry1za3eWrL3yNv/O//RK5AMuxyVSG43ssHznCU297K19+9nnCOMVqN1BhRM3ycR2bjd4OJ2sW\nKkkYXlkjaLdIagFWGHL2hW8QD0OcKGXer2HPOESWoBgmrMwvMFA5y3WP7vY2P/6e9/Grv/Mb3Pzm\nZX7sne9kZWmFLB5SZCmWotz3VSiUtSdf+bi/dNHREubErpWSVsBViqzaz6ZMmUrOnBzSNEXvhmNa\nXlU0bsqU7/v7Y0grXBPs6es05WOOEbO+paU65sClnASUZqRI9X0bjZkDjl5df/1Xv3sYDicsXTPs\n16R7pq2InhizrxcC/+Jn/mBiVq1mNNMdoB1607KQVU20PMlxaz55UW7+gJR8+pnPEPh1Tp64fc95\nYTMIQ/JQke/tsC6FIKj5JVsm9ORQzsZZnpGmRbnKLaiBAqFK6iGMRnR7PYQNtmOBhNgp+NS/+yR/\n80N/GZlkOLZA2BLyHCsrcKTcQ+AglcAqJlNOTnPw6f9Nc1Y7dMwl0GZXmiaqieOQTwkAACAASURB\nVH6qirekCQ8upJgWtqXTYZqmr0mJmAuJyvtMcuvm4D9oXh9MZm9mTDTbY+jmBGHBjPT53d/7fV54\n/gVOnjiFaDRwaj5rZ89xyG+QZQk3wj5xAUvHbufwydNcXdsk8ANslXPhhS8zXLtMy0rxRUoRNJBR\njlAWm2R4h5Zp1JswjHno4Yc5fvoOzn796ywIj8zKiXd3sYYhXuCyGnYZWIJme56N6xv40sLKco4s\nLfI7n/0j3v/n/nPe/r1vL/N5C0VeFDiOhSUsojDkk3/wSSxpITzB+o3riM6QWi7oZwlpAT/46Nto\nOHV6yy3ORT1mI5uGJfmeJx7nynPfIBSKKyIi8m08ZWEnitSG1JbIAqRt4beb7A46vPraK/zE+36U\nlu9jxwmuKpCUYyYVZc5vqcaZ+Kooe5pVVUW81fPH4zafoAdMZD8t+kNHcAATSLZ63yqdaspz1Zot\niumWnin7+l5FkSKlhTAc6eZkUz6vjDcvisl2MieTasiiBkKmxa2VuakLiqLgTW//0e8+BF6NJqlG\nm0D5ojo0x+wYfZ5+wX3TyRVIUWB7Lp4QICWPPfIEH//Ev8V260jhMVObodlsESwG2Hbp4c2yjHA0\nIk5i8jTDDzwajQae59Hr9cj7HXq9Hba21rClTbPZotlssbg4x6HDy4zCAVs72+zubqN8h5/6qf+C\n3/3EJ3jfD/8Qju1CFCGVLDdidstnerLMoKfEGL0CEyaiHkBmu+gshSayrgpjFf1oNCvEZA6VMWfH\n/nXVATQZWVCurizPNRcOlehN7kXy6FLlK7WQTqNopCxXY5bvq/tV4jg6RE3tLR5JAQcrCOh3Ii6/\nco6GG9AddAmKjJsXNgn7fWqzBTPCRjkezZkW7aXDXLy+znZ3xH33HqOzsUouLGw/IB7F+IGHysWe\nGVyiqfpsG7uAleVD1Aq4/NJLBChkGBIVCUEtoDvsszuISOsud5+5nyuXrpLECckwxHYkv/VHz/Ff\n/62f474HHzRoCVUmSssy4jzH9QLmlpfY2drm5JGjuI7N1bPniHZHBLaNV3PY2d0iynt0O5tc314n\nbcyTL7TZ6u7QXlpEdXdp2mB5FiJX1AMf5Tgo1yIOYxpejeEo5MjiIexawP/y9/4v/vpf/RDzrgcI\nAsuBNMQChBTkxeRYNFNdVJW4GQqnP9W88rq/s6zYdzaWERqaQhV78dl6Cbm990yNuMFxDq4NGCvM\nSQenlnEzW2c1Oqo6Vqr0bEnNpCiV7J9r2/Z+W+ioqKLQiN1cXj8JvsyEdKXc51OfPflOYmJtzLTy\nulIo5qw3naua5ECnOfccx9lXMllehuCoPAUEyrJxXZdRGNGeWyKNFTdvbLB6fQen5uC4DrZl4bgu\nliWxbQvPD8Cy6fSHiMGILMtwPYe5+cPUggYAg/6AcDSi1+ugMxi5js2xY8cZxTHDbh9lObx66TIP\n3XGamrCwpEA6gnSP+3SSgrwoyk2bGZt52gNdLVUOsmqmmW1imreTpuNkfHgVdWikUFW6VdrD7ANT\nKSsl0Mn/YXL3FX3NNG6yfP7BPOoYu8GU/eOVDiipCLOcQW9Aqz6LF9RY7e+QbW8xWl0jseB6HDMn\nXHZrAQ/fdR9prkjChLl2mxvXrnHz8jnsLKE52yYVOb3hgFyN8C2HOE05dOI4QaPJzUuX+d53PEbU\n73P27Ku06jU8t057tskgT9jIQsIs4c6lu7jw0msMRwmNmRm2RyO+eeU8f/Xn/jr3P/wgaZ6X++eU\n88Pei5dy0R8M6Q9DVo4cY2N1i1qjgd9qkvUj6m5AbgnOnnuNt97/GN2tbfLrawwXC9yazSAOObbQ\nxq77uKR84+oFcinJZEwhJLkSKAHRYESjUSfqDkiGQ/7aX/wr/MlnnuHtb30T7SBAydIinHE8kjQt\nQ14r/LAupuIx5Wzaql+z7zUFYcpPVb7Na3QCuHEk1sGIrapSrqJ6LXP6N72M3ZTvKro16xYE9QPj\nsPyUeXfG58o9+ZcTIMykY8xnpWmGXiVq1rXaJt/OifldsZBHV7I6uM1ONRWU+TG9w67jIQrAQIGt\nmQZz83O8fPYVjh05ybFjR5lrLTDMQrq9Dtvb2+S9HM/zqNfrKARNzyOLE6JoRJKkFNmQ3U4XKS1q\ntRqe5+HWfGZmZ3FdlzAMGQ5HRGGC53gMopCHHnyI3/zNf8n8T36QkwuLuI5FmmeU+weBIyAVlHG5\nxvtWzbSqUGuh05y2plDMc6oI20QLVSEpzx3TJdWshlW+s3rMtB6qfVY1ZauI3hzk2qw2eUyzmPyh\nnQvsRoNPf+0zDJKM2cUWVr9D2O2x2JghtAsGcUK3P8BfaLO7s8NuJ2Tp8HEaMzNcvnQOR8WoPCEH\n/EaLzPIZ9LtsDkfMLS1h+zW217eYrbdoz7bZGY7wC+itrZM5Du5WQM+DkQ9zS4c49+JZHBz6ccJ9\njz/CxRtX+Ct/86/z0BMPEqf53nZ7lFkri/Jvmqa0WrOce+0CwzCklRdsbe0gdwV33nk316OCzo01\nBqMhKHjh7Dd42yNvYmdri9cunsefn+Hy1SusWzc5c/puTs6tsLGxxno0JJXQqs8QjSIyKXA8jyhL\nsAubeafOxrkr/OA7foBf/8i/4od++N14KysEwLA3wnMchD0JBnSfmBSGebxK41UnaP3dXHqu/5qg\nYrp8Ti62MZVy9T5CjPOLT6PsTHk05dO0eCefP5kDvTpB6OeXsjnms6vjpto2tm1RFAfXSVSV/a2o\nE11e1zjwagWr5pEWIK2oqlEqB/IXFxKV55CXuaJBgeNw972n+INP/BGPP/4o61fX2Vi/gbJtmq0W\nd9xxklqtRhTFhGFIvz9gNBoxGg3xPJ9ms4lnNxFAlMTEccHO9hZhGJaK3HWRliTwfer1Bq7rUBOS\nfq/PL/z83+aTv/8xFt/+NmpKICxRrvSLQnLAsu19BK/bpPp/tQOrlkg1Eb5uQzMsajwJjhfITCLp\ng6EB1QFiTqRmmx+kWab3cXUiMutbvsfkYqLqrimaY8/zHF+4hLsDvv7CyxDBfKHYublJI8/JSSBR\neJ5LHMDK/BwqjQg72wwti7XzrzIcdmgEEiuwiMIRmXCRbgN/RiKas2SBTy9OyVOFW/e4dvMGW1ev\nIJIUF8EgGpBHMbgztJpt1s5fZkEEDDoDlk4cRTg2hQWPPfEonbCLtP3yfQUUeUGudHiazWgUcvXq\nVZrNFnmmOHTsODeuXCHuj3DqNXbiIa4FIi9Y21xlc/Um9x47zo3eFt/42leZPbTCsQfPUNgS2Rkw\nrxz6lsXAtkiTBCtXCCFJs6SkBxsz2MJG5Tnrl6/zI9/3p3jhK1+n9oTDrOewODvDcBQiKz4nLVNV\nRVad3HV/VqNUzHQZ05SSqRinWXn6U/WzmIpd5zsx5WfsjBxbCRobmDSLWQ/z2ZY1KYd64wjz3TTd\nVw0gMOtf9W3lebofzmj6evS7WZaF67rfvblQqhy42hdqyzCZxIFZ33xJswghKFRpylhSIAXkFOR5\nzB2nT5B9fESU9lg53EIWbbrDlDhNWVu9geO6eK6H63osLc5jWzbD0Ygoiuh2dhHCwXFc6rUa9XpA\nrdEqww2jkG6nw6g/IPQz8txGOCGuLZFhxhf/6LO05+Z46eI5HnrgPmQcI8IET1gIR5CqgnLJ5mS4\nVtVxV91CzuzUKhrRilUXc4CV4U4lZ2cqYBMxmwNU16na9trxcivUoss0usdc7mya5tWkWKbQm/SS\nZVl4wufZz3yRmXqLo2dOc+4rLxAIm8C1oEhIez16/QHO8gI2Bb3ONjOuhZ2MiLdvEg13sWc8Wq0W\nfr1GlECe27iNFisnj9JcXuT6hYvk/YhabYbXLpxn9+o1FhybcDQg9SWNo0vsDvv0zl/B3h2Ry5R3\n/uC7OfbEw9SOLvMnn/hDSHMKu/RzSMp9K5ESW2i/T8FL33gRzwtwbQ/f8xlEI06eOMmLX3qWQyeO\n4M61iHd2sPOcVqPB2W++zIP3PMChpXlOHj3Gu971LvK6y8ZL53n1Sy9y7PhxmjWfbh5iSRdH2oRK\n4Tsu2JJhOMK2HYSQJN0h2SDk7Q8+ybNffJa7H7wbUfcJZjzq8fRd3k15qU7w0+RQg68q2Koi9Krs\n6vuYeYSqOYRMRW7mE5mmX0xkq5Os6WJGclUR9oEIqCnWqFlnM8hATzZV5/w0/WW2rSn737UUSnUF\n1K3SO1ZJfCFACN3hmq/diwO1fKSQWBZYKITIyIWi7vs8/vjDfOXLz3LnidvJopRm6zCzMw1qS3Us\ny2Y4GjIaRaxtb+8pTYd2u82R5RXcYIbhcER/MGBra5vBcIDnusw0Z7j33vvxXI9Op8Pu7g6DsE8/\nimg4Dp60uf3OO/mNj/0bgkbAwydP4Yq83HxXSoq8KDeFqKBSz/PGdIphDsLk6rSqealDnEykUuZf\nGLe55pQnObeDA9JEQybVYUYNmYtvqmXaYDVXmJlcoZSSNI0rpnmZG7y0rMqoDfaS4UdxzLXLV1ho\nzxHMNIjimJbtIYqYFEBYzDQa2POLRGEIecGhxWWuXbyACvsstQIUKZvr15hbPILj1vGEx+KhwzSP\nLNNNRxw5eoTFO+7m2vnz3Lh+A4YDfMdmNBrhNWbpKwXSwk0LVvwZ3vE972AjU1xdW+X2mRlaXpPh\n7oC0lWNbe2a1nrD23rHfH7Czs0O/N2Judp44HjCz2KZz9ToPPPAQn/vy57nz3lPcTGKy7oDusE8W\nKrZ2t/FqNb7vnd/P/MICF3fWWF1bpSlt5Chmbr5NrxCko5Sm20RJRUaBZdkQWGQIAjfAsxwCy2Lt\nm5f4z37kvXz0s58kmXE5urhITZQblYhxdRF7udrVnm9C5QUIjH4U+w7KMdLW6JkDcmcqrFIu2N8c\nBdSBNNJVftkEOVXkPgYeegWo6ewfjx/T6Wrb9kTyPABVjHUO+3lVoMjVnjxStomaDEDQ76rHgtZd\nuh1M9G6CJDN6bJovrFpetzDCz/7R7+03tNmZVbRXLv9m4jf9vYretHNCF6UUSpROnDhL+dVfe5rv\n+4F3keU5vfXS6WDZEscr80BYltwLrs9xpI1juaRJiu2C45QmTb1e31d0w0EftddZWpkFvk+apkRp\nubVamCbMtJpcv3aJxx95kPlmDZkliCKDvYXM+n320cJebmxUgSjjFikUKHWQE5/G7+lwPyklruvu\nm5VwMOa1qrT1p8pzmoinin7Me5p1qypw0zwsaZNxqs48T/cmp/GErcNI0zRFMuYsL+52+crnn8dP\nJdHmgLDbRYiMzrBDL4zY7oW05o/i+U1Gw3V8xycdDLCHm7SdhKKI2YkSeli0l1ewKEgGfXJ3Ebqb\nDDavUtRchpaDJGDe8bnt0AK7URdch2ZtDlsdojabYSWrHE4cnFGDS0pwvWlzY3WDk16b/+5vfYjd\nuQ4qtXCUUypwY+K9fn2VL3zxORaXDuO6DfygTjcasdCeRXU7bL/2GkU8xGq6XLx2mfDaNu0iIK3X\n+ODf+R+YX1kh7g64dOES0eU17lA+jaRAzgas2zkbRUrk11mPU+bmZiEZkRZpuby7yAksC5kpHNcj\nkw6i2eAbFy9w4o7jnD4yR8tycMIYK8/K3Y9cSWZLCmEhcoFdSCSQqHHSJZNWM5WmBlzayT2BWsVe\n4iAUgvE4zyrhfqblN032TItumr/GLFU/UXVMjf8eDOtVanIji7Hsj4GO+cxpaPvbqV1TwT/y1A/c\n8vzXNQrFRHgaQeqPLkmS7Dd2lk2GwI1pB31sMqbUsiyUENiuQzrMqdVqXLx4kdZsi7vueoSaXyfN\nMjq9Dp1ehyxPyfOMRqNOu9nGsWx6vT67nW263ZJSsSyLRqNBrebTnm1Tr9VQShGGIYPBgN1OB8dx\n8PyA+dkWSZay2+1w9NBRPve5z/Nnfvx9jLIUgShzVjtynwPL84xCFXtJcUp0g5BIBOXauEmzU0o5\ngc612QblJq8avVTN2yrHWJ1E9exvOleqqEJfD+yvJpvsk0nuu6rIzXspNU7gpc/TCzlWV1c5cuQI\n6+vrzM3NofICt3Ucq3ENK8zpDjsMtjssLbVwhMS1BA+fuR8nmOXK1XUax2rENzsMN6+zOO+wNdol\nHSryxKGwJVGsqNeatBrLWKebXH1+m6C2wGC0w/xKwGZvi7RxiIudAY12G98rePXVZ3HtFYKGxLVG\njIIWSV+yldusbo948exXefOf/ynC2i6jBHxlkRcFlhRYRnidH/g0GnV2dnc5dGgGKSXtoMaMZZMo\nwcXXXmWpUWNetjm9dIS1VHLl7GXe/6M/xZ0z83Q7Az7yL3+TO5YOc8ehYySdLjd6uyyFBceWF9lc\nv4GoBdx55+1sXrvBYlAnJaNwBLZj49gSkRd4XkCY5WSOxR0nT/Dq2Zc4PPsImRLMei7u3i52aRyT\nxQIlJJa0QDpYQlKmbdVWdWm9TltNLKVFThkFYhkT8lgPQLE3zrWOqPLH02TJXKRmUo1mMScCU3FX\ndVAVtOhdp0xqqKSFDm5daIbQViPAphWzvnpiMulRc33MrcrrpsBNLlQXpRRxHBPH8f534IBSNkt1\nttP0wf53IVBRSNCoc9edd3H56hXuuece1m9eIstzFBI/CGjUfBzXJc8LRqMRnc42AkWaJjQaNZaX\nl5BS0O/3ieOILMtYvXmz5GQ9F9dzieIRflBDIYiTiHQnJ4oiHNfFtW0Cr8ZnPvdZHnnoDJbtYEkb\nCoVlS6QlcLBRqiBJ4n36QFoWSpaJr6oIeFr0TrlRhg5zEhO8oaaeboUOTOGdlvy/auJVJwN9fpW7\nNvvGrO8kLyoZJ04a5704fPgwcRyztLRMOBph2TbWYB073CGQNdJkgN+osd3vo2yJN9vixN2nuLG2\nxUNPPECY7XJ19SXmZpfpxzsMkpym72NJD6c1S+OOE/S7fYJEsHHtNbrhLnUvIC9aRNspdzQOsdQ8\nRD7T4mqnT9a3OOaeYcgqu5sdmnNzbLguiZcxiob0169zz+2HefStj6IaLnYssQsLS+5Nv2r8/q7j\n0ul0mZtbIvC90grxbHaTPv24wx2PP8ilr3+N3rU+XrsBy01O3/E93P49Z+htrnP+1XPMJVDvRmS1\nLkMRs6uGdC6ucai7w9GjK1xOQ65dPU/TCUijkFQo0lRRlICXOIoIfB9pW0RFhl3zefTMg/z2Rz7K\nD//gu3F9l0GaUHMsbNvB3kOFFBDnexQAGgjs5fAnQwiwLQcEFHm5LR5CYFtuqbCKgjQrE2ZZ0ogZ\nF2IvTqtsrFtl3NQyWFXaprxWHZJa1sZKeLyhwrR1FaYVYT7bpDe+FUipghizVMeCHi8m+PlOyJHX\nTYGbStZ0UsF4Vs2yDN/3J2bXqqLQ36smkxn7KW2LrfUNzpw5w7/5yG/z+OOPMzdfo1GbIc0Kdnd7\nRKMhlrRwbJvF+Xl83yXNYrrdLjs7Pfr9PkVRovilpSVaM03SLCYKI9bW1xgM+6XSk1ALfDwvwLE9\n+r1eicz7fRbmFuiPulxf3+LY8SNQJNhSZwnUHaZwnD2nhywFulBaQCYdPmYH6+O6XW1bGoicA4Ju\ntlU1kkUjYlMZm6ax/j6NKpk2kPQ55n100d+TJJ1w1kop8TyPwWCAlBZZOtpLw+ly6cufwer1WN+N\nkGqEV29AIdnobrE4v8jOcIdMDfH8hM4goBEoVmybmyMfe+EurBSCLMduz9I+dpJXoks4nsvp5YBa\n6pDvJHh1m5/5qQ9w8aWvYduS7//T7+O19W2uXbyJvZsyF8Bvf/Tj7KYKggWuXXsNOx9yZHae/+nv\n/BJFa4Zet6DhOcA4FE3u+xsEvudTr9eZn2ujigzPDShEgee4jGyL9uEV1q/MIvpDwt0+raU5vIUW\ny0cPceHjX2Dn/BVWz13g4Xe+C1RGlickMmMQ7tCMXNxNcGd8bjt1kt3hkNx2yQsFQiIKge+6zHh1\niizDDTyCIgVHMur3+bH3/QT/zz/+FT70l3+Wes0nyjLcosAtBLawwLb2kmCBo8ahp3riHYXDCW5X\nSomQAlVkSGFhS4mwLQoh9vjkMT+sfVtiby/UKgV3cL0AB3xluj5V3lxfaya7MvVKdfOVck6ZBCEl\nDXQweZwQB1NhV88xx4OppLX+0uB12hiaVl73XCimIlZqcncO27ZJk8mENroTdUOboXLSmpwJpRBY\nQhAlCe12m0wpnnz8Cb721a9xbGUe1/HxvQae12CmXsfzAoajiCwd0e12KFSK5zkcWlnG8wKKIicK\nI3Z3ttjcWMN1XYLAZ2lpkcAPiJOIKM1Ii5ze5gZ5mhN4NQLXp1GvE6URYRLz9Zdepr28hJtnpFmK\nJcHaixLRm/oW+20DirxMfCUmN2jQ7WS2Y1FMbodlWixmhjh9DYyzs+lSHTAmitAx3+b5Jh1mFnNw\nVKNMqihKO6zKwQ5Qvo/nediOy2A4pNGa5d//+2e5eHEbT9hcPH+Vph9AGJNLRdtzOXPnXWx2dkoF\nIQVrqzewigFdNcCqN3BVA8cuUGGHIPDwC49lf5ZTCzOc713BzwMOtRe549gKz332kwgnJZib59Lm\nOpe2NjnzlrvpXD3P8o1Z/o//8Zd5/so5Xtm+yU53k5Y9x5/58fdSq88Ri4CVhkfYWUP51n5/akuK\nvXTCSVRu5TfTnMUPahRJyrA/YLDVYe74IU7dex9//K9/h6brEqnLPHniNB97+jd5qnkMqxcSuDY3\nhzt4ss7q+k12u5uESZdofchj8w/SRrFx5RLeyiKbg108J6Dm1MmTHInAUqUvKBtFOL5FliRYStHZ\nHfB97/4hPvnpz/DDf+rdNGseMoyRKERRkKQFiVX6mIpsL2oqS9G7zihUmTs+LfeBdBwHW9iIQqGk\nQiEplKBQBWmSokQZsrdP0amDSrkqY1W0a8q2aZ1OU/jTFtyYIHGMyCc3TzeLBpp67Nn2ZDhuFdRU\nf6ueJ4TY3whD1+vbcuXqO1Hz/5FLWcH/5I99o7xR3ihvlO/a4roFTzy+yy/+wivMtcdbKX6rTY2/\n9Z71b5Q3yhvljfJG+U9SkkTy+S/M8wv/073f8TWv64YOb5Q3yhvljfJGmSzPfbn9baNPdHkDgb9R\n3ihvlDfKd1FJksnUId+qvIHA3yhvlDfKG+W7rOhggWkRLGZ53RX4Fz/zh1MXlugSx8MJL63+3Ux2\npEMOhXAQiDJFrOOUIVtCIGQZtRIlMcKyyIuc2LG5fOUK/y9zbx4jSXbfd37ee3HnWXd1VXfPdE9P\nz91z8BBNcniJh2nxmJVoeklhZZHSGtAubAgSVsDakCDuApKxtiCTAmxqJdFa6rBsmddKlCWeQ1IS\nyRmSM+TcM313V3XXmWfc8d7bPyKzOrtnKC0EC1IAjU5kRUZlRlZ84/d+v+/x3/74j3nPQw/hOQ5Z\nltFptmk2OlSVIU1yNq5eYX9/D8/ziKKIRqNBp90mDDyCICDLMvr9PqPRCMdxaLc6+F5Q0/BUHfqw\n3+uxv79fi5SUQ6fVYnFxiRyHndGIp773OK995ctYWegSuQpdFkghkEJiqA2QKltzaWdFOzf6nlw7\nRzAbvHCNQvhiTvbUNGc6yZ+m6kyfm4qMZvmqs9LmA8HUS7BLHEceqEBvpIPNMo+MMaAlWZEhJDQa\nEXmVEwQhrhdSVYao0SVJcj73uc/zkU+cBlPSakSgNVpb0tQwHOdICYFrEDqh2VB0zJdwgCs9za5z\nG9pfp2NiXnvqVi7tlzzx7GVW04u891bLK95+G3vPb/HCYxepOqtEtxynt3+GhdFzvGoV5r2SY7fc\nQ2/koOcWOby2T9WIecK8lifHP8yZR67wnjtzbl/b4KI0dO55HVf2R0ReRFVWWG0RFhzl4SgHa0EK\nRZqmeJ6PMRZpYqS0KMeDYUWDgLIZsRNYKk/hxDlukjGOR2RxxbnLW5y5eplPffqTmDzB9yx33nKY\nZQNH3YjYFFwKKjaKfRrSsmB97p4/zKrbZr65wOKRYyRBg+6J22gcuZmhtvhRxIVzp7ll/Sjj3pCo\n2aKfpfTKDOF7CClxSgNxTp4VWD/C9Tws01BfQdSo2RRlWaewt1otqqqirBK0rggCH89VBJ5DFARs\nblzg8sULSGFYWlqi1Yyo4vF1TJQbBWPAARbMBj9M/y7LsjwAwllRWprmN1wv13Bl1iai/ru9Rgmc\nJgXV3vT6RVjlONc7Nc6KDm+8RmZphPe87A0vwsVZ5t1ftf21LJQPfvCDfPazn2V5eZknnngCgF/8\nxV/kN3/zN1laWgLgl37pl3j7298OwC//8i/zsY99DKUUH/nIR3jrW9/6km9uykL5y4f/CNd1D8xo\nZknx8NKeKdOTM6sSnJ6QWSP5qfR29suabpHxiIXhN/7rf8JvNbn96HHaxqcRhHitBqOiwFEODcdH\neQ6O5x5wR3u93gEAOY5Do9FASklRFFRVcfAeZjnNYRhOAgkKsiyr37dVjOIxYeDx53/+Zd7/P76H\nViPCERZdVvh+gC7rUAvfD0DUZ+2A6z4BaWeSFl7bAShMVSEx1EJgO6GwgevUdEEJCFMbMxgLlZZY\nW6EUIDRCmJqzay1VBUK6KOVhtUGWI4T0sI6HdlxKI9CmwlEWZQs8VYDOURis8bBQK2itRbgeruNT\nGQkopPSo7QEUpacJXIfNy5c5d/oMV7d2GYxyMi05d/ESrVYHIWs14BMbispo3MADKRnE45qKVllE\naXCMoBO1Ge73ycOUShtQHq7ro6sCpRNWWpK1lsUOLnHTvMdcQ7FZ9CApeODoCVqDBLO9jwldtv2I\npDVP2Ghw87zPTV1Y7+zTWhnTdBXOuIMI5tkcSjwWaQXw7ctb7AR3cfKBN6OcmKAwKFUyihIyERAR\nQTVAyArfOFgDfcdDGpem9GrJvdY4Uh2Yj4Gtv8+J/4grIlwpwYw4c/p5/uNv/C4XzlxlYb7LPaeO\nMRhu4no+Tz11jkIGiMjnSLfNsuvgZgmdKODue++jubTG985s4HSWwW9xB0D72AAAIABJREFU5NjN\n3H3fbRRlyWAYM7+0yqA/pNSaRtQkz3LKsiAMQ0bjmNxoorBZ8/nzAiEcBLXystIlzWaDIAgoqhKT\n1WlXYRhSlCWVKfH9Oum92W5irabX67O7u0O7qcnzDGM1zWaIlAKFBVPiQG1HYQxKSkzhIqSkqkq8\nwCPNUnzfrZWdtk6Bt9SU2UC4aFP7uRgh68c1vxNjNEqAnLiZWuR12DI15pp6gc9u05CG2cJktliZ\n/n+jdezdD7z+RRj51GNfPXh81/2v/75A/tcC+Ne+9jWazSY/9mM/dgDgH/rQh2i1WvzMz/zMdfs+\n/fTTvP/97+fRRx9lY2ODN7/5zTz//PMv6uXMAviff+kzL+Ihz4pBphLr6etmgXFWNTWVlc8qPKf7\nuO418J3u2/Fb5MIyFoZ/++F/x4+84yECIwmdgBJLbzyiyMs6pVtJgqAWXXieV0vlfZ80TRkMBpRl\nSRAEdDodut0OQsBgMCCOY+I4PrjZNBoNFhYWDsQzSVqglMPW9iZCWJ595kn+yXvfg0DTjproqsJq\nQ5EXRI0GBkulK7Q2E3gGRzk1sM+Ibqb8dyuuWaNbAbrSCEAxqRQsNQ/X5mCuGdRLIShLjdEGRzk4\nTq1Q1cYgfZdWI6S3t0MYeOiywPE8DJJSKKzywfWJmh2ytE7p0bakKHLyIqXSJUIaKqOJRwN29/ZI\nkpjByCdLYh7/1rdIkoSitHhBAydoI5RDlmaMR33Goz74N1EZg+MHWCGJswxjKrCadDQg8iTrSwsc\nWl3GUwE7e/ucPnuJwkiMkGBLQqWJVIEs+/hVzNJcg62ixXioOb7U4q0nDa3xYwz6I0byECuHDzPv\nXOV4O6ctfNrRKu7RPtrPIW8SLLTYKgrmRUgzGbER38mjV++ic0vEiZN3IpQhyXZotX3GZYbgME7l\n4zjnyZ0SXR7B04CKsYKJb4rGuUF9rK1B22riMOWSpxlRIPGU4qnvPsO/+aVfQZcVJ25ZZ2W1w97e\nHrs7A4ZWEWN51Z13UO5t0XEErdDnrnvv5eS9L6OzfjO//JGP8q0nnuP22+9g/dAi9933AK9+7YOk\neYnnBcRJShzHiBrrsNYQBAGlMawdWmdraxujbR2KYmsbWynr/EddCxpQysN1a8VxFDXY2d/FURJj\nDZ7vImTNhbbWstD1UUqSpAkvvPAcRZGzvDBX71/kVHlOp9Vg0B/QbrXJ0oxKF3iBf2AxoZTCItDT\n6lcKHG3QxmKtwAiBZXZlaiYADmAQ8ppT4bQKB0EQhAcYdU2Mll1Xxc9qNG505pzFrpeqwJ9+/GsH\nhempl7/p+wL4X9tCefDBBzl//vyLnn+pA37mM5/hfe97H67rcvPNN3PixAkeeeQRXvWqV33f40+X\n6tM72+zxp/9uFKtM979RfTl9buqNMPUxmBX+HJzsSqOtRWF48+tex3PPPcODr3otu1e3qbKSw+vr\nOJ6LlRJjLOPxmPF4zO7uDq7r4XneBNQjwjBEiDq/78KFC8C06o5YXIxQSuE4iizLuHTpMlrX1bXj\nenguLC+tsLu3w/ziCs+fvcDJE8cZphmmLAgmiUPjZIyYKDSFlLjKuS5b0HWv+U8oqWqnOGNrKTMW\nayyO606uvMk5pq7phKwwQiCwSOEgUERhg7LIMUYj0HiuAOXQMw6iLPECB9fmhK4hTvps9TJ2M4fn\nNgec2RwwSCrSdHxNpcmkArEGIWvnOiXrJaqjHHCaZEmCaB+nNeeSZgWVVeS2fq32UzwR4ehanen5\nIZ7rUxlFFPhkeUaRj2m2WlRFj8XDHSozwslKTBaDzhDWwQiXvIQUSeKFKOEyN3cT6UKXk+GQnVhz\n+sIFFrcDXru8zlG/x+ZOytaVfQZhh7TX45i7R9WaR7opYRNaWQ4mx+8YcDdRc2OG8Sla6w/xyNOf\nZGXhaZzWYaLGLajxDvOqYigSHCmJqhaBKchsga8rCqHJ1WQ5LjyYZKZWVQkTpZ+Sqv7upKHRicjz\nFIHk9rvv5jWvfzVf/NznidMMWKHIJUVRkZYZbqvNYDTi5kNrDLcu0/Rcgm6br3/3W3zvk5/gO8+e\nppfnPHX6aa5sNDly5CaGwyHNdhchBIcOrVCVE/m7kjhKUhUFQkji8Yi15XnKokTK2pt7NBriCJdW\n6BKEdVsxLasDhfDu3iZzc3MMRwO6cx2SJMVzfYoiZWFxgeFgVKc6CYfbT56i0iUXL5wHXeAqhe+1\nGaeGsLFEUvTwAhdlw7pom7SkrK2dBN2pgrkyoGq7aWMFSkiYmGlZa+tKZ1KJY6lXbBNAdl13glf1\nMWdX/lMsm+LRbBJP3UoqrxPh3dj+fKltVqj3/ba/cQ/8137t1/j4xz/Oy1/+cn7lV36FbrfL5ubm\ndWB9+PBhNjY2/srjTKvTaR97us2q/2YluVMQn/ZWZ/uws9aMN3oNzPa6rLU4noesDA3l8cp77+Nj\n3/s4z517nluOHkPkJTpLERKu7O8w150nikLa7RbG1Enm4/GYJBkzHtfmU1EUAaCUQ1EUpGlKlhUH\nn6Xb7RKGISsrqwefNRnHCMcjjhMWl1Zxg5A/+8IXaHW7HFpeptNqkgz6BJ6DqRTMqBmLosBYixQz\ngcLGgLVoqTB68geiJnmionaOs9eJqCYVgZGISVsDqTAW0jLFdSSOIzC2RDgW6bh4JiBJxyx1Ozz1\n2LeRUnD8xB20fY9P/uGfspc6ZLZBe34NJ9yp7UWtg8TBGklVGKyZMThCYAxkeYZy57DGEOclSSFw\nvQDleXUAhq7QZYqrPKIwYzjeResCP5zDVQFppXGki8Rw/PitnD9/ASkrGjZiZ79HXMT16kAFNDoN\ntLE4rodyFbtZxt7VPaRzlfVDbY7evsDm2Q3O2ybzc5bjaxnfHqT8Zf8I+WieH5jrs76T4WUVx1bn\nOKkiynJIUXbxmy7eukuMx1eevMpG7tBMHkcnPZ7Yu8r9p+7GqRxakSG2PTI6KFuB2mVkI4T0EKJE\nVxozsXTVpjYos8YipKxvhliyPMb1PRwnIM81oefwgX/248wttPlvf/Q5Xr12gp3dEY12QTYcko8T\nysEI0a5DHVaWVzEGhsMxzzz3LM1GFzfqUBWG22+/g9VDa7SabaRS7O3tc+HiRbrdDivLy2xsXKTV\niPA9jysbG+zs7HLnnXcRRg2CwGc0GrO2tozRZnKDsZR5iut7+F5EVuQsLd1Cr9/n6OE1kjSl3YxA\nCCI3oCxyGmGTOE5pNBuURUGlYX3tGFEYossChWBjc4NRUhC1fCrqeRGy9m2R2h7Q7OTknCFqcK69\nd+prwEzk8fWqVGGtxk6KDkx5ANTX2ifiwHBrFmCr6vrB4/Rns3YW02t3Fs9eaiuKYoInfwtmVj/1\nUz/FL/zCLwDw8z//8/zsz/4sv/Vbv/WS+/51d5ler3dQhc+C8WyfaFphv5RnOFyfrHHj3W1afc96\nZ3uex6jK8ZAUcYbjuDz00Lv5yK9/lB9510OU+yOOrq6hhM+Jk7cw2B9hjKHX6x2YPM3PzxNF0cGA\nbzweTz6LT6PRpNvt0mw2ybKMJEnI85y9vb2DXv+hQ4fwXJciz1haXOLy5hWa8/O89e3v4A/+8BP8\nzx/8AGkyouE7FGVRt0MqgxS1KZVSoKzAkeqgT2emJlZW4E/NqGplPmDJk3Ti3ywnS7y6AlG6MTE9\nBqEkldUgoJSGvEgRwuAplyrNwAg2L1/hmecS7nvg1XznyWf51f/zP5AWcOyWOylyzVzXZ/fSGcL5\nFo5yUdJDCQeQENS/pygysirDcRSOqzDjum9qhMCRlkbg1vFz1uIrifRdhPBJyhjfqwe1eTEijXPm\n5lZphxJtFP1+ynceeZzV1UXG8Ri3oaiEj98NEVJSVhrlgEKgdUaRapRyMMbyxPgk+cV9Xn88pLuu\nGRS7bCJZ1hlrzSFX9D6PDLp88vIhmkWO2gu49dyIH16ruPWQRdBGJg36e0/SnbN87Ru/y4lXHsMv\ncxzzJFqf4N/85zHve++7WCyex7oFPddglUVZB60UbmVxp4skbVCOoiorlOPUPVZjmbom+56LowRZ\nUtFpLZClA7Qteegf/wjj2BK0FjFOg7ALd60dohplrMx10MMxh5dW0VlFMkq5cPYivgjRRnH0yHHe\n+09+lJfd9wCXN6+wublNq9NFCIfl5RWwljNnzjDXbeP7Pr5fh2Xff/+99feLJc3SSZvEkucpuqoA\nw8LyEv3RmL29bZaXlynylPn5OfIsp9utQ1KGoxGB59ctFXyChYgsy+oBr3BwXZckqb3jledz6213\nYYzl8Se+RODXg1NHSoqqlvxjLRILtq6opRRIR9VGctSB3oraSdAag5ASrEDYKRaZ61b717yGvOta\nvfVz16fJ3+iFcmM1/VdV4Tca1X2/7W8E4MvLywePf/Inf5J3vvOdAKyvr3Pp0qWDn12+fJn19fXv\nc5RfBOD3/uAJ7r/vbu47ddeMreq1RBrHqSval2Kq3FhpT/vdjuMchB3PntDpF5FlGSJQOMJF6Rrk\n1lZWePdD7+a5557jHa9/M/lwTFkW7G9eJlARvhsgrAVjqaoSXVZkSUoYhvi+R6fVptNqkeUlSZww\nynPKvL7TOlLSmV9geXGJwWBAmqaUeUGV51RVRW9/n06nQ380QmO5/Y67ePb55zl11+3kVY4X+phS\n4yt34iddX+EHNy0pwYCrXJATg6qyQKprjBWwNKKQsiowWqO1wVoJRuLaEGsN1tEIBVpXlMogHIe9\nXsF4nDI3v0wUtFFFztrhW/nuV7/BZ//9H9BeWufQ3Q8ikZTxGMWAhohpLTnsIQ+MuYQ0VJVGY1Ce\ni/QkohTIQCFdh64MAUsyTrBao5QEeS1+q6oKlKhoRi7jQhG6iipPSeI+PR0jpIvnhjR9QWf9MG97\ny9v48pcfZs96SFlS5BnKgitBajBVibQCV/nYEiyK/VbEmSKhdekqrzrhYQcVT14W3LlwF4fVPm/w\nN7HNEV937+epcg3PXUcPzrLTGbM+SpCiR1Jq3JbD8889TKc5j9nrkJdt1o/scapreHJ8Gx//2gU+\n8O5V2sUZfNujKiVNx0Gby6RFgPAXabY6E2aFAU3tlK0kYur9AzjSoPOSQEYUaYGuLEYItJD8s3/+\nL/jyF79BPy+oTEJVxqz4IXkypOnX3jILq6v0hgn7+zE/+Ka3c98PvJawOQdCcvbsJdIsp9FqkmX1\nANBxIU9T2u0OSwtLhKHH0089xfraYfK8IogiSmOofV4so2SA73qgFHlaoivwvYC5OYeqqmcuyTip\nB/xlhZXQajRxHZeyqqCyuErgtxrYRkiS5Qct0qqqGCcJ4yQBAceO34MUMB6P6O/vY3RBt9VCVzmY\n2rbZGI0Ulqw0OE7dTdRV7dFeR6fVISvX2igGoa63hK1BWFxXEE6xSOvyoKqetlxmW8EHoDsTK/fS\nAP0w//dv/e5/HxYKwPnz53nnO995MMS8cuUKhw4dAuBXf/VXefTRR/n93//9gyHmI488cjDEPH36\n9IvuMrNDzIc/94nrbB1nlxX1YKA4+CCzZjNTUPY8D8dx2N3dZWlpiTiOD6rkNE2Rsg410Fof9K0d\nx+H5S2dZbM+xELYJo5BcwpiKL33xy4RWctOhddqdDn6nzWA/xlUuaZpO7GP9g3DjOI4ZDocURTEJ\nRm4RBAG+76O1Jo7HZFldfbdazTqabdJy0WXBlatXsEKSaU1lLGGrwdb2NmdPP8db3/xGDq+tUBUZ\nJi9oBw2yLDsY7CqlKMsSz/Oum2rXvbj653EcH+wvHXWwSrECyqrCd3xE6WKVJs5jVKToJzHPnj6H\nEQHahCQxGOMzHiW03JR+VnJpe0Tn0E3gRSRFRREPUFWCHu5RjPZYaDcQ84cwKKrK4noRxjgUWpBV\nFqtcSiuIsxzl+wT5EDtxoQv9EG2g0HWrDFNiyxTKGKELbAWNZkhnro0QJfu9Hfb29+gPRvT2xygZ\ncPzYSa5c2WFoI9IkwZGgqxJhNEZXkwzFGmyU64OFvlfgqIIwvcQD7ZT7V+apMpdsf8hx9jgcDtny\nWnzdO0GSaroLJ9G9EYd0n8DNMN2CB074LLLP1587x7e2G3Qab+Jn3jvHLXOnEV6Db/ffyGf3foCn\n9i7wv7znZpbTLeZkiLEJqc350y98i4e/8g2azQanTt3DnXfeydGjN2FMDVqzq6ci7rG0uMD+Xgr4\nKB+anYCv/vlf8OnPfI4f+qH38J//y3/CipTh1XOshj7z7Q6+cmg0mozykmO338X9r3qQVz74Rs5d\n3KIo60uz3+9xz6m72dreo91uU+mcvb19jh5dxWjLaDhg6+omt912EiEharTpD0eEUYQxtT++4zjE\n4zGddhtHSrI0w2+ERFHE6RdOc+utx9nZ2iWKapZKWZYURYHnukSNBkWe0GiE5HlJlmVI4eCHdb5o\nnGZ1ZU5NCJDUhlm+59GMAi5dOs+FC2fotiIaTQ9sgdYFnispq2ust8DzKYsCo6er28nyZpJyL91Z\nmvK1WZrW18NmXVhes7+9jiLL9Z2IaU98up16+RtfhLnf+cbnD4rV+1755r85C+V973sfX/nKV9jd\n3WVlZYUPfehDPPzwwzz++OMIITh27Bi//uu/zsrKClBTCj/2sY/hOA4f/vCHedvb3vbiX3oDjfDG\nZv0sR/nG56+FO1Qv8tKd5SdPf+667kHVPaUBaa3JraYdNpCTkIhKQuUItnZ2+bM/+VNe/9oHmet0\niJMEz21i9PWhrtPqfzaYdLoKmHVT9H0fIWqf7izLDoYZAI5SGGtwfR/luCR5Rl6WDEYDpBQ8+eQT\nvOXNb6ppWHXPAM/z6j9yz6PSFY7jUukKMbFfrftz1cHNrSzr8NSimAxRlDwIhtDWUBUV8SglbIVk\nVUEpLI3uPMKJ+PSnP8doBHniUOQKzw+x7pC80qwfOcbO3oBGq02eFwhbEXlgijFFOiJPR5gyIc9K\nms12XSH7DRyvBU5IiUdWSCqhMEJRioSqyCmzDN91ybIC5YVUxqKriqrI0HmMrjKczCdJR2gdY0xM\nWQ3J8xG+77F1dZvAbyKEg6N8ikLWYQqT5W3dKhIgnDrxRSik61OWhsg45J7BcVLmyj3uXw65qQtz\nXkIrvorsX8V6PsnCKrcWQ4ascFEc5ulRk7/cLEkbBbe0Y26vNOveAFOdZS5a5Yd/8F7ChYq4mSK8\nk/ze10/xR1dOwlyf+fy73KT6hK6iO3cng+0zXLn8FEIKFhYX69We0eR5XrOcOl2iMCAKQ7pNn06z\nybFjJ/nmo9/m4sYlnnjqe+z1+2xv9zl16n42NzcwtsCTGQ3PkMUp8915tIHW3CIf/Kn/lbA9T9Ra\nwKIIg5D9nR7NIKLSBs/3yfMc6Ui6nSb9fp9G6HL16hZrh1ZQStJstdjd79PudNjvD2i2O/V1ICxp\nnBJ4HmpSdHmToicKQ4wpKcv6+gsDnzhOWF1dYuvqLv3+HmWVEDUiFuaXaqquECjlkpclUtbBEkVZ\nIqUizzRZmqKkpCpL2p0IozWD/g6D4Q5FHhNGLlWVY6ra+dJznZpyKwR6wmCTonaJ5OAamWGzHKzm\n6+HojcWkUtdbXs9i2WwFP9thsNa+JIA//sgXD451z8ve+DcH8L+NbRbAv/bFTwPXTsYsAE7BeJbD\nPa3Gp+AE0Gg0eOKJJ7j33nvJsmxSqdS98CzLDtops6/PygIpJLoscVUN7E4QIAKPnd4+v/e7v8sH\nf/THGO/3KXUNevNz80SNelhpjOHy5csHU+W6+m7Q6dR/vNvb2wwGA4qiIM9zWq3WJMknwlEO2uiD\n91+WdZyYG7gEQQBSsrW9zWA85vSZM/zIe/8xASCytL4pOQ6F1ni+h1SSURzT6XbJJy0Zx/UoCl1X\nNUWB69TnynE8pKO4vLFJf1RP+I2FpbUjgOTq3i57/TFPP/M8e70xg37K6soRdA5R2MQg6ZkRUkha\nUYtG0CAZjwk9n6Io0LbESENeZKRZgt+7zDgeoZQhScZIJSiqku7CMnNLq3hRh9JAXlTssIxrIY9H\nzHXaWG1xgwZGelSTSrwoMsqioOxXYEuELUiSPfK0h7UpcTzAm/igt1ptxkmKHQ+xQuK4LqMkwUrF\n0soajh+y3x+S5SVZVqItWNfUrJUkR0mBqzTrYckDC4bb5wo8MSBJCgaDir47Tyfo4Lht+uoIl8wK\nZ9MRz198hnameOtNDrfybe5e9/APP8DlQ3ext3CSoFScPHIfv/KH59jt3EWWP4uz/Qhy8zJ3LDYZ\njy+A0lRa0+q0wVpGSVKnRHU6AIyHI4o8x3cEAk1e5pTa0Gh02N3rkWUZge/jOIKqLFHCoTIJfiSI\nR2OyNOdf/st/xeb2Lp//ylf43/73f8XWdo8Tx09iK4MnLWEQcuXqDq1Wi+3dHbrdDs2oRZoOOfPC\n86yvr3F4fY0w8Kk0xGmOFQLP9ym1YTyO65WooxgNBoR+QCMKKaqKMAxJkoTd3R2OHD48YShpPvmp\nT/Bnf/ZndDsdevs9UCVXr15FlxXHj9/Cu971bt7ylrfQaDYxxrDfH9DtzlFUFa6sqa7W1ICa5RlS\nWlxXImQdbv7t7zzKoUPL+I6H1Zo0Teg0GwwHfVxn0loVEsG1AaNQN/rsO0ipmAL8gSbDGF6KMDKt\ntqct3CkzblaQdNf9r3vR65793l8c/N6/1wD+za/9yXV3smmU1rSKvjGEAK6nDU6PN71DTjnZUC99\ngiA4ALYphzvPc6gMRkm0NXiOh8knKivfIZaGz3/h84RGcvvaTYSdeZR3LWi4qqoDLviNysiqKvH9\nAKXkpJVRZ23WIp+KOI4PPpeQCsd162grLLqq90EISmCcFlzc3KQ/HPPm1z/I+nwXx3Uoywoha26r\npk6zkUphRM297Q+GFLmhKkrm5ufZ2dolDBsIIWm0WvSHY6Tr4AchSV6xuVtw4eIG+/0xlQZrBK6j\nyJMhadxDl2OWF9qkeUrZWMKRLg2/ha9CqtyiK4N0XIQj6Mcj3NCtRRNxjyIfs7OzgRIFUFKWWc1k\nlC6d+RWywjC/uIJpruNgyIYDWoFPWZTkpaWwDpVVFNpSVhNqljAk4zHSWpSt0EWOsCUSje8pOt0W\nWRaTFxlOUZHmOXlZ4oUBUbOFtZY4SUFIAs/DVHVW454e0LaCEEmiLbujhDnp0En6rESayo5AWob7\nQ74ybnL3SsR9S016/YwruzFb/RFjL0Q2XO5qWY47MQvdkOHSTXxnM2KnugMjLJ3mFm/4wR/iLx8v\noBmy1im5+q0/pZs/jkFhZIfhaITjOmjqYiXLUhylkAKUVCgp0UYzTgYk2YDjx28m9DsUKVRFidUx\njVCiUOhcErYa9NM+H/7wv+PJ736PO+++m8JovEbIb3zsY9xx2x3cfcddrC4sMRiNGKUxYRiRZTlB\nGNYCNeXU4dNY5rtthKw1BVUpJsKqgMrWvvxxkuIqSZVXeEphdH3NlMbQ7bYZDAb4fkBVFjz99FM8\n9dRTeK5DI4oAi+e7pHlKlqRsb2+zcfES+/v7gGRubo53vPtdvPVtb6OcXItGG0BgjUA5ijStV6FJ\nluIHHlbUAqKNjUuUyQBTlYShj61KAtchTka4zsSTXoq6hULdZpzFp2kxacw18sS152qQnxaL05bm\nFMtmA8mnLRwpJfe+4k0vwsjvfOPzB/v/vQbwR//iT4Hr47dmQXF2iTJ97ZTOM6XPTU/K7FJnGpgL\ndYU7ZYzkeV5Xu1ahlaCSELgeqtAIBKkwxB7s9Xv81//4O/zwG97K3Oph/Ebj4ObS7/fJsow8z3Fd\n90BiH0UR1hqyLGVvb++g5TJVa07ThaayXwOkaY7vO/iOAGPQuqKoNDv9HspvYpXLuYsb7Fy5xHve\n8Q8ZDofMLy5MpMsgXcUojsmKggsXL9Dr99ja2mU0zJFC8ra3/kOWllbQ2tJudegNhiRpjhuGnD1/\nnrMXrzKqFtEafK9JUZh6sm1SArfElNuk6SbCDMiymP1qicX5FYQNWZk/QpELPL/BOMnIqpygGZAW\nCYPhEM/1WF6aQ0lN5AuKNMYaTbs1j9YulpAkNeSZwbjbtAKPfDSgTMY4UoEKyLQi1YK0MGRFRaUr\nUjUmGWcoI6G0mELjWGgEPocPrzIa93A9SZKO8E3IYDSirCq00biBS6fVoixzdF5Q5Sm2KlEC2uI4\nI7NNGQzBKViZb9MNJPPdeZpzJxmVEaur83hyFxknPPn1P6Gf9um1FglbHVarBueffJZx8hwdO6bb\nuYX99mFKxqyP5pnTHTZbBZvNFqZs0HEiYuPTWPAZ7HyDpTBDxpLQaRBnKX4QTJhFljRNCIMAayxW\n14Ks0sIg2Wd5tYWSAlt6NLx5TFYQeAWRr+mELZKh4YFX/gNe9vpXEDo+//b/+rf89M/+NFoY2otz\nPP30U/z6R/899911D+9517vRAnKlSLMMIaDUFYEfsHZojf/yh3/AW970BqwxdNot+vs9GkEbP2qQ\nZBnK9UjzetiIBd9xEBaKLCXwfazrsLm5waFDq1SV5vHHvs3p0y9w88030Wo2EELQbtfsLeW6WG0Y\nDgacO3OWSxcuk2UZg9GQc+fP89rXPciP/dN/yvLKMsKaiUrZQVcWqRQIqDQUpcHzoaw0WV7ScHIu\nb1xib/sqnWYDTFXTZbVGiHpgbKnzOY25Pv6xvp5dpoPMWZCGawlYs21guD6BbJY7bq3lgVe95UUY\n+d+tB/63sd3YA7+xtzwF7ml/eZYSOOszMH3umnjHue6Dzh5z1s9DSkmAgwG0hEpaKqMpyxxPuWht\n8BsRn/jUp0A6vPaVr8Nqw3jUx/cUrgOddhOwWClIs5IsL4mTDFPlhIGP47i4nk+aZnS682R5yXA0\nxnG8mg7puBhdD9Qstq4WPZeyyA96+UWZEfkhQRhyZWuPz335q/xP738/w8GAl7/8ZZhSU5QVZWXJ\n8pLPf/FhOvOLvPzlP0Czvch/+I2P4kcOD9x3Nz9w6m62z19idXHmYRJjAAAgAElEQVSVcSGpGl0e\nfuxp9uIUXeUYUyEw+J5DWRQ4QqJLizCSIrc40mPY30cX5yh1yeraEYKoQ6k9TBWidS3sSLMeSuWU\nZcw9J+6grKra58Maev0hUrlUxlAUFUmW02q38DyPZBRTmYpWO8JzYdDbwlWGIkspK4ORAfujHJyA\nMpVY6iG177kEkU9e5ESRzziJ8XyXJE2wxqDTIWEYEvoetirp93oEvkeRZ/hBVPOqZV0QZGndD5VY\nbj5ymE67ha0KptFaw+EIIWtV7s2Hb2Zz8yLbW5soVZLlI6wu8Xyfzc1toqiLVCFB0CA1Gi8I6iGZ\nBasrpLX1eZYKYyrKLEcIwdJygFKGvZ1ddGHwvBAlPaQM0DikhcH1G1jpIs2QyKu5/qtrh/HCiKwo\nkIDvSqwp+B/e9UNEvovnCYaDMfu9ff7o//0jfvwDP06cJCwvL+J5Pp4n+e3f/h36/QF33X03nfk5\ngsDDcx0cR5DGMV/64hd48DWvYXl5lcX5BXRVkecFBkGz3SJJc7KsIIgaFEVR55ceeImUtKIGg0Gf\nKIyQQnD1yiZf+9pXuevuO+tVtOMglKQeLLtQ1H3p6Ryn3+9z9uxZtra2GA6HPPbYY7z97W/nJ37i\nJ/AC/6DYu7Zqrzm0s20OgKQoCQKPOI7Z390iTUb1CkHauu1U1C00Yyok19KmkiwjihroCd3wgFwh\na/qktNd0KFOMmiVmTNvDdTUuDhh3D7zqxXPCx775hQMs+3uZSn/wBmYy6GYDeIFJ39Y5AN9Zys60\nsp1VQdXUpGsDhFnJPVw/Cc6LAqREuBIx4VM7foC0AiktRmve+MY38pu//f/w8ntehu94RGFAmsaU\nheHKlas4roMfhjSbHSyKIIjARvUXJH16/Ziw0aQ/TGu5rvTpDcZkeW2yk2cljvRAQNQISbMxxtbL\neVcpxuOULN2n2+lw6oH7ecVr3sSnP/VJ8iTBDyJCP+DUqVNYJDs7ezz44IPcettJdnZ7OK7C9wOS\nNObLD3+VjTPniITkda/p4IQdNrauUhYpCkOpodOeI89TiiKjEbbI8wLl1mZLhlpW70Y+nuwQSs3V\nq1d42csOEScVWZaQJ4bAjZBW0m7N0ekcpixqI6NK1kMfowvC0CcbJUgpWV9bJstrGXyzFda9/61N\norCu2tI4pdtq0B+OmJvrUukhVroU2qBNxfpNq2xubiK0oR165EXM8lyrXnrqvObqBkHt/YKgM7+A\nJ30EMLIjHOlgqb//dnOeKMgIgoBuu8142GfQL+m0W5R5Da6dTps4TqjKkosblwh8j8NHjhBFLsbk\n9Pt77O/3WVxaJU4KHM8lajUJVEAcJyhHMTc3RzIaMtft0N/fQ+sKz3NAGxqNCGMTPE+ijcOwP6TV\nbOM4LotLq/QGY/LSooIIhMTk0PAkw3FMu92k1x/gejUTo6pyTt5yjGeeeZp41KfVbNJud3nhhecZ\njIacPXeOZrPJpUs5y8sLVJXmoYceotfrc/rMGR555FF832N1ZYlmM2LQ6/Hlh7/CD/2jd4CQZHlB\nu9VGqIKsyEmSlCQt0MbSDXyYeOmMxiMC38fzfOIsJQwbGKOxAr7whS9w5113XgNCcy2D0pqaFTIL\nep1Oh1OnTnH16lX29vYwxvDYY4/x0Y9+lH/x0/8ca6/R++prf9JelADyIPJMOIpKa0Lf5+abbiLL\nYra3tojjAbooCf2QeDzC9VyUqo2y/MAn4Nr7mZIRjJ1Riutrua6zPe5ZZ8FrNxjz16bOz2Lh99v+\nzivwb3z1s9eZUk2rz2mb5EaTq+mddAr2cK3l4nnBwT6zVMRZR7NpFa/zmrlhZc3GqOXvAle6pEWO\n8j1UGPB//Ot/zY/+o4fwJ/xylMT1PPxGk6Io6fUHDAdDpg5ljhOijeXyxiZ+EAACzw8oigLH83Fc\nFykUSZYSeC2sUehKIx1JNbmYpRCkSUyr0SDwPW47eYKd/j533HMn3/zmN7h04QIXz59lYW4epRQf\n+MCP8/TTz9Jst/G8AM+PMCrk248/xhNPP4mQMN7fxcQJr7jvZRw5foLzW7tc7o/xW3NI1WBvbw8/\ncJmfW6Df35swbDy0rhgORxMArugoS6VjkmSXIh9y7OZjtFtdbKWQwsfzwoMbq6M0QgrSLMdxXEZx\njDYWpTyk49DvDzDYia9MTqU1URTWAb8OmCKlSGPCMGBrr49G4QUNDi2usLe7R1EWaGs4tHqIPM/w\nPJfdXo/t7W2OHj3CYDBEEBEGAbs726yvrXLm9GmwhmPHjjMajuqqT9ZD6LSKKbKUtdVVWq0GjcDH\ndz02NzbotNpsXrnC3PwC5WRQPBwNSJMRnudgJquYetUIBoVUDq1WF02DJMlxHInnKooyr6twBXme\nAhpT1bMbz3OQFlxHEoUho8GQStdaAeW4LK2uYhAkRU4gBHPNBqPxmPXDh0mSGGMq8jxnrtumEYW4\nrsPy8gIg6fdHXDh/FmMMW9vbPPja15CmKYuLC0SBz4ULF1hfX2e/1ydqNOj1epw5e5pGI+L82bOs\nri5z3333sb52mHarhXIcrmxcBSAvS9qdLotL8+zuD3BclzxLcV2F70+Ln5T+Xp+5Tpdnnn0SYWtN\nhZ1QXuWk5ysmNhHK1oyqdqdDPB4fXLue5zEaj9nc2ODZZ59lNBoxGPX5uZ/7OY4cOUKe5wRB9H2r\nViMkUtR6B60rHCmxaJ743vcwppbpe55Tt2cnRmLGGFzPw0wAfBoabrSpNRQz+HJtqCmvY8QppQ7o\niNZem/Xd/cCLWSjfffRLB4/vfcUP/v2twGf71VOwnYL5FLxntyk9b/raKbBXVfWiBPZZ6fwsoEsp\na2WjuEayV4AytRw2CgLissB1XJZWlhmNB6wdO0F/NMZaSZrD7qhHpS15YRingAGtIc6TuufWWML1\nPOIkYXd3zOL8IuM8Ryf55CJXZEWB0bL2SYljDh8+jO+7NKOQKAiQAjzHoTO/QCFdLm7u0RtkdOZX\nWTOSqshJkjGf+swfc9+pe2mEIZ7vM45TSqu5+eab2R+O2d7boTPvMLa7fOt7T2C8gN3BkOWlFeIi\nx1iFJwy+cijSjCKvQ2lLnZOmcT0EsgasJI0rrFFEUQtX5excPUc6iAi9Jnfcdi/aOhSZRjpOzaE1\nulZVGkPoKfwwYjyOwRpOHDtCmmUkacLcXIuy0gwHY8IgJPIdSmHJRwOUNRxa6LI3HFHmI/avjonH\nY44evYnBYEggUpZWukRhyPpyF3H7LQwGfTq+Is3rcN2j63MU2ZCVpTrAWoqc9UNz9Pt9XM/FdaHR\nalNVIdaWYCp6+yMcpWi3IpQSrCwvUVWaqsjZ2dtmZWUZKQ2NKKLIC4qy5uhPh9fjOKGqcvIclHBw\nZA1Q7XabOBnjugov9CbSbotyJFWu0cZSZAXD8T6jfo8kGdPutCh0TmVyHM+lGI+Jmi3iLAE0mxvn\n8F2HwPeYb4c0Qoe5uRZxknDm7FkWF1c4d+ECeVGbujWaLZ565hk2Nza44/bbeeCB+wmjiN3dXUCy\nt9cnjhNuvuk4URRw7twFllfX2drZx/MbPPPsCxw+chRrLe1Wm5brsbO7x+5gSJ7nhGHA2toh4njE\neG+fJAxIkoSVhbqid12PYb+Hcuogb2P0wTUtJ20P5XogBaPx6AAXtNakeYbjOqwdXmecxJw9exY9\nqPjOY9/m0KHVyfWlZ7BDXPe/NROsUVNLaoMuLfecOsVoNOTq1av0evsEYUjgANW1PrictEuKfGKT\nYaYmXRIjuG729lIinWvYda0i/364+P9n+zsH8OlyY/oYZuWq1xs2zaqaZkH6ms3srPKw3m5UM03v\njspzDx4LAdIYhLb4rk+SpFjHYbe3T9BosL29TTqK8aMW0m+y0x9TWkWS5kjXRSHwPZ9Bf0heKpZX\n1tjv9WgIRaO9zJGbb2d/0EcZQ5qmjCfVhB8owlZIp9NBSslg0Kc/KukHLulojOsIjqytcf7SGZZX\n1+gsrjKMC8bDAfNzXXZHV7G47O0NuHDpEkfX1xn0e6yuHaHVabJ1/iJ5XmKNIsk1uZHcctvtbO3t\nsLi8yqWNC0RRE03K0twcg8EY33No+i6lsfiBT5rGtFstBqM+eV4QBE2KJGOclPjSQ9mKUa+PDTXp\nuIe1Lo3WHEI4GGFBa1zPYTRO0GVBJSSB61AZw+bGRcIwxFWK3e1NrIDlpXVMpdnf2ubIygKL7Qbz\n3Tb9YZ92O6LV6ZKNe3juUYSQ3HfPbbRabZ566mkqoTnzwhk8z2dubp7Dywt0Fjq1D7vrMhju09vr\nceXKJiBoRgFR2GI8HpGlA6BFp92m3WwSj0f4vkMWx2xcusDq8iHKsmI8HpMmOUvrCxR5zPxcF6M1\ngdNkfv4oeVH7w+/2djl65DAbm5dxlawtdwWMhglB6FOUBVlWG3sFQVAbJfkeviMY9gdcuXIVgWFt\nZZmbbjrM3FyH3b1dKl3RabUYDQc0o4jA9+g0GzQCF2E1WTrmzOkz3HPqHs6fPU2zM8f60SOAg3Q8\nHCtwsYxGI/b3B/yDV7+am4/exLlz5/AnFaYQAl1BFDWxtuKFF87y+c9/gVtPnqQsK+648x6yXJNX\ntS3slSu7DEZDDq2vkQwSlleWMNbw3PPPU1UlZVGwMNflxInj7G3t8cwzz9BqNBipATCtVmtmhzUG\nRT3Qr4q6gGs0Ggde3FNbZtd1GY1GLC8v0+/3KauURx75Jt1uhze+4Y2MxiOajfb0qr9u1S+kQOsK\npRyUVIACRzAaD2m152i2uqRpxnPPPYtRAsfzah1HkiIdh6ooJm6LdY9dCIuUoBz3urYuzKz4J/3u\naUtX6/KAFfdS21TPMSv4ecn9/q5bKN/82p8cLC2mtJnph32pHtAslWfW96Qeenq1zHxy7FnDmBt5\n5FbWgzCrDUpYHGqzJ11ZpOeRmArZiviNj/8Oy45PK4xw/CajTCO8BnFhKCpwXJ/QD1FKYI1GiJCy\nrHtvvX6PZrN58D4935sYE4E/4ekKRxxw133Pp9mI6Pf2sabClbB+aIUjh9eRbkRvmPP888/TajUJ\ngoD+/g5pMkZYTbsRsbI4z91338lwPCbTgke+/T2GSVEPvIRE2Ir5ToMw9CjyFG3txCNZcvnyVZrN\neZZXDnP+0hWKypJrQ6PV+P/Ye/Mfy9Lzvu/zvmff7lZ1a+vqru6ehcPN4hZKgiKS4iLJUiwCoRJB\nhCwFSCLDRqAf9SdE1C8xJNhOEGsxEEgyaDkQFTu2bNkyJVIjUeTQIjnkcNbeqrqq7n7PvueH99zb\nNcMZSohgUHHmBRpo3NrvPfc5z/N9vgtoKH9l26Esoa0yXK3FbAuqZIXW5KRxyDve/g5sx6NqlVHU\nRuyQphlCqudAN03iOKWqK3q9Aev1iizL0eyWqhFkWYkuDYZ+gGtoDHwLQUWSRFiOQ1LkZMkaKRSe\nPJ8vCMOIfm9AlhUEgSrYi8US27ZJigWWZZKmMYN+H9fzKKuCthVkWU4Uxri2S5KmhJkyMFJjuo6h\n692I3JJEKUmcMhyNMHULoRXEcURVN+hSLayzLN/CBUWVkaYxVVWiCYs4znB9D80wKKqaVmgd3S3H\nNC3VwRelEpw4FjvDIbZpUJfqezab6VIKwnWEbhpKiFUV9H2HLAkxBFiWhtu5Y0rD5N7pOcFgyMXF\nnDQv2RmOcFybwPexTJMyz3Aci9FgSFkWqLemoCzBMHSiOOT3fu9fU7c1P/VTP8W9+w/w/ADDNAnD\niOFgiGxFByvp1G1NURYMBn0Ggx40LYKaLM2Yz+c4psNbn3qCZ5/9Gkm8xjTUEndT+IR8xLPWdI26\nbnjuuW8Qxwl1XXP79m1u376lxGOdjfLl5SVfeuYLTCYTnnzyST75yU8y3h2r60+8eoJvoTMIgra7\neYiucRQI5QsuVWec5zmzi7us12scy6YsM6XByDJ0KTE0qXzGG8U3T8vqVQvMTa3bGO+9Wsn5CBd/\n53u/lUb4lS/+wbZ2/ZXsZP9Tnw029FrZ6dVlwFVL2I1sfPPYa2k7m0598702IptHfM1uudl5X9eU\nV7p0gWlbIDVcw6TUDB4+POPwsaeQlkPZCoSmxC9tC8PhkFYom9i8rGnqGkOTuK6Prkk89wC/Ex2s\no5CiLEiTiF6vT7heMhwNoQXXdonjhKZuOT+7xDR1LN1Cipbx7j51BWG44HK2Yjjo0x8MSZIEzXA4\nurbDbHrBxcWU1WLJO9/+TrIsJ0wzNAHf/33fx937D5nO5/i+RxzOOZ+cszMcUhYZUjRgVAR2y3ho\nUyRzPBOoKjzPJU4SRuNdbN1kNpviDnr0hrusp3PW65qBu8Ps4gG0OgiDsqmpm4qGirrUCcMQ13WJ\nk7VSs+qaUkZqOtFqhWmaaFKjbiMMXSINJfPe392hSiLyLGM2PcM0DZq2wQ08jg+fYDabURRF9+aQ\nnF+co2tKJLG3d8C1a0d4rkeWByrxRmrMpnPOTh9uAzgCv8/J9ROWixWOJZC6ZDFXo/N6OcdxHOhE\nJqZpUpWS+eU5o9GIg4MB+zt9PD+gyCrqtuXu3bsk8YosWVNkMa5nY2mC68d7TKZTFsslUVjTSonn\nB4jGpO87VFnFcrXm6PAIhIOmtZRFzCLMsHQDagtTV4lQmmbg6B6W5SBMKOqE9WKB4zhkUUgUrZm1\nU5oWZos1Nx97nGeffZZrxzcxTYeqLilLjclkyuHBPq7jouuSZ599lps3T6jrhjhKaFuJ6zkURcFX\nv/oV/puf+G85OzvDDwKk1AjXyro3ihKKrMDzXS6ml+RFzvXr19B1nQcPHrC7s8vpgwcc7u/z2GNP\n8ODeA165c5+TG8fcvXuHNAm75WLXWDXKM8cwTJarFZ/73OeYTqcIIZjP5zzz5Wd46qmn+PjHP05V\nVcruNgjY2dkhz3P+5E/+hO//L78f3/exLbfbb3WdMK9uBgWdNe+VhyUqBUtKDcdxOTy6jh+E3Lt3\nDylaNKmaNklD1Ta0CExL8dCvigU3BVsIgeM4W6h4U8wty3yVQOj16uJVuuEbne94B/70Z//FtlN+\nbUf9WkeuDVSy+fjVbfBm4fkIp3r0s66KgQTKT7uuakVZEoAQGEJTVqxlg+15xGVJIQWf+vv/Cz/8\ngQ9j6gam6ZBkFdPFCssOqFqB5/doETiOS922iEYQrcNuVOoUiFIl8jiO08n7ddIsRQqddRhjWy5R\nkuI6rnrRmpamyjm5cZ3xzpDVas7u3h5lXVOUNbbr8uDhBXlREoUholFSc8vQKLOE0c6A2295jOdf\nuEOvv09RQpypuLKqyhkOAmbzGRqCnu+QRudEUcpqFbNYJVw7voUbjJgv15iWA0JnFUbs7I5Y53Oi\nVUzPGuCbHnWesb/bx9QbDLMhSda4gaNMtypdUa6EYL1es7s7Zr1eI6WmxthGhVWokbhAN21sx1c3\nyKrGtw3KPGU08KnqmqKuVbhDEnfdSYPn+ZSlshQQSJq6pipr4jimKEt6nkdVV1i2tX0zKxVvRZJk\nCCRJkqJrBtIQVHXFdDZhvDfm3r27jMdjFvM5e3tKwTca7ZKnGdF6Rt00WKZFmMTousFwOOzw1wbT\n0CnyTJkrLRdkecb44IDBzi5ZViI1k+VyTVvDdDJn2B8y7A3Q7RohG3RNWQDTtlRVQ1U0ik9d1MRR\nSprmeEMXt2fRVBW6JqBtqMuSuiopSmX21DSKcfXg7CHf/4EPMZvPcCybvb0xmtRI4xjbthj0+6zX\nqw7O0DAME2h55plnePbZr/GRj32EXk/dsNI0QzdUBx74Pr2gz2Q6xXEdGmrW6xWOoxbAQgiuH11j\nPp9TlRV13XJ5+ZCT60esVzMMHZrmESGhrlXQgpAaf/pnX+Dy8nLbiYZhiBCCNE352Mc+xlNPPbWF\nJB48uMvLL7/E3bv3sCybX/iff4FebwOhbDpuVVy113Tlm4+93qk7eu16vWY+m1LkmTJCE51ozzRo\nOhtZhfw8YsG8lhGX5/n2/0I84oG/3hLzK1/8g+0S9G3v+sBf3w78tSEOV//wDQd0A61c5YC/Vmb/\n2kCIq5DJZvML6oVqamXLqYzFlKKxUsFjaKZSv4HgS1/8Ens7ewgpyLMMAZxcu8bxwRghJPPFijiN\nSPOCuFjTtC2OaTMMLCzTVD4lVUAYrgmjiCyKthekY9t4/oCTowN0w2a9jsnynKKoQLSkZc43n/0K\n0bVDbMsgtjQmsymt0OgNdimLjChOkZpiUWhtiyYF/mjMy3deoGhTPG+IbErSdUxRVriBR0XLw4sL\nfM+nLmvOzia4BjhOD9vp03BJ0xTMZg8ZDFWxsR0LXQ/QZUOglxxeP0A0FrbuoIseRR5SNiVlWlKW\nBUWmoIeizJTJl22h6ZLVerGlhmqt3vm16JiGxtD3kIaFYTlYjk0chcwm5/Q8lwcPT/E8H810EQJc\nN2C1XBFFIW2jOjPXcZBSw/c8bMvCc0ZIKUjikiKPODu7ZLQzxA8ClSDU5hxf36fIKqIo4e6dO7iu\nEq889eSTvPDiCzi2RZqEtG1FP/CIo5iL81MCz+PakVrA5mVB1TakecY6WpHEMYaho2s6O4MRruOz\nDhNaWfPg9JwXX77HYrWiKEpc12NnuMMwGFBla0pTsl6HSF3guC62ZW0ZV6PRmDhKsKTEdV3SNMN0\nDFbRAhBkAqIwpihyHMfhYG+M7RZICa+88jLXDg+o8gTfsRACyjzDcF2Cno9lWsRJiqYZjEZ9Vqsl\nSRJRVQXPP/8Nej2fXhBgWwZ5liAQmJrG7nBIkqTcv3cP23NYLKYEgYehawggz3N2hkPuvHJXeSWZ\ngvliQRD0yAslqKuaAtoa2arirWxjDS4vLrrr1CPLc9I45ujaNe7dv4/UNJ57/nmeePJJNYXXNbbt\nUBQVrqt2VmVVUZSbhK5HE/ob0fIEr784FGjUDQwGu1iWTdtUXF5ekGcplq6rm49lUWQ5mnjUMF5l\n1m2JEpp2xdr6USLP653XQjFvdL7jBfwqDLIp1q81fnlk1/jIata27e3/N1FHm4XA5o69Oa8XcSTK\nGqSkFipbsouIVGyWPMdxezz9uc/z7vd/N7ujEYKGeLWmTBYErkdb11zfdSlrB812QBoUdUWa5BR5\nTlXFiNpAB0Y9i1vHe2i6TlHc3C5mFqsFl5dTkkj5cdi2x+F4pJ4H0WNnd0CZp2RZQlOu8UyJ4bjU\nrYJifL9HVXZ4vxAYumS5WuI4LvPFlHAVsTs8wpA60tLRREu/H9Cjx3K5IlwlHO7fwNJaRalqa45v\n+JRVxVuOr3Hnzl3SPEY3NcIooed7BDoYdUp/4GNbKmOy8ZQEOc8axqM9krjAcTym8ZSgp+Ae27aR\nmiSOa9q2pkFxucvOGH81awkGA+IoJskzgp7LwcEeSbzm5OSELK9YRRlFWmKLBl1q9Ht9+kGfa4eH\ntE1DkacqTCNeE8cxbduys7PPeG/I+HBEnMQIveH84hSpSe6f3qPMS6qi5vDoiL7noJs6Qgre/1+8\nlziNKMqSB/fusb8/ZmFoDII+0+mUxTpmNp/Ttg3D3QGW52JaOqO9XWXdkFXcPztH5S7qtMLBC/oE\nI43+aI+yyjl98ACpNfR6FtcPb5FEEYY7Uth2WVLWKshB1wzW4YKiqKjKTrAGNHnJ4f4Bp+cXOI7P\nYhnxoQ//EE8//TRoFq6r4sve8tgTnJ2fkUQhVaXMn9qqUota28b3FO8/CALWYUhV1+iGpChqdEPj\nb3zXOxBCKYwd20UISRKHSKn2BAcH+6RZjGUGrMNVpzhWBbmqKgzdIFpFRHFMKwVB0IM27/Bn8Spr\n5KZpqRFcnF9QliVJ52poWoqCu5m0T09P0bogcoTyyO/1eqzXEfP5kj//86/woQ9+CIGgbZU9BKgd\nAq9XM9v2WwAWUL+LlDpFUeE4PnVdcnB4xN07LxPGGbat4BOh67T1o4XjawvvBip+1HAab9hVX/36\nv4gH/h0v4Bv631X64FUMybKsbWd9VVIP6kU3TXO7BL2ag7cp7Bs8/GoXDmC2amnZiFZ13F3wgWbo\n+EHAc8+/xHK5JHA9qiqnrSoMDWYXD3EPD7rQgR6FqEmzFbU0QOp4joZnu2id82EURig1WE60XuE6\nDmmcImoVAPyWJ/aJ4oQWVSSLfIljWUTRgjxv0GTLbH6XxWSFafYJhsopMPA8VkmCabrEcYquCaI4\nxXY8NKNmHUf0+gPapiJNUhwvQIqWKAqZLlbcvPkYvlejS52mkWRlrKhXmkFdFrz8ykuM9/dwfU9x\nfKWHJmC/HxCGGbLKScsMr2eT5wmWbVAWaqRMw5IsKTAMjSxPcD2VJ1oUObY9RPnDaNvX2nVdmmWI\nYVtUQqBHK5IkJomX9HsBYRxRNxp+r09ethCvKcqCpinJ04wkUjjqeHcHTai8GiFqdE1nsTzHdh00\nw0BIWIcr/L5NGMXYruT6jRtoSIqiZL1eo+kaWZ5h2jqu56DrGtevX+Ps7AG9IODs7JSTGzdIcwPL\ndknzhDANadsarRCYhklZVOyNDzB0hyzNObuYM1+GWI6F7dpohmC8f8jh0QE7gx7hbMbl5BRDauRN\nTYNE05WXTivZpi7pVq3Eaq3iMOdZwf379zFMh6fe+jY00+He6Tl7B9fIi4K2LhBJTLhYYJkmlxcP\n6fcH+EMf0zCxLFuZQZUVg8GANE2p64aiyDAMlZLkeS77+8r/XwiBYSovntFwQLiOaJuGyeSC8XjM\nKlximDpSE93v3nRuhDaaptHzA8Is5eHDhzz1ltvkadS916EoC2g7j5FaCd02ds0bllaWZdud0uHh\nIWmabouglBqLxWpbR7761a9ycnKT3Z0xnude2bO1SO1KUWwB0bIN+rz6OJ2QrW3RDZ2qqrv6YnDz\n5m3uP7jDdDbBsgw0IdGvqC43CMHm525q2iaN5yrZ4vXOq2mLb3y+4wV849e96ZDLsnzVYnLjX7L5\n3E1gw2Yk2UjupZRIlYWuCn3bInVzuwhtO3xqs5WuNaGkukQNphUAACAASURBVE1LLSWZru7SelmR\nJjG///k/5vDmLdqsJG5sorjA0FrSsABmHB/tM5nPCTrBBFKSFkUnphCsV2vKoqLfV8k9Atjb2aWo\nSzRDZzqbIpqWycO5YqQYGju9Hs7YBQTG8XWWizVhGONZe/RO9pBaw3IVstN3Wa4vGBkmQpZIvaCq\nW2oJtmMytPfYHfXp9/vEcYxmSRbLS5xWSZzz5Zx07mNaDtPLmVr4OA5JUrAMUxzTJAh6NFHBwDJB\nQiVT8jwnLlyu3byFkDpJkpImJU1tsopLZpMF5oGJbQscR7JMHEpKZKsRr5PtDbiua7JMqfdAmf5c\nO+jTpA1tC+PxLvu7OxiGoRwfDZhOzjFNVXSEJXFcC8vwaOoSvYEiTzh7GFO3DQINr9fDdT0OTm6Q\nZTl1K5hM51xO5riuwHf72L5JuFySpRF932f3cGdrAZwmCev1isVsRVUUvOUtb2G5WOC5Ni+99AIg\nsR2HvfGYG/YI0zRZLpeYlsVLr7zMYvKQ5WrFarUiCHxOjvxuEpRURUl8cUpdNxjVEfsHR4rVESuv\nmLIoSdOYOFbFWgihFKFZgSYEnuuhSUmuZ7z08CGj42Om91/E1yqaOqdtCgLfoilb8jyj9aEWkne+\n9z0gBLbtohkWAg2h6XiuRpamGIZFnkXomkma5kxnCzzPwdRVjODuaExTl6wXc9Joje2o3MnhwGc+\nu0DXdWzdosgKDFunbEvKMsfzXLJCMa0MU/Lkk48zm8/Ia9DQEFKjKEoMU6OqMuqmQuolo50es/kF\nRqpsZKP1kiAI8ByHG8fHtHVNkZcqKKRrAs/PJxiWy3D3gCSvOZ/O8dKcwA8wTQ1L3zSDV5hpzaZh\n3BRLVdCVPkSl1APIDqGVukGr6dw8eRLPHaoQG8OgEQqGNTRJW1XIVoWtNFUNtFRFiUCqaUC8mkL9\n2vN66vHXO38tCvgGAtncda4mWQBbRkrbttsCv7kzXYVRmoZtEoa+oSCKR/4pVfPIYMaqG6TnsYhD\nPMfCRpJWNbgmn//Dz7M8u+BjH/0Yumtj6b7qIPME0RQs1xGWZbI7HrFex3iBpGlbyqZWqfRIRsMB\nZVEjWoVrVk1DnmcqJUcT3ZLHovY20tyCyWSilFtlheP7zGfKsW1v74CyzGmp8YMBVVXT7/XIi5rl\nOsSyXaqqJPBcfM9htVriWAar5RLZhc8e7O1hmCaLxYre7ZsIoWPpkr3dXaQuieKYXq+H3j1/RZHj\nOzaCCse1qSuhIrwci8uLc4SQOK6PaUiKVmB7LrrYx3EcomjFbHaJPzyibWuqqkaTWoeNqvHf0HV6\ngY8Q6qYs2oy6VpPSbLZA03TaFjxPfc5jjz2hjIxWKzR0irxEtkDd0O/7uLarfKs1jTwviaOExWKJ\nlC1258Ro6AZSCHZ3dhAIDEPi7OzQNgPaVnH0QeURKun8gMD3ydOUNE27vy1iMOhTlBVCSO7fv7f1\nYbdtmySJOdjbYzqfI4BbN2/iOCrqyzQMFTTdqmYiyzKkprFYzHBcT0GAGlimTq/nIVEUNEPXKTrj\ntKZpCMM1eZaTxTG7uzvcuHHMxeUEw7K276MsUQpP3/PxXJfpYoFlGliWQ1FW0DQIqZEmsbI3riq1\n55EqpV03TDSpo+kGQtMxTZuqqhFCY29vn6LIQTRbTw/f97suWRlZLZdLTNPE932iKMIwFJxTtzUv\nvfQi164doRs6eRKR5alabG/YZUgM3cRB48bxdeaLOUkSKugmSXj729/JzZs3Wa/XGN2idD5fUFUV\nZ2enjPcPODo6QtM0VqsVvV6P5WJB2zYMej3cbg+grrGr3inqXEUAXq9Lvio6PDg4IAgCzs7OWIUR\nnut0/kaCvCy3VGUhBSqpqKauQUgV5/ZGBfyqdci3O9/xAr6BNl5rXLXpyDeLzA2hfdNtb75u80cq\nvvGjyCWumFddpSlKKTszGo15uETvB4COUQtqQ/JHX/4Sv/pPfpWf+9v/I0YNCMFsPiWPEw4O93Ht\nfXz/JpPLS+7df8iNk2NW64j9/TFZlrFeLRXW1yirzWvXrnN0tKc8FcqSrMhZrJaEUYSpGzimAwL8\nwGN3d9QFNlRIqfH4Y4/z3Def5/LyoRImBD5nZ2eMRrvs7O7R67tYto1hWiyXIb4fIHWda4cHGLpG\nWRakWUIYx6zTFcvlEtcNODw6Ik0L4jBVz58ucS2LaDVDkzqr+YK2bkjXDXWZo0soi5SyzPkb7343\ng2BElpWsQmWN21Y1UV1R5CmSHrquc/36dbJKY2+8w2w2YzabbW+yWZYpAY+hUo56vR62ZTAcjrau\njRs3x/v375OlBbZt4zgOO6MxlmORpgnTyQVJHDGdSI6PjlgtI6X2Ozzg+rW9rquD2XTOfLWiLGuO\nDw4puoxS0cEtvu/iBx6jjiUThiFRFG5x0Z7vkcQxvu+pgF4h2Nvbw/d9fN9XsXjLJd98/ptqCm8F\nk8mEw8NDRFORxRF5nqvCmqbdJGFB25JmFYdHRyzXK3KpuvOyS6EyTTVB9vt9dnaG26am7ibU9WJB\nuF4xmZzjuC7j8RjTcojjkDzPKcuMJFaLzaaqyZMEz3VpW42qriiKgn5/RF1VPLi8xLEdyjynbqBu\nYDJfMuwPybKS0WBE27bMZyuqao3veyphytSZzcNXpV+p1Cl/S/PTdZ0sUx+3XYfd3R1WqxVCQM/3\nkRLiaK1CR1BopqbpNHnBzmhE4PvEcYxlOezv7zMe71EVpWKTdO/pMAq5e+cetuvxiU/8uKLlNg03\nbtzglZdfZjgYsrMzoKpKFouEwWBAkoT0+2ofNBz2X1WXHnmZfPv6Vdc1rutycnLCn3/tkiQr6AUe\nNA2uZZLFMa1WdRTJDv8Wjxgxb3Q2hfsv6sC/4zTCL3zuXwGvvuNcZZxcXWK+9o959Z0SEKroy414\nRz1I2zbbkUnXFV5eykZ1562kbGqkpvGHn/0cn/vDz/HhD3+EIBhweHBAGicYhqloWXGM1CRFnqNp\nkjSN0TXYHQ2gbTjY20XoSgxg6iZRGFNXilcrhFCJ77RYXTHK8xxDN4nCkCxP0Q1l+G7bLk0DZ2cP\ncRwPwzQ7WX7KYDCgqhru3ruHEJI4Trlx8yZ5VnT0L50WtZmvqor5fM7BwT7z+YyirPBcj1s3bzNf\nLKmqbkwzJXlWoElNGfIXJT3fZzab4Jg6k8k5h/t7tG1D3dSYhoXQdKTU0Q0LXTOUp0VTEkURpqmT\nJjFlq9JNDEPfwmRZlnN8fIMoihRH3HG4e+8eJ9eOt5hm3iUPqRuvCpCuqoooUvTBqm22HbSpaaxW\nC4LAx3FsqqpSvPyiQEiJrlWAVCZLgx3yokJoBgJ1raVZrBJ/qhLRTW2WpRJkNF2jrirKPCPNUjzP\nYdgf0DQNy+WUulHZooahKIyapm/N+6MoxjItdUMuVfdcd570jmV3eLMydfICT/nmSLANm7SDlgzD\nIE0yWiBJkivK5JayqsiSkIPxDnSPSd0gzwqyPKdpVGGpygLLMplMp1w/OSFNVYEejnZJ0pyirLsE\n9M4FtIWqbinKmqef/kPe8fYn2d/bQRfQC/rqpmiYhOGKosjRTR3T8rZTxWZKBrrr0VBL07alqlUk\nXFkUmJaJEGDqGnVVMl/MaLuPI1pmsxlJkryqcesFKihcRSQ+EutVVcUfP/0nXE4mjPcP+MQnfpzh\naEcVwbbGMCxVA8oKy7IYDQYsFgqO0XUdqYFtOQi5oSqLLRz+RvXzKtFiA/UmVczF+UNW8ym6rlGX\nOZ5t09QlkquhNJ13eXdDfr1Ah6//xz/aogvveM+H/vrSCNV4pXDtq2pMeLSk3FwUjyTzr07C2C4M\nDElb19SAaFtF1Nc0TMPcfo0QAs3UiLQKmZb0dI2Shn/8W/8HLz/7Av/T3/7vsSybeZVzvphiZjXC\ndXFcF/CompYoyTi7f5/xeBe/F3DvwRkn14742le/jhtYOJ6D53gYuoHnevR7PZVdadlkea5SPgAw\n0TWdXt/nyD+gKHLyvODi8oIoijk6usZqFaJrFo5j47gebdtyenrKoN9jMplQVzlVnuDaNoahs15H\nOI5HUZacnz2g3+/j2RaHT70VKRXjZzI5x3U8WkMipMDxXeI4Jo4StEai6XB5dpc0Tdi7dYPHb7+P\nui4UN1mqGKvlYs3lZEaaZji2i2nbmKbBwcGYOIrw3F2iTFnjrtdr5vMpcRyTpRm//2//DWVZslop\nxsK73/1uyv1dpNZy48YNZfM5WxKGIUmSMJ9PuXnzJoahlkCe46vnKk5ZZjGHh/tIqUbaXi8gSWPK\nsqIsC/J4SRgn6LrF9PIC0BRttGrYGe/SC3oYhk5ZFpR1w3K5ZHIxpW1rev0A13FwXY+Tkxtkecr5\nw4fs7uxwfHxIURYslyvquuXBgweUZYXn+lTdtepaJrqARkJVZoyGQ2hA0wT+7gjD1FiuVni+SxTH\n5EXOw8Vaecw7LiIIumLScHztgKpuuxtcTl4UFNmaokyxDWVtbFsaum7iuhZpmtI0FVKq98DR0SFJ\nqKa0VRiyWs6Rmk5T1ZR5QRjH+H5AlikXR91QNNjhYEiW5bi2xWq17ooxjMd7JEnMKlwSx9F2H2Vb\nyhpitVp1U0CBpmk4jodpmLSturFcXJxzcvOEKAy5fu2IxXKONEyyJO7CUhw8z9tO5oZhkaUqkjBN\nVcKVIikIpNSYzxfsHx7y/vd/D0W5MQYzee655xj2BxxdO8SQgpdfvkORFdy+fcLl5ZQsyzg6OmI2\nm3dCKYnj2leaxTfubzeQz1Y1LnWu3zjB9wPuvPwShq6RZDm0DbomuvDxutOCWMC3JtVvzlVb7W93\nvuMd+Of+/WceFdYrgaCbX/yqG+Em03JzrjJWpJTkZbclVsRPpFBiCDWSaRQd4V7TNQpbfZ8yL/iN\n3/hNwnXIj/3I3yKLU5XhKAS+F0DTkHVvmFZIwjjF6w0wLYtwvSKNQ9o6h6rgcH+M7RnohkSXOlVZ\nkaUpdfXICN5yHHRDmTsZuoGh6WSFGufrpt5CRIZhKHOsJAEkqzCiatquO2sZjXbQuyi4qny0ta/q\nWnXmaUGapNi2ulCauiFN1TRhW+oxWvADn7ItydKMtlXFIo0THNdhdzSiaSrKMkfQkiRK5i3Q8LyA\nVkg0adAKhXk6rsP9+/c72KsgKTYpLgV13TAcDruL3cBxHNI0xbaVU2MShYzHY87OzhgMBhimiet4\nDAZ9mqbl7OwM11U4d1U0BIHfLbtbRRlEeU7rumQ83lUUtCRhGHi0LSpuCw3b8UmzTNn7IsiKnDTL\n0DSpJgpd7xSYDUVRkGUJZa5sZrM8xTB0bNtGtIrj7rj+Vg3qeT5ZkvHss1/lHW99G7ZtURUlCEFD\ni2kayqO8euRzXbc1uqEjNI3BcIjWalSlcq8rioI4iclL1bkjlBikaRocx8XQW9I4xLQsLNsmz8tt\ngTVMU01GdQ2oabEoUqI4YTjY4fj6dZariLppycsNW0vtjPJCURV/+5/9Fp/4r/8Wq+Wcvd1d2kYR\nNdQklWFaOqZlIKWhbINL5W1fFCWWaW09s7UNkQC6bh/6gz5JV6yTOOL46Ij1esXl5LLD8TWaMu/2\nWBLTtLbPs2reoKoVzPHMl55BGDpPPvkk73jHO9ENFV6+Wq05ObmxZSq5js3OaIc8LzsIDcbjHS4u\nJuzu7m5hWddz2PinmOYj86mOsrLtyjfqbSnVA0Vbd++1kvl8xv07L2M7pnK5ayu1DO30GhvKshDi\ndTMxv/LFP9h24N8u0OE73oGri9F5Ff1mU7Q3ONTVJeZmwbTJuSyKgrqut138VkpfN7Si7XIYK0yp\n8vrSLEWgQVJwFs75h7/yv/P2k8f5mx/4AXzLxun3mF1OcUo4O5tg7vQZ9HvYtkVWqrSc5WKBadvo\nmqTfH2DpknC54JU79xnuBHi+wncHQZ+dXR/Riq3su65V8nZRVehSUzFOUqhCKwVlqWxV0yRT04Np\nIYSGaDvryk7WTSdZL7LOxa2uSaucNE0RQrIMY05OTjBNS8XJ6QaWdUiR54gOZiqKnCgOifOMy/ML\nojhipz/k+Poxe+M98jylbBpsQ6MF+ocHXexbjSZbFfNlC9oGdoY9Ts8f8s1vfoP3ve99mKaO49nk\necF4d6C8udsWz/WQmqJR7e+NaGplW1tWKVmWc+v2dXw/YLGYI2TNdHbR3ZhyTCugLBt0vSVO1ggg\n6AWUlUZ/sENdl0wml7z00otYlsljjz+GgejyF6ccHB4xnV7ieT50vhemaVCUxXYMVkq/BF3X8DwP\n33eJw0i9LmXd5S6CppUdxVXStAa6btC0qjm4desWaZqg0dI2NY3smFKA1DUcy0TXNFqhLE/zslD2\nuosFhlRwjK7rxHHMcDSi7VzuiqLEtPSukLWs1ytEqyx4LcNQ7noIsrxAl5I8V+IcdX2pUd53XV54\n/ht84Qt/yu7uHrdvPwZSx/V9FosVluXgWDplUVGVGVK0BL5DliaYuk1/METTNZq2Zh2taFY1rq32\nFrZlq/deVROuV51Jl6mSha5YZtR1zWRyidUtXfuDIQ8vLwlcj6PDa9y9e5e6aikLZV5V1zVRFGOa\nFnle4HoeWZYDkvPzS7727Nf5H/7Oz6pkLF/ZPDcNGIbJnTt3ODo4oKoqfN9nOptweHCN3d0Bk8mc\n8/NLDg/3SZKMNE05PNwnThKSJO6gu5rVas1oNEIIyPOyc5tsO9ZKu11GCq3z0tFMdnfGNHXN5PJc\n8eU1g6rIKMtcUQ7/Ml7fr9GzvO7nfKc78P/4hX8HPMKUNtztq0rKqyrLDWyykdBf7dxbTUcTEkPX\nlQS5KKFVHFOpSfKyxPYVDDGbzfgnv/kb3Hr8Md79tnfgmy5RFKE7NkHQx5FKTZhUBeFqTZbnSM2g\nPxhgmA5Zniu8s644fXCf8WiHssip2hwhGobDATTgOSZZmqFJiWlbnazew3IcqqJE13SiKETTte3d\nvu7W1J4fcH5+zpe+9AxtKzi+foPBYEC/P+iepxpd0ygKNVImcULbtkymUxBSpf9IdRNI02wbBF3X\nVScqyrsw4pbRcMjOaMRgMGC9XCrL0zjuXNs06qYkimOauiZNUy7OL0mSlDCMsW2H88sLxnv7vPs9\n78Hv9XBshyxL0TZTQqV8qlXItEWWZViWxWq1Up24qzDRoig710OFZ49GI1W4ukxS27ZxTYesKJjN\nZuiGgWXZTKYThBCMRgNGowGLxQLaln/ze/+Kj3/84zS18msP/B6r1RqEIElysiJHGroKm5ZqB1E3\nDVEYqtCPtsE0DPwgUNS9PEU3DIp8TZ4XpFnGfL6kKFR3eHiwj+dYWLrGoOeThCEYusLjpUYYhtA0\nylulrtUeRpOUdY1lW+RpoWT9UlLWKkP18vKSIFDJRUfH1zrWh/IrL4uMqqppW5VypEmJ47pkaYkX\nBKzWoVpK1hV5HrNcLEnTlNFoxN7eAWmWUVVqL1NWDUWpXt+mqvjjP/4jfvRHfxhT1zBMi7qGcB2T\n5jlCg17fVxa4maL6FkWBadpb10DDMLY7CeVvYmDY5tZyVxXmaNvR7+7uMpvN8DyP+WyOqAuaFuqq\nJklTev0eVaXYQtPZnG889xx7e/v86I/+CHVT0+/3ieIYwzDJspzbt28znUzxHIskjhj2+4pfb9iE\nYcjx8bUtBBLHMf2BuuZ1XSMIAkCwXq+6hafaSwSBv7XD2ODY246cKxi3UI9Mpxe88vJL9AOPuirQ\npapH2hVo5h3v+eC31Mivfuk/bP//1zoT85k/+bdbHGhD4/K6/MmNf8BV7HsDl2y67TzPt1mVSFUA\nte5z2lq9OLZjqxR2xyYrch5enPO//dqv8V1PPMUH3v+9SMvAGvSo4hwtq7Adl8ssxLRt+oZNkhVI\nXSMKQ2bzOZZpoek6w8FQJbs3EMWJenOKmratyLOUpi65dXIDTarFmK6r7X/doJZcholj2tiOjdQU\nhFJVJVmWqY3/YkV/MMBx3C0kVBRFR2eLUYkjjy6as7NTXnzhRdIs5aMf/ZhKCTdt5XmRlrieh24Y\n3SZ+juPYBEFvC1kZuk4SRfiet2VNWJal+MltQ0PLYjFX1qemie8GtC0dXJFStw2aoTpRlWaiXjNd\nN7ZGU1WlFknqbwS6ZWLTVKxWa9ZhSJZl6LpOURSEYcjtW49xOZng+z51VXHt8Ej5zgj1NphMZxwe\nHWEYBrPZhDhSfOrz84c8/fQfcnJywg//4A919MiSqqxVoG5TI6RaardSkIQprqswb02TXddbKPZQ\nllGUJY7tIKVAUnL//n1miyWe30MIqTp3CdQlb3vLkwx8B0OX1AJaoayUDMNEExpt3dIKwWodYrtO\nt3TVWK1CmrblhRdeUB4lgz7j8S4dI1bRIU0d13Voqnor6nFtB8uyts+ZadlUVYPluuRFyWQyZbmc\n43kuN65fR3Qe1iBohaBuFIRSNy0tLYvJJS++8E2+613vwHVc2lagGy5l1VC3LVmZ0aAojrbuAmzf\ni0mSXpkexfZ9q+s6y3CtYArXVdawpWoQ1us1ILaCIikltq4xGAy4c/ce9x/c56WXXuZyMuXmrVsc\nX7/OrVu30XUVUm47BkEQEAQ9TNNiMplQliW7ox3SJEITShB1cnKdhw/P8XyP5XLFzZs3uXfvLr7v\n4boevu+RZSlJopgqWapeF9e1yVIFX9mOg2kYSE0iRefdItS1DC1CE50/j5pUFvMJL7/0Ar5rU9fK\nW0VduWoaeT0I5WvPfHaLSHw7N8LveAH/3L//zDYyzTTNbbe2KepbXndXsDcf2zh8bbb+uq78joui\noMgU/GB3F9B8tcT2XAzHJkxifuOf/hY3rl3n+9/7fvJ1TEnL6WyKKTRsJAjB6OQYNEk0mZOXNaZl\n4dg2fd+jzHOSJOHhw3OqRvHLbcdnd2+ftq3JsoSHZ/dZLWaYpsbJjeukcYSmSUzHwjQs+oMBfX9A\nGik1WlHmmKaB1OUVW92WLMs4f3jBeHeXIFB4a5pn9Ps92k7kUdc18/mcr3/963zkIx/BMDTiOO7S\nugWX0xmD4YjzhxcITSeMQvrDPkWhQitoGqbTqbIataxugkGJPhwbw7K4mFwyn8+59fhjaiJC5TtG\nUbTtYoJeH8tR04lm6LQNNK0SgWzgB4GCA9ZhiGPbCClZrUIWixVCCI6OjhiNdlSRlBpxpG6MTdOw\ns7OD53mcPzxjNNrh4fkFtudx/8FDlqv1tugeHR4hRMOtk5vM12f0/ICL83OefPwJVssllmWRxCkN\nXbPQqrSVnt9ntVozmUxJkgTDMPD8AMd16ff7lGVFluVMJhMml6cMBiP8Xg/bcVUYdVEwu7zAsXRM\n0XD9cI+6Liha1eHWVUUSZ90NU7lkSqlRlA3L1ZoHp6c0CK4dH2OYOoNBH8+xadq6s8RNGO/skKQx\ndV1jGcqGtq5qyiInidXN1w8CxURqWk7Pzvn800/zwQ9+kJ2dHXRdg7ahKUta1PI/imLyosDze5SV\nctR8+YXn8DwHx7UYj/fI85owTNFMG9v10Eydqi4oypx4mW6FJ77vMxgMCMOwE2wV2wbBtCw0Q03T\nm4696CCJXq+HlFJJ5w2DMIxwdV09b03D7du3uHv/FMdxOD07x7Zt4iwDWg4OjijzEMdRjUcYhrzt\nbW9jNBrQVC1np/cZj0bUtRLppXnGjRs3WCwW1HVNGIbcuHGsaMDrNePxmMFgwGRyiaE7KtO1E6H1\n+/0t2WCzmwsCn7Zt0HVJXVZoutZZ0iqjLilbVusFLzz/HI5pIESLIR+J2t753g99S43cJPIojPyv\ncQF/+rP/AmBbhDcS682Ts8Elr37tVcn9Bh9XlKzOa6BtMXSVDYiAoqrIqpLZesmn/89/zs54l4++\n53vJqhLNNvF1G0eazFYLpGtTViVGq5ZBpSEAjbqsaMsCy9ShKjE0Hcf1KGuYLtZUaGR5iWUZCBrK\nMicKl2jUCNGohJh+H6/rgv3Ap0gLknXMeLxDWRVIKSjrcot9OY6Ce2gFjm1hmnonWpirbrRpyIuC\n+/cf0LYtTzzxhMK7TZ2mLrAdlyTJsW0P3bBYhepmoVtmlyGqjPGbskGIFlPTaZtmO7k4rktRFsRp\nyr3799jb30eahvLJrmrsjt1T1zWWZdG0LUmWY5gmLS1t84jHqqYmc3uz3nDAq6ri7PScoDdSvthF\njut6HR++oNfrKQ+QSkndHcdBtA2aYVJWNeeTKY6rHAyFkMjO3tPQ1DRmOmppZmpqme3YDkkcU5Yl\nRVkq8VDTKNaCYW8nP103SNOMoqyoOo+ONM22y+jxWFEb67ZFSo28KGmbCtk2yq+8LtDakn4/wPJd\n6i7dXBNKit62UFcNSZbz4kuvsLM7Jgh61EIQ9AKkEFRlgZBgaBJEo4yqbLPbAWnUZau8yDvDpqrM\nVRxfmqIZJpfTKWGU8pa3qnDpLCuUiIUGXQpVbLp9RN00CE0nihNsU+e3/+lv8Dd/5IeAVqlyWwnC\nICshShLQwXFNyqrC0R2qst5OY6D87jfxcnWtJuWWbgfQ4dpCCIq83PqbeL6P1NTklSQJliY5PDzi\n/PwSwzJVUr1uslgukboSeg2GKnyh56n8ysFgSJplNN1ULhEEvst6sWA4HCBocTyVPHT9+nVms4ma\nMsOQ3d1dwjCk3++T56nym5+tGQ530HVJlhVX6pOgrpst1AfgO+aW2SO6vZGiDKp/s9klr7z8Iq5t\noYtHavPXoxH+f6aAf/lPf/9VaqRN8QZVGLRWYYQNLVXHm7068re1KjjKp0FDKxqkbTLLU3qGTRuX\nRJ7G0tf41b//D3jf/mN88P3fS2GrYNNHKlCJbujYtrX1YJhOp5RljmF4jIY7BEHQFdAZq9VyCzH0\nej1sx2KxmLNaxMxmSiF24/otoiQlDCNaIUjSkCgJsW2Dx3o+7/8H/4jhM19B/gWpG2+eN8+b5z/f\nI15P7XnFWuuNnBLhLyjg9+/f56d/+qe3Fqg/+7M/Ct9LZgAAIABJREFUy8/93M8xn8/5iZ/4Ce7e\nvcvNmzf59Kc/zWAwAOAXfuEX+LVf+zU0TeOXf/mX+cEf/MFv/aFXCvifff5fb8eTqwk6W0OYjsvd\nduol5CN/b13TtjigJiVFliFMHVGDoWlUUpJqMF0v+e1P/zOuHx7zvne+i2K+ptAkmq4zGAwwO4Me\ntSWv1MKk16NtGhzXJU1y0jTbbtCVRNhVHht1tcXpASzLo6mhqhryvGIVxkipYVomZVWArImiNf/V\nP/xfufWNb/4lX+I3z5vnzfOf6/lPVsDPz885Pz/nXe96F1EU8d73vpff+Z3f4dd//dfZ3d3l53/+\n5/nFX/xFFosFn/rUp/j617/OJz/5Sf7sz/6M09NTPvrRj/L8889/C1n9agH/4//wf20Xk5tf5aoP\nymZBKaUE7ZHTYFmW6FLSVApX1aWG1DVWWUzf8iApmLUFa1/nN//xr3PTHvHBD/8Azs6AKk4py5Yo\njrm4uMA0DY6OrrG7qzyk0zTt4rEsxSUeDhQ+l6jlxsZcq9/vbxd1G9lwmmRITVeJKdLAMCzCKGE2\nm9G0NaatYxga/93f+Xvob5DG8eZ587x5/v9z/ioF/NvywA8ODjg4OADUcuKtb30rp6en/O7v/i6f\n/exnAfiZn/kZPvShD/GpT32Kz3zmM/zkT/4khmFw8+ZNHn/8cb7whS/wPd/zPW/4MzZsh6tLSrgS\ncFwrcUArBJRsZfXUjcLBOnOguoW4rtAaSRQl2P2AMqv5d7/7fzN0e3zgB36AIs5w3ZpFktCzPHaG\nIw729rdqwee+/k0Mw6Tf7ykRR6JkyOfn55imgWVZ+L5Hv99HCMFqte4wPtWVDwYjhiNFFygKFRSQ\nZjWmpXPj5nXl6VLlFEX2ZvF+87x53jx/5fOXFvLcuXOHL3/5y3z3d383FxcXKmED2N/f5+LiAoCz\ns7NXFevj42NOT0+/7fd1XfdVisoNA2MbaFw98kHZ+qUI5egnhaQRDbKjKFkIHFOnMCWXbc5n/uW/\nxFxmfPj7vo9CwPjgAL1S8vwwXqELg8FAsQv6/R6j0Yj1es1sNmc0Gikqk+1g2jpxHHF+ft650nm4\nrovr+gR+j6LIWS5XXF5M0UyB1CSe67N/MGaxWLNaheRFhmGanXDJ+3/zWr153jxvnjfPq85fqoBH\nUcQnPvEJfumXfqkjuD86rzKTep3zF6mNriY2b7iim6/TNA15JS9va3bVPvI1uWp01cckbCvWsuUL\nX3yGh6dn/PgHfpCB46FZDrNwhee4eIaFved2sEhCWZVUtbIzDfwenneEphlkWUYYrkFWOI5LEAQd\nh1mJSBbzOacPTtE0nZ2dXY6Pr1PUKXVTkmUF33juG+i6Sb8/wNAt6qbGtgOicP26z8Wv/Mo/oixT\nJhcXNHXJ7Zs3FdYvJS++/AKOa7O7u8vFxQVf/OIXGQwGHB8fdV4Tyur04OCA57/5PL7j8/Ef+zHl\njNg2W08J27Y7d7gE0zRJkoTFasnp6SlxnHLz5Cbj8bh7XlUquGIMKD53FEV4nsdkMgPg4cOH9HsD\n+n3lPw6P9hjzdahuluGKOIkQApaLBeF6zTPPfJm/+3f/HkVRIoXEcfvYtg1CECUqTWe9DmmaFk3X\nVYq6beP7PqvlAl03mM/n5EVBGMaMx2M0TSMI+pRVRRAEihI4j2hpEaJVlgBCIETDYj5DSuUBfXp6\nhq4b9F2d6XRGmeW8733vYzRSPH8h2XKToy6J3ev10Q0VqVWkGUmSsF6HTCZTbj32BEWp5O6aYVAV\nMVL1HEwnE6I4VM6TuoHneUSRCm/2XZ+qKkAoZovQNaXYkwLTsEHAiy++iGXbXLt2jaoqt3mSyoq3\nJggCTh/8P8y9acwl2Xnf9zu1V9393nftbXp6OCuHs1DmIpJiZMk2JRmGZdMiowSKA1j5kMDJhxiw\nPxgBAtuwDQSOY8NBEthWIgdG4I2xFK0WKUciTVJDcsjhDDVbz0xvb3e//W53rf3UyYfnVHWTM5QB\nfwhVANkYcvpd6lY953n+z385YDqb4rm+lZQv6A8SHNV0YQKeJ+lVWZ7j+z5VJYEoaZqhHIdf/qVf\n4tOf/rSIwVwJMU4Gfeq6AePYAAbhkpd1jjENw8FYnsNNKl+vqsmynKZpGI5GjMdjGl2xWq1ELWkZ\nPEmSdJTgKI4Fisxyzs0mJL0eaZrSAFku03pv0CfLxGDM8T32985RFRVxHDOfz7u6EoZhd3/b98Pz\nPHp94fpfe+cd4qTHaDjiwoXzvPXWOziOQ38wIPB9NuuM/rgnbCOtcYGdnV2Ojo4py5LJbMZ8KfTX\n/miIsiIjCS8R+uTW9oT1Ou32Z57niYujo6AxvP7aK/CBd9eC33/xty3JooGPfur71s9/bwGvqopP\nf/rT/NzP/Rw//dM/DUjXfffuXfb29rhz5w47O5LYcf78eTE3t9etW7c4f/789/nK/z0Av/CLr/Oh\nH3qOD3/o+c7LOAzDDmcWrqy1iO0CGQxhGHXF3xhDowxNqal7Hv/u977CK1/9Gh9+5nnKQBRt4aZg\nMu5TRT5qlVOWOY1VTA2GCUVeUtcl682yg3QENulTVgXGaBbzlUiDg4jd7R2yLGc2VWw2qfBwATeQ\nn0eMcTy0NlZ+Lv4QNJrK+kF87zUaj1gtGxH9uCHr9Zqjo3ucHh/zvkcfwfGc7iE4d+4c4/GYxx57\nAt/3Wa1WHB8f89JLLzEajgnCiIM7d9mazYjCkKLMrQrSZ7VaUtcVd+7cIc1SZltbXLx4kSAQD4nV\naoXn+l0UXXsIbzYr4lj8SCaTEUqJ1WmW5WAQP5S4x+3btyUpJhoIj9x32d3dZrFYcPHCZfq9Hh/9\n6I/I7zyUhuD0bCGWs8aQ9BKUUt10VJZVJ1DJsowoDDq6l27EY2W12lilZyEiHavUnUxmKOWQ5ymF\n63H79i3CMMBRAYPBgN2dXR555HGSOKbK1gCcHB/huS7Xrt0kSRLe//6ncJDdRc9yjc/ORAUaW5l4\n29jEcWJ9vj0MDavlkjAQW9Kirtne3WFcjlDKEAUBuqkZ9Pv4NuzB0NpBgK40jTWwyqwnOTTs7m7j\nOIKdBr4UaZBAEsdx6Pd6KAONrlkuFsymEzYbcYlsGvERaZoG175rxhjxlqkqwiAgjmOSJOmCqPM8\np9dPyLOMMIrw/AA/sA6ISpG44q1T1zWrlRheRaGPikIGA6HC1lVDkaU0RuO5Dl4iP3NjDFUlzZPr\neqSbFVEY0EtifBeaMsfoEoMhDhw83+H43m2qsqI36OOiKdMlcW8swRF5xnq9xnHEKsLzXDzPIU03\nKCXRgW7ocbo4ZbK9Jc6S6znLN9bs7u5x/dp1/CgkSmLCfsRqtRRKpA2ZPj4+Znt7SpbloqcYDFms\nlmxWK/a2d7tksCRJyIuco6OT7hCZTicUhaVIegHKGCbTrfeoBP8v/9M/+Eed58sfdP2BS0xjDH/+\nz/95ZrMZf/fv/t3uf//Lf/kvM5vN+Ct/5a/wt//232Y+n3/XEvOFF17olphXr159Vxf+4BLzi1/4\n110n3T4EQBeT5vhyxjjWRMdV0uG1ij7JDRRsfFPUzPM1/+QX/wmf+PDH2L9wHr+fUJ+tqI4WbMqc\ncHvMZDplazIQ46YsZzGfk/QShoNx93uXZWl9VqQDHY/H9Hp9tG44OTmhLO5HuO3tnbNpIHdplCYr\nMhzlkvR61lI0oigKDg5udxFxn/3Zn3vX/f6n//QXuHd0hzgKWJydcnJ8xM72Nhf2z7FYLSirnCTp\nd/uCNE15++23SZIexjRMp1Nu3LjBQ5cuYzSUVcXly5fxXFG+TadT0nSN90D60Ww2oaprojjGmKZz\nu+v3+1y/fl3ucxDbl038WMIwJMsKlsslV6++zdWrV3Edj5OTEy5fvsIP/dAP8cwzz6C8hHv3DnE9\nl816xee/8Hk+9sMfJQojtDYcHR0xHk/Y2dnBDSXRZrVaEYYBp6enKOWIx3UQ4Lo+vu9z9849siLt\nbEQfeeQR60bpdQ51y+VSLAWOjshL8SupdUlZ5KAMH3j6/RhA1zVnZ2esNxuyLGOUyFQ2mY4Jg4Ak\ninEcUZ/2k4TT01PCyOf8+fOUuma1XrNaLCmscvT09JTt7R3xZfcDK9DS5FlKWWYUhQhedFVw69ZN\nPvkjHxcufFFS1zXD4QhQ9/MQH8hy9QOXo6MjTk5O2N7ZkSIbJbab9jr+em0tawtrKet6Hq7jojDo\nusIoRa1r4Wsr1bl9btYbppMZAEWR868+9y/5kU/+KHEc0+/38TzfOvb1xMs+vO+m9712z223+b1X\nqz14UBsgcKlHVYv9sTDSNL4f0A8cdN1QVhXKNnFKiTGYMUjwSRzjui6bSnf+7EdHR9auOacs5R04\nOjqiaURU9thTT3J0dES62YBy+PAf+RDf+vZLPPzwFY6Pj2nsszmdzkiigCLLKYqCKw8/TFEUHB+f\nsLd/jiRJuHd8RJwkBGHIwc2b7OzskvQSbty4wWQ8pteLZULc2eb4+EQESwqSOKTIJd0+SeJ33auv\n/7tfE26+cvnwJ37qP4yF8qUvfYlPfvKT8jLam/63/tbf4sMf/jCf+cxnuHHjxrtohH/zb/5NfuEX\nfgHP8/h7f+/v8alPvbv9f7CA/85vfa77MFvDm7a4tGIO15WUa6UUnut1lEPH96h03UnrN6OEf/H3\n/yHnhhOeeP5ZGt8hcFxcz2OY9HA2JevVimvpKaqsmYzGTCYTDPddD1sXslZUpJSiqsWfJU1TgiDE\n9wP7oEmxUyiUI7x0PxBBS1mW3QOpdSMCHk+8IbI046d/5mffdV/+l//176CUQdFgdM21d94h8H0m\nwyFe4IEj3f1qtaKxwQF5lnF0dMJT738/87MzNmkq47RN5HYdh8FgSL+X0DSaR648TNNolssFD126\nxPHJSffi13XVJWfnec5oPJQ/RyPSNKWX9AjDmM1mw/Xr15lMZjSNYTbb4s7tu4RhyPGx2HKOx2Ma\n47K1LT7iVVWSZSmj8YjGKEwj4/Nmk7FYLfEDj/l8ju/7XLnysCy0K3GNMwayTc5sNpPvX0iX1doP\nt7AQ1vM9jESh6DoOmxwrDFmT9BLiOLQWq17n+miMPH9VUXbB2UWR4SjpYgPfZTqdsFmvxM+6qvCi\nUNhProvreGRZxltXr/K+Rx9FKfGYV5Y1hZHn3fc8smxDGHislnNcx2FrMpGGpKoxTUNVa5Gzyw91\n34nTl+6v1xPvcMdxMA04yumKcGs94XkOtZbilqYpYPAcD4zCsalXRVV2MKXWGqMbSeQBgjDgC//2\nC/zYj/0Y88WCJOl1ZlqO9Yz3/KAT4DiO26kKm0YSmHQbyOyJF7xuNK6jCLyAqirxg5AojjroR6At\nB8eVwBXHcSiyrDOta90ZV0txrawqCUtGKYoiJ4hFml+WJev1isFgYIVqAUkSs1gsrUd6Dz+MbJfu\ndLVmMpl0h4uxcvnlcsnuzjbLxQrXcayvUECSJMwXC3F+bJN9lCL0fCpruyA/Y0WWpfT7Mp0ZYzpv\nINfzaHQFpmY4HL6rFnzjy7+GQp7R5z76x//wCnl+9/P/d/cQzedz+v0+0+mUPBfVm9H38dtGN+Kp\nawtZVuQo34Z/+h7/52/+KtXNI/7Cz/wn5Fh7y6ri7uKUyoOB8djrTwi2Je7s3uEh9+6JfeXOzg67\nu7v4vuDtrdTX8zx5ER+AdpbLpZgqJUmXz2mMYT6fs1xlDAYDojDEdaQYrtdrzs7ORLru+4zHY37y\nT//Mu+7LL/7iPyAIXI6P7/HSt77FT37qU3zkwx8hXa359d/8de6dHNHr9VBKsdlkgsUfn9DvD20Y\nbUm/N0Rbw6ler0evn3B6esJoOGRvd5eDg1u89fZV/uJ/+V/h++KfXdi4rjYarH0Ztdb4gSxwfV8w\n09//zu9z8+aBleyL0+GdO4dUZc3e3h5hKEVDPmOxmS3KHG2sWMtagwZBRFmLy1yv12e9XnLhwgWu\nXbtGmm4wVrH5zDPPMJ1M0RquXn2Lt956mwtXLncTRRSI93WRp2xvb0uW5XzR3Xc3FI/nc+f20Vbl\nul6vu+etqrQ9sAMGwzFRFMpLu1ziYEizNUeHdzFGs7+/z3QsDKRVlrNarVicnZHnOUPrpX3h4gVW\n6xXaCHZ/Nj8j8COyPKcXR4BhNOzhWOVk4Pn0koSyKMUrJUjQ2tCYmob75m1FmZHngiePxmNQDYEf\noytj3ynTPY+r9cI+Iyv6fbFfMFpRZBVVXVM3mgZjdwZ9XEdRlRXj4VBUtqbhc7/0r/jRH/1RNpuU\n9SZlNpvR7/dZLldMJlNA9IWbNCUKk65z1lrTGI3ve5Q2BxPEubKua+qivu/M2TRog3i3BIFNHhLB\nnOu6GDfkbD7vJrM4CNnf3ycMQ+JI7lk7MYaRWBO0Lo6tUZrn+cRxxHA45OTkxJYdz8JGA3zfZz4X\n0kLbDMhz70roRi1ff9Dv38/jbRTb29ukecZ6s2G2NeP45ITIl/DujVX67u7uyo5oNOosQqJIpnE8\nH60rqmzN3t7+u2rBK9/4Aq7jA4onn//kH94C/qXf/qXvkse33W/7Y7nK6YypWrxbNw1FJfmHqS3i\n3/jmi7zw1a/zYz/5KS7u7ROXhli5qCigdqFyQDug85JoWVB4EZ7F+8qyIM8z69uxxpjGSoE9O7I1\n4t9tTZxa35bVamWlsve5644KMEY6uzSVwicfvHQr2OL4pz/7n73rvvzzf/YPeevtN+gP+jz7gacZ\nDgZURUkvjmmA+XLOq6++RlmWnJ0trLmOSLsXiyW9pC/4pjFESSQdXVmSxBGNaSiLgrqqUA4MhwO2\nZ1uAIYnvy4HbLiQMw66I19bfWPIgx3zoQx+xC6qC4XBEFMUUeWnTeKKuqxmPhtKdGS1p72WJbsQv\nOs1L0jRns0nJ8pzlQoquyOsrmlr2BFWlicKIOO5z5coVrlx5Hzfu3CHLUoajIcvFkjAIJJvRxrs1\nTc0777zDeDTi3KVLgnk7ijyXXMuqqvB8sYD1fQnwVcphvclRChqjwcguQmGIoxBlxB5huVzQSxKC\nuI/nekRhaG1+G5bLJf1Bn1pr/NDH8z2qusZzxZ7BcaAqCmu1kOG7HlWRE0exyOFRFGVD0yiUA8q9\nH2zSBuqKxeyYWteSL6qVtTeFosgoipwoDikK+V3W6zX7+3vkaYmDjx3crFeHLN0CPyAMAsosw1ES\nsfcrv/7LfOYzn6EoCoEzra+4Uko82W2aU5T00BpLPpDDRjmyNBaigZEJzHrxF5sC5VjCguvjBwFp\nnllYVNvfoyTNMkw0kGCMOKGuKuIoIl1vKIsKkIAEsUAIqKslSZIQRZJ2FIbhdz3TnufR6wn7a360\n7OTz/X7PLvlb33ptJ4RAzN2qCoVM/kVRsDWZSh2yE0NelCT9HgZDZV00EwvFrVYrdnZ2yfPCdt9i\nUVxVFVVjqOuCdLPgmaeffVctePnrnwcje4ZnP/Lj/2E88P+/rhYvazHluq7JsowkScgoGMZ9PKDJ\na7wgoMZgfB9CWbod3T3k5ju3+FM//immoy28qsEPI6qmoalrNosNQRgQRCGDqE/jhOh6Q60z1umK\nOIqZzHrkeU7Sm7BerWld8uI4wnE9fM9nsZxzfO+IwPfF4GgwxPfdrisX/xLry+y59PpTVqsNZ/OT\nDhecjMf4baDC91zX336LH/vkH2UwGMiWvzEEQULdiM/HaDjjox/5BFrXvPTSt7l27Rp1XfP8889w\n/foN8jzn4OAAP/BwaymWfuiDq7l3eE8eRC/AMS53Do9J85qHHrrEI08+gbx8NRjx7M7SlLsHd3nx\nxRf5+Mc+xqOPPMbBrQNGwz69MCQYDjk7OyPbLDi8e4s46ZMkPaqmZLXYkOc563zdmY0lSYxBSQEt\nNfP5iu3tXYqyIkLhuBG+57EfxQRhwGy6JRa7fsDh4T2uXr3K2Srn6y+9wtbuPv1xD88P2NoR97qT\nkxPyIuX4+IjFYi4ZjL0h2VtXefbZZ+n1EvEdryrSNKeu5aCOIsF4kzhie2uHLE3RjaGua+bzM9JN\nyhkrUDCdjpltn5cw3mqNG/nU1GjkgAqSmLwoSTcZ6/WG0XAsQdZJSFXX9BMxPUqiHr4fEwUenlOi\nlISGGNMQ90d4QUCZy5K9aeTQ36zXbNZLicyrQ3xH5B/aKpMVin4vxHPkRe9FPRogzTRXr97i4qVL\n+FFIWZVURYljHDw/YBhGwjaqNf1xjKMcDBqUy3y5YLNZs7O9QxBGqMbF90PiwKBNRZFnZGlGpQUW\nFFaMwfOUXYA7xFGM53ty2EURkSN4r+u6oOT7DhMJIjGeETMtJyZwYxoVCqRYg4fCFAWjXoTqxzQN\nuI5LVWlAE1jhXFk1RL2RTJQWPnUdl7TIcHyB2iZegK410/1tVuuVpB85il4Uc+vWAXuXHrKpSDG+\n28NxBSLNsjVnZ6eMRkM26QblOLi5NDF5XrCzsyUum9rg+y5bW1O0LgHN1taWBE03Gl2VlDqjrkr6\nveA9a4EgaP/+3voH3oF/5Xd+5T2phC1jovYadF7SD2IcbQCHUhlUHFFoyUn88u9+idDz+OD7n+k6\nx3YReT9h5X6CfVEU4NSW7eCQ5zmyPDJEkfy7nutb72xtrVsBmm7M6oKVbRhDS38sLV6e59KlRFHU\nnb5tErnWmj/72f/8XfflG7/3bzrc/cHMvZYx0CpQW9qVjH9z3n77bba3tzk6OuLGjRvopgZHElAc\n5XB49x5lKQZKxpo+1bUm8H1mWzPO5kcUec4jVx7uDK48x6OX9Njb3aUsKoaDoQ0jFs+X5WKOAiaT\nCdOtmRhOabEvdV054NrfGwPrzdqOu6Ut4GIS5Lo+/cEQbZ0Vy7JkuVxZHFV8psdjCbZtjCHPcvxe\nnxvXb9ighlO7g4Bz5/aZTEZEUYjjOlRVQTo/7gJD2i6sXSS3U1PrU65ruTctpOJ54g9uFPYzTanr\nynZQuSQcuR7b29sslyuW8wU72ztsTWe4rkeZFziuQ65Laq0p8hLPCymy/L4HjyMMGcdRNBba0E2D\nwlAWBdAwnYyJ44DxYEAUh5SFJC0ZfHA8HEdRlyXafnaO45GXFZs06/y9b94+YDAcsL29Q+j79hmo\nLWZb4DguLnKPlANx7NIYwdE3mzWmcSiyGtfxaRqDH7hEkYRFBPZ+ts+4MQ2Y+/mYVVV136uqahrd\n4Hm+DW32UHa88P3AiuLE/a9uhL0jy3VtU5dqdNMAjoVe7aTuO2K57Lo4rgf2c2zs7yTTvUetNYHX\ndM+msl/HaTNSswyFBG9PplN8Rzr5vb1dXN+xpnP3F7BlJSZhVa1JQpd0k3Y1qL08zxPmSRAJNAz4\nscvpyTGz2ZjHHn3mXbXgpRd+S35+84c8kefBRQq825Uw22QkNk4pT3OiJMGJQupGU+maN19/g5vX\nr/OTf/xTGAw4Ct8VU/6myNlkMqIPh0MxmQ98giKgqDL74UvEVxAIXXF+Nme93hBFgmclifC/syzr\nxsl2+eW6Yts6P5tb3icEkWykh8Oh9Z8uLb1qRZJEnJ2d2ESY97466MgW8gfvjUy+Sk55u9SJwpBH\nrlyR4pQknNvfZ71ZU5Q5d+/elcy//XM02rBcLslzYdf0hwOSJGExP6OuShqtefvtd6iqivc/9X4m\n0xnr1Zo3rr4tAcBKRsIPPP0U4PDIo4/juQ5Nozk6kmzB3b19PMdlsVjKAlfLIRRFEQ6K1KbRH9+7\nxf7eeUBRVZo7B7fwrNG/HBoz8iLHdeTFe+edd7h3WDMYDpmMx9R5zsnRIdvbOwwuXSAvxKcm8j2h\nmhqDarQE5oZhl2ifpmJp4DgOYRh2I3frTa2UJDzNz5aU1aZbJoZBTK8f47oSHlHpiuOjClf1uHH9\nBq+99g6+6zAej7h37zt88hMfB91QNTmB6xH7DrUybO1ts9lkDHoxeV6B41LkJXghdV1RlyUHt94h\nigK7kwjYmu2wWS1ojGExX3Lu3D6NrtGNMFPiJJDkHU/CrLVpfbkNynG5e3gb5XhszbbxQwk93qxS\nSxkVO9vJZCoMkUIWm0HgkRcb+x6EjMdjFC6uCjGNEk91XaK1FGbD/SCWwMKSSt3XgHi2+WjhOaUU\nCgm8rmtNA12GKUCarqlrTRjGEnihhDLZKPlaVikicnN7CFeFwB55lgnRIAipyhI/jCktvXQ0HkGl\nabS4TZpGoFLTGMJAiu5wMGG5XJLEA3RlyMsVShkWyzlptuHC+XPUjYSFN01DGMZEUUxVbshSsZht\n33nf97umrZckNuBEfp+7d2+TpSlB4L5nHWhzBt47MfOB+vmD7sC/+IV/3Ql1Wj+Utni7rotyFcZi\nWY0GJ/CpATyX6zdv8Bu/+us8++TTJF7A+YsXpLOpJenG932iOMZzXdIs4/DwUAIRoohev1VTiqm8\nWHyabhHSYliyxafzLG550m1XIUkpofX9lezKqqqZL+bUdc2FCxfs76W5fft2Nw38uZ/9+Xfdl29/\n7QsdDtwubtsH3/U8PN+TSC/ToGxnXpYlvV6PLM1w3PsJ2e3vkaYp165ds53tUjyrT07EmD6KxHSe\nGqVczs7OuHTpEnfvHHF6etb5s8dxYkUXDp5SPP/889w+uMXJ8RHjkYibtre3GU+m8tI5jjB1tMS+\nNUYeYkc5LBZL+v0hGAgD4convR5ZlbJcSuFfrdeEQYTjugyHA4aDIY1pyNKCt956i9Fkr6OdKuXi\nKIfhaIRSkKYbNpslaZqimxrX2hzs7OzQ6/VYLCRh5cGEoBbmcQOfMJIIuySRRJamgc065ezsjKIq\nO3iGJmBvb5d+P8FxFJv1EmNqwsjDcyCKfKbTCWWVo4uSsijJCxn16wYa46C8gJPTOZs0Zb3ZsL2z\ny3gYEPoOURh1zKuRTYGp64rNao1uNGEQsk5FwFSWhYVRxHpYN5rlasONGzfpD0cMhiNGo4mdNjLq\nSqAOXYtNhedJIlG+aQt7QK0L7tw54H2PPswqWiQ0AAAgAElEQVRqtcJxfFTj4roSxO16ygYuG5Tr\ndHzyopBYv7LIOrZYO+m0KTztu+65wmQJo5Bat8tYRaMNutFgWkpijbYL0tZL3YBYCCOTlB/Iclws\nZz2CMEQ3EoLSGEs9bBocywqqq5p+r9e9Q63FsQI2a1mCSmpSbpsqQxiKdmCz2RCEIX4QsNmk5Jm8\ng1WZWt/4qtORnBwfdRbKrissm8APwDFs1mug4WMf+/H3qAWfByXN4h+USv8DL+Bf/d1ffWDEUd9l\nJyueJ5qiLIl7PYqqpHEVfpTw9vVrfOsbL7Iz3WKc9Nnd2mFtH5r2hrXUwDiOiWNJuW6XHFVeWaYL\ntEkagPV8NgyHw87TuKpESIBRnZCm9QEuCuGKi0ezTxDG6EaWoZKOXiHBwBWDQZ97R/f41re+yf/2\nj//Fu+7Lq9/6XbIs6w6Slh/fTiWt5cD9eygPeCscaP+u1gK5tA98GAp9sShLirxgs1nzyisvY4yE\nGhvVcHJySn8wZLVcM1+sKKuKMIoxxnZQjmI8npCu1xweHnJ+b5fpeMygnxAEPuvVmjTLqKqK4XDE\n3v45drZnHR1UciY9JJRWXrzFYkmvNxC2kGcsjNCQxJLSXhayGJ3NZt2hdHp2RqMifC/g9OyMMAgJ\nA1kSTiYTQNgQYRRQVyX5JqW2UNvWbMZgOLChu0JtM42M3aZpyHXZ+X47ygVcgiBiuVizXK86TnQY\nhqgmsArYiLLKKfMUP3BZb+Zk6ZowcJlMxvT7CY3WlEVJGPdYLTcY5bBOC5bLDddv3uKRRx8Tlelg\ngKk3uKrpip2rHFzXx5iGsijFB76StHnlKssVFjhNVyV5LofSm1ff5vyFC8RJH8d1CcNYfIMQWK3R\ndffOVVZoFliKLhgaU3FwcINnn3uasixxHI/AjdCa7v2CRmCNB+i/vu/Lc2vpeAJ7SNFuU5bajryl\n4hqUfdaF+eG6QmJwH1R5PxhbZpSlNdZgnyeQBkwCw+UzdR2h6xnTMjnvfz3P80mSRIqxDRJXCKsm\njqVr17UGVX7XIWSAMIzu+9s3QtlVjku/J4lIxt7XPE87qC6KfNJNKipMz0V5HkdHh7iO4qM//O4C\n/s2v/huMjWz7oR/+1B9eCKXlSj8oyW//bJoGT7liKO8qTKNRNpapKApe/va3+U8/+7PsjKZURdkJ\nBRy7ne73+wSBCEQWiwVvv/02xhh6vR7n9y8yHI4JgoDlck6aSoHZ2dmxQpUNZ2dnEgZrlXZJIiq7\nNE1F3ttIostgIDzOoig4PDrED0J6vRjPd1ks5qRpjue5vPXWW7zx5mv8N3/xv4b3KOBVVVnMcdNZ\n7IL1RX8w6AI5blqYpRVuOErhBwGO65LnJX7odxx5V1UEngTrRqHPT/7ETwg8ce0djk+PmU1nlJXA\nSZtNyv65C9w8uEUYhoRKsZqvJAEnjIh7Pc4WCxzXoSwLjo/udVDTcDhkd3eXwPd59dVXmU6n7O7u\nEIayrCmLkkYbhsMx+489xmq1pmkMm1KCh5umwTQGz3GZ7e91B+Vms+Hg4ECEH6qhF3u87+HnKArB\nzFs46+TkyLJMJMlpf38f3/fp9yXFfr1eA4aqltT6VnLtOA6TLeGoz2YzlHK5e+ceJyfHFHnVHegX\nLlyQJftGOvfjkzlVVdJLYsBlb/ccw1Gf9WIBGA4P75EMBnhBglYe/cmY46NTid+j4bln3s/Fi5dY\nrlb0kh5V7eI4Mu0sl0uCKKIsKqqypN8fcnBwu3tfyrqgqguMlobBdV2uPPwwSa/PdDbl0uXLNrdT\nDsLSprHXpaS+u67TdZdb2zOaSmhuQeBT1yW+H3B4eIRSonze6Azfj2QZGYWiBpX/oihy0nTTqWV1\nJXBJrStc1+kmynaqa3MxQ9spS6PgkiTxfUYaNqC8MdKV60b0D7aaS2xbYBu+pqsZAA1iKicahLoL\nQFeOSxyFFPmGxVyENU1jWFnb514cgylxHI1xtYUq5RlZrzdcunTJducRy9UKcKisbD7dbOxBpCyT\nTQ7hOAoxjSaJA4o8JRwOGY0GXLv2NrPJ+D3roh/4GHN/F/b9rh94B/47v/W5bgHSQgYt99XzrLd3\nFJE3NTUNNYaDgwM+9y/+JX/ix/4Ye1vbRHarrIL7OZmdayGCwbbdbMuRluV+60EOYLoA1rb7bWmE\nyuLOZVmSJAlbW1udsKE9MLrcTtelqivOzs5wXEVTC1725ptvcOWRh/nUH/tjbDYr/sjH/+S77ss3\nvvwb37VcazE0Y2yws83EbLHx9moFFe3PoiT/HMe9fxA6DtZrIgRamEpTlAVYUct8vub07Iw0K7h5\n64D5ciFS48WCxXJOkoiydD6fM52MUKYh9DweunSR4WBIEscUVcnh4RGr5Yraeo/keUa/3+te2I//\n8CdI00y44EVJ4Ic4vtdRv9pFbztZtPzZdrrKSlkK6rohDCPKsqKX9OxnBZ7v2QSfBfOFcKG7BBj7\nGQ4Gwn9XSjEcCnfe8aX7zrKcoqhJ05zpdBuQZZfBoBwRzgS+XWKbVrkrNLmyLAgCsSFO4pjNJkVF\nAavNhvnZKYHnkWVrzu8JpzwIfBvYIl238WTcl2QbeWZdxydLMxHu2M5vsVhQ1QVFlRH4Ho5yWC4X\n3Lp5k9F4zLPPPkdRlfh+iEHhewGmsT9rXuD7HkWR23dF4fku6Kb75yDw2aRLhqOeneA0umrw3BBw\nqOtK3hux9Oj2VuPxiLqu8By343srR1HkuVAf1X2ls9aaMIhwbXF/EDoFLN2yIook8QcUynVxLLTQ\nLjsdx6GuxHrDIN25biSftZXoSxaswJyOa2i0Jo5ii1e3zZ8mDH0wAsX5vk9tp/ggkALuB4FMCJZR\n1S7H5Wep8H1fqJxRxGa9oiwyO3U4BGFAFErq1P75C7z26quMhgM+8NzH31ULXnrh890i9g9K5PmB\nd+Atn7rlSLbFu4MLlMPZck48GlKVJWES86UvfpEPffCHeOzKIzS2g3d9l3Uqi8aW69t2n0WbIN80\nxHHMZDLBRTo2kW7LQksp1UEsy+WSMIy6Qrq3t49Sijt37vDKK9+hKArOnTvHxYsXMcZwcnJEUZTM\nVwscTx7A7e1tvv3SS7z4jW/w8//Fz/PIw5dZr1aEYfSe96IdP+UhKLpC3opP4jimwRAGQXewaC3M\nBT8MMEq6dd93aepGmAWuy2azRmuxHmh0I8Iau3RyXRff9dlsMsbjEX4Q4rkBlx++QhAEfOnLX0Sp\nhrLKMKam0oo4Cdls1uzubHFud4+7d+9w89YNdG1wPZe9vX3CKOKZZ5/lGy9+jceeeIKL5y+ws7PF\nyfGphM1ubUlnpBxOT0+pTRuIETIcDuj3gw6XXCwWzOfzTpw03Z52UIyuJdz43r17neowigSTP7e/\nx8NXrsiieT6nqiqKImO1WnB8LFPDzo6It4zRpOulZENGfcp8yaDfpyozdnbO0e/1cV2XdSpf697Z\nsTCSQvEN2RqNCUOBW9I0x3V95os1WZaRLpZsshQazXyxYNRPuPTQBTzloBpNU1foqoYqIytqlB92\nC/MyL+37IM/I7YO7vPLKK3zwgx/EcR3iJCKJE6s38Gi0sIvCMKCsK6q6Epiv1phGbKHb4u37PpPJ\nhKouWK/XhFHUNQPGKJJEfF+iKGA6naKMg6NEXCKMEoFrsqIkz3POzs44PT2lLAt8z8V1XGvB3CeK\n4u59fBCScBwH3WhKm0r1IKW4ncTzrKAoqw7T9n3f7pIMtdE0VUU/ijoFdGDfj9o0+H6A7dC6g8Ho\nCoUsS13XJQhFFelgaOrGdv+aqqwJPBvMbGB7ewvX8Tr2ly4LKl3b+FhFmm6IwpDcQqe+71uZvMH3\nJTA88DzuHd6j1+8zHPRFmPgeV9LrycTxh70D/8rv/ErXKbdFpRXyyEmqcAIfLw5Jy5zf/I3f5O3X\n3+DP/Mk/xfZ0htY1946PiQd9+tazo6Uvtaqolg4GdLQmyfobEEUxq5XQ1lpMu8W4JY+xBMSToV2M\n9fs9HEfZANVFJ68tipxC12gjvN2XXnqJ/b19/tyn/ywoQ11WskhxHJ56/j9613359guft/Jwz2Lt\nboeHr1YrEisCaBWh7UTRTi1hGNqlTEUUiHinhaikSIHWlcWTA4t/1kivo3AcTyTmrrjk/dYXPs+v\n/8avMZmN2d6ZcufOHRo8JuMRDpKCNOjFwpk+mzObbTGZTIkT60cRRmxvbZEkMWfzU4osZzgYsLe7\nx6A/tEHHmtAP8CJhLsgkUXW/V5JIYLAUnYK8KEj6fdljVDVFXjGbbYl6sRFBRl1XRFHIcrWgaaAs\nK/r276w3a+lmjXTzq/UajLzjQQj37h2zv3+O7e1dfC8kins0Tfu0GpFThwFBJAycshSnOl0ba7pV\ncXDrDnHSw1EOs9k2BVrMzRyFaSqMrpmMBlR5Do3kU3qWSlfQ4IVtZykslTAMuXHjBm+8cZUnnniS\nra1tsjSTbhaD5zk0WuMoOD4+ZjgcMplOhR1iFaFaG5tgr9HWXa9d5LZ7ll4U23soMMfBwTWSfsju\nrkyccZiga0lcj+NYaI+NxrMMLqDDuE2jqe1U+N1FSKTvSdKzzp45nu+hHEeokUbuc1UJ9KKtmVej\nTdddV1VF3WhbM6yvvtao1nbA80RABpJR2ZiuszcoPEdCYBrdEARy8GlrydEJkICmqVH49p0SR1JA\ncHatsSiN/fsax4HlUhbojiNhx71ejyxLiexBtl6LNsL1BOLDGJ754I+8qxa8/I3fRqZol/c/9/2V\nmD/wDrwNQW2XIEC3FfY8Dy8MMK7D6dkZR6cnfPXLX+Enf/yP05Q1+SbF830uXLxIbmqixmW5WAov\n1ZGE6F6ckBcFjdbSEViTosV6zsHtAxaLBbPpjH5/wGDQJ00POT09tbzfnMuXH2Y4mDAeT+n1+hwf\nH1GWFXEckiQxSRIxX5xx6+CmdL9JSJrnvPTNb/Iff/aznD9/gdV6SRSGOCgaY9pq8K7Lcz1CO357\nrohYjDEEfkAcJ98FC7UwSrvsauEHrbXFJxWBF1GWRdepC0bud7LnxhiCQDoM5XmUhWY2m3F6Oudz\nn/scb159ncsPXWC9WXJ2dI9+HDLf5ByfHNLUDbouee7ZZ8jzlDiJ8QKf49NTwjTj4qWL6LLm7r17\nFJZGNZlKqPOrr71OkRd8/Ic/LvsNbahNJSOu6zDqD8BAXuRk2Yosz+x04pAksfXbECZFrSveeust\ni69GzGYTC9VE9E1D4Iedb8pL3/oWjz/+OP2ehHK0XP5Obm4y3nflEfKiRCmX6WQs+KfltGd5Rpal\n5PkGtVHdXqTtKrO04I033mBre0y/L3YKummI3QDfl6Veb9hnvVgK08TvURYyGVbaoMuaispOpA5B\nGJPnOTevv8OdwyM++clPUFUV/X5Mlm3wfK9TKRsM/X5fhCJGPptNVnSmU22XGIQ+fi8mSzM8rwcY\ntJaCqcuqc3R0XU+88AOvYzolUUyjFUq14SsluhF4sZ0YBfKI8F0Pz3M6zUQLj8g+SNgbi8WyM9XC\nUgQ7PxI7eYZhiOsHtuBHRO3z7wAKsnRDWVX4CksmcKi1xnFdVGPQRoyxQGGUdPZxYDMIXIPjgGmg\nabo9KaBwfQ+M7Jsc18OJPHzfs18LPEuVrHSJ1jWbdMlicYYx95O6tK7YrFfMZrOOVLFarZjP51y8\neJEwCDro53uv9iATB8rvf/3AC3iH2z7wobX/cV2XvBFvhd1z+/xf//yf8eEPf5inn3wKU1ScHR/T\nOLA5KPH7CUMCxuOxPb3dbhwcj0Yd5qZrTalLRqMR0+nEGgBVZFnG0fERruvy0EMPMZ2KZeR6veHO\nnbtcv34DYwz9fo/pdGJlug0nJ8e4nsPFi+dZLpd846Vvgav4+Z//C6JozFKCMLRdcYBSDr733uqr\n9vLtCd8W5CzL8MOA2jQ0CkpdU+qaynpB+77PKm0XKA5lXaGsx0R7EAIdPNPCVDLCujSmocoywiDh\n6OiI3/u9r/HmG28wmYxZreckUUCtazzfpdeTpaLvuuzt7lJrjRf49MME5bhUdcqlvYc5uH0boxWN\nrnjqqadwHIfje/d4487bTMdj9nb3eP3110kS6VLOXdwlCEOKomS1WgAyScVJyGCYiBud7eY265zJ\nZErTGBFiZe3Y7Ip4J0158803EW8OcY/M85wPfvCDHUxX13VHuWw9ahrtECQxruNSFhWr1QLXDSgW\nZ53J19ZUPK+LWlPVNYv5ilrX5LlQDff395nNprYQCRtCVYYgEEpnujijyFMcNWS9Fq654/g4ToAK\nffpBTKzLzrIhDHxeefllnnrySfJ8QxBEHBzcFPuCokA1hjiOcIOAxdkZVSXWu9vb20S9RGxcq7Lb\np6TpWhZz9uDwLTac5wU7sy07xUEURqTZgtXmlCSZcnJywqk5xRiHwI/tpCzTSBiGxHHcmc81jTBm\n2vc7y7LOliGKejiOi+8rfD9gMPBELWyfzwcZV47n22XumjTNODsVH/0G60/vu4AUYTcIMaah1o21\nSfAoqxpPOdY22umoxWjHTh26Y8SIoMehdeRUuChlaOpKMHeULJOtb3pr/KZNhVKGTbomikL7XGma\nxjAdTwhDoR23XuVlUchGxUY3Bv5788DF8sIl+j5wa3v9wAt41Iuso5i4+jVlTegH6Loiq0rKOCEK\nQr78xS+RLzfMroyZny6YDMdcvPw+jONQ6pq0yKmzjEa5GAcW64w0PaUoKoLgpEvc8V3pGI5O5oK3\nDgbUDfT6I+JkxHq9RrkeB3fu4nk+49GI4XjCJt1QlYKlV6bk9PTEehu7FHXJS6+8zEsvvcRP/dRP\n8cwHxKFdlxWB59JobYu3IgqDbur43qu23ZPriYl/0wi2HVi3Q9fSsJRSOAHWFdGOh8rtHkaNZIX6\nfmhfSG1HvAcWxK54lIBBV9o+/BUvfP3rfOs732ayLR7SfhSA0fjG0FQavU7Z3dlmb2+f0WhEGMZM\nZlv81ud/G9cPCOMeL7/6GsYYBsmQ7a0Z128eUNku/PH3PUYQBly79g7Xr1+nLArLX5fO5eLF81y6\ndMmyA+Rwz/OcuhIowXEdhqMBWleAQ5alwtLxBCpxXZd+P2Fvf5fVasU3X3iBhx66xM72NkVREDjG\nsnogTTMi38PUBZtsjeNqlCO7kfFojOP61NbhUDc1Zd3gVIpKVziOKAmD4RBjIEtD8nWGY2Azl0Lj\nOIowioj8gHyTM+z1yVwP33UESmkqVCMeJsr1KMuaJIpQGIpUKLGlrhgNe0zG8n2U47K/u0dZVhT5\nxhZhRVlp/CAQ4zXf4/j4mL29HdCaXBeEUUgQJ+SuR6MbmrDpDi/XU8TDgOXyDNMY4Tdna5Ik4sbN\n2zx85X2MR3RYsNbCFErTjEZrapN2u6IWfvQ8kbH7nkfS62MwVqNRSsfsBhhk2talJrfwk+8F+H5A\nWWsmQ9EJ9HoRSrkwgha3EBDWQitNgy4yPMclDKSLr/KcIJQJVlsVpzINCgWucNaV19BQg27QTYVS\njngVGY1pxDFTOcKkcZSoMMuyBKVotLbFXmCY0FPUKKIkkcnFCyjKmqpKiaIYXVcs5ieURcGF8/v0\nElFlfj9oxA9aL6I/OHrxB17A8zzHKhZkKRT0KPMCcOglCW4ccHDrFv/PL/8yf/RHfpQnHn0ctOHg\nzh3Koka5Lv1Bn6TfF94yiqKsCMKQyXTWyVqXyyXvXLtOmq7Z3t5mb3+HVla/XK6oyhrPJqSAsriq\n5u7hIUlfTHAGowFxHPPa66+SJDFZlmIUfOWrX8HzPP7bv/SXiPwAbacKz8rIXed+8HErdnivq/0w\njTE0RhaCYhxUd9QroJOYm0YWeK0XRpHL4ku+nyIKY6paFkwiWpIFrVivNtRlSRgEYjXqOrzy+6/x\ntRe/xnS2RVGVaNPgKQddaVylWC5XXH7oMvv7+1y69BCPPvoYN27eYjSZ8pmf+QzffPllDo+OiKKY\nxWKBPwk4OT2jzFNCzycMAt65ds0601U89NBDPPzwZUvzG7Bey9Lv7t27vPDCC0wmY554/DHquubc\nuX3yPCPPcsrG3hM3IPB9mgbW65VMFEqxSTMO7x2yWq14/9NPCt83TQmDgPVmRRRFlFWJ6znE8X0b\nBKNqmgbiOLaUwxVpnhMEsuT2g+iB6ajqKKWN1tw+uM2lSxfYmkwFvrMdp/h11CjHIStyjo+PUcrg\n+R5Dm3CTKPDt4tJzPOqqQmuBxzZZysWLF0BeEZbzhVBFHVc8bxx5b+qqRtmDPwyFj3x6esp0NsHN\nJYwjXadW/ShFwfdFvSv4r0sSR8LFB6pGEwYBV69e5SMf+iM2ialvYQZDkvQ6WKO0uLQ8qxWr1VL4\n3XYHUxSl7cw1URSAI1GKYRThuQrlemwNh2AUWS5c/CByOT097jjn8qfpJiGlpPsWGDEk8UOqsrDW\ntPI+KITO57seym0N50C5BiXKDkyjcRxD4HoYI7i9sarPsigJo4A8T2ka2cm09hrtpOG6LmE4ppfE\nGF/IE+0B5Xk+joI8zzg9PsJzHXYvnqffi8ntVPL9rhZKepBt9l7XD7yAD5KeWFs6LkVheZrWMtVY\n746rb1zl2Weeo98bEEYR2WrN5cuXqaqa9WZDXhQURd4xEspCBA2Hh4ed74XQ/2ZkWYyh4Y033ui8\nMSaTCVEUUxZVB1mkadpt0FGiDDs6OrI3VjoHYaS8wk/8xJ/gmWee6fDIFqdurS3bh+5BaOS9rpYx\nA3Qezw9K6h+kV7YpLJ7nd1SqyLIIFEpodk3dSZvLsrQiCdd2rB6Rk6Ab8Uypi4qv/N5XGY/H3RIm\niWLS9QKahkpXPPnEk8xmWzz33HNEUcx6vWFna5trN27yyKOP8/QTT3Lz5k3KNOXCuX2xP9UNFy5c\nYNTvk6UbVouC8+fPdcrWg4Pb4Dj04pjd3V3r3Z3z/PPPMxoOUUrgjqtX32I2m9Lv94mVh+s6HB2d\n8uqbb9qiKJMVlmmRZSlPP/0BvMhnmQqUVKQF/V6fvBIaneM4GCUdmq41jalwHKEztp9H4PsoJVRM\nKRAtbdRlOEwYDXscHBwQhj6j0YjlYtHRy8By9e1SXmsJF8jylE26QacGg+xaPC8gDGNy+z2Uo/AA\n3Wgry3ZxHJfBsEfgRyJBNxVYf5DGaHzl4nq+df5rcJTH21evMp6MSeKehIG4bhdggdHUusBRDkWZ\n4igHZU2yXM8FU3Fuf4dr19/hkUeuSLfvBRhrMaB1Q1Vv0E1lnz8f1xV5fsskuy/6AYwh28gCuSxL\nlumZVVQa4ighCAWacR2fwHOZzWbdu/GgY2nrI9Pyu5vGsNmkwt7KCnGf9KQOtC6HStlAGM+jqiWp\nhwZ0XVE2NTSNQPGm9fIXGHKTrgDbaCKHe2uj8eDEoZQjzpOeJx7xrkMQ+hjdMJ9vKMuSnZ0tKwys\nMMb9A4tzO03/oS/g8zPBFwM/EuOfNCNMYrRpaDDkacOX/91X+Kmf+AnO7+2zXq2o8oI8F5xwNB6y\nHYQ0puHg4KZwh+M+Dz2021HNDg8POTo6FIphEnL+/HkuX36I+XzOnTt3ePPNN1FKllKPPPIIrqdw\nGpflcsm9e/e4ePG8QBpa4yjZ5v+b3/hNLlw4z9/4a3+dk9MjSrvEGQ+H4uhmRQtwP4mk/dDb4vu9\nl1KK8Xjc5S+2ktx2CdmKdtrRqxWgtF1By3NuDa/E0jbtHoRWsSfKsMgu5nLCMOJ/+Dt/h62dbbI8\nx3Vlelgu54wHA+Ynxzxy+TJJlDAcjtnd3RdhSFmD6/DsB57h6y++yBNPPcVnP/1p/vH/8b+ThAFl\nmbNcLrlzp+adPMd3XJ5/9jmqquJsseStd64xnU7pRTGOcnn52y+z3qx55plnmExm/P4rL/Paa6+x\ntT3lYx/7mGUtFKzWCyu7L3n++WfJsozxcERVVqzWKyqLv65WK4Io5OTstDu4oqYmzdP7/udG4eLS\n0NCLY+pas9lsbHfU0EsSEptI0xYPMVvLWC3F4zoMAzabJduzCScnZ93n1cIKpbWvVY7DyfyMMBSL\nhzSVJfxsa4eyFJpnkedWTdgapUXURUH1gCUpTU0UeKA8iiq3n63T0fMao4jjAY2uOHdun+985zu8\n+OI3WS7XPP7441y6dEmotK5rzcLGncWDsuIXrSvS1Zqnn3qCX/3VX+PK5YfEZ2QY4Po+TaNQLmLs\n5LZLzZrM+n63V7vfahfww54kStWN+IS3fkFplrJaLcQErtLoRuMFQdf8PChaE6aJy7Af22fbwVEJ\nZVkynk4oy4LNak0ch7heGwsoXW1d1QShaw/sWpanjqLSDUWWU9cSINNoTbpJcTx5p/Jc9i6tJW3L\ndhFTLSuYMw1ru3caDoe4jpKJ0e4kgkAyZWUZGnVUyu9XC4DvC7d2/94Pmkb4td/9FQHscfHCgFLX\naEdB4OG4Ln//f/yf2Z5t8eSTTzIdjoijEN+573xWFAWbXGCB8XiE1rKQaXRDZYUgrutalVtpP4zM\nyq/b/99hY1VUy+WyY8S0xVEEH3KzX3/9dXzf54MffJ6nn37adk8ueSEcZV1VXQF/0FGwxa5BuM6P\nP/OJd92X1176IkD3MjwYFtEuodpOvO3WOwjFdifGiNikTTF6UBwlwb7i/NgYwzrdEMUx33zp23zt\nha8xmow5OxMPlLoq8RyHk3uHPPHoozz2vkf4yIc/wrqouHXjJpcvX0bXNtauqqm08I17wz6H9+7x\n2uuv8/atG8L+cV0W8yXjwZDz584T+AFaN5bPLhS2dL3G9zx2d3ep6opXX/194ijiwoXzbM1mnJ6e\nMJ/PieKQ4WhGa3eg65rQetF49kX1XPGQnp+dce3oLhcuXJDpp9ZkdrLybVEwBhoLbUE7Ziv7e1VW\nrt26ZPod1Od5DlUpnWdVVty+fZt+b8BoNML3g+5zCYIA0yoLHXHWaxqN4zniYLhY2ig1R2TiTYNj\nR/XWV7uuaqFt2klSnBobHM+xNEJxCNCwZIcAACAASURBVJSUHlFWNo3G6JrDe4e8+up3eP/TH+Ch\nhx62oqbaPjuOpW7WFiJwWkIIWhf0+pLE81f/6n/HX/sbf53NOiMMY3QDMv0rfM/HceuOaeK6Xlcw\nWyZFG0vYNA1NblCOsfTC2u56Wn8U134OwhdvlItj7XLlYJJpw3PdjoGilMLzfUxzf4Hq+17nI9M0\nWmyvlPiHO8qhNPeZHbquyfMM3Wh8y1NXSjpzkIVpGEZi6GahSWOwv6dQFH1LW3Qjv4NpqrK0gRMN\ncRgymYxscIpQFh9kVj79wfegFH/938rPpzXPf+T7J/L8wDtw3/MwZUMQhiJSCWMqBYt8w+9942sc\nHR7xZ//0nyFJEso04+zsjM1yxXQ6ZTKZMBoNCUIfbRpWq6WlHsWMx+MOozw7O2W5FL727u42vV5M\nXRny+YK7dw5JelFH/RmN/j/m3jzIsuuu8/zcfXn7yz0rs7KqVFVSlapKJcmyJMubkCVjYwFtg/ew\nacbBBBM9RIehpxtmIJjGmJ7oAWNo6GbCMG3jtmFwgBeQANtYtpFVliyXtipJtS+ZWbm+fPt7dz3z\nx7nn5ktJJRP9R5vrqJCjKvOt5/7O73x/36VCr9djbW0tE4742LbMO3zooYd45zvfyT33vE4O1uI4\nw0ujPLjUzTw2FEVN8mXTXN2pOrhXupJEdn+lUinneqsvTuHnanNRG5jqSNQf9by9fh8780MRaYpt\n2YSxTN5JhIRN/EKBMIo4c/YsXsGn3elkn9sA0zDYWF3l+NGjaAJuOXqcxmaD1LQ5eNMhXjh9mtuO\nH2dlZQXf90GYLK0vUa2WmKjXWXjLfTxz7gVOPvl9zp+/KGloxSLtXo+iJwAN1/UzmCWRjAPLZGnl\nGo1Gg8nJKXbvnmdlZYUTjz/OVqPB3XffxdzcHEvLy1SrcrMpFYvYjkkcSXWdrkNjo0Ez23yLvkez\nsUkQBOyanSWNQwq1igyWTiRmHieyEDuuj4olGw6H6LqO77v0+wN03SQYSlVdoVgkCmLq9RppCpGp\nMzMzjaZJJkMYDgmCTBmLRpRKonmx5FMoFqWQaiBTayqVKp1ul0qtTppAGAeYhvTvMS2dKApzWt61\n5WU6HemjXigUCeOQVEi+uK5nVFzkzCVJIrq9DhcunOe1r30ts7OzUhgU9DOWRYqhZ+vRgAQwDOVP\nIsVgSRTiuA7FgsvWxgbVsXEs00HTLZJE0Ol06fW6hMG2IZiu65LeqOn5idOypUe+rutYtgWanO8k\nqGbEJU0kq2c4kCEf8nW5mdDGzrpWk2JB0Uhj6vVqzjNvdwYYpo6WChxHKkWjMJKeLEKASEk0nVSD\nRIuzjTQFNEoFP2elaJkYDiGhykRkvjOaxNRBFu+cmpgmGavGYpDZBCvjsSSRFhylYiE/kUsY1cwh\nmesJdfr9fn7qeLXrh96BP/a1v0ITEnsbhBFWwSMkpRsG/Of/54+4+8gdzMzM4DkOpqEzUR/Lu1sZ\nWhpk/FYbw3JycUEYyrglz/N2dNODwUAOQ3Q7/7ckjXK6UxiGuK6TP24URVy+eJEkiXnggQeYnJyk\n2+3m8uxtIU2yswvWpOhA0cHUa1D/fvNtb37Z53L+9In8+PlSi91RCb0q4PJzlB7GufosjjFMCQmI\nrMsVqSAKQ0xTshTQpFxbNw0efewE33vySZnoPRig6xpREJDGMbVKmZmpKW45cpQ0TljYvUA785xp\nbTUxdY1KpZK/Zt3UOHf+HHNzc6RC0BeJNMifnubJJ0+CgEF/yK7ZWWkMFkuP7DRN0TWIojD3gfcL\nHhsbG2xubDA9Ncn09DS9XjcLrHXZs2eB6emZ3NGt1WphmQamoTM9Pc3WVpNqtUx3OJQWspqGYztM\njEvTfdOQGZqWlVHWNI0wk8VL0VcmyU6kgKvZbDIcDmk1W3S7HeLMAOrGG29kbGycZrPF/NxuhNAo\nlcoM+n0Mw0IzdDTDyEJ5I8IoxPOlxF52+BHtdpfGVpN2u0saJ1SrlcwXxKXf62HbFv1+D5Xj6Xo+\npYxnXigqCwE9k5THICTjqNfrsLm5wW233UqcnT5HT3NKrTy68cv5TVbwMifHL//1V5iemuHAjYcy\nwRRouuRJW5Yt9QUj2ZWjFE1FuZNRa5nLICITlMXSJlak2QZkZsVRYsuWZuXdN4w4G5Ki3P0UKSBM\nBaVSUSpORYqZnUbUe5Onilh28JlQScFFqpinij8+UqckZVHLBuTKWsPI/mSfu/JWT+P8hBxFIY5j\nUfQLO+qeqgmjhl43HXu5lP6Z730jv9cP3fKGf74duMh8aBIh8IoFgoxX/PzJp5gYm+CO229DpDL6\n6dq1aywvLuHYNuPj45RKRWq1sbz4xmmAIv8LIUnwnU4nz8ir18cYG5sgSRKaW+0Mj7IwTMlGkcdK\nUKb9Dz/8MJOTk7ztR9/K/NwcnU4nL9gqx08tVNUBW5aRm0+pL0wJHPKFcB35rCrYUv257Y2uCrd6\nzNGNQkWYqU1CfelCCAxbuhDG6qgfRcRBIjFMTUCi841HvsG+fTfQaDQyK9seIo5xHZtKucxdd97J\n9NQ0w16fp595hptvvQ1d16lUyly+eJHx8fH8RCEZE/P0+n1uuOEGVttt2u0Xuffem2lstvid3/kd\nJienCIKImZlZojCSgg/dBF2gAyXXo1gq0GlKHxa/WGCr3WZlfY0kSdi3bx+1apneYMBDf/swszPT\n+L6XwyQCuLy8RBInrG9tZsIpwVi9jud5mS+7j6HpxCJm2M+Cc20bZ8TxUdc1lpeXeO65U3S7HdzM\nge7QoUMcPHADExNjRJkni5dJ2VOR0O10cRxbRqgZejYUizL2m4Zjy5RzEFk03hbXlleZnJxirF6n\n0+5SLlfypCfLljmW6Ab9QMJUWxev5jDFxFgdx3GZnJyUXbZp4joFOp0mnid9exT0ZltG3pSgaVim\nDBCOwu21qVlWThUEjeEw5MANB7l0+TI3mwahSCj4Ps2tFn6xBCJBE5aU/Ge4PyD1AYaBY9m4lkzc\nSdMEs2ARhYGk9WkSOEKTkI90C9SzzSXGAESqeC8yag4gTWRWq0glzIhtkkYBhp5gmFkouWFkTo1g\nZTOCNNVJEgMhpBWuvMekf4pmGLmsXcGWSZKgmxoiRToPJrLbVjOtJEkxTRkmAvI0l2bQUKlQyE7f\nMbpubrOSDIM4TonjYf6Zv9Kl7vHr1Qp1/dALeJxxkg3DZBiGkr7TDXj0W9/mR+67n3ZrizizJJ2d\nmsxMjaRk9dKlyySZKY3ve+iWIwcauk4UJTkDY2JiQubs9QcsLi4jUnK/X4lxD0GThXVjY53V1VXS\nNOE973k3hw8fhjQhCkMmxseyfLuUcqlEP2N16BkOJ/HQIP/gRztxxUIZtYR96aUGFkr2r7BuJZlX\nl3pcIFtUet5BhWGIbujopuw6LGOb/eI4DoaQRbEfDHn47/6OarVGY7NBEkeIVJec5Yy3vmdhganp\naTrtDo5ls/eGG1hbW2FychIQzC/s5uzZs8zPz5MIKQpJMRlsNXnh7Dmmds3TaXf52G/8Jk8/+yzV\n2hhBGHHy6WcoFst4boFUaBi6RhBH+YB2sNEgCgNsz6c2Ps7GxjobjQZpHHP58hUuX45pNLaoVquE\nScyhffu4ePEi7U47H+6Wy2XGxieo1Sp0Ox2uXL5CEATsnpvD8TxMU8dgOzgkCEN63a40Ier1uHr1\nKpubm8zMTLN7/g6q1SqFQiH3gu91u/JIbJrYlkmtUiYRAtcdAxJcz8LQNeI4S20XEpuOY2kW1e90\nOPviGcqVKjfddJBet0+v20PTYH19NYfT0uxIH4YhUZwZM9kuUSTpb2ubDRqNJmnyLPv27mXf3r0Y\nhsZWY4MzZ15g754FypUyrmNh6OQB1mpArmfDOHXii8IQ15UFulAoEIYBExNTPPbYCVZXV5mZ2YVr\n24yNVUHIpHaZ2yGxY7U+5RrPMmyTGBHHxHHI2tIKURzhmCaOayNl8xn9VdOJE+lTDuDbkg2Upimp\nyKx/s2alUimj6Tq6Jk9yBdfFyQb0egZlyVcASZwQJxI2QZN2utudvXzReoYKRFnDpetajnHHcUyh\n4GQUzIw7bxgYhpbRJIdZoZWPVSgUsuYuyeX56rRgGFZ230Z5w/VKl2SC/aA4h38GBdywTBIB/eGA\nUqVCEET84yPfwsBgbnqagmlkg8eAOJGdqWJrFAoFEBBHkmsqlXmCMIyz7rtNmiaZ5LmIYZgUCyWi\nKCGKgjw5I4oiOt0WrVaL8fE6d955J/PzczmLwzZ0RConzKZpIpKEToapawLSVCaCpGlKijzaKcaC\nMqZSRyulrrzeNTqQVDeuKtLqMYC8Ix/F0KQ/tYdmaKALDM0kiaIcf1MngXa7wzAOuXL1Kq7n0Wt3\nsSwbkSYMhwOq5RIi84nY3NykWCgihCYFGYbO+fPnufnQYRnaOj1FnCnadFOaSu1eWODK1av8wX/6\nQ1bW1gCdmeldNLa2KFeqTIxNsLK6xoH9BzCyjkvTdeI0lfi8tN1jGAxZv3KFbqfNMAwYHxvH831c\nG+pjNdqdDhcvXaLd7TEI5LRfKkMdqpUuz7zwIkXfYnJykpnpGeIoptFpsdnaYnJ8Ajfz8AiHAZ7n\nMjExwdbWVv55HzhwIBtQ6fT7fYbDAZVyGdKUYDBAxAmWbbGxsSF5zY7MvtxqblGt1kiFHNhZZibv\n1qX9bpwkXDh3hvn5OWr1MZpbbZIkwnNthoHkbzcaDTTT4OriImkqqNbqrK1vIgDPL1GpVNlcX0ET\nSe7y+MKLZ7h85Qq+53Hz4Zt48Md/gjSJuHJ1iZsO3kCUMVaUX8moj46yqLVtm69//Rv4fg3HsWRD\nZEAwDBn0hqyuLKPrkqJX8IukqQDdQDf0fE0qvxjpYSKLpu1I6McvucRphKkpM7YI06gQBCEiFXhe\nkTRJ6HY6lMou2b4nh83ZawzDANuyUYHhMsczRRdZN438HXkiUDYC0qFTIBCZLW2aypOC2nDU4+u6\nDkIK6WRYhctwKGtPsViUnkiJ8jiXcEqapiRxSq1WQTckXJam2/exvFchTUNMQwazjFpGv/QaZZi9\n2vVDL+CJEKQCvGKBQRDQ2Nzi9KlT3Pume9FT2NxYx3YcSqVSJncWmWGMpGu5jkehUKRWswkSmfwy\nGEgDq4WFPdi2ybVrKywuLhIMQzyvmHW4EgM8c+YMzWaTI0cP88Y3vp59+/YBIncwdFyZD9nNQpZH\nC6nkbOt50VVQR5Js+zmoLlp1zK9GI4Tt7lph2kC+Eyu64Cg2Pvr/dT2LerOMPPJKRyMRKc1mk0Kh\nJI9/IpWnjEwwZFk2OilBFIEQ+J7Pa15zK0cOH2FjY5PzF84zP7dAkiZ4nsOxY0d4/tRpZmd3YVkW\n58+fZ9/+GxgOBoxPTvGtf3yUz37uc5RLYxRLZTkYEoI33/uWXAW6ub7BE088wVhNQht+tZy9dps0\nkcIX35A3ayoEURrT7rQplUv0+x1SIbjr7rvZu+8GHNflE7/7u+iGyUajgWlZNDttev0+iIjzV65S\nLBQwdIOi71Ov1hgGMRMTE5SKRWKg2e2ztLSSncI22LNnDxsbG9RrNTlEtAz6vR5nz5yR+gC/gJ/J\nxy3LptHckmKXUiHrygb5qUeGFkiWhV9wWV+XtNP5XbP0+0PGx2WG5pNPPglIFeWRI0eo1Gu8c3YW\noRsyiDkVmJbD0vIqhmkh4iG+Y9Ltdrm2fI0LF86ztr6BY1tcvXKZp56a4JZbjjI9NcnlK1dYmJ/N\n15PqIpVHer/fZ3V1lQsXLvDWt74DQy8SxUHGZzaYnZ7h1KnnmJo+RNEvZPmoHo7rQtYVx3GSnTBl\nyIQU2mx792gaaCnYug1aikiV+VhCyffRNYNgEGJoGlPjEwzjXn4/qKIKygd/e6akMHMgx9rjWEJA\nmm6gayCEonamiBjQ5T0jEklXVveVuodU5+0VCqRpwthYPYdj1XMq1ou8rw0cy5LCrTAkFTGGZYCW\nSmw+SYmihMEgYJAMcRxzx0n6pZdqzn6QG+EPvYCncQymSb/fIwhjnnnmacbqdWZnpoijCN8vousa\n3cz43/O8LK/SzNVZ7W4LXdMIEo1SuYSVSLvHzc2GpP8Bnu/huA6gsbW1zqXz5ymVihw4sIdbjx+X\nv2eZhMOBdAozDYyM4hREQ0qlErDN/1RshFRI9oNUdcmuNUn0vFMexeSvJ+DJP4s0zTsB9bs5no68\nAeSXmnnG6FJJZhgmpilvxk67L7sh00AkKZoAy7QolytE2YDJsC3OnjlLwXbp9nuyZ9E1guGAerVM\nqVBgfnaObrcrQxoqFbq9AakQrK9tMOwPmZmdYbOxydz8PDfefBMra+tousl//W9/xpmz5/AKVUyv\nQm1imrH6GI5rEwYBnu9BKljYvYcbDx7MjuApnaDFVmOTldU1gjCgXCnjez6OZ7OxfIVOp4lGSmWs\nwDvufYCpqSksxfhJE+5+zW08+tgJxislur0uveaWNLxKEgatDr/36d/h6uXL9Hs9/uHr/8BjzzyL\n0DT8YpHpmWn8YoGqaTO7aw4aDS5eucLuhQV0w+DKtWXiOMCzHaZmp6RXdhiw3F4nCmIMTaNcLDE5\nMYGp6wx6fVrXNui1pRe5XvAo16poQhCFEZtrm9xx+x30e1Iu3+1ukSQptUqZlbU17rv39dTqdfqD\nAKKIMBpgaQamrmPqGvvmpkHX0bQUnZQwrLAwv4t7XncnIFhZWaHV3OLatWW+c+IEK9eusXfvAgf3\n38AttxxjenqKRmND0hkNDdM2Wbu8hm7ovO3tb8ugwK4UuxATheB6UsVarVRl4IZl4/kFqVkwpbDK\nNA3SNBuCZutVURNVM6Nnqmu1qYG0iIiTFEgwHInTh2mYRwQipLhImq9LkZFc/9sBMHHG/tAzCJVE\nk2k9yEAIRZrQdYPUlNJ6NNmv6wI03ZJhyIBuyCF2koCmyXut2WwhhIwhDIIg3wAV48RxpAK71+9h\nGLo8Ucayox8MgqzR0jI2Dfkp/QdxSBScer3rh17Ai8UiUSJAi6lU6zx18ikeuP9+EFlCea9LrVbL\nuLJSESXTTGQn4WV4pud5EKQ8//xpbNuSHbstMbFhMCAVEh549NFHcRyH97zrXczN7cq75jAYINIs\niX6Q5IVUCJEZ2ewcLBqGQRoGL8v5G6UHqWDTURxcdQ2vdKndeFR8kx/tkDxWRSVUj+XY8rER0ifc\ndRzJkR0OJWshFZDKRSyZEDElv8Bzzz5HrVLBMS00y6DdalGulOm028zfcUfmG97HMELpb67LINjJ\niUk6nQ7nLpzj2LFjnL1wjrn53USp4H//lV/mxptuxi1WuPHgITTTp1wu0+408UwX15DHXdMxQYMo\nFiTZlN9zXczJSXbv2cNzp07T7nRYXV+n5LusrF3jve/+KQ7deIAwHDJdGJcnoH4f2zalI+TGBpYG\naRQQ9qUQx7YshGFy8fIV1paWKTkeE+UqLzx3CnSdYrVMu9ul3e2x3mry3TNn0XWDSq3GLbfexpnz\n51leuUa9VmVyYoLyWA3NNLiydBW9F1H0C7i6weT0NK1Wm2Ea0Oy0+fa3H8UvFWl12hKGa7YIBgOK\nxRL33PN6XNdlYnKCTqcjxWVuTa6ZOGR8vMLmxgph0Gd2dp5mo8HY+JQcygcBQXeA7TpopkG/30P5\nvKsBt2majI3VqNUq7N23F9+/n3PnzvHNR77BP37nBH//ta+ze36OD3zgvRi6ThyFPPHEE7iuy913\n3QUCer2+DDYgZRvSE7zpTW+kP+hn9520nFA4t+JwO46ZD+5H17TCmzV9xLRuZE6kW9uFTJV2kcih\nphoAK2hRQR1yECpF8VYWfK7uryQbHor8uUFZLaBnGH1GhtOymDZdSfAzAaBhWtnvyiGrFHAN80ZM\nOS9WKtUd8y1VDyxz+95XVEP1HtRrvV5TNyp+erXrh04j/M43v0w/CPGKJf7iC1+gudXkR+69D9dy\nsoGKnaVsJ/kgUB2X1N+32zJizTSd3PtECEn/Wr62TK/XQYiUhYUFbjp0E/v27iXMgoMVJqjM5tXH\nkdvZmtIkP4mTfDFDpq7MTKLU4Eyl4ii8efT/KyxLQStHbr/3ZZ/L6ZPfBLbDnUcVmGkqu2nYtt5V\nGPkorVDKsOWi16WnGqCRAKmukWrQ7vb47Gc+g58lkuiOxMp1kXLo4AFpOnVgv+wiEsnRL1YqUrQh\nssVnGCwuLRPECWfPX+RLX/5rCqUa1foE0zNzOK4PRkbVSlN0XcO2LOm3gUaapBjZIjVMgygNkfSy\nmGE4zLjUPZqNDcrFAu/56XfR67YoFgoMGi2szAWv0+lgOVKW/4lPfpLVtdVcyNTpdgmGEu+99Zbj\n7N41B0LwvSeflKdx3UCz5PC8NjbG3NQYz59+kSiOKZYqCA1mZ2cZGxvLjuEJ3VZTeum4ZQq+T5ym\nrDUapAh64ZB+GFAbHwdTx/V9Ot0uXpKwurTM1NQUjuOyubHBrbfeJod7mXBIpQMtXlti0OtTqdQZ\nH5/EMmws05amWroupY+aIBHyM1UiFQWLqLU7OthWa2lra4vLly7SajVZXLzEnoXdjNVrjI+Ns3/f\nPuKM9mcaJmTrVK2rKIrRNJ2trS3q9TpCyOFguVLJgxSU7mFUlwDsaIYU11z9XZqmWcF+eVNjaNus\nkJf+N6cHaqoL3x4GbkMTkmUz2jBJXHmY/Zz6eQWvZGKizCpA03V0XeT1ZpQVok7Wo5CqgqR22GGP\nQDvq90ZhH0kTfLmo7/RT387v/VcLNX7VDvzq1at86EMfYm1tDU3T+Lmf+zl+4Rd+gV//9V/nU5/6\nFBMTEwB8/OMf521vexsAv/Vbv8Wf/MmfYBgGv/d7v8cDDzzwak9BlKQITQ7GnnrqaR64/wFMU6aw\nyCGg9CkxTYvhcEij0aTXk3CKikqTwzufbjbFT9OYixcvsri4yO7d83zwAx/AL8ijT7FYJAgCKhWp\njFIFdpTpMbqLBkFAGif5IDCf1kcRuik7YcUvh20PE/WFBkGQf5lqqHm9ybMq3C91KTNNpSzb3pGV\nYEBJ49WOLuS0hxSBadr584ZBiG6ZJLrGYycey/w+BK7nEpMSxDGOZdJsNrn/vvvQEHLAg2TYbDS2\nuHzlMiXP4eDBm0DT2bNvP1/5m4f5+iPfRreKzM0fQLc8xid3s7S8TH2iRKvdYmpyil6ng+OVCKKY\nNE4yRZp8b0kEplMhigIGwzaVUo2L6y8yOV7j/POnWZi5hW//wyPccvQok7sm8SZn6Pa6PPvccyRp\nwiPf/CZG5jsTZjYCaBrVeo3N9SaVcpnFxWUeuP+tGALu+5H78Qo+Dz38EE+ePEkwGNJttfj6qad5\n3T1v4Pz587xw5kVqtRr9IOTM2XM4toWh60xMjHHp6lMsHD+CNthiYqxOXDApuh431PexdOkKM7Vx\n1peuoXUj/CCiEbQYmxqnUq8QhxETk+NcW11idmYXw8Ew23glz39qapJ/fPQ76LrBzMwMri0FI1qK\nPEGRZtFkOrZpIjI3yjRO0NHy71ti271cr9Bud6hWK3g33kicRNx4YB8nHnuUaqnEwu7dcp6TpFQr\nFTYaDbzCNn9ZrmWdNCUX7JimmYWCyE5TCJGFbyTXLTbqsUYvxcV+pUt12qPFehR2eGl3qhqe/D7O\nVJOjJ2R5b0t8fPulqA0hy7PUJTFBiATLcnbw59X9r2kyUm30pKHwcyXeU3VA2Wio15tTFF/CLhu9\ner1eviG/2vWqBdyyLD7xiU9w/Phxut0ut99+O/fffz+apvHRj36Uj370ozt+/vTp0/z5n/85p0+f\nZmlpibe85S2cOXPmVY8BwyCkNj7OYye+y7FjxxAIlq8tI1JBuVDMFUlRFNEfDEjiGMuyc/yp1W7L\noWavR9Dv4XkutuNw08EDfOB976FcLkupbJLgOy4ijrB0GVGm61IEI/nbcoNQu7WCSQzDwHBlavko\nfCGEIErizJhG7DCqUoKI0S9NdfiapuUwy0svVZRVDqRaBKZpSu+GEWxcbTpqkavXC/JGF5pAxGnG\nP5dGV0LXsGyTy5cvY2hScei4LmEUEEYhC3O76LaaaJrMMExTgcjwxXK5zLFjx9AS6bZ2dekqj554\nnG/943ep1Kc5fOQQtfFJbLvAVmvA1NQeumEbv1il0w9wvRKdfoRtmuimBZqBMAWxkLODsJuiGxam\n4RJFMVMTk2ysLRIMepBEbK03+YvP/zlbmw327N9Nr9fD8zwKpSK7du1iZnaW2w2DL//1V+j3Bwhd\nDrCKpRLDYcALF19geXmZO25/DbZpoQm4+867uHDhIsPhkHqxjLF7N888/TRHjx0jDGMaW1sYhjwa\n9zo90iSh1Wpz6Kab6DbahGHEi8+cIo0TpscnWbp6lUqxyP59+zB0TUamCY3dN+zFMGX4bbvfJQpj\nkkh+v8VCCUM38o5uZX2N/fv3sXhlEd/3mZudI05SKpUaw2CYeUTbxElCMBiiIZORNE2etuKMDaXr\nGr7jYmT3cXF6isbWFr7v0+935XzBcbnx4EG0VGQKRpduq0O1VKGf5ZkqVzxZsFwKBT+HDoJARrGV\nK9WMligH+IonvV0kRf4nzbrb/E/GBx+NU8ivEXbISwvdqJ5CFtHtoaa6TyRMMtqla9m96ux4HFCz\npYTt04G0K1DFd9RTv1Qq5XMw1WWrk87oyWL0FKJ+d5Tfrfj5r3SNCvNe7XrVAj49Pc309DQgsepD\nhw6xtLS0442PXl/60pd43/veh2VZ7Nmzh/379/P4449z1113Xfc5PNfHtm0uXbrEkaNHpceDpiN0\nQT9TRqpdzDRN4iTh2soKFy5cIEkSxsfHmZub4/Dhw8zNjOM4mRexYSJESqOxiW1b6LqEbdJU5Jl/\nqiCPGkepv1Nc2TRNyaKw851X4d5JuLOoqu5j9NikhhCqcL8aBj6q2lSbB2Rdqi6jsNTCVcc2tfmo\n7yQIApI0kf7hugRQ0mxBaqaVhCUb5gAAIABJREFUR8qZjiud2cJQhs72h1i2zete97pcXafofUma\nkgjpPx5EEV6xyOK1Vb5z4kkmZxaYXzhAoVzHsEoEscAtVNnYauGUTPmcYUiCiRBgOUXiMCJBIwkj\nTFMHYWCaNmkS4Hk+adIhjoZoIuaj//pfkQQBtXIVAyn6cStuzsiJ04Rms83K2ioXL1+m3x8QxQm9\nfp9arYZu2Agtolqt85nPfJZ77n49SRTyzDPPcPz4cT7w3vfx/Asv8MT3nsBAo14p88zJkxw+fDMv\nBCHdrS05wE7lwGtl8Rrv+NG3E3eHfO3E13ntnXfy3/7sc7z2I/8TvVaHZ59/jq89+m327d/H7r0L\nBGGAe/4UIowwdZ362BhxKIvChfMXefOb30yx4DEYDOl1+oCWlTpBq91kcmIi6+hCIMl8dqRy1XUk\njXUwGOQnxCSOKJfLxEmiIF7CUHquF4tFwnCIZehojk09M7FKkwTPcQmGAZZhEgyHGHY20NO3fX2C\nIMD3pRiqUJBpPaoAm6aesU9gu2CT3XeqkGs7ihxamlMEd14iX/c7ir0Y9fXZWRxloZOnhJwBhvTF\nERnVF1RXn2VjKspq9py6rrQVKZqWYppgZeEVqvtW96QQIoeLFPylOup8YDvSxOW1JKsLoyLAV7p2\nUBpf5fonDzEvXbrEyZMnueuuu3j00Uf5/d//fT7zmc/wmte8ht/+7d+mWq2yvLy8o1jPzc3lBf96\nl2VbmJpOY3MTkaQkUUScBhSLJaIwIhgGqOy+Tkd6PtfrdX7yJx5kbGyMer2+jfVFUqSg65J+ZloG\npaL02wjCeAeMgWbkFqtq8atdT3XVqhinsfyQlc2osmXNh5kjXM9RSTFsF/Ycxx4puC+91G6rvny1\nEKMoyuXK6jHUFYZhjrnlR0szi2NLBGkUZ/agJug6QU+eOEzLoj8cYpkm3X6XUqlIs9HgzIsvYpsW\n01OTuK6T+z+IVJqDCdMiSjX+8otf4YaDR/CKNdxijRgLIoHQTPRUo1wfJ0q6xHGK4xaIk5RisZL5\nu8iF5xWLRMMhghQhYoSQnemg3+LShTP8yw+/H9fUaIUBjY11Oq0uSZxi+Ba+X8D1fGq1GmNj40zN\nzHLX6+7h1HPP4xULrK2t02q18PyCHMr5Hs3NLT72m7/J3oV9eK7F+Pg4n//85/nYxz5GEsc8/LWH\nSJOEsUqVxcuXeP1dd3Ly5ElarbZk+hgG87OzrC5f45vfPcHW1hbpsyd520/+OF/44l/x4Q99iGtL\nVxm0OvyvH/4IR48cASFY21wjFYJysUS1WqPflaG3z5w8yXdPfI99e/YxMTHJ+toWjc4m19aWEUmK\n70nPeeXFYRommo6M4dINwnCILqSvt0gFiER6jEeBDP8IQkxTsld63a60aRYyeq3danLjwRuJowiv\n4NDr9fA9X0I6hk6aqTMTyDtKZc7lWFJNWiwWIdugNU0O2RGSlcUIfq6Kad7RI9A12TWL7H/y/tgO\n2lb2EepeUH9eCaKRMyg7u+e2rSzM7HHk6VcniuQwUcvoiGhGxohR944gCKQ5nVJlKkVsHAuKxWqO\nBkhlZzyisIYgiHc4kL4UBlX1YBReud6lrDMUYeN61z+pgHe7XX7qp36KT37ykxSLRX7+53+eX/u1\nXwPgV3/1V/nFX/xF/viP//gVf/d6PEd1xVHIH/6nPyAYDHjm6acoFmVSs+u6TE5OMTs7k8lXZWEs\nFkty4WRS5DCUBUGIlDQOQAdLM7PMPOm6pxsGBhm3MiuImi4LoUqjj+OY4XCYBwOr1x2GIQXPzzsQ\nVbhHC+boQpXD1CwgINsAVCeh8gGv14GPdidqseYbg2khtO3F/NJjoXrNaZoSpwnDIJCYqC5v4Bi5\nkahuIcm41km2+bh+gTRJ+dCHPsTG2jpJFBIMhkRJykZjk/GJSfxCEcP0+Pe/8R8w7SKlyjhOsYpm\nOOiGi25KznMqBLZhEKcaBb8omQQyQwLlkphm4cpkKrU06uN5Fv1ul42NFX72Zz+MpaeYhsbU9CSm\nZjLoB9imTWroslt0bAzDJEEQBDFoAdXaGFGSMD4+yZUri2w2mqRpSrlYotvvc+XqIm9/x49z7eoV\n/uN//L9ZWlrk4x//OG94wxtorK+zb+9eLl++wsT4GFcunuf1d93JxUsXOX/uAp7tILQUg5Sjx4/x\nrW9/m8tXr1Asyui85uYmtxw6wtWzF/ntj/0Hjh09yvve825Kk+NESUKawOrKGrqm49oOd975Om4+\ndIxCocDly5eZuXWWMBnwp5/7U3bPzVEqldjc3GBsrI4QSQ7nJIl0zvNshzibKSijJLX+wqGcvUTZ\nzxd8D8PUiQ2p0C0WC7iOTb/XxTIsbNOSIcCmhe3YJCNDxVF2RRBKG4CNzXV0Q8t0DR69XhchLEbD\ngZU5VM5A0WQhVfTfnfixhhA7a8Xo8PCluPeoziIfhuYFMZPCx9sd7/Z9KsVFuq7uB2lApRtkSUJm\ndpLQ8mKt4uLSVFp6DIeSVqw6c9V4heE260zBoaP3uzo1KAriqwn61Psrl8vX/Rn4JxTwKIp417ve\nxQc/+EF+8id/EiCTUsvrIx/5CA8++CAAu3bt4urVq/m/LS4usmvXrus88q8D8Md/8gK33XaMn/mZ\nD2WMCjPP1gMQGaPEMMy8MyZLx3AsKz+iSUhhG0pIUsGg26XX6+N57g5oIkHLswDVl1AoFFBOdIrZ\noWiK/W7vZfSfJEnQDD0fIiojrEqlkh1VQzqdTo6rK3/uVzsWqYGoCig2TTM33DJ0Q8a3ZU55apCk\ndny1iBzHwdJtIpFAxgPXDAPbMGj1ujSz4bDCXNV8wPN9TEPLcT1Ll4n1pq2ze/duwijm+edf4Onn\nL9IbxOzZf4hyfZJUszGcAq1Wh5Lj0ek0GR+vc215if0HFmhsbuE6LkmUYJuZh3KaYGgmIgkwDQ3T\n0rFsk7XVJRob13jXOx9krDpGu7VBGCYYjhy+ppoOpiULliP94weDAD2z89Q0g1ptjEe++S0ANM2U\n7BfbxLIcSuUy5y9e4L/85z/g6M03U6pWuGPXLKdOnZJDOM9DIIjiIXv2zHPp0kVOPft9bjl+nHLJ\n58KFS2xsbnLhwhl279rLoaldnH7+eS4+d4p3ve3H+NJf/SVvetObuOn4Uc6cPcM3Tz7Oo899n3/3\ni/+Gm286JAtsr49hWViGSb/Xx3VlZuL8/LxkcRTHWF5aZnZ6hjNnzrAwv5t+v08xc7vUNOniKNBI\n0gS/4JIkiVQHpvKY3u90KRQKGBgjMASkcYJj2USxXN+2ZTM0DFrZydY0ZSFn0EeQ7hi4CyGwbBPf\nLdLr9ZiYmKDZbDI1Nc3FixfYs2dPnvaUJHFeCBV0obpU1R2rAiaHeRppquWbhOqytQy/Hm1kRl+P\nKogvdffMZ0SajqbpeTFVhVTXjB0nb8Pc9t5WgeU5nKlts3nUa5CBIYMdJ+ltlWi445Q9enoZZQcp\n0sMrO5M+wp98+s92vObrXa9KIxRC8OEPf5ixsTE+8YlP5H9/7do1ZmZmAPjEJz7BE088wec+9zlO\nnz7N+9//fh5//PF8iHnu3LmXdeE7aITf+BJRJCPQlBRZzh4khpvGSVZohjKpPWNP5IZNhlwwYRhi\n2cotTCamqKGiWiyqgKpjWbvdzndYtdsqTq0q5gBJFO/AwBTMEkRh7iymcPogCHBdN4db1FGw3+/n\nCkwhBMfvvP9ln/fzT30LJdsefa1CiIwM+PKFoRbL6KIZBgGYOqamQ5KSCoHQDWKRcvbiBf7m4Ycp\nuB5pJIUHg+GQ6clJqqUiH/nZn2HY6yOSmChOEJpM6JZhsQ6/+18+x8pGg6O33E6UmlhukTAWWK5H\nEAzxXRnkUKtW6XbagIaODJR1MpWlaeikaUAQ9HEcE0g5e+r7bGysEYVDbrvlCCKRbm5JFGdqPj1X\njQYZF1lDQzdl5iJo6IbJ6RdfoNWSA8bVtTUc16LVajI3N0e72WJ+bpZz585hmQa1SpXZ6SkOHz6E\nSAXf/s7XKZaKtLYaTEzU+e6Jx7jhhhuo1+vs338AwzS5srjE8vI1aoUJqpUqYRQhNNi9dw+GafL0\ns8+x2Wxw6dJl0GWRNcKIN77+9bzvfe+jXCpjWbbUFWTe29IONoP3PIu/+uJf0dhcx/c8Cp7HrceP\n5wVXhoFpJIkcgA0G/R0zG0W1VbQ+1QxoaERhgm4qKp00bpPzp8v0ej0WF5cwTYv7fuRHQIwMArNL\n2e6C/L3BoE+n06WexQ8WsiQtBfWprvilTcsopi3X8isP6IXYduVUDctod6uuV/IKkgNMjThWEIqZ\nfz5pnBLFUa6YBnbAobLYyvcj0HJqsirgqvao2qKaO9g+NavPQL029fzqudTfAdx218trwfe+83ey\nLto2t9/91uvCLa/agT/66KN89rOf5dixY9x6662ApAx+/vOf56mnnkLTNPbu3csf/dEfAXD48GHe\n/W5pAGWaJn/4h3/4AyEU0zRoNDapGlUGihebf6kCz3VptXr4vk8UB/T6nR0e20EwyPnWnf5guzvO\ndmZlbJWmKbFQxyyNki+d2mAnR1MNG1RH63keVtHMh39KnFMoFCjoxbz7ieOYUklmZqqNQy0u1e2q\nQnu9z6TT6eT+DoqilMM2hszXHL0ZhBD577TbbRnGW63iFXzCRLqikRkAoWUy+/wziQj6A0CDTGHW\n7Xb51Kc+xa7pGXzXwTBMKrU6XtGnUq0RpxonHn+Sn/gXP40wLDTDIoxTquOTXLp0kampSYJhH993\nWVq+QsUvUygU6PcHWI5Lv9/DsUw0UyrcPM9m9doST37/CVwjZH11jTSOeaSxydrKcn4Sc10XTZd+\n3ega85PjOQc+jBNa7Xam5NOwPY9up4flqJNJBEJuxt1elxfPnCWOI/q9kHa7zbPPPcNf/OUXKPkF\n3LLJm9/8RlJKhFHIXffczZVLlyiW5/j+009y4OBBdu+bxy979FoxV1YXqY+PMTMzQ6cvk4Seevpp\ngmGAl5liFQtFCkWNJ777GE8+8V3+7f/2bzl27Ba2ttqUSqXsu0wJwl4GRfTZt28fp557lrlds9im\njP1LkoROR84A/GJRwkmOievaGfd4iGma9PtdQGZOCiFThXQd2s02lXKNVMS02i3q9RqtVpP19XUc\nz+Wx757AMh0efPBBUiEwR7QFkjYrmxW1Hvt9qdJNkoSNjQ0WFhbodrt4npcbZo1eqgCp4bNqPgxj\nu7t9KbdbhVSPQiUvPRWov1cnUbVxyAG85IKrJko9jzTwMjJyw6ixFXkEYq1Wk5uPvu1rrl6/gkbD\nMMSypF/Q2NgYcZyytbWFruvs2iVFgt1ul1arJRW5I8Kc0QbslS5F//xnn8jzd1/+U8bHx/MkHDXd\nhexNJiJ3EFO/gyaFILqRdSVqR9c1HNfJu+ckTkANEZF+w2T/TWIJ0ch0D2nWrhvbHGuQZjimZRGF\nUeZrYWdWmwm2Y0uzIuSAME0yv+ORzUDt6pKKpefdhK7rHL71TS/7XE49+Q8jjBgJD6XqvaXZH5SR\nlZ51KkZ+5HRdDxAMgyG6ITtfyzJB04kSiY1fXlrmbx56CEPLOoI0pddtMz01iWeb/MK/+l9oN1sY\nGpKCZRh0+wNSdP7s//sCL1ztcMOBmyhVJtEsn14/wnJshsMBlqVTcGWHXCoWiBPpCheFIbZpYBoa\nhiYQSUijscbqyiKXLl9gGPRpbKzIQa2hMej2qVXKdLudfOCaiFRuAoMBVjSQm3ySoBuWlHKbFnES\n557nmm4wGA4Jhn0812Mw7El/dNNkMBjgew6GaeIVPPbu25t1qRFj9WomGS+zePUqhmWwZ2GBOIlZ\n39ykUq2i6wbdZsD83DyPP/EExWKR1951F1/58le4/61v5atf/Wqej1goFHG0FDsrhpubm9zz+tfz\nwP33MzY2RhDIYGnVrUZITcPHfuM3GKtXmZgY47WvvYMkiigWSzmHOQjkCSVKQow8yUaT3X0YEsUx\nBb/AYDigp/jgcYKmCWzHptvt4vseYRDyzLPPcvnKZe5/ywOUyiUJA4hMtSgEtimLpnLd7A96uL5H\nb9AnCEI6XVn0pqamJF3XL2Td7PZQUpE9lDnsNo1wmx0iRiT4um4g4ozFools0J05G4pMuJNmEnsB\nKdtsknxQiCzu6v41TBk0Ydrbqklly9vvyTVVLZcplcq5r76i8452/GoDUnRJdWIolcoIIWdz6vQ+\nCodGkYyR227uZDDzXW98x8tqwYlv/XW+Udxxz9v/+zrw/xFXuVym0+nkxVsdBZULXxxtq5+szLJV\ncVMB8LW8aw3jgGCw3YUrnqWemdUYehZVlUWGlcvlbRzOMOh1O/kQUz0fIsWyjGyIpOcQi+M4dLvd\nTLkmkz5M0yQYCV6ADDu0THR9G9++3pehupEoCrNFbGWpKdK3wbCt/H2PChvszDBJPbdjOwghJ+RJ\nGpNq2QDRNPE9TzqtCQCTIMyCjuMEYRm0O12SJEJoGr3BEMf1SNEQms7y6iql6gxuoYjQTPrdENcv\nMRj0qVfrdNtbaKnANWxszaY16FIsFigWC9iGRjDo4FgG3/jWIyAi1jdWaLaadDot/KInue5hRBgF\nXFtZzrp0j7XNNcrVMhutTfqDIX4ckaQpnu/RHwTy/WfD6rHxcUrFcrZGUtlxthpUy2W6rZaUQkcR\n3TAgFjHesAiGzvTMDGXHxnN8Xnz+DDOv2UUUwt4bDhCEEYbhMDUlPeF1PaVUqfD8mTM4nofQNC5e\nvMj87nk2VlcxNQ2/UMBxZOK9gQyMsG2bUrnMI488wlMnT3Lvvffy9re/XToeotFqtbA9l9XVdVzX\no1SqMBwGhFEk7RCETLM3DakWbDQadLptPM9jenoGIQT9/gDX9XCcAiurK5RKJYZBhOPE+L6z43Sq\nNg8deP9737uNM+saumHQaTZJwxivZNFvybSrKI4plIoEwxDbdimVq1SrEUEYspmZgPV6fZKs25Qh\nvyZCz6yVw2gH1p0Kmc4jhAx2kOEOOlqaooU6kSY9UlKynExThjtLWFHDQNozBGKAocuoNQWHpmmK\noesY7rbviKZpxKlK0pJ15NKlS9Tr4+zdvSDnYmgkqaDX7mIYWj47276ft5lmo4SG5eUlbNvOhqFG\n5vzZyho3c8eGIYfR18e4RRao8QMAjB9+AVc4kTqqqQGdKpKea+Y73DaFZxseUd22DHSIcohitNjD\nqEex7I7L5XIeXqtEOOqYpDpl1T2rXVgdw0bhEfUeFHanhhOjAh4pEgp3DDRe6VJDFQXDqNdrmmbm\nZif9SOJ4G6JRg1VpaJVhhGmCUBavukzgiYVAN0wc22bYH0hGSZbOE0bSftexLTm80TUWFxeZmJC2\nr2EsmN+zl0Zji7mJfVnBjzFMi263mdHSNikWXExDdkVJGlDxNUw9IB50WN1YY2N9lYvnX0SkMc3m\npvSkIKY+VsF1bExdo+QXOHL4Rl5z2604tkUUxTRbLfxykUq1iu26mEGE63t4nsdgOCRJwS/4BGHI\n//tfP81gOKTX62HaNo5lksQxzcYW7/noR3nzG97IcDikWC7S63elk54QPPz3f8c3v/YwtbTO9MwM\nQRiyd/8NkoI6DGTsWbWCEDpJKri6usTGxgae6/Lggw/y0EMPcd999/G1r341hxpULJ5nW6CRK3/n\n5+fZWF/nb//2b/nSl76E67q8653v5I1veCNBHFMqFpmemmBlZRnPd6XsWlesBY1eb0A4DInjlGp1\nPIMtYgzdxDQcut0BtpPieQWE0Bgbm5B0uHAojbj6PTTk4Htrawt0nW63h2XJBkSdEm3XxfR0wjTF\nq8jOvOyUCaOAYDjAwZWQomkQhyFJHHP2zBnGx8cl+wiNNE5I0fL7xzEtyU/Xt5OAkkTOWlQdyAUv\ngKkBWpo5Z6YZNp9NhDQNkUREaYrjS29/mUQvT+uatk3bU34kpmmCJtXOahO54447pWp6MERkEXW2\nbWeq7UG+GaiOGMg1HaqhEkIwOTm5g+k16lEzWiMUjq6K//XqooJsX+36oRfwUZ6nGgaq4mmaJsEw\nyHZmjVQYCJFuew2LmDQBNA3bsbCytzPKHx3FnEeL9GA4zLmaLx2IAPkQQl2jvgajmJmaPKsNQ3XY\n6vnV6QDCfLj0avJZ9YWPdthxLIN1VYGWf4x8mi2fM2U4HGAYZhYkIOlcInvfsRA4rke5VJJJ5LU6\nzWYnW0waYRxTKBT49Kc/zT2vex1TkxP0BwPqY2MITWLwtUqZNIkw9JQw6JEIk2qpil806Wk6hhZS\n9H3J1dZhfWWZ7z3xPSxTo9/t0GxuEUeBhFscE5IUw7aAiHgY0hsM+OB7f57DB2+ktdXAtk327J6l\n2+vRbLdIwj4brQaz9SmGA2k3MMyi4lZXVqnUqrTbbTRdx3YcwijEseWQ2/c8CoUCV69exbYtWq0t\nHN9jGAZ4hQJf/OIXsfSE3nDA9K5Zer0+hm4QJynnL15C1w0G/YB6vU6pVGJs3MRxpYf4pStXOH78\nOEtLSyRZoaqP1TEzhaWppQTBgMFgmGVwWkzPTiESyT3WNJ3+oEciYuI4pbHVzK2LVccsmxaLbreH\nbTsIItJUxzRcbMsnTmKCUA7Ry+WyVG1mA/1+b4Dn2Qyy8G/Q6fY7bDUa9AcDev0+QRjiFwroSZxz\nj7vdLpquMzk1SbebzWcMGA5lByvl3gGObWVzIY/V1fU83UneEwZxIvMh0yQBy8rsW2NEyk54Qjey\noqugUgmxpBn8l6ZIaC9T/2iahqFpmIZGp9PKPyfJatlWfRqGgev6eeFcXFmSsyLPw/cLrKysSF+b\nDHOP420BYbHo5/UgDMM8/FypL1VhV/ei0pMo2vEoo01RDkdnbdc7jSv22Q9iofzQC7ii3ajhoG3b\neRFVu5xrSfaGeuNSaWnkZlZa9qUqWfwOylD2WGryq1SUIk3pBwHtdntkt5WLf5T3qhaieo3qsZQJ\nluu6OYUwCKSAAraHNmohlEqVH5iyoYZCCsYZVXaOctjlotQZ5ZYrEx0hpIezY5s7OLOWbtDt9ahU\n6+g69HpdNE1g2TaGViQM5BBsOAiZ372bYDjIaVxpKhWtBw8cYKMbYYqIsu8gsED0uXb1KvO7puh2\nW1xbXOLCufMMen2KbsrtR/fx4IPvYDAY8iu/8sukcUi55BBEAV7Bl4wZLMbrVf7lz/xrSgWPYNCl\nWPTodTtcvHiBhYUFCkVfmvALwaVzF5mdnWM4HFKuVLBdh2KxyKnnT1PwfTYaDQqFAtVqlUGvh2Va\nrF1bZd/evVknK2+eYDhEN3TWVle58447ePb0U0RpSqPVouD5NDYb+H6B/fsPoqFTKZVzrUCMNDA7\ncuQIS0tL+K7L97//fWZmZmg2m3S73ZwvXHAMCoUClUplxynOMi0cx2Y4DPiLL/wFp06d4t/98q9i\nWhY/9mNv41d/7f/gxhtv3NFkmKZFvzfAshzGxkrYjstwOKTX62DbFpVKhW6vsw3HhSGmZbLVatIb\ndDn1wvPy5BmFVCplul3p9omusXRtmYLv43oew8EAwzTpDvt0hwO6femD7Q37eI7DMAwy1onUjvqF\nApsbG+yam6W5tcXS8hK1ao1arUYcy02oWC4SRjK4IRWClBRNSDMzNCOry2IkRk2GtCRCoJGl/ySJ\ntKQVAhGHxKn0sLc9Gf6QpoI4iUniBNf1stP09mBTCMGRI8eywPICvV6PvXv20mw08Bw3w9f1nEkW\nBAN6vV5+75dKpRwJGBUYwU6n0tHvbPu7M3c0rIpX/kqX9HfyfmD9/KEX8FGaEJCpnratV2UIcZqz\nTtSupz4k2JaQK0622r1GbR9VF652Pjv72Wq1Cmx354MsuEEVbznZ7+9QV6rnVLupwtZM05ShqNlz\nbavAtn0TduD3L7kGg+3jmnoOVcgNw0DPIB31b/Kx7fzzUFx2IVKpdhuhV+kaVEolNDSOHj3Kiy+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zF5YbSEPdd4BFpHnyDwQGsC/41vuxncBnUrxQpfLWYsMM++7XdeRNGAOWRKVQKm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\nt0utdc4P1PaMJWMteglFk3xfuB+CDj7aA0IIZH6eq4s7Ko1gqI6yx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T8YSN9fUzn8fGq5f4q3d/xGRe8cbtO1Qedp8/YWKmPHv6iPTSkHkjSLMRjXXs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9BK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\ngg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4684b10>"
+        "<matplotlib.figure.Figure at 0x10e077310>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Remove the temp directory to clean up."
+      "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
+      "\n",
+      "You should try out detection on an image of your own next!"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "(Remove the temp directory to clean up, and we're done.)"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import shutil\n",
-      "shutil.rmtree('_temp')"
+      "!rm -rf _temp"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 1
     }
    ],
    "metadata": {}

From 206bf8687244bf21754231569532f9c1af0765c4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 9 Jun 2014 20:43:47 -0700
Subject: [PATCH 0106/2053] make selective search proposals with R-CNN
 configuration

---
 python/caffe/detector.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index b4e96029e98..56c26aefd45 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -107,7 +107,10 @@ def detect_selective_search(self, image_fnames):
         import selective_search_ijcv_with_python as selective_search
         # Make absolute paths so MATLAB can find the files.
         image_fnames = [os.path.abspath(f) for f in image_fnames]
-        windows_list = selective_search.get_windows(image_fnames)
+        windows_list = selective_search.get_windows(
+            image_fnames,
+            cmd='selective_search_rcnn'
+        )
         # Run windowed detection on the selective search list.
         return self.detect_windows(zip(image_fnames, windows_list))
 

From 9882d47dad8ee3f0b5fa0c0831fdc3355ee3255d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 9 Jun 2014 20:56:19 -0700
Subject: [PATCH 0107/2053] finish R-CNN detection example

- run through and save new output
- collect region proposals with R-CNN configuration (see sergeyk/selective_search_ijcv_with_python)
- call detect.py in GPU mode
- fix NMS plotting: X and Y coords were accidentally exchanged. print scores too.
---
 examples/detection.ipynb | 448 ++++++++++++++++++---------------------
 1 file changed, 212 insertions(+), 236 deletions(-)

diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index e4eedfef38c..ff0b7a7ba69 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -34,7 +34,7 @@
      "input": [
       "!mkdir -p _temp\n",
       "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt _temp/det_input.txt _temp/det_output.h5"
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt --gpu _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
@@ -44,7 +44,7 @@
        "stream": "stdout",
        "text": [
         "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0609 19:20:40.532173 1915982608 net.cpp:36] Initializing net from parameters: \r\n",
+        "I0610 10:12:49.299607 25530 net.cpp:36] Initializing net from parameters: \r\n",
         "name: \"R-CNN-ilsvrc13\"\r\n",
         "layers {\r\n",
         "  bottom: \"data\"\r\n",
@@ -251,165 +251,148 @@
         "input_dim: 3\r\n",
         "input_dim: 227\r\n",
         "input_dim: 227\r\n",
-        "I0609 19:20:40.533190 1915982608 net.cpp:77] Creating Layer conv1\r\n",
-        "I0609 19:20:40.533205 1915982608 net.cpp:87] conv1 <- data\r\n",
-        "I0609 19:20:40.533215 1915982608 net.cpp:113] conv1 -> conv1\r\n",
-        "I0609 19:20:40.533259 1915982608 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0609 19:20:40.533267 1915982608 net.cpp:154] conv1 needs backward computation.\r\n",
-        "I0609 19:20:40.533274 1915982608 net.cpp:77] Creating Layer relu1\r\n",
-        "I0609 19:20:40.533279 1915982608 net.cpp:87] relu1 <- conv1\r\n",
-        "I0609 19:20:40.533285 1915982608 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
-        "I0609 19:20:40.533293 1915982608 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0609 19:20:40.533299 1915982608 net.cpp:154] relu1 needs backward computation.\r\n",
-        "I0609 19:20:40.533306 1915982608 net.cpp:77] Creating Layer pool1\r\n",
-        "I0609 19:20:40.533311 1915982608 net.cpp:87] pool1 <- conv1\r\n",
-        "I0609 19:20:40.533316 1915982608 net.cpp:113] pool1 -> pool1\r\n",
-        "I0609 19:20:40.533330 1915982608 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0609 19:20:40.533336 1915982608 net.cpp:154] pool1 needs backward computation.\r\n",
-        "I0609 19:20:40.533344 1915982608 net.cpp:77] Creating Layer norm1\r\n",
-        "I0609 19:20:40.533349 1915982608 net.cpp:87] norm1 <- pool1\r\n",
-        "I0609 19:20:40.533399 1915982608 net.cpp:113] norm1 -> norm1\r\n",
-        "I0609 19:20:40.533423 1915982608 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0609 19:20:40.533432 1915982608 net.cpp:154] norm1 needs backward computation.\r\n",
-        "I0609 19:20:40.533442 1915982608 net.cpp:77] Creating Layer conv2\r\n",
-        "I0609 19:20:40.533447 1915982608 net.cpp:87] conv2 <- norm1\r\n",
-        "I0609 19:20:40.533452 1915982608 net.cpp:113] conv2 -> conv2\r\n",
-        "I0609 19:20:40.534168 1915982608 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0609 19:20:40.534180 1915982608 net.cpp:154] conv2 needs backward computation.\r\n",
-        "I0609 19:20:40.534188 1915982608 net.cpp:77] Creating Layer relu2\r\n",
-        "I0609 19:20:40.534193 1915982608 net.cpp:87] relu2 <- conv2\r\n",
-        "I0609 19:20:40.534199 1915982608 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
-        "I0609 19:20:40.534205 1915982608 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0609 19:20:40.534210 1915982608 net.cpp:154] relu2 needs backward computation.\r\n",
-        "I0609 19:20:40.534216 1915982608 net.cpp:77] Creating Layer pool2\r\n",
-        "I0609 19:20:40.534221 1915982608 net.cpp:87] pool2 <- conv2\r\n",
-        "I0609 19:20:40.534227 1915982608 net.cpp:113] pool2 -> pool2\r\n",
-        "I0609 19:20:40.534235 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0609 19:20:40.534240 1915982608 net.cpp:154] pool2 needs backward computation.\r\n",
-        "I0609 19:20:40.534246 1915982608 net.cpp:77] Creating Layer norm2\r\n",
-        "I0609 19:20:40.534251 1915982608 net.cpp:87] norm2 <- pool2\r\n",
-        "I0609 19:20:40.534261 1915982608 net.cpp:113] norm2 -> norm2\r\n",
-        "I0609 19:20:40.534270 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0609 19:20:40.534275 1915982608 net.cpp:154] norm2 needs backward computation.\r\n",
-        "I0609 19:20:40.534373 1915982608 net.cpp:77] Creating Layer conv3\r\n",
-        "I0609 19:20:40.534381 1915982608 net.cpp:87] conv3 <- norm2\r\n",
-        "I0609 19:20:40.534386 1915982608 net.cpp:113] conv3 -> conv3\r\n",
-        "I0609 19:20:40.536221 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0609 19:20:40.536238 1915982608 net.cpp:154] conv3 needs backward computation.\r\n",
-        "I0609 19:20:40.536247 1915982608 net.cpp:77] Creating Layer relu3\r\n",
-        "I0609 19:20:40.536252 1915982608 net.cpp:87] relu3 <- conv3\r\n",
-        "I0609 19:20:40.536258 1915982608 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
-        "I0609 19:20:40.536264 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0609 19:20:40.536269 1915982608 net.cpp:154] relu3 needs backward computation.\r\n",
-        "I0609 19:20:40.536277 1915982608 net.cpp:77] Creating Layer conv4\r\n",
-        "I0609 19:20:40.536281 1915982608 net.cpp:87] conv4 <- conv3\r\n",
-        "I0609 19:20:40.536286 1915982608 net.cpp:113] conv4 -> conv4\r\n",
-        "I0609 19:20:40.537760 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0609 19:20:40.537786 1915982608 net.cpp:154] conv4 needs backward computation.\r\n",
-        "I0609 19:20:40.537803 1915982608 net.cpp:77] Creating Layer relu4\r\n",
-        "I0609 19:20:40.537809 1915982608 net.cpp:87] relu4 <- conv4\r\n",
-        "I0609 19:20:40.537814 1915982608 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
-        "I0609 19:20:40.537821 1915982608 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0609 19:20:40.537825 1915982608 net.cpp:154] relu4 needs backward computation.\r\n",
-        "I0609 19:20:40.537832 1915982608 net.cpp:77] Creating Layer conv5\r\n",
-        "I0609 19:20:40.537837 1915982608 net.cpp:87] conv5 <- conv4\r\n",
-        "I0609 19:20:40.537842 1915982608 net.cpp:113] conv5 -> conv5\r\n",
-        "I0609 19:20:40.538887 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0609 19:20:40.538899 1915982608 net.cpp:154] conv5 needs backward computation.\r\n",
-        "I0609 19:20:40.538908 1915982608 net.cpp:77] Creating Layer relu5\r\n",
-        "I0609 19:20:40.538913 1915982608 net.cpp:87] relu5 <- conv5\r\n",
-        "I0609 19:20:40.538918 1915982608 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
-        "I0609 19:20:40.538923 1915982608 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0609 19:20:40.538928 1915982608 net.cpp:154] relu5 needs backward computation.\r\n",
-        "I0609 19:20:40.538934 1915982608 net.cpp:77] Creating Layer pool5\r\n",
-        "I0609 19:20:40.538939 1915982608 net.cpp:87] pool5 <- conv5\r\n",
-        "I0609 19:20:40.538945 1915982608 net.cpp:113] pool5 -> pool5\r\n",
-        "I0609 19:20:40.538954 1915982608 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0609 19:20:40.538959 1915982608 net.cpp:154] pool5 needs backward computation.\r\n",
-        "I0609 19:20:40.538965 1915982608 net.cpp:77] Creating Layer fc6\r\n",
-        "I0609 19:20:40.538970 1915982608 net.cpp:87] fc6 <- pool5\r\n",
-        "I0609 19:20:40.538975 1915982608 net.cpp:113] fc6 -> fc6\r\n"
+        "I0610 10:12:49.300204 25530 net.cpp:77] Creating Layer conv1\r\n",
+        "I0610 10:12:49.300214 25530 net.cpp:87] conv1 <- data\r\n",
+        "I0610 10:12:49.300220 25530 net.cpp:113] conv1 -> conv1\r\n",
+        "I0610 10:12:49.300283 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0610 10:12:49.300294 25530 net.cpp:154] conv1 needs backward computation.\r\n",
+        "I0610 10:12:49.300302 25530 net.cpp:77] Creating Layer relu1\r\n",
+        "I0610 10:12:49.300308 25530 net.cpp:87] relu1 <- conv1\r\n",
+        "I0610 10:12:49.300314 25530 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
+        "I0610 10:12:49.300323 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0610 10:12:49.300328 25530 net.cpp:154] relu1 needs backward computation.\r\n",
+        "I0610 10:12:49.300335 25530 net.cpp:77] Creating Layer pool1\r\n",
+        "I0610 10:12:49.300341 25530 net.cpp:87] pool1 <- conv1\r\n",
+        "I0610 10:12:49.300348 25530 net.cpp:113] pool1 -> pool1\r\n",
+        "I0610 10:12:49.300357 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0610 10:12:49.300365 25530 net.cpp:154] pool1 needs backward computation.\r\n",
+        "I0610 10:12:49.300372 25530 net.cpp:77] Creating Layer norm1\r\n",
+        "I0610 10:12:49.300379 25530 net.cpp:87] norm1 <- pool1\r\n",
+        "I0610 10:12:49.300384 25530 net.cpp:113] norm1 -> norm1\r\n",
+        "I0610 10:12:49.300393 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0610 10:12:49.300400 25530 net.cpp:154] norm1 needs backward computation.\r\n",
+        "I0610 10:12:49.300406 25530 net.cpp:77] Creating Layer conv2\r\n",
+        "I0610 10:12:49.300412 25530 net.cpp:87] conv2 <- norm1\r\n",
+        "I0610 10:12:49.300420 25530 net.cpp:113] conv2 -> conv2\r\n",
+        "I0610 10:12:49.300925 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0610 10:12:49.300935 25530 net.cpp:154] conv2 needs backward computation.\r\n",
+        "I0610 10:12:49.300941 25530 net.cpp:77] Creating Layer relu2\r\n",
+        "I0610 10:12:49.300947 25530 net.cpp:87] relu2 <- conv2\r\n",
+        "I0610 10:12:49.300954 25530 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
+        "I0610 10:12:49.300961 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0610 10:12:49.300967 25530 net.cpp:154] relu2 needs backward computation.\r\n",
+        "I0610 10:12:49.300974 25530 net.cpp:77] Creating Layer pool2\r\n",
+        "I0610 10:12:49.300981 25530 net.cpp:87] pool2 <- conv2\r\n",
+        "I0610 10:12:49.300987 25530 net.cpp:113] pool2 -> pool2\r\n",
+        "I0610 10:12:49.300994 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.301000 25530 net.cpp:154] pool2 needs backward computation.\r\n",
+        "I0610 10:12:49.301007 25530 net.cpp:77] Creating Layer norm2\r\n",
+        "I0610 10:12:49.301013 25530 net.cpp:87] norm2 <- pool2\r\n",
+        "I0610 10:12:49.301019 25530 net.cpp:113] norm2 -> norm2\r\n",
+        "I0610 10:12:49.301026 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.301033 25530 net.cpp:154] norm2 needs backward computation.\r\n",
+        "I0610 10:12:49.301041 25530 net.cpp:77] Creating Layer conv3\r\n",
+        "I0610 10:12:49.301048 25530 net.cpp:87] conv3 <- norm2\r\n",
+        "I0610 10:12:49.301054 25530 net.cpp:113] conv3 -> conv3\r\n",
+        "I0610 10:12:49.302455 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.302467 25530 net.cpp:154] conv3 needs backward computation.\r\n",
+        "I0610 10:12:49.302477 25530 net.cpp:77] Creating Layer relu3\r\n",
+        "I0610 10:12:49.302484 25530 net.cpp:87] relu3 <- conv3\r\n",
+        "I0610 10:12:49.302490 25530 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
+        "I0610 10:12:49.302496 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.302503 25530 net.cpp:154] relu3 needs backward computation.\r\n",
+        "I0610 10:12:49.302510 25530 net.cpp:77] Creating Layer conv4\r\n",
+        "I0610 10:12:49.302515 25530 net.cpp:87] conv4 <- conv3\r\n",
+        "I0610 10:12:49.302521 25530 net.cpp:113] conv4 -> conv4\r\n",
+        "I0610 10:12:49.303639 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.303650 25530 net.cpp:154] conv4 needs backward computation.\r\n",
+        "I0610 10:12:49.303658 25530 net.cpp:77] Creating Layer relu4\r\n",
+        "I0610 10:12:49.303663 25530 net.cpp:87] relu4 <- conv4\r\n",
+        "I0610 10:12:49.303670 25530 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
+        "I0610 10:12:49.303676 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.303683 25530 net.cpp:154] relu4 needs backward computation.\r\n",
+        "I0610 10:12:49.303691 25530 net.cpp:77] Creating Layer conv5\r\n",
+        "I0610 10:12:49.303697 25530 net.cpp:87] conv5 <- conv4\r\n",
+        "I0610 10:12:49.303704 25530 net.cpp:113] conv5 -> conv5\r\n",
+        "I0610 10:12:49.304410 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.304420 25530 net.cpp:154] conv5 needs backward computation.\r\n",
+        "I0610 10:12:49.304427 25530 net.cpp:77] Creating Layer relu5\r\n",
+        "I0610 10:12:49.304433 25530 net.cpp:87] relu5 <- conv5\r\n",
+        "I0610 10:12:49.304440 25530 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
+        "I0610 10:12:49.304446 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.304471 25530 net.cpp:154] relu5 needs backward computation.\r\n",
+        "I0610 10:12:49.304478 25530 net.cpp:77] Creating Layer pool5\r\n",
+        "I0610 10:12:49.304484 25530 net.cpp:87] pool5 <- conv5\r\n",
+        "I0610 10:12:49.304491 25530 net.cpp:113] pool5 -> pool5\r\n",
+        "I0610 10:12:49.304498 25530 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
+        "I0610 10:12:49.304504 25530 net.cpp:154] pool5 needs backward computation.\r\n",
+        "I0610 10:12:49.304512 25530 net.cpp:77] Creating Layer fc6\r\n",
+        "I0610 10:12:49.304517 25530 net.cpp:87] fc6 <- pool5\r\n",
+        "I0610 10:12:49.304523 25530 net.cpp:113] fc6 -> fc6\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0609 19:20:40.628031 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0609 19:20:40.628060 1915982608 net.cpp:154] fc6 needs backward computation.\r\n",
-        "I0609 19:20:40.628075 1915982608 net.cpp:77] Creating Layer relu6\r\n",
-        "I0609 19:20:40.628082 1915982608 net.cpp:87] relu6 <- fc6\r\n",
-        "I0609 19:20:40.628089 1915982608 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
-        "I0609 19:20:40.628096 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0609 19:20:40.628101 1915982608 net.cpp:154] relu6 needs backward computation.\r\n",
-        "I0609 19:20:40.628109 1915982608 net.cpp:77] Creating Layer drop6\r\n",
-        "I0609 19:20:40.628114 1915982608 net.cpp:87] drop6 <- fc6\r\n",
-        "I0609 19:20:40.628119 1915982608 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
-        "I0609 19:20:40.628136 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0609 19:20:40.628142 1915982608 net.cpp:154] drop6 needs backward computation.\r\n",
-        "I0609 19:20:40.628149 1915982608 net.cpp:77] Creating Layer fc7\r\n",
-        "I0609 19:20:40.628154 1915982608 net.cpp:87] fc7 <- fc6\r\n",
-        "I0609 19:20:40.628160 1915982608 net.cpp:113] fc7 -> fc7\r\n",
-        "I0609 19:20:40.666213 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0609 19:20:40.666244 1915982608 net.cpp:154] fc7 needs backward computation.\r\n",
-        "I0609 19:20:40.666255 1915982608 net.cpp:77] Creating Layer relu7\r\n",
-        "I0609 19:20:40.666261 1915982608 net.cpp:87] relu7 <- fc7\r\n",
-        "I0609 19:20:40.666267 1915982608 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
-        "I0609 19:20:40.666273 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0609 19:20:40.666280 1915982608 net.cpp:154] relu7 needs backward computation.\r\n",
-        "I0609 19:20:40.666286 1915982608 net.cpp:77] Creating Layer drop7\r\n",
-        "I0609 19:20:40.666290 1915982608 net.cpp:87] drop7 <- fc7\r\n",
-        "I0609 19:20:40.666296 1915982608 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
-        "I0609 19:20:40.666302 1915982608 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0609 19:20:40.666307 1915982608 net.cpp:154] drop7 needs backward computation.\r\n",
-        "I0609 19:20:40.666314 1915982608 net.cpp:77] Creating Layer fc-rcnn\r\n",
-        "I0609 19:20:40.666321 1915982608 net.cpp:87] fc-rcnn <- fc7\r\n",
-        "I0609 19:20:40.666326 1915982608 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
-        "I0609 19:20:40.668617 1915982608 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0609 19:20:40.668654 1915982608 net.cpp:154] fc-rcnn needs backward computation.\r\n",
-        "I0609 19:20:40.668663 1915982608 net.cpp:165] This network produces output fc-rcnn\r\n",
-        "I0609 19:20:40.668689 1915982608 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0609 19:20:40.668720 1915982608 net.cpp:176] Network initialization done.\r\n",
-        "I0609 19:20:40.668737 1915982608 net.cpp:177] Memory required for Data 41950840\r\n"
+        "I0610 10:12:49.364333 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.364372 25530 net.cpp:154] fc6 needs backward computation.\r\n",
+        "I0610 10:12:49.364387 25530 net.cpp:77] Creating Layer relu6\r\n",
+        "I0610 10:12:49.364420 25530 net.cpp:87] relu6 <- fc6\r\n",
+        "I0610 10:12:49.364429 25530 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
+        "I0610 10:12:49.364437 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.364444 25530 net.cpp:154] relu6 needs backward computation.\r\n",
+        "I0610 10:12:49.364455 25530 net.cpp:77] Creating Layer drop6\r\n",
+        "I0610 10:12:49.364461 25530 net.cpp:87] drop6 <- fc6\r\n",
+        "I0610 10:12:49.364467 25530 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
+        "I0610 10:12:49.364480 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.364487 25530 net.cpp:154] drop6 needs backward computation.\r\n",
+        "I0610 10:12:49.364495 25530 net.cpp:77] Creating Layer fc7\r\n",
+        "I0610 10:12:49.364501 25530 net.cpp:87] fc7 <- fc6\r\n",
+        "I0610 10:12:49.364507 25530 net.cpp:113] fc7 -> fc7\r\n",
+        "I0610 10:12:49.391316 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.391350 25530 net.cpp:154] fc7 needs backward computation.\r\n",
+        "I0610 10:12:49.391361 25530 net.cpp:77] Creating Layer relu7\r\n",
+        "I0610 10:12:49.391369 25530 net.cpp:87] relu7 <- fc7\r\n",
+        "I0610 10:12:49.391377 25530 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
+        "I0610 10:12:49.391384 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.391391 25530 net.cpp:154] relu7 needs backward computation.\r\n",
+        "I0610 10:12:49.391398 25530 net.cpp:77] Creating Layer drop7\r\n",
+        "I0610 10:12:49.391427 25530 net.cpp:87] drop7 <- fc7\r\n",
+        "I0610 10:12:49.391433 25530 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
+        "I0610 10:12:49.391440 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.391446 25530 net.cpp:154] drop7 needs backward computation.\r\n",
+        "I0610 10:12:49.391454 25530 net.cpp:77] Creating Layer fc-rcnn\r\n",
+        "I0610 10:12:49.391459 25530 net.cpp:87] fc-rcnn <- fc7\r\n",
+        "I0610 10:12:49.391466 25530 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
+        "I0610 10:12:49.392812 25530 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
+        "I0610 10:12:49.392823 25530 net.cpp:154] fc-rcnn needs backward computation.\r\n",
+        "I0610 10:12:49.392829 25530 net.cpp:165] This network produces output fc-rcnn\r\n",
+        "I0610 10:12:49.392850 25530 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0610 10:12:49.392868 25530 net.cpp:176] Network initialization done.\r\n",
+        "I0610 10:12:49.392875 25530 net.cpp:177] Memory required for Data 41950840\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Loading input...\r\n"
+        "GPU mode\r\n",
+        "Loading input...\r\n",
+        "selective_search_rcnn({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpo7yOum.mat')\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "selective_search({'/Users/shelhamer/h/desk/caffe/caffe/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpCk4v4e.mat')\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Processed 190 windows in 14.397 s.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
+        "Processed 1570 windows in 35.012 s.\r\n",
+        "/home/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
         "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
         "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
         "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Saved to _temp/det_output.h5 in 0.068 s.\r\n"
+        "  warnings.warn(ws, PerformanceWarning)\r\n",
+        "Saved to _temp/det_output.h5 in 0.035 s.\r\n"
        ]
       }
      ],
@@ -419,7 +402,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "This run was in CPU mode. For GPU mode detection, call `detect.py` with the `--gpu` argument.\n",
+      "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
       "\n",
       "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
       "(We only ran on one image, so the filenames will all be the same.)"
@@ -442,13 +425,13 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "(190, 5)\n",
-        "prediction    [-2.31462, -1.8712, -2.74564, -2.41909, -1.863...\n",
-        "ymin                                                          0\n",
-        "xmin                                                          0\n",
-        "ymax                                                        323\n",
-        "xmax                                                        481\n",
-        "Name: /Users/shelhamer/h/desk/caffe/caffe/examples/images/fish-bike.jpg, dtype: object\n"
+        "(1570, 5)\n",
+        "prediction    [-2.64547, -2.88455, -2.85903, -3.17038, -1.92...\n",
+        "ymin                                                     79.846\n",
+        "xmin                                                       9.62\n",
+        "ymax                                                     246.31\n",
+        "xmax                                                    339.624\n",
+        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
        ]
       }
      ],
@@ -458,7 +441,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "190 regions were proposed with this light configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant. Note that the full R-CNN system's selective search configuration is more exhaustive and generates ~2,000 proposals per image.\n",
+      "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
       "\n",
       "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
       "Simply list an image per line in the `images_file`, and it will process all of them.\n",
@@ -492,37 +475,37 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "accordion      -2.314622\n",
-        "airplane       -1.871199\n",
-        "ant            -2.745639\n",
-        "antelope       -2.419094\n",
-        "apple          -1.863528\n",
-        "armadillo      -1.965794\n",
-        "artichoke      -2.634531\n",
-        "axe            -1.960974\n",
-        "baby bed       -0.765412\n",
-        "backpack       -1.186638\n",
-        "bagel          -2.278808\n",
-        "balance beam   -2.417408\n",
-        "banana         -1.467084\n",
-        "band aid       -1.598927\n",
-        "banjo          -2.113579\n",
+        "accordion      -2.645470\n",
+        "airplane       -2.884554\n",
+        "ant            -2.859026\n",
+        "antelope       -3.170383\n",
+        "apple          -1.924201\n",
+        "armadillo      -2.493925\n",
+        "artichoke      -2.235427\n",
+        "axe            -2.378177\n",
+        "baby bed       -2.757855\n",
+        "backpack       -2.160120\n",
+        "bagel          -2.715738\n",
+        "balance beam   -2.716172\n",
+        "banana         -2.418939\n",
+        "band aid       -1.604563\n",
+        "banjo          -2.329196\n",
         "...\n",
-        "trombone        -1.890996\n",
-        "trumpet         -2.309392\n",
-        "turtle          -1.685296\n",
-        "tv or monitor   -1.517442\n",
-        "unicycle        -1.536263\n",
-        "vacuum          -1.304291\n",
-        "violin          -2.140619\n",
-        "volleyball      -2.413140\n",
-        "waffle iron     -1.830395\n",
-        "washer          -1.806998\n",
-        "water bottle    -1.686736\n",
-        "watercraft      -1.740314\n",
-        "whale           -2.516271\n",
-        "wine bottle     -2.607729\n",
-        "zebra           -1.917919\n",
+        "trombone        -2.531519\n",
+        "trumpet         -2.382109\n",
+        "turtle          -2.378510\n",
+        "tv or monitor   -2.777433\n",
+        "unicycle        -2.263807\n",
+        "vacuum          -1.894700\n",
+        "violin          -2.797967\n",
+        "volleyball      -2.807812\n",
+        "waffle iron     -2.418155\n",
+        "washer          -2.429423\n",
+        "water bottle    -2.163465\n",
+        "watercraft      -2.803971\n",
+        "whale           -3.094172\n",
+        "wine bottle     -2.830827\n",
+        "zebra           -2.791829\n",
         "Name: 0, Length: 200, dtype: float32\n"
        ]
       }
@@ -553,22 +536,22 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x10da7df50>"
+        "<matplotlib.text.Text at 0x4e2c090>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x10da5c910>"
+        "<matplotlib.figure.Figure at 0x4d008d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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fGQwGXlcqkKQ1JpeXYm1erxfdbpetJQBsnTabTUmdmyiKyGazmJqaQiKR4EA5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lkkT/l19+GblcTuwRHA6HpDmDwUBYeYTfOp2O5NapVEq6sSz2AEiwaLfbiMVi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+9rdtp/vggw90d3env/zLv9Rv/uZv/oc/A89ggmDAc0EHgOSoAdEJXl/fpzoBRWFFRVpU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\nZp1t5rPVM+s/AIIOk5coA2g1AAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10da5c8d0>"
+        "<matplotlib.figure.Figure at 0x4d00a10>"
        ]
       }
      ],
@@ -597,16 +580,16 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "bicycle         0.800025\n",
-        "person          0.545095\n",
-        "unicycle       -0.210109\n",
-        "motorcycle     -0.233815\n",
-        "lizard         -0.366079\n",
-        "cart           -0.590633\n",
-        "electric fan   -0.664937\n",
-        "purse          -0.681369\n",
-        "baby bed       -0.693025\n",
-        "snowmobile     -0.787456\n",
+        "person          1.883164\n",
+        "bicycle         0.936994\n",
+        "unicycle        0.016907\n",
+        "banjo           0.013019\n",
+        "motorcycle     -0.024704\n",
+        "electric fan   -0.193420\n",
+        "turtle         -0.243857\n",
+        "cart           -0.289637\n",
+        "lizard         -0.307945\n",
+        "baby bed       -0.582180\n",
         "dtype: float32\n"
        ]
       }
@@ -617,7 +600,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The positive detections are in fact a bicycle and person.\n",
+      "The top detections are in fact a person and bicycle.\n",
       "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
      ]
     },
@@ -625,11 +608,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Find, print, and display max detection.\n",
-      "window_order = pd.Series(predictions_df.values.max(1)).order(ascending=False)\n",
-      "\n",
-      "i = window_order.index[0]\n",
-      "j = window_order.index[2]\n",
+      "# Find, print, and display the top detections: person and bicycle.\n",
+      "i = predictions_df['person'].argmax()\n",
+      "j = predictions_df['bicycle'].argmax()\n",
       "\n",
       "# Show top predictions for top detection.\n",
       "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
@@ -664,33 +645,20 @@
        "text": [
         "Top detection:\n",
         "name\n",
-        "bicycle        0.800025\n",
-        "unicycle      -0.473696\n",
-        "scorpion      -0.932076\n",
-        "soccer ball   -1.147575\n",
-        "cart          -1.187498\n",
-        "dtype: float32\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Second-best detection:"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
+        "person             1.883164\n",
+        "swimming trunks   -1.136701\n",
+        "rubber eraser     -1.251888\n",
+        "plastic bag       -1.286928\n",
+        "snowmobile        -1.304962\n",
+        "dtype: float32\n",
         "\n",
+        "Second-best detection:\n",
         "name\n",
-        "person             0.545095\n",
-        "swimming trunks   -1.098802\n",
-        "plastic bag       -1.130418\n",
-        "rubber eraser     -1.185333\n",
-        "tie               -1.195762\n",
+        "bicycle     0.936994\n",
+        "unicycle   -0.372841\n",
+        "scorpion   -0.812350\n",
+        "lobster    -1.041506\n",
+        "lamp       -1.118889\n",
         "dtype: float32\n"
        ]
       },
@@ -699,15 +667,15 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "<matplotlib.patches.Rectangle at 0x10e0b0b90>"
+        "<matplotlib.patches.Rectangle at 0x4f59f10>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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F0SsuRLYBflWBkSIdgvBhK+fvj6TOf7/n7GPEK9UnaE16Rl1Ss0s6gjgy0yU+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H7Zdxfn7Ot771FkoJbp/cYTQaMRyO+udJYpkmda1HyjzLUUpxNZ2CMHT6j6EPgaIot0HQUra9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Z7nE2VJ432EUrXVb7sRGOmJQyllUVCUeROvYO5ptLg3F3taRki12JQ0MWLRV8bAVIvc8Kqq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\nqw5AOL05Qjhz5gwG/SkItA8cl1X44WnFj95s+z/2qXd20Sc2sStpl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+/ud/Dmsds/mMqqz5a1/4HPNyDgiUiuHtwzuPePz4MW+99RZt2/LBnTt84xt/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\n8upRmOoyHX+bg6qLbyvaU7fTu36Gq5efQkjBzOxssdvTOXEc0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FhGAe9973uI4pI0bcji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pNGwkyzBgeP0INMnickpWVJiWRafb5ehgnyLLiOOYe/dOKWso6wbb8Rnt7tM0FWkaE4YnLGYL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/iex9mNTcIw5Pe/+Ht89KMf1fS7wRIHd/bwPJ84zvEDn/FojB8GONh0goAkiamq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\njRz4yEY2spG9Q+3/AULZeneOI2OUAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10e077650>"
+        "<matplotlib.figure.Figure at 0x3f5b2d0>"
        ]
       }
      ],
@@ -779,7 +747,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -793,13 +761,13 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 24
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Show top 3 NMS'd detections for 'bicycle' in the image."
+      "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
      ]
     },
     {
@@ -811,23 +779,31 @@
       "colors = ['r', 'b', 'y']\n",
       "for c, det in zip(colors, nms_dets[:3]):\n",
       "    currentAxis.add_patch(\n",
-      "        Rectangle((det[0], det[1]), det[2], det[3],\n",
+      "        Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
       "        fill=False, edgecolor=c, linewidth=5)\n",
-      "    )"
+      "    )\n",
+      "print 'scores:', nms_dets[:3, 4]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "scores: [ 0.93699419 -0.65612102 -1.32907355]\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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T8YSN9fUzn8fGq5f4q3d/xGRe8cbtO1Qedp8/YWKmPHv6iPTSkHkjSLMRjXXs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9BK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\nLU296DGf5P16Gv99IQTefug3tqzJYwBWv/+/grPjiJ2hUVma+iZYN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3uHXnNs/fvI1d1szOS8x0QuVbgm1YPHnMVpHTtseMsjF/+xf/Dn/53ofMhaCY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Qmmqk1kxWQ32OTm9QucvL2Fa1foDnyYrrFRE6iNFWTosrsZYnsW//CHfozHH/84RxcWeN1D59gY\n+Tgqw6l6xElEFIfYlnMA+eqLsf689YW8aIPBYF9Re56nycVYPgqZGw6HE7IxVm4Ho010x3nxedmR\nXfTvMJnQ+6krYsOw+GZRKPrGonKCteK6+mKjBwvoMnqYzBa/G3PpxSsjTceLQEFfFUxDETp9WCm2\ncvtbocD1JEQ6YlUqz7OsI1JdwcGkQrNck8yPGOzucM+Ze1hevcH52+9le6eLH4fIMOWe03fTlwmm\nqBJFCWtra2zt7GBIg+Eg30Az3WqRplHOczsWJoI0SciimHDkExsJKTB97Cj/+6/9Mh/4+Z+H0Qg1\n9Cdyw5W55ML0gnFxBd1kKwZMr+Sj84TFOXUlp/ODZSRe/K7gAPVoEx1Zl89R9KGM7vOxmowzL84N\nkxOvbFKXrQgoh06mB+5Nv6eCIoK9BV+AUhL2wgkt02FtdYPPf/bznL7tTjbX15hpz6AGfVIi/FjR\nsEx8KRlkCbWKw/JLL5C5FoE/RbC6gYp9XMMk8n0sx0U4Lp4xTRL7VBsNYiUYdLs0vAq9YZ8wHJFv\nosywpcTCREQxmzeW8RPFY/c8gG2YIA3CfsDXPv9FZlsNvuUNb+CP/ugP+JZveQ3hbgchBUEUUFEG\nRj+i1qjy7OYVHnnPm/jcF77IbadPMrV0it2VDvP3nab9FodWL8aJFJ3tLaR06HdDzt35AJ1wyK98\n+D/w99/3bqQlMZIYK46pOi5xNpkWVafW9Gee/z+JHsupDnRaqwhf1R2NrusesOzy+Tx2guuyPwkS\nJi0xXRbK0VvjhUFNpK8YW/Wv7MTUz63TIrpVUO5bOQCg6EPhz8ufzZj/LipI5U7Owq/n7M+HYgz0\niLAoiibKGt6qveoKvBAEfXNO8UDLSK04vvy++D8OfKqWQ5Ql3H77bVy98hlmZ9q4rkciDGSY8vLL\nLxK6Fv4wxTJtvEqFB+6/DyENskyxu7NNEPr0en1IU4SskUpJpeJhSglCEaQBnltl8cQxhCn5tx/6\nEO/5nnfQtG1IUuIoQUlAgCEk2d4ONgwDJYBMkUT5qiuMSeRRXrR0ZQ5jk03nz8uKsowMdKVcOHUs\nqxD2wx2Y5UolYwUcH5h4+hjofS0cNbfybRTH5+NI/oz2K6jrJeR05KNwDIuIDAWkUQJSEkQhH/29\nP4TUYmunR38wwEDiD3xqM01U0CdNM6I0pt5q4dqSrdVtzGaV57/6ZcKNDTzHxLYFludiYhIFIdJy\nmJ6b4dhtJ1hfvokyRhhujcFok63lm7Qdj3rFQ2YpFemShQmWMuhu7/LySy9iNJrMzi9w9bkXaHoV\nXrrwMkfvPcH3/eB7+fjjjzPdbmNbBlkQ0pIWtuehkpQBMSv9bYJen+e+8CVqb51h4dht7GztcvL8\n/aw+/SKD9V2qtkU/8Kl6Ht2dPkqkPHT2If7PX/4V/t57/i6nZmdwnSrxyMdybFKlEIbIF8xMQZbt\npYMwclpqb5zlnhNTT8YmRL7Q5oqtUPIKpQ735+hgYQzCDqZfvdX7cj6eLMsmeHhdvg3DQkqxT+eM\nle8r03J6niEdZBT33Wg0ybKUJM4d+Uk6nn9ZloEqHLmKarUKsOdoLRLPFbtac+ujyPVvWdEEPVrc\nX2FxF9bAZJK6g+1VV+C6iXAYR6u/102K8iBKKbFESiRjMCWztTpGknJjZRmZGJjCZnpmHvNYBeE4\nxMmQ0XBIEPhcvfwcjuPQqE9RdU1a9Qaz03V63T7D0RA/SfGzAMd2mKo1aFSqgEL0FQ+dPs8z0Vd5\n6qWXOPvgWRpJim1ajFSM5eZpQ20hkYZBKGGUxEghcMnzR5SVoa6AdRNSF7JyWNUYbUw+L10w8vPl\n4WuTz1BgGOahAl6+bnE9nbfT+3wwGdXYcihzmPq45xOwiDIquHZ54HoFWqkkNspMSVWGkUgMt8Z/\n/PSXuPSNLaa9JrXmIjd3Omxdvkw2EoSbQ5SM2DUyhFAcrdcg9HFMxZRlsrWzQ6+7jS0M1HQLr9GE\nYYaTWQxjhVFpYkzN4PVjLKvJiJjR6nXqoaAaBZi1lJmTR1FhRrTWw8Tl6NIJuv6AqN8h6GyxfPM6\nQRRwefkKP3TyR4nikKMnjrKzvkG7UaFTNxmEEa3IIElShGPz8osXOTmzRP/yCqvb6xgPn0b4DuHl\nbcypBdIgwg4HOBVJ6oKdZDSUib8T8uYHXs8LT1/kC4OnePc73kW7UiGJhghbEmURiAxTZBgoDCQo\nQYpBgpHX2sxilMq55VxW8kW9iALRQ+oM4/CNa0Ub0yNqn0LRreky9Ve0wwqKlJ3448ishDSd9JON\nAcGtCyXo9KxO4+z3L03JVIY0JI7hYCt7vxZpuhdpojJFkhYFwid3khsGSJnuswymae9F/UQT1FGa\npvuIu7BoXmnhKdqrrsD1MKPySlg29yaVw6SzT0qJJV3CJMWpukw1HI4fO8Lm5hqPnHuYtZvrfOPC\nc1ieR6hS2s0pHNth9sgilUqFke9jmSbdbo+V5WtkSlGtVJhu1ak0pnKUN/KJgoidzS36gz6t6Sbu\nyOG1r3kdv/pvf4WpqSnuO34bvu9Tq1QYjUZIQxBLgzgMEVJgs2cG7u3WlHBAIMucv24W6lSIrrR1\nGqRoZdO4mDz6eXNUFR94vuVt/IWw6WbmeGE4GDucfy4OKHp9UdHpkjJ3DgdzkhfjHKYpjjBIhcJu\nNYhiwec//glMp4qvItpTdawoxnBsUsdGmoowhSSKqbemGIUpw6DDyRMnCIMRW2vLuDIjUYrRoE+U\nJDQqLXBNqp7DyRO3sbGxQRT4nLnvXuI0pkOCuThE+QN2elvE/YCZ2hRh2ySpWIjpGt21daaw+MrT\nz3L8jpN85vOf4Yd+9Ifx/QBpwOte/3o++ru/h1upcve993Lh5QuExEhLEvgBSIFxZJ6KaWILQQOL\ntGKzutHh9tN342YhAyG4urqCZQucqSaeVSEJI1oyw+jtcPvJE/yP//3P8uP/8Ee4/Y4lrCiiJiVZ\nGGNKBaYkEQKFQGVZjsoBpW1E0ZVM8ZkuezqdUozhYY5LyBcB3Tej0yHl+T0ZwTFOmaAfpytcnVLR\nN7fp5z6sFQtScY4ibFaXTZ0h0MHTfiTOXlbQ4nzFMzIMA8/z9j8fO3djhDjI9xc0kE4rv1J71RW4\nfgPlVbL4Tg9ZKiNPfWCksICQOIxJhGJxYZ5nn36OTneb6dkGs4ttTMclTGNGOz5RGHHz2lWqtRqG\nlFi2hSkk7UYFz/MwTZNut8vWmo9p20hhUHUrtI8tYRiCwWjIMOizvrLBD//9H+azX/gczUqFuWaD\n0I9oVWr0gxG+SpCmxM7yYrSpglAopAAjm0wuVE5DWyDboum8dPG+aGUlWg5RKmpt6k1HymUaRn+N\nQxDz35WRU5kmyZ2m5i3N6nJ/y/db/k7/PrAyqkmGzDIyK+N3P/w7HGk0yZwqqRBcufgiU4aB8GqY\nVQdhCnr+gGqrQa0xRaczwETSG43YWL5C1ZKQhAjTJY1D4jjBD2LsRoMTt53CMQ2IAs6efwBXGuxe\nW8OzTBoz0zS9RZwbNmkS093toCyXu8/dx9XuNrY0WH/hIrfdfoyXL3wDt1Lhda9/HUESkkkwLJu3\nvv3tfPavPkWMYvboEa5duYKRZriOTaPRQDZr3HXmDq68fJH2bbcRtKcYKRg98wyvO3M/zy0vM4XF\n7m6fviUZWCNknCdYa1Y8Bt0+//yf/jwf+4s/Zjsd8vAdd2HbFUbdIXbVI8oyMkORyjzkUO6VOCyr\nDT1lQxltlxfp4nPd4hqDgcNzv+sWXKFM9cIdui4oI3chBGEY7l9T39SmB0Yc1vQCE/qmpkJhj6NG\ndOq2WEhyizHntlOSZOw3KF5RFOVATotikVLiOPbEQmFZ1v7u6/I8fMX+v+K3/z81fWUrT9xiA49e\nzAAm8xIUx+52Oni1av5ADMndd93Ji889y053i/7Qot/vY9kulucy5c5w/Pjx/fMPh316vR6d7i6G\nYRBGGdVam9PzJ4lSgyCI6Ox02N5Yx/fzjRLTM23a002EZbCxusnrX/NaPvPlz/Lu7/1e5ChiMBxh\n2BaRirEdCzvOMDJIVUacZjiWhaVF2uiIV1fE+uJWHtBy7T9doesmoR6FcqumX7egq8r5vw3jIAVS\n9FvPnCiE2J8QZWR+2AJR5DXXFYBeuEFvyhKQZARDn9FgyNVvXOTM6Qew5+aITcm1L32duUaN1FJs\nbm+QpAK32eLu84/QGwasdS/SrNbAyKMl4uGAqYpJ6hgQKrIMNvodLEeykAVU/BFTnsvMdBMPg8tf\n2WJjfRnf83Bn53JrKwq5dPMmCydP8dUXnsfyHLrL69y7MM+V3V2++vTX+MX/7QOkAgzLJFEJlusw\nf/Qoj73hW/nKk1/GD2OMVg3lB1QMF9sy2ejtcLJioKKI4bU1vFaTqOJh+D4vPv0M4dDHCmKm3Qpm\n3SIwBNkwYmF6hoFKma86dLe3+b53vIvf/IPfZuUbV/neN72JhbkFknBIlsQYirzuq1AoY0++0vF4\nFa2IltAX1HJoXZkiK4+zLlMHwcE4V09RDUe3vMpoXJcp13X351ChcHWwdyu5L1uuZQ5cyklGQI8U\nKd9vrVY/4Ogt+l/8Le7d94cTC6Ie9qsHIBy2I1pvr7oCLya5vjkH0FCcnLhBXTDKJpplWXk8bZaR\nxAmmlMzMzBBFMUePLLG4uISUJgPfJ/UVyzdX8msIgVdxMU2L9vT0nkApwjBiZXWVOM6wLId6vUK9\n5iFUTj34wYjd7R2ECaZlsLm6xu0nTvGB/+Nf8o9//CeQmcJKYkxTEgcRWZJhSUkmJYYUZElKmCUT\n1MRhDj79PnVOuHh2upAUTTfjdIEqK16NJtxvuqBNCr7K+VkNGegKuUzZFNy6PtnLaK2YQDpa0s3w\nIsRRfx6JkRKqjEajzif+6E9QQUiv30GoBKvikiU+pmtAGJEakiiDqdY8oXJY7/VYOHkXpkq59PRX\nGPohTa+CIEYKA0m+ycL1HJxGje3NDbav3uDc+fPYacpzX3+K26bbJHNNwt1dBv0+jucwTAJad5/C\naU3TXd6AIKbuuGRVm6ee+ho/8VM/yanTdxDFUZ4/J8vAEmQqpdFuE6WKWnuaexZnWL+5jOgMEakg\njkI++elP8daH3oCXZPRurHAh6DEVmNQMyWsffYRrX3oGP4zoRD0C18RRBjdvLBObEJsSmeUhgG/7\n1jezO+jwe088wfe/63toui5mKLBUhlQZicpIRJ7zW8rJYgYFMj4oE0wo4TK9WR7n3A/DBL1RHFOc\nX08dEYbhvkItFvlCT+jX08GDDly+GYrV6Rp9fo1jtGOkNBB7jvQoGiP98WacPN68cDzq1y3AZzm/\nfb7zeAxSdBpRXyRfCXDB3wIFXnSwHG0Ce+byXmiOPijFccUqu2862QIpMkzHxhECpOThBx/l8Sf+\nAtOuIoVDvVKn0WjizXqYZs6/J0mCPxoRRiFpnOB6DrVaDcdx6PV6pP0Ovd4OW1trmNKk0WjSaDSZ\nnW2zeGSekT9ga2eb3d1tlGvx/vf/1/zhE0/wru9+G5ZpQxAglcwLMdv5NR2ZZ9BTYoxeYbK4RcEt\n6s+lyFKoI+vDzNfiORXf5chhMofKmLMbj4UueMWk0kaLwqklhG4m5+hN7kXyFK3MV+pKQO8f5Egn\n21vM8hwWeTUhyypC1NTe5pEYsDA8j34n4OoLF6jZHt1BFy9LWLm0id/vU5nKqAsTZTk06k1ac0e4\nvLzOdnfEffcu0dlYJRUGpusRjkJcz0GlOW8POZqqTrUwM1iYX6SSwdXnnsNDIX2fIIvwKh7dYZ/d\nQUBctbn7zP1cu3KdKIyIhj6mJfm9v/wS/+0/+VnuO3s2z9FN/qxMyyBOEsI0xXY82vNz7Gxtc/Lo\nMWzL5PqLFwh2R3imiVOx2NndIkh7dDubLG+vE9emSWdabHV3aM3Norq7NEwwHAORKqqei7IslG0Q\n+iE1p8Jw5HN0dhGz4vE//6v/i5/+yR9n2nYAgWdYEPsYgJCCNJuci3qqi7IS10ysruEAACAASURB\nVEPhilc5r3wx3kmS7Tsb8wiNAmTkFaKUKraQm3vXLBA3WNZBhTZWmJMOzkLGdernsFbOzZ/L4njO\nJUmMUtG+DJumiU4jZVm+4zNX0Pr2+kkLQU9Il8v9ZDqJsk9Bv94rtVddgZeRNxzkPIu/hzn3LMva\nH5wkzUNwVBoDAmWY2LbNyA9oteeIQ8XKzQ1Wl3ewKlbOeRsGlm1jGBLTNHBcDwyTTn+IGIxIkgTb\nsWhPH6Hi1QAY9Af4oxG9Xocig5FtmSwtHWcUhgy7fZRh8dKVq5w7dZqKMDCkQFqC2IAsTrCiLEdh\n5qTXu/BAl1shUGUT9jAnoY54dYVsGJPx4fo5i4lXjiTRz30Yf60rZaUERfJ/mNwkUfzmsEiF/PoH\n86ijVYPJx8fJwxulwk9SBr0BzeoUjldhtb9Dsr3FaHWNyIDlMKQtbHYrHufvuo84VUR+RLvV4uaN\nG6xcvYCZRDSmWsQipTcckKoRrmERxjGLJ47j1RqsXLnK3/m2hwn6fV588SWa1QqOXaU11WCQRmwk\nPn4ScefcXVx67mWGo4havc72aMQ3rl3kJ3/2p7n//FniNM3r5+Trw96N53LRHwzpD30Wji6xsbpF\npVbDbTZI+gFV2yM1BC9eeJlvvf9hulvbpMtrDGcz7IrJIPRZmmlhVl1sYp65folUShIZkglJqgRK\nQDAYUatVCboDouGQn/oH/4i/+tSneeO3fgstz0NJkEpQtxyiOM5DXjWKQ0exuuLR5eywXb/62BcU\nhC4/ZfnWf1MkgBsj/4MRW+WQVX0+3CrSRW+Hfaf3zfOqE9+N6aA87874WLkn/3IChOl0jD5/4jjn\n0Mt9LT+Tv/VOTBgPdnly64OqKyj9pXuHbctBZICGApv1Gu3pNs+/+AJLR0+ytHSMdnOGYeLT7XXY\n3t4m7aU4jkO1WkUhaDgOSRgRBCOiKCZLhux2ukhpUKlUcBwHu+JSn5rCtm1832c4HBH4EY7lMAh8\nzp09x+/8zr9n+j3v4+TMLLZlEKcJef0gsATEgjwuV7tf3UNfdgIVz6E4Tqde9GPKCFtHC2UhyY8d\nm7/lrIZlCqT8mW49lMdMd0qXz6+Pd7EI6AtIWXB1WsdMBWatxie/9ikGUcLUbBOj38Hv9pit1fHN\njEEY0e0PcGda7O7ssNvxmTtynFq9ztUrF7BUiEojUsCtNUkMl0G/y+ZwRHtuDtOtsL2+xVS1SWuq\nxc5whJtBb22dxLKwtzx6DoxcaM8tcuHZF7Gw6IcR9z3yIJdvXuMf/eOf5tyjZwnjdK/cHnnWyiz/\nG8cxzeYUF16+xND3aaYZW1s7yF3BnXfezXKQ0bm5xmA0BAVPv/gMb3jwW9jZ2uLlyxdxp+tcvX6N\ndWOFM6fv5mR7gY2NNdaDIbGEZrVOMApIpMByHIIkwsxMpq0qGxeu8dZv+04+/NH/wNu++y04Cwt4\nwLA3wrEshDkJBooxKYeDlqkzXU71sdunv7St58VfHVQcLp+Tm210pVw+jxBigm75ZlSEjtj1aJrx\n9Scd6uUForh+LptjPrs8b8rPxjQNsuzgPomysn8l+gf+Fijwwzhf/X1xg+WqHYUy0M19lUlUmkKa\n54oGBZbF3ffewZ8+8Zc88shDrF9fZ2P9Jso0aTSbnDp1kkqlQhCE+L5Pvz9gNBoxGg1xHJdGo4Fj\nNhBAEIWEYcbO9ha+7+eK3LaRhsRzXarVGrZtURGSfq/Pz//cP+PP/+SPmX3jG6gogTBEvtMv8EkB\nwzT3ETwcjG0vf1a81y2RciL84hnqYVHjRXC8QWYSSR+MkS1PEH0hLXOetzJPdSEsL0R6f/P7mIym\n0dFc0Z+CS3SFjb874OtPPw8BTGeKnZVNamlKSgSRwnFsQg8WptuoOMDvbDM0DNYuvsRw2KHmSQzP\nIPBHJMJG2jXcukQ0pkg8l14Yk8YKu+pwY+UmW9evIaIYG8EgGJAGIdh1mo0WaxevMiM8Bp0BcyeO\nISyTzICHH32Ijt9Fmm5+vwKyNCNVRXiayWjkc/36dRqNJmmiWFw6zs1r1wj7I6xqhZ1wiG2ASDPW\nNlfZXF3h3qXj3Oxt8czXvsrU4gJLZ8+QmRLZGTCtLPqGwcA0iKMII1UIIYmTKKcHa3VMYaLSlPWr\ny7z927+Lp5/6OpVHLaYci9mpOsORjyz5nAqZKiuy8uJejGc5SkVPl3GYUtIV42FWXvG6VQnGYqdo\n4TPRi6Ho/Ss3HSEX/dCvbRiTclgUjtDvraD7ygEEev/Lei5N4/3NckUfdIvXMAxs2/4vIxcKjCe7\nHjJYPKCy4+KwFVUIQaZyU8aQAikgJSNNQ06dPkHy+Igg7rFwpInMWnSHMWEcs7Z6E8u2cWwH23aY\nm53GNEyGoxFBENDt7CKEhWXZVCsVqlWPSq2JFBI/8Ol2Ooz6A3w3IU1NhOVjmxLpJ3zhLz9Dq93m\nucsXOPfAfcgwRPgRjjAQliBWGfmWzbGJqfN2xeflEnL6oJbRyKGOSoqIlKISfTqhgHXErE/Qok/l\nZ1/EBN8KtRTtMLpHD5PUzddyUixd6HV6yTAMHOHy5Ke+QL3a5NiZ01x46mk8YeLZBmQRca9Hrz/A\nmp/BJKPX2aZuG5jRiHB7hWC4i1l3aDabuNUKQQRpamLXmiycPEZjfpblS5dJ+wGVSp2XL11k9/oN\nZiwTfzQgdiW1Y3PsDvv0Ll7D3B2Rypg3vfUtLD16nsqxef7qiT+DOCUzcz+HJK9biZSYovD7ZDz3\nzLM4jodtOriOyyAYcfLESZ794pMsnjiK3W4S7uxgpinNWo0Xv/E8Z+95gMW5aU4eW+LNb34zadVm\n47mLvPTFZ1k6fpxGxaWb+hjSxpImvlK4lg2mZOiPME0LISRRd0gy8Hnj2dfw5Bee5O6zdyOqLl7d\noRoenvxJl5fyAn+YHBbgqwy2ygi9LLvFefQ8QuUcQroi1/OJFMfreuJWyayKkMGxlTqJsA9EQB1i\njep91oMMisWm7Jy/lUVwmML/W0+hFApAV9xwcACLJkS+Sy+/yYKv3csfYrhIITEMMFAIkZAKRdV1\neeSR8zz1lSe588TtJEFMo3mEqXqNylwVwzAZjoaMRgFr29t7StOi1WpxdH4B26szHI7oDwZsbW0z\nGA5wbJt6o869996PYzt0Oh12d3cY+H36QUDNsnCkye133slv//Hv49U8zp+8A1ukefFdKcnSLC8K\nUUKljuOM6RTNHITJ3Wll87IIcdKRSh6jmp+7cAbCJEIoSNmySVoWpuL/Yqz0zTfldthk1XeY6Vyh\nlJI4DkumeZ4bPLes9qI29pLhB2HIjavXmGm18eo1gjCkaTqILCQGEAb1Wg1zepbA9yHNWJyd58bl\nSyi/z1zTQxGzuX6D9uxRLLuKIxxmF4/QODpPNx5x9NhRZk/dzY2LF7m5fBOGA1zLZDQa4dSm6CsF\n0sCOMxbcOt/22m9jI1FcX1vl9nqdptNguDsgbqaYxp5ZXSxYe/fY7w/Y2dmh3xvRnpomDAfUZ1t0\nri/zwAPn+OxXPsed997BShSSdAd0h30SX7G1u41TqfDtb/oOpmdmuLyzxuraKg1pIkch7ekWvUwQ\nj2IadgMlFQkZhmGCZ5Ag8GwPx7DwDIO1b1zhv3r7O/nYZ/6cqG5zbHaWisgLlYhxdxF7udrVnm9C\npRlocd0FQChARy4bBXrmgNzpCiuXC/aLo4A6kEa6zC/rIKeM3MfAo9gBergC19M0m6Z5MGIkG+sc\n9vOqQJYqil3DkM8xvW/FvRZzIe8X+89BR+86SNpnE9St+6y3Vz0f+Oc/+acTg1lGe0XYkf5d8b6M\n3ooIiaIppVAid+KEScxv/taH+PbvfDNJmtJbz50OhimxnDwPhGHIveD6FEuaWIZNHMWYNlhWbtJU\nq9V9RTcc9FF7g1UoM891ieOYIM5Lq/lxRL3ZYPnGFR558CzTjQoyiRBZAnsbmYv72UcLe7mxURkC\nBUKRKVDqICd+GG9YVL+WUmLbthaKdzDmtay0i1eZ59QRTxn96OfU+3ZYXH+BpAv6Z4zo473Fabxg\nZ1m233/J2GF1ebfLU5/7Mm4sCTYH+N0uQiR0hh16fsB2z6c5fQzHbTAaruNaLvFggDncpGVFZFnI\nThDRw6A1v4BBRjTok9qz0N1ksHmdrGIzNCwkHtOWy22LM+wGXbAtGpU2plqkMpVgRKsciSysUY0r\nSrDcMLm5usFJp8X/8E9+nN12BxUbWMrKFbi28C4vr/L5L3yJ2bkj2HYN16vSDUbMtKZQ3Q7bL79M\nFg4xGjaXb1zFv7FNK/OIqxXe94v/lOmFBcLugCuXrhBcXeOUcqlFGXLKY91M2chiArfKehjTbk9B\nNCLO4nx7d5biGQYyUVi2QyItRKPGM5cvceLUcU4fbdM0LCw/xEiTvPqRLUlMSSYMRCowM4kEIjVO\nuqTTanqEVAG4Cif3BGoVe4mDUAjG8zzJJuVbt/wOkz3dojvMX3PP2ddSbi98/fMH5Hby70GKV6nJ\nQhZj2Z/cyFP+nd6+mdrVFfyDj33nLY9/1RE4jCd6UYFDT+ASRdH+wCTJZAjcmHYoPpvMMW0YBkoI\nTNsiHqZUKhUuX75Mc6rJXXc9SMWtEicJnV6HTq9DksakaUKtVqXVaGEZJr1en93ONt1uTqkYhkGt\nVqNScWlNtahWKiil8H2fwWDAbqeT76pyPaanmkRJzG63w7HFY3z2s5/jB77vXYySGIHIc1Zbcp8D\nS9OETGV7SXFydIOQSASQ7RVvy1txjzo6L8w2yIu8FuilbN6WOcYyKipWf925UkYVxe+hSN6TlcZk\nkvsuK3L9XEqNE3gVxxUbOVZXVzl69Cjr6+u0221UmmE3j2PUbmD4Kd1hh8F2h7m5JpaQ2Ibg/Jn7\nsbwprl1fp7ZUIVzpMNxcZnbaYmu0SzxUpJFFZkqCUFGtNGjW5jFON7j+5W28ygyD0Q7TCx6bvS3i\n2iKXOwNqrRauk/HSS09imwt4NYltjBh5TaK+ZCs1Wd0e8eyLX+V1P/J+/MouowhcZZBmGYYUGFp4\nneu51GpVdnZ3WVysI6Wk5VWoGyaRElx++SXmahWmZYvTc0dZiyXXXrzKu7/n/dxZn6bbGfDRf/87\nnJo7wqnFJaJOl5u9Xeb8jKX5WTbXbyIqHnfeeTubN24y61WJScgsgWmZWKZEpBmO4+EnKYllcOrk\nCV568TmOTD1IogRTjo29V8UuDkOSUKCExJAGSAtDSPK0rQVNkVuvejTTeLwN0r04e0NbkMcKHrK9\neQ5M0C56K8uSvkmtvB+iaLdSgGX6pgxaiqpTOjWU00IHSxfqIbTlCLDDWpk2LazbohVW7iu1V12B\n6xSJUoowDAnDcP89cEAp66282hX0wf57IVCBj1erctedd3H1+jXuuece1leukKQpConredQqLpZt\nk6YZo9GITmcbgSKOI2q1CvPzc0gp6Pf7hGFAkiSsrqzknKxjYzs2QTjC9SooBGEUEO+kBEGAZdvY\nponnVPjUZz/Dg+fOYJgWhjQhUximRBoCCxOlMqIo3KcPpGGgZJ74qoyAD4veybfjFmFOYoI3LKin\nW6EDXXgPS/5fNvHKi0FxfJm71sdG7+8kLyoZJ04a5704cuQIYRgyNzePPxphmCbGYB3T38GTFeJo\ngFursN3vo0yJM9XkxN13cHNti3OPPoCf7HJ99TnaU/P0wx0GUUrDdTGkg9WconbqBP1uHy8SbNx4\nma6/S9XxSLMmwXbMqdoic41F0nqT650+Sd9gyT7DkFV2Nzs02m02bJvISRgFQ/rry9xz+xEe+taH\nUDUbM5SYmYEh95ZfNb5/27LpdLq023N4rpNbIY7JbtSnH3Y49chZrnz9a/Ru9HFaNZhvcPrUa7n9\ntWfoba5z8aULtCOodgOSSpehCNlVQzqX11js7nDs2AJXY58b1y/SsDziwCcWijhWZHtGcBgEeK6L\nNA2CLMGsuDx05iwf+ejH+O63vgXbtRnEERXLwDQtzD1USAZhukcBUACBvRz+JAgBpmGBgCzNy+Ih\nBKaRp3NIs4w4yRNmGVKLGRdiL04rf1i3yrhZyGBZaevyWnZIHtaK6KfD9lXoVoR+bZ3eeCWQUgYx\neivPhWK+6ODnb0KOvOoKXN9CrQ9WEQrkuu7E6lpWFMX7sslUrJpZliFNg631Dc6cOcPvf/QjPPLI\nI7SnK9QqdeIkY3e3RzAaYkgDyzSZnZ7GdW3iJKTb7bKz06Pf75NlOYqfm5ujWW8QJyGBH7C2vsZg\n2M+VnoSK5+I4Hpbp0O/1cmTe7zPTnqE/6rK8vsXS8aOQRZiyyBJYDJjCsvacHjIX6EwVAjKJGPQB\nLj4vFi/TlBoi54Cg68+qHMlSIGJdGeumcfH+MKrksIlUHKOfp2jF+yiKJ5y1Ukocx2EwGCClQRKP\n9tJw2lz5yqcwej3WdwOkGuFUa5BJNrpbzE7PsjPcIVFDHDeiM/CoeYoF02Rl5GLO3IURg5ekmK0p\nWksneSG4guXYnJ73qMQW6U6EUzX5ofe/l8vPfQ3TlHzH330XL69vc+PyCuZuTNuDj3zscXZjBd4M\nN268jJkOOTo1zT//xQ+QNev0uhk1xwLGoWhy398gcB2XarXKdLuFyhIc2yMTGY5lMzINWkcWWL82\nhegP8Xf7NOfaODNN5o8tcunxz7Nz8RqrFy5x/k1vBpWQpBGRTBj4OzQCG3sT7LrLbXecZHc4JDVt\n0kyBkIhM4No2dadKliTYnoOXxWBJRv0+3/uu7+f//jcf5Md/4seoVlyCJMHOMuxMYAoDTGMvCRZY\nahx6Wiy8I384we1KKRFSoLIEKQxMKRGmQSbEHp885ocL35ZQ49wnhUzp1qAuY4dteCn8Ra+EYssp\nHMrFV/I1ZRKE5DTQweRxQhxMhV0+Rp8PupIu9FcBXsv3d6v2qivwwiFXTH49xaJpmsTRuBadvqIV\nD1oPlZPG5EoohcAQgiCKaLVaJErxmkce5Wtf/RpLC9PYlovr1HCcGvVqFcfxGI4CknhEt9shUzGO\nY7G4MI/jeGRZSuAH7O5ssbmxhm3beJ7L3NwsnusRRgFBnBBnKb3NDdI4xXMqeLZLrVoliAP8KOTr\nzz1Pa34OO02IkxhDgrEXJVIU9c0oFilQpEjy3OI6OiieE4wXtCybLIelC+9hSaKA/XCmopUnjI4i\niphv/Xg9MkBvOq1SjjIpo6jCYZVPdoD8fhzHwbRsBsMhteYUf/3XT3L58jaOMLl88ToN1wM/JJWK\nlmNz5s672Ozs5ApCCtZWb2JkA7pqgFGtYasalpmh/A6e5+BmDvPuFHfM1LnYu4abeiy2Zjm1tMCX\nPvPnCCvGa09zZXOdK1ubnHn93XSuX2T+5hT/8hf+V7587QIvbK+w092kabb5ge97J5Vqm1B4LNQc\n/M4ayjX2x7OwpNhLJxwFeSm/emMK16uQRTHD/oDBVof28UXuuPc+PvG7f0DDtgnUVV5z4jR//KHf\n4bHGEkbPx7NNVoY7OLLK6voKu91N/KhLsD7k4emztFBsXLuCszDL5mAXx/KoWFXSKEUiMFTuC0pG\nAZZrkEQRhlJ0dgd8+1vexp9/8lN893e9hUbFQfohEoXIMqI4IzJyH1O2l16BJKaoOqNQee74OK8D\naVkWpjARmUJJhUKSKUGmMuIoRok8ZG+folMHlXJZxspoV5dt3Tp9pUiOYtu+7lwv/uZzJr7lbwug\nWcw905wMVSyDmvJ35eOEEPuFMIq+fzMl/qorcF0R64paj0zJ72HsyS6Q6P6A7W+Q0AYqy93nCkXF\n8/KdTxK+713v4tc++EHe8NjDeWRJz6ff36LRmEHiUqtU94IyMpIsotfv4GcRjhNiWxaWbTIzM41S\nim63Q6ezi8jyZO6u6+JVPaRlUbFd+t0BWRITpzFBoDBci3Z7hkqzzu/+/kf40b/3Xow43Bd6IfL8\nw1IKBBK1P8aT8dBF07mzYqDHO1sP5go/LJTrMApFf/9KnKDeFx1hF8fmmxVyXrSgc5SaTNhfIBwp\nxxVfTFPm2eVkimU7pFlGs9niC1/4ax7/syc4f+q1dDbWcDMb048IBptkhqCxsEg6UiRZlcr0LL3M\nxbv5GUI/YUtUSTMD0h7KDBmlIW6jybXuDjudZY6363gjiZ9mbPlbbD53g1bN4fTSbYS7MGVM8ZXP\nP86v/tIv8R3nzvLtC/dCJcasCB67437+8s8/wtu/593c/eBDxA7E6TZW7OBKg1AaiL2kSGR5BEeW\n5sn7fX/E8o0bzM3HuI5DZMC238M34NnlZdI4xL3jBFuXlwmurFD5yjM8fN9ZYttkNxgxIOHplSsI\nCaPNLdI0JBQBA89hdbDF7e3bSaOYZ555hh0JtuXheXVMw6HRaOE5FTKV0mzUGPZ3SdOEGEG9PUso\nFF5tiic+/pe85+3fQ5iGSJXfg5ICw7JASgwx9k3lPicb0zSI4nC/vCAiR9aGyudmFOU5TtircC+F\ngSEypGDv+aTEqsixXSDpXK7zDWnFvBgXbhijdYUQY0s8l83DKZQyStbjxvNrjncTj4FMHnWiUy/5\n/xyYF7o+06kXPTWBfn19UfgvAoGXH4KuHIrv9C2nuolyK25LqDEGleSxO2mS4FY8TNviwfPn+dJT\nT3FkcYnW1Azt6Qoog93dXfr9Pil56adGs4ppWVRr3h6lkE+6aC/UqNGoM9tuU6lWyLKMTqfD5uYm\nQ99HKknz/2XuzYMsO88yz9/3fWc/d8vMyqUyqyRVqVTaymUt3saWbGNkuT14azAwNtE0NjQRdEd3\nEBATMUMPhD0dY4gGmjZEdMOABUMDNpsXaIPxvoAXLZbskspSqapUa1bletezn/N988e5N/NmSoIO\nojvc5x+Vbp48N8/2fO/7vM/7vM0mc7MzAFi2wzCJiLKMIAhxHZfTz5zh1iM3ooTCCzySeIS9YyI0\ncc6QIEDsW+Am18hxnClwnAA6O8A4/QBNtv1db9MP0TT/PPls+ncmn+3/3ReOjPYa/+/ye3Uz0d6H\ndjctLooCQ10bgFovrSyLDz30u7RbHTquIBM5jipBVzhhwCArGOSax546Q7/UzB8u6Q1HzCdr3BC0\ncXVFLjXXtM2FJCC1XZbbcwSih3vTLOUNL2X1yb8mdAQ3HllmYWmewWaPC+euMm85bDzyJT7wnh+g\n+Gdv4T/8P7/MRnudc48/g1i4lwVjoRzBG//pg1zc7CF9MEVEoRMqMUtlxj4vjMHPgEAQeD6j0ZDu\ndp/r16/z5KlTOAszjEYjZsMmMvQphGH55AmW51boX7rKcLXH1swWHz33GIdvPMR8OM+IEmELButr\n6DRB2hXDvOILp75BKQzHF2/kjqDFM91NGm4DL/QhDLm4cY3L6xs0Gg2KNCO0bY6uHEIql/PnLqIt\ng0LR8Ns899wVDrbaNIIQR0miLKUsKypT1Z2mVNiWjbBsKgkGSYEAaTFRZ5S6wqNWrijLqvXo4ynt\nRlfkaVYrpqSsa0pJPrZjqGtRZblbjJ9ovica8xovqnHAsPucT8D/xSR5+xtspqPmWlhQ7HnOJ8ec\nPL/T1ORk3xfKNvcD8vR7vJ8KerGo/YW27zqAT5/A/hOqb9a0w12tx9wPOpOIc3IvJPtSLSmxLQuJ\nIEtSbj1+nD/5s29x78tfzbUr13CdnGbYYWamxcLiAdIkIysyKl0xGkakSUwYBliWRZqm5HlGWVaM\nhgPUmKv1PI8g8DnUbGAQFHnBaDhiFI2QSkKW1uoSKYiyhFfc/TI+/7nPcPzoMUpdEac5UlpYtkVV\nlrU80dRG+yDQlUZPtbzvSK1eoDW5jnL3Rw3PtyqYvAzThj+TY++vKUwvBC8G3PvvY1XtUmMvxJNP\nL8aOZZOXOUrVkjIjxs0ZjoVUktEo5vWvfwOnvn2KXtllNe3S92wGsWCYlgyikuWGZqboUw6uUFan\nCdMu52ULk2zT6q+zVSR8s5rlVHQjLTegWT3OLbNrXN3Mefo5l05YsL7eo7kp2Vq7gJARs0uziGCB\nb24BV3PE6Fn+1b/4XvyDZ9kYnOTzX23x+GNb/Oz/9Ytc7K1R2T66kggCtBJUXog2US2Vm9yLcWr1\n7LPPIgycPHkSIRWNsMEgG9E8cjOHFg4yd/Ag/kyb0A1o4/DlP/sLPveJv6DoxbTn5rj3TQ8wc/gg\nSElmcp599mn++k8+jFcV+IFDTMHfnXqU4eXrnLj5Nu6am6eXZWT9LkduOcJdr7ybgTAY5WAqg1UY\nmspDCIvIQKFLKlNPimkEHr600UUGloUlLIb9Hq4XUMqi5t/T2pEvyxMAms0QNwjGwYVG2ham0uid\n+15RlRVSyBq0PQvH82ogrjSe5+2MGJv2UJmu3UxHzNPP2R5q9UWUKTv48ALP/C5/v/f5rr+/NuSa\n/v36+C/c1DZd1N9vfTH9LsBe3/X/lu27DuCTCHK/nGZyckWRT6VLu5HcNG872d+2a0N0JeSeiLPK\nC1zPRZs6ND8wO8fJu+/hq1/7Oi9/+Sso85Iki9i4/hyNRgvP8+h0Zml1ZsZp7pC8yBmNhhRFQRAE\ntNserUZjzAlrRqMR585dJS81ruPSarfpdDq4rlt3dA769AbbJEmCshxsz+X1r38DP/fz7+OXPvDv\ncKQAXZCmCbaSCETNeYu6Kq+FxujdbsTJgzIxsp++4fUDVu7h/+p00N7zcE2u8fT0nWnZ4QRgJ983\nff2nCzUvBMr1titjnNQ6JpIv2JuuZmlGRTV+UQ2IsX1nWdQZBRavevWrOffcRR7rFTy9kbM5TCml\nT6k9HD8gGSTc4WhunTN00vM4Zo2/OLPE02Kek7e+jkym9DdXWbYkIu5z+ux5gtskx+cdnORhun7J\n+XJI5Cyx0ppjNsu4YXmBj5/WfJVD/MpHNniVusZ/uM8ha11lxj+GryWutcQXvnGGn/yX38+1s2ew\ny4pShfSLFMvV2EZjdIE1dtgTqh5h9s3Hv4llWVy8cIEDC/NsbKxx6OZDczLHDgAAIABJREFU+E2H\n1CRQlpisoJf12SorXvm2B7m8ucogL/g/3vfz5J7Ndq/H8vxBBkXM4tEbsKThMx/+MNkoJjMFtrJ4\nZvUCWpfcePRGbj1+K5c3N/nO177CSu922keOYLc7pKVGSY8KBRocZdXDHYzEEhrLWMRZjrIcSm2I\nU43nthGA5fg4UowtgeuRbLat2N7e5JkzlxgMBiwsLDAz2yawPMqqRAqDlBaO66Fk7fqXlwXGVFjC\nwnIEeV4SjBeAycDf6SLjZCDCfgnrNBhP9pvGg+lt4nu/H/z3q96mqZHJ58+nM194SPP04jH5m6dr\nPtMB2f7v+4c6Mb/rjTzf+MqnngcA09Xrskz36JGnQX4/b27bfl3K3qlm1xfAtm2yIkcqhVQKISWV\n5/DQhx7i+LFjHFxcxB0XWbI0r7vJEARBg7QoCQIPx9nVuhpjMFU9186YmsaYcMMCVU90SRLSIsd2\nbKRSeJ6H67pU44g5imLWtnt05hdYv3aVN7z2NZgyw2QJSoixjHCcrlGDsp6qyk+u1/4JPfUDwTgj\n2e/etldBMr0Awl5qZb9N7f6sZz+dMznWXp242Wkiml4kXigjkMZCWJNFoUCL2oM9TjIaYYthlBOG\nTT70oYf42Kmo5kkR2E5AnOQUUYyVDZmzRhyfNRyZKZixU7bmbuXCdc3lUZNuDk7V5QYnJ1SKp9e2\n6cwF3DcX8woucBGbhBkSOuiy5KCrCbGJw2OcD46wnSTcWa3xdr9P694NGp0Z4mqFz5xr8sj1Fq+6\n615eebCkY22QNALWhYexfSyTI41BIhFG1BEnku3tLk9/5xmWlg5SlrVboZYZhShIogyRgid9rgx7\nbFc5RZFzY2eOWS9AuYpKW/hOSCWg0WngBRYNV/KZP/0IW2fP0HZc+klELA1YhnbgcvuNN3P88FGS\nQUJSgndggc4NR2gs34BxQwZRhrQsHMeiSgukBttxyDBUtiLKshpMK4NdGpRQaMdFqonNwuT91kgl\nEOPCvAAGgz5VmRLHCUuL8/iew9XLF3FsC99zUBJ0VaLHz7Rv7S76f5+3ySSY2P8sToO9lJI7737t\n87Do1GNffl6wMvme+rv2UrU15tQF6Gm8qp/rF4bS6Xd0fxDzYhH49Lv2knu/50UziO96BL7fvnQa\npAGyLHkeiExSEdgrOSyKGngtpWp+TCpQFkIKmmGDNM9q8NYVeVFw3+tey1//1//KO9/xDpwxPTIz\n26IRtilLTRJnbPWucfnyBRzHIQgCwjCk3WoRhD6eN0OapvR6Pba3t7Esi1azjed6tJpNjKqHPmx3\nu1y6tFY3KSmLdrPJwoF52nOLbAyHbG53WV29xuJch8APqIocKeoXXQOm0pSm2qGTYFrm9ELV7Brw\npzntyUM+2aaLnr7vT2U8tTG+53lTPOBew5/p1ub9Mq3dNNOg1D4Z2VRUUlft93pQpKMUISEMA7Iy\nw7IUc3NzlKVmefkgcZxx9OhR7MfPgi5ohgFUFS3LIXEsBrlL3xzg1EDzZDemESralz6LBaTdisi6\nlYG7QjEque/kEQ66Bzn19BVGZ/q0bunw8jffytaZNZ59/Cxle4no5qNc2T7H3OU/5h1LMOsUHDn+\nErpDi2z7APPeGnZ4nltvvY9i5QGeevgat9+Rsbx8lc1Ys/yS13Jte4jnBJRFialMTaNIC0tZzM/P\ns7iwRJIkOI6L1gapI6Q0KMuBQUmIR9EI2PAMpaOwogw7ThlFQ9Ko5Lkra5y7foWPffyj6CzGdQx3\n3HyIBQ1hpcDyGHglV/NtwnRI7+khw/VNluwWs405DjTniS+vYdwO4eFZqtDBDQIuPneWm1duYNQd\n4FkuaZrQT0YI10E6LqrQ6CwjSROMK7AdB8PEFgOCMEAIQVGk5HlFs9mk1XYoyhi/UaJcFyzFzbfe\nQeB5rF69yJVLF5FCMz8/T6MRUEajPY19E7pw+lmaYMFEfjcdABZFsaOaerEIfGJZuz8KnjADSu1m\nmkVRjCP+3cEm08e1rL1OjdN8/bQ8dhK9v1DWOnkn/lsLmf9gBP7e976XT37ykywsLHDq1CkA3ve+\n9/E7v/M7zM/PA/CBD3yAN7/5zQD84i/+Ig899BBKKX7913+dBx988PlfOhWBf/WLf4lt2ztmNNMr\nDzx/avR0UWy6S3ByQaaN5KeLF3tTewi0QyQ0v/1nH8ZtNrjthqO0tEvo+TjNkGGeYymL0HJRjoXl\n2DucW7fb3QFPy7IIwxApJXmeU5b5zt8wfdN83x8PJMhJ07T+u41iGI3wPYe//dsv8O7/7Z00wwBL\nGKqixHU9qqIeauG6HtSCm50bO+HErfG0cMZprC5LJJqalTNjCRvYVk07SUDourirDZSVxJgSpQBR\nIYSuNbvGUJYgpI1SDqbSyGKIkA7Gcqgsm0ILKl1iKYMyOY7KocpQaIx2MFB30BqDsB1sy6XUElBI\n6VDbAygKp8KzLVavXOG5s+e4vrZJf5iRVpLnLl2m2WwjZN0NeOqqotQVtueAlPSjUS1FKw2i0Fha\n0A5aDLZ7ZH5CWWlQDrbtUpU5qopZbEqWmwbTv8yNsw4zoWI170Kcc88Nx2j2Y/T6Ntq3WXcD4uYs\nfhhy06zLjR1YaW/TXBzRsBXWqI3wZlkdSBwO0PTgsStrbHh3cvyeB1BWhJdrlCoYBjGp8AgIoOwj\nZImrLYyGnuUgtU1DOnXLfVVhScXEfAxMfT/HigtbBNhSgh5y7uwZfve3/4CL564zN9vhJSeP0B+s\nYjsuTz31HLn0EIHL4U6LBdvCTmPagceJl95FY36Zb5+7itVeALfJ4SM3ceKuW8mLgv4gYnZ+iX5v\nQFFVhEGDLM0oihzf9xmOIjJdEfgNhBDkWY4QFoK687KsChqNWqGVlwU6radd+b5PXhSUusB160nv\njVYDYyq63R6bmxu0GhVZlqJNRaPhI6VAYUAXWFDbUWiNkhKd2wgpKcsCx3NI0gTXtevOTlNnyy97\n5Zufh0XfevRLVNqM6dW63V8JkGM3U4Pcgy2TounEC3x6mwxpmKZkpqmZyX/3W8dOMG2/I+Fku/Pu\n170okP+DAP6Vr3yFRqPBj/7oj+4A+Pvf/36azSY/8zM/s2ff06dP8+53v5tHHnmEq1ev8sADD3Dm\nzJnnrX7TAP63n//E83TI0yT/pMV68nv7+fLpQkVZlnuquJN9bHsXfCf7tt0mmTCMhOZXPvgf+YG3\nvANPS3zLo8DQHQ3Js6Ke0q0knlc3XUzoEtd1SZKEfr9PURR4nke73abTaSME9Pt9oigiiqKdxSYM\nQ+bm5nYKMHGSo5TF2voqQhie/s6T/PAPvRNBRSto1MXMSpNnOUEYojGUVVlzbePrZymrBvZpjk7U\n+vex4qu+lgKqskIAinGkYKh1uCYDvWtQL4WgKOpCkqUsLKvuUK20Rro2zdCnu7WB7zlURY7lOGgk\nhVAY5YLtEjTapEk9pacyBXmekeUJZVUgpKbUFdGwz+bWFnEc0R+6pHHEE48+ShzH5IXB8UIsr4VQ\nFmmSMhr2GA174N5IqTWW62GEJEpTtC7BVCTDPoEjWZmf4+DSAo7y2Nja5uz5y+RaooUEU+CrikDl\nyKKHW0bMz4Ss5U1Gg4qj800ePK5pjh6n3xsylAdZPHSIWes6R1sZLeHSCpawb+hRuRlkDby5Jmt5\nzqzwacRDrkZ38Mj1O2nfHHDs+B0IpYnTDZotl1GRIjiEVbpY1gUyq6AqDuNUgIowgrFvSoW1L8Op\njKYyZc2RYZMlKYEncZTiqW99h1/+wK9SFSXHbl5hcanN1tYWmxt9BkYRYXjVHbdTbK3RtgRN3+XO\nl76U4y+9l/bKTfzir/8mj556httuu52Vgwe46657ePV995NkBY7jEcUJURTVKi9TZ3me51FozfLB\nFdbW1tGVqYeiGMNk4k6SRLUZmQGlHGy77jgOgpCN7U0sJdFG47i1KmWS/c11XJSSxEnMs88+Q55n\nLMzN1PvnGWWW0W6G9Ht9Ws0WaZJSVjmO5+7w3kopDILKGE7e/brn4du3Hv3SWCgwwR49BnAAjZC7\nWeIkCgeB5/k7GDXBpTzfpXun60aTIG46ot6PXfsz6enM9OTL3vCPp1Duv/9+Lly48LzPX+iAn/jE\nJ3jXu96FbdvcdNNNHDt2jIcffphXvepVL3r8ScffZGWbPv608mHy2fRquF8pMflswjHt8NJit/Fn\n52KXFZUxKDQPvPa1PPPMd7j/VfexeX2dMi04tLKC5dgYKdHaMBqNGI1GbG5uYNsOjuOMQb2eYD8p\nKF68eBGYRN0BBw4EO8WdNE25fPkKVVVH15bt4NiwML/I5tYGswcWOXP+IsePHWWQpOgixxtPHBrF\nI8S4Q1NIia2m5VM1zz95KJRUtVOcrouBGoPRBsu299QI6hhDI2RZKwMwSGEhUAR+SJHXGnVBhWML\nUBZdbSGKAsezsE2Gb2uiuMdaN2UztXhmtc+51T79uCRJRrv8IuMIxGiErJ3rlKxTVEtZYDVI4xjR\nOkpzxiZJc0qjyEz9u5Wb4IgAq6q7Mx3Xx7FdSq0IPJc0S8mzEY1mkzLvcuBQm1IPsdICnUZQpQhj\noYVNVkCCJHZ8lLCZmbmRZK7DcX/ARlRx9uJFDqx73Lewwg1ul9WNhLVr2/T9Nkm3yxF7i7I5i7QT\n/AY00wx0htvWYK+iZkYMopM0V97Bw6c/yuLcaazmIYLwZtRog1lVMhAxlpQEZRNP56Qmx61KclGR\nqXE6LhzQZhycFDDu9FOTuojUhO2gphmR3HbiBK953av53Kc/Q5SkwCJ5JsnzkqRIsZst+sMhNx1c\nZrB2hYZj43VafO1bj/Ltj/4533z6LN0s46mzp7l2tcHhwzcyGAxotDoIITh4cJGyGLe/K4mlJGWe\nI4QkGg1ZXpilyIsdTf9wOMASNk3fxvO9evhJUe4UFje3VpmZmWEw7NOZaRPHCY7tkucJcwfmGPSH\ntYxQWNx2/CRlVXDp4gWocmylcJ0Wo0Tjh/PEeRfHs1HGr4O2MSVlTK1cs1+EQlFKwthMyxhTRzrj\nSBxDnbGNAdm27TFe1ceczvwnWDbBo2nasaaSih3Anry3E9CeFFKnqRbYO5T8xbZ/NAf+G7/xG/z+\n7/8+L3vZy/jVX/1VOp0Oq6ure8D60KFDXL169e89ziQ6nfDYk206jZguak5AfFIcm+ZhJ8WK6Yuw\nP3qfHMNyHGSpCZXDK156Fw99+/d55rkz3HzDEURWUKUJQsK17Q1mOrMEgU+r1UTrepL5aDQijkeM\nRrX5VBAEAChljZUrCWma75xLp9PB930WF5d2zjUeRQjLIYpiDswvYXs+f/PZz9LsdDi4sEC72SDu\n9/AcC10qmKpQ53mONgY5Ts3KHemhoZIKPZbwoSaNErX+1kxlP0za97VEjGkNpEIbSIoE25JYlkCb\nAmEZpGXjaI84GTHfafPU448hpeDosdtpuQ4f/dNPsZVYpCakNbuM5W/UBWFjIbEwWlLmGqN3AwCJ\nQGtIsxRlz2C0JsoK4lxgOx7KceoBGFVJVSTYyiHwUwajTaoqx/VnsJVHUlZY0kaiOXr0Fi5cuIiU\nJaEJ2NjuEuVRnR0oj7AdUmmDZTsoW7GZpmxd30Ja11k52OKG2+ZYPX+VC6bB7Izh6HLKY/2Er/YO\nkw1neeVMj5WNFCctObI0w3EVUBQD8qKD27BxVmwiHL705HWuZhaN+AmquMuprevcffIEVmnRDDSR\n6ZLSRpkS1CZDEyCkgxAFVVmhx5aula4Nyow2dT8AAo0hzSJs18GyPLKswncs3vOTP8bMXIu//stP\n8+rlY2xsDglbOelgQDaKKfpDRKse6rC4sITWMBiM+M4zT9MIO9hBmzLX3Hbb7SwdXKbZaCGVYmtr\nm4uXLtHptFlcWODq1Us0wwDXcbh29SobG5vccced+EGI57kMhyOWlxfQlR4vMIYiS7BdB9cJSPOM\n+fmb6fZ63HBomThJaDUCEILA9ijyjNBvEEUJYSOkyHPKClaWjxD4PlWRoxBcXb3KMM4Jmi4l9cAM\nZO3bIivDBP4ke+mOySao6ZO6C1oijMKYakf9hS52gHqXPhE7hlvTAFuWxQ7WTP9suqdi8u7uF2BM\nsw77G/P+vu0fBeA/9VM/xS/8wi8A8PM///P87M/+LB/60Ide+AKJF75wk63b7e5E4dNgPM0TTcvd\n9nsjwF498/7VbRJ9T/YRoh4wOiwzHCR5lGJZNu94x9v59d/6TX7gbe+g2B5yw9IySrgcO34z/e0h\nWmu63e5OQWV2dnZH4qR1LSOsz8UlDBt0Oh0ajQZpmhLHMVmWsbW1tcP1Hzx4EMe2ybOU+QPzXFm9\nRmN2lgff/BY+8qd/zr9473tI4iGha5EXeU2HlBopanWKUqCMwJJqh6fTExMrI3AnZlS1jBwwZHFC\n7d8sxyleLVVUVTju/AGhJKWpQEAhNVmeIITGUTZlkoIWrF65xneeibnrnlfzzSef5tf+3X8myeHI\nzXeQZxUzHZfNy+fwZ5tYykZJByUsQIJXf0+ep6RlimUpLFuhRzVvqoXAkobQs+vxc8bgKol0bYRw\niYsI16kLtVk+JIkyZmaWaPmSSit6vYRvPvwES0sHGEUj7FBRChe34yOkpCgrlAUKQVWl5EmFUhZa\nG06NjpNd2uZ1R306KxX9fJNVJAtVynJjwLVqm4f7HT565SCNPENtedzy3JDvXy655aBB0ELGIb2t\nJ+nMGL7y9T/g2CuO4BYZln6SqjrGL//xiHf90Ns4kJ/B2DldW2OUQRmLSins0mBPkqRKoyxFWZQo\ny6o5Vm2YuCa7jo2lBGlc0m7OkSZ9KlPwjh/8AUaRwWseQFshfgfuXD5IOUxZnGlTDUYcml+iSkvi\nYcLF85dwhU+lFTccPsoP/fCPcO9d93Bl9Rqrq+s02x2EsFhYWARjOHfuHDOdFq7r4rr1sOy7735p\nfX8xJGkypkkMWZaMlVeauYV5esMRW1vrLCwskGcJs7MzZGlGp1MPSRkMh3iOW1MquHhzAWma1gVe\nYWHbNnFce8crx+WWW+9Ea8MTpz6P59Zt6JaU5GXd8o8xSAyYF27kUUqhqLs7jdYIWUs8hZlgkd6T\n7e96DTl7QLf+bFeLPqFcpjFwfzS9H6emwXt/APpi2z8KwBcWFnb+/RM/8RO89a1vBWBlZYXLly/v\n/OzKlSusrKy8yFHeB8AffuQUd991grtO3rlH8TBdlZ2I+Scn+kLpxuSCTbqzJsOOpy/o5EakaYrw\nFJawUVUNcsuLi7z9HW/nmWee4S2ve4BsMKIocrZXr+CpANf2EMaANpRlQVWUpHGC7/u4rkO72aLd\nbJJmBXEUM8wyiqxeaS0pac/OsXBgnn6/T5IkFFlOOZZkdbe3abfb9MZdoLfdfidPnznDyTtvIysz\nHN9FFxWussd+0vUbvrNoSQkabGWDHDc5FHndQMTkITCEgU9R5uiqoqo0xkjQEtv4tcLFqhCKup1a\naYRlsdXNGY0SZmYXCLwWKs9YPnQL3/ry1/nkf/oIrfkVDp64H4mkiEYo+oQiojlvsYXcMeYSUted\ne2iUYyMdiSgE0lNI26IjfcAQj2JMVdWprdxteijLHCVKGoHNKFf4tqLMEuKoR7eKENLGsX0arqC9\ncog3vfFNfOELX2TLOEhZkGcpyoAtQVagywJpBLZyMQUYFNvNgHN5TPPydV51zMH0S568Irhj7k4O\nqW1e765iGkO+Zt/NU8Uyjr1C1T/PRnvEyjBGii5xUWE3Lc4880XajVn0VpusaLFyeIuTHc2To1v5\n/a9c5D1vX6KVn8M1XcpC0rAsKn2FJPcQ7gEazTZZlmGMhoraKVvVDS/G1KytJTVVVuDJgDzJqUqD\nFoJKSH7yX/8bvvC5r9PLckodUxYRi65PFg9ouLW3zNzSEt1BzPZ2xPe+4c3c9cr78BszICTnz18m\nSTPCZoM0zTGAZUOWJLRabebn5vF9h9NPPcXK8iGyrMQLAgqtqX1eDMO4j2s7oBRZUlCV4DoeMzMW\nZVnXXOJRXBf4x1YXzbCBbdkUZQmlwVYCtxliQp84zXYo0rIsGcUxozgGAUeOvgQpYDQa0tveRlc5\nnWaTqsxA17bNL7RleY7R9bALIeohK7s0ikaovZawNQiLPQHhBIsmnZiTYHNS29uvJplu5tkP2Eop\nHnn0CR557In/PioUgAsXLvDWt751p4h57do1Dh48CMCv/dqv8cgjj/BHf/RHO0XMhx9+eKeIefbs\n2edF4dNFzC9++s93gHWyIk2D9QS89xP+E1B2HAfLstjc3GR+fp4oinai5CRJkLIealBV1Q5vbVkW\nZy6f50Brhjm/hR/4ZBJGlHz+c1/AN5IbD67Qardx2y362xG2susmHKVwXXdnuHEURQwGA/I8Hw9G\nbu5qvquKKBqRpnX03Ww26tFsY8qlKnKuXb+GEZK0qii1wW+GrK2vc/7sMzz4wPdwaHmRMk/RWU7L\nC0nTdKewq5SiKIo97fQw4eLqn0dRtLO/tNROlmIEFGWJa7mIwsaoiiiLUIGiF0c8ffY5tPCotE8c\ngdYuo2FM007opQWX14e0D94ITkCcl+RRH1XGVIMt8uEWc60QMXsQjaIsDbYToLVFXgnS0mCUTWEE\nUZqhXBcvG2DGLnS+61NpyKuaKkMXmCKBIkJUOaaEsOHTnmkhRMF2d4Ot7S16/SHd7RFKehw9cpxr\n1zYYmIAkjrEkVGWB0BW6KsczFGuwUbYLBnpOjqVy/OQy97QS7l6cpUxt0u0BR9nikD9gzWnyNecY\ncVLRmTtO1R1ysOrh2Sm6k3PPMZcDbPO1Z57j0fWQdvgGfuaHZrh55izCCXms9z18cuuVPLV1kX/5\nzptYSNaYkT7axCQm41OffZQvfunrNBohJ0++hDvuuIMbbrgRrSfNLLvZUx51mT8wx/ZWArgoFxpt\njy//7d/x8U98mu/7vnfyx3/yYYxIGFx/jiXfZbbVxlUWYdhgmBUcue1O7n7V/bzi/u/huUtr5EX9\navZ6XV5y8gRr61u0Wi3KKmNra5sbblhCV4bhoM/a9VVuvfU4QkIQtugNhvhBgNa1P75lWUSjEe1W\nC0tK0iTFDX2CIODss2e55ZajbKxtEgS1SqUoCvI8x7FtgjAkz2LC0CfLCtI0RQoL16/ni0ZJWkfm\n1IIASW2Y5ToOjcDj8uULXLx4jk4zIGw4YHJuP3H/87DtzFNfp8hzdDXJbsfpzXjKvbTZWTCma2nT\nnZgTrBJi1/52OqLexbzdfacHgcMuTTyhQ6e16Xe94oF/vArlXe96F1/60pfY3NxkcXGR97///Xzx\ni1/kiSeeQAjBkSNH+K3f+i0WFxeBWlL40EMPYVkWH/zgB3nTm970/C/dJyPcT9ZPr0b7P5+sWJOT\nnL4A0/rkyc9t296JuicyoKqqyExFyw+R4yERpYTSEqxtbPI3f/UpXnff/cy020RxjGM30NXeoa6T\n6H96MOm0SH+iT3VdFyHETkfmpJgBYCmFNhrbdVGWTZylZEVBf9hHSsGTT57ijQ+8oZZh1ZwBjuPU\nD7njUFYllmVTViVCyp3vmpz7ZJqNbdvk+biIouTOYIjKaMq8JBom+E2ftMwphCHszCKsgI9//NMM\nh5DFFnmmcFwfYw/IyoqVw0fY2OoTNltkWY4wJYEDOh+RJ0OyZIguYrK0oNFo1RGyG2I5TbB8ChzS\nXFIKhRaKQsSUeUaRpri2TZrmKMen1LWPTZmnVFlEVaZYqUucDKmqCK0jinJAlg1xXYe16+t4bgMh\nLCzlkueyHqYwTm9rqkiAsOqJL0IhbZei0ATaInM0lpUwU2xx94LPjR2YcWKa0XVk7zrGcYnnlrgl\nHzBgkUviEKeHDb66WpCEOTe3Im4rK1acPro8z0ywxPd/70vx50qiRoJwjvOHXzvJX147DjM9ZrNv\ncaPq4duKzswd9NfPce3KUwgpmDtwoM72dEWWZbXKqd0h8D0C36fTcGk3Ghw5cpxvPPIYl65e5tRT\n32ar12N9vcfJk3ezunoVbXIcmRI6mjRKmO3MUmlozhzgvT/1r/BbswTNOQwK3/PZ3ujS8ALKSuO4\nLlmWIS1Jp92g1+sR+jbXr6+xfHARpSSNZpPN7R6tdpvtXp9Gq12/B8KQRAme46DGQZczDnoC30fr\ngqKo3z/fc4mimKWledaub9LrbVGUMUEYMDc7X0t1hUApm6wokLIeLJEXBVIqsrQiTRKUlJRFQasd\noKuKfm+D/mCDPIt45ave+Dws+tajX0AKQTVWsMmdJp3JOzKlZtnJ5id2FXuDSaWeryjZT4VMcGqa\nBp4OWPdH9v9QI893vRPzK5/7OLB7MaYBcALG0xruyQlPwAkgDENOnTrFS1/6UtI03Wm71VrXAxXG\ndMr076dFjhSSqqjbjS3LwvI8hOew0d3mD//gD3jvj/woo+0eRVWD3uxMbVwF9Q25cuXKTlW5jr5D\n2u364V1fX6ff75PnOVmW0Ww2x5N8AixlUelq5+8vinqcmO3ZeJ4HUrK2vk5/NOLsuXP8wA/9IB4g\n0qRelCyLvKpwXAepJMMoot3pkI0pGct2yPOqjmryHNuqr5VlOUhLceXqKr1hXeHXBuaXDwOS61ub\nbPVGnP7OGba6I/q9hKXFw1QZBH4DjaSrh0ghaQZNQi8kHo3wHZc8z6lMgZaaLE9J0hi3e4VRNEQp\nTRyPkEqQlwWduQVm5pdwgjaFhiwv2WAB20AWDZlptzCVwfZCtHQox5F4nqcUeU7RK8EUCJMTx1tk\nSRdjEqKojzPu3ms2W4ziBDMaYITEsm2GcYyRivnFZSzXZ7s3IM0K0rSgMmBsXatW4gwlBbaqWPEL\n7pnT3DaT44g+cZzT75f07FnaXhvLbtFTh7msFzmfDDlz6Tu0UsWDN1rcwmOcWHFwD93DlYN3sjV3\nHK9QHD98F7/6p8+x2b6TNHsaa/1h5OoVbj/QYDS6CKqirCqa7RZ36Cv7AAAgAElEQVQYwzCO6ylR\n7TYAo8GQPMtwLYGgIisyikoThm02t7qkaYrnuliWoCwKlLAodYwbCKLhiDTJ+Lmf+7esrm/ymS99\nif/9//y3rK13OXb0OKbUONLgez7Xrm/QbDZZ39yg02nTCJokyYBzz55hZWWZQyvL+J5LWUGUZBgh\ncFyXotKMRlGdiVqKYb+P73qEgU9elvi+TxzHbG5ucPjQobFCqeKjH/tz/uZv/oZOu013uwuq4Pr1\n61RFydGjN/O2t72dN77xjYSNBlprtnt9Op0Z8rLElrXU1egaBNMsRUqDbUuErIebd1oHnodFl859\ni0G/h22NqdVxl+yOHlvt99m3kFIxAfidngxduxG+ENZNz92cKOP2W1JMAH66xX7yvf9TA/g3vvJX\ne05k0jY7iaKnee7JNi0bnBxvskJONNlQpz6e5+0A20TDnWUZlBqtJJXROJaDzoo6inYtIqn5zGc/\ng68lty3fiN+eRTm7g4YnXgau6+5kCJObUJYFruuhlBxTGfWszbrJpySKol0TeamwbLsebYWhKut9\nEIICGCU5l1ZX6Q1GPPC6+1mZ7WDZFkVR1i5uxlBRT7ORSqFFrb3t9QfkmabMC2ZmZ9lY28T3Q4SQ\nhM0mvcEIaVu4nk+claxu5ly8dJXt3oiyAqMFtqXI4gFJ1KUqRizMtUiyhCKcx5I2odvEVT5lZqhK\njbRshCXoRUNs366N/qMueTZiY+MqSuRAQVGktZJR2rRnF0lzzeyBRXRjBQtNOujT9FyKvCArDLmx\nKI0irwxFOZZmCU08GiGNQZmSKs8QpkBS4TqKdqdJmkZkeYqVlyRZRlYUOL5H0GhijCGKExASz3HQ\nZW3hu1X1aRmBjySuDJvDmBlp0Y57LAYVpRmCNAy2B3xp1ODEYsBd8w26vZRrmxFrvSEjx0eGNnc2\nDUetiLmOz2D+Rr65GrBR3o4WhnZjjdd/7/fx1SdyaPgstwuuP/opOtkTaBRathkMh7WxGXWwkqYJ\nllJIAUoq1LijeBT3idM+R4/ehO+2yRMo8wJTRYS+RKGoMonfDOklPT74wf/Ik9/6NnecOEGuK5zQ\n57cfeojbb72dE7ffydLcPP3hkGES4fsBaZrh+X7doKasevg0htlOCyHrnoKyEOPGKo/S1L78UVx7\n+pRZiaMUuqrfmUJrOp0W/X4f1/Uoi5zTp5/iqaeewrEtwiAADI5rk2QJaZywvr7O1UuX2d7eBiQz\nMzO85e1v48E3vYli/C7qqraMNVqgLEWS1FlonCa4noMRmtlm+DwseuqJv8OzLaJ4WLspirr9vzaS\nq2nGaXza7WpmT7Rcf1aD/HRjDrCDZdMDySdihv0t9dNMwmT//6kB/JG/+xTw/PZY2K3S7vf0mLaQ\nnIDhZEWbAPlkYC6wY0BVVXUqqpTCNopKCUoJnu2g8gqBIBGayIGtXpc/+93/wve//kFmlg7hhuHO\n4tLr9UjTlCzLsG17p8U+CAKM0aRpwtbW1g7lMunWnEwXqotTdYdkkmS4roVr1XaKVVWSlxUbvS7K\nbWCUzXOXrrJx7TLvfMs/YTAYMHtgbty6DNJWDKOINM+5eOki3V6XtbVNhoMMKSRvevCfMD+/SFUZ\nWs023f6AOMmwfZ/zFy5w/tJ1huUBqgpcp0Ge67qyrRM8u0AX6yTJKkL3SdOI7XKeA7OLCOOzOHuY\nPBM4bsgoTknLDK/hkeQx/cEAx3ZYmJ9ByYrAFeRJhNEVreYsVWVj8IkTTZZqtL1O03PIhn2KeDS2\nQfBIK0VSCZJck+YlZVWSqBHxKEVpCYVB5xWWgdBzOXRoieGoi+1I4mSIq336wyFFWVLpCtuzaTeb\nFEVGleWUWYIpC5SAljjKUK9TeAOwchZnW3Q8yWxnlsbMcYZFwNLSLI7cREYxT37tr+glPbrNA/jN\nNktlyIUnn2YUP0PbjOi0b2a7dYiCESvDWWaqNqvNnNVGE12EtK2ASLuEcy79ja8z76fISOJbIVGa\n4HreWFlkSJIY3/MwuvbhEUBhoB9vs7DUREmBKRxCZxad5nhOTuBWtP0m8UBzzyv+F+593cvxLZdf\n+fe/wk//7E9TCU3rwAynTz/Fb/3mf+KuO1/CO9/2dioBmVIkaYoQUFQlnuuxfHCZP/nTj/DGN7we\nozXtVpPedpfQa+EGIXGaomyHJKuLjRhwLQthIE8TPNfF2Barq1c5eHCJsqx44vHHOHv2WW666Uaa\njRAhBK1Wrd5Sto2pNIN+n+fOnefyxSu1MdxwwHMXLnDfa+/nR//5P2dhcQFh9LhL2aIqDVIpEFBW\nkBcaxwX/BQqZTz3+d6DLWi479qc39ShxtKkHqkw3B9bvs82kkLnXm2V3AtZ+GeC0RHpaJri/vmdZ\n1n9fDvx/xLafA9/PLU/++Am/PC21mfx7jxHSTvOOtedEp4854agn/+9hoYFKQikNpa4oigxH2VSV\nxg0D/vxjHwNpcd8rXoupNKNhD9dR2Ba0Ww2gNrZP0oI0K4jiFF1m+J6LZdnYjkuSpLQ7s6RZwWA4\nwrKcWrpk2eiqLqgZTB0tOjbF2OjeGENepASuj+f7XFvb4tNf+DL/7N3vZtDv87KX3YsuKvKipCgN\naVbwmc99kfbsAV72slfSaB3gP//2b+IGFvfcdYJXnjzB+oXLLB1YYpRLyrDDFx8/zVaUUJUZWpcI\nNK5jUeQ5lpBUhUFoSZ4ZLOkw6G1T5c9RVAVLy4fxgjZF5aBLn6qqGzuStItSGUUR8ZJjt1OUZe3z\nYTTd3gCpbEqtyfOSOM1otpo4jkM8jCh1SbMV4NjQ765hK02eJhSlRkuP7WEGlkeRSAx1kdp1bLzA\nJcszgsBlFEc4rk2cxBitqZIBvu/juw6mLOh1u3iuQ56luF5Q66rHLoFpUtYeNBhuOnyIdquJKfPx\n8wiDwRAh667cmw7dxOrqJdbXVlGqIM2GmKrAcV1WV9cJgg5S+XheSKIrHM+ri2QGTFUijamvs1Ro\nXVKktavk/IKHUpqtjU2qXOM4Pko6SOlRYZHkGtsNMdJG6gGBU2v9l5YP4fgBaZ4jAdeWGJ3zT9/2\nfQSujeMIBv0R291t/vIv/pIfe8+PEcUxCwsHcBwXx5H83u/9F3q9PneeOEF7dgbPc3BsC8sSJFHE\n5z/3We5/zWtYWFjiwOwcVVmSZTkaQaPVJE4y0jTHC0LyPK/nl6pdg7JmENLv9wj8ACkE16+t8pWv\nfJk7T9xRZ9GWVTs1IuthEXnNS0/qOL1ej/Pnz7O2tsZgMODxxx/nzW9+Mz/+4z+O47k7wd5u1l5r\naCcg+UJKlDNnnyWJh3WGIE1NO+U1hVZ7uO+ax8VpShCEVGO54Q74ylo+KY3eE1VP+xVNsvfdaHzX\nMGuCYQDGPJ8b//um0n/XAfzhv/1rYFcGuDuAV+zRcE8uxHRTz7Qcp74wezWTkxs5Ae49UsRcg5QI\nW6KlGFs0a6SpqQmUpDcc8ju/9//xw2/7QVzLQeuCJIkQaPr9HpZt4fo+jUYbjdiR95VlWRdrRhF+\n2KiLHgiKUjMcjkiz2mQnSwss6YCAIPRJ0gRt6nTeVorRaESaJHTabU7eczetuTk+/rGPksUx99xz\nN77rcfLkSQySjY0tuv0Rt9x6nI3NLo7f4f/90IeI84giizh++DCBkLz2Nfdj+W0ubo/49oWrRIUm\nzQrarSZZlpDnKaHvjwuTIIUiTyt0ZcjSBJFeA1kxiIbce+89RHFJmhqyWOP7AXme0Gp6tNshOs/H\nXXF191qvPyBsNOkPRyAk7c4MaZaiTa3dRUrW1lYJfAuhc8osotMM6Q2GtOeWWNscYKRNHmkqXTIz\nO8Pq6iqOV3fIZUVKs9lEKUW3t11bm5a1OlkgaDdDBr0uAhgOh/XzM+Y7W51ZyjKtrYRbLUaDHo6S\ntFtN0iQe9w/Uhbb63x6eayOpCAIbrTN6vS22t3ukaU4U57heg1Z7Fq08oijGthUzMzPEwwEznTa9\n7S2qqsRxLKg0YRigTYzjSIa9AYPegFajhWXZHJhfotsfkRUG5QUgJDobEjqSwSji2PHjdHt9bKdW\nYpRlxvGbb0JJiIY9mo0GrVaHZ589w7dPfYu3vvWtNBoNLMtiYWGOsqxwXZdut8fZc+d45uxZXNdh\nabE2lup3u/ze7z3Er/z7X8bzfBpBWLevZzlpXhtJxUlOpQ3zC/OkY/XYaDTEc10cx6YsC2xpYXQd\n6X74D/+QO+68A9etVWMTABdCoayxne14m1AKVVVx/fp1tra2OH36NGtra5w4cYJ/89P/mtq+eL8f\neD0rACaj+vZueZGTphHra2tEUR90ie+6RKMhtmPjqNooy/XceqrXuC5XjDN8baY6xavdua7THPf+\n4Sj1tteIbiJPzLJ8z2dCiL+3lf67DuBf//In95hS7Y7VsnZkctP0ygSQJ2APu5SL43g7+0yD9fRs\nvcnCUGW1csPIWo1Rt78LbGmT5BnKdVC+x//9S7/Ej/yv76jtZi0LlMR2HNywQZ4XdHt9Bv0BE4cy\ny/KptOHK1VVczwMEjlsb01uOi2XbSKGI0wTPaWK0qk3tLUk5fpmlECRxRDMM8VyHW48fY6O3ze0v\nuYNvfOPrXL54kUsXzjM3M4tSive858c4ffppGq0WjuPhuAFa+Tz2xOOcOv0kQsJoexMdxbz8rns5\nfPQYF9Y2udIb4TZnkCpka2sL17OZnZmj19saRywOVVUyGAwxprb6bCtDWUXE8SZ5NuDITUdoNTuY\nUiGFi+P4OwurpSqEFCRphmXZDKOIShuUcpCWRa/XR2PGvjIZZVURBH494NcCnSfkSYTve6xt9ahQ\nOF7IwQOLbG1ukRc5ldEcXDpIlqU4js1mt8v6+jo33HCYfn+AIMD3PDY31llZXuLc2bNgNEeOHGU4\nGNagIesidFJG5GnC8tISzWZI6Lm4tsPq1au0my1Wr11jZnaOYlwoHgz7JPEQx7HQ4yymzhpBo5DK\notnsUBESxxmWJXHsetSYqUqkqt02oUKXde3GcSykAduSBL7PsD+grOpeAWXZzC8toRHEeYYnBDON\nkOFoxMqhQ8RxhNYlWZYx02kRBj62XQM0SHq9IRcvnEdrzdr6Ovff9xqSJOHAgTkCz+XixYusrKyw\n3e0RhCHdbpdz588ShgEXzp9naWmBu+66i5XlQ7SaTZRlce3qdQCyoqDV7nBgfpbN7T6WbZOlCbat\ncF2HwPdIkoTeVo+ZdofvPP0kwtQ9FWYseZUTn/uxTYQyNfi12m2i0Wjn3XUch+FoxOrVqzz99NMM\nh0P6/z97bxZra3rW+f3ebx7XtPfa49nnnKpyja7ygLGhmwYMpoC2kqCk00FCNKGVNFcRl1aUvkly\nEYwUBYmopUgoIKUF3RDSrZZQRMd0JMBgsHHRHqpcruHMZ49r/ubxzcX7rXXWqXLZiaKkiFLv1dba\ne6+99re+9bzP+3/+Q7Tgc5/7HCcnJxRFgeN47yp6uv7uCp4VOYamIWn4xte/Ttsqmb5lKShD64zE\n2rbFtKwu8lDbhIa3TRc6s1VfHg01tccYcbqub/mPPz7rW9czXTcfq18AH/3kZ96zgL/vdrLbePW6\n2K6L+bp4b681TrT+3W0/gXcmsG9Pc7cLuqapvMlWPCLZ64DeKjms5zgkVYlpmIz394jiJUdPfIhF\nFCOlRlbAJJpTN5KibIkzoIWmgaRIFebmjzEtiyRNmUxidke7xEVBkxbdh1wnL0vaRlM+KUnCtWvX\nsG2TwHPxHAdNgGUY9Ec7lJrJvdMp82VOf3TAUatRlwVpGvMv/9Uf8LGPfBTfdbFsmzjJqGTDzZs3\nma1iLqdX9EcGsZzwV1//Bq3lMFmu2Bvvk5QFrdSxRIutG5RZTlmoUNqqKciyRA2BZAtSI0tqZKvj\neSGmXnB1fpts6eFaAc8/+1EaaVDmDZphKA5t2yhVZdviWjq26xHHCciWDz1xQpbnpFnKcBhS1Q2r\nZYzruHi2QSUkRbREly2HOwOmq4iqiJidxyRxzPXrN1guVzgiY7w/wHNdjvcGiOeeYrlc0Ld1skJF\nXV0/HlLmK/bHKsBaEwXHh0MWiwWmZWKa4Ic96tpFygramvkswtB1eqGHrgv298bUdUNdFlxNL9nf\n30PTWnzPoyxKykpx9NfD6zhJqeuCogBdGBhdvFev1yNJY0xTx3ItVB6qipCri4amlZR5ySqeES3m\npGlMrx9SNgV1W2BYJmUc4wUhSZ4CDacPb2ObBo5tMeq5+K7BcBiSpClv37rF7u4+t+/epShVsfCD\nkFe/9S1OHz7k+eeeUyc6z2MymQAa0+mCJEm5eeNJPM/h9u277B0cc3E1w7J9vvX6m1w7uY6Ukl7Y\nIzQtriZTJssVRVHgug5HR4ckSUQ8nZG6Dmmasr+jOnrTtFgt5uiG8gtfR6FJKTeJWrppgSaI4ugx\nP/CsyDFMg6Nrx8Rpwq1bt2iWNa/89Vc5PDzoPl/NVu14bzW4wrNbmkry0kc+QhStOD8/Zz6f4bgu\njgHUj3BwrYNLyq5TVvYVanNoBY/N3r6TivJR7eKxpnUbCv6/st73Ar4+bqy/hm256uOGTduqpu0i\nvR5ewrbyUK137mbr3VG3zM3XQoDWtohGYps2aZohDYPJfIbj+1xeXpJFCbYXotkBV4uYSuqkWYFm\nmugIbMtmuVhRVDp7+0fM5nN8oeP39ji5+Ryz5QK9bcmyjLjrJmxHxw1d+v0+mqapkOSoYuGYZFGM\naQhOjo64c/9t9g6O6O8esEpK4tWS0XDAJDpHYjKdLrl7/z7Xj49ZLuYcHJ0Q9gMu7tyjKCpkq5MW\nDUWr8dSzz3ExvWJ374D7D+/ieQENGePhkOUyxrYMAtukaiW2Y5NlCb0wZBktKIoSxwko05w4rbA1\nC13WRPMF0m3I4jlSmvjhECEMWiGhaTAtgyhOaaqSWmg4pkHdtpw+vIfrupi6zuTyFClgb3xMWzfM\nLi452d9ht+czGvRYrBb0eh5hf0Aez7HM6wih8bGXniUMe7z66mvUouHtN9/GsmyGwxHX9nbo7/Q3\nntLL1Yz5dM7Z2SkgCDwHzw2J44g8WwIh/V6PXhCQxBG2bZAnCQ/v3+Vg75CqqonjmCwtGB/vUBYJ\no+GAtmlwjIDR6DpFqfzhJ/MJ10+u8fD0AaauKctdAdEqxXHt7uiujL0cx1FGSbaFbQhWiyVnZ+cI\nWo7297hx4xrDYZ/JdELd1PTDkGi1JPA8HNuiH/j4jomQDXkW8/Zbb/PSR17izq23CPpDjq+fAAaa\nYWFIgYkkiiJmsyV/62//bW5ev8Ht27exuw5TCEFTg+cFSFnz5pu3+MIX/oinn3mGqqp5/oWXyIuG\nola2sGdnE5bRisPjI9Jlyt7+mFa2fPuNN6jriqos2RkO+NCHnmR6MeVb3/oWoe8T6Utg3a0qZods\nW3SVgExdqgbO932qSrHE1rbMpmkSRRF7e3ssFguqOuPLX/5LBoM+P/bpHyOKIwK/t/7Ud43euwu5\nrumADoYgileEvSFBOCDLcr797ddpdRXSbNs2eZqhGQZ1WXZui+o5hVBBFrphPgbrwtaJv8O711h4\n01QbVtwaGlLIw6PgGJVxUL3rNW+v9x1C+cs//V83R4s1bWb9z24PN9drezq77Xuihp6WwqF5hItv\nT49hy+VQU4Mw2bToQmJ0YatNLdEsi7St0UKP3/if/il7hk3oehh2QJQ3CMsnKVvKGgzTxrVddF10\n2J5LVSlq33wxJwiCzeu0bKszJgK74+kKQ2y467ZlE/gei/kM2daYGhwf7nNy7RjN9JivCt544w3C\nMMBxHBazK7I0RsiGnu+xvzvixRdfYBXH5I3gy1/9Oqu0VAMvoSFkzajv47oWZZHRSKnofqbGgwfn\nBMGIvf1r3Ll/RllLiqbFD33QUf7KjktVgaxzPF1iyZI6XaK3BVkS8eKHX8RxfWqpjKLWYocsyxFd\nWLNhWSRJRt3U9HoDVqsleV6gO5K6FeR5haGZDIMQz9QZBDaCmjSNsV2XtCzI0xWaUHjybDYnimL6\nvQF5XhKGqmDP5wscxyEt59i2RZYlDPp9PN+nqkukFOR5QRwleI5HmmVEuTIwUsd0A9MwuiOyJI0z\n0iRjOBphGTZCL0mSmLppMTQ1sM7zYgMXlHVOliXUdYUubJIkxwt8dNOkrBuk0Du6W4Fl2aqDLyvK\nIsF1bXaGQxzLpKnUc7br06UmiFYxhmUqIVZd0g9c8jTCFGDbOl7njqmZFvcenhMOhlxczMiKip3h\nCNdzCIMA27KoihzXtRkNho/iC6WgqsA0DeIk4l//6z+kkQ0///M/z737D/CDENOyiKKY4WCIJkUH\nKxkqQ7MqGQz6DAY9aCWChjzLmc1muJbL8889zauvfpM0WWGZaoi7LnxCe8Sz1g2dpml5/fVvkSQp\nTdPw5JNP8uSTT6gZTWejfHl5yVdf+TJXV1c888wz/NzP/Rzj3bG6/8R2cs+7IZSqrrshpJqTtOqX\naNtW+Rdd3GW1WuHaDlWVKw1GrmAXU9dUKlerBqRZ9Si6bZtksTbee1zJ+bjx1XqV5SPh0Lp2/d+y\nk/1/eq2xoXfKTreHAduWsGvZ+Pqxd9J21p36+rnWIptHfM1uuNl5XzdUW126wHJs0HQ806LSTc7O\nTjl86jk026WSAqEr8YuUMBwOkULZxBZVQ9s0mLqG5wUYuobvHRB0ooNVrHI1szSm1+sTrRYMR0OQ\n4DkeSZLSNpLz00ssy8A2bDQhGe/u09QQRXMup0uGgz79wZA0TdFNl6PjHaaTCy4uJiznC1768Evk\neUGU5egCfviHfoi798+YzGYEgU8SzTi/OmdnOKQqczTRglkTOpLx0KFMZ/gWUNf4vkeSpozGuziG\nxXQ6wRv06A13WU1mrFYNA2+H6cUDkAYIk6ptaNqalpqmMoiiCM/zSNKVUrMaulJG6gbxcollWeia\nTiNjTENDM5XMe393hzqNKfKc6eQUyzJpZYsX+lw7fJrpdEpZlt2HQ+P84hxDVyKJvb0Djo+P8D2f\nvAhV4o2mM53MOH14tgngCIM+N05usJgvcW2BZmjMZ+rovFrMcF0XOpGJZVnUlcbs8pzRaMTBwYD9\nnT5+EFLmNY2U3L17lzRZkqcryjzB8x1sXXBybY+ryYT5YkEcNUhNww9CRGvRD1zqvGaxXHF0eATC\nRdclVZkwj3Jsw4TGxjJUIpSum7iGj227CAvKJmU1n+O6LnkcEccrpnJCK2E6X3HzqQ/x6quvcnzt\nJpblUjcVVaVzdTXh8GAfz/UwDI1XX32Vmzdv0DQtSZwipYbnu5RlyTe+8XX+/s/+R5yenhKEIZqm\nE62UdW8cp5R5iR94XEwuKcqCk5NjDMPgwYMH7O7s8vDBAw7393nqqad5cO8Bt+/c58b1a9y9e4cs\njTAMBUtIqQyl6rrBNC0WyyVf/OIXmUwmCCGYzWa88tev8Nxzz/EzP/Mz1HWt7G7DkJ2dHYqi4C/+\n4i/44b/zwwRBgGN73XzrvU31dE1fZ0wDoKFSsDRNx3U9Do9OCMKIe/fuoQmJrqmmTaOlli0SgWUr\nHvq2WHCbiLFOvFp31EqZbW0eU4EdXf3pjP3eaVX7Xut978C/9Md/sOmU39lRr4vwNsSyzVLRNG0j\nTV8PPLcpOeu/tS0GEig/7aZuFGVJAEJgCl1ZsVYtju+TVBWlJvj8r/13/PSP/DiWYWJZLmleM5kv\nsZ2QWgr8oIdE4LoejZSIVhCvou6o1CkQNZXI47puJ+83yPIMTRisogTH9ojTDM/11JvWStq64Mb1\nE8Y7Q5bLGbt7e1RNQ1k1OJ7Hg7MLirIijiJEq6TmtqlT5SmjnQFPPvsUb7x5h15/n7KCJFdxZXVd\nMByETGdTdAS9wCWLz4njjOUyYb5MOb72BF44YrZYYdkuCINlFLOzO2JVzIiXCT17QGD5NEXO/m4f\ny2gxrZY0XeGFrjLdqg1FuRKC1WrF7u6Y1WqFpulKiNSqsAp1JC4xLAfHDdQGWTcEjklVZIwGAXXT\nUDaNCndIk647afH9gKpSlgICjbZpqKuGJEkoq4qe71M3NbZjbz7MSsVbk6Y5Ao00zTB0E80U1E3N\nZHrFeG/MvXt3GY/HzGcz9vb2sA2L0WiXIsuJV1OaVgVYR2mCYZgMh8MOf22xTIOyyJW50mJOXuSM\nDw4Y7OyS5xWabrFYrJANTK5mDPtDhr0BhtMgtBZD1zZJ9nXdUpet4lOXDUmckWUF/tDD69m0dY2h\nC5AtTVXR1BVlpcye2lYZNj04PeOHf+TTTGdTXNthb2+MrulkSYLj2Az6fVarZQdn6JimBUheeeUV\nXn31m3zm5c/Q66kNK8tyDFN14GEQ0Av7XE0muJ5LS8NqtcR11QBYCMHJ0TGz2Yy6qmkayeXlGTdO\njlgtp5gGtO0jQkLTSKQUCE3nL7/yZS4vLzedaBRFCCHIsoyXX36Z5557bgNJPHhwl1u33ubu3XvY\ntsOv/De/Qq+3hlA6OvF3KOLr6vdeRbDp6LWr1YrZdEJZ5MoITXSiPcuk7WxkFfLzKEbtnSyTdeyb\n+t7jTobrn62q5rH6pmkaL3zsR/7mduDvDHHY/sfXHNDtFHZ490XaFves1zZksp78gnqj2kbZcirm\njqIN1ip4DN1S6jcQfPWvvsrezh5CExR5jgBuHB9z7WCMEBqz+ZIki8mKkqRc0UqJazkMQxvbspRP\nSR0SRSuiOCaP480N6ToOfjDgxtEBhumwWiXkRUFZ1iAkWVXw7Ve/Tnx8iGObJLbO1XSCFDq9wS5V\nmRMnGZquWBS6lOiaIBiNuXXnTUqZ4ftDtLYiWyWUVY0X+tRIzi4uCPyApmo4Pb3CM8F1ezhun5ZL\n2rZkOj1jMFTFxnFtDCPE0FpCo+Lw5ADR2jiGiyF6lEVE1VZUWUVVlZS5gh7KKlcmX46NbmgsV/NO\n0m+gS6PzazGwTJ1h4KOZNqbtYrsOSRwxvTqn53s8OHuI716MtlUAACAASURBVAfolocQ4Hkhy8WS\nOI6QrerMPNdF03QC38exbXx3hKYJ0qSiLGJOTy8Z7QwJwlAlCMmCayf7lHlNHKfcvXMHz1Pileee\neYY333oT17HJ0ggpa/qhTxInXJw/JPR9jo/UALaoSmrZkhU5q3hJmiSYpsq83BmM8NyAVZQitYYH\nD89569Y95sslZVnheT47wx2G4YA6X1FZGqtVhGYIXM/Dse3NkGs0GpPEKbam4XkeWZZjuSbLeA4I\ncgFxlFCWBa7rcrA3xvFKNA1u377F8eEBdZESuDZCQFXkmJ5H2AuwLZskzdB1k9Goz3K5IE1j6rrk\njTe+Ra8X0AtDHNukyFMEAkvX2R0OSdOM+/fu4fgu8/mEMPQxDR0BFEXBznDIndt3lVeSJZjN54Rh\nj6JUgrq6LUE2aFIVb2Uba3J5cdHdpz55UZAlCUfHx9y7fx9N13n9jTd4+pln1Cm8aXAct0uxVzOr\nqq4pq3VCF5sT9nstAd+xSAp0mhYGg11s20G2NZeXFxR5hm0YavOxbcq8QBePGsZtZt2GKKHrW9bW\njxJ5Hs3ixAYteGfz+l7rfS/g2zDIuli/0/jlkV3jI+K74zibr9dRR2sXwvVFWa/vFHEkqgY0jUao\nbMkuIlKxWYoC1+vxpS/+GR//1A+wOxohaEmWK6p0Tuj5yKbhZNejalx0xwXNpGxqsrSgLArqOkE0\nJgYw6tk8cW0P3TAoy5ubwcx8OefyckIaKz8Ox/E5HI/UdRA9dnYHVEVGnqe01Qrf0jBdj0YqKCYI\netRVh/cLgWloLJYLXNdjNp8QLWN2h0eYmoFmG+hC0u+H9OixWCyJlimH+9exdakCE2TDtesBVV3z\n7LVj7ty5S1YkGJZOFKf0Ap/QALPJ6A8CHFtlTLa+kiAXect4tEealLiuzySZEPYU3OM4DpqukSQN\nUja0KC531XH3l1NJOBiQxAlpkRP2PA4O9kiTFTdu3CAvapZxTplVOKLF0HT6vT79sM/x4SGybSmL\nTIVpJCuSJEFKyc7OPuO9IePDEUmaIIyW84uHaLrG/Yf3qIqKumw4PDqi77sYlgrB/tQnP0GSxZRV\nxYN799jfHzM3dQZhn8lkwnyVMJ3NkLJluDvA9j0s22C0t6usG/Ka+6fntK2kFQZSuPhhn3Ck0x/t\nUdUFDx88QNNbej2bk8MnSOMY0xspbLuqqBoV5GDoJqtoTlnW1FUnWAPaouJw/4CH5xe4bsB8EfPp\nH/8pvvSlL4Fu43kqvuzZp57m9PyUNI6oa2X+JOtaDWodh8APKYqSMAxZRRF102CYGmXZYJg6H/no\niwihFMau4yGERppEaJqaExwc7JPlCbYVsoqWneJYFeS6rjENk3gZEycJUhOEYQ9koYqWJh6zRm5b\nSYPg4vyCqqpIO1dDy1YU3PVJ++HDh+hdEDlCeeT3ej1Wq5jZbMHXvvZ1Pv2jn0YgkFLZQ3zXJeU7\nyrtCCRoEmmZQljWuG9A0FQeHR9y9c4soyXEcBZ8Iw0A2jwaO7yy8a6j4UcP5KEbyUZF/FLX2zs78\nvdb7XsDX9L9t+uA2hmTb9qaz3pbUg3rTLcvaDEG3c/DWhX29m2134QCWVEPLVkjVcXdDat00CMKQ\n1994m8ViQej51HWBrGtMHaYXZ3iHB13oQI9SNGT5kkYzQTPwXR3f8dA758M4ilFqsIJ4tcRzXbIk\nQzQqAPjZp/eJkxSJKpJlscC1beJ4TlG06JpkOrvL/GqJZfUJh8opMPR9lmmKZXkkSYahC+Ikw3F9\ndLNhlcT0+gNkW5OlGa4fogklrJjMl9y8+RSB32BoBm2rkVeJol7pJk1Vcuv224z39/ACX3F8NR9d\nwH4/JIpytLogq3L8nkNRpNiOSVWqI2UWVeRpiWnq5EWK56s80bIscJwhyh/mUTK353m0iwjTsamF\nwIiXpGlCmizo90KiJKZpdYJen6KSkKwoq5K2rSiynDRWOOp4dwddqLwaIRoM3WC+OMfxXHTTRGiw\nipYEfYcoTnA8jZPr19HRKMuK1WqFbujkRY7lGHi+i2HonJwcc3r6gF4Ycnr6kBvXr5MVJrbjkRUp\nURYhZYNeCizToipr9sYHmIZLnhWcXsyYLSJs18bxHHRTMN4/5PDogJ1Bj2g65fLqIaamU7QNLRq6\nobx0pMYmdcmwG+WjLZVne5GX3L9/H9Nyee75F9Atl3sPz9k7OFY+102JSBOi+Rzbsri8OKPfHxAM\nAyzTwrYdZQZV1QwGA7Iso2layjLHNFVKku977O8r/38hBKalvHhGwwHRKka2LVdXF4zHY5bRAtMy\n0PQOy9Xazo3QQdd1ekFIlGecnZ3x3LNPUmRx91lXghpk5zHSKKHb2q55zdLK83wzUzo8PCTLsi0h\nn858vtzUkW984xvcuHGT3Z0xvu+9d7KNBIRkE/S5/ThKyNZKiWEa1HXT1ReTmzef5P6DO0ymV9i2\niS40jC3V5RohWM/31jVtncazTbZ4vNk0Hive76RRv3O97wV87de97pCrqnpsMLn2L1n/7DqwYX0k\nUewTZRmrqSx0VeilRDOszSBUdvjUeird6EJJdVtJo2nkhtqljaomSxP+6M/+nMObTyDziqR1iJMS\nU5dkUQlMuXa0z9VsRtgJJtA0srLsxBSC1XJFVdb0+yq5RwB7O7uUTYVuGkymE0QruTqbKUaKqbPT\n6+GOPUBgXjthMV8RRQm+vUfvxh6a3rJYRuz0PRarC0amhdAqNKOkbiSNBo5rMXT22B316ff7JEmC\nbmvMF5e4Ukmci8WMbBZg2S6Ty6ka+LguaVqyiDJcyyIMe7RxycC2QINayyiKgqT0OL75BEIzSNOM\nLK1oG4tlUjG9mmMdWDiOwHU1FqlLRYUmdZJVutmAm6YhzwvSNFPvRdNwfNCnzVqkhPF4l/3dHUzT\nVI6PJkyuzrEsVXSEreF6Nrbp0zYVRgtlkXJ6ltDIFoGO3+vheT4HN66T5wWNFFxNZlxezfA8QeD1\ncQKLaLEgz2L6QcDu4c7GAjhLU1arJfPpkrosefbZZ1nM5/iew9tvvwloOK7L3njMdWeEZVksFgss\n2+bt27eYX52xWC5ZLpeEYcCNo6A7CWrUZUVy8ZCmaTHrI/YPjhSrI1FeMVWpFL9Jooq1EIJ+L6TK\nS3Qh8D0fXdMojJy3z84YXbvG5P5bBHpN2xTItiQMbNpKUhQ5MoBGaLz0ie8DIXAcD920EegI3cD3\ndPIswzRtijzG0C2yrGAyneP7LpahYgR3R2PapmI1n5HFKxxX5U4OBwGz6QWGYeAYNmVeYjoGlayo\nqgLf98hLxbQyLY1nnvkQ09mUogEdHaHplGWFaenUdU7T1mhGxWinx3R2gZkpG9l4tSAMQ3zX5fq1\na8imoSwqFRTSNYHn51eYtsdw94C0aDifzPCzgjAIAftd9UeCiqrruvjNo2KtD1Ep9QBatwdohonU\nDW7eeAbfG6oQG9OkFQqGNXUNWddoUoWttHUDSOqyQqCp04B4RKFe12/FvGs2DSl87w78fR9ifuXP\n/vAx9eX65WzvWvBIFv9O74BtG9q2ZZOEIdaY+Racsu7CdV3Hblo0x2aeRPiuh4ZGVjdgW/zxn/wZ\nr/z5X/LyT7yM4TnYRkBT1ZRFSrKaUWQx++MRu+ORyu4LQ1opqdoG3w/VmyShKhuEVLhm3bYYpqFS\ncnRB0zbYpk1TNd3GVRLHauBZVzVuEDCbKse2vb0DqqpQBzpNp67VMLMoGxarCNvxSHNl2BUEIcvl\nAtc21U2ia0gkum5gWhbz+RLdtBDCxLZsmkaiGRpxkhAEPdIk6/zDCwLXQaPGtU2auqCuKgxXqSaF\n0HC9AE0op0XLMCjSmJ7vEsdL4nhJMDxSnRVq2q9pKs9Rwha1Sm3KQuY0TbsRQqhOBHxfHdnXfurL\n5RLDsZCywbFMaBr6fR/ZKNqXpusqAKCsKKsSTZM4nRMjmsm9+w+5du0EgcA0uy2/bZCypawfUTyF\nEMqvvakpsgzLstCE6N4jQVnVCKGRJMnm5x3H6bpUi8lsxnK5Yjgc4rrqccs0VdC0VM1EnuediyS4\nnt9BgFuWESgKmmkYlEWxsSWtq4oiL8iTBNdxeOqZp7m4vMK0bcWXRtJUJdDguw5Stkzmc/aOTrBt\nl7Kq0Q0LTVPOnIZuKF+TPFevDzVgPn14h9n8gh/8ge+jzAs8x0PJ3C3KsgDRdj+rb15znhcKNmqV\nH73rup2ls2qmGtmwnM85Pj6iqiuKNCYvMmUoZWpkSQLA/fv3yOuGxWLBbD6jzMtNgf3wh1/i+eef\n78zkLJCCi4szbt+5zSuv/FvG+wf8x7/4Dzk6OiaOY05OTijzgieuH7yrFlXVWvDzONV4zVj7TuPN\nd+pKkiTh9PSUZXSB77nK36iuaesKyzSQzRoq6mpYC0JTtvRr7Hvdqa8h4+3HP/zxH33PIeb7XsC/\n9Md/sBlCbr+U7VCEqqo2hPZ1p/7OJGdQzn7bw8xtKuG2vFUIga/pzKsMox/iYGA3ghzJn37tFf7J\nf/9P+OV/8I8IvQB6LtEyo0hSDg730WkJApery0vKPOH6jWtkWcb+/ljlX+a5wvpaZbV5fHyCaVro\nukFRVeRlwXy5IIpjLMPEtVwQEIQ+rut2gQ21ojF5Pq9/+w2KvFTChDDg9PSU0WiXnd09DMumKCpM\ny2axiAiCEM1QuKRp6FRVSZanREmiZMyLBZ4Xcnh0RJaVj1g7hobZCZh0zVD4cdMiREtTFRgaVGVG\nVRV85OMfx3Ec8rxiGaWq4NaNclEsMnaGPQxdJww98lonDEOm0ynT6XSzyeZ5rgQ8pko56vV6OLZi\ncaxdG9dujvfv3yfPShzHwXVdwjDEdm2yLGVydUGaxBi6xrWjIxaLhVL7HR4wGo26rg6mkxmzZURV\nNQyH441Hu+jgliDwCEKfIOyxWq2Ioog4jja4aC/wkW1LEPhMp4rStre3RxAEBEGgYvEWC779x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UUnmRd4ZNdVWoOL4sQzctLicTojjj2edVuHSel3iujaDF0ARNpWh7bSuVa6RuECcpjmXw+//8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\no47LNOoEXkcdddRxmcb/AwKQdm5yYaZEAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10e077310>"
+        "<matplotlib.figure.Figure at 0x5a3a290>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -854,7 +830,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 10
     }
    ],
    "metadata": {}

From 36edcbd87437843fe4b90095bdca01d1055399ad Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 10 Jun 2014 11:35:08 -0700
Subject: [PATCH 0108/2053] fix clang compilation problem w/ DummyDataLayer

---
 src/caffe/layers/dummy_data_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 8925772f3a5..58044f4c952 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -75,7 +75,7 @@ void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     (*top)[i]->Reshape(num, channels, height, width);
   }
   // Run Forward once, with refill_ inverted, to fill the constant Blobs.
-  Forward(bottom, top);
+  this->Forward(bottom, top);
   // Invert the inverted refill_ values to refill the desired (non-constant)
   // Blobs in every usual forward pass.
   for (int i = 0; i < refill_.size(); ++i) {

From 9241c68540329aa26323f85bd00fd356e47379ab Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 10 Jun 2014 14:46:53 -0700
Subject: [PATCH 0109/2053] groom install docs

- make OS X boost compilation more clear
- make punctuation more sincere
---
 docs/installation.md | 32 +++++++++++++++-----------------
 1 file changed, 15 insertions(+), 17 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index b84ae3b83e4..1d2c77ec35b 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -21,8 +21,8 @@ Caffe depends on several software packages.
 * [OpenCV](http://opencv.org/).
 * [Boost](http://www.boost.org/) (we have only tested 1.55)
 * `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`
-* For the python wrapper
-    * `python`, `numpy (>= 1.7)`, Boost-provided `boost.python`
+* For the Python wrapper
+    * `Python`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For the MATLAB wrapper
     * MATLAB with the `mex` compiler.
 
@@ -48,8 +48,7 @@ The choice is yours:
 
 ### Python and/or Matlab wrappers (optional)
 
-This is only a requirement if you'd like the Python wrapper for Caffe.
-The main required package is `numpy`, and `Boost` (in "Other dependencies" below) must be compiled with Python support.
+Python: The main requirements are `numpy` and `boost.python` (provided by boost). `pandas` is useful too and needed for some examples.
 
 For **OS X**, we highly recommend using the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 If you don't, please use Homebrew -- but beware of potential linking errors!
@@ -58,7 +57,7 @@ Note that if you use the **Ubuntu** default python, you will need to `apt-get in
 
     pip install -r /path/to/caffe/python/requirements.txt
 
-If you would like to have the MATLAB wrapper, install MATLAB, and make sure that its `mex` is in your `$PATH`.
+MATLAB: install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
 ### The rest of the dependencies
 
@@ -99,12 +98,12 @@ In other `ENV` settings, things may not work as expected.
 
 #### 10.8-specific Instructions
 
-Simply run the following.
+Simply run the following:
 
-    brew install --build-from-source boost
+    brew install --build-from-source --with-python boost
     for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew install $x; done
 
-Building boost from source is needed to link against your local python (exceptions might be raised during some OS X installs, but **ignore** these and continue).
+Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
 
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
@@ -139,18 +138,17 @@ To edit the formulae in turn, run
 
 After this, run
 
-    for x in snappy leveldb protobuf gflags glog szip boost homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    brew install --build-from-source --with-python --fresh -vd boost
+
+**Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
 
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
 
-**Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
-
-    brew install --build-from-source --with-python --fresh -vd boost
-
 #### Windows
 
-There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng).
+There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
 
 ## Compilation
 
@@ -165,9 +163,9 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
 
 Note that if there is no GPU in your machine, building and running CPU-only works, but GPU tests will naturally fail.
 
-To compile the python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
-Be sure to set your MATLAB and python paths in `Makefile.config` first!
-For Python support, you must add the compiled module to your `PYTHONPATH` (as `/path/to/caffe/python` or the like).
+To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
+Be sure to set your MATLAB and Python paths in `Makefile.config` first!
+For Python support, you must add the compiled module to your `$PYTHONPATH` (as `/path/to/caffe/python` or the like).
 
 *Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
 

From 300f770ac018edad95d582137945d6a643433236 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 11 Jun 2014 09:55:54 -0700
Subject: [PATCH 0110/2053] drop learning rates and decays from deploy model

---
 examples/imagenet/imagenet_deploy.prototxt | 32 ----------------------
 1 file changed, 32 deletions(-)

diff --git a/examples/imagenet/imagenet_deploy.prototxt b/examples/imagenet/imagenet_deploy.prototxt
index fbff3adbe18..4e494f420b5 100644
--- a/examples/imagenet/imagenet_deploy.prototxt
+++ b/examples/imagenet/imagenet_deploy.prototxt
@@ -9,10 +9,6 @@ layers {
   type: CONVOLUTION
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -52,10 +48,6 @@ layers {
   type: CONVOLUTION
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 256
     pad: 2
@@ -96,10 +88,6 @@ layers {
   type: CONVOLUTION
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 384
     pad: 1
@@ -117,10 +105,6 @@ layers {
   type: CONVOLUTION
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 384
     pad: 1
@@ -139,10 +123,6 @@ layers {
   type: CONVOLUTION
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 256
     pad: 1
@@ -172,10 +152,6 @@ layers {
   type: INNER_PRODUCT
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   inner_product_param {
     num_output: 4096
   }
@@ -200,10 +176,6 @@ layers {
   type: INNER_PRODUCT
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   inner_product_param {
     num_output: 4096
   }
@@ -228,10 +200,6 @@ layers {
   type: INNER_PRODUCT
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   inner_product_param {
     num_output: 1000
   }

From 8dc270e691cb15e540c1b5ac074774163bc6faf1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 10 Jun 2014 15:01:41 -0700
Subject: [PATCH 0111/2053] pycaffe: leave grayscale images gray according to
 arg

---
 python/caffe/io.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 0bd2f812bec..1fc97231ca6 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -5,19 +5,24 @@
 from caffe.proto import caffe_pb2
 
 
-def load_image(filename):
+def load_image(filename, color=True):
     """
     Load an image converting from grayscale or alpha as needed.
 
     Take
     filename: string
+    color: flag for color format. True (default) loads as RGB while False
+        loads as intensity (if image is already grayscale).
 
     Give
-    image: an image of size (H x W x 3) with RGB channels of type uint8.
+    image: an image with type np.float32 of size (H x W x 3) in RGB or
+        of size (H x W x 1) in grayscale.
     """
     img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32)
     if img.ndim == 2:
-        img = np.tile(img[:, :, np.newaxis], (1, 1, 3))
+        img = img[:, :, np.newaxis]
+        if color:
+            img = np.tile(img, (1, 1, 3))
     elif img.shape[2] == 4:
         img = img[:, :, :3]
     return img

From 6a5b2c19c75dd9f6965ca9a377164bf8b6c96688 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 25 May 2014 23:49:51 -0700
Subject: [PATCH 0112/2053] save from python for net surgery

0. Scheme desired parameters.
1. Do surgery on the net through `net.params['name'][idx].data[...] = `.
2. Save post-operation net params by `net.save('fname')`.

Handwoven deep nets, anyone?
---
 python/caffe/_caffe.cpp | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 9f190096ace..e9fe5cd3b05 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -224,6 +224,13 @@ struct CaffeNet {
         PyArray_DIMS(data_arr)[0]);
   }
 
+  // save the network weights to binary proto for net surgeries.
+  void save(string filename) {
+    NetParameter net_param;
+    net_->ToProto(&net_param, false);
+    WriteProtoToBinaryFile(net_param, filename.c_str());
+  }
+
   // The caffe::Caffe utility functions.
   void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
   void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
@@ -315,7 +322,8 @@ BOOST_PYTHON_MODULE(_caffe) {
       .add_property("layers",   &CaffeNet::layers)
       .add_property("inputs",   &CaffeNet::inputs)
       .add_property("outputs",  &CaffeNet::outputs)
-      .def("_set_input_arrays", &CaffeNet::set_input_arrays);
+      .def("_set_input_arrays", &CaffeNet::set_input_arrays)
+      .def("save",              &CaffeNet::save);
 
   boost::python::class_<CaffeBlob, CaffeBlobWrap>(
       "Blob", boost::python::no_init)

From f0ee675e2ef3e5519e43e903d86570ca634a0f5d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 11 Jun 2014 09:57:07 -0700
Subject: [PATCH 0113/2053] define fully-convolutional imagenet model

---
 examples/imagenet/imagenet_full_conv.prototxt | 215 ++++++++++++++++++
 1 file changed, 215 insertions(+)
 create mode 100644 examples/imagenet/imagenet_full_conv.prototxt

diff --git a/examples/imagenet/imagenet_full_conv.prototxt b/examples/imagenet/imagenet_full_conv.prototxt
new file mode 100644
index 00000000000..6473c1f78fc
--- /dev/null
+++ b/examples/imagenet/imagenet_full_conv.prototxt
@@ -0,0 +1,215 @@
+name: "CaffeNetConv"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 454
+input_dim: 454
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6-conv"
+  type: CONVOLUTION
+  bottom: "pool5"
+  top: "fc6-conv"
+  convolution_param {
+    num_output: 4096
+    kernel_size: 6
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6-conv"
+  top: "fc6-conv"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6-conv"
+  top: "fc6-conv"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7-conv"
+  type: CONVOLUTION
+  bottom: "fc6-conv"
+  top: "fc7-conv"
+  convolution_param {
+    num_output: 4096
+    kernel_size: 1
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7-conv"
+  top: "fc7-conv"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7-conv"
+  top: "fc7-conv"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8-conv"
+  type: CONVOLUTION
+  bottom: "fc7-conv"
+  top: "fc8-conv"
+  convolution_param {
+    num_output: 1000
+    kernel_size: 1
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8-conv"
+  top: "prob"
+}

From c3440fa48b651159ab41d96d8a6c4efdeeb4ffb2 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 12 Jun 2014 14:41:25 -0700
Subject: [PATCH 0114/2053] make notebook for net surgery of
 fully-convolutional model

---
 examples/net_surgery.ipynb | 327 +++++++++++++++++++++++++++++++++++++
 1 file changed, 327 insertions(+)
 create mode 100644 examples/net_surgery.ipynb

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
new file mode 100644
index 00000000000..6cba8bb9a0c
--- /dev/null
+++ b/examples/net_surgery.ipynb
@@ -0,0 +1,327 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Net Surgery\n",
+      "\n",
+      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we translate the inner product classifier layers of the Caffe Reference ImageNet model into convolutional layers. This yields a fully-convolutional model that generates a classification map for any given input size instead of a single classification. In particular, a classification will be made for every 6 $\\times$ 6 region of the `pool5` layer, giving a 8 $\\times$ 8 classification map for our example 454 $\\times$ 454 input dimensions.\n",
+      "\n",
+      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (While planned, this is currently an exercise for the reader.)\n",
+      "\n",
+      "Roll up your sleeves for net surgery with pycaffe!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!diff imagenet/imagenet_full_conv.prototxt imagenet/imagenet_deploy.prototxt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1c1\r\n",
+        "< name: \"CaffeNetConv\"\r\n",
+        "---\r\n",
+        "> name: \"CaffeNet\"\r\n",
+        "3c3\r\n",
+        "< input_dim: 1\r\n",
+        "---\r\n",
+        "> input_dim: 10\r\n",
+        "5,6c5,6\r\n",
+        "< input_dim: 454\r\n",
+        "< input_dim: 454\r\n",
+        "---\r\n",
+        "> input_dim: 227\r\n",
+        "> input_dim: 227\r\n",
+        "151,152c151,152\r\n",
+        "<   name: \"fc6-conv\"\r\n",
+        "<   type: CONVOLUTION\r\n",
+        "---\r\n",
+        ">   name: \"fc6\"\r\n",
+        ">   type: INNER_PRODUCT\r\n",
+        "154,155c154,155\r\n",
+        "<   top: \"fc6-conv\"\r\n",
+        "<   convolution_param {\r\n",
+        "---\r\n",
+        ">   top: \"fc6\"\r\n",
+        ">   inner_product_param {\r\n",
+        "157d156\r\n",
+        "<     kernel_size: 6\r\n",
+        "163,164c162,163\r\n",
+        "<   bottom: \"fc6-conv\"\r\n",
+        "<   top: \"fc6-conv\"\r\n",
+        "---\r\n",
+        ">   bottom: \"fc6\"\r\n",
+        ">   top: \"fc6\"\r\n",
+        "169,170c168,169\r\n",
+        "<   bottom: \"fc6-conv\"\r\n",
+        "<   top: \"fc6-conv\"\r\n",
+        "---\r\n",
+        ">   bottom: \"fc6\"\r\n",
+        ">   top: \"fc6\"\r\n",
+        "176,180c175,179\r\n",
+        "<   name: \"fc7-conv\"\r\n",
+        "<   type: CONVOLUTION\r\n",
+        "<   bottom: \"fc6-conv\"\r\n",
+        "<   top: \"fc7-conv\"\r\n",
+        "<   convolution_param {\r\n",
+        "---\r\n",
+        ">   name: \"fc7\"\r\n",
+        ">   type: INNER_PRODUCT\r\n",
+        ">   bottom: \"fc6\"\r\n",
+        ">   top: \"fc7\"\r\n",
+        ">   inner_product_param {\r\n",
+        "182d180\r\n",
+        "<     kernel_size: 1\r\n",
+        "188,189c186,187\r\n",
+        "<   bottom: \"fc7-conv\"\r\n",
+        "<   top: \"fc7-conv\"\r\n",
+        "---\r\n",
+        ">   bottom: \"fc7\"\r\n",
+        ">   top: \"fc7\"\r\n",
+        "194,195c192,193\r\n",
+        "<   bottom: \"fc7-conv\"\r\n",
+        "<   top: \"fc7-conv\"\r\n",
+        "---\r\n",
+        ">   bottom: \"fc7\"\r\n",
+        ">   top: \"fc7\"\r\n",
+        "201,205c199,203\r\n",
+        "<   name: \"fc8-conv\"\r\n",
+        "<   type: CONVOLUTION\r\n",
+        "<   bottom: \"fc7-conv\"\r\n",
+        "<   top: \"fc8-conv\"\r\n",
+        "<   convolution_param {\r\n",
+        "---\r\n",
+        ">   name: \"fc8\"\r\n",
+        ">   type: INNER_PRODUCT\r\n",
+        ">   bottom: \"fc7\"\r\n",
+        ">   top: \"fc8\"\r\n",
+        ">   inner_product_param {\r\n",
+        "207d204\r\n",
+        "<     kernel_size: 1\r\n",
+        "213c210\r\n",
+        "<   bottom: \"fc8-conv\"\r\n",
+        "---\r\n",
+        ">   bottom: \"fc8\"\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The only differences needed in the architecture are to change the fully-connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import caffe\n",
+      "\n",
+      "# Load the original network and extract the fully-connected layers' parameters.\n",
+      "net = caffe.Net('imagenet/imagenet_deploy.prototxt', 'imagenet/caffe_reference_imagenet_model')\n",
+      "params = ['fc6', 'fc7', 'fc8']\n",
+      "# fc_params = {name: (weights, biases)}\n",
+      "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
+      "\n",
+      "for fc in params:\n",
+      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "fc6 weights are (1, 1, 4096, 9216) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
+        "fc7 weights are (1, 1, 4096, 4096) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
+        "fc8 weights are (1, 1, 1000, 4096) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Consider the shapes of the inner product parameters. For weights and biases the zeroth and first dimensions are both 1. The second and third weight dimensions are the output and input sizes while the last bias dimension is the output size."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Load the fully-convolutional network to transplant the parameters.\n",
+      "net_full_conv = caffe.Net('imagenet/imagenet_full_conv.prototxt', 'imagenet/caffe_reference_imagenet_model')\n",
+      "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
+      "# conv_params = {name: (weights, biases)}\n",
+      "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
+      "\n",
+      "for conv in params_full_conv:\n",
+      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
+        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
+        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we need to roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices.\n",
+      "\n",
+      "The biases are identical to those of the inner product -- let's transplant these first since no reshaping is needed."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for pr, pr_conv in zip(params, params_full_conv):\n",
+      "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The output channels have the leading dimension of both the inner product and convolution weights, so the parameters are translated by reshaping the flat input dimensional parameter vector from the inner product into the channel $\\times$ height $\\times$ width filter shape."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for pr, pr_conv in zip(params, params_full_conv):\n",
+      "    out, in_, h, w = conv_params[pr_conv][0].shape\n",
+      "    W = fc_params[pr][0].reshape((out, in_, h, w))\n",
+      "    conv_params[pr_conv][0][...] = W"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next, save the new model weights."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "net_full_conv.save('imagenet/caffe_imagenet_full_conv')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To conclude, let's make a classification map from the example cat image. This gives an 8-by-8 labeling of overlapping image regions."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# load input and configure preprocessing\n",
+      "im = caffe.io.load_image('images/cat.jpg')\n",
+      "plt.imshow(im)\n",
+      "net_full_conv.set_mean('data', '../python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+      "net_full_conv.set_channel_swap('data', (2,1,0))\n",
+      "net_full_conv.set_input_scale('data', 255.0)\n",
+      "# make classification map by forward pass and show top prediction index per location\n",
+      "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
+      "out['prob'][0].argmax(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "array([[278, 151, 259, 281, 282, 259, 282, 282],\n",
+        "       [283, 259, 283, 282, 283, 281, 259, 277],\n",
+        "       [283, 283, 283, 287, 287, 287, 287, 282],\n",
+        "       [283, 283, 283, 281, 281, 259, 259, 333],\n",
+        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
+        "       [283, 283, 283, 283, 283, 259, 283, 333],\n",
+        "       [283, 356, 359, 371, 368, 368, 259, 852],\n",
+        "       [356, 335, 358, 151, 283, 263, 277, 744]])"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\nMcdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1193b2c10>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The classifications include various cats -- 282 = tabby, 283 = tiger, 281 = persian -- and foxes and other mammals.\n",
+      "\n",
+      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "*A thank you to Rowland Depp for first suggesting this trick.*"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 4de3b94e67725b663601e6dac89e8a68e2b11009 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 12 Jun 2014 14:58:07 -0700
Subject: [PATCH 0115/2053] make test_net use DUMMY_DATA instead of leveldb

---
 src/caffe/test/test_net.cpp | 101 +++++++++++++-----------------------
 1 file changed, 36 insertions(+), 65 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 4c7f0e7f7ac..1e43a038455 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1,8 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
-#include <sstream>
 #include <string>
 
 #include "gtest/gtest.h"
@@ -14,43 +12,28 @@
 
 namespace caffe {
 
-
 template <typename Dtype>
 class NetTest : public ::testing::Test {
  protected:
-  virtual void SetUp() {  // Create the leveldb
-    filename_ = tmpnam(NULL);  // get temp name
-    LOG(INFO) << "Using temporary leveldb " << filename_;
-    leveldb::DB* db;
-    leveldb::Options options;
-    options.error_if_exists = true;
-    options.create_if_missing = true;
-    leveldb::Status status = leveldb::DB::Open(options, filename_, &db);
-    CHECK(status.ok());
-    for (int i = 0; i < 5; ++i) {
-      Datum datum;
-      datum.set_label(i);
-      datum.set_channels(2);
-      datum.set_height(3);
-      datum.set_width(4);
-      std::string* data = datum.mutable_data();
-      for (int j = 0; j < 24; ++j) {
-        data->push_back((uint8_t)i);
-      }
-      std::stringstream ss;
-      ss << i;
-      db->Put(leveldb::WriteOptions(), ss.str(), datum.SerializeAsString());
-    }
-    delete db;
-
-    const string& proto_prefix =
+  virtual void SetUp() {
+    const string& proto =
         "name: 'TestNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DATA "
-        "  data_param { ";
-    const string& proto_suffix =
-        "    batch_size: 1 "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    num: 5 "
+        "    channels: 1 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
         "  } "
         "  top: 'data' "
         "  top: 'label' "
@@ -82,55 +65,43 @@ class NetTest : public ::testing::Test {
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
         "} ";
-    proto_ = proto_prefix + "source: '" + string(this->filename_) +
-        "' " + proto_suffix;
+    NetParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    net_.reset(new Net<Dtype>(param));
   }
 
-  char* filename_;
-  string proto_;
+  shared_ptr<Net<Dtype> > net_;
 };
 
 typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(NetTest, Dtypes);
 
 TYPED_TEST(NetTest, TestHasBlob) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_TRUE(net.has_blob("data"));
-  EXPECT_TRUE(net.has_blob("label"));
-  EXPECT_TRUE(net.has_blob("innerproduct"));
-  EXPECT_FALSE(net.has_blob("loss"));
+  EXPECT_TRUE(this->net_->has_blob("data"));
+  EXPECT_TRUE(this->net_->has_blob("label"));
+  EXPECT_TRUE(this->net_->has_blob("innerproduct"));
+  EXPECT_FALSE(this->net_->has_blob("loss"));
 }
 
 TYPED_TEST(NetTest, TestGetBlob) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_EQ(net.blob_by_name("data"), net.blobs()[0]);
-  EXPECT_EQ(net.blob_by_name("label"), net.blobs()[1]);
-  EXPECT_EQ(net.blob_by_name("innerproduct"), net.blobs()[2]);
-  EXPECT_FALSE(net.blob_by_name("loss"));
+  EXPECT_EQ(this->net_->blob_by_name("data"), this->net_->blobs()[0]);
+  EXPECT_EQ(this->net_->blob_by_name("label"), this->net_->blobs()[1]);
+  EXPECT_EQ(this->net_->blob_by_name("innerproduct"), this->net_->blobs()[2]);
+  EXPECT_FALSE(this->net_->blob_by_name("loss"));
 }
 
 TYPED_TEST(NetTest, TestHasLayer) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_TRUE(net.has_layer("data"));
-  EXPECT_TRUE(net.has_layer("innerproduct"));
-  EXPECT_TRUE(net.has_layer("loss"));
-  EXPECT_FALSE(net.has_layer("label"));
+  EXPECT_TRUE(this->net_->has_layer("data"));
+  EXPECT_TRUE(this->net_->has_layer("innerproduct"));
+  EXPECT_TRUE(this->net_->has_layer("loss"));
+  EXPECT_FALSE(this->net_->has_layer("label"));
 }
 
 TYPED_TEST(NetTest, TestGetLayerByName) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_EQ(net.layer_by_name("data"), net.layers()[0]);
-  EXPECT_EQ(net.layer_by_name("innerproduct"), net.layers()[1]);
-  EXPECT_EQ(net.layer_by_name("loss"), net.layers()[2]);
-  EXPECT_FALSE(net.layer_by_name("label"));
+  EXPECT_EQ(this->net_->layer_by_name("data"), this->net_->layers()[0]);
+  EXPECT_EQ(this->net_->layer_by_name("innerproduct"), this->net_->layers()[1]);
+  EXPECT_EQ(this->net_->layer_by_name("loss"), this->net_->layers()[2]);
+  EXPECT_FALSE(this->net_->layer_by_name("label"));
 }
 
 }  // namespace caffe

From 383d92d3a47b09fd5464d4bc0892591cbe0fd617 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 10 Jun 2014 13:19:34 -0700
Subject: [PATCH 0116/2053] make Net::Init loop indices clearer

---
 src/caffe/net.cpp | 77 ++++++++++++++++++++++++-----------------------
 1 file changed, 40 insertions(+), 37 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 9f84b0978f4..0c5ca7d9dbd 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -69,41 +69,42 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_vecs_.resize(param.layers_size());
   bottom_id_vecs_.resize(param.layers_size());
   top_id_vecs_.resize(param.layers_size());
-  for (int i = 0; i < param.layers_size(); ++i) {
+  for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
     bool in_place = false;
-    const LayerParameter& layer_param = param.layers(i);
+    const LayerParameter& layer_param = param.layers(layer_id);
     layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
     bool need_backward = param.force_backward();
     // Figure out this layer's input and output
-    for (int j = 0; j < layer_param.bottom_size(); ++j) {
-      const string& blob_name = layer_param.bottom(j);
+    for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
+         ++bottom_id) {
+      const string& blob_name = layer_param.bottom(bottom_id);
       const int blob_id = blob_name_to_idx[blob_name];
       if (available_blobs.find(blob_name) == available_blobs.end()) {
         LOG(FATAL) << "Unknown blob input " << blob_name <<
-            " to layer" << j;
+            " to layer" << bottom_id;
       }
       LOG(INFO) << layer_param.name() << " <- " << blob_name;
-      bottom_vecs_[i].push_back(
+      bottom_vecs_[layer_id].push_back(
           blobs_[blob_id].get());
-      bottom_id_vecs_[i].push_back(blob_id);
+      bottom_id_vecs_[layer_id].push_back(blob_id);
       // If a blob needs backward, this layer should provide it.
       need_backward |= blob_need_backward_[blob_id];
       available_blobs.erase(blob_name);
     }
-    for (int j = 0; j < layer_param.top_size(); ++j) {
-      const string& blob_name = layer_param.top(j);
+    for (int top_id = 0; top_id < layer_param.top_size(); ++top_id) {
+      const string& blob_name = layer_param.top(top_id);
       // Check if we are doing in-place computation
-      if (layer_param.bottom_size() > j &&
-          blob_name == layer_param.bottom(j)) {
+      if (layer_param.bottom_size() > top_id &&
+          blob_name == layer_param.bottom(top_id)) {
         // In-place computation
         LOG(INFO) << layer_param.name() << " -> " << blob_name << " (in-place)";
         in_place = true;
         available_blobs.insert(blob_name);
-        top_vecs_[i].push_back(
+        top_vecs_[layer_id].push_back(
             blobs_[blob_name_to_idx[blob_name]].get());
-        top_id_vecs_[i].push_back(blob_name_to_idx[blob_name]);
+        top_id_vecs_[layer_id].push_back(blob_name_to_idx[blob_name]);
       } else if (blob_name_to_idx.find(blob_name) != blob_name_to_idx.end()) {
         // If we are not doing in-place computation but has duplicated blobs,
         // raise an error.
@@ -117,33 +118,34 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
         blob_need_backward_.push_back(param.force_backward());
         blob_name_to_idx[blob_name] = blob_names_.size() - 1;
         available_blobs.insert(blob_name);
-        top_vecs_[i].push_back(blobs_[blob_names_.size() - 1].get());
-        top_id_vecs_[i].push_back(blob_names_.size() - 1);
+        top_vecs_[layer_id].push_back(blobs_[blob_names_.size() - 1].get());
+        top_id_vecs_[layer_id].push_back(blob_names_.size() - 1);
       }
     }
     // After this layer is connected, set it up.
-    // LOG(INFO) << "Setting up " << layer_names_[i];
-    layers_[i]->SetUp(bottom_vecs_[i], &top_vecs_[i]);
-    for (int topid = 0; topid < top_vecs_[i].size(); ++topid) {
-      LOG(INFO) << "Top shape: " << top_vecs_[i][topid]->num() << " "
-          << top_vecs_[i][topid]->channels() << " "
-          << top_vecs_[i][topid]->height() << " "
-          << top_vecs_[i][topid]->width() << " ("
-          << top_vecs_[i][topid]->count() << ")";
+    // LOG(INFO) << "Setting up " << layer_names_[layer_id];
+    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
+    for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
+      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
+          << top_vecs_[layer_id][top_id]->channels() << " "
+          << top_vecs_[layer_id][top_id]->height() << " "
+          << top_vecs_[layer_id][top_id]->width() << " ("
+          << top_vecs_[layer_id][top_id]->count() << ")";
       if (!in_place)
-        memory_used += top_vecs_[i][topid]->count();
+        memory_used += top_vecs_[layer_id][top_id]->count();
     }
     DLOG(INFO) << "Memory  required for Data " << memory_used*sizeof(Dtype);
-    int blobs_lr_size = layers_[i]->layer_param().blobs_lr_size();
-    CHECK(blobs_lr_size == layers_[i]->blobs().size() || blobs_lr_size == 0)
+    int blobs_lr_size = layers_[layer_id]->layer_param().blobs_lr_size();
+    CHECK(blobs_lr_size == layers_[layer_id]->blobs().size() || blobs_lr_size == 0)
         << "Incorrect blobs lr size: should be either 0 or the same as "
            "the number of the layer's parameter blobs.";
     if (blobs_lr_size) {
       // Check if this layer needs backward operation itself
-      for (int j = 0; j < blobs_lr_size; ++j) {
-        need_backward |= (layers_[i]->layer_param().blobs_lr(j) > 0);
+      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
+        need_backward |=
+            (layers_[layer_id]->layer_param().blobs_lr(param_id) > 0);
       }
-    } else if (layers_[i]->blobs().size()) {
+    } else if (layers_[layer_id]->blobs().size()) {
       // catch: if a layer param does not specify blobs_lr, we should assume the
       // learning rate to be 1. Thus we will need to perform backward.
       need_backward = true;
@@ -151,12 +153,13 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     // Finally, set the backward flag
     layer_need_backward_.push_back(need_backward);
     if (need_backward) {
-      LOG(INFO) << layer_names_[i] << " needs backward computation.";
-      for (int j = 0; j < top_id_vecs_[i].size(); ++j) {
-        blob_need_backward_[top_id_vecs_[i][j]] = true;
+      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
+      for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {
+        blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;
       }
     } else {
-      LOG(INFO) << layer_names_[i] << " does not need backward computation.";
+      LOG(INFO) << layer_names_[layer_id]
+                << " does not need backward computation.";
     }
   }
   // In the end, all remaining blobs are considered output blobs.
@@ -166,11 +169,11 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
     net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
   }
-  for (size_t i = 0; i < blob_names_.size(); ++i) {
-    blob_names_index_[blob_names_[i]] = i;
+  for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {
+    blob_names_index_[blob_names_[blob_id]] = blob_id;
   }
-  for (size_t i = 0; i < layer_names_.size(); ++i) {
-    layer_names_index_[layer_names_[i]] = i;
+  for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
+    layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
   GetLearningRateAndWeightDecay();
   LOG(INFO) << "Network initialization done.";

From 653aa25c262080e1b8a6fdf30e9d0dc4b1cfa6f9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 10 Jun 2014 19:22:16 -0700
Subject: [PATCH 0117/2053] refactor Net::Init to call helpers AppendBottom and
 AppendTop

---
 include/caffe/net.hpp |  13 ++++
 src/caffe/net.cpp     | 149 ++++++++++++++++++++++++------------------
 2 files changed, 98 insertions(+), 64 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 46d72a6b0a1..5b5dd22a282 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -4,6 +4,7 @@
 #define CAFFE_NET_HPP_
 
 #include <map>
+#include <set>
 #include <string>
 #include <vector>
 
@@ -14,6 +15,7 @@
 
 using std::map;
 using std::vector;
+using std::set;
 using std::string;
 
 namespace caffe {
@@ -100,6 +102,15 @@ class Net {
   const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name);
 
  protected:
+  // Helpers for Init.
+  // Append a new input or top blob to the net.
+  void AppendTop(const NetParameter& param, const int layer_id,
+                 const int top_id, set<string>* available_blobs,
+                 map<string, int>* blob_name_to_idx);
+  // Append a new bottom blob to the net.
+  int AppendBottom(const NetParameter& param, const int layer_id,
+                   const int bottom_id, set<string>* available_blobs,
+                   map<string, int>* blob_name_to_idx);
   // Function to get misc parameters, e.g. the learning rate multiplier and
   // weight decay.
   void GetLearningRateAndWeightDecay();
@@ -135,6 +146,8 @@ class Net {
   vector<float> params_lr_;
   // the weight decay multipliers
   vector<float> params_weight_decay_;
+  // The bytes of memory used by this net
+  size_t memory_used_;
   DISABLE_COPY_AND_ASSIGN(Net);
 };
 
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 0c5ca7d9dbd..41ae7520356 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -44,26 +44,14 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   set<string> available_blobs;
   int num_layers = param.layers_size();
   CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-      << "Incorrect bottom blob dimension specifications.";
-  size_t memory_used = 0;
+      << "Incorrect input blob dimension specifications.";
+  memory_used_ = 0;
   // set the input blobs
-  for (int i = 0; i < param.input_size(); ++i) {
-    const string& blob_name = param.input(i);
-    shared_ptr<Blob<Dtype> > blob_pointer(
-        new Blob<Dtype>(param.input_dim(i * 4),
-                        param.input_dim(i * 4 + 1),
-                        param.input_dim(i * 4 + 2),
-                        param.input_dim(i * 4 + 3)));
-    blobs_.push_back(blob_pointer);
-    blob_names_.push_back(blob_name);
-    blob_need_backward_.push_back(param.force_backward());
-    net_input_blob_indices_.push_back(i);
-    net_input_blobs_.push_back(blob_pointer.get());
-    blob_name_to_idx[blob_name] = i;
-    available_blobs.insert(blob_name);
-    memory_used += blob_pointer->count();
+  for (int input_id = 0; input_id < param.input_size(); ++input_id) {
+    const int layer_id = -1;  // inputs have fake layer ID -1
+    AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
   }
-  DLOG(INFO) << "Memory required for Data" << memory_used*sizeof(Dtype);
+  DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
   // For each layer, set up their input and output
   bottom_vecs_.resize(param.layers_size());
   top_vecs_.resize(param.layers_size());
@@ -79,48 +67,13 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     // Figure out this layer's input and output
     for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
          ++bottom_id) {
-      const string& blob_name = layer_param.bottom(bottom_id);
-      const int blob_id = blob_name_to_idx[blob_name];
-      if (available_blobs.find(blob_name) == available_blobs.end()) {
-        LOG(FATAL) << "Unknown blob input " << blob_name <<
-            " to layer" << bottom_id;
-      }
-      LOG(INFO) << layer_param.name() << " <- " << blob_name;
-      bottom_vecs_[layer_id].push_back(
-          blobs_[blob_id].get());
-      bottom_id_vecs_[layer_id].push_back(blob_id);
+      const int blob_id = AppendBottom(param, layer_id, bottom_id,
+                                       &available_blobs, &blob_name_to_idx);
       // If a blob needs backward, this layer should provide it.
       need_backward |= blob_need_backward_[blob_id];
-      available_blobs.erase(blob_name);
     }
     for (int top_id = 0; top_id < layer_param.top_size(); ++top_id) {
-      const string& blob_name = layer_param.top(top_id);
-      // Check if we are doing in-place computation
-      if (layer_param.bottom_size() > top_id &&
-          blob_name == layer_param.bottom(top_id)) {
-        // In-place computation
-        LOG(INFO) << layer_param.name() << " -> " << blob_name << " (in-place)";
-        in_place = true;
-        available_blobs.insert(blob_name);
-        top_vecs_[layer_id].push_back(
-            blobs_[blob_name_to_idx[blob_name]].get());
-        top_id_vecs_[layer_id].push_back(blob_name_to_idx[blob_name]);
-      } else if (blob_name_to_idx.find(blob_name) != blob_name_to_idx.end()) {
-        // If we are not doing in-place computation but has duplicated blobs,
-        // raise an error.
-        LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
-      } else {
-        // Normal output.
-        LOG(INFO) << layer_param.name() << " -> " << blob_name;
-        shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
-        blobs_.push_back(blob_pointer);
-        blob_names_.push_back(blob_name);
-        blob_need_backward_.push_back(param.force_backward());
-        blob_name_to_idx[blob_name] = blob_names_.size() - 1;
-        available_blobs.insert(blob_name);
-        top_vecs_[layer_id].push_back(blobs_[blob_names_.size() - 1].get());
-        top_id_vecs_[layer_id].push_back(blob_names_.size() - 1);
-      }
+      AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
     }
     // After this layer is connected, set it up.
     // LOG(INFO) << "Setting up " << layer_names_[layer_id];
@@ -131,14 +84,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
           << top_vecs_[layer_id][top_id]->height() << " "
           << top_vecs_[layer_id][top_id]->width() << " ("
           << top_vecs_[layer_id][top_id]->count() << ")";
-      if (!in_place)
-        memory_used += top_vecs_[layer_id][top_id]->count();
     }
-    DLOG(INFO) << "Memory  required for Data " << memory_used*sizeof(Dtype);
-    int blobs_lr_size = layers_[layer_id]->layer_param().blobs_lr_size();
-    CHECK(blobs_lr_size == layers_[layer_id]->blobs().size() || blobs_lr_size == 0)
-        << "Incorrect blobs lr size: should be either 0 or the same as "
-           "the number of the layer's parameter blobs.";
+    DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+    const int blobs_lr_size = layers_[layer_id]->layer_param().blobs_lr_size();
+    CHECK(blobs_lr_size == layers_[layer_id]->blobs().size() ||
+          blobs_lr_size == 0) << "Incorrect blobs lr size: should be either 0 "
+        << "or the same as the number of the layer's parameter blobs.";
     if (blobs_lr_size) {
       // Check if this layer needs backward operation itself
       for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
@@ -177,9 +128,79 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   }
   GetLearningRateAndWeightDecay();
   LOG(INFO) << "Network initialization done.";
-  LOG(INFO) << "Memory required for Data " << memory_used*sizeof(Dtype);
+  LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+}
+
+// Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
+// layer_id == -1, tops have layer_id >= 0.)
+template <typename Dtype>
+void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
+                           const int top_id, set<string>* available_blobs,
+                           map<string, int>* blob_name_to_idx) {
+  shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
+    (new LayerParameter(param.layers(layer_id))) : NULL);
+  const string& blob_name = layer_param ?
+      layer_param->top(top_id) : param.input(top_id);
+  // Check if we are doing in-place computation
+  if (layer_param && layer_param->bottom_size() > top_id &&
+      blob_name == layer_param->bottom(top_id)) {
+    // In-place computation
+    LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
+    top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
+    top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
+  } else if (blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
+    // If we are not doing in-place computation but have duplicated blobs,
+    // raise an error.
+    LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
+  } else {
+    // Normal output.
+    if (layer_param) {
+      LOG(INFO) << layer_param->name() << " -> " << blob_name;
+    } else {
+      LOG(INFO) << "Input " << top_id << " -> " << blob_name;
+    }
+    shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
+    const int blob_id = blobs_.size();
+    blobs_.push_back(blob_pointer);
+    blob_names_.push_back(blob_name);
+    blob_need_backward_.push_back(param.force_backward());
+    (*blob_name_to_idx)[blob_name] = blob_id;
+    if (layer_id == -1) {
+      // Set the (explicitly specified) dimensions of the input blob.
+      blob_pointer->Reshape(param.input_dim(top_id * 4),
+                            param.input_dim(top_id * 4 + 1),
+                            param.input_dim(top_id * 4 + 2),
+                            param.input_dim(top_id * 4 + 3));
+      net_input_blob_indices_.push_back(blob_id);
+      net_input_blobs_.push_back(blob_pointer.get());
+    } else {
+      top_id_vecs_[layer_id].push_back(blob_id);
+      top_vecs_[layer_id].push_back(blob_pointer.get());
+    }
+    memory_used_ += blob_pointer->count();
+  }
+  available_blobs->insert(blob_name);
 }
 
+// Helper for Net::Init: add a new bottom blob to the net.
+template <typename Dtype>
+int Net<Dtype>::AppendBottom(const NetParameter& param,
+    const int layer_id, const int bottom_id,
+    set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
+  const LayerParameter& layer_param = param.layers(layer_id);
+  const string& blob_name = layer_param.bottom(bottom_id);
+  if (available_blobs->find(blob_name) == available_blobs->end()) {
+    LOG(FATAL) << "Unknown blob input " << blob_name
+               << " (at index " << bottom_id << ") to layer " << layer_id;
+  }
+  const int blob_id = (*blob_name_to_idx)[blob_name];
+  LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
+  bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
+  bottom_id_vecs_[layer_id].push_back(blob_id);
+  available_blobs->erase(blob_name);
+  bool need_backward = param.force_backward() || blob_need_backward_[blob_id];
+  return blob_id;
+}
 
 template <typename Dtype>
 void Net<Dtype>::GetLearningRateAndWeightDecay() {

From 12f6fd52a8cd5e4b5972f3bf78bb526403c05f77 Mon Sep 17 00:00:00 2001
From: linmin <mavenlin@gmail.com>
Date: Thu, 22 May 2014 00:15:32 +0800
Subject: [PATCH 0118/2053] add option for lmdb

---
 Makefile                        |   1 +
 include/caffe/data_layers.hpp   |  10 +++
 src/caffe/layers/data_layer.cpp | 142 ++++++++++++++++++++++++++------
 src/caffe/proto/caffe.proto     |   5 ++
 4 files changed, 132 insertions(+), 26 deletions(-)

diff --git a/Makefile b/Makefile
index c43b30ee33a..943165a4642 100644
--- a/Makefile
+++ b/Makefile
@@ -129,6 +129,7 @@ LIBRARY_DIRS += $(CUDA_LIB_DIR)
 LIBRARIES := cudart cublas curand \
 	pthread \
 	glog protobuf leveldb snappy \
+	lmdb \
 	boost_system \
 	hdf5_hl hdf5 \
 	opencv_core opencv_highgui opencv_imgproc
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 3d3ce94bc88..2b4c278eafe 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -8,6 +8,7 @@
 #include <vector>
 
 #include "leveldb/db.h"
+#include "lmdb.h"
 #include "pthread.h"
 #include "hdf5.h"
 #include "boost/scoped_ptr.hpp"
@@ -133,8 +134,17 @@ class DataLayer : public Layer<Dtype> {
   virtual unsigned int PrefetchRand();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
+
+  // LEVELDB
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::Iterator> iter_;
+  // LMDB
+  MDB_env* mdb_env_;
+  MDB_dbi mdb_dbi_;
+  MDB_txn* mdb_txn_;
+  MDB_cursor* mdb_cursor_;
+  MDB_val mdb_key_, mdb_value_;
+
   int datum_channels_;
   int datum_height_;
   int datum_width_;
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 348753e5db9..3b307ec7743 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -12,6 +12,7 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
+#include "caffe/proto/caffe.pb.h"
 
 using std::string;
 
@@ -46,9 +47,22 @@ void* DataLayerPrefetch(void* layer_pointer) {
   const Dtype* mean = layer->data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
-    CHECK(layer->iter_);
-    CHECK(layer->iter_->Valid());
-    datum.ParseFromString(layer->iter_->value().ToString());
+    switch (layer->layer_param_.data_param().backend()) {
+    case DataParameter_DB_LEVELDB:
+      CHECK(layer->iter_);
+      CHECK(layer->iter_->Valid());
+      datum.ParseFromString(layer->iter_->value().ToString());
+      break;
+    case DataParameter_DB_LMDB:
+      CHECK_EQ(mdb_cursor_get(layer->mdb_cursor_, &layer->mdb_key_,
+              &layer->mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
+      datum.ParseFromArray(layer->mdb_value_.mv_data,
+          layer->mdb_value_.mv_size);
+      break;
+    default:
+      LOG(FATAL) << "Unknown database backend";
+    }
+
     const string& data = datum.data();
     if (crop_size) {
       CHECK(data.size()) << "Image cropping only support uint8 data";
@@ -110,11 +124,26 @@ void* DataLayerPrefetch(void* layer_pointer) {
       top_label[item_id] = datum.label();
     }
     // go to the next iter
-    layer->iter_->Next();
-    if (!layer->iter_->Valid()) {
-      // We have reached the end. Restart from the first.
-      DLOG(INFO) << "Restarting data prefetching from start.";
-      layer->iter_->SeekToFirst();
+    switch (layer->layer_param_.data_param().backend()) {
+    case DataParameter_DB_LEVELDB:
+      layer->iter_->Next();
+      if (!layer->iter_->Valid()) {
+        // We have reached the end. Restart from the first.
+        DLOG(INFO) << "Restarting data prefetching from start.";
+        layer->iter_->SeekToFirst();
+      }
+      break;
+    case DataParameter_DB_LMDB:
+      if (mdb_cursor_get(layer->mdb_cursor_, &layer->mdb_key_,
+              &layer->mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
+        // We have reached the end. Restart from the first.
+        DLOG(INFO) << "Restarting data prefetching from start.";
+        CHECK_EQ(mdb_cursor_get(layer->mdb_cursor_, &layer->mdb_key_,
+                &layer->mdb_value_, MDB_FIRST), MDB_SUCCESS);
+      }
+      break;
+    default:
+      LOG(FATAL) << "Unknown database backend";
     }
   }
 
@@ -124,6 +153,19 @@ void* DataLayerPrefetch(void* layer_pointer) {
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   JoinPrefetchThread();
+  // clean up the database resources
+  switch (this->layer_param_.data_param().backend()) {
+  case DataParameter_DB_LEVELDB:
+    break; // do nothing
+  case DataParameter_DB_LMDB:
+    mdb_cursor_close(mdb_cursor_);
+    mdb_close(mdb_env_, mdb_dbi_);
+    mdb_txn_abort(mdb_txn_);
+    mdb_env_close(mdb_env_);
+    break;
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
 }
 
 template <typename Dtype>
@@ -135,35 +177,83 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   } else {
     output_labels_ = true;
   }
-  // Initialize the leveldb
-  leveldb::DB* db_temp;
-  leveldb::Options options;
-  options.create_if_missing = false;
-  options.max_open_files = 100;
-  LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
-  leveldb::Status status = leveldb::DB::Open(
-      options, this->layer_param_.data_param().source(), &db_temp);
-  CHECK(status.ok()) << "Failed to open leveldb "
-      << this->layer_param_.data_param().source() << std::endl
-      << status.ToString();
-  db_.reset(db_temp);
-  iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
-  iter_->SeekToFirst();
+  // Initialize DB
+  switch (this->layer_param_.data_param().backend()) {
+  case DataParameter_DB_LEVELDB:
+    {
+    leveldb::DB* db_temp;
+    leveldb::Options options;
+    options.create_if_missing = false;
+    options.max_open_files = 100;
+    LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
+    leveldb::Status status = leveldb::DB::Open(
+        options, this->layer_param_.data_param().source(), &db_temp);
+    CHECK(status.ok()) << "Failed to open leveldb "
+                       << this->layer_param_.data_param().source() << std::endl
+                       << status.ToString();
+    db_.reset(db_temp);
+    iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
+    iter_->SeekToFirst();
+    }
+    break;
+  case DataParameter_DB_LMDB:
+    CHECK_EQ(mdb_env_create(&mdb_env_), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env_, 1099511627776), MDB_SUCCESS); // 1TB
+    CHECK_EQ(mdb_env_open(mdb_env_,
+            this->layer_param_.data_param().source().c_str(),
+            MDB_RDONLY|MDB_NOTLS, 0664), MDB_SUCCESS) << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn_), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn_, NULL, 0, &mdb_dbi_), MDB_SUCCESS)
+        << "mdb_open failed";
+    CHECK_EQ(mdb_cursor_open(mdb_txn_, mdb_dbi_, &mdb_cursor_), MDB_SUCCESS)
+        << "mdb_cursor_open failed";
+    LOG(INFO) << "Opening lmdb " << this->layer_param_.data_param().source();
+    CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_FIRST),
+        MDB_SUCCESS) << "mdb_cursor_get failed";
+    break;
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
+
   // Check if we would need to randomly skip a few data points
   if (this->layer_param_.data_param().rand_skip()) {
     unsigned int skip = caffe_rng_rand() %
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      iter_->Next();
-      if (!iter_->Valid()) {
-        iter_->SeekToFirst();
+      switch (this->layer_param_.data_param().backend()) {
+      case DataParameter_DB_LEVELDB:
+        iter_->Next();
+        if (!iter_->Valid()) {
+          iter_->SeekToFirst();
+        }
+        break;
+      case DataParameter_DB_LMDB:
+        if(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_NEXT)
+            != MDB_SUCCESS) {
+          CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_,
+                  MDB_FIRST), MDB_SUCCESS);
+        }
+        break;
+      default:
+        LOG(FATAL) << "Unknown database backend";
       }
     }
   }
   // Read a data point, and use it to initialize the top blob.
   Datum datum;
-  datum.ParseFromString(iter_->value().ToString());
+  switch (this->layer_param_.data_param().backend()) {
+  case DataParameter_DB_LEVELDB:
+    datum.ParseFromString(iter_->value().ToString());
+    break;
+  case DataParameter_DB_LMDB:
+    datum.ParseFromArray(mdb_value_.mv_data, mdb_value_.mv_size);
+    break;
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
+
   // image
   int crop_size = this->layer_param_.data_param().crop_size();
   if (crop_size > 0) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 60c7daa9031..b85b49c77f7 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -229,6 +229,10 @@ message ConvolutionParameter {
 
 // Message that stores parameters used by DataLayer
 message DataParameter {
+  enum DB {
+    LEVELDB = 0;
+    LMDB = 1;
+  }
   // Specify the data source.
   optional string source = 1;
   // For data pre-processing, we can do simple scaling and subtracting the
@@ -247,6 +251,7 @@ message DataParameter {
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
   optional uint32 rand_skip = 7 [default = 0];
+  optional DB backend = 8 [default = LEVELDB];
 }
 
 // Message that stores parameters used by DropoutLayer

From f43f555dc03a8bf616c21f9ae6225012bad3ad1b Mon Sep 17 00:00:00 2001
From: linmin <mavenlin@gmail.com>
Date: Fri, 13 Jun 2014 10:19:33 +0800
Subject: [PATCH 0119/2053] add tests for lmdb of datalayer (copied from
 test_data_layer.cpp)

---
 src/caffe/test/test_data_layer_lmdb.cpp | 565 ++++++++++++++++++++++++
 1 file changed, 565 insertions(+)
 create mode 100644 src/caffe/test/test_data_layer_lmdb.cpp

diff --git a/src/caffe/test/test_data_layer_lmdb.cpp b/src/caffe/test/test_data_layer_lmdb.cpp
new file mode 100644
index 00000000000..9b5b7d2bfc6
--- /dev/null
+++ b/src/caffe/test/test_data_layer_lmdb.cpp
@@ -0,0 +1,565 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <string>
+#include <vector>
+#include <sys/stat.h>
+
+#include "cuda_runtime.h"
+#include "leveldb/db.h"
+#include "lmdb.h"
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::string;
+using std::stringstream;
+
+namespace caffe {
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class DataLayerTestLmdb : public ::testing::Test {
+ protected:
+  DataLayerTestLmdb()
+      : blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()),
+        filename_(new string(tmpnam(NULL))),
+        seed_(1701) {}
+  virtual void SetUp() {
+    blob_top_vec_.push_back(blob_top_data_);
+    blob_top_vec_.push_back(blob_top_label_);
+  }
+
+  // Fill the LMDB with data: if unique_pixels, each pixel is unique but
+  // all images are the same; else each image is unique but all pixels within
+  // an image are the same.
+
+  void FillLMDB(const bool unique_pixels) {
+    LOG(INFO) << "Using temporary lmdb " << *filename_;
+    CHECK_EQ(mkdir(filename_->c_str(), 0744), 0) << "mkdir " << filename_
+                                                 << "failed";
+    MDB_env *env;
+    MDB_dbi dbi;
+    MDB_val mdbkey, mdbdata;
+    MDB_txn *txn;
+    CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS) // 1TB
+        << "mdb_env_set_mapsize failed";
+    CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
+        << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(env, NULL, 0, &txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(txn, NULL, 0, &dbi), MDB_SUCCESS) << "mdb_open failed";
+
+    for (int i = 0; i < 5; ++i) {
+      Datum datum;
+      datum.set_label(i);
+      datum.set_channels(2);
+      datum.set_height(3);
+      datum.set_width(4);
+      std::string* data = datum.mutable_data();
+      for (int j = 0; j < 24; ++j) {
+        int datum = unique_pixels ? j : i;
+        data->push_back(static_cast<uint8_t>(datum));
+      }
+      stringstream ss;
+      ss << i;
+
+      string value;
+      datum.SerializeToString(&value);
+      mdbdata.mv_size = value.size();
+      mdbdata.mv_data = reinterpret_cast<void*>(&value[0]);
+      string keystr = ss.str();
+      mdbkey.mv_size = keystr.size();
+      mdbkey.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      CHECK_EQ(mdb_put(txn, dbi, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
+          << "mdb_put failed";
+    }
+    CHECK_EQ(mdb_txn_commit(txn), MDB_SUCCESS) << "mdb_txn_commit failed";
+    mdb_close(env, dbi);
+    mdb_env_close(env);
+  }
+
+  virtual ~DataLayerTestLmdb() { delete blob_top_data_; delete blob_top_label_; }
+
+  shared_ptr<string> filename_;
+  Blob<Dtype>* const blob_top_data_;
+  Blob<Dtype>* const blob_top_label_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+  int seed_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(DataLayerTestLmdb, Dtypes);
+
+TYPED_TEST(DataLayerTestLmdb, TestReadCPU) {
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = false;  // all pixels the same; images different
+  this->FillLMDB(unique_pixels);
+  const TypeParam scale = 3;
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_scale(scale);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  DataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 5);
+  EXPECT_EQ(this->blob_top_data_->channels(), 2);
+  EXPECT_EQ(this->blob_top_data_->height(), 3);
+  EXPECT_EQ(this->blob_top_data_->width(), 4);
+  EXPECT_EQ(this->blob_top_label_->num(), 5);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+
+  for (int iter = 0; iter < 100; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 24; ++j) {
+        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
+            << "debug: iter " << iter << " i " << i << " j " << j;
+      }
+    }
+  }
+}
+
+TYPED_TEST(DataLayerTestLmdb, TestReadGPU) {
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = false;  // all pixels the same; images different
+  this->FillLMDB(unique_pixels);
+  const TypeParam scale = 3;
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_scale(scale);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  DataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 5);
+  EXPECT_EQ(this->blob_top_data_->channels(), 2);
+  EXPECT_EQ(this->blob_top_data_->height(), 3);
+  EXPECT_EQ(this->blob_top_data_->width(), 4);
+  EXPECT_EQ(this->blob_top_label_->num(), 5);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+
+  for (int iter = 0; iter < 100; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 24; ++j) {
+        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
+            << "debug: iter " << iter << " i " << i << " j " << j;
+      }
+    }
+  }
+}
+
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainCPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  const TypeParam scale = 3;
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_scale(scale);
+  data_param->set_crop_size(1);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  DataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 5);
+  EXPECT_EQ(this->blob_top_data_->channels(), 2);
+  EXPECT_EQ(this->blob_top_data_->height(), 1);
+  EXPECT_EQ(this->blob_top_data_->width(), 1);
+  EXPECT_EQ(this->blob_top_label_->num(), 5);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+
+  for (int iter = 0; iter < 2; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    int num_with_center_value = 0;
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        const TypeParam center_value = scale * (j ? 17 : 5);
+        num_with_center_value +=
+            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
+      }
+    }
+    // Check we did not get the center crop all 10 times (occurs with
+    // probability 1-1/12^10 in working implementation).
+    EXPECT_LT(num_with_center_value, 10);
+  }
+}
+
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainGPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  const TypeParam scale = 3;
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_scale(scale);
+  data_param->set_crop_size(1);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  DataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 5);
+  EXPECT_EQ(this->blob_top_data_->channels(), 2);
+  EXPECT_EQ(this->blob_top_data_->height(), 1);
+  EXPECT_EQ(this->blob_top_data_->width(), 1);
+  EXPECT_EQ(this->blob_top_label_->num(), 5);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+
+  for (int iter = 0; iter < 2; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    int num_with_center_value = 0;
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        const TypeParam center_value = scale * (j ? 17 : 5);
+        num_with_center_value +=
+            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
+      }
+    }
+    // Check we did not get the center crop all 10 times (occurs with
+    // probability 1-1/12^10 in working implementation).
+    EXPECT_LT(num_with_center_value, 10);
+  }
+}
+
+// Test that the sequence of random crops is consistent when using
+// Caffe::set_random_seed.
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceSeededCPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_crop_size(1);
+  data_param->set_mirror(true);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  // Get crop sequence with Caffe seed 1701.
+  Caffe::set_random_seed(this->seed_);
+  vector<vector<TypeParam> > crop_sequence;
+  {
+    DataLayer<TypeParam> layer1(param);
+    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int iter = 0; iter < 2; ++iter) {
+      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+      }
+      vector<TypeParam> iter_crop_sequence;
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          iter_crop_sequence.push_back(
+              this->blob_top_data_->cpu_data()[i * 2 + j]);
+        }
+      }
+      crop_sequence.push_back(iter_crop_sequence);
+    }
+  }  // destroy 1st data layer and unlock the leveldb
+
+  // Get crop sequence after reseeding Caffe with 1701.
+  // Check that the sequence is the same as the original.
+  Caffe::set_random_seed(this->seed_);
+  DataLayer<TypeParam> layer2(param);
+  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int iter = 0; iter < 2; ++iter) {
+    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
+                  this->blob_top_data_->cpu_data()[i * 2 + j])
+            << "debug: iter " << iter << " i " << i << " j " << j;
+      }
+    }
+  }
+}
+
+// Test that the sequence of random crops is consistent when using
+// Caffe::set_random_seed.
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceSeededGPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_crop_size(1);
+  data_param->set_mirror(true);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  // Get crop sequence with Caffe seed 1701.
+  Caffe::set_random_seed(this->seed_);
+  vector<vector<TypeParam> > crop_sequence;
+  {
+    DataLayer<TypeParam> layer1(param);
+    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int iter = 0; iter < 2; ++iter) {
+      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+      }
+      vector<TypeParam> iter_crop_sequence;
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          iter_crop_sequence.push_back(
+              this->blob_top_data_->cpu_data()[i * 2 + j]);
+        }
+      }
+      crop_sequence.push_back(iter_crop_sequence);
+    }
+  }  // destroy 1st data layer and unlock the leveldb
+
+  // Get crop sequence after reseeding Caffe with 1701.
+  // Check that the sequence is the same as the original.
+  Caffe::set_random_seed(this->seed_);
+  DataLayer<TypeParam> layer2(param);
+  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int iter = 0; iter < 2; ++iter) {
+    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
+                  this->blob_top_data_->cpu_data()[i * 2 + j])
+            << "debug: iter " << iter << " i " << i << " j " << j;
+      }
+    }
+  }
+}
+
+// Test that the sequence of random crops differs across iterations when
+// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceUnseededCPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_crop_size(1);
+  data_param->set_mirror(true);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  // Get crop sequence with Caffe seed 1701, srand seed 1701.
+  Caffe::set_random_seed(this->seed_);
+  srand(this->seed_);
+  vector<vector<TypeParam> > crop_sequence;
+  {
+    DataLayer<TypeParam> layer1(param);
+    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int iter = 0; iter < 2; ++iter) {
+      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+      }
+      vector<TypeParam> iter_crop_sequence;
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          iter_crop_sequence.push_back(
+              this->blob_top_data_->cpu_data()[i * 2 + j]);
+        }
+      }
+      crop_sequence.push_back(iter_crop_sequence);
+    }
+  }  // destroy 1st data layer and unlock the leveldb
+
+  // Get crop sequence continuing from previous Caffe RNG state;
+  // reseed srand with 1701. Check that the sequence differs from the original.
+  srand(this->seed_);
+  DataLayer<TypeParam> layer2(param);
+  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int iter = 0; iter < 2; ++iter) {
+    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    int num_sequence_matches = 0;
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
+                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
+      }
+    }
+    EXPECT_LT(num_sequence_matches, 10);
+  }
+}
+
+// Test that the sequence of random crops differs across iterations when
+// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceUnseededGPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_crop_size(1);
+  data_param->set_mirror(true);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  // Get crop sequence with Caffe seed 1701, srand seed 1701.
+  Caffe::set_random_seed(this->seed_);
+  srand(this->seed_);
+  vector<vector<TypeParam> > crop_sequence;
+  {
+    DataLayer<TypeParam> layer1(param);
+    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int iter = 0; iter < 2; ++iter) {
+      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+      }
+      vector<TypeParam> iter_crop_sequence;
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          iter_crop_sequence.push_back(
+              this->blob_top_data_->cpu_data()[i * 2 + j]);
+        }
+      }
+      crop_sequence.push_back(iter_crop_sequence);
+    }
+  }  // destroy 1st data layer and unlock the leveldb
+
+  // Get crop sequence continuing from previous Caffe RNG state;
+  // reseed srand with 1701. Check that the sequence differs from the original.
+  srand(this->seed_);
+  DataLayer<TypeParam> layer2(param);
+  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int iter = 0; iter < 2; ++iter) {
+    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    int num_sequence_matches = 0;
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
+                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
+      }
+    }
+    EXPECT_LT(num_sequence_matches, 10);
+  }
+}
+
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTestCPU) {
+  Caffe::set_phase(Caffe::TEST);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  const TypeParam scale = 3;
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_scale(scale);
+  data_param->set_crop_size(1);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  DataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 5);
+  EXPECT_EQ(this->blob_top_data_->channels(), 2);
+  EXPECT_EQ(this->blob_top_data_->height(), 1);
+  EXPECT_EQ(this->blob_top_data_->width(), 1);
+  EXPECT_EQ(this->blob_top_label_->num(), 5);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+
+  for (int iter = 0; iter < 2; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        const TypeParam center_value = scale * (j ? 17 : 5);
+        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
+            << "debug: iter " << iter << " i " << i << " j " << j;
+      }
+    }
+  }
+}
+
+TYPED_TEST(DataLayerTestLmdb, TestReadCropTestGPU) {
+  Caffe::set_phase(Caffe::TEST);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  const TypeParam scale = 3;
+  LayerParameter param;
+  DataParameter* data_param = param.mutable_data_param();
+  data_param->set_batch_size(5);
+  data_param->set_scale(scale);
+  data_param->set_crop_size(1);
+  data_param->set_source(this->filename_->c_str());
+  data_param->set_backend(DataParameter_DB_LMDB);
+  DataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 5);
+  EXPECT_EQ(this->blob_top_data_->channels(), 2);
+  EXPECT_EQ(this->blob_top_data_->height(), 1);
+  EXPECT_EQ(this->blob_top_data_->width(), 1);
+  EXPECT_EQ(this->blob_top_label_->num(), 5);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+
+  for (int iter = 0; iter < 2; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    for (int i = 0; i < 5; ++i) {
+      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+    }
+    for (int i = 0; i < 5; ++i) {
+      for (int j = 0; j < 2; ++j) {
+        const TypeParam center_value = scale * (j ? 17 : 5);
+        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
+            << "debug: iter " << iter << " i " << i << " j " << j;
+      }
+    }
+  }
+}
+
+}  // namespace caffe

From c77c6e12e49048e4cfcfac82dee0a8dae3e69d56 Mon Sep 17 00:00:00 2001
From: linmin <mavenlin@gmail.com>
Date: Fri, 13 Jun 2014 10:26:38 +0800
Subject: [PATCH 0120/2053] fixed cpplint error

---
 src/caffe/test/test_data_layer_lmdb.cpp | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/caffe/test/test_data_layer_lmdb.cpp b/src/caffe/test/test_data_layer_lmdb.cpp
index 9b5b7d2bfc6..f3f9d723b5b 100644
--- a/src/caffe/test/test_data_layer_lmdb.cpp
+++ b/src/caffe/test/test_data_layer_lmdb.cpp
@@ -1,8 +1,8 @@
 // Copyright 2014 BVLC and contributors.
 
+#include <sys/stat.h>
 #include <string>
 #include <vector>
-#include <sys/stat.h>
 
 #include "cuda_runtime.h"
 #include "leveldb/db.h"
@@ -48,7 +48,7 @@ class DataLayerTestLmdb : public ::testing::Test {
     MDB_val mdbkey, mdbdata;
     MDB_txn *txn;
     CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS) // 1TB
+    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS)  // 1TB
         << "mdb_env_set_mapsize failed";
     CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
         << "mdb_env_open failed";
@@ -85,7 +85,10 @@ class DataLayerTestLmdb : public ::testing::Test {
     mdb_env_close(env);
   }
 
-  virtual ~DataLayerTestLmdb() { delete blob_top_data_; delete blob_top_label_; }
+  virtual ~DataLayerTestLmdb() {
+    delete blob_top_data_;
+    delete blob_top_label_;
+  }
 
   shared_ptr<string> filename_;
   Blob<Dtype>* const blob_top_data_;

From c17e413b305c29f64fdaa348d070478414a208a6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 12 Jun 2014 22:29:59 -0700
Subject: [PATCH 0121/2053] lint

---
 src/caffe/layers/data_layer.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 3b307ec7743..f12ae1c12e1 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -156,7 +156,7 @@ DataLayer<Dtype>::~DataLayer<Dtype>() {
   // clean up the database resources
   switch (this->layer_param_.data_param().backend()) {
   case DataParameter_DB_LEVELDB:
-    break; // do nothing
+    break;  // do nothing
   case DataParameter_DB_LMDB:
     mdb_cursor_close(mdb_cursor_);
     mdb_close(mdb_env_, mdb_dbi_);
@@ -198,10 +198,10 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     break;
   case DataParameter_DB_LMDB:
     CHECK_EQ(mdb_env_create(&mdb_env_), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env_, 1099511627776), MDB_SUCCESS); // 1TB
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env_, 1099511627776), MDB_SUCCESS);  // 1TB
     CHECK_EQ(mdb_env_open(mdb_env_,
-            this->layer_param_.data_param().source().c_str(),
-            MDB_RDONLY|MDB_NOTLS, 0664), MDB_SUCCESS) << "mdb_env_open failed";
+             this->layer_param_.data_param().source().c_str(),
+             MDB_RDONLY|MDB_NOTLS, 0664), MDB_SUCCESS) << "mdb_env_open failed";
     CHECK_EQ(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn_), MDB_SUCCESS)
         << "mdb_txn_begin failed";
     CHECK_EQ(mdb_open(mdb_txn_, NULL, 0, &mdb_dbi_), MDB_SUCCESS)
@@ -230,10 +230,10 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
         }
         break;
       case DataParameter_DB_LMDB:
-        if(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_NEXT)
+        if (mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_NEXT)
             != MDB_SUCCESS) {
           CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_,
-                  MDB_FIRST), MDB_SUCCESS);
+                   MDB_FIRST), MDB_SUCCESS);
         }
         break;
       default:

From f8ebd09d067d893289e34b0ccc8ac2d2ac2cca8f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 12 Jun 2014 22:51:33 -0700
Subject: [PATCH 0122/2053] unify test_data_layer tests

---
 src/caffe/test/test_data_layer.cpp | 573 ++++++++++-------------------
 1 file changed, 191 insertions(+), 382 deletions(-)

diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 1eae1a4e068..98ac60d47b4 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -63,6 +63,187 @@ class DataLayerTest : public ::testing::Test {
     delete db;
   }
 
+  void TestRead() {
+    const Dtype scale = 3;
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_scale(scale);
+    data_param->set_source(filename_->c_str());
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 5);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_data_->height(), 3);
+    EXPECT_EQ(blob_top_data_->width(), 4);
+    EXPECT_EQ(blob_top_label_->num(), 5);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
+    for (int iter = 0; iter < 100; ++iter) {
+      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+      }
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 24; ++j) {
+          EXPECT_EQ(scale * i, blob_top_data_->cpu_data()[i * 24 + j])
+              << "debug: iter " << iter << " i " << i << " j " << j;
+        }
+      }
+    }
+  }
+
+  void TestReadCrop() {
+    const Dtype scale = 3;
+    LayerParameter param;
+    Caffe::set_random_seed(1701);
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_scale(scale);
+    data_param->set_crop_size(1);
+    data_param->set_source(filename_->c_str());
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 5);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_data_->height(), 1);
+    EXPECT_EQ(blob_top_data_->width(), 1);
+    EXPECT_EQ(blob_top_label_->num(), 5);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
+    for (int iter = 0; iter < 2; ++iter) {
+      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+      }
+      int num_with_center_value = 0;
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          const Dtype center_value = scale * (j ? 17 : 5);
+          num_with_center_value +=
+              (center_value == blob_top_data_->cpu_data()[i * 2 + j]);
+          // At TEST time, check that we always get center value.
+          if (Caffe::phase() == Caffe::TEST) {
+            EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
+                << "debug: iter " << iter << " i " << i << " j " << j;
+          }
+        }
+      }
+      // At TRAIN time, check that we did not get the center crop all 10 times.
+      // (This check fails with probability 1-1/12^10 in a correct 
+      // implementation, so we call set_random_seed.)
+      if (Caffe::phase() == Caffe::TRAIN) {
+        EXPECT_LT(num_with_center_value, 10);
+      }
+    }
+  }
+
+  void TestReadCropTrainSequenceSeeded() {
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_crop_size(1);
+    data_param->set_mirror(true);
+    data_param->set_source(filename_->c_str());
+
+    // Get crop sequence with Caffe seed 1701.
+    Caffe::set_random_seed(seed_);
+    vector<vector<Dtype> > crop_sequence;
+    {
+      DataLayer<Dtype> layer1(param);
+      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      for (int iter = 0; iter < 2; ++iter) {
+        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        for (int i = 0; i < 5; ++i) {
+          EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+        }
+        vector<Dtype> iter_crop_sequence;
+        for (int i = 0; i < 5; ++i) {
+          for (int j = 0; j < 2; ++j) {
+            iter_crop_sequence.push_back(
+                blob_top_data_->cpu_data()[i * 2 + j]);
+          }
+        }
+        crop_sequence.push_back(iter_crop_sequence);
+      }
+    }  // destroy 1st data layer and unlock the leveldb
+
+    // Get crop sequence after reseeding Caffe with 1701.
+    // Check that the sequence is the same as the original.
+    Caffe::set_random_seed(seed_);
+    DataLayer<Dtype> layer2(param);
+    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    for (int iter = 0; iter < 2; ++iter) {
+      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+      }
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          EXPECT_EQ(crop_sequence[iter][i * 2 + j],
+                    blob_top_data_->cpu_data()[i * 2 + j])
+              << "debug: iter " << iter << " i " << i << " j " << j;
+        }
+      }
+    }
+  }
+
+  void TestReadCropTrainSequenceUnseeded() {
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_crop_size(1);
+    data_param->set_mirror(true);
+    data_param->set_source(filename_->c_str());
+
+    // Get crop sequence with Caffe seed 1701, srand seed 1701.
+    Caffe::set_random_seed(seed_);
+    srand(seed_);
+    vector<vector<Dtype> > crop_sequence;
+    {
+      DataLayer<Dtype> layer1(param);
+      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      for (int iter = 0; iter < 2; ++iter) {
+        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        for (int i = 0; i < 5; ++i) {
+          EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+        }
+        vector<Dtype> iter_crop_sequence;
+        for (int i = 0; i < 5; ++i) {
+          for (int j = 0; j < 2; ++j) {
+            iter_crop_sequence.push_back(
+                blob_top_data_->cpu_data()[i * 2 + j]);
+          }
+        }
+        crop_sequence.push_back(iter_crop_sequence);
+      }
+    }  // destroy 1st data layer and unlock the leveldb
+
+    // Get crop sequence continuing from previous Caffe RNG state;
+    // reseed srand with 1701. Check that the sequence differs from the original.
+    srand(seed_);
+    DataLayer<Dtype> layer2(param);
+    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    for (int iter = 0; iter < 2; ++iter) {
+      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+      }
+      int num_sequence_matches = 0;
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 2; ++j) {
+          num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
+                                   blob_top_data_->cpu_data()[i * 2 + j]);
+        }
+      }
+      EXPECT_LT(num_sequence_matches, 10);
+    }
+  }
+
   virtual ~DataLayerTest() { delete blob_top_data_; delete blob_top_label_; }
 
   shared_ptr<string> filename_;
@@ -80,70 +261,14 @@ TYPED_TEST(DataLayerTest, TestReadCPU) {
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = false;  // all pixels the same; images different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 3);
-  EXPECT_EQ(this->blob_top_data_->width(), 4);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 100; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 24; ++j) {
-        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestRead();
 }
 
 TYPED_TEST(DataLayerTest, TestReadGPU) {
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = false;  // all pixels the same; images different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 3);
-  EXPECT_EQ(this->blob_top_data_->width(), 4);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 100; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 24; ++j) {
-        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestRead();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTrainCPU) {
@@ -151,41 +276,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainCPU) {
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_with_center_value = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        num_with_center_value +=
-            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    // Check we did not get the center crop all 10 times (occurs with
-    // probability 1-1/12^10 in working implementation).
-    EXPECT_LT(num_with_center_value, 10);
-  }
+  this->TestReadCrop();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTrainGPU) {
@@ -193,41 +284,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainGPU) {
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_with_center_value = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        num_with_center_value +=
-            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    // Check we did not get the center crop all 10 times (occurs with
-    // probability 1-1/12^10 in working implementation).
-    EXPECT_LT(num_with_center_value, 10);
-  }
+  this->TestReadCrop();
 }
 
 // Test that the sequence of random crops is consistent when using
@@ -237,53 +294,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededCPU) {
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence after reseeding Caffe with 1701.
-  // Check that the sequence is the same as the original.
-  Caffe::set_random_seed(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
-                  this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestReadCropTrainSequenceSeeded();
 }
 
 // Test that the sequence of random crops is consistent when using
@@ -293,53 +304,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededGPU) {
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence after reseeding Caffe with 1701.
-  // Check that the sequence is the same as the original.
-  Caffe::set_random_seed(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
-                  this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestReadCropTrainSequenceSeeded();
 }
 
 // Test that the sequence of random crops differs across iterations when
@@ -349,55 +314,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededCPU) {
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701, srand seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  srand(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence continuing from previous Caffe RNG state;
-  // reseed srand with 1701. Check that the sequence differs from the original.
-  srand(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_sequence_matches = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
-                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    EXPECT_LT(num_sequence_matches, 10);
-  }
+  this->TestReadCropTrainSequenceUnseeded();
 }
 
 // Test that the sequence of random crops differs across iterations when
@@ -407,55 +324,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededGPU) {
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701, srand seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  srand(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence continuing from previous Caffe RNG state;
-  // reseed srand with 1701. Check that the sequence differs from the original.
-  srand(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_sequence_matches = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
-                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    EXPECT_LT(num_sequence_matches, 10);
-  }
+  this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestCPU) {
@@ -463,37 +332,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTestCPU) {
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestReadCrop();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestGPU) {
@@ -501,37 +340,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTestGPU) {
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestReadCrop();
 }
 
 }  // namespace caffe

From 1dd0d94bbea53bd080ad48ae15d53e4e9d47cad7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 12 Jun 2014 23:07:23 -0700
Subject: [PATCH 0123/2053] unify data layer tests: was copied four times for
 all combinations of cpu/gpu and leveldb/lmdb; now just one copy of each test
 body

---
 src/caffe/test/test_data_layer.cpp      | 167 ++++++-
 src/caffe/test/test_data_layer_lmdb.cpp | 568 ------------------------
 2 files changed, 154 insertions(+), 581 deletions(-)
 delete mode 100644 src/caffe/test/test_data_layer_lmdb.cpp

diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 98ac60d47b4..68f2618d323 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -27,6 +27,7 @@ class DataLayerTest : public ::testing::Test {
       : blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()),
         filename_(new string(tmpnam(NULL))),
+        backend_(DataParameter_DB_LEVELDB),
         seed_(1701) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
@@ -37,6 +38,7 @@ class DataLayerTest : public ::testing::Test {
   // all images are the same; else each image is unique but all pixels within
   // an image are the same.
   void FillLevelDB(const bool unique_pixels) {
+    backend_ = DataParameter_DB_LEVELDB;
     LOG(INFO) << "Using temporary leveldb " << *filename_;
     leveldb::DB* db;
     leveldb::Options options;
@@ -63,6 +65,54 @@ class DataLayerTest : public ::testing::Test {
     delete db;
   }
 
+  // Fill the LMDB with data: unique_pixels has same meaning as in FillLevelDB.
+  void FillLMDB(const bool unique_pixels) {
+    backend_ = DataParameter_DB_LMDB;
+    LOG(INFO) << "Using temporary lmdb " << *filename_;
+    CHECK_EQ(mkdir(filename_->c_str(), 0744), 0) << "mkdir " << filename_
+                                                 << "failed";
+    MDB_env *env;
+    MDB_dbi dbi;
+    MDB_val mdbkey, mdbdata;
+    MDB_txn *txn;
+    CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS)  // 1TB
+        << "mdb_env_set_mapsize failed";
+    CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
+        << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(env, NULL, 0, &txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(txn, NULL, 0, &dbi), MDB_SUCCESS) << "mdb_open failed";
+
+    for (int i = 0; i < 5; ++i) {
+      Datum datum;
+      datum.set_label(i);
+      datum.set_channels(2);
+      datum.set_height(3);
+      datum.set_width(4);
+      std::string* data = datum.mutable_data();
+      for (int j = 0; j < 24; ++j) {
+        int datum = unique_pixels ? j : i;
+        data->push_back(static_cast<uint8_t>(datum));
+      }
+      stringstream ss;
+      ss << i;
+
+      string value;
+      datum.SerializeToString(&value);
+      mdbdata.mv_size = value.size();
+      mdbdata.mv_data = reinterpret_cast<void*>(&value[0]);
+      string keystr = ss.str();
+      mdbkey.mv_size = keystr.size();
+      mdbkey.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      CHECK_EQ(mdb_put(txn, dbi, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
+          << "mdb_put failed";
+    }
+    CHECK_EQ(mdb_txn_commit(txn), MDB_SUCCESS) << "mdb_txn_commit failed";
+    mdb_close(env, dbi);
+    mdb_env_close(env);
+  }
+
   void TestRead() {
     const Dtype scale = 3;
     LayerParameter param;
@@ -70,6 +120,7 @@ class DataLayerTest : public ::testing::Test {
     data_param->set_batch_size(5);
     data_param->set_scale(scale);
     data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
     DataLayer<Dtype> layer(param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
@@ -104,6 +155,7 @@ class DataLayerTest : public ::testing::Test {
     data_param->set_scale(scale);
     data_param->set_crop_size(1);
     data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
     DataLayer<Dtype> layer(param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
@@ -134,7 +186,7 @@ class DataLayerTest : public ::testing::Test {
         }
       }
       // At TRAIN time, check that we did not get the center crop all 10 times.
-      // (This check fails with probability 1-1/12^10 in a correct 
+      // (This check fails with probability 1-1/12^10 in a correct
       // implementation, so we call set_random_seed.)
       if (Caffe::phase() == Caffe::TRAIN) {
         EXPECT_LT(num_with_center_value, 10);
@@ -149,6 +201,7 @@ class DataLayerTest : public ::testing::Test {
     data_param->set_crop_size(1);
     data_param->set_mirror(true);
     data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
 
     // Get crop sequence with Caffe seed 1701.
     Caffe::set_random_seed(seed_);
@@ -199,6 +252,7 @@ class DataLayerTest : public ::testing::Test {
     data_param->set_crop_size(1);
     data_param->set_mirror(true);
     data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
 
     // Get crop sequence with Caffe seed 1701, srand seed 1701.
     Caffe::set_random_seed(seed_);
@@ -223,8 +277,8 @@ class DataLayerTest : public ::testing::Test {
       }
     }  // destroy 1st data layer and unlock the leveldb
 
-    // Get crop sequence continuing from previous Caffe RNG state;
-    // reseed srand with 1701. Check that the sequence differs from the original.
+    // Get crop sequence continuing from previous Caffe RNG state; reseed
+    // srand with 1701. Check that the sequence differs from the original.
     srand(seed_);
     DataLayer<Dtype> layer2(param);
     layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
@@ -246,6 +300,7 @@ class DataLayerTest : public ::testing::Test {
 
   virtual ~DataLayerTest() { delete blob_top_data_; delete blob_top_label_; }
 
+  DataParameter_DB backend_;
   shared_ptr<string> filename_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
@@ -257,21 +312,21 @@ class DataLayerTest : public ::testing::Test {
 typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(DataLayerTest, Dtypes);
 
-TYPED_TEST(DataLayerTest, TestReadCPU) {
+TYPED_TEST(DataLayerTest, TestReadLevelDBCPU) {
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = false;  // all pixels the same; images different
   this->FillLevelDB(unique_pixels);
   this->TestRead();
 }
 
-TYPED_TEST(DataLayerTest, TestReadGPU) {
+TYPED_TEST(DataLayerTest, TestReadLevelDBGPU) {
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = false;  // all pixels the same; images different
   this->FillLevelDB(unique_pixels);
   this->TestRead();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTrainCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDBCPU) {
   Caffe::set_phase(Caffe::TRAIN);
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -279,7 +334,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainCPU) {
   this->TestReadCrop();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTrainGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDBGPU) {
   Caffe::set_phase(Caffe::TRAIN);
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -289,7 +344,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainGPU) {
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDBCPU) {
   Caffe::set_phase(Caffe::TRAIN);
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -299,7 +354,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededCPU) {
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDBGPU) {
   Caffe::set_phase(Caffe::TRAIN);
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -309,7 +364,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededGPU) {
 
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDBCPU) {
   Caffe::set_phase(Caffe::TRAIN);
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -319,7 +374,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededCPU) {
 
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDBGPU) {
   Caffe::set_phase(Caffe::TRAIN);
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -327,7 +382,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededGPU) {
   this->TestReadCropTrainSequenceUnseeded();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTestCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTestLevelDBCPU) {
   Caffe::set_phase(Caffe::TEST);
   Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -335,7 +390,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTestCPU) {
   this->TestReadCrop();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTestGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTestLevelDBGPU) {
   Caffe::set_phase(Caffe::TEST);
   Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -343,4 +398,90 @@ TYPED_TEST(DataLayerTest, TestReadCropTestGPU) {
   this->TestReadCrop();
 }
 
+TYPED_TEST(DataLayerTest, TestReadLMDBCPU) {
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = false;  // all pixels the same; images different
+  this->FillLMDB(unique_pixels);
+  this->TestRead();
+}
+
+TYPED_TEST(DataLayerTest, TestReadLMDBGPU) {
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = false;  // all pixels the same; images different
+  this->FillLMDB(unique_pixels);
+  this->TestRead();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTrainLMDBCPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCrop();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTrainLMDBGPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCrop();
+}
+
+// Test that the sequence of random crops is consistent when using
+// Caffe::set_random_seed.
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDBCPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCropTrainSequenceSeeded();
+}
+
+// Test that the sequence of random crops is consistent when using
+// Caffe::set_random_seed.
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDBGPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCropTrainSequenceSeeded();
+}
+
+// Test that the sequence of random crops differs across iterations when
+// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDBCPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCropTrainSequenceUnseeded();
+}
+
+// Test that the sequence of random crops differs across iterations when
+// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDBGPU) {
+  Caffe::set_phase(Caffe::TRAIN);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCropTrainSequenceUnseeded();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTestLMDBCPU) {
+  Caffe::set_phase(Caffe::TEST);
+  Caffe::set_mode(Caffe::CPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCrop();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTestLMDBGPU) {
+  Caffe::set_phase(Caffe::TEST);
+  Caffe::set_mode(Caffe::GPU);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCrop();
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_layer_lmdb.cpp b/src/caffe/test/test_data_layer_lmdb.cpp
deleted file mode 100644
index f3f9d723b5b..00000000000
--- a/src/caffe/test/test_data_layer_lmdb.cpp
+++ /dev/null
@@ -1,568 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <sys/stat.h>
-#include <string>
-#include <vector>
-
-#include "cuda_runtime.h"
-#include "leveldb/db.h"
-#include "lmdb.h"
-#include "gtest/gtest.h"
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/test/test_caffe_main.hpp"
-
-using std::string;
-using std::stringstream;
-
-namespace caffe {
-
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-template <typename Dtype>
-class DataLayerTestLmdb : public ::testing::Test {
- protected:
-  DataLayerTestLmdb()
-      : blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()),
-        filename_(new string(tmpnam(NULL))),
-        seed_(1701) {}
-  virtual void SetUp() {
-    blob_top_vec_.push_back(blob_top_data_);
-    blob_top_vec_.push_back(blob_top_label_);
-  }
-
-  // Fill the LMDB with data: if unique_pixels, each pixel is unique but
-  // all images are the same; else each image is unique but all pixels within
-  // an image are the same.
-
-  void FillLMDB(const bool unique_pixels) {
-    LOG(INFO) << "Using temporary lmdb " << *filename_;
-    CHECK_EQ(mkdir(filename_->c_str(), 0744), 0) << "mkdir " << filename_
-                                                 << "failed";
-    MDB_env *env;
-    MDB_dbi dbi;
-    MDB_val mdbkey, mdbdata;
-    MDB_txn *txn;
-    CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(env, NULL, 0, &txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(txn, NULL, 0, &dbi), MDB_SUCCESS) << "mdb_open failed";
-
-    for (int i = 0; i < 5; ++i) {
-      Datum datum;
-      datum.set_label(i);
-      datum.set_channels(2);
-      datum.set_height(3);
-      datum.set_width(4);
-      std::string* data = datum.mutable_data();
-      for (int j = 0; j < 24; ++j) {
-        int datum = unique_pixels ? j : i;
-        data->push_back(static_cast<uint8_t>(datum));
-      }
-      stringstream ss;
-      ss << i;
-
-      string value;
-      datum.SerializeToString(&value);
-      mdbdata.mv_size = value.size();
-      mdbdata.mv_data = reinterpret_cast<void*>(&value[0]);
-      string keystr = ss.str();
-      mdbkey.mv_size = keystr.size();
-      mdbkey.mv_data = reinterpret_cast<void*>(&keystr[0]);
-      CHECK_EQ(mdb_put(txn, dbi, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
-          << "mdb_put failed";
-    }
-    CHECK_EQ(mdb_txn_commit(txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-    mdb_close(env, dbi);
-    mdb_env_close(env);
-  }
-
-  virtual ~DataLayerTestLmdb() {
-    delete blob_top_data_;
-    delete blob_top_label_;
-  }
-
-  shared_ptr<string> filename_;
-  Blob<Dtype>* const blob_top_data_;
-  Blob<Dtype>* const blob_top_label_;
-  vector<Blob<Dtype>*> blob_bottom_vec_;
-  vector<Blob<Dtype>*> blob_top_vec_;
-  int seed_;
-};
-
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(DataLayerTestLmdb, Dtypes);
-
-TYPED_TEST(DataLayerTestLmdb, TestReadCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLMDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 3);
-  EXPECT_EQ(this->blob_top_data_->width(), 4);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 100; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 24; ++j) {
-        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
-}
-
-TYPED_TEST(DataLayerTestLmdb, TestReadGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLMDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 3);
-  EXPECT_EQ(this->blob_top_data_->width(), 4);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 100; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 24; ++j) {
-        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
-}
-
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_with_center_value = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        num_with_center_value +=
-            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    // Check we did not get the center crop all 10 times (occurs with
-    // probability 1-1/12^10 in working implementation).
-    EXPECT_LT(num_with_center_value, 10);
-  }
-}
-
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_with_center_value = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        num_with_center_value +=
-            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    // Check we did not get the center crop all 10 times (occurs with
-    // probability 1-1/12^10 in working implementation).
-    EXPECT_LT(num_with_center_value, 10);
-  }
-}
-
-// Test that the sequence of random crops is consistent when using
-// Caffe::set_random_seed.
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceSeededCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  // Get crop sequence with Caffe seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence after reseeding Caffe with 1701.
-  // Check that the sequence is the same as the original.
-  Caffe::set_random_seed(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
-                  this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
-}
-
-// Test that the sequence of random crops is consistent when using
-// Caffe::set_random_seed.
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceSeededGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  // Get crop sequence with Caffe seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence after reseeding Caffe with 1701.
-  // Check that the sequence is the same as the original.
-  Caffe::set_random_seed(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
-                  this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
-}
-
-// Test that the sequence of random crops differs across iterations when
-// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceUnseededCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  // Get crop sequence with Caffe seed 1701, srand seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  srand(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence continuing from previous Caffe RNG state;
-  // reseed srand with 1701. Check that the sequence differs from the original.
-  srand(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_sequence_matches = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
-                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    EXPECT_LT(num_sequence_matches, 10);
-  }
-}
-
-// Test that the sequence of random crops differs across iterations when
-// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTrainSequenceUnseededGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  // Get crop sequence with Caffe seed 1701, srand seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  srand(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence continuing from previous Caffe RNG state;
-  // reseed srand with 1701. Check that the sequence differs from the original.
-  srand(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_sequence_matches = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
-                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    EXPECT_LT(num_sequence_matches, 10);
-  }
-}
-
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTestCPU) {
-  Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
-}
-
-TYPED_TEST(DataLayerTestLmdb, TestReadCropTestGPU) {
-  Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  data_param->set_backend(DataParameter_DB_LMDB);
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
-}
-
-}  // namespace caffe

From 27ece9381607baf058ae14b151a789b4d7be1a6a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 13 Jun 2014 11:42:40 -0700
Subject: [PATCH 0124/2053] add net surgery link to docs (+ drop old comment)

---
 docs/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 6857c4e5319..4665425e6ad 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -10,8 +10,6 @@ Caffe is released under [the BSD 2-Clause license](https://github.com/BVLC/caffe
 
 Check out the [classification demo](http://demo.caffe.berkeleyvision.org/)!
 
-<!-- BVLC hosts a quick [classification demo](http://demo.caffe.berkeleyvision.org/) using Caffe. -->
-
 ## Why Caffe?
 
 Caffe aims to provide computer vision scientists and practitioners with a **clean and modifiable implementation** of state-of-the-art deep learning algorithms.
@@ -35,6 +33,7 @@ Even in CPU mode, computing predictions on an image takes only 20 ms when images
 * [Image Classification \[notebook\]][imagenet_classification]: classify images with the pretrained ImageNet model by the Python interface.
 * [Detection \[notebook\]][detection]: run a pretrained model as a detector in Python.
 * [Visualizing Features and Filters \[notebook\]][visualizing_filters]: extracting features and visualizing trained filters with an example image, viewed layer-by-layer.
+* [Editing Model Parameters \[notebook\]][net_surgery]: how to do net surgery and manually change model parameters.
 * [LeNet / MNIST Demo](/mnist.html): end-to-end training and testing of LeNet on MNIST.
 * [CIFAR-10 Demo](/cifar10.html): training and testing on the CIFAR-10 data.
 * [Training ImageNet](/imagenet_training.html): recipe for end-to-end training of an ImageNet classifier.
@@ -43,6 +42,7 @@ Even in CPU mode, computing predictions on an image takes only 20 ms when images
 [imagenet_classification]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_classification.ipynb
 [detection]:   http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb
 [visualizing_filters]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb
+[net_surgery]:  http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/net_surgery.ipynb
 
 ## Citing Caffe
 

From e0ce93383e7b09d1f90db89b10f269a47fec360e Mon Sep 17 00:00:00 2001
From: linmin <mavenlin@gmail.com>
Date: Sat, 14 Jun 2014 16:15:18 +0800
Subject: [PATCH 0125/2053] add lmdb support for convert_imageset

---
 tools/convert_imageset.cpp | 112 ++++++++++++++++++++++++++++++-------
 1 file changed, 92 insertions(+), 20 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 524d1950cb0..a381eaf4c03 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -17,6 +17,8 @@
 #include <glog/logging.h>
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
+#include <lmdb.h>
+#include <sys/stat.h>
 
 #include <algorithm>
 #include <fstream>  // NOLINT(readability/streams)
@@ -33,12 +35,13 @@ using std::string;
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 4 || argc > 8) {
+  if (argc < 4 || argc > 9) {
     printf("Convert a set of images to the leveldb format used\n"
         "as input for Caffe.\n"
         "Usage:\n"
         "    convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME"
-        " RANDOM_SHUFFLE_DATA[0 or 1] [resize_height] [resize_width]\n"
+        " RANDOM_SHUFFLE_DATA[0 or 1] DB_BACKEND[leveldb or lmdb]"
+        " [resize_height] [resize_width]\n"
         "The ImageNet dataset for the training demo is at\n"
         "    http://www.image-net.org/download-images\n");
     return 1;
@@ -60,33 +63,71 @@ int main(int argc, char** argv) {
     std::random_shuffle(lines.begin(), lines.end());
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
-  int resize_height = 0;
-  int resize_width = 0;
+
+  string db_backend = "leveldb";
   if (argc >= (arg_offset+6)) {
-    resize_height = atoi(argv[arg_offset+5]);
+    db_backend = string(argv[arg_offset+5]);
+    if (db_backend != "leveldb" || db_backend != "lmdb") {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
   }
+
+  int resize_height = 0;
+  int resize_width = 0;
   if (argc >= (arg_offset+7)) {
-    resize_width = atoi(argv[arg_offset+6]);
+    resize_height = atoi(argv[arg_offset+6]);
+  }
+  if (argc >= (arg_offset+8)) {
+    resize_width = atoi(argv[arg_offset+7]);
   }
 
+  // Open new db
+  // lmdb
+  MDB_env *mdb_env;
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_data;
+  MDB_txn *mdb_txn;
+  // leveldb
   leveldb::DB* db;
   leveldb::Options options;
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  LOG(INFO) << "Opening leveldb " << argv[arg_offset+3];
-  leveldb::Status status = leveldb::DB::Open(
-      options, argv[arg_offset+3], &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << argv[arg_offset+3];
+  leveldb::WriteBatch* batch;
 
+  // Open db
+  if (db_backend == "leveldb") {  // leveldb
+    LOG(INFO) << "Opening leveldb " << argv[arg_offset+3];
+    leveldb::Status status = leveldb::DB::Open(
+        options, argv[arg_offset+3], &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << argv[arg_offset+3];
+    batch = new leveldb::WriteBatch();
+  } else if (db_backend == "lmdb") {  // lmdb
+    LOG(INFO) << "Opening lmdb " << argv[arg_offset+3];
+    CHECK_EQ(mkdir(argv[arg_offset+3], 0744), 0)
+        << "mkdir " << argv[arg_offset+3] << "failed";
+    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
+        << "mdb_env_set_mapsize failed";
+    CHECK_EQ(mdb_env_open(mdb_env, argv[3], 0, 0664), MDB_SUCCESS)
+        << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
+        << "mdb_open failed";
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
+
+  // Storing to db
   string root_folder(argv[arg_offset+1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;
   char key_cstr[kMaxKeyLength];
-  leveldb::WriteBatch* batch = new leveldb::WriteBatch();
   int data_size;
   bool data_size_initialized = false;
+
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     if (!ReadImageToDatum(root_folder + lines[line_id].first,
         lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
@@ -104,23 +145,54 @@ int main(int argc, char** argv) {
     snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
     string value;
-    // get the value
     datum.SerializeToString(&value);
-    batch->Put(string(key_cstr), value);
+    string keystr(key_cstr);
+
+    // Put in db
+    if (db_backend == "leveldb") {  // leveldb
+      batch->Put(keystr, value);
+    } else if (db_backend == "lmdb") {  // lmdb
+      mdb_data.mv_size = value.size();
+      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
+      mdb_key.mv_size = keystr.size();
+      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
+          << "mdb_put failed";
+    } else {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
+
     if (++count % 1000 == 0) {
-      db->Write(leveldb::WriteOptions(), batch);
+      // Commit txn
+      if (db_backend == "leveldb") {  // leveldb
+        db->Write(leveldb::WriteOptions(), batch);
+        delete batch;
+        batch = new leveldb::WriteBatch();
+      } else if (db_backend == "lmdb") {  // lmdb
+        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
+            << "mdb_txn_commit failed";
+        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+            << "mdb_txn_begin failed";
+      } else {
+        LOG(FATAL) << "Unknown db backend " << db_backend;
+      }
       LOG(ERROR) << "Processed " << count << " files.";
-      delete batch;
-      batch = new leveldb::WriteBatch();
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    db->Write(leveldb::WriteOptions(), batch);
+    if (db_backend == "leveldb") {  // leveldb
+      db->Write(leveldb::WriteOptions(), batch);
+      delete batch;
+      delete db;
+    } else if (db_backend == "lmdb") {  // lmdb
+      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
+      mdb_close(mdb_env, mdb_dbi);
+      mdb_env_close(mdb_env);
+    } else {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
     LOG(ERROR) << "Processed " << count << " files.";
   }
-
-  delete batch;
-  delete db;
   return 0;
 }

From 01481d581b7b333c15f00f8c3597194eb285eeb9 Mon Sep 17 00:00:00 2001
From: linmin <mavenlin@gmail.com>
Date: Sat, 14 Jun 2014 16:15:44 +0800
Subject: [PATCH 0126/2053] add lmdb support for compute_image_mean

---
 tools/compute_image_mean.cpp | 145 +++++++++++++++++++++++++++--------
 1 file changed, 115 insertions(+), 30 deletions(-)

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 7cf5fe5f222..5b212b66edd 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -2,6 +2,7 @@
 
 #include <glog/logging.h>
 #include <leveldb/db.h>
+#include <lmdb.h>
 #include <stdint.h>
 
 #include <algorithm>
@@ -16,28 +17,69 @@ using std::max;
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc != 3) {
-    LOG(ERROR) << "Usage: compute_image_mean input_leveldb output_file";
+  if (argc < 3 || argc > 4) {
+    LOG(ERROR) << "Usage: compute_image_mean input_leveldb output_file"
+               << " db_backend[leveldb or lmdb]";
     return 1;
   }
 
+  string db_backend = "leveldb";
+  if (argc == 4) {
+    db_backend = string(argv[3]);
+  }
+
+  // leveldb
   leveldb::DB* db;
   leveldb::Options options;
   options.create_if_missing = false;
+  leveldb::Iterator* it;
+  // lmdb
+  MDB_env* mdb_env;
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_value;
+  MDB_txn* mdb_txn;
+  MDB_cursor* mdb_cursor;
 
-  LOG(INFO) << "Opening leveldb " << argv[1];
-  leveldb::Status status = leveldb::DB::Open(
-      options, argv[1], &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
+  // Open db
+  if (db_backend == "leveldb") {  // leveldb
+    LOG(INFO) << "Opening leveldb " << argv[1];
+    leveldb::Status status = leveldb::DB::Open(
+        options, argv[1], &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
+    leveldb::ReadOptions read_options;
+    read_options.fill_cache = false;
+    it = db->NewIterator(read_options);
+    it->SeekToFirst();
+  } else if (db_backend == "lmdb") {  // lmdb
+    LOG(INFO) << "Opening lmdb " << argv[1];
+    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS); // 1TB
+    CHECK_EQ(mdb_env_open(mdb_env, argv[1], MDB_RDONLY, 0664),
+        MDB_SUCCESS) << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, MDB_RDONLY, &mdb_txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
+        << "mdb_open failed";
+    CHECK_EQ(mdb_cursor_open(mdb_txn, mdb_dbi, &mdb_cursor), MDB_SUCCESS)
+        << "mdb_cursor_open failed";
+    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
+        MDB_SUCCESS);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
 
-  leveldb::ReadOptions read_options;
-  read_options.fill_cache = false;
-  leveldb::Iterator* it = db->NewIterator(read_options);
-  it->SeekToFirst();
   Datum datum;
   BlobProto sum_blob;
   int count = 0;
-  datum.ParseFromString(it->value().ToString());
+  // load first datum
+  if (db_backend == "leveldb") {
+    datum.ParseFromString(it->value().ToString());
+  } else if (db_backend == "lmdb") {
+    datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
+
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());
   sum_blob.set_height(datum.height());
@@ -49,28 +91,61 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  for (it->SeekToFirst(); it->Valid(); it->Next()) {
-    // just a dummy operation
-    datum.ParseFromString(it->value().ToString());
-    const string& data = datum.data();
-    size_in_datum = std::max<int>(datum.data().size(), datum.float_data_size());
-    CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
-        size_in_datum;
-    if (data.size() != 0) {
-      for (int i = 0; i < size_in_datum; ++i) {
-        sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
+  if (db_backend == "leveldb") {  // leveldb
+    for (it->SeekToFirst(); it->Valid(); it->Next()) {
+      // just a dummy operation
+      datum.ParseFromString(it->value().ToString());
+      const string& data = datum.data();
+      size_in_datum = std::max<int>(datum.data().size(),
+          datum.float_data_size());
+      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
+          size_in_datum;
+      if (data.size() != 0) {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
+        }
+      } else {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) +
+              static_cast<float>(datum.float_data(i)));
+        }
       }
-    } else {
-      for (int i = 0; i < size_in_datum; ++i) {
-        sum_blob.set_data(i, sum_blob.data(i) +
-            static_cast<float>(datum.float_data(i)));
+      ++count;
+      if (count % 10000 == 0) {
+        LOG(ERROR) << "Processed " << count << " files.";
       }
     }
-    ++count;
-    if (count % 10000 == 0) {
-      LOG(ERROR) << "Processed " << count << " files.";
-    }
+  } else if (db_backend == "lmdb") {  // lmdb
+    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
+        MDB_SUCCESS);
+    do {
+      // just a dummy operation
+      datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
+      const string& data = datum.data();
+      size_in_datum = std::max<int>(datum.data().size(),
+          datum.float_data_size());
+      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
+          size_in_datum;
+      if (data.size() != 0) {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
+        }
+      } else {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) +
+              static_cast<float>(datum.float_data(i)));
+        }
+      }
+      ++count;
+      if (count % 10000 == 0) {
+        LOG(ERROR) << "Processed " << count << " files.";
+      }
+    } while (mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_NEXT)
+        == MDB_SUCCESS);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
   }
+
   if (count % 10000 != 0) {
     LOG(ERROR) << "Processed " << count << " files.";
   }
@@ -81,6 +156,16 @@ int main(int argc, char** argv) {
   LOG(INFO) << "Write to " << argv[2];
   WriteProtoToBinaryFile(sum_blob, argv[2]);
 
-  delete db;
+  // Clean up
+  if (db_backend == "leveldb") {
+    delete db;
+  } else if (db_backend == "lmdb") {
+    mdb_cursor_close(mdb_cursor);
+    mdb_close(mdb_env, mdb_dbi);
+    mdb_txn_abort(mdb_txn);
+    mdb_env_close(mdb_env);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
   return 0;
 }

From dbc5ebf31b529db5690dc0d1dad1498ff269e799 Mon Sep 17 00:00:00 2001
From: linmin <mavenlin@gmail.com>
Date: Sat, 14 Jun 2014 16:35:48 +0800
Subject: [PATCH 0127/2053] fix string compare error

---
 tools/convert_imageset.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index a381eaf4c03..aa9515514c3 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -67,7 +67,7 @@ int main(int argc, char** argv) {
   string db_backend = "leveldb";
   if (argc >= (arg_offset+6)) {
     db_backend = string(argv[arg_offset+5]);
-    if (db_backend != "leveldb" || db_backend != "lmdb") {
+    if (!(db_backend == "leveldb") && !(db_backend == "lmdb")) {
       LOG(FATAL) << "Unknown db backend " << db_backend;
     }
   }

From 40837161e382812b331213f9694fa529b4ae7a4b Mon Sep 17 00:00:00 2001
From: leelurch <jyegerlehner@yahoo.com>
Date: Sun, 15 Jun 2014 20:07:04 -0500
Subject: [PATCH 0128/2053] Add comment for how to set the CUDA path when cuda
 tools are installed by the package manager.

---
 Makefile.config.example | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/Makefile.config.example b/Makefile.config.example
index f9d8be80100..9754129beb7 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -3,6 +3,9 @@
 
 # CUDA directory contains bin/ and lib/ directories that we need.
 CUDA_DIR := /usr/local/cuda
+# On Ubuntu 14.04, if cuda tools are installed via
+# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
+# CUDA_DIR := /usr
 
 # CUDA architecture setting: going with all of them.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \

From 47ef5629d8ad73aba4a262612ea4c3f537b08c17 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 16 Jun 2014 15:20:17 -0700
Subject: [PATCH 0129/2053] in Caffe::SetDevice, call cudaSetDevice before Get

Otherwise initialization will be performed on whichever device is
default.
---
 src/caffe/common.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 7edb3a455ac..218feb7d877 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -69,11 +69,13 @@ void Caffe::SetDevice(const int device_id) {
   if (current_device == device_id) {
     return;
   }
+  // The call to cudaSetDevice must come before any calls to Get, which
+  // may perform initialization using the GPU.
+  CUDA_CHECK(cudaSetDevice(device_id));
   if (Get().cublas_handle_) CUBLAS_CHECK(cublasDestroy(Get().cublas_handle_));
   if (Get().curand_generator_) {
     CURAND_CHECK(curandDestroyGenerator(Get().curand_generator_));
   }
-  CUDA_CHECK(cudaSetDevice(device_id));
   CUBLAS_CHECK(cublasCreate(&Get().cublas_handle_));
   CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
       CURAND_RNG_PSEUDO_DEFAULT));

From e43947a44cddaf975fd6a969eb4dd47ee40e7f9c Mon Sep 17 00:00:00 2001
From: Craig Quiter <cquiter@gmail.com>
Date: Tue, 17 Jun 2014 00:00:01 -0700
Subject: [PATCH 0130/2053] Add comment to Makefile.config.example about DEBUG
 flag issue in OSX per #171.

---
 Makefile.config.example | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index f9d8be80100..9a75f5e55d5 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -46,7 +46,7 @@ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 BUILD_DIR := build
 DISTRIBUTE_DIR := distribute
 
-# Uncomment for debugging.
+# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
 # DEBUG := 1
 
 # The ID of the GPU that 'make runtest' will use to run unit tests.

From 3686f872f26e92e5c48e04e24bb89029b225984e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 19 Jun 2014 12:55:48 -0700
Subject: [PATCH 0131/2053] Set device_id at the begining of Solver.Init() to
 avoid using memory in the default GPU

---
 src/caffe/solver.cpp | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 6a8f18f1331..769618175ac 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -36,6 +36,11 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   LOG(INFO) << "Initializing solver from parameters: " << std::endl
             << param.DebugString();
   param_ = param;
+  if (param_.solver_mode() == SolverParameter_SolverMode_GPU &&
+      param_.has_device_id()) {
+    Caffe::SetDevice(param_.device_id());
+  }
+  Caffe::set_mode(Caffe::Brew(param_.solver_mode()));
   if (param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
@@ -74,14 +79,8 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   LOG(INFO) << "Solver scaffolding done.";
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
-  Caffe::set_mode(Caffe::Brew(param_.solver_mode()));
-  if (param_.solver_mode() == SolverParameter_SolverMode_GPU &&
-      param_.has_device_id()) {
-    Caffe::SetDevice(param_.device_id());
-  }
   Caffe::set_phase(Caffe::TRAIN);
   LOG(INFO) << "Solving " << net_->name();
   PreSolve();

From f5853bc2eb85dda3e44269e6de8a539d0f54d5a1 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 19 Jun 2014 16:40:32 -0700
Subject: [PATCH 0132/2053] Now Loss layers would return the loss in the top
 blob if requested

---
 include/caffe/loss_layers.hpp                         | 4 ++--
 src/caffe/layers/euclidean_loss_layer.cpp             | 3 +++
 src/caffe/layers/infogain_loss_layer.cpp              | 3 +++
 src/caffe/layers/loss_layer.cpp                       | 4 ++++
 src/caffe/layers/multinomial_logistic_loss_layer.cpp  | 3 +++
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp | 3 +++
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu  | 3 +++
 src/caffe/layers/softmax_loss_layer.cpp               | 3 +++
 8 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 381bf0f4f8f..dd06ca07187 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -37,7 +37,7 @@ class LossLayer : public Layer<Dtype> {
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {}
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
+  virtual inline int MaxTopBlobs() const { return 1; }
 };
 
 /* SigmoidCrossEntropyLossLayer
@@ -166,7 +166,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
 
 /* AccuracyLayer
   Note: not an actual loss layer! Does not implement backwards step.
-  Computes the accuracy and logprob of a with respect to b.
+  Computes the accuracy of a with respect to b.
 */
 template <typename Dtype>
 class AccuracyLayer : public Layer<Dtype> {
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index a894d470c64..766294997f0 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -35,6 +35,9 @@ Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       diff_.mutable_cpu_data());
   Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data());
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss;
+  }
   return loss;
 }
 
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index ab6e67d73b1..3e7fc4f812b 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -48,6 +48,9 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 14ea975ad0d..ac8ad216732 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -20,6 +20,10 @@ void LossLayer<Dtype>::SetUp(
   Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
+  if (top->size() == 1) {
+  // Layers should copy the loss in the top blob
+    (*top)[0]->Reshape(1, 1, 1, 1);
+  }
   FurtherSetUp(bottom, top);
 }
 
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 6486621d8aa..5a408795d6d 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -35,6 +35,9 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
     Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
+  if (top->size() == 1){
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index a638684f3b6..955581d8875 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -41,6 +41,9 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 61004541fce..0caed2b83bd 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -29,6 +29,9 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index ef6eebabadd..8a37efee395 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -37,6 +37,9 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     loss += -log(max(prob_data[i * dim + static_cast<int>(label[i])],
                      Dtype(FLT_MIN)));
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 

From 03cd25ce658c0270dd8bcf4c4cc0ff206f87aa05 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 19 Jun 2014 16:45:08 -0700
Subject: [PATCH 0133/2053] Modified test_net to check loss layer with top

---
 src/caffe/test/test_net.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 1e43a038455..7f2f67b59c6 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -64,6 +64,7 @@ class NetTest : public ::testing::Test {
         "  type: SOFTMAX_LOSS "
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
+        "  top: 'top_loss' "
         "} ";
     NetParameter param;
     CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
@@ -81,6 +82,7 @@ TYPED_TEST(NetTest, TestHasBlob) {
   EXPECT_TRUE(this->net_->has_blob("label"));
   EXPECT_TRUE(this->net_->has_blob("innerproduct"));
   EXPECT_FALSE(this->net_->has_blob("loss"));
+  EXPECT_TRUE(this->net_->has_blob("top_loss"));
 }
 
 TYPED_TEST(NetTest, TestGetBlob) {
@@ -88,6 +90,7 @@ TYPED_TEST(NetTest, TestGetBlob) {
   EXPECT_EQ(this->net_->blob_by_name("label"), this->net_->blobs()[1]);
   EXPECT_EQ(this->net_->blob_by_name("innerproduct"), this->net_->blobs()[2]);
   EXPECT_FALSE(this->net_->blob_by_name("loss"));
+  EXPECT_EQ(this->net_->blob_by_name("top_loss"), this->net_->blobs()[3]);
 }
 
 TYPED_TEST(NetTest, TestHasLayer) {

From 3d0bc1acbd98de289f038fd836dbab27ffadb4cc Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 20 Jun 2014 01:05:41 -0700
Subject: [PATCH 0134/2053] remove erroneous comment in ArgMaxLayer

---
 src/caffe/layers/argmax_layer.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 70ccfff5a36..cc31c0f52d8 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -31,7 +31,6 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
   for (int i = 0; i < num; ++i) {
-    // Accuracy
     Dtype max_val = -FLT_MAX;
     int max_ind = 0;
     for (int j = 0; j < dim; ++j) {

From bb5f9e67522064eeabf915188788bd27535f377a Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 17 Jun 2014 11:43:10 +0800
Subject: [PATCH 0135/2053] Fix the condition prefetch_needs_rand in the
 ImageDataLayer

---
 src/caffe/layers/image_data_layer.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 9a79e72fedc..1f7368e7e4d 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -236,9 +236,7 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
   phase_ = Caffe::phase();
   const bool prefetch_needs_rand =
       this->layer_param_.image_data_param().shuffle() ||
-          ((phase_ == Caffe::TRAIN) &&
-           (this->layer_param_.image_data_param().mirror() ||
-            this->layer_param_.image_data_param().crop_size()));
+      this->layer_param_.image_data_param().crop_size();
   if (prefetch_needs_rand) {
     const unsigned int prefetch_rng_seed = caffe_rng_rand();
     prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));

From ea77c96356a0a432ea990b9dad579a85c5dfe6fa Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 17 Jun 2014 13:35:59 +0800
Subject: [PATCH 0136/2053] Initialize the RNG generator with an orthogonally
 newed Generator

---
 src/caffe/common.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 218feb7d877..6eadfc7917e 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -126,7 +126,7 @@ class Caffe::RNG::Generator {
   shared_ptr<caffe::rng_t> rng_;
 };
 
-Caffe::RNG::RNG() : generator_(new Generator) { }
+Caffe::RNG::RNG() : generator_(new Generator()) { }
 
 Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }
 

From 21e4a502e7b81d82aa0be8acb58dd2ba7f384500 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Mon, 2 Jun 2014 13:12:48 +0800
Subject: [PATCH 0137/2053] Extract GPU code out of
 SyncedMemoryTest::TestCPUWrite

---
 src/caffe/test/test_syncedmem.cpp | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index cd7475898d1..d0d6c31517c 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -39,6 +39,20 @@ TEST_F(SyncedMemoryTest, TestCPUWrite) {
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 1);
   }
+  // do another round
+  cpu_data = mem.mutable_cpu_data();
+  EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
+  memset(cpu_data, 2, mem.size());
+  for (int i = 0; i < mem.size(); ++i) {
+    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 2);
+  }
+}
+
+TEST_F(SyncedMemoryTest, TestGPURead) {
+  SyncedMemory mem(10);
+  void* cpu_data = mem.mutable_cpu_data();
+  EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
+  memset(cpu_data, 1, mem.size());
   const void* gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
@@ -52,9 +66,6 @@ TEST_F(SyncedMemoryTest, TestCPUWrite) {
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 2, mem.size());
-  for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 2);
-  }
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same

From db93727325fa0091dd505975c852e39850f0b99d Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Mon, 2 Jun 2014 20:07:48 +0800
Subject: [PATCH 0138/2053] Separate TestForward into Test{CPU, GPU}Forward in
 HDF5OutputLayerTest

---
 src/caffe/test/test_hdf5_output_layer.cpp | 129 +++++++++++++++-------
 1 file changed, 91 insertions(+), 38 deletions(-)

diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 9f793f2fce6..45cf11ba897 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -19,7 +19,7 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
+template<typename Dtype>
 class HDF5OutputLayerTest : public ::testing::Test {
  protected:
   HDF5OutputLayerTest()
@@ -30,7 +30,8 @@ class HDF5OutputLayerTest : public ::testing::Test {
         num_(5),
         channels_(8),
         height_(5),
-        width_(5) {}
+        width_(5) {
+  }
 
   virtual ~HDF5OutputLayerTest() {
     delete blob_data_;
@@ -51,9 +52,9 @@ class HDF5OutputLayerTest : public ::testing::Test {
   int width_;
 };
 
-template <typename Dtype>
-void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(
-    const Blob<Dtype>& b1, const Blob<Dtype>& b2) {
+template<typename Dtype>
+void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(const Blob<Dtype>& b1,
+                                                const Blob<Dtype>& b2) {
   EXPECT_EQ(b1.num(), b2.num());
   EXPECT_EQ(b1.channels(), b2.channels());
   EXPECT_EQ(b1.height(), b2.height());
@@ -72,8 +73,61 @@ void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(
 typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(HDF5OutputLayerTest, Dtypes);
 
-TYPED_TEST(HDF5OutputLayerTest, TestForward) {
+/*
+ * TestCPUForward and TestGPUForward are almost identical except for the mode.
+ * They are separated to use with `test_all.testbin --gtest_filter="*CPU*"`.
+ */
+TYPED_TEST(HDF5OutputLayerTest, TestCPUForward) {
+  LOG(INFO) << "Loading HDF5 file " << this->input_file_name_;
+  hid_t file_id = H5Fopen(this->input_file_name_.c_str(), H5F_ACC_RDONLY,
+                          H5P_DEFAULT);
+  ASSERT_GE(file_id, 0)<< "Failed to open HDF5 file" <<
+      this->input_file_name_;
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
+                       this->blob_data_);
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
+                       this->blob_label_);
+  herr_t status = H5Fclose(file_id);
+  EXPECT_GE(status, 0)<< "Failed to close HDF5 file " <<
+      this->input_file_name_;
+  this->blob_bottom_vec_.push_back(this->blob_data_);
+  this->blob_bottom_vec_.push_back(this->blob_label_);
+
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
+  // This code block ensures that the layer is deconstructed and
+  //   the output hdf5 file is closed.
+  {
+    HDF5OutputLayer<TypeParam> layer(param);
+    EXPECT_EQ(layer.file_name(), this->output_file_name_);
+    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  }
+  file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
+                          H5P_DEFAULT);
+  ASSERT_GE(
+    file_id, 0)<< "Failed to open HDF5 file" <<
+          this->input_file_name_;
+
+  Blob<TypeParam>* blob_data = new Blob<TypeParam>();
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
+                       blob_data);
+  this->CheckBlobEqual(*(this->blob_data_), *blob_data);
+
+  Blob<TypeParam>* blob_label = new Blob<TypeParam>();
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
+                       blob_label);
+  this->CheckBlobEqual(*(this->blob_label_), *blob_label);
+
+  status = H5Fclose(file_id);
+  EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
+      this->output_file_name_;
+}
+
+TYPED_TEST(HDF5OutputLayerTest, TestGPUForward) {
   LOG(INFO) << "Loading HDF5 file " << this->input_file_name_;
+
   hid_t file_id = H5Fopen(this->input_file_name_.c_str(), H5F_ACC_RDONLY,
                           H5P_DEFAULT);
   ASSERT_GE(file_id, 0) << "Failed to open HDF5 file" <<
@@ -88,38 +142,37 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
   this->blob_bottom_vec_.push_back(this->blob_data_);
   this->blob_bottom_vec_.push_back(this->blob_label_);
 
-  Caffe::Brew modes[] = { Caffe::CPU, Caffe::GPU };
-  for (int m = 0; m < 2; ++m) {
-    Caffe::set_mode(modes[m]);
-    LayerParameter param;
-    param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
-    // This code block ensures that the layer is deconstructed and
-    //   the output hdf5 file is closed.
-    {
-      HDF5OutputLayer<TypeParam> layer(param);
-      EXPECT_EQ(layer.file_name(), this->output_file_name_);
-      layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-      layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    }
-    hid_t file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
-                            H5P_DEFAULT);
-    ASSERT_GE(file_id, 0) << "Failed to open HDF5 file" <<
-        this->input_file_name_;
-
-    Blob<TypeParam>* blob_data = new Blob<TypeParam>();
-    hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                         blob_data);
-    this->CheckBlobEqual(*(this->blob_data_), *blob_data);
-
-    Blob<TypeParam>* blob_label = new Blob<TypeParam>();
-    hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                         blob_label);
-    this->CheckBlobEqual(*(this->blob_label_), *blob_label);
-
-    herr_t status = H5Fclose(file_id);
-    EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
-        this->output_file_name_;
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter param;
+  param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
+  // This code block ensures that the layer is deconstructed and
+  //   the output hdf5 file is closed.
+  {
+    HDF5OutputLayer<TypeParam> layer(param);
+    EXPECT_EQ(layer.file_name(), this->output_file_name_);
+    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
   }
+  file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
+                          H5P_DEFAULT);
+  ASSERT_GE(
+    file_id, 0)<< "Failed to open HDF5 file" <<
+          this->input_file_name_;
+
+  Blob<TypeParam>* blob_data = new Blob<TypeParam>();
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
+                       blob_data);
+  this->CheckBlobEqual(*(this->blob_data_), *blob_data);
+
+  Blob<TypeParam>* blob_label = new Blob<TypeParam>();
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
+                       blob_label);
+  this->CheckBlobEqual(*(this->blob_label_), *blob_label);
+
+  status = H5Fclose(file_id);
+  EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
+      this->output_file_name_;
 }
 
-}  // namespace caffe
+}
+  // namespace caffe

From 485a96df6c6b2e0774ae90f7df40083cd77b45dd Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sun, 8 Jun 2014 00:00:26 +0800
Subject: [PATCH 0139/2053] Suppress redundant log messages of unavailable
 curand

---
 src/caffe/common.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 218feb7d877..f5600690212 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -50,6 +50,7 @@ void Caffe::set_random_seed(const unsigned int seed) {
   // Curand seed
   // Yangqing's note: simply setting the generator seed does not seem to
   // work on the tesla K20s, so I wrote the ugly reset thing below.
+  static bool curand_availability_logged = false;
   if (Get().curand_generator_) {
     CURAND_CHECK(curandDestroyGenerator(curand_generator()));
     CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
@@ -57,7 +58,11 @@ void Caffe::set_random_seed(const unsigned int seed) {
     CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(curand_generator(),
         seed));
   } else {
-    LOG(ERROR) << "Curand not available. Skipping setting the curand seed.";
+    if (!curand_availability_logged) {
+        LOG(ERROR) <<
+            "Curand not available. Skipping setting the curand seed.";
+        curand_availability_logged = true;
+    }
   }
   // RNG seed
   Get().random_generator_.reset(new RNG(seed));

From b61070e309976146afdd94c5d95c78f43bef2e02 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 10 Jun 2014 09:24:39 +0800
Subject: [PATCH 0140/2053] Revert the namespace ending comment to the same
 line of the bracket

---
 src/caffe/test/test_hdf5_output_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 45cf11ba897..5b396ef551e 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -174,5 +174,4 @@ TYPED_TEST(HDF5OutputLayerTest, TestGPUForward) {
       this->output_file_name_;
 }
 
-}
-  // namespace caffe
+}  // namespace caffe

From 9aa085863762d8b582159bafedc2559608e447c0 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 10 Jun 2014 09:26:31 +0800
Subject: [PATCH 0141/2053] Rename curand_availability_logged according to the
 Google style guide

---
 src/caffe/common.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index f5600690212..88fd3a9c5b6 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -50,7 +50,7 @@ void Caffe::set_random_seed(const unsigned int seed) {
   // Curand seed
   // Yangqing's note: simply setting the generator seed does not seem to
   // work on the tesla K20s, so I wrote the ugly reset thing below.
-  static bool curand_availability_logged = false;
+  static bool g_curand_availability_logged = false;
   if (Get().curand_generator_) {
     CURAND_CHECK(curandDestroyGenerator(curand_generator()));
     CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
@@ -58,10 +58,10 @@ void Caffe::set_random_seed(const unsigned int seed) {
     CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(curand_generator(),
         seed));
   } else {
-    if (!curand_availability_logged) {
+    if (!g_curand_availability_logged) {
         LOG(ERROR) <<
             "Curand not available. Skipping setting the curand seed.";
-        curand_availability_logged = true;
+        g_curand_availability_logged = true;
     }
   }
   // RNG seed

From a3ab2c7aa954d28a88e3c863d28a8287e166776f Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 17 Jun 2014 09:36:46 +0800
Subject: [PATCH 0142/2053] Verify the result of memtest in
 SyncedMemoryTest::TestGPURead

---
 src/caffe/test/test_syncedmem.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index d0d6c31517c..7bbbbab6a57 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -66,6 +66,9 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 2, mem.size());
+  for (int i = 0; i < mem.size(); ++i) {
+    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 2);
+  }
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same

From f2452d8966823bcb993ce64e6045f544d96c3ace Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 18:26:05 -0700
Subject: [PATCH 0143/2053] Merge HingeLoss and L2HingeLoss by adding
 hinge_norm to params

Conflicts:
	src/caffe/layers/loss_layer.cpp
	src/caffe/proto/caffe.proto
	src/caffe/test/test_l2_hinge_loss_layer.cpp
---
 src/caffe/proto/caffe.proto              | 10 ++++++++++
 src/caffe/test/test_hinge_loss_layer.cpp | 24 ++++++++++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b85b49c77f7..f1e82f1bfda 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -194,6 +194,7 @@ message LayerParameter {
   optional PowerParameter power_param = 21;
   optional WindowDataParameter window_data_param = 20;
   optional ThresholdParameter threshold_param = 25;
+  optional HingeLossParameter hinge_loss_param = 29;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -305,6 +306,15 @@ message HDF5OutputParameter {
   optional string file_name = 1;
 }
 
+message HingeLossParameter {
+  enum Norm {
+    L1 = 0;
+    L2 = 1;    
+  }
+  // Specify the Norm to use L1 or L2
+  optional Norm hinge_norm = 1 [default = L1];
+}
+
 // Message that stores parameters used by ImageDataLayer
 message ImageDataParameter {
   // Specify the data source.
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 1725827755f..e862e1eb191 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -70,4 +70,28 @@ TYPED_TEST(HingeLossLayerTest, TestGradientGPU) {
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
+
+TYPED_TEST(HingeLossLayerTest, TestGradientCPUL2) {
+  LayerParameter layer_param;
+  HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
+  hinge_loss_param->set_hinge_norm(HingeLossParameter_Norm_L2);
+  Caffe::set_mode(Caffe::CPU);
+  HingeLossLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701, 1, 0.01);
+  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0, -1, -1);
+}
+
+TYPED_TEST(HingeLossLayerTest, TestGradientGPUL2) {
+  LayerParameter layer_param;
+  HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
+  hinge_loss_param->set_hinge_norm(HingeLossParameter_Norm_L2);
+  Caffe::set_mode(Caffe::GPU);
+  HingeLossLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701, 1, 0.01);
+  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0, -1, -1);
+}
 }  // namespace caffe

From f25687e1be1915d04171c6f5e9c1cdb0e03731c0 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 18:29:14 -0700
Subject: [PATCH 0144/2053] Removed L2HingeLoss class now a case within
 HingeLoss class

Conflicts:
	include/caffe/vision_layers.hpp
	src/caffe/layers/loss_layer.cpp
	src/caffe/proto/caffe.proto
	src/caffe/test/test_l2_hinge_loss_layer.cpp
---
 src/caffe/proto/caffe.proto | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f1e82f1bfda..b15172fa1ef 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -307,9 +307,9 @@ message HDF5OutputParameter {
 }
 
 message HingeLossParameter {
-  enum Norm {
-    L1 = 0;
-    L2 = 1;    
+  enum Norm {    
+    L1 = 1;
+    L2 = 2;    
   }
   // Specify the Norm to use L1 or L2
   optional Norm hinge_norm = 1 [default = L1];

From eaa30176f47e7c57009caf6a4755e67253a6bf9d Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 18:32:49 -0700
Subject: [PATCH 0145/2053] Remove spaces and merge tests into one file

Conflicts:
	src/caffe/layers/loss_layer.cpp
	src/caffe/test/test_hinge_loss_layer.cpp
---
 src/caffe/layers/loss_layer.cpp          | 1 +
 src/caffe/test/test_hinge_loss_layer.cpp | 8 ++++++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 14ea975ad0d..a8e63c443cc 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -21,6 +21,7 @@ void LossLayer<Dtype>::SetUp(
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   FurtherSetUp(bottom, top);
+
 }
 
 INSTANTIATE_CLASS(LossLayer);
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index e862e1eb191..ec74b03c5d7 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -71,8 +71,9 @@ TYPED_TEST(HingeLossLayerTest, TestGradientGPU) {
 }
 
 
-TYPED_TEST(HingeLossLayerTest, TestGradientCPUL2) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL2CPU) {
   LayerParameter layer_param;
+  // Set norm to L2
   HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
   hinge_loss_param->set_hinge_norm(HingeLossParameter_Norm_L2);
   Caffe::set_mode(Caffe::CPU);
@@ -83,8 +84,10 @@ TYPED_TEST(HingeLossLayerTest, TestGradientCPUL2) {
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
-TYPED_TEST(HingeLossLayerTest, TestGradientGPUL2) {
+
+TYPED_TEST(HingeLossLayerTest, TestGradientL2GPU) {
   LayerParameter layer_param;
+  // Set norm to L2
   HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
   hinge_loss_param->set_hinge_norm(HingeLossParameter_Norm_L2);
   Caffe::set_mode(Caffe::GPU);
@@ -94,4 +97,5 @@ TYPED_TEST(HingeLossLayerTest, TestGradientGPUL2) {
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
+
 }  // namespace caffe

From 7be676cfb743bd2dddb2b070da5bf5c52a9cd8d5 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 18:34:24 -0700
Subject: [PATCH 0146/2053] Fixed switch and test l2hingeloss

Conflicts:
	src/caffe/layers/loss_layer.cpp
	src/caffe/test/test_hinge_loss_layer.cpp
---
 src/caffe/test/test_hinge_loss_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index ec74b03c5d7..64657c88dd7 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -79,7 +79,7 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2CPU) {
   Caffe::set_mode(Caffe::CPU);
   HingeLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701, 1, 0.01);
+  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
@@ -93,7 +93,7 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2GPU) {
   Caffe::set_mode(Caffe::GPU);
   HingeLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701, 1, 0.01);
+  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }

From 4094170e85d349c821ce3bf68cf95840ba188745 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 18:35:25 -0700
Subject: [PATCH 0147/2053] Fix the loss to follow the convention

Conflicts:
	src/caffe/layers/loss_layer.cpp
---
 src/caffe/layers/loss_layer.cpp | 246 ++++++++++++++++++++++++++++++++
 1 file changed, 246 insertions(+)

diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index a8e63c443cc..33a221f50d2 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -20,8 +20,254 @@ void LossLayer<Dtype>::SetUp(
   Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
+<<<<<<< HEAD
   FurtherSetUp(bottom, top);
 
+=======
+  CHECK_EQ(bottom[1]->channels(), 1);
+  CHECK_EQ(bottom[1]->height(), 1);
+  CHECK_EQ(bottom[1]->width(), 1);
+}
+
+template <typename Dtype>
+Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* bottom_label = bottom[1]->cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  Dtype loss = 0;
+  for (int i = 0; i < num; ++i) {
+    int label = static_cast<int>(bottom_label[i]);
+    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+    loss -= log(prob);
+  }
+  return loss / num;
+}
+
+template <typename Dtype>
+void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
+    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  int num = (*bottom)[0]->num();
+  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+  memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
+  for (int i = 0; i < num; ++i) {
+    int label = static_cast<int>(bottom_label[i]);
+    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+    bottom_diff[i * dim + label] = -1. / prob / num;
+  }
+}
+
+
+template <typename Dtype>
+void InfogainLossLayer<Dtype>::SetUp(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 0) << "Loss Layer takes no output.";
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
+      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[1]->channels(), 1);
+  CHECK_EQ(bottom[1]->height(), 1);
+  CHECK_EQ(bottom[1]->width(), 1);
+  BlobProto blob_proto;
+  ReadProtoFromBinaryFile(this->layer_param_.infogain_loss_param().source(),
+                          &blob_proto);
+  infogain_.FromProto(blob_proto);
+  CHECK_EQ(infogain_.num(), 1);
+  CHECK_EQ(infogain_.channels(), 1);
+  CHECK_EQ(infogain_.height(), infogain_.width());
+}
+
+
+template <typename Dtype>
+Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* bottom_label = bottom[1]->cpu_data();
+  const Dtype* infogain_mat = infogain_.cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  CHECK_EQ(infogain_.height(), dim);
+  Dtype loss = 0;
+  for (int i = 0; i < num; ++i) {
+    int label = static_cast<int>(bottom_label[i]);
+    for (int j = 0; j < dim; ++j) {
+      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+      loss -= infogain_mat[label * dim + j] * log(prob);
+    }
+  }
+  return loss / num;
+}
+
+template <typename Dtype>
+void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const bool propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+  const Dtype* infogain_mat = infogain_.cpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  int num = (*bottom)[0]->num();
+  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+  CHECK_EQ(infogain_.height(), dim);
+  for (int i = 0; i < num; ++i) {
+    int label = static_cast<int>(bottom_label[i]);
+    for (int j = 0; j < dim; ++j) {
+      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+      bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+    }
+  }
+}
+
+
+template <typename Dtype>
+void EuclideanLossLayer<Dtype>::SetUp(
+  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 0) << "Loss Layer takes no as output.";
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
+      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
+  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
+  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
+  difference_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+}
+
+template <typename Dtype>
+Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  int count = bottom[0]->count();
+  int num = bottom[0]->num();
+  caffe_sub(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(),
+      difference_.mutable_cpu_data());
+  Dtype loss = caffe_cpu_dot(
+      count, difference_.cpu_data(), difference_.cpu_data()) / num / Dtype(2);
+  return loss;
+}
+
+template <typename Dtype>
+void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+  int count = (*bottom)[0]->count();
+  int num = (*bottom)[0]->num();
+  // Compute the gradient
+  caffe_cpu_axpby(count, Dtype(1) / num, difference_.cpu_data(), Dtype(0),
+      (*bottom)[0]->mutable_cpu_diff());
+}
+
+template <typename Dtype>
+void AccuracyLayer<Dtype>::SetUp(
+  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "Accuracy Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 1) << "Accuracy Layer takes 1 output.";
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
+      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[1]->channels(), 1);
+  CHECK_EQ(bottom[1]->height(), 1);
+  CHECK_EQ(bottom[1]->width(), 1);
+  (*top)[0]->Reshape(1, 2, 1, 1);
+}
+
+template <typename Dtype>
+Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  Dtype accuracy = 0;
+  Dtype logprob = 0;
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* bottom_label = bottom[1]->cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  for (int i = 0; i < num; ++i) {
+    // Accuracy
+    Dtype maxval = -FLT_MAX;
+    int max_id = 0;
+    for (int j = 0; j < dim; ++j) {
+      if (bottom_data[i * dim + j] > maxval) {
+        maxval = bottom_data[i * dim + j];
+        max_id = j;
+      }
+    }
+    if (max_id == static_cast<int>(bottom_label[i])) {
+      ++accuracy;
+    }
+    Dtype prob = max(bottom_data[i * dim + static_cast<int>(bottom_label[i])],
+                     Dtype(kLOG_THRESHOLD));
+    logprob -= log(prob);
+  }
+  // LOG(INFO) << "Accuracy: " << accuracy;
+  (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
+  (*top)[0]->mutable_cpu_data()[1] = logprob / num;
+  // Accuracy layer should not be used as a loss function.
+  return Dtype(0);
+}
+
+template <typename Dtype>
+void HingeLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "Hinge Loss Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 0) << "Hinge Loss Layer takes no output.";
+  C_ =  this->layer_param_.hinge_loss_param().c_param();
+}
+
+template <typename Dtype>
+Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  const Dtype* label = bottom[1]->cpu_data();
+  int num = bottom[0]->num();
+  int count = bottom[0]->count();
+  int dim = count / num;
+
+  caffe_copy(count, bottom_data, bottom_diff);
+  for (int i = 0; i < num; ++i) {
+    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
+  }
+  for (int i = 0; i < num; ++i) {
+    for (int j = 0; j < dim; ++j) {
+      bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
+    }
+  }
+  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
+  case HingeLossParameter_Norm_L1:
+    return caffe_cpu_asum(count, bottom_diff) / num * C_;
+  case HingeLossParameter_Norm_L2:
+    return caffe_cpu_dot(count, bottom_diff, bottom_diff) / num * C_;
+  default:
+    LOG(FATAL) << "Unknown hinge_norm";
+  }
+}
+
+template <typename Dtype>
+void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  const Dtype* label = (*bottom)[1]->cpu_data();
+  int num = (*bottom)[0]->num();
+  int count = (*bottom)[0]->count();
+  int dim = count / num;
+  
+  for (int i = 0; i < num; ++i) {
+    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
+  }
+
+  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
+  case HingeLossParameter_Norm_L1:
+    caffe_cpu_sign(count, bottom_diff, bottom_diff);
+    caffe_scal(count, Dtype(C_ / num), bottom_diff);
+    break;
+  case HingeLossParameter_Norm_L2:
+    caffe_scal(count, Dtype(2 * C_ / num), bottom_diff);
+    break;
+  default:
+    LOG(FATAL) << "Unknown hinge_norm";
+  }  
+>>>>>>> 161e7cb... Fix the loss to follow the convention
 }
 
 INSTANTIATE_CLASS(LossLayer);

From 4c711140543dc88eb2e9c3a46eff8bfb77c1aae0 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 18:37:34 -0700
Subject: [PATCH 0148/2053] Unify L1 and L2 Hinge_Loss to follow convention

---
 src/caffe/layers/hinge_loss_layer.cpp |  26 ++-
 src/caffe/layers/loss_layer.cpp       | 246 --------------------------
 2 files changed, 22 insertions(+), 250 deletions(-)

diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 24329fba028..f0f8521ff16 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -14,6 +14,7 @@ using std::max;
 
 namespace caffe {
 
+
 template <typename Dtype>
 Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -33,7 +34,14 @@ Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
-  return caffe_cpu_asum(count, bottom_diff) / num;
+  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
+  case HingeLossParameter_Norm_L1:
+    return caffe_cpu_asum(count, bottom_diff) / num;
+  case HingeLossParameter_Norm_L2:
+    return caffe_cpu_dot(count, bottom_diff, bottom_diff) / num;
+  default:
+    LOG(FATAL) << "Unknown hinge_norm";
+  }
 }
 
 template <typename Dtype>
@@ -44,12 +52,22 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   int num = (*bottom)[0]->num();
   int count = (*bottom)[0]->count();
   int dim = count / num;
-
-  caffe_cpu_sign(count, bottom_diff, bottom_diff);
+  
   for (int i = 0; i < num; ++i) {
     bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
   }
-  caffe_scal(count, Dtype(1. / num), bottom_diff);
+
+  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
+  case HingeLossParameter_Norm_L1:
+    caffe_cpu_sign(count, bottom_diff, bottom_diff);
+    caffe_scal(count, Dtype(C_ / num), bottom_diff);
+    break;
+  case HingeLossParameter_Norm_L2:
+    caffe_scal(count, Dtype(2 * C_ / num), bottom_diff);
+    break;
+  default:
+    LOG(FATAL) << "Unknown hinge_norm";
+  }  
 }
 
 INSTANTIATE_CLASS(HingeLossLayer);
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 33a221f50d2..a8e63c443cc 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -20,254 +20,8 @@ void LossLayer<Dtype>::SetUp(
   Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
-<<<<<<< HEAD
   FurtherSetUp(bottom, top);
 
-=======
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-}
-
-template <typename Dtype>
-Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-    loss -= log(prob);
-  }
-  return loss / num;
-}
-
-template <typename Dtype>
-void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-    bottom_diff[i * dim + label] = -1. / prob / num;
-  }
-}
-
-
-template <typename Dtype>
-void InfogainLossLayer<Dtype>::SetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "Loss Layer takes no output.";
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  BlobProto blob_proto;
-  ReadProtoFromBinaryFile(this->layer_param_.infogain_loss_param().source(),
-                          &blob_proto);
-  infogain_.FromProto(blob_proto);
-  CHECK_EQ(infogain_.num(), 1);
-  CHECK_EQ(infogain_.channels(), 1);
-  CHECK_EQ(infogain_.height(), infogain_.width());
-}
-
-
-template <typename Dtype>
-Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* bottom_label = bottom[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
-  Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      loss -= infogain_mat[label * dim + j] * log(prob);
-    }
-  }
-  return loss / num;
-}
-
-template <typename Dtype>
-void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
-    }
-  }
-}
-
-
-template <typename Dtype>
-void EuclideanLossLayer<Dtype>::SetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "Loss Layer takes no as output.";
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
-  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
-  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
-  difference_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
-}
-
-template <typename Dtype>
-Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  int count = bottom[0]->count();
-  int num = bottom[0]->num();
-  caffe_sub(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(),
-      difference_.mutable_cpu_data());
-  Dtype loss = caffe_cpu_dot(
-      count, difference_.cpu_data(), difference_.cpu_data()) / num / Dtype(2);
-  return loss;
-}
-
-template <typename Dtype>
-void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  int count = (*bottom)[0]->count();
-  int num = (*bottom)[0]->num();
-  // Compute the gradient
-  caffe_cpu_axpby(count, Dtype(1) / num, difference_.cpu_data(), Dtype(0),
-      (*bottom)[0]->mutable_cpu_diff());
-}
-
-template <typename Dtype>
-void AccuracyLayer<Dtype>::SetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Accuracy Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 1) << "Accuracy Layer takes 1 output.";
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  (*top)[0]->Reshape(1, 2, 1, 1);
-}
-
-template <typename Dtype>
-Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  Dtype accuracy = 0;
-  Dtype logprob = 0;
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  for (int i = 0; i < num; ++i) {
-    // Accuracy
-    Dtype maxval = -FLT_MAX;
-    int max_id = 0;
-    for (int j = 0; j < dim; ++j) {
-      if (bottom_data[i * dim + j] > maxval) {
-        maxval = bottom_data[i * dim + j];
-        max_id = j;
-      }
-    }
-    if (max_id == static_cast<int>(bottom_label[i])) {
-      ++accuracy;
-    }
-    Dtype prob = max(bottom_data[i * dim + static_cast<int>(bottom_label[i])],
-                     Dtype(kLOG_THRESHOLD));
-    logprob -= log(prob);
-  }
-  // LOG(INFO) << "Accuracy: " << accuracy;
-  (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
-  (*top)[0]->mutable_cpu_data()[1] = logprob / num;
-  // Accuracy layer should not be used as a loss function.
-  return Dtype(0);
-}
-
-template <typename Dtype>
-void HingeLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Hinge Loss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "Hinge Loss Layer takes no output.";
-  C_ =  this->layer_param_.hinge_loss_param().c_param();
-}
-
-template <typename Dtype>
-Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-  const Dtype* label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int count = bottom[0]->count();
-  int dim = count / num;
-
-  caffe_copy(count, bottom_data, bottom_diff);
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
-  }
-  for (int i = 0; i < num; ++i) {
-    for (int j = 0; j < dim; ++j) {
-      bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
-    }
-  }
-  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
-  case HingeLossParameter_Norm_L1:
-    return caffe_cpu_asum(count, bottom_diff) / num * C_;
-  case HingeLossParameter_Norm_L2:
-    return caffe_cpu_dot(count, bottom_diff, bottom_diff) / num * C_;
-  default:
-    LOG(FATAL) << "Unknown hinge_norm";
-  }
-}
-
-template <typename Dtype>
-void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  const Dtype* label = (*bottom)[1]->cpu_data();
-  int num = (*bottom)[0]->num();
-  int count = (*bottom)[0]->count();
-  int dim = count / num;
-  
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
-  }
-
-  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
-  case HingeLossParameter_Norm_L1:
-    caffe_cpu_sign(count, bottom_diff, bottom_diff);
-    caffe_scal(count, Dtype(C_ / num), bottom_diff);
-    break;
-  case HingeLossParameter_Norm_L2:
-    caffe_scal(count, Dtype(2 * C_ / num), bottom_diff);
-    break;
-  default:
-    LOG(FATAL) << "Unknown hinge_norm";
-  }  
->>>>>>> 161e7cb... Fix the loss to follow the convention
 }
 
 INSTANTIATE_CLASS(LossLayer);

From 8d4dacf6d9b5151937927eea34f96ebc6912eb31 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 19:55:59 -0700
Subject: [PATCH 0149/2053] Now AccuracyLayer only computes accuracy, one
 should use LossLayers to compute loss Changed all val.prototxt in examples to
 add a LossLayer to compute loss in Test

---
 examples/cifar10/cifar10_full_test.prototxt   | 15 +++++-----
 examples/cifar10/cifar10_quick_test.prototxt  | 15 +++++-----
 .../feature_extraction/imagenet_val.prototxt  |  7 +++++
 examples/imagenet/alexnet_val.prototxt        | 15 +++++-----
 examples/imagenet/imagenet_val.prototxt       | 15 +++++-----
 .../mnist/lenet_consolidated_solver.prototxt  | 30 ++++++++++---------
 examples/mnist/lenet_test.prototxt            | 15 +++++-----
 .../mnist/mnist_autoencoder_test.prototxt     |  1 +
 .../pascal_finetune_val.prototxt              | 16 +++++-----
 include/caffe/loss_layers.hpp                 |  5 +++-
 src/caffe/layers/accuracy_layer.cpp           |  8 ++---
 11 files changed, 79 insertions(+), 63 deletions(-)

diff --git a/examples/cifar10/cifar10_full_test.prototxt b/examples/cifar10/cifar10_full_test.prototxt
index 0e1957a9045..1f77b4f0348 100644
--- a/examples/cifar10/cifar10_full_test.prototxt
+++ b/examples/cifar10/cifar10_full_test.prototxt
@@ -166,16 +166,17 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "ip1"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "ip1"
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/cifar10/cifar10_quick_test.prototxt b/examples/cifar10/cifar10_quick_test.prototxt
index a154b9a0ea7..aa82c32aa24 100644
--- a/examples/cifar10/cifar10_quick_test.prototxt
+++ b/examples/cifar10/cifar10_quick_test.prototxt
@@ -160,16 +160,17 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "ip2"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "ip2"
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 14bfe770ef8..b0451a1a114 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -227,3 +227,10 @@ layers {
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/imagenet/alexnet_val.prototxt b/examples/imagenet/alexnet_val.prototxt
index 3fd6296ef9d..1d8d86b78ff 100644
--- a/examples/imagenet/alexnet_val.prototxt
+++ b/examples/imagenet/alexnet_val.prototxt
@@ -213,15 +213,16 @@ layers {
   top: "fc8"
 }
 layers {
-  name: "prob"
-  type: SOFTMAX
+  name: "accuracy"
+  type: ACCURACY
   bottom: "fc8"
-  top: "prob"
+  bottom: "label"
+  top: "accuracy"
 }
 layers {
-  top: "accuracy"
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "prob"
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8"
   bottom: "label"
+  top: "loss"
 }
diff --git a/examples/imagenet/imagenet_val.prototxt b/examples/imagenet/imagenet_val.prototxt
index dd26f40ea14..8be5150cdd2 100644
--- a/examples/imagenet/imagenet_val.prototxt
+++ b/examples/imagenet/imagenet_val.prototxt
@@ -212,16 +212,17 @@ layers {
     num_output: 1000
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "fc8"
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
\ No newline at end of file
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 07cbc211414..ef851e0f656 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -262,19 +262,20 @@ test_net_param {
       }
     }
   }
-  layers {
-    name: "prob"
-    type: SOFTMAX
-    bottom: "ip2"
-    top: "prob"
-  }
   layers {
     name: "accuracy"
     type: ACCURACY
-    bottom: "prob"
+    bottom: "ip2"
     bottom: "label"
     top: "accuracy"
   }
+  layers {
+    name: "loss"
+    type: SOFTMAX_LOSS
+    bottom: "ip2"
+    bottom: "label"
+    top: "loss"
+  }
 }
 
 # The train set has 60K images, so we run 600 test iters (600 * 100 = 60K).
@@ -385,19 +386,20 @@ test_net_param {
       }
     }
   }
-  layers {
-    name: "prob"
-    type: SOFTMAX
-    bottom: "ip2"
-    top: "prob"
-  }
   layers {
     name: "accuracy"
     type: ACCURACY
-    bottom: "prob"
+    bottom: "ip2"
     bottom: "label"
     top: "accuracy"
   }
+  layers {
+    name: "loss"
+    type: SOFTMAX_LOSS
+    bottom: "ip2"
+    bottom: "label"
+    top: "loss"
+  }
 }
 
 # Expected results for first and last 500 iterations:
diff --git a/examples/mnist/lenet_test.prototxt b/examples/mnist/lenet_test.prototxt
index 3b59b75513d..2497f02ae86 100644
--- a/examples/mnist/lenet_test.prototxt
+++ b/examples/mnist/lenet_test.prototxt
@@ -102,16 +102,17 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "ip2"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "ip2"
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/mnist/mnist_autoencoder_test.prototxt b/examples/mnist/mnist_autoencoder_test.prototxt
index 5090e82fe0a..b52364c17fc 100644
--- a/examples/mnist/mnist_autoencoder_test.prototxt
+++ b/examples/mnist/mnist_autoencoder_test.prototxt
@@ -142,4 +142,5 @@ layers {
   bottom: "flatdata"
   name: "loss"
   type: EUCLIDEAN_LOSS
+  top: "loss"
 }
diff --git a/examples/pascal-finetuning/pascal_finetune_val.prototxt b/examples/pascal-finetuning/pascal_finetune_val.prototxt
index ff898fe7376..91ded585d85 100644
--- a/examples/pascal-finetuning/pascal_finetune_val.prototxt
+++ b/examples/pascal-finetuning/pascal_finetune_val.prototxt
@@ -313,16 +313,18 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8_pascal"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "fc8_pascal"
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "prob"
+  type: SOFTMAX_LOSS
+  bottom: "fc8_pascal"
+  bottom: "label"
+  top: "loss"
+}
+
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index dd06ca07187..2f98a1246be 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -166,7 +166,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
 
 /* AccuracyLayer
   Note: not an actual loss layer! Does not implement backwards step.
-  Computes the accuracy of a with respect to b.
+  Computes the accuracy of argmax(a) with respect to b.
 */
 template <typename Dtype>
 class AccuracyLayer : public Layer<Dtype> {
@@ -180,6 +180,9 @@ class AccuracyLayer : public Layer<Dtype> {
     return LayerParameter_LayerType_ACCURACY;
   }
 
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index fbc943eaf2d..899750f869a 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -23,14 +23,13 @@ void AccuracyLayer<Dtype>::SetUp(
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
-  (*top)[0]->Reshape(1, 2, 1, 1);
+  (*top)[0]->Reshape(1, 1, 1, 1);
 }
 
 template <typename Dtype>
 Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   Dtype accuracy = 0;
-  Dtype logprob = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   int num = bottom[0]->num();
@@ -48,13 +47,10 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     if (max_id == static_cast<int>(bottom_label[i])) {
       ++accuracy;
     }
-    Dtype prob = max(bottom_data[i * dim + static_cast<int>(bottom_label[i])],
-                     Dtype(kLOG_THRESHOLD));
-    logprob -= log(prob);
   }
   // LOG(INFO) << "Accuracy: " << accuracy;
   (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
-  (*top)[0]->mutable_cpu_data()[1] = logprob / num;
+
   // Accuracy layer should not be used as a loss function.
   return Dtype(0);
 }

From 909fb3924098874a5c68bea4aac7cc1fffe3d220 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 20:22:34 -0700
Subject: [PATCH 0150/2053] Remove C_ mentions, extra spaces and change
 hinge_norm to norm

---
 src/caffe/layers/hinge_loss_layer.cpp | 15 +++++++--------
 src/caffe/layers/loss_layer.cpp       |  1 -
 src/caffe/proto/caffe.proto           |  2 +-
 3 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index f0f8521ff16..e47d3c90114 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -14,7 +14,6 @@ using std::max;
 
 namespace caffe {
 
-
 template <typename Dtype>
 Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -34,13 +33,13 @@ Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
-  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
+  switch (this->layer_param_.hinge_loss_param().norm()) {
   case HingeLossParameter_Norm_L1:
     return caffe_cpu_asum(count, bottom_diff) / num;
   case HingeLossParameter_Norm_L2:
     return caffe_cpu_dot(count, bottom_diff, bottom_diff) / num;
   default:
-    LOG(FATAL) << "Unknown hinge_norm";
+    LOG(FATAL) << "Unknown Norm";
   }
 }
 
@@ -52,21 +51,21 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   int num = (*bottom)[0]->num();
   int count = (*bottom)[0]->count();
   int dim = count / num;
-  
+
   for (int i = 0; i < num; ++i) {
     bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
   }
 
-  switch (this->layer_param_.hinge_loss_param().hinge_norm()) {
+  switch (this->layer_param_.hinge_loss_param().norm()) {
   case HingeLossParameter_Norm_L1:
     caffe_cpu_sign(count, bottom_diff, bottom_diff);
-    caffe_scal(count, Dtype(C_ / num), bottom_diff);
+    caffe_scal(count, Dtype(1. / num), bottom_diff);
     break;
   case HingeLossParameter_Norm_L2:
-    caffe_scal(count, Dtype(2 * C_ / num), bottom_diff);
+    caffe_scal(count, Dtype(2. / num), bottom_diff);
     break;
   default:
-    LOG(FATAL) << "Unknown hinge_norm";
+    LOG(FATAL) << "Unknown Norm";
   }  
 }
 
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index a8e63c443cc..14ea975ad0d 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -21,7 +21,6 @@ void LossLayer<Dtype>::SetUp(
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   FurtherSetUp(bottom, top);
-
 }
 
 INSTANTIATE_CLASS(LossLayer);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b15172fa1ef..954f1033e97 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -312,7 +312,7 @@ message HingeLossParameter {
     L2 = 2;    
   }
   // Specify the Norm to use L1 or L2
-  optional Norm hinge_norm = 1 [default = L1];
+  optional Norm norm = 1 [default = L1];
 }
 
 // Message that stores parameters used by ImageDataLayer

From 60ff8bacdde3ac37c2942efd307aa644425add1e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 20 Jun 2014 20:26:51 -0700
Subject: [PATCH 0151/2053] Change hinge_norm to norm in test_hinge_loss

---
 src/caffe/test/test_hinge_loss_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 64657c88dd7..2e8db475914 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -75,7 +75,7 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2CPU) {
   LayerParameter layer_param;
   // Set norm to L2
   HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
-  hinge_loss_param->set_hinge_norm(HingeLossParameter_Norm_L2);
+  hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
   Caffe::set_mode(Caffe::CPU);
   HingeLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
@@ -89,7 +89,7 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2GPU) {
   LayerParameter layer_param;
   // Set norm to L2
   HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
-  hinge_loss_param->set_hinge_norm(HingeLossParameter_Norm_L2);
+  hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
   Caffe::set_mode(Caffe::GPU);
   HingeLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);

From 8042be6966653b3290d3c763c53727384e93d723 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 21 Jun 2014 14:08:15 -0700
Subject: [PATCH 0152/2053] fix whitespace error in HingeLossLayer

---
 src/caffe/layers/hinge_loss_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index e47d3c90114..fd00d8c6930 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -66,7 +66,7 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     break;
   default:
     LOG(FATAL) << "Unknown Norm";
-  }  
+  }
 }
 
 INSTANTIATE_CLASS(HingeLossLayer);

From bddd10ae03ae63cea6651463520ba8a4b89f3dad Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 21 Jun 2014 14:08:30 -0700
Subject: [PATCH 0153/2053] explicitly name L1 hinge test

---
 src/caffe/test/test_hinge_loss_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 2e8db475914..318030b67a3 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -50,7 +50,7 @@ typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(HingeLossLayerTest, Dtypes);
 
 
-TYPED_TEST(HingeLossLayerTest, TestGradientCPU) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL1CPU) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
   HingeLossLayer<TypeParam> layer(layer_param);
@@ -60,7 +60,7 @@ TYPED_TEST(HingeLossLayerTest, TestGradientCPU) {
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
-TYPED_TEST(HingeLossLayerTest, TestGradientGPU) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL1GPU) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::GPU);
   HingeLossLayer<TypeParam> layer(layer_param);

From 93049bd7d5e788ca95ee36c4630ba96b7f6a840b Mon Sep 17 00:00:00 2001
From: Craig Quiter <cquiter@gmail.com>
Date: Sat, 21 Jun 2014 15:22:05 -0700
Subject: [PATCH 0154/2053] There are 256 filters in conv2.

AlexNet paper splits conv2 into two GPU's, each with 128 filters. Reference paper: http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf Applying #528 to dev instead of master per @Yangqing.
---
 examples/filter_visualization.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 6f494821028..130df97074a 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -298,7 +298,7 @@
      "source": [
       "The second layer filters, `conv2`\n",
       "\n",
-      "There are 128 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
+      "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
      ]
     },
     {
@@ -611,4 +611,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}

From da0f89bbd495c604f63744374165cdc9f3cbba9f Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 17 Jun 2014 14:53:00 +0800
Subject: [PATCH 0155/2053] Extract multiple features in a single Forward pass

---
 tools/extract_features.cpp | 123 +++++++++++++++++++++++--------------
 1 file changed, 76 insertions(+), 47 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index cdad6676d7f..99eb0dbce26 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -5,6 +5,7 @@
 #include <google/protobuf/text_format.h>
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
+#include <boost/algorithm/string.hpp>
 #include <string>
 #include <vector>
 
@@ -32,9 +33,14 @@ int feature_extraction_pipeline(int argc, char** argv) {
     LOG(ERROR)<<
     "This program takes in a trained network and an input data layer, and then"
     " extract features of the input data produced by the net.\n"
-    "Usage: demo_extract_features  pretrained_net_param"
-    "  feature_extraction_proto_file  extract_feature_blob_name"
-    "  save_feature_leveldb_name  num_mini_batches  [CPU/GPU]  [DEVICE_ID=0]";
+    "Usage: extract_features  pretrained_net_param"
+    "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
+    "  save_feature_leveldb_name1[,name2,...]  num_mini_batches  [CPU/GPU]"
+    "  [DEVICE_ID=0]\n"
+    "Note: you can extract multiple features in one pass by specifying"
+    " multiple feature blob names and leveldb names seperated by ','."
+    " The names cannot contain white space characters and the number of blobs"
+    " and leveldbs must be equal.";
     return 1;
   }
   int arg_pos = num_required_args;
@@ -91,29 +97,46 @@ int feature_extraction_pipeline(int argc, char** argv) {
       new Net<Dtype>(feature_extraction_proto));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
-  string extract_feature_blob_name(argv[++arg_pos]);
-  CHECK(feature_extraction_net->has_blob(extract_feature_blob_name))
-      << "Unknown feature blob name " << extract_feature_blob_name
-      << " in the network " << feature_extraction_proto;
+  string extract_feature_blob_names(argv[++arg_pos]);
+  vector<string> blob_names;
+  boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
+
+  string save_feature_leveldb_names(argv[++arg_pos]);
+  vector<string> leveldb_names;
+  boost::split(leveldb_names, save_feature_leveldb_names,
+               boost::is_any_of(","));
+  CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
+      " the number of blob names and leveldb names must be equal";
+  size_t num_features = blob_names.size();
+
+  for (size_t i = 0; i < num_features; i++) {
+    CHECK(feature_extraction_net->has_blob(blob_names[i]))
+        << "Unknown feature blob name " << blob_names[i]
+        << " in the network " << feature_extraction_proto;
+  }
 
-  string save_feature_leveldb_name(argv[++arg_pos]);
-  leveldb::DB* db;
   leveldb::Options options;
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  LOG(INFO)<< "Opening leveldb " << save_feature_leveldb_name;
-  leveldb::Status status = leveldb::DB::Open(options,
-                                             save_feature_leveldb_name.c_str(),
-                                             &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << save_feature_leveldb_name;
+  vector<leveldb::DB*> feature_dbs;
+  for (size_t i = 0; i < num_features; ++i) {
+    LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
+    leveldb::DB* db;
+    leveldb::Status status = leveldb::DB::Open(options,
+                                               leveldb_names[i].c_str(),
+                                               &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << leveldb_names[i];
+    feature_dbs.push_back(db);
+  }
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  leveldb::WriteBatch* batch = new leveldb::WriteBatch();
+  vector<leveldb::WriteBatch*> feature_batches(
+      num_features, new leveldb::WriteBatch());
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
   int num_bytes_of_binary_code = sizeof(Dtype);
@@ -121,45 +144,51 @@ int feature_extraction_pipeline(int argc, char** argv) {
   int image_index = 0;
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
-    const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
-        ->blob_by_name(extract_feature_blob_name);
-    int num_features = feature_blob->num();
-    int dim_features = feature_blob->count() / num_features;
-    Dtype* feature_blob_data;
-    for (int n = 0; n < num_features; ++n) {
-      datum.set_height(dim_features);
-      datum.set_width(1);
-      datum.set_channels(1);
-      datum.clear_data();
-      datum.clear_float_data();
-      feature_blob_data = feature_blob->mutable_cpu_data() +
-          feature_blob->offset(n);
-      for (int d = 0; d < dim_features; ++d) {
-        datum.add_float_data(feature_blob_data[d]);
-      }
-      string value;
-      datum.SerializeToString(&value);
-      snprintf(key_str, kMaxKeyStrLength, "%d", image_index);
-      batch->Put(string(key_str), value);
-      ++image_index;
-      if (image_index % 1000 == 0) {
-        db->Write(leveldb::WriteOptions(), batch);
-        LOG(ERROR)<< "Extracted features of " << image_index <<
-            " query images.";
-        delete batch;
-        batch = new leveldb::WriteBatch();
-      }
-    }  // for (int n = 0; n < num_features; ++n)
+    for (int i = 0; i < num_features; ++i) {
+      const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
+          ->blob_by_name(blob_names[i]);
+      int batch_size = feature_blob->num();
+      int dim_features = feature_blob->count() / batch_size;
+      Dtype* feature_blob_data;
+      for (int n = 0; n < batch_size; ++n) {
+        datum.set_height(dim_features);
+        datum.set_width(1);
+        datum.set_channels(1);
+        datum.clear_data();
+        datum.clear_float_data();
+        feature_blob_data = feature_blob->mutable_cpu_data() +
+            feature_blob->offset(n);
+        for (int d = 0; d < dim_features; ++d) {
+          datum.add_float_data(feature_blob_data[d]);
+        }
+        string value;
+        datum.SerializeToString(&value);
+        snprintf(key_str, kMaxKeyStrLength, "%d", image_index);
+        feature_batches[i]->Put(string(key_str), value);
+        ++image_index;
+        if (image_index % 1000 == 0) {
+          feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i]);
+          LOG(ERROR)<< "Extracted features of " << image_index <<
+              " query images.";
+          delete feature_batches[i];
+          feature_batches[i] = new leveldb::WriteBatch();
+        }
+      }  // for (int n = 0; n < batch_size; ++n)
+    }  // for (int i = 0; i < num_features; ++i)
   }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
   // write the last batch
   if (image_index % 1000 != 0) {
-    db->Write(leveldb::WriteOptions(), batch);
+    for (int i = 0; i < num_features; ++i) {
+      feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i]);
+    }
     LOG(ERROR)<< "Extracted features of " << image_index <<
         " query images.";
   }
 
-  delete batch;
-  delete db;
+  for (int i = 0; i < num_features; ++i) {
+    delete feature_batches[i];
+    delete feature_dbs[i];
+  }
   LOG(ERROR)<< "Successfully extracted the features!";
   return 0;
 }

From 4415f8c0453cd272fb2439711f74efd3f88aab1d Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 17 Jun 2014 16:27:24 +0800
Subject: [PATCH 0156/2053] Progress should be reported for each feature blob

---
 tools/extract_features.cpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 99eb0dbce26..aa665a00084 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -141,7 +141,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   char key_str[kMaxKeyStrLength];
   int num_bytes_of_binary_code = sizeof(Dtype);
   vector<Blob<float>*> input_vec;
-  int image_index = 0;
+  vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
     for (int i = 0; i < num_features; ++i) {
@@ -163,13 +163,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
         }
         string value;
         datum.SerializeToString(&value);
-        snprintf(key_str, kMaxKeyStrLength, "%d", image_index);
+        snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
         feature_batches[i]->Put(string(key_str), value);
-        ++image_index;
-        if (image_index % 1000 == 0) {
+        ++image_indices[i];
+        if (image_indices[i] % 1000 == 0) {
           feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i]);
-          LOG(ERROR)<< "Extracted features of " << image_index <<
-              " query images.";
+          LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
+              " query images for feature blob " << blob_names[i];
           delete feature_batches[i];
           feature_batches[i] = new leveldb::WriteBatch();
         }
@@ -177,12 +177,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
     }  // for (int i = 0; i < num_features; ++i)
   }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
   // write the last batch
-  if (image_index % 1000 != 0) {
-    for (int i = 0; i < num_features; ++i) {
+  for (int i = 0; i < num_features; ++i) {
+    if (image_indices[i] % 1000 != 0) {
       feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i]);
     }
-    LOG(ERROR)<< "Extracted features of " << image_index <<
-        " query images.";
+    LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
+        " query images for feature blob " << blob_names[i];
   }
 
   for (int i = 0; i < num_features; ++i) {

From 738be6fdfc5faec5fc111e040c2436628d1b9989 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 17 Jun 2014 16:56:30 +0800
Subject: [PATCH 0157/2053] No need to manually delete the pointers which are
 managed by std::vector

---
 tools/extract_features.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index aa665a00084..66d4be1eaf1 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -185,10 +185,6 @@ int feature_extraction_pipeline(int argc, char** argv) {
         " query images for feature blob " << blob_names[i];
   }
 
-  for (int i = 0; i < num_features; ++i) {
-    delete feature_batches[i];
-    delete feature_dbs[i];
-  }
   LOG(ERROR)<< "Successfully extracted the features!";
   return 0;
 }

From 4a276b03fa94bd040a3596e77e34ae0468665a10 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 20 Jun 2014 10:14:19 +0800
Subject: [PATCH 0158/2053] Replace the raw pointers with shared_ptr to ensure
 memory is released

---
 tools/extract_features.cpp | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 66d4be1eaf1..1bff7ca4f1d 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -119,7 +119,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  vector<leveldb::DB*> feature_dbs;
+  vector<shared_ptr<leveldb::DB> > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
     leveldb::DB* db;
@@ -127,7 +127,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
                                                leveldb_names[i].c_str(),
                                                &db);
     CHECK(status.ok()) << "Failed to open leveldb " << leveldb_names[i];
-    feature_dbs.push_back(db);
+    feature_dbs.push_back(shared_ptr<leveldb::DB>(db));
   }
 
   int num_mini_batches = atoi(argv[++arg_pos]);
@@ -135,8 +135,9 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  vector<leveldb::WriteBatch*> feature_batches(
-      num_features, new leveldb::WriteBatch());
+  vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
+      num_features,
+      shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
   int num_bytes_of_binary_code = sizeof(Dtype);
@@ -167,11 +168,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
         feature_batches[i]->Put(string(key_str), value);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
-          feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i]);
+          feature_dbs[i]->Write(leveldb::WriteOptions(),
+                                feature_batches[i].get());
           LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
               " query images for feature blob " << blob_names[i];
-          delete feature_batches[i];
-          feature_batches[i] = new leveldb::WriteBatch();
+          feature_batches[i].reset(new leveldb::WriteBatch());
         }
       }  // for (int n = 0; n < batch_size; ++n)
     }  // for (int i = 0; i < num_features; ++i)
@@ -179,7 +180,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   // write the last batch
   for (int i = 0; i < num_features; ++i) {
     if (image_indices[i] % 1000 != 0) {
-      feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i]);
+      feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i].get());
     }
     LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
         " query images for feature blob " << blob_names[i];

From dda092c84e35943ced8be41fd545aea4ab3c6409 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 24 Jun 2014 22:14:54 +0800
Subject: [PATCH 0159/2053] Init google logging

---
 tools/extract_features.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 1bff7ca4f1d..becc1f86d47 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -28,6 +28,7 @@ int main(int argc, char** argv) {
 
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv) {
+  ::google::InitGoogleLogging(argv[0]);
   const int num_required_args = 6;
   if (argc < num_required_args) {
     LOG(ERROR)<<

From adab413fc65f760f9bc2097f379c2703e3641de4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 25 Jun 2014 11:01:10 +0800
Subject: [PATCH 0160/2053] fix SOFTMAX_LOSS to work with loss top blob
 interface

---
 include/caffe/vision_layers.hpp         |  2 +-
 src/caffe/layers/softmax_loss_layer.cpp | 14 +++++++++++++-
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index fc3dbbe1938..b7e1068c7d3 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -425,7 +425,7 @@ class SoftmaxWithLossLayer : public Layer<Dtype> {
     return LayerParameter_LayerType_SOFTMAX_LOSS;
   }
   virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
+  virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 8a37efee395..bdb3272ee25 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -20,6 +20,15 @@ void SoftmaxWithLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.push_back(&prob_);
   softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
+  if (top->size() >= 1) {
+    // softmax loss (averaged across batch)
+    (*top)[0]->Reshape(1, 1, 1, 1);
+  }
+  if (top->size() == 2) {
+    // softmax output
+    (*top)[1]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+        bottom[0]->height(), bottom[0]->width());
+  }
 }
 
 template <typename Dtype>
@@ -37,9 +46,12 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     loss += -log(max(prob_data[i * dim + static_cast<int>(label[i])],
                      Dtype(FLT_MIN)));
   }
-  if (top->size() == 1) {
+  if (top->size() >= 1) {
     (*top)[0]->mutable_cpu_data()[0] = loss / num;
   }
+  if (top->size() == 2) {
+    (*top)[1]->ShareData(prob_);
+  }
   return loss / num;
 }
 

From ba09a9c3484096827c9f966beebdd9ef34764f8f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 26 May 2014 02:02:42 -0700
Subject: [PATCH 0161/2053] actually check status values from all HDF5 calls

---
 src/caffe/layers/hdf5_data_layer.cpp | 1 +
 src/caffe/util/io.cpp                | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index d5c64f05696..50863b8d93a 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -45,6 +45,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
     file_id, "label", MIN_LABEL_DIM, MAX_LABEL_DIM, &label_blob_);
 
   herr_t status = H5Fclose(file_id);
+  CHECK_GE(status, 0) << "Failed to close HDF5 file " << filename;
   CHECK_EQ(data_blob_.num(), label_blob_.num());
   LOG(INFO) << "Successully loaded " << data_blob_.num() << " rows";
 }
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 65f82547071..ff65bd64d88 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -123,6 +123,7 @@ void hdf5_load_nd_dataset_helper(
   herr_t status;
   int ndims;
   status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
+  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
   CHECK_GE(ndims, min_dim);
   CHECK_LE(ndims, max_dim);
 
@@ -131,6 +132,7 @@ void hdf5_load_nd_dataset_helper(
   H5T_class_t class_;
   status = H5LTget_dataset_info(
       file_id, dataset_name_, dims.data(), &class_, NULL);
+  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
   CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
 
   blob->Reshape(
@@ -146,6 +148,7 @@ void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
   hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
   herr_t status = H5LTread_dataset_float(
     file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
 }
 
 template <>
@@ -154,6 +157,7 @@ void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
   hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
   herr_t status = H5LTread_dataset_double(
     file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
 }
 
 template <>

From c0bcf68f8d2b063dd6b92acdfc30c7d5a5fb44ea Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 10 Jun 2014 15:34:52 -0700
Subject: [PATCH 0162/2053] check if window file is empty in WindowDataLayer

Also note that window files containing windows with different numbers of
channels may not work correctly.
---
 src/caffe/layers/window_data_layer.cpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index e08bed7d56f..fd4860f98be 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -291,7 +291,10 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 
   string hashtag;
   int image_index, channels;
-  while (infile >> hashtag >> image_index) {
+  if (!(infile >> hashtag >> image_index)) {
+    LOG(FATAL) << "Window file is empty";
+  }
+  do {
     CHECK_EQ(hashtag, "#");
     // read image path
     string image_path;
@@ -347,7 +350,7 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
           << image_size[2] << " "
           << "windows to process: " << num_windows;
     }
-  }
+  } while (infile >> hashtag >> image_index);
 
   LOG(INFO) << "Number of images: " << image_index+1;
 

From 636d51c4a61464908f9236ae158b49aca0686da4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 26 May 2014 02:31:05 -0700
Subject: [PATCH 0163/2053] initialize in declared order

---
 include/caffe/blob.hpp | 4 ++--
 src/caffe/common.cpp   | 5 ++---
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 75101462faf..c04375a10e2 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -13,8 +13,8 @@ template <typename Dtype>
 class Blob {
  public:
   Blob()
-       : num_(0), channels_(0), height_(0), width_(0), count_(0), data_(),
-       diff_() {}
+       : data_(), diff_(), num_(0), channels_(0), height_(0), width_(0),
+       count_(0) {}
   explicit Blob(const int num, const int channels, const int height,
     const int width);
   void Reshape(const int num, const int channels, const int height,
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 6eadfc7917e..387d008c614 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -22,9 +22,8 @@ int64_t cluster_seedgen(void) {
 
 
 Caffe::Caffe()
-    : mode_(Caffe::CPU), phase_(Caffe::TRAIN), cublas_handle_(NULL),
-      curand_generator_(NULL),
-      random_generator_() {
+    : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
+    mode_(Caffe::CPU), phase_(Caffe::TRAIN) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {

From 1ee22bf761c7d6af2dc53c3ec34a4171d749cf72 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 11 Jun 2014 19:31:22 -0700
Subject: [PATCH 0164/2053] initialize in declared order in tests

---
 src/caffe/test/test_data_layer.cpp              | 4 ++--
 src/caffe/test/test_hdf5data_layer.cpp          | 6 +++---
 src/caffe/test/test_image_data_layer.cpp        | 6 +++---
 src/caffe/test/test_lrn_layer.cpp               | 6 +++---
 src/caffe/test/test_memory_data_layer.cpp       | 7 ++++---
 src/caffe/test/test_random_number_generator.cpp | 4 ++--
 6 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 68f2618d323..782fe1212c6 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -24,9 +24,9 @@ template <typename Dtype>
 class DataLayerTest : public ::testing::Test {
  protected:
   DataLayerTest()
-      : blob_top_data_(new Blob<Dtype>()),
+      : filename_(new string(tmpnam(NULL))),
+        blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()),
-        filename_(new string(tmpnam(NULL))),
         backend_(DataParameter_DB_LEVELDB),
         seed_(1701) {}
   virtual void SetUp() {
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index a0ed113b36e..dd2027c5591 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -24,9 +24,9 @@ template <typename Dtype>
 class HDF5DataLayerTest : public ::testing::Test {
  protected:
   HDF5DataLayerTest()
-      : blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()),
-        filename(NULL) {}
+      : filename(NULL),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index fea5793d4be..2278f4e2eb8 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -27,10 +27,10 @@ template <typename Dtype>
 class ImageDataLayerTest : public ::testing::Test {
  protected:
   ImageDataLayerTest()
-      : blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()),
+      : seed_(1701),
         filename_(new string(tmpnam(NULL))),
-        seed_(1701) {}
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 1923128dd71..47c46ee1dfe 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -25,9 +25,9 @@ template <typename Dtype>
 class LRNLayerTest : public ::testing::Test {
  protected:
   LRNLayerTest()
-      : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()),
-        epsilon_(Dtype(1e-5)) {}
+      : epsilon_(Dtype(1e-5)),
+        blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1701);
     blob_bottom_->Reshape(2, 7, 3, 3);
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 15f01bd41e3..9781439f2fe 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -12,9 +12,10 @@ template <typename Dtype>
 class MemoryDataLayerTest : public ::testing::Test {
  protected:
   MemoryDataLayerTest()
-    : data_blob_(new Blob<Dtype>()),
-      label_blob_(new Blob<Dtype>()),
-      data_(new Blob<Dtype>()), labels_(new Blob<Dtype>()) {}
+    : data_(new Blob<Dtype>()),
+      labels_(new Blob<Dtype>()),
+      data_blob_(new Blob<Dtype>()),
+      label_blob_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     batch_size_ = 8;
     batches_ = 12;
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 62daf6087fd..4116de4c252 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -16,9 +16,9 @@ template <typename Dtype>
 class RandomNumberGeneratorTest : public ::testing::Test {
  protected:
   RandomNumberGeneratorTest()
-     : sample_size_(10000),
+     : mean_bound_multiplier_(3.8),  // ~99.99% confidence for test failure.
+       sample_size_(10000),
        seed_(1701),
-       mean_bound_multiplier_(3.8),  // ~99.99% confidence for test failure.
        data_(new SyncedMemory(sample_size_ * sizeof(Dtype))),
        data_2_(new SyncedMemory(sample_size_ * sizeof(Dtype))),
        int_data_(new SyncedMemory(sample_size_ * sizeof(int))),

From a65cb967e66c6ec6248ea0923b4398c708af959d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 26 May 2014 02:31:34 -0700
Subject: [PATCH 0165/2053] remove unused variables

---
 src/caffe/layers/tanh_layer.cpp | 1 -
 src/caffe/net.cpp               | 3 ---
 tools/extract_features.cpp      | 1 -
 3 files changed, 5 deletions(-)

diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 66f530f82d5..77b4410411c 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -33,7 +33,6 @@ void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
-    Dtype exp2x;
     Dtype tanhx;
     for (int i = 0; i < count; ++i) {
       tanhx = top_data[i];
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 41ae7520356..d76d284df2e 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -42,7 +42,6 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  int num_layers = param.layers_size();
   CHECK_EQ(param.input_size() * 4, param.input_dim_size())
       << "Incorrect input blob dimension specifications.";
   memory_used_ = 0;
@@ -58,7 +57,6 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   bottom_id_vecs_.resize(param.layers_size());
   top_id_vecs_.resize(param.layers_size());
   for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
-    bool in_place = false;
     const LayerParameter& layer_param = param.layers(layer_id);
     layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
     layer_names_.push_back(layer_param.name());
@@ -198,7 +196,6 @@ int Net<Dtype>::AppendBottom(const NetParameter& param,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  bool need_backward = param.force_backward() || blob_need_backward_[blob_id];
   return blob_id;
 }
 
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index cdad6676d7f..3a670d96c9a 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -116,7 +116,6 @@ int feature_extraction_pipeline(int argc, char** argv) {
   leveldb::WriteBatch* batch = new leveldb::WriteBatch();
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
-  int num_bytes_of_binary_code = sizeof(Dtype);
   vector<Blob<float>*> input_vec;
   int image_index = 0;
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {

From 88472c9b173f9885af192d8b78ebf9b18b89dee5 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 11 Jun 2014 19:31:40 -0700
Subject: [PATCH 0166/2053] remove unused variables from tests

---
 src/caffe/test/test_hdf5data_layer.cpp                   | 1 -
 src/caffe/test/test_lrn_layer.cpp                        | 1 -
 src/caffe/test/test_neuron_layer.cpp                     | 2 --
 src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp | 1 -
 src/gtest/gtest-all.cpp                                  | 1 -
 5 files changed, 6 deletions(-)

diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index dd2027c5591..1cbca0090fd 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -61,7 +61,6 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   int batch_size = 5;
   hdf5_data_param->set_batch_size(batch_size);
   hdf5_data_param->set_source(*(this->filename));
-  int num_rows = 10;
   int num_cols = 8;
   int height = 5;
   int width = 5;
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 47c46ee1dfe..7f0a211a50b 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -55,7 +55,6 @@ void LRNLayerTest<Dtype>::ReferenceLRNForward(
     Blob<Dtype>* blob_top) {
   blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
       blob_bottom.height(), blob_bottom.width());
-  const Dtype* bottom_data = blob_bottom.cpu_data();
   Dtype* top_data = blob_top->mutable_cpu_data();
   LRNParameter lrn_param = layer_param.lrn_param();
   Dtype alpha = lrn_param.alpha();
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 2210b4612ad..ca70e1acd9b 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -189,7 +189,6 @@ TYPED_TEST(NeuronLayerTest, TestDropoutCPUTestPhase) {
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     if (top_data[i] != 0) {
       EXPECT_EQ(top_data[i], bottom_data[i]);
@@ -244,7 +243,6 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGPUTestPhase) {
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     if (top_data[i] != 0) {
       EXPECT_EQ(top_data[i], bottom_data[i]);
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index d8018be0c25..089d5950967 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -69,7 +69,6 @@ class SigmoidCrossEntropyLossLayerTest : public ::testing::Test {
     targets_filler_param.set_max(1.0);
     UniformFiller<Dtype> targets_filler(targets_filler_param);
     Dtype eps = 2e-2;
-    int num_inf = 0;
     for (int i = 0; i < 100; ++i) {
       // Fill the data vector
       data_filler.Fill(this->blob_bottom_data_);
diff --git a/src/gtest/gtest-all.cpp b/src/gtest/gtest-all.cpp
index 5ced66a90f1..926197419fc 100644
--- a/src/gtest/gtest-all.cpp
+++ b/src/gtest/gtest-all.cpp
@@ -601,7 +601,6 @@ class GTestFlagSaver {
   bool list_tests_;
   String output_;
   bool print_time_;
-  bool pretty_;
   internal::Int32 random_seed_;
   internal::Int32 repeat_;
   bool shuffle_;

From e84dfb26bbda927e591cf9b15a752d44004c7c8f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 11 Jun 2014 15:29:04 -0700
Subject: [PATCH 0167/2053] add missing const qualifiers to MemoryDataLayer
 ExactNum* functions

---
 include/caffe/data_layers.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 2b4c278eafe..fc93daacd3c 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -248,8 +248,8 @@ class MemoryDataLayer : public Layer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_MEMORY_DATA;
   }
-  virtual inline int ExactNumBottomBlobs() { return 0; }
-  virtual inline int ExactNumTopBlobs() { return 2; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
 
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable

From c41c15a138de23e0bba80c31ad7813f556be6be4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 10 Jun 2014 14:45:36 -0700
Subject: [PATCH 0168/2053] move CUDA 6.0 check into switch statement itself

This allows -Wswitch to be turned on so that the compiler can check
exhaustiveness.
---
 include/caffe/common.hpp | 5 -----
 src/caffe/common.cpp     | 2 ++
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 7bfa5d402bd..bd4e39f136d 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -56,11 +56,6 @@ private:\
 // CUDA: check for error after kernel execution and exit loudly if there is one.
 #define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
 
-// Define not supported status for pre-6.0 compatibility.
-#if CUDA_VERSION < 6000
-#define CUBLAS_STATUS_NOT_SUPPORTED 831486
-#endif
-
 namespace caffe {
 
 // We will use the boost shared_ptr instead of the new C++11 one mainly
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 387d008c614..2964b2ce1d0 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -156,8 +156,10 @@ const char* cublasGetErrorString(cublasStatus_t error) {
     return "CUBLAS_STATUS_EXECUTION_FAILED";
   case CUBLAS_STATUS_INTERNAL_ERROR:
     return "CUBLAS_STATUS_INTERNAL_ERROR";
+#if CUDA_VERSION >= 6000
   case CUBLAS_STATUS_NOT_SUPPORTED:
     return "CUBLAS_STATUS_NOT_SUPPORTED";
+#endif
   }
   return "Unknown cublas status";
 }

From 1334c3fa58eb935d38e2c648f1b05dd995661a70 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 10 Jun 2014 14:50:24 -0700
Subject: [PATCH 0169/2053] initialize and comment variables that the compiler
 finds suspicious

---
 src/caffe/layers/data_layer.cpp    | 2 +-
 src/caffe/layers/pooling_layer.cpp | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index f12ae1c12e1..29c4fec8ca4 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -26,7 +26,7 @@ void* DataLayerPrefetch(void* layer_pointer) {
   Datum datum;
   CHECK(layer->prefetch_data_);
   Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
-  Dtype* top_label;
+  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
   if (layer->output_labels_) {
     top_label = layer->prefetch_label_->mutable_cpu_data();
   }
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 8f5f82d6ff7..ba84edeab5d 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -87,8 +87,8 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int top_count = (*top)[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top->size() > 1;
-  int* mask;
-  Dtype* top_mask;
+  int* mask = NULL;  // suppress warnings about uninitalized variables
+  Dtype* top_mask = NULL;
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more code.
   switch (this->layer_param_.pooling_param().pool()) {
@@ -195,8 +195,8 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
-  const int* mask;
-  const Dtype* top_mask;
+  const int* mask = NULL;  // suppress warnings about uninitialized variables
+  const Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // The main loop

From cd81969b245a245fb72e9e33c9f5ffdbfdf1389f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 10 Jun 2014 15:34:01 -0700
Subject: [PATCH 0170/2053] don't end comments with \, so that -Wcomment can be
 used

---
 tools/convert_imageset.cpp | 2 +-
 tools/dump_network.cpp     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index aa9515514c3..c4bc0b32439 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -2,7 +2,7 @@
 // This program converts a set of images to a leveldb by storing them as Datum
 // proto buffers.
 // Usage:
-//   convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1] \
+//   convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1]
 //                     [resize_height] [resize_width]
 // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
 // should be a list of files as well as their labels, in the format as
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index f29e150b048..8ed8edac284 100644
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -4,7 +4,7 @@
 // all the intermediate blobs produced by the net to individual binary
 // files stored in protobuffer binary formats.
 // Usage:
-//    dump_network input_net_param trained_net_param \
+//    dump_network input_net_param trained_net_param
 //        input_blob output_prefix 0/1
 // if input_net_param is 'none', we will directly load the network from
 // trained_net_param. If the last argv is 1, we will do a forward-backward pass

From ff5e35ee776bae64ce2433f514fd3533fced02b1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 10 Jun 2014 15:38:06 -0700
Subject: [PATCH 0171/2053] upgrade warnings to -Wall -Werror -Wno-sign-compare

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 943165a4642..e821b20d13b 100644
--- a/Makefile
+++ b/Makefile
@@ -134,7 +134,7 @@ LIBRARIES := cudart cublas curand \
 	hdf5_hl hdf5 \
 	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
-WARNINGS := -Wall
+WARNINGS := -Wall -Werror -Wno-sign-compare
 
 ##############################
 # Set build directories

From 8490718f4c3f00329a3236ba9ed2795d585f8f3c Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 10 Jun 2014 15:39:44 -0700
Subject: [PATCH 0172/2053] add WARNINGS to CXXFLAGS

---
 Makefile | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/Makefile b/Makefile
index e821b20d13b..8ab82b91a3c 100644
--- a/Makefile
+++ b/Makefile
@@ -218,7 +218,7 @@ LIBRARY_DIRS += $(BLAS_LIB)
 
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
-CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS)
+CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
 		$(foreach library,$(LIBRARIES),-l$(library))
@@ -283,7 +283,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 		exit 1; \
 	fi
 	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) $(STATIC_NAME) \
-			CXXFLAGS="\$$CXXFLAGS $(CXXFLAGS) $(WARNINGS)" \
+			CXXFLAGS="\$$CXXFLAGS $(CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(LDFLAGS)" -o $@
 	@ echo
 
@@ -306,7 +306,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
 $(NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
-	$(CXX) -shared -o $@ $(OBJS) $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+	$(CXX) -shared -o $@ $(OBJS) $(CXXFLAGS) $(LDFLAGS)
 	@ echo
 
 $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
@@ -321,21 +321,21 @@ $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+		-o $@ $(CXXFLAGS) $(LDFLAGS)
 	@ echo
 
 $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+		-o $@ $(CXXFLAGS) $(LDFLAGS)
 	@ echo
 
 $(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS)
 	@ echo
 
 $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \

From aa7371147e42e80a24da5cfc8d40bb207337a7f3 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 13 Jun 2014 19:59:33 -0700
Subject: [PATCH 0173/2053] turn off some warnings for older compilers

---
 Makefile | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 8ab82b91a3c..d62f4150c2e 100644
--- a/Makefile
+++ b/Makefile
@@ -168,6 +168,11 @@ endif
 
 ifeq ($(LINUX), 1)
 	CXX := /usr/bin/g++
+	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
+	# older versions of gcc are too dumb to build boost with -Wuninitalized
+	ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
+		WARNINGS += -Wno-uninitialized
+	endif
 endif
 
 # OS X:
@@ -175,6 +180,8 @@ endif
 # libstdc++ instead of libc++ for CUDA compatibility on 10.9
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
+	# clang throws this warning for cuda headers
+	WARNINGS += -Wno-unneeded-internal-declaration
 	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)
 		CXXFLAGS += -stdlib=libstdc++
 	endif
@@ -220,6 +227,8 @@ LIBRARY_DIRS += $(BLAS_LIB)
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
 CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
+# mex may invoke an older gcc that is too liberal with -Wuninitalized
+MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
@@ -283,7 +292,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 		exit 1; \
 	fi
 	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) $(STATIC_NAME) \
-			CXXFLAGS="\$$CXXFLAGS $(CXXFLAGS)" \
+			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(LDFLAGS)" -o $@
 	@ echo
 

From 73001f2fc0548a034bcd6398361207c9aac31ce4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 25 Jun 2014 17:32:27 -0700
Subject: [PATCH 0174/2053] fix test data layer post-lmdb

---
 src/caffe/test/test_data_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 782fe1212c6..8ba5f29ff91 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -24,10 +24,10 @@ template <typename Dtype>
 class DataLayerTest : public ::testing::Test {
  protected:
   DataLayerTest()
-      : filename_(new string(tmpnam(NULL))),
+      : backend_(DataParameter_DB_LEVELDB),
+        filename_(new string(tmpnam(NULL))),
         blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()),
-        backend_(DataParameter_DB_LEVELDB),
         seed_(1701) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);

From 30ad1e237907be354541b1fb79761d909d07728f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 25 Jun 2014 17:35:40 -0700
Subject: [PATCH 0175/2053] make clang++ happy on OSX by not linking with
 pthread

---
 Makefile | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/Makefile b/Makefile
index d62f4150c2e..32ce4722aa6 100644
--- a/Makefile
+++ b/Makefile
@@ -184,6 +184,7 @@ ifeq ($(OSX), 1)
 	WARNINGS += -Wno-unneeded-internal-declaration
 	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)
 		CXXFLAGS += -stdlib=libstdc++
+		LINKFLAGS += -stdlib=libstdc++
 	endif
 endif
 
@@ -229,6 +230,7 @@ CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
+LINKFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
@@ -278,7 +280,7 @@ py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
 $(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC)
 	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(STATIC_NAME) $(CXXFLAGS) $(PYTHON_LDFLAGS)
+		$(STATIC_NAME) $(LINKFLAGS) $(PYTHON_LDFLAGS)
 	@ echo
 
 mat$(PROJECT): mat
@@ -315,7 +317,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
 $(NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
-	$(CXX) -shared -o $@ $(OBJS) $(CXXFLAGS) $(LDFLAGS)
+	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
@@ -330,21 +332,21 @@ $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(CXXFLAGS) $(LDFLAGS)
+		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(CXXFLAGS) $(LDFLAGS)
+		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS)
+	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS)
+	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \

From 89fd7e333480d0eb4d78fb14dc6f435e76c5638c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 25 Jun 2014 17:58:38 -0700
Subject: [PATCH 0176/2053] content ourselves to -Wall without -Werror for now

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 32ce4722aa6..77e2ff5edbe 100644
--- a/Makefile
+++ b/Makefile
@@ -134,7 +134,7 @@ LIBRARIES := cudart cublas curand \
 	hdf5_hl hdf5 \
 	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
-WARNINGS := -Wall -Werror -Wno-sign-compare
+WARNINGS := -Wall -Wno-sign-compare
 
 ##############################
 # Set build directories

From 61e1ef03537461f708bcb110e9decb69ca4de544 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 26 Jun 2014 10:21:19 -0700
Subject: [PATCH 0177/2053] file SoftmaxWithLoss in with loss layers

---
 include/caffe/loss_layers.hpp   | 43 +++++++++++++++++++++++++++++++++
 include/caffe/vision_layers.hpp | 40 ------------------------------
 2 files changed, 43 insertions(+), 40 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 2f98a1246be..db7c63edca1 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -40,6 +40,49 @@ class LossLayer : public Layer<Dtype> {
   virtual inline int MaxTopBlobs() const { return 1; }
 };
 
+// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
+template <typename Dtype> class SoftmaxLayer;
+
+/* SoftmaxWithLossLayer
+  Implements softmax and computes the loss.
+
+  It is preferred over separate softmax + multinomiallogisticloss
+  layers due to more numerically stable gradients.
+
+  In test, this layer could be replaced by simple softmax layer.
+*/
+template <typename Dtype>
+class SoftmaxWithLossLayer : public Layer<Dtype> {
+ public:
+  explicit SoftmaxWithLossLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX_LOSS;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
+  // prob stores the output probability of the layer.
+  Blob<Dtype> prob_;
+  // Vector holders to call the underlying softmax layer forward and backward.
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  vector<Blob<Dtype>*> softmax_top_vec_;
+};
+
 /* SigmoidCrossEntropyLossLayer
 */
 template <typename Dtype>
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index b7e1068c7d3..3fd7e2f8bdb 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -405,46 +405,6 @@ class SoftmaxLayer : public Layer<Dtype> {
   Blob<Dtype> scale_;
 };
 
-/* SoftmaxWithLossLayer
-  Implements softmax and computes the loss.
-
-  It is preferred over separate softmax + multinomiallogisticloss
-  layers due to more numerically stable gradients.
-
-  In test, this layer could be replaced by simple softmax layer.
-*/
-template <typename Dtype>
-class SoftmaxWithLossLayer : public Layer<Dtype> {
- public:
-  explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX_LOSS;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
-  // prob stores the output probability of the layer.
-  Blob<Dtype> prob_;
-  // Vector holders to call the underlying softmax layer forward and backward.
-  vector<Blob<Dtype>*> softmax_bottom_vec_;
-  vector<Blob<Dtype>*> softmax_top_vec_;
-};
-
 /* SplitLayer
 */
 template <typename Dtype>

From 74443591d62feb2755d98ed87fb4aecc22d78297 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 10 Jun 2014 12:38:59 -0700
Subject: [PATCH 0178/2053] change Backward interface: propagate_down is a
 vector -- use to fix long-standing issue with how this is handled in loss
 layers (esp. EuclideanLossLayer)

---
 include/caffe/data_layers.hpp                 |  28 ++--
 include/caffe/layer.hpp                       |   8 +-
 include/caffe/loss_layers.hpp                 |  27 ++--
 include/caffe/net.hpp                         |   4 +
 include/caffe/neuron_layers.hpp               |  26 ++--
 include/caffe/vision_layers.hpp               |  48 +++---
 src/caffe/layers/bnll_layer.cpp               |   4 +-
 src/caffe/layers/bnll_layer.cu                |   4 +-
 src/caffe/layers/concat_layer.cpp             |  22 +--
 src/caffe/layers/concat_layer.cu              |  22 +--
 src/caffe/layers/conv_layer.cpp               |   4 +-
 src/caffe/layers/conv_layer.cu                |   4 +-
 src/caffe/layers/dropout_layer.cpp            |   4 +-
 src/caffe/layers/dropout_layer.cu             |   4 +-
 src/caffe/layers/eltwise_layer.cpp            |  12 +-
 src/caffe/layers/eltwise_layer.cu             |  12 +-
 src/caffe/layers/euclidean_loss_layer.cpp     |  24 +--
 src/caffe/layers/euclidean_loss_layer.cu      |  45 ++++++
 src/caffe/layers/flatten_layer.cpp            |   2 +-
 src/caffe/layers/flatten_layer.cu             |   2 +-
 src/caffe/layers/hdf5_data_layer.cpp          |   5 -
 src/caffe/layers/hdf5_data_layer.cu           |   5 -
 src/caffe/layers/hdf5_output_layer.cpp        |   2 +-
 src/caffe/layers/hdf5_output_layer.cu         |   2 +-
 src/caffe/layers/hinge_loss_layer.cpp         |  44 +++---
 src/caffe/layers/im2col_layer.cpp             |   2 +-
 src/caffe/layers/im2col_layer.cu              |   2 +-
 src/caffe/layers/infogain_loss_layer.cpp      |  32 ++--
 src/caffe/layers/inner_product_layer.cpp      |   4 +-
 src/caffe/layers/inner_product_layer.cu       |   4 +-
 src/caffe/layers/lrn_layer.cpp                |  20 +--
 src/caffe/layers/lrn_layer.cu                 |   4 +-
 .../multinomial_logistic_loss_layer.cpp       |  28 ++--
 src/caffe/layers/pooling_layer.cpp            |   4 +-
 src/caffe/layers/pooling_layer.cu             |   4 +-
 src/caffe/layers/power_layer.cpp              |   4 +-
 src/caffe/layers/power_layer.cu               |   4 +-
 src/caffe/layers/relu_layer.cpp               |   4 +-
 src/caffe/layers/relu_layer.cu                |   4 +-
 .../sigmoid_cross_entropy_loss_layer.cpp      |  28 ++--
 .../sigmoid_cross_entropy_loss_layer.cu       |  28 ++--
 src/caffe/layers/sigmoid_layer.cpp            |   4 +-
 src/caffe/layers/sigmoid_layer.cu             |   4 +-
 src/caffe/layers/softmax_layer.cpp            |   2 +-
 src/caffe/layers/softmax_layer.cu             |   2 +-
 src/caffe/layers/softmax_loss_layer.cpp       |  29 ++--
 src/caffe/layers/softmax_loss_layer.cu        |   2 +-
 src/caffe/layers/split_layer.cpp              |   4 +-
 src/caffe/layers/split_layer.cu               |   4 +-
 src/caffe/layers/tanh_layer.cpp               |   4 +-
 src/caffe/layers/tanh_layer.cu                |   4 +-
 src/caffe/net.cpp                             |   6 +-
 src/caffe/test/test_euclidean_loss_layer.cpp  |  14 +-
 src/caffe/test/test_gradient_check_util.hpp   |   6 +-
 src/caffe/test/test_lrn_layer.cpp             |  10 +-
 .../test/test_maxpool_dropout_layers.cpp      |  20 ++-
 src/caffe/test/test_net.cpp                   | 143 +++++++++++++++++-
 tools/net_speed_benchmark.cpp                 |   5 +-
 58 files changed, 522 insertions(+), 281 deletions(-)
 create mode 100644 src/caffe/layers/euclidean_loss_layer.cu

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index fc93daacd3c..2c6be551d8f 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -47,9 +47,9 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void SaveBlobs();
 
   std::string file_name_;
@@ -80,9 +80,9 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void LoadHDF5FileData(const char* filename);
 
   std::vector<std::string> hdf_filenames_;
@@ -125,9 +125,9 @@ class DataLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
@@ -175,9 +175,9 @@ class DummyDataLayer : public Layer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   vector<shared_ptr<Filler<Dtype> > > fillers_;
   vector<bool> refill_;
@@ -211,9 +211,9 @@ class ImageDataLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   virtual void ShuffleImages();
 
@@ -263,9 +263,9 @@ class MemoryDataLayer : public Layer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   Dtype* data_;
   Dtype* labels_;
@@ -306,9 +306,9 @@ class WindowDataLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index af3d5441b34..12e7610d756 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -46,7 +46,7 @@ class Layer {
   inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   inline void Backward(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down,
+      const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom);
 
   // Returns the vector of blobs.
@@ -104,10 +104,10 @@ class Layer {
   // Backward functions: compute the gradients for any parameters and
   // for the bottom blobs if propagate_down is true.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down,
+      const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom) = 0;
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down,
+      const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom) {
     // LOG(WARNING) << "Using CPU code as backup.";
     Backward_cpu(top, propagate_down, bottom);
@@ -172,7 +172,7 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   switch (Caffe::mode()) {
   case Caffe::CPU:
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index db7c63edca1..b8adc996be5 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -62,7 +62,6 @@ class SoftmaxWithLossLayer : public Layer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SOFTMAX_LOSS;
   }
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
@@ -71,9 +70,9 @@ class SoftmaxWithLossLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
   // prob stores the output probability of the layer.
@@ -105,9 +104,9 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
   // sigmoid_output stores the output of the sigmoid layer.
@@ -138,8 +137,12 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Blob<Dtype> diff_;
 };
@@ -162,7 +165,7 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Blob<Dtype> infogain_;
 };
@@ -183,7 +186,7 @@ class HingeLossLayer : public LossLayer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* MultinomialLogisticLossLayer
@@ -204,7 +207,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* AccuracyLayer
@@ -230,15 +233,11 @@ class AccuracyLayer : public Layer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
   }
 };
 
-/* Also see
-- SoftmaxWithLossLayer in vision_layers.hpp
-*/
-
 }  // namespace caffe
 
 #endif  // CAFFE_LOSS_LAYERS_HPP_
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 5b5dd22a282..d05ca09f20a 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -81,6 +81,9 @@ class Net {
   // this unless you do per-layer checks such as gradients.
   inline vector<vector<Blob<Dtype>*> >& bottom_vecs() { return bottom_vecs_; }
   inline vector<vector<Blob<Dtype>*> >& top_vecs() { return top_vecs_; }
+  inline vector<vector<bool> >& bottom_need_backward() {
+    return bottom_need_backward_;
+  }
   // returns the parameters
   inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
   // returns the parameter learning rate multipliers
@@ -131,6 +134,7 @@ class Net {
   // pointers.
   vector<vector<Blob<Dtype>*> > bottom_vecs_;
   vector<vector<int> > bottom_id_vecs_;
+  vector<vector<bool> > bottom_need_backward_;
   // top_vecs stores the vectors containing the output for each layer
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index ed664df7570..e52e395e24b 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -64,9 +64,9 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* DropoutLayer
@@ -97,9 +97,9 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   shared_ptr<Blob<unsigned int> > rand_vec_;
   Dtype threshold_;
@@ -131,9 +131,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Dtype power_;
   Dtype scale_;
@@ -167,9 +167,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
       vector<Blob<Dtype>*>* top);
 
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* SigmoidLayer
@@ -199,9 +199,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* TanHLayer
@@ -227,9 +227,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* ThresholdLayer
@@ -260,7 +260,7 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
   }
 
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 3fd7e2f8bdb..b68dcbf6e83 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -44,7 +44,7 @@ class ArgMaxLayer : public Layer<Dtype> {
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
   }
   bool out_max_val_;
@@ -74,9 +74,9 @@ class ConcatLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Blob<Dtype> col_bob_;
   int count_;
@@ -109,9 +109,9 @@ class ConvolutionLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int kernel_size_;
   int stride_;
@@ -153,9 +153,9 @@ class EltwiseLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   EltwiseParameter_EltwiseOp op_;
   vector<Dtype> coeffs_;
@@ -183,9 +183,9 @@ class FlattenLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int count_;
 };
@@ -212,9 +212,9 @@ class Im2colLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int kernel_size_;
   int stride_;
@@ -246,9 +246,9 @@ class InnerProductLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int M_;
   int K_;
@@ -284,9 +284,9 @@ class LRNLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   virtual Dtype CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -295,11 +295,11 @@ class LRNLayer : public Layer<Dtype> {
   virtual Dtype WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int size_;
   int pre_pad_;
@@ -356,9 +356,9 @@ class PoolingLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int max_top_blobs_;
   int kernel_size_;
@@ -395,9 +395,9 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   // sum_multiplier is just used to carry out sum using blas
   Blob<Dtype> sum_multiplier_;
@@ -427,9 +427,9 @@ class SplitLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int count_;
 };
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index d08adc49eef..95e6bd8748c 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -28,9 +28,9 @@ Dtype BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 75bea00e993..7849d11c25d 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -45,9 +45,9 @@ __global__ void BNLLBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 4541ee74215..4c894ddffc4 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -70,26 +70,30 @@ Dtype ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   if (concat_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_cpu_diff();
-      caffe_copy(blob->count(),
-        top_diff+top[0]->offset(offset_num), bottom_diff);
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_cpu_diff();
+        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
+                   bottom_diff);
+      }
       offset_num += blob->num();
     }
   } else if (concat_dim_ == 1) {
     int offset_channel = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_cpu_diff();
-      int num_elem = blob->channels()*blob->height()*blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, top_diff+top[0]->offset(n, offset_channel),
-          bottom_diff+blob->offset(n));
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_cpu_diff();
+        int num_elem = blob->channels()*blob->height()*blob->width();
+        for (int n = 0; n < num_; ++n) {
+          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
+                     bottom_diff + blob->offset(n));
+        }
       }
       offset_channel += blob->channels();
     }
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 2820bf0dfdf..ca0cf0c1b5b 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -41,26 +41,30 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   if (concat_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_gpu_diff();
-      caffe_gpu_copy(blob->count(),
-        top_diff + top[0]->offset(offset_num), bottom_diff);
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_gpu_diff();
+        caffe_gpu_copy(blob->count(), top_diff + top[0]->offset(offset_num),
+                       bottom_diff);
+      }
       offset_num += blob->num();
     }
   } else if (concat_dim_ == 1) {
     int offset_channel = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_gpu_diff();
-      int num_elem = blob->channels()*blob->height()*blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_gpu_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
-          bottom_diff + blob->offset(n));
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_gpu_diff();
+        int num_elem = blob->channels()*blob->height()*blob->width();
+        for (int n = 0; n < num_; ++n) {
+          caffe_gpu_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
+                         bottom_diff + blob->offset(n));
+        }
       }
       offset_channel += blob->channels();
     }
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 880b83afccb..9ec8da47e1a 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -109,7 +109,7 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
@@ -148,7 +148,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         weight_diff + weight_offset * g);
     }
     // gradient w.r.t. bottom data, if necessary
-    if (propagate_down) {
+    if (propagate_down[0]) {
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
           (Dtype)1., weight + weight_offset * g,
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 51f5d159879..85f95fd32c9 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -43,7 +43,7 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
@@ -84,7 +84,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         weight_diff + weight_offset * g);
     }
     // gradient w.r.t. bottom data, if necessary
-    if (propagate_down) {
+    if (propagate_down[0]) {
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
           (Dtype)1., weight + weight_offset * g,
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index f1e54112986..e9a1a524d63 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -47,10 +47,10 @@ Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   CHECK(Caffe::phase() == Caffe::TRAIN);
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const unsigned int* mask = rand_vec_->cpu_data();
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 3c25d6a1292..2c7226402e8 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -56,10 +56,10 @@ __global__ void DropoutBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   CHECK(Caffe::phase() == Caffe::TRAIN);
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const unsigned int* mask =
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 5e5d760c993..2c265f6678f 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -67,12 +67,12 @@ Dtype EltwiseLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    const int count = top[0]->count();
-    const Dtype* top_data = top[0]->cpu_data();
-    const Dtype* top_diff = top[0]->cpu_diff();
-    for (int i = 0; i < bottom->size(); ++i) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int count = top[0]->count();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  for (int i = 0; i < bottom->size(); ++i) {
+    if (propagate_down[i]) {
       const Dtype* bottom_data = (*bottom)[i]->cpu_data();
       Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
       switch (op_) {
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 75827badb80..3860944889c 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -36,12 +36,12 @@ Dtype EltwiseLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    const int count = top[0]->count();
-    const Dtype* top_data = top[0]->gpu_data();
-    const Dtype* top_diff = top[0]->gpu_diff();
-    for (int i = 0; i < bottom->size(); ++i) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int count = top[0]->count();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  for (int i = 0; i < bottom->size(); ++i) {
+    if (propagate_down[i]) {
       const Dtype* bottom_data = (*bottom)[i]->gpu_data();
       Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
       switch (op_) {
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 766294997f0..2478a514cac 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -1,8 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <algorithm>
-#include <cmath>
-#include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
@@ -10,8 +7,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -43,13 +38,18 @@ Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  caffe_cpu_axpby(
-      (*bottom)[0]->count(),              // count
-      Dtype(1) / (*bottom)[0]->num(),     // alpha
-      diff_.cpu_data(),                   // a
-      Dtype(0),                           // beta
-      (*bottom)[0]->mutable_cpu_diff());  // b
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const Dtype sign = (i == 0) ? 1 : -1;
+      caffe_cpu_axpby(
+          (*bottom)[i]->count(),              // count
+          sign / (*bottom)[i]->num(),         // alpha
+          diff_.cpu_data(),                   // a
+          Dtype(0),                           // beta
+          (*bottom)[i]->mutable_cpu_diff());  // b
+    }
+  }
 }
 
 INSTANTIATE_CLASS(EuclideanLossLayer);
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
new file mode 100644
index 00000000000..b070ea96ff8
--- /dev/null
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -0,0 +1,45 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/io.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Dtype EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  int count = bottom[0]->count();
+  caffe_gpu_sub(
+      count,
+      bottom[0]->gpu_data(),
+      bottom[1]->gpu_data(),
+      diff_.mutable_gpu_data());
+  Dtype dot;
+  caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
+  Dtype loss = dot / bottom[0]->num() / Dtype(2);
+  return loss;
+}
+
+template <typename Dtype>
+void EuclideanLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const Dtype sign = (i == 0) ? 1 : -1;
+      caffe_gpu_axpby(
+          (*bottom)[i]->count(),              // count
+          sign / (*bottom)[i]->num(),         // alpha
+          diff_.gpu_data(),                   // a
+          Dtype(0),                           // beta
+          (*bottom)[i]->mutable_gpu_diff());  // b
+    }
+  }
+}
+
+INSTANTIATE_CLASS(EuclideanLossLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 95f4859f8d9..9494da9a255 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -29,7 +29,7 @@ Dtype FlattenLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   (*bottom)[0]->ShareDiff(*top[0]);
 }
 
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index 157eeb1dcdc..68add383c48 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -17,7 +17,7 @@ Dtype FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   (*bottom)[0]->ShareDiff(*top[0]);
 }
 
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 50863b8d93a..2ba7fa77f45 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -114,11 +114,6 @@ Dtype HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
-// The backward operations are dummy - they do not carry any computation.
-template <typename Dtype>
-void HDF5DataLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { }
-
 INSTANTIATE_CLASS(HDF5DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 9c5bb5a818f..b2b09ef7dd1 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -54,11 +54,6 @@ Dtype HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
-template <typename Dtype>
-void HDF5DataLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-}
-
 INSTANTIATE_CLASS(HDF5DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 0961b9b73a3..3a513b9c366 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -67,7 +67,7 @@ Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   return;
 }
 
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index b9948252285..59505ee6acf 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -40,7 +40,7 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   return;
 }
 
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index fd00d8c6930..8097761d22b 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -45,27 +45,33 @@ Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  const Dtype* label = (*bottom)[1]->cpu_data();
-  int num = (*bottom)[0]->num();
-  int count = (*bottom)[0]->count();
-  int dim = count / num;
-
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
   }
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* label = (*bottom)[1]->cpu_data();
+    int num = (*bottom)[0]->num();
+    int count = (*bottom)[0]->count();
+    int dim = count / num;
 
-  switch (this->layer_param_.hinge_loss_param().norm()) {
-  case HingeLossParameter_Norm_L1:
-    caffe_cpu_sign(count, bottom_diff, bottom_diff);
-    caffe_scal(count, Dtype(1. / num), bottom_diff);
-    break;
-  case HingeLossParameter_Norm_L2:
-    caffe_scal(count, Dtype(2. / num), bottom_diff);
-    break;
-  default:
-    LOG(FATAL) << "Unknown Norm";
+    for (int i = 0; i < num; ++i) {
+      bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
+    }
+
+    switch (this->layer_param_.hinge_loss_param().norm()) {
+    case HingeLossParameter_Norm_L1:
+      caffe_cpu_sign(count, bottom_diff, bottom_diff);
+      caffe_scal(count, Dtype(1. / num), bottom_diff);
+      break;
+    case HingeLossParameter_Norm_L2:
+      caffe_scal(count, Dtype(2. / num), bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown Norm";
+    }
   }
 }
 
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index f0c26c9a1cb..e047dfb80a7 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -38,7 +38,7 @@ Dtype Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 26bc1b97959..9cfb74e815c 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -23,7 +23,7 @@ Dtype Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 3e7fc4f812b..a72874e4bb4 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -56,20 +56,26 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+    const Dtype* infogain_mat = infogain_.cpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    int num = (*bottom)[0]->num();
+    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    CHECK_EQ(infogain_.height(), dim);
+    for (int i = 0; i < num; ++i) {
+      int label = static_cast<int>(bottom_label[i]);
+      for (int j = 0; j < dim; ++j) {
+        Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+        bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+      }
     }
   }
 }
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 971254c9c45..ddf55e49b63 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -74,7 +74,7 @@ Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* bottom_data = (*bottom)[0]->cpu_data();
@@ -87,7 +87,7 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), (Dtype)0.,
         this->blobs_[1]->mutable_cpu_diff());
   }
-  if (propagate_down) {
+  if (propagate_down[0]) {
     // Gradient with respect to bottom data
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index f139c23c310..5b95a57b23b 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -31,7 +31,7 @@ Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = (*bottom)[0]->gpu_data();
@@ -44,7 +44,7 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
         (Dtype)0., this->blobs_[1]->mutable_gpu_diff());
   }
-  if (propagate_down) {
+  if (propagate_down[0]) {
     // Gradient with respect to bottom data
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 071e7198544..a86c1d4c59d 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -173,7 +173,7 @@ Dtype LRNLayer<Dtype>::WithinChannelForward(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_cpu(top, propagate_down, bottom);
@@ -188,7 +188,7 @@ void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
@@ -243,14 +243,16 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::WithinChannelBackward(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    product_layer_->Backward(top, true, &product_bottom_vec_);
-    power_layer_->Backward(power_top_vec_, true, &pool_top_vec_);
-    pool_layer_->Backward(pool_top_vec_, true, &square_top_vec_);
-    square_layer_->Backward(square_top_vec_, true, &square_bottom_vec_);
-    split_layer_->Backward(split_top_vec_, true, bottom);
+  if (propagate_down[0]) {
+    vector<bool> product_propagate_down(2, true);
+    product_layer_->Backward(top, product_propagate_down, &product_bottom_vec_);
+    power_layer_->Backward(power_top_vec_, propagate_down, &pool_top_vec_);
+    pool_layer_->Backward(pool_top_vec_, propagate_down, &square_top_vec_);
+    square_layer_->Backward(square_top_vec_, propagate_down,
+                            &square_bottom_vec_);
+    split_layer_->Backward(split_top_vec_, propagate_down, bottom);
   }
 }
 
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index b2097eb99cf..237a42b2860 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -104,7 +104,7 @@ Dtype LRNLayer<Dtype>::CrossChannelForward_gpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_gpu(top, propagate_down, bottom);
@@ -180,7 +180,7 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_gpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   int n_threads = num_ * height_ * width_;
   // NOLINT_NEXT_LINE(whitespace/operators)
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 5a408795d6d..013d4034240 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -43,18 +43,24 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-    bottom_diff[i * dim + label] = -1. / prob / num;
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    int num = (*bottom)[0]->num();
+    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
+    for (int i = 0; i < num; ++i) {
+      int label = static_cast<int>(bottom_label[i]);
+      Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+      bottom_diff[i * dim + label] = -1. / prob / num;
+    }
   }
 }
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index ba84edeab5d..bc002078814 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -184,8 +184,8 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (!propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->cpu_diff();
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index abba6252cc6..f07fe3c48ac 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -322,8 +322,8 @@ __global__ void StoPoolBackward(const int nthreads,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (!propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->gpu_diff();
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 85c84423aa2..5ff3392968e 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -49,9 +49,9 @@ Dtype PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
     const Dtype* top_diff = top[0]->cpu_diff();
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 9a25de72d36..6d699636e21 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -38,9 +38,9 @@ Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const int count = (*bottom)[0]->count();
     const Dtype* top_diff = top[0]->gpu_diff();
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 7a33e556268..d7a8509b247 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -24,9 +24,9 @@ Dtype ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index 51e5ef26c81..a5b24afab60 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -45,9 +45,9 @@ __global__ void ReLUBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 955581d8875..8cb830ff248 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -16,7 +16,7 @@ template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::FurtherSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
-      "SigmoidCrossEntropyLoss Layer inputs must have same count.";
+      "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
   sigmoid_bottom_vec_.clear();
   sigmoid_bottom_vec_.push_back(bottom[0]);
   sigmoid_top_vec_.clear();
@@ -49,17 +49,23 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  // First, compute the diff
-  const int count = (*bottom)[0]->count();
-  const int num = (*bottom)[0]->num();
-  const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
-  const Dtype* target = (*bottom)[1]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  caffe_sub(count, sigmoid_output_data, target, bottom_diff);
-  // Scale down gradient
-  caffe_scal(count, Dtype(1) / num, bottom_diff);
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    // First, compute the diff
+    const int count = (*bottom)[0]->count();
+    const int num = (*bottom)[0]->num();
+    const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
+    const Dtype* target = (*bottom)[1]->cpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    caffe_sub(count, sigmoid_output_data, target, bottom_diff);
+    // Scale down gradient
+    caffe_scal(count, Dtype(1) / num, bottom_diff);
+  }
 }
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 0caed2b83bd..8f7275827e2 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -37,18 +37,24 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  // First, compute the diff
-  const int count = (*bottom)[0]->count();
-  const int num = (*bottom)[0]->num();
-  const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
-  const Dtype* target = (*bottom)[1]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  caffe_gpu_copy(count, sigmoid_output_data, bottom_diff);
-  caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
-  // Scale down gradient
-  caffe_gpu_scal(count, Dtype(1) / num, bottom_diff);
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    // First, compute the diff
+    const int count = (*bottom)[0]->count();
+    const int num = (*bottom)[0]->num();
+    const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
+    const Dtype* target = (*bottom)[1]->gpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    caffe_gpu_copy(count, sigmoid_output_data, bottom_diff);
+    caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
+    // Scale down gradient
+    caffe_gpu_scal(count, Dtype(1) / num, bottom_diff);
+  }
 }
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 88a7920fc18..50139d863dd 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -28,9 +28,9 @@ Dtype SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index aa8568abb3f..d55968e58c2 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -47,9 +47,9 @@ __global__ void SigmoidBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index dbe16da2343..57847d005f6 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -60,7 +60,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index a264a819b78..f53883c9206 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -79,7 +79,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 // TODO(Yangqing): implement the GPU version of softmax.
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index bdb3272ee25..1a3601aa9e6 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -57,20 +57,25 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  // Compute the diff
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  const Dtype* prob_data = prob_.cpu_data();
-  memcpy(bottom_diff, prob_data, sizeof(Dtype) * prob_.count());
-  const Dtype* label = (*bottom)[1]->cpu_data();
-  int num = prob_.num();
-  int dim = prob_.count() / num;
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* prob_data = prob_.cpu_data();
+    memcpy(bottom_diff, prob_data, sizeof(Dtype) * prob_.count());
+    const Dtype* label = (*bottom)[1]->cpu_data();
+    int num = prob_.num();
+    int dim = prob_.count() / num;
+    for (int i = 0; i < num; ++i) {
+      bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
+    }
+    // Scale down gradient
+    caffe_scal(prob_.count(), Dtype(1) / num, bottom_diff);
   }
-  // Scale down gradient
-  caffe_scal(prob_.count(), Dtype(1) / num, bottom_diff);
 }
 
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 24a3c384c96..e46be6ba85d 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -21,7 +21,7 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   // TODO(Yangqing): implement the GPU version of softmax.
   Backward_cpu(top, propagate_down, bottom);
 }
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 2f99ca1842c..28abd95f5ff 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -37,8 +37,8 @@ Dtype SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[0]) {
     (*bottom)[0]->ShareDiff(*top[0]);
     // Add remaining top blob diffs.
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index e2269b8beab..4c921d39f17 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -19,8 +19,8 @@ Dtype SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[0]) {
     (*bottom)[0]->ShareDiff(*top[0]);
     // Add remaining top blob diffs.
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 77b4410411c..6b5166d53e9 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -26,9 +26,9 @@ Dtype TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index aa822d84f38..9307cab2ec5 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -42,9 +42,9 @@ __global__ void TanHBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index d76d284df2e..8687d0070ce 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -56,6 +56,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_vecs_.resize(param.layers_size());
   bottom_id_vecs_.resize(param.layers_size());
   top_id_vecs_.resize(param.layers_size());
+  bottom_need_backward_.resize(param.layers_size());
   for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
     const LayerParameter& layer_param = param.layers(layer_id);
     layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
@@ -196,6 +197,8 @@ int Net<Dtype>::AppendBottom(const NetParameter& param,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
+  const bool need_backward = param.force_backward() || blob_need_backward_[blob_id];
+  bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
 
@@ -288,7 +291,8 @@ template <typename Dtype>
 void Net<Dtype>::Backward() {
   for (int i = layers_.size() - 1; i >= 0; --i) {
     if (layer_need_backward_[i]) {
-      layers_[i]->Backward(top_vecs_[i], true, &bottom_vecs_[i]);
+      layers_[i]->Backward(
+          top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
     }
   }
 }
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index d5e4107ac5e..02945b655de 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -47,13 +47,23 @@ typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(EuclideanLossLayerTest, Dtypes);
 
 TYPED_TEST(EuclideanLossLayerTest, TestGradientCPU) {
-  LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  EuclideanLossLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), -1, -1, -1);
+}
+
+TYPED_TEST(EuclideanLossLayerTest, TestGradientGPU) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
   EuclideanLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+      &(this->blob_top_vec_), -1, -1, -1);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_check_util.hpp b/src/caffe/test/test_gradient_check_util.hpp
index bcf03973dd3..ff104b90eb9 100644
--- a/src/caffe/test/test_gradient_check_util.hpp
+++ b/src/caffe/test/test_gradient_check_util.hpp
@@ -86,6 +86,7 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
   }
   // First, figure out what blobs we need to check against.
   vector<Blob<Dtype>*> blobs_to_check;
+  vector<bool> propagate_down(bottom->size(), check_bottom < 0);
   for (int i = 0; i < layer->blobs().size(); ++i) {
     blobs_to_check.push_back(layer->blobs()[i].get());
   }
@@ -94,8 +95,9 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
       blobs_to_check.push_back((*bottom)[i]);
     }
   } else {
-    CHECK(check_bottom < bottom->size());
+    CHECK_LT(check_bottom, bottom->size());
     blobs_to_check.push_back((*bottom)[check_bottom]);
+    propagate_down[check_bottom] = true;
   }
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
@@ -103,7 +105,7 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
   Dtype computed_objective = layer->Forward(*bottom, top);
   // Get additional loss from the objective
   computed_objective += GetObjAndGradient(top, top_id, top_data_id);
-  layer->Backward(*top, true, bottom);
+  layer->Backward(*top, propagate_down, bottom);
   // Store computed gradients for all checked blobs
   vector<shared_ptr<Blob<Dtype> > >
       computed_gradient_blobs(blobs_to_check.size());
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 7f0a211a50b..6996a234fc6 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -164,7 +164,9 @@ TYPED_TEST(LRNLayerTest, TestCPUGradientAcrossChannels) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
   //   std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
   //       << std::endl;
@@ -183,7 +185,9 @@ TYPED_TEST(LRNLayerTest, TestGPUGradientAcrossChannels) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
   //   std::cout << "GPU diff " << this->blob_bottom_->cpu_diff()[i]
   //       << std::endl;
@@ -254,7 +258,6 @@ TYPED_TEST(LRNLayerTest, TestCPUGradientWithinChannel) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
@@ -272,7 +275,6 @@ TYPED_TEST(LRNLayerTest, TestGPUGradientWithinChannel) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 3862e12697b..ac6f86f0cf8 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -130,7 +130,9 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
   TypeParam sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -141,8 +143,10 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
   DropoutLayer<TypeParam> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Backward(this->blob_top_vec_, true, &(this->blob_top_vec_));
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  dropout_layer.Backward(this->blob_top_vec_, propagate_down,
+                         &(this->blob_top_vec_));
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   TypeParam sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -164,7 +168,9 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
   TypeParam sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -175,8 +181,10 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
   DropoutLayer<TypeParam> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Backward(this->blob_top_vec_, true, &(this->blob_top_vec_));
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  dropout_layer.Backward(this->blob_top_vec_, propagate_down,
+                         &(this->blob_top_vec_));
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   TypeParam sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 7f2f67b59c6..eb368cd6c22 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1,7 +1,9 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <google/protobuf/text_format.h>
 #include <string>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
 
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
@@ -15,9 +17,15 @@ namespace caffe {
 template <typename Dtype>
 class NetTest : public ::testing::Test {
  protected:
-  virtual void SetUp() {
-    const string& proto =
-        "name: 'TestNetwork' "
+  virtual void InitNetFromProtoString(const string& proto) {
+    NetParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    net_.reset(new Net<Dtype>(param));
+  }
+
+  virtual void InitTinyNet(const bool force_backward = false) {
+    string proto =
+        "name: 'TinyTestNetwork' "
         "layers: { "
         "  name: 'data' "
         "  type: DUMMY_DATA "
@@ -66,9 +74,84 @@ class NetTest : public ::testing::Test {
         "  bottom: 'label' "
         "  top: 'top_loss' "
         "} ";
-    NetParameter param;
-    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
-    net_.reset(new Net<Dtype>(param));
+    if (force_backward) {
+      proto += "force_backward: true ";
+    }
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitTrickyNet() {
+    const string& proto =
+        "name: 'TrickyTestNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    num: 5 "
+        "    channels: 1 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "  top: 'label' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 1000 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
+        "  } "
+        "  blobs_lr: 1. "
+        "  blobs_lr: 2. "
+        "  weight_decay: 1. "
+        "  weight_decay: 0. "
+        "  bottom: 'data' "
+        "  top: 'transformed_data' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 1 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
+        "  } "
+        "  blobs_lr: 1. "
+        "  blobs_lr: 2. "
+        "  weight_decay: 1. "
+        "  weight_decay: 0. "
+        "  bottom: 'label' "
+        "  top: 'transformed_label' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: SOFTMAX_LOSS "
+        "  bottom: 'transformed_data' "
+        "  bottom: 'transformed_label' "
+        "} ";
+    InitNetFromProtoString(proto);
   }
 
   shared_ptr<Net<Dtype> > net_;
@@ -78,6 +161,7 @@ typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(NetTest, Dtypes);
 
 TYPED_TEST(NetTest, TestHasBlob) {
+  this->InitTinyNet();
   EXPECT_TRUE(this->net_->has_blob("data"));
   EXPECT_TRUE(this->net_->has_blob("label"));
   EXPECT_TRUE(this->net_->has_blob("innerproduct"));
@@ -86,6 +170,7 @@ TYPED_TEST(NetTest, TestHasBlob) {
 }
 
 TYPED_TEST(NetTest, TestGetBlob) {
+  this->InitTinyNet();
   EXPECT_EQ(this->net_->blob_by_name("data"), this->net_->blobs()[0]);
   EXPECT_EQ(this->net_->blob_by_name("label"), this->net_->blobs()[1]);
   EXPECT_EQ(this->net_->blob_by_name("innerproduct"), this->net_->blobs()[2]);
@@ -94,6 +179,7 @@ TYPED_TEST(NetTest, TestGetBlob) {
 }
 
 TYPED_TEST(NetTest, TestHasLayer) {
+  this->InitTinyNet();
   EXPECT_TRUE(this->net_->has_layer("data"));
   EXPECT_TRUE(this->net_->has_layer("innerproduct"));
   EXPECT_TRUE(this->net_->has_layer("loss"));
@@ -101,10 +187,53 @@ TYPED_TEST(NetTest, TestHasLayer) {
 }
 
 TYPED_TEST(NetTest, TestGetLayerByName) {
+  this->InitTinyNet();
   EXPECT_EQ(this->net_->layer_by_name("data"), this->net_->layers()[0]);
   EXPECT_EQ(this->net_->layer_by_name("innerproduct"), this->net_->layers()[1]);
   EXPECT_EQ(this->net_->layer_by_name("loss"), this->net_->layers()[2]);
   EXPECT_FALSE(this->net_->layer_by_name("label"));
 }
 
+TYPED_TEST(NetTest, TestBottomNeedBackward) {
+  this->InitTinyNet();
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(3, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(false, bottom_need_backward[1][0]);
+  EXPECT_EQ(2, bottom_need_backward[2].size());
+  EXPECT_EQ(true, bottom_need_backward[2][0]);
+  EXPECT_EQ(false, bottom_need_backward[2][1]);
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
+  const bool force_backward = true;
+  this->InitTinyNet(force_backward);
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(3, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(true, bottom_need_backward[1][0]);
+  EXPECT_EQ(2, bottom_need_backward[2].size());
+  EXPECT_EQ(true, bottom_need_backward[2][0]);
+  EXPECT_EQ(true, bottom_need_backward[2][1]);
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
+  this->InitTrickyNet();
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(4, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(false, bottom_need_backward[1][0]);
+  EXPECT_EQ(1, bottom_need_backward[2].size());
+  EXPECT_EQ(false, bottom_need_backward[2][0]);
+  EXPECT_EQ(2, bottom_need_backward[3].size());
+  EXPECT_EQ(true, bottom_need_backward[3][0]);
+  EXPECT_EQ(true, bottom_need_backward[3][1]);
+}
+
 }  // namespace caffe
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index 36a00779f60..4b65cfd3dcc 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -64,6 +64,8 @@ int main(int argc, char** argv) {
   const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
   vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
   vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
+  const vector<vector<bool> >& bottom_need_backward =
+      caffe_net.bottom_need_backward();
   LOG(ERROR) << "*** Benchmark begins ***";
   Timer total_timer;
   total_timer.Start();
@@ -87,7 +89,8 @@ int main(int argc, char** argv) {
     const string& layername = layers[i]->layer_param().name();
     timer.Start();
     for (int j = 0; j < total_iter; ++j) {
-      layers[i]->Backward(top_vecs[i], true, &bottom_vecs[i]);
+      layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
+                          &bottom_vecs[i]);
     }
     LOG(ERROR) << layername << "\tbackward: "
         << timer.MilliSeconds() << " milli seconds.";

From a04834f6ceee13310b7f4e17ccf639c1e7abd459 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 16 Jun 2014 16:37:17 -0700
Subject: [PATCH 0179/2053] force_backward works properly with
 non-backproppable things

---
 include/caffe/layer.hpp       |  8 ++++
 include/caffe/loss_layers.hpp | 15 +++++++
 src/caffe/net.cpp             | 21 ++++++++--
 src/caffe/test/test_net.cpp   | 73 +++++++++++++++++++++++++++++++++++
 4 files changed, 114 insertions(+), 3 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 12e7610d756..690c36ba23f 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -84,6 +84,14 @@ class Layer {
   virtual inline int MinTopBlobs() const { return -1; }
   virtual inline int MaxTopBlobs() const { return -1; }
 
+  // Declare for each bottom blob whether to allow force_backward -- that is,
+  // if AllowForceBackward(i) == false, we will ignore the force_backward
+  // setting and backpropagate to blob i only if it needs gradient information
+  // (as is done when force_backward == false).
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return true;
+  }
+
  protected:
   // The protobuf that stores the layer parameters
   LayerParameter layer_param_;
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index b8adc996be5..bb03f637450 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -38,6 +38,11 @@ class LossLayer : public Layer<Dtype> {
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
   virtual inline int MaxTopBlobs() const { return 1; }
+  // We usually cannot backpropagate to the labels; ignore force_backward for
+  // these inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
+  }
 };
 
 // Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
@@ -63,6 +68,11 @@ class SoftmaxWithLossLayer : public Layer<Dtype> {
     return LayerParameter_LayerType_SOFTMAX_LOSS;
   }
   virtual inline int MaxTopBlobs() const { return 2; }
+  // We cannot backpropagate to the labels; ignore force_backward for these
+  // inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
+  }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -133,6 +143,11 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_EUCLIDEAN_LOSS;
   }
+  // Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+  // to both inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return true;
+  }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 8687d0070ce..a6537619a8b 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -62,7 +62,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
-    bool need_backward = param.force_backward();
+    bool need_backward = false;
     // Figure out this layer's input and output
     for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
          ++bottom_id) {
@@ -112,6 +112,21 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
                 << " does not need backward computation.";
     }
   }
+  // Handle force_backward if needed.
+  if (param.force_backward()) {
+    for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
+      layer_need_backward_[layer_id] = true;
+      for (int bottom_id = 0;
+           bottom_id < bottom_need_backward_[layer_id].size(); ++bottom_id) {
+        bottom_need_backward_[layer_id][bottom_id] =
+            bottom_need_backward_[layer_id][bottom_id] ||
+            layers_[layer_id]->AllowForceBackward(bottom_id);
+        blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] =
+            blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] ||
+            bottom_need_backward_[layer_id][bottom_id];
+      }
+    }
+  }
   // In the end, all remaining blobs are considered output blobs.
   for (set<string>::iterator it = available_blobs.begin();
       it != available_blobs.end(); ++it) {
@@ -162,7 +177,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     const int blob_id = blobs_.size();
     blobs_.push_back(blob_pointer);
     blob_names_.push_back(blob_name);
-    blob_need_backward_.push_back(param.force_backward());
+    blob_need_backward_.push_back(false);
     (*blob_name_to_idx)[blob_name] = blob_id;
     if (layer_id == -1) {
       // Set the (explicitly specified) dimensions of the input blob.
@@ -197,7 +212,7 @@ int Net<Dtype>::AppendBottom(const NetParameter& param,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  const bool need_backward = param.force_backward() || blob_need_backward_[blob_id];
+  const bool need_backward = blob_need_backward_[blob_id];
   bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index eb368cd6c22..8fb02fcb7a7 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -80,6 +80,62 @@ class NetTest : public ::testing::Test {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitTinyNetEuclidean(const bool force_backward = false) {
+    string proto =
+        "name: 'TinyTestEuclidLossNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    num: 5 "
+        "    channels: 1 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "  top: 'label' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 1 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
+        "  } "
+        "  blobs_lr: 1. "
+        "  blobs_lr: 2. "
+        "  weight_decay: 1. "
+        "  weight_decay: 0. "
+        "  bottom: 'data' "
+        "  top: 'innerproduct' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'innerproduct' "
+        "  bottom: 'label' "
+        "} ";
+    if (force_backward) {
+      proto += "force_backward: true ";
+    }
+    InitNetFromProtoString(proto);
+  }
+
   virtual void InitTrickyNet() {
     const string& proto =
         "name: 'TrickyTestNetwork' "
@@ -218,6 +274,20 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
   EXPECT_EQ(true, bottom_need_backward[1][0]);
   EXPECT_EQ(2, bottom_need_backward[2].size());
   EXPECT_EQ(true, bottom_need_backward[2][0]);
+  EXPECT_EQ(false, bottom_need_backward[2][1]);
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackwardEuclideanForce) {
+  const bool force_backward = true;
+  this->InitTinyNetEuclidean(force_backward);
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(3, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(true, bottom_need_backward[1][0]);
+  EXPECT_EQ(2, bottom_need_backward[2].size());
+  EXPECT_EQ(true, bottom_need_backward[2][0]);
   EXPECT_EQ(true, bottom_need_backward[2][1]);
 }
 
@@ -233,6 +303,9 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
   EXPECT_EQ(false, bottom_need_backward[2][0]);
   EXPECT_EQ(2, bottom_need_backward[3].size());
   EXPECT_EQ(true, bottom_need_backward[3][0]);
+  // The label input to the SoftmaxLossLayer should say it "needs backward"
+  // since it has weights under it, even though we expect this to cause a crash
+  // at training/test time.
   EXPECT_EQ(true, bottom_need_backward[3][1]);
 }
 

From 41685ac81b6bab42c4fa47cc6c8faa203a4d0c54 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Jun 2014 19:53:45 -0700
Subject: [PATCH 0180/2053] weight sharing

---
 include/caffe/net.hpp       |  10 +-
 src/caffe/net.cpp           | 111 ++++++++-
 src/caffe/proto/caffe.proto |  12 +
 src/caffe/test/test_net.cpp | 469 ++++++++++++++++++++++++++++++++++++
 4 files changed, 594 insertions(+), 8 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index d05ca09f20a..aa540edf27e 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -6,6 +6,7 @@
 #include <map>
 #include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "caffe/blob.hpp"
@@ -14,9 +15,10 @@
 #include "caffe/proto/caffe.pb.h"
 
 using std::map;
-using std::vector;
+using std::pair;
 using std::set;
 using std::string;
+using std::vector;
 
 namespace caffe {
 
@@ -103,6 +105,7 @@ class Net {
   const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name);
   bool has_layer(const string& layer_name);
   const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name);
+  const map<string, int>& param_names_index() { return param_names_index_; }
 
  protected:
   // Helpers for Init.
@@ -114,6 +117,8 @@ class Net {
   int AppendBottom(const NetParameter& param, const int layer_id,
                    const int bottom_id, set<string>* available_blobs,
                    map<string, int>* blob_name_to_idx);
+  void AppendParam(const NetParameter& param, const int layer_id,
+                   const int param_id);
   // Function to get misc parameters, e.g. the learning rate multiplier and
   // weight decay.
   void GetLearningRateAndWeightDecay();
@@ -138,6 +143,9 @@ class Net {
   // top_vecs stores the vectors containing the output for each layer
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
+  vector<int> param_owners_;
+  vector<pair<int, int> > param_net_indices_;
+  map<string, int> param_names_index_;
   // blob indices for the input and the output of the net
   vector<int> net_input_blob_indices_;
   vector<int> net_output_blob_indices_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a6537619a8b..fc532b7b376 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -11,10 +11,12 @@
 #include "caffe/net.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/insert_splits.hpp"
+#include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
-using std::pair;
+using std::make_pair;
 using std::map;
+using std::pair;
 using std::set;
 
 namespace caffe {
@@ -86,8 +88,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
     DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
     const int blobs_lr_size = layers_[layer_id]->layer_param().blobs_lr_size();
-    CHECK(blobs_lr_size == layers_[layer_id]->blobs().size() ||
-          blobs_lr_size == 0) << "Incorrect blobs lr size: should be either 0 "
+    const int num_param_blobs = layers_[layer_id]->blobs().size();
+    CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)
+        << "Incorrect blobs lr size: should be either 0 "
         << "or the same as the number of the layer's parameter blobs.";
     if (blobs_lr_size) {
       // Check if this layer needs backward operation itself
@@ -100,6 +103,17 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       // learning rate to be 1. Thus we will need to perform backward.
       need_backward = true;
     }
+    const int blob_name_size = layer_param.blob_name_size();
+    CHECK(blob_name_size == num_param_blobs || blob_name_size == 0)
+        << "Incorrect blob_name size: should be either 0 or the same as "
+           "the number of the layer's parameter blobs: " << num_param_blobs;
+    const int blob_share_mode_size = layer_param.blob_share_mode_size();
+    CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)
+        << "Incorrect blob_share_mode size: should be either 0 or the same as "
+           "the number of the layer's parameter blobs: " << num_param_blobs;
+    for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
+      AppendParam(param, layer_id, param_id);
+    }
     // Finally, set the backward flag
     layer_need_backward_.push_back(need_backward);
     if (need_backward) {
@@ -217,14 +231,69 @@ int Net<Dtype>::AppendBottom(const NetParameter& param,
   return blob_id;
 }
 
+template <typename Dtype>
+void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
+                             const int param_id) {
+  const LayerParameter& layer_param = layers_[layer_id]->layer_param();
+  const int blob_name_size = layer_param.blob_name_size();
+  string param_name;
+  if (blob_name_size) {
+    param_name = layer_param.blob_name(param_id);
+  }
+  const int net_param_id = params_.size();
+  params_.push_back(layers_[layer_id]->blobs()[param_id]);
+  param_net_indices_.push_back(make_pair(layer_id, param_id));
+  if (!blob_name_size || !param_name.size() || (param_name.size() &&
+      param_names_index_.find(param_name) == param_names_index_.end())) {
+    // This layer "owns" this parameter blob -- it is either anonymous
+    // (i.e., not given a param_name) or explicitly given a name that we
+    // haven't already seen.
+    param_owners_.push_back(-1);
+    if (blob_name_size) {
+      param_names_index_[param_name] = net_param_id;
+    }
+  } else {
+    // Named param blob with name we've seen before: share params
+    const int owner_net_param_id = param_names_index_[param_name];
+    param_owners_.push_back(owner_net_param_id);
+    const pair<int, int>& owner_index =
+        param_net_indices_[owner_net_param_id];
+    const int owner_layer_id = owner_index.first;
+    const int owner_param_id = owner_index.second;
+    LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
+              << "layer '" << layer_names_[owner_layer_id] << "', param "
+              << "index " << owner_param_id;
+    Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
+    Blob<Dtype>* owner_blob =
+        layers_[owner_layer_id]->blobs()[owner_param_id].get();
+    const int blob_share_mode_size = layer_param.blob_share_mode_size();
+    if (blob_share_mode_size > param_id &&
+        (layer_param.blob_share_mode(param_id) ==
+         LayerParameter_DimCheckMode_PERMISSIVE)) {
+      // Permissive dimension checking -- only check counts are the same.
+      CHECK_EQ(this_blob->count(), owner_blob->count())
+          << "Shared parameter blobs must have the same count.";
+    } else {
+      // Strict dimension checking -- all dims must be the same.
+      CHECK_EQ(this_blob->num(), owner_blob->num())
+          << "Shared parameter blobs must have the same num.";
+      CHECK_EQ(this_blob->channels(), owner_blob->channels())
+          << "Shared parameter blobs must have the same channels.";
+      CHECK_EQ(this_blob->height(), owner_blob->height())
+          << "Shared parameter blobs must have the same height.";
+      CHECK_EQ(this_blob->width(), owner_blob->width())
+          << "Shared parameter blobs must have the same width.";
+    }
+    layers_[layer_id]->blobs()[param_id]->ShareData(
+        *layers_[owner_layer_id]->blobs()[owner_param_id]);
+  }
+}
+
 template <typename Dtype>
 void Net<Dtype>::GetLearningRateAndWeightDecay() {
   LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
   for (int i = 0; i < layers_.size(); ++i) {
     vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
-    for (int j = 0; j < layer_blobs.size(); ++j) {
-      params_.push_back(layer_blobs[j]);
-    }
     // push the learning rate mutlipliers
     if (layers_[i]->layer_param().blobs_lr_size()) {
       CHECK_EQ(layers_[i]->layer_param().blobs_lr_size(), layer_blobs.size());
@@ -403,8 +472,36 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) {
 
 template <typename Dtype>
 void Net<Dtype>::Update() {
+  // First, accumulate the diffs of any shared parameters into their owner's
+  // diff. (Assumes that the learning rate, weight decay, etc. have already been
+  // accounted for in the current diff.)
   for (int i = 0; i < params_.size(); ++i) {
-    params_[i]->Update();
+    if (param_owners_[i] < 0) {
+      continue;
+    }
+    const int count = params_[i]->count();
+    const Dtype* this_diff;
+    Dtype* owner_diff;
+    switch (Caffe::mode()) {
+    case Caffe::CPU:
+      this_diff = params_[i]->cpu_diff();
+      owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
+      caffe_add(count, this_diff, owner_diff, owner_diff);
+      break;
+    case Caffe::GPU:
+      this_diff = params_[i]->gpu_diff();
+      owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
+      caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+    }
+  }
+  // Now, update the owned parameters.
+  for (int i = 0; i < params_.size(); ++i) {
+    if (param_owners_[i] < 0) {
+      params_[i]->Update();
+    }
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 954f1033e97..76b643d5ff4 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -170,6 +170,18 @@ message LayerParameter {
 
   // The blobs containing the numeric parameters of the layer
   repeated BlobProto blobs = 6;
+  // The names of the parameter blobs -- useful for sharing parameters among
+  // layers (but never required).
+  repeated string blob_name = 1001;
+  // Whether to require shared weights to have the same shape, or just the same
+  // count -- defaults to STRICT if unspecified.
+  repeated DimCheckMode blob_share_mode = 1002;
+  enum DimCheckMode {
+    // STRICT (default) requires that num, channels, height, width each match.
+    STRICT = 0;
+    // PERMISSIVE requires only the count (num*channels*height*width) to match.
+    PERMISSIVE = 1;
+  }
   // The ratio that is multiplied on the global learning rate. If you want to
   // set the learning ratio for one blob, you need to set it for all blobs.
   repeated float blobs_lr = 7;
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 8fb02fcb7a7..1eb7b077cdf 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1,6 +1,7 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "google/protobuf/text_format.h"
@@ -8,6 +9,7 @@
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
 #include "caffe/net.hpp"
+#include "caffe/util/math_functions.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -17,6 +19,8 @@ namespace caffe {
 template <typename Dtype>
 class NetTest : public ::testing::Test {
  protected:
+  NetTest() : seed_(1701) {}
+
   virtual void InitNetFromProtoString(const string& proto) {
     NetParameter param;
     CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
@@ -210,6 +214,245 @@ class NetTest : public ::testing::Test {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitUnsharedWeightsNet() {
+    const string& proto =
+        "name: 'UnsharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  blob_name: 'unsharedweights1' "
+        "  bottom: 'data' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  blob_name: 'unsharedweights2' "
+        "  bottom: 'data' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'innerproduct1' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitSharedWeightsNet() {
+    const string& proto =
+        "name: 'SharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  blob_name: 'sharedweights' "
+        "  bottom: 'data' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  blob_name: 'sharedweights' "
+        "  bottom: 'data' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'innerproduct1' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitDiffDataUnsharedWeightsNet() {
+    const string& proto =
+        "name: 'DiffDataUnsharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  top: 'data1' "
+        "  top: 'data2' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  blob_name: 'unsharedweights1' "
+        "  bottom: 'data1' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  blob_name: 'unsharedweights2' "
+        "  bottom: 'innerproduct1' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'data2' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitDiffDataSharedWeightsNet() {
+    const string& proto =
+        "name: 'DiffDataSharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  top: 'data1' "
+        "  top: 'data2' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  blob_name: 'sharedweights' "
+        "  bottom: 'data1' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  blob_name: 'sharedweights' "
+        "  bottom: 'innerproduct1' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'data2' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
 
@@ -309,4 +552,230 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
   EXPECT_EQ(true, bottom_need_backward[3][1]);
 }
 
+TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
+  this->InitUnsharedWeightsNet();
+  vector<Blob<TypeParam>*> bottom;
+  TypeParam loss;
+  this->net_->Forward(bottom, &loss);
+  EXPECT_GT(loss, 0);
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
+  this->InitSharedWeightsNet();
+  vector<Blob<TypeParam>*> bottom;
+  TypeParam loss;
+  this->net_->Forward(bottom, &loss);
+  EXPECT_FLOAT_EQ(loss, 0);
+}
+
+TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
+  this->InitUnsharedWeightsNet();
+  vector<Blob<TypeParam>*> bottom;
+  Net<TypeParam>* net = this->net_.get();
+  net->Forward(bottom);
+  net->Backward();
+  Layer<TypeParam>* ip1_layer = net->layer_by_name("innerproduct1").get();
+  Layer<TypeParam>* ip2_layer = net->layer_by_name("innerproduct2").get();
+  const int count = ip1_layer->blobs()[0]->count();
+  const TypeParam* grad1 = ip1_layer->blobs()[0]->cpu_diff();
+  const TypeParam* grad2 = ip2_layer->blobs()[0]->cpu_diff();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_GT(fabs(grad1[i]), 0);
+    EXPECT_FLOAT_EQ(-1 * grad1[i], grad2[i]);
+  }
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
+  this->InitSharedWeightsNet();
+  vector<Blob<TypeParam>*> bottom;
+  Net<TypeParam>* net = this->net_.get();
+  TypeParam loss;
+  net->Forward(bottom, &loss);
+  net->Backward();
+  EXPECT_FLOAT_EQ(loss, 0);
+  Layer<TypeParam>* ip1_layer = net->layer_by_name("innerproduct1").get();
+  Layer<TypeParam>* ip2_layer = net->layer_by_name("innerproduct2").get();
+  const int count = ip1_layer->blobs()[0]->count();
+  const TypeParam* grad1 = ip1_layer->blobs()[0]->cpu_diff();
+  const TypeParam* grad2 = ip2_layer->blobs()[0]->cpu_diff();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_FLOAT_EQ(0, grad1[i]);
+    EXPECT_FLOAT_EQ(0, grad2[i]);
+  }
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsUpdateCPU) {
+  Caffe::set_random_seed(this->seed_);
+  Caffe::set_mode(Caffe::CPU);
+  this->InitDiffDataSharedWeightsNet();
+  vector<Blob<TypeParam>*> bottom;
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  Blob<TypeParam>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  Blob<TypeParam>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  // Check that diff blobs of shared weights are at different locations in
+  // locations.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  // Compute the expected update as the data minus the two diffs.
+  Blob<TypeParam> shared_params;
+  const bool reshape = true;
+  const bool copy_diff = false;
+  shared_params.CopyFrom(*ip1_weights, copy_diff, reshape);
+  shared_params.CopyFrom(*ip1_weights, !copy_diff, reshape);
+  const int count = ip1_weights->count();
+  // Make sure the diffs are non-trivial.
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
+    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
+    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
+  }
+  caffe_axpy(count, TypeParam(1), ip2_weights->cpu_diff(),
+             shared_params.mutable_cpu_diff());
+  caffe_axpy(count, TypeParam(-1), shared_params.cpu_diff(),
+             shared_params.mutable_cpu_data());
+  const TypeParam* expected_updated_params = shared_params.cpu_data();
+  this->net_->Update();
+  const TypeParam* actual_updated_params = ip1_weights->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(expected_updated_params[i], actual_updated_params[i]);
+  }
+  // Check that data blobs of shared weights STILL point to the same memory
+  // location (because ... who knows).
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataUnsharedWeightsNet();
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data and diff blobs of unshared weights are at different
+  // locations in memory.
+  EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  // Compute the expected update.
+  Blob<TypeParam> unshared_params1;
+  unshared_params1.CopyFrom(*ip1_weights, copy_diff, reshape);
+  unshared_params1.CopyFrom(*ip1_weights, !copy_diff, reshape);
+  Blob<TypeParam> unshared_params2;
+  unshared_params2.CopyFrom(*ip2_weights, copy_diff, reshape);
+  unshared_params2.CopyFrom(*ip2_weights, !copy_diff, reshape);
+  // Make sure the diffs are non-trivial and sum to the diff in the shared net.
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
+    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
+    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
+    EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
+              shared_params.cpu_diff()[i]);
+  }
+  caffe_axpy(count, TypeParam(-1), ip1_weights->cpu_diff(),
+             unshared_params1.mutable_cpu_data());
+  caffe_axpy(count, TypeParam(-1), ip2_weights->cpu_diff(),
+             unshared_params2.mutable_cpu_data());
+  const TypeParam* expected_updated_params1 = unshared_params1.cpu_data();
+  const TypeParam* expected_updated_params2 = unshared_params2.cpu_data();
+  this->net_->Update();
+  const TypeParam* actual_updated_params1 = ip1_weights->cpu_data();
+  const TypeParam* actual_updated_params2 = ip2_weights->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(expected_updated_params1[i], actual_updated_params1[i]);
+    EXPECT_EQ(expected_updated_params2[i], actual_updated_params2[i]);
+    EXPECT_NE(actual_updated_params1[i], actual_updated_params2[i]);
+    EXPECT_NE(expected_updated_params, expected_updated_params1);
+  }
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsUpdateGPU) {
+  Caffe::set_random_seed(this->seed_);
+  Caffe::set_mode(Caffe::GPU);
+  this->InitDiffDataSharedWeightsNet();
+  vector<Blob<TypeParam>*> bottom;
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  Blob<TypeParam>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  Blob<TypeParam>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  // Check that diff blobs of shared weights are at different locations in
+  // locations.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  // Compute the expected update as the data minus the two diffs.
+  Blob<TypeParam> shared_params;
+  const bool reshape = true;
+  const bool copy_diff = false;
+  shared_params.CopyFrom(*ip1_weights, copy_diff, reshape);
+  shared_params.CopyFrom(*ip1_weights, !copy_diff, reshape);
+  const int count = ip1_weights->count();
+  // Make sure the diffs are non-trivial.
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
+    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
+    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
+  }
+  caffe_axpy(count, TypeParam(1), ip2_weights->cpu_diff(),
+             shared_params.mutable_cpu_diff());
+  caffe_axpy(count, TypeParam(-1), shared_params.cpu_diff(),
+             shared_params.mutable_cpu_data());
+  const TypeParam* expected_updated_params = shared_params.cpu_data();
+  this->net_->Update();
+  const TypeParam* actual_updated_params = ip1_weights->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(expected_updated_params[i], actual_updated_params[i]);
+  }
+  // Check that data blobs of shared weights STILL point to the same memory
+  // location (because ... who knows).
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataUnsharedWeightsNet();
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data and diff blobs of unshared weights are at different
+  // locations in memory.
+  EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  // Compute the expected update.
+  Blob<TypeParam> unshared_params1;
+  unshared_params1.CopyFrom(*ip1_weights, copy_diff, reshape);
+  unshared_params1.CopyFrom(*ip1_weights, !copy_diff, reshape);
+  Blob<TypeParam> unshared_params2;
+  unshared_params2.CopyFrom(*ip2_weights, copy_diff, reshape);
+  unshared_params2.CopyFrom(*ip2_weights, !copy_diff, reshape);
+  // Make sure the diffs are non-trivial and sum to the diff in the shared net.
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
+    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
+    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
+    EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
+              shared_params.cpu_diff()[i]);
+  }
+  caffe_axpy(count, TypeParam(-1), ip1_weights->cpu_diff(),
+             unshared_params1.mutable_cpu_data());
+  caffe_axpy(count, TypeParam(-1), ip2_weights->cpu_diff(),
+             unshared_params2.mutable_cpu_data());
+  const TypeParam* expected_updated_params1 = unshared_params1.cpu_data();
+  const TypeParam* expected_updated_params2 = unshared_params2.cpu_data();
+  this->net_->Update();
+  const TypeParam* actual_updated_params1 = ip1_weights->cpu_data();
+  const TypeParam* actual_updated_params2 = ip2_weights->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(expected_updated_params1[i], actual_updated_params1[i]);
+    EXPECT_EQ(expected_updated_params2[i], actual_updated_params2[i]);
+    EXPECT_NE(actual_updated_params1[i], actual_updated_params2[i]);
+    EXPECT_NE(expected_updated_params, expected_updated_params1);
+  }
+}
+
 }  // namespace caffe

From 26e022acedcb53b2c45147b635fc9081b66396af Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 26 Jun 2014 12:49:00 -0700
Subject: [PATCH 0181/2053] change weight blob field name to param

---
 src/caffe/net.cpp           | 16 ++++++++--------
 src/caffe/proto/caffe.proto |  6 +++---
 src/caffe/test/test_net.cpp | 16 ++++++++--------
 3 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index fc532b7b376..e25c754bda6 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -103,9 +103,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       // learning rate to be 1. Thus we will need to perform backward.
       need_backward = true;
     }
-    const int blob_name_size = layer_param.blob_name_size();
-    CHECK(blob_name_size == num_param_blobs || blob_name_size == 0)
-        << "Incorrect blob_name size: should be either 0 or the same as "
+    const int param_size = layer_param.param_size();
+    CHECK(param_size == num_param_blobs || param_size == 0)
+        << "Incorrect param size: should be either 0 or the same as "
            "the number of the layer's parameter blobs: " << num_param_blobs;
     const int blob_share_mode_size = layer_param.blob_share_mode_size();
     CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)
@@ -235,21 +235,21 @@ template <typename Dtype>
 void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                              const int param_id) {
   const LayerParameter& layer_param = layers_[layer_id]->layer_param();
-  const int blob_name_size = layer_param.blob_name_size();
+  const int param_size = layer_param.param_size();
   string param_name;
-  if (blob_name_size) {
-    param_name = layer_param.blob_name(param_id);
+  if (param_size) {
+    param_name = layer_param.param(param_id);
   }
   const int net_param_id = params_.size();
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
   param_net_indices_.push_back(make_pair(layer_id, param_id));
-  if (!blob_name_size || !param_name.size() || (param_name.size() &&
+  if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
     // This layer "owns" this parameter blob -- it is either anonymous
     // (i.e., not given a param_name) or explicitly given a name that we
     // haven't already seen.
     param_owners_.push_back(-1);
-    if (blob_name_size) {
+    if (param_size) {
       param_names_index_[param_name] = net_param_id;
     }
   } else {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 76b643d5ff4..fd71f45fab4 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -172,7 +172,7 @@ message LayerParameter {
   repeated BlobProto blobs = 6;
   // The names of the parameter blobs -- useful for sharing parameters among
   // layers (but never required).
-  repeated string blob_name = 1001;
+  repeated string param = 1001;
   // Whether to require shared weights to have the same shape, or just the same
   // count -- defaults to STRICT if unspecified.
   repeated DimCheckMode blob_share_mode = 1002;
@@ -319,9 +319,9 @@ message HDF5OutputParameter {
 }
 
 message HingeLossParameter {
-  enum Norm {    
+  enum Norm {
     L1 = 1;
-    L2 = 2;    
+    L2 = 2;
   }
   // Specify the Norm to use L1 or L2
   optional Norm norm = 1 [default = L1];
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 1eb7b077cdf..18b03476996 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -243,7 +243,7 @@ class NetTest : public ::testing::Test {
         "      std: 10 "
         "    } "
         "  } "
-        "  blob_name: 'unsharedweights1' "
+        "  param: 'unsharedweights1' "
         "  bottom: 'data' "
         "  top: 'innerproduct1' "
         "} "
@@ -258,7 +258,7 @@ class NetTest : public ::testing::Test {
         "      std: 10 "
         "    } "
         "  } "
-        "  blob_name: 'unsharedweights2' "
+        "  param: 'unsharedweights2' "
         "  bottom: 'data' "
         "  top: 'innerproduct2' "
         "} "
@@ -300,7 +300,7 @@ class NetTest : public ::testing::Test {
         "      std: 10 "
         "    } "
         "  } "
-        "  blob_name: 'sharedweights' "
+        "  param: 'sharedweights' "
         "  bottom: 'data' "
         "  top: 'innerproduct1' "
         "} "
@@ -315,7 +315,7 @@ class NetTest : public ::testing::Test {
         "      std: 10 "
         "    } "
         "  } "
-        "  blob_name: 'sharedweights' "
+        "  param: 'sharedweights' "
         "  bottom: 'data' "
         "  top: 'innerproduct2' "
         "} "
@@ -362,7 +362,7 @@ class NetTest : public ::testing::Test {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  blob_name: 'unsharedweights1' "
+        "  param: 'unsharedweights1' "
         "  bottom: 'data1' "
         "  top: 'innerproduct1' "
         "} "
@@ -377,7 +377,7 @@ class NetTest : public ::testing::Test {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  blob_name: 'unsharedweights2' "
+        "  param: 'unsharedweights2' "
         "  bottom: 'innerproduct1' "
         "  top: 'innerproduct2' "
         "} "
@@ -424,7 +424,7 @@ class NetTest : public ::testing::Test {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  blob_name: 'sharedweights' "
+        "  param: 'sharedweights' "
         "  bottom: 'data1' "
         "  top: 'innerproduct1' "
         "} "
@@ -439,7 +439,7 @@ class NetTest : public ::testing::Test {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  blob_name: 'sharedweights' "
+        "  param: 'sharedweights' "
         "  bottom: 'innerproduct1' "
         "  top: 'innerproduct2' "
         "} "

From a71354f87d9b4cf205180e5339b958fc684e8a51 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 26 Jun 2014 13:05:44 -0700
Subject: [PATCH 0182/2053] rename layer -> param mapping for clarity

---
 include/caffe/net.hpp | 2 +-
 src/caffe/net.cpp     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index aa540edf27e..cbd5becde2d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -144,7 +144,7 @@ class Net {
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
   vector<int> param_owners_;
-  vector<pair<int, int> > param_net_indices_;
+  vector<pair<int, int> > layer_param_indices_;
   map<string, int> param_names_index_;
   // blob indices for the input and the output of the net
   vector<int> net_input_blob_indices_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index e25c754bda6..f364e6767c6 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -242,7 +242,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
   }
   const int net_param_id = params_.size();
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
-  param_net_indices_.push_back(make_pair(layer_id, param_id));
+  layer_param_indices_.push_back(make_pair(layer_id, param_id));
   if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
     // This layer "owns" this parameter blob -- it is either anonymous
@@ -257,7 +257,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     const int owner_net_param_id = param_names_index_[param_name];
     param_owners_.push_back(owner_net_param_id);
     const pair<int, int>& owner_index =
-        param_net_indices_[owner_net_param_id];
+        layer_param_indices_[owner_net_param_id];
     const int owner_layer_id = owner_index.first;
     const int owner_param_id = owner_index.second;
     LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "

From 6450e0f0c8e19cde6c5e3c26ffb50deba4308bc7 Mon Sep 17 00:00:00 2001
From: James Thewlis <jamt9000@gmail.com>
Date: Thu, 26 Jun 2014 18:12:30 +0100
Subject: [PATCH 0183/2053] Test for im2col kernel

With associated Makefile changes for .cu tests

This tests that the grid-stride loop works for im2col,
using the CPU version as a reference.
---
 Makefile                             |  14 ++-
 src/caffe/test/test_im2col_kernel.cu | 125 +++++++++++++++++++++++++++
 2 files changed, 136 insertions(+), 3 deletions(-)
 create mode 100644 src/caffe/test/test_im2col_kernel.cu

diff --git a/Makefile b/Makefile
index 77e2ff5edbe..14072747b19 100644
--- a/Makefile
+++ b/Makefile
@@ -30,11 +30,12 @@ CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
 # HXX_SRCS are the header files
 HXX_SRCS := $(shell find include/$(PROJECT) -name "*.hpp")
 # CU_SRCS are the cuda source files
-CU_SRCS := $(shell find src/$(PROJECT) -name "*.cu")
+CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
 # TEST_SRCS are the test source files
 TEST_MAIN_SRC := src/$(PROJECT)/test/test_caffe_main.cpp
 TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
 TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
+TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
 GTEST_SRC := src/gtest/gtest-all.cpp
 # TEST_HDRS are the test header files
 TEST_HDRS := $(shell find src/$(PROJECT) -name "test_*.hpp")
@@ -101,7 +102,9 @@ OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
 TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
 TOOL_BUILD_DIR := $(BUILD_DIR)/tools
 TEST_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
-TEST_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
+TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
+TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo})
+TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
 GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
 GTEST_BUILD_DIR := $(dir $(GTEST_OBJ))
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
@@ -329,13 +332,18 @@ $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 	$(CXX) $< $(CXXFLAGS) -c -o $@
 	@ echo
 
+$(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HDRS) \
+		| $(TEST_BUILD_DIR)
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
+	@ echo
+
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
+$(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.*o $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
new file mode 100644
index 00000000000..f49a5b7e4d9
--- /dev/null
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -0,0 +1,125 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <cstring>
+#include <vector>
+
+#include "cuda_runtime.h"
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/im2col.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+// Forward declare kernel functions
+template <typename Dtype>
+__global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
+    const int height, const int width, const int ksize, const int pad,
+    const int stride, const int height_col, const int width_col,
+    Dtype* data_col);
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class Im2colKernelTest : public ::testing::Test {
+ protected:
+  Im2colKernelTest()
+        // big so launches > 1024 threads
+      : blob_bottom_(new Blob<Dtype>(5, 500, 10, 10)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_cpu_(new Blob<Dtype>()) {
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+
+    height_ = blob_bottom_->height();
+    width_ = blob_bottom_->width();
+    channels_ = blob_bottom_->channels();
+    pad_ = 0;
+    stride_ = 2;
+    kernel_size_ = 3;
+    height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+    width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+  }
+
+  virtual ~Im2colKernelTest() {
+      delete blob_bottom_;
+      delete blob_top_;
+      delete blob_top_cpu_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_cpu_;
+  int height_;
+  int width_;
+  int channels_;
+  int pad_;
+  int stride_;
+  int kernel_size_;
+  int height_col_;
+  int width_col_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(Im2colKernelTest, Dtypes);
+
+TYPED_TEST(Im2colKernelTest, TestGPU) {
+  Caffe::set_mode(Caffe::GPU);
+
+  // Reshape the blobs to correct size for im2col output
+  this->blob_top_->Reshape(this->blob_bottom_->num(),
+          this->channels_ * this->kernel_size_ * this->kernel_size_,
+          this->height_col_,
+          this->width_col_);
+
+  this->blob_top_cpu_->Reshape(this->blob_bottom_->num(),
+          this->channels_ * this->kernel_size_ * this->kernel_size_,
+          this->height_col_,
+          this->width_col_);
+
+  const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
+  TypeParam* top_data = this->blob_top_->mutable_gpu_data();
+  TypeParam* cpu_data = this->blob_top_cpu_->mutable_cpu_data();
+
+  // CPU Version
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n),
+      this->channels_, this->height_, this->width_, this->kernel_size_,
+      this->pad_, this->stride_, cpu_data + this->blob_top_cpu_->offset(n));
+  }
+
+  // GPU version
+  int num_kernels = this->channels_ * this->height_col_ * this->width_col_;
+  int default_grid_dim = CAFFE_GET_BLOCKS(num_kernels);
+
+  // Launch with different grid sizes
+  for (int grid_div = 2; grid_div <= 8; grid_div++) {
+    for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+      int grid_dim = default_grid_dim/grid_div;
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      im2col_gpu_kernel<TypeParam><<<grid_dim, CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, bottom_data + this->blob_bottom_->offset(n),
+        this->height_, this->width_, this->kernel_size_, this->pad_,
+        this->stride_, this->height_col_, this->width_col_,
+        top_data + this->blob_top_->offset(n));
+      CUDA_POST_KERNEL_CHECK;
+    }
+
+    // Compare results against CPU version
+    for (int i = 0; i < this->blob_top_->count(); ++i) {
+      TypeParam cpuval = cpu_data[i];
+      TypeParam gpuval = this->blob_top_->cpu_data()[i];
+      EXPECT_EQ(cpuval, gpuval);
+      if (cpuval != gpuval) {
+        break;
+      }
+    }
+  }
+}
+
+}  // namespace caffe

From 423808dd60c5077dda16432080caa69a2e6f8d13 Mon Sep 17 00:00:00 2001
From: James Thewlis <jamt9000@gmail.com>
Date: Fri, 27 Jun 2014 10:49:08 +0100
Subject: [PATCH 0184/2053] Make resizing & cropping with PIL work

Previously it was trying to use undefined variables
---
 tools/extra/resize_and_crop_images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tools/extra/resize_and_crop_images.py b/tools/extra/resize_and_crop_images.py
index 0ab75dc2ae5..c844f590c06 100755
--- a/tools/extra/resize_and_crop_images.py
+++ b/tools/extra/resize_and_crop_images.py
@@ -3,7 +3,7 @@
 import gflags
 import os
 import cv2
-import PIL
+from PIL import Image
 
 # gflags
 gflags.DEFINE_string('image_lib', 'opencv',
@@ -37,9 +37,10 @@ def resize_and_crop_image(self, input_file, output_file, output_side_length = 25
 
 class PILResizeCrop:
 ## http://united-coders.com/christian-harms/image-resizing-tips-every-coder-should-know/
-    def resize_and_crop_image(self, input_file, output_file, output_side_length = 256):
+    def resize_and_crop_image(self, input_file, output_file, output_side_length = 256, fit = True):
         '''Downsample the image.
         '''
+        img = Image.open(input_file)
         box = (output_side_length, output_side_length)
         #preresize image with factor 2, 4, 8 and fast algorithm
         factor = 1

From 180a75bb960b8494a7d1096d2d7557fe5f19192a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 27 Jun 2014 08:47:42 -0700
Subject: [PATCH 0185/2053] add latest CUDA arch to fix invalid device function
 errors

...on devices of that architecture.
---
 Makefile.config.example | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index 9754129beb7..c41bada9ac2 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -7,11 +7,15 @@ CUDA_DIR := /usr/local/cuda
 # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
 # CUDA_DIR := /usr
 
-# CUDA architecture setting: going with all of them.
+# CUDA architecture setting: going with all of them (up to CUDA 5.5 compatible).
+# For the latest architecture, you need to install CUDA >= 6.0 and uncomment
+# the *_50 lines below.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
 		-gencode arch=compute_35,code=sm_35
+		#-gencode=arch=compute_50,code=sm_50 \
+		#-gencode=arch=compute_50,code=compute_50
 
 # BLAS choice:
 # atlas for ATLAS (default)

From 5890a353e6a158ff1b30a12565a6864f4c047f55 Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Fri, 13 Jun 2014 18:16:04 -0700
Subject: [PATCH 0186/2053] Add parameter for AccuracyLayer in proto.

---
 src/caffe/proto/caffe.proto | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index fd71f45fab4..48d6fe27ed8 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -188,6 +188,7 @@ message LayerParameter {
   // The weight decay that is multiplied on the global weight decay.
   repeated float weight_decay = 8;
 
+  optional AccuracyParameter accuracy_param = 27;
   optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
   optional ConvolutionParameter convolution_param = 10;
@@ -214,6 +215,14 @@ message LayerParameter {
   optional V0LayerParameter layer = 1;
 }
 
+// Message that stores parameters used by AccuracyLayer
+message AccuracyParameter {
+  // When computing accuracy, count as correct by comparing the true label to
+  // the top k scoring classes.  By default, only compare to the top scoring
+  // class (i.e. argmax).
+  optional uint32 compare_to_top_k = 1 [default = 1];
+}
+
 // Message that stores parameters used by ArgMaxLayer
 message ArgMaxParameter {
   // If true produce pairs (argmax, maxval)

From 1c640c976b113839514ca5b70a28f6a921cf9eb2 Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Fri, 13 Jun 2014 19:07:38 -0700
Subject: [PATCH 0187/2053] Incorporate top_k param into AccuracyLayer and
 check it's value.

---
 include/caffe/loss_layers.hpp       | 2 ++
 src/caffe/layers/accuracy_layer.cpp | 3 +++
 src/caffe/proto/caffe.proto         | 2 +-
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index bb03f637450..3a4d41662fd 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -251,6 +251,8 @@ class AccuracyLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
   }
+
+  int top_k_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 899750f869a..55f620d8af6 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -18,8 +18,11 @@ template <typename Dtype>
 void AccuracyLayer<Dtype>::SetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   Layer<Dtype>::SetUp(bottom, top);
+  top_k_ = this->layer_param_.accuracy_param().top_k();
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
+  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
+      << "top_k must be less than the number of classes.";
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 48d6fe27ed8..936a215e131 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -220,7 +220,7 @@ message AccuracyParameter {
   // When computing accuracy, count as correct by comparing the true label to
   // the top k scoring classes.  By default, only compare to the top scoring
   // class (i.e. argmax).
-  optional uint32 compare_to_top_k = 1 [default = 1];
+  optional uint32 top_k = 1 [default = 1];
 }
 
 // Message that stores parameters used by ArgMaxLayer

From 5a887fc30fba742501ad395165d3f0968738db04 Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Mon, 16 Jun 2014 13:35:46 -0700
Subject: [PATCH 0188/2053] Compute top-k accuracy in AccuracyLayer.

---
 src/caffe/layers/accuracy_layer.cpp | 34 +++++++++++++++++++----------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 55f620d8af6..a284f6b191a 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -22,7 +22,7 @@ void AccuracyLayer<Dtype>::SetUp(
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
-      << "top_k must be less than the number of classes.";
+      << "top_k must be less than or equal to the number of classes.";
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
@@ -37,20 +37,32 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_label = bottom[1]->cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
+  Dtype* maxval = new Dtype[top_k_+1];
+  int* max_id = new int[top_k_+1];
   for (int i = 0; i < num; ++i) {
-    // Accuracy
-    Dtype maxval = -FLT_MAX;
-    int max_id = 0;
-    for (int j = 0; j < dim; ++j) {
-      if (bottom_data[i * dim + j] > maxval) {
-        maxval = bottom_data[i * dim + j];
-        max_id = j;
+    // Top-k accuracy
+    std::fill_n(maxval, top_k_, -FLT_MAX);
+    std::fill_n(max_id, top_k_, 0);
+    for (int j = 0, k; j < dim; ++j) {
+      // insert into (reverse-)sorted top-k array
+      Dtype val = bottom_data[i * dim + j];
+      for (k = top_k_; k > 0 && maxval[k-1] < val; k--) {
+        maxval[k] = maxval[k-1];
+        max_id[k] = max_id[k-1];
       }
+      maxval[k] = val;
+      max_id[k] = j;
     }
-    if (max_id == static_cast<int>(bottom_label[i])) {
-      ++accuracy;
-    }
+    // check if true label is in top k predictions
+    for (int k = 0; k < top_k_; k++)
+      if (max_id[k] == static_cast<int>(bottom_label[i])) {
+        ++accuracy;
+        break;
+      }
   }
+  delete[] maxval;
+  delete[] max_id;
+
   // LOG(INFO) << "Accuracy: " << accuracy;
   (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
 

From fde99cce7735a7362f458a8592e4662965734482 Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Thu, 19 Jun 2014 15:12:56 -0700
Subject: [PATCH 0189/2053] Use vectors instead of arrays.

---
 src/caffe/layers/accuracy_layer.cpp | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index a284f6b191a..409965519ca 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -37,12 +37,12 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_label = bottom[1]->cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  Dtype* maxval = new Dtype[top_k_+1];
-  int* max_id = new int[top_k_+1];
+  vector<Dtype> maxval(top_k_+1);
+  vector<int> max_id(top_k_+1);
   for (int i = 0; i < num; ++i) {
     // Top-k accuracy
-    std::fill_n(maxval, top_k_, -FLT_MAX);
-    std::fill_n(max_id, top_k_, 0);
+    std::fill_n(maxval.begin(), top_k_, -FLT_MAX);
+    std::fill_n(max_id.begin(), top_k_, 0);
     for (int j = 0, k; j < dim; ++j) {
       // insert into (reverse-)sorted top-k array
       Dtype val = bottom_data[i * dim + j];
@@ -60,8 +60,6 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         break;
       }
   }
-  delete[] maxval;
-  delete[] max_id;
 
   // LOG(INFO) << "Accuracy: " << accuracy;
   (*top)[0]->mutable_cpu_data()[0] = accuracy / num;

From cca6500874f35fc051eab76230e96eda2ff24688 Mon Sep 17 00:00:00 2001
From: cypof <cypof@yahoo-inc.com>
Date: Thu, 19 Jun 2014 15:52:10 -0700
Subject: [PATCH 0190/2053] Next LayerParameter proto id

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 936a215e131..e42c208ecb5 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -115,7 +115,7 @@ message SolverState {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 27 (last added: dummy_data_param)
+// LayerParameter next available ID: 28 (last added: dummy_data_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs

From e2e443b27e032f04e08fe2469363897a7702036f Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Fri, 20 Jun 2014 14:37:07 -0700
Subject: [PATCH 0191/2053] Add unit test for accuracy layer.

---
 src/caffe/test/test_accuracy_layer.cpp | 90 ++++++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 src/caffe/test/test_accuracy_layer.cpp

diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
new file mode 100644
index 00000000000..f87089b6c8b
--- /dev/null
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -0,0 +1,90 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <cmath>
+#include <cstring>
+#include <cfloat>
+#include <vector>
+
+#include "cuda_runtime.h"
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class AccuracyLayerTest : public ::testing::Test {
+ protected:
+  AccuracyLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(2, 10, 1, 1)),
+        blob_bottom_label_(new Blob<Dtype>(2, 1, 1, 1)),
+        blob_top_(new Blob<Dtype>()),
+        top_k_(3) {
+    // fill the probability values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_data_);
+
+    // set the labels; first label is set to max-probability index
+    Dtype m_val = -FLT_MAX;
+    int m_id = 0;
+    for (int i = 0; i < 10; i++)
+      if (blob_bottom_data_->data_at(0, i, 0, 0) > m_val) {
+        m_val = blob_bottom_data_->data_at(0, i, 0, 0);
+        m_id = i;
+      }
+    Dtype* label_data = blob_bottom_label_->mutable_cpu_data();
+    int offset = blob_bottom_label_->offset(0);
+    label_data[offset] = m_id;
+
+    // set the labels; first label is set to min-probability index
+    m_val = FLT_MAX;
+    m_id = 0;
+    for (int i = 0; i < 10; i++)
+      if (blob_bottom_data_->data_at(1, i, 0, 0) < m_val) {
+        m_val = blob_bottom_data_->data_at(1, i, 0, 0);
+        m_id = i;
+      }
+    offset = blob_bottom_label_->offset(1);
+    label_data[offset] = m_id;
+
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~AccuracyLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_label_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+  int top_k_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(AccuracyLayerTest, Dtypes);
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
+  LayerParameter layer_param;
+  AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
+  accuracy_param->set_top_k(this->top_k_);
+  Caffe::set_mode(Caffe::CPU);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+
+  // Output accuracy should be ~0.5
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0), 0.5, 1e-4);
+}
+
+}  // namespace caffe

From 4a57e726c83d54c251392101d5bb9888cd1c1d78 Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Fri, 20 Jun 2014 14:39:58 -0700
Subject: [PATCH 0192/2053] Update name of last added param.

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e42c208ecb5..adb75264f8e 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -115,7 +115,7 @@ message SolverState {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 28 (last added: dummy_data_param)
+// LayerParameter next available ID: 28 (last added: accuracy_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs

From 228c3266377330cd46b36cec3343a0fc1980976f Mon Sep 17 00:00:00 2001
From: Rob Hess <hess@yahoo-inc.com>
Date: Mon, 23 Jun 2014 17:22:43 -0700
Subject: [PATCH 0193/2053] Comment-fix.

---
 src/caffe/test/test_accuracy_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index f87089b6c8b..deb72af34ec 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -43,7 +43,7 @@ class AccuracyLayerTest : public ::testing::Test {
     int offset = blob_bottom_label_->offset(0);
     label_data[offset] = m_id;
 
-    // set the labels; first label is set to min-probability index
+    // set the labels; second label is set to min-probability index
     m_val = FLT_MAX;
     m_id = 0;
     for (int i = 0; i < 10; i++)

From a63feadb088f1fdcfc0cdc5d1ec73978f574375e Mon Sep 17 00:00:00 2001
From: James Thewlis <jamt9000@gmail.com>
Date: Sat, 28 Jun 2014 00:49:53 +0100
Subject: [PATCH 0194/2053] Fix building tests with parallel make

The changes for .cu tests meant that creating
TEST_BUILD_DIR wasn't happening first
---
 Makefile | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 14072747b19..6d8cb905128 100644
--- a/Makefile
+++ b/Makefile
@@ -116,8 +116,11 @@ TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
 # Put the test binaries in build/test for convenience.
 TEST_BIN_DIR := $(BUILD_DIR)/test
-TEST_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
-		$(foreach obj,$(TEST_OBJS),$(basename $(notdir $(obj))))))
+TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
+		$(foreach obj,$(TEST_CU_OBJS),$(basename $(notdir $(obj))))))
+TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
+		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
+TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 
 ##############################
@@ -343,7 +346,13 @@ $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.*o $(GTEST_OBJ) $(STATIC_NAME) \
+$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
+		| $(TEST_BIN_DIR)
+	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS)
+	@ echo
+
+$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)

From aa654a5daea5e6f6f5c5514687ed75662637901e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 27 Jun 2014 18:54:08 -0700
Subject: [PATCH 0195/2053] Modify Dropout to allow backward pass in TEST phase

Conflicts:
	src/caffe/layers/dropout_layer.cpp
	src/caffe/layers/dropout_layer.cu
---
 src/caffe/layers/dropout_layer.cpp | 13 ++++++++-----
 src/caffe/layers/dropout_layer.cu  | 19 +++++++++++--------
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index e9a1a524d63..a3501bf518c 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -49,14 +49,17 @@ template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  CHECK(Caffe::phase() == Caffe::TRAIN);
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const unsigned int* mask = rand_vec_->cpu_data();
-    const int count = (*bottom)[0]->count();
-    for (int i = 0; i < count; ++i) {
-      bottom_diff[i] = top_diff[i] * mask[i] * scale_;
+    if (Caffe::phase() == Caffe::TRAIN) {
+      const unsigned int* mask = rand_vec_->cpu_data();
+      const int count = (*bottom)[0]->count();
+      for (int i = 0; i < count; ++i) {
+        bottom_diff[i] = top_diff[i] * mask[i] * scale_;
+      }
+    } else {
+      caffe_copy(top[0]->count(), top_diff, bottom_diff);
     }
   }
 }
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 2c7226402e8..0040d263803 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -58,17 +58,20 @@ template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  CHECK(Caffe::phase() == Caffe::TRAIN);
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const unsigned int* mask =
-        static_cast<const unsigned int*>(rand_vec_->gpu_data());
-    const int count = (*bottom)[0]->count();
-    // NOLINT_NEXT_LINE(whitespace/operators)
-    DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, mask, uint_thres_, scale_, bottom_diff);
-    CUDA_POST_KERNEL_CHECK;
+    if (Caffe::phase() == Caffe::TRAIN) {
+      const unsigned int* mask =
+          static_cast<const unsigned int*>(rand_vec_->gpu_data());
+      const int count = (*bottom)[0]->count();
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+          count, top_diff, mask, uint_thres_, scale_, bottom_diff);
+      CUDA_POST_KERNEL_CHECK;
+    } else {
+      caffe_gpu_copy(top[0]->count(), bottom_data, top_data);
+    }
   }
 }
 

From ae83a22e5805612f07264b7f2ac30986317e7624 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 13 Jun 2014 18:54:28 -0700
Subject: [PATCH 0196/2053] Fix var names in Dropout.cu

---
 src/caffe/layers/dropout_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 0040d263803..2e0b0d3aa2c 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -70,7 +70,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           count, top_diff, mask, uint_thres_, scale_, bottom_diff);
       CUDA_POST_KERNEL_CHECK;
     } else {
-      caffe_gpu_copy(top[0]->count(), bottom_data, top_data);
+      caffe_gpu_copy(top[0]->count(), top_diff, bottom_diff);
     }
   }
 }

From 06da1a7de277763164c8bf0c700429785b531c09 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 14 Jun 2014 08:39:35 -0700
Subject: [PATCH 0197/2053] Added test to Dropout to check gradients during
 Test phase

---
 src/caffe/layers/dropout_layer.cu    |  3 ++-
 src/caffe/test/test_neuron_layer.cpp | 25 +++++++++++++++++++++++++
 2 files changed, 27 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 2e0b0d3aa2c..225e0919409 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -66,7 +66,8 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           static_cast<const unsigned int*>(rand_vec_->gpu_data());
       const int count = (*bottom)[0]->count();
       // NOLINT_NEXT_LINE(whitespace/operators)
-      DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(
           count, top_diff, mask, uint_thres_, scale_, bottom_diff);
       CUDA_POST_KERNEL_CHECK;
     } else {
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index ca70e1acd9b..c7c0dc1675d 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -178,6 +178,15 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradientCPU) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(NeuronLayerTest, TestDropoutGradientCPUTest) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  Caffe::set_phase(Caffe::TEST);
+  DropoutLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
 
 TYPED_TEST(NeuronLayerTest, TestDropoutCPUTestPhase) {
   LayerParameter layer_param;
@@ -232,6 +241,22 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPU) {
   }
 }
 
+TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPUTest) {
+  if (CAFFE_TEST_CUDA_PROP.major >= 2) {
+    LayerParameter layer_param;
+    Caffe::set_mode(Caffe::GPU);
+    Caffe::set_phase(Caffe::TEST);
+    DropoutLayer<TypeParam> layer(layer_param);
+    GradientChecker<TypeParam> checker(1e-2, 1e-3);
+    // it is too expensive to call curand multiple times, so we don't do an
+    // exhaustive gradient check.
+    checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
+        &(this->blob_top_vec_));
+  } else {
+    LOG(ERROR) << "Skipping test to spare my laptop.";
+  }
+}
+
 
 TYPED_TEST(NeuronLayerTest, TestDropoutGPUTestPhase) {
   LayerParameter layer_param;

From 2dd0bc13b51d456a1cd1aaa2dbaf7372baeda7b3 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 18 Jun 2014 18:49:14 -0700
Subject: [PATCH 0198/2053] Check that pointers are different before copying in
 caffe_copy and caffe_gpu_copy

---
 src/caffe/util/math_functions.cpp | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 67274ef73cc..90df51248e2 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -151,22 +151,30 @@ void caffe_add_scalar(const int N, const double alpha, double* Y) {
 
 template <>
 void caffe_copy<float>(const int N, const float* X, float* Y) {
-  cblas_scopy(N, X, 1, Y, 1);
+  if (X != Y) {
+    cblas_scopy(N, X, 1, Y, 1);
+  }
 }
 
 template <>
 void caffe_copy<double>(const int N, const double* X, double* Y) {
-  cblas_dcopy(N, X, 1, Y, 1);
+  if (X != Y) {
+    cblas_dcopy(N, X, 1, Y, 1);
+  }
 }
 
 template <>
 void caffe_gpu_copy<float>(const int N, const float* X, float* Y) {
-  CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+  if (X != Y) {
+    CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+  }
 }
 
 template <>
 void caffe_gpu_copy<double>(const int N, const double* X, double* Y) {
-  CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+  if (X != Y) {
+    CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+  }
 }
 
 template <>

From 5316ca0a06efe71eed59dc4eef1f17606c8f2b56 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 28 Jun 2014 10:22:13 -0700
Subject: [PATCH 0199/2053] Remove Cuda.major >= 2 check on Dropout test

---
 src/caffe/test/test_neuron_layer.cpp | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index c7c0dc1675d..a439128a13b 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -226,7 +226,6 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGPU) {
 
 
 TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPU) {
-  if (CAFFE_TEST_CUDA_PROP.major >= 2) {
     LayerParameter layer_param;
     Caffe::set_mode(Caffe::GPU);
     Caffe::set_phase(Caffe::TRAIN);
@@ -236,13 +235,9 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPU) {
     // exhaustive gradient check.
     checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
         &(this->blob_top_vec_));
-  } else {
-    LOG(ERROR) << "Skipping test to spare my laptop.";
-  }
 }
 
 TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPUTest) {
-  if (CAFFE_TEST_CUDA_PROP.major >= 2) {
     LayerParameter layer_param;
     Caffe::set_mode(Caffe::GPU);
     Caffe::set_phase(Caffe::TEST);
@@ -252,9 +247,6 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPUTest) {
     // exhaustive gradient check.
     checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
         &(this->blob_top_vec_));
-  } else {
-    LOG(ERROR) << "Skipping test to spare my laptop.";
-  }
 }
 
 

From e6a46c2be6f5917c84198de49848887daa28098a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 28 Jun 2014 13:54:58 -0700
Subject: [PATCH 0200/2053] lint

---
 src/caffe/layers/multinomial_logistic_loss_layer.cpp | 2 +-
 src/caffe/net.cpp                                    | 1 +
 tools/compute_image_mean.cpp                         | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 013d4034240..dd5cae44d6d 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -35,7 +35,7 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
     Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
-  if (top->size() == 1){
+  if (top->size() == 1) {
     (*top)[0]->mutable_cpu_data()[0] = loss / num;
   }
   return loss / num;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index f364e6767c6..c0f124b0ea7 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -3,6 +3,7 @@
 #include <map>
 #include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "caffe/common.hpp"
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 5b212b66edd..a7cbc8bd6b1 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -53,7 +53,7 @@ int main(int argc, char** argv) {
   } else if (db_backend == "lmdb") {  // lmdb
     LOG(INFO) << "Opening lmdb " << argv[1];
     CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS); // 1TB
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS);  // 1TB
     CHECK_EQ(mdb_env_open(mdb_env, argv[1], MDB_RDONLY, 0664),
         MDB_SUCCESS) << "mdb_env_open failed";
     CHECK_EQ(mdb_txn_begin(mdb_env, NULL, MDB_RDONLY, &mdb_txn), MDB_SUCCESS)

From fae9b13f717789084275c546619adc79452fe44e Mon Sep 17 00:00:00 2001
From: yzhuan <465022947@qq.com>
Date: Mon, 30 Jun 2014 16:08:55 +0800
Subject: [PATCH 0201/2053] Update Makefile.config.example

fix some typing error
---
 Makefile.config.example | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index 73c3740b1c7..6c3948fc2d7 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -13,9 +13,9 @@ CUDA_DIR := /usr/local/cuda
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
-		-gencode arch=compute_35,code=sm_35
-		#-gencode=arch=compute_50,code=sm_50 \
-		#-gencode=arch=compute_50,code=compute_50
+		-gencode arch=compute_35,code=sm_35 \
+		#-gencode arch=compute_50,code=sm_50 \
+		#-gencode arch=compute_50,code=compute_50
 
 # BLAS choice:
 # atlas for ATLAS (default)

From 9dc87346005c5676aaaa62b17d6d1749ab56bb6c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 1 Jul 2014 10:15:29 -0700
Subject: [PATCH 0202/2053] fix uninitialized variable warnings in tools

---
 tools/compute_image_mean.cpp | 2 +-
 tools/convert_imageset.cpp   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index a7cbc8bd6b1..0c0f0f3f7e5 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -32,7 +32,7 @@ int main(int argc, char** argv) {
   leveldb::DB* db;
   leveldb::Options options;
   options.create_if_missing = false;
-  leveldb::Iterator* it;
+  leveldb::Iterator* it = NULL;
   // lmdb
   MDB_env* mdb_env;
   MDB_dbi mdb_dbi;
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index c4bc0b32439..28de653b2c7 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -93,7 +93,7 @@ int main(int argc, char** argv) {
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  leveldb::WriteBatch* batch;
+  leveldb::WriteBatch* batch = NULL;
 
   // Open db
   if (db_backend == "leveldb") {  // leveldb

From eada9dbdb5084fdcfb89b9da4bbcf825c1a84477 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 3 Jul 2014 20:15:53 +0800
Subject: [PATCH 0203/2053] Separate layers relatively independent of images
 out of vision_layers

---
 include/caffe/common_layers.hpp | 182 ++++++++++++++++++++++++++++++++
 include/caffe/vision_layers.hpp | 165 +----------------------------
 2 files changed, 185 insertions(+), 162 deletions(-)
 create mode 100644 include/caffe/common_layers.hpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
new file mode 100644
index 00000000000..761c19fa7e2
--- /dev/null
+++ b/include/caffe/common_layers.hpp
@@ -0,0 +1,182 @@
+// Copyright 2014 BVLC and contributors.
+
+#ifndef CAFFE_COMMON_LAYERS_HPP_
+#define CAFFE_COMMON_LAYERS_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/loss_layers.hpp"
+#include "caffe/data_layers.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/* ArgmaxLayer
+  Compute the index of the max value across all (channels x height x width).
+  [In the future, can take specific dimension.]
+  Intended for use after a classification layer to produce prediction.
+  If parameter out_max_val is set to true, then output is a vector of pairs
+  (max_ind, max_val) for each image.
+
+  NOTE: does not implement Backwards operation.
+*/
+template <typename Dtype>
+class ArgMaxLayer : public Layer<Dtype> {
+ public:
+  explicit ArgMaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ARGMAX;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+  bool out_max_val_;
+};
+
+/* ConcatLayer
+  Takes at least two blobs and concatenates them along either num or
+  channel dim, outputting the result.
+*/
+template <typename Dtype>
+class ConcatLayer : public Layer<Dtype> {
+ public:
+  explicit ConcatLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONCAT;
+  }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> col_bob_;
+  int count_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+  int concat_dim_;
+};
+
+/* FlattenLayer
+*/
+template <typename Dtype>
+class FlattenLayer : public Layer<Dtype> {
+ public:
+  explicit FlattenLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_FLATTEN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  int count_;
+};
+
+/* SoftmaxLayer
+*/
+template <typename Dtype>
+class SoftmaxLayer : public Layer<Dtype> {
+ public:
+  explicit SoftmaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  // sum_multiplier is just used to carry out sum using blas
+  Blob<Dtype> sum_multiplier_;
+  // scale is an intermediate blob to hold temporary results.
+  Blob<Dtype> scale_;
+};
+
+/* SplitLayer
+*/
+template <typename Dtype>
+class SplitLayer : public Layer<Dtype> {
+ public:
+  explicit SplitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SPLIT;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  int count_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_COMMON_LAYERS_HPP_
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index b68dcbf6e83..ef04772a98c 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -9,84 +9,15 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
 #include "caffe/loss_layers.hpp"
-#include "caffe/data_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
-/* ArgmaxLayer
-  Compute the index of the max value across all (channels x height x width).
-  [In the future, can take specific dimension.]
-  Intended for use after a classification layer to produce prediction.
-  If parameter out_max_val is set to true, then output is a vector of pairs
-  (max_ind, max_val) for each image.
-
-  NOTE: does not implement Backwards operation.
-*/
-template <typename Dtype>
-class ArgMaxLayer : public Layer<Dtype> {
- public:
-  explicit ArgMaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ARGMAX;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-    NOT_IMPLEMENTED;
-  }
-  bool out_max_val_;
-};
-
-/* ConcatLayer
-  Takes at least two blobs and concatenates them along either num or
-  channel dim, outputting the result.
-*/
-template <typename Dtype>
-class ConcatLayer : public Layer<Dtype> {
- public:
-  explicit ConcatLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONCAT;
-  }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  Blob<Dtype> col_bob_;
-  int count_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-  int concat_dim_;
-};
-
 /* ConvolutionLayer
 */
 template <typename Dtype>
@@ -161,35 +92,6 @@ class EltwiseLayer : public Layer<Dtype> {
   vector<Dtype> coeffs_;
 };
 
-/* FlattenLayer
-*/
-template <typename Dtype>
-class FlattenLayer : public Layer<Dtype> {
- public:
-  explicit FlattenLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_FLATTEN;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  int count_;
-};
-
 /* Im2colLayer
 */
 template <typename Dtype>
@@ -373,67 +275,6 @@ class PoolingLayer : public Layer<Dtype> {
   shared_ptr<Blob<int> > max_idx_;
 };
 
-/* SoftmaxLayer
-*/
-template <typename Dtype>
-class SoftmaxLayer : public Layer<Dtype> {
- public:
-  explicit SoftmaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  // sum_multiplier is just used to carry out sum using blas
-  Blob<Dtype> sum_multiplier_;
-  // scale is an intermediate blob to hold temporary results.
-  Blob<Dtype> scale_;
-};
-
-/* SplitLayer
-*/
-template <typename Dtype>
-class SplitLayer : public Layer<Dtype> {
- public:
-  explicit SplitLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SPLIT;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  int count_;
-};
-
 }  // namespace caffe
 
 #endif  // CAFFE_VISION_LAYERS_HPP_

From 8e1689faa55f4e7dc57a222792eb80e0afe1a3a7 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 3 Jul 2014 20:16:54 +0800
Subject: [PATCH 0204/2053] Arrange the data layers to be in alphabetical order

---
 include/caffe/data_layers.hpp | 137 +++++++++++++++++-----------------
 1 file changed, 68 insertions(+), 69 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 2c6be551d8f..06c90ea9923 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -24,75 +24,6 @@ namespace caffe {
 #define HDF5_DATA_DATASET_NAME "data"
 #define HDF5_DATA_LABEL_NAME "label"
 
-template <typename Dtype>
-class HDF5OutputLayer : public Layer<Dtype> {
- public:
-  explicit HDF5OutputLayer(const LayerParameter& param);
-  virtual ~HDF5OutputLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HDF5_OUTPUT;
-  }
-  // TODO: no limit on the number of blobs
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
-
-  inline std::string file_name() const { return file_name_; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void SaveBlobs();
-
-  std::string file_name_;
-  hid_t file_id_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
-};
-
-
-template <typename Dtype>
-class HDF5DataLayer : public Layer<Dtype> {
- public:
-  explicit HDF5DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~HDF5DataLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HDF5_DATA;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void LoadHDF5FileData(const char* filename);
-
-  std::vector<std::string> hdf_filenames_;
-  unsigned int num_files_;
-  unsigned int current_file_;
-  hsize_t current_row_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
-};
-
 // TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
 // same basic structure and a lot of duplicated code.
 
@@ -183,6 +114,74 @@ class DummyDataLayer : public Layer<Dtype> {
   vector<bool> refill_;
 };
 
+template <typename Dtype>
+class HDF5DataLayer : public Layer<Dtype> {
+ public:
+  explicit HDF5DataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual ~HDF5DataLayer();
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HDF5_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+  virtual void LoadHDF5FileData(const char* filename);
+
+  std::vector<std::string> hdf_filenames_;
+  unsigned int num_files_;
+  unsigned int current_file_;
+  hsize_t current_row_;
+  Blob<Dtype> data_blob_;
+  Blob<Dtype> label_blob_;
+};
+
+template <typename Dtype>
+class HDF5OutputLayer : public Layer<Dtype> {
+ public:
+  explicit HDF5OutputLayer(const LayerParameter& param);
+  virtual ~HDF5OutputLayer();
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HDF5_OUTPUT;
+  }
+  // TODO: no limit on the number of blobs
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+  inline std::string file_name() const { return file_name_; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void SaveBlobs();
+
+  std::string file_name_;
+  hid_t file_id_;
+  Blob<Dtype> data_blob_;
+  Blob<Dtype> label_blob_;
+};
+
 // This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
 void* ImageDataLayerPrefetch(void* layer_pointer);

From 97deb9101b5a555b135ddc9826ab2925536be69a Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 3 Jul 2014 20:17:41 +0800
Subject: [PATCH 0205/2053] Organize the loss layers in alphabetical order

---
 include/caffe/loss_layers.hpp | 206 +++++++++++++++++-----------------
 1 file changed, 103 insertions(+), 103 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 3a4d41662fd..fb7f5539674 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -22,6 +22,36 @@ namespace caffe {
 
 const float kLOG_THRESHOLD = 1e-20;
 
+/* AccuracyLayer
+  Note: not an actual loss layer! Does not implement backwards step.
+  Computes the accuracy of argmax(a) with respect to b.
+*/
+template <typename Dtype>
+class AccuracyLayer : public Layer<Dtype> {
+ public:
+  explicit AccuracyLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ACCURACY;
+  }
+
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  int top_k_;
+};
+
 /* LossLayer
   Takes two inputs of same num (a and b), and has no output.
   The gradient is propagated to a.
@@ -45,67 +75,27 @@ class LossLayer : public Layer<Dtype> {
   }
 };
 
-// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
-template <typename Dtype> class SoftmaxLayer;
-
-/* SoftmaxWithLossLayer
-  Implements softmax and computes the loss.
-
-  It is preferred over separate softmax + multinomiallogisticloss
-  layers due to more numerically stable gradients.
+/* EuclideanLossLayer
+  Compute the L_2 distance between the two inputs.
 
-  In test, this layer could be replaced by simple softmax layer.
+  loss = (1/2 \sum_i (a_i - b_i)^2)
+  a' = 1/I (a - b)
 */
 template <typename Dtype>
-class SoftmaxWithLossLayer : public Layer<Dtype> {
+class EuclideanLossLayer : public LossLayer<Dtype> {
  public:
-  explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  explicit EuclideanLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
+  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX_LOSS;
+    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
   }
-  virtual inline int MaxTopBlobs() const { return 2; }
-  // We cannot backpropagate to the labels; ignore force_backward for these
-  // inputs.
+  // Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+  // to both inputs.
   virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return bottom_index != 1;
-  }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
-  // prob stores the output probability of the layer.
-  Blob<Dtype> prob_;
-  // Vector holders to call the underlying softmax layer forward and backward.
-  vector<Blob<Dtype>*> softmax_bottom_vec_;
-  vector<Blob<Dtype>*> softmax_top_vec_;
-};
-
-/* SigmoidCrossEntropyLossLayer
-*/
-template <typename Dtype>
-class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
- public:
-  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param),
-          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
-          sigmoid_output_(new Blob<Dtype>()) {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
+    return true;
   }
 
  protected:
@@ -118,48 +108,26 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
-  // sigmoid_output stores the output of the sigmoid layer.
-  shared_ptr<Blob<Dtype> > sigmoid_output_;
-  // Vector holders to call the underlying sigmoid layer forward and backward.
-  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
-  vector<Blob<Dtype>*> sigmoid_top_vec_;
+  Blob<Dtype> diff_;
 };
 
-/* EuclideanLossLayer
-  Compute the L_2 distance between the two inputs.
-
-  loss = (1/2 \sum_i (a_i - b_i)^2)
-  a' = 1/I (a - b)
+/* HingeLossLayer
 */
 template <typename Dtype>
-class EuclideanLossLayer : public LossLayer<Dtype> {
+class HingeLossLayer : public LossLayer<Dtype> {
  public:
-  explicit EuclideanLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), diff_() {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  explicit HingeLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
 
   virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
-  }
-  // Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
-  // to both inputs.
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return true;
+    return LayerParameter_LayerType_HINGE_LOSS;
   }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  Blob<Dtype> diff_;
 };
 
 /* InfogainLossLayer
@@ -185,16 +153,18 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   Blob<Dtype> infogain_;
 };
 
-/* HingeLossLayer
+/* MultinomialLogisticLossLayer
 */
 template <typename Dtype>
-class HingeLossLayer : public LossLayer<Dtype> {
+class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
  public:
-  explicit HingeLossLayer(const LayerParameter& param)
+  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
+  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HINGE_LOSS;
+    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
   }
 
  protected:
@@ -204,55 +174,85 @@ class HingeLossLayer : public LossLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* MultinomialLogisticLossLayer
+/* SigmoidCrossEntropyLossLayer
 */
 template <typename Dtype>
-class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
+class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
  public:
-  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
+  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param),
+          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+          sigmoid_output_(new Blob<Dtype>()) {}
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
+    return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
   }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+  // sigmoid_output stores the output of the sigmoid layer.
+  shared_ptr<Blob<Dtype> > sigmoid_output_;
+  // Vector holders to call the underlying sigmoid layer forward and backward.
+  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+  vector<Blob<Dtype>*> sigmoid_top_vec_;
 };
 
-/* AccuracyLayer
-  Note: not an actual loss layer! Does not implement backwards step.
-  Computes the accuracy of argmax(a) with respect to b.
+// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
+template <typename Dtype> class SoftmaxLayer;
+
+/* SoftmaxWithLossLayer
+  Implements softmax and computes the loss.
+
+  It is preferred over separate softmax + multinomiallogisticloss
+  layers due to more numerically stable gradients.
+
+  In test, this layer could be replaced by simple softmax layer.
 */
 template <typename Dtype>
-class AccuracyLayer : public Layer<Dtype> {
+class SoftmaxWithLossLayer : public Layer<Dtype> {
  public:
-  explicit AccuracyLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+  explicit SoftmaxWithLossLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ACCURACY;
+    return LayerParameter_LayerType_SOFTMAX_LOSS;
+  }
+  virtual inline int MaxTopBlobs() const { return 2; }
+  // We cannot backpropagate to the labels; ignore force_backward for these
+  // inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
   }
-
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-    NOT_IMPLEMENTED;
-  }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int top_k_;
+  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
+  // prob stores the output probability of the layer.
+  Blob<Dtype> prob_;
+  // Vector holders to call the underlying softmax layer forward and backward.
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  vector<Blob<Dtype>*> softmax_top_vec_;
 };
 
 }  // namespace caffe

From 872e6c5bc794c8535f4c6d7211f12e8c597dabf7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 3 Jul 2014 14:39:35 -0700
Subject: [PATCH 0206/2053] add EqualNumBottomTopBlobs() property for layers;
 use in ConvolutionLayer

---
 include/caffe/layer.hpp         | 9 +++++++++
 include/caffe/vision_layers.hpp | 5 +++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 690c36ba23f..b8afcfe37cf 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -84,6 +84,10 @@ class Layer {
   virtual inline int MinTopBlobs() const { return -1; }
   virtual inline int MaxTopBlobs() const { return -1; }
 
+  // EqualNumBottomTopBlobs should return true for layers requiring an equal
+  // number of bottom and top blobs.
+  virtual inline bool EqualNumBottomTopBlobs() const { return false; }
+
   // Declare for each bottom blob whether to allow force_backward -- that is,
   // if AllowForceBackward(i) == false, we will ignore the force_backward
   // setting and backpropagate to blob i only if it needs gradient information
@@ -156,6 +160,11 @@ class Layer {
           << type_name() << " Layer produces at most " << MaxTopBlobs()
           << " top blob(s) as output.";
     }
+    if (EqualNumBottomTopBlobs()) {
+      CHECK_EQ(bottom.size(), top.size())
+          << type_name() << " Layer produces one top blob as output for each "
+          << "bottom blob input.";
+    }
   }
 
   DISABLE_COPY_AND_ASSIGN(Layer);
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index b68dcbf6e83..c9beebe6849 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -100,8 +100,9 @@ class ConvolutionLayer : public Layer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_CONVOLUTION;
   }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,

From a2b287472d7d85f997d2621bc4a2486c3837f6ba Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 3 Jul 2014 15:33:12 -0700
Subject: [PATCH 0207/2053] ConvolutionLayer can take N bottom blobs and N top
 blobs

---
 src/caffe/layers/conv_layer.cpp           | 141 ++++++++++++----------
 src/caffe/layers/conv_layer.cu            | 124 ++++++++++---------
 src/caffe/test/test_convolution_layer.cpp |  60 +++++++--
 3 files changed, 195 insertions(+), 130 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 9ec8da47e1a..67913bfa574 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -22,6 +22,16 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
+  // TODO: generalize to handle inputs of different shapes.
+  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
+    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
+    CHECK_EQ(channels_, bottom[bottom_id]->channels())
+        << "Inputs must have same channels.";
+    CHECK_EQ(height_, bottom[bottom_id]->height())
+        << "Inputs must have same height.";
+    CHECK_EQ(width_, bottom[bottom_id]->width())
+        << "Inputs must have same width.";
+  }
   num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
   CHECK_EQ(channels_ % group_, 0);
@@ -39,7 +49,9 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   M_ = num_output_ / group_;
   K_ = channels_ * kernel_size_ * kernel_size_ / group_;
   N_ = height_out * width_out;
-  (*top)[0]->Reshape(bottom[0]->num(), num_output_, height_out, width_out);
+  for (int top_id = 0; top_id < top->size(); ++top_id) {
+    (*top)[top_id]->Reshape(num_, num_output_, height_out, width_out);
+  }
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
@@ -56,7 +68,7 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, intiialize and fill the bias term
+    // If necessary, initialize and fill the bias term
     if (bias_term_) {
       this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
@@ -79,29 +91,31 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  Dtype* col_data = col_buffer_.mutable_cpu_data();
-  const Dtype* weight = this->blobs_[0]->cpu_data();
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  for (int n = 0; n < num_; ++n) {
-    // First, im2col
-    im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // Second, innerproduct with groups
-    for (int g = 0; g < group_; ++g) {
-      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-        (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-        (Dtype)0., top_data + (*top)[0]->offset(n) + top_offset * g);
-    }
-    // third, add bias
-    if (bias_term_) {
-      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-          N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
-          (Dtype)1., top_data + (*top)[0]->offset(n));
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* top_data = (*top)[i]->mutable_cpu_data();
+    Dtype* col_data = col_buffer_.mutable_cpu_data();
+    const Dtype* weight = this->blobs_[0]->cpu_data();
+    int weight_offset = M_ * K_;
+    int col_offset = K_ * N_;
+    int top_offset = M_ * N_;
+    for (int n = 0; n < num_; ++n) {
+      // First, im2col
+      im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+                        width_, kernel_size_, pad_, stride_, col_data);
+      // Second, innerproduct with groups
+      for (int g = 0; g < group_; ++g) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
+          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+      }
+      // third, add bias
+      if (bias_term_) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
+            reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
+            (Dtype)1., top_data + (*top)[i]->offset(n));
+      }
     }
   }
   return Dtype(0.);
@@ -110,54 +124,57 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  Dtype* col_data = col_buffer_.mutable_cpu_data();
-  Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-  // bias gradient if necessary
+  memset(weight_diff, 0, sizeof(Dtype) * this->blobs_[0]->count());
   Dtype* bias_diff = NULL;
-
   if (bias_term_) {
     bias_diff = this->blobs_[1]->mutable_cpu_diff();
     memset(bias_diff, 0, sizeof(Dtype) * this->blobs_[1]->count());
-    for (int n = 0; n < num_; ++n) {
-      caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-          1., top_diff + top[0]->offset(n),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), 1.,
-          bias_diff);
-    }
   }
+  const int weight_offset = M_ * K_;
+  const int col_offset = K_ * N_;
+  const int top_offset = M_ * N_;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const Dtype* bottom_data = (*bottom)[i]->cpu_data();
+    Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+    Dtype* col_data = col_buffer_.mutable_cpu_data();
+    Dtype* col_diff = col_buffer_.mutable_cpu_diff();
 
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  memset(weight_diff, 0, sizeof(Dtype) * this->blobs_[0]->count());
-  for (int n = 0; n < num_; ++n) {
-    // since we saved memory in the forward pass by not storing all col data,
-    // we will need to recompute them.
-    im2col_cpu(bottom_data + (*bottom)[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // gradient w.r.t. weight. Note that we will accumulate diffs.
-    for (int g = 0; g < group_; ++g) {
-      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-        (Dtype)1., top_diff + top[0]->offset(n) + top_offset * g,
-        col_data + col_offset * g, (Dtype)1.,
-        weight_diff + weight_offset * g);
+    // Bias gradient, if necessary.
+    if (bias_term_) {
+      for (int n = 0; n < num_; ++n) {
+        caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
+            1., top_diff + top[0]->offset(n),
+            static_cast<const Dtype*>(bias_multiplier_->cpu_data()), 1.,
+            bias_diff);
+      }
     }
-    // gradient w.r.t. bottom data, if necessary
-    if (propagate_down[0]) {
+    for (int n = 0; n < num_; ++n) {
+      // Since we saved memory in the forward pass by not storing all col data,
+      // we will need to recompute them.
+      im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                 width_, kernel_size_, pad_, stride_, col_data);
+      // gradient w.r.t. weight. Note that we will accumulate diffs.
       for (int g = 0; g < group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-          (Dtype)1., weight + weight_offset * g,
-          top_diff + top[0]->offset(n) + top_offset * g,
-          (Dtype)0., col_diff + col_offset * g);
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+          (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+          col_data + col_offset * g, (Dtype)1.,
+          weight_diff + weight_offset * g);
+      }
+      // gradient w.r.t. bottom data, if necessary
+      if (propagate_down[i]) {
+        for (int g = 0; g < group_; ++g) {
+          caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+            (Dtype)1., weight + weight_offset * g,
+            top_diff + top[i]->offset(n) + top_offset * g,
+            (Dtype)0., col_diff + col_offset * g);
+        }
+        // col2im back to the data
+        col2im_cpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
+            stride_, bottom_diff + (*bottom)[i]->offset(n));
       }
-      // col2im back to the data
-      col2im_cpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-          stride_, bottom_diff + (*bottom)[0]->offset(n));
     }
   }
 }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 85f95fd32c9..71b00c9566b 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -13,29 +13,31 @@ namespace caffe {
 template <typename Dtype>
 Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  Dtype* col_data = col_buffer_.mutable_gpu_data();
-  const Dtype* weight = this->blobs_[0]->gpu_data();
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  for (int n = 0; n < num_; ++n) {
-    // First, im2col
-    im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // Second, innerproduct with groups
-    for (int g = 0; g < group_; ++g) {
-      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-        (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-        (Dtype)0., top_data + (*top)[0]->offset(n) + top_offset * g);
-    }
-    // third, add bias
-    if (bias_term_) {
-      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-          N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-          (Dtype)1., top_data + (*top)[0]->offset(n));
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* top_data = (*top)[i]->mutable_gpu_data();
+    Dtype* col_data = col_buffer_.mutable_gpu_data();
+    const Dtype* weight = this->blobs_[0]->gpu_data();
+    int weight_offset = M_ * K_;
+    int col_offset = K_ * N_;
+    int top_offset = M_ * N_;
+    for (int n = 0; n < num_; ++n) {
+      // First, im2col
+      im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+                        width_, kernel_size_, pad_, stride_, col_data);
+      // Second, innerproduct with groups
+      for (int g = 0; g < group_; ++g) {
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
+          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+      }
+      // third, add bias
+      if (bias_term_) {
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+            N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
+            reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
+            (Dtype)1., top_data + (*top)[i]->offset(n));
+      }
     }
   }
   return Dtype(0.);
@@ -44,56 +46,58 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  CUDA_CHECK(cudaMemset(weight_diff, 0,
+      sizeof(Dtype) * this->blobs_[0]->count()));
   Dtype* col_data = col_buffer_.mutable_gpu_data();
   Dtype* col_diff = col_buffer_.mutable_gpu_diff();
-  // bias gradient if necessary
   Dtype* bias_diff = NULL;
-
   if (bias_term_) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
     CUDA_CHECK(cudaMemset(bias_diff, 0,
         sizeof(Dtype) * this->blobs_[1]->count()));
-    for (int n = 0; n < num_; ++n) {
-      caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-          1., top_diff + top[0]->offset(n),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-          1., bias_diff);
-    }
   }
-
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  CUDA_CHECK(cudaMemset(weight_diff, 0,
-      sizeof(Dtype) * this->blobs_[0]->count()));
-  for (int n = 0; n < num_; ++n) {
-    // since we saved memory in the forward pass by not storing all col data,
-    // we will need to recompute them.
-    im2col_gpu(bottom_data + (*bottom)[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // gradient w.r.t. weight. Note that we will accumulate diffs.
-    for (int g = 0; g < group_; ++g) {
-      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-        (Dtype)1., top_diff + top[0]->offset(n) + top_offset * g,
-        col_data + col_offset * g, (Dtype)1.,
-        weight_diff + weight_offset * g);
+  const int weight_offset = M_ * K_;
+  const int col_offset = K_ * N_;
+  const int top_offset = M_ * N_;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    const Dtype* bottom_data = (*bottom)[i]->gpu_data();
+    Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+    // Bias gradient, if necessary.
+    if (bias_term_) {
+      for (int n = 0; n < num_; ++n) {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
+            1., top_diff + top[0]->offset(n),
+            static_cast<const Dtype*>(bias_multiplier_->gpu_data()),
+            1., bias_diff);
+      }
     }
-    // gradient w.r.t. bottom data, if necessary
-    if (propagate_down[0]) {
+    for (int n = 0; n < num_; ++n) {
+      // since we saved memory in the forward pass by not storing all col data,
+      // we will need to recompute them.
+      im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                        width_, kernel_size_, pad_, stride_, col_data);
+      // gradient w.r.t. weight. Note that we will accumulate diffs.
       for (int g = 0; g < group_; ++g) {
-        caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-          (Dtype)1., weight + weight_offset * g,
-          top_diff + top[0]->offset(n) + top_offset * g,
-          (Dtype)0., col_diff + col_offset * g);
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+          (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+          col_data + col_offset * g, (Dtype)1.,
+          weight_diff + weight_offset * g);
+      }
+      // gradient w.r.t. bottom data, if necessary
+      if (propagate_down[i]) {
+        for (int g = 0; g < group_; ++g) {
+          caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+            (Dtype)1., weight + weight_offset * g,
+            top_diff + top[i]->offset(n) + top_offset * g,
+            (Dtype)0., col_diff + col_offset * g);
+        }
+        // col2im back to the data
+        col2im_gpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
+            stride_, bottom_diff + (*bottom)[i]->offset(n));
       }
-      // col2im back to the data
-      col2im_gpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-          stride_, bottom_diff + (*bottom)[0]->offset(n));
     }
   }
 }
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index b08486e10a3..f7401015538 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -21,22 +21,32 @@ template <typename Dtype>
 class ConvolutionLayerTest : public ::testing::Test {
  protected:
   ConvolutionLayerTest()
-      : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()) {}
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
   virtual void SetUp() {
-    blob_bottom_->Reshape(2, 3, 6, 4);
     // fill the values
     FillerParameter filler_param;
     filler_param.set_value(1.);
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_2_);
     blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
   }
 
-  virtual ~ConvolutionLayerTest() { delete blob_bottom_; delete blob_top_; }
+  virtual ~ConvolutionLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_2_;
+    delete blob_top_;
+    delete blob_top_2_;
+  }
+
   Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
   Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_2_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -51,6 +61,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(4);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<TypeParam> > layer(
       new ConvolutionLayer<TypeParam>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -58,6 +70,10 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->channels(), 4);
   EXPECT_EQ(this->blob_top_->height(), 2);
   EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 4);
+  EXPECT_EQ(this->blob_top_2_->height(), 2);
+  EXPECT_EQ(this->blob_top_2_->width(), 1);
   // setting group should not change the shape
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
@@ -67,14 +83,24 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 2);
   EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 3);
+  EXPECT_EQ(this->blob_top_2_->height(), 2);
+  EXPECT_EQ(this->blob_top_2_->width(), 1);
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
   // We will simply see if the convolution layer carries out averaging well.
+  shared_ptr<ConstantFiller<TypeParam> > filler;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
+  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  filler_param.set_value(2.);
+  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_2_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -95,14 +121,24 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     EXPECT_NEAR(top_data[i], 27.1, 1e-4);
   }
+  top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
+  }
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolution) {
   // We will simply see if the convolution layer carries out averaging well.
+  shared_ptr<ConstantFiller<TypeParam> > filler;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
+  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  filler_param.set_value(2.);
+  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_2_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -123,6 +159,10 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolution) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     EXPECT_NEAR(top_data[i], 27.1, 1e-4);
   }
+  top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
+  }
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolutionGroup) {
@@ -223,6 +263,8 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUGradient) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(2);
@@ -256,6 +298,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUGradient) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(2);

From 1fcd29fb9aaedb763f58a1702dedc4a1e3904422 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 27 Jun 2014 15:01:02 -0700
Subject: [PATCH 0208/2053] report UVA in platform test

---
 src/caffe/test/test_platform.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/test/test_platform.cpp b/src/caffe/test/test_platform.cpp
index c3868f34d9f..7cf8306e8c9 100644
--- a/src/caffe/test/test_platform.cpp
+++ b/src/caffe/test/test_platform.cpp
@@ -47,6 +47,8 @@ TEST_F(PlatformTest, TestInitialization) {
          CAFFE_TEST_CUDA_PROP.multiProcessorCount);
   printf("Kernel execution timeout:      %s\n",
          (CAFFE_TEST_CUDA_PROP.kernelExecTimeoutEnabled ? "Yes" : "No"));
+  printf("Unified virtual addressing:    %s\n",
+         (CAFFE_TEST_CUDA_PROP.unifiedAddressing ? "Yes" : "No"));
   EXPECT_TRUE(true);
 }
 

From 9f74b6b129ab6ae161d569ce73d346553e04ec49 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 27 Jun 2014 18:36:48 -0700
Subject: [PATCH 0209/2053] switch to unified virtual addressing CUDA memcpy

Host / device copies are distinguished by the virtual address of the
pointers instead of explicit memcpy modes.
---
 matlab/caffe/matcaffe.cpp             | 10 +++++-----
 src/caffe/blob.cpp                    |  4 ++--
 src/caffe/layers/data_layer.cu        |  4 ++--
 src/caffe/layers/hdf5_data_layer.cu   |  4 ++--
 src/caffe/layers/hdf5_output_layer.cu |  4 ++--
 src/caffe/layers/image_data_layer.cu  |  4 ++--
 src/caffe/layers/window_data_layer.cu |  4 ++--
 src/caffe/syncedmem.cpp               |  4 ++--
 src/caffe/test/test_syncedmem.cpp     |  4 ++--
 9 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 21f51e83994..1b2b65ec7d8 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -59,7 +59,7 @@ static mxArray* do_forward(const mxArray* const bottom) {
       break;
     case Caffe::GPU:
       cudaMemcpy(input_blobs[i]->mutable_gpu_data(), data_ptr,
-          sizeof(float) * input_blobs[i]->count(), cudaMemcpyHostToDevice);
+          sizeof(float) * input_blobs[i]->count(), cudaMemcpyDefault);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -82,7 +82,7 @@ static mxArray* do_forward(const mxArray* const bottom) {
       break;
     case Caffe::GPU:
       cudaMemcpy(data_ptr, output_blobs[i]->gpu_data(),
-          sizeof(float) * output_blobs[i]->count(), cudaMemcpyDeviceToHost);
+          sizeof(float) * output_blobs[i]->count(), cudaMemcpyDefault);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -109,7 +109,7 @@ static mxArray* do_backward(const mxArray* const top_diff) {
       break;
     case Caffe::GPU:
       cudaMemcpy(output_blobs[i]->mutable_gpu_diff(), data_ptr,
-        sizeof(float) * output_blobs[i]->count(), cudaMemcpyHostToDevice);
+        sizeof(float) * output_blobs[i]->count(), cudaMemcpyDefault);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -134,7 +134,7 @@ static mxArray* do_backward(const mxArray* const top_diff) {
       break;
     case Caffe::GPU:
       cudaMemcpy(data_ptr, input_blobs[i]->gpu_diff(),
-          sizeof(float) * input_blobs[i]->count(), cudaMemcpyDeviceToHost);
+          sizeof(float) * input_blobs[i]->count(), cudaMemcpyDefault);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -211,7 +211,7 @@ static mxArray* do_get_weights() {
           break;
         case Caffe::GPU:
           CUDA_CHECK(cudaMemcpy(weights_ptr, layer_blobs[j]->gpu_data(),
-              sizeof(float) * layer_blobs[j]->count(), cudaMemcpyDeviceToHost));
+              sizeof(float) * layer_blobs[j]->count(), cudaMemcpyDefault));
           break;
         default:
           LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index e603712fd82..eb1075a0273 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -150,10 +150,10 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   case Caffe::GPU:
     if (copy_diff) {
       CUDA_CHECK(cudaMemcpy(diff_->mutable_gpu_data(), source.gpu_diff(),
-          sizeof(Dtype) * count_, cudaMemcpyDeviceToDevice));
+          sizeof(Dtype) * count_, cudaMemcpyDefault));
     } else {
       CUDA_CHECK(cudaMemcpy(data_->mutable_gpu_data(), source.gpu_data(),
-          sizeof(Dtype) * count_, cudaMemcpyDeviceToDevice));
+          sizeof(Dtype) * count_, cudaMemcpyDefault));
     }
     break;
   case Caffe::CPU:
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 2ff9a292b3e..5a8bb0bfc65 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -23,11 +23,11 @@ Dtype DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // Copy the data
   CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
       prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyHostToDevice));
+      cudaMemcpyDefault));
   if (output_labels_) {
     CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
         prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-        cudaMemcpyHostToDevice));
+        cudaMemcpyDefault));
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index b2b09ef7dd1..232f55fd677 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -44,12 +44,12 @@ Dtype HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
             &(*top)[0]->mutable_gpu_data()[i * data_count],
             &data_blob_.cpu_data()[current_row_ * data_count],
             sizeof(Dtype) * data_count,
-            cudaMemcpyHostToDevice));
+            cudaMemcpyDefault));
     CUDA_CHECK(cudaMemcpy(
             &(*top)[1]->mutable_gpu_data()[i * label_data_count],
             &label_blob_.cpu_data()[current_row_ * label_data_count],
             sizeof(Dtype) * label_data_count,
-            cudaMemcpyHostToDevice));
+            cudaMemcpyDefault));
   }
   return Dtype(0.);
 }
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 59505ee6acf..19567c12b2b 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -29,10 +29,10 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom[0]->num(); ++i) {
     CUDA_CHECK(cudaMemcpy(&data_blob_.mutable_cpu_data()[i * data_datum_dim],
            &bottom[0]->gpu_data()[i * data_datum_dim],
-           sizeof(Dtype) * data_datum_dim, cudaMemcpyDeviceToHost));
+           sizeof(Dtype) * data_datum_dim, cudaMemcpyDefault));
     CUDA_CHECK(cudaMemcpy(&label_blob_.mutable_cpu_data()[i * label_datum_dim],
            &bottom[1]->gpu_data()[i * label_datum_dim],
-           sizeof(Dtype) * label_datum_dim, cudaMemcpyDeviceToHost));
+           sizeof(Dtype) * label_datum_dim, cudaMemcpyDefault));
   }
   SaveBlobs();
   return Dtype(0.);
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index 98047297d80..3f905bc461a 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -29,10 +29,10 @@ Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // Copy the data
   CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
       prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyHostToDevice));
+      cudaMemcpyDefault));
   CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
       prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-      cudaMemcpyHostToDevice));
+      cudaMemcpyDefault));
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index bc49fef6545..5e8cdb7841d 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -30,10 +30,10 @@ Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // Copy the data
   CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
       prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyHostToDevice));
+      cudaMemcpyDefault));
   CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
       prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-      cudaMemcpyHostToDevice));
+      cudaMemcpyDefault));
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index fec37d6e9ec..ec4c528f46d 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -32,7 +32,7 @@ inline void SyncedMemory::to_cpu() {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
-    CUDA_CHECK(cudaMemcpy(cpu_ptr_, gpu_ptr_, size_, cudaMemcpyDeviceToHost));
+    CUDA_CHECK(cudaMemcpy(cpu_ptr_, gpu_ptr_, size_, cudaMemcpyDefault));
     head_ = SYNCED;
     break;
   case HEAD_AT_CPU:
@@ -52,7 +52,7 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
-    CUDA_CHECK(cudaMemcpy(gpu_ptr_, cpu_ptr_, size_, cudaMemcpyHostToDevice));
+    CUDA_CHECK(cudaMemcpy(gpu_ptr_, cpu_ptr_, size_, cudaMemcpyDefault));
     head_ = SYNCED;
     break;
   case HEAD_AT_GPU:
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index 7bbbbab6a57..e741f0f70da 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -58,7 +58,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   // check if values are the same
   char* recovered_value = new char[10];
   cudaMemcpy(reinterpret_cast<void*>(recovered_value), gpu_data, 10,
-             cudaMemcpyDeviceToHost);
+             cudaMemcpyDefault);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 1);
   }
@@ -73,7 +73,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
   cudaMemcpy(reinterpret_cast<void*>(recovered_value), gpu_data, 10,
-             cudaMemcpyDeviceToHost);
+             cudaMemcpyDefault);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 2);
   }

From 803a7a94f3033e4d389de57c18524c98ac744b3f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 27 Jun 2014 18:38:39 -0700
Subject: [PATCH 0210/2053] replace softmax cudaMemcpy with caffe_gpu_copy

---
 src/caffe/layers/softmax_layer.cu | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index f53883c9206..5ec4412ccfb 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -50,8 +50,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* scale_data = scale_.mutable_gpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  CUDA_CHECK(cudaMemcpy(top_data, bottom_data,
-      sizeof(Dtype) * bottom[0]->count(), cudaMemcpyDeviceToDevice));
+  caffe_gpu_copy(bottom[0]->count(), bottom_data, top_data);
   // we need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   // Compute max
@@ -85,8 +84,7 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int num = top[0]->num();
   int dim = top[0]->count() / top[0]->num();
-  CUDA_CHECK(cudaMemcpy(bottom_diff, top_diff,
-      sizeof(Dtype) * top[0]->count(), cudaMemcpyDeviceToDevice));
+  caffe_gpu_copy(top[0]->count(), top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
   // cuda dot returns the result to cpu, so we temporarily change the pointer
   // mode

From aaa9f24d369798cfa268f465ec5a1c35257ed500 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 27 Jun 2014 21:25:36 -0700
Subject: [PATCH 0211/2053] do all caffe_copy() as UVA mem copy, and drop
 caffe_gpu_copy()

Do all memory copies by `cudaMemcpy` in UVA mode so that the same
`caffe_copy()` interface works for all transfers.

`cudaMemcpy()` is used in lieu of BLAS copies because they do not
understand UVA.

Drop the now unnecessary `caffe_gpu_copy()` since location of the
pointers is now irrelevant to the interface.
---
 include/caffe/util/math_functions.hpp |  5 ++---
 src/caffe/util/math_functions.cpp     | 21 +++++++++++++--------
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 99519974243..b5f4dfbd1a8 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -59,15 +59,14 @@ void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
 
+void caffe_copy(const size_t N, const void *X, void *Y);
+
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
 
 template <typename Dtype>
 void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_copy(const int N, const Dtype *X, Dtype *Y);
-
 template <typename Dtype>
 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
 
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 90df51248e2..b1b62edb0d2 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -150,30 +150,35 @@ void caffe_add_scalar(const int N, const double alpha, double* Y) {
 }
 
 template <>
-void caffe_copy<float>(const int N, const float* X, float* Y) {
+void caffe_copy<int>(const int N, const int* X, int* Y) {
   if (X != Y) {
-    cblas_scopy(N, X, 1, Y, 1);
+    CUDA_CHECK(cudaMemcpy(Y, X, sizeof(int) * N, cudaMemcpyDefault));
   }
 }
 
 template <>
-void caffe_copy<double>(const int N, const double* X, double* Y) {
+void caffe_copy<unsigned int>(const int N, const unsigned int* X,
+    unsigned int* Y) {
   if (X != Y) {
-    cblas_dcopy(N, X, 1, Y, 1);
+  CUDA_CHECK(cudaMemcpy(Y, X, sizeof(unsigned int) * N, cudaMemcpyDefault));
   }
 }
 
 template <>
-void caffe_gpu_copy<float>(const int N, const float* X, float* Y) {
+void caffe_copy<float>(const int N, const float* X, float* Y) {
   if (X != Y) {
-    CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+    CUDA_CHECK(cudaMemcpy(Y, X, sizeof(float) * N, cudaMemcpyDefault));
   }
 }
 
 template <>
-void caffe_gpu_copy<double>(const int N, const double* X, double* Y) {
+void caffe_copy<double>(const int N, const double* X, double* Y) {
+  CUDA_CHECK(cudaMemcpy(Y, X, sizeof(double) * N, cudaMemcpyDefault));
+}
+
+void caffe_copy(const size_t N, const void* X, void* Y) {
   if (X != Y) {
-    CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
   }
 }
 

From 1da30a555827e778e36c5db72ef9cc672ad85675 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 27 Jun 2014 19:51:33 -0700
Subject: [PATCH 0212/2053] replace all memcpy by caffe_copy

---
 include/caffe/blob.hpp                        |  1 +
 include/caffe/syncedmem.hpp                   |  1 +
 matlab/caffe/matcaffe.cpp                     | 38 +++++++++----------
 src/caffe/blob.cpp                            | 16 ++++----
 src/caffe/layers/concat_layer.cu              |  8 ++--
 src/caffe/layers/data_layer.cu                | 10 ++---
 src/caffe/layers/dropout_layer.cu             |  2 +-
 src/caffe/layers/eltwise_layer.cu             |  2 +-
 src/caffe/layers/hdf5_data_layer.cu           | 16 +++-----
 src/caffe/layers/hdf5_output_layer.cpp        | 10 ++---
 src/caffe/layers/hdf5_output_layer.cu         | 10 ++---
 src/caffe/layers/image_data_layer.cu          | 10 ++---
 src/caffe/layers/power_layer.cu               |  4 +-
 .../sigmoid_cross_entropy_loss_layer.cu       |  2 +-
 src/caffe/layers/softmax_layer.cpp            |  4 +-
 src/caffe/layers/softmax_layer.cu             |  4 +-
 src/caffe/layers/softmax_loss_layer.cpp       |  2 +-
 src/caffe/layers/window_data_layer.cu         | 10 ++---
 src/caffe/solver.cpp                          |  2 +-
 src/caffe/syncedmem.cpp                       |  5 ++-
 src/caffe/test/test_gradient_check_util.hpp   |  4 +-
 src/caffe/test/test_math_functions.cpp        |  2 +-
 src/caffe/test/test_syncedmem.cpp             |  7 ++--
 src/caffe/test/test_util_blas.cpp             | 16 ++++----
 24 files changed, 86 insertions(+), 100 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index c04375a10e2..bbea86aea69 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -6,6 +6,7 @@
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index bed55c3806e..2b7f349025a 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -6,6 +6,7 @@
 #include <cstdlib>
 
 #include "caffe/common.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 1b2b65ec7d8..957ebea008e 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -54,12 +54,12 @@ static mxArray* do_forward(const mxArray* const bottom) {
         reinterpret_cast<const float* const>(mxGetPr(elem));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(input_blobs[i]->mutable_cpu_data(), data_ptr,
-          sizeof(float) * input_blobs[i]->count());
+      caffe_copy(input_blobs[i]->count(), data_ptr,
+          input_blobs[i]->mutable_cpu_data());
       break;
     case Caffe::GPU:
-      cudaMemcpy(input_blobs[i]->mutable_gpu_data(), data_ptr,
-          sizeof(float) * input_blobs[i]->count(), cudaMemcpyDefault);
+      caffe_copy(input_blobs[i]->count(), data_ptr,
+          input_blobs[i]->mutable_gpu_data());
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -77,12 +77,12 @@ static mxArray* do_forward(const mxArray* const bottom) {
     float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(data_ptr, output_blobs[i]->cpu_data(),
-          sizeof(float) * output_blobs[i]->count());
+      caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(),
+          data_ptr);
       break;
     case Caffe::GPU:
-      cudaMemcpy(data_ptr, output_blobs[i]->gpu_data(),
-          sizeof(float) * output_blobs[i]->count(), cudaMemcpyDefault);
+      caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(),
+          data_ptr);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -104,12 +104,12 @@ static mxArray* do_backward(const mxArray* const top_diff) {
         reinterpret_cast<const float* const>(mxGetPr(elem));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(output_blobs[i]->mutable_cpu_diff(), data_ptr,
-        sizeof(float) * output_blobs[i]->count());
+      caffe_copy(output_blobs[i]->count(), data_ptr,
+          output_blobs[i]->mutable_cpu_diff());
       break;
     case Caffe::GPU:
-      cudaMemcpy(output_blobs[i]->mutable_gpu_diff(), data_ptr,
-        sizeof(float) * output_blobs[i]->count(), cudaMemcpyDefault);
+      caffe_copy(output_blobs[i]->count(), data_ptr,
+          output_blobs[i]->mutable_gpu_diff());
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -129,12 +129,10 @@ static mxArray* do_backward(const mxArray* const top_diff) {
     float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(data_ptr, input_blobs[i]->cpu_diff(),
-          sizeof(float) * input_blobs[i]->count());
+      caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr);
       break;
     case Caffe::GPU:
-      cudaMemcpy(data_ptr, input_blobs[i]->gpu_diff(),
-          sizeof(float) * input_blobs[i]->count(), cudaMemcpyDefault);
+      caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -206,12 +204,12 @@ static mxArray* do_get_weights() {
 
         switch (Caffe::mode()) {
         case Caffe::CPU:
-          memcpy(weights_ptr, layer_blobs[j]->cpu_data(),
-              sizeof(float) * layer_blobs[j]->count());
+          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(),
+              weights_ptr);
           break;
         case Caffe::GPU:
-          CUDA_CHECK(cudaMemcpy(weights_ptr, layer_blobs[j]->gpu_data(),
-              sizeof(float) * layer_blobs[j]->count(), cudaMemcpyDefault));
+          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(),
+              weights_ptr);
           break;
         default:
           LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index eb1075a0273..69ff49efc46 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -149,20 +149,20 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   switch (Caffe::mode()) {
   case Caffe::GPU:
     if (copy_diff) {
-      CUDA_CHECK(cudaMemcpy(diff_->mutable_gpu_data(), source.gpu_diff(),
-          sizeof(Dtype) * count_, cudaMemcpyDefault));
+      caffe_copy(count_, source.gpu_diff(),
+          reinterpret_cast<Dtype*>(diff_->mutable_gpu_data()));
     } else {
-      CUDA_CHECK(cudaMemcpy(data_->mutable_gpu_data(), source.gpu_data(),
-          sizeof(Dtype) * count_, cudaMemcpyDefault));
+      caffe_copy(count_, source.gpu_data(),
+          reinterpret_cast<Dtype*>(data_->mutable_gpu_data()));
     }
     break;
   case Caffe::CPU:
     if (copy_diff) {
-      memcpy(diff_->mutable_cpu_data(), source.cpu_diff(),
-          sizeof(Dtype) * count_);
+      caffe_copy(count_, source.cpu_diff(),
+          reinterpret_cast<Dtype*>(diff_->mutable_cpu_data()));
     } else {
-      memcpy(data_->mutable_cpu_data(), source.cpu_data(),
-        sizeof(Dtype) * count_);
+      caffe_copy(count_, source.cpu_data(),
+          reinterpret_cast<Dtype*>(data_->mutable_cpu_data()));
     }
     break;
   default:
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index ca0cf0c1b5b..2643d7441c8 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -16,7 +16,7 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     int offset_num = 0;
     for (int i = 0; i < bottom.size(); ++i) {
       const Dtype* bottom_data = bottom[i]->gpu_data();
-      caffe_gpu_copy(bottom[i]->count(), bottom_data,
+      caffe_copy(bottom[i]->count(), bottom_data,
         top_data + (*top)[0]->offset(offset_num));
       offset_num += bottom[i]->num();
     }
@@ -27,7 +27,7 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       int num_elem =
         bottom[i]->channels() * bottom[i]->height() * bottom[i]->width();
       for (int n = 0; n < num_; ++n) {
-        caffe_gpu_copy(num_elem, bottom_data+bottom[i]->offset(n),
+        caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
           top_data + (*top)[0]->offset(n, offset_channel));
       }
       offset_channel += bottom[i]->channels();
@@ -49,7 +49,7 @@ void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       Blob<Dtype>* blob = (*bottom)[i];
       if (propagate_down[i]) {
         Dtype* bottom_diff = blob->mutable_gpu_diff();
-        caffe_gpu_copy(blob->count(), top_diff + top[0]->offset(offset_num),
+        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
                        bottom_diff);
       }
       offset_num += blob->num();
@@ -62,7 +62,7 @@ void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         Dtype* bottom_diff = blob->mutable_gpu_diff();
         int num_elem = blob->channels()*blob->height()*blob->width();
         for (int n = 0; n < num_; ++n) {
-          caffe_gpu_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
+          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
                          bottom_diff + blob->offset(n));
         }
       }
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 5a8bb0bfc65..40316a16772 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -21,13 +21,11 @@ Dtype DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
-      prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyDefault));
+  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+      (*top)[0]->mutable_gpu_data());
   if (output_labels_) {
-    CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
-        prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-        cudaMemcpyDefault));
+    caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+        (*top)[1]->mutable_gpu_data());
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 225e0919409..11a426312ce 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -40,7 +40,7 @@ Dtype DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         count, bottom_data, mask, uint_thres_, scale_, top_data);
     CUDA_POST_KERNEL_CHECK;
   } else {
-    caffe_gpu_copy(count, bottom_data, top_data);
+    caffe_copy(count, bottom_data, top_data);
   }
   return Dtype(0);
 }
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 3860944889c..99c14feace1 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -51,7 +51,7 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         break;
       case EltwiseParameter_EltwiseOp_SUM:
         if (coeffs_[i] == Dtype(1.)) {
-          caffe_gpu_copy(count, top_diff, bottom_diff);
+          caffe_copy(count, top_diff, bottom_diff);
         } else {
           caffe_gpu_scale(count, coeffs_[i], top_diff, bottom_diff);
         }
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 232f55fd677..3c27f37e360 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -40,16 +40,12 @@ Dtype HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       }
       current_row_ = 0;
     }
-    CUDA_CHECK(cudaMemcpy(
-            &(*top)[0]->mutable_gpu_data()[i * data_count],
-            &data_blob_.cpu_data()[current_row_ * data_count],
-            sizeof(Dtype) * data_count,
-            cudaMemcpyDefault));
-    CUDA_CHECK(cudaMemcpy(
-            &(*top)[1]->mutable_gpu_data()[i * label_data_count],
-            &label_blob_.cpu_data()[current_row_ * label_data_count],
-            sizeof(Dtype) * label_data_count,
-            cudaMemcpyDefault));
+    caffe_copy(data_count,
+        &data_blob_.cpu_data()[current_row_ * data_count],
+        &(*top)[0]->mutable_gpu_data()[i * data_count]);
+    caffe_copy(label_data_count,
+        &label_blob_.cpu_data()[current_row_ * label_data_count],
+        &(*top)[1]->mutable_gpu_data()[i * label_data_count]);
   }
   return Dtype(0.);
 }
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 3a513b9c366..8307ad7c184 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -54,12 +54,10 @@ Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int label_datum_dim = bottom[1]->count() / bottom[1]->num();
 
   for (int i = 0; i < bottom[0]->num(); ++i) {
-    memcpy(&data_blob_.mutable_cpu_data()[i * data_datum_dim],
-           &bottom[0]->cpu_data()[i * data_datum_dim],
-           sizeof(Dtype) * data_datum_dim);
-    memcpy(&label_blob_.mutable_cpu_data()[i * label_datum_dim],
-           &bottom[1]->cpu_data()[i * label_datum_dim],
-           sizeof(Dtype) * label_datum_dim);
+    caffe_copy(data_datum_dim, &bottom[0]->cpu_data()[i * data_datum_dim],
+        &data_blob_.mutable_cpu_data()[i * data_datum_dim]);
+    caffe_copy(label_datum_dim, &bottom[0]->cpu_data()[i * label_datum_dim],
+        &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();
   return Dtype(0.);
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 19567c12b2b..744b8fe1128 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -27,12 +27,10 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const int label_datum_dim = bottom[1]->count() / bottom[1]->num();
 
   for (int i = 0; i < bottom[0]->num(); ++i) {
-    CUDA_CHECK(cudaMemcpy(&data_blob_.mutable_cpu_data()[i * data_datum_dim],
-           &bottom[0]->gpu_data()[i * data_datum_dim],
-           sizeof(Dtype) * data_datum_dim, cudaMemcpyDefault));
-    CUDA_CHECK(cudaMemcpy(&label_blob_.mutable_cpu_data()[i * label_datum_dim],
-           &bottom[1]->gpu_data()[i * label_datum_dim],
-           sizeof(Dtype) * label_datum_dim, cudaMemcpyDefault));
+    caffe_copy(data_datum_dim, &bottom[0]->gpu_data()[i * data_datum_dim],
+        &data_blob_.mutable_cpu_data()[i * data_datum_dim]);
+    caffe_copy(label_datum_dim, &bottom[0]->gpu_data()[i * label_datum_dim],
+        &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();
   return Dtype(0.);
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index 3f905bc461a..dd5bdbc2068 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -27,12 +27,10 @@ Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
-      prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyDefault));
-  CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
-      prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-      cudaMemcpyDefault));
+  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+      (*top)[0]->mutable_gpu_data());
+  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+      (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 6d699636e21..e7f9831eb13 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -23,7 +23,7 @@ Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     return Dtype(0);
   }
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  caffe_gpu_copy(count, bottom_data, top_data);
+  caffe_copy(count, bottom_data, top_data);
   if (scale_ != Dtype(1)) {
     caffe_gpu_scal(count, scale_, top_data);
   }
@@ -68,7 +68,7 @@ void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
         caffe_gpu_scal(count, power_, bottom_diff);
       } else {
-        caffe_gpu_copy(count, bottom_data, bottom_diff);
+        caffe_copy(count, bottom_data, bottom_diff);
         if (scale_ != Dtype(1)) {
           caffe_gpu_scal(count, scale_, bottom_diff);
         }
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 8f7275827e2..0c858cdfc19 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -50,7 +50,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
     const Dtype* target = (*bottom)[1]->gpu_data();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    caffe_gpu_copy(count, sigmoid_output_data, bottom_diff);
+    caffe_copy(count, sigmoid_output_data, bottom_diff);
     caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
     // Scale down gradient
     caffe_gpu_scal(count, Dtype(1) / num, bottom_diff);
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 57847d005f6..5d60d9df1ba 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -34,7 +34,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  memcpy(top_data, bottom_data, sizeof(Dtype) * bottom[0]->count());
+  caffe_copy(bottom[0]->count(), bottom_data, top_data);
   // we need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   for (int i = 0; i < num; ++i) {
@@ -68,7 +68,7 @@ void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = top[0]->num();
   int dim = top[0]->count() / top[0]->num();
-  memcpy(bottom_diff, top_diff, sizeof(Dtype) * top[0]->count());
+  caffe_copy(top[0]->count(), top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
   for (int i = 0; i < num; ++i) {
     scale_data[i] = caffe_cpu_dot<Dtype>(dim, top_diff + i * dim,
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 5ec4412ccfb..ceeaff5b020 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -50,7 +50,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* scale_data = scale_.mutable_gpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  caffe_gpu_copy(bottom[0]->count(), bottom_data, top_data);
+  caffe_copy(bottom[0]->count(), bottom_data, top_data);
   // we need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   // Compute max
@@ -84,7 +84,7 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int num = top[0]->num();
   int dim = top[0]->count() / top[0]->num();
-  caffe_gpu_copy(top[0]->count(), top_diff, bottom_diff);
+  caffe_copy(top[0]->count(), top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
   // cuda dot returns the result to cpu, so we temporarily change the pointer
   // mode
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 1a3601aa9e6..37c5ebc45be 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -66,7 +66,7 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const Dtype* prob_data = prob_.cpu_data();
-    memcpy(bottom_diff, prob_data, sizeof(Dtype) * prob_.count());
+    caffe_copy(prob_.count(), prob_data, bottom_diff);
     const Dtype* label = (*bottom)[1]->cpu_data();
     int num = prob_.num();
     int dim = prob_.count() / num;
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index 5e8cdb7841d..ca664fcef41 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -28,12 +28,10 @@ Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
-      prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyDefault));
-  CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
-      prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-      cudaMemcpyDefault));
+  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+      (*top)[0]->mutable_gpu_data());
+  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+      (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 769618175ac..ca1d92525c9 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -310,7 +310,7 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
             history_[param_id]->mutable_gpu_data());
       }
       // copy
-      caffe_gpu_copy(net_params[param_id]->count(),
+      caffe_copy(net_params[param_id]->count(),
           history_[param_id]->gpu_data(),
           net_params[param_id]->mutable_gpu_diff());
     }
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index ec4c528f46d..011d35976a5 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -6,6 +6,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
@@ -32,7 +33,7 @@ inline void SyncedMemory::to_cpu() {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
-    CUDA_CHECK(cudaMemcpy(cpu_ptr_, gpu_ptr_, size_, cudaMemcpyDefault));
+    caffe_copy(size_, gpu_ptr_, cpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_CPU:
@@ -52,7 +53,7 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
-    CUDA_CHECK(cudaMemcpy(gpu_ptr_, cpu_ptr_, size_, cudaMemcpyDefault));
+    caffe_copy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_GPU:
diff --git a/src/caffe/test/test_gradient_check_util.hpp b/src/caffe/test/test_gradient_check_util.hpp
index ff104b90eb9..2d551f82428 100644
--- a/src/caffe/test/test_gradient_check_util.hpp
+++ b/src/caffe/test/test_gradient_check_util.hpp
@@ -230,7 +230,7 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(vector<Blob<Dtype>*>* top,
         loss += top_blob_data[j] * top_blob_data[j];
       }
       // set the diff: simply the data.
-      memcpy(top_blob_diff, top_blob_data, sizeof(Dtype) * top_blob->count());
+      caffe_copy(top_blob->count(), top_blob_data, top_blob_diff);
     }
     loss /= 2.;
   } else {
@@ -238,7 +238,7 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(vector<Blob<Dtype>*>* top,
     for (int i = 0; i < top->size(); ++i) {
       Blob<Dtype>* top_blob = (*top)[i];
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
-      memset(top_blob_diff, 0, sizeof(Dtype) * top_blob->count());
+      caffe_set(top_blob->count(), Dtype(0), top_blob_diff);
     }
     loss = (*top)[top_id]->cpu_data()[top_data_id];
     (*top)[top_id]->mutable_cpu_diff()[top_data_id] = 1.;
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index d0265767c07..941d8b9479a 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -219,7 +219,7 @@ TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
   TypeParam* top_data = this->blob_top_->mutable_gpu_data();
-  caffe_gpu_copy(n, bottom_data, top_data);
+  caffe_copy(n, bottom_data, top_data);
   bottom_data = this->blob_bottom_->cpu_data();
   top_data = this->blob_top_->mutable_cpu_data();
   for (int i = 0; i < n; ++i) {
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index e741f0f70da..a7a5131578e 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -7,6 +7,7 @@
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -57,8 +58,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
   char* recovered_value = new char[10];
-  cudaMemcpy(reinterpret_cast<void*>(recovered_value), gpu_data, 10,
-             cudaMemcpyDefault);
+  caffe_copy(size_t(10), gpu_data, reinterpret_cast<void*>(recovered_value));
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 1);
   }
@@ -72,8 +72,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
-  cudaMemcpy(reinterpret_cast<void*>(recovered_value), gpu_data, 10,
-             cudaMemcpyDefault);
+  caffe_copy(size_t(10), gpu_data, reinterpret_cast<void*>(recovered_value));
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 2);
   }
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 2e4c6795952..5b4c48eaf80 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -30,8 +30,8 @@ TYPED_TEST(GemmTest, TestGemm) {
   TypeParam A_reshape_data[6] = {1, 4, 2, 5, 3, 6};
   TypeParam B_reshape_data[12] = {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12};
   TypeParam result[8] = {38, 44, 50, 56, 83, 98, 113, 128};
-  memcpy(A.mutable_cpu_data(), data, 6 * sizeof(TypeParam));
-  memcpy(B.mutable_cpu_data(), data, 12 * sizeof(TypeParam));
+  caffe_copy(6, data, A.mutable_cpu_data());
+  caffe_copy(12, data, B.mutable_cpu_data());
 
   if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
     // [1, 2, 3; 4 5 6] * [1, 2, 3, 4; 5, 6, 7, 8; 9, 10, 11, 12];
@@ -48,7 +48,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 
     // Test when we have a transposed A
     A.Reshape(1, 1, 3, 2);
-    memcpy(A.mutable_cpu_data(), A_reshape_data, 6 * sizeof(TypeParam));
+    caffe_copy(6, A_reshape_data, A.mutable_cpu_data());
     caffe_cpu_gemm<TypeParam>(CblasTrans, CblasNoTrans, 2, 4, 3, 1.,
         A.cpu_data(), B.cpu_data(), 0., C.mutable_cpu_data());
     for (int i = 0; i < 8; ++i) {
@@ -62,7 +62,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 
     // Test when we have a transposed A and a transposed B too
     B.Reshape(1, 1, 4, 3);
-    memcpy(B.mutable_cpu_data(), B_reshape_data, 12 * sizeof(TypeParam));
+    caffe_copy(12, B_reshape_data, B.mutable_cpu_data());
     caffe_cpu_gemm<TypeParam>(CblasTrans, CblasTrans, 2, 4, 3, 1.,
         A.cpu_data(), B.cpu_data(), 0., C.mutable_cpu_data());
     for (int i = 0; i < 8; ++i) {
@@ -76,7 +76,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 
     // Test when we have a transposed B
     A.Reshape(1, 1, 2, 3);
-    memcpy(A.mutable_cpu_data(), data, 6 * sizeof(TypeParam));
+    caffe_copy(6, data, A.mutable_cpu_data());
     caffe_cpu_gemm<TypeParam>(CblasNoTrans, CblasTrans, 2, 4, 3, 1.,
         A.cpu_data(), B.cpu_data(), 0., C.mutable_cpu_data());
     for (int i = 0; i < 8; ++i) {
@@ -100,8 +100,8 @@ TYPED_TEST(GemmTest, TestGemv) {
   TypeParam data[6] = {1, 2, 3, 4, 5, 6};
   TypeParam result_2[2] = {14, 32};
   TypeParam result_3[3] = {9, 12, 15};
-  memcpy(A.mutable_cpu_data(), data, 6 * sizeof(TypeParam));
-  memcpy(x.mutable_cpu_data(), data, 3 * sizeof(TypeParam));
+  caffe_copy(6, data, A.mutable_cpu_data());
+  caffe_copy(3, data, x.mutable_cpu_data());
 
   if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
     caffe_cpu_gemv<TypeParam>(CblasNoTrans, 2, 3, 1., A.cpu_data(),
@@ -116,7 +116,7 @@ TYPED_TEST(GemmTest, TestGemv) {
     }
 
     // Test transpose case
-    memcpy(y.mutable_cpu_data(), data, 2 * sizeof(TypeParam));
+    caffe_copy(2, data, y.mutable_cpu_data());
     caffe_cpu_gemv<TypeParam>(CblasTrans, 2, 3, 1., A.cpu_data(),
         y.cpu_data(), 0., x.mutable_cpu_data());
     for (int i = 0; i < 3; ++i) {

From af624faa55eb75f31de5f612b3d6b1553d652563 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 28 Jun 2014 01:52:49 -0700
Subject: [PATCH 0213/2053] replace all memset with caffe_set() /
 caffe_gpu_set()

...except for `SyncedMem` since it has no type.
---
 src/caffe/layers/conv_layer.cpp               |  4 ++--
 src/caffe/layers/conv_layer.cu                |  6 ++----
 src/caffe/layers/lrn_layer.cpp                |  6 +++---
 .../multinomial_logistic_loss_layer.cpp       |  2 +-
 src/caffe/layers/window_data_layer.cpp        |  2 +-
 src/caffe/util/im2col.cpp                     |  3 ++-
 src/caffe/util/im2col.cu                      |  2 --
 src/caffe/util/math_functions.cu              | 21 +++++++------------
 8 files changed, 18 insertions(+), 28 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 67913bfa574..963dc688a0e 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -126,11 +126,11 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  memset(weight_diff, 0, sizeof(Dtype) * this->blobs_[0]->count());
+  caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   Dtype* bias_diff = NULL;
   if (bias_term_) {
     bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    memset(bias_diff, 0, sizeof(Dtype) * this->blobs_[1]->count());
+    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   const int weight_offset = M_ * K_;
   const int col_offset = K_ * N_;
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 71b00c9566b..59ec58dfebe 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -48,15 +48,13 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  CUDA_CHECK(cudaMemset(weight_diff, 0,
-      sizeof(Dtype) * this->blobs_[0]->count()));
+  caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   Dtype* col_data = col_buffer_.mutable_gpu_data();
   Dtype* col_diff = col_buffer_.mutable_gpu_diff();
   Dtype* bias_diff = NULL;
   if (bias_term_) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    CUDA_CHECK(cudaMemset(bias_diff, 0,
-        sizeof(Dtype) * this->blobs_[1]->count()));
+    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   const int weight_offset = M_ * K_;
   const int col_offset = K_ * N_;
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index a86c1d4c59d..2bda0430c15 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -123,7 +123,7 @@ Dtype LRNLayer<Dtype>::CrossChannelForward_cpu(
   }
   Blob<Dtype> padded_square(1, channels_ + size_ - 1, height_, width_);
   Dtype* padded_square_data = padded_square.mutable_cpu_data();
-  memset(padded_square_data, 0, sizeof(Dtype) * padded_square.count());
+  caffe_set(padded_square.count(), Dtype(0), padded_square_data);
   Dtype alpha_over_size = alpha_ / size_;
   // go through the images
   for (int n = 0; n < num_; ++n) {
@@ -201,7 +201,7 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
   Dtype* accum_ratio_data = accum_ratio.mutable_cpu_data();
   // We hack a little bit by using the diff() to store an additional result
   Dtype* accum_ratio_times_bottom = accum_ratio.mutable_cpu_diff();
-  memset(padded_ratio_data, 0, sizeof(Dtype) * padded_ratio.count());
+  caffe_set(padded_ratio.count(), Dtype(0), padded_ratio_data);
   Dtype cache_ratio_value = 2. * alpha_ * beta_ / size_;
 
   caffe_powx<Dtype>(scale_.count(), scale_data, -beta_, bottom_diff);
@@ -220,7 +220,7 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
         scale_data + block_offset,
         padded_ratio_data + padded_ratio.offset(0, inverse_pre_pad));
     // Now, compute the accumulated ratios and the bottom diff
-    memset(accum_ratio_data, 0, sizeof(Dtype) * accum_ratio.count());
+    caffe_set(accum_ratio.count(), Dtype(0), accum_ratio_data);
     for (int c = 0; c < size_ - 1; ++c) {
       caffe_axpy<Dtype>(height_ * width_, 1.,
           padded_ratio_data + padded_ratio.offset(0, c), accum_ratio_data);
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index dd5cae44d6d..8687784d6ab 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -55,7 +55,7 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     int num = (*bottom)[0]->num();
     int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-    memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
+    caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
       Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index fd4860f98be..5dbdff330ff 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -59,7 +59,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  memset(top_data, 0, sizeof(Dtype)*layer->prefetch_data_->count());
+  caffe_set(layer->prefetch_data_->count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 037410e29b7..ce4e18848bf 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -5,6 +5,7 @@
 #include <cstring>
 
 #include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
@@ -45,7 +46,7 @@ template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int ksize, const int pad,
     const int stride, Dtype* data_im) {
-  memset(data_im, 0, sizeof(Dtype) * height * width * channels);
+  caffe_set(height * width * channels, Dtype(0), data_im);
   int height_col = (height + 2 * pad - ksize) / stride + 1;
   int width_col = (width + 2 * pad - ksize) / stride + 1;
   int channels_col = channels * ksize * ksize;
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index ec4465eff66..79faa6cb5c9 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -106,8 +106,6 @@ template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int ksize, const int pad,
     const int stride, Dtype* data_im) {
-  // CUDA_CHECK(cudaMemset(data_im, 0,
-  //            sizeof(Dtype) * height * width * channels));
   int height_col = (height + 2 * pad - ksize) / stride + 1;
   int width_col = (width + 2 * pad - ksize) / stride + 1;
   int num_kernels = channels * height * width;
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 63c8fac69c5..849e53b9ca4 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -20,27 +20,20 @@ __global__ void set_kernel(const int n, const Dtype alpha, Dtype* y) {
   }
 }
 
-template <>
-void caffe_gpu_set(const int N, const float alpha, float* Y) {
+template <typename Dtype>
+void caffe_gpu_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
-    CUDA_CHECK(cudaMemset(Y, 0, sizeof(float) * N));
+    CUDA_CHECK(cudaMemset(Y, 0, sizeof(Dtype) * N));
     return;
   }
   // NOLINT_NEXT_LINE(whitespace/operators)
-  set_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+  set_kernel<Dtype><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
       N, alpha, Y);
 }
 
-template <>
-void caffe_gpu_set(const int N, const double alpha, double* Y) {
-  if (alpha == 0) {
-    CUDA_CHECK(cudaMemset(Y, 0, sizeof(double) * N));
-    return;
-  }
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  set_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
-      N, alpha, Y);
-}
+template void caffe_gpu_set<int>(const int N, const int alpha, int* Y);
+template void caffe_gpu_set<float>(const int N, const float alpha, float* Y);
+template void caffe_gpu_set<double>(const int N, const double alpha, double* Y);
 
 template <typename Dtype>
 __global__ void add_scalar_kernel(const int n, const Dtype alpha, Dtype* y) {

From 6461365565bd8a661522a267c7caae1a37313935 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 28 Jun 2014 13:48:37 -0700
Subject: [PATCH 0214/2053] reduce caffe_copy to instantiations, split off
 caffe_memcpy for void*

---
 include/caffe/util/math_functions.hpp |  2 +-
 src/caffe/layers/dropout_layer.cu     |  2 +-
 src/caffe/syncedmem.cpp               |  4 ++--
 src/caffe/test/test_syncedmem.cpp     |  4 ++--
 src/caffe/util/math_functions.cpp     | 32 ++++++++-------------------
 5 files changed, 15 insertions(+), 29 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index b5f4dfbd1a8..97a057103db 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -59,7 +59,7 @@ void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
 
-void caffe_copy(const size_t N, const void *X, void *Y);
+void caffe_memcpy(const size_t N, const void *X, void *Y);
 
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 11a426312ce..c9f3ecd2dd5 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -71,7 +71,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           count, top_diff, mask, uint_thres_, scale_, bottom_diff);
       CUDA_POST_KERNEL_CHECK;
     } else {
-      caffe_gpu_copy(top[0]->count(), top_diff, bottom_diff);
+      caffe_copy(top[0]->count(), top_diff, bottom_diff);
     }
   }
 }
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 011d35976a5..9fe55280de9 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -33,7 +33,7 @@ inline void SyncedMemory::to_cpu() {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
-    caffe_copy(size_, gpu_ptr_, cpu_ptr_);
+    caffe_memcpy(size_, gpu_ptr_, cpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_CPU:
@@ -53,7 +53,7 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
-    caffe_copy(size_, cpu_ptr_, gpu_ptr_);
+    caffe_memcpy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_GPU:
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index a7a5131578e..3aaeafc353e 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -58,7 +58,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
   char* recovered_value = new char[10];
-  caffe_copy(size_t(10), gpu_data, reinterpret_cast<void*>(recovered_value));
+  caffe_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 1);
   }
@@ -72,7 +72,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
-  caffe_copy(size_t(10), gpu_data, reinterpret_cast<void*>(recovered_value));
+  caffe_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 2);
   }
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index b1b62edb0d2..918bb3c361c 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -149,34 +149,20 @@ void caffe_add_scalar(const int N, const double alpha, double* Y) {
   }
 }
 
-template <>
-void caffe_copy<int>(const int N, const int* X, int* Y) {
-  if (X != Y) {
-    CUDA_CHECK(cudaMemcpy(Y, X, sizeof(int) * N, cudaMemcpyDefault));
-  }
-}
-
-template <>
-void caffe_copy<unsigned int>(const int N, const unsigned int* X,
-    unsigned int* Y) {
-  if (X != Y) {
-  CUDA_CHECK(cudaMemcpy(Y, X, sizeof(unsigned int) * N, cudaMemcpyDefault));
-  }
-}
-
-template <>
-void caffe_copy<float>(const int N, const float* X, float* Y) {
+template <typename Dtype>
+void caffe_copy(const int N, const Dtype* X, Dtype* Y) {
   if (X != Y) {
-    CUDA_CHECK(cudaMemcpy(Y, X, sizeof(float) * N, cudaMemcpyDefault));
+    CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
   }
 }
 
-template <>
-void caffe_copy<double>(const int N, const double* X, double* Y) {
-  CUDA_CHECK(cudaMemcpy(Y, X, sizeof(double) * N, cudaMemcpyDefault));
-}
+template void caffe_copy<int>(const int N, const int* X, int* Y);
+template void caffe_copy<unsigned int>(const int N, const unsigned int* X,
+    unsigned int* Y);
+template void caffe_copy<float>(const int N, const float* X, float* Y);
+template void caffe_copy<double>(const int N, const double* X, double* Y);
 
-void caffe_copy(const size_t N, const void* X, void* Y) {
+void caffe_memcpy(const size_t N, const void* X, void* Y) {
   if (X != Y) {
     CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
   }

From 19e909ba982baeaf5505812ea49aba7066967576 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 28 Jun 2014 14:05:35 -0700
Subject: [PATCH 0215/2053] fix casts (static for void*)

---
 src/caffe/blob.cpp | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 69ff49efc46..8df46323eee 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -75,25 +75,25 @@ const Dtype* Blob<Dtype>::gpu_diff() const {
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_cpu_data() {
   CHECK(data_);
-  return reinterpret_cast<Dtype*>(data_->mutable_cpu_data());
+  return static_cast<Dtype*>(data_->mutable_cpu_data());
 }
 
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_gpu_data() {
   CHECK(data_);
-  return reinterpret_cast<Dtype*>(data_->mutable_gpu_data());
+  return static_cast<Dtype*>(data_->mutable_gpu_data());
 }
 
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_cpu_diff() {
   CHECK(diff_);
-  return reinterpret_cast<Dtype*>(diff_->mutable_cpu_data());
+  return static_cast<Dtype*>(diff_->mutable_cpu_data());
 }
 
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_gpu_diff() {
   CHECK(diff_);
-  return reinterpret_cast<Dtype*>(diff_->mutable_gpu_data());
+  return static_cast<Dtype*>(diff_->mutable_gpu_data());
 }
 
 template <typename Dtype>
@@ -121,15 +121,15 @@ void Blob<Dtype>::Update() {
   case SyncedMemory::HEAD_AT_CPU:
     // perform computation on CPU
     caffe_axpy<Dtype>(count_, Dtype(-1),
-        reinterpret_cast<const Dtype*>(diff_->cpu_data()),
-        reinterpret_cast<Dtype*>(data_->mutable_cpu_data()));
+        static_cast<const Dtype*>(diff_->cpu_data()),
+        static_cast<Dtype*>(data_->mutable_cpu_data()));
     break;
   case SyncedMemory::HEAD_AT_GPU:
   case SyncedMemory::SYNCED:
     // perform computation on GPU
     caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
-        reinterpret_cast<const Dtype*>(diff_->gpu_data()),
-        reinterpret_cast<Dtype*>(data_->mutable_gpu_data()));
+        static_cast<const Dtype*>(diff_->gpu_data()),
+        static_cast<Dtype*>(data_->mutable_gpu_data()));
     break;
   default:
     LOG(FATAL) << "Syncedmem not initialized.";
@@ -150,19 +150,19 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   case Caffe::GPU:
     if (copy_diff) {
       caffe_copy(count_, source.gpu_diff(),
-          reinterpret_cast<Dtype*>(diff_->mutable_gpu_data()));
+          static_cast<Dtype*>(diff_->mutable_gpu_data()));
     } else {
       caffe_copy(count_, source.gpu_data(),
-          reinterpret_cast<Dtype*>(data_->mutable_gpu_data()));
+          static_cast<Dtype*>(data_->mutable_gpu_data()));
     }
     break;
   case Caffe::CPU:
     if (copy_diff) {
       caffe_copy(count_, source.cpu_diff(),
-          reinterpret_cast<Dtype*>(diff_->mutable_cpu_data()));
+          static_cast<Dtype*>(diff_->mutable_cpu_data()));
     } else {
       caffe_copy(count_, source.cpu_data(),
-          reinterpret_cast<Dtype*>(data_->mutable_cpu_data()));
+          static_cast<Dtype*>(data_->mutable_cpu_data()));
     }
     break;
   default:

From 18a4e2d1fc7a269e03be24f3a23bc59bc071a03e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 3 Jul 2014 18:15:38 -0700
Subject: [PATCH 0216/2053] customize compiler setting in Makefile.config

in case of issues with default compilers or exotic platforms.
---
 Makefile                | 5 +++++
 Makefile.config.example | 4 ++++
 2 files changed, 9 insertions(+)

diff --git a/Makefile b/Makefile
index 6d8cb905128..f8abc1b2b88 100644
--- a/Makefile
+++ b/Makefile
@@ -194,6 +194,11 @@ ifeq ($(OSX), 1)
 	endif
 endif
 
+# Custom compiler 
+ifdef CUSTOM_CXX
+	CXX := $(CUSTOM_CXX)
+endif
+
 # Debugging
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS := -DDEBUG -g -O0
diff --git a/Makefile.config.example b/Makefile.config.example
index 6c3948fc2d7..17c90016f75 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -1,6 +1,10 @@
 ## Refer to http://caffe.berkeleyvision.org/installation.html
 # Contributions simplifying and improving our build system are welcome!
 
+# To customize your choice of compiler, uncomment and set the following.
+# N.B. the default for Linux is g++ and the default for OSX is clang++
+# CUSTOM_CXX := g++
+
 # CUDA directory contains bin/ and lib/ directories that we need.
 CUDA_DIR := /usr/local/cuda
 # On Ubuntu 14.04, if cuda tools are installed via

From 4e5ef959abd2f5a3f18010963f12537425ce091e Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 3 Jul 2014 20:30:25 -0700
Subject: [PATCH 0217/2053] Update caffe.proto

Add pad_h, pad_w, kernel_size_h, kernel_size_w, stride_h, stride_w to support pooling on rectangle regions.
---
 src/caffe/proto/caffe.proto | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index adb75264f8e..effebd09a38 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -405,11 +405,17 @@ message PoolingParameter {
     STOCHASTIC = 2;
   }
   optional PoolMethod pool = 1 [default = MAX]; // The pooling method
-  optional uint32 kernel_size = 2; // The kernel size
-  optional uint32 stride = 3 [default = 1]; // The stride
-  // The padding size -- currently implemented only for average and max pooling.
-  // average pooling zero pads. max pooling -inf pads.
-  optional uint32 pad = 4 [default = 0];
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 2; // The kernel size (square)
+  optional uint32 kernel_size_h = 5; // The kernel height
+  optional uint32 kernel_size_w = 6; // The kernel width
+  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 7; // The stride height
+  optional uint32 stride_w = 8; // The stride width
 }
 
 // Message that stores parameters used by PowerLayer

From 9905c20130783046248b8c17a3cbc17847019d22 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 3 Jul 2014 20:33:50 -0700
Subject: [PATCH 0218/2053] Update vision_layers.hpp

Replace pad_, kernel_size_, stride_ with pad_h_, pad_w_, kernel_size_h_, kernel_size_w_, stride_h_, stride_w_ to support pooling on rectangle regions.
---
 include/caffe/vision_layers.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index b68dcbf6e83..62b65bc1780 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -361,9 +361,9 @@ class PoolingLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int max_top_blobs_;
-  int kernel_size_;
-  int stride_;
-  int pad_;
+  int kernel_size_h_, kernel_size_w_;
+  int stride_h_, stride_w_;
+  int pad_h_, pad_w_;
   int channels_;
   int height_;
   int width_;

From dd82d97190a2c14ebe3799a7ed01ff2c38578f96 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 3 Jul 2014 20:42:58 -0700
Subject: [PATCH 0219/2053] Update pooling_layer.cpp

Replace pad_, kernel_size_, stride_ with pad_h_, pad_w_, kernel_size_h_, kernel_size_w_, stride_h_, stride_w_ to support pooling on rectangle regions.
---
 src/caffe/layers/pooling_layer.cpp | 81 +++++++++++++++++++++---------
 1 file changed, 57 insertions(+), 24 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index bc002078814..3b64741fa5f 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -28,35 +28,68 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     max_top_blobs_ = 1;
   }
   Layer<Dtype>::SetUp(bottom, top);
-  kernel_size_ = this->layer_param_.pooling_param().kernel_size();
-  stride_ = this->layer_param_.pooling_param().stride();
-  pad_ = this->layer_param_.pooling_param().pad();
-  if (pad_ != 0) {
+  PoolingParameter pool_param = this->layer_param_.pooling_param();
+  CHECK(!pool_param.has_kernel_size() !=
+      !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(pool_param.has_kernel_size() ||
+      (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((pool_param.has_pad()
+      && !(pool_param.has_pad_h() || pool_param.has_pad_w()))
+      || (!pool_param.has_pad()
+      && (pool_param.has_pad_h() && pool_param.has_pad_w())
+      || (!pool_param.has_pad_h() && !pool_param.has_pad_w())))
+      << "Padding size is pad OR pad_h and pad_w; not both";
+  CHECK(!pool_param.has_stride() !=
+      !(pool_param.has_stride_h() && pool_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (pool_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = pool_param.kernel_size();
+  } else {
+    kernel_h_ = pool_param.kernel_h();
+    kernel_w_ = pool_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_ * kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (pool_param.has_pad()) {
+    pad_h_ = pad_w_ = pool_param.pad();
+  } else {
+    pad_h_ = pool_param.pad_h();
+    pad_w_ = pool_param.pad_w();
+  }
+  if (pool_param.has_stride()) {
+    stride_h_ = stride_w_ = pool_param.stride();
+  } else {
+    stride_h_ = pool_param.stride_h();
+    stride_w_ = pool_param.stride_w();
+  }
+  if (pad_h_ != 0 || pad_w_ != 0) {
     CHECK(this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_AVE
         || this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_MAX)
         << "Padding implemented only for average and max pooling.";
-    CHECK_LT(pad_, kernel_size_);
+    CHECK_LT(pad_h_, kernel_size_h_);
+    CHECK_LT(pad_w_, kernel_size_w_);
   }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   pooled_height_ = static_cast<int>(ceil(static_cast<float>(
-      height_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
+      height_ + 2 * pad_h_ - kernel_size_h_) / stride_h_)) + 1;
   pooled_width_ = static_cast<int>(ceil(static_cast<float>(
-      width_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
-  if (pad_) {
+      width_ + 2 * pad_w_ - kernel_size_w_) / stride_w_)) + 1;
+  if (pad_h_ || pad_w_) {
     // If we have padding, ensure that the last pooling starts strictly
     // inside the image (instead of at the padding); otherwise clip the last.
-    if ((pooled_height_ - 1) * stride_ >= height_ + pad_) {
+    if ((pooled_height_ - 1) * stride_h_ >= height_ + pad_h_) {
       --pooled_height_;
     }
-    if ((pooled_width_ - 1) * stride_ >= width_ + pad_) {
+    if ((pooled_width_ - 1) * stride_w_ >= width_ + pad_w_) {
       --pooled_width_;
     }
-    CHECK_LT((pooled_height_ - 1) * stride_, height_ + pad_);
-    CHECK_LT((pooled_width_ - 1) * stride_, width_ + pad_);
+    CHECK_LT((pooled_height_ - 1) * stride_h_, height_ + pad_h_);
+    CHECK_LT((pooled_width_ - 1) * stride_w_, width_ + pad_w_);
   }
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
@@ -107,10 +140,10 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_ - pad_;
-            int wstart = pw * stride_ - pad_;
-            int hend = min(hstart + kernel_size_, height_);
-            int wend = min(wstart + kernel_size_, width_);
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_size_h_, height_);
+            int wend = min(wstart + kernel_size_w_, width_);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
             const int pool_index = ph * pooled_width_ + pw;
@@ -149,10 +182,10 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_ - pad_;
-            int wstart = pw * stride_ - pad_;
-            int hend = min(hstart + kernel_size_, height_ + pad_);
-            int wend = min(wstart + kernel_size_, width_ + pad_);
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_size_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_size_w_, width_ + pad_w_);
             int pool_size = (hend - hstart) * (wend - wstart);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
@@ -231,10 +264,10 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_ - pad_;
-            int wstart = pw * stride_ - pad_;
-            int hend = min(hstart + kernel_size_, height_ + pad_);
-            int wend = min(wstart + kernel_size_, width_ + pad_);
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_size_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_size_w_, width_ + pad_w_);
             int pool_size = (hend - hstart) * (wend - wstart);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);

From ce88f9f966d68db43a7c0a7563c27c59e8433cdc Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 3 Jul 2014 21:05:23 -0700
Subject: [PATCH 0220/2053] Update pooling_layer.cu

Replace pad_, kernel_size_, stride_ with pad_h_, pad_w_, kernel_size_h_, kernel_size_w_, stride_h_, stride_w_ to support pooling on rectangle regions.
---
 src/caffe/layers/pooling_layer.cu | 117 ++++++++++++++++--------------
 1 file changed, 64 insertions(+), 53 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index f07fe3c48ac..3ac74f8bbb3 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,17 +17,18 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, const int pad, Dtype* top_data,
+    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
     int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride - pad;
-    int wstart = pw * stride - pad;
-    int hend = min(hstart + kernel_size, height);
-    int wend = min(wstart + kernel_size, width);
+    int hstart = ph * stride_h - pad_h;
+    int wstart = pw * stride_w - pad_w;
+    int hend = min(hstart + kernel_size_h, height);
+    int wend = min(wstart + kernel_size_w, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
@@ -54,16 +55,17 @@ template <typename Dtype>
 __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, const int pad, Dtype* top_data) {
+    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride - pad;
-    int wstart = pw * stride - pad;
-    int hend = min(hstart + kernel_size, height + pad);
-    int wend = min(wstart + kernel_size, width + pad);
+    int hstart = ph * stride_h - pad_h;
+    int wstart = pw * stride_w - pad_w;
+    int hend = min(hstart + kernel_size_h, height + pad_h);
+    int wend = min(wstart + kernel_size_w, width + pad_w);
     int pool_size = (hend - hstart) * (wend - wstart);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
@@ -85,16 +87,17 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* rand_idx, Dtype* top_data) {
+    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int stride_w, Dtype* rand_idx, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride;
-    int hend = min(hstart + kernel_size, height);
-    int wstart = pw * stride;
-    int wend = min(wstart + kernel_size, width);
+    int hstart = ph * stride_h;
+    int hend = min(hstart + kernel_size_h, height);
+    int wstart = pw * stride_w;
+    int wend = min(wstart + kernel_size_w, width);
     Dtype cumsum = 0.;
     bottom_data += (n * channels + c) * height * width;
     // First pass: get sum
@@ -125,16 +128,17 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* top_data) {
+    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int stride_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride;
-    int hend = min(hstart + kernel_size, height);
-    int wstart = pw * stride;
-    int wend = min(wstart + kernel_size, width);
+    int hstart = ph * stride_h;
+    int hend = min(hstart + kernel_size_h, height);
+    int wstart = pw * stride_w;
+    int wend = min(wstart + kernel_size_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
@@ -171,15 +175,16 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        pad_, top_data, mask, top_mask);
+        height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
+        kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data, 
+        mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        pad_, top_data);
+        height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
+        kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     if (Caffe::phase() == Caffe::TRAIN) {
@@ -190,15 +195,16 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
+          height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
+          kernel_size_w_, stride_h_, stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-          top_data);
+          height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
+          kernel_size_w_, stride_h_, stride_w_, top_data);
     }
     break;
   default:
@@ -213,8 +219,9 @@ template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int* mask, const Dtype* top_mask, const int num, const int channels,
     const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_size, const int stride,
-    const int pad, Dtype* bottom_diff) {
+    const int pooled_width, const int kernel_size_h, const int kernel_size_w, 
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w, 
+    Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -223,11 +230,11 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
     int phstart =
-        (h + pad < kernel_size) ? 0 : (h + pad - kernel_size) / stride + 1;
-    int phend = min((h + pad) / stride + 1, pooled_height);
+        (h + pad_h < kernel_size_h) ? 0 : (h + pad_h - kernel_size_h) / stride_h + 1;
+    int phend = min((h + pad_h) / stride_h + 1, pooled_height);
     int pwstart =
-        (w + pad < kernel_size) ? 0 : (w + pad - kernel_size) / stride + 1;
-    int pwend = min((w + pad) / stride + 1, pooled_width);
+        (w + pad_w < kernel_size_w) ? 0 : (w + pad_w - kernel_size_w) / stride_w + 1;
+    int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     int offset = (n * channels + c) * pooled_height * pooled_width;
     top_diff += offset;
@@ -258,28 +265,29 @@ template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, const int pad,
+    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int stride_w, const int pad_h, const int pad_w, 
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width + pad;
-    int h = (index / width) % height + pad;
+    int w = index % width + pad_w;
+    int h = (index / width) % height + pad_h;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
+    int phstart = (h < kernel_size_h) ? 0 : (h - kernel_size_h) / stride_h + 1;
+    int phend = min(h / stride_h + 1, pooled_height);
+    int pwstart = (w < kernel_size_w) ? 0 : (w - kernel_size_w) / stride_w + 1;
+    int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         // figure out the pooling size
-        int hstart = ph * stride - pad;
-        int wstart = pw * stride - pad;
-        int hend = min(hstart + kernel_size, height + pad);
-        int wend = min(wstart + kernel_size, width + pad);
+        int hstart = ph * stride_h - pad_h;
+        int wstart = pw * stride_w - pad_w;
+        int hend = min(hstart + kernel_size_h, height + pad_h);
+        int wend = min(wstart + kernel_size_w, width + pad_w);
         int pool_size = (hend - hstart) * (wend - wstart);
         gradient += top_diff[ph * pooled_width + pw] / pool_size;
       }
@@ -294,7 +302,8 @@ __global__ void StoPoolBackward(const int nthreads,
     const Dtype* rand_idx, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* bottom_diff) {
+    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int stride_w, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -302,10 +311,10 @@ __global__ void StoPoolBackward(const int nthreads,
     int h = (index / width) % height;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
+    int phstart = (h < kernel_size_h) ? 0 : (h - kernel_size_h) / stride_h + 1;
+    int phend = min(h / stride_h + 1, pooled_height);
+    int pwstart = (w < kernel_size_w) ? 0 : (w - kernel_size_w) / stride_w + 1;
+    int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     rand_idx += (n * channels + c) * pooled_height * pooled_width;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
@@ -345,21 +354,23 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, mask, top_mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
-        kernel_size_, stride_, pad_, bottom_diff);
+        kernel_size_h_, kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, 
+        bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        pad_, bottom_diff);
+        height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
+        kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     // NOLINT_NEXT_LINE(whitespace/operators)
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, rand_idx_.gpu_data(), top_diff,
         top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_size_, stride_, bottom_diff);
+        pooled_width_, kernel_size_h_, kernel_size_w_, stride_h_, stride_w_, 
+        bottom_diff);
     break;
   default:
     LOG(FATAL) << "Unknown pooling method.";

From 7b727b020d1159dea63ef56daf98eaa147fd0ebb Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 4 Jul 2014 17:29:56 -0700
Subject: [PATCH 0221/2053] fixing pooling SetUp() to allow default values for
 stride and pad

---
 src/caffe/layers/pooling_layer.cpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 3b64741fa5f..d4feaad178f 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -35,15 +35,15 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK(pool_param.has_kernel_size() ||
       (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((pool_param.has_pad()
-      && !(pool_param.has_pad_h() || pool_param.has_pad_w()))
-      || (!pool_param.has_pad()
-      && (pool_param.has_pad_h() && pool_param.has_pad_w())
-      || (!pool_param.has_pad_h() && !pool_param.has_pad_w())))
-      << "Padding size is pad OR pad_h and pad_w; not both";
-  CHECK(!pool_param.has_stride() !=
-      !(pool_param.has_stride_h() && pool_param.has_stride_w()))
+  CHECK((!pool_param.has_pad() && pool_param.has_pad_h() 
+      && pool_param.has_pad_w())
+      || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!pool_param.has_stride() && pool_param.has_stride_h() 
+      && pool_param.has_stride_w())
+      || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";
+
   if (pool_param.has_kernel_size()) {
     kernel_h_ = kernel_w_ = pool_param.kernel_size();
   } else {
@@ -51,13 +51,13 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     kernel_w_ = pool_param.kernel_w();
   }
   CHECK_GT(kernel_h_ * kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (pool_param.has_pad()) {
+  if (!pool_param.has_pad_h()) {
     pad_h_ = pad_w_ = pool_param.pad();
   } else {
     pad_h_ = pool_param.pad_h();
     pad_w_ = pool_param.pad_w();
   }
-  if (pool_param.has_stride()) {
+  if (!pool_param.has_stride_h()) {
     stride_h_ = stride_w_ = pool_param.stride();
   } else {
     stride_h_ = pool_param.stride_h();

From f74979e7de2532ae4eccffa8aabad46c31a90285 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 5 Jul 2014 08:21:50 -0700
Subject: [PATCH 0222/2053] add tests for rectangular pooling regions

---
 include/caffe/vision_layers.hpp       |   2 +-
 src/caffe/layers/pooling_layer.cpp    |  24 +--
 src/caffe/layers/pooling_layer.cu     |  70 +++----
 src/caffe/proto/caffe.proto           |   4 +-
 src/caffe/test/test_pooling_layer.cpp | 270 +++++++++++++++++++++++++-
 5 files changed, 314 insertions(+), 56 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 62b65bc1780..450524e2a7d 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -361,7 +361,7 @@ class PoolingLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int max_top_blobs_;
-  int kernel_size_h_, kernel_size_w_;
+  int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
   int pad_h_, pad_w_;
   int channels_;
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index d4feaad178f..d8cd2e2139d 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -43,14 +43,14 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       && pool_param.has_stride_w())
       || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";
-
   if (pool_param.has_kernel_size()) {
     kernel_h_ = kernel_w_ = pool_param.kernel_size();
   } else {
     kernel_h_ = pool_param.kernel_h();
     kernel_w_ = pool_param.kernel_w();
   }
-  CHECK_GT(kernel_h_ * kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
   if (!pool_param.has_pad_h()) {
     pad_h_ = pad_w_ = pool_param.pad();
   } else {
@@ -69,16 +69,16 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
         || this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_MAX)
         << "Padding implemented only for average and max pooling.";
-    CHECK_LT(pad_h_, kernel_size_h_);
-    CHECK_LT(pad_w_, kernel_size_w_);
+    CHECK_LT(pad_h_, kernel_h_);
+    CHECK_LT(pad_w_, kernel_w_);
   }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   pooled_height_ = static_cast<int>(ceil(static_cast<float>(
-      height_ + 2 * pad_h_ - kernel_size_h_) / stride_h_)) + 1;
+      height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
   pooled_width_ = static_cast<int>(ceil(static_cast<float>(
-      width_ + 2 * pad_w_ - kernel_size_w_) / stride_w_)) + 1;
+      width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1;
   if (pad_h_ || pad_w_) {
     // If we have padding, ensure that the last pooling starts strictly
     // inside the image (instead of at the padding); otherwise clip the last.
@@ -142,8 +142,8 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           for (int pw = 0; pw < pooled_width_; ++pw) {
             int hstart = ph * stride_h_ - pad_h_;
             int wstart = pw * stride_w_ - pad_w_;
-            int hend = min(hstart + kernel_size_h_, height_);
-            int wend = min(wstart + kernel_size_w_, width_);
+            int hend = min(hstart + kernel_h_, height_);
+            int wend = min(wstart + kernel_w_, width_);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
             const int pool_index = ph * pooled_width_ + pw;
@@ -184,8 +184,8 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           for (int pw = 0; pw < pooled_width_; ++pw) {
             int hstart = ph * stride_h_ - pad_h_;
             int wstart = pw * stride_w_ - pad_w_;
-            int hend = min(hstart + kernel_size_h_, height_ + pad_h_);
-            int wend = min(wstart + kernel_size_w_, width_ + pad_w_);
+            int hend = min(hstart + kernel_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_w_, width_ + pad_w_);
             int pool_size = (hend - hstart) * (wend - wstart);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
@@ -266,8 +266,8 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           for (int pw = 0; pw < pooled_width_; ++pw) {
             int hstart = ph * stride_h_ - pad_h_;
             int wstart = pw * stride_w_ - pad_w_;
-            int hend = min(hstart + kernel_size_h_, height_ + pad_h_);
-            int wend = min(wstart + kernel_size_w_, width_ + pad_w_);
+            int hend = min(hstart + kernel_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_w_, width_ + pad_w_);
             int pool_size = (hend - hstart) * (wend - wstart);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 3ac74f8bbb3..e38028dd061 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,7 +17,7 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h, 
     const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
     int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
@@ -27,8 +27,8 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
-    int hend = min(hstart + kernel_size_h, height);
-    int wend = min(wstart + kernel_size_w, width);
+    int hend = min(hstart + kernel_h, height);
+    int wend = min(wstart + kernel_w, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
@@ -55,7 +55,7 @@ template <typename Dtype>
 __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h, 
     const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -64,8 +64,8 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
-    int hend = min(hstart + kernel_size_h, height + pad_h);
-    int wend = min(wstart + kernel_size_w, width + pad_w);
+    int hend = min(hstart + kernel_h, height + pad_h);
+    int wend = min(wstart + kernel_w, width + pad_w);
     int pool_size = (hend - hstart) * (wend - wstart);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
@@ -87,7 +87,7 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h, 
     const int stride_w, Dtype* rand_idx, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -95,9 +95,9 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_size_h, height);
+    int hend = min(hstart + kernel_h, height);
     int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_size_w, width);
+    int wend = min(wstart + kernel_w, width);
     Dtype cumsum = 0.;
     bottom_data += (n * channels + c) * height * width;
     // First pass: get sum
@@ -128,7 +128,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h, 
     const int stride_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -136,9 +136,9 @@ __global__ void StoPoolForwardTest(const int nthreads,
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_size_h, height);
+    int hend = min(hstart + kernel_h, height);
     int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_size_w, width);
+    int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
@@ -175,16 +175,16 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
-        kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data, 
         mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
-        kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
+        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     if (Caffe::phase() == Caffe::TRAIN) {
@@ -195,16 +195,16 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
-          kernel_size_w_, stride_h_, stride_w_,
+          height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+          kernel_w_, stride_h_, stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
-          kernel_size_w_, stride_h_, stride_w_, top_data);
+          height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+          kernel_w_, stride_h_, stride_w_, top_data);
     }
     break;
   default:
@@ -219,7 +219,7 @@ template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int* mask, const Dtype* top_mask, const int num, const int channels,
     const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_size_h, const int kernel_size_w, 
+    const int pooled_width, const int kernel_h, const int kernel_w, 
     const int stride_h, const int stride_w, const int pad_h, const int pad_w, 
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
@@ -230,10 +230,10 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
     int phstart =
-        (h + pad_h < kernel_size_h) ? 0 : (h + pad_h - kernel_size_h) / stride_h + 1;
+        (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
     int phend = min((h + pad_h) / stride_h + 1, pooled_height);
     int pwstart =
-        (w + pad_w < kernel_size_w) ? 0 : (w + pad_w - kernel_size_w) / stride_w + 1;
+        (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
     int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     int offset = (n * channels + c) * pooled_height * pooled_width;
@@ -265,7 +265,7 @@ template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h, 
     const int stride_w, const int pad_h, const int pad_w, 
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
@@ -275,9 +275,9 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     int h = (index / width) % height + pad_h;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size_h) ? 0 : (h - kernel_size_h) / stride_h + 1;
+    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
     int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_size_w) ? 0 : (w - kernel_size_w) / stride_w + 1;
+    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
     int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
@@ -286,8 +286,8 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
         // figure out the pooling size
         int hstart = ph * stride_h - pad_h;
         int wstart = pw * stride_w - pad_w;
-        int hend = min(hstart + kernel_size_h, height + pad_h);
-        int wend = min(wstart + kernel_size_w, width + pad_w);
+        int hend = min(hstart + kernel_h, height + pad_h);
+        int wend = min(wstart + kernel_w, width + pad_w);
         int pool_size = (hend - hstart) * (wend - wstart);
         gradient += top_diff[ph * pooled_width + pw] / pool_size;
       }
@@ -302,7 +302,7 @@ __global__ void StoPoolBackward(const int nthreads,
     const Dtype* rand_idx, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size_h, const int kernel_size_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h, 
     const int stride_w, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -311,9 +311,9 @@ __global__ void StoPoolBackward(const int nthreads,
     int h = (index / width) % height;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size_h) ? 0 : (h - kernel_size_h) / stride_h + 1;
+    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
     int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_size_w) ? 0 : (w - kernel_size_w) / stride_w + 1;
+    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
     int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     rand_idx += (n * channels + c) * pooled_height * pooled_width;
@@ -354,22 +354,22 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, mask, top_mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
-        kernel_size_h_, kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, 
+        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, 
         bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_h_, 
-        kernel_size_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
+        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     // NOLINT_NEXT_LINE(whitespace/operators)
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, rand_idx_.gpu_data(), top_diff,
         top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_size_h_, kernel_size_w_, stride_h_, stride_w_, 
+        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_, 
         bottom_diff);
     break;
   default:
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index effebd09a38..70a4ab2e8e9 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -411,8 +411,8 @@ message PoolingParameter {
   optional uint32 pad_h = 9 [default = 0]; // The padding height
   optional uint32 pad_w = 10 [default = 0]; // The padding width
   optional uint32 kernel_size = 2; // The kernel size (square)
-  optional uint32 kernel_size_h = 5; // The kernel height
-  optional uint32 kernel_size_w = 6; // The kernel width
+  optional uint32 kernel_h = 5; // The kernel height
+  optional uint32 kernel_w = 6; // The kernel width
   optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
   optional uint32 stride_h = 7; // The stride height
   optional uint32 stride_w = 8; // The stride width
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index b13d11f6c6d..651d20305b4 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -44,8 +44,8 @@ class PoolingLayerTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_mask_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
-
-  void TestForward() {
+  // Test for 2x 2 square pooling layer
+  void TestForwardSquare() {
     LayerParameter layer_param;
     PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
     pooling_param->set_kernel_size(2);
@@ -116,6 +116,256 @@ class PoolingLayerTest : public ::testing::Test {
       }
     }
   }
+  // Test for 3x 2 rectangular pooling layer with kernel_h > kernel_w
+  void TestForwardRectHigh() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_h(3);
+    pooling_param->set_kernel_w(2);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 6, 6);
+    // Input: 2x 2 channels of:
+    // [35     1     6    26    19    24]
+    // [ 3    32     7    21    23    25]
+    // [31     9     2    22    27    20]
+    // [ 8    28    33    17    10    15]
+    // [30     5    34    12    14    16]
+    // [ 4    36    29    13    18    11] (this is generated by magic(6) in MATLAB)
+    for (int i = 0; i < 36 * num * channels; i += 36) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 35;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 6;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 26;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 19;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 24;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 32;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 7;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 21;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 23;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 25;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 31;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 9;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 15] = 22;
+      blob_bottom_->mutable_cpu_data()[i + 16] = 27;
+      blob_bottom_->mutable_cpu_data()[i + 17] = 20;
+      blob_bottom_->mutable_cpu_data()[i + 18] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 19] = 28;
+      blob_bottom_->mutable_cpu_data()[i + 20] = 33;
+      blob_bottom_->mutable_cpu_data()[i + 21] = 17;
+      blob_bottom_->mutable_cpu_data()[i + 22] = 10;
+      blob_bottom_->mutable_cpu_data()[i + 23] = 15;
+      blob_bottom_->mutable_cpu_data()[i + 24] = 30;
+      blob_bottom_->mutable_cpu_data()[i + 25] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 26] = 34;
+      blob_bottom_->mutable_cpu_data()[i + 27] = 12;
+      blob_bottom_->mutable_cpu_data()[i + 28] = 14;
+      blob_bottom_->mutable_cpu_data()[i + 29] = 16;
+      blob_bottom_->mutable_cpu_data()[i + 30] = 4;
+      blob_bottom_->mutable_cpu_data()[i + 31] = 36;
+      blob_bottom_->mutable_cpu_data()[i + 32] = 29;
+      blob_bottom_->mutable_cpu_data()[i + 33] = 13;
+      blob_bottom_->mutable_cpu_data()[i + 34] = 18;
+      blob_bottom_->mutable_cpu_data()[i + 35] = 11;
+    }
+    PoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 4);
+    EXPECT_EQ(blob_top_->width(), 5);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 4);
+      EXPECT_EQ(blob_top_mask_->width(), 5);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    // [35    32    26    27    27]
+    // [32    33    33    27    27]
+    // [31    34    34    27    27]
+    // [36    36    34    18    18]
+    for (int i = 0; i < 20 * num * channels; i += 20) {
+      EXPECT_EQ(blob_top_->cpu_data()[i +  0], 35);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  1], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  2], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  3], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  4], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  5], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  6], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  7], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  8], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  9], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 10], 31);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 11], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 13], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 14], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 15], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 17], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 18], 18);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
+    }
+    if (blob_top_vec_.size() > 1) {
+        // [ 1     8     4    17    17]
+        // [ 8    21    21    17    17]
+        // [13    27    27    17    17]
+        // [32    32    27    35    35]
+      for (int i = 0; i < 20 * num * channels; i += 20) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  0],  0);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  1],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  2],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  3], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  4], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  5],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  6], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  7], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  8], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  9], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 34);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
+      }
+    }
+  }
+  // Test for rectangular pooling layer with kernel_w > kernel_h
+  void TestForwardRectWide() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_h(2);
+    pooling_param->set_kernel_w(3);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 6, 6);
+    // Input: 2x 2 channels of:
+    // [35     1     6    26    19    24]
+    // [ 3    32     7    21    23    25]
+    // [31     9     2    22    27    20]
+    // [ 8    28    33    17    10    15]
+    // [30     5    34    12    14    16]
+    // [ 4    36    29    13    18    11] (this is generated by magic(6) in MATLAB)
+    for (int i = 0; i < 36 * num * channels; i += 36) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 35;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 6;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 26;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 19;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 24;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 32;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 7;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 21;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 23;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 25;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 31;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 9;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 15] = 22;
+      blob_bottom_->mutable_cpu_data()[i + 16] = 27;
+      blob_bottom_->mutable_cpu_data()[i + 17] = 20;
+      blob_bottom_->mutable_cpu_data()[i + 18] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 19] = 28;
+      blob_bottom_->mutable_cpu_data()[i + 20] = 33;
+      blob_bottom_->mutable_cpu_data()[i + 21] = 17;
+      blob_bottom_->mutable_cpu_data()[i + 22] = 10;
+      blob_bottom_->mutable_cpu_data()[i + 23] = 15;
+      blob_bottom_->mutable_cpu_data()[i + 24] = 30;
+      blob_bottom_->mutable_cpu_data()[i + 25] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 26] = 34;
+      blob_bottom_->mutable_cpu_data()[i + 27] = 12;
+      blob_bottom_->mutable_cpu_data()[i + 28] = 14;
+      blob_bottom_->mutable_cpu_data()[i + 29] = 16;
+      blob_bottom_->mutable_cpu_data()[i + 30] = 4;
+      blob_bottom_->mutable_cpu_data()[i + 31] = 36;
+      blob_bottom_->mutable_cpu_data()[i + 32] = 29;
+      blob_bottom_->mutable_cpu_data()[i + 33] = 13;
+      blob_bottom_->mutable_cpu_data()[i + 34] = 18;
+      blob_bottom_->mutable_cpu_data()[i + 35] = 11;
+    }
+    PoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 5);
+    EXPECT_EQ(blob_top_->width(), 4);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 5);
+      EXPECT_EQ(blob_top_mask_->width(), 4);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    // [35    32    26    26]
+    // [32    32    27    27]
+    // [33    33    33    27]
+    // [34    34    34    17]
+    // [36    36    34    18]
+    for (int i = 0; i < 20 * num * channels; i += 20) {
+      EXPECT_EQ(blob_top_->cpu_data()[i +  0], 35);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  1], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  2], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  3], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  4], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  5], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  6], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  7], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  8], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  9], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 10], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 11], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 13], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 14], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 15], 17);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 17], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 18], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
+    }
+    if (blob_top_vec_.size() > 1) {
+        // [ 1     8     4     4]
+        // [ 8     8    17    17]
+        // [21    21    21    17]
+        // [27    27    27    22]
+        // [32    32    27    35]
+      for (int i = 0; i < 20 * num * channels; i += 20) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  0],  0);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  1],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  2],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  3],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  4],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  5],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  6], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  7], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  8], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  9], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 21);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
+      }
+    }
+  }
 };
 
 typedef ::testing::Types<float, double> Dtypes;
@@ -207,24 +457,32 @@ TYPED_TEST(PoolingLayerTest, PrintCPUBackward) {
 
 TYPED_TEST(PoolingLayerTest, TestCPUForwardMax) {
   Caffe::set_mode(Caffe::CPU);
-  this->TestForward();
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
 }
 
 TYPED_TEST(PoolingLayerTest, TestGPUForwardMax) {
   Caffe::set_mode(Caffe::GPU);
-  this->TestForward();
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
 }
 
 TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxTopMask) {
   Caffe::set_mode(Caffe::CPU);
   this->blob_top_vec_.push_back(this->blob_top_mask_);
-  this->TestForward();
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
 }
 
 TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxTopMask) {
   Caffe::set_mode(Caffe::GPU);
   this->blob_top_vec_.push_back(this->blob_top_mask_);
-  this->TestForward();
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
 }
 
 TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {

From e10977f8a598f6753c28d1388e0900e7805280ac Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Mon, 7 Jul 2014 11:32:58 -0700
Subject: [PATCH 0223/2053] fixed style errors

---
 src/caffe/layers/pooling_layer.cpp    |  4 ++--
 src/caffe/layers/pooling_layer.cu     | 34 +++++++++++++--------------
 src/caffe/test/test_pooling_layer.cpp |  6 +++--
 3 files changed, 23 insertions(+), 21 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index d8cd2e2139d..9151ff23674 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -35,11 +35,11 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK(pool_param.has_kernel_size() ||
       (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!pool_param.has_pad() && pool_param.has_pad_h() 
+  CHECK((!pool_param.has_pad() && pool_param.has_pad_h()
       && pool_param.has_pad_w())
       || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
       << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!pool_param.has_stride() && pool_param.has_stride_h() 
+  CHECK((!pool_param.has_stride() && pool_param.has_stride_h()
       && pool_param.has_stride_w())
       || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index e38028dd061..a44b1770ee3 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,7 +17,7 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
     int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
@@ -55,7 +55,7 @@ template <typename Dtype>
 __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -87,7 +87,7 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, Dtype* rand_idx, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -128,7 +128,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -175,15 +175,15 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
-        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data,
         mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
@@ -195,7 +195,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
           kernel_w_, stride_h_, stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
@@ -203,7 +203,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
           kernel_w_, stride_h_, stride_w_, top_data);
     }
     break;
@@ -219,8 +219,8 @@ template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int* mask, const Dtype* top_mask, const int num, const int channels,
     const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_h, const int kernel_w, 
-    const int stride_h, const int stride_w, const int pad_h, const int pad_w, 
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -265,8 +265,8 @@ template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
-    const int stride_w, const int pad_h, const int pad_w, 
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, const int pad_h, const int pad_w,
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -302,7 +302,7 @@ __global__ void StoPoolBackward(const int nthreads,
     const Dtype* rand_idx, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -354,14 +354,14 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, mask, top_mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
-        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, 
+        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_,
         bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
@@ -369,7 +369,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, rand_idx_.gpu_data(), top_diff,
         top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_, 
+        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_,
         bottom_diff);
     break;
   default:
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 651d20305b4..bb21113a666 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -132,7 +132,8 @@ class PoolingLayerTest : public ::testing::Test {
     // [31     9     2    22    27    20]
     // [ 8    28    33    17    10    15]
     // [30     5    34    12    14    16]
-    // [ 4    36    29    13    18    11] (this is generated by magic(6) in MATLAB)
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
     for (int i = 0; i < 36 * num * channels; i += 36) {
       blob_bottom_->mutable_cpu_data()[i +  0] = 35;
       blob_bottom_->mutable_cpu_data()[i +  1] = 1;
@@ -256,7 +257,8 @@ class PoolingLayerTest : public ::testing::Test {
     // [31     9     2    22    27    20]
     // [ 8    28    33    17    10    15]
     // [30     5    34    12    14    16]
-    // [ 4    36    29    13    18    11] (this is generated by magic(6) in MATLAB)
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
     for (int i = 0; i < 36 * num * channels; i += 36) {
       blob_bottom_->mutable_cpu_data()[i +  0] = 35;
       blob_bottom_->mutable_cpu_data()[i +  1] = 1;

From 45e201d42d2c2243a06ceb9376998903ac227f4b Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Mon, 7 Jul 2014 12:12:59 -0700
Subject: [PATCH 0224/2053] added gradient check for non-square pooling

---
 src/caffe/test/test_pooling_layer.cpp | 214 +++++++++++++++-----------
 1 file changed, 128 insertions(+), 86 deletions(-)

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index bb21113a666..fdc3ea4cafa 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -488,31 +488,41 @@ TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxTopMask) {
 }
 
 TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(1);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-4, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(1);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      Caffe::set_mode(Caffe::CPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(1);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-4, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(1);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      Caffe::set_mode(Caffe::GPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxPadded) {
@@ -608,31 +618,43 @@ TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxPadded) {
 
 
 TYPED_TEST(PoolingLayerTest, TestCPUGradientMaxTopMask) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  this->blob_top_vec_.push_back(this->blob_top_mask_);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-4, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      this->blob_top_vec_.push_back(this->blob_top_mask_);
+      Caffe::set_mode(Caffe::CPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+      this->blob_top_vec_.pop_back();
+    }
+  }
 }
 
 TYPED_TEST(PoolingLayerTest, TestGPUGradientMaxTopMask) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  this->blob_top_vec_.push_back(this->blob_top_mask_);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-4, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      this->blob_top_vec_.push_back(this->blob_top_mask_);
+      Caffe::set_mode(Caffe::GPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+      this->blob_top_vec_.pop_back();
+    }
+  }
 }
 
 
@@ -703,60 +725,80 @@ TYPED_TEST(PoolingLayerTest, TestGPUForwardAve) {
 
 
 TYPED_TEST(PoolingLayerTest, TestCPUGradientAve) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      Caffe::set_mode(Caffe::CPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 
 TYPED_TEST(PoolingLayerTest, TestGPUGradientAve) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      Caffe::set_mode(Caffe::GPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 
 TYPED_TEST(PoolingLayerTest, TestCPUGradientAvePadded) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      Caffe::set_mode(Caffe::CPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 
 TYPED_TEST(PoolingLayerTest, TestGPUGradientAvePadded) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      Caffe::set_mode(Caffe::GPU);
+      PoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 

From e5be75459a706d0e1261851894d11c223d07d4d6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 8 Jul 2014 15:02:00 -0700
Subject: [PATCH 0225/2053] Don't make clean when running linecount

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index f8abc1b2b88..c8b25d935bf 100644
--- a/Makefile
+++ b/Makefile
@@ -266,7 +266,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 
 all: $(NAME) $(STATIC_NAME) tools examples
 
-linecount: clean
+linecount:
 	cloc --read-lang-def=$(PROJECT).cloc src/$(PROJECT)/
 
 lint: $(LINT_REPORT)

From ba8a2bb8c99f39b66cd915b5cfab0d3991b1488f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 8 Jul 2014 15:58:12 -0700
Subject: [PATCH 0226/2053] Dump compiler warnings to *.warnings.txt; use "make
 warn" to print them

---
 Makefile | 68 ++++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 56 insertions(+), 12 deletions(-)

diff --git a/Makefile b/Makefile
index c8b25d935bf..4d67aa32802 100644
--- a/Makefile
+++ b/Makefile
@@ -123,6 +123,22 @@ TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 
+##############################
+# Derive compiler warning dump locations
+##############################
+WARNS_EXT := warnings.txt
+CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.${WARNS_EXT}})
+CU_WARNS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo.${WARNS_EXT}})
+TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.${WARNS_EXT}})
+EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.${WARNS_EXT}})
+TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.${WARNS_EXT}})
+TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo.${WARNS_EXT}})
+ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
+ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
+ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)
+
+ALL_WARNS_FILENAME := $(BUILD_DIR)/$(WARNS_EXT)
+
 ##############################
 # Derive include and lib directories
 ##############################
@@ -262,7 +278,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 ##############################
 .PHONY: all test clean linecount lint tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
-	superclean supercleanlist supercleanfiles
+	superclean supercleanlist supercleanfiles warn
 
 all: $(NAME) $(STATIC_NAME) tools examples
 
@@ -312,6 +328,23 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 runtest: $(TEST_ALL_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle
 
+warn: $(ALL_WARNS_FILENAME)
+
+$(ALL_WARNS_FILENAME): $(ALL_WARNS) | $(BUILD_DIR)
+	@ touch $(ALL_WARNS)
+	@ cat $(ALL_WARNS) > $@
+	@ # If the concatenated warning file is non-empty, touch all dependencies
+	@ # so that "make warn" always prints the error list and only says
+	@ # "nothing to be done" when there are no warnings.
+	@ if [ -s "$@" ]; then touch $(ALL_WARNS); else echo "No compiler warnings!"; fi
+	@ cat $@
+
+$(ALL_CXX_WARNS): %.o.${WARNS_EXT} : %.o
+	@ cat $@
+
+$(ALL_CU_WARNS): %.cuo.${WARNS_EXT} : %.cuo
+	@ cat $@
+
 $(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
 
 # Create a target ".linked" in this BUILD_DIR to tell Make that the "build" link
@@ -337,12 +370,14 @@ $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
 
 $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
@@ -373,42 +408,51 @@ $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
 		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 $(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
+	@ cat $@.${WARNS_EXT}
 	@ echo
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)

From 933dc6e7d9fc190ad64bf61d53217c2af9465b1d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 8 Jul 2014 17:52:49 -0700
Subject: [PATCH 0227/2053] Output a lint report for every source file linted;
 use to lint incrementally

---
 Makefile | 27 ++++++++++++++++++++-------
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/Makefile b/Makefile
index 4d67aa32802..74e4a944bc3 100644
--- a/Makefile
+++ b/Makefile
@@ -62,6 +62,9 @@ NONGEN_CXX_SRCS := $(shell find \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
+LINT_OUTPUT_DIR := $(BUILD_DIR)/lint
+LINT_EXT := lint.txt
+LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, ${NONGEN_CXX_SRCS}))
 LINT_REPORT := $(BUILD_DIR)/cpp_lint.log
 FAILED_LINT_REPORT := $(BUILD_DIR)/cpp_lint.error_log
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
@@ -173,6 +176,7 @@ ALL_BUILD_DIRS := $(sort \
 		$(LAYER_BUILD_DIR) $(UTIL_BUILD_DIR) $(TOOL_BUILD_DIR) \
 		$(TEST_BUILD_DIR) $(TEST_BIN_DIR) $(GTEST_BUILD_DIR) \
 		$(EXAMPLE_BUILD_DIRS) \
+		$(LINT_OUTPUT_DIR) \
 		$(PROTO_BUILD_DIR) $(PROTO_BUILD_INCLUDE_DIR) $(PY_PROTO_BUILD_DIR) \
 		$(DISTRIBUTE_SUBDIRS))
 
@@ -287,13 +291,22 @@ linecount:
 
 lint: $(LINT_REPORT)
 
-$(LINT_REPORT): $(NONGEN_CXX_SRCS) | $(BUILD_DIR)
-	@ (python ./scripts/cpp_lint.py $(NONGEN_CXX_SRCS) > $(LINT_REPORT) 2>&1 \
-		&& ($(RM) $(FAILED_LINT_REPORT); echo "No lint errors!")) || ( \
-			mv $(LINT_REPORT) $(FAILED_LINT_REPORT); \
-			grep -v "^Done processing " $(FAILED_LINT_REPORT); \
-			echo "Found 1 or more lint errors; see log at $(FAILED_LINT_REPORT)"; \
-			exit 1)
+$(LINT_REPORT): $(LINT_OUTPUTS)
+	@ cat $(LINT_OUTPUTS) > $@
+	@ if [ -s "$@" ]; then \
+		cat $@; mv $@ $(FAILED_LINT_REPORT); \
+	  else \
+		echo "No lint errors!"; \
+	  fi
+
+$(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % | $(LINT_OUTPUT_DIR)
+	@ mkdir -p $(dir $@)
+	@ python ./scripts/cpp_lint.py $< 2>&1 \
+		| grep -v "^Done processing " \
+		| grep -v "^Total errors found: 0" \
+		> $@ \
+		|| true
+	@ if [ -s "$@" ]; then touch $<; cat $@; fi
 
 test: $(TEST_ALL_BIN) $(TEST_BINS)
 

From d86735ed3bc1d84bb5e0e01958f8a3bab277b503 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 11:37:14 -0700
Subject: [PATCH 0228/2053] Makefile: cleanup lint/warn report logic (and make
 the two more consistent)

---
 Makefile | 99 ++++++++++++++++++++++++++++----------------------------
 1 file changed, 50 insertions(+), 49 deletions(-)

diff --git a/Makefile b/Makefile
index 74e4a944bc3..a55e44b0836 100644
--- a/Makefile
+++ b/Makefile
@@ -62,11 +62,11 @@ NONGEN_CXX_SRCS := $(shell find \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
-LINT_OUTPUT_DIR := $(BUILD_DIR)/lint
+LINT_OUTPUT_DIR := $(BUILD_DIR)/.lint
 LINT_EXT := lint.txt
-LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, ${NONGEN_CXX_SRCS}))
-LINT_REPORT := $(BUILD_DIR)/cpp_lint.log
-FAILED_LINT_REPORT := $(BUILD_DIR)/cpp_lint.error_log
+LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, $(NONGEN_CXX_SRCS)))
+EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
+NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
@@ -130,17 +130,18 @@ TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 # Derive compiler warning dump locations
 ##############################
 WARNS_EXT := warnings.txt
-CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.${WARNS_EXT}})
-CU_WARNS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo.${WARNS_EXT}})
-TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.${WARNS_EXT}})
-EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.${WARNS_EXT}})
-TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.${WARNS_EXT}})
-TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo.${WARNS_EXT}})
+CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.$(WARNS_EXT)})
+CU_WARNS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.$(WARNS_EXT)})
+EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.$(WARNS_EXT)})
+TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.$(WARNS_EXT)})
+TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
 ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
 ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
 ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)
 
-ALL_WARNS_FILENAME := $(BUILD_DIR)/$(WARNS_EXT)
+EMPTY_WARN_REPORT := $(BUILD_DIR)/.$(WARNS_EXT)
+NONEMPTY_WARN_REPORT := $(BUILD_DIR)/$(WARNS_EXT)
 
 ##############################
 # Derive include and lib directories
@@ -289,13 +290,15 @@ all: $(NAME) $(STATIC_NAME) tools examples
 linecount:
 	cloc --read-lang-def=$(PROJECT).cloc src/$(PROJECT)/
 
-lint: $(LINT_REPORT)
+lint: $(EMPTY_LINT_REPORT)
 
-$(LINT_REPORT): $(LINT_OUTPUTS)
+$(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
 	@ cat $(LINT_OUTPUTS) > $@
 	@ if [ -s "$@" ]; then \
-		cat $@; mv $@ $(FAILED_LINT_REPORT); \
+		cat $@; \
+		mv $@ $(NONEMPTY_LINT_REPORT); \
 	  else \
+		$(RM) $(NONEMPTY_LINT_REPORT); \
 		echo "No lint errors!"; \
 	  fi
 
@@ -306,7 +309,6 @@ $(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % | $(LINT_OUTPUT_DIR)
 		| grep -v "^Total errors found: 0" \
 		> $@ \
 		|| true
-	@ if [ -s "$@" ]; then touch $<; cat $@; fi
 
 test: $(TEST_ALL_BIN) $(TEST_BINS)
 
@@ -341,22 +343,21 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 runtest: $(TEST_ALL_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle
 
-warn: $(ALL_WARNS_FILENAME)
+warn: $(EMPTY_WARN_REPORT)
 
-$(ALL_WARNS_FILENAME): $(ALL_WARNS) | $(BUILD_DIR)
-	@ touch $(ALL_WARNS)
+$(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
 	@ cat $(ALL_WARNS) > $@
-	@ # If the concatenated warning file is non-empty, touch all dependencies
-	@ # so that "make warn" always prints the error list and only says
-	@ # "nothing to be done" when there are no warnings.
-	@ if [ -s "$@" ]; then touch $(ALL_WARNS); else echo "No compiler warnings!"; fi
-	@ cat $@
+	@ if [ -s "$@" ]; then \
+		cat $@; \
+		mv $@ $(NONEMPTY_WARN_REPORT); \
+	  else \
+		$(RM) $(NONEMPTY_WARN_REPORT); \
+		echo "No compiler warnings!"; \
+	  fi
 
-$(ALL_CXX_WARNS): %.o.${WARNS_EXT} : %.o
-	@ cat $@
+$(ALL_CXX_WARNS): %.o.$(WARNS_EXT) : %.o
 
-$(ALL_CU_WARNS): %.cuo.${WARNS_EXT} : %.cuo
-	@ cat $@
+$(ALL_CU_WARNS): %.cuo.$(WARNS_EXT) : %.cuo
 
 $(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
 
@@ -383,14 +384,14 @@ $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
 
 $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
@@ -421,51 +422,51 @@ $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
 		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.${WARNS_EXT}
-	@ cat $@.${WARNS_EXT}
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)

From 37329cbc2c283a4701c18170a1f19c69d92e5168 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 15:47:21 -0700
Subject: [PATCH 0229/2053] fix bug introduced by warning logs: errors didn't
 print because they are logged to the warnings file

---
 Makefile | 33 ++++++++++++++++++++++-----------
 1 file changed, 22 insertions(+), 11 deletions(-)

diff --git a/Makefile b/Makefile
index a55e44b0836..839532eff56 100644
--- a/Makefile
+++ b/Makefile
@@ -384,13 +384,15 @@ $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
 
 $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT)
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
@@ -422,50 +424,59 @@ $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT)
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT)
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
 		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| cat $@.$(WARNS_EXT); exit 1
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 

From 1679815f678f5bf78951d415a04a12b4f64173f3 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 16:39:17 -0700
Subject: [PATCH 0230/2053] makefile hotfix

---
 Makefile | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/Makefile b/Makefile
index 839532eff56..829602b7946 100644
--- a/Makefile
+++ b/Makefile
@@ -385,14 +385,14 @@ $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
 $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HDRS) \
 		| $(TEST_BUILD_DIR)
 	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
@@ -425,58 +425,58 @@ $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
 	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
 	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
 		| $(EXAMPLE_BUILD_DIRS)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| cat $@.$(WARNS_EXT); exit 1
+		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 

From 3bd49bac72bbaeea225a1e5e5a730482b366241a Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Mon, 7 Jul 2014 15:26:24 +0800
Subject: [PATCH 0231/2053] Avoid using cudaMemcpy for memcpy when there is no
 GPU and CUDA driver

---
 src/caffe/test/test_math_functions.cpp |  2 ++
 src/caffe/util/math_functions.cpp      | 12 ++++++++++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index 941d8b9479a..ddb9f060972 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -209,6 +209,7 @@ TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   TypeParam* top_data = this->blob_top_->mutable_cpu_data();
+  Caffe::set_mode(Caffe::CPU);
   caffe_copy(n, bottom_data, top_data);
   for (int i = 0; i < n; ++i) {
     EXPECT_EQ(bottom_data[i], top_data[i]);
@@ -219,6 +220,7 @@ TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
   TypeParam* top_data = this->blob_top_->mutable_gpu_data();
+  Caffe::set_mode(Caffe::GPU);
   caffe_copy(n, bottom_data, top_data);
   bottom_data = this->blob_bottom_->cpu_data();
   top_data = this->blob_top_->mutable_cpu_data();
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 918bb3c361c..df32093238f 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -152,7 +152,11 @@ void caffe_add_scalar(const int N, const double alpha, double* Y) {
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype* X, Dtype* Y) {
   if (X != Y) {
-    CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
+    if (Caffe::mode() == Caffe::CPU) {
+      memcpy(Y, X, sizeof(Dtype) * N);
+    } else {
+      CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
+    }
   }
 }
 
@@ -164,7 +168,11 @@ template void caffe_copy<double>(const int N, const double* X, double* Y);
 
 void caffe_memcpy(const size_t N, const void* X, void* Y) {
   if (X != Y) {
-    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
+    if (Caffe::mode() == Caffe::CPU) {
+      memcpy(Y, X, N);
+    } else {
+      CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
+    }
   }
 }
 

From 4d16ed5969d8963babd63ae650289dfb8fd633f4 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Wed, 9 Jul 2014 09:47:28 +0800
Subject: [PATCH 0232/2053] Check the GPU mode to decide which memcpy to use

---
 src/caffe/util/math_functions.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index df32093238f..9311a398721 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -152,10 +152,10 @@ void caffe_add_scalar(const int N, const double alpha, double* Y) {
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype* X, Dtype* Y) {
   if (X != Y) {
-    if (Caffe::mode() == Caffe::CPU) {
-      memcpy(Y, X, sizeof(Dtype) * N);
-    } else {
+    if (Caffe::mode() == Caffe::GPU) {
       CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
+    } else {
+      memcpy(Y, X, sizeof(Dtype) * N);
     }
   }
 }
@@ -168,10 +168,10 @@ template void caffe_copy<double>(const int N, const double* X, double* Y);
 
 void caffe_memcpy(const size_t N, const void* X, void* Y) {
   if (X != Y) {
-    if (Caffe::mode() == Caffe::CPU) {
-      memcpy(Y, X, N);
-    } else {
+    if (Caffe::mode() == Caffe::GPU) {
       CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
+    } else {
+      memcpy(Y, X, N);
     }
   }
 }

From 00433d8082796bdaa2cafa067c158a5f31245985 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Wed, 9 Jul 2014 10:12:07 +0800
Subject: [PATCH 0233/2053] Switch to GPU mode when pointer is move to or from
 GPU in SyncedMemory

---
 src/caffe/syncedmem.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 9fe55280de9..5d6d0585222 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -33,6 +33,7 @@ inline void SyncedMemory::to_cpu() {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
+    Caffe::set_mode(Caffe::GPU);
     caffe_memcpy(size_, gpu_ptr_, cpu_ptr_);
     head_ = SYNCED;
     break;
@@ -53,6 +54,7 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
+    Caffe::set_mode(Caffe::GPU);
     caffe_memcpy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
     break;

From ac0dd39252812aba1f67b9b0b0e18e62ea1742e6 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 10 Jul 2014 01:57:16 +0800
Subject: [PATCH 0234/2053] Implement @Yangqing's solution to copy memory in
 the SyncedMemory

---
 src/caffe/syncedmem.cpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 5d6d0585222..3f9a3be9ebf 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -33,8 +33,7 @@ inline void SyncedMemory::to_cpu() {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
-    Caffe::set_mode(Caffe::GPU);
-    caffe_memcpy(size_, gpu_ptr_, cpu_ptr_);
+    CUDA_CHECK(cudaMemcpy(cpu_ptr_, gpu_ptr_, size_, cudaMemcpyDefault));
     head_ = SYNCED;
     break;
   case HEAD_AT_CPU:
@@ -54,8 +53,7 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
-    Caffe::set_mode(Caffe::GPU);
-    caffe_memcpy(size_, cpu_ptr_, gpu_ptr_);
+    CUDA_CHECK(cudaMemcpy(gpu_ptr_, cpu_ptr_, size_, cudaMemcpyDefault));
     head_ = SYNCED;
     break;
   case HEAD_AT_GPU:

From 904c2ce69b728c988004cef6796fbdf07ecb4c1e Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 10 Jul 2014 07:50:31 +0800
Subject: [PATCH 0235/2053] Replace cudaMemcpy with caffe_gpu_memcpy in
 SyncedMemory per @longjon

---
 include/caffe/util/math_functions.hpp | 2 +-
 src/caffe/syncedmem.cpp               | 4 ++--
 src/caffe/test/test_syncedmem.cpp     | 4 ++--
 src/caffe/util/math_functions.cpp     | 8 ++------
 4 files changed, 7 insertions(+), 11 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 97a057103db..2df0fc983f9 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -59,7 +59,7 @@ void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
 
-void caffe_memcpy(const size_t N, const void *X, void *Y);
+void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
 
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 3f9a3be9ebf..77dfe7a4636 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -33,7 +33,7 @@ inline void SyncedMemory::to_cpu() {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
-    CUDA_CHECK(cudaMemcpy(cpu_ptr_, gpu_ptr_, size_, cudaMemcpyDefault));
+    caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_CPU:
@@ -53,7 +53,7 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
-    CUDA_CHECK(cudaMemcpy(gpu_ptr_, cpu_ptr_, size_, cudaMemcpyDefault));
+    caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_GPU:
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index 3aaeafc353e..3a757088e89 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -58,7 +58,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
   char* recovered_value = new char[10];
-  caffe_memcpy(10, gpu_data, recovered_value);
+  caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 1);
   }
@@ -72,7 +72,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
-  caffe_memcpy(10, gpu_data, recovered_value);
+  caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 2);
   }
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 9311a398721..b989ca2ab69 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -166,13 +166,9 @@ template void caffe_copy<unsigned int>(const int N, const unsigned int* X,
 template void caffe_copy<float>(const int N, const float* X, float* Y);
 template void caffe_copy<double>(const int N, const double* X, double* Y);
 
-void caffe_memcpy(const size_t N, const void* X, void* Y) {
+void caffe_gpu_memcpy(const size_t N, const void* X, void* Y) {
   if (X != Y) {
-    if (Caffe::mode() == Caffe::GPU) {
-      CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
-    } else {
-      memcpy(Y, X, N);
-    }
+    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
   }
 }
 

From e07a2c0eea903e8d7c7050e14d177bbd0f8f611a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 11 Jul 2014 06:09:41 +0200
Subject: [PATCH 0236/2053] fix link for caffe_rcnn_imagenet_model

---
 examples/imagenet/get_caffe_rcnn_imagenet_model.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
index 7cd47fa33a6..c7f36edcbf0 100755
--- a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
+++ b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
@@ -23,5 +23,5 @@ fi
 
 echo "Downloading..."
 
-wget --no-check-certificate https://www.dropbox.com/s/0i3etlgmsmgf5ei/$MODEL
+wget http://dl.caffe.berkeleyvision.org/$MODEL
 echo "Done. Please run this command again to verify that checksum = $CHECKSUM."

From e5ce15d29d183f8a2cdc48ab12e90d254cf4fad1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 12 Jul 2014 06:58:35 -0700
Subject: [PATCH 0237/2053] Move test headers to include/. Add a test param to
 test both CPU and GPU (with both float and double Dtypes).

---
 Makefile                                      |   9 +-
 include/caffe/test/test_caffe_main.hpp        |  62 ++++
 .../caffe/test/test_gradient_check_util.hpp   |   0
 src/caffe/test/test_accuracy_layer.cpp        |   4 +-
 src/caffe/test/test_argmax_layer.cpp          |   4 +-
 src/caffe/test/test_benchmark.cpp             | 106 +------
 src/caffe/test/test_blob.cpp                  |   5 +-
 src/caffe/test/test_caffe_main.cpp            |   2 +-
 src/caffe/test/test_caffe_main.hpp            |  20 --
 src/caffe/test/test_common.cpp                |   6 +-
 src/caffe/test/test_concat_layer.cpp          |  39 +--
 src/caffe/test/test_convolution_layer.cpp     | 185 ++----------
 src/caffe/test/test_data_layer.cpp            | 125 +-------
 src/caffe/test/test_dummy_data_layer.cpp      |   3 +-
 src/caffe/test/test_eltwise_layer.cpp         | 172 +++--------
 src/caffe/test/test_euclidean_loss_layer.cpp  |  26 +-
 src/caffe/test/test_filler.cpp                |  10 +-
 src/caffe/test/test_flatten_layer.cpp         |  48 +--
 src/caffe/test/test_hdf5_output_layer.cpp     |  78 +----
 src/caffe/test/test_hdf5data_layer.cpp        |  88 +++---
 src/caffe/test/test_hinge_loss_layer.cpp      |  49 +--
 src/caffe/test/test_im2col_kernel.cu          |   3 +-
 src/caffe/test/test_im2col_layer.cpp          |  55 +---
 src/caffe/test/test_image_data_layer.cpp      |  24 +-
 src/caffe/test/test_inner_product_layer.cpp   |  78 ++---
 src/caffe/test/test_lrn_layer.cpp             | 123 ++------
 src/caffe/test/test_math_functions.cpp        |   3 +-
 .../test/test_maxpool_dropout_layers.cpp      | 108 ++-----
 src/caffe/test/test_memory_data_layer.cpp     |   3 +-
 .../test_multinomial_logistic_loss_layer.cpp  |   3 +-
 src/caffe/test/test_net.cpp                   | 170 +++--------
 src/caffe/test/test_neuron_layer.cpp          | 251 ++++------------
 src/caffe/test/test_pooling_layer.cpp         | 281 +++---------------
 src/caffe/test/test_power_layer.cpp           | 189 ++++--------
 .../test/test_random_number_generator.cpp     |   5 +-
 .../test_sigmoid_cross_entropy_loss_layer.cpp |  36 +--
 src/caffe/test/test_softmax_layer.cpp         |   9 +-
 .../test/test_softmax_with_loss_layer.cpp     |  27 +-
 src/caffe/test/test_split_layer.cpp           |  89 ++----
 src/caffe/test/test_stochastic_pooling.cpp    |   3 +-
 src/caffe/test/test_syncedmem.cpp             |  28 +-
 src/caffe/test/test_tanh_layer.cpp            |  54 +---
 src/caffe/test/test_threshold_layer.cpp       |  83 ++----
 src/caffe/test/test_util_blas.cpp             |  10 +-
 44 files changed, 684 insertions(+), 1992 deletions(-)
 create mode 100644 include/caffe/test/test_caffe_main.hpp
 rename {src => include}/caffe/test/test_gradient_check_util.hpp (100%)
 delete mode 100644 src/caffe/test/test_caffe_main.hpp

diff --git a/Makefile b/Makefile
index 829602b7946..c17d4980b79 100644
--- a/Makefile
+++ b/Makefile
@@ -28,7 +28,7 @@ STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
 # CXX_SRCS are the source files excluding the test ones.
 CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
 # HXX_SRCS are the header files
-HXX_SRCS := $(shell find include/$(PROJECT) -name "*.hpp")
+HXX_SRCS := $(shell find include/$(PROJECT) ! -name "test_*.hpp" -name "*.hpp")
 # CU_SRCS are the cuda source files
 CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
 # TEST_SRCS are the test source files
@@ -38,7 +38,7 @@ TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
 TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
 GTEST_SRC := src/gtest/gtest-all.cpp
 # TEST_HDRS are the test header files
-TEST_HDRS := $(shell find src/$(PROJECT) -name "test_*.hpp")
+TEST_HDRS := $(shell find include/$(PROJECT) -name "test_*.hpp")
 # TOOL_SRCS are the source files for the tool binaries
 TOOL_SRCS := $(shell find tools -name "*.cpp")
 # EXAMPLE_SRCS are the source files for the example binaries
@@ -282,7 +282,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 # Define build targets
 ##############################
 .PHONY: all test clean linecount lint tools examples $(DIST_ALIASES) \
-	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
+	py mat py$(PROJECT) mat$(PROJECT) proto runtest runtestnogpu \
 	superclean supercleanlist supercleanfiles warn
 
 all: $(NAME) $(STATIC_NAME) tools examples
@@ -343,6 +343,9 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 runtest: $(TEST_ALL_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle
 
+runtestnogpu: $(TEST_ALL_BIN)
+	$(TEST_ALL_BIN) --gtest_shuffle --gtest_filter="-*GPU*:*/2.*:*/3.*"
+
 warn: $(EMPTY_WARN_REPORT)
 
 $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
new file mode 100644
index 00000000000..e1a76455bed
--- /dev/null
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -0,0 +1,62 @@
+// Copyright 2014 BVLC and contributors.
+
+// The main caffe test code. Your test cpp code should include this hpp
+// to allow a main function to be compiled into the binary.
+#ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
+#define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
+
+#include <cuda_runtime.h>
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+
+#include <cstdlib>
+#include <cstdio>
+
+#include "caffe/common.hpp"
+
+using std::cout;
+using std::endl;
+
+int main(int argc, char** argv);
+
+namespace caffe {
+
+template <typename TypeParam>
+class MultiDeviceTest : public ::testing::Test {
+ public:
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  MultiDeviceTest() {
+    Caffe::set_mode(TypeParam::device);
+  }
+  virtual ~MultiDeviceTest() {}
+};
+
+typedef ::testing::Types<float, double> TestDtypes;
+
+struct FloatCPU {
+  typedef float Dtype;
+  static const Caffe::Brew device = Caffe::CPU;
+};
+
+struct DoubleCPU {
+  typedef double Dtype;
+  static const Caffe::Brew device = Caffe::CPU;
+};
+
+struct FloatGPU {
+  typedef float Dtype;
+  static const Caffe::Brew device = Caffe::GPU;
+};
+
+struct DoubleGPU {
+  typedef double Dtype;
+  static const Caffe::Brew device = Caffe::GPU;
+};
+
+typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
+    TestDtypesAndDevices;
+
+}  // namespace caffe
+
+#endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/src/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
similarity index 100%
rename from src/caffe/test/test_gradient_check_util.hpp
rename to include/caffe/test/test_gradient_check_util.hpp
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index deb72af34ec..355a36b3c95 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -71,14 +71,12 @@ class AccuracyLayerTest : public ::testing::Test {
   int top_k_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(AccuracyLayerTest, Dtypes);
+TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
 
 TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(this->top_k_);
-  Caffe::set_mode(Caffe::CPU);
   AccuracyLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index c4150e5abe0..44a13b99b1a 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -37,9 +37,7 @@ class ArgMaxLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ArgMaxLayerTest, Dtypes);
-
+TYPED_TEST_CASE(ArgMaxLayerTest, TestDtypes);
 
 TYPED_TEST(ArgMaxLayerTest, TestSetup) {
   LayerParameter layer_param;
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index 40eee9c80f9..82880088073 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -12,26 +12,19 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-class BenchmarkTest : public ::testing::Test {};
+template <typename TypeParam>
+class BenchmarkTest : public MultiDeviceTest<TypeParam> {};
 
-TEST_F(BenchmarkTest, TestTimerConstructorCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  Timer timer;
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-}
+TYPED_TEST_CASE(BenchmarkTest, TestDtypesAndDevices);
 
-TEST_F(BenchmarkTest, TestTimerConstructorGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(BenchmarkTest, TestTimerConstructor) {
   Timer timer;
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerStartCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(BenchmarkTest, TestTimerStart) {
   Timer timer;
   timer.Start();
   EXPECT_TRUE(timer.initted());
@@ -48,26 +41,7 @@ TEST_F(BenchmarkTest, TestTimerStartCPU) {
   EXPECT_TRUE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerStartGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Timer timer;
-  timer.Start();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_TRUE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-  timer.Stop();
-  timer.Start();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_TRUE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-  timer.Start();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_TRUE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerStopCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(BenchmarkTest, TestTimerStop) {
   Timer timer;
   timer.Stop();
   EXPECT_TRUE(timer.initted());
@@ -84,83 +58,31 @@ TEST_F(BenchmarkTest, TestTimerStopCPU) {
   EXPECT_TRUE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerStopGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Timer timer;
-  timer.Stop();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-  timer.Start();
-  timer.Stop();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-  timer.Stop();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerMilliSecondsCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  Timer timer;
-  CHECK_EQ(timer.MilliSeconds(), 0);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-  timer.Start();
-  usleep(300 * 1000);
-  CHECK_GE(timer.MilliSeconds(), 298);
-  CHECK_LE(timer.MilliSeconds(), 302);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerMilliSecondsGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Timer timer;
-  CHECK_EQ(timer.MilliSeconds(), 0);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-  timer.Start();
-  usleep(300 * 1000);
-  CHECK_GE(timer.MilliSeconds(), 298);
-  CHECK_LE(timer.MilliSeconds(), 302);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerSecondsCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   Timer timer;
-  CHECK_EQ(timer.Seconds(), 0);
+  EXPECT_EQ(timer.MilliSeconds(), 0);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  CHECK_GE(timer.Seconds(), 0.298);
-  CHECK_LE(timer.Seconds(), 0.302);
+  EXPECT_GE(timer.MilliSeconds(), 298);
+  EXPECT_LE(timer.MilliSeconds(), 302);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerSecondsGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   Timer timer;
-  CHECK_EQ(timer.Seconds(), 0);
+  EXPECT_EQ(timer.Seconds(), 0);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  CHECK_GE(timer.Seconds(), 0.298);
-  CHECK_LE(timer.Seconds(), 0.302);
+  EXPECT_GE(timer.Seconds(), 0.298);
+  EXPECT_LE(timer.Seconds(), 0.302);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index 5d38e54ff75..a5240940fd8 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -23,8 +23,7 @@ class BlobSimpleTest : public ::testing::Test {
   Blob<Dtype>* const blob_preshaped_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(BlobSimpleTest, Dtypes);
+TYPED_TEST_CASE(BlobSimpleTest, TestDtypes);
 
 TYPED_TEST(BlobSimpleTest, TestInitialization) {
   EXPECT_TRUE(this->blob_);
@@ -41,7 +40,7 @@ TYPED_TEST(BlobSimpleTest, TestInitialization) {
   EXPECT_EQ(this->blob_->count(), 0);
 }
 
-TYPED_TEST(BlobSimpleTest, TestPointers) {
+TYPED_TEST(BlobSimpleTest, TestPointersCPUGPU) {
   EXPECT_TRUE(this->blob_preshaped_->gpu_data());
   EXPECT_TRUE(this->blob_preshaped_->cpu_data());
   EXPECT_TRUE(this->blob_preshaped_->mutable_gpu_data());
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index ecc117e3b36..07e6b8d5ef2 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -3,7 +3,7 @@
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 
-#include "test_caffe_main.hpp"
+#include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
   cudaDeviceProp CAFFE_TEST_CUDA_PROP;
diff --git a/src/caffe/test/test_caffe_main.hpp b/src/caffe/test/test_caffe_main.hpp
deleted file mode 100644
index df64cbb41cc..00000000000
--- a/src/caffe/test/test_caffe_main.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-// The main caffe test code. Your test cpp code should include this hpp
-// to allow a main function to be compiled into the binary.
-#ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
-#define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
-
-#include <cuda_runtime.h>
-#include <glog/logging.h>
-#include <gtest/gtest.h>
-
-#include <cstdlib>
-#include <cstdio>
-
-using std::cout;
-using std::endl;
-
-int main(int argc, char** argv);
-
-#endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index 13c2d9514f7..a452b612890 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -13,7 +13,7 @@ namespace caffe {
 
 class CommonTest : public ::testing::Test {};
 
-TEST_F(CommonTest, TestCublasHandler) {
+TEST_F(CommonTest, TestCublasHandlerGPU) {
   int cuda_device_id;
   CUDA_CHECK(cudaGetDevice(&cuda_device_id));
   EXPECT_TRUE(Caffe::cublas_handle());
@@ -53,10 +53,10 @@ TEST_F(CommonTest, TestRandSeedGPU) {
   SyncedMemory data_b(10 * sizeof(unsigned int));
   Caffe::set_random_seed(1701);
   CURAND_CHECK(curandGenerate(Caffe::curand_generator(),
-        reinterpret_cast<unsigned int*>(data_a.mutable_gpu_data()), 10));
+        static_cast<unsigned int*>(data_a.mutable_gpu_data()), 10));
   Caffe::set_random_seed(1701);
   CURAND_CHECK(curandGenerate(Caffe::curand_generator(),
-        reinterpret_cast<unsigned int*>(data_b.mutable_gpu_data()), 10));
+        static_cast<unsigned int*>(data_b.mutable_gpu_data()), 10));
   for (int i = 0; i < 10; ++i) {
     EXPECT_EQ(((const unsigned int*)(data_a.cpu_data()))[i],
         ((const unsigned int*)(data_b.cpu_data()))[i]);
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 72e3c902cf1..ff208a90d3a 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -17,8 +17,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class ConcatLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   ConcatLayerTest()
       : blob_bottom_0(new Blob<Dtype>(2, 3, 6, 5)),
@@ -55,13 +57,13 @@ class ConcatLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ConcatLayerTest, Dtypes);
+TYPED_TEST_CASE(ConcatLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(ConcatLayerTest, TestSetupNum) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_concat_param()->set_concat_dim(0);
-  ConcatLayer<TypeParam> layer(layer_param);
+  ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_1, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(),
     this->blob_bottom_0->num() + this->blob_bottom_2->num());
@@ -71,8 +73,9 @@ TYPED_TEST(ConcatLayerTest, TestSetupNum) {
 }
 
 TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ConcatLayer<TypeParam> layer(layer_param);
+  ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0->num());
   EXPECT_EQ(this->blob_top_->channels(),
@@ -82,10 +85,10 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
 }
 
 
-TYPED_TEST(ConcatLayerTest, TestCPUNum) {
+TYPED_TEST(ConcatLayerTest, TestNum) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ConcatLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_0, &(this->blob_top_vec_));
   for (int n = 0; n < this->blob_top_->num(); ++n) {
@@ -108,21 +111,11 @@ TYPED_TEST(ConcatLayerTest, TestCPUNum) {
   }
 }
 
-
-TYPED_TEST(ConcatLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ConcatLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
-    &(this->blob_top_vec_));
-}
-
-TYPED_TEST(ConcatLayerTest, TestGPUGradient) {
+TYPED_TEST(ConcatLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ConcatLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
     &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index f7401015538..6f3e3146117 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -17,8 +17,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class ConvolutionLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class ConvolutionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   ConvolutionLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
@@ -51,10 +53,10 @@ class ConvolutionLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ConvolutionLayerTest, Dtypes);
+TYPED_TEST_CASE(ConvolutionLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(ConvolutionLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -63,8 +65,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
@@ -77,7 +79,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   // setting group should not change the shape
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
-  layer.reset(new ConvolutionLayer<TypeParam>(layer_param));
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
@@ -89,15 +91,16 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_2_->width(), 1);
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
+TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   // We will simply see if the convolution layer carries out averaging well.
-  shared_ptr<ConstantFiller<TypeParam> > filler;
+  typedef typename TypeParam::Dtype Dtype;
+  shared_ptr<ConstantFiller<Dtype> > filler;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler.reset(new ConstantFiller<Dtype>(filler_param));
   filler->Fill(this->blob_bottom_);
   filler_param.set_value(2.);
-  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler.reset(new ConstantFiller<Dtype>(filler_param));
   filler->Fill(this->blob_bottom_2_);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -111,13 +114,12 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
   convolution_param->mutable_weight_filler()->set_value(1);
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::CPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 27.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     EXPECT_NEAR(top_data[i], 27.1, 1e-4);
   }
@@ -127,98 +129,14 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
   }
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolution) {
-  // We will simply see if the convolution layer carries out averaging well.
-  shared_ptr<ConstantFiller<TypeParam> > filler;
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  filler.reset(new ConstantFiller<TypeParam>(filler_param));
-  filler->Fill(this->blob_bottom_);
-  filler_param.set_value(2.);
-  filler.reset(new ConstantFiller<TypeParam>(filler_param));
-  filler->Fill(this->blob_bottom_2_);
-  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
-  this->blob_top_vec_.push_back(this->blob_top_2_);
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(4);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
-  convolution_param->mutable_bias_filler()->set_type("constant");
-  convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::GPU);
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 27.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
-  }
-  top_data = this->blob_top_2_->cpu_data();
-  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
-  }
-}
-
-TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolutionGroup) {
-  // We will simply see if the convolution layer carries out averaging well.
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
-  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
-      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
-        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
-          bottom_data[this->blob_bottom_->offset(n, c, h, w)] = c;
-        }
-      }
-    }
-  }
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(3);
-  convolution_param->set_group(3);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
-  convolution_param->mutable_bias_filler()->set_type("constant");
-  convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::CPU);
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 9.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int n = 0; n < this->blob_top_->num(); ++n) {
-    for (int c = 0; c < this->blob_top_->channels(); ++c) {
-      for (int h = 0; h < this->blob_top_->height(); ++h) {
-        for (int w = 0; w < this->blob_top_->width(); ++w) {
-          TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
-          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
-        }
-      }
-    }
-  }
-}
-
-
-TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolutionGroup) {
+TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   // We will simply see if the convolution layer carries out averaging well.
+  typedef typename TypeParam::Dtype Dtype;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
+  ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
-  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
+  Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
   for (int n = 0; n < this->blob_bottom_->num(); ++n) {
     for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
@@ -239,18 +157,17 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolutionGroup) {
   convolution_param->mutable_weight_filler()->set_value(1);
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::GPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 9.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int n = 0; n < this->blob_top_->num(); ++n) {
     for (int c = 0; c < this->blob_top_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
-          TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
+          Dtype data = top_data[this->blob_top_->offset(n, c, h, w)];
           EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
         }
       }
@@ -258,43 +175,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolutionGroup) {
   }
 }
 
-
-TYPED_TEST(ConvolutionLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
-  this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(2);
-  convolution_param->mutable_weight_filler()->set_type("gaussian");
-  convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::CPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(ConvolutionLayerTest, TestCPUGradientGroup) {
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(3);
-  convolution_param->set_group(3);
-  convolution_param->mutable_weight_filler()->set_type("gaussian");
-  convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::CPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(ConvolutionLayerTest, TestGPUGradient) {
+TYPED_TEST(ConvolutionLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -305,14 +187,14 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUGradient) {
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::GPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestGPUGradientGroup) {
+TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -322,9 +204,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUGradientGroup) {
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::GPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 8ba5f29ff91..8cd157dd98a 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -20,8 +20,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class DataLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class DataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   DataLayerTest()
       : backend_(DataParameter_DB_LEVELDB),
@@ -309,54 +311,25 @@ class DataLayerTest : public ::testing::Test {
   int seed_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(DataLayerTest, Dtypes);
+TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(DataLayerTest, TestReadLevelDBCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
   this->FillLevelDB(unique_pixels);
   this->TestRead();
 }
 
-TYPED_TEST(DataLayerTest, TestReadLevelDBGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLevelDB(unique_pixels);
-  this->TestRead();
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDBCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
   this->TestReadCrop();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDBGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  this->TestReadCrop();
-}
-
-// Test that the sequence of random crops is consistent when using
-// Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDBCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  this->TestReadCropTrainSequenceSeeded();
-}
-
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDBGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
   this->TestReadCropTrainSequenceSeeded();
@@ -364,65 +337,28 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDBGPU) {
 
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDBCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
-// Test that the sequence of random crops differs across iterations when
-// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDBGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  this->TestReadCropTrainSequenceUnseeded();
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTestLevelDBCPU) {
-  Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  this->TestReadCrop();
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTestLevelDBGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
   Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
   this->TestReadCrop();
 }
 
-TYPED_TEST(DataLayerTest, TestReadLMDBCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(DataLayerTest, TestReadLMDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
   this->FillLMDB(unique_pixels);
   this->TestRead();
 }
 
-TYPED_TEST(DataLayerTest, TestReadLMDBGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLMDB(unique_pixels);
-  this->TestRead();
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTrainLMDBCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  this->TestReadCrop();
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTrainLMDBGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLMDB(unique_pixels);
   this->TestReadCrop();
@@ -430,55 +366,24 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainLMDBGPU) {
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDBCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLMDB(unique_pixels);
   this->TestReadCropTrainSequenceSeeded();
 }
 
-// Test that the sequence of random crops is consistent when using
-// Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDBGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  this->TestReadCropTrainSequenceSeeded();
-}
-
-// Test that the sequence of random crops differs across iterations when
-// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDBCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  this->TestReadCropTrainSequenceUnseeded();
-}
-
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDBGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLMDB(unique_pixels);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTestLMDBCPU) {
-  Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
-  this->TestReadCrop();
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTestLMDBGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
   Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLMDB(unique_pixels);
   this->TestReadCrop();
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 7d9287e86ab..3a83a79772c 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -46,8 +46,7 @@ class DummyDataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(DummyDataLayerTest, Dtypes);
+TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes);
 
 TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
   Caffe::set_mode(Caffe::CPU);
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 5f72f625da4..66490d2bc4e 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -16,8 +16,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class EltwiseLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class EltwiseLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   EltwiseLayerTest()
       : blob_bottom_a_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -49,15 +51,15 @@ class EltwiseLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(EltwiseLayerTest, Dtypes);
+TYPED_TEST_CASE(EltwiseLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(EltwiseLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
@@ -65,185 +67,99 @@ TYPED_TEST(EltwiseLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->width(), 5);
 }
 
-TYPED_TEST(EltwiseLayerTest, TestProdCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(EltwiseLayerTest, TestProd) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
+  const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
     EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
   }
 }
 
-TYPED_TEST(EltwiseLayerTest, TestSumCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(EltwiseLayerTest, TestSum) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
+  const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
     EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
   }
 }
 
-TYPED_TEST(EltwiseLayerTest, TestSumCoeffCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(EltwiseLayerTest, TestSumCoeff) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   eltwise_param->add_coeff(1);
   eltwise_param->add_coeff(-0.5);
   eltwise_param->add_coeff(2);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
+  const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
     EXPECT_NEAR(data[i], in_data_a[i] - 0.5*in_data_b[i] + 2*in_data_c[i],
         1e-4);
   }
 }
 
-TYPED_TEST(EltwiseLayerTest, TestProdGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(EltwiseLayerTest, TestProdGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
-  }
-}
-
-TYPED_TEST(EltwiseLayerTest, TestSumGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  LayerParameter layer_param;
-  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
-  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
-  }
-}
-
-TYPED_TEST(EltwiseLayerTest, TestSumCoeffGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  LayerParameter layer_param;
-  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
-  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
-  eltwise_param->add_coeff(1);
-  eltwise_param->add_coeff(-0.5);
-  eltwise_param->add_coeff(2);
-  shared_ptr<EltwiseLayer<TypeParam> > layer(
-      new EltwiseLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_NEAR(data[i], in_data_a[i] - 0.5*in_data_b[i] + 2*in_data_c[i],
-        1e-4);
-  }
-}
-
-TYPED_TEST(EltwiseLayerTest, TestProdCPUGradient) {
-  Caffe::set_mode(Caffe::CPU);
-  LayerParameter layer_param;
-  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
-  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
-  EltwiseLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(EltwiseLayerTest, TestSumCPUGradient) {
-  Caffe::set_mode(Caffe::CPU);
-  LayerParameter layer_param;
-  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
-  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
-  EltwiseLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(EltwiseLayerTest, TestSumCoeffCPUGradient) {
-  Caffe::set_mode(Caffe::CPU);
-  LayerParameter layer_param;
-  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
-  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
-  eltwise_param->add_coeff(1);
-  eltwise_param->add_coeff(-0.5);
-  eltwise_param->add_coeff(2);
-  EltwiseLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(EltwiseLayerTest, TestSumGPUGradient) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(EltwiseLayerTest, TestSumGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
-  EltwiseLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(EltwiseLayerTest, TestSumCoeffGPUGradient) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   eltwise_param->add_coeff(1);
   eltwise_param->add_coeff(-0.5);
   eltwise_param->add_coeff(2);
-  EltwiseLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 02945b655de..8c796945b89 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -19,8 +19,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class EuclideanLossLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   EuclideanLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -43,27 +45,17 @@ class EuclideanLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(EuclideanLossLayerTest, Dtypes);
+TYPED_TEST_CASE(EuclideanLossLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(EuclideanLossLayerTest, TestGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(EuclideanLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  EuclideanLossLayer<TypeParam> layer(layer_param);
+  EuclideanLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), -1, -1, -1);
 }
 
-TYPED_TEST(EuclideanLossLayerTest, TestGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  LayerParameter layer_param;
-  EuclideanLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), -1, -1, -1);
-}
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index e8b556a66b5..93eda7e8330 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -10,8 +10,6 @@
 
 namespace caffe {
 
-typedef ::testing::Types<float, double> Dtypes;
-
 template <typename Dtype>
 class ConstantFillerTest : public ::testing::Test {
  protected:
@@ -28,7 +26,7 @@ class ConstantFillerTest : public ::testing::Test {
   shared_ptr<ConstantFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(ConstantFillerTest, Dtypes);
+TYPED_TEST_CASE(ConstantFillerTest, TestDtypes);
 
 TYPED_TEST(ConstantFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
@@ -57,7 +55,7 @@ class UniformFillerTest : public ::testing::Test {
   shared_ptr<UniformFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(UniformFillerTest, Dtypes);
+TYPED_TEST_CASE(UniformFillerTest, TestDtypes);
 
 TYPED_TEST(UniformFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
@@ -84,7 +82,7 @@ class PositiveUnitballFillerTest : public ::testing::Test {
   shared_ptr<PositiveUnitballFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(PositiveUnitballFillerTest, Dtypes);
+TYPED_TEST_CASE(PositiveUnitballFillerTest, TestDtypes);
 
 TYPED_TEST(PositiveUnitballFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
@@ -123,7 +121,7 @@ class GaussianFillerTest : public ::testing::Test {
   shared_ptr<GaussianFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(GaussianFillerTest, Dtypes);
+TYPED_TEST_CASE(GaussianFillerTest, TestDtypes);
 
 TYPED_TEST(GaussianFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 52c567b0295..e6e777e69c0 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -17,8 +17,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class FlattenLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class FlattenLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   FlattenLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -38,12 +39,12 @@ class FlattenLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(FlattenLayerTest, Dtypes);
+TYPED_TEST_CASE(FlattenLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(FlattenLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  FlattenLayer<TypeParam> layer(layer_param);
+  FlattenLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
@@ -51,10 +52,10 @@ TYPED_TEST(FlattenLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 1);
 }
 
-TYPED_TEST(FlattenLayerTest, TestCPU) {
+TYPED_TEST(FlattenLayerTest, Test) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  FlattenLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  FlattenLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int c = 0; c < 3 * 6 * 5; ++c) {
@@ -65,34 +66,11 @@ TYPED_TEST(FlattenLayerTest, TestCPU) {
   }
 }
 
-TYPED_TEST(FlattenLayerTest, TestGPU) {
+TYPED_TEST(FlattenLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  FlattenLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int c = 0; c < 3 * 6 * 5; ++c) {
-    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
-        this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
-    EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
-        this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
-  }
-}
-
-TYPED_TEST(FlattenLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  FlattenLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(FlattenLayerTest, TestGPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  FlattenLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 5b396ef551e..6fd9a2fc194 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -19,8 +19,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template<typename Dtype>
-class HDF5OutputLayerTest : public ::testing::Test {
+template<typename TypeParam>
+class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   HDF5OutputLayerTest()
       : output_file_name_(tmpnam(NULL)),
@@ -52,9 +54,9 @@ class HDF5OutputLayerTest : public ::testing::Test {
   int width_;
 };
 
-template<typename Dtype>
-void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(const Blob<Dtype>& b1,
-                                                const Blob<Dtype>& b2) {
+template<typename TypeParam>
+void HDF5OutputLayerTest<TypeParam>::CheckBlobEqual(const Blob<Dtype>& b1,
+                                                    const Blob<Dtype>& b2) {
   EXPECT_EQ(b1.num(), b2.num());
   EXPECT_EQ(b1.channels(), b2.channels());
   EXPECT_EQ(b1.height(), b2.height());
@@ -70,14 +72,10 @@ void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(const Blob<Dtype>& b1,
   }
 }
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(HDF5OutputLayerTest, Dtypes);
+TYPED_TEST_CASE(HDF5OutputLayerTest, TestDtypesAndDevices);
 
-/*
- * TestCPUForward and TestGPUForward are almost identical except for the mode.
- * They are separated to use with `test_all.testbin --gtest_filter="*CPU*"`.
- */
-TYPED_TEST(HDF5OutputLayerTest, TestCPUForward) {
+TYPED_TEST(HDF5OutputLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LOG(INFO) << "Loading HDF5 file " << this->input_file_name_;
   hid_t file_id = H5Fopen(this->input_file_name_.c_str(), H5F_ACC_RDONLY,
                           H5P_DEFAULT);
@@ -93,62 +91,12 @@ TYPED_TEST(HDF5OutputLayerTest, TestCPUForward) {
   this->blob_bottom_vec_.push_back(this->blob_data_);
   this->blob_bottom_vec_.push_back(this->blob_label_);
 
-  Caffe::set_mode(Caffe::CPU);
-  LayerParameter param;
-  param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
-  // This code block ensures that the layer is deconstructed and
-  //   the output hdf5 file is closed.
-  {
-    HDF5OutputLayer<TypeParam> layer(param);
-    EXPECT_EQ(layer.file_name(), this->output_file_name_);
-    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-  }
-  file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
-                          H5P_DEFAULT);
-  ASSERT_GE(
-    file_id, 0)<< "Failed to open HDF5 file" <<
-          this->input_file_name_;
-
-  Blob<TypeParam>* blob_data = new Blob<TypeParam>();
-  hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                       blob_data);
-  this->CheckBlobEqual(*(this->blob_data_), *blob_data);
-
-  Blob<TypeParam>* blob_label = new Blob<TypeParam>();
-  hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                       blob_label);
-  this->CheckBlobEqual(*(this->blob_label_), *blob_label);
-
-  status = H5Fclose(file_id);
-  EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
-      this->output_file_name_;
-}
-
-TYPED_TEST(HDF5OutputLayerTest, TestGPUForward) {
-  LOG(INFO) << "Loading HDF5 file " << this->input_file_name_;
-
-  hid_t file_id = H5Fopen(this->input_file_name_.c_str(), H5F_ACC_RDONLY,
-                          H5P_DEFAULT);
-  ASSERT_GE(file_id, 0) << "Failed to open HDF5 file" <<
-      this->input_file_name_;
-  hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                       this->blob_data_);
-  hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                       this->blob_label_);
-  herr_t status = H5Fclose(file_id);
-  EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
-      this->input_file_name_;
-  this->blob_bottom_vec_.push_back(this->blob_data_);
-  this->blob_bottom_vec_.push_back(this->blob_label_);
-
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter param;
   param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
   // This code block ensures that the layer is deconstructed and
   //   the output hdf5 file is closed.
   {
-    HDF5OutputLayer<TypeParam> layer(param);
+    HDF5OutputLayer<Dtype> layer(param);
     EXPECT_EQ(layer.file_name(), this->output_file_name_);
     layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
     layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
@@ -159,12 +107,12 @@ TYPED_TEST(HDF5OutputLayerTest, TestGPUForward) {
     file_id, 0)<< "Failed to open HDF5 file" <<
           this->input_file_name_;
 
-  Blob<TypeParam>* blob_data = new Blob<TypeParam>();
+  Blob<Dtype>* blob_data = new Blob<Dtype>();
   hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
                        blob_data);
   this->CheckBlobEqual(*(this->blob_data_), *blob_data);
 
-  Blob<TypeParam>* blob_label = new Blob<TypeParam>();
+  Blob<Dtype>* blob_label = new Blob<Dtype>();
   hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
                        blob_label);
   this->CheckBlobEqual(*(this->blob_label_), *blob_label);
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 1cbca0090fd..3eb24210d99 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -20,8 +20,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class HDF5DataLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   HDF5DataLayerTest()
       : filename(NULL),
@@ -49,10 +51,10 @@ class HDF5DataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(HDF5DataLayerTest, Dtypes);
+TYPED_TEST_CASE(HDF5DataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(HDF5DataLayerTest, TestRead) {
+  typedef typename TypeParam::Dtype Dtype;
   // Create LayerParameter with the known parameters.
   // The data file we are reading has 10 rows and 8 columns,
   // with values from 0 to 10*8 reshaped in row-major order.
@@ -66,7 +68,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   int width = 5;
 
   // Test that the layer setup got the correct parameters.
-  HDF5DataLayer<TypeParam> layer(param);
+  HDF5DataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), batch_size);
   EXPECT_EQ(this->blob_top_data_->channels(), num_cols);
@@ -78,49 +80,41 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_label_->height(), 1);
   EXPECT_EQ(this->blob_top_label_->width(), 1);
 
-  for (int t = 0; t < 2; ++t) {
-    // TODO: make this a TypedTest instead of this silly loop.
-    if (t == 0) {
-      Caffe::set_mode(Caffe::CPU);
-    } else {
-      Caffe::set_mode(Caffe::GPU);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+
+  // Go through the data 10 times (5 batches).
+  const int data_size = num_cols * height * width;
+  for (int iter = 0; iter < 10; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+
+    // On even iterations, we're reading the first half of the data.
+    // On odd iterations, we're reading the second half of the data.
+    int label_offset = (iter % 2 == 0) ? 0 : batch_size;
+    int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
+
+    // Every two iterations we are reading the second file,
+    // which has the same labels, but data is offset by total data size,
+    // which is 2000 (see generate_sample_data).
+    int file_offset = (iter % 4 < 2) ? 0 : 2000;
+
+    for (int i = 0; i < batch_size; ++i) {
+      EXPECT_EQ(
+        label_offset + i,
+        this->blob_top_label_->cpu_data()[i]);
     }
-    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-
-    // Go through the data 10 times (5 batches).
-    const int data_size = num_cols * height * width;
-    for (int iter = 0; iter < 10; ++iter) {
-      layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-
-      // On even iterations, we're reading the first half of the data.
-      // On odd iterations, we're reading the second half of the data.
-      int label_offset = (iter % 2 == 0) ? 0 : batch_size;
-      int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
-
-      // Every two iterations we are reading the second file,
-      // which has the same labels, but data is offset by total data size,
-      // which is 2000 (see generate_sample_data).
-      int file_offset = (iter % 4 < 2) ? 0 : 2000;
-
-      for (int i = 0; i < batch_size; ++i) {
-        EXPECT_EQ(
-          label_offset + i,
-          this->blob_top_label_->cpu_data()[i]);
-      }
-      for (int i = 0; i < batch_size; ++i) {
-        for (int j = 0; j < num_cols; ++j) {
-          for (int h = 0; h < height; ++h) {
-            for (int w = 0; w < width; ++w) {
-              int idx = (
-                i * num_cols * height * width +
-                j * height * width +
-                h * width + w);
-              EXPECT_EQ(
-                file_offset + data_offset + idx,
-                this->blob_top_data_->cpu_data()[idx])
-                << "debug: i " << i << " j " << j
-                << " iter " << iter << " t " << t;
-            }
+    for (int i = 0; i < batch_size; ++i) {
+      for (int j = 0; j < num_cols; ++j) {
+        for (int h = 0; h < height; ++h) {
+          for (int w = 0; w < width; ++w) {
+            int idx = (
+              i * num_cols * height * width +
+              j * height * width +
+              h * width + w);
+            EXPECT_EQ(
+              file_offset + data_offset + idx,
+              this->blob_top_data_->cpu_data()[idx])
+              << "debug: i " << i << " j " << j
+              << " iter " << iter;
           }
         }
       }
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 318030b67a3..868e9e9ae16 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -19,8 +19,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class HingeLossLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class HingeLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   HingeLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -46,56 +48,31 @@ class HingeLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(HingeLossLayerTest, Dtypes);
+TYPED_TEST_CASE(HingeLossLayerTest, TestDtypesAndDevices);
 
 
-TYPED_TEST(HingeLossLayerTest, TestGradientL1CPU) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL1) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  HingeLossLayer<TypeParam> layer(layer_param);
+  HingeLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 1, 0.01);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 1, 0.01);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
-TYPED_TEST(HingeLossLayerTest, TestGradientL1GPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  HingeLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 1, 0.01);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
-}
-
-
-TYPED_TEST(HingeLossLayerTest, TestGradientL2CPU) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   // Set norm to L2
   HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
   hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
-  Caffe::set_mode(Caffe::CPU);
-  HingeLossLayer<TypeParam> layer(layer_param);
+  HingeLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701);
+  GradientChecker<Dtype> checker(1e-2, 2e-3, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
 
-TYPED_TEST(HingeLossLayerTest, TestGradientL2GPU) {
-  LayerParameter layer_param;
-  // Set norm to L2
-  HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
-  hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
-  Caffe::set_mode(Caffe::GPU);
-  HingeLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 2e-3, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
-}
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index f49a5b7e4d9..bd4404a0000 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -65,8 +65,7 @@ class Im2colKernelTest : public ::testing::Test {
   int width_col_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(Im2colKernelTest, Dtypes);
+TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
 
 TYPED_TEST(Im2colKernelTest, TestGPU) {
   Caffe::set_mode(Caffe::GPU);
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 7f677ca03d6..5be191742fc 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -17,8 +17,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class Im2colLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   Im2colLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -37,16 +38,16 @@ class Im2colLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(Im2colLayerTest, Dtypes);
+TYPED_TEST_CASE(Im2colLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(Im2colLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  Im2colLayer<TypeParam> layer(layer_param);
+  Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 27);
@@ -54,31 +55,14 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
-TYPED_TEST(Im2colLayerTest, TestCPU) {
+TYPED_TEST(Im2colLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  Im2colLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // We are lazy and will only check the top left block
-  for (int c = 0; c < 27; ++c) {
-    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
-        this->blob_bottom_->data_at(0, (c / 9), (c / 3) % 3, c % 3));
-  }
-}
-
-TYPED_TEST(Im2colLayerTest, TestGPU) {
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  Im2colLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
+  Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // We are lazy and will only check the top left block
@@ -88,28 +72,15 @@ TYPED_TEST(Im2colLayerTest, TestGPU) {
   }
 }
 
-TYPED_TEST(Im2colLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  Caffe::set_mode(Caffe::CPU);
-  Im2colLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(Im2colLayerTest, TestGPUGradient) {
+TYPED_TEST(Im2colLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  Caffe::set_mode(Caffe::GPU);
-  Im2colLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 2278f4e2eb8..fbd4a1ca9d4 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -23,8 +23,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class ImageDataLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   ImageDataLayerTest()
       : seed_(1701),
@@ -57,16 +59,16 @@ class ImageDataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ImageDataLayerTest, Dtypes);
+TYPED_TEST_CASE(ImageDataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(ImageDataLayerTest, TestRead) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
   image_data_param->set_source(this->filename_->c_str());
   image_data_param->set_shuffle(false);
-  ImageDataLayer<TypeParam> layer(param);
+  ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
@@ -86,6 +88,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
 }
 
 TYPED_TEST(ImageDataLayerTest, TestResize) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
@@ -93,7 +96,7 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   image_data_param->set_new_height(256);
   image_data_param->set_new_width(256);
   image_data_param->set_shuffle(false);
-  ImageDataLayer<TypeParam> layer(param);
+  ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
@@ -113,12 +116,13 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
 }
 
 TYPED_TEST(ImageDataLayerTest, TestShuffle) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
   image_data_param->set_source(this->filename_->c_str());
   image_data_param->set_shuffle(true);
-  ImageDataLayer<TypeParam> layer(param);
+  ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
@@ -131,14 +135,14 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
     layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    map<TypeParam, int> values_to_indices;
+    map<Dtype, int> values_to_indices;
     int num_in_order = 0;
     for (int i = 0; i < 5; ++i) {
-      TypeParam value = this->blob_top_label_->cpu_data()[i];
+      Dtype value = this->blob_top_label_->cpu_data()[i];
       // Check that the value has not been seen already (no duplicates).
       EXPECT_EQ(values_to_indices.find(value), values_to_indices.end());
       values_to_indices[value] = i;
-      num_in_order += (value == TypeParam(i));
+      num_in_order += (value == Dtype(i));
     }
     EXPECT_EQ(5, values_to_indices.size());
     EXPECT_GT(5, num_in_order);
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index 91917df6cae..ad4783f991a 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -17,8 +17,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class InnerProductLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class InnerProductLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   InnerProductLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -37,16 +38,16 @@ class InnerProductLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(InnerProductLayerTest, Dtypes);
+TYPED_TEST_CASE(InnerProductLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(InnerProductLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   InnerProductParameter* inner_product_param =
       layer_param.mutable_inner_product_param();
   inner_product_param->set_num_output(10);
-  shared_ptr<InnerProductLayer<TypeParam> > layer(
-      new InnerProductLayer<TypeParam>(layer_param));
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -54,43 +55,23 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->channels(), 10);
 }
 
-TYPED_TEST(InnerProductLayerTest, TestCPU) {
-  LayerParameter layer_param;
-  InnerProductParameter* inner_product_param =
-      layer_param.mutable_inner_product_param();
-  Caffe::set_mode(Caffe::CPU);
-  inner_product_param->set_num_output(10);
-  inner_product_param->mutable_weight_filler()->set_type("uniform");
-  inner_product_param->mutable_bias_filler()->set_type("uniform");
-  inner_product_param->mutable_bias_filler()->set_min(1);
-  inner_product_param->mutable_bias_filler()->set_max(2);
-  shared_ptr<InnerProductLayer<TypeParam> > layer(
-      new InnerProductLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], 1.);
-  }
-}
-
-TYPED_TEST(InnerProductLayerTest, TestGPU) {
-  if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
+TYPED_TEST(InnerProductLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
     LayerParameter layer_param;
     InnerProductParameter* inner_product_param =
         layer_param.mutable_inner_product_param();
-    Caffe::set_mode(Caffe::GPU);
     inner_product_param->set_num_output(10);
     inner_product_param->mutable_weight_filler()->set_type("uniform");
     inner_product_param->mutable_bias_filler()->set_type("uniform");
     inner_product_param->mutable_bias_filler()->set_min(1);
     inner_product_param->mutable_bias_filler()->set_max(2);
-    shared_ptr<InnerProductLayer<TypeParam> > layer(
-      new InnerProductLayer<TypeParam>(layer_param));
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
     layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
     layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    const TypeParam* data = this->blob_top_->cpu_data();
+    const Dtype* data = this->blob_top_->cpu_data();
     const int count = this->blob_top_->count();
     for (int i = 0; i < count; ++i) {
       EXPECT_GE(data[i], 1.);
@@ -100,34 +81,21 @@ TYPED_TEST(InnerProductLayerTest, TestGPU) {
   }
 }
 
-TYPED_TEST(InnerProductLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  InnerProductParameter* inner_product_param =
-      layer_param.mutable_inner_product_param();
-  Caffe::set_mode(Caffe::CPU);
-  inner_product_param->set_num_output(10);
-  inner_product_param->mutable_weight_filler()->set_type("gaussian");
-  inner_product_param->mutable_bias_filler()->set_type("gaussian");
-  inner_product_param->mutable_bias_filler()->set_min(1);
-  inner_product_param->mutable_bias_filler()->set_max(2);
-  InnerProductLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(InnerProductLayerTest, TestGPUGradient) {
-  if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
+TYPED_TEST(InnerProductLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
     LayerParameter layer_param;
     InnerProductParameter* inner_product_param =
         layer_param.mutable_inner_product_param();
-    Caffe::set_mode(Caffe::GPU);
     inner_product_param->set_num_output(10);
     inner_product_param->mutable_weight_filler()->set_type("gaussian");
     inner_product_param->mutable_bias_filler()->set_type("gaussian");
-    InnerProductLayer<TypeParam> layer(layer_param);
-    GradientChecker<TypeParam> checker(1e-2, 1e-2);
-    checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    InnerProductLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
         &(this->blob_top_vec_));
   } else {
     LOG(ERROR) << "Skipping test due to old architecture.";
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 6996a234fc6..a627c97f40f 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -21,8 +21,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class LRNLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class LRNLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   LRNLayerTest()
       : epsilon_(Dtype(1e-5)),
@@ -49,10 +51,11 @@ class LRNLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-template <typename Dtype>
-void LRNLayerTest<Dtype>::ReferenceLRNForward(
+template <typename TypeParam>
+void LRNLayerTest<TypeParam>::ReferenceLRNForward(
     const Blob<Dtype>& blob_bottom, const LayerParameter& layer_param,
     Blob<Dtype>* blob_top) {
+  typedef typename TypeParam::Dtype Dtype;
   blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
       blob_bottom.height(), blob_bottom.width());
   Dtype* top_data = blob_top->mutable_cpu_data();
@@ -111,12 +114,12 @@ void LRNLayerTest<Dtype>::ReferenceLRNForward(
   }
 }
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(LRNLayerTest, Dtypes);
+TYPED_TEST_CASE(LRNLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
@@ -124,28 +127,13 @@ TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
-TYPED_TEST(LRNLayerTest, TestCPUForwardAcrossChannels) {
-  LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
-  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
-      &top_reference);
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
-                this->epsilon_);
-  }
-}
-
-TYPED_TEST(LRNLayerTest, TestGPUForwardAcrossChannels) {
+TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
+  Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -154,11 +142,11 @@ TYPED_TEST(LRNLayerTest, TestGPUForwardAcrossChannels) {
   }
 }
 
-TYPED_TEST(LRNLayerTest, TestCPUGradientAcrossChannels) {
+TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::CPU);
+  LRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
@@ -175,33 +163,13 @@ TYPED_TEST(LRNLayerTest, TestCPUGradientAcrossChannels) {
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(LRNLayerTest, TestGPUGradientAcrossChannels) {
-  LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }
-  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
-  layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
-  // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-  //   std::cout << "GPU diff " << this->blob_bottom_->cpu_diff()[i]
-  //       << std::endl;
-  // }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
 TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_lrn_param()->set_norm_region(
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
@@ -209,16 +177,16 @@ TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
-TYPED_TEST(LRNLayerTest, TestCPUForwardWithinChannel) {
+TYPED_TEST(LRNLayerTest, TestForwardWithinChannel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_lrn_param()->set_norm_region(
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
+  Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -227,49 +195,14 @@ TYPED_TEST(LRNLayerTest, TestCPUForwardWithinChannel) {
   }
 }
 
-TYPED_TEST(LRNLayerTest, TestGPUForwardWithinChannel) {
-  LayerParameter layer_param;
-  layer_param.mutable_lrn_param()->set_norm_region(
-      LRNParameter_NormRegion_WITHIN_CHANNEL);
-  layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
-  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
-      &top_reference);
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
-                this->epsilon_);
-  }
-}
-
-TYPED_TEST(LRNLayerTest, TestCPUGradientWithinChannel) {
-  LayerParameter layer_param;
-  layer_param.mutable_lrn_param()->set_norm_region(
-      LRNParameter_NormRegion_WITHIN_CHANNEL);
-  layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(LRNLayerTest, TestGPUGradientWithinChannel) {
+TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_lrn_param()->set_norm_region(
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::GPU);
+  LRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index ddb9f060972..2a70c9c4aba 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -65,8 +65,7 @@ class MathFunctionsTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MathFunctionsTest, Dtypes);
+TYPED_TEST_CASE(MathFunctionsTest, TestDtypes);
 
 TYPED_TEST(MathFunctionsTest, TestNothing) {
   // The first test case of a test suite takes the longest time
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index ac6f86f0cf8..eef375acea7 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -16,8 +16,9 @@
 
 namespace caffe {
 
-template <typename Dtype>
-class MaxPoolingDropoutTest : public ::testing::Test {
+template <typename TypeParam>
+class MaxPoolingDropoutTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   MaxPoolingDropoutTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -40,17 +41,17 @@ class MaxPoolingDropoutTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MaxPoolingDropoutTest, Dtypes);
+TYPED_TEST_CASE(MaxPoolingDropoutTest, TestDtypesAndDevices);
 
 TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> max_layer(layer_param);
+  PoolingLayer<Dtype> max_layer(layer_param);
   max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  DropoutLayer<Dtype> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -59,27 +60,27 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
 }
 
 
-TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  TypeParam sum = 0.;
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  Dtype sum = 0.;
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     sum += top_data[i];
   }
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
-  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  DropoutLayer<Dtype> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
   sum = 0.;
-  TypeParam scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
+  Dtype scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     sum += top_data[i];
@@ -88,81 +89,14 @@ TYPED_TEST(MaxPoolingDropoutTest, CPUForward) {
   EXPECT_LE(sum, this->blob_top_->count()*scale);
 }
 
-TYPED_TEST(MaxPoolingDropoutTest, GPUForward) {
-  Caffe::set_mode(Caffe::GPU);
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  TypeParam sum = 0.;
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    sum += top_data[i];
-  }
-  EXPECT_EQ(sum, this->blob_top_->count());
-
-  DropoutLayer<TypeParam> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
-  sum = 0.;
-  TypeParam scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
-  top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    sum += top_data[i];
-  }
-  EXPECT_GE(sum, 0);
-  EXPECT_LE(sum, this->blob_top_->count()*scale);
-}
-
-TYPED_TEST(MaxPoolingDropoutTest, CPUBackward) {
-  Caffe::set_mode(Caffe::CPU);
-  Caffe::set_phase(Caffe::TRAIN);
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }
-  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
-  layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
-  const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
-  TypeParam sum = 0.;
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    sum += bottom_diff[i];
-  }
-  EXPECT_EQ(sum, this->blob_top_->count());
-  // Dropout in-place
-  DropoutLayer<TypeParam> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Backward(this->blob_top_vec_, propagate_down,
-                         &(this->blob_top_vec_));
-  layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
-  TypeParam sum_with_dropout = 0.;
-  bottom_diff = this->blob_bottom_->cpu_diff();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    sum_with_dropout += bottom_diff[i];
-  }
-  EXPECT_GE(sum_with_dropout, sum);
-}
-
-TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
+  typedef typename TypeParam::Dtype Dtype;
   Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
@@ -171,21 +105,21 @@ TYPED_TEST(MaxPoolingDropoutTest, GPUBackward) {
   vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
   layer.Backward(this->blob_top_vec_, propagate_down,
                  &(this->blob_bottom_vec_));
-  const TypeParam* bottom_diff = this->blob_bottom_->cpu_diff();
-  TypeParam sum = 0.;
+  const Dtype* bottom_diff = this->blob_bottom_->cpu_diff();
+  Dtype sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     sum += bottom_diff[i];
   }
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
-  DropoutLayer<TypeParam> dropout_layer(layer_param);
+  DropoutLayer<Dtype> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
   dropout_layer.Backward(this->blob_top_vec_, propagate_down,
                          &(this->blob_top_vec_));
   layer.Backward(this->blob_top_vec_, propagate_down,
                  &(this->blob_bottom_vec_));
-  TypeParam sum_with_dropout = 0.;
+  Dtype sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     sum_with_dropout += bottom_diff[i];
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 9781439f2fe..c9d588986c2 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -55,8 +55,7 @@ class MemoryDataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MemoryDataLayerTest, Dtypes);
+TYPED_TEST_CASE(MemoryDataLayerTest, TestDtypes);
 
 TYPED_TEST(MemoryDataLayerTest, TestSetup) {
   LayerParameter layer_param;
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index aa475ca27c7..d73347e8e2e 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -46,8 +46,7 @@ class MultinomialLogisticLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, Dtypes);
+TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes);
 
 
 TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 18b03476996..129d4447c41 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -16,8 +16,10 @@
 
 namespace caffe {
 
-template <typename Dtype>
+template <typename TypeParam>
 class NetTest : public ::testing::Test {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   NetTest() : seed_(1701) {}
 
@@ -456,8 +458,7 @@ class NetTest : public ::testing::Test {
   shared_ptr<Net<Dtype> > net_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(NetTest, Dtypes);
+TYPED_TEST_CASE(NetTest, TestDtypesAndDevices);
 
 TYPED_TEST(NetTest, TestHasBlob) {
   this->InitTinyNet();
@@ -507,6 +508,7 @@ TYPED_TEST(NetTest, TestBottomNeedBackward) {
 }
 
 TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
+  typedef typename TypeParam::Dtype Dtype;
   const bool force_backward = true;
   this->InitTinyNet(force_backward);
   const vector<vector<bool> >& bottom_need_backward =
@@ -521,6 +523,7 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
 }
 
 TYPED_TEST(NetTest, TestBottomNeedBackwardEuclideanForce) {
+  typedef typename TypeParam::Dtype Dtype;
   const bool force_backward = true;
   this->InitTinyNetEuclidean(force_backward);
   const vector<vector<bool> >& bottom_need_backward =
@@ -553,32 +556,35 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
+  typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<TypeParam>*> bottom;
-  TypeParam loss;
+  vector<Blob<Dtype>*> bottom;
+  Dtype loss;
   this->net_->Forward(bottom, &loss);
   EXPECT_GT(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
+  typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<TypeParam>*> bottom;
-  TypeParam loss;
+  vector<Blob<Dtype>*> bottom;
+  Dtype loss;
   this->net_->Forward(bottom, &loss);
   EXPECT_FLOAT_EQ(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
+  typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<TypeParam>*> bottom;
-  Net<TypeParam>* net = this->net_.get();
+  vector<Blob<Dtype>*> bottom;
+  Net<Dtype>* net = this->net_.get();
   net->Forward(bottom);
   net->Backward();
-  Layer<TypeParam>* ip1_layer = net->layer_by_name("innerproduct1").get();
-  Layer<TypeParam>* ip2_layer = net->layer_by_name("innerproduct2").get();
+  Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
+  Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
   const int count = ip1_layer->blobs()[0]->count();
-  const TypeParam* grad1 = ip1_layer->blobs()[0]->cpu_diff();
-  const TypeParam* grad2 = ip2_layer->blobs()[0]->cpu_diff();
+  const Dtype* grad1 = ip1_layer->blobs()[0]->cpu_diff();
+  const Dtype* grad2 = ip2_layer->blobs()[0]->cpu_diff();
   for (int i = 0; i < count; ++i) {
     EXPECT_GT(fabs(grad1[i]), 0);
     EXPECT_FLOAT_EQ(-1 * grad1[i], grad2[i]);
@@ -586,120 +592,34 @@ TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
 }
 
 TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
+  typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<TypeParam>*> bottom;
-  Net<TypeParam>* net = this->net_.get();
-  TypeParam loss;
+  vector<Blob<Dtype>*> bottom;
+  Net<Dtype>* net = this->net_.get();
+  Dtype loss;
   net->Forward(bottom, &loss);
   net->Backward();
   EXPECT_FLOAT_EQ(loss, 0);
-  Layer<TypeParam>* ip1_layer = net->layer_by_name("innerproduct1").get();
-  Layer<TypeParam>* ip2_layer = net->layer_by_name("innerproduct2").get();
+  Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
+  Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
   const int count = ip1_layer->blobs()[0]->count();
-  const TypeParam* grad1 = ip1_layer->blobs()[0]->cpu_diff();
-  const TypeParam* grad2 = ip2_layer->blobs()[0]->cpu_diff();
+  const Dtype* grad1 = ip1_layer->blobs()[0]->cpu_diff();
+  const Dtype* grad2 = ip2_layer->blobs()[0]->cpu_diff();
   for (int i = 0; i < count; ++i) {
     EXPECT_FLOAT_EQ(0, grad1[i]);
     EXPECT_FLOAT_EQ(0, grad2[i]);
   }
 }
 
-TYPED_TEST(NetTest, TestSharedWeightsUpdateCPU) {
-  Caffe::set_random_seed(this->seed_);
-  Caffe::set_mode(Caffe::CPU);
-  this->InitDiffDataSharedWeightsNet();
-  vector<Blob<TypeParam>*> bottom;
-  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
-  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
-  Blob<TypeParam>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
-  Blob<TypeParam>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
-  // Check that data blobs of shared weights share the same location in memory.
-  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-  // Check that diff blobs of shared weights are at different locations in
-  // locations.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
-  this->net_->Backward();
-  // Compute the expected update as the data minus the two diffs.
-  Blob<TypeParam> shared_params;
-  const bool reshape = true;
-  const bool copy_diff = false;
-  shared_params.CopyFrom(*ip1_weights, copy_diff, reshape);
-  shared_params.CopyFrom(*ip1_weights, !copy_diff, reshape);
-  const int count = ip1_weights->count();
-  // Make sure the diffs are non-trivial.
-  for (int i = 0; i < count; ++i) {
-    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
-    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
-    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
-  }
-  caffe_axpy(count, TypeParam(1), ip2_weights->cpu_diff(),
-             shared_params.mutable_cpu_diff());
-  caffe_axpy(count, TypeParam(-1), shared_params.cpu_diff(),
-             shared_params.mutable_cpu_data());
-  const TypeParam* expected_updated_params = shared_params.cpu_data();
-  this->net_->Update();
-  const TypeParam* actual_updated_params = ip1_weights->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(expected_updated_params[i], actual_updated_params[i]);
-  }
-  // Check that data blobs of shared weights STILL point to the same memory
-  // location (because ... who knows).
-  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-
-  Caffe::set_random_seed(this->seed_);
-  this->InitDiffDataUnsharedWeightsNet();
-  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
-  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
-  ip1_weights = this->net_->layers()[1]->blobs()[0].get();
-  ip2_weights = this->net_->layers()[2]->blobs()[0].get();
-  // Check that data and diff blobs of unshared weights are at different
-  // locations in memory.
-  EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
-  this->net_->Backward();
-  // Compute the expected update.
-  Blob<TypeParam> unshared_params1;
-  unshared_params1.CopyFrom(*ip1_weights, copy_diff, reshape);
-  unshared_params1.CopyFrom(*ip1_weights, !copy_diff, reshape);
-  Blob<TypeParam> unshared_params2;
-  unshared_params2.CopyFrom(*ip2_weights, copy_diff, reshape);
-  unshared_params2.CopyFrom(*ip2_weights, !copy_diff, reshape);
-  // Make sure the diffs are non-trivial and sum to the diff in the shared net.
-  for (int i = 0; i < count; ++i) {
-    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
-    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
-    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
-    EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
-              shared_params.cpu_diff()[i]);
-  }
-  caffe_axpy(count, TypeParam(-1), ip1_weights->cpu_diff(),
-             unshared_params1.mutable_cpu_data());
-  caffe_axpy(count, TypeParam(-1), ip2_weights->cpu_diff(),
-             unshared_params2.mutable_cpu_data());
-  const TypeParam* expected_updated_params1 = unshared_params1.cpu_data();
-  const TypeParam* expected_updated_params2 = unshared_params2.cpu_data();
-  this->net_->Update();
-  const TypeParam* actual_updated_params1 = ip1_weights->cpu_data();
-  const TypeParam* actual_updated_params2 = ip2_weights->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(expected_updated_params1[i], actual_updated_params1[i]);
-    EXPECT_EQ(expected_updated_params2[i], actual_updated_params2[i]);
-    EXPECT_NE(actual_updated_params1[i], actual_updated_params2[i]);
-    EXPECT_NE(expected_updated_params, expected_updated_params1);
-  }
-}
-
-TYPED_TEST(NetTest, TestSharedWeightsUpdateGPU) {
+TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
   Caffe::set_random_seed(this->seed_);
-  Caffe::set_mode(Caffe::GPU);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<TypeParam>*> bottom;
+  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
-  Blob<TypeParam>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
-  Blob<TypeParam>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
   // Check that data blobs of shared weights share the same location in memory.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   // Check that diff blobs of shared weights are at different locations in
@@ -708,7 +628,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdateGPU) {
   this->net_->Forward(bottom);
   this->net_->Backward();
   // Compute the expected update as the data minus the two diffs.
-  Blob<TypeParam> shared_params;
+  Blob<Dtype> shared_params;
   const bool reshape = true;
   const bool copy_diff = false;
   shared_params.CopyFrom(*ip1_weights, copy_diff, reshape);
@@ -720,13 +640,13 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdateGPU) {
     EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
     EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
   }
-  caffe_axpy(count, TypeParam(1), ip2_weights->cpu_diff(),
+  caffe_axpy(count, Dtype(1), ip2_weights->cpu_diff(),
              shared_params.mutable_cpu_diff());
-  caffe_axpy(count, TypeParam(-1), shared_params.cpu_diff(),
+  caffe_axpy(count, Dtype(-1), shared_params.cpu_diff(),
              shared_params.mutable_cpu_data());
-  const TypeParam* expected_updated_params = shared_params.cpu_data();
+  const Dtype* expected_updated_params = shared_params.cpu_data();
   this->net_->Update();
-  const TypeParam* actual_updated_params = ip1_weights->cpu_data();
+  const Dtype* actual_updated_params = ip1_weights->cpu_data();
   for (int i = 0; i < count; ++i) {
     EXPECT_EQ(expected_updated_params[i], actual_updated_params[i]);
   }
@@ -747,10 +667,10 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdateGPU) {
   this->net_->Forward(bottom);
   this->net_->Backward();
   // Compute the expected update.
-  Blob<TypeParam> unshared_params1;
+  Blob<Dtype> unshared_params1;
   unshared_params1.CopyFrom(*ip1_weights, copy_diff, reshape);
   unshared_params1.CopyFrom(*ip1_weights, !copy_diff, reshape);
-  Blob<TypeParam> unshared_params2;
+  Blob<Dtype> unshared_params2;
   unshared_params2.CopyFrom(*ip2_weights, copy_diff, reshape);
   unshared_params2.CopyFrom(*ip2_weights, !copy_diff, reshape);
   // Make sure the diffs are non-trivial and sum to the diff in the shared net.
@@ -761,15 +681,15 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdateGPU) {
     EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
               shared_params.cpu_diff()[i]);
   }
-  caffe_axpy(count, TypeParam(-1), ip1_weights->cpu_diff(),
+  caffe_axpy(count, Dtype(-1), ip1_weights->cpu_diff(),
              unshared_params1.mutable_cpu_data());
-  caffe_axpy(count, TypeParam(-1), ip2_weights->cpu_diff(),
+  caffe_axpy(count, Dtype(-1), ip2_weights->cpu_diff(),
              unshared_params2.mutable_cpu_data());
-  const TypeParam* expected_updated_params1 = unshared_params1.cpu_data();
-  const TypeParam* expected_updated_params2 = unshared_params2.cpu_data();
+  const Dtype* expected_updated_params1 = unshared_params1.cpu_data();
+  const Dtype* expected_updated_params2 = unshared_params2.cpu_data();
   this->net_->Update();
-  const TypeParam* actual_updated_params1 = ip1_weights->cpu_data();
-  const TypeParam* actual_updated_params2 = ip2_weights->cpu_data();
+  const Dtype* actual_updated_params1 = ip1_weights->cpu_data();
+  const Dtype* actual_updated_params2 = ip2_weights->cpu_data();
   for (int i = 0; i < count; ++i) {
     EXPECT_EQ(expected_updated_params1[i], actual_updated_params1[i]);
     EXPECT_EQ(expected_updated_params2[i], actual_updated_params2[i]);
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index a439128a13b..f44471843a9 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -17,8 +17,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class NeuronLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   NeuronLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -38,97 +39,41 @@ class NeuronLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(NeuronLayerTest, Dtypes);
+TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(NeuronLayerTest, TestReLUCPU) {
+TYPED_TEST(NeuronLayerTest, TestReLU) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ReLULayer<TypeParam> layer(layer_param);
+  ReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestReLUGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ReLULayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestReLUGPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ReLULayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestReLUGradientGPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ReLULayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestSigmoidCPU) {
+TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_FLOAT_EQ(top_data[i], 1. / (1 + exp(-bottom_data[i])));
-    // check that we squashed the value between 0 and 1
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_LE(top_data[i], 1.);
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestSigmoidGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  ReLULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(NeuronLayerTest, TestSigmoidGPU) {
+TYPED_TEST(NeuronLayerTest, TestSigmoid) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
+  SigmoidLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_FLOAT_EQ(top_data[i], 1. / (1 + exp(-bottom_data[i])));
     // check that we squashed the value between 0 and 1
@@ -137,28 +82,25 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGPU) {
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestSigmoidGradientGPU) {
+TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  SigmoidLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-
-
-TYPED_TEST(NeuronLayerTest, TestDropoutCPU) {
+TYPED_TEST(NeuronLayerTest, TestDropout) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<TypeParam> layer(layer_param);
+  DropoutLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     if (top_data[i] != 0) {
@@ -167,37 +109,16 @@ TYPED_TEST(NeuronLayerTest, TestDropoutCPU) {
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGradientCPUTest) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  Caffe::set_phase(Caffe::TEST);
-  DropoutLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(NeuronLayerTest, TestDropoutCPUTestPhase) {
+TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   Caffe::set_phase(Caffe::TEST);
-  DropoutLayer<TypeParam> layer(layer_param);
+  DropoutLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     if (top_data[i] != 0) {
       EXPECT_EQ(top_data[i], bottom_data[i]);
@@ -205,116 +126,46 @@ TYPED_TEST(NeuronLayerTest, TestDropoutCPUTestPhase) {
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGPU) {
+TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
   Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    if (top_data[i] != 0) {
-      EXPECT_EQ(top_data[i], bottom_data[i] * scale);
-    }
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPU) {
-    LayerParameter layer_param;
-    Caffe::set_mode(Caffe::GPU);
-    Caffe::set_phase(Caffe::TRAIN);
-    DropoutLayer<TypeParam> layer(layer_param);
-    GradientChecker<TypeParam> checker(1e-2, 1e-3);
-    // it is too expensive to call curand multiple times, so we don't do an
-    // exhaustive gradient check.
-    checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
-}
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPUTest) {
-    LayerParameter layer_param;
-    Caffe::set_mode(Caffe::GPU);
-    Caffe::set_phase(Caffe::TEST);
-    DropoutLayer<TypeParam> layer(layer_param);
-    GradientChecker<TypeParam> checker(1e-2, 1e-3);
-    // it is too expensive to call curand multiple times, so we don't do an
-    // exhaustive gradient check.
-    checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
+  DropoutLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGPUTestPhase) {
+TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
   Caffe::set_phase(Caffe::TEST);
-  DropoutLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    if (top_data[i] != 0) {
-      EXPECT_EQ(top_data[i], bottom_data[i]);
-    }
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestBNLLCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  BNLLLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_GE(top_data[i], bottom_data[i]);
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestBNLLGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  BNLLLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  DropoutLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestBNLLGPU) {
+TYPED_TEST(NeuronLayerTest, TestBNLL) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  BNLLLayer<TypeParam> layer(layer_param);
+  BNLLLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_GE(top_data[i], bottom_data[i]);
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestBNLLGradientGPU) {
+TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  BNLLLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  BNLLLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index fdc3ea4cafa..b209d821a32 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -17,8 +17,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class PoolingLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   PoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -370,15 +372,15 @@ class PoolingLayerTest : public ::testing::Test {
   }
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(PoolingLayerTest, Dtypes);
+TYPED_TEST_CASE(PoolingLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(PoolingLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -387,13 +389,14 @@ TYPED_TEST(PoolingLayerTest, TestSetup) {
 }
 
 TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -402,41 +405,11 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
 }
 
 /*
-TYPED_TEST(PoolingLayerTest, PrintGPUBackward) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
-  }
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    cout << "top data " << i << " " << this->blob_top_->cpu_data()[i] << endl;
-  }
-
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
-  }
-}
-*/
-
-/*
-TYPED_TEST(PoolingLayerTest, PrintCPUBackward) {
+TYPED_TEST(PoolingLayerTest, PrintBackward) {
   LayerParameter layer_param;
   layer_param.set_kernelsize(3);
   layer_param.set_stride(2);
   layer_param.set_pool(LayerParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::CPU);
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -457,56 +430,21 @@ TYPED_TEST(PoolingLayerTest, PrintCPUBackward) {
 }
 */
 
-TYPED_TEST(PoolingLayerTest, TestCPUForwardMax) {
-  Caffe::set_mode(Caffe::CPU);
-  this->TestForwardSquare();
-  this->TestForwardRectHigh();
-  this->TestForwardRectWide();
-}
-
-TYPED_TEST(PoolingLayerTest, TestGPUForwardMax) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(PoolingLayerTest, TestForwardMax) {
   this->TestForwardSquare();
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
 }
 
-TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxTopMask) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(PoolingLayerTest, TestForwardMaxTopMask) {
   this->blob_top_vec_.push_back(this->blob_top_mask_);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
 }
 
-TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxTopMask) {
-  Caffe::set_mode(Caffe::GPU);
-  this->blob_top_vec_.push_back(this->blob_top_mask_);
-  this->TestForwardSquare();
-  this->TestForwardRectHigh();
-  this->TestForwardRectWide();
-}
-
-TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {
-  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
-    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
-      LayerParameter layer_param;
-      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-      pooling_param->set_kernel_h(kernel_h);
-      pooling_param->set_kernel_w(kernel_w);
-      pooling_param->set_stride(2);
-      pooling_param->set_pad(1);
-      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-      Caffe::set_mode(Caffe::CPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
-    }
-  }
-}
-
-TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
+TYPED_TEST(PoolingLayerTest, TestGradientMax) {
+  typedef typename TypeParam::Dtype Dtype;
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -516,23 +454,22 @@ TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
       pooling_param->set_stride(2);
       pooling_param->set_pad(1);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-      Caffe::set_mode(Caffe::GPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-4, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
     }
   }
 }
 
-TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxPadded) {
+TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pad(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::CPU);
   this->blob_bottom_->Reshape(1, 1, 3, 3);
   // Input:
   //     [ 1 2 4 ]
@@ -547,14 +484,14 @@ TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxPadded) {
   this->blob_bottom_->mutable_cpu_data()[6] = 4;
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  TypeParam epsilon = 1e-8;
+  Dtype epsilon = 1e-8;
   // Output:
   //     [ 1 4 4 ]
   //     [ 4 4 4 ]
@@ -570,54 +507,8 @@ TYPED_TEST(PoolingLayerTest, TestCPUForwardMaxPadded) {
   EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
 }
 
-
-TYPED_TEST(PoolingLayerTest, TestGPUForwardMaxPadded) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::GPU);
-  this->blob_bottom_->Reshape(1, 1, 3, 3);
-  // Input:
-  //     [ 1 2 4 ]
-  //     [ 2 3 2 ]
-  //     [ 4 2 1 ]
-  this->blob_bottom_->mutable_cpu_data()[0] = 1;
-  this->blob_bottom_->mutable_cpu_data()[1] = 2;
-  this->blob_bottom_->mutable_cpu_data()[2] = 4;
-  this->blob_bottom_->mutable_cpu_data()[3] = 2;
-  this->blob_bottom_->mutable_cpu_data()[4] = 3;
-  this->blob_bottom_->mutable_cpu_data()[5] = 2;
-  this->blob_bottom_->mutable_cpu_data()[6] = 4;
-  this->blob_bottom_->mutable_cpu_data()[7] = 2;
-  this->blob_bottom_->mutable_cpu_data()[8] = 1;
-  PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), 1);
-  EXPECT_EQ(this->blob_top_->channels(), 1);
-  EXPECT_EQ(this->blob_top_->height(), 3);
-  EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  TypeParam epsilon = 1e-8;
-  // Output:
-  //     [ 1 4 4 ]
-  //     [ 4 4 4 ]
-  //     [ 4 4 1 ]
-  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 1, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestCPUGradientMaxTopMask) {
+TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
+  typedef typename TypeParam::Dtype Dtype;
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -627,9 +518,8 @@ TYPED_TEST(PoolingLayerTest, TestCPUGradientMaxTopMask) {
       pooling_param->set_stride(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       this->blob_top_vec_.push_back(this->blob_top_mask_);
-      Caffe::set_mode(Caffe::CPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-4, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
       this->blob_top_vec_.pop_back();
@@ -637,48 +527,27 @@ TYPED_TEST(PoolingLayerTest, TestCPUGradientMaxTopMask) {
   }
 }
 
-TYPED_TEST(PoolingLayerTest, TestGPUGradientMaxTopMask) {
-  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
-    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
-      LayerParameter layer_param;
-      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-      pooling_param->set_kernel_h(kernel_h);
-      pooling_param->set_kernel_w(kernel_w);
-      pooling_param->set_stride(2);
-      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-      this->blob_top_vec_.push_back(this->blob_top_mask_);
-      Caffe::set_mode(Caffe::GPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
-      this->blob_top_vec_.pop_back();
-    }
-  }
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestCPUForwardAve) {
+TYPED_TEST(PoolingLayerTest, TestForwardAve) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(1);
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::CPU);
   this->blob_bottom_->Reshape(1, 1, 3, 3);
   FillerParameter filler_param;
-  filler_param.set_value(TypeParam(2));
-  ConstantFiller<TypeParam> filler(filler_param);
+  filler_param.set_value(Dtype(2));
+  ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  TypeParam epsilon = 1e-5;
+  Dtype epsilon = 1e-5;
   EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
@@ -690,79 +559,8 @@ TYPED_TEST(PoolingLayerTest, TestCPUForwardAve) {
   EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
 }
 
-
-TYPED_TEST(PoolingLayerTest, TestGPUForwardAve) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(1);
-  pooling_param->set_pad(1);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::GPU);
-  this->blob_bottom_->Reshape(1, 1, 3, 3);
-  FillerParameter filler_param;
-  filler_param.set_value(TypeParam(2));
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), 1);
-  EXPECT_EQ(this->blob_top_->channels(), 1);
-  EXPECT_EQ(this->blob_top_->height(), 3);
-  EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  TypeParam epsilon = 1e-5;
-  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 2.0    , epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestCPUGradientAve) {
-  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
-    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
-      LayerParameter layer_param;
-      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-      pooling_param->set_kernel_h(kernel_h);
-      pooling_param->set_kernel_w(kernel_w);
-      pooling_param->set_stride(2);
-      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      Caffe::set_mode(Caffe::CPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
-    }
-  }
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestGPUGradientAve) {
-  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
-    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
-      LayerParameter layer_param;
-      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-      pooling_param->set_kernel_h(kernel_h);
-      pooling_param->set_kernel_w(kernel_w);
-      pooling_param->set_stride(2);
-      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      Caffe::set_mode(Caffe::GPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
-    }
-  }
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestCPUGradientAvePadded) {
+TYPED_TEST(PoolingLayerTest, TestGradientAve) {
+  typedef typename TypeParam::Dtype Dtype;
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -770,19 +568,17 @@ TYPED_TEST(PoolingLayerTest, TestCPUGradientAvePadded) {
       pooling_param->set_kernel_h(kernel_h);
       pooling_param->set_kernel_w(kernel_w);
       pooling_param->set_stride(2);
-      pooling_param->set_pad(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      Caffe::set_mode(Caffe::CPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-2, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
     }
   }
 }
 
-
-TYPED_TEST(PoolingLayerTest, TestGPUGradientAvePadded) {
+TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
+  typedef typename TypeParam::Dtype Dtype;
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -792,9 +588,8 @@ TYPED_TEST(PoolingLayerTest, TestGPUGradientAvePadded) {
       pooling_param->set_stride(2);
       pooling_param->set_pad(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      Caffe::set_mode(Caffe::GPU);
-      PoolingLayer<TypeParam> layer(layer_param);
-      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-2, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
     }
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 99b127d3dbc..a1b716ad531 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -20,8 +20,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class PowerLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class PowerLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   PowerLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -90,166 +92,85 @@ class PowerLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(PowerLayerTest, Dtypes);
-
-TYPED_TEST(PowerLayerTest, TestPowerCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerGradientShiftZeroCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = 0.0;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerZeroCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerZeroGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerOneCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerOneGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerTwoCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.34;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerTwoGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerTwoScaleHalfGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.5;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
+TYPED_TEST_CASE(PowerLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(PowerLayerTest, TestPowerGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPower) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.37;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.37;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerGradientShiftZeroGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = 0.0;
+TYPED_TEST(PowerLayerTest, TestPowerGradientShiftZero) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.37;
+  Dtype scale = 0.83;
+  Dtype shift = 0.0;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerZeroGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerZero) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerZeroGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerZeroGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerOneGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerOne) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 1.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerOneGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerOneGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 1.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerTwoGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.34;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerTwo) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 2.0;
+  Dtype scale = 0.34;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerTwoGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerTwoGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 2.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerTwoScaleHalfGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.5;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerTwoScaleHalfGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 2.0;
+  Dtype scale = 0.5;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 4116de4c252..3ab4680e976 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -202,10 +202,7 @@ class RandomNumberGeneratorTest : public ::testing::Test {
   shared_ptr<SyncedMemory> int_data_2_;
 };
 
-
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(RandomNumberGeneratorTest, Dtypes);
-
+TYPED_TEST_CASE(RandomNumberGeneratorTest, TestDtypes);
 
 TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussian) {
   const TypeParam mu = 0;
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 089d5950967..76bbfb48a17 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -18,8 +18,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class SigmoidCrossEntropyLossLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   SigmoidCrossEntropyLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -95,36 +97,18 @@ class SigmoidCrossEntropyLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SigmoidCrossEntropyLossLayerTest, Dtypes);
-
-
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLossCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  this->TestForward();
-}
+TYPED_TEST_CASE(SigmoidCrossEntropyLossLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLossGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLoss) {
   this->TestForward();
 }
 
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SigmoidCrossEntropyLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
-}
-
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradientGPU) {
+TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SigmoidCrossEntropyLossLayer<TypeParam> layer(layer_param);
+  SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 3ba302d4c60..4a9c0f2867d 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -38,12 +38,10 @@ class SoftmaxLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SoftmaxLayerTest, Dtypes);
+TYPED_TEST_CASE(SoftmaxLayerTest, TestDtypes);
 
-TYPED_TEST(SoftmaxLayerTest, TestForwardCPU) {
+TYPED_TEST(SoftmaxLayerTest, TestForward) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   SoftmaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -73,9 +71,8 @@ TYPED_TEST(SoftmaxLayerTest, TestForwardCPU) {
   }
 }
 
-TYPED_TEST(SoftmaxLayerTest, TestGradientCPU) {
+TYPED_TEST(SoftmaxLayerTest, TestGradient) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   SoftmaxLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 8b8be8e8b6d..efd6e33c2f7 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -19,8 +19,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class SoftmaxWithLossLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   SoftmaxWithLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -46,26 +48,15 @@ class SoftmaxWithLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SoftmaxWithLossLayerTest, Dtypes);
-
+TYPED_TEST_CASE(SoftmaxWithLossLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SoftmaxWithLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
-}
 
-TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientGPU) {
+TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SoftmaxWithLossLayer<TypeParam> layer(layer_param);
+  SoftmaxWithLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 327bcf937ac..455fb59d4d3 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -20,8 +20,10 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class SplitLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class SplitLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   SplitLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -47,12 +49,12 @@ class SplitLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SplitLayerTest, Dtypes);
+TYPED_TEST_CASE(SplitLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(SplitLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
+  SplitLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_a_->num(), 2);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
@@ -64,91 +66,46 @@ TYPED_TEST(SplitLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_b_->width(), 5);
 }
 
-TYPED_TEST(SplitLayerTest, TestCPU) {
+TYPED_TEST(SplitLayerTest, Test) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  SplitLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
+    Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
     EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
     EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
   }
 }
 
-TYPED_TEST(SplitLayerTest, TestGPU) {
-  LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
-    EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
-    EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
-  }
-}
-
-TYPED_TEST(SplitLayerTest, TestCPUInPlace) {
-  LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
-  this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
-    EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
-  }
-}
-
-TYPED_TEST(SplitLayerTest, TestGPUInPlace) {
+TYPED_TEST(SplitLayerTest, TestInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
+  SplitLayer<Dtype> layer(layer_param);
   this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
+    Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
     EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
   }
 }
 
-TYPED_TEST(SplitLayerTest, TestCPUGradient) {
+TYPED_TEST(SplitLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(SplitLayerTest, TestGPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(SplitLayerTest, TestCPUGradientInPlace) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
+  SplitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(SplitLayerTest, TestGPUGradientInPlace) {
+TYPED_TEST(SplitLayerTest, TestGradientInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  SplitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 0ad8123f881..7a931d224d8 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -49,8 +49,7 @@ class StochasticPoolingLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(StochasticPoolingLayerTest, Dtypes);
+TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes);
 
 TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   LayerParameter layer_param;
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index 3a757088e89..20bd86138b5 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -24,7 +24,7 @@ TEST_F(SyncedMemoryTest, TestInitialization) {
   delete p_mem;
 }
 
-TEST_F(SyncedMemoryTest, TestAllocation) {
+TEST_F(SyncedMemoryTest, TestAllocationCPUGPU) {
   SyncedMemory mem(10);
   EXPECT_TRUE(mem.cpu_data());
   EXPECT_TRUE(mem.gpu_data());
@@ -32,20 +32,32 @@ TEST_F(SyncedMemoryTest, TestAllocation) {
   EXPECT_TRUE(mem.mutable_gpu_data());
 }
 
+TEST_F(SyncedMemoryTest, TestAllocationCPU) {
+  SyncedMemory mem(10);
+  EXPECT_TRUE(mem.cpu_data());
+  EXPECT_TRUE(mem.mutable_cpu_data());
+}
+
+TEST_F(SyncedMemoryTest, TestAllocationGPU) {
+  SyncedMemory mem(10);
+  EXPECT_TRUE(mem.gpu_data());
+  EXPECT_TRUE(mem.mutable_gpu_data());
+}
+
 TEST_F(SyncedMemoryTest, TestCPUWrite) {
   SyncedMemory mem(10);
   void* cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 1, mem.size());
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 1);
+    EXPECT_EQ((static_cast<char*>(cpu_data))[i], 1);
   }
   // do another round
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 2, mem.size());
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 2);
+    EXPECT_EQ((static_cast<char*>(cpu_data))[i], 2);
   }
 }
 
@@ -60,21 +72,21 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   char* recovered_value = new char[10];
   caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 1);
+    EXPECT_EQ((static_cast<char*>(recovered_value))[i], 1);
   }
   // do another round
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 2, mem.size());
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 2);
+    EXPECT_EQ((static_cast<char*>(cpu_data))[i], 2);
   }
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
   caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 2);
+    EXPECT_EQ((static_cast<char*>(recovered_value))[i], 2);
   }
   delete[] recovered_value;
 }
@@ -86,7 +98,7 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
   CUDA_CHECK(cudaMemset(gpu_data, 1, mem.size()));
   const void* cpu_data = mem.cpu_data();
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<const char*>(cpu_data))[i], 1);
+    EXPECT_EQ((static_cast<const char*>(cpu_data))[i], 1);
   }
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
 
@@ -95,7 +107,7 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
   CUDA_CHECK(cudaMemset(gpu_data, 2, mem.size()));
   cpu_data = mem.cpu_data();
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<const char*>(cpu_data))[i], 2);
+    EXPECT_EQ((static_cast<const char*>(cpu_data))[i], 2);
   }
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
 }
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 9c9f8a74ae2..171eb4e472e 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -19,8 +19,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class TanHLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class TanHLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   TanHLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 1, 1)),
@@ -39,13 +40,12 @@ class TanHLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(TanHLayerTest, Dtypes);
+TYPED_TEST_CASE(TanHLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(TanHLayerTest, TestForwardCPU) {
+TYPED_TEST(TanHLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  TanHLayer<TypeParam> layer(layer_param);
+  TanHLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test exact values
@@ -65,43 +65,11 @@ TYPED_TEST(TanHLayerTest, TestForwardCPU) {
   }
 }
 
-TYPED_TEST(TanHLayerTest, TestGradientCPU) {
+TYPED_TEST(TanHLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  TanHLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(TanHLayerTest, TestForwardGPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  TanHLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Test exact values
-  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
-      for (int k = 0; k < this->blob_bottom_->height(); ++k) {
-        for (int l = 0; l < this->blob_bottom_->width(); ++l) {
-          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
-          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
-        }
-      }
-    }
-  }
-}
-
-TYPED_TEST(TanHLayerTest, TestGradientGPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  TanHLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  TanHLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 8303e442007..46519ff2094 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -16,8 +16,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class ThresholdLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class ThresholdLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   ThresholdLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -37,13 +38,13 @@ class ThresholdLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ThresholdLayerTest, Dtypes);
+TYPED_TEST_CASE(ThresholdLayerTest, TestDtypesAndDevices);
 
 
 TYPED_TEST(ThresholdLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ThresholdLayer<TypeParam> layer(layer_param);
+  ThresholdLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -51,16 +52,16 @@ TYPED_TEST(ThresholdLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_->width());
 }
 
-TYPED_TEST(ThresholdLayerTest, TestCPU) {
+TYPED_TEST(ThresholdLayerTest, Test) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ThresholdLayer<TypeParam> layer(layer_param);
+  ThresholdLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  const Dtype threshold_ = layer_param.threshold_param().threshold();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_LE(top_data[i], 1.);
@@ -73,67 +74,19 @@ TYPED_TEST(ThresholdLayerTest, TestCPU) {
   }
 }
 
-TYPED_TEST(ThresholdLayerTest, TestCPU2) {
+TYPED_TEST(ThresholdLayerTest, Test2) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   ThresholdParameter* threshold_param =
     layer_param.mutable_threshold_param();
   threshold_param->set_threshold(0.5);
-  ThresholdLayer<TypeParam> layer(layer_param);
+  ThresholdLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer_param.threshold_param().threshold();
-  EXPECT_FLOAT_EQ(threshold_, 0.5);
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_LE(top_data[i], 1.);
-    if (top_data[i] == 0) {
-      EXPECT_LE(bottom_data[i], threshold_);
-    }
-    if (top_data[i] == 1) {
-      EXPECT_GT(bottom_data[i], threshold_);
-    }
-  }
-}
-
-TYPED_TEST(ThresholdLayerTest, TestGPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ThresholdLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer_param.threshold_param().threshold();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_LE(top_data[i], 1.);
-    if (top_data[i] == 0) {
-      EXPECT_LE(bottom_data[i], threshold_);
-    }
-    if (top_data[i] == 1) {
-      EXPECT_GT(bottom_data[i], threshold_);
-    }
-  }
-}
-
-TYPED_TEST(ThresholdLayerTest, TestGPU2) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ThresholdParameter* threshold_param =
-    layer_param.mutable_threshold_param();
-  threshold_param->set_threshold(0.5);
-  ThresholdLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  const TypeParam threshold_ = layer_param.threshold_param().threshold();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  const Dtype threshold_ = layer_param.threshold_param().threshold();
   EXPECT_FLOAT_EQ(threshold_, 0.5);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 5b4c48eaf80..725d24e21d3 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -15,14 +15,12 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-typedef ::testing::Types<float, double> Dtypes;
-
-template <typename Dtype>
+template <typename TypeParam>
 class GemmTest : public ::testing::Test {};
 
-TYPED_TEST_CASE(GemmTest, Dtypes);
+TYPED_TEST_CASE(GemmTest, TestDtypes);
 
-TYPED_TEST(GemmTest, TestGemm) {
+TYPED_TEST(GemmTest, TestGemmCPUGPU) {
   Blob<TypeParam> A(1, 1, 2, 3);
   Blob<TypeParam> B(1, 1, 3, 4);
   Blob<TypeParam> C(1, 1, 2, 4);
@@ -93,7 +91,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 }
 
 
-TYPED_TEST(GemmTest, TestGemv) {
+TYPED_TEST(GemmTest, TestGemvCPUGPU) {
   Blob<TypeParam> A(1, 1, 2, 3);
   Blob<TypeParam> x(1, 1, 1, 3);
   Blob<TypeParam> y(1, 1, 1, 2);

From 00a8f3af825e9083b4674437e6890d4159a0ec1d Mon Sep 17 00:00:00 2001
From: huy <huy@huyng.com>
Date: Wed, 9 Jul 2014 23:02:06 -0700
Subject: [PATCH 0238/2053] @huyng's .travis.yml integration configuration file
 to install and test Caffe.

---
 .travis.yml | 41 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 00000000000..1dd8a2d8a78
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,41 @@
+language: cpp
+compiler:
+  - gcc
+  - clang
+before_install:
+  - echo $LANG
+  - echo $LC_ALL
+  - sudo apt-get -y update
+install:
+  - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev
+  - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
+  - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
+  - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
+  - sudo apt-get -y update && sudo apt-get -y install cuda
+  - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
+  - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
+before_script:
+  - mv Makefile.config.example Makefile.config
+script:
+  - make all
+  - make test
+  - LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64 make runtest
+branches:
+  only:
+    - master
+    - dev
+    - travisci
+notifications:
+  recipients:
+    - huy@huyng.com
+  email:
+    on_success: change
+    on_failure: always
+  irc:
+    channels:
+      - "chat.freenode.net#caffe"
+    template:
+      - "%{repository}/%{branch} (%{commit} - %{author}): %{message}"
+os:
+  - linux
+  - osx

From 24bfd03da3da5342034be7ba18116dcf0596216c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 12 Jul 2014 10:00:44 -0700
Subject: [PATCH 0239/2053] -Override the default compiler by specifying a CXX
 in Makefile.config instead of CUSTOM_CXX, as Travis exports CXX as the
 compiler env variable name. -Change TEST_HDFS -> TEST_HXX_SRCS.

---
 Makefile                | 17 ++++++-----------
 Makefile.config.example |  2 +-
 2 files changed, 7 insertions(+), 12 deletions(-)

diff --git a/Makefile b/Makefile
index c17d4980b79..fc3ffe6a30b 100644
--- a/Makefile
+++ b/Makefile
@@ -37,8 +37,8 @@ TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
 TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
 TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
 GTEST_SRC := src/gtest/gtest-all.cpp
-# TEST_HDRS are the test header files
-TEST_HDRS := $(shell find include/$(PROJECT) -name "test_*.hpp")
+# TEST_HXX_SRCS are the test header files
+TEST_HXX_SRCS := $(shell find include/$(PROJECT) -name "test_*.hpp")
 # TOOL_SRCS are the source files for the tool binaries
 TOOL_SRCS := $(shell find tools -name "*.cpp")
 # EXAMPLE_SRCS are the source files for the example binaries
@@ -194,7 +194,7 @@ else ifeq ($(UNAME), Darwin)
 endif
 
 ifeq ($(LINUX), 1)
-	CXX := /usr/bin/g++
+	CXX ?= /usr/bin/g++
 	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
 	# older versions of gcc are too dumb to build boost with -Wuninitalized
 	ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
@@ -206,7 +206,7 @@ endif
 # clang++ instead of g++
 # libstdc++ instead of libc++ for CUDA compatibility on 10.9
 ifeq ($(OSX), 1)
-	CXX := /usr/bin/clang++
+	CXX ?= /usr/bin/clang++
 	# clang throws this warning for cuda headers
 	WARNINGS += -Wno-unneeded-internal-declaration
 	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)
@@ -215,11 +215,6 @@ ifeq ($(OSX), 1)
 	endif
 endif
 
-# Custom compiler 
-ifdef CUSTOM_CXX
-	CXX := $(CUSTOM_CXX)
-endif
-
 # Debugging
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS := -DDEBUG -g -O0
@@ -385,14 +380,14 @@ $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
 	ar rcs $@ $(PROTO_OBJS) $(OBJS)
 	@ echo
 
-$(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
+$(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HXX_SRCS) \
 		| $(TEST_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
-$(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HDRS) \
+$(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HXX_SRCS) \
 		| $(TEST_BUILD_DIR)
 	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
diff --git a/Makefile.config.example b/Makefile.config.example
index 17c90016f75..81effb481f3 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -3,7 +3,7 @@
 
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
-# CUSTOM_CXX := g++
+# CXX := g++
 
 # CUDA directory contains bin/ and lib/ directories that we need.
 CUDA_DIR := /usr/local/cuda

From 89d50ca20746517f71e7c7c121401d09d40e3ebc Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 12 Jul 2014 04:27:36 -0700
Subject: [PATCH 0240/2053] @jeffdonahue's tweaks to .travis.yml config: -Add
 (CPU-only) test, lint, warn and parallel (-j 4) to travis CI build. -Add
 /usr/local/lib to travis config (seems needed for LMDB). -Put export in
 "before_script"; disable clang build -- doesn't work on Linux. -Cache Ubuntu
 apt packages. -Install bc package to hopefully suppress "bc: not found"
 errors -Get apt packages before_install as suggested by Travis official docs
 -Remove specified email address and IRC notifications (emails are sent to the
 committer by default; others can view build results in public Travis feed, on
 pull requests, etc.).

---
 .travis.yml | 44 ++++++++++++++++++++++++++++----------------
 1 file changed, 28 insertions(+), 16 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 1dd8a2d8a78..fcea38c8f47 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,13 +1,18 @@
 language: cpp
+# Cache Ubuntu apt packages.
+cache: apt
 compiler:
   - gcc
-  - clang
+# Disable clang build: doesn't seem to work on Linux.
+# (@jeffdonahue: Travis buildbot's failure behavior is similar to what I see
+# building on Linux.)
+#   - clang
 before_install:
   - echo $LANG
   - echo $LC_ALL
   - sudo apt-get -y update
+  - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
 install:
-  - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
@@ -16,26 +21,33 @@ install:
   - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
 before_script:
   - mv Makefile.config.example Makefile.config
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/lib
+  - export NUM_THREADS=4
 script:
-  - make all
-  - make test
-  - LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64 make runtest
+  - make -j $NUM_THREADS all test warn lint
+  - make runtestnogpu
+  - make -j $NUM_THREADS all
+  - make -j $NUM_THREADS test
+  - make -j $NUM_THREADS warn
+  - make -j $NUM_THREADS lint
 branches:
   only:
     - master
     - dev
     - travisci
 notifications:
-  recipients:
-    - huy@huyng.com
+# Emails are sent to the committer's git-configured email address by default,
+# but only if they have access to the repository.  To enable Travis on your
+# public fork of Caffe, just go to travis-ci.org and flip the switch on for
+# your Caffe fork.  To configure your git email address, use:
+#     git config --global user.email me@example.com
   email:
-    on_success: change
+    on_success: always
     on_failure: always
-  irc:
-    channels:
-      - "chat.freenode.net#caffe"
-    template:
-      - "%{repository}/%{branch} (%{commit} - %{author}): %{message}"
-os:
-  - linux
-  - osx
+# IRC notifications disabled by default.
+# Uncomment next 5 lines to send notifications to chat.freenode.net#caffe
+#   irc:
+#     channels:
+#       - "chat.freenode.net#caffe"
+#     template:
+#       - "%{repository}/%{branch} (%{commit} - %{author}): %{message}"

From c962e7de51be765584504b11c84eb7023a9b9e89 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 12 Jul 2014 22:37:10 -0700
Subject: [PATCH 0241/2053] Travis build failure wasn't working for lint/warn
 because they didn't exit with a non-zero code -- this fixes that.

---
 Makefile | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/Makefile b/Makefile
index fc3ffe6a30b..38c180c28da 100644
--- a/Makefile
+++ b/Makefile
@@ -292,10 +292,11 @@ $(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
 	@ if [ -s "$@" ]; then \
 		cat $@; \
 		mv $@ $(NONEMPTY_LINT_REPORT); \
-	  else \
-		$(RM) $(NONEMPTY_LINT_REPORT); \
-		echo "No lint errors!"; \
-	  fi
+		echo "Found one or more lint errors."; \
+		exit 1; \
+	  fi; \
+	  $(RM) $(NONEMPTY_LINT_REPORT); \
+	  echo "No lint errors!";
 
 $(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % | $(LINT_OUTPUT_DIR)
 	@ mkdir -p $(dir $@)
@@ -348,10 +349,11 @@ $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
 	@ if [ -s "$@" ]; then \
 		cat $@; \
 		mv $@ $(NONEMPTY_WARN_REPORT); \
-	  else \
-		$(RM) $(NONEMPTY_WARN_REPORT); \
-		echo "No compiler warnings!"; \
-	  fi
+		echo "Found one or more lint errors."; \
+		exit 1; \
+	  fi; \
+	  $(RM) $(NONEMPTY_WARN_REPORT); \
+	  echo "No compiler warnings!";
 
 $(ALL_CXX_WARNS): %.o.$(WARNS_EXT) : %.o
 

From 717b6b0c1f396923e6380f3745db3b141e7d5f19 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 14 Jul 2014 02:44:28 -0700
Subject: [PATCH 0242/2053] Seed HingeLossLayerTest; bad values can cause test
 (and therefore Travis CI build) to fail

---
 src/caffe/test/test_hinge_loss_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 868e9e9ae16..df6d8e25803 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -28,6 +28,7 @@ class HingeLossLayerTest : public MultiDeviceTest<TypeParam> {
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
         blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)) {
     // fill the values
+    Caffe::set_random_seed(1701);
     FillerParameter filler_param;
     filler_param.set_std(10);
     GaussianFiller<Dtype> filler(filler_param);

From 856f06d44fbaae09c12d060d078b81e257159d50 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 14 Jul 2014 19:54:48 +0200
Subject: [PATCH 0243/2053] fix Makefile comment about library names

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 38c180c28da..ad3c1bb41c0 100644
--- a/Makefile
+++ b/Makefile
@@ -17,7 +17,7 @@ else
 	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
 endif
 
-# The target static library and shared library name
+# The target shared library and static library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
 NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a

From 9045fd20eec25b8a59f8a196bacf69d0b157c6a5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 14 Jul 2014 13:31:13 -0700
Subject: [PATCH 0244/2053] Add --keep-going flag to first make so that any
 targets that can be built are built; write out full -j flag as --jobs for
 clarity.

---
 .travis.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index fcea38c8f47..7951b2e78f2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -24,12 +24,12 @@ before_script:
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/lib
   - export NUM_THREADS=4
 script:
-  - make -j $NUM_THREADS all test warn lint
+  - make --keep-going --jobs=$NUM_THREADS all test warn lint
   - make runtestnogpu
-  - make -j $NUM_THREADS all
-  - make -j $NUM_THREADS test
-  - make -j $NUM_THREADS warn
-  - make -j $NUM_THREADS lint
+  - make --jobs=$NUM_THREADS all
+  - make --jobs=$NUM_THREADS test
+  - make --jobs=$NUM_THREADS warn
+  - make --jobs=$NUM_THREADS lint
 branches:
   only:
     - master

From 89159f395c02d5dab59d4658e4121fd981d89363 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 14 Jul 2014 13:37:13 -0700
Subject: [PATCH 0245/2053] Run Travis build on all branches (remove whitelist
 from .travis.yml).

---
 .travis.yml | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 7951b2e78f2..19ef68375e4 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -30,11 +30,6 @@ script:
   - make --jobs=$NUM_THREADS test
   - make --jobs=$NUM_THREADS warn
   - make --jobs=$NUM_THREADS lint
-branches:
-  only:
-    - master
-    - dev
-    - travisci
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
 # but only if they have access to the repository.  To enable Travis on your

From 9c65fbaa865829e4b479e43eae72cf8f5f09afcc Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 14 Jul 2014 13:43:08 -0700
Subject: [PATCH 0246/2053] Replace CUSTOM_CXX env var to specify non-default
 C++ compiler.

---
 Makefile                | 5 +++++
 Makefile.config.example | 2 +-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index ad3c1bb41c0..00f67c978dc 100644
--- a/Makefile
+++ b/Makefile
@@ -215,6 +215,11 @@ ifeq ($(OSX), 1)
 	endif
 endif
 
+# Custom compiler
+ifdef CUSTOM_CXX
+	CXX := $(CUSTOM_CXX)
+endif
+
 # Debugging
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS := -DDEBUG -g -O0
diff --git a/Makefile.config.example b/Makefile.config.example
index 81effb481f3..17c90016f75 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -3,7 +3,7 @@
 
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
-# CXX := g++
+# CUSTOM_CXX := g++
 
 # CUDA directory contains bin/ and lib/ directories that we need.
 CUDA_DIR := /usr/local/cuda

From 97d6ccfda5ec371b8113713f01b86ac54135ff58 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 14 Jul 2014 13:46:09 -0700
Subject: [PATCH 0247/2053] Add blank lines between fields for readability.

---
 .travis.yml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 19ef68375e4..1f780471b3b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,17 +1,21 @@
 language: cpp
+
 # Cache Ubuntu apt packages.
 cache: apt
+
 compiler:
   - gcc
 # Disable clang build: doesn't seem to work on Linux.
 # (@jeffdonahue: Travis buildbot's failure behavior is similar to what I see
 # building on Linux.)
 #   - clang
+
 before_install:
   - echo $LANG
   - echo $LC_ALL
   - sudo apt-get -y update
   - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
+
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
@@ -19,10 +23,12 @@ install:
   - sudo apt-get -y update && sudo apt-get -y install cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
   - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
+
 before_script:
   - mv Makefile.config.example Makefile.config
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/lib
   - export NUM_THREADS=4
+
 script:
   - make --keep-going --jobs=$NUM_THREADS all test warn lint
   - make runtestnogpu
@@ -30,6 +36,7 @@ script:
   - make --jobs=$NUM_THREADS test
   - make --jobs=$NUM_THREADS warn
   - make --jobs=$NUM_THREADS lint
+
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
 # but only if they have access to the repository.  To enable Travis on your
@@ -39,6 +46,7 @@ notifications:
   email:
     on_success: always
     on_failure: always
+
 # IRC notifications disabled by default.
 # Uncomment next 5 lines to send notifications to chat.freenode.net#caffe
 #   irc:

From c28823f7829b91c5dd56060579dda2cecbc22d7b Mon Sep 17 00:00:00 2001
From: Ross Girshick <rbg@eecs.berkeley.edu>
Date: Mon, 14 Jul 2014 17:04:43 -0700
Subject: [PATCH 0248/2053] fix bug that breaks MATLAB 2014a compilation

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 00f67c978dc..855d948f230 100644
--- a/Makefile
+++ b/Makefile
@@ -336,9 +336,9 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			"to build mat$(PROJECT)."; \
 		exit 1; \
 	fi
-	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) $(STATIC_NAME) \
+	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
-			CXXLIBS="\$$CXXLIBS $(LDFLAGS)" -o $@
+			CXXLIBS="\$$CXXLIBS $(STATIC_NAME) $(LDFLAGS)" -output $@
 	@ echo
 
 runtest: $(TEST_ALL_BIN)

From 304be2fec6440aef246ac19b4832d3d5e72fc6c9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 14 Jul 2014 23:49:36 -0700
Subject: [PATCH 0249/2053] Fix SoftmaxLayerTest: forgot to change this one to
 use DtypesAndDevices; was causing Travis build to randomly fail if a previous
 test had set the mode to GPU (which no test that is run by 'make
 runtestnogpu' should, so I guess there's another bug somewhere).

---
 src/caffe/test/test_softmax_layer.cpp | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 4a9c0f2867d..f0be279bbca 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -18,8 +18,9 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-template <typename Dtype>
-class SoftmaxLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class SoftmaxLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   SoftmaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 1, 1)),
@@ -38,16 +39,17 @@ class SoftmaxLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-TYPED_TEST_CASE(SoftmaxLayerTest, TestDtypes);
+TYPED_TEST_CASE(SoftmaxLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(SoftmaxLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SoftmaxLayer<TypeParam> layer(layer_param);
+  SoftmaxLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    TypeParam sum = 0;
+    Dtype sum = 0;
     for (int j = 0; j < this->blob_top_->channels(); ++j) {
       sum += this->blob_top_->data_at(i, j, 0, 0);
     }
@@ -56,7 +58,7 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
   }
   // Test exact values
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    TypeParam scale = 0;
+    Dtype scale = 0;
     for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
       scale += exp(this->blob_bottom_->data_at(i, j, 0, 0));
     }
@@ -72,9 +74,10 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
 }
 
 TYPED_TEST(SoftmaxLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SoftmaxLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  SoftmaxLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }

From 6a0883d45ffc277aa67d0746acc1e82718316104 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 15 Jul 2014 10:20:10 +0200
Subject: [PATCH 0250/2053] fix choice of clang++ in OS X build

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 855d948f230..f7e58700b67 100644
--- a/Makefile
+++ b/Makefile
@@ -206,7 +206,7 @@ endif
 # clang++ instead of g++
 # libstdc++ instead of libc++ for CUDA compatibility on 10.9
 ifeq ($(OSX), 1)
-	CXX ?= /usr/bin/clang++
+	CXX := /usr/bin/clang++
 	# clang throws this warning for cuda headers
 	WARNINGS += -Wno-unneeded-internal-declaration
 	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)

From 0271224513e02c1d1a0421d784b02d8a0e900baf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 15 Jul 2014 16:06:59 +0200
Subject: [PATCH 0251/2053] [example] elaborate net surgery description

---
 examples/net_surgery.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 550f4112830..0a6e208f6d7 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "Editing model parameters",
-  "description": "How to do net surgery and manually change model parameters.",
+  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "include_in_docs": true
  },
  "nbformat": 3,

From 3e35a160e284f9a2da1fcc7df9b6ad15eaf20480 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 15 Jul 2014 13:47:14 -0700
Subject: [PATCH 0252/2053] Fix Makefile warning error message.

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index f7e58700b67..ad13bb949e7 100644
--- a/Makefile
+++ b/Makefile
@@ -354,7 +354,7 @@ $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
 	@ if [ -s "$@" ]; then \
 		cat $@; \
 		mv $@ $(NONEMPTY_WARN_REPORT); \
-		echo "Found one or more lint errors."; \
+		echo "Compiler produced one or more warnings."; \
 		exit 1; \
 	  fi; \
 	  $(RM) $(NONEMPTY_WARN_REPORT); \

From 8a0ba0fd405ebdd1aa247695edf20b991b9e7b98 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 14:25:55 -0700
Subject: [PATCH 0253/2053] use layer_param instead of
 layers_[layer_id]->layer_param()

---
 src/caffe/net.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index c0f124b0ea7..0b9cc8412cb 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -88,7 +88,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
           << top_vecs_[layer_id][top_id]->count() << ")";
     }
     DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-    const int blobs_lr_size = layers_[layer_id]->layer_param().blobs_lr_size();
+    const int blobs_lr_size = layer_param.blobs_lr_size();
     const int num_param_blobs = layers_[layer_id]->blobs().size();
     CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)
         << "Incorrect blobs lr size: should be either 0 "
@@ -96,8 +96,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     if (blobs_lr_size) {
       // Check if this layer needs backward operation itself
       for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
-        need_backward |=
-            (layers_[layer_id]->layer_param().blobs_lr(param_id) > 0);
+        need_backward |= layer_param.blobs_lr(param_id) > 0;
       }
     } else if (layers_[layer_id]->blobs().size()) {
       // catch: if a layer param does not specify blobs_lr, we should assume the

From b2a9a3c7fea3022cccecf1cd729b44db3fd9dffd Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 14:47:46 -0700
Subject: [PATCH 0254/2053] Add param_propagate_down_ vector to layer, populate
 according to blobs_lr in Net::Init

---
 include/caffe/layer.hpp | 17 +++++++++++++++++
 src/caffe/net.cpp       | 12 +++++++++++-
 2 files changed, 28 insertions(+), 1 deletion(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index b8afcfe37cf..d4d10aafa31 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -96,11 +96,28 @@ class Layer {
     return true;
   }
 
+  // param_propagate_down specifies whether the layer should compute gradients
+  // in Backward.  You can safely ignore false and always compute gradients
+  // for all parameters, but possibly with wasteful computation.
+  inline bool param_propagate_down(const int param_id) {
+    return (param_propagate_down_.size() > param_id) ?
+        param_propagate_down_[param_id] : false;
+  }
+  inline void set_param_propagate_down(const int param_id, const bool value) {
+    if (param_propagate_down_.size() <= param_id) {
+      param_propagate_down_.resize(param_id + 1, true);
+    }
+    param_propagate_down_[param_id] = value;
+  }
+
+
  protected:
   // The protobuf that stores the layer parameters
   LayerParameter layer_param_;
   // The vector that stores the parameters as a set of blobs.
   vector<shared_ptr<Blob<Dtype> > > blobs_;
+  // Vector indicating whether to compute the diff of each param blob.
+  vector<bool> param_propagate_down_;
 
   // Forward functions: compute the layer output
   // (and loss layers return the loss; other layers return the dummy value 0.)
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 0b9cc8412cb..edea726fb51 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -96,12 +96,18 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     if (blobs_lr_size) {
       // Check if this layer needs backward operation itself
       for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
-        need_backward |= layer_param.blobs_lr(param_id) > 0;
+        const bool param_need_backward = layer_param.blobs_lr(param_id) > 0;
+        need_backward |= param_need_backward;
+        layers_[layer_id]->set_param_propagate_down(param_id,
+                                                    param_need_backward);
       }
     } else if (layers_[layer_id]->blobs().size()) {
       // catch: if a layer param does not specify blobs_lr, we should assume the
       // learning rate to be 1. Thus we will need to perform backward.
       need_backward = true;
+      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
+        layers_[layer_id]->set_param_propagate_down(param_id, true);
+      }
     }
     const int param_size = layer_param.param_size();
     CHECK(param_size == num_param_blobs || param_size == 0)
@@ -139,6 +145,10 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
             blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] ||
             bottom_need_backward_[layer_id][bottom_id];
       }
+      for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
+           ++param_id) {
+        layers_[layer_id]->set_param_propagate_down(param_id, true);
+      }
     }
   }
   // In the end, all remaining blobs are considered output blobs.

From d5351bc99eb40bcf2de750b5b3aeeb43cafce82f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 15:05:57 -0700
Subject: [PATCH 0255/2053] Make ConvolutionLayer and InnerProductLayer abide
 by param_propagate_down_

---
 src/caffe/layers/conv_layer.cpp          | 76 +++++++++++++----------
 src/caffe/layers/conv_layer.cu           | 78 ++++++++++++++----------
 src/caffe/layers/inner_product_layer.cpp | 17 ++++--
 src/caffe/layers/inner_product_layer.cu  | 20 +++---
 4 files changed, 112 insertions(+), 79 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 963dc688a0e..0d6871d6ac4 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -85,6 +85,7 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
         bias_multiplier_data[i] = 1.;
     }
   }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
 
@@ -124,11 +125,15 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* weight = this->blobs_[0]->cpu_data();
-  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->cpu_data();
+    weight_diff = this->blobs_[0]->mutable_cpu_diff();
+    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
   Dtype* bias_diff = NULL;
-  if (bias_term_) {
+  if (bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_cpu_diff();
     caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
@@ -136,14 +141,10 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   const int col_offset = K_ * N_;
   const int top_offset = M_ * N_;
   for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = top[i]->cpu_diff();
-    const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
-    Dtype* col_data = col_buffer_.mutable_cpu_data();
-    Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-
+    const Dtype* top_diff = NULL;
     // Bias gradient, if necessary.
-    if (bias_term_) {
+    if (bias_term_ && this->param_propagate_down_[1]) {
+      top_diff = top[i]->cpu_diff();
       for (int n = 0; n < num_; ++n) {
         caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
@@ -151,29 +152,40 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
             bias_diff);
       }
     }
-    for (int n = 0; n < num_; ++n) {
-      // Since we saved memory in the forward pass by not storing all col data,
-      // we will need to recompute them.
-      im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                 width_, kernel_size_, pad_, stride_, col_data);
-      // gradient w.r.t. weight. Note that we will accumulate diffs.
-      for (int g = 0; g < group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-          (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-          col_data + col_offset * g, (Dtype)1.,
-          weight_diff + weight_offset * g);
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      if (!top_diff) {
+        top_diff = top[i]->cpu_diff();
       }
-      // gradient w.r.t. bottom data, if necessary
-      if (propagate_down[i]) {
-        for (int g = 0; g < group_; ++g) {
-          caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-            (Dtype)1., weight + weight_offset * g,
-            top_diff + top[i]->offset(n) + top_offset * g,
-            (Dtype)0., col_diff + col_offset * g);
+      Dtype* col_data = col_buffer_.mutable_cpu_data();
+      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
+      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        // Since we saved memory in the forward pass by not storing all col
+        // data, we will need to recompute them.
+        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                   width_, kernel_size_, pad_, stride_, col_data);
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+                col_data + col_offset * g, (Dtype)1.,
+                weight_diff + weight_offset * g);
+          }
+        }
+        // gradient w.r.t. bottom data, if necessary
+        if (propagate_down[i]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+                (Dtype)1., weight + weight_offset * g,
+                top_diff + top[i]->offset(n) + top_offset * g,
+                (Dtype)0., col_diff + col_offset * g);
+          }
+          // col2im back to the data
+          col2im_cpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
+              stride_, bottom_diff + (*bottom)[i]->offset(n));
         }
-        // col2im back to the data
-        col2im_cpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-            stride_, bottom_diff + (*bottom)[i]->offset(n));
       }
     }
   }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 59ec58dfebe..74067886c6a 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -46,13 +46,15 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* weight = this->blobs_[0]->gpu_data();
-  Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  Dtype* col_data = col_buffer_.mutable_gpu_data();
-  Dtype* col_diff = col_buffer_.mutable_gpu_diff();
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->gpu_data();
+    weight_diff = this->blobs_[0]->mutable_gpu_diff();
+    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
   Dtype* bias_diff = NULL;
-  if (bias_term_) {
+  if (bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
     caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
@@ -60,41 +62,51 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const int col_offset = K_ * N_;
   const int top_offset = M_ * N_;
   for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = top[i]->gpu_diff();
-    const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+    const Dtype* top_diff = NULL;
     // Bias gradient, if necessary.
-    if (bias_term_) {
+    if (bias_term_ && this->param_propagate_down_[1]) {
+      top_diff = top[i]->gpu_diff();
       for (int n = 0; n < num_; ++n) {
         caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
-            static_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-            1., bias_diff);
+            static_cast<const Dtype*>(bias_multiplier_->gpu_data()), 1.,
+            bias_diff);
       }
     }
-    for (int n = 0; n < num_; ++n) {
-      // since we saved memory in the forward pass by not storing all col data,
-      // we will need to recompute them.
-      im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                        width_, kernel_size_, pad_, stride_, col_data);
-      // gradient w.r.t. weight. Note that we will accumulate diffs.
-      for (int g = 0; g < group_; ++g) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-          (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-          col_data + col_offset * g, (Dtype)1.,
-          weight_diff + weight_offset * g);
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      if (!top_diff) {
+        top_diff = top[i]->gpu_diff();
       }
-      // gradient w.r.t. bottom data, if necessary
-      if (propagate_down[i]) {
-        for (int g = 0; g < group_; ++g) {
-          caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-            (Dtype)1., weight + weight_offset * g,
-            top_diff + top[i]->offset(n) + top_offset * g,
-            (Dtype)0., col_diff + col_offset * g);
+      Dtype* col_data = col_buffer_.mutable_gpu_data();
+      Dtype* col_diff = col_buffer_.mutable_gpu_diff();
+      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        // Since we saved memory in the forward pass by not storing all col
+        // data, we will need to recompute them.
+        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                   width_, kernel_size_, pad_, stride_, col_data);
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+                col_data + col_offset * g, (Dtype)1.,
+                weight_diff + weight_offset * g);
+          }
+        }
+        // gradient w.r.t. bottom data, if necessary
+        if (propagate_down[i]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+                (Dtype)1., weight + weight_offset * g,
+                top_diff + top[i]->offset(n) + top_offset * g,
+                (Dtype)0., col_diff + col_offset * g);
+          }
+          // col2im back to the data
+          col2im_gpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
+              stride_, bottom_diff + (*bottom)[i]->offset(n));
         }
-        // col2im back to the data
-        col2im_gpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-            stride_, bottom_diff + (*bottom)[i]->offset(n));
       }
     }
   }
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index ddf55e49b63..29df6bf2924 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -54,6 +54,7 @@ void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
         bias_multiplier_data[i] = 1.;
     }
   }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
 template <typename Dtype>
@@ -76,18 +77,22 @@ template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  // Gradient with respect to weight
-  caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-      top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
-  if (bias_term_) {
+  if (this->param_propagate_down_[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    // Gradient with respect to weight
+    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
+        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
         reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), (Dtype)0.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bottom data
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 5b95a57b23b..f3e494679d2 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -33,18 +33,22 @@ template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  // Gradient with respect to weight
-  caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-      top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
-  if (bias_term_) {
+  if (this->param_propagate_down_[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    // Gradient with respect to weight
+    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
+        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-        (Dtype)0., this->blobs_[1]->mutable_gpu_diff());
+        reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()), (Dtype)0.,
+        this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bottom data
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,

From fb8c0616f4827795f50e0b665805403eb3719966 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 9 Jul 2014 15:34:44 -0700
Subject: [PATCH 0256/2053] Use Blobs instead of SyncedMemorys for the
 bias_multiplier_'s.

---
 include/caffe/vision_layers.hpp          |  4 ++--
 src/caffe/layers/conv_layer.cpp          | 12 ++++--------
 src/caffe/layers/conv_layer.cu           |  4 ++--
 src/caffe/layers/inner_product_layer.cpp | 12 ++++--------
 src/caffe/layers/inner_product_layer.cu  |  4 ++--
 5 files changed, 14 insertions(+), 22 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 1016f761af5..c8b4609c20f 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -55,7 +55,7 @@ class ConvolutionLayer : public Layer<Dtype> {
   int num_output_;
   int group_;
   Blob<Dtype> col_buffer_;
-  shared_ptr<SyncedMemory> bias_multiplier_;
+  shared_ptr<Blob<Dtype> > bias_multiplier_;
   bool bias_term_;
   int M_;
   int K_;
@@ -157,7 +157,7 @@ class InnerProductLayer : public Layer<Dtype> {
   int K_;
   int N_;
   bool bias_term_;
-  shared_ptr<SyncedMemory> bias_multiplier_;
+  shared_ptr<Blob<Dtype> > bias_multiplier_;
 };
 
 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 0d6871d6ac4..d7099e5511d 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -78,12 +78,8 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // Set up the bias filler
   if (bias_term_) {
-    bias_multiplier_.reset(new SyncedMemory(N_ * sizeof(Dtype)));
-    Dtype* bias_multiplier_data =
-        reinterpret_cast<Dtype*>(bias_multiplier_->mutable_cpu_data());
-    for (int i = 0; i < N_; ++i) {
-        bias_multiplier_data[i] = 1.;
-    }
+    bias_multiplier_.reset(new Blob<Dtype>(1, 1, 1, N_));
+    caffe_set(N_, Dtype(1), bias_multiplier_->mutable_cpu_data());
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
@@ -114,7 +110,7 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (bias_term_) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-            reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
+            bias_multiplier_->cpu_data(),
             (Dtype)1., top_data + (*top)[i]->offset(n));
       }
     }
@@ -148,7 +144,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       for (int n = 0; n < num_; ++n) {
         caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
-            static_cast<const Dtype*>(bias_multiplier_->cpu_data()), 1.,
+            bias_multiplier_->cpu_data(), 1.,
             bias_diff);
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 74067886c6a..0b6e912b639 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -35,7 +35,7 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       if (bias_term_) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
-            reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
+            bias_multiplier_->gpu_data(),
             (Dtype)1., top_data + (*top)[i]->offset(n));
       }
     }
@@ -69,7 +69,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       for (int n = 0; n < num_; ++n) {
         caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
-            static_cast<const Dtype*>(bias_multiplier_->gpu_data()), 1.,
+            bias_multiplier_->gpu_data(), 1.,
             bias_diff);
       }
     }
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 29df6bf2924..a92b56a333c 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -47,12 +47,8 @@ void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }  // parameter initialization
   // Setting up the bias multiplier
   if (bias_term_) {
-    bias_multiplier_.reset(new SyncedMemory(M_ * sizeof(Dtype)));
-    Dtype* bias_multiplier_data =
-        reinterpret_cast<Dtype*>(bias_multiplier_->mutable_cpu_data());
-    for (int i = 0; i < M_; ++i) {
-        bias_multiplier_data[i] = 1.;
-    }
+    bias_multiplier_.reset(new Blob<Dtype>(1, 1, 1, M_));
+    caffe_set(M_, Dtype(1), bias_multiplier_->mutable_cpu_data());
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
@@ -67,7 +63,7 @@ Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
+        bias_multiplier_->cpu_data(),
         this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
   }
   return Dtype(0);
@@ -88,7 +84,7 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), (Dtype)0.,
+        bias_multiplier_->cpu_data(), (Dtype)0.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index f3e494679d2..453593cf552 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -23,7 +23,7 @@ Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
+        bias_multiplier_->gpu_data(),
         this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
   }
   return Dtype(0);
@@ -44,7 +44,7 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()), (Dtype)0.,
+        bias_multiplier_->gpu_data(), (Dtype)0.,
         this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {

From 5b974e05f28a4950fdf5aa6f7604ff207fe7684e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 15 Jul 2014 13:15:36 -0700
Subject: [PATCH 0257/2053] Add Net Test to verify correct param_propagate_down
 behavior.

---
 src/caffe/test/test_net.cpp | 119 +++++++++++++++++++++++++++++++++---
 1 file changed, 111 insertions(+), 8 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 129d4447c41..cd6daeb6ade 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -14,6 +14,8 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
+using std::ostringstream;
+
 namespace caffe {
 
 template <typename TypeParam>
@@ -216,8 +218,11 @@ class NetTest : public ::testing::Test {
     InitNetFromProtoString(proto);
   }
 
-  virtual void InitUnsharedWeightsNet() {
-    const string& proto =
+  virtual void InitUnsharedWeightsNet(const bool bias_term = false,
+      const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
+      const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
+    ostringstream proto;
+    proto <<
         "name: 'UnsharedWeightsNetwork' "
         "layers: { "
         "  name: 'data' "
@@ -236,16 +241,25 @@ class NetTest : public ::testing::Test {
         "} "
         "layers: { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+       "  type: INNER_PRODUCT "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: false "
+        "    bias_term: " << bias_term <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'unsharedweights1' "
+        "  param: 'unsharedweights1' ";
+    if (bias_term) {
+      proto << "  param: '' ";
+    }
+    proto <<
+        "  blobs_lr: " << blobs_lr_w1;
+    if (bias_term) {
+      proto << "  blobs_lr: " << blobs_lr_b1;
+    }
+    proto <<
         "  bottom: 'data' "
         "  top: 'innerproduct1' "
         "} "
@@ -254,14 +268,23 @@ class NetTest : public ::testing::Test {
         "  type: INNER_PRODUCT "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: false "
+        "    bias_term: " << bias_term <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'unsharedweights2' "
+        "  param: 'unsharedweights2' ";
+    if (bias_term) {
+      proto << "  param: '' ";
+    }
+    proto <<
         "  bottom: 'data' "
+        "  blobs_lr: " << blobs_lr_w2;
+    if (bias_term) {
+      proto << "  blobs_lr: " << blobs_lr_b2;
+    }
+    proto <<
         "  top: 'innerproduct2' "
         "} "
         "layers: { "
@@ -270,7 +293,7 @@ class NetTest : public ::testing::Test {
         "  bottom: 'innerproduct1' "
         "  bottom: 'innerproduct2' "
         "} ";
-    InitNetFromProtoString(proto);
+    InitNetFromProtoString(proto.str());
   }
 
   virtual void InitSharedWeightsNet() {
@@ -698,4 +721,84 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   }
 }
 
+TYPED_TEST(NetTest, TestParamPropagateDown) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<Blob<Dtype>*> bottom;
+  const bool kBiasTerm = true;
+
+  // Run the net with all params learned; check that gradients are non-zero.
+  Caffe::set_random_seed(this->seed_);
+  Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
+  const int num_params = params.size();
+  ASSERT_EQ(4, num_params);
+  const Dtype kNonZeroTestMin = 1e-3;
+  vector<Dtype> param_asums(params.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params[i]->count(), params[i]->cpu_diff());
+    param_asums[i] = param_asum;
+    EXPECT_GT(param_asum, kNonZeroTestMin);
+  }
+
+  // Change the learning rates to different non-zero values; should see same
+  // gradients.
+  Caffe::set_random_seed(this->seed_);
+  blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
+  ASSERT_EQ(num_params, params2.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params2[i]->count(), params2[i]->cpu_diff());
+    EXPECT_EQ(param_asum, param_asums[i]);
+  }
+
+  // Change a subset of the learning rates to zero; check that we see zero
+  // gradients for those.
+  Caffe::set_random_seed(this->seed_);
+  blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
+  ASSERT_EQ(num_params, params3.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params3[i]->count(), params3[i]->cpu_diff());
+    if (i == 1 || i == 2) {
+      EXPECT_EQ(0, param_asum);
+    } else {
+      EXPECT_EQ(param_asum, param_asums[i]);
+    }
+  }
+
+  // Change the opposite subset of the learning rates to zero.
+  Caffe::set_random_seed(this->seed_);
+  blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
+  ASSERT_EQ(num_params, params4.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params4[i]->count(), params4[i]->cpu_diff());
+    if (i == 0 || i == 3) {
+      EXPECT_EQ(0, param_asum);
+    } else {
+      EXPECT_EQ(param_asum, param_asums[i]);
+    }
+  }
+}
+
 }  // namespace caffe

From 53825cd3c71fb044d1ee1683f2041d5b4a65b9cc Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 15 Jul 2014 15:12:51 -0700
Subject: [PATCH 0258/2053] Another bugfix related to my CPU/GPU test changes:
 make NetTest a MultiDeviceTest (Caffe:set_mode(Caffe::CPU/GPU) isn't run
 without this).

---
 src/caffe/test/test_net.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index cd6daeb6ade..c30f4168edc 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -19,7 +19,7 @@ using std::ostringstream;
 namespace caffe {
 
 template <typename TypeParam>
-class NetTest : public ::testing::Test {
+class NetTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
 
  protected:

From 7dccb008c1422acf4a51036bea49c274788dc939 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Wed, 16 Jul 2014 16:15:09 -0700
Subject: [PATCH 0259/2053] move using statements inside namespace caffe to
 avoid polluting the whole name space.

---
 include/caffe/layer.hpp                | 4 ++--
 include/caffe/net.hpp                  | 5 ++---
 include/caffe/util/insert_splits.hpp   | 4 ++--
 include/caffe/util/io.hpp              | 6 +++---
 include/caffe/util/upgrade_proto.hpp   | 4 ++--
 src/caffe/layer_factory.cpp            | 3 +--
 src/caffe/layers/accuracy_layer.cpp    | 3 ++-
 src/caffe/layers/bnll_layer.cpp        | 4 ++--
 src/caffe/layers/bnll_layer.cu         | 4 ++--
 src/caffe/layers/data_layer.cpp        | 2 --
 src/caffe/layers/data_layer.cu         | 2 --
 src/caffe/layers/hdf5_data_layer.cu    | 2 --
 src/caffe/layers/image_data_layer.cpp  | 5 ++---
 src/caffe/layers/image_data_layer.cu   | 5 ++---
 src/caffe/layers/window_data_layer.cpp | 7 +++----
 src/caffe/layers/window_data_layer.cu  | 7 +++----
 16 files changed, 28 insertions(+), 39 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index d4d10aafa31..3e386b362c8 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -10,11 +10,11 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+namespace caffe {
+
 using std::string;
 using std::vector;
 
-namespace caffe {
-
 template <typename Dtype>
 class Layer {
  public:
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index cbd5becde2d..ce82e28602c 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -14,15 +14,14 @@
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+namespace caffe {
+
 using std::map;
 using std::pair;
 using std::set;
 using std::string;
 using std::vector;
 
-namespace caffe {
-
-
 template <typename Dtype>
 class Net {
  public:
diff --git a/include/caffe/util/insert_splits.hpp b/include/caffe/util/insert_splits.hpp
index e25cdd7faf1..45c3a051021 100644
--- a/include/caffe/util/insert_splits.hpp
+++ b/include/caffe/util/insert_splits.hpp
@@ -7,11 +7,11 @@
 
 #include "caffe/proto/caffe.pb.h"
 
+namespace caffe {
+
 using std::pair;
 using std::string;
 
-namespace caffe {
-
 // Copy NetParameters with SplitLayers added to replace any shared bottom
 // blobs with unique bottom blobs provided by the SplitLayer.
 void InsertSplits(const NetParameter& param, NetParameter* param_split);
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 4458096ef78..accb0947a9c 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -12,13 +12,13 @@
 
 #include "caffe/blob.hpp"
 
-using std::string;
-using ::google::protobuf::Message;
-
 #define HDF5_NUM_DIMS 4
 
 namespace caffe {
 
+using std::string;
+using ::google::protobuf::Message;
+
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
 
 inline bool ReadProtoFromTextFile(const string& filename, Message* proto) {
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index a1ac060970f..0772f803057 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -8,10 +8,10 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/proto/caffe_pretty_print.pb.h"
 
-using std::string;
-
 namespace caffe {
 
+using std::string;
+
 // Return true iff any layer contains parameters specified using
 // deprecated V0LayerParameter.
 bool NetNeedsUpgrade(const NetParameter& net_param);
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index d6e506dfb9f..173c870f6ae 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -9,10 +9,9 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-using std::string;
-
 namespace caffe {
 
+using std::string;
 
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 409965519ca..ddfe38ab38e 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -10,10 +10,11 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-using std::max;
 
 namespace caffe {
 
+using std::max;
+
 template <typename Dtype>
 void AccuracyLayer<Dtype>::SetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 95e6bd8748c..a00de9e302e 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -6,10 +6,10 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::min;
-
 namespace caffe {
 
+using std::min;
+
 const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 7849d11c25d..8fbe7f79eed 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -6,10 +6,10 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
+using std::max;
+
 const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 29c4fec8ca4..5ff1a440d64 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -14,8 +14,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-using std::string;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 40316a16772..4409f47f21c 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -11,8 +11,6 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 3c27f37e360..d50d9616771 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -15,8 +15,6 @@ TODO:
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 1f7368e7e4d..90ecadfd335 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -16,12 +16,11 @@
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
 
+namespace caffe {
+
 using std::iterator;
-using std::string;
 using std::pair;
 
-namespace caffe {
-
 template <typename Dtype>
 void* ImageDataLayerPrefetch(void* layer_pointer) {
   CHECK(layer_pointer);
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index dd5bdbc2068..2b8c8ed0a35 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -16,11 +16,10 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-using std::pair;
-
 namespace caffe {
 
+using std::pair;
+
 template <typename Dtype>
 Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 5dbdff330ff..53467e8d55c 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -22,16 +22,15 @@
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-using std::map;
-using std::pair;
-
 // caffe.proto > LayerParameter > WindowDataParameter
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
 
 namespace caffe {
 
+using std::map;
+using std::pair;
+
 template <typename Dtype>
 void* WindowDataLayerPrefetch(void* layer_pointer) {
   WindowDataLayer<Dtype>* layer =
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index ca664fcef41..d4efeca06c1 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -12,16 +12,15 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-using std::map;
-using std::pair;
-
 // caffe.proto > LayerParameter > WindowDataParameter
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
 
 namespace caffe {
 
+using std::map;
+using std::pair;
+
 template <typename Dtype>
 Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {

From b3ba56b23e8447ff4b50b3aa7d873572612fcabf Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Wed, 16 Jul 2014 16:22:52 -0700
Subject: [PATCH 0260/2053] neuron_layers.hpp should not need to include
 leveldb

---
 include/caffe/neuron_layers.hpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index e52e395e24b..070b89044d0 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -7,7 +7,6 @@
 #include <utility>
 #include <vector>
 
-#include "leveldb/db.h"
 #include "pthread.h"
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"

From b6d881d1e7eeb75515e70254fc5168f7854f7c42 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 29 Jun 2014 20:45:41 -0700
Subject: [PATCH 0261/2053] configure Makefile for CPU-only build

---
 Makefile                | 16 +++++++++++++---
 Makefile.config.example |  3 +++
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index ad13bb949e7..ef3aa4ba3f3 100644
--- a/Makefile
+++ b/Makefile
@@ -202,6 +202,16 @@ ifeq ($(LINUX), 1)
 	endif
 endif
 
+# CPU-only configuration
+ifeq ($(CPU_ONLY), 1)
+	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
+	TEST_OBJS := $(TEST_CXX_OBJS)
+	TEST_BINS := $(TEST_CXX_BINS)
+	ALL_WARNS := $(ALL_CXX_WARNS)
+	TEST_FILTER := --gtest_filter="-*GPU*:*/2.*:*/3.*"
+	COMMON_FLAGS += -DCPU_ONLY
+endif
+
 # OS X:
 # clang++ instead of g++
 # libstdc++ instead of libc++ for CUDA compatibility on 10.9
@@ -222,9 +232,9 @@ endif
 
 # Debugging
 ifeq ($(DEBUG), 1)
-	COMMON_FLAGS := -DDEBUG -g -O0
+	COMMON_FLAGS += -DDEBUG -g -O0
 else
-	COMMON_FLAGS := -DNDEBUG -O2
+	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
 # BLAS configuration (default = ATLAS)
@@ -342,7 +352,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 	@ echo
 
 runtest: $(TEST_ALL_BIN)
-	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle
+	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
 runtestnogpu: $(TEST_ALL_BIN)
 	$(TEST_ALL_BIN) --gtest_shuffle --gtest_filter="-*GPU*:*/2.*:*/3.*"
diff --git a/Makefile.config.example b/Makefile.config.example
index 17c90016f75..708a39c7841 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -1,6 +1,9 @@
 ## Refer to http://caffe.berkeleyvision.org/installation.html
 # Contributions simplifying and improving our build system are welcome!
 
+# CPU-only switch (uncomment to build without GPU support).
+# CPU_ONLY := 1
+
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
 # CUSTOM_CXX := g++

From 41e063f77647a489aaabb26d795e27c8850a816f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 29 Jun 2014 20:47:40 -0700
Subject: [PATCH 0262/2053] stub out GPU layer methods to crash loudly in
 CPU-only mode

---
 include/caffe/layer.hpp                       |  1 +
 include/caffe/util/cpu_only.hpp               | 37 +++++++++++++++++++
 src/caffe/layers/bnll_layer.cpp               |  3 ++
 src/caffe/layers/concat_layer.cpp             |  4 ++
 src/caffe/layers/conv_layer.cpp               |  4 ++
 src/caffe/layers/data_layer.cpp               |  4 ++
 src/caffe/layers/dropout_layer.cpp            |  4 ++
 src/caffe/layers/eltwise_layer.cpp            |  4 ++
 src/caffe/layers/euclidean_loss_layer.cpp     |  4 ++
 src/caffe/layers/flatten_layer.cpp            |  4 ++
 src/caffe/layers/hdf5_data_layer.cpp          |  4 ++
 src/caffe/layers/hdf5_output_layer.cpp        |  4 ++
 src/caffe/layers/im2col_layer.cpp             |  4 ++
 src/caffe/layers/image_data_layer.cpp         |  4 ++
 src/caffe/layers/inner_product_layer.cpp      |  4 ++
 src/caffe/layers/lrn_layer.cpp                |  6 +++
 src/caffe/layers/pooling_layer.cpp            |  4 ++
 src/caffe/layers/power_layer.cpp              |  4 ++
 src/caffe/layers/relu_layer.cpp               |  4 ++
 src/caffe/layers/relu_layer.cu                |  1 +
 .../sigmoid_cross_entropy_loss_layer.cpp      |  4 ++
 src/caffe/layers/sigmoid_layer.cpp            |  4 ++
 src/caffe/layers/softmax_layer.cpp            |  4 ++
 src/caffe/layers/softmax_loss_layer.cpp       |  4 ++
 src/caffe/layers/split_layer.cpp              |  4 ++
 src/caffe/layers/tanh_layer.cpp               |  4 ++
 src/caffe/layers/threshold_layer.cpp          |  4 ++
 src/caffe/layers/window_data_layer.cpp        |  4 ++
 28 files changed, 140 insertions(+)
 create mode 100644 include/caffe/util/cpu_only.hpp

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index d4d10aafa31..31689a4fe6f 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -9,6 +9,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/cpu_only.hpp"
 
 using std::string;
 using std::vector;
diff --git a/include/caffe/util/cpu_only.hpp b/include/caffe/util/cpu_only.hpp
new file mode 100644
index 00000000000..1ae6ec9dac2
--- /dev/null
+++ b/include/caffe/util/cpu_only.hpp
@@ -0,0 +1,37 @@
+// Copyright 2014 BVLC and contributors.
+
+#ifndef CAFFE_UTIL_CPU_ONLY_H_
+#define CAFFE_UTIL_CPU_ONLY_H_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+
+// For CPU-only Caffe, stub out GPU calls as unavailable.
+#ifdef CPU_ONLY
+
+#define NO_GPU LOG(FATAL) << "CPU-only Mode"
+
+#define STUB_GPU(classname) \
+template <typename Dtype> \
+Dtype classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
+    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+template <typename Dtype> \
+void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
+    const vector<bool>& propagate_down, \
+    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+
+#define STUB_GPU_FORWARD(classname, funcname) \
+template <typename Dtype> \
+Dtype classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
+    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+
+#define STUB_GPU_BACKWARD(classname, funcname) \
+template <typename Dtype> \
+void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
+    const vector<bool>& propagate_down, \
+    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+
+#endif  // CPU_ONLY
+#endif  // CAFFE_UTIL_CPU_ONLY_H_
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 95e6bd8748c..0e30b8aacbf 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -43,6 +43,9 @@ void BNLLLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(BNLLLayer);
+#endif
 
 INSTANTIATE_CLASS(BNLLLayer);
 
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 4c894ddffc4..dd899a57c85 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -100,6 +100,10 @@ void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }  // concat_dim_ is guaranteed to be 0 or 1 by SetUp.
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(ConcatLayer);
+#endif
+
 INSTANTIATE_CLASS(ConcatLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index d7099e5511d..e8f08c7e3cd 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -187,6 +187,10 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(ConvolutionLayer);
+#endif
+
 INSTANTIATE_CLASS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 29c4fec8ca4..e9ec15c501c 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -362,6 +362,10 @@ Dtype DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(DataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index a3501bf518c..a9372977693 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -65,6 +65,10 @@ void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(DropoutLayer);
+#endif
+
 INSTANTIATE_CLASS(DropoutLayer);
 
 
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 2c265f6678f..44bad932a11 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -94,6 +94,10 @@ void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(EltwiseLayer);
+#endif
+
 INSTANTIATE_CLASS(EltwiseLayer);
 
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 2478a514cac..43e9989f513 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -52,6 +52,10 @@ void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(EuclideanLossLayer);
+#endif
+
 INSTANTIATE_CLASS(EuclideanLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 9494da9a255..7e106d26f79 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -33,6 +33,10 @@ void FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   (*bottom)[0]->ShareDiff(*top[0]);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(FlattenLayer);
+#endif
+
 INSTANTIATE_CLASS(FlattenLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 2ba7fa77f45..81cfdc096df 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -114,6 +114,10 @@ Dtype HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(HDF5DataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(HDF5DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 8307ad7c184..ffa240f15cc 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -69,6 +69,10 @@ void HDF5OutputLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   return;
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(HDF5OutputLayer);
+#endif
+
 INSTANTIATE_CLASS(HDF5OutputLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index e047dfb80a7..2c86412bb15 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -47,6 +47,10 @@ void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(Im2colLayer);
+#endif
+
 INSTANTIATE_CLASS(Im2colLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 1f7368e7e4d..9d09d3c4583 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -287,6 +287,10 @@ Dtype ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(ImageDataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(ImageDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index a92b56a333c..4da8578d76f 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -96,6 +96,10 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(InnerProductLayer);
+#endif
+
 INSTANTIATE_CLASS(InnerProductLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 2bda0430c15..0e2df2ff4bb 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -256,6 +256,12 @@ void LRNLayer<Dtype>::WithinChannelBackward(
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(LRNLayer);
+STUB_GPU_FORWARD(LRNLayer, CrossChannelForward);
+STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
+#endif
+
 INSTANTIATE_CLASS(LRNLayer);
 
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 9151ff23674..d6fef07ba46 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -296,6 +296,10 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(PoolingLayer);
+#endif
+
 INSTANTIATE_CLASS(PoolingLayer);
 
 
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 5ff3392968e..4607457a460 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -99,6 +99,10 @@ void PowerLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(PowerLayer);
+#endif
+
 INSTANTIATE_CLASS(PowerLayer);
 
 
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index d7a8509b247..6d6017442d4 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -38,6 +38,10 @@ void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(ReLULayer);
+#endif
+
 INSTANTIATE_CLASS(ReLULayer);
 
 
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index a5b24afab60..2ad689126a3 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -59,6 +59,7 @@ void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+
 INSTANTIATE_CLASS(ReLULayer);
 
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 8cb830ff248..074fa6cef3c 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -68,6 +68,10 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(SigmoidCrossEntropyLossLayer);
+#endif
+
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
 
 
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 50139d863dd..e25db04c42a 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -42,6 +42,10 @@ void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(SigmoidLayer);
+#endif
+
 INSTANTIATE_CLASS(SigmoidLayer);
 
 
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 5d60d9df1ba..665cfaec3ad 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -82,6 +82,10 @@ void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(SoftmaxLayer);
+#endif
+
 INSTANTIATE_CLASS(SoftmaxLayer);
 
 
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 37c5ebc45be..c87270eab22 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -79,6 +79,10 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(SoftmaxWithLossLayer);
+#endif
+
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
 
 
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 28abd95f5ff..6bae4ef22fb 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -50,6 +50,10 @@ void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(SplitLayer);
+#endif
+
 INSTANTIATE_CLASS(SplitLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 6b5166d53e9..0a6ec68d1f9 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -41,6 +41,10 @@ void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(TanHLayer);
+#endif
+
 INSTANTIATE_CLASS(TanHLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index e6ed8a6b40e..2c8546c957a 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -27,6 +27,10 @@ Dtype ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(ThresholdLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(ThresholdLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 5dbdff330ff..51e0dc09e35 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -453,6 +453,10 @@ Dtype WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(WindowDataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(WindowDataLayer);
 
 }  // namespace caffe

From b882c3b4707eeb072a04490c6f36bd7c4deb03df Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 29 Jun 2014 21:11:44 -0700
Subject: [PATCH 0263/2053] add guards to drop GPU code in CPU-only mode

---
 Makefile                                      |   2 +-
 include/caffe/test/test_caffe_main.hpp        |   8 ++
 src/caffe/net.cpp                             |   2 +
 src/caffe/test/test_math_functions.cpp        | 129 +++++++++---------
 .../test/test_random_number_generator.cpp     |  42 +++---
 5 files changed, 102 insertions(+), 81 deletions(-)

diff --git a/Makefile b/Makefile
index ef3aa4ba3f3..69f2c30e49c 100644
--- a/Makefile
+++ b/Makefile
@@ -208,7 +208,7 @@ ifeq ($(CPU_ONLY), 1)
 	TEST_OBJS := $(TEST_CXX_OBJS)
 	TEST_BINS := $(TEST_CXX_BINS)
 	ALL_WARNS := $(ALL_CXX_WARNS)
-	TEST_FILTER := --gtest_filter="-*GPU*:*/2.*:*/3.*"
+	TEST_FILTER := --gtest_filter="-*GPU*"
 	COMMON_FLAGS += -DCPU_ONLY
 endif
 
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index e1a76455bed..3541c0b2c8a 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -44,6 +44,12 @@ struct DoubleCPU {
   static const Caffe::Brew device = Caffe::CPU;
 };
 
+#ifdef CPU_ONLY
+
+typedef ::testing::Types<FloatCPU, DoubleCPU> TestDtypesAndDevices;
+
+#else
+
 struct FloatGPU {
   typedef float Dtype;
   static const Caffe::Brew device = Caffe::GPU;
@@ -57,6 +63,8 @@ struct DoubleGPU {
 typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
     TestDtypesAndDevices;
 
+#endif
+
 }  // namespace caffe
 
 #endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index edea726fb51..aba4cc2f4f2 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -498,11 +498,13 @@ void Net<Dtype>::Update() {
       owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
       caffe_add(count, this_diff, owner_diff, owner_diff);
       break;
+#ifndef CPU_ONLY
     case Caffe::GPU:
       this_diff = params_[i]->gpu_diff();
       owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
       caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
       break;
+#endif
     default:
       LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
     }
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index 2a70c9c4aba..502b77e07a8 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -80,18 +80,6 @@ TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
             caffe_cpu_hamming_distance<TypeParam>(n, x, y));
 }
 
-// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
-  int n = this->blob_bottom_->count();
-  const TypeParam* x = this->blob_bottom_->cpu_data();
-  const TypeParam* y = this->blob_top_->cpu_data();
-  int reference_distance = this->ReferenceHammingDistance(n, x, y);
-  x = this->blob_bottom_->gpu_data();
-  y = this->blob_top_->gpu_data();
-  int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
-  EXPECT_EQ(reference_distance, computed_distance);
-}
-
 TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
@@ -103,91 +91,116 @@ TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
+TYPED_TEST(MathFunctionsTest, TestSignCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  TypeParam std_asum = 0;
+  caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* signs = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    std_asum += std::fabs(x[i]);
+    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
   }
-  TypeParam gpu_asum;
-  caffe_gpu_asum<TypeParam>(n, this->blob_bottom_->gpu_data(), &gpu_asum);
-  EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignCPU) {
+TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* signs = this->blob_bottom_->cpu_diff();
+  caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
+    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignGPU) {
+TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
-                            this->blob_bottom_->mutable_gpu_diff());
-  const TypeParam* signs = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
+  caffe_cpu_fabs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
+    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
+TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
   int n = this->blob_bottom_->count();
+  TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
+                                                   this->blob_bottom_->count()];
+  caffe_cpu_scale<TypeParam>(n, alpha, this->blob_bottom_->cpu_data(),
+                             this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* scaled = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
+    EXPECT_EQ(scaled[i], x[i] * alpha);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
+TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
+  const int n = this->blob_bottom_->count();
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  TypeParam* top_data = this->blob_top_->mutable_cpu_data();
+  Caffe::set_mode(Caffe::CPU);
+  caffe_copy(n, bottom_data, top_data);
+  for (int i = 0; i < n; ++i) {
+    EXPECT_EQ(bottom_data[i], top_data[i]);
+  }
+}
+
+#ifndef CPU_ONLY
+
+// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
+TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
+  int n = this->blob_bottom_->count();
+  const TypeParam* x = this->blob_bottom_->cpu_data();
+  const TypeParam* y = this->blob_top_->cpu_data();
+  int reference_distance = this->ReferenceHammingDistance(n, x, y);
+  x = this->blob_bottom_->gpu_data();
+  y = this->blob_top_->gpu_data();
+  int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
+  EXPECT_EQ(reference_distance, computed_distance);
+}
+
+TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
-                            this->blob_bottom_->mutable_gpu_diff());
-  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
+  TypeParam std_asum = 0;
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
+    std_asum += std::fabs(x[i]);
   }
+  TypeParam gpu_asum;
+  caffe_gpu_asum<TypeParam>(n, this->blob_bottom_->gpu_data(), &gpu_asum);
+  EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
+TYPED_TEST(MathFunctionsTest, TestSignGPU) {
   int n = this->blob_bottom_->count();
+  caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
+                            this->blob_bottom_->mutable_gpu_diff());
+  const TypeParam* signs = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_fabs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
+    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
+TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_fabs<TypeParam>(n, this->blob_bottom_->gpu_data(),
+  caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
-  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
+  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
+    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
+TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
   int n = this->blob_bottom_->count();
-  TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
-                                                   this->blob_bottom_->count()];
-  caffe_cpu_scale<TypeParam>(n, alpha, this->blob_bottom_->cpu_data(),
-                             this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* scaled = this->blob_bottom_->cpu_diff();
+  caffe_gpu_fabs<TypeParam>(n, this->blob_bottom_->gpu_data(),
+                            this->blob_bottom_->mutable_gpu_diff());
+  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(scaled[i], x[i] * alpha);
+    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
   }
 }
 
@@ -204,17 +217,6 @@ TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
-  const int n = this->blob_bottom_->count();
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  TypeParam* top_data = this->blob_top_->mutable_cpu_data();
-  Caffe::set_mode(Caffe::CPU);
-  caffe_copy(n, bottom_data, top_data);
-  for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(bottom_data[i], top_data[i]);
-  }
-}
-
 TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
@@ -228,4 +230,7 @@ TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
   }
 }
 
+#endif
+
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 3ab4680e976..3cd77da9876 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -65,11 +65,6 @@ class RandomNumberGeneratorTest : public ::testing::Test {
     caffe_rng_gaussian(sample_size_, mu, sigma, rng_data);
   }
 
-  void RngGaussianFillGPU(const Dtype mu, const Dtype sigma, void* gpu_data) {
-    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
-    caffe_gpu_rng_gaussian(sample_size_, mu, sigma, rng_data);
-  }
-
   void RngGaussianChecks(const Dtype mu, const Dtype sigma,
                          const void* cpu_data, const Dtype sparse_p = 0) {
     const Dtype* rng_data = static_cast<const Dtype*>(cpu_data);
@@ -114,19 +109,6 @@ class RandomNumberGeneratorTest : public ::testing::Test {
     caffe_rng_uniform(sample_size_, lower, upper, rng_data);
   }
 
-  void RngUniformFillGPU(const Dtype lower, const Dtype upper, void* gpu_data) {
-    CHECK_GE(upper, lower);
-    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
-    caffe_gpu_rng_uniform(sample_size_, lower, upper, rng_data);
-  }
-
-  // Fills with uniform integers in [0, UINT_MAX] using 2 argument form of
-  // caffe_gpu_rng_uniform.
-  void RngUniformIntFillGPU(void* gpu_data) {
-    unsigned int* rng_data = static_cast<unsigned int*>(gpu_data);
-    caffe_gpu_rng_uniform(sample_size_, rng_data);
-  }
-
   void RngUniformChecks(const Dtype lower, const Dtype upper,
                         const void* cpu_data, const Dtype sparse_p = 0) {
     const Dtype* rng_data = static_cast<const Dtype*>(cpu_data);
@@ -188,6 +170,28 @@ class RandomNumberGeneratorTest : public ::testing::Test {
     EXPECT_NEAR(sample_mean, true_mean, bound);
   }
 
+#ifndef CPU_ONLY
+
+  void RngGaussianFillGPU(const Dtype mu, const Dtype sigma, void* gpu_data) {
+    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
+    caffe_gpu_rng_gaussian(sample_size_, mu, sigma, rng_data);
+  }
+
+  void RngUniformFillGPU(const Dtype lower, const Dtype upper, void* gpu_data) {
+    CHECK_GE(upper, lower);
+    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
+    caffe_gpu_rng_uniform(sample_size_, lower, upper, rng_data);
+  }
+
+  // Fills with uniform integers in [0, UINT_MAX] using 2 argument form of
+  // caffe_gpu_rng_uniform.
+  void RngUniformIntFillGPU(void* gpu_data) {
+    unsigned int* rng_data = static_cast<unsigned int*>(gpu_data);
+    caffe_gpu_rng_uniform(sample_size_, rng_data);
+  }
+
+#endif
+
   int num_above_mean;
   int num_below_mean;
 
@@ -393,6 +397,7 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngBernoulliTimesBernoulli) {
   EXPECT_NEAR(true_mean, sample_p, bound);
 }
 
+#ifndef CPU_ONLY
 
 TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussianGPU) {
   const TypeParam mu = 0;
@@ -512,5 +517,6 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngUniformTimesUniformGPU) {
   this->RngUniformChecks(lower_prod, upper_prod, uniform_data_1);
 }
 
+#endif
 
 }  // namespace caffe

From e52d91ea331dfd1430384808db272926561b116f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 15 Jul 2014 15:56:36 +0200
Subject: [PATCH 0264/2053] collect CUDA includes and calls, separate from
 CPU-only mode, leave out

- collect CUDA includes in device_alternate.hpp
- add guards for CUDA code
- move GPU code into cu from cpp
- make CUDA includes and libraries conditional in Makefile
- drop CUDA dependency from travis-ci build
---
 .travis.yml                                   |   4 +-
 Makefile                                      |  12 +-
 include/caffe/common.hpp                      |  17 +-
 include/caffe/layer.hpp                       |   2 +-
 include/caffe/test/test_caffe_main.hpp        |   1 -
 include/caffe/util/benchmark.hpp              |   5 +-
 .../{cpu_only.hpp => device_alternate.hpp}    |  23 +-
 include/caffe/util/math_functions.hpp         | 208 +++++++++---------
 src/caffe/blob.cpp                            |   7 +-
 src/caffe/common.cpp                          |  49 ++++-
 src/caffe/layers/image_data_layer.cu          |   1 -
 src/caffe/layers/inner_product_layer.cu       |   2 -
 src/caffe/solver.cpp                          |   4 +
 src/caffe/syncedmem.cpp                       |  20 +-
 src/caffe/test/test_accuracy_layer.cpp        |   3 -
 src/caffe/test/test_argmax_layer.cpp          |   3 -
 src/caffe/test/test_benchmark.cpp             |   3 -
 src/caffe/test/test_blob.cpp                  |   1 -
 src/caffe/test/test_caffe_main.cpp            |   6 +
 src/caffe/test/test_common.cpp                |   9 +-
 src/caffe/test/test_concat_layer.cpp          |   3 -
 src/caffe/test/test_convolution_layer.cpp     |   3 -
 src/caffe/test/test_data_layer.cpp            |   3 -
 src/caffe/test/test_dummy_data_layer.cpp      |   2 -
 src/caffe/test/test_eltwise_layer.cpp         |   3 -
 src/caffe/test/test_euclidean_loss_layer.cpp  |   3 -
 src/caffe/test/test_filler.cpp                |   1 -
 src/caffe/test/test_flatten_layer.cpp         |   3 -
 src/caffe/test/test_hdf5_output_layer.cpp     |   3 -
 src/caffe/test/test_hdf5data_layer.cpp        |   3 -
 src/caffe/test/test_hinge_loss_layer.cpp      |   3 -
 src/caffe/test/test_im2col_kernel.cu          |   1 -
 src/caffe/test/test_im2col_layer.cpp          |   3 -
 src/caffe/test/test_image_data_layer.cpp      |   4 -
 src/caffe/test/test_inner_product_layer.cpp   |  15 +-
 src/caffe/test/test_lrn_layer.cpp             |   3 -
 .../test/test_maxpool_dropout_layers.cpp      |   2 -
 .../test_multinomial_logistic_loss_layer.cpp  |   3 -
 src/caffe/test/test_neuron_layer.cpp          |   3 -
 src/caffe/test/test_platform.cpp              |   5 +-
 src/caffe/test/test_pooling_layer.cpp         |   3 -
 src/caffe/test/test_power_layer.cpp           |   3 -
 .../test/test_random_number_generator.cpp     |   1 -
 .../test_sigmoid_cross_entropy_loss_layer.cpp |   2 -
 src/caffe/test/test_softmax_layer.cpp         |   3 -
 .../test/test_softmax_with_loss_layer.cpp     |   3 -
 src/caffe/test/test_split_layer.cpp           |   3 -
 src/caffe/test/test_stochastic_pooling.cpp    |   3 -
 src/caffe/test/test_syncedmem.cpp             |  14 +-
 src/caffe/test/test_tanh_layer.cpp            |   3 -
 src/caffe/test/test_threshold_layer.cpp       |   3 -
 src/caffe/test/test_upgrade_proto.cpp         |   1 -
 src/caffe/test/test_util_blas.cpp             |   8 +-
 src/caffe/util/benchmark.cpp                  |  21 +-
 src/caffe/util/math_functions.cpp             | 135 +-----------
 src/caffe/util/math_functions.cu              | 131 +++++++++++
 tools/dump_network.cpp                        |   1 -
 tools/extract_features.cpp                    |   1 -
 tools/finetune_net.cpp                        |   2 -
 tools/net_speed_benchmark.cpp                 |   1 -
 tools/test_net.cpp                            |   2 -
 tools/train_net.cpp                           |   2 -
 62 files changed, 422 insertions(+), 373 deletions(-)
 rename include/caffe/util/{cpu_only.hpp => device_alternate.hpp} (71%)

diff --git a/.travis.yml b/.travis.yml
index 1f780471b3b..c0119756c6b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -19,14 +19,12 @@ before_install:
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
-  - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
-  - sudo apt-get -y update && sudo apt-get -y install cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
   - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
 
 before_script:
   - mv Makefile.config.example Makefile.config
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/lib
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
   - export NUM_THREADS=4
 
 script:
diff --git a/Makefile b/Makefile
index 69f2c30e49c..417ff868218 100644
--- a/Makefile
+++ b/Makefile
@@ -149,11 +149,13 @@ NONEMPTY_WARN_REPORT := $(BUILD_DIR)/$(WARNS_EXT)
 CUDA_INCLUDE_DIR := $(CUDA_DIR)/include
 CUDA_LIB_DIR := $(CUDA_DIR)/lib64 $(CUDA_DIR)/lib
 
-INCLUDE_DIRS += $(BUILD_INCLUDE_DIR)
-INCLUDE_DIRS += ./src ./include $(CUDA_INCLUDE_DIR)
-LIBRARY_DIRS += $(CUDA_LIB_DIR)
-LIBRARIES := cudart cublas curand \
-	pthread \
+INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./include
+ifneq ($(CPU_ONLY), 1)
+	INCLUDE_DIRS += $(CUDA_INCLUDE_DIR)
+	LIBRARY_DIRS += $(CUDA_LIB_DIR)
+	LIBRARIES := cudart cublas curand
+endif
+LIBRARIES += pthread \
 	glog protobuf leveldb snappy \
 	lmdb \
 	boost_system \
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index bd4e39f136d..0ee49f36030 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -4,12 +4,10 @@
 #define CAFFE_COMMON_HPP_
 
 #include <boost/shared_ptr.hpp>
-#include <cublas_v2.h>
-#include <cuda.h>
-#include <curand.h>
-#include <driver_types.h>  // cuda driver types
 #include <glog/logging.h>
 
+#include "caffe/util/device_alternate.hpp"
+
 // Disable the copy and assignment operator for a class.
 #define DISABLE_COPY_AND_ASSIGN(classname) \
 private:\
@@ -25,6 +23,8 @@ private:\
 // is executed we will see a fatal log.
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
 
+#ifndef CPU_ONLY
+
 // CUDA: various checks for different function calls.
 #define CUDA_CHECK(condition) \
   /* Code block avoids redefinition of cudaError_t error */ \
@@ -56,6 +56,8 @@ private:\
 // CUDA: check for error after kernel execution and exit loudly if there is one.
 #define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
 
+#endif  // CPU_ONLY
+
 namespace caffe {
 
 // We will use the boost shared_ptr instead of the new C++11 one mainly
@@ -99,10 +101,12 @@ class Caffe {
     }
     return *(Get().random_generator_);
   }
+#ifndef CPU_ONLY
   inline static cublasHandle_t cublas_handle() { return Get().cublas_handle_; }
   inline static curandGenerator_t curand_generator() {
     return Get().curand_generator_;
   }
+#endif
 
   // Returns the mode: running on CPU or GPU.
   inline static Brew mode() { return Get().mode_; }
@@ -125,8 +129,10 @@ class Caffe {
   static void DeviceQuery();
 
  protected:
+#ifndef CPU_ONLY
   cublasHandle_t cublas_handle_;
   curandGenerator_t curand_generator_;
+#endif
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
@@ -140,6 +146,8 @@ class Caffe {
   DISABLE_COPY_AND_ASSIGN(Caffe);
 };
 
+#ifndef CPU_ONLY
+
 // NVIDIA_CUDA-5.5_Samples/common/inc/helper_cuda.h
 const char* cublasGetErrorString(cublasStatus_t error);
 const char* curandGetErrorString(curandStatus_t error);
@@ -158,6 +166,7 @@ inline int CAFFE_GET_BLOCKS(const int N) {
   return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS;
 }
 
+#endif  // CPU_ONLY
 
 }  // namespace caffe
 
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 31689a4fe6f..06518c2eef1 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -9,7 +9,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/cpu_only.hpp"
+#include "caffe/util/device_alternate.hpp"
 
 using std::string;
 using std::vector;
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 3541c0b2c8a..9e7b85944da 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -5,7 +5,6 @@
 #ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
 #define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
 
-#include <cuda_runtime.h>
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 
diff --git a/include/caffe/util/benchmark.hpp b/include/caffe/util/benchmark.hpp
index 1d26314c62f..8ee76140d9a 100644
--- a/include/caffe/util/benchmark.hpp
+++ b/include/caffe/util/benchmark.hpp
@@ -4,7 +4,8 @@
 #define CAFFE_UTIL_BENCHMARK_H_
 
 #include <boost/date_time/posix_time/posix_time.hpp>
-#include <cuda_runtime.h>
+
+#include "caffe/util/device_alternate.hpp"
 
 namespace caffe {
 
@@ -27,8 +28,10 @@ class Timer {
   bool initted_;
   bool running_;
   bool has_run_at_least_once_;
+#ifndef CPU_ONLY
   cudaEvent_t start_gpu_;
   cudaEvent_t stop_gpu_;
+#endif
   boost::posix_time::ptime start_cpu_;
   boost::posix_time::ptime stop_cpu_;
   float elapsed_milliseconds_;
diff --git a/include/caffe/util/cpu_only.hpp b/include/caffe/util/device_alternate.hpp
similarity index 71%
rename from include/caffe/util/cpu_only.hpp
rename to include/caffe/util/device_alternate.hpp
index 1ae6ec9dac2..abee4f57dd1 100644
--- a/include/caffe/util/cpu_only.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -1,15 +1,13 @@
 // Copyright 2014 BVLC and contributors.
 
-#ifndef CAFFE_UTIL_CPU_ONLY_H_
-#define CAFFE_UTIL_CPU_ONLY_H_
+#ifndef CAFFE_UTIL_DEVICE_ALTERNATE_H_
+#define CAFFE_UTIL_DEVICE_ALTERNATE_H_
 
-#include <vector>
+#ifdef CPU_ONLY  // CPU-only Caffe.
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
+#include <vector>
 
-// For CPU-only Caffe, stub out GPU calls as unavailable.
-#ifdef CPU_ONLY
+// Stub out GPU calls as unavailable.
 
 #define NO_GPU LOG(FATAL) << "CPU-only Mode"
 
@@ -33,5 +31,14 @@ void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
     const vector<bool>& propagate_down, \
     vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
 
+#else  // Normal GPU + CPU Caffe.
+
+#include <cublas_v2.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <curand.h>
+#include <driver_types.h>  // cuda driver types
+
 #endif  // CPU_ONLY
-#endif  // CAFFE_UTIL_CPU_ONLY_H_
+
+#endif  // CAFFE_UTIL_DEVICE_ALTERNATE_H_
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 2df0fc983f9..5036a573033 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -3,12 +3,12 @@
 #ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_
 #define CAFFE_UTIL_MATH_FUNCTIONS_H_
 
-#include <cublas_v2.h>
 #include <stdint.h>
 #include <cmath>  // for std::fabs and std::signbit
 
 #include "glog/logging.h"
 
+#include "caffe/util/device_alternate.hpp"
 #include "caffe/util/mkl_alternate.hpp"
 
 namespace caffe {
@@ -21,97 +21,49 @@ void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
     const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
     Dtype* C);
 
-// Decaf gpu gemm provides an interface that is almost the same as the cpu
-// gemm function - following the c convention and calling the fortran-order
-// gpu code under the hood.
-template <typename Dtype>
-void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
-    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-    const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
-    Dtype* C);
-
 template <typename Dtype>
 void caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
     const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
     Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
-    const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
-    Dtype* y);
-
 template <typename Dtype>
 void caffe_axpy(const int N, const Dtype alpha, const Dtype* X,
     Dtype* Y);
 
-template <typename Dtype>
-void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
-    Dtype* Y);
-
 template <typename Dtype>
 void caffe_cpu_axpby(const int N, const Dtype alpha, const Dtype* X,
     const Dtype beta, Dtype* Y);
 
-template <typename Dtype>
-void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
-    const Dtype beta, Dtype* Y);
-
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
 
-void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
-
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
-
 template <typename Dtype>
 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
-
 template <typename Dtype>
 void caffe_scal(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
-
 template <typename Dtype>
 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
 
 template <typename Dtype>
 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
 template <typename Dtype>
 void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
 template <typename Dtype>
 void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
 template <typename Dtype>
 void caffe_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
 template <typename Dtype>
 void caffe_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
-
 unsigned int caffe_rng_rand();
 
 template <typename Dtype>
@@ -120,58 +72,29 @@ Dtype caffe_nextafter(const Dtype b);
 template <typename Dtype>
 void caffe_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
 
-// caffe_gpu_rng_uniform with two arguments generates integers in the range
-// [0, UINT_MAX].
-void caffe_gpu_rng_uniform(const int n, unsigned int* r);
-
-// caffe_gpu_rng_uniform with four arguments generates floats in the range
-// (a, b] (strictly greater than a, less than or equal to b) due to the
-// specification of curandGenerateUniform.  With a = 0, b = 1, just calls
-// curandGenerateUniform; with other limits will shift and scale the outputs
-// appropriately after calling curandGenerateUniform.
-template <typename Dtype>
-void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
-
 template <typename Dtype>
 void caffe_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
                         Dtype* r);
 
-template <typename Dtype>
-void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
-                            Dtype* r);
-
 template <typename Dtype>
 void caffe_rng_bernoulli(const int n, const Dtype p, int* r);
 
 template <typename Dtype>
 void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
 
-template <typename Dtype>
-void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
-
 template <typename Dtype>
 void caffe_exp(const int n, const Dtype* a, Dtype* y);
 
 template <typename Dtype>
 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
-
 template <typename Dtype>
 int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y);
 
-template <typename Dtype>
-uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
-                                    const Dtype* y);
-
 // Returns the sum of the absolute values of the elements of vector x
 template <typename Dtype>
 Dtype caffe_cpu_asum(const int n, const Dtype* x);
 
-template <typename Dtype>
-void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
-
 // the branchless, type-safe version from
 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
 template<typename Dtype>
@@ -200,6 +123,109 @@ inline char caffe_sign(Dtype val) {
   template <> \
   void caffe_cpu_##name<double>(const int n, const double* x, double* y)
 
+// output is 1 for the positives, 0 for zero, and -1 for the negatives
+DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
+
+// This returns a nonzero value if the input has its sign bit set.
+// The name sngbit is meant to avoid conflicts with std::signbit in the macro
+using std::signbit;
+DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = signbit(x[i]));
+
+DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
+
+template <typename Dtype>
+void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
+
+#ifndef CPU_ONLY  // GPU
+
+// Decaf gpu gemm provides an interface that is almost the same as the cpu
+// gemm function - following the c convention and calling the fortran-order
+// gpu code under the hood.
+template <typename Dtype>
+void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
+    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+    const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
+    Dtype* C);
+
+template <typename Dtype>
+void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
+    const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
+    Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
+    Dtype* Y);
+
+template <typename Dtype>
+void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
+    const Dtype beta, Dtype* Y);
+
+void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
+
+template <typename Dtype>
+void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
+
+template <typename Dtype>
+void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
+
+template <typename Dtype>
+void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
+
+template <typename Dtype>
+void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
+
+// caffe_gpu_rng_uniform with two arguments generates integers in the range
+// [0, UINT_MAX].
+void caffe_gpu_rng_uniform(const int n, unsigned int* r);
+
+// caffe_gpu_rng_uniform with four arguments generates floats in the range
+// (a, b] (strictly greater than a, less than or equal to b) due to the
+// specification of curandGenerateUniform.  With a = 0, b = 1, just calls
+// curandGenerateUniform; with other limits will shift and scale the outputs
+// appropriately after calling curandGenerateUniform.
+template <typename Dtype>
+void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
+
+template <typename Dtype>
+void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
+                            Dtype* r);
+
+template <typename Dtype>
+void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
+
+template <typename Dtype>
+void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
+
+template <typename Dtype>
+uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
+                                    const Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
+
+template<typename Dtype>
+void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
+
+template<typename Dtype>
+void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
 
 #define DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(name, operation) \
 template<typename Dtype> \
@@ -221,32 +247,8 @@ void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
       n, x, y); \
 }
 
-// output is 1 for the positives, 0 for zero, and -1 for the negatives
-DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
-
-template<typename Dtype>
-void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
-
-// This returns a nonzero value if the input has its sign bit set.
-// The name sngbit is meant to avoid conflicts with std::signbit in the macro
-using std::signbit;
-DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = signbit(x[i]));
-
-template<typename Dtype>
-void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
-
-DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
-
-template <typename Dtype>
-void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
-
-template <typename Dtype>
-void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
-
-template <typename Dtype>
-void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
+#endif  // CPU_ONLY
 
 }  // namespace caffe
 
-
 #endif  // CAFFE_UTIL_MATH_FUNCTIONS_H_
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 8df46323eee..1051eaa1c2c 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -1,8 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
-#include <cublas_v2.h>
-
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
@@ -126,10 +123,14 @@ void Blob<Dtype>::Update() {
     break;
   case SyncedMemory::HEAD_AT_GPU:
   case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
     // perform computation on GPU
     caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
         static_cast<const Dtype*>(diff_->gpu_data()),
         static_cast<Dtype*>(data_->mutable_gpu_data()));
+#else
+    NO_GPU;
+#endif
     break;
   default:
     LOG(FATAL) << "Syncedmem not initialized.";
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 631c8afd068..e8765eebff1 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -10,8 +10,7 @@ namespace caffe {
 
 shared_ptr<Caffe> Caffe::singleton_;
 
-
-// curand seeding
+// random seeding
 int64_t cluster_seedgen(void) {
   int64_t s, seed, pid;
   pid = getpid();
@@ -20,6 +19,50 @@ int64_t cluster_seedgen(void) {
   return seed;
 }
 
+#ifdef CPU_ONLY  // CPU-only Caffe.
+
+Caffe::Caffe()
+    : random_generator_(), mode_(Caffe::CPU), phase_(Caffe::TRAIN) { }
+
+Caffe::~Caffe() { }
+
+void Caffe::set_random_seed(const unsigned int seed) {
+  // RNG seed
+  Get().random_generator_.reset(new RNG(seed));
+}
+
+void Caffe::SetDevice(const int device_id) {
+  NO_GPU;
+}
+
+void Caffe::DeviceQuery() {
+  NO_GPU;
+}
+
+
+class Caffe::RNG::Generator {
+ public:
+  Generator() : rng_(new caffe::rng_t(cluster_seedgen())) {}
+  explicit Generator(unsigned int seed) : rng_(new caffe::rng_t(seed)) {}
+  caffe::rng_t* rng() { return rng_.get(); }
+ private:
+  shared_ptr<caffe::rng_t> rng_;
+};
+
+Caffe::RNG::RNG() : generator_(new Generator()) { }
+
+Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }
+
+Caffe::RNG& Caffe::RNG::operator=(const RNG& other) {
+  generator_.reset(other.generator_.get());
+  return *this;
+}
+
+void* Caffe::RNG::generator() {
+  return static_cast<void*>(generator_->rng());
+}
+
+#else  // Normal GPU + CPU Caffe.
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
@@ -201,4 +244,6 @@ const char* curandGetErrorString(curandStatus_t error) {
   return "Unknown curand status";
 }
 
+#endif  // CPU_ONLY
+
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index dd5bdbc2068..4026709151f 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -1,6 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
 #include <stdint.h>
 #include <leveldb/db.h>
 #include <pthread.h>
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 453593cf552..6ef75788665 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -1,7 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cublas_v2.h>
-
 #include <vector>
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ca1d92525c9..edfa9c0063e 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -295,6 +295,7 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
     }
     break;
   case Caffe::GPU:
+#ifndef CPU_ONLY
     for (int param_id = 0; param_id < net_params.size(); ++param_id) {
       // Compute the value to history, and then copy them to the blob's diff.
       Dtype local_rate = rate * net_params_lr[param_id];
@@ -314,6 +315,9 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
           history_[param_id]->gpu_data(),
           net_params[param_id]->mutable_gpu_diff());
     }
+#else
+    NO_GPU;
+#endif
     break;
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 77dfe7a4636..844f639e013 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -1,7 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
-
 #include <cstring>
 
 #include "caffe/common.hpp"
@@ -15,9 +13,11 @@ SyncedMemory::~SyncedMemory() {
     CaffeFreeHost(cpu_ptr_);
   }
 
+#ifndef CPU_ONLY
   if (gpu_ptr_) {
     CUDA_CHECK(cudaFree(gpu_ptr_));
   }
+#endif  // CPU_ONLY
 }
 
 inline void SyncedMemory::to_cpu() {
@@ -29,12 +29,16 @@ inline void SyncedMemory::to_cpu() {
     own_cpu_data_ = true;
     break;
   case HEAD_AT_GPU:
+#ifndef CPU_ONLY
     if (cpu_ptr_ == NULL) {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
     caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
     head_ = SYNCED;
+#else
+    NO_GPU;
+#endif
     break;
   case HEAD_AT_CPU:
   case SYNCED:
@@ -43,6 +47,7 @@ inline void SyncedMemory::to_cpu() {
 }
 
 inline void SyncedMemory::to_gpu() {
+#ifndef CPU_ONLY
   switch (head_) {
   case UNINITIALIZED:
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
@@ -60,6 +65,9 @@ inline void SyncedMemory::to_gpu() {
   case SYNCED:
     break;
   }
+#else
+  NO_GPU;
+#endif
 }
 
 const void* SyncedMemory::cpu_data() {
@@ -78,8 +86,12 @@ void SyncedMemory::set_cpu_data(void* data) {
 }
 
 const void* SyncedMemory::gpu_data() {
+#ifndef CPU_ONLY
   to_gpu();
   return (const void*)gpu_ptr_;
+#else
+  NO_GPU;
+#endif
 }
 
 void* SyncedMemory::mutable_cpu_data() {
@@ -89,9 +101,13 @@ void* SyncedMemory::mutable_cpu_data() {
 }
 
 void* SyncedMemory::mutable_gpu_data() {
+#ifndef CPU_ONLY
   to_gpu();
   head_ = HEAD_AT_GPU;
   return gpu_ptr_;
+#else
+  NO_GPU;
+#endif
 }
 
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 355a36b3c95..40b08748d1f 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -5,7 +5,6 @@
 #include <cfloat>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -16,8 +15,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class AccuracyLayerTest : public ::testing::Test {
  protected:
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 44a13b99b1a..d3bdfbde658 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -2,7 +2,6 @@
 
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -14,8 +13,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class ArgMaxLayerTest : public ::testing::Test {
  protected:
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index 82880088073..b613a873ea4 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -1,7 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <unistd.h>  // for usleep
-#include <cuda_runtime.h>
 #include <gtest/gtest.h>
 
 #include "caffe/common.hpp"
@@ -10,8 +9,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class BenchmarkTest : public MultiDeviceTest<TypeParam> {};
 
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index a5240940fd8..aec88f322e6 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -2,7 +2,6 @@
 
 #include <cstring>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
 #include "caffe/blob.hpp"
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index 07e6b8d5ef2..bb5e6b46afb 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -6,14 +6,19 @@
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
+#ifndef CPU_ONLY
   cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+#endif
 }
 
+#ifndef CPU_ONLY
 using caffe::CAFFE_TEST_CUDA_PROP;
+#endif
 
 int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
   ::google::InitGoogleLogging(argv[0]);
+#ifndef CPU_ONLY
   // Before starting testing, let's first print out a few cuda defice info.
   int device;
   cudaGetDeviceCount(&device);
@@ -27,6 +32,7 @@ int main(int argc, char** argv) {
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
   cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device);
+#endif
   // invoke the test.
   return RUN_ALL_TESTS();
 }
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index a452b612890..a8e2eb15270 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -2,7 +2,6 @@
 
 #include <cstring>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
@@ -13,12 +12,16 @@ namespace caffe {
 
 class CommonTest : public ::testing::Test {};
 
+#ifndef CPU_ONLY  // GPU Caffe singleton test.
+
 TEST_F(CommonTest, TestCublasHandlerGPU) {
   int cuda_device_id;
   CUDA_CHECK(cudaGetDevice(&cuda_device_id));
   EXPECT_TRUE(Caffe::cublas_handle());
 }
 
+#endif
+
 TEST_F(CommonTest, TestBrewMode) {
   Caffe::set_mode(Caffe::CPU);
   EXPECT_EQ(Caffe::mode(), Caffe::CPU);
@@ -48,6 +51,8 @@ TEST_F(CommonTest, TestRandSeedCPU) {
   }
 }
 
+#ifndef CPU_ONLY  // GPU Caffe singleton test.
+
 TEST_F(CommonTest, TestRandSeedGPU) {
   SyncedMemory data_a(10 * sizeof(unsigned int));
   SyncedMemory data_b(10 * sizeof(unsigned int));
@@ -63,4 +68,6 @@ TEST_F(CommonTest, TestRandSeedGPU) {
   }
 }
 
+#endif
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index ff208a90d3a..0550bb2d5c9 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,8 +14,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 6f3e3146117..d0522b13cd0 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,8 +14,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class ConvolutionLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 8cd157dd98a..ed7337c5eca 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -3,7 +3,6 @@
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "leveldb/db.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
@@ -18,8 +17,6 @@ using std::stringstream;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class DataLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 3a83a79772c..5b5f2025737 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -15,8 +15,6 @@ using std::stringstream;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class DummyDataLayerTest : public ::testing::Test {
  protected:
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 66490d2bc4e..53bff96ed6d 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -2,7 +2,6 @@
 
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -14,8 +13,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class EltwiseLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 8c796945b89..dd27670da13 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -5,7 +5,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -17,8 +16,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 93eda7e8330..1b145f2418e 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -2,7 +2,6 @@
 
 #include <cstring>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/filler.hpp"
 
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index e6e777e69c0..bea099b1f92 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,8 +14,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class FlattenLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 6fd9a2fc194..221d62aa2d2 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -1,6 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
 #include <string>
 #include <vector>
 
@@ -17,8 +16,6 @@ using std::vector;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template<typename TypeParam>
 class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 3eb24210d99..e606e209661 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -3,7 +3,6 @@
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "leveldb/db.h"
 
 #include "gtest/gtest.h"
@@ -18,8 +17,6 @@ using std::string;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index df6d8e25803..84374e9579a 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -5,7 +5,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -17,8 +16,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class HingeLossLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index bd4404a0000..5671968b56d 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 5be191742fc..a40f59df2e8 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,8 +14,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index fbd4a1ca9d4..ae9a2dbc17d 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,7 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
-
 #include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
 #include <map>
@@ -21,8 +19,6 @@ using std::string;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index ad4783f991a..de194f2d63f 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,7 +14,9 @@
 
 namespace caffe {
 
+#ifndef CPU_ONLY
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+#endif
 
 template <typename TypeParam>
 class InnerProductLayerTest : public MultiDeviceTest<TypeParam> {
@@ -57,8 +58,12 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
 
 TYPED_TEST(InnerProductLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
   if (Caffe::mode() == Caffe::CPU ||
-      sizeof(Dtype) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
     LayerParameter layer_param;
     InnerProductParameter* inner_product_param =
         layer_param.mutable_inner_product_param();
@@ -83,8 +88,12 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
 
 TYPED_TEST(InnerProductLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
   if (Caffe::mode() == Caffe::CPU ||
-      sizeof(Dtype) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
     LayerParameter layer_param;
     InnerProductParameter* inner_product_param =
         layer_param.mutable_inner_product_param();
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index a627c97f40f..5bd5533f955 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -4,7 +4,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -19,8 +18,6 @@ using std::max;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class LRNLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index eef375acea7..733bbf4ad1f 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -3,8 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
-
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index d73347e8e2e..ec53d40e53e 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -5,7 +5,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -17,8 +16,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class MultinomialLogisticLossLayerTest : public ::testing::Test {
  protected:
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index f44471843a9..246832d226d 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,8 +14,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_platform.cpp b/src/caffe/test/test_platform.cpp
index 7cf8306e8c9..e1c4dafc7bb 100644
--- a/src/caffe/test/test_platform.cpp
+++ b/src/caffe/test/test_platform.cpp
@@ -1,9 +1,10 @@
 // Copyright 2014 BVLC and contributors.
 
+#ifndef CPU_ONLY
+
 #include <cstdlib>
 #include <cstdio>
 
-#include "cuda_runtime.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"
 #include "caffe/test/test_caffe_main.hpp"
@@ -53,3 +54,5 @@ TEST_F(PlatformTest, TestInitialization) {
 }
 
 }  // namespace caffe
+
+#endif  // CPU_ONLY
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index b209d821a32..b9cec54af39 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,8 +14,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index a1b716ad531..c9992d57f65 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -3,7 +3,6 @@
 #include <algorithm>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
@@ -18,8 +17,6 @@ using std::isnan;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class PowerLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 3cd77da9876..abe21c2523e 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -1,6 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
 #include <cmath>
 #include <cstring>
 
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 76bbfb48a17..a5388db092f 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -16,8 +16,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index f0be279bbca..fa899f92db3 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -4,7 +4,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -16,8 +15,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class SoftmaxLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index efd6e33c2f7..6f45c3889c2 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -5,7 +5,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -17,8 +16,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 455fb59d4d3..bbee6d28855 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -4,7 +4,6 @@
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
@@ -18,8 +17,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class SplitLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 7a931d224d8..66e9b2d7d88 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -4,7 +4,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -18,8 +17,6 @@ using std::min;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class StochasticPoolingLayerTest : public ::testing::Test {
  protected:
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index 20bd86138b5..f07682a2aa5 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -3,10 +3,10 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/device_alternate.hpp"
 #include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -24,6 +24,8 @@ TEST_F(SyncedMemoryTest, TestInitialization) {
   delete p_mem;
 }
 
+#ifndef CPU_ONLY  // GPU test
+
 TEST_F(SyncedMemoryTest, TestAllocationCPUGPU) {
   SyncedMemory mem(10);
   EXPECT_TRUE(mem.cpu_data());
@@ -32,18 +34,24 @@ TEST_F(SyncedMemoryTest, TestAllocationCPUGPU) {
   EXPECT_TRUE(mem.mutable_gpu_data());
 }
 
+#endif
+
 TEST_F(SyncedMemoryTest, TestAllocationCPU) {
   SyncedMemory mem(10);
   EXPECT_TRUE(mem.cpu_data());
   EXPECT_TRUE(mem.mutable_cpu_data());
 }
 
+#ifndef CPU_ONLY  // GPU test
+
 TEST_F(SyncedMemoryTest, TestAllocationGPU) {
   SyncedMemory mem(10);
   EXPECT_TRUE(mem.gpu_data());
   EXPECT_TRUE(mem.mutable_gpu_data());
 }
 
+#endif
+
 TEST_F(SyncedMemoryTest, TestCPUWrite) {
   SyncedMemory mem(10);
   void* cpu_data = mem.mutable_cpu_data();
@@ -61,6 +69,8 @@ TEST_F(SyncedMemoryTest, TestCPUWrite) {
   }
 }
 
+#ifndef CPU_ONLY  // GPU test
+
 TEST_F(SyncedMemoryTest, TestGPURead) {
   SyncedMemory mem(10);
   void* cpu_data = mem.mutable_cpu_data();
@@ -112,4 +122,6 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
 }
 
+#endif
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 171eb4e472e..7fc443f6d6d 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -5,7 +5,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -17,8 +16,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class TanHLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 46519ff2094..7006dd11880 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -2,7 +2,6 @@
 
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -14,8 +13,6 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename TypeParam>
 class ThresholdLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 9203f5583be..72ee838931d 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -4,7 +4,6 @@
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 725d24e21d3..d74f9f017ee 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -1,12 +1,12 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cstring>
+#ifndef CPU_ONLY  // CPU-GPU test
 
-#include "cuda_runtime.h"
-#include "cublas_v2.h"
+#include <cstring>
 
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
+#include "caffe/util/device_alternate.hpp"
 #include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -131,3 +131,5 @@ TYPED_TEST(GemmTest, TestGemvCPUGPU) {
 }
 
 }  // namespace caffe
+
+#endif  // CPU_ONLY
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 0bd852182c8..009b118a35a 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -1,7 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <boost/date_time/posix_time/posix_time.hpp>
-#include <cuda_runtime.h>
 
 #include "caffe/common.hpp"
 #include "caffe/util/benchmark.hpp"
@@ -17,15 +16,23 @@ Timer::Timer()
 
 Timer::~Timer() {
   if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
     CUDA_CHECK(cudaEventDestroy(start_gpu_));
     CUDA_CHECK(cudaEventDestroy(stop_gpu_));
+#else
+    NO_GPU;
+#endif
   }
 }
 
 void Timer::Start() {
   if (!running()) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaEventRecord(start_gpu_, 0));
+#else
+      NO_GPU;
+#endif
     } else {
       start_cpu_ = boost::posix_time::microsec_clock::local_time();
     }
@@ -37,8 +44,12 @@ void Timer::Start() {
 void Timer::Stop() {
   if (running()) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaEventRecord(stop_gpu_, 0));
       CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
+#else
+      NO_GPU;
+#endif
     } else {
       stop_cpu_ = boost::posix_time::microsec_clock::local_time();
     }
@@ -55,8 +66,12 @@ float Timer::MilliSeconds() {
     Stop();
   }
   if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
+#else
+      NO_GPU;
+#endif
   } else {
     elapsed_milliseconds_ = (stop_cpu_ - start_cpu_).total_milliseconds();
   }
@@ -70,8 +85,12 @@ float Timer::Seconds() {
 void Timer::Init() {
   if (!initted()) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaEventCreate(&start_gpu_));
       CUDA_CHECK(cudaEventCreate(&stop_gpu_));
+#else
+      NO_GPU;
+#endif
     }
     initted_ = true;
   }
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index b989ca2ab69..36d8877d8c5 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -2,7 +2,6 @@
 
 #include <boost/math/special_functions/next.hpp>
 #include <boost/random.hpp>
-#include <cublas_v2.h>
 
 #include <limits>
 
@@ -34,38 +33,6 @@ void caffe_cpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
       ldb, beta, C, N);
 }
 
-template <>
-void caffe_gpu_gemm<float>(const CBLAS_TRANSPOSE TransA,
-    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-    const float alpha, const float* A, const float* B, const float beta,
-    float* C) {
-  // Note that cublas follows fortran order.
-  int lda = (TransA == CblasNoTrans) ? K : M;
-  int ldb = (TransB == CblasNoTrans) ? N : K;
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  cublasOperation_t cuTransB =
-      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  CUBLAS_CHECK(cublasSgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
-      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
-}
-
-template <>
-void caffe_gpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
-    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-    const double alpha, const double* A, const double* B, const double beta,
-    double* C) {
-  // Note that cublas follows fortran order.
-  int lda = (TransA == CblasNoTrans) ? K : M;
-  int ldb = (TransB == CblasNoTrans) ? N : K;
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  cublasOperation_t cuTransB =
-      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  CUBLAS_CHECK(cublasDgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
-      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
-}
-
 template <>
 void caffe_cpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M,
     const int N, const float alpha, const float* A, const float* x,
@@ -80,26 +47,6 @@ void caffe_cpu_gemv<double>(const CBLAS_TRANSPOSE TransA, const int M,
   cblas_dgemv(CblasRowMajor, TransA, M, N, alpha, A, N, x, 1, beta, y, 1);
 }
 
-template <>
-void caffe_gpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M,
-    const int N, const float alpha, const float* A, const float* x,
-    const float beta, float* y) {
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
-  CUBLAS_CHECK(cublasSgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
-      A, N, x, 1, &beta, y, 1));
-}
-
-template <>
-void caffe_gpu_gemv<double>(const CBLAS_TRANSPOSE TransA, const int M,
-    const int N, const double alpha, const double* A, const double* x,
-    const double beta, double* y) {
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
-  CUBLAS_CHECK(cublasDgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
-      A, N, x, 1, &beta, y, 1));
-}
-
 template <>
 void caffe_axpy<float>(const int N, const float alpha, const float* X,
     float* Y) { cblas_saxpy(N, alpha, X, 1, Y, 1); }
@@ -108,18 +55,6 @@ template <>
 void caffe_axpy<double>(const int N, const double alpha, const double* X,
     double* Y) { cblas_daxpy(N, alpha, X, 1, Y, 1); }
 
-template <>
-void caffe_gpu_axpy<float>(const int N, const float alpha, const float* X,
-    float* Y) {
-  CUBLAS_CHECK(cublasSaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
-}
-
-template <>
-void caffe_gpu_axpy<double>(const int N, const double alpha, const double* X,
-    double* Y) {
-  CUBLAS_CHECK(cublasDaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
-}
-
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
@@ -153,7 +88,11 @@ template <typename Dtype>
 void caffe_copy(const int N, const Dtype* X, Dtype* Y) {
   if (X != Y) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
+#else
+      NO_GPU;
+#endif
     } else {
       memcpy(Y, X, sizeof(Dtype) * N);
     }
@@ -166,12 +105,6 @@ template void caffe_copy<unsigned int>(const int N, const unsigned int* X,
 template void caffe_copy<float>(const int N, const float* X, float* Y);
 template void caffe_copy<double>(const int N, const double* X, double* Y);
 
-void caffe_gpu_memcpy(const size_t N, const void* X, void* Y) {
-  if (X != Y) {
-    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
-  }
-}
-
 template <>
 void caffe_scal<float>(const int N, const float alpha, float *X) {
   cblas_sscal(N, alpha, X, 1);
@@ -182,30 +115,6 @@ void caffe_scal<double>(const int N, const double alpha, double *X) {
   cblas_dscal(N, alpha, X, 1);
 }
 
-template <>
-void caffe_gpu_scal<float>(const int N, const float alpha, float *X) {
-  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), N, &alpha, X, 1));
-}
-
-template <>
-void caffe_gpu_scal<double>(const int N, const double alpha, double *X) {
-  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
-}
-
-template <>
-void caffe_gpu_axpby<float>(const int N, const float alpha, const float* X,
-    const float beta, float* Y) {
-  caffe_gpu_scal<float>(N, beta, Y);
-  caffe_gpu_axpy<float>(N, alpha, X, Y);
-}
-
-template <>
-void caffe_gpu_axpby<double>(const int N, const double alpha, const double* X,
-    const double beta, double* Y) {
-  caffe_gpu_scal<double>(N, beta, Y);
-  caffe_gpu_axpy<double>(N, alpha, X, Y);
-}
-
 template <>
 void caffe_cpu_axpby<float>(const int N, const float alpha, const float* X,
                             const float beta, float* Y) {
@@ -407,18 +316,6 @@ double caffe_cpu_dot<double>(const int n, const double* x, const double* y) {
   return cblas_ddot(n, x, 1, y, 1);
 }
 
-template <>
-void caffe_gpu_dot<float>(const int n, const float* x, const float* y,
-    float* out) {
-  CUBLAS_CHECK(cublasSdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
-}
-
-template <>
-void caffe_gpu_dot<double>(const int n, const double* x, const double* y,
-    double * out) {
-  CUBLAS_CHECK(cublasDdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
-}
-
 template <>
 int caffe_cpu_hamming_distance<float>(const int n, const float* x,
                                   const float* y) {
@@ -451,16 +348,6 @@ double caffe_cpu_asum<double>(const int n, const double* x) {
   return cblas_dasum(n, x, 1);
 }
 
-template <>
-void caffe_gpu_asum<float>(const int n, const float* x, float* y) {
-  CUBLAS_CHECK(cublasSasum(Caffe::cublas_handle(), n, x, 1, y));
-}
-
-template <>
-void caffe_gpu_asum<double>(const int n, const double* x, double* y) {
-  CUBLAS_CHECK(cublasDasum(Caffe::cublas_handle(), n, x, 1, y));
-}
-
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sign);
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sgnbit);
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(fabs);
@@ -479,18 +366,4 @@ void caffe_cpu_scale<double>(const int n, const double alpha, const double *x,
   cblas_dscal(n, alpha, y, 1);
 }
 
-template <>
-void caffe_gpu_scale<float>(const int n, const float alpha, const float *x,
-                            float* y) {
-  CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), n, x, 1, y, 1));
-  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), n, &alpha, y, 1));
-}
-
-template <>
-void caffe_gpu_scale<double>(const int n, const double alpha, const double *x,
-                             double* y) {
-  CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), n, x, 1, y, 1));
-  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), n, &alpha, y, 1));
-}
-
 }  // namespace caffe
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 849e53b9ca4..1e934931462 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -4,6 +4,7 @@
 #include <thrust/device_vector.h>
 #include <thrust/functional.h>  // thrust::plus
 #include <thrust/reduce.h>
+
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
@@ -13,6 +14,136 @@
 
 namespace caffe {
 
+template <>
+void caffe_gpu_gemm<float>(const CBLAS_TRANSPOSE TransA,
+    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+    const float alpha, const float* A, const float* B, const float beta,
+    float* C) {
+  // Note that cublas follows fortran order.
+  int lda = (TransA == CblasNoTrans) ? K : M;
+  int ldb = (TransB == CblasNoTrans) ? N : K;
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransB =
+      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  CUBLAS_CHECK(cublasSgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
+      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
+}
+
+template <>
+void caffe_gpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
+    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+    const double alpha, const double* A, const double* B, const double beta,
+    double* C) {
+  // Note that cublas follows fortran order.
+  int lda = (TransA == CblasNoTrans) ? K : M;
+  int ldb = (TransB == CblasNoTrans) ? N : K;
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransB =
+      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  CUBLAS_CHECK(cublasDgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
+      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
+}
+
+template <>
+void caffe_gpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M,
+    const int N, const float alpha, const float* A, const float* x,
+    const float beta, float* y) {
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
+  CUBLAS_CHECK(cublasSgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
+      A, N, x, 1, &beta, y, 1));
+}
+
+template <>
+void caffe_gpu_gemv<double>(const CBLAS_TRANSPOSE TransA, const int M,
+    const int N, const double alpha, const double* A, const double* x,
+    const double beta, double* y) {
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
+  CUBLAS_CHECK(cublasDgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
+      A, N, x, 1, &beta, y, 1));
+}
+
+template <>
+void caffe_gpu_axpy<float>(const int N, const float alpha, const float* X,
+    float* Y) {
+  CUBLAS_CHECK(cublasSaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
+}
+
+template <>
+void caffe_gpu_axpy<double>(const int N, const double alpha, const double* X,
+    double* Y) {
+  CUBLAS_CHECK(cublasDaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
+}
+
+void caffe_gpu_memcpy(const size_t N, const void* X, void* Y) {
+  if (X != Y) {
+    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
+  }
+}
+
+template <>
+void caffe_gpu_scal<float>(const int N, const float alpha, float *X) {
+  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+}
+
+template <>
+void caffe_gpu_scal<double>(const int N, const double alpha, double *X) {
+  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+}
+
+template <>
+void caffe_gpu_axpby<float>(const int N, const float alpha, const float* X,
+    const float beta, float* Y) {
+  caffe_gpu_scal<float>(N, beta, Y);
+  caffe_gpu_axpy<float>(N, alpha, X, Y);
+}
+
+template <>
+void caffe_gpu_axpby<double>(const int N, const double alpha, const double* X,
+    const double beta, double* Y) {
+  caffe_gpu_scal<double>(N, beta, Y);
+  caffe_gpu_axpy<double>(N, alpha, X, Y);
+}
+
+template <>
+void caffe_gpu_dot<float>(const int n, const float* x, const float* y,
+    float* out) {
+  CUBLAS_CHECK(cublasSdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
+}
+
+template <>
+void caffe_gpu_dot<double>(const int n, const double* x, const double* y,
+    double * out) {
+  CUBLAS_CHECK(cublasDdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
+}
+
+template <>
+void caffe_gpu_asum<float>(const int n, const float* x, float* y) {
+  CUBLAS_CHECK(cublasSasum(Caffe::cublas_handle(), n, x, 1, y));
+}
+
+template <>
+void caffe_gpu_asum<double>(const int n, const double* x, double* y) {
+  CUBLAS_CHECK(cublasDasum(Caffe::cublas_handle(), n, x, 1, y));
+}
+
+template <>
+void caffe_gpu_scale<float>(const int n, const float alpha, const float *x,
+                            float* y) {
+  CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), n, x, 1, y, 1));
+  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), n, &alpha, y, 1));
+}
+
+template <>
+void caffe_gpu_scale<double>(const int n, const double alpha, const double *x,
+                             double* y) {
+  CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), n, x, 1, y, 1));
+  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), n, &alpha, y, 1));
+}
+
 template <typename Dtype>
 __global__ void set_kernel(const int n, const Dtype alpha, Dtype* y) {
   CUDA_KERNEL_LOOP(index, n) {
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index 8ed8edac284..c5aac604079 100644
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -13,7 +13,6 @@
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "fcntl.h"
 #include "google/protobuf/text_format.h"
 
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 22e74908a3f..22b0e61e227 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -1,7 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <stdio.h>  // for snprintf
-#include <cuda_runtime.h>
 #include <google/protobuf/text_format.h>
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index c1cd788a1fb..4d80be9f4da 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -4,8 +4,6 @@
 // Usage:
 //    finetune_net solver_proto_file pretrained_net
 
-#include <cuda_runtime.h>
-
 #include <string>
 
 #include "caffe/caffe.hpp"
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index 4b65cfd3dcc..bdec391fbb6 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -1,6 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <cuda_runtime.h>
 #include <fcntl.h>
 #include <google/protobuf/text_format.h>
 
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
index c5819ec71b7..9d37536c758 100644
--- a/tools/test_net.cpp
+++ b/tools/test_net.cpp
@@ -6,8 +6,6 @@
 // Usage:
 //    test_net net_proto pretrained_net_proto iterations [CPU/GPU]
 
-#include <cuda_runtime.h>
-
 #include <cstring>
 #include <cstdlib>
 #include <vector>
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 7c6f23e6240..41a33247854 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -5,8 +5,6 @@
 // Usage:
 //    train_net net_proto_file solver_proto_file [resume_point_file]
 
-#include <cuda_runtime.h>
-
 #include <cstring>
 
 #include "caffe/caffe.hpp"

From 79a769c5841960dc755d0f233cd90a79a240fc2f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 17 Jul 2014 11:57:56 +0200
Subject: [PATCH 0265/2053] switch travis build to CPU-only flag, drop
 runtestnogpu target

---
 .travis.yml | 3 ++-
 Makefile    | 5 +----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index c0119756c6b..17b483caaa3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,10 +26,11 @@ before_script:
   - mv Makefile.config.example Makefile.config
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
   - export NUM_THREADS=4
+  - export CPU_ONLY=1
 
 script:
   - make --keep-going --jobs=$NUM_THREADS all test warn lint
-  - make runtestnogpu
+  - make runtest
   - make --jobs=$NUM_THREADS all
   - make --jobs=$NUM_THREADS test
   - make --jobs=$NUM_THREADS warn
diff --git a/Makefile b/Makefile
index 417ff868218..109894dd7c7 100644
--- a/Makefile
+++ b/Makefile
@@ -294,7 +294,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 # Define build targets
 ##############################
 .PHONY: all test clean linecount lint tools examples $(DIST_ALIASES) \
-	py mat py$(PROJECT) mat$(PROJECT) proto runtest runtestnogpu \
+	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn
 
 all: $(NAME) $(STATIC_NAME) tools examples
@@ -356,9 +356,6 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 runtest: $(TEST_ALL_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
-runtestnogpu: $(TEST_ALL_BIN)
-	$(TEST_ALL_BIN) --gtest_shuffle --gtest_filter="-*GPU*:*/2.*:*/3.*"
-
 warn: $(EMPTY_WARN_REPORT)
 
 $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)

From 5b302f09dc844fe62dec653596838678214d777c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 16 Jul 2014 15:24:48 +0200
Subject: [PATCH 0266/2053] relax benchmark test timing for cheap hardware / CI
 build

---
 src/caffe/test/test_benchmark.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index b613a873ea4..6555b270ec4 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -63,8 +63,8 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.MilliSeconds(), 298);
-  EXPECT_LE(timer.MilliSeconds(), 302);
+  EXPECT_GE(timer.MilliSeconds(), 295);
+  EXPECT_LE(timer.MilliSeconds(), 305);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
@@ -78,8 +78,8 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.Seconds(), 0.298);
-  EXPECT_LE(timer.Seconds(), 0.302);
+  EXPECT_GE(timer.Seconds(), 0.295);
+  EXPECT_LE(timer.Seconds(), 0.305);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());

From f9f65e50dd44dc5fb43466f4364b2f414a1978f6 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 17 Jul 2014 11:32:29 +0200
Subject: [PATCH 0267/2053] check CPU-only everything, CPU + GPU build with
 travis

---
 .travis.yml | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 17b483caaa3..c5445fcdb90 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -19,6 +19,8 @@ before_install:
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
+  - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
+  - sudo apt-get -y update && sudo apt-get -y install cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
   - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
 
@@ -26,9 +28,14 @@ before_script:
   - mv Makefile.config.example Makefile.config
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
   - export NUM_THREADS=4
-  - export CPU_ONLY=1
 
 script:
+  # CPU-GPU: build only.
+  - export CPU_ONLY=0
+  - make --keep-going --jobs=$NUM_THREADS all
+  - make clean
+  # CPU-only: comprehensive.
+  - export CPU_ONLY=1
   - make --keep-going --jobs=$NUM_THREADS all test warn lint
   - make runtest
   - make --jobs=$NUM_THREADS all

From d6f3adee56ef408c9c11b44ed74dac9d245a68a0 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 17 Jul 2014 12:18:27 -0700
Subject: [PATCH 0268/2053] compute_image_mean namespace fix.

---
 tools/compute_image_mean.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 0c0f0f3f7e5..79bfdc7c4bb 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -13,6 +13,7 @@
 
 using caffe::Datum;
 using caffe::BlobProto;
+using std::string;
 using std::max;
 
 int main(int argc, char** argv) {

From 8ad32ec1c4c790c5af629e424c453ca5c1cbefc9 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 17 Jul 2014 17:01:17 -0700
Subject: [PATCH 0269/2053] lint

---
 include/caffe/common.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 0ee49f36030..cf4fd457318 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -6,6 +6,10 @@
 #include <boost/shared_ptr.hpp>
 #include <glog/logging.h>
 
+#include <map>
+#include <set>
+#include <string>
+
 #include "caffe/util/device_alternate.hpp"
 
 // Disable the copy and assignment operator for a class.

From fa6397e891ccfa01cfadb11402b0ac3b62e60c72 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 18 Jul 2014 13:32:25 -0700
Subject: [PATCH 0270/2053] cosmetics: add syntax = proto2

---
 src/caffe/proto/caffe.proto              | 2 ++
 src/caffe/proto/caffe_pretty_print.proto | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 70a4ab2e8e9..e4b5ad312c4 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -1,5 +1,7 @@
 // Copyright 2014 BVLC and contributors.
 
+syntax = "proto2";
+
 package caffe;
 
 message BlobProto {
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
index cfdce82c79f..f34988c9645 100644
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ b/src/caffe/proto/caffe_pretty_print.proto
@@ -1,5 +1,7 @@
 // Copyright 2014 BVLC and contributors.
 
+syntax = "proto2";
+
 package caffe;
 
 import "caffe/proto/caffe.proto";

From 7683986be557fe9d6f44ec1cdd6a2cfd889c07ed Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 18 Jul 2014 15:18:14 -0700
Subject: [PATCH 0271/2053] reapply namespace change

---
 include/caffe/common.hpp                              | 11 +++++++++++
 include/caffe/layer.hpp                               |  3 ---
 include/caffe/net.hpp                                 |  6 ------
 include/caffe/test/test_gradient_check_util.hpp       |  6 ++----
 include/caffe/util/insert_splits.hpp                  |  3 ---
 include/caffe/util/io.hpp                             |  1 -
 include/caffe/util/upgrade_proto.hpp                  |  2 --
 src/caffe/layer_factory.cpp                           |  2 --
 src/caffe/layers/accuracy_layer.cpp                   |  2 --
 src/caffe/layers/bnll_layer.cpp                       |  4 +---
 src/caffe/layers/bnll_layer.cu                        |  2 --
 src/caffe/layers/dropout_layer.cu                     |  2 --
 src/caffe/layers/hdf5_output_layer.cpp                |  1 -
 src/caffe/layers/hdf5_output_layer.cu                 |  1 -
 src/caffe/layers/hinge_loss_layer.cpp                 |  5 ++---
 src/caffe/layers/image_data_layer.cpp                 |  3 ---
 src/caffe/layers/image_data_layer.cu                  |  2 --
 src/caffe/layers/infogain_loss_layer.cpp              |  6 ++----
 src/caffe/layers/loss_layer.cpp                       |  2 --
 src/caffe/layers/multinomial_logistic_loss_layer.cpp  |  8 ++++----
 src/caffe/layers/pooling_layer.cpp                    |  6 +++---
 src/caffe/layers/pooling_layer.cu                     |  3 ---
 src/caffe/layers/power_layer.cpp                      |  2 --
 src/caffe/layers/power_layer.cu                       |  2 --
 src/caffe/layers/relu_layer.cpp                       |  4 +---
 src/caffe/layers/relu_layer.cu                        |  2 --
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp |  2 --
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu  |  2 --
 src/caffe/layers/sigmoid_layer.cu                     |  2 --
 src/caffe/layers/softmax_layer.cpp                    |  4 +---
 src/caffe/layers/softmax_layer.cu                     |  2 --
 src/caffe/layers/softmax_loss_layer.cpp               |  6 ++----
 src/caffe/layers/softmax_loss_layer.cu                |  2 --
 src/caffe/layers/threshold_layer.cu                   |  2 --
 src/caffe/layers/window_data_layer.cpp                |  3 ---
 src/caffe/layers/window_data_layer.cu                 |  3 ---
 src/caffe/net.cpp                                     |  4 ----
 src/caffe/solver.cpp                                  |  3 ---
 src/caffe/test/test_power_layer.cpp                   |  4 ++--
 src/caffe/util/insert_splits.cpp                      |  5 -----
 src/caffe/util/io.cpp                                 |  8 ++------
 src/caffe/util/upgrade_proto.cpp                      |  3 ---
 42 files changed, 33 insertions(+), 113 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index cf4fd457318..2c79f2bc326 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -68,6 +68,17 @@ namespace caffe {
 // because cuda does not work (at least now) well with C++11 features.
 using boost::shared_ptr;
 
+// Common functions and classes from std that caffe often uses.
+using std::fstream;
+using std::ios;
+using std::iterator;
+using std::make_pair;
+using std::map;
+using std::ostringstream;
+using std::pair;
+using std::set;
+using std::string;
+using std::vector;
 
 // A singleton class to hold common caffe stuff, such as the handler that
 // caffe is going to use for cublas, curand, etc.
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 6d2374fc9ab..aac82447a41 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -13,9 +13,6 @@
 
 namespace caffe {
 
-using std::string;
-using std::vector;
-
 template <typename Dtype>
 class Layer {
  public:
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index ce82e28602c..8a2df2bf17f 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -16,12 +16,6 @@
 
 namespace caffe {
 
-using std::map;
-using std::pair;
-using std::set;
-using std::string;
-using std::vector;
-
 template <typename Dtype>
 class Net {
  public:
diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 2d551f82428..034aeccb90b 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -13,8 +13,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
 
-using std::max;
-
 namespace caffe {
 
 // The gradient checker adds a L2 normalization loss function on top of the
@@ -160,8 +158,8 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
           || fabs(feature) > kink_ + kink_range_) {
         // We check relative accuracy, but for too small values, we threshold
         // the scale factor by 1.
-        Dtype scale = max(
-            max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
+        Dtype scale = std::max(
+            std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
         EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
           << "debug: (top_id, top_data_id, blob_id, feat_id)="
           << top_id << "," << top_data_id << "," << blob_id << "," << feat_id;
diff --git a/include/caffe/util/insert_splits.hpp b/include/caffe/util/insert_splits.hpp
index 45c3a051021..4da1ea81502 100644
--- a/include/caffe/util/insert_splits.hpp
+++ b/include/caffe/util/insert_splits.hpp
@@ -9,9 +9,6 @@
 
 namespace caffe {
 
-using std::pair;
-using std::string;
-
 // Copy NetParameters with SplitLayers added to replace any shared bottom
 // blobs with unique bottom blobs provided by the SplitLayer.
 void InsertSplits(const NetParameter& param, NetParameter* param_split);
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index accb0947a9c..631dbe7ca64 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -16,7 +16,6 @@
 
 namespace caffe {
 
-using std::string;
 using ::google::protobuf::Message;
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 0772f803057..39e4f4ce22c 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -10,8 +10,6 @@
 
 namespace caffe {
 
-using std::string;
-
 // Return true iff any layer contains parameters specified using
 // deprecated V0LayerParameter.
 bool NetNeedsUpgrade(const NetParameter& net_param);
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 173c870f6ae..e6cbe187c3c 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -11,8 +11,6 @@
 
 namespace caffe {
 
-using std::string;
-
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
 // but we will leave it this way for now.
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index ddfe38ab38e..95439b9af85 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -13,8 +13,6 @@
 
 namespace caffe {
 
-using std::max;
-
 template <typename Dtype>
 void AccuracyLayer<Dtype>::SetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 992c7e09526..9b7a0642779 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -8,8 +8,6 @@
 
 namespace caffe {
 
-using std::min;
-
 const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
@@ -37,7 +35,7 @@ void BNLLLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const int count = (*bottom)[0]->count();
     Dtype expval;
     for (int i = 0; i < count; ++i) {
-      expval = exp(min(bottom_data[i], Dtype(kBNLL_THRESHOLD)));
+      expval = exp(std::min(bottom_data[i], Dtype(kBNLL_THRESHOLD)));
       bottom_diff[i] = top_diff[i] * expval / (expval + 1.);
     }
   }
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 8fbe7f79eed..bc6a201e9b4 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -8,8 +8,6 @@
 
 namespace caffe {
 
-using std::max;
-
 const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index c9f3ecd2dd5..2316c9e0211 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -10,8 +10,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index ffa240f15cc..accc9f9ccdd 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -12,7 +12,6 @@
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
-using std::vector;
 
 template <typename Dtype>
 HDF5OutputLayer<Dtype>::HDF5OutputLayer(const LayerParameter& param)
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 744b8fe1128..5c790555e50 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -12,7 +12,6 @@
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
-using std::vector;
 
 template <typename Dtype>
 Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 8097761d22b..91fe1534da9 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -10,8 +10,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -30,7 +28,8 @@ Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
   for (int i = 0; i < num; ++i) {
     for (int j = 0; j < dim; ++j) {
-      bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
+      bottom_diff[i * dim + j] = std::max(
+        Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
   switch (this->layer_param_.hinge_loss_param().norm()) {
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 408452f5d80..e7779ae4980 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -18,9 +18,6 @@
 
 namespace caffe {
 
-using std::iterator;
-using std::pair;
-
 template <typename Dtype>
 void* ImageDataLayerPrefetch(void* layer_pointer) {
   CHECK(layer_pointer);
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index cf74853d902..0fa5842c570 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -17,8 +17,6 @@
 
 namespace caffe {
 
-using std::pair;
-
 template <typename Dtype>
 Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index a72874e4bb4..204f6c305ca 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -10,8 +10,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -44,7 +42,7 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < num; ++i) {
     int label = static_cast<int>(bottom_label[i]);
     for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+      Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
@@ -73,7 +71,7 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
       for (int j = 0; j < dim; ++j) {
-        Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+        Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
         bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
       }
     }
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index ac8ad216732..92eb06f7b0c 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -10,8 +10,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 8687784d6ab..beea747c31b 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -10,8 +10,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -32,7 +30,8 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
     int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+    Dtype prob = std::max(
+        bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
   if (top->size() == 1) {
@@ -58,7 +57,8 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
     caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
-      Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+      Dtype prob = std::max(
+          bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
       bottom_diff[i * dim + label] = -1. / prob / num;
     }
   }
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index d6fef07ba46..37da4d1795a 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -10,11 +10,11 @@
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-using std::min;
-
 namespace caffe {
 
+using std::min;
+using std::max;
+
 template <typename Dtype>
 void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index a44b1770ee3..4aa94921a31 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -8,9 +8,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-using std::min;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 4607457a460..55ace207b7a 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -7,8 +7,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index e7f9831eb13..0aa00272eea 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -7,8 +7,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 6d6017442d4..1e1195501e8 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -6,8 +6,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -17,7 +15,7 @@ Dtype ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
-    top_data[i] = max(bottom_data[i], Dtype(0));
+    top_data[i] = std::max(bottom_data[i], Dtype(0));
   }
   return Dtype(0);
 }
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index 2ad689126a3..e8b0fbeea57 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -6,8 +6,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 074fa6cef3c..31522dd1fe6 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -8,8 +8,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 0c858cdfc19..1d1f8cd51ff 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -8,8 +8,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index d55968e58c2..28cc7f1daa6 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -7,8 +7,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 665cfaec3ad..0de1403585a 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -7,8 +7,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -40,7 +38,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < num; ++i) {
     scale_data[i] = bottom_data[i*dim];
     for (int j = 0; j < dim; ++j) {
-      scale_data[i] = max(scale_data[i], bottom_data[i * dim + j]);
+      scale_data[i] = std::max(scale_data[i], bottom_data[i * dim + j]);
     }
   }
   // subtraction
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index ceeaff5b020..d471ba51cd0 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -10,8 +10,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index c87270eab22..4e957cee9b4 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -8,8 +8,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -43,8 +41,8 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   int dim = prob_.count() / num;
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
-    loss += -log(max(prob_data[i * dim + static_cast<int>(label[i])],
-                     Dtype(FLT_MIN)));
+    loss += -log(std::max(prob_data[i * dim + static_cast<int>(label[i])],
+                          Dtype(FLT_MIN)));
   }
   if (top->size() >= 1) {
     (*top)[0]->mutable_cpu_data()[0] = loss / num;
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index e46be6ba85d..ae7060d90bd 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -8,8 +8,6 @@
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 03eb3f32d49..fb973b2ba82 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -6,8 +6,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index c45a1521db4..8c8de348628 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -28,9 +28,6 @@
 
 namespace caffe {
 
-using std::map;
-using std::pair;
-
 template <typename Dtype>
 void* WindowDataLayerPrefetch(void* layer_pointer) {
   WindowDataLayer<Dtype>* layer =
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index d4efeca06c1..49f67d2b607 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -18,9 +18,6 @@
 
 namespace caffe {
 
-using std::map;
-using std::pair;
-
 template <typename Dtype>
 Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index aba4cc2f4f2..520ee587828 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -15,10 +15,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
-using std::make_pair;
-using std::map;
-using std::pair;
-using std::set;
 
 namespace caffe {
 
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index edfa9c0063e..a33149721bf 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -12,9 +12,6 @@
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 
-using std::max;
-using std::min;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index c9992d57f65..d4e6fc5b05e 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -55,8 +55,8 @@ class PowerLayerTest : public MultiDeviceTest<TypeParam> {
       if (isnan(expected_value)) {
         EXPECT_TRUE(isnan(top_data[i]));
       } else {
-        Dtype precision = max(Dtype(abs(expected_value * 0.0001)),
-                              min_precision);
+        Dtype precision = std::max(
+          Dtype(std::abs(expected_value * Dtype(1e-4))), min_precision);
         EXPECT_NEAR(expected_value, top_data[i], precision);
       }
     }
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index b9aeb37c71b..4de546810e5 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -8,11 +8,6 @@
 #include "caffe/common.hpp"
 #include "caffe/util/insert_splits.hpp"
 
-using std::map;
-using std::ostringstream;
-using std::pair;
-using std::make_pair;
-
 namespace caffe {
 
 void InsertSplits(const NetParameter& param, NetParameter* param_split) {
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index ff65bd64d88..4f200474459 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -19,10 +19,8 @@
 #include "caffe/util/io.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-using std::fstream;
-using std::ios;
-using std::max;
-using std::string;
+namespace caffe {
+
 using google::protobuf::io::FileInputStream;
 using google::protobuf::io::FileOutputStream;
 using google::protobuf::io::ZeroCopyInputStream;
@@ -31,8 +29,6 @@ using google::protobuf::io::ZeroCopyOutputStream;
 using google::protobuf::io::CodedOutputStream;
 using google::protobuf::Message;
 
-namespace caffe {
-
 bool ReadProtoFromTextFile(const char* filename, Message* proto) {
   int fd = open(filename, O_RDONLY);
   CHECK_NE(fd, -1) << "File not found: " << filename;
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index e079b422dfb..a17beed70a7 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -12,9 +12,6 @@
 #include "caffe/util/upgrade_proto.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-using std::map;
-using std::string;
-
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {

From ccc4b30c04632416393ef261fcd17945e161aaee Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 17 Feb 2014 16:35:38 -0800
Subject: [PATCH 0272/2053] add Net::Forward/Backward From/To

---
 include/caffe/net.hpp |  6 +++++
 src/caffe/net.cpp     | 56 ++++++++++++++++++++++++++++++++++---------
 2 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index ce82e28602c..d7282f551b8 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -35,6 +35,9 @@ class Net {
   // Run forward with the input blobs already fed separately. You can get the
   // input blobs using input_blobs().
   const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+  Dtype ForwardFromTo(int start, int end);
+  Dtype ForwardFrom(int start);
+  Dtype ForwardTo(int end);
   // Run forward using a set of bottom blobs, and return the result.
   const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
       Dtype* loss = NULL);
@@ -46,6 +49,9 @@ class Net {
   // computes the gradient w.r.t the parameters, and the data has already
   // been provided during the forward pass.
   void Backward();
+  void BackwardFromTo(int start, int end);
+  void BackwardFrom(int start);
+  void BackwardTo(int end);
 
   Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
     Dtype loss;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index aba4cc2f4f2..cadcdcdf109 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -335,16 +335,34 @@ void Net<Dtype>::GetLearningRateAndWeightDecay() {
 }
 
 template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
-  if (loss != NULL) {
-    *loss = Dtype(0.);
-  }
-  for (int i = 0; i < layers_.size(); ++i) {
+Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
+  CHECK_GE(start, 0);
+  CHECK_LT(end, layers_.size());
+  Dtype loss = 0;
+  for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
-    if (loss != NULL) {
-      *loss += layer_loss;
-    }
+    loss += layer_loss;
+  }
+  return loss;
+}
+
+template <typename Dtype>
+Dtype Net<Dtype>::ForwardFrom(int start) {
+  return ForwardFromTo(start, layers_.size() - 1);
+}
+
+template <typename Dtype>
+Dtype Net<Dtype>::ForwardTo(int end) {
+  return ForwardFromTo(0, end);
+}
+
+template <typename Dtype>
+const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
+  if (loss != NULL) {
+    *loss = ForwardFromTo(0, layers_.size() - 1);
+  } else {
+    ForwardFromTo(0, layers_.size() - 1);
   }
   return net_output_blobs_;
 }
@@ -380,10 +398,11 @@ string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
   return output;
 }
 
-
 template <typename Dtype>
-void Net<Dtype>::Backward() {
-  for (int i = layers_.size() - 1; i >= 0; --i) {
+void Net<Dtype>::BackwardFromTo(int start, int end) {
+  CHECK_GE(end, 0);
+  CHECK_LT(start, layers_.size());
+  for (int i = start; i >= end; --i) {
     if (layer_need_backward_[i]) {
       layers_[i]->Backward(
           top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
@@ -422,6 +441,21 @@ void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::BackwardFrom(int start) {
+  BackwardFromTo(start, 0);
+}
+
+template <typename Dtype>
+void Net<Dtype>::BackwardTo(int end) {
+  BackwardFromTo(layers_.size() - 1, end);
+}
+
+template <typename Dtype>
+void Net<Dtype>::Backward() {
+  BackwardFromTo(layers_.size() - 1, 0);
+}
+
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
   int num_source_layers = param.layers_size();

From 3ef43e41e74f4cebf8b1be006bf0b596a6b37e5a Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 18 Jun 2014 21:03:28 -0700
Subject: [PATCH 0273/2053] pycaffe: expose Forward/Backward From/To as kwargs
 start and end

---
 python/caffe/_caffe.cpp |  8 ++++----
 python/caffe/pycaffe.py | 43 ++++++++++++++++++++++++++++++++---------
 2 files changed, 38 insertions(+), 13 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index e9fe5cd3b05..10fc23b7974 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -181,12 +181,12 @@ struct CaffeNet {
     }
   }
 
-  void Forward() {
-    net_->ForwardPrefilled();
+  void Forward(int start, int end) {
+    net_->ForwardFromTo(start, end);
   }
 
-  void Backward() {
-    net_->Backward();
+  void Backward(int start, int end) {
+    net_->BackwardFromTo(start, end);
   }
 
   void set_input_arrays(object data_obj, object labels_obj) {
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 5c1512cd8b9..0ac1868663d 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -34,8 +34,7 @@ def _Net_params(self):
     return OrderedDict([(lr.name, lr.blobs) for lr in self.layers
                         if len(lr.blobs) > 0])
 
-
-def _Net_forward(self, blobs=None, **kwargs):
+def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
 
@@ -44,6 +43,8 @@ def _Net_forward(self, blobs=None, **kwargs):
     kwargs: Keys are input blob names and values are blob ndarrays.
             For formatting inputs for Caffe, see Net.preprocess().
             If None, input is taken from data layers.
+    start: optional name of layer at which to begin the forward pass
+    end: optional name of layer at which to finish the forward pass (inclusive)
 
     Give
     outs: {blob name: blob ndarray} dict.
@@ -51,6 +52,18 @@ def _Net_forward(self, blobs=None, **kwargs):
     if blobs is None:
         blobs = []
 
+    if start is not None:
+        start_ind = [lr.name for lr in self.layers].index(start)
+    else:
+        start_ind = 0
+
+    if end is not None:
+        end_ind = [lr.name for lr in self.layers].index(end)
+        outputs = set([end] + blobs)
+    else:
+        end_ind = len(self.layers) - 1
+        outputs = set(self.outputs + blobs)
+
     if kwargs:
         if set(kwargs.keys()) != set(self.inputs):
             raise Exception('Input blob arguments do not match net inputs.')
@@ -63,14 +76,13 @@ def _Net_forward(self, blobs=None, **kwargs):
                 raise Exception('{} blob is not 4-d'.format(in_))
             self.blobs[in_].data[...] = blob
 
-    self._forward()
+    self._forward(start_ind, end_ind)
 
     # Unpack blobs to extract
-    outs = {out: self.blobs[out].data for out in set(self.outputs + blobs)}
-    return outs
+    return {out: self.blobs[out].data for out in outputs}
 
 
-def _Net_backward(self, diffs=None, **kwargs):
+def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
     """
     Backward pass: prepare diffs and run the net backward.
 
@@ -78,6 +90,8 @@ def _Net_backward(self, diffs=None, **kwargs):
     diffs: list of diffs to return in addition to bottom diffs.
     kwargs: Keys are output blob names and values are diff ndarrays.
             If None, top diffs are taken from forward loss.
+    start: optional name of layer at which to begin the backward pass
+    end: optional name of layer at which to finish the backward pass (inclusive)
 
     Give
     outs: {blob name: diff ndarray} dict.
@@ -85,6 +99,18 @@ def _Net_backward(self, diffs=None, **kwargs):
     if diffs is None:
         diffs = []
 
+    if start is not None:
+        start_ind = [lr.name for lr in self.layers].index(start)
+    else:
+        start_ind = len(self.layers) - 1
+
+    if end is not None:
+        end_ind = [lr.name for lr in self.layers].index(end)
+        outputs = set([end] + diffs)
+    else:
+        end_ind = 0
+        outputs = set(self.inputs + diffs)
+
     if kwargs:
         if set(kwargs.keys()) != set(self.outputs):
             raise Exception('Top diff arguments do not match net outputs.')
@@ -97,11 +123,10 @@ def _Net_backward(self, diffs=None, **kwargs):
                 raise Exception('{} diff is not 4-d'.format(top))
             self.blobs[top].diff[...] = diff
 
-    self._backward()
+    self._backward(start_ind, end_ind)
 
     # Unpack diffs to extract
-    outs = {out: self.blobs[out].diff for out in set(self.inputs + diffs)}
-    return outs
+    return {out: self.blobs[out].diff for out in outputs}
 
 
 def _Net_forward_all(self, blobs=None, **kwargs):

From a27076315e0bacb9216ab3074a281ad017bef98b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 15:36:39 -0700
Subject: [PATCH 0274/2053] comment in net.hpp to explain subtleties of From/To
 on DAGs

---
 include/caffe/net.hpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index d7282f551b8..44f28b2a04f 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -35,6 +35,13 @@ class Net {
   // Run forward with the input blobs already fed separately. You can get the
   // input blobs using input_blobs().
   const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+
+  // The From and To variants of Forward and Backward operate on the
+  // (topological) ordering by which the net is specified. For general DAG
+  // networks, note that (1) computing from one layer to another might entail
+  // extra computation on unrelated branches, and (2) computation starting in
+  // the middle may be incorrect if all of the layers of a fan-in are not
+  // included.
   Dtype ForwardFromTo(int start, int end);
   Dtype ForwardFrom(int start);
   Dtype ForwardTo(int end);

From 1c2328606b7f8b78c0ea5af8637cec09b1e7aa8b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 15:39:17 -0700
Subject: [PATCH 0275/2053] test consistency of From/To Forward/Backward

---
 src/caffe/test/test_net.cpp | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index c30f4168edc..e84701d941c 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -801,4 +801,38 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   }
 }
 
+TYPED_TEST(NetTest, TestFromTo) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->InitTinyNet();
+
+  // Run Forward and Backward, recording the data diff and loss.
+  Blob<Dtype> data;
+  data.ReshapeLike(*this->net_->blob_by_name("data"));
+  this->net_->ForwardPrefilled();
+  this->net_->Backward();
+  data.CopyFrom(*this->net_->blob_by_name("data"), true, true);
+  const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data();
+  Dtype loss = *loss_ptr;
+
+  // Check that combining partial Forwards gives the same loss.
+  for (int i = 1; i < this->net_->layers().size(); ++i) {
+    // Note that we skip layer zero to keep the same data.
+    this->net_->ForwardFromTo(1, 1);
+    if (i < this->net_->layers().size() - 1) {
+      this->net_->ForwardFrom(i + 1);
+    }
+    EXPECT_EQ(loss, *loss_ptr);
+  }
+
+  // Check that combining partial Backwards gives the same data diff.
+  for (int i = 1; i < this->net_->layers().size(); ++i) {
+    this->net_->BackwardTo(i);
+    this->net_->BackwardFrom(i - 1);
+    for (int j = 0; j < data.count(); ++j) {
+      EXPECT_EQ(data.cpu_diff()[j],
+          this->net_->blob_by_name("data")->cpu_diff()[j]);
+    }
+  }
+}
+
 }  // namespace caffe

From 49381cc22c360b0c4c8c8e0df93ec6a445db3f30 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 16:13:24 -0700
Subject: [PATCH 0276/2053] use /usr instead of /usr/local for default Python
 include/lib

This makes the default settings usable with Travis/Ubuntu Python
---
 Makefile.config.example | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index 708a39c7841..7c96d8a9356 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -42,15 +42,15 @@ BLAS := atlas
 
 # NOTE: this is required only if you will compile the python interface.
 # We need to be able to find Python.h and numpy/arrayobject.h.
-PYTHON_INCLUDE := /usr/local/include/python2.7 \
-		/usr/local/lib/python2.7/dist-packages/numpy/core/include
+PYTHON_INCLUDE := /usr/include/python2.7 \
+		/usr/lib/python2.7/dist-packages/numpy/core/include
 # Anaconda Python distribution is quite popular. Include path:
 # PYTHON_INCLUDE := $(HOME)/anaconda/include \
 		# $(HOME)/anaconda/include/python2.7 \
 		# $(HOME)/anaconda/lib/python2.7/site-packages/numpy/core/include
 
 # We need to be able to find libpythonX.X.so or .dylib.
-PYTHON_LIB := /usr/local/lib
+PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(HOME)/anaconda/lib
 
 # Whatever else you find you need goes here.

From e8b23c47a9fe1b0cd6b707ced1b4ace202fd0e37 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 15:48:36 -0700
Subject: [PATCH 0277/2053] Travis builds pycaffe

---
 .travis.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index c5445fcdb90..4a65090b7c4 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -14,7 +14,7 @@ before_install:
   - echo $LANG
   - echo $LC_ALL
   - sudo apt-get -y update
-  - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
+  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
 
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
@@ -42,6 +42,7 @@ script:
   - make --jobs=$NUM_THREADS test
   - make --jobs=$NUM_THREADS warn
   - make --jobs=$NUM_THREADS lint
+  - make --jobs=$NUM_THREADS pycaffe
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,

From fc48d5de5c95255ad98eea8666910482235b1c75 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 16:31:16 -0700
Subject: [PATCH 0278/2053] pycaffe: allow unspecified mean. Fixes #671.

---
 python/caffe/pycaffe.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 5c1512cd8b9..a2fb16ba06f 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -259,7 +259,6 @@ def _Net_preprocess(self, input_name, input_):
     caffe_in = input_.astype(np.float32)
     input_scale = self.input_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    mean = self.mean.get(input_name)
     in_size = self.blobs[input_name].data.shape[2:]
     if caffe_in.shape[:2] != in_size:
         caffe_in = caffe.io.resize_image(caffe_in, in_size)
@@ -268,8 +267,8 @@ def _Net_preprocess(self, input_name, input_):
     if channel_order:
         caffe_in = caffe_in[:, :, channel_order]
     caffe_in = caffe_in.transpose((2, 0, 1))
-    if mean is not None:
-        caffe_in -= mean
+    if hasattr(self, 'mean'):
+        caffe_in -= self.mean.get(input_name, 0)
     return caffe_in
 
 
@@ -280,9 +279,8 @@ def _Net_deprocess(self, input_name, input_):
     decaf_in = input_.copy().squeeze()
     input_scale = self.input_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    mean = self.mean.get(input_name)
-    if mean is not None:
-        decaf_in += mean
+    if hasattr(self, 'mean'):
+        decaf_in += self.mean.get(input_name, 0)
     decaf_in = decaf_in.transpose((1,2,0))
     if channel_order:
         channel_order_inverse = [channel_order.index(i)

From 32fefa96f9ba585bbbf4fff19fef89c3ec125a89 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 16:32:12 -0700
Subject: [PATCH 0279/2053] pycaffe: reorder exceptions

It doesn't make sense to complain about input not being batch-sized if
it isn't even 4-d in the first place.
---
 python/caffe/pycaffe.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index a2fb16ba06f..dde838d6600 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -57,10 +57,10 @@ def _Net_forward(self, blobs=None, **kwargs):
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
-                raise Exception('Input is not batch sized')
             if blob.ndim != 4:
                 raise Exception('{} blob is not 4-d'.format(in_))
+            if blob.shape[0] != self.blobs[in_].num:
+                raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
     self._forward()
@@ -91,10 +91,10 @@ def _Net_backward(self, diffs=None, **kwargs):
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for top, diff in kwargs.iteritems():
-            if diff.shape[0] != self.blobs[top].num:
-                raise Exception('Diff is not batch sized')
             if diff.ndim != 4:
                 raise Exception('{} diff is not 4-d'.format(top))
+            if diff.shape[0] != self.blobs[top].num:
+                raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
     self._backward()

From fb2f7c1c2757e2a7e48860f75b6a091f7351fc68 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 18 Jul 2014 16:45:17 -0700
Subject: [PATCH 0280/2053] pycaffe: test channel_order and input_scale against
 None

An input_scale of zero should produce warnings/errors/infinities rather
than being the same as an input_scale of one. A channel_order of () or 0
should not be equivalent to the default ordering.
---
 python/caffe/pycaffe.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index dde838d6600..5f04a95246b 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -262,9 +262,9 @@ def _Net_preprocess(self, input_name, input_):
     in_size = self.blobs[input_name].data.shape[2:]
     if caffe_in.shape[:2] != in_size:
         caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if input_scale:
+    if input_scale is not None:
         caffe_in *= input_scale
-    if channel_order:
+    if channel_order is not None:
         caffe_in = caffe_in[:, :, channel_order]
     caffe_in = caffe_in.transpose((2, 0, 1))
     if hasattr(self, 'mean'):
@@ -282,11 +282,11 @@ def _Net_deprocess(self, input_name, input_):
     if hasattr(self, 'mean'):
         decaf_in += self.mean.get(input_name, 0)
     decaf_in = decaf_in.transpose((1,2,0))
-    if channel_order:
+    if channel_order is not None:
         channel_order_inverse = [channel_order.index(i)
                                  for i in range(decaf_in.shape[2])]
         decaf_in = decaf_in[:, :, channel_order_inverse]
-    if input_scale:
+    if input_scale is not None:
         decaf_in /= input_scale
     return decaf_in
 

From 7722514cddc127ba782ad97732532e6afea0db6e Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 4 Jul 2014 17:19:24 +0800
Subject: [PATCH 0281/2053] Extend the ArgMaxLayer to output top k results

---
 include/caffe/common_layers.hpp      |  1 +
 src/caffe/layers/argmax_layer.cpp    | 54 ++++++++++++++++++------
 src/caffe/proto/caffe.proto          |  1 +
 src/caffe/test/test_argmax_layer.cpp | 62 +++++++++++++++++++++++++++-
 4 files changed, 103 insertions(+), 15 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 761c19fa7e2..395ea7d6c98 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -48,6 +48,7 @@ class ArgMaxLayer : public Layer<Dtype> {
     NOT_IMPLEMENTED;
   }
   bool out_max_val_;
+  size_t top_k_;
 };
 
 /* ConcatLayer
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index cc31c0f52d8..1fe2a89e755 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -1,12 +1,12 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <vector>
 #include <cfloat>
+#include <queue>
+#include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-
 namespace caffe {
 
 template <typename Dtype>
@@ -14,15 +14,29 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Layer<Dtype>::SetUp(bottom, top);
   out_max_val_ = this->layer_param_.argmax_param().out_max_val();
+  top_k_ = this->layer_param_.argmax_param().top_k();
+  CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
+  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
+      << "top_k must be less than or equal to the number of classes.";
   if (out_max_val_) {
     // Produces max_ind and max_val
-    (*top)[0]->Reshape(bottom[0]->num(), 2, 1, 1);
+    (*top)[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
   } else {
     // Produces only max_ind
-    (*top)[0]->Reshape(bottom[0]->num(), 1, 1, 1);
+    (*top)[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
   }
 }
 
+template <typename Dtype>
+class IDAndValueComparator {
+ public:
+  bool operator() (const std::pair<size_t, Dtype>& lhs,
+                   const std::pair<size_t, Dtype>& rhs) const {
+    return lhs.second < rhs.second || (lhs.second == rhs.second &&
+        lhs.first < rhs.first);
+  }
+};
+
 template <typename Dtype>
 Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -30,20 +44,35 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
+  Dtype value;
   for (int i = 0; i < num; ++i) {
-    Dtype max_val = -FLT_MAX;
-    int max_ind = 0;
+    std::priority_queue<std::pair<size_t, Dtype>,
+        std::vector<std::pair<size_t, Dtype> >, IDAndValueComparator<Dtype> >
+        top_k_results;
     for (int j = 0; j < dim; ++j) {
-      if (bottom_data[i * dim + j] > max_val) {
-        max_val = bottom_data[i * dim + j];
-        max_ind = j;
+      value = -(bottom_data[i * dim + j]);
+      if (top_k_results.size() >= top_k_) {
+        if (value < top_k_results.top().second) {
+          top_k_results.pop();
+          top_k_results.push(std::make_pair(j, value));
+        }
+      } else {
+        top_k_results.push(std::make_pair(j, value));
       }
     }
     if (out_max_val_) {
-      top_data[i * 2] = max_ind;
-      top_data[i * 2 + 1] = max_val;
+      for (int j = 0; j < top_k_; ++j) {
+        top_data[i * 2 * top_k_ + (top_k_ - 1 - j) * 2] =
+            top_k_results.top().first;
+        top_data[i * 2 * top_k_ + (top_k_ - 1 - j) * 2 + 1] =
+            -(top_k_results.top().second);
+        top_k_results.pop();
+      }
     } else {
-      top_data[i] = max_ind;
+      for (int j = 0; j < top_k_; ++j) {
+        top_data[i * top_k_ + (top_k_ - 1 - j)] = top_k_results.top().first;
+        top_k_results.pop();
+      }
     }
   }
   return Dtype(0);
@@ -51,5 +80,4 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 INSTANTIATE_CLASS(ArgMaxLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e4b5ad312c4..10e52d2ad47 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -229,6 +229,7 @@ message AccuracyParameter {
 message ArgMaxParameter {
   // If true produce pairs (argmax, maxval)
   optional bool out_max_val = 1 [default = false];
+  optional uint32 top_k = 2 [default = 1];
 }
 
 // Message that stores parameters used by ConcatLayer
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index d3bdfbde658..3cd65651c9d 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -17,8 +17,9 @@ template <typename Dtype>
 class ArgMaxLayerTest : public ::testing::Test {
  protected:
   ArgMaxLayerTest()
-      : blob_bottom_(new Blob<Dtype>(20, 10, 1, 1)),
-        blob_top_(new Blob<Dtype>()) {
+      : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
+        blob_top_(new Blob<Dtype>()),
+        top_k_(10) {
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
@@ -32,6 +33,7 @@ class ArgMaxLayerTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  size_t top_k_;
 };
 
 TYPED_TEST_CASE(ArgMaxLayerTest, TestDtypes);
@@ -105,4 +107,60 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   }
 }
 
+TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_top_k(this->top_k_);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(top_data[i], 0);
+    EXPECT_LE(top_data[i], dim);
+    max_ind = top_data[i * this->top_k_];
+    max_val = bottom_data[i * dim + max_ind];
+    EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
+    for (int j = 0; j < dim; ++j) {
+      EXPECT_LE(bottom_data[i * dim + j], max_val);
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_out_max_val(true);
+  argmax_param->set_top_k(this->top_k_);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(top_data[i], 0);
+    EXPECT_LE(top_data[i], dim);
+    max_ind = top_data[i * 2 * this->top_k_];
+    max_val = top_data[i * 2 * this->top_k_ + 1];
+    EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
+    for (int j = 0; j < dim; ++j) {
+      EXPECT_LE(bottom_data[i * dim + j], max_val);
+    }
+  }
+}
+
+
 }  // namespace caffe

From b9a9c588be6286ecc4bc899e20b5f040f8d14ae7 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sat, 5 Jul 2014 17:17:12 +0800
Subject: [PATCH 0282/2053] Add the test cases for the mulitple top predictions
 argmax layer

---
 src/caffe/test/test_argmax_layer.cpp | 27 ++++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 3cd65651c9d..11b6712797a 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -1,5 +1,7 @@
 // Copyright 2014 BVLC and contributors.
 
+#include <algorithm>
+#include <utility>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -19,7 +21,7 @@ class ArgMaxLayerTest : public ::testing::Test {
   ArgMaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
         blob_top_(new Blob<Dtype>()),
-        top_k_(10) {
+        top_k_(5) {
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
@@ -107,6 +109,12 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   }
 }
 
+template<typename Dtype>
+bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
+                            std::pair<int, Dtype> b) {
+  return a.second > b.second || (a.second == b.second && a.first > b.first);
+}
+
 TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
@@ -128,8 +136,15 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
     max_ind = top_data[i * this->top_k_];
     max_val = bottom_data[i * dim + max_ind];
     EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
+    std::vector<std::pair<int, TypeParam> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
       EXPECT_LE(bottom_data[i * dim + j], max_val);
+      bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
+    }
+    std::sort(bottom_data_vector.begin(), bottom_data_vector.end(),
+              int_Dtype_pair_greater<TypeParam>);
+    for (int j = 0; j < this->top_k_; ++j) {
+      EXPECT_EQ(bottom_data_vector[j].first, top_data[i * this->top_k_ + j]);
     }
   }
 }
@@ -156,8 +171,18 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
     max_ind = top_data[i * 2 * this->top_k_];
     max_val = top_data[i * 2 * this->top_k_ + 1];
     EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
+    std::vector<std::pair<int, TypeParam> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
       EXPECT_LE(bottom_data[i * dim + j], max_val);
+      bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
+    }
+    std::sort(bottom_data_vector.begin(), bottom_data_vector.end(),
+              int_Dtype_pair_greater<TypeParam>);
+    for (int j = 0; j < this->top_k_; ++j) {
+      EXPECT_EQ(bottom_data_vector[j].first,
+                top_data[i * 2 * this->top_k_ + 2 * j]);
+      EXPECT_EQ(bottom_data_vector[j].second,
+                top_data[i * 2 * this->top_k_ + 2 * j + 1]);
     }
   }
 }

From dfe69b2f06d68633a37a99b56b8878e7fcad14e6 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 8 Jul 2014 22:24:20 +0800
Subject: [PATCH 0283/2053] Simplify the top-k argmax layer using std::sort

---
 include/caffe/common_layers.hpp      |  6 +++
 src/caffe/layers/argmax_layer.cpp    | 38 ++++-------------
 src/caffe/test/test_argmax_layer.cpp | 63 ++++++++++------------------
 3 files changed, 37 insertions(+), 70 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 395ea7d6c98..26bfb5d0217 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -17,6 +17,12 @@
 
 namespace caffe {
 
+template<typename Dtype>
+bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
+                            std::pair<int, Dtype> b) {
+  return a.second > b.second || (a.second == b.second && a.first > b.first);
+}
+
 /* ArgmaxLayer
   Compute the index of the max value across all (channels x height x width).
   [In the future, can take specific dimension.]
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 1fe2a89e755..00c17f1c554 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -1,5 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
+#include <algorithm>
 #include <cfloat>
 #include <queue>
 #include <vector>
@@ -27,16 +28,6 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
-template <typename Dtype>
-class IDAndValueComparator {
- public:
-  bool operator() (const std::pair<size_t, Dtype>& lhs,
-                   const std::pair<size_t, Dtype>& rhs) const {
-    return lhs.second < rhs.second || (lhs.second == rhs.second &&
-        lhs.first < rhs.first);
-  }
-};
-
 template <typename Dtype>
 Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -44,34 +35,21 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  Dtype value;
   for (int i = 0; i < num; ++i) {
-    std::priority_queue<std::pair<size_t, Dtype>,
-        std::vector<std::pair<size_t, Dtype> >, IDAndValueComparator<Dtype> >
-        top_k_results;
+    std::vector<std::pair<int, Dtype> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
-      value = -(bottom_data[i * dim + j]);
-      if (top_k_results.size() >= top_k_) {
-        if (value < top_k_results.top().second) {
-          top_k_results.pop();
-          top_k_results.push(std::make_pair(j, value));
-        }
-      } else {
-        top_k_results.push(std::make_pair(j, value));
-      }
+      bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
     }
+    std::sort(bottom_data_vector.begin(), bottom_data_vector.end(),
+              int_Dtype_pair_greater<Dtype>);
     if (out_max_val_) {
       for (int j = 0; j < top_k_; ++j) {
-        top_data[i * 2 * top_k_ + (top_k_ - 1 - j) * 2] =
-            top_k_results.top().first;
-        top_data[i * 2 * top_k_ + (top_k_ - 1 - j) * 2 + 1] =
-            -(top_k_results.top().second);
-        top_k_results.pop();
+        top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].first;
+        top_data[(*top)[0]->offset(i, 1, j)] = bottom_data_vector[j].second;
       }
     } else {
       for (int j = 0; j < top_k_; ++j) {
-        top_data[i * top_k_ + (top_k_ - 1 - j)] = top_k_results.top().first;
-        top_k_results.pop();
+        top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].first;
       }
     }
   }
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 11b6712797a..5286a0a390a 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -1,6 +1,5 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <algorithm>
 #include <utility>
 #include <vector>
 
@@ -109,12 +108,6 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   }
 }
 
-template<typename Dtype>
-bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
-                            std::pair<int, Dtype> b) {
-  return a.second > b.second || (a.second == b.second && a.first > b.first);
-}
-
 TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
@@ -124,27 +117,23 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
   int max_ind;
   TypeParam max_val;
   int num = this->blob_bottom_->num();
   int dim = this->blob_bottom_->count() / num;
   for (int i = 0; i < num; ++i) {
-    EXPECT_GE(top_data[i], 0);
-    EXPECT_LE(top_data[i], dim);
-    max_ind = top_data[i * this->top_k_];
-    max_val = bottom_data[i * dim + max_ind];
-    EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
-    std::vector<std::pair<int, TypeParam> > bottom_data_vector;
-    for (int j = 0; j < dim; ++j) {
-      EXPECT_LE(bottom_data[i * dim + j], max_val);
-      bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
-    }
-    std::sort(bottom_data_vector.begin(), bottom_data_vector.end(),
-              int_Dtype_pair_greater<TypeParam>);
+    EXPECT_GE(this->blob_top_->data_at(i, 0, 0, 0), 0);
+    EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
     for (int j = 0; j < this->top_k_; ++j) {
-      EXPECT_EQ(bottom_data_vector[j].first, top_data[i * this->top_k_ + j]);
+      max_ind = this->blob_top_->data_at(i, 0, j, 0);
+      max_val = this->blob_bottom_->data_at(i, max_ind, 0, 0);
+      int count = 0;
+      for (int k = 0; k < dim; ++k) {
+        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+          ++count;
+        }
+      }
+      EXPECT_EQ(j, count);
     }
   }
 }
@@ -159,30 +148,24 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
   int max_ind;
   TypeParam max_val;
   int num = this->blob_bottom_->num();
   int dim = this->blob_bottom_->count() / num;
   for (int i = 0; i < num; ++i) {
-    EXPECT_GE(top_data[i], 0);
-    EXPECT_LE(top_data[i], dim);
-    max_ind = top_data[i * 2 * this->top_k_];
-    max_val = top_data[i * 2 * this->top_k_ + 1];
-    EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
-    std::vector<std::pair<int, TypeParam> > bottom_data_vector;
-    for (int j = 0; j < dim; ++j) {
-      EXPECT_LE(bottom_data[i * dim + j], max_val);
-      bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
-    }
-    std::sort(bottom_data_vector.begin(), bottom_data_vector.end(),
-              int_Dtype_pair_greater<TypeParam>);
+    EXPECT_GE(this->blob_top_->data_at(i, 0, 0, 0), 0);
+    EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
     for (int j = 0; j < this->top_k_; ++j) {
-      EXPECT_EQ(bottom_data_vector[j].first,
-                top_data[i * 2 * this->top_k_ + 2 * j]);
-      EXPECT_EQ(bottom_data_vector[j].second,
-                top_data[i * 2 * this->top_k_ + 2 * j + 1]);
+      max_ind = this->blob_top_->data_at(i, 0, j, 0);
+      max_val = this->blob_top_->data_at(i, 1, j, 0);
+      EXPECT_EQ(this->blob_bottom_->data_at(i, max_ind, 0, 0), max_val);
+      int count = 0;
+      for (int k = 0; k < dim; ++k) {
+        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+          ++count;
+        }
+      }
+      EXPECT_EQ(j, count);
     }
   }
 }

From 4697b3f2140a89648d2ebbce9d099dbffefe0c97 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Wed, 9 Jul 2014 17:30:05 +0800
Subject: [PATCH 0284/2053] Use std::partial_sort in the ArgMaxLayer as
 suggested by @shuokay

---
 src/caffe/layers/argmax_layer.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 00c17f1c554..f4d9b453314 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -40,8 +40,9 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     for (int j = 0; j < dim; ++j) {
       bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
     }
-    std::sort(bottom_data_vector.begin(), bottom_data_vector.end(),
-              int_Dtype_pair_greater<Dtype>);
+    std::partial_sort(
+        bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
+        bottom_data_vector.end(), int_Dtype_pair_greater<Dtype>);
     if (out_max_val_) {
       for (int j = 0; j < top_k_; ++j) {
         top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].first;

From 7b1a2ca345df0abf36e1863a4b53e9766d6f88ef Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 10 Jul 2014 09:06:11 +0800
Subject: [PATCH 0285/2053] Move compararing function from common_layers to
 argmax_layer

---
 include/caffe/common_layers.hpp   |  6 ------
 src/caffe/layers/argmax_layer.cpp | 10 +++++++++-
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 26bfb5d0217..395ea7d6c98 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -17,12 +17,6 @@
 
 namespace caffe {
 
-template<typename Dtype>
-bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
-                            std::pair<int, Dtype> b) {
-  return a.second > b.second || (a.second == b.second && a.first > b.first);
-}
-
 /* ArgmaxLayer
   Compute the index of the max value across all (channels x height x width).
   [In the future, can take specific dimension.]
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index f4d9b453314..44476cc0f27 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -3,6 +3,7 @@
 #include <algorithm>
 #include <cfloat>
 #include <queue>
+#include <utility>
 #include <vector>
 
 #include "caffe/layer.hpp"
@@ -28,6 +29,12 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
+template<typename Dtype>
+bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
+                            std::pair<int, Dtype> b) {
+  return a.second > b.second;
+}
+
 template <typename Dtype>
 Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -38,7 +45,8 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < num; ++i) {
     std::vector<std::pair<int, Dtype> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
-      bottom_data_vector.push_back(std::make_pair(j, bottom_data[i * dim + j]));
+      bottom_data_vector.push_back(
+          std::make_pair(j, bottom_data[i * dim + j]));
     }
     std::partial_sort(
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,

From 907c78a44f4717167206440e4eebf32a371a1162 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 10 Jul 2014 09:37:34 +0800
Subject: [PATCH 0286/2053] Refactor the accuracy layer with std::partial_sort

---
 src/caffe/layers/accuracy_layer.cpp | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index ddfe38ab38e..86ded06a7ec 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -30,6 +30,12 @@ void AccuracyLayer<Dtype>::SetUp(
   (*top)[0]->Reshape(1, 1, 1, 1);
 }
 
+template<typename Dtype>
+bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
+                            std::pair<int, Dtype> b) {
+  return a.second > b.second;
+}
+
 template <typename Dtype>
 Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -42,21 +48,17 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   vector<int> max_id(top_k_+1);
   for (int i = 0; i < num; ++i) {
     // Top-k accuracy
-    std::fill_n(maxval.begin(), top_k_, -FLT_MAX);
-    std::fill_n(max_id.begin(), top_k_, 0);
-    for (int j = 0, k; j < dim; ++j) {
-      // insert into (reverse-)sorted top-k array
-      Dtype val = bottom_data[i * dim + j];
-      for (k = top_k_; k > 0 && maxval[k-1] < val; k--) {
-        maxval[k] = maxval[k-1];
-        max_id[k] = max_id[k-1];
-      }
-      maxval[k] = val;
-      max_id[k] = j;
+    std::vector<std::pair<int, Dtype> > bottom_data_vector;
+    for (int j = 0; j < dim; ++j) {
+      bottom_data_vector.push_back(
+          std::make_pair(j, bottom_data[i * dim + j]));
     }
+    std::partial_sort(
+        bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
+        bottom_data_vector.end(), int_Dtype_pair_greater<Dtype>);
     // check if true label is in top k predictions
     for (int k = 0; k < top_k_; k++)
-      if (max_id[k] == static_cast<int>(bottom_label[i])) {
+      if (bottom_data_vector[k].first == static_cast<int>(bottom_label[i])) {
         ++accuracy;
         break;
       }

From 6abdb003708d6eb82bdd35aa7082113f7b9bf6b4 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 10 Jul 2014 09:38:03 +0800
Subject: [PATCH 0287/2053] Add more test cases for the accuracy layer

---
 src/caffe/test/test_accuracy_layer.cpp | 106 +++++++++++++++++++------
 1 file changed, 81 insertions(+), 25 deletions(-)

diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 40b08748d1f..5023a809d17 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -9,6 +9,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -19,8 +20,8 @@ template <typename Dtype>
 class AccuracyLayerTest : public ::testing::Test {
  protected:
   AccuracyLayerTest()
-      : blob_bottom_data_(new Blob<Dtype>(2, 10, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(2, 1, 1, 1)),
+      : blob_bottom_data_(new Blob<Dtype>(100, 10, 1, 1)),
+        blob_bottom_label_(new Blob<Dtype>(100, 1, 1, 1)),
         blob_top_(new Blob<Dtype>()),
         top_k_(3) {
     // fill the probability values
@@ -28,28 +29,14 @@ class AccuracyLayerTest : public ::testing::Test {
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_data_);
 
-    // set the labels; first label is set to max-probability index
-    Dtype m_val = -FLT_MAX;
-    int m_id = 0;
-    for (int i = 0; i < 10; i++)
-      if (blob_bottom_data_->data_at(0, i, 0, 0) > m_val) {
-        m_val = blob_bottom_data_->data_at(0, i, 0, 0);
-        m_id = i;
-      }
+    const unsigned int prefetch_rng_seed = caffe_rng_rand();
+    shared_ptr<Caffe::RNG> rng(new Caffe::RNG(prefetch_rng_seed));
+    caffe::rng_t* prefetch_rng =
+          static_cast<caffe::rng_t*>(rng->generator());
     Dtype* label_data = blob_bottom_label_->mutable_cpu_data();
-    int offset = blob_bottom_label_->offset(0);
-    label_data[offset] = m_id;
-
-    // set the labels; second label is set to min-probability index
-    m_val = FLT_MAX;
-    m_id = 0;
-    for (int i = 0; i < 10; i++)
-      if (blob_bottom_data_->data_at(1, i, 0, 0) < m_val) {
-        m_val = blob_bottom_data_->data_at(1, i, 0, 0);
-        m_id = i;
-      }
-    offset = blob_bottom_label_->offset(1);
-    label_data[offset] = m_id;
+    for (int i = 0; i < 100; ++i) {
+      label_data[i] = (*prefetch_rng)() % 10;
+    }
 
     blob_bottom_vec_.push_back(blob_bottom_data_);
     blob_bottom_vec_.push_back(blob_bottom_label_);
@@ -70,7 +57,57 @@ class AccuracyLayerTest : public ::testing::Test {
 
 TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
 
+TYPED_TEST(AccuracyLayerTest, TestSetup) {
+  LayerParameter layer_param;
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
+  LayerParameter layer_param;
+  AccuracyParameter* accuracy_param =
+      layer_param.mutable_accuracy_param();
+  accuracy_param->set_top_k(5);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
 TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  for (int i = 0; i < 100; ++i) {
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+    }
+  }
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / 100.0, 1e-4);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(this->top_k_);
@@ -78,8 +115,27 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
 
-  // Output accuracy should be ~0.5
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0), 0.5, 1e-4);
+  TypeParam current_value;
+  int current_rank;
+  int num_correct_labels = 0;
+  for (int i = 0; i < 100; ++i) {
+    for (int j = 0; j < 10; ++j) {
+      current_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+      current_rank = 0;
+      for (int k = 0; k < 10; ++k) {
+        if (this->blob_bottom_data_->data_at(i, k, 0, 0) > current_value) {
+          ++current_rank;
+        }
+      }
+      if (current_rank < this->top_k_ &&
+          j == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
+      }
+    }
+  }
+
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / 100.0, 1e-4);
 }
 
 }  // namespace caffe

From cf26171b34ec767291abcd4dff1a7d1eeef15250 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 11 Jul 2014 10:22:01 +0800
Subject: [PATCH 0288/2053] Limit the comparison functions to have file scope

---
 src/caffe/layers/accuracy_layer.cpp | 2 +-
 src/caffe/layers/argmax_layer.cpp   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 86ded06a7ec..916059e8d98 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -31,7 +31,7 @@ void AccuracyLayer<Dtype>::SetUp(
 }
 
 template<typename Dtype>
-bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
+static bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
                             std::pair<int, Dtype> b) {
   return a.second > b.second;
 }
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 44476cc0f27..580e4b0f3e6 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -30,7 +30,7 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template<typename Dtype>
-bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
+static bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
                             std::pair<int, Dtype> b) {
   return a.second > b.second;
 }

From fe3f6aa062d8d44cb89992854c7e1a97197f12a7 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sun, 13 Jul 2014 00:29:54 +0800
Subject: [PATCH 0289/2053] Include <utility> for pair in the accuracy layer

---
 src/caffe/layers/accuracy_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 916059e8d98..b244c51d321 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -3,6 +3,7 @@
 #include <algorithm>
 #include <cmath>
 #include <cfloat>
+#include <utility>
 #include <vector>
 
 #include "caffe/layer.hpp"

From a9281488f0d263667204db0ab4b538cfb9ac1ccc Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sun, 20 Jul 2014 00:42:12 +0800
Subject: [PATCH 0290/2053] Fix style issues in accuracy & argmax layer

---
 src/caffe/layers/accuracy_layer.cpp | 18 +++++++-----------
 src/caffe/layers/argmax_layer.cpp   | 23 ++++++++---------------
 2 files changed, 15 insertions(+), 26 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index b244c51d321..3ccba3b50d1 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -3,6 +3,7 @@
 #include <algorithm>
 #include <cmath>
 #include <cfloat>
+#include <functional>
 #include <utility>
 #include <vector>
 
@@ -31,12 +32,6 @@ void AccuracyLayer<Dtype>::SetUp(
   (*top)[0]->Reshape(1, 1, 1, 1);
 }
 
-template<typename Dtype>
-static bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
-                            std::pair<int, Dtype> b) {
-  return a.second > b.second;
-}
-
 template <typename Dtype>
 Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -49,20 +44,21 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   vector<int> max_id(top_k_+1);
   for (int i = 0; i < num; ++i) {
     // Top-k accuracy
-    std::vector<std::pair<int, Dtype> > bottom_data_vector;
+    std::vector<std::pair<Dtype, int> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
       bottom_data_vector.push_back(
-          std::make_pair(j, bottom_data[i * dim + j]));
+          std::make_pair(bottom_data[i * dim + j], j));
     }
     std::partial_sort(
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
-        bottom_data_vector.end(), int_Dtype_pair_greater<Dtype>);
+        bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
     // check if true label is in top k predictions
-    for (int k = 0; k < top_k_; k++)
-      if (bottom_data_vector[k].first == static_cast<int>(bottom_label[i])) {
+    for (int k = 0; k < top_k_; k++) {
+      if (bottom_data_vector[k].second == static_cast<int>(bottom_label[i])) {
         ++accuracy;
         break;
       }
+    }
   }
 
   // LOG(INFO) << "Accuracy: " << accuracy;
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 580e4b0f3e6..bd1a2689f3e 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -2,6 +2,7 @@
 
 #include <algorithm>
 #include <cfloat>
+#include <functional>
 #include <queue>
 #include <utility>
 #include <vector>
@@ -29,12 +30,6 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
-template<typename Dtype>
-static bool int_Dtype_pair_greater(std::pair<int, Dtype> a,
-                            std::pair<int, Dtype> b) {
-  return a.second > b.second;
-}
-
 template <typename Dtype>
 Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -43,22 +38,20 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
   for (int i = 0; i < num; ++i) {
-    std::vector<std::pair<int, Dtype> > bottom_data_vector;
+    std::vector<std::pair<Dtype, int> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
       bottom_data_vector.push_back(
-          std::make_pair(j, bottom_data[i * dim + j]));
+          std::make_pair(bottom_data[i * dim + j], j));
     }
     std::partial_sort(
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
-        bottom_data_vector.end(), int_Dtype_pair_greater<Dtype>);
+        bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
+    for (int j = 0; j < top_k_; ++j) {
+      top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
+    }
     if (out_max_val_) {
       for (int j = 0; j < top_k_; ++j) {
-        top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].first;
-        top_data[(*top)[0]->offset(i, 1, j)] = bottom_data_vector[j].second;
-      }
-    } else {
-      for (int j = 0; j < top_k_; ++j) {
-        top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].first;
+        top_data[(*top)[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
       }
     }
   }

From 00ecb24ce011b7c06416cb2a6279526856df8a8f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 30 Jun 2014 23:11:11 -0700
Subject: [PATCH 0291/2053] use Blob directly instead of shared_ptr for
 ConvolutionLayer::bias_multiplier_

This will make layer reshaping easier and more uniform, and is
consistent with col_buffer_.
---
 include/caffe/vision_layers.hpp |  2 +-
 src/caffe/layers/conv_layer.cpp | 10 +++++-----
 src/caffe/layers/conv_layer.cu  |  4 ++--
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index c8b4609c20f..a261ac35360 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -55,7 +55,7 @@ class ConvolutionLayer : public Layer<Dtype> {
   int num_output_;
   int group_;
   Blob<Dtype> col_buffer_;
-  shared_ptr<Blob<Dtype> > bias_multiplier_;
+  Blob<Dtype> bias_multiplier_;
   bool bias_term_;
   int M_;
   int K_;
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index e8f08c7e3cd..11f29e783db 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -76,10 +76,10 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
-  // Set up the bias filler
+  // Set up the all ones "bias multiplier" for adding bias using blas
   if (bias_term_) {
-    bias_multiplier_.reset(new Blob<Dtype>(1, 1, 1, N_));
-    caffe_set(N_, Dtype(1), bias_multiplier_->mutable_cpu_data());
+    bias_multiplier_.Reshape(1, 1, 1, N_);
+    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
@@ -110,7 +110,7 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (bias_term_) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-            bias_multiplier_->cpu_data(),
+            bias_multiplier_.cpu_data(),
             (Dtype)1., top_data + (*top)[i]->offset(n));
       }
     }
@@ -144,7 +144,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       for (int n = 0; n < num_; ++n) {
         caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
-            bias_multiplier_->cpu_data(), 1.,
+            bias_multiplier_.cpu_data(), 1.,
             bias_diff);
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 0b6e912b639..d328fbde9ae 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -35,7 +35,7 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       if (bias_term_) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
-            bias_multiplier_->gpu_data(),
+            bias_multiplier_.gpu_data(),
             (Dtype)1., top_data + (*top)[i]->offset(n));
       }
     }
@@ -69,7 +69,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       for (int n = 0; n < num_; ++n) {
         caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
-            bias_multiplier_->gpu_data(), 1.,
+            bias_multiplier_.gpu_data(), 1.,
             bias_diff);
       }
     }

From 40054a70cd6458e74ba8d42f80d3e534e9312a1d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 19 Jul 2014 18:02:33 -0700
Subject: [PATCH 0292/2053] use Blob directly instead of shared_ptr for
 InnerProductLayer::bias_multiplier_

---
 include/caffe/vision_layers.hpp          | 2 +-
 src/caffe/layers/inner_product_layer.cpp | 8 ++++----
 src/caffe/layers/inner_product_layer.cu  | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index a261ac35360..c995992a252 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -157,7 +157,7 @@ class InnerProductLayer : public Layer<Dtype> {
   int K_;
   int N_;
   bool bias_term_;
-  shared_ptr<Blob<Dtype> > bias_multiplier_;
+  Blob<Dtype> bias_multiplier_;
 };
 
 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 4da8578d76f..5505d08587a 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -47,8 +47,8 @@ void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }  // parameter initialization
   // Setting up the bias multiplier
   if (bias_term_) {
-    bias_multiplier_.reset(new Blob<Dtype>(1, 1, 1, M_));
-    caffe_set(M_, Dtype(1), bias_multiplier_->mutable_cpu_data());
+    bias_multiplier_.Reshape(1, 1, 1, M_);
+    caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
@@ -63,7 +63,7 @@ Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        bias_multiplier_->cpu_data(),
+        bias_multiplier_.cpu_data(),
         this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
   }
   return Dtype(0);
@@ -84,7 +84,7 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_->cpu_data(), (Dtype)0.,
+        bias_multiplier_.cpu_data(), (Dtype)0.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 6ef75788665..513e4eb7fa8 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -21,7 +21,7 @@ Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        bias_multiplier_->gpu_data(),
+        bias_multiplier_.gpu_data(),
         this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
   }
   return Dtype(0);
@@ -42,7 +42,7 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_->gpu_data(), (Dtype)0.,
+        bias_multiplier_.gpu_data(), (Dtype)0.,
         this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {

From 03544062c447019e5a86865ae6c3b12826211553 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 1 Jul 2014 13:20:01 -0700
Subject: [PATCH 0293/2053] use Blob directly instead of shared_ptr for
 PoolingLayer::max_idx_

Again, this is more uniform, and will simplify the implementation of
Reshape.
---
 include/caffe/vision_layers.hpp    | 2 +-
 src/caffe/layers/pooling_layer.cpp | 8 ++++----
 src/caffe/layers/pooling_layer.cu  | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index c995992a252..05c7b45e783 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -273,7 +273,7 @@ class PoolingLayer : public Layer<Dtype> {
   int pooled_height_;
   int pooled_width_;
   Blob<Dtype> rand_idx_;
-  shared_ptr<Blob<int> > max_idx_;
+  Blob<int> max_idx_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index d6fef07ba46..feb82669e08 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -99,8 +99,8 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // If max pooling, we will initialize the vector index part.
   if (this->layer_param_.pooling_param().pool() ==
       PoolingParameter_PoolMethod_MAX && top->size() == 1) {
-    max_idx_.reset(new Blob<int>(bottom[0]->num(), channels_,
-                                 pooled_height_, pooled_width_));
+    max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
+        pooled_width_);
   }
   // If stochastic pooling, we will initialize the random index part.
   if (this->layer_param_.pooling_param().pool() ==
@@ -131,7 +131,7 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       top_mask = (*top)[1]->mutable_cpu_data();
       caffe_set(top_count, Dtype(-1), top_mask);
     } else {
-      mask = max_idx_->mutable_cpu_data();
+      mask = max_idx_.mutable_cpu_data();
       caffe_set(top_count, -1, mask);
     }
     caffe_set(top_count, Dtype(-FLT_MAX), top_data);
@@ -236,7 +236,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     if (use_top_mask) {
       top_mask = top[1]->cpu_data();
     } else {
-      mask = max_idx_->cpu_data();
+      mask = max_idx_.cpu_data();
     }
     for (int n = 0; n < top[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index a44b1770ee3..42a6c1e5ec7 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -170,7 +170,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     if (use_top_mask) {
       top_mask = (*top)[1]->mutable_gpu_data();
     } else {
-      mask = max_idx_->mutable_gpu_data();
+      mask = max_idx_.mutable_gpu_data();
     }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -348,7 +348,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     if (use_top_mask) {
       top_mask = top[1]->gpu_data();
     } else {
-      mask = max_idx_->gpu_data();
+      mask = max_idx_.gpu_data();
     }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(

From f6696d8b5d8c43c7de41c0c7be7af97ba09160a0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 1 Jul 2014 12:52:48 -0700
Subject: [PATCH 0294/2053] use Blob directly instead of shared_ptr for
 DropoutLayer::rand_vec_

This will simplify the implementation of Reshape by making allocation
automatic. There is no need for shared_ptr here, and this is more
uniform with buffers used by other layers.
---
 include/caffe/neuron_layers.hpp    | 2 +-
 src/caffe/layers/dropout_layer.cpp | 8 ++++----
 src/caffe/layers/dropout_layer.cu  | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 070b89044d0..8f0588d4771 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -100,7 +100,7 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  shared_ptr<Blob<unsigned int> > rand_vec_;
+  Blob<unsigned int> rand_vec_;
   Dtype threshold_;
   Dtype scale_;
   unsigned int uint_thres_;
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index a9372977693..65360d67ada 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -17,8 +17,8 @@ void DropoutLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   NeuronLayer<Dtype>::SetUp(bottom, top);
   // Set up the cache for random number generation
-  rand_vec_.reset(new Blob<unsigned int>(bottom[0]->num(),
-      bottom[0]->channels(), bottom[0]->height(), bottom[0]->width()));
+  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
   threshold_ = this->layer_param_.dropout_param().dropout_ratio();
   DCHECK(threshold_ > 0.);
   DCHECK(threshold_ < 1.);
@@ -31,7 +31,7 @@ Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  unsigned int* mask = rand_vec_->mutable_cpu_data();
+  unsigned int* mask = rand_vec_.mutable_cpu_data();
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
     // Create random numbers
@@ -53,7 +53,7 @@ void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     if (Caffe::phase() == Caffe::TRAIN) {
-      const unsigned int* mask = rand_vec_->cpu_data();
+      const unsigned int* mask = rand_vec_.cpu_data();
       const int count = (*bottom)[0]->count();
       for (int i = 0; i < count; ++i) {
         bottom_diff[i] = top_diff[i] * mask[i] * scale_;
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index c9f3ecd2dd5..a53eb5e4bed 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -32,7 +32,7 @@ Dtype DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
     unsigned int* mask =
-        static_cast<unsigned int*>(rand_vec_->mutable_gpu_data());
+        static_cast<unsigned int*>(rand_vec_.mutable_gpu_data());
     caffe_gpu_rng_uniform(count, mask);
     // set thresholds
     // NOLINT_NEXT_LINE(whitespace/operators)
@@ -63,7 +63,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     if (Caffe::phase() == Caffe::TRAIN) {
       const unsigned int* mask =
-          static_cast<const unsigned int*>(rand_vec_->gpu_data());
+          static_cast<const unsigned int*>(rand_vec_.gpu_data());
       const int count = (*bottom)[0]->count();
       // NOLINT_NEXT_LINE(whitespace/operators)
       DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count),

From b8159f772a63d541894fcd3c8bedeec81e8b1273 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 19 Jul 2014 18:14:53 -0700
Subject: [PATCH 0295/2053] use Blob directly instead of shared_ptr for
 DataLayer buffers

---
 include/caffe/data_layers.hpp   |  4 ++--
 src/caffe/layers/data_layer.cpp | 28 +++++++++++++---------------
 src/caffe/layers/data_layer.cu  |  4 ++--
 3 files changed, 17 insertions(+), 19 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 06c90ea9923..069f0c2eea2 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -81,8 +81,8 @@ class DataLayer : public Layer<Dtype> {
   int datum_width_;
   int datum_size_;
   pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
   bool output_labels_;
   Caffe::Phase phase_;
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index a688e197f02..b6afaa972f9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -22,11 +22,11 @@ void* DataLayerPrefetch(void* layer_pointer) {
   DataLayer<Dtype>* layer = static_cast<DataLayer<Dtype>*>(layer_pointer);
   CHECK(layer);
   Datum datum;
-  CHECK(layer->prefetch_data_);
-  Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
+  CHECK(layer->prefetch_data_.count());
+  Dtype* top_data = layer->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
   if (layer->output_labels_) {
-    top_label = layer->prefetch_label_->mutable_cpu_data();
+    top_label = layer->prefetch_label_.mutable_cpu_data();
   }
   const Dtype scale = layer->layer_param_.data_param().scale();
   const int batch_size = layer->layer_param_.data_param().batch_size();
@@ -257,16 +257,14 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   if (crop_size > 0) {
     (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
                        datum.channels(), crop_size, crop_size);
-    prefetch_data_.reset(new Blob<Dtype>(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        crop_size, crop_size));
+    prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
+        datum.channels(), crop_size, crop_size);
   } else {
     (*top)[0]->Reshape(
         this->layer_param_.data_param().batch_size(), datum.channels(),
         datum.height(), datum.width());
-    prefetch_data_.reset(new Blob<Dtype>(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        datum.height(), datum.width()));
+    prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
+        datum.channels(), datum.height(), datum.width());
   }
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
@@ -274,8 +272,8 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   if (output_labels_) {
     (*top)[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
-    prefetch_label_.reset(
-        new Blob<Dtype>(this->layer_param_.data_param().batch_size(), 1, 1, 1));
+    prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
+        1, 1, 1);
   }
   // datum size
   datum_channels_ = datum.channels();
@@ -303,9 +301,9 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_->mutable_cpu_data();
+  prefetch_data_.mutable_cpu_data();
   if (output_labels_) {
-    prefetch_label_->mutable_cpu_data();
+    prefetch_label_.mutable_cpu_data();
   }
   data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
@@ -349,10 +347,10 @@ Dtype DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              (*top)[0]->mutable_cpu_data());
   if (output_labels_) {
-    caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
                (*top)[1]->mutable_cpu_data());
   }
   // Start a new prefetch thread
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 4409f47f21c..e275e9d913e 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -19,10 +19,10 @@ Dtype DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       (*top)[0]->mutable_gpu_data());
   if (output_labels_) {
-    caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
         (*top)[1]->mutable_gpu_data());
   }
   // Start a new prefetch thread

From 6761735ca027e89230826d02e434723dbd3c5bb9 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 19 Jul 2014 18:18:03 -0700
Subject: [PATCH 0296/2053] use Blob directly instead of shared_ptr for
 ImageDataLayer buffers

---
 include/caffe/data_layers.hpp         |  4 ++--
 src/caffe/layers/image_data_layer.cpp | 23 +++++++++++------------
 src/caffe/layers/image_data_layer.cu  |  4 ++--
 3 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 069f0c2eea2..4f3136dc27f 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -228,8 +228,8 @@ class ImageDataLayer : public Layer<Dtype> {
   int datum_width_;
   int datum_size_;
   pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
   Caffe::Phase phase_;
 };
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 408452f5d80..e41baaf63d5 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -28,9 +28,9 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
       reinterpret_cast<ImageDataLayer<Dtype>*>(layer_pointer);
   CHECK(layer);
   Datum datum;
-  CHECK(layer->prefetch_data_);
-  Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
-  Dtype* top_label = layer->prefetch_label_->mutable_cpu_data();
+  CHECK(layer->prefetch_data_.count());
+  Dtype* top_data = layer->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = layer->prefetch_label_.mutable_cpu_data();
   ImageDataParameter image_data_param = layer->layer_param_.image_data_param();
   const Dtype scale = image_data_param.scale();
   const int batch_size = image_data_param.batch_size();
@@ -183,20 +183,19 @@ void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   const string& mean_file = this->layer_param_.image_data_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    prefetch_data_.reset(new Blob<Dtype>(batch_size, datum.channels(),
-                                         crop_size, crop_size));
+    prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
   } else {
     (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
                        datum.width());
-    prefetch_data_.reset(new Blob<Dtype>(batch_size, datum.channels(),
-                                         datum.height(), datum.width()));
+    prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
+        datum.width());
   }
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  prefetch_label_.reset(new Blob<Dtype>(batch_size, 1, 1, 1));
+  prefetch_label_.Reshape(batch_size, 1, 1, 1);
   // datum size
   datum_channels_ = datum.channels();
   datum_height_ = datum.height();
@@ -222,8 +221,8 @@ void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_->mutable_cpu_data();
-  prefetch_label_->mutable_cpu_data();
+  prefetch_data_.mutable_cpu_data();
+  prefetch_label_.mutable_cpu_data();
   data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
   CreatePrefetchThread();
@@ -277,9 +276,9 @@ Dtype ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              (*top)[0]->mutable_cpu_data());
-  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
              (*top)[1]->mutable_cpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index cf74853d902..b7ee02fe155 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -25,9 +25,9 @@ Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       (*top)[0]->mutable_gpu_data());
-  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
       (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();

From 21b019097af1f001fc9707c45c3fa1a31af47d67 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 19 Jul 2014 18:22:28 -0700
Subject: [PATCH 0297/2053] use Blob directly instead of shared_ptr for
 WindowDataLayer buffers

---
 include/caffe/data_layers.hpp          |  4 ++--
 src/caffe/layers/window_data_layer.cpp | 20 +++++++++-----------
 src/caffe/layers/window_data_layer.cu  |  4 ++--
 3 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 4f3136dc27f..1bb6bceb8c8 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -315,8 +315,8 @@ class WindowDataLayer : public Layer<Dtype> {
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
   pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
   vector<std::pair<std::string, vector<int> > > image_database_;
   enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index c45a1521db4..c59cac37c0c 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -39,8 +39,8 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
 
-  Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
-  Dtype* top_label = layer->prefetch_label_->mutable_cpu_data();
+  Dtype* top_data = layer->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = layer->prefetch_label_.mutable_cpu_data();
   const Dtype scale = layer->layer_param_.window_data_param().scale();
   const int batch_size = layer->layer_param_.window_data_param().batch_size();
   const int crop_size = layer->layer_param_.window_data_param().crop_size();
@@ -58,7 +58,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  caffe_set(layer->prefetch_data_->count(), Dtype(0), top_data);
+  caffe_set(layer->prefetch_data_.count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
@@ -370,16 +370,14 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  prefetch_data_.reset(
-      new Blob<Dtype>(batch_size, channels, crop_size, crop_size));
+  prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  prefetch_label_.reset(
-      new Blob<Dtype>(batch_size, 1, 1, 1));
+  prefetch_label_.Reshape(batch_size, 1, 1, 1);
 
   // check if we want to have mean
   if (this->layer_param_.window_data_param().has_mean_file()) {
@@ -400,8 +398,8 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_->mutable_cpu_data();
-  prefetch_label_->mutable_cpu_data();
+  prefetch_data_.mutable_cpu_data();
+  prefetch_label_.mutable_cpu_data();
   data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
   CreatePrefetchThread();
@@ -443,9 +441,9 @@ Dtype WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              (*top)[0]->mutable_cpu_data());
-  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
              (*top)[1]->mutable_cpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index d4efeca06c1..b7658f6ff4f 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -27,9 +27,9 @@ Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       (*top)[0]->mutable_gpu_data());
-  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
       (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();

From 29fd174ec68006bc35d7820b3172c73dd8d2b623 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 19 Jul 2014 18:51:15 -0700
Subject: [PATCH 0298/2053] add an "everything" target to make for
 comprehensive build testing

The everything target builds all components, checks warnings, runs lint,
and runs tests.
---
 Makefile | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 109894dd7c7..b25d1ba798d 100644
--- a/Makefile
+++ b/Makefile
@@ -295,7 +295,9 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 ##############################
 .PHONY: all test clean linecount lint tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
-	superclean supercleanlist supercleanfiles warn
+	superclean supercleanlist supercleanfiles warn everything
+
+everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
 
 all: $(NAME) $(STATIC_NAME) tools examples
 

From 968f82661d8030717d0737a40b1a5fbaea91389e Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 20 Jul 2014 02:29:15 -0700
Subject: [PATCH 0299/2053] remove unused includes in AccuracyLayer and
 ArgMaxLayer

---
 src/caffe/layers/accuracy_layer.cpp | 3 ---
 src/caffe/layers/argmax_layer.cpp   | 2 --
 2 files changed, 5 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 3ccba3b50d1..58ea6ae10e2 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -1,8 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <algorithm>
-#include <cmath>
-#include <cfloat>
 #include <functional>
 #include <utility>
 #include <vector>
@@ -12,7 +10,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
 
-
 namespace caffe {
 
 using std::max;
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index bd1a2689f3e..732f7bf15d1 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -1,9 +1,7 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <algorithm>
-#include <cfloat>
 #include <functional>
-#include <queue>
 #include <utility>
 #include <vector>
 

From 0193012e5cccb3e1a8d74f84f988bce8966640f7 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 19 Jul 2014 13:24:01 -0700
Subject: [PATCH 0300/2053] leaky relu + unit test

---
 src/caffe/layers/relu_layer.cpp      |  8 ++++++--
 src/caffe/layers/relu_layer.cu       | 16 ++++++++++------
 src/caffe/proto/caffe.proto          | 13 ++++++++++++-
 src/caffe/test/test_neuron_layer.cpp | 26 ++++++++++++++++++++++++++
 4 files changed, 54 insertions(+), 9 deletions(-)

diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 1e1195501e8..57ccf97e046 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -14,8 +14,10 @@ Dtype ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
+  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   for (int i = 0; i < count; ++i) {
-    top_data[i] = std::max(bottom_data[i], Dtype(0));
+    top_data[i] = std::max(bottom_data[i], Dtype(0))
+        + negative_slope * std::min(bottom_data[i], Dtype(0));
   }
   return Dtype(0);
 }
@@ -29,8 +31,10 @@ void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
+    Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     for (int i = 0; i < count; ++i) {
-      bottom_diff[i] = top_diff[i] * (bottom_data[i] > 0);
+      bottom_diff[i] = top_diff[i] * (bottom_data[i] >= 0)
+          + top_diff[i] * negative_slope * (bottom_data[i] < 0);
     }
   }
 }
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index e8b0fbeea57..f5a24a9a466 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -9,9 +9,10 @@
 namespace caffe {
 
 template <typename Dtype>
-__global__ void ReLUForward(const int n, const Dtype* in, Dtype* out) {
+__global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
+    Dtype negative_slope) {
   CUDA_KERNEL_LOOP(index, n) {
-    out[index] = in[index] > 0 ? in[index] : 0;
+    out[index] = in[index] > 0 ? in[index] : in[index] * negative_slope;
   }
 }
 
@@ -21,9 +22,10 @@ Dtype ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
+  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   // NOLINT_NEXT_LINE(whitespace/operators)
   ReLUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-      count, bottom_data, top_data);
+      count, bottom_data, top_data, negative_slope);
   CUDA_POST_KERNEL_CHECK;
   // << " count: " << count << " bottom_data: "
   //     << (unsigned long)bottom_data
@@ -35,9 +37,10 @@ Dtype ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 __global__ void ReLUBackward(const int n, const Dtype* in_diff,
-    const Dtype* in_data, Dtype* out_diff) {
+    const Dtype* in_data, Dtype* out_diff, Dtype negative_slope) {
   CUDA_KERNEL_LOOP(index, n) {
-    out_diff[index] = in_diff[index] * (in_data[index] > 0);
+    out_diff[index] = in_diff[index] * (in_data[index] >= 0)
+        + in_diff[index] * (in_data[index] < 0) * negative_slope;
   }
 }
 
@@ -50,9 +53,10 @@ void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const int count = (*bottom)[0]->count();
+    Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     // NOLINT_NEXT_LINE(whitespace/operators)
     ReLUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, bottom_data, bottom_diff);
+        count, top_diff, bottom_data, bottom_diff, negative_slope);
     CUDA_POST_KERNEL_CHECK;
   }
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 10e52d2ad47..9c3ddfe7ed5 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -117,7 +117,7 @@ message SolverState {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 28 (last added: accuracy_param)
+// LayerParameter next available ID: 31 (last added: relu_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -207,6 +207,7 @@ message LayerParameter {
   optional MemoryDataParameter memory_data_param = 22;
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
+  optional ReLUParameter relu_param = 30;
   optional WindowDataParameter window_data_param = 20;
   optional ThresholdParameter threshold_param = 25;
   optional HingeLossParameter hinge_loss_param = 29;
@@ -429,6 +430,16 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+// Message that stores parameters used by ReLULayer
+message ReLUParameter {
+  // Allow non-zero slope for negative inputs to speed up optimization
+  // Described in:
+  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+  // improve neural network acoustic models. In ICML Workshop on Deep Learning
+  // for Audio, Speech, and Language Processing.
+  optional float negative_slope = 1 [default = 0];
+}
+
 // Message that stores parameters used by WindowDataLayer
 message WindowDataParameter {
   // Specify the data source.
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 246832d226d..db9b934fb5b 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -62,6 +62,32 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  ReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ReLULayer<Dtype> layer(layer_param);
+  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From ba3ea2b6b1733a330057aff7e94e64834b2df439 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 19 Jul 2014 17:28:52 -0700
Subject: [PATCH 0301/2053] reduced multiplications & fixed unit test

---
 src/caffe/layers/relu_layer.cpp      | 4 ++--
 src/caffe/layers/relu_layer.cu       | 4 ++--
 src/caffe/test/test_neuron_layer.cpp | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 57ccf97e046..3a3e8a21afe 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -33,8 +33,8 @@ void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const int count = (*bottom)[0]->count();
     Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     for (int i = 0; i < count; ++i) {
-      bottom_diff[i] = top_diff[i] * (bottom_data[i] >= 0)
-          + top_diff[i] * negative_slope * (bottom_data[i] < 0);
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] >= 0)
+          + negative_slope * (bottom_data[i] < 0));
     }
   }
 }
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index f5a24a9a466..503147c713d 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -39,8 +39,8 @@ template <typename Dtype>
 __global__ void ReLUBackward(const int n, const Dtype* in_diff,
     const Dtype* in_data, Dtype* out_diff, Dtype negative_slope) {
   CUDA_KERNEL_LOOP(index, n) {
-    out_diff[index] = in_diff[index] * (in_data[index] >= 0)
-        + in_diff[index] * (in_data[index] < 0) * negative_slope;
+    out_diff[index] = in_diff[index] * ((in_data[index] >= 0)
+        + (in_data[index] < 0) * negative_slope);
   }
 }
 
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index db9b934fb5b..7a7af3188fe 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -81,8 +81,8 @@ TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
 TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReLULayer<Dtype> layer(layer_param);
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));

From 324973aa6cc9909fc0b09c939f6c0056942cfc04 Mon Sep 17 00:00:00 2001
From: bhack <s.fabri@email.it>
Date: Sun, 13 Jul 2014 01:09:10 +0200
Subject: [PATCH 0302/2053] Add split dim layer

Differentiate top test blob vector size

Rename to SplitLayer
Add slicing points
---
 include/caffe/common_layers.hpp     |  38 ++++++++
 src/caffe/layer_factory.cpp         |   2 +
 src/caffe/layers/slice_layer.cpp    | 135 ++++++++++++++++++++++++++++
 src/caffe/layers/slice_layer.cu     |  74 +++++++++++++++
 src/caffe/proto/caffe.proto         |  13 ++-
 src/caffe/test/test_slice_layer.cpp | 131 +++++++++++++++++++++++++++
 6 files changed, 392 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/slice_layer.cpp
 create mode 100644 src/caffe/layers/slice_layer.cu
 create mode 100644 src/caffe/test/test_slice_layer.cpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 395ea7d6c98..643c0ee4147 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -178,6 +178,44 @@ class SplitLayer : public Layer<Dtype> {
   int count_;
 };
 
+/* SliceLayer
+  Takes one blobs and slice it along either num or
+  channel dim, outputting the result.
+*/
+template <typename Dtype>
+class SliceLayer : public Layer<Dtype> {
+ public:
+  explicit SliceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SLICE;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 2; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> col_bob_;
+  int count_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+  int slice_dim_;
+  google::protobuf::RepeatedField<google::uint32> slice_point_;
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_COMMON_LAYERS_HPP_
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index e6cbe187c3c..de960214153 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -71,6 +71,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new SigmoidLayer<Dtype>(param);
   case LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
     return new SigmoidCrossEntropyLossLayer<Dtype>(param);
+  case LayerParameter_LayerType_SLICE:
+    return new SliceLayer<Dtype>(param);
   case LayerParameter_LayerType_SOFTMAX:
     return new SoftmaxLayer<Dtype>(param);
   case LayerParameter_LayerType_SOFTMAX_LOSS:
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
new file mode 100644
index 00000000000..c6a9b05e221
--- /dev/null
+++ b/src/caffe/layers/slice_layer.cpp
@@ -0,0 +1,135 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
+  slice_dim_ = this->layer_param_.slice_param().slice_dim();
+  slice_point_ = this->layer_param_.slice_param().slice_point();
+  CHECK_GE(slice_dim_, 0) <<
+    "slice_dim should be >= 0";
+  CHECK_LE(slice_dim_, 1) <<
+    "For now slice_dim <=1, it can only slice num and channels";
+  count_ = 0;
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  if (slice_point_.size() != 0) {
+    CHECK_EQ(slice_point_.size(), top->size()-1);
+    if (slice_dim_ == 0) {
+      CHECK_LE(top->size(), num_);
+    } else {
+      CHECK_LE(top->size(), channels_);
+    }
+    int prev = 0;
+    vector<int> slices;
+    for (int i = 0; i < slice_point_.size(); ++i) {
+      CHECK_GT(slice_point_.Get(i), prev);
+      slices.push_back(slice_point_.Get(i) - prev);
+      prev = slice_point_.Get(i);
+    }
+    if (slice_dim_ == 0) {
+      slices.push_back(num_ - prev);
+      for (int i = 0; i < top->size(); ++i) {
+        (*top)[i]->Reshape(slices[i], channels_, height_, width_);
+         count_ += (*top)[i]->count();
+      }
+    } else {
+      slices.push_back(channels_ - prev);
+      for (int i = 0; i < top->size(); ++i) {
+        (*top)[i]->Reshape(num_, slices[i], height_, width_);
+         count_ += (*top)[i]->count();
+      }
+    }
+
+  } else {
+    if (slice_dim_ == 0) {
+      int remainder = num_%top->size();
+      CHECK_EQ(remainder, 0);
+      num_ = num_/top->size();
+    } else {
+      int remainder = channels_%top->size();
+      CHECK_EQ(remainder, 0);
+      channels_ = channels_/top->size();
+    }
+    for (int i = 0; i < top->size(); ++i) {
+      (*top)[i]->Reshape(num_, channels_, height_, width_);
+       count_ += (*top)[i]->count();
+    }
+  }
+  CHECK_EQ(count_, bottom[0]->count());
+}
+
+template <typename Dtype>
+Dtype SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->mutable_cpu_data();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_cpu_data();
+      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
+                 top_data);
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_cpu_data();
+      int num_elem = blob->channels()*blob->height()*blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
+                     top_data + blob->offset(n));
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetU
+  return Dtype(0.);
+}
+
+template <typename Dtype>
+void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      if (propagate_down[i]) {
+        const Dtype* top_diff = blob->cpu_diff();
+        caffe_copy(blob->count(), top_diff,
+                   bottom_diff + (*bottom)[0]->offset(offset_num));
+      }
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      if (propagate_down[i]) {
+        const Dtype* top_diff = blob->cpu_diff();
+        int num_elem = blob->channels()*blob->height()*blob->width();
+        for (int n = 0; n < num_; ++n) {
+          caffe_copy(num_elem, top_diff + blob->offset(n),
+                     bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
+        }
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+}
+
+INSTANTIATE_CLASS(SliceLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
new file mode 100644
index 00000000000..c2eeb5c7d95
--- /dev/null
+++ b/src/caffe/layers/slice_layer.cu
@@ -0,0 +1,74 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Dtype SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_gpu_data();
+      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
+                 top_data);
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_gpu_data();
+      int num_elem = blob->channels()*blob->height()*blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
+                     top_data + blob->offset(n));
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetU
+  return Dtype(0.);
+}
+
+template <typename Dtype>
+void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      if (propagate_down[i]) {
+        const Dtype* top_diff = blob->gpu_diff();
+        caffe_copy(blob->count(), top_diff,
+                   bottom_diff + (*bottom)[0]->offset(offset_num));
+      }
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      if (propagate_down[i]) {
+        const Dtype* top_diff = blob->gpu_diff();
+        int num_elem = blob->channels()*blob->height()*blob->width();
+        for (int n = 0; n < num_; ++n) {
+          caffe_copy(num_elem, top_diff + blob->offset(n),
+                     bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
+        }
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+}
+
+INSTANTIATE_CLASS(SliceLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9c3ddfe7ed5..d1d5336a9c8 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -129,7 +129,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 33 (last added: DUMMY_DATA)
+  // LayerType next available ID: 34 (last added: SLICE)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -164,6 +164,7 @@ message LayerParameter {
     SOFTMAX = 20;
     SOFTMAX_LOSS = 21;
     SPLIT = 22;
+    SLICE = 33;
     TANH = 23;
     WINDOW_DATA = 24;
     THRESHOLD = 31;
@@ -211,6 +212,7 @@ message LayerParameter {
   optional WindowDataParameter window_data_param = 20;
   optional ThresholdParameter threshold_param = 25;
   optional HingeLossParameter hinge_loss_param = 29;
+  optional SliceParameter slice_param=30;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -440,6 +442,15 @@ message ReLUParameter {
   optional float negative_slope = 1 [default = 0];
 }
 
+// Message that stores parameters used by SliceLayer
+message SliceParameter {
+  // Slice Layer needs to specify the dimension along the slice will happen,
+  // the other dimensions must be the same for all the bottom blobs
+  // By default it will slice blobs along channels dimension
+  optional uint32 slice_dim = 1 [default = 1];
+  repeated uint32 slice_point = 2;
+}
+
 // Message that stores parameters used by WindowDataLayer
 message WindowDataParameter {
   // Specify the data source.
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
new file mode 100644
index 00000000000..8f7bb60171b
--- /dev/null
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -0,0 +1,131 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <cstring>
+#include <vector>
+
+#include "cuda_runtime.h"
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class SliceLayerTest : public ::testing::Test {
+ protected:
+  SliceLayerTest()
+      : blob_bottom_(new Blob<Dtype>(6, 12, 6, 5)),
+        blob_top_0(new Blob<Dtype>()),
+        blob_top_1(new Blob<Dtype>()),
+        blob_top_2(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    ConstantFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_top_vec_0.push_back(blob_top_0);
+    blob_top_vec_0.push_back(blob_top_1);
+    blob_top_vec_1.push_back(blob_top_0);
+    blob_top_vec_1.push_back(blob_top_1);
+    blob_top_vec_1.push_back(blob_top_2);
+    blob_bottom_vec_.push_back(blob_bottom_);
+  }
+
+  virtual ~SliceLayerTest() {
+    delete blob_top_0; delete blob_top_1;
+    delete blob_top_2; delete blob_bottom_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_0;
+  Blob<Dtype>* const blob_top_1;
+  Blob<Dtype>* const blob_top_2;
+  vector<Blob<Dtype>*> blob_top_vec_0, blob_top_vec_1;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+};
+
+typedef ::testing::Types<float, double> Dtypes;
+TYPED_TEST_CASE(SliceLayerTest, Dtypes);
+
+TYPED_TEST(SliceLayerTest, TestCPUSetupNum) {
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->set_slice_dim(0);
+  SliceLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1));
+  EXPECT_EQ(this->blob_bottom_->num(),
+    this->blob_top_0->num() + this->blob_top_1->num() +
+    this->blob_top_2->num());
+  EXPECT_EQ(this->blob_bottom_->channels(), this->blob_top_0->channels());
+  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0->height());
+  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0->width());
+}
+
+TYPED_TEST(SliceLayerTest, TestCPUSetupChannels) {
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->add_slice_point(3);
+  SliceLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0));
+  EXPECT_EQ(this->blob_top_0->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_0->channels(), 3);
+  EXPECT_EQ(this->blob_top_1->channels(), 9);
+  EXPECT_EQ(this->blob_bottom_->channels(),
+    this->blob_top_0->channels()+this->blob_top_1->channels());
+  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0->height());
+  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0->width());
+}
+
+
+TYPED_TEST(SliceLayerTest, TestCPUNum) {
+  LayerParameter layer_param;
+  SliceLayer<TypeParam> layer(layer_param);
+  Caffe::set_mode(Caffe::CPU);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_0));
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_0->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c, h, w),
+            this->blob_top_vec_0[0]->data_at(n, c, h, w));
+        }
+      }
+    }
+    for (int c = 0; c < this->blob_top_1->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c+3, h, w),
+            this->blob_top_vec_0[1]->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+}
+
+
+TYPED_TEST(SliceLayerTest, TestCPUGradient) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  SliceLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
+    &(this->blob_top_vec_0));
+}
+
+TYPED_TEST(SliceLayerTest, TestGPUGradient) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::GPU);
+  SliceLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
+    &(this->blob_top_vec_0));
+}
+
+}  // namespace caffe

From 5db5b31c5928eb18a4d77d49d09d84c6289c0414 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 22 Jul 2014 00:44:53 -0700
Subject: [PATCH 0303/2053] SliceLayer: post-rebase fixes, cleanup, etc. (some
 from changes suggested by @sguada).  Test for both num & channels in forward
 & backward; use GaussianFiller so that tests are non-trivial.

---
 include/caffe/common_layers.hpp     |   6 +-
 src/caffe/layers/slice_layer.cpp    |  69 +++++-----
 src/caffe/layers/slice_layer.cu     |  27 ++--
 src/caffe/proto/caffe.proto         |  11 +-
 src/caffe/test/test_slice_layer.cpp | 187 ++++++++++++++++++----------
 5 files changed, 181 insertions(+), 119 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 643c0ee4147..5d872452ae2 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -179,8 +179,8 @@ class SplitLayer : public Layer<Dtype> {
 };
 
 /* SliceLayer
-  Takes one blobs and slice it along either num or
-  channel dim, outputting the result.
+  Takes one blobs and slices it along either num or channel dim,
+  outputting the result into as many top blobs as needed.
 */
 template <typename Dtype>
 class SliceLayer : public Layer<Dtype> {
@@ -213,7 +213,7 @@ class SliceLayer : public Layer<Dtype> {
   int height_;
   int width_;
   int slice_dim_;
-  google::protobuf::RepeatedField<google::uint32> slice_point_;
+  vector<int> slice_point_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index c6a9b05e221..e895e4f0b8a 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -12,19 +12,21 @@ template <typename Dtype>
 void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Layer<Dtype>::SetUp(bottom, top);
-  slice_dim_ = this->layer_param_.slice_param().slice_dim();
-  slice_point_ = this->layer_param_.slice_param().slice_point();
-  CHECK_GE(slice_dim_, 0) <<
-    "slice_dim should be >= 0";
-  CHECK_LE(slice_dim_, 1) <<
-    "For now slice_dim <=1, it can only slice num and channels";
+  const SliceParameter& slice_param = this->layer_param_.slice_param();
+  slice_dim_ = slice_param.slice_dim();
+  CHECK_GE(slice_dim_, 0);
+  CHECK_LE(slice_dim_, 1) << "Can only slice num and channels";
+  slice_point_.clear();
+  for (int i = 0; i < slice_param.slice_point_size(); ++i) {
+    slice_point_.push_back(slice_param.slice_point(i));
+  }
   count_ = 0;
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   if (slice_point_.size() != 0) {
-    CHECK_EQ(slice_point_.size(), top->size()-1);
+    CHECK_EQ(slice_point_.size(), top->size() - 1);
     if (slice_dim_ == 0) {
       CHECK_LE(top->size(), num_);
     } else {
@@ -33,9 +35,9 @@ void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     int prev = 0;
     vector<int> slices;
     for (int i = 0; i < slice_point_.size(); ++i) {
-      CHECK_GT(slice_point_.Get(i), prev);
-      slices.push_back(slice_point_.Get(i) - prev);
-      prev = slice_point_.Get(i);
+      CHECK_GT(slice_point_[i], prev);
+      slices.push_back(slice_point_[i] - prev);
+      prev = slice_point_[i];
     }
     if (slice_dim_ == 0) {
       slices.push_back(num_ - prev);
@@ -53,17 +55,19 @@ void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 
   } else {
     if (slice_dim_ == 0) {
-      int remainder = num_%top->size();
-      CHECK_EQ(remainder, 0);
-      num_ = num_/top->size();
+      CHECK_EQ(num_ % top->size(), 0)
+          << "Number of top blobs (" << top->size() << ") "
+          << "should evenly divide input num ( " << num_ << ")";
+      num_ = num_ / top->size();
     } else {
-      int remainder = channels_%top->size();
-      CHECK_EQ(remainder, 0);
-      channels_ = channels_/top->size();
+      CHECK_EQ(channels_ % top->size(), 0)
+          << "Number of top blobs (" << top->size() << ") "
+          << "should evenly divide input channels ( " << channels_ << ")";
+      channels_ = channels_ / top->size();
     }
     for (int i = 0; i < top->size(); ++i) {
       (*top)[i]->Reshape(num_, channels_, height_, width_);
-       count_ += (*top)[i]->count();
+      count_ += (*top)[i]->count();
     }
   }
   CHECK_EQ(count_, bottom[0]->count());
@@ -87,49 +91,50 @@ Dtype SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     for (int i = 0; i < top->size(); ++i) {
       Blob<Dtype>* blob = (*top)[i];
       Dtype* top_data = blob->mutable_cpu_data();
-      int num_elem = blob->channels()*blob->height()*blob->width();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
       for (int n = 0; n < num_; ++n) {
         caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
-                     top_data + blob->offset(n));
+                   top_data + blob->offset(n));
       }
       offset_channel += blob->channels();
     }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetU
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
   return Dtype(0.);
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) { return; }
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   if (slice_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < top.size(); ++i) {
       Blob<Dtype>* blob = top[i];
-      if (propagate_down[i]) {
-        const Dtype* top_diff = blob->cpu_diff();
-        caffe_copy(blob->count(), top_diff,
-                   bottom_diff + (*bottom)[0]->offset(offset_num));
-      }
+      const Dtype* top_diff = blob->cpu_diff();
+      caffe_copy(blob->count(), top_diff,
+                 bottom_diff + (*bottom)[0]->offset(offset_num));
       offset_num += blob->num();
     }
   } else if (slice_dim_ == 1) {
     int offset_channel = 0;
     for (int i = 0; i < top.size(); ++i) {
       Blob<Dtype>* blob = top[i];
-      if (propagate_down[i]) {
-        const Dtype* top_diff = blob->cpu_diff();
-        int num_elem = blob->channels()*blob->height()*blob->width();
-        for (int n = 0; n < num_; ++n) {
-          caffe_copy(num_elem, top_diff + blob->offset(n),
-                     bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
-        }
+      const Dtype* top_diff = blob->cpu_diff();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, top_diff + blob->offset(n),
+                   bottom_diff + (*bottom)[0]->offset(n, offset_channel));
       }
       offset_channel += blob->channels();
     }
   }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(SliceLayer);
+#endif
+
 INSTANTIATE_CLASS(SliceLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index c2eeb5c7d95..3ea8d57659d 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -26,43 +26,40 @@ Dtype SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     for (int i = 0; i < top->size(); ++i) {
       Blob<Dtype>* blob = (*top)[i];
       Dtype* top_data = blob->mutable_gpu_data();
-      int num_elem = blob->channels()*blob->height()*blob->width();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
       for (int n = 0; n < num_; ++n) {
         caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
-                     top_data + blob->offset(n));
+                   top_data + blob->offset(n));
       }
       offset_channel += blob->channels();
     }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetU
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
   return Dtype(0.);
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) { return; }
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   if (slice_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < top.size(); ++i) {
       Blob<Dtype>* blob = top[i];
-      if (propagate_down[i]) {
-        const Dtype* top_diff = blob->gpu_diff();
-        caffe_copy(blob->count(), top_diff,
-                   bottom_diff + (*bottom)[0]->offset(offset_num));
-      }
+      const Dtype* top_diff = blob->gpu_diff();
+      caffe_copy(blob->count(), top_diff,
+                 bottom_diff + (*bottom)[0]->offset(offset_num));
       offset_num += blob->num();
     }
   } else if (slice_dim_ == 1) {
     int offset_channel = 0;
     for (int i = 0; i < top.size(); ++i) {
       Blob<Dtype>* blob = top[i];
-      if (propagate_down[i]) {
-        const Dtype* top_diff = blob->gpu_diff();
-        int num_elem = blob->channels()*blob->height()*blob->width();
-        for (int n = 0; n < num_; ++n) {
-          caffe_copy(num_elem, top_diff + blob->offset(n),
-                     bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
-        }
+      const Dtype* top_diff = blob->gpu_diff();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, top_diff + blob->offset(n),
+                   bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
       }
       offset_channel += blob->channels();
     }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d1d5336a9c8..34cf8443f56 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -117,7 +117,7 @@ message SolverState {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 31 (last added: relu_param)
+// LayerParameter next available ID: 32 (last added: slice_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -212,7 +212,7 @@ message LayerParameter {
   optional WindowDataParameter window_data_param = 20;
   optional ThresholdParameter threshold_param = 25;
   optional HingeLossParameter hinge_loss_param = 29;
-  optional SliceParameter slice_param=30;
+  optional SliceParameter slice_param = 31;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -444,9 +444,10 @@ message ReLUParameter {
 
 // Message that stores parameters used by SliceLayer
 message SliceParameter {
-  // Slice Layer needs to specify the dimension along the slice will happen,
-  // the other dimensions must be the same for all the bottom blobs
-  // By default it will slice blobs along channels dimension
+  // SliceLayer needs to know which dimension to slice across.
+  // Currently, SliceLayer only supports slicing across num (dim 0)
+  // and channels (dim 1).
+  // By default, SliceLayer slices across channels.
   optional uint32 slice_dim = 1 [default = 1];
   repeated uint32 slice_point = 2;
 }
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index 8f7bb60171b..59f6a8d4c66 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -3,7 +3,6 @@
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -15,117 +14,177 @@
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class SliceLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class SliceLayerTest : public ::testing::Test {
  protected:
   SliceLayerTest()
-      : blob_bottom_(new Blob<Dtype>(6, 12, 6, 5)),
-        blob_top_0(new Blob<Dtype>()),
-        blob_top_1(new Blob<Dtype>()),
-        blob_top_2(new Blob<Dtype>()) {}
+      : blob_bottom_(new Blob<Dtype>(6, 12, 2, 3)),
+        blob_top_0_(new Blob<Dtype>()),
+        blob_top_1_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     // fill the values
+    Caffe::set_random_seed(1701);
     FillerParameter filler_param;
-    filler_param.set_value(1.);
-    ConstantFiller<Dtype> filler(filler_param);
+    GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
-    blob_top_vec_0.push_back(blob_top_0);
-    blob_top_vec_0.push_back(blob_top_1);
-    blob_top_vec_1.push_back(blob_top_0);
-    blob_top_vec_1.push_back(blob_top_1);
-    blob_top_vec_1.push_back(blob_top_2);
+    blob_top_vec_0_.push_back(blob_top_0_);
+    blob_top_vec_0_.push_back(blob_top_1_);
+    blob_top_vec_1_.push_back(blob_top_0_);
+    blob_top_vec_1_.push_back(blob_top_1_);
+    blob_top_vec_1_.push_back(blob_top_2_);
     blob_bottom_vec_.push_back(blob_bottom_);
   }
 
+  virtual void ReduceBottomBlobSize() {
+    blob_bottom_->Reshape(4, 5, 2, 2);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+  }
+
   virtual ~SliceLayerTest() {
-    delete blob_top_0; delete blob_top_1;
-    delete blob_top_2; delete blob_bottom_;
+    delete blob_top_0_; delete blob_top_1_;
+    delete blob_top_2_; delete blob_bottom_;
   }
 
   Blob<Dtype>* const blob_bottom_;
-  Blob<Dtype>* const blob_top_0;
-  Blob<Dtype>* const blob_top_1;
-  Blob<Dtype>* const blob_top_2;
-  vector<Blob<Dtype>*> blob_top_vec_0, blob_top_vec_1;
+  Blob<Dtype>* const blob_top_0_;
+  Blob<Dtype>* const blob_top_1_;
+  Blob<Dtype>* const blob_top_2_;
+  vector<Blob<Dtype>*> blob_top_vec_0_, blob_top_vec_1_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SliceLayerTest, Dtypes);
+TYPED_TEST_CASE(SliceLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(SliceLayerTest, TestCPUSetupNum) {
+TYPED_TEST(SliceLayerTest, TestSetupNum) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_slice_param()->set_slice_dim(0);
-  SliceLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1));
-  EXPECT_EQ(this->blob_bottom_->num(),
-    this->blob_top_0->num() + this->blob_top_1->num() +
-    this->blob_top_2->num());
-  EXPECT_EQ(this->blob_bottom_->channels(), this->blob_top_0->channels());
-  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0->height());
-  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0->width());
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  EXPECT_EQ(this->blob_bottom_->num(), 3 * this->blob_top_0_->num());
+  EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_1_->num());
+  EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_2_->num());
+  EXPECT_EQ(this->blob_bottom_->channels(), this->blob_top_0_->channels());
+  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0_->height());
+  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
 }
 
-TYPED_TEST(SliceLayerTest, TestCPUSetupChannels) {
+TYPED_TEST(SliceLayerTest, TestSetupChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_slice_param()->add_slice_point(3);
-  SliceLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0));
-  EXPECT_EQ(this->blob_top_0->num(), this->blob_bottom_->num());
-  EXPECT_EQ(this->blob_top_0->channels(), 3);
-  EXPECT_EQ(this->blob_top_1->channels(), 9);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  EXPECT_EQ(this->blob_top_0_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_0_->channels(), 3);
+  EXPECT_EQ(this->blob_top_1_->channels(), 9);
   EXPECT_EQ(this->blob_bottom_->channels(),
-    this->blob_top_0->channels()+this->blob_top_1->channels());
-  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0->height());
-  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0->width());
+    this->blob_top_0_->channels() + this->blob_top_1_->channels());
+  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0_->height());
+  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
 }
 
+TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->set_slice_dim(0);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  const int top_num = this->blob_bottom_->num() / 2;
+  ASSERT_EQ(top_num, this->blob_top_0_->num());
+  ASSERT_EQ(top_num, this->blob_top_1_->num());
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  for (int n = 0; n < top_num; ++n) {
+    for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c, h, w),
+                    this->blob_top_0_->data_at(n, c, h, w));
+        }
+      }
+    }
+    for (int c = 0; c < this->blob_top_1_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n + 3, c, h, w),
+                    this->blob_top_1_->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+}
 
-TYPED_TEST(SliceLayerTest, TestCPUNum) {
+TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SliceLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_0));
+  // Slice at 2, 8: should produce output blobs with #channels 2, 6, 4.
+  const int kSlicePoint0 = 2;
+  const int kSlicePoint1 = 8;
+  layer_param.mutable_slice_param()->add_slice_point(kSlicePoint0);
+  layer_param.mutable_slice_param()->add_slice_point(kSlicePoint1);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  ASSERT_EQ(kSlicePoint0, this->blob_top_0_->channels());
+  ASSERT_EQ(kSlicePoint1 - kSlicePoint0, this->blob_top_1_->channels());
+  ASSERT_EQ(this->blob_bottom_->channels() - kSlicePoint1,
+            this->blob_top_2_->channels());
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
   for (int n = 0; n < this->blob_bottom_->num(); ++n) {
-    for (int c = 0; c < this->blob_top_0->channels(); ++c) {
+    for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
         for (int w = 0; w < this->blob_bottom_->width(); ++w) {
           EXPECT_EQ(this->blob_bottom_->data_at(n, c, h, w),
-            this->blob_top_vec_0[0]->data_at(n, c, h, w));
+              this->blob_top_0_->data_at(n, c, h, w));
         }
       }
     }
-    for (int c = 0; c < this->blob_top_1->channels(); ++c) {
+    for (int c = 0; c < this->blob_top_1_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
         for (int w = 0; w < this->blob_bottom_->width(); ++w) {
-          EXPECT_EQ(this->blob_bottom_->data_at(n, c+3, h, w),
-            this->blob_top_vec_0[1]->data_at(n, c, h, w));
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c + kSlicePoint0, h, w),
+              this->blob_top_1_->data_at(n, c, h, w));
+        }
+      }
+    }
+    for (int c = 0; c < this->blob_top_2_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c + kSlicePoint1, h, w),
+              this->blob_top_2_->data_at(n, c, h, w));
         }
       }
     }
   }
 }
 
-
-TYPED_TEST(SliceLayerTest, TestCPUGradient) {
+TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Gradient checks are slow; reduce blob size.
+  this->ReduceBottomBlobSize();
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SliceLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-    &(this->blob_top_vec_0));
+  layer_param.mutable_slice_param()->set_slice_dim(0);
+  SliceLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+    &(this->blob_top_vec_0_));
 }
 
-TYPED_TEST(SliceLayerTest, TestGPUGradient) {
+TYPED_TEST(SliceLayerTest, TestGradientAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Gradient checks are slow; reduce blob size.
+  this->ReduceBottomBlobSize();
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SliceLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-    &(this->blob_top_vec_0));
+  const int kSlicePoint = 4;
+  layer_param.mutable_slice_param()->add_slice_point(kSlicePoint);
+  SliceLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+    &(this->blob_top_vec_0_));
 }
 
 }  // namespace caffe

From 4bd3624c3e85b9d78449a047af1a2f7e274ddd3b Mon Sep 17 00:00:00 2001
From: bhack <s.fabri@email.it>
Date: Tue, 22 Jul 2014 19:25:45 +0200
Subject: [PATCH 0304/2053] Fix repeated field copy loop

Fix lint algorithm include

Fix spaces
---
 src/caffe/layers/slice_layer.cpp | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index e895e4f0b8a..e8231e357d9 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -1,6 +1,7 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <vector>
+#include <algorithm>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
@@ -17,9 +18,9 @@ void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GE(slice_dim_, 0);
   CHECK_LE(slice_dim_, 1) << "Can only slice num and channels";
   slice_point_.clear();
-  for (int i = 0; i < slice_param.slice_point_size(); ++i) {
-    slice_point_.push_back(slice_param.slice_point(i));
-  }
+  std::copy(slice_param.slice_point().begin(),
+      slice_param.slice_point().end(),
+      std::back_inserter(slice_point_));
   count_ = 0;
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();

From 0ade27b6eda30062ee7a204563d8f28808372012 Mon Sep 17 00:00:00 2001
From: Ross Girshick <rbg@eecs.berkeley.edu>
Date: Tue, 22 Jul 2014 12:51:48 -0700
Subject: [PATCH 0305/2053] Correctly apply padding to pooling layers when
 upgrading from V0

---
 src/caffe/util/upgrade_proto.cpp | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index a17beed70a7..e1a48f194e1 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -82,11 +82,13 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
       LayerParameter source_layer = param.layers(top_idx);
       if (source_layer.layer().type() == "padding") {
         // This layer has a padding layer as input -- check that it is a conv
-        // layer and takes only one input.  Also check that the padding layer
-        // input has only one input and one output.  Other cases have undefined
-        // behavior in Caffe.
-        CHECK_EQ(layer_param.type(), "conv") << "Padding layer input to "
-            "non-convolutional layer type " << layer_param.type();
+        // layer or a pooling layer and takes only one input.  Also check that
+        // the padding layer input has only one input and one output.  Other
+        // cases have undefined behavior in Caffe.
+        CHECK((layer_param.type() == "conv") || (layer_param.type() == "pool"))
+            << "Padding layer input to "
+            "non-convolutional / non-pooling layer type "
+            << layer_param.type();
         CHECK_EQ(layer_connection.bottom_size(), 1)
             << "Conv Layer takes a single blob as input.";
         CHECK_EQ(source_layer.bottom_size(), 1)
@@ -186,6 +188,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
     if (v0_layer_param.has_pad()) {
       if (type == "conv") {
         layer_param->mutable_convolution_param()->set_pad(v0_layer_param.pad());
+      } else if (type == "pool") {
+        layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
       } else {
         LOG(ERROR) << "Unknown parameter pad for layer type " << type;
         is_fully_compatible = false;

From 1ceb791ab0c2bee3344ed3f7c724f3e19429cd62 Mon Sep 17 00:00:00 2001
From: Ross Girshick <rbg@eecs.berkeley.edu>
Date: Tue, 22 Jul 2014 20:06:48 -0700
Subject: [PATCH 0306/2053] Use the same CXX for matcaffe as for the rest of
 the build

---
 Makefile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile b/Makefile
index b25d1ba798d..058d3d9c26e 100644
--- a/Makefile
+++ b/Makefile
@@ -351,6 +351,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 		exit 1; \
 	fi
 	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
+			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_NAME) $(LDFLAGS)" -output $@
 	@ echo

From bd6527b0f306d923f5bea03502483408dab46edf Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Wed, 23 Jul 2014 11:48:19 -0700
Subject: [PATCH 0307/2053] numerical stability improvement

---
 src/caffe/layers/relu_layer.cpp | 4 ++--
 src/caffe/layers/relu_layer.cu  | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 3a3e8a21afe..b94086df98c 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -33,8 +33,8 @@ void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const int count = (*bottom)[0]->count();
     Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     for (int i = 0; i < count; ++i) {
-      bottom_diff[i] = top_diff[i] * ((bottom_data[i] >= 0)
-          + negative_slope * (bottom_data[i] < 0));
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+          + negative_slope * (bottom_data[i] <= 0));
     }
   }
 }
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index 503147c713d..e0d6aa1b5c3 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -39,8 +39,8 @@ template <typename Dtype>
 __global__ void ReLUBackward(const int n, const Dtype* in_diff,
     const Dtype* in_data, Dtype* out_diff, Dtype negative_slope) {
   CUDA_KERNEL_LOOP(index, n) {
-    out_diff[index] = in_diff[index] * ((in_data[index] >= 0)
-        + (in_data[index] < 0) * negative_slope);
+    out_diff[index] = in_diff[index] * ((in_data[index] > 0)
+        + (in_data[index] <= 0) * negative_slope);
   }
 }
 

From 15bc0f767fa0b8f12302f81e86654bd11daf8bea Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Wed, 23 Jul 2014 22:47:25 -0700
Subject: [PATCH 0308/2053] add gflags dependency to caffe.

---
 Makefile                 |  4 +--
 include/caffe/common.hpp |  4 +++
 src/caffe/common.cpp     | 57 ++++++++++++++++++++++++----------------
 3 files changed, 41 insertions(+), 24 deletions(-)

diff --git a/Makefile b/Makefile
index 109894dd7c7..6f0da361a69 100644
--- a/Makefile
+++ b/Makefile
@@ -155,8 +155,8 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARY_DIRS += $(CUDA_LIB_DIR)
 	LIBRARIES := cudart cublas curand
 endif
-LIBRARIES += pthread \
-	glog protobuf leveldb snappy \
+LIBRARIES += \
+	glog gflags pthread protobuf leveldb snappy \
 	lmdb \
 	boost_system \
 	hdf5_hl hdf5 \
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 2c79f2bc326..ff8649ad2a1 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -80,6 +80,10 @@ using std::set;
 using std::string;
 using std::vector;
 
+// A global initialization function that you should call in your main function.
+// Currently it initializes google flags and google logging.
+void GlobalInit(int* pargc, char*** pargv);
+
 // A singleton class to hold common caffe stuff, such as the handler that
 // caffe is going to use for cublas, curand, etc.
 class Caffe {
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index e8765eebff1..5894a961dcc 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,11 +1,14 @@
 // Copyright 2014 BVLC and contributors.
 
+#include <gflags/gflags.h>
+#include <glog/logging.h>
 #include <cstdio>
 #include <ctime>
 
 #include "caffe/common.hpp"
 #include "caffe/util/rng.hpp"
 
+
 namespace caffe {
 
 shared_ptr<Caffe> Caffe::singleton_;
@@ -19,6 +22,14 @@ int64_t cluster_seedgen(void) {
   return seed;
 }
 
+
+void GlobalInit(int* pargc, char*** pargv) {
+  // Google flags.
+  ::gflags::ParseCommandLineFlags(pargc, pargv, true);
+  // Google logging.
+  ::google::InitGoogleLogging(*(pargv)[0]);
+}
+
 #ifdef CPU_ONLY  // CPU-only Caffe.
 
 Caffe::Caffe()
@@ -138,28 +149,30 @@ void Caffe::DeviceQuery() {
     return;
   }
   CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
-  printf("Device id:                     %d\n", device);
-  printf("Major revision number:         %d\n", prop.major);
-  printf("Minor revision number:         %d\n", prop.minor);
-  printf("Name:                          %s\n", prop.name);
-  printf("Total global memory:           %lu\n", prop.totalGlobalMem);
-  printf("Total shared memory per block: %lu\n", prop.sharedMemPerBlock);
-  printf("Total registers per block:     %d\n", prop.regsPerBlock);
-  printf("Warp size:                     %d\n", prop.warpSize);
-  printf("Maximum memory pitch:          %lu\n", prop.memPitch);
-  printf("Maximum threads per block:     %d\n", prop.maxThreadsPerBlock);
-  printf("Maximum dimension of block:    %d, %d, %d\n",
-      prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
-  printf("Maximum dimension of grid:     %d, %d, %d\n",
-      prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
-  printf("Clock rate:                    %d\n", prop.clockRate);
-  printf("Total constant memory:         %lu\n", prop.totalConstMem);
-  printf("Texture alignment:             %lu\n", prop.textureAlignment);
-  printf("Concurrent copy and execution: %s\n",
-      (prop.deviceOverlap ? "Yes" : "No"));
-  printf("Number of multiprocessors:     %d\n", prop.multiProcessorCount);
-  printf("Kernel execution timeout:      %s\n",
-      (prop.kernelExecTimeoutEnabled ? "Yes" : "No"));
+  LOG(INFO) << "Device id:                     " << device;
+  LOG(INFO) << "Major revision number:         " << prop.major;
+  LOG(INFO) << "Minor revision number:         " << prop.minor;
+  LOG(INFO) << "Name:                          " << prop.name;
+  LOG(INFO) << "Total global memory:           " << prop.totalGlobalMem;
+  LOG(INFO) << "Total shared memory per block: " << prop.sharedMemPerBlock;
+  LOG(INFO) << "Total registers per block:     " << prop.regsPerBlock;
+  LOG(INFO) << "Warp size:                     " << prop.warpSize;
+  LOG(INFO) << "Maximum memory pitch:          " << prop.memPitch;
+  LOG(INFO) << "Maximum threads per block:     " << prop.maxThreadsPerBlock;
+  LOG(INFO) << "Maximum dimension of block:    "
+      << prop.maxThreadsDim[0] << ", " << prop.maxThreadsDim[1] << ", "
+      << prop.maxThreadsDim[2];
+  LOG(INFO) << "Maximum dimension of grid:     "
+      << prop.maxGridSize[0] << ", " << prop.maxGridSize[1] << ", "
+      << prop.maxGridSize[2];
+  LOG(INFO) << "Clock rate:                    " << prop.clockRate;
+  LOG(INFO) << "Total constant memory:         " << prop.totalConstMem;
+  LOG(INFO) << "Texture alignment:             " << prop.textureAlignment;
+  LOG(INFO) << "Concurrent copy and execution: "
+      << (prop.deviceOverlap ? "Yes" : "No");
+  LOG(INFO) << "Number of multiprocessors:     " << prop.multiProcessorCount;
+  LOG(INFO) << "Kernel execution timeout:      "
+      << (prop.kernelExecTimeoutEnabled ? "Yes" : "No");
   return;
 }
 

From d13d6a2a642c2753588e867614a8bf6e3d81e80e Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Wed, 23 Jul 2014 22:47:58 -0700
Subject: [PATCH 0309/2053] add gflags dependency doc

---
 docs/installation.md | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index c32f2d0a43c..6ff1887574b 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -71,12 +71,22 @@ And on **CentOS or RHEL**, you can install via yum using:
 
     sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
 
-The only exception being the google logging library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install it, do:
+The only exceptions being the google logging library and the google flags library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install them, do:
 
+    # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
     ./configure
     make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    cmake ..
+    make && make install
+
+Note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
 
 #### OS X
 

From 09f196bf26f7ae95c0ace981c78389a4578c5bed Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Wed, 23 Jul 2014 22:49:16 -0700
Subject: [PATCH 0310/2053] gflags should preferrably be installed before glog

---
 docs/installation.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 6ff1887574b..5f092594dec 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -71,13 +71,8 @@ And on **CentOS or RHEL**, you can install via yum using:
 
     sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
 
-The only exceptions being the google logging library and the google flags library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install them, do:
+The only exceptions being the google flags library and the google logging library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install them, do:
 
-    # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
-    ./configure
-    make && make install
     # gflags
     wget https://github.com/schuhschuh/gflags/archive/master.zip
     unzip master.zip
@@ -85,6 +80,11 @@ The only exceptions being the google logging library and the google flags librar
     mkdir build && cd build
     cmake ..
     make && make install
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    ./configure
+    make && make install
 
 Note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
 

From 57d7ecbc464c36cbf635ebb8bc510b003f7944f1 Mon Sep 17 00:00:00 2001
From: TANGUY Arnaud <arn.tanguy@gmail.com>
Date: Wed, 16 Jul 2014 15:20:15 +0200
Subject: [PATCH 0311/2053] Cleanup pthread code for data layers

Get rid of the ugly "friend" thread function. Simplify the creation of the
thread by inheriting from a base class. Thus, defining the thread is as
simple as overriding a virtual function.
---
 include/caffe/data_layers.hpp          | 36 +++--------
 include/caffe/internal_thread.hpp      | 46 ++++++++++++++
 src/caffe/layers/data_layer.cpp        | 83 ++++++++++++--------------
 src/caffe/layers/image_data_layer.cpp  | 60 +++++++++----------
 src/caffe/layers/window_data_layer.cpp | 56 ++++++++---------
 src/caffe/test/test_power_layer.cpp    |  2 -
 6 files changed, 144 insertions(+), 139 deletions(-)
 create mode 100644 include/caffe/internal_thread.hpp

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 1bb6bceb8c8..107d7800d1e 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -9,7 +9,6 @@
 
 #include "leveldb/db.h"
 #include "lmdb.h"
-#include "pthread.h"
 #include "hdf5.h"
 #include "boost/scoped_ptr.hpp"
 
@@ -18,6 +17,7 @@
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/internal_thread.hpp"
 
 namespace caffe {
 
@@ -27,15 +27,8 @@ namespace caffe {
 // TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
 // same basic structure and a lot of duplicated code.
 
-// This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* DataLayerPrefetch(void* layer_pointer);
-
-template <typename Dtype>
-class DataLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* DataLayerPrefetch<Dtype>(void* layer_pointer);
-
+class DataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit DataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
@@ -63,6 +56,8 @@ class DataLayer : public Layer<Dtype> {
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
   virtual unsigned int PrefetchRand();
+  // The thread's function
+  virtual void InternalThreadEntry();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
 
@@ -80,7 +75,6 @@ class DataLayer : public Layer<Dtype> {
   int datum_height_;
   int datum_width_;
   int datum_size_;
-  pthread_t thread_;
   Blob<Dtype> prefetch_data_;
   Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
@@ -182,15 +176,8 @@ class HDF5OutputLayer : public Layer<Dtype> {
   Blob<Dtype> label_blob_;
 };
 
-// This function is used to create a pthread that prefetches the data.
-template <typename Dtype>
-void* ImageDataLayerPrefetch(void* layer_pointer);
-
 template <typename Dtype>
-class ImageDataLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* ImageDataLayerPrefetch<Dtype>(void* layer_pointer);
-
+class ImageDataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit ImageDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
@@ -219,6 +206,7 @@ class ImageDataLayer : public Layer<Dtype> {
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
   virtual unsigned int PrefetchRand();
+  virtual void InternalThreadEntry();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
   vector<std::pair<std::string, int> > lines_;
@@ -227,7 +215,6 @@ class ImageDataLayer : public Layer<Dtype> {
   int datum_height_;
   int datum_width_;
   int datum_size_;
-  pthread_t thread_;
   Blob<Dtype> prefetch_data_;
   Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
@@ -277,15 +264,8 @@ class MemoryDataLayer : public Layer<Dtype> {
   int pos_;
 };
 
-// This function is used to create a pthread that prefetches the window data.
-template <typename Dtype>
-void* WindowDataLayerPrefetch(void* layer_pointer);
-
 template <typename Dtype>
-class WindowDataLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* WindowDataLayerPrefetch<Dtype>(void* layer_pointer);
-
+class WindowDataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit WindowDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
@@ -312,9 +292,9 @@ class WindowDataLayer : public Layer<Dtype> {
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
   virtual unsigned int PrefetchRand();
+  virtual void InternalThreadEntry();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
-  pthread_t thread_;
   Blob<Dtype> prefetch_data_;
   Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
new file mode 100644
index 00000000000..46f67360f0c
--- /dev/null
+++ b/include/caffe/internal_thread.hpp
@@ -0,0 +1,46 @@
+// Copyright 2014 BVLC and contributors.
+
+#ifndef CAFFE_INTERNAL_THREAD_HPP_
+#define CAFFE_INTERNAL_THREAD_HPP_
+
+#include <pthread.h>
+
+namespace caffe {
+
+/**
+ * Virutal class encapsulate pthread for use in base class
+ * The child class will acquire the ability to run a single pthread,
+ * by reimplementing the virutal function InternalThreadEntry.
+ */
+class InternalThread {
+ public:
+  InternalThread() {}
+  virtual ~InternalThread() {}
+
+  /** Returns true if the thread was successfully started **/
+  bool StartInternalThread() {
+    return pthread_create(&_thread, NULL, InternalThreadEntryFunc, this);
+  }
+
+  /** Will not return until the internal thread has exited. */
+  bool WaitForInternalThreadToExit() {
+    return pthread_join(_thread, NULL);
+  }
+
+ protected:
+  /* Implement this method in your subclass 
+      with the code you want your thread to run. */
+  virtual void InternalThreadEntry() = 0;
+
+ private:
+  static void * InternalThreadEntryFunc(void * This) {
+    reinterpret_cast<InternalThread *>(This)->InternalThreadEntry();
+    return NULL;
+  }
+
+  pthread_t _thread;
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index b6afaa972f9..ef27c1a3f1f 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -2,7 +2,6 @@
 
 #include <stdint.h>
 #include <leveldb/db.h>
-#include <pthread.h>
 
 #include <string>
 #include <vector>
@@ -16,46 +15,44 @@
 
 namespace caffe {
 
+// This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* DataLayerPrefetch(void* layer_pointer) {
-  CHECK(layer_pointer);
-  DataLayer<Dtype>* layer = static_cast<DataLayer<Dtype>*>(layer_pointer);
-  CHECK(layer);
+void DataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
-  CHECK(layer->prefetch_data_.count());
-  Dtype* top_data = layer->prefetch_data_.mutable_cpu_data();
+  CHECK(prefetch_data_.count());
+  Dtype* top_data = prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
-  if (layer->output_labels_) {
-    top_label = layer->prefetch_label_.mutable_cpu_data();
+  if (output_labels_) {
+    top_label = prefetch_label_.mutable_cpu_data();
   }
-  const Dtype scale = layer->layer_param_.data_param().scale();
-  const int batch_size = layer->layer_param_.data_param().batch_size();
-  const int crop_size = layer->layer_param_.data_param().crop_size();
-  const bool mirror = layer->layer_param_.data_param().mirror();
+  const Dtype scale = this->layer_param_.data_param().scale();
+  const int batch_size = this->layer_param_.data_param().batch_size();
+  const int crop_size = this->layer_param_.data_param().crop_size();
+  const bool mirror = this->layer_param_.data_param().mirror();
 
   if (mirror && crop_size == 0) {
     LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
         << "set at the same time.";
   }
   // datum scales
-  const int channels = layer->datum_channels_;
-  const int height = layer->datum_height_;
-  const int width = layer->datum_width_;
-  const int size = layer->datum_size_;
-  const Dtype* mean = layer->data_mean_.cpu_data();
+  const int channels = datum_channels_;
+  const int height = datum_height_;
+  const int width = datum_width_;
+  const int size = datum_size_;
+  const Dtype* mean = data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
-    switch (layer->layer_param_.data_param().backend()) {
+    switch (this->layer_param_.data_param().backend()) {
     case DataParameter_DB_LEVELDB:
-      CHECK(layer->iter_);
-      CHECK(layer->iter_->Valid());
-      datum.ParseFromString(layer->iter_->value().ToString());
+      CHECK(iter_);
+      CHECK(iter_->Valid());
+      datum.ParseFromString(iter_->value().ToString());
       break;
     case DataParameter_DB_LMDB:
-      CHECK_EQ(mdb_cursor_get(layer->mdb_cursor_, &layer->mdb_key_,
-              &layer->mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
-      datum.ParseFromArray(layer->mdb_value_.mv_data,
-          layer->mdb_value_.mv_size);
+      CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
+              &mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
+      datum.ParseFromArray(mdb_value_.mv_data,
+          mdb_value_.mv_size);
       break;
     default:
       LOG(FATAL) << "Unknown database backend";
@@ -66,14 +63,14 @@ void* DataLayerPrefetch(void* layer_pointer) {
       CHECK(data.size()) << "Image cropping only support uint8 data";
       int h_off, w_off;
       // We only do random crop when we do training.
-      if (layer->phase_ == Caffe::TRAIN) {
-        h_off = layer->PrefetchRand() % (height - crop_size);
-        w_off = layer->PrefetchRand() % (width - crop_size);
+      if (phase_ == Caffe::TRAIN) {
+        h_off = PrefetchRand() % (height - crop_size);
+        w_off = PrefetchRand() % (width - crop_size);
       } else {
         h_off = (height - crop_size) / 2;
         w_off = (width - crop_size) / 2;
       }
-      if (mirror && layer->PrefetchRand() % 2) {
+      if (mirror && PrefetchRand() % 2) {
         // Copy mirrored version
         for (int c = 0; c < channels; ++c) {
           for (int h = 0; h < crop_size; ++h) {
@@ -118,34 +115,32 @@ void* DataLayerPrefetch(void* layer_pointer) {
       }
     }
 
-    if (layer->output_labels_) {
+    if (output_labels_) {
       top_label[item_id] = datum.label();
     }
     // go to the next iter
-    switch (layer->layer_param_.data_param().backend()) {
+    switch (this->layer_param_.data_param().backend()) {
     case DataParameter_DB_LEVELDB:
-      layer->iter_->Next();
-      if (!layer->iter_->Valid()) {
+      iter_->Next();
+      if (!iter_->Valid()) {
         // We have reached the end. Restart from the first.
         DLOG(INFO) << "Restarting data prefetching from start.";
-        layer->iter_->SeekToFirst();
+        iter_->SeekToFirst();
       }
       break;
     case DataParameter_DB_LMDB:
-      if (mdb_cursor_get(layer->mdb_cursor_, &layer->mdb_key_,
-              &layer->mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
+      if (mdb_cursor_get(mdb_cursor_, &mdb_key_,
+              &mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
         // We have reached the end. Restart from the first.
         DLOG(INFO) << "Restarting data prefetching from start.";
-        CHECK_EQ(mdb_cursor_get(layer->mdb_cursor_, &layer->mdb_key_,
-                &layer->mdb_value_, MDB_FIRST), MDB_SUCCESS);
+        CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
+                &mdb_value_, MDB_FIRST), MDB_SUCCESS);
       }
       break;
     default:
       LOG(FATAL) << "Unknown database backend";
     }
   }
-
-  return static_cast<void*>(NULL);
 }
 
 template <typename Dtype>
@@ -323,14 +318,12 @@ void DataLayer<Dtype>::CreatePrefetchThread() {
   } else {
     prefetch_rng_.reset();
   }
-  // Create the thread.
-  CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
-        static_cast<void*>(this))) << "Pthread execution failed.";
+  CHECK(!StartInternalThread()) << "Pthread execution failed";
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 30050ddd3c6..1ba2a3b2456 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -2,7 +2,6 @@
 
 #include <stdint.h>
 #include <leveldb/db.h>
-#include <pthread.h>
 
 #include <string>
 #include <vector>
@@ -18,17 +17,14 @@
 
 namespace caffe {
 
+// This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* ImageDataLayerPrefetch(void* layer_pointer) {
-  CHECK(layer_pointer);
-  ImageDataLayer<Dtype>* layer =
-      reinterpret_cast<ImageDataLayer<Dtype>*>(layer_pointer);
-  CHECK(layer);
+void ImageDataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
-  CHECK(layer->prefetch_data_.count());
-  Dtype* top_data = layer->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = layer->prefetch_label_.mutable_cpu_data();
-  ImageDataParameter image_data_param = layer->layer_param_.image_data_param();
+  CHECK(prefetch_data_.count());
+  Dtype* top_data = prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = prefetch_label_.mutable_cpu_data();
+  ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const Dtype scale = image_data_param.scale();
   const int batch_size = image_data_param.batch_size();
   const int crop_size = image_data_param.crop_size();
@@ -41,17 +37,17 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
         << "set at the same time.";
   }
   // datum scales
-  const int channels = layer->datum_channels_;
-  const int height = layer->datum_height_;
-  const int width = layer->datum_width_;
-  const int size = layer->datum_size_;
-  const int lines_size = layer->lines_.size();
-  const Dtype* mean = layer->data_mean_.cpu_data();
+  const int channels = datum_channels_;
+  const int height = datum_height_;
+  const int width = datum_width_;
+  const int size = datum_size_;
+  const int lines_size = lines_.size();
+  const Dtype* mean = data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
-    CHECK_GT(lines_size, layer->lines_id_);
-    if (!ReadImageToDatum(layer->lines_[layer->lines_id_].first,
-          layer->lines_[layer->lines_id_].second,
+    CHECK_GT(lines_size, lines_id_);
+    if (!ReadImageToDatum(lines_[lines_id_].first,
+          lines_[lines_id_].second,
           new_height, new_width, &datum)) {
       continue;
     }
@@ -60,14 +56,14 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
       CHECK(data.size()) << "Image cropping only support uint8 data";
       int h_off, w_off;
       // We only do random crop when we do training.
-      if (layer->phase_ == Caffe::TRAIN) {
-        h_off = layer->PrefetchRand() % (height - crop_size);
-        w_off = layer->PrefetchRand() % (width - crop_size);
+      if (phase_ == Caffe::TRAIN) {
+        h_off = PrefetchRand() % (height - crop_size);
+        w_off = PrefetchRand() % (width - crop_size);
       } else {
         h_off = (height - crop_size) / 2;
         w_off = (width - crop_size) / 2;
       }
-      if (mirror && layer->PrefetchRand() % 2) {
+      if (mirror && PrefetchRand() % 2) {
         // Copy mirrored version
         for (int c = 0; c < channels; ++c) {
           for (int h = 0; h < crop_size; ++h) {
@@ -114,18 +110,16 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
 
     top_label[item_id] = datum.label();
     // go to the next iter
-    layer->lines_id_++;
-    if (layer->lines_id_ >= lines_size) {
+    lines_id_++;
+    if (lines_id_ >= lines_size) {
       // We have reached the end. Restart from the first.
       DLOG(INFO) << "Restarting data prefetching from start.";
-      layer->lines_id_ = 0;
-      if (layer->layer_param_.image_data_param().shuffle()) {
-        layer->ShuffleImages();
+      lines_id_ = 0;
+      if (this->layer_param_.image_data_param().shuffle()) {
+        ShuffleImages();
       }
     }
   }
-
-  return reinterpret_cast<void*>(NULL);
 }
 
 template <typename Dtype>
@@ -239,8 +233,7 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
     prefetch_rng_.reset();
   }
   // Create the thread.
-  CHECK(!pthread_create(&thread_, NULL, ImageDataLayerPrefetch<Dtype>,
-        static_cast<void*>(this))) << "Pthread execution failed.";
+  CHECK(!StartInternalThread()) << "Pthread execution failed";
 }
 
 template <typename Dtype>
@@ -255,9 +248,10 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
   }
 }
 
+
 template <typename Dtype>
 void ImageDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 9b4a23d22a8..fe7f61c91df 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -3,7 +3,6 @@
 // Based on data_layer.cpp by Yangqing Jia.
 
 #include <stdint.h>
-#include <pthread.h>
 
 #include <algorithm>
 #include <string>
@@ -28,34 +27,32 @@
 
 namespace caffe {
 
+// Thread fetching the data
 template <typename Dtype>
-void* WindowDataLayerPrefetch(void* layer_pointer) {
-  WindowDataLayer<Dtype>* layer =
-      reinterpret_cast<WindowDataLayer<Dtype>*>(layer_pointer);
-
+void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
 
-  Dtype* top_data = layer->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = layer->prefetch_label_.mutable_cpu_data();
-  const Dtype scale = layer->layer_param_.window_data_param().scale();
-  const int batch_size = layer->layer_param_.window_data_param().batch_size();
-  const int crop_size = layer->layer_param_.window_data_param().crop_size();
-  const int context_pad = layer->layer_param_.window_data_param().context_pad();
-  const bool mirror = layer->layer_param_.window_data_param().mirror();
+  Dtype* top_data = prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = prefetch_label_.mutable_cpu_data();
+  const Dtype scale = this->layer_param_.window_data_param().scale();
+  const int batch_size = this->layer_param_.window_data_param().batch_size();
+  const int crop_size = this->layer_param_.window_data_param().crop_size();
+  const int context_pad = this->layer_param_.window_data_param().context_pad();
+  const bool mirror = this->layer_param_.window_data_param().mirror();
   const float fg_fraction =
-      layer->layer_param_.window_data_param().fg_fraction();
-  const Dtype* mean = layer->data_mean_.cpu_data();
-  const int mean_off = (layer->data_mean_.width() - crop_size) / 2;
-  const int mean_width = layer->data_mean_.width();
-  const int mean_height = layer->data_mean_.height();
+      this->layer_param_.window_data_param().fg_fraction();
+  const Dtype* mean = data_mean_.cpu_data();
+  const int mean_off = (data_mean_.width() - crop_size) / 2;
+  const int mean_width = data_mean_.width();
+  const int mean_height = data_mean_.height();
   cv::Size cv_crop_size(crop_size, crop_size);
-  const string& crop_mode = layer->layer_param_.window_data_param().crop_mode();
+  const string& crop_mode = this->layer_param_.window_data_param().crop_mode();
 
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  caffe_set(layer->prefetch_data_.count(), Dtype(0), top_data);
+  caffe_set(prefetch_data_.count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
@@ -66,24 +63,24 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
       // sample a window
-      const unsigned int rand_index = layer->PrefetchRand();
+      const unsigned int rand_index = PrefetchRand();
       vector<float> window = (is_fg) ?
-          layer->fg_windows_[rand_index % layer->fg_windows_.size()] :
-          layer->bg_windows_[rand_index % layer->bg_windows_.size()];
+          fg_windows_[rand_index % fg_windows_.size()] :
+          bg_windows_[rand_index % bg_windows_.size()];
 
       bool do_mirror = false;
-      if (mirror && layer->PrefetchRand() % 2) {
+      if (mirror && PrefetchRand() % 2) {
         do_mirror = true;
       }
 
       // load the image containing the window
       pair<std::string, vector<int> > image =
-          layer->image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
+          image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
 
       cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
       if (!cv_img.data) {
         LOG(ERROR) << "Could not open or find file " << image.first;
-        return reinterpret_cast<void*>(NULL);
+        return;
       }
       const int channels = cv_img.channels();
 
@@ -210,7 +207,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
       // useful debugging code for dumping transformed windows to disk
       string file_id;
       std::stringstream ss;
-      ss << layer->PrefetchRand();
+      ss << PrefetchRand();
       ss >> file_id;
       std::ofstream inf((string("dump/") + file_id +
           string("_info.txt")).c_str(), std::ofstream::out);
@@ -242,8 +239,6 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
       item_id++;
     }
   }
-
-  return reinterpret_cast<void*>(NULL);
 }
 
 template <typename Dtype>
@@ -415,13 +410,12 @@ void WindowDataLayer<Dtype>::CreatePrefetchThread() {
     prefetch_rng_.reset();
   }
   // Create the thread.
-  CHECK(!pthread_create(&thread_, NULL, WindowDataLayerPrefetch<Dtype>,
-        static_cast<void*>(this))) << "Pthread execution failed.";
+  CHECK(!StartInternalThread()) << "Pthread execution failed.";
 }
 
 template <typename Dtype>
 void WindowDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed.";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index d4e6fc5b05e..44fcbd9117c 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -13,8 +13,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::isnan;
-
 namespace caffe {
 
 template <typename TypeParam>

From 5fbf4af1387bf31b194ba61175c905ea92a80adb Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 24 Jul 2014 09:28:46 -0700
Subject: [PATCH 0312/2053] travis

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index c5445fcdb90..a61e5084f87 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -17,6 +17,7 @@ before_install:
   - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
 
 install:
+  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb

From 6136f5dcb7b840e00eb1efcda5640f992fe10cf7 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 24 Jul 2014 10:28:06 -0700
Subject: [PATCH 0313/2053] travis - does adding cflags help? want to keep
 minimal invasion into gflags installation.

---
 .travis.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index a61e5084f87..5d8beb13902 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -15,6 +15,8 @@ before_install:
   - echo $LC_ALL
   - sudo apt-get -y update
   - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
+  - export CFLAGS="-fPIC"
+  - export CXXFLAGS="-fPIC"
 
 install:
   - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd

From 781630d0e979192ce34c2485d49d40bb772b5349 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 24 Jul 2014 10:48:04 -0700
Subject: [PATCH 0314/2053] turns out that glog does not compile with gflags
 2.1 - compiling glog first and then gflags.

---
 .travis.yml          |  4 +---
 docs/installation.md | 12 ++++++------
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 5d8beb13902..fd04ac247ac 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -15,12 +15,10 @@ before_install:
   - echo $LC_ALL
   - sudo apt-get -y update
   - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
-  - export CFLAGS="-fPIC"
-  - export CXXFLAGS="-fPIC"
 
 install:
-  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
+  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
   - sudo apt-get -y update && sudo apt-get -y install cuda
diff --git a/docs/installation.md b/docs/installation.md
index 5f092594dec..a74071f8ecb 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -73,6 +73,11 @@ And on **CentOS or RHEL**, you can install via yum using:
 
 The only exceptions being the google flags library and the google logging library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install them, do:
 
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    ./configure
+    make && make install
     # gflags
     wget https://github.com/schuhschuh/gflags/archive/master.zip
     unzip master.zip
@@ -80,13 +85,8 @@ The only exceptions being the google flags library and the google logging librar
     mkdir build && cd build
     cmake ..
     make && make install
-    # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
-    ./configure
-    make && make install
 
-Note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
+Note that glog does not compile with the most recent gflags version (2.1), so before that is solved you will need to build with gflags first. Also note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
 
 #### OS X
 

From ca7d992c30d0284e7bbba3c4823baca1b9ed979c Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 24 Jul 2014 10:53:53 -0700
Subject: [PATCH 0315/2053] I am really bad at debugging travis.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index fd04ac247ac..d2c850b3964 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -18,8 +18,8 @@ before_install:
 
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
-  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
+  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
   - sudo apt-get -y update && sudo apt-get -y install cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz

From e8dc0ee5f76876caf7993a476aecf1f91c6fd1fc Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 24 Jul 2014 11:05:58 -0700
Subject: [PATCH 0316/2053] travis: gflags still needs -fPIC, otherwise it
 makes caffe cry.

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index d2c850b3964..72291e40c7f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -19,7 +19,7 @@ before_install:
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
-  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && cmake .. && make && sudo make install && popd
+  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
   - sudo apt-get -y update && sudo apt-get -y install cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz

From 6f46bb90294c9f5f544da76594d49c111788612c Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 25 Jul 2014 09:02:03 -0700
Subject: [PATCH 0317/2053] include benchmark.hpp

---
 include/caffe/caffe.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index ada0695472a..172fc5f3bf7 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -5,15 +5,15 @@
 #ifndef CAFFE_CAFFE_HPP_
 #define CAFFE_CAFFE_HPP_
 
-#include "caffe/common.hpp"
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-#include "caffe/proto/caffe.pb.h"
-
 #endif  // CAFFE_CAFFE_HPP_

From a916ef49fad7b7a3c5faee7df46af3d327aecee9 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 25 Jul 2014 09:02:37 -0700
Subject: [PATCH 0318/2053] Adding new caffe binary that does everything in one
 binary; deprecating device_query, finetune_net, net_speed_benchmark,
 train_net

---
 tools/caffe.cpp               | 176 ++++++++++++++++++++++++++++++++++
 tools/device_query.cpp        |  17 +---
 tools/finetune_net.cpp        |  26 +----
 tools/net_speed_benchmark.cpp | 100 +------------------
 tools/train_net.cpp           |  30 +-----
 5 files changed, 184 insertions(+), 165 deletions(-)
 create mode 100644 tools/caffe.cpp

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
new file mode 100644
index 00000000000..48352485bc6
--- /dev/null
+++ b/tools/caffe.cpp
@@ -0,0 +1,176 @@
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+
+#include <cstring>
+#include <map>
+
+#include "caffe/caffe.hpp"
+
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Net;
+using caffe::Layer;
+using caffe::shared_ptr;
+using caffe::Timer;
+using caffe::string;
+using std::vector;
+
+
+// Used in device query
+DEFINE_int32(device_id, 0,
+             "[devicequery,speedtest] The device id to use.");
+// Used in training
+DEFINE_string(solver_proto_file, "",
+              "[train] The protobuf containing the solver definition.");
+DEFINE_string(net_proto_file, "",
+              "[speedtest] The net proto file to use.");
+DEFINE_string(resume_point_file, "",
+              "[train] (optional) The snapshot from which to resume training.");
+DEFINE_string(pretrained_net_file, "",
+              "[train] (optional) A pretrained network to run finetune from. "
+              "Cannot be set simultaneously with resume_point_file.");
+DEFINE_int32(run_iterations, 50,
+             "[speedtest] The number of iterations to run.");
+DEFINE_bool(speedtest_with_gpu, false,
+            "[speedtest] Test the model with GPU.");
+
+// A simple registry for caffe commands.
+typedef int (*BrewFunction)();
+typedef std::map<std::string, BrewFunction> BrewMap;
+BrewMap g_brew_map;
+
+#define RegisterBrewFunction(func) \
+  namespace { \
+  class __Registerer_##func { \
+   public: \
+    __Registerer_##func() { \
+      g_brew_map[#func] = &func; \
+    } \
+  }; \
+  __Registerer_##func g_registerer_##func; \
+  }
+
+static BrewFunction GetBrewFunction(const std::string& name) {
+  if (g_brew_map.count(name)) {
+    return g_brew_map[name];
+  } else {
+    LOG(ERROR) << "Available caffe actions:";
+    for (BrewMap::iterator it = g_brew_map.begin();
+         it != g_brew_map.end(); ++it) {
+      LOG(ERROR) << "\t" << it->first;
+    }
+    LOG(FATAL) << "Unknown action: " << name;
+    return NULL;  // not reachable, just to suppress old compiler warnings.
+  }
+}
+
+// caffe actions that could be called in the form
+//     caffe.bin action
+// To do so, define actions as "int action()" functions, and register it with
+// RegisterBrewFunction(action);
+
+int devicequery() {
+  LOG(INFO) << "Querying device_id = " << FLAGS_device_id;
+  caffe::Caffe::SetDevice(FLAGS_device_id);
+  caffe::Caffe::DeviceQuery();
+  return 0;
+}
+RegisterBrewFunction(devicequery);
+
+int train() {
+  CHECK_GT(FLAGS_solver_proto_file.size(), 0);
+
+  caffe::SolverParameter solver_param;
+  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver_proto_file, &solver_param);
+
+  LOG(INFO) << "Starting Optimization";
+  caffe::SGDSolver<float> solver(solver_param);
+  if (FLAGS_resume_point_file.size()) {
+    LOG(INFO) << "Resuming from " << FLAGS_resume_point_file;
+    solver.Solve(FLAGS_resume_point_file);
+  } else if (FLAGS_pretrained_net_file.size()) {
+    LOG(INFO) << "Finetuning from " << FLAGS_pretrained_net_file;
+    solver.net()->CopyTrainedLayersFrom(FLAGS_pretrained_net_file);
+    solver.Solve();
+  } else {
+    solver.Solve();
+  }
+  LOG(INFO) << "Optimization Done.";
+  return 0;
+}
+RegisterBrewFunction(train);
+
+int speedtest() {
+  // Set device id and mode
+  if (FLAGS_speedtest_with_gpu) {
+    LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
+    Caffe::SetDevice(FLAGS_device_id);
+    Caffe::set_mode(Caffe::GPU);
+  } else {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+  // Instantiate the caffe net.
+  Caffe::set_phase(Caffe::TRAIN);
+  Net<float> caffe_net(FLAGS_net_proto_file);
+
+  // Do a clean forward and backward pass, so that memory allocation are done
+  // and future iterations will be more stable.
+  LOG(INFO) << "Performing Forward";
+  // Note that for the speed benchmark, we will assume that the network does
+  // not take any input blobs.
+  float initial_loss;
+  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+  LOG(INFO) << "Initial loss: " << initial_loss;
+  LOG(INFO) << "Performing Backward";
+  caffe_net.Backward();
+
+  const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
+  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
+  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
+  const vector<vector<bool> >& bottom_need_backward =
+      caffe_net.bottom_need_backward();
+  LOG(INFO) << "*** Benchmark begins ***";
+  LOG(INFO) << "Testing for " << FLAGS_run_iterations << " iterations.";
+  Timer total_timer;
+  total_timer.Start();
+  Timer forward_timer;
+  forward_timer.Start();
+  Timer timer;
+  for (int i = 0; i < layers.size(); ++i) {
+    const string& layername = layers[i]->layer_param().name();
+    timer.Start();
+    for (int j = 0; j < FLAGS_run_iterations; ++j) {
+      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
+    }
+    LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
+        " milli seconds.";
+  }
+  LOG(INFO) << "Forward pass: " << forward_timer.MilliSeconds() <<
+      " milli seconds.";
+  Timer backward_timer;
+  backward_timer.Start();
+  for (int i = layers.size() - 1; i >= 0; --i) {
+    const string& layername = layers[i]->layer_param().name();
+    timer.Start();
+    for (int j = 0; j < FLAGS_run_iterations; ++j) {
+      layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
+                          &bottom_vecs[i]);
+    }
+    LOG(INFO) << layername << "\tbackward: "
+        << timer.MilliSeconds() << " milli seconds.";
+  }
+  LOG(INFO) << "Backward pass: " << backward_timer.MilliSeconds() <<
+      " milli seconds.";
+  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
+      " milli seconds.";
+  LOG(INFO) << "*** Benchmark ends ***";
+  return 0;
+}
+RegisterBrewFunction(speedtest);
+
+int main(int argc, char** argv) {
+  caffe::GlobalInit(&argc, &argv);
+  CHECK_EQ(argc, 2);
+  return GetBrewFunction(std::string(argv[1]))();
+}
diff --git a/tools/device_query.cpp b/tools/device_query.cpp
index 5040b8ee9d1..224a3145308 100644
--- a/tools/device_query.cpp
+++ b/tools/device_query.cpp
@@ -1,23 +1,8 @@
 // Copyright 2014 BVLC and contributors.
 
-
 #include "caffe/common.hpp"
-#include "caffe/net.hpp"
-
-
-using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
-  if (argc > 2) {
-    LOG(ERROR) << "device_query [device_id=0]";
-    return 1;
-  }
-  if (argc == 2) {
-    LOG(INFO) << "Querying device_id=" << argv[1];
-    Caffe::SetDevice(atoi(argv[1]));
-    Caffe::DeviceQuery();
-  } else {
-    Caffe::DeviceQuery();
-  }
+  LOG(ERROR) << "Deprecated. Use caffe.bin devicequery [--device_id=0] instead.";
   return 0;
 }
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index 4d80be9f4da..dff97215f5e 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -1,31 +1,9 @@
 // Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly finetune a network.
-// Usage:
-//    finetune_net solver_proto_file pretrained_net
-
-#include <string>
 
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  ::google::InitGoogleLogging(argv[0]);
-  if (argc != 3) {
-    LOG(ERROR) << "Usage: finetune_net solver_proto_file pretrained_net";
-    return 1;
-  }
-
-  SolverParameter solver_param;
-  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
-
-  LOG(INFO) << "Starting Optimization";
-  SGDSolver<float> solver(solver_param);
-  LOG(INFO) << "Loading from " << argv[2];
-  solver.net()->CopyTrainedLayersFrom(string(argv[2]));
-  solver.Solve();
-  LOG(INFO) << "Optimization Done.";
-
+  LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+                "[pretrained_net_file=...] instead.";
   return 0;
 }
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index bdec391fbb6..9e6bb4cbe95 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -1,103 +1,9 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <fcntl.h>
-#include <google/protobuf/text_format.h>
-
-#include <cstring>
-#include <ctime>
-#include <string>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/net.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/benchmark.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/solver.hpp"
-
-using namespace caffe;  // NOLINT(build/namespaces)
+#include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  int total_iter = 50;
-  if (argc < 2 || argc > 5) {
-    LOG(ERROR) << "net_speed_benchmark net_proto [iterations=50]"
-        " [CPU/GPU] [Device_id=0]";
-    return 1;
-  }
-
-  if (argc >=3) {
-    total_iter = atoi(argv[2]);
-  }
-
-  LOG(ERROR) << "Testing for " << total_iter << "Iterations.";
-
-  if (argc >= 4 && strcmp(argv[3], "GPU") == 0) {
-    LOG(ERROR) << "Using GPU";
-    uint device_id = 0;
-    if (argc >= 5 && strcmp(argv[3], "GPU") == 0) {
-      device_id = atoi(argv[4]);
-    }
-    LOG(ERROR) << "Using Device_id=" << device_id;
-    Caffe::SetDevice(device_id);
-    Caffe::set_mode(Caffe::GPU);
-  } else {
-    LOG(ERROR) << "Using CPU";
-    Caffe::set_mode(Caffe::CPU);
-  }
-
-  Caffe::set_phase(Caffe::TRAIN);
-  Net<float> caffe_net(argv[1]);
-
-  // Run the network without training.
-  LOG(ERROR) << "Performing Forward";
-  // Note that for the speed benchmark, we will assume that the network does
-  // not take any input blobs.
-  float initial_loss;
-  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
-  LOG(ERROR) << "Initial loss: " << initial_loss;
-  LOG(ERROR) << "Performing Backward";
-  caffe_net.Backward();
-
-  const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
-  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
-  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
-  const vector<vector<bool> >& bottom_need_backward =
-      caffe_net.bottom_need_backward();
-  LOG(ERROR) << "*** Benchmark begins ***";
-  Timer total_timer;
-  total_timer.Start();
-  Timer forward_timer;
-  forward_timer.Start();
-  Timer timer;
-  for (int i = 0; i < layers.size(); ++i) {
-    const string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < total_iter; ++j) {
-      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
-    }
-    LOG(ERROR) << layername << "\tforward: " << timer.MilliSeconds() <<
-        " milli seconds.";
-  }
-  LOG(ERROR) << "Forward pass: " << forward_timer.MilliSeconds() <<
-      " milli seconds.";
-  Timer backward_timer;
-  backward_timer.Start();
-  for (int i = layers.size() - 1; i >= 0; --i) {
-    const string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < total_iter; ++j) {
-      layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
-                          &bottom_vecs[i]);
-    }
-    LOG(ERROR) << layername << "\tbackward: "
-        << timer.MilliSeconds() << " milli seconds.";
-  }
-  LOG(ERROR) << "Backward pass: " << backward_timer.MilliSeconds() <<
-      " milli seconds.";
-  LOG(ERROR) << "Total Time: " << total_timer.MilliSeconds() <<
-      " milli seconds.";
-  LOG(ERROR) << "*** Benchmark ends ***";
+  LOG(ERROR) << "Deprecated. Use caffe.bin speedtest --net_proto_file=... "
+             "[--run_iterations=50] [--speedtest_with_gpu] [--device_id=0]";
   return 0;
 }
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 41a33247854..568260c7b45 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,35 +1,9 @@
 // Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly train a network whose
-// parameters are specified by text format protocol buffers.
-// Usage:
-//    train_net net_proto_file solver_proto_file [resume_point_file]
-
-#include <cstring>
 
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  ::google::InitGoogleLogging(argv[0]);
-  if (argc < 2 || argc > 3) {
-    LOG(ERROR) << "Usage: train_net solver_proto_file [resume_point_file]";
-    return 1;
-  }
-
-  SolverParameter solver_param;
-  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
-
-  LOG(INFO) << "Starting Optimization";
-  SGDSolver<float> solver(solver_param);
-  if (argc == 3) {
-    LOG(INFO) << "Resuming from " << argv[2];
-    solver.Solve(argv[2]);
-  } else {
-    solver.Solve();
-  }
-  LOG(INFO) << "Optimization Done.";
-
+  LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+                "[resume_point_file=...] instead.";
   return 0;
 }

From 2ced4c9ab9021f7df3b03c604248a300510bfeba Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 25 Jul 2014 09:52:30 -0700
Subject: [PATCH 0319/2053] lint

---
 tools/caffe.cpp        | 29 ++++++++++++++++-------------
 tools/device_query.cpp |  3 ++-
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 48352485bc6..dde58c68b3f 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -1,8 +1,11 @@
+// Copyright 2014 BVLC and contributors.
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cstring>
 #include <map>
+#include <string>
+#include <vector>
 
 #include "caffe/caffe.hpp"
 
@@ -13,7 +16,7 @@ using caffe::Layer;
 using caffe::shared_ptr;
 using caffe::Timer;
 using caffe::string;
-using std::vector;
+using caffe::vector;
 
 
 // Used in device query
@@ -36,21 +39,21 @@ DEFINE_bool(speedtest_with_gpu, false,
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
-typedef std::map<std::string, BrewFunction> BrewMap;
+typedef std::map<string, BrewFunction> BrewMap;
 BrewMap g_brew_map;
 
 #define RegisterBrewFunction(func) \
-  namespace { \
-  class __Registerer_##func { \
-   public: \
-    __Registerer_##func() { \
-      g_brew_map[#func] = &func; \
-    } \
-  }; \
-  __Registerer_##func g_registerer_##func; \
-  }
+namespace { \
+class __Registerer_##func { \
+ public: /* NOLINT */ \
+  __Registerer_##func() { \
+    g_brew_map[#func] = &func; \
+  } \
+}; \
+__Registerer_##func g_registerer_##func; \
+}
 
-static BrewFunction GetBrewFunction(const std::string& name) {
+static BrewFunction GetBrewFunction(const string& name) {
   if (g_brew_map.count(name)) {
     return g_brew_map[name];
   } else {
@@ -172,5 +175,5 @@ RegisterBrewFunction(speedtest);
 int main(int argc, char** argv) {
   caffe::GlobalInit(&argc, &argv);
   CHECK_EQ(argc, 2);
-  return GetBrewFunction(std::string(argv[1]))();
+  return GetBrewFunction(string(argv[1]))();
 }
diff --git a/tools/device_query.cpp b/tools/device_query.cpp
index 224a3145308..a87e23c8722 100644
--- a/tools/device_query.cpp
+++ b/tools/device_query.cpp
@@ -3,6 +3,7 @@
 #include "caffe/common.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin devicequery [--device_id=0] instead.";
+  LOG(ERROR) << "Deprecated. Use caffe.bin devicequery "
+                "[--device_id=0] instead.";
   return 0;
 }

From 2cfbda510c27e103cf060f4a9b10d5d51806358e Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 25 Jul 2014 10:11:26 -0700
Subject: [PATCH 0320/2053] oops, wrong deprecation message (should have --)

---
 tools/finetune_net.cpp | 2 +-
 tools/train_net.cpp    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index dff97215f5e..17b7f3a7bce 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -4,6 +4,6 @@
 
 int main(int argc, char** argv) {
   LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
-                "[pretrained_net_file=...] instead.";
+                "[--pretrained_net_file=...] instead.";
   return 0;
 }
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 568260c7b45..35b1c776470 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -4,6 +4,6 @@
 
 int main(int argc, char** argv) {
   LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
-                "[resume_point_file=...] instead.";
+                "[--resume_point_file=...] instead.";
   return 0;
 }

From 7de3382561bbbe235358f4259328093ecf048e2d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 23 Jul 2014 19:22:11 -0700
Subject: [PATCH 0321/2053] Add gradient checks for infogain loss layer,
 letting it take the infogain matrix as the third bottom blob.

---
 src/caffe/layers/infogain_loss_layer.cpp    | 51 +++++++++++-----
 src/caffe/test/test_infogain_loss_layer.cpp | 67 +++++++++++++++++++++
 2 files changed, 104 insertions(+), 14 deletions(-)
 create mode 100644 src/caffe/test/test_infogain_loss_layer.cpp

diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 204f6c305ca..4b02f4e61db 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -18,14 +18,24 @@ void InfogainLossLayer<Dtype>::FurtherSetUp(
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
-
-  BlobProto blob_proto;
-  ReadProtoFromBinaryFile(
-    this->layer_param_.infogain_loss_param().source(), &blob_proto);
-  infogain_.FromProto(blob_proto);
-  CHECK_EQ(infogain_.num(), 1);
-  CHECK_EQ(infogain_.channels(), 1);
-  CHECK_EQ(infogain_.height(), infogain_.width());
+  Blob<Dtype>* infogain = NULL;
+  if (bottom.size() < 3) {
+    CHECK(this->layer_param_.infogain_loss_param().has_source())
+        << "Infogain matrix source must be specified.";
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFile(
+      this->layer_param_.infogain_loss_param().source(), &blob_proto);
+    infogain_.FromProto(blob_proto);
+    infogain = &infogain_;
+  } else {
+    infogain = bottom[2];
+  }
+  const int num = bottom[0]->num();
+  const int dim = bottom[0]->count() / num;
+  CHECK_EQ(infogain->num(), 1);
+  CHECK_EQ(infogain->channels(), 1);
+  CHECK_EQ(infogain->height(), dim);
+  CHECK_EQ(infogain->width(), dim);
 }
 
 
@@ -34,10 +44,14 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
+  const Dtype* infogain_mat = NULL;
+  if (bottom.size() < 3) {
+    infogain_mat = infogain_.cpu_data();
+  } else {
+    infogain_mat = bottom[2]->cpu_data();
+  }
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
     int label = static_cast<int>(bottom_label[i]);
@@ -46,10 +60,11 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
+  loss /= num;
   if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+    (*top)[0]->mutable_cpu_data()[0] = loss;
   }
-  return loss / num;
+  return loss;
 }
 
 template <typename Dtype>
@@ -60,14 +75,22 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
   }
+  if (propagate_down.size() > 2 && propagate_down[2]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to infogain inputs.";
+  }
   if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->cpu_data();
     const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-    const Dtype* infogain_mat = infogain_.cpu_data();
+    const Dtype* infogain_mat = NULL;
+    if (bottom->size() < 3) {
+      infogain_mat = infogain_.cpu_data();
+    } else {
+      infogain_mat = (*bottom)[2]->cpu_data();
+    }
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     int num = (*bottom)[0]->num();
     int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-    CHECK_EQ(infogain_.height(), dim);
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
       for (int j = 0; j < dim; ++j) {
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
new file mode 100644
index 00000000000..99bad263e88
--- /dev/null
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -0,0 +1,67 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/loss_layers.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  InfogainLossLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
+        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+        blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)) {
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    PositiveUnitballFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    for (int i = 0; i < blob_bottom_label_->count(); ++i) {
+      blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+    }
+    blob_bottom_vec_.push_back(blob_bottom_label_);
+    filler_param.set_min(0.1);
+    filler_param.set_max(2.0);
+    UniformFiller<Dtype> infogain_filler(filler_param);
+    infogain_filler.Fill(this->blob_bottom_infogain_);
+    blob_bottom_vec_.push_back(blob_bottom_infogain_);
+  }
+  virtual ~InfogainLossLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_label_;
+    delete blob_bottom_infogain_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_bottom_infogain_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(InfogainLossLayerTest, TestDtypesAndDevices);
+
+
+TYPED_TEST(InfogainLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  InfogainLossLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
+  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0, -1, -1);
+}
+
+}  // namespace caffe

From 70b01f2164dd5e53605fc58c25530da3a5cccbd3 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 25 Jul 2014 17:58:35 -0700
Subject: [PATCH 0322/2053] using caffe::string to be consistent with other
 string definition.

---
 tools/caffe.cpp | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index dde58c68b3f..ec414298ea4 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -15,7 +15,6 @@ using caffe::Net;
 using caffe::Layer;
 using caffe::shared_ptr;
 using caffe::Timer;
-using caffe::string;
 using caffe::vector;
 
 
@@ -39,7 +38,7 @@ DEFINE_bool(speedtest_with_gpu, false,
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
-typedef std::map<string, BrewFunction> BrewMap;
+typedef std::map<caffe::string, BrewFunction> BrewMap;
 BrewMap g_brew_map;
 
 #define RegisterBrewFunction(func) \
@@ -53,7 +52,7 @@ class __Registerer_##func { \
 __Registerer_##func g_registerer_##func; \
 }
 
-static BrewFunction GetBrewFunction(const string& name) {
+static BrewFunction GetBrewFunction(const caffe::string& name) {
   if (g_brew_map.count(name)) {
     return g_brew_map[name];
   } else {
@@ -141,7 +140,7 @@ int speedtest() {
   forward_timer.Start();
   Timer timer;
   for (int i = 0; i < layers.size(); ++i) {
-    const string& layername = layers[i]->layer_param().name();
+    const caffe::string& layername = layers[i]->layer_param().name();
     timer.Start();
     for (int j = 0; j < FLAGS_run_iterations; ++j) {
       layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
@@ -154,7 +153,7 @@ int speedtest() {
   Timer backward_timer;
   backward_timer.Start();
   for (int i = layers.size() - 1; i >= 0; --i) {
-    const string& layername = layers[i]->layer_param().name();
+    const caffe::string& layername = layers[i]->layer_param().name();
     timer.Start();
     for (int j = 0; j < FLAGS_run_iterations; ++j) {
       layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
@@ -175,5 +174,5 @@ RegisterBrewFunction(speedtest);
 int main(int argc, char** argv) {
   caffe::GlobalInit(&argc, &argv);
   CHECK_EQ(argc, 2);
-  return GetBrewFunction(string(argv[1]))();
+  return GetBrewFunction(caffe::string(argv[1]))();
 }

From 9862d5a4dcc70edba94291a64c303b659e7bd362 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Sat, 26 Jul 2014 08:37:05 -0700
Subject: [PATCH 0323/2053] LOG(ERROR)->LOG(FATAL), and misc script changes.

---
 examples/cifar10/readme.md                 |  2 +-
 examples/cifar10/train_full.sh             | 16 ++++++++--------
 examples/cifar10/train_quick.sh            |  7 +++++--
 examples/imagenet/resume_training.sh       |  5 +++--
 examples/imagenet/train_alexnet.sh         |  3 ++-
 examples/imagenet/train_imagenet.sh        |  3 ++-
 examples/mnist/readme.md                   |  2 +-
 examples/mnist/train_lenet.sh              |  3 ++-
 examples/mnist/train_lenet_consolidated.sh |  3 ++-
 examples/mnist/train_mnist_autoencoder.sh  |  3 ++-
 tools/device_query.cpp                     |  2 +-
 tools/finetune_net.cpp                     |  2 +-
 tools/net_speed_benchmark.cpp              |  2 +-
 tools/train_net.cpp                        |  2 +-
 14 files changed, 32 insertions(+), 23 deletions(-)

diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 9d5bd7b2b0c..315713f7022 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -42,7 +42,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/cifar10
     ./train_quick.sh
 
-`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with the `train` action, and the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 4db7b9a98f1..930b27a1a32 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -2,15 +2,15 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver.prototxt
 
 #reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr1.prototxt \
-    cifar10_full_iter_60000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver_lr1.prototxt \
+    --resume_point_file=cifar10_full_iter_60000.solverstate
 
 #reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr2.prototxt \
-    cifar10_full_iter_65000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+    --solver_proto_file=cifar10_full_solver_lr2.prototxt \
+    --resume_point_file=cifar10_full_iter_65000.solverstate
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 1d954b5935e..5e8fffccd48 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -2,7 +2,10 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=cifar10_quick_solver.prototxt
 
 #reduce learning rate by fctor of 10 after 8 epochs
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver_lr1.prototxt cifar10_quick_iter_4000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=cifar10_quick_solver_lr1.prototxt \
+  --resume_point_file=cifar10_quick_iter_4000.solverstate
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index 2b3b4031b5d..b43a9b1bf7c 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -2,7 +2,8 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    imagenet_solver.prototxt caffe_imagenet_train_10000.solverstate
+GLOG_logtostderr=1 $TOOLS/caffe.bin train\
+    --solver_proto_file=imagenet_solver.prototxt \
+    --resume_point_file=caffe_imagenet_train_10000.solverstate
 
 echo "Done."
diff --git a/examples/imagenet/train_alexnet.sh b/examples/imagenet/train_alexnet.sh
index c91e9108ae5..d43a235dba1 100755
--- a/examples/imagenet/train_alexnet.sh
+++ b/examples/imagenet/train_alexnet.sh
@@ -2,6 +2,7 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin alexnet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=alexnet_solver.prototxt
 
 echo "Done."
diff --git a/examples/imagenet/train_imagenet.sh b/examples/imagenet/train_imagenet.sh
index 0d2563b0c9d..90dcdfaddb8 100755
--- a/examples/imagenet/train_imagenet.sh
+++ b/examples/imagenet/train_imagenet.sh
@@ -2,6 +2,7 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin imagenet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=imagenet_solver.prototxt
 
 echo "Done."
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index d609cfff9aa..4f3f4d9ce12 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -212,7 +212,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh
 
-`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with action `train`, with the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index c30fc3e02e1..4d0f1eea051 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -2,4 +2,5 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=lenet_solver.prototxt
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
index ff6897e263d..802c801e31f 100755
--- a/examples/mnist/train_lenet_consolidated.sh
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -2,4 +2,5 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_consolidated_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=lenet_consolidated_solver.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index af2245e07f0..3fb493534db 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin mnist_autoencoder_solver.prototxt
+GLOG_logtostderr=1 $TOOLS/caffe.bin train \
+  --solver_proto_file=mnist_autoencoder_solver.prototxt
diff --git a/tools/device_query.cpp b/tools/device_query.cpp
index a87e23c8722..efdbbbbc90e 100644
--- a/tools/device_query.cpp
+++ b/tools/device_query.cpp
@@ -3,7 +3,7 @@
 #include "caffe/common.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin devicequery "
+  LOG(FATAL) << "Deprecated. Use caffe.bin devicequery "
                 "[--device_id=0] instead.";
   return 0;
 }
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index 17b7f3a7bce..213ff01702a 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -3,7 +3,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
                 "[--pretrained_net_file=...] instead.";
   return 0;
 }
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index 9e6bb4cbe95..f520e1d7785 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -3,7 +3,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin speedtest --net_proto_file=... "
+  LOG(FATAL) << "Deprecated. Use caffe.bin speedtest --net_proto_file=... "
              "[--run_iterations=50] [--speedtest_with_gpu] [--device_id=0]";
   return 0;
 }
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 35b1c776470..08602d4a7ca 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -3,7 +3,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(ERROR) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
+  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
                 "[--resume_point_file=...] instead.";
   return 0;
 }

From 149a176dc2f35f2218cd7a639157e90c59b12e72 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 25 Jul 2014 20:40:07 -0700
Subject: [PATCH 0324/2053] Print blob L1 norms during forward/backward passes
 and updates if new "debug_info" field in SolverParameter is set.

---
 include/caffe/blob.hpp      |  4 ++
 include/caffe/net.hpp       | 18 +++++--
 src/caffe/blob.cpp          | 70 +++++++++++++++++++++++++++
 src/caffe/net.cpp           | 95 ++++++++++++++++++++++++++++++++-----
 src/caffe/proto/caffe.proto |  4 ++
 src/caffe/solver.cpp        | 10 +++-
 6 files changed, 186 insertions(+), 15 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index bbea86aea69..ab7a0f60f80 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -76,6 +76,10 @@ class Blob {
   void FromProto(const BlobProto& proto);
   void ToProto(BlobProto* proto, bool write_diff = false) const;
 
+  // Compute the sum of absolute values (L1 norm) of the data or diff.
+  Dtype asum_data() const;
+  Dtype asum_diff() const;
+
   // Set the data_/diff_ shared_ptr to point to the SyncedMemory holding the
   // data_/diff_ of Blob other -- useful in layers which simply perform a copy
   // in their forward or backward pass.
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 481a64979f1..7548011d973 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -95,8 +95,9 @@ class Net {
   // returns the parameters
   inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
   // returns the parameter learning rate multipliers
-  inline vector<float>& params_lr() {return params_lr_; }
+  inline vector<float>& params_lr() { return params_lr_; }
   inline vector<float>& params_weight_decay() { return params_weight_decay_; }
+  const map<string, int>& param_names_index() { return param_names_index_; }
   // Input and output blob numbers
   inline int num_inputs() { return net_input_blobs_.size(); }
   inline int num_outputs() { return net_output_blobs_.size(); }
@@ -111,7 +112,8 @@ class Net {
   const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name);
   bool has_layer(const string& layer_name);
   const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name);
-  const map<string, int>& param_names_index() { return param_names_index_; }
+
+  void set_debug_info(const bool value) { debug_info_ = value; }
 
  protected:
   // Helpers for Init.
@@ -125,6 +127,12 @@ class Net {
                    map<string, int>* blob_name_to_idx);
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
+
+  // Helpers for displaying debug info.
+  void ForwardDebugInfo(const int layer_id);
+  void BackwardDebugInfo(const int layer_id);
+  void UpdateDebugInfo(const int param_id);
+
   // Function to get misc parameters, e.g. the learning rate multiplier and
   // weight decay.
   void GetLearningRateAndWeightDecay();
@@ -150,7 +158,8 @@ class Net {
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
   vector<int> param_owners_;
-  vector<pair<int, int> > layer_param_indices_;
+  vector<string> param_display_names_;
+  vector<pair<int, int> > param_layer_indices_;
   map<string, int> param_names_index_;
   // blob indices for the input and the output of the net
   vector<int> net_input_blob_indices_;
@@ -166,6 +175,9 @@ class Net {
   vector<float> params_weight_decay_;
   // The bytes of memory used by this net
   size_t memory_used_;
+  // Whether to compute and display debug info for the net.
+  bool debug_info_;
+
   DISABLE_COPY_AND_ASSIGN(Net);
 };
 
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 1051eaa1c2c..738b549e10c 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -137,6 +137,76 @@ void Blob<Dtype>::Update() {
   }
 }
 
+template <> unsigned int Blob<unsigned int>::asum_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::asum_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::asum_data() const {
+  if (!data_) { return 0; }
+  switch (data_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    return caffe_cpu_asum(count_, cpu_data());
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+  {
+    Dtype asum;
+    caffe_gpu_asum(count_, gpu_data(), &asum);
+    return asum;
+  }
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+  return 0;
+}
+
+template <> unsigned int Blob<unsigned int>::asum_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::asum_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::asum_diff() const {
+  if (!diff_) { return 0; }
+  switch (diff_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    return caffe_cpu_asum(count_, cpu_diff());
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+  {
+    Dtype asum;
+    caffe_gpu_asum(count_, gpu_diff(), &asum);
+    return asum;
+  }
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
+  }
+  return 0;
+}
+
 template <typename Dtype>
 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   if (num_ != source.num() || channels_ != source.channels() ||
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 7673608a5ed..612cbb1b60d 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -163,6 +163,8 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   GetLearningRateAndWeightDecay();
   LOG(INFO) << "Network initialization done.";
   LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+  // Don't display debug info by default.
+  debug_info_ = false;
 }
 
 // Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
@@ -242,13 +244,17 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                              const int param_id) {
   const LayerParameter& layer_param = layers_[layer_id]->layer_param();
   const int param_size = layer_param.param_size();
-  string param_name;
-  if (param_size) {
-    param_name = layer_param.param(param_id);
+  string param_name = param_size ? layer_param.param(param_id) : "";
+  if (param_name.size()) {
+    param_display_names_.push_back(param_name);
+  } else {
+    ostringstream param_display_name;
+    param_display_name << param_id;
+    param_display_names_.push_back(param_display_name.str());
   }
   const int net_param_id = params_.size();
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
-  layer_param_indices_.push_back(make_pair(layer_id, param_id));
+  param_layer_indices_.push_back(make_pair(layer_id, param_id));
   if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
     // This layer "owns" this parameter blob -- it is either anonymous
@@ -263,7 +269,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     const int owner_net_param_id = param_names_index_[param_name];
     param_owners_.push_back(owner_net_param_id);
     const pair<int, int>& owner_index =
-        layer_param_indices_[owner_net_param_id];
+        param_layer_indices_[owner_net_param_id];
     const int owner_layer_id = owner_index.first;
     const int owner_param_id = owner_index.second;
     LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
@@ -339,6 +345,7 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
     loss += layer_loss;
+    if (debug_info_) { ForwardDebugInfo(i); }
   }
   return loss;
 }
@@ -402,10 +409,75 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
     if (layer_need_backward_[i]) {
       layers_[i]->Backward(
           top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
+      if (debug_info_) { BackwardDebugInfo(i); }
     }
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
+  for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
+    const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
+    const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
+    const Dtype asum = blob.asum_data();
+    const Dtype asum_mean = asum / blob.count();
+    LOG(INFO) << "    [Forward] "
+       << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
+       << " data: " << asum << " (" << asum_mean << ")";
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
+  const vector<Blob<Dtype>*>& bottom_vec = bottom_vecs_[layer_id];
+  for (int bottom_id = 0; bottom_id < bottom_vec.size(); ++bottom_id) {
+    if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }
+    const Blob<Dtype>& blob = *bottom_vec[bottom_id];
+    const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+    const Dtype asum = blob.asum_diff();
+    const Dtype asum_mean = asum / blob.count();
+    LOG(INFO) << "    [Backward] "
+        << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
+        << " diff: " << asum << " (" << asum_mean << ")";
+  }
+  for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
+       ++param_id) {
+    if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
+    const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
+    const Dtype asum = blob.asum_diff();
+    const Dtype asum_mean = asum / blob.count();
+    LOG(INFO) << "    [Backward] "
+        << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
+        << " diff: " << asum << " (" << asum_mean << ")";
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::UpdateDebugInfo(const int param_id) {
+  const Blob<Dtype>& blob = *params_[param_id];
+  const int param_owner = param_owners_[param_id];
+  const string& layer_name = layer_names_[param_layer_indices_[param_id].first];
+  const string& param_display_name = param_display_names_[param_id];
+  const Dtype asum_diff = blob.asum_diff();
+  const Dtype asum_diff_mean = asum_diff / blob.count();
+  if (param_owner < 0) {
+    const Dtype asum_data = blob.asum_data();
+    const Dtype asum_data_mean = asum_data / blob.count();
+    LOG(INFO) << "    [Update] Layer " << layer_name
+        << ", param " << param_display_name
+        << " data: " << asum_data << " (" << asum_data_mean << ");"
+        << " diff: " << asum_diff << " (" << asum_diff_mean << ")";
+  } else {
+    const string& owner_layer_name =
+        layer_names_[param_layer_indices_[param_owner].first];
+    LOG(INFO) << "    [Update] Layer " << layer_name
+        << ", param blob " << param_display_name
+        << " (owned by layer " << owner_layer_name << ", "
+        << "param " << param_display_names_[param_owners_[param_id]] << ")"
+        << " diff: " << asum_diff << " (" << asum_diff_mean << ")";
+  }
+}
+
 template <typename Dtype>
 void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
   int num_source_layers = other->layers().size();
@@ -516,9 +588,8 @@ void Net<Dtype>::Update() {
   // diff. (Assumes that the learning rate, weight decay, etc. have already been
   // accounted for in the current diff.)
   for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] < 0) {
-      continue;
-    }
+    if (param_owners_[i] < 0) { continue; }
+    if (debug_info_) { UpdateDebugInfo(i); }
     const int count = params_[i]->count();
     const Dtype* this_diff;
     Dtype* owner_diff;
@@ -534,6 +605,8 @@ void Net<Dtype>::Update() {
       owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
       caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
       break;
+#else
+      NO_GPU;
 #endif
     default:
       LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
@@ -541,9 +614,9 @@ void Net<Dtype>::Update() {
   }
   // Now, update the owned parameters.
   for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] < 0) {
-      params_[i]->Update();
-    }
+    if (param_owners_[i] >= 0) { continue; }
+    if (debug_info_) { UpdateDebugInfo(i); }
+    params_[i]->Update();
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 34cf8443f56..5e437dff327 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -105,6 +105,10 @@ message SolverParameter {
   // random number generator -- useful for reproducible results. Otherwise,
   // (and by default) initialize using a seed derived from the system clock.
   optional int64 random_seed = 20 [default = -1];
+
+  // If true, print information about the state of the net that may help with
+  // debugging learning problems.
+  optional bool debug_info = 23 [default = false];
 }
 
 // A message that stores the solver snapshots
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a33149721bf..0dfc7f0a52d 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -53,6 +53,8 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
     LOG(INFO) << "Creating training net from file: " << param_.train_net();
     net_.reset(new Net<Dtype>(param_.train_net()));
   }
+  CHECK(net_) << "Training net uninitialized.";
+  net_->set_debug_info(param_.debug_info());
   const int num_test_net_params = param_.test_net_param_size();
   const int num_test_net_files = param_.test_net_size();
   const int num_test_nets = num_test_net_params + num_test_net_files;
@@ -100,11 +102,17 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // should be given, and we will just provide dummy vecs.
   vector<Blob<Dtype>*> bottom_vec;
   while (iter_++ < param_.max_iter()) {
+    const bool display = param_.display() && iter_ % param_.display() == 0;
+    if (display) {
+      net_->set_debug_info(param_.debug_info());
+    } else {
+      net_->set_debug_info(false);
+    }
     Dtype loss = net_->ForwardBackward(bottom_vec);
     ComputeUpdateValue();
     net_->Update();
 
-    if (param_.display() && iter_ % param_.display() == 0) {
+    if (display) {
       LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
     }
     if (param_.test_interval() && iter_ % param_.test_interval() == 0) {

From 69997119747d0c61424a8338ae70c0a805b74f0d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 26 Jul 2014 15:18:41 -0700
Subject: [PATCH 0325/2053] Print just the mean absolute value (no sum/l1norm)

---
 src/caffe/net.cpp | 26 ++++++++++----------------
 1 file changed, 10 insertions(+), 16 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 612cbb1b60d..8e99d307f32 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -419,11 +419,10 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
     const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
     const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
-    const Dtype asum = blob.asum_data();
-    const Dtype asum_mean = asum / blob.count();
+    const Dtype asum_mean = blob.asum_data() / blob.count();
     LOG(INFO) << "    [Forward] "
        << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
-       << " data: " << asum << " (" << asum_mean << ")";
+       << " data: " << asum_mean;
   }
 }
 
@@ -434,21 +433,19 @@ void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
     if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }
     const Blob<Dtype>& blob = *bottom_vec[bottom_id];
     const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
-    const Dtype asum = blob.asum_diff();
-    const Dtype asum_mean = asum / blob.count();
+    const Dtype asum_mean = blob.asum_diff() / blob.count();
     LOG(INFO) << "    [Backward] "
         << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
-        << " diff: " << asum << " (" << asum_mean << ")";
+        << " diff: " << asum_mean;
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
     if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
     const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
-    const Dtype asum = blob.asum_diff();
-    const Dtype asum_mean = asum / blob.count();
+    const Dtype asum_mean = blob.asum_diff() / blob.count();
     LOG(INFO) << "    [Backward] "
         << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
-        << " diff: " << asum << " (" << asum_mean << ")";
+        << " diff: " << asum_mean;
   }
 }
 
@@ -458,15 +455,12 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
   const int param_owner = param_owners_[param_id];
   const string& layer_name = layer_names_[param_layer_indices_[param_id].first];
   const string& param_display_name = param_display_names_[param_id];
-  const Dtype asum_diff = blob.asum_diff();
-  const Dtype asum_diff_mean = asum_diff / blob.count();
+  const Dtype asum_diff_mean = blob.asum_diff() / blob.count();
   if (param_owner < 0) {
-    const Dtype asum_data = blob.asum_data();
-    const Dtype asum_data_mean = asum_data / blob.count();
+    const Dtype asum_data_mean = blob.asum_data() / blob.count();
     LOG(INFO) << "    [Update] Layer " << layer_name
         << ", param " << param_display_name
-        << " data: " << asum_data << " (" << asum_data_mean << ");"
-        << " diff: " << asum_diff << " (" << asum_diff_mean << ")";
+        << " data: " << asum_data_mean << "; diff: " << asum_diff_mean;
   } else {
     const string& owner_layer_name =
         layer_names_[param_layer_indices_[param_owner].first];
@@ -474,7 +468,7 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
         << ", param blob " << param_display_name
         << " (owned by layer " << owner_layer_name << ", "
         << "param " << param_display_names_[param_owners_[param_id]] << ")"
-        << " diff: " << asum_diff << " (" << asum_diff_mean << ")";
+        << " diff: " << asum_diff_mean;
   }
 }
 

From 4fe16db831ee8d5aa47c8798b4cb5ff83637944d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 26 Jul 2014 19:53:15 -0700
Subject: [PATCH 0326/2053] Fix my nonsensical variable names.

---
 src/caffe/net.cpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 8e99d307f32..228f826a8b9 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -419,10 +419,10 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
     const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
     const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
-    const Dtype asum_mean = blob.asum_data() / blob.count();
+    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
     LOG(INFO) << "    [Forward] "
        << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
-       << " data: " << asum_mean;
+       << " data: " << data_abs_val_mean;
   }
 }
 
@@ -433,19 +433,19 @@ void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
     if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }
     const Blob<Dtype>& blob = *bottom_vec[bottom_id];
     const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
-    const Dtype asum_mean = blob.asum_diff() / blob.count();
+    const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
     LOG(INFO) << "    [Backward] "
         << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
-        << " diff: " << asum_mean;
+        << " diff: " << diff_abs_val_mean;
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
     if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
     const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
-    const Dtype asum_mean = blob.asum_diff() / blob.count();
+    const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
     LOG(INFO) << "    [Backward] "
         << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
-        << " diff: " << asum_mean;
+        << " diff: " << diff_abs_val_mean;
   }
 }
 
@@ -455,12 +455,12 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
   const int param_owner = param_owners_[param_id];
   const string& layer_name = layer_names_[param_layer_indices_[param_id].first];
   const string& param_display_name = param_display_names_[param_id];
-  const Dtype asum_diff_mean = blob.asum_diff() / blob.count();
+  const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
   if (param_owner < 0) {
-    const Dtype asum_data_mean = blob.asum_data() / blob.count();
+    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
     LOG(INFO) << "    [Update] Layer " << layer_name
         << ", param " << param_display_name
-        << " data: " << asum_data_mean << "; diff: " << asum_diff_mean;
+        << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;
   } else {
     const string& owner_layer_name =
         layer_names_[param_layer_indices_[param_owner].first];
@@ -468,7 +468,7 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
         << ", param blob " << param_display_name
         << " (owned by layer " << owner_layer_name << ", "
         << "param " << param_display_names_[param_owners_[param_id]] << ")"
-        << " diff: " << asum_diff_mean;
+        << " diff: " << diff_abs_val_mean;
   }
 }
 

From 84c424b50e5cc2c21e298ee45180b74fefa0d81e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 26 Jul 2014 17:03:27 -0700
Subject: [PATCH 0327/2053] Make training iterations 0-indexed.

---
 src/caffe/solver.cpp | 51 ++++++++++++++++++++++++--------------------
 1 file changed, 28 insertions(+), 23 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 0dfc7f0a52d..b44948eda58 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -89,25 +89,24 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     LOG(INFO) << "Restoring previous solver status from " << resume_file;
     Restore(resume_file);
   }
-
-  // Run a test pass before doing any training to avoid waiting a potentially
-  // very long time (param_.test_interval() training iterations) to report that
-  // there's not enough memory to run the test net and crash, etc.; and to gauge
-  // the effect of the first training iterations.
-  if (param_.test_interval()) {
-    TestAll();
-  }
+  // Remember the initial iter_ value; will be non-zero if we loaded from a
+  // resume_file above.
+  const int start_iter = iter_;
 
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.
   vector<Blob<Dtype>*> bottom_vec;
-  while (iter_++ < param_.max_iter()) {
-    const bool display = param_.display() && iter_ % param_.display() == 0;
-    if (display) {
-      net_->set_debug_info(param_.debug_info());
-    } else {
-      net_->set_debug_info(false);
+  for (; iter_ < param_.max_iter(); ++iter_) {
+    // Save a snapshot if needed.
+    if (param_.snapshot() && iter_ > start_iter &&
+        iter_ % param_.snapshot() == 0) {
+      Snapshot();
+    }
+    if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
+      TestAll();
     }
+    const bool display = param_.display() && iter_ % param_.display() == 0;
+    net_->set_debug_info(display && param_.debug_info());
     Dtype loss = net_->ForwardBackward(bottom_vec);
     ComputeUpdateValue();
     net_->Update();
@@ -115,17 +114,23 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     if (display) {
       LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
     }
-    if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
-      TestAll();
-    }
-    // Check if we need to do snapshot
-    if (param_.snapshot() && iter_ % param_.snapshot() == 0) {
-      Snapshot();
-    }
   }
-  // After the optimization is done, always do a snapshot.
-  iter_--;
+  // Always save a snapshot after optimization.
   Snapshot();
+  // After the optimization is done, run an additional train and test pass to
+  // display the train and test loss/outputs if appropriate (based on the
+  // display and test_interval settings, respectively).  Unlike in the rest of
+  // training, for the train net we only run a forward pass as we've already
+  // updated the parameters "max_iter" times -- this final pass is only done to
+  // display the loss, which is computed in the forward pass.
+  if (param_.display() && iter_ % param_.display() == 0) {
+    Dtype loss;
+    net_->Forward(bottom_vec, &loss);
+    LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+  }
+  if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
+    TestAll();
+  }
   LOG(INFO) << "Optimization Done.";
 }
 

From 91ed16ba7ec96ccfe0dee27ca8923d566d28371a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 26 Jul 2014 20:57:07 -0700
Subject: [PATCH 0328/2053] Move loss display before lr display in
 ComputeUpdateValue.

---
 src/caffe/solver.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index b44948eda58..91738817875 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -102,18 +102,20 @@ void Solver<Dtype>::Solve(const char* resume_file) {
         iter_ % param_.snapshot() == 0) {
       Snapshot();
     }
+
     if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
       TestAll();
     }
+
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
     Dtype loss = net_->ForwardBackward(bottom_vec);
-    ComputeUpdateValue();
-    net_->Update();
-
     if (display) {
       LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
     }
+
+    ComputeUpdateValue();
+    net_->Update();
   }
   // Always save a snapshot after optimization.
   Snapshot();

From 6d71c516bc5f8179e319d501436681c1ea0aa9d4 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 27 Jul 2014 12:50:13 -0700
Subject: [PATCH 0329/2053] Symlink to tool bins without the .bin extension

---
 Makefile | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 7f778d364c7..a788f5a394c 100644
--- a/Makefile
+++ b/Makefile
@@ -117,6 +117,8 @@ EXAMPLE_BUILD_DIRS += $(foreach obj,$(EXAMPLE_OBJS),$(dir $(obj)))
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
+# symlinks to tool bins without the ".bin" extension
+TOOL_BIN_LINKS := ${TOOL_BINS:.bin=}
 # Put the test binaries in build/test for convenience.
 TEST_BIN_DIR := $(BUILD_DIR)/test
 TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
@@ -327,7 +329,7 @@ $(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % | $(LINT_OUTPUT_DIR)
 
 test: $(TEST_ALL_BIN) $(TEST_BINS)
 
-tools: $(TOOL_BINS)
+tools: $(TOOL_BINS) $(TOOL_BIN_LINKS)
 
 examples: $(EXAMPLE_BINS)
 
@@ -431,6 +433,11 @@ $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ)
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
+# Target for extension-less symlinks to tool binaries with extension '*.bin'.
+$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
+	@ $(RM) $@
+	@ ln -s $(abspath $<) $@
+
 $(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
 	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo

From 0aa8dca699d8029c5698a724e1c668377dc27d1c Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sun, 27 Jul 2014 15:52:59 -0700
Subject: [PATCH 0330/2053] relaxed timer test requirements

---
 src/caffe/test/test_benchmark.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index 6555b270ec4..fe0cc82a1d8 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -63,8 +63,8 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.MilliSeconds(), 295);
-  EXPECT_LE(timer.MilliSeconds(), 305);
+  EXPECT_GE(timer.MilliSeconds(), 290);
+  EXPECT_LE(timer.MilliSeconds(), 310);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
@@ -78,8 +78,8 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.Seconds(), 0.295);
-  EXPECT_LE(timer.Seconds(), 0.305);
+  EXPECT_GE(timer.Seconds(), 0.290);
+  EXPECT_LE(timer.Seconds(), 0.310);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());

From 4f12cc7391b05c351ae73fe39410f69c8658ab54 Mon Sep 17 00:00:00 2001
From: alfredtofu <alfredtofu@gmail.com>
Date: Mon, 28 Jul 2014 10:19:51 +0800
Subject: [PATCH 0331/2053] Update image_data_layer.cpp

fix bug for getting new width
---
 src/caffe/layers/image_data_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 30050ddd3c6..d3cc7ff17bf 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -138,7 +138,7 @@ void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Layer<Dtype>::SetUp(bottom, top);
   const int new_height  = this->layer_param_.image_data_param().new_height();
-  const int new_width  = this->layer_param_.image_data_param().new_height();
+  const int new_width  = this->layer_param_.image_data_param().new_width();
   CHECK((new_height == 0 && new_width == 0) ||
       (new_height > 0 && new_width > 0)) << "Current implementation requires "
       "new_height and new_width to be set at the same time.";

From 19e87d946829cb91e74a03463cda166f79cfe7ae Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 27 Jul 2014 19:02:58 -0700
Subject: [PATCH 0332/2053] Only install the minimal CUDA subpackages necessary
 for building.

---
 .travis.yml | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index db115173f47..7721de112e3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,7 +21,14 @@ install:
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
   - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
-  - sudo apt-get -y update && sudo apt-get -y install cuda
+  - sudo apt-get -y update
+  # Install the minimal CUDA subpackages required to test Caffe build.
+  # For a full CUDA installation, add 'cuda' to the list of packages.
+  - sudo apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
+  # Create CUDA symlink at /usr/local/cuda
+  # (This would normally be created by the CUDA installer, but we create it
+  # manually since we did a partial installation.)
+  - sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
   - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
 

From 6388b049ff02f3d5cb371efa11e8b06b8cdde673 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 27 Jul 2014 15:02:38 -0700
Subject: [PATCH 0333/2053] set_mode to CPU in ArgMaxLayerTest constructor to
 avoid random Travis failures.

---
 src/caffe/test/test_argmax_layer.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 5286a0a390a..51d68c42f9a 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -21,6 +21,7 @@ class ArgMaxLayerTest : public ::testing::Test {
       : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
         blob_top_(new Blob<Dtype>()),
         top_k_(5) {
+    Caffe::set_mode(Caffe::CPU);
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
@@ -59,7 +60,6 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
 
 TYPED_TEST(ArgMaxLayerTest, TestCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   ArgMaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -83,7 +83,6 @@ TYPED_TEST(ArgMaxLayerTest, TestCPU) {
 
 TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
@@ -110,7 +109,6 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
 
 TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_top_k(this->top_k_);
   ArgMaxLayer<TypeParam> layer(layer_param);
@@ -140,7 +138,6 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
 
 TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   argmax_param->set_top_k(this->top_k_);

From 9fc6ff3586ed8d8e341d25c2a2fb2e41c8e22e56 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Mon, 28 Jul 2014 13:38:38 -0700
Subject: [PATCH 0334/2053] gflags 2.1 bugfix (or rather a hack).

---
 src/caffe/common.cpp | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 5894a961dcc..9eea0cacf12 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -8,6 +8,17 @@
 #include "caffe/common.hpp"
 #include "caffe/util/rng.hpp"
 
+// gflags 2.1 issue: namespace google was changed to gflags without warning.
+// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// 2.1. If it is not, we will add a temporary solution to redirect the
+// namespace.
+// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
+// remove the following hack.
+#ifdef GFLAGS_GFLAGS_H_
+namespace google {
+using namespace gflags; 
+}  // namespace google
+#endif  // GFLAGS_GFLAGS_H_
 
 namespace caffe {
 
@@ -25,7 +36,7 @@ int64_t cluster_seedgen(void) {
 
 void GlobalInit(int* pargc, char*** pargv) {
   // Google flags.
-  ::gflags::ParseCommandLineFlags(pargc, pargv, true);
+  ::google::ParseCommandLineFlags(pargc, pargv, true);
   // Google logging.
   ::google::InitGoogleLogging(*(pargv)[0]);
 }

From ad5ce91c6d16b25cae911f475867f05279197aaa Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Mon, 28 Jul 2014 13:44:40 -0700
Subject: [PATCH 0335/2053] fixing comment

---
 src/caffe/common.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 9eea0cacf12..ed46b3aae95 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -10,8 +10,7 @@
 
 // gflags 2.1 issue: namespace google was changed to gflags without warning.
 // Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
-// 2.1. If it is not, we will add a temporary solution to redirect the
-// namespace.
+// 2.1. If yes , we will add a temporary solution to redirect the namespace.
 // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
 // remove the following hack.
 #ifdef GFLAGS_GFLAGS_H_

From cd1313fac76a2127006e4ab905cc1a522ecd6cbb Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Mon, 28 Jul 2014 14:16:25 -0700
Subject: [PATCH 0336/2053] lint

---
 src/caffe/common.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index ed46b3aae95..1810bb70b61 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -15,7 +15,7 @@
 // remove the following hack.
 #ifdef GFLAGS_GFLAGS_H_
 namespace google {
-using namespace gflags; 
+using ::gflags::ParseCommandLineFlags;
 }  // namespace google
 #endif  // GFLAGS_GFLAGS_H_
 

From cbc507e71fd2615747dd60edf00d7be702aa5aa9 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 28 Jul 2014 14:54:03 -0700
Subject: [PATCH 0337/2053] FIX: tests now catch bug reported in #750 (in HDF5
 output layer)

---
 .../test/test_data/generate_sample_data.py    |   5 ++++-
 src/caffe/test/test_data/sample_data.h5       | Bin 10184 -> 10184 bytes
 .../test/test_data/sample_data_2_gzip.h5      | Bin 9992 -> 9992 bytes
 src/caffe/test/test_hdf5_output_layer.cpp     |   2 +-
 4 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 0d8f5aa98e3..0516eff9158 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -14,7 +14,10 @@
 data = np.arange(total_size)
 data = data.reshape(num_rows, num_cols, height, width)
 data = data.astype('float32')
-label = np.arange(num_rows)[:, np.newaxis]
+
+# We had a bug where data was copied into label, but the tests weren't
+# catching it, so let's make label 1-indexed.
+label = 1 + np.arange(num_rows)[:, np.newaxis]
 label = label.astype('float32')
 
 print data
diff --git a/src/caffe/test/test_data/sample_data.h5 b/src/caffe/test/test_data/sample_data.h5
index a1f923a71ae51bf568810679b01e98ab67adaccf..cb327573cae5de98cb1008eb38a3372c229a499c 100644
GIT binary patch
delta 41
wcmX@%f5Lym9cJb?N3U(Z&uq%X4rBx~Fl202WOHSjcz|tl8i$Si<TMU70FN&acmMzZ

delta 41
wcmX@%f5Lym9cJct32K|~Gn+E80~x^#3>ljh*<6_>9$?#?#$h8rIf+9J09A4hP5=M^

diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5
index 56c0a740ec2a5163d2200826c3748bb930d26b78..b3d187cb1da4445a6a2f5510dc98ba4b25e8ec75 100644
GIT binary patch
delta 79
zcmeD1>+sva!@~UL=(WxKEM4MC85`0Q5(*fU6!I7zu{zW+bjULpHY+nXGYDu~Ffr5{
ju^CU^Eq#(5s6Lp1!C~S?*U1Jf5{zmSH_9*OkmUjZ7UCHA

delta 79
zcmeD1>+sva!@~S7L2WZXOP9D3LnB+Y!b1iZhdT^MxEp>j2-q_uvAMIcF}gFP=dxa6
jU|^WMTlyqBP<=21gTus)u9FQ|Bp8(^Zj@ilA<G2-y1Ew;

diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 221d62aa2d2..ec48fc4fc32 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -62,7 +62,7 @@ void HDF5OutputLayerTest<TypeParam>::CheckBlobEqual(const Blob<Dtype>& b1,
     for (int c = 0; c < b1.channels(); ++c) {
       for (int h = 0; h < b1.height(); ++h) {
         for (int w = 0; w < b1.width(); ++w) {
-          EXPECT_EQ(b1.data_at(n, c, h, w), b1.data_at(n, c, h, w));
+          EXPECT_EQ(b1.data_at(n, c, h, w), b2.data_at(n, c, h, w));
         }
       }
     }

From 65c9c4a4c89f9d5112f265d56cc842e47f698afb Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 28 Jul 2014 14:55:05 -0700
Subject: [PATCH 0338/2053] FIX: both data and label now copied correctly in
 HDF5 output layer

---
 src/caffe/layers/hdf5_output_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index accc9f9ccdd..30e4997854b 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -55,7 +55,7 @@ Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom[0]->num(); ++i) {
     caffe_copy(data_datum_dim, &bottom[0]->cpu_data()[i * data_datum_dim],
         &data_blob_.mutable_cpu_data()[i * data_datum_dim]);
-    caffe_copy(label_datum_dim, &bottom[0]->cpu_data()[i * label_datum_dim],
+    caffe_copy(label_datum_dim, &bottom[1]->cpu_data()[i * label_datum_dim],
         &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();

From 416a7a1747fd95bb4564268627bf9a88fbb3928d Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 28 Jul 2014 15:09:47 -0700
Subject: [PATCH 0339/2053] FIX: updating HDF5 input test for new test data

---
 src/caffe/test/test_hdf5data_layer.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index e606e209661..f3d7c8c6974 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -86,7 +86,8 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
 
     // On even iterations, we're reading the first half of the data.
     // On odd iterations, we're reading the second half of the data.
-    int label_offset = (iter % 2 == 0) ? 0 : batch_size;
+    // NB: label is 1-indexed
+    int label_offset = 1 + ((iter % 2 == 0) ? 0 : batch_size);
     int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
 
     // Every two iterations we are reading the second file,

From dff2023a67dad3e59450dfdaf6c5042c1c6b0ccb Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 15:22:43 -0700
Subject: [PATCH 0340/2053] make "all" the default target

"all" is the right default since it builds the library itself.
define "everything" after it.
---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index a788f5a394c..391d3c08e5c 100644
--- a/Makefile
+++ b/Makefile
@@ -299,10 +299,10 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
-
 all: $(NAME) $(STATIC_NAME) tools examples
 
+everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
+
 linecount:
 	cloc --read-lang-def=$(PROJECT).cloc src/$(PROJECT)/
 

From 2e48c3fc4fdfd73d9bfac13829ef78da0e5ad474 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 19:45:09 -0700
Subject: [PATCH 0341/2053] [docs] fix missing glog install cd

---
 docs/installation.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/installation.md b/docs/installation.md
index a74071f8ecb..9cf9700235a 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -76,6 +76,7 @@ The only exceptions being the google flags library and the google logging librar
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
     ./configure
     make && make install
     # gflags

From fdb190358ed5045d79a8d7de872aaa9682cc81e5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 19:49:10 -0700
Subject: [PATCH 0342/2053] [docs] install glog first given incompatibility

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 9cf9700235a..820b918d64a 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -87,7 +87,7 @@ The only exceptions being the google flags library and the google logging librar
     cmake ..
     make && make install
 
-Note that glog does not compile with the most recent gflags version (2.1), so before that is solved you will need to build with gflags first. Also note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
+Note that glog does not compile with the most recent gflags version (2.1), so before that is solved you will need to build with glog first. Also note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
 
 #### OS X
 

From f3140227a8a91aafeac2933c85ff94d40458c876 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 19:53:44 -0700
Subject: [PATCH 0343/2053] [docs] compile gflags with -fPIC for linking

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 820b918d64a..fa29a0e6d90 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -84,7 +84,7 @@ The only exceptions being the google flags library and the google logging librar
     unzip master.zip
     cd gflags-master
     mkdir build && cd build
-    cmake ..
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is solved you will need to build with glog first. Also note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.

From edf438a4a6609f2e32eeeca4ce353ff9c9c4a905 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Jun 2014 00:38:50 -0700
Subject: [PATCH 0344/2053] add h/w kernel size, stride, and pad for non-square
 filtering

while keeping everything working as-is.
---
 src/caffe/proto/caffe.proto | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5e437dff327..c27e9e5aebe 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -251,10 +251,18 @@ message ConcatParameter {
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
-  optional uint32 pad = 3 [default = 0]; // The padding size
-  optional uint32 kernel_size = 4; // The kernel size
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 4; // The kernel size (square)
+  optional uint32 kernel_h = 11; // The kernel height
+  optional uint32 kernel_w = 12; // The kernel width
   optional uint32 group = 5 [default = 1]; // The group size for group conv
-  optional uint32 stride = 6 [default = 1]; // The stride
+  optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 13; // The stride height
+  optional uint32 stride_w = 14; // The stride width
   optional FillerParameter weight_filler = 7; // The filler for the weight
   optional FillerParameter bias_filler = 8; // The filler for the bias
 }

From 4d44fe7b5ecf94411db34484e7e86e07f6e735ab Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 30 Jun 2014 12:06:27 -0700
Subject: [PATCH 0345/2053] im2col + convolve non-square filters, padding, and
 stride

---
 include/caffe/util/im2col.hpp        | 20 +++---
 include/caffe/vision_layers.hpp      | 12 ++--
 src/caffe/layers/conv_layer.cpp      | 58 ++++++++++++++----
 src/caffe/layers/conv_layer.cu       | 11 ++--
 src/caffe/layers/im2col_layer.cpp    | 51 +++++++++++++---
 src/caffe/layers/im2col_layer.cu     |  6 +-
 src/caffe/test/test_im2col_kernel.cu | 17 ++++--
 src/caffe/util/im2col.cpp            | 62 ++++++++++---------
 src/caffe/util/im2col.cu             | 91 ++++++++++++++++------------
 9 files changed, 216 insertions(+), 112 deletions(-)

diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index a649d8cc4e8..c263997ae66 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -7,23 +7,27 @@ namespace caffe {
 
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, Dtype* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im);
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_col);
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, Dtype* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im);
 
 }  // namespace caffe
 
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 05c7b45e783..80dc37543ee 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -45,11 +45,11 @@ class ConvolutionLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
   int num_;
   int channels_;
-  int pad_;
+  int pad_h_, pad_w_;
   int height_;
   int width_;
   int num_output_;
@@ -119,12 +119,12 @@ class Im2colLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
   int channels_;
   int height_;
   int width_;
-  int pad_;
+  int pad_h_, pad_w_;
 };
 
 /* InnerProductLayer
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 11f29e783db..79b040f08d8 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -14,10 +14,42 @@ template <typename Dtype>
 void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Layer<Dtype>::SetUp(bottom, top);
-  kernel_size_ = this->layer_param_.convolution_param().kernel_size();
-  stride_ = this->layer_param_.convolution_param().stride();
+  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  CHECK(!conv_param.has_kernel_size() !=
+      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(conv_param.has_kernel_size() ||
+      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
+      && conv_param.has_pad_w())
+      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
+      && conv_param.has_stride_w())
+      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (conv_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  } else {
+    kernel_h_ = conv_param.kernel_h();
+    kernel_w_ = conv_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!conv_param.has_pad_h()) {
+    pad_h_ = pad_w_ = conv_param.pad();
+  } else {
+    pad_h_ = conv_param.pad_h();
+    pad_w_ = conv_param.pad_w();
+  }
+  if (!conv_param.has_stride_h()) {
+    stride_h_ = stride_w_ = conv_param.stride();
+  } else {
+    stride_h_ = conv_param.stride_h();
+    stride_w_ = conv_param.stride_w();
+  }
   group_ = this->layer_param_.convolution_param().group();
-  pad_ = this->layer_param_.convolution_param().pad();
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -37,17 +69,18 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_EQ(channels_ % group_, 0);
   // The im2col result buffer would only hold one image at a time to avoid
   // overly large memory usage.
-  int height_out = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
-  int width_out = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+  int height_out =
+      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
+  int width_out = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
   col_buffer_.Reshape(
-      1, channels_ * kernel_size_ * kernel_size_, height_out, width_out);
+      1, channels_ * kernel_h_ * kernel_w_, height_out, width_out);
   // Set the parameters
   CHECK_EQ(num_output_ % group_, 0)
       << "Number of output should be multiples of group.";
   bias_term_ = this->layer_param_.convolution_param().bias_term();
   // Figure out the dimensions for individual gemms.
   M_ = num_output_ / group_;
-  K_ = channels_ * kernel_size_ * kernel_size_ / group_;
+  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
   N_ = height_out * width_out;
   for (int top_id = 0; top_id < top->size(); ++top_id) {
     (*top)[top_id]->Reshape(num_, num_output_, height_out, width_out);
@@ -63,7 +96,7 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     }
     // Intialize the weight
     this->blobs_[0].reset(new Blob<Dtype>(
-        num_output_, channels_ / group_, kernel_size_, kernel_size_));
+        num_output_, channels_ / group_, kernel_h_, kernel_w_));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
@@ -99,7 +132,8 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     for (int n = 0; n < num_; ++n) {
       // First, im2col
       im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                        width_, kernel_size_, pad_, stride_, col_data);
+                        width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_data);
+        stride_h_, stride_w_, col_data);
       // Second, innerproduct with groups
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
@@ -160,7 +194,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
         im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_size_, pad_, stride_, col_data);
+                   width_, kernel_h_, kernel_w_ pad_h_, pad_w_, stride_h_, stride_w_, col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
@@ -179,8 +213,8 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_cpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-              stride_, bottom_diff + (*bottom)[i]->offset(n));
+          col2im_cpu(col_diff, channels_, height_, width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index d328fbde9ae..86c047b4307 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -24,7 +24,8 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     for (int n = 0; n < num_; ++n) {
       // First, im2col
       im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                        width_, kernel_size_, pad_, stride_, col_data);
+                        width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_data);
+                      pad_w_, stride_h_, stride_w_, col_data);
       // Second, innerproduct with groups
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
@@ -65,6 +66,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = NULL;
     // Bias gradient, if necessary.
     if (bias_term_ && this->param_propagate_down_[1]) {
+                      width_, kernel_h_, kernel_w_, pad_h_,
       top_diff = top[i]->gpu_diff();
       for (int n = 0; n < num_; ++n) {
         caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
@@ -85,7 +87,8 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
         im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_size_, pad_, stride_, col_data);
+                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                   stride_h_, stride_w_, col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
@@ -104,8 +107,8 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_gpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-              stride_, bottom_diff + (*bottom)[i]->offset(n));
+          col2im_gpu(col_diff, channels_, height_, width_, kernel_h_, kernel_w, pad_h_, pad_w_,
+              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 2c86412bb15..fabce0a6ad6 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -13,15 +13,48 @@ template <typename Dtype>
 void Im2colLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Layer<Dtype>::SetUp(bottom, top);
-  kernel_size_ = this->layer_param_.convolution_param().kernel_size();
-  stride_ = this->layer_param_.convolution_param().stride();
-  pad_ = this->layer_param_.convolution_param().pad();
+  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  CHECK(!conv_param.has_kernel_size() !=
+      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(conv_param.has_kernel_size() ||
+      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
+      && conv_param.has_pad_w())
+      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
+      && conv_param.has_stride_w())
+      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (conv_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  } else {
+    kernel_h_ = conv_param.kernel_h();
+    kernel_w_ = conv_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!conv_param.has_pad_h()) {
+    pad_h_ = pad_w_ = conv_param.pad();
+  } else {
+    pad_h_ = conv_param.pad_h();
+    pad_w_ = conv_param.pad_w();
+  }
+  if (!conv_param.has_stride_h()) {
+    stride_h_ = stride_w_ = conv_param.stride();
+  } else {
+    stride_h_ = conv_param.stride_h();
+    stride_w_ = conv_param.stride_w();
+  }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
-  (*top)[0]->Reshape(bottom[0]->num(), channels_ * kernel_size_ * kernel_size_,
-      (height_ + 2 * pad_ - kernel_size_) / stride_ + 1,
-      (width_ + 2 * pad_ - kernel_size_) / stride_ + 1);
+  (*top)[0]->Reshape(
+      bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
+      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
+      (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
 }
 
 template <typename Dtype>
@@ -31,7 +64,8 @@ Dtype Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_size_, pad_, stride_, top_data + (*top)[0]->offset(n));
+        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
   }
   return Dtype(0.);
 }
@@ -43,7 +77,8 @@ void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_size_, pad_, stride_, bottom_diff + (*bottom)[0]->offset(n));
+        kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
   }
 }
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 9cfb74e815c..84439742250 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -16,7 +16,8 @@ Dtype Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_size_, pad_, stride_, top_data + (*top)[0]->offset(n));
+        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
   }
   return Dtype(0.);
 }
@@ -28,7 +29,8 @@ void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_size_, pad_, stride_, bottom_diff + (*bottom)[0]->offset(n));
+        kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
   }
 }
 
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 5671968b56d..37d1a1523af 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -17,8 +17,10 @@ namespace caffe {
 // Forward declare kernel functions
 template <typename Dtype>
 __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_col);
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
@@ -87,8 +89,10 @@ TYPED_TEST(Im2colKernelTest, TestGPU) {
   // CPU Version
   for (int n = 0; n < this->blob_bottom_->num(); ++n) {
     im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n),
-      this->channels_, this->height_, this->width_, this->kernel_size_,
-      this->pad_, this->stride_, cpu_data + this->blob_top_cpu_->offset(n));
+      this->channels_, this->height_, this->width_,
+      this->kernel_size_, this->kernel_size_, this->pad_, this->pad_,
+      this->stride_, this->stride_,
+      cpu_data + this->blob_top_cpu_->offset(n));
   }
 
   // GPU version
@@ -102,8 +106,9 @@ TYPED_TEST(Im2colKernelTest, TestGPU) {
       // NOLINT_NEXT_LINE(whitespace/operators)
       im2col_gpu_kernel<TypeParam><<<grid_dim, CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, bottom_data + this->blob_bottom_->offset(n),
-        this->height_, this->width_, this->kernel_size_, this->pad_,
-        this->stride_, this->height_col_, this->width_col_,
+        this->height_, this->width_, this->kernel_size_, this->kernel_size_,
+        this->pad_, this->pad_, this->stride_, this->stride_,
+        this->height_col_, this->width_col_,
         top_data + this->blob_top_->offset(n));
       CUDA_POST_KERNEL_CHECK;
     }
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index ce4e18848bf..ca1dc797985 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -11,19 +11,21 @@ namespace caffe {
 
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col) {
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
-  int channels_col = channels * ksize * ksize;
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    Dtype* data_col) {
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  int channels_col = channels * kernel_h * kernel_w;
   for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % ksize;
-    int h_offset = (c / ksize) % ksize;
-    int c_im = c / ksize / ksize;
+    int w_offset = c % kernel_h;
+    int h_offset = (c / kernel_h) % kernel_h;
+    int c_im = c / kernel_h / kernel_w;
     for (int h = 0; h < height_col; ++h) {
       for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride - pad + h_offset;
-        int w_pad = w * stride - pad + w_offset;
+        int h_pad = h * stride_h - pad_h + h_offset;
+        int w_pad = w * stride_w - pad_w + w_offset;
         if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
           data_col[(c * height_col + h) * width_col + w] =
             data_im[(c_im * height + h_pad) * width + w_pad];
@@ -36,28 +38,32 @@ void im2col_cpu(const Dtype* data_im, const int channels,
 
 // Explicit instantiation
 template void im2col_cpu<float>(const float* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, float* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_col);
 template void im2col_cpu<double>(const double* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, double* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int ksize, const int pad,
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
     const int stride, Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
-  int channels_col = channels * ksize * ksize;
+  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
+  int channels_col = channels * patch_h * patch_w;
   for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % ksize;
-    int h_offset = (c / ksize) % ksize;
-    int c_im = c / ksize / ksize;
+    int w_offset = c % patch_h;
+    int h_offset = (c / patch_h) % patch_h;
+    int c_im = c / patch_h / patch_w;
     for (int h = 0; h < height_col; ++h) {
       for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride - pad + h_offset;
-        int w_pad = w * stride - pad + w_offset;
+        int h_pad = h * stride_h - pad_h + h_offset;
+        int w_pad = w * stride_w - pad_w + w_offset;
         if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
           data_im[(c_im * height + h_pad) * width + w_pad] +=
               data_col[(c * height_col + h) * width_col + w];
@@ -68,10 +74,12 @@ void col2im_cpu(const Dtype* data_col, const int channels,
 
 // Explicit instantiation
 template void col2im_cpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, float* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_im);
 template void col2im_cpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, double* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_im);
 
 }  // namespace caffe
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 79faa6cb5c9..b565d2d3843 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -12,23 +12,25 @@ namespace caffe {
 
 template <typename Dtype>
 __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
     int w_out = index % width_col;
     int h_index = index / width_col;
     int h_out = h_index % height_col;
     int channel_in = h_index / height_col;
-    int channel_out = channel_in * ksize * ksize;
-    int h_in = h_out * stride - pad;
-    int w_in = w_out * stride - pad;
+    int channel_out = channel_in * kernel_h * kernel_w;
+    int h_in = h_out * stride_h - pad_h;
+    int w_in = w_out * stride_w - pad_w;
     Dtype* data_col_ptr = data_col;
     data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
     const Dtype* data_im_ptr = data_im;
     data_im_ptr += (channel_in * height + h_in) * width + w_in;
-    for (int i = 0; i < ksize; ++i) {
-      for (int j = 0; j < ksize; ++j) {
+    for (int i = 0; i < kernel_h; ++i) {
+      for (int j = 0; j < kernel_w; ++j) {
         int h = h_in + i;
         int w = w_in + j;
         *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
@@ -41,17 +43,20 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col) {
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    Dtype* data_col) {
   // We are going to launch channels * height_col * width_col kernels, each
   // kernel responsible for copying a single-channel grid.
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
   int num_kernels = channels * height_col * width_col;
   // NOLINT_NEXT_LINE(whitespace/operators)
   im2col_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_im, height, width, ksize, pad, stride, height_col,
+      num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h,
+      pad_w, stride_h, stride_w, height_col,
       width_col, data_col);
   CUDA_POST_KERNEL_CHECK;
 }
@@ -59,40 +64,46 @@ void im2col_gpu(const Dtype* data_im, const int channels,
 
 // Explicit instantiation
 template void im2col_gpu<float>(const float* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, float* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    float* data_col);
 template void im2col_gpu<double>(const double* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, double* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    double* data_col);
 
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
-    const int height, const int width, const int channels, const int ksize,
-    const int pad, const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int channels,
+    const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
     Dtype val = 0;
-    int w = index % width + pad;
-    int h = (index / width) % height + pad;
+    int w = index % width + pad_w;
+    int h = (index / width) % height + pad_h;
     int c = index / (width * height);
     // compute the start and end of the output
-    int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1;
-    int w_col_end = min(w / stride + 1, width_col);
-    int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1;
-    int h_col_end = min(h / stride + 1, height_col);
+    int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1;
+    int w_col_end = min(w / stride_w + 1, width_col);
+    int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1;
+    int h_col_end = min(h / stride_h + 1, height_col);
     /*
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         // the col location: [c * width * height + h_out, w_out]
-        int c_col = c * ksize * ksize + (h - h_col * stride) * ksize + (w - w_col * stride);
+        int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize
+            + (w - w_col * stride_w);
         val += data_col[(c_col * height_col + h_col) * width_col + w_col];
       }
     }
     */
     // equivalent implementation
-    int offset = (c * ksize * ksize + h * ksize + w) * height_col * width_col;
-    int coeff_h_col = (1 - stride * ksize * height_col) * width_col;
-    int coeff_w_col = (1 - stride * height_col * width_col);
+    int offset = (c * patch_h * patch_w + h * patch_h + w) * height_col * width_col;
+    int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
+    int coeff_w_col = (1 - stride_w * height_col * width_col);
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
@@ -104,29 +115,31 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_im) {
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im) {
+  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
   int num_kernels = channels * height * width;
   // To avoid involving atomic operations, we will launch one kernel per
   // bottom dimension, and then in the kernel add up the top dimensions.
   // NOLINT_NEXT_LINE(whitespace/operators)
   col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_col, height, width, channels, ksize, pad, stride,
+      num_kernels, data_col, height, width, channels, patch_h, patch_w,
+      pad_h, pad_w, stride_h, stride_w,
       height_col, width_col, data_im);
   CUDA_POST_KERNEL_CHECK;
 }
 
-
 // Explicit instantiation
 template void col2im_gpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, float* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_im);
 template void col2im_gpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, double* data_im);
-
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_im);
 
 }  // namespace caffe

From 909e4d83e3f19df92316b7bbd85225ef2a3156c3 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 17 Jun 2014 22:08:00 -0700
Subject: [PATCH 0346/2053] test rectangular im2col

---
 src/caffe/test/test_im2col_layer.cpp | 33 ++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index a40f59df2e8..0463cee7040 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -83,4 +83,37 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
 }
 
 
+TYPED_TEST(Im2colLayerTest, TestRect) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_h(5);
+  convolution_param->set_kernel_w(3);
+  convolution_param->set_stride(2);
+  Im2colLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // We are lazy and will only check the top left block
+  for (int c = 0; c < 45; ++c) {
+    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
+        this->blob_bottom_->data_at(0, (c / 15), (c / 5) % 5, c % 5));
+  }
+}
+
+
+TYPED_TEST(Im2colLayerTest, TestRectGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_h(5);
+  convolution_param->set_kernel_w(3);
+  convolution_param->set_stride(2);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 }  // namespace caffe

From ca4482c5ae0ef7ec62a55ebdc920519eb2f63cd5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 21:47:34 -0700
Subject: [PATCH 0347/2053] fix GPU indexing

Thanks to @ejaz-izy's debugging in
https://github.com/BVLC/caffe/pull/505#issuecomment-50287608
---
 src/caffe/layers/conv_layer.cpp      | 10 ++++++----
 src/caffe/layers/conv_layer.cu       | 10 +++++-----
 src/caffe/test/test_im2col_layer.cpp |  2 +-
 src/caffe/util/im2col.cpp            | 10 +++++-----
 src/caffe/util/im2col.cu             |  3 ++-
 5 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 79b040f08d8..c7949e049cd 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -132,8 +132,8 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     for (int n = 0; n < num_; ++n) {
       // First, im2col
       im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                        width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_data);
-        stride_h_, stride_w_, col_data);
+          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+          col_data);
       // Second, innerproduct with groups
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
@@ -194,7 +194,8 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
         im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_ pad_h_, pad_w_, stride_h_, stride_w_, col_data);
+                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                   stride_h_, stride_w_, col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
@@ -213,7 +214,8 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_cpu(col_diff, channels_, height_, width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+          col2im_cpu(col_diff, channels_, height_, width_,
+              kernel_h_, kernel_w_, pad_h_, pad_w_,
               stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
         }
       }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 86c047b4307..fc114b0b328 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -24,8 +24,8 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     for (int n = 0; n < num_; ++n) {
       // First, im2col
       im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                        width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_data);
-                      pad_w_, stride_h_, stride_w_, col_data);
+          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+          col_data);
       // Second, innerproduct with groups
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
@@ -66,7 +66,6 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = NULL;
     // Bias gradient, if necessary.
     if (bias_term_ && this->param_propagate_down_[1]) {
-                      width_, kernel_h_, kernel_w_, pad_h_,
       top_diff = top[i]->gpu_diff();
       for (int n = 0; n < num_; ++n) {
         caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
@@ -107,8 +106,9 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_gpu(col_diff, channels_, height_, width_, kernel_h_, kernel_w, pad_h_, pad_w_,
-              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
+          col2im_gpu(col_diff, channels_, height_, width_,
+              kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+              bottom_diff + (*bottom)[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 0463cee7040..aaee3d81bf7 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -97,7 +97,7 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
   // We are lazy and will only check the top left block
   for (int c = 0; c < 45; ++c) {
     EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
-        this->blob_bottom_->data_at(0, (c / 15), (c / 5) % 5, c % 5));
+        this->blob_bottom_->data_at(0, (c / 15), (c / 3) % 5, c % 3));
   }
 }
 
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index ca1dc797985..01df8c89182 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -19,8 +19,8 @@ void im2col_cpu(const Dtype* data_im, const int channels,
   int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
   int channels_col = channels * kernel_h * kernel_w;
   for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % kernel_h;
-    int h_offset = (c / kernel_h) % kernel_h;
+    int w_offset = c % kernel_w;
+    int h_offset = (c / kernel_w) % kernel_h;
     int c_im = c / kernel_h / kernel_w;
     for (int h = 0; h < height_col; ++h) {
       for (int w = 0; w < width_col; ++w) {
@@ -51,14 +51,14 @@ void col2im_cpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int patch_h, const int patch_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
-    const int stride, Dtype* data_im) {
+    Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
   int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
   int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
   int channels_col = channels * patch_h * patch_w;
   for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % patch_h;
-    int h_offset = (c / patch_h) % patch_h;
+    int w_offset = c % patch_w;
+    int h_offset = (c / patch_w) % patch_h;
     int c_im = c / patch_h / patch_w;
     for (int h = 0; h < height_col; ++h) {
       for (int w = 0; w < width_col; ++w) {
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index b565d2d3843..653ce49e7f8 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -101,7 +101,8 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     }
     */
     // equivalent implementation
-    int offset = (c * patch_h * patch_w + h * patch_h + w) * height_col * width_col;
+    int offset =
+        (c * patch_h * patch_w + h * patch_w + w) * height_col * width_col;
     int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
     int coeff_w_col = (1 - stride_w * height_col * width_col);
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {

From dadeb990ade280de90a8d4e0295005ee8d895189 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 22:43:06 -0700
Subject: [PATCH 0348/2053] test non-square filters by separable convolution of
 Sobel operator

Compute the G_x kernel of the Sobel operator as a full filter and as
separable filters to check the rectangular filter output.
---
 src/caffe/test/test_convolution_layer.cpp | 96 +++++++++++++++++++++++
 1 file changed, 96 insertions(+)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index d0522b13cd0..f3cfb680990 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -172,6 +172,102 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
+  // Test separable convolution by computing the Sobel operator
+  // as a single filter then comparing the result
+  // as the convolution of two rectangular filters.
+  typedef typename TypeParam::Dtype Dtype;
+  // Fill bottoms with identical Gaussian noise.
+  shared_ptr<GaussianFiller<Dtype> > filler;
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  filler.reset(new GaussianFiller<Dtype>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  this->blob_bottom_2_->CopyFrom(*this->blob_bottom_);
+  // Compute Sobel G_x operator as 3 x 3 convolution.
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 3));
+  Dtype* weights = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 9;  // 3 x 3 filter
+    weights[i +  0] = -1;
+    weights[i +  1] =  0;
+    weights[i +  2] =  1;
+    weights[i +  3] = -2;
+    weights[i +  4] =  0;
+    weights[i +  5] =  2;
+    weights[i +  6] = -1;
+    weights[i +  7] =  0;
+    weights[i +  8] =  1;
+  }
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
+  // (1) the [1 2 1] column filter
+  vector<Blob<Dtype>*> sep_blob_bottom_vec;
+  vector<Blob<Dtype>*> sep_blob_top_vec;
+  shared_ptr<Blob<Dtype> > blob_sep(new Blob<Dtype>());
+  sep_blob_bottom_vec.push_back(this->blob_bottom_2_);
+  sep_blob_top_vec.push_back(this->blob_top_2_);
+  convolution_param->clear_kernel_size();
+  convolution_param->clear_stride();
+  convolution_param->set_kernel_h(3);
+  convolution_param->set_kernel_w(1);
+  convolution_param->set_stride_h(2);
+  convolution_param->set_stride_w(1);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 1));
+  Dtype* weights_1 = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 3;  // 3 x 1 filter
+    weights_1[i +  0] = 1;
+    weights_1[i +  1] = 2;
+    weights_1[i +  2] = 1;
+  }
+  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  // (2) the [-1 0 1] row filter
+  blob_sep->CopyFrom(*this->blob_top_2_, false, true);
+  sep_blob_bottom_vec.clear();
+  sep_blob_bottom_vec.push_back(blob_sep.get());
+  convolution_param->set_kernel_h(1);
+  convolution_param->set_kernel_w(3);
+  convolution_param->set_stride_h(1);
+  convolution_param->set_stride_w(2);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
+  Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 3;  // 1 x 3 filter
+    weights_2[i +  0] = -1;
+    weights_2[i +  1] =  0;
+    weights_2[i +  2] =  1;
+  }
+  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  // Test equivalence of full and separable filters.
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  const Dtype* sep_top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], sep_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From 374218a07a4bbe9826da8b0d9edf2e6c51042224 Mon Sep 17 00:00:00 2001
From: TANGUY Arnaud <arn.tanguy@gmail.com>
Date: Tue, 29 Jul 2014 14:08:42 +0200
Subject: [PATCH 0349/2053] Fix choice MKL directory from Makefile.config

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 391d3c08e5c..8e8ebedbc57 100644
--- a/Makefile
+++ b/Makefile
@@ -247,7 +247,7 @@ ifeq ($(BLAS), mkl)
 	# MKL
 	LIBRARIES += mkl_rt
 	COMMON_FLAGS += -DUSE_MKL
-	MKL_DIR = /opt/intel/mkl
+	MKL_DIR ?= /opt/intel/mkl
 	BLAS_INCLUDE ?= $(MKL_DIR)/include
 	BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
 else ifeq ($(BLAS), open)

From 5271c029a2d0d81be4bb2a9553344d2b1d431eca Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 29 Jul 2014 09:21:39 -0700
Subject: [PATCH 0350/2053] [example] standardize imagenet leveldb names

---
 examples/imagenet/create_imagenet.sh | 4 ++--
 examples/imagenet/readme.md          | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index a1bcb7bfc5a..11fc0999059 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -10,11 +10,11 @@ echo "Creating leveldb..."
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
     /path/to/imagenet/train/ \
     $DATA/train.txt \
-    imagenet_train_leveldb 1
+    ilsvrc12_train_leveldb 1
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
     /path/to/imagenet/val/ \
     $DATA/val.txt \
-    imagenet_val_leveldb 1
+    ilsvrc12_val_leveldb 1
 
 echo "Done."
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index cdc8ecb78c4..734ce74d92f 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -41,7 +41,7 @@ You will also need to resize the images to 256x256: we do not explicitly do this
 
 Go to `$CAFFE_ROOT/examples/imagenet/` for the rest of this guide.
 
-Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `imagenet_train_leveldb` and `imagenet_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
+Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `ilsvrc12_train_leveldb` and `ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
 
 Compute Image Mean
 ------------------

From c2b74c3cf66b229c0ad926cf052c7e7ed522038d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 17 Jul 2014 20:12:44 -0700
Subject: [PATCH 0351/2053] Add NetState message with phase, level, stage;
 NetStateRule message with filtering rules for Layers.

---
 src/caffe/proto/caffe.proto | 95 +++++++++++++++++++++++++++++++------
 1 file changed, 81 insertions(+), 14 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index c27e9e5aebe..d2f61e57f34 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -56,24 +56,55 @@ message NetParameter {
   // If set False, then whether to carry out backward is determined
   // automatically according to the net structure and learning rates.
   optional bool force_backward = 5 [default = false];
+  // The current "state" of the network, including the phase, level, and stage.
+  // Some layers may be included/excluded depending on this state and the states
+  // specified in the layers' include and exclude fields.
+  optional NetState state = 6;
 }
 
+// NOTE
+// Update the next available ID when you add a new SolverParameter field.
+//
+// SolverParameter next available ID: 27 (last added: test_state)
 message SolverParameter {
-  // {train,test}_net specify a path to a file containing the {train,test} net
-  // parameters; {train,test}_net_param specify the net parameters directly
-  // inside the SolverParameter.
+  //////////////////////////////////////////////////////////////////////////////
+  // Specifying the train and test networks
   //
-  // Only either train_net or train_net_param (not both) should be specified.
-  // You may specify 0 or more test_net and/or test_net_param.  All
-  // nets specified using test_net_param will be tested first, followed by all
-  // nets specified using test_net (each processed in the order specified in
-  // the prototxt).
-  optional string train_net = 1; // The proto filename for the train net.
-  repeated string test_net = 2; // The proto filenames for the test nets.
-  optional NetParameter train_net_param = 21; // Full params for the train net.
-  repeated NetParameter test_net_param = 22; // Full params for the test nets.
-  // The number of iterations for each testing phase.
+  // Exactly one train net must be specified using one of the following fields:
+  //     train_net_param, train_net, net_param, net
+  // One or more test nets may be specified using any of the following fields:
+  //     test_net_param, test_net, net_param, net
+  // If more than one test net field is specified (e.g., both net and
+  // test_net are specified), they will be evaluated in the field order given
+  // above: (1) test_net_param, (2) test_net, (3) net_param/net.
+  // A test_iter must be specified for each test_net.
+  // A test_level and/or a test_stage may also be specified for each test_net.
+  //////////////////////////////////////////////////////////////////////////////
+
+  // Proto filename for the train net, possibly combined with one or more
+  // test nets.
+  optional string net = 24;
+  // Inline train net param, possibly combined with one or more test nets.
+  optional NetParameter net_param = 25;
+
+  optional string train_net = 1; // Proto filename for the train net.
+  repeated string test_net = 2; // Proto filenames for the test nets.
+  optional NetParameter train_net_param = 21; // Inline train net params.
+  repeated NetParameter test_net_param = 22; // Inline test net params.
+
+  // The states for the train/test nets. Must be unspecified or
+  // specified once per net.
+  //
+  // By default, all states will have solver = true;
+  // train_state will have phase = TRAIN,
+  // and all test_state's will have phase = TEST.
+  // Other defaults are set according to the NetState defaults.
+  optional NetState train_state = 26;
+  repeated NetState test_state = 27;
+
+  // The number of iterations for each test net.
   repeated int32 test_iter = 3;
+
   // The number of iterations between two testing phases.
   optional int32 test_interval = 4 [default = 0];
   optional bool test_compute_loss = 19 [default = false];
@@ -118,15 +149,51 @@ message SolverState {
   repeated BlobProto history = 3; // The history for sgd solvers
 }
 
+enum Phase {
+   TRAIN = 0;
+   TEST = 1;
+}
+
+message NetState {
+  optional Phase phase = 1 [default = TEST];
+  optional int32 level = 2 [default = 0];
+  repeated string stage = 3;
+}
+
+message NetStateRule {
+  // Set phase to require the NetState have a particular phase (TRAIN or TEST)
+  // to meet this rule.
+  optional Phase phase = 1;
+
+  // Set the minimum and/or maximum levels in which the layer should be used.
+  // Leave undefined to meet the rule regardless of level.
+  optional int32 min_level = 2;
+  optional int32 max_level = 3;
+
+  // A customizable set of stages.
+  // The net must have ALL of the specified stages to meet the rule.
+  // (Use multiple NetStateRules to specify conjunctions of stages.)
+  repeated string stage = 4;
+}
+
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 32 (last added: slice_param)
+// LayerParameter next available ID: 34 (last added: exclude)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
   optional string name = 4; // the layer name
 
+  // Rules controlling whether and when a layer is included in the network,
+  // based on the current NetState.  You may specify a non-zero number of rules
+  // to include OR exclude, but not both.  If no include or exclude rules are
+  // specified, the layer is always included.  If the current NetState meets
+  // ANY (i.e., one or more) of the specified rules, the layer is
+  // included/excluded.
+  repeated NetStateRule include = 32;
+  repeated NetStateRule exclude = 33;
+
   // NOTE
   // Add new LayerTypes to the enum below in lexicographical order (other than
   // starting with NONE), starting with the next available ID in the comment

From b8833565f8234903d33c880017c3768cd02e6fd9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 29 Jul 2014 11:22:52 -0700
Subject: [PATCH 0352/2053] Add unit tests and skeleton code for Net/Solver
 filtering functionality.

---
 include/caffe/net.hpp          |   8 +
 include/caffe/solver.hpp       |   5 +
 src/caffe/net.cpp              |  11 +
 src/caffe/test/test_net.cpp    | 820 +++++++++++++++++++++++++++++++++
 src/caffe/test/test_solver.cpp |  98 ++++
 5 files changed, 942 insertions(+)
 create mode 100644 src/caffe/test/test_solver.cpp

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 7548011d973..808b244e47d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -115,6 +115,14 @@ class Net {
 
   void set_debug_info(const bool value) { debug_info_ = value; }
 
+  // Helpers for Init.
+  // Remove layers that the user specified should be excluded given the current
+  // phase, level, and stage.
+  static void FilterNet(const NetParameter& param,
+      NetParameter* param_filtered);
+  static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
+      const string& layer_name);
+
  protected:
   // Helpers for Init.
   // Append a new input or top blob to the net.
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 3112c59e0fc..52dcd1936c5 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -6,6 +6,8 @@
 #include <string>
 #include <vector>
 
+#include "caffe/net.hpp"
+
 namespace caffe {
 
 template <typename Dtype>
@@ -20,6 +22,9 @@ class Solver {
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
   virtual ~Solver() {}
   inline shared_ptr<Net<Dtype> > net() { return net_; }
+  inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
+    return test_nets_;
+  }
 
  protected:
   // PreSolve is run before any solving iteration starts, allowing one to
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 228f826a8b9..a02b1118826 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -167,6 +167,17 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   debug_info_ = false;
 }
 
+template <typename Dtype>
+void Net<Dtype>::FilterNet(const NetParameter& param,
+    NetParameter* param_filtered) {
+}
+
+template <typename Dtype>
+bool Net<Dtype>::StateMeetsRule(const NetState& state,
+    const NetStateRule& rule, const string& layer_name) {
+  return true;
+}
+
 // Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
 // layer_id == -1, tops have layer_id >= 0.)
 template <typename Dtype>
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index e84701d941c..c4181345573 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -835,4 +835,824 @@ TYPED_TEST(NetTest, TestFromTo) {
   }
 }
 
+class FilterNetTest : public ::testing::Test {
+ protected:
+  void RunFilterNetTest(
+      const string& input_param_string, const string& filtered_param_string) {
+    NetParameter input_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        input_param_string, &input_param));
+    NetParameter expected_filtered_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        filtered_param_string, &expected_filtered_param));
+    NetParameter actual_filtered_param;
+    Net<float>::FilterNet(input_param, &actual_filtered_param);
+    EXPECT_EQ(expected_filtered_param.DebugString(),
+        actual_filtered_param.DebugString());
+    // Also test idempotence.
+    NetParameter double_filtered_param;
+    Net<float>::FilterNet(actual_filtered_param, &double_filtered_param);
+    EXPECT_EQ(actual_filtered_param.DebugString(),
+       double_filtered_param.DebugString());
+  }
+};
+
+TEST_F(FilterNetTest, TestNoFilter) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
+  const string& input_proto =
+      "name: 'LeNet' "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-train-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 64 "
+      "  } "
+      "  include: { phase: TRAIN } "
+      "} "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-test-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 100 "
+      "  } "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: CONVOLUTION "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  convolution_param { "
+      "    num_output: 20 "
+      "    kernel_size: 5 "
+      "    stride: 1 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'ip1' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'conv1' "
+      "  top: 'ip1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'accuracy' "
+      "  type: ACCURACY "
+      "  bottom: 'ip1' "
+      "  bottom: 'label' "
+      "  top: 'accuracy' "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'ip2' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "} ";
+  const string input_proto_train = "state: { phase: TRAIN } " + input_proto;
+  const string input_proto_test = "state: { phase: TEST } " + input_proto;
+  const string& output_proto_train =
+      "state: { phase: TRAIN } "
+      "name: 'LeNet' "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-train-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 64 "
+      "  } "
+      "  include: { phase: TRAIN } "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: CONVOLUTION "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  convolution_param { "
+      "    num_output: 20 "
+      "    kernel_size: 5 "
+      "    stride: 1 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'ip1' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'conv1' "
+      "  top: 'ip1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'ip2' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "} ";
+  const string& output_proto_test =
+      "state: { phase: TEST } "
+      "name: 'LeNet' "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-test-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 100 "
+      "  } "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: CONVOLUTION "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  convolution_param { "
+      "    num_output: 20 "
+      "    kernel_size: 5 "
+      "    stride: 1 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'ip1' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'conv1' "
+      "  top: 'ip1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'accuracy' "
+      "  type: ACCURACY "
+      "  bottom: 'ip1' "
+      "  bottom: 'label' "
+      "  top: 'accuracy' "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'ip2' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "} ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train);
+  this->RunFilterNetTest(input_proto_test, output_proto_test);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByStage) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByStage2) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByStage2) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  exclude: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' stage: 'myotherstage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  const string& output_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'myotherstage' } "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMultipleStage2) {
+  const string& input_proto =
+      "state: { stage: 'mystage' stage: 'myotherstage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' stage: 'myotherstage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { max_level: -3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMinLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 0 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
+  const string& input_proto =
+      "state: { level: 7 } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMaxLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { max_level: 0 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
+  const string& input_proto =
+      "state: { level: -7 } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { max_level: -3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 2  phase: TRAIN } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { min_level: 2  phase: TEST } "
+      "} ";
+  const string& input_proto_train =
+      "state: { level: 4  phase: TRAIN } " + input_proto;
+  const string& input_proto_test =
+      "state: { level: 4  phase: TEST } " + input_proto;
+  const string& output_proto_train =
+      "state: { level: 4  phase: TRAIN } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 2  phase: TRAIN } "
+      "} ";
+  const string& output_proto_test =
+      "state: { level: 4  phase: TEST } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { min_level: 2  phase: TEST } "
+      "} ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train);
+  this->RunFilterNetTest(input_proto_test, output_proto_test);
+}
+
+TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 2  phase: TRAIN } "
+      "  include: { phase: TEST } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { min_level: 2  phase: TEST } "
+      "  include: { phase: TRAIN } "
+      "} ";
+  const string& input_proto_train =
+      "state: { level: 2  phase: TRAIN } " + input_proto;
+  const string& input_proto_test =
+      "state: { level: 2  phase: TEST } " + input_proto;
+  this->RunFilterNetTest(input_proto_train, input_proto_train);
+  this->RunFilterNetTest(input_proto_test, input_proto_test);
+}
+
+TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  exclude: { min_level: 2  phase: TRAIN } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  exclude: { min_level: 2  phase: TEST } "
+      "} ";
+  const string& input_proto_train =
+      "state: { level: 4  phase: TRAIN } " + input_proto;
+  const string& input_proto_test =
+      "state: { level: 4  phase: TEST } " + input_proto;
+  const string& output_proto_train =
+      "state: { level: 4  phase: TRAIN } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  exclude: { min_level: 2  phase: TEST } "
+      "} ";
+  const string& output_proto_test =
+      "state: { level: 4  phase: TEST } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  exclude: { min_level: 2  phase: TRAIN } "
+      "} ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train);
+  this->RunFilterNetTest(input_proto_test, output_proto_test);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
new file mode 100644
index 00000000000..7380ab86308
--- /dev/null
+++ b/src/caffe/test/test_solver.cpp
@@ -0,0 +1,98 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
+
+#include "gtest/gtest.h"
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::ostringstream;
+
+namespace caffe {
+
+template <typename TypeParam>
+class SolverTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolverFromProtoString(const string& proto) {
+    SolverParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    solver_.reset(new SGDSolver<Dtype>(param));
+  }
+
+  shared_ptr<Solver<Dtype> > solver_;
+};
+
+TYPED_TEST_CASE(SolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(SolverTest, TestInitTrainTestNets) {
+  const string& proto =
+     "test_interval: 10 "
+     "test_iter: 10 "
+     "test_state: { stage: 'with-softmax' }"
+     "test_iter: 10 "
+     "test_state: {}"
+     "net_param { "
+     "  name: 'TestNetwork' "
+     "  layers: { "
+     "    name: 'data' "
+     "    type: DUMMY_DATA "
+     "    dummy_data_param { "
+     "      num: 5 "
+     "      channels: 3 "
+     "      height: 10 "
+     "      width: 10 "
+     "      num: 5 "
+     "      channels: 1 "
+     "      height: 1 "
+     "      width: 1 "
+     "    } "
+     "    top: 'data' "
+     "    top: 'label' "
+     "  } "
+     "  layers: { "
+     "    name: 'innerprod' "
+     "    type: INNER_PRODUCT "
+     "    inner_product_param { "
+     "      num_output: 10 "
+     "    } "
+     "    bottom: 'data' "
+     "    top: 'innerprod' "
+     "  } "
+     "  layers: { "
+     "    name: 'accuracy' "
+     "    type: ACCURACY "
+     "    bottom: 'innerprod' "
+     "    bottom: 'label' "
+     "    top: 'accuracy' "
+     "    exclude: { phase: TRAIN } "
+     "  } "
+     "  layers: { "
+     "    name: 'loss' "
+     "    type: SOFTMAX_LOSS "
+     "    bottom: 'innerprod' "
+     "    bottom: 'label' "
+     "    include: { phase: TRAIN } "
+     "    include: { phase: TEST stage: 'with-softmax' } "
+     "  } "
+     "} ";
+  this->InitSolverFromProtoString(proto);
+  ASSERT_TRUE(this->solver_->net());
+  EXPECT_TRUE(this->solver_->net()->has_layer("loss"));
+  EXPECT_FALSE(this->solver_->net()->has_layer("accuracy"));
+  ASSERT_EQ(2, this->solver_->test_nets().size());
+  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("loss"));
+  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("accuracy"));
+  EXPECT_FALSE(this->solver_->test_nets()[1]->has_layer("loss"));
+  EXPECT_TRUE(this->solver_->test_nets()[1]->has_layer("accuracy"));
+}
+
+}  // namespace caffe

From cb4555c052a6ac2ba65a7d71681d9320a6afefb6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 17 Jul 2014 20:38:53 -0700
Subject: [PATCH 0353/2053] Incorporate NetState{,Rule} into Solver/Net. 
 Net::FilterNet includes/excludes layers based on whether the NetState meets
 each layer's NetStateRule(s).

---
 include/caffe/solver.hpp |   2 +
 src/caffe/net.cpp        |  78 ++++++++++++++++++++++-
 src/caffe/solver.cpp     | 133 +++++++++++++++++++++++++++++++--------
 3 files changed, 185 insertions(+), 28 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 52dcd1936c5..811d70372a4 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -16,6 +16,8 @@ class Solver {
   explicit Solver(const SolverParameter& param);
   explicit Solver(const string& param_file);
   void Init(const SolverParameter& param);
+  void InitTrainNet();
+  void InitTestNets();
   // The main entry of the solver function. In default, iter will be zero. Pass
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a02b1118826..a4d1f235a5a 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -32,11 +32,15 @@ Net<Dtype>::Net(const string& param_file) {
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  // Filter layers based on their include/exclude rules and
+  // the current NetState.
+  NetParameter filtered_param;
+  FilterNet(in_param, &filtered_param);
   LOG(INFO) << "Initializing net from parameters: " << std::endl
-            << in_param.DebugString();
-  // Create a copy of in_param with splits added where necessary.
+            << filtered_param.DebugString();
+  // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
-  InsertSplits(in_param, &param);
+  InsertSplits(filtered_param, &param);
   // Basically, build all the layers and set up its connections.
   name_ = param.name();
   map<string, int> blob_name_to_idx;
@@ -170,11 +174,79 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
 template <typename Dtype>
 void Net<Dtype>::FilterNet(const NetParameter& param,
     NetParameter* param_filtered) {
+  const NetState& net_state = param.state();
+  param_filtered->CopyFrom(param);
+  param_filtered->clear_layers();
+  for (int i = 0; i < param.layers_size(); ++i) {
+    const LayerParameter& layer_param = param.layers(i);
+    const string& layer_name = layer_param.name();
+    CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)
+          << "Specify either include rules or exclude rules; not both.";
+    // If no include rules are specified, the layer is included by default and
+    // only excluded if it meets one of the exclude rules.
+    bool layer_included = (layer_param.include_size() == 0);
+    for (int j = 0; layer_included && j < layer_param.exclude_size(); ++j) {
+      if (StateMeetsRule(net_state, layer_param.exclude(j), layer_name)) {
+        layer_included = false;
+      }
+    }
+    for (int j = 0; !layer_included && j < layer_param.include_size(); ++j) {
+      if (StateMeetsRule(net_state, layer_param.include(j), layer_name)) {
+        layer_included = true;
+      }
+    }
+    if (layer_included) {
+      param_filtered->add_layers()->CopyFrom(layer_param);
+    }
+  }
 }
 
 template <typename Dtype>
 bool Net<Dtype>::StateMeetsRule(const NetState& state,
     const NetStateRule& rule, const string& layer_name) {
+  // Check whether the rule is broken due to phase.
+  if (rule.has_phase()) {
+      if (rule.phase() != state.phase()) {
+        LOG(INFO) << "The NetState phase (" << state.phase()
+          << ") differed from the phase (" << rule.phase()
+          << ") specified by a rule in layer " << layer_name;
+        return false;
+      }
+  }
+  // Check whether the rule is broken due to min level.
+  if (rule.has_min_level()) {
+    if (state.level() < rule.min_level()) {
+      LOG(INFO) << "The NetState level (" << state.level()
+          << ") is above the min_level (" << rule.min_level()
+          << " specified by a rule in layer " << layer_name;
+      return false;
+    }
+  }
+  // Check whether the rule is broken due to max level.
+  if (rule.has_max_level()) {
+    if (state.level() > rule.max_level()) {
+      LOG(INFO) << "The NetState level (" << state.level()
+          << ") is above the max_level (" << rule.max_level()
+          << " specified by a rule in layer " << layer_name;
+      return false;
+    }
+  }
+  // Check whether the rule is broken due to stage. If stage is specified,
+  // the NetState must contain ALL of the rule's stages to meet it.
+  if (rule.stage_size()) {
+    for (int i = 0; i < rule.stage_size(); ++i) {
+      // Check that the NetState contains the rule's ith stage.
+      bool has_stage = false;
+      for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
+        if (rule.stage(i) == state.stage(j)) { has_stage = true; }
+      }
+      if (!has_stage) {
+        LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
+                  << "' specified by a rule in layer " << layer_name;
+        return false;
+      }
+    }
+  }
   return true;
 }
 
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 91738817875..11795f058cd 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -11,6 +11,7 @@
 #include "caffe/solver.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/util/upgrade_proto.hpp"
 
 namespace caffe {
 
@@ -42,40 +43,122 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
     Caffe::set_random_seed(param_.random_seed());
   }
   // Scaffolding code
+  InitTrainNet();
+  InitTestNets();
+  LOG(INFO) << "Solver scaffolding done.";
+}
+
+template <typename Dtype>
+void Solver<Dtype>::InitTrainNet() {
+  const int num_train_nets = param_.has_net() + param_.has_net_param() +
+      param_.has_train_net() + param_.has_train_net_param();
+  const string& field_names = "net, net_param, train_net, train_net_param";
+  CHECK_GE(num_train_nets, 1) << "SolverParameter must specify a train net "
+      << "using one of these fields: " << field_names;
+  CHECK_LE(num_train_nets, 1) << "SolverParameter must not contain more than "
+      << "one of these fields specifying a train_net: " << field_names;
+  NetParameter net_param;
   if (param_.has_train_net_param()) {
-    CHECK(!param_.has_train_net()) << "Either train_net_param or train_net may "
-                                   << "be specified, but not both.";
-    LOG(INFO) << "Creating training net specified in SolverParameter.";
-    net_.reset(new Net<Dtype>(param_.train_net_param()));
-  } else {
-    CHECK(param_.has_train_net())
-        << "Neither train_net nor train_net_param were specified.";
-    LOG(INFO) << "Creating training net from file: " << param_.train_net();
-    net_.reset(new Net<Dtype>(param_.train_net()));
+    LOG(INFO) << "Creating training net specified in train_net_param.";
+    net_param.CopyFrom(param_.train_net_param());
+  } else if (param_.has_train_net()) {
+    LOG(INFO) << "Creating training net from train_net file: "
+              << param_.train_net();
+    ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);
   }
-  CHECK(net_) << "Training net uninitialized.";
-  net_->set_debug_info(param_.debug_info());
+  if (param_.has_net_param()) {
+    LOG(INFO) << "Creating training net specified in net_param.";
+    net_param.CopyFrom(param_.net_param());
+  }
+  if (param_.has_net()) {
+    LOG(INFO) << "Creating training net from net file: " << param_.net();
+    ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);
+  }
+  // Set the correct NetState.  We start with the solver defaults (lowest
+  // precedence); then, merge in any NetState specified by the net_param itself;
+  // finally, merge in any NetState specified by the train_state (highest
+  // precedence).
+  NetState net_state;
+  net_state.set_phase(TRAIN);
+  net_state.MergeFrom(net_param.state());
+  net_state.MergeFrom(param_.train_state());
+  net_param.mutable_state()->CopyFrom(net_state);
+  net_.reset(new Net<Dtype>(net_param));
+}
+
+template <typename Dtype>
+void Solver<Dtype>::InitTestNets() {
+  const bool has_net_param = param_.has_net_param();
+  const bool has_net_file = param_.has_net();
+  const int num_generic_nets = has_net_param + has_net_file;
+  CHECK_LE(num_generic_nets, 1)
+      << "Both net_param and net_file may not be specified.";
   const int num_test_net_params = param_.test_net_param_size();
   const int num_test_net_files = param_.test_net_size();
   const int num_test_nets = num_test_net_params + num_test_net_files;
-  if (num_test_nets) {
-    CHECK_EQ(param_.test_iter_size(), num_test_nets)
-        << "test_iter must be specified for each test network.";
+  if (num_generic_nets) {
+      CHECK_GE(param_.test_iter_size(), num_test_nets)
+          << "test_iter must be specified for each test network.";
+  } else {
+      CHECK_EQ(param_.test_iter_size(), num_test_nets)
+          << "test_iter must be specified for each test network.";
+  }
+  // If we have a generic net (specified by net or net_param, rather than
+  // test_net or test_net_param), we may have an unlimited number of actual
+  // test networks -- the actual number is given by the number of remaining
+  // test_iters after any test nets specified by test_net_param and/or test_net
+  // are evaluated.
+  const int num_generic_net_instances = param_.test_iter_size() - num_test_nets;
+  const int num_test_net_instances = num_test_nets + num_generic_net_instances;
+  if (param_.test_state_size()) {
+    CHECK_EQ(param_.test_state_size(), num_test_net_instances)
+        << "test_state must be unspecified or specified once per test net.";
+  }
+  if (num_test_net_instances) {
     CHECK_GT(param_.test_interval(), 0);
   }
-  test_nets_.resize(num_test_nets);
-  for (int i = 0; i < num_test_net_params; ++i) {
-      LOG(INFO) << "Creating testing net (#" << i
-                << ") specified in SolverParameter.";
-      test_nets_[i].reset(new Net<Dtype>(param_.test_net_param(i)));
+  int test_net_id = 0;
+  vector<string> sources(num_test_net_instances);
+  vector<NetParameter> net_params(num_test_net_instances);
+  for (int i = 0; i < num_test_net_params; ++i, ++test_net_id) {
+      sources[test_net_id] = "test_net_param";
+      net_params[test_net_id].CopyFrom(param_.test_net_param(i));
   }
-  for (int i = 0, test_net_id = num_test_net_params;
-       i < num_test_net_files; ++i, ++test_net_id) {
-      LOG(INFO) << "Creating testing net (#" << test_net_id
-                << ") from file: " << param.test_net(i);
-      test_nets_[test_net_id].reset(new Net<Dtype>(param_.test_net(i)));
+  for (int i = 0; i < num_test_net_files; ++i, ++test_net_id) {
+      sources[test_net_id] = "test_net file: " + param_.test_net(i);
+      ReadNetParamsFromTextFileOrDie(param_.test_net(i),
+          &net_params[test_net_id]);
+  }
+  const int remaining_test_nets = param_.test_iter_size() - test_net_id;
+  if (has_net_param) {
+    for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {
+      sources[test_net_id] = "net_param";
+      net_params[test_net_id].CopyFrom(param_.net_param());
+    }
+  }
+  if (has_net_file) {
+    for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {
+      sources[test_net_id] = "net file: " + param_.net();
+      ReadNetParamsFromTextFileOrDie(param_.net(), &net_params[test_net_id]);
+    }
+  }
+  test_nets_.resize(num_test_net_instances);
+  for (int i = 0; i < num_test_net_instances; ++i) {
+    // Set the correct NetState.  We start with the solver defaults (lowest
+    // precedence); then, merge in any NetState specified by the net_param
+    // itself; finally, merge in any NetState specified by the test_state
+    // (highest precedence).
+    NetState net_state;
+    net_state.set_phase(TEST);
+    net_state.MergeFrom(net_params[i].state());
+    if (param_.test_state_size()) {
+      net_state.MergeFrom(param_.test_state(i));
+    }
+    net_params[i].mutable_state()->CopyFrom(net_state);
+    LOG(INFO)
+        << "Creating testing net (#" << i << ") specified by " << sources[i];
+    test_nets_[i].reset(new Net<Dtype>(net_params[i]));
   }
-  LOG(INFO) << "Solver scaffolding done.";
 }
 
 template <typename Dtype>

From e526e2ddd71c0881b71f31bf522806f85f687648 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 18 Jul 2014 15:07:13 -0700
Subject: [PATCH 0354/2053] Use unified train/test nets in examples.

---
 examples/cifar10/cifar10_full_solver.prototxt |   6 +-
 .../cifar10/cifar10_full_solver_lr1.prototxt  |   6 +-
 .../cifar10/cifar10_full_solver_lr2.prototxt  |   6 +-
 examples/cifar10/cifar10_full_train.prototxt  | 174 ------------
 ...totxt => cifar10_full_train_test.prototxt} |  16 +-
 .../cifar10/cifar10_quick_solver.prototxt     |   6 +-
 .../cifar10/cifar10_quick_solver_lr1.prototxt |   6 +-
 examples/cifar10/cifar10_quick_train.prototxt | 168 ------------
 ...otxt => cifar10_quick_train_test.prototxt} |  16 +-
 examples/imagenet/alexnet_solver.prototxt     |   3 +-
 ...in.prototxt => alexnet_train_val.prototxt} |  23 ++
 examples/imagenet/alexnet_val.prototxt        | 228 ---------------
 examples/imagenet/imagenet_solver.prototxt    |   3 +-
 ...n.prototxt => imagenet_train_val.prototxt} |  23 ++
 examples/imagenet/imagenet_val.prototxt       | 228 ---------------
 examples/imagenet/readme.md                   |   7 +-
 .../mnist/lenet_consolidated_solver.prototxt  | 259 ++----------------
 examples/mnist/lenet_solver.prototxt          |   6 +-
 examples/mnist/lenet_test.prototxt            | 118 --------
 ...ain.prototxt => lenet_train_test.prototxt} |  23 ++
 ...in.prototxt => mnist_autoencoder.prototxt} |  28 ++
 .../mnist/mnist_autoencoder_solver.prototxt   |   3 +-
 .../mnist/mnist_autoencoder_test.prototxt     | 146 ----------
 examples/mnist/readme.md                      |   6 +-
 24 files changed, 177 insertions(+), 1331 deletions(-)
 delete mode 100644 examples/cifar10/cifar10_full_train.prototxt
 rename examples/cifar10/{cifar10_full_test.prototxt => cifar10_full_train_test.prototxt} (89%)
 delete mode 100644 examples/cifar10/cifar10_quick_train.prototxt
 rename examples/cifar10/{cifar10_quick_test.prototxt => cifar10_quick_train_test.prototxt} (89%)
 rename examples/imagenet/{alexnet_train.prototxt => alexnet_train_val.prototxt} (91%)
 delete mode 100644 examples/imagenet/alexnet_val.prototxt
 rename examples/imagenet/{imagenet_train.prototxt => imagenet_train_val.prototxt} (91%)
 delete mode 100644 examples/imagenet/imagenet_val.prototxt
 delete mode 100644 examples/mnist/lenet_test.prototxt
 rename examples/mnist/{lenet_train.prototxt => lenet_train_test.prototxt} (82%)
 rename examples/mnist/{mnist_autoencoder_train.prototxt => mnist_autoencoder.prototxt} (88%)
 delete mode 100644 examples/mnist/mnist_autoencoder_test.prototxt

diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt
index 0a0b456308d..49de3f58803 100644
--- a/examples/cifar10/cifar10_full_solver.prototxt
+++ b/examples/cifar10/cifar10_full_solver.prototxt
@@ -1,10 +1,8 @@
 # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
 # then another factor of 10 after 10 more epochs (5000 iters)
 
-# The training protocol buffer definition
-train_net: "cifar10_full_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_full_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_full_solver_lr1.prototxt b/examples/cifar10/cifar10_full_solver_lr1.prototxt
index 4376de5493f..746f4fba15a 100644
--- a/examples/cifar10/cifar10_full_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr1.prototxt
@@ -1,10 +1,8 @@
 # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
 # then another factor of 10 after 10 more epochs (5000 iters)
 
-# The training protocol buffer definition
-train_net: "cifar10_full_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_full_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_full_solver_lr2.prototxt b/examples/cifar10/cifar10_full_solver_lr2.prototxt
index 19580c5184a..5a549ffc96d 100644
--- a/examples/cifar10/cifar10_full_solver_lr2.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr2.prototxt
@@ -1,10 +1,8 @@
 # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
 # then another factor of 10 after 10 more epochs (5000 iters)
 
-# The training protocol buffer definition
-train_net: "cifar10_full_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_full_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_full_train.prototxt b/examples/cifar10/cifar10_full_train.prototxt
deleted file mode 100644
index 25c76060991..00000000000
--- a/examples/cifar10/cifar10_full_train.prototxt
+++ /dev/null
@@ -1,174 +0,0 @@
-name: "CIFAR10_full_train"
-layers {
-  name: "cifar"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "cifar10-leveldb/cifar-train-leveldb"
-    mean_file: "mean.binaryproto"
-    batch_size: 100
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.0001
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "pool1"
-  top: "pool1"
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    norm_region: WITHIN_CHANNEL
-    local_size: 3
-    alpha: 5e-05
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    norm_region: WITHIN_CHANNEL
-    local_size: 3
-    alpha: 5e-05
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "pool3"
-  type: POOLING
-  bottom: "conv3"
-  top: "pool3"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "ip1"
-  type: INNER_PRODUCT
-  bottom: "pool3"
-  top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
-  inner_product_param {
-    num_output: 10
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "ip1"
-  bottom: "label"
-}
diff --git a/examples/cifar10/cifar10_full_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
similarity index 89%
rename from examples/cifar10/cifar10_full_test.prototxt
rename to examples/cifar10/cifar10_full_train_test.prototxt
index 1f77b4f0348..4fd52fec133 100644
--- a/examples/cifar10/cifar10_full_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -1,4 +1,16 @@
-name: "CIFAR10_full_test"
+name: "CIFAR10_full"
+layers {
+  name: "cifar"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "cifar10-leveldb/cifar-train-leveldb"
+    mean_file: "mean.binaryproto"
+    batch_size: 100
+  }
+  include: { phase: TRAIN }
+}
 layers {
   name: "cifar"
   type: DATA
@@ -9,6 +21,7 @@ layers {
     mean_file: "mean.binaryproto"
     batch_size: 100
   }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -172,6 +185,7 @@ layers {
   bottom: "ip1"
   bottom: "label"
   top: "accuracy"
+  include: { phase: TEST }
 }
 layers {
   name: "loss"
diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index 4b547cc96f4..cdd0722b3a0 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -1,9 +1,7 @@
 # reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
 
-# The training protocol buffer definition
-train_net: "cifar10_quick_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_quick_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_quick_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_quick_solver_lr1.prototxt b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
index d4ba3d525d9..2ed54ad980f 100644
--- a/examples/cifar10/cifar10_quick_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
@@ -1,9 +1,7 @@
 # reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
 
-# The training protocol buffer definition
-train_net: "cifar10_quick_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_quick_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_quick_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_quick_train.prototxt b/examples/cifar10/cifar10_quick_train.prototxt
deleted file mode 100644
index de5b6c32c5d..00000000000
--- a/examples/cifar10/cifar10_quick_train.prototxt
+++ /dev/null
@@ -1,168 +0,0 @@
-name: "CIFAR10_quick_train"
-layers {
-  name: "cifar"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "cifar10-leveldb/cifar-train-leveldb"
-    mean_file: "mean.binaryproto"
-    batch_size: 100
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.0001
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "pool1"
-  top: "pool1"
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "pool1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "pool2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "pool3"
-  type: POOLING
-  bottom: "conv3"
-  top: "pool3"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "ip1"
-  type: INNER_PRODUCT
-  bottom: "pool3"
-  top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  inner_product_param {
-    num_output: 64
-    weight_filler {
-      type: "gaussian"
-      std: 0.1
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "ip2"
-  type: INNER_PRODUCT
-  bottom: "ip1"
-  top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
-  inner_product_param {
-    num_output: 10
-    weight_filler {
-      type: "gaussian"
-      std: 0.1
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "ip2"
-  bottom: "label"
-}
diff --git a/examples/cifar10/cifar10_quick_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
similarity index 89%
rename from examples/cifar10/cifar10_quick_test.prototxt
rename to examples/cifar10/cifar10_quick_train_test.prototxt
index aa82c32aa24..b34d1cd2fcb 100644
--- a/examples/cifar10/cifar10_quick_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -1,4 +1,16 @@
-name: "CIFAR10_quick_test"
+name: "CIFAR10_quick"
+layers {
+  name: "cifar"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "cifar10-leveldb/cifar-train-leveldb"
+    mean_file: "mean.binaryproto"
+    batch_size: 100
+  }
+  include: { phase: TRAIN }
+}
 layers {
   name: "cifar"
   type: DATA
@@ -9,6 +21,7 @@ layers {
     mean_file: "mean.binaryproto"
     batch_size: 100
   }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -166,6 +179,7 @@ layers {
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
+  include: { phase: TEST }
 }
 layers {
   name: "loss"
diff --git a/examples/imagenet/alexnet_solver.prototxt b/examples/imagenet/alexnet_solver.prototxt
index 75d0d5dffa7..8581e99c6a7 100644
--- a/examples/imagenet/alexnet_solver.prototxt
+++ b/examples/imagenet/alexnet_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "alexnet_train.prototxt"
-test_net: "alexnet_val.prototxt"
+net: "alexnet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
diff --git a/examples/imagenet/alexnet_train.prototxt b/examples/imagenet/alexnet_train_val.prototxt
similarity index 91%
rename from examples/imagenet/alexnet_train.prototxt
rename to examples/imagenet/alexnet_train_val.prototxt
index 32a96cfd4d9..f65f3e7f87e 100644
--- a/examples/imagenet/alexnet_train.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -2,6 +2,8 @@ name: "AlexNet"
 layers {
   name: "data"
   type: DATA
+  top: "data"
+  top: "label"
   data_param {
     source: "ilsvrc12_train_leveldb"
     mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
@@ -9,8 +11,21 @@ layers {
     crop_size: 227
     mirror: true
   }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: DATA
   top: "data"
   top: "label"
+  data_param {
+    source: "ilsvrc12_val_leveldb"
+    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    batch_size: 50
+    crop_size: 227
+    mirror: false
+  }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -308,6 +323,14 @@ layers {
   bottom: "fc7"
   top: "fc8"
 }
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
 layers {
   name: "loss"
   type: SOFTMAX_LOSS
diff --git a/examples/imagenet/alexnet_val.prototxt b/examples/imagenet/alexnet_val.prototxt
deleted file mode 100644
index 1d8d86b78ff..00000000000
--- a/examples/imagenet/alexnet_val.prototxt
+++ /dev/null
@@ -1,228 +0,0 @@
-name: "AlexNet"
-layers {
-  name: "data"
-  type: DATA
-  data_param {
-    source: "ilsvrc12_val_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 50
-    crop_size: 227
-    mirror: false
-  }
-  top: "data"
-  top: "label"
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-  bottom: "data"
-  top: "conv1"
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "norm1"
-  type: LRN
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-  bottom: "conv1"
-  top: "norm1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "norm1"
-  top: "pool1"
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-  bottom: "pool1"
-  top: "conv2"
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "norm2"
-  type: LRN
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-  bottom: "conv2"
-  top: "norm2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "norm2"
-  top: "pool2"
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-  bottom: "pool2"
-  top: "conv3"
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-  bottom: "conv3"
-  top: "conv4"
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-  bottom: "conv4"
-  top: "conv5"
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-  bottom: "conv5"
-  top: "pool5"
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 4096
-  }
-  bottom: "pool5"
-  top: "fc6"
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 4096
-  }
-  bottom: "fc6"
-  top: "fc7"
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 1000
-  }
-  bottom: "fc7"
-  top: "fc8"
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "fc8"
-  bottom: "label"
-  top: "accuracy"
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
diff --git a/examples/imagenet/imagenet_solver.prototxt b/examples/imagenet/imagenet_solver.prototxt
index e543ba66cad..aedec4104a6 100644
--- a/examples/imagenet/imagenet_solver.prototxt
+++ b/examples/imagenet/imagenet_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "imagenet_train.prototxt"
-test_net: "imagenet_val.prototxt"
+net: "imagenet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
diff --git a/examples/imagenet/imagenet_train.prototxt b/examples/imagenet/imagenet_train_val.prototxt
similarity index 91%
rename from examples/imagenet/imagenet_train.prototxt
rename to examples/imagenet/imagenet_train_val.prototxt
index 519d4509be9..af28c1495e5 100644
--- a/examples/imagenet/imagenet_train.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -11,6 +11,21 @@ layers {
     crop_size: 227
     mirror: true
   }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "ilsvrc12_val_leveldb"
+    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    batch_size: 50
+    crop_size: 227
+    mirror: false
+  }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -308,6 +323,14 @@ layers {
     }
   }
 }
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
 layers {
   name: "loss"
   type: SOFTMAX_LOSS
diff --git a/examples/imagenet/imagenet_val.prototxt b/examples/imagenet/imagenet_val.prototxt
deleted file mode 100644
index 8be5150cdd2..00000000000
--- a/examples/imagenet/imagenet_val.prototxt
+++ /dev/null
@@ -1,228 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "ilsvrc12_val_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 50
-    crop_size: 227
-    mirror: false
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8"
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "fc8"
-  bottom: "label"
-  top: "accuracy"
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}
\ No newline at end of file
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index e74e6b86996..0b5b8452188 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -72,12 +72,11 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 
-Sound good? This is implemented in `examples/imagenet/imagenet_solver.prototxt`. Again, you will need to change the first two lines:
+Sound good? This is implemented in `examples/imagenet/imagenet_solver.prototxt`. Again, you will need to change the first line:
 
-    train_net: "imagenet_train.prototxt"
-    test_net: "imagenet_val.prototxt"
+    net: "imagenet_train_val.prototxt"
 
-to point to the actual path if you have changed them.
+to point to the actual path if you have changed it.
 
 Training ImageNet
 -----------------
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index ef851e0f656..980f9382066 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -30,8 +30,19 @@ random_seed: 1701
 # solver mode: CPU or GPU
 solver_mode: GPU
 
-# The training protocol buffer definition
-train_net_param {
+# We test on both the test and train set using "stages".  The TEST DATA layers
+# each have a stage, either 'test-on-train-set' or 'test-on-test-set'.
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+test_state: { stage: "test-on-test-set" }
+# The train set has 60K images, so we run 600 test iters (600 * 100 = 60K).
+test_iter: 600
+test_state: { stage: "test-on-train-set" }
+
+# The net protocol buffer definition
+net_param {
   name: "LeNet"
   layers {
     name: "mnist"
@@ -43,122 +54,8 @@ train_net_param {
       scale: 0.00390625
       batch_size: 64
     }
+    include: { phase: TRAIN }
   }
-  layers {
-    name: "conv1"
-    type: CONVOLUTION
-    bottom: "data"
-    top: "conv1"
-    blobs_lr: 1
-    blobs_lr: 2
-    convolution_param {
-      num_output: 20
-      kernel_size: 5
-      stride: 1
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "pool1"
-    type: POOLING
-    bottom: "conv1"
-    top: "pool1"
-    pooling_param {
-      pool: MAX
-      kernel_size: 2
-      stride: 2
-    }
-  }
-  layers {
-    name: "conv2"
-    type: CONVOLUTION
-    bottom: "pool1"
-    top: "conv2"
-    blobs_lr: 1
-    blobs_lr: 2
-    convolution_param {
-      num_output: 50
-      kernel_size: 5
-      stride: 1
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "pool2"
-    type: POOLING
-    bottom: "conv2"
-    top: "pool2"
-    pooling_param {
-      pool: MAX
-      kernel_size: 2
-      stride: 2
-    }
-  }
-  layers {
-    name: "ip1"
-    type: INNER_PRODUCT
-    bottom: "pool2"
-    top: "ip1"
-    blobs_lr: 1
-    blobs_lr: 2
-    inner_product_param {
-      num_output: 500
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "relu1"
-    type: RELU
-    bottom: "ip1"
-    top: "ip1"
-  }
-  layers {
-    name: "ip2"
-    type: INNER_PRODUCT
-    bottom: "ip1"
-    top: "ip2"
-    blobs_lr: 1
-    blobs_lr: 2
-    inner_product_param {
-      num_output: 10
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "loss"
-    type: SOFTMAX_LOSS
-    bottom: "ip2"
-    bottom: "label"
-  }
-}
-
-# test_iter specifies how many forward passes the test should carry out.
-# In the case of MNIST, we have test batch size 100 and 100 test iterations,
-# covering the full 10,000 testing images.
-test_iter: 100
-# The testing protocol buffer definition
-test_net_param {
-  name: "LeNet-test"
   layers {
     name: "mnist"
     type: DATA
@@ -169,120 +66,11 @@ test_net_param {
       scale: 0.00390625
       batch_size: 100
     }
-  }
-  layers {
-    name: "conv1"
-    type: CONVOLUTION
-    bottom: "data"
-    top: "conv1"
-    convolution_param {
-      num_output: 20
-      kernel_size: 5
-      stride: 1
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "pool1"
-    type: POOLING
-    bottom: "conv1"
-    top: "pool1"
-    pooling_param {
-      pool: MAX
-      kernel_size: 2
-      stride: 2
-    }
-  }
-  layers {
-    name: "conv2"
-    type: CONVOLUTION
-    bottom: "pool1"
-    top: "conv2"
-    convolution_param {
-      num_output: 50
-      kernel_size: 5
-      stride: 1
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "pool2"
-    type: POOLING
-    bottom: "conv2"
-    top: "pool2"
-    pooling_param {
-      pool: MAX
-      kernel_size: 2
-      stride: 2
-    }
-  }
-  layers {
-    name: "ip1"
-    type: INNER_PRODUCT
-    bottom: "pool2"
-    top: "ip1"
-    inner_product_param {
-      num_output: 500
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
+    include: {
+      phase: TEST
+      stage: "test-on-test-set"
     }
   }
-  layers {
-    name: "relu1"
-    type: RELU
-    bottom: "ip1"
-    top: "ip1"
-  }
-  layers {
-    name: "ip2"
-    type: INNER_PRODUCT
-    bottom: "ip1"
-    top: "ip2"
-    inner_product_param {
-      num_output: 10
-      weight_filler {
-        type: "xavier"
-      }
-      bias_filler {
-        type: "constant"
-      }
-    }
-  }
-  layers {
-    name: "accuracy"
-    type: ACCURACY
-    bottom: "ip2"
-    bottom: "label"
-    top: "accuracy"
-  }
-  layers {
-    name: "loss"
-    type: SOFTMAX_LOSS
-    bottom: "ip2"
-    bottom: "label"
-    top: "loss"
-  }
-}
-
-# The train set has 60K images, so we run 600 test iters (600 * 100 = 60K).
-test_iter: 600
-# The protocol buffer definition to test on the train set
-test_net_param {
-  name: "LeNet-test-on-train"
   layers {
     name: "mnist"
     type: DATA
@@ -293,12 +81,18 @@ test_net_param {
       scale: 0.00390625
       batch_size: 100
     }
+    include: {
+      phase: TEST
+      stage: "test-on-train-set"
+    }
   }
   layers {
     name: "conv1"
     type: CONVOLUTION
     bottom: "data"
     top: "conv1"
+    blobs_lr: 1
+    blobs_lr: 2
     convolution_param {
       num_output: 20
       kernel_size: 5
@@ -327,6 +121,8 @@ test_net_param {
     type: CONVOLUTION
     bottom: "pool1"
     top: "conv2"
+    blobs_lr: 1
+    blobs_lr: 2
     convolution_param {
       num_output: 50
       kernel_size: 5
@@ -355,6 +151,8 @@ test_net_param {
     type: INNER_PRODUCT
     bottom: "pool2"
     top: "ip1"
+    blobs_lr: 1
+    blobs_lr: 2
     inner_product_param {
       num_output: 500
       weight_filler {
@@ -376,6 +174,8 @@ test_net_param {
     type: INNER_PRODUCT
     bottom: "ip1"
     top: "ip2"
+    blobs_lr: 1
+    blobs_lr: 2
     inner_product_param {
       num_output: 10
       weight_filler {
@@ -392,6 +192,7 @@ test_net_param {
     bottom: "ip2"
     bottom: "label"
     top: "accuracy"
+    include: { phase: TEST }
   }
   layers {
     name: "loss"
diff --git a/examples/mnist/lenet_solver.prototxt b/examples/mnist/lenet_solver.prototxt
index 7947f2d6a73..a3b33090472 100644
--- a/examples/mnist/lenet_solver.prototxt
+++ b/examples/mnist/lenet_solver.prototxt
@@ -1,7 +1,5 @@
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
+# The train/test net protocol buffer definition
+net: "lenet_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/mnist/lenet_test.prototxt b/examples/mnist/lenet_test.prototxt
deleted file mode 100644
index 2497f02ae86..00000000000
--- a/examples/mnist/lenet_test.prototxt
+++ /dev/null
@@ -1,118 +0,0 @@
-name: "LeNet-test"
-layers {
-  name: "mnist"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "mnist-test-leveldb"
-    scale: 0.00390625
-    batch_size: 100
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 20
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "pool1"
-  top: "conv2"
-  convolution_param {
-    num_output: 50
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-  }
-}
-layers {
-  name: "ip1"
-  type: INNER_PRODUCT
-  bottom: "pool2"
-  top: "ip1"
-  inner_product_param {
-    num_output: 500
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "ip1"
-  top: "ip1"
-}
-layers {
-  name: "ip2"
-  type: INNER_PRODUCT
-  bottom: "ip1"
-  top: "ip2"
-  inner_product_param {
-    num_output: 10
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "ip2"
-  bottom: "label"
-  top: "accuracy"
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "ip2"
-  bottom: "label"
-  top: "loss"
-}
diff --git a/examples/mnist/lenet_train.prototxt b/examples/mnist/lenet_train_test.prototxt
similarity index 82%
rename from examples/mnist/lenet_train.prototxt
rename to examples/mnist/lenet_train_test.prototxt
index e8a1e74e40b..3c77452130c 100644
--- a/examples/mnist/lenet_train.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -9,7 +9,21 @@ layers {
     scale: 0.00390625
     batch_size: 64
   }
+  include: { phase: TRAIN }
 }
+layers {
+  name: "mnist"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "mnist-test-leveldb"
+    scale: 0.00390625
+    batch_size: 100
+  }
+  include: { phase: TEST }
+}
+
 layers {
   name: "conv1"
   type: CONVOLUTION
@@ -110,9 +124,18 @@ layers {
     }
   }
 }
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
 layers {
   name: "loss"
   type: SOFTMAX_LOSS
   bottom: "ip2"
   bottom: "label"
+  top: "loss"
 }
diff --git a/examples/mnist/mnist_autoencoder_train.prototxt b/examples/mnist/mnist_autoencoder.prototxt
similarity index 88%
rename from examples/mnist/mnist_autoencoder_train.prototxt
rename to examples/mnist/mnist_autoencoder.prototxt
index 90d2cff99b8..ad1e7665bf2 100644
--- a/examples/mnist/mnist_autoencoder_train.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -8,6 +8,18 @@ layers {
     scale: 0.0039215684
     batch_size: 100
   }
+  include: { phase: TRAIN }
+}
+layers {
+  top: "data"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "mnist-test-leveldb"
+    scale: 0.0039215684
+    batch_size: 100
+  }
+  include: { phase: TEST }
 }
 layers {
   bottom: "data"
@@ -232,4 +244,20 @@ layers {
   bottom: "flatdata"
   name: "loss"
   type: SIGMOID_CROSS_ENTROPY_LOSS
+  include: { phase: TRAIN }
+}
+layers {
+  bottom: "decode1"
+  top: "decode1neuron"
+  name: "decode1neuron"
+  type: SIGMOID
+  include: { phase: TEST }
+}
+layers {
+  bottom: "decode1neuron"
+  bottom: "flatdata"
+  name: "loss"
+  type: EUCLIDEAN_LOSS
+  top: "loss"
+  include: { phase: TEST }
 }
diff --git a/examples/mnist/mnist_autoencoder_solver.prototxt b/examples/mnist/mnist_autoencoder_solver.prototxt
index 06e057d53a4..ae1ddebccd2 100644
--- a/examples/mnist/mnist_autoencoder_solver.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "mnist_autoencoder_train.prototxt"
-test_net: "mnist_autoencoder_test.prototxt"
+net: "mnist_autoencoder.prototxt"
 test_iter: 50
 test_interval: 100
 test_compute_loss: true
diff --git a/examples/mnist/mnist_autoencoder_test.prototxt b/examples/mnist/mnist_autoencoder_test.prototxt
deleted file mode 100644
index b52364c17fc..00000000000
--- a/examples/mnist/mnist_autoencoder_test.prototxt
+++ /dev/null
@@ -1,146 +0,0 @@
-name: "MNISTAutoencoder"
-layers {
-  top: "data"
-  name: "data"
-  type: DATA
-  data_param {
-    source: "mnist-test-leveldb"
-    scale: 0.0039215684
-    batch_size: 100
-  }
-}
-layers {
-  bottom: "data"
-  top: "flatdata"
-  name: "flatdata"
-  type: FLATTEN
-}
-layers {
-  bottom: "data"
-  top: "encode1"
-  name: "encode1"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  bottom: "encode1"
-  top: "encode1neuron"
-  name: "encode1neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "encode1neuron"
-  top: "encode2"
-  name: "encode2"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 500
-  }
-}
-layers {
-  bottom: "encode2"
-  top: "encode2neuron"
-  name: "encode2neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "encode2neuron"
-  top: "encode3"
-  name: "encode3"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 250
-  }
-}
-layers {
-  bottom: "encode3"
-  top: "encode3neuron"
-  name: "encode3neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "encode3neuron"
-  top: "encode4"
-  name: "encode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 30
-  }
-}
-layers {
-  bottom: "encode4"
-  top: "decode4"
-  name: "decode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 250
-  }
-}
-layers {
-  bottom: "decode4"
-  top: "decode4neuron"
-  name: "decode4neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode4neuron"
-  top: "decode3"
-  name: "decode3"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 500
-  }
-}
-layers {
-  bottom: "decode3"
-  top: "decode3neuron"
-  name: "decode3neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode3neuron"
-  top: "decode2"
-  name: "decode2"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  bottom: "decode2"
-  top: "decode2neuron"
-  name: "decode2neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode2neuron"
-  top: "decode1"
-  name: "decode1"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 784
-  }
-}
-layers {
-  bottom: "decode1"
-  top: "decode1neuron"
-  name: "decode1neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode1neuron"
-  bottom: "flatdata"
-  name: "loss"
-  type: EUCLIDEAN_LOSS
-  top: "loss"
-}
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 4f3f4d9ce12..65a780714ae 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -177,10 +177,8 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 Check out the comments explaining each line in the prototxt:
 
-    # The training protocol buffer definition
-    train_net: "lenet_train.prototxt"
-    # The testing protocol buffer definition
-    test_net: "lenet_test.prototxt"
+    # The train/test net protocol buffer definition
+    net: "lenet_train_test.prototxt"
     # test_iter specifies how many forward passes the test should carry out.
     # In the case of MNIST, we have test batch size 100 and 100 test iterations,
     # covering the full 10,000 testing images.

From 41a56376816b8c59c4777c1ab2d3230f8a785241 Mon Sep 17 00:00:00 2001
From: yajiedesign <yajiedesign@gmail.com>
Date: Sat, 26 Jul 2014 00:01:15 +0800
Subject: [PATCH 0355/2053] fix some namespace with std::signbit

---
 include/caffe/util/math_functions.hpp |  6 ++++--
 src/caffe/util/math_functions.cpp     | 12 ++++++++++++
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 5036a573033..001950113a1 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -128,8 +128,10 @@ DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
 
 // This returns a nonzero value if the input has its sign bit set.
 // The name sngbit is meant to avoid conflicts with std::signbit in the macro
-using std::signbit;
-DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = signbit(x[i]));
+bool caffe_signbit(float arg);
+bool caffe_signbit(double arg);
+bool caffe_signbit(long double arg);
+DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = caffe_signbit(x[i]));
 
 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
 
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 36d8877d8c5..2fdcf91b34b 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -366,4 +366,16 @@ void caffe_cpu_scale<double>(const int n, const double alpha, const double *x,
   cblas_dscal(n, alpha, y, 1);
 }
 
+
+using std::signbit;
+bool caffe_signbit(float arg) {
+    return signbit(arg);
+}
+bool caffe_signbit(double arg) {
+    return signbit(arg);
+}
+bool caffe_signbit(long double arg) {
+    return signbit(arg);
+}
+
 }  // namespace caffe

From 6485be076bdebd2ea311e8a06778a55b72a1d095 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 29 Jul 2014 15:59:49 -0700
Subject: [PATCH 0356/2053] Set correct solver_mode in SolverTest so Travis
 build doesn't randomly fail.

---
 src/caffe/test/test_solver.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index 7380ab86308..9e087b08c53 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -25,6 +25,17 @@ class SolverTest : public MultiDeviceTest<TypeParam> {
   virtual void InitSolverFromProtoString(const string& proto) {
     SolverParameter param;
     CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    // Set the solver_mode according to current Caffe::mode.
+    switch (Caffe::mode()) {
+      case Caffe::CPU:
+        param.set_solver_mode(SolverParameter_SolverMode_CPU);
+        break;
+      case Caffe::GPU:
+        param.set_solver_mode(SolverParameter_SolverMode_GPU);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
+    }
     solver_.reset(new SGDSolver<Dtype>(param));
   }
 

From afa09ecfa5c09c30824cd0aacf7a7f4e48e70a39 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 29 Jul 2014 18:13:32 -0700
Subject: [PATCH 0357/2053] Also apply HDF5OutputLayer fix to GPU version.

---
 src/caffe/layers/hdf5_output_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 5c790555e50..9b3dd135841 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -28,7 +28,7 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom[0]->num(); ++i) {
     caffe_copy(data_datum_dim, &bottom[0]->gpu_data()[i * data_datum_dim],
         &data_blob_.mutable_cpu_data()[i * data_datum_dim]);
-    caffe_copy(label_datum_dim, &bottom[0]->gpu_data()[i * label_datum_dim],
+    caffe_copy(label_datum_dim, &bottom[1]->gpu_data()[i * label_datum_dim],
         &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();

From 44962584b2f71937dbfd2dadaeeed211d4e49598 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 29 Jul 2014 22:40:45 -0700
Subject: [PATCH 0358/2053] [docs] install update: dependencies, CPU-only,
 Ubuntu 14.04

- update dependency versions
- document CPU-only build
- add Ubuntu 14.04 details
- include LMDB
- warn about defective CUDA driver series 331.*
---
 docs/installation.md | 76 ++++++++++++++++++++++++++++----------------
 1 file changed, 49 insertions(+), 27 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index fa29a0e6d90..ff0760dd060 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -6,7 +6,7 @@ title: Caffe
 # Installation
 
 Prior to installing, it is best to read through this guide and take note of the details for your platform.
-We have successfully compiled and run Caffe on Ubuntu 12.04, OS X 10.8, and OS X 10.9.
+We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
@@ -16,20 +16,22 @@ We have successfully compiled and run Caffe on Ubuntu 12.04, OS X 10.8, and OS X
 
 Caffe depends on several software packages.
 
-* [CUDA](https://developer.nvidia.com/cuda-zone) (5.0, 5.5, or 6.0).
+* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
 * [OpenCV](http://opencv.org/).
-* [Boost](http://www.boost.org/) (we have only tested 1.55)
-* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`
+* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 is tested)
+* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
 * For the Python wrapper
-    * `Python`, `numpy (>= 1.7)`, boost-provided `boost.python`
+    * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For the MATLAB wrapper
     * MATLAB with the `mex` compiler.
 
+**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
+
 ### CUDA and BLAS
 
-Caffe requires the CUDA `nvcc` compiler to compile its GPU code.
-To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cuda-downloads) and follow installation instructions there. **Note:** you can install the CUDA libraries without a CUDA card or driver, in order to build and run Caffe on a CPU-only machine.
+Caffe requires the CUDA `nvcc` compiler to compile its GPU code and CUDA driver for GPU operation.
+To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cuda-downloads) and follow installation instructions there. Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date. **Warning!** The 331.* CUDA driver series has a critical performance issue: do not use it.
 
 Caffe requires BLAS as the backend of its matrix and vector computations.
 There are several implementations of this library.
@@ -46,32 +48,49 @@ The choice is yours:
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
 
-### Python and/or Matlab wrappers (optional)
+### Python and/or MATLAB wrappers (optional)
 
-Python: The main requirements are `numpy` and `boost.python` (provided by boost). `pandas` is useful too and needed for some examples.
+#### Python
 
-For **OS X**, we highly recommend using the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
-If you don't, please use Homebrew -- but beware of potential linking errors!
+The main requirements are `numpy` and `boost.python` (provided by boost). `pandas` is useful too and needed for some examples.
 
-Note that if you use the **Ubuntu** default python, you will need to `apt-get install` the `python-dev` package to have the python headers. You can install any remaining dependencies with
+You can install the dependencies with
 
     pip install -r /path/to/caffe/python/requirements.txt
 
-MATLAB: install MATLAB, and make sure that its `mex` is in your `$PATH`.
+but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
+
+For **Ubuntu**, if you use the default Python you will need to `apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
+
+For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
+
+To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
+
+*Caffe's Python interface works with Python 2.7. Python 3 or earlier Pythons are your own adventure.*
+
+#### MATLAB
+
+Install MATLAB, and make sure that its `mex` is in your `$PATH`.
+
+*Caffe's MATLAB interface works with versions 2012b, 2013a/b, and 2014a.*
 
 ### The rest of the dependencies
 
 #### Linux
 
-On **Ubuntu**, the remaining dependencies can be installed with
+On **Ubuntu**, most of the dependencies can be installed with
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
 
-And on **CentOS or RHEL**, you can install via yum using:
+And on **CentOS / RHEL**, you can install via yum with
 
     sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
 
-The only exceptions being the google flags library and the google logging library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install them, do:
+and for **Ubuntu 14.04** the rest of the dependencies can be installed with
+
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+
+For **Ubuntu 12.04 and CentOS / RHEL** the only exceptions to package installation are the Google flags library, Google logging library, and LMDB. To install these, do:
 
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
@@ -86,12 +105,16 @@ The only exceptions being the google flags library and the google logging librar
     mkdir build && cd build
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
 
-Note that glog does not compile with the most recent gflags version (2.1), so before that is solved you will need to build with glog first. Also note that if you have newer versions of Ubuntu, like 13.04 or 14.04, gflags may be available under apt-get.
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
 
 #### OS X
 
-On **OS X**, we highly recommend using the [homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
+On **OS X**, we highly recommend using the [Homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
 In the following, we assume that you're using Anaconda Python and Homebrew.
 
 To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:
@@ -112,7 +135,7 @@ In other `ENV` settings, things may not work as expected.
 Simply run the following:
 
     brew install --build-from-source --with-python boost
-    for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew install $x; done
+    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
 
 Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
 
@@ -125,12 +148,12 @@ In OS X 10.9, clang++ is the default C++ compiler and uses `libc++` as the stand
 However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
 This makes it necessary to change the compilation settings for each of the dependencies.
 
-We do this by modifying the homebrew formulae before installing any packages.
-Make sure that homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
+We do this by modifying the Homebrew formulae before installing any packages.
+Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
 
-The prerequisite homebrew formulae are
+The prerequisite Homebrew formulae are
 
-    boost snappy leveldb protobuf gflags glog szip homebrew/science/opencv
+    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
 
 For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
 
@@ -145,11 +168,11 @@ For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as
 
 To edit the formulae in turn, run
 
-    for x in snappy leveldb protobuf gflags glog szip boost homebrew/science/opencv; do brew edit $x; done
+    for x in snappy leveldb protobuf gflags glog szip boost lmdb homebrew/science/opencv; do brew edit $x; done
 
 After this, run
 
-    for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
     brew install --build-from-source --with-python --fresh -vd boost
 
 **Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
@@ -176,11 +199,10 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
     make test
     make runtest
 
-Note that if there is no GPU in your machine, building and running CPU-only works, but GPU tests will naturally fail.
+If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
 Be sure to set your MATLAB and Python paths in `Makefile.config` first!
-For Python support, you must add the compiled module to your `$PYTHONPATH` (as `/path/to/caffe/python` or the like).
 
 *Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
 

From 62bbc41e0e2cf2f22f0d30ff795aa09c99d3c63e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 29 Jul 2014 22:57:47 -0700
Subject: [PATCH 0359/2053] link OpenBLAS as blas

OpenBLAS is most easily configured by `update-alternatives` or other
symlink strategies.

See https://github.com/BVLC/caffe/issues/314#issuecomment-40703048
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 8e8ebedbc57..62e5158b9bd 100644
--- a/Makefile
+++ b/Makefile
@@ -252,7 +252,7 @@ ifeq ($(BLAS), mkl)
 	BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
 else ifeq ($(BLAS), open)
 	# OpenBLAS
-	LIBRARIES += openblas
+	LIBRARIES += blas
 else
 	# ATLAS
 	ifeq ($(LINUX), 1)

From a08faf645dc8c223718c99a6695fc4072ed3164c Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 15 Jul 2014 08:13:00 -0700
Subject: [PATCH 0360/2053] [docs] contact us on webpage

---
 docs/index.md | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 6ffab7a0675..b8e58cf52dc 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -41,7 +41,7 @@ A 4-page report for the ACM Multimedia Open Source competition.
 Tested on Ubuntu, Red Hat, OS X.
 * [Pre-trained models](/getting_pretrained_models.html)<br />
 BVLC provides ready-to-use models for non-commercial use.
-* [Development](/development.html)<br />
+* [Developing & Contributing](/development.html)<br />
 Guidelines for development and contributing to Caffe.
 
 ### Examples
@@ -85,4 +85,13 @@ Additionally, the open-source community plays a large and growing role in Caffe'
 Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity, and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for a sorted list.
 
 We sincerely appreciate your interest and contributions!
-If you'd like to contribute, please read the [development guide](development.html).
+If you'd like to contribute, please read the [developing & contributing](development.html) guide.
+
+## Contacting us
+
+All questions about installation, code, future development, and applications should be searched for and asked at [GitHub Issues](https://github.com/BVLC/caffe/issues).
+
+If you have a proposal that may not be suited for public discussion *and an ability to act on it*, please email us [directly](mailto:caffe-dev@googlegroups.com).
+Requests for features, explanations, or personal help will be ignored; post such matters publicly as issues.
+
+Some developers may be able to provide [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.

From 0428913a3b1c7acc3538557ed0ea0e0db6d69c9c Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 15 Jul 2014 18:29:19 -0700
Subject: [PATCH 0361/2053] [fix] adding requirements.txt for web_demo. Closes
 #704

---
 examples/web_demo/requirements.txt | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 examples/web_demo/requirements.txt

diff --git a/examples/web_demo/requirements.txt b/examples/web_demo/requirements.txt
new file mode 100644
index 00000000000..8fb1d2ccbb2
--- /dev/null
+++ b/examples/web_demo/requirements.txt
@@ -0,0 +1,6 @@
+werkzeug
+flask
+tornado
+numpy
+pandas
+pillow

From 4f0e38add5f64ef28ee6a5a6c827923dbfeaf38f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 29 Jul 2014 09:21:39 -0700
Subject: [PATCH 0362/2053] [example] standardize imagenet leveldb names

---
 examples/imagenet/create_imagenet.sh | 4 ++--
 examples/imagenet/readme.md          | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index 8767f120a83..24f961bee4c 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -38,7 +38,7 @@ echo "Creating train leveldb..."
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
     $TRAIN_DATA_ROOT \
     $DATA/train.txt \
-    imagenet_train_leveldb 1 \
+    ilsvrc12_train_leveldb 1
     $RESIZE_HEIGHT $RESIZE_WIDTH
 
 echo "Creating val leveldb..."
@@ -46,7 +46,7 @@ echo "Creating val leveldb..."
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
     $VAL_DATA_ROOT \
     $DATA/val.txt \
-    imagenet_val_leveldb 1 \
+    ilsvrc12_val_leveldb 1
     $RESIZE_HEIGHT $RESIZE_WIDTH
 
 echo "Done."
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 0b5b8452188..2273c877ca5 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -41,7 +41,7 @@ You may want to resize the images to 256x256 in advance. By default, we do not e
 
 Go to `$CAFFE_ROOT/examples/imagenet/` for the rest of this guide.
 
-Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set "RESIZE=true" to resize all images to 256x256 if you haven't resized the images in advance. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `imagenet_train_leveldb` and `imagenet_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
+Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set "RESIZE=true" to resize all images to 256x256 if you haven't resized the images in advance. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `ilsvrc12_train_leveldb` and `ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
 
 Compute Image Mean
 ------------------

From d726338b22665d053d6665f4d6ffae01fecb5e17 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 29 Jul 2014 23:37:09 -0700
Subject: [PATCH 0363/2053] [docs] announce caffe-users

---
 docs/index.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index b8e58cf52dc..333f5f7e048 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -89,7 +89,9 @@ If you'd like to contribute, please read the [developing & contributing](develop
 
 ## Contacting us
 
-All questions about installation, code, future development, and applications should be searched for and asked at [GitHub Issues](https://github.com/BVLC/caffe/issues).
+All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
+
+All development discussion should be carried out at [GitHub Issues](https://github.com/BVLC/caffe/issues).
 
 If you have a proposal that may not be suited for public discussion *and an ability to act on it*, please email us [directly](mailto:caffe-dev@googlegroups.com).
 Requests for features, explanations, or personal help will be ignored; post such matters publicly as issues.

From e4dece55f5975ff1dd5d9c0a062d02378acefe8f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 30 Jul 2014 00:48:35 -0700
Subject: [PATCH 0364/2053] [example] include image dimensions for oversampling

---
 examples/imagenet_classification.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index 7ac140d9e79..9a7113e6892 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -60,7 +60,8 @@
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
       "                       mean_file=caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy',\n",
       "                       channel_swap=(2,1,0),\n",
-      "                       input_scale=255)"
+      "                       input_scale=255,\n"
+      "                       image_dims=(256, 256))"
      ],
      "language": "python",
      "metadata": {},

From ee03edea23edd611b6a3fc5e3d7a440b24f780ed Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 31 Jul 2014 18:14:12 -0700
Subject: [PATCH 0365/2053] [example] fix imagenet classification typo

---
 examples/imagenet_classification.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index 9a7113e6892..e7fe9f84677 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -60,7 +60,7 @@
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
       "                       mean_file=caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy',\n",
       "                       channel_swap=(2,1,0),\n",
-      "                       input_scale=255,\n"
+      "                       input_scale=255,\n",
       "                       image_dims=(256, 256))"
      ],
      "language": "python",

From 8c15185812efc58c32dca00a97e93edfbddd1dce Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Thu, 31 Jul 2014 16:43:21 -0700
Subject: [PATCH 0366/2053] Included cmath in common.hpp to avoid isnan
 complaints from the xcode compiler

---
 include/caffe/common.hpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index ff8649ad2a1..58f500d0984 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -10,6 +10,8 @@
 #include <set>
 #include <string>
 
+#include <cmath>
+
 #include "caffe/util/device_alternate.hpp"
 
 // Disable the copy and assignment operator for a class.

From 9f2b8f305d87974b5f3c3090286075e265fb006c Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Fri, 1 Aug 2014 08:09:18 -0700
Subject: [PATCH 0367/2053] turns out you need using::isnan too

---
 include/caffe/common.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 58f500d0984..789b3db023f 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -81,6 +81,7 @@ using std::pair;
 using std::set;
 using std::string;
 using std::vector;
+using std::isnan;
 
 // A global initialization function that you should call in your main function.
 // Currently it initializes google flags and google logging.

From 7eb8a6fe451317292180ac9474329a5a072c61b4 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Fri, 1 Aug 2014 08:10:19 -0700
Subject: [PATCH 0368/2053] the compiler complains about solver tests using
 pointers as bool

---
 src/caffe/test/test_solver.cpp | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index 9e087b08c53..8b241daeda1 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -96,14 +96,14 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  } "
      "} ";
   this->InitSolverFromProtoString(proto);
-  ASSERT_TRUE(this->solver_->net());
-  EXPECT_TRUE(this->solver_->net()->has_layer("loss"));
-  EXPECT_FALSE(this->solver_->net()->has_layer("accuracy"));
+  ASSERT_TRUE((bool)this->solver_->net());
+  EXPECT_TRUE((bool)this->solver_->net()->has_layer("loss"));
+  EXPECT_FALSE((bool)this->solver_->net()->has_layer("accuracy"));
   ASSERT_EQ(2, this->solver_->test_nets().size());
-  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("loss"));
-  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("accuracy"));
-  EXPECT_FALSE(this->solver_->test_nets()[1]->has_layer("loss"));
-  EXPECT_TRUE(this->solver_->test_nets()[1]->has_layer("accuracy"));
+  EXPECT_TRUE((bool)this->solver_->test_nets()[0]->has_layer("loss"));
+  EXPECT_TRUE((bool)this->solver_->test_nets()[0]->has_layer("accuracy"));
+  EXPECT_FALSE((bool)this->solver_->test_nets()[1]->has_layer("loss"));
+  EXPECT_TRUE((bool)this->solver_->test_nets()[1]->has_layer("accuracy"));
 }
 
 }  // namespace caffe

From d78a8de102111525ae66a4536093d58823fd7129 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Fri, 1 Aug 2014 08:28:43 -0700
Subject: [PATCH 0369/2053] lint

---
 src/caffe/test/test_solver.cpp | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index 8b241daeda1..20bad95f878 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -96,14 +96,18 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  } "
      "} ";
   this->InitSolverFromProtoString(proto);
-  ASSERT_TRUE((bool)this->solver_->net());
-  EXPECT_TRUE((bool)this->solver_->net()->has_layer("loss"));
-  EXPECT_FALSE((bool)this->solver_->net()->has_layer("accuracy"));
+  ASSERT_TRUE(static_cast<bool>(this->solver_->net()));
+  EXPECT_TRUE(static_cast<bool>(this->solver_->net()->has_layer("loss")));
+  EXPECT_FALSE(static_cast<bool>(this->solver_->net()->has_layer("accuracy")));
   ASSERT_EQ(2, this->solver_->test_nets().size());
-  EXPECT_TRUE((bool)this->solver_->test_nets()[0]->has_layer("loss"));
-  EXPECT_TRUE((bool)this->solver_->test_nets()[0]->has_layer("accuracy"));
-  EXPECT_FALSE((bool)this->solver_->test_nets()[1]->has_layer("loss"));
-  EXPECT_TRUE((bool)this->solver_->test_nets()[1]->has_layer("accuracy"));
+  EXPECT_TRUE(static_cast<bool>(
+      this->solver_->test_nets()[0]->has_layer("loss")));
+  EXPECT_TRUE(static_cast<bool>(
+      this->solver_->test_nets()[0]->has_layer("accuracy")));
+  EXPECT_FALSE(static_cast<bool>(
+      this->solver_->test_nets()[1]->has_layer("loss")));
+  EXPECT_TRUE(static_cast<bool>(
+      this->solver_->test_nets()[1]->has_layer("accuracy")));
 }
 
 }  // namespace caffe

From 21d1d91ed63e4ffb486560c0d22278357c3aa896 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Fri, 1 Aug 2014 14:24:22 -0700
Subject: [PATCH 0370/2053] fixed unnecessary conversions in test_solver, and
 rearraged common.hpp a bit

---
 include/caffe/common.hpp       |  5 ++---
 src/caffe/test/test_solver.cpp | 18 +++++++-----------
 2 files changed, 9 insertions(+), 14 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 789b3db023f..d3dbd471e67 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -6,12 +6,11 @@
 #include <boost/shared_ptr.hpp>
 #include <glog/logging.h>
 
+#include <cmath>
 #include <map>
 #include <set>
 #include <string>
 
-#include <cmath>
-
 #include "caffe/util/device_alternate.hpp"
 
 // Disable the copy and assignment operator for a class.
@@ -73,6 +72,7 @@ using boost::shared_ptr;
 // Common functions and classes from std that caffe often uses.
 using std::fstream;
 using std::ios;
+using std::isnan;
 using std::iterator;
 using std::make_pair;
 using std::map;
@@ -81,7 +81,6 @@ using std::pair;
 using std::set;
 using std::string;
 using std::vector;
-using std::isnan;
 
 // A global initialization function that you should call in your main function.
 // Currently it initializes google flags and google logging.
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index 20bad95f878..feefb51210a 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -96,18 +96,14 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  } "
      "} ";
   this->InitSolverFromProtoString(proto);
-  ASSERT_TRUE(static_cast<bool>(this->solver_->net()));
-  EXPECT_TRUE(static_cast<bool>(this->solver_->net()->has_layer("loss")));
-  EXPECT_FALSE(static_cast<bool>(this->solver_->net()->has_layer("accuracy")));
+  ASSERT_TRUE(this->solver_->net() != NULL);
+  EXPECT_TRUE(this->solver_->net()->has_layer("loss"));
+  EXPECT_FALSE(this->solver_->net()->has_layer("accuracy"));
   ASSERT_EQ(2, this->solver_->test_nets().size());
-  EXPECT_TRUE(static_cast<bool>(
-      this->solver_->test_nets()[0]->has_layer("loss")));
-  EXPECT_TRUE(static_cast<bool>(
-      this->solver_->test_nets()[0]->has_layer("accuracy")));
-  EXPECT_FALSE(static_cast<bool>(
-      this->solver_->test_nets()[1]->has_layer("loss")));
-  EXPECT_TRUE(static_cast<bool>(
-      this->solver_->test_nets()[1]->has_layer("accuracy")));
+  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("loss"));
+  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("accuracy"));
+  EXPECT_FALSE(this->solver_->test_nets()[1]->has_layer("loss"));
+  EXPECT_TRUE(this->solver_->test_nets()[1]->has_layer("accuracy"));
 }
 
 }  // namespace caffe

From fc4e1d11e6b8a44417085113fda4a8f9b3e7d072 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Fri, 1 Aug 2014 14:53:35 -0700
Subject: [PATCH 0371/2053] fix compiler complaint in matcaffe

---
 matlab/caffe/matcaffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 957ebea008e..2aad3192a65 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -183,7 +183,7 @@ static mxArray* do_get_weights() {
       mxArray* mx_layer_cells = NULL;
       if (layer_names[i] != prev_layer_name) {
         prev_layer_name = layer_names[i];
-        const mwSize dims[2] = {layer_blobs.size(), 1};
+        const mwSize dims[2] = {static_cast<mwSize>(layer_blobs.size()), 1};
         mx_layer_cells = mxCreateCellArray(2, dims);
         mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
         mxSetField(mx_layers, mx_layer_index, "layer_names",

From 737b79ea72a0193bc7767fc0b7054e78714a9e9a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 1 Aug 2014 19:10:16 -0700
Subject: [PATCH 0372/2053] Test that DropoutLayer obeys dropout_ratio.

---
 src/caffe/test/test_neuron_layer.cpp | 57 ++++++++++++++++++++--------
 1 file changed, 41 insertions(+), 16 deletions(-)

diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 7a7af3188fe..b3a536a7bfd 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -17,6 +17,7 @@ namespace caffe {
 template <typename TypeParam>
 class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
+
  protected:
   NeuronLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -34,6 +35,38 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
   Blob<Dtype>* const blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+
+  void TestDropoutForward(const float dropout_ratio) {
+    LayerParameter layer_param;
+    // Fill in the given dropout_ratio, unless it's 0.5, in which case we don't
+    // set it explicitly to test that 0.5 is the default.
+    if (dropout_ratio != 0.5) {
+      layer_param.mutable_dropout_param()->set_dropout_ratio(dropout_ratio);
+    }
+    Caffe::set_phase(Caffe::TRAIN);
+    DropoutLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
+    const int count = this->blob_bottom_->count();
+    // Initialize num_kept to count the number of inputs NOT dropped out.
+    int num_kept = 0;
+    for (int i = 0; i < count; ++i) {
+      if (top_data[i] != 0) {
+        ++num_kept;
+        EXPECT_EQ(top_data[i], bottom_data[i] * scale);
+      }
+    }
+    const Dtype std_error = sqrt(dropout_ratio * (1 - dropout_ratio) / count);
+    // Fail if the number dropped was more than 1.96 * std_error away from the
+    // expected number -- requires 95% confidence that the dropout layer is not
+    // obeying the given dropout_ratio for test failure.
+    const Dtype empirical_dropout_ratio = 1 - num_kept / Dtype(count);
+    EXPECT_NEAR(empirical_dropout_ratio, dropout_ratio, 1.96 * std_error);
+  }
 };
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
@@ -114,22 +147,14 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(NeuronLayerTest, TestDropout) {
-  typedef typename TypeParam::Dtype Dtype;
-  LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
-  const Dtype* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    if (top_data[i] != 0) {
-      EXPECT_EQ(top_data[i], bottom_data[i] * scale);
-    }
-  }
+TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
+  const float kDropoutRatio = 0.5;
+  this->TestDropoutForward(kDropoutRatio);
+}
+
+TYPED_TEST(NeuronLayerTest, TestDropoutThreeQuarters) {
+  const float kDropoutRatio = 0.75;
+  this->TestDropoutForward(kDropoutRatio);
 }
 
 TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {

From 8880ac8706a205623b830a0f1edb5d2dbbe590d8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 2 Aug 2014 13:17:09 -0700
Subject: [PATCH 0373/2053] Add script to speedtest imagenet (currently broken
 as FilterNet ignores Caffe::phase()).

---
 examples/imagenet/speedtest_imagenet.sh | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100755 examples/imagenet/speedtest_imagenet.sh

diff --git a/examples/imagenet/speedtest_imagenet.sh b/examples/imagenet/speedtest_imagenet.sh
new file mode 100755
index 00000000000..c790d835c14
--- /dev/null
+++ b/examples/imagenet/speedtest_imagenet.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env sh
+
+TOOLS=../../build/tools
+
+if [ -z "$1" ]; then
+  echo "Using CPU! To run GPU speedtest, use:"
+  echo "    ./speedtest_imagenet.sh <device ID>"
+  echo "(Try ./speedtest_imagenet.sh 0 if you have just one GPU.)"
+  sleep 3  # Let the user read
+  WITH_GPU=false
+  DEVICE_ID=0
+else
+  WITH_GPU=true
+  DEVICE_ID=$1
+fi
+
+GLOG_logtostderr=1 $TOOLS/caffe.bin speedtest \
+  --net_proto_file=imagenet_train_val.prototxt \
+  --speedtest_with_gpu=${WITH_GPU} \
+  --device_id=${DEVICE_ID}
+
+echo "Done."

From 81e240b351e7fbbc9d6dab2eab3970389ef1ce60 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 2 Aug 2014 13:10:46 -0700
Subject: [PATCH 0374/2053] Add tests for phase filtering according to Caffe
 singleton phase (currently failing as FilterNet ignores the singleton phase).

---
 src/caffe/test/test_net.cpp | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index c4181345573..a0a387929be 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -961,8 +961,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} ";
   const string input_proto_train = "state: { phase: TRAIN } " + input_proto;
   const string input_proto_test = "state: { phase: TEST } " + input_proto;
-  const string& output_proto_train =
-      "state: { phase: TRAIN } "
+  const string output_proto_train =
       "name: 'LeNet' "
       "layers { "
       "  name: 'mnist' "
@@ -1020,7 +1019,6 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'loss' "
       "} ";
   const string& output_proto_test =
-      "state: { phase: TEST } "
       "name: 'LeNet' "
       "layers { "
       "  name: 'mnist' "
@@ -1085,8 +1083,26 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  bottom: 'label' "
       "  top: 'loss' "
       "} ";
-  this->RunFilterNetTest(input_proto_train, output_proto_train);
-  this->RunFilterNetTest(input_proto_test, output_proto_test);
+  const string output_proto_train_explicit =
+      output_proto_train + " state: { phase: TRAIN } ";
+  const string output_proto_test_explicit =
+      output_proto_test + " state: { phase: TEST } ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
+  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
+
+  // Also check that nets are filtered according to the Caffe singleton phase,
+  // if not explicitly specified in the input proto.
+  Caffe::set_phase(Caffe::TRAIN);
+  this->RunFilterNetTest(input_proto, output_proto_train);
+  Caffe::set_phase(Caffe::TEST);
+  this->RunFilterNetTest(input_proto, output_proto_test);
+
+  // Finally, check that the current Caffe singleton phase is ignored if the
+  // phase is explicitly specified in the input proto.
+  Caffe::set_phase(Caffe::TEST);
+  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
+  Caffe::set_phase(Caffe::TRAIN);
+  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
 }
 
 TEST_F(FilterNetTest, TestFilterOutByStage) {

From 4cece16571e39e74c7a12bf677b044c9f5341180 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 2 Aug 2014 13:17:15 -0700
Subject: [PATCH 0375/2053] Fix speedtest (and possibly other tools) by setting
 the net phase to the current Caffe::phase() unless explicitly specified in
 the state.

---
 src/caffe/net.cpp | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a4d1f235a5a..b80bbba8479 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -174,7 +174,21 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
 template <typename Dtype>
 void Net<Dtype>::FilterNet(const NetParameter& param,
     NetParameter* param_filtered) {
-  const NetState& net_state = param.state();
+  NetState net_state(param.state());
+  // Let the phase of the net be the current global phase provided in the Caffe
+  // singleton, unless explicitly provided by the state.
+  if (!net_state.has_phase()) {
+    switch (Caffe::phase()) {
+      case Caffe::TRAIN:
+        net_state.set_phase(TRAIN);
+        break;
+      case Caffe::TEST:
+        net_state.set_phase(TEST);
+        break;
+      default:
+        LOG(FATAL) << "Unknown phase: " << Caffe::phase();
+    }
+  }
   param_filtered->CopyFrom(param);
   param_filtered->clear_layers();
   for (int i = 0; i < param.layers_size(); ++i) {

From db286e430d282d04e222a50cfca7875867b55df8 Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Mon, 4 Aug 2014 12:28:21 +0800
Subject: [PATCH 0376/2053] Fix and improve multiple places about random number
 generation and shuffling

---
 include/caffe/util/rng.hpp            | 30 ++++++++++++++++++++++++---
 src/caffe/common.cpp                  | 13 +++++++++++-
 src/caffe/layers/image_data_layer.cpp | 11 +++-------
 tools/convert_imageset.cpp            |  3 ++-
 4 files changed, 44 insertions(+), 13 deletions(-)

diff --git a/include/caffe/util/rng.hpp b/include/caffe/util/rng.hpp
index 5909d1715ed..2b79ef1801b 100644
--- a/include/caffe/util/rng.hpp
+++ b/include/caffe/util/rng.hpp
@@ -4,16 +4,40 @@
 #define CAFFE_RNG_CPP_HPP_
 
 #include <boost/random/mersenne_twister.hpp>
+#include <boost/random/uniform_int.hpp>
+#include <iterator>
+#include <algorithm>
 #include "caffe/common.hpp"
 
 namespace caffe {
 
-  typedef boost::mt19937 rng_t;
+typedef boost::mt19937 rng_t;
 
-  inline rng_t* caffe_rng() {
-    return static_cast<caffe::rng_t*>(Caffe::rng_stream().generator());
+inline rng_t* caffe_rng() {
+  return static_cast<caffe::rng_t*>(Caffe::rng_stream().generator());
+}
+
+// Fisher–Yates algorithm
+template <class RandomAccessIterator, class RandomGenerator>
+inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end,
+                    RandomGenerator* gen) {
+  typedef typename std::iterator_traits<RandomAccessIterator>::difference_type
+      difference_type;
+  typedef typename boost::uniform_int<difference_type> dist_type;
+
+  difference_type length = std::distance(begin, end);
+  if (length <= 0) return;
+
+  for (difference_type i = length - 1; i > 0; --i) {
+    dist_type dist(0, i);
+    std::iter_swap(begin + i, begin + dist(*gen));
   }
+}
 
+template <class RandomAccessIterator>
+inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end) {
+  shuffle(begin, end, caffe_rng());
+}
 }  // namespace caffe
 
 #endif  // CAFFE_RNG_HPP_
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 1810bb70b61..019370b1dd7 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -26,6 +26,17 @@ shared_ptr<Caffe> Caffe::singleton_;
 // random seeding
 int64_t cluster_seedgen(void) {
   int64_t s, seed, pid;
+  FILE* f = fopen("/dev/urandom", "rb");
+  if (f && fread(&seed, 1, sizeof(seed), f) == sizeof(seed)) {
+    fclose(f);
+    return seed;
+  }
+
+  LOG(INFO) << "System entropy source not available, "
+              "using fallback algorithm to generate seed instead.";
+  if (f)
+    fclose(f);
+
   pid = getpid();
   s = time(NULL);
   seed = abs(((s * 181) * ((pid - 83) * 359)) % 104729);
@@ -75,7 +86,7 @@ Caffe::RNG::RNG() : generator_(new Generator()) { }
 Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }
 
 Caffe::RNG& Caffe::RNG::operator=(const RNG& other) {
-  generator_.reset(other.generator_.get());
+  generator_ = other.generator_;
   return *this;
 }
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index efd593808ec..a0f91af29fa 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -238,14 +238,9 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
 
 template <typename Dtype>
 void ImageDataLayer<Dtype>::ShuffleImages() {
-  const int num_images = lines_.size();
-  for (int i = 0; i < num_images; ++i) {
-    const int max_rand_index = num_images - i;
-    const int rand_index = PrefetchRand() % max_rand_index;
-    pair<string, int> item = lines_[rand_index];
-    lines_.erase(lines_.begin() + rand_index);
-    lines_.push_back(item);
-  }
+  caffe::rng_t* prefetch_rng =
+      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
+  shuffle(lines_.begin(), lines_.end(), prefetch_rng);
 }
 
 
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 28de653b2c7..21ff0d38104 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -28,6 +28,7 @@
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
+#include "caffe/util/rng.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
@@ -60,7 +61,7 @@ int main(int argc, char** argv) {
   if (argc >= (arg_offset+5) && argv[arg_offset+4][0] == '1') {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";
-    std::random_shuffle(lines.begin(), lines.end());
+    shuffle(lines.begin(), lines.end());
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 

From 5f520af6da1ae70f1a9b3a7fb5b29765927c4fae Mon Sep 17 00:00:00 2001
From: zszhong <zszhong5@gmail.com>
Date: Sun, 3 Aug 2014 13:55:06 +0800
Subject: [PATCH 0377/2053] modified test_concat_layer.cpp

---
 src/caffe/test/test_concat_layer.cpp | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 0550bb2d5c9..4cb28b1e24e 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -26,14 +26,17 @@ class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     // fill the values
+    shared_ptr<ConstantFiller<Dtype> > filler;
     FillerParameter filler_param;
     filler_param.set_value(1.);
-    ConstantFiller<Dtype> filler(filler_param);
-    filler.Fill(this->blob_bottom_0);
+    filler.reset(new ConstantFiller<Dtype>(filler_param));
+    filler->Fill(this->blob_bottom_0);
     filler_param.set_value(2.);
-    filler.Fill(this->blob_bottom_1);
+    filler.reset(new ConstantFiller<Dtype>(filler_param));
+    filler->Fill(this->blob_bottom_1);
     filler_param.set_value(3.);
-    filler.Fill(this->blob_bottom_2);
+    filler.reset(new ConstantFiller<Dtype>(filler_param));
+    filler->Fill(this->blob_bottom_2);
     blob_bottom_vec_0.push_back(blob_bottom_0);
     blob_bottom_vec_0.push_back(blob_bottom_1);
     blob_bottom_vec_1.push_back(blob_bottom_0);
@@ -112,7 +115,7 @@ TYPED_TEST(ConcatLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
     &(this->blob_top_vec_));
 }

From 36fd64cfc44cef04d98d98bbd9fb773df3a3093e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 4 Aug 2014 13:09:00 -0700
Subject: [PATCH 0378/2053] Add 'snapshot_after_train' to SolverParameter to
 override the final snapshot.

---
 src/caffe/proto/caffe.proto | 3 +++
 src/caffe/solver.cpp        | 5 +++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d2f61e57f34..4e77863e7e6 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -140,6 +140,9 @@ message SolverParameter {
   // If true, print information about the state of the net that may help with
   // debugging learning problems.
   optional bool debug_info = 23 [default = false];
+
+  // If false, don't save a snapshot after training finishes.
+  optional bool snapshot_after_train = 28 [default = true];
 }
 
 // A message that stores the solver snapshots
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 11795f058cd..6049ddfec7c 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -200,8 +200,9 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     ComputeUpdateValue();
     net_->Update();
   }
-  // Always save a snapshot after optimization.
-  Snapshot();
+  // Always save a snapshot after optimization, unless overridden by setting
+  // snapshot_after_train := false.
+  if (param_.snapshot_after_train()) { Snapshot(); }
   // After the optimization is done, run an additional train and test pass to
   // display the train and test loss/outputs if appropriate (based on the
   // display and test_interval settings, respectively).  Unlike in the rest of

From b08729e366623245cf632681a0f747192f3b914a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 3 Aug 2014 16:00:46 -0700
Subject: [PATCH 0379/2053] Add (momentum) SGD solver tests to check that
 learning rate, weight decay, and momentum are implemented properly on a least
 squares problem.

---
 src/caffe/test/test_sgd_solver.cpp | 346 +++++++++++++++++++++++++++++
 1 file changed, 346 insertions(+)
 create mode 100644 src/caffe/test/test_sgd_solver.cpp

diff --git a/src/caffe/test/test_sgd_solver.cpp b/src/caffe/test/test_sgd_solver.cpp
new file mode 100644
index 00000000000..a28ed7b1817
--- /dev/null
+++ b/src/caffe/test/test_sgd_solver.cpp
@@ -0,0 +1,346 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <algorithm>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
+
+#include "gtest/gtest.h"
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::ostringstream;
+
+namespace caffe {
+
+template <typename TypeParam>
+class SGDSolverTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  SGDSolverTest() :
+      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+
+  // MockSGDSolver: an SGDSolver with public history.
+  class MockSGDSolver : public SGDSolver<Dtype> {
+   public:
+    explicit MockSGDSolver(const SolverParameter& param) :
+        SGDSolver<Dtype>(param) {}
+    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
+  };
+
+  shared_ptr<MockSGDSolver> solver_;
+  int seed_;
+  int num_, channels_, height_, width_;
+
+  virtual void InitSolverFromProtoString(const string& proto) {
+    SolverParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    // Disable saving a final snapshot so the tests don't pollute the user's
+    // working directory with useless snapshots.
+    param.set_snapshot_after_train(false);
+    // Set the solver_mode according to current Caffe::mode.
+    switch (Caffe::mode()) {
+      case Caffe::CPU:
+        param.set_solver_mode(SolverParameter_SolverMode_CPU);
+        break;
+      case Caffe::GPU:
+        param.set_solver_mode(SolverParameter_SolverMode_GPU);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
+    }
+    solver_.reset(new MockSGDSolver(param));
+  }
+
+  void RunLeastSquaresSolver(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+    ostringstream proto;
+    proto <<
+       "max_iter: " << num_iters << " "
+       "base_lr: " << learning_rate << " "
+       "lr_policy: 'fixed' "
+       "net_param { "
+       "  name: 'TestNetwork' "
+       "  layers: { "
+       "    name: 'data' "
+       "    type: DUMMY_DATA "
+       "    dummy_data_param { "
+       "      num: " << num_ << " "
+       "      channels: " << channels_ << " "
+       "      height: " << height_ << " "
+       "      width: " << width_ << " "
+       "      channels: 1 "
+       "      height: 1 "
+       "      width: 1 "
+       "      data_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    top: 'data' "
+       "    top: 'targets' "
+       "  } "
+       "  layers: { "
+       "    name: 'innerprod' "
+       "    type: INNER_PRODUCT "
+       "    inner_product_param { "
+       "      num_output: 1 "
+       "      weight_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "      bias_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    bottom: 'data' "
+       "    top: 'innerprod' "
+       "  } "
+       "  layers: { "
+       "    name: 'loss' "
+       "    type: EUCLIDEAN_LOSS "
+       "    bottom: 'innerprod' "
+       "    bottom: 'targets' "
+       "  } "
+       "} ";
+    if (weight_decay != 0) {
+      proto << "weight_decay: " << weight_decay << " ";
+    }
+    if (momentum != 0) {
+      proto << "momentum: " << momentum << " ";
+    }
+    Caffe::set_random_seed(this->seed_);
+    this->InitSolverFromProtoString(proto.str());
+    this->solver_->Solve();
+  }
+
+  // Compute an update value given the current state of the train net,
+  // using the analytical formula for the least squares gradient.
+  // updated_params will store the updated weight and bias results,
+  // using the blobs' diffs to hold the update values themselves.
+  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum,
+      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
+    const int N = num_;
+    const int D = channels_ * height_ * width_;
+
+    // Run a forward pass, and manually compute the update values from the
+    // result.
+    Net<Dtype>& net = *this->solver_->net();
+    vector<Blob<Dtype>*> empty_bottom_vec;
+    net.Forward(empty_bottom_vec);
+    ASSERT_TRUE(net.has_blob("data"));
+    const Blob<Dtype>& data = *net.blob_by_name("data");
+    ASSERT_TRUE(net.has_blob("targets"));
+    const Blob<Dtype>& targets = *net.blob_by_name("targets");
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    const int num_param_blobs = 2;
+    ASSERT_EQ(num_param_blobs, param_blobs.size());
+    const Blob<Dtype>& weights = *param_blobs[0];
+    const Blob<Dtype>& bias = *param_blobs[1];
+    ASSERT_EQ(D * N, data.count());
+    ASSERT_EQ(N, targets.count());
+    ASSERT_EQ(D, weights.count());
+    ASSERT_EQ(1, bias.count());
+
+    updated_params->clear();
+    updated_params->resize(num_param_blobs);
+    for (int i = 0; i < num_param_blobs; ++i) {
+      (*updated_params)[i].reset(new Blob<Dtype>());
+    }
+    Blob<Dtype>& updated_weights = *(*updated_params)[0];
+    updated_weights.ReshapeLike(weights);
+    Blob<Dtype>& updated_bias = *(*updated_params)[1];
+    updated_bias.ReshapeLike(bias);
+
+    for (int i = 0; i <= D; ++i) {
+      // Compute the derivative with respect to the ith weight (i.e., the ith
+      // element of the gradient).
+      Dtype grad = 0;
+      for (int j = 0; j <= D; ++j) {
+        // Compute element (i, j) of X^T * X.
+        Dtype element = 0;
+        for (int k = 0; k < N; ++k) {
+          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
+          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
+          element += element_i * element_j;
+        }
+        if (j == D) {
+          grad += element * bias.cpu_data()[0];
+        } else {
+          grad += element * weights.cpu_data()[j];
+        }
+      }
+      for (int k = 0; k < N; ++k) {
+        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+        grad -= element_i * targets.cpu_data()[k];
+      }
+      // Scale the gradient over the N samples.
+      grad /= N;
+      // Add the weight decay to the gradient.
+      grad += weight_decay *
+          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
+      // Finally, add any momentum.
+      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      Dtype update_value = learning_rate * grad;
+      if (i == D) {
+        update_value += momentum * history[1]->cpu_data()[0];
+        updated_bias.mutable_cpu_diff()[0] = update_value;
+        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
+      } else {
+        update_value += momentum * history[0]->cpu_data()[i];
+        updated_weights.mutable_cpu_diff()[i] = update_value;
+        updated_weights.mutable_cpu_data()[i] =
+            weights.cpu_data()[i] - update_value;
+      }
+    }
+  }
+
+  void CheckLeastSquaresUpdate(
+      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
+    const int D = channels_ * height_ * width_;
+
+    const Blob<Dtype>& updated_weights = *updated_params[0];
+    const Blob<Dtype>& updated_bias = *updated_params[1];
+
+    Net<Dtype>& net = *this->solver_->net();
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    ASSERT_EQ(2, param_blobs.size());
+    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
+    ASSERT_EQ(D, solver_updated_weights.count());
+    const double kPrecision = 1e-3;
+    const double kMinPrecision = 1e-7;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
+      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
+      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
+    }
+    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
+    ASSERT_EQ(1, solver_updated_bias_blob.count());
+    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
+    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
+    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
+
+    // Check the solver's history -- should contain the previous update value.
+    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
+    ASSERT_EQ(2, history.size());
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_history = updated_weights.cpu_diff()[i];
+      const Dtype solver_history = history[0]->cpu_data()[i];
+      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_history), fabs(solver_history)));
+      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+    }
+    const Dtype expected_history = updated_bias.cpu_diff()[0];
+    const Dtype solver_history = history[1]->cpu_data()[0];
+    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+        std::min(fabs(expected_history), fabs(solver_history)));
+    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+  }
+
+  // Test that the correct update is computed for a regularized least squares
+  // problem:
+  //
+  //            E = (1/(2n)) || X w - y ||^2 + (lambda / 2) || w ||^2
+  //   \nabla_w E = (1/n) (X^T X w - X^T y) + lambda * w
+  //
+  // X \in R^{n x (d+1)} (each example is a row, (d+1)th element is always 1)
+  // w \in R^{(d+1) x 1} ((d+1)th element is the bias)
+  // y \in R^{n x 1}
+  // lambda is weight_decay
+  //
+  // TestLeastSquaresUpdate works "inductively", assuming that the solver
+  // correctly updates the net K (= iter_to_check) times, then given the history
+  // from the Kth update, we compute the (K+1)th update and check that it
+  // matches the solver's (K+1)th update.
+  void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
+      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+      const int iter_to_check = 0) {
+    // Initialize the solver and run K (= iter_to_check) solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+
+    // Compute the (K+1)th update using the analytic least squares gradient.
+    vector<shared_ptr<Blob<Dtype> > > updated_params;
+    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+                              &updated_params);
+
+    // Reinitialize the solver and run K+1 solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                          iter_to_check + 1);
+
+    // Check that the solver's solution matches ours.
+    CheckLeastSquaresUpdate(updated_params);
+  }
+};
+
+TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->TestLeastSquaresUpdate();
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneTenth) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+}  // namespace caffe

From b1c4f121b4f01b538eef6997ba3af6c9a71afd31 Mon Sep 17 00:00:00 2001
From: Vik Kamath <vikram.kmth@gmail.com>
Date: Mon, 7 Jul 2014 15:01:56 +0300
Subject: [PATCH 0380/2053] Changed path in description to point to
 parse_log.sh

It seems that parse_log.sh has moved from /path/to/caffe/scripts/ to /path/to/caffe/tools/extra. The edit changes the description to reflect this change. Nothing major really.
---
 tools/extra/plot_log.gnuplot.example | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example
index 76715c589f8..334ff1f2858 100644
--- a/tools/extra/plot_log.gnuplot.example
+++ b/tools/extra/plot_log.gnuplot.example
@@ -5,7 +5,7 @@
 # You had better check the data files before designing your own plots.
 
 # Please generate the neccessary data files with 
-# /path/to/caffe/scripts/parse_log.sh before plotting.
+# /path/to/caffe/tools/extra/parse_log.sh before plotting.
 # Example usage: 
 #     ./parse_log.sh mnist.log
 # Now you have mnist.log.train and mnist.log.test.

From 6786385c82f48ef7c48ad488ab674ff3d750cb26 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 1 Aug 2014 19:42:41 -0700
Subject: [PATCH 0381/2053] Add "lintclean" target to remove current lint
 outputs -- forces lint to be run again next time "make lint" is run.

---
 Makefile | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 62e5158b9bd..84f8db26abb 100644
--- a/Makefile
+++ b/Makefile
@@ -295,7 +295,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 ##############################
 # Define build targets
 ##############################
-.PHONY: all test clean linecount lint tools examples $(DIST_ALIASES) \
+.PHONY: all test clean linecount lint lintclean tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
@@ -308,6 +308,9 @@ linecount:
 
 lint: $(EMPTY_LINT_REPORT)
 
+lintclean:
+	@ $(RM) -r $(LINT_OUTPUT_DIR) $(EMPTY_LINT_REPORT) $(NONEMPTY_LINT_REPORT)
+
 $(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
 	@ cat $(LINT_OUTPUTS) > $@
 	@ if [ -s "$@" ]; then \

From bd288793ff425a6753e9f60b28f7554524f6e63c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 1 Aug 2014 19:58:56 -0700
Subject: [PATCH 0382/2053] Enable the 'build/include_alpha' rules to make lint
 check that the includes are correctly alphabetized.

---
 scripts/cpp_lint.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 76eee4b2dbe..3f0ca1c3527 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -210,7 +210,6 @@
 # off by default (i.e., categories that must be enabled by the --filter= flags).
 # All entries here should start with a '-' or '+', as in the --filter= flag.
 _DEFAULT_FILTERS = [
-  '-build/include_alpha',
   '-build/include_dir',
   '-readability/todo',
   ]

From 92f00dd3a6a84315a302e09eead55506369c1104 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 1 Aug 2014 20:14:42 -0700
Subject: [PATCH 0383/2053] Fix header alphabetization lint errors.

---
 examples/cifar10/convert_cifar_data.cpp               | 10 +++++-----
 examples/mnist/convert_mnist_data.cpp                 | 10 +++++-----
 include/caffe/blob.hpp                                |  2 +-
 include/caffe/common_layers.hpp                       |  4 ++--
 include/caffe/data_layers.hpp                         |  6 +++---
 include/caffe/filler.hpp                              |  4 ++--
 include/caffe/loss_layers.hpp                         |  4 ++--
 include/caffe/neuron_layers.hpp                       |  2 +-
 include/caffe/test/test_caffe_main.hpp                |  2 +-
 include/caffe/util/io.hpp                             |  2 +-
 include/caffe/util/rng.hpp                            |  8 +++++---
 matlab/caffe/matcaffe.cpp                             |  1 +
 src/caffe/layer_factory.cpp                           |  2 +-
 src/caffe/layers/accuracy_layer.cpp                   |  4 ++--
 src/caffe/layers/concat_layer.cpp                     |  2 +-
 src/caffe/layers/concat_layer.cu                      |  2 +-
 src/caffe/layers/conv_layer.cpp                       |  4 ++--
 src/caffe/layers/conv_layer.cu                        |  4 ++--
 src/caffe/layers/data_layer.cpp                       |  4 ++--
 src/caffe/layers/data_layer.cu                        |  8 ++++----
 src/caffe/layers/dropout_layer.cpp                    |  2 +-
 src/caffe/layers/dropout_layer.cu                     |  2 +-
 src/caffe/layers/eltwise_layer.cpp                    |  2 +-
 src/caffe/layers/eltwise_layer.cu                     |  2 +-
 src/caffe/layers/euclidean_loss_layer.cpp             |  4 ++--
 src/caffe/layers/euclidean_loss_layer.cu              |  4 ++--
 src/caffe/layers/flatten_layer.cpp                    |  2 +-
 src/caffe/layers/flatten_layer.cu                     |  2 +-
 src/caffe/layers/hdf5_data_layer.cpp                  |  4 ++--
 src/caffe/layers/hinge_loss_layer.cpp                 |  6 +++---
 src/caffe/layers/im2col_layer.cpp                     |  2 +-
 src/caffe/layers/im2col_layer.cu                      |  2 +-
 src/caffe/layers/image_data_layer.cpp                 |  9 +++------
 src/caffe/layers/image_data_layer.cu                  |  7 -------
 src/caffe/layers/infogain_loss_layer.cpp              |  6 +++---
 src/caffe/layers/inner_product_layer.cpp              |  2 +-
 src/caffe/layers/inner_product_layer.cu               |  2 +-
 src/caffe/layers/loss_layer.cpp                       |  6 +++---
 src/caffe/layers/lrn_layer.cpp                        |  2 +-
 src/caffe/layers/lrn_layer.cu                         |  2 +-
 src/caffe/layers/multinomial_logistic_loss_layer.cpp  |  6 +++---
 src/caffe/layers/pooling_layer.cpp                    |  2 +-
 src/caffe/layers/pooling_layer.cu                     |  2 +-
 src/caffe/layers/power_layer.cpp                      |  2 +-
 src/caffe/layers/power_layer.cu                       |  2 +-
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp |  2 +-
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu  |  2 +-
 src/caffe/layers/slice_layer.cpp                      |  4 ++--
 src/caffe/layers/slice_layer.cu                       |  2 +-
 src/caffe/layers/softmax_layer.cpp                    |  2 +-
 src/caffe/layers/softmax_layer.cu                     |  2 +-
 src/caffe/layers/softmax_loss_layer.cpp               |  2 +-
 src/caffe/layers/softmax_loss_layer.cu                |  2 +-
 src/caffe/layers/split_layer.cpp                      |  2 +-
 src/caffe/layers/split_layer.cu                       |  2 +-
 src/caffe/layers/window_data_layer.cpp                |  6 +++---
 src/caffe/layers/window_data_layer.cu                 |  2 +-
 src/caffe/net.cpp                                     |  5 +++--
 src/caffe/test/test_accuracy_layer.cpp                |  3 ++-
 src/caffe/test/test_argmax_layer.cpp                  |  2 +-
 src/caffe/test/test_benchmark.cpp                     |  4 +++-
 src/caffe/test/test_blob.cpp                          |  3 ++-
 src/caffe/test/test_common.cpp                        |  2 ++
 src/caffe/test/test_concat_layer.cpp                  |  3 ++-
 src/caffe/test/test_convolution_layer.cpp             |  3 ++-
 src/caffe/test/test_data_layer.cpp                    |  5 ++++-
 src/caffe/test/test_dummy_data_layer.cpp              |  4 +++-
 src/caffe/test/test_eltwise_layer.cpp                 |  3 ++-
 src/caffe/test/test_euclidean_loss_layer.cpp          |  3 ++-
 src/caffe/test/test_filler.cpp                        |  1 +
 src/caffe/test/test_flatten_layer.cpp                 |  3 ++-
 src/caffe/test/test_hdf5_output_layer.cpp             |  4 +++-
 src/caffe/test/test_hdf5data_layer.cpp                |  4 +++-
 src/caffe/test/test_hinge_loss_layer.cpp              |  3 ++-
 src/caffe/test/test_im2col_kernel.cu                  |  3 ++-
 src/caffe/test/test_im2col_layer.cpp                  |  3 ++-
 src/caffe/test/test_image_data_layer.cpp              |  6 ++++--
 src/caffe/test/test_infogain_loss_layer.cpp           |  3 ++-
 src/caffe/test/test_inner_product_layer.cpp           |  3 ++-
 src/caffe/test/test_lrn_layer.cpp                     |  3 ++-
 src/caffe/test/test_math_functions.cpp                |  1 +
 src/caffe/test/test_maxpool_dropout_layers.cpp        |  3 ++-
 src/caffe/test/test_memory_data_layer.cpp             |  1 +
 .../test/test_multinomial_logistic_loss_layer.cpp     |  3 ++-
 src/caffe/test/test_net.cpp                           |  5 ++---
 src/caffe/test/test_neuron_layer.cpp                  |  3 ++-
 src/caffe/test/test_platform.cpp                      |  3 ++-
 src/caffe/test/test_pooling_layer.cpp                 |  3 ++-
 src/caffe/test/test_power_layer.cpp                   |  2 +-
 src/caffe/test/test_protobuf.cpp                      |  4 +++-
 src/caffe/test/test_random_number_generator.cpp       |  2 ++
 src/caffe/test/test_sgd_solver.cpp                    |  1 +
 .../test/test_sigmoid_cross_entropy_loss_layer.cpp    |  3 ++-
 src/caffe/test/test_slice_layer.cpp                   |  3 ++-
 src/caffe/test/test_softmax_layer.cpp                 |  3 ++-
 src/caffe/test/test_softmax_with_loss_layer.cpp       |  3 ++-
 src/caffe/test/test_solver.cpp                        |  2 +-
 src/caffe/test/test_split_layer.cpp                   |  5 +++--
 src/caffe/test/test_stochastic_pooling.cpp            |  3 ++-
 src/caffe/test/test_syncedmem.cpp                     |  1 +
 src/caffe/test/test_tanh_layer.cpp                    |  3 ++-
 src/caffe/test/test_threshold_layer.cpp               |  2 +-
 src/caffe/test/test_upgrade_proto.cpp                 |  1 +
 src/caffe/test/test_util_blas.cpp                     |  1 +
 src/caffe/util/insert_splits.cpp                      |  2 +-
 src/caffe/util/io.cpp                                 | 10 +++++-----
 src/caffe/util/upgrade_proto.cpp                      |  6 +++---
 tools/dump_network.cpp                                |  4 ++--
 tools/extract_features.cpp                            | 11 ++++++-----
 tools/test_net.cpp                                    |  2 +-
 tools/upgrade_net_proto_binary.cpp                    |  2 +-
 tools/upgrade_net_proto_text.cpp                      |  2 +-
 112 files changed, 210 insertions(+), 165 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 8e223b212ad..d1f3f3e0e9f 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -7,14 +7,14 @@
 // The CIFAR dataset could be downloaded at
 //    http://www.cs.toronto.edu/~kriz/cifar.html
 
-#include <google/protobuf/text_format.h>
-#include <glog/logging.h>
-#include <leveldb/db.h>
-
-#include <stdint.h>
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "glog/logging.h"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "stdint.h"
+
 #include "caffe/proto/caffe.pb.h"
 
 using std::string;
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 593a2d23313..c2cf4b3af33 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -7,14 +7,14 @@
 // The MNIST dataset could be downloaded at
 //    http://yann.lecun.com/exdb/mnist/
 
-#include <google/protobuf/text_format.h>
-#include <glog/logging.h>
-#include <leveldb/db.h>
-
-#include <stdint.h>
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "glog/logging.h"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "stdint.h"
+
 #include "caffe/proto/caffe.pb.h"
 
 uint32_t swap_endian(uint32_t val) {
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index ab7a0f60f80..2086c5119bc 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -4,8 +4,8 @@
 #define CAFFE_BLOB_HPP_
 
 #include "caffe/common.hpp"
-#include "caffe/syncedmem.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 5d872452ae2..ed11487c6f3 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -9,10 +9,10 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
 #include "caffe/loss_layers.hpp"
-#include "caffe/data_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 107d7800d1e..f868b2869da 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -7,17 +7,17 @@
 #include <utility>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
+#include "hdf5.h"
 #include "leveldb/db.h"
 #include "lmdb.h"
-#include "hdf5.h"
-#include "boost/scoped_ptr.hpp"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/internal_thread.hpp"
 
 namespace caffe {
 
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index 242f11a3513..beeee01dcb9 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -9,11 +9,11 @@
 
 #include <string>
 
-#include "caffe/common.hpp"
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index fb7f5539674..73a677b624a 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -7,10 +7,10 @@
 #include <utility>
 #include <vector>
 
-#include "leveldb/db.h"
-#include "pthread.h"
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
+#include "leveldb/db.h"
+#include "pthread.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 8f0588d4771..4b3f6f4394d 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -7,9 +7,9 @@
 #include <utility>
 #include <vector>
 
-#include "pthread.h"
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
+#include "pthread.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 9e7b85944da..759e01a2629 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -8,8 +8,8 @@
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 
-#include <cstdlib>
 #include <cstdio>
+#include <cstdlib>
 
 #include "caffe/common.hpp"
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 631dbe7ca64..4f517ff1d71 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -8,9 +8,9 @@
 #include "google/protobuf/message.h"
 #include "hdf5.h"
 #include "hdf5_hl.h"
-#include "caffe/proto/caffe.pb.h"
 
 #include "caffe/blob.hpp"
+#include "caffe/proto/caffe.pb.h"
 
 #define HDF5_NUM_DIMS 4
 
diff --git a/include/caffe/util/rng.hpp b/include/caffe/util/rng.hpp
index 2b79ef1801b..f774d704569 100644
--- a/include/caffe/util/rng.hpp
+++ b/include/caffe/util/rng.hpp
@@ -3,10 +3,12 @@
 #ifndef CAFFE_RNG_CPP_HPP_
 #define CAFFE_RNG_CPP_HPP_
 
-#include <boost/random/mersenne_twister.hpp>
-#include <boost/random/uniform_int.hpp>
-#include <iterator>
 #include <algorithm>
+#include <iterator>
+
+#include "boost/random/mersenne_twister.hpp"
+#include "boost/random/uniform_int.hpp"
+
 #include "caffe/common.hpp"
 
 namespace caffe {
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 2aad3192a65..d0e98f8319d 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -8,6 +8,7 @@
 #include <vector>
 
 #include "mex.h"
+
 #include "caffe/caffe.hpp"
 
 #define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index de960214153..1165abc6da8 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -6,8 +6,8 @@
 #include <string>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 7c913114abc..f99b01dc105 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -6,9 +6,9 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index dd899a57c85..32203a8d980 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 2643d7441c8..1a97f02a62a 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index c7949e049cd..ca63e976e80 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -2,11 +2,11 @@
 
 #include <vector>
 
+#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/filler.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index fc114b0b328..616a81a1388 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -2,11 +2,11 @@
 
 #include <vector>
 
+#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/filler.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index ef27c1a3f1f..ddc7f770b46 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,17 +1,17 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <stdint.h>
 #include <leveldb/db.h>
+#include <stdint.h>
 
 #include <string>
 #include <vector>
 
 #include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index e275e9d913e..fcbf2346840 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -1,12 +1,12 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <stdint.h>
-#include <leveldb/db.h>
-#include <pthread.h>
-
 #include <string>
 #include <vector>
 
+#include "leveldb/db.h"
+#include "pthread.h"
+#include "stdint.h"
+
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 65360d67ada..605aa151196 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -5,9 +5,9 @@
 #include <vector>
 
 #include "caffe/common.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 158456f9bc5..8befa4d87fb 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -7,8 +7,8 @@
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/syncedmem.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 44bad932a11..09ca66f38e8 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 99c14feace1..d47d24afad7 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 43e9989f513..2a814f0150a 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -3,9 +3,9 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index b070ea96ff8..e1358f0fb51 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -3,9 +3,9 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 7e106d26f79..f1fdd322eb4 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index 68add383c48..60b67f06e05 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 81cfdc096df..38b2b8d23f3 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -7,13 +7,13 @@
   :: don't forget to update hdf5_daa_layer.cu accordingly
 - add ability to shuffle filenames if flag is set
 */
-#include <stdint.h>
+#include <fstream>  // NOLINT(readability/streams)
 #include <string>
 #include <vector>
-#include <fstream>  // NOLINT(readability/streams)
 
 #include "hdf5.h"
 #include "hdf5_hl.h"
+#include "stdint.h"
 
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 91fe1534da9..a7ec061eb28 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -1,14 +1,14 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index fabce0a6ad6..fb3dad43844 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -2,10 +2,10 @@
 
 #include <vector>
 
+#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/common.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 84439742250..c9802541aaa 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -2,10 +2,10 @@
 
 #include <vector>
 
+#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/common.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index a0f91af29fa..a10f02c37a8 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,13 +1,10 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <stdint.h>
-#include <leveldb/db.h>
-
-#include <string>
-#include <vector>
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
+#include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index fc7e13b338a..8a449d010e2 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -1,13 +1,6 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <stdint.h>
-#include <leveldb/db.h>
-#include <pthread.h>
-
-#include <string>
 #include <vector>
-#include <iostream>  // NOLINT(readability/streams)
-#include <fstream>  // NOLINT(readability/streams)
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 4b02f4e61db..c972c032607 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -1,14 +1,14 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 5505d08587a..6493d0bd497 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -6,8 +6,8 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 513e4eb7fa8..4b670d644c2 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -6,8 +6,8 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 92eb06f7b0c..656e85a75d8 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -1,14 +1,14 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 0e2df2ff4bb..50ad6464449 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 237a42b2860..7a19350549c 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index beea747c31b..e5eafd65078 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -1,14 +1,14 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 9415a15a094..75a74dac9ec 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -6,9 +6,9 @@
 
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index efa1aaa2ab1..6f3a7205091 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -5,8 +5,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 55ace207b7a..86cb3d76291 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -4,8 +4,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 0aa00272eea..db88fee8f3b 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -4,8 +4,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 31522dd1fe6..2bf931d004b 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -5,8 +5,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 1d1f8cd51ff..698ec1dbde1 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -5,8 +5,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index e8231e357d9..fc86a2d4307 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -1,11 +1,11 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <vector>
 #include <algorithm>
+#include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index 3ea8d57659d..70702148f5d 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 0de1403585a..6a98b320208 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -4,8 +4,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index d471ba51cd0..6d65fbce957 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -7,8 +7,8 @@
 #include "thrust/device_vector.h"
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 4e957cee9b4..43e8fcac44c 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -5,8 +5,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index ae7060d90bd..f55b8c7adf3 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -5,8 +5,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 6bae4ef22fb..d13b8d12358 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index 4c921d39f17..a89c5d61883 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -3,8 +3,8 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index fe7f61c91df..cbd269685ce 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -5,11 +5,11 @@
 #include <stdint.h>
 
 #include <algorithm>
-#include <string>
-#include <vector>
-#include <map>
 #include <fstream>  // NOLINT(readability/streams)
+#include <map>
+#include <string>
 #include <utility>
+#include <vector>
 
 #include "opencv2/core/core.hpp"
 #include "opencv2/highgui/highgui.hpp"
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index d95e5b20e88..5efcc3f4adc 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -2,8 +2,8 @@
 //
 // Based on data_layer.cpp by Yangqing Jia.
 
-#include <stdint.h>
 #include <pthread.h>
+#include <stdint.h>
 
 #include <string>
 #include <vector>
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index b80bbba8479..6fab0e3a6b4 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -7,14 +7,15 @@
 #include <vector>
 
 #include "caffe/common.hpp"
-#include "caffe/proto/caffe.pb.h"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/insert_splits.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
+#include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 5023a809d17..95217df3f1f 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -1,11 +1,12 @@
 // Copyright 2014 BVLC and contributors.
 
+#include <cfloat>
 #include <cmath>
 #include <cstring>
-#include <cfloat>
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 51d68c42f9a..3f55edaac22 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -4,11 +4,11 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index fe0cc82a1d8..e6d9ebf9a05 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -1,10 +1,12 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <unistd.h>  // for usleep
-#include <gtest/gtest.h>
+
+#include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
 #include "caffe/util/benchmark.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index aec88f322e6..89f4a161c9c 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -3,8 +3,9 @@
 #include <cstring>
 
 #include "gtest/gtest.h"
-#include "caffe/common.hpp"
+
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index a8e2eb15270..455f5d8a32d 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -3,9 +3,11 @@
 #include <cstring>
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 4cb28b1e24e..b07c5727a79 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index f3cfb680990..41951cfb940 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index ed7337c5eca..be478b2746c 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -4,12 +4,15 @@
 #include <vector>
 
 #include "leveldb/db.h"
+
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 5b5f2025737..aa6cc55364e 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -4,10 +4,12 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 53bff96ed6d..fe3269bb3a2 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -3,13 +3,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index dd27670da13..6dd631e55e8 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 1b145f2418e..d0d750fff47 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -3,6 +3,7 @@
 #include <cstring>
 
 #include "gtest/gtest.h"
+
 #include "caffe/filler.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index bea099b1f92..928db849296 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index ec48fc4fc32..a36a8cbd9a8 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -4,11 +4,13 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index f3d7c8c6974..1f3bf9f3cd0 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -6,11 +6,13 @@
 #include "leveldb/db.h"
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 84374e9579a..c6be1d73262 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 37d1a1523af..89008ac7e39 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -4,11 +4,12 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/im2col.hpp"
+#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index aaee3d81bf7..6884b059fde 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index ae9a2dbc17d..bbeedb8dce5 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,17 +1,19 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 #include <map>
 #include <string>
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::map;
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index 99bad263e88..7b3705084a5 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/loss_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index de194f2d63f..31d87afc7ce 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 5bd5533f955..fbb03dfecb5 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -5,13 +5,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 using std::min;
 using std::max;
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index 502b77e07a8..c89ca027adf 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -7,6 +7,7 @@
 #include <cstdlib>  // for rand_r
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 733bbf4ad1f..e939d0c6e11 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index c9d588986c2..60e75ebbc1a 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -4,6 +4,7 @@
 
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index ec53d40e53e..9b6bb067805 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index a0a387929be..6787ba956ec 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -7,14 +7,13 @@
 #include "google/protobuf/text_format.h"
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/net.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
-
-using std::ostringstream;
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index b3a536a7bfd..59c9b3d34f5 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_platform.cpp b/src/caffe/test/test_platform.cpp
index e1c4dafc7bb..a4f4b4260c4 100644
--- a/src/caffe/test/test_platform.cpp
+++ b/src/caffe/test/test_platform.cpp
@@ -2,11 +2,12 @@
 
 #ifndef CPU_ONLY
 
-#include <cstdlib>
 #include <cstdio>
+#include <cstdlib>
 
 #include "glog/logging.h"
 #include "gtest/gtest.h"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index b9cec54af39..4636dd773a4 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 44fcbd9117c..e35d539b776 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -9,9 +9,9 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_protobuf.cpp b/src/caffe/test/test_protobuf.cpp
index 182af2e4611..da1a0d3f5eb 100644
--- a/src/caffe/test/test_protobuf.cpp
+++ b/src/caffe/test/test_protobuf.cpp
@@ -6,9 +6,11 @@
 
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
-#include "caffe/test/test_caffe_main.hpp"
+
 #include "caffe/proto/caffe.pb.h"
 
+#include "caffe/test/test_caffe_main.hpp"
+
 namespace caffe {
 
 class ProtoTest : public ::testing::Test {};
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index abe21c2523e..8e3919157af 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -4,9 +4,11 @@
 #include <cstring>
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
diff --git a/src/caffe/test/test_sgd_solver.cpp b/src/caffe/test/test_sgd_solver.cpp
index a28ed7b1817..14dcac25e8e 100644
--- a/src/caffe/test/test_sgd_solver.cpp
+++ b/src/caffe/test/test_sgd_solver.cpp
@@ -8,6 +8,7 @@
 #include "google/protobuf/text_format.h"
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index a5388db092f..7205ce9311b 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index 59f6a8d4c66..e61f3e4f2c9 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -4,13 +4,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index fa899f92db3..a305c647585 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -5,13 +5,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 6f45c3889c2..2994a9117af 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index feefb51210a..133d01c5446 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -5,8 +5,8 @@
 #include <vector>
 
 #include "google/protobuf/text_format.h"
-
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index bbee6d28855..a4e5857877e 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -6,14 +6,15 @@
 
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 #include "caffe/util/insert_splits.hpp"
+#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 66e9b2d7d88..600b909a638 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -5,13 +5,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 using std::min;
 
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index f07682a2aa5..5ccc480112d 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -4,6 +4,7 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/device_alternate.hpp"
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 7fc443f6d6d..1dac5a57043 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -6,13 +6,14 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 7006dd11880..b06d697ab0b 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -3,11 +3,11 @@
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 72ee838931d..a16b835f864 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -6,6 +6,7 @@
 
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/util/upgrade_proto.hpp"
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index d74f9f017ee..841edefc66c 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -5,6 +5,7 @@
 #include <cstring>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/util/device_alternate.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 4de546810e5..b88b320ff3d 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -1,8 +1,8 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <map>
-#include <string>
 #include <sstream>
+#include <string>
 #include <utility>
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 4f200474459..575ea3d63c2 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -1,23 +1,23 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <stdint.h>
 #include <fcntl.h>
-#include <google/protobuf/text_format.h>
-#include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/io/coded_stream.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/text_format.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
 #include <opencv2/imgproc/imgproc.hpp>
+#include <stdint.h>
 
 #include <algorithm>
+#include <fstream>  // NOLINT(readability/streams)
 #include <string>
 #include <vector>
-#include <fstream>  // NOLINT(readability/streams)
 
 #include "caffe/common.hpp"
-#include "caffe/util/io.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index e1a48f194e1..a6e9c404df7 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -1,16 +1,16 @@
 // Copyright 2014 BVLC and contributors.
 
-#include <google/protobuf/text_format.h>
-#include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/io/coded_stream.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/text_format.h>
 
 #include <map>
 #include <string>
 
 #include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/upgrade_proto.hpp"
-#include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index c5aac604079..8984016916a 100644
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -18,11 +18,11 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/net.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/io.hpp"
 #include "caffe/solver.hpp"
+#include "caffe/util/io.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 22b0e61e227..06e07fe6cba 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -1,19 +1,20 @@
 // Copyright 2014 BVLC and contributors.
 
 #include <stdio.h>  // for snprintf
-#include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
-#include <leveldb/write_batch.h>
-#include <boost/algorithm/string.hpp>
 #include <string>
 #include <vector>
 
+#include "boost/algorithm/string.hpp"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/net.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
+#include "caffe/vision_layers.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
index 9d37536c758..b6f827e6285 100644
--- a/tools/test_net.cpp
+++ b/tools/test_net.cpp
@@ -6,8 +6,8 @@
 // Usage:
 //    test_net net_proto pretrained_net_proto iterations [CPU/GPU]
 
-#include <cstring>
 #include <cstdlib>
+#include <cstring>
 #include <vector>
 
 #include "caffe/caffe.hpp"
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index 928fc52dc27..ed703f027cd 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -5,8 +5,8 @@
 //    upgrade_net_proto_binary v0_net_proto_file_in net_proto_file_out
 
 #include <cstring>
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 8a77f752cc2..d05d1ac5883 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -5,8 +5,8 @@
 //    upgrade_net_proto_text v0_net_proto_file_in net_proto_file_out
 
 #include <cstring>
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"

From c1faaa36ca76d15c078f6873c507b499429280db Mon Sep 17 00:00:00 2001
From: Seiya Tokui <beam.web@gmail.com>
Date: Tue, 5 Aug 2014 18:07:19 +0900
Subject: [PATCH 0384/2053] Fix conflict on setting of LRN layers between
 train/test net and deploy net

---
 examples/cifar10/cifar10_full.prototxt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 237a7a0a0ed..9e7016362fc 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -43,6 +43,7 @@ layers {
   bottom: "pool1"
   top: "norm1"
   lrn_param {
+    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
@@ -74,6 +75,7 @@ layers {
   bottom: "conv2"
   top: "pool2"
   pooling_param {
+    norm_region: WITHIN_CHANNEL
     pool: AVE
     kernel_size: 3
     stride: 2

From 99f27fc082612745c8d8cc932be42fdfd035e789 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 14:55:13 -0700
Subject: [PATCH 0385/2053] define caffe.Net input preprocessing members by
 boost::python

define `Net.{mean, input_scale, channel_swap}` on the boost::python side
so that the members always exist. drop ugly initialization logic.
---
 python/caffe/_caffe.cpp | 34 +++++++++++++++++++++-------------
 python/caffe/pycaffe.py | 16 ++++++----------
 2 files changed, 27 insertions(+), 23 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 10fc23b7974..30c86aeb384 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -25,6 +25,7 @@
 
 
 using namespace caffe;  // NOLINT(build/namespaces)
+using boost::python::dict;
 using boost::python::extract;
 using boost::python::len;
 using boost::python::list;
@@ -274,6 +275,10 @@ struct CaffeNet {
 
   // The pointer to the internal caffe::Net instant.
   shared_ptr<Net<float> > net_;
+  // Input preprocessing configuration attributes.
+  dict mean_;
+  dict input_scale_;
+  dict channel_swap_;
   // if taking input from an ndarray, we need to hold references
   object input_data_;
   object input_labels_;
@@ -311,19 +316,22 @@ BOOST_PYTHON_MODULE(_caffe) {
   boost::python::class_<CaffeNet, shared_ptr<CaffeNet> >(
       "Net", boost::python::init<string, string>())
       .def(boost::python::init<string>())
-      .def("_forward",          &CaffeNet::Forward)
-      .def("_backward",         &CaffeNet::Backward)
-      .def("set_mode_cpu",      &CaffeNet::set_mode_cpu)
-      .def("set_mode_gpu",      &CaffeNet::set_mode_gpu)
-      .def("set_phase_train",   &CaffeNet::set_phase_train)
-      .def("set_phase_test",    &CaffeNet::set_phase_test)
-      .def("set_device",        &CaffeNet::set_device)
-      .add_property("_blobs",   &CaffeNet::blobs)
-      .add_property("layers",   &CaffeNet::layers)
-      .add_property("inputs",   &CaffeNet::inputs)
-      .add_property("outputs",  &CaffeNet::outputs)
-      .def("_set_input_arrays", &CaffeNet::set_input_arrays)
-      .def("save",              &CaffeNet::save);
+      .def("_forward",              &CaffeNet::Forward)
+      .def("_backward",             &CaffeNet::Backward)
+      .def("set_mode_cpu",          &CaffeNet::set_mode_cpu)
+      .def("set_mode_gpu",          &CaffeNet::set_mode_gpu)
+      .def("set_phase_train",       &CaffeNet::set_phase_train)
+      .def("set_phase_test",        &CaffeNet::set_phase_test)
+      .def("set_device",            &CaffeNet::set_device)
+      .add_property("_blobs",       &CaffeNet::blobs)
+      .add_property("layers",       &CaffeNet::layers)
+      .add_property("inputs",       &CaffeNet::inputs)
+      .add_property("outputs",      &CaffeNet::outputs)
+      .add_property("mean",         &CaffeNet::mean_)
+      .add_property("input_scale",  &CaffeNet::input_scale_)
+      .add_property("channel_swap", &CaffeNet::channel_swap_)
+      .def("_set_input_arrays",     &CaffeNet::set_input_arrays)
+      .def("save",                  &CaffeNet::save);
 
   boost::python::class_<CaffeBlob, CaffeBlobWrap>(
       "Blob", boost::python::no_init)
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 870dec4f45e..64747f30483 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -211,8 +211,6 @@ def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
     mode: elementwise = use the whole mean (and check dimensions)
           channel = channel constant (e.g. mean pixel instead of mean image)
     """
-    if not hasattr(self, 'mean'):
-        self.mean = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     in_shape = self.blobs[input_].data.shape
@@ -240,8 +238,6 @@ def _Net_set_input_scale(self, input_, scale):
     input_: which input to assign this scale factor
     scale: scale coefficient
     """
-    if not hasattr(self, 'input_scale'):
-        self.input_scale = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     self.input_scale[input_] = scale
@@ -257,8 +253,6 @@ def _Net_set_channel_swap(self, input_, order):
     order: the order to take the channels.
            (2,1,0) maps RGB to BGR for example.
     """
-    if not hasattr(self, 'channel_swap'):
-        self.channel_swap = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     self.channel_swap[input_] = order
@@ -282,6 +276,7 @@ def _Net_preprocess(self, input_name, input_):
     caffe_inputs: (K x H x W) ndarray
     """
     caffe_in = input_.astype(np.float32)
+    mean = self.mean.get(input_name)
     input_scale = self.input_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
     in_size = self.blobs[input_name].data.shape[2:]
@@ -292,8 +287,8 @@ def _Net_preprocess(self, input_name, input_):
     if channel_order is not None:
         caffe_in = caffe_in[:, :, channel_order]
     caffe_in = caffe_in.transpose((2, 0, 1))
-    if hasattr(self, 'mean'):
-        caffe_in -= self.mean.get(input_name, 0)
+    if mean is not None:
+        caffe_in -= mean
     return caffe_in
 
 
@@ -302,10 +297,11 @@ def _Net_deprocess(self, input_name, input_):
     Invert Caffe formatting; see Net.preprocess().
     """
     decaf_in = input_.copy().squeeze()
+    mean = self.mean.get(input_name)
     input_scale = self.input_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    if hasattr(self, 'mean'):
-        decaf_in += self.mean.get(input_name, 0)
+    if mean is not None:
+        decaf_in += mean
     decaf_in = decaf_in.transpose((1,2,0))
     if channel_order is not None:
         channel_order_inverse = [channel_order.index(i)

From 6c87b92c76931e0b0aa4e2af135812112306e89b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 20:10:16 -0700
Subject: [PATCH 0386/2053] [example] add caffe to pythonpath in all examples

---
 examples/imagenet_classification.ipynb | 7 +++++--
 examples/net_surgery.ipynb             | 7 ++++++-
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index e7fe9f84677..40e69aea138 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -31,9 +31,12 @@
       "import matplotlib.pyplot as plt\n",
       "%matplotlib inline\n",
       "\n",
-      "import caffe\n",
-      "\n",
+      "# Make sure that caffe is on the python path:\n",
       "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
+      "import caffe\n",
       "\n",
       "# Set the right path to your model definition file, pretrained model weights,\n",
       "# and the image you would like to classify.\n",
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 0a6e208f6d7..d64ae853ace 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -135,6 +135,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "# Make sure that caffe is on the python path:\n",
+      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
       "import caffe\n",
       "\n",
       "# Load the original network and extract the fully-connected layers' parameters.\n",
@@ -326,4 +331,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file

From 943b98ea4927db22c6aad821972aa5c2d20b4af1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 16:08:44 -0700
Subject: [PATCH 0387/2053] [example] fix example outputs

With the right input processing, the actual image classification output
is sensible.

- filter visualization example's top prediction is "tabby cat"
- net surgery fully-convolutional output map is better

Fix incorrect class names too.
---
 examples/filter_visualization.ipynb | 75 +++++++++++++----------------
 examples/net_surgery.ipynb          | 20 ++++----
 2 files changed, 44 insertions(+), 51 deletions(-)

diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 56f6b8d30e4..0fe863bc7e5 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,8 +1,8 @@
 {
  "metadata": {
-  "name": "Filter visualization",
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "include_in_docs": true
+  "include_in_docs": true,
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -229,9 +229,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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RmTD/h556AXIpjitBH7mezTJ2KT8g3eQzP1ASIcpYwXpKY0nYtggRZhMUpUJfa2aDkqif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T58NKqsizwd2ItQNMeYyKHd3UgCwgkmLHkmRU7hLVVMmoDg2ycWb58Efi9pamOMouShlu/C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\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/+U/+2oYmIR4QKRzLI02sjSBSw1WNe4mIrCZurFdXbU++fNkFKIxGi5kgWNsJ2kNVGrum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\nCf7X//oXERFp1G0+d3Q+zwlN7uveViX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677sZausTsgLUkjKCiPQkQVnziTnp+Sw1UCw93swJ5bPOU/SdS6jIeRmAIGsn\nvS1p9pBws+3KAwWYoaNWaXNec9KclBmRypYtW7Zs2bJl+75YfpHKli1btmzZsmW7o90f2bwfTCmb\nUYdU0Fl6DFSxVlWWaRPxSU3QeEKdlFoVFfVsxI0UXYUJsZI6Jno1wM2DQ32EqivVRwIsOCo8atSH\ngRbJRlwBNYjFhbidmPhVbmIuCEV6112UIEUKKZtuPiYPNjM7wM3Y96yP3D8TdQo3cL8LBz545Odl\nk9Tibqjr4L7ZCLRfQg34jdf9fGwKTULLOhdF+O1RYH8Syw/uiYg6P0PnhSNuaBANpAruiV7kUXbw\n964E769JyoyQvffrfoDGjmrxxESiAs9HHRslMaJ/pO+IvKureI5QNeB8hefLJRTOWIj1VgjI6P/1\n6kEs+wNf/FfDdzvpUJBYf9fv+WOx6D8Gyfdv/O2/ZmZmH18+j989uV4qmw/lEffsp2U99f6jGy9x\nQYBYLWNntQWhmc0wKZwPIrBKzJwg+1JvaFZea3TLicuAARUy7uNI4HVrpQcs3YMkwqobg/N57pdU\ngbETFxRcNfVK1zCsSXW5KIs0B9UYQl+r7k1U0RZtpwGu75uXvv4cn2JN2KveHlxL4qqne51yV3qv\nBcb1du3adg/Ogy7ZZusukxIu4FXjrt3n0zMzM9tXQizGGidT12qsDwMzAYjbkW2sLmBOp8K0nUI9\nVSuviG7BRBgulIlXbg9ttc1ZuB9hUUQ3n2phkdCtpHT2v+rCTewznf9GV5lfo8fSARmxSEkJN8FM\nDFqnIvnOzGyGq7BV1x76WGkZTEKsmQo8KEMfnuFjZLCPciDw5TCKAj4DVZKoDFJwJACAa2uxXE+V\nllMjomKMLlUlkS+DUuj6nYTE3kKVfS0q5qvVkoDP5+5RngV9cr9Ly4hUtmzZsmXLli3bHe3eEKmy\nKBIiWNx1a1inf7v4a1bCWvxuuSPUEE6+6cY8PUpijztcDVfFDrpYvpmnoZbhfEpA5G5Wd9jTQDkF\nEKFVagEhzqXsVolEKTm5AslPiXgF3vQbCZM/K0IoctP4LqHGtq8HSnU4usJxvH3ZQtzehB3s7a3v\ntM4uww5vM/rukwhLLWq/3Dmfn3n+t8ePDfW88eu+ErrcbgT9AWF+nK9jGQm1qmLMndAkoeskRXZy\nP3vc5ErCrxmVUAARmI/eJyTF9xqai/oqIhhlJARV4A4qkV8ASqK5xghicqerCtdxRyjE6glj8sGZ\nq5PvkYcLafg3AAAgAElEQVTqzc1bsezwcUDsPvjwN2LZi0+Cynm78R3Zj/8Hf87MzL72tX/NzMz+\nq7/4n/m10IedBBs8eRmCB3QHFqeuzJ2pXN5rAeV7KtabuZwDibKaG5IbV82r5deU3feJbAen8u/F\n3HW6TkTtBN7D0uYT/0uvn36aCbCdbsnxsSQKq0XUI2Z7UPiPHzKGiCZL2x2O4e+DyB9QDb6QMHnm\n/5xk7ZiIBOL41hQtCH13uX0Yy167ChN7cyaINPp6s7qIZQPOu7t+6vezQvBGK2tnRGljxEL8jn/V\nCQEc8gNC2K6AdK4EpetvQ6cUnYbO81PGBNaH3W2o2/pKZB0o9SHzmgElOiYYxJCSqCHTIkgLEZlE\nuQa/oUqGLGsROVLHDIeYDn/mlaukX5mBomol2AMoTZIUsaAUjo+dDnIeLTwmpc4rIPxdtyTFJzI1\n47JN2KNJUE5JlMyP6oDsRk+ASq0AfVSUsMGzaLWR5xQWlLb1svUaCK/OMdRPHxPDoMrvS8uIVLZs\n2bJly5Yt2x3t/nLtDUPyZsqQzCnhPjDUWnPIxRj+WFbE4/38zLWU6JEVFJPD27Ls4LhznkWkkq75\n4kTm5znhY0F8TZCjKRI7VDoB54Pvtdv5vdZFeJtfrTTUeLmrjvWVv3lZFUnjEYpmtahAxdx4Epp+\nQH2HTkI+sSN99sx5Fuvz8JuLS9n9na15F3517Fw3wgO7ugzolKJu+/0ed4hdmGwDIqfkoKHBx+T4\ncN9ABAVNYq6nUuUv0J6d5A5bAwkk+jMk3Afcj4aGU9SuXob/aqq5uDtVfhO1N2VMkK4Vh5huK5kv\ncNRdXejjC5GauECysytz9O8f/cP/w8zMjjtH/zao4L57Gcv+yn/7X5qZ2e//4/+JmZn9qT/95+J3\nf/Gn/wszM5sE/Xp2+5Q3LfeK+5J5ynxlOnIpJrkRRMxDkbnj1PmyxIdOoT/kuSinIx4n5xj5vea/\nBF+riqK+goPjHlWQl6SqROgzZp+XsRPBRN3pgt+jYr4ME9e8o9x0c/3T5GgRktIcZvhU8V8MrEk4\nWiwrp3bx20Rflin2sBSUwv26fC3w8M43PiY264BOXV04R488uKr0sdZhbdmd+/x7fgicvMYpV9bv\nIRNz+O2lLsiBMnMk8uxCELEzcl8cTTo8AB/pmZ9vfx3WExWTpLBmiWGqyExBpFkRGcCPnZwjjiNF\nidCfg+bzJJqqiBgQGXKfas2ht6VcyylUR6U+IGuxFuQM634hyLmDfuqJCR+Dhv/HMYBnSKXPZKK0\ntrA01yyRUxXfJOzsv6HXZRQ0MYKUzImanJdf6vEQOhX0aQVuVCNc4k27tleN7wUquq3z7ZRlRCpb\ntmzZsmXLlu2Oll+ksmXLli1btmzZ7mj3RzafUhxwpgK1uscYfVwqZAoYU+WeoSw9JkR1qsgq3E/8\nMHxWAmcS2i+V2M3vy6V7JuGk414GkzxxOGAsFYIMzT2gHqW44o77UHbTepfU+DvJ4TQAqhQ30hjh\nfm1Tku3FBcAcSsaQfw3XD79tJNfagFDX461D8bfPQz8dHsq1Xgvn6Y7issBpGlGR3V7AVVKLCwJ+\nBLpFylKUsGNOQA/Jp/tG8991dI+ofwLQrvb/Ciju5kzcAg0IqGxCIZFOAwnQfnwF2FdzWFVos7rx\ndppjmPoS2lZlZfdL41Ni/Rm8sF5d+jmG0BaThHV/9XO/28zMXnzoquT982vUSYiVbfCf9ELU7+H6\n+YW/9E0zM/v6n/nP43c//mMhT99f+rt/PZY1m9CISQ5Bqn2XEiaPtpvE396C0Nqu5Lc1QvdRp7ZV\nlwE+E1cIXXFSRJdZMimxTqi7mcfpNIErcRlULW7J6VSpSA1gDBfCjmWwSTMIsZnK4zLEJ7qyK3XV\nYd5TpkXdSDPDz/0czAAwdnIc3NzzUZZ45vo0JwVzQRs87sTqhuN+KauwgqzJ5tJJ5A8fvxbKVuIm\nQTeOMk+PXXABnm/c3Xy4QDuJAnoBd/81Mgqs9tJgsSpCLIbMf3Xh9WzPQ11aDfXvQtnmTO4Hyu/X\n15L/Dv4u5v3U/KukW2j+xX0f6ltpYEVFt5y3P6VrGgk2GLkWS1lZc+3GuUTCoIqZEIQWUjGwRcYa\n/qyERkGJjUmpCnRViluekgGjaFIwu4UHgHh7UYnchBbB4JFExZwuO1Hgn0Fpocs0nPuEu5vSKaQU\njEqBwXguRaYHruWtSB1suU7P/tsG66MGL01Ys9c6do9Z/iBbtmzZsmXLlu37YvcnyDl3CWM0vswL\n0sQ37PlEnptEkI0Ij37P7PMiksnrUUyylPMWcziuFiJeBCcEVaCYZBKSTOBK0QyQkftedzokpeIY\n2VUOfKm+cYLfbbF8q7c2HFgpEY/EZ80Jxu2M5qliDqOB0gyC1hFpUUkKCOdprr09iKC3t76r3SPE\n+qJ2YicJe8Xku9Szdfh71B0Zdr1HZK6vRNSuOnSoR5LsDL/T/sf5L/z6JdChVnZkW+xEW0H4ihYo\n0UAEQ8Ti0D7aT0SsVBKjrBlWK9dnAIJyx6P4nxCgGYDAHZciKBh3w+y79cdtIPlOL70N3/odYaf/\n7V//pVjGnHwrQRh3h1szMzs793pugVI9ffGxmZl99Lf+p/jdn/wT/6mZmX347X8Uyz7sXphZmpst\nzjW52ZF5tWRQrrahLpXusCl7gMPmUonVEF8VcuqpHF4W829KEeumxxkFOXXegyhLwrag34TE5xMk\n9kSQNAqyShmQg0kDQAYQhWVOzhQJlUFWVmmdikQmJrSPIiJDnLsa1s/caH55VjohwKOlRgmooWTL\nhJD3SdbktgmE7oszR0mvrsL42258rndY0PS8m80ex5152QGIxCwE4DfCuevhUzMzuz66+C6DEQS4\ntHVEn/wcLfJ/UgbAzOwS434QkeJ6C5mGC//tAbIHXMNUQHZAEEs1O9I7Y3wmdOSJx0teQaBTlSAi\nnuPTfz0gGImipoUICPMchVytYl5RGZQUn0zy6kWBWX3GMZ+gyung8wTMQimSIlnDGewjiDy8GaUG\nagDZqyX/5AgpDiXbHzFndN7F2B0WyfUNa7cGjxGJX4mH5+x8i09fE2OOX1l3KFKqGhNN9b0xp4xI\nZcuWLVu2bNmy3dHyi1S2bNmyZcuWLdsd7d5ce9MwJLoXEZiXfF2RPC6wmqsoCxQefyvHxTKH+wjP\nlcBd61a0Q0hiF9InuaPlCcK66ogQ5qwSLxrP55DxQMIe/n+Qc0x7KHHf+vVvQAoVL55tN+H6a8lr\nVVUBUi2F2BgJgArZR5cePhN9IvpW5L7QFQr77vfBzXT90uH2Z88CefRs6zpGVLaehQBZocorIQD2\nY4DlO7g0E8Vy/K3u0W6Ca1OUeLfnoUXPNssAhO25t0m7RTt500VF3yKq3i+V5RNpJ0DAlSqWw42m\nRHnwVK1Scano2hNtnwJ5AuHGq2u9GHI+jZKvEf3z7hs/EstuPgrutlbzH+ImVR2YbqFCGoC5Ax9d\nBHXq3/rg1+N3F+//ipmZ/eRP/plY9rd/JmhLDZW7cdg+N+IyovaPukBr+GPUtReHFvWMpLHpZh4H\n9a0y2EBoAWiTMsk2QL+YlOE3q7XXPeYOiy4IDSyBK0yV6GNevyXdQB2JFd2NmjsQXvtqKBdlbSvu\nPurdUUdMrlDAjaJaPPQ9FxI8wmCbqtE1AWc6kWNUyc70RlY93bMSbIFJTBdfqHsY99utl1E9nC57\nM7M1cnHWquMT+0I04ODufPR6GJPT8Vn87vrDoEul6yrXqWajgTrINbeVOYnAl/bCx38N1+L+1utJ\nvSWyQtTtxKmT6A3ia6UscM2ckjUWQQmabaOia0ncbaBUcExWQphn7FBRqruV2lJST65Jsv7GHHpJ\nnBe1ncS1howSSp9hG9NVqC57uvY0r2TUR1PNKLpAewlKiZkiZO1CAFB/QiuLCvTqdozK8lJG1+b5\nhbuR6XpeyfirG+Z69d9SN6yUNmnqpNEWlhGpbNmyZcuWLVu2O9r9kc2n2bOrmzkRUvOa8S094Uby\n3U+VZckA1V0qd8RyCewiSMRN9ILxFtwIOZG7ikpRmoKKvarAvAyenpHCWyUWBgvbma4gmU52MEA4\n+r3szBDCXEho7oQQ5+7gZZRJKAWlYU4mlYRgPC2zn2v+IBKgC6lvCVilELI9SX4vbm79Wk+emJnZ\nGw9f90tNlB8QdVzc7yw5sYiicQd9PDqJve8D0pVkdcf1NUz+7MEZruk7nc1ZqHuzkXyGG7SxIjwY\nXMydN6g6NbPPq7IwkRbZEZOoWMoutag2OE7yWoGMrwEQYxXq0pfYrSqJEu11PjuCssFuaiX5sqKc\nhmhiNFH2wM9H4uU8LAMwiPoWtZ/j27/4t83M7Ef+0J+OZV/5Kz9oZma/3Em+NCAIjchf9FXo9yR4\ng4iZyhnEXTJJ5zL+iZzoGD6RV68oTyxjzBcmZNc1dqRvvP6a1Cn89snH4X5ublyJO5JdE6RruSMm\nYqO7b27hSwnoiFIcSiwn6ia733pKCbCzZlYgYp9skEn2FwQBO+jVVtA05B8bZZweIG2iU7xHeDqR\nkLOtE8vrljnMFJEkWiA14nhKSPFYYytHhDYNAlB0LaTKPUjhFw/8+HIfZBf6nRDQB8o6+L1umGtO\ngk1aoPiVPBRa5GIral8TqlVYgwastbMgLaylyjpUkHNp1SNQEJFRrILEflljmL3h3NuzwW9WR+Zw\nldD8qDQg6A9up0zUxvmHIux8xmpPAeE9Lp+7nVwj5ufD3C1Uagjzvz+IhAdU0QsNKItBFksF+F6n\nOPux1nvE8yRmB9DMIrwTb+vNWUAiN1tpVyCSKvHDeZJkWSESvF+i3r+dZUQqW7Zs2bJly5btjpZf\npLJly5YtW7Zs2e5o96dsPk8JYh+RukrcI9Ri0eTCdA8luheA50RZldoaSgov59RVIAiz1YBYG3GP\nlYD9alsSzVKifHCHKPxHaLcQuDW6OaJKrEOMJIw34jLc70LZUWDXESTXQbRFZriFVMeKhF4lG5I0\nT9Kpkp7p0ZuVnAz4OnUt0LXoLrgXLwLZ+eNPn8Sy119/Mxw+iYoyPEorgVuZaHkEoVzP2/cn9ESg\nsXN+JVpI6xb3J2TTNRKUigtsrqnsLmTb2GZFcn4z1RgRiBfjadAghqi7IomMZ7b/copVQhQl3ExX\n3Sjtxetv5b7WSDT9cONZXncfBRXzTULAhWtFFIipHqzjlC6aDppda3EtNkiq/UJ0pP74N/5DMzP7\nr3/2L8SyPeZRIcltOe7WogvW1kz4upxPkQctqsd9Rz0lcaPzD9Vsoo6TFqL9G9GRubwKbqHtmY+/\ncxCkL7ZhPD156mP4kyeB5Nzr+oOqJ96RKHKj2mZLbZ84x9zbbdOKxGZfjMYKQSbxh348p/Ms+kRs\nH3VZMRjl/NLnydiHsTPs/bfXTXCR3b7wcReTO6NKmllhuw1udA3AIYl60CwKTPwqSdAP0IMqxLW7\nhvZTrwEIeCy1D9F3ByHCvwAtoXcp9qlDIlvxD3HetStNkA26hayT1C0jOdvMrNmFOu12wd03ShAH\nKRDJGEbHKmHck2WrGwuf0nYNg1c04Tfav0QfnlVLKoQOQLrD1d3FdbUXRe4ezwzVsYtuMVmTqAel\nfUzP8wS9L3XZTT3XcHFZM1OI0C16PEeKJKCJbmGhr7BOyTKR6lfpM5nPs3OZ11toRam2GAMQCulr\nagAmpHg0j9ICeqVDnLCMSGXLli1btmzZst3R7k/+YCoiwdhMUo0pSlLE1+VYFlW2e3/TjMK7SU4k\nvMHWunVNPqxqRAkVO6JWVZeBYBSy0+NOs9Iw6Rjq7JcaUfdqEsVcoG0NEKyyEtVbbP8mkV8gEnIQ\nEh93cMqv3QHF6XZLEuFKdlMkEg8T0S9/Mx+AhDTmO2NyoutEbZqv64IcgPj+wdOPYtkbrwUphKPm\nv4tkaNlhWtixdn2He5WdJkiWGn7coC82Euq8wk6zETSTxMtayNPdHHbEMpxieqhIxBSkMZJ9hRwc\nOZyygxoxjWRDFsNldUyQvF4K6sU8WXXD/Foy/hAOsRFy/lkRdl0PV4/9+kUgSs8aVoyw71nIpi3Q\noec7DxSYoV7NMf7suYear9/4ATMze/JP/kEs+4M/8R+Zmdl/93P/TSz7LaCJhXnfEQlar2RHGLnb\ngjBFMm4aCGFmNvVUFpdGnJch3BGJUpAA93q2dUTm/DKgKUqUXmOXerYhEdnPsUPo/vMn3iacVwn1\nFDviWeVXIjqlaAZKVFn8iOAVUcmYsGOO+dKk/6codaBke+QplN33qg79fynI5aYJc3IQ5P76kzAW\nvl06Ejcg/HsDkncrO/2rCxDPZRIRHVf0qcfft7c+1nb4u++9rIw5KeV8QIw4d6+uPK/f8Rr33Xm+\nvhGI1LjzOTkMRDA0JxtQQgkzYlDOthLvQM3Ai/B52DtadzgElGqSNixPKOsTTlH0i8hJW3t7VhMD\nFXyMt7w+A5skOSPR9KoQdwrarhWpC0qG7I9Oon/xHAR9cRIcse6u1z5P+DBOMgpgbWHbTeoRmRkc\nIM9frI+tyKQwiKCQABQG6GgGCKr2Tyrdw+c5JWmktS9R9+2Zt9PFFfLqSZpGeidU7b3ChNfAN5Ld\nZ0Gkpinp3YVlRCpbtmzZsmXLlu2Oll+ksmXLli1btmzZ7mj3RzYvRvUORThNFVsJ6auycISUVR0W\nTLhJtWWodlqI+wynrqkcLGgdvytFnpzus0TZmvowJwizSmKjCyqBR6NrMdzDkJAIQWLuRDMJUPSj\nS9dxoT6QQuEXOPHuxl0rz18E6HsvcHcN/SrCzkqfi+R40UchYVkl28uGquiacTl8HA+u7XKLpKWt\ntAn1oCbpE8ood1BM73qF55H4UvyYNVx6640oFuPrplFtJ/SxqNdbx+SymsgT7c7Er0IEpmZUKe5e\nKlpPg5f1JNmKsvrQUwFYCOhw6ZXqgiwJN8MVKZB9ib44u3TF6C+MXwnXvBYlZo51ceMalbcFkn5x\nG8bEw0dvxbLrZ8EtuMKYaFcOxb+8DUljX3/dx9/LXwtuvn/zR/6NWPYzf++vmpnZrbpgC7pbvUqR\nky3HMWikB1G4E1LnjLHWKhGWhHYlu45LUnpRhvY5u3C1/YtNcAGsZUxSP2xFPSGZVw+eB7fYTe1u\npO5AsqsknsV41nESCdtKNqZmmspoTUsdGy44BdT5NWk31x91LTUoayt3z1xcBa2sBxfed5v6Asf7\nGOvOQt03Z/7bpy+Cm291Hs579eCRXwvHqWYaMyb0e3cj3dyGv188u45lz56H8XQcfJ3YrkIbK3ug\nwZjouF6Ie/oM834vrqj+ABL1tfusGsyPYu26YOdVGAtTKcRmzN1W1g6u9xXmUCUq9mXH7BCqMcQf\n+j1El55SBeDGq1VvkNk2JNtAi7/j+qPrVRyfSbSDmaXkaGr2rVZ+3kePwm9vb/05YbulpuAMgvis\nLnio8R+wQM7i2uuh/adrYoVnzdh4/zdwPa5auVcmV9a1i97bhADOSA1onGnSauhCnZ+7C/gCf282\nvnby+aABIP5o8RMeQaXRhNtV/b0xp4xIZcuWLVu2bNmy3dHuj2w+z68wRrFbV0kEoiOjvv2Ht88i\nIYcF0zfNOaIfiWRq+MBPFemKMglSAVeZVnXY5b3E3aQyi6P8gb6lh/PEl2954yf/WN9sV9h9tY3v\nvohINUKirqFY3D/s5bcBafjkw09j2RFEyWIkwU5IfyDla74kkvMm5YuXJPtJPbHDKEUeed+FneDm\n7EEsm0uE5ErodgfUiTuiQRApT5e2lJDQcGXmTFS1aQYlKMLQAXXS++Y4GUF6rqSvGVassgpEUyYh\n0fdg5U9CSq8h8aBtQtK+EiULogNok6L2XTXb9Sgqzm98AarcvyC7b6AquiMlUVKHP/NKSZS6TZQY\nwdxRuZALkDc/eeJj6OwsXP8n/+C/F8v+h5//X8J3QorlPErkN+LoXgZqkIivORSdgC1lxh20oKTs\nH7nXISKNfrMMhW9FEqFZAyVAG7ZXPq8evR52tU+fu4r7MITdfCodAvRRdq3FSKK82pIUT2QjIaBj\nXRqx7qmswRylUySHIYMSBKUlQfvh1SMpexiOH/3+b14G4nfhQKRtLqDyj/l6ceE7fa6xoywKHdCJ\nY6fE5hc4vxP1jwgkuT06wse4+s3Gc6Ix/pyzaZJ1/Yi51iVi9+H+e5FJePkUgSWSAaFdBTRlJbo3\n7RrPE1nYawy8pkEexpWSuMPfhzl5UIV6KCJEBXxB+Kl2r1gSf6M5KWO2BdQzyaFXEelWIjY/l0rc\n84msHOcXjtIMGJO7naNUzIahBHg+2gZkheiOEmyDoBBVCFgDnW0UfaYkj8q5IP/ixbmPUwZ59abS\nGWHMnEp2ssYzcSNBJMznyv418ywGda3rOdBUyQnJYKx+8DVWAylOWUaksmXLli1btmzZ7mj5RSpb\ntmzZsmXLlu2Odm+uvaJItRnqeUm6nF75NHNIc1IvWnGCgAdXwShEQXiRrG6oEyGK4YAxhdfpLsMi\nAWNxfSFlR2Vr1WUak+PNXHmcRFV1RdIVUlUKRdMV4a69NcqUWN1AIX2Qe6UEspI4P/3ouZmZ7W6W\nCSUrQMbC63TXmiRUpVC2apZs4cZanXnZvnuO84k+SUX1bncLUCGciTxnTdAZ3R1KtgVhXNXGUWd1\no7BPelHKPgKenUTHaYKbc5iK5HdmZnXR4BjVAoK7L/EYI8mnwN1V4W4OL0Oggqidsy5ThNN1DEHj\nSFw7N09D2z0UYmc3kYAppHy68aStmSz59vpFLDu7eIQ64VwHJ4deXwf3bC3n/e53f9XMzH74i1+N\nZb/3C18zM7O/+ct/N5YxQKAUt6wHDai7G+M+TiJxxWGeHjUAIgabCBEWY6iUNmGyWJKezTwxbi1w\n/xrK4hVc5aosz3H95udcs2uag1ba0Os4XdxWdFkNou1l0Eya1LWLuvcSZFKDeF83J/a5MWmzuOXp\nChI31gr3+OCBJ2g+fxjcZ61oxdXQnipEx6pck+we/n8pWlwt1slGkjGPIBs//9RdoNe3Yexcv3ge\ny3Yo24uOFDMZ9EJebmu6u7FeiHt8ohK1zr+JGTD8/m+fH3F/omL/EO7WQX+LZ4H0HfWmuF5pMmAu\nSZpkecC6ksREof8rHRN4uOiyT1d1pesZxsTAhN5KWeBYlwagmzeJV+DxcnneRyGBAufnDLIQt+gt\nXGtCs1gE5eizBo3SStL0Kkm+Hqwuwzhate7G3a6D23gtQS6kNOgaX1w8RDVAo9BgM4wPJdavkO2i\nWauKOaqr2SswP1Wzikr9SdmQXXvZsmXLli1btmzfF7s3RMrmVEDA0SfNtQdUJSGW9/jUvEYkBUtZ\nJNtJWc8cUnjj1Ldq7FLGRt70SfaUVjqRQsn4PjqO/aJMdwncfbBGjZDeRqiy6y6UpMezMycHrtvw\n5r7e+C6xxqv2oDstvM1rmDRDWK0Iu0Sq9JqZFQ2J0MscRtMkMBXPl0hHhN+cbb2eBqL6ofcdfosd\nHpWTzcw6IEYkOUt1I9mWqudmrkDfy81G4rmQ2K3ocC3ZVWDXOw+yw7QU4dB4gSrusGRMjsxX6P1E\n8nLX+fX7bpmvqgEpWHPtDSBXz0A4NDR9vw/3eGXeru+89raZme3qD6VOS8V4ju1ElR/3dhBS8Ar3\nRvkJJWd2OE7R1zV2tfvv/los+1N/5KfMzOzv/V8/73W6QE422f0S4dA8ldz0FeibQrINEParpciR\nQ9nB4xxTEhQCZee9E/VfQv7hzUcuiUACagy20MAKIAgrUdG/ehx20DtRuyZgrWsXkfBK8g9yrA3C\nlCYpuUrWuHR/q7vvGv2ZgOQl87/J2oE2Xj9wlOASiFRCIq4wtgSm6UAyPwcysB38vh6BWH/zwtuV\n63QtKF2PEPvDtcsP7JFr7yDyB0TxRxkT5+sQoMKsEP3RidD9RKTd+2SAVPck6B+DR148c/Tr8jr0\ne9X6elLsR5zDxz3BfsqVqKxIzGFZJgu7foTv+ewSqI/yHKPm+ozsaenjIn12KTIUc+NJf3EMJzI9\nVK45gZxrYYm1fSvPE5LNi8Lb/YD1YQbqO8u4toE5TAWRR04+E0SoxbPrfOvzb70KY1LRdFa+kbLN\n9pwVNjOzfpIMGEhe2Whe2zXXWpFkKSkdovkHoYovTPk4juRhVFffG3PKiFS2bNmyZcuWLdsdLb9I\nZcuWLVu2bNmy3dHuz7VnsyVJhulSEsZe9IqoPoZRxXhZ9Ukg6Ei30wS1PYmdcCcJFjsQgR30+vQ7\n+HF0S6h7whMeym9PaCDRLUj3WSO6M7zDWRWLmUhZiOWXD0AYbdzdU8O1NYxJRUPdBILtjgGyPPYB\nWq9WQhiNCYLdXFney0jKH4XY2dbQAjlzyJZ5OZUoSe0lhaqPUCWOLrhZ65smNDZzzRiFgo8HkOcL\nd7fMcO0lfEm4Cga9yZi0F8lT1T1M12avriiSopWAjnsWtxxvQ4MnOhC/C9VAgpuxaHldTWga/n7n\nrS/6ve7CfZXiWpigxFsL2TJyt205xh488H4q4dLdQ4PncO2ukMePAsFzJ0mOOXe+/Uu/FMt++F/6\nM2Zmdqb+XihWz6W7ceiXGJUoSzcH1cHFtR3J4dJfY3HCt+4pA5Ym4+njZ0Gx+/133vDrwy84g+w9\nCTm/aOF2W/k51tBYarbS1zPV/r1fh5m6aOJugmv9uFcNJtAMRO3eXakIAElUtJdJm6n6PIoLlsTb\nWrSl1tDZGUW+nz+5rXzuvIO+e3gMv/3B7cP43Qbz6SDurh7z9FclufoTBF58uvPghZmBQr26FvHZ\ni7u5Ceeju+UoZHPqknWaNBwL1KABCLjF+dbr9PyjEGSxvfTggT2yQbSayTwGA9HFJvpwNV3BQvbH\nWlhKPzHxbjlrsAVcSzL+e/Rd17urqoB+0YSAFXU3U+28kXndIDH4Zi1BBBXrrq59BmoJLYKfrZ9v\nM5JSnTkAACAASURBVJLsL2sXXNkj6REaRIH1cRRqRc+MDYMERUEXqxLRsgIuQKWU0B3ailZipF7E\n5MXisqsZgCXZBpaSbdGllwQq4D+j9B2fhVOStPjU4uKWEals2bJly5YtW7Y72v0hUsWYKDxzV5eE\nBoPYOKs6KsN/zd+Io0zBiS2pEjWHHmgGfloNfvsVdoaam41SCKpYO+JNW15g49/p/WBHICTCcmZe\nJYTLy06HpEvd/TB3Vy27hTVCkc/OXG2YkgkaQs236t3adzpb5MnaHhh+6t/N3Mwo+lfwzVxVX7kj\nkN1PCRVvGU4b5MJqhLw/zWGHNcjuk6TsnvmNREJgOIAIqCrS6OJBdv9R5b7QENVQduwUJcA1pT9L\nUESp6F4KsZ7k3fGo/QpEUpFOm5Lvwm95nKjXlyRAKnK0wnHYrU2OND58EJCBj587qvNFICGjkOgb\nKN/rTrepmWtS5hPQzlshYK9Amq/X4borIUy/vA5ogqaZYrs3Z96GL3/p75uZ2R/71//dWPZXfzkQ\nz/uVEOsH7qYldx53f1TsFnJ8jfpKbIDHEyix9kT+Ocok1IKcHEFa/uDpb8Wyt8qQi44In0qdlPjt\n5lLQpxrh0ooIYP7NSiKeqIDv9dxjV9+pJAfG+/7oyA2Fx4ngKKrL8V+eWBOnBLkPNogiM3+hyOkV\nENaHg6M0b2Mx2AJ96Z/5fK2AelwNUikg0mPp6tT/ePeLZmb2WORPnu4hEyDzZIAXYTg46nnDeTKF\nayVSEx3Qd0E/jmhkDbaJQSF7b+tPvxtU1i9fl7XzjFIHEngD5NpaojoqYQFiv+Zro/yA5oajTIGg\naRbztFbL386KpoTr9pQakGcYA1r20iblIdRld/BznCFAYtVIXkU2kDxjGhDAlYDdtgxUEgX4OvTj\nYUbwgHg/iMSOvY+JDvc977xdd7tw/bOt5H+tgCZp3llC0IoCMbgHk6EXVH+FYCtF7pgp4SjjhF6k\nWdsT8++wU2J9mKdDIsWzlHNQy4hUtmzZsmXLli3bHe1+OVInEKni1LudOjXjb1Q5c8mvslekBkIR\nffTYwUoYLHkIvYQmM3SykLfRAjvSMvGpl0nN9H5SKgfeuuP5ysXxmhuOh9XCZSJfql1LvjCEKdeC\npu0gdKe8mRo+5BbHJ9nCx6XvmRuCQSCBCmHCq/VWfkulM8m1NjInoslxDDGXfsffPflbB++THn74\nQX36DeskHAVKPUhjc3M4yRAvHc7y43CTA1CC6SC7JbS1cjrIkUjyWpXc6UidcK1a+G2UaSiE39Wu\ngNy1AX1alc4fmLvQxg83Lqo4dmHn3o9+3hlCm6Z5/WJOShHJK8NOfBA+xg5h6utzhLV3wtXA+Y57\n31U+fi3U5ebonJrzDz8wM7Of+NE/Esv+t1/930PdSh8TE7kvo/92oHApaU6K0nJOyk6T/dUp+sS5\nI2N3tQGqIYgod98vdiJIug/3vY4512S3jn7aiKgjB/QgA7vFoCxL4YOgr0eBSa7GgH4dZIzd3IT+\nXHc+xw7I53c05qQTpAtr3CRlRPEUuZjAZRn2Pp9e3gQ04WzwNeGdp+HeNk80r1lALD+CXITu4Il6\nTSL18u4775qZ2XtvfM7LngYe3vObJ7GMAsQ6nwqieTKfDhifBa7RCadsOCD/pKwhRCJGRWnoERBE\nkJ6Na0F4rzAmVBCUAqA1ARzlXk1cw1QnA9dU9PkE55ffuzCtP2NKkR0pgL6UWAsPsibW5FzJWGd7\njTInDXykWnhTTQP0WybUNCznTgWOUiXo9ArjmWjyQeU/IhLlx/fwGM23jki+eHqDc7kgZ43nSS1I\ncFWyTuJh4TpWLp+/fHbPMicpdTP28jzHWFOOWHeETMpO8r/Ci3E4yv0o2faEZUQqW7Zs2bJly5bt\njpZfpLJly5YtW7Zs2e5o9+jaS4nhDi0KORkQnJITT4V1j4nrLz2f5pOL6un4YxB4tgcE2FbiMhmY\nh8evxRxXmuuPecKSWuC44lQZxcHVPYFfq6oxXVWafy8Sv1XZlu4mIUqTZFtLSCiPoyqwKmxHIrQq\nYUPNteuFCIk6na3P/bcFCdsiCQDkdxRS5gQXYX8QWJ5h57gdzVcXZSpklBKy1fuPYa+Jsj2uKdA+\nc+ZpO7HfSSgXXn3sOx1rdAEo2ZSqxEn+J5G9iIcVzKslLgCMt3UTXAy1uvZw/OW5SF18inBphbYB\n1aurtgDxvpUIYrrqVBW/rKmeH+D2Q+eNzbyWoyrw4/5bce3ewD34tozTd87fMjOz75qTqF+C7K0B\nFdFVhP5KcmMiAGDspb/2J9ziqGcrN8sMAcLrj0rVkyjQU/mcEiaNkNOprK55LdlOvdISTsiE1HCf\nt5JDjG77c7nHGnVuDn6P6z64O17u6LLQscZgG/VZcbB7/x+RG0xJucWnoT3ffCZE+Y8/NjOzT0Xt\nvhi4doY61bImdl2Y2JUQpr/zQXDtvt/6mPjqa18xM7NffPYbUifSN/y3McdmmjzPzMxKRPsUtayT\n/KloYnDeKWE7Bh4oVQFNcXvtY/Lhm6Gte1G2LnBuz2KhcxljSOkeJJGfkE4pZPGqW1IrNNcjXcoy\nxzA+mk0YQytxo93CFdxJf63hvtPAJib5myR35cUFggKOogqPOaaEfj6LlL3CtYPPk/XKx8SBwRM6\nAdDsvVAFdtfhuGv5bR1pET52BpDcV52s8Zvw2waZD+ZSgihi+4uKPDNljKfcc17PGAAiLnC63kd1\n9x6zay9btmzZsmXLlu37YveISL2yC6HJDiLuIJPQQ2TG1ojU8hTZG+TphNAePgeQc2sRlRwh+jhJ\nYi/mxJJoYSfFqkrgiYzslGJIyNZEMYhIpTHci3sgKTwphXCh5jUaEa6ryJzviPytmm3CtEF1Izst\n5rCS+4r59wS5OoLErtm6SU7UVINEpwa5xyN2Lt2tlCE8uT+SHCjEWuwSR810X5HEuxwT6baAec2U\nqFsurhHF11B3iQK2ETsTRZC4w9HdJ8X8FISK4nsKXcbBo6HrFF3d4NOJmES9jkIOPd6AKCtzgn9q\nWD3rN6r8AcoUTKO0B8Uvq9p3y/0BaI2AHzuIuWpisQ65Ew+/4bICP/YjP25mZt/8W/99LCPYc5Sd\n3jAy2AA7yIPOP3xKCLvnSZR1gicWMU9GS9e6wkH0spJl78WLgE5ssFvdbGRNgPzKWel9wlyElWhC\nMK9akusS7a7BHlwnBiGKX1yFAID1me+mb24DKXw2Cuj68Ydxl5zLTBAEJQxzXD33Sfk25tiFjKcO\n/b+ufD6XqHOH62q+thZSLLOI5PK4Dz/4Tix7+KVAPN93TuyeOSZnnaiR0e1FmITlaolIMfGils2o\noMqaRH1TQdgr5mSVMuZMXAvqSHQ8gkmzojUMtVdfA+aVzJOakhxyGMeCIsJrBJtY8jhJ+/P8gaM1\nZ/tw/IsXz2LZLZBefU5ugHqp6DSR81pyR9aQRzjeihQO83RK4EXM3Ye2KzX3XAnpILlZroWV5s4E\nwngrIq183qgg6QbCsSsJslqfo2wTjm8kril21ImgNJUOiXn1RiWbd8l3ZmYDnjuDkPyH/TK4QC0j\nUtmyZcuWLVu2bHe0/CKVLVu2bNmyZct2R7tX156aK5Yvy1LYk7mmlmRPO6HsqwrEJMxVJLgqmawm\nPK8+Q8KYqliN86qyNsne4oKrSio1C3md2kbF0sVD95QWkWNfColwhlbNKH60Hi4oJZYOYE33o8CT\nhnxJVCfX3IAVyeaCBZMcLW6MqgKMK9BuSbL36GXDEfC8+IBubgJBcncjrirod+xu4UaSPiHJW3Vc\nBpDDa/FPzQxKUGI7XZtCFGWuwVIgaLr0yDVNyLm8ZkKiJOnRi+jaVNEa6sxU4lvq4FIpa1G2Zv5B\n9Mm2cRfLBpB1Ke4uCmn1ogRNd6cq5a/RZpWQXeeWJFrRLNqFdn/ySdD7efTA1am7mbkBfaydnW9w\nX46tP34cctf95recWPzVn/jD4fi//jOxbA/35U7GJDVb7EgleglEgLtPxLmjK3KWccVceKOS/WOw\nwTJQ5KD0Aajsj1Bdrgt3uzBQZWhEbw5abZW0IV1LqoRMDaym9rFODTKd422LeSftaUUI5GDXPRf3\n3AR3g/Z1g/yMm8pdkBW0fd5/6fV8HW3WTUKsxvpUyhzr0Y4bEKF1rVvB3d/JnGDO0KeHm1j2mkHH\nSOgD1S1cK5q7bqS7z29/hgtoh6CASubrSGrDJEE0XLsSnzV1pKSeZ8ynKcdhHZ3UcwMXMTXo0vyb\n/EvGGhMryHEjggE2jY8nutGq1ZKArmsH11aqfqtmWIt7WJ37PdxAlbsTHamWzyRdk3GeM1m8WNZu\nZU14CZqFqIIXHqoV7rmS+2cGDnWj1tWibIY7VnNCPn/+Ap9Pve5wPV5eXcaySwvuzQ1oLKvR27AH\nRUdz8/GZLZJ9nkVDXeVw7failTdRv1DI7v1R3YZLy4hUtmzZsmXLli3bHe1+lc2Lk6WLvxPcJiqA\nL9Gc2U68NSahw0RimHJ8SfqdBJEasNMqZJdcE52RV1CGcNeC5hTzqZ0WVYlxioScToKfVn4Z1szM\n8J2SsvGpufb2+0Do28sucQ/F4oHZxVWxnERdJbFSiVe5nthh7g5OIr28eoD7kvDrA97wJay1R5bw\nvbz9U9Gct9gnYbjMuSTEajTQIDsitqgiZ6yKbMhcRV2uQTmDaeAOdtmulaJv1bKsH4hmeD2pyl43\nSpQG+iTExm4fdpO3beins9WVV5i7eVFxZ1i3kthJ2NaM9DdHtLEQ5RuoCCtR/OphQKAuLsJ1R9ma\nzwj5rSTX4+4Q6rs/ODmUisqfe+f1WFY8+8jMzH7fl/9ALPsff+Gvh3tOdoQIHkGuxWk4cV+a629a\nzv9xPIG+QepC0RTmM5wSpXCgD+ivTtDCFQi7k6gjU66jkKAUIsKdoKkkwDeq3UHUQfO0Yeeuat+c\ngjXUqR8KSji9QHDGrY+hLcZMI2Hl//IY5t0XGvktLrHfOZp5tglkd80KUAP1pDSHIuKMACAh2Mxs\njzFcytqxacL1SwkKmbjeakBHTPYgc4wID0W89QdR9kHmxAmAf0LhLEz5mkiQSFwwaEiReP4d1z95\nAnUnFM25TpXC7OY6qesE5UkUuZpLSncIwsYbLzmupb7MtiHq4BV+O02OfnHc96LOzaCIUp57W+Ru\n3UuuuakK81mVwldou/4sjLt59iCKYVrOUz4nGw1owveaJ5X11Hy2E/q4G/w5cTi8mjtTJFQ4AFaS\n2aFlBgq/PK+VeKIocSTj/4j5fBRJBJVHOGUZkcqWLVu2bNmyZbuj5RepbNmyZcuWLVu2O9q9ufbm\nYkwI4/xTYc8I+wk8RwVuTaTJ70+59krRwogK6RXPK7AzrjUKPEtXTDEJszjWX4jl8wm3EN146heb\nScojdLs4bZKMkXUa5F6PUIotRLKZZHtNbnnogmvv2DuMT62OA9wyrRAhqQqctCDbVbnOJd1jkngW\nhGnV2mCyWCUWMtHpoDpCaOOo56SKwayGajEVdNlIPasTLgPWXTRDiMrP4qpZuPSm5RhSMTC6D1sd\nJ0xkqkJa+HoUzR6Op0qg9QFsyN11cJXuK9dYadahTqvxodcX96P6NHSzKor+1lshufDzp5/GMrpe\n6q2P5z367LXX3+BB8bs2Jq31ezjsgsZRv/d6rlbBtTsIKf7Zh0FT6A/+/n8nlv2NX/g7Zmb2ya2P\nnamj+wKuosldxhW00sZECZ7+HpWsR2OIH30cmIRbldrhFtb+R59RF6me/FpnW7hPxLU60BUt56DO\n0050vMY5uCWmc2nPTWj3eiXE8jiQZe3AnyRZV5Uff7YJbjwlTF9Be+y96UEs+9rD3xHOKsOZyW0v\nztx93IFQuznzTAWsUhnXWnGPRW07HxOrNqwja3NXzEUdXIatiPANzJ4gASB01ZKcHOqc0hymRG+P\ngkaagWF8pUZmM3Xx1GUHN/tmK0moqWKuek8cb1xDlJtO95CsSQ2DklQJ3MrFcfxtL0nTKwQvtJUk\nl4fafUzyLAsw9d5WK6dMVJj44yzzCuO5FWJ77FfxUlGrbl14m9zuwzOjXKkuIdrusl6ct2pYTwmA\naDh21QWP42Weko5SmYo1BlP19mGA+xLJ2FXvj+Np0mAzBoqICJ63vwRvTMsy/jkIwXw46ANnaRmR\nypYtW7Zs2bJlu6PdGyJVFkXcyZtJvjpVkS2XSMN8KjcTj5ddGnefyWEUFmdovEr2RkBCypDjq698\nrzNNVLHWUHu8VWtIPt7ONUyfJPeoNq07kyjOKm/VICLuj04E5AZvFhldhjofDoISMddWJ7sUSCIw\nvHiUMPQxqumKwjHbS/NVxXBl/+2zFyF0frv1XRI3J4WQh/mbwyDXBTpFxXBFsMhxVWX1+OqfCCGD\ngD3qDop/qCo5PiWs9VXBjERFnyTWJK8W7l+2RBV2taUo8RYlUSpV8R5Rt+UOm5IEHyuJeh9Qpcu3\n3vXjISGhKCWV17ved3CffhqUj3/g/S/Est/49V82M7OV7JLX5wHt+uTjgCBdvfZG/O6td98zM7Pr\nly9jGdHUQ6L2Hs739NrRzy88DHVfXXuww9fe/xEzM/sHv/k/x7ILKDvvQfLeiAwAQ7N1/FFQWqcp\nFaCLhG0MSQRBiQYgsZ2Mv2rguCNc+SJ+9+j1gPAUK0FLMMZGDRgASnV97XPtyfOQw+7Rax7CvUbu\ntO2FI8HnZ2HOrIQozoFKAqxKstQYlI/XjlI+RBDBT7zzNT8F8lkOkuushWRAX/r51hvMWYFdaqBD\nRCtmDVjAeJ0FuRvBDl8V3ndEvTetI112eG5maY7PuJ5Lpgb2I+d1sttnNQU5LeNjTNbEaYlcVisQ\n6xv5bVTsFuSSiRIw1pXqzmcHCfnhHmxhcUyqRwJ574ZJ1uk+nKcTjQ+iODW8DqXkS43yH7L+l3ye\nyLpS1WGMjUI2p5dEG7TA+bSaXMe1TscprC1Fwet7f23Qnyqnw8APdSY0GNeDqMJHB5QCPhinhQR+\n+HsB5BokswblN9SbE3PtSv69kc8/8RxEsnkvz24i0JO2+/d+VcqIVLZs2bJly5Yt2x0tv0hly5Yt\nW7Zs2bLd0e7NtTfNU5LQNyYXVtVh+IfGxGVX8g+32b9dlElRVCXnd0kySsC46h7CLwrVrIAboxBo\nu8Tfs+j48BqTJNes432gTNjBUWJGE3TO1NNwKLg6kOwokDUItZ248eiqUBmbFiTGrgGJ96hKzAH2\nHIUISpfWrLonaLNZXFb7482i7lT2nUSDhtDqIAlPe/wdSdk6JgCnKrHQ9bZExwltkfTdSFV00SeZ\nlxB41CWL0Q4mNi3KWBfVACMunSQtxn1PQtTuOibNlF/ib5IuFYrfbIKrZP0578Qdie3isqTezyAB\nANRA+eSTj2PZg0cgoD9399W8D313dRVcRepu/+Db3w73pdEGcAcXQo69vglunNc+9ziWfQyl9M/B\nnWJm9ju/8jvNzOzy5/9aLKvrANHvDqFOqjtTQ4toPLjLsowuU3U3L8dOVKzXDAQzXQDiMojJmlN3\nkpkTsRXip1cgyYBAzTJxGT/5KNx/L3W/OA/38/K5993Vg+D6u7x0FyB9RR3cUqUETGwRIHImrqUf\nvwrE8vPRXev7PvRrIYOyRrLcRl2g6EfVUWpjNgYQcSU4JLaTuEJakOeLvbt2X74Ibjwl5W+RGLkr\n/H64PEzibiQBPbrHEh03lIk+GzW9KtGH4jImzWQ1ElIXctxmDRdYsk6AZE/KgKlBz6hWVyzOr2zz\nmcra3q4N/ta1c+xCm+xnccFCqX8NpfpanisxiEbacIJrbZSgKAZFlK2SzUmsFmoD1k4lZdPPV2sS\n8DXcwnDx6WOqQVvo2uGJn+XZgTEza6ACXG+T6I2VxsTgWqcp+W0hz5WyXGYWmfj800WZbmnTABQ8\nY0WD8Yi2299IBop9OgpetYxIZcuWLVu2bNmy3dHuj2xelqmKNhABVbGlYmoKPgFBkB25v4kKIlGQ\nqKjq4SAF1yTdLY9PyN54Wy+VbI2dhor9kqCsb+Sen29JQCcBTnfGDOfVcF3eq6pN9wN3zrJLByI1\n6a4i5vry8222UGrGOfZCeiTANQoRN6I5SZ1wD7W0CXaQtztHOqhOOwkpn8RGJTHGOrPpBOkjciSn\niOjDfAKl0tsfqBR9AvVM+ORNZKXbqz8g6FQmOfxiwkQ/Drs1k101v01yHbLvZIc/gSBedFTR9/M+\nAin5XELId9hi6/gnmjcp2RRq5MylZ2a2qgMiVbce/v7gUVC+7hAU8PLJR/G7tg275fUJREDV4dll\n+52T0i/W4beFoKlffO9LZmb2o5//Siz7O9/6FTNzknkvQQzMF9bLrrIHiqrBHuxrzY11BMm8bZwA\nvd5SsdrPN46hfQgwTDL+jt0tzq8SFkt27Lgcatagfw4vHKWZoJgsigx23KH/Zy9sgcodMMivpF8f\nYA7/vofvxbLPtQH1uxbFcsqj1ImcCs4v+R85ZhpNHglEhm2sit0c2ZqSswPxvlEEA2P8/NwRyQ2D\nYQSRZt7LufT1LK7BVCGQdaqsiaDJutou1/MJkgjbcyf222pY3E8kKKs3oeRzB4R9UTWJchoagEJS\nvjwnVhh3KpNCr8csQUb7YwgK6eV8lOnZcJ7KGF5hLs6NrwlUxx+ERH2AjIgG7/SUE1CZHq5JglL1\nBlK2rGcl1skSx6v8AZXoNVPGCDQ1eZ6UDKyRfuJCXiyf04r6rc+wPjLYRN0UrFu5fCZpUA7fJyT+\nyeAkSBDWGFCl2SPSlCMLy4hUtmzZsmXLli3bHS2/SGXLli1btmzZst3R7jVpsaqDJ74aGLWlNKEg\nUd9RlYABBabgG+FWdd+lKrbzieMLTYaJa6iKdiQlL+WGIsHNzOVLlBTbwN1CWHTdKnQZysSLElVs\nlTA6gpQ3CIm5VlIerIQuSivXIDpJt4ySSKeOejpCGJ/jTSzOP0v7k9CnxOp+poq5KpUvAwri35EI\nKBAr+0LItjOynCaaQcxMrPxfuBtKUzcGYeRX1aPMyoZ6NuLGQieqsrfBHTtq4lkGJcgY7iuOHQ2U\nwPeD90l/xG8RADBJluXtKsD3e1ERPyJBcKFzIkrmnBBNq/xaL26C6+fNt96JZc9eBnfs+UXQTPoc\nFNHNzA641rp1N8KTTz8xM7NNK76dOVzj+TOv5/mbgSi7v3V30wqaRn/46z8Wy375k98ws8hhT5O8\nFqFNVpMQoeHavD2IFkxPDTBJOAw3QiP1vICrtFm7u2EPV2EJd5eSoyPpe/JrzdGNL3pfuO4oZH9S\nlNWNMO74vf/2cKR+lrtFWyQE7pE89nJ299Qf+sEfMjOzt0t397zcBzeOuvbpvlN9pMi8ljFRRl0o\nUeqmUjbmXV2pezBcQ519dAvOk4+TFq63tnYCfF1y3RMSL9xBnSYtxkRmT4x6NczFUeZJXLLE3Vdu\ncP0rGTvQAyslUOKIa6m2VXTVwcU2yvpDBfBa2pUBNbWsSet1aJNmLSr2XH/F3T1CsXslbqz5GMpu\nr4MrrhEV8wI+1Vrcs1GrSta1ulnqONXon0EZIFjjqtHbZIV1bGxE28uoM0gtPA1AAjldE/8ate38\nvAws0mcCm3YlUVExaEAzn2Adb3Bfldz/gPlZSFAItdA02wGpJbNqG+IZKKLs1rN+4m5s1Jd9wjIi\nlS1btmzZsmXLdke7v1x7ryBQfCHUt3rukpaB5vbK2yp3VSeuo8hVkRLblJzG7UKSaq1Idyavntnr\nSaK6oDQgL+r5KA/AHUQjaBEVjlsJV6VSuYZaT6fuEWVKzmM4cym7SaI47Ro7Pgk5PUCaQBVm+TKf\nhHpHDr30E9XJRQGebZ20XNxoiCr3K2/604n/FbL7n7lzFbZ/DGs3LcM9aIPN6a7KzKwCkbsoKYPg\nh0fFYCW2MlxXQ20Zpl5IDifev+xqItoi42kEebMH+rPf+251hfvSnFMxh1ci7b1U9mUI+UbQh3Ub\nUKKbWw+1fu/9HwzXB3nz9saREZ6PyJSZ2cVlIA/vbzywYIVd4iy72t0+XOPqysnGu5eBWPveD3w5\nlv0rrwfS9P/6LMg0VKpEjNxh7fosll0jn+RRdpUD5lUrYe1nTUAOmo2fb3MOEu/G67m5CPU7YuAf\nhbBtVOLvXZ196rnTVUSKCO9SfkSV9QvMz5V5nXZo25tPhah/Hs73vA8SCn/y6/9W/O7tMiiFDzIm\nqCxfJ5LVuLzMQJLsVytHuIhwttImA9adqiSqqqHmWFf8SpFQfZDcZETVPjo4SlkBnSmVbAxCs6ap\ntCH8hvIrisgbFNBVfoSEYg1AWJ2HNl5fOSLEcP5So1cop1DKPWJNOh6Rf1HQJ+bJbKROa9xXU3sb\nRpROCPg9UPJKNBlqSJzoGlsAzadi/lHlP4rwnCiSrCD4lLW0xnNEAyWiFINIjDBmpJV6RoK4BoOh\nfYj0KHLeU4lf1r9hXAZgMcsAvQqhLNSvV6V2rCeKAlHigGtoXetCTSV+HZV4duvyj6bQtisxF6cT\nOflGkZhYXwiyeMIyIpUtW7Zs2bJly3ZHyy9S2bJly5YtW7Zsd7T7I5vPdjLx8JRoS1EJdWkK7Y6E\nEW3520p1VOwVuFEzNc50uymJ8YQ+1anKxGvKb+E2U2VX6n0UTQpTmjmhcyXuvg0g49tbdy10gGq1\nHoSK1bVXLPl6NuD6NcmkjeOe1ICZZoU4cQ+CrJOgfqqf1C9I92V5gpQ9ywnJD45KtYnuDVwmfvl4\nPwoZF1HbS+BunkMjBWIfS1HUjCHsLxo3TFqqyTDRZoUq9gIzrleSIPeEBtaAeqpmDCtKAmQhxNqq\np56KuwwOx2tW3M87LIn1TsoVZXW4nirpu2fPgrttDyX0afbWvr2lu8nHyfvvvW9mZt/59NNY9uhh\n0LupxS39HC7C7bUTZSu4Ox6998VY9vgy6Fh9GfVU1XtqkJ0LxP/rw9Nwr2u//10RXB/Xo2gR6dpt\nhQAAIABJREFUFeE8733OkzA/uf3AzMx+UHSsnu/CfTxHQl11hc2Ya+Ps5+26cI/l6HONrt1B2jWS\nl08kzd6cuWtt9yy4QFfi7i1fBLfpT3zld5mZ2e99/f34naF/xs77adXCZZZo1vH6Mp6qpT4OXVCJ\n+47tjXGqLiPq/qgbhW02ymJz/igEL6zP3SXy2jqMhWdHCUAoGTyTLFThg/6+SpXoWUc/nGvXLFJY\na5S1rZ+3AQG9bHVNxtohARV07VR0MQmJmaTo89aT5q5buva8X8eZgVI+ngu42TvVlsM81aAoPqf6\nLnwed97X6xU08ySwiarstarTo07qgj5Qo60SsnVBbT1xy03UO1RtJdIi8DtZVyfMk0Mnbjz4DOW0\nMSikO7gLvAV5P2HPFNR79D5hnaMGWKJsvsxiMWI97SRTBNtzEFrA/haZNUZdz3Et1c9bqXt5aRmR\nypYtW7Zs2bJlu6PdI9m8MKUikyisb5VEeF4lppulBOQKx40JXIQdQapdEM7H/8s5BOqQOi5VjHmO\nQnd/JLadkETQF+26oSovQzhlC0XyouwCmGNpe+ZkW4MS8kHe6iOyJ9dn+KvuHFrUZoCK9NAI0taQ\nbC73j7+HUXc/86uHeaS9NBNDnVWpPraJoFQknjOfn7YXJRw01J+7r7HXHQQ+VW2cStEJixU7rcZ3\nkwXIrkQia1F4jl0isyRGTsuujrta5bWX3JFOOp6haF8sd/MkYDa1H//aY+SuE1Rjswm7+uvnz/yu\nQFTVXI8zJAPq1hGhi8sHKPP777GbfPj4Ee7V23BzHYjNT37rN2PZL//iL5mZ2ftfcGXtf/Jrv2hm\nZo8fPYxl5LUeRBZ6Yj1F7fjz737ezMw+3QXUtdo6Of3hg4B0vXjm97pbh/H/WuX38BJyBp+aq7jf\nlNhpFj5P3kI+wVbm+DuvB7mH7uOAAg299/8R46nrT+y+ZQK0NYMi/F4roHMCHNjcMShBxhjCyFtB\njh4BMf4Tv+cPh3u4ccL2DqHzrawd7MNa1K4b5L+rhVhOhKGUhYph9LPM8YJkZKKkys2m6rQSloEi\n1kKiPvQBaXvz8edj2ZMpIFGdoFldzCjhF9khZ+jMxkvysDEPns4h3IvwgRtIh8jUiSjyrLlTqbY9\nq5xGuO4WJ1xJqP0Z2ngtBHT2hcbNMFBK1wSiSKOi/gPzbwqawjbGwlNIEMd0wBrWyrOTnhWVOqDs\nR+uNQtmbqfT5d8rfE5F9Rc6JpmMuDKKhQGV7GyTbwAGBMjtB346UHxBvwgron8iUcBhLUo7kOWL2\n2wSASRmnuHouemTUOOwlryPq0glKNWM+J88Ckew4ZRmRypYtW7Zs2bJlu6PlF6ls2bJly5YtW7Y7\n2v259l75P0G50k5AdidN3XJ0C3pRVEVXHRf+SVeQEqZt6UZ0VFDVqcni1gSJ1DtRbQu4G0VZNSrA\ngkRXJM2/dAYSvqyEgNwAUh5Ed4OE+qpYkrgVAvW6g1gtx0eSvRDs5pJuN6+lN4kqxpOw6MeR0Fdo\nPQGtV5VA9ewUks4FH6cbsdF2pdqwQMskvs8Jjs8P0bYitC8ugBJ1qSKJXE4BiLmsl/dQaKACublK\nFOc9qLL5TKKy/5RK8myHragY1/Ajlv8UVd2iYrs6FH12Ec6zPXsQy8gFPexcR2qN5MJsw9uXHtiw\nArR9Ju42uso1A8ARejcvRINqu25xvM+nwxDccqMkN37v8ZtmZnb95MNwXvHPvH1xGeq0ddd21Nk6\n9+tfw1Xy+Y27Z16CxNubE5vPHwZX6buvubL79TGQ1x+cBbff3D/3+8dYO5i7x26GUHfNrHAA8XuS\ncfoQunDPd6L3hLajnpCZ2cNtaP/3H3o//ft/6MfNzGy4Di69QZJ8d3SVigs2utREHXqOBFyZa1R7\nlzlObaExCaih3l4s8Gvh+E6CAug+acVlvt6GNptvJOEuCODrSdw9hxZ18t82Fcjb0CUaB6/vQM06\nccUw0XQtWkgF6jmXup4yeEYDing+zUyMD9y2Jn6vPWuu1wku66JYtrUG+zCRuyqb99RWEgoG69nh\nOXErum9VFcbVRnIxj1zDJAMA18LNxtuf6/9ek7tD+0ua2NcRWU9Jm+DQSBgztqRxMLn8oMR60Bdm\nScI+ct3bitp+TJbtF2ngXmUbKz0kBgfI+ke6w6DP3yiups8JagVqAAaeu+K+blbfI8rMMiKVLVu2\nbNmyZct2Z7tH+YM5QaUY8pmG8PIIRZ/wsVQuSMM1qYpenSoDwVCuX0QEa3GpVB045oTT8P/l9U/V\niaGWwwPu6vX6p86BN/NKkaMOn35cU4bdXEpiZgX8GjPezslnThSGI0wnlSr5nSir86U+QYmm9Acm\nbZ10VJF+mqNpUSZBd0FVj3rrtYjgKNJGCQOVsXWMk0byfiGseOZiKyFdMMtOv8QuZC50pzsnn+FA\ntrXKX2CXZKIYTeXnUfJ0oU+2yLX42sO3vL6I5x4l119L8nDxNJaRJ6wh4XEjJsReEnCvzh1hur0O\nCNSL50EGoBf0Y49zXGwdJXvxMlz317/1a7Hsy1/9YTMzu37+JJZtoCh+OPhuuq4CoZ0Kx2ZmF2+8\nbWZmb34rkNjr1tGnNW6sERL1V98Oxz+7dmL529vw/Xjl5/0U97FZe1sfIW1xMTr68agK7d2tqMTu\nuQafHcP9tBePYtmHCJNWVG8HqO985eeNa8zR++4AAvobK2//L7zzOTMz+8mv/Z5Y9iZkAkYgF7dH\n2S3HAAzZkWP8r6SdWgQUqNRBeQKl4s69OCEBzewEk+zqmTtSz8HsETrVG4zTRqQe1ljHup0oqyPI\npRcF7tIhXnzKnDyxrlLRvJK5XrWUWvE6UWKhTLJSEImSMhK/QdgvJBOCRfRbECRIbYySazAmWy1l\n7QSKp4h8h2CIUcjuRCwPL6F6v3cEd98HhPOy8MCO7TmuK6jKCtdQhHuzxVo3iOeiK3ANR2LLgsFQ\njmaN+4Ds0vsyqWJ5LNN1EtIRa2kTjhPpJ/K5awl8IupZVvp8GnCNZf5bHpVw4xkUJST+oV9KIgyQ\nhOmOvu4xFqcVAvz6LMsfZMuWLVu2bNmyfV/s/hApSxGZGe90Y/L9ki/FnYDuCGIuJkWTmNbslbDJ\ntAIn0K9EEQFl8ro5xbjKJUqlFZhiiZcdDxBYA8+jvxBfMXYJvYRaMxS9EV8teU2tZisnmDPpLpHH\ni+8Z3d2xdnL/lJA4yg52ZA4laZRJuUHxvJQuUL81SU/qN6cvW/P/8STc1S7rdJBzGEP8hSPl6JvG\nyyKrufAmDDucUtAk7lirdZH838zBnEnzOrI5Vf4gHi/hvxR4E94K80Vp7rot0J633ghIyHnliAzb\npix8B9+02MFKqDH5Tf3Rd7XX/zd7b9Zr25JWicXsV7Pb059z783bZE+TFK1dD1UWNoksl43JF3hA\nVkquQvwBC+QnLJUsJb/B4gGVGwlZFsaU7KKwhCywiwskCZVk3uxvf/rdrn52fogx4htzr5WZ1k6h\ng0vxvey1Y801Z0TMiJgzxje+8a39DnMlofP3XvLoy1mjYfq+QWNwWh68bPyhd99+xznn3LNT2a0C\nEcmlrTfveJ7TuUgyEB1R+Y3pvpdTKAZohj/PIbhavYQcdxuKT1pf3z/0kgh3D41TtMIOvhCC2xwh\n9NnI+q7POP5s3lHEdv3Un+Oks/566abnaK0LO+/pw8f47lYoq5H/72Bku/816lwKSvkcu97X96xO\nP/n6J5xzzn3ivvX7Zu2lGC7BjRkJIri6QP1UfBHf54UhCOQ+ZYrmcs4ompy0W0Wcp2GtkzW0bchR\nUX5pgjbbrp7yJ9Ppnp239+hnKfekWKxxfcknybxq4H41veYw9H/7WslHRH+U4Ih2yb3mOtFIXrUg\nLCzPE6bdW4GjNUqtX5uCc0fQLyAxXWf1ZP+vN1a2bvz9nNeGpna1R3pqyVNI+YUl6plmdv0FZCXS\nSq6PNSOVe0IUVzlK7M9MRaIpRKz8OvaxLvVYGCmN0AnPj56OtQhy0mOQytypkLsu0+dO+FoQSSKn\ng3E65JLu4v7VgnRzSGyk/ylmrWgWx0IhgpvkSKnnIi++96tSRKSiRYsWLVq0aNGuafFFKlq0aNGi\nRYsW7Zr24lx7SXIFO9w2fjsgNpKArW65HZ+6XaRkKpt324q9u8jZJpOg1QYpXdW2mZNKiL0D5uWV\nup+fe2i3FDiRhMlacMeDKUjkctqc/4gLxAUVbalTIIhq3/E7yhqoG81/LsU9sGEOQ3Wj4iTqnitJ\nct1BYtXcfayLqv02qEtBuQj1xKaEh62I3TNQVoa7oW0GWLT/rRIWIWPQS1nCHINwBZQC4dKNqRAv\n3a2ZuGzobtWwZtf7e5c4qzy7ZzI29939O57IfOeGd+2NE3OFlFAvFq55CCvWQUF4fj1bbB3Xi7T2\n+akniu8fHYSyMYjndNltGjueY3G10XBt71pQcvI777ztnHOuGpsL6gjyC3uiyn+G6+cKo8MFQhL9\nUkjHrWOovbl97t2+jfpqrkES+0U6A+6oy6X9dlwVW79NEYqdHnoF7nc7U3E/Gvt78Y1Hj0LZP/7k\njzjnnHv0+Gkou3/X12l/z9yND9HWUu7/rdb33afumCr8j8O116xMpmG5Ye5EuLZnJkkRsiJIbjhm\nSOh2EctlTSjg0tGAnq6ju0NI0R3n/Y410V11+5lrr5G1q4BkRV9rUAwVwG2OhXx2pbpbIRMC93Uh\nNAaqguu6XpSUFVFcAGucUhUoNSKk6MAQEEI93UcLuJuK1NxYCa5fDsYQ1rWNKGbDjbtsLChhtYH8\nh7hK+9D/GqjCddJfa72yMUyv/HhsbvQRAipKCTbo4GbbrKxORbWtSj8a+Xuy0PynHSUhVOJhGFCg\nz8Qg56GZHXiPlaoBGkGa2oEkluciMUEPvboqSZ/RXLg0ynW04rJt6PrdQUBXukeGDBnqAS/GvgLC\n03eZ5v3bYRGRihYtWrRo0aJFu6a9wFx7/VWoZ8dB/s8gXxl2PzuAo6EgJkPipYxvrOmOt1qWNAMi\nLK8vxxGJ0EuR/yy7NO6ONSSTZ5zPGtTDdiv85cGhvfnyLXwy0rxe3NVZG0g8bnVXGRA5e9NvuJ3p\nmQdqAP/gKwlDxS4ll91Kj3YVgtwwg7xmH++wm9kISsMQX800H3YsRJ8EVciByJSyW6Mgn1Y9IQFW\nwK8gvyDEyiBdIJIYJCBTJkPJ5n3WbB+PXWqaa1uxg2yF2I9tle40HcbfzSMLXb5/0xO1p1DY2y8O\nw3d7IIAXC2vsDIjRwbGhSpfn56i7IA1AAgbEXtyfVCC+9973CMwdCGM+bR6H7z72CY+WfOX5v7X2\noyrLpSEo+zO/O/7pf/wfhrJvf/kv/HmPra3PnnvSerO0unctdukgu+4d2Dbw8ROP+qgkA1uoopaT\nsUfVliJcyPHfLA3NGY896lfL4NlHSHgOAviouxu+O8J9SuZ2rRxSC3uCfh6g7vuCyE0xZyaZha5v\n0I4ffvCJUFY6X7/FxlAHCgvWIBZPD21MrM582VgI65QOqCpFFXz9CiFbJ2h3XookxBJzUtCkFgRl\nyq5osAkh4052/0Rni9zkH9ZALoup5HqDIGuqwTNEeCV33D7QrM75cyzlftVYpxT9D3NRYAVKQihK\nQUmSRhQpW3oYpP0jrLGUPVhuRMKj8+1pJdikTzGGVcwZv90oUZ6yEgLdNAhK6FU4kmLKDCgRRLoB\ngnVyZuN6/8DPp5EEO5Cw3qxs7BLFUyFoemKKXF8FgLBv7Ldcd4lmqlpGEB+VUxAJzMRzUiQM9hBE\nEvdfESne2oEnBs+7xHSCtq41fIXAmtwrcghETKVrMD438tzL8H2mQqxZRKSiRYsWLVq0aNH+Tiy+\nSEWLFi1atGjRol3TXqiOlHq9djj27AjB7Mg/a/TX27JI4YwDF1BwtzFhkEDB1GLKtn12gxxCOxjo\n1D5SHSVCpkP9dn7pj7u8FJcFCNCFKLyWhFt7yWtVwt2juYYASzbiRut7wJIDZXeSSHcoAaNM0EwH\nIdigq+Gcwf3U33HOuRGIonkvhEGows5ru8Zq5T83TupJuJl9mG7D84kQu7OC2lbSTx1dm1JPuNS6\nHUR5dfemWTsoUxV3jpcBjk29s3b7vqapaNag3UriLUBYPD4yAvbhgXdLTUFAPxiLy27h3Wft0qbp\n0bF3T52fG9m0hZurkfxnKQiTWssKas/52FwwH7tzx7cVfXhxdh6++9svf9nX8Ya55+olFMBvWD3v\now1/++W/tHrC9fvBB++GsgO4qOZad7h7qLZeSw61xQLkeQ0KwViYzc1ltlr58bQWdeIVCLqJ9MBi\njTKZEwtoei3Rrr09uzcVPt+/dSeU8Z589KWXQ1mNsn3Ja7a35/unkhxy5xf+uONDUzZPoGztEiHZ\nY+KNQR5eXFhbR3BjXkpOxNEedYRExwx9oR6JFK43HSck73aiS8a1g7o8m1r0kXB8Wao+k//tdM/G\nRApl+2ZmvyVFIJM1brLn+6wXtfnV2vfTBArwiawrLU7Xisuqq6ltZW2oqaItrr0+BM9IXwd3k+iX\n0c2Deb+Rc8yhFVWK36mH3lQv7eL9VGI5ifJKn6AGorIdQu5WkOJ7+ZLPv9Xc9M7OofN2IBkLlvPV\n1rVCTkDpzxru8LVo24XsHfqIpfvMpYP/nZPABjt8kDM1HMe1Ptluv9JtgvdOgyL4fKCe1SDXH8aV\nBiWg/wtxwboS7l7Ru6I+lAbPUHk+TbbH+HeziEhFixYtWrRo0aJd0/7eKJsnOz4F1dkd2NUw/12y\n4zhKm2+H/4e3akWViD7tCOvt5G2ZSIgqcKc78u+FMN10+808iOnKbmE+87uayVhkBTKiH0r2xGch\njDJMVTOyJyDWKdmyBtk85DpSpCWhhIMVhTBZISfnOxCpEMLubEceyIOy+2cm7n6jTEWQLa3i4auw\nM9Ss6o5EQDlF5f/RjPA9dnNrudYuBfoQVsupoGOCJPId+Q9blVDANlFRMpIyU9HqJxlzPLGdzt7U\nfx5N/I580xmxtR/5Hf7qwsqOJ15R++JCxgkI5fVTy793cfoU9RUVc9Tv6LYhLE8eesToI294AvSd\nO6bYfX7qkaNbt61sAaV05aYm2OFrmHaBnHX13EjpizmI1bKd7DF2jw48WrW4NKRljnx20z0jW1PO\nQHNonZ/5dqti9Qjk/Y0QZi8hI7B/aKTcp8+RTw+h2dPKxvAK9T0WYn+BsbYWcvizS48Y5bJOVDjP\n89pU4UmiLoVYvZgDpXBmHYi9NdpTCYLYoL/KSuQPuMYoYZbrpITkM8uAqjQvF8jdKUEpHDNEKTSI\npaeEQq1BIVgTZE1e4xKaQ65He5gHzznnSpynHVt7xrXvu3rjx44qcedAjgT8cyFbnqDfAc1WVIXN\nUGVv5vrUp1FA7nEuQV8IZtQ6hoGSJyIXEHLHSVYGkpcVYaLaR9/ruuf/NpQw0WAblKWCdD1/6ufp\neGRo6vHx0PvgnHMbZD7oa0GTsLat1yKxEDIfKAHb1539qehfQJOkLEMAhnoEOGL6HQi/ol+UKZCq\nhxyTfCYMEKwdkgxGNpexy/ZIPXl/SnmeGnImwRsDXZ5ti4hUtGjRokWLFi3aNS2+SEWLFi1atGjR\nol3T/t6QzQnVqSsqQHuiREtYMFFto5ZkQztft0tb4qprp9t2zwy8XZQ4UtiVpLeBC4paRHKtkPhR\nWkkJquBOElcgiJ3rpUGRi4Kwv5LY14NrOudcQiVageDZxr5TtxT79oomhzRWIVMK0KqyMCFWPa7M\nPc5eCtk6gwuwlGvsjfz1Z6pATc2ahP+LEnnHcwkBXfFeXqugZpJq4cAF16naNftn+xzkWhZCOk1z\nQOuqxQV9JiXMJgXuSSt6Q8xuPEg4jTEpSu3F1P9TghyuY22x8q6tO5UpZnPcP7h3P5SdnXvX0vmp\nkZLXK0/Uvjw1Uupo6t1dzx8/tHrCpbVAMtxFZq61Q5CHG3HZjY+gWG5NcCfnT/zxE3NB5RgnrSgC\nr6lAX5urkqOIhNlTIbtTx2YyNRLt2Tnql9q93gN5e3ZudX+OvtjfN3cHieyXl9YeksxvQzE9ENyd\ncxdwS2oyYvZXI4vNFNkAWlGFp35VK6rwr338k865ofu8JZF7R+RNDvfgWjSz9iZHOER1d0DY1UTq\n1FsqrO4JXDYrcUEFVXKlADB4Ae1RzSauz726drmOSuLbp2uMJ1Fs52kaUa+nBpBMU5eDFFxN/PmW\nF5qgnOdSaoPvw1QeZ+Z5l/UE66N6aYLOnLqloHzdY7yq7lXPZMiijk0FermouTR1nU64Jml7EAAl\nlbqagaJTdXCssY1oq7WdH7PPHj8LZTnWLLqJnbM52StVApetNbkv3Naqd0W3WBp0twbOaJx/m4A/\nWGsTulHtPoVHvPYJHsJFoVkhhsE9jcw1urbVBUtNR31chLGjKupBs0qpOld/8d2C4cwiIhUtWrRo\n0aJFi3ZNe2GIVNq7gXSpqZPKrqrfJoeRO6ZE7R0CA4ZE7VJAJcFwIKuA3fKA9LZ9Bf6m35FLr9W3\n9HT7HTbsGHFifasnOrW4tDftccUdoYTQUp29s10qX5cbIVZn6XZOKu56crzp69s6+z1VqQH0UyM7\nWJoq4Y6Qw6zKjAEaQCwlylPtV8mbrDPzBbYi9RCI2kpspKyEIoLop1x2bqhe0uh9IAFfQn03ULtF\nu0OOJie7VSGMOoTTalgzm6p9x7aqsjwBu8YJKRs727QCqtlJyC0Aprs3jeydjPz5VpJXLyjgS/j5\n08cf+rJ9Q7NmIGofHpicAcfp+cmJ/+62fTdfelQhEXXmT3zc55p7+tbXQ9negUd9Zo9MFX0FiG88\nMuwqA2JXy27ykCjZmSfM1oJWZEACnp/bTvv2TY8crQXVYji3opXFCOiYTEkSkDX/3wKoE0P99foX\nqFN108Z6NfFj/OyxkciZbWAp8gtp7u/nK6+aTMIe8g+endpviSZpiD1HVobohEllSF+N+aLE9hRE\n2USCQoisEkFzzkjLaWt9t0GdlQC8BtpBZCob2bwmcDBYV0gAlxyST4GInpzYvWMuuI3cO6JEmuOR\nZybqPRHF+BWQ4MtLQ7qIurcaxBJcAoKm5dvPGK5Tuk50wBcKqmmLij3R5F7uF4nXKidDNfhMCcsN\n8//Jswtra9or+sJAFbRLCdPwKmSJHu+vO59Zn5wiUESRFuYw3PWQU4Rr3VCpXfMvNoNrab5KEroH\nGUDYsUr2xxjLdmQWaTRQiI9JmYsV0SzKdch47ZkHUFBa+6weGRD1xcNBlQZdp0OlB/l8I9k8WrRo\n0aJFixbt78R+IETqtddecwcHBy7LMlcUhXvzzTfdycmJ++Vf/mX3zjvvuNdee8397u/+rjs6Ovr+\nJ4sWLVq0aNGiRfv/mf1AL1JJkrg//uM/djdu3AhlX/jCF9xnP/tZ9+u//uvut37rt9wXvvAF94Uv\nfGHrt/1V11cydHs5Z8kwlVjNTwNSOPWe+m13WycwJhFFEtfSQR0AD6o6NznpqUCx1LjYQXpOVYEX\nbiklyQW3YBCSEoId4Om1JEhdT6DxIm3ISgqZSDJK6Lj0jeit0LWlLkgSuqE3pS0IVZKy4KmSc5DQ\nl4trj2Q/VYwuSNAUzZgW7saxkOJbEGA3EGjR+8U61Z0dTyKqtouJT3tNUFnhOHEZuNrXOVNV3jUS\nlKYkggqMXrMTRQsF1+8k8SUTVAuy7NrEw+OjkSSNBRm1bo0U3ackwIIw2xhkX7b+t31u15+de/he\nE/SuV749q5W5+/qWWkBCgAcpeu/Q5uv5wpO79+Biu3Fo7pkJtZjWdt5v/tVfOeec++TPfCaUPf7L\nL+ECmuTT34u7Lz0IZR98w7sD1d3UYs4soGatyXA5P6lc7pxzKxBhb4m7c45kyJpcd4SEz8cTcwu9\n9+EHzjnnjm7axm4fausPHz9yzg1d1i368EiU3Z888e7LtdSpPITukejzZNBvyoSAvEKi50GMB8e4\njNMSbskVEgoXonFD/bZUMsRmIPSra5m9qAt8ARdhK/Wkm0m6PfRBXVNHStpFjR2NrAFVYFKaC/Dk\n3PfTydOTULaH8TRQIIebfa0u/Y4K3AzsEWI/3IfN1Pp1fenvf6JJizO622ysJS0T6Vp7Cuq9abZy\nBkVgLcpKde1vaxZy3euEAtHQs6W6hKSPiKswUB8GXkkmkKc6tyQZZgCUBmAxUYesncuV709qp/mq\n+9+MhIJBUnzb2DrBAKBadAnNLbe91tPNXEoEAt2j6sakBqCOJ2a2UNcuyf2awJ7uZhLQeyXxh5M5\nsy7fun7QoBy8J+DZqfeTz8JhpJr7XvYDu/ausul///d/333+8593zjn3+c9/3v3e7/3eD3qJaNGi\nRYsWLVq0v5f2AyNSP/dzP+eyLHO/9mu/5n71V3/VPX782N29e9c559zdu3fd48ePd/62u/IKl4Zc\nOlbWBClyIX3hTTgRtdfw1r+DKL6D8211GISmguyrOxOeUIiIzCHUdxpCTIRLtnVEvZRseOWlU3da\n3K0oie7i3L+FN6J2zpD8emR1KrETzHNN2MSti95itDHdJpY33CVoHYMkgoT/8nhpvwuXks6m2rnk\nXOJuaiV5sgqgJDWUdYXf6JoN+jrblsQoBBFiWSfIUSAUJrpzIUolO3e2g+NFdn9tvWP8YQeZV5ob\niv0pu+qcBGgrm2In2EigwKz2O8aDzBPF23M7/o07r/pjFiYJ8OyRJ+8qIrtAXq2NoJRkfrai7E0F\n/GfPn4ay1z76Ef8B7T45M3X01HlZAZWf6IEgnD80CYVFx5277CCxI374zvuhbANC7UTu3Rq73snU\nt//h+++E76jAnZZGWCeYqSjlBrvqRAiwGVAVnf7TqUenTk4s19/h1KNzqxp9LB17/4FH0+aC/hHN\nmYliO+s5EgVyTqPFhd27aoLcbXKbFnOP9lWl/XYDdCDdgdITfdf1hxkLUkmsR+XrRsLTq78IAAAg\nAElEQVTfSYbO5Frc4V9I/kPOgRHkFza1omUlK2JtxVib7Bn6962//bZzzrnT53bebuTPk1c2xxpK\nkuSigN4z7B95NRXB7hgab22dQDF+JR3LwJoklc7G+pz1itwDkZb2BGVrLAqNSrIQpVJUEdVLy+37\npKH+fVh/7bdFBoRNUGfXEPX3desGSuBEyQXpQWVKCUpgG1ci9VFWCFSprayqruSfdSbjoZIEJHnT\nE6Cp9DjXMkV/KMnT6UBhvwpyiPvUKnKH39Sdob4ck5T60UCtID8kCJbJ/qhHKhn8HZhKh6Ssp75j\n7AppM/uBXqT+9E//1N2/f989ffrUffazn3Wf+tSnBt8nSTLUKooWLVq0aNGiRft3yH6gF6n7970o\n4O3bt93nPvc59+abb7q7d++6R48euXv37rmHDx+6O3fu7PztTMOHp6MgFhgtWrRo0aJFi/Yi7eT9\nM3f6PhHl7y1/cO0XqcVi4dq2dfv7+24+n7s//MM/dL/5m7/pfuEXfsH9zu/8jvuN3/gN9zu/8zvu\nF3/xF3f+/uDu4YAcGuA51ZHagWYl3baOhNtGwF1Q9h5g0Dh8B3QXEhkPEuTyr7jn6MZQDayE+iBy\neRLb1d8Y6kk2m8CTgTFohy8vPASbt6IEW/KnQuyDm0PyCAfiX5IYtJ3B9VG3hJjt+k3ofztHv4uc\n11J3RGD08XZ7CItn4j4kabISsvGmGKonC//etUyyqaOUysKStJQk7oECfE8oWgjQVOVttjVg2Ebx\nGIV7p4q5OaDwXvqOUtWZ3ACSgRNxIxAOzzNJWgpC7zL1Lr7bvRHBKyZjXoq2FC47Fx2pxaXXe1Je\nPd3ItcLzcJFMJ9Z5l9Bounn/JeeccwdTu/7TJ16LqpCxPgLx+esgnTvn3E/+o3/knHPuK1/8olUA\nfXx+am7Ej7zqlb3V3UJV6PkCCY3X5jKbpvs4xtpQlL4vOiEWE+UvhSi+RMLhaWYJjw/2/fk24haY\nLfz1mOT42dMn4btb97xmlY7/i9kF2mDXv4Sbr5Ib8NIr3i07n5lrjy6by3MrIwF83Yi2GcjjCdqT\nijo8gxK0/Qz8UG2zntkTRIOu5vdL6U8m0pYswA10hGpQGtSNk1KrSob/AQjgyZEp0L/11lvOOeee\nrm3DvBz781aStLsvURdxbZVwETMTgQZMtFj/k26H20UzW1CXSNzdxYjaTnZchbGV71LRZuJzp0Rs\n6gg6Maz/ooXEZafTe4KPeaLBFnRf7tAgRABOnouOV5DnljUJLt2qMDCCGljpoE/aKydxbg2ahZKy\nGwRZKPUlXBadp4RxUj96fU5yzsrzlFpRw6wgrJPVk+5zDWgKQUD4o1lBjG4iWmQM7HLbzyTVFgtJ\nkJNt+sjNlyfu5ssTfJ+6b79pNIWrdu0XqcePH7vPfe5zvgFN437lV37F/fzP/7z7qZ/6KfdLv/RL\n7rd/+7eD/EG0aNGiRYsWLdq/i3btF6nXX3/dfelLX9oqv3HjhvujP/qj7/v7pB++VQYEKdkqGhb2\nDH+XHQlJxPqWSvRHL8p/uu3jGeuvCqYddgbdANYDmjVAn5iwR45iXjW9POtOxVpVx6WsghL28Na/\nXohicse3f1WW9d+Pnez0AilT377rQT1bIex36ItOw5p3aSJQCVdUnGsQi8eV7ZyI8KVK9qNisITJ\n8zNz82W6W0H/NGuRX6Cyu6rYgryskhph95NqG4kcyl0Jt4Aqwttqtor+ERwsldgYNsSSa5B/ldCP\nj3qLm9qjGRuQ7u9MLIfeAqjKgSiWn4x8OPlyboTR2SWJ0tb/AUQRmJQ7zIszQwnu3P+oc865S5DM\nkyMjdldQtCbi5U+H0HghAL/5J/+Xc865GzdvhrIC/bhY22+zIFOhuQuBfkAJPZeAhctLT/I+lLB6\nhkafnFgbctwnRRqnQJg0d16FXIDzpaE/N4BSrZBPcP/ApBFWUDsvRX5gA/J2IejjpvHjP89MOmIO\nlGojoe6LDZTiBU1YQ86hEzmDBHOc6M9SVKd7qo1LTkiHPGlrkamYQr09S+1+9g6yJzLFA9ldiPpV\nNUYbFmiXjesCc1ylNkYjZkqwE19c+PY/F/mDixEQ6am1tdwDcltZn0z2IOdQUv7G6suAicQJYR4p\nAy5WopiebCNHjMVRJD7HmlFKWVgngBxtZKyTuz9U5waJXdbklCryqqLtSJjekQEh3UZJiCppEtcQ\nWCVrGD8lqpiOZ0cj4y/LSMrWa6EFMnc4xtXrE65REOkXEj3zmg6Qph3eDD53BCXnc0eDzMwTot4p\noJTBm+LEtsUHQj8NcuiBFC/reZazPYpmWU3CcSp8vsOisnm0aNGiRYsWLdo1Lb5IRYsWLVq0aNGi\nXdNeWNLi3l1xe1GLSAq7K64w5yRpYTc4EB+UbEzytLrA6O5xO76De6jfdvskAvsGCFKwyFAkeGOy\no+7BpUdi90D3iteXMlZDiKVJjeS6Q9+i/04gU3oetD0km+8i24f2pAqnUrNGXGtUHRYYl1BtuyOR\n5yBBKD5mAlVTFZwJOjXYgB3AhLLOOZfyHkrdKdTbCYk5gZttUCe6gLRP0Da6vXq9h/ic51ZG3mcu\nLtOQeFhI5KYKr+3xbW3ELViD2Fkt/XG375uKdn3iic1dpYRZf2MLIexzXA1JuRhjmd5jVsnKlksq\nBvv/H77/XvjuNlI7qRtjNvfuqVLcsxu4546Ore6P3vdE9XJsBNgG92wFwrZzzo0KKnVTn00DO/zf\n6dRcm5s1XSHWrzX0vnpVB3dUG5dgi2ZbA+4Ubk6qeU+mRpgmYXspumct2rBZmctysufdaMuluZYy\nkMhLITGvQmJ2W3brpXeRZSPrpxL3bAlttWpsdapbjlebk/XCXzeTcdpAeb1dmlswh1u0lCTEE+hC\ntaJivYSbk8nKR9InLdxcuRDgC+p9TawNb73ribmX4pbMkLUhn1v7pxvMsamQfXHv9m/QLSnzD23I\nJdokm8AFN7E+ObnA3ClEbw/EbtbXOecy6PL1sp6WCIohob8QcnKgfgzWX0YxqRsL7WnELUc3lkzJ\nnOuUjGe6GeuSmkma0BnrqugDpkHbUAOwGOyitcSzYxDk5D+vVpKgGPdY3WdpuF5y5a+wB3bk/VVS\nOrW9+kFQEs4/WPYZ5CPrPk6T7UiGbGusVJhrvGYAgJtV5Q4Lat+l28+dRAMFvreMVESkokWLFi1a\ntGjRrmsvDJHaSl2DN8hmQE721klMPN+gUw0hDclx9PTbyqZER6ic2u0gpyuCFBAxeat2VxRenTNV\n9kxQMr7fa04ivmmHdiXb13LfJ4dQgxxeaaeKuTyJvv3jOyEMEkVJgSYIqBDyOul52V+6+6WsQC9I\nR7fd/eG6ej9JBlcCNncJWTr86yuD3bduB2rsPiVclbIGGtfcUyJdSZm8/4omBkkMIGOiuk0wIS8F\nwcJGuJDdvymbW99V/L7Ylr+oBSWra+SuwuEjQbrG+35HfnFiauMViL+jysZ/gXxyy43l1WqpjqzB\nA1QHlhvfoJ86BAwUkhvu2YmXLnj1Ix8JZW9/5+s4zvpkMfPE4re+9rVQ9uC+VwU/PtyX4zyKowrs\nRGyY12whgRVU+1ZUbQ6l8EyUrZvaIzJTCeHneZ/OTc6ghOzDuBJS+MLX6ejA11NxhjUI8POZEds3\nGyBIIsFPdf5e+N/7+1524dlTU4A/OPbSEsuZSTyk3XZOzhrk9Zt3fIaIrrN2zXrf1l4Iy4FQryTm\nHjIRjaFkM0hMFDOr6AK3sRK18fMzf9wISvCVoGVnZ14u48H9u3YpXlfU/on05RKAwhUiFYS9B++/\nLYXQXUPZut5WbKd0Sq6PLqA/05Epqy+Aqs1bG2sL5C48PJYbhQpkiaiCZ5zPQD91YaPEgbQhiI3v\nCJRS5DTHPc4k2CLDnGwFiW2RyYFIUy1EcKKpu7w0A2I36zmoE705qhTP4/U4rEnSoILBDXzWbTfV\nNZpXDwjzgABvvptQFq4xcEXx+O0gMy6duv4yeGgYgOYtE+SOkj3zpa2Th8gtqvk/DYkTb8b3ERaP\niFS0aNGiRYsWLdo17cUhUi4ZvEHvlCtIySnafjNUNIlv/QNRM1g38McOUaqB35Ooyo4Xz10SCsku\nnYZWOTLbaA65VjxqmBk72a5vAKmUewQ/t+xSkP7sSv6/fKtBvF62I6+h7Qj6reOVt0Q/uwoCMp/X\noE5AEdNuu5+UI0XEJMcOphBOyajyO9H1QrKq47oCSLlA7xikScTObcARgo9cNqTkSwUBQ5kRxRh1\nqmQHWVCmQRApiAnmkmsuoFgiNJgGZMvGyWbjPy9ajxycPDf0aa/0SIDmcJshd5aOqwL9NNm34y5O\n/HGrtYT63/Kcp0Z26S04LAzDzzVfJHZkjx5+EEpu3b7nnHPu9MSQnsN9IBeSw20MbtSTRybI+dJL\nHtl6fip9AnRsg7GTCX+FAnsnp49CGTl1N26/FMqC+KjwfD74kHW2ftpAsoMcIOeszzJwflZL428V\nGAzrhZV1yDtXjQ3V4rozklxzS9TlUHhjz555NEdlQsb7/jwbQSmmE49E1msIUm50Xvt6EgVzzrnq\nGDkRhbfTQui1T1XM0bd7LMgNkeOlyETkue+TUenrputvjbmuiOgSl3369rdCWQMh4FGpSA+Ea2VK\nNoAYChEdZm67BThnRaljHfOvs3tNNFkFHDOGy4skBJei5dx+uwdESJfzjogQUI9U5holFHoh2iSU\n9RA4k+K8qfQd0aeRyJQQiEo65W1d4fIp0kKgXbwPvD+K3DJfnqI6DcukoiHvp0g8sJ4DNNvRYwCk\nSelguGzdXz3a8vA551wBNWnN00rOnUpnUCZlmAuXMjnJVruCJJGcg12mMj2XZ5eDujnnXD2G/EWp\nyqWBkGVFA7XtbYuIVLRo0aJFixYt2jUtvkhFixYtWrRo0aJd016c/EHfD8Kq2xAaLGH1gOxzgewI\n4zcDTjJI4SpnsON8ZoQCpT5u2y1ozq5tWQV1rRCK7dy2r3Cn+/BKLifnnEvoslLCeA+ypbrWGKa/\nI4S0k/fiDUN9xS3Fb9nvba8uFg83d+IeCGhmru3ndeUdHCq+6u7soajbirIuyZPJwKUJtxTcDbkq\nhoNYPytERR34cbMWqLUGiVqI2kCnByG8dBs1GhIfUHacTwizKYjiuYRQp+yLXnMY+r9lYXUnVJwq\n1xbuU1U7JqF2BVfFfGHk4Bv73hV3/txcS4EIO+CQs/9VideX7Uv+s03rr5EKTE0XzbymOra5AjMQ\nz1s574cfeLL3Ky+9bBUA2ftQVMG//fVvOOece/DSK6HsyRNPvD6+8WooWy68K7OCrMNeb+fYMIRf\nZB1WcEWUM5MfqEjKFQL4Bmrfk4m5sajYvNqIAnji3WgzSDK0IqFQ4B4+f2bu1oJh/4kphudQAl9t\nrF8LqIifXxpR/QDBAypTkUCVe28q7jb8pRvl5g3LF1hjzZhdWBvml548m3bbrr2us/vZtZA1sKLQ\nT73M06MbtwffNZd2rcQqF8roCvr9P/yDUDa65dtaicvOIZAjE5kQBq1oTkqucRvM/yYzcjDUCtwo\ns/6nyrvOP9ZpkCkC8295IcR2uMU7IRbnYQ2AXIPMf0oHdJn6sdAuCeLowd7OhewcaAzS1hpzsRjk\nbkXgFc6byrpKV2EmZWRUtM12TkJ9/lDqpJdgFzLpBxIvxTbNhFQG83ppsFMgq4QyBlLlqsAP9fCR\nlDWklMgznu5efT7z2U73ncrf9OgnpfZkIWOGHTdCMIrKHpXImsDx4pxzLdbiLFUOSHTtRYsWLVq0\naNGi/Z3YiyObp8lO+YFdNpAJoKiZao+FN8xtAvouong4x47vdtZDSfFb17TwSz0h8/gNBNG4EeBF\nlIhOQU5FyUIE64BFPayIkxxjuv0gT1EQHgIhKUis6VjDgHeE0LJB8jbOUOBBNwX5CfttkGlQ6ITt\nkb5jbjWoALjRyMipK4Tkl6WhNPXMn7cVciYr02teK+wwNK8WUUrdfbBreZQSVoMUguw+ExJRZUyW\nI3+SciLEWoRzJ7LT61JfpjudZuXPN4aY4VLIkWenHv2RU7iLM4+c5JmiX35XtR4Z1FA1vkzF58oR\n+loIuMvZqa8nx5+G/GLs6JCYIhT+5NRQmpfveAL6c5FpeOP1j/k2C/rF7PAh56NzLgehnkjvoRCh\n33/fI1j7En6/mTM3n6EK+7c8YtNIWDODMlSQ9fzC1+/WvXuhLAWh/Xy2RJsF1Vkh2EFyuB3f9/kE\nCyGMUx5gs7R+ZUh6JcdR9kDFFJeNr3Ou+QQpU4H7vqztvFyyUwkKGGMeLwXNZPsHedUgK9AI6kjU\naTQS1AvIRo0ABKJbzjk33vdt7QS523/ZI1jffPftUEbFglyI8uTTJ4PljETtXeHvFFC275YNxF9H\nKmvDoBgrq0BsziT/pUOd1ys7bg5ke1/Q7BoBGBXQx0QiWxh2vxJyNlGnQhdv1DmXuZ6hTgpuYDkZ\nPAvt2caFbfCwwx+RVUg5r23tYDBQqygl7oUKdxIdzwSJT9JtfIWBRBSuHSBCDF6SuZYG9F3uCfpz\n8IRhOwaDgh4THRNDknky6GqUqZg0BZ7lvo6O/RjPcn1PYPCUnS+Hd0YcNu77YU4RkYoWLVq0aNGi\nRbumxRepaNGiRYsWLVq0a9oL1JG64nbb6VLzf5TmRSgwVwVyFxhw9lNqIOlx7ZAwNjzv/7d6EkYc\neNFauoyU7E73odZz+NsBsdttKxwT9u52uPb0uEAG3qHjoe7DIGgLf08jUGyR0mUgVwo6VlZGN1M2\nyEnH84kWCXRUBmRz/ESJjeE7uqrkvKOJ17EZV+ayWBfePdLVio+jrwXGJaSbi29L3TxWeaoY87fS\nruBjULI/PutYC0kE5bzlUHfFXwqkSM2dBVJk0cAFIYRl5rBbrsy1s0L+Nc2/SLdYJq7NkmrL4qpL\nMUAODo2oS7J7CzVtnQctCNNKBCbsXYkS8CkI1S+9/FooW0AxfCJ6S5PJAdpl97OBi7aF6vDekeXV\n27vhlcBL8c6QWNyKFpbbQTZnTjzVQFrC9bZamquqh1t2BlflWLSgNrjXqm3Wdf5zI+6WAkrpmZLi\ncZ96VaWGr2AjufvG+76984WpnTvnP+eVr281umXXAilf71OHca2unZ7uucbK6NpSd98cWk1lZf2e\nYfDQe6ok6smeHzuJEIYLqMI7dXfCpZ6JBhS12gZ5Snlq5WqQPoA26rLd4beX6/NQVvZQpZd7UpWc\n/7Imr0lAt+NmM7qstnWhkg3bIMFOmGOF3ACuhdkgKGnQFP85Ya47KzPSuAQZdcP1uVXXXjixPLpD\nAJToQzHYZ5B/j+uPjb8Urrpu4Mdie3UtpLYT8v8N3H/Jlb/mDlUKQpJt57/lbwb9RDeeuMBD3lc3\nfIb7WtK1p+cljUA0wAIBfRBltvXb0MVKn/neXPOISEWLFi1atGjRol3XXhgilaTJUPU7vPEKSkMe\nmiICPF52BPx+EK4JJKDbiUIMrzmwgZoqDxzEYTrnrqQGYkjsIE8edsQaEuooXcDjZLeE0M1UNx/Y\nQQwUy5PtsNaUpOBWd3/+L0nfvs4g1mEXkkkOOar3KmGPO8FU3tZJItT+Z16lXpALdkVdq2Iu+852\nswlUfgM5X3NTgRRYlDZMi2Jb/oKRsyorQJJ5OthpUBVeiKLsO0cldN2t8L7qxa6w050qutvun7ne\nZEPmktSjHa3IL8wXfmc9Kz0ioDvtdOaP69dKACeJ1NS5c8gtTwSRmxyCFCwK0BuobddCHuYUIMFZ\nd2vjyqtyV6UhWATYMiGRtggK+PADU0A/gtr2RuSOb1bICbiyOq02Hn0Z4RYvFtauV172+fouZ1Z2\nvnzfn2Nh/TTKvGL6xcb6lUjbuDKiusMO9+DYJBbe+c47+IFHZsb79l2G0PhENCymB/4+7Qty9sFD\nr1heZDauSqI6a6s7d/NlZf25XiEoQaX6cVPyMSRBJDiC6uy9zKtnj6AyL+OUIe6KyKWQ7Ggkr1sK\n+YUk216niFyPRJ2d46QTpPUS5PXDe4acHc6BXAlKvd4Q9ZT5lHGdFAI00YSQ106UsPF3mYokA9YT\niaB3TKe4v7bCGc6nCCvrVItMQ0G0F+T8ShC5DHMsKVRqAoiQLAokoKsXJMjNKLrRbq/nfWg2kEaR\nNQgPHpXJaSgXIGsnif3yiK9bBgDJ9SHT0CjZuqN3QhG2ISKkzzoG7ySJJuxDddVLwEAdechxnVbE\nh48xlY7gM9PeBTRQa0egFHPyytxJdzw7TYpo250TpE6czInvYhGRihYtWrRo0aJFu6bFF6lo0aJF\nixYtWrRr2osjmydJcF05J245JaIFDE5de9tMMDtKiN27lM2vvjbuOIe68Xa5EXf81GxwXLJVpyQQ\nyndpNm1fq293uBEB3yc7fpuqAjzVYcW3RFJ8Tbi5ViI8tai0LkyeKf3UM/GoEAHpbhokEvYwby2Y\ncVDFTQQyvXKPdyWD1uSZo4l3s7Qrc081TFDs1BJ3tZBjJ5OB0BJH5uGNQOYkqioBu6UrRoiYxZVz\nOXMpqsskJNduxLUCd9DDi8fOOeceHN4N3x2k3j3SOIP2R3DLMrGs/wx9Kklu3ELFWt3CdBGsRG9p\nDVdRBYVzJfbS3dn2RoRm3ccjc21N4Q47PbVExqPRFHUz1xoT+WrCW86Z5QVca1Nzo9HFUVXWruND\n72a6PHsWymYg4w8Tmfq6L5eSoHjq67yWsbO+8Od56Y1POOecOzi6Eb7LMGBv3b1tdcKadfLcXIv7\nSNpcr0xtfQXyuLqsqsq7O1cbI9tzDUhk4h0f30XdEUQgOmp56vvzQgjzUxDWFzO7T4FSIG7kNca2\nJpLlepvKOkE3xhQk8nJi93CCezc5tn762gfv+vP31tdUuW/WVidqqnXCtq6DT0czKqBPgoySyviD\nsC/XGmOeFKqFhN/ujWxMNPh+LfOUwROtXKPFGsuMGcIEcFnKIBJV1sbfQTJgzDtpawc3WqqBQs13\nX2M3Ld1u4kaje0oI41x3Wlm7EquUnZdZMVQrjkmId6yTiYogJnTpkQphnRKIKvJ85Xn12RWU59W1\nF5IFy5q4ortPokyumKrYM6Cp1X6iinyhrzik1lgJ+y6RbPVcuweJ6b9XNJqLiFS0aNGiRYsWLdq1\n7YUhUl3fD4jA9ja9A/0ZqC0z546ejF8q+mO/vvpp11HJ9uFGxB0QlnGAvmpTFVrz31FtdSBBvk0U\ntEttE+aDiq3mdSLpbxCSjzd4JcThrT/dQQAnOTqrhYgIsmsiO7MM6sm5vP2z/1VWIiFKJu/lDL/V\n3WdT8xpGNg4K9DxO0CeS51VCIAeJO1cpWm7OdKdXk2xuh4WcgK3eJ+xSOkpNyOE470aIvbzHqtie\nIDdVLcTqNWQMRqqAz/PKvWsR/83cXRvZaS4WHmmZSvz/auV34szb5pxzI6iBtxLqXuP6qjTRLlCW\nG+pDhJOAxP6BKVxTWf1gz9CnMfJVUf3ZOecuZ/7z7duGpu3veUQiLQ1N4vCoZxK6jn68xP1XYu14\n5Nu9PDVUowQ6VZaKapHsauN5tvCo2ygzNOXBy17R/N13vhXKplPfj3fu+LqTYO6cc+vnJ8455w4P\njGzNKVzI9WdnXjpBkT7Ws5rafZqde1X6THbkmw0DKkx24Ry58yhxMdmze3KBHIP7h4YIrUnel2WC\naMblXFTRgTArAZjrycWl1f3B0T2cjgiuoAVo/6Wsf2++/WVf78YQOWYqmEj7exB/N6J23jFAQNCP\nHAEPHYnISuIG6lMr+oMgj1LIwSXm1XQiKvJAOpZzDSjydWkGyzSQM9SpGZDIgcgI+uN2kZjR/7r+\nZXiO6fJP5L5vFc7xdV5hPg9yrXKZ1McEg61UsZx1Gkgy8NmhDzle146rsQb1op5PaQfLCiHrL6Ur\nBNUkKb0Q9JkZILJcyeaUqbA1KYdMQt/IHL+KBEqfMLCkyO34aqRJTlkn/hWkiZ4rkfNgnTQoSZHt\nXRYRqWjRokWLFi1atGtafJGKFi1atGjRokW7pr1YZfNdhGnVVgqZd7cVw1X2wRIUK2GPJDLRsUiG\nEKi+RXZBd2KbYNb3Ss7bVnENv021nrvcd9/dWN+BjFGyrQ+SFYCim8GBg3M4Z64qdYFS5ZukSOGh\nBvdpniuxk3CuXB/wrfY/21rvSOTZyoHUwFGvXAVXCu97LZUyd6/qWDFppti2tIjL4KIUL2JIQllv\nFJbvBz/NCzvzmr49GRNsT9+rPpavU7Mnrr0l3ZJSd5D7OyH5Z64YXD8ZyKOD9CqQ+TE0fRTap96M\nkl2ZyHizMdcOoWodJyVdWdTMau1ad27fd845VwvETd2hQ3EtHh5619+Txw9DWQ33zQ/9+E+EsgsQ\nylezi1C2QELgA5CTO3E7UUX6SHSfzp97raqm3lYiLsUtF3inokpOhfLLS3Mt3nrjU84558bQbErF\njb2BK0zd8z00dhZLIXa3fpDtHZoLsMb8vDyztvY4biODcn/iiezdYET7zzdv3UT77Pp7e95l2LR2\njtMzn3j67n1zrZ489+5G1dGhRtp8ZmNiD7pYo8raPb/wdZ4c+3t8IO2aYH58/dzI/u+tfJBBL96U\nSetdqkx87JxzGd086kfLSdUQzSAG5fB/oSdkPfTZ7AwhoCKtJBk2urMUsvn0ACr2ostm3SOuQuhC\nFQXXJrsaKQuFjJMMg03F2YN+mtSdQT4qGVajLuIpdx014sL6b9eiAnurNBY+a+Q5Rc0qXWupY6YE\n7EDoH1BgGKgkrlK42an7pGsN1cs1i8QEyeclTsQVlT+vjrUESZ21nuMJNSAl4fOVJORaX94nTRDO\nZ6ZKALLOOid4H9NBwmNvGmQwkoCLXRYRqWjRokWLFi1atGvai0Ok2k5TmIU3405eTbfJ4foGvYNO\nLtt0koj1TdfOlAz+c87eSNNBDCfeflWBnaRI2SUMQ/av1FNRIpLHA9Klar7bCCxA7ycAACAASURB\nVBJDSDVauee2R8iZ3OBpuiRKIWS6I0X/BJBOVXdDu6T/SUQWlMbCtaVOgUUthVRAlyHWksS40u0X\nyOtoqxKmSaxuBur0vL6S7aldIGRzoo6d7Ag3HBOy++B9SrYVlkNsgJJjQ2OFMIwcXquFjJMKoc4y\nTjL2v6go97gHVeERkaN9U4ceg4Ccyn0yqQkJP2buPhlrVFTu5bcFdo7cLfr2DPMJqlwFB8qRENCJ\nTuVCgF+DjH/r7oNQ9vTxh8455549NUmEmzc9wnX2dBu57QOqbOfNQ75GQcQwnj/5qR8JZfO1R1hU\nsb0qvfzCbVHbLlDnVEjJhwjjz0GKL2WsX2QYr43Vl6hWc2nXKie+fyrp18XpBc5nZQn7TFXBzz1B\nOxHUc7LnZQdOLzzSNBpb/29AHs86G0NHQI7G+3ItdHsva+JsBkJ5I9Ihjf9NvRGZgNZ/X+75HX5x\nIHn4Ej8/FyePQ9kScg6alYAoTipIU83FWNpKIvsgKIfn6IiMaP9jTZQxUWDel4KcF4hAaYTYTC6y\nkp0zoDONBADUIKpnVEKX6+fMdZrqnPSmLQhL+yDXqS9sBBFvkdBwU+uzAGsRUG/NIkCVe1Vnbzn/\npZ6OdddnYkpJIMHzuJ7LcRzHmpWhgEwBgTiVsCHSqUFJRM6SXLI9lNvPEz5uFTna6c1hnlb8QI8p\nQBTXRze9T4neV+bEzRWRZ53lPjF3qWbZSL73q1JEpKJFixYtWrRo0a5p8UUqWrRo0aJFixbtmvbC\nXHu9S4auiG1kN8B3uxRGOyG2EUcdqLPiNwoZd3hvpGZRn+xwWe3wI2aq+ktIURI07iIKW5lAsMGN\ngmv1+h6L80lRBqg2zQc+M1RtWzG8E3IcYdS8EB0TJtrsqZ0ksPt62xUFhDu4yXwbtsn+JIqKpzJA\n+4o2Zzh3o2rLULumAntdGzl1ufEuhlQalgLGV7I1PQZKSqWbr1FiK0je7QAWxm+Da0kVi3liKaOb\nUcZkDd7xqtpOxpmJv7UApF2koo+CKZigbmVipMYSmVcLSXxLnaVMyZZIkNsIiZbaO6qFsisAY2/i\n3ULUu0mEiEqtplYIw/cevOKcG6qNc04qKbzBAHn722+HMiYfrkRbZgNXLr29B8fT8F3LZKwSANH2\nvn6zlZG9STbWJOgV3GiXQmzPQZ69f/8Va/+RrzNdPEshkc+W3u1WZdauy9mZb+uRudv61PfFam46\nSpOpLwvuNGd6a5uV9edoCnejuEqYNYBuRPXZB/22RNTuoZW0XJgC+vzCt2M+F2VxuNbHE3HtQlOt\nWcn54CJcnHtS/kRce+XY1/PJ238RygJ5uZfHSQq3qCZSZhJ0WWSpFTWkNMB90zPYQlx7nIs71m49\nb0igK+ukufE1CTLVtu36TDxQw32vc43J3VtxxXHtGq7nvJi4sdHFqi3FpSgdUBUYAEO6iRLxqe0l\n7rGMzwRZu7lAKu0ELttB9gqse+XI5uQYGRIkV7yjzFIJjadCEznjsyrmZ0ExXe/JDtwGibQ12IDr\nuQYeqTv8qtWYH1onjiGlhXCc9JrwOfBR9DmNeyzPnUEQ1g6LiFS0aNGiRYsWLdo17cUhUn3iBpRl\nvi3KMUSCBsRm/E0l1jQQVuXtm/nUWqdvpFfI5gNUZZsySAK6vkinQQFbdgQJd1UaQrktyRC+77av\nRYRJCat9wrxugiqRlC1v1eST14LwkVBZlEKYK5inCSrajeofQLFXk62l2zkBgwKv7LRC6Kgid9gl\nZBJCOxp7tGUj92mx8LukFnIBrdSpAYlZyYEk22v+qX5DJVwlUfprNBq8wJx8iRLgcZ4SocEqfwG4\nar3W9nMHK+TcFUKYDXxwCVFSGX8NiI1tZjudo30fWv7SyP+9d3QzfNcBiVJiOT9PD0wJm7uuXHaE\nm5b9r0RdkIfLbZmAgP4qWod8eeN9U9HegMRaltbXC6AZq7URyx/cf8kfV5lMwle+9pZzzrnDA1FW\nx98U4yQv7LuGyvYy13NIHBzsWZ2ePfLE9pXkn6sQrn3r2CQBNo3vz0MJ52fwQOhDGa9F6ZEo3emP\nc98nZWrHzTCfqpHdkycPfZ00JHsJUryOiSyd4vp2j2v258rDAGUpUhto/3hqyF299khU3Vr7Ly+8\nKruiWSuoveeVEfBz7LpbUdTPMuRJZFh7Ze1KkddP898t0K+qSs953ApMTRKvBhRx7VT1cgZ8MFND\nUiuqgVyTzvo1w7o6eCZQnmQQ0LQdlEK1DVH9sDp1zDUpCBrO0UgZwLdBoFLa+/u0lmwHDKzpWi2j\nN2U7YGmMeToIGCLQ1Olzip+sbIR5pKgO54R6glLkHxyNrT8n5bacAUF0qvIP2sqgKEW/GhLLDenq\ngI43Ih2TZxXOKwEIISeprHtXnkWDZxLq1EmwAdXRu97az/vZSbqHhrkO5bj+yvH+8w6XmVhEpKJF\nixYtWrRo0a5p8UUqWrRo0aJFixbtmvbikha3/SDxa78TiiXpUMjGIAcq0BYSBGuC2ADFaYJOamZQ\ni2LbjadupIyquzsS6bYCzzYCC9r5diSyBLRNwFBdi1RlZqJW51xIVtxokl1yLaVddbMNDydoRymu\nPXotOsDyq5W48aAYrJAxyY5a1oAcPNACgQtAFWPpMqqKbdfSuDQYebPxCszLmSfFtjsSBCei+0LC\nrroi2P1KAA16U4OtAl3F2wRYdtN4LBAz+ivJtpOBruZ2fWpl1TNxAcBlogT8ch/XrOwaq8S3++jw\nNX8uIaKuVt4V0yxNiXs89idZCzl4Hyrj54vTUNbyBohbKhDEZdzxntXUkapVx8u7ig5HRoA/OvYk\n64szu9azJ96NdXZqatdfx318+fUfCmUf/dhHnXPO/R9/+Aeh7Md+9DO+Sgm0YMZ2rTWDDYQwWoEU\nuxTC9s1j7w599OF7oWyyNxm22Tm3rr0LjGRa5+x+buBGrSR5agISeSeBJSO4KtVlfRvX+vDdb4ey\nPZDNz05PQlmDgJJDSUJc19TlEpI/hsdq4evUiRtrhHVClc3HCBjYXIi2ElwqF3J9/jYRHa3ZpSfP\n371r7r4EY+LOPSYvligWfFeJPtbs0vfrem11WtAtrZkKmKBXz8dFUKJHGri7WsyFTOa6Y7JoGcOk\nOwwT+eIwjT/Z4UYM+e41Cbyx4n19ZP3NSQsZJEegivYuCoQdF7JyyG9DpgiZ9ylcVCXWSw0AYsNa\neSZy3afWk3PO5Sm1kOyndC1mIyF2s9m5PgvQ70Jyp1ZXklY4v5y259+BjLo/lXRUFhIPi6uSwVOd\nukURZCWL92ozVLvX9Z/P87XQPUrMp9RtPxOa1gIwsppkd9EgKxg8pZqK7ntaRKSiRYsWLVq0aNGu\naS8MkUqSK8reIfzcrHfbSEtvewg7V/is4d80CYlMSEoHYS3TXYD/O1JEAuTdVvLatTuuT56c5usJ\nREnNycfQffxANxoQVnZ52UoZ83rZcS12c6mG1Vf+GuvB3cTbv4bkZ0OEKc2NCBh2i72U4f7kA2J5\nP/jrnPTrICTfn6eSsFESGpVrWXeevLpEzrVapejRdalIOHSoJ9VsnXNuE9AfhVpCrPNWkZbxHoym\nvvPGBxpC69s12jPC9OVzkPidXZ/oVLuyuhNh6EXEvZ35+uVCwHywx9x5vo+Xos7NcVXKfSKhXAnz\nz596VK8YkCi35SwCUVdyXTH/YcKQe2dIQ7309+RSZtM73/qWc865k+eGdHz44SN/3NJyuOXY6n7r\nPSOg//S//w+dc8597GM/Gsref+Tr/tIDjyptZK6X2NWPBK0hYXt/avfk9InP8Xd8w5CeENcha8zN\nG56gTqTNOSP+jg48EraY206fyMh6ZVEEJQj4N25aUADJ3geSj+vRc9/u0UjG/9TXL0sNYaIiyWJh\nfZcC7aWK9SCH52Z7rq2cv/7F6Vkoy4AEH960fhqPPHl+pmjeLd8nG9nNHwBhKyd+3K1nhj6Wt3wQ\nwfu45845t7i8QBtEpmPt+3gjfU0pmlzWSYf1ocvsOAaFUM6kFbV9Bo9U6iVgrj1BMNh3mbgEcsDO\nqYT6U+IlHz54/B9K7ajaP54nGmzEjAXqpXA94X9bADoqpmtAS8eAIiHgBzCLgU0KawEtUvkfSiKo\nsjtQx2Fggz9xPtasBL5+a1moEkpirA31KXM/tkc5UFqF1ZjXT6UekA0g1XQXVJGX7BUZ77EgrOuN\n76flytpzBhkP9onmcHRBJkKfU0DfJACna6mUb/1ZYi2uZEyUIf+jBnltBwOoRUQqWrRo0aJFixbt\nmvbicu25ZMAf4kv3ANXB23eSKfp05QfOhe2nyh/09OnuEEkL4a3iv2XocjVWlAy7D0FE1kt+lh0J\n/dyCEuU5Q11lR8Do27CrsbfwETgVxVh2ASPwNwZ8IIbwC+bWISN6LtwDCj0O3qqxcwrZvW1Xx5xY\nA94YWyooQcj+7SSsFEemmZWNx+hPQY4mU7+rUXGzEruUNXb1z0/tHMu536UqqkYxvUL2AOUeuE9L\nFQ7lzl3FXP1fFVil6tz4JkLN94V7glx3bW3n2ABVSgc5BBEmLTvXPUCMeSloEnaixxNDTvZzjxKU\n4Nwsa+NDZeBBjYSPwrovRGixQ+r4RtEv1IXjyjkRbhWRRMptcJyuVjLWoZK5kNxgF9glvvvYUJqv\nv+2FKBdLg04J4j5YWp/M//jfOOec+9mf/Q9C2Xfeft8559y9B75dz58bqvIqEKSLpZUdgw+kpIUU\n6MtmY2Pn8ABipsIboUjv3rHxgfb3/XEd5vrJuaEvNeZCLetKBX7VRub6euPvxfmzp6FsegBRQ5FJ\n2JAjI2OS4yPfGCJTVNi5t9uIGPk4uvteYu6UA/QLvLkTXWT9tbrWREonBx5ZU93a0dSPe3JOko2K\nD9eoo42rDZDI5cwGYAskqt5IrjUioTInAuosc7KHFEQC9L0XAdsa47TVNQmIaSq8tbDuq5hpuo3m\n8LOiFH2oCua6IEghhF67td1GP+ogZ6EyPbh3bgdHSNdz5tNjnrhB5P0unhXOJWMiJw+z1WccUGfr\npiBmmQmaRgQsl3vSBumYbaHRBvIwtfBbG3CacuHoUhB0PBY0tfdrR99YPWcbf+7zczvf2WzYn/pM\nyoHwltL/SyBWs5Wtp5xPKolQYf0nCuucc9UIkgzC7x08M3ZYRKSiRYsWLVq0aNGuafFFKlq0aNGi\nRYsW7Zr2YnPtXSlx7oqCaDL4812tC24c/Sl1AoTsFnyJIN0pOQ8Ez7EovPIUbWvvmy2g5W6jZG8o\nVg/yL1GSQcm+/i/dHhotWk6Y80hcVqhKLqS7eltpwWUIzxcvmkHmSlSFBC/JdCtBKzv0U66hqYBv\nB+rs+E2mBHCS0gd5AtGf4loawS1SCbY8haum7uAK6I1EuO7h0qj0PsGNl0pIPFDfSvqOObS6gSz9\nUMXaOeeqqT9ufOy/OzgUIjCmx+pSJBxK3mNxmcJ7sXewH8rG1RjHGwG5g6L5sxNzH/VwgbQg2+c3\nze10q0KONyHWruDGUXJyizIN3jg89L9dKSkTLuJqo6RcuGVxT/Lc7tey8XX/4Im5gr6K3HmlkL0/\n8iOfds45d3lpdXr/0bvOOecenls/PXnmSeF9/v+Esk//8Md9GeZfNbK8bhtMwNXK3EMQAnep9H+O\nQTkV6QTOrVxC/TO4lFTZnNIlPfLE5YWd4/LCuxRHItdRQAqiHhBm/fUPxWXYIT9mosRyuBYWc3PV\nbUDKnojEBKMiOoz/XnJ+bRp/L1QJegTl8URdYAhaOJAxefLMj4WJzEkS6m/cMqV4ho4HcrK0v4ar\n5N0PTOphPffnqBdCGIerptM1kefRWHJQDzJ9FCVQgGcbxRXHwJJOgojCdE53uOzk+pxGGqjSY+1M\ni+11Inj0BhI2OL/0P581bS1uxNZff6OyBgw2Ut0b9EUmi2fOBKB8/skDkHln1bXHnHCqYt6PKKEg\nP8ZY7+R5VjBThqyJzFmotBh+buHmH+SGxPlU7b3F/NBcpwkmpRLQmXazblXiiEEWQtUJbjmsV9L/\nG7gUlYLBc0xyW08u4SJfSwaEtkSgkEgiVEtf91TuSTbQkdi2iEhFixYtWrRo0aJd014c2bzv3YA6\nzlf9Qbbo3g4NZYECHcr44qohmSFfjqIpV3YYqSJSlX8LzSUzPXcwrZBzlzUFxJSUvUP8k+RBuQZ3\nMT1JsYKWZAVIf4WQLnN/XCaSDMxJVyqJMSfZe7vvOkHkSIYkiXA0ltvPN34hFge1NnkbT0tuybZz\n2DW1oR8NdklKLK+C6KjtvnMgIbc6hNqvL8N3M/fYX1N2nySWak487laKsYqP+rZtJE8exQTbVrKU\ng/g4OfC/VfmLBDvMXHZ1+Rg7ONlVEqSa7BthcTL20MlIRErTwn9fJ4bcHB95dOQYueNakTXYgDCe\npbaDaoA0bKSvmZPRya5yA/LyYmn9WQPNmKbW/wy7Jul5b//l8N2tex4t+sZ7XwllBUL4byGXnnPO\n3QAp/KOf+Aeh7F/89//COefcLP0wlC0vfT/+zVtvh7I3Pu5FOhMgM5/+hAl4Pn3+gW+/rAmUFell\nB8t51Qlh9eZdX89c0M8gHCsocbnn0ZlmjXmfW792yVCY0jnnFhhPeq/XGCeHL3/UyigZIDvnFsTr\n9VJQQgohym6eqPDhoUf9lgtDsLg+KIKTI2BjKvILFFp99wMTSS3HlHOx9k/3ffsnE1v3OCaIqvYi\n4Hj+3Lfr5NzG1Xx2jjZrYI1v93RqiMAR2qNikhSkVeCYMiYt11MJzW9I2Be0JAgHyzOBihm6Tqxr\nilTacQ1J5pp/j+tpkFCxurmQ92/bTzLQo8Tc7QX9CcNYBF53Ecq5njrKlei6zurq+o9r1BIAVDdE\npFTiB6LLqu/ZbQuMtnjglnLdFCLOWcM5tI3INfL85bq/XFun1GvmfxWyN0RHlSifoh1K7242RIz8\nvOtVEgnPGkUVMyLR0q6bR36dmk5sTJ5d+vE8kD3BOiKqM64sIiIVLVq0aNGiRYv2d2LxRSpatGjR\nokWLFu2a9sJce2nWuqYW6DDdzoOWBM0I+10SNKNE94NK5YOEOIAgBTIkZMzzleJGKyoQDAfK4syD\nJK69BUnM2yrWreCjLaBnJanx2xyYYdKqOivgTNFCISlR8+9tUM9uo9BqjzZIR6GthahCZ+B6puin\nSvmtgHHXF+pu9W2k1pP/TLK3tbWBq3K5lBxGGUjkewaZloBbNa9YVYIoDLj1+NgUo8/W0PgRtW/m\npMpKhYJ9f473xN02gd7SSkiJDTRwRhJQwOPH3n2gavddTdeqkXNLQNW1YMYjagGJPsseXCbjyq7V\nw1VwLq6aVevdfA0VkNNtzSglxzd094muyQrq5Jlg9heX3t3SdtZ3NfSA1ksju4/h2joc+/t1//VP\nhu8uEw+Fn0v+vYNb/v68/Z33Q1k58e7Jlz9yP5R9831PNt/Mjaj+o5/2rq/vvPVWKHv40Cukf+be\nK84555Zrqy/Vy9eSa5BabfXaxtUaed0Oj0yfi+6+MZTInXNBvjpXtxyV3OGyLid2vz71Qz/inHNu\nfm46VrOZd2nNz6xd470S35mK+/G+7xMlBRcTf9xIlO0XPJ/MnQ3GO5cuXUNSupun1q7JnieUj8bW\nrhKk+dffsOM+eM/rXCXibjo69N+3osBNraoSC8To4Ch899Zf/rlzzrlnTx+Hsgp92HZC2AVFYiLk\n/RHUsSuZfxc1+s5JAAbWTrIn0k7WH6xT6vZhXjkV+2a+wLywG5BS20iDcuBaHZCJA6UE46Xfds/p\ns6YhBUJz7ZGon6rPkq5KqWia6B//mfQFuic1D2FLWoiegmR3ef40DHbQYCdmgNDsEb4zWn10wlWm\nNAMGZSzRAZUQscvM39dpImvd2s/ZVW/aclR+bxpz7W2wxnaJlVGOT/XWqIE4n/v1MpMMDBPonk0r\nG+uk3lSVurt9X9we2W8fHN9xzjn36MTG8xkCKjLpuzSJrr1o0aJFixYtWrS/E3thiNTe3shdXqg6\ntrdkQDanhMBA2nXrOIbnp5p/qSdytZ22OcHuQ164yUN2WWnXYoh/LyxCKuCqYnUgCg52Fdv1TK/q\nOQjpkzuiXEhtCdAp3S0x/H6tGcnRP3UhIazh+oLS4HxFHrZ61gTUqRXCdocQ3kLJ1jl3UEqiB3Ii\nytIXp353euNAQk2h5J5JiD03aVTYLiuDdaiOvhKyNcmjuSB3JfpkJDmkChBk84mVrZZAmEq7PkNy\nmeMwl/5qsPtVcmwLkn1X2A6eea1UMXi6B/kDGZMNfrsn7Q9kWPRxoeO/Z84ruxbRik7UpgtcWHd6\nK+Q9KzJF/zyy0LSaO86jPkdAhNKphcGffjjDNe1at2955PDhE0Ok/uyv/q1zzrn/5Q/+VSibXfhd\nnSpbn0Ee4aWPfDyUPTlFSPLc71zHgqrUaPdMELy7xx71SuWelKlHZJQQOoF6fF0bSpKjrUlhyFUO\nbKPufb/evWuo2le++BfOOefee+edULZc+HYtLuW82Oke3bAd8Ruf8mjW7VsmiUACeC8IQ9lhjNeS\nUBP5Ljl3M5HQaDacQxIuHyAWGztrBBvo+vPyq7f9teaSE6+B2vTI+j2sCkQ9WxtXHz729/0TL71m\nZTN/vq+dG9I4qXBPBP4pMRfGgnBvnG/bqrEAjBLQeQdlc0WaUsyJdkBsRr9KUFBohUb/Ax0q5IRU\nu88yfRTieYJnR9Jvz8l++5EkgVDyLFKgix6WTtb9dPsR3AEeSlOSs+34HM+Mjahzsy4bCcCoG/bT\ndg6/QZ2vyBo459wSMhaNoHkHB358ME9oIg0Ln6RfKwT0TKXuK9yzVurOB0/vhICPfk8HhH4GNJFt\nLyglxngp/T8BYptLZoEK4yodZCVBYMdLRkD/4NSP8dOFBGp8nzeliEhFixYtWrRo0aJd0+KLVLRo\n0aJFixYt2jXthbn2RuNyoAUyB9ys5PAkJTlcFFMD7Kdka8KDohmVbP+WkC1JfIUkVKTLLs0keWRO\n96DqPfmyWojyJdx8qm3DBKpDfRTWggRHYXsTxlYl2uDa1GSUTPIrSrAJNahExwXQcppbn9AFNap8\n3VT3g7obnTPYP+3Gg++ccy4HFJpJX1PvpWmsnrMTTyK+JUrd1DSaiCo2BUxIIg6JTZ1zI7hims6I\nvUF3pJMggpwq6kZ2JNm1FFI+3Vyp9DuVoktA0Jm4MRK4T9a9uRYzuB4LIXZn+K2SzSnBohpIlH7v\nRMcopwJy6102q9RcHBuqswsBO7gW9Ly4FZ0oYNOlvRK3YANi58v3jdB//95HnHPO3XvNk8y73Prw\n/NQTxo9umLtvCZL1SgjoLYidJ8+MsMlp/E/+s18IZb/2Tz/vnHPut/75b4WyJ2fv+eOpsC4uuwzz\nOpEAENeAxDy1zl6c+3YtRFk7hz7YnpDHkwkTk5sLLm3oMvD99d63vh6++/Lffs2fK7Ux/OEzf/xy\nafU8QDDIRrIdnJ3838455/7Bz/xoKHv11U/4a/XbCa81yIYJpDlyxxNTJ18VfnwUos/GT5u1zd0c\n86gQNzbHSX5o82+N8aFrDBPUTo+8u6NfmGbUzSNfl6IQwvjck9iP9oyUTkXzfE91+eBaE3dPiVaW\nMp/GHVx7dNnIGtbCBdq2QjcPbk65Vgj8EWoHM2D0uk7CtTd4nvB4UED0EcL1asDOBt1AHqcc/4kk\nks+obN5tP5OUPs+gqCzfTvxLBXBFQNZYO1qhsdRBWk6eXXRjynOCGl2ZdhOCqxailD6dog5wnw9V\ntNiv1taqoDq5qOKv52ifuCV5z6Sepvxu5yMBnpqBibhii6DLZWtSDzL6wYES0KEZJ8E7THSsGmR3\nQEAvxbW52FgwxC6LiFS0aNGiRYsWLdo17YUhUkWZu33ZVSUg4i4Wgghg565IU0BpBmcjciO7um6b\nbEiZAL7h843fOVNiHaAKqJNKLYwgiSAv3y7LSQoVsjN2Xaq2TrRtsdjgux3nyO2WJBZrG8oqsN5W\nglwlPSUJRM4BO4JcdnMdkJWOZHuVf8Bb/VSGRBnIeduk+ESkHrirVcLiCpvYcwkTn839bna6J6gX\n4n65wVNyPK8xLo0IWzToYyFpFiBAjlVWYezLNoWQGNe4J0Ie5a6XpGMn8gdERKVbXYJ8hdVEyrBz\nKTJFn4A0DJTNcZwQhY+BjqUghy5a21WN8bnrtQ2+bDKxnRZJ5o0oe2vKStrdB/fQBisrRiSREgXQ\ntm5HUcyQh+ryYiEH+jZqUALH/bvvm7L562+86pxz7uj27VB29tSjXkQ9B8gAlJgHAQBcCySHF+s+\nklx7+1Ax7lZGCp+OPGLSjwyR6TFQK6DK3/zGe+G795/5a1x0T0PZ4U2P5o1v2jkuZh4leXpuffIA\n6Ne3v2qk/GrkJRH294RQj3aUI1Wb93+JVm1koaBifiO5/pbIBrC3p/IP2OnrzWZQiqCUlB+pGxtj\nh1DbryCr8ESQxq+87VG6tz98N5SR0Hs4seufQdm9LHVNygZ/nXOuIiLVGpq0BrJNInSq62TiUYVE\n6puNIPUiGgI5kNWRxARtOCedhORjbhWD/H/D54k+fzivBNQI6KA+k6ieXurzJKUqv4xnIHdK3ibH\nv2DOP5Gm6HGOWucE9QIE6SbC1Qpy1rIjhetNFHugdp5wjbfOW2HJnpTb44rZBnTutiFfoeRVbSC1\nkRhy7BBIlKosPEwDdaZjf43ZDIFFKmGBc/Safw/DY9PaPD0ce4S1EdUhyvhkEhQx6f3YaSXvp3pq\ndllEpKJFixYtWrRo0a5p8UUqWrRo0aJFixbtmvYCXXtZIPo551xOpereoPjVCoTtQYJiJP5V3wU9\nYJ26hbb1jqjRtA8157wyyJTnVYFbQuEKT5fgztVr1aKABpC4Ks1VpRgkIGPAviL747KMiRKLq4eH\nBJj+tzyvHUbXmyqxjkGyLUTviDBuCxw3z4wIWFYtjldyIuor5Ni8oMtS+KXqfgAAIABJREFUyMZ0\nQdVyTwDLr+YG416cez2gycTIqxWUv2ucoxbXFt2tWS9JjvHunyZCIoT7rMi2XaCdkE2pCt3pOAmQ\nPv6KbysQUQVGp+c1lftEsnuu+HhwqSqxlXC3jBO4DSpg1aNaCLMbuKI18S1w/9VKiNVwFVbi2izy\nKa5lZculd2ns3zDImqRtErA1sILaMRdftfu1jyS0lbigZ+hD5d9yPn3n638byv7Vv/zX/oO4qmbL\nNeqxh+trX0Ofad9cRoFkL329WnuX2u2DO6Gs6beDTYJ+mfhb+rk/7vH73n334TNzz80xP26+aq5I\nV/t++uuvvhmKXr7j3X2vfebToeybf+21te6/ZHV6+tS7uQvVVoILfrOyxYBuhhzuuaFmm//bido3\nlap1nchH25o5LiiWW98xKGY0NXfj3j5I5nDZfulLXwzffXD+yB9TGIm9QhaDRBbPBjSCRvwodIf1\nnbWfnu9S1iJmKuCym8paw7mjbqQs2e4nZlHQQBm61GtZO87g2u3F3UmyfYbxN5AixPqQiY4RMwro\n2sm6V5I0mG1VvT3X8llgrsoc504dXZGyhqGNhegYktLQtLr+8hqitg76RtvpQ44ZQIS+kfP6EpSD\nZN2Lla9TVSkFghdQbS//txa6AXXEUnGZpaWfE/rsqPFg1OTS1chfZMrsALL+jJCMuxxtR3ZdzEWB\nH3ps40J4GZwfsnTTzd80NiY32XY2DLWISEWLFi1atGjRol3TXhgiVVWZK2VXM3Ek7Nmr4dmZ3y2s\nV7bTMhK55oTbzslnpHQhikP5ejyFwq4Qdl1CYvlGyvhXVJRH3EGKdADDejWvU8jrp+H3vr18+1+L\nYvcYuebyXNTesdPsB7tqIDKqzotw1VyJnejaTFSEKRmQp5RwsPqSnN3LTr/lLkHVcfH1gNgM4ud6\nLWhCBeSwFkmES787ne8b6sidwxphzSo1kADiKGScpIGwqeGyVNvV3EjYkaZW994t0BxBbkBkJ5jY\nyW6RStS5MEuz0rehEpmAFAhCVwtKQEanENCDtIcoW5O9moBgO85sp8kdbiayEqsASdg5VgvOE6v7\nCGM7Hdlx+whFZw5J55w7efLcOefcSx8DiXVl9b13wyMtz589D2WvfOYzzjnnXn/9QSh7+uZXfX0n\ntoNdQ7LhlddfCWV//ua/cc45961vfyuUZW6IMBXCIl2BsNwJgsBu17yCRK4UETsGqtLIUMv2QXZd\nCVEZZNh33vFIy1981er2xme8dMF/+fl/Fsr+1//tz5xzzn34J/86lH39K99wzjn3HwtK+emf+DHn\nnHNfeesboewXPuERK6reO+fcau7z86kqPdG+DORhIm7OOTcZM9uBBDGgUzpBUxkmXkiuxxqoRyJh\n5WNkFJhKTrISY2e59nV6dG4BAwxKOb5xHMrOZ/64VgJbiDDOGyPq15AWqQVNS1K/3o4EEaFS/IhS\nN5IyoG4QHJAaqjFmDk/pf/Ku94TEX2BNSkXZ/rj2n5+eWj7FOZT0N61fk1Rqh8hZKYhQ4nxZVamH\nBXk9hVhNz0UueVpTEJs7CZ5IAuruz5EK2z4FmjLIPzvxdakl2IdOhFSzQgAdawX17yEJkMsaO0af\npU6CF7juQ35hLnNofwIEayzo1y45H0qcCHLJcdyK54ZSOGkpCBeeI5TuqSp5/jIrRa4aDsjJKXI2\n7z/9jnPOudfufiqUVVhvN40FICR4ZpSyFpWiRr/LIiIVLVq0aNGiRYt2TYsvUtGiRYsWLVq0aNe0\nF+faG5euKpQISMjUykgyPzsXLaJLwNPJ934HJGm1qERbCHBgnm+2rpUGZXMV4IEbSV17IGrmhR4H\nCFYgyySjKroSxQlLwmUj7iGi0oPzdr5+6u7qgnq6KOEGxXaDIkdw1aWZkT2DYi5ce50cn6XQZ1Fi\nI96zlQCcgMRYN6r3hfNpJk+QKBMl4G+oY2N9skpWOJ9vY+22NaaCJo5zLkP9NgKFk0SYyZjogxaN\nuECYoHgQUeD7c4R7UYt7JKgnC4kyBcZdSiJZKsbXci0S2nMRr6eicCtCLjmUfzP8thd4ftNSn8XK\nqBnTCTm1xnHTQ1OWbtk/g2AHf435TDRRJr69X/rzP3HOOfeRN34yfDUd+7b+zA8bFP7ec0/efOVl\nc+3NLv18enxixM67H33DOefcT/2EKXs/fuRddU+fmLvnxz/xKioMOL9VwrCv+1oI00xQmwg5uOQ9\nGZl7IrgMRIG7ZhJucQGsFr4f3/3QJyi9D/Vx55x79PAD55xz/9V//c9D2cNHvo2nT42AzyTAf/M3\nXwllFZKmHkuC5jqDu7GR4A1+p1pFmAst+mQk69QISZ3PL8wVVRbUDLI+4fBIVuJugpu/79UthXVP\nE37f8ImbG2hw3TgyZfdHp94FOpbk5hnc6CtRws4htNatbAJkCHxJUht/TKCuOkplDZpHzwAYIR1j\nLRjJmkDy/lhcexUIyJWogmdjPz6ykbmKRxhHlSTLPgWl5PzCu7Q1EGDEa4lifAl3WyUu87LyN6AR\n33KCtU2GuGuRIUCT63YgjTNZcjdQYseYkMTnGbM4yNOc2mN5anUP8l16/YYZJeTZhWCAtJcE8ghK\nYQ+vJIiAyZLXK3HtjpkgWugeHJMy//gIKlSXCuttKe7eCn1ycMTAAhkvdPerCxRjspfO3kAz7Pnl\nw1B2/8Y9VEozNfjPqdRzPNZ1dNsiIhUtWrRo0aJFi3ZNe2GI1HhUhR2nc84V2FUqSkUkqOkMkWE4\n5UpyXQVJBEVEKF0gob7lCATMkuRseTOG/IBG8PPNXVW8ExAFcyFxc/efZPpG7K/b1LKbCEgIdgvy\nVj2eMNRe2lWQgG+7jxnIg9kgJyEJ+Lr7BCIjIaQd3s75oj/SkE6EveelvnkzXtlKNtiJKDmWXOxu\nsNUC+iUhwVMqlAuatMF9JHe8kXxxE8hU1KoOj9DgToICSArXPI0ZiZpS+YJK9kIeT0D2ZA47Rb/C\nztDJbh2fR6Up5pJEWibWnx3Qt0zI5oEfLERx7khJilS0oMe462sZ/8yN5swmUNFWRJSyEiTuOmfk\n5UzIxivkP6R6/hkQB+ecmyAk/mf/oaFK/93//HvOOefu3LWw/k9++qPOOec+1n8klL3ysv/85Int\nXL/2lid7ps7a82Of+SHnnHMtiL3z2Uza6uum8iMkTKuKfIbggclEFMsxyGX6uREI1b0Er7zzzbed\nc879n3/qifAf/4mfDt89Qx7AP//iX9u1sPes50YA/x/+p//ROefcP/un/0Uo+9FL38b82Or+DhTC\n7+xbP50/97vj5ZmhdC1ykZVAGvYOjdjdYO043Df08fzcIydKQCcZWdcpjoUis/FMFGt8YORtB0Xz\ndOPv3UgkNF6+4+/7QlDlZ6jT3sRQnScgyBe1ho2TPO22ynIJsmDaiBHqPi5lrauA9Euof5oB1RH0\nm4iUonl97utXbCQnJ9a7slbU04+TqvLrz2omEQuYi6NKnl0lx594OIDENI0ESqB+i0uRqVhiHRXU\nm0EjBXO9thocgLUmVc8N5nUmkhSVv0+rxpT6M+fnVpvZHGsRcDQSNLfH+qwZBcZ4ju5Vfo6tOuvD\nkws/dhO5sSkDmVKr+6jkM0GlFuC5UPkB5s5dyvWRz3LE9wNBy3hdVVFPg2SOPDt4zwTNn818Tthq\nZO3p4XVJpf1F+b1flSIiFS1atGjRokWLdk2LL1LRokWLFi1atGjXtBfn2isyNxKNDyowC4rpxoDb\nDg4Nsl+C0Nhp4scN5b6FAI2/rbiK6O6ia29saKYjebsXsnEKYl8iuj/U9MgrOy+VYntxmbRwQaoA\n+6b2ULkR61ULh8RSg4ypPF6rwit+M0+N7MpkjcUAxmeFRYGXekslXUYGexYZ1YQFCgfsv1Z14qCP\nJDcKMKsS1RMkC55Iew4OvS6JKnVnINcvqQUm+lAZyPZdoklGoS0jbtSeZPx+W7FWBHBd2TJQQJSd\n6aKAa7NT1d+Erj3tJ7igRTGZiHEvxO42oe6J7lWomCx9h1M3GNcXomM1QvvztZDDG45JO+9y4d0o\no7HB07z/vQQlbODnWooq+gSVnx5799HDt98K360xdl59wxS7f/k/+Y+cc8792Rf/KpRdAgrPxLX+\nzb/xiub1xtwYP/NDnlj+Y5/5+VCWgdh6fuoh9vVtUT2GSzcrdbxAxXltbdjf8+6TWpS9O7je9pC8\n2P8IpNgL68+zOYjyl97d8WlxDzUgz6rGUUPXnkzsjyIZ83/6T/7zUNYiy+taCNjLuR+T031b92aP\nfftXS3O3cHu7XPtxv5Q+vHET7nlRoKe7txPNNKqiX16aBtgB3IHVaN8uRQqA5uyFm3m98EE+pbhC\nMrR7s7IAoDuTu845585b0UxquMbaItsgsETZEzmCXDQoh24k+ifHlbqxcH5RFk8zJq3V7MZIhiyL\nfFb6cTKa2DxZIbijqO0ed+lieH1ZVzn/OtEnIh2lFGJ7ApdVJu52Bu8UclyPR/BG1uIatIAlaA/7\nh+LaxDNuNJI+AR1gH/fBOeeYF33dmY5bUvgAhVVrbuQ5iNV9b+2ZInuBE/26CjQcskFKWSf3pn48\nP1udhrKsBjldgqc6ZM9QtXe6numedU7I9ZpRA31HScNE7n/fUzNS7hN0n0g6d865tmFieqGl9NAK\nSyUqCGt316u21I4s8GIRkYoWLVq0aNGiRbumvbhce0UWcoQ5Z6HuTgheJRRwK5EwYJhmsme7/9mF\nf3NsZKNPsh/fVnlN55wrSyIDoo6Kuixnu0jMmv8OYdqCKnQ9w48l11RNhEMUwLFjYt1KCWFNqXpb\n2m6JIa6KiByAgL1aGSI1n4GArrsfvHyrJEJ4m8dxbSvoF8PwBVUhmrPc2LUodZBJWGsKNfZCiP0Z\nAmWPbhpRdg/IgeaaYvWYk6sX9I2E+k6YiOxOJZaToD7IyQVoU9EMShf0Tu4xOopgiuZ1TIDSZSqJ\n0TOEXtTRM+Z/1OAJf58qIaV2/Qz1lR0pVeZJNhUS9QTTMxVUL8VvF3NDMKhi7lrrJyIMvY4/7PRm\nIh2wXEFRHujj/Qe2gz0Acvrud74Zyu7c87IH/95nPhbKGPY8E1JuAaLunoyJpvY7/UsharsMCuyY\n1+uFjbUx+m6zFrV55tAb5J9E/kdVkcc6slqIKjiCHS7O7BqXcxKg/Y60FRX/4xsewdF5lQAJ39sz\n5Oz3/uX/7pxzbn5h92T/GEiL1DPkv9yzeh7f9lIDq5khR0HaALeuFaTv2VOP3N27J8r62J2r2nW9\n8fc1KXTugMQrmNAYMh46TkjGTYCS7R8bgnVW+3ruCyJyWvv6zvSe4F7sCXK7aEZolubT9Pc/l6wQ\n45a/AbF6oCKO9Vdz7WGeqkegBnk8FRLxCErVvaAPRc35ZOhDA+SCsg4bgRuY17BvVLEcf2VN5JLV\niYo5x0KWbHs9FGGkdErbUB1cZF3gESil/6cjH8KfOkPkGdDR1nYcJSmK/mYoqzdeWX9dm5zG3p5H\ntjol4MPD0GfsG0EpIesybhVp89ffyJq8x3GaSQQIELZM7n+BgZ/Ks4Bk/ND/EoDmEDCk85TRS2uR\nkyEZvZc5zlyXTWdrFz1hq/Ugeaj7XhYRqWjRokWLFi1atGvaC0OkksRQGOdMTEs5RWXObOX2vjdi\nGKLsCPb2/RvkfL6dp27wUstwfiAMvRID0u08QMzhN0SksCMQP/cGQp+t5JVrgYi0wuWqUSd+V400\nYZJvVyEKjgV877287x5AGG2ztvDnzcbvJopSfM/gQeXCJWrg503BfdHM6CWECztBBMnDGcmubrn2\nu1SVNRhBfG0ufXL3luemEIVyzrhJ6QD1wX1qCrRBdtq4vqJEzKHUSbhwQ+6Zitoxg7u0h2hnL4OM\nyB03jhpCSymErtL7un1ejrFOULIcu16V8+iwY06EX3G58DvhCr8te0WwCCFaEyinoPzCCfLKaQxx\nQuFKZ7uvDnUvhHPiguyC/24jaOFNcCVy4dmdPfPClf3Y7muHcPUHwm9qEuY/s/t5fuJ3fRMRM5wD\nWVsAkelviNQHhEP3hdPCHI5LQdVGqMtauDwddv8Hx1bPNZCes0tFqZBVHsjZam470/sv+Z374dQQ\nmefgcmm4/hf+2//GOefcT/7kj4eyozte2DMTRPgWZAUOblsOs9HK7/7bhSECHXhtcwhCav8zr2Qt\nvEXuhxXhzwoip8J5DIQ84TKiu9NMeUB+bU2R/zIRjti48v15uj4PZRug49OJ8dGICAv24DZAxPKx\nyBQUlA6w9azLPf9qufT3Ipd1LYGXQkV9Q2S6rAk5kWbpuxxSCKWIb66RM64XflcfZCe7wR/nDFWq\nVVQ3g/xIrki7b2sr3KcWqFOq3B/MWeWNpi3FlJHDUNaLCYRee2cctXLkUc1OtD7Ide2E85qmvp7r\nzrhMo5KcO82T6e/tZGxCrG0DHh74erk+k+Ex6oW3tAIi6jTXaUpEVFxHWHhVkHUMGY31cnvdbyDN\nkLpCvkIb5HCC/llv10ocpWP+X/bepNmyLK0S+05/m9e4+/M2wqPLnoAERFKQqIqCMpHITAMKmcxS\nlpiJNNCYCQPAcsiE5A+gEZLlRBjITKIxTSisJLCyGiCySAoIKjsiMiK893D319zm9Brstc63bj6v\nTLMnS7lUtr+JPz/33tPsvc8+Z6+1vvVpnVygf97E1vZgR9TNZ9CRfD4iIhUjRowYMWLEiHHBiC9S\nMWLEiBEjRowYF4wXRu31QzfVfDMzSwD3DQLFMYVVHXtLQPF9p3gr0lQH/15ds66aUDAQWRaguxJ1\nEQdUr+LgjsdQZ/OUNeR8G0WMm/68KFCpyhEpox1qo7Wd0BOEIkXEnKJ2VCHdVMBtumlcxE0ouhtc\nRFtVTJOXWnusq5SBWlAbAoijW6kNRaG+ut4OEBGqs3mxDPs9vOKQ/bVrgRbZqXWFfswE7iX1SfuL\nonTYPeFva0lDBaU2iDt5BxG30iisWZdVtfwWbSIUANnlaYssLVJQoVrXkbYLpaREk4LLRdieA+bO\nBYI20Cy12Ofvod0JVWejWPzCRT3VY1FYL6nerGdYSQ2xAWNXIXOjY7akhDeoNTcgXfj01GH/5X6w\nHdFU45cOw2+Lhe9jfnAtHF9qoh1eD6L19an33Sncu4fRx9jpSXD7LkDFdI2P180m/PZo/+VpG+9h\nFUc3DRILZD5ZrVe4Bm+TDu2+EVH4tWvh3H/q06HG4F/Dfd3M7PJRqBf4A5/0+nt/+VdfNjOzWtzR\nf/gHgz3E7dt+npcuhf2uIQ43M3vjR384XOueU3sNangtD/08F+tAkSZIvxYNt3UYa6cnTu3sgdot\npP5jkjKtXMYfxnMrNEWCe2YQV/L2SRAgd5tAgV5a+lyz2gQK8sbgtM8MteYaoZZPcV+vxNZgDzRv\ntvBrXRbh3Ku506fFIlzHsyyMl2bwMcTakckotd4wj6YiWO6m4zot3OP+y4QCnS/D/npJ55/kDXDM\nzySFvtmSivJgbUSTMUmbglRq3fUUVku1AQrpS6EviyLcWyXupzT1vmaVjUFqkjZDuIfmpTvms+5p\nIeeeQAw/iCt4hwoIiXyvht3KKOn/BpnDiIoStGgwMysw/+yJ/UqCMdaI/RBF9KOppAT1D6VB04QV\nDaTuKvo970nFSmUR0KeJjDVmVhXS1l41Q1978DxPVRZEqty/10l1iedFRKRixIgRI0aMGDEuGC8M\nkRqtmWpKmZmNNIFrBEHI+Ubu2woIu7WC+oA0UTW4TLA6SgX9YOr+JHDbQWRoainnOJl6KayE76uw\nDSscTdek8FOFzQnejmkSqiLCAULEXVFb2J/WATKsHA4PPYV1PgtCzW3jq9+0eIpj+jmVEHbT6iDP\npDYZEDRdwTR4C09SR86ona52rB7CNe7f8hXxEgLgvPLrIYqiK8J6FY4xYLWWqyVEwnaVVUW3wfd9\ntdTg+NXgxx+Qkp+Vfu4tUma1P7myyiZxtsckitwRsfIvqaGYsK6eyffOIwJpGtq7FCR0BnH9sgpC\n3VISFhZAoupjR3C2eVit9bL6ZfKCCoY5tOuNo5TbbUAYUlFR7i/D8fs6nPxy6WjJ8VlYCb908/a0\nrSDqIEuwA3RZL0L9Zw/vmJnZ2bGLqBtaG+ysHEP/XDkKCMezJ4+nj67COkOruh8/CftTS4Aeppdb\nWTXy3lrL8dtNOOnm1Ntz/zAgR//VT/+ImZlt1n78p/fD/r7v+93qYTFnarSjWq++8oaZmb10yw0R\nv/n2XTMz++FX3U5i/6XQ/71YEkx14swjAz46R+3A47PzwvKNJAXsAS2Yzb3viOwrSpsTkRE7AQqE\nd+wEYMRZYAwvxYzwShHOaTETmxogTY9OH0zblhi7gybv4LrKpSclzKswd5WVJBTgPGfzcKy7d/9x\n+qw3mM/K91ewaUilrtsIdmIUlsKQyLEVk1RarMwXPncQfWBOhFrIrDEnjGJWOeQwddyxOkCyjyJS\nQPPVdoe2D7kg95MVxMjaqGoITQRZkZ7QJr0gVzmQ7l7sB3og92pcXSHxpEgdkZzhOaogzCTKBhKY\njMr00JBZkD4yO3746VmYDmLJgGdRKQ/eFAlXY+/37pgRiUf9XTV/ppm2sBQpnvU75XcTovRitQBz\n0HZ7PsmqEiamlKSN50VEpGLEiBEjRowYMS4Y8UUqRowYMWLEiBHjgvHCqL08z6xuHB7PQOPsiJ1p\noyOUGRHAXnwsCN8mAq2W8AzZcfuGz0cyUUwidmcNPdk2dNiHUAuk3tKd+nv4V52VKZTPhcaYKBWK\n2QTin67fsdCRLs5yrAww+kz8hugEu1x4d1LP3I1C91VhYw4YNzOn9hoIgAsRjPYr0Ei9QNE5ayid\nd/bdUwf6YovzlXMaKFT3PjmFf84c3i6JiLMLQOpNr144gLTV44MCRPEnoSi9qVVYG467Fadssnwp\n+jWR8VLAg6UvxcUXkHHfizs6+jMtdJySKhZfKog9z7a+v00f7oES1O4oNsr1Kcap4tO4KeaVw855\nCVH4uKPYNDOzVj2DsgCZJ71TRWtQqx2up1xJf4EyT1QcisHeCz3y9F6gsRqhsUvcC8tD9weq0CYn\nK6GqcEO/f/9euC5pr7M1KCv12ClY7cCpmGfHgcYuhTKm2LaXvn7760FIvlNrbnjMH5iZ2X/zM5+e\nPvvyW980M7N3HjmNfP16oAKzxMXWiYVzeXDX3clfhaD/ox/y7119JbTF+qnThz3O/fjMfZkSyBFK\n0ONShsyGhoJl76fTs1P8zvt/Caoq2fGFC/9eFm+v4iDQp+rAPtRhLthCsN+sfU7eQ78m2v7rcIKZ\neIuNSMrQagOkjCR3wWbzsJ9F6bQk/ftKeNCt952e/uCDO/hLJQOYO3fq1VHsrAJ0ehDJPI25c+iU\n7sO4T0DFiY8gqcBcePwGMgd1bCe1Z+ribfTxE7kFnmMLmbspaB9w/J2EJZz7IGL7DXyf2kZd7OFL\np3KXNPSj+l0xuWqQ8TR2EKCL2zolLRUSC3o5KVaK0GQP0sKjzP9FSm8z9YACtSzPDlaFGEQWkVi4\nT+qGNKrUWp3qiurzF+2kxXtJLYqzPP+sZD6dqpHIcy9SezFixIgRI0aMGN+jeIGIVG5bSWtvscLo\nencd7iCAVrfpgkK8YUftbWZmlSBCOUXTIh4vKorXKQQWZII6dEGfuHJITERnFVPYpdI1UKcd9Csh\nIiUXDQdWvv1qaaAEbrdaVZwiw7r2FeGVxUv4nrfTbEFnXRcMNkCszmRFzrYr8yBsViEmVx9166tl\nnnvXKKoGpEVWED1Wc4UIBrMKwsdEXITrsLJsBc1p4J68txdQlUJdnDOetzRiTbsEEcdyxSg2BUlO\n+wlZzmHV3YlQM0XVc9awm5Wy8sDKWGvtMZ1W9O9WoKEUEaA9Q7tT1TysIrvR++5kuG9mXn9uP3HB\n8tUCgn1BC7ZYda8730fesCbkc+p1yaoqQ+28XlDPlkLtbTi3S5dECI9xupYaerc/9nEzM3v4+OG0\njXtb7DnC2aKv14I+rU4D+rZt1EWayBltTfyzG9fC2JnNfL9bIHJyWVZh3ClK2EMp+/ih3ONAdrut\no7R9E/q7Wl43M7NrR57EUW/DSv/jrzmC87f/EFCqB8987rp6OXyuqMKHXwtWCJ/40e/zE30c5ozN\nQ69deAw0b7vx/mwxT+zBvX4pdUXbOlxXLfd1g1qYmcx/bFd1lqezdyJWByP6eNw4IpbmrDIQPmtk\n/iFinEpSzKIKCN+p3idJ6Pe5JIBsMY9vekeY8vw1M5OKFebO7jPYeaQ3fVxvgL5tGxci86etVJEY\ncH8QwTAz65KACCajjzFO9+0OcgF0GuxI3fr8TxftWemC+Qwo/lYSAOr2GOcmaHZO9EufJ2AT9nxb\n24Zr3ACdHqSGZtvDbV7YBIOIfcgE6e3JyPhvC8yTqUlN1JT/qig+NOg883HSAdmvYLFBuxQzswZ1\n+hJhM2ZgGHqpdZmkZA4ETeLf4vY+TkiYMDFkUZB4Nsqxxj6M8VTsPwa4w/OZb+ZO/W3rY6IsYO0h\nSVZD/xx7pPE7vypFRCpGjBgxYsSIEeOCEV+kYsSIESNGjBgxLhgvjNozSyxNFB6Em6oaTxiL/Ao9\nAU5lEGFpAZFvXqoJFIpBFirsg98QINZMBIsJBGalOOEmCaBCoYfoWZFLMcge9BDFxGZmBu+lVFxk\n05z+FBBsC4tEkfOgRWYncbbvd10Hr5ayvOY/hthxPvfrr0BjjKkXEh1IYxHGzM6L3QWdnkSvqVzX\nAFhcBZN07E3EWZhGW10nxU1BW/adQ6b0ORmTAxxLi3yGf4vcYWwmAwwC+9JbzBLxIMMaYRTBZAf6\nrhWvJgr1aWyc7Xgx0QtFfMxAe+RCC7d9oDETEZaSjhatq1MKkhSRQXm7AI1RiWMv6btG+n9kH4qw\nnEzlptFEhXDgs7XTKCz0PJOixXtwyl8ehDZ+8swTQBILfXfp0J0wbBE9AAAgAElEQVSt//bv/tbM\nzN74kHsrHT8N37t+w+mOzTqcX71ybxsWI101DvdvN/DsAQU1l3u4BH3SZU57ZKA5pVltQF83a++n\n09NwToVQRjkKE2cC0++Ro0V/5lIg+fb3B1ruwVf/Ydr2T34oUFGpeLAlqGxweNm37cED6uSBO6V/\n8P47Zma2fuR+SyO89PKZ3Lv4bd9SMiDXilMvRNqwbVjw3cfJfHKZFgoOYvDiSOYOHGuE67yZWY65\noABVnI3nKUP1fVogaeZgLf0EkfOpCMDn6ONCPADX2zDeLu35OXFqL2ZwzO79s4+98UNmZnb/obfr\nww/CPvJS6CkU5h2FAu/W8EBaigO8MQFJqCVIOWr8NhMqbESbdI0Iq8d9XLPek0xK8XYdjZ510qFo\n23ImlTIWpFZRMUNo3H6LOaxUF3XMiZ06gYdtWSfUHuQYnbSJTYkk+jyFzKRVqQaouo7POi0aDcpU\nE7CYADX3uWNISHeKZ1RC/0RJ3sHYVk/FFufM+VQpyyTBWJS5M8O2QiqV1HBlH8THrm5YcFsexi36\nUedYncifExGRihEjRowYMWLEuGC8MEQqSfwN3cyshTi3V4EZ39zV7RtIRya1kSqsNHNJU6WLail2\nBkxZZRpm0msaZGiKXhy7s4yrLn37B5qVyLFSugj7eeZwTB1EAJ1jNUNhZVmcP7dx8FX1FvYQuQi2\n19uQYl0duCiWCFO6o17fFbabmdVYufLtv8h9VdmNrFclacUdU5jFRRqIgDq2EyUaxTGXyJpaUowU\nRUqdqIxtkBDpOv/m7/1gVmFV1YtguyjpACwrx8n+wffTA6VsRdBM1K3AZ7ryKLA0zipP66agORG3\nfdYOHMzRlz4JvxlFKFoCfUg0dZvH7Siil+8D1crE1qFHTa5a6sW1ELErIrGByJMopJnXq+pl5ZgT\nzcGqV+sKlrArOD52VJHj6u1vfXXa9JEPfcLMzJ48vjdtu3o5rFKXIso9gUP3vtSa2zShzUak6R9e\nd7uA+QHQVHUCB6qXa2IH5oQd5A792AiaPaOzt1z/6VkQnr/6SkjiaLfeh8uXgmP59dden7adPQvC\n2pXUJLyyCEL1euMi9odPQxLB5sy31R+ERI5U+rPF6r9vfNt6FYT8TB3fX7pdA6fstvXv78EmgEL0\n8Hdo68WBo2SzReiLTKoiMGsil/u5Rdt1QDgVfeYqPRdEbB/3Z1+piBiCXhHs1hhrhfTd8SbMZ83o\nqNMiw3livO779GfWhu/dOtKkkHAvnNbf9E1pOH5Te/t3HcadIGJ5iQQUQZj8cYPrEqQ3H4HMKFox\nMv3fnwl7s9DGaj/S9B/g+yJsRySSzs/6ezZV9vA+7JAcMkhiBV3OC0HzKexuW6lKUSPZQJDzHvdM\nWUr1DPRTojYBSDJgnUatV1oBrcp25trQJ8kgiRLG2oWSFETWSRCpoWFNQE2KUaZq120+n/yH9P7n\necqxwIAlcv91OO629udJCfd+k3MfJZHheRERqRgxYsSIESNGjAtGfJGKESNGjBgxYsS4YLxYsbnQ\nGMkEJ/q2nCK684yVDamKjeE2OwoFADhafZzos5JkFCerwBDi5Od4bHRCNxL3HQelAOlUroUUAWOK\nj0gJCHwGylKpPVpsD7LfyStKHZtB46237ve0qG6ZmVndOmRbzbFvccqmyI7UViK0Z2vBM6UX2qko\nKMoUsSmaYoeW7Sjic8quHcO17omLdAKn2nornjGrM+wDgu3UfZQy9GcqlOmsDHRPrYJJenapK/vz\nxIGTyFiuB7BwOrHIQk8Azh1HpYAxTkUwSjHuIP40Np4vTE23ZUGWbQEK8HAZritrxNsLh83FWbw/\nA8QtlCkpnfVavYXC341UHt0/DMdYC1XXrgJVNbn9Cu1SbwI90kkx0MNLgbK7+fLLvm0/UDGleGZd\nQsFhekeZmXWg9hqBydMy0AKsRJAmTqN2EH1eExH3GnRnLjc2i8GWQlk1KCrMAqhmZgn6vdm6B9HB\nMvwmA8Wx3XrbDO8F+rI7cXrooIS3lyR23L/zdTPbLaR7/CyM56tHTmOmly/j+9+atrGAdSWu4MtZ\n+M0Gcw2LcpuZrbfhWjfiwUdB+Z7Q+KuzcP3LfT9+sgDdLJRJBgH2jlM/bwZ6domLfYJz0iKzpF0q\noYLmuNfaxM9pi3shLcVHCe1+evzetG04CEWy96swrgqlnTCdtL2PqyU8wLJK6NHu/XCsrbdTD6+u\nXPyGuoEFb6VSBuj4LGWBaim8zXwRSeJxvznfVOC6E/H7641t7ec0ThSZiPeR8NDhvh57b68OxcV3\nki0oUdkRYCMpJhX5BqUaMnfQxy9PfZ4e+dwVCUaCe3GqwNH4GezNMf/KvMrhNAziGI9na995342Y\nJyuhRZlIpN5erB7A5KFS2j/FM3anFjorReyI6Oe45vMu9pvakwKyOaVCPk7GPorNY8SIESNGjBgx\nvifxwhCpYei1XJqnlcuqhiLiRoRmFJmqJQIFzepAnQPFEV3b5Pw81WaS1FDWXxpEdEmd4CDNxPR8\nrRc0EGKQV+JJJy/u4aytR7QslZRbo7OsXANXjq2gGgmWZCsRtpZ5WGkmYslANGXoVVgIYSURJEnr\nZ00oRXL4Bt/LPuZwMe7EbZtv7lqvqkStMRUvV8UM+5UVFlAcrjD7wVcrvC5F6VIgEYXWhsKqZhRh\ntX8myCEQqzLZ8Z3gmeD74jCMa9TEgjQ/j2pNoswdE3XYOvS7IkkzXxmauXg0x/UvRxEWDxRR9+e+\nv9163w1YuW5FbNwh7byV++TpaUBJZjNpfyQ+jB2tMfyemAPBOhVhezEPKEEvbv9rIG3FwhGhu0hJ\nv7TvYvMbt+Gon7sr+ntf+0czM8uAXD19+O702RKI6ckHr03bLl+9Gb5f+GqxABI0SFJGVwTkbD+X\nxAbcf2cr9aQIx9isAxK1vPXK9NFQhzHx9NgRpE1GBFXGOuakTtr/6tXQFvfvOtLSAh1US4YCiEEq\nCRXbbjfxYlao/QesTiSte6qJKI79FCwvl4505ai/N669Pweck9p+NN0G+wjbNnIPD7QrEWH/Ekiz\niqgXQHjyVOckICKCSNJipl456nOWBmuDzfwSzsP7Oh2ZlOHjrwJilZXeTsOWyLUjjB1QymTj9xhd\n1HNhR1i7s+O8ImL7DfZXyDxNO41RrG4SJO0MCpxjbqHlgpkj+yroT7BtAWuKrvX+IjnR7zhtcx9S\np5TVFnaE3fxXbIcaWEKY3OOYnwdJxhoxJjP0V9pJmwBp3tv39ud46pVhIUq049ROhN3vyQEo7ZCI\nAL2noH6BK9Zai0wUkmcnfitOO9b1YGIEkUtS2l/4sbZNSBSZlYLSCdr4vIiIVIwYMWLEiBEjxgUj\nvkjFiBEjRowYMWJcMF4gtdftiM1zQNtKTyXwJarEn2Toz0OhI6DSQkSMacrvqY8E4E6jsPu8iFiF\nxdN7psKjU3FjOT4Ei5mIx9MtBHjqHk7PFvjiKJyeZBAni+8KvUCGzqFtgyg86b2dTuE7c5C5AG8L\ngWqv3k70VqIvSipFGSdYVOBRIKBVIUYuaLNKXsFrUK8KgRMCHkQwmIECSDNxCgct00Morc72PV1v\ne6XikDAgFNQk6Bdod4KR1f6D7vVKLX4btJ4J3dqjDRNxdicCraL0aXiojwp9TAb1kQHcL9QGWdMR\n9KEWnuYYNqG2s4yeNQLPgyqsROxcwlttVFq6ZJKDb3uCIrAsGt2LP9HpNvTJ5eueAJDjGLfFW+mV\n1wL1tt74ee6j4e/fdW+pGy8Fgfri0L+32Av0zVf/5itmZvbhmz6GZyg4+ujuP07b7nwtfE+9qPau\nB3FyufDflgeBUsyFxq1A8136kJ87O4BzjXrcdCz8m/n9Vw9hHyyybGZ25VrwvlJh/d27QeysdPey\nYhFmp9sa3GSZjF0O7Rb36fGpC2Gn8e9bJuorE8qETvmJzD89fcm2MiecIaFAKwBgbNcoZJ2JsDy1\n827r7YZj0sf6gvOp3BQHoDEX4guYIsnmTKgV0nxP54FiEb3y5Jmn80QGul0L6RZIMugkAaZvMHdu\nxAEc97sUO5ikBA2Sd5RG6jH/bkXEvqhAaYlUxDCfW6q0POZkuf6pCLQUnGYiRQ4KTr0Au4YJMFLF\nAy7eVelzB4XdiSSqsLixJkrRR8lGv54Mz6dR+Sy0rYvT/SMWNa+Ebub1pArVYB8qC2GSVS9UcUMK\nThK1Jtpy4ie9/ytQuo1Qy5xju176vyeNJ8ku9JssZE4eURhbflsVWiT6fEREKkaMGDFixIgR44Lx\n4uwP0nESeJuZ5ajDpBACnboTsSno8SY6DP4GXQNF6sRtmrXw0kRF6ahT1J133TauDETEN6Fe6o46\nMDVUxYF0bBX0hdYCgiZltotSJamK3uiYLuLogm6yfk4d0mnHHQF6WAmrULqH8LGtReyMtutTpjX7\nGzetGHQFSdRD61AlKZEeOafJEkIdqCne98OXJfpHrAMo8isWSEPtVtNnFWwqRhHRUoCf57LSmlAd\nWUHh71TGU4dVjabJFtOYIdQoQnAuhWWlOaL/M7EJ6CFYHGScMCV6lAbou/PJAxS59+P55IgK4ukh\nFQRhDruAWlKT0Sd14xdGt+Nerr/ZhrFzJjYJOUTbZ2dBiD5IskdJ8W7mosvv/8FQf+7wkjuQP3wS\nEIT5ngibIQb+/k/9iF8QEJFWUN8Pvx7O/e77of7cl//a69pd3Qvfe/2NS35OQNOSmaBEQBhONu9P\n2/bSV83MbLGQ9GcIrwtJHi8WEAATYhEX9dmN4HZ+dOBI18Ov/52ZmY2Z1zA8PQlWJFpC7TL2u1qJ\nTQUTQKTvmLTSy49n89Du1f6u67yZC+bVdXq7CgLoRtAXgglzESCn23DOiViSZJuwrTlxp/YcNxSd\n1TvT+4815PxacyARR7nPCR+sQzLMQmFaVhYQRLhKwpic9Y6mtGkYi0+fvYPviyVLGZDIvpFxDaRn\nMJ2n6dguySOb0O+JIOI1kocSQdPohk3bh+1K7AqASCqqsWno4u7HYqKO2t9UQDE7tf/gPKHJM7QC\nQl8XM0nYGTHu9f7H84dVH8JO0E+dVmAA+i8u4gmO24lNQ1uH6xhHn4uJUiasdSlif85ZJ2eeRLKY\nh/NsB99HD6RplOceJ+Nhxzkcg7fXZzHaZJqn/Z6oiTRKAsw4VdYQOxv06zAIIllgnpTnVD69bwgT\nJm3xvIiIVIwYMWLEiBEjxgUjvkjFiBEjRowYMWJcMF4YtZdbYqrOnSgt5TZISyjdxa8JPGsQBWrB\n2R6fl5lD2xndZnHZjcCZWXoedp3cxkUwTS8U9ZGaqD9RgJYZqS2huxIKgEmZqZswqDWBjEkB9gqF\n9uehzRQC5G3jXizleIhjCY2Dc2KzqhcOCxir7xUpPWERLDWKPdUfK/y2qb39N5vQ/ipA7FCQOZVi\nkPQ0akB71SjKbGaWNMGxOBVRfA9hdSIu8pP30U5iAV3xBbOFt5ImIBQF+wKFWsUxvcH3xTJlKpaZ\nKBYMwWK/I6w/Lwqe5PwqigRt11mgWDaNFOME3aoFRTtQn7l4EW2ptdfzRKHlpQibj66E9jy87FRV\nh74gmi1ac7t0PXghbUWcmwOyH2X8z+HKfef+g2lbARrv3fvud3Z0FOjAW7c/PG3bOwzn9I1vBb+l\n5thplOZJ8G9arX1bdSmIyB88cqj9/XtB0P6JH/jktO36frjGS5f9/rt6FM7z+ImfUwNq5crRR8KG\n0ovnjnm4h9JDp8KuXAvHvfeNL0/b7jwMlEYh/XoAv6vFFf9tC7f5WlzJC7Td/ODytI02+2UV2nCm\nTAioEE2ioIt1v/U2GUCjdTPvJ5LhfS3O0qD+19LHpKDnOZNzZA7DnKQFsqmAGGQ+L3hPCo+egtrp\nhapOMY8VsqanyH8zhPGUD077HR8/wzaRMWDOLir1BQz3zCg0zgjqs6uFpoFjdy7qaYqx6ffWdDL+\njf5YalAIat+ExoJ4OytcgsCEgrI/71VH3zczp+UreIGNIqzOc3pWaYF2JMyIt9Uk3k6VMgvH0L7L\n2Z8yxlo4j1MIb2bWYRJ0d3If6zmSWKzzcU35QidzIqsM5Jlfv6GSQdO63xe9DFO9RshwWAFlJ7EH\nxxIFiPX4z6Dfo6O/FhTBOMnkGTsZ+0uSwaAC+edERKRixIgRI0aMGDEuGC8MkcrSdMfZm2XiUrUE\n4MtnorWJwr+5ICcVVz+JiiLD6odoiZlZib+7Bt/PfKXBlZOKk+kKrm/wfCMfxJ2ajuVa621yoJWV\ni9ew+jbhnPlbdbbTI0Tkzm8aRQDqNfQE4UKbqdsrBdA8pczSb//IZAFhLY6RycpgAEpXygouRfpt\nJu252dLOQNoJIscy9RVmWQaEYTkPyMTj1l2kt1ilaPo5haAm6bJsMxXgMlGh11UaTkWtA+iGPyas\njSeO4fhpIzBNMcCmQ+oU0oF6B6QCcpinutKke7xYPMD6uLaAxGltwpo1ERMfw8ujgBLNj277PpA0\nsF47msa/T9Yn07a3775jZmYHJ476LYHc7B0GRCQVC4UnjyGilmSP8gpsBdRqBB2QykqT+ytlfwn6\n5P1HjggVWDH/05/+qXDMe15DchwD+qDO9hvYitx8/aPTtjd/4s1wfFl9l/uhz45uudt6voTFw5m6\nIqP+VhXQpwzjMJwwxnXiwvLqZkCa0vv+vTkQs0ru/w/uB+F7Iw70y324eG/FsRkIqKJUrEW4twzf\nny889bp/jtUIV9+1JI8cXgnIWi+CXSI3w+DnVCLJZzEXixP8hnOSupg7wuTHpxDZ1P6EtUsFkcpo\nnSD1Hxe4P/b0t0A7B6AQ69ZFzF0bxti21ZqstHpQpJkO2H5PDEzyUEQOk6Hc9hNjMSXRiLKe898o\n1z+D7UCaevsPTAASRoB0Si4WP3VHOxdhOFgTEq7jWsOTVTRyQbo4ee8IqykK1zKxRjuL87Y/CrDV\nLZISRr93J5uInqyGsATbcNzF3L9P9DWVenUZrjsvlDlAklXu8962DvP+kEg/EW5PWO1BLFSAvncC\nyXcNGAnpuxHoZyKvPUyaSsQmJeEzVmC6Qdir50VEpGLEiBEjRowYMS4YL04jlWc7poo9kQs1pExo\n6uVv6z1S0pXTpnHZViCBDKvoXDRSEzqFFW4y+iqMJlxq0sl6aZm8raZIsRRPQ8vyGr8V5KIk6uRB\nbQ4RrEQsBMbJfFIQnIxIj5j1EaUT6IiV27sdlCxcdyPuBzTgpCGc1lKqe3LKatIZ/i4T19kYjPC0\nrh21acops83qzlfzLVZOpaAurKdXZmHVvZi5+eMHz2DmKOfkthaygpgsDFRMQo2S1i4Ewibmd2xP\natR60ch1WMGqboh1BzPRKFGvliaK0gB9EqO7MeXY9Z8SHJhBe9QI0vjSrWBgOeulDhu0LMdPHVV6\nfO+OmZmtT10jl8Ee4upNb8+r18J4f/qBo1QPH4bf3nk/1LgrFn5PVFjpXbvqVgdPTnmN3tbsu1sw\n3DTze3Z5xa0LLu0HdOjRB66laoGw9NDq/Of/4qenz/7V//zVcB6CqkwyGFnBF0BVNu3dadurV8K5\nUCtmZlYeBRTp2tzNPHsgp/n8MnYr93pxvg7c6eNw7vcf+jXYJmhjnj72unr7y3Ddsz0f6zksGxb7\nYqbKtPLExx1Ruho18QbRo1SzcH5dLZYAGFD717yvDy4FpJE1L80c6Wklnb8G6qH3E5HtBqaTozhi\n5gktac5rStpWtE9MVxdLEGpeFoI+GawNlqJRWWFeSjGHFjLXtAnnTkHTjVYjYknSs/6aH4rnl+rc\nhcOqvpbaoMGA6nfn8YZUkPaeBsszmadxXYnc/zQ2zuRYGyBSvTiCVhh3tPhRVM9oxCnPSc7raknD\n61EdbIo5IVGBHfWqidgpENka/TypQ00L2iD4/ZegfbZitTODRlIdGYgmqUn19LwRLXGeAU1MHIkb\n0Rccm2or0fIa5D2Bz9NU0D+SA0JITPVnR3nuDz37Qt47FJV9TkREKkaMGDFixIgR44IRX6RixIgR\nI0aMGDEuGC+Q2qvMTAWDSDXfSSGHEEzT/wkPihMwU+KFAbMS9Yno5mrm0O+0XxNqDxxYkqhdAA/p\nxypz7FfgwXJGEblQdXk450bddgmLg/DrBLLv8L1UhMVDF/7O5RoyCPAGSYnt+/Niw7Mzp9Smc0pI\nN+FfoQLJgCmNNWmME61NleJ7Qi0UtHqQ/aUUtoqLOSi1InNqhemndPguRXQ49qCvRERKLk5rHfL6\nUxF2jwNF5JqmTfpU0olL1B8DLNwKZdEjXXqn/iL+1HpZPD21riCn0InYlWbsbeON/Ortj5uZ2X4Z\nqKh+6+LM+98KItv9uUDWOEQmlOXyUqCs8spFydeuBmrpm994e9rWNIHGu3//zrTtU//kn5uZ2esf\n/SEzM0ulrteDh4Gq2khdsduvBeuCt7/x1rRtexporuEdH/97EEhfuuZp/RR0XrvmFgMTHYQ+vPry\nK9NnP/Av/kszM3vrX/8f/v0siN3Lfd9HNg+/vS00Ju0hnj0VweoqtHsi92S+DOOtIu1R+ZxAQXFW\niOgWXhNj721yH+L5mzdenbY9e4rUfWm7Cu7dJ2d+Tnug+7alpsSHYxwehmu9fOj3SwtR+lz66dlx\nEOirKH15GETx7Z6myYdrXBz4eGpPwrk0vafu895uQfvlMtdONTQTsVroSZkL3TbyXvd7Z44J+kxc\nuTtQZImoomdj6JMO82mdixIc1gU7tySooL7XexJJIeZ9R/F8L1UxOI8oLUl6n5UadpJYElKGMlGC\n2pstnEaiyH82lwQoWJeotOEQNdw2x/7bbgs7B1hjdDJfNTh3SgfMzDq6l0v9vZa18WSOL/C4z6Wu\nXMbajSIAH4Y5rkG2gcovMtKDMq+jMxqRpbANO32eYHyMUj0gZbUDTfJBAk6vlCaqXJQp7Rf8WG2K\nsSiVPUYI5VUwP5276m1wXZ3WP0x27S9wAvadIiJSMWLEiBEjRowYF4wXhkglaTq9IZqZJVhNqFki\nRYSZWiIkFBv7myaNNVXDyDfdUapff7uZ2SirIKIZgwjsKIorVNgKAXhRnvckWCzEwKzFW/Wg4nmK\nvZFeK6gO34hzEbglSCdXsT2rmu+IoiEo1FXVtMLU+nMpUTeY1YkUfjyvL7QtBK2tGpfmRPXki1h1\nqpcpjU3bVt/qw7bVxlPcKWgmSqTooxtRiogTK61RkCPaU2iaMFcaQ6+i8PMmdQlXWkhhrhtBkJCo\n0EpqejpBkSKY5PdlVcP6ZFrX6epRqN22uOmoy9nTgByePQvozzL1tPpLlwPqUsmYIErQCvxaYYFb\nLnyl+813glB7K+aDFI3/xD//Gf/e2/9oZmb/5i/+OOxr7uLsNz4WUKqjK24h8JWv/E04zz2//974\nyMfCsWT5yVpoWv8sRfvfF+NOooQNLAR6cT+d74f2Osv8nG7fDm13cMNF7MvLTCLxlTvR3mbj98Rm\nBZRW5pNFS0QijMlrYuGQzMPcUcwdJVxevmVmZh998wenbTnQoUFqKB7cRF9Iqj9tLw5KRcKZ0KA1\nLoHEQAi/3Z43Di5KHxOH+wH1m+95m4y4T3Mxvx2PH5mZ2dmZ729k/TOZpzLcd3tlaH8V22/bMJ60\nrhsRK507aeLZ6PXDsLcQOH/Oe0ascM4Mdeqwu05S3SckQtCnHPdzIipi2r5ko1qnwHRY5ol0Ml3W\nNkFNNiR5qBUj66mmYhK9gXi/lBqCy4Pn1HqFQFprks6A8NlcLBGYcMLz7RzB7IfQF32vDzueuAim\nIdRWUKXrgEj1wgRheKjYOkto8SIJBehPInJqNcHP5oXYdOBe2IjRa4Jkr52xTuNi2d/Qw/Zhx34A\n6DDGSSYZO9ttaJ9SE4CIRAojwee/1s6l+Wey85jif3zcjTvPlvMREakYMWLEiBEjRowLRnyRihEj\nRowYMWLEuGC8OGovSSwVj6eUQrjM4TR6zKjvRAGXa/UxScYA/W9ElEfmI09V2EYX0/Cv+oOMrEOn\nHj8D6EHxWBmN0LqIGAGLp4V6cYRtxSguxj3dYfk7P1jTkNp0KJo0h2qt8+fUcGsn/Fb2N5CWc1ie\ntFQx0XLqmYV/W4GHCXsKFDvDdeeFOHaTxhEMnH5cWhOKliYrceDeX0I8OtWrE8oqpbO0t0kHylIZ\nQ7r9aqOkGak12ciaVALLJzxPsjJyvhkotVJMhFO4tydCrRWgW9QJdwuq6M2P/uS07ew4iOfv3XMB\n+N48CIqrkskRfm41and1G6nXhc9TOSfSvQ/vuoj8+q3XzMzs6LLTfe+/G9y2//c/+V+mbUvUQnzz\nRz4dzueyi8P/6st/bWZm+/ve/nkZaL7P/Bc/PW3rmvP13xpQLzf2va7fk6dPzcxsu5b6Z/S7qSEE\nF7qbY3h+KIL1MtAH33zb6cFb69B5t645BbhaBa+s5Z7TLZfo47T0c2rT8HlZsg6nDGLQEq2YTudH\nQfh9YC9N217CfNI8c8f2GlkbnbiN56AZ1iunW589CdfRrJ2+WS44FrAvcafPQR8VMq/tof7g8kBo\nLIp3N05JtOinmQjbWePtVOg+3qisJ1poHcAR9KHQ3UPLKgpSbYF0vAiQ91E7cNv5OdEOS2ucLkEV\nrikY38pzAofYtE/9nPowJuYzmadZxWH0bftVGLubWrzlYORWCH3cwtuISSGNULak/Vgj1Mysb0BZ\nroRGgnh7Nne6i8yTyh1aeHRlqe9vBqq4biAjyZyyXQ1hjA3Srm59ptUWpoJxU0yC/kQf+3RvF0kL\nqzaIUJyi9FmxxPfFMR8eWGptVSJ5Yyvj5OQ0UMuzTiol0G9R6i+O047ExwryliJf4F8f63wn2Ml1\nmt4ZfD6lZ6HYohlfAdTbjzVTE9WqaEM+JyIiFSNGjBgxYsSIccF4YYhUN2aWy1se3zi1Ns5kOyDi\nMAIMOwJwpHNmta+q6KLQZf76OYkBE66gRQgIUeYoCBLrJDzea8gAACAASURBVKnVAlN800SFfeFz\nOpGbmfV5eBPXBqbuckJLxHW8b8MbfC8oXYsfK/rDRYfWRmKK7079J7jjprL66Iawihsn51p1c6X9\nhNSmo4u3pDATTRvEaqDenK9hNLlYSEpuzppsIop/chzcqPdQ62uQlNscabKFrNao/1MRNdOkU6n1\nN1ULF1F8P61+/XoqrLon2w3pV6bBFtKve/uoyVb4qrppA+pX5L76vXbjI2Zm9o/f+nf+W9Sdm4l7\ndgG7h6kM2OhV0K2DiDN3cTITNLgyNDP74INgk/D6G15/7snTsPr7+tvf9OtpQpvdQr04M7MfABL1\nlX8XznPz938zfbY8DGL3JHFU6yd+/MfMzOzRI69/9uhJGFdXLznSk6LN7t27N20jwnl25n1XTHX6\nQj9oCnsHIfbxI3dx7157PfxOUqifnYQ2o12Fmdk+UtEfPfHjHwBZ2b/kY/L6DaA5qMNXS2r+fC+0\nu6JkdEIelo40XMI99lDu5xwC2JNjR6noEC63k12+HNCGZ3KMBKtpIvHrMz+nrAz70DGUoe1KQUnY\ndom4eJf76EdZkrfPwpw5avo/Ei6Y2GGCIBFhSWY+/oYSte7EVmBCcxT1RuJFIyJeXvYOcwBUvAJi\nX8hEeQyx+2br7dpYQPXUOmUgIyD3ZJLCvV6eHW1/hvPQOp18jkCw3frxOccNowqxgSBt/RpWqOdY\nyvHzwxa/1fqbFK+LdQ4SpHIyIQKXLIqANG8Tv/+GISCWpZbbgAC8KqVdKRjXZ0dC5sC3TVUetHYf\nK1pAxJ6q2Dzh+co8nRD99+SZdR/uxfXGEVZaHKS5tDF2XWYy75Vgooyskorjw5jMdmChYWf/Yb+w\n6ZA2MYx7fcZNlT92qqzYd4yISMWIESNGjBgxYlwwvuuL1C//8i/bjRs37JOf/OS07cmTJ/aZz3zG\nPvaxj9nP/uzP2rNnrsP5rd/6LfvoRz9qn/jEJ+xP//RPvzdnHSNGjBgxYsSI8f+B+K7U3i/90i/Z\nr/zKr9gv/uIvTtu++MUv2mc+8xn7tV/7Nfvt3/5t++IXv2hf/OIX7a233rLf//3ft7feesvu3Llj\nP/MzP2Nf+9rXdryhGElS7LqYTz4iAkUm9H3SQr6g4MQDqijCtio/mLadbcLLXZI6LE7vFXo1DbKP\nAZSWGtZS9KaO3XSv7nageIiN1cUcdFReqAJ791hq8URxZtvIdaFJOqE7ChG++nXBHbYRwSB2MxNR\n3qYPkCphTPWM8nNRISa+IJhpB3Gmms4Sxu3EM4biYR1iJfxz1G14CzqOXjBl5sLWxSxAtk2t/QS4\nu/d+bdBmWSWu9ClpSTW3yr/tCs3qpt45N6U7WfCyEtffDJQhKUEzs+UiOFong4+/t9/+OzMzmwvd\nMg4QNAu1wqLGCTxYcqEdsuy8ALuHOHS18cXLyygW/N7770/b+JvLB15weLMNx/jEh9yB+//6t38R\njot2InVpZjbgXP7lz/3X07b/9X/7fTMz+/Ef/4lp22uvvmFmZo/ue9Hgli7O4gqegb5Rp+wVhNcN\nqKiTp05t1qegnYRuPjkJXlBHUlx5YJ9JUkiPeWS570Ldsy2SJ9aSlPI4HK8DjVouZLxOygIfEzkE\nzcNainujssHRq+4P9vReaItLncsN6jNQGpKUMJuRFvMxtt0EuqluSc+JiBn9qkzDogzjLqm87/JL\n8EASH58UIvJGqDqKgeuNn2cBD6oG1R4G8XabfNHUgw+O+umOODjso5z5/EP/pFqKkHN0NOrfhnl/\nNlQ4bxeWJ7j/txu/r8qMRdv9vuIzQf2GEtB3UlvXsnSJc/fjJ7jGFPeEjlfOxepxlOB66q1fP3OR\nVqdCrWJ46rzbgDZMtbjwCKoKvaxeiPP8EMcXCQpczgdRh9N7cVaJ3KRBMehUn52oVNHqMxrzaS4y\nj46+VOH+WxTer6T2Mtk2ggtra6FRt4H6T6VPBvhiJb0+90k3iqAeHlRJcv5dguep/c9n+w4lh+dU\nLb5sNWjGQcZuDgH8jnpexuzz4rsiUj/5kz9plyWTx8zsj//4j+3zn/+8mZl9/vOftz/8wz80M7M/\n+qM/ss997nNWFIW9/vrr9pGPfMT+8i//8rsdIkaMGDFixIgR4/+XcSGx+YMHD+zGjVDX6saNG/bg\nQRD73b171z796U9P37t9+7bduXPnufvom84GqWvH+jsq4uNCXAVj8yKsRBtBmoj+zOb+XrjCCndd\nu7CNaE45CXUF6YCwfAd9wraNeWoyBdN9K2/1SL/sRSjMNN1SRWyoWZfAbVdrU6U50sAFfWg7IGhi\nYZDQsV2F8khXHTqBuPB5UfibdDEJv1lr0K+hwRv5uANTQVgv18C/RhFndqxhJTa663W7831ckZmZ\nlYpQ4hq327DCKqQPE6y6ChGRtkiTTbXW4lT/TFbEuI5c66qhvbtBxfNYpXGFLc7aPEYhrstFGRCU\nmdS1o2XFw5OvTdtmVVh8qHh624RxlIijfw/xbJmEMbmc+QqOY7EZXVg7r143M7ODQ1/9vfet4E4+\nNFLrbhlWrgJw2Qz99I2vvzNtO0AdNwqVT04crfiXP//fmpnZ//g//Q/Ttv/uF/97MzP7yl+5KP3x\nk4C+HV31Bdcbb4TzfPTAx26PFd5X/4O3U1lSqBraeiUyAabpv/qJj0/bzk7D/dyLTcFrcDsfpFDk\nk21YOV+75ijN9ZeCyH5vz/uO99MApFFFxwMFszKuhjUSVfQ+rUOb9Svvp/2jILLdu+Rt8u7fBTuJ\n9pkL4E+Q3LKUGn9ckTdApJai4T28HNDH2x/6Pt+IdPn2zOef/gTVHgTp2rB6hCAy3Tacu9bprGGF\nMK9Ym0znOlRbqAQ5pf2HJuCgzWqxf6Arf6WoLywOhlLQdDwWtsb7WhNgwvkKIGY955CNz9NtG+6P\nqvS+npAWqV1KR/NBmIBJ7cyEFUFmeGAVZxvQrL4XAX6L+2/m89Qp+qQofZy2eI4MgpJUEO9XeUBT\nU5nDWlpTiCXMhvO+JNvQTiEt1P4Fc51QIUQYtZ7oZOcj/T6y7ijGU9N6wsj+XkhKWYqFBCmOs97H\n5ABU6XSrynbcY9KgKR+ekijWobZlWaJdpdYgE9W0/m3XbXc+MzNrgL5qQlWN+3gUhI/JCOlOQcfv\njDn9PxabJ0myo6B/3ucxYsSIESNGjBj/KcaFEKkbN27Y/fv37ebNm3bv3j27fj2svl5++WV77733\npu+9//779vLLLz93H1/+87emAjcvvXZkr3701ed+L0aMGDFixIgR4//NuPONE7vzTTJaw3f87oVe\npH7u537OvvSlL9mv//qv25e+9CX7+Z//+Wn7L/zCL9iv/uqv2p07d+zrX/+6/diP/dhz9/HDP/Wx\nqbComdkKsGwlNtIUO46JQ5YVFNiluPOmoNsWAmPWs0AHHW+d2muHcIwMUGgvjUMKRosMUxSeyfHp\ngZHMRewKx2B1R03ofSUwKs8zhS9ULp5JM9BIjVALLWDxQkR0pKcSFQySqhIPqo4QtAj6yfMkINw6\nLQZszwuK7aXIJa4hz0WA3rEY8/m2Uwic27SQ5rajeJv/dyi4KgK0PYgXDm1ZZmJB36Dw8aZzuJlC\n+UGdqtklcj0jXYwp+pTzbWF7u1g4PZCl4e/Z3Mff09NAVRXimbICZaCUCUXRG6E7MtCdOUSvdevw\n+BwU8Kx0emgGsfO9e75gWcAdPRMOaIR7soptT0CbVUtxwJ4FuH3VBNj7k5/6p9Nn/+df/GszM/vU\nj/5n07Y/+ZOgh3zzE29O2y4dBbH5t955d9p25/0gtt5unSo8Pgn3zkYomEt7wSGclQIKobsLiI6L\nuVO7BUTmrXjGvft+aIsbN9zHag538NONFPcemYAibsegvpeLMNbKudM+HNeF0AOsgHC28uvq4KKu\ng/3kzjvh+HL9tz8RCh2fPbwxbXv03t+amdnTJ04LVihuW4Di6VRsDsp6I35XB9dDG6aSbNI9C22s\nlEm3hlO+3DubTaBAm617QFUYMxTg5jKHsTC6+j5Ns4eMdYrNUy2Gi787kQ9sMD+3Jr5sOB7zVXop\nWpzhfk5FgkFPo9XGv7eYgype+fyfTVUxxFsP4vW6kUK2BYugo0C7+i6VTNTx9jfcp4V4e434bSWu\n5AmOb+K31bXBI63r/DwTFm1mMWxxAqd8YSd/i30s9BM9yLK8Ovc9LTg/sj2FF8vwnOgHn0/pVUa5\nS5op3YokptLnP8oSMqVsce65PJI2qJqRyzhh4kkuSR59HeRDszmTzbytO5xTLwlIXUcaV3jx8bwA\nnT5bwyCifJzfrQ8d2a0PBTf8xEb7qz91Sv7b47u+SH3uc5+zP//zP7fHjx/bK6+8Yr/5m79pv/Eb\nv2Gf/exn7Xd/93ft9ddftz/4gz8wM7M333zTPvvZz9qbb75peZ7b7/zO70RqL0aMGDFixIjxn2x8\n1xep3/u933vu9j/7sz977vYvfOEL9oUvfOG7Hri1s516aTnektte3anDv5vWBagFijPN5x+atjGt\nWooO2bIK+zs5cwfYnsgJ0mV3XJT5mWxjhmsjKcys8dd1KkrFKqH15uQp7WRfEhGhwE5WcHlBEbms\njIDwqE1Ej7fqXN3G8b1S3K47rDB72d8kBuUCRkWHI0SnKpuDOLiTmoT8bVlJWjH3Ky/NdCXvajkG\noCgV9DNlOIFT+mie6pzCpmJRiYtzElYOlfhAzHqI/c1RwnE4xvUI6pYxdVyc8qc6gmwUv9S+C224\nrv3712+9bmZmJ8fvTNtYC6sWFW+KFdOQ+qquYRtrokIfVkzbhKJzH+tnSEm/8arT3g/uBNRnX+rF\nGepePX7oqMb1oyAAPVbx9hy1pmT1RSHvzZcCqrQSwfLZOqyWj3pH5N54/cPhWI/dbfzhw7Ba/MEf\n/CE/z4f3zcxsvfLVN1f2N2+6JUOKFfHhIWpoHTn6dvosjIW5WBJcOriE8/T+PzsL6Mu9e36v334N\nbVb7WJtB+Ku1HmeXQ/uPI+1P/F5bQ0R+9cgRpBpp4Fev+DXc34Y2e/db35q27VdhTF6d+YD6yr/5\nV2Zm9urH3JPv8OatcHxBeJJN2B9d7Pev+Er/4Ch8//DQ+78H6tJK/bNiHkS5J8+8TZZImtisfUzm\nrD8qTICLbNEmouwmwqrp/znQDE1N5zyqGt0ZENO28TY5QDp/0TsiVaAvWqAUH4gTdgWR9VxgDVpd\ndJKU1ACx0zkuMQqQxWqC9g+CEg/4Xl5RCO3XkDxnnKRAcGaSAFNBjL8R5HSOOTsXt/OBKNFOpYSA\nEo64/qJwRLYBq9IrIkcnctk2zXFap3V6Psr3UlrSyDam+gvDMaJteyS05HMRcQMJbUqfu/OUiQp+\n/Om5J+eUoX86qZM7WUKYMxFMJHt2HJLXZpU/68aEz3MZpz3uTyUkcPxeq5fgeT7siO3DsbTKRiaV\nPJ4X0dk8RowYMWLEiBHjghFfpGLEiBEjRowYMS4YL6xo8dC3O/bYpNZSoWcyCJpTEWA3LaiCxKFQ\nFqtUYTML/eapC2vbNkDERRGgWxVR851SrDMmYV8isB6Fba0U+aQYcxzEtCcnVaVUIWBEUFwUn5u5\ni/lOdgCd0AUf7UYK8QSKB7TbboVGQnvmIvZj8WdCnOo7Vab7uD4t5ExnXxGMgvsaGhWA4jy08aZ3\ndBE2wsdKRZn076rpxSN9nSSBPipzFwzOQel1o0Lr7CfxlkLBU9ErWsYz1ZqVON7kldMrFYljHvr5\n3r3/lpmZ5YW3dTrs4VrEC4hFOOUEWo7xHWgbwt4kjOuTZ378H/r4PzMzs4d33LF8MQvUV711KqKG\nt9GNa9embSwWPLn0mlkJYflyT3y5WLQTYu+78KQyM/vUp4In3N/8zb/340PsfXDgLu7MzH37nbf9\ne/Nw3x0euth2C6+cVCD4fRbSxbhuRJx95Ur4bCEFgms4dS/nR9O2w8vhe/fv3p+2naKQ8dU9pwDo\n3r4vPlJzJA3wHlLaZwZvrdVaEiDglP70gYj9kfhyIAko7XEYu48bv55b1wMd9/T9r/r1H4Z2vHTN\n6btsBC2Bwr/Xrzm1uH8Z37vsfZ3UpKz8nujgGZ48eTRte/QACQBSgYCeQoXMMfR7yipWgJg+mrzv\nslwlA6RsfB8s1puJiLiHRCETyoj5Qa0mxYDameHxdFB4fw2Yu85q/36NEyxURA4KNuv0EYd5r9d5\ngv6B4u2E36ZFOA91NicFrLKMSQyfersysWdQIzc4gPeD0318ZumzK2FFDRRU3ooXWDMG2k+rXSR4\n1iiN2cH3qZDJLmPx81GpWtKyPp/3PQXlMlFBDjDm9FvT+T/s7+mJ08iXDsKcMEiiVocqDoP6N07X\nJufeQlCuFU3Ir9KXqhd6GnPXOPqznk7siTyTcwjvR3lOtZBvDDIndfAls973Z+Lb9byIiFSMGDFi\nxIgRI8YF44UhUlliO6t1uolqfRuulmZSw4crjXYQcWJOREBcvFumekqaPtCHdnJAFvQFb6ma6kx7\nhFFEZw1WTnnqIlouuhKpVzSlieaCZlFsjZffHQEb3HN7WcENPZ19tdYV9i9ic0Zd66qCCJ+4CKM9\nuejOU3H9xY6TUt7MIXbNdxAcpPWr2BGjaGjl+LA1SE3e5Ec6qrvYeRKNA01ScWaOFfmpidj0MsSR\n4tieUPjZejvRqXeQ1VyO61HrCI6PDueu9RKrMSAdSeuIGFPn00LE9gPHjjjmos127RfQJokmJRQ4\n9bD6/Pir/2z67OvfhGN4dXva1tVB0Nlt/Twv7wWU4vFDXxFeuRyQk630CeuPbTc+dq8cBWRnA7To\n1k0/1nvvBmHnqyJ2p4j4vffe8eNfRg2tnWQDVAAQ644GSMdS6n8x/X4OpOtIylGtUYdvNnP0iQiG\nJizsXwqC5UxWmhscK5fxPFucT1Ony/kWCN8g99BsTqsFcT2GTcRSUKIPvvUPZmZ26/br07bjgjX5\nHM1q4Gy/d9PRpLOTkAyQS0LFZYjLe1RxUKuRDMdVS4RiEdosGUWwjL8XV1+atuUQ/p8++Pq0rcKc\nkM/E7R3C8xHzXyYoHcdQuoNgAU3W/kctSq1KMYnWFTlpMZ+YxxJz8IjvHUoK+xMwEkRQzcxSuGyn\nktbf16y/5/dahvu/EZTQUCkhl/uZDADRSXV2J/oySpsMGd3WHSXhPJ5ljv4Sdc93rANYE9TbhOUp\n6zOIzgVp7CHyJ+JkZpZAqJ+pTQdT/WU+SzHvDjuidFxr5m3cjugTrX+HVwXOobW4k88KJmB5uz49\nDohtXog7esI5Vl3EcUxFLjFOsl4RMbj9d7TVSb/9o12HAM77khQxIKEgE1d0qtH7QZ8d/MPHWPJd\nMKeISMWIESNGjBgxYlww4otUjBgxYsSIESPGBeOFUXuj9RPVYaYOpA77UZyb504PNSguWYtQegRF\nRt8h3Y/CfYQ7JzGdFKMdKYBT3w1wWqP4s9A9tRHfk6nwZK2VNOEZUvXyW1CFdN0WyJbCc3WiHht+\nLlAwzLeGRAV7OMZwHjJOxQOqgvdTAfF217kQtwdVmifi2ZQHqDoRf6Q+obOsVD6FUDARwWAC2nYQ\nupMOwKO6HbOAKKDyqnA4dQunYh2k63U4z+W+uK2DisikaG+zxfGl74gjqyg9zViEFo7Raz/fl49C\nkdvjUxd7L1EsWLSmUkBX6Eb0bSKi+AxtsgO3Y0eXlh8xM7N37//V9FmVBcqqa079YICbZ5lTMRRo\nX7nkwm6ywUojFmjbS1JI9+wsUCXPjgNleOO603jHk1eWU4EsYH2kfk+n4fyqyu/TGhSt3pMU9HZC\nwZYlqM06jLUs9Wugs/9m615YVy4FGrEWL6QUXjDXr3uB4iS/youeth3sH+JfFdvD2RyCdoX4SUvO\n9sUJHoLZMnO6cX4UjjV84GL3K6+EQsrP3hVhMeaxPPExfnQ9iM0rofZS9G0GumnvtQ/7OZ2Bnpj7\n9w3zWbd1GrU6QFucSnFlUJsLoUrpsl0LLUNKq6A/VKqSBfwr1GIFmk3FyaSMRmVR8J9BJBWc71qh\ngEmtkQo8Ec8mzn/zmfc1XbnVl3DA/L9RHzEUvFX5yLbG2BUD7CnJiZcjoucpoUZpzATtL+7kOe7T\nuvN7h7WvEynM3sE9PE39+nu0cg4X9a04u9dbUOaJVweoKs5nOv+G/hzFi4k9pkkBk3+a0Gie8CNz\n11QVg/O/H2u1WmObyCjw7CgG9RuEtMRk7OL4LHwfvgdJiVDaTNCygpSd76LZ8jxF2pNx/he/s4Ly\nGTkWT0N+686Cfk8MkdqLESNGjBgxYsT43sQLQ6TabmWpoB8UKqqwnOn6naxqMnwvS3wJwbfFRBAZ\nvp2OO/X0+GYP0bG4yQ7jeRFlMuL8VBSOFX7dampm2E+99uvJlxBvN7Ii4Js+bQ1kuTbmVKDLm3HK\nlY6sfiiiFgF4D9SjF5sC6ymU9GOkcLEtDQ7X8h6dIDV2spcwF5b28ma+AKrVSFsPSKEvVLAI4XUj\n6b9ziGcXpffxPtLPtxCMqjiR7S9OE1bD9Xm+9BVphZXLUtC8NUWhrdRQIrLWiU1BhuvFyuiqiJ0f\nPQ7i7cVS+pV2GoL0jGlY1erqJ4OgNOml1hVTx1NBRCCKPbwekJhu4+jLAijtKCvoHk7oc7EwePYk\n/KaRlXsDpGVvz20Knj4N32sbb+O9/XBtBcbuyYmny1+/HpCWWuq6bSGYXYljOVeaw6go6fnxTDFs\nNZPkjS1QR9RG6yQNfQ6ksRNxcIaVZtv7/XflCkTZmSOsJcTrxdzHWoHxUcx8G8HJbBZ+m0tyQFnx\nfpIaarivhrU7u1+GoPuxOKZv8fflV79v2nZ6/51wTKl/VlVInihl5Y5pucK/w6m3fw5n90EQpPUK\nqIqgD+uvBZf1cs/Hc5nh/lv49XDspFoTFIp+VlHQOmxMjihKreGGc5P7j6hWvXHkcAAD0DaC0kzo\ni8wdSBQ4bUIbLgqxxMFYUEuWyT5d5tOiCP2zWfux6oZp8orco9amFABlgkiPOayQOamf6gqKiHog\nIqf2A5gTZEyskWK/7fy3Rc62FoQJ1jYt2rORGoIjkLtR0L8BzxVaqJi5ZRCF42ZmYxL+LmVOJkvS\ny/OsAduQl/LsGkP/JECOhlZsYvhTffziGOmgzzMcS2x/+gzXo84R+Fym2IkJKvDg00M1qHWrSVn5\nDOeeCNIH5qgX66Qe19q3kpSF547OZ53Ue31eREQqRowYMWLEiBHjgvHiNFJjO6FAZr6o6IRnTcH5\nDrL6nNIeBabK8GNNNZ/SdEfVTYGjTfjGqahSWFX0vXL1hm3K/WJVpfXvsErIJCW/qfljNf2k+xrR\nIuG0+aatKBVW9U3tb9AVVpONrCBYL6hXUzusCBWky8Zdfrss1cAMxxX6mteTCPyVsfq5rOBaLCdU\nj0VpSiNF0qmNEBmKlSX1IECmal9VGdKJNdWWPHwjKekF0DwaTpqZ5TXM72T1N/YBxeml30voUFj/\nal37yjADWpCmmgYLVENNAqkXEzSzgiGnDGdLx4AOzCuv07aAncOju3fxmXfAugNa0PnxmRreaa0z\nrI7X0nZc1aXiXXHtMKTV37vrNglPnobffPzjHzczs3ffc6NJ1k5biiHmCfRQWkSNmgtd6VPzMhMz\nVZp0MuXdzGyGPjs7C3199UjqpQFBPlg6+nYG9EV1DtSZsb6ZmdmCppty/XMcP5X1I03/qmnsSGo4\nkM5RkD7aBGiqfwPd3tVXXctUA32y0lGyvRuhPmi79jFGs9FiJvXUcG/nsE4ZVoIqzMI1ZgvXki0x\nr23vea2/GaC2d//+307bbtyAbuup10RjKnwplhQ1UJ9KGAM/t9DuiSAiNC4dBSdYo06kjgmCOHmi\n+sZw3BNpE+pgZrBO2Yim7hDnuRJ96TOgr7mgRDynVmp9bhsix4IIAX3rGp13w78p7jW9h0fUaVO7\nBA6ZVlANWiZ0YhzaQeu0ES3bFdR6LARh6w0oHu0MRJDZ0NR5EC0rER5FyWA1M+zUlQv77aXWJvtf\n50nWnRNvXCsyTORsjNHbtUHtxMXc73UinFq7dsJt9AHQE2FS3SzPXeZp6AsnA1PZR4NnV1OL1QHs\nIbJCkKaM7a59jblLnnE1agcWMnclOwYd5yMiUjFixIgRI0aMGBeM+CIVI0aMGDFixIhxwXhh1F5e\nlZZIGjz/kjJQVhWEB9XtHO9+Wn8PYjyFAilarWYuVNx0M+wPu9AydIBFc7VEgOgvUXQSMZdUb8sC\njN21Qu0BWh1VxAa6LaeYTpx4mRrei2NrS8hS9ruF7cOobsd4H9b6TwPEcZ20Uw/ItgRkrHWI6Oit\nNdwKiMxTcVHvafsg0DodgwcRNqZoO3WWJwRN2NnMrIVlxHLvEP8XypICQ7nWBvD4vBXKANReJvBr\nRWftXujjEXTP6HYCPWD+w4NAu9179GD6rIR4PsvERZmUjtITSdjvpvf053kZaLROBlky9ZNDxntL\nOJCfBRG91qFbgiqZpbembTeuBXro/rueap9MNfz8nBbzQCnRmsDMab7r1zz9/e79QOU9eRLonitw\nKTczO1sFuoU0jZkLtm3HaiREJxTYlNbe+LZywXYUsSmohcUy3Kdncr68UdWJvAO1pKnRrBhQCi2a\noS1y+S0pvUTSv0np8nx7saswWmfkwo8nmE8qcZZes3alfy2fBwfyVqji+eWbuB4fJ13DWl9CVc/D\neOJRsxtOBdvx4/D91MXuBkq9FBF5swq/vnLZf3uMsa21xkh3qnyCLtv8dxR9QF5SguDbGtbdVBoP\n92KZ+zihQ3oromzakxwufRtpYaakD+Li/XQdtqmtAB3tW2nDElKB5dLn/6YNVPm28fFMmn8QO5sc\nyRAjqCKlLHnqrVZR6DH/qf0CE4qG87KUTJ4x2y2dzSXxilIRzLuFJCdwjGmyD60JNAGA+ygraVc8\nXHsZqLQHaoQq47bMlAJkpQxa+Ci1Hr7X1L5tsQjtDKZ7rAAAIABJREFUX+be/nWHfpRnHB8teo/3\n6G+V5Uzynek8xf4IbdHJM6FhFQmhNosS1Q5k/un4TlCLeH+kBEju++8COUVEKkaMGDFixIgR44Lx\nwhCpotyzRETcTKdUcXSHVUIhJo01TPpWG1+5zpBiq4IwpozraiqFwWEPEfM4+lvwgO93orZOsEoo\nxvNCM32Dpog6ySStsuc+RJRMIfuE0uhqhSZ0fgyaVY6Dr2o3QN+WV2T1zVT/nVpjWM0NKiINq7hZ\nEYSt5Q76hpWmWD1QgFnMRLAIdDApxKRuWh3INmwqRezHdNJMDAnbBisNoBmlCIbXrALenRf2b7ay\ngsJKdzLcM7OKKKEYMiZT34qdAdLOV01Y4c91VUckMBdTQxjMZVrXCUajmUCX+RhWYpXYedDgLzNf\npa2ehjGzh/T7QcSxS1Qcb8/8fGlcabL6Zt5Bmfv3iCxeveKIxH3YObx0+5Vp29GVUPftCQTIr7zi\nhpw5bA92kCaiFLpapflrqqgGVrU6xljPUlDiHvczzRwbSfYoMIi2W72vgNLNJYUbw76RVfIciGQq\nZpI02NWalLyPmw3EqaUYEyJNOhf0rR+wqhajVzZ7IvU/2wWMNgX92qwC6ljuuVDcVqHNtpImn0LQ\nzvu/q91CoLwWrBbab3xl2jYCza2fuXXFuA59nYqtwqJirVGZJzHhjIrSYd4bOcYFaZrqxYnYuKqQ\nxCHMAZHDRNEX/DaT9PfJJFLm7gLXPQPCPpc5cQ9WCBsxqVwPZCR8W455dzkTS4JZ2FGz8fv0ePXE\nzMzK0g+yPQvnvrcfziNRsfdIM2c/J46xRuYkIvKW+DllNHgWRIYoSi/MAREwJj6psJz18lpJ4skw\n/+r8z0QRcaSYEorUzJlzsaK0Xk9R69SGZwctBhL5bJySuPxYFGpXgoixhp4K0NM0jPtM0Ke247zj\n+yOLQgshNdCk8H0UNJm2BnmqKCm+rmbKQB07rZPL85QxNl94MsjzIiJSMWLEiBEjRowYF4z4IhUj\nRowYMWLEiHHBeGHUXjbmVogTcQ5BbyPC5gFwotYL6yCsU7+h9Rp+O1pDaNrJebdvg9i5UhgfVEkm\nxzd4Z+Spn+cklFYo/DkeVBPbpXo1wr2k4vTcJqGwwJ4U/QkU7rikCmbP138qQZWoKHLA3y0ou7nU\nfKogaFx24mcDvxOlVsmUtoNjy23HWksqFIcAV+DWzTZQFJ3ApB2cuknfFCIYJsSdqLNyF9ribKMO\n7OH8KqHx9ueBslpl4vY7CfUVbqYvTLF7gaYUpHQiPk/k1ily+p3519KUdJ8Ly/cXENSvve0m+hpC\n+ERoxPUKdKf4Y43wx5pXPiZJmY5CozQ9a1hpokYYH6szp4roizZDsTH156IXjDpR07FcxyT3UQg8\nz+8VpYhCp2aUtptoPni3CGeyBO3WyhjKMa5ToTGLGcXZfqyzs0CHX77swnpSekqBdG3YNlGL4kVT\nzeH2L+7cCcZELhT0AOFzLvNUlrKunNyTM7hY74idUT1g42Pi7E7wFLv84ZBYMJ76vNJuAwWb3HLP\nquzu34d9iLB7W4U2Wb37H6ZtS9Ri3Mq9Q5F9IT5SHekbzE+Z3P8LtIkuwevmfF3TaZaSpIzZDE71\nMk+yzdQ9v2YfYE6ciYyihKP7sFXH8tDXudDyGcZHL4k6C1CQtXCFz1ApoV4LLY0kp2YL0bkYSfWg\n9lmj08xdt/XZwWSIQeZ4Jp4UmSbAwDOpU18qeMWxooRMP3xmnQrd3eK3g1Qs4L2+41ifkMZVb6/z\nspUMkpdc5lNW4ehwrV0v9wTG+sHhzWnbJH0RHq2Cp9pOUhaeCZn4aFEh0YpX4haTq8t31Fkd1KJe\nF641k4QuHqPfSAIYZRZCt7JPdhzwu/PtpBERqRgxYsSIESNGjAvGi0OkLLc8ddFpgZW7ruq2cLTW\nOjcU+Q5Sf+zkNAgGZeFkeUa3XQ+KGJnqv18p0hKOUQ+emjxYePvecQBvnpPqjDf3NpWVBt6IB0nJ\nzNDcXLntuKXybVprE2Fdl6liEPW09G2ZgE0ib+QFVi5Skss6vOETpFIxXzFnuqxv61tW/HaUgmie\nptDyMnpBc+g822kNKxz3+MTryRUYAx1QnU7q+s1Qf6sVe/QM6EMjK8img02FODHP8L20FAH+KVES\nR4lO67DCJ8BRqsMw0IJc0EcKS1tx1i8hKM91pTmJscVtvQgrsjqT+k/sTyRbdN3x9Nl8CNdwrfSV\n3rNnx/i+rEiZui7jj+jUZu0rxxTjiAiCmQj1WX9OhmQ3paHLShffH3fuLDr1i/0EEBu1KeCxElm/\nTZ9jGaqoYgmUNJcbm47VxY79CNAHTdTA+RGZMvOU7ExQLyKhFNHv1NeC2Fxr/XF8tCIOzjDutU0G\njI9Erqc7DX3XbryPq6Pb4Xr2HGHkyOruw4G+knTxxyEpYhSbihY3liZlDEi82GhNzJMw1q8dXZu2\nPTsL813TKhIJOwUgoakgnQSdWzlWAuRoXgkiB/uBaiZJOeggTdRoBVmafou+YOLBVqDes23oz9O1\nzyG09WjlGvKpTqLck0B/dd6lULuVfi8wTxOFUGE1q3G0Mq8TCFSrlwGIfSL3P5FzrRQxwDphdeLX\nSMuGqVKHtNf0TFDmBp2SdGJ1QJeWQZMiwLpIUlCJZARFfXtWqtipnVnvHKuXZJcKSGtZeltXqJNZ\nb/17A54FKrYf8bxX5KjA/dwKOr4Bwp5P96laB+H65DlZct6X94QEfTer/N6pUTVikDnBLVA0ySYi\nUjFixIgRI0aMGN+TiC9SMWLEiBEjRowYF4wXRu31bWq5UDH0oEkTgewBz55txcUX4j1Sd2ZmJ2vA\n3SJiOzoIztJF6j5OFWDMBNSG+mQQ4jSBhzs4dWe5iDMBY9crdSAPx22Fgqwy0n1aNBIOtFNBSYEz\nQR/lImyknjcTGiHFbwuBHd2J1oPnaeJ2vQXMupgH2LkZXPSaDwFOViqmoKBWUE2izKl4QSXJGf4V\nsR+dzdVbCefSCFUy5vCbgbfTOAjFwILCcqyKBVKFAk7g7KwC6Ao0WiEu6tvkjpmZdVpcNKV4HA7D\npQvhh2mdIYJNiMj73sfEWGywL/9tAa+uIvVxSv8aLVDa4RgZ6NOic7p7SrIQiH2xwLhqZEwAMtdi\nqCyqqs7qpK3WQnfdvBEcuJ88DfS4+q5RnK33SZqRRhEBNO4ZpcxmoHlaoSDLit4y3ia8NHo85SJO\nNxaGFbExOVhh8SZKWa81x99DJpQFtikpyeK7K7i3z+dOsbUQXWey3txCeN9Lge4SRZiHTP1xIN4X\nsX9zRh8foe9Pw9xVSBuT3S4ugdqW8TJQxH7yxI+/DFTF42/83bTt4ErwsWquuyv+KQopL/d9Tuww\nB2nfTfQ15slCfZ8y+HgV3ocZaByxW5oSMFK5d0gHjVrIFvNept5iG46Z8L2N+IPNIHa/NN6Ytj2A\nB5yO6yoPlQp2KCAUEu/FKZ3C70Qd7TEoU9CCnQiNmfgySmUH3jKDFJwvce+mcvyUNNvgz7hmC2dx\nlYCkdJanFETPDc8QEZHX9AIb9JwgrFZ6aqI5xe8Lz7hExi730/filI97fKLnxDORc/Fo3q5pGsZk\nWQktifbRpKy0ZJKJyEIwdrYyJhv4Io4dinaraRXbWu6hDM9dmTqtKEOyRZV5Agr1AOp3x3mikQoY\nRf6dX5UiIhUjRowYMWLEiHHBeGGI1Ko9tUWprt9YrZYqWA7/lqWsVoD+JIkL25aLsPo43Tz0/TFN\nttD064BszIE6dJ0jMtmk2JbVB419E38z5Zt7JqvkmqJseatmim+iAuBpxQIxnVgTlBBl6spwTnGy\npAazFmAmSBMbahRR5rQfrX+F1XmDdO25+cqIIkp17J1qAYqLcQKEKROkhehYkYv9Aw47irNtCjdo\ndZYd6NpMJEJWhiOE/fO5j5MRLr+XDw6mbW3LWnN+PSUckPsdYWNYpWxExJjNQttSsDurfL9T08mC\nkBeW5ipYRv23rLFvjyRVB3CsiFUUPIS+oBN0abpaRVr1vJXvU+wpYnsgfOpA3uHkc02owPAoZRsd\nzelOvpVai0Q6VcS7B3sKdbE+2A/3k9bka3A9lQj7KTLPJU1/as8JBREEE6JTTeHeIHVfBcAUym/k\n+FOStKApa7iHLxaSPNLzLFDXUuwyWKezlrZePQtI0N5Vvy62u6baFwXF/j53ZLgXUnFR3qDG4ezl\nN/w8n34zbNsDMrHvjv10IE9rRwS6dRCvLwRpOn0ats3F4mWA7cjpsTilQww/DOcREVpC2HN0ttr/\nPeaxQtgECoAVfWYfsw6mmVmF35yJ6DxNd5GLUub6WQmk5UTqj8KeQq0GTlahXTP5bY25ZpT7NCvC\n39VcH4W0LkByiiDiA9p/TAVVS9b4lcyTsDDQGnrjEMbMIK74U+q+/Ha9QgIEfppLe3FOEkcYq8Ds\nKPo0WcIImJvi814Qvs0WSKwggg3mqU4sFjpMHkPH+9rbmlUGEmmTBHOt1tqknUHb6ZgAE6FicybA\nVGLdg76gPYs+/2kj1Iv9TtsA6ZK5roDbvQKii0W47jbV+xkI++jnWRTK95yPiEjFiBEjRowYMWJc\nMOKLVIwYMWLEiBEjxgXjhVF7dXNim8L9HEqI/nJxWm0BHys8msNbRp1I92co2il+QzVErqlQa00d\n4MG9PYiCO6Xd4E+lkDWhPRH7EWXNSqHR8FsW4DVz+DTLRO02EkYHFScQI6FI9asg7K50H/2jWmXR\nRNDt504PEt9Gqq7DdfW9OixTsKc/gD+NtDXFuIM4y++VQbw3dO7BNfYBCk06pRYhnhS6MaW3yyQA\nFd8TnG8q/b+EsFZFwSykqs72CYSVgxZSBty9PRMfn5xuw6DMpPBsASde9YzZgmbdMZtn84/eJhsI\nFfOlQ+Y54OhRVLkZPIJqeja1Pib3svDbrdBDY0YvNHHdxVhnQWczp4DVuZh/VgJ3n56FfR/gnjg9\n9WOVEBGrOJuC8V0fobDjwwOnoB49CgV0r0jRZNKRzyskTCG8UpbujuzHp+hUPWsaOmvLYOf5rdfq\nSg5/JPE7YtFYemYNpvcSPLMksWHAseozF3vvoUBxs/JjDbjHCqGAezr1q1N1G8bM43vvT9sOQQF2\ncH22tVNm2cgKDL7fs+K8sNkwj+WDJkrAsVrkE6R0OynQOmLfpEoyof0o7Femgz53Y6fUDkTRQuMm\nGasdaKWAcAx1L2f7JKCiZnOnXfJtGJ/Jzr0OzzyRapzgeyzQHj6HYNmERoRoOpVqB3S2JgWejir2\nx7G0XemFJHRzkcALqvdtLb3iai0GjHEnfnsd5lZWCpC6y9P8nEvReBbV7tVFH0kZuYjiSe2tOqHv\n09BO28bnjhr0+notzzgWQeYh5J6g8D6VZ90a/nzrtRZohohbHl7VAmNHa8BjzpZb3BIWZsazqBfP\nLBa8HkRszwQB+s6Fawh/J5kktPWcE8TFHaL5mVbUyCO1FyNGjBgxYsSI8T2JF4ZIdV1vm0ZqflGU\nKunKBpFf2zrSUeQQjLUiFIcY+srSV78nm3vhe7JK6+E2u23DW/go7qxMXVUR+TCGVcUob/B9AqRJ\nxGlJGlYQlaQE11usPkY5BtxZJ6RNBHMlUBddaY1wVs5kVddh5aA1wXoIATMR5TJdtZXlfJEHhIW1\noVoRh6aonVTpdfG8k/OokoqIM6y6l6Lia2usnBa+rdlSqO5tTAQuHZmaPH1kPVZ9vdQGu3olrDD3\nKl9pLqvwo9Otux1TlVmlLh5PUTNxb+bIyQkQhgrX0Inrd1EALRscwWlrCCal/UekEFcLSatNQhuv\n156SzVpjpqm+WInOS9pfiOgRK8JxKSvynmJXSQ2m268gVwn6cZAVqQFpWIr9wBptO3AM5YpgYBUq\nju1ERxcLRwRXK1z/gZwnUBKiWuEYcGCW1SdRJyJCqaBlRKu0NhxX5PO5iPJxTywXjv4RnWqkPxdY\nnXYigG0gqCVKN4rVCFfme5LC3cFRPZf76gwC/T2xbmjr0BeKZvRIPGiOn07bMqaEP/i6H/dVCM9X\n4bqXufdrewlJFPcFET4M+30k17XEmFmt/HuHh2HuPJXafT1dUuS+p0M0ETwV7NOdXhGJBEk8ZSWo\nCm0vBCWYarftWC1AUC/zNBGuGY5/78QR5Ltn983MbN2KszlrPIqtQIt6hm0vlSo4x6t1DBHDVJBr\nbJsXYe7QGqKsYZnsYBBzfE/E0UC1ErmfM6DzvVRvoFB6EOsc1t2bNOFSc45WE6UI0HvOdbknG/CR\nodUmDPdxKUzMGU6l7X1MMrdkFCuWFvYsCRihnfyDNoz/vvJnctewUoPaCkCwLue0xf25ECZmRL1R\ndQQpZ2HbYiSr4J+xL7SGIWHsTKqnEGFdaAIO2r/ZyHMac7e6rkz2SP+RiIhUjBgxYsSIESPGBSO+\nSMWIESNGjBgxYlwwXhi1N1hrTeu+Tx1osaETeBK+QO3gGFs3UOyrAmwUPBUKbAH6pm2dlhsg8j0+\nfYT9+/mwyOVMhNsZYMxB6TnAg3nu8CSFr+qtwmK96sCclnSHxTFNBJb4fjbTa6VjtHdTC5pRrY2S\nlII9gZaxv1QLCYM2Je2irtN08U3FY2UqULvjRQV/IikGWgD2VBolAVS9VbE3hIKJilcBwY6A1kcR\n9ve4nkH8eWYFi1wKPQAMeKbQfhfokLmIDasx0IG9eJbM6U+CsajXShE3PWHMzDagkcZeKTN+T5x9\nQdWenbkr/9gGOiwrRGyZh/NLRwr7va8P9gK1kIk/FgtI972KbcNv5+KPlMJbZhSqeAE/rs3a6R5S\neqRxtPAw26KaORRO9/BeONh9+EitVo63z+FAnYlgkwVHk+eIzZMdrgDXgO9tNz5ODw4Pzp0ni7yq\n2JyU4k4xXhZNTvW3oU1ILVbiYJxDgKqO0cs9FM1upNoBzqXrXKpAurE+dVqKdMPy4LJ/70mYi1K5\no9vTx+F78H1qn97xYz3FnPSSO3uvHwa66+Da9Wnbyb33wvHlPHtQS1txwGc7dVrdHF2bTn0ngmHc\n47OZSAZA/a23Wsgb3kJayJvyhUopGFL74mOFZBCOk6Ol0/NPcI89fOY0Ug1fvEYSBeZ5GBPr3sfk\niGtMRBRdFji+zLHFNCZJd/v59pCR9FKMOMO8OtQy19KfbNCBjXl6VAqQiT9aSByfdfRH8v3OMIfK\n9DclnmSF0Kh0+zadJzFPy30yUAAv88mI6hrqt8QbdOrORBMLwhy2qX38t5vw/c1aqD08kxLxQOQc\nk4p8ZLGEBCURCnokfQ/frd7n9dUKBaKVMgbdmnbeT3z+qKSHw06rDQwoJN+JAzuF//+xiIhUjBgx\nYsSIESPGBeOFIVJjb9YkYlcwBAShbH31USLVWwVj63VYrS1m6nYd3o5TedOd5UCzxKm5A7JRA6Vq\nRGA4h7KsEqQlx/FlQTqlGOsKgiLLYetfLGdIv2/1XTW8pSeTwFpEhBSljrrUYLq0n1OanV8lD1CM\nDoMK2+lE+5z095Lon7gew7Fd38JTiP76RJE2OstLSjgEhVXhIu7TBunhkurPFYOiD0xTX+FfFfun\nSCEu5/6DDUSkVeX9P4lic//tM7g3l7KtsLDSycWmILfQFus2jJdE+qSlFYK6I0PQq+7kxQz2F2In\nwbqCWn+wgQBzX92+JzQV56boD5CYURx2DavqptNVLZ3t/WsJEIFCbQKQ4j6IJQhF2yUskBVVZCzm\nIjrtaOuh4xTjX1bOiwVSjaWz6Sje1LoiBEqzpOtwf+77Tz9wqwG6bi+XLnafhOLqzg00ZZ6LiBrJ\nGFUhqc5AaYiqqTs8r7AQVG0Fd/Rc0+WT3e+HHaNNWh9r3UlAPbO5I1IHQIROxL38DOLqZYX6l42u\nzIPIOt1IZQXU3ZuL23QNNe7RZT/W4wcPcY2CHMH5XBEZjicibYOIqHmETqw26izMp3npfTJinhpU\nMYx233E/mJJnfIwnGAMzJirs+TPhQ7AwyeWZkPUBsatrZzhaoFNpL87WBW0avE8K3Nt5rk7l+AdM\nhN5Xk/BeEFkmMaWFJAARRR28XYlEKcNB93a1MyHrwbk+kc84PvNCS1CwAoV/L59qUgr6hOQdrVM7\nDqzJeR7hH+TcqxmZmPB/tfVgAkhTu7B/vUKlgFquf2D1DGFieF213Kd7YRwllVpHrHGJYb+a2MA6\nheqinkyoohwf3dPLe8eASho6x/I1Qp3SGxkzz4uISMWIESNGjBgxYlww4otUjBgxYsSIESPGBeOF\nUXuJZTaKd8YW0O4sdYiNLqqF+JiwkOm6cRixhBg3EQqKfhuLygW4NaiNFD4+iUB3dABW2JEOq6la\nKxMKHJSygcdG5tBukbBAr7go96RqQO2J7xNpN6Unree5OKw4wtOol+K+/VRQUn5Kx2IpkFmRooAr\n7LxyKH6As/hGxKmkncZUbNTx57wQd9iB1KKfewk6aHMmMGoOF2HxZSLcSvaiFCEoxdujiK0fPwkO\n0HuLm35KoOA6cSWn2/kqc7FvhzZpxRfM0I//N3vvEmtJllUJbvub3d/7+i88IjPyE5BkFerOboSQ\nWqjVElMYIiUDJFJMGDBJZjmCESNGSEhMmSBGiBESUqtAqknT1VWpaioaIjIjIzI8wsPd3/f+7G9W\ng7O27XXrOZnSk7K9u3T2xJ/bvdc+5xw7ZmfttddSQ+tDgqX7h81oVYvmUAtHCZOUxlQCKG3TFPTY\nkuEntI9iqP2GpARfA7KexfZ9VS8OWXUX6VAm9gvuo5FSNhHUgXPSYNIUmJoBzxeWMtHxyUrUw+Q2\nQClwHJ/HrjoQzEjbSdNhNaX2EuQKEqR9MrpflQjNOka6v4RSwJra49SitvViQcUrk6aTfU+J9JqC\nzFK+/6H7w0RsiPt0JRFRc5d62u9I265XzTQbJ5vetfHuk/9o+zt9gmslvaHQpeVq1QwiF4EaKaX5\npRUxLJfu+G25pW3uum/Wpg/06Etfddtemop6hzmQU8B63RMRmK5BW/CgrzEW2Nh1mke5eIScJ6Zt\nSDOONHZbjFO9h0ZKj2cYQzmN62Wu+nDW1y9uXfsMpE+k6fOM1a5BFO8ptdM2h0U+CVERVLNpGDi1\njZQZjasR919Pc+JkVkyPE6UlsNtBCNK4UiE6mpSKwo3XfW3aYurekVK6vcV4TkjbUHD8gO6nFM+A\nkdTzK+g7ZpQ+nGE/M6SPIyrY6XDftQc0EtBCqHhG+zggbbs8O3bXFdszoQZFJsuPp21K+djuXPo2\nICJ6mmmf2KUGKLZhGS1VVmdajGpWcVGOIC184MpBKf/XhUekfPjw4cOHDx8+7hlvDJGKwlACsbdg\nfeFjD68kdiuDhN6009S9uZYleWhFUConmYQCBO2Q3nRzrHZVCbWllWakpfnMOUTZKxNr69q9Cce0\n+lKfvDBiYjX8egpbJXUd/Mxu3XVVIaE/AmSCylAFiEDI5ECsiGoCiZLkLtlYm7GnVUKPFdYARCog\nhdcEiEzZG2EzUEQuZpQEqxUicbetvtXTigjEakbd2gHXyysy9I+iakxENWK79dMOK7GqttW3kh0r\nIgS2vSPlvrr+Yto2mznl+5KV8uFrlnSKSNIKDsGrRV24j6RYvVyAsCk2JkegjxH5OoHXa8igGPG0\nApqmflAipApOgzLB+K9KJkyqii8VD6gANZFNrS+onH1qYy0D589A2KWlnn6uKIiIyMuXL7HNCNOK\niLCyeTLJbtg9noHkrqhSTONqxIBerUzFXqU7EiKMq59WGPB9qmOGCbhADui3SazuCSDik9dfhNV/\n3xjSpArH1ZZQotp9HlIByu2lc1ZYECn+eOUkC/aVjeeXQJa++uUn07YtENO4cWP4+qXJH+Snro25\n1HuNY52cGrF8qN18Mp+RKwNQ7IwQyUDbiVbk2hatSr3QsdQ9gRFJlTpgr1MBwh2zh57OpyQxkUDu\nJSPyfodzakHUvyEZ650it3RPqGTFERVF7IAq7ajYSPuf5Qy08GCkQp22cihe06BtCMFR+YMosOvq\nkTGo+XmC39Q0UasrAjXn9GxpOTsChfxx0MISUuLHjV3SfrNUn12EtPQ6J1BRhKrIU5FREZ7hGmg+\nxT2T0DwRQ5U/RsYgpcKGFukEsmuc0OGW0DSVP2mpoGmlpHBqk149awlNi4CONa2T9eDiAEWx24aJ\n5W6/uy0jV+7fvGAPQfX4ZDkHSPzQmGTJiteFR6R8+PDhw4cPHz7uGf5FyocPHz58+PDh457x5sjm\nQSBRTPpEg4PHK1KiTQcH1bZE2FZcNKKURYnfFERULgEzj/SuqGS/GCmukKA7JZiROPUE8Y2U2osA\ncbYE9wv21xC2mReAsVnFFXDwybFLi1zfkjovSN4xE9ZBMlZY1e3X/VvVlNqBxkZAebGxV30YMo3F\ntgB6SuNIaSSk76LI0ihV6/RpUtY4wd81aXEsQLbdkY5WC6JezebKKC6gbN+UZg0n+NzaWgnFCfXr\niBTsvjTT0ix1x2dtr6p2n++bV9O2cnTX0w02xobRpaBUW4VV5O0ciViMVEBHhHHdlrAqeOuOEVBR\nQBSotg2RZ3uY0Oqm0NpLSaYRpVbXG3cNnMbS1FYv1LBoTxKxlkyVuonYqzpSe6gSsz6WajvFlApR\nE+brayMx6z40Tef+zg7+FTHtoTRlHSuQWKEZNY6cnqvwHUvZFLgBON0evMbIWNOIA0Hy2ma3tzZ2\nzs8eYn+qWM3fR7ozuUvEZmK7IKVyCzVxd93u35fPfmTHeviOO1+6xx8/PcM5GXn45Nhte/bSpezO\niLBfwoQ4IBLvHGnW9ZXpbdUgqOfElC5BPD8g6ud6/9nlaEpjhAJ+zBa1oxLwbZxstm5M5AW5CKQ4\nZyJ7x6nqCFEKRukGlFLVVKJuOlvYnBShYOW2tPbaY/67JBX5GppuQUqEYWhGzYnErPT5jBwlZsu3\nRURku3Fp15tLm6eD1J1UNiMHCtAx9lQ8pRqZU8w3AAAgAElEQVREXChRqY4UEdUHEKBZvi1G+nhA\nyjCitOu+Uj0l0rbDvBf2TC1x112StlMSq7YbqX1j/jkgyuvYDjgtrhpg0L0iGomm6plao84KARfg\nBFq8cPd6TopTO08dM5SC7KH3l0b4XmjXpdSHOCFzeUyo3YGOI7TdRnt2KVE9pnTzRG9gHS3SSHtd\neETKhw8fPnz48OHjnvHGEKkwEBEiZ6coF+16W2lUlXsLTCMqyda3WZYpwAKv5hUB9hcHTNRWtVP1\nK6LVZ4c37pBQjTXIlgtbriWQX2jprXYiyNNrqap9R6TOOq0I4Pk2L8wb6+LmhYiINK1df5Kc4zyN\nxBwnC3xm57lD6Tot/mQQVQAmsjne/pOVI6yywm6Hky9iW9VvtlDWDchXDKjHdrRzktitdMeRFLBR\nHt61tHJPtb2JxJgcqp2Pg/VhjGKEkRCcyTuPVl/Sq4owXStWaU1vq9R279o2ooaKFBFD32S0glS1\nW9b6DuCrFxFKGIqWGtv3aizxR0IOe9xuQ2Sk1AErt3BCuvho7nsloXrzpVtNxzkRhrH6ZQL6Zu/6\nZ744m7bFKA9PMxvPV2v3PfXhYvmB1cohHZcXVmqvK2xGiXTbcmErfUVEBuq7ob9bkq2/6YDqaHm3\niJHNmTCeAPXiFaSiSUxsVyQ4YsLopApN7Y9dX128wj6IbF/Dw5GQlv3WjaE8IfkTkIIzIrFevXDj\nbkGI0Pb6c3eNRw+mbTtIJsyp1HxduTGQqYTKia3Wwyt3r+33Nk+kmfs8ohW5YCww0qWIXU3IdYbf\n5BkXj7h2Us85Rp9U9ZzlGtT4jVHSUdF0KrbQEvMgoj7WUnSSLpmU4oEqxoSqLtAXRyRrsbx1f89z\nQykutUyekYYEitmk7D+Dj988IaX8BIUUR18TEZHyoSFSnz7/gTvdwNpV0dGRxtUGDhwhuWfo/H9A\nbB5VRtsuUj370pVrp65nxwq9GFI7byB1Q8+zAG4LPJ+MqlRPkjiDzreEvkTq5BDR81Sfmb37rCGo\nu2vcfmtSFtf7L2aoUzM8BGc1KBrqcnaqcEUTHaFpLe6JMdBnKKnDixaAMdvdnSfVtUivGZnGxl+D\n648Lku5QT0hytIhCQ0VfFx6R8uHDhw8fPnz4uGe8MUQqjTJpCX0Y4H7OXmcN8txlR+Jj+uJIaJYK\notX09q1V/7y/EG/dmiPvSBogwht+R75Wmj+vtpTnXbi31SKjUmMANklsq3StZu0oz7uE2GEcuLfv\nLLIV/NHSrVKfv/whHR+cIir1DYGYpYl13XYHNI18hQKU52eRrTQbIBx16VZLaWCrsER5OAG3CdA3\nSuBPXBJaJW12bvWdENJSQopgGAkRQFlrlNP1YLkZiVsFzmaUi271molnhcYeSaRUxruCkOrFt6Ec\neQiuVUACo5ovHyFXMYyc53dtkWZ2/Ao+jXlOrvYonY6IU6ACbh2hZDHGbE1cugDIXgBn9JzK/4uV\nQxqWkckKVBWEM6msXsuP29au6+TY/TYkr7kI3IvNllCK3I1JRelY1mC3A+rIihxYmR4f3+WZxITm\nCeQ0Qlr96zGyjFACdFn0mjXdCsjpixef3/mMESkFWPj69VwikprQv89ODaVTccIc3oBNZW2jGhLl\n1lBN5Qu+eGXntHrgkOXtNYlkAv1h6QydbpvW7pOTB+6+v3j+fNpWoE57KRgLM1tBZxBOXK9tH1/g\nt+ckf7A6cuN/x4gIUMyEuHQhPNP2ld1PymFTj70DWQ2d2AgRVtCbeU7q9Skpj3/tMy4l198wmgw/\nNRWaJW/ACtIRPF4y9Uml8aecp5ogiR733X5naPpq5VD/+czGxOmxE/vV7MPtxjh1irS8uP5o2tbg\n+dQPhlqEmJ9CEp9UZc+Rrj8KCvyWUCKgs12l6B8JUic6rokPKCoNQJJAOifQ82+P/cbEuTQVS/K4\nzN0xUkJTA8xjLea4gJDmBtmXkF4nVBCTpR56UZ/Suzzg263dO7lCkCNzqN22DL6r48jcN4UwaUxC\naDWluUYzRyxm3UK6pCfU+yh292RBKBkjdq8Lj0j58OHDhw8fPnzcM/yLlA8fPnz48OHDxz3jjaX2\nYslkICXoQYlwRDpskfaoGkoPQMW7Y7Ix8MOAYHz17utIbXeROPg2h+dSyD58IOduiexeg9CWkSTA\ngFRdSmTfZKEwMqnDQvm8JQJeD+L1AkrNOamuj737+/zkS9O2m53zxOKScE1pJXStMeD5kN6LFYJO\nYioTVxgVat8n6VfsWuF/llHKaj6DrEFJvkpQXQ5i29Z3Dh5tOC2KNM5AfkUqtzAMXGKuCuhKbKdU\nDGDscbR2HQMoUI+U2ovmd67/9Pg9ERFZI40pItL2Sopn+XoUFMTuuhoqTc6RHuRxGoRKNqeSeKQP\nxwPCJsqPKY03wmstovLzGaQbMqTMciI4Qi1gKm8WEZmv3LU25FfXt5oCJkkGEK8HSvfK5Alo17iA\nArmmEziNq3urSIn7K+86BW5OmWm6oSL/ueMTl/pjFXM9bE5eZ4qoq0wCK9triolhfC024dSejqeG\nzn1M7ioWz5RQTe2k+1G/uIEUu9vK9ftIUieb0hFgB952CTcAUpaePObIAUHviZr8LOs9VNGpAKKY\nu3Rrc+PGa0ptWEFtu5jZ/BM3mm639i9RWJKxAjxK2DmNl6AUviWlbGU89DrH0v1qas8k9YG0ND9N\ntPy/p3Sr3ncByZlohx8UqWPbpDYvd9M4fF0zHU90X2vRSk/nPqJsZE2p7bNGixy4yMKllI+RWp/N\njbKhabzNzu7hWmU9yH90uXDp3h3NPwnSnAkVT+k9npH/6KCedKPOK3YNKYoDMklpmzvuLGG/vC2u\n3/q6f40DgvZimrF3Jjz5yPe2h0xDp/IXNIUOOJecikg6jImMpE7UlWKgcdrhOdXTfB7hORURzWIq\nWkGbHD5DVNaAnjWgsTDXPVT3DLpPQ5VRCW1M7Cp3P2cJ+XQmXv7Ahw8fPnz48OHjZxJvDJGaRbnE\n9GZ+0zlCX0ueRzHeiIfGyiBTCCiObIqnQSWhAtE74vpOpHQlVs/nVoZc4lxuyddJaycPBOlalF8T\ncpAVQERoNdvpdZAkw9W1E8xbQPYgiOwtN9A3YxJaUxG0ngiDEYhyUWL7jbBiy+ntP5kcyS0UiYjw\n9r++MYKrIggJCSgez93KbE6rv6ud+w2Lug0TKZ4QKfRTxNbpo5bfWh83DfoiVxE+IvbjPX8k/7sE\nbVyVNiaW6MeQynrnOPf25BenbT969b+7/REBfmhUJA7XQEib6lDyCnIogb5VRkCOA4hq0jJNScYd\njz+M2Y7G7rZy434Q9YsyBHWe3e3/FgjX+sYQkWMQi3t2K8e/jEju4Tv2+MnjaVuj9f9AGmpCdVRo\n8cG53Se2IqTrwtGePH5r2qIl2z05rSsQlGdElFdft0Yd38kFHqgbSy0EIEoPdAI9vB5VQFREpMVv\nGbnqcD/zb1X8MQaCcX1rpNcGBNiM5pUGROWBCLNhhAIQLuxQTzgSDlUw52hlcgbVHtIJjLChLfIz\nd0+WNzYnBWhP9bcUEYkw1gJhlBAILx2/AaE4pPFUoABmR96VAWCSFqh+TeintueM2lqlEKKUUHqg\nf4xSJ6GSmEkSQT3ZmCiNcZIAfWGhxRrjb0sTe1IscUxCDTAX7GieSJEBiEkm4/LSkcYfn7xjv4XY\n6emxa5uHKyv2aFp4eO5+PG1DnYp05LWnSEga2zkp2T0h9DEBAp+QSGtbYyzgOdWQIHCK8v+wpyIS\nyA+wTFADf9KGxIdVCiikAqgIY5YRqQQipjwXjxBbHnG/9uQJG4YooiICfICxlpAnYYB2v+leTNuG\n0SFBGaFZAbICTceEdswTexTlzEgSBW3ImYYSRUER3RMj5qmmo/E/w30ycpu4Y233z6Ztx6u7Hqwc\nHpHy4cOHDx8+fPi4Z/gXKR8+fPjw4cOHj3vGm/Pak0HYrmqYUgak8TDCVyggZdcW/k+kj6ME1JBI\njCE0e0YizCm3PAUEvToyyFa9uV7dkmI6YOymt3TLLHMwctvYO2iKtBTrTkyQMqVWekDlz199JiIi\nD84MHtV0R0gQb9cgPUQEfEH6jLWQikJ1VEhtGmTniODmGgQ8TTvttgZ7L47cdYVETk6gjp7NrK0b\npGzK3rRVAoWqAztWlAJSJR0n5fj1RKxsABmHUBGfHWgMKTmaNYNc+/REQN9Ujjx/unx32pZBe+Vk\nZWmsy+3X8O+/2DEACzcg2xakRK8k55RSi1nkyOFtzyq+7vORxm6YqIeZ9XFVq0+VpQWXkeo4YQzN\nTQuoKTXdZfvYbOBrlltfv4Iq99GRaUBVE1HaYOzTh44oXpPaeqJpaaS4VGlbxIjFKaVsIuQ769r6\neg7tr47aRFNAEaURlCBKXO9J+0uJ4kl6N2XPautKQO+JgK3nvlnbeM4zqEgT2blBqoKJ+kqAV6rA\ngojwL166duqF7j+kAAdKWc9w/5WUblkduTYpS0tZjTjnem/9r6mVhPznJo8/jKc5EctvNo6A3pRG\nzj1euXlnu7V7IkeKviXNnBW0x5rWxsRuC7cDmk/VR1C1eDh0THQdj3+3LeNrENdPOSnVq4ccz4kj\nUpQjF2+AqtDiPJoDxWy3D1b724C8X9O1NiA0d9R3W8x387md5xqp+hfXNiccn57i++5+Ol7aPfn4\nofPhe3FrtIjLly4F1A82/gZQOhLS0Qqhts0Or6rLFrPbQo5UlfqZkgFsvXdjd5ER3aBHupv8F3PQ\nDbj/m06pEuTUgbEbcU0EUltC/dniPHvoA3bUJykKO1I6pwDjhD0hUxRecKHOrn2OYzJRHNqCpHel\n6Wh9JoyjtdcwpXnvkujrhp5J6oZCen/qxRmRs4B6AIeUFl1vP5SfFB6R8uHDhw8fPnz4uGe8MURK\ngu6grFl9uJjX3cNDbSDCtvrpBPSmHQbqHM8HANktJVZs4N4wUzg550SwC4EM5DNDlSqsHEfymuvF\nrWDa1lb/TQ0VVfLriUBsDIiArKv5V7cOQWnJL2gO76g0sLf6GPvYVbb66Xt4o9ES4vgYK6aW/d+A\nyFGjKNlVHe5bggT3G0eEf3T+9rRNSbkFkThTECHbhkpd8VbP6IuuqntSJR+wIhuIqDiqTxzQipRX\nIToY2MMLSsF1Z6u0Ye9IwQ9OvkrXCpSOVkmn858TEZFdZYTiEoUM6qvHMKn6inEBQBA45C4Qch/X\nz1nZHNc9oxX5ogApfE99ApL5PHX77cnDKsbaNaLrV/8zlil4/MRJZlxeGUqocgLLY1tNl1pqT/IL\n7R5yFiBUj8LjxX2PEZEAq9nNhlzlsdJcrkwmRBGLgKUWFm4cban8fD53v1FE7oAIrsUWVJyQJEqO\ntv4vQFTl21+PcXJkCuwT6kXk5dtbN+4DoBoxee2tzl3bba+ohB3K8yxhUAJpPDp9ZMcCEhaR2r+W\npHP9wYBVb0/+h4vCnfPVhbuG2cquoVB1cpr/KkgdRIQ0aIHCjFDyauuuP0wJftCxSwTcLNX51LVo\nvLLxr4rlxczuKz0ndacQERmx+qfulwEoAiPMOo8Vc0Iup/vO/RORr2GHz1YkHbPY4VqTL6ZtRzi/\ndrBrVcIye9cpEvTxM0McjqB2vliCnE2EfZVnWS2sAGMGsvtN+ZntN8Hzp6c5Ge3as3QPbvf2AGGF\nejfaKaEipmYHsndC81+D9iS3i8VEcqdCoQAZnoNiGxQ2kHehToE9SQH1isTq/UnzZBZoAZChr4vC\n3TsBeZJqh54sjNhf4Px2pfWdts9AY1ybIAGqqSRxEfMT5AIMlQzSdwi3EYicWPvr+Mzo/tPMSszt\n3lER2mvCI1I+fPjw4cOHDx/3DP8i5cOHDx8+fPjwcc94Y6m9chhlJC2mBCmOODA4rwMsHhG02oG8\nzGkp1SVKSO9I/2QtjO3ewXNKWOsp7VUgtbJcWCpk3Tlj0oSg6E3tUgErIkXf3rpjHAdmfJkgfVKP\nRDZFakuRxav1x9Nn4ehI0R0ZHweT7hPB00Clj4/sWHECE2ZKGfU1COWxHb9SVWCYonbU1qpsvN8T\nYTd1+z1Qm4bCa0OpNdX9CAJL96lBZUgwahRD24eup2uR7kO6sSEScwKz1pFVlHEyTFiuO3fOm4rI\n8/kjnO9dUviD1TenbZ++/L47j8HB3gz7Bw1Uv4nYr4ajfW/9lEZI45JpZhBpqpL0XqDsnrBSfu3a\nLoHJ73JpaZwRar7kxS0B0lgrguKVWMuaTTFUefel9VMG7Z/dxmBqTRtd37i0IOupPHnidKFYxfx2\n7dLdKWmW6ed7IqrnhTvPjDSDcqRZX61f3Pmewv5Mdl+tXPp8TSTyAqnSjlIh+0ELUDhl5K6jJl0u\nlYOqm6tpm6pi75Cqu3hlad9EBxlRC4bWXevi2O6/eu9+W5HenWgBAqU21KCaVcm3a9wnpV3PBlpF\np2cufbQvqWAEOlZnVFiwR2qvrImCgDGzWpGOFtKtfD9rNr6j+3QHl4cOk42mDkVEchChSRxdBOMu\np0KJAYT2NOa0uJ4c3096ImQWDo0uNQoY6Z5Ug25OGc8X7n6aH1m6Lbr6kdsHmyGj8CYoqKABxOeR\n6Osffvx/i4jI8akrzohprjnKXYHSMrR7OENqr6ZnzQrPtpAcMFpNn1IBhrpdTLwLERkHnfdBhI6o\niCR3fXGztTT+InftPuPcNlKrq4x0nDr3944MisN0Jv9tzBJ3jT0RyjV91lfqUG3jup3oCHavaccu\nl5bG09Qz63gtg7dxjTZP7qEsvquYvD+dibsW6n8tjuhaIqD3+hkXKiFVndK7A/q9ozGplILugObw\nkzEnj0j58OHDhw8fPnzcM94cIlWPk5eZiEgOsnfL/nsT6YxQAixrWH6gV68dkglQddaAVaGBOux7\ntyJNa3obx0vyrCAV7RKrFVrV6qpmRyXsuorf1/aWXkDRdWBpa6xYVM1XCMHoIOtAfG1p8VrNKtIn\nyy+LiMj5mZX1CwiV695WjmXr3uZ7Xn1iNT/27rNlzm/wbijc7olYCwSBVwZKOkxo9T+2d1XUdZHC\niyT1eBqorHpSyEUbNi2RiEGEDWjPw7Q0IZSudX1S1kaOrBr3d0KrnzBU8qbt72T1VEREXt7+s9tH\naW04E4e+sUyHUoUTUvEd0XYRrVJVRT4h8nKkJbvsCRZoUQBI9LUdvwKhOh1pTKoUAxHbX3zhihHO\nzs6nbeod1pInVQ1iOSMiKjeiqMPbT9+dPpvPIfVAEhot2nqxMOkQVXRnrytFP1htnJXfLbCa1HJ1\nkitQ1fOM1PZvrl35f0iQpMo5nJ4aSlQBnenJu1ERu4HkTEqQ0lW9n2zIjORPG3UM8/jX1tnvDaaZ\n6zlTCbkyZss9qddDOkLlKkSstFtLtweSGgiBXOwIElJUL6V+VSmIhr63fOQQxu1LQwT7qSiHFeDd\n332Jwh66fv2L5S96VUInEvVs4cZOzP6T0+RG9+TkpzhtkiED6gCUrqO2iXHvxtT/IcZOQPdVXMAv\nriSiODwJD+Q/ADel5Im43ji05xMQ0E+XD6fP6hoyGXO7J7/26BdEROTVjSlhl7VDVR4uDGHWuWO7\nsevZN+450tcsHZLrhYnI4X2VAeHqqDS/xH2yeg1hP6PCihVkbOLa+kkRKeLzy4j7NBIbTz2ed61K\nZ/R2rBok/iCg5xmKcfr4pR1/7uYnldUREUng7nGc09yJ8dG1P7RjgDSuz/+6uivN0VHBhLY1FzvY\n+bHXqsrp2HhK8ewOCKVleYrXhUekfPjw4cOHDx8+7hn+RcqHDx8+fPjw4eOe8cZSe5tqlIyIbiof\nFJIWSojTW5ev7IdKjiR1XlXbbSrbZtkzu8QGaY5PLxwRscgMsm0aVf022C+F2umeNHs6pAX2W1IM\nBmH3Zm2ppSRx6riqcSUiUu6dVoZ5oJJ2h2h6yK6/xjVGZPz4lS85LSQ2fk1AUN/vTQulRDpA1bRF\nZOrtGLB00LHCtZoG23WVtYNnq8ZgTW3NgNJzIaDSmPSWItWqsqNLrCrWgR03BXl1ABGSMiYTATwc\n7fp7USVgMpFUdWQyTV5DK+rh/F07T1WMJsmQo5lLkdZIre4rIyJvAWfnBelY4Yqy2NJIg6ZxezKy\nDh2MncZEVMZv+5bHpPs3zVyqrFpbKkbTkikbfyJV9eLF57YtVKK2pQxeIn2zWFoK7vzc6U1VPafP\nXD8+euSItScnRhhWQvlmc0Pb0oPPRESqCvcO5aAzKItHIbediw0pcD96rARh1zcdmQFXMFlOqYhk\nvXbXGBMBXlN/rLatE8BI+xs1zUkw/nbt+jtT/TgyTR9jd+9ywYAKmtdkxqvsgYR06W6uPxURkZNT\n02WL0BfVNRkjoy9WZIxb1Wr47P6dLexev712Y7IhysLJzM0119fcTyiAoHZav3TzaEIqzprtaKmi\nQdPnquOTUtpPNb24ACSB0fSMNPhUsZ3TuRHGzEBt3ENZPaJ5NwxtvIscpt2ublx/lQM5UIB6MSdi\n92ru2mRVXk/bXm1cu/Mc37aq7WVtrGTjjz/9zyIi8vbjd6fPZuc/786XcmEPzhyh+uuP/6dp2w9f\n/Ce3r9BSe6coEJrndp9ejC4duA1t3tmBXjErdPzRvYY0akC6R+XWXX9LRREzzI98n8SgdhArQEKk\nhUOhZxx2c0BLQZFXiOKJitLTAc4vLawN68Zd4/7GrquHAnuSWRqvKNy4n5OjRAyaR1vZPDGMjr4Q\nYNwx3UDNoFs2Ep/Sc3axWhRzYCQeKnmeKTgonqJxOAQH/I474REpHz58+PDhw4ePe8YbQ6TaapCu\nIIIXyqQHZkePqqZKqw9dLZFibafKrqSsO5VukrK2IlI3N+7tdndmq5VggrCIsIdX847Yxi3I0R35\nyinJeUmeYMqZLmiVNMvcKul279CELCLVVyBn3WCr3xylq1wSH+EN+vjIkIMZSnJvN0ZUfvYF/J9o\npSEdCJhAi0I63+kSY2uvEoTtiIjV7eguLGqtTRItFz0oCgByRL5KM3h9hVQlO6FuuK6YVnoqJzAS\nYXoI1cOJEIlU1b6p1B2Lo31NpfNzdx3bC0I98efJ3KE1EfmlVSp/QX2SgHTYEHKlXodxYH2Sg3ie\nkPzCvnW/6cVQpzR2q/iqv0vOVkXjhMqfOwysJLR7R8nA6i8nItJhxXbESAdWeIywavufnzpkKDwo\nK0ZpPh0rS9QVwI6lxOOAiN2pklyZ7QkUZaR73IoH4NdF6FMDlETRFRErYY5Y6gDXX5H/nBaZ9K8h\njG63dp8ssIpWwm5IRQx9wzX+Liqgn2lEiJSO+55W/1Bgv70y5HBx7O7/6EA6AirWhJKZjAPuDUIV\nCiAIFclaKPG2IBX9Aavq7c6uYQHEqGX5ARB0e0KkmknuAK4DRITOIfsSE4l+UokYuXgG45RW/00F\npX4u9Ud7DqyADq83RYYiQgSL3N0Lr0htvsJ45nGVQs7maG73zm2yxXnY9SSp2zejmTrGBsDF//KD\n/2v67ASK5nNyoFC1+7ceWql/F0I6Ym/tGkH25PzcikIyOCV8/uKfrAG08AS+diMVJUUoAEp4aGau\nDa8bQyTnGH+MtPS418OY7gk4RXAZiCKRARH6g8llwX3GRTTHS9cmcUoq+g1kGqh46dWVeyblOaF0\nyAj0RIDXIo9ZZnPXrnRz54DSDpa16PAsGtjrM1UVcxprotIZhFxN2haEUqJoQe9hkZ/+ouQRKR8+\nfPjw4cOHj3uGf5Hy4cOHDx8+fPi4Z7y51F4zCgnmSr5ysDQrYavGg3RMNsYpE9lc4fuA4L4GJNqO\n4GZN4+ygt/Tq9uPps0XhYDzWk6igxcRq12pGKqQArcS6R6cGQQ8gFFd0/BxQpRKhI4LHswRmuKQZ\npKK0aWyptRhptiQyGLUGoTyi7pyrWelgMH6HFEwIUjibJqcwvGRyaAsyetcZZK7mvjnJ/apSfDta\nh8bJGY5lcGuA9/YFKSCH0IjpQBgMqU00PRsS2XSOAoWUFHuVT9/Upu2lHEPW7NEea0mxt5i7tsix\n3/jY4PnrmxH7NXg6XYKwSNzEfelSBjkRQMcR50kpKD3qEBrZOIjVcNO1Q0nGm33o+npHKt713sH3\nnJ55C9pPdWX9OYMuS0NpvOcv3HWcnpsCtJrPqkFnP3DKyl1PSlpcmna6uaE0AsjQ7CwQ63giAqiS\nvCMyV9Xzi6APxPvokdJVMimf757U2dNjlypIYk7LuevYbGxMqEYc1RpMxPejpdtvVVP7I7XXk7aV\npttbIpsn6NmBdGwWSCPvNra/zY0rAHjwwAjotzcYOzMbUAVU6ysUr7BmkwYXBagbgBZuiIgUSAFx\navf21rXFjrTSctxvIY1TTaWO2NZ2dq0V0ucFpXZC9N1I16+p14GdAvAT1Q4SEYnn7jxHvqEC1QDC\n9dDxC+hundK2zSs3roeKdAThRhFTUc4CBtnVQArwaozMOkEomlBS/rNn/zx99OGR0517793/YdqW\nZm4ny7mlgh6OrnhjX9j802E+TYg8vkBfryorXumRvsLUIDURq1VvjN0h1Nyd1bnXNe4PmpMizCMR\nzZ0l5q6Q0s1aeNNRGwfaPpjrj1ZPps9WC6dtOJKOVALKwi2lltXw+uUrM3d+dOzSnDG5h0z0DnI+\nyTK4d+DZ3ZGJ8Ch3nx06hpiWMGIb63JNBRWJfU8LJSLCmbyOlA8fPnz48OHDx88o3hgiVdUb2VD5\n/wKkQFai7vGWOptZueTVjXurD8nrR8veO1ZOhncZqxiPIK8XM/eme7M1hd8gdETQILLvN4N7+29b\neqvGSzcTsJWodrs1AvIZVhojl1UCWUshV8ClmQUI47PCVpoVPLcqQhU2G4eSrRZ2nutbt3ItWeoA\n5zSnFUkLKQZVM+6p+5XYycTGDUi5dU1v/zjljtC/AI3SECm/C905ZVRW3IG0GhXWx1GMslpd1ZJc\nhRYeBLQtkQT7tfZPsMLaVhf0W9dnIWyA4DUAACAASURBVPkJyohVamwr8iCATEWonn/kl7Vyq8TN\njspwUWygCKKISInS4I6UnZU9O3Y2TuPR/YZsGqUBmjlg5dRHto+ydee5yljZ3J1vnrMSNRBZlhpA\nYcHVhY3JUPuWVmlKGtf7juUKelwP+1XeXDo0bbWy62+Azpwck68gCNJM4tX7M6MVcT99rgrTtgpV\nKZSRiKAxPm8IJVLkin87oh9bundGoCgdrfADlDWron1B57YGZN4TqqJK/AFLUuQotiDC9mavJe5U\nko623m6tAGJ1Cj9LKq/ugWLH8OtkErVWhfB1qfwD36cqmbKjdlL0o1gYcnL50vVnV5OfHWQnFH1j\nvZAid78Nae7SAqB4RsUzOP5Iq3+Vs2FivU4oIyGxqsqtKAh7vk0EaCpAyTB24x0p0KM989TG6fmp\nu65tSIUSQHEDGuM15owYJPKAniv/zwf/KCIiKTlgfPnRe+4aqLBmDkRYkUERkWvcOw2h5KJK7SQF\npM2tcyMjqCEUxQN6rmhyJCBU56p2468n9GvZu/6J+Jmk8znNcXpvtXTvBor2dm5/M8hLiIisjt2z\nq2tYMRxo3sWn06YCnoA5OWrcbF0xRkoK7AmKNnrKZiQgfg9Af/vG7mEtSpmR/II+W3u6d3qM04DR\npUClc9jrEE4pPJ8H3At3wyNSPnz48OHDhw8f9wz/IuXDhw8fPnz48HHPeGOpvaHupQ+JxA2T0QWZ\nPCqMF61I92bvYLmyNMha1cNrIqD10ILIKI2kkGUEyJbVeXs1vgyJbAtYPjrQ0VFtE4Ii8fe2tNTS\nyYkzCCURX1EcVQ1vk5h0LwAdJqSmGhYutXR9a2aYLwYHlZaNwY5Br5o9Bq0GIJIPdIwI5L0AqaA4\npPQkSIRRRGTfmZL4aVuvaUFKmQBmD4iwp30RElE+gPZX3LOOicK4UKwdKe2Cdho6e9/vQJgMWG0c\nfR2S3s6udGaZBSkLDyBDRgTVB0jbJinMQEceV64v5vNH9n0dV5lB20Hktm32RmxOYMgdlQQjx3q+\n1p450tYd0qic4lksXbo7qKkoYebOKaFrbWEum1Fq5cXnn+NYpHdWun2fcqYIRO7N2p370YldVw2y\ndVmSyWrp0lJ5bsdiQrOGkszZIHYGojAXheg9qFpQnE7QiCk9oSmtGaU721I1i+7qg4WUqlSibkKp\nKi0GqHCNPaURlQA/UMpGjVHjhG5sKO9nuaVn6tLth8n+vd7/OadPkUYisq+myibiPaXCyr07/q4m\nc/dA9b6sTzQdy3QHNetmsv9kCM3m1kiH3ML4eLkkFwXMD3lO6WYocM+WVsTQD65P2HB90gqkPtEC\nGJYbi5C+6ysdO9ZfGzwnrqjY4AtoSm33ZFoeoN1TKgBAoUJE+kjbtWuLek/9joILLViIYupXENr/\n5YP/ZNcVuPv0ydlbtg+krzpy25iDjP78pZGtQ6ToczJBTis3J/Ql2obSUxmwj4YpC3qtZMYe4nu7\nxsaJpv4zmn8TTcFS+rRBSq0kXb5E9cMwTudLS5lqsclua8dq1DWD+n+OIoacuA0VTLWb1vozhsp5\nwu4VB0pXIk3NzzXQE2IbpxnaoqF0ez39bVSdiRZBQmaTkTwVVHhlcx8+fPjw4cOHj59RvDFEKs9i\nycjDqdyjhPehIRiRqmy39qa7XLq3yZpJrPCCYzkBJRnniSE8RQH0A2WdKa3MdNXHq9pYV07EM4si\nRWnojRjICYlSy6tb5+eXkSp1CsJcBGPBOGayN0qO6c1bPb4qWi1c3zq/vs/l42nbMnfIFas4K7H4\ngBQKUv4Isl3ICBaQgZ7UYSP8tiBic6uyE7T6VfXYsraS+BjkwX6wVeKANq4qWmFpyTZIpx2RY8NR\nCYYWFVY6OY0J7bKRVt/rnVuljgmTkqGeTu1eQb1dUPIajLSq76Dmm1jHzkHUDQNCJOE7uCOfvJtr\n109Vber5i8U59mttnGmpr/J6aeGlZO+M0IoYK63rG0M/iwKlxuS1pighl64/eQyvPS5/ByKWQB3+\n6sp8LVVF+/raCOtaiv/ooa2+5wu3j4HgB/WzGgjpUGSXAIGpxHoOtKCsuDgEZHciseZAUAZakZYg\nhTOJewn5AZZuuLhw13Y8IzRLfdpArK531jZawh/TSl8hFvYVVDeAlhSzC6A0C3I20NX3SA2QYo5h\nlGQ6N9yL7OF3dApl70tbaat6+cnKrmu7d9dxfGRoYQnUk1fXA+5xPrqiVDmU0vfUrglQUlZbT3CN\nvA8tReexPnGcCZFSiYORJC7UvUB9IHuSkNFd5ETiztVXcGdzTQOUsidPUCU053STjZN0gCG3ww5K\n2RPZnd023LldXFih0vvR/yEiIlnyv9o5Ne5Y7GuoWRJGH3c7+HnmdozTIyctsI70eWXnpl6vWWzf\n7zCHsEzG9NhjsjXQp4j8X/V5w3Nngrl429r+whTkfYxrJcKLiAShosrTJtkpwktyKnHkxiK7XcSx\nZiIMzdeiDX4+6xAYe32uMbEcfq0VIdKYuzJSxe9adwweT02piDAXr7l2Yj9LdtJ4XXhEyocPHz58\n+PDh457xxhCpxSyQikouE+Sh96TSeXrm3n6zwFCdonCrr5xWf6UKRhKXIIKPHYv0aYl3jXzsSEKH\nU16WkIYUfmk1oR+6cIxolaoePxEJRwbw4hoDFqmET5xyANiZHpyjYWD0ByX0JP65rdzqsO1sv1tw\nc/KZvekfzx7hGOQcDk9AkxpgrAc+fCELaGK1SgsyQ6e4hNQdI46I84ZrS1MqXQdvi0VHdbWnKF1E\niJCWZA/Ur1pq3weG/liZNokpwhNwV5GonLjjFjGvZlwbbHcOrRhb+0w5B+FIIqE4REEcIend56dH\nD6dNzy9+7I5PJekl+BKruR0Di1SZo3Q3TW21GKKcuSXPtzW4TB1JcuTgnjSENM2AurStjcmjlSs/\nvt3ayn21gvgjyvWrve1XqYFcwq5IS0C8QeWNLJaG3H3xhUPkVrRNOYpahi9ipe7KjWK3+puNO88Z\nlZrv4adXFIZIbPeujTviI6lgZE+r6kXq2vjm1lC3DMjZFm0cE/dEhTi1DF7EUKq2s3ZSLlOYkiAo\nkLaE+DWLY3f8qrT2VwSYGRgJEItRfepY1BXyC2cPbKzdAEXsCE2egcOkIpwiIkfw2utGu0+6SrlM\njCaCrwlEIDkmr0/8y+hThK0No8TgerJ3o4puhsSvihSd6G2eEvDQ1Ap0uyPuTavZBOK5gbfFD7ME\nk1bHWqYpBBkJidcfhYRcSaxjB3MHoTrKXwtpTv7hxx+KiEheGPr3zXf/R3e+JNxaA01iTzjBuKfb\neWr3HDxLFjCuA4i0kviowjUd+0QCCabbaRKVZES+h/xDT2Re1dXMaDy3QJYyiKmygKmKWhL1UEa0\nMUv8rDdO/iElfqmOOn6eKtdXeXbuElViRn31bP7NIB3RESKHR6eE7N0a6G/p3Bs3FhvaX6jfY4mV\nu9TNg/CIlA8fPnz48OHDxz3Dv0j58OHDhw8fPnzcM95Yai9L5lKTiniA1NbVpWGcx4DC44BVX+GX\nRTIFi5WDdrc3BgFrGi9JmFjnPs8A8TcE+yuxtetT+r77eyDCXIWS1DwzyDCGengUEYwNaDUnxLpu\nXApClZ1rSg9sSvfFglRfk8FtY0mEFrIH+8p+W4BZWGR2nkq2ffTgW9O2D37oSnY/v3ZE+FQMztTU\nCqdscpQOszqvpgc4FaCq3PFAxPrOpRRCylloNenAfn4NCJVKqCfF+gZSBA2pLqvUQLkmrzeQbGdE\nCk8AXzdUFDCC+N63rJ6N/W3dWNT0p4hInoGIHVoqbti6/WXUJ6qAm1Ja+ASl4B+9/PG0ra6h4kxw\n92oGRf8Aqb0jSmNdQ52dPLSUjF/kVn6ssHwxt3NK0WfHubXJDikl9iT88Q9Btj52BQunZ+fTZzc3\njijPqd0MxOKSSNmrhUtpDAOl25FmnM+JvI8+TohEqqTldpImoXsIwH9CRSk3N+7c0xXJhOC+C3pK\nC8Knjguogxj9RGXVk1Iy5AQ47aMpu+BguXlX2bwDHSEimYa8cOmwgQirSigvqCqlh6J3SHmRRgn4\nyG1tb21MLiEPM1tauicAsXt3ZR6OIUi8SzqWzhkR+WQWaNucnBpur12/NyBgq2+giMjRyo2PmiVE\nMP5iGmst6BYRFftMBR2syp3j88jGc4e+iGuQyFtSAkdK7ZIU+yvMD1wok4Bk3JLETovzHEniRQR9\nx5IUa9AHOhChqWBD1UQCKmwKcE7//MF/nrapOs4JKYBrai0h/CJDmr9pmJQPSkXjjpGKtY0g9TwE\nTAFBKoq8U1ucO9dTKWuEKR1abCBEpu7Uu47Td7i3e1BKusHu/27UogC7rnnq5rWzhc0nL26dJEvZ\nEKUHxVsJyQlp4VN74FSCIi/IBKV0v/TwxxxIwqBF+q6ImbCuqvi0W3yvJjkhvT0GIvQPBzSYu+ER\nKR8+fPjw4cOHj3vGG0Ok4mCQZWZvobutE1A8y43YeH0NsunCVhBF5lZiaUZlrQPQp6W9parDN5uK\nhyCN9p1KGBiq06D89oCcpiTj0VZVe6y0SyIRr4CI5IRSqO9XHJEnF1bOSpwbCC25wtv6OZWmClaL\nIfv8YFkRDCTgh9XEcm4l6auZc+SeJYYS/duf+1UREbn5PyEh0VtZcwdyeEWsxx5SB7OCiXiuQWdz\nWyVVQBMyaux94xCjIuNadxDwexJEA4rQYWXeEelUyfgD+ZUFQKkY1Wogp5CQSGiEFUtAJFItxW9a\n8trDUkIJ1Q35FTYVroFc7ROQXLve0M/jpRuzBZX15iBlnhSE8Ny6VXQcW7sXqZb9u2MsF4aWZIW7\nntu1SShkIMpviDB+duLQrxndTypOGNNq+urSkZIZ4VmtIGYLEvdua+NaQ6UJRAyxZMKqrtb2jFLB\ni68iQU719WNPQC280PZfr61t9BhM2FUhyMNSb9fuuzWR7QEFr2+t7c6BcF9SkUuKFfESIoEbEnrU\n85wREbZWRJqKDbrOndMQkNclhn1C7VSDDH9A1AXqExNyk8Xunq2wci+oOEFFUvtLIpYDdTp+8GTa\nppIFIS2/I4yPrrZ2ur5xY5zJ2wv03Q4SClVJJP65O18Wvx0VTSCUJkpUVuFuX7Mkhp7eGFFRispp\nwBNzFRv6NtzifGkM73vMyVQ81GFM9iQ/0KnA8pzv5wT7s3l/NnN/X1059LNtrL+0oGEYiB0eaPGO\ntdPHP/qBO/7TL0/bjiEdIeQrp9mWJaF+uz2QYPX/bAktQ/FQRwhK16JfW9tWY04OC9umno3NeJfE\nzR6jHSQRYoKzYqDiTaMCmjaGmg5CtxlJF6EpGCU/BbI/EolcRaGZlK4VXR0VY6mwrspqsDffFkK7\nPRVRhJgfYvJVVNkFnk8GeBeOLaNP2AeJWbNkwuvCI1I+fPjw4cOHDx/3DP8i5cOHDx8+fPjwcc94\nY6m9bVvLjHzYVMW0bY0I20IddiACeJw42Hc+IwVwQJqcWtkqzBvYtnnhUiC76hX+NXiyRWqJUxa6\nO86saXquJYGSEjBikrGHFnQvCO1XAugALRJWca6QZlu25us2wuMuIGJhnAAeJf+zFkTFQKydFgX2\nQ3DnCFj47QdfERGRj1788/SZ+h9VFXuIIWVB2lYKhSZErB3Rxgz3zibypm1LJl8vO0QgDtq+uQU8\n23N6BGkfIqeGMT4fDIpV36UiIQI2COAxMaVVq6olmLYHRK7Zu57SA5OycmdjMgChP6ZmyqAiv5pZ\nCmaGlFEuXIAw3LnGsnH9/vTx27gWG2zqIRWnlp69uXTpwfMT0xFSSeGLy5d2XSCehjR2ZtBeKma2\nvx00mJTsuaE0oqYWWPep7rUowa5LCeKcljs+RjqGEHstaGD/O9U+MuievPGQbuT0mBLVaxqnFZS9\nKWMmI8ixc0rBXVw4NfiTUyMA3750PpbXOzgr0PeVsMwpk2n/BwR8105ty9o60BsixaUZNLh2O2un\nDEUxFXmH5idQm26R2icibo05I6NUXIn05WxlKTAVEGItnGNoT21emo5WeOrO/dknH03bqplriwKp\nWPYL3G2gWVfYmCiWaE8qFFDy/EAaTEpeHkkXTpAqjeg8xxQFRRgbTCJXp4rH1Idb3E9XG9JsQpfN\nUvvtBlNh31n7r+CPOI6kVTWHptnGXc/1jRHbB/RJRNSKASm9fGlpJHW+eHllvno5FOq5KCnGOE2J\n7D9gnrzBuJaAH9NwwKDU4ggP03EgYr8S1um3AQprqCZjcg1grbg+eF0a61AXinUMr67dNT48+5Jd\nA+afjIqyjlD4VVPxgHoCBuTooefC5HHFfNSpI6AKkADP1YTcAQLMyQM9//RWDLl4C9pnCT3k00yL\nTIiUTx6srwuPSPnw4cOHDx8+fNwz3hgiFabBgefWCcqFbwlpUZFnXv0pUbwgNEsXQiO7OkOpNC6M\nvJ4DzVG13Q25hZd4Mw2IbK6KuQtSVt7l7k283xsi1WNVsSPF4g4SC2PDZHP3bxThrZpU1wNc5M3W\nVj8rlAan5BdU4K1+T4hQi7L6ly/M/+nn33Urgq6z1WyE0m4lTMa0hI+BKuUzLhgHEZHkAtTjrCvp\nTR/Xw25EHcpvw8Te6lv0XUQK5PMliMyhK6HffGEruB6ok6rEi4g0aPYoZhIlSOlETuy1TJ1K8hPI\nTgwpkdfRPjVWLoy0hCjrDciHq6xUAdu+dw3F3iMqUw5Q2JDQ/hKspg+8zka3v83G9d2jxTemz5Tj\nnOaGCCiakpOsR4/z29F4Vk+snIjiKrvA5c8xbp4WDcv9FYJYXpP8xBxK6HzvqnTIkhzhtRigmLHX\nXIXfEnlXFc1B+mWkczdgLqDvK5rLRSQxvO6akjzJBnWQt++lIKpvLm/oex3O3aF0Y0vSHCrJwL5y\nUHseCVbtgQhwSfzkNUgr4hboTJyw/IKLOaEZKvHw9IlDKb94+Xz67GTl5rPthtE/d+77yrY1IIg3\nJJOiqvBHR4bmaNn9EamXa5vs8NuE2loLCw5QJSUHU6FKEsGTkuvvVTKClu89yujHHUG8kD8IQV4O\nF6YYnmKO3xL6qUjHV8+tsOMTFGPcio2/GYjnazrPEHIuyyXJ3qB4Z4c5/uqaJFlKeL1Fd5Xgj+Z2\nnmaPaN9TV4LTYysKSFGMNBLqluJBkadKLKcCKIwxShJIiPGXUOZGJQRybn887lNCOMfCXWPNINSg\nmRN2hYCfLe5P7muFnV9efzJtmaGIZqBinwIP9Dinm1LtbFMbFCXmMX1OulCJAzxrIp5XQRints4w\n1x5YWAKd60ebJ4IE8z7tL0owJ9EzRrzXng8fPnz48OHDx88m/IuUDx8+fPjw4cPHPeONpfb6YZAg\nJYVpkC5PKRVRg+TLSrzj4FJmQ0dQKFJfNcHoynEsEtIlAmStsHNHRMgBkG0QGWQcpQ72zYnD9+jc\nbXt1ZVB0r6rgncF/NVIElIGZ4FFNrTA6HkN3qKwNMk1DEIEpFaY6Rim1XQ2tnvXWfvvRJ05l9+HZ\nV+0a9XJDEFZzItBBqyRLKWUBku2hYixMfikFq3DvSKrQmtoQMvyN8b2ctMJikM3zU7dtt7dz+vEX\nH+H7BkWrBk1AKroR5NOblhVzdRwRKR6QNXENRWKVKoaKMkmxL9HxGcHOOQjYZUepCEDrN7dG9k5z\n158djdNhUA0eguAB6V/duNTeIrdigzkGT0D6UDUUu/u9EeA1ZkQif+edd0VE5LPPntlvQWx/QKTQ\nIyhlP3/m0keTAbSIdCDxsjq9pnZmM9KA27lzykgBX41+mahsJFIbY5pKVX0ylhFXrZiGDFong2Ai\ncffQhUmJlL69dum7oTdSrBLg9z0p5eO4msab53YNGm3LRFN3fmyaPCKpTVlcaeGawGlMS0fc1VZi\nc1/d99WVS/MvqIhhC0X/k1NLY6n2VUHaSgOcHwJWFsexmOx+dubSfE1F+mGq7aZtTP3VIT3PSRcl\ntAdkRjzNcZRaDyJNgZIrhCqvl6xt5Nqux1wYkGZQhGPElJ6SEXqDVOxzHkDHbG1zYonU/yw1Un4q\n7p7JFjZ3h9AN1EKMkwekLYZ0V0puB+dnjsSf0/UL2j8msnUN7cGLS0vVPjhy93tA43mALlMAVfb+\ngLIC43caV5PRNz0nA8yTIc3JmirLaEyMmqIkU15Vnq9p7oqCQ01D1gILAndOOyqe6lpoplF6LAGV\nJSVl+RhzHDsAxBj/O5rjmvbm4LrZ5DiCaBWbdgueNey2MKUniVqRxuooYm2SJA3Ol8e9/MTwiJQP\nHz58+PDhw8c9440hUp0EEsX0Zoy31eXKCKvlpCJOpalYYSbklxXDf6ij18IKq+kZlclHWDKqN1ld\n2UozTO6qqSphNirsdXSJVZ8hHiK7HeQMdlSS36qKNhEwR5RVgsw2ULl0CFIkE8DzDOgblaPmIOrF\ne+u6AghPeWsrpw8+ctIGa1JxHsStGMJQyeG0CkiB9MR3icA9aThsUC67oDVpCzXsJCMVZSXsDdZP\nYepWkRkpFWexWxGPuMYnj74yfXa1ceXqbUuohhIhiW0cZ6q2TX0dqXQErf5BSkzYkyrW8lusIKmE\neybqYXgXkSoKW8GqoHEb2oqsvHGr/pqKF9QnKxjvkpLr3v32YmcFAzJ3K91stNVvGLo2bDvylcTq\n/Mnjp9O250Ciqr0Rqx8/fCwiIiP5VH38I+e7+ODcqeK/vLTjf+29XxARketrWxmeA4mak9r2FTze\nCMySCP3DfpZaks2rWfXEVCI8K9ZnhftMfRBFbPWdMtKiis2ENJ0cOaTjk09s9T/HCnO1MuTu8tKh\nGSGO22U2XrU4gREZJbsyIqP/y+i3bePmqTBgNN39m9IYU1+9kuYpRWCbTv0/6b5CX2/3dl9nkC7Z\nr21M6D3eU1sr6sXtdHXhUNTTE0O4Pn/h2qzI1FeTETmUyzOqiEuMEhsTiioFNHdpUQITewegeUFm\nRG31XY1x7j0V8Uju+i49O5s2NZB/2FFRiEp3zKitb3DZcUAyJSnQJFJAj5eu7dQbtSG5kixwbRwN\nJv/w4NzNvymlHxQ5GkbOnLj9XlyY1ITgvufiEUWRukElTMh/FSz2jjQMFOE98HrVuT3kLA1cIcgB\nQhHeIaPvqZJ7x9Ixbt8R5i5GkHpIARUkIdDjOc3FO6oQn2YsyQDyPj28w8iN8Twhj0V9LmpGgtCn\nxRx+qSUr22/xL8lv4NmRkkySktxzyjrEKLiJSTqDmuy14REpHz58+PDhw4ePe4Z/kfLhw4cPHz58\n+LhnvLHUnvSh1EQEKwCtdgQjp4DKn18ZiTeAgMZsQWagQCVbUh9tkUbp6Rgj3hv3IDGOdPmKVJNg\n+QTtzgdijCvsmJKKLdKBGZGya8CDFSm2DjinCTIltrmSrhdErI5B4ksTIrYG7gSPSbNkB7PUqLXf\nrrcO7n7WGdk4gdHzYuWgzXlORHxRg2JKO8FQU02BRUR2UGDejaxZAriZjJwThZtjO/cOZPzN1tSz\ni1NHtmxbNYi2Dnh05NJNn778dNqWgZTPAiEJdISKgq5HCcLUd5FqkFAKsEK6U5Du6tnkVTmcwqko\n1+7xaPvNkUZqdhfTtnpw19ixYjUuMSOy7YgCCT3Wq8uPp8+OoB4dCaWxcY0JHV+1xS4u7fhKFF+s\nLAXy8TOXxjs9NbK5ppKDEHpKM0utX2N/GRGwM2ggffHcUmZffvddERG5urI0YgaCfEzEUtWRIlFo\nERQNaEovT20MqxL6nFLGDdK8yxmZ/CLdsbmxFNCIioJTUvu+XbvrOT6y+3S1cv3Z93cVnkXV8Snd\nHmHc1eSKkOdI84xMI8D5UUGJ/nZPelfaTmpGLCKyWKiAnmp8kfEzqAU9pbsbjPViaWOihfZQQAa1\nEegTHaVAdtCWamiePDl1bbbbIj3JaXTo4yWUMlOWPad7AhQAjZ1dl6pi91RsoUa/UW7zzqgafVDA\njniehsZQSppdK/TxFaWR1xs3Fi+pXTUtGlMBSop5nPjHkiUuVbecuflsNbPnT/akwKmRkXeu+6fi\nKUwZNQk0KUUhTS0FtoFuYBnfbRMt2IiJHtBPhUq2jw4PLcq2SoeUKhdlhRgLPVEF1NKB96eOGilR\nMKJE7w+Q2CllnMLFImiIgN67MVPSMyFFMUrfGFUgxr0b05hQJfswIw3AFgUd0CIribKgOnoj6b01\nFWgURO3Q/mEjY03pj0QpGEBL6YkCEghPWnfDI1I+fPjw4cOHDx/3jDeGSMVhIe2eSNwPHImQlVC1\nFLqubVu1BkpEK2f1ogpoRajeOXsqiVcCXlnBVy/isnoQ+0jBtK0O1VRFRIJpBW9vqAXeqnNqzhwo\nFS20plVfC9Xh6mC15FY6p0BhRKyEWgJb/aZYsZ8ckzcQ2uyEFNi7Z1AAJmXjGuR2LSHlzp8fu9+m\nRLrssMJISB46AyK4L61dYyBXpHUuqaIoO1sRL7Fy3O7snIpUVydQwj0gx+J3JImhvmcpMZvDGF5r\ndEGxKgCTT5+qMbcdlc6PqlQMwjJBkhVQqoT8Ghdo/yI2RDCCdEIRGmFXUc+BiNJanh+x/56qp2NJ\nnJBcR1m5VVeUGbE1xmq2r21MruF/VhABPEafXYMILmJK8ayUXwUoyYYP3bvvENn/1hHmQ0JkFJ3g\ncu148lWbNk1k9M3GUCKVM2DQJ0anNUB/DqQu9uqDSIggVp1NY/2k4yQgRLqG7EIQ2jZdkbZESk6A\nsAS4hpjIsZMig52uzOZu3lHJERGRGvIMaUoorZaJE0qjyE1MatMVxsl8bmP88tKhmW89dgrYF0Qs\nX3fuuEfU1zV8AseA5k60cUQyKWv0J/sP1nt3/JbmyS3mWJVhCInErIr5rBitfo4j3YD68UgOFIoO\nRIRwjSrxQaTsUQtegOYw+qZIa0sE/FDc9R/RPLHDPBHdWl83KgUwEClaYRzanxL0ZyDA53S/SIr9\nhdZek/yL8PNEBzl54mF88hjvR5lqxwAAIABJREFU4Cdb13aeOu9H8BVl+RW9d1iRQ+dplvvX8cyF\nQj2eSSWN3RxFW+FB9QT6iRAeBSCzApkj2q86PzQHjg0owKrZV9TNMTN2W9i4dp+TdIuS5wMiyseQ\nm1A0KSP09frazXEJne8IFFt9aEXoeT9wNsPtt2mtUEOATvGzIAkpK/Wa8IiUDx8+fPjw4cPHPcO/\nSPnw4cOHDx8+fNwz3lhqL0lEjkgLKgI8W6RkfAqy5SI1wmwXf+x+HxiMriTOkJTKC5Bc45SVmvE5\nINOGoFAgwUJelJKCRdiVlEaKkGbhV1DAmPHIWhhI2ZAxsaYbVW6qZu0MkFNTuv6z43dFROR6/cNp\n29A7qLigNF6OdFA0EIkQJ/jhJx9MW7a1S7OonkdDRMh5r8q1RKydDG05teNOPididwtTW9YnUp2T\nltJiqlrOOiafvfxYRESOFg9xTgTxAyrOidjaTyrqto8M6RnKQMiANF5OattV664/ILXfrlUFYvf/\nOCciOrRgQlIxj0GALQKDfScCMLE9c4ydLY2JGOlgJs8qkVnNbdPU0nibrSO55rmlDIvMQeAhDcCq\n2+D7RuJUlL3tDbJ+64lL29W1fW8H0uZbT50C/qsLI6wfqbYQEVFV5fvJEzNeffnyJc7dUhZqkMvk\nbU0VsCq4GiIrJ5TJoUoE7ohsrVo5I6XMVIsqpNRSCQPfjtIYi+URPrN0S4zzm8NcuSODZtWUS1mx\nGuMqowKQGiTetiFdOpxnRPdThfRNnDCxV90OiBSLuUv1uQ7TaFDWJ2LvbO7OvS6NApBhflDCuIhp\n+lxevLLrxz3bUapUk/QVtNVS6q8K5OHV+dv2ddyTIc11/R6adUSsHucgoJMGkrbT2LPeG+ZOGKiH\nnfVrA02zDTlA3EArryHdoyJ3bZKSQfQO2l6c7t+DZrBYkDEx5sUEadHF6uH0WQdz5T6zdtV7bOyI\nbC+qd0epfcwtccxq29BFCsiEHKnFqlEtKrHvY/5lcnSj2of0TIyVosHEfjzkxoxSgEjLthUXZemY\nJE1DUGlUF6zgFHzrjlFXlsbX1KPSaEREUsyTHemYJbHSLewald6RZ0Z2H6HbOIJa0tX2XJnP3N+s\nmdbu3DgJ+ZmEYqecxqm+AjB9p93BmJw00LqA74+74REpHz58+PDhw4ePe8YbQ6TmeSAr8hBL4JfD\nb7o3W7ctIw8tRWxCWumqAndHr7X6VpsRSqWryAZqqjWtwgZ9WyfSY4DVZzAQqtW6t9+MyM77BiW5\nVEKcYKXVEwVbzyQGeTAlxewCq9SQasMTqFgX+em07Xr7oYiIRJ2109HCIXZja+d0DlmBF6RU3Sdu\n5aREwYFK6KsdVoFExCzQPyzOrQrUjVBZP/qMyeaCFXbfW99ttg796ENbzY0D1OuxWoyFFauhRE5F\nAYG++1NZ/USKpFWFqjGz15ISiYPA9jcDKVHbZDG3VdhmDW88QpWG7RfuvA04kgSq+M3AysruX/aE\nS0BaZfKqKvvWgjah1aoSNpvRVnpd6MZwQihFD4RtILXfPZCI83NbTav8xyvy+nr80Kmh34CwuVgY\n+qvE8oQUu2dAbq6vr6ZtF1cOxfo3/+bfTtuuLt2K8GhpCGuP6+lJKbrFPauuAyMVOwTRXUkCRUxj\ngmkUm+AVtBK/h5pWxCgnr0liJcbqtIQkQULHV9SLj6+SDAnPSUB/GJHSwpeAFbMjRYJtRT4rHJpa\nVYSwZoc+hTEdq1b/OUL/eqA+A5lIblDkMT9A/9z9N5vZ4L25ccT2orDf1pAYUOA4JPR3QBFHR2zn\ndOXkAug2kQDzGRcljLhnB0KJApi8DSQnoaj3iKKQgyKGB64Yp7+1IoprEIprQhACoDNRRvNEr0iL\n7W/yiZxbH5e1O5cWKt4xef1pwQTPKzUKAFq6/xQ5YZ9Svf9shhVJ8Axg71QtUBghScLnq+hsQAhe\njGPU9PyLdMxQn4yYobkAQmU31FdVRGSPv0OxsXMMLrgWzPD1t0B62tY6qsZzl2UVFFqNSepB4DZQ\n0niKRjxPSKbgZOFcGVbLB+4cdzYnbiEhsSttTlrA47CLbL8VnjHsqKLX3VMB0h5SCxJZhicJfjLm\n5BEpHz58+PDhw4ePe4Z/kfLhw4cPHz58+LhnvLHU3mqZS0Ygp2rrxBHB+PibBGMn00bVieEod2Ru\nC8BflXjd/pSU52DJjJS9a6QFgpGJoFBxpfMMkXpqW9uWQltlfUmK3ZMsNpH9kCpUWHQYGXZ0qZCu\nM3Kiop1MzlPCap4xORPJjcGg2DnUkR+cGSm4vQQpE8abLZk2N0pKpPRIAN2pjAiLKdqwZPJdp7pc\nNpx6VawmaD8EoXIk9fAWqb2qgvExQcER4NQ4sPSQGp7GAadbUWxAuiOaR+16Mo1O7iowZ0gVz2Yw\ndCXNmgGpgJbUySsQS2+2RsrukY7tqZ+C16SqCuinhAGPJ7dvVSKOQtoH4P7La1NWzk8cxh5QylS1\nzTi1sFy49A2bcPcb18YnJw+mbbdrR8puAXEfHbFqkp673ZPPP3dpwS99ycjGIa71xQtLGT565FKG\ntzekdp7qvWNHUIK+Gi8L6wkh3coFEAHunZrukw6K6culpayuLt1xV0em99VMjgYWqtmjysoNpecK\npM+YMhCEarxq4y+BCWtAxNYG9+l2a3pDswKm3ZSWK0HK5xTYFmmLAoryrC1WgPj+6qWNiRppyYzS\nWKqfU9N4UrPmyxeW7l9gnGwoVZKlOk6VCE1FMbEqq1v7T8TqlbW13CJVR3PcgLaN53b9WqAy0H0X\nNe4eC1D5U+2sOKIv3XGT0O7hIxhpM19+g/n58YNH07b9Z+4aS0oj9pgf1qTVNcApYuxdH3ekDt41\nIOdXVNiANGpIRSw2xbCRLxS7A0qVz/U+tVGp5us6/gN6TEfY30j6SBnuIU4PdpgLB9IW0/ERk97T\niLzpjjQNIXMoy9zup0oLNFQzLLTGVnPllsSoVBU8I2L7AqTwkO5AvbaIfptCM2qeW999+a2fFxGR\nNcZVRhSIaIlCpZY1u6DsTs+pGUQdY65KUvcCunekc3831Mb9gZrc3fCIlA8fPnz48OHDxz3jjSFS\nxTyWiMrq1Wtru7bVh77Vk2Dp9PdAb7Wqsn11YwREfcHsxVbEp7qI1BUuvQVHKE1nNdUBvm9Vwyq2\nbjXBSqdpBqQlsVLz7d79TVx3ibGy3OMteBhtZXaLVdeTh/ZWvSsd6tH0jFKh/JYIiLcoe88DQ986\nrDCWCyLstW5Fui/VG8/OV1fQrPq7TNz3Y1qRBliRxqQEO8CvbhhJWR3SDhGxovMU5ayBrXS6BCgZ\nCPshUdaVsBsSgtUNW5ymoW8DSKENldqOk08Skc1X7riqxOx+DDQL17XMSf7hyG3bxdb+u41r15CI\niHusXLqRjo9+GglNUC8o9i5LAiWqgxzPhGWMMVbn3ZVuPKeRSSKoTEhC6KuWR0eHtfM4d1q59tgf\nZCLy3Mb1buf6+IxUtI+P3Oe7va3qj44d2Zh99RIgsQGjSTj+Qak9bvJsruXN5A2mw47kJ9K5KjFT\nH4LY3JF0hkoxNCSd0EJlmZErnQOGyWvPxpqivxGtRnOQ7XsqP+8w7jLy39QS/0hYEsK12Wpp30sS\nTEpEaI8xPtWbUPtBxBCk+cL65OKVQ5gWvfWdqkOXe0OaVFF6sSKy+bXr/4L8FNW0LdTJllbwiua2\njNwCVQsrzhK4vztqzxio60jZhE4RgQXdzxsUeaDf8xMrgNhcu+u53RixuMR9F8fUrziXJKbCAmQp\nopTcHga3v+2ekHNI18QR+rqiEvq9O8+KXDmqvSKtNq5HFJ7w+I+AehYkcaJSC3FC0imFO2e1RKxJ\nriMC6s9FEeoxGxw8J4EmcoZH56eBfBpr96MtOVDMZ++IiMjx4p1p24PTYxzLPWuu1h9Nn1WQU+no\n2aH+e+yKEUPqIKL5V9uHAE7JVy5LMKd2Ws7dfBf2rr++uLD5Z712Y7glX0dVhRdCE0XHHz0TVCak\np0KFmXoRVlQowUaGrwmPSPnw4cOHDx8+fNwz3pzXXhweeJg1W/fmvN6QWBx4CENnb7Vz8JEG4oio\nON16Z2+NKXKo+9oQqSB3b6kRVvrsqj2VOjOCgTL5mtAX6PzJcmb52wGCkbO5+QXdgsPRs69T5b5X\nQrhN5RVERHqUs768+GTatsjh1xbb27d6TdWM5o3u87r/fNrWNsg90zJlrs7t4CXsKFdet24fHSEC\nA0RSg9BWvw2uK3qN/2BNTt/D6FYVMfEBVDgzJtXTEShWrR5qrfHMFJxqB3KQ1xJq4j6FEHZlUbW9\nCgIGzHlxq8nVsXE5SiAWKv7Z0wpOVReKwa5hOXP5eALzpITvXcMwIVbkPYmJ7tHGq9SQAy0ZVt5Y\nL8Q9geN6QH5l13vXx3lhqzUt649YkmKm6JsdPwFvbntlq3lFTs4eufJi9Y1zxwWngvlA6KeqtFXt\nfOna9elbxsf77LNPRUTk5MyQs/UNkFNC6ZT/1GFJmhKqs9u5m60gQcwBq/6MYGpFApvGOiVBX+92\ndq0hSq33FaM0rp1yIDID+fCp/1mW3+U0MaqoTvfdSP5/oZakk+jt7Bj7JR4cdpOT/EDbjDg3d9yb\nGzvfy1dOfuPs7PG07RTCqZu1zXXKkVOxVhGRBjygq621k857jGak4KulkDoIhFEFnTsJfdM5s7T9\ndkD/kpPjaZsiVwEhrOnKoU3Djd33gcqDqDcjrfdXj9x1bT+1uabaut9uOpsnB6BJw2Dfy3I8J2p7\nxvSj+7za3c1OxMhOjI09a7bwhrtak4dfC04TzeeKCIchPWKBHC1m5FMZ6XPH5rMOqMsISZSIZACG\nWn39aKyNmBNZV2HAPE18xBrzYxsxmuX6oihM/PLtx++JiMj50ZenbSt42x0tXH+Gyf8yffbjZ/8k\nIiI/eP4fpm25Xg/xBlUSoqfBVjcqBE3yE8gwvXVu3NgSCPhXn35dREQCylJcXv3YXQuJ74YQTI0T\n4lzqWKc5scXzTOcGEZEYz2LmHAr7WL4mPCLlw4cPHz58+PBxz/ipL1Lf+c535NGjR/KLv/iL07Y/\n/MM/lLffflu+9a1vybe+9S3527/92+mzP/7jP5b33ntPvvGNb8jf/d3f/WzO2ocPHz58+PDh4/8D\n8VNTe7/zO78jv//7vy+//du/PW0LgkC++93vyne/+92D777//vvyV3/1V/L+++/LZ599Jr/2a78m\nH3zwwQRzczRde6AYXOPvPcG+mkbQ1JGIyAwE6H6wdMc8VzkBI5vXKO1v2P/olYNbFwvAzuQDFUze\nWHSSgPjazoiQO6jeSmCQeQFPqIR0GlYrB72rmreIyACoOO7hw7azz1IQ8DZEolRFYy5hjyNNNxIU\nmbq2224MHleyd55YykAVZaPYwbMH9LlaCfi2dRB3PS31Uw9YtCcYW9McTGxuUVbbU7pLy04TVraF\nanyC0vDbziDrPnSpgoFSwDnSgz2RCHuQJ9nDbPI6E0uBtbXb1u2pPZFGaPYu7RRkdnxNbaSZpWxD\nnO+YEgEXKbU6tL5TlXUmz2uBREkyBT1yhKrAG2fUKyCCS2iw/x6l201iqZ0Y5MjFwqBwTTM3JcsE\nuOPvqCT/7MylKpX0yeTgkyO4CHC/oiSbvR41tff555ZaXoDQ3ZV27jOkCLeUWjo/c6r9O/il5UR6\n1nLugIjde6T74hmpDiPd21O+tVi6lNFu5DSy4HpoPCvxHQrnXOo+R2n6bkv3OtKMTDbXlHJH2xYg\ndkeUglTyOCuVT2XalJbW6y6RiuNqbT279a2NtZWSc+fW/7fo45iI8nqt6isoIrKGJEZCB+lAJFdv\ntJhSsSrrEbCzgCp/UxorzkHLoHzTmLk+Y/mTEYRyLn8PVB8jvlucMSK1++iRyW+UaLuyskKlqwqF\nOq1tGwI3Fvc7SguiZH6/5wIIkPILlRrhMYQ0Ms8/+DOgeUofea/z/9xT8YBAbifOSvqeyk6orAC7\nI7i/t0Tt0IIFLpSqUIwRkpxIBPmZiFS8NY0ZEn1gQIUUe2I+eOCI50uMnRuSZJnDeePB8q1pW1s+\nc+fEUjdIQYYHMj0uLZ3SfXINl4WQHC0iPFvVPaCIbR85zv2aUrsj5tMgtNzmqHSL9jVpWZZuEJVE\nkDu//dfipyJSv/qrvyonJyd3tvNLkMbf/M3fyLe//W1JkkTeffdd+frXvy7/+I//+NMO4cOHDx8+\nfPjw8f/LuDfZ/E//9E/lL/7iL+SXfumX5E/+5E/k+PhYPv/8c/mVX/mV6Ttvv/22fPbZZ6/9fT82\nIgG/Qbt/B3oL7QMV8LI3yBXKIEsqTTyaY+UU/8B+i5UQcadlv4XXT+LeXAMi8en7cEwviPMExEYW\nqYT44nZtq5rjpVu5zJaGXGgpbkirlFfwaUuxwotpFZABrQj5hOEc3rfWTVmmb+JUwquIFa1cSnVH\nH8klG4TWNHLnduANhVfqjnzIQpTr9j0TsEHKJeRoelt/zcs1b1LvsIgRSpUfAKE9TowIvq/cym1O\nS4MRxO8sszapcC7DgYcaCJhEtlSn+5HE9GotfwfSNnC5LKQbophL8kFYpEKFCKhCOjJKoT8g6Qac\np6I6bptDWEIQywdefcJrKyD5hRDnVNZEmEaZ8IwI6JPDPZHn9xDfXFLpvJbkB0AJj1a0aFKUhOQH\nIrRhFNt51rUSxQ1pzOHPd3tpKPEcKBZLHHSTJ6L7d0YEzxqyAhHBxA1Qp4H6XwVzWRAxCFRMl9FH\n1z4NFY/kmfvteuOQixmtwnW8hrRa1gVkzygx7mce17e3INaTnIQKaw4HpdTuN3sqP49iN04U/TqQ\nkBj03OwaVImC0Ty9jusrQ6lzILa7yo6l5P6W6s8VAVKiNJ+tCqgKy3ooeZfnSfh0joT+hRgfTF7v\nkTkYaIxNXGQgaD2hCiHafUMFA5FmFhpr/7p18/Nub4hUCq/VVAy5e3Xp5slrEnMu0CYZ4KT0AG7A\n2A2tvSbZDyrrV08+RmTUO7MlhKvCGA9J4kaJ573KhfRMdEa/U1FGi/lnSYj8PHb3eJSyxJD6vxLZ\nutEsBXnNVU5O4/LG5okM93MaOjFf9ah05wfkjISLBRkTSohM7ZQWVqgVYr4fyKe0wBzwyfP3p21P\nnziBX0WO5nO7r85O3D5utnZdmjkR8lXVsduTdEcPIdSqvzvHhiSJMIl5/ytxL7L57/3e78mPfvQj\n+f73vy9PnjyRP/iDP/hXv8uTgA8fPnz48OHDx39PcS9E6uFDc5T/3d/9Xfn1X/91ERF5+vSpfPrp\np9Nnz549k6dPn752H//h330qgbgy1KdfOZOT87PXfs+HDx8+fPjw4eP/zXj14528+jHsisK72RaO\ne71IPX/+XJ48cZoxf/3Xfz1V9P3Gb/yG/NZv/ZZ897vflc8++0w+/PBD+eVf/uXX7uN//t+eSkck\nbuncqRyfWGqnBdwfjpQygAZPQ6kltXhLyOtthC5EMNhvA6SgUqhiB6SOHEdIOzVE+gQ8vSJPviRy\nqY9Na15r12v3QniyJMgS53K2spfOTefIwzsowSakxZIDWk1J9buHPlYxt/NMEpAII/ttDZh/IM0O\n9bgjCyepALemUO/OqP2HACkeUidXv6RxNOAyULJfamnM7R7EQtbWUZIr5fa2e5da2lNa6mjuiO+B\nwteDHSsLHQRfZKZF08FDa6S+y0LVUTEIfF+6lEY7rKdtEqpWEF0PyJglGqojEn2duN/OCoN1c6T0\n9gRjtyAZL2emmbRcuXF6RZpNHTStmtp+qz596jV24HQHjZmYUkYJ0ozDQWoJ6RlKY+1uHRm0IRVp\n1TuiDIzkGbwGWdlarwtptKOF9fUGukxJRvpgSKM2tZE9b0DoTA8KTVzb5VTkoenODON6t7VU1B6E\n6cdnlm7U1OpA16rX2FKbBKUbYwERuwOkmYKDaQ/pFozrgcj+qiLN6TlNafRkGKgpTdWdEhHJQTa/\nurR5YgkvuvhAWwdFBlyAgXPSO2c2IwqCqv1TyrhWRXFSdtYufnBiaaz1Dimj1vqkRAqsIK2wbCL2\nunQHe5KmaM+AtIBUsT8aOWWrPqn22wHXyOn+MHV9xt51WjwzIlXG3nAV7pMtKbbfbHGPUbFPhblz\nX1ubLDHv55HNJ7uNS2OVtbXJPHdtluDZEZHuXQIfvJiuoUPaJyT/zyiFd6YQKV/7jlKAI7zgOnru\ndOj5QRNGKX2GJk5jG+tPlm5cPSTF/hy+cjXlZTfQfrsmVfYWPpI7und1nq5La2MBKXtz63QOAzqn\n568+FBGRm9pAlBmuK6JiAx2TQ2/HUo28kTTwAmg/PXv+T9O2y+tvuOteQUeLiggiPHd6ola0oAMl\n4d13jJ7SeF2rqvik1QUtubfeXclb72Luiyp5/98bTeG/jZ/6IvXtb39b/uEf/kEuLi7knXfekT/6\noz+Sv//7v5fvf//7EgSBfOUrX5E///M/FxGRb37zm/Kbv/mb8s1vflPiOJY/+7M/86k9Hz58+PDh\nw8d/t/FTX6T+8i//8s6273znO//q97/3ve/J9773vZ964Lq5mkppRURikONyXulCFbsjFeUEq7+U\n3rR3a+c6n82pJB/s9aEh/zOsBHoopSdEmAuwmghiJtZCLmFmaccCsgtxSeqsg1v97ypDH06XXxIR\nkRk5aD86/qqIiHz0/D/i+LaCTRKgL1TWOQB9YAkBAdIW0+pL7dnahoiNkB9QJXQRkYW6g3c5ro+u\nFY7X4QFh3e0joRVhjCETEEo4zx06uSsNTRgCKMaKreYVMCi3tiKJBKs+JYJTuXoUqlu3XWuGAoBd\nZWRbJa9GhPCt4Ml2S5DcGlIUY0Hlv7gOJSK2hDRMbTfaCmYAobOuCblEHxeprXTnQOySM9t2e+NW\nv9veVnqKhGy3QL/mtIJFHzPpMcH4jFIq4R0hobAn9Amr37wwwmgJhEdRKBGRFVaziqqN5E331hOH\npn7x3GQNFLBbPiACOPqi3Bn6tzwCskyKwMpTHgm5qLbuOo6PHep0cWFl1SorcntLqteYMypSYJ+O\nSchZj+tXVFnEZAUYYekbdakHEb5ncrDrm57Wglr+PraE/u1cf2opuYhIiPF8cvpg2vbq0l0bK7sv\nUE7OXl7RhKxjpU0o7YS60HkWMx0nJBMA6GJPauMZULKAkLseF1fvyTsTh8iAxJU7m+tmMzd2IvI6\nDbVgg1ByRe4Gup8F/odCThWKxCY0TrVCZMzCg/+LiMSp+trZNVzBp7TPbK6ZYc5uCX1Tr79ZRp6U\nICWPdD0qRRHgX74ElaJPGOlEQU/EkhBKVCf0KYfaekyP3RbEcnZFqOHK0OH5ENXWr8vISQ28TZmb\nr527TIj6YLofuf5/tbb7WSUbeip22eq9u7V7t0db9NTuR83Hbh8btD+hSl/c/IvbR2Pz2oD7LiX0\nOcS1BoEhzKuFe3bs6RkfwAGiyO0e/+jT/+L2i0dx09iztgGqxl6jWiDF42QEOlYk9oyNJp9Ce551\n6LMg5AKIn0wn98rmPnz48OHDhw8f9wz/IuXDhw8fPnz48HHPeGOmxWEwk7oxImCag0RKxp8d4M7Z\nzCD7o4VLN+Qzg+w+AiktmxsUFyfut13JpFT3bw+183Ek0itSgVlKmkVIC80oPVCEC/zW9puJg1tv\nN0YsfXr+CyIiEtD3zpdP8T2XilxvX0yfRSCPRgnD2O64FSkmK+eMjRdFoLbckeEt0l0HRo4rpFRw\nTllEaR+FagNrwxCs5Lq1fsqgQRQQAT3BfuYFqSODsBiFREAFyXsgtrMq0KeZatxYimF1pDo6dKXQ\nAtqKQbsRdKa4KGGZO+ibYdyb9cfu+JFdTwmyc43BURFhWMneIWnBqAL6SCTKBinQs4WZfKrIdkxC\nKu88cmagr15+NG2r9i5tFWFN01XWXhuoFx+fcmoXKZPcoPVycPvII4PMZ0iVlDv73sOHDkYfqO8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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR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\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\nAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10d0961d0>"
+        "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
        ]
       }
      ],
@@ -260,7 +260,7 @@
        "output_type": "display_data",
        "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly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\nRK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13612df90>"
+        "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
        ]
       }
      ],
@@ -286,9 +286,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+aB+3BWsBGl82mzUZH7AZvHbwGViUZrOp7ANMHZxRGUin09oWsEFWButsNuuZ\njTZt2mS2z82/xJncLUBL7evri9QPFGmxDGDeOsF1B+A9ABPW/Py8tsEtoOvCDZlfj3mFHZNFoqwD\n7jc/P6+mKyCdTutz8byjR4+aZleYHDH2PC/YCzt37vQYqVKppGxXr8z1RpydLVcG/I02jY+PeznN\nduzYoXuEz0ewbMgM3mg0zLWM8YWpsFAoeLmvzp496zncr66uRhy8AYwlHNXfeOMNL9iEWXL8HvtI\npM1iccZydkvBGPBZgusrlUrHuofnC4uV6QaLKUf7EOSwsLDgBS+w9QbPGx4eVtaOzwusZfxbLBa9\nM9pyc7H6Zpkt2RWI74M14b7X4hAYqYCAgICAgICADeI3hpGyJEfL1s5aAidYFGk7pYq0Jf2lpSWV\nLDlZmsuOZDIZveZDH/qQiEQTGcb5daRSKS85JDM17Azn9s2yRfPnrFG7DFMikdBnMJsBTR5arQtr\nfF2wn5M1D1a2dvd6EYk4vrsa9datW1WLgU8BI45xYwYBY8BsFMagr6/PrI1m1WSMGw92RnS12Hw+\nbyZkdbXxwcFB/QxjUSqVdO1A287n85HM9yIt5gQaOvq+e/fuiL8MnsU1IEWi/n+WrxV8QVhTdn2v\n3LEAkxMX0MAh2xgz9kFhsB+USGt8wJi4jHIc4mo7Yv9zpm+wVJ0YKdwPzA8nf3TTpbhw7/n5z39e\n/vEf/9H7HbR24KqrrpIXXnhBRKKJaqG1c5JJ1MZ75ZVXRCTqu4NxttIcrCfhKcYIe8oKQWewzyfa\n4Gb3ZszMzGiVAjBrJ0+e9Pwh2ZcK87B3716dV7YQwG8JY3bxxRfr/uK0IHiHXH755SLSqjcIdgKM\nIvtr8dpxHd9HR0cjcyPSGiuXfarX6xGnepHWHLn1/ETaZ8fy8vJ7xkgBlmN5J79Odw9ns1mdp5//\n/OciEh03C2Db+XcYe4sd67bu4mBVq+jEjm/kORdckIorV2Klx8e/HIXhOqAy2PsfLwUu6QKUSiUV\nPCBAbdmyRR1yOVeV63DYzRQUl3mVYRUq5rT8LPThvrg3H5Y4RM6njEavBSLT6bTXPzZ/AuzIjHtP\nTU1pnhcUWj116pQ3/4lEQnbt2iUi7Tlk0y5Hp2Fs8Jm1KTh/Fdpi5XHh6D42g0IYQTs7OYLje/w7\nODioa5ZNd66peHh42Mu5tbS05JVMKhaL+rLG7/r6+vR3LAThRYWXfr1e12cgE/rk5KTeDxFTDI5g\nhACHw9AghTISAAAgAElEQVQyrwwNDem4YW9NTU1p3/FiqNfrXoROo9GIlO8RsSPwdu/eHVmrWCeu\nGUfEdijt5kCNciZYn+fOnVMBCoKFJUhxQAue9cUvftEUpAA4mH/mM5+Rp59+OvIdOypjTQ8MDKip\n6d577xWR1hrjTOV4PudTAuJyWVnXxkV08lnpRueK2AIUC1cQWr70pS9pf1xhj82TWDtvvPGGzgfW\nPRQJtEukdabD5ARBipV2ZK7/0pe+JN/5znciz2UlhoUwRBLDXG6ND+fS4vMPAhSE1MXFRd2jUIA4\nu/dFF11k7rF3A27pFG6rSG8Rf2tra15pt+XlZdM5H8CYdhK20AbsHy4Aze4tvZZF64VAYLhuE3EI\npr2AgICAgICAgA3igjFSQ0NDMjc3ZzqWu5popVLxiuBWKhWVdrkgqotGo+ExHCxhQ9qdmZlRqfnQ\noUMiEq1RxSHC0PRxLTuRMvAZNL9Go+FlhLaKOM7MzHg5W7iWETS14eFhZZ34+d0caHtBJ+c8l4Vh\nLSwuz8za2ppXB23z5s2aw4jnxApxhWYGrZLNrmzetK61qHWrfS66aWLdnDNxPTSbbDarGi3GiNkA\njC1ncgfbMTo6qusD5oV33nlHtXSMLTsRMyNp1dP6+Mc/LiJtrf3cuXPmmsG9Yf546qmntF24n8Vq\nNJtNXRNgdDjsHiagd955R9kbhLXzOQBwxnzOLcWM0HXXXScibXMZa9nMaEDLtEyTyAEElkJE5LHH\nHhOR1ryB8YgzjzHjjb9ff/11M9UJcM8994hIO1M2wxrf8fFx1dDBNOXzeW+9z8/PayoGdtaGGQ33\nzufz3nM4516cZn7u3Dk1EXPQgXUmYO54brA+/+d//kdEWuvFNZMxMPZ9fX16HuO+PLZvv/22iIhc\nffXVsm/fPhGxaw9ibX/3u9/Vzzg/lWUlwdkFcDoKa22gv7t379Z2oa3ValXfPzxmmMNTp06pgz3Q\nyQnaDXKyrAtsxuv03oyD61KSz+c9xlzEZ6JSqZTucQ7GwPmA9clmN3bCX281Cey3bgEVVjUQqz8d\nr+/5lwEBAQEBAQEBARFcMEZqbm4u4udkJbLEd2yjZr8Z1zmvU00+NzUB+yfx/aAdwC+Cf8eOm9Bs\nuKo7qqZD02CJnzOqci0+7rdI1NcCY8BO8dByIGXPzs6afg6WBmlleGWNxnUotOzInZzzeslsnkql\nPG2XHTcBa8zHxsZUy7Xm12qTpdXFIZFIxGabZkZyvekneCyxpq1EgZiDYrGo6x1t2rRpk/bTYpx4\nLl2fg2w269WPHBkZ0TpfSDo5Pz+v/h6MO+64IzIGTz31lLaVAzwAMMVjY2PaXw5Xxlyib8yCoR+L\ni4seA5LNZiPJQ/E7Hl/sL7SB783sk8sIZrNZLwEgg7V2+AJZrEOcT8hXv/pVOXDggIjYPmhIg+Cm\nTRAROXjwoJ5LwMLCggZpoG9LS0vqawU/sRMnTqgfHM6pXbt2eT6Ua2trXhCOSJvdA+Nk+RNy7Utm\nbzDOnDYAa8JaO5yAFOvDWicAr3UOl//EJz4hIiIPPfSQiLSc05k9QTsxT88//7yItMb52WefFZF2\nHbyLLrrICwhAnxm5XE6uuOIKEWklZEVbXEf7VCrl+RPlcjnti+uILtJary5D18nXp5fzzvKHymQy\nHovViXV3z9zl5WW9DywiBw8e1Hck5o794Zi9Q9/YeuMybtZ+s975fG7wOwnzgD5ymp711KW1cEGd\nzSuVikeZrq6uepvZitDiPE34XSqVinWgtFLEA/wC50Xk0tDLy8sRAUqktQkgQPHzXafLVCqlmwUv\nz0ql4rXVKn/Cgh5/Z5nRXDOoiB9FJxLdiOstbcJlG3px3isWi2pawUbjnCIwLx0/ftzLGcYCFwub\nfDi7iBsXK7BBxF8zHD2DcbPGyQoOYHCuEuvwdcvQTE9P61hhDhcXFzuWDuE2zM/PKz0eNy9ra2ve\nyxzzwti7d6+OC/8eAgPWMb9ceY25+avK5XKskynaPj8/rwIN/k0mk56S5QaOvPjiiyISnUuOfHOB\nPo2NjekatF7YENDy+bypAABYH6Ojo9oXPm+wVt2SRyIif/7nfy4itmnPiryrVqsavYZnzM7OqkmK\nHe4x/riPlZeom6ka/eF8Yww3dxLvVThKX3fddXLkyBFtA4B9iM84Qg/rkl0ZODgF4BczlAP+DO3B\n2hgbG4uc6yIizz77rFx99dUi0o4M5AoScfnJ5ubmZM+ePSLSFqTcfmIsYEqEgMZrDgJUsViMCOTW\nmFtwzzk+n3APPhusChwWuFwVB0GJRM3uWCePPvpo7P0wr/v379f3pxW4wWPu9i2urJoLdx647BoH\nk7nyQS+544JpLyAgICAgICBgg0g0e40FfDcf+v/mpmg0GqaTFxDHLrEGzA6cXIdOpLOWBY2atVm3\nkGmpVPJYgP7+ftVemDlxn8cmIIZL6Vo0uYVcLqfPsEKJLbYllUqZKRX4e5HOLAszAZ2eiz6I2IWM\nuT0Yc4wfjyW3AWHA0AyWl5d1fUATnZqaUg2Kn4U1g2s7rSs3X4nFPrF5ltcpBxmIRM04nE8MvwN7\nl0qlVFvDMzZv3qzt59QE7jiWSiVTc8Qz0L5uWiVYvImJCTWxgcXB2IiIOiefPHnSzMMFx1fk8HHN\nTujveplOTkcQl4PGQrPZVPMTtxlzgnVlmS+z2aymTsC48LyCeRscHDRTK7g4dOiQPPPMMyJi76+7\n7rpLREQeeOAB77vh4WE5ePCgiLTHtVwue2t5165dOk84p44cOaL9xP49duyYOsijLQMDAzouYAN6\n3d/pdFoZNThcp9PpSJZuEdvkXiwWdQ+jyDAD63jHjh2eOY1rRmKdZ7NZ7/2wbds2bRfnh8JY4Qw+\nfPiwV/BYxHaQtxhE4KMf/aiIiPzkJz/Rz+LYcrRbpD1WfCZhLkdGRvRdxH3k9856na8tYMyz2aze\nh+fOTQFjpbyxMDAwoIwgu+b0msMsLicc5ojfn/yui2OsGK7TPO8xt7KKFYAFBEYqICAgICAgIGCD\nuGA+Uq4zmVVrD1K4pdky+2HV22Hp2bJvQ9u0HCOB+fl51UTw+3K5rNdAKi6Xy177mOHg73Aftqtb\njI7LFnViIfA518Cz0jwAvUrrzWbTZDfAWLDPkMt6WX3nMQcWFxeVQYAWVqlUzPpufI3bdoxfIpGI\n9XMC2H8tLmVDo9HQ/vLzrASwVlg7PmPfPMwrvltZWdFnW/OBuVpYWDDDz+Mc7eHfNzc3p+uYWUGX\nbU0mk3LNNddE2meFAOdyOZ1rTlDoYiNacq8sVCcm29IYXcbP0ixrtZr68cQ5ns7Pz8euGYar7TLA\njjB7AoyPj8stt9wiIqKJOa02DQwMqLM5zpOrr75a+4G9MDw8rHON8T1z5oxMTEyISNuniZN1MuLO\nXv4Nn2ki0cChOJ9KHgMk/zx+/LjndF0ul5XNwmcrKyue5YKd2MG2bt++XV566SUREfnd3/1dEWk5\nz7us2IEDB/TeYFNmZmY6snUibSbq5ptvlkceeSTyHVe94HQ4bvoY9jFi9guMb5zPby/AucP149xU\nPPwMq+4rO2kDmPOtW7dqW+G4v7Cw4PWzUCjomY99yeufgzXi/LAsdp6rT1gWGPbxQn/c85P90Hqt\nuSpygZ3NO5XlcA+qRCKhm8UVHETaC4oFLvxrRYGJ+C++/v7+SCkCkdamt6hc16Q4NDTkFdis1Wre\nAcTtY+dCtJGdjvE7jhbjwo8AtxVtizPZdaQmnRdTKpXS9nBkE/62BBQee/fZpVLJNOO5meO5yDQO\nsrW1NT2crZIlcTlCrO/YodCig7EJrUhD/p4zAltUN5sIRaLlInDYrydXSdzBiftyuRp8JtI2SWD/\n1Go1r2+NRkOFXYy3dbjmcjndo1Y0U6+Iy46fSqV0f/GL0o3ydZUs7GHOHO8K8Dt27NDDmwuf43fW\nOPOLwCoy7mJyclJfiJaAglxGbhZykVY0MNYW8mKx2QhIp9Ma+HLxxReLiMgll1yi++anP/2piIjs\n2bPHdPBHP2C2nJycNM8MrFW+1uqzeyawIIXzrlQqaZs53x3GivcDBBouTQPncphhjx8/7gl2R48e\nlZtuuklE2iY7XscYl9XV1UjZMJFW8AHm9corrxSRlgM/1lVcniiYckXaJr2xsTFdvzChrqyseKa9\n1dVVz7TH2eKtXIXsLM3ntlteLJFIxObQsxQD6z0Bs3sqldJ5wjlRqVS84JVisRipHCHSWkOuUDoy\nMuKVe2NCAJ9ls1n9jIkDSxHFGcjveatyhTsePAYWMdAJwbQXEBAQEBAQELBBXFBGiiVM1nDYkVAk\nKk1CW+ACqyyRujXsmKHhYoWuBmyFbKdSKXXShFaZTqe1XazlQap3TV/c5nq9HnHsQ1tcZiCZTEZM\nP7jWvV+j0fAyW6+srEQ0EcDK8GqZU9l50WXFrJQTVqFLroOH+zEbw86o0IaZaXK1Z8sUMzIyEmHh\nRFpzw/l0AFezqFarkSzi/CyMh4hNEfNYseZnZcjGtay9cjHt9QIaIbcVz8C8cD4Xzq/m3sNKpQB2\nU6SdjmJ5eVm1YvT3oosu0nnj7N+WiToOLsvMYC0Qe5U12U7maYw19uOOHTs8TZmz7KNP+Xze2yO8\nJnHfoaEhz9Rhrc9Tp05pziYLP/7xj7UtLmq1WiQtgwuYBSuVio4NzJapVErz3MFRfXl52awpyLVH\nRWy2Ip/P6xnIhcVdpndoaCjCnoi0AhHARGF8uF4as/IYX5gZr7rqKmXtMEd9fX36N5jQ/fv3mwXP\nkYmec/5hftHO22+/XX72s59p+0VaDAueARNVJpNRVoyzqLvMytzcnNxwww0iIvLEE0+ISItVwxxi\nnLkeJr+HXMf3ZrOpOcGs3GJW9n+LKeTqDvwOBPh9i/bwesZexLmTTCZN1xOX2ep2xrkm906IK2TM\n7xpev64Z0qqvytdyYJVb+7QXx/rASAUEBAQEBAQEbBAXLP2Bi7gaVBa4unqc3TqTyXhhudVq1bQB\nQ6N268m537khogMDAz3VtbPYG/bN4me5DFwn/xg3RNStl2UlZXOdM5PJpBdGa0nw7zas5Gfng07h\n9szIiNiJ3UTamjT6a/lX5fN5XUeWfxMn34T/hZtBuhe4+6Gvr0/vB2f8arWqv2N/LbAP6M/Zs2c9\ndsoK7d+1a5c6+wIvvPCCav/wYzlw4IAytJwKAIwWxuW9WjedgBBlkbZfzaFDhzwHYJG2fw47w+Ia\nMEmcFgIoFotebU8+iyxgD4+MjJg+URZuvvlmEWmzAE8++aR+9+Uvf1lEWqyWmxH61ltv1ZB/JIV8\n++23Y7Ox816wWEUrRQCA/XPgwAH9nn2frCoQcWCfT+taa98i1QDWKZ8pnIwV88BMI9Y+xnl1dTXW\naZ79teJenWDxJicn1ccMzy+Xy7pH2VfTOn8wHv39/Zq2g/0D3T1msaPWucjvsfMRAdixH/dDn6za\njblcLvZ85cAh9wzkBM5Wm3HGWVYDKwlzIpGIrdTBVqO1tbXY9AcXzLQHYcHN/p3L5bzOsbMcm5kA\nLp3ivpjr9brn6W85UouIZ2bkPB1x9CILURxZxfdBW1yUy2WvyDCPi+UMzePiOj6vrKzEZsDN5XJe\nO6wM6FZbWRDEc3O5XOzYMNw+sYkt7louVsnz7poruLgxm7zcvvA6sSIg49q8srJiOqBbOc/ifsfU\nszuv2WzWUzbYGZWzbLv9YPMHvuMDg80RWHc4rOv1ur6Y8RLhArRYny+//LLp9G9FJL6b6CR4W9GM\nmPPJyUlTSXPLRVWrVRUAETlkYWlpSV++EJC7FUTFfPTitArAtMdrA87PWBssoGOsOSM8BMJyudwx\nIs99hrUPMUZW6RKABTQ+DyHcWHOAXGSIMhSJjpGrgGQyGdM0DcduK6M61sHc3FzE3CsSLdLNZ6Ab\nicrnAkcGxkWYYn+USiVdV2w655e5SPTM57FEPztVanAFC+tF361IO8alE8HgghUHnCfJZNJ771jP\ntfrBFQdYOcUzrMoRbJp3A4c69cEya7rjx++49UTtBdNeQEBAQEBAQMAGccEYKdSxcx32OkmBLlPC\nDtlcg879zDIvNBqNSEg/fu+a0ZgFslglzvjqFj+06vjwfdhpG1oRJPq1tTVPmues3Vzw2KUw+/r6\nTE3JCnHltkD6Z2neldaZSnZzfXGfeMyZbuUQfZGWdh+XawjMQKVS0eeBSmZmyNLG4nJlNZvNWHMw\n1gTnyOLQf7foarVaNdkX6zM3fYdFu9dqNS9ogrUyNkth7eBZ5XLZpLUx9vwv7mOxMmAdmEHA3xYb\nxW3sxkhZe8D9TsSfu06Zsi1HatyH0zNw8VjMK8aA9wyYEK7txn1E6H2cydZikK2M6p2AdBVWYV9m\nrl0sLS3J+Pi4iLS1ey4AHWcaEbHdJFwHfgsnT540TXFYAzCXceF2yzSOfl966aWRQAaR1nyAbeK0\nBZhDfFcsFiNpPgB8BpZn06ZNauLmVDtuSgF+R3GAjgXOpI77on14Lq8rjMXw8LBZ9JlTgLj7iYOX\nMG8rKyveukgmk97ZbAV3dQLOcLcmKKPRaHif8znG57G7BhcXF72ULel0OuImgWe4Oa8KhUJsZQ2A\n35U8v1auKheua4iFwEgFBAQEBAQEBGwQFzT9QaVSMW3n0HxYm3B9c5hVYh8JN1leOp32EllazAU/\ng8GZYPEbaBHQ1JrNpsf4WI7lIn5Nn76+vkjCRhesSVjJMN3kaxz+bmkd6XTa0ywsLaETUwJYWhOH\nOMdVFLcSv1lttsYDbWbfA04pYdXfc31oarWax25kMhkzcy/GirVT9NfSzPhZltbqfpbL5TwG0bpu\nbW1NxwM+BVYGeF6LrMmB2UDbc7mcsgQ89vge48g+UnHg9RKnwfX396v2bDleu1mguc2Wf1S1WjWZ\nKvR3dnZWv+cUFXgOHOi5n/C5wXeMvr4+M9WFC16LGwGyP3NCYLBrnMDXHZPZ2VllcuGDdPLkSW0z\n/IMWFha8ZMOJRMI720TaAQVggToBTBQnywSs8x3zPzY25vldTU5Oaug//LvOnDnjpVDJZrNeoE+j\n0Yj1W8M6feeddzwf00KhoPezUg9wElM8g1kM+BhyX91zNJFIeDVXk8mkngPwRZucnNTnFYtFj6VG\nX0XaZyUzaoCVJoEZG7ascHohXGuxe27VBk4zBDCjz+AkuCKtMWVfK9zP2j+ulYfPY8tvCuhWq9Y6\nW+JqRrq4oIKUiE/fs9MvNjXnfeIJdoWSVCqlLwfQvN2iwiwzFAtc7iAWi0VdtHFO5JY5z8oFxS9R\nOIeeO3dOFyCXWAA1bAk0DCtSj7+DQMa0q9sHq0+ZTEb7YkXGYIFaCy+ZTJqbudfIQDeicnl5OSLc\nithZaZvNpufsaz2rU5sth0f3eg6QiHPWZCGWc8q4zpI8zpZpwnKGZvrdzZrcbDZ1L+EzpsTZNI72\n9xpdBvDLizMgu+to27ZteoCyIIiXKwtNVoZ2dy912t9oP5s12TQCoQACC0foYQwsE3mhUOip6OpG\nhCj0fd++fWbuLLx0ER2Xy+W8tpw5c0YFJAg05XJZx+CrX/2qiIg89NBDniDVbDZjc/rApNvJfQAm\nRQhQQ0NDOg6W6ZSjAQ8cOCAibSf72dlZderHfOzdu1eFXAhenP0b4LMB1/L5yWvGdV5eWlrSfQEB\namhoSB3Q8fvV1dXYckT4l7Onc348rEUWOjH/yLq/b98+FU5nZma8vGSWE7Z1ljMssxb3wypaDKWE\nzXMYQ4yVFdTD4LPXcoPhYu8u+B3nCtIrKytecI2FbvuRiRecw70IUHp9z78MCAgICAgICAiI4DeG\nkbJoNM5szY6i+L3LjtTr9YjDoUg0oza0DivTtPtsF2jf0tKSGcbcS+g8U6esCeF71GLivCV4Vqdi\nqbgWob/z8/MmnWqZR/h3aBcYvXq9rhocOwW7Y5RMJk2q1M24Xa/XvfZb2XO5TayJxGXkdteGSDzr\nxGH03G/XibfRaJjr0h1fy4HfQn9/vz4DfbPMl41Gw2QaXTqdzcfMPrGWKNLaR1jz2AO5XE4ZDuwZ\nq6AswzK/8mcYfzBOlpZ64sQJXWNAJpPx6k1u2bJFxwr37evr0991y9vG/QAjwMwFGA205cCBA8qG\noA08RsD09LTmLXq3gT28efPmSN02AGOJuYQTM4OLft94443etTBXTkxM6HiwWQ17FPfm79A+S/Pf\nu3evx3BxoeCPfOQjIiLy6KOP6vdvvfWWiIiMjo7q2OPc2L59u3euWOcaM7BcT46ZKJHW2nbr6nH+\nPzBEjUbDy4dmnW/ZbNZkJnHvb37zmyIi8kd/9EfKMPFZiH6y+RMmPTBhr732WsS8iPcDYO1VDk5g\nU5brAiLSHmuuMGCd23EFm7mqhHvm8vnEc8dO5iLRNENx560V1MXfW+9Hvq+1bl03oo3mNQyMVEBA\nQEBAQEDABnFBE3Ky5sASqJUkC5Ki66QnYqcNgKbP9nxoEJ0yYLtsEdcUYkmV68yJRJkLSMecKoCl\nbPe5KysrEa1JJBoyC00plUp5jtTFYtGrtdXf329qbpYDNTN1GBsObXe1g0wmo312q3UzeHyZcbRq\nKALsi+Tay5PJpMeOsX8Va0wuG8O+Nswkumxhp5B+l4FjJ3cgkUhEsuZ3ArNpfF9o+ujH8vJyLDOE\nMeVUDJiHYrHo1aBkFpV9C9AGtx5aJ7CfmpWd2M2ybmF1ddXTcNnRFn3LZrNeuDf7JzLwXBHxkmW6\nbQSw3uFz8+lPf1pZEcyh5QfEfpMWE3o+wJhOTU3FJnsEwPy5ADOEc3H79u3qBP3Hf/zHIiLy2c9+\nVj74wQ+KiMj3vvc9vRZnkcWsTkxMiEjL+dvdA8xcXXPNNSIi8vTTT6sPEJioK6+8UmvYYZy3bt2q\nLBrW4Llz53T+kYj0+eefl0svvVRERF5//XURaTmMu+uuVCqpTxOPIzNReL6b7iGXy+na4QSOlm+e\ny3Ax/uIv/kJEWmN7xx13iEh7fkulkjJN7JsHJuqTn/ykiIg8+OCD+lkymfRSYaTTae+dVa1WzXeb\nlSLAOj+BOH8nC9YYWelyGHE+xtls1mOzOIWBBcsqxPKF5YfpXsNJTtdTmeGCCVKpVCqSDRUbk529\n3Izf/Lvh4WHdLJ2cjEVaG4nNfC54UN2B40OThSZ3oFOplP6Oc1+4eXIsQUSk/YJlx0PXdJHNZr2I\nJj7MrBeI5WzOWWnxr5XvZ3x8XA8XtK9er8em1LeECcwvbwDOv+TOjeXg38nExn3Cd3FUMs+XJWyi\nXWjz3Nyc90LhMecit5YA5R5eliBQKpW84ssi4kVoMjXtCk/8+2QyaUazuhGJtVrNMxV2AmexF4kK\n9XwI4iWMceR9wXDXtpU5mseChT9LwLBM1FyiI07QwfOee+65yDUitiAl0ha+zqe8ESszrhJ29OjR\nWDcDmGI6VQPAukMx3xtvvFEeeOABEWnP1/e//3353Oc+JyLtIsgvvfSSmvs4Xx/Wh6vMMHhfWEE1\nWEMsDCFPFAQrkZbQJ9KKfkPhbDbJ4hpEH548edJT2rZu3apnF86X2dlZFZqw/gqFguemsbq66p2l\nVgmgcrkcu67wjPvvvz8iCIq0xhH3w5qbnZ3Vtfjggw+KSKu8EQpPNxoNL49Xo9GIzUSP8RgYGNC5\n471s5VoEUcGKEhf2FomaCuMqYXAeKSu/Ytz+6fSdm+OLg0n4XePmOeToQysimJ/rRpL3UpEgmPYC\nAgICAgICAjaIC8ZIra6uSiaTUQ2dHcXwN2uu0HIh4TLzAuZqdXVVpU38fnV1VZ9hUfEs+bqZmTvl\nO3I1eM4wy6Ysi1lxc9pwugfQ27t27Yo4IaKdFsMEQANjh3aGlUWa+4s2gK7mLMxWHTnOQItrue4b\nxtpyCoZ2lM/ndW5Yi7L6GceEWfnG4sDFL7lGHRhODmjA76x0D0w5W6YQjBval8vlPEaqXC7Hthlr\nkeco7vfMorDJ2w3ptRyVOwFaPZtLLRqdvxfpbipEAIHF/HDRbPy7sLBgZubm8YCJmx3C8RyrzWjr\n888/bzqRw+zK97cCHtYLzMPw8LDuOSubvAXsLWvc2HGfmZyPf/zjIiLy8MMP67VPP/20iLSZ8Jde\neslMR+PWJeXAEQt8Nrvafb1eV4YG7NLBgwfl2WefFZFoHiYwnGzeQpoEXPvhD39YfvrTn4pIO3Dg\n7bff1j7hflaahHq9Hsn7J9IaP7TfOvfQ705BNm5Axv333y9f+cpXRKSdD2tmZkbnDr9fWVmRQ4cO\niYgoC4UxEWmZcV0TYqdzgNOoiLTOW05JIhLNJs6MvpVtHuBcVZhXXFuv12Nr+nG+rrhCy1xBwt3/\n1WrVq9DApsw4c3invI5xe209NfcCIxUQEBAQEBAQsEFcMEaKK9KLtKW+4eFh1fgg/W/btk2z6wJc\newisR7FYjGQ8BVzfh07gZGsAJF9I1PV63XSSs1gqgDVvaIuWXxJXrEeiQM647Gr4/f39ykRBsu6U\nLA/tKxQKXh067i9rPfBRAFPDbJebDJVhpUmwHPytEFYOcY1zRhTxtb9uDIjlS8PJKAHX+d99ptse\ni33kdlu2eawJ1tCstqDNVqj1pk2bdL5YU3NZT8sZn/0mrLQGAFesx7rq5L/jMomdfHjwO/hjZDIZ\nb6zX1tbMcGa0nx2BrXkHuzs+Ph6bXJQd6OFTxMDYIUv49PS0GfCyURSLxQiT0gswX9aaSCQSyqhw\nAAlC67ntvTyP2UysZ87qDXD6CL4v/sY6Wl1dlfvvv19ERO6++24RabE2SM6Js+b06dNehvlz584p\nE4Ux++Uvf2n6NLlMt1UXsdlsRlJriEQzZfMZ7frrjoyMdKw5JxLdU3//938vIqKM0+LiYiQVCwCG\nEMwo+o/29ZIIlmGlEmK4gV59fX3aJ65BZzl9A53qaXZ6lsUANZtNz++4Uqno3GGMEomEl7aGM8fj\n2tXA4a0AACAASURBVGw2q/PFKSz4PiLRc4Uz5bupEOKCZ4ALJkjl83mpVCrey5IXPDp67Ngxr+zA\nysqKbiYsEjabYKAHBwc9AaoTzWc5NFvFb/E3OxO7JodGo2E6uXNUn/sMdvCDAIWDz9pE5XJZXzYc\nacRmNxfriTByX0AcCGA5tzMV7raLzZ9Ap1IsbqmRbsIr5+SyclRx+0Vac+AKa/l8XuedHTNdyjmd\nTqtpxco0z8KENdauAzoXcbXGg4smu2sW5nG+b61Wiy3YjAOGD6A4QYr71y3Tr+u83kkARvvcsiDu\nb7DG8Nzp6Wl1Rsa9uXyHSCsnkUj7gJ+ZmTFzn6GNOEPGx8cjTs8AzhaYZfjeQKco4F7AOfI6CZ6d\n0KngNj5HO2u1mkaHYfxeffVVbbNVBgjraf/+/ZoLCufohz70oYjZCfdzBQAWrjDePHYQqCYmJjRb\nO3DdddfJkSNHRCTqHmApxZdccomItM8izhOHseXAESt7N5/f7rXJZDIiQLnXWrj11ltFROTHP/6x\nmm5hsmPhidcN9gX6xiZPK+P85s2bPXM/72E+2/AcPKNcLnt9YGGrm0O4e77zu6HX6F+3IDxfy/ez\nFGAWhtz3Xb1e17XAJc/iBCPrjFtPVG4w7QUEBAQEBAQEbBAXjJGqVCqRYpVs/oLWDOmw0WgoE8Wh\n86CBATZbQfOZnZ1VLQJmEEvzdk2NgGVm4sKKgOuAzKHuXH/J1da5ThvXvnPTQnAmd9bqrNBUq6gu\nS9f428oey/PganDcX0j8rDlwu1wHvU4mR8BNM8HP5b8tlorrEsZpQ+yEiVBo1pShsbAjrRsi3KlI\nLsatG9XNWq5IS9vu5DQsEt0DnL3cbYs1fhzm7TqWciqBOCZkZWWlJ6YkmUzqvnX35XrAWc/RJ66b\nZzlSM8DgwikZjsiMgYEBbSPnXLJCqwHukzvW9XrddEzuBQsLC5HgBr5/p765IdsMdg7HuXfJJZfI\nQw89JCJ2ChgwUszkuNmiGf39/RGTL4D5xxoHGyXSPsf279+v/Tt69GjkO5F2rqojR47ofKC/w8PD\nnnVhfHw8whaKtNgWK5jCqmwBtohZL9cknkqldH/3UlNTRJTFE2m/dzAfvJYwzrfffrv88Ic/jLSP\n+2WlP3AzyXeCNeci7fcSB3JhHDhoynVH4VQCWGv5fF7PE04X4NZ1ZVMxwzWnWr/h9ydnZV9vVnLc\ne9++fcpgw6y/tLSkbVhPepPASAUEBAQEBAQEbBCJZjfR+r14KIXdu45fzHAA7FjOgPYKCZ41OWga\nHNLJNZmsMF9XKkb2dZGoBuL6lBQKBdOOyjWM3Hsw4sLALcdWaDaNRsNzonTHCs+MS51gwfLJYY01\nDtbvrErl/DmH00LzYk3Kqqfl3gNJXkXWnyyR/aEY0LyRifiVV17RZ+O5/f39ntbXbDY9h8exsTHV\n1rAm2LmfHajdJJic/RvodT7Gx8d1fTJzYjngumBfqjhmanR0VB2y4ej99ttv9+Q7tGnTJi+z+Xoq\nrwPstPoHf/AHIiLyrW99y/vdtm3b9Hns5G4xQpb/GM4WtHV+fl73qduPbujr65Px8XERabO8lj8M\nPxcMAqcKAAYHB+X3f//3RUTk2muvFZHWmv23f/s3EYmyRG5/d+7c6fmcWdizZ4+yRfBt4v0NVunY\nsWPqRI7fjYyMaP8uv/xyEYlm9wY6tQU+XnCe/9WvfqUJRcFW8Z7CO2JlZUX3HPbUysqK59C8urqq\nc2dlLke/x8bGIili3O9xD/YTY/AYibTeNfAxw3y8+eab8rWvfU1ERP7u7/5Or40705PJpL5P+Dzp\nJXVKN2Dc+vv79R0TZ2Ww3uXpdNpMW+M+g9MCWW3GvkylUupLifVcqVT0XEQ7c7mcrjewhZ2YOhc4\n3zu9I0QucNFifglYAgMf+tgQoEVTqZT+jQHkKDYs/nQ6HYn6EIlGn7GTnptNmieancNdR7xKpWJG\nGMXR/Jh8kfYBijFIJpM6DpzxHW3mg9btRzeBaXh4WJ+HfhQKBR03tzyLiJ2xHKYxjq5g8xcXfhWx\nFy2b+7hUg7XB8Gw2k7pt7WaCwnoaGhrSDWFlx8dB1NfXp2sGL9yhoSFtM0d5WsKwK1yXy2VtP8ab\nDwm0r9FomM65LnoVFjkTOZ7LuYBYmcF6wouFr40DZ1nmAIM4YE8vLS2dV5ZwC3FFjScnJ2X37t0i\nEs1pBqGZneDxGQudOFtgPqxWqxuO4Gs0Gl7kpYivYIi013eckNlsNuXgwYMi0na8//a3v22+9HEf\nRAiz4GJlFQes6EYutA7hIJFIeOt3eXlZ1x0EqEQioQIXhI4TJ06oYA5B8/HHH1fhEQ7mIu0XIwKS\nlpaWdP9gf+fzec/tQ0Q8h+bR0VFVBFiAQptx5pw+fdqco9/6rd8SkXZJnFdffVWvveKKK0Sk5XTu\nCgdra2sqCGLfDA0NqQA1Pj7uzWGhUIgICiKtOe1VQMB5gz6Vy2VPULBcSno5D9AnnIscWecKm/wZ\n3xvBWsDy8rL3nqvVajpf3dry5JNPiki7370qor1ESwbTXkBAQEBAQEDABnFBTXvpdNqURK3fo5nM\n/FgsFtgnsAHseGhpqd1Cv92QfSu8tNFomDmeuN6fe003bd0Nf0+n054mysVy2eTFTB6Hf6KtFnvm\ngjU4zr7rOnh36hM+4+y0Vl0ml8nppiVYGdWB4eFh1ZqYDeLUCriHy3CNjIzo99BEmTGzWEqLhWIT\nwXrNqRYsrZe1Y4wp7wGwe+w872qpyWRSf4e+cXFjPGN2dtZb2xbFvXPnTmVvoFl3MlH1go2kFGDT\nnpsl2sWePXtEpM2upFIpNd9yzjr3fGBzBc6aq666Sv77v/97XW1luDW9duzYoWcbpxmAOQvPt8a3\nr69Pfvazn4lIW/P+8Ic/rL/Fs/r7+3WPwMz0xhtv6DVY23Nzc7HmIKyDa6+9Vsca7Zybm9M9ymfw\n9ddfLyIihw8fFpEoCwRzWiaT8dJjfPKTn9Q6dABXgWAWDWkScOZ3qofpYsuWLbrurGLEYPlyuZw6\n6VsMIVi0arWqrDafF26uOjZ5ApdddpmybbhWJFrFwi0An0wmlWHiGnQckCMSfY9xXjf8zmJYrbMI\nyGazXl6qbrACNPBZp7quLjKZjFp3sMYqlYqupzjzYb1ej83ryMDZ0klcCoxUQEBAQEBAQMAGccF8\npFD7CBJwnCSay+VUYuTK8pCaWQOH9A+NLp/Pq1bCvjxW9loXnTJWA24tIAbbVVlDsFggSMjo28rK\niteuWq2mbYZvyZkzZ1QTYWdsN+RUpK2BZDIZT0tcXl7We7IDLbQgK6Eoa+oWc2B9Bo2Gr3WdFbme\nknUPTjbpakiW8zRn+rYSseFaK5SY22atA4uJYq3XTTzXDfAJmJiY0L5AY+U5wFxZPih8H1y7srLi\ntY/ZYM5cjDbH1cvjz8CiDA4OelUAzgcTExOqVXbzP7JC9BE6ztnfgWw2633G2il8n06ePOkxeWtr\na8p2oF3rzTjtwq3ptWvXLvVbYkaK57MTRkZGlClD6gdm1fEsOM+K2M7ZWEP1er2n2oIcqg8mae/e\nvZqck8cMTBRw5swZdSLHup+ZmZHf+Z3fERGRH/zgByIi8uCDD3rO68ePH9drwYRy4BD2bSKRiFhC\n+DsG+8pajDPWJDs5W2cH/Jm2bNki+/btExGR1157TUSiVQVw5k9PT3u+vKdOnYowecxKAS7L1mg0\nvM96TcLM5yen2HATlIr4VRu4FiynL7Ke7TqbczZxPlvc+rqVSiWSsBPXYh/CH47fIRb4HWydzes9\nt0UuoCAFJ8Y4gcbKns05ODBY1kHGTtrWwLgvCC6SyhnGLbjp7K00+gz+Dm3Gpjl9+rROLG96C1jQ\n2MybN2/WQ9CirbktfHjgbzZ1WoeBW+z5/e9/v7zwwguRtqZSKc8xutlsegV76/W6OTaWycwSuPA7\nzmyLZ/DLzhXCOO8X+pFIJLQtbO5zo/EsIYLzg3FOs27j78ISFnG/mZkZ0znYFQQ5BxWba7H2rRIX\nXIbGioCMi8KxwLme3JeXhU5VBVxUKpWeTHv5fD6StwZgk7iL8fFx08yPfQgTnzUHIuJlop+bm9M1\nBgEkztm9GzZt2qSKIKOXHFUf+MAH1On6n/7pn0QkaurCfV977TVd3yxI4SxFYeGVlRWdhzgn5qmp\nKS8giHM+QfBJpVJy5513iohoJKFIOwIR8zU8POxF8/X390fKpohEhTWYBZeWlrQ0DcyWlUpF1yX6\ny3sZmJ6e1vcOxmpqakrXGPZHqVTyzsyxsTF9r2GNnz17VscZa4Nf8jAxv/jiizpuEAxPnTqlfUqn\n097eTKfTkcLkAD7jXIQQ5j74wQ+KSGv+H3nkEekEPs9wzlpnA5vJ0C+s/Xw+71XmaDabZjFigKP3\nXTeSbDbrmShrtZp3niSTST3DMebNZjOi9KHN2ONWuTKglxIxwbQXEBAQEBAQELBBXFBnc3aws+rS\n8e+hIT333HMiEtVsOfTTzfTNWhw0ybW1NS+LteUo3Wg0enac40yrIi0pFs/mmmZuHTQr1wbnAoHk\nzbmF2PziZjnme7ETvOVsznk33JQO/f39EY2sFzAj4S6roaEhL12FNbasdTBb5Obk4n5yMco4B3ou\nBO3WiuLn8lroNbcQAFZpeXnZZBbd9cb9iMuOnUqlvKKbrCHGXZtMJlULY6aE88z0AvQtl8t5JuXt\n27fr2MO0s5GUAMw4uoxUPp/3xi+RSERSOqBPcHg+c+aMtxa3bdsWqXzgAnnaujnLYx/u3btX5wL/\n9pp12sIdd9yh/XOdq7vhvvvuU8aHCwXjfP3Qhz4kIq0acHFH/1VXXSUirT5Ca7dyb3FOI4tlveGG\nG0RE5IknnhCRaJqEu+66S0REHnjgAe/8Hxwc1OfE5ZsS8Yv88rVwDufap1ifbO5jq4a1l8AM4Uw8\nfvy4mRYGY4DnMiuHe8zPz+tZBCaO09IAiURC55ArL1jvkzh0CuBxx7xQKHhWET73wO7Nz8/3lIOu\nE9x0OZyOqNf3LZursdfBBq7HJMcph3CtZeEKzuYBAQEBAQEBAe8RLpiPVC6Xi2h8kIo5Mzck/lQq\npUwUUCqVVOtgFsLVJjmzLDQqZgq4lpnLNLATHGD5UrFmw851rq9NMplUbYJr31kakMvMFQoFvZa1\nC7QB/V5eXtY2W0kml5aW9HP8y6GrYBjK5bJK5vgsl8t5Dv7lctnTbPr6+iLh8yJ2rTKGVW2c2Qm0\nxU1HIWLb2nl80VZmY9gWj/+7YejcTtwjm82a2phbi6sTLNYLYH9B9r/C7y3HTTw3ztcwk8lo3/D7\nRqPh+T7U6/WeUk9Uq1W9Fpp1qVTSvq+Xicpms6qtc+CAy/Ju3bpV/XAwly5r7DK5lvNqrVaL1ahx\ndkxMTERSIbjAXG7btk0diQFLe+2W0gHa8dzcnHe/XvHbv/3b8p//+Z/aBgB7E3NdLBZj5wnnbS6X\nU58dJJR89dVXPcaN0xBw1m52lnfbxAkS0b5bb71VRCSSTgJ7b/Pmzfrcj33sYyLSYtZcv6mtW7fq\nuwH+pFxHDv5CzHDxeeaeA/39/epXhTHr6+vryFyI2LVIsSbr9boyURjbvXv3ymOPPebdC+fm1q1b\nvfni9YV0C4lEwgtQsRIpl8tl/Z4ZItzTYnV4L/B9AE6WjPsBeB9wsmH8rl6ve6l22HKC/T8/P69t\n5QoWmGOcgVyTk2vbWrUqMb7o79DQkJ4nXPezGy6YIOVGjqHxXFQXL45arRbJwi3SGlQ4UPJmwSGI\nrLicnp8j2ywq0Z1gzsxqlX7APZLJZMQhTiQaxcALxhWahoaGdKFDGOL8IJy1G4uXneWwubhcBBYA\n7icSFczc7NUrKyumg7Wbf6tarXoZjxOJhCf0LS8v66LluXRNXalUyhQs0C58l0gkvI3LTu6W2Y2F\nDvQDNPu+ffvk9ddfj/SXBXjcN5lMekWB+eDgtdGrk7b1IuUCxoA1LjzvIq15w3pih2H3RcCRSECz\n2dRxxj2skiPuNSKtscdc4r7lcjk2OgjRVhxVhvsNDAzoWOKzxcVF/QwH6tzcnD4DL46xsbFIAV1r\nPABcy0XBcQ5t3brVM2Hv3r3bE6RKpZL3QltcXNSxw3ofHR318iB1imTlPou0TFSdHN074eqrrxaR\n1pw//vjjItLOq8QZy3FW7t69W4sG4/zZt2+fvPLKKyISzdqPdmEP7Nixw3PAZXMfxuz666/3IvSq\n1aoXefe1r31NM3hDgGLBDAJXqVSSL3zhCyIi8t3vfldEotUsMPeWgMiCAc6rgwcPqqDH+xdnDc44\nPqPxOzzLBRzV8R7K5/P6bEvwwro5deqUOp6/9dZbIhIVRCz3itHRUb03BMxOihDOArQB5jBuq+UW\nUSqVdH/hGSsrK15f2HyI82dgYEDnAvdOpVJetLD7N+7n7otEIqH7lh3Rsa/4/LH2Wi/Rp6xgYa9Y\nwSwugmkvICAgICAgIGCDuGDO5lu2bJH5+flYUwJTq2waEmlpYL/61a+8a1zz3MTEhErzltmDi43i\nGiuLNDSQhYUFM0O3Cyt/DQNsQH9//3k57nUDFwMWibIs1tRzZmsOWY2Dy1J1yr8Vl1bAclTl2nPu\nOmGnVcAKre9UULpXxLWZWTK0n7NEn08+JeQywrqfmppSFoa1T5e9FWkzkZzyws3JMj8/r2sbplmr\nhlomk9E9Au23v79f74NnLS0tqWbL6xnOzahtxwwCB2NwagqRlgbrFng+ffq0MgKcfw576Re/+IXs\n2LFDRLqza5wFWaR1Drj79aabbvLMLZztns2lLiu7d+9eDcGPM/fefPPNOg8PP/xwbJvjcPfdd4tI\nyzT2t3/7tyLSdqqen5/33BouvfRS1dAxX1xHktcTxh/z0dfXp/uR2SAwF1z7jEP5ATCNmIOpqSkt\nPIxM3swWufmVRNoO5vPz8149wIGBAR1TfGedA4cOHVJWDKwHp0RgJ3bsa85mHnf+c07Ad/sVG1e0\neGRkRPcDzsKFhYWezsChoSE907AmqtWqZ07jwKduBc17ycnEmdddt5lOwFocHh72UuOkUil9LvpT\nKBR07jjfGH7H5yL3EwjO5gEBAQEBAQEB7xEuaPoDBiR4djJjn5G49AiwS2cymYhmIRKtXg6NiR2y\nu2Udd9vcabgsB2WwFOyP5TrBseQN7b5SqZg+N3Eh7gxmKSztBWODcTtx4oRqiexH4tqy0+m0sk/Q\nOvv7+1WzhfS/urq67pB61pg4nQU/n7/rNFdWegE8A75j5XJZNWo8Y2BgQMfKZT/xvftc/D00NKRj\nwMnmLJ+wOC0W4zc2NqZ+JNDQp6am5ODBgyLS9v+ZnZ3V+eBgDXf/sHM4nn/mzBmdc3zHfhgYq76+\nPmU2cN/h4WFdQ3j+wsKC+p3xXIKR4FBxzoYsEvVPYWf49WYMbzabGiqPTNvz8/NeXTMLzESA1dq+\nfbv65wDMqGFf5/N5b78ODAzo91wbzWVWb775Zvnwhz8sIiLf+MY39PO4FCEWvvzlL4tIy4HarS9Y\nLBZ7ZpexzrF/O/n+uay85cfILBDW7nPPPee14XOf+5zcd999ItIe+5MnT6ofD+4xOjrqOZaLtBOo\nsi8YgHW6tramY8o+VJdeeqmIiLz88ssiYrPpXAsw7rlWvTyRNhuL36XTaa/W59jYmK4//MtrTaR9\nXmP/W2PeCW4y0HQ6HclyL9L7WrMwNjam84RzJ5FIeFnkmS3iYCfXh8u6Fu0WsRku610C5HI5HT8e\nZ05XJNLaHxtJf3DBnM1FovmSsGE5moiFJ3QeFOvCwoIuQtCQnAGbBShseusgZafKuPxA1gDy762J\nxSHDjuNxKel5E+JFhpfX6dOnVYDCguBcO1g8a2tr5kJiJ3h3ww4NDXkOm1Zm6Vqt5jnB8pjiHmwO\n4kMB84lDZGZmJmJawT3YMdGFm0+Kf1ev1z0Biw/zOHp7fn5e590qDWEJ9ZzZmsvtAFiLmN+hoSEd\nU0Sp9ff36wGAF0c6ndbxwPi+733vU9MeBO6ZmZlIDiWRaCZltLlQKGhb2ezGUbEueI3g3lxI26XE\nq9WqubbjTGxYk2xudoUsF/wiEGntPRZYsYdgDmJBCvdsNBreOmEFDePLjub4DKWtRNrjy3OOeWUz\nIUxEzWbTM59OTk6aZ0uvLzWchxAI4GjO4HIwDEs5dM24q6urpvCPFzjDXU+VSkX3Bcx0119/vUYE\nYj9CiBKJKjGus/7i4mLkhSfSWvcsWLj94cARC27f1tbWPGVtYWHBy9ou0lYmsS/5bGQBByZeIJFI\nRCIbRaJKDKIKjx07porK8vKy6QKC/Y9xTiaTpiKI9Y0xYiEN87Z582ZtN1ekYDNfJ/BcsbCD+7Dg\njfOOi6VbpcdwLc78TCaj7cd8FAoF7S//3hUOV1dXdYxxNqRSKd27eLcmEgndA26EehyCaS8gICAg\nICAgYIM4L9PexMSEDAwMSCqVkkwmI0eOHJGZmRm566675O2335aJiQn5l3/5Fy8fA6RKDpnEZ4VC\nQaVEMC9c/NAKk4+TlLPZrFlTLM5UZ30HSZlzVHAWWPc+1mfj4+MqXcPhbXV1tafaYyK9Oe5xWgCu\na4Q+5XI503RqAWYZXHvq1Ckv7LfZbGp7oGkwdc7sjtv+bDYbceLHsyxnfqvuYlxxY84nBm0NWkqx\nWPRSXVQqFX0GtJNSqaRaKdZfr2kOms2mjgfue+DAAa/wNGcJx71ffvllb45TqZS2H063ncyE7vod\nGxvTsWKtGWOE8XaddgFojpwFGBo1nvHCCy/E7sPzARjAUqnkZXdnp9/JycnI2SLSOhtcdjSXy6mW\nGZe93Ep1sH37dr0fruVx4+z5wM0336y/f/755yP327Ztm3z+858XEZF//dd/FRHRsP/14Nvf/raI\niDz22GPyz//8z+u+3oXrliBis2SYfzZvdwMYF2ZJ3Wutig/cLsw5m2RdlocxNDSk+5pdI8AWYp5P\nnz4dcapHO7H/UWGD5xFmy1//+tfqNA8GjitEoM3Ly8va3zizJMZBxK4Tu2XLFjODPlgsTn/DaYDW\nCyunHZhXnMeZTMYMCHLdM7q5NzDcqh29ni9WQfZCoWC+77hag0hrHWD8mDF/66233jtn80QiIY8+\n+qg888wzcuTIERER+eY3vykf+9jH5LXXXpNbb71VvvnNb57PIwICAgICAgICfmNxXozU+973Pnnq\nqaciku+BAwfksccek7GxMZmampKPfOQjnjSMxFqdHIYtXyVo9ZA0WRK3mAl8lkqlTEnWddxOJBIq\nnQIW+8ASNSfctCpQs/9IJ1iaF4eXw2/mtdde8+z0VvVykahDNqbX1cpF2v4ciURC7xmnLaRSKWUH\n8IylpSWdL4zfwsJCrOM+kEgkVOuzHPzYH8bNXs51oXj8XA2uk2a7UbgOoGiL+/yFhQX9fNeuXSLS\nclp1Q+crlYoyGtCKK5XKup2NMY7JZDJSGV3EdoLdtGmTtgsMiJWuo1AoaOJbtH1oaEg1afj8HD16\n1AyGcIMr2Kkfv2s2mzqv6Hcmk/GyHXcLYrAc/DsBZxb75rmMHCcARFuTyaQynOxfibG2suij3tzh\nw4e9/bVp0yZNMonxR7LJbuD6lX/4h38oIi3H5j/5kz8REdsnFHuKa+P1slc7AWPADu0W4iwAN9xw\ngwYHsD+Rm1bFAqc86RaMg5QicFjntcT1/LBmsacKhYL6AN14440iIvLzn/9cn8vpGTgJrkhnVh2s\nHDLYs58iZ7jnACmLvcdv8TxrDvr7+z1H+0Qi4VkXLL/YTsA6x/MXFha85MUMTuCKv/ndi/tZyTUZ\nbFkRaZ0nODtw38HBwUhmeZEo89xrHxndnM3PS5C66KKLZHBwUFKplHz1q1+Vr3zlK5Hii81mUzZt\n2mQWYxSJmqG4wUyp4ffuwA4NDXllRTZSJDUOO3bs0IOKBRA3B00mk/GK4HY6VNBmvIiOHTum48GR\nTb2Y8fh+WBz9/f2RdvFiBeLubQmxcdEQnYDxwLXFYlEPS2zgZDJpCqu9FAXu1hZ29rQOt7hnsBDj\nOp4Xi0VvvhYWFnTN4vdnz55VUwMi8M6dO6cvS8xXuVzWNcZtweHM0Z2YaxYqsd7Wa1a78cYb9cBD\nJuWFhQXvJXTw4EGNpOJM5Gj/f/3Xf4lIVKDmfrBJXKT1cnTLlXRDNzM855taryCFcZ6dnTUjs7BW\nWehAO3BtJpPRee3meI71gUzkU1NT8rnPfU5ERKMe77///ti2o4833HCDOpdDSPj0pz8tP/jBD0Sk\nLZRYVQ14PfX6YrF+j7HYuXOn7m92CLdy81lmestNw81f1Gw2dc2ywI0xx57fuXNnrCkrrryJtcb2\n7t2rJnGcO0NDQ957rVQq6fcQAqenp7VvltLLAVUYX/zePeOsSEkX/JnVl27vFVyPtcvlvnDN9PS0\n965lwZwrK6APfCZg/DGHlnM8o9czn8kTVzao1+u6XzlinwPV4vCeRu098cQTsm3bNjl79qx87GMf\n080McA26gICAgICAgID/azgvQQqU/5YtW+TOO++UI0eOqElv69atMjk5KaOjo+a1MO1B2GKnMKb8\nrevwneUo6oa54vd8v2w2q9It2CW+FzuEQ/rnkEjXaa1Wq3naNYdJM3A/OESOjIxEUj+ItDQq1/lw\n9+7d6sAKKt4yj7KW1Ymyj2O50IZcLucVYrbMZMViUT9jjQBjhDbMzc2ZGY0BNkdCA+GiydAs0JZi\nseiFzGezWdXwoDHt3LlT2wJThzVunPcH64RDawFLqxSx8+7ALOsW0hZpa0WLi4ueNjQ6Oqrrjes+\nwrxwPpnawYTmcjkt2opx5r0C5PN5j4mYm5vTtc3jYWl1+AwmCg5hZsBsjXnm57IZ32IO1svG9fX1\n6TrBWWOxUSLtMcHanZ+f13HAOcE5b4BisaiO58xIYX1AGx8aGpKbbrpJRER++tOf9tR+ZDHf2sOI\nvwAAIABJREFUuXOnMlJgYPL5vN7byr2HGqRvvPGG9sOtqdkJcVr71NRUpP6ZC97fYAF4XWEseS7j\nzinsH85BhPu9+uqr8pGPfERERJ5++mkRae0z15wmInLVVVeJSLtIc39/v94bbeHcVXBOHxoa0nlF\nzcLFxUVtA4KJRNrvpOuvv15EWiZesCNYf/l8PlJvFp9hzKvVqpnCwS1GXqvVvLMtnU5HgjNcgAFj\nczrOPesda6VL4aAjnHdzc3PmO9y11gwMDJhrjytb9AIrqIxhOeZzKgSRqEsGLDpra2vy9a9/PfbZ\nG3Y2X15e1pdVuVyWH/3oR3LFFVfIpz71Kbn33ntFROTee++VO+64w7yeKfNOOT4CAgICAgICAv63\nUSgUZHh4WEqlUldBasOM1OnTp+XOO+8UkZbm8IUvfEFuu+02OXTokHz2s5+Vf/iHf9D0BxbADnAo\nvEhnbZsdTvGvmzgtnU7r71wGg7GysqISOiRuq6I9I84Zul6ve1r72bNntU+wl586dcrTEqenpz3f\nkhMnTqhUjL6xEybaaUnq27Zti2hDAATXXC5nOqFCq4M2YVUbTyaT2n4eK0vLce3q+Xxex9CqtM7j\ni7/xHWcCBpaWljwWoNFoeA7TzOyBHT1z5kykVhM+c8HjhH6zzx+zmrgP9w3Xow1vvvlmrD8AtMpE\nIqHjv97s3iIS6/AKpmtyctJzSrfmMZ/Pa/uZ2bDWGHzBsFZ5H2H+VlZWvDpYe/bs0c84dB1jxVqt\nq+FuxMUzkUjo3Fjzwf46bgX6Wq3mZc+v1Wq6h6HhWlnMGajxduWVV8o111zTsS0WECQAiwCD01uw\nDw/XIXOBs2FtbS3iCygSnXP24XQxMDDg7T0Of2fWGNdjbDkYAn2anJzU/cB12Fyn/nK5HHF4BvA3\nmM5kMhlhogDsL/afQfvwjEqlonsJ7NTExISuc/yur69PmVpuH/bB4cOHRaTFVLvJRkXaexPvi6NH\nj2qyz0wm4yX2FGnvcU64CSaUiQ6saXbIdhP3ct+BbDbrvWc4IS/Gd2hoSPvELJbrDG8FjHRiQnEN\n1iIYfu4b+5ZZ7234euXzeb0P9u309LSyVLznca7H1cp1ccFKxGzdutV8WVsOXbz52JHRzbhcq9VM\n6t91GJ6YmNBIJS4fYmVh7lYaphOYquUXmUtDWw7XVhp9kbZwwhFf3ZwHrRIxODixYTlDNsDRMFZu\nHIab6TuRSKw7EojHA+1DaQWYoLqBnf4xptlstmtJHRdYE9lsVttlOYTHodlsqs8g5t8qbyHSnk9s\n5vW2lzEwMBDJfyMSZX2xXiqVSuxBgX5ff/31mhPHchxlIMcOXuYLCws6H3ix8MuQy0a4JXYYWLvJ\nZDLWMXo9UXsuOAoUz+DAFy6mzGZKkZbJAL+DY/7i4qLuybii5Pfcc498+tOfFhGRz372syLSff4R\nOXbRRRfJd77znch3W7du1bXPLyjO0ycSFfi5j5Zii2stgQXzde2116pwDWGiWq2aVQLciNRkMumZ\n80X8clDdInC5FBDWORz5f/3rX2uKHisiFpaThx9+WM8zNkFaAoYlULpttnKRWZ9ZpWnYxaNQKOgY\nQVDZtGmTV+qK24TzfWVlRZ8XFw3MgTRxgVscuW4FmGCvb9u2zTOdPfvsszrHfOa7JduGh4dNxduC\n+462ZAiroD0DijC7tECon5mZeW/zSAUEBAQEBAQE/P8ZF7RocSaT8cJjU6mU57y8trbmhcfmcjlP\nO6nX6550yswXO4y79XRE2k6XyFR75swZdSTk7N0XX3yxiLSd1+bm5pRatSR+/qxb0V0RW9uJk4YZ\nfX19EQke18CsZeV4SqfT2newFMVi0TP9MOPTyenaBWsYVp4etA9jxE6LDGg50BYLhYK2BXPpFmt1\n2wDtuNls6ryD/RoZGdG1CI2btWOsP667BI11cHBQNTSMS7PZVEdWPPell17acIoOztbLDIFrxmNW\nAWOby+UiDvQirfHG+rWcuW+99VYRaa17pEdA3+bn5705yuVy6sgORorXIYdEn0/eIhc8LmyyXy+s\nYsSswbLpHusOZo1z587pc7GeOL8N1gmzaVjHt9xyi663Rx99dF1tZsYCbUkkEtoPPkNc087y8rKa\nStjM5J6ffX19HgvI88csAJvlRKIsCzu0u2c518PkczkuPQP2VDqd9kxDFvswMjKiz0V/+Tdo386d\nO5WdwH0XFhZ0j2AP8Dlr1eHjz1z3haGhIb03njU3N+fVZq1Wq2bov2VlADrlFnTRid1z1z7Gmb8r\nl8veu2h4eLhr6h+R1lkJxrXXcwBrglP7YPy4HTCDLi8vm2ZyrE+MD+8PDiBxx290dFROnz4dGKmA\ngICAgICAgPcCF4yRugCPDQgICAgICAhYN96zhJznA3YgFenuxNvL70qlktKtG0kD/26C88ggEmk9\npUpgsoGpoFKpbKjUiUsDW0WBOXEqm4isRYP2gLJvNpt6PzZ/urQtzxvMCxzpg3skk8lYyhlt4ufi\n3mNjY9ou3LdTQWl3DPL5vH4W59w4NjamEX64r0WTd3N8ZvreCmjA/LNzuIuRkREv+pWvwWfJZDLW\nkb3XbMdxxaQ5Eq7Xvcd5mKz+4blw4O5U2JUdqP83EgD3GoCy0UCV80E3p9p3G2w+5j0s0l1h5hJA\n7rX1et1z+0gmk172bD67+DOsZY4QdNeGVZSe28K/5/MTv3PLVtVqNa+/qVRK9zKurVarnvk9k8mY\nUch8fqNdMFtx3/+vERMcXcrrOS5inRE313Gw9o9VAs67rqe7BwQEBAQEBAQEeLhgjJRIS/qDtM7O\nvJDwOTy7Fy1rcXHR006YLXg3NLVkMullgGZtGs+/+eab1VEPqRbWA7QZzI7FFmQyGbNGVVyhW0sy\nz2QynnM7S+Z8jZuzg7OE89jjWivZKkJiOZ3C/8Peu/xIll3Vwzsy3pHPelc/yi66bctuLCOBQUgI\nyRIPiQliBPIIwZC/gCFigscMmIHkGTADCckSeGAjS8jiYdqm3bT7Ue3qdlVX17vyGRmR8Rvkt06u\n2HedR0RmOdt8Z00qK+LGveece+65Z6+99t7MaqXCntnS8PeTi4sCvV4v/JbD2v049Pv9ZMZwZsT8\nb5dhCrnt6C+H3+NvhDBPJpPGfX3w4IEMmvBotVr26quvmtmx4N1D3SO2dDkQBO0DYB0PBoMQfo7x\nYBGwYpRhgcfmK7OPKbD1WhIKzUhZ9Mw6LGrx85xVjB9fV7VhWaZBMbWKcT7NNUraoP72bWHmx+e5\n4lQXPN+4Tiv+j2twfwHPZMfayefnCg04zrOsnKIGODo6kutUagz8v/449ZtUH04jm4mxMd6rEZun\nYIa5MknJHFNtjrHaqbUoV2cwxVanji8Zz3PbSGES4gWOl+Z4PG64NRZ5UXFVaLPjTc6iJSQUeCHC\n+dQLCAnA3nvvveCKWOb6qeR3wNWrV0MbEBmUi9hotVqNSao2mLGcPX5ScRQWNnUcteHvs9l8Xip/\njzmSE5hMJsmCuHiAlevn5ZdftnfeeafxuQdvXhRKczyhLYsA51TRPzAmXnnllZDEkYF7xAuLv2/T\n6TRstG7+f4WUOfFlbiMFcN4n3pSaHT9naD8MCN5IwY3HblPf9hhSJYrMTp4V394SpDYvMcOrNHoW\nzyK3Wc3j0pd8CdRGX3121jpVvoZyiaWOU/efN7G84VKuPS6jYjbvYsPc4POpDRd/p+QQfr5zG3AN\n9VJfWVlpjAFvHNVxsZd6aiNwFpvi3CZbnRvv21ii52WNGP4e4I1eybvJn0ttCP1GNvdcxlBdexUV\nFRUVFRUVS+LcGCm4k/yu7+DgoCFUvnLlylwZA7PjnSOYD87GCuv+NNmhGYpCVJQzLAxknVXFHhdB\nCYu1uroaxqqEiTLTFlXsM9V3jD9yo/B1wdrw/eMdPu4hMmXz+YC1tTWZCRosB67HFhy77ryrk/M+\nMbw1eXBwkBTXYwxyuU/gtjRrjjm3WbFAas7is263K8t2KGG8ug9wL6McCTNSChCR8zxWol9ca3t7\ney5PjgfaeeHChUaJnX6/n2Sf+TlXedpylH2pu49dOfyvWT4IA8B8efr0achrA/zkJz9puLwXkRuU\nsA657N/AadmoFNPEx6ix9204OjpqMAJKgM6fqfNxGRLPZvV6vcZ9ZTcot0mJ3L0bbzabNSQj/Fwo\ntk2xn/x/P368XuSCKJa5n+oeLjov1fsWawKzewC/S0rbjOP4txhXdQ2z5rMSY9b8PFGflQSwVEaq\noqKioqKiomJJnBsjBaF5Sai0z4gNoMAuBLnvvfdegzFgzc0yKRFKd+Ylx33xi18Mf3/wwQdmdmw9\npUTOKezu7kbHxiMmpvXfseXtC0m3Wq1gmbEeyosMGWwdg1HBcay1AdQ9Yg0Mtx2WgmKJwAxxwAJb\nKb5WlNJHqRpbOeskZXGxxYfvmEGA3kCN4yIFNH2dRg4I+I//+A8zOy4wDM2VYjDAKilmlccKBXTf\nfvvtoIkC+6Tu7/r6ejgO3/EYsxWI73k8wJQxa6m0KSltR8wSTjE5OaGtsuSR4VtdT2n9mJVT60lK\naFvSh1g/lkFKh8VsUYpRSa1JsdQDKbZQrfNcO1CJ8D37wHUpVbu4byoYx7eZz5V6R6i+sZYqdp1l\nEUuTgc+YWVP6MD+P2u12Q6vW6XTkHC1NTZDqb+pdXhpgwtdIjUUJzm0jNZ1Ord1uh5crOnd4eFjs\nlsMij8X15ZdfDos8zre/vx9cIanU9bGJ5d0k6uVQislkEvqL3DiTyST0FxsWVRlcIec+fOWVV4rO\no/o9HA7lYqlesKnNCANuD0CV9FGVwFdXVxsp/4+Ojhp0e7/fbxS3RrkS/ozbqYSuXHYFY4N5wC8q\nLsiKc6fmGL9w1eKEeaxEm4eHh41zxwqn+mAINV8R7ef7BGCDHqtUr6DKWXAhWbPj+4t+ouwGL8L4\nl93W3D78jbm0v7+/lLsgBb7//j7lXuY89vgMG+QLFy7Iwt/+tzlwf1XOsFTk1aLjozYbsReMd1vG\nXGex6+SghMA5ATLGZTKZNPI5cdv4MyVox5rB7jwfFBWLGlNRin4TxhtRfhb8xuaswGPJYnkuy+b7\nwcDnWBdZ4O/zCsZQOhd5/FKbV56fKYMhtSFcFtW1V1FRUVFRUVGxJM6NkTo8PJwTB3NRTW+h9fv9\n8D3ny3nppZfM7CTVAVvs2FmrbLM4J0O5hzgrLXa7ly9fDuzIogVob926NVc0Fm326QNKwZY/GK6N\njY3QVrBfHiW78LW1NclEKHdaSoDNokAliC9pC3/HeZO8xXPlypXgMsU9iuWHUiJD36bpdNrIb8Os\nAbsccVyOVShJPRFDqYsK1jMXRAUw719//fXg/uSixQCYQS5ayoVnAcyRwWAgrWbf3+3tbTkvcV/B\nXO3s7DQCTMxOnjk8R6urq0n3wSKWJrPiMSjxOkOJ5vF83L17NxQPR3Z8/g1Qmu6l1Wo1gj92dnZC\n+3J5ukqgWLfS37BwO+ee8/eQ3VqKceR74NkiBn47mUwazFGMrfDnYfcW1pLpdBrmIM6rxPB8Pf6/\nD2zIhfOfmjFJpI9h9mnRXIuKWc+dIxV4kDo+5wlKpW+IuftKXYA5VEaqoqKioqKiomJJnKtGajqd\nhh0hrHsWoGNHePny5XAcrI/Nzc2Q2BEao6dPn4bzQPDK1jgYrMlkEpgDWLix7KqeRbl9+3YQuX/l\nK18Jbf7nf/7nbJ/39/eT+q9lMmQDYGJWVlaCjolD8RnKqlPHqM9VuH3KX81Wpbe82eetBLRg6J48\neSLDib3lw+fHPFlbWwtWJLdTCdhZdwP4rOPcBmYkVVi+greqWGyO8yntk6rJd3h4mNStoO2DwSCM\nAdq+u7trr732mpmZffvb3462l4MZwERduXIlfA4WmKsU8Fj4fsxms8YzMJlMwrgw45hi99CfjY0N\nedwyFrxfiyaTycJWLGe29sdtbW0FJorvW8lzr7RPfH5m4/1a+dOAst65vSxeTgnPlRCc++nF6Myi\npJiQw8PDsD4oJllplbieKFhFZt/9s8z6KpX0U/2dEpMz+8m6qmXgz83nyyX69O8L/i0Y60W9M9ym\nmO6Yn0PVhxRSz0ppiodFrneuRYv5IcDiOh6PG4P64Ycfhr95EPA5NkuHh4fh5aFennwej5WVlZCV\nPCfixvff/e53zSxd5Pa0wKbt0aNHUkTq6e+jo6Mwll6g7aEmEtwu/Fve5HgxOAuFuV3KrYANHlxJ\nGxsbyTaycNeLlhmf//znzczszTffbGxo+HieV2gLbxT8Rq/dbjc2IOgfn4/dxItuho+OjoKLAJFo\no9GosTnY29trCEHxewa/cLlUA6DKuyiROB/vFx4131V05qNHj+RGCm1gNxgMFuRems1m2dxo+O1p\nDBCFZTYgfoxUZYDHjx/bz//8z5uZLtWT2ozHqg94Q6Q0r84in5UitUFSLzJ/DJ+DX+Cq1E2q9IvC\ndDoNc5FLwPjzsMCb81Ip+YLvG7v2UsdxP1WOLIBF08uA72Vqkxb7jQf3A+sxB3eVGOhmTfc3PydY\nS3u9XvHmzAcH8Hqg2vA8inpX115FRUVFRUVFxZI41zxSvFvknS12pSmRWSx/0qLsEHbWg8FAZtRO\nIXetUusOO3QwA+vr60EoDObk0aNHwS3A7gPVZpxPiYhz7cJnBwcH0kJWIm1/HmZ2+DvP9DHjwHXX\nfDqFbrcr+/mZz3zGzI6ZKFy3VLTurf61tbXG/eRgCP7MMyCluWIUjo6Ogog7lRNsPB433KW53Cfo\nI7vSMN/v3btn3//+983shAVixpZZKn8ddiNxW8BoXbt2zcxM3jO2+FNi8uvXr9vbb7/d+D3AmbCX\nYU9SuZhKn1vlXgL4vJwmQzFRimlUNSj9nD06Okr2g1HqoiwRAiu3ZYyN8mPJcg6Gyh/l26JSE5Sy\nNrPZLDBSuFedTieMs5IM4PhcAEmKbfPf+8+8jIXBDFyMQVJQ8xf95OvyM4TflbJKWCt5TqYYVb6/\nvrCzWpdzFSQYqeLWOUF7SoS/yFpeGamKioqKioqKiiVxboyU8hubHe8goVUCK7OyshK0OSmdk0Iu\ny6kSt2JH+sILLwSrehkdlA+PHQ6HMjkoGDiEMA+Hw7AjB0uxtrYW9BU4h7LY2+122NXHwkGV1enT\nGnQ6HWlZeAGg2rVzAjvgM5/5jL3zzjtzn7EFDnZiZWUljDXGL2YRer3WbNZMAHj16tW5UHOAgwxw\nDdUXrp3mwRnElSVaCtZGmemEcixK9/UEuR8813HcdDoNrBRbejgWTCFfA9ZdLOknwPMBv8H9UyxK\n7Bw4Dvfl6tWrsrag7++yegcVbh9bk/h6uZB0tmYx/qlkiqo23mAwaAjyY2uYCq0vCemezZq1NHMJ\nD3NtSTFXKjxesQYp3VaMkUndN3Uc5hoHazBj4tmi2Hl9ItB2uy3HIFVBIrZ+ngaqDUpHqATZKm2A\nP2/sufbJsHu93lz6FLN5lprPh3WEdZqlzFDps6mOP21Gc+BcN1JqkrGgGW6ItbW10DlsfJRIWWXj\nNtMlN+B+YJE7Nm64+XiRm80/aPgebeCXCNDv98NnOO+v/dqvhZcShOp37twJmyq8gPi3mJzr6+v2\n2c9+ttEPTAoW6Z0mignnywl4U1mzVe6ue/fuNVxIqswLuwU5v5a/ztraWtgg8YvKP+Cf+tSn5EYK\n5+PyNqlFnJEq5rwMfDZ23hDiGpcuXQo5m1T2bHV/EaiAiE6zk40Kjz2X7EEbfJvM9ILFG0h2Q5sd\nGwaYl1xsGm3GvVIbjY8++qhozqoNkW+jh4oSim2e/Es65tZSGzv8rUSz7Dr1QQ7KraHml4qAiwnH\n1bionGZnIUBn4baKivMbo1LXuHrxlb48YyiJ+Cs9h6rUoAoPx8r/5DZQy26wcnM79Zn67eHhYXgP\ne+OJf6NE+oPBIPwG79GnT5821u1clCIbk0pukptHKSwTcFFdexUVFRUVFRUVS+LcGCmzOH0L6wsu\nB2Y4OAM615fif83ydfVwPrBf3W43WImwIB8+fNjIPdJut0Nb4fbZ3d2do3fNzC5evBh26WCkOGcM\ndszdbjdY/2iTYtCePXsW6sbh+ru7u43flNYp5OthTMzmc3aonbnK8K3OqzL3evHx5uZmSE+Qsir2\n9/fnMjfH+qGKZCq3EDMSitHhfqdEvPjNMvUXFWXOjKS3Ppktwj1SqScYmJ/KpWA2L4I2O75HPM/5\nWnwefvY4jxRc8rjPOzs79sILL5jZyX3gGmUpRmp7e3suY7TZfIbpWA6cZVgsj2XCwvE5j6lnkS5c\nuDCXqsWfm9m7VEoKvi6uh+eI17YUixbrU4kAfRFmitkpf10e59J0Br6e3yLteV5QbeH56ddCZtZy\nLijlYjsLpO4zX08xZSy/UG5hhl9j+P2kUtmk2md2UiQd68nDhw8l68ltjfWD52cKJXUOKyNVUVFR\nUVFRUbEkPnFic7aowAwdHBw0mJDpdBosOG8d5zAajRq6mfF4nPw99B9HR0ehgrvKxo4dOO/aYVV+\n5zvfCdZ6LlsrdtJg4IbDYaOfy2YuVoJCfz9iWcfRHiW+Z3GgZ+hWVlYaWpGdnZ1wDTBOMaYLfcb5\nmGnCfWB/PO7Xm2++aVeuXDGzEwuImQIWSCrLxs8J1qAxQ7SsjkRZmpx6Anj69GlgfDB31f1ntgj9\n3djYkKkV8HvWG+AzTkHiLU220Hgsff1KsxN2Ct8dHBwEpkyJfvlannFmBi4nMF5G53AafRDmAp4L\nxVI+evSowaxycAiO73a7Yc3gOabGCPeYx9ejVByeQ0wbZxYPM1f3KSUeT423ukZOW5QLfz8NvMYr\nxiB5lmo6nSaZZB7b59l+f42YF8L3KVYXUCWlXTZZNY8l5jaz3j/+8Y9lGzx4nEsE6Oq4Em/Dubr2\nFI6OjuZS85vN04H8ssMig5fm3t5eI3KA3SRw45VkTPZQ4nbOc4QFkkvU+Eik3d3dItfbaDQKQnu0\n/dmzZwvnuYpBbV5TDzbGmXMoKcE1P0De9aLKqfCmiRdGlfME91jdO4wzXxfHP378uJG/Si3qvIFP\nPXzKRcHHLRq9F1u8/Dw+ODgoEsSqnEwxatpHHfX7/fAiVhtq4ODgoFHwODaHMN+xALIAVfU7lbts\nPB7L73NRX+q7Rd1a6lr8nc+NM51O5TPiN+bT6bTh/lCbyZiUwQelMPj6JaJ0FvjmIqZSc7F086lc\nXanoYhbXq4i6lDvqLDfcZvP57lLt8/2MXYsNudgmbFn3ntrgxZ4Blendt8WsmedMucm4NJXqMwTr\nrVZLRmCrkl3f+973Gn3zyL3PUljGiDKrrr2KioqKioqKiqXxiWOkzNI7QN5pwrpjwavficaoRWVx\noWbbq6++ambHbiGf+0iBr+GtS7MTsfn169ftrbfeMrO0KLzVagU2ATv6HL0IBuvSpUsh/44Pq8a5\n/fgqa4LdKGztot2wFpQ7lNkdWCwbGxvB6mC2BcwGu57AXjx8+DB8BsaPx9W7dnmMXn75ZTMzu3Xr\nVmNOMPOi3JEAswDMyvnrshun1AXEzJESdgOwijY3N6OZ6vk4xbDEXNZoMzKrr66u2p07d+Z+yyHs\nuZDiUqYXTCSzS8w+mmnXMrtQ/bXV3ykopqnktzmLlZk87ovZ8fgxU2p2vF5gTmNu9Pv9pMyAn62U\nSy/FrKr+qtQNi8Dfm5jrroQ1VGt5zLUXSydQ0l7FXJUwEZzWQLVPCdAXdXPyulwqjFbtUX2Ksbil\nQTNqrfIM7O7ubviM5RfXr183MwsSGTUeONa3z4P7ljouNp9SLOEi410ZqYqKioqKioqKJfGJZKSW\nxSJWFMIowXqMRqPASGFHDevcA4klsQPmbOspcd39+/eLNFKlonmzk8R+SL44Go0CcxHTkQAqpQCs\nYv4t6xdgifiMtXyN9fX1oOeCRcJ1rdiv7mvZmVlg1HBvtra2gs6J73GqHhOLtX0/laWe0x8wK+K1\nKkrPEwP6m6qrxkwYs4LekmOoBKlKP4NEtEjuydfg32LMHj9+HMYS93x7e7sxz9vtdjbliEcqqzBn\n9+a0JP48o9FIpgjIWeOKlU1BsXL4jRLBM9vBuiPcay9O5zarcVQBDSsrK8lnIKcT88fFtCWKeUmx\nRTkxeanWB2PE51MsWopVyEH99ixE3aw18teIMSE5nWXpuJXo/0rne6t1knKE2UJ8xmsX5gTrJ30d\nvNFoFNaeFAtUyhDzb0o9WepZXlYrB/xMb6Sw2GMhXWQjhRc93E1Pnz61f/3XfzWzZnFdxpUrV+x3\nfud3zMxCXqe9vb05N5QHNmSqzMhpgb6/++67yeOUwFu555TrCVhbWwsuR7zcuE9wM/DLgcu8+HNu\nbW01Sv688sor8mXpxf6xLLb4nDfB/nz8sGCDdHBwEPrOGwv+3iz/IlAbwxz8+RS9/uTJE7tx44aZ\nnWykVEkXBtrM44PNGG+kfNSY2fyY4XvO4eI3Uuvr640NALtQcy65WNvN5rOs+37u7u4Gd1ns3Ool\nkprni0Zj8sucN7R+3nEOLRWZxVGUqjSRbwNvzFS2ewVlOChhceo3SvjM7nyO1FVtVhtblWEcx/kN\nFR9XKg5W47LMS5M3GOp6ys2kBPyp49ilrb7PYVkXIG9eUpuM6XQqnx98D0N+OBzaL//yL5uZ2Y9+\n9CMzOzbMcO6rV6+GfuJ9DONJGUDXr18P7zvOm5Zy2SnEAoZiKAkgqq69ioqKioqKiool8TPNSC3i\nAvNQWVVTTBTwhS98IbAy3/nOd8zs2GoEE4Hd6/7+fvj7eTBRi0LtqpWbMUWBsnid6xDic3U+WBA+\nL1Lss3fffTeIzYHr168H9g9ot9tzqSZ8u958881wjZgg3mzeovKZ7c1O3Fn4bDQahfNx+3MFltFm\nxYDgGhwqrML8/fxUBW8ZSgCPtrP4H+wPu2tVygS+5wAsukuXLjXax9Ye7tHly5eDxarKlZU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FhY+MWH36TKaSyDVqtlL7/8crie2bHrxr8cThPxZ6bbrVwdvmgx59fKRU3gJafy1vBDgb/ZDQH0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bk5bTCm1j03idXkYXjfcQoIe3IkzV2DFxq/gOxm6eqrrw5OfDF27typmj7D2txupPL5fLA5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\nFu2DxVhs1WkEwITLGBoa8gKf+60Zx0BbSqWSyiXaZcUEsGUFzOr79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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD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\ngICAgICAgC0ibKQCAgICAgICAraI/wdvk9M+hXkCqAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024fe090>"
+        "<matplotlib.figure.Figure at 0x7fd621d91150>"
        ]
       }
      ],
@@ -318,7 +318,7 @@
        "output_type": "display_data",
        "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu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\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg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\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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7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\ni8T/A2aklexSCttuAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024db950>"
+        "<matplotlib.figure.Figure at 0x7fd630658d90>"
        ]
       }
      ],
@@ -344,9 +344,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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1mv45OXPmTNGHU3mMRAEAAATgJgoAACAA6bwcDAwMlPK6M2fOLOV1UR9mzJjh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\nXnllxP38RVWPHz8+4n7+LJQiU3iopv3795d9CLm44447XMz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44sWLqd6jk/OahTXn6zYWLlwYeU2rRmu6rL29PbLeq6++6uJDhw4Nux//99rc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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13061bad0>"
+        "<matplotlib.figure.Figure at 0x7fd621d73990>"
        ]
       }
      ],
@@ -372,9 +372,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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XuF5UztrYKEBBYg5ASeP4KErcl9/HmjVrBs4PzOF9ZbG1jfWZFF33iqvFlU3a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FrI3Pc1n5+1dtCq/YEhOuywwyR1z43d8Bw+ToAI4BKcUYtyNALWrL5zMrIP2PPf+Z3fkSTdcMMN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Okec+WKymeN9jDGirKHE81xm3v7CvTg1LbQRziVSkkiRJkiRJOrJDKFLOM57x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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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C/MCPAMUmVb+sXzmRIFU/rJTol8H1sc5YD/w/vo4ptQh6HecjifX9cv6R+FZQLQDlAn83LFp8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\nVOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024c1750>"
+        "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
        ]
       }
      ],
@@ -400,9 +400,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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DikZxoXypnBtE4XnEB8oKEQ+8eoSJR0Ckct/k8kgR4YFaQD95bhKsXdqVSBTq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\nntJ3vvMdvelNb5IkvelNb9K3v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/uRcjLUk+c69t5770HHmTZ9Y8gcFDvPJqsFFYD+jrxr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\nRUoIIYQQIpLSFKks1mhe676E95LWf2Cm4IqwoDXW09MDoK5UxSonrQrrN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3h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024f9490>"
+        "<matplotlib.figure.Figure at 0x7fd621d25190>"
        ]
       }
      ],
@@ -428,9 +428,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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yT6dF9SKz11Nvb++Ir9nrUCrP1b/m2traJJU31JbKm/IeOHAggRk3D3vtppnO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\nKP6yyy7TwMCA7r//fq1bt06SdMstt+jNb36zbr/9dt13332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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nxEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13062c050>"
+        "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
        ]
       }
      ],
@@ -456,9 +456,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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trc2I3XXXXUYsOzvbMbvo046Bn/z+ftC2Nz4+bvXaJUuWGLGhoSEjZtvkvnr1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\n794t9fX1cu+993I+QlBbWyt79+6V4eFhicfjsnv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iIzY5OWnELr77L27m+rOnqzAameOkt7c35dsYGRmxWi4d9712I4LG9++WlZVltZzW9Ay/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\nIqDoAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13063e810>"
+        "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
        ]
       }
      ],
@@ -489,9 +489,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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4GFM5nE/dUhNXsN5+e+CtgLT7YJWT9x3EZgfhSnWYmDavKq3/sOsn6BAD5WK4\n6KLep6yiMHXSpk0c9id/knYEZmRpm5a7wpylZUCvRBMsvwN5Eo1m5Upp3ry+9eUpkXFhmIZSYZMo\nm9viH/9RWr3aXvl+XDgg9vRIN96YfL0mtmXUUebzegJauvTw8csFNtdz//ZTbViYsNLu5G5bnDtF\nWVrOrKiJK1hZkvZJIu36TSg9UHzrW9KiRenFkpT+r+VpbZU+9zn/aU33werpkf75n+OVkQWVEmeb\nJ6ebb7bfGT/KiTkPxwoXpbVe2Z7m0QerDNud3LMq6nZ0baDRLD9FuH69tHdv8vVW8vnPx5vfxPhX\naXZOz2uHb1t1BC3XhWE0XDlHxRkHi3NYOlK7gvW7MUtT48pOkxcuHcBcOpjYiGXCBLPlJbUvLFli\nt3yXtnvWubouTd5+37hR6uqKX045114rBRkU/MABad++w/9Pqw8W50TzEkuwvvOdvv8vvkLB9R05\nLSbHwerpCf5uLhMDjWZhJHebB60k246JuiqVEbT8INuw/+uTXOX6VYAkn4BNq9+sLaWxjxsnXX55\ntHmD+OpXpQULqk83e3blV+eEkbU7RXmXWIK1YAEbOQgb62j58urvQAtab5j49u49PH2S296Vdta/\nX5RtNrZhUly6ZVttelcTjP/6r2DTmerwnPUfJ1Lvq2vStnattGXL4f/zBHt+ZLqT+/XXS//zP9Hm\ndbUx2biy8vbbweuNym997t4drB5Xt0VUrp6Ai2y+Kqdoz57owy0gmqxcIUxDHl43Qx+s7Ek9wYrT\naK+4Qvryl+3Um6dO7kHj/973pHPOiVZW6ed+0yTZyd2l7WU6lgsvLF9Pmsvdfxt+5Su9L7WNgnGr\ngild7htvlF5+Ob1Ywgp6K8v0MA1RJJ1YhXnK98IL+96GrNV9wVWpJ1i2XHZZ8H5HfrLUUAcPNlPO\nL35hppwscOmWVBj33Re/DJuvyikKe+slC9vD5WNCuSE5ggrTd8d2vz/TddksL0l+sd9xh7R4cfKx\nIJhBaQdgq8Hfcot07LHSpEl2yo8rzBU023WFqS9uXGHfRWiqjnJ4MseMpNtrmrJ8kpbcWL9Rn5Sr\n9v1732u+X9WePWbLq8ZG+3Jhm9ei3F7BqibtBpfkQfqCC6pPY/JpsUr9rFx5VU4ST2IlcduutPyo\n69HGYJKvvx5tvjh1mlrXSRwbXE/SbOxDW7dK3d1myqw0nd/QC66vb1Pog+WW1F+Vk1aik+QVpDDS\nTvyqidroZ1mYAAAgAElEQVQnwrWBRvPIpYPosmXhpjfRBoL0D8yDJIdpMOm44w6P+J+F4QT6x1j8\n/803xy/7scekn/3M/7s442C5NB4havgKVhhZG9fIplo5icVh4qpSWJs2BZ/WRh+sNG8RVhvJPays\ntHGb8dh6RY7fk8Wmyg4iTPlPPOH/+f/5P/HjuOQS6ROf8P8ubPtzrV3isNQTrKQbR1qdK2tJ1IFG\n07jF49IBvb/ubumHPww27W23Ra/HNWnfIiyaPdtePS498enHleNfmk8RHjyYfJ22ZDn2LEs0wVq1\nynyZUQ8ENLhkBflFnOTTXll4au2114K/4y8LtwbSuk0cdd1EHWMvT5J62bPtqzNByrn3XulTn4pX\nRpzpoyrdRtX2o0cfldra7MaDw1K/gpUWEqxobP6idOVXsylJL0/UbVApzrAPP5hoB2nsm6U//pK4\nFfP1r0sdHebKiyuJwWcr1Wd7viD+/d+l//xPe+UHZaMPVvHzP/sz6dOfjh4bwkk9wbLx2L8Jtg8w\nLgygF0WQp1TCDjRqWrV1+M1v2n1FRnFZr72290WuNlVb737SulpUKEhvvVU9sUgj0X7uueTrXL8+\n+TpNyGon+7jlh91vgkzvd3zoH+NZZwWvM86YWK6fe7Io9QQrrasWYZ56sxFjVq/W2FwXSa2T665L\npp4FC6SNG5OpK8y6S6vteZ50xhnSyScP/M6lpwjjPBGbpFpNdEzMn0SdQab/x3+sPs2vfx28PL8h\nKpCe1BMs9FXcid5+O9yTYXG50A8njTpIns3e1qtW1ubN1cfIivoUYa35ylek5mazCWTa+46t45Cr\nbeTVV5Opx8Z6XbTIfJl5U7MJVpgGZ6NxBinzlFPM11tO3ANQtXc3Jnn5+T/+Q/q7v0uuPhfEac+2\nOyuHHUYh7QdXXH8cvhjHjTdK3/pWsEFdf/KT8sMO+CnXRnbskDZsCF5O2PKT7gsWho1bhCbnq8RG\n2337bfNl5k3mEqy77pKmTrVfjwsve967N516bbA9SN7atVJra++/f/zjwwMapiGNWzem62xrk/72\nb5N5Ukxy6xZhtemTPB7s31950NagscyZI33pS8HrLVfuxRf33uaNK+i2KjedjQc6XME4WPmReoIV\ndkdZsUL6zW+CTZvWY+F5FvdVDLZeHvsnfyJ98pPxy0GvBx7oTVSDqnSLMOyPlTyfPMN65JG0I+hV\nXLem+vi4+BRhknVHKSuP7TvvMv+yZ1uNznYn93KSfJ3QnDmH+wC40Acr7nqu9p6zcnX++te9Lwc3\nxWZ7sfGuMZPbvlIczz0n/cEfxCujkqz9aAqynDNmVP4+yjIH+ZFjevDfoPLeByutfmumpkE4qV/B\nsqlSg8lSY6q0U771VvRy77uvd+C5anWE5crBrJz+8V155eHbi64LegulEpt9sPyUlm9jeJJqT6Ga\nWr6k2vUPfyhdfnkydZUTpJ3ZSOqDlpnGMSZOG120KPhQIDYe8nD9mJxXmU+wgjwR89vfDvzMVB2m\nhY3vQx+yE0dYcV6enfbO39JitrysHfxN1p/VgUbDMJVslLuS9P3vp/eEVlJtt1wC5/LwEHHmvfxy\nadKkePWkfZxEeKknWHEPpkEa3Re/GL5eFzq5B/HKK+nUG/ZKiku/slw/gVdi4pZQmCe14vSti8p0\nO+jsTLf+LOp/izCtdZLlfbUSk+szr+soD1JPsNLorxKESwfZJPolmBzWwKV1l4a4y9/TIz39tJlY\npOoJVelJ1MbI80F+rNg8SVx7rZlyknhoJuwPka4uadq0cHW4/LJnW7evTfzAs/Gka5jlc6WfJYJL\nPcGKy3TD8RsrJmrDbmuTnn2272evvy594QvhykniYLdli/06qknjoO5iMnjPPdKUKeHmCbMc5faZ\nf/kX6fd+L1y9QYRdx1Ef9CjWM316vLGaknrCy2+eT35S2ro1XDlJXMU23Qcr6m2w4vdpJAw2jhV+\n7dTGgyxBkISZl/kEK+oYNyYa03PPST/6UfnvL7hAmjy572f33lv5VS1JNnKTdWW5D5ZpcZ9ArTT+\nmYn+GeWmfeGF4GX0Z/KdomFP5v2/a2mRHnooXjyV2Gyva9faKzuKPO+b/Zk+9kZ9YjrqOS3uNEGs\nXx/vwapak/kEq5I4O1Pp9+Wm/eY3pc98pnwZ+/ZVrsNPkge0nh475Zr+tVtrwoyQHOQHQ5TkN4vb\nK2u/wF2KN+htZNPuvNNMOUm0V5uD4WbFhAnS5z6XdhTZkXqCZfKXbxBRHgNOo99IEvWE7fxbiYnH\nq9N6gsn0PHGXY+7c8t9FaQtBb7u89trA7558Mnx9ldgcjyfNJ9BcSpZMSGpd/u//Ha6+FSvCTd+/\nvjhM3Oq2Iem2t3t3svVlWeoJls0dxcWDXlL9O7IkzeVLu1/Df/xHuOlNxFuujtJ+eMVpPvaxeGVW\n+85vmqjLmOd2ZFKY2/WVniK0sczVrqRV+vFhum6b5Zk8DwQpy8VzYS1IPcGyydRBwcYVLFMvykzr\nwP7UU+W/i/qkTNhl6e6W9uwJN49k/2ATpg9WU1P5eaPWmabiFYZSUWPbtCnc9D09Zl5E3D/eI/od\nJZ95Rvr5z83X4xJXTshJJj1JlJPWNne5reVZ5hKspEehtiXsU2KuueSSvv+v1o/HxgF7/nzpD//Q\nfLlBBF0e0+0zysMCcR5AMK1/jB0dA6cpFKRt26T3vc+/jIMHpTvu8P/uxBODxxJ1G/7d30kf/3i0\nskxI4pgXZziBIOKO6G/jPFCt323YbZy1W4SuJNV5knqCVa4hmxiPp9JJP8yYJzZe7xGk3iwJ2s/H\n5K2GOE+9hRXmhBMnsbRxizCJDsxR2+2vfz1wfs+T9u8vX/6vfiVdeGHf6aMw2T0hSgxp7etRrp5P\nmND7AnBTTDwNa6K+MNOHfTo46rKEHaYhqxcZaoGzL3s+eFAaPLj6/Gl2crchDztLUrcIXe0MHUe5\n2H7+894x1MpN73nuJuelcfU/uX/4w8HKSLJTs8vtw4QFC4JPW1wXmzZJDz5oLoYwfb3yJMhy2kik\nTBwbTD4UVStSv4JVTlq3YLLG1ZOqZOZqVZL9pUwpHZ7DVPmf/WzlIUFMC3vCMznOme1t3tnZu43y\nmKCXuvdes8fRPHTPiHMV0tVbhEGY2FarVsUvo9Y4m2CZYLuTexQm3iXnotIYv/c9/6st1YTdXlHX\ni42hDkp96lPh67K5jbPWfmzfshw2TPrrv07nUX+b5fWX5C30uO6+u/fvqAlh1HX5s58FnzapW4RJ\ny0qcWVQ1wbr00ktVV1enySVDknd0dKixsVHjx4/XzJkz1Wnh2mHQ+9Bp/gpN6sSVxR3gpZcO/zto\n/6y8aG8PP09xHRw8WHm6KFeK8rZ+TXj55fLfZSEhtaVS4mJzvRQHr0yzD1YS2z3IleGwfbBqub26\nrmqCdckll2hFv2evm5ub1djYqA0bNmjGjBlqbm4OVem995b/zuSvSlMjVNOAq/M86b//e+BnccUd\nXyltYdfBrl3RyguzLsq9RsaVROzqq6WZM4NPH/WqdNR3vrmynmwK0p7uucfuK4lMeewx82XabANp\nX1mFOVUTrDPPPFPDhg3r89nSpUvV9LsBfJqamrRkyZJQlZZ2bH3llb5jKpXL8N/3PmnHjuplv/zy\n4YOD7TFPeIqwr/4vtvYTtg+EjVuEJh5gML2d4pZXKWGIMkxD0u3wzTcP/3vZMmnjRvt1csWvPL91\n0L9NXHRR8IFoq5Uft5+e6QdpXDwO2+rkTnu3J1IfrPb2dtXV1UmS6urq1B7lnkiJ0hcml3s/3qZN\n0ubNQWKr/L3rjckvvrADLqYhzg7s+jaRzPX5qFZ+1DGr4iQLJoYfiHtCqjYsS7nyBw2SWlvD15d2\nX0wpG53sk6qrXD1R3udqIgbby22y/QVpR3nqM5YlsTu5FwoFFQym+9XuUZuqKkxjSvvedxbf/eR5\n/v03wp7Mbd8itL0NTQ0jUm6/KHdSSPJkkYRyy9DdHeyHl5+w26a0X6EJLm+XoFc1s9LxP2653d3R\nyysd9d9GXzCX21GtizQOVl1dndra2jRy5Ei1trZqxIgRFaZeUPLvht/9Ka9SgmXi6k6WLxH7cWXn\nqpY4hX3sP0i5JvzkJ9HmK9c+qnVSzwpX2pUtcZYvj+smyBVam8dEk+vUxG3//st6003SCSdEK7f/\nqP/VJN2+8tiew2hpaVFLS4uVsiMlWOeee64WL16sq666SosXL9Z5551XYeoFocoubux166QzzogS\nnRlBnp7JShKWhijrZtky6eKL7dbRf75KD1yUCnqLsH95SR28Kt06TOJVOWn28aj1E4RNtm7vx73l\nXmn+0v581eYr913/6Xbs6JtgxW1zSd+iNrGP53U/a2hoUENDw6H/X3PNNcbKrnqLcM6cOTrjjDP0\n4osv6rjjjtOtt96q+fPn6/7779f48eP14IMPav78+cYCKm7EP/uzgd8VCv4b+dlnpTVrwpUfJaYg\nenqy2xBNJ4xhO576Pe1jI4m1uX3K9SEMKk6/nNJ5bT+F159rt1qrefxxfiD5SeKp7LD1hjVpkvny\nXVzOoldeka6/vnq9Qa+uv/e90he+ULksBFP1Ctbtt9/u+/nKlSuNByNF23jTp0vbtweb98c/lj7w\nAXsnlfe8R5o7V/rhD82VmZQ46+Shh6Q/+IN45folJzaeInSZqf5+69cfLi8Pt8NM9ymrtF7SvupY\ni6J2IUjKN74hLV9urjzTx7Urrqj8/S23SGeeGSyWLVuk1avNxFXrnBjJ/V//tffWyn//d+WGd999\nvQlSUH4N4rbbwscXZqfv6Dj8AtsoXD3AVPPd7w78LGg/uiKTB7BStTKcRlbbTq3Kwvaq1lUirSs7\naVwxDZvkR326z0biH2RU/yy0x6xJ/WXPRRdf3JucdHWVn2bpUv/P0/ol6uJJNouK28nvgQUTTxFG\naQe/+Y105JHSiSf6l2GrbYVtU8XL/q+9JpUbji6Jdpq1W4SVyrTV78iUtOs3wfST2WG7I0SZ7pxz\ngsdTroz+n7lwDnHtwaM8ceIKVikXN54LO0E5ScR2553Sd74Tbh5TQwbYaA9B1tnUqWYesrA9IOlN\nN/X+vW6d//RRbxGajNvULT3TXDzWuIJhGoJ3iE9KknWuWcP+YYLzCdaOHdJvf9v7b5NP84VpPKZ/\nbWXNP/yDdNVV4eb5xS/cuYIYddTnMGWWe8o3aF2VxgmrlKBu3x6s/LCydHDNUqxpeOWV6tNEvUKb\n1i3CX/yi96qtLWmOsRf1yqpp+/cnW18eOZ9gzZkjjR/v/125eUz76U/tlp9HN9xgphxXHi+udgJ6\n9FGz5Qf9/pZb4tVbTdgEMUuyGHMUY8ZEn7daHywT5XpetKdvS4/LNq6kmXh9VbnPXLo6lnQyXUuc\nT7CqvfzWltKdq9zOH2YH/Pu/l159NTuX1E2K06el0nf9Xy6dB6VtylRimIdEwsY4W7bme+ih7LbN\npG7hl7uqbPt45vJwCy7Yu7f6NHlddhuc6eReFLe/SJD5N2yo3Jk+ahyV3HCDNG6c9K53mS3XpCTG\nwUpj58zCU4TVbhHGecdm2gdEV/tg2arrU58K9mPq/vt7h4ypRaXr5pvfTLa+IGz/KDHdJ6yaoE80\nlhtqB9E4cwWr2HiiXCoO2/BOPFH60pfC19NfHq4MlIqyA5cbWiFOx/ZSWblFGHa6avNJ0h//cbB5\nXZDkvtC/LtN9XZLy9a+nW38QtoZpKJ3vq19N/0dAWKaufia13C7/AMszZxKsotKN3dPTd3TvIAfE\nSu+TKrVzZ/jYXOB5vR3/XWHj0eVy311xhfSrX4WrLw5XrrzEKSMvB0+XbhGmzUbczzxTfRqTCVa5\n7fmb30QrL6tdL9JO8ivJ6v7hEmcSLL9bJA8/HG7e/lx5is2kFSukY46xU3Zar8oJ6vrrpZtvDj9f\nmldXnnwy3HxpDUtRjiud3F26RWjy1vfzz5d/hUmST3FVe9WK5NZAo67XU025OLZuDT9PWEuWBDsu\npfVQWZ44k2D5dXIM+u6kWtrgr79++N9BRucNw9Z6NHmLMMm+C1EGOY17NcXGNkh7/7B9dcHG9k3K\nxInlfzT8/u8nG4ufpI4JrnVur9Y2KpVXKTGuFsdxx0lvvVV5GhNK7wy58iMqj5xJsIqydK84zQb3\n0kvSySenV79JWdjmSZ0Qol4d6e4OXh56mVw3cY4Fe/aYiyOuSuvE5PHu+eeD12vSJz4h/dd/VZ8u\nTjxxE+NyFxaSTkI5dsTnzFOEcW6R2B5otNT8+dKECek3PtcHgSsU7DxFGKd9rFkjLVuWTF1JzVdU\n6dZCEk9RpvmqnKR/6BQHPq5FfleUi/tJ6XfV+rj2f19rUseF4kvpL744Xn1xJf0UYZRyi5+/853S\nyy/bjyOPnEmwiqL8grKxwZua/D+/9trD//6bvzFfbyWrV6ffuJNMZv3qK720Hab+AwekH/ygd3DC\nYcOix1MurtK6oqh0izBsmU88ES0Gl9m4ymlyG5bO8+qr0eIxafdu6a67os9/883SBz848ClCv6sc\nRxwhLVzY++OzaPPm6HW7IM4twkrKXWmWpNNOi1ambW+/3fue2LTPPVnkzC3CIH1QktzANl/DEJXt\nMXOivv293HQmkoX+07/4Yrj5i8aPDz8if6Xx1Vw+2JS+NLvcdggq7acgbZa/fr3ZOFzy7/8urVoV\nr4yFC6tPU1z3a9f2/fyIkGcW19qIrXiOP778d/2v6qXN5WNcVjiTYBWVbtSgj+Sn1RcqjXpt1plW\nJ3fbO/LTT0f7RZ2lW4S2+K0Dv4cr0rxFGFVnp7myXNt+YeMxvQ+6lmCZZjPepJ+K52lBe5xJsPz6\nYP3TPwWbN68NwbWDdhj9Y7/11mDz9d+WJtaB6actpXgHwXvukT7zGf/5bPRbM92O3nzTbHl54NIw\nEmFt2xbsXZphhmkIm2DFZXqdZPnYG1WWrtJnhTMJls3H1CvVF0fWxv65+urK37swDlaUkfyLgsZf\nqR9ElPLC+r//V3rqKf/vbA9pkFVh+2amfVs0Sx55pPx3+/ZVntdUglVr6zxKV5g0b6OWfldr2yoO\nZxKsoiRvy9Sa5ubK35db90FHxy9XVrWds/SzOAlW0LYTdBT/SnHb+sXsypOXYaW5D9pcvqBX6/Jy\nDOq/Lks7rodZxv4J1k9+Eq5e00z3wcp6kuF3bOt/7M36MrrAmQQrzjANaTWENA+qSdYd5ddL1Pji\nJFhIRtjO4WlcLTa1fwQdo6pWTkZB12v/BGvOnMrTJ/kjYsWKysOaJMHF9sItQvOcSbDKZdG268ua\nNDpXRp3fL9Z16yqXkZUEy9btVBu3CE3HOm+e2fKCcOXpYht27Uo7An9x+kO6fouwvb3y97aGaYjD\nZJ1+yxdnWA/4cybBKopyIM3KU4R///fmyrexg1frm1QtOQpyW7Cjo3IZLiVYrgzTkGYCYfuKpYuS\nXt/33ZdsfVFF3cZZTIBd3OdsP0X4L/+Sbhx55EyCFeQWYbkdfPv23r/PP3/gd+VGPM/TCcGU1asr\nf3/OOXbqNdUHy7QkDyS22mPccbBcUVw/H/5w8IcUsuK559KOwJ/faP1B21LYbgWu9cGKq9pt9KQf\n6oqiNLZKx6c33pC+/3378WSRMwmWiQa3ZEnf/z/9dPn3QmXhKcKk6wxy4gpaf5iDcdgYXFBuPcT9\nlW+jk3slhUL0wVuDMhn/vfdKe/eaK88FQR+6KDLdHi66yGx5UvJPnSU9TEO1+qoNDRP1lvcHPyhd\neGHlssOKe6XqjjukL33JXDx5kqlX5YR9/16l++xZeLIqSIxJvHm9lO2HEMpdwdq4sfq8LlyVjPtr\n3US7DNt3pq1t4Gff+Ea4OrM2ZAnCC7ONXbuClSWVEp6HH5be/e5k4/GLA8E4cwWrqNLGKzd2UFr6\nH3Bc7awaV5irVlEEuUU4bly0sk2xfVCJ8xRtJVHL27DBbBw2ubDO8nLSMbUcpeX87GfJ1ZsUU089\nJn3FOgxX4siyTF3BclWh0Ps499Chhz875hhpxw7zdbn89Er/X61Rbvu61AcrSTavaoWdv9in0UTd\nprhwdRLBb42Xtpsf/chOLGHaZtLt+PHHe18wX06UBMvmk8ZhPkdwmbqCZZKJA3ZpGf3Hy7GRXNWC\nJAYaRWWf/nT4eWw/8ZqXzvpxHXNMbz+0NNi8RRhF0v28wrj22srfV4q39GXtaXJtnWZRzSZYWWg8\nlQ5orv+ij3owdukKVpLDNNgayT3MOFjFeg4ePPxZV1eweZMYUqRYjkttJGk7dgR7b2DaktinbR7D\nq5Vt6qry008P/K7aUDZpH/uzcO50BQlWDKUN/bOf9f88D6K8KkfK/i1Cxroyw8Q2LRQOl2M72a3l\nPliV2BymIYpaWOeluEWYPc4kWFkYF6SS0ndtZXUZygl6sCx3xWf48OB1uZRgVRIniU56ANlqsvIE\nYP9jRP+BEW0IG/vkyXbicMEVVwSfNu4VrNdf73sl1U+YcsNuR9sjuWchic/beSwNznRy7+zs/TtL\nGzWNE1NWro5FHQcrToKVlXXjx+YtwiRUWvcmkubSPljF8v7mb+KXG6TeIPbsSa79ZekYKUWLt65O\n+va3g5dbC+skTVmL1xXOXMEqcuGE4HLZaUgykXT1CpYrt66zeKAztU3L3SK0KWhdeRmiJeq67eiQ\nFi70L+f116OV6Tc+W6ks7gtFlZ4wrKarq/pry8IIe4swy+s9ac4lWFl62XO5xMNUAzx4cOD6cPlW\nqolflFkZyT1JWf6lLpnb18J0cjd11S9Kn6M8K3fMu/9+6R/+4fD/S9eHrafi0lznQR/+MDl/cXm7\nu6X6+nj1h6kP0TmXYLlypcAVixf7f57Wr/gwV7PWrg1fV5wEe82a6PMmKey2W7Ysubqisn2LsLQc\n2wmWjXJqTRLrrbQdJH3lN26C5ZKvf93/c9p+fDWbYJlg+n10fspdXk9rPQXt5N7VJV1wQfgy45yM\n3347+rzVpJn4X3xx8nGYZCrBKnf11nS/tSjlZHG72GRifVQrY/ly6fTT49dT9NOfmivLBltt7Lrr\nqk/zxhvl49iyxWw8eUKCZUESy5DkekqyT5mrfbBckcX9w3aCVWlaU3UinCTW269+1TtiehrSaBdp\nPkV43nnlp6s2qGotI8GKIW+d2V1Q6wmW6VsdSSUjL70knXWWvfKl5G4R0gcrPtZH9rEN4yPBsiCJ\nxCuNPlhJ96tIS5ptMMtPET7wgP/npodpSPIKVtDYk3w4w+X2GXY6U2zsN5XmycsVrHHjzJeJw0iw\nYrD9FGElaa0n08njG2/0HVDQhQSrOCZb0fr10tixydSd5QSrnFq4RVhtUMysqJVbq3HjS+NpZxvr\ndOPG8t+VeyWT69vWJTWbYGW1kRQ7cqfRByvq4KGVbNsmfeELh//vQoLV35o11d8PlhQbtwijlh2n\n3LDjRpW+Kqfc0CXVPosiaDn79pmpz3VB34OY1eNrqUrLUAvDyVx0UdoRZF/uEiwXOn/bvEX45S/3\n/p2HA1jRc88d/rcLCVb/E3me1rUf28vnt01nzQpfTlJXsKL0wdq/P3p9eZT3fSZrV7Dyvj1cFSvB\nWrFihU466SSNGzdO1xp6lCBoQxgyxP9zF07QnZ0txsrqn6zt3t37d9QdptroyHFs3twSab7SX/9R\ntp/phLZ4uyf4aMstxupO8xZh+LJbAk2Vv2EaWgZ8kmSCld7JsiXwlK73wQoXX8uAT9I4zyS/3VuS\nrjB3IidY3d3d+uxnP6sVK1bo+eef1+23367169fHDijoL4NyA70l2fDLndjfeqvFWB3ldqqoO5vN\nV3pETbBKuZAgFxOs4P1qWixFMpDNpwhdT7DcGWi0ZcAntXEFqyXwlK5fMQl3FbRlwHdpXMF6882+\n/7e/jlus/nipBZETrDVr1uiEE07QmDFjNHjwYF100UW66667Ygf0Z38Wb34XNn6cKyr9T+qrV/tP\nF3U5466fSstmYt270LeheOUqjY7LeezkbjrZSf8K1kBJ9sHKwvAwJta/W1dr+yo9Tt1wQ7yygvre\n9/r+P4njUxaPNy4ZFHXGbdu26bjjjjv0/9GjR+vxtEZ9K7Fz5+F/V/pVWen2T/+nyMp9vndvsLJL\n5yve4iunvb3v/+++23+6IFei9uwZ+NmOHdXnk/rGXDx57N1b+UQS5wWmRW+95V93FOW2Y7V5ih3a\ne3p6/++3zcpdQa32Co3OTv8R54ufVVveIAfV0njLxV+q2I6L+4tfu4mqs9PMVdOurt62IQ1cHr/l\n27Nn4PYP2h5K9+tinX5Ky/ObLkr7q6RYXpB9oto271+m3//LHd+ClGdimwdth52dlbeT1Pe80P//\nftupq0v6gz8oX15pfX//99VjrCTqMW77dv/P/Zan/7mwqytY+/Q7d5VrF56XjeQ/SQXPi5aj/vzn\nP9eKFSt08803S5Juu+02Pf7447qhJJ0/4YQT9NJLL5mJFAAAwKKxY8dqY6XxK0KIfAXr2GOP1ZaS\nlxBt2bJFo0eP7jONqSABAACyJHIfrFNPPVW//e1vtXnzZu3fv18//elPde6555qMDQAAIJMiX8Ea\nNGiQbrzxRp199tnq7u7WvHnzdPLJJ5uMDQAAIJMi98ECAACAPysjudsYgNQlY8aM0SmnnKL6+nqd\ndtppkqSOjg41NjZq/PjxmjlzpjpLHr9YuHChxo0bp5NOOkn33XdfWmGHdumll6qurk6TJ08+9FmU\n5XzyySc1efJkjRs3TpdffnmiyxCF33IvWLBAo0ePVn19verr63XPPfcc+i4vy71lyxZNnz5dEydO\n1KRJk7Ro0SJJ+d/m5ZY779t87969mjZtmqZOnaoJEybo6quvlpT/7V1uufO+vYu6u7tVX1+vWb97\nnULet3dR/+VOZHt7hh08eNAbO3ast2nTJm///v3elClTvOeff950NakaM2aMt2PHjj6fffnLX/au\nvcezGPcAABuTSURBVPZaz/M8r7m52bvqqqs8z/O85557zpsyZYq3f/9+b9OmTd7YsWO97u7uxGOO\n4pe//KX31FNPeZMmTTr0WZjl7Onp8TzP8/70T//Ue/zxxz3P87yPfOQj3j333JPwkoTjt9wLFizw\nvv/97w+YNk/L3dra6q1du9bzPM/btWuXN378eO/555/P/TYvt9y1sM13797teZ7nHThwwJs2bZr3\n8MMP5357e57/ctfC9vY8z/v+97/vXXzxxd6sWbM8z6uNY7rnDVzuJLa38StYtgYgdY3X787q0qVL\n1dTUJElqamrSkiVLJEl33XWX5syZo8GDB2vMmDE64YQTtGbNmsTjjeLMM8/UsGHD+nwWZjkff/xx\ntba2ateuXYeu9H3qU586NI+r/JZbGrjNpXwt98iRIzV16lRJ0lFHHaWTTz5Z27Zty/02L7fcUv63\n+Tvf+U5J0v79+9Xd3a1hw4blfntL/sst5X97b926VcuXL9dll112aFlrYXv7Lbfneda3t/EEy28A\n0uLBKi8KhYLOOussnXrqqYfGAWtvb1ddXZ0kqa6uTu2/GzH0tdde6zN8RdbXR9jl7P/5sccem9nl\nv+GGGzRlyhTNmzfv0GX0vC735s2btXbtWk2bNq2mtnlxuU8//XRJ+d/mPT09mjp1qurq6g7dJq2F\n7e233FL+t/fnP/95ffe739URRxw+9dfC9vZb7kKhYH17G0+wCjUwlOvq1au1du1a3XPPPbrpppv0\n8MMP9/m+UChUXA95WUfVljNPPv3pT2vTpk1at26dRo0apS9+8Ytph2RNV1eXZs+ereuvv15D+r1V\nPc/bvKurSx//+Md1/fXX66ijjqqJbX7EEUdo3bp12rp1q375y19q1apVfb7P6/buv9wtLS25397L\nli3TiBEjVF9f73vlRsrn9i633Elsb+MJVpABSLNu1KhRkqT3vOc9Ov/887VmzRrV1dWpra1NktTa\n2qoRI0ZIGrg+tm7dqmOPPTb5oA0Js5yjR4/Wscceq61bt/b5PIvLP2LEiEMHn8suu+zQbd68LfeB\nAwc0e/ZszZ07V+edd56k2tjmxeX+y7/8y0PLXSvbXJLe9a536ZxzztGTTz5ZE9u7qLjcTzzxRO63\n96OPPqqlS5fq+OOP15w5c/Tggw9q7ty5ud/efsv9qU99KpntbaT3WIkDBw5473vf+7xNmzZ5+/bt\ny10n9927d3s7d+70PM/zurq6vDPOOMO79957vS9/+ctec3Oz53met3DhwgEdBfft2+e9/PLL3vve\n975DHeayYNOmTQM6uYddztNOO8177LHHvJ6ensx0iOy/3K+99tqhf//gBz/w5syZ43levpa7p6fH\nmzt3rnfFFVf0+Tzv27zccud9m7/xxhvem2++6Xme5+3Zs8c788wzvZUrV+Z+e5db7tbW1kPT5HF7\nl2ppafE++tGPep6X//27VOlyJ7F/G0+wPM/zli9f7o0fP94bO3as9+1vf9tGFal5+eWXvSlTpnhT\npkzxJk6ceGj5duzY4c2YMcMbN26c19jYeGgH9jzP+9a3vuWNHTvWO/HEE70VK1akFXpoF110kTdq\n1Chv8ODB3ujRo71/+7d/i7ScTzzxhDdp0iRv7Nix3uc+97k0FiWU/st9yy23eHPnzvUmT57snXLK\nKd7HPvYxr62t7dD0eVnuhx9+2CsUCt6UKVO8qVOnelOnTvXuueee3G9zv+Vevnx57rf5008/7dXX\n13tTpkzxJk+e7H3nO9/xPC/asSwPy5337V2qpaXl0NN0ed/epVatWnVouf/yL//S+vZmoFEAAADD\nrAw0CgAAUMtIsAAAAAwjwQIAADCMBAsAAMAwEiwAAADDSLAAAAAMI8ECAAAwjAQLAADAMBIsAAAA\nw0iwAAAADCPBAgAAMIwECwAAwDASLAAAAMNIsAAAAAwjwQIAADCMBAsAAMAwEiwAAADDSLAAAAAM\nI8ECAAAwjAQLAADAMBIsAAAAw0iwAAAADCPBAgAAMIwECwAAwDASLAAAAMNIsAAAAAwjwQIAADCM\nBAsAAMAwEiwAAADDSLAAAAAMI8ECAAAwjAQLAADAMBIsAAAAw6omWFu2bNH06dM1ceJETZo0SYsW\nLZIkLViwQKNHj1Z9fb3q6+u1YsUK68ECAABkQcHzPK/SBG1tbWpra9PUqVPV1dWl97///VqyZInu\nuOMODRkyRF/4wheSihUAACATBlWbYOTIkRo5cqQk6aijjtLJJ5+sbdu2SZKq5GYAAAA1KVQfrM2b\nN2vt2rU6/fTTJUk33HCDpkyZonnz5qmzs9NKgAAAAJnjBbRr1y7v/e9/v3fnnXd6nud57e3tXk9P\nj9fT0+N95Stf8S699NIB84wdO9aTxB/+8Ic//OEPf/jj/J+xY8cGTYuqqtoHS5IOHDigj370o/rI\nRz6iK664YsD3mzdv1qxZs/TMM8/0+bxQKHAbMaMWLFigBQsWpB0GImL7ZRvbL7vYdtlmMm+peovQ\n8zzNmzdPEyZM6JNctba2Hvr3nXfeqcmTJxsJCAAAIOuqdnJfvXq1brvtNp1yyimqr6+XJH3729/W\n7bffrnXr1qlQKOj444/Xj3/8Y+vBAgAAZEHVBOsDH/iAenp6Bnz+kY98xEpAcENDQ0PaISAGtl+2\nsf2yi22HokB9sCIXTh8sAACQEYn2wQIAAEA4JFgAAACGkWABAAAYRoIFAABgGAlWQoYOHa5CodDn\nz9Chw9MOCwAAWMBThAkpFArqHYm/z6esHwAAHMFThAAAAA4jwQIAADCMBAsAAMAwEiwAAADDSLAA\nAAAMI8ECAAAwjAQLAADAMBIsAAAAw2oiwWIUdQAAkKSaGMndhVHUXYgBAACUx0juAAAADiPBAgAA\nMIwECwAAwDASLAAAAMNIsAAAAAwjwQIAADCMBAsAAMAwEiwAAADDSLAAAAAMI8EKwO9VO7xuBwAA\nlMOrciLPb6IMN9YPAADgVTkAAABOI8ECAAAwjAQLAADAMBIsAAAAw0iwAAAADCPBAgAAMIwECwAA\nwDASLAAAAMNIsFI1iBHiAQDIoUFpB1DbDspvhPhduwrJhwIAAIzhChYAAIBhJFgAAACGkWABAAAY\nVjXB2rJli6ZPn66JEydq0qRJWrRokSSpo6NDjY2NGj9+vGbOnKnOzk7rwQIAAGRBwfO8gb2sS7S1\ntamtrU1Tp05VV1eX3v/+92vJkiW69dZbdcwxx+jKK6/UtddeqzfffFPNzc19Cy8UVKX4RBQKBQ3s\nTB48Nv/5TZQRv1wAAGCGybyl6hWskSNHaurUqZKko446SieffLK2bdumpUuXqqmpSZLU1NSkJUuW\nGAkIAAAg60L1wdq8ebPWrl2radOmqb29XXV1dZKkuro6tbe3WwkQAAAgawInWF1dXZo9e7auv/56\nDRkypM93xQEyAQAAEHCg0QMHDmj27NmaO3euzjvvPEm9V63a2to0cuRItba2asSIEb7zLliw4NC/\nGxoa1NDQEDtoAACAuFpaWtTS0mKl7Kqd3D3PU1NTk44++mhdd911hz6/8sordfTRR+uqq65Sc3Oz\nOjs76eQeIQY6uQMA4AaTeUvVBOuRRx7RBz/4QZ1yyimHbgMuXLhQp512mi644AK9+uqrGjNmjO64\n4w69+93vthZoHCRYAACgmkQTrFiFk2BVjYEECwAANyQ6TAMAAADCIcECAAAwjAQLAADAMBIsAAAA\nw0iwAAAADCPBAgAAMCzQSO4oZ5DPK4IGSzqQRjAAAMARJFixHFSYsa0AAEBt4BYhAACAYSRYAAAA\nhpFgAQAAGEaCBQAAYBgJFgAAgGEkWAAAAIaRYAEAABhGggUAAGBYrhKsoUOHq1AoDPgDAACQpILn\neX7DjpspvFCQxeJ96ys/ivrAEdeDxha2XBPTJrneAACA2bwlV1ewAAAAXECCBQAAYBgJFgAAgGEk\nWAAAAIaRYAEAABhGggUAAGAYCRYAAIBhJFgAAACGOZNglRuFfejQ4YGnD2dQqPryIOw6BgAA0Tgz\nknul0dL9yvCf3s4o6nkZyT3sOgYAoJYwkjsAAIDDSLAAAAAMI8ECAAAwjAQLAADAMBIsAAAAw0iw\nAAAADCPBAgAAMIwECwAAwDASLAAAAMNIsAAAAAwjwQIAADCMBAsAAMAwEiwAAADDSLAAAAAMq5pg\nXXrppaqrq9PkyZMPfbZgwQKNHj1a9fX1qq+v14oVK6wGCQAAkCVVE6xLLrlkQAJVKBT0hS98QWvX\nrtXatWv14Q9/2FqAAAAAWVM1wTrzzDM1bNiwAZ97nmclIAAAgKyL3Afrhhtu0JQpUzRv3jx1dnaa\njAkAACDTIiVYn/70p7Vp0yatW7dOo0aN0he/+MWy0x599Hv7/DnmmPdq2bJlkQMGAABw3aAoM40Y\nMeLQvy+77DLNmjWr7LQdHbNL/ne6fv/3l+nVV1+NUi1CGDp0uHbtejPlugZLOtDnkyFDhmnnzo5E\n4gIAoJKWlha1tLRYKTtSgtXa2qpRo0ZJku68884+TxgOdF2f/xUKD0WpEiH1Jjz9+8kVEqyrWJ/X\nb1o7MQAAEFZDQ4MaGhoO/f+aa64xVnbVBGvOnDl66KGHtH37dh133HG65ppr1NLSonXr1qlQKOj4\n44/Xj3/8Y2MBAQAAZF3VBOv2228f8Nmll15qJRgAAIA8YCR3AAAAw0iwAAAADCPBAgAAMIwECwAA\nwDASLAAAAMNIsAAAAAwjwYIThg4drkKh0OfP0KHD0w4LAIBIIo3kDpjmNxo8o74DALKKK1gAAACG\nkWABAAAYRoIFAABgGAkWAACAYSRYAAAAhpFgAQAAGEaCBQAAYBgJFgAAgGEFz/O86pNFLLxQUP/B\nI4888jPyvP/Uvn1dPnP4hVKQX4h+ZUt+n5X7vPy0/evzryt8ucGnHSzpYJ9PhgwZpp07O3ym9Rd2\n/cRpBmHXT5jtabF5AgDQR6Fg7ryTykjuvcmV38kfvQ6KUc0BAMgubhECAAAYRoIFAABgGAkWAACA\nYSRYAAAAhpFgAQAAGEaCBQAAYBgJFgAAgGEkWAAAAIaRYGXc0KHDVSgUBvwJZ9CA+YcOHW4lXgAA\nakEqI7nDnF273lT519QExcjxAACYxBUsAAAAw0iwAAAADCPBAgAAMIwECwAAwDASLAAAAMNIsAAA\nAAwjwQIAADCMBAsAAMAwEqwBBo5q7oaBcdmNzb8+RnivzG9kfdYZANQeRnIfYOCo5uFGRbfFLy7J\nXmz+9THCe2V+I+uzzgCg9nAFCwAAwDASLAAAAMNIsAAAAAwjwQIAADCsaoJ16aWXqq6uTpMnTz70\nWUdHhxobGzV+/HjNnDlTnZ2dVoMEAADIkqoJ1iWXXKIVK1b0+ay5uVmNjY3asGGDZsyYoebmZmsB\nAgAAZE3VBOvMM8/UsGHD+ny2dOlSNTU1SZKampq0ZMkSO9EBAABkUKQ+WO3t7aqrq5Mk1dXVqb29\n3WhQAAAAWRZ7oNHqI4ovKPl3Q9zqkLpBMUeQjzu/GUOHDv/doKCHDRkyTDt3dliozX+Z49bntwwm\nyjXB5dgAoKilpUUtLS1Wyi54nuc3PHgfmzdv1qxZs/TMM89Ikk466SS1tLRo5MiRam1t1fTp0/XC\nCy8MLLxQUP9RrY888jPau/dHAz7vHZHcf6RyvxD9yq5URm1N63Js4aYN0DwjKdd+4tYXtl3Gqc+/\nrvjlmuBybABQTqFg7hgV6Rbhueeeq8WLF0uSFi9erPPOO89IMAAAAHlQNcGaM2eOzjjjDL344os6\n7rjjdOutt2r+/Pm6//77NX78eD344IOaP39+ErECAABkQqBbhJEL5xZhStO6HBu3CE3V5/JtOJdj\nA4ByUr9FCAAAgPJIsAAAAAwjwQIAADCMBAsAAMAwEiwAAADDMpBgDTo0WnzpH9SuoUOH+7SJ3/Nt\nJ0OHDk87XABADYr9qhz7Dqr8Y/2oRb2vYAk2FMKuXbQTAEDyMnAFCwAAIFtIsAAAAAwjwQIAADCM\nBAsAAMAwEiwAAADDSLAAAAAMI8ECAAAwjAQLAADAMBIsAAAAw0iwkHNhXrXkP63fa3jy8goe/9cO\nubF8frG5EBcABJGBV+UAcYR51VKlaft+npdX8Pi/dsiN5fOLzYW4ACAIrmABAAAYRoIFAABgGAkW\nAACAYSRYAAAAhpFgAQAAGEaCBQAAYBgJFgAAgGEkWAAAAIaRYMFhYUZhzwdbI6uXKxcAYAcjucNh\nYUZhzwdbI6uXKzfP6xIA0sQVLAAAAMNIsAAAAAwjwQIAADCMBAsAAMAwEiwAAADDSLAAAAAMI8EC\nAAAwjAQLAADAMBIsIJKkR5kfWF/SXB5l3q+McnHFndbEMrsgz8sGuKDgeZ7f8M5mCi8U1H/06COP\n/Iz27v3RgM97R5QuN9J00M+Z1v3YXJjWndj6735++0zy5YYvw9a0fsvsd8jyL8PWtOWnz5I8LxsQ\nVaFgrv1zBQsAAMAwEiwAAADDSLAAAAAMGxRn5jFjxmjo0KF6xzveocGDB2vNmjWm4gIAAMisWAlW\noVBQS0uLhg/nqRMAAICi2LcIedoEAACgr1gJVqFQ0FlnnaVTTz1VN998s6mYAAAAMi3WLcLVq1dr\n1KhReuONN9TY2KiTTjpJZ555pqnYAAAAMilWgjVq1ChJ0nve8x6df/75WrNmjU+CtaDk3w1xqgMQ\nyKBURnoPxuXYwhi4HEOGDNPOnR0pxQMgipaWFrW0tFgpO/JI7nv27FF3d7eGDBmi3bt3a+bMmfra\n176mmTNnHi6ckdxTmtbl2FyY1p3YbI3kbmL9JDk6exZHcg9ahqsYyR0YyORI7pGvYLW3t+v888+X\nJB08eFCf/OQn+yRXAAAAtSpygnX88cdr3bp1JmMBAADIBUZyBwAAMIwECwAAwDASLAAAAMNIsAAA\nAAwjwQIAADCMBAsAAMAwEiwgNb2jgZf+cYfLsblq4DorFAoaOnR4rFKHDh1updy881tvrDMkKdar\ncgDEcVD+o5q7wOXYXOW3zqRdu+Ktt1273rRSbt75rTfWGZLEFSwAAADDSLAAAAAMI8ECAAAwjAQL\nAADAMBIsAAAAw0iwAAAADCPBAgAAMIwECwAAwDASLAAZ4j9aem0auC7KjVTuN6o5zGLkePTHSO4A\nMsR/tPTaHGV+4LooN1K5/2jwtbjO7GHkePTHFSwAAADDSLAAAAAMI8ECAAAwjAQLAADAMBIsAAAA\nw0iwAAAADCPBAgAAMIwECwAAwDASLAAAAMNIsADENPCVLdn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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\ni4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024f8d90>"
+        "<matplotlib.figure.Figure at 0x7fd63064b890>"
        ]
       }
      ],
@@ -520,9 +520,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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TLgXgz65de37mYgcEk7YB7Zzr6ZPa4KqmRnr99eDrX3ihVPBwY7u0bUFrT3HVuG/y3zJV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\nwCCCKwAAAIMIrgAAAAwiuAIAADCI4AoAAMAggis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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\nGERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10b0e8910>"
+        "<matplotlib.figure.Figure at 0x7fd6306fb450>"
        ]
       }
      ],
@@ -550,15 +550,15 @@
        "output_type": "pyout",
        "prompt_number": 18,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x12b260710>]"
+        "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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lyH37zIQBkC8RRs2wHAlASkQYNaNUqixB9r860nIkAHkSYRTa0HPCIixHApAG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\nbGyMxsbGmDVrVkREfOYzn4n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+acS6dRH/+q8RU6akPhoAJpqx7Ja6I2ENDQ2xcuXKWLRoUVSr1Vi2bFl0dHTEqlWrIiJi\n+fLl8eUvfzleeOGFuPnmmyMiorGxMXp7ew+7LwAARxgJOyEHYCSME2hwJOzZZyPOPjv10QAw0Yxl\nt1gxn6zofQDKQoSRJTEGQGoiDAAgARFGVvwBbwDKQoQBACQgwsiSkTAAUhNhAAAJiDCy4powAMpC\nhAEAJCDCAAASEGFkxXQkAGUhwgAAEhBhZMlIGACpiTCyIr4AKAsRRpbEGACpiTAAgAREGFkyEgZA\naiKMrIgvAMpChJElMQZAaiIMACABEUZWjIABUBYijCyJMQBSE2EAAAmIMLLiD3gDUBYiDAAgARFG\nloyEAZCaCAMASECEkRXXhAFQFiIMACABEUaWjIQBkJoIIyviC4CyEGEAAAmIMLJkRAyA1EQYAEAC\nIoysWKICgLIQYQAACYgwsmQkDIDURBhZEV8AlIUII0tiDIDURBgAQAIijKx4diQAZSHCAAASEGEA\nAAmIMLJkOhKA1EQYWRFfAJSFCCNLYgyA1EQYAEACIoysWKICgLIQYQAACYgwsmQkDIDURBhZEV8A\nlIUII0tiDIDURBgAQAIiDAAgARFGVixRAUBZiDAAgAREGFkyEgZAaiKMrIgvAMpChJElMQZAaiIM\nACABEUZWPDsSgLIQYQAACYgwsmQkDIDURBgAQAIijKy4JgyAshBhAAAJiDAAgAREGFkxHQlAWYgw\nAIAERBhZMhIGQGoijKyILwDK4ogR1t3dHe3t7dHW1hYrVqw45P7HH388Lrvssnj7298eX//61w+6\nr6WlJWbPnh1z586N+fPnj91Rw3ESYwCk1lDvzmq1GrfcckusX78+mpqaYt68ebF48eLo6OgY2mbK\nlCnxzW9+M+65555D9q9UKtHT0xNnnXXW2B85AMAEVnckrLe3N1pbW6OlpSUaGxtj6dKlsXbt2oO2\nmTp1anR2dkZjY+OIj1EYcqCEfFkCkFrdCOvv748ZM2YM3W5ubo7+/v5RP3ilUonLL788Ojs74847\n7zz2o4QxIr4AKIu605GVSuW4Hvyhhx6KadOmxbPPPhsLFy6M9vb2WLBgwXE9JhyvSkWMAZBe3Qhr\namqKvr6+odt9fX3R3Nw86gefNm1aRNSmLJcsWRK9vb0jRtitt9469HZXV1d0dXWN+mPA0TrO3y0A\nyEhPT0/09PSMy2PXjbDOzs7YunVr7Ny5M6ZPnx5r1qyJ1atXj7jt8Gu/9u3bF9VqNU499dTYu3dv\n3HffffGlL31pxH0PjDAYT0bAADgawweHbrvttjF77LoR1tDQECtXroxFixZFtVqNZcuWRUdHR6xa\ntSoiIpYvXx67d++OefPmxUsvvRQnnXRS3HHHHbFly5Z45pln4tprr42IiIGBgbjuuuviiiuuGLMD\nh2NlOhKAMqgUiZ++WKlUPIOSE+bd747YvDniwQcj3vOe1EcDwEQzlt1ixXyyUhRGwgAoBxFGdlyY\nD0AZiDCyZCQMgNREGNkxEgZAGYgwsuKaMADKQoSRHSNhAJSBCCNLRsIASE2EkZXB6UgASE2EkR0R\nBkAZiDCyZDoSgNREGFkxHQlAWYgwsmOJCgDKQISRHSNhAJSBCCNLRsIASE2EkRXXhAFQFiKM7Lgm\nDIAyEGFkx0gYAGUgwsiKP+ANQFmIMLJjJAyAMhBhAAAJiDCyYjoSgLIQYWTHdCQAZSDCyJKRMABS\nE2Fkx0gYAGUgwsjK4DVhb7wRsWtX6qMBIGcijOxUKhEPPBBx002pjwSAnIkwsvTGG7UXAEhFhJEV\nS1QAUBYijOwMRpgQAyAlEUZ2KpWIN98UYQCkJcLIwnPPHRxeAgyA1EQYWfiLv4j4+c9rb5uOBKAM\nRBhZeP312kuECAOgHEQY2XjzzbfeFmAApCbCyMLgyNeBS1QIMQBSEmFkoSjeGgkTYQCUgQgjCwdG\nlyUqACgDEUY2BsPLivkAlIEIIwvDR8JMRwKQmggjC8OjS4QBkJoIIxumIwEoExFGFkxHAlA2Iows\nHLhExeBtEQZASiKMbAxfogIAUhJhZMGK+QCUjQgjC1bMB6BsRBhZGGmJCgBISYSRjeFLVAgxAFIS\nYWTBEhUAlI0IIwsj/QFvAEhJhJEN64QBUCYijCxYogKAshFhZGGkJSoAICURRjaGXxMmxABISYSR\nhQOnIw98HwCkIsLIghXzASgbEUYWLFEBQNmIMLJhxXwAykSEkQUr5gNQNiKMLLgoH4CyEWFkw4X5\nAJSJCCMLVswHoGxEGFkYvkSFZ0cCkJoIIxvDrwkzEgZASiKMLJiOBKBsRBhZ8Ae8ASgbEUYWRlqi\nQogBkJIIIxtWzAegTEQYWRhpxXwASEmEkYWR/oC3EAMgJRFGNg5cG8x0JACpHTHCuru7o729Pdra\n2mLFihWH3P/444/HZZddFm9/+9vj61//+lHtCyfKSEtUAEBKdSOsWq3GLbfcEt3d3bFly5ZYvXp1\nPPbYYwdtM2XKlPjmN78Zn/vc5456XzhRRlqiQogBkFLdCOvt7Y3W1tZoaWmJxsbGWLp0aaxdu/ag\nbaZOnRqdnZ3R2Nh41PvCiTLShfkiDICU6kZYf39/zJgxY+h2c3Nz9Pf3j+qBj2dfGA/Dl6gAgJQa\n6t1ZqVSO+YGPZt9bb7116O2urq7o6uo65o8LIxk+ElatCjEAjqynpyd6enrG5bHrRlhTU1P09fUN\n3e7r64vm5uZRPfDR7HtghMF4sGI+AMdi+ODQbbfdNmaPXXc6srOzM7Zu3Ro7d+6M/fv3x5o1a2Lx\n4sUjblsM+4l2NPvCiWDFfADKpO5IWENDQ6xcuTIWLVoU1Wo1li1bFh0dHbFq1aqIiFi+fHns3r07\n5s2bFy+99FKcdNJJcccdd8SWLVvilFNOGXFfSMGK+QCUTaUYPoR1og+gUjlkFA3G2nnnRfz5n0d8\n73sRF1wQ8eqrEbt3R+zcmfrIAJhIxrJbrJhPNixRAUCZiDCyYMV8AMpGhJGF4Svm+wPeAKQmwsiC\nJSoAKBsRRjasmA9AmYgwsuBvRwJQNiKMLAyPMNeEAZCaCCMbwguAMhFhZMF0JABlI8LIgiUqACgb\nEUY2hi9RAQApiTCyMNKK+UIMgJREGFkYPh0pwgBITYSRhZEuzAeAlEQY2Ri+Yr4QAyAlEUYWLNYK\nQNmIMLIw0h/wBoCURBjZMB0JQJmIMLJgxXwAykaEkYUDl6gYvC3CAEhJhJENS1QAUCYijCxYMR+A\nshFhZMEf8AagbEQYWbBEBQBlI8LIhiUqACgTEUYWLFEBQNmIMLIw0jVhAJCSCCMbnh0JQJmIMLJg\nOhKAshFhZGGkFfMBICURRhaMhAFQNiKMbBy4QKsIAyA1EUYWRhoJA4CURBhZMB0JQNmIMLJhxXwA\nykSEkYXhI2EWawUgNRFGFkZaosJIGAApiTCyYcV8AMpEhJEFF+YDUDYijCyM9Ae8BwYi7rkn7XEB\nkC8RRhaGj3wNvv35z6c5HgAQYWRj+BIVEaYkAUhHhJGFw62YL8IASEWEkYXDTUeKMABSEWFkw3Qk\nAGUiwsjC4aYjrZwPQCoijGwcuESFkTAAUhNhZGP4ivmD7wOAFEQYk97w4KpU3rrPdCQAqYgwJr16\n13+JMABSEWFMekbCACgjEUY2Dlyi4sD3AUAKIoxJz0gYAGUkwpj06j0TUoQBkIoIIxumIwEoExHG\npFdvOtI6YQCkIsKY9OotUVGtnthjAYBBIoxsHLhi/iDTkQCkIsKY9ExHAlBGIoxJb/h0pJEwAMpA\nhDHp1VuiwjVhAKQiwsiGJSoAKBMRxqTnmjAAykiEMenVuyYMAFIRYWRjcIkKACgDEcakV286EgBS\nEWFMeqYjASgjEcakV2+JCgBIRYSRjZGWqACAVEQYk55rwgAooyNGWHd3d7S3t0dbW1usWLFixG0+\n/elPR1tbW8yZMyceeeSRofe3tLTE7NmzY+7cuTF//vyxO2o4CiIMgDJqqHdntVqNW265JdavXx9N\nTU0xb968WLx4cXR0dAxts27duti2bVts3bo1Nm7cGDfffHNs2LAhIiIqlUr09PTEWWedNb6fBYyC\n6UgAyqTuSFhvb2+0trZGS0tLNDY2xtKlS2Pt2rUHbXPvvffGjTfeGBERl156aezZsyeefvrpofsL\nV0OT2JFGwnyJApBC3Qjr7++PGTNmDN1ubm6O/v7+UW9TqVTi8ssvj87OzrjzzjvH8rhh1IYvUTGc\nvx8JQAp1pyMro5y3Odxo189//vOYPn16PPvss7Fw4cJob2+PBQsWHP1RwnE66aS3Vsw3EgZAGdSN\nsKampujr6xu63dfXF83NzXW32bVrVzQ1NUVExPTp0yMiYurUqbFkyZLo7e0dMcJuvfXWobe7urqi\nq6vrqD8ROJyiiHjb20xHAnD0enp6oqenZ1weu26EdXZ2xtatW2Pnzp0xffr0WLNmTaxevfqgbRYv\nXhwrV66MpUuXxoYNG+KMM86Ic845J/bt2xfVajVOPfXU2Lt3b9x3333xpS99acSPc2CEwVgbHP06\n3LSjCAPgcIYPDt12221j9th1I6yhoSFWrlwZixYtimq1GsuWLYuOjo5YtWpVREQsX748rrrqqli3\nbl20trbGySefHN/+9rcjImL37t1x7bXXRkTEwMBAXHfddXHFFVeM2YHDaBXFW9OREUbCACiHSpH4\n6YuVSsUzKBlXzzwTcf75EdVqxP79Ef/5P0f81//61v379kW84x3pjg+AiWMsu8WK+Ux6RsIAKCMR\nxqQ3PMJGuh8ATjQRRhZOOunwK+aLMABSEGFMekeajrRYKwApiDAmvcEIG4wtI2EAlEHdJSpgsnBh\nPgBlYySMSW9wJGyQCAOgDEQYk95IF+MfyDVhAKQgwpj0BiPspMN8tRsJAyAFEUY2BkfDTEcCUAYi\njElv+EiYCAOgDEQYk95ghB1uJMw1YQCkIMLIxuEuzjcSBkAKIoxJ70gjYSIMgBREGJPe8Agb6X4A\nONFEGNlwTRgAZSLCmPSMhAFQRiKMSc81YQCUkQhj0rNiPgBlJMLIhmvCACgTEcak55owAMpIhDHp\niTAAykiEkQ0X5gNQJiKMSe9II2GuCQMgBRHGpGeJCgDKSIQx6bkmDIAyEmFkw0gYAGUiwpj0XBMG\nQBmJMCa94SvmGwkDoAxEGNlwTRgAZSLCmPQ8OxKAMhJhTHqeHQlAGYkwsuHCfADKRIQx6ZmOBKCM\nRBiTnulIAMpIhDHpGQkDoIxEGNlwTRgAZSLCmPSMhAFQRiKMSW/4ivkj3Q8AJ5oIIxtGwgAoExHG\npOcPeANQRiKMSc81YQCUkQgjC9YJA6BsRBiT3mBkiTAAykSEMekdaTrSNWEApCDCmPT82SIAykiE\nkQ0X5gNQJiKMSc9irQCUkQhj0nNNGABlJMLIgmvCACgbEcakN3yJCteEAVAGIoxJz7MjASgjEUYW\nXBMGQNmIMCY9K+YDUEYijEnPdCQAZSTCmPSOtESFCAMgBRFGNg43EuaaMABSEGFMesNXzDcSBkAZ\niDAmPdeEAVBGIowsuCYM4Ojcf3/E+vWpj2JyE2FMelbMBzh669eLsPHWkPoAYLwNn448+eSD73dh\nPsChXn01olpNfRSTmwhj0hseYaeeeuj9ABxMhI0/EUY2Xnut9lqEARyZCBt/IoxJb3AkbN++2u3T\nTjv0fgAOJsLGnwhj0huMsFdfrd0ePhLmmjCAQ4mw8SfCmNTWrIn4b/+tFl6HizAjYQCHEmHjzxIV\nTGj/7/9FvPHG4e//l3+J2Lat9vbgdKQIAziyV19965fXY3HXXW9di8vIRBgT2sc+FvGLXxz+/hdf\njNiz5+DpyLe//eBtxivCBgYienrG57Enm8FQBsrjtdeOL8L+43/0vX0kIowJ7V//NeK55w5//4sv\n1obTK5WI118f+c8Xjdc1YT/7WcT73mek7UiKImL27PrnETjxjmckrCginn++9sLhiTAmtOefr//D\ne8+eg2+/4x2HbjNekbR/f+317343Po8/Wbz0Uu0/+meeSX0k8JaBgYjf/Cb1UYyvrq6I/v7D3388\nEfbyy7VfgEVYfUeMsO7u7mhvb4+2trZYsWLFiNt8+tOfjra2tpgzZ0488sgjR7UvHKvXX4/Yu7cW\nYffcU/vAg9n7AAALaElEQVQ7Z8O9+GLt9eDo12CETZ361jbjFWHPPlt7/Xd/ZzSsnsH4EmGUyQMP\nRHzoQ6mPYvwURcSGDfWnC48nwgbjS4TVVzfCqtVq3HLLLdHd3R1btmyJ1atXx2OPPXbQNuvWrYtt\n27bF1q1b4x/+4R/i5ptvHvW+THw9CS96GhwBe/752rMg1649dJvBkbDhEXbgD/zxjLDLLqtNS/6n\n/xTxv//36Pe9//6Ivr7xOa5BKc/dgUYTYa+9Vv8JGDkqy/mbrHbsqL2M1/8Pqc/fc8/VfpEdr5Gw\nwfgqy2UGr7wSceedqY/iUHUjrLe3N1pbW6OlpSUaGxtj6dKlsXbYT7p77703brzxxoiIuPTSS2PP\nnj2xe/fuUe3L2HrhhYi2thP7wyrlfyQHfpNv2xaxdeuh2wyOhA0+zfrf/ttDtxmva8KeeSbiz/4s\n4gtfiFixIuJrXxv9vl/8YsQPfjA+xzUo9Q+BQaOJsE99KuKOO07M8UwUZTl/ZfPqqxH/5b8c/+P8\n9re1H9zjNZKT+vwNxtdoIuxYQrRsI2EPPhjxmc/UppnLpG6E9ff3x4wZM4ZuNzc3R/+wM3a4bZ58\n8skj7puLN9889Nqk8fDzn9diZNOmsXm822+vTaWNlzffjPgf/+Ota6eO1uBvWM89F7F9+6HD6r/6\nVcSWLbW3X3qp9nqkCBvPkbB3vrN20XlExCOPjC74qtXaths2jM9xjYf/9b8iHnro2PYdnLYdfD2S\nn/2sNj00HnburP/b/t69tW2OpKcn4qabxuigovZ9vHjxxJ7KTnHs998f8ZWv1P5PGMnzz4986cJw\nv/1t7fVozv1ENPjj+MknR76/Wq39Ql+pjO4X+8cfj/jEJ9769xrLCHvlleP/GfrII7Xv87JNyNVd\nrLUy/Glkh1Ec53fa1Vcf1+6l99RTtUD4d/9ufD/O9u215Rc++cmIGTNqI2NPPx3R3n74feqdusEf\nqv/n/0Q0Nta+eM84I2L69Le2+c1varFzLF58sba8xH//7xHTpkW87W1Ht//TT0dMmVL74fzGGxFP\nPBHxp39aC5033zw4Rl9/vfb6/PMPfZyVKyN+9KNj+xzqefjhiGuuiZg1K+Lf/Jvab2BXXll7u57X\nXqudx56e2uczym/Do/bEE7VjHK1qtfY5DH89MFD7+n7HOyLmzRvdY73+eu3C3SlTal+3Z54Z8Z3v\n1I7njTciNm6MmDu39m8xZUrErl21Hxb//t/XPu5IL297W+1xTvr9r5bDv7Z37Kh9Xwz/9//Zz2rv\nb2kZ+Vj/5V9q38MLFtSmiBsbI84559DtHn20tt1vfxvR0FA7juM5d48/XvuBdvnlBz+hZPDzeuKJ\niP/7f+t/Dw8+K/jll2vHdOaZx348R2vnztq/x3veU/t+3Lev9kPwne888r7PPx9xyilH/l4Z9PTT\nta+Vwem1P/iDiD//89r/K8Nt21b7Wrj88vrnZ+PG2v5/8RcRM2cefN+bb9Z+qF9ySe3fNaJ2Hp56\nKuLcc9/6Ghy0d2/tuNra3nrfSN9/v/517f+o005767yO1euI2tduQ0Pt8+7vjzj99Ij/+T9Hvi6s\nWq390vq2t0X8h/9Qe/3ii7VzMtIvs48+WjvuRYsiLrig9oSks8+u/d+6a9eh29cz/Lxs2VL7Zf2S\nS47ucQ60eXPteD784dq/cVEc28uYK+r45S9/WSxatGjo9le/+tXi9ttvP2ib5cuXF6tXrx66PWvW\nrGL37t2j2rcoimLmzJlFRHjx4sWLFy9evJT+ZebMmfXS6ajUHQnr7OyMrVu3xs6dO2P69OmxZs2a\nWL169UHbLF68OFauXBlLly6NDRs2xBlnnBHnnHNOTJky5Yj7RkRss5IbAJChuhHW0NAQK1eujEWL\nFkW1Wo1ly5ZFR0dHrFq1KiIili9fHldddVWsW7cuWltb4+STT45vf/vbdfcFACCiUhQT+bJPAICJ\nKemK+RZzLbe+vr543/veF+9617vioosuim984xsREfH888/HwoUL44ILLogrrrgi9hzwtJWvfe1r\n0dbWFu3t7XHfffelOnR+r1qtxty5c+Pq3z/7xbmbOPbs2RMf/OAHo6OjIy688MLYuHGj8zeBfO1r\nX4t3vetdcfHFF8dHP/rReP31152/kvr4xz8e55xzTlx88cVD7zuWc/WrX/0qLr744mhra4vPfOYz\no/vgY3Z12VEaGBgoZs6cWezYsaPYv39/MWfOnGLLli2pDocRPPXUU8UjjzxSFEVRvPzyy8UFF1xQ\nbNmypfibv/mbYsWKFUVRFMXtt99efOELXyiKoij++Z//uZgzZ06xf//+YseOHcXMmTOLarWa7Pgp\niq9//evFRz/60eLqq68uiqJw7iaQG264ofjWt75VFEVRvPHGG8WePXucvwlix44dxXnnnVe89tpr\nRVEUxYc//OHiH//xH52/knrggQeKTZs2FRdddNHQ+47mXL355ptFURTFvHnzio0bNxZFURRXXnll\n8eMf//iIHzvZSJjFXMvv3HPPjUt+/5zgU045JTo6OqK/v/+gBXpvvPHGuOeeeyIiYu3atfGRj3wk\nGhsbo6WlJVpbW6O3tzfZ8edu165dsW7duvjEJz4xtIyMczcxvPjii/Hggw/Gxz/+8YioXWN7+umn\nO38TxGmnnRaNjY2xb9++GBgYiH379sX06dOdv5JasGBBnDls/ZajOVcbN26Mp556Kl5++eWYP39+\nRETccMMNQ/vUkyzCRrMQLOWxc+fOeOSRR+LSSy+Np59+Os75/UJJ55xzTjz99NMREfHkk09Gc3Pz\n0D7OaVqf/exn42//9m/jpAMWLXLuJoYdO3bE1KlT42Mf+1j80R/9Udx0002xd+9e52+COOuss+Kv\n//qv4w//8A9j+vTpccYZZ8TChQudvwnkaM/V8Pc3NTWN6hwmi7DRLgRLeq+88kp84AMfiDvuuCNO\nPfXUg+6rVCp1z6XznMY//dM/xTvf+c6YO3fuYRdTdu7Ka2BgIDZt2hR/+Zd/GZs2bYqTTz45br/9\n9oO2cf7Ka/v27fH3f//3sXPnznjyySfjlVdeie9///sHbeP8TRxHOlfHI1mENTU1Rd8Bf6G4r6/v\noIqkHN544434wAc+ENdff31cc801EVH7rWD37t0REfHUU0/FO3+/BPbwc7pr165oamo68QdN/OIX\nv4h77703zjvvvPjIRz4SP/3pT+P666937iaI5ubmaG5ujnm//xMEH/zgB2PTpk1x7rnnOn8TwMMP\nPxx//Md/HFOmTImGhoa49tpr45e//KXzN4Eczf+Vzc3N0dTUFLsO+NMAoz2HySLswIVg9+/fH2vW\nrInFixenOhxGUBRFLFu2LC688ML4q7/6q6H3L168OL7zne9ERMR3vvOdoThbvHhx3H333bF///7Y\nsWNHbN26dWh+nBPrq1/9avT19cWOHTvi7rvvjve///3xve99z7mbIM4999yYMWNGPPHEExERsX79\n+njXu94VV199tfM3AbS3t8eGDRvi1VdfjaIoYv369XHhhRc6fxPI0f5fee6558Zpp50WGzdujKIo\n4nvf+97QPnWN4RMMjtq6deuKCy64oJg5c2bx1a9+NeWhMIIHH3ywqFQqxZw5c4pLLrmkuOSSS4of\n//jHxXPPPVf8yZ/8SdHW1lYsXLiweOGFF4b2+cpXvlLMnDmzmDVrVtHd3Z3w6BnU09Mz9OxI527i\n+PWvf110dnYWs2fPLpYsWVLs2bPH+ZtAVqxYUVx44YXFRRddVNxwww3F/v37nb+SWrp0aTFt2rSi\nsbGxaG5uLu66665jOlcPP/xwcdFFFxUzZ84sPvWpT43qY1usFQAggaSLtQIA5EqEAQAkIMIAABIQ\nYQAACYgwAIAERBgAQAIiDAAgAREGAJDA/wckYxa5Es1/mgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13119c350>"
+        "<matplotlib.figure.Figure at 0x7fd630627290>"
        ]
       }
      ],
@@ -575,22 +575,15 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "# load labels\n",
       "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
       "try:\n",
       "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
       "except:\n",
       "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
+      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+      "\n",
+      "# sort top k predictions from softmax output\n",
       "top_k = net.blobs['prob'].data[4].flatten().argsort()[-1:-6:-1]\n",
       "print labels[top_k]"
      ],
@@ -601,16 +594,16 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "['n02115913 dhole, Cuon alpinus' 'n02119022 red fox, Vulpes vulpes'\n",
-        " 'n02119789 kit fox, Vulpes macrotis' 'n02123159 tiger cat'\n",
-        " 'n02123045 tabby, tabby cat']\n"
+        "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+        " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+        " 'n02127052 lynx, catamount']\n"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 19
     }
    ],
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index d64ae853ace..bf3d1140559 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -290,14 +290,14 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "array([[278, 151, 259, 281, 282, 259, 282, 282],\n",
-        "       [283, 259, 283, 282, 283, 281, 259, 277],\n",
-        "       [283, 283, 283, 287, 287, 287, 287, 282],\n",
-        "       [283, 283, 283, 281, 281, 259, 259, 333],\n",
-        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
-        "       [283, 283, 283, 283, 283, 259, 283, 333],\n",
-        "       [283, 356, 359, 371, 368, 368, 259, 852],\n",
-        "       [356, 335, 358, 151, 283, 263, 277, 744]])"
+        "array([[282, 281, 281, 281, 281, 282, 282, 278],\n",
+        "       [281, 283, 281, 283, 281, 287, 281, 282],\n",
+        "       [283, 283, 283, 281, 283, 283, 283, 259],\n",
+        "       [283, 283, 281, 281, 283, 283, 287, 259],\n",
+        "       [283, 283, 285, 283, 283, 283, 283, 283],\n",
+        "       [283, 283, 333, 283, 283, 283, 283, 282],\n",
+        "       [905, 358, 259, 283, 371, 259, 259, 852],\n",
+        "       [335, 335, 371, 185, 186, 263, 185, 356]])"
        ]
       },
       {
@@ -305,7 +305,7 @@
        "output_type": "display_data",
        "png": 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IKqyrUEsKkeG59FMkhNGdvjnnh86+C5dLZ+zbU4KSKciGT6otrpz5YIggA9gbUTM5aVFq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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\nMcdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1193b2c10>"
+        "<matplotlib.figure.Figure at 0x7f85901260d0>"
        ]
       }
      ],
@@ -315,7 +315,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The classifications include various cats -- 282 = tabby, 283 = tiger, 281 = persian -- and foxes and other mammals.\n",
+      "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
       "\n",
       "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself."
      ]

From f1eb9821ba717a55b684d42ef8c87125e855b402 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 28 Jul 2014 20:16:52 -0700
Subject: [PATCH 0388/2053] [example] include prediction in classification,
 time on GTX 770

---
 examples/imagenet_classification.ipynb | 66 +++++++++++---------------
 1 file changed, 28 insertions(+), 38 deletions(-)

diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index 40e69aea138..8ab65fda02f 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -112,15 +112,15 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.image.AxesImage at 0xa7ebfd0>"
+        "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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CGz2I6GEUheHG9DIS4f13V9KSycT3Vw14CDfMB3pdN3EgRSW4HXoeBGUQx4Ef+3XPCanO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\nMZX4O/OUkKSsrXFeYrzgdQ5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VTTG3Y79uagVhAFurUb6yDlvunjzimUtLnBJrsdEKnGbQ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K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\n5+crK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hg4za+43F6waRuscignebT5NK3ZoJstsnc0jSWXiRAn5ilymkfe+94zZGN49OQB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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\nMcdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x3fb41d0>"
+        "<matplotlib.figure.Figure at 0x7fda517da210>"
        ]
       }
      ],
@@ -139,7 +139,8 @@
      "input": [
       "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
       "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
+      "plt.plot(prediction[0])\n",
+      "print 'predicted class:', prediction[0].argmax()"
      ],
      "language": "python",
      "metadata": {},
@@ -148,23 +149,16 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0xc8b1bd0>]"
+        "prediction shape: (1000,)\n",
+        "predicted class: 281\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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j8dTduHFj/HlFRYUqKiom/LpIjxEBcHkKhUIKhUKTsu+0QeByuRSJROLLkUhEbrd7TDse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\nBABgOYIAACxHEACA5QgCALDc/wN1ylfVql6MNwAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xa7f9290>"
+        "<matplotlib.figure.Figure at 0x7fda20473950>"
        ]
       }
      ],
@@ -174,6 +168,10 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
+      "\n",
+      "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
+      "\n",
       "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
      ]
     },
@@ -183,7 +181,8 @@
      "input": [
       "prediction = net.predict([input_image], oversample=False)\n",
       "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
+      "plt.plot(prediction[0])\n",
+      "print 'predicted class:', prediction[0].argmax()"
      ],
      "language": "python",
      "metadata": {},
@@ -192,23 +191,16 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0xc8e7090>]"
+        "prediction shape: (1000,)\n",
+        "predicted class: 281\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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iLd0g8Pv9sFqtsFgsMBqNqK6uRmtra1yZrKwsOJ1OGI3GKdelmccgICIt3SAIhULIycmJ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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xc8b9550>"
+        "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
        ]
       }
      ],
@@ -218,9 +210,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
       "\n",
-      "Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are foxes, cats, and other cute mammals. Not unreasonable predictions, right?\n",
       "\n",
       "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
      ]
@@ -238,7 +228,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1 loops, best of 3: 492 ms per loop\n"
+        "1 loops, best of 3: 355 ms per loop\n"
        ]
       }
      ],
@@ -271,7 +261,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1 loops, best of 3: 327 ms per loop\n"
+        "1 loops, best of 3: 210 ms per loop\n"
        ]
       }
      ],
@@ -325,15 +315,15 @@
        "output_type": "pyout",
        "prompt_number": 10,
        "text": [
-        "[<matplotlib.lines.Line2D at 0xcbba250>]"
+        "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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wjsdTd+PGjfHnFRUVqqiomPDrIj1GBMDlKRQKKRQKTcq+0waBy+VSJBKJL0ciEbnd7jHt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\nIAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xa686ed0>"
+        "<matplotlib.figure.Figure at 0x7fda1ab53650>"
        ]
       }
      ],
@@ -343,7 +333,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a K20 GPU:"
+      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
      ]
     },
     {
@@ -360,7 +350,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 192 ms per loop\n"
+        "10 loops, best of 3: 174 ms per loop\n"
        ]
       }
      ],
@@ -380,7 +370,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 25.2 ms per loop\n"
+        "10 loops, best of 3: 34.2 ms per loop\n"
        ]
       }
      ],
@@ -390,7 +380,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual convnet computation itself!\n",
+      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
       "\n",
       "To fully utilize the power of GPUs, you really want to:\n",
       "\n",

From a0a5bb180f1b8d9fec5a8dd9523743bc088b47a4 Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Tue, 5 Aug 2014 12:57:03 +0800
Subject: [PATCH 0389/2053] convert_imageset now uses gflags; optional
 arguments can be positioned arbitrarily now

---
 tools/convert_imageset.cpp | 61 +++++++++++++++++---------------------
 1 file changed, 28 insertions(+), 33 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 21ff0d38104..bfbd2a1d542 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -15,6 +15,7 @@
 // converted to color.
 
 #include <glog/logging.h>
+#include <gflags/gflags.h>
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
 #include <lmdb.h>
@@ -34,53 +35,47 @@ using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
 using std::string;
 
+DEFINE_bool(gray, false, "When this option is on, treat images as grayscale ones; otherwise colored");
+DEFINE_bool(shuffle, false, "Randomly shuffle the order of images and their labels");
+DEFINE_string(backend, "leveldb", "The backend for storing the result");
+DEFINE_int32(resize_width, 0, "Width images are resized to");
+DEFINE_int32(resize_height, 0, "Height images are resized to");
+
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 4 || argc > 9) {
-    printf("Convert a set of images to the leveldb format used\n"
+  gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb format used\n"
         "as input for Caffe.\n"
         "Usage:\n"
-        "    convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME"
-        " RANDOM_SHUFFLE_DATA[0 or 1] DB_BACKEND[leveldb or lmdb]"
-        " [resize_height] [resize_width]\n"
+        "    convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME\n"
         "The ImageNet dataset for the training demo is at\n"
         "    http://www.image-net.org/download-images\n");
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  if (argc != 4) {
+    fputs("Use --help to see detailed description of usage\n", stdout);
     return 1;
   }
 
-  // Test whether argv[1] == "-g"
-  bool is_color= !(string("-g") == string(argv[1]));
-  int  arg_offset = (is_color ? 0 : 1);
-  std::ifstream infile(argv[arg_offset+2]);
+  bool is_color = !FLAGS_gray;
+  std::ifstream infile(argv[2]);
   std::vector<std::pair<string, int> > lines;
   string filename;
   int label;
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
   }
-  if (argc >= (arg_offset+5) && argv[arg_offset+4][0] == '1') {
+  if (FLAGS_shuffle) {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";
     shuffle(lines.begin(), lines.end());
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  string db_backend = "leveldb";
-  if (argc >= (arg_offset+6)) {
-    db_backend = string(argv[arg_offset+5]);
-    if (!(db_backend == "leveldb") && !(db_backend == "lmdb")) {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
-  }
+  const string& db_backend = FLAGS_backend;
+  const char* db_path = argv[3];
 
-  int resize_height = 0;
-  int resize_width = 0;
-  if (argc >= (arg_offset+7)) {
-    resize_height = atoi(argv[arg_offset+6]);
-  }
-  if (argc >= (arg_offset+8)) {
-    resize_width = atoi(argv[arg_offset+7]);
-  }
+  int resize_height = FLAGS_resize_height;
+  int resize_width = FLAGS_resize_width;
 
   // Open new db
   // lmdb
@@ -98,19 +93,19 @@ int main(int argc, char** argv) {
 
   // Open db
   if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << argv[arg_offset+3];
+    LOG(INFO) << "Opening leveldb " << db_path;
     leveldb::Status status = leveldb::DB::Open(
-        options, argv[arg_offset+3], &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << argv[arg_offset+3];
+        options, db_path, &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << db_path;
     batch = new leveldb::WriteBatch();
   } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << argv[arg_offset+3];
-    CHECK_EQ(mkdir(argv[arg_offset+3], 0744), 0)
-        << "mkdir " << argv[arg_offset+3] << "failed";
+    LOG(INFO) << "Opening lmdb " << db_path;
+    CHECK_EQ(mkdir(db_path, 0744), 0)
+        << "mkdir " << db_path << "failed";
     CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
     CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
         << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(mdb_env, argv[3], 0, 0664), MDB_SUCCESS)
+    CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
         << "mdb_env_open failed";
     CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
         << "mdb_txn_begin failed";
@@ -121,7 +116,7 @@ int main(int argc, char** argv) {
   }
 
   // Storing to db
-  string root_folder(argv[arg_offset+1]);
+  string root_folder(argv[1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;

From 42ebee599ba39884342e472f0355c0fc78e22d5c Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Tue, 5 Aug 2014 13:07:28 +0800
Subject: [PATCH 0390/2053] Handles gflags's change of namespace

---
 tools/convert_imageset.cpp | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index bfbd2a1d542..976f1b546c7 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -43,6 +43,11 @@ DEFINE_int32(resize_height, 0, "Height images are resized to");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
+
+#ifndef GFLAGS_GFLAGS_H_
+  namespace gflags = google;
+#endif
+  
   gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb format used\n"
         "as input for Caffe.\n"
         "Usage:\n"
@@ -74,8 +79,8 @@ int main(int argc, char** argv) {
   const string& db_backend = FLAGS_backend;
   const char* db_path = argv[3];
 
-  int resize_height = FLAGS_resize_height;
-  int resize_width = FLAGS_resize_width;
+  int resize_height = std::max<int>(0, FLAGS_resize_height);
+  int resize_width = std::max<int>(0, FLAGS_resize_width);
 
   // Open new db
   // lmdb

From e1d6655480714f8d381ad9b38aabe2e264dcfb3d Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Tue, 5 Aug 2014 13:15:22 +0800
Subject: [PATCH 0391/2053] Fix lint errors

---
 tools/convert_imageset.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 976f1b546c7..70f57d6ec6a 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -35,8 +35,10 @@ using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
 using std::string;
 
-DEFINE_bool(gray, false, "When this option is on, treat images as grayscale ones; otherwise colored");
-DEFINE_bool(shuffle, false, "Randomly shuffle the order of images and their labels");
+DEFINE_bool(gray, false,
+    "When this option is on, treat images as grayscale ones");
+DEFINE_bool(shuffle, false,
+    "Randomly shuffle the order of images and their labels");
 DEFINE_string(backend, "leveldb", "The backend for storing the result");
 DEFINE_int32(resize_width, 0, "Width images are resized to");
 DEFINE_int32(resize_height, 0, "Height images are resized to");
@@ -47,9 +49,9 @@ int main(int argc, char** argv) {
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
 #endif
-  
-  gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb format used\n"
-        "as input for Caffe.\n"
+
+  gflags::SetUsageMessage("Convert a set of images to the leveldb/lmdb\n"
+        "format used as input for Caffe.\n"
         "Usage:\n"
         "    convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME\n"
         "The ImageNet dataset for the training demo is at\n"

From 75f293871213b705df027d4a16d3584df92bd137 Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Wed, 6 Aug 2014 10:38:26 +0800
Subject: [PATCH 0392/2053] Reordering of header includes for
 convert_imageset.cpp

---
 tools/convert_imageset.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 70f57d6ec6a..b628a357185 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -14,8 +14,8 @@
 // single-channel grayscale. If omitted, grayscale images will be
 // converted to color.
 
-#include <glog/logging.h>
 #include <gflags/gflags.h>
+#include <glog/logging.h>
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
 #include <lmdb.h>

From d5c3cef47155b5c2aad146465187364a2b41fd99 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 31 Jul 2014 16:19:20 -0700
Subject: [PATCH 0393/2053] fix pycaffe input processing

- load an image as [0,1] single / np.float32 according to Python convention
- fix input scaling during preprocessing:
  - scale input for preprocessing by `raw_scale` e.g. to map an image
    to [0, 255] for the CaffeNet and AlexNet ImageNet models
  - scale feature space by `input_scale` after mean subtraction
  - switch examples to raw scale for ImageNet models
  - fix #525
- preserve type after resizing.
- resize 1, 3, or K channel images with special casing between
  skimage.transform (1 and 3) and scipy.ndimage (K) for speed
---
 examples/detection.ipynb               |  2 +-
 examples/filter_visualization.ipynb    | 19 +++------
 examples/imagenet_classification.ipynb |  4 +-
 examples/net_surgery.ipynb             |  4 +-
 python/caffe/_caffe.cpp                |  2 +
 python/caffe/classifier.py             | 15 +++++---
 python/caffe/detector.py               | 17 +++++----
 python/caffe/io.py                     | 18 +++++++--
 python/caffe/pycaffe.py                | 53 ++++++++++++++++++--------
 python/classify.py                     |  8 +++-
 python/detect.py                       | 11 ++++--
 11 files changed, 98 insertions(+), 55 deletions(-)

diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 3f2cf71ac57..3b0a5b2e705 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -36,7 +36,7 @@
      "input": [
       "!mkdir -p _temp\n",
       "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt --gpu _temp/det_input.txt _temp/det_output.h5"
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 0fe863bc7e5..ea99f06b148 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -66,8 +66,8 @@
       "net.set_mode_cpu()\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
       "net.set_mean('data', caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')  # ImageNet mean\n",
-      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB\n",
-      "net.set_input_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]"
+      "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
      ],
      "language": "python",
      "metadata": {},
@@ -178,12 +178,6 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# our network takes BGR images, so we need to switch color channels\n",
-      "def showimage(im):\n",
-      "    if im.ndim == 3:\n",
-      "        im = im[:, :, ::-1]\n",
-      "    plt.imshow(im)\n",
-      "    \n",
       "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
       "#  and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
       "def vis_square(data, padsize=1, padval=0):\n",
@@ -199,7 +193,7 @@
       "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
       "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
       "    \n",
-      "    showimage(data)"
+      "    imshow(data)"
      ],
      "language": "python",
      "metadata": {},
@@ -218,10 +212,7 @@
      "collapsed": false,
      "input": [
       "# index four is the center crop\n",
-      "image = net.blobs['data'].data[4].copy()\n",
-      "image -= image.min()\n",
-      "image /= image.max()\n",
-      "showimage(image.transpose(1, 2, 0))"
+      "imshow(net.deprocess('data', net.blobs['data'].data[4]))"
      ],
      "language": "python",
      "metadata": {},
@@ -606,4 +597,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index 8ab65fda02f..60e8bd070fb 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -53,7 +53,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean file, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and input scaling multiplies the feature scale from the input [0,1] to [0,255]."
+      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean file, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
      ]
     },
     {
@@ -63,7 +63,7 @@
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
       "                       mean_file=caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy',\n",
       "                       channel_swap=(2,1,0),\n",
-      "                       input_scale=255,\n",
+      "                       raw_scale=255,\n",
       "                       image_dims=(256, 256))"
      ],
      "language": "python",
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index bf3d1140559..31847b2418e 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -277,7 +277,7 @@
       "plt.imshow(im)\n",
       "net_full_conv.set_mean('data', '../python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
-      "net_full_conv.set_input_scale('data', 255.0)\n",
+      "net_full_conv.set_raw_scale('data', 255.0)\n",
       "# make classification map by forward pass and show top prediction index per location\n",
       "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
       "out['prob'][0].argmax(axis=0)"
@@ -331,4 +331,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 30c86aeb384..59317727914 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -278,6 +278,7 @@ struct CaffeNet {
   // Input preprocessing configuration attributes.
   dict mean_;
   dict input_scale_;
+  dict raw_scale_;
   dict channel_swap_;
   // if taking input from an ndarray, we need to hold references
   object input_data_;
@@ -329,6 +330,7 @@ BOOST_PYTHON_MODULE(_caffe) {
       .add_property("outputs",      &CaffeNet::outputs)
       .add_property("mean",         &CaffeNet::mean_)
       .add_property("input_scale",  &CaffeNet::input_scale_)
+      .add_property("raw_scale",    &CaffeNet::raw_scale_)
       .add_property("channel_swap", &CaffeNet::channel_swap_)
       .def("_set_input_arrays",     &CaffeNet::set_input_arrays)
       .def("save",                  &CaffeNet::save);
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index f347be42a93..48835bab842 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -14,13 +14,14 @@ class Classifier(caffe.Net):
     by scaling, center cropping, or oversampling.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
-                 gpu=False, mean_file=None, input_scale=None, channel_swap=None):
+                 gpu=False, mean_file=None, input_scale=None, raw_scale=None,
+                 channel_swap=None):
         """
         Take
         image_dims: dimensions to scale input for cropping/sampling.
-                    Default is to scale to net input size for whole-image crop.
-        gpu, mean_file, input_scale, channel_swap: convenience params for
-            setting mode, mean, input scale, and channel order.
+            Default is to scale to net input size for whole-image crop.
+        gpu, mean_file, input_scale, raw_scale, channel_swap: params for
+            preprocessing options.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
         self.set_phase_test()
@@ -32,9 +33,11 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
 
         if mean_file:
             self.set_mean(self.inputs[0], mean_file)
-        if input_scale:
+        if input_scale is not None:
             self.set_input_scale(self.inputs[0], input_scale)
-        if channel_swap:
+        if raw_scale is not None:
+            self.set_raw_scale(self.inputs[0], raw_scale)
+        if channel_swap is not None:
             self.set_channel_swap(self.inputs[0], channel_swap)
 
         self.crop_dims = np.array(self.blobs[self.inputs[0]].data.shape[2:])
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 56c26aefd45..a9b06cd17c7 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -25,11 +25,12 @@ class Detector(caffe.Net):
     selective search proposals.
     """
     def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
-                 input_scale=None, channel_swap=None, context_pad=None):
+                 input_scale=None, raw_scale=None, channel_swap=None,
+                 context_pad=None):
         """
         Take
-        gpu, mean_file, input_scale, channel_swap: convenience params for
-            setting mode, mean, input scale, and channel order.
+        gpu, mean_file, input_scale, raw_scale, channel_swap: params for
+            preprocessing options.
         context_pad: amount of surrounding context to take s.t. a `context_pad`
             sized border of pixels in the network input image is context, as in
             R-CNN feature extraction.
@@ -44,9 +45,11 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
 
         if mean_file:
             self.set_mean(self.inputs[0], mean_file)
-        if input_scale:
+        if input_scale is not None:
             self.set_input_scale(self.inputs[0], input_scale)
-        if channel_swap:
+        if raw_scale is not None:
+            self.set_raw_scale(self.inputs[0], raw_scale)
+        if channel_swap is not None:
             self.set_channel_swap(self.inputs[0], channel_swap)
 
         self.configure_crop(context_pad)
@@ -180,7 +183,7 @@ def configure_crop(self, context_pad):
         """
         self.context_pad = context_pad
         if self.context_pad:
-            input_scale = self.input_scale.get(self.inputs[0])
+            raw_scale = self.raw_scale.get(self.inputs[0])
             channel_order = self.channel_swap.get(self.inputs[0])
             # Padding context crops needs the mean in unprocessed input space.
             self.crop_mean = self.mean[self.inputs[0]].copy()
@@ -188,4 +191,4 @@ def configure_crop(self, context_pad):
             channel_order_inverse = [channel_order.index(i)
                                      for i in range(self.crop_mean.shape[2])]
             self.crop_mean = self.crop_mean[:,:, channel_order_inverse]
-            self.crop_mean /= input_scale
+            self.crop_mean /= raw_scale
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 1fc97231ca6..aabcfddbbeb 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -1,6 +1,7 @@
 import numpy as np
 import skimage.io
-import skimage.transform
+from scipy.ndimage import zoom
+from skimage.transform import resize
 
 from caffe.proto import caffe_pb2
 
@@ -15,7 +16,8 @@ def load_image(filename, color=True):
         loads as intensity (if image is already grayscale).
 
     Give
-    image: an image with type np.float32 of size (H x W x 3) in RGB or
+    image: an image with type np.float32 in range [0, 1]
+        of size (H x W x 3) in RGB or
         of size (H x W x 1) in grayscale.
     """
     img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32)
@@ -40,7 +42,17 @@ def resize_image(im, new_dims, interp_order=1):
     Give
     im: resized ndarray with shape (new_dims[0], new_dims[1], K)
     """
-    return skimage.transform.resize(im, new_dims, order=interp_order)
+    if im.shape[-1] == 1 or im.shape[-1] == 3:
+        # skimage is fast but only understands {1,3} channel images in [0, 1].
+        im_min, im_max = im.min(), im.max()
+        im_std = (im - im_min) / (im_max - im_min)
+        resized_std = resize(im_std, new_dims, order=interp_order)
+        resized_im = resized_std * (im_max - im_min) + im_min
+    else:
+        # ndimage interpolates anything but more slowly.
+        scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
+        resized_im = zoom(im, scale + (1,), order=interp_order)
+    return resized_im.astype(np.float32)
 
 
 def oversample(images, crop_dims):
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 64747f30483..43648d0af34 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -216,12 +216,10 @@ def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
     in_shape = self.blobs[input_].data.shape
     mean = np.load(mean_f)
     if mode == 'elementwise':
-        if mean.shape != in_shape[1:]:
-            # Resize mean (which requires H x W x K input in range [0,1]).
-            m_min, m_max = mean.min(), mean.max()
-            normal_mean = (mean - m_min) / (m_max - m_min)
-            mean = caffe.io.resize_image(normal_mean.transpose((1,2,0)),
-                    in_shape[2:]).transpose((2,0,1)) * (m_max - m_min) + m_min
+        if mean.shape[1:] != in_shape[2:]:
+            # Resize mean (which requires H x W x K input).
+            mean = caffe.io.resize_image(mean.transpose((1,2,0)),
+                                         in_shape[2:]).transpose((2,0,1))
         self.mean[input_] = mean
     elif mode == 'channel':
         self.mean[input_] = mean.mean(1).mean(1).reshape((in_shape[1], 1, 1))
@@ -229,10 +227,11 @@ def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
         raise Exception('Mode not in {}'.format(['elementwise', 'channel']))
 
 
-
 def _Net_set_input_scale(self, input_, scale):
     """
-    Set the input feature scaling factor s.t. input blob = input * scale.
+    Set the scale of preprocessed inputs s.t. the blob = blob * scale.
+    N.B. input_scale is done AFTER mean subtraction and other preprocessing
+    while raw_scale is done BEFORE.
 
     Take
     input_: which input to assign this scale factor
@@ -243,6 +242,22 @@ def _Net_set_input_scale(self, input_, scale):
     self.input_scale[input_] = scale
 
 
+def _Net_set_raw_scale(self, input_, scale):
+    """
+    Set the scale of raw features s.t. the input blob = input * scale.
+    While Python represents images in [0, 1], certain Caffe models
+    like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
+    of these models must be 255.
+
+    Take
+    input_: which input to assign this scale factor
+    scale: scale coefficient
+    """
+    if input_ not in self.inputs:
+        raise Exception('Input not in {}'.format(self.inputs))
+    self.raw_scale[input_] = scale
+
+
 def _Net_set_channel_swap(self, input_, order):
     """
     Set the input channel order for e.g. RGB to BGR conversion
@@ -263,10 +278,11 @@ def _Net_preprocess(self, input_name, input_):
     Format input for Caffe:
     - convert to single
     - resize to input dimensions (preserving number of channels)
-    - scale feature
     - reorder channels (for instance color to BGR)
-    - subtract mean
+    - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
     - transpose dimensions to K x H x W
+    - subtract mean
+    - scale feature
 
     Take
     input_name: name of input blob to preprocess for
@@ -275,20 +291,23 @@ def _Net_preprocess(self, input_name, input_):
     Give
     caffe_inputs: (K x H x W) ndarray
     """
-    caffe_in = input_.astype(np.float32)
+    caffe_in = input_.astype(np.float32, copy=False)
     mean = self.mean.get(input_name)
     input_scale = self.input_scale.get(input_name)
+    raw_scale = self.raw_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
     in_size = self.blobs[input_name].data.shape[2:]
     if caffe_in.shape[:2] != in_size:
         caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if input_scale is not None:
-        caffe_in *= input_scale
     if channel_order is not None:
         caffe_in = caffe_in[:, :, channel_order]
     caffe_in = caffe_in.transpose((2, 0, 1))
+    if raw_scale is not None:
+        caffe_in *= raw_scale
     if mean is not None:
         caffe_in -= mean
+    if input_scale is not None:
+        caffe_in *= input_scale
     return caffe_in
 
 
@@ -299,16 +318,19 @@ def _Net_deprocess(self, input_name, input_):
     decaf_in = input_.copy().squeeze()
     mean = self.mean.get(input_name)
     input_scale = self.input_scale.get(input_name)
+    raw_scale = self.raw_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
+    if input_scale is not None:
+        decaf_in /= input_scale
     if mean is not None:
         decaf_in += mean
+    if raw_scale is not None:
+        decaf_in /= raw_scale
     decaf_in = decaf_in.transpose((1,2,0))
     if channel_order is not None:
         channel_order_inverse = [channel_order.index(i)
                                  for i in range(decaf_in.shape[2])]
         decaf_in = decaf_in[:, :, channel_order_inverse]
-    if input_scale is not None:
-        decaf_in /= input_scale
     return decaf_in
 
 
@@ -364,6 +386,7 @@ def _Net_batch(self, blobs):
 Net.forward_backward_all = _Net_forward_backward_all
 Net.set_mean = _Net_set_mean
 Net.set_input_scale = _Net_set_input_scale
+Net.set_raw_scale = _Net_set_raw_scale
 Net.set_channel_swap = _Net_set_channel_swap
 Net.preprocess = _Net_preprocess
 Net.deprocess = _Net_deprocess
diff --git a/python/classify.py b/python/classify.py
index fdaeeb01be4..417f8b5442c 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -66,8 +66,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        default=255,
-        help="Multiply input features by this scale before input to net"
+        help="Multiply input features by this scale to finish input preprocessing."
+    )
+    parser.add_argument(
+        "--raw_scale",
+        type=float,
+        help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(
         "--channel_swap",
diff --git a/python/detect.py b/python/detect.py
index a3bee5c5cf8..4cfe0825dd9 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -76,8 +76,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        default=255,
-        help="Multiply input features by this scale before input to net"
+        help="Multiply input features by this scale to finish input preprocessing."
+    )
+    parser.add_argument(
+        "--raw_scale",
+        type=float,
+        help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(
         "--channel_swap",
@@ -99,7 +103,8 @@ def main(argv):
     # Make detector.
     detector = caffe.Detector(args.model_def, args.pretrained_model,
             gpu=args.gpu, mean_file=args.mean_file,
-            input_scale=args.input_scale, channel_swap=channel_swap,
+            input_scale=args.input_scale, raw_scale=args.raw_scale,
+            channel_swap=channel_swap,
             context_pad=args.context_pad)
 
     if args.gpu:

From e1b3413708ffc51f5ccc07f9c81f748fc36864c0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 1 Aug 2014 17:55:30 -0700
Subject: [PATCH 0394/2053] take array in pycaffe `Net.set_mean()` instead of
 file path

---
 examples/filter_visualization.ipynb    |  2 +-
 examples/imagenet_classification.ipynb |  4 ++--
 examples/net_surgery.ipynb             |  2 +-
 python/caffe/classifier.py             |  8 ++++----
 python/caffe/detector.py               |  8 ++++----
 python/caffe/pycaffe.py                |  5 ++---
 python/classify.py                     | 13 +++++++++----
 python/detect.py                       |  8 ++++++--
 8 files changed, 29 insertions(+), 21 deletions(-)

diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index ea99f06b148..bbc919ba68b 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -65,7 +65,7 @@
       "net.set_phase_test()\n",
       "net.set_mode_cpu()\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "net.set_mean('data', caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')  # ImageNet mean\n",
+      "net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'))  # ImageNet mean\n",
       "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
       "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
      ],
diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index 60e8bd070fb..fc08c743093 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -53,7 +53,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean file, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
+      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
      ]
     },
     {
@@ -61,7 +61,7 @@
      "collapsed": false,
      "input": [
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean_file=caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy',\n",
+      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
       "                       channel_swap=(2,1,0),\n",
       "                       raw_scale=255,\n",
       "                       image_dims=(256, 256))"
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 31847b2418e..5f151e4be3a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -275,7 +275,7 @@
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
       "plt.imshow(im)\n",
-      "net_full_conv.set_mean('data', '../python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+      "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
       "net_full_conv.set_raw_scale('data', 255.0)\n",
       "# make classification map by forward pass and show top prediction index per location\n",
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 48835bab842..61e916ccb44 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -14,13 +14,13 @@ class Classifier(caffe.Net):
     by scaling, center cropping, or oversampling.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
-                 gpu=False, mean_file=None, input_scale=None, raw_scale=None,
+                 gpu=False, mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
         """
         Take
         image_dims: dimensions to scale input for cropping/sampling.
             Default is to scale to net input size for whole-image crop.
-        gpu, mean_file, input_scale, raw_scale, channel_swap: params for
+        gpu, mean, input_scale, raw_scale, channel_swap: params for
             preprocessing options.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
@@ -31,8 +31,8 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
         else:
             self.set_mode_cpu()
 
-        if mean_file:
-            self.set_mean(self.inputs[0], mean_file)
+        if mean is not None:
+            self.set_mean(self.inputs[0], mean)
         if input_scale is not None:
             self.set_input_scale(self.inputs[0], input_scale)
         if raw_scale is not None:
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index a9b06cd17c7..ccb5a1f24f2 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -24,12 +24,12 @@ class Detector(caffe.Net):
     Detector extends Net for windowed detection by a list of crops or
     selective search proposals.
     """
-    def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
+    def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
                  input_scale=None, raw_scale=None, channel_swap=None,
                  context_pad=None):
         """
         Take
-        gpu, mean_file, input_scale, raw_scale, channel_swap: params for
+        gpu, mean, input_scale, raw_scale, channel_swap: params for
             preprocessing options.
         context_pad: amount of surrounding context to take s.t. a `context_pad`
             sized border of pixels in the network input image is context, as in
@@ -43,8 +43,8 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
         else:
             self.set_mode_cpu()
 
-        if mean_file:
-            self.set_mean(self.inputs[0], mean_file)
+        if mean is not None:
+            self.set_mean(self.inputs[0], mean)
         if input_scale is not None:
             self.set_input_scale(self.inputs[0], input_scale)
         if raw_scale is not None:
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 43648d0af34..9a230a72b3c 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -201,20 +201,19 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     return all_outs, all_diffs
 
 
-def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
+def _Net_set_mean(self, input_, mean, mode='elementwise'):
     """
     Set the mean to subtract for data centering.
 
     Take
     input_: which input to assign this mean.
-    mean_f: path to mean .npy with ndarray (input dimensional or broadcastable)
+    mean: mean K x H x W ndarray (input dimensional or broadcastable)
     mode: elementwise = use the whole mean (and check dimensions)
           channel = channel constant (e.g. mean pixel instead of mean image)
     """
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     in_shape = self.blobs[input_].data.shape
-    mean = np.load(mean_f)
     if mode == 'elementwise':
         if mean.shape[1:] != in_shape[2:]:
             # Resize mean (which requires H x W x K input).
diff --git a/python/classify.py b/python/classify.py
index 417f8b5442c..d3d239cbb7d 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -78,7 +78,6 @@ def main(argv):
         default='2,1,0',
         help="Order to permute input channels. The default converts " +
              "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
-
     )
     parser.add_argument(
         "--ext",
@@ -89,12 +88,18 @@ def main(argv):
     args = parser.parse_args()
 
     image_dims = [int(s) for s in args.images_dim.split(',')]
-    channel_swap = [int(s) for s in args.channel_swap.split(',')]
+
+    mean, channel_swap = None, None
+    if args.mean_file:
+        mean = np.load(args.mean_file)
+    if args.channel_swap:
+        channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
     # Make classifier.
     classifier = caffe.Classifier(args.model_def, args.pretrained_model,
-            image_dims=image_dims, gpu=args.gpu, mean_file=args.mean_file,
-            input_scale=args.input_scale, channel_swap=channel_swap)
+            image_dims=image_dims, gpu=args.gpu, mean=mean,
+            input_scale=args.input_scale, raw_scale=args.raw_scale,
+            channel_swap=channel_swap)
 
     if args.gpu:
         print 'GPU mode'
diff --git a/python/detect.py b/python/detect.py
index 4cfe0825dd9..77f4676aec1 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -98,11 +98,15 @@ def main(argv):
     )
     args = parser.parse_args()
 
-    channel_swap = [int(s) for s in args.channel_swap.split(',')]
+    mean, channel_swap = None, None
+    if args.mean_file:
+        mean = np.load(args.mean_file)
+    if args.channel_swap:
+        channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
     # Make detector.
     detector = caffe.Detector(args.model_def, args.pretrained_model,
-            gpu=args.gpu, mean_file=args.mean_file,
+            gpu=args.gpu, mean=mean,
             input_scale=args.input_scale, raw_scale=args.raw_scale,
             channel_swap=channel_swap,
             context_pad=args.context_pad)

From 4f7726916cb965c79975c44e833e71347fa1822c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 1 Aug 2014 18:46:17 -0700
Subject: [PATCH 0395/2053] fix pycaffe context cropping with or without mean

---
 python/caffe/detector.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index ccb5a1f24f2..2fc23db0c04 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -186,9 +186,16 @@ def configure_crop(self, context_pad):
             raw_scale = self.raw_scale.get(self.inputs[0])
             channel_order = self.channel_swap.get(self.inputs[0])
             # Padding context crops needs the mean in unprocessed input space.
-            self.crop_mean = self.mean[self.inputs[0]].copy()
-            self.crop_mean = self.crop_mean.transpose((1,2,0))
-            channel_order_inverse = [channel_order.index(i)
-                                     for i in range(self.crop_mean.shape[2])]
-            self.crop_mean = self.crop_mean[:,:, channel_order_inverse]
-            self.crop_mean /= raw_scale
+            mean = self.mean.get(self.inputs[0])
+            if mean is not None:
+                crop_mean = mean.copy().transpose((1,2,0))
+                if channel_order is not None:
+                    channel_order_inverse = [channel_order.index(i)
+                                            for i in range(crop_mean.shape[2])]
+                    crop_mean = crop_mean[:,:, channel_order_inverse]
+                if raw_scale is not None:
+                    crop_mean /= raw_scale
+                self.crop_mean = crop_mean
+            else:
+                self.crop_mean = np.zeros(self.blobs[self.inputs[0]].data.shape,
+                                          dtype=np.float32)

From 0db94786a7a463fed49825811fac903f1f1fc3c8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 5 Aug 2014 10:14:35 -0700
Subject: [PATCH 0396/2053] drop np.asarray() in favor of declaration (~1.75x
 speedup)

---
 python/caffe/classifier.py | 17 +++++++++++------
 python/caffe/detector.py   |  7 +++++--
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 61e916ccb44..fe471ca13b1 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -60,12 +60,15 @@ def predict(self, inputs, oversample=True):
                      for N images and C classes.
         """
         # Scale to standardize input dimensions.
-        inputs = np.asarray([caffe.io.resize_image(im, self.image_dims)
-                             for im in inputs])
+        input_ = np.zeros((len(inputs),
+            self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
+            dtype=np.float32)
+        for ix, in_ in enumerate(inputs):
+            input_[ix] = caffe.io.resize_image(in_, self.image_dims)
 
         if oversample:
             # Generate center, corner, and mirrored crops.
-            inputs = caffe.io.oversample(inputs, self.crop_dims)
+            input_ = caffe.io.oversample(input_, self.crop_dims)
         else:
             # Take center crop.
             center = np.array(self.image_dims) / 2.0
@@ -73,11 +76,13 @@ def predict(self, inputs, oversample=True):
                 -self.crop_dims / 2.0,
                 self.crop_dims / 2.0
             ])
-            inputs = inputs[:, crop[0]:crop[2], crop[1]:crop[3], :]
+            input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
-        caffe_in = np.asarray([self.preprocess(self.inputs[0], in_)
-                    for in_ in inputs])
+        caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+                            dtype=np.float32)
+        for ix, in_ in enumerate(input_):
+            caffe_in[ix] = self.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 2fc23db0c04..f219b6105e1 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -76,8 +76,11 @@ def detect_windows(self, images_windows):
                 window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
-        caffe_in = np.asarray([self.preprocess(self.inputs[0], window_in)
-                    for window_in in window_inputs])
+        caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+                            + self.blobs[self.inputs[0]].data.shape[2:],
+                            dtype=np.float32)
+        for ix, window_in in enumerate(window_inputs):
+            caffe_in[ix] = self.preprocess(self.inputs[0], window_in)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 

From 9410e93480f232e9bcce052414b268d8b8b6af3a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 00:16:36 -0700
Subject: [PATCH 0397/2053] clarify the license and copyright terms of the
 project

As has been the case, contributions are copyright their respective
contributors and the project is BSD-2 licensed. By contributing to the
project, contributors release their contributions under these copyright
and license terms as declared in LICENSE.
---
 CONTRIBUTORS.md |  2 ++
 LICENSE         | 22 ++++++++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 2de2a717eff..8db66ea82c6 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -15,3 +15,5 @@ to see line-by-line credits and
 to see the change log even across renames and rewrites.
 
 Please refer to the [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements) on the Caffe site for further details.
+
+**Copyright** is held by the original contributor according to the versioning history; see LICENSE.
diff --git a/LICENSE b/LICENSE
index bac9c99fd41..efcc5c5b6b0 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,22 @@
+COPYRIGHT
+
+All contributions by the University of California:
 Copyright (c) 2014, The Regents of the University of California (Regents)
 All rights reserved.
 
+All other contributions:
+Copyright (c) 2014, the respective contributors
+All rights reserved.
+
+Caffe uses a shared copyright model: each contributor holds copyright over
+their contributions to Caffe. The project versioning records all such
+contribution and copyright details. If a contributor wants to further mark
+their specific copyright on a particular contribution, they should indicate
+their copyright solely in the commit message of the change when it is
+committed.
+
+LICENSE
+
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met: 
 
@@ -20,3 +36,9 @@ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+CONTRIBUTION AGREEMENT
+
+By contributing to the BVLC/caffe repository through pull-request, comment,
+or otherwise, the contributor releases their content to the
+license and copyright terms herein.

From b97b88f0c2b7e32ab9e17d4325ef6bc4251c0cc8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 00:43:17 -0700
Subject: [PATCH 0398/2053] LICENSE governs the whole project so strip file
 headers

---
 examples/cifar10/convert_cifar_data.cpp                  | 1 -
 examples/mnist/convert_mnist_data.cpp                    | 1 -
 include/caffe/blob.hpp                                   | 2 --
 include/caffe/caffe.hpp                                  | 1 -
 include/caffe/common.hpp                                 | 2 --
 include/caffe/common_layers.hpp                          | 2 --
 include/caffe/data_layers.hpp                            | 2 --
 include/caffe/filler.hpp                                 | 2 --
 include/caffe/internal_thread.hpp                        | 4 +---
 include/caffe/layer.hpp                                  | 2 --
 include/caffe/loss_layers.hpp                            | 2 --
 include/caffe/net.hpp                                    | 2 --
 include/caffe/neuron_layers.hpp                          | 6 ++----
 include/caffe/solver.hpp                                 | 2 --
 include/caffe/syncedmem.hpp                              | 2 --
 include/caffe/test/test_caffe_main.hpp                   | 2 --
 include/caffe/test/test_gradient_check_util.hpp          | 2 --
 include/caffe/util/benchmark.hpp                         | 2 --
 include/caffe/util/device_alternate.hpp                  | 2 --
 include/caffe/util/im2col.hpp                            | 2 --
 include/caffe/util/insert_splits.hpp                     | 2 --
 include/caffe/util/io.hpp                                | 2 --
 include/caffe/util/math_functions.hpp                    | 2 --
 include/caffe/util/mkl_alternate.hpp                     | 2 --
 include/caffe/util/rng.hpp                               | 2 --
 include/caffe/util/upgrade_proto.hpp                     | 2 --
 include/caffe/vision_layers.hpp                          | 2 --
 matlab/caffe/matcaffe.cpp                                | 1 -
 python/caffe/_caffe.cpp                                  | 1 -
 src/caffe/blob.cpp                                       | 2 --
 src/caffe/common.cpp                                     | 2 --
 src/caffe/layer_factory.cpp                              | 2 --
 src/caffe/layers/accuracy_layer.cpp                      | 2 --
 src/caffe/layers/argmax_layer.cpp                        | 2 --
 src/caffe/layers/bnll_layer.cpp                          | 2 --
 src/caffe/layers/bnll_layer.cu                           | 2 --
 src/caffe/layers/concat_layer.cpp                        | 2 --
 src/caffe/layers/concat_layer.cu                         | 2 --
 src/caffe/layers/conv_layer.cpp                          | 2 --
 src/caffe/layers/conv_layer.cu                           | 2 --
 src/caffe/layers/data_layer.cpp                          | 2 --
 src/caffe/layers/data_layer.cu                           | 2 --
 src/caffe/layers/dropout_layer.cpp                       | 2 --
 src/caffe/layers/dropout_layer.cu                        | 2 --
 src/caffe/layers/dummy_data_layer.cpp                    | 2 --
 src/caffe/layers/eltwise_layer.cpp                       | 2 --
 src/caffe/layers/eltwise_layer.cu                        | 2 --
 src/caffe/layers/euclidean_loss_layer.cpp                | 2 --
 src/caffe/layers/euclidean_loss_layer.cu                 | 2 --
 src/caffe/layers/flatten_layer.cpp                       | 2 --
 src/caffe/layers/flatten_layer.cu                        | 2 --
 src/caffe/layers/hdf5_data_layer.cpp                     | 1 -
 src/caffe/layers/hdf5_data_layer.cu                      | 1 -
 src/caffe/layers/hdf5_output_layer.cpp                   | 2 --
 src/caffe/layers/hdf5_output_layer.cu                    | 2 --
 src/caffe/layers/hinge_loss_layer.cpp                    | 2 --
 src/caffe/layers/im2col_layer.cpp                        | 2 --
 src/caffe/layers/im2col_layer.cu                         | 2 --
 src/caffe/layers/image_data_layer.cpp                    | 2 --
 src/caffe/layers/image_data_layer.cu                     | 2 --
 src/caffe/layers/infogain_loss_layer.cpp                 | 2 --
 src/caffe/layers/inner_product_layer.cpp                 | 2 --
 src/caffe/layers/inner_product_layer.cu                  | 2 --
 src/caffe/layers/loss_layer.cpp                          | 2 --
 src/caffe/layers/lrn_layer.cpp                           | 2 --
 src/caffe/layers/lrn_layer.cu                            | 2 --
 src/caffe/layers/memory_data_layer.cpp                   | 2 --
 src/caffe/layers/multinomial_logistic_loss_layer.cpp     | 2 --
 src/caffe/layers/neuron_layer.cpp                        | 2 --
 src/caffe/layers/pooling_layer.cpp                       | 2 --
 src/caffe/layers/pooling_layer.cu                        | 2 --
 src/caffe/layers/power_layer.cpp                         | 2 --
 src/caffe/layers/power_layer.cu                          | 2 --
 src/caffe/layers/relu_layer.cpp                          | 2 --
 src/caffe/layers/relu_layer.cu                           | 2 --
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp    | 2 --
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu     | 2 --
 src/caffe/layers/sigmoid_layer.cpp                       | 2 --
 src/caffe/layers/sigmoid_layer.cu                        | 2 --
 src/caffe/layers/slice_layer.cpp                         | 2 --
 src/caffe/layers/slice_layer.cu                          | 2 --
 src/caffe/layers/softmax_layer.cpp                       | 1 -
 src/caffe/layers/softmax_layer.cu                        | 2 --
 src/caffe/layers/softmax_loss_layer.cpp                  | 2 --
 src/caffe/layers/softmax_loss_layer.cu                   | 2 --
 src/caffe/layers/split_layer.cpp                         | 2 --
 src/caffe/layers/split_layer.cu                          | 2 --
 src/caffe/layers/tanh_layer.cpp                          | 1 -
 src/caffe/layers/tanh_layer.cu                           | 1 -
 src/caffe/layers/threshold_layer.cpp                     | 2 --
 src/caffe/layers/threshold_layer.cu                      | 2 --
 src/caffe/layers/window_data_layer.cpp                   | 1 -
 src/caffe/layers/window_data_layer.cu                    | 1 -
 src/caffe/net.cpp                                        | 2 --
 src/caffe/proto/caffe.proto                              | 2 --
 src/caffe/proto/caffe_pretty_print.proto                 | 2 --
 src/caffe/solver.cpp                                     | 2 --
 src/caffe/syncedmem.cpp                                  | 2 --
 src/caffe/test/test_accuracy_layer.cpp                   | 2 --
 src/caffe/test/test_argmax_layer.cpp                     | 2 --
 src/caffe/test/test_benchmark.cpp                        | 2 --
 src/caffe/test/test_blob.cpp                             | 2 --
 src/caffe/test/test_caffe_main.cpp                       | 2 --
 src/caffe/test/test_common.cpp                           | 2 --
 src/caffe/test/test_concat_layer.cpp                     | 2 --
 src/caffe/test/test_convolution_layer.cpp                | 2 --
 src/caffe/test/test_data_layer.cpp                       | 2 --
 src/caffe/test/test_dummy_data_layer.cpp                 | 2 --
 src/caffe/test/test_eltwise_layer.cpp                    | 2 --
 src/caffe/test/test_euclidean_loss_layer.cpp             | 2 --
 src/caffe/test/test_filler.cpp                           | 2 --
 src/caffe/test/test_flatten_layer.cpp                    | 2 --
 src/caffe/test/test_hdf5_output_layer.cpp                | 2 --
 src/caffe/test/test_hdf5data_layer.cpp                   | 2 --
 src/caffe/test/test_hinge_loss_layer.cpp                 | 2 --
 src/caffe/test/test_im2col_kernel.cu                     | 2 --
 src/caffe/test/test_im2col_layer.cpp                     | 2 --
 src/caffe/test/test_image_data_layer.cpp                 | 2 --
 src/caffe/test/test_infogain_loss_layer.cpp              | 2 --
 src/caffe/test/test_inner_product_layer.cpp              | 2 --
 src/caffe/test/test_lrn_layer.cpp                        | 2 --
 src/caffe/test/test_math_functions.cpp                   | 2 --
 src/caffe/test/test_maxpool_dropout_layers.cpp           | 2 --
 src/caffe/test/test_memory_data_layer.cpp                | 2 --
 src/caffe/test/test_multinomial_logistic_loss_layer.cpp  | 2 --
 src/caffe/test/test_net.cpp                              | 2 --
 src/caffe/test/test_neuron_layer.cpp                     | 2 --
 src/caffe/test/test_platform.cpp                         | 2 --
 src/caffe/test/test_pooling_layer.cpp                    | 2 --
 src/caffe/test/test_power_layer.cpp                      | 2 --
 src/caffe/test/test_protobuf.cpp                         | 2 --
 src/caffe/test/test_random_number_generator.cpp          | 2 --
 src/caffe/test/test_sgd_solver.cpp                       | 2 --
 src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp | 2 --
 src/caffe/test/test_slice_layer.cpp                      | 2 --
 src/caffe/test/test_softmax_layer.cpp                    | 2 --
 src/caffe/test/test_softmax_with_loss_layer.cpp          | 2 --
 src/caffe/test/test_solver.cpp                           | 2 --
 src/caffe/test/test_split_layer.cpp                      | 2 --
 src/caffe/test/test_stochastic_pooling.cpp               | 2 --
 src/caffe/test/test_syncedmem.cpp                        | 2 --
 src/caffe/test/test_tanh_layer.cpp                       | 1 -
 src/caffe/test/test_threshold_layer.cpp                  | 2 --
 src/caffe/test/test_upgrade_proto.cpp                    | 2 --
 src/caffe/test/test_util_blas.cpp                        | 2 --
 src/caffe/util/benchmark.cpp                             | 2 --
 src/caffe/util/im2col.cpp                                | 2 --
 src/caffe/util/im2col.cu                                 | 2 --
 src/caffe/util/insert_splits.cpp                         | 2 --
 src/caffe/util/io.cpp                                    | 2 --
 src/caffe/util/math_functions.cpp                        | 2 --
 src/caffe/util/math_functions.cu                         | 2 --
 src/caffe/util/upgrade_proto.cpp                         | 2 --
 tools/caffe.cpp                                          | 1 -
 tools/compute_image_mean.cpp                             | 2 --
 tools/convert_imageset.cpp                               | 1 -
 tools/device_query.cpp                                   | 2 --
 tools/dump_network.cpp                                   | 2 --
 tools/extract_features.cpp                               | 2 --
 tools/finetune_net.cpp                                   | 2 --
 tools/net_speed_benchmark.cpp                            | 2 --
 tools/test_net.cpp                                       | 2 --
 tools/train_net.cpp                                      | 2 --
 tools/upgrade_net_proto_binary.cpp                       | 2 --
 tools/upgrade_net_proto_text.cpp                         | 2 --
 165 files changed, 3 insertions(+), 318 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index d1f3f3e0e9f..2d5589bd30a 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // This script converts the CIFAR dataset to the leveldb format used
 // by caffe to perform classification.
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index c2cf4b3af33..a97a9285a19 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // This script converts the MNIST dataset to the leveldb format used
 // by caffe to perform classification.
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 2086c5119bc..eb0c25ba394 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_BLOB_HPP_
 #define CAFFE_BLOB_HPP_
 
diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index 172fc5f3bf7..0af9ef04c43 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // caffe.hpp is the header file that you need to include in your code. It wraps
 // all the internal caffe header files into one for simpler inclusion.
 
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index d3dbd471e67..97a34962c63 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_COMMON_HPP_
 #define CAFFE_COMMON_HPP_
 
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index ed11487c6f3..1e282d33d7a 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_COMMON_LAYERS_HPP_
 #define CAFFE_COMMON_LAYERS_HPP_
 
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index f868b2869da..31774828a69 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_DATA_LAYERS_HPP_
 #define CAFFE_DATA_LAYERS_HPP_
 
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index beeee01dcb9..a8366801132 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 // Fillers are random number generators that fills a blob using the specified
 // algorithm. The expectation is that they are only going to be used during
 // initialization time and will not involve any GPUs.
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 46f67360f0c..8520899ada6 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_INTERNAL_THREAD_HPP_
 #define CAFFE_INTERNAL_THREAD_HPP_
 
@@ -28,7 +26,7 @@ class InternalThread {
   }
 
  protected:
-  /* Implement this method in your subclass 
+  /* Implement this method in your subclass
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() = 0;
 
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index aac82447a41..dca5f8709af 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_
 
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 73a677b624a..bd2618d6d36 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_LOSS_LAYERS_HPP_
 #define CAFFE_LOSS_LAYERS_HPP_
 
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 808b244e47d..8c22137e514 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_NET_HPP_
 #define CAFFE_NET_HPP_
 
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 4b3f6f4394d..6fe425229e8 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_NEURON_LAYERS_HPP_
 #define CAFFE_NEURON_LAYERS_HPP_
 
@@ -233,12 +231,12 @@ class TanHLayer : public NeuronLayer<Dtype> {
 
 /* ThresholdLayer
   Outputs 1 if value in input is above threshold, 0 otherwise.
-  The defult threshold = 0, which means positive values would become 1 and 
+  The defult threshold = 0, which means positive values would become 1 and
   negative or 0, would become 0
 
   y = 1 if x > threshold
   y = 0 if x <= threshold
-  
+
   y' = don't differenciable
 */
 template <typename Dtype>
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 811d70372a4..cdf583a1429 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
 #define CAFFE_OPTIMIZATION_SOLVER_HPP_
 
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 2b7f349025a..b2fc287b23e 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_SYNCEDMEM_HPP_
 #define CAFFE_SYNCEDMEM_HPP_
 
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 759e01a2629..40b9effc5f1 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 #ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 034aeccb90b..4cf2cbc9dc8 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_TEST_GRADIENT_CHECK_UTIL_H_
 #define CAFFE_TEST_GRADIENT_CHECK_UTIL_H_
 
diff --git a/include/caffe/util/benchmark.hpp b/include/caffe/util/benchmark.hpp
index 8ee76140d9a..f7ef8eaf3ee 100644
--- a/include/caffe/util/benchmark.hpp
+++ b/include/caffe/util/benchmark.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_BENCHMARK_H_
 #define CAFFE_UTIL_BENCHMARK_H_
 
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index abee4f57dd1..cf1aef759cc 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_DEVICE_ALTERNATE_H_
 #define CAFFE_UTIL_DEVICE_ALTERNATE_H_
 
diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index c263997ae66..0051e2fa067 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef _CAFFE_UTIL_IM2COL_HPP_
 #define _CAFFE_UTIL_IM2COL_HPP_
 
diff --git a/include/caffe/util/insert_splits.hpp b/include/caffe/util/insert_splits.hpp
index 4da1ea81502..4ca933b9203 100644
--- a/include/caffe/util/insert_splits.hpp
+++ b/include/caffe/util/insert_splits.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef _CAFFE_UTIL_INSERT_SPLITS_HPP_
 #define _CAFFE_UTIL_INSERT_SPLITS_HPP_
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 4f517ff1d71..cdf4d9e724e 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 001950113a1..744dc849b94 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_
 #define CAFFE_UTIL_MATH_FUNCTIONS_H_
 
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index c30eab8d3d4..d72bcd2814d 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_MKL_ALTERNATE_H_
 #define CAFFE_UTIL_MKL_ALTERNATE_H_
 
diff --git a/include/caffe/util/rng.hpp b/include/caffe/util/rng.hpp
index f774d704569..8f1cf0d17c2 100644
--- a/include/caffe/util/rng.hpp
+++ b/include/caffe/util/rng.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_RNG_CPP_HPP_
 #define CAFFE_RNG_CPP_HPP_
 
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 39e4f4ce22c..1ee184a34e9 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_UPGRADE_PROTO_H_
 #define CAFFE_UTIL_UPGRADE_PROTO_H_
 
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 80dc37543ee..0797065081f 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_VISION_LAYERS_HPP_
 #define CAFFE_VISION_LAYERS_HPP_
 
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index d0e98f8319d..70652e26e49 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
 // caffe::Caffe functions so that one could easily call it from matlab.
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 59317727914..3103d0267be 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // pycaffe provides a wrapper of the caffe::Net class as well as some
 // caffe::Caffe functions so that one could easily call it from Python.
 // Note that for Python, we will simply use float as the data type.
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 738b549e10c..9fd1232a9ad 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 019370b1dd7..99b2292461e 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <cstdio>
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 1165abc6da8..118929fbff5 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_LAYER_FACTORY_HPP_
 #define CAFFE_LAYER_FACTORY_HPP_
 
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index f99b01dc105..76889d8b70a 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <functional>
 #include <utility>
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 732f7bf15d1..b2ef91eab67 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <functional>
 #include <utility>
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 9b7a0642779..4cb852031af 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index bc6a201e9b4..9895a06107a 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 32203a8d980..b76d4b2ca14 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 1a97f02a62a..aea8b77e37f 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index ca63e976e80..df3e31ba84a 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/filler.hpp"
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 616a81a1388..04ae13932d9 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/filler.hpp"
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index ddc7f770b46..c089c9ba5df 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <leveldb/db.h>
 #include <stdint.h>
 
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index fcbf2346840..2ae1a640319 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 605aa151196..0621b56ec6e 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 // TODO (sergeyk): effect should not be dependent on phase. wasted memcpy.
 
 #include <vector>
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 8befa4d87fb..9bcd687bb5d 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <limits>
 #include <vector>
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 58044f4c952..98b437eea98 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/filler.hpp"
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 09ca66f38e8..8085b4644cc 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index d47d24afad7..eec8857c577 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 2a814f0150a..17180d40b69 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index e1358f0fb51..dd14f1995c8 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index f1fdd322eb4..81a506a8177 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index 60b67f06e05..7233afb3718 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 38b2b8d23f3..5bad5f6a1f0 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 /*
 TODO:
 - load file in a separate thread ("prefetch")
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index d50d9616771..1f682d5726d 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 /*
 TODO:
 - only load parts of the file, in accordance with a prototxt param "max_mem"
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 30e4997854b..0d7590b1b12 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "hdf5.h"
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 9b3dd135841..d2f20b3fc79 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "hdf5.h"
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index a7ec061eb28..bc3a593c769 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <cmath>
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index fb3dad43844..2dd7476237a 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index c9802541aaa..6b4c701073e 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index a10f02c37a8..a0f03a8266d 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
 #include <string>
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index 8a449d010e2..f61409cc38a 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index c972c032607..fa01116ee36 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <cmath>
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 6493d0bd497..a9e0f353e9e 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 4b670d644c2..e02107200cf 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 656e85a75d8..48665221f1d 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <cmath>
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 50ad6464449..e77f6857c85 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 7a19350549c..eee12e66cec 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 15eedb317e3..d1717fd4258 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index e5eafd65078..a9c7de6595d 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <cmath>
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index 1b8fcecd482..3343b26cfc5 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 75a74dac9ec..30657b6c804 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 6f3a7205091..58f1997c38e 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 86cb3d76291..8b5d8d16bd4 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index db88fee8f3b..0950b78b4ce 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index b94086df98c..fca10a5aad3 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index e0d6aa1b5c3..a74428bfce1 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 2bf931d004b..24ab6a85c6a 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 698ec1dbde1..0e4dab76b79 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index e25db04c42a..0f8b582da2f 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cmath>
 #include <vector>
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index 28cc7f1daa6..039796e1b22 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cmath>
 #include <vector>
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index fc86a2d4307..e182837c3aa 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index 70702148f5d..8e01131ec01 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 6a98b320208..61990ed9d97 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 #include <algorithm>
 #include <vector>
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 6d65fbce957..65b0e229d1a 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 43e8fcac44c..98cf14c4a28 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index f55b8c7adf3..32f3e670f48 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index d13b8d12358..2786d3f7694 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index a89c5d61883..1cf15a79314 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 0a6ec68d1f9..0c8be3fac60 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index 9307cab2ec5..b3daad1e638 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index 2c8546c957a..c932356019b 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index fb973b2ba82..398d56e8765 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index cbd269685ce..ddff5555417 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // Based on data_layer.cpp by Yangqing Jia.
 
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index 5efcc3f4adc..6e8fa8b349a 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // Based on data_layer.cpp by Yangqing Jia.
 
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 6fab0e3a6b4..db6b4ffefc4 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <map>
 #include <set>
 #include <string>
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 4e77863e7e6..44bfb631a22 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 syntax = "proto2";
 
 package caffe;
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
index f34988c9645..225138a4274 100644
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ b/src/caffe/proto/caffe_pretty_print.proto
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 syntax = "proto2";
 
 package caffe;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 6049ddfec7c..d96d03942f3 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstdio>
 
 #include <algorithm>
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 844f639e013..7d25183701d 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 95217df3f1f..e11e3f2a981 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cfloat>
 #include <cmath>
 #include <cstring>
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 3f55edaac22..fb3951c3098 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <utility>
 #include <vector>
 
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index e6d9ebf9a05..dbbee08d667 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <unistd.h>  // for usleep
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index 89f4a161c9c..adf7a4d38e9 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index bb5e6b46afb..df188fd319b 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index 455f5d8a32d..0b3639c7706 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index b07c5727a79..c60b7f744cc 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 41951cfb940..5a7ea80467e 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index be478b2746c..208beed91d1 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index aa6cc55364e..4188bb68c9e 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index fe3269bb3a2..4c17dfd341c 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 6dd631e55e8..511d38ccab5 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index d0d750fff47..e04b0fd22af 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 928db849296..cbd01f245f2 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index a36a8cbd9a8..696165cdaa2 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 1f3bf9f3cd0..f903afb736f 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index c6be1d73262..8f6f6f78cf1 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 89008ac7e39..ee684c00255 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 6884b059fde..32cf6369361 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index bbeedb8dce5..5232c1187c1 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
 #include <map>
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index 7b3705084a5..162d0e6c589 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index 31d87afc7ce..5f9729c4f90 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index fbb03dfecb5..3bd62fd9e18 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cstring>
 #include <vector>
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index c89ca027adf..d10e702e79a 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <stdint.h>  // for uint32_t & uint64_t
 #include <time.h>
 #include <climits>
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index e939d0c6e11..311c7781be5 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 60e75ebbc1a..f401bb0f4f2 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/filler.hpp"
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 9b6bb067805..3d1037baed0 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 6787ba956ec..18bc9ad7838 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <utility>
 #include <vector>
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 59c9b3d34f5..649f8f626d9 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_platform.cpp b/src/caffe/test/test_platform.cpp
index a4f4b4260c4..f3513e08814 100644
--- a/src/caffe/test/test_platform.cpp
+++ b/src/caffe/test/test_platform.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CPU_ONLY
 
 #include <cstdio>
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 4636dd773a4..5be01f77b32 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index e35d539b776..0c104c20b7c 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/test/test_protobuf.cpp b/src/caffe/test/test_protobuf.cpp
index da1a0d3f5eb..0c502d6dd36 100644
--- a/src/caffe/test/test_protobuf.cpp
+++ b/src/caffe/test/test_protobuf.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 // This is simply a script that tries serializing protocol buffer in text
 // format. Nothing special here and no actual code is being tested.
 #include <string>
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 8e3919157af..98424c06bfc 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstring>
 
diff --git a/src/caffe/test/test_sgd_solver.cpp b/src/caffe/test/test_sgd_solver.cpp
index 14dcac25e8e..1ec24b134c6 100644
--- a/src/caffe/test/test_sgd_solver.cpp
+++ b/src/caffe/test/test_sgd_solver.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <string>
 #include <utility>
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 7205ce9311b..f5716c9e9a7 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index e61f3e4f2c9..ee8818781f5 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index a305c647585..37685af6b12 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstring>
 #include <vector>
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 2994a9117af..bd39bd4420b 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index 133d01c5446..a7dbf77fd95 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <utility>
 #include <vector>
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index a4e5857877e..711669bacb8 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <string>
 #include <vector>
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 600b909a638..4f13981bd82 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cstring>
 #include <vector>
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index 5ccc480112d..b658871b3ee 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 1dac5a57043..9b8e745b8a7 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // Adapted from other test files
 
 #include <cmath>
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index b06d697ab0b..32dfbeeac92 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index a16b835f864..2abcadc0862 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <string>
 #include <vector>
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 841edefc66c..8770f309951 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CPU_ONLY  // CPU-GPU test
 
 #include <cstring>
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 009b118a35a..566d06a8f5f 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <boost/date_time/posix_time/posix_time.hpp>
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 01df8c89182..c48f31f35d4 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 653ce49e7f8..c90f93eb67b 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cmath>
 #include <cstdlib>
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index b88b320ff3d..270568cb5dc 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <map>
 #include <sstream>
 #include <string>
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 575ea3d63c2..fd7454d4f98 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <fcntl.h>
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 2fdcf91b34b..974adf55e7d 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <boost/math/special_functions/next.hpp>
 #include <boost/random.hpp>
 
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 1e934931462..cec051e6bef 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <math_functions.h>  // CUDA's, not caffe's, for fabs, signbit
 #include <thrust/device_vector.h>
 #include <thrust/functional.h>  // thrust::plus
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index a6e9c404df7..5415ca83b60 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ec414298ea4..ee12bca8c36 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 79bfdc7c4bb..fe3497fa87d 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <glog/logging.h>
 #include <leveldb/db.h>
 #include <lmdb.h>
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 21ff0d38104..0ddafa0c5fe 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // This program converts a set of images to a leveldb by storing them as Datum
 // proto buffers.
 // Usage:
diff --git a/tools/device_query.cpp b/tools/device_query.cpp
index efdbbbbc90e..2678154cbb4 100644
--- a/tools/device_query.cpp
+++ b/tools/device_query.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include "caffe/common.hpp"
 
 int main(int argc, char** argv) {
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index 8984016916a..90895fdc969 100644
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This program takes in a trained network and an input blob, and then dumps
 // all the intermediate blobs produced by the net to individual binary
 // files stored in protobuffer binary formats.
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 06e07fe6cba..49e8f98971c 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <stdio.h>  // for snprintf
 #include <string>
 #include <vector>
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index 213ff01702a..2966de48d17 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index f520e1d7785..ff112bdff55 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
index b6f827e6285..de97ab8924c 100644
--- a/tools/test_net.cpp
+++ b/tools/test_net.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This is a simple script that allows one to quickly test a network whose
 // structure is specified by text format protocol buffers, and whose parameter
 // are loaded from a pre-trained network.
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 08602d4a7ca..26aeb40bd05 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index ed703f027cd..d7a62e32441 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This is a script to upgrade "V0" network prototxts to the new format.
 // Usage:
 //    upgrade_net_proto_binary v0_net_proto_file_in net_proto_file_out
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index d05d1ac5883..1176585b02a 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This is a script to upgrade "V0" network prototxts to the new format.
 // Usage:
 //    upgrade_net_proto_text v0_net_proto_file_in net_proto_file_out

From 72effc037f60c4acdbc2f172064fc6bef6820009 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 00:34:05 -0700
Subject: [PATCH 0399/2053] [docs] detail attribution, license, and copyright
 for development

---
 docs/development.md | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/docs/development.md b/docs/development.md
index ff734d17219..d586a2b1c30 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -7,6 +7,19 @@ title: Development
 Caffe is developed with active participation of the community.<br />
 The [BVLC](http://bvlc.eecs.berkeley.edu/) maintainers welcome all contributions!
 
+The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
+This method is impartial and always up-to-date.
+
+## License
+
+Caffe is licensed under the terms in [LICENSE](https://github.com/BVLC/caffe/blob/master/LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
+
+## Copyright
+
+Caffe uses a shared copyright model: each contributor holds copyright over their contributions to Caffe. The project versioning records all such contribution and copyright details.
+
+If a contributor wants to further mark their specific copyright on a particular contribution, they should indicate their copyright solely in the commit message of the change when it is committed. Do not include copyright notices in files for this purpose.
+
 ### Documentation
 
 This website, written with [Jekyll](http://jekyllrb.com/), functions as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
@@ -109,12 +122,3 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 - Wrap lines at 80 chars.
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
 - **Run `make lint` to check C++ code.**
-
-### Copyright
-
-Assign copyright jointly to BVLC and contributors like so:
-
-    // Copyright 2014 BVLC and contributors.
-
-The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
-This method is impartial and always up-to-date.

From e788eae37e746c512a5618c673b04eacaa3fff6a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 00:36:00 -0700
Subject: [PATCH 0400/2053] lint for copyright

---
 scripts/cpp_lint.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 3f0ca1c3527..3f80e723643 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -445,7 +445,7 @@
 _error_suppressions = {}
 
 # Finds Copyright.
-_RE_COPYRIGHT = re.compile(r'Copyright \d\d\d\d BVLC and contributors.')
+_RE_COPYRIGHT = re.compile(r'Copyright')
 
 # The root directory used for deriving header guard CPP variable.
 # This is set by --root flag.
@@ -1368,16 +1368,15 @@ def ReverseCloseExpression(clean_lines, linenum, pos):
 
 
 def CheckForCopyright(filename, lines, error):
-  """Logs an error if no Copyright message appears at the top of the file."""
+  """Logs an error if a Copyright message appears at the top of the file."""
 
-  # We'll say it should occur by line 10. Don't forget there's a
+  # We'll check up to line 10. Don't forget there's a
   # dummy line at the front.
   for line in xrange(1, min(len(lines), 11)):
-    if _RE_COPYRIGHT.search(lines[line], re.I): break
-  else:                       # means no copyright line was found
-    error(filename, 0, 'legal/copyright', 5,
-          'BVLC copyright message not found.  '
-          'You should have a line: "Copyright [year] BVLC and contributors."')
+    if _RE_COPYRIGHT.search(lines[line], re.I):
+      error(filename, 0, 'legal/copyright', 5,
+            'Copyright message found.  '
+            'You should not include a copyright line.')
 
 
 def GetHeaderGuardCPPVariable(filename):

From 51074e91d25659764148ccd02646d24b3b96ddf5 Mon Sep 17 00:00:00 2001
From: Alireza Shafaei <shafaei@cs.ubc.ca>
Date: Tue, 5 Aug 2014 21:34:45 -0700
Subject: [PATCH 0401/2053] Painless binary mean conversion to matlab matrices.

---
 matlab/caffe/matcaffe.cpp | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 70652e26e49..5888676c744 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -311,6 +311,31 @@ static void is_initialized(MEX_ARGS) {
   }
 }
 
+static void read_mean(MEX_ARGS) {
+    if (nrhs != 1) {
+        mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'");
+        return;
+    }
+    const string& mean_file = mxArrayToString(prhs[0]);
+    Blob<float> data_mean;
+    LOG(INFO) << "Loading mean file from" << mean_file;
+    BlobProto blob_proto;
+    bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
+    if (!result) {
+        mexErrMsgTxt("Couldn't read the file");
+        return;
+    }
+    data_mean.FromProto(blob_proto);
+    mwSize dims[4] = {data_mean.width(), data_mean.height(),
+                      data_mean.channels(), data_mean.num() };
+    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
+    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
+    caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
+    mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]"
+                  " format and channels are also BGR!");
+    plhs[0] = mx_blob;
+}
+
 /** -----------------------------------------------------------------
  ** Available commands.
  **/
@@ -333,6 +358,7 @@ static handler_registry handlers[] = {
   { "get_weights",        get_weights     },
   { "get_init_key",       get_init_key    },
   { "reset",              reset           },
+  { "read_mean",          read_mean       },
   // The end.
   { "END",                NULL            },
 };

From de6123c4f4f0e24220a874247971d452ae43874e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 19:22:58 -0700
Subject: [PATCH 0402/2053] rename tools

---
 tools/caffe.cpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ee12bca8c36..1672b600d74 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -19,21 +19,21 @@ using caffe::vector;
 
 // Used in device query
 DEFINE_int32(device_id, 0,
-             "[devicequery,speedtest] The device id to use.");
+             "[device_query,time] The device id to use.");
 // Used in training
 DEFINE_string(solver_proto_file, "",
               "[train] The protobuf containing the solver definition.");
 DEFINE_string(net_proto_file, "",
-              "[speedtest] The net proto file to use.");
+              "[time] The net proto file to use.");
 DEFINE_string(resume_point_file, "",
               "[train] (optional) The snapshot from which to resume training.");
 DEFINE_string(pretrained_net_file, "",
               "[train] (optional) A pretrained network to run finetune from. "
               "Cannot be set simultaneously with resume_point_file.");
 DEFINE_int32(run_iterations, 50,
-             "[speedtest] The number of iterations to run.");
-DEFINE_bool(speedtest_with_gpu, false,
-            "[speedtest] Test the model with GPU.");
+             "[time] The number of iterations to run.");
+DEFINE_bool(time_with_gpu, false,
+            "[time] Test the model with GPU.");
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
@@ -70,13 +70,13 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
 // To do so, define actions as "int action()" functions, and register it with
 // RegisterBrewFunction(action);
 
-int devicequery() {
+int device_query() {
   LOG(INFO) << "Querying device_id = " << FLAGS_device_id;
   caffe::Caffe::SetDevice(FLAGS_device_id);
   caffe::Caffe::DeviceQuery();
   return 0;
 }
-RegisterBrewFunction(devicequery);
+RegisterBrewFunction(device_query);
 
 int train() {
   CHECK_GT(FLAGS_solver_proto_file.size(), 0);
@@ -101,9 +101,9 @@ int train() {
 }
 RegisterBrewFunction(train);
 
-int speedtest() {
+int time() {
   // Set device id and mode
-  if (FLAGS_speedtest_with_gpu) {
+  if (FLAGS_time_with_gpu) {
     LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
     Caffe::SetDevice(FLAGS_device_id);
     Caffe::set_mode(Caffe::GPU);
@@ -168,7 +168,7 @@ int speedtest() {
   LOG(INFO) << "*** Benchmark ends ***";
   return 0;
 }
-RegisterBrewFunction(speedtest);
+RegisterBrewFunction(time);
 
 int main(int argc, char** argv) {
   caffe::GlobalInit(&argc, &argv);

From 8eb05e1f47860cc18b8af2ed5ebc8d5169c03eaf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 19:45:05 -0700
Subject: [PATCH 0403/2053] consolidaet GPU flag for caffe cli

---
 tools/caffe.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 1672b600d74..26db1785a28 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -32,8 +32,8 @@ DEFINE_string(pretrained_net_file, "",
               "Cannot be set simultaneously with resume_point_file.");
 DEFINE_int32(run_iterations, 50,
              "[time] The number of iterations to run.");
-DEFINE_bool(time_with_gpu, false,
-            "[time] Test the model with GPU.");
+DEFINE_bool(gpu, false,
+            "[time] Run in GPU mode when true.");
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
@@ -103,7 +103,7 @@ RegisterBrewFunction(train);
 
 int time() {
   // Set device id and mode
-  if (FLAGS_time_with_gpu) {
+  if (FLAGS_gpu) {
     LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
     Caffe::SetDevice(FLAGS_device_id);
     Caffe::set_mode(Caffe::GPU);

From fa084945d43eed3d2942f4c28def2e87bbe1c92d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 20:10:27 -0700
Subject: [PATCH 0404/2053] output INFO from caffe cli to stderr by default

---
 tools/caffe.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 26db1785a28..b201f8ecaff 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -171,6 +171,9 @@ int time() {
 RegisterBrewFunction(time);
 
 int main(int argc, char** argv) {
+  // Print output to stderr (while still logging).
+  FLAGS_alsologtostderr = 1;
+
   caffe::GlobalInit(&argc, &argv);
   CHECK_EQ(argc, 2);
   return GetBrewFunction(caffe::string(argv[1]))();

From 3655aa93952c470b2fc04ca622b03f7a956be9cb Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 20:17:00 -0700
Subject: [PATCH 0405/2053] give usage message for caffe cli

- call format
- commands
- flags
---
 tools/caffe.cpp | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index b201f8ecaff..3bf18650b4d 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -173,8 +173,18 @@ RegisterBrewFunction(time);
 int main(int argc, char** argv) {
   // Print output to stderr (while still logging).
   FLAGS_alsologtostderr = 1;
-
+  // Usage message.
+  gflags::SetUsageMessage("command line brew\n"
+      "usage: caffe <command> <args>\n\n"
+      "commands:\n"
+      "  train           train or finetune a model\n"
+      "  device_query    show GPU diagnostic information\n"
+      "  time            benchmark model execution time");
+  // Run tool or show usage.
   caffe::GlobalInit(&argc, &argv);
-  CHECK_EQ(argc, 2);
-  return GetBrewFunction(caffe::string(argv[1]))();
+  if (argc == 2) {
+    return GetBrewFunction(caffe::string(argv[1]))();
+  } else {
+    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
+  }
 }

From 8634885d83dabfb38e8ca5f0d0d037335127cfba Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 23:22:13 -0700
Subject: [PATCH 0406/2053] rename caffe cli args and revise text

---
 tools/caffe.cpp | 52 ++++++++++++++++++++++++-------------------------
 1 file changed, 25 insertions(+), 27 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 3bf18650b4d..1320b7a5e25 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -17,23 +17,21 @@ using caffe::Timer;
 using caffe::vector;
 
 
-// Used in device query
 DEFINE_int32(device_id, 0,
-             "[device_query,time] The device id to use.");
-// Used in training
-DEFINE_string(solver_proto_file, "",
-              "[train] The protobuf containing the solver definition.");
-DEFINE_string(net_proto_file, "",
-              "[time] The net proto file to use.");
-DEFINE_string(resume_point_file, "",
-              "[train] (optional) The snapshot from which to resume training.");
-DEFINE_string(pretrained_net_file, "",
-              "[train] (optional) A pretrained network to run finetune from. "
-              "Cannot be set simultaneously with resume_point_file.");
-DEFINE_int32(run_iterations, 50,
-             "[time] The number of iterations to run.");
+    "The GPU device ID to use.");
 DEFINE_bool(gpu, false,
-            "[time] Run in GPU mode when true.");
+    "Run in GPU mode when true.");
+DEFINE_string(solver, "",
+    "The solver definition protocol buffer text file.");
+DEFINE_string(model, "",
+    "The model definition protocol buffer text file..");
+DEFINE_string(snapshot, "",
+    "Optional; the snapshot solver state to resume training.");
+DEFINE_string(weights, "",
+    "Optional; the pretrained weights to initialize finetuning. "
+    "Cannot be set simultaneously with snapshot.");
+DEFINE_int32(iterations, 50,
+    "The number of iterations to run.");
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
@@ -79,19 +77,19 @@ int device_query() {
 RegisterBrewFunction(device_query);
 
 int train() {
-  CHECK_GT(FLAGS_solver_proto_file.size(), 0);
+  CHECK_GT(FLAGS_solver.size(), 0);
 
   caffe::SolverParameter solver_param;
-  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver_proto_file, &solver_param);
+  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
   LOG(INFO) << "Starting Optimization";
   caffe::SGDSolver<float> solver(solver_param);
-  if (FLAGS_resume_point_file.size()) {
-    LOG(INFO) << "Resuming from " << FLAGS_resume_point_file;
-    solver.Solve(FLAGS_resume_point_file);
-  } else if (FLAGS_pretrained_net_file.size()) {
-    LOG(INFO) << "Finetuning from " << FLAGS_pretrained_net_file;
-    solver.net()->CopyTrainedLayersFrom(FLAGS_pretrained_net_file);
+  if (FLAGS_snapshot.size()) {
+    LOG(INFO) << "Resuming from " << FLAGS_snapshot;
+    solver.Solve(FLAGS_snapshot);
+  } else if (FLAGS_weights.size()) {
+    LOG(INFO) << "Finetuning from " << FLAGS_weights;
+    solver.net()->CopyTrainedLayersFrom(FLAGS_weights);
     solver.Solve();
   } else {
     solver.Solve();
@@ -113,7 +111,7 @@ int time() {
   }
   // Instantiate the caffe net.
   Caffe::set_phase(Caffe::TRAIN);
-  Net<float> caffe_net(FLAGS_net_proto_file);
+  Net<float> caffe_net(FLAGS_model);
 
   // Do a clean forward and backward pass, so that memory allocation are done
   // and future iterations will be more stable.
@@ -132,7 +130,7 @@ int time() {
   const vector<vector<bool> >& bottom_need_backward =
       caffe_net.bottom_need_backward();
   LOG(INFO) << "*** Benchmark begins ***";
-  LOG(INFO) << "Testing for " << FLAGS_run_iterations << " iterations.";
+  LOG(INFO) << "Testing for " << FLAGS_iterations << " iterations.";
   Timer total_timer;
   total_timer.Start();
   Timer forward_timer;
@@ -141,7 +139,7 @@ int time() {
   for (int i = 0; i < layers.size(); ++i) {
     const caffe::string& layername = layers[i]->layer_param().name();
     timer.Start();
-    for (int j = 0; j < FLAGS_run_iterations; ++j) {
+    for (int j = 0; j < FLAGS_iterations; ++j) {
       layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
     }
     LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
@@ -154,7 +152,7 @@ int time() {
   for (int i = layers.size() - 1; i >= 0; --i) {
     const caffe::string& layername = layers[i]->layer_param().name();
     timer.Start();
-    for (int j = 0; j < FLAGS_run_iterations; ++j) {
+    for (int j = 0; j < FLAGS_iterations; ++j) {
       layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
                           &bottom_vecs[i]);
     }

From 5c46d79da98cc5a090bffd489cfbaca1b6f9e35d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 23:22:40 -0700
Subject: [PATCH 0407/2053] check required caffe cli args

---
 tools/caffe.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 1320b7a5e25..089fef39e1b 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -77,7 +77,10 @@ int device_query() {
 RegisterBrewFunction(device_query);
 
 int train() {
-  CHECK_GT(FLAGS_solver.size(), 0);
+  CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
+  CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
+      << "Give a snapshot to resume training or weights to finetune "
+      "but not both.";
 
   caffe::SolverParameter solver_param;
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
@@ -100,6 +103,8 @@ int train() {
 RegisterBrewFunction(train);
 
 int time() {
+  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
+
   // Set device id and mode
   if (FLAGS_gpu) {
     LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;

From 7dcf592a506754ea5faed1cf510410b41ef19f61 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 23:22:56 -0700
Subject: [PATCH 0408/2053] comment caffe cli

---
 tools/caffe.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 089fef39e1b..57e7a5ebdae 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -63,11 +63,13 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
   }
 }
 
-// caffe actions that could be called in the form
-//     caffe.bin action
-// To do so, define actions as "int action()" functions, and register it with
+// caffe commands to call by
+//     caffe <command> <args>
+//
+// To add a command, define a function "int command()" and register it with
 // RegisterBrewFunction(action);
 
+// Device Query: show diagnostic information for a GPU device.
 int device_query() {
   LOG(INFO) << "Querying device_id = " << FLAGS_device_id;
   caffe::Caffe::SetDevice(FLAGS_device_id);
@@ -76,6 +78,8 @@ int device_query() {
 }
 RegisterBrewFunction(device_query);
 
+
+// Train / Finetune a model.
 int train() {
   CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
   CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
@@ -102,6 +106,8 @@ int train() {
 }
 RegisterBrewFunction(train);
 
+
+// Time: benchmark the execution time of a model.
 int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
 

From 76633d335142741d99f24fa326f774ecbeaf311b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 23:38:17 -0700
Subject: [PATCH 0409/2053] consolidate test into caffe cli

---
 tools/caffe.cpp    | 35 ++++++++++++++++++++++++++++++++
 tools/test_net.cpp | 50 ++--------------------------------------------
 2 files changed, 37 insertions(+), 48 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 57e7a5ebdae..313803da654 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -107,6 +107,40 @@ int train() {
 RegisterBrewFunction(train);
 
 
+// Test: score a model.
+int test() {
+  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
+  CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
+
+  // Set device id and mode
+  if (FLAGS_gpu) {
+    LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
+    Caffe::SetDevice(FLAGS_device_id);
+    Caffe::set_mode(Caffe::GPU);
+  } else {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+  // Instantiate the caffe net.
+  Caffe::set_phase(Caffe::TEST);
+  Net<float> caffe_net(FLAGS_model);
+  caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
+  LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
+
+  double test_score = 0;
+  for (int i = 0; i < FLAGS_iterations; ++i) {
+    const vector<Blob<float>*>& result = caffe_net.ForwardPrefilled();
+    test_score += result[0]->cpu_data()[0];
+    LOG(INFO) << "Batch " << i << ", score: " << result[0]->cpu_data()[0];
+  }
+  test_score /= FLAGS_iterations;
+  LOG(INFO) << "Score: " << test_score;
+
+  return 0;
+}
+RegisterBrewFunction(test);
+
+
 // Time: benchmark the execution time of a model.
 int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
@@ -187,6 +221,7 @@ int main(int argc, char** argv) {
       "usage: caffe <command> <args>\n\n"
       "commands:\n"
       "  train           train or finetune a model\n"
+      "  test            score a model\n"
       "  device_query    show GPU diagnostic information\n"
       "  time            benchmark model execution time");
   // Run tool or show usage.
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
index de97ab8924c..cebbc4b1efc 100644
--- a/tools/test_net.cpp
+++ b/tools/test_net.cpp
@@ -1,53 +1,7 @@
-// This is a simple script that allows one to quickly test a network whose
-// structure is specified by text format protocol buffers, and whose parameter
-// are loaded from a pre-trained network.
-// Usage:
-//    test_net net_proto pretrained_net_proto iterations [CPU/GPU]
-
-#include <cstdlib>
-#include <cstring>
-#include <vector>
-
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  if (argc < 4 || argc > 6) {
-    LOG(ERROR) << "test_net net_proto pretrained_net_proto iterations "
-        << "[CPU/GPU] [Device ID]";
-    return 1;
-  }
-
-  Caffe::set_phase(Caffe::TEST);
-
-  if (argc >= 5 && strcmp(argv[4], "GPU") == 0) {
-    Caffe::set_mode(Caffe::GPU);
-    int device_id = 0;
-    if (argc == 6) {
-      device_id = atoi(argv[5]);
-    }
-    Caffe::SetDevice(device_id);
-    LOG(ERROR) << "Using GPU #" << device_id;
-  } else {
-    LOG(ERROR) << "Using CPU";
-    Caffe::set_mode(Caffe::CPU);
-  }
-
-  Net<float> caffe_test_net(argv[1]);
-  caffe_test_net.CopyTrainedLayersFrom(argv[2]);
-
-  int total_iter = atoi(argv[3]);
-  LOG(ERROR) << "Running " << total_iter << " iterations.";
-
-  double test_accuracy = 0;
-  for (int i = 0; i < total_iter; ++i) {
-    const vector<Blob<float>*>& result = caffe_test_net.ForwardPrefilled();
-    test_accuracy += result[0]->cpu_data()[0];
-    LOG(ERROR) << "Batch " << i << ", accuracy: " << result[0]->cpu_data()[0];
-  }
-  test_accuracy /= total_iter;
-  LOG(ERROR) << "Test accuracy: " << test_accuracy;
-
+  LOG(FATAL) << "Deprecated. Use caffe.bin test --model=... "
+      "--weights=... instead.";
   return 0;
 }

From e3de3d55de460e440c514cf3ac33dc406101ab3e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 23:48:29 -0700
Subject: [PATCH 0410/2053] fix deprecation warnings

---
 tools/device_query.cpp        | 2 +-
 tools/finetune_net.cpp        | 4 ++--
 tools/net_speed_benchmark.cpp | 4 ++--
 tools/test_net.cpp            | 2 +-
 tools/train_net.cpp           | 4 ++--
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/tools/device_query.cpp b/tools/device_query.cpp
index 2678154cbb4..03799e52b53 100644
--- a/tools/device_query.cpp
+++ b/tools/device_query.cpp
@@ -1,7 +1,7 @@
 #include "caffe/common.hpp"
 
 int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe.bin devicequery "
+  LOG(FATAL) << "Deprecated. Use caffe device_query "
                 "[--device_id=0] instead.";
   return 0;
 }
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index 2966de48d17..81c0c354dbf 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -1,7 +1,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
-                "[--pretrained_net_file=...] instead.";
+  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
+                "[--weights=...] instead.";
   return 0;
 }
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index ff112bdff55..cd16e8d0984 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -1,7 +1,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe.bin speedtest --net_proto_file=... "
-             "[--run_iterations=50] [--speedtest_with_gpu] [--device_id=0]";
+  LOG(FATAL) << "Deprecated. Use caffe time --model=... "
+             "[--iterations=50] [--gpu] [--device_id=0]";
   return 0;
 }
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
index cebbc4b1efc..92e14eeebaf 100644
--- a/tools/test_net.cpp
+++ b/tools/test_net.cpp
@@ -1,7 +1,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe.bin test --model=... "
+  LOG(FATAL) << "Deprecated. Use caffe test --model=... "
       "--weights=... instead.";
   return 0;
 }
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 26aeb40bd05..622bca311c8 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,7 +1,7 @@
 #include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe.bin train --solver_proto_file=... "
-                "[--resume_point_file=...] instead.";
+  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
+                "[--snapshot=...] instead.";
   return 0;
 }

From 13d625b556ab473f98bd70ac1b13087c50fedddf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 6 Aug 2014 23:56:06 -0700
Subject: [PATCH 0411/2053] update cli usage in examples

---
 examples/cifar10/train_full.sh                | 19 +++++++++----------
 examples/cifar10/train_quick.sh               | 11 +++++------
 examples/imagenet/resume_training.sh          |  6 +++---
 ...speedtest_imagenet.sh => time_imagenet.sh} |  6 +++---
 examples/imagenet/train_alexnet.sh            |  3 +--
 examples/imagenet/train_imagenet.sh           |  3 +--
 examples/mnist/train_lenet.sh                 |  3 +--
 examples/mnist/train_lenet_consolidated.sh    |  3 +--
 examples/mnist/train_mnist_autoencoder.sh     |  3 +--
 9 files changed, 25 insertions(+), 32 deletions(-)
 rename examples/imagenet/{speedtest_imagenet.sh => time_imagenet.sh} (73%)

diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 930b27a1a32..9dd9ad79822 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -2,15 +2,14 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-    --solver_proto_file=cifar10_full_solver.prototxt
+$TOOLS/caffe train --solver=cifar10_full_solver.prototxt
 
-#reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-    --solver_proto_file=cifar10_full_solver_lr1.prototxt \
-    --resume_point_file=cifar10_full_iter_60000.solverstate
+# reduce learning rate by factor of 10
+$TOOLS/caffe train \
+    --solver=cifar10_full_solver_lr1.prototxt \
+    --snapshot=cifar10_full_iter_60000.solverstate
 
-#reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-    --solver_proto_file=cifar10_full_solver_lr2.prototxt \
-    --resume_point_file=cifar10_full_iter_65000.solverstate
+# reduce learning rate by factor of 10
+$TOOLS/caffe train \
+    --solver=cifar10_full_solver_lr2.prototxt \
+    --snapshot=cifar10_full_iter_65000.solverstate
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 5e8fffccd48..e348e12fd94 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -2,10 +2,9 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=cifar10_quick_solver.prototxt
+$TOOLS/caffe.bin train --solver=cifar10_quick_solver.prototxt
 
-#reduce learning rate by fctor of 10 after 8 epochs
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=cifar10_quick_solver_lr1.prototxt \
-  --resume_point_file=cifar10_quick_iter_4000.solverstate
+# reduce learning rate by fctor of 10 after 8 epochs
+$TOOLS/caffe.bin train \
+  --solver=cifar10_quick_solver_lr1.prototxt \
+  --snapshot=cifar10_quick_iter_4000.solverstate
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index b43a9b1bf7c..9e00d93c469 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -2,8 +2,8 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train\
-    --solver_proto_file=imagenet_solver.prototxt \
-    --resume_point_file=caffe_imagenet_train_10000.solverstate
+$TOOLS/caffe train\
+    --solver=imagenet_solver.prototxt \
+    --snapshot=caffe_imagenet_train_10000.solverstate
 
 echo "Done."
diff --git a/examples/imagenet/speedtest_imagenet.sh b/examples/imagenet/time_imagenet.sh
similarity index 73%
rename from examples/imagenet/speedtest_imagenet.sh
rename to examples/imagenet/time_imagenet.sh
index c790d835c14..c83b0c021c7 100755
--- a/examples/imagenet/speedtest_imagenet.sh
+++ b/examples/imagenet/time_imagenet.sh
@@ -14,9 +14,9 @@ else
   DEVICE_ID=$1
 fi
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin speedtest \
-  --net_proto_file=imagenet_train_val.prototxt \
-  --speedtest_with_gpu=${WITH_GPU} \
+$TOOLS/caffe time \
+  --model=imagenet_train_val.prototxt \
+  --gpu=${WITH_GPU} \
   --device_id=${DEVICE_ID}
 
 echo "Done."
diff --git a/examples/imagenet/train_alexnet.sh b/examples/imagenet/train_alexnet.sh
index d43a235dba1..6a7c0577413 100755
--- a/examples/imagenet/train_alexnet.sh
+++ b/examples/imagenet/train_alexnet.sh
@@ -2,7 +2,6 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=alexnet_solver.prototxt
+$TOOLS/caffe train --solver=alexnet_solver.prototxt
 
 echo "Done."
diff --git a/examples/imagenet/train_imagenet.sh b/examples/imagenet/train_imagenet.sh
index 90dcdfaddb8..008b96c01a1 100755
--- a/examples/imagenet/train_imagenet.sh
+++ b/examples/imagenet/train_imagenet.sh
@@ -2,7 +2,6 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=imagenet_solver.prototxt
+$TOOLS/caffe train --solver=imagenet_solver.prototxt
 
 echo "Done."
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index 4d0f1eea051..b93e48fb629 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -2,5 +2,4 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=lenet_solver.prototxt
+$TOOLS/caffe train --solver=lenet_solver.prototxt
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
index 802c801e31f..83fe895ba0b 100755
--- a/examples/mnist/train_lenet_consolidated.sh
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -2,5 +2,4 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=lenet_consolidated_solver.prototxt
+$TOOLS/caffe train --solver=lenet_consolidated_solver.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index 3fb493534db..628c74b969a 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,5 +1,4 @@
 #!/bin/bash
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/caffe.bin train \
-  --solver_proto_file=mnist_autoencoder_solver.prototxt
+$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver.prototxt

From 3b07be06a459f655b8cfa3e9d87bd534be5247d4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 7 Aug 2014 11:56:45 -0700
Subject: [PATCH 0412/2053] consolidate gpu and device_id args in caffe tool

---
 examples/imagenet/time_imagenet.sh | 17 ++++++-----------
 tools/caffe.cpp                    | 23 +++++++++++------------
 2 files changed, 17 insertions(+), 23 deletions(-)

diff --git a/examples/imagenet/time_imagenet.sh b/examples/imagenet/time_imagenet.sh
index c83b0c021c7..c448b4977ce 100755
--- a/examples/imagenet/time_imagenet.sh
+++ b/examples/imagenet/time_imagenet.sh
@@ -3,20 +3,15 @@
 TOOLS=../../build/tools
 
 if [ -z "$1" ]; then
-  echo "Using CPU! To run GPU speedtest, use:"
-  echo "    ./speedtest_imagenet.sh <device ID>"
-  echo "(Try ./speedtest_imagenet.sh 0 if you have just one GPU.)"
+  echo "Using CPU! To time GPU mode, use:"
+  echo "    ./time_imagenet.sh <device ID>"
+  echo "(Try ./time_imagenet.sh 0 if you have just one GPU.)"
   sleep 3  # Let the user read
-  WITH_GPU=false
-  DEVICE_ID=0
+  GPU=""
 else
-  WITH_GPU=true
-  DEVICE_ID=$1
+  GPU="--gpu=$1"
 fi
 
-$TOOLS/caffe time \
-  --model=imagenet_train_val.prototxt \
-  --gpu=${WITH_GPU} \
-  --device_id=${DEVICE_ID}
+$TOOLS/caffe time --model=imagenet_train_val.prototxt ${GPU}
 
 echo "Done."
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 313803da654..22d9eb19912 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -17,10 +17,8 @@ using caffe::Timer;
 using caffe::vector;
 
 
-DEFINE_int32(device_id, 0,
-    "The GPU device ID to use.");
-DEFINE_bool(gpu, false,
-    "Run in GPU mode when true.");
+DEFINE_int32(gpu, -1,
+    "Run in GPU mode on given device ID.");
 DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
@@ -71,8 +69,9 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
 
 // Device Query: show diagnostic information for a GPU device.
 int device_query() {
-  LOG(INFO) << "Querying device_id = " << FLAGS_device_id;
-  caffe::Caffe::SetDevice(FLAGS_device_id);
+  CHECK_GT(FLAGS_gpu, -1) << "Need a device ID to query.";
+  LOG(INFO) << "Querying device ID = " << FLAGS_gpu;
+  caffe::Caffe::SetDevice(FLAGS_gpu);
   caffe::Caffe::DeviceQuery();
   return 0;
 }
@@ -113,9 +112,9 @@ int test() {
   CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
 
   // Set device id and mode
-  if (FLAGS_gpu) {
-    LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
-    Caffe::SetDevice(FLAGS_device_id);
+  if (FLAGS_gpu >= 0) {
+    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+    Caffe::SetDevice(FLAGS_gpu);
     Caffe::set_mode(Caffe::GPU);
   } else {
     LOG(INFO) << "Use CPU.";
@@ -146,9 +145,9 @@ int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
 
   // Set device id and mode
-  if (FLAGS_gpu) {
-    LOG(INFO) << "Use GPU with device id " << FLAGS_device_id;
-    Caffe::SetDevice(FLAGS_device_id);
+  if (FLAGS_gpu >= 0) {
+    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+    Caffe::SetDevice(FLAGS_gpu);
     Caffe::set_mode(Caffe::GPU);
   } else {
     LOG(INFO) << "Use CPU.";

From c9fd6608095348feb20f2a7fbfdcbe1a2353d57d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 8 Aug 2014 11:18:46 -0700
Subject: [PATCH 0413/2053] [example] fix broken links in ImageNet recipe

---
 examples/imagenet/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index ec2a39bfd23..883e09272f1 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -12,7 +12,7 @@ Yangqing's Recipe on Brewing ImageNet
     "All your braincells are belong to us."
         - Caffeine
 
-We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf). Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming. If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](getting_pretrained_models.html) page. *Note that the pretrained model is for academic research / non-commercial use only*.
+We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf). Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming. If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page. *Note that the pretrained model is for academic research / non-commercial use only*.
 
 To clarify, by ImageNet we actually mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.
 
@@ -102,4 +102,4 @@ Parting Words
 
 Hope you liked this recipe! Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network. The recent ILSVRC 2013 challenge suggests that there are quite some room for improvement. **Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it: [Running Pretrained ImageNet](getting_pretrained_models.html). This time we will use Python, but if you have wrappers for other languages, please kindly send a pull request!
+And since now you have a trained network, check out how to use it: [Running Pretrained ImageNet](../../getting_pretrained_models.html). This time we will use Python, but if you have wrappers for other languages, please kindly send a pull request!

From 70a6b0b71d2ec894689c85291bae5e64d44b0584 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 8 Aug 2014 12:01:39 -0700
Subject: [PATCH 0414/2053] [docs] fix find complaint in example gathering
 script

---
 scripts/gather_examples.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/gather_examples.sh b/scripts/gather_examples.sh
index dd6cef0df1b..3fc726065ba 100755
--- a/scripts/gather_examples.sh
+++ b/scripts/gather_examples.sh
@@ -22,7 +22,7 @@ for README_FILENAME in $(find examples -iname "readme.md"); do
 done
 
 # Gather docs from examples/*.ipynb and add YAML front-matter.
-for NOTEBOOK_FILENAME in $(find examples -d 1 -iname "*.ipynb"); do
+for NOTEBOOK_FILENAME in $(find examples -depth -iname "*.ipynb"); do
     DOCS_FILENAME=$GATHERED_DIR/$NOTEBOOK_FILENAME
     mkdir -p `dirname $DOCS_FILENAME`
     python scripts/copy_notebook.py $NOTEBOOK_FILENAME $DOCS_FILENAME

From 2243aa5007cf92acf44bb995f796653490caec5e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 8 Aug 2014 12:40:42 -0700
Subject: [PATCH 0415/2053] [example] fix example names

---
 .../{imagenet_classification.ipynb => classification.ipynb}   | 2 +-
 examples/filter_visualization.ipynb                           | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)
 rename examples/{imagenet_classification.ipynb => classification.ipynb} (99%)

diff --git a/examples/imagenet_classification.ipynb b/examples/classification.ipynb
similarity index 99%
rename from examples/imagenet_classification.ipynb
rename to examples/classification.ipynb
index fc08c743093..302bdea0e00 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/classification.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
+  "name": "ImageNet classification",
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "name": "ImageNet Classification",
   "include_in_docs": true
  },
  "nbformat": 3,
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index bbc919ba68b..5165d840b7b 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,8 +1,8 @@
 {
  "metadata": {
+  "name": "Filter visualization",
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "include_in_docs": true,
-  "name": ""
+  "include_in_docs": true
  },
  "nbformat": 3,
  "nbformat_minor": 0,

From c5a1aa51a5737bfac41f521df2decb67ab71f6b4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 8 Aug 2014 12:40:42 -0700
Subject: [PATCH 0416/2053] [example] fix example names

---
 .../{imagenet_classification.ipynb => classification.ipynb}   | 2 +-
 examples/filter_visualization.ipynb                           | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)
 rename examples/{imagenet_classification.ipynb => classification.ipynb} (99%)

diff --git a/examples/imagenet_classification.ipynb b/examples/classification.ipynb
similarity index 99%
rename from examples/imagenet_classification.ipynb
rename to examples/classification.ipynb
index fc08c743093..302bdea0e00 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/classification.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
+  "name": "ImageNet classification",
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "name": "ImageNet Classification",
   "include_in_docs": true
  },
  "nbformat": 3,
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index bbc919ba68b..5165d840b7b 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,8 +1,8 @@
 {
  "metadata": {
+  "name": "Filter visualization",
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "include_in_docs": true,
-  "name": ""
+  "include_in_docs": true
  },
  "nbformat": 3,
  "nbformat_minor": 0,

From c8b6457de5b9ba194c5539c81a05591bca6b3e1b Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 8 Aug 2014 15:47:23 -0700
Subject: [PATCH 0417/2053] add necessary input checks for matcaffe

---
 matlab/caffe/matcaffe.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 957ebea008e..f4a2425c6c1 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -50,6 +50,10 @@ static mxArray* do_forward(const mxArray* const bottom) {
       input_blobs.size());
   for (unsigned int i = 0; i < input_blobs.size(); ++i) {
     const mxArray* const elem = mxGetCell(bottom, i);
+    CHECK(mxIsSingle(elem))
+        << "MatCaffe require single-precision float point data";
+    CHECK_EQ(mxGetNumberOfElements(elem), input_blobs[i]->count())
+        << "MatCaffe input size does not match the input size of the network";
     const float* const data_ptr =
         reinterpret_cast<const float* const>(mxGetPr(elem));
     switch (Caffe::mode()) {

From 148eb67d66e584041c499b6ef4ae9e47916dd12b Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sat, 9 Aug 2014 20:11:38 +0800
Subject: [PATCH 0418/2053] Fix the gflags namespace issue

---
 include/caffe/common.hpp | 10 ++++++++++
 src/caffe/common.cpp     | 14 +-------------
 tools/caffe.cpp          |  1 -
 3 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 97a34962c63..9008eae02bf 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -2,6 +2,7 @@
 #define CAFFE_COMMON_HPP_
 
 #include <boost/shared_ptr.hpp>
+#include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cmath>
@@ -11,6 +12,15 @@
 
 #include "caffe/util/device_alternate.hpp"
 
+// gflags 2.1 issue: namespace google was changed to gflags without warning.
+// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// 2.1. If yes , we will add a temporary solution to redirect the namespace.
+// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
+// remove the following hack.
+#ifndef GFLAGS_GFLAGS_H_
+namespace gflags = google;
+#endif  // GFLAGS_GFLAGS_H_
+
 // Disable the copy and assignment operator for a class.
 #define DISABLE_COPY_AND_ASSIGN(classname) \
 private:\
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 99b2292461e..e6c0a4f56a3 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,4 +1,3 @@
-#include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <cstdio>
 #include <ctime>
@@ -6,17 +5,6 @@
 #include "caffe/common.hpp"
 #include "caffe/util/rng.hpp"
 
-// gflags 2.1 issue: namespace google was changed to gflags without warning.
-// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
-// 2.1. If yes , we will add a temporary solution to redirect the namespace.
-// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
-// remove the following hack.
-#ifdef GFLAGS_GFLAGS_H_
-namespace google {
-using ::gflags::ParseCommandLineFlags;
-}  // namespace google
-#endif  // GFLAGS_GFLAGS_H_
-
 namespace caffe {
 
 shared_ptr<Caffe> Caffe::singleton_;
@@ -44,7 +32,7 @@ int64_t cluster_seedgen(void) {
 
 void GlobalInit(int* pargc, char*** pargv) {
   // Google flags.
-  ::google::ParseCommandLineFlags(pargc, pargv, true);
+  ::gflags::ParseCommandLineFlags(pargc, pargv, true);
   // Google logging.
   ::google::InitGoogleLogging(*(pargv)[0]);
 }
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 22d9eb19912..13398ae8577 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -1,4 +1,3 @@
-#include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cstring>

From e114edf205dbec65653b45cef14c1ee4b7a47081 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sat, 9 Aug 2014 20:41:58 +0800
Subject: [PATCH 0419/2053] Distribute the generated proto header files

---
 Makefile | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/Makefile b/Makefile
index 84f8db26abb..2c071c8a8ce 100644
--- a/Makefile
+++ b/Makefile
@@ -558,6 +558,8 @@ $(DIST_ALIASES): $(DISTRIBUTE_DIR)
 $(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
 	# add include
 	cp -r include $(DISTRIBUTE_DIR)/
+	mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto
+	cp $(PROTO_GEN_HEADER_SRCS) $(DISTRIBUTE_DIR)/include/caffe/proto
 	# add tool and example binaries
 	cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin

From 7dc646398064cdf7ca1e636edd285f73ea69592f Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sat, 9 Aug 2014 20:11:38 +0800
Subject: [PATCH 0420/2053] Fix the gflags namespace issue

---
 include/caffe/common.hpp | 10 ++++++++++
 src/caffe/common.cpp     | 14 +-------------
 tools/caffe.cpp          |  1 -
 3 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 97a34962c63..9008eae02bf 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -2,6 +2,7 @@
 #define CAFFE_COMMON_HPP_
 
 #include <boost/shared_ptr.hpp>
+#include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cmath>
@@ -11,6 +12,15 @@
 
 #include "caffe/util/device_alternate.hpp"
 
+// gflags 2.1 issue: namespace google was changed to gflags without warning.
+// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// 2.1. If yes , we will add a temporary solution to redirect the namespace.
+// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
+// remove the following hack.
+#ifndef GFLAGS_GFLAGS_H_
+namespace gflags = google;
+#endif  // GFLAGS_GFLAGS_H_
+
 // Disable the copy and assignment operator for a class.
 #define DISABLE_COPY_AND_ASSIGN(classname) \
 private:\
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 99b2292461e..e6c0a4f56a3 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,4 +1,3 @@
-#include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <cstdio>
 #include <ctime>
@@ -6,17 +5,6 @@
 #include "caffe/common.hpp"
 #include "caffe/util/rng.hpp"
 
-// gflags 2.1 issue: namespace google was changed to gflags without warning.
-// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
-// 2.1. If yes , we will add a temporary solution to redirect the namespace.
-// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
-// remove the following hack.
-#ifdef GFLAGS_GFLAGS_H_
-namespace google {
-using ::gflags::ParseCommandLineFlags;
-}  // namespace google
-#endif  // GFLAGS_GFLAGS_H_
-
 namespace caffe {
 
 shared_ptr<Caffe> Caffe::singleton_;
@@ -44,7 +32,7 @@ int64_t cluster_seedgen(void) {
 
 void GlobalInit(int* pargc, char*** pargv) {
   // Google flags.
-  ::google::ParseCommandLineFlags(pargc, pargv, true);
+  ::gflags::ParseCommandLineFlags(pargc, pargv, true);
   // Google logging.
   ::google::InitGoogleLogging(*(pargv)[0]);
 }
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 22d9eb19912..13398ae8577 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -1,4 +1,3 @@
-#include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cstring>

From f8b612d8cdfae97c004db307ab84df78d25e679b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 9 Aug 2014 20:43:40 -0700
Subject: [PATCH 0421/2053] default raw_scale in python scripts to ImageNet
 model value

---
 python/classify.py | 3 ++-
 python/detect.py   | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/python/classify.py b/python/classify.py
index d3d239cbb7d..ddc5429feac 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -66,11 +66,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        help="Multiply input features by this scale to finish input preprocessing."
+        help="Multiply input features by this scale to finish preprocessing."
     )
     parser.add_argument(
         "--raw_scale",
         type=float,
+        default=255.0,
         help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(
diff --git a/python/detect.py b/python/detect.py
index 77f4676aec1..4598fc7a327 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -76,11 +76,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        help="Multiply input features by this scale to finish input preprocessing."
+        help="Multiply input features by this scale to finish preprocessing."
     )
     parser.add_argument(
         "--raw_scale",
         type=float,
+        default=255.0,
         help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(

From b123fcc635317853cc9861f57c71d2f5e70ea8bc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 9 Aug 2014 22:24:15 -0700
Subject: [PATCH 0422/2053] output loss for caffenet and alexnet train/val
 models

---
 examples/imagenet/alexnet_train_val.prototxt  | 1 +
 examples/imagenet/imagenet_train_val.prototxt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/examples/imagenet/alexnet_train_val.prototxt b/examples/imagenet/alexnet_train_val.prototxt
index f65f3e7f87e..8d7977d981e 100644
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -336,4 +336,5 @@ layers {
   type: SOFTMAX_LOSS
   bottom: "fc8"
   bottom: "label"
+  top: "loss"
 }
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/examples/imagenet/imagenet_train_val.prototxt
index af28c1495e5..ea1f907f00d 100644
--- a/examples/imagenet/imagenet_train_val.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -336,4 +336,5 @@ layers {
   type: SOFTMAX_LOSS
   bottom: "fc8"
   bottom: "label"
+  top: "loss"
 }

From 6c4dd5aa989cee6793233552b872ff199c451c77 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 9 Aug 2014 22:24:15 -0700
Subject: [PATCH 0423/2053] output loss for caffenet and alexnet train/val
 models

---
 examples/imagenet/alexnet_train_val.prototxt  | 1 +
 examples/imagenet/imagenet_train_val.prototxt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/examples/imagenet/alexnet_train_val.prototxt b/examples/imagenet/alexnet_train_val.prototxt
index f65f3e7f87e..8d7977d981e 100644
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -336,4 +336,5 @@ layers {
   type: SOFTMAX_LOSS
   bottom: "fc8"
   bottom: "label"
+  top: "loss"
 }
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/examples/imagenet/imagenet_train_val.prototxt
index af28c1495e5..ea1f907f00d 100644
--- a/examples/imagenet/imagenet_train_val.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -336,4 +336,5 @@ layers {
   type: SOFTMAX_LOSS
   bottom: "fc8"
   bottom: "label"
+  top: "loss"
 }

From 2288d282fbf63ede3c6fd0fc590aafed3ecb4e39 Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Sun, 10 Aug 2014 23:31:29 +0800
Subject: [PATCH 0424/2053] Use gflags to show help when the arguments not
 correct

---
 tools/convert_imageset.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index b628a357185..0f122ea57cc 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -59,7 +59,7 @@ int main(int argc, char** argv) {
   gflags::ParseCommandLineFlags(&argc, &argv, true);
 
   if (argc != 4) {
-    fputs("Use --help to see detailed description of usage\n", stdout);
+    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/convert_imageset");
     return 1;
   }
 

From 1d4d1d668a3f3714c514b9cabad8b97375609c97 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 10 Aug 2014 21:23:34 -0700
Subject: [PATCH 0425/2053] fix formatting error in blob.hpp

---
 include/caffe/blob.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index eb0c25ba394..69600290858 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -23,7 +23,7 @@ class Blob {
   inline int channels() const { return channels_; }
   inline int height() const { return height_; }
   inline int width() const { return width_; }
-  inline int count() const {return count_; }
+  inline int count() const { return count_; }
   inline int offset(const int n, const int c = 0, const int h = 0,
       const int w = 0) const {
     CHECK_GE(n, 0);

From f072600bb1ae60cd9dc97ad4b8deb829a62cfdc4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 11 Aug 2014 00:27:38 -0700
Subject: [PATCH 0426/2053] [docs] fix example links from install guide

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index ff0760dd060..73a35395c4e 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -208,7 +208,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 *Speed*: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
 
-Now that you have installed Caffe, check out the [MNIST demo](mnist.html) and the pretrained [ImageNet example](imagenet.html).
+Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
 ## Hardware Questions
 

From b1d6da5740a3031a633d7fa6e4c0f7fa4dc77ebf Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Mon, 11 Aug 2014 09:16:51 -0700
Subject: [PATCH 0427/2053] Add a leveldb option function in io.hpp/cpp

---
 include/caffe/util/io.hpp       | 6 ++++++
 src/caffe/layers/data_layer.cpp | 3 +--
 src/caffe/util/io.cpp           | 9 +++++++++
 3 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index cdf4d9e724e..8ff8b0c25ae 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -12,6 +12,11 @@
 
 #define HDF5_NUM_DIMS 4
 
+namespace leveldb {
+// Forward declaration for leveldb::Options to be used in GetlevelDBOptions().
+class Options;
+}
+
 namespace caffe {
 
 using ::google::protobuf::Message;
@@ -70,6 +75,7 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, datum);
 }
 
+leveldb::Options GetLevelDBOptions();
 
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index c089c9ba5df..8f17c45e5f6 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -173,9 +173,8 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   case DataParameter_DB_LEVELDB:
     {
     leveldb::DB* db_temp;
-    leveldb::Options options;
+    leveldb::Options options = GetLevelDBOptions();
     options.create_if_missing = false;
-    options.max_open_files = 100;
     LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
     leveldb::Status status = leveldb::DB::Open(
         options, this->layer_param_.data_param().source(), &db_temp);
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index fd7454d4f98..48952a53f96 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,6 +2,7 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
+#include <leveldb/db.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
@@ -108,6 +109,14 @@ bool ReadImageToDatum(const string& filename, const int label,
   return true;
 }
 
+leveldb::Options GetLevelDBOptions() {
+  // In default, we will return the leveldb option and set the max open files
+  // in order to avoid using up the operating system's limit.
+  leveldb::Options options;
+  options.max_open_files = 100;
+  return options;
+}
+
 // Verifies format of data stored in HDF5 file and reshapes blob accordingly.
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(

From b04aa0036979d0c34ef96c150abb34e21bdf1a7b Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sun, 3 Aug 2014 18:19:58 -0700
Subject: [PATCH 0428/2053] mean-variance normalization layer

---
 include/caffe/vision_layers.hpp   |  33 +++++++
 src/caffe/layers/mvn_layer.cpp    | 151 ++++++++++++++++++++++++++++++
 src/caffe/layers/mvn_layer.cu     | 129 +++++++++++++++++++++++++
 src/caffe/proto/caffe.proto       |   3 +-
 src/caffe/test/test_mvn_layer.cpp |  85 +++++++++++++++++
 5 files changed, 400 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/mvn_layer.cpp
 create mode 100644 src/caffe/layers/mvn_layer.cu
 create mode 100644 src/caffe/test/test_mvn_layer.cpp

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 0797065081f..a6844cd6930 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -234,6 +234,39 @@ class LRNLayer : public Layer<Dtype> {
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
+/* MVNLayer
+ */
+template <typename Dtype>
+class MVNLayer : public Layer<Dtype> {
+ public:
+  explicit MVNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MVN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> mean_, variance_, temp_;
+
+  // sum_multiplier is just used to carry out sum using blas
+  Blob<Dtype> sum_multiplier_;
+};
+
+
 /* PoolingLayer
 */
 template <typename Dtype>
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
new file mode 100644
index 00000000000..7e6a97c16dd
--- /dev/null
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -0,0 +1,151 @@
+// Copyright 2014 BVLC and contributors.
+//
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void MVNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
+  (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+  mean_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      1, 1);
+  variance_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      1, 1);
+  temp_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+  sum_multiplier_.Reshape(1, 1,
+      bottom[0]->height(), bottom[0]->width());
+  Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
+  for (int i = 0; i < sum_multiplier_.count(); ++i) {
+    multiplier_data[i] = 1.;
+  }
+}
+
+template <typename Dtype>
+Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  int num = bottom[0]->num() * bottom[0]->channels();
+  int dim = bottom[0]->count() / num;
+  Dtype eps = 1e-10;
+
+  // put the squares of bottom into temp_
+  caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
+      temp_.mutable_cpu_data());
+
+  // computes variance using var(X) = E(X^2) - (EX)^2
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.cpu_data(),
+      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());
+  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), mean_.cpu_data(),
+      mean_.mutable_cpu_data());  // EX
+  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), variance_.cpu_data(),
+      variance_.mutable_cpu_data());  // E(X^2)
+  caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
+      temp_.mutable_cpu_data());  // (EX)^2
+  caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
+      variance_.mutable_cpu_data());  // variance
+
+  // do mean and variance normalization
+  // subtract mean
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+          mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+          temp_.mutable_cpu_data());
+
+  caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
+
+  // normalize variance
+  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+        variance_.mutable_cpu_data());
+
+  caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+        variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+        temp_.mutable_cpu_data());
+
+  caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
+
+  return Dtype(0);
+}
+
+template <typename Dtype>
+void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+
+  int num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+  int dim = (*bottom)[0]->count() / num;
+  Dtype eps = 1e-10;
+
+  caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
+        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+        mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+        bottom_diff);
+  caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
+          sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+          mean_.cpu_data(), sum_multiplier_.cpu_data(), 1.,
+          bottom_diff);
+
+  caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
+      bottom_diff);
+
+  // put the squares of bottom into temp_
+  caffe_powx(temp_.count(), bottom_data, Dtype(2),
+      temp_.mutable_cpu_data());
+
+  // computes variance using var(X) = E(X^2) - (EX)^2
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.cpu_data(),
+      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());
+  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), mean_.cpu_data(),
+      mean_.mutable_cpu_data());  // EX
+  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), variance_.cpu_data(),
+      variance_.mutable_cpu_data());  // E(X^2)
+  caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
+      temp_.mutable_cpu_data());  // (EX)^2
+  caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
+      variance_.mutable_cpu_data());  // variance
+
+  // normalize variance
+  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+        variance_.mutable_cpu_data());
+
+  caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+      variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+      temp_.mutable_cpu_data());
+
+  caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(MVNLayer);
+#endif
+
+INSTANTIATE_CLASS(MVNLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
new file mode 100644
index 00000000000..3714d49f2b1
--- /dev/null
+++ b/src/caffe/layers/mvn_layer.cu
@@ -0,0 +1,129 @@
+// Copyright 2014 BVLC and contributors.
+//
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  int num = bottom[0]->num() * bottom[0]->channels();
+  int dim = bottom[0]->count() / num;
+
+  // put the squares of bottom into temp_
+  caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
+      temp_.mutable_gpu_data());
+
+  // computes variance using var(X) = E(X^2) - (EX)^2
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.gpu_data(),
+      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());
+  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), mean_.gpu_data(),
+      mean_.mutable_gpu_data());  // EX
+  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), variance_.gpu_data(),
+      variance_.mutable_gpu_data());  // E(X^2)
+  caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
+      temp_.mutable_gpu_data());  // (EX)^2
+  caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
+      variance_.mutable_gpu_data());  // variance
+
+  Dtype eps = 1e-10;
+
+  // do mean and variance normalization
+  // subtract mean
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+          mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+          temp_.mutable_gpu_data());
+
+  caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
+
+  // normalize variance
+  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+        variance_.mutable_gpu_data());
+
+  caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+        variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+        temp_.mutable_gpu_data());
+
+  caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
+
+  return Dtype(0);
+}
+
+template <typename Dtype>
+void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+
+  int num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+  int dim = (*bottom)[0]->count() / num;
+
+  Dtype eps = 1e-10;
+
+  caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
+        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+        mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+        bottom_diff);
+  caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
+          sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+          mean_.gpu_data(), sum_multiplier_.gpu_data(), 1.,
+          bottom_diff);
+
+  caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
+      bottom_diff);
+
+  // put the squares of bottom into temp_
+  caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
+      temp_.mutable_gpu_data());
+
+  // computes variance using var(X) = E(X^2) - (EX)^2
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.gpu_data(),
+      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());
+  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), mean_.gpu_data(),
+      mean_.mutable_gpu_data());  // EX
+  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), variance_.gpu_data(),
+      variance_.mutable_gpu_data());  // E(X^2)
+  caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
+      temp_.mutable_gpu_data());  // (EX)^2
+  caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
+      variance_.mutable_gpu_data());  // variance
+
+  // normalize variance
+  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+        variance_.mutable_gpu_data());
+
+  caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+      variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+      temp_.mutable_gpu_data());
+
+  caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
+}
+
+
+INSTANTIATE_CLASS(MVNLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 44bfb631a22..fce56945bb3 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -201,7 +201,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 34 (last added: SLICE)
+  // LayerType next available ID: 35 (last added: MVN)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -228,6 +228,7 @@ message LayerParameter {
     LRN = 15;
     MEMORY_DATA = 29;
     MULTINOMIAL_LOGISTIC_LOSS = 16;
+    MVN = 34;
     POOLING = 17;
     POWER = 26;
     RELU = 18;
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
new file mode 100644
index 00000000000..d5bea3f952c
--- /dev/null
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -0,0 +1,85 @@
+// Copyright 2014 BVLC and contributors.
+
+#include <cmath>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class MVNLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  MVNLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~MVNLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(MVNLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(MVNLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  MVNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Test mean
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      Dtype sum = 0, var = 0;
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          sum += data;
+          var += data * data;
+        }
+      }
+      sum /= height * width;
+      var /= height * width;
+
+      // expect zero mean
+      EXPECT_GE(sum, -0.001);
+      EXPECT_LE(sum, 0.001);
+      // expect unit variance
+      EXPECT_GE(var, 0.999);
+      EXPECT_LE(var, 1.001);
+    }
+  }
+}
+
+TYPED_TEST(MVNLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  MVNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+}  // namespace caffe

From e7c11e1992cfbb6e654ee81bedcb0d5a1dc999b9 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 4 Aug 2014 09:03:34 -0700
Subject: [PATCH 0429/2053] reduced blas calls

---
 src/caffe/layers/mvn_layer.cpp | 24 ++++++++----------------
 src/caffe/layers/mvn_layer.cu  | 24 ++++++++----------------
 2 files changed, 16 insertions(+), 32 deletions(-)

diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 7e6a97c16dd..c9c075c4cda 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -43,14 +43,10 @@ Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       temp_.mutable_cpu_data());
 
   // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
-      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.cpu_data(),
-      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());
-  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), mean_.cpu_data(),
-      mean_.mutable_cpu_data());  // EX
-  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), variance_.cpu_data(),
-      variance_.mutable_cpu_data());  // E(X^2)
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
+      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data()); // E(X^2)
   caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
       temp_.mutable_cpu_data());  // (EX)^2
   caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
@@ -114,14 +110,10 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       temp_.mutable_cpu_data());
 
   // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
-      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.cpu_data(),
-      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());
-  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), mean_.cpu_data(),
-      mean_.mutable_cpu_data());  // EX
-  caffe_cpu_scale(mean_.count(), Dtype(1. / dim), variance_.cpu_data(),
-      variance_.mutable_cpu_data());  // E(X^2)
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
+      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data()); // E(X^2)
   caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
       temp_.mutable_cpu_data());  // (EX)^2
   caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 3714d49f2b1..be5f25bdf0d 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -22,14 +22,10 @@ Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       temp_.mutable_gpu_data());
 
   // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
-      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.gpu_data(),
-      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());
-  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), mean_.gpu_data(),
-      mean_.mutable_gpu_data());  // EX
-  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), variance_.gpu_data(),
-      variance_.mutable_gpu_data());  // E(X^2)
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
+      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data()); // E(X^2)
   caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
       temp_.mutable_gpu_data());  // (EX)^2
   caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
@@ -96,14 +92,10 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       temp_.mutable_gpu_data());
 
   // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_data,
-      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., temp_.gpu_data(),
-      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());
-  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), mean_.gpu_data(),
-      mean_.mutable_gpu_data());  // EX
-  caffe_gpu_scale(mean_.count(), Dtype(1. / dim), variance_.gpu_data(),
-      variance_.mutable_gpu_data());  // E(X^2)
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
+      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data()); // E(X^2)
   caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
       temp_.mutable_gpu_data());  // (EX)^2
   caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),

From cc352bfcaf4af04fd0efd2cf44dc7a9e8b09839a Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 4 Aug 2014 09:27:14 -0700
Subject: [PATCH 0430/2053] lint

---
 src/caffe/layers/mvn_layer.cpp | 8 ++++----
 src/caffe/layers/mvn_layer.cu  | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index c9c075c4cda..e0360b6cd1e 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -44,9 +44,9 @@ Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   // computes variance using var(X) = E(X^2) - (EX)^2
   caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
   caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data()); // E(X^2)
+      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
   caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
       temp_.mutable_cpu_data());  // (EX)^2
   caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
@@ -111,9 +111,9 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 
   // computes variance using var(X) = E(X^2) - (EX)^2
   caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
   caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data()); // E(X^2)
+      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
   caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
       temp_.mutable_cpu_data());  // (EX)^2
   caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index be5f25bdf0d..fb2380c176c 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -23,9 +23,9 @@ Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   // computes variance using var(X) = E(X^2) - (EX)^2
   caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
   caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data()); // E(X^2)
+      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
   caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
       temp_.mutable_gpu_data());  // (EX)^2
   caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
@@ -93,9 +93,9 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   // computes variance using var(X) = E(X^2) - (EX)^2
   caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
   caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data()); // E(X^2)
+      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
   caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
       temp_.mutable_gpu_data());  // (EX)^2
   caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),

From 1e11ef71369fc7b579a10eda04f2ed4310d485e6 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 4 Aug 2014 11:29:54 -0700
Subject: [PATCH 0431/2053] addressed Jeff's comment

---
 src/caffe/test/test_mvn_layer.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index d5bea3f952c..ded7fd5d361 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -63,12 +63,11 @@ TYPED_TEST(MVNLayerTest, TestForward) {
       sum /= height * width;
       var /= height * width;
 
+      const Dtype kErrorBound = 0.001;
       // expect zero mean
-      EXPECT_GE(sum, -0.001);
-      EXPECT_LE(sum, 0.001);
+      EXPECT_NEAR(0, sum, kErrorBound);
       // expect unit variance
-      EXPECT_GE(var, 0.999);
-      EXPECT_LE(var, 1.001);
+      EXPECT_NEAR(1, var, kErrorBound);
     }
   }
 }

From 978c81a4abf0fc35d37c92bd40cb023f1e523a5b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 9 Aug 2014 20:43:40 -0700
Subject: [PATCH 0432/2053] default raw_scale in python scripts to ImageNet
 model value

---
 python/classify.py | 3 ++-
 python/detect.py   | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/python/classify.py b/python/classify.py
index d3d239cbb7d..ddc5429feac 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -66,11 +66,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        help="Multiply input features by this scale to finish input preprocessing."
+        help="Multiply input features by this scale to finish preprocessing."
     )
     parser.add_argument(
         "--raw_scale",
         type=float,
+        default=255.0,
         help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(
diff --git a/python/detect.py b/python/detect.py
index 77f4676aec1..4598fc7a327 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -76,11 +76,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        help="Multiply input features by this scale to finish input preprocessing."
+        help="Multiply input features by this scale to finish preprocessing."
     )
     parser.add_argument(
         "--raw_scale",
         type=float,
+        default=255.0,
         help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(

From 9e903ef67544d6892ffc907ab945bba4e3182fa8 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 11 Aug 2014 10:44:27 -0700
Subject: [PATCH 0433/2053] added cross-channel MVN, Mean-only normalization,
 added to layer factory, moved to common_layers

---
 include/caffe/common_layers.hpp   |  32 ++++++
 include/caffe/vision_layers.hpp   |  32 ------
 src/caffe/layer_factory.cpp       |   2 +
 src/caffe/layers/mvn_layer.cpp    | 160 +++++++++++++++-----------
 src/caffe/layers/mvn_layer.cu     | 182 +++++++++++++++++-------------
 src/caffe/proto/caffe.proto       |  12 +-
 src/caffe/test/test_mvn_layer.cpp |  95 +++++++++++++++-
 7 files changed, 329 insertions(+), 186 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 1e282d33d7a..5d7c21db622 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -115,6 +115,38 @@ class FlattenLayer : public Layer<Dtype> {
   int count_;
 };
 
+/* MVNLayer
+ */
+template <typename Dtype>
+class MVNLayer : public Layer<Dtype> {
+ public:
+  explicit MVNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MVN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> mean_, variance_, temp_;
+
+  // sum_multiplier is just used to carry out sum using blas
+  Blob<Dtype> sum_multiplier_;
+};
+
 /* SoftmaxLayer
 */
 template <typename Dtype>
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index a6844cd6930..76b9f245643 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -234,38 +234,6 @@ class LRNLayer : public Layer<Dtype> {
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
-/* MVNLayer
- */
-template <typename Dtype>
-class MVNLayer : public Layer<Dtype> {
- public:
-  explicit MVNLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MVN;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  Blob<Dtype> mean_, variance_, temp_;
-
-  // sum_multiplier is just used to carry out sum using blas
-  Blob<Dtype> sum_multiplier_;
-};
-
 
 /* PoolingLayer
 */
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 118929fbff5..0d5b19d92a6 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -57,6 +57,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new LRNLayer<Dtype>(param);
   case LayerParameter_LayerType_MEMORY_DATA:
     return new MemoryDataLayer<Dtype>(param);
+  case LayerParameter_LayerType_MVN:
+    return new MVNLayer(param);
   case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
     return new MultinomialLogisticLossLayer<Dtype>(param);
   case LayerParameter_LayerType_POOLING:
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index e0360b6cd1e..2686ba6d275 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-//
 #include <algorithm>
 #include <vector>
 
+#include "caffe/common_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -24,9 +22,7 @@ void MVNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   sum_multiplier_.Reshape(1, 1,
       bottom[0]->height(), bottom[0]->width());
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
-  for (int i = 0; i < sum_multiplier_.count(); ++i) {
-    multiplier_data[i] = 1.;
-  }
+  caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
 }
 
 template <typename Dtype>
@@ -34,43 +30,60 @@ Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  int num = bottom[0]->num() * bottom[0]->channels();
+  int num;
+  if (this->layer_param_.mvn_param().across_channels())
+    num = bottom[0]->num();
+  else
+    num = bottom[0]->num() * bottom[0]->channels();
+
   int dim = bottom[0]->count() / num;
   Dtype eps = 1e-10;
 
-  // put the squares of bottom into temp_
-  caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
-      temp_.mutable_cpu_data());
-
-  // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
-  caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-      temp_.mutable_cpu_data());  // (EX)^2
-  caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-      variance_.mutable_cpu_data());  // variance
-
-  // do mean and variance normalization
-  // subtract mean
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-          mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-          temp_.mutable_cpu_data());
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    // put the squares of bottom into temp_
+    caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
+        temp_.mutable_cpu_data());
 
-  caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
+    // computes variance using var(X) = E(X^2) - (EX)^2
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
+        sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
+    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
+        temp_.mutable_cpu_data());  // (EX)^2
+    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
+        variance_.mutable_cpu_data());  // variance
 
-  // normalize variance
-  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
-        variance_.mutable_cpu_data());
+    // do mean and variance normalization
+    // subtract mean
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+            mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+            temp_.mutable_cpu_data());
 
-  caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+    caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
 
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-        variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-        temp_.mutable_cpu_data());
+    // normalize variance
+    caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+          variance_.mutable_cpu_data());
+
+    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+          variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+          temp_.mutable_cpu_data());
+
+    caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
+  } else {
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+            sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
 
-  caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
+    // subtract mean
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+            mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+            temp_.mutable_cpu_data());
+
+    caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
+  }
 
   return Dtype(0);
 }
@@ -84,52 +97,61 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   const Dtype* bottom_data = (*bottom)[0]->cpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
 
-  int num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+  int num;
+  if (this->layer_param_.mvn_param().across_channels())
+    num = (*bottom)[0]->num();
+  else
+    num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+
   int dim = (*bottom)[0]->count() / num;
   Dtype eps = 1e-10;
 
-  caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
-        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-        mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-        bottom_diff);
-  caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
-
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
           sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-          mean_.cpu_data(), sum_multiplier_.cpu_data(), 1.,
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+          mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
           bottom_diff);
+    caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
 
-  caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
-      bottom_diff);
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
+            sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+            mean_.cpu_data(), sum_multiplier_.cpu_data(), 1.,
+            bottom_diff);
 
-  // put the squares of bottom into temp_
-  caffe_powx(temp_.count(), bottom_data, Dtype(2),
-      temp_.mutable_cpu_data());
+    caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
+        bottom_diff);
 
-  // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-      sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
-  caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-      temp_.mutable_cpu_data());  // (EX)^2
-  caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-      variance_.mutable_cpu_data());  // variance
+    // put the squares of bottom into temp_
+    caffe_powx(temp_.count(), bottom_data, Dtype(2),
+        temp_.mutable_cpu_data());
 
-  // normalize variance
-  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
-        variance_.mutable_cpu_data());
+    // computes variance using var(X) = E(X^2) - (EX)^2
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
+        sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
+    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
+        temp_.mutable_cpu_data());  // (EX)^2
+    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
+        variance_.mutable_cpu_data());  // variance
 
-  caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+    // normalize variance
+    caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+          variance_.mutable_cpu_data());
 
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-      variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-      temp_.mutable_cpu_data());
+    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
 
-  caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+        variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+        temp_.mutable_cpu_data());
+
+    caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
+  } else {
+    caffe_copy(temp_.count(), top_diff, bottom_diff);
+  }
 }
 
 
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index fb2380c176c..6d1d71570fb 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-//
 #include <algorithm>
 #include <vector>
 
+#include "caffe/common_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -14,44 +12,61 @@ Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  int num = bottom[0]->num() * bottom[0]->channels();
-  int dim = bottom[0]->count() / num;
-
-  // put the squares of bottom into temp_
-  caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
-      temp_.mutable_gpu_data());
-
-  // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
-  caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-      temp_.mutable_gpu_data());  // (EX)^2
-  caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-      variance_.mutable_gpu_data());  // variance
-
-  Dtype eps = 1e-10;
-
-  // do mean and variance normalization
-  // subtract mean
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-          mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-          temp_.mutable_gpu_data());
+  int num;
+  if (this->layer_param_.mvn_param().across_channels())
+    num = bottom[0]->num();
+  else
+    num = bottom[0]->num() * bottom[0]->channels();
 
-  caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
-
-  // normalize variance
-  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
-        variance_.mutable_gpu_data());
-
-  caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+  int dim = bottom[0]->count() / num;
 
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-        variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    // put the squares of bottom into temp_
+    caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
-
-  caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
+  
+    // computes variance using var(X) = E(X^2) - (EX)^2
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
+        sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
+    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
+        temp_.mutable_gpu_data());  // (EX)^2
+    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
+        variance_.mutable_gpu_data());  // variance
+  
+    Dtype eps = 1e-10;
+  
+    // do mean and variance normalization
+    // subtract mean
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+            temp_.mutable_gpu_data());
+  
+    caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
+  
+    // normalize variance
+    caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+          variance_.mutable_gpu_data());
+  
+    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+  
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+          variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+          temp_.mutable_gpu_data());
+  
+    caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
+  } else {
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+            sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+    
+    // subtract mean
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+            temp_.mutable_gpu_data());
+  
+    caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
+  }
 
   return Dtype(0);
 }
@@ -65,53 +80,62 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* bottom_data = (*bottom)[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
 
-  int num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+  int num;
+  if (this->layer_param_.mvn_param().across_channels())
+    num = (*bottom)[0]->num();
+  else
+    num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+
   int dim = (*bottom)[0]->count() / num;
 
   Dtype eps = 1e-10;
 
-  caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
-        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-        mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-        bottom_diff);
-  caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
-
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
           sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-          mean_.gpu_data(), sum_multiplier_.gpu_data(), 1.,
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+          mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
           bottom_diff);
-
-  caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
-      bottom_diff);
-
-  // put the squares of bottom into temp_
-  caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
-      temp_.mutable_gpu_data());
-
-  // computes variance using var(X) = E(X^2) - (EX)^2
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-      sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
-  caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-      temp_.mutable_gpu_data());  // (EX)^2
-  caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-      variance_.mutable_gpu_data());  // variance
-
-  // normalize variance
-  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
-        variance_.mutable_gpu_data());
-
-  caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
-      variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-      temp_.mutable_gpu_data());
-
-  caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
+    caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+  
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
+            sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+            mean_.gpu_data(), sum_multiplier_.gpu_data(), 1.,
+            bottom_diff);
+  
+    caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
+        bottom_diff);
+  
+    // put the squares of bottom into temp_
+    caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
+        temp_.mutable_gpu_data());
+  
+    // computes variance using var(X) = E(X^2) - (EX)^2
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
+        sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
+    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
+        temp_.mutable_gpu_data());  // (EX)^2
+    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
+        variance_.mutable_gpu_data());  // variance
+  
+    // normalize variance
+    caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+          variance_.mutable_gpu_data());
+  
+    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+  
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
+        variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+        temp_.mutable_gpu_data());
+  
+    caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
+  } else {
+    caffe_copy(temp_.count(), top_diff, bottom_diff);
+  }
 }
 
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index fce56945bb3..acc038326b3 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -180,7 +180,7 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 34 (last added: exclude)
+// LayerParameter next available ID: 35 (last added: mvn_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -286,6 +286,7 @@ message LayerParameter {
   optional ThresholdParameter threshold_param = 25;
   optional HingeLossParameter hinge_loss_param = 29;
   optional SliceParameter slice_param = 31;
+  optional MVNParameter mvn_param = 34;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -484,6 +485,15 @@ message MemoryDataParameter {
   optional uint32 width = 4;
 }
 
+// Message that stores parameters used by MVNLayer
+message MVNParameter {
+  // This parameter can be set to false to normalize mean only
+  optional bool normalize_variance = 1 [default = true];
+  
+  // This parameter can be set to true to perform DNN-like MVN
+  optional bool across_channels = 2 [default = false];
+}
+
 // Message that stores parameters used by PoolingLayer
 message PoolingParameter {
   enum PoolMethod {
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index ded7fd5d361..d3d8012f09f 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -1,17 +1,15 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstring>
 #include <vector>
 
-#include "gtest/gtest.h"
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
+#include "gtest/gtest.h"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
@@ -72,6 +70,73 @@ TYPED_TEST(MVNLayerTest, TestForward) {
   }
 }
 
+TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+  MVNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Test mean
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      Dtype sum = 0, var = 0;
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          sum += data;
+          var += data * data;
+        }
+      }
+      sum /= height * width;
+
+      const Dtype kErrorBound = 0.001;
+      // expect zero mean
+      EXPECT_NEAR(0, sum, kErrorBound);
+    }
+  }
+}
+
+TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.ParseFromString("mvn_param{across_channels: true}");
+  MVNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Test mean
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    Dtype sum = 0, var = 0;
+    for (int j = 0; j < channels; ++j) {
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          sum += data;
+          var += data * data;
+        }
+      }
+    }
+    sum /= height * width * channels;
+    var /= height * width * channels;
+
+    const Dtype kErrorBound = 0.001;
+    // expect zero mean
+    EXPECT_NEAR(0, sum, kErrorBound);
+    // expect unit variance
+    EXPECT_NEAR(1, var, kErrorBound);
+  }
+}
+
 TYPED_TEST(MVNLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -81,4 +146,24 @@ TYPED_TEST(MVNLayerTest, TestGradient) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+  MVNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.ParseFromString("mvn_param{across_channels: true}");
+  MVNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 }  // namespace caffe

From a5f49623c19e4d453ff3073491b4bdb1ff592f90 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 11 Aug 2014 10:48:57 -0700
Subject: [PATCH 0434/2053] minor fix for layer factory

---
 src/caffe/layer_factory.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 0d5b19d92a6..2170c19f539 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -58,7 +58,7 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   case LayerParameter_LayerType_MEMORY_DATA:
     return new MemoryDataLayer<Dtype>(param);
   case LayerParameter_LayerType_MVN:
-    return new MVNLayer(param);
+    return new MVNLayer<Dtype>(param);
   case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
     return new MultinomialLogisticLossLayer<Dtype>(param);
   case LayerParameter_LayerType_POOLING:

From a3083f82acf2232f2286f6aa58be01dd0f89787c Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 11 Aug 2014 11:09:57 -0700
Subject: [PATCH 0435/2053] lint

---
 src/caffe/layers/mvn_layer.cpp |  6 +++--
 src/caffe/layers/mvn_layer.cu  | 42 ++++++++++++++++++----------------
 2 files changed, 26 insertions(+), 22 deletions(-)

diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 2686ba6d275..30235b3f467 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -48,7 +48,8 @@ Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
+        sum_multiplier_.cpu_data(), 0.,
+        variance_.mutable_cpu_data());  // E(X^2)
     caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
         temp_.mutable_cpu_data());  // (EX)^2
     caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
@@ -132,7 +133,8 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
+        sum_multiplier_.cpu_data(), 0.,
+        variance_.mutable_cpu_data());  // E(X^2)
     caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
         temp_.mutable_cpu_data());  // (EX)^2
     caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 6d1d71570fb..dd823984dd2 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -24,47 +24,48 @@ Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     // put the squares of bottom into temp_
     caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
-  
+
     // computes variance using var(X) = E(X^2) - (EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
+        sum_multiplier_.gpu_data(), 0.,
+        variance_.mutable_gpu_data());  // E(X^2)
     caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
         temp_.mutable_gpu_data());  // (EX)^2
     caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
         variance_.mutable_gpu_data());  // variance
-  
+
     Dtype eps = 1e-10;
-  
+
     // do mean and variance normalization
     // subtract mean
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
             mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
             temp_.mutable_gpu_data());
-  
+
     caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
-  
+
     // normalize variance
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
-  
+
     caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-  
+
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
           temp_.mutable_gpu_data());
-  
+
     caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
   } else {
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
             sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-    
+
     // subtract mean
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
             mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
             temp_.mutable_gpu_data());
-  
+
     caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
   }
 
@@ -98,40 +99,41 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
           bottom_diff);
     caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
-  
+
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
             sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
             mean_.gpu_data(), sum_multiplier_.gpu_data(), 1.,
             bottom_diff);
-  
+
     caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
         bottom_diff);
-  
+
     // put the squares of bottom into temp_
     caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
-  
+
     // computes variance using var(X) = E(X^2) - (EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
+        sum_multiplier_.gpu_data(), 0.,
+        variance_.mutable_gpu_data());  // E(X^2)
     caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
         temp_.mutable_gpu_data());  // (EX)^2
     caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
         variance_.mutable_gpu_data());  // variance
-  
+
     // normalize variance
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
-  
+
     caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-  
+
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
         temp_.mutable_gpu_data());
-  
+
     caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
   } else {
     caffe_copy(temp_.count(), top_diff, bottom_diff);

From 9c3710eddbcb42175e71e759554f618b3e5d9009 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 11 Aug 2014 15:09:53 -0700
Subject: [PATCH 0436/2053] =?UTF-8?q?[docs]=20sorting=20of=20examples.=20i?=
 =?UTF-8?q?f=20doesn=E2=80=99t=20work=20for=20you,=20update=20jekyll.?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 docs/index.md                         | 10 ++++------
 examples/cifar10/readme.md            |  1 +
 examples/feature_extraction/readme.md |  1 +
 examples/imagenet/readme.md           |  1 +
 examples/mnist/readme.md              |  1 +
 examples/web_demo/readme.md           |  1 +
 6 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 0450edea6ee..e7a819824de 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -46,18 +46,16 @@ Guidelines for development and contributing to Caffe.
 
 ### Examples
 
-{% for page in site.pages %}
-{% if page.category == 'example' %}
+{% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
+{% for page in examples %}
 - <div><a href="{{page.url}}">{{page.title}}</a><br />{{page.description}}</div>
-{% endif %}
 {% endfor %}
 
 ### Notebook examples
 
-{% for page in site.pages %}
-{% if page.category == 'notebook' %}
+{% assign notebooks = site.pages | where:'category','notebook' %}
+{% for page in notebooks %}
 - <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br />{{page.description}}</div>
-{% endif %}
 {% endfor %}
 
 ## Citing Caffe
diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 315713f7022..d0b0b766217 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -4,6 +4,7 @@ category: example
 description: Train and test Caffe on CIFAR-10 data.
 include_in_docs: true
 layout: default
+priority: 5
 ---
 
 Alex's CIFAR-10 tutorial, Caffe style
diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index c336e71855f..357f31daed5 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -4,6 +4,7 @@ description: Extract AlexNet features using the Caffe binary.
 category: example
 include_in_docs: true
 layout: default
+priority: 10
 ---
 
 Extracting Features
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 883e09272f1..d139ad84af8 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -4,6 +4,7 @@ description: Train and test "CaffeNet" on ImageNet challenge data.
 category: example
 include_in_docs: true
 layout: default
+priority: 1
 ---
 
 Yangqing's Recipe on Brewing ImageNet
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 65a780714ae..266ff4c9689 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -4,6 +4,7 @@ description: Train and test "LeNet" on MNIST data.
 category: example
 include_in_docs: true
 layout: default
+priority: 1
 ---
 
 # Training MNIST with Caffe
diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index 559c41e049c..873b4fd210d 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -4,6 +4,7 @@ description: Image classification demo running as a Flask web server.
 category: example
 layout: default
 include_in_docs: true
+priority: 10
 ---
 
 # Web Demo

From 8f47e7dc14555357ec341f0520e69cdf180cad3c Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 11 Aug 2014 15:09:53 -0700
Subject: [PATCH 0437/2053] =?UTF-8?q?[docs]=20sorting=20of=20examples.=20i?=
 =?UTF-8?q?f=20doesn=E2=80=99t=20work=20for=20you,=20update=20jekyll.?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 docs/index.md                         | 10 ++++------
 examples/cifar10/readme.md            |  1 +
 examples/feature_extraction/readme.md |  1 +
 examples/imagenet/readme.md           |  1 +
 examples/mnist/readme.md              |  1 +
 examples/web_demo/readme.md           |  1 +
 6 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 0450edea6ee..e7a819824de 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -46,18 +46,16 @@ Guidelines for development and contributing to Caffe.
 
 ### Examples
 
-{% for page in site.pages %}
-{% if page.category == 'example' %}
+{% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
+{% for page in examples %}
 - <div><a href="{{page.url}}">{{page.title}}</a><br />{{page.description}}</div>
-{% endif %}
 {% endfor %}
 
 ### Notebook examples
 
-{% for page in site.pages %}
-{% if page.category == 'notebook' %}
+{% assign notebooks = site.pages | where:'category','notebook' %}
+{% for page in notebooks %}
 - <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br />{{page.description}}</div>
-{% endif %}
 {% endfor %}
 
 ## Citing Caffe
diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 315713f7022..d0b0b766217 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -4,6 +4,7 @@ category: example
 description: Train and test Caffe on CIFAR-10 data.
 include_in_docs: true
 layout: default
+priority: 5
 ---
 
 Alex's CIFAR-10 tutorial, Caffe style
diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index c336e71855f..357f31daed5 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -4,6 +4,7 @@ description: Extract AlexNet features using the Caffe binary.
 category: example
 include_in_docs: true
 layout: default
+priority: 10
 ---
 
 Extracting Features
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 883e09272f1..d139ad84af8 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -4,6 +4,7 @@ description: Train and test "CaffeNet" on ImageNet challenge data.
 category: example
 include_in_docs: true
 layout: default
+priority: 1
 ---
 
 Yangqing's Recipe on Brewing ImageNet
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 65a780714ae..266ff4c9689 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -4,6 +4,7 @@ description: Train and test "LeNet" on MNIST data.
 category: example
 include_in_docs: true
 layout: default
+priority: 1
 ---
 
 # Training MNIST with Caffe
diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index 559c41e049c..873b4fd210d 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -4,6 +4,7 @@ description: Image classification demo running as a Flask web server.
 category: example
 layout: default
 include_in_docs: true
+priority: 10
 ---
 
 # Web Demo

From 91b2e3cedb5a5b9fbaefa00bf75a4aad3c618635 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 11 Aug 2014 15:52:56 -0700
Subject: [PATCH 0438/2053] [docs] [fix] closes #899

---
 examples/web_demo/app.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index 9bc4ed5c673..f7f46ce6c02 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -113,15 +113,16 @@ class ImagenetClassifier(object):
             raise Exception(
                 "File for {} is missing. Should be at: {}".format(key, val))
     default_args['image_dim'] = 227
-    default_args['gpu_mode'] = True
+    default_args['raw_scale'] = 255.
+    default_args['gpu_mode'] = False
 
     def __init__(self, model_def_file, pretrained_model_file, mean_file,
-                 class_labels_file, bet_file, image_dim, gpu_mode=False):
+                 raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
         logging.info('Loading net and associated files...')
         self.net = caffe.Classifier(
-            model_def_file, pretrained_model_file, input_scale=255,
-            image_dims=(image_dim, image_dim), gpu=gpu_mode,
-            mean_file=mean_file, channel_swap=(2, 1, 0)
+            model_def_file, pretrained_model_file,
+            image_dims=(image_dim, image_dim), raw_scale=raw_scale,
+            mean=np.load(mean_file), channel_swap=(2, 1, 0), gpu=gpu_mode
         )
 
         with open(class_labels_file) as f:
@@ -197,7 +198,13 @@ def start_from_terminal(app):
         '-p', '--port',
         help="which port to serve content on",
         type='int', default=5000)
+    parser.add_option(
+        '-g', '--gpu',
+        help="use gpu mode",
+        action='store_true', default=False)
+
     opts, args = parser.parse_args()
+    ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})
 
     # Initialize classifier
     app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)

From aaad9e43425cf16bc6db22e92c58de2a1f7b45f5 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 11 Aug 2014 15:59:48 -0700
Subject: [PATCH 0439/2053] =?UTF-8?q?[docs]=20=E2=80=98maximally=20accurat?=
 =?UTF-8?q?e=E2=80=99=20in=20the=20web=20demo=20explanation.=20closes=20#9?=
 =?UTF-8?q?05?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 examples/web_demo/readme.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index 873b4fd210d..b0c14e379b2 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -29,3 +29,10 @@ You can enable debug mode of the web server, or switch to a different port:
       -h, --help            show this help message and exit
       -d, --debug           enable debug mode
       -p PORT, --port=PORT  which port to serve content on
+
+## How are the "maximally accurate" results generated?
+
+In a nutshell: ImageNet predictions are made at the leaf nodes, but the organization of the project allows leaf nodes to be united via more general parent nodes, with 'entity' at the very top.
+To give "maximally accurate" results, we "back off" from maximally specific predictions to maintain a high accuracy.
+The `bet_file` that is loaded in the demo provides the graph structure and names of all relevant ImageNet nodes as well as measures of information gain between them.
+Please see the "Hedging your bets" paper from [CVPR 2012](http://www.image-net.org/projects/hedging/) for further information.

From fc27faf81f98b15e31350271aafb806a6b1cde78 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 11 Aug 2014 15:52:56 -0700
Subject: [PATCH 0440/2053] [docs] [fix] closes #899

---
 examples/web_demo/app.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index 9bc4ed5c673..f7f46ce6c02 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -113,15 +113,16 @@ class ImagenetClassifier(object):
             raise Exception(
                 "File for {} is missing. Should be at: {}".format(key, val))
     default_args['image_dim'] = 227
-    default_args['gpu_mode'] = True
+    default_args['raw_scale'] = 255.
+    default_args['gpu_mode'] = False
 
     def __init__(self, model_def_file, pretrained_model_file, mean_file,
-                 class_labels_file, bet_file, image_dim, gpu_mode=False):
+                 raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
         logging.info('Loading net and associated files...')
         self.net = caffe.Classifier(
-            model_def_file, pretrained_model_file, input_scale=255,
-            image_dims=(image_dim, image_dim), gpu=gpu_mode,
-            mean_file=mean_file, channel_swap=(2, 1, 0)
+            model_def_file, pretrained_model_file,
+            image_dims=(image_dim, image_dim), raw_scale=raw_scale,
+            mean=np.load(mean_file), channel_swap=(2, 1, 0), gpu=gpu_mode
         )
 
         with open(class_labels_file) as f:
@@ -197,7 +198,13 @@ def start_from_terminal(app):
         '-p', '--port',
         help="which port to serve content on",
         type='int', default=5000)
+    parser.add_option(
+        '-g', '--gpu',
+        help="use gpu mode",
+        action='store_true', default=False)
+
     opts, args = parser.parse_args()
+    ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})
 
     # Initialize classifier
     app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)

From 39d1e340126955d98fb76d6377ad65e6596b9eb8 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 11 Aug 2014 15:59:48 -0700
Subject: [PATCH 0441/2053] =?UTF-8?q?[docs]=20=E2=80=98maximally=20accurat?=
 =?UTF-8?q?e=E2=80=99=20in=20the=20web=20demo=20explanation.=20closes=20#9?=
 =?UTF-8?q?05?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 examples/web_demo/readme.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index 873b4fd210d..b0c14e379b2 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -29,3 +29,10 @@ You can enable debug mode of the web server, or switch to a different port:
       -h, --help            show this help message and exit
       -d, --debug           enable debug mode
       -p PORT, --port=PORT  which port to serve content on
+
+## How are the "maximally accurate" results generated?
+
+In a nutshell: ImageNet predictions are made at the leaf nodes, but the organization of the project allows leaf nodes to be united via more general parent nodes, with 'entity' at the very top.
+To give "maximally accurate" results, we "back off" from maximally specific predictions to maintain a high accuracy.
+The `bet_file` that is loaded in the demo provides the graph structure and names of all relevant ImageNet nodes as well as measures of information gain between them.
+Please see the "Hedging your bets" paper from [CVPR 2012](http://www.image-net.org/projects/hedging/) for further information.

From 2d2055a4b2708045b15a0ef637eae95c2a202d43 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 11 Aug 2014 16:40:02 -0700
Subject: [PATCH 0442/2053] [examples] fix links in feature extraction

---
 examples/feature_extraction/readme.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index 357f31daed5..4aab26c941e 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -1,6 +1,6 @@
 ---
 title: Feature extraction with Caffe C++ code.
-description: Extract AlexNet features using the Caffe binary.
+description: Extract CaffeNet / AlexNet features using the Caffe utility.
 category: example
 include_in_docs: true
 layout: default
@@ -10,8 +10,8 @@ priority: 10
 Extracting Features
 ===================
 
-In this tutorial, we will extract features using a pre-trained model.
-Follow instructions for [setting up caffe](installation.html) and for [getting](getting_pretrained_models.html) the pre-trained ImageNet model.
+In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
+Follow instructions for [installing Caffe](../../installation.html) and for [downloading the reference model](../../getting_pretrained_models.html) for ImageNet.
 If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
 
 Select data to run on

From b445d4830eb531c18cf0251fd10d6d163181e203 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 11 Aug 2014 16:40:02 -0700
Subject: [PATCH 0443/2053] [examples] fix links in feature extraction

---
 examples/feature_extraction/readme.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index 357f31daed5..4aab26c941e 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -1,6 +1,6 @@
 ---
 title: Feature extraction with Caffe C++ code.
-description: Extract AlexNet features using the Caffe binary.
+description: Extract CaffeNet / AlexNet features using the Caffe utility.
 category: example
 include_in_docs: true
 layout: default
@@ -10,8 +10,8 @@ priority: 10
 Extracting Features
 ===================
 
-In this tutorial, we will extract features using a pre-trained model.
-Follow instructions for [setting up caffe](installation.html) and for [getting](getting_pretrained_models.html) the pre-trained ImageNet model.
+In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
+Follow instructions for [installing Caffe](../../installation.html) and for [downloading the reference model](../../getting_pretrained_models.html) for ImageNet.
 If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
 
 Select data to run on

From dd883bdda70c11101badf194661770d7f358577c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 5 Aug 2014 16:19:31 -0700
Subject: [PATCH 0444/2053] lint targets should depend on the lint script
 itself

---
 Makefile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 2c071c8a8ce..287120a5d22 100644
--- a/Makefile
+++ b/Makefile
@@ -62,6 +62,7 @@ NONGEN_CXX_SRCS := $(shell find \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
+LINT_SCRIPT := scripts/cpp_lint.py
 LINT_OUTPUT_DIR := $(BUILD_DIR)/.lint
 LINT_EXT := lint.txt
 LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, $(NONGEN_CXX_SRCS)))
@@ -322,9 +323,9 @@ $(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
 	  $(RM) $(NONEMPTY_LINT_REPORT); \
 	  echo "No lint errors!";
 
-$(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % | $(LINT_OUTPUT_DIR)
+$(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % $(LINT_SCRIPT) | $(LINT_OUTPUT_DIR)
 	@ mkdir -p $(dir $@)
-	@ python ./scripts/cpp_lint.py $< 2>&1 \
+	@ python $(LINT_SCRIPT) $< 2>&1 \
 		| grep -v "^Done processing " \
 		| grep -v "^Total errors found: 0" \
 		> $@ \

From 9572a68b8832a09f716017742fbd3b4f1da3d131 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 5 Aug 2014 16:01:11 -0700
Subject: [PATCH 0445/2053] Add caffe/alt_fn rule to lint checks to check for
 functions (like memset & memcpy) with caffe_* alternatives that should be
 used instead.

---
 scripts/cpp_lint.py | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 3f80e723643..61b8b0cd320 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -154,6 +154,7 @@
   'build/namespaces',
   'build/printf_format',
   'build/storage_class',
+  'caffe/alt_fn',
   'caffe/random_fn',
   'legal/copyright',
   'readability/alt_tokens',
@@ -1559,6 +1560,37 @@ def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
           'Use C++11 raw strings or concatenation instead.')
 
 
+caffe_alt_function_list = (
+    ('memset', ['caffe_set', 'caffe_memset']),
+    ('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
+    ('memcpy', ['caffe_copy', 'caffe_memcpy']),
+    ('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
+    )
+
+
+def CheckCaffeAlternatives(filename, clean_lines, linenum, error):
+  """Checks for C(++) functions for which a Caffe substitute should be used.
+
+  For certain native C functions (memset, memcpy), there is a Caffe alternative
+  which should be used instead.
+
+  Args:
+    filename: The name of the current file.
+    clean_lines: A CleansedLines instance containing the file.
+    linenum: The number of the line to check.
+    error: The function to call with any errors found.
+  """
+  line = clean_lines.elided[linenum]
+  for function, alts in caffe_alt_function_list:
+    ix = line.find(function + '(')
+    if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
+                                line[ix - 1] not in ('_', '.', '>'))):
+      disp_alts = ['%s(...)' % alt for alt in alts]
+      error(filename, linenum, 'caffe/alt_fn', 2,
+            'Use Caffe function %s instead of %s(...).' %
+                (' or '.join(disp_alts), function))
+
+
 c_random_function_list = (
     'rand(',
     'rand_r(',
@@ -4560,6 +4592,7 @@ def ProcessLine(filename, file_extension, clean_lines, line,
   CheckForNonStandardConstructs(filename, clean_lines, line,
                                 nesting_state, error)
   CheckVlogArguments(filename, clean_lines, line, error)
+  CheckCaffeAlternatives(filename, clean_lines, line, error)
   CheckCaffeRandom(filename, clean_lines, line, error)
   CheckPosixThreading(filename, clean_lines, line, error)
   CheckInvalidIncrement(filename, clean_lines, line, error)

From b8be6b1ba425acaf540d23160cb364e3c9d9974e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 5 Aug 2014 16:59:14 -0700
Subject: [PATCH 0446/2053] Create caffe_{,gpu_}memset functions to replace
 {m,cudaM}emset's.

---
 include/caffe/util/math_functions.hpp | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 744dc849b94..6004e93f2e8 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -6,6 +6,7 @@
 
 #include "glog/logging.h"
 
+#include "caffe/common.hpp"
 #include "caffe/util/device_alternate.hpp"
 #include "caffe/util/mkl_alternate.hpp"
 
@@ -38,6 +39,10 @@ void caffe_copy(const int N, const Dtype *X, Dtype *Y);
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
 
+inline void caffe_memset(const size_t N, const int alpha, void* X) {
+  memset(X, alpha, N);
+}
+
 template <typename Dtype>
 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
 
@@ -165,6 +170,14 @@ void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
 template <typename Dtype>
 void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
 
+inline void caffe_gpu_memset(const size_t N, const int alpha, void* X) {
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaMemset(X, alpha, N));  // NOLINT(caffe/alt_fn)
+#else
+  NO_GPU;
+#endif
+}
+
 template <typename Dtype>
 void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
 

From b2b1ee63171e09b430297f419e9f38cb518da0aa Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 5 Aug 2014 17:33:19 -0700
Subject: [PATCH 0447/2053] Fix caffe/alt_fn lint errors.

---
 include/caffe/util/math_functions.hpp |  2 +-
 src/caffe/layers/hdf5_data_layer.cpp  | 11 +++++------
 src/caffe/syncedmem.cpp               |  4 ++--
 src/caffe/test/test_syncedmem.cpp     | 12 ++++++------
 src/caffe/util/math_functions.cpp     |  5 +++--
 src/caffe/util/math_functions.cu      |  4 ++--
 6 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 6004e93f2e8..90a1a86d8e8 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -40,7 +40,7 @@ template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
 
 inline void caffe_memset(const size_t N, const int alpha, void* X) {
-  memset(X, alpha, N);
+  memset(X, alpha, N);  // NOLINT(caffe/alt_fn)
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 5bad5f6a1f0..938d8435f08 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -103,12 +103,11 @@ Dtype HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       }
       current_row_ = 0;
     }
-    memcpy(&(*top)[0]->mutable_cpu_data()[i * data_count],
-           &data_blob_.cpu_data()[current_row_ * data_count],
-           sizeof(Dtype) * data_count);
-    memcpy(&(*top)[1]->mutable_cpu_data()[i * label_data_count],
-            &label_blob_.cpu_data()[current_row_ * label_data_count],
-            sizeof(Dtype) * label_data_count);
+    caffe_copy(data_count, &data_blob_.cpu_data()[current_row_ * data_count],
+               &(*top)[0]->mutable_cpu_data()[i * data_count]);
+    caffe_copy(label_data_count,
+               &label_blob_.cpu_data()[current_row_ * label_data_count],
+               &(*top)[1]->mutable_cpu_data()[i * label_data_count]);
   }
   return Dtype(0.);
 }
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 7d25183701d..7617ccfb27f 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -22,7 +22,7 @@ inline void SyncedMemory::to_cpu() {
   switch (head_) {
   case UNINITIALIZED:
     CaffeMallocHost(&cpu_ptr_, size_);
-    memset(cpu_ptr_, 0, size_);
+    caffe_memset(size_, 0, cpu_ptr_);
     head_ = HEAD_AT_CPU;
     own_cpu_data_ = true;
     break;
@@ -49,7 +49,7 @@ inline void SyncedMemory::to_gpu() {
   switch (head_) {
   case UNINITIALIZED:
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
-    CUDA_CHECK(cudaMemset(gpu_ptr_, 0, size_));
+    caffe_gpu_memset(size_, 0, gpu_ptr_);
     head_ = HEAD_AT_GPU;
     break;
   case HEAD_AT_CPU:
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index b658871b3ee..b946233d07c 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -55,14 +55,14 @@ TEST_F(SyncedMemoryTest, TestCPUWrite) {
   SyncedMemory mem(10);
   void* cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
-  memset(cpu_data, 1, mem.size());
+  caffe_memset(mem.size(), 1, cpu_data);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((static_cast<char*>(cpu_data))[i], 1);
   }
   // do another round
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
-  memset(cpu_data, 2, mem.size());
+  caffe_memset(mem.size(), 2, cpu_data);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((static_cast<char*>(cpu_data))[i], 2);
   }
@@ -74,7 +74,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   SyncedMemory mem(10);
   void* cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
-  memset(cpu_data, 1, mem.size());
+  caffe_memset(mem.size(), 1, cpu_data);
   const void* gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
@@ -86,7 +86,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   // do another round
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
-  memset(cpu_data, 2, mem.size());
+  caffe_memset(mem.size(), 2, cpu_data);
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((static_cast<char*>(cpu_data))[i], 2);
   }
@@ -104,7 +104,7 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
   SyncedMemory mem(10);
   void* gpu_data = mem.mutable_gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_GPU);
-  CUDA_CHECK(cudaMemset(gpu_data, 1, mem.size()));
+  caffe_gpu_memset(mem.size(), 1, gpu_data);
   const void* cpu_data = mem.cpu_data();
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((static_cast<const char*>(cpu_data))[i], 1);
@@ -113,7 +113,7 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
 
   gpu_data = mem.mutable_gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_GPU);
-  CUDA_CHECK(cudaMemset(gpu_data, 2, mem.size()));
+  caffe_gpu_memset(mem.size(), 2, gpu_data);
   cpu_data = mem.cpu_data();
   for (int i = 0; i < mem.size(); ++i) {
     EXPECT_EQ((static_cast<const char*>(cpu_data))[i], 2);
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 974adf55e7d..e10f0191362 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -56,7 +56,7 @@ void caffe_axpy<double>(const int N, const double alpha, const double* X,
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
-    memset(Y, 0, sizeof(Dtype) * N);
+    memset(Y, 0, sizeof(Dtype) * N);  // NOLINT(caffe/alt_fn)
     return;
   }
   for (int i = 0; i < N; ++i) {
@@ -87,12 +87,13 @@ void caffe_copy(const int N, const Dtype* X, Dtype* Y) {
   if (X != Y) {
     if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
+      // NOLINT_NEXT_LINE(caffe/alt_fn)
       CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
 #else
       NO_GPU;
 #endif
     } else {
-      memcpy(Y, X, sizeof(Dtype) * N);
+      memcpy(Y, X, sizeof(Dtype) * N);  // NOLINT(caffe/alt_fn)
     }
   }
 }
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index cec051e6bef..eacbb4728dd 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -78,7 +78,7 @@ void caffe_gpu_axpy<double>(const int N, const double alpha, const double* X,
 
 void caffe_gpu_memcpy(const size_t N, const void* X, void* Y) {
   if (X != Y) {
-    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
+    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));  // NOLINT(caffe/alt_fn)
   }
 }
 
@@ -152,7 +152,7 @@ __global__ void set_kernel(const int n, const Dtype alpha, Dtype* y) {
 template <typename Dtype>
 void caffe_gpu_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
-    CUDA_CHECK(cudaMemset(Y, 0, sizeof(Dtype) * N));
+    CUDA_CHECK(cudaMemset(Y, 0, sizeof(Dtype) * N));  // NOLINT(caffe/alt_fn)
     return;
   }
   // NOLINT_NEXT_LINE(whitespace/operators)

From eea74c30e6f85634f6cbb47528b05f640db97155 Mon Sep 17 00:00:00 2001
From: Alireza Shafaei <alireza@shafaei.net>
Date: Tue, 12 Aug 2014 13:54:51 -0700
Subject: [PATCH 0448/2053] Fixed the GPU implementation of EuclideanLoss to
 report the loss to the top layer

---
 src/caffe/layers/euclidean_loss_layer.cu | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index dd14f1995c8..f4dfd0b5883 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -19,6 +19,9 @@ Dtype EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype dot;
   caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss;
+  }
   return loss;
 }
 

From b8c03e368b7833284dd7f963bd6d4dba1adcaa7a Mon Sep 17 00:00:00 2001
From: Alireza Shafaei <alireza@shafaei.net>
Date: Tue, 12 Aug 2014 13:54:51 -0700
Subject: [PATCH 0449/2053] Fixed the GPU implementation of EuclideanLoss to
 report the loss to the top layer

---
 src/caffe/layers/euclidean_loss_layer.cu | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index dd14f1995c8..f4dfd0b5883 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -19,6 +19,9 @@ Dtype EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype dot;
   caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss;
+  }
   return loss;
 }
 

From da2138c75d2253b0982bee11179a58b968dd4d72 Mon Sep 17 00:00:00 2001
From: Clemens Korner <clemens.corny@gmail.com>
Date: Wed, 13 Aug 2014 12:05:30 +0200
Subject: [PATCH 0450/2053] use plt namespace for imshow in
 filter_visualization.ipynb

---
 examples/filter_visualization.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 5165d840b7b..765aab52228 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -193,7 +193,7 @@
       "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
       "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
       "    \n",
-      "    imshow(data)"
+      "    plt.imshow(data)"
      ],
      "language": "python",
      "metadata": {},
@@ -212,7 +212,7 @@
      "collapsed": false,
      "input": [
       "# index four is the center crop\n",
-      "imshow(net.deprocess('data', net.blobs['data'].data[4]))"
+      "plt.imshow(net.deprocess('data', net.blobs['data'].data[4]))"
      ],
      "language": "python",
      "metadata": {},

From bb903025df83450dd09d1ddd077aa99b74bac5da Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Aug 2014 09:22:42 -0700
Subject: [PATCH 0451/2053] [example] fix plt commands in detection

---
 examples/detection.ipynb | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 3b0a5b2e705..1f6af25ff42 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -414,7 +414,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import numpy as np\n",
       "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
       "\n",
       "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
       "print(df.shape)\n",
@@ -525,10 +528,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "gray()\n",
-      "matshow(predictions_df.values)\n",
-      "xlabel('Classes')\n",
-      "ylabel('Windows')"
+      "plt.gray()\n",
+      "plt.matshow(predictions_df.values)\n",
+      "plt.xlabel('Classes')\n",
+      "plt.ylabel('Windows')"
      ],
      "language": "python",
      "metadata": {},
@@ -626,17 +629,17 @@
       "print(f.order(ascending=False)[:5])\n",
       "\n",
       "# Show top detection in red, second-best top detection in blue.\n",
-      "im = imread('images/fish-bike.jpg')\n",
-      "imshow(im)\n",
+      "im = plt.imread('images/fish-bike.jpg')\n",
+      "plt.imshow(im)\n",
       "currentAxis = plt.gca()\n",
       "\n",
       "det = df.iloc[i]\n",
       "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
       "\n",
       "det = df.iloc[j]\n",
       "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
      ],
      "language": "python",
      "metadata": {},
@@ -776,12 +779,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "imshow(im)\n",
+      "plt.imshow(im)\n",
       "currentAxis = plt.gca()\n",
       "colors = ['r', 'b', 'y']\n",
       "for c, det in zip(colors, nms_dets[:3]):\n",
       "    currentAxis.add_patch(\n",
-      "        Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+      "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
       "        fill=False, edgecolor=c, linewidth=5)\n",
       "    )\n",
       "print 'scores:', nms_dets[:3, 4]"

From 944361e27e8269634343e9e947185102b15e746d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Aug 2014 09:27:56 -0700
Subject: [PATCH 0452/2053] [example] change notebook name metadata to avoid
 conflict

see https://github.com/ipython/ipython/issues/5686
---
 examples/classification.ipynb       | 7 ++++---
 examples/detection.ipynb            | 5 +++--
 examples/filter_visualization.ipynb | 7 ++++---
 examples/net_surgery.ipynb          | 7 ++++---
 4 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 302bdea0e00..4d4a738a9f3 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "ImageNet classification",
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "include_in_docs": true
+  "example_name": "ImageNet classification",
+  "include_in_docs": true,
+  "signature": "sha256:4f8d4c079c30d20ef4b6818e9672b1741fd1377354e5b83e291710736cecd24f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -403,4 +404,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 1f6af25ff42..beae000a11b 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "ImageNet detection",
   "description": "Run a pretrained model as a detector in Python.",
-  "include_in_docs": true
+  "example_name": "R-CNN detection",
+  "include_in_docs": true,
+  "signature": "sha256:8a744fbbb9ed80acab471247eaf50c27dcbd652105404df9feca599939f0c0ee"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 765aab52228..5fdcbe25fb4 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "Filter visualization",
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "include_in_docs": true
+  "example_name": "Filter visualization",
+  "include_in_docs": true,
+  "signature": "sha256:b1b0457e2b10110aca847a718a3fe631ebcfce63a61cbc33653244f52b1ff4af"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -597,4 +598,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 5f151e4be3a..dc9c7e84954 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "Editing model parameters",
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
-  "include_in_docs": true
+  "example_name": "Editing model parameters",
+  "include_in_docs": true,
+  "signature": "sha256:0b2ad61622122fa34a40c250be2c0799a85fb65c149b802ce844c46eceba066e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -331,4 +332,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file

From 41986fbcffd6c51eb46a7f72e197021ca13f0600 Mon Sep 17 00:00:00 2001
From: Clemens Korner <clemens.corny@gmail.com>
Date: Wed, 13 Aug 2014 12:05:30 +0200
Subject: [PATCH 0453/2053] use plt namespace for imshow in
 filter_visualization.ipynb

---
 examples/filter_visualization.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 5165d840b7b..765aab52228 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -193,7 +193,7 @@
       "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
       "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
       "    \n",
-      "    imshow(data)"
+      "    plt.imshow(data)"
      ],
      "language": "python",
      "metadata": {},
@@ -212,7 +212,7 @@
      "collapsed": false,
      "input": [
       "# index four is the center crop\n",
-      "imshow(net.deprocess('data', net.blobs['data'].data[4]))"
+      "plt.imshow(net.deprocess('data', net.blobs['data'].data[4]))"
      ],
      "language": "python",
      "metadata": {},

From 20adacb5d164fed0f463e538e1591141359aedf1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Aug 2014 09:22:42 -0700
Subject: [PATCH 0454/2053] [example] fix plt commands in detection

---
 examples/detection.ipynb | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 3b0a5b2e705..1f6af25ff42 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -414,7 +414,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import numpy as np\n",
       "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
       "\n",
       "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
       "print(df.shape)\n",
@@ -525,10 +528,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "gray()\n",
-      "matshow(predictions_df.values)\n",
-      "xlabel('Classes')\n",
-      "ylabel('Windows')"
+      "plt.gray()\n",
+      "plt.matshow(predictions_df.values)\n",
+      "plt.xlabel('Classes')\n",
+      "plt.ylabel('Windows')"
      ],
      "language": "python",
      "metadata": {},
@@ -626,17 +629,17 @@
       "print(f.order(ascending=False)[:5])\n",
       "\n",
       "# Show top detection in red, second-best top detection in blue.\n",
-      "im = imread('images/fish-bike.jpg')\n",
-      "imshow(im)\n",
+      "im = plt.imread('images/fish-bike.jpg')\n",
+      "plt.imshow(im)\n",
       "currentAxis = plt.gca()\n",
       "\n",
       "det = df.iloc[i]\n",
       "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
       "\n",
       "det = df.iloc[j]\n",
       "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
      ],
      "language": "python",
      "metadata": {},
@@ -776,12 +779,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "imshow(im)\n",
+      "plt.imshow(im)\n",
       "currentAxis = plt.gca()\n",
       "colors = ['r', 'b', 'y']\n",
       "for c, det in zip(colors, nms_dets[:3]):\n",
       "    currentAxis.add_patch(\n",
-      "        Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+      "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
       "        fill=False, edgecolor=c, linewidth=5)\n",
       "    )\n",
       "print 'scores:', nms_dets[:3, 4]"

From 445ad1fd7972ab7edd0a1452d0d7eecbd2d93e95 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Aug 2014 09:27:56 -0700
Subject: [PATCH 0455/2053] [example] change notebook name metadata to avoid
 conflict

see https://github.com/ipython/ipython/issues/5686
---
 examples/classification.ipynb       | 7 ++++---
 examples/detection.ipynb            | 5 +++--
 examples/filter_visualization.ipynb | 7 ++++---
 examples/net_surgery.ipynb          | 7 ++++---
 4 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 302bdea0e00..4d4a738a9f3 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "ImageNet classification",
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "include_in_docs": true
+  "example_name": "ImageNet classification",
+  "include_in_docs": true,
+  "signature": "sha256:4f8d4c079c30d20ef4b6818e9672b1741fd1377354e5b83e291710736cecd24f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -403,4 +404,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 1f6af25ff42..beae000a11b 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "ImageNet detection",
   "description": "Run a pretrained model as a detector in Python.",
-  "include_in_docs": true
+  "example_name": "R-CNN detection",
+  "include_in_docs": true,
+  "signature": "sha256:8a744fbbb9ed80acab471247eaf50c27dcbd652105404df9feca599939f0c0ee"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 765aab52228..5fdcbe25fb4 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "Filter visualization",
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "include_in_docs": true
+  "example_name": "Filter visualization",
+  "include_in_docs": true,
+  "signature": "sha256:b1b0457e2b10110aca847a718a3fe631ebcfce63a61cbc33653244f52b1ff4af"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -597,4 +598,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 5f151e4be3a..dc9c7e84954 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "name": "Editing model parameters",
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
-  "include_in_docs": true
+  "example_name": "Editing model parameters",
+  "include_in_docs": true,
+  "signature": "sha256:0b2ad61622122fa34a40c250be2c0799a85fb65c149b802ce844c46eceba066e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -331,4 +332,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file

From cb6144a8dde71b64846e6bab13755cae241f6183 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Aug 2014 10:42:30 -0700
Subject: [PATCH 0456/2053] [docs] update docs generation for notebook metadata

---
 scripts/copy_notebook.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/copy_notebook.py b/scripts/copy_notebook.py
index 4f60cfd0e68..e4c6385bef8 100755
--- a/scripts/copy_notebook.py
+++ b/scripts/copy_notebook.py
@@ -19,7 +19,7 @@
 if 'include_in_docs' in content['metadata'] and content['metadata']['include_in_docs']:
     yaml_frontmatter = ['---']
     for key, val in content['metadata'].iteritems():
-        if key == 'name':
+        if key == 'example_name':
             key = 'title'
             if val == '':
                 val = os.path.basename(filename)

From e32496aaad00438fb2c9926aad96a37e7394a0b0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Aug 2014 10:42:30 -0700
Subject: [PATCH 0457/2053] [docs] update docs generation for notebook metadata

---
 scripts/copy_notebook.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/copy_notebook.py b/scripts/copy_notebook.py
index 4f60cfd0e68..e4c6385bef8 100755
--- a/scripts/copy_notebook.py
+++ b/scripts/copy_notebook.py
@@ -19,7 +19,7 @@
 if 'include_in_docs' in content['metadata'] and content['metadata']['include_in_docs']:
     yaml_frontmatter = ['---']
     for key, val in content['metadata'].iteritems():
-        if key == 'name':
+        if key == 'example_name':
             key = 'title'
             if val == '':
                 val = os.path.basename(filename)

From d0cae53dd5291331ca7da0dfef0e7ff54e8e0bac Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 01:44:49 -0700
Subject: [PATCH 0458/2053] Add loss_weight to proto, specifying coefficients
 for each top blob in the objective function.

---
 src/caffe/proto/caffe.proto | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index acc038326b3..971a291cad4 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -180,10 +180,14 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 35 (last added: mvn_param)
+// LayerParameter next available ID: 36 (last added: loss_weight)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
+  // The amount of weight to assign each top blob in the objective.
+  // Each layer assigns a default value, usually of either 0 or 1,
+  // to each top blob.
+  repeated float loss_weight = 35;
   optional string name = 4; // the layer name
 
   // Rules controlling whether and when a layer is included in the network,

From 7a3ed9b8edf43895770b63cb4d9f5cacf0dba047 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 01:48:36 -0700
Subject: [PATCH 0459/2053] Add net tests for loss_weight.

Check that the loss and gradients throughout the net are appropriately scaled
for a few loss_weight values, assuming a default weight of 1 in the loss layer
only.  Also modify test_gradient_check_util to associate a loss of 2 rather
than 1 with the top blob, so that loss layer tests fail if they don't scale
their diffs.
---
 .../caffe/test/test_gradient_check_util.hpp   |  37 ++--
 src/caffe/test/test_net.cpp                   | 166 ++++++++++++++++--
 2 files changed, 176 insertions(+), 27 deletions(-)

diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 4cf2cbc9dc8..5a8d382ff26 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -57,8 +57,8 @@ class GradientChecker {
       const vector<Blob<Dtype>*>& input);
 
  protected:
-  Dtype GetObjAndGradient(vector<Blob<Dtype>*>* top, int top_id = -1,
-      int top_data_id = -1);
+  Dtype GetObjAndGradient(const Layer<Dtype>& layer, vector<Blob<Dtype>*>* top,
+      int top_id = -1, int top_data_id = -1);
   Dtype stepsize_;
   Dtype threshold_;
   unsigned int seed_;
@@ -97,10 +97,11 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
   }
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
-  // Get any loss from the layer
-  Dtype computed_objective = layer->Forward(*bottom, top);
+  // Ignore the loss from the layer (it's just the weighted sum of the losses
+  // from the top blobs, whose gradients we may want to test individually).
+  layer->Forward(*bottom, top);
   // Get additional loss from the objective
-  computed_objective += GetObjAndGradient(top, top_id, top_data_id);
+  GetObjAndGradient(*layer, top, top_id, top_data_id);
   layer->Backward(*top, propagate_down, bottom);
   // Store computed gradients for all checked blobs
   vector<shared_ptr<Blob<Dtype> > >
@@ -131,18 +132,22 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
       // i != top_data_id, we know the derivative is 0 by definition, and simply
       // check that that's true.
       Dtype estimated_gradient = 0;
+      Dtype positive_objective = 0;
+      Dtype negative_objective = 0;
       if (!element_wise || (feat_id == top_data_id)) {
         // Do finite differencing.
         // Compute loss with stepsize_ added to input.
         current_blob->mutable_cpu_data()[feat_id] += stepsize_;
         Caffe::set_random_seed(seed_);
-        Dtype positive_objective = layer->Forward(*bottom, top);
-        positive_objective += GetObjAndGradient(top, top_id, top_data_id);
+        layer->Forward(*bottom, top);
+        positive_objective =
+            GetObjAndGradient(*layer, top, top_id, top_data_id);
         // Compute loss with stepsize_ subtracted from input.
         current_blob->mutable_cpu_data()[feat_id] -= stepsize_ * 2;
         Caffe::set_random_seed(seed_);
-        Dtype negative_objective = layer->Forward(*bottom, top);
-        negative_objective += GetObjAndGradient(top, top_id, top_data_id);
+        layer->Forward(*bottom, top);
+        negative_objective =
+            GetObjAndGradient(*layer, top, top_id, top_data_id);
         // Recover original input value.
         current_blob->mutable_cpu_data()[feat_id] += stepsize_;
         estimated_gradient = (positive_objective - negative_objective) /
@@ -160,7 +165,10 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
             std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
         EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
           << "debug: (top_id, top_data_id, blob_id, feat_id)="
-          << top_id << "," << top_data_id << "," << blob_id << "," << feat_id;
+          << top_id << "," << top_data_id << "," << blob_id << "," << feat_id
+          << "; feat = " << feature
+          << "; objective+ = " << positive_objective
+          << "; objective- = " << negative_objective;
       }
       // LOG(ERROR) << "Feature: " << current_blob->cpu_data()[feat_id];
       // LOG(ERROR) << "computed gradient: " << computed_gradient
@@ -212,8 +220,8 @@ void GradientChecker<Dtype>::CheckGradientNet(
 }
 
 template <typename Dtype>
-Dtype GradientChecker<Dtype>::GetObjAndGradient(vector<Blob<Dtype>*>* top,
-    int top_id, int top_data_id) {
+Dtype GradientChecker<Dtype>::GetObjAndGradient(const Layer<Dtype>& layer,
+    vector<Blob<Dtype>*>* top, int top_id, int top_data_id) {
   Dtype loss = 0;
   if (top_id < 0) {
     // the loss will be half of the sum of squares of all outputs
@@ -236,8 +244,9 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(vector<Blob<Dtype>*>* top,
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
       caffe_set(top_blob->count(), Dtype(0), top_blob_diff);
     }
-    loss = (*top)[top_id]->cpu_data()[top_data_id];
-    (*top)[top_id]->mutable_cpu_diff()[top_data_id] = 1.;
+    const Dtype loss_weight = 2;
+    loss = (*top)[top_id]->cpu_data()[top_data_id] * loss_weight;
+    (*top)[top_id]->mutable_cpu_diff()[top_data_id] = loss_weight;
   }
   return loss;
 }
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 18bc9ad7838..acd3bcdd8eb 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -141,7 +141,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
-  virtual void InitTrickyNet() {
+  virtual void InitTrickyNet(Dtype* loss_weight = NULL) {
+    ostringstream loss_weight_stream;
+    if (loss_weight) {
+      loss_weight_stream << "  top_loss_weight: " << *loss_weight << " ";
+    }
     const string& proto =
         "name: 'TrickyTestNetwork' "
         "layers: { "
@@ -208,19 +212,27 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: SOFTMAX_LOSS "
+        "  type: SOFTMAX_LOSS " +
+        loss_weight_stream.str() +
         "  bottom: 'transformed_data' "
         "  bottom: 'transformed_label' "
         "} ";
     InitNetFromProtoString(proto);
   }
 
-  virtual void InitUnsharedWeightsNet(const bool bias_term = false,
+  virtual void InitUnsharedWeightsNet(Dtype* loss_weight,
+      const bool force_backward = false, const bool bias_term = false,
       const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
       const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
     ostringstream proto;
+    if (loss_weight) {
+      loss_weight_stream << "  top_loss_weight: " << *loss_weight << " ";
+    }
+    proto << "name: 'UnsharedWeightsNetwork' ";
+    if (force_backward) {
+      proto << "force_backward: true ";
+    }
     proto <<
-        "name: 'UnsharedWeightsNetwork' "
         "layers: { "
         "  name: 'data' "
         "  type: DUMMY_DATA "
@@ -286,7 +298,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: EUCLIDEAN_LOSS ";
+    if (loss_weight) {
+      proto << "  top_loss_weight: " << *loss_weight << " ";
+    }
+    proto <<
         "  bottom: 'innerproduct1' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -575,6 +591,128 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
   EXPECT_EQ(true, bottom_need_backward[3][1]);
 }
 
+TYPED_TEST(NetTest, TestLossWeightCPU) {
+  Caffe::set_mode(Caffe::CPU);
+  // First, compute the loss and gradients with no top_loss_weight specified.
+  // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default
+  // to 1.
+  vector<Blob<TypeParam>*> bottom;
+  Caffe::set_random_seed(this->seed_);
+  const bool kForceBackward = true;
+  this->InitUnsharedWeightsNet(NULL, kForceBackward);
+  const TypeParam loss = this->net_->ForwardBackward(bottom);
+  const bool kCopyDiff = true;
+  const bool kReshape = true;
+  const vector<shared_ptr<Blob<TypeParam> > >& net_blobs = this->net_->blobs();
+  vector<shared_ptr<Blob<TypeParam> > > blob_grads(net_blobs.size());
+  for (int i = 0; i < net_blobs.size(); ++i) {
+    blob_grads[i].reset(new Blob<TypeParam>());
+    blob_grads[i]->CopyFrom(*net_blobs[i], kCopyDiff, kReshape);
+  }
+  const vector<shared_ptr<Blob<TypeParam> > >& net_params =
+      this->net_->params();
+  vector<shared_ptr<Blob<TypeParam> > > param_grads(net_params.size());
+  for (int i = 0; i < net_params.size(); ++i) {
+    param_grads[i].reset(new Blob<TypeParam>());
+    param_grads[i]->CopyFrom(*net_params[i], kCopyDiff, kReshape);
+  }
+  // Check that the loss is non-trivial, otherwise the test doesn't prove much.
+  const TypeParam kMinLossAbsValue = 1e-2;
+  ASSERT_GE(fabs(loss), kMinLossAbsValue);
+  const TypeParam kErrorMargin = 1e-5;
+  const int kNumLossWeights = 6;
+  TypeParam kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
+  for (int i = 0; i < kNumLossWeights; ++i) {
+    Caffe::set_random_seed(this->seed_);
+    this->InitUnsharedWeightsNet(&kLossWeights[i], kForceBackward);
+    const TypeParam weighted_loss = this->net_->ForwardBackward(bottom);
+    const TypeParam error_margin = kErrorMargin * fabs(kLossWeights[i]);
+    EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
+        << "loss weight = " << kLossWeights[i];
+    const vector<shared_ptr<Blob<TypeParam> > >& weighted_blobs =
+        this->net_->blobs();
+    ASSERT_EQ(blob_grads.size(), weighted_blobs.size());
+    for (int j = 0; j < blob_grads.size(); ++j) {
+      ASSERT_EQ(blob_grads[j]->count(), weighted_blobs[j]->count());
+      for (int k = 0; k < blob_grads[j]->count(); ++k) {
+        EXPECT_NEAR(blob_grads[j]->cpu_diff()[k] * kLossWeights[i],
+                    weighted_blobs[j]->cpu_diff()[k], error_margin);
+      }
+    }
+    const vector<shared_ptr<Blob<TypeParam> > >& weighted_params =
+        this->net_->params();
+    ASSERT_EQ(param_grads.size(), weighted_params.size());
+    for (int j = 0; j < param_grads.size(); ++j) {
+      ASSERT_EQ(param_grads[j]->count(), weighted_params[j]->count());
+      for (int k = 0; k < param_grads[j]->count(); ++k) {
+        EXPECT_NEAR(param_grads[j]->cpu_diff()[k] * kLossWeights[i],
+                    weighted_params[j]->cpu_diff()[k], error_margin);
+      }
+    }
+  }
+}
+
+TYPED_TEST(NetTest, TestLossWeightGPU) {
+  Caffe::set_mode(Caffe::GPU);
+  // First, compute the loss and gradients with no top_loss_weight specified.
+  // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default
+  // to 1.
+  vector<Blob<TypeParam>*> bottom;
+  Caffe::set_random_seed(this->seed_);
+  const bool kForceBackward = true;
+  this->InitUnsharedWeightsNet(NULL, kForceBackward);
+  const TypeParam loss = this->net_->ForwardBackward(bottom);
+  const bool kCopyDiff = true;
+  const bool kReshape = true;
+  const vector<shared_ptr<Blob<TypeParam> > >& net_blobs = this->net_->blobs();
+  vector<shared_ptr<Blob<TypeParam> > > blob_grads(net_blobs.size());
+  for (int i = 0; i < net_blobs.size(); ++i) {
+    blob_grads[i].reset(new Blob<TypeParam>());
+    blob_grads[i]->CopyFrom(*net_blobs[i], kCopyDiff, kReshape);
+  }
+  const vector<shared_ptr<Blob<TypeParam> > >& net_params =
+      this->net_->params();
+  vector<shared_ptr<Blob<TypeParam> > > param_grads(net_params.size());
+  for (int i = 0; i < net_params.size(); ++i) {
+    param_grads[i].reset(new Blob<TypeParam>());
+    param_grads[i]->CopyFrom(*net_params[i], kCopyDiff, kReshape);
+  }
+  // Check that the loss is non-trivial, otherwise the test doesn't prove much.
+  const TypeParam kMinLossAbsValue = 1e-2;
+  ASSERT_GE(fabs(loss), kMinLossAbsValue);
+  const Dtype kErrorMargin = 1e-4;
+  const int kNumLossWeights = 6;
+  TypeParam kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
+  for (int i = 0; i < kNumLossWeights; ++i) {
+    Caffe::set_random_seed(this->seed_);
+    this->InitUnsharedWeightsNet(&kLossWeights[i], kForceBackward);
+    const TypeParam weighted_loss = this->net_->ForwardBackward(bottom);
+    const TypeParam error_margin = kErrorMargin * fabs(kLossWeights[i]);
+    EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
+        << "loss weight = " << kLossWeights[i];
+    const vector<shared_ptr<Blob<TypeParam> > >& weighted_blobs =
+        this->net_->blobs();
+    ASSERT_EQ(blob_grads.size(), weighted_blobs.size());
+    for (int j = 0; j < blob_grads.size(); ++j) {
+      ASSERT_EQ(blob_grads[j]->count(), weighted_blobs[j]->count());
+      for (int k = 0; k < blob_grads[j]->count(); ++k) {
+        EXPECT_NEAR(blob_grads[j]->cpu_diff()[k] * kLossWeights[i],
+                    weighted_blobs[j]->cpu_diff()[k], error_margin);
+      }
+    }
+    const vector<shared_ptr<Blob<TypeParam> > >& weighted_params =
+        this->net_->params();
+    ASSERT_EQ(param_grads.size(), weighted_params.size());
+    for (int j = 0; j < param_grads.size(); ++j) {
+      ASSERT_EQ(param_grads[j]->count(), weighted_params[j]->count());
+      for (int k = 0; k < param_grads[j]->count(); ++k) {
+        EXPECT_NEAR(param_grads[j]->cpu_diff()[k] * kLossWeights[i],
+                    weighted_params[j]->cpu_diff()[k], error_margin);
+      }
+    }
+  }
+}
+
 TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
@@ -722,12 +860,14 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
   vector<Blob<Dtype>*> bottom;
   const bool kBiasTerm = true;
+  const bool kForceBackward = false;
+  const Dtype* kLossWeight = NULL;
 
   // Run the net with all params learned; check that gradients are non-zero.
   Caffe::set_random_seed(this->seed_);
   Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
-  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
-                               blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
+      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
@@ -746,8 +886,8 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   // gradients.
   Caffe::set_random_seed(this->seed_);
   blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
-  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
-                               blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
+      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
@@ -762,8 +902,8 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   // gradients for those.
   Caffe::set_random_seed(this->seed_);
   blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
-  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
-                               blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
+      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
@@ -781,8 +921,8 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   // Change the opposite subset of the learning rates to zero.
   Caffe::set_random_seed(this->seed_);
   blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
-  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
-                               blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
+      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();

From 512a626fc71c69ed4460024b31c5fe8dff1e668c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 01:55:17 -0700
Subject: [PATCH 0460/2053] Generalize loss by allowing any top blob to be used
 as a loss in which its elements are summed with a scalar coefficient.

Forward for layers no longer returns a loss; instead all loss layers must have
top blobs.  Existing loss layers are given a top blob automatically by
Net::Init, with an associated top_loss_weight of 1 (set in
LossLayer::FurtherSetUp).  Due to the increased amount of common SetUp logic,
the SetUp interface is modified such that all subclasses should normally
override FurtherSetUp only, which is called by SetUp.
---
 include/caffe/common_layers.hpp               |  40 ++---
 include/caffe/data_layers.hpp                 |  38 ++---
 include/caffe/layer.hpp                       |  83 ++++++++--
 include/caffe/loss_layers.hpp                 |  64 ++++----
 include/caffe/neuron_layers.hpp               |  36 ++---
 include/caffe/util/device_alternate.hpp       |   4 +-
 include/caffe/vision_layers.hpp               |  50 +++---
 src/caffe/layers/accuracy_layer.cpp           |   7 +-
 src/caffe/layers/argmax_layer.cpp             |   6 +-
 src/caffe/layers/bnll_layer.cpp               |   3 +-
 src/caffe/layers/bnll_layer.cu                |   3 +-
 src/caffe/layers/concat_layer.cpp             |  10 +-
 src/caffe/layers/concat_layer.cu              |   3 +-
 src/caffe/layers/conv_layer.cpp               |   6 +-
 src/caffe/layers/conv_layer.cu                |   3 +-
 src/caffe/layers/data_layer.cpp               |   6 +-
 src/caffe/layers/data_layer.cu                |   3 +-
 src/caffe/layers/dropout_layer.cpp            |   7 +-
 src/caffe/layers/dropout_layer.cu             |   3 +-
 src/caffe/layers/dummy_data_layer.cpp         |   7 +-
 src/caffe/layers/eltwise_layer.cpp            |   6 +-
 src/caffe/layers/eltwise_layer.cu             |   3 +-
 src/caffe/layers/euclidean_loss_layer.cpp     |  13 +-
 src/caffe/layers/euclidean_loss_layer.cu      |  10 +-
 src/caffe/layers/flatten_layer.cpp            |   6 +-
 src/caffe/layers/flatten_layer.cu             |   3 +-
 src/caffe/layers/hdf5_data_layer.cpp          |   6 +-
 src/caffe/layers/hdf5_data_layer.cu           |   3 +-
 src/caffe/layers/hdf5_output_layer.cpp        |   3 +-
 src/caffe/layers/hdf5_output_layer.cu         |   3 +-
 src/caffe/layers/hinge_loss_layer.cpp         |  14 +-
 src/caffe/layers/im2col_layer.cpp             |   6 +-
 src/caffe/layers/im2col_layer.cu              |   3 +-
 src/caffe/layers/image_data_layer.cpp         |   8 +-
 src/caffe/layers/image_data_layer.cu          |   3 +-
 src/caffe/layers/infogain_loss_layer.cpp      |  16 +-
 src/caffe/layers/inner_product_layer.cpp      |   6 +-
 src/caffe/layers/inner_product_layer.cu       |   3 +-
 src/caffe/layers/loss_layer.cpp               |  11 +-
 src/caffe/layers/lrn_layer.cpp                |  19 +--
 src/caffe/layers/lrn_layer.cu                 |  12 +-
 src/caffe/layers/memory_data_layer.cpp        |   6 +-
 .../multinomial_logistic_loss_layer.cpp       |  13 +-
 src/caffe/layers/mvn_layer.cpp                |   7 +-
 src/caffe/layers/mvn_layer.cu                 |   4 +-
 src/caffe/layers/neuron_layer.cpp             |   6 +-
 src/caffe/layers/pooling_layer.cpp            |  15 +-
 src/caffe/layers/pooling_layer.cu             |   3 +-
 src/caffe/layers/power_layer.cpp              |   8 +-
 src/caffe/layers/power_layer.cu               |   4 +-
 src/caffe/layers/relu_layer.cpp               |   3 +-
 src/caffe/layers/relu_layer.cu                |   3 +-
 .../sigmoid_cross_entropy_loss_layer.cpp      |  13 +-
 .../sigmoid_cross_entropy_loss_layer.cu       |  10 +-
 src/caffe/layers/sigmoid_layer.cpp            |   3 +-
 src/caffe/layers/sigmoid_layer.cu             |   3 +-
 src/caffe/layers/slice_layer.cpp              |   6 +-
 src/caffe/layers/slice_layer.cu               |   3 +-
 src/caffe/layers/softmax_layer.cpp            |   6 +-
 src/caffe/layers/softmax_layer.cu             |   3 +-
 src/caffe/layers/softmax_loss_layer.cpp       |  28 ++--
 src/caffe/layers/softmax_loss_layer.cu        |   5 +-
 src/caffe/layers/split_layer.cpp              |   6 +-
 src/caffe/layers/split_layer.cu               |   3 +-
 src/caffe/layers/tanh_layer.cpp               |   3 +-
 src/caffe/layers/tanh_layer.cu                |   3 +-
 src/caffe/layers/threshold_layer.cpp          |   7 +-
 src/caffe/layers/threshold_layer.cu           |   4 +-
 src/caffe/layers/window_data_layer.cpp        |   8 +-
 src/caffe/layers/window_data_layer.cu         |   3 +-
 src/caffe/net.cpp                             |  32 +++-
 src/caffe/test/test_euclidean_loss_layer.cpp  |  42 ++++-
 src/caffe/test/test_hinge_loss_layer.cpp      |  21 +--
 src/caffe/test/test_infogain_loss_layer.cpp   |  10 +-
 .../test_multinomial_logistic_loss_layer.cpp  |  10 +-
 src/caffe/test/test_net.cpp                   | 143 ++++++------------
 .../test_sigmoid_cross_entropy_loss_layer.cpp |  16 +-
 .../test/test_softmax_with_loss_layer.cpp     |  12 +-
 src/caffe/test/test_split_layer.cpp           |   1 +
 79 files changed, 504 insertions(+), 511 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 5d7c21db622..979fd9a6dae 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -29,7 +29,7 @@ class ArgMaxLayer : public Layer<Dtype> {
  public:
   explicit ArgMaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -39,7 +39,7 @@ class ArgMaxLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
@@ -58,7 +58,7 @@ class ConcatLayer : public Layer<Dtype> {
  public:
   explicit ConcatLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -68,9 +68,9 @@ class ConcatLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -93,7 +93,7 @@ class FlattenLayer : public Layer<Dtype> {
  public:
   explicit FlattenLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -103,9 +103,9 @@ class FlattenLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -122,7 +122,7 @@ class MVNLayer : public Layer<Dtype> {
  public:
   explicit MVNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -132,9 +132,9 @@ class MVNLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -154,7 +154,7 @@ class SoftmaxLayer : public Layer<Dtype> {
  public:
   explicit SoftmaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -164,9 +164,9 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -186,7 +186,7 @@ class SplitLayer : public Layer<Dtype> {
  public:
   explicit SplitLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -196,9 +196,9 @@ class SplitLayer : public Layer<Dtype> {
   virtual inline int MinTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -217,7 +217,7 @@ class SliceLayer : public Layer<Dtype> {
  public:
   explicit SliceLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -227,9 +227,9 @@ class SliceLayer : public Layer<Dtype> {
   virtual inline int MinTopBlobs() const { return 2; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 31774828a69..8ab92bfc0a5 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -31,7 +31,7 @@ class DataLayer : public Layer<Dtype>, public InternalThread {
   explicit DataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual ~DataLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -42,9 +42,9 @@ class DataLayer : public Layer<Dtype>, public InternalThread {
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
@@ -85,7 +85,7 @@ class DummyDataLayer : public Layer<Dtype> {
  public:
   explicit DummyDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -95,7 +95,7 @@ class DummyDataLayer : public Layer<Dtype> {
   virtual inline int MinTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
@@ -112,7 +112,7 @@ class HDF5DataLayer : public Layer<Dtype> {
   explicit HDF5DataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual ~HDF5DataLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -122,9 +122,9 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
@@ -145,7 +145,7 @@ class HDF5OutputLayer : public Layer<Dtype> {
  public:
   explicit HDF5OutputLayer(const LayerParameter& param);
   virtual ~HDF5OutputLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {}
 
   virtual inline LayerParameter_LayerType type() const {
@@ -158,9 +158,9 @@ class HDF5OutputLayer : public Layer<Dtype> {
   inline std::string file_name() const { return file_name_; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -180,7 +180,7 @@ class ImageDataLayer : public Layer<Dtype>, public InternalThread {
   explicit ImageDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual ~ImageDataLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -190,9 +190,9 @@ class ImageDataLayer : public Layer<Dtype>, public InternalThread {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
@@ -226,7 +226,7 @@ class MemoryDataLayer : public Layer<Dtype> {
  public:
   explicit MemoryDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -244,7 +244,7 @@ class MemoryDataLayer : public Layer<Dtype> {
   int batch_size() { return batch_size_; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
@@ -268,7 +268,7 @@ class WindowDataLayer : public Layer<Dtype>, public InternalThread {
   explicit WindowDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual ~WindowDataLayer();
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -278,9 +278,9 @@ class WindowDataLayer : public Layer<Dtype>, public InternalThread {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index dca5f8709af..2881fe25807 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -1,6 +1,7 @@
 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_
 
+#include <algorithm>
 #include <string>
 #include <vector>
 
@@ -29,12 +30,17 @@ class Layer {
       }
     }
   virtual ~Layer() {}
-  // SetUp: your function should implement this, and call Layer::SetUp for
-  // common SetUp functionality.
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+  // SetUp: implements common layer setup functionality, and calls
+  // LayerSetUp to do special layer setup for individual layer types.
+  // This method may not be overridden.
+  void SetUp(const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
     CheckBlobCounts(bottom, *top);
+    LayerSetUp(bottom, top);
+    SetLossWeights(top);
   }
+  // LayerSetUp: your layer should implement this.
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) { NOT_IMPLEMENTED; }
 
   // Forward and backward wrappers. You should implement the cpu and
   // gpu specific implementations instead, and should not change these
@@ -51,10 +57,37 @@ class Layer {
   }
 
   // Returns the layer parameter
-  const LayerParameter& layer_param() { return layer_param_; }
+  const LayerParameter& layer_param() const { return layer_param_; }
   // Writes the layer parameter to a protocol buffer
   virtual void ToProto(LayerParameter* param, bool write_diff = false);
 
+  inline Dtype loss(const int top_index) const {
+    return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
+  }
+  inline void set_loss(const int top_index, const Dtype value) {
+    if (loss_.size() <= top_index) {
+      loss_.resize(top_index + 1, Dtype(0));
+    }
+    loss_[top_index] = value;
+  }
+  // Setup the weights associated with each top blob in the loss function.
+  // Store non-zero loss weights in the diff blob.
+  inline void SetLossWeights(vector<Blob<Dtype>*>* top) {
+    const int num_loss_weights = layer_param_.loss_weight_size();
+    if (num_loss_weights) {
+      CHECK_EQ(top->size(), num_loss_weights) << "loss_weight must be "
+          "unspecified or specified once per top blob.";
+      for (int top_id = 0; top_id < top->size(); ++top_id) {
+        const Dtype loss_weight = layer_param_.loss_weight(top_id);
+        if (loss_weight == Dtype(0)) { continue; }
+        this->set_loss(top_id, loss_weight);
+        const int count = (*top)[top_id]->count();
+        Dtype* loss_multiplier = (*top)[top_id]->mutable_cpu_diff();
+        caffe_set(count, loss_weight, loss_multiplier);
+      }
+    }
+  }
+
   // Returns the layer type as an enum value.
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_NONE;
@@ -80,6 +113,11 @@ class Layer {
   virtual inline int MinTopBlobs() const { return -1; }
   virtual inline int MaxTopBlobs() const { return -1; }
 
+  // AutoTopBlobs may be overridden with a positive integer to automatically
+  // create enough "anonymous" top blobs to fulfill the requirement specified
+  // by ExactNumTopBlobs() or MinTopBlobs().
+  virtual inline bool AutoTopBlobs() const { return false; }
+
   // EqualNumBottomTopBlobs should return true for layers requiring an equal
   // number of bottom and top blobs.
   virtual inline bool EqualNumBottomTopBlobs() const { return false; }
@@ -115,12 +153,16 @@ class Layer {
   // Vector indicating whether to compute the diff of each param blob.
   vector<bool> param_propagate_down_;
 
+  // The vector that indicates whether each top blob has a non-zero weight in
+  // the objective function.
+  vector<Dtype> loss_;
+
   // Forward functions: compute the layer output
   // (and loss layers return the loss; other layers return the dummy value 0.)
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) = 0;
   // If no gpu code is provided, we will simply use cpu code.
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
     // LOG(WARNING) << "Using CPU code as backup.";
     return Forward_cpu(bottom, top);
@@ -189,15 +231,36 @@ class Layer {
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
+  Dtype loss = 0;
   switch (Caffe::mode()) {
   case Caffe::CPU:
-    return Forward_cpu(bottom, top);
+    Forward_cpu(bottom, top);
+    for (int top_id = 0; top_id < top->size(); ++top_id) {
+      if (!this->loss(top_id)) { continue; }
+      const int count = (*top)[top_id]->count();
+      const Dtype* data = (*top)[top_id]->cpu_data();
+      const Dtype* loss_weights = (*top)[top_id]->cpu_diff();
+      loss += caffe_cpu_dot(count, data, loss_weights);
+    }
+    break;
   case Caffe::GPU:
-    return Forward_gpu(bottom, top);
+    Forward_gpu(bottom, top);
+#ifndef CPU_ONLY
+    for (int top_id = 0; top_id < top->size(); ++top_id) {
+      if (!this->loss(top_id)) { continue; }
+      const int count = (*top)[top_id]->count();
+      const Dtype* data = (*top)[top_id]->gpu_data();
+      const Dtype* loss_weights = (*top)[top_id]->gpu_diff();
+      Dtype blob_loss = 0;
+      caffe_gpu_dot(count, data, loss_weights, &blob_loss);
+      loss += blob_loss;
+    }
+#endif
+    break;
   default:
     LOG(FATAL) << "Unknown caffe mode.";
-    return Dtype(0);
   }
+  return loss;
 }
 
 template <typename Dtype>
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index bd2618d6d36..937da0ecda5 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -29,7 +29,7 @@ class AccuracyLayer : public Layer<Dtype> {
  public:
   explicit AccuracyLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -40,7 +40,7 @@ class AccuracyLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
@@ -59,13 +59,15 @@ class LossLayer : public Layer<Dtype> {
  public:
   explicit LossLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
-  virtual void SetUp(
+  virtual void LayerSetUp(
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
-  virtual void FurtherSetUp(
-      const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {}
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int MaxTopBlobs() const { return 1; }
+  // For convenience and backwards compatibility, LossLayer's automatically
+  // allocate a singleton top blob into which they output their singleton loss,
+  // (even if the user didn't specify one in the prototxt, etc.).
+  virtual inline bool AutoTopBlobs() const { return true; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
   // We usually cannot backpropagate to the labels; ignore force_backward for
   // these inputs.
   virtual inline bool AllowForceBackward(const int bottom_index) const {
@@ -84,7 +86,7 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
  public:
   explicit EuclideanLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), diff_() {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -97,9 +99,9 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -122,7 +124,7 @@ class HingeLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -135,15 +137,22 @@ class InfogainLossLayer : public LossLayer<Dtype> {
  public:
   explicit InfogainLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), infogain_() {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  // InfogainLossLayer takes 2-3 bottom blobs; if there are 3 the third should
+  // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
+  // file specified by LayerParameter.)
+  virtual inline int ExactNumBottomBlobs() const { return -1; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MaxBottomBlobs() const { return 3; }
+
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_INFOGAIN_LOSS;
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -158,7 +167,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
  public:
   explicit MultinomialLogisticLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -166,7 +175,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -181,7 +190,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
       : LossLayer<Dtype>(param),
           sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
           sigmoid_output_(new Blob<Dtype>()) {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -189,9 +198,9 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -218,27 +227,28 @@ template <typename Dtype> class SoftmaxLayer;
   In test, this layer could be replaced by simple softmax layer.
 */
 template <typename Dtype>
-class SoftmaxWithLossLayer : public Layer<Dtype> {
+class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
   explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      : LossLayer<Dtype>(param),
+        softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SOFTMAX_LOSS;
   }
+  virtual inline int ExactNumBottomBlobs() const { return -1; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MaxBottomBlobs() const { return 3; }
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return 1; }
   virtual inline int MaxTopBlobs() const { return 2; }
-  // We cannot backpropagate to the labels; ignore force_backward for these
-  // inputs.
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return bottom_index != 1;
-  }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 6fe425229e8..20f7f6d58e8 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -28,7 +28,7 @@ class NeuronLayer : public Layer<Dtype> {
  public:
   explicit NeuronLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -56,9 +56,9 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -81,7 +81,7 @@ class DropoutLayer : public NeuronLayer<Dtype> {
  public:
   explicit DropoutLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -89,9 +89,9 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -115,7 +115,7 @@ class PowerLayer : public NeuronLayer<Dtype> {
  public:
   explicit PowerLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -123,9 +123,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -158,9 +158,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
@@ -191,9 +191,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -219,9 +219,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -244,7 +244,7 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
  public:
   explicit ThresholdLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -252,9 +252,9 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index cf1aef759cc..4141c3d60a3 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -11,7 +11,7 @@
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \
-Dtype classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
+void classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
     vector<Blob<Dtype>*>* top) { NO_GPU; } \
 template <typename Dtype> \
 void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
@@ -20,7 +20,7 @@ void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
 
 #define STUB_GPU_FORWARD(classname, funcname) \
 template <typename Dtype> \
-Dtype classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
+void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
     vector<Blob<Dtype>*>* top) { NO_GPU; } \
 
 #define STUB_GPU_BACKWARD(classname, funcname) \
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 76b9f245643..31df136af9b 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -23,7 +23,7 @@ class ConvolutionLayer : public Layer<Dtype> {
  public:
   explicit ConvolutionLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -34,9 +34,9 @@ class ConvolutionLayer : public Layer<Dtype> {
   virtual inline bool EqualNumBottomTopBlobs() const { return true; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -68,7 +68,7 @@ class EltwiseLayer : public Layer<Dtype> {
  public:
   explicit EltwiseLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -78,9 +78,9 @@ class EltwiseLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -98,7 +98,7 @@ class Im2colLayer : public Layer<Dtype> {
  public:
   explicit Im2colLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -108,9 +108,9 @@ class Im2colLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -132,7 +132,7 @@ class InnerProductLayer : public Layer<Dtype> {
  public:
   explicit InnerProductLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -142,9 +142,9 @@ class InnerProductLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -170,7 +170,7 @@ class LRNLayer : public Layer<Dtype> {
  public:
   explicit LRNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -180,20 +180,20 @@ class LRNLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  virtual Dtype CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
+  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
@@ -242,7 +242,7 @@ class PoolingLayer : public Layer<Dtype> {
  public:
   explicit PoolingLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -250,19 +250,23 @@ class PoolingLayer : public Layer<Dtype> {
   }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return max_top_blobs_; }
+  // MAX POOL layers can output an extra top blob for the mask;
+  // others can only output the pooled inputs.
+  virtual inline int MaxTopBlobs() const {
+    return (this->layer_param_.pooling_param().pool() ==
+            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+  }
 
  protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int max_top_blobs_;
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
   int pad_h_, pad_w_;
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 76889d8b70a..062e927183b 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -11,9 +11,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void AccuracyLayer<Dtype>::SetUp(
+void AccuracyLayer<Dtype>::LayerSetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   top_k_ = this->layer_param_.accuracy_param().top_k();
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
@@ -26,7 +25,7 @@ void AccuracyLayer<Dtype>::SetUp(
 }
 
 template <typename Dtype>
-Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   Dtype accuracy = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
@@ -56,9 +55,7 @@ Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   // LOG(INFO) << "Accuracy: " << accuracy;
   (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
-
   // Accuracy layer should not be used as a loss function.
-  return Dtype(0);
 }
 
 INSTANTIATE_CLASS(AccuracyLayer);
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index b2ef91eab67..4b67f24cb9a 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -9,9 +9,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   out_max_val_ = this->layer_param_.argmax_param().out_max_val();
   top_k_ = this->layer_param_.argmax_param().top_k();
   CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
@@ -27,7 +26,7 @@ void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -51,7 +50,6 @@ Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       }
     }
   }
-  return Dtype(0);
 }
 
 INSTANTIATE_CLASS(ArgMaxLayer);
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 4cb852031af..ef98326a23e 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
-Dtype BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -19,7 +19,6 @@ Dtype BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         bottom_data[i] + log(1. + exp(-bottom_data[i])) :
         log(1. + exp(bottom_data[i]));
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 9895a06107a..b940133b4b3 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -18,7 +18,7 @@ __global__ void BNLLForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-Dtype BNLLLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void BNLLLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -27,7 +27,6 @@ Dtype BNLLLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   BNLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
       count, bottom_data, top_data);
   CUDA_POST_KERNEL_CHECK;
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index b76d4b2ca14..73d28b17850 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -7,9 +7,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void ConcatLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   concat_dim_ = this->layer_param_.concat_param().concat_dim();
   CHECK_GE(concat_dim_, 0) <<
     "concat_dim should be >= 0";
@@ -39,7 +38,7 @@ void ConcatLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   if (concat_dim_== 0) {
@@ -61,9 +60,8 @@ Dtype ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           top_data+(*top)[0]->offset(n, offset_channel));
       }
       offset_channel += bottom[i]->channels();
-    }  // concat_dim_ is guaranteed to be 0 or 1 by SetUp.
+    }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
@@ -95,7 +93,7 @@ void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       }
       offset_channel += blob->channels();
     }
-  }  // concat_dim_ is guaranteed to be 0 or 1 by SetUp.
+  }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index aea8b77e37f..99c55da25cb 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   if (concat_dim_ == 0) {
@@ -34,7 +34,6 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
       " not implemented yet";
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index df3e31ba84a..1a1248f3dda 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -9,9 +9,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
@@ -117,7 +116,7 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 
 
 template <typename Dtype>
-Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->cpu_data();
@@ -147,7 +146,6 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       }
     }
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 04ae13932d9..f7f393badba 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -9,7 +9,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
@@ -39,7 +39,6 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       }
     }
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 8f17c45e5f6..c2b0c73a53b 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -160,9 +160,8 @@ DataLayer<Dtype>::~DataLayer<Dtype>() {
 }
 
 template <typename Dtype>
-void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   if (top->size() == 1) {
     output_labels_ = false;
   } else {
@@ -332,7 +331,7 @@ unsigned int DataLayer<Dtype>::PrefetchRand() {
 }
 
 template <typename Dtype>
-Dtype DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
@@ -345,7 +344,6 @@ Dtype DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
-  return Dtype(0.);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 2ae1a640319..467b146fa1f 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -12,7 +12,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
@@ -25,7 +25,6 @@ Dtype DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
-  return Dtype(0.);
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 0621b56ec6e..52537d1aba9 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -11,9 +11,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void DropoutLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  NeuronLayer<Dtype>::SetUp(bottom, top);
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   // Set up the cache for random number generation
   rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
@@ -25,7 +25,7 @@ void DropoutLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -40,7 +40,6 @@ Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   } else {
     caffe_copy(bottom[0]->count(), bottom_data, top_data);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 9bcd687bb5d..9756c862183 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -21,7 +21,7 @@ __global__ void DropoutForward(const int n, const Dtype* in,
 }
 
 template <typename Dtype>
-Dtype DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -38,7 +38,6 @@ Dtype DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   } else {
     caffe_copy(count, bottom_data, top_data);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 98b437eea98..883f2528ef8 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const int num_top = top->size();
   const DummyDataParameter& param = this->layer_param_.dummy_data_param();
@@ -32,7 +32,7 @@ void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // If refill_[i] is false, Forward does nothing for Blob i. We use this to
   // avoid wastefully refilling "constant" Blobs in every forward pass.
   // We first fill refill_ in with the INVERSE of its final values.
-  // The first time we run Forward from the SetUp method, we'll fill only the
+  // The first time we run Forward from the LayerSetUp method, we'll fill only
   // Blobs for which refill_ is normally false.  These Blobs will never be
   // filled again.
   refill_.clear();
@@ -82,7 +82,7 @@ void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < top->size(); ++i) {
     const int filler_id = (fillers_.size() > 1) ? i : 0;
@@ -90,7 +90,6 @@ Dtype DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       fillers_[filler_id]->Fill((*top)[i]);
     }
   }
-  return Dtype(0.);
 }
 
 INSTANTIATE_CLASS(DummyDataLayer);
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 8085b4644cc..ec6a46ff8f5 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -7,9 +7,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   CHECK(this->layer_param().eltwise_param().coeff_size() == 0
       || this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<
       "Eltwise Layer takes one coefficient per bottom blob.";
@@ -39,7 +38,7 @@ void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype EltwiseLayer<Dtype>::Forward_cpu(
+void EltwiseLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   const int count = (*top)[0]->count();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -60,7 +59,6 @@ Dtype EltwiseLayer<Dtype>::Forward_cpu(
   default:
     LOG(FATAL) << "Unknown elementwise operation.";
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index eec8857c577..4b38949da09 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype EltwiseLayer<Dtype>::Forward_gpu(
+void EltwiseLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   const int count = (*top)[0]->count();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -29,7 +29,6 @@ Dtype EltwiseLayer<Dtype>::Forward_gpu(
   default:
     LOG(FATAL) << "Unknown elementwise operation.";
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 17180d40b69..be83601f5a1 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -8,8 +8,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void EuclideanLossLayer<Dtype>::FurtherSetUp(
+void EuclideanLossLayer<Dtype>::LayerSetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::LayerSetUp(bottom, top);
   CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
   CHECK_EQ(bottom[0]->height(), bottom[1]->height());
   CHECK_EQ(bottom[0]->width(), bottom[1]->width());
@@ -18,7 +19,7 @@ void EuclideanLossLayer<Dtype>::FurtherSetUp(
 }
 
 template <typename Dtype>
-Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   int count = bottom[0]->count();
   caffe_sub(
@@ -28,10 +29,7 @@ Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       diff_.mutable_cpu_data());
   Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data());
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
-  if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss;
-  }
-  return loss;
+  (*top)[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
@@ -40,9 +38,10 @@ void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / (*bottom)[i]->num();
       caffe_cpu_axpby(
           (*bottom)[i]->count(),              // count
-          sign / (*bottom)[i]->num(),         // alpha
+          alpha,                              // alpha
           diff_.cpu_data(),                   // a
           Dtype(0),                           // beta
           (*bottom)[i]->mutable_cpu_diff());  // b
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index f4dfd0b5883..70b1b9ee9ea 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -8,7 +8,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   int count = bottom[0]->count();
   caffe_gpu_sub(
@@ -19,10 +19,7 @@ Dtype EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype dot;
   caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
-  if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss;
-  }
-  return loss;
+  (*top)[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
@@ -31,9 +28,10 @@ void EuclideanLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / (*bottom)[i]->num();
       caffe_gpu_axpby(
           (*bottom)[i]->count(),              // count
-          sign / (*bottom)[i]->num(),         // alpha
+          alpha,                              // alpha
           diff_.gpu_data(),                   // a
           Dtype(0),                           // beta
           (*bottom)[i]->mutable_gpu_diff());  // b
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 81a506a8177..8c1fc74e159 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -7,9 +7,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void FlattenLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void FlattenLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   int channels_out = bottom[0]->channels() * bottom[0]->height()
       * bottom[0]->width();
   (*top)[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
@@ -19,10 +18,9 @@ void FlattenLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype FlattenLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void FlattenLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->ShareData(*bottom[0]);
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index 7233afb3718..ff23f523fee 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -7,10 +7,9 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->ShareData(*bottom[0]);
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 938d8435f08..1f2a83582f2 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -50,9 +50,8 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 }
 
 template <typename Dtype>
-void HDF5DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
   LOG(INFO) << "Loading filename from " << source;
@@ -85,7 +84,7 @@ void HDF5DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
   const int data_count = (*top)[0]->count() / (*top)[0]->num();
@@ -109,7 +108,6 @@ Dtype HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
                &label_blob_.cpu_data()[current_row_ * label_data_count],
                &(*top)[1]->mutable_cpu_data()[i * label_data_count]);
   }
-  return Dtype(0.);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 1f682d5726d..79cc536eb28 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -17,7 +17,7 @@ TODO:
 namespace caffe {
 
 template <typename Dtype>
-Dtype HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
   const int data_count = (*top)[0]->count() / (*top)[0]->num();
@@ -44,7 +44,6 @@ Dtype HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         &label_blob_.cpu_data()[current_row_ * label_data_count],
         &(*top)[1]->mutable_gpu_data()[i * label_data_count]);
   }
-  return Dtype(0.);
 }
 
 INSTANTIATE_CLASS(HDF5DataLayer);
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 0d7590b1b12..3cdbbb31a6a 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -39,7 +39,7 @@ void HDF5OutputLayer<Dtype>::SaveBlobs() {
 }
 
 template <typename Dtype>
-Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK_GE(bottom.size(), 2);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num());
@@ -57,7 +57,6 @@ Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index d2f20b3fc79..0813c02a440 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -12,7 +12,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK_GE(bottom.size(), 2);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num());
@@ -30,7 +30,6 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index bc3a593c769..8022aae279c 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -11,7 +11,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
@@ -30,11 +30,14 @@ Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
+  Dtype* loss = (*top)[0]->mutable_cpu_data();
   switch (this->layer_param_.hinge_loss_param().norm()) {
   case HingeLossParameter_Norm_L1:
-    return caffe_cpu_asum(count, bottom_diff) / num;
+    loss[0] = caffe_cpu_asum(count, bottom_diff) / num;
+    break;
   case HingeLossParameter_Norm_L2:
-    return caffe_cpu_dot(count, bottom_diff, bottom_diff) / num;
+    loss[0] = caffe_cpu_dot(count, bottom_diff, bottom_diff) / num;
+    break;
   default:
     LOG(FATAL) << "Unknown Norm";
   }
@@ -58,13 +61,14 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
     }
 
+    const Dtype loss_weight = top[0]->cpu_diff()[0];
     switch (this->layer_param_.hinge_loss_param().norm()) {
     case HingeLossParameter_Norm_L1:
       caffe_cpu_sign(count, bottom_diff, bottom_diff);
-      caffe_scal(count, Dtype(1. / num), bottom_diff);
+      caffe_scal(count, loss_weight / num, bottom_diff);
       break;
     case HingeLossParameter_Norm_L2:
-      caffe_scal(count, Dtype(2. / num), bottom_diff);
+      caffe_scal(count, loss_weight * 2 / num, bottom_diff);
       break;
     default:
       LOG(FATAL) << "Unknown Norm";
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 2dd7476237a..02f33f1cff4 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -8,9 +8,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void Im2colLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
@@ -56,7 +55,7 @@ void Im2colLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -65,7 +64,6 @@ Dtype Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
         stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 6b4c701073e..8df061d88e1 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -8,7 +8,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -17,7 +17,6 @@ Dtype Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
         stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index a0f03a8266d..c72bf9c036f 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -123,10 +123,9 @@ ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
 }
 
 template <typename Dtype>
-void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
-  const int new_height  = this->layer_param_.image_data_param().new_height();
+  const int new_height = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_width();
   CHECK((new_height == 0 && new_width == 0) ||
       (new_height > 0 && new_width > 0)) << "Current implementation requires "
@@ -252,7 +251,7 @@ unsigned int ImageDataLayer<Dtype>::PrefetchRand() {
 }
 
 template <typename Dtype>
-Dtype ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
@@ -263,7 +262,6 @@ Dtype ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
              (*top)[1]->mutable_cpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
-  return Dtype(0.);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index f61409cc38a..30a22100dc3 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -9,7 +9,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
@@ -20,7 +20,6 @@ Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
-  return Dtype(0.);
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index fa01116ee36..91dd89240e4 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -11,8 +11,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void InfogainLossLayer<Dtype>::FurtherSetUp(
+void InfogainLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::LayerSetUp(bottom, top);
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
@@ -38,7 +39,7 @@ void InfogainLossLayer<Dtype>::FurtherSetUp(
 
 
 template <typename Dtype>
-Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
@@ -58,11 +59,7 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
-  loss /= num;
-  if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss;
-  }
-  return loss;
+  (*top)[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
@@ -89,11 +86,12 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     int num = (*bottom)[0]->num();
     int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    const Dtype scale = - top[0]->cpu_diff()[0] / num;
     for (int i = 0; i < num; ++i) {
-      int label = static_cast<int>(bottom_label[i]);
+      const int label = static_cast<int>(bottom_label[i]);
       for (int j = 0; j < dim; ++j) {
         Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-        bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+        bottom_diff[i * dim + j] = scale * infogain_mat[label * dim + j] / prob;
       }
     }
   }
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index a9e0f353e9e..3ba0e1f29f6 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -10,9 +10,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
   // Figure out the dimensions
@@ -52,7 +51,7 @@ void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -64,7 +63,6 @@ Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         bias_multiplier_.cpu_data(),
         this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index e02107200cf..3a0d4388352 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -10,7 +10,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -22,7 +22,6 @@ Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         bias_multiplier_.gpu_data(),
         this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 48665221f1d..89d8c91e342 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -11,16 +11,15 @@
 namespace caffe {
 
 template <typename Dtype>
-void LossLayer<Dtype>::SetUp(
+void LossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
-  if (top->size() == 1) {
-  // Layers should copy the loss in the top blob
-    (*top)[0]->Reshape(1, 1, 1, 1);
+  (*top)[0]->Reshape(1, 1, 1, 1);
+  // LossLayers have a non-zero (1) loss by default.
+  if (this->layer_param_.loss_weight_size() == 0) {
+    this->layer_param_.add_loss_weight(Dtype(1));
   }
-  FurtherSetUp(bottom, top);
 }
 
 INSTANTIATE_CLASS(LossLayer);
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index e77f6857c85..c76ca95dfcd 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -7,9 +7,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -96,21 +95,22 @@ void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
-    return CrossChannelForward_cpu(bottom, top);
+    CrossChannelForward_cpu(bottom, top);
+    break;
   case LRNParameter_NormRegion_WITHIN_CHANNEL:
-    return WithinChannelForward(bottom, top);
+    WithinChannelForward(bottom, top);
+    break;
   default:
     LOG(FATAL) << "Unknown normalization region.";
-    return Dtype(0);
   }
 }
 
 template <typename Dtype>
-Dtype LRNLayer<Dtype>::CrossChannelForward_cpu(
+void LRNLayer<Dtype>::CrossChannelForward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -154,19 +154,16 @@ Dtype LRNLayer<Dtype>::CrossChannelForward_cpu(
   // In the end, compute output
   caffe_powx<Dtype>(scale_.count(), scale_data, -beta_, top_data);
   caffe_mul<Dtype>(scale_.count(), top_data, bottom_data, top_data);
-
-  return Dtype(0.);
 }
 
 template <typename Dtype>
-Dtype LRNLayer<Dtype>::WithinChannelForward(
+void LRNLayer<Dtype>::WithinChannelForward(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   split_layer_->Forward(bottom, &split_top_vec_);
   square_layer_->Forward(square_bottom_vec_, &square_top_vec_);
   pool_layer_->Forward(square_top_vec_, &pool_top_vec_);
   power_layer_->Forward(pool_top_vec_, &power_top_vec_);
   product_layer_->Forward(product_bottom_vec_, top);
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index eee12e66cec..d6cb23bf245 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -54,16 +54,17 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
 
 
 template <typename Dtype>
-Dtype LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
-    return CrossChannelForward_gpu(bottom, top);
+    CrossChannelForward_gpu(bottom, top);
+    break;
   case LRNParameter_NormRegion_WITHIN_CHANNEL:
-    return WithinChannelForward(bottom, top);
+    WithinChannelForward(bottom, top);
+    break;
   default:
     LOG(FATAL) << "Unknown normalization region.";
-    return Dtype(0);
   }
 }
 
@@ -77,7 +78,7 @@ __global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
 }
 
 template <typename Dtype>
-Dtype LRNLayer<Dtype>::CrossChannelForward_gpu(
+void LRNLayer<Dtype>::CrossChannelForward_gpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   // First, compute scale
   const Dtype* bottom_data = bottom[0]->gpu_data();
@@ -96,7 +97,6 @@ Dtype LRNLayer<Dtype>::CrossChannelForward_gpu(
   LRNComputeOutput<<<CAFFE_GET_BLOCKS(n_threads), CAFFE_CUDA_NUM_THREADS>>>(
       n_threads, bottom_data, scale_data, -beta_, top_data);
   CUDA_POST_KERNEL_CHECK;
-  return Dtype(0.);
 }
 
 
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index d1717fd4258..fda92976c49 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -6,9 +6,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void MemoryDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void MemoryDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
   datum_channels_ = this->layer_param_.memory_data_param().channels();
   datum_height_ = this->layer_param_.memory_data_param().height();
@@ -34,13 +33,12 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
 }
 
 template <typename Dtype>
-Dtype MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
   (*top)[0]->set_cpu_data(data_ + pos_ * datum_size_);
   (*top)[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
-  return Dtype(0.);
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index a9c7de6595d..cf96bfe73ed 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -11,15 +11,16 @@
 namespace caffe {
 
 template <typename Dtype>
-void MultinomialLogisticLossLayer<Dtype>::FurtherSetUp(
+void MultinomialLogisticLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::LayerSetUp(bottom, top);
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
 }
 
 template <typename Dtype>
-Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
+void MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
@@ -32,10 +33,7 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
         bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
-  if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss / num;
-  }
-  return loss / num;
+  (*top)[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
@@ -53,11 +51,12 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
     int num = (*bottom)[0]->num();
     int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
     caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+    const Dtype scale = - top[0]->cpu_diff()[0] / num;
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
       Dtype prob = std::max(
           bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-      bottom_diff[i * dim + label] = -1. / prob / num;
+      bottom_diff[i * dim + label] = scale / prob;
     }
   }
 }
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 30235b3f467..4d90702fd55 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -8,9 +8,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void MVNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void MVNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   mean_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -26,7 +25,7 @@ void MVNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -85,8 +84,6 @@ Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
     caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
   }
-
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index dd823984dd2..2c02dfe1ddc 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -8,7 +8,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -68,8 +68,6 @@ Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
     caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
   }
-
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index 3343b26cfc5..eff7948a616 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -6,14 +6,12 @@
 namespace caffe {
 
 template <typename Dtype>
-void NeuronLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void NeuronLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   // NeuronLayer allows in-place computations. If the computation is not
   // in-place, we will need to initialize the top blob.
   if ((*top)[0] != bottom[0]) {
-    (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-        bottom[0]->height(), bottom[0]->width());
+    (*top)[0]->ReshapeLike(*bottom[0]);
   }
 }
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 30657b6c804..9e77fa28a45 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -14,18 +14,8 @@ using std::min;
 using std::max;
 
 template <typename Dtype>
-void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  // Set the max number of top blobs before calling base Layer::SetUp.
-  // If doing MAX pooling, we can optionally output an extra top Blob
-  // for the mask.  Otherwise, we only have one top Blob.
-  if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_MAX) {
-    max_top_blobs_ = 2;
-  } else {
-    max_top_blobs_ = 1;
-  }
-  Layer<Dtype>::SetUp(bottom, top);
   PoolingParameter pool_param = this->layer_param_.pooling_param();
   CHECK(!pool_param.has_kernel_size() !=
       !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
@@ -111,7 +101,7 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 // TODO(Yangqing): Is there a faster way to do pooling in the channel-first
 // case?
 template <typename Dtype>
-Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -210,7 +200,6 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   default:
     LOG(FATAL) << "Unknown pooling method.";
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 58f1997c38e..e64128b87f2 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -151,7 +151,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
 
 
 template <typename Dtype>
-Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -206,7 +206,6 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     LOG(FATAL) << "Unknown pooling method.";
   }
   CUDA_POST_KERNEL_CHECK;
-  return Dtype(0.);
 }
 
 
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 8b5d8d16bd4..a332c4d272a 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -8,9 +8,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void PowerLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void PowerLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  NeuronLayer<Dtype>::SetUp(bottom, top);
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   power_ = this->layer_param_.power_param().power();
   scale_ = this->layer_param_.power_param().scale();
   shift_ = this->layer_param_.power_param().shift();
@@ -19,7 +19,7 @@ void PowerLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 
 // Compute y = (shift + scale * x)^power
 template <typename Dtype>
-Dtype PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
@@ -27,7 +27,6 @@ Dtype PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   if (diff_scale_ == Dtype(0)) {
     Dtype value = (power_ == 0) ? Dtype(1) : pow(shift_, power_);
     caffe_set(count, value, top_data);
-    return Dtype(0);
   }
   const Dtype* bottom_data = bottom[0]->cpu_data();
   caffe_copy(count, bottom_data, top_data);
@@ -40,7 +39,6 @@ Dtype PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   if (power_ != Dtype(1)) {
     caffe_powx(count, top_data, power_, top_data);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 0950b78b4ce..eaf63c1f48a 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -8,7 +8,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
@@ -16,7 +16,6 @@ Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   if (diff_scale_ == Dtype(0)) {
     Dtype value = (power_ == 0) ? Dtype(1) : pow(shift_, power_);
     caffe_gpu_set(count, value, top_data);
-    return Dtype(0);
   }
   const Dtype* bottom_data = bottom[0]->gpu_data();
   caffe_copy(count, bottom_data, top_data);
@@ -29,7 +28,6 @@ Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   if (power_ != Dtype(1)) {
     caffe_gpu_powx(count, top_data, power_, top_data);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index fca10a5aad3..b50352f8526 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -17,7 +17,6 @@ Dtype ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     top_data[i] = std::max(bottom_data[i], Dtype(0))
         + negative_slope * std::min(bottom_data[i], Dtype(0));
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index a74428bfce1..def2bbcd7b9 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -15,7 +15,7 @@ __global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
 }
 
 template <typename Dtype>
-Dtype ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -30,7 +30,6 @@ Dtype ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   //     << " top_data: " << (unsigned long)top_data
   //     << " blocks: " << CAFFE_GET_BLOCKS(count)
   //     << " threads: " << CAFFE_CUDA_NUM_THREADS;
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 24ab6a85c6a..6e440a820c4 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -9,8 +9,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void SigmoidCrossEntropyLossLayer<Dtype>::FurtherSetUp(
+void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::LayerSetUp(bottom, top);
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
       "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
   sigmoid_bottom_vec_.clear();
@@ -21,7 +22,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::FurtherSetUp(
 }
 
 template <typename Dtype>
-Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
+void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   // The forward pass computes the sigmoid outputs.
   sigmoid_bottom_vec_[0] = bottom[0];
@@ -37,10 +38,7 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
-  if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss / num;
-  }
-  return loss / num;
+  (*top)[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
@@ -60,7 +58,8 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     caffe_sub(count, sigmoid_output_data, target, bottom_diff);
     // Scale down gradient
-    caffe_scal(count, Dtype(1) / num, bottom_diff);
+    const Dtype loss_weight = top[0]->cpu_diff()[0];
+    caffe_scal(count, loss_weight / num, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 0e4dab76b79..8d0fdc6fac4 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -9,7 +9,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
+void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   // The forward pass computes the sigmoid outputs.
   sigmoid_bottom_vec_[0] = bottom[0];
@@ -25,10 +25,7 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
-  if (top->size() == 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss / num;
-  }
-  return loss / num;
+  (*top)[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
@@ -49,7 +46,8 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     caffe_copy(count, sigmoid_output_data, bottom_diff);
     caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
     // Scale down gradient
-    caffe_gpu_scal(count, Dtype(1) / num, bottom_diff);
+    const Dtype loss_weight = top[0]->cpu_diff()[0];
+    caffe_gpu_scal(count, loss_weight / num, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 0f8b582da2f..d7bba7fbfc3 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -13,7 +13,7 @@ inline Dtype sigmoid(Dtype x) {
 }
 
 template <typename Dtype>
-Dtype SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -21,7 +21,6 @@ Dtype SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < count; ++i) {
     top_data[i] = sigmoid(bottom_data[i]);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index 039796e1b22..e1ebb1f6c41 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -15,7 +15,7 @@ __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-Dtype SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -29,7 +29,6 @@ Dtype SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   //     << " top_data: " << (unsigned long)top_data
   //     << " blocks: " << CAFFE_GET_BLOCKS(count)
   //     << " threads: " << CAFFE_CUDA_NUM_THREADS;
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index e182837c3aa..9fa127528a0 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -8,9 +8,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   const SliceParameter& slice_param = this->layer_param_.slice_param();
   slice_dim_ = slice_param.slice_dim();
   CHECK_GE(slice_dim_, 0);
@@ -73,7 +72,7 @@ void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->mutable_cpu_data();
   if (slice_dim_ == 0) {
@@ -98,7 +97,6 @@ Dtype SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       offset_channel += blob->channels();
     }
   }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index 8e01131ec01..f64e5754890 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
   if (slice_dim_ == 0) {
@@ -32,7 +32,6 @@ Dtype SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       offset_channel += blob->channels();
     }
   }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 61990ed9d97..fa2ba17a151 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -9,9 +9,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void SoftmaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   sum_multiplier_.Reshape(1, bottom[0]->channels(),
@@ -24,7 +23,7 @@ void SoftmaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -52,7 +51,6 @@ Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < num; ++i) {
     caffe_scal<Dtype>(dim, Dtype(1.) / scale_data[i], top_data + i * dim);
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 65b0e229d1a..6b853099a3f 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -39,7 +39,7 @@ __global__ void kernel_exp(const int num, const Dtype* data, Dtype* out) {
 }
 
 template <typename Dtype>
-Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -68,7 +68,6 @@ Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   kernel_softmax_div<Dtype><<<CAFFE_GET_BLOCKS(num * dim),
                               CAFFE_CUDA_NUM_THREADS>>>(
       num, dim, scale_data, top_data);
-  return Dtype(0);
 }
 
 // TODO(Yangqing): implement the GPU version of softmax.
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 98cf14c4a28..0fa83ccddac 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -9,26 +9,22 @@
 namespace caffe {
 
 template <typename Dtype>
-void SoftmaxWithLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
+void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::LayerSetUp(bottom, top);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
+  softmax_top_vec_.clear();
   softmax_top_vec_.push_back(&prob_);
   softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
-  if (top->size() >= 1) {
-    // softmax loss (averaged across batch)
-    (*top)[0]->Reshape(1, 1, 1, 1);
-  }
-  if (top->size() == 2) {
+  if (top->size() >= 2) {
     // softmax output
-    (*top)[1]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-        bottom[0]->height(), bottom[0]->width());
+    (*top)[1]->ReshapeLike(*bottom[0]);
   }
 }
 
 template <typename Dtype>
-Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
+void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   // The forward pass computes the softmax prob values.
   softmax_bottom_vec_[0] = bottom[0];
@@ -42,13 +38,10 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     loss += -log(std::max(prob_data[i * dim + static_cast<int>(label[i])],
                           Dtype(FLT_MIN)));
   }
-  if (top->size() >= 1) {
-    (*top)[0]->mutable_cpu_data()[0] = loss / num;
-  }
+  (*top)[0]->mutable_cpu_data()[0] = loss / num;
   if (top->size() == 2) {
     (*top)[1]->ShareData(prob_);
   }
-  return loss / num;
 }
 
 template <typename Dtype>
@@ -69,8 +62,9 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     for (int i = 0; i < num; ++i) {
       bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
     }
-    // Scale down gradient
-    caffe_scal(prob_.count(), Dtype(1) / num, bottom_diff);
+    // Scale gradient
+    const Dtype loss_weight = top[0]->cpu_diff()[0];
+    caffe_scal(prob_.count(), loss_weight / num, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 32f3e670f48..9ef8dd23615 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -9,10 +9,9 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype SoftmaxWithLossLayer<Dtype>::Forward_gpu(
+void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  // The forward pass computes the softmax prob values.
-  return Forward_cpu(bottom, top);
+  Forward_cpu(bottom, top);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 2786d3f7694..c223d475917 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -7,9 +7,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void SplitLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void SplitLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   count_ = bottom[0]->count();
   for (int i = 0; i < top->size(); ++i) {
     // Allow the 0th top blob to be 'in-place', but no others.
@@ -25,12 +24,11 @@ void SplitLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-Dtype SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < top->size(); ++i) {
     (*top)[i]->ShareData(*bottom[0]);
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index 1cf15a79314..2d2b3c2b089 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -7,12 +7,11 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < top->size(); ++i) {
     (*top)[i]->ShareData(*bottom[0]);
   }
-  return Dtype(0.);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 0c8be3fac60..8dae0054aed 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -10,7 +10,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -20,7 +20,6 @@ Dtype TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     exp2x = exp(2 * bottom_data[i]);
     top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index b3daad1e638..bdb7a94978e 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -18,7 +18,7 @@ __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-Dtype TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -27,7 +27,6 @@ Dtype TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   TanHForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
       count, bottom_data, top_data);
   CUDA_POST_KERNEL_CHECK;
-  return Dtype(0);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index c932356019b..180ea6a322b 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -7,14 +7,14 @@
 namespace caffe {
 
 template <typename Dtype>
-void ThresholdLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void ThresholdLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  NeuronLayer<Dtype>::SetUp(bottom, top);
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   threshold_ = this->layer_param_.threshold_param().threshold();
 }
 
 template <typename Dtype>
-Dtype ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -22,7 +22,6 @@ Dtype ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < count; ++i) {
     top_data[i] = (bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
   }
-  return Dtype(0);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 398d56e8765..93430815900 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -15,7 +15,7 @@ __global__ void ThresholdForward(const int n, const Dtype threshold,
 }
 
 template <typename Dtype>
-Dtype ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -24,8 +24,6 @@ Dtype ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   ThresholdForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
       count, threshold_, bottom_data, top_data);
   CUDA_POST_KERNEL_CHECK;
-
-  return Dtype(0);
 }
 
 
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index ddff5555417..8138234ebee 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -246,10 +246,9 @@ WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
 }
 
 template <typename Dtype>
-void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+void WindowDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
-  // SetUp runs through the window_file and creates two structures
+  // LayerSetUp runs through the window_file and creates two structures
   // that hold windows: one for foreground (object) windows and one
   // for background (non-object) windows. We use an overlap threshold
   // to decide which is which.
@@ -426,7 +425,7 @@ unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
 }
 
 template <typename Dtype>
-Dtype WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
@@ -437,7 +436,6 @@ Dtype WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
              (*top)[1]->mutable_cpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
-  return Dtype(0.);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index 6e8fa8b349a..475ec265a6b 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -18,7 +18,7 @@
 namespace caffe {
 
 template <typename Dtype>
-Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
@@ -29,7 +29,6 @@ Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
-  return Dtype(0.);
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index db6b4ffefc4..68a80261e32 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <map>
 #include <set>
 #include <string>
@@ -73,11 +74,26 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       // If a blob needs backward, this layer should provide it.
       need_backward |= blob_need_backward_[blob_id];
     }
-    for (int top_id = 0; top_id < layer_param.top_size(); ++top_id) {
+    int num_top = layer_param.top_size();
+    for (int top_id = 0; top_id < num_top; ++top_id) {
       AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
     }
+    // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
+    // specified fewer than the required number (as specified by
+    // ExactNumTopBlobs() or MinTopBlobs()), allocate them here.
+    Layer<Dtype>* layer = layers_[layer_id].get();
+    if (layer->AutoTopBlobs()) {
+      const int needed_num_top =
+          std::max(layer->MinTopBlobs(), layer->ExactNumTopBlobs());
+      for (; num_top < needed_num_top; ++num_top) {
+        // Add "anonymous" top blobs -- do not modify available_blobs or
+        // blob_name_to_idx as we don't want these blobs to be usable as input
+        // to other layers.
+        AppendTop(param, layer_id, num_top, NULL, NULL);
+      }
+    }
     // After this layer is connected, set it up.
-    // LOG(INFO) << "Setting up " << layer_names_[layer_id];
+    LOG(INFO) << "Setting up " << layer_names_[layer_id];
     layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
@@ -272,15 +288,17 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
   shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
     (new LayerParameter(param.layers(layer_id))) : NULL);
   const string& blob_name = layer_param ?
-      layer_param->top(top_id) : param.input(top_id);
+      (layer_param->top_size() > top_id ?
+          layer_param->top(top_id) : "(automatic)") : param.input(top_id);
   // Check if we are doing in-place computation
-  if (layer_param && layer_param->bottom_size() > top_id &&
+  if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
       blob_name == layer_param->bottom(top_id)) {
     // In-place computation
     LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
     top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
     top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
-  } else if (blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
+  } else if (blob_name_to_idx &&
+             blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
     // If we are not doing in-place computation but have duplicated blobs,
     // raise an error.
     LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
@@ -296,7 +314,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     blobs_.push_back(blob_pointer);
     blob_names_.push_back(blob_name);
     blob_need_backward_.push_back(false);
-    (*blob_name_to_idx)[blob_name] = blob_id;
+    if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
     if (layer_id == -1) {
       // Set the (explicitly specified) dimensions of the input blob.
       blob_pointer->Reshape(param.input_dim(top_id * 4),
@@ -311,7 +329,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     }
     memory_used_ += blob_pointer->count();
   }
-  available_blobs->insert(blob_name);
+  if (available_blobs) { available_blobs->insert(blob_name); }
 }
 
 // Helper for Net::Init: add a new bottom blob to the net.
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 511d38ccab5..d7d2de7e9d4 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -22,7 +22,8 @@ class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   EuclideanLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 5, 1, 1)) {
+        blob_bottom_label_(new Blob<Dtype>(10, 5, 1, 1)),
+        blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
@@ -30,28 +31,61 @@ class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
     blob_bottom_vec_.push_back(blob_bottom_data_);
     filler.Fill(this->blob_bottom_label_);
     blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_loss_);
   }
   virtual ~EuclideanLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
+    delete blob_top_loss_;
   }
+
+  void TestForward() {
+    // Get the loss without a specified objective weight -- should be
+    // equivalent to explicitly specifiying a weight of 1.
+    LayerParameter layer_param;
+    EuclideanLossLayer<Dtype> layer_weight_1(layer_param);
+    layer_weight_1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    const Dtype loss_weight_1 =
+        layer_weight_1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+
+    // Get the loss again with a different objective weight; check that it is
+    // scaled appropriately.
+    const Dtype kLossWeight = 3.7;
+    layer_param.add_loss_weight(kLossWeight);
+    EuclideanLossLayer<Dtype> layer_weight_2(layer_param);
+    layer_weight_2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    const Dtype loss_weight_2 =
+        layer_weight_2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    const Dtype kErrorMargin = 1e-5;
+    EXPECT_NEAR(loss_weight_1 * kLossWeight, loss_weight_2, kErrorMargin);
+    // Make sure the loss is non-trivial.
+    const Dtype kNonTrivialAbsThresh = 1e-1;
+    EXPECT_GE(fabs(loss_weight_1), kNonTrivialAbsThresh);
+  }
+
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_top_loss_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
 TYPED_TEST_CASE(EuclideanLossLayerTest, TestDtypesAndDevices);
 
+TYPED_TEST(EuclideanLossLayerTest, TestForward) {
+  this->TestForward();
+}
+
 TYPED_TEST(EuclideanLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  const Dtype kLossWeight = 3.7;
+  layer_param.add_loss_weight(kLossWeight);
   EuclideanLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), -1, -1, -1);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
 }
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 8f6f6f78cf1..3c11b9ac491 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -22,7 +22,8 @@ class HingeLossLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   HingeLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)) {
+        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+        blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     Caffe::set_random_seed(1701);
     FillerParameter filler_param;
@@ -34,13 +35,16 @@ class HingeLossLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
     }
     blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_loss_);
   }
   virtual ~HingeLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
+    delete blob_top_loss_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_top_loss_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -52,10 +56,9 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL1) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   HingeLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 1, 0.01);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+  GradientChecker<Dtype> checker(1e-2, 2e-3, 1701, 1, 0.01);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
 }
 
 TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
@@ -65,11 +68,9 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
   HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
   hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
   HingeLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<Dtype> checker(1e-2, 2e-3, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
 }
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index 162d0e6c589..de2f901af31 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -23,7 +23,8 @@ class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
   InfogainLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
         blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
-        blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)) {
+        blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)),
+        blob_top_loss_(new Blob<Dtype>()) {
     Caffe::set_random_seed(1701);
     FillerParameter filler_param;
     PositiveUnitballFiller<Dtype> filler(filler_param);
@@ -38,15 +39,18 @@ class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
     UniformFiller<Dtype> infogain_filler(filler_param);
     infogain_filler.Fill(this->blob_bottom_infogain_);
     blob_bottom_vec_.push_back(blob_bottom_infogain_);
+    blob_top_vec_.push_back(blob_top_loss_);
   }
   virtual ~InfogainLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
     delete blob_bottom_infogain_;
+    delete blob_top_loss_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
   Blob<Dtype>* const blob_bottom_infogain_;
+  Blob<Dtype>* const blob_top_loss_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -59,8 +63,8 @@ TYPED_TEST(InfogainLossLayerTest, TestGradient) {
   LayerParameter layer_param;
   InfogainLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 3d1037baed0..1fc4c42f0f4 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -20,7 +20,8 @@ class MultinomialLogisticLossLayerTest : public ::testing::Test {
  protected:
   MultinomialLogisticLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)) {
+        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+        blob_top_loss_(new Blob<Dtype>()) {
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
@@ -31,13 +32,16 @@ class MultinomialLogisticLossLayerTest : public ::testing::Test {
       blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
     }
     blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_loss_);
   }
   virtual ~MultinomialLogisticLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
+    delete blob_top_loss_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_top_loss_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -51,8 +55,8 @@ TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
   MultinomialLogisticLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   GradientChecker<TypeParam> checker(1e-2, 2*1e-2, 1701, 0, 0.05);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index acd3bcdd8eb..497f11d4ac5 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -144,7 +144,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitTrickyNet(Dtype* loss_weight = NULL) {
     ostringstream loss_weight_stream;
     if (loss_weight) {
-      loss_weight_stream << "  top_loss_weight: " << *loss_weight << " ";
+      loss_weight_stream << "  loss_weight: " << *loss_weight << " ";
     }
     const string& proto =
         "name: 'TrickyTestNetwork' "
@@ -220,14 +220,16 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
-  virtual void InitUnsharedWeightsNet(Dtype* loss_weight,
+  // loss_weight is the loss weight for the EUCLIDEAN_LOSS layer output.
+  // midnet_loss_weight is the loss weight for the first INNER_PRODUCT layer
+  // output.  Should both default to 0.0 if unspecified (i.e., if NULL is
+  // passed to this function).
+  virtual void InitUnsharedWeightsNet(const Dtype* loss_weight = NULL,
+      const Dtype* midnet_loss_weight = NULL,
       const bool force_backward = false, const bool bias_term = false,
       const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
       const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
     ostringstream proto;
-    if (loss_weight) {
-      loss_weight_stream << "  top_loss_weight: " << *loss_weight << " ";
-    }
     proto << "name: 'UnsharedWeightsNetwork' ";
     if (force_backward) {
       proto << "force_backward: true ";
@@ -270,7 +272,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     }
     proto <<
         "  bottom: 'data' "
-        "  top: 'innerproduct1' "
+        "  top: 'innerproduct1' ";
+    if (midnet_loss_weight) {
+      proto << "  loss_weight: " << *midnet_loss_weight << " ";
+    }
+    proto <<
         "} "
         "layers: { "
         "  name: 'innerproduct2' "
@@ -300,7 +306,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  name: 'loss' "
         "  type: EUCLIDEAN_LOSS ";
     if (loss_weight) {
-      proto << "  top_loss_weight: " << *loss_weight << " ";
+      proto << "  loss_weight: " << *loss_weight << " ";
     }
     proto <<
         "  bottom: 'innerproduct1' "
@@ -544,7 +550,6 @@ TYPED_TEST(NetTest, TestBottomNeedBackward) {
 }
 
 TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
-  typedef typename TypeParam::Dtype Dtype;
   const bool force_backward = true;
   this->InitTinyNet(force_backward);
   const vector<vector<bool> >& bottom_need_backward =
@@ -559,7 +564,6 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
 }
 
 TYPED_TEST(NetTest, TestBottomNeedBackwardEuclideanForce) {
-  typedef typename TypeParam::Dtype Dtype;
   const bool force_backward = true;
   this->InitTinyNetEuclidean(force_backward);
   const vector<vector<bool> >& bottom_need_backward =
@@ -591,106 +595,45 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
   EXPECT_EQ(true, bottom_need_backward[3][1]);
 }
 
-TYPED_TEST(NetTest, TestLossWeightCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  // First, compute the loss and gradients with no top_loss_weight specified.
-  // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default
-  // to 1.
-  vector<Blob<TypeParam>*> bottom;
-  Caffe::set_random_seed(this->seed_);
-  const bool kForceBackward = true;
-  this->InitUnsharedWeightsNet(NULL, kForceBackward);
-  const TypeParam loss = this->net_->ForwardBackward(bottom);
-  const bool kCopyDiff = true;
-  const bool kReshape = true;
-  const vector<shared_ptr<Blob<TypeParam> > >& net_blobs = this->net_->blobs();
-  vector<shared_ptr<Blob<TypeParam> > > blob_grads(net_blobs.size());
-  for (int i = 0; i < net_blobs.size(); ++i) {
-    blob_grads[i].reset(new Blob<TypeParam>());
-    blob_grads[i]->CopyFrom(*net_blobs[i], kCopyDiff, kReshape);
-  }
-  const vector<shared_ptr<Blob<TypeParam> > >& net_params =
-      this->net_->params();
-  vector<shared_ptr<Blob<TypeParam> > > param_grads(net_params.size());
-  for (int i = 0; i < net_params.size(); ++i) {
-    param_grads[i].reset(new Blob<TypeParam>());
-    param_grads[i]->CopyFrom(*net_params[i], kCopyDiff, kReshape);
-  }
-  // Check that the loss is non-trivial, otherwise the test doesn't prove much.
-  const TypeParam kMinLossAbsValue = 1e-2;
-  ASSERT_GE(fabs(loss), kMinLossAbsValue);
-  const TypeParam kErrorMargin = 1e-5;
-  const int kNumLossWeights = 6;
-  TypeParam kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
-  for (int i = 0; i < kNumLossWeights; ++i) {
-    Caffe::set_random_seed(this->seed_);
-    this->InitUnsharedWeightsNet(&kLossWeights[i], kForceBackward);
-    const TypeParam weighted_loss = this->net_->ForwardBackward(bottom);
-    const TypeParam error_margin = kErrorMargin * fabs(kLossWeights[i]);
-    EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
-        << "loss weight = " << kLossWeights[i];
-    const vector<shared_ptr<Blob<TypeParam> > >& weighted_blobs =
-        this->net_->blobs();
-    ASSERT_EQ(blob_grads.size(), weighted_blobs.size());
-    for (int j = 0; j < blob_grads.size(); ++j) {
-      ASSERT_EQ(blob_grads[j]->count(), weighted_blobs[j]->count());
-      for (int k = 0; k < blob_grads[j]->count(); ++k) {
-        EXPECT_NEAR(blob_grads[j]->cpu_diff()[k] * kLossWeights[i],
-                    weighted_blobs[j]->cpu_diff()[k], error_margin);
-      }
-    }
-    const vector<shared_ptr<Blob<TypeParam> > >& weighted_params =
-        this->net_->params();
-    ASSERT_EQ(param_grads.size(), weighted_params.size());
-    for (int j = 0; j < param_grads.size(); ++j) {
-      ASSERT_EQ(param_grads[j]->count(), weighted_params[j]->count());
-      for (int k = 0; k < param_grads[j]->count(); ++k) {
-        EXPECT_NEAR(param_grads[j]->cpu_diff()[k] * kLossWeights[i],
-                    weighted_params[j]->cpu_diff()[k], error_margin);
-      }
-    }
-  }
-}
-
-TYPED_TEST(NetTest, TestLossWeightGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  // First, compute the loss and gradients with no top_loss_weight specified.
+TYPED_TEST(NetTest, TestLossWeight) {
+  typedef typename TypeParam::Dtype Dtype;
+  // First, compute the loss and gradients with no loss_weight specified.
   // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default
   // to 1.
-  vector<Blob<TypeParam>*> bottom;
+  vector<Blob<Dtype>*> bottom;
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
-  this->InitUnsharedWeightsNet(NULL, kForceBackward);
-  const TypeParam loss = this->net_->ForwardBackward(bottom);
+  this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
+  const Dtype loss = this->net_->ForwardBackward(bottom);
   const bool kCopyDiff = true;
   const bool kReshape = true;
-  const vector<shared_ptr<Blob<TypeParam> > >& net_blobs = this->net_->blobs();
-  vector<shared_ptr<Blob<TypeParam> > > blob_grads(net_blobs.size());
+  const vector<shared_ptr<Blob<Dtype> > >& net_blobs = this->net_->blobs();
+  vector<shared_ptr<Blob<Dtype> > > blob_grads(net_blobs.size());
   for (int i = 0; i < net_blobs.size(); ++i) {
-    blob_grads[i].reset(new Blob<TypeParam>());
+    blob_grads[i].reset(new Blob<Dtype>());
     blob_grads[i]->CopyFrom(*net_blobs[i], kCopyDiff, kReshape);
   }
-  const vector<shared_ptr<Blob<TypeParam> > >& net_params =
+  const vector<shared_ptr<Blob<Dtype> > >& net_params =
       this->net_->params();
-  vector<shared_ptr<Blob<TypeParam> > > param_grads(net_params.size());
+  vector<shared_ptr<Blob<Dtype> > > param_grads(net_params.size());
   for (int i = 0; i < net_params.size(); ++i) {
-    param_grads[i].reset(new Blob<TypeParam>());
+    param_grads[i].reset(new Blob<Dtype>());
     param_grads[i]->CopyFrom(*net_params[i], kCopyDiff, kReshape);
   }
   // Check that the loss is non-trivial, otherwise the test doesn't prove much.
-  const TypeParam kMinLossAbsValue = 1e-2;
+  const Dtype kMinLossAbsValue = 1e-2;
   ASSERT_GE(fabs(loss), kMinLossAbsValue);
   const Dtype kErrorMargin = 1e-4;
   const int kNumLossWeights = 6;
-  TypeParam kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
+  Dtype kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
   for (int i = 0; i < kNumLossWeights; ++i) {
     Caffe::set_random_seed(this->seed_);
-    this->InitUnsharedWeightsNet(&kLossWeights[i], kForceBackward);
-    const TypeParam weighted_loss = this->net_->ForwardBackward(bottom);
-    const TypeParam error_margin = kErrorMargin * fabs(kLossWeights[i]);
+    this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward);
+    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
-    const vector<shared_ptr<Blob<TypeParam> > >& weighted_blobs =
+    const vector<shared_ptr<Blob<Dtype> > >& weighted_blobs =
         this->net_->blobs();
     ASSERT_EQ(blob_grads.size(), weighted_blobs.size());
     for (int j = 0; j < blob_grads.size(); ++j) {
@@ -700,7 +643,7 @@ TYPED_TEST(NetTest, TestLossWeightGPU) {
                     weighted_blobs[j]->cpu_diff()[k], error_margin);
       }
     }
-    const vector<shared_ptr<Blob<TypeParam> > >& weighted_params =
+    const vector<shared_ptr<Blob<Dtype> > >& weighted_params =
         this->net_->params();
     ASSERT_EQ(param_grads.size(), weighted_params.size());
     for (int j = 0; j < param_grads.size(); ++j) {
@@ -859,15 +802,15 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
 TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
   vector<Blob<Dtype>*> bottom;
-  const bool kBiasTerm = true;
-  const bool kForceBackward = false;
-  const Dtype* kLossWeight = NULL;
+  const bool kBiasTerm = true, kForceBackward = false;
+  const Dtype* kLossWeight1 = NULL;
+  const Dtype* kLossWeight2 = NULL;
 
   // Run the net with all params learned; check that gradients are non-zero.
   Caffe::set_random_seed(this->seed_);
   Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
-  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
-      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
+      kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
@@ -886,8 +829,8 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   // gradients.
   Caffe::set_random_seed(this->seed_);
   blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
-  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
-      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
+      kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
@@ -902,8 +845,8 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   // gradients for those.
   Caffe::set_random_seed(this->seed_);
   blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
-  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
-      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
+      kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
@@ -921,8 +864,8 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   // Change the opposite subset of the learning rates to zero.
   Caffe::set_random_seed(this->seed_);
   blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
-  this->InitUnsharedWeightsNet(kLossWeight, kForceBackward, kBiasTerm
-      blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
+  this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
+      kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
   this->net_->Forward(bottom);
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index f5716c9e9a7..47ccdea1538 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -22,7 +22,8 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   SigmoidCrossEntropyLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_targets_(new Blob<Dtype>(10, 5, 1, 1)) {
+        blob_bottom_targets_(new Blob<Dtype>(10, 5, 1, 1)),
+        blob_top_loss_(new Blob<Dtype>()) {
     // Fill the data vector
     FillerParameter data_filler_param;
     data_filler_param.set_std(1);
@@ -36,10 +37,12 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
     UniformFiller<Dtype> targets_filler(targets_filler_param);
     targets_filler.Fill(blob_bottom_targets_);
     blob_bottom_vec_.push_back(blob_bottom_targets_);
+    blob_top_vec_.push_back(blob_top_loss_);
   }
   virtual ~SigmoidCrossEntropyLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_targets_;
+    delete blob_top_loss_;
   }
 
   Dtype SigmoidCrossEntropyLossReference(const int count, const int num,
@@ -60,6 +63,8 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestForward() {
     LayerParameter layer_param;
+    const Dtype kLossWeight = 3.7;
+    layer_param.add_loss_weight(kLossWeight);
     FillerParameter data_filler_param;
     data_filler_param.set_std(1);
     GaussianFiller<Dtype> data_filler(data_filler_param);
@@ -82,7 +87,7 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
       const Dtype* blob_bottom_data = this->blob_bottom_data_->cpu_data();
       const Dtype* blob_bottom_targets =
           this->blob_bottom_targets_->cpu_data();
-      Dtype reference_loss = this->SigmoidCrossEntropyLossReference(
+      Dtype reference_loss = kLossWeight * SigmoidCrossEntropyLossReference(
           count, num, blob_bottom_data, blob_bottom_targets);
       EXPECT_NEAR(reference_loss, layer_loss, eps) << "debug: trial #" << i;
     }
@@ -90,6 +95,7 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
 
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_targets_;
+  Blob<Dtype>* const blob_top_loss_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -103,11 +109,13 @@ TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLoss) {
 TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  const Dtype kLossWeight = 3.7;
+  layer_param.add_loss_weight(kLossWeight);
   SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
 }
 
 
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index bd39bd4420b..0f0adbba621 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -22,7 +22,8 @@ class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   SoftmaxWithLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)) {
+        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+        blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
     filler_param.set_std(10);
@@ -33,13 +34,16 @@ class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
     }
     blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_loss_);
   }
   virtual ~SoftmaxWithLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
+    delete blob_top_loss_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_top_loss_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -50,11 +54,11 @@ TYPED_TEST_CASE(SoftmaxWithLossLayerTest, TestDtypesAndDevices);
 TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  layer_param.add_loss_weight(3);
   SoftmaxWithLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 711669bacb8..bf634f58a19 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -8,6 +8,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/insert_splits.hpp"
 #include "caffe/vision_layers.hpp"
 

From 6176f5bad668deda6bae32a772aa8775603e9a27 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 03:17:53 -0700
Subject: [PATCH 0461/2053] Make multiple losses work by inserting split layers
 and add some tests for it. Test that we can call backward with an ACCURACY
 layer.  This currently fails, but should be possible now that we explicitly
 associate a loss weight with each top blob.

---
 include/caffe/util/insert_splits.hpp |   2 +-
 src/caffe/test/test_net.cpp          | 236 +++++++++++++++++++++++++--
 src/caffe/test/test_split_layer.cpp  | 133 ++++++++++++++-
 src/caffe/util/insert_splits.cpp     |  36 +++-
 4 files changed, 385 insertions(+), 22 deletions(-)

diff --git a/include/caffe/util/insert_splits.hpp b/include/caffe/util/insert_splits.hpp
index 4ca933b9203..446abb817be 100644
--- a/include/caffe/util/insert_splits.hpp
+++ b/include/caffe/util/insert_splits.hpp
@@ -12,7 +12,7 @@ namespace caffe {
 void InsertSplits(const NetParameter& param, NetParameter* param_split);
 
 void ConfigureSplitLayer(const string& layer_name, const string& blob_name,
-    const int blob_idx, const int split_count,
+    const int blob_idx, const int split_count, const float loss_weight,
     LayerParameter* split_layer_param);
 
 string SplitLayerName(const string& layer_name, const string& blob_name,
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 497f11d4ac5..fa5735f2a2d 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -28,7 +28,34 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     net_.reset(new Net<Dtype>(param));
   }
 
-  virtual void InitTinyNet(const bool force_backward = false) {
+  virtual void CopyNetBlobs(const bool copy_diff,
+      vector<shared_ptr<Blob<Dtype> > >* blobs_copy) {
+    CHECK(net_);
+    const vector<shared_ptr<Blob<Dtype> > >& net_blobs = net_->blobs();
+    blobs_copy->clear();
+    blobs_copy->resize(net_blobs.size());
+    const bool kReshape = true;
+    for (int i = 0; i < net_blobs.size(); ++i) {
+      (*blobs_copy)[i].reset(new Blob<Dtype>());
+      (*blobs_copy)[i]->CopyFrom(*net_blobs[i], copy_diff, kReshape);
+    }
+  }
+
+  virtual void CopyNetParams(const bool copy_diff,
+      vector<shared_ptr<Blob<Dtype> > >* params_copy) {
+    CHECK(net_);
+    const vector<shared_ptr<Blob<Dtype> > >& net_params = net_->params();
+    params_copy->clear();
+    params_copy->resize(net_params.size());
+    const bool kReshape = true;
+    for (int i = 0; i < net_params.size(); ++i) {
+      (*params_copy)[i].reset(new Blob<Dtype>());
+      (*params_copy)[i]->CopyFrom(*net_params[i], copy_diff, kReshape);
+    }
+  }
+
+  virtual void InitTinyNet(const bool force_backward = false,
+                           const bool accuracy_layer = false) {
     string proto =
         "name: 'TinyTestNetwork' "
         "layers: { "
@@ -79,6 +106,16 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  bottom: 'label' "
         "  top: 'top_loss' "
         "} ";
+    if (accuracy_layer) {
+      proto +=
+          "layers: { "
+          "  name: 'loss' "
+          "  type: ACCURACY "
+          "  bottom: 'innerproduct' "
+          "  bottom: 'label' "
+          "  top: 'accuracy' "
+          "} ";
+    }
     if (force_backward) {
       proto += "force_backward: true ";
     }
@@ -606,20 +643,10 @@ TYPED_TEST(NetTest, TestLossWeight) {
   this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
   const Dtype loss = this->net_->ForwardBackward(bottom);
   const bool kCopyDiff = true;
-  const bool kReshape = true;
-  const vector<shared_ptr<Blob<Dtype> > >& net_blobs = this->net_->blobs();
-  vector<shared_ptr<Blob<Dtype> > > blob_grads(net_blobs.size());
-  for (int i = 0; i < net_blobs.size(); ++i) {
-    blob_grads[i].reset(new Blob<Dtype>());
-    blob_grads[i]->CopyFrom(*net_blobs[i], kCopyDiff, kReshape);
-  }
-  const vector<shared_ptr<Blob<Dtype> > >& net_params =
-      this->net_->params();
-  vector<shared_ptr<Blob<Dtype> > > param_grads(net_params.size());
-  for (int i = 0; i < net_params.size(); ++i) {
-    param_grads[i].reset(new Blob<Dtype>());
-    param_grads[i]->CopyFrom(*net_params[i], kCopyDiff, kReshape);
-  }
+  vector<shared_ptr<Blob<Dtype> > > blob_grads;
+  this->CopyNetBlobs(kCopyDiff, &blob_grads);
+  vector<shared_ptr<Blob<Dtype> > > param_grads;
+  this->CopyNetParams(kCopyDiff, &param_grads);
   // Check that the loss is non-trivial, otherwise the test doesn't prove much.
   const Dtype kMinLossAbsValue = 1e-2;
   ASSERT_GE(fabs(loss), kMinLossAbsValue);
@@ -656,6 +683,185 @@ TYPED_TEST(NetTest, TestLossWeight) {
   }
 }
 
+TYPED_TEST(NetTest, TestLossWeightMidNet) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<Blob<Dtype>*> bottom;
+  Caffe::set_random_seed(this->seed_);
+  const bool kForceBackward = true;
+  Dtype loss_weight = 0;
+  Dtype midnet_loss_weight = 1;
+  this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
+                               kForceBackward);
+  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const bool kCopyDiff = true;
+  const bool kReshape = true;
+  Blob<Dtype> data_grad;
+  data_grad.CopyFrom(*this->net_->blob_by_name("data"), kCopyDiff, kReshape);
+  // Check that the loss is non-trivial, otherwise the test doesn't prove much.
+  const Dtype kMinLossAbsValue = 1e-2;
+  ASSERT_GE(fabs(loss), kMinLossAbsValue);
+  const Dtype kErrorMargin = 1e-4;
+  const int kNumLossWeights = 6;
+  Dtype kLossWeights[kNumLossWeights] = {2, 0, 1, -1, -2.5, 3.7};
+  for (int i = 0; i < kNumLossWeights; ++i) {
+    Caffe::set_random_seed(this->seed_);
+    this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i],
+                                 kForceBackward);
+    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
+    EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
+        << "loss weight = " << kLossWeights[i];
+    const shared_ptr<Blob<Dtype> >& weighted_blob =
+        this->net_->blob_by_name("data");
+    ASSERT_EQ(data_grad.count(), weighted_blob->count());
+    for (int j = 0; j < data_grad.count(); ++j) {
+      EXPECT_NEAR(data_grad.cpu_diff()[j] * kLossWeights[i],
+                  weighted_blob->cpu_diff()[j], error_margin);
+    }
+  }
+}
+
+TYPED_TEST(NetTest, TestComboLossWeight) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<Blob<Dtype>*> bottom;
+  Dtype loss_weight;
+  Dtype midnet_loss_weight;
+  const bool kForceBackward = true;
+  const Dtype kErrorMargin = 1e-4;
+
+  // Get the loss and gradients with EUCLIDEAN_LOSS weight 1,
+  // INNER_PRODUCT weight 1.
+  loss_weight = 1;
+  midnet_loss_weight = 1;
+  Caffe::set_random_seed(this->seed_);
+  this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
+                               kForceBackward);
+  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const bool kCopyDiff = true;
+  vector<shared_ptr<Blob<Dtype> > > blob_grads;
+  this->CopyNetBlobs(kCopyDiff, &blob_grads);
+  vector<shared_ptr<Blob<Dtype> > > param_grads;
+  this->CopyNetParams(kCopyDiff, &param_grads);
+
+  loss_weight = 2;
+  midnet_loss_weight = 1;
+  Caffe::set_random_seed(this->seed_);
+  this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
+                               kForceBackward);
+  const Dtype loss_main_2 = this->net_->ForwardBackward(bottom);
+  vector<shared_ptr<Blob<Dtype> > > blob_grads_loss_2;
+  this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
+  vector<shared_ptr<Blob<Dtype> > > param_grads_loss_2;
+  this->CopyNetParams(kCopyDiff, &param_grads_loss_2);
+
+  loss_weight = 3;
+  midnet_loss_weight = 1;
+  Caffe::set_random_seed(this->seed_);
+  this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
+                               kForceBackward);
+  const Dtype loss_main_3 = this->net_->ForwardBackward(bottom);
+  const vector<shared_ptr<Blob<Dtype> > >& blob_grads_loss_3 =
+      this->net_->blobs();
+  ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size());
+  ASSERT_EQ(blob_grads_loss_2.size(), blob_grads_loss_3.size());
+  for (int j = 0; j < blob_grads.size(); ++j) {
+    const string& blob_name = this->net_->blob_names()[j];
+    bool grad_should_change = true;
+    if (blob_name == "innerproduct1_innerproduct1_0_split_0") {
+      grad_should_change = false;
+    }
+    ASSERT_EQ(blob_grads[j]->count(), blob_grads_loss_3[j]->count());
+    ASSERT_EQ(blob_grads_loss_2[j]->count(), blob_grads_loss_3[j]->count());
+    for (int k = 0; k < blob_grads[j]->count(); ++k) {
+      const Dtype grad_diff_2 = blob_grads_loss_2[j]->cpu_diff()[k] -
+                                    blob_grads[j]->cpu_diff()[k];
+      const Dtype grad_diff_3 = blob_grads_loss_3[j]->cpu_diff()[k] -
+                                    blob_grads[j]->cpu_diff()[k];
+      if (grad_should_change) {
+        // Test non-triviality.
+        const Dtype kMinGradDiffAbsValue = 1e-4;
+        EXPECT_GT(fabs(grad_diff_2), kMinGradDiffAbsValue) << blob_name;
+        EXPECT_NEAR(2 * grad_diff_2, grad_diff_3, kErrorMargin) << blob_name;
+      } else {
+        EXPECT_EQ(0, grad_diff_2) << blob_name;
+        EXPECT_EQ(0, grad_diff_3) << blob_name;
+      }
+    }
+  }
+
+  loss_weight = 1;
+  midnet_loss_weight = 2;
+  Caffe::set_random_seed(this->seed_);
+  this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
+                               kForceBackward);
+  const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom);
+  this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
+  this->CopyNetParams(kCopyDiff, &param_grads_loss_2);
+
+  loss_weight = 1;
+  midnet_loss_weight = 3;
+  Caffe::set_random_seed(this->seed_);
+  this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
+                               kForceBackward);
+  const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom);
+  const vector<shared_ptr<Blob<Dtype> > >& blob_grads_midnet_loss_3 =
+      this->net_->blobs();
+  ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size());
+  ASSERT_EQ(blob_grads_loss_2.size(), blob_grads_midnet_loss_3.size());
+  const vector<string>& blob_names = this->net_->blob_names();
+  for (int j = 0; j < blob_grads.size(); ++j) {
+    const string& blob_name = blob_names[j];
+    bool grad_should_change = false;
+    if (blob_name == "innerproduct1" ||
+        blob_name == "innerproduct1_innerproduct1_0_split_0" ||
+        blob_name == "data_data_0_split_0" || blob_name == "data") {
+      grad_should_change = true;
+    }
+    ASSERT_EQ(blob_grads[j]->count(), blob_grads_midnet_loss_3[j]->count());
+    ASSERT_EQ(blob_grads[j]->count(), blob_grads_loss_2[j]->count());
+    for (int k = 0; k < blob_grads[j]->count(); ++k) {
+      const Dtype grad_diff_2 = blob_grads_loss_2[j]->cpu_diff()[k] -
+                                    blob_grads[j]->cpu_diff()[k];
+      const Dtype grad_diff_3 = blob_grads_midnet_loss_3[j]->cpu_diff()[k] -
+                                    blob_grads[j]->cpu_diff()[k];
+      if (grad_should_change) {
+        // Test non-triviality.
+        const Dtype kMinGradDiffAbsValue = 1e-4;
+        EXPECT_GT(fabs(grad_diff_2), kMinGradDiffAbsValue) << blob_name;
+        EXPECT_NEAR(2 * grad_diff_2, grad_diff_3, kErrorMargin) << blob_name;
+      } else {
+        EXPECT_EQ(0, grad_diff_2) << blob_name;
+        EXPECT_EQ(0, grad_diff_3) << blob_name;
+      }
+    }
+  }
+
+  const Dtype kMinLossDiffAbsValue = 1e-4;
+
+  Dtype loss_diff_2 = loss_main_2 - loss;
+  // Test non-triviality.
+  EXPECT_GT(fabs(loss_diff_2), kMinLossDiffAbsValue);
+  Dtype loss_diff_3 = loss_main_3 - loss;
+  EXPECT_NEAR(2 * loss_diff_2, loss_diff_3, kErrorMargin);
+
+  loss_diff_2 = loss_midnet_2 - loss;
+  // Test non-triviality.
+  EXPECT_GT(fabs(loss_diff_2), kMinLossDiffAbsValue);
+  loss_diff_3 = loss_midnet_3 - loss;
+  EXPECT_NEAR(2 * loss_diff_2, loss_diff_3, kErrorMargin);
+}
+
+TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  const bool kForceBackward = false;
+  const bool kAccuracyLayer = true;
+  this->InitTinyNet(kForceBackward, kAccuracyLayer);
+  EXPECT_TRUE(this->net_->has_blob("accuracy"));
+  vector<Blob<Dtype>*> bottom;
+  // Test that we can do Backward even though we have an ACCURACY layer.
+  this->net_->ForwardBackward(bottom);
+}
+
 TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index bf634f58a19..448e13c5924 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -124,12 +124,12 @@ class SplitLayerInsertionTest : public ::testing::Test {
         output_param_string, &expected_output_param));
     NetParameter actual_output_param;
     InsertSplits(input_param, &actual_output_param);
-    EXPECT_EQ(expected_output_param.DebugString(),
+    CHECK_EQ(expected_output_param.DebugString(),
         actual_output_param.DebugString());
     // Also test idempotence.
     NetParameter double_split_insert_param;
     InsertSplits(actual_output_param, &double_split_insert_param);
-    EXPECT_EQ(actual_output_param.DebugString(),
+    CHECK_EQ(actual_output_param.DebugString(),
        double_split_insert_param.DebugString());
   }
 };
@@ -546,6 +546,135 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionWithInPlace) {
   this->RunInsertionTest(input_proto, input_proto);
 }
 
+TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
+  const string& input_proto =
+      "name: 'UnsharedWeightsNetwork' "
+      "force_backward: true "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DUMMY_DATA "
+      "  dummy_data_param { "
+      "    num: 5 "
+      "    channels: 2 "
+      "    height: 3 "
+      "    width: 4 "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "} "
+      "layers: { "
+      "  name: 'innerproduct1' "
+      "  type: INNER_PRODUCT "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    bias_term: false "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 10 "
+      "    } "
+      "  } "
+      "  param: 'unsharedweights1' "
+      "  bottom: 'data' "
+      "  top: 'innerproduct1' "
+      "  loss_weight: 2.5 "
+      "} "
+      "layers: { "
+      "  name: 'innerproduct2' "
+      "  type: INNER_PRODUCT "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    bias_term: false "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 10 "
+      "    } "
+      "  } "
+      "  param: 'unsharedweights2' "
+      "  bottom: 'data' "
+      "  top: 'innerproduct2' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: EUCLIDEAN_LOSS "
+      "  bottom: 'innerproduct1' "
+      "  bottom: 'innerproduct2' "
+      "} ";
+  const string& expected_output_proto =
+      "name: 'UnsharedWeightsNetwork' "
+      "force_backward: true "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DUMMY_DATA "
+      "  dummy_data_param { "
+      "    num: 5 "
+      "    channels: 2 "
+      "    height: 3 "
+      "    width: 4 "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "} "
+      "layers: { "
+      "  name: 'data_data_0_split' "
+      "  type: SPLIT "
+      "  bottom: 'data' "
+      "  top: 'data' "
+      "  top: 'data_data_0_split_1' "
+      "} "
+      "layers: { "
+      "  name: 'innerproduct1' "
+      "  type: INNER_PRODUCT "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    bias_term: false "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 10 "
+      "    } "
+      "  } "
+      "  param: 'unsharedweights1' "
+      "  bottom: 'data' "
+      "  top: 'innerproduct1' "
+      "} "
+      "layers: { "
+      "  name: 'innerproduct1_innerproduct1_0_split' "
+      "  type: SPLIT "
+      "  bottom: 'innerproduct1' "
+      "  top: 'innerproduct1' "
+      "  top: 'innerproduct1_innerproduct1_0_split_1' "
+      "  loss_weight: 2.5 "
+      "  loss_weight: 0 "
+      "} "
+      "layers: { "
+      "  name: 'innerproduct2' "
+      "  type: INNER_PRODUCT "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    bias_term: false "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 10 "
+      "    } "
+      "  } "
+      "  param: 'unsharedweights2' "
+      "  bottom: 'data_data_0_split_1' "
+      "  top: 'innerproduct2' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: EUCLIDEAN_LOSS "
+      "  bottom: 'innerproduct1_innerproduct1_0_split_1' "
+      "  bottom: 'innerproduct2' "
+      "} ";
+  this->RunInsertionTest(input_proto, expected_output_proto);
+}
+
 TEST_F(SplitLayerInsertionTest, TestInsertion) {
   const string& input_proto =
       "name: 'TestNetwork' "
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 270568cb5dc..e917e356f95 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <map>
 #include <sstream>
 #include <string>
@@ -15,6 +16,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
   map<string, pair<int, int> > blob_name_to_last_top_idx;
   map<pair<int, int>, pair<int, int> > bottom_idx_to_source_top_idx;
   map<pair<int, int>, int> top_idx_to_bottom_count;
+  map<pair<int, int>, float> top_idx_to_loss_weight;
   map<pair<int, int>, int> top_idx_to_bottom_split_idx;
   map<int, string> layer_idx_to_layer_name;
   layer_idx_to_layer_name[-1] = "input";
@@ -41,6 +43,18 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       const string& blob_name = layer_param.top(j);
       blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
     }
+    // A use of a top blob as a loss should be handled similarly to the use of
+    // a top blob as an input (bottom) blob to another layer.
+    const int last_loss =
+        std::min(layer_param.loss_weight_size(), layer_param.top_size());
+    for (int j = 0; j < last_loss; ++j) {
+      const string& blob_name = layer_param.top(j);
+      const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
+      top_idx_to_loss_weight[top_idx] = layer_param.loss_weight(j);
+      if (top_idx_to_loss_weight[top_idx]) {
+        ++top_idx_to_bottom_count[top_idx];
+      }
+    }
   }
   // Create split layer for any input blobs used by other layers as bottom
   // blobs more than once.
@@ -50,8 +64,9 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       const string& layer_name = layer_idx_to_layer_name[-1];
       const string& blob_name = param.input(i);
       LayerParameter* split_layer_param = param_split->add_layers();
+      const float kZeroLossWeight = 0;
       ConfigureSplitLayer(layer_name, blob_name, i, split_count,
-          split_layer_param);
+          kZeroLossWeight, split_layer_param);
     }
   }
   for (int i = 0; i < param.layers_size(); ++i) {
@@ -72,20 +87,26 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
     // Create split layer for any top blobs used by other layers as bottom
     // blobs more than once.
     for (int j = 0; j < layer_param->top_size(); ++j) {
-      const int split_count = top_idx_to_bottom_count[make_pair(i, j)];
+      const pair<int, int>& top_idx = make_pair(i, j);
+      const int split_count = top_idx_to_bottom_count[top_idx];
       if (split_count > 1) {
         const string& layer_name = layer_idx_to_layer_name[i];
         const string& blob_name = layer_param->top(j);
         LayerParameter* split_layer_param = param_split->add_layers();
+        const float loss_weight = top_idx_to_loss_weight[top_idx];
         ConfigureSplitLayer(layer_name, blob_name, j, split_count,
-            split_layer_param);
+            loss_weight, split_layer_param);
+        if (loss_weight) {
+          layer_param->clear_loss_weight();
+          top_idx_to_bottom_split_idx[top_idx]++;
+        }
       }
     }
   }
 }
 
 void ConfigureSplitLayer(const string& layer_name, const string& blob_name,
-    const int blob_idx, const int split_count,
+    const int blob_idx, const int split_count, const float loss_weight,
     LayerParameter* split_layer_param) {
   split_layer_param->Clear();
   split_layer_param->add_bottom(blob_name);
@@ -94,6 +115,13 @@ void ConfigureSplitLayer(const string& layer_name, const string& blob_name,
   for (int k = 0; k < split_count; ++k) {
     split_layer_param->add_top(
         SplitBlobName(layer_name, blob_name, blob_idx, k));
+    if (loss_weight) {
+      if (k == 0) {
+        split_layer_param->add_loss_weight(loss_weight);
+      } else {
+        split_layer_param->add_loss_weight(0);
+      }
+    }
   }
 }
 

From e01c867af8ad1038bf4a53e4e4993c4edd8be128 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 17:19:17 -0700
Subject: [PATCH 0462/2053] Net::Init can determine that layers don't need
 backward if they are not used to compute the loss.

---
 src/caffe/net.cpp | 30 +++++++++++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 68a80261e32..e70c23bec0a 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -138,14 +138,42 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     // Finally, set the backward flag
     layer_need_backward_.push_back(need_backward);
     if (need_backward) {
-      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
       for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {
         blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;
       }
+    }
+  }
+  // Go through the net backwards to determine which blobs contribute to the
+  // loss.  We can skip backward computation for blobs that don't contribute
+  // to the loss.
+  set<string> blobs_under_loss;
+  for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
+    bool layer_contributes_loss = false;
+    for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
+      const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
+      if (layers_[layer_id]->has_loss(top_id) ||
+          (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
+        layer_contributes_loss = true;
+        break;
+      }
+    }
+    if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
+    if (layer_need_backward_[layer_id]) {
+      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
     } else {
       LOG(INFO) << layer_names_[layer_id]
                 << " does not need backward computation.";
     }
+    for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
+         ++bottom_id) {
+      if (layer_contributes_loss) {
+        const string& blob_name =
+            blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+        blobs_under_loss.insert(blob_name);
+      } else {
+        bottom_need_backward_[layer_id][bottom_id] = false;
+      }
+    }
   }
   // Handle force_backward if needed.
   if (param.force_backward()) {

From 7c5dc2091f24306a5379cc67b737acde2c3be7bb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 17:21:27 -0700
Subject: [PATCH 0463/2053] AccuracyLayer only dies when necessary.

---
 include/caffe/loss_layers.hpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 937da0ecda5..f24b1b749f3 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -44,7 +44,9 @@ class AccuracyLayer : public Layer<Dtype> {
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-    NOT_IMPLEMENTED;
+    for (int i = 0; i < propagate_down.size(); ++i) {
+      if (propagate_down[i]) { NOT_IMPLEMENTED; }
+    }
   }
 
   int top_k_;

From f50b233a2607cac2472c1799c332eabf0093c4ae Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 22:52:02 -0700
Subject: [PATCH 0464/2053] Disallow in-place computation in SPLIT layer -- has
 strange effects in backward pass when input into a loss.

---
 include/caffe/vision_layers.hpp     |  2 +-
 src/caffe/layers/lrn_layer.cpp      |  4 +-
 src/caffe/layers/split_layer.cpp    | 31 +++++++++------
 src/caffe/layers/split_layer.cu     | 18 +++++----
 src/caffe/test/test_split_layer.cpp | 61 +++++++++--------------------
 src/caffe/util/insert_splits.cpp    |  5 ---
 6 files changed, 51 insertions(+), 70 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 31df136af9b..d2d9ab3f783 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -230,7 +230,7 @@ class LRNLayer : public Layer<Dtype> {
   Blob<Dtype> power_output_;
   vector<Blob<Dtype>*> power_top_vec_;
   shared_ptr<EltwiseLayer<Dtype> > product_layer_;
-  Blob<Dtype> product_data_input_;
+  Blob<Dtype> product_input_;
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index c76ca95dfcd..e81a32ba84f 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -26,7 +26,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     {
       // Set up split_layer_ to use inputs in the numerator and denominator.
       split_top_vec_.clear();
-      split_top_vec_.push_back(bottom[0]);
+      split_top_vec_.push_back(&product_input_);
       split_top_vec_.push_back(&square_input_);
       LayerParameter split_param;
       split_layer_.reset(new SplitLayer<Dtype>(split_param));
@@ -76,7 +76,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       // Set up a product_layer_ to compute outputs by multiplying inputs by the
       // inverse demoninator computed by the power layer.
       product_bottom_vec_.clear();
-      product_bottom_vec_.push_back(bottom[0]);
+      product_bottom_vec_.push_back(&product_input_);
       product_bottom_vec_.push_back(&power_output_);
       LayerParameter product_param;
       EltwiseParameter* eltwise_param = product_param.mutable_eltwise_param();
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index c223d475917..3bed347ea6c 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -11,12 +11,13 @@ void SplitLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   count_ = bottom[0]->count();
   for (int i = 0; i < top->size(); ++i) {
-    // Allow the 0th top blob to be 'in-place', but no others.
-    if (i == 0 && (*top)[i] == bottom[0]) {
-      continue;
-    } else {
-      CHECK_NE((*top)[i], bottom[0]) << "Only 0th top blob may be in place.";
-    }
+    // Do not allow in-place computation in the SplitLayer.  Instead, share data
+    // by reference in the forward pass, and keep separate diff allocations in
+    // the backward pass.  (Technically, it should be possible to share the diff
+    // blob of the first split output with the input, but this seems to cause
+    // some strange effects in practice...)
+    CHECK_NE((*top)[i], bottom[0]) << this->type_name() << " Layer does not "
+        "allow in-place computation.";
     (*top)[i]->Reshape(bottom[0]->num(), bottom[0]->channels(),
                        bottom[0]->height(), bottom[0]->width());
     CHECK_EQ(count_, (*top)[i]->count());
@@ -34,14 +35,18 @@ void SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down[0]) {
-    (*bottom)[0]->ShareDiff(*top[0]);
-    // Add remaining top blob diffs.
+  if (!propagate_down[0]) { return; }
+  if (top.size() == 1) {
+    caffe_copy(count_, top[0]->cpu_diff(), (*bottom)[0]->mutable_cpu_diff());
+    return;
+  }
+  caffe_add(count_, top[0]->cpu_diff(), top[1]->cpu_diff(),
+            (*bottom)[0]->mutable_cpu_diff());
+  // Add remaining top blob diffs.
+  for (int i = 2; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    for (int i = 1; i < top.size(); ++i) {
-      const Dtype* top_diff = top[i]->cpu_diff();
-      caffe_axpy(count_, Dtype(1.), top_diff, bottom_diff);
-    }
+    caffe_axpy(count_, Dtype(1.), top_diff, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index 2d2b3c2b089..fcc0917e67d 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -17,14 +17,18 @@ void SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down[0]) {
-    (*bottom)[0]->ShareDiff(*top[0]);
-    // Add remaining top blob diffs.
+  if (!propagate_down[0]) { return; }
+  if (top.size() == 1) {
+    caffe_copy(count_, top[0]->gpu_diff(), (*bottom)[0]->mutable_gpu_diff());
+    return;
+  }
+  caffe_gpu_add(count_, top[0]->gpu_diff(), top[1]->gpu_diff(),
+                (*bottom)[0]->mutable_gpu_diff());
+  // Add remaining top blob diffs.
+  for (int i = 2; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    for (int i = 1; i < top.size(); ++i) {
-      const Dtype* top_diff = top[i]->gpu_diff();
-      caffe_gpu_axpy(count_, Dtype(1.), top_diff, bottom_diff);
-    }
+    caffe_gpu_axpy(count_, Dtype(1.), top_diff, bottom_diff);
   }
 }
 
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 448e13c5924..92a3ec77b93 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -76,19 +76,6 @@ TYPED_TEST(SplitLayerTest, Test) {
   }
 }
 
-TYPED_TEST(SplitLayerTest, TestInPlace) {
-  typedef typename TypeParam::Dtype Dtype;
-  LayerParameter layer_param;
-  SplitLayer<Dtype> layer(layer_param);
-  this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
-    EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
-  }
-}
-
 TYPED_TEST(SplitLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -98,16 +85,6 @@ TYPED_TEST(SplitLayerTest, TestGradient) {
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(SplitLayerTest, TestGradientInPlace) {
-  typedef typename TypeParam::Dtype Dtype;
-  LayerParameter layer_param;
-  SplitLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-2, 1e-2);
-  this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
 
 class SplitLayerInsertionTest : public ::testing::Test {
  protected:
@@ -124,12 +101,12 @@ class SplitLayerInsertionTest : public ::testing::Test {
         output_param_string, &expected_output_param));
     NetParameter actual_output_param;
     InsertSplits(input_param, &actual_output_param);
-    CHECK_EQ(expected_output_param.DebugString(),
+    EXPECT_EQ(expected_output_param.DebugString(),
         actual_output_param.DebugString());
     // Also test idempotence.
     NetParameter double_split_insert_param;
     InsertSplits(actual_output_param, &double_split_insert_param);
-    CHECK_EQ(actual_output_param.DebugString(),
+    EXPECT_EQ(actual_output_param.DebugString(),
        double_split_insert_param.DebugString());
   }
 };
@@ -624,7 +601,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "  name: 'data_data_0_split' "
       "  type: SPLIT "
       "  bottom: 'data' "
-      "  top: 'data' "
+      "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
       "layers: { "
@@ -639,14 +616,14 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "    } "
       "  } "
       "  param: 'unsharedweights1' "
-      "  bottom: 'data' "
+      "  bottom: 'data_data_0_split_0' "
       "  top: 'innerproduct1' "
       "} "
       "layers: { "
       "  name: 'innerproduct1_innerproduct1_0_split' "
       "  type: SPLIT "
       "  bottom: 'innerproduct1' "
-      "  top: 'innerproduct1' "
+      "  top: 'innerproduct1_innerproduct1_0_split_0' "
       "  top: 'innerproduct1_innerproduct1_0_split_1' "
       "  loss_weight: 2.5 "
       "  loss_weight: 0 "
@@ -726,14 +703,14 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "  name: 'data_data_0_split' "
       "  type: SPLIT "
       "  bottom: 'data' "
-      "  top: 'data' "
+      "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "  top: 'data_data_0_split_2' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
       "  type: INNER_PRODUCT "
-      "  bottom: 'data' "
+      "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
@@ -746,7 +723,7 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "  name: 'innerprod2_innerprod2_0_split' "
       "  type: SPLIT "
       "  bottom: 'innerprod2' "
-      "  top: 'innerprod2' "
+      "  top: 'innerprod2_innerprod2_0_split_0' "
       "  top: 'innerprod2_innerprod2_0_split_1' "
       "} "
       "layers: { "
@@ -759,7 +736,7 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "  name: 'loss1' "
       "  type: EUCLIDEAN_LOSS "
       "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
+      "  bottom: 'innerprod2_innerprod2_0_split_0' "
       "} "
       "layers: { "
       "  name: 'loss2' "
@@ -827,26 +804,26 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
       "  name: 'data_data_0_split' "
       "  type: SPLIT "
       "  bottom: 'data' "
-      "  top: 'data' "
+      "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
       "layers: { "
       "  name: 'label_data_1_split' "
       "  type: SPLIT "
       "  bottom: 'label' "
-      "  top: 'label' "
+      "  top: 'label_data_1_split_0' "
       "  top: 'label_data_1_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
       "  type: INNER_PRODUCT "
-      "  bottom: 'data' "
+      "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
       "  type: INNER_PRODUCT "
-      "  bottom: 'label' "
+      "  bottom: 'label_data_1_split_0' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
@@ -913,13 +890,13 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "  name: 'data_input_0_split' "
       "  type: SPLIT "
       "  bottom: 'data' "
-      "  top: 'data' "
+      "  top: 'data_input_0_split_0' "
       "  top: 'data_input_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
       "  type: INNER_PRODUCT "
-      "  bottom: 'data' "
+      "  bottom: 'data_input_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
@@ -988,13 +965,13 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
       "  name: 'data_data_0_split' "
       "  type: SPLIT "
       "  bottom: 'data' "
-      "  top: 'data' "
+      "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
       "  type: INNER_PRODUCT "
-      "  bottom: 'data' "
+      "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
@@ -1007,13 +984,13 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
       "  name: 'innerprod1_relu1_0_split' "
       "  type: SPLIT "
       "  bottom: 'innerprod1' "
-      "  top: 'innerprod1' "
+      "  top: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod1_relu1_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
       "  type: INNER_PRODUCT "
-      "  bottom: 'innerprod1' "
+      "  bottom: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index e917e356f95..f20efdae8c6 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -135,11 +135,6 @@ string SplitLayerName(const string& layer_name, const string& blob_name,
 
 string SplitBlobName(const string& layer_name, const string& blob_name,
     const int blob_idx, const int split_idx) {
-  // 0th split top blob is given the same name as the bottom blob so that
-  // computation is done 'in-place', saving a bit of time and memory.
-  if (split_idx == 0) {
-    return blob_name;
-  }
   ostringstream split_blob_name;
   split_blob_name << blob_name << "_" << layer_name << "_" << blob_idx
       << "_split_" << split_idx;

From 7d590a8de82233db763fed2e487add5a2491f230 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 23:06:41 -0700
Subject: [PATCH 0465/2053] Also display outputs in the train net.  (Otherwise,
 why have them?)

---
 src/caffe/solver.cpp | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index d96d03942f3..18269b0a2dc 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -193,6 +193,17 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     Dtype loss = net_->ForwardBackward(bottom_vec);
     if (display) {
       LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+      const vector<Blob<Dtype>*>& result = net_->output_blobs();
+      vector<Dtype> score;
+      for (int j = 0; j < result.size(); ++j) {
+        const Dtype* result_vec = result[j]->cpu_data();
+        for (int k = 0; k < result[j]->count(); ++k) {
+          score.push_back(result_vec[k]);
+        }
+      }
+      for (int i = 0; i < score.size(); ++i) {
+        LOG(INFO) << "    Training score #" << i << ": " << score[i];
+      }
     }
 
     ComputeUpdateValue();
@@ -267,7 +278,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
     LOG(INFO) << "Test loss: " << loss;
   }
   for (int i = 0; i < test_score.size(); ++i) {
-    LOG(INFO) << "Test score #" << i << ": "
+    LOG(INFO) << "    Test score #" << i << ": "
         << test_score[i] / param_.test_iter(test_net_id);
   }
   Caffe::set_phase(Caffe::TRAIN);

From f7b4507314258590e805eb974cdf8a8020f35ab2 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 11 Jul 2014 23:22:21 -0700
Subject: [PATCH 0466/2053] Add ACCURACY layer and softmax_error output to
 lenet_consolidated_solver example.

---
 examples/mnist/lenet_consolidated_solver.prototxt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 980f9382066..42f54e4e93a 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -192,7 +192,6 @@ net_param {
     bottom: "ip2"
     bottom: "label"
     top: "accuracy"
-    include: { phase: TEST }
   }
   layers {
     name: "loss"

From 415123b7ba93550d940faed92297aec5f4a65825 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 13 Jul 2014 16:56:42 -0700
Subject: [PATCH 0467/2053] Store loss coefficients in layer; use for prettier
 training output.

---
 include/caffe/net.hpp    |  6 ++++++
 include/caffe/solver.hpp |  1 +
 src/caffe/net.cpp        |  9 ++++++++-
 src/caffe/solver.cpp     | 39 ++++++++++++++++++++++++++++++---------
 4 files changed, 45 insertions(+), 10 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 8c22137e514..cc238e721b6 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -90,6 +90,9 @@ class Net {
   inline vector<vector<bool> >& bottom_need_backward() {
     return bottom_need_backward_;
   }
+  inline vector<Dtype>& blob_loss_weights() {
+    return blob_loss_weights_;
+  }
   // returns the parameters
   inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
   // returns the parameter learning rate multipliers
@@ -163,6 +166,9 @@ class Net {
   // top_vecs stores the vectors containing the output for each layer
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
+  // Vector of weight in the loss (or objective) function of each net blob,
+  // indexed by blob_id.
+  vector<Dtype> blob_loss_weights_;
   vector<int> param_owners_;
   vector<string> param_display_names_;
   vector<pair<int, int> > param_layer_indices_;
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index cdf583a1429..9012c5d50d9 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -46,6 +46,7 @@ class Solver {
   // function that restores the state from a SolverState protocol buffer.
   void Restore(const char* resume_file);
   virtual void RestoreSolverState(const SolverState& state) = 0;
+  void DisplayOutputBlobs(const int net_id);
 
   SolverParameter param_;
   int iter_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index e70c23bec0a..5a49a23e8f4 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -96,11 +96,18 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     LOG(INFO) << "Setting up " << layer_names_[layer_id];
     layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
+      if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
+        blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
+      }
+      blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
       LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
           << top_vecs_[layer_id][top_id]->channels() << " "
           << top_vecs_[layer_id][top_id]->height() << " "
           << top_vecs_[layer_id][top_id]->width() << " ("
           << top_vecs_[layer_id][top_id]->count() << ")";
+      if (layer->loss(top_id)) {
+        LOG(INFO) << "    with loss weight " << layer->loss(top_id);
+      }
     }
     DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
     const int blobs_lr_size = layer_param.blobs_lr_size();
@@ -151,7 +158,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     bool layer_contributes_loss = false;
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
-      if (layers_[layer_id]->has_loss(top_id) ||
+      if (layers_[layer_id]->loss(top_id) ||
           (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
         layer_contributes_loss = true;
         break;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 18269b0a2dc..80582b312fe 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -154,7 +154,7 @@ void Solver<Dtype>::InitTestNets() {
     }
     net_params[i].mutable_state()->CopyFrom(net_state);
     LOG(INFO)
-        << "Creating testing net (#" << i << ") specified by " << sources[i];
+        << "Creating test net (#" << i << ") specified by " << sources[i];
     test_nets_[i].reset(new Net<Dtype>(net_params[i]));
   }
 }
@@ -194,16 +194,24 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     if (display) {
       LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
-      vector<Dtype> score;
+      int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
         const Dtype* result_vec = result[j]->cpu_data();
+        const string& output_name =
+            net_->blob_names()[net_->output_blob_indices()[j]];
+        const Dtype loss_weight =
+            net_->blob_loss_weights()[net_->output_blob_indices()[j]];
         for (int k = 0; k < result[j]->count(); ++k) {
-          score.push_back(result_vec[k]);
+          ostringstream loss_msg_stream;
+          if (loss_weight) {
+            loss_msg_stream << " (* " << loss_weight
+                            << " = " << loss_weight * result_vec[k] << " loss)";
+          }
+          LOG(INFO) << "    Train net output #"
+              << score_index++ << ": " << output_name << " = "
+              << result_vec[k] << loss_msg_stream.str();
         }
       }
-      for (int i = 0; i < score.size(); ++i) {
-        LOG(INFO) << "    Training score #" << i << ": " << score[i];
-      }
     }
 
     ComputeUpdateValue();
@@ -247,12 +255,14 @@ void Solver<Dtype>::Test(const int test_net_id) {
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
+  vector<int> test_score_output_id;
   vector<Blob<Dtype>*> bottom_vec;
+  const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
   Dtype loss = 0;
   for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
-        test_nets_[test_net_id]->Forward(bottom_vec, &iter_loss);
+        test_net->Forward(bottom_vec, &iter_loss);
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }
@@ -261,6 +271,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
         const Dtype* result_vec = result[j]->cpu_data();
         for (int k = 0; k < result[j]->count(); ++k) {
           test_score.push_back(result_vec[k]);
+          test_score_output_id.push_back(j);
         }
       }
     } else {
@@ -278,8 +289,18 @@ void Solver<Dtype>::Test(const int test_net_id) {
     LOG(INFO) << "Test loss: " << loss;
   }
   for (int i = 0; i < test_score.size(); ++i) {
-    LOG(INFO) << "    Test score #" << i << ": "
-        << test_score[i] / param_.test_iter(test_net_id);
+    const string& output_name = test_net->blob_names()[
+        test_net->output_blob_indices()[test_score_output_id[i]]];
+    const Dtype loss_weight =
+        test_net->blob_loss_weights()[test_net->output_blob_indices()[i]];
+    ostringstream loss_msg_stream;
+    const Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);
+    if (loss_weight) {
+      loss_msg_stream << " (* " << loss_weight
+                      << " = " << loss_weight * mean_score << " loss)";
+    }
+    LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
+        << mean_score << loss_msg_stream.str();
   }
   Caffe::set_phase(Caffe::TRAIN);
 }

From f3083f55c42c7a97e2c4a5bf0baa941c1fa94354 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 13 Aug 2014 14:54:59 -0700
Subject: [PATCH 0468/2053] Fix from loss-generalization: accidentally removed
 mid-Forward return from PowerLayer (caused bad performance for trivial
 PowerLayer cases...)

---
 src/caffe/layers/power_layer.cpp | 1 +
 src/caffe/layers/power_layer.cu  | 1 +
 2 files changed, 2 insertions(+)

diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index a332c4d272a..bf61955d065 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -27,6 +27,7 @@ void PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   if (diff_scale_ == Dtype(0)) {
     Dtype value = (power_ == 0) ? Dtype(1) : pow(shift_, power_);
     caffe_set(count, value, top_data);
+    return;
   }
   const Dtype* bottom_data = bottom[0]->cpu_data();
   caffe_copy(count, bottom_data, top_data);
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index eaf63c1f48a..a40bc75829d 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -16,6 +16,7 @@ void PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   if (diff_scale_ == Dtype(0)) {
     Dtype value = (power_ == 0) ? Dtype(1) : pow(shift_, power_);
     caffe_gpu_set(count, value, top_data);
+    return;
   }
   const Dtype* bottom_data = bottom[0]->gpu_data();
   caffe_copy(count, bottom_data, top_data);

From b5added8327eebb36b6bf1b3eb885e4752ec0916 Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Wed, 13 Aug 2014 17:58:04 -0600
Subject: [PATCH 0469/2053] Updated ImageNet Tutorial to reflect new merged
 train+val prototxt format. Also corrected 4,500,000 iterations  -> 450,000
 iterations.

---
 examples/imagenet/readme.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index d139ad84af8..dc607e6846e 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -57,17 +57,17 @@ which will make `data/ilsvrc12/imagenet_mean.binaryproto`.
 Network Definition
 ------------------
 
-The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
+The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
 
     source: "ilvsrc12_train_leveldb"
     mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
 
-to point to your own leveldb and image mean. Likewise, do the same for `examples/imagenet/imagenet_val.prototxt`.
+to point to your own leveldb and image mean.
 
-If you look carefully at `imagenet_train.prototxt` and `imagenet_val.prototxt`, you will notice that they are largely the same, with the only difference being the data layer sources, and the last layer: in training, we will be using a `softmax_loss` layer to compute the loss function and to initialize the backpropagation, while in validation we will be using an `accuracy` layer to inspect how well we do in terms of accuracy.
+If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include: { phase: TRAIN }` and `include: { phase: TEST }` sections. These sections allow us to define two related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for the input and output layers. The `TRAIN` network has an input layer, `data`, which randomly mirrors input images, whereas the `TEST` network's `data` layer does not. Both networks output the softmax loss, but only the `TEST` network also outputs accuracy. In training, the `softmax_loss` layer is used to compute the loss function and to initialize the backpropagation, while in validation we will be using an `accuracy` layer to inspect how well we do in terms of accuracy.
 
 We will also lay out a protocol buffer for running the solver. Let's make a few plans:
-* We will run in batches of 256, and run a total of 4,500,000 iterations (about 90 epochs).
+* We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
 * Information will be displayed every 20 epochs.

From 1d322ad6ab741b60ef5e383f3648907dea039cd3 Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Wed, 13 Aug 2014 17:32:48 -0700
Subject: [PATCH 0470/2053] Tried to clarify function of `include' lines and
 train vs. test network differences

---
 examples/imagenet/readme.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index dc607e6846e..4c716d9ce0b 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -57,16 +57,21 @@ which will make `data/ilsvrc12/imagenet_mean.binaryproto`.
 Network Definition
 ------------------
 
-The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
+The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer --- if you have not followed the exact paths in this guide you will need to change the following lines:
 
     source: "ilvsrc12_train_leveldb"
     mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
 
 to point to your own leveldb and image mean.
 
-If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include: { phase: TRAIN }` and `include: { phase: TEST }` sections. These sections allow us to define two related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for the input and output layers. The `TRAIN` network has an input layer, `data`, which randomly mirrors input images, whereas the `TEST` network's `data` layer does not. Both networks output the softmax loss, but only the `TEST` network also outputs accuracy. In training, the `softmax_loss` layer is used to compute the loss function and to initialize the backpropagation, while in validation we will be using an `accuracy` layer to inspect how well we do in terms of accuracy.
+If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include` sections specifying either `phase: TRAIN` or `phase: TEST`. These sections allow us to define two closely related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for those marked with `include { phase: TRAIN }` or `include { phase: TEST }`. In this case, only the input layers and one output layer are different.
+
+**Input layer differences:** The training network's `data` input layer draws its data from `ilsvrc12_train_leveldb` and randomly mirrors the input image. The testing network's `data` layer takes data from `ilsvrc12_val_leveldb` and does not perform random mirroring.
+
+**Output layer differences:** Both networks output the `softmax_loss` layer, which in training is used to compute the loss function and to initialize the backpropagation, while in validation this loss is simply reported. The testing network also has a second output layer, `accuracy`, which is used to report the accuracy on the test set. In the process of training, the test network will occasionally be instantiated and tested on the test set, producing lines like `Test score #0: xxx` and `Test score #1: xxx`. In this case score 0 is the accuracy (which will start around 1/1000 = 0.001 for an untrained network) and score 1 is the loss (which will start around 7 for an untrained network).
 
 We will also lay out a protocol buffer for running the solver. Let's make a few plans:
+
 * We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).

From d9f2d6cbcddcf3a2ae35751ec1cafe586d28c457 Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Wed, 13 Aug 2014 17:58:04 -0600
Subject: [PATCH 0471/2053] Updated ImageNet Tutorial to reflect new merged
 train+val prototxt format. Also corrected 4,500,000 iterations  -> 450,000
 iterations.

---
 examples/imagenet/readme.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index d139ad84af8..dc607e6846e 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -57,17 +57,17 @@ which will make `data/ilsvrc12/imagenet_mean.binaryproto`.
 Network Definition
 ------------------
 
-The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
+The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
 
     source: "ilvsrc12_train_leveldb"
     mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
 
-to point to your own leveldb and image mean. Likewise, do the same for `examples/imagenet/imagenet_val.prototxt`.
+to point to your own leveldb and image mean.
 
-If you look carefully at `imagenet_train.prototxt` and `imagenet_val.prototxt`, you will notice that they are largely the same, with the only difference being the data layer sources, and the last layer: in training, we will be using a `softmax_loss` layer to compute the loss function and to initialize the backpropagation, while in validation we will be using an `accuracy` layer to inspect how well we do in terms of accuracy.
+If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include: { phase: TRAIN }` and `include: { phase: TEST }` sections. These sections allow us to define two related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for the input and output layers. The `TRAIN` network has an input layer, `data`, which randomly mirrors input images, whereas the `TEST` network's `data` layer does not. Both networks output the softmax loss, but only the `TEST` network also outputs accuracy. In training, the `softmax_loss` layer is used to compute the loss function and to initialize the backpropagation, while in validation we will be using an `accuracy` layer to inspect how well we do in terms of accuracy.
 
 We will also lay out a protocol buffer for running the solver. Let's make a few plans:
-* We will run in batches of 256, and run a total of 4,500,000 iterations (about 90 epochs).
+* We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
 * Information will be displayed every 20 epochs.

From d2566dd40a180ec6fcc02c7e48e2e8f3a26e3e72 Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Wed, 13 Aug 2014 17:32:48 -0700
Subject: [PATCH 0472/2053] Tried to clarify function of `include' lines and
 train vs. test network differences

---
 examples/imagenet/readme.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index dc607e6846e..4c716d9ce0b 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -57,16 +57,21 @@ which will make `data/ilsvrc12/imagenet_mean.binaryproto`.
 Network Definition
 ------------------
 
-The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer - if you have not followed the exact paths in this guide you will need to change the following lines:
+The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer --- if you have not followed the exact paths in this guide you will need to change the following lines:
 
     source: "ilvsrc12_train_leveldb"
     mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
 
 to point to your own leveldb and image mean.
 
-If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include: { phase: TRAIN }` and `include: { phase: TEST }` sections. These sections allow us to define two related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for the input and output layers. The `TRAIN` network has an input layer, `data`, which randomly mirrors input images, whereas the `TEST` network's `data` layer does not. Both networks output the softmax loss, but only the `TEST` network also outputs accuracy. In training, the `softmax_loss` layer is used to compute the loss function and to initialize the backpropagation, while in validation we will be using an `accuracy` layer to inspect how well we do in terms of accuracy.
+If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include` sections specifying either `phase: TRAIN` or `phase: TEST`. These sections allow us to define two closely related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for those marked with `include { phase: TRAIN }` or `include { phase: TEST }`. In this case, only the input layers and one output layer are different.
+
+**Input layer differences:** The training network's `data` input layer draws its data from `ilsvrc12_train_leveldb` and randomly mirrors the input image. The testing network's `data` layer takes data from `ilsvrc12_val_leveldb` and does not perform random mirroring.
+
+**Output layer differences:** Both networks output the `softmax_loss` layer, which in training is used to compute the loss function and to initialize the backpropagation, while in validation this loss is simply reported. The testing network also has a second output layer, `accuracy`, which is used to report the accuracy on the test set. In the process of training, the test network will occasionally be instantiated and tested on the test set, producing lines like `Test score #0: xxx` and `Test score #1: xxx`. In this case score 0 is the accuracy (which will start around 1/1000 = 0.001 for an untrained network) and score 1 is the loss (which will start around 7 for an untrained network).
 
 We will also lay out a protocol buffer for running the solver. Let's make a few plans:
+
 * We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).

From 1991826a79e7f6a4bc99a8ecc85878cacac92a53 Mon Sep 17 00:00:00 2001
From: Alireza Shafaei <alireza@shafaei.net>
Date: Sat, 9 Aug 2014 22:44:12 -0700
Subject: [PATCH 0473/2053] Added absolute value layer, useful for
 implementation of siamese networks! This commit also replaces the default
 caffe_fabs with MKL/non-MKL implementation of Abs.

---
 include/caffe/neuron_layers.hpp        | 30 +++++++++++++++++
 include/caffe/util/math_functions.hpp  |  6 ++++
 include/caffe/util/mkl_alternate.hpp   |  1 +
 src/caffe/layer_factory.cpp            |  2 ++
 src/caffe/layers/absval_layer.cpp      | 46 ++++++++++++++++++++++++++
 src/caffe/layers/absval_layer.cu       | 34 +++++++++++++++++++
 src/caffe/proto/caffe.proto            |  3 +-
 src/caffe/test/test_math_functions.cpp |  4 +--
 src/caffe/test/test_neuron_layer.cpp   | 23 +++++++++++++
 src/caffe/util/math_functions.cpp      | 11 +++++-
 src/caffe/util/math_functions.cu       | 23 ++++++++++++-
 11 files changed, 178 insertions(+), 5 deletions(-)
 create mode 100644 src/caffe/layers/absval_layer.cpp
 create mode 100644 src/caffe/layers/absval_layer.cu

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 20f7f6d58e8..d17120fa73a 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -38,6 +38,36 @@ class NeuronLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 };
 
+/* AbsVal Layer
+  y = |x|
+
+  y' = 1    if x > 0
+     = -1   if x < 0
+*/
+template <typename Dtype>
+class AbsValLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit AbsValLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ABSVAL;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+};
+
 /* BNLLLayer
 
   y = x + log(1 + exp(-x))  if x > 0
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 90a1a86d8e8..6a608d5cfa3 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -88,6 +88,9 @@ void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
 template <typename Dtype>
 void caffe_exp(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_abs(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
 
@@ -196,6 +199,9 @@ void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index d72bcd2814d..32fdbf79932 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -33,6 +33,7 @@ extern "C" {
 
 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
+DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
 
 // A simple way to define the vsl unary functions with singular parameter b.
 // The operation should be in the form e.g. y[i] = pow(a[i], b)
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 2170c19f539..d18d24627a9 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -19,6 +19,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   switch (type) {
   case LayerParameter_LayerType_ACCURACY:
     return new AccuracyLayer<Dtype>(param);
+  case LayerParameter_LayerType_ABSVAL:
+    return new AbsValLayer<Dtype>(param);
   case LayerParameter_LayerType_ARGMAX:
     return new ArgMaxLayer<Dtype>(param);
   case LayerParameter_LayerType_BNLL:
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
new file mode 100644
index 00000000000..ce9d05cc764
--- /dev/null
+++ b/src/caffe/layers/absval_layer.cpp
@@ -0,0 +1,46 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AbsValLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  CHECK_NE((*top)[0], bottom[0]) << this->type_name() << " Layer does not "
+    "allow in-place computation.";
+}
+
+template <typename Dtype>
+void AbsValLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const int count = (*top)[0]->count();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  caffe_abs(count, bottom[0]->cpu_data(), top_data);
+}
+
+template <typename Dtype>
+void AbsValLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int count = top[0]->count();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    caffe_div(count, top_data, bottom_data, bottom_diff);
+    caffe_mul(count, bottom_diff, top_diff, bottom_diff);
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(AbsValLayer);
+#endif
+
+INSTANTIATE_CLASS(AbsValLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
new file mode 100644
index 00000000000..46778aa79b1
--- /dev/null
+++ b/src/caffe/layers/absval_layer.cu
@@ -0,0 +1,34 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AbsValLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const int count = (*top)[0]->count();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  caffe_gpu_abs(count, bottom[0]->gpu_data(), top_data);
+}
+
+template <typename Dtype>
+void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int count = top[0]->count();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+    caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
+  }
+}
+
+INSTANTIATE_CLASS(AbsValLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 971a291cad4..9cc0de9fe9b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -205,12 +205,13 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 35 (last added: MVN)
+  // LayerType next available ID: 36 (last added: ABSVAL)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
     // the type is unspecified).
     NONE = 0;
+    ABSVAL = 35;
     ACCURACY = 1;
     ARGMAX = 30;
     BNLL = 2;
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index d10e702e79a..667f744bdd7 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -113,7 +113,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
 TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_fabs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  caffe_abs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
   const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
     EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
@@ -194,7 +194,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
 
 TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_fabs<TypeParam>(n, this->blob_bottom_->gpu_data(),
+  caffe_gpu_abs<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
   const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 649f8f626d9..29dcec5d889 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -70,6 +70,29 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
 
+TYPED_TEST(NeuronLayerTest, TestAbsVal) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  AbsValLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data    = this->blob_top_->cpu_data();
+  const int count = this->blob_bottom_->count();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(top_data[i], fabs(bottom_data[i]));
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestAbsGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  AbsValLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 TYPED_TEST(NeuronLayerTest, TestReLU) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index e10f0191362..bac06f8d025 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -206,6 +206,16 @@ void caffe_exp<double>(const int n, const double* a, double* y) {
   vdExp(n, a, y);
 }
 
+template <>
+void caffe_abs<float>(const int n, const float* a, float* y) {
+    vsAbs(n, a, y);
+}
+
+template <>
+void caffe_abs<double>(const int n, const double* a, double* y) {
+    vdAbs(n, a, y);
+}
+
 unsigned int caffe_rng_rand() {
   return (*caffe_rng())();
 }
@@ -349,7 +359,6 @@ double caffe_cpu_asum<double>(const int n, const double* x) {
 
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sign);
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sgnbit);
-INSTANTIATE_CAFFE_CPU_UNARY_FUNC(fabs);
 
 template <>
 void caffe_cpu_scale<float>(const int n, const float alpha, const float *x,
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index eacbb4728dd..4ae4bba6029 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -281,6 +281,28 @@ void caffe_gpu_div<double>(const int N, const double* a,
       N, a, b, y);
 }
 
+template <typename Dtype>
+__global__ void abs_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = abs(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_abs<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  abs_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_abs<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  abs_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+
 template <typename Dtype>
 __global__ void powx_kernel(const int n, const Dtype* a,
     const Dtype alpha, Dtype* y) {
@@ -308,7 +330,6 @@ void caffe_gpu_powx<double>(const int N, const double* a,
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
-DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(fabs, y[index] = fabs(x[index]));
 
 __global__ void popc_kernel(const int n, const float* a,
     const float* b, uint8_t* y) {

From 16f23eb95a123885f818f76f7475d4eaccc6ce19 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 15 Aug 2014 14:04:59 -0700
Subject: [PATCH 0474/2053] [example] include imports in net surgery

---
 examples/net_surgery.ipynb | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index dc9c7e84954..2b5a8611d33 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:0b2ad61622122fa34a40c250be2c0799a85fb65c149b802ce844c46eceba066e"
+  "signature": "sha256:de67ad69a180b5a6b350d897042fe0bd424f7db63112ca3b7faba259ccbb9be6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -273,6 +273,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
+      "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
       "plt.imshow(im)\n",

From d41be0b39ae8bca576b826175ef876984c8ea165 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 15 Aug 2014 14:04:59 -0700
Subject: [PATCH 0475/2053] [example] include imports in net surgery

---
 examples/net_surgery.ipynb | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index dc9c7e84954..2b5a8611d33 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:0b2ad61622122fa34a40c250be2c0799a85fb65c149b802ce844c46eceba066e"
+  "signature": "sha256:de67ad69a180b5a6b350d897042fe0bd424f7db63112ca3b7faba259ccbb9be6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -273,6 +273,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
+      "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
       "plt.imshow(im)\n",

From 02aadf0576f432ba985de0ed4a81e80ce722f895 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 15 Aug 2014 14:13:22 -0700
Subject: [PATCH 0476/2053] [example] set phase test for fully-convolutional
 model

---
 examples/net_surgery.ipynb | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 2b5a8611d33..4710236c6e9 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:de67ad69a180b5a6b350d897042fe0bd424f7db63112ca3b7faba259ccbb9be6"
+  "signature": "sha256:742fe1bbeabccae59725a5fd6e97a0a0b2b90842d1dd5953aecd353b2d87cab3"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -280,6 +280,7 @@
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
       "plt.imshow(im)\n",
+      "net_full_conv.set_phase_test()\n",
       "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
       "net_full_conv.set_raw_scale('data', 255.0)\n",
@@ -295,22 +296,22 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "array([[282, 281, 281, 281, 281, 282, 282, 278],\n",
-        "       [281, 283, 281, 283, 281, 287, 281, 282],\n",
-        "       [283, 283, 283, 281, 283, 283, 283, 259],\n",
-        "       [283, 283, 281, 281, 283, 283, 287, 259],\n",
-        "       [283, 283, 285, 283, 283, 283, 283, 283],\n",
-        "       [283, 283, 333, 283, 283, 283, 283, 282],\n",
-        "       [905, 358, 259, 283, 371, 259, 259, 852],\n",
-        "       [335, 335, 371, 185, 186, 263, 185, 356]])"
+        "array([[282, 281, 281, 281, 281, 281, 281, 282],\n",
+        "       [283, 283, 283, 281, 281, 281, 281, 282],\n",
+        "       [283, 283, 283, 283, 283, 283, 287, 282],\n",
+        "       [283, 283, 283, 281, 283, 283, 283, 283],\n",
+        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
+        "       [283, 283, 283, 283, 283, 283, 283, 259],\n",
+        "       [283, 283, 283, 283, 283, 259, 259, 852],\n",
+        "       [335, 335, 283, 283, 283, 263, 263, 331]])"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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IKqyrUEsKkeG59FMkhNGdvjnnh86+C5dLZ+zbU4KSKciGT6otrpz5YIggA9gbUTM5aVFq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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt\n6zikMKhUcM3/D3EXDx8+5OnTpzx69IgPP/yQBw8eAPD666/z/vvvH7/u+9//Pq+//voPfI67Jzu5\nkQv4padZ+WNVqJR4pMtBi6mUWKuSohjJDT+wLYUTqOsELtdS/vCoerAiE7dSEmSxWMaDyOkwCSTX\naaRGm7ojK4NqZGGLIZNDwWpH04rbxji5oLTWhH0kpULT1J/dlOqSSKSkK1XFCkjDaKxxWA1RCdx4\nDjPZiIsppsA0z4R5QhXJadZV0xqSLIpZH/BdhjxLdaoVwg9VwjQsRqAK0uYQOUaJMG1hZ8sRlkKU\ndoJRGp09UUsomJrBny5oncbNMyUXxjjKz+IMOUZK1sxB+JA5JxZ9j9aG09NzciyElLj/+E3G3a1c\nI2+8idKW1Xotv/+m5VPvfI4Pnnyf9959j+1mplt6qbZjYIozTnesml7iPTDs9jte++TbkuGtIiGk\n6nDxbLY3tG0HSvPaozdZn5+TgaZtGO+u2Q43MLeYvuN285KLizOM6/nekw/RKtEvBd3m3IJhGJjD\ngHWFdXeKxkPWdI2rA5FQY09mXGMw3nJ9ec3uZstut8PogrcZ7x0lK+ZRMspt79jfbXjzE4bu8Rvo\nuwXv/9XfEM2MQYYyRVX5TDKkPImLTReMqqJvJQM+U8BhsEqBCoJGVGKWMCmiVBRrsU4UHcnKCjZP\nF7GwKtBZFnqdIM+RZORkpuosoOTD0KmQgmQCkWXwKTi2cqziZHZQsFpegzGWFBKN90zzWJGQMuxx\nrcG3nq5vsQYxY1hwthzbVd45lNXi7KvidK1r642KStRCGZujYOw+coYk5CiRv0pjjJWN4OgwFGtp\nIvLdv3nGu3/94Q+dwRwe/6JF85d+6Zf46le/ym/+5m/y1a9+lX/7b//t8eO//Mu/zG/8xm/w5MkT\nvv3tb/MzP/MzP/A5Vo9XVfIjFSdksV8ddySEr1elA3Jw1cRa7hcRdtUjuyx6xoBSEimQ84E8pKpt\nsFbFQFQfHRgdUG4zrmkkOVfN8sYoA0p6J9Za5jmjdcQ6L5ZEK0d+ozI5S3pmSQWxIZQ6SZTpPFQ/\nrlYi7jXifGjblrlMYu8bdhIfkITunUuufmbpR+aUyVNCZU2pvvNcokicauUcVUI5och7a4gFQpBc\nl5w1WhfiPjGbgioOFQNlNhhvcDrR+syyPeXUtnzm4ac471bEcU9OmWke8VaxWq8YpxHvHFMlehsj\nnFBrLM43pPgq42XYb3nw6BNobbi9veVs1RGjZg6RaR65f++Cvuu4vLzhO999n2gG3rh4yJwT2jSg\nDs9ZKKowTBPOedanp3zn77/FovEMw0DXLTg7O2fYT7z+xuvc3m7Q1lHIzPOM1g5t13jvmXNitTzD\nG8vlZsv5w8dcf/A9bm7uWCwXjONIyTNKJRq/JIaZGGa58ULAupZs9pLd03eUEthubpiGPdfXLwDY\n3N2hSsE1vQw2w0xSDq8aSky0fYvq1ijjoFhSgXGaiDGRYiAVIfhIQF9B61ytgoLnO3jWXRECu0Na\nBU6rWv8dHG6vrkmrjBynDQKoyAe8oa3uPNBRKkGlDEY1zLGQ54N9OAtLU2oZ6acai/fNkfcgdU71\nhVPo2p4UM646lUB6srp19AtH0xisAWUkQlergFJgncMYhVUahRGJYs6kOZLTLImWSlxHWsnwNs6B\nYowMyZATZizglUdXRUkusR7PxVmlMbz+2TNe/+yZIPKA/+t//Ma/fNH80pe+xNe//nUuLy958803\n+e3f/m1+67d+iy9+8Yv83u/9Hm+99RZ/+Id/CMDnP/95vvjFL/L5z38eay2/+7u/+08ez4+aqrrg\nCZC4UrLR9XhZ5UPVN3rYXcm8gnqkmuOti6TU1STHksrxWB5zPvZCAUzJpFLnMiVJRdA29F2H8VF6\nL0XIJ1YbYgAz136rivi24BuFtkUC7XN9jdnKwqZleFUq+T2XGetkd1RGKuoYM41r8N7RdR3DduDl\ndMU0BvKcpbF+yIuKEFKQDPQ5k6JCKMmHY7ipAyC56o3NtI2jWyhiKYxzJk6GaS/oOa0tYaMoKRLH\nTJgKrs94VyBqmjLJZFt15HlgHG5J0xZrPVq3oqdcLLi7vhVkWJQWgvdtrU6ksg4xSnWQC3fbLYvl\nik++9QmmcWAeBm5vXnL18il/8xd7Ht2/z+rsdX7ycz/J//F//weefO97/MSn3yYXR9c2BDTDFGhM\npm1blsueYRj51Cc+zbe+9Q26rkFpw6Jfc3v9HsZYuq6TSW5p0CkyDAFtehIR5zy+6VEYXn/rPsN2\nR86FRWvZ3lwS4oz1lkW3JMRE36+Y9B6tYRp25JKwbc+6OSWVSNt2xDBDEpr/9vaGEiaWp/fI2rHd\n3rFcNCRr0M5ycn7CYrkmF0mYNMpxd33Hbh4Yx5FhGgU0nKJkJ6ksiNxKJwopShtKS3WIl7gMp01t\nNRVUyWJ4MNXiSyKpLHpiFKUkZG4p75EwvVW1Hh6SDV5N0UtSpMoAUFEwN8pomsZhjaGUuinHVPkL\nGu8NzmSckX+fi8J4g3GGoDLFCM/TaIO38u/RPVZXPkSWe/GwDSgtAYJWVYedE3ScqcPWOeXaz5Sh\ncs4JlQrTQX+qFBCgCHoxxIBxogeVGBppt/2wxz+7aP7+7//+D/z4n/7pn/7Aj3/5y1/my1/+8j/3\ntK8m4NUNJJVgkXlLERqRcDJTnWAXstIiydE1bkIBTkg9Tafw3pCTkaEHWY45RSo0rZVcZIXK0xSC\ndcoK1zgan3FNxncKimc/BrKKQELbhG0NeiygDcoUrAdlCphMyV7CqGIkZ7l4UxFWoSKhTcaYXPuV\nGaOl6mwbg/eeohS+MSSVuHz6kjkEAoKK0y6jYqmVhGY8QDqqXEtVRpNUmiLlaHvD6tSgfcZrw1It\n2N4kGq2YhpEwZfJYyFp+8XGXKXNBLRznbcujfoFDs58msNJ3RmucteL7nQMvty/pu4btMBJDBZUo\n6R0ZbZhjlSkpzzSMoLdYlfi7Zx9wfXXN9dUVz5+/YDdIFev1t1mfLvmpf/Vf89/963/Dv/v3/ytP\nnl/x+MHbqG7BTEJPhbaXSt16h1WGzdUl/aKl5IjSjpgi5xcXtL6RIUYWC2lKgawbUt4wT3usbWkX\nltPzB6QUMe3E1jtuNldYW+i6nqZfoopBm1xRfAVdijhknFjvYpDB11hGwhS5fnGFsZrVyZI5QL84\n5W7Y0PYNy/Ua27W0y5azi1M2u0su5k9Cu+bDZ8+5vb5miJFxHIhzYB4nWShVPYtpAzqRmBA8oEIX\ny36YaJzCIu6hkhPikbAEXcSh5DWq9RyQlyrVe00rmXLPAUxDVkoWBV1zgIqjxICYLsU0obMs2IqC\njBI0WjtSFoShdUokczpjncbbhpIj1ovTx7aerDIlTqLFTEWye3BY29SvzxIFkyX+IicFSuzBRTli\nmtHKUOZCiZF5NkyzZbubGMIEWgkNPgoj8pCDbg6U/hRBlZqoIL1Nd6DXp/BD164fKbCjFJATq6qN\nYXH/qIM48iPi9EMCpVGCqJIcHzmqd31D24qPtWTBuY17GbikRKWdxOoBp5LeqagqhfbQLh2ujWhT\n8N4ypkn6NrqQdMImi7UyDFJolI4YrchESpnluZMcC3IpGCQVUlPQJgoCDukjde0Kpx1N4+m6Duct\ny9jSOokOuLqNbG4mlAuYVgLGhF6taZaWMCa5iSszS+kqyDUR7TX9PY9fB5yDtumJsyLuExaNMZ69\nlmCvRjmWbY9y4lo67dc8XN3jnj9l3ZyQ1MQ4RlSINLaVI3ISLWSeZ7Z3W5rGUUqmaXrZrV1DKYqm\nkdbFOAa0dQy7kWnO7KbEbtS8++Gev/+HW242G5JS+Kbh9QeRy9s/5zNvveCX/vv/gf/5j/8nFosJ\nvRtYeMPJYsFmuCMn8MUxM4ht1cL+LvLo0QV977FuSbdeMyvNarmsCAnNy+vn5JK52+559PiEZtET\nEAvmzfVLtrdXLFYdXq0oWjPWSXDTL9hvZeEjwzQWYgr4GJiTbP7TfgtoFqsWf7Hg2Ycfcu/8TbZb\nQdidP3zEEGZWi56mb5hzJk2JtL/FLs9p+zXDuGUcAvshUKZACAGlCr4xKGelX6ml5RGTBOOFLEfd\nORWsKSiJepTrIWvJO9eqWl9FjWKdwIbLMSZIevmpJKEcYY60olgzkkKYyLmtw9ODdTKitUPZQirz\nQbRHqRFfh7Ay14ipxBhF03is0+zDhEkVcJPF2lmqy+tQpYaQmWcxoqQgVLE4R9om0fUwjwOBgRIT\nm6DZD4qbzcR2lKm9DKk1TjdgJbxwt79lLodKNGKUEkvtosd7h/WWQ2DcP/X4EaLhxOJU6gtQSoYg\nRx9UFWaWckj9Oeg2D+HvgCo0XmOdZrHw1TJoCbGQs0Skmiy8yBSrDZNSM3KAotCpoHShWQqCX9si\nu/ZeMY0HEbkSYbCDFKQDqo1ESxircFaO7UXJMUOha09FYbwFLYi2WApeC2jDGk2/6GjblsY5jDGs\nlyswCd0WpjxQVEA1IkBnjLgg0o12YSp9SZMr5i3lgDbQriz9RWa9drKDZssImObwW1XYAF2/oG8b\n2qbHWEdUkd0w8e6T99mtbnltfcp+85I3Lh7RGw/aMM0zaZoYdgMlzOQc0GaBd43QhXJExZ0MF6xw\nCr1zTMEw0/L0ww03+4Fv/8O79CcnPP7cZ7kIgeeXz7nd3vHdZ5F333/C1d3ELibefucdJh1ZLk7J\naSag2Q8z99Yrht2OptFYrQXs3HXMY8CcONq2JQOnJ+eEPGHs/8fcu/xalt33fZ/13o9z7rOqu7r6\nzSbFpkhaEvVwYDgJLAhOAliInVGcQQaZZJSBPbATJAYyyT8RBHk7GWaQIDCSSHAUKxLsmBIpUXw0\nm81mV3W97+vcc/be65nBb1eLA4sZBc0DFLpRqH7ce89Ze631+34/n446RXy/5fLFQ6wLhG7A+kBM\nC8SCwfDqvftkJUoNcsXTOOwP5BhxWqC6ymmJPOVMLjfg5DAcQicDSdMxT3tev3+PB59c0ppiczRS\nNRyfHXN6fkyuQvLqug7jHMvNA3708fdY9o04F7FeVsixYL2mZWimop2Q4i2GyILSYJQBU1lqojMO\nlSt1VaqY1jAWYlshwkbaSM0UrLXSaRe1IyCDRWHBriWQJJAVsT2CUQLDkNhbxTmDcxalxYrpjbjU\nBXotvp+YIsGDs4JIpCVaVXTGkVWVmrRePzfr1UBeCkY5TLWYWiglU1IiJfkstlUJ01lJ2sScoDkU\nVhbYtVmkjZH1oxayyRIRsxtUymIgiEmmGX1mmaUGq435jIvyF70+Z+95+wz1Jt/sP0fSr2A1WAO3\nL3+3rTEGVYUFaWzFdwkb5GjYlILcWIpoIIwHlREqi1pJSUbqjTlXtBZPugtip2tmRgeD6RRlFiCG\nImH9+u9vhqZWYruTqaU1gWEDeYESMzQn1VBTwRaMl6M0LUl7Ind4FwjB0XeeEHqckxxZVpFsMvt6\nzX56IZrcJhi6MkFXGjaA7WXHnRbQLpBKoSroNjCeKIaNXXFfmRw1rpNFrJqMpxG6wPHJOcZoAhrr\nHXOb8V3h7Oics/GEwVgOseF0QetITYllEk4lREqOtEPDHVmuby6I6YZBC6GpkbHe4t0R2zvvEY7P\neXH7Kd/5o9/nS1/9Oq7r6LrAl97/Bh/88EN+7/f+T6q/pMUT/tn3f0CMld/8rbukktDWc/f8LhfX\nj1HKkMtEHzakODN0HfvrG4KznN09Wz8omhgnNuOI2XakRUAkS70lFs27b71F6I642k2c3DkjNU0f\nerQylPl2zQg3pts9mgNohesH5mlh3AZOjqXiSU1y3FuD2j5Yxr6nHvU8fnZN1QtDOKK0iA+GO+db\nqJFgFJvgKB7wgYcffI9Hz58zTTccrpMsGlrR9T2bTc849CxlJppJdoDak+uNkOQlWAbVUFpAN71i\n2xTegY+VXAyKitMQ1ysipatMrLXsxqgNlWSw+nJ+3JIwF1KR4zamAQml1kSH0aVrHpcAACAASURB\nVDQysEK1Jf8n12vNkHNB6cjiKnZYJ/LKUldNtWT0oLRK1U1qlsViq5XcZpVKcq4Fo8UXXzLoVtF2\nwVuPxYKpGJWwFJwq5GWPVgMUS6kJ1Qq4Jn6kYggaxjuvEHPh8nrHHPekXHFertLszyu5vRSZZsqd\nnFrzjGv+8qf65K29zFiugVOlXraqcM4SuoIPDe0LXS8Edpc1cTbSDW8F443UCpu8QawxEjlSBpct\n2qR198jKnWwMfaAsipYk+F5awwaZbDvv8J2RWFJrGF0Yj0V0P++gpor1iuDXi3AnU9+cI2mKtGqF\nPpMS7ugE5y2hF6PkZjzmPBcu98+xfqbmRM1CoWlORFXdoOm2kJthmjW5REZnJKtoCmFIaCeu65wz\nph8IW1gOGaMrfefx1tBvDMEFOhdECZIKy7JnKntS66H5FYispBJXJINKa8Rc0cpRauPFxTNKXUgp\n0XIkp0I/BgZruXvnNV778td5elV4dPVdXvvSe5QK3/uT7/MLv/g1fvO3/hX+n29+k0+eP2feXfDF\nd9/gS1/9Ot/842/y5a98iXffe4/gPcZaxn5kOeyYpz2b7QmHQyIue8btiJHhMNvxiMOyMHYdZjOy\nVIfpNOWQOTo/5o3XX2XaXbHbz6S4MN08xPiG1h7vLJ0ZBUZmJV429gPTYcfN7oZuMzDFBa0R1umc\nMK1huw7f9YzjBmM83gXcsGXcnnB9cYVVA3fvnNIocmVUwXjH0d17tJz40z/9No8+/ZQ6FTau43ba\nE7xn4wc6O7DpRo7tKfs6cR2fktSy8hKURI0I2CLXlcZUnMpY5VEYKWGohrEO48R/o53BrNhV+TA2\nuQ992chTRsRkGmhaej9KUG2yG9QYJ5pqsSQUOm9RiIJXK0k+a2TXmLxCKScnQZWoTYFyEhlMDWoW\nGEhNtCKAYqMqfRjwrseYnkPckXMirnCUkjLRRqq2FCZy1fL3bYZWmKY9Rsl132YY6EMnAyOrcXZA\nawhdx9vH97mYdlzuXnCz7NGe1UH1F78+t0XTB0uKbUVArcfml0H5NWb1Mhz9EtaxBieAFcNvFNo1\njAcbGtrJMKm1l80HjTfSxilajJXyTzeslrYBTVD31qywXmtQBkKviRMsrdCK7DiNK7heoY3wMp11\nOCPH+1RkCOObJk0N4xXOKZxhrbjJlDtHxcX+lqCPmDcLpUas6yROYaDre7p54OhoQ1ZX5JhQTiqY\nrgvUMdN1DhsMyji6ZJnnjHFS5TQ6Y8MeY0UP0GxbFRsQ9QE68MbjHdig2Pa9ELYNdN7QdVuUlye/\nVnL9L1GOSM6JZZqwSuGsW5tLkcN+x9D1UPt1Ejzhui1Hd+9zcv9dZj3w8NGPuLy65v57r/P8yVN+\n8vDH/LNv/Qm/+3/9IRcXz4nLRIqJHz98yF/+pd/g6M5jHj+/4c37C1pprHM4a7hOM1pD7yzZW5zZ\n4BxY5dgenzIvB45OR7qjc4w/wVbZNfphw4nd8L1vf4snj55x8eIBaVmY9gec9Yxjz9mrp7z9hV9g\nu93Shy37NGP7wKA1lcQ8J+xKj3LayIOpCSHcOIu1gVIKSxLL5J27W8bRUXOlqYRRFqu05Hi1wjjH\nzcVTPnn0Y9699xp3230+fPCIfdszhi3eeoJRBKNxztIY2ecOtMLrmUVsgVgqqgnqzVu56pG2TRVz\n6Vqtc1atPhwBXb9s4em25pVfTsl5GRtS6+S9odVab24SxdOr2kVc5w1ri4TKtSGmJHnjWslVeAcp\nN7QpKGPXBl6FaohTZlkSk4ocbccVGCKxQVVlUKysIlRH6yx6Fh9nqolQJSPaVERVRa0RrfLKh5AB\n0tiPOBRBWbquxxmHVQFnnQC1vWc7HnP/9BUe3zzmYvecpuLPXLs+t0Xz+GTgdrcQUxZPzcsLYP4c\n4stP4d4AUNKAQAvFxHsJmSstbQZlGs4aSqmr7mE9NllLzXKx/vKeFORSnGbQZgAq3stCDOA9uN6x\nRHnymlYwfjUIBolGeBPwfkEZTSoG1wA9Y7zF6x5tLM4K0FVlJT/sotk927HtDoybA7eHK4btQMuS\nc6ytYD30fSAsHTATtIcoH8yqG8PoQVXG/oT9fpYpu+mpLZFLxnmLUwtNW2ZdiWbGhICNkmY2TsCv\n1ii8tZIosApvBJhrqyKTOJQdTlUWBsosQXtnDLFkAoZaktT7FEzTgjEdyjnunG157d67vPruF1Fu\nwyFNPH36BNf1nB+f8Gff/CapVWqFBx//CDRMhwP/xt/4bf77/+a/4O//nb+P/tqv8emTD5jmSQZh\nRhofxjd06Zj3B4bBc31xJQCCznF1mDg/GdFO0Z28giagUmF/mLlzdpd/8r/9z3z3ex8w2Fe5nF7h\n2fMLUq4M1nKeNM+fP+LTjx/wi7/yPl94+8sMR1tKzizTAaUNNU/CvQS095ycbjikPc5YOmuI87S+\nNzPOBQwNezRQqyLGiDWWuCSq0WxPT9A1UQ/XfOHNt3iy3/HRRz8GBdtuQ0sVf9TjjAwyfRBlrrcG\npwNRdygTsdqgkLxuLprm9FposCglXAXVBD7dWhH+6hrRAbWqRwQFh6prAw10s2gEhahsxdoKpf4U\n4Ud2nKnW9YQmdtVUM1o1Ui3EJlnjlCsxa5wzlMZaCknYplC50BJMJaN1wnuPMnq9nKsoVait4K1e\nDaWN62VHrQs0TWsGihFmZs2oJnGiVioVjcngbQC1kIvm6PiIwQ60FuQUZhUai3eee+4eY7BcTU9+\n5tr1uS2awxhw3nB7e2Besjy5SgEr942q/XQUSa1h1NXDoxrOOYxLEqh2Gucb1jWMyWhb6HsheiuE\n7qK9J1QJJmtt0cqwLGnNZymMqWgjfnOaHKe74IheqC66GZQyhKBxTmMs1LZQjYAEjG+4FUCsek/n\nA7WsrMyWASWiq1gpB3j2+BofDJvjgO93bMfGNJUVUxXX+1rDoAZc8TQnaDnnLMFo+o28uZRqzKkJ\n7q4KZ9BqhcLKw6hmQYaYgg8V5QzKVpyWIz9mvUe2BauVuMbNiCuyyz6khKoTtUZYEt4Y+q4jRdGu\nxtW7XltjGMQPNIyn2G6gKcPp6Qm7h8+RDnLj+cUNMSmuLm9Ad1gteVrnO37040+otfLbf/O3+S//\nq3/IvDT6oxP6IIMZqx01JU7PXkF3hpxuGTZbWs5sjo4YTu5Q5z0ta1rTlNDTmOi6jo++/wGPH+15\n8Awe3XzE6at3GO69SscJ0xL54GKHSZ63tOKjP31AjI53v/AOqlZiqmjX04+NmtOqa5CHYHCOUiqH\naY8PPb0fUUrG0k0BtcoO0BliWghDx3B8ynByxhIXfvDwh3zrwz/l+z/5EbXA6XjKrB03h2uMKhjr\ncc5InK5W+uRYioAtVDWopjE6YDGonDBNTlROGwmSe0VulUknNJGsKraBWjcO3ggVXcwGMjh+WVZQ\nVUyT3stASSNRvxgzVgVKE06s0w2vqzBZdaUoyKWJ3tk6yVhmwcm95HXINimhrDT6jLaUbFiiIRgH\n2kjqELk6qLqimlwnbetWhIntIImRKjXOYA2da4x9YZrAKQFuKz1Lxls1DtMz+uO36J0nzU4aVl6W\nwT4m1HAE7ed0pzkMjloNobdcXx2YJiGfNNYIUv3puFFba1NiO3ReM246fC8cPa0kS6hVA93oe7+6\nYNS6YKg1qwc+dNJ9rQ3tNM6vE3IjrR9RCwhAQmmBvCrVoAkbsh86hlGhzUKtiVI02hmCl6GUKwaa\nwakGbV2EWeuUOdJW588yRfa3M5eXN4S+R9xRilwjSxF3SrBeYhtao5RH6Q5tDM42uiDTz1wNpiTJ\npc0FRZEPRJOprjOO1OSoHYJFd4amFQ6L0o1qRNeqkF2MqpB0o3eWPjt80Zgsu4hq5ft2OEw4q+XX\nZkPKGW89tcF2M2CsYhwD/bDBe8/gLGfnW27+7Pu8+dY79F1PMJacG0XpzwLhD370ff7hf/s/UpeF\n/e6W3X5iszlhHHqJjljD8WaLMdIKGq3nZnfFyXaD7TrKelS2fkS7Hpwn7W9Jh8R3v/MB3/zuAw6+\n45VffIXT8Q0ePnzEhx//kDvHI+//4teY9ws/+PBDju/cY7drPH1yyenJiHOOuExy4nAKq+Glg0bK\nZdLgUbVQc8UHUdDqpigUfDCklCQE3nm2x6coHXjw4Xf5J//093mRbtlYj9EBZTs6Z2m6kNNM7TW1\nRGo1KJ3pQ0edQStHkCIdpq4+cmXQzQg7QSm8C/SdwwWDs6BD5Ho+UNNCSUmKB3ZVVFQls0tlaUVh\nrBF7pWmEFSv30lOkqqKkLKcjYzG6rgYDOS1ar+mzo2QrNWaiXMGpTKNQysusk7h9jLXY1qNVYL9L\nGDLWBjkJqoquoFzFAjkVgnb04QTtDU3vaU0iYFopnFV0vWYzGgyWcXC4oPBBiGW57Hmx+4Q7x2/R\nhwFT22rWNPTeAp7iNz9z7frcFs0QAs7JHZA1jt3uwP5WlJ4vgRq8LEz+1G7TOE0/eMaNoRuMuG90\nlvsMI6FV6xq+M4RexPQheIyR6FLJFec8VnvsnFhUpLTMuDnB+0xT6bOKmLEK6y25zdSoscFiOnBB\ng7LoamRjnApKa7rBQ9MrLagILNYoaq7ElKipsCyQdWF0jjYbbq8X9ts9ZOh6y7RMxJrIRIzSWNdj\nSqWgsKbjM5J17VE+YXUBZkqWuIi1A9YYalpoTeGV9G2TW9bduZfKXLQYxI1u7LojFiqKzE+zplMO\nry3WK/SsiCtCrFE47G8pSViK25MTmmr0vYUSUXrDxdPnvPb2O8R5z52zM774ZuEfpT9kOSTefvct\nnt0850++/REgWUCjNF/+4mt8+4/+Kbe7iR9+8AO2RkMRpe322HP1/AZlBpZS6b1DqUzfeWJtnNhA\nt9mQ9w1/ciJUpDljbceHP/4JnzzZ83yuvPHOa/zb/87f5s75u/zdv/ef8uD5Nd/7zg958viCv/pX\nfo2v/Pqv8sff+hP+tfvf4Oj4dUKXSdMlU1zojUFjMEoTYxSCjvNQM1o5nFuHe84SvAw68pLRSpw0\ng+9x/YDfbEna8L2PvkOsezpVOD8/58X1Itlb69mwYZ+ek8oVcx6wBRoJrxVZ289UFMY0MhHVEk5t\n2dhO+tvOSZSu8/ihw3lFNzju5sJuOnB9c8M0H1jqAd0ke9yZDucttQh/0nmFdRmnEzDjjBEWrPLE\nJpVKhUK3QqsZYy1Gd+jScH2PqWsN2hoMk7zPXKOUSM2Wisf5AVqAOhDchsZCLpXbaUEph/ca1Uv+\n1JoV6OPXarQB6zqimmipUqjYDlzW9J0g5cxQ5RRq/jx7OcULPn66cP/8C5wNJ+haSXkBJVdb3f/H\nsvi5LZr96HHWYe1I3/d0/gZjrjgcFva3cpyV1qNM1Guta/WtEbqCcQ0XLMFLmF0ZiSLVlkFlQteT\nJsVcpW4odxfSQddKjpTOy1SxNodzDesqqA6aYNtKzryknqPAGkfXebqukss6BHDI/asKYntUbYUr\nyNepSGtv+eUxVuIYxgcRryWIU8GwQC0scWYpM9XIXalZwR6sldAubISEvfqCFAbdMt6IgtWoRrCB\nhiYhcBOQnacLgeAHjLYktABQKAIidoWiBdXkzIitDpUUMSWhINGIKUFp8vWUyjBs6LQllyo9YR3I\nOTJNM9lm/vD3f4e3vvB1+u3rHA2av/Vb/yr/0//xj/nSL7zDF997B4PnweNP6fuee/fu8fWvfZm4\nND768SdcPH/GN/7KN0QzXKHGSrCOpDRdCDjdqPNMS4mTk3OUlxplVQbjN+QqErDDTeGTTx7zB9/+\ngK/9yi/zw598n//g7/wndP2WDz78gPmwZ9kv/PG3v8PdOye880XFe3/p1/nkOvKV8R7kSyyXONWo\nK2A6z2IB9V7urdMyEXzHkma0giXKdYVzDmsstam10VPo77yKPrtHTjNHJ+ecbk7YPf2Evh+4q0dq\nysyt4sII80TmgPEFpSeMlpZbDYqQDaZVgtOkIgMmr4S63nsrv8a1mugMOnR0uSemzHg0cLQ95mZ3\ny+72ksN+xqAI1tE5h/cb4U96hQuZVtedbo0yU6iRqhsZvaZHDJBBFbQu9DqQGthNL8PCatA64taY\nk1rvPIPVVKNEj1ECPnj6LqCryNqmZaJZx3So9J0G3SCArQplRThnnUJbQ1MLyiqqUvRViXe+WZyr\neC9XC0Z7sSboyrwkLnaP8KYRXAdqkayzbjjf/oVr1svX57doBo93HqOt/KCcpZmInNYa8yFRqxT1\nGy/955m+9/iu4AM43wh9FcJJlcXVGPlzWmV8F8jr4sI6IUc1ako45yXvWcBqTT+IoldhMFqOKJOW\naZy1ilzEL2StxgVoy8vJPqu9z4mveY1zxJZQupFLpNSFnCulKKmDNcU0HTg5O2PTDeikaLYxlURZ\nnT/OOrQR7p/SBqUqS5oZx9O1RVUxqqBbXZtKUmFzyqErNGVRukDVaDwGg9U9gx9AVYJ2kOVOx6zD\nBvneZ3JeiMXR2gAKcs2oZaLEQlwWnDUMw4ANFopmGAYhJDXQ1jDf7lG9ZRy3PPrJj3n9vQ7dMr/0\npdeY6q/zO7/7+7z/9S/T9Rve/fJbbLqO09Nznj6LfP+7P+BHH36fr73/Dr/6K18n51tyilxfLKS4\nwzuN9YY4z6Igrl4yltZhXKCyx2yOKMnQlsxH3/shf/BH3+Z6aSjfSMvEd7/7LYoCVw3T7pr/6B/8\nA/7yb3yD//jv/V3unt/n9LhwnW451MqQG9fPP2W6ekxOC40qtdtuJMaBzck5J9tTrm8uqLWseVvx\nJ2ljsM4Anlwiwzigjo6hH6BERhc48h3v3X+dw1KYOs9+Mgza8Dwe8FnkX239MMtubyQXRauJ4Axj\nCDTA6kbJDeclVmadwXeBzjuM6/BqJCdPypUua4au0XUbhnFk2S+keY+qRQoRVkl+uFMoU6lVMH5W\nJw77HSXLsIcqiw9V0Q2FlAvbsRfcm7XEWeP9Fu97Urui8ARDouodxkqluet65grKVrypbMcNXncU\nVdlNF+ymW1TQtAjKKqwuWCdYN6011q8AHyWcz1YtGs8QPIqwxheVXH3QQTFUlfEqYIrh+uoJ52d3\nZPixXh8Y83O6aG76NdCtjMBya+Ps7JRSILW8VtMEgMrK66ssoJxEebyiD4ouFHI2tKLWO03kiWcq\nNiRMFOWDkG6KtBVoxDThrCcEjzYNZwvBO5yR3JsmsPSJeZopRdOMHBWcl0CwIPEtygvNR9zQYgAs\nVLIppCyRomqjPA0nYWi6AK1Gzk+2nJ6dQoNgDM0UplZoqaGrwuHIaiZXycwZnal1xuhe4AhK0Fah\nOaHeNCTIi7ixFW6twQkyK1hH7+SqokSLsT2siC7B6FWyBdMaNWVu0h4VG33z2LiQp0XUqTkx7TOh\neHyQgZeyipgKeUlSIa2ezThgmfjuP/89su94/c0v8o333+W0s3zvhx9i0gFXMjePdlx88phSKu/e\nGfg3//2/zZ2796hzIS3XPHvygPv3z6EWtF6PxkZTS2McB2JrdLkQdzs2J2eUacH4nsvHL7g+FL71\ng4+49+Z7pCWTpkqnFRGDMpqlwNnZKb/x67+GdT2tNQ7zhI6iCDk6GZkuO/YVbndX+NCxnw6ELrM9\n0jSuSV0ip8SyHDCbYzKRmCaC79kenUjxAiM7nZRQOTLtrki7K0almWohLTuGcIwfey7iHmsUm2HD\nbZJjbQWsHtf7+cIYLE1B54VUbnXF9p5SZ5TJglA0Ct91hG5L1R0QSKWyRMc0z2C0gDP8TIkdqlZp\neWFEEREktldKlWBTjTgPSjvB9qVGikAViI21DaWSzBSsx9uOo82rWN1YUqDZHuV2LDlwe9iRUsS6\nxmazoWaNN06aV53cnw4qkNrMVdqhqsNGjeoq1RacrWuDp6xDngoqY7TDmxWajNz9irDN0srq7LIj\nNWmUFUndFK8Zh1O0scQ0r3nxv/j1uS2azkv9EJCFrhmiMvSDIyxGNBGqME9xJZJL9bC0ggua0AnP\n0jrJTc77RKmyEzS2p7UolkhdaMWTM7TqSEkupYU7WNCu4L2T+xEb8N6jlYdWONqMlBS5vsnUrNc7\nGcRKycrkM+IX0drjjBezo67YmlnSLYKWU7hQiVPEeU1Vmlfuvspmu2Wz3UgWU1W013L5r2X4Y1cS\nea1if8bIrk+3BCVjVlOnLprcDlhnKRHaOgQCjXUeE2cKUXD/1mF1IxuDNR1UK7peDlSiBPo1JNUo\nLdE7j60e7TOuNlIsLPNMP46oYiTuYySvp1sl1cIUE/vDnsvbK95++13Oj04wXeDFowdcXt5w92TD\nX/3Vr5BiY5lneWNrRW+hlJllitxMz+jdBlPAuca0v8Fby3xYON4OTIcDvQEzeEw/cJgPbE7OmA4H\nNqFnuYxc3+y4uV0wdsvN7S3O3+fOK3fwXaDOGVrl5PiEf/S//y6qFbbjBtMbjIIWNcErNnd72vwm\nh90Fp0bx/OICq2V09vzqkm4/8corr6KUIoRBuKJNpsTBD2I+VY3edeKaqmKodNMtm03HJm6YLg+0\nznOxXBH1wpwjyhp6pVB0JCf1Ya07uk6MAlMLUgv0hqYrugmk2DmLd4qSIzkq2mmPHR3OBJoeyAXc\ntKp7lVx5RQ25M1AarcgDSWthJgiLM0kkqVYMCquyBMerPLjKSidzToNKGKcYuzOcOYNqMTqBKiTE\nVOq9JseCUgspX7AdTmjFrWH0mboOX63PuNQYlELVSFLSdRqURtsqR3KdsG6FgWtDUgqlsmxkEIkh\nhJUUbyhZfOxOW1rVkppRjlIP9H0AJbi4n/X63BZNay1o8XDXLOpaXRpdZ+iCZc4T4ybgTGY/JUqu\nn4WtG/mzPndTQv0wVgvWScslMQqUroReYkq1WLHplSKZtYroWNVA6BogObDgB5qydA1s66gloWjk\ncrv+wCvaK3QV1YQAYQPBBLwPQKNUsM4yqI45TqRUsUHTDYZ50vih4/U3XmXcdDgnVTitGlUlDI4Q\neqxX8jU2yZW+pNekdMBqh9LSNzduHZTVdadtFZS6LoiyaNZOo7XBG9lRvsTkybXCEcE3GpXU5Ii3\nnwoB2PqROkWKlu+BtZYUM5vtlu7oiFI0ZmUeqpZJSYYBtVVMU3QedjfXvPH6mfiqtz1LSVw8fc4Y\nArlmjjcdoe9oxmH9wO7qGTomTscNNzfXHPY3TFNhfOcdrm9v2Y6BGjOaJpPU0IOSae50u2N7fEpe\ndjz7dMfuUBmC4rXzI77zk4+pX32P19865+3X3uDPfvADjDZYY/jHv/O/8qMPvsmXv/T+ykrt8LbS\nO8twukFdn/Puu1/kxbNPyc1y2F3QmmHTBZncpnlttOn1IW6wrqMBuSRc8uAaug/i2Zl25NsLnLFY\n29OFnpu0A5XAaLabgaUWmWprRykL3ajpjaLvT8FodLhhng4YD2gLGeIc6Xyj1SJd8fWBbrzH2YDv\nB1KqGH1YYduW/X597yyALXKPnw3WeJSVHKMpUJIE0xMOoyPBGg5UdHEol0Qlo8D5QOMa609lk5My\nzjqqahiV0NoQ281q2fTE2XGYLjg5fo08Z1ItECVxYK1m7B3WOZYYiSWitFkD+3+u2VDKMvQD2Ypu\nJtlF1gttaFlgzlKXl79mPa951baeLOW9NC+3Mu3/C3CWn61d/7+ujD/jJeShArVKuV9brLUYI/Gd\nWgRY0CqMyrHEBdUMVpfVDCnys0bEuoG+98zzywnZOn1fp2GtGuaDApU+oyvVqqkolpToq5aGh9Vg\nwCiH9oZgBmo9xVmDdSO17QleKCulVlQuOLOh5p7gPFYHlrJfw/iWvpcAcs6WohphmznGcbo55+ho\nKzY8rTFBKEwtKbSy+H5DbQmMIscZVIUKMU0S3DcOVQET6WwGnagpoZR72Q+hqroi/leVr1I4o3DW\nUlvGBo1Vspt1K2gipQOqNI5tj6odaoLebzAEbEhUnfG6k2+xNozjSKvSvrDaYpwjl4LzgZpmctEs\nS+Lxo4+5c/9NSoHTYWTjE8PRlvHkFaiF4zt3GY/OuX5xzWF3QUkHOle4uXpIKYUvv/8+F8+eoI3m\nbDvScsUqOT7Pc6Y3iZZmbueJrvM43TPd3FJj4Pz8Lr/9r/81Hv7X/wOf/OQpb75zl9/4l36F2A48\nePCYWitf+8oXefvdN7h//11OTs958vED/sYv/wqnr56gN0foo552Bcenp9wcJqyONDRTWtAYrq6v\nGccNm80RxmmUshgbcM5RFcRW6Y0FF1Bec3j8kHLYM4YNZ5tESbc4/SrbfmYphbkVDrUxk6nOoHVP\n6HsGd4T1HSEE+nHDzf6S/byjUuQIrysxzjSV0M6QSkGpQG4d1sppKlgPJlNVBAW6Wpy1JGeIy56s\nIDPLe7guKDVjdKSqeY0IaawRipX3mezteo+/R2vppVciqT2nFU3n7pFrwXlDbR5lNeDJfYerFas9\nKWWW+QpvNkJKapF5Fv5lsOBdwCpgiUIpq41aHcF7vDOSteQW5w+0ujraW6ZmRdN23fgYAXYrTTBO\nOunKgPIS09OVWhXBO8Eu/ozX57Zozsse5yw5JeYkk6/WCtosuFDJ1aKbQHwVZsVUZZkYa6FOC3rK\nShTBNVLSq2BNtBbSNRU8lbXSHqhVtBTOGErJtJYpRa9g4EwLUtnT1kF0bLfH9P2GTZeJ5YqmL3B+\nljC5bTjbQzkWt7KKBNfT5pnSFlALzje6wZBTo1o4GjtxWgeDcVEqnNaSSmWeD1SV0aoHbWUDUQw5\nHagkSjtIUDoIoqxNGUwht5lSo0Bei+wuYVWAVAEBWwOKjKLInSOCwKMqtHZYs8VnMG5gNANdf0IY\nHFY1HBB3EzlNLHEGVium0lhvMc4yH3bM+1tKkhjQ2HfEqdF3R9jecXl9ydtvvkONlXG7pdSMbxMm\nbHj+6QOunj1h2d+yu3hM33mePXuMVpnt8cDFi6csy8LdO2cYldjvd1itCDA5DgAAIABJREFUSVKE\n58WzJ3ivWeaFw/U1tw+fkPaNkgqb7RFvnAX+s//w3+U//+/+Fy4edrz11Xv89d/8a3zy8BOMabxy\n9zXeuP8Wu/mWxx9/yq+/8zavvL3l7ntvENOO2nYMznN58wJvNH7csp8TNUJTijgnXGgY19H1PbUm\njBH7YdNKHtzGY+xIvd1hlz1VGXql2SpPsyNJOYJzXM97VFogGNCS7w1e0/UndO6YMHhMcxx5y2Zz\nwtNnn3K9e7bGrzr2+8g8LWgTaNVBtXInj2NOGWOk6tj1jlqiYNycpnnDrUoc8oy1CUUBFamp4UxG\nWVjyAWtlAGt9wfdmtRnI/6tICQu0Qk4HtNtRWo9xQfxUdU+rC5WJ0BlKMXgzUqsmR6lXagO5zhgD\nNUds8Cg0g3e05qh5Amsx1WOUxZse50CZDbncktUe40TMlpug9MraFnK+k2poquJEbwrvRNmiKqCN\n6GzG7meuXZ/bohnTRENyeDHvqVl0F8ZIRKBVTQSUEuhtwGJcJ9Uwm0W25gWwga4Yi1BMELxbKcKv\nbKVgrFu9IhKmhZdUTCONBiv5u5yjkL6LvOmtCxhtmKcDfhhRJlAIxPKcqsTD4swGyga0Jq/k6sqB\nJc1CNNcW7wAS2jtG7wlmwYcJpXpimah4puWWm9tLjo+PBebrLNpAyh5SpSk5VueSPzP61VaIS0Fb\nQ4pK9BLrG8Q4g2qS16str+0kyHmRlpSuWGMw1mOtxXvFIWWWekWOL6h6ppYOE4XJaIG8zITQybHW\nu7WTr0lZuJnKDAyuEuc9Lw4HIIPNDGUglUaKH/HK2V0ePPgJJRceeYPxnWRdafTbY4KRxS9XqMby\n6MULKJU7Z2fs9rd4pWQ40aDvB168eI6zQrSquZCnmZsXF1w+u+L8lXvoAo7Ckd3w7/1b/zIfPXzO\ndz++4MDM66+9QWuJki0vntywzJf80uv3eP+9u/zCL71NPFxx8/wB5uIFF7tLpsOtRNfMgFUJ1xQl\nSvsspsjzi6cMw8B2M6JS46AawTt8Bbc9po0D+dFT2rJnOUxM0wGdIyddx20x7KaCcQXtoOkMpTAE\nT7YNZQSC4s2Gznfkkuk2W5zqiNPCNF/LvbjW1GaISyYtkcM8448hN6l/UiKtIVc7KpHVhFGe3MQT\n1DnPNEUaiaZEC6Faoa4blqqU+MZ7zdHW08pMZkGpRllBzbktUCAXzUSiFXmAh07T8i3GZUInylxl\nDa14kjKUJBuhbtys5soElFWeqBlcRyHijRPvUtbgBVVndQfN0/meXK8peVo3DQVjwtqZBqc0tS7r\n51+WP2tlSFeLlEN0/TnNaWpdxcNCJpcDiYhuDioSKUBBlQZQ5wXIoHWguYnQW6yptDavaCtR7zpr\nKElo1sZ7UrkGEs1KnSsXoVlrLWR4FwasT8LXxK6K30xpkRorw3CXlGTh6e0GZQaU3bCbHXMWIIc3\n5wKqMI1UHCndoE3GFE2pGZrC2gBKpuzGWvlv+j0tWWJaiAV2tztyjSgtu1aJT4H3niUp6ewa4Wc2\nGtqtDqLaKDHJhL0sco9U5B7JWCs6Y9NWe6a8d4wWZmBrmZxn0E52ACoxc8tSr9lPC12+w5Ee2FgP\nRaqDu5uduFmsGPuU0kLsBrR1HOYbye3pxnSY6XoRWXnvSDFyfXPNq/ff5uLyOcZa+n4kx4VaFqZp\nYd7PlFqIy57aDK0FmorEVLnjRpwfscGzpETOMHQdtUZKnEkx8uR2xnUd6Jk0PacuYMKWzXZkON9i\ndeH9t+/zycOnPH52w9WhcXQUMK3yyhtv8PZbb/De19+FJVGvP2L+9IfcPnvMze4KZzsyho0fGLU4\nbOIhk2Jmmm9wzhC8kJ+stoSuF6Tg0TH0IzlFvNZEJ2HzlGZSVVANWhX60DMDUWuKiuhciTagVGJJ\nt+SaODs6EgbAygTt/REUePjoA/aHF3inIMuCU/ItOU8sKWKa0NM9AmRuFQFYl4m4FEoVgLczA3bs\niHHHkjMxXeNMxNpMKQalPdZHhtZDVZSmOExaNhQ5SUi8jRSVoO1FkFYDBkNrBq0DqlkUhuDFclow\n9ENPjpZaBHitnZfvT9mjtJPscZ05LAeRB7aCMnI6Va2X+0oEOqJVh9FJ2lrKgupQKtBqWIEhhVoj\nOcvGwPuOlj2tgGoR6s9pjdIYQ4xNIjlNpms0yShaYylruNYag1UGmmgLjAsYK64YZwMtTWhrsM1h\n6ClaiRLaQjOe3MS0aB2kHCEJpspYjXWGfpRBhjaGSmFJB3ptSWni+vYnbPu36foBimIcenAa48+5\nvp2lNaS9YK4UKGeobQdzQrsiCdOyUJvBWJFgaVfASrUs1SuZlKpGinvAgJqxrhCzaEyd2WKtIbVI\n6KRKp8xCa2LYqxUKhiVK5lTZgpzFtag0qoK4oLVYFGO6JeaE1zMJLcO1FigtAguaIkcU1zHYjhob\n+3TAZkF4lZro1EDLhXlZqLWSSuRmd71mPRVD13G8GTi9c87N1SUX1zvSsuf46JScMod55gvvfYmH\nDx7S9QOvvfE6F8+eMh92jGPP1e4GbTXLbqbrNjjb04eRrj+nWSdA6QpVa3I1tNS4vdkxzwvOGXot\nQNlGoWsVPzpivOH8zffp7r3Ch3/wf/PmGz3vvH2OMj26t2yPTzk+7qmxsH/xEy4fP+D5xx/R5oiy\nim7jsb6nU4Z5lnB/qzCOPQd1YIry4LDWMo69UNBpnBydoc5fgeNT1IsnlFzXHZ0jayO6Z20INnDU\nJIJH0nhvOajKbCpHpmeXPLc3V5wd3af3G4nJBYPzPefxNTbdEdfXT/n44T9HuwPGKmq9JOdLynJC\nPHhaH4C0CtosVXXM2UFVWBVANXKFli2dOcYbj2k9MT2RoUqTzLJKgeIK/WCJ2RJCR8x7piVR5obu\nBEJMS9Qyr0kTvy66cqIzSWGNk567ttRk8bYHq0lpwdgkLFnVr0fpjPEBuItyO4LvX24eSWXCak2t\nmSVONGZKlbt/o3vJLFcDyqKUwfYj8yIAk1QWYmr04QyyktPvT2FJ/kWvz23RrFVQ96kUytr91LD6\naCq6VZzXaOWwTdzlxoAz0ot1Roldztj13sauHDwLWix8unqsTqSa0EYyYbWKMdGg6JyROp6uq3St\nUsokd6M6MMdLunCK1SdY72kCCsQYQxf6z3KYRksFsdZFyDMeWoaildgoyYD4VKqKNO0pTWAM2lRy\n3mO7Ri1Q24RSSTwprWCto+sGCjuSSrgmKlKFIdcijd9myFmm56U1nFoJ1qXgXUCrgRQTpR7k+AfU\nektplkqgpkLThUZEo3AtYJrDNkcXPDpWdC0UK62l2+tLUYW0yjJn5uWAptCyoTs6ocVIOizslkjJ\n9bOd79X1FVrf0lSltIWvfuWXePH0Mc8fTZyennB21HF5c8upOyU9ayydZZonjk7vcPfVN+m6EyE9\nxYQvWSbyXjHlmYahtURqBpcFUG2NZnd7wRv37tKsZrl8gj67yxd/+S9xuLzi6vKSZX7GWE+YLw/c\nPJ2J88J8uEEtM94o2mA45Mh8dWAzCHyjOsGMeec53EykHOmHHtXg5uqSVhPD0YZNv8EcnaKOz2go\n9IpMW3JiOczUIj+LluVr6Y2hGUtVPddKCEO6ZpK1wr9Mt1wfPsX61wl6xChH5yz2aOSw71HV0+5n\nrvZ/xpye0uxMXq5ZDs9xfSBnoYLV6oQ1mxUwUtIt1AVTe+mFV9FFGGMZ/AmhGFK5IOUdjUxDtMJK\nK/ou0ErF6A6rDSVngZqstCSjG0pVai4sU0S1tVefK615tArCRFTSW6daWhMyu3cG321Qpch9fkkE\n7cjVoKoUOlByoqux0SjUFslFct3OeKwNsoM0DZqE31EapZw8BBzMywGaJfhjVPOU+HOqu1BK3tgq\nG7SWjJbVegUFQ6wCcVCARQvJ2YppUhuDMgVp8ldas9SqpTutxZHeapbpWDNYW6muEhdhIWoT8Xoj\nYjM0xitqFQ+RapWUrlFuizKB6/1Djke5K22x4buXwidFaYWSK1EpDJXKxFInrO8BMd9pZYl5QmHJ\ndaaWiZID2lnG0Et3NlnmQ+JwuCbVayrnaNzafy90HJPrgaYupfOOXrFdkuAVG6UciZuWqJUCSiwU\n5I1idKCxrLU+QW61lqAmStPkKJpglS0dhqNui8uOkjLKqhXkkIVNSqRMkTlm5qmQcyKVxGZ7wsWz\n5xjdOPy/zL1brG7pVab3jO8wD/9pnfehdh1N2RhDuZ1uh0MIJCg0EUlDK4mEZBQhYaEkROLG3FkC\ngRLBFTcQISEBUhMpBDURQQpK0lHSdtJpCQeBSbnLUGW7ylV7V9U+rb0O///POb9jLsasouk2Jgpp\nwX9Vqlq1dtVac47vG2O87/N6FEmGChmmcSLGxOZgzfrgiKOjM3IqvPDCs+zGHavNIeN+INQ9MSVy\ndVRnOL15BLXh+nLEhIFbTx0TnaNkwTlHJmDWJ7hcMXGvpoNqGKdrbPGsVyfUccIddEzbC8y45fLd\nu9im4Wx1wGVxXDy5R9e07C6vKbVyfLghL1vefecBl+f3WbcGfKdAZtcTayElyFMk5kSqM6jD6R1l\nHAb65ZLF+pDatdRwjc2VGgdKSnjXEGVPDpGcktKlqMQQMLnQW0ssFpGWnIWpFLzvWYpl2l1zWb9M\nv3ma3i1orMMsFhgimZHt2HNkn2efWoZ4nxICDDvSfqs6xqDvW04VyQZrOpAd15fnODZYezyjGS3W\nOhpr1RSSRyKJmCeEiOBAHH3vyTFgRaliOSudLMakl5hqscZjrUKRY1R9dUwT02Rp/UL5VzWSwg6K\nx+BJAQyVVa+yqZoMtRQlgBkDZSDGgWI9Xgy5VlLVDbjUhUJzzCGWDtPuNEkzKXlKELzvNBa7BITM\nMD1SWZ1Z657k63z+GjOC5kB5r7nWmsEjs/hc3t9mOycqZzFKpX5PD2fm63jKf5ZoGYPyojMN1im5\nyNCSMlAz1hS6pgeqwoaNqP3OVEQmqmQwmVInYk4IC6RmLq/e4fDgBuDIRXT+VxI5JVLKpLxTgrYU\nckq6RHJqgWxblcCUAsUtqDVibY+zepO0pqPvT3F+S2RLLDtiuqDvnJ7oWelKIgbnvMaZlkipmSI6\nhzIUWtNoBAcWqnIUS6nkpA+JGEFEN/zvKSpSiZgSKAmN8ciVGDO5JCTvWNPgm4a0D6SQsKUQxokw\nBmpK7Pd7chZNZDSW3TjirKPdrBXskRMAXbfA9ZGr6y3FdmxObhFKg+1WnO8musUhX757ztnRMXee\nv8F2u6e0D7n7ypc5f3BFzg/ZtI6lF+69fsTJ2W0WywPEeprFEu97olmS3AlLH/C+4DZrSgTEsNs/\n4cbpDTha4oZAazsePHqXEgL7aUByxswz477teP0rX4YaadqOg8MjnDEKl0HIJCgRb1pa17NceIZB\n/eW1JqRUGu9ZLdeYtgNjMPstNU6YUqjWEseJrmuZRk8qkRgnjDgWtqERy7YEVhha47G1YlIilIqn\nY5smhu05sY4smobGPo3FIRSkOPpuwRguWDVHyJSZ0iU5XRPHLUVavFgsaY6/zqSUNVnSV/bjE0wa\n6Zo1OYGd419qjXMn0yLonF55CwqVWy5WDDISgyoWUlE6OrViTYt3DWBneDGAI8WRfd1SS4NrWkpu\ndOEU1a5sciGlwJY96/VqhtG8N1vPxKBxwKVkcoFMBJOpMlFKxbuOXFXor/G/aoKJaU9JmZh02VWq\ndqG1JKb0LtFczDXiL/78pUXzk5/8JL/3e7/HjRs3ePnllwH4mZ/5GX71V3+Vs7MzAH7u536O7//+\n7wfg53/+5/n1X/91rLX84i/+It/3fd/3Nb+viC4nKt3s305gC4JSjpwR8pzFY5v3tt/yXnKQMi9R\niY1YzctxzlKyzEVmxsph3kd62QlKcTNuTdmUzluMVep0ISNNpERDzZYiW2qqlNKw3cJqfYgkUYAB\nmVK19ZzSSI5q/apkataxQtuojtE53a6mFKnG4Eyjsakl4myHcUZD19qWFCdiCdi0V2xXcmAyRty8\n7WcmQFlynAnbiJ7mTuVWxugIoyQV8DurDqsc0dmxoEWcQs2JWjzTOGhJKBliofUecR1UQ9s21DxQ\ncqBpO+IwEXJksdxoiJWx5GJ0tmxVuTDlQtf2iAhXY8BSOTy9ycFmzWpzwLMf+AZOT4/BONq25+Do\nNk8eP6bajnbTsYqJk5MbvPbwVcouIl1ldbpg2j3h9Qdv0fmWbrlieXQD3y9JtmNxeIy3idZOVGNY\nr9dIqSwWjmF/xfL4iJAM3fqYdYpc73f4pufsxjHbJxeEkNltH9F2HbZ2NL7BOa83yRw1frbRW9A4\nbnWGPRSsc7TekSZVKKyWG3yrQOZsG8yUYHdNiokcJ1IMTMM1UGkbjbMVa8gxYhH6WlVbWwpL4/E4\nkghXFIZiCCYTtw94bJbUpaN1C3JRrKAmpToEza7KeUFMetMEVYDEOqn0BiFlCOMEJErdM8VrQrmi\n9z01Cg36zNSayblSSppvmZqoYK3yI9qq7X+pou191SDDvl9iMPqezrNCTYwVfXem/ZyfBbVUcnZI\njhoYR2W/nelIbn7sJapZZU6xtGJIUUdVxhWwGiUtoo4nIwHnNEUzJV30pvnSAQXrtBuyAqWOlDoS\nyvBXK5o/+qM/yk/8xE/wIz/yI/9cwRM+9alP8alPferPfe0rr7zCb/3Wb/HKK69w7949vvd7v5dX\nX311nhf++U+tVbl/ttHYUCNQC9Zape0wkqvVxDqCbnhJNKI3rxjTzOkDmQuKVNHi957uigrGYygq\nircg1WGKx2LmRL74fia6sqwFcZaCJeWJWjOpDkyx0BeDoyekRAwjSCKTKZIJYaTxnoJGY3jjqGKU\nvJPVt15KoWSjVvr5T7M2Yp0QksH6AC4wpiv9b8yNZsGkNLunWphzZkSMivQFUi6zrEpUJ0dRmRGG\nmCKlWGy0+FZjQnJVelOuFakRi1KsS43EMuKlIcZMcYWmXcJYsDZQ2w5iQdaJ5bLjYjtQykTTAzmz\n3+6IKekhUISLqyd60zLCcnmDao84e+oZPvrSN9H6lkcXW/bjyFO3b5Oy4YN/62N4t+Dy+gorHmc3\nXFxPfOXz1/zR73+BF24t+JaPPoUlMeFZtisuL3c0IXCVPM+uDtlNe45vbog5YFLEdZ4qlrbrKX1H\nd/sG5tYzHN+/x/jqFxjHS3Z3H9N4w+1bSx7e23G12zHFxNAf0blC23bQ9fSLHus62sYxjYFxGui6\nnlwy26vHQCRknQ93scDVpb7o/QozteTrc0ouWCKLpiVadfE470i50jjtUDo8aTfSkjFVD9nRGLwJ\n9HhibsnumsvdVynZ4lhi7IYadSkSoiY/1mxxtOSSiEOiykBtqjqPqnYvOWWmKZB2O4QRa4WQ99Ti\n6KXHu1ap/m1FopAmsFKJRWN/a7FIY3BY2oUF02GmaY4BcTRe4d1TCKoksT0xB8RaSgnEOmKDByIl\ntYTksSlTcTS+o5TI5dU7aqSQgSlcKlzD6OVKRMhlrzKq7HAiiK1zLIe+B1PYIXTknBATZ+Kk0tGQ\npJbMKjjjYUYy/5WK5nd913fxxhtvfM2i9y9+fvd3f5dPfOITeO95/vnnefHFF/nc5z7Ht3/7t/9L\nX+uNvvRGBGyjt0OTKSXibEPOAbFgiuipUKNqKv0KQTFkMSdy0V90KoWU1VObS6amjPVG0+oEIoHW\nVlIxiFiM6OnnPVQ0LIuZ0QeGkPIcE6wLErHCMF2oDSvpD1tT60S3/jWr+DZ7sjiMc6S5ODnX6QLK\nFVItTONE13dUKYS8x+aEMZX16pgQd+z3e8ZwjrMLjGm0fTZRRxjG6MNahWqFWNRDrbkvGp0rkrUN\nyUKIELnCGk9xhhIVaZfShHULRNOz5t9pIZcJTMDWR+yCpaSJlVvhmoZp3BFKQpwCpFdHvYKSjbbx\n65UaAXKBmAFnODg8ZsqFwyO9lbZOKDRELP36iKlccf/xjt12z9uPLrHOcfvOM7THt7l9dJMPXW11\nsRUmDpuJ633keNVzevMp/uS1h0yp5dadO9x57gOYruWp0yOOT9bUFNhuBxKVg6NbNLc+CLYjhx5q\nojt8hoPlQ3Yh8nAI7Act+vSHLLtDzfrZXrMb9Sa0OTymGkOqajtslits15NToml6DmxHGa8xs20x\nHqxpjtb6uxq35HHPFBJTmFMTjZBzVZwgWfOcaiVXzdVp51a2lsw+XVMEnC3Y+Z9FK0S27MI9pK7I\n6SGmNNRkQTKd8zizYAhxfi4jebzUpY+gJK8qlCKkOBJzoHEeEPVgl4lq9qSyxUmL8Zpe0JVeo28L\nSrSXgLEVIVHKpLduseSUEaPgnJzUpGKtJeegkjkRGt+QkzCFa4yxMxW/o+L0WYwTvrHkEgjxShe2\n0mO9R2zByBFIZgyXhDhhTNUcsBn9WEqdF1JCjhM5F0rN5CqkOlAp2NohUtQdZRW/WP+qRfMv+vzS\nL/0Sv/Ebv8HHP/5xfuEXfoHDw0PefvvtP1cgn376ae7du/e1/2BrEeOYYp2lKrrFVqlExTlLa3ty\nLlTRjG3bqlfbmo4ULMYmzWQmYavoILqEORTKo1kpKmpXrFuFbGakm26gQUnUqSZsowNwEYuxBUmW\nlANWGp2fJiHEHSXb2YqZlTCPLmNKhpw1j2WUQWODEaxJc6H2GDMRAux3ezAR5zO+eLpuTetPWPVP\nI/Uu17sLYtjibY9U+/4c0tiZYiX6M8xZPfgiyjW01qqEi0KpGjuQc9B4WrF0c3ZMqUIJ2pZUSaQ8\nIaK3zVoSsXqkOyVLItSMsR0HN26zuLNApsqwv+TJxQPyZLi4esiUIvshsGgdq4NjlssjusWK6/GC\nguXeW4nOt9y8ecaqc3T9jBBrMuv1Ac5fc3LrBl3bcb0duNzuaUzhw9/yEgeHJ7z9lS/TDQ8o2XP+\neMCvRl76+Hdw69mPcbU9x/vM8e2Ws9MD2s0B5xeX1NxTs2WwRyAdvjsF72jMligThy++yMM/PCeb\nx4y7rVoYt5eEMbA4WOvopqpDZXf+gCJCzJnVoqftOnWN5cQ021R3uyusF9YHN5HgmLYT1idSSXTW\n0y/WWDNAmZT0Po+EUoogRu2BSaOYqcpisLXi502xKZVDJzgMe3Fkk9QcURMxOnIQvKxxzpCix/ke\n7xK77agtcBj0MRJQu4Ih10gKATu3rwY7O+garNXCGeuEbxtyTjOQxkLRGbkYQwwR6wKYmRFh1IZc\na0Yo1JSQrIsakYRrdLRmnWoxU94CDlPXWNeSY9ZuMaFJmX03L5YMYj1iFT5uZgxkLxus25LLJTBS\n1OxDSAHnwcqKWiGGogCcGpWKxHzbzrPppWgumYrq/38umj/+4z/OT//0TwPwUz/1U/zkT/4kv/Zr\nv/Y1v/a9ON5/8WOtnTfoWa/IxmJMM5vwA9Zq6h3WzqFLHm86naFIA85i6grJlVx3FBEgUquhcUul\nPjuLdZPawWrFiH7PnMBZg2+8puxVbaVTAcl+ZmpWHVybCERCKO+HUzm7JEwRbCTmQsxRs31CJJWi\nPvbq8Y1FJLHdbWmadhbPR20p5jYup0grG7xd422Pk451/xTDkNlPO6Tm2e/bAQNGkpKTcPP2XDeA\n1ugiyljBiNWFTp6dV8XqwshZoo0qLZJWgckm6AbaZowpNFkYh8LyYM1ivWDV3uTQnTJdDVxtdzwJ\n19Rhy7i9Zhqu2F0N3H76aZquo2B4/O4lj999xL38FhXP089+AyenJxyenUJNXA7XHIVDLt58AycL\nDo6OwRoODk8Yc2bTb7i9OeH+w3dxVuUvB0enfM/f+wH+19/+B7QhUcdATi37sRDjRCpPONxsOOpb\njOtwfsnpc6cQoaaM+DXVOmhWWFGL7vnddxievMO7b9+ltwmXA5dPHrLslvjNCuMdxjX43lCqYYoj\nfb/AWa8EoahMz8P1ijQGas2s12uOjs/YnJzSHB6Abagxk7ePmcIEUeVhecqUaWAqI63oZrmWqA6w\nmHR05bRDMqniqtfDvC20thKz5To4aDydh33Ql9xKo6qJohCcVM3srMmkvFfCUoGUE1QN1CsykpNV\nEnzJWlBQ+pFoQAHWRGqNtIueahLRWBgsMQmNW2BcYZwG7cAwlBowRlvjVDKlCGMcEGuxThF072mM\nMQWLoRSZY208+KhxKJPDUNiPmvNkrcyLJDNnqKs7jllPgniqKKshhAnnM/tpoG+1jmAMgsfZFskt\nxuiFQsShO0tl5FL9161//5+K5o0bN97/6x/7sR/jB37gBwC4c+cOb7311vv/7O7du9y5c+drfo/P\n/eM3qRVSKTz1wiHPvniqEI85O8S5npJVY2ktONfg3FLnelUJPYYGpKGEgEiac350Xul9jzGC9wbj\nG6wplDqR7Lxln617OWVyiSTUSeRqg/MK381moFpLiorGSjGTkm4TEV2ipAKIRlpQIcailBSXyEVb\nIWcs03SN80IuFZlDnlKsOK9F3JoVtRpqHbVlMw2gkASZ2xVrPDGN81zGUYujSsYaQ6lxPvk1SraW\nqENzdHhvRJcDuWj2UZ3lSrlEKhNIZtGsOexucvr8HeLQIKFnex6ZhndZ9g0GtaTWdkUuHmkOufW0\n5+LinDe/9Ab73QXtwnF4+BQf/eC/wWp9xPVuz8PHjxjjE1YHa77xQ99MHPfce/iQTX/E4+2W7sGC\nWoVu1fL2vXcxxnHj7AbX045xu2WzOebkzvP87b/793n5s58hJE9ZnnL81B3cBm4cHrJZLDHec/7o\nios3H2C7Hu8cTbfm8LShXy+xNVGNxTcd6/URnD/i6GTNW19+nX7V4w9vsQ8DZRppqeQpEKdJRxC+\nYbvdUYoWx265pm87ckos+p6LS4UQu25JuzwhWdF5bs74/oAigWQm0u4aMZaQi3ZEVrshXcyAGKFx\nDbUUMELrhBQqYy6klJFSaMTSySG1joQ8gBkoVZ041rYgfoZXN0C/ZgpTAAAgAElEQVRPrU6f8zxh\nrGbYl3ylMr3iKRSyUR4BFWTUILWVayg5YN1E4zMljJjOcdgvCM4x7D0pZKztsPaQGJ+Q8ohIIZdM\nKhFqQy5CrIkaGjwJYytNM8dSe0cMy5leNsfVWIupomyErP77lNQs0EkD0VKsxo4IhlQmSt2T614V\nE1ZZm3lWRUzhCmpLpdP8I2kwTuO8pS5IKfD6K+e89eql3lL/VbTn77zzDrdv3wbgd37nd3jppZcA\n+MEf/EF++Id/mE996lPcu3eP1157jW/91m/9mt/j3/73v4mSLcXO5vqU5jmhQ2hwVsiibWlMSUXa\n1QCt+k6LI5WCE0vI+u9b22Do1IZFo6mRjSeXHV1vqDkxEkGqnlJJiKkorTwXXGmg7xDrtOA2hTJp\nBnhKkZQLuQamXBTDZpMSocVQUO0mYhlDJKSIiQXn3ay3TDTGULLKqaYIJWsfcV0u6PsVy/6UGHVT\naXH0fkkpdT5FC7mIbrxLmhdeWbfr8wbdSEVqwdmebMCZTOM6hrgn1R25Nljmr6/Ky6ypIibxkQ+/\nhPWW+++8zZdff4XjzS18OWLhT1m2h5hs6FxiGAeSeI5vHZND4atv/CmXjx7gTOVbv+M7uXX7w7zz\nzlf5Pz7zP/L4yQNOb93k3/t7/yG2P+Ltu2/xv/zP/4iTm2f87Y+9RO8UwzeNE+MYWB+uMMayu97x\nR3/4RxwfH2Oq4dE7j7nc7tlsTnBHN3HrAxY3bmAWHuMNi/6IkkaGYSCFhHGVcfeY1K7Adjx5/ISQ\nLKuasZ3HtC3d8U18SSQbwXekNFFCIFahwRCmHVUycRjJYSKHiHcObx0xRMKoSyAaj3HCwWbNen1K\nvzmgmozPLRJH0sUjdtdXpLDHOEPI0DUdp7efZ7jest8/JoWB1aLDWIMJ6EHpHCYF4n5AaqUzKp0x\nxutMtQoRyxBVZ1tzJadJl57WIuqkwJSG3h0Spy2IJrkaosbCFKEkQ1YzsxobrCXs37MROtYH2pF4\nV0gZSjaKL2w6pLYkFwnRYUxHKZDynpIdvtHn2xqdMUIllqQ/R++UkWsV2+adI9UIWCV/Va85WFY7\nz6XrVQsctuR8Tb8y1AnaWikUxEKOI5hKiAlXHdU1eL8ghsxuesyyb+etv1f6mVOQdywJsYYXPnLE\ncx8+QKowhC3/9H9462vWrf9XRfMTn/gEn/3sZ3n06BHPPPMMP/uzP8tnPvMZPv/5zyMivPDCC/zK\nr/wKAB/5yEf4oR/6IT7ykY/gnOOXf/mX/8L2vMgcipSTesffw9I7BeVConEdORmkRkqqiPuz+SEG\nlRsUwDjEZ1KqWNOQq0EsGiJmCkjA+kh1FY/mm9TsEAc1CTm1GjcqC1JQ6Ib3YEW0fS8wBdVUZqNy\nhZgHyBVvq1ows84PU1BtJKIRHW1u8M08O6oVisbLhqjxqCkZELi8ejzHbLznoAFXe4SemPbEFEgV\nclVBv3W6jSxzWqcK2i0UQ5oSJcssU6q0zZKuB3GaLe3m2zaSuPnUs3hOePfuu2SuEWs4O7kFucEY\nmPKWkgJtWugJbVsOT3qur7ZcPH7EnTsv8JFveYm7b36Jr7z+FV794hd58OQRd24/w3f/O/8uu/0V\n/+Sz/5TqLN572u6YF198kTdef5M7d55mHCecbbHWE6fEOG6ZJpVJxRC4utpzujnA24Htbou4hoPN\nAY/P90z5Psv+CTfONrQu4UzFeej7jpOTm7SLNdV1rFanGNPpIq0IaT9Q6khdLlie3eHZzSnj9RXD\n9SXjuGcct6zmm0owHaNcEseRadKQvkW7YBq2pDDSL1YU69gcbug2HTXuyUPBNAM5QWk6+o0hx54Y\nRmydSNOOy7CjGEO3OqJpn4IcidMOjCPnqEaDKrRNgxTLEAdKyowpgTesxBOqILXDDpkxXjDFJ4xD\nZLV4RouuiUryMSsOl08zxUsqo8qCbCCTSLEyxVEXN2gGFrVh3KksTySyMUvw4FwkxURI+3kZOy9x\n65JMT24qIYAQSHFAjM5HmRUvalLR/QWS0YztoiYXo4QjY4TO95QIgiHUSs1C4zqkVqZ4xeXlfayD\nfqnvqpgAkqi54F0z15AeZ1qa3lFYoFJ5g7hCpcWZTsHNTdSkBCI5q/ba/SVV8S8tmr/5m7/5L/29\nT37yk3/h13/605/m05/+9F/2bYmlYpyGlcU0v/RZaSapZhppMM6xWDjGFEghKKjABYpzGKObOKmG\nmqxawqrOZJxfKonHWdVyWYc1hmyCYuQmIU5A1gF7qoacHGGyODQxrzoza9EADM6uqLVqtojrSbkh\n1nN1Jc3a0VqKbuxKVsmFE1KpaOx5hFgBSwwZZmiBtyr83Q9bUglslgsMjW7NrcXiqWVFyjvCNFBw\niNtTUNKRcVlvAKUSo2K6StGM8YrON7vG03pH1zvE7hjGa5aLDacnL/LmW6+T6+ucrI+QamjoFb5Q\nITPhbYtNPUaWNNbRNR3biz3Wej70Td/MeL3j5Ve+QOcMfdfx4OKa7/7u78G1K/7P//2zxLjj1s2n\nGYqwWJzxw//xJ/iHv/UP+Q/+o7/P7/9ff8hzT9/h6upaSThiSSkSQqDrOq6vr2lcwzaO7Ic9YRh5\n67WvIN/wHIfPnCHeEXPmajeR88CN40Me3H+A9x0HRx7rJ+7cfkZZpCXAJNjFzLdKUJuO9vgU2e51\nE2w8hkfE7SXTtKdQcVI5ONpALJQc5meqkFNlCnqYnR2fIdXQr49w/ZI0RcZxS9uv8G0PNTKeP2bY\nbRl3V0jNNK3DiiOnSIgjWI/3K4pMLG0l5UoIIyEZhW+UTK2FphqmkiEbGtPT5USolTjt2A2JWi5w\n5gDX9+/bcL1dUoP63UuN1NqyD+eITIhMM6NSI3Bz0flmKY5hp++HlBYrmb53FPTZLjlhaHS2Xg2m\ndLTmlFXbMsaH5CqUule1B0LfeyIoC1Z0ZiuE+R0TBcjkQJGJUrdYv0RqZWF7alpQyTR+iY0dU3lI\nSFt2+y2+KRgbEWOwRm2/zneA0o8QMKihxXghpkmBOkbf1ThumR0gmmprdIn69T5/fY6gbCgygTQz\n8NSSqTTV4n3LNAVaa2jcir4tXG4fkSUgU5mlB41uHnMC9AdQcsa4oNxNsYzDiPMC1ulySToKAeej\nUtCz/rKcF6YxE2Mh2Y4YIxjNQwHVvJms1rOUIq1vlTifVSjvnCFRkFyBMoOOtYDlEqBYEL0RxhjI\nsRCniHWGtikgSn6JEhhMZdEssEbHECnOA/PiKWlkShHxgabNiFFRu2TVfOZcGadA0/zZdtM3LV4E\nZwYaByI9d57/Zu69fY+vfPWPqUUwsmLcQ99lSh4JZYtzKxwdhkRM51AHSlpTa+Xm7UN22z1fffNN\nJBuevfUcFxfnLI6f4sN/69v4/B98jjhds1lYmsVtrvaBb/nYt/Hxj38b/+V/8TP8Z//pT/Bf/4P/\nhm//ju/k1Ve/hFA5O7vJbruj6zpqrTx++Ihpmtjt9ioFipk4BA6Ojhl2E/HeWzz97B265YIQlOCe\nMRyenOHbJbVaFosNF5dbQqpgLSdnB8RBA+TEClbAJGidw5wcY8eBtm+RWhkuH9BZy7Db0TjPjds9\n1xdPiGEkpYiVwmrhNUveGbp2Qc0CpsUedjAtVYQ97tmdPyHur4lBpW2N8aQQKTON3wZlH9RuQSmV\nUHSTLUZwXU+I4LKng3mmLjqvNJ79NJHjBTV7pqnHSmHYD3Re7YZ91+ozbB1hnB11pcFWxaxZVzEp\n46rSqqCS0pZaW0qx+MYTbeHySdQZek2EMJIHi6kTzjQYY7TdnaCxPTUdkmwhSyHViJVWDRadJ6Wi\nIXwqpHx/UWWtxmbkvCWRcK2nMQdI7WgWPYIlhKDQ8nSEMiYs5J3alSnMvkCcPaDooBhrdQyQUyZE\n3WVQCqkGQkiMYSKFLaVWXGPUVPCvSnL0V/3EkAjTiJvjGnIqhJqQMeNNi/MNVcA1SxZFmEJhzBfs\npi3WRxq7whg/pz0mck6UbAgM1HpB9Su8OIzpwSpk2MSilBMTSJIR4/QWmg3eiQq6KeyHPb0bITYq\nLaoTvuk059k1FCrGFHzV+AJbzfxjLv9cu/xeXLBF5ozolLOCC8bAOBS865CaMWbS7BVvmaaEYWTR\nGMQKxjRYBx5Dmx1TZn7QoFSwaJ5QxpCLbktJSrfPuSLGzeR5jxg4Ojzi0ePXCGGHtR0xGYpkprRD\nksG6hIREi6Pxa8R6nGuoudL3K45Wxzx6cI/d9YC3DYt2yZQTdz7wQbZXF7z8xy/T9Au805z4YYQP\nffgFnrn9AX7lV/4r/pP//Mf5zP/2Wb773/wuXv3Sq2w2a8ZhQIxmT5ch8cbrX+XR/UecnpxinMfk\nQq2Zg4M17vSMkBLjbse7b7/D4eaQk9MDrFjeuX/F0dGGzvQcbNa0y56K0K06uq5lNw50bUsOgabt\nmcaBEAM5BFLcUYYtIcPqqedYnN7m4s1XGcdHnD95GxPBt7phjiFQbWbReY4Pb3P7+Q/DZoOzjuni\nMTlc0EjLlDP7ccRkQxj25BQZ91uysbTeYrzRXJ9mgTiPEXBujlV2lpgScRqQFOhqxSBEgUollUDK\nE7lGTA7kvJs5sC0lwn63xVqPkYauW1Cyw9kFOQ+kkIkBqjXkojALg1DS7PgRg9CQkyWFJc1yiacy\nbHcKxk6BEAakXNJ6S9ecaLyGabEkvPWk7Ei1YJ06gxrjKEYPd71ZJvLM6FRaewVUG13rRIxbjFuz\nXh2wENW7jrKnZqWB7YbCNE2IK7iawaV5plkwztN3R/pnWYHaIpKodZiXXwZhAvSgiOqzxiSVfRlr\nv27t+usrmiUyjiNtSTTek0tmCDtSzIjNLBdHONtpzo/vWC832LFwNV2TUsDUESNxFr4Gcs7EGDUT\nGWGfEr3vMTYjOKwpeJkzU7zHWkimYm2msUJpHWY+bXOZaHKkOnUNWDMvWholqk/TqCOB2a6prUhB\nyTCGnCI5B1LqcdbTti2lTKScybXMG+1CzoaUMzU7oq2UKdFgKJyrUNc26lX3lloNrfMsuzW7MBHT\nVmlKUjRUSixFmBcCajfNRU/YvtlQSsuN0zPOn9wF+5i2OSCMnpIHjEtghXGaMCVi8h5rWqbc4Rdr\n4li4cfQ8ve/5kz/9v7l5csZ6fUzXrrj31bf5pg9/I6+9/iW8t6yOj5CSkXrAoycP+ZaX/g7eH/Df\n/fe/yd/9vn+L3/nt3+a5Zz/A5/7g9/nGb/wQr7zyxXnG+VVCCIQQuLq65GCjMN/N5pCcEturK6RU\nuuWas9WCxw8ekuLEw0cPuLw+5/T0iNMbZ+yCoV16Lq52iLUa5RA9uSRWBxv2+z2b9SExRFzXcbHb\nUcdL7t9/h+ODNY3AG1/8PDHD3/m27+H83Xe4eueLnJ/fI11fQplYrU91+WcdpUSeXL3DwapB/A3a\nBUAi7LfUkDhoWmKBq8d79rtLdcE5S0Xm5aVlGAZsm+maDubBijGWFEdqyph5dj2OW6Y8gZup/Bly\nmHAZGlEHulRhypk07Fl2C/bD1fsCb+sqOVtisgyhII0+j84pyMK5TIoTxmgIXLvo53QFo4J3YzFS\n2E/3lXNZLLV6apm100kLaqma1aU3Ce2GfNO+L7w3tkKNeN+r7ZmsSapSFa5dKk3TUstECFes+mPl\nJFhd+jhf8dkwDjJv3IWUIyYXXFfYDXexLuHsmpiEPMdo5+TJcUnbWmU3lEDXGZxtKNkwTbrYDePf\n0GC1qVyDKeQ6ErNHxFIZGdOAnzKr5SHOepyt2OrJ3qggPHliHLFmIr0XOlaqRlcUodSorbLRdMqU\nLVAwrqG6pNnec0vPHMokBhonSIvaIst7zoyBRkPGtSUznnGaCCniHag4VnN+SBZKpWajPmURaq4U\nKmlSx4/MWlFrLN5rzChZZkBJYdpPVNyMk1Oh/LI5wpleTz9vSaWlNGumEig5YlwFU6FWpBiqVIyp\n1Fmom9LAdl947rlnefvRG1h2UNfEIroYSoYqhSkN5DzRsMTnlkkCrrnk4vGWF5/+19hdXbCLV5yc\nHENtmabI44d3ufXUU7z8z15muV5QK3T9gtZ1XFxc8tI3/+vEEHn5C5/l27/zW7n/8DE3zm6SU+aD\nH/wgX/zin/Dss89y9+5d2lZVCKvVCu8skirGZEoZOTo80JgFI7RNT6Vy+/YJVMPl9SVXF1ecn1/g\nugWb444QE32ni5zlYgFU+r7Hzo1XihN2sSCnyNFiwa4OrPsFD+6/RdjuONtsuLx4zD/5n/5bbjz1\nFL6D06eeY7g8Z395TuM965MbHB6esD68oQsnMileUfFE14GfGK63jPsd18MexLJaHjAN16qysGa2\nA8d5vqejHZm35KUW+kVHLiqvm8aBputY5ol9HNmWwqGz7GImEVnWSmggYohVw/rGcYvIUpkHBUQs\nMUWG8Zox7hC2tF1WCdqcJeVQqVzruzkmV0h1UoC3/vRm91zB2EAM19QSadwRpQqBnapMZMJaNY/k\nEjDFYEw/R1+XOVl2whqoRa2kahax6ihLic57pjFwUe+x6I8pEsEkMgmxMrflnpQCY87UMtKIp6Vw\nffkO3j/COqvx1tki9YCaOu0ygVwTMQ/EGGdjiiNGKPFv6E2TrPDdnLK6dSx0nWcaEyFOxHLNwqxZ\nNGcEM5CLpfEL7ORJcUtyI8Z4YswouF6jbpGqyXw1YcqIsR6pBZ8d2WScq+SsLoycDGAVBlDnM74k\njAipVqQkBZnalpwrw7BlioFiMsVokdKMZIOVBc4bTAbJjlwi3gtQsEXtcM5aUlG8mjbWKlMSGrpm\nllmFcbZ/JShVXTOdwxl1LZkUqVkfrpLVe2uZ/we8JUwqSUIEyYa0h8PTY9746p+yPBgxlHnjuJqh\nKYUsdXYyaeZOqZ4k1+z3hg/c/jj7/Y4ahb5Zs9+NeoueLM8/8wyvfenLrJYLhv2OzcEhi37Fdrfl\n2eeeo1a4/85dTk5usOg3vPbaGzz3zHNA4Qv/7GWevnOHR48eaWyyMSz7Fftxh9TKwcGGtm2pAuOw\npXVC03gODtbst9c69jCew5M7PHzQcvXkknHcsswrqJ6+W9I0DavVipwTMSg9q+97Lq4uOHAG41qe\n7C6RGJUcHo9ADE/Oz+lax7O3Trm8eEBtW+g8x2c3uXHnWcIQOD48YLM+wK6PMAfHlKEgjOzTRLs+\nY3d3R9pPbC8fkqo6srx1WLGUnCmmQPmzMMGma2dhesEaozKckqhZC4hm2SuN3GZoMiQRjmeaUBTh\nyiVSjTQoKWiaRkIccLadZ/4z19UaTGGW2k10Tm3MRipZQEqmMlLZk0ulaRZshx3rZTN3UKoTLlVB\nGjls2UehWkcou7mbmt5HKBZJ1JIVeGJ7PeDJOKOHRapBUw1MS6094oLO/8OEAa53T8jZqgqFHanu\ndHmGKlVymRe5sUFqxVLIdk9KQuN7jHOIqCQql4EyKjFMjCGXSEwDIKRkoQo1t1+3dP01RviaeQ7S\nKKKJSOs7pCyZpoHtOHKwajGuxVGxblLXkBVSEUJIWFtBVBDsrCOZjFRtGWKcEDNSR1FakCt0uRJT\nwMyc1VLyvEEUbYExpAQlZ2pW8lE2GtAmaOpiipHqC756jNGtvJEFIk4hqbbBeI+RhHOVkPbEnBQt\nVrRtLwXiBN50c/FToXrjlpi2ZUwDMURGChd5i0jLctHiOvXimwIkBSqn6LDOg2hL5LzeXlLOTIPn\nQ89/lIeP32KzPsSWazADtVQsFRFN4as1YlymJH0kjLVIgeeeeZHHF/eRMLGuZ7iiAWteOk6P7vDG\nV17n5umZio77BdUIpcLpjTO22z3GgnUNH3vp4/zjz/wjPvyhb+T8/AnWF87Ojjh/8gjB0nXKDh3G\nHcO4Q1JkHK8IQfM5bhydslouefLkCSenx5yeHXF9ecli4agi3Lp5yGbdIc5iuobVao1vGpbLJdaq\n1CnnTAiBtmlZbw6o0x6RzOlzL/LojS/QWsvh+gM8fniPrvWMwxUYy53100zTiPctzjiadsPJnZsI\nhdy2uNWhFrrGEEbP6mBDyBPrm8+zDEBJXFzen2d9FeySXMZ55q2diogwjRpYJyIU+54CQsEsJRUQ\nS6lFlyZiaEUo1tOkQtpFsjP0qwMIV4SUMaaS9iMhTCwXB4AlxEiuI0imbR1RHE2j89Guaahp0pC/\nmonhgiJWOZXFYnCEKeC9YQrTLMszOBOgRGIKZFpiyGT03ZOis1GRoss3FC9XSsJURwoyRz7vKGlC\nTMWagtARoyOlMPNfJ652d/HhPUyiBriJcdQYiThSEVrnkVygeFKdKKZipSWWALMOVYojlUCpCesK\nlIyYNM//PWHc4+RvaEZQygPWtojxWHFAIcWCwWKko5YFpQqZCUQzTTRgScXoZHW3eN9oVKlRkWxO\n7/nCYRyjwoSqo+yBRnBZ/d4xJmqpqiEzBUyedZuaUEmGUova10WF4GIgprmY+qQRoq6j5E7BHKK8\nzda2GKcLJOfNvL1PupSxLcU6ohUEjzcLGtvSOE/jIeRA53pijcSQcWLYb/e0jcE0dt6oW8Y556Wm\nhjKT76sxQAAKYaqcnT7H/XffxjaBxi0QseRiqSYhcxiW85USVf1qrCK0ah443HyA7W7HdvcWB+55\nakmkOCI4bp09y8NHjzg9OSGmeZBuhOVmw9XVBSKWRb/g3r03uXXrDn/0x3/A8y88z+XlltPTGzx4\ncE+p9zljrTCMe7xvFAdWBWsqzgklRxrXYGaKlKGw357jDg9ZrRfUkvGNY7064OD4kLZfINbRtgsW\niwVd24ERmn6hpgmjywZrPbUBKRH2W47ufIAHb75C3T5hc/MWw2WDua6EZHCupV2fUtLI4vAUsiEN\nI4uT27THx6TthLhMjpPCda+22q3gKd2SxcGxAnfHa6ZYKPmarms0d9xYpVNZR9M2egAblbqJiOIE\nQ8F6LSDGGozRg6LWwhgnplKYxFKrw7mGRWtpmqK5QK4S4kBMHdBQyUxhwHswNapzJyWaRjFtSmjT\nAmd9VdK/tUxBLyPjvtIvFH6d0h7nEoUM3s2Yt5E4WZIMNK2mBxhTVLeZBExHqRNWhDpbPWtOqtU2\nEVOVzCTFkoIup8I0qYun0bGadQYMiOkQM5JKUbaCabAuYUwmx4TxLbZWUkiMMWJNpvUVI3OBbCwp\nR1KNs+e/zHlaUMr+69auv772vCZS7sBA5xtsFUKs1GKhqHDdmlZBGCXjnNJ93nMPCS3VFQwJsLPM\nx82g3YSxnpwLY5gwNlOLV1hxVp5aLUp+0ZO9Yo3CE4ypuuWOCduo9KMQlQotAjMcQzX7HlNaqB7v\nLUUqJe6wzilphULXtGAt++lKmZvGaaywEUqGxnm8bVl2a7rWM8UdU4o0pmFXB8JUaAlM4zWtXSkd\nvG/ZB8MYKiU2GmIlBicNzlViyNw4O2F7caXpgbaB4mjajlAKyE7J5+8jtBrILY3bYMQxDomD9YZ3\n732VVb+BFJgkkPMa7AGPLh9TpXJ5dUFIyg04OT3j6uqKp59+hkcPLxmnLauVjgCaxlJroW09T548\nnscPlff0raoxDVTn8NbjDHRtw/XVSLaW/bDn8KCja73696vaOa+udtw6WrFYHFMEuuUK27T0i15F\n1IAY8G2H9w0pjzSLjriLtIs1aRwo4xYxS25+4KNs33qVOI3cfP5DPL7b0qYBI57FcoWjoS48aZ9o\nxOCmQLm6wm6OKTkzXu4pV/dZmEx69JBtrSzXR0zDgDE6R+X/Ye7NlW1J0zLN55/dfQ17n32miCQg\ns4ukDVrowgwBRASSG0DAUEBDROQWSCQMLgBk0OAOUFpAwdq6O7FKoMkhpjPsca3l7v/cwufnVFV3\nVQqUtWW62bYIizixYw/uv3/D+z5v1zhnWNeIsQYTmtzjenvRd3HRhBBEC9nZjBOdnGXkYrRDaUta\nnphTpGnPYdhJHO5gWZJYIbtRuNWQk2VeT9D9ds+KesPRKFo4Ca3KLDU3SX5sTaMZaF2itoUvq2gt\nc1kru92IcYXaTjgnBY+2jTqLz7wiJDBrO62tKG2ofaW1E96MgIauaaWQcyVRML6gmhzmtIzuEymD\nJK9a6F46w54Fa7hNYbGGXkT/2VWXOOLSpaBQss+Iq4Je6H7BDxJG2FKhfUjcrJ1WNIogyy31MzrT\nbBvqTXVFjhqtPYpCqVWsV6nTcqVuqYuyUXMYNciNg6c3yFFKcKUyylqWRbblWoEJltI1ikqtmbhK\nlk9JbWuL5O2OUpIE3iHnCCB6RzdAkSiCtiG7lO503clFMziPUpJzYqwn94w1Ha0LxjlqCyhdBEpg\nFLordJUMdmsVpWu6FvJ5SR2722HtDrWcRRxtoaGovZETOCcthTWGYZjIbaZWsPVIo1BVRgH7/RXn\n+cycCzv/EmcCtI7WAa+vyF0R88Jgd9TWJPXFBLx9jlaBX/z5l3z+w39hMoGaKr2dUfWKYbrmG598\nk68/f0OZV7qWyInr6xsen554/vIF3//+P/Pi+Svubu/55V/+Ff71X/+VX/qlX+KLL77g6uqKGFcR\nHOsPQmdFSkkywjuEwaM6nE4nhmHk6fTENE6s64W0rLw8TtRaxZpqBLJ82DesGxiHEesDKSamacI5\nR6kF6x1ohTEO2oAZO117TAi0oKhLwWXN/tU36U93XO4fufrGLxMvt9S4+f9DwAwHFBk7OXGAXRa8\nuUfvj+xfvCRbg56fCFeK+ct/4uuv/o3p+jmPj3d46+RB3iRp1li0spRc0ErjtNogNop1XaUwUBIs\n2BVoNdHxLGmGXrk+7FF5x12qtH5mbZDTTK+Sj6V6YZomtAqcTpF1jZSaBeirNXGJmMFQomzhhQon\nM3rNjpREwtc7Qh0qAoQhN6xZGLXfOq8L2ngheqlGzI3aJZxMdSf604o4cVqm1PMm+2lbThiAIkWH\ncQ3NIvdq06QkdDBjhMerlNsWNtszvum7lb6IDhrp3owR+/AbxAAAACAASURBVHWvAtxOuYg7q1XW\nMqNtwdoslXU1lGLpzVFLxRoveUI/4fqpHZp+2AnduUHJjaajBJRtwnA5SCJzF/pJ2fBmwe+oXMki\npxvZVHvxoIPB2RG1zYOs8wQcqFXmOUXkFtrtKC1vURqajuQ61y7SnlIaOVfOc2S/G2TOojpm433S\npVItLuOdxikv0onuWOKZ1i5MUwOdgYIEQDVJ3WsrdIv3AarYR60bKdmS5sJuvyPYRjaR0mQTXkpk\nXTXeR6CjlRV3jpM3sO576CsKodeHwfJ4XnHO01VGmRFl7AaI8Oi6hy62UWc/6FjLlkJ5zeUpMgwT\n1BGfO0EPOG7Y2WfcvbkjXy5oFKV0xmnk9HRiHCd6E6dHCIFp2vHu3TumaSLGyDAMzPMsdjpjhWfa\nt5ZIa4knbmKpM9oTnCHGlWEnkN+YVnqTQC4Gxel8wRhFro1GYwgB50Xb+2Gx1HrDh4FaZcxhg6Om\nhvaetm2Te9cYP9DWSB8CanrJLs3M64ndzWfky4kWn1hrweeIP1yRY0aHgJue0R8+h3c/og0HejpT\n0z3r03v8/gbTFKf7222LLCaJRscZs7045FBkMz2AYAx7b4I+0x9iXRrSTWn2g8jg1iR4udorFcNd\nbbSSQXcqmVoiRg9Mg4OaKOmBGCtLWrFGqEWmNmzQ+CAVGXTK5k8vRdGKEeZml46sYzBaM8+yXFIK\nDFXmpOww2tFLISZFKxYVBkxQqCwyI6UaKS0SZ6GEHlWoEmWhBzoC/daA6uJb781RUsEZxRB2tB5F\nYtg6ujesK3QlBQTNCpRZO4FwN9GE9mYpBVFjULEqg4o4ZSjZkbOmpUZXmpYLdvgZrTQHfy0WyOwo\nKFqPeOPQxrCmyBwv3J9umYYj0KXKo1ArKDWBmjddl7Tv3QaqlhmXqp3et4fTGIFxaKEP9cK2HVOA\nEKJbU9sDJDeGNQH6hbgW9vuOG/Q2MO7krIix0bUixogzCWf3eOvQ2rDbR5Y509uKdaLZbVU4oSkn\neVEgeUcuKHEfYaA7atEiLfEGHQ0oRUVuonVNKLsyDQO78cBoNc1pqvZYHEp3Yl1xRnOJD0yjJUdP\nK4VuMqUqbHc4rTFtxFkwBHTb4d0gVVksHMOOy9OZgz3SVJMOIBse5hPXV6+5e7zDKYGlDMOIQuGd\nYxwG3r95SxhHvn7zJS9evODh4Y6XL19zf/+Ic4ac8ybR2iJHtmiTEAK5iK+70+jaUir43Z7eKrU1\nNIpxN1FqZZ4XEf47jzKB1BqmV1IpDONI2WQ8y7Kg0HKYaygRsHqbl1W6CqjuZAY5WEzLtGGCDr4/\nkk7vsYcrWl6xqXJ+/46bb+ywuwnSSp9n7KtvMv/433DvvxT4SF55eHrPQOHF8xvm+T09RpQL4hLb\nwgFr6+Q1Mg4GoyzeSpT0B8j1sqx4v8G5FYCSw1MV0TJjMaax844lZ56Ne0qurOXCmmZCcGizE5vj\nYNFHg+meLx++IFKElFY7YevKaq7kmqQj0pnWRDrnnYwP6GKfrE0zhhsRoVdIpQsQGLEGD25giYW4\nyjxTbMgdnTb+ZWvbjsCgrZDFvDV4Z2isxHihtYzqBZA5bvCBcdhht0iX3gxWw5wixhQqQgRbo8w2\nU0bAHKiNd2vRulFrxgfRoxqzjRyylo8mlXHpmVx/RoPVDJ5gDmgr8bOlLJS0bjnnlkzhfLkXa6SW\nqE6lNVa5LREvMPmA1ppS1y0ULWP1jGk7ehW3g9YWp8IWwCRZLKAwymHUxs3rRqjtqm5JjVKtSFUp\n6C5rJZxsqEbiR0uh5UYunao0zhq8t7Q+SDVZJXWwdkPOiVaykJJKlcTLFElRlk1392959dwyL9Ji\nlGaxdkRl2R5qFDFLtrgzA9UZhvAcEwaWdKavHW0c2npifmQYnLTqXaHsHoPCWaFYm+oxRuOqpleL\ncTvAcnV8xXxeOJ9OKBI1GxSNuD6h88Cr5z+HKo79eKCuM2prgWrrxLTgvRx8g9lJfksVa2PvneO2\nICqlbGF5cttp7bYqSmaYzmvJvbcOY6VNMgZoFacNgx+2vCiDHwd8mBj2V3Q8rXUenx4BmRvO84zS\nipQiznviUjHe4ZTjclkIVtFVx45OmKR1E4H1SLcKFTVNr8S7hJ5uaJcTo3W8/+G/8uyTz6RdX26J\nvRBePKf+4McYgO45fOOXePt//m88vPkRLz77D8RZtIDaGMmX6h3nPGGaCEHGCKlmgfkag9EWPzr8\n9r0C1JIxppGy8FNjSlIEKC2Fxnrm8fw1l5pAG5TzaGeFgNQUjoCzEiq3JOEjYDU5Su66OHGCaBiV\nsD2tVXS1opQjZzAmiPssy8Go9wGlB1pdSB9g3KajjYjVY0qYZlBNQdViR9ZCendWGJqqK1T7z+kB\ntQticc0XtG5Y17m6eo1zA7VsYyozoLTicr5I4eQTJSlS7SyXLM/MpuQAaF1jtMI6hTENpbYqtgac\nOdKLoSB2Za0lhO0nXT+1Q9OpPaYZtAk4N5BxzLVTW0aj8S6wxJU1nTFesQs7DAeUdgz+CGklaIcP\nGnTeAqFWrE54HajFoaISnJIWR4XzGt0967yKda1rdGvycGpopQq0wDS0lQNatQ2nZbbMEe+wRpGy\nYl0TzhfWPuO8wdFxrjMER1qlUmTTZNLlZumqySKqI5lGrXE5X1j2F1Qw9BYoOVNNZxpGYlwJYUTF\nzGVRlLyjIVCC4Ce8c5yLAGC1dfQu0QnBj2QMrUhOumhNxbERgsfoAaUHvN2DgnXtWBfI/kRaErZp\nVK2YlnFqx9AmYpzJdUVr0EozrwnrPNdXVzydTgzTtOU0qY9SH2OkwvwA1wUIIXC5XND6gzlAMQyB\nTsVaK2OYMCBZ9wpUZxwG7BYlEfwgIW7DhDKe1BoqJva7A5fLiePxKG1u65QqyYveWWiOukS8NeT5\ngp08ywK+5S0pUkGXl/MyZ3pMrOnCZB3T1TPq0z0vvvENyuUkP7PcCfmO0s7oZ695/Ofvoawldc3u\nk5/n4cf/F+fLLdevXvD27VtqlReGdyKxqimTlHjZldabg8nLyEgrwiBZ6jFK0FeMK7VVtLWMNnBO\nkXldeZjPrCWjTGAwikstzPPC3h7FnUijI1tmo7SMl/qm70UOwdYzyjSqACkJZuMo0FG1MbgBqyUX\nvRdDKZr728TVcRIYNkUqxNZxWuawpUpFV0qDnnCILrX3iDMNYw3oSm0rusmIqNVVKkDElaONQemM\ns0estqIGSBIVrIxke3kDuED1nRihlLqlvYo0ySiZaTsnWVdKO5Qa2U0vyGaQxaI21CpuQO1+Rmea\nkk1TofdN+DvhfWNNT+KdNhbvR56Wd7QS8UZt9HPwbsSbidzWTfsFzg60okS64B5BDbQaKLFTtAhs\na9XC9nNQKDg1UreHxRtDaxlvLao5isloFN5orO6iXELhtPxdR0Lq1+XCeB1o3dFaANWxQZOrge1r\ns8pRlMAEjJYb1XoHraKLcAPn+ZGbqyNaiW6y5YK2kjSosRwOB5ENaYPVO4awxxiLVp2zuqepRFwi\nKkTZK5pRkhNVp7VOR6RVrWdSrih2eDOizIDVgbwmrG1sCmBUy9hoUHqgl443UHLlMI3E1GTUgMSs\nPj4+YKwlx4Q10qpba7m7u+Pq6or7+/tNXiRV04dq80Ni6DhOrOuCUshh2TshDKSUtj8HtSn5mRlL\nVYq4JmrjowbTasPlcmEcR5ZlYRwlCbM2eeiXMjOZgU6j5UaKhbXeY4Zn1HVBlRk3TvQMl/nEOO2p\nrGgF8f6O6Rf+Fx7fvWF8e8f0/IrqwexeU8pKffsVJhb218+5/fF/4urFS+IYKDevWc5PtKoJw7At\nsDYiVu8EL9G7wcsipSGc0941Tlvymj62s9YY3OFI7o35MpNSpG0pjmG346palqfKaX3LEmfS2jFq\nQuuBVC4ilLcJ6zp7Y+lNwYevpXaMlTmgNisKAfH2DrZ7yT1SAaqHaqjZU3OlVsNJty0tcoPGoDG6\nC1bRCFXJmwFtG0rLi1ObSiqPBBPoutMpxGRoFEHf1YZSWaJtbKerFW36xh4SF19vRbSuVlIWgtUk\nJ+T7WjspCsy406FLuF+viao6VlmG6RlKTRgUe/bkbOmtkcojVSV+0vVTOzTnfMtgrtC9ojbCiTay\n6OlV0uEmt8d5x93Tl6QU8fpMV54YFVO4olQoKeOHQK1P8mYhSQSu3WEHCTYrZZD5YK2gIygJs+pd\n5BklNoqRXObRB1RqLMwYZSQqootz6YMsROkuVBVTqIjrInjJk9EKEd8a+eUaJzeSUoa9tvQy05um\nakOqCJCiG3qT4Ks+jiKf8ZZUErvdDaqPeD9ymDQlCxLO6B3GgGEH1jEvd0DDtSQRAz0RwohWipY2\nao6pgCGuBWMWaCe0GnBmwJgglrhe0LYQ14WmRlyxeKXpaRbKTNfkUknrSk4JlMFqLfkq2hBjZH91\n5O27dx8rypQS3nuW5cJ+v8d7z+l0YrfbkXMmhMDT04mXL19QqyxBlFIfK9UPWUygoAnspdZCrY3d\nbk/vjXmetxmgVLmC5pMo5bxmlviIdo7BD6TSucQFc04ML8SJlWOjR3lpOmeIcaHWxM4NnO8e+fL7\n/8jNy5+jvPu/adFhwp5iQD9F3PGKy/07rFIMw8jdm7ccryZGF2DY0Uve7k2E1xiCAChixDtHTAml\nKy5Im26bPOTGGKyXWeCHq5ZKCAFjLct6lphcPOt2pOSaxXFE493T14zuSuI5aqT0SDcL1hT0Rifq\nvWGUp9OpathqywtOG6ySzkwrRy2WViRZNadG75rWDJfTgh3k0LReSRFkOj4I1xZjsMbibGdwndpW\nYl3pXCQpVgVSziyLQG5ayWggBCEUDZOXhV95IkVLTk30oCmBVRgVaFay2IfSUWNl1Zp1rdRuMC0I\n0awIXm/cG6zaY9WO1py08mHCWUdKUSKA3f9g7vn/X9fp8ojZH7AUUJ2GJsaMtRrndtSS5Bdn9lyN\nrzjPb6jB0FslxotkjWi2llMeql5Fv9W7QrmIUhZMxaKgaBHL90JXmVTYHsRN/4lCtYZz8suy24NX\nSmOZq1Sw6C0TqFK1QmNoPdF0Yq0z2IrtssyATteaMHi6lh9zp6KVIy6Fbi2WQi8Kqy0WR68LuimU\n8YKUQ1My3Fw/Y5qODM6RYmEpi1RX1kNrWHvAu3vOl4WqFAfrqClS1IK1E3FplJjItUhueis059E6\nMS93aDRTONBKw3gjej0ndkzTPWVNzDYyhonSqryI6CJaTgqFpQdNbBmLxj+/kQwkYJ5nhmHYZEVB\nsmeU4ng8CrAlBJZlEbjvtP+4LFJKMQyDuKmMJ6XEOE7kmGhSkxFC4P7+npcvX7LOy391GNecOVwd\nuL99x/X1M5ZLZH8s3J4fuLm6RiEba7eeMeFImAZaKuS84rUhq0Zm4PHrH3E87Lj/8l94Gi07N5Lm\ni1TTJZL2Gn54z3R9ze0XP6CkwmE3cT4tPLs6kNJM12L0Lbl9nOHK+CJsml8ZZWhtsWic3WZ8XcDS\nvUs2t1bS6Sjr6EoxuoGzi1we77mfH0jlLAdu0ZRUeYgnZp/QGmrNaJU+OsCUg94dk79CYcglYowm\n10rvHqMLRjmc3knhUA0tKzEzZNFK9l5IJZPRTIMB3bHO43SVPYF10D3WC1KOrWPTBVIrQi3qhpw6\nKRnohl6rdHuDB1Up+QzuyBobcQ7kLMvTulmTp2AkGkQVlC0EZbFOobUjzhLlba2Xdp5Or2DMQO8G\n7wa0ChQKKINRgg9T7mfUEbSmR5Z1ZBeeb5KFCWcCKZ9EzqC32IduOQ5HtIrkItqqSuXh8pbReUqJ\nuA7eKazZ0zr0ulJUxqpCGAbWtRC0I66NUjvNJNCN2CKaCY8sLGT5M6OtQRtLWiK0DbRhJVI150Ip\nmdaEValUY00n8XPTaVoOM60VvWe6dttG0kur0RtUqaR072S1Bb51IcBIbnmhNJHMzOsjn4ZfwJsg\nEAVT0dkwr7eEEBj8Adt3jO45hMjcIKcFZTqxrTgzC4k7RnICrRONhUHdYFyj5ZleHshpQetCbRWL\nx7qGriOmj0yHPd5fk5cVb7WI6WnUkhmcRKo2YAoDVhtxFXVZDGj9n6EUtVa8DwJX6bKkG4aBN2/e\ncHPzDLdBG5xzWGs+VozeuY9V0RpXrLFY53DOMY7j9nk9zm2LpdZQVmJkj8cDJSd24w4qjONEuTxy\nOFyRtUF1ETsv8yODdZS4ELPEn4TjDdEZ7h/uOYwTt1/8E/rqG4zGkG8L1Ir95FPKFFCnB24++ZSv\nHt/TrQHVuL29YzcdOF2eRGvYJc9J5nqiN9RagxYW67AtyHLOoJBFYt0E7dYAGmUaVhuK8HtRKdNa\nY24rl7qSeiOiaMoxmj2xLJS6oFXHqI61nW7M9nU4jHVYHNZ5lnRCa0/vGqUiJQtxyfQAzWB6p6pl\nG9VnecF1gav0UtFG461k+5QGWNH/OqOoTZ4HYz2mRGoyxFxBJWqFWgaBzKgmErhWoFl6zazpTMsT\nOWvWdQP0tI6ycD4XdqPGeEXTG5WsN3ZTwDTHujRUUzhjaShsC+ju6AhRiarRtpOWZdufVMxPXp7/\nFMXtbSHmO5x1uBSw2tONgTwyL3dMwyizlS5zsv3wnKfLV+QCtVXWtLBGAXRMm4TFeS2zzKagOpqR\nOIphkAfXDdCzJrfN2VPESmfVgGl92+LJNlVLUgxKd2qVhUXpUQSwKGJt5FwYxrAh5SCXBmRUQ97o\nCJ/QOgvdoLB4pyj5JAN322QY35QwGlUndYEs126xztPqSswzV4dPUKbLzFBrni7vWdcz03jFoK9J\nJRF0pPREzFluhtqJbUYDS8ykKNlI1ncos2zSlWdeH+h1xFnFNDq8HWlREkDDMKGyaAlRnZIbtYm7\nx9gdcXbUZkTq5RzLecHN80dLoLyIGikl9nuh37dtrbnbTayb5zqEAVDs9zu0tqS0fmzRx2nClSJt\n/MMTGD6K1/f7PTFGpnHcBPId7zxdS8Rxb3A47jmfThjAaIV3lrXJdj+WiGqVwVlakQz5h9uvIK3M\n737M1WffZtDCn0zv7qnXL1DK4lJh6RH/byfc//xt3v7v/8LNp8/ZjYHzwwO7aeTd4z2n8+NGtZKv\nrVUxcAzDAMh8d11npv2O3iraeozzBC9RtV0Zuh8lCKw2lLHk0wMqZ7w2vLh+jgk79DDA3UiNtzy1\ne7QCbyy9CjUJIroVvHIYv3EKrAZkL6CUbO178xQWcfg0JH6lCRWsNhlx5RIFbdiqIOBwsvRp0KqM\nvLTptB7Fc648Vn769FpRykN15GI3ZoKl1UBvHTcYjG04L0WKRnOZ35PmHTmOxLXJjNJ6dDM0ldFt\n4MrvQHkST7Re8V6j9xrVNfMaZYNPk2Vr0ZhmqGmFFilkqi5gsgC/8/oTz66fno1Sw5qfcG6P1TuU\nndBqZPCKFBtrOuN0gKowoWOdZXI75rISa6SRUKVuIAArc0qt6RgoFmM7kMi5YCwonbFuFGlFtuKx\nNUJ/Nmhx62jZXtPWbVEVRCjcEyVv4VUtUaumFiOtUnUEtxPykM7EtG1szULXDVKTA6FqdA+ovsOH\nziU+UVSkmYyzshkuOXOJDesbxgbAYJzh9uErXtz8Aik7CcXScnA9nS54E6BKK616geoobQLdsE1J\n+FpbKa0TS8FqGfw7Mq0t6C6ynlI1hgnTHbrBLniBJ8dEqQrVEzXLR1eKmhOldvHWu471Qt4P4yia\nOrulHFY57D4Qh0TgvmXRa9Ee7vd7Wus4J3ZUrS0xLh9b9BAGrG0Mg8zyhmHAWMM0TRhjOBwO0PqW\nFLknWEspBeckisFay36/E0xYl9gKYzzn85PELFdLywpNpZXG6Acent7gW+bu/Z6XQ8COBz55/T9x\n+/4d7ue+RX16ZBhhiSf8j/6F558+Z/76nZDCVSYuhW/+h1/k/dfvyXnevOaaopRoDXunlYK2hnHc\nIaFpSuRgLqB1oPeCsQpKomYhX5Wt01FKScSDMRzGEW0dqnu8GegEPn94y5wWUm9oHRjchCJhTaG3\nTKcIPamL+kRCVWXT3XWjVqlMW51lkdgtrYkoX+J3RWHSexZwRt/o80qE93wQ8BuRC1qv6N1Qm0fr\ninEDKmd6DeI9R+GcYRccIXSsEUhJbiJfyzWSqwLlaV2UAKObMHovFKeicf451u4p5URKYqQIo6fS\nSKlvTkKDVp6SK2x5W1VLBr21kjjb1c/q9hxPa4o1NbwqGApD6Fg0IYw8rrd4XXDW0EvE64Hgr2n9\nkdzF8dN6wiiFqSIPao0tpElRyirC9p5lnmM6Sq14H7DWU0plXcVFYXyTz7NxCFs3KJFnUkoTkbeS\nGWjJ4pUuWYkLyUo1MI47Sl1ZyoXYErpLoJtE5jaCuqYWkT7RA9ZN9HShq0JuCxpLN3KTqKbopaD0\ngm6OmO+5vXvLfnwFOpG2lus8v6e3GWsmYl7prOJe6hatnQibVREUl1KbW8cwjNB1pLVE61oAI7Xj\ntcY0jQ0QlxnXxARQc2W/M3g9osJILpm4gVOUaXRdNqsr2CBkoVIETXZ1dSClzDAMIjbfYj92uz3r\nulJrY5p2HA5Hdrsd1op97oOzyFpxT8nmvTOOAyGEjx/ee7z3lJSZlxnrLd44qe6MwbmBjmyrVRea\nVV1FD2lNJ81n3NghK+L6QFsj3hnCbiQ/PpKfvuTHj4Gfe31D1pnnXlH6SmkrRCT1MxcuD2/ZP7+m\nrhee3bzmdP+WXBIvPvuM09svyFXo4GEMpCTQbBMsHwTYWlsJBTMGyII81HK4am3khY5Ceydqg2XG\nKcNazqytMNdE7wXbO/tp4lW/4evHewGJWIt1Fm+D6BSRDmRNM4oi4nVlqB2sGSkUcswbxEbRS2aZ\nE7oPtN7QSqGNJrWI851OpKlOTBk/aOZ4gSyide0SPWUGdcCa8JEnG+wONUzM55WUZ5zpjMYzmhFv\nK86Lrri2TlyQbqUWepOK3VrhPxz2V+ieiWuCZgnhiLKNeX4AEtYcCWOnd0OvAzl3es843/G+ir1T\nic+/94rRSqKMf8L10zs0FXSlSG0m1hljLwxKAp2s8wx1YIknum/YNkGS+IeKwZqBsXsqia4KBkVb\nKm70wqjUGtUqpURJOcwFctvym4Novxjxe8e79480FqnscmfQRuhDNlDsQoqZ0jTBAV1EuqWA5I47\n0bg5D21i0CNJi2zKtoaxmtwzSwLvj2gl4WdrljjgXhW5JKE86UbDbfNAjbV+i+NtoDR3t29wrweM\ncaScSOWRtXzF093KtH9G7hdyi1g/4LqW7BZlZX5qOmaUWZPqnXGwdO1QNWJUQbWC1RqrKipXWulY\n5xDnu2VSE9bA6XLmcpGFizGKaTdSK4T9kdOcGIYgUJMudspRDdQqsQRirZy2KgkeHx8BmKYdu518\neO8JwZHzmXEcGcdRljq1E0JgXdeP9CJrRfP3ca5pHdNuJOWIUrCuM8PwjN6bzFFLFluldbC5t4Zx\nZD6f6CZzefcVe1+ZT48s85lvvP4mLdywdM903HP75T/xySefcvf5VxjT2SnHl1+95XD9guiaZK6/\n+RHP9s95eHfL1fXI/d0bJr+yuzqIRKoWtNGUUkgpg24E5+RFirTqc4p47whWMoDEe23Q1goDUll6\nq3gNy3Jh5wOJjitS3HVVBa24nDFGMypP6xVrDc4OaIwkduoXxBC5f3zL+SyONeUs1lusgmwqS75g\nnXQDFZjjLFwIoHUlraxydH0RVQmG03oSY4RRYCq6bpK3dmIc5eVllMW3IyWDqQrdheVpjRH+rZIx\nilKVZY7QPb2JySPVKPCVloglsafSssbg0X2AakAdaNVj3Ap6wSAb/NI6KXZyUpTSNi9+xgdJjnDb\nDN2Z6SeeXT89R5Br6C4zpFge0XiMsiI5oqGapavOvC5iGUTLJrE0oTAri3OKXuWfobbEQatRAUFz\n9coaIygoPTHqCa0cznmaHtBq5LNPb7jMicvyQAiNMN7gCEBhnI7kAvG0sPaM7o3S1QYXUZQU2Y2f\nYNgLIkvDGI5c1kRKJ2yvKKMoqfOQ37OfOqVaaqqkXIm5bjT5gtEHBBIv+jnVtWg6G4xDwKiOplNz\nkr+WzLw8kvoT69OdiMC3MYN1OwYT6JQta0baMR8MLYPVFuMHgSakAmSUMmhd8d7hzEAps+DwSmJe\nH+mpU3vn2fNrOUq1IZWKKo3z+ZEQjlgn/u9hCDyezqRU8Erx/PkL9vud5JJvEQ/DMG4H3n7TWgpE\npWSZFV9dHZimHcZotG7sdjvWdcUHoRjZTY5kjCGEQNUGpRBrZ63sDgeeTiee37zg4f4e7w0lixV3\nNw6cLxd0a0yDJ7aMSjO3T3eoBn1duP36x5jBUO1I5omrqxuens7cvP4Gn7/7Avv8E477Z8QUqTVz\nPF5xONzw7t3XKNu5vb0n+EA3iqfTheD8RqeXw7H3TsuZ0jYtgPak3rHaMPgRawPaOLrpIkvTjtaF\nlqQ3yrpC4Yzj5d4z+MBaklSrStHpzOs9RTfGcKR3wzAcGcKEphH8EWMGPrkpLJeVd7dvWeo9RhWC\nfYbe7L8xztgtHdVaoEv2eU6ZXCF4jZ1E7dF6kQhfJfNhpQR47Wyj1UxbMt5NtLrdq2iMGpBcjErX\nCrSltUYpIvPLqZPTB/aDRPaiOlY5es88Pb3n+fFTDB7VusiRasWaG7R+BCWfW0LTJNYiZ7VV8Uqe\nmyaYR8MHXefwE8+un3ho/vjHP+b3f//3efv2LUop/vAP/5A/+qM/4u7ujt/93d/lhz/8Id/61rf4\nm7/5G66vrwH4kz/5E/7yL/8SYwx/8Rd/wW//9m//Nz+36orWq0Rp1kwqC75OqCIOELxFN0uqmiVG\nnNFQQVdDa1pwZ13TKjirqemM1pq4dHTtTOOAM0bkQaUzhAOtSvrdECzDOEKfCPaKF892XJYH3t99\nTm4LNIdwKR3jMDLPE2s8YY2nZkuwHqcNKRXScsF5i1ZY7wAAIABJREFUaVm1FjeLVY5zFL+u84Za\nt/xwFE6PLHGhFkWpHaONbMxVxLoBowLOOnSWUQF0equMe0uKZ4z2oLqANnSllSjJkxpkiORQ2tN1\nwiAb+a4auWSRfmhp/yydpkT034hUHKlHzukJy1lSohs4PH4I7PwBeqOguLl+xjxfYF3J6cJu3OHD\nhA97wuS5vX+idTgcrlCqc331nDWeWNcVrcXtI9g4mMYjp9PTBmfQNKWYxon9/kAInpTyVmEpnj17\nxt1dxWwV5gexfGsN7yXV1DpZOdQqmTvaWnKOzJcLtSR208Td+4XdOJDnMzUvDMcXvHn4isMhcHla\nuTruWZeFyd6gwkA4HHn/wx9w9ewZb96+55PjC+o0YlYIKnH77i21FA5XR6ZxR4ozTVvWmBmN5zAd\nRGtKYxxHcimiojDSfovovaOVAFdKzegP35/RMnvf7gatRRpH7/hR6FV3T4903dlPI4dhz36c2ccT\nT0mTU6GVClYq/nG6Yh+uOeyu2E/PUN0SS+QXPvslvrr9IXePn5PiBW/2NFuJMZNVkQWa9xKGaHe0\nZkQutBZx9li9LZY6zu2gdlqJ5JrQo4y41lzw2aKVkxl5k1lo8I4Q5EBsVSSDtRbJHMqWmkFbISdZ\n3alNXjoyW83McUT3HdRKZyG2jPONro0I6mkYW+ktCwxcNaHLl04plVITpRb2uyucPQhE5d97aDrn\n+LM/+zN+9Vd/lfP5zK/92q/xne98h7/6q7/iO9/5Dn/8x3/Mn/7pn/Ld736X7373u3zve9/jr//6\nr/ne977HF198wW/91m/x/e9/X2QV/6/LYKh903O1TOHCZVU0taertJGPGk4HcpZUvkakRdj5a47h\nhaD2XSLHldgiS5rx4UBchAu42+252u2J8YFORjtDK5116YRjYAzXeHvEENiFa6Zw5P3jj2g9QhE6\nUisNbwaKXVBN4Y3HmkYwBpUr8+kdfhzpeBQrzhvCMFCqIpVHEbmXSm6Juj6gmKk0YumorkmLgdGg\nbRHvevEYHRh2I70kIIJaSOVBbHVaAA678RXH/beID6t45muWoXZNGL2QS6WrA8ZWlJlRJBqFcdhR\nY4Ju0L3QcpbwKazE2jrPNO5RpTEazeCuoVr53sPAYRhJRUKMWmvsD3uUCyjlURYRwANXV1fUWhhH\nyS9XunwUsj9//nwTsEtK5ziO22adDYzsNreQHIYfDsZpmnj7NhPCFa21TbrTpYI1MgPNKWO8/Pdh\nHDDOc7i6Iq+GWiJ5XbG9sTzcUtcz58d77GXB9Mz8eGE/HokloYPjtF7o68qL0ePGgct84ebFCy7v\n3uF3Ab0fiY8Lz25umC8X7u/uuDkcCKMXPWw3hOA30IYs+h7u77cZK/Qm1fUHhUHPldzAB0tKCaU9\n1riPv9vWq+h4lf6IMCylQJdIjF4aow84RN6kdUeZTiORkmY5F6bxGXaaOB6vuT6+4rB7xhIX3rz5\nkqvpGSVfeGgXNAGln9GaY0l3aFXQrmLM5rxTMkpSJtFKQiNqBR8CTnmcG8TH3RIlz0JLV52CAKZ1\nlRli8I7RGYxr9J7oWVGsGNNK06I4CRI3Y4wHMqUs1NzQxmOM5f3jO3bDjGpGzBy9YbSn1jMKYUl8\nyO2yXuM0lFRQ6kOsTCHVC+c5c9wNeHf49x+an3zyCZ988gkA+/2eX/mVX+GLL77g7/7u7/j7v/97\nAP7gD/6A3/zN3+S73/0uf/u3f8vv/d7v4ZzjW9/6Ft/+9rf5h3/4B37jN37j//O5JzvSWyK1TGli\nV2tVUZe66e0qTYkQWGlFJYmoto1b+h3spmthL/LAU5xJtdNyYhoOkBWmeXb7ieA8a7yAltzndZ15\n7E8ML59htcFbQ8kGo0fGcENuj/QegSeabTiXULEzBI9XFt0VrgutPPaV27efc7x5QQsabSfGMDG5\na86LYcm3lO1Gbz3JlhsvM8MipCFqh6rJCcJHaYoCZUFVnPHkvLIsDxwPn6AqTGHPZy/+IyVV7i//\nSeZH6oMkI0qchfXshh25LqIAKKvQopqnFIM10JDo5KoSXg+UXrisMwOW0h2Pl3u83qO8pqyJyzLT\nmiZ4s0UDS9SALN+EAfnq1SvKVi30bQm1LAvGWH7+s2+RS6Q3LZG4RnM8HolReI+dzm7aM4SBXArW\nKEoteO+ptQqCbjuwpZ3PMtPMZSNGyU2ttKLUJhZdOo+nE5BklEEhzRfIkV4SukpIXXCOuKzowaGt\nYzCOUg2X04x3CrosnMabK+7e36L2N3TVySUz7Sdomdoy9/f3XB8PInPDMM8Xrp+/YM0rOWWBEpdK\niuvHEYPzAsPQxklr7iy1ZtoqG3bjLEZ7So7kKtG3Wmv8BufI85lh0EwEhtMD5tFChTVGmqlMo0MZ\ny/3jV7y4fkWvhiHsuLk5EOwLnu0nvv+jhbm845Q0eVnoNELwKP2MuDyilUU7K9k6WgZmgl+zdCUW\nUa8HfAuophl0ABWYlSL2JMnkCUyX58E6w+Ac1gMqkVKlFU3NTYwozWH1bkt2kJbc2iAsU06UdkH3\nhNFB8orMnpKFiNbmFT/UjZMg64jSIjU3UAPaKNKa0FYJPV+tdHVmXj/HaPfvPzT/y+sHP/gB//iP\n/8iv//qv8+bNG16/fg3A69evefPmDQBffvnlf3VAfvbZZ3zxxRf/7f+xcijlqBRSTRhjqO2JSiRV\nv7Vkkhipbcf0Tm4ZqsU5jfeGw/4oMAtreVgf2NsdMSe0ajizoxdNTYohHHB2YM1nUok4P/F0eqTz\nBd94MdA9aCuzxNYqKWeULliF/HmXGaxjcCNOd0wV0EfsGaM763xPf+y8fPmK1mBwE6PbswuB20fF\npT6hraLrKHxLFGMYKWuGbih0cqwSiDZIjk1FeI9KDTQ0qJllecNuv8e7a4bg2ZmBb776X0lfPvC4\n/hCtZEPbe2I3DpvMoxHcHq3gcjkB201eC1V1lJfwq2VZqRmmkBn7me6P1DJw5SdUU+RaGQbL/d0D\nx+MzTqcneu8Mg8P6A4+PD9y8es311TWXNRLCSK0S6XB7e0drleurl2LnxEiQW1NcXV1tNB/NZS6k\nlDDabAu8ytaJopTaFkoj3gfmOdM2J9jp8Ynj8Sgb/FqZt4N0WVactWhVmLzhy8+/YHm4Z/LCIKgx\n4vcTu9GRfMCYAT1o1rTQc0Gnxv7qOQ3QXWOMIzjPeHAYF7h9eKQvJ3opXB33LJcTZRj49NNPuX+4\n58XrV8znM7tJ9KjDuBdjREnkFDc1gWKaJjoKHwLBObQJUmEqUYP4wYMSwjqqSmLqFq/SNnG+dxPv\nL4/MpdCCpVlLSsLPXGtDK0/wisuy8vmX/we7ceI4Hyl5x/PjDU5rUv9FLvXE+9uvUf1CJ1GaZGgN\nfkdKCa278E+9kpyhJFEukUyMib0RuLfqMkoz2uF1xemVljO6D2Iv9g7nxfDhQgXtaAykBj0tpJKh\nBUzwjG5P02xyIcewv+Hkbrms70At4uFvidwu9A65J9GLFoX9oFelkjfKliLJfkQrIZ/Z7RD3HcqF\n8/zP/+OH5vl85nd+53f48z//c9HE/RfXB8vbf+/67/673ii1UrYYn1pWKo1mVxnO9gGjPNrIW0Yp\nacdKO+NDYhiOsmEd9pSW2T0deZxPaKM45/eEyZHrHr1WyUr2Ft0HbBP/uHMr727fEHPjsDswjBMG\njzOdOTZifYR2odPxg8W5PaYNIpnohbJErG20WMmt0taF+fLEzfUrCXejE8KB/f6GckpUpWndbGgs\nJe2TVdSkaNVSeqF3Ta9ZiNeuYLRDY+gqU2sh1RN3p5Gff30jLhljGMKB11f/kVo7T+vnQKeUwjTu\naF3SDZ11OH2F1VUwWcpJHISKWOeYumfyE2WttFpRekQj8Rg5NXQrTNOe8/mJw37PMl9wfmC3P3B3\n/57RFl6+ekFXmmVd0MayrCvOWR4fH8URZQYOhz3DMHK5nJnnCyE8p5SM924jGhlp0+ms64pzbnNg\nlc1hJXKlEAKlZHIpuG2+ef/wQPCe3ThRUmJdVuhwe/cWXSNadW5uXjJrRTzdk0vDGst8ufD/MPcu\nrZZ1973eM+7zttbat6q3pFeWZNmcY78kJibELUO+gTAY1DC45S9g3LHRB3HPDYG/RMAEQo4gB9xw\nGrId2ZYlvZe67NqXdZtzjnsaY1WdkNg6EHOQV6vYULuovdcac4zx//2eZ+yv2dy8gKLbzkRr1uVE\nUorT4T2ygpu65iHPjsfHI8O04+52y/qUOe+fmU97XNfaSe/u75nGDfN+RilNFokiFMFHFAbtDErp\nj4H3Vsxw9F3rPAtZGwbxwxVGlQgtEQiUbci3dtfdSESyNpr/Vt3w/v0jMUk0msE5+tz66j7MgEJp\neL//Cv3F36CtwnaSimDqtwzdlpdX3+Dh7jWff/6IjzPCtFaesQYjRiiFGtvcIfrWfhNIUI4cBefD\ngt45VNHMOWGNolcdur7gKZ9ZRWlZ6iqa+cC0jVHJ7T2eC6RcCD4w2h55qTJb07cCTLUgKy+mDdvp\nijl8SSoeZEURyLJiddvFWqeRMreadS2XO1Ao5YxSQwMeqwqioK1AiBXnRkr+N4bbY4z8/u//Pn/4\nh3/I7/3e7wFtd/nmzRtevXrF69evefnyJQCffvopn3/++ce/+8UXX/Dpp5/+i9/3h//rW2JNxJK4\n+9Ty8lcEVEMthZgEVUeEVReat7oEsFeUrszr/jItBmU007Bju7nllN5SCVijOK73TE6SF0WuLdxs\npCXXgJKVWh1VrNw//YSzH9gNtw1cIQ1CJEQphBRQyrDbXiHERF41JVQonkWciKLhvKw1CFVZjgdi\nWNogR0iMNjjtMKZvRCOhkUpSLvoNUQFRkKK0BlOBVFocxcgOaL/QIvPlcnxl9s8clxOje4EWLW7j\nzBUvdr9ByoXT8p5KIMTIqGWjVYs2IMrZYoRDyg5YqUJgNFg9oMUARmPLRE3QiQEdO6ZhwKoOsmEY\nd7iuY3vlGKaev/+7v2e3u8ZYx7ysjNuOEAPr6Uw/NobmujRYx+3tLc51CFF4fHzPOLZp+jRNbXep\nFKZ29H1HLpHT6ch2uyOlALWyrh5rLc/Pz0xTc5nHED6S32spl7ZKQCuFjw3m3BnN4WlPCSs5htbb\nlgWRweieYbzhvMz0zmKcJJemIckVRIwMux1P9+/x6wnbdWAT4zBy3O8JMWBy4Wq343n/RCmF1c9M\n0w6hNUoYYggNM2gsOa9kUdoA0XtC9CjVfFG1aKiZrnOkmiklI7NoaZAKorQKrtIGkE1r4VQ7pudE\nDgETNbdXdyzWsT8f2S8PjKnjED2lrgg1gGjYti/f/iNSKazrSXnlbnuHEYZBbunMSLWW6CObItGm\n5XWFlKQkkFmRL3lNShvgODmQtSbkxNPxPb3Z4OiQ2aJ1h7MSJXr2cSaWBvsuQtJbjWZkCXsomVJm\ncqio3CGqJa2ROezJNtL3V1gzIWTGdgNOOUa34RBfk+LCGmeKqJc6skBpUFISfCalSow0r3pq7xep\na8PAicRX/7Ty1U+W1s//r6yJv3DRrLXyR3/0R3z22Wf88R//8cevf/e73+UHP/gBf/qnf8oPfvCD\nj4vpd7/7Xf7gD/6AP/mTP+HLL7/kH/7hH/id3/mdf/F7/4//8zfJpRBrJuWFXFdSWdCqxYtibHcb\nQrYJtNL1cjdROPs9S36mzxMmdWip2W22PCwjtcyoAD6vPB/fMdoNPoJQ7TghadNUAeTY4B3ePxGl\nQVjPcmEAlizQ3HF3c8fY3TVwh/QEAqufqVpSrKFmz0brtkusijfvf87XXv065yAoqtnu+q6HdabU\n5mePRTQdgdJIIxEytyhEahPU6FdWAcM4UoS89LgFBcUaZ87+ieN8ze3uZfPKSIUSjk9ufpV8Hzmu\nX+L9kSC3qOwoJVy4ghaKpaa2g7fGUvWpJROKpDcj4dAmlbJUdtOG1XtOpwd0sYgL0GEzGX70ox8x\njgPDONIPIz4lfPCICs5ZYlp5enqk70fu7l4yjVukhOPpCaUk0zQB7RSz27XBTtf1tLB3y69WEss6\ns9lseHh44Pb2FiEEj4+P1NwWy+PxyMuXLy809ERYoSpNDGfC0hQpndGsK5yWGSdVU+4qWmSnc6zL\nyuG4x6UOpzegHNYO+POeZVnZXd8yn54pue2Au2FouUc0i5+RznB9cwOykfZP85nOGPq7G8QaiOcj\nKfiW8kiSkPMlydFskENvWdelmQDMCduPdH2mG9rPTMomA8Q6Pqh8MYUaEuG8UksAZZpHvCaS9yhn\nwQqqKaAFKSZCWDG6Q0mNUvD5F/+M667oht+hlzOdcYxW851Xv8H+/Mhxfk+qnmnYYK1hWTzeJ2Ke\nCUu7vza6R4hCyjQYi5asy8LRn+m3GwSSXAtaaqZuRPc9SyxU1zXOaxFICVZMnMuKXzPRV2qWrCVi\nRLrIFCO+FHZXDis7qgAtBmy3xfY3nJY31PPn+HJoGWSRiaGgOgsIcmr5YWhai0qDfEAipsCr7zi+\n+R8HlJCImvk//pf9/79F84c//CF/+Zd/yW/91m/x27/920CLFP3Zn/0Z3/ve9/iLv/iLj5EjgM8+\n+4zvfe97fPbZZ2it+fM///N/9XjuQ4czFikKCkepM+ckiGmhkknRIGjw35wT2l7qZ7EQi+fLx58w\nbW953qt2SS8zfW8ISyHXuf2Q54AQCSU7nvaK3Wak73vO54hfT0gSqkxIHS7cHIGolXUNdGbkV7/1\nm/TDruUAQ+V1fssy+5bDTBXdSaSwlLDgtKJURxWS4/Kewd0Qz8fWuKitoytLRVfRKoNSkWnKYmpB\nmBWnHSUbEIXKSqVVz1IGrSRGj8zeU2rgsL6j74fLfe1jO2IIy4urXyU/FaI/c8jP1G5Cp4qQBSkc\n2hhyKtSqMGXCygGlM1Jl4joTpKIsgo2zHI9PreVhHUo259I4DLx9+0UjqovK8/MDX73+OV0/sd18\ngh07UinUXBjGka+/+pSrq2uMdhxPe0KI9EN/OXpHaq3EGNntdh8BHfMcLsdzTd/37bQh5UdO5vG4\nJ/rQdtnOMc8zwziwzDNaN5htColaEufTPTfjhtP+EeUUFMnQb4j+jDaKsAREbazW+XAg65V+c0Pt\nBnyKxGVFhMD26go/e7TRLbtoNLl4csnMMXO3vWVdA/2wxcT29efHR8ZpS5LtM9Cwd5okFbM/o4yk\n5EqKsXm7bX8ZDLUHpQ8eJ1U7ndRMDhklaJ4JKRrwVwm8j6SQ8FWSlwUrC8ZoNtMVaz7g18ISPSmH\nVnfEYo1GyMI//uSv2W6uGL/1PyDoMJ3gdvc1/uM3/idKFnz19GMEI+Nwze1uYH/Yc1LvEOKE92fW\n/ICzFzygvOw8naLkzNN8pNs4pDQNENJptLPoc6Fae7kyS42BGUQLpqfmBQo+tCsrY1DWscaIkCfm\n+8+53n7ClXFoO2DcyMYILA5ZBI/hn6mybT5yDm2hDyshFqQwH2lRSsumRFY09XcpiNq0M/LfIlb7\n3d/93Y8oq//366/+6q/+xa9///vf5/vf//4v/EcBQig47dpCVUCJgiiJnCIV0RSvpd3N5JCIPmN0\nm4zFUnm/f83d0xsm93XmpVUmye0NFXIlpkSshf3ZM3Y3lKgJtsNpxdhtmJdnUjzgOo2SrvnSjUPb\nHiETV7trxqHj+uqWq90OqiNEeH3/jjXNxDxf2hodZmgEHlEFQsHZH1DCkmtFCY8VmlLSxXxZESRq\nURin6VyrF8pSqEVSs0QbgXWCnFZaorhN86SwGJM4nZ64+frXOPnnRjLvO+pBIqTG6Q1fu/5Nnvfv\nOK9foeUjgxyaAKt4Fu/ph5coeYWqE05YtPOs8YksV6TusK5HSIVUltubHRKFMztC8CynM1bDUtpA\nx/uFnDxT/xKt4HTaE1LhfPZ859e+Q98PCCEoNeD9Quf6jz30+/t7hGhd664bPvbF5/kdp+MMyI8E\n+PP5TM4fCEj6Y0BcKdWwc0rizzPdVraf46XYsBkG5vnEMA6cTvtWTJCCHFuGWYgmrjPKMA098zyz\nvv+Sq5sbbq5v2L/9CttZ5uPMze0tx+MRZzTOdsQYCV6xzCt5J+j79m/UonheVl7cjPjz8UJNL5ew\n+Bk/z1TRrlza/90QQuMoKGVw1l52Z1ByJoX2IPDzjFatPYMAbR3KGjrbcz4eeD6d8aWRiaZ+yznc\nEvPCMkfWJTYfuJIUmQkRlKoUEj/6ux+yGya+/fX/jhIVWldeXL8kpP+erhs47U84c03fD+yuvsHx\n4Ve4f/gJ++NXHM6PeB9wncbYDi070twUFN4vHOOxDXykRlbVwNG6UnXFWNG87qLVRMmKkgRaSZIE\nWSrXV3dkekKa8XFhvz4xrzPIntF9gmFA5IXBbhCinQjW/Eyi5WLX1V/uvzPOfsgzf1BsV5QuiNbQ\nJaRMZ3uQ/17RcOfMaGwjc9PYeLV4cu4oVUIW5MQHrCA5Z3yBVNtRu8SZr97/mE9fCKiJimKNz4S0\nkGvB5+bXyyWR88zN9R2iWPws0dYyjS9ZQrvDsMqhVU8uht6OSJcY+h1Kdk06prbM80zvJl7dvuJ0\nfI8SO3JMUCOhNMGbkvJSiSss8XyZ/C50ykINjbaUF9AOoVvOTSrDbtfjw8p5XcEU9AWYrKSCojC2\nw2iHVBUZKynOlOxRYuR0PDQajNSE6HGuVe/udt9i6Eaej1+iZGQcBFIYivCs65HN+ALBgLFbjI4U\nLGt6BBEQUrGWGajUOWDUwHwukD2H4x4pC1c3Lxj7Dc+lMPYtP/ru/Vuej0du716243e5AIFpQWVj\n2uJvreX9+/eXhoy41DIVKSViiHRdj9IS7/3HAHvXtUWqlIyzjsfHR/SkLsf6jhQjSkgO+0e6ziFq\nRgvB6Ximc5pcazPQxhN+nRnHER884FHVU0MgKUvXD6zzidPTezCK65s7jvsjt3fXxBSZthPrPJPi\nmb4f2U4bttOWdfWYouiHphARQjCfT6ScCMljRcsQCpp5UymLD5HFR2a/MriOrhuoJbdrBaVJYgGT\nkVXj3Ii5urqAMhQN1NVsrKob2AjJQmV92rPMR1IJ7Rje9YzTyLIE4hIRNAB3aSU6lNaczvf89d/8\nb5Ss+PrLb7d2jFDcXO2o5lusV4HT6YC+xKF22xsqM6kcUVqwhiM+r0S/UomoqrDSgk7s/RGNxSnX\n4LcopJaI4gk+UoQghJYr5jKEoxS6fuRq801GewuiEFLi7I+cl4hfV758/VOuxyt0jUQhkSqRksLq\nTdP81pmQJRdv8uVqr/XV1eWar7ZdFsj2NakUVStk+TfsNP9bvvbHGacDV9cdRjmKTAwyEQ+V85KQ\nuQnlc1kuR1RNSp5UC9oUtJY8Hx7oup/Qu5EqAnPZs/gjJTUFcC0OqWCNCz7OTHaklsQ6F5TpGPot\nPh7QemDsb7B2aMeldCDGxHz21BvFYb/ncDxxPjcV8Nh1aFGIoZBqpXHnWzNCqMqy+Eu8pznbawl0\ntt1lxRIhFXrXQ8qkNaJ6h9EO1zUZWi2KnNs9kawNrzapAbSgUz3n5Z4YjigGCisCjZaegKfi0Moi\nq2BSX7vcC74hpUpne4QYWVeNjwnrBDUbZOlRCHpjWOQjiQI54LRh9YE1eHq1peZGHdpuBlAdb+/f\n4OxEjoJ3h7ec5hM3n3wNJRWd69vRNCWmqT10Qkht0v30TN+3qpq79MihkY989BcknL6I6Nrfcc5d\noNARqSQ3Nzc83L/Hdc0XFGNAKcm6BnJccVpTSiCXiPft3rBc7Jc5R87zM5vNFcEXVKcbbtCvdB2N\nNKQkKWdCDOzubgn5kqm0FpcrNRfOxxkloO97hqHHh4WwJmTXkUtmu9sRU2L1C4f9kZrzZYCi0VLg\nOn1JBWTIzTJpO4nqDLU2JqUulSpbYFt+EH/FSK6Vai/RsdwIUVPXU64Fh1LZ79+jRaXTGzbjgq6K\n02HF+4gVklASWVaUMdhcuH//M/7PH/0nfPZ848W3ccqhtWHbXzEOhdENHA/PZALaaLQZGadrQj5j\n0SjRsXhPyAUtJKVUlNLUKnhaTxTl6I2mzw6pLqSitSnMvPctDlQTQgtyaMbLu5tvoUXXShI0OEp9\ntvTdhLWCh+efoeQrSslYZ5CyIIzF6S2pKPyyknIml4y1HUqJy7C1zQjgQyIjooxDCkWOYH5xTPOX\niIZLkfl8YDM5hrEDmdAqYa4gh6bjNMYSM/gwk2rGmB6n5kbctiPGdZyWGaUkQq2kegbaNNoZSy2O\nabJ4v/D+6QuGr18ha22MzFoROLRyl1qkoTcTZuh4OmWO85lKZv4nz83mU06nBUSl5hWlc0OvCYPK\niVxFIwApLioKhdKSdW6KjBAXIpWYC0JLYjpTo8SwQxSDjAnjGii1ArkmBBJRNGRLCZViKlp0F43F\nwNPhLS+vu0akl0Cd0SWha0SpnpBadbLvJrT+hFoDUg50/Q6hAotfGIis89owZFJSssO5a4QplCUQ\n14BxPeM4YVNPiJXr6Q5VC8fZ8yvf+hXevXnP+XRqLY0cWY5n3O2IVpV1OaOt5enpkZQCSld+/vN/\n5Pq6NYKstYxj25WllFiWGWiLkD3b1pqp5eNdJ4BSbSciEfSuw88LSrQdU83LJXDdVCZaSaTRzOcj\ncTkxdo41ZpSybUcXPO0wa3Fdh5Aa70/EGMi+0ZdKicTk2YwDawjEktsdWC1oVck5cpwPOGcxylKF\npdT2sDydz4zDyDhtCD4RvWc5zIS13dtvNiOqSoxqdk1pNNurO0w3kLIn54rWDmUciDa4pMS2U0WS\nlxVBJWQ4nw48HA68ftrzbj7gkyeIRDG5/TwVOGOZz4G4JChdyzOGhNCOodO8efszUg6sv1759ovv\n0HcO63S75pi2pOh53D8gnUF2EpcGtO/I3iNyRFJxolWXhbokGWwPUfO4PLJhRxKgZcQo00AePrGS\nCNG3WddgqFIy9Fu2VxMSxTpLUo2EJ09vOzrSWSKjAAAgAElEQVTnmlCxZp4Ob6gioYPCWkcVCi47\n6RAj0Ue0tXTdBqvatU7MzQrbGnQRoSD6hBQSK3qK+sXz81/aoqmlhFqZTytXu2us1cTSnMtXW8Vj\ngZwCAtOo56WpGly3oTjT1AxjwVrTvNZGokoDq9bSlBVC1Qt6bCLlhbeHn7Ht76Cohk5T9UIbtyhp\n2yW5qFg3cP/wnv3hnnX9O17efpMcaFRp2eqISjXcfwVUvdxVivYhp2Ss0qjBEmLEuYlcPRWIaUbr\nZtWTukns269BoXTziecUSRG0cKRcWVLAJok0AlEiJSV8mdmfvsLWgXTZhZTk8Su4XiOwkAWlOIxq\nx0YhLEoqpiHyuDzy7t2XTOOZJVq6YUPJDZXXOUlSDWnXS0mMLdw6biZiSuyPDxg7cnw68vz8gKzt\nvrTvNtxeX/O8f0Ybiw0Lm92WL7/6nOvra16//ooQPafzkevrW8Zx+phVTKl5fz6oMT5YLJ1rmubz\n+dx2Bfm/MDq9Wojec8yBcTNhjcTatpNNutIZTY4eZzRpXTkdnrGuQ0nVMqy5otRF7LVmlFGM44jo\nR5JfiSXhpMJeIk1Om1ZZzAnjDMopSrEcjnuO55mrjWn0nDQzbTacj0eSDyBFu7pRsu0wNxtiipxO\nJ6Z+YOgmXNdhhrEtkFKjPmgpmga16XoL7YGQMyBJoYW4s9BUJEIalNYIWQnLypJX/OTprEJ3XfOM\na8WiI/EUKKugSHFRv2is1ty/e8sc/xM5BL756tdQoX2m5vMJaRVCS/aHR/pBtkrszTeYl4Gnxy8x\nKlFqBFlBZ6SthLDgbEUlSUpnghUYYREUSszUlFAUkvck2XgBrncM49DsnLLHq4CfF47HAzkVrO7o\nrEFRKTUT80yh2TWUdK0EUAs1GbTaojAUb9D9gFEFLSvndGxBeB8oGVAtIlaVYKn/ThfNHI9gtoQQ\nCCEwTA7NhoRnmjwRzfPDSsmghUAqS28t1gq0Nvi4UHxBd2CUwilATCQ/E3K41MzEx12M0gahInN6\ni6k9m34HF8dPygZtRLt7y56aKyEk9qcHUjmy//k7jDKY3jDaHVaNzMcVqWjVthTaUSSLFh8qqWXY\nZGTsNaVWau2QqhJjZQkLvTYYs9CytVuCr9jSpFSyGIiBUDxCKM4pkTkzpAPWmuY5qonj/LYRmYrF\nKoftDPO6EE8RUUeCV6hOI9DkpNqOUUpKjNxsFW/ev+Hx8AZ1MvT9ytiPaFnwteLcgLUghENpg8qw\nPzwRvCfFE7d24Hw4YmQzgcYsub75GjFHpmni1atPmFfPmy9+xm5q5HTvW9TLdR1StshN8JFxMjw9\nPV3yl+31AcDxYfhTSpOnTcPA6XxmK1ts6f79PUZK9scnbqeJnAO3V9e8ffMFXiRkbeFvZyRa0abm\nWrc8roSUVrpeUzIICjlGejsyXG9Z4kIJTbFbasEaw4VbRhQVp027q1OSw+GJ0/HIq699yuFw4N3b\nt83t5DoO5yMhBtLlYaC0ph8GnGs09hhbttQiSDEg6oVxWVV7L1mHVB0oi46+pS1qq64GH0ihDTu2\n04RPiYf5QCclOkXWOVOkQbu28NNVdKwImcA051b2hSpohW/gq89/wrwEYg584+V30KpQiKyp7Ww7\nCeG0UI3E6R7Z31J3iXV9wi8ngo+X5Ac032ZsgjVdUbJgVNOMiOSpBQwtkJ5JpBwbxNrIS98+YK2k\nzvmikPas3uC6DRh5GZY1sHEunkqhpEoMmZqb6loWSVojUQiU0hQRm0yvKkTdkM4BZXqKEFQ84r9y\nPv+lLZr90OP9E30/8PhwYhhU252oDauakST6YUD0hvPhhFGGrhsuDp2INY6YAn71dKYnZUsuLfUQ\nfSHGjNECa8XHAHGpoETFGoEPp8tgInFa3qOtonNXH2VrStoW1PYNlNt3CWU8dph4dfdNBBM/+/Jv\nebf/ClmbyTKn0o7tSuC0xGhL13Ut2hJjy0ZagekkoiYkFS1qo7NUQwoNuV+rAjp88uTmfOIQTvS9\npB8dne2ggNAaH32DHaeKQ1KF5zwfSXFPyZqBLcpZQgItJML2lJwxquP2+hPun97wcHzGnheuNjcM\n00iXHZ3u6KcemTVhXolxJYVMSTC4HTEG1nDk+uaalGDrOsZp4t27NyiT+ed/+DGuH7HWYTrL/PiA\nURqjWz70eDxeXEqS8zk3/mXfcH5KN9ybMYZ1XS84OUEtGUTFOcu6rvRdj7aanBJSVR73z5hSwcgW\nGM9ND5piJPuVvu8uAFt58XFnapXE+YyUCu16ZLdp/p5S6bqR3Ar6SGVJJaJMg0+XUvDBo7qO67tX\nDNsrnt+84Xm/Z+x7us7h13AxcepmKTWSYwyksJKjp+s143aLVh1VgpECaRRVqubp0QqhJBUDCIQz\n0PeNy3BxJclcyXElicLTwz0n38oRpWac6YhCQinEWkFJiImqE9ppyuovVdW20DQQh0QKx+P7e/7m\nb/8zPp24Gq+RSiAu71XlWjIkIVC0qJAsDqc2SC0QKbLGGZGb6ydnqFUgVaW3rS1XayXlBn5JJSFk\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\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ozanF2Lc9Wwp3MF+4v/uI+9NH3Bw+4PZ0x+nmFivweH6CEHzAfhm87Z8RvVN3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WwYM2AwCBbvDetVI9mh20w+V5QtGBtoJlJVJWeNVgY39DuHNAO3mqczwhw02jq02fHzLx2/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\nJ0mv0B4CKXaWvrO4+fjtB943aObiqdKewUyOaXQEfkAchsL6m53un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SayLEFp6PuW0Jh5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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f85901260d0>"
+        "<matplotlib.figure.Figure at 0x7f318eee8ad0>"
        ]
       }
      ],

From 4288b2b5fc1fea600a336fc56fbaacaae5c94877 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 15 Aug 2014 14:13:22 -0700
Subject: [PATCH 0477/2053] [example] set phase test for fully-convolutional
 model

---
 examples/net_surgery.ipynb | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 2b5a8611d33..4710236c6e9 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:de67ad69a180b5a6b350d897042fe0bd424f7db63112ca3b7faba259ccbb9be6"
+  "signature": "sha256:742fe1bbeabccae59725a5fd6e97a0a0b2b90842d1dd5953aecd353b2d87cab3"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -280,6 +280,7 @@
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
       "plt.imshow(im)\n",
+      "net_full_conv.set_phase_test()\n",
       "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
       "net_full_conv.set_raw_scale('data', 255.0)\n",
@@ -295,22 +296,22 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "array([[282, 281, 281, 281, 281, 282, 282, 278],\n",
-        "       [281, 283, 281, 283, 281, 287, 281, 282],\n",
-        "       [283, 283, 283, 281, 283, 283, 283, 259],\n",
-        "       [283, 283, 281, 281, 283, 283, 287, 259],\n",
-        "       [283, 283, 285, 283, 283, 283, 283, 283],\n",
-        "       [283, 283, 333, 283, 283, 283, 283, 282],\n",
-        "       [905, 358, 259, 283, 371, 259, 259, 852],\n",
-        "       [335, 335, 371, 185, 186, 263, 185, 356]])"
+        "array([[282, 281, 281, 281, 281, 281, 281, 282],\n",
+        "       [283, 283, 283, 281, 281, 281, 281, 282],\n",
+        "       [283, 283, 283, 283, 283, 283, 287, 282],\n",
+        "       [283, 283, 283, 281, 283, 283, 283, 283],\n",
+        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
+        "       [283, 283, 283, 283, 283, 283, 283, 259],\n",
+        "       [283, 283, 283, 283, 283, 259, 259, 852],\n",
+        "       [335, 335, 283, 283, 283, 263, 263, 331]])"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\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ozanF2Lc9Wwp3MF+4v/uI+9NH3Bw+4PZ0x+nmFivweH6CEHzAfhm87Z8RvVN3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WwYM2AwCBbvDetVI9mh20w+V5QtGBtoJlJVJWeNVgY39DuHNAO3mqczwhw02jq02fHzLx2/\n/+bXXNeVjx++5zo883D3BAjry3fUKjzdv+Pd28+ZxpkhTPzss39HyoXr5YW4/CPn88IrmtfxI2/n\nXTf7DdDcnnWBoAo73SHGqymsZGpOzE6jm8dr8LIRVaOoFarmeGnkMlJLRdeBh4dH7sZfsB8O/OUX\n/y3vX7/i//jb/4lj+ooUB7aSCEHITtNUnyEqoFRF1gZaY1Dg5okxjFyWS4carKlvVY3GOMPkAhSF\nhMAaE1tZMSiC0RhjcDogqbDGDe89iHA9LTirej99OVFrwfmBmjTDfkaJMM8eQfFwP7GljKYwDoHR\njRyvR8IQWGM/HHd3B2KMuNZYS7ltsvsNc1s7iq7eDtPWGi8vn0B3oIpGQAoijSkEnO3P6v2049PH\nb/jp23ecj69477uV0tm+8GiVGhe4UXJSKkyTBa0xuts5rXco6HPquMEwYq3uTaaasdYgLXNZjgQ7\nMh6mvtjwjuYDpnXivG6GdHnh6z/+lvfHjzyfXvj201edSm8dzhnu75+4O0w424gx8t13r1w3QdPb\nLoIh1hUxCmsGcoS0Sv//jZEwgDV9DJO8JlmFSuDI5NpQWeMbTM5A7qMrrTRFFZx3VCraOaoYlLbo\nWtFVKLmiSyXFhXDwVHGs10bMhfWaiKlh9cabrWdwr/tPPN4/cjdNXcMxaJ7mLxnDHhfucLsJFzTG\nVHKJrNvG8/ORT5+eQeD+4Q4bdqTjK6oWhoNldAMihViEdIlIEqiG1gxxbchaGLVj0h2sY+VPH4t/\n+kj9/+/rf/jv/vu/YotCaiA3P0qSTFUCzqOHAfNDhVIJVYSUCqUocuvLo9yEFBPXbSPn2y2i1S5+\naomYEilv5FTIuSC5dTyIOGrSpCjUalHKAhrre7zE2D7b1CYxTorSLtzdvUGaJ3iPUYbr9UStG5fj\nwrKsLMuZy/XEuq48HD7Dmzu++OznfPb0c+53b26RlzMvLx8w2nF6yVyOiesxUlMlGN8ZndlxPVWU\nTDQVUGpHqoFSNLpqgtvj3D3rtQeLNYJR/RNUa8EYjxTD+byw3z3xxU//ks+f/g2fP/6Mv/j5f2Rb\nhU/HXxNT6nW7lPDKEozH2NvoImWW1Jsg1oSbtbLbE3NuxEXQ1TLrPaObsXiUDhg99adzczjTP7C0\nKOraoRvSug53v99xd3+HUZqYNwY/UGpmngfC4NBaYY3mYb9j9IEYrxjVjZhaK9blwm636/NZet4y\nbfHHjKbzrttAl36b9K5T319fX4HGul1QSlFyREmjlIpRCu8sShpT8HjVD9OWth9hHrUV7u8O5Lyh\nWsW7/jpQSt2215265KzrtzJ1++BwvZpqnGGcdtTW2GLGmN6pLzUhVTHPM+IMOoywn2mioRTEG9Ky\n8n/+8j/zN//w1/z+w685Xc9sKdGq4u3jzN29ZRr7Vvjjx1e++e6FGBWgf2z5FCmU1hcnP7yyQKE0\nGKvQuhN+WlWUKOjUbZnOerwYBgyjNzjVc6koQbvW/17nQTs0I1YaroJNvSAQRkfYO2wwpAIpR9IF\ntrNh2zSx9EyxHTPKZUQSKV07sSns8W5GK9ed9L7inMIPmhAc47jn4f6R+8e3WD9zvV65rK+4IDw+\nzuz2E5OdUNWzbY3Tc+L1VbicCssJ1guQNIMKjHbqkGvd+Jtffg/wP/5zh9efMdyuQQYGP6BU7pEe\nM1FyJqXuOXfeYfRE2gKt9flYzJnBOmLOvXGRCzmCPkds0IxrZb5rzJNHWg+r5iqdpagyaxVUFpxW\nKCxbbljX0A6MbWit+tDeKZRUwpwxOnJZv2Nyv2BbK1ppgh1IErG2sW2d+N2aoVXheT7x0ze/gBJ4\nc/8F825P/vQ1WjmMrxymkbjA+2/OLCkhLXM/BnZuQJRma8JxuTLbO/bDPaO1yOUTLS+sIngJ7MfP\nYXvFeY1zjpTXG7RA0ZQmx8yv/v5vcWFgnmbePr7B5YG/+Pl/5LJ+5Nd/+FvOx42SGxeVOQxXgvEM\nesda+izomCOzt4xTJ7jnnGmFHgdrilw0ZuvLg1y7xdOPFucD1RiuNaO3xKw027YQt43Hx0e8URjV\nwFsmGWilsN/PDOOOVjXaa8Yw4I2hpguBBK0vjOLyTLCGvJyQMHG9nBnnQwdcONctm6bPxGsrPzIz\nY4xoDctyQRtBNYfcRGjqNkPuGlePprKtZ0pcWGL3O8W49jhR7XNSafXmc2q0WjEaFI2aIoRAE8Eq\nhTGGwQYkGKqGnDsebnKBdU2U3DO+3vb/Hq89TTQqC4wzkjba8ZUaG86OLNdCWRTl2q2nT2/2TJPB\nOmjG8nJ64ePxynUttLjAwWGMxfq+BFImYNWKsYbSPC3N5JhILuEDaNWQFnFa02jQFMHfIB1V9QOy\nto7Lo0d4DIDSPZerdFdylIyxgh8syoBWlmuOna6+CPmlQ0GwCeUa48FivEHrwJZ6fpOkyZdMCCve\nKuppY2PFSGWaA4f9I/vdEyE4nBl5+xTYTXc8vr7n+fSJWoX9/Ja8rZwlYktBhRPv7IgPN5iPthht\nmYxiGgaMNz1+xt/+v55df7ZDczuX7pkG5mmAVsgJcr5gTMFq25ckw4xVu94bHgWjPE3AuowWISrP\nZjPrunK5JHJUtLbSiuCdwTWLmEamk5CaFFqrQGU3aLT00HdNDZogqtO+vekHQGLBmYHT8jVqGKjr\nQKmNw/wGYy3vP35HXVbWBaQatDZ89fXvOdw94NyOdV1wfiTGC8fr99zv77ph8ycDZc3E14azjloX\nJjWjxfAxe2KKiESmKbALD+ii+fjhVwz7HTo3WhVme+B+3He7Ylm4xAtrSixloRbNy/WF/+2v/1eO\nryf+w7/7K6w2UIXD/i0//+Ln/IFveX3daKoRSybkDeV37OYnzjnRWgLVWLcTWxFqs32WZDJK9VfA\nOW1I7B8kzW/oChSFrR7XIEjjnCuT8cz3M6MfCL6Tg3K+GUGtYxhnvBlpdiGnhDMOqwo5RwYbUbZR\nyobUXgFc14LfNZQeeD2faZJp5w51Ocw7jq8nnHdsW89mblunlDf5gWqzUGu70d8tpWW8s8ACmu4n\ncoblmijblfvdAWeEZbnwcP+O5Xwk5RODtZSUyXFFmdCXgkoYdg3rDqAcW0vc7Z7Q2uCHiaoc5+tC\nigvWBoYwoiWj8TRlUbaC6zZLjyVvid/+4fecLgt7PzIazzzSl5G2b7trcZzOV9ZL7oej9tjQb5DG\nDP83c2/yY9ue5Xd9fv1uThMR997XZVeoGpVKQraMxAgJIcEUZjBEghkDpuA/wEgwYMqAEY1AeASW\nGNBJgBjQCBsbnLZFFtVlvua+e6M5cXb3axmsHZGvqMq0IIWyjhSKF/Eizj0Rsff6rbW+Hcaon8tO\nd/lyHzy1ZmLR1LlQiwHEGxNdqUZTtaNpGUm1lZFcKYX1jSxfTamKmjWqaZRroHvsQdN3VhzhjWAR\naplJywStYkYlYOII5mToR03otAggakPhsfoNwd3i7UCMket1Ylky2mim0ZCj4B637sDQHwg2EWyh\n92e+ePs7GDpas9RiKFGxLYX6W4rBeTrfYZ1DG0UwTtzSrH39Hf01/sNfWLt+fTzN9ZmUnyXJkAOq\nybg0zwu1Rvo+0vUHVINaFTVncXDeXadpIsq31jKiJCaXSqyZtBkWs1GsEGq19vQhyK6uLaSa0E6D\nSxw7wzQbtlmjsxbdsK0Ym2nW0XQj5ogh8P7jn+A5o/SBJq2FOGK7no0sZrbB4Yznpz/7Y0I3cDoe\neZ6vXJYHLtMDxm4EFeid5vZ85poWVFPo1rHElc4lPlGVP1wSsfOc+lvO509YlGV+/JI4zSxEQXud\nx7nGbXB4N2Jj4jgo1qkRW8Faz3R94n/62/89f/cn/wtvbm85HjqszQz9yNu3nxDTR3xdRQO981F7\nfeLzm4Hn6RsGr8g5MwTDxEpr4h+6pcJSJkmBNImcMw1H0jJGq5YJWiz5+mHAZYXRZv/bb2xxwTnL\nPE8Mw8jxeCTFwjqtjP1IKxNaiS2fc0esEd6hKYn7y8SyZZQ3oAulOZ6f7yUFYOi5f3rg4emJwziy\nbStKaeb5SilCR1JKrqeUxDmp1UwfOrqgGZyld5bL9MQ2r+SUoVSWZWJdG+N4pERxa68BcXkaNOu2\nMlgPNNZ5plWFUxajNN57ctwwPmCcwRjPoAyKxrpukr7oemoraO8oraEa+JIoy8I333zgH/zkJ/xv\nP/lfeZp+RvMrb98cabbQWmF6zmzpypQrWykY7+hGD6VwPI2MfYemktTOyji8w3fCzXROE0tD68TN\n8UDvRgwdqgR0sxh6Ss2UOHO93BPjilKO03FEKdi2jUpDW4/SFt8N9P3IMHichi70GNtRmyLGxDqv\npJh2Ir6WjKhe0+3KK1EMOXS1WOXo/IBz4jmrmiiKUkrCNlAST+zxnN2R02GQg3LZJGYmNzkUdYCi\nsdrjlEf3SBb9HtFhtXnlabaaRUjySx6/tqJZ24LViVhXni+Rbau0mF67z7Ks5DGyLBaFou3O3U1l\nUI6GEx25UtgKwTSSdxjtMXmlrFBdQYWItoZWEtY47CAqoq6DflAE6+g1XOsV0kZbFXMsrKojbUmc\nb2rB2oqqK7FkjK0UPCknlNIEHyi9R+kFVWS0N6bwx1/9BKUzp+ENuV2Ypm9xIaJdRueOwRjcYSCn\nypbE4qwcHMZAZ6HzR97e/JAvvvcbfGv+hOuHP+bpeaOVwnWa6IYz4dAxT5nh2DN0hVInWoBYGq1U\nut6hU+Lbb/+YL7/8e7y9O/DZ5+8Y+jeEcODuTSU+fY3WmZifGQ8DkRmtTxxvP4f6iOsaiix69ZTJ\nRlHNSqmFTEZViYPdzMq6ZGJJGBVQ4QgEbINge2qTQ26dpWBeLk+cz2fu7u64Xq/M04RqK8P5hsfn\nC93NW5SyhN4L8Tg3pm2hFkNK98T1SmyGLWXKmlmUomlxL2ooVC1sWW7QlNIewVFeQaKXrsJ7x7u3\ntxy9paWFnGfWNfLm7SfEORDXmbbHXlAiOa74fsR2Ay1l2W+ixHuyOWrNUC88lYiz0PQRqxvH44Hr\ntFBaIlWxqhMjCYfWXhII6oY5vJF75PmJ+6/e8+VX3/DN4wPT0xXtDCZ4FI2SCzHCfGmUrBgOJwav\nWE1Dh4HOB+7OdxyHUVyJjCf4juPNAe8l+TNlcacPNnAcD6Ky8p3cF6FDK02KhefpytPTI1sUZ6p+\nkKlo3Tauk+j7x6EnhI4QpHtWTaGM2AWqXVIc142YMltascrg3IlxrPjeYbUhZyMsBRpaN5rSeNPh\nney6jbagITZxqhLvzyYk+6ohGgY3ymFqC82IaKELA8F4tFIUlV+5vDkV1G6mAoB1P//vX/D4hxXN\nDvjvEFaBB/4z4K8Cd8B/AvwI+EPgnwce9+/5q8C/hGRh/avAf/nnPXHvNIQAqhJzIWVYSmXNBQro\nCiUvaCPuKw3ZN0qes6M1R9sqKW54F9DOcz7JKaJ1T0sLNUc0FYpQh0qW/dH5dIf3jqELqBZBZUKu\nhOIZbOLL58SUMqUotiWKr7GeJV4jNxrvicXBjhL3w4D1FTeLY43rHeOx43h+x7pu3F9+Qi3P1LJh\nY2Rh422we9fjCK7SSkTZkWtKdKFxOjg+/fQtb99+wm13w9Y9ogrYUmi18OZ0RJmBGjXD6UhOEdsH\nSJW+FLLamFGUsmFN43wYuDxF3n/znsrCJ28T3gYOtjA5SyuVWlbW5T16fEfNFdedJCWTR4zxKA1T\nukcZy5sxgNHMzxv38ZmlZUITh/R1iwTrwFvOh7vd9k0TnGdbF1KJeK05jANv7j7lw7dfMa9XnLXc\nnSQG9zAesVajtcV7kctRG64PeBvQyvHhciGtkXXbWK4bWIMNlrhEfHBc5mfESzoBYjILilorOQtI\nYa3h7nzHEAaMyhRjWS6JfhiI28rxfMdjTahaMHtOlKFiW8VUQ7WemjaU0pS6sKWN25tblnmhpQtP\n9xn79kekMtA0hE6s+KgKozU5pV2Z0qE6j3Ke6k/yOnng28sTT9PKu5u3+N/+R5nXC42Edx3Wdixb\nIn667gkIjS0u5FLRynPoDwzD8ColHccTh8PIsR/xRkjr2grS75xDKemwtFF0XUcIHd55SkncrAO3\nR+ncnfMcj6fdA6CQ4kYumS50gqTnQs5lDz0E52TE99oSl8SWt12QoOmGQN+N+E44mhRNzqvspPdE\nS6UaxgjrwFpHUUgBjhulJJw11P3fMkYRQocxhiZHJ8roPcxOC1E/N0pNbHElbpFUAt5LdpPWSlZ0\nv0LRXIF/Cpj3r/0fgH8C+GeB/wr4t4B/DfjX97ffA/6F/f33gP8a+B3Em+lPPWoqhGOg1IjxkkxH\nkeV/WsVWrJVGy1pCpFrGGY0pDT9YWqsYb8AEOufoDp7hEFC6UFHUImpqVRtbmilAKQ6KwpoDNVtK\nVigC21oYWkUzc3MoPMXG89qEt2l6oFJbZF0LLWtqzTvyqNHOoTvwweBsL27qzvHZJ1/w27/5l/HW\n8wc//QN+/w9/zDopQpMY0zmBNp7jzRmVKnm98Pw4Q6/IumGHgPGVWiPP14X7+ysf3j/hW6MaUKZy\nPAW0FVfyqi2tlN2dXvPZJ2dKNVzTzPP0QGZlCBanR+bnmWf3yOlwEKCi64jXDNZQVSQu7yXxcHvH\n2H+PWCy5PuGsx9kjWif6YOmGA52OxAQP04oh0PseXyqqeTSWlAu2NZp1XJcVVRvj4Qw5MRw9T48f\neXh44PbuyLEf0UbAheBHQuhoyhBC9zpSQ6MbB25SJQGxPjKviEFDSszWSsaLauSYsF14NSN21u05\nOA2HwlrFMAz03vN8eaTEZ7q+R2mLM4b5emEYwfkB8iZgB41SIq16nHHU3e08KkfUkvOdc+V8c8s0\nPdGFnlY2UZ5sK9oEdEU6HKzYz8WNdjhhwkCzHtKKKpWUG91w4jhmmpEwt3U90w2BN2/uOIxnamw8\nPz5Kx6UU12Vi2yJWv7gECYc1xoRqhbguPC4L1noOw4AzAduMHCql7N1cYOgHvA+s60pKG1ppbk4n\nch6EkK+0jLFGeHtsIm8AACAASURBVKm96amtkWLa3frFaq/rhOivd0d122lsG+l7kfR679De4Z2X\nidI2iZtpRriVxuxFv9Aq0s1buRZqLVgnvp7GmN07VfbRxpjvfG+lpLzLlsUmTsIMxUGKvcCWklDK\notSvztOc9/ce2Qc/7EXzn9w//+8B/+1eNP854D9GjvY/BH4C/OPA//hnimap6FzxFpaocC5QfRLi\ns1Zco6JWLaqRLAiosoIEettRKATf463FG8nGyTVTUkJn2JZKjZDrFZwn1UzLgc1LZzuOB0oK6GpR\nFKa0ontFK8/oEEhZUWrGWck0GWxHaonLNZOroSGmAKk1Wp0IgxOTU1NxI/zgh7/N7/3WX8KqitOO\nr99/zeUyo6N0GznBzemM1RZbC6VZ0orko7fEEDQpLzzcf8lH9cQfvv/7fHn/J9y6QNf3VBWZpgvj\nTeA6X/DekdKCVoXx0PHm9h3OHfjq2y/JccIdAqWbWVMg58S6vqfrG04PDP6AOw2YotC1sSwPaHvF\nqHf448jp9BvcP/0BedvQ7RarL/Rdz9if2a5P1JixuuKs5aZ3iI19xXU9NEMwsOXEOBzoQ0deZrTT\nXK8TT5cPDKPjcDhxdz7w8f4DVhVsF6QLD0HydpwDazBN4Z2hP4wcaiaVRNwK05S4zs8wzfT9QIoC\nltkmK5t+HKhYOQxKxWnR/o/DgCLLqL8toAQhjlsi9CeaBqUN2jpiE8oTrZBL5Hpt9OcBpfZYie6W\nnArearQ1fP+Hv8O6LZzffMpw+ylKB65rZamgjPBtbfA4rTFK05ynmoBKF1QBrQe8P9CHiXWbqUbR\nHU/c3txwe3PLMBwk6tho+q7HOse6rpRcxVVKKVKKTNPEsky73j4xzzMxPvEcOk6nI4fjgG+it+5C\nx/l8ZhzH1++HnQZmLdM0Y2oTp6jdo1L06rIGEV3/JiNuBaVkDTDPYvtorf05BctIIcVompJIihZl\nPSdxxzIqZ9UoVZOreAmQwHmPMkY6RO9elWEpJVQT4E9r+TuoJkYx2hoBmA2YIgBVrRW0eTV1sda+\ndty/StHUwN8EfhP4d4C/C3wKfLP//2/2jwG++H8UyJ8iHeefedTcKHMmO09pK0ED2lCKx/jA1mZB\n3LSi7yWC1DqHUyJ5lEIpnMxUxMGkLImnZWWbVxwWk8EaT3UFrEK3Qlwnnp+vPH4M9F0QMiuNViOx\nBrQJVLPSHRTP946KxnQV24SvWPuJKSZq7SiqEHN9LbBhWKlec1Y3fPbZp9zdvKXkDVUN5/HI+5bR\nSGBYcRLoY/rArDKPlyv3y4QxAa1AmcLUX3gffsaSKveXP6I7G2JLgCHYkYIhriuxXbDWk0si5ZnP\n+YISCmHc8EHYAypnumNPVyK5KGqb5Q1HVLL3KaqyzoVp9TxfP/DpJ28YhxPGGcbzF8zPH3GmkCZF\nXhWXFMWSr1p0dnT9gFeDnN7WEcyRg+kx1XJ3cNQ0sU6PBNfxcH+PMY03b78PNdN3PcuW5VD0I877\nvevRNMTBPUeFoYER02H52g7XRfqwkLKh1bzvxIDdlPpwODKeTgTniSlScxEAqGS0bjvNTXxBW2n4\nTnw0+/4ENWJtobZGrzQqJppX5LSybU9Yf4sNpz2gT+Itur7HG4/per7/+ffpzzdo76EohmBJy8q2\nrrRWOZ4PEBwYBznKDjAr8pJZ1kjZFUeAIMTBQ9N8/PjAh/sHWpEccO8dTjnGcaA1CCEIWFXLXjQX\nUsr7gbm+au9l0pDRXCmh283z/Gr63JqsMqy1LMvCuq7UKhQuYwylZCG675EeUhQtYkUjxUtpvXsF\naLxzeL/vFsWXThyKdhpTzoVa2z76i/9p0+JV8OJMZI1m2zb5EzfZZ2q1u1nVJvaPWnAQpRoa/Zow\nW1JGeb9HkWhxejcao/cGTb288l+taFbgLwNn4L9AxvXvPhq//N/5c/9fzAWjsxgSpIhVHms0KVZa\nK3izh6BV4f8pI7sWpYGSSTFTjaNWh66J9Tkx3W98eF7IqdB1ikPvqU3yYJQWMGCNhWo0iY0YLavr\nCTbQBceH+5n+xlFVYxgy5EqcErSAsg7rOlzRUJ4kf7mCUpZaMnEt5FZptXI43PDZ2x/Rh4GHvPDl\n/R/hfeZ46pmXBZMMWNmOOR/ILqOTYXA9OnViBqss82awzxNbeiYuH2hmRVlDbpFgerHV05lWZ+bl\nSkIxrSstvqdsiaGzrCpJ3vTYoUyiuSJmy8ZRWkW3nlY8TQVZpmvH0d2gu5ntCewbh9s0pWrG+nY3\nJjnRNUdLYHWCm88waqTzht558T+tYEoR96phYJuvpLjiVePh8SPaaW5ub4hbkxEWWNaJFDcOg2MY\nBmptxLiCkn2ZUpqW1122mOi9JTrPoe95Mk/SFSmLKUV4gsbgjJhca63xRgyQVbCAQdkGZWXdc8YP\nhyPOWWKOqKaYrg/cHg8o20E1WKvQXd2fs6FzY5sjVmesEyqO0kYUQcERhh60AaPBGGqq5JJRNUuy\nZY7Edaa/uRGFks4Qm2Qr5czz5Yl5mncgUopN3w+s68rDwz1KiU1d38sIvCzrHuRmOZ9PdF2QFcfe\nIb90bykVrtdnkZvurvetVVqDabqyLt/w9PRACIEQwqtDlNq19/JvyAhbyku3CV0nfqFa6x1MUbvo\nQj42Zg9G2ztCKXxqPyCFESMmOY1SMqVWtrhRTcUoCVNTxqJao1Z5PbVWtmWl1fpaZJUxkr304k1Q\nMnGrr8wJ4oq1jpcx3XsP2tC0Jrf6alL9ix7/b9DzJ+A/B/4xpLv8DPga+Bx4v3/Nz4AffOd7vr9/\n7s88/o8ff4PTnmbg9gvHJ28zxnvmpwu1NIxSryqUba1kUylFSUHJkUbF2iqRtaViVo/D09XGmhO6\nsGdgezSJHBu5SpwqsZJx1Axx2Zj1joIbS46GmxuPt+AGxVPJXKYn+qHHWkc/3PI8F7Z4gT3eyTlD\na46cDUtcuBnvuPEH4jbz1bdf883DH6Dagg+Oh4cHmhET23engAmeQ3BY13h4fpZ9yraSlacYw5ZW\natowwdMP4hi+LZHKE7Z2hCqJnrEtpNng2oEpwr3aMKXH9QN98OIg3yIYyQW3xkDR1GRoOojj+DjC\nYJnDSh2vUAxqUljVM2SD0Y1oK1GtGCwheJx1/Ohdh8JSSqLWwrLOXC4TNDB9xxo3uYCb5bpM1LLS\nhQNrlKKqrBxUj9cnjn0nWeZNzuKUMt4bWhF6S9xmnBMKjVEV7zVOw+FwYM2Fx6dnpumCVoBzxBjZ\nthWumrmJEshY0cW3Grk5dmL2cDpLWqYWilXnLNvjI+3goDZ63eN6R8obtqm9QIiiqDTJZu96j8Kg\ndsfx480Z2x3QSuSVpVWucSPFjEfRB4vTDWKhvbulWYO+bqTLlesioFJOEp1hrRN6F41c8n7D75LM\nnJmvV9ZtE5ciZ9mWK/M80XWBhnSLL+Oxwu07wMrlckEptRezSimNdZ1ZV5G9frdoDsMgoXzevxag\nvh+EDqSEB/qqgNrH3G3b+bhWiuyL8fO6rqRNxu2YEl3XvSLatUiGfM6CclfT8FYK+DB4jAVrh1dW\nRCmSTKqNQWlN3fs0ux+Son6T6cMYg2lNEPxtJedCjJG/9Xd+zN/8Oz8WKmP91YCgt0BGkPEe+GcQ\nadHfAP5F4N/c3/+n+9f/DeA/Av5tZCz/beB//vOe+Pf+ykCv3op+djTUOkHO+A7ev98ITtN1Hgts\ncWPeGl4VlCooo1FGoYYGJqKMx3kto1UAZS2us5JdXgXRVs4SHJStMG8FY72ABaqRSkW3vCc9Gmxt\n9IOhmIjzGZxDW82xf4NVBV0fmJ8WvAV2jbU1YGrFdobUIl8/fUPnZt5/+VPu7z+gzZXSNNpmcl1Y\nVnhcBg5p4LY/YTrFXCrPyzPKGLTxhGDl59WNTju0tcS4gVYi/UM8Rzvzhr4TUMqZIyGMHMNRwryM\nAVWpGYxh/x4FqdEZcfnGKLz2uODJObH4lZxGjJFuoTWFtw6jFKbP6K1AU3htCaGTnZxWFGVYUkH0\nATIeaRRKdSQFa3qm1orB4VR4NUbw1jOvH0ip4m8Pr4bAoumOUKH3npwWStn2/GxR21hVsQ5KntGt\nYDXkWAR5tI7WCiBqlVwzzlrJMDcWH0bWZUbrwv39B25vTlgrtBhjApVCjJJrPhw7dFP0rqPmQm4N\n2wWcE1OL1hq5FE7HI8Z7lPV8/PCRTz511PGIKo1WNkrMqFLIdcOaHm01sNCsRdkblJu5LB/58mdf\n8c3TPUkAf5z3WGPYVkPMSQANbVjSht114CiNGwOj61A0Hh4eXjtM7yVuQxFfUelSxHk/50xrgqCH\nIHHZMUoS5TzP++f8q/HJy3uQTtdYgzGabYus20rOsmYR2pCM8Nu2vtK8cimkHKlFkkCNtaxbwGhD\nboWSKj44Us1cnp8pdaMLI6fTia7r6V0g5czz5ZnHx0eepyvGWvq+4+Z8Qx+CGCmjyDGilAgVUGqn\nUUlSw/N15fHxgZwyv/Nb3+P3fvc3dgmu5t/99//6/+ei+TkC9Oj97T8A/hvgbwF/HfiX+TnlCODH\n++d/vBfbf4VfMJ43P2NN5BhuWYyi1D3jWFuWJXN5KAQD2iZ6G9AlMW0rRon5qMkKNziGzlOSwgUL\nDW6tZc3yfLk0Uiu0IjuzIWjxKKxW8m2qFecTstjd05iLgZzZksaOBddbFKDaBjUDlUpjnRPVG5qR\nWIzOZDqneXO4g1T52Td/H2cPfPX+p7RiuEwJa8AFw3xdMKbx8fEDvRVPTesRUm/0dG4k9CduhgPO\nADVL5o63bBGEOVPwdkTXDqvFmEHXiveBrj8wjCO8nPTbgkbR+x60Ynq+sqwzSSe6vpfdzmihVfqu\ngybxxs7a1zGqVilEL5nhSkFTou9+iuKbGbzfTZUb1gqJW2tRilQs3ThSo6UzQh9LKfHJu3csy8LX\nX32L8wajAqlUrLFcn59JaWEIZzHkMLLz1hiJnQBqWzFG0w8d7799FFpya0IUVwqjNM467u7eQdto\nWRx3nLHEZaXVyDo9s24LWr3l/sMHxq5jXmdC6Ckl0XvPFiesg4ZhCOF1FWNML0GA+45sW1eO/YjV\n0A8HrJM4WprBjo47V8S5vmVC1xMGIcmrZkAFlLeYw1vm9i2P18jz0wPee25ubijWkrRGG8vheKLW\nRqcN3nUEbYSP7F4OGCG5v4zSfd9j9r2f1g3nHMMwcjrdyMpA632va3cHMknvdHvH+JLFJMbO6ZXL\nuCwLzotvQc5i7kwTZHocB8bxyO3dHcs8s22RUjKd1dTaMU0TMV2ZpmfWVYqztW43/qjEFJmWiZw2\natO40OHnmbQtuwLwyvV6Zdkibt+Vliou/EYbahX1kPNyeHrrYN+/xrzhvOf27lZ2mezWgkZhXfdL\ni+I/rGj+78Bf+XM+fw/807/ge/6N/e2XPlI0tD6Qi0FpqFXTh0g4BcyP3vL763uuT4XzMRBsQwVw\noyeXgqxCDS1ldLb0/YCmEpxwN7dlosTGVitx3hh6Q1cavjmqLQzHSsmatBTWJaGUOCJVrShlZU0a\nszlMb1DovZsKaB14c+h49zsn0m/8Ls4Y2E9yUUVobm+PHE+ewQSmGQZ7xyfH3+BZn+g6wxh6dBP3\n+GAdnfecjid88Lw7FMxbg9eGYRwYeqGElJwoWc4eRZHC1bLkr0Q5zYLpRPFSJdJ0W2aMNZQU96LS\nY41lXheWuFGAlAqlLgxDv99M+nXpv16vbLvjObCrMBoxJWiNEMKuusjM64w2llQyugkK+tKN+D3O\nRBmDcwdSMZQWicvK3c2B999+w+PlPX04YZ3aZX+WuDUulwvWFaZJczrfys7LSMfQWpOd7E4dsUZG\nyWm5UGm4nWUwHo/0Yy8+jcqwqgVXAxpNKVcuTx+oaeLdmzuu14l+GIGK1YbTYaTkiNaGrvPEWDif\nB0oVu7eUEmqSMfV0vkEbKTAtR6zzwi+1gXoaUeYAMaFyh0srzhaa7SGISUTTDt1E4aSMZhh7Pv3s\nU4auex3Da6koJCHVaEvnHaiGsqIPN9h9hyqgqU7CBe68l0Mibvt+U0vcinM4Z2lNTE+Grt/3yBFr\nLDfnG7quo9tpWy+poClFas2s6yZcyT0eQmtRPwVnKRjQPeOx53i65XQ+kWKWSUlV1lX2kDVLYqlk\nO0Uk5UOTUmZZrszThbTnR1ljyNsqGv8iaLlzluE4ytogeIxV5BzJKeJDgFZoVZF3x6vQBbrugHOe\nw2Gk1g6lKnF7ccFCpotf8vj1mRBfFTo/c+w76paZ8gPGOQ7W8+7cs/zwlj/6Px+Z5sowGLqDo4aE\nLg7TFKaCUbtSBEXTUHUTl3ClyevGMkcsCtPAo/Ba0YxFd7BtjVoitlU5lXOjRYn1NEFTXCW3xk24\n4zS+5e35c27Pn3Ae7+isiPyd93jrsUYRs4yBPlh8MFQq+VT5/Lzyw9s35JLFFsyIWW3Nhc4HlG0c\nhsNusS9jrmkvXEW/ZyjJnk0cmxJpjTRd6UpjbhsghHfrDBrk+Vtim2aM0YReuKHrOvP8fCFmiXcY\nexm7nPevsRAvFJKcM9M8C5CmxXF9WRfQmj4Esc3TWpzZrUXvb7VUlJbC36hCONcWUKQyU1Rj2xac\nLnx8+Mi2zPT9gXVeGW2ApvE+8OHhvbgGWQGtUq7yWndXfdk7Cbew1UZNmS5Y+i5QlorSEmUwHM5Y\nY9mWiblCLhkfJKFTgtY0wR8IboSyios/jdvjAbS4nw/9SFVNoqKVEq/MKtxBozVKK6brM4fzjfiH\naoNxHrWu+2TTZFWhD3AYMfoWEMCi7ZI9RaPmyPrxnunpGaWgdwF7vpGkx5J3ZD4Iooyi1SL2eKqy\nOEtNlbhtHG/PnI5naSK0gZ1yk4uAPbVUUl5BSTF52Tvq/eNcJNgsjAPj6Sg0sV1ymnOiKekw53nm\n+fmZp6enVxDGWo8OgcNxxHcDKReul2e0AY3s0l/WC1UrTOe47e/2Qi4rl5STTJ5aSYfMRsuFvG14\nI1xZs+epg5K4Fm0lTgUEt0CzbROlZtKWWXe0fRgGDgf5Xa7LQoxJop+T8D6VUntW+y9+/NqKpm8n\npqdMW78SQnNXePhW078DH+BHP3iDwfDVV484b1B+j111lbPtGNFgDZdpJS4z3diha0KVjJ5WbMqc\njUX5HYUvlet8xRqDth5vLWZ0OG9Y1420ZpyHZDfc2NGNjnfnd3zv5h/hezc/5HRzFk8/q8EpvBUQ\n5HA44IL4RFolS+iSMqVmckoErzi/C+S4URsUJDpAaSXmFV0n6OJ+wdRadnKw6GxfFurOiRVWTJno\n113VkiV/KGVqyTI+IymJOck6XGnJjo9blCLkPOw3gO+7V91tKYW08yFFTSE3xv3jAylnuq4TE90g\nyommFZlG0wrrHMaKRM9ojd55by8yRaWUEM2dw2RDswbVKmuc0LoyTQXnDMfDCRRcnh6xunE4jChV\ndzpVRefMMAamKVFKBiz1Oxnn67KI05IPhG7A9QfmZWXdEqEbSbWwLRuHw4A3AtY0sgTC7TzDZZ15\nd3cixwjW0gdx3sp5Yzz0eyfucc6zrSu683IwOb8Twj3aSuSFP52hP6DsSJ4TWjcII9r0tKrFyxOo\nFZQWys3HD/f87MsvmbcZSmaZJ6b5SXZ5/YjW6jvqFpluUspY47isE+tW6KaKGg05JSmY8J1Y4/bq\nBPXyt17XlcvlwjzPbDHSDT1ffPEFw2HEOEvcNvIW2XZGgrGGYRhe0fXz+Qz8vNO0LyMxsEwTj+s9\nIQRxtbdWzEBqpXeWIZxe0X0QFyiDouhEcB6GgdaPrx2ygFlCEaqtMU8TseRdHVQoWdYRQ9/LdaxE\nXfgCFMUYWZaZ1hrLsrCtAlJaY7FOXp/9i1o0e9ujVaOkK0NwqO4sY+Eacd5ztoEffP8OFzreP3xA\naYVtim3dSD24G/FadM7y9LxCihJd2mXyWLCdoRZNVZZYFVU7XNB0PnDoR/q+x4cepRVrXlnnSEyZ\nqhvYTB88n55/xLubzzkfDgxjT9OFRMZWyxpncrWYTVGKZIBbZ3G2p+aC1uzoYwcts66WbV2wrQli\nrBRiPVIFqNp3ls24PUjs57QH4cPJ8r4LjnHoZSyeZ2oQP82UItO88Hh5FESURtdLQuH1Kn6QL8Fw\nL5xXhfDo+r4X/8edGvJShKy1nI4nSqt0fcc4DBgjfL3HpyexOOsHxvFAFwa0lptZKEHyHKUWSsnU\nV2KamDxLMNyFznUYI4BS1x1RSn53p9OZdZlRSopi6P3O48v7a2yUVmTnpmWERmlKTjjX0XdHet9T\nlGLdCk0lnp7kdzMMI29vbiTXx8KyzYxjIJeVQxforGe5PlO04XB7S0oby3bFBVHLbKXhnMc4S0mJ\n4Abpdqt0uBbN0A9wPtP8SHMd7nQWyWRONCJYmSxACdKPwhjP8XhL19+TW6WmyOXyFQ+P9zuVx+60\nHUGnQ/B446hUOi96b39zJnjLdXpCCATutYt7CZV7Ocheiqhzjndv39LYDTha43g4YLXhcnlmerrs\n+1tZG8nBLEi3IOg9XejQRssaJyfqHqd8nWc5kFOi7wdhMrT8em03GrWIcXKjif68yXpn7HuO4wjq\nJbNr52LSZPVUiqD3+8H5kl6aSyGVyjAMDONIt/NEX39mreR30wW0t/s9JtSnzgec/guae955TU0J\nUmDwA/50ZHCeJc7kWElWMwwj3//c463i6fFCzpWcG8uWWWpjsIY+gC4daU3i0HNwHI53WNMJOGId\nTo0Y1+GspQsDfSdmBt53kputi4xlWfKdPzx/TS2Zu/ENw9ijnSbXsisc1O6jadm2mVqiyL9ohG7k\n1EPwga7vJDgsRVIUL0eR4zeJ96iQS2OaV6AxDD2k/LpvAgFbRIOb0caC2ikVe2Rt2ikZIPvD0Af6\nNDLPK5nCcDqhSyNtkQosO82iVtHst32PucSNbd9lul0MYbzjcD5xUMLdaq2htGHdEtfrFYDgRrw7\ncjrcvCp3corkWqlIsc8pMV8XShFji5YKW9pIuWBcj3Md1nne3n0qYXdrRKnKtq10PrBsE6iMVRYb\nPFuOKK3ofM+UIrmBMZ5+kBv6umU5TINn2iptW7DGE1NGKTH30OvGuk2cDob5snLwQcjmKTKMJ1rN\ndF3P5fKEdu8k7TFvkB0VQ/WalCLH0w3LdKU2hNGxK1r06Gmhx9/+kGIPkDIE0MrBEmF5QI2e6u+o\nTWFaFiNs4zl88QP+0udfkJdnfv/v/W2e1w/U0Pj45bfcX77E2MDd7c0r6GKVZRwOHIeRghSql1WL\n947PP/8RxjtiWvfXKOBY5z3bKpJL5wRoOowjh1F4oC1n5suFLSZiSiitKKVR5qvE+NZG2tM+nTNs\ny7zvScWVvlTZVQp3VeG8Evu+Wnh+ugi5vlWcDzRlXjtfP/bonBn7w+th/l1q0cv0UqvEfx/GE23n\nZbpXs422H8ThtflQSgn42xTaSEKDMQ6FemUC1FoxO6n/lz1+bUUz14xWCesNqnYMesCohZYVz9dM\nqwtDP1KKwrue4AvT9CgdXPO4NNDygeN44m7w1KL3scmhXC+uKigBIExAG4c1FpQoCLwTlHAcj9Ix\nlEKKmWWeacDzfEEZUSUoxN37RTWhlNppGpW0icSz0VAVsrV0ztFqoeQo3oHGMAw9IXgh7RZxZyml\nsOzZ3FIoFULxkY6qtkarQoL23tP14yuSvW0r2yZ7uZwz4zjSdQNdFzgcBnLJGBrd0OGdJeVMnBOl\nFVlRaCOjbcmvXZLspOzr2GaMIW4bcduk41OWddmotdJ1A9Zb+n1st8aw7Ya/L4jtSzfcWiXGjWV6\nouWIUobgezrrGbtBft4YaS2ja0WwHk1MoqBSeGIpGNfY1plhOJDSKg5XukIVcroPjuPhQBdGjA98\n/eGBy/WZnCqn0w3f++Izbk4n5nlmmicoMHQe7zxxXemcxRkLTTP0HTlF5mVGKcXxfEuJkma4bYvQ\nwHqRHlrn6IcB4wyu7+i8x+5GIU2D7kZaXinPM8vH654Y6unuIvZ4R9P+1YzMWk3DYN0bvvit3+X+\n+sjz08IwHLHeEnxHF/x+7cwC7tTCuq3ElMl5EyAtrUwTdOGeru+5ThdaawTvBRDcC8M8z3jfc71e\nZXSG14nDOc8w9BjVSGsUmeachFa0q3/6fqDW9J19oEwB03QlRrluX66rruv2a0tzPB5fO96mfj7u\ne+9f9+ovhTTvQNELFzQlUTVZI9ew3r/eadBWOnYxHAmySgKg/fx5MCglhVY7T7NaUmd3gw/Ur+Zy\n9P/b43L9iFOVo3tLK4H4XFCukddMi5VvvnrgdCwcjwe86Tn1hjgVWql8cvcpdzefEsYTN/6ENQGt\nhAfThQ5jO5RG3Faa0F+staBkTEj76CCFS/Kixa25Yq3m0I+7FEzGiJzLLuZXr/KyLUZoDdMaucrr\nIhUG70lG02qmZckZ8sHvKLQixbJ7T4o0z1i7I8BiJlF3WketVUjeVtQaGAew8+c2lGIvXjIWGWNY\nlolpmsQTUEPLhZgnUs5sRX4GjZz8tVXSlvbluxbwqAqQ8CKRk49lvKJBKiulvBB/ZcfkQxBd+P65\nWn+uqHhB3NVuXPtyKJRSMYizz7ItqAZGaQbXEzpHTgutVuKWxJfRBXFOt4paRNusFKhScMbgjWXW\nDSg4bwldR1GKbYvMy0baMpfLxDhIJMV1mmlZ4286dO9eOxfxE3WkmNhiYRgPgrh6j2qG0+mWmht2\nJ3znIjdsrRXrLMpaqtIMbz+l3ryl2YCaZmgzKE9JlZ/87P+i84Z3w1EQYdfThvCdO0Mij2lwevMZ\nX3zvN5nuL3SuR6kmUepeS2rBJnu8aZG/sfeeYRj3aI5NxtVl4TiIs9GyLLRSWZM4FRmj9xiWaVcH\nFYah53A4COdyl3ou60QumcPxII1HhcPhxDBonp8vzMuVWvNO+wm0Jtep3mlMcnC2V3J85/x+zQjw\nVKqQ3WPMvpy0tQAAIABJREFUjGPd95/6dQXwsiryXr7v5bkb7FQvOXRL3EhRSl/aVjYXJFBvZ4D4\nXXlUTJYOvxpMjRA1L5xSiZz+5Y9fW9GMW6GoRm8UzSjiltmmlXl9JOVCTJGvp5UUG+/efooZek5n\nQ+cHvvfme9wc34LWkq2jG9ZosfXfF9WtwUbGYX5uL2UN1hpxVtGiskBVchLFBIB18PbdCW2MEHCr\nnMbrur7uVVoTvbKMRwpjDakI2pxiwp6dKEaMxYcObUSylfJGbWL0QdPCq3SOVgoxbjxPV9ZlZVkX\njocjgxpeqTvODK9qixdFhpDx5TlQVQi8Tc5V7zwxR9IWWbYVtMIbg3eWuvMXjQe3d53flb1tu3oi\npcSWiui8tSFYuZBLKWirORxOHMbjTiuSDvNansn7DfyirBCgqe0BZ6IHfxnpOhew2tB7z3HsuL//\nGqrjZuy5pkcGPK0k8jphh4Gh6yi5ELwg6mXLONfhXcHZGbWJz2ehA2Ww2qGDleji998yjiN912GM\nHCJ9d6TEyHg8oah0w0hMDxQiaZtBQYwbx2EUepkVTp82jlYNte7FYU3c3h0Zh4FcFf78OZgjeMvl\nZ3+Cbs+kljDVsFwWJuVwYyEvK374+W0qccS8HjJvPvmCn3b/AOcdyzZTc8FYRUqFqqAbR/SelBlC\nkJTIUphXCJ1Yv12WSf4GKPoQOPj+VdnzMg28KGZeOkK7c3Q/fnxgmq5oo6BC13UcTyOhs2jTuP/4\nyNfffMW0XLk53/HDH3yPPvQcdlAMBSnKhLPFjWmayK4wDgPj8ci459G/jN4vBy3wugsFKXjjKNSi\nBqAN3nq59mkUm2WfJb/FnVRfXws38Mot1UbTKhQK1VQyjZST+BzURuf8L61dv0Yg6IRzGm87tJZf\nbE4R3SRjhCSmoulayKO4uHzx9o7jeMt5vBFbrlZk30KjGQUetFbUJAal1mhy02gF1jqc16+/uBeE\nuDXJeLH7PsR5L0HyVlr4nCN9L8DLuq6v9IyXHBrr7a6LVlynia/ef0OuldP5JHvTuOJ9gH2Ef+nQ\n2j6ev4war7sXLUj2dboyLxIMdjweZRx5HYdk6T3P867QsOSysW1RJJY7sl5qRiN6Y1F/aJwPgILW\ncHb3MESh9Z4BVDV27wTM2bBGiYvwXkbk6TqzrhvjOHI6HXbNtST01ipUoJcx6+UGkI4lv2qLW5XM\noNtDR02V082J3lrm60SrDa0qMa30new8p/lC3GN+dVNUpXh+nug6yQRXe8734DuWJbItC6kptFaM\nY09KBaXkRowxyhjmGuNwomybdOra0IWep+uFeZlw9vAayma17Fqz85gXsMlqtMp4J4eDMYqUV1JW\n9GOg1Fk6GB04ff+HrJd70uMjX339Fc4UDmOPCxYTDFCgGWnE2x7opiWKJfQHirXMj0+UVni6PFCL\ngCrn8y13b97CbrJRSmGeJ1prjP1AWjfm5yv+fEN/GLGjGHqI7FEsBA+HA2/eiOnxyzUo9CbN6XTi\nk08+YdsW0Yk3uL29Yxh7SsnElBjGA6fzHVssPD/PfPPNB+5ub/nk3TvG05HaGl1rbOsqvrlpgiZd\nYgGGfvhTevVt215TQ19G8pcO83U8L3mn4S3UWgg+iGt/FeaITGJqp/EZrN3dn8pMrQ1j9wZLa4IR\nSmLKiYJ0rmv+C8rTvD31bHOilsxymTAG0pZRThHCgMuabAOuHSgL2OHA7fiO0xiwXtzUS5b9oLda\nGP25UF8KUqv73q7sbswi9Xpp119oNuKvqPHe7KO37GRSLtCEvG13lUTnA+8/fPvq5KIJOGMJzhFz\nwgTHw7NQN/phENlXkMIkyJ3Zd4VS8M2OBk7zjHGWg+8oO9Ini/LGMI6vF1Iumb7v0MowTVehHLlO\nnGhKFM/Axuvy3Lx0kEWiQFCi3ddKdr1KsUc+hD9lstBK+b+Ze5NY2bI0S+vbe5++M7Pbv87dn3tE\nZGQkCSQkJVRiQMGEWUkICYkBA0BiBAgxqhrRDBFMmTABJBgjhFAhBoVUgypVRoUqMyIyMyI8PML9\ndbex/vT7nH0Y/MfMPbJVKYUiTXL51Xv33cbMzj57r3+tb5FGEb7ns8iy2Rjdc+hqbCT66XJ1RTIb\nwe3QUdcttuvQWtIngEzMUWdYQ933eDjcqIjCFKaJi4sFnmdo+g7f03S2Z7XMsX2DH2i61tE0Hb5n\n6LpWUG2IpixYL8XoHDgwWhN7Pm3bU9YW23VUbYMdxUTd1BVucox25NXzJdOYij0mlLx7VdX4gSII\nQsZpoqorrOdztVxR7Y8EccgwTRhGoU2NlrxYCdDXQZqkmCAFLUmgcVDYwwZLgB+ATjxWFzl//MM/\noEgzbH9LPI1MM40JZIc5NHtUvGCaZML76rPP+PCzgf3xwDQ5ymNFWTZMk8ZojzSKiX2fqFiw0Yr1\nekNb1Yz9gG07hrgnMAuWRYHnewLJcU6m1n1PnqUkSXo+omutZ0CHZrVa4lxOWR5RSnF9fS0pomGg\nrivSpCQMY7IsZ7PZst7seHh84H7zxNX1NavFgkW2JM+W5Nni7O+UoaGlHI9orc9UJqWneaBk5nBE\niNanG/9EVe1lFpGk9G5ktKNk2I3C2oGqKum6jizLMMY7U52+Dm8M9P04XyMTkRXISNe2aGPOscu/\n6PHrK1bTPa2qGexAMIyoXjOZDl+D0QYvigm9FUEYE0cRkefhez0jhrGXo8To5EiqfYOesVZ2chjf\nwAjjJFpTGPp4nuyEYDqbdGXB5LzLHMeRvm9lO386hg/jXMc6YW0viKnZKzdoNU+NpzkJEWAyxPto\nBEclL5p0b2dZRttanKvPnjOlYLCWqiw5cKJcK6IwlKpX29GPA742+HiUxxKjpakzCuddI8AMYRDr\nkDlrnvJmEZlbzUbnMAzOKZC2bWfNdh4+uZEgCNnu97jRkcTiOui6jlHJDl6bgMCP5uGYXGCH/Q4Y\nCfwIrSeaphXzdVPT9dIznxcrlBuIopA4mFimAWNrGWyDH0YwDrx89hw3TFhbC7OyegsjxH7CMDls\nLy2QmgE7tPhhitEKxQCzrm1HS9N2HNueuuoYR9HFhDPZExlD4CmCyCdIc3ztqPaPLNOYzXrHs9tb\n4TGOijRLMJ4hTBPqrmNRLDHKl0isMTRdS5ylxFlK2znubi/h4hnjFKD8kHC5oH665/H+gcD3ePbs\nGb//T3+fx92al22F3m3J45zJS8WqrYDBoModU7ZEqYCXzz/h7Rc/41AfydOcNCpo+wZrezbbe46+\nT+AlXF6reUcttbqLVUE/dByOexarBZ1tGd1cJOgG1ARKa5jGc1ooDKOZlC/DuDAMadtmlrM8DocD\nVVXPgOGI1cUVy4trRkbqqma73bDePGFtJzYe4+NGAE2eZ4RhwjQpuq4lDIOzjeikN0ZhhookqiwV\nFwZrB7quQwFBGGBxDEiYo+5bxv7kZhE4i7XDjL5Tc5ppZBzdvAaIfFFV1ZkBehro1bVQn3zvmxrz\nn378+gZBdUlnG2J/YvQdalRoa3GTIw4XqDjCmEhytb4kQfpxwHWdlCLNO6MgkMzpaaLNzMMznlCx\nw+DEFXRY28nE2369oJyp4PORdhiErejNuuc0iXZ4PM5H6abBD2XCd0rsmLn3ZurtfHyTI0Lfn5iD\n43mHKyRse54Saq1pmhZrJT87MhFFMYHnSyZ4gigMiKIYo81ZtFfwjYlkQFVVIrJH4ax5zhrtPBGV\n792JedgICNbafrZmCLvSuYmm7nDTeD5eV01D3dT0vcXzNV4Y4QcTve1hlAW7a1v6rqOqjxT5kmEY\nZ2HfcjzOFJ0g4PpyReB5RHHAIktIk0i00kj8suX2nsPTL7GHA/2kUWjyPKQtW7ywoLITzna4MUAr\nTwz8ZsQPQ0IX4fWOkV4irRqiICT0pXbX2l7M/i4iCQx5nuFpD6MUfTNQpCnl4UCc+Fhn6boGHRgs\nCrwErRGXhdOowGfSmiCL8LRH249cBKJn2rbFQ6P1JaOz6Kkn9ASScqxKinzJ977326zX76jqBhO2\n+OWBeBmjJoNiRHs+1cM9iReiYp8gSfG8ENt1NHZgGmB1tZRjrUG6vss19+t74igmDAKiObiQpglK\nKZJEGAO97eWqn5xsOrRP0zQcDgecG7m6kooV8eNK4ue0+9RazOHH41a8nddXcnOepCTN15pFlhOH\nHrbv8TyPPM/QSrLfXddKqyxfD2VOzQFnsrvn4/mS0DmlgoSLqvA9D+ahopplHF9pJiNDv2EOYTjX\ncDgc5xMl83U9zrxPM58U83lIVbFZ72naGhDJiviv6fR8ahEzsgLl+wQamqbDjgNDW5P7BV4Q4pkI\n4/mM0wiDRjHiGe+cEADOL+opA840ScNfGJN8w+slXSPmfPyAGWI6m2kFGKDPX3tSzDSYATeMsyYp\nU/kTMiuOBfM/zFqKr2XXG/oBk+Jsqv16qiwlZKchifxcgjbzPI/ROdF0HXhaE3kBcSK/Rz9afD8n\nTdNzrrsoFmcxX2vxtkZROPtC5+raYZA355wkqeuaphGtsmsaGt8nCCQCNwwTx1JI8FEc0/U92vcJ\nPcNoO4ZhwPZCAzdGoVHUtQAU7j88oPAJAssw9BzLA/3QkiYJUejhRsvl9SWeZxiGkX3VojQ8zxdc\nLC64u33B+vGa/eMTSV8ytR37x0eSRUxrrWyqewkEnCKnmol26NEmAM+i5ohhmqWkRBgjtSWnGOpo\nW9I4IIkjlllOtduS5TldVxHFMdY69vuG0WniJCSML9HBiiS5AiXkLcaGobXsjzWLfMHF6pK2G4kX\nHkGxkOpbOhSafv+I6iYWixXddqDvW9wo/USb9Qa8gMX1HTgrchIwGU25PRKZA+ZlAdoQJwVhHLNc\nBkx2YphvbGEY8e7tA+vNe5ybuLy45urqivV2S55Ky2ff9+z3cqy11oKBPM9RSrqAfC+cb94N1o4c\nj8ez7cf3fdI0xbmBqqoAWCwWAj32fdaPT9zfv5XTVOCzyHI++ugV+d0zSRqVRw7HpzkpJEi+r5kE\nE13Xo7U3G9VD4jikbsrZChUwjY6uszM1aQZMj45pjEUGmo/51o74vpTaaa0oihRrOx4eBEUXxzF5\nns/Dpp71ek3bdvi+N8sLxTxo8v/6+jSNCzBWgTLSRa6kQGsYFNpLIIjwQw/fk0GD1lLredbpkCcf\nrc4LkOd5eEqGQVEUkaYJIAisaUJSQIFsxWU4MYnuPjGDCSKck6OcmMct4/g1HOA0wbPzjlYphW07\nqrqmOVYEvmiiWRQTRRGdE3qMN8cKtVYCRJ1NwUb7wpz0PfTcD41STLOZl1l/dG6gqsuz+TYMAjw/\nxPMMbStevSSRY5VospIxlvpaMe8O8xTe82QaXFUVXuAzDpa23FNWNcPg8AKJB3qex2Kx+BXPXBgK\nWWdyPV0n/r6u6+itZRhH1DRSHjayMDf1DOrwUMYXMKxnKOuS0PcxQUCWFXjaYxgVT7s9vj8SRCkX\nz3xufU15PBCmGfvdmrZaM441xo3oQNBzgxswo4FB2hincRL5Fh9FiJ0snvJxo5C/jS9Q3iTwiZOQ\n2pYMwOF4kF2ihrjIWORXOAft0HF5dc1ysSK/uhZ5Z5pQTctgO7J8gVYw9Q1RIfUcgxdJwktrpgmC\ndEHdbvBNwNXqgsNhR2Mb+n7g5z//AoXik29/m0n78rWBoalJlxmEMKkRBYQefPryI3prKY9HDtWB\nrmvI0oTPPn6Fpxzr7RN26ADNanWJ0TJ4G9w436hFGpmUwTMhMFGVLXEMeb5gtUxnJqfDGE2SRIRh\njFKK43FPVR9o247V6pooimmals4OGN/Dd1I7MUwjHx6fiLOMq9sb+lHsXlXZnCEhck2ORJG8Z60d\naNuGw2FHmoW0tWVyjvQqRWnDFE20Tct6c6QqS7SnicN4bs706bqeNE0BGcaNrj1f14MVjfKwbzke\ndmitOVQVdVUThD5+vMQLQyGX2R6MJgv/mh7PL26vsJ2ja1ucnWgGi1UNepRYmWdOXR1u3iXFYl4P\nTnzAYKa0+OeL+nSBT9M3rEHDNxYpk5yjX6cEi/g1QStvzjl7ZFnG6BxlJYZfadgbzpLAycYw6YlR\ni11pUeSEgeg/wyjVoReLC3mTThNdbymPB7SSiWUYxORZBhMEUciI+ATHWR6w4wDjiFYnb2Z/Puo7\n5+i6hqoaRDyfd7xaG8qyous60iSiaRvxtBlDb3vpG+9amroRYsxgMb68BWzfzXqO/L6n+GVRFCgl\nrYMn/VQBTVvOmubsh7UjbdtRHrcoz6cbLKskx40j292W+O6GMAjEBpL4LFcXxElKEiWSTuktTVdS\n7Z8oDxsYW1aLJa9ffEzz8iOmQfOLt1/gjjv6viGOIoa+oW5bhmn+2ZTHOIMIy/JIXCSEUcTkHKFK\nhAZkDL4yTNPAOCkmAtJlAUqRFUs++uTbOKdIkpS6PnJ7c0uxWMprBbRNy2ClrRIFqe9jWxk+RIHG\nmIBJT0yqw5iCZv2Bqe14Vz7R24FVHjPMCSw3OJI0oe8tMcIa6Ms921/+lEWe4BWJTJrVyCKLic0l\n1rXEcUDeSymeBsLA5zvRd/j5Fx73j2u22ycWiwVhmOKc0IvCUI7hGk1la/SkyBYZvvbp5h4h2f05\nRtcThjl+YLBDw2F/ZL15EgycHxCF0bnlsiyP5EVBEPiUZSl8y2N5lgMktuvRVELd0mGAmzuW2l5h\nhp5h6CjLg8hUbwVFuFgt8WJDHMUwlwX6fkAYxqRZPJO+HGp0It318t6W7+nNNsIRzw8kqqkMeZqh\njeLi8gY/9DEzg9QLA4zWjE5khij6a7poXj2/IwoiNoeSxw8PHDaNROdMQOr74rVEgVbEcUSWp3gm\nxA/UmUANnMP+p2GOUtPZl3U6fpw+PqVUTgOTk8boBxFhFDLYHtv3BEFEaNSshciU1gujc062bRra\npuZidSFm4EgWrKaRF62xDcPBEngKT8GkPaqyZLvd8erFM9IkIYxi8qzgeDwSRBFB6KO0kKarupZa\nBhzMCLLTonla9Nu+p2kasiw7WzVOMU1jNGVVYq3l6ekJbQxV19A2DZ42GKWxw4CtytlF4KSKwg/R\nTBhPUhSHw+E8sDqRu083qWEY6Lr9PHCCY3lgvz+wXq/J0hQdehinCJKYq+UlRbEgiAIWecpyeUmS\nLYniGKOkn9wPfOwY0mPwjcd+W6OGATXsSZcvUCrmo49fsH3M0UosRH1n0Yctw+MD5TjQDQPWQtv1\njG7E9pbSlRJG6DqiNCLKckmfRLJrz6Ir4jwhSWJevnxJli85HA8M8wTW8+b3mpL33eAGtKckaRVG\nkBYYFFFncVPDODbocUKZEKaeEUsQ51wnKT/8ve9j+5y6rtnv96yygjAMsW1FPNQoE3DYPsrE32ie\nKR9PadRkWV0s+PKXa7q2mo+ysfxba1HTxGJREMQJ/udfsNl84P6+Zrm8xLmRIssIAo80TTnuj9hh\nYN2v6YZOThOeoa5L9gfRG9tWeumzLD8PTY7HCoXHzY1Mzne79awhhgSh6KZd15GmCePo+MEPfsAf\n/MEf8N3vfpeut1RtTRRGuLrGuZ4wDM7fRwjtmjy7xE0yDA2SkEPZsd4cKdKYJEnI84QsS6iqEucg\nTUOKIsd4Ie5kt3KOtheO5ipbiF1wcgzzzlYrNXfAm9l9IuxOx0QchhI00X9Nj+dKGXw/5PIyBkZ0\n4DgeFKN1eDrEoQnjkGWxIisWZEVGOB9JT2mV4Rt+qnG0BIHol9/U/E6m3ZNOeQJTnDxgYvk5EkY+\nYRSCkrTMOMqdTGDGI6HyzllqEHP15AZs28gx3XUz8SWn71uqumK337HebUFNNHVLHCX4YTxHJif8\nyCeZUqq6ZmIgDCPyNMUNPU9Pa7lQg4ggijBznEw5EfBP0cm+78/+UaUMi8UCz9O0rUwcgyBgUorE\nJBItMx62tzS2QzMRGCFy14OjaY4EQcfq4pIiz9FMTEpTluV5N3/yXjZtI9PKVr7vertmXx3ZbHdU\nZUUYRXjP7kgXEauLFavlUgZQYYTxPDwzkYQCts3yBW5S+LFM1dsQjBtQQ0lz2NIcG6L0gkE73BCi\nowV4Dt8MxCiqrmF62mEHqaqdnONpfY93jPjs9evz8VyjGGxP0zfE2SVhHONHgaSaghCUpqkbRjvQ\njyN2nBgPe5KiEIhE20p7qPExfoQOEibPY9I+KshRQ4PSA1ongAw/wuyK9uGeD08fsNrw05/+nIfH\nR6YBbDRyqEuKpqXfrukHy+HpnnYYmNqWpzdfsLy4wzZ7bH3kcKgFM6ckGHGK4Xq+oYgXXHgeSn3C\nl1/6PDx8mK064Yx/s8TxfAMcR9q+Z7ffkRY5Hz9/jrU9x+OepqlwztL3lsfHe6qqpqpqrB1ZLS/Y\n7R959/6XOAc318+4u7tjsB3rzZqyPOD7UmkchiFlWfIHP/yhWKd8jzxJMSg8X8M4EAQJXhgSpflc\nkaEJw5jVaoXn+dR1PZe8fT0s2m63eF5CmoWAo2p6gsgj8gLyPIDBEcQDbnAEUSgtnYHPxIRWZpaX\nRpo57aSdEQKS1qiZkzr8JZ6jX98giBhrDaGvWSYXGHz6ZgIffEKWiyVXF5dcLG9ZLgqiJMD3JeFx\n2mWeqkL7OfN88nqlaXrenZ00lFMO9tR3cqLlRJF02PR9PwMCzNwr01FXFbYXzqAbJ/rZ6L3MF7Rd\ng+f7XF1eysLVCQk6CMRAfLm8wgQ+x+rIV199yegcynh8eHygSCJeffQxAC9ePmccR75880vuHz9w\nCEIUQptRWpNkKRPubIKvqhqt1fn3ORVphaFoVmV5OMsRp122myYSX1BZbhiZEghsz2BH0jRltANp\nFnM8HmdtV2SMJEnoeksYhmdxfBgGmaqXDWEY0rUdddvw4f49Hz68Zfsk0/IsK6ianixbcnXxQp5X\n2zBOIXVdMQ4W2/dcXFzghzIAG4+W1eqKPgowk6PeWTYfPtD2ez5KLhnHiWWmMaGjG2OmQBwRgZfh\nmYphtEzOgpMBhh/4QgyfTx5ucHhpQJLkxGkqN97RnfP2ggvrpBY38MmyZJZCBH5R1w0X15ekec7k\nRzjjIUwjM/MyEwEXOQN6kty5CbD9wP5pzXG94+ndhq7spf7ZDty/e8LzIsryyP5QstuuiXwfV+Rs\nv3hg+/YXBJHhUNYY7bG6vOBYHejtSJ7nHI9Hpkmx3W4krZPnvHjxfLbgtGRZQhrLTm0cR4Kg5/ru\nDj8I2B32PDw98vL5S/JsMRPUW6wdmSZN11mapgU0SSIblt1ufyZFxXFMUSwwRuGcpLys7WianiwT\n4EZd1/hzv4+1lmEcGJxmGLVwXb2Q1cUVQRBwPEpe/enpCaUUNzc3JEnCbreebXvCMI2ThCxJ6Ac5\nyvu+P5+iPIokI/QUzLKRG0f6Tq6faE5C1UOL73vCIChL0jghTVOBWFfVOZH05z1+bYvmOA04RDSP\nwgRPxbhnEV1dkqqEu+tbbq6fk0UpYSQTaewE4XTGXTEjqk6T81OUr6qqcz71FAmT1r74HNkC5jdR\nQFEU5zTFYKWcapifcH/WVtXk6BsxyaYLj9ura3zPnxc3eYGbpuFpvebly4+4u76RMnrvFTc3t2w2\nG5re8vj0wKRjikXB5Cb2+z1t1/D09CCao1EEfgQYyrJkf9xRFIWkiuAM7Miy7BtSg1zQ3wS5nqKf\ngefRW0tb1agJMLIQ50HKNGnauqWshHZzuhCmibkMSzqGovkoc45Qas1qtaKbb0phHKGNochz9pcy\npGCayNKMwXUcyzVKieF9GCwMUB6PJEk7a19KdjNdR+x7KBOTLpbY9sCbD/cE0SVeskR5yJHYOJIg\nwQ0aZx1V7Jimx/nkYOkHRxTHeEFwBuUaY7i5vsH3BWNmlM/NzSVhEKK0piobstSdUqiEobg2ghlc\nESep/D9IGAeF8Q0TBqUSJkQumCah2qMjQBI9k294u9/wj77/j3n7/i0jGmdb0iimtw1ls+d+844k\njSiylDRdEGciF2Rpiu06fvLVT7l//8Bvf/c3qWsB0WRZwWp1QRRF/OSnfzybuhuyLOfq6pZnz27Z\n73cYY9jt9zL48zyeP3/Oze0tdhjRnqEdBn75yy/xjMH3PdJkidaie4+DxjlN21as12tBrSUR3szT\nnKaJ4/EwX4sG20/0dkRrjzCUifjF8oI4TImShDCO6AYrDQO9ZbG4IAh8kjg581pF4vqaPNQ0FZvt\n0znD7vs+Wb7kYrlidfuccrfnZz//CZqJPMs57HbkWU4QR4xWwMgjDmM8CcEMJ4D17JwByrLkhI1j\nmijneuM/7/FrWzQfH58oso7Wm83nGPKLTCohXMYyv2SRZUTawwG2FThBrCZ65kKnmavI6LAzxWQY\nBpTRuNGRzsmUE2TDGH/OQHsEgYAwlNLirZwGpmEEKxYWN4xgh3N6CDjXAXgmIPQjhnGkamqSMJY0\nzjyQCQIPPxKwwNh2eFpzuVrSNA2J79M0NY8Pj1ytFgyjZbde09cdwWzvAFDK0rSKcdT0/SDi9jgQ\nhFL9O4wSBxTKi0ec+PT9QDine04EmThO2Gw2RLHsPKYJykOF54k+d3IRhGEgA7DR0dkBP47o2p5y\nf8QYjyxLaN0kBnNrRbvTGt/TQjq6ueZiUTA8H+aQgDBGkziWWGQnlpDRyXOptaGsK0YnRvjID1Cz\nEK+1QS9ywnpJEN3w6W/985h4KWT2PEMp2Wn3OJwfoCIPC2g83DRSt41wBYaRuuvxPAkWONfP/lWJ\n5Qa+3Ih8z+M4BwzCIsNDqjGapiGYuaPFYiWdSU3NMHSEXKDjgskb5g4kDdrj5PCbYAaVwGff+x1+\n8fMvKduGPEtl1z10tE2FCRR1XRIGIYvlJVeXV5xgvlp7hHFEul7w6lVCPCdxkjSByfHm7S9lSBrG\njMNEni0IgoCqrIiihMvLa7EaRZHETJOcZ3cveff+LY/rNddXV3z87AWHw/4seTVNI51KLy64vb3l\n6WkZdBf+AAAgAElEQVTN+/cfCPwK3/dkt7+44Prq5gzPOME0rq5uSdOUJIln+5KdNzjmTDiy1lJV\nJf7CnzcrAmZ5u35L1zUYAzc3t2RZJpuY3qInzdPDGi8IePnqNdfLa+I8w/g+6SI7nyyrrmW0I7QN\nyYxVjHV8ZitoY+iGDj1qglAGdn7o03YV2/VGNjRTh63/am2U/7893nz5liwNUIFHHEYkcUwSRQTa\nJ0mv0B4CKXaWvrO4+fjtB943aObiqdKewUyOaXQEfkAchsL6m53un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SayLEFp6PuW0Jh5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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f85901260d0>"
+        "<matplotlib.figure.Figure at 0x7f318eee8ad0>"
        ]
       }
      ],

From c6e9c592a1305ebf993500131825eb8cd83b341a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 15 Aug 2014 14:29:39 -0700
Subject: [PATCH 0478/2053] Add "not_stage" to NetStateRule to exclude
 NetStates with certain stages.

---
 src/caffe/net.cpp           | 44 ++++++++++++++++----------
 src/caffe/proto/caffe.proto |  6 ++--
 src/caffe/test/test_net.cpp | 63 +++++++++++++++++++++++++++++++++++++
 3 files changed, 95 insertions(+), 18 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 5a49a23e8f4..1ee60bfe5e3 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -282,7 +282,7 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
     if (state.level() < rule.min_level()) {
       LOG(INFO) << "The NetState level (" << state.level()
           << ") is above the min_level (" << rule.min_level()
-          << " specified by a rule in layer " << layer_name;
+          << ") specified by a rule in layer " << layer_name;
       return false;
     }
   }
@@ -291,24 +291,36 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
     if (state.level() > rule.max_level()) {
       LOG(INFO) << "The NetState level (" << state.level()
           << ") is above the max_level (" << rule.max_level()
-          << " specified by a rule in layer " << layer_name;
+          << ") specified by a rule in layer " << layer_name;
       return false;
     }
   }
-  // Check whether the rule is broken due to stage. If stage is specified,
-  // the NetState must contain ALL of the rule's stages to meet it.
-  if (rule.stage_size()) {
-    for (int i = 0; i < rule.stage_size(); ++i) {
-      // Check that the NetState contains the rule's ith stage.
-      bool has_stage = false;
-      for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
-        if (rule.stage(i) == state.stage(j)) { has_stage = true; }
-      }
-      if (!has_stage) {
-        LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
-                  << "' specified by a rule in layer " << layer_name;
-        return false;
-      }
+  // Check whether the rule is broken due to stage. The NetState must
+  // contain ALL of the rule's stages to meet it.
+  for (int i = 0; i < rule.stage_size(); ++i) {
+    // Check that the NetState contains the rule's ith stage.
+    bool has_stage = false;
+    for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
+      if (rule.stage(i) == state.stage(j)) { has_stage = true; }
+    }
+    if (!has_stage) {
+      LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
+                << "' specified by a rule in layer " << layer_name;
+      return false;
+    }
+  }
+  // Check whether the rule is broken due to not_stage. The NetState must
+  // contain NONE of the rule's not_stages to meet it.
+  for (int i = 0; i < rule.not_stage_size(); ++i) {
+    // Check that the NetState contains the rule's ith not_stage.
+    bool has_stage = false;
+    for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
+      if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }
+    }
+    if (has_stage) {
+      LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
+                << "' specified by a rule in layer " << layer_name;
+      return false;
     }
   }
   return true;
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9cc0de9fe9b..428ba2b406d 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -171,10 +171,12 @@ message NetStateRule {
   optional int32 min_level = 2;
   optional int32 max_level = 3;
 
-  // A customizable set of stages.
-  // The net must have ALL of the specified stages to meet the rule.
+  // Customizable sets of stages to include or exclude.
+  // The net must have ALL of the specified stages and NONE of the specified
+  // "not_stage"s to meet the rule.
   // (Use multiple NetStateRules to specify conjunctions of stages.)
   repeated string stage = 4;
+  repeated string not_stage = 5;
 }
 
 // NOTE
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index fa5735f2a2d..395e3523418 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1618,6 +1618,69 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage2) {
   this->RunFilterNetTest(input_proto, input_proto);
 }
 
+TEST_F(FilterNetTest, TestFilterInByNotStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { not_stage: 'myotherstage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { not_stage: 'myotherstage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByNotStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { not_stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { not_stage: 'mystage' } "
+      "} ";
+  const string& output_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
 TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "

From da3e9b1e51671b8350d6b8909da4bb608688ddbc Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Sat, 16 Aug 2014 23:18:12 -0700
Subject: [PATCH 0479/2053] Updated documentation to include instructions to
 install protobuf with Python support on Mac OS X

---
 docs/installation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index ff0760dd060..bc36f7ebb89 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -135,7 +135,8 @@ In other `ENV` settings, things may not work as expected.
 Simply run the following:
 
     brew install --build-from-source --with-python boost
-    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
+    brew install --with-python protobuf
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
 
 Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
 

From 5e8e19b1387de73c1de01b80a052c71c4195b317 Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Sat, 16 Aug 2014 23:41:52 -0700
Subject: [PATCH 0480/2053] Updated installation docs for OS X 10.9 brew
 install protobuf as well

---
 docs/installation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index bc36f7ebb89..e13f02add4c 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -173,7 +173,8 @@ To edit the formulae in turn, run
 
 After this, run
 
-    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
     brew install --build-from-source --with-python --fresh -vd boost
 
 **Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.

From ececfc0c0ca98e242ef3b7634f1bfcb431028bd8 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Tue, 1 Jul 2014 09:56:20 +0200
Subject: [PATCH 0481/2053] cmake build system

---
 .gitignore                                    |   6 +
 CMakeLists.txt                                |  72 +++++++
 CMakeScripts/FindAtlas.cmake                  |  61 ++++++
 CMakeScripts/FindGlog.cmake                   |  48 +++++
 CMakeScripts/FindLAPACK.cmake                 | 190 ++++++++++++++++++
 CMakeScripts/FindLMDB.cmake                   |  28 +++
 CMakeScripts/FindLevelDB.cmake                |  37 ++++
 CMakeScripts/FindMKL.cmake                    | 113 +++++++++++
 CMakeScripts/FindOpenBLAS.cmake               |  62 ++++++
 Makefile                                      |   4 +-
 matlab/CMakeLists.txt                         |   1 +
 python/CMakeLists.txt                         |  20 ++
 src/caffe/CMakeLists.txt                      | 105 ++++++++++
 src/caffe/proto/CMakeLists.txt                |  35 ++++
 src/caffe/proto/caffe_pretty_print.proto      |   2 +-
 src/caffe/test/CMakeLists.txt                 |  46 +++++
 src/caffe/test/cmake_test_defines.hpp.in      |   5 +
 src/caffe/test/test_caffe_main.cpp            |   4 +
 src/caffe/test/test_caffe_main.hpp            |  29 +++
 .../test/test_data/sample_data_list.txt.in    |   2 +
 src/caffe/test/test_hdf5_output_layer.cpp     |   2 +-
 src/caffe/test/test_hdf5data_layer.cpp        |   2 +-
 src/caffe/test/test_image_data_layer.cpp      |   2 +-
 src/gtest/CMakeLists.txt                      |   6 +
 tools/CMakeLists.txt                          |  18 ++
 25 files changed, 894 insertions(+), 6 deletions(-)
 create mode 100644 CMakeLists.txt
 create mode 100644 CMakeScripts/FindAtlas.cmake
 create mode 100644 CMakeScripts/FindGlog.cmake
 create mode 100644 CMakeScripts/FindLAPACK.cmake
 create mode 100644 CMakeScripts/FindLMDB.cmake
 create mode 100644 CMakeScripts/FindLevelDB.cmake
 create mode 100644 CMakeScripts/FindMKL.cmake
 create mode 100644 CMakeScripts/FindOpenBLAS.cmake
 create mode 100644 matlab/CMakeLists.txt
 create mode 100644 python/CMakeLists.txt
 create mode 100644 src/caffe/CMakeLists.txt
 create mode 100644 src/caffe/proto/CMakeLists.txt
 create mode 100644 src/caffe/test/CMakeLists.txt
 create mode 100644 src/caffe/test/cmake_test_defines.hpp.in
 create mode 100644 src/caffe/test/test_caffe_main.hpp
 create mode 100644 src/caffe/test/test_data/sample_data_list.txt.in
 create mode 100644 src/gtest/CMakeLists.txt
 create mode 100644 tools/CMakeLists.txt

diff --git a/.gitignore b/.gitignore
index 7a10d94bafd..c1989306c27 100644
--- a/.gitignore
+++ b/.gitignore
@@ -62,3 +62,9 @@ _site
 *.sublime-workspace
 *.sublime-project
 
+# Eclipse Project settings
+*.*project
+
+
+# CMake generated files
+*.gen.cmake
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 00000000000..cc50a3a141d
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,72 @@
+cmake_minimum_required(VERSION 2.8)
+project( Caffe )
+
+###    Build Options     ##########################################################################
+
+option(BUILD_PYTHON "Build Python wrapper" OFF)
+option(BUILD_MATLAB "Build Matlab wrapper" OFF)
+option(BUILD_EXAMPLES "Build examples" ON)
+option(BUILD_SHARED_LIBS "Build SHARED libs if ON and STATIC otherwise" OFF)
+
+if(NOT BLAS)
+    set(BLAS atlas)
+endif()
+
+if(NOT CUDA_TEST_DEVICE)
+    set(CUDA_TEST_DEVICE -1)
+endif()
+
+#    Install Prefix
+if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
+    set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE )
+endif()
+
+###    Configuration    ###########################################################################
+
+#    Enable CTest from top build directory
+enable_testing()
+
+#    Compiler Flags
+set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
+set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC)
+set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} -O3)
+
+#    Include Directories
+set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
+include_directories(${${PROJECT_NAME}_INCLUDE_DIRS})
+include_directories(${CMAKE_SOURCE_DIR}/src)
+
+#	CMake module path for custom module finding
+set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SOURCE_DIR}/CMakeScripts)
+
+#    CUDA is required globally
+find_package(CUDA 5.5 REQUIRED)
+include_directories(${CUDA_INCLUDE_DIRS})
+
+
+###    Subdirectories    ##########################################################################
+
+add_subdirectory(src/gtest)
+add_subdirectory(src/caffe)
+add_subdirectory(tools)
+
+if(BUILD_EXAMPLES)
+    message(STATUS "Examples enabled")
+    add_subdirectory(examples)
+endif()
+
+if(BUILD_PYTHON)
+    message(STATUS "Python enabled")
+    add_subdirectory(python)
+endif()
+
+if(BUILD_MATLAB)
+    message(STATUS "Matlab enabled")
+    add_subdirectory(matlab)
+endif()
+
+###    Install    #################################################################################
+
+#    Install Includes
+file(GLOB folders ${${PROJECT_NAME}_INCLUDE_DIRS}/*)
+install(DIRECTORY ${folders} DESTINATION include)
diff --git a/CMakeScripts/FindAtlas.cmake b/CMakeScripts/FindAtlas.cmake
new file mode 100644
index 00000000000..27657a6c7d7
--- /dev/null
+++ b/CMakeScripts/FindAtlas.cmake
@@ -0,0 +1,61 @@
+# Find the Atlas (and Lapack) libraries
+#
+# The following variables are optionally searched for defaults
+#  Atlas_ROOT_DIR:            Base directory where all Atlas components are found
+#
+# The following are set after configuration is done:
+#  Atlas_FOUND
+#  Atlas_INCLUDE_DIRS
+#  Atlas_LIBRARIES
+#  Atlas_LIBRARYRARY_DIRS
+
+set(Atlas_INCLUDE_SEARCH_PATHS
+  /usr/include/atlas
+  /usr/include/atlas-base
+  $ENV{Atlas_ROOT_DIR}
+  $ENV{Atlas_ROOT_DIR}/include
+)
+
+set(Atlas_LIB_SEARCH_PATHS
+  /usr/lib/atlas
+  /usr/lib/atlas-base
+  $ENV{Atlas_ROOT_DIR}
+  $ENV{Atlas_ROOT_DIR}/lib
+)
+
+find_path(Atlas_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
+find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
+find_library(Atlas_CBLAS_LIBRARY NAMES ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_BLAS_LIBRARY NAMES atlas_r atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
+
+set(LOOKED_FOR
+
+  Atlas_CBLAS_INCLUDE_DIR
+  Atlas_CLAPACK_INCLUDE_DIR
+
+  Atlas_CBLAS_LIBRARY
+  Atlas_BLAS_LIBRARY
+  Atlas_LAPACK_LIBRARY
+)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(Atlas DEFAULT_MSG ${LOOKED_FOR})
+
+if(ATLAS_FOUND)
+
+  mark_as_advanced(${LOOKED_FOR})
+
+  set(Atlas_INCLUDE_DIR
+    ${Atlas_CBLAS_INCLUDE_DIR}
+    ${Atlas_CLAPACK_INCLUDE_DIR}
+  )
+
+  set(Atlas_LIBRARIES
+    ${Atlas_LAPACK_LIBRARY}
+    ${Atlas_CBLAS_LIBRARY}
+    ${Atlas_BLAS_LIBRARY}
+  )
+
+endif(ATLAS_FOUND)
+
diff --git a/CMakeScripts/FindGlog.cmake b/CMakeScripts/FindGlog.cmake
new file mode 100644
index 00000000000..0dc30abdbf5
--- /dev/null
+++ b/CMakeScripts/FindGlog.cmake
@@ -0,0 +1,48 @@
+# - Try to find Glog
+#
+# The following variables are optionally searched for defaults
+#  GLOG_ROOT_DIR:            Base directory where all GLOG components are found
+#
+# The following are set after configuration is done:
+#  GLOG_FOUND
+#  GLOG_INCLUDE_DIRS
+#  GLOG_LIBRARIES
+#  GLOG_LIBRARYRARY_DIRS
+
+include(FindPackageHandleStandardArgs)
+
+set(GLOG_ROOT_DIR "" CACHE PATH "Folder contains Google glog")
+
+if(WIN32)
+    find_path(GLOG_INCLUDE_DIR glog/logging.h
+        PATHS ${GLOG_ROOT_DIR}/src/windows)
+else()
+    find_path(GLOG_INCLUDE_DIR glog/logging.h
+        PATHS ${GLOG_ROOT_DIR})
+endif()
+
+if(MSVC)
+    find_library(GLOG_LIBRARY_RELEASE libglog_static
+        PATHS ${GLOG_ROOT_DIR}
+        PATH_SUFFIXES Release)
+
+    find_library(GLOG_LIBRARY_DEBUG libglog_static
+        PATHS ${GLOG_ROOT_DIR}
+        PATH_SUFFIXES Debug)
+
+    set(GLOG_LIBRARY optimized ${GLOG_LIBRARY_RELEASE} debug ${GLOG_LIBRARY_DEBUG})
+else()
+    find_library(GLOG_LIBRARY glog
+        PATHS ${GLOG_ROOT_DIR}
+        PATH_SUFFIXES
+            lib
+            lib64)
+endif()
+
+find_package_handle_standard_args(GLOG DEFAULT_MSG
+    GLOG_INCLUDE_DIR GLOG_LIBRARY)
+
+if(GLOG_FOUND)
+    set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
+    set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+endif()
diff --git a/CMakeScripts/FindLAPACK.cmake b/CMakeScripts/FindLAPACK.cmake
new file mode 100644
index 00000000000..9641c45d196
--- /dev/null
+++ b/CMakeScripts/FindLAPACK.cmake
@@ -0,0 +1,190 @@
+# - Find LAPACK library
+# This module finds an installed fortran library that implements the LAPACK
+# linear-algebra interface (see http://www.netlib.org/lapack/).
+#
+# The approach follows that taken for the autoconf macro file, acx_lapack.m4
+# (distributed at http://ac-archive.sourceforge.net/ac-archive/acx_lapack.html).
+#
+# This module sets the following variables:
+#  LAPACK_FOUND - set to true if a library implementing the LAPACK interface is found
+#  LAPACK_LIBRARIES - list of libraries (using full path name) for LAPACK
+
+# Note: I do not think it is a good idea to mixup different BLAS/LAPACK versions
+# Hence, this script wants to find a Lapack library matching your Blas library
+
+# Do nothing if LAPACK was found before
+IF(NOT LAPACK_FOUND)
+
+SET(LAPACK_LIBRARIES)
+SET(LAPACK_INFO)
+
+IF(LAPACK_FIND_QUIETLY OR NOT LAPACK_FIND_REQUIRED)
+  FIND_PACKAGE(BLAS)
+ELSE(LAPACK_FIND_QUIETLY OR NOT LAPACK_FIND_REQUIRED)
+  FIND_PACKAGE(BLAS REQUIRED)
+ENDIF(LAPACK_FIND_QUIETLY OR NOT LAPACK_FIND_REQUIRED)
+
+# Old search lapack script
+include(CheckFortranFunctionExists)
+
+macro(Check_Lapack_Libraries LIBRARIES _prefix _name _flags _list _blas)
+  # This macro checks for the existence of the combination of fortran libraries
+  # given by _list.  If the combination is found, this macro checks (using the
+  # Check_Fortran_Function_Exists macro) whether can link against that library
+  # combination using the name of a routine given by _name using the linker
+  # flags given by _flags.  If the combination of libraries is found and passes
+  # the link test, LIBRARIES is set to the list of complete library paths that
+  # have been found.  Otherwise, LIBRARIES is set to FALSE.
+  # N.B. _prefix is the prefix applied to the names of all cached variables that
+  # are generated internally and marked advanced by this macro.
+  set(_libraries_work TRUE)
+  set(${LIBRARIES})
+  set(_combined_name)
+  foreach(_library ${_list})
+    set(_combined_name ${_combined_name}_${_library})
+    if(_libraries_work)
+      if (WIN32)
+        find_library(${_prefix}_${_library}_LIBRARY
+          NAMES ${_library} PATHS ENV LIB PATHS ENV PATH)
+      else (WIN32)
+        if(APPLE)
+          find_library(${_prefix}_${_library}_LIBRARY
+            NAMES ${_library}
+            PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64
+            ENV DYLD_LIBRARY_PATH)
+        else(APPLE)
+          find_library(${_prefix}_${_library}_LIBRARY
+            NAMES ${_library}
+            PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64
+            ENV LD_LIBRARY_PATH)
+        endif(APPLE)
+      endif(WIN32)
+      mark_as_advanced(${_prefix}_${_library}_LIBRARY)
+      set(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
+      set(_libraries_work ${${_prefix}_${_library}_LIBRARY})
+    endif(_libraries_work)
+  endforeach(_library ${_list})
+  if(_libraries_work)
+    # Test this combination of libraries.
+    set(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}} ${_blas})
+    if (CMAKE_Fortran_COMPILER_WORKS)
+      check_fortran_function_exists(${_name} ${_prefix}${_combined_name}_WORKS)
+    else (CMAKE_Fortran_COMPILER_WORKS)
+      check_function_exists("${_name}_" ${_prefix}${_combined_name}_WORKS)
+    endif (CMAKE_Fortran_COMPILER_WORKS)
+    set(CMAKE_REQUIRED_LIBRARIES)
+    mark_as_advanced(${_prefix}${_combined_name}_WORKS)
+    set(_libraries_work ${${_prefix}${_combined_name}_WORKS})
+  endif(_libraries_work)
+  if(NOT _libraries_work)
+    set(${LIBRARIES} FALSE)
+  endif(NOT _libraries_work)
+endmacro(Check_Lapack_Libraries)
+
+
+if(BLAS_FOUND)
+
+  # Intel MKL
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "mkl"))
+    IF(MKL_LAPACK_LIBRARIES)
+      SET(LAPACK_LIBRARIES ${MKL_LAPACK_LIBRARIES} ${MKL_LIBRARIES})
+    ELSE(MKL_LAPACK_LIBRARIES)
+      SET(LAPACK_LIBRARIES ${MKL_LIBRARIES})
+    ENDIF(MKL_LAPACK_LIBRARIES)
+    SET(LAPACK_INCLUDE_DIR ${MKL_INCLUDE_DIR})
+    SET(LAPACK_INFO "mkl")
+  ENDIF()
+
+  # OpenBlas
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "open"))
+    SET(CMAKE_REQUIRED_LIBRARIES ${BLAS_LIBRARIES})
+    check_function_exists("cheev_" OPEN_LAPACK_WORKS)
+    if(OPEN_LAPACK_WORKS)
+      SET(LAPACK_INFO "open")
+    else()
+      message(STATUS "It seems OpenBlas has not been compiled with Lapack support")
+    endif()
+  endif()
+
+  # GotoBlas
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "goto"))
+    SET(CMAKE_REQUIRED_LIBRARIES ${BLAS_LIBRARIES})
+    check_function_exists("cheev_" GOTO_LAPACK_WORKS)
+    if(GOTO_LAPACK_WORKS)
+      SET(LAPACK_INFO "goto")
+    else()
+      message(STATUS "It seems GotoBlas has not been compiled with Lapack support")
+    endif()
+  endif()
+
+  # ACML
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "acml"))
+    SET(CMAKE_REQUIRED_LIBRARIES ${BLAS_LIBRARIES})
+    check_function_exists("cheev_" ACML_LAPACK_WORKS)
+    if(ACML_LAPACK_WORKS)
+      SET(LAPACK_INFO "acml")
+    else()
+      message(STATUS "Strangely, this ACML library does not support Lapack?!")
+    endif()
+  endif()
+
+  # Accelerate
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "accelerate"))
+    SET(CMAKE_REQUIRED_LIBRARIES ${BLAS_LIBRARIES})
+    check_function_exists("cheev_" ACCELERATE_LAPACK_WORKS)
+    if(ACCELERATE_LAPACK_WORKS)
+      SET(LAPACK_INFO "accelerate")
+    else()
+      message(STATUS "Strangely, this Accelerate library does not support Lapack?!")
+    endif()
+  endif()
+
+  # vecLib
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "veclib"))
+    SET(CMAKE_REQUIRED_LIBRARIES ${BLAS_LIBRARIES})
+    check_function_exists("cheev_" VECLIB_LAPACK_WORKS)
+    if(VECLIB_LAPACK_WORKS)
+      SET(LAPACK_INFO "veclib")
+    else()
+      message(STATUS "Strangely, this vecLib library does not support Lapack?!")
+    endif()
+  endif()
+
+  # Generic LAPACK library?
+  IF((NOT LAPACK_INFO) AND (BLAS_INFO STREQUAL "generic"))
+    check_lapack_libraries(
+      LAPACK_LIBRARIES
+      LAPACK
+      cheev
+      ""
+      "lapack"
+      "${BLAS_LIBRARIES}"
+      )
+    if(LAPACK_LIBRARIES)
+      SET(LAPACK_INFO "generic")
+    endif(LAPACK_LIBRARIES)
+  endif()
+
+else(BLAS_FOUND)
+  message(STATUS "LAPACK requires BLAS")
+endif(BLAS_FOUND)
+
+if(LAPACK_INFO)
+  set(LAPACK_FOUND TRUE)
+else(LAPACK_INFO)
+  set(LAPACK_FOUND FALSE)
+endif(LAPACK_INFO)
+
+IF (NOT LAPACK_FOUND AND LAPACK_FIND_REQUIRED)
+  message(FATAL_ERROR "Cannot find a library with LAPACK API. Please specify library location.")
+ENDIF (NOT LAPACK_FOUND AND LAPACK_FIND_REQUIRED)
+IF(NOT LAPACK_FIND_QUIETLY)
+  IF(LAPACK_FOUND)
+    MESSAGE(STATUS "Found a library with LAPACK API. (${LAPACK_INFO})")
+  ELSE(LAPACK_FOUND)
+    MESSAGE(STATUS "Cannot find a library with LAPACK API. Not using LAPACK.")
+  ENDIF(LAPACK_FOUND)
+ENDIF(NOT LAPACK_FIND_QUIETLY)
+
+# Do nothing if LAPACK was found before
+ENDIF(NOT LAPACK_FOUND)
diff --git a/CMakeScripts/FindLMDB.cmake b/CMakeScripts/FindLMDB.cmake
new file mode 100644
index 00000000000..e615f542335
--- /dev/null
+++ b/CMakeScripts/FindLMDB.cmake
@@ -0,0 +1,28 @@
+# Try to find the LMBD libraries and headers
+#  LMDB_FOUND - system has LMDB lib
+#  LMDB_INCLUDE_DIR - the LMDB include directory
+#  LMDB_LIBRARIES - Libraries needed to use LMDB
+
+# FindCWD based on FindGMP by:
+# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
+#
+# Redistribution and use is allowed according to the terms of the BSD license.
+
+# Adapted from FindCWD by:
+# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
+# Aug 31, 2013
+
+if (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
+  # Already in cache, be silent
+  set(LMDB_FIND_QUIETLY TRUE)
+endif (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
+
+find_path(LMDB_INCLUDE_DIR NAMES "lmdb.h" HINTS "$ENV{LMDB_DIR}/include")
+find_library(LMDB_LIBRARIES NAMES lmdb HINTS $ENV{LMDB_DIR}/lib )
+MESSAGE(STATUS "LMDB lib: " ${LMDB_LIBRARIES} )
+MESSAGE(STATUS "LMDB include: " ${LMDB_INCLUDE} )
+
+include(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
diff --git a/CMakeScripts/FindLevelDB.cmake b/CMakeScripts/FindLevelDB.cmake
new file mode 100644
index 00000000000..f3386f26dbf
--- /dev/null
+++ b/CMakeScripts/FindLevelDB.cmake
@@ -0,0 +1,37 @@
+# - Find LevelDB
+#
+#  LEVELDB_INCLUDE - Where to find leveldb/db.h
+#  LEVELDB_LIBS    - List of libraries when using LevelDB.
+#  LEVELDB_FOUND   - True if LevelDB found.
+
+get_filename_component(module_file_path ${CMAKE_CURRENT_LIST_FILE} PATH)
+
+# Look for the header file.
+find_path(LEVELDB_INCLUDE NAMES leveldb/db.h PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include DOC "Path in which the file leveldb/db.h is located." )
+mark_as_advanced(LEVELDB_INCLUDE)
+
+# Look for the library.
+# Does this work on UNIX systems? (LINUX)
+find_library(LEVELDB_LIBS NAMES leveldb PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib DOC "Path to leveldb library." )
+mark_as_advanced(LEVELDB_LIBS)
+
+# Copy the results to the output variables.
+if (LEVELDB_INCLUDE AND LEVELDB_LIBS)
+  message(STATUS "Found leveldb in ${LEVELDB_INCLUDE} ${LEVELDB_LIBS}")
+  set(LEVELDB_FOUND 1)
+  include(CheckCXXSourceCompiles)
+  set(CMAKE_REQUIRED_LIBRARY ${LEVELDB_LIBS} pthread)
+  set(CMAKE_REQUIRED_INCLUDES ${LEVELDB_INCLUDE})
+ else ()
+   set(LEVELDB_FOUND 0)
+ endif ()
+
+ # Report the results.
+ if (NOT LEVELDB_FOUND)
+   set(LEVELDB_DIR_MESSAGE "LEVELDB was not found. Make sure LEVELDB_LIBS and LEVELDB_INCLUDE are set.")
+   if (LEVELDB_FIND_REQUIRED)
+     message(FATAL_ERROR "${LEVELDB_DIR_MESSAGE}")
+   elseif (NOT LEVELDB_FIND_QUIETLY)
+     message(STATUS "${LEVELDB_DIR_MESSAGE}")
+   endif ()
+ endif ()
\ No newline at end of file
diff --git a/CMakeScripts/FindMKL.cmake b/CMakeScripts/FindMKL.cmake
new file mode 100644
index 00000000000..eb2d9f8868b
--- /dev/null
+++ b/CMakeScripts/FindMKL.cmake
@@ -0,0 +1,113 @@
+# - Find Intel MKL
+# Find the MKL libraries
+#
+# Options:
+#
+#   MKL_STATAIC       :   use static linking
+#   MKL_MULTI_THREADED:   use multi-threading
+#   MKL_SDL           :   Single Dynamic Library interface
+#
+# This module defines the following variables:
+#
+#   MKL_FOUND            : True if MKL_INCLUDE_DIR are found
+#   MKL_INCLUDE_DIR      : where to find mkl.h, etc.
+#   MKL_INCLUDE_DIRS     : set when MKL_INCLUDE_DIR found
+#   MKL_LIBRARIES        : the library to link against.
+
+
+include(FindPackageHandleStandardArgs)
+
+set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
+set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
+
+# Find include dir
+find_path(MKL_INCLUDE_DIR mkl.h
+    PATHS ${MKL_ROOT}/include)
+
+# Find include directory
+#  There is no include folder under linux
+if(WIN32)
+    find_path(INTEL_INCLUDE_DIR omp.h
+        PATHS ${INTEL_ROOT}/include)
+    set(MKL_INCLUDE_DIR ${MKL_INCLUDE_DIR} ${INTEL_INCLUDE_DIR})
+endif()
+
+# Find libraries
+
+# Handle suffix
+set(_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
+
+if(WIN32)
+    if(MKL_STATAIC)
+        set(CMAKE_FIND_LIBRARY_SUFFIXES .lib)
+    else()
+        set(CMAKE_FIND_LIBRARY_SUFFIXES _dll.lib)
+    endif()
+else()
+    if(MKL_STATAIC)
+        set(CMAKE_FIND_LIBRARY_SUFFIXES .a)
+    else()
+        set(CMAKE_FIND_LIBRARY_SUFFIXES .so)
+    endif()
+endif()
+
+
+# MKL is composed by four layers: Interface, Threading, Computational and RTL
+
+if(MKL_SDL)
+    find_library(MKL_LIBRARY mkl_rt
+        PATHS ${MKL_ROOT}/lib/ia32/)
+
+    set(MKL_MINIMAL_LIBRARY ${MKL_LIBRARY})
+else()
+    ######################### Interface layer #######################
+    if(WIN32)
+        set(MKL_INTERFACE_LIBNAME mkl_intel_c)
+    else()
+        set(MKL_INTERFACE_LIBNAME mkl_intel)
+    endif()
+
+    find_library(MKL_INTERFACE_LIBRARY ${MKL_INTERFACE_LIBNAME}
+        PATHS ${MKL_ROOT}/lib/ia32/)
+
+    ######################## Threading layer ########################
+    if(MKL_MULTI_THREADED)
+        set(MKL_THREADING_LIBNAME mkl_intel_thread)
+    else()
+        set(MKL_THREADING_LIBNAME mkl_sequential)
+    endif()
+
+    find_library(MKL_THREADING_LIBRARY ${MKL_THREADING_LIBNAME}
+        PATHS ${MKL_ROOT}/lib/ia32/)
+
+    ####################### Computational layer #####################
+    find_library(MKL_CORE_LIBRARY mkl_core
+        PATHS ${MKL_ROOT}/lib/ia32/)
+    find_library(MKL_FFT_LIBRARY mkl_cdft_core
+        PATHS ${MKL_ROOT}/lib/ia32/)
+    find_library(MKL_SCALAPACK_LIBRARY mkl_scalapack_core
+        PATHS ${MKL_ROOT}/lib/ia32/)
+
+    ############################ RTL layer ##########################
+    if(WIN32)
+        set(MKL_RTL_LIBNAME libiomp5md)
+    else()
+        set(MKL_RTL_LIBNAME libiomp5)
+    endif()
+    find_library(MKL_RTL_LIBRARY ${MKL_RTL_LIBNAME}
+        PATHS ${INTEL_RTL_ROOT}/lib)
+
+    set(MKL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_FFT_LIBRARY} ${MKL_SCALAPACK_LIBRARY} ${MKL_RTL_LIBRARY})
+    set(MKL_MINIMAL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_RTL_LIBRARY})
+endif()
+
+set(CMAKE_FIND_LIBRARY_SUFFIXES ${_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
+
+find_package_handle_standard_args(MKL DEFAULT_MSG
+    MKL_INCLUDE_DIR MKL_LIBRARY MKL_MINIMAL_LIBRARY)
+
+if(MKL_FOUND)
+    set(MKL_INCLUDE_DIRS ${MKL_INCLUDE_DIR})
+    set(MKL_LIBRARIES ${MKL_LIBRARY})
+    set(MKL_MINIMAL_LIBRARIES ${MKL_LIBRARY})
+endif()
diff --git a/CMakeScripts/FindOpenBLAS.cmake b/CMakeScripts/FindOpenBLAS.cmake
new file mode 100644
index 00000000000..297a7b6bc43
--- /dev/null
+++ b/CMakeScripts/FindOpenBLAS.cmake
@@ -0,0 +1,62 @@
+
+
+SET(Open_BLAS_INCLUDE_SEARCH_PATHS
+  /usr/include
+  /usr/include/openblas-base
+  /usr/local/include
+  /usr/local/include/openblas-base
+  /opt/OpenBLAS/include
+  $ENV{OpenBLAS_HOME}
+  $ENV{OpenBLAS_HOME}/include
+)
+
+SET(Open_BLAS_LIB_SEARCH_PATHS
+        /lib/
+        /lib/openblas-base
+        /lib64/
+        /usr/lib
+        /usr/lib/openblas-base
+        /usr/lib64
+        /usr/local/lib
+        /usr/local/lib64
+        /opt/OpenBLAS/lib
+        $ENV{OpenBLAS}cd
+        $ENV{OpenBLAS}/lib
+        $ENV{OpenBLAS_HOME}
+        $ENV{OpenBLAS_HOME}/lib
+ )
+
+FIND_PATH(OpenBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${ATLAS_POSSIBLE_INCLUDE_PATHS})
+FIND_LIBRARY(OpenBLAS_LIB NAMES openblas PATHS ${Open_BLAS_LIB_SEARCH_PATHS})
+
+SET(OpenBLAS_FOUND ON)
+
+#    Check include files
+IF(NOT OpenBLAS_INCLUDE_DIR)
+    SET(OpenBLAS_FOUND OFF)
+    MESSAGE(STATUS "Could not find OpenBLAS include. Turning OpenBLAS_FOUND off")
+ENDIF()
+
+#    Check libraries
+IF(NOT OpenBLAS_LIB)
+    SET(OpenBLAS_FOUND OFF)
+    MESSAGE(STATUS "Could not find OpenBLAS lib. Turning OpenBLAS_FOUND off")
+ENDIF()
+
+IF (OpenBLAS_FOUND)
+  IF (NOT OpenBLAS_FIND_QUIETLY)
+    MESSAGE(STATUS "Found OpenBLAS libraries: ${OpenBLAS_LIB}")
+    MESSAGE(STATUS "Found OpenBLAS include: ${OpenBLAS_INCLUDE_DIR}")
+  ENDIF (NOT OpenBLAS_FIND_QUIETLY)
+ELSE (OpenBLAS_FOUND)
+  IF (OpenBLAS_FIND_REQUIRED)
+    MESSAGE(FATAL_ERROR "Could not find OpenBLAS")
+  ENDIF (OpenBLAS_FIND_REQUIRED)
+ENDIF (OpenBLAS_FOUND)
+
+MARK_AS_ADVANCED(
+    OpenBLAS_INCLUDE_DIR
+    OpenBLAS_LIB
+    OpenBLAS
+)
+
diff --git a/Makefile b/Makefile
index 287120a5d22..2c3ce5415bd 100644
--- a/Makefile
+++ b/Makefile
@@ -512,12 +512,12 @@ proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
 $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
 		$(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
-	protoc --proto_path=src --cpp_out=$(BUILD_DIR)/src $<
+	protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<
 	@ echo
 
 $(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
 		$(PY_PROTO_INIT) | $(PY_PROTO_BUILD_DIR)
-	protoc --proto_path=src --python_out=python $<
+	protoc --proto_path=$(PROTO_SRC_DIR) --python_out=$(PY_PROTO_BUILD_DIR) $<
 	@ echo
 
 $(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
new file mode 100644
index 00000000000..f6a03ee4625
--- /dev/null
+++ b/matlab/CMakeLists.txt
@@ -0,0 +1 @@
+project( Matlab )
\ No newline at end of file
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
new file mode 100644
index 00000000000..e09257c5d7a
--- /dev/null
+++ b/python/CMakeLists.txt
@@ -0,0 +1,20 @@
+project( Python )
+
+#    Python
+find_package(PythonLibs REQUIRED)
+include_directories(${PYTHON_INCLUDE_DIRS})
+
+#    Boost.Python
+find_package(Boost 1.49 COMPONENTS python REQUIRED)
+include_directories(${Boost_INCLUDE_DIRS})
+
+file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+add_library(pycaffe SHARED ${Python_SOURCES})
+target_link_libraries(pycaffe caffe ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+
+###    Install    #################################################################################
+
+install(DIRECTORY caffe DESTINATION python)
+install(FILES requirements.txt DESTINATION python)
+install(TARGETS pycaffe DESTINATION python/caffe)
\ No newline at end of file
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
new file mode 100644
index 00000000000..d953d0b2c10
--- /dev/null
+++ b/src/caffe/CMakeLists.txt
@@ -0,0 +1,105 @@
+project( CaffeSrc )
+
+#    Threads
+find_package(Threads REQUIRED)
+
+#	Google-glog
+find_package(Glog REQUIRED)
+include_directories(${GLOG_INCLUDE_DIRS})
+
+#    CUDA
+set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
+        -gencode arch=compute_20,code=sm_20
+		-gencode arch=compute_20,code=sm_21
+		-gencode arch=compute_30,code=sm_30
+		-gencode arch=compute_35,code=sm_35
+)
+
+#   BLAS
+if(BLAS STREQUAL "atlas")
+
+    find_package(Atlas REQUIRED)
+    include_directories(${Atlas_INCLUDE_DIR})
+    set(BLAS_LIBRARIES ${Atlas_LIBRARIES})
+
+elseif(BLAS STREQUAL "open")
+
+    find_package(OpenBLAS REQUIRED)
+    include_directories(${OpenBLAS_INCLUDE_DIR})
+    set(BLAS_LIBRARIES ${OpenBLAS_LIB})
+
+elseif(BLAS STREQUAL "mkl")
+
+    find_package(MKL REQUIRED)
+    include_directories(${MKL_INCLUDE_DIR})
+    set(BLAS_LIBRARIES ${MKL_LIBRARIES})
+
+endif()
+
+#    HDF5
+find_package(HDF5 COMPONENTS HL REQUIRED)
+include_directories(${HDF5_INCLUDE_DIRS})
+
+#    OpenCV
+find_package(OpenCV COMPONENTS core highgui imgproc REQUIRED)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+#    LevelDB
+find_package(LevelDB REQUIRED)
+include_directories(${LEVELDB_INCLUDE})
+
+#    LMDB
+find_package(LMDB REQUIRED)
+include_directories(${LMDB_INCLUDE_DIR})
+
+#    Boost
+find_package(Boost 1.46 COMPONENTS system REQUIRED)
+include_directories( ${Boost_INCLUDE_DIR} )
+link_directories( ${Boost_LIBRARY_DIRS} )
+
+add_subdirectory(proto)
+
+#    Recursively find source files
+
+#    cuda sources
+file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
+
+#    test sources
+file(GLOB_RECURSE TEST_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
+
+#    all cpp sources
+file(GLOB_RECURSE CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+#    remove test sources from cpp sources
+list(REMOVE_ITEM CPP_SOURCES ${TEST_SOURCES})
+
+add_library(caffe ${CPP_SOURCES})
+cuda_add_library(caffe_cu ${CU_SOURCES})
+
+# both depend on proto
+add_dependencies(caffe proto)
+add_dependencies(caffe_cu proto)
+
+target_link_libraries(caffe caffe_cu proto
+        ${GLOG_LIBRARIES}
+        ${CUDA_curand_LIBRARY}
+        ${HDF5_LIBRARIES}
+        ${OpenCV_LIBS}
+        ${LEVELDB_LIBS}
+        ${LMDB_LIBRARIES} 
+        ${CUDA_CUBLAS_LIBRARIES}
+        ${BLAS_LIBRARIES}
+        ${CMAKE_THREAD_LIBS_INIT}
+)
+
+#set output directory
+set_target_properties(caffe PROPERTIES
+        ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
+        LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
+)
+
+add_subdirectory(test)
+
+###    Install    #################################################################################
+
+install(TARGETS caffe DESTINATION lib)
diff --git a/src/caffe/proto/CMakeLists.txt b/src/caffe/proto/CMakeLists.txt
new file mode 100644
index 00000000000..a4b2c00e393
--- /dev/null
+++ b/src/caffe/proto/CMakeLists.txt
@@ -0,0 +1,35 @@
+cmake_minimum_required(VERSION 2.8)
+project( Proto )
+
+#	Google Protocol Buffers
+find_package( Protobuf REQUIRED )
+
+# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package and should be installed
+# separately as in: sudo apt-get install protobuf-compiler
+if(PROTOBUF_PROTOC_EXECUTABLE)
+	message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
+else()
+	message(FATAL_ERROR "Could not find PROTOBUF Compiler")
+endif()
+
+include_directories(${PROTOBUF_INCLUDE_DIR})
+file(GLOB ProtoFiles "${CMAKE_CURRENT_SOURCE_DIR}/*.proto")
+PROTOBUF_GENERATE_CPP(ProtoSources ProtoHeaders ${ProtoFiles})
+
+add_library(proto
+        ${ProtoSources}
+        ${ProtoHeaders}
+)
+
+target_link_libraries(proto ${PROTOBUF_LIBRARIES})
+
+# Copy proto headers to include/caffe/proto/
+foreach(header ${ProtoHeaders})
+
+    ADD_CUSTOM_COMMAND(TARGET proto
+        COMMAND cmake -E copy ${header}
+        ${Caffe_INCLUDE_DIRS}/caffe/proto/
+        DEPENDS ${header}
+)
+
+endforeach(header)
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
index 225138a4274..6f0a5f6b8df 100644
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ b/src/caffe/proto/caffe_pretty_print.proto
@@ -2,7 +2,7 @@ syntax = "proto2";
 
 package caffe;
 
-import "caffe/proto/caffe.proto";
+import "caffe.proto";
 
 // A near-duplicate of NetParameter with fields re-numbered to beautify
 // automatic prototext dumps.  The main practical purpose is to print inputs
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
new file mode 100644
index 00000000000..721df27d0f4
--- /dev/null
+++ b/src/caffe/test/CMakeLists.txt
@@ -0,0 +1,46 @@
+#
+#
+#    All test files' names must begin with a "test_" prefix
+#
+#
+cmake_minimum_required(VERSION 2.8)
+project( Test )
+
+#    Set CUDA Device number and CMAKE_SOURCE_DIR
+add_definitions(-DCMAKE_BUILD)
+configure_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.in
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.gen.cmake
+)
+
+configure_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.in
+    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.gen.cmake
+)
+
+include_directories(
+        ${Caffe_SOURCE_DIR}
+)
+
+set(TEST_MAIN test_caffe_main.cpp)
+list(REMOVE_ITEM TEST_SOURCES ${TEST_MAIN})
+
+
+foreach(source ${TEST_SOURCES})
+
+    get_filename_component(name ${source} NAME_WE)
+    add_executable(${name}.test ${TEST_MAIN} ${source} ../blob.cpp)
+    target_link_libraries(${name}.test gtest caffe)
+    add_test(${name} ${name}.test)
+
+    #    output dir
+    set_target_properties(${name}.test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+
+endforeach()
+
+add_executable(test_all.test ${TEST_SOURCES} ${TEST_MAIN})
+target_link_libraries(test_all.test gtest caffe)
+
+#    output dir
+set_target_properties(test_all.test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+
diff --git a/src/caffe/test/cmake_test_defines.hpp.in b/src/caffe/test/cmake_test_defines.hpp.in
new file mode 100644
index 00000000000..617968dfa9c
--- /dev/null
+++ b/src/caffe/test/cmake_test_defines.hpp.in
@@ -0,0 +1,5 @@
+
+#define CUDA_TEST_DEVICE @CUDA_TEST_DEVICE@
+#define CMAKE_SOURCE_DIR "@CMAKE_SOURCE_DIR@/src/"
+#define Examples_SOURCE_DIR "@Examples_SOURCE_DIR@/"
+#define CMAKE_EXT ".gen.cmake"
\ No newline at end of file
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index df188fd319b..ad6e189a657 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -26,6 +26,10 @@ int main(int argc, char** argv) {
     device = atoi(argv[1]);
     cudaSetDevice(device);
     cout << "Setting to use device " << device << endl;
+  } else if (CUDA_TEST_DEVICE >= 0) {
+
+	  // Use the device assigned in build configuration; but with a lower priority
+	  device = CUDA_TEST_DEVICE;
   }
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
diff --git a/src/caffe/test/test_caffe_main.hpp b/src/caffe/test/test_caffe_main.hpp
new file mode 100644
index 00000000000..76f50be83c0
--- /dev/null
+++ b/src/caffe/test/test_caffe_main.hpp
@@ -0,0 +1,29 @@
+// Copyright 2014 BVLC and contributors.
+
+// The main caffe test code. Your test cpp code should include this hpp
+// to allow a main function to be compiled into the binary.
+#ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
+#define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
+
+#include <cuda_runtime.h>
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+
+#include <cstdlib>
+#include <cstdio>
+
+using std::cout;
+using std::endl;
+
+#ifdef CMAKE_BUILD
+	#include "cmake_test_defines.hpp.gen.cmake"
+#else
+	#define CUDA_TEST_DEVICE -1
+	#define CMAKE_SOURCE_DIR "src/"
+	#define Examples_SOURCE_DIR "examples/"
+	#define CMAKE_EXT ""
+#endif
+
+int main(int argc, char** argv);
+
+#endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/src/caffe/test/test_data/sample_data_list.txt.in b/src/caffe/test/test_data/sample_data_list.txt.in
new file mode 100644
index 00000000000..9860ef583ab
--- /dev/null
+++ b/src/caffe/test/test_data/sample_data_list.txt.in
@@ -0,0 +1,2 @@
+@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data.h5
+@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data_2_gzip.h5
\ No newline at end of file
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 696165cdaa2..f3ddfcd9625 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -23,7 +23,7 @@ class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   HDF5OutputLayerTest()
       : output_file_name_(tmpnam(NULL)),
-        input_file_name_("src/caffe/test/test_data/sample_data.h5"),
+        input_file_name_(CMAKE_SOURCE_DIR  "caffe/test/test_data/sample_data.h5"),
         blob_data_(new Blob<Dtype>()),
         blob_label_(new Blob<Dtype>()),
         num_(5),
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index f903afb736f..1de82330b79 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -31,7 +31,7 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
     blob_top_vec_.push_back(blob_top_label_);
 
     // Check out generate_sample_data.py in the same directory.
-    filename = new string("src/caffe/test/test_data/sample_data_list.txt");
+    filename = new string(CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data_list.txt" CMAKE_EXT);
     LOG(INFO) << "Using sample HDF5 data file " << filename;
   }
 
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 5232c1187c1..17d67bd13b3 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -37,7 +37,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
     std::ofstream outfile(filename_->c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << *filename_;
     for (int i = 0; i < 5; ++i) {
-      outfile << "examples/images/cat.jpg " << i;
+      outfile << Examples_SOURCE_DIR "images/cat.jpg " << i;
     }
     outfile.close();
   }
diff --git a/src/gtest/CMakeLists.txt b/src/gtest/CMakeLists.txt
new file mode 100644
index 00000000000..82a4120ca3f
--- /dev/null
+++ b/src/gtest/CMakeLists.txt
@@ -0,0 +1,6 @@
+project(gtest CXX C)
+cmake_minimum_required(VERSION 2.6.2)
+
+add_library(gtest gtest-all.cpp)
+add_library(gtest_main gtest_main.cc)
+target_link_libraries(gtest_main gtest)
\ No newline at end of file
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
new file mode 100644
index 00000000000..81ecf7cd41a
--- /dev/null
+++ b/tools/CMakeLists.txt
@@ -0,0 +1,18 @@
+project( Tools )
+
+#    Find all source files
+file(GLOB_RECURSE Tools_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+#    Build each source file independently
+foreach(source ${Tools_SOURCES})
+    get_filename_component(name ${source} NAME_WE)
+    add_executable(${name} ${source})
+    target_link_libraries(${name} caffe)
+
+###    Install    #################################################################################
+
+    install(TARGETS ${name} DESTINATION tools)
+
+
+endforeach(source)
+

From ed39454b8decee5b9278b94e25e4aedf147ee70d Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Tue, 1 Jul 2014 11:42:24 +0200
Subject: [PATCH 0482/2053] examples CMake lists

---
 examples/CMakeLists.txt | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 examples/CMakeLists.txt

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
new file mode 100644
index 00000000000..1f13a0d9c75
--- /dev/null
+++ b/examples/CMakeLists.txt
@@ -0,0 +1,17 @@
+project( Examples )
+
+file(GLOB_RECURSE Examples_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+# message(${Examples_SOURCES})
+foreach(source ${Examples_SOURCES})
+    #    get file name
+    get_filename_component(name ${source} NAME_WE)  
+    
+    #get folder name
+    get_filename_component(path ${source} PATH)      
+    get_filename_component(folder ${path} NAME_WE)      
+    
+    add_executable(${name} ${source})
+    target_link_libraries(${name} caffe)
+    set_target_properties(${name} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/${folder})
+endforeach(source)

From bf55cb9a56bf707908e047c5a4bce33442b7224f Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Tue, 22 Jul 2014 14:44:19 +0200
Subject: [PATCH 0483/2053] fixed CMake dependant header file generation

---
 include/caffe/test/test_caffe_main.hpp |  9 +++++++
 src/caffe/test/CMakeLists.txt          | 37 +++++++++++++++++++++-----
 src/caffe/test/test_caffe_main.hpp     | 29 --------------------
 3 files changed, 39 insertions(+), 36 deletions(-)
 delete mode 100644 src/caffe/test/test_caffe_main.hpp

diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 40b9effc5f1..e02e67ece98 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -14,6 +14,15 @@
 using std::cout;
 using std::endl;
 
+#ifdef CMAKE_BUILD
+  #include <cmake_test_defines.hpp.gen.cmake>
+#else
+  #define CUDA_TEST_DEVICE -1
+  #define CMAKE_SOURCE_DIR "src/"
+  #define Examples_SOURCE_DIR "examples/"
+  #define CMAKE_EXT ""
+#endif
+
 int main(int argc, char** argv);
 
 namespace caffe {
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 721df27d0f4..f64d5da0c8d 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -8,18 +8,41 @@ project( Test )
 
 #    Set CUDA Device number and CMAKE_SOURCE_DIR
 add_definitions(-DCMAKE_BUILD)
-configure_file(
-    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.in
-    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.gen.cmake
-)
 
-configure_file(
-    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.in
-    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.gen.cmake
+#    Generate config files
+set(IN_EXT .in)
+set(GEN_EXT .gen.cmake)
+set(TEST_DEFINES_FILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp)
+set(TEST_DATA_FILE ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt)
+
+set(IN_FILES 
+    ${TEST_DEFINES_FILE}
+    ${TEST_DATA_FILE}
 )
 
+foreach(in_file ${IN_FILES})
+    
+    configure_file(
+        ${in_file}${IN_EXT}
+        ${in_file}${GEN_EXT}
+    )
+    
+endforeach()       
+
+
+#configure_file(
+#    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.in
+#    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.gen.cmake
+#)
+#
+#configure_file(
+#    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.in
+#    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.gen.cmake
+#)
+
 include_directories(
         ${Caffe_SOURCE_DIR}
+        ${CMAKE_CURRENT_SOURCE_DIR}
 )
 
 set(TEST_MAIN test_caffe_main.cpp)
diff --git a/src/caffe/test/test_caffe_main.hpp b/src/caffe/test/test_caffe_main.hpp
deleted file mode 100644
index 76f50be83c0..00000000000
--- a/src/caffe/test/test_caffe_main.hpp
+++ /dev/null
@@ -1,29 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-// The main caffe test code. Your test cpp code should include this hpp
-// to allow a main function to be compiled into the binary.
-#ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
-#define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
-
-#include <cuda_runtime.h>
-#include <glog/logging.h>
-#include <gtest/gtest.h>
-
-#include <cstdlib>
-#include <cstdio>
-
-using std::cout;
-using std::endl;
-
-#ifdef CMAKE_BUILD
-	#include "cmake_test_defines.hpp.gen.cmake"
-#else
-	#define CUDA_TEST_DEVICE -1
-	#define CMAKE_SOURCE_DIR "src/"
-	#define Examples_SOURCE_DIR "examples/"
-	#define CMAKE_EXT ""
-#endif
-
-int main(int argc, char** argv);
-
-#endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_

From c357810c2eed2f677c2a9b22416460a4bca14470 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Wed, 23 Jul 2014 09:12:44 +0200
Subject: [PATCH 0484/2053] fixed lint issues

---
 src/caffe/test/test_caffe_main.cpp        | 5 ++---
 src/caffe/test/test_hdf5_output_layer.cpp | 3 ++-
 src/caffe/test/test_hdf5data_layer.cpp    | 5 +++--
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index ad6e189a657..bff0c4ed20e 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -27,9 +27,8 @@ int main(int argc, char** argv) {
     cudaSetDevice(device);
     cout << "Setting to use device " << device << endl;
   } else if (CUDA_TEST_DEVICE >= 0) {
-
-	  // Use the device assigned in build configuration; but with a lower priority
-	  device = CUDA_TEST_DEVICE;
+    // Use the device assigned in build configuration; but with a lower priority
+    device = CUDA_TEST_DEVICE;
   }
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index f3ddfcd9625..b2a511d1d09 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -23,7 +23,8 @@ class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   HDF5OutputLayerTest()
       : output_file_name_(tmpnam(NULL)),
-        input_file_name_(CMAKE_SOURCE_DIR  "caffe/test/test_data/sample_data.h5"),
+        input_file_name_(
+        CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data.h5"),
         blob_data_(new Blob<Dtype>()),
         blob_label_(new Blob<Dtype>()),
         num_(5),
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 1de82330b79..acca75b19b1 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -31,8 +31,9 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
     blob_top_vec_.push_back(blob_top_label_);
 
     // Check out generate_sample_data.py in the same directory.
-    filename = new string(CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data_list.txt" CMAKE_EXT);
-    LOG(INFO) << "Using sample HDF5 data file " << filename;
+    filename = new string(
+    CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data_list.txt" CMAKE_EXT);
+    LOG(INFO)<< "Using sample HDF5 data file " << filename;
   }
 
   virtual ~HDF5DataLayerTest() {

From a2a4d636c9cb4a8f071b1703acfe0aeb6f8d7771 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Wed, 23 Jul 2014 09:41:36 +0200
Subject: [PATCH 0485/2053] cmake build configuration for travis-ci

---
 .travis.yml                    | 47 ++++++++++++++++++++++++----------
 CMakeLists.txt                 |  5 ++--
 CMakeScripts/FindSnappy.cmake  | 33 ++++++++++++++++++++++++
 python/CMakeLists.txt          |  2 +-
 src/caffe/CMakeLists.txt       | 10 +++++++-
 src/caffe/proto/CMakeLists.txt |  4 ++-
 src/caffe/test/CMakeLists.txt  |  1 -
 7 files changed, 81 insertions(+), 21 deletions(-)
 create mode 100644 CMakeScripts/FindSnappy.cmake

diff --git a/.travis.yml b/.travis.yml
index 7721de112e3..e5969764564 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -17,8 +17,12 @@ before_install:
   - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
 
 install:
+  # CMake 2.8.12
+  - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz
+  - cd /tmp/cmake-2.8.12 && ./bootstrap --prefix=/usr && make -j4 &&  sudo make install -j4 &&  cd - #&& rm -r /tmp/cmake-2.8.12
+    #
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
-  - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
+  - cd /tmp/glog-0.3.3 && ./configure && make -j4 && sudo make install -j4 && cd -
   - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
   - sudo apt-get -y update
@@ -30,7 +34,7 @@ install:
   # manually since we did a partial installation.)
   - sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
-  - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
+  - cd /tmp/mdb-mdb/libraries/liblmdb/ && make -j4 && sudo make install -j4 && cd -
 
 before_script:
   - mv Makefile.config.example Makefile.config
@@ -38,19 +42,34 @@ before_script:
   - export NUM_THREADS=4
 
 script:
-  # CPU-GPU: build only.
-  - export CPU_ONLY=0
-  - make --keep-going --jobs=$NUM_THREADS all
+  # CMake build.
+  - mkdir build
+  - cd build
+  ## CPU-only build
+    #- cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+    #- make --keep-going --jobs=$NUM_THREADS
+    #- make clean
+    #- rm -rf *
+  ## GPU + CPU build
+  - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
+  - make --keep-going --jobs=$NUM_THREADS
   - make clean
-  # CPU-only: comprehensive.
-  - export CPU_ONLY=1
-  - make --keep-going --jobs=$NUM_THREADS all test warn lint
-  - make runtest
-  - make --jobs=$NUM_THREADS all
-  - make --jobs=$NUM_THREADS test
-  - make --jobs=$NUM_THREADS warn
-  - make --jobs=$NUM_THREADS lint
-  - make --jobs=$NUM_THREADS pycaffe
+  - cd ..
+  - rm -r build
+
+#  # CPU-GPU: build only.
+#  - export CPU_ONLY=0
+#  - make --keep-going --jobs=$NUM_THREADS all
+#  - make clean
+#  # CPU-only: comprehensive.
+#  - export CPU_ONLY=1
+#  - make --keep-going --jobs=$NUM_THREADS all test warn lint
+#  - make runtest
+#  - make --jobs=$NUM_THREADS all
+#  - make --jobs=$NUM_THREADS test
+#  - make --jobs=$NUM_THREADS warn
+#  - make --jobs=$NUM_THREADS lint
+#  - make --jobs=$NUM_THREADS pycaffe
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
diff --git a/CMakeLists.txt b/CMakeLists.txt
index cc50a3a141d..f65046d6998 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 2.8)
+cmake_minimum_required(VERSION 2.8.8)
 project( Caffe )
 
 ###    Build Options     ##########################################################################
@@ -29,7 +29,7 @@ enable_testing()
 #    Compiler Flags
 set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
 set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC)
-set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} -O3)
+set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} )#-O3)
 
 #    Include Directories
 set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
@@ -43,7 +43,6 @@ set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SOURCE_DIR}/CMakeScripts)
 find_package(CUDA 5.5 REQUIRED)
 include_directories(${CUDA_INCLUDE_DIRS})
 
-
 ###    Subdirectories    ##########################################################################
 
 add_subdirectory(src/gtest)
diff --git a/CMakeScripts/FindSnappy.cmake b/CMakeScripts/FindSnappy.cmake
new file mode 100644
index 00000000000..d769b442812
--- /dev/null
+++ b/CMakeScripts/FindSnappy.cmake
@@ -0,0 +1,33 @@
+# Find the Snappy libraries
+#
+# The following variables are optionally searched for defaults
+#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
+#
+# The following are set after configuration is done:
+#  Snappy_FOUND
+#  Snappy_INCLUDE_DIRS
+#  Snappy_LIBS
+
+find_path(SNAPPY_INCLUDE_DIR
+    NAMES snappy.h
+    HINTS ${SNAPPY_ROOT_DIR}
+          ${SNAPPY_ROOT_DIR}/include
+)
+
+find_library(SNAPPY_LIBS
+    NAMES snappy
+    HINTS ${SNAPPY_ROOT_DIR}
+          ${SNAPPY_ROOT_DIR}/lib
+)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(Snappy
+    DEFAULT_MSG
+    SNAPPY_LIBS
+    SNAPPY_INCLUDE_DIR
+)
+
+mark_as_advanced(
+    SNAPPY_LIBS
+    SNAPPY_INCLUDE_DIR
+)
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index e09257c5d7a..8642c39367a 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -5,7 +5,7 @@ find_package(PythonLibs REQUIRED)
 include_directories(${PYTHON_INCLUDE_DIRS})
 
 #    Boost.Python
-find_package(Boost 1.49 COMPONENTS python REQUIRED)
+find_package(Boost 1.46 COMPONENTS python REQUIRED)
 include_directories(${Boost_INCLUDE_DIRS})
 
 file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index d953d0b2c10..300188e5b83 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -47,6 +47,14 @@ include_directories(${OpenCV_INCLUDE_DIRS})
 #    LevelDB
 find_package(LevelDB REQUIRED)
 include_directories(${LEVELDB_INCLUDE})
+if(LEVELDB_FOUND)
+    find_package(Snappy REQUIRED)
+    include_directories(${SNAPPY_INCLUDE_DIR})
+    set(LEVELDB_LIBS
+        ${LEVELDB_LIBS}
+        ${SNAPPY_LIBS}
+    )
+endif()
 
 #    LMDB
 find_package(LMDB REQUIRED)
@@ -82,12 +90,12 @@ add_dependencies(caffe_cu proto)
 
 target_link_libraries(caffe caffe_cu proto
         ${GLOG_LIBRARIES}
-        ${CUDA_curand_LIBRARY}
         ${HDF5_LIBRARIES}
         ${OpenCV_LIBS}
         ${LEVELDB_LIBS}
         ${LMDB_LIBRARIES} 
         ${CUDA_CUBLAS_LIBRARIES}
+        ${CUDA_curand_LIBRARY}
         ${BLAS_LIBRARIES}
         ${CMAKE_THREAD_LIBS_INIT}
 )
diff --git a/src/caffe/proto/CMakeLists.txt b/src/caffe/proto/CMakeLists.txt
index a4b2c00e393..186a856509b 100644
--- a/src/caffe/proto/CMakeLists.txt
+++ b/src/caffe/proto/CMakeLists.txt
@@ -1,4 +1,3 @@
-cmake_minimum_required(VERSION 2.8)
 project( Proto )
 
 #	Google Protocol Buffers
@@ -23,6 +22,9 @@ add_library(proto
 
 target_link_libraries(proto ${PROTOBUF_LIBRARIES})
 
+# Create proto include directory
+file(MAKE_DIRECTORY ${CMAKE_SOURCE_DIR}/include/caffe/proto)
+
 # Copy proto headers to include/caffe/proto/
 foreach(header ${ProtoHeaders})
 
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index f64d5da0c8d..4eb9aa30119 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -3,7 +3,6 @@
 #    All test files' names must begin with a "test_" prefix
 #
 #
-cmake_minimum_required(VERSION 2.8)
 project( Test )
 
 #    Set CUDA Device number and CMAKE_SOURCE_DIR

From 68ae1ab7f2f486d82c9d683d9e1db21ecd329505 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Thu, 24 Jul 2014 13:44:05 +0200
Subject: [PATCH 0486/2053] cmake from binaries

---
 .travis.yml    | 5 +++++
 CMakeLists.txt | 1 +
 2 files changed, 6 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index e5969764564..f6b21136cf9 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -18,8 +18,13 @@ before_install:
 
 install:
   # CMake 2.8.12
+<<<<<<< HEAD
   - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz
   - cd /tmp/cmake-2.8.12 && ./bootstrap --prefix=/usr && make -j4 &&  sudo make install -j4 &&  cd - #&& rm -r /tmp/cmake-2.8.12
+=======
+  - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.2-Linux-i386.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz 
+  - cd /tmp/cmake-2.8.12.2-Linux-i386 && sudo cp * /usr/ -r && cd - && rm -r /tmp/cmake-2.8.12.2-Linux-i386
+>>>>>>> cmake from binaries
     #
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make -j4 && sudo make install -j4 && cd -
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f65046d6998..b7db2c98fc7 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -3,6 +3,7 @@ project( Caffe )
 
 ###    Build Options     ##########################################################################
 
+option(CPU_ONLY "Build Caffe without GPU support" OFF)
 option(BUILD_PYTHON "Build Python wrapper" OFF)
 option(BUILD_MATLAB "Build Matlab wrapper" OFF)
 option(BUILD_EXAMPLES "Build examples" ON)

From 75e3f56b98bbe6480277ad8ee24a99df8a9f56f0 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Thu, 24 Jul 2014 14:31:52 +0200
Subject: [PATCH 0487/2053] restoring travis.yml

---
 .travis.yml | 32 +++++++++++++-------------------
 1 file changed, 13 insertions(+), 19 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index f6b21136cf9..da60258fae8 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -50,31 +50,25 @@ script:
   # CMake build.
   - mkdir build
   - cd build
-  ## CPU-only build
-    #- cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
-    #- make --keep-going --jobs=$NUM_THREADS
-    #- make clean
-    #- rm -rf *
-  ## GPU + CPU build
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
   - make --keep-going --jobs=$NUM_THREADS
   - make clean
   - cd ..
   - rm -r build
 
-#  # CPU-GPU: build only.
-#  - export CPU_ONLY=0
-#  - make --keep-going --jobs=$NUM_THREADS all
-#  - make clean
-#  # CPU-only: comprehensive.
-#  - export CPU_ONLY=1
-#  - make --keep-going --jobs=$NUM_THREADS all test warn lint
-#  - make runtest
-#  - make --jobs=$NUM_THREADS all
-#  - make --jobs=$NUM_THREADS test
-#  - make --jobs=$NUM_THREADS warn
-#  - make --jobs=$NUM_THREADS lint
-#  - make --jobs=$NUM_THREADS pycaffe
+  # CPU-GPU: build only.
+  - export CPU_ONLY=0
+  - make --keep-going --jobs=$NUM_THREADS all
+  - make clean
+  # CPU-only: comprehensive.
+  - export CPU_ONLY=1
+  - make --keep-going --jobs=$NUM_THREADS all test warn lint
+  - make runtest
+  - make --jobs=$NUM_THREADS all
+  - make --jobs=$NUM_THREADS test
+  - make --jobs=$NUM_THREADS warn
+  - make --jobs=$NUM_THREADS lint
+  - make --jobs=$NUM_THREADS pycaffe
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,

From edc493a3e4b221c7114c9c64e94bfc3b8c85e435 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Thu, 24 Jul 2014 13:58:37 +0200
Subject: [PATCH 0488/2053] cpu only

---
 CMakeLists.txt           | 11 ++++++++--
 src/caffe/CMakeLists.txt | 46 +++++++++++++++++++++-------------------
 2 files changed, 33 insertions(+), 24 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b7db2c98fc7..3158cfff2b3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -32,6 +32,11 @@ set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
 set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC)
 set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} )#-O3)
 
+#    Global Definitions
+if(CPU_ONLY)
+    add_definitions(-DCPU_ONLY)
+endif()
+
 #    Include Directories
 set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
 include_directories(${${PROJECT_NAME}_INCLUDE_DIRS})
@@ -41,8 +46,10 @@ include_directories(${CMAKE_SOURCE_DIR}/src)
 set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SOURCE_DIR}/CMakeScripts)
 
 #    CUDA is required globally
-find_package(CUDA 5.5 REQUIRED)
-include_directories(${CUDA_INCLUDE_DIRS})
+if(NOT CPU_ONLY)
+    find_package(CUDA 5.5 REQUIRED)
+    include_directories(${CUDA_INCLUDE_DIRS})
+endif()
 
 ###    Subdirectories    ##########################################################################
 
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 300188e5b83..8b8542d8d30 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -7,14 +7,6 @@ find_package(Threads REQUIRED)
 find_package(Glog REQUIRED)
 include_directories(${GLOG_INCLUDE_DIRS})
 
-#    CUDA
-set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
-        -gencode arch=compute_20,code=sm_20
-		-gencode arch=compute_20,code=sm_21
-		-gencode arch=compute_30,code=sm_30
-		-gencode arch=compute_35,code=sm_35
-)
-
 #   BLAS
 if(BLAS STREQUAL "atlas")
 
@@ -68,34 +60,44 @@ link_directories( ${Boost_LIBRARY_DIRS} )
 add_subdirectory(proto)
 
 #    Recursively find source files
-
-#    cuda sources
-file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
-
-#    test sources
+##    test sources
 file(GLOB_RECURSE TEST_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
 
-#    all cpp sources
+##    all cpp sources
 file(GLOB_RECURSE CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
-#    remove test sources from cpp sources
+##    remove test sources from cpp sources
 list(REMOVE_ITEM CPP_SOURCES ${TEST_SOURCES})
 
 add_library(caffe ${CPP_SOURCES})
-cuda_add_library(caffe_cu ${CU_SOURCES})
-
 # both depend on proto
 add_dependencies(caffe proto)
-add_dependencies(caffe_cu proto)
 
-target_link_libraries(caffe caffe_cu proto
+#    CUDA
+if(NOT CPU_ONLY)
+    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
+            -gencode arch=compute_20,code=sm_20
+            -gencode arch=compute_20,code=sm_21
+            -gencode arch=compute_30,code=sm_30
+            -gencode arch=compute_35,code=sm_35
+    )
+    
+    #    cuda sources
+    file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
+    cuda_add_library(caffe_cu ${CU_SOURCES})
+    add_dependencies(caffe_cu proto)
+    target_link_libraries(caffe caffe_cu
+        ${CUDA_CUBLAS_LIBRARIES}
+        ${CUDA_curand_LIBRARY}
+    )
+endif()
+
+target_link_libraries(caffe proto
         ${GLOG_LIBRARIES}
         ${HDF5_LIBRARIES}
         ${OpenCV_LIBS}
         ${LEVELDB_LIBS}
-        ${LMDB_LIBRARIES} 
-        ${CUDA_CUBLAS_LIBRARIES}
-        ${CUDA_curand_LIBRARY}
+        ${LMDB_LIBRARIES}
         ${BLAS_LIBRARIES}
         ${CMAKE_THREAD_LIBS_INIT}
 )

From f83407d4c0abc8b9cc4180be8947cdd03ebd3926 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Thu, 24 Jul 2014 15:27:53 +0200
Subject: [PATCH 0489/2053] cpu only build works

---
 CMakeLists.txt                |  5 ++++-
 examples/CMakeLists.txt       |  1 -
 src/caffe/test/CMakeLists.txt | 26 +++++++++++---------------
 3 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3158cfff2b3..5d8e82daee0 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,7 +25,8 @@ endif()
 ###    Configuration    ###########################################################################
 
 #    Enable CTest from top build directory
-enable_testing()
+#disable ctest
+#enable_testing()
 
 #    Compiler Flags
 set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
@@ -60,6 +61,8 @@ add_subdirectory(tools)
 if(BUILD_EXAMPLES)
     message(STATUS "Examples enabled")
     add_subdirectory(examples)
+else()
+    set(Examples_SOURCE_DIR ${CMAKE_SOURCE_DIR}/examples)
 endif()
 
 if(BUILD_PYTHON)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 1f13a0d9c75..ec3cc470fb4 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -2,7 +2,6 @@ project( Examples )
 
 file(GLOB_RECURSE Examples_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
-# message(${Examples_SOURCES})
 foreach(source ${Examples_SOURCES})
     #    get file name
     get_filename_component(name ${source} NAME_WE)  
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 4eb9aa30119..52c146f5f89 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -28,17 +28,6 @@ foreach(in_file ${IN_FILES})
     
 endforeach()       
 
-
-#configure_file(
-#    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.in
-#    ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp.gen.cmake
-#)
-#
-#configure_file(
-#    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.in
-#    ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt.gen.cmake
-#)
-
 include_directories(
         ${Caffe_SOURCE_DIR}
         ${CMAKE_CURRENT_SOURCE_DIR}
@@ -53,16 +42,23 @@ foreach(source ${TEST_SOURCES})
     get_filename_component(name ${source} NAME_WE)
     add_executable(${name}.test ${TEST_MAIN} ${source} ../blob.cpp)
     target_link_libraries(${name}.test gtest caffe)
-    add_test(${name} ${name}.test)
+    #disable ctest
+    #add_test(${name} ${name}.test)
 
     #    output dir
     set_target_properties(${name}.test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
 
 endforeach()
 
-add_executable(test_all.test ${TEST_SOURCES} ${TEST_MAIN})
-target_link_libraries(test_all.test gtest caffe)
+add_executable(test ${TEST_SOURCES} ${TEST_MAIN})
+target_link_libraries(test gtest caffe)
+#    Test command
+set(TEST_ARGS --gtest_shuffle)
+if(CPU_ONLY)
+    set(TEST_ARGS ${TEST_ARGS} --gtest_filter="-*GPU*")
+endif()
+add_custom_command(TARGET test POSTBUILD COMMAND test ${TEST_ARGS})
 
 #    output dir
-set_target_properties(test_all.test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+set_target_properties(test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
 

From d69b0be45b33d82d622c58afd2d0e0ff993ca626 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Thu, 24 Jul 2014 15:37:26 +0200
Subject: [PATCH 0490/2053] enable both GPU and CPU builds + testing in travis

---
 .travis.yml | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index da60258fae8..1883c086da4 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -50,9 +50,16 @@ script:
   # CMake build.
   - mkdir build
   - cd build
+    ## CPU-GPU build
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
   - make --keep-going --jobs=$NUM_THREADS
-  - make clean
+  - make clean && rm -rf *
+    ## CPU-only build
+  - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+  - make --keep-going --jobs=$NUM_THREADS
+  - cd test && ./test --gtest_shuffle --gtest_filter="-*GPU*" && cd -
+  - make clean && rm -rf *
+    ## Cleaning
   - cd ..
   - rm -r build
 

From 15f0a5ed52a7f6858ba9b7817b229c493e7b12f4 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Fri, 25 Jul 2014 09:38:42 +0200
Subject: [PATCH 0491/2053] Examples_SOURCE_DIR cmake variable bugfix * it was
 set only when BUILD_EXAMPLES==OFF

---
 .travis.yml                              | 11 ++++++-----
 CMakeLists.txt                           |  2 --
 src/caffe/test/cmake_test_defines.hpp.in |  2 +-
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 1883c086da4..26d435b28f2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -50,11 +50,11 @@ script:
   # CMake build.
   - mkdir build
   - cd build
-    ## CPU-GPU build
+  ## CPU-GPU: build only.
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
   - make --keep-going --jobs=$NUM_THREADS
   - make clean && rm -rf *
-    ## CPU-only build
+   ## CPU-only: comprehensive
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
   - make --keep-going --jobs=$NUM_THREADS
   - cd test && ./test --gtest_shuffle --gtest_filter="-*GPU*" && cd -
@@ -62,12 +62,13 @@ script:
     ## Cleaning
   - cd ..
   - rm -r build
-
-  # CPU-GPU: build only.
+  
+  # Make build
+  ## CPU-GPU: build only.
   - export CPU_ONLY=0
   - make --keep-going --jobs=$NUM_THREADS all
   - make clean
-  # CPU-only: comprehensive.
+  ## CPU-only: comprehensive.
   - export CPU_ONLY=1
   - make --keep-going --jobs=$NUM_THREADS all test warn lint
   - make runtest
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 5d8e82daee0..0cf08fe0900 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -61,8 +61,6 @@ add_subdirectory(tools)
 if(BUILD_EXAMPLES)
     message(STATUS "Examples enabled")
     add_subdirectory(examples)
-else()
-    set(Examples_SOURCE_DIR ${CMAKE_SOURCE_DIR}/examples)
 endif()
 
 if(BUILD_PYTHON)
diff --git a/src/caffe/test/cmake_test_defines.hpp.in b/src/caffe/test/cmake_test_defines.hpp.in
index 617968dfa9c..2d67df4ac39 100644
--- a/src/caffe/test/cmake_test_defines.hpp.in
+++ b/src/caffe/test/cmake_test_defines.hpp.in
@@ -1,5 +1,5 @@
 
 #define CUDA_TEST_DEVICE @CUDA_TEST_DEVICE@
 #define CMAKE_SOURCE_DIR "@CMAKE_SOURCE_DIR@/src/"
-#define Examples_SOURCE_DIR "@Examples_SOURCE_DIR@/"
+#define Examples_SOURCE_DIR "@CMAKE_SOURCE_DIR@/examples/"
 #define CMAKE_EXT ".gen.cmake"
\ No newline at end of file

From bad9d1ea0dddf2d639ca98d93a57692b92976de5 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Fri, 25 Jul 2014 12:22:00 +0200
Subject: [PATCH 0492/2053] added proper 'runtest' target

---
 .travis.yml                   |  2 +-
 CMakeLists.txt                |  5 ----
 src/caffe/test/CMakeLists.txt | 48 +++++++++++++++++------------------
 3 files changed, 25 insertions(+), 30 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 26d435b28f2..e8e9c8a3526 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -57,7 +57,7 @@ script:
    ## CPU-only: comprehensive
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
   - make --keep-going --jobs=$NUM_THREADS
-  - cd test && ./test --gtest_shuffle --gtest_filter="-*GPU*" && cd -
+  - make runtest --keep-going --jobs=$NUM_THREADS
   - make clean && rm -rf *
     ## Cleaning
   - cd ..
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0cf08fe0900..7bc6d07bc2f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -23,11 +23,6 @@ if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
 endif()
 
 ###    Configuration    ###########################################################################
-
-#    Enable CTest from top build directory
-#disable ctest
-#enable_testing()
-
 #    Compiler Flags
 set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
 set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC)
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 52c146f5f89..f916ac0b0dd 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -5,27 +5,29 @@
 #
 project( Test )
 
-#    Set CUDA Device number and CMAKE_SOURCE_DIR
-add_definitions(-DCMAKE_BUILD)
+# Configuration
+set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are going to be placed there
+set(TEST_EXT .bin)               # test executable extension
+set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
+set(RUN_TEST runtest)            # dummy target for running tests
 
 #    Generate config files
-set(IN_EXT .in)
-set(GEN_EXT .gen.cmake)
+add_definitions(-DCMAKE_BUILD)    # definition needed in order to include CMake's generated files
+set(IN_EXT .in)            # generator input file extension
+set(GEN_EXT .gen.cmake)    # generated output file extension
 set(TEST_DEFINES_FILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp)
 set(TEST_DATA_FILE ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt)
 
-set(IN_FILES 
+set(IN_FILES             # generator input files
     ${TEST_DEFINES_FILE}
     ${TEST_DATA_FILE}
 )
 
-foreach(in_file ${IN_FILES})
-    
+foreach(in_file ${IN_FILES})    
     configure_file(
         ${in_file}${IN_EXT}
         ${in_file}${GEN_EXT}
-    )
-    
+    )    
 endforeach()       
 
 include_directories(
@@ -36,29 +38,27 @@ include_directories(
 set(TEST_MAIN test_caffe_main.cpp)
 list(REMOVE_ITEM TEST_SOURCES ${TEST_MAIN})
 
-
+#    Build each test separately
 foreach(source ${TEST_SOURCES})
-
     get_filename_component(name ${source} NAME_WE)
-    add_executable(${name}.test ${TEST_MAIN} ${source} ../blob.cpp)
-    target_link_libraries(${name}.test gtest caffe)
-    #disable ctest
-    #add_test(${name} ${name}.test)
-
+    set(TEST_NAME ${name}${TEST_EXT})
+    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_MAIN} ${source} ../blob.cpp)
+    target_link_libraries(${TEST_NAME} gtest caffe)
     #    output dir
-    set_target_properties(${name}.test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-
+    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
 endforeach()
 
-add_executable(test ${TEST_SOURCES} ${TEST_MAIN})
-target_link_libraries(test gtest caffe)
+#    Build a compound test excluded from the ALL target
+add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_SOURCES} ${TEST_MAIN})
+target_link_libraries(${ALL_TEST} gtest caffe)
+add_dependencies(${ALL_TEST} ${TEST_TARGETS})
+#    output dir
+set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})
 #    Test command
 set(TEST_ARGS --gtest_shuffle)
 if(CPU_ONLY)
     set(TEST_ARGS ${TEST_ARGS} --gtest_filter="-*GPU*")
 endif()
-add_custom_command(TARGET test POSTBUILD COMMAND test ${TEST_ARGS})
-
-#    output dir
-set_target_properties(test PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
 
+add_custom_target(${RUN_TEST} COMMAND ${ALL_TEST} ${TEST_ARGS})

From f2f3b4c71210958125d4945442641e0305fbeba9 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Mon, 28 Jul 2014 14:01:45 +0200
Subject: [PATCH 0493/2053] Added lint target

---
 .travis.yml             |  1 +
 CMakeLists.txt          | 16 +++++++++++++-
 CMakeScripts/lint.cmake | 48 +++++++++++++++++++++++++++++++++++++++++
 3 files changed, 64 insertions(+), 1 deletion(-)
 create mode 100644 CMakeScripts/lint.cmake

diff --git a/.travis.yml b/.travis.yml
index e8e9c8a3526..c94d9cdebc1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -58,6 +58,7 @@ script:
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
   - make --keep-going --jobs=$NUM_THREADS
   - make runtest --keep-going --jobs=$NUM_THREADS
+  - make lint --jobs=$NUM_THREADS
   - make clean && rm -rf *
     ## Cleaning
   - cd ..
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7bc6d07bc2f..8df7412b2cd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -38,8 +38,11 @@ set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
 include_directories(${${PROJECT_NAME}_INCLUDE_DIRS})
 include_directories(${CMAKE_SOURCE_DIR}/src)
 
+#    CMake Scripts dir
+set(CMAKE_SCRIPT_DIR ${CMAKE_SOURCE_DIR}/CMakeScripts)
+
 #	CMake module path for custom module finding
-set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SOURCE_DIR}/CMakeScripts)
+set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SCRIPT_DIR})
 
 #    CUDA is required globally
 if(NOT CPU_ONLY)
@@ -68,8 +71,19 @@ if(BUILD_MATLAB)
     add_subdirectory(matlab)
 endif()
 
+###    Lint Target Setup    ##########################################################################
+
+set(LINT_TARGET lint)
+set(LINT_SCRIPT ${CMAKE_SCRIPT_DIR}/lint.cmake)
+add_custom_target(
+    ${LINT_TARGET} 
+    COMMAND ${CMAKE_COMMAND} -P ${LINT_SCRIPT}
+)
+
 ###    Install    #################################################################################
 
 #    Install Includes
 file(GLOB folders ${${PROJECT_NAME}_INCLUDE_DIRS}/*)
 install(DIRECTORY ${folders} DESTINATION include)
+
+
diff --git a/CMakeScripts/lint.cmake b/CMakeScripts/lint.cmake
new file mode 100644
index 00000000000..04df3409e84
--- /dev/null
+++ b/CMakeScripts/lint.cmake
@@ -0,0 +1,48 @@
+
+set(CMAKE_SOURCE_DIR ../)
+set(LINT_COMMAND ${CMAKE_SOURCE_DIR}/scripts/cpp_lint.py)
+set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc)
+set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
+set(LINT_DIRS include src/caffe examples tools python matlab)
+
+# find all files of interest
+foreach(ext ${SRC_FILE_EXTENSIONS})
+    foreach(dir ${LINT_DIRS})
+        file(GLOB_RECURSE FOUND_FILES ${CMAKE_SOURCE_DIR}/${dir}/*.${ext})
+        set(LINT_SOURCES ${LINT_SOURCES} ${FOUND_FILES})
+    endforeach()
+endforeach()
+
+# find all files that should be excluded
+foreach(ext ${EXCLUDE_FILE_EXTENSTIONS})
+    file(GLOB_RECURSE FOUND_FILES ${CMAKE_SOURCE_DIR}/*.${ext})
+    set(EXCLUDED_FILES ${EXCLUDED_FILES} ${FOUND_FILES})
+endforeach()
+
+# exclude generated pb files
+list(REMOVE_ITEM LINT_SOURCES ${EXCLUDED_FILES})
+
+execute_process(
+    COMMAND ${LINT_COMMAND} ${LINT_SOURCES}
+    ERROR_VARIABLE LINT_OUTPUT 
+    ERROR_STRIP_TRAILING_WHITESPACE
+)
+
+string(REPLACE "\n" ";" LINT_OUTPUT ${LINT_OUTPUT})
+
+list(GET LINT_OUTPUT -1 LINT_RESULT)
+list(REMOVE_AT LINT_OUTPUT -1)
+string(REPLACE " " ";" LINT_RESULT ${LINT_RESULT})
+list(GET LINT_RESULT -1 NUM_ERRORS)
+if(NUM_ERRORS GREATER 0)
+    foreach(msg ${LINT_OUTPUT})
+        string(FIND ${msg} "Done" result)
+        if(result LESS 0)
+            message(STATUS ${msg})
+        endif()
+    endforeach()
+    message(FATAL_ERROR "Lint found ${NUM_ERRORS} errors!")
+else()
+    message(STATUS "Lint did not find any errors!")
+endif()
+

From 9f9b013813dc02c0ab86043e0b7a385c69e1b4f7 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <a.kosiorek@samsung.com>
Date: Tue, 29 Jul 2014 10:40:40 +0200
Subject: [PATCH 0494/2053] added gflags + bugfixes + rebase on bvlc/caffe *
 added gflags requirement in CMake * fixed a bug that linked some tests into
 caffe lib * renamed tools/caffe due to conflicting target names with caffe
 lib * rebased onto bvlc/caffe

---
 .travis.yml                         |  9 ++----
 CMakeLists.txt                      |  5 +--
 CMakeScripts/FindGFlags.cmake       | 48 +++++++++++++++++++++++++++++
 src/caffe/CMakeLists.txt            | 11 +++++--
 src/caffe/test/CMakeLists.txt       | 10 ++++--
 tools/{caffe.cpp => caffe_tool.cpp} |  0
 6 files changed, 69 insertions(+), 14 deletions(-)
 create mode 100644 CMakeScripts/FindGFlags.cmake
 rename tools/{caffe.cpp => caffe_tool.cpp} (100%)

diff --git a/.travis.yml b/.travis.yml
index c94d9cdebc1..fd82c99f308 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -18,13 +18,8 @@ before_install:
 
 install:
   # CMake 2.8.12
-<<<<<<< HEAD
-  - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz
-  - cd /tmp/cmake-2.8.12 && ./bootstrap --prefix=/usr && make -j4 &&  sudo make install -j4 &&  cd - #&& rm -r /tmp/cmake-2.8.12
-=======
-  - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.2-Linux-i386.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz 
+  - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.2-Linux-i386.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz
   - cd /tmp/cmake-2.8.12.2-Linux-i386 && sudo cp * /usr/ -r && cd - && rm -r /tmp/cmake-2.8.12.2-Linux-i386
->>>>>>> cmake from binaries
     #
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make -j4 && sudo make install -j4 && cd -
@@ -63,7 +58,7 @@ script:
     ## Cleaning
   - cd ..
   - rm -r build
-  
+
   # Make build
   ## CPU-GPU: build only.
   - export CPU_ONLY=0
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8df7412b2cd..64250eea0f2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,8 +25,9 @@ endif()
 ###    Configuration    ###########################################################################
 #    Compiler Flags
 set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
-set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC)
-set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} )#-O3)
+set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} )#set global flags here)
+set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG} )#set debug flags here)
+set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} )#set release flags here)
 
 #    Global Definitions
 if(CPU_ONLY)
diff --git a/CMakeScripts/FindGFlags.cmake b/CMakeScripts/FindGFlags.cmake
new file mode 100644
index 00000000000..f93c57136a1
--- /dev/null
+++ b/CMakeScripts/FindGFlags.cmake
@@ -0,0 +1,48 @@
+# - Try to find GFLAGS
+#
+# The following variables are optionally searched for defaults
+#  GFLAGS_ROOT_DIR:            Base directory where all GFLAGS components are found
+#
+# The following are set after configuration is done:
+#  GFLAGS_FOUND
+#  GFLAGS_INCLUDE_DIRS
+#  GFLAGS_LIBRARIES
+#  GFLAGS_LIBRARYRARY_DIRS
+
+include(FindPackageHandleStandardArgs)
+
+set(GFLAGS_ROOT_DIR "" CACHE PATH "Folder contains Gflags")
+
+# We are testing only a couple of files in the include directories
+if(WIN32)
+    find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h
+        PATHS ${GFLAGS_ROOT_DIR}/src/windows)
+else()
+    find_path(GFLAGS_INCLUDE_DIR gflags/gflags.h
+        PATHS ${GFLAGS_ROOT_DIR})
+endif()
+
+if(MSVC)
+    find_library(GFLAGS_LIBRARY_RELEASE
+        NAMES libgflags
+        PATHS ${GFLAGS_ROOT_DIR}
+        PATH_SUFFIXES Release)
+
+    find_library(GFLAGS_LIBRARY_DEBUG
+        NAMES libgflags-debug
+        PATHS ${GFLAGS_ROOT_DIR}
+        PATH_SUFFIXES Debug)
+
+    set(GFLAGS_LIBRARY optimized ${GFLAGS_LIBRARY_RELEASE} debug ${GFLAGS_LIBRARY_DEBUG})
+else()
+    find_library(GFLAGS_LIBRARY gflags)
+endif()
+
+find_package_handle_standard_args(GFLAGS DEFAULT_MSG
+    GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+
+
+if(GFLAGS_FOUND)
+    set(GFLAGS_INCLUDE_DIRS ${GFLAGS_INCLUDE_DIR})
+    set(GFLAGS_LIBRARIES ${GFLAGS_LIBRARY})
+endif()
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 8b8542d8d30..4fd170a24db 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -7,6 +7,10 @@ find_package(Threads REQUIRED)
 find_package(Glog REQUIRED)
 include_directories(${GLOG_INCLUDE_DIRS})
 
+#   Google-gflags
+find_package(GFlags REQUIRED)
+include_directories(${GFLAGS_INCLUDE_DIRS})
+
 #   BLAS
 if(BLAS STREQUAL "atlas")
 
@@ -61,13 +65,13 @@ add_subdirectory(proto)
 
 #    Recursively find source files
 ##    test sources
-file(GLOB_RECURSE TEST_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
+file(GLOB_RECURSE TEST_CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
 
 ##    all cpp sources
 file(GLOB_RECURSE CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
 ##    remove test sources from cpp sources
-list(REMOVE_ITEM CPP_SOURCES ${TEST_SOURCES})
+list(REMOVE_ITEM CPP_SOURCES ${TEST_CPP_SOURCES})
 
 add_library(caffe ${CPP_SOURCES})
 # both depend on proto
@@ -84,6 +88,8 @@ if(NOT CPU_ONLY)
     
     #    cuda sources
     file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
+    file(GLOB_RECURSE TEST_CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cu)
+    list(REMOVE_ITEM CU_SOURCES ${TEST_CU_SOURCES})
     cuda_add_library(caffe_cu ${CU_SOURCES})
     add_dependencies(caffe_cu proto)
     target_link_libraries(caffe caffe_cu
@@ -94,6 +100,7 @@ endif()
 
 target_link_libraries(caffe proto
         ${GLOG_LIBRARIES}
+        ${GFLAGS_LIBRARIES}
         ${HDF5_LIBRARIES}
         ${OpenCV_LIBS}
         ${LEVELDB_LIBS}
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index f916ac0b0dd..cbad0ccdeee 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -36,10 +36,14 @@ include_directories(
 )
 
 set(TEST_MAIN test_caffe_main.cpp)
-list(REMOVE_ITEM TEST_SOURCES ${TEST_MAIN})
+list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
+
+if(NOT CPU_ONLY)
+    set(TEST_CPP_SOURCES ${TEST_CPP_SOURCES} ${TEST_CU_SOURCES})
+endif()
 
 #    Build each test separately
-foreach(source ${TEST_SOURCES})
+foreach(source ${TEST_CPP_SOURCES})
     get_filename_component(name ${source} NAME_WE)
     set(TEST_NAME ${name}${TEST_EXT})
     add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_MAIN} ${source} ../blob.cpp)
@@ -50,7 +54,7 @@ foreach(source ${TEST_SOURCES})
 endforeach()
 
 #    Build a compound test excluded from the ALL target
-add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_SOURCES} ${TEST_MAIN})
+add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_CPP_SOURCES} ${TEST_MAIN})
 target_link_libraries(${ALL_TEST} gtest caffe)
 add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 #    output dir
diff --git a/tools/caffe.cpp b/tools/caffe_tool.cpp
similarity index 100%
rename from tools/caffe.cpp
rename to tools/caffe_tool.cpp

From fca1e623023b4b8396e5de44c9bd115f6fc0f307 Mon Sep 17 00:00:00 2001
From: bhack <s.fabri@email.it>
Date: Tue, 29 Jul 2014 20:25:50 +0200
Subject: [PATCH 0495/2053] Add ppa for CMake for fix 32bit precompiled cmake
 on 64bit

---
 .travis.yml | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index fd82c99f308..37864c16423 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,17 +10,15 @@ compiler:
 # building on Linux.)
 #   - clang
 
+  
 before_install:
   - echo $LANG
   - echo $LC_ALL
+  - sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
   - sudo apt-get -y update
-  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
+  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc cmake
 
 install:
-  # CMake 2.8.12
-  - wget http://www.cmake.org/files/v2.8/cmake-2.8.12.2-Linux-i386.tar.gz -O /tmp/cmake.tar.gz && tar -C /tmp -xvf /tmp/cmake.tar.gz && rm /tmp/cmake.tar.gz
-  - cd /tmp/cmake-2.8.12.2-Linux-i386 && sudo cp * /usr/ -r && cd - && rm -r /tmp/cmake-2.8.12.2-Linux-i386
-    #
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make -j4 && sudo make install -j4 && cd -
   - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd

From db211a27fd811f827ffb684f5f4e4d3f8c94a262 Mon Sep 17 00:00:00 2001
From: bhack <s.fabri@email.it>
Date: Wed, 30 Jul 2014 01:15:39 +0200
Subject: [PATCH 0496/2053] Reduce packages

---
 .travis.yml              | 2 +-
 src/caffe/CMakeLists.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 37864c16423..baed23ccc40 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -16,7 +16,7 @@ before_install:
   - echo $LC_ALL
   - sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
   - sudo apt-get -y update
-  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc cmake
+  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-dev libboost-system-dev libboost-python-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc cmake
 
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 4fd170a24db..ec756bede71 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -37,7 +37,7 @@ find_package(HDF5 COMPONENTS HL REQUIRED)
 include_directories(${HDF5_INCLUDE_DIRS})
 
 #    OpenCV
-find_package(OpenCV COMPONENTS core highgui imgproc REQUIRED)
+find_package(OpenCV REQUIRED core highgui imgproc)
 include_directories(${OpenCV_INCLUDE_DIRS})
 
 #    LevelDB

From f135665a205490540721471dc4c5ea54cb3dc22f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 16 Aug 2014 23:47:25 -0700
Subject: [PATCH 0497/2053] [docs] CMake build steps and Ubuntu 12.04 install
 instructions

---
 docs/installation.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/docs/installation.md b/docs/installation.md
index ff0760dd060..c2bd64d866e 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -210,6 +210,27 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 Now that you have installed Caffe, check out the [MNIST demo](mnist.html) and the pretrained [ImageNet example](imagenet.html).
 
+### Compilation using CMake (beta)
+
+In lieu of manually editing `Makefile.config` to tell Caffe where dependencies are located, Caffe also provides a CMake-based build system (currently in "beta").
+It requires CMake version >= 2.8.8.
+The basic installation steps are as follows:
+
+    mkdir build
+    cd build
+    cmake ..
+    make all
+    make runtest
+
+#### Ubuntu 12.04
+
+Note that in Ubuntu 12.04, Aptitude will install version CMake 2.8.7 by default, which is not supported by Caffe's CMake build (requires at least 2.8.8).
+As a workaround, if you are using Ubuntu 12.04 you can try the following steps to install (or upgrade to) CMake 2.8.9:
+
+    sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
+    sudo apt-get -y update
+    sudo apt-get install cmake
+
 ## Hardware Questions
 
 **Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.

From f923a242733733a159be2d46e1e94e77419b763e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 17 Aug 2014 00:03:28 -0700
Subject: [PATCH 0498/2053] .travis.yml and .gitignore: various minor cleanup

---
 .gitignore  |  1 -
 .travis.yml | 44 ++++++++++++++++++++++----------------------
 2 files changed, 22 insertions(+), 23 deletions(-)

diff --git a/.gitignore b/.gitignore
index c1989306c27..d162cb42bb0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -65,6 +65,5 @@ _site
 # Eclipse Project settings
 *.*project
 
-
 # CMake generated files
 *.gen.cmake
diff --git a/.travis.yml b/.travis.yml
index baed23ccc40..ed021b61fda 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,17 +10,18 @@ compiler:
 # building on Linux.)
 #   - clang
 
-  
 before_install:
   - echo $LANG
   - echo $LC_ALL
+  - export NUM_THREADS=4
+  - alias make="make --jobs=$NUM_THREADS"
   - sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
   - sudo apt-get -y update
   - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-dev libboost-system-dev libboost-python-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc cmake
 
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
-  - cd /tmp/glog-0.3.3 && ./configure && make -j4 && sudo make install -j4 && cd -
+  - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
   - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
   - sudo apt-get -y update
@@ -32,45 +33,44 @@ install:
   # manually since we did a partial installation.)
   - sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
-  - cd /tmp/mdb-mdb/libraries/liblmdb/ && make -j4 && sudo make install -j4 && cd -
+  - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
 
 before_script:
   - mv Makefile.config.example Makefile.config
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
-  - export NUM_THREADS=4
 
 script:
-  # CMake build.
+  # CMake build
   - mkdir build
   - cd build
-  ## CPU-GPU: build only.
+  ## CPU-GPU: build only
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
-  - make --keep-going --jobs=$NUM_THREADS
+  - make --keep-going
   - make clean && rm -rf *
    ## CPU-only: comprehensive
   - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
-  - make --keep-going --jobs=$NUM_THREADS
-  - make runtest --keep-going --jobs=$NUM_THREADS
-  - make lint --jobs=$NUM_THREADS
-  - make clean && rm -rf *
-    ## Cleaning
+  - make --keep-going
+  - make runtest
+  - make lint
+  - make clean
+    ## Cleanup CMake build
   - cd ..
-  - rm -r build
+  - rm -rf build
 
   # Make build
-  ## CPU-GPU: build only.
+  ## CPU-GPU: build only
   - export CPU_ONLY=0
-  - make --keep-going --jobs=$NUM_THREADS all
+  - make --keep-going all
   - make clean
-  ## CPU-only: comprehensive.
+  ## CPU-only: comprehensive
   - export CPU_ONLY=1
-  - make --keep-going --jobs=$NUM_THREADS all test warn lint
+  - make --keep-going all test warn lint
   - make runtest
-  - make --jobs=$NUM_THREADS all
-  - make --jobs=$NUM_THREADS test
-  - make --jobs=$NUM_THREADS warn
-  - make --jobs=$NUM_THREADS lint
-  - make --jobs=$NUM_THREADS pycaffe
+  - make all
+  - make test
+  - make warn
+  - make lint
+  - make pycaffe
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,

From ac1bb6c98e1f9e2b5dba07d06a3805e9aa6d88e2 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 17 Aug 2014 00:26:35 -0700
Subject: [PATCH 0499/2053] use all caps for global preprocess vars (e.g.
 EXAMPLES_SOURCE_DIR), and other minor cleanup

---
 examples/CMakeLists.txt                  | 4 ++--
 include/caffe/test/test_caffe_main.hpp   | 2 +-
 src/caffe/test/cmake_test_defines.hpp.in | 5 ++---
 src/caffe/test/test_image_data_layer.cpp | 2 +-
 tools/CMakeLists.txt                     | 4 ++--
 5 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index ec3cc470fb4..055f4ef0d35 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,8 +1,8 @@
 project( Examples )
 
-file(GLOB_RECURSE Examples_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+file(GLOB_RECURSE EXAMPLES_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
-foreach(source ${Examples_SOURCES})
+foreach(source ${EXAMPLES_SOURCES})
     #    get file name
     get_filename_component(name ${source} NAME_WE)  
     
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index e02e67ece98..438acf2bf17 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -19,7 +19,7 @@ using std::endl;
 #else
   #define CUDA_TEST_DEVICE -1
   #define CMAKE_SOURCE_DIR "src/"
-  #define Examples_SOURCE_DIR "examples/"
+  #define EXAMPLES_SOURCE_DIR "examples/"
   #define CMAKE_EXT ""
 #endif
 
diff --git a/src/caffe/test/cmake_test_defines.hpp.in b/src/caffe/test/cmake_test_defines.hpp.in
index 2d67df4ac39..870eaf5c26e 100644
--- a/src/caffe/test/cmake_test_defines.hpp.in
+++ b/src/caffe/test/cmake_test_defines.hpp.in
@@ -1,5 +1,4 @@
-
 #define CUDA_TEST_DEVICE @CUDA_TEST_DEVICE@
 #define CMAKE_SOURCE_DIR "@CMAKE_SOURCE_DIR@/src/"
-#define Examples_SOURCE_DIR "@CMAKE_SOURCE_DIR@/examples/"
-#define CMAKE_EXT ".gen.cmake"
\ No newline at end of file
+#define EXAMPLES_SOURCE_DIR "@CMAKE_SOURCE_DIR@/examples/"
+#define CMAKE_EXT ".gen.cmake"
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 17d67bd13b3..73be5da7b23 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -37,7 +37,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
     std::ofstream outfile(filename_->c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << *filename_;
     for (int i = 0; i < 5; ++i) {
-      outfile << Examples_SOURCE_DIR "images/cat.jpg " << i;
+      outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
     }
     outfile.close();
   }
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 81ecf7cd41a..bb88cfa44b0 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -1,10 +1,10 @@
 project( Tools )
 
 #    Find all source files
-file(GLOB_RECURSE Tools_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
 #    Build each source file independently
-foreach(source ${Tools_SOURCES})
+foreach(source ${TOOLS_SOURCES})
     get_filename_component(name ${source} NAME_WE)
     add_executable(${name} ${source})
     target_link_libraries(${name} caffe)

From 246f510d4e04424bdba501dba4dd8606f52d8cfe Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 17 Aug 2014 00:28:14 -0700
Subject: [PATCH 0500/2053] restore .testbin extension, and move caffe_tool
 back to "caffe". (Required as I had to change the tool target names to '.bin'
 but give them an OUTPUT_NAME, but the .bin made the test_net tool collide
 with the test_net unit test.)

---
 src/caffe/test/CMakeLists.txt       | 2 +-
 tools/CMakeLists.txt                | 7 ++++---
 tools/{caffe_tool.cpp => caffe.cpp} | 0
 3 files changed, 5 insertions(+), 4 deletions(-)
 rename tools/{caffe_tool.cpp => caffe.cpp} (100%)

diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index cbad0ccdeee..ffcc58f5463 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -7,7 +7,7 @@ project( Test )
 
 # Configuration
 set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are going to be placed there
-set(TEST_EXT .bin)               # test executable extension
+set(TEST_EXT .testbin)           # test executable extension
 set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
 set(RUN_TEST runtest)            # dummy target for running tests
 
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index bb88cfa44b0..0da19fbadc8 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -6,12 +6,13 @@ file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 #    Build each source file independently
 foreach(source ${TOOLS_SOURCES})
     get_filename_component(name ${source} NAME_WE)
-    add_executable(${name} ${source})
-    target_link_libraries(${name} caffe)
+    add_executable(${name}.bin ${source})
+    set_target_properties(${name}.bin PROPERTIES OUTPUT_NAME ${name})
+    target_link_libraries(${name}.bin caffe)
 
 ###    Install    #################################################################################
 
-    install(TARGETS ${name} DESTINATION tools)
+    install(TARGETS ${name}.bin DESTINATION tools)
 
 
 endforeach(source)
diff --git a/tools/caffe_tool.cpp b/tools/caffe.cpp
similarity index 100%
rename from tools/caffe_tool.cpp
rename to tools/caffe.cpp

From a909f916e2e9e89674498459f83b0e851a5d1e15 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 17 Aug 2014 03:11:59 -0700
Subject: [PATCH 0501/2053] Travis build matrix to do parallel builds for make
 and CMake; CUDA-less and CUDA-ful.  Move bits of functionality into scripts
 under scripts/travis for readability. Only generate CUDA compute_50 for
 perfomance.

---
 .travis.yml                                   | 72 ++++-------------
 scripts/travis/travis_build.sh                | 18 +++++
 scripts/travis/travis_build_and_test.sh       | 22 ++++++
 scripts/travis/travis_install.sh              | 79 +++++++++++++++++++
 .../travis/travis_setup_makefile_config.sh    | 12 +++
 5 files changed, 146 insertions(+), 57 deletions(-)
 create mode 100755 scripts/travis/travis_build.sh
 create mode 100755 scripts/travis/travis_build_and_test.sh
 create mode 100755 scripts/travis/travis_install.sh
 create mode 100755 scripts/travis/travis_setup_makefile_config.sh

diff --git a/.travis.yml b/.travis.yml
index ed021b61fda..46dfc117975 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,76 +1,34 @@
+# Use a build matrix to do two builds in parallel:
+# one using CMake, and one using make.
+env:
+  matrix:
+    - WITH_CUDA=false WITH_CMAKE=false
+    - WITH_CUDA=false WITH_CMAKE=true
+    - WITH_CUDA=true WITH_CMAKE=false
+    - WITH_CUDA=true WITH_CMAKE=true
+
 language: cpp
 
 # Cache Ubuntu apt packages.
 cache: apt
 
-compiler:
-  - gcc
-# Disable clang build: doesn't seem to work on Linux.
-# (@jeffdonahue: Travis buildbot's failure behavior is similar to what I see
-# building on Linux.)
-#   - clang
+compiler: gcc
 
 before_install:
-  - echo $LANG
-  - echo $LC_ALL
   - export NUM_THREADS=4
   - alias make="make --jobs=$NUM_THREADS"
-  - sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
-  - sudo apt-get -y update
-  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-dev libboost-system-dev libboost-python-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc cmake
+  - export SCRIPTS=./scripts/travis
+  - set -e  # fail when a script fails
 
 install:
-  - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
-  - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
-  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd
-  - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
-  - sudo apt-get -y update
-  # Install the minimal CUDA subpackages required to test Caffe build.
-  # For a full CUDA installation, add 'cuda' to the list of packages.
-  - sudo apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
-  # Create CUDA symlink at /usr/local/cuda
-  # (This would normally be created by the CUDA installer, but we create it
-  # manually since we did a partial installation.)
-  - sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda
-  - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
-  - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
+  - source $SCRIPTS/travis_install.sh
 
 before_script:
-  - mv Makefile.config.example Makefile.config
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
+  - if ! $WITH_CMAKE; then source $SCRIPTS/travis_setup_makefile_config.sh; fi
 
 script:
-  # CMake build
-  - mkdir build
-  - cd build
-  ## CPU-GPU: build only
-  - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
-  - make --keep-going
-  - make clean && rm -rf *
-   ## CPU-only: comprehensive
-  - cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
-  - make --keep-going
-  - make runtest
-  - make lint
-  - make clean
-    ## Cleanup CMake build
-  - cd ..
-  - rm -rf build
-
-  # Make build
-  ## CPU-GPU: build only
-  - export CPU_ONLY=0
-  - make --keep-going all
-  - make clean
-  ## CPU-only: comprehensive
-  - export CPU_ONLY=1
-  - make --keep-going all test warn lint
-  - make runtest
-  - make all
-  - make test
-  - make warn
-  - make lint
-  - make pycaffe
+  - if $WITH_CUDA; then source $SCRIPTS/travis_build.sh; else source $SCRIPTS/travis_build_and_test.sh; fi
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
diff --git a/scripts/travis/travis_build.sh b/scripts/travis/travis_build.sh
new file mode 100755
index 00000000000..06b4e24bb98
--- /dev/null
+++ b/scripts/travis/travis_build.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# Script called by Travis to do a full build of Caffe,
+# including CUDA functionality.
+
+if $WITH_CMAKE; then
+  mkdir build
+  cd build
+  cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
+  make --keep-going
+  cd -
+else
+  export CPU_ONLY=0
+  make --keep-going all test pycaffe warn
+  make all
+  make test
+  make pycaffe
+  make warn
+fi
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
new file mode 100755
index 00000000000..6b7c85e9d2d
--- /dev/null
+++ b/scripts/travis/travis_build_and_test.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+# Script called by Travis to do a CPU-only build of and test Caffe.
+
+if $WITH_CMAKE; then
+  mkdir build
+  cd build
+  cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+  make --keep-going
+  make runtest
+  make lint
+  make clean
+  cd -
+else
+  export CPU_ONLY=1
+  make --keep-going all test pycaffe warn lint
+  make runtest
+  make all
+  make test
+  make pycaffe
+  make warn
+  make lint
+fi
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
new file mode 100755
index 00000000000..f6a357b5c57
--- /dev/null
+++ b/scripts/travis/travis_install.sh
@@ -0,0 +1,79 @@
+#!/bin/bash
+
+# Install apt packages where the Ubuntu 12.04 default works for Caffe
+sudo apt-get -y update
+sudo apt-get install \
+    wget git curl \
+    python-dev python-numpy \
+    libleveldb-dev libsnappy-dev libopencv-dev \
+    libboost-dev libboost-system-dev libboost-python-dev \
+    libprotobuf-dev protobuf-compiler \
+    libatlas-dev libatlas-base-dev \
+    libhdf5-serial-dev \
+    bc
+
+# Add a special apt-repository to install CMake 2.8.9 for CMake Caffe build,
+# if needed.  By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
+# Caffe requires a minimum CMake version of 2.8.8.
+if $WITH_CMAKE; then
+    sudo add-apt-repository -y ppa:ubuntu-sdk-team/ppa
+    sudo apt-get -y update
+    sudo apt-get -y install cmake || (echo "CMake install failed"; exit 1)
+fi
+
+# Install glog
+GLOG_URL=https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+GLOG_FILE=/tmp/glog-0.3.3.tar.gz
+pushd .
+wget $GLOG_URL -O $GLOG_FILE && \
+    tar -C /tmp -xzvf $GLOG_FILE && \
+    rm $GLOG_FILE && \
+    cd /tmp/glog-0.3.3 && \
+    ./configure && make && sudo make install || \
+    (echo "glog install failed"; exit 1)
+popd
+
+# Install gflags
+GFLAGS_URL=https://github.com/schuhschuh/gflags/archive/master.zip
+GFLAGS_FILE=/tmp/gflags-master.zip
+pushd .
+wget $GFLAGS_URL -O $GFLAGS_FILE && \
+    cd /tmp/ && unzip gflags-master.zip && \
+    cd gflags-master && \
+    mkdir build && \
+    cd build && \
+    export CXXFLAGS="-fPIC" && \
+    cmake .. && make VERBOSE=1 && sudo make install || \
+    (echo "gflags install failed"; exit 1)
+popd
+
+# Install CUDA, if needed
+CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb
+CUDA_FILE=/tmp/cuda_install.deb
+if $WITH_CUDA; then
+    curl $CUDA_URL -o $CUDA_FILE && \
+        sudo dpkg -i $CUDA_FILE ||
+        (echo "CUDA install failed"; exit 1)
+    rm -f $CUDA_FILE
+    sudo apt-get -y update
+    # Install the minimal CUDA subpackages required to test Caffe build.
+    # For a full CUDA installation, add 'cuda' to the list of packages.
+    sudo apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
+    # Create CUDA symlink at /usr/local/cuda
+    # (This would normally be created by the CUDA installer, but we create it
+    # manually since we did a partial installation.)
+    sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda ||
+        (echo "CUDA symlink creation failed"; exit 1)
+fi
+
+# Install LMDB
+LMDB_URL=https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz
+LMDB_FILE=/tmp/mdb.tar.gz
+pushd .
+curl $LMDB_URL -o $LMDB_FILE && \
+    tar -C /tmp -xzvf $LMDB_FILE && \
+    cd /tmp/mdb-mdb/libraries/liblmdb/ && \
+    make && sudo make install || \
+    (echo "LMDB install failed"; exit 1)
+popd
+rm -f $LMDB_FILE
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
new file mode 100755
index 00000000000..fa86e92a131
--- /dev/null
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+mv Makefile.config.example Makefile.config
+
+if $WITH_CUDA; then
+  # Remove default gencode set; only generate compute_50.
+  sed -i 's/-gencode arch=.*\\//' Makefile.config
+  sed -i 's/CUDA_ARCH :=//' Makefile.config
+  GENCODE="-gencode arch=compute_50,code=sm_50"
+  GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
+  echo "CUDA_ARCH := $GENCODE" >> Makefile.config
+fi

From bf61d4f957e7a0a5af00f9bc70089b58ea40873a Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 17 Aug 2014 22:01:24 -0700
Subject: [PATCH 0502/2053] milliseconds is a word

---
 tools/caffe.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 13398ae8577..bc404d3b318 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -186,10 +186,10 @@ int time() {
       layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
     }
     LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
-        " milli seconds.";
+        " milliseconds.";
   }
   LOG(INFO) << "Forward pass: " << forward_timer.MilliSeconds() <<
-      " milli seconds.";
+      " milliseconds.";
   Timer backward_timer;
   backward_timer.Start();
   for (int i = layers.size() - 1; i >= 0; --i) {
@@ -200,12 +200,12 @@ int time() {
                           &bottom_vecs[i]);
     }
     LOG(INFO) << layername << "\tbackward: "
-        << timer.MilliSeconds() << " milli seconds.";
+        << timer.MilliSeconds() << " milliseconds.";
   }
   LOG(INFO) << "Backward pass: " << backward_timer.MilliSeconds() <<
-      " milli seconds.";
+      " milliseconds.";
   LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
-      " milli seconds.";
+      " milliseconds.";
   LOG(INFO) << "*** Benchmark ends ***";
   return 0;
 }

From e957bbc2f7b14134a27a5587352e4cc8e77f8a39 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 17 Aug 2014 23:15:03 -0700
Subject: [PATCH 0503/2053] add caffe_cpu_strided_dot for strided dot products

This provides a more direct interface to the cblas_?dot functions.
This is useful, for example, for taking dot products across channels.
---
 include/caffe/util/math_functions.hpp |  4 ++++
 src/caffe/util/math_functions.cpp     | 21 +++++++++++++++++----
 2 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 6a608d5cfa3..f4310bf4417 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -94,6 +94,10 @@ void caffe_abs(const int n, const Dtype* a, Dtype* y);
 template <typename Dtype>
 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
 
+template <typename Dtype>
+Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
+    const Dtype* y, const int incy);
+
 template <typename Dtype>
 int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y);
 
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index bac06f8d025..7016e635318 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -315,14 +315,27 @@ void caffe_rng_bernoulli<double>(const int n, const double p, unsigned int* r);
 template
 void caffe_rng_bernoulli<float>(const int n, const float p, unsigned int* r);
 
+template <typename Dtype>
+Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y) {
+  return caffe_cpu_strided_dot(n, x, 1, y, 1);
+}
+
+template
+float caffe_cpu_dot<float>(const int n, const float* x, const float* y);
+
+template
+double caffe_cpu_dot<double>(const int n, const double* x, const double* y);
+
 template <>
-float caffe_cpu_dot<float>(const int n, const float* x, const float* y) {
-  return cblas_sdot(n, x, 1, y, 1);
+float caffe_cpu_strided_dot<float>(const int n, const float* x, const int incx,
+    const float* y, const int incy) {
+  return cblas_sdot(n, x, incx, y, incy);
 }
 
 template <>
-double caffe_cpu_dot<double>(const int n, const double* x, const double* y) {
-  return cblas_ddot(n, x, 1, y, 1);
+double caffe_cpu_strided_dot<double>(const int n, const double* x,
+    const int incx, const double* y, const int incy) {
+  return cblas_ddot(n, x, incx, y, incy);
 }
 
 template <>

From cf42598d60a121404105fadb1e8d097df1f65beb Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 14 Aug 2014 19:20:30 -0700
Subject: [PATCH 0504/2053] softmax and softmax loss layers work across
 channels

---
 src/caffe/layers/softmax_layer.cpp      | 63 +++++++++++++++----------
 src/caffe/layers/softmax_loss_layer.cpp | 19 +++++---
 2 files changed, 50 insertions(+), 32 deletions(-)

diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index fa2ba17a151..ab74dc7cfbf 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -13,13 +13,12 @@ void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
-  sum_multiplier_.Reshape(1, bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
+  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
   for (int i = 0; i < sum_multiplier_.count(); ++i) {
     multiplier_data[i] = 1.;
   }
-  scale_.Reshape(bottom[0]->num(), 1, 1, 1);
+  scale_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
 }
 
 template <typename Dtype>
@@ -29,27 +28,34 @@ void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = bottom[0]->num();
+  int channels = bottom[0]->channels();
   int dim = bottom[0]->count() / bottom[0]->num();
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
   caffe_copy(bottom[0]->count(), bottom_data, top_data);
-  // we need to subtract the max to avoid numerical issues, compute the exp,
+  // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   for (int i = 0; i < num; ++i) {
-    scale_data[i] = bottom_data[i*dim];
-    for (int j = 0; j < dim; ++j) {
-      scale_data[i] = std::max(scale_data[i], bottom_data[i * dim + j]);
+    // initialize scale_data to the first plane
+    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
+    for (int j = 0; j < channels; j++) {
+      for (int k = 0; k < spatial_dim; k++) {
+        scale_data[k] = std::max(scale_data[k],
+            bottom_data[i * dim + j * spatial_dim + k]);
+      }
+    }
+    // subtraction
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
+    // exponentiation
+    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
+    // sum after exp
+    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
+        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
+    // division
+    for (int j = 0; j < channels; j++) {
+      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
+          top_data + (*top)[0]->offset(i, j));
     }
-  }
-  // subtraction
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-    scale_data, sum_multiplier_.cpu_data(), 1., top_data);
-  // Perform exponentiation
-  caffe_exp<Dtype>(num * dim, top_data, top_data);
-  // sum after exp
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_data,
-      sum_multiplier_.cpu_data(), 0., scale_data);
-  // Do division
-  for (int i = 0; i < num; ++i) {
-    caffe_scal<Dtype>(dim, Dtype(1.) / scale_data[i], top_data + i * dim);
   }
 }
 
@@ -62,18 +68,23 @@ void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = top[0]->num();
+  int channels = top[0]->channels();
   int dim = top[0]->count() / top[0]->num();
+  int spatial_dim = top[0]->height() * top[0]->width();
   caffe_copy(top[0]->count(), top_diff, bottom_diff);
-  // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
   for (int i = 0; i < num; ++i) {
-    scale_data[i] = caffe_cpu_dot<Dtype>(dim, top_diff + i * dim,
-        top_data + i * dim);
+    // compute dot(top_diff, top_data) and subtract them from the bottom diff
+    for (int k = 0; k < spatial_dim; ++k) {
+      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
+          bottom_diff + i * dim + k, spatial_dim,
+          top_data + i * dim + k, spatial_dim);
+    }
+    // subtraction
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
+        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
   }
-  // subtraction
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-      scale_data, sum_multiplier_.cpu_data(), 1., bottom_diff);
   // elementwise multiplication
-  caffe_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
+  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }
 
 
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 0fa83ccddac..e07c8dc9df9 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -27,18 +27,21 @@ template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   // The forward pass computes the softmax prob values.
-  softmax_bottom_vec_[0] = bottom[0];
   softmax_layer_->Forward(softmax_bottom_vec_, &softmax_top_vec_);
   const Dtype* prob_data = prob_.cpu_data();
   const Dtype* label = bottom[1]->cpu_data();
   int num = prob_.num();
   int dim = prob_.count() / num;
+  int spatial_dim = prob_.height() * prob_.width();
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
-    loss += -log(std::max(prob_data[i * dim + static_cast<int>(label[i])],
-                          Dtype(FLT_MIN)));
+    for (int j = 0; j < spatial_dim; j++) {
+      loss -= log(std::max(prob_data[i * dim +
+          static_cast<int>(label[i * spatial_dim + j]) * spatial_dim + j],
+                           Dtype(FLT_MIN)));
+    }
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  (*top)[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
   if (top->size() == 2) {
     (*top)[1]->ShareData(prob_);
   }
@@ -59,12 +62,16 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* label = (*bottom)[1]->cpu_data();
     int num = prob_.num();
     int dim = prob_.count() / num;
+    int spatial_dim = prob_.height() * prob_.width();
     for (int i = 0; i < num; ++i) {
-      bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
+      for (int j = 0; j < spatial_dim; ++j) {
+        bottom_diff[i * dim + static_cast<int>(label[i * spatial_dim + j])
+            * spatial_dim + j] -= 1;
+      }
     }
     // Scale gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_scal(prob_.count(), loss_weight / num, bottom_diff);
+    caffe_scal(prob_.count(), loss_weight / num / spatial_dim, bottom_diff);
   }
 }
 

From 7f3ebcb8a8756675c197f40b0a3c100ad2acf593 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 14 Aug 2014 19:21:28 -0700
Subject: [PATCH 0505/2053] test softmax and softmax with loss across channels

---
 src/caffe/test/test_softmax_layer.cpp         | 44 ++++++++++---------
 .../test/test_softmax_with_loss_layer.cpp     |  4 +-
 2 files changed, 25 insertions(+), 23 deletions(-)

diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 37685af6b12..9f45f762cdb 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -19,7 +19,7 @@ class SoftmaxLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
  protected:
   SoftmaxLayerTest()
-      : blob_bottom_(new Blob<Dtype>(2, 10, 1, 1)),
+      : blob_bottom_(new Blob<Dtype>(2, 10, 2, 3)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
@@ -45,26 +45,28 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    Dtype sum = 0;
-    for (int j = 0; j < this->blob_top_->channels(); ++j) {
-      sum += this->blob_top_->data_at(i, j, 0, 0);
-    }
-    EXPECT_GE(sum, 0.999);
-    EXPECT_LE(sum, 1.001);
-  }
-  // Test exact values
-  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    Dtype scale = 0;
-    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
-      scale += exp(this->blob_bottom_->data_at(i, j, 0, 0));
-    }
-    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
-      EXPECT_GE(this->blob_top_->data_at(i, j, 0, 0) + 1e-4,
-          exp(this->blob_bottom_->data_at(i, j, 0, 0)) / scale)
-          << "debug: " << i << " " << j;
-      EXPECT_LE(this->blob_top_->data_at(i, j, 0, 0) - 1e-4,
-          exp(this->blob_bottom_->data_at(i, j, 0, 0)) / scale)
-          << "debug: " << i << " " << j;
+    for (int k = 0; k < this->blob_bottom_->height(); ++k) {
+      for (int l = 0; l < this->blob_bottom_->width(); ++l) {
+        Dtype sum = 0;
+        for (int j = 0; j < this->blob_top_->channels(); ++j) {
+          sum += this->blob_top_->data_at(i, j, k, l);
+        }
+        EXPECT_GE(sum, 0.999);
+        EXPECT_LE(sum, 1.001);
+        // Test exact values
+        Dtype scale = 0;
+        for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
+          scale += exp(this->blob_bottom_->data_at(i, j, k, l));
+        }
+        for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
+          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
+              exp(this->blob_bottom_->data_at(i, j, k, l)) / scale)
+              << "debug: " << i << " " << j;
+          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
+              exp(this->blob_bottom_->data_at(i, j, k, l)) / scale)
+              << "debug: " << i << " " << j;
+        }
+      }
     }
   }
 }
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 0f0adbba621..246d64e116a 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -21,8 +21,8 @@ class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   SoftmaxWithLossLayerTest()
-      : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+      : blob_bottom_data_(new Blob<Dtype>(10, 5, 2, 3)),
+        blob_bottom_label_(new Blob<Dtype>(10, 1, 2, 3)),
         blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;

From 5528fa932747af96dffbe03860266ae597f6e90a Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 16 Aug 2014 14:52:19 -0700
Subject: [PATCH 0506/2053] implement GPU version of Softmax

---
 src/caffe/layers/softmax_layer.cpp |   2 +-
 src/caffe/layers/softmax_layer.cu  | 141 +++++++++++++++++++----------
 2 files changed, 96 insertions(+), 47 deletions(-)

diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index ab74dc7cfbf..29767ac775c 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -18,7 +18,7 @@ void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < sum_multiplier_.count(); ++i) {
     multiplier_data[i] = 1.;
   }
-  scale_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 6b853099a3f..f97eafcedb0 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -11,33 +11,80 @@
 namespace caffe {
 
 template <typename Dtype>
-__global__ void kernel_get_max(const int num, const int dim,
-    const Dtype* data, Dtype* out) {
-  CUDA_KERNEL_LOOP(index, num) {
+__global__ void kernel_channel_max(const int num, const int channels,
+    const int spatial_dim, const Dtype* data, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
+    int n = index / spatial_dim;
+    int s = index % spatial_dim;
     Dtype maxval = -FLT_MAX;
-    for (int i = 0; i < dim; ++i) {
-      maxval = max(data[index * dim + i], maxval);
+    for (int c = 0; c < channels; ++c) {
+      maxval = max(data[(n * channels + c) * spatial_dim + s], maxval);
     }
     out[index] = maxval;
   }
 }
 
 template <typename Dtype>
-__global__ void kernel_softmax_div(const int num, const int dim,
-    const Dtype* scale, Dtype* data) {
-  CUDA_KERNEL_LOOP(index, num * dim) {
-    int n = index / dim;
-    data[index] /= scale[n];
+__global__ void kernel_channel_subtract(const int num, const int channels,
+    const int spatial_dim, Dtype* data, const Dtype* channel_max) {
+  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
+    int n = index / spatial_dim;
+    int s = index % spatial_dim;
+    for (int c = 0; c < channels; ++c) {
+      data[(n * channels + c) * spatial_dim + s] -= channel_max[index];
+    }
   }
 }
 
 template <typename Dtype>
-__global__ void kernel_exp(const int num, const Dtype* data, Dtype* out) {
-  CUDA_KERNEL_LOOP(index, num) {
+__global__ void kernel_exp(const int count, const Dtype* data, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, count) {
     out[index] = exp(data[index]);
   }
 }
 
+template <typename Dtype>
+__global__ void kernel_channel_sum(const int num, const int channels,
+    const int spatial_dim, const Dtype* data, Dtype* channel_sum) {
+  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
+    int n = index / spatial_dim;
+    int s = index % spatial_dim;
+    Dtype sum = 0;
+    for (int c = 0; c < channels; ++c) {
+      sum += data[(n * channels + c) * spatial_dim + s];
+    }
+    channel_sum[index] = sum;
+  }
+}
+
+template <typename Dtype>
+__global__ void kernel_channel_div(const int num, const int channels,
+    const int spatial_dim, Dtype* data, const Dtype* channel_sum) {
+  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
+    int n = index / spatial_dim;
+    int s = index % spatial_dim;
+    for (int c = 0; c < channels; ++c) {
+      data[(n * channels + c) * spatial_dim + s] /= channel_sum[index];
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void kernel_channel_dot(const int num, const int channels,
+    const int spatial_dim, const Dtype* data_1, const Dtype* data_2,
+    Dtype* channel_dot) {
+  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
+    int n = index / spatial_dim;
+    int s = index % spatial_dim;
+    Dtype dot = 0;
+    for (int c = 0; c < channels; ++c) {
+      dot += (data_1[(n * channels + c) * spatial_dim + s]
+          * data_2[(n * channels + c) * spatial_dim + s]);
+    }
+    channel_dot[index] = dot;
+  }
+}
+
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
@@ -45,56 +92,58 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
   int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  int channels = bottom[0]->channels();
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
   caffe_copy(bottom[0]->count(), bottom_data, top_data);
-  // we need to subtract the max to avoid numerical issues, compute the exp,
+  // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
-  // Compute max
+  // compute max
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  kernel_channel_max<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+      scale_data);
+  // subtract
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_get_max<Dtype><<<CAFFE_GET_BLOCKS(num), CAFFE_CUDA_NUM_THREADS>>>(
-      num, dim, bottom_data, scale_data);
-  // subtraction
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-      scale_data, sum_multiplier_.gpu_data(), 1., top_data);
-  // Perform exponentiation
+  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+      scale_data);
+  // exponentiate
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(num * dim), CAFFE_CUDA_NUM_THREADS>>>(
-      num * dim, top_data, top_data);
+  kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(num * channels * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num * channels * spatial_dim, top_data,
+      top_data);
   // sum after exp
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_data,
-      sum_multiplier_.gpu_data(), 0., scale_data);
-  // Do division
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_softmax_div<Dtype><<<CAFFE_GET_BLOCKS(num * dim),
-                              CAFFE_CUDA_NUM_THREADS>>>(
-      num, dim, scale_data, top_data);
+  kernel_channel_sum<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+      scale_data);
+  // divide
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  kernel_channel_div<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+      scale_data);
 }
 
-// TODO(Yangqing): implement the GPU version of softmax.
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  Dtype* scale_data = scale_.mutable_gpu_data();
   int num = top[0]->num();
-  int dim = top[0]->count() / top[0]->num();
+  int channels = top[0]->channels();
+  int spatial_dim = top[0]->height() * top[0]->width();
   caffe_copy(top[0]->count(), top_diff, bottom_diff);
-  // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
-  // cuda dot returns the result to cpu, so we temporarily change the pointer
-  // mode
-  CUBLAS_CHECK(cublasSetPointerMode(Caffe::cublas_handle(),
-      CUBLAS_POINTER_MODE_DEVICE));
-  Dtype* scale_data = scale_.mutable_gpu_data();
-  for (int i = 0; i < num; ++i) {
-    caffe_gpu_dot<Dtype>(dim, top_diff + i * dim,
-        top_data + i * dim, scale_data + i);
-  }
-  CUBLAS_CHECK(cublasSetPointerMode(Caffe::cublas_handle(),
-      CUBLAS_POINTER_MODE_HOST));
-  // subtraction
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-      scale_.gpu_data(), sum_multiplier_.gpu_data(), 1., bottom_diff);
+  // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff.
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  kernel_channel_dot<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_diff, top_data,
+      scale_data);
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
+      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, bottom_diff,
+      scale_data);
   // elementwise multiplication
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }

From 1b5ef7402d9e486f1c00af5e7f0e735633d94118 Mon Sep 17 00:00:00 2001
From: "R. Gokberk Cinbis" <gokberkcinbis@gmail.com>
Date: Wed, 20 Aug 2014 07:53:31 +0300
Subject: [PATCH 0507/2053] Lock the mex file to avoid Matlab crashes.

Commands like "clear all" and "clear function" causes segmentation faults or errors like these (especially after an 'init'):
 libprotobuf ERROR google/protobuf/descriptor_database.cc:57] File already exists in database: caffe/proto/caffe.proto
 libprotobuf FATAL google/protobuf/descriptor.cc:862] CHECK failed: generated_database_->Add(encoded_file_descriptor, size):
 [libprotobuf ERROR google/protobuf/message.cc:333] Type appears to be in generated pool but wasn't registered: caffe.LayerParameter
 Attempt to restart MATLAB?
To avoid these, lock the the mex file.
---
 matlab/caffe/matcaffe.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 83786caed0d..db8896da6ae 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -372,6 +372,7 @@ static handler_registry handlers[] = {
  ** matlab entry point: caffe(api_command, arg1, arg2, ...)
  **/
 void mexFunction(MEX_ARGS) {
+  mexLock(); // Avoid clearing the mex file.
   if (nrhs == 0) {
     LOG(ERROR) << "No API command given";
     mexErrMsgTxt("An API command is requires");

From f6ffd8ef0f14c6889c09efe2423c56118b0c3539 Mon Sep 17 00:00:00 2001
From: TANGUY Arnaud <arn.tanguy@gmail.com>
Date: Wed, 20 Aug 2014 18:37:54 +0200
Subject: [PATCH 0508/2053] Refactor DataLayer using a new DataTransformer

Start the refactoring of the datalayers to avoid data transformation
code duplication. So far, only DataLayer has been done.
---
 include/caffe/data_layers.hpp         |   8 +-
 include/caffe/data_transformer.hpp    |  55 +++++++++++++
 src/caffe/data_transformer.cpp        | 114 ++++++++++++++++++++++++++
 src/caffe/layers/data_layer.cpp       |  98 +++-------------------
 src/caffe/proto/caffe.proto           |  33 +++++---
 src/caffe/test/test_data_layer.cpp    |  29 +++++--
 src/caffe/test/test_net.cpp           |  16 +++-
 src/caffe/test/test_split_layer.cpp   |   8 +-
 src/caffe/test/test_upgrade_proto.cpp |  26 +++---
 src/caffe/util/upgrade_proto.cpp      |  14 ++--
 10 files changed, 267 insertions(+), 134 deletions(-)
 create mode 100644 include/caffe/data_transformer.hpp
 create mode 100644 src/caffe/data_transformer.cpp

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 8ab92bfc0a5..06508eabf57 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -12,6 +12,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/data_transformer.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
@@ -24,12 +25,12 @@ namespace caffe {
 
 // TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
 // same basic structure and a lot of duplicated code.
-
 template <typename Dtype>
 class DataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+      : Layer<Dtype>(param),
+        data_transformer_(param.data_param().transform_param()) {}
   virtual ~DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -53,11 +54,10 @@ class DataLayer : public Layer<Dtype>, public InternalThread {
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
-  virtual unsigned int PrefetchRand();
   // The thread's function
   virtual void InternalThreadEntry();
 
-  shared_ptr<Caffe::RNG> prefetch_rng_;
+  DataTransformer<Dtype> data_transformer_;
 
   // LEVELDB
   shared_ptr<leveldb::DB> db_;
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
new file mode 100644
index 00000000000..302bf70e606
--- /dev/null
+++ b/include/caffe/data_transformer.hpp
@@ -0,0 +1,55 @@
+#ifndef CAFFE_DATA_TRANSFORMER_HPP
+#define CAFFE_DATA_TRANSFORMER_HPP
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Applies common transformations to the input data, such as
+ * scaling, mirroring, substracting the image mean...
+ */
+template <typename Dtype>
+class DataTransformer {
+ public:
+  explicit DataTransformer(const TransformationParameter& param)
+    : param_(param) {
+    phase_ = Caffe::phase();
+  }
+  virtual ~DataTransformer() {}
+
+  void InitRand();
+
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to the data.
+   *
+   * @param batch_item_id
+   *    Datum position within the batch. This is used to compute the
+   *    writing position in the top blob's data
+   * @param datum
+   *    Datum containing the data to be transformed.
+   * @param mean
+   * @param top_data
+   *    This is meant to be the top blob's data. The transformed data will be
+   *    written at the appropriate place within the blob's data.
+   */
+  void Transform(const int batch_item_id, const Datum& datum,
+                 const Dtype* mean, Dtype* transformed_data);
+
+ protected:
+  virtual unsigned int Rand();
+
+  // Tranformation parameters
+  TransformationParameter param_;
+
+
+  shared_ptr<Caffe::RNG> rng_;
+  Caffe::Phase phase_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_TRANSFORMER_HPP_
+
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
new file mode 100644
index 00000000000..609c06d6c31
--- /dev/null
+++ b/src/caffe/data_transformer.cpp
@@ -0,0 +1,114 @@
+#include <string>
+
+#include "caffe/data_transformer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/util/rng.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const int batch_item_id,
+                                       const Datum& datum,
+                                       const Dtype* mean,
+                                       Dtype* transformed_data) {
+
+  const string& data = datum.data();
+  const int channels = datum.channels();
+  const int height = datum.height();
+  const int width = datum.width();
+  const int size = datum.channels() * datum.height() * datum.width();
+
+  const int crop_size = param_.crop_size();
+  const bool mirror = param_.mirror();
+  const Dtype scale = param_.scale();
+
+
+
+  if (mirror && crop_size == 0) {
+    LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
+               << "set at the same time.";
+  }
+
+  if (crop_size) {
+    CHECK(data.size()) << "Image cropping only support uint8 data";
+    int h_off, w_off;
+    // We only do random crop when we do training.
+    if (phase_ == Caffe::TRAIN) {
+      h_off = Rand() % (height - crop_size);
+      w_off = Rand() % (width - crop_size);
+    } else {
+      h_off = (height - crop_size) / 2;
+      w_off = (width - crop_size) / 2;
+    }
+    if (mirror && Rand() % 2) {
+      // Copy mirrored version
+      for (int c = 0; c < channels; ++c) {
+        for (int h = 0; h < crop_size; ++h) {
+          for (int w = 0; w < crop_size; ++w) {
+            int data_index = (c * height + h + h_off) * width + w + w_off;
+            int top_index = ((batch_item_id * channels + c) * crop_size + h)
+                * crop_size + (crop_size - 1 - w);
+            Dtype datum_element =
+                static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+            transformed_data[top_index] =
+                (datum_element - mean[data_index]) * scale;
+          }
+        }
+      }
+    } else {
+      // Normal copy
+      for (int c = 0; c < channels; ++c) {
+        for (int h = 0; h < crop_size; ++h) {
+          for (int w = 0; w < crop_size; ++w) {
+            int top_index = ((batch_item_id * channels + c) * crop_size + h)
+                * crop_size + w;
+            int data_index = (c * height + h + h_off) * width + w + w_off;
+            Dtype datum_element =
+                static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+            transformed_data[top_index] =
+                (datum_element - mean[data_index]) * scale;
+          }
+        }
+      }
+    }
+  } else {
+    // we will prefer to use data() first, and then try float_data()
+    if (data.size()) {
+      for (int j = 0; j < size; ++j) {
+        Dtype datum_element =
+            static_cast<Dtype>(static_cast<uint8_t>(data[j]));
+        transformed_data[j + batch_item_id * size] =
+            (datum_element - mean[j]) * scale;
+      }
+    } else {
+      for (int j = 0; j < size; ++j) {
+        transformed_data[j + batch_item_id * size] =
+            (datum.float_data(j) - mean[j]) * scale;
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void DataTransformer<Dtype>::InitRand() {
+  const bool needs_rand = (phase_ == Caffe::TRAIN) &&
+      (param_.mirror() || param_.crop_size());
+  if (needs_rand) {
+    const unsigned int rng_seed = caffe_rng_rand();
+    rng_.reset(new Caffe::RNG(rng_seed));
+  } else {
+    rng_.reset();
+  }
+}
+
+template <typename Dtype>
+unsigned int DataTransformer<Dtype>::Rand() {
+  CHECK(rng_);
+  caffe::rng_t* rng =
+      static_cast<caffe::rng_t*>(rng_->generator());
+  return (*rng)();
+}
+
+INSTANTIATE_CLASS(DataTransformer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index c2b0c73a53b..b6ac5d6b351 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -23,20 +23,8 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   if (output_labels_) {
     top_label = prefetch_label_.mutable_cpu_data();
   }
-  const Dtype scale = this->layer_param_.data_param().scale();
   const int batch_size = this->layer_param_.data_param().batch_size();
-  const int crop_size = this->layer_param_.data_param().crop_size();
-  const bool mirror = this->layer_param_.data_param().mirror();
 
-  if (mirror && crop_size == 0) {
-    LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
-        << "set at the same time.";
-  }
-  // datum scales
-  const int channels = datum_channels_;
-  const int height = datum_height_;
-  const int width = datum_width_;
-  const int size = datum_size_;
   const Dtype* mean = data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
@@ -56,66 +44,13 @@ void DataLayer<Dtype>::InternalThreadEntry() {
       LOG(FATAL) << "Unknown database backend";
     }
 
-    const string& data = datum.data();
-    if (crop_size) {
-      CHECK(data.size()) << "Image cropping only support uint8 data";
-      int h_off, w_off;
-      // We only do random crop when we do training.
-      if (phase_ == Caffe::TRAIN) {
-        h_off = PrefetchRand() % (height - crop_size);
-        w_off = PrefetchRand() % (width - crop_size);
-      } else {
-        h_off = (height - crop_size) / 2;
-        w_off = (width - crop_size) / 2;
-      }
-      if (mirror && PrefetchRand() % 2) {
-        // Copy mirrored version
-        for (int c = 0; c < channels; ++c) {
-          for (int h = 0; h < crop_size; ++h) {
-            for (int w = 0; w < crop_size; ++w) {
-              int top_index = ((item_id * channels + c) * crop_size + h)
-                              * crop_size + (crop_size - 1 - w);
-              int data_index = (c * height + h + h_off) * width + w + w_off;
-              Dtype datum_element =
-                  static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-              top_data[top_index] = (datum_element - mean[data_index]) * scale;
-            }
-          }
-        }
-      } else {
-        // Normal copy
-        for (int c = 0; c < channels; ++c) {
-          for (int h = 0; h < crop_size; ++h) {
-            for (int w = 0; w < crop_size; ++w) {
-              int top_index = ((item_id * channels + c) * crop_size + h)
-                              * crop_size + w;
-              int data_index = (c * height + h + h_off) * width + w + w_off;
-              Dtype datum_element =
-                  static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-              top_data[top_index] = (datum_element - mean[data_index]) * scale;
-            }
-          }
-        }
-      }
-    } else {
-      // we will prefer to use data() first, and then try float_data()
-      if (data.size()) {
-        for (int j = 0; j < size; ++j) {
-          Dtype datum_element =
-              static_cast<Dtype>(static_cast<uint8_t>(data[j]));
-          top_data[item_id * size + j] = (datum_element - mean[j]) * scale;
-        }
-      } else {
-        for (int j = 0; j < size; ++j) {
-          top_data[item_id * size + j] =
-              (datum.float_data(j) - mean[j]) * scale;
-        }
-      }
-    }
+    // Apply data transformations (mirror, scale, crop...)
+    data_transformer_.Transform(item_id, datum, mean, top_data);
 
     if (output_labels_) {
       top_label[item_id] = datum.label();
     }
+
     // go to the next iter
     switch (this->layer_param_.data_param().backend()) {
     case DataParameter_DB_LEVELDB:
@@ -244,7 +179,7 @@ void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
 
   // image
-  int crop_size = this->layer_param_.data_param().crop_size();
+  int crop_size = this->layer_param_.data_param().transform_param().crop_size();
   if (crop_size > 0) {
     (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
                        datum.channels(), crop_size, crop_size);
@@ -274,8 +209,9 @@ void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(datum_height_, crop_size);
   CHECK_GT(datum_width_, crop_size);
   // check if we want to have mean
-  if (this->layer_param_.data_param().has_mean_file()) {
-    const string& mean_file = this->layer_param_.data_param().mean_file();
+  if (this->layer_param_.data_param().transform_param().has_mean_file()) {
+    const string& mean_file =
+        this->layer_param_.data_param().transform_param().mean_file();
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
@@ -305,15 +241,9 @@ void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void DataLayer<Dtype>::CreatePrefetchThread() {
   phase_ = Caffe::phase();
-  const bool prefetch_needs_rand = (phase_ == Caffe::TRAIN) &&
-      (this->layer_param_.data_param().mirror() ||
-       this->layer_param_.data_param().crop_size());
-  if (prefetch_needs_rand) {
-    const unsigned int prefetch_rng_seed = caffe_rng_rand();
-    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
-  } else {
-    prefetch_rng_.reset();
-  }
+
+  data_transformer_.InitRand();
+
   CHECK(!StartInternalThread()) << "Pthread execution failed";
 }
 
@@ -322,14 +252,6 @@ void DataLayer<Dtype>::JoinPrefetchThread() {
   CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
-template <typename Dtype>
-unsigned int DataLayer<Dtype>::PrefetchRand() {
-  CHECK(prefetch_rng_);
-  caffe::rng_t* prefetch_rng =
-      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
-  return (*prefetch_rng)();
-}
-
 template <typename Dtype>
 void DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 428ba2b406d..b7c6bca8194 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -344,6 +344,20 @@ message ConvolutionParameter {
   optional FillerParameter bias_filler = 8; // The filler for the bias
 }
 
+// Message that stores parameters used to apply transformation
+// to the data layer's data 
+message TransformationParameter {
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 1 [default = 1];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 2 [default = false];
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 3 [default = 0];
+  optional string mean_file = 4;
+}
+
 // Message that stores parameters used by DataLayer
 message DataParameter {
   enum DB {
@@ -352,23 +366,18 @@ message DataParameter {
   }
   // Specify the data source.
   optional string source = 1;
-  // For data pre-processing, we can do simple scaling and subtracting the
-  // data mean, if provided. Note that the mean subtraction is always carried
-  // out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
   // Specify the batch size.
-  optional uint32 batch_size = 4;
-  // Specify if we would like to randomly crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // Specify if we want to randomly mirror data.
-  optional bool mirror = 6 [default = false];
+  optional uint32 batch_size = 3;
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
-  optional uint32 rand_skip = 7 [default = 0];
-  optional DB backend = 8 [default = LEVELDB];
+  optional uint32 rand_skip = 4 [default = 0];
+
+  // Parameters for data pre-processing.
+  optional TransformationParameter transform_param = 5;
+
+  optional DB backend = 6 [default = LEVELDB];
 }
 
 // Message that stores parameters used by DropoutLayer
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 208beed91d1..5c21f2e745d 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -118,9 +118,13 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     LayerParameter param;
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
-    data_param->set_scale(scale);
     data_param->set_source(filename_->c_str());
     data_param->set_backend(backend_);
+
+    TransformationParameter* transform_param =
+        data_param->mutable_transform_param();
+    transform_param->set_scale(scale);
+
     DataLayer<Dtype> layer(param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
@@ -150,12 +154,17 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     const Dtype scale = 3;
     LayerParameter param;
     Caffe::set_random_seed(1701);
+
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
-    data_param->set_scale(scale);
-    data_param->set_crop_size(1);
     data_param->set_source(filename_->c_str());
     data_param->set_backend(backend_);
+
+    TransformationParameter* transform_param =
+        data_param->mutable_transform_param();
+    transform_param->set_scale(scale);
+    transform_param->set_crop_size(1);
+
     DataLayer<Dtype> layer(param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
@@ -198,11 +207,14 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     LayerParameter param;
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
-    data_param->set_crop_size(1);
-    data_param->set_mirror(true);
     data_param->set_source(filename_->c_str());
     data_param->set_backend(backend_);
 
+    TransformationParameter* transform_param =
+        data_param->mutable_transform_param();
+    transform_param->set_crop_size(1);
+    transform_param->set_mirror(true);
+
     // Get crop sequence with Caffe seed 1701.
     Caffe::set_random_seed(seed_);
     vector<vector<Dtype> > crop_sequence;
@@ -249,11 +261,14 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     LayerParameter param;
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
-    data_param->set_crop_size(1);
-    data_param->set_mirror(true);
     data_param->set_source(filename_->c_str());
     data_param->set_backend(backend_);
 
+    TransformationParameter* transform_param =
+        data_param->mutable_transform_param();
+    transform_param->set_crop_size(1);
+    transform_param->set_mirror(true);
+
     // Get crop sequence with Caffe seed 1701, srand seed 1701.
     Caffe::set_random_seed(seed_);
     srand(seed_);
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 395e3523418..f0a368245a5 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1177,7 +1177,9 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-train-leveldb' "
-      "    scale: 0.00390625 "
+      "    transform_param { "
+      "      scale: 0.00390625 "
+      "    } "
       "    batch_size: 64 "
       "  } "
       "  include: { phase: TRAIN } "
@@ -1189,7 +1191,9 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-test-leveldb' "
-      "    scale: 0.00390625 "
+      "    transform_param { "
+      "      scale: 0.00390625 "
+      "    } "
       "    batch_size: 100 "
       "  } "
       "  include: { phase: TEST } "
@@ -1256,7 +1260,9 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-train-leveldb' "
-      "    scale: 0.00390625 "
+      "    transform_param { "
+      "      scale: 0.00390625 "
+      "    } "
       "    batch_size: 64 "
       "  } "
       "  include: { phase: TRAIN } "
@@ -1313,7 +1319,9 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-test-leveldb' "
-      "    scale: 0.00390625 "
+      "    transform_param { "
+      "      scale: 0.00390625 "
+      "    } "
       "    batch_size: 100 "
       "  } "
       "  include: { phase: TEST } "
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 92a3ec77b93..bee73f64568 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -180,10 +180,12 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
-      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "    batch_size: 256 "
-      "    crop_size: 227 "
-      "    mirror: true "
+      "    transform_param { "
+      "      crop_size: 227 "
+      "      mirror: true "
+      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "    } "
       "  } "
       "  top: 'data' "
       "  top: 'label' "
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 2abcadc0862..3e9ab2194ee 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1189,10 +1189,12 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
-      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "    batch_size: 256 "
-      "    crop_size: 227 "
-      "    mirror: true "
+      "    transform_param { "
+      "      crop_size: 227 "
+      "      mirror: true "
+      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "    } "
       "  } "
       "  top: 'data' "
       "  top: 'label' "
@@ -1523,11 +1525,13 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
-      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "    batch_size: 256 "
-      "    crop_size: 227 "
-      "    mirror: true "
-      "    scale: 0.25 "
+      "    transform_param { "
+      "      crop_size: 227 "
+      "      mirror: true "
+      "      scale: 0.25 "
+      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "    } "
       "    rand_skip: 73 "
       "  } "
       "  top: 'data' "
@@ -2119,10 +2123,12 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
-      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "    batch_size: 256 "
-      "    crop_size: 227 "
-      "    mirror: true "
+      "    transform_param { "
+      "      crop_size: 227 "
+      "      mirror: true "
+      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "    } "
       "  } "
       "  top: 'data' "
       "  top: 'label' "
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 5415ca83b60..48eb579fe00 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -307,7 +307,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_scale()) {
       if (type == "data") {
-        layer_param->mutable_data_param()->set_scale(v0_layer_param.scale());
+        layer_param->mutable_data_param()->mutable_transform_param()->
+            set_scale(v0_layer_param.scale());
       } else if (type == "images") {
         layer_param->mutable_image_data_param()->set_scale(
            v0_layer_param.scale());
@@ -318,8 +319,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_meanfile()) {
       if (type == "data") {
-        layer_param->mutable_data_param()->set_mean_file(
-            v0_layer_param.meanfile());
+        layer_param->mutable_data_param()->mutable_transform_param()->
+            set_mean_file(v0_layer_param.meanfile());
       } else if (type == "images") {
         layer_param->mutable_image_data_param()->set_mean_file(
             v0_layer_param.meanfile());
@@ -351,8 +352,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_cropsize()) {
       if (type == "data") {
-        layer_param->mutable_data_param()->set_crop_size(
-            v0_layer_param.cropsize());
+        layer_param->mutable_data_param()->mutable_transform_param()->
+            set_crop_size(v0_layer_param.cropsize());
       } else if (type == "images") {
         layer_param->mutable_image_data_param()->set_crop_size(
             v0_layer_param.cropsize());
@@ -366,7 +367,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_mirror()) {
       if (type == "data") {
-        layer_param->mutable_data_param()->set_mirror(v0_layer_param.mirror());
+        layer_param->mutable_data_param()->mutable_transform_param()->
+            set_mirror(v0_layer_param.mirror());
       } else if (type == "images") {
         layer_param->mutable_image_data_param()->set_mirror(
             v0_layer_param.mirror());

From 0ac0f20413ea44847af7c8c7bcca655f499b0b60 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 20 Aug 2014 20:06:11 -0700
Subject: [PATCH 0509/2053] clean up cpu signbit definition

The redundant "caffe_signbit" function was used as a circumlocution
around CUDA's signbit macro; we can just add extra parens to prevent
macro expansion.
---
 include/caffe/util/math_functions.hpp |  9 ++++-----
 src/caffe/util/math_functions.cpp     | 12 ------------
 2 files changed, 4 insertions(+), 17 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index f4310bf4417..62467fb5c52 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -137,11 +137,10 @@ inline char caffe_sign(Dtype val) {
 DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
 
 // This returns a nonzero value if the input has its sign bit set.
-// The name sngbit is meant to avoid conflicts with std::signbit in the macro
-bool caffe_signbit(float arg);
-bool caffe_signbit(double arg);
-bool caffe_signbit(long double arg);
-DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = caffe_signbit(x[i]));
+// The name sngbit is meant to avoid conflicts with std::signbit in the macro.
+// The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
+// and we don't want that to expand here when CUDA headers are also included.
+DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = (std::signbit)(x[i]));
 
 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
 
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 7016e635318..0338155e1f3 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -387,16 +387,4 @@ void caffe_cpu_scale<double>(const int n, const double alpha, const double *x,
   cblas_dscal(n, alpha, y, 1);
 }
 
-
-using std::signbit;
-bool caffe_signbit(float arg) {
-    return signbit(arg);
-}
-bool caffe_signbit(double arg) {
-    return signbit(arg);
-}
-bool caffe_signbit(long double arg) {
-    return signbit(arg);
-}
-
 }  // namespace caffe

From ac64a7b3d35a0c52468fbf9725a8517a389d7a47 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 20 Aug 2014 20:38:29 -0700
Subject: [PATCH 0510/2053] specialize cpu_strided_dot before instantiation to
 fix clang++ build

---
 src/caffe/util/math_functions.cpp | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 7016e635318..86b7e866fd3 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -315,17 +315,6 @@ void caffe_rng_bernoulli<double>(const int n, const double p, unsigned int* r);
 template
 void caffe_rng_bernoulli<float>(const int n, const float p, unsigned int* r);
 
-template <typename Dtype>
-Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y) {
-  return caffe_cpu_strided_dot(n, x, 1, y, 1);
-}
-
-template
-float caffe_cpu_dot<float>(const int n, const float* x, const float* y);
-
-template
-double caffe_cpu_dot<double>(const int n, const double* x, const double* y);
-
 template <>
 float caffe_cpu_strided_dot<float>(const int n, const float* x, const int incx,
     const float* y, const int incy) {
@@ -338,6 +327,17 @@ double caffe_cpu_strided_dot<double>(const int n, const double* x,
   return cblas_ddot(n, x, incx, y, incy);
 }
 
+template <typename Dtype>
+Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y) {
+  return caffe_cpu_strided_dot(n, x, 1, y, 1);
+}
+
+template
+float caffe_cpu_dot<float>(const int n, const float* x, const float* y);
+
+template
+double caffe_cpu_dot<double>(const int n, const double* x, const double* y);
+
 template <>
 int caffe_cpu_hamming_distance<float>(const int n, const float* x,
                                   const float* y) {

From a4460972333507aec0c42db7f7cbaebcf16edcdf Mon Sep 17 00:00:00 2001
From: TANGUY Arnaud <arn.tanguy@gmail.com>
Date: Thu, 21 Aug 2014 15:50:39 +0200
Subject: [PATCH 0511/2053] Refactor ImageDataLayer to use DataTransformer

---
 include/caffe/data_layers.hpp         |  6 +-
 src/caffe/data_transformer.cpp        |  1 -
 src/caffe/layers/image_data_layer.cpp | 97 +++------------------------
 src/caffe/proto/caffe.proto           | 25 +++----
 src/caffe/test/test_upgrade_proto.cpp | 10 +--
 src/caffe/util/upgrade_proto.cpp      | 16 ++---
 6 files changed, 38 insertions(+), 117 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 06508eabf57..bc29429832e 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -178,7 +178,8 @@ template <typename Dtype>
 class ImageDataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit ImageDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+      : Layer<Dtype>(param),
+        data_transformer_(param.data_param().transform_param()) {}
   virtual ~ImageDataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -203,10 +204,11 @@ class ImageDataLayer : public Layer<Dtype>, public InternalThread {
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
-  virtual unsigned int PrefetchRand();
   virtual void InternalThreadEntry();
 
+  DataTransformer<Dtype> data_transformer_;
   shared_ptr<Caffe::RNG> prefetch_rng_;
+
   vector<std::pair<std::string, int> > lines_;
   int lines_id_;
   int datum_channels_;
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 609c06d6c31..bdde028435b 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -11,7 +11,6 @@ void DataTransformer<Dtype>::Transform(const int batch_item_id,
                                        const Datum& datum,
                                        const Dtype* mean,
                                        Dtype* transformed_data) {
-
   const string& data = datum.data();
   const int channels = datum.channels();
   const int height = datum.height();
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index c72bf9c036f..85ee9885b5c 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -20,22 +20,11 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   Dtype* top_data = prefetch_data_.mutable_cpu_data();
   Dtype* top_label = prefetch_label_.mutable_cpu_data();
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
-  const Dtype scale = image_data_param.scale();
   const int batch_size = image_data_param.batch_size();
-  const int crop_size = image_data_param.crop_size();
-  const bool mirror = image_data_param.mirror();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
 
-  if (mirror && crop_size == 0) {
-    LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
-        << "set at the same time.";
-  }
   // datum scales
-  const int channels = datum_channels_;
-  const int height = datum_height_;
-  const int width = datum_width_;
-  const int size = datum_size_;
   const int lines_size = lines_.size();
   const Dtype* mean = data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
@@ -46,62 +35,9 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
           new_height, new_width, &datum)) {
       continue;
     }
-    const string& data = datum.data();
-    if (crop_size) {
-      CHECK(data.size()) << "Image cropping only support uint8 data";
-      int h_off, w_off;
-      // We only do random crop when we do training.
-      if (phase_ == Caffe::TRAIN) {
-        h_off = PrefetchRand() % (height - crop_size);
-        w_off = PrefetchRand() % (width - crop_size);
-      } else {
-        h_off = (height - crop_size) / 2;
-        w_off = (width - crop_size) / 2;
-      }
-      if (mirror && PrefetchRand() % 2) {
-        // Copy mirrored version
-        for (int c = 0; c < channels; ++c) {
-          for (int h = 0; h < crop_size; ++h) {
-            for (int w = 0; w < crop_size; ++w) {
-              int top_index = ((item_id * channels + c) * crop_size + h)
-                              * crop_size + (crop_size - 1 - w);
-              int data_index = (c * height + h + h_off) * width + w + w_off;
-              Dtype datum_element =
-                  static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-              top_data[top_index] = (datum_element - mean[data_index]) * scale;
-            }
-          }
-        }
-      } else {
-        // Normal copy
-        for (int c = 0; c < channels; ++c) {
-          for (int h = 0; h < crop_size; ++h) {
-            for (int w = 0; w < crop_size; ++w) {
-              int top_index = ((item_id * channels + c) * crop_size + h)
-                              * crop_size + w;
-              int data_index = (c * height + h + h_off) * width + w + w_off;
-              Dtype datum_element =
-                  static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-              top_data[top_index] = (datum_element - mean[data_index]) * scale;
-            }
-          }
-        }
-      }
-    } else {
-      // Just copy the whole data
-      if (data.size()) {
-        for (int j = 0; j < size; ++j) {
-          Dtype datum_element =
-              static_cast<Dtype>(static_cast<uint8_t>(data[j]));
-          top_data[item_id * size + j] = (datum_element - mean[j]) * scale;
-        }
-      } else {
-        for (int j = 0; j < size; ++j) {
-          top_data[item_id * size + j] =
-              (datum.float_data(j) - mean[j]) * scale;
-        }
-      }
-    }
+
+    // Apply transformations (mirror, crop...) to the data
+    data_transformer_.Transform(item_id, datum, mean, top_data);
 
     top_label[item_id] = datum.label();
     // go to the next iter
@@ -163,9 +99,11 @@ void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK(ReadImageToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
                          new_height, new_width, &datum));
   // image
-  const int crop_size = this->layer_param_.image_data_param().crop_size();
+  const int crop_size = this->layer_param_.image_data_param()
+      .transform_param().crop_size();
   const int batch_size = this->layer_param_.image_data_param().batch_size();
-  const string& mean_file = this->layer_param_.image_data_param().mean_file();
+  const string& mean_file = this->layer_param_.image_data_param()
+      .transform_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
     prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
@@ -189,7 +127,7 @@ void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(datum_height_, crop_size);
   CHECK_GT(datum_width_, crop_size);
   // check if we want to have mean
-  if (this->layer_param_.image_data_param().has_mean_file()) {
+  if (this->layer_param_.image_data_param().transform_param().has_mean_file()) {
     BlobProto blob_proto;
     LOG(INFO) << "Loading mean file from" << mean_file;
     ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
@@ -217,15 +155,9 @@ void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ImageDataLayer<Dtype>::CreatePrefetchThread() {
   phase_ = Caffe::phase();
-  const bool prefetch_needs_rand =
-      this->layer_param_.image_data_param().shuffle() ||
-      this->layer_param_.image_data_param().crop_size();
-  if (prefetch_needs_rand) {
-    const unsigned int prefetch_rng_seed = caffe_rng_rand();
-    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
-  } else {
-    prefetch_rng_.reset();
-  }
+
+  data_transformer_.InitRand();
+
   // Create the thread.
   CHECK(!StartInternalThread()) << "Pthread execution failed";
 }
@@ -243,13 +175,6 @@ void ImageDataLayer<Dtype>::JoinPrefetchThread() {
   CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
-template <typename Dtype>
-unsigned int ImageDataLayer<Dtype>::PrefetchRand() {
-  caffe::rng_t* prefetch_rng =
-      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
-  return (*prefetch_rng)();
-}
-
 template <typename Dtype>
 void ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b7c6bca8194..75f4b2862b0 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -345,7 +345,7 @@ message ConvolutionParameter {
 }
 
 // Message that stores parameters used to apply transformation
-// to the data layer's data 
+// to the data layer's data
 message TransformationParameter {
   // For data pre-processing, we can do simple scaling and subtracting the
   // data mean, if provided. Note that the mean subtraction is always carried
@@ -444,27 +444,20 @@ message HingeLossParameter {
 message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
-  // For data pre-processing, we can do simple scaling and subtracting the
-  // data mean, if provided. Note that the mean subtraction is always carried
-  // out before scaling.
-  optional float scale = 2 [default = 1];
-  optional string mean_file = 3;
   // Specify the batch size.
-  optional uint32 batch_size = 4;
-  // Specify if we would like to randomly crop an image.
-  optional uint32 crop_size = 5 [default = 0];
-  // Specify if we want to randomly mirror data.
-  optional bool mirror = 6 [default = false];
+  optional uint32 batch_size = 2;
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
-  optional uint32 rand_skip = 7 [default = 0];
+  optional uint32 rand_skip = 3 [default = 0];
   // Whether or not ImageLayer should shuffle the list of files at every epoch.
-  optional bool shuffle = 8 [default = false];
+  optional bool shuffle = 4 [default = false];
   // It will also resize images if new_height or new_width are not zero.
-  optional uint32 new_height = 9 [default = 0];
-  optional uint32 new_width = 10 [default = 0];
+  optional uint32 new_height = 5 [default = 0];
+  optional uint32 new_width = 6 [default = 0];
+  // Parameters for data pre-processing.
+  optional TransformationParameter transform_param = 7;
 }
 
 // Message that stores parameters InfogainLossLayer
@@ -505,7 +498,7 @@ message MemoryDataParameter {
 message MVNParameter {
   // This parameter can be set to false to normalize mean only
   optional bool normalize_variance = 1 [default = true];
-  
+
   // This parameter can be set to true to perform DNN-like MVN
   optional bool across_channels = 2 [default = false];
 }
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 3e9ab2194ee..9d0cd58d84c 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1542,11 +1542,13 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "  type: IMAGE_DATA "
       "  image_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-images' "
-      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "    batch_size: 256 "
-      "    crop_size: 227 "
-      "    mirror: true "
-      "    scale: 0.25 "
+      "    transform_param {"
+      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "      crop_size: 227 "
+      "      mirror: true "
+      "      scale: 0.25 "
+      "    } "
       "    rand_skip: 73 "
       "    shuffle: true "
       "    new_height: 40 "
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 48eb579fe00..21fc038a15d 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -310,8 +310,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
         layer_param->mutable_data_param()->mutable_transform_param()->
             set_scale(v0_layer_param.scale());
       } else if (type == "images") {
-        layer_param->mutable_image_data_param()->set_scale(
-           v0_layer_param.scale());
+        layer_param->mutable_image_data_param()->mutable_transform_param()->
+            set_scale(v0_layer_param.scale());
       } else {
         LOG(ERROR) << "Unknown parameter scale for layer type " << type;
         is_fully_compatible = false;
@@ -322,8 +322,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
         layer_param->mutable_data_param()->mutable_transform_param()->
             set_mean_file(v0_layer_param.meanfile());
       } else if (type == "images") {
-        layer_param->mutable_image_data_param()->set_mean_file(
-            v0_layer_param.meanfile());
+        layer_param->mutable_image_data_param()->mutable_transform_param()->
+            set_mean_file(v0_layer_param.meanfile());
       } else if (type == "window_data") {
         layer_param->mutable_window_data_param()->set_mean_file(
             v0_layer_param.meanfile());
@@ -355,8 +355,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
         layer_param->mutable_data_param()->mutable_transform_param()->
             set_crop_size(v0_layer_param.cropsize());
       } else if (type == "images") {
-        layer_param->mutable_image_data_param()->set_crop_size(
-            v0_layer_param.cropsize());
+        layer_param->mutable_image_data_param()->mutable_transform_param()->
+            set_crop_size(v0_layer_param.cropsize());
       } else if (type == "window_data") {
         layer_param->mutable_window_data_param()->set_crop_size(
             v0_layer_param.cropsize());
@@ -370,8 +370,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
         layer_param->mutable_data_param()->mutable_transform_param()->
             set_mirror(v0_layer_param.mirror());
       } else if (type == "images") {
-        layer_param->mutable_image_data_param()->set_mirror(
-            v0_layer_param.mirror());
+        layer_param->mutable_image_data_param()->mutable_transform_param()->
+            set_mirror(v0_layer_param.mirror());
       } else if (type == "window_data") {
         layer_param->mutable_window_data_param()->set_mirror(
             v0_layer_param.mirror());

From 3b0f7a57f0ad6cd33ded7d148c7ad7e8f2a29e4d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 21 Aug 2014 12:53:43 -0700
Subject: [PATCH 0512/2053] If specified, --gpu flag overrides SolverParameter
 solver_mode.

---
 tools/caffe.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index bc404d3b318..77e031ef3c1 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -89,6 +89,17 @@ int train() {
 
   LOG(INFO) << "Starting Optimization";
   caffe::SGDSolver<float> solver(solver_param);
+
+  // Set device id and mode
+  if (FLAGS_gpu >= 0) {
+    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+    Caffe::SetDevice(FLAGS_gpu);
+    Caffe::set_mode(Caffe::GPU);
+  } else if (!solver_param.has_solver_mode()) {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
     solver.Solve(FLAGS_snapshot);

From 3e20ca98888aa1e47acc3e6100a7a66095e04183 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 Aug 2014 14:19:33 -0700
Subject: [PATCH 0513/2053] [docs] fix citation bibtex

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index e7a819824de..27ca28d2753 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -66,7 +66,7 @@ Please cite Caffe in your publications if it helps your research:
        Author = {Yangqing Jia},
        Title = { {Caffe}: An Open Source Convolutional Architecture for Fast Feature Embedding},
        Year  = {2013},
-       Howpublished = {\url{http://caffe.berkeleyvision.org/}
+       Howpublished = {\url{http://caffe.berkeleyvision.org/}}
     }
 
 If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).

From b497475e2958a2ce79621da2978b885e88bf109e Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Sat, 16 Aug 2014 23:18:12 -0700
Subject: [PATCH 0514/2053] Updated documentation to include instructions to
 install protobuf with Python support on Mac OS X

---
 docs/installation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 73a35395c4e..863800f77dd 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -135,7 +135,8 @@ In other `ENV` settings, things may not work as expected.
 Simply run the following:
 
     brew install --build-from-source --with-python boost
-    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
+    brew install --with-python protobuf
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
 
 Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
 

From b84f2d255e9bbda6939cd96ac7e338a2671ab6ad Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Sat, 16 Aug 2014 23:41:52 -0700
Subject: [PATCH 0515/2053] Updated installation docs for OS X 10.9 brew
 install protobuf as well

---
 docs/installation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 863800f77dd..6306f6338e4 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -173,7 +173,8 @@ To edit the formulae in turn, run
 
 After this, run
 
-    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
     brew install --build-from-source --with-python --fresh -vd boost
 
 **Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.

From ac85f8136b287f231ea495ecba5a72341018a1ae Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 21 Aug 2014 12:53:43 -0700
Subject: [PATCH 0516/2053] If specified, --gpu flag overrides SolverParameter
 solver_mode.

---
 tools/caffe.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 13398ae8577..eb32128351e 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -89,6 +89,17 @@ int train() {
 
   LOG(INFO) << "Starting Optimization";
   caffe::SGDSolver<float> solver(solver_param);
+
+  // Set device id and mode
+  if (FLAGS_gpu >= 0) {
+    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+    Caffe::SetDevice(FLAGS_gpu);
+    Caffe::set_mode(Caffe::GPU);
+  } else if (!solver_param.has_solver_mode()) {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
     solver.Solve(FLAGS_snapshot);

From 3141e719443353e84a56538732d1688a81cffa24 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 Aug 2014 19:03:09 -0700
Subject: [PATCH 0517/2053] restore old data transformation parameters for
 compatibility

---
 src/caffe/proto/caffe.proto | 44 +++++++++++++++++++++++++++----------
 1 file changed, 32 insertions(+), 12 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 75f4b2862b0..9c0825284d8 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -367,17 +367,26 @@ message DataParameter {
   // Specify the data source.
   optional string source = 1;
   // Specify the batch size.
-  optional uint32 batch_size = 3;
+  optional uint32 batch_size = 4;
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
-  optional uint32 rand_skip = 4 [default = 0];
-
+  optional uint32 rand_skip = 7 [default = 0];
+  optional DB backend = 8 [default = LEVELDB];
   // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 5;
-
-  optional DB backend = 6 [default = LEVELDB];
+  optional TransformationParameter transform_param = 9;
+  // DEPRECATED. See TransformParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
 }
 
 // Message that stores parameters used by DropoutLayer
@@ -445,19 +454,30 @@ message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
   // Specify the batch size.
-  optional uint32 batch_size = 2;
+  optional uint32 batch_size = 4;
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
-  optional uint32 rand_skip = 3 [default = 0];
+  optional uint32 rand_skip = 7 [default = 0];
   // Whether or not ImageLayer should shuffle the list of files at every epoch.
-  optional bool shuffle = 4 [default = false];
+  optional bool shuffle = 8 [default = false];
   // It will also resize images if new_height or new_width are not zero.
-  optional uint32 new_height = 5 [default = 0];
-  optional uint32 new_width = 6 [default = 0];
+  optional uint32 new_height = 9 [default = 0];
+  optional uint32 new_width = 10 [default = 0];
   // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 7;
+  optional TransformationParameter transform_param = 11;
+  // DEPRECATED. See TransformParameter. For data pre-processing, we can do
+  // simple scaling and subtracting the data mean, if provided. Note that the
+  // mean subtraction is always carried out before scaling.
+  optional float scale = 2 [default = 1];
+  optional string mean_file = 3;
+  // DEPRECATED. See TransformParameter. Specify if we would like to randomly
+  // crop an image.
+  optional uint32 crop_size = 5 [default = 0];
+  // DEPRECATED. See TransformParameter. Specify if we want to randomly mirror
+  // data.
+  optional bool mirror = 6 [default = false];
 }
 
 // Message that stores parameters InfogainLossLayer

From af1d065a4ffa42e6c60812d2e90ddeb65acd447f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 Aug 2014 19:02:41 -0700
Subject: [PATCH 0518/2053] compact net parameter upgrade

---
 include/caffe/util/upgrade_proto.hpp |  3 +++
 src/caffe/util/upgrade_proto.cpp     | 35 ++++++++++------------------
 2 files changed, 15 insertions(+), 23 deletions(-)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 1ee184a34e9..3b1062479f2 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -34,6 +34,9 @@ LayerParameter_LayerType UpgradeV0LayerType(const string& type);
 void NetParameterToPrettyPrint(const NetParameter& param,
                                NetParameterPrettyPrint* pretty_param);
 
+// Check for deprecations and upgrade the NetParameter as needed.
+void UpgradeNetAsNeeded(NetParameter* param);
+
 // Read parameters from a file into a NetParameter proto message.
 void ReadNetParamsFromTextFileOrDie(const string& param_file,
                                     NetParameter* param);
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 21fc038a15d..c9c57a21eef 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -567,10 +567,7 @@ void NetParameterToPrettyPrint(const NetParameter& param,
   }
 }
 
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
-                                    NetParameter* param) {
-  CHECK(ReadProtoFromTextFile(param_file, param))
-      << "Failed to parse NetParameter file: " << param_file;
+void UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
   if (NetNeedsUpgrade(*param)) {
     // NetParameter was specified using the old style (V0LayerParameter); try to
     // upgrade it.
@@ -585,32 +582,24 @@ void ReadNetParamsFromTextFileOrDie(const string& param_file,
                 << "V0LayerParameter";
     }
     LOG(ERROR) << "Note that future Caffe releases will not support "
-        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text.bin to "
-        << "upgrade this and any other network proto files to the new format.";
+        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
+        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
+        << "weights upgrade this and any other net protos to the new format.";
   }
 }
 
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+                                    NetParameter* param) {
+  CHECK(ReadProtoFromTextFile(param_file, param))
+      << "Failed to parse NetParameter file: " << param_file;
+  UpgradeNetAsNeeded(param_file, param);
+}
+
 void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
                                       NetParameter* param) {
   CHECK(ReadProtoFromBinaryFile(param_file, param))
       << "Failed to parse NetParameter file: " << param_file;
-  if (NetNeedsUpgrade(*param)) {
-    // NetParameter was specified using the old style (V0LayerParameter); try to
-    // upgrade it.
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "V0LayerParameter: " << param_file;
-    NetParameter original_param(*param);
-    if (!UpgradeV0Net(original_param, param)) {
-      LOG(ERROR) << "Warning: had one or more problems upgrading "
-          << "V0NetParameter to NetParameter (see above); continuing anyway.";
-    } else {
-      LOG(INFO) << "Successfully upgraded file specified using deprecated "
-                << "V0LayerParameter";
-    }
-    LOG(ERROR) << "Note that future Caffe releases will not support "
-        << "V0NetParameter; use ./build/tools/upgrade_net_proto_binary.bin to "
-        << "upgrade this and any other network proto files to the new format.";
-  }
+  UpgradeNetAsNeeded(param_file, param);
 }
 
 }  // namespace caffe

From c14c8be5f123ab650922750976f380d461a6d5cd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 Aug 2014 21:22:44 -0700
Subject: [PATCH 0519/2053] upgrade net parameter data transformation fields
 automagically

Convert DataParameter and ImageDataParameter data transformation fields
into a TransformationParameter.
---
 include/caffe/util/upgrade_proto.hpp |  7 +++
 src/caffe/util/upgrade_proto.cpp     | 79 ++++++++++++++++++++++++++++
 tools/upgrade_net_proto_text.cpp     |  5 ++
 3 files changed, 91 insertions(+)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 3b1062479f2..45483685133 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -29,6 +29,13 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
 
 LayerParameter_LayerType UpgradeV0LayerType(const string& type);
 
+// Return true iff any layer contains deprecated data transformation parameters.
+bool NetNeedsDataUpgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade old transformation fields
+// into a TransformationParameter.
+void UpgradeNetDataTransformation(NetParameter* net_param);
+
 // Convert a NetParameter to NetParameterPrettyPrint used for dumping to
 // proto text files.
 void NetParameterToPrettyPrint(const NetParameter& param,
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index c9c57a21eef..9045abbe17d 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -547,6 +547,75 @@ LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
   }
 }
 
+bool NetNeedsDataUpgrade(const NetParameter& net_param) {
+  for (int i = 0; i < net_param.layers_size(); ++i) {
+    if (net_param.layers(i).type() == LayerParameter_LayerType_DATA) {
+      DataParameter layer_param = net_param.layers(i).data_param();
+      if (layer_param.has_scale()) { return true; }
+      if (layer_param.has_mean_file()) { return true; }
+      if (layer_param.has_crop_size()) { return true; }
+      if (layer_param.has_mirror()) { return true; }
+    }
+    if (net_param.layers(i).type() == LayerParameter_LayerType_IMAGE_DATA) {
+      ImageDataParameter layer_param = net_param.layers(i).image_data_param();
+      if (layer_param.has_scale()) { return true; }
+      if (layer_param.has_mean_file()) { return true; }
+      if (layer_param.has_crop_size()) { return true; }
+      if (layer_param.has_mirror()) { return true; }
+    }
+  }
+  return false;
+}
+
+void UpgradeNetDataTransformation(NetParameter* net_param) {
+  for (int i = 0; i < net_param->layers_size(); ++i) {
+    if (net_param->layers(i).type() == LayerParameter_LayerType_DATA) {
+      DataParameter* layer_param =
+          net_param->mutable_layers(i)->mutable_data_param();
+      TransformationParameter* transform_param =
+          layer_param->mutable_transform_param();
+      if (layer_param->has_scale()) {
+        transform_param->set_scale(layer_param->scale());
+        layer_param->clear_scale();
+      }
+      if (layer_param->has_mean_file()) {
+        transform_param->set_mean_file(layer_param->mean_file());
+        layer_param->clear_mean_file();
+      }
+      if (layer_param->has_crop_size()) {
+        transform_param->set_crop_size(layer_param->crop_size());
+        layer_param->clear_crop_size();
+      }
+      if (layer_param->has_mirror()) {
+        transform_param->set_mirror(layer_param->mirror());
+        layer_param->clear_mirror();
+      }
+    }
+    if (net_param->layers(i).type() == LayerParameter_LayerType_IMAGE_DATA) {
+      ImageDataParameter* layer_param =
+          net_param->mutable_layers(i)->mutable_image_data_param();
+      TransformationParameter* transform_param =
+          layer_param->mutable_transform_param();
+      if (layer_param->has_scale()) {
+        transform_param->set_scale(layer_param->scale());
+        layer_param->clear_scale();
+      }
+      if (layer_param->has_mean_file()) {
+        transform_param->set_mean_file(layer_param->mean_file());
+        layer_param->clear_mean_file();
+      }
+      if (layer_param->has_crop_size()) {
+        transform_param->set_crop_size(layer_param->crop_size());
+        layer_param->clear_crop_size();
+      }
+      if (layer_param->has_mirror()) {
+        transform_param->set_mirror(layer_param->mirror());
+        layer_param->clear_mirror();
+      }
+    }
+  }
+}
+
 void NetParameterToPrettyPrint(const NetParameter& param,
                                NetParameterPrettyPrint* pretty_param) {
   pretty_param->Clear();
@@ -586,6 +655,16 @@ void UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
         << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
         << "weights upgrade this and any other net protos to the new format.";
   }
+  // NetParameter uses old style data transformation fields; try to upgrade it.
+  if (NetNeedsDataUpgrade(*param)) {
+    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
+               << "transformation parameters: " << param_file;
+    UpgradeNetDataTransformation(param);
+    LOG(INFO) << "Successfully upgraded file specified using deprecated "
+              << "data transformation parameters.";
+    LOG(ERROR) << "Note that future Caffe releases will only support "
+               << "transform_param messages for transformation fields.";
+  }
 }
 
 void ReadNetParamsFromTextFileOrDie(const string& param_file,
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 1176585b02a..2f290fc5e14 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -29,6 +29,7 @@ int main(int argc, char** argv) {
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
+  bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
     NetParameter v0_net_param(net_param);
@@ -37,6 +38,10 @@ int main(int argc, char** argv) {
     LOG(ERROR) << "File already in V1 proto format: " << argv[1];
   }
 
+  if (need_data_upgrade) {
+    UpgradeNetDataTransformation(&net_param);
+  }
+
   // Convert to a NetParameterPrettyPrint to print fields in desired
   // order.
   NetParameterPrettyPrint net_param_pretty;

From 45262c8099e6d0265f7e3d8d13d5045a4679b2c1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 Aug 2014 22:19:09 -0700
Subject: [PATCH 0520/2053] upgrade model definitions for transformation params

---
 .../cifar10/cifar10_full_train_test.prototxt     |  8 ++++++--
 .../cifar10/cifar10_quick_train_test.prototxt    |  8 ++++++--
 .../feature_extraction/imagenet_val.prototxt     |  8 +++++---
 examples/imagenet/alexnet_train_val.prototxt     | 16 ++++++++++------
 examples/imagenet/imagenet_train_val.prototxt    | 16 ++++++++++------
 .../mnist/lenet_consolidated_solver.prototxt     | 12 +++++++++---
 examples/mnist/lenet_train_test.prototxt         |  8 ++++++--
 examples/mnist/mnist_autoencoder.prototxt        |  8 ++++++--
 8 files changed, 58 insertions(+), 26 deletions(-)

diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index 4fd52fec133..c01fef46ac0 100644
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -6,8 +6,10 @@ layers {
   top: "label"
   data_param {
     source: "cifar10-leveldb/cifar-train-leveldb"
-    mean_file: "mean.binaryproto"
     batch_size: 100
+    transform_param {
+      mean_file: "mean.binaryproto"
+    }
   }
   include: { phase: TRAIN }
 }
@@ -18,8 +20,10 @@ layers {
   top: "label"
   data_param {
     source: "cifar10-leveldb/cifar-test-leveldb"
-    mean_file: "mean.binaryproto"
     batch_size: 100
+    transform_param {
+      mean_file: "mean.binaryproto"
+    }
   }
   include: { phase: TEST }
 }
diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index b34d1cd2fcb..779dacf6758 100644
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -6,8 +6,10 @@ layers {
   top: "label"
   data_param {
     source: "cifar10-leveldb/cifar-train-leveldb"
-    mean_file: "mean.binaryproto"
     batch_size: 100
+    transform_param {
+      mean_file: "mean.binaryproto"
+    }
   }
   include: { phase: TRAIN }
 }
@@ -18,8 +20,10 @@ layers {
   top: "label"
   data_param {
     source: "cifar10-leveldb/cifar-test-leveldb"
-    mean_file: "mean.binaryproto"
     batch_size: 100
+    transform_param {
+      mean_file: "mean.binaryproto"
+    }
   }
   include: { phase: TEST }
 }
diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index b0451a1a114..8d5b07626b5 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -6,12 +6,14 @@ layers {
   top: "label"
   image_data_param {
     source: "$CAFFE_DIR/examples/_temp/file_list.txt"
-    mean_file: "$CAFFE_DIR/data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 50
-    crop_size: 227
-    mirror: false
     new_height: 256
     new_width: 256
+    transform_param {
+      crop_size: 227
+      mean_file: "$CAFFE_DIR/data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: false
+    }
   }
 }
 layers {
diff --git a/examples/imagenet/alexnet_train_val.prototxt b/examples/imagenet/alexnet_train_val.prototxt
index 8d7977d981e..8ffdb7a8f69 100644
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -6,10 +6,12 @@ layers {
   top: "label"
   data_param {
     source: "ilsvrc12_train_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 256
-    crop_size: 227
-    mirror: true
+    transform_param {
+      crop_size: 227
+      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: true
+    }
   }
   include: { phase: TRAIN }
 }
@@ -20,10 +22,12 @@ layers {
   top: "label"
   data_param {
     source: "ilsvrc12_val_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 50
-    crop_size: 227
-    mirror: false
+    transform_param {
+      crop_size: 227
+      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: false
+    }
   }
   include: { phase: TEST }
 }
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/examples/imagenet/imagenet_train_val.prototxt
index ea1f907f00d..5f9c83949f5 100644
--- a/examples/imagenet/imagenet_train_val.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -6,10 +6,12 @@ layers {
   top: "label"
   data_param {
     source: "ilsvrc12_train_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 256
-    crop_size: 227
-    mirror: true
+    transform_param {
+      crop_size: 227
+      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: true
+    }
   }
   include: { phase: TRAIN }
 }
@@ -20,10 +22,12 @@ layers {
   top: "label"
   data_param {
     source: "ilsvrc12_val_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 50
-    crop_size: 227
-    mirror: false
+    transform_param {
+      crop_size: 227
+      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: false
+    }
   }
   include: { phase: TEST }
 }
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 42f54e4e93a..2e69589c67f 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -51,8 +51,10 @@ net_param {
     top: "label"
     data_param {
       source: "mnist-train-leveldb"
-      scale: 0.00390625
       batch_size: 64
+      transform_param {
+        scale: 0.00390625
+      }
     }
     include: { phase: TRAIN }
   }
@@ -63,8 +65,10 @@ net_param {
     top: "label"
     data_param {
       source: "mnist-test-leveldb"
-      scale: 0.00390625
       batch_size: 100
+      transform_param {
+        scale: 0.00390625
+      }
     }
     include: {
       phase: TEST
@@ -78,8 +82,10 @@ net_param {
     top: "label"
     data_param {
       source: "mnist-train-leveldb"
-      scale: 0.00390625
       batch_size: 100
+      transform_param {
+        scale: 0.00390625
+      }
     }
     include: {
       phase: TEST
diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index 3c77452130c..578641d5b2d 100644
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -6,8 +6,10 @@ layers {
   top: "label"
   data_param {
     source: "mnist-train-leveldb"
-    scale: 0.00390625
     batch_size: 64
+    transform_param {
+      scale: 0.00390625
+    }
   }
   include: { phase: TRAIN }
 }
@@ -18,8 +20,10 @@ layers {
   top: "label"
   data_param {
     source: "mnist-test-leveldb"
-    scale: 0.00390625
     batch_size: 100
+    transform_param {
+      scale: 0.00390625
+    }
   }
   include: { phase: TEST }
 }
diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index ad1e7665bf2..3af6b7afcb3 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -5,8 +5,10 @@ layers {
   type: DATA
   data_param {
     source: "mnist-train-leveldb"
-    scale: 0.0039215684
     batch_size: 100
+    transform_param {
+      scale: 0.0039215684
+    }
   }
   include: { phase: TRAIN }
 }
@@ -16,8 +18,10 @@ layers {
   type: DATA
   data_param {
     source: "mnist-test-leveldb"
-    scale: 0.0039215684
     batch_size: 100
+    transform_param {
+      scale: 0.0039215684
+    }
   }
   include: { phase: TEST }
 }

From 27555cdc403698134a729fa74d5b3d45d26090ec Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 22 Aug 2014 00:02:17 -0700
Subject: [PATCH 0521/2053] fix parameter for transformation in ImageDataLayer
 constructor

---
 include/caffe/data_layers.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index bc29429832e..f972965b45b 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -179,7 +179,7 @@ class ImageDataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit ImageDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param),
-        data_transformer_(param.data_param().transform_param()) {}
+        data_transformer_(param.image_data_param().transform_param()) {}
   virtual ~ImageDataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);

From 3aa2fc0e3eaa75e254ed49eb848093bbd31cf4d0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 23 Aug 2014 19:25:56 -0700
Subject: [PATCH 0522/2053] move {InnerProduct,Eltwise}Layer to common instead
 of vision

---
 include/caffe/common_layers.hpp | 64 +++++++++++++++++++++++++++++++++
 include/caffe/vision_layers.hpp | 64 ---------------------------------
 2 files changed, 64 insertions(+), 64 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 979fd9a6dae..ed47dbc2620 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -86,6 +86,37 @@ class ConcatLayer : public Layer<Dtype> {
   int concat_dim_;
 };
 
+/* EltwiseLayer
+  Compute elementwise operations like product or sum.
+*/
+template <typename Dtype>
+class EltwiseLayer : public Layer<Dtype> {
+ public:
+  explicit EltwiseLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ELTWISE;
+  }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  EltwiseParameter_EltwiseOp op_;
+  vector<Dtype> coeffs_;
+};
+
 /* FlattenLayer
 */
 template <typename Dtype>
@@ -115,6 +146,39 @@ class FlattenLayer : public Layer<Dtype> {
   int count_;
 };
 
+/* InnerProductLayer
+*/
+template <typename Dtype>
+class InnerProductLayer : public Layer<Dtype> {
+ public:
+  explicit InnerProductLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_INNER_PRODUCT;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  Blob<Dtype> bias_multiplier_;
+};
+
 /* MVNLayer
  */
 template <typename Dtype>
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index d2d9ab3f783..02f9e43b6d9 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -60,37 +60,6 @@ class ConvolutionLayer : public Layer<Dtype> {
   int N_;
 };
 
-/* EltwiseLayer
-  Compute elementwise operations like product or sum.
-*/
-template <typename Dtype>
-class EltwiseLayer : public Layer<Dtype> {
- public:
-  explicit EltwiseLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ELTWISE;
-  }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  EltwiseParameter_EltwiseOp op_;
-  vector<Dtype> coeffs_;
-};
-
 /* Im2colLayer
 */
 template <typename Dtype>
@@ -125,39 +94,6 @@ class Im2colLayer : public Layer<Dtype> {
   int pad_h_, pad_w_;
 };
 
-/* InnerProductLayer
-*/
-template <typename Dtype>
-class InnerProductLayer : public Layer<Dtype> {
- public:
-  explicit InnerProductLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_INNER_PRODUCT;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  int M_;
-  int K_;
-  int N_;
-  bool bias_term_;
-  Blob<Dtype> bias_multiplier_;
-};
-
 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
 template <typename Dtype> class PoolingLayer;
 template <typename Dtype> class SplitLayer;

From 76fd37263aa844a92b5f2fdde29c294c1e87a810 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 23 Aug 2014 22:41:30 -0700
Subject: [PATCH 0523/2053] fix internal thread interface confusion

thanks @ronghanghu for pointing this out in #964
---
 include/caffe/internal_thread.hpp      | 4 ++--
 src/caffe/layers/data_layer.cpp        | 2 +-
 src/caffe/layers/image_data_layer.cpp  | 2 +-
 src/caffe/layers/window_data_layer.cpp | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 8520899ada6..bb0f932566d 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -15,9 +15,9 @@ class InternalThread {
   InternalThread() {}
   virtual ~InternalThread() {}
 
-  /** Returns true if the thread was successfully started **/
+  /** Returns true if the thread was successfully started. **/
   bool StartInternalThread() {
-    return pthread_create(&_thread, NULL, InternalThreadEntryFunc, this);
+    return (pthread_create(&_thread, NULL, InternalThreadEntryFunc, this) == 0);
   }
 
   /** Will not return until the internal thread has exited. */
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index b6ac5d6b351..0cc2769bfc9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -244,7 +244,7 @@ void DataLayer<Dtype>::CreatePrefetchThread() {
 
   data_transformer_.InitRand();
 
-  CHECK(!StartInternalThread()) << "Pthread execution failed";
+  CHECK(StartInternalThread()) << "Pthread execution failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 85ee9885b5c..dc39e694bbb 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -159,7 +159,7 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
   data_transformer_.InitRand();
 
   // Create the thread.
-  CHECK(!StartInternalThread()) << "Pthread execution failed";
+  CHECK(StartInternalThread()) << "Pthread execution failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 8138234ebee..bed03d8e8d2 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -408,7 +408,7 @@ void WindowDataLayer<Dtype>::CreatePrefetchThread() {
     prefetch_rng_.reset();
   }
   // Create the thread.
-  CHECK(!StartInternalThread()) << "Pthread execution failed.";
+  CHECK(StartInternalThread()) << "Pthread execution failed.";
 }
 
 template <typename Dtype>

From 113a21ab70180b4be7b7087f72421cff3b1047c7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 24 Aug 2014 15:36:52 -0700
Subject: [PATCH 0524/2053] [example] add fully-convolutional efficiency note +
 confidence map

- spell out fully-convolutional efficiency
- add confidence map
- fix input size: 451 x 451 is correct for an 8 x 8 output map by the
  equation input size = 227 + 32(d-1) for output map dimension of d
---
 examples/imagenet/imagenet_full_conv.prototxt |  4 +-
 examples/net_surgery.ipynb                    | 57 ++++++++++++-------
 2 files changed, 37 insertions(+), 24 deletions(-)

diff --git a/examples/imagenet/imagenet_full_conv.prototxt b/examples/imagenet/imagenet_full_conv.prototxt
index 6473c1f78fc..570efae5901 100644
--- a/examples/imagenet/imagenet_full_conv.prototxt
+++ b/examples/imagenet/imagenet_full_conv.prototxt
@@ -2,8 +2,8 @@ name: "CaffeNetConv"
 input: "data"
 input_dim: 1
 input_dim: 3
-input_dim: 454
-input_dim: 454
+input_dim: 451
+input_dim: 451
 layers {
   name: "conv1"
   type: CONVOLUTION
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 4710236c6e9..2d8bbb106be 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:742fe1bbeabccae59725a5fd6e97a0a0b2b90842d1dd5953aecd353b2d87cab3"
+  "signature": "sha256:10c551b31a64c2210f6094dbb603f26c206a7b72cd99032f475cb5023edcdc43"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -14,11 +14,11 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Net Surgery\n",
+      "## Net Surgery for a Fully-Convolutional Model\n",
       "\n",
-      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we translate the inner product classifier layers of the Caffe Reference ImageNet model into convolutional layers. This yields a fully-convolutional model that generates a classification map for any given input size instead of a single classification. In particular, a classification will be made for every 6 $\\times$ 6 region of the `pool5` layer, giving a 8 $\\times$ 8 classification map for our example 454 $\\times$ 454 input dimensions.\n",
+      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully-convolutional model for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional neural network (CNN) structure by dynamic programming in the forward pass from shallow to deep layers.\n",
       "\n",
-      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (While planned, this is currently an exercise for the reader.)\n",
+      "To do so we translate the inner product classifier layers of CaffeNet into convolutional layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding.\n",
       "\n",
       "Roll up your sleeves for net surgery with pycaffe!"
      ]
@@ -45,8 +45,8 @@
         "---\r\n",
         "> input_dim: 10\r\n",
         "5,6c5,6\r\n",
-        "< input_dim: 454\r\n",
-        "< input_dim: 454\r\n",
+        "< input_dim: 451\r\n",
+        "< input_dim: 451\r\n",
         "---\r\n",
         "> input_dim: 227\r\n",
         "> input_dim: 227\r\n",
@@ -266,12 +266,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "To conclude, let's make a classification map from the example cat image. This gives an 8-by-8 labeling of overlapping image regions."
+      "To conclude, let's make a classification map from the example cat image and visualize the confidence as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
      ]
     },
     {
      "cell_type": "code",
-     "collapsed": false,
+     "collapsed": true,
      "input": [
       "import numpy as np\n",
       "import matplotlib.pyplot as plt\n",
@@ -279,39 +279,50 @@
       "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
-      "plt.imshow(im)\n",
       "net_full_conv.set_phase_test()\n",
       "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
       "net_full_conv.set_raw_scale('data', 255.0)\n",
-      "# make classification map by forward pass and show top prediction index per location\n",
+      "# make classification map by forward and print prediction indices at each location\n",
       "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
-      "out['prob'][0].argmax(axis=0)"
+      "print out['prob'][0].argmax(axis=0)\n",
+      "# show net input and confidence map (probability of the top prediction at each location)\n",
+      "plt.subplot(1, 2, 1)\n",
+      "plt.imshow(net_full_conv.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+      "plt.subplot(1, 2, 2)\n",
+      "plt.imshow(out['prob'][0].max(axis=0))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[282 282 281 281 281 281 277 282]\n",
+        " [281 283 281 281 281 281 281 282]\n",
+        " [283 283 283 283 283 283 281 282]\n",
+        " [283 283 283 281 283 283 283 259]\n",
+        " [283 283 283 283 283 283 283 259]\n",
+        " [283 283 283 283 283 283 259 259]\n",
+        " [283 283 283 283 259 259 259 277]\n",
+        " [335 335 283 283 263 263 263 277]]\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "array([[282, 281, 281, 281, 281, 281, 281, 282],\n",
-        "       [283, 283, 283, 281, 281, 281, 281, 282],\n",
-        "       [283, 283, 283, 283, 283, 283, 287, 282],\n",
-        "       [283, 283, 283, 281, 283, 283, 283, 283],\n",
-        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
-        "       [283, 283, 283, 283, 283, 283, 283, 259],\n",
-        "       [283, 283, 283, 283, 283, 259, 259, 852],\n",
-        "       [335, 335, 283, 283, 283, 263, 263, 331]])"
+        "<matplotlib.image.AxesImage at 0x14c8050d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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ozanF2Lc9Wwp3MF+4v/uI+9NH3Bw+4PZ0x+nmFivweH6CEHzAfhm87Z8RvVN3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WwYM2AwCBbvDetVI9mh20w+V5QtGBtoJlJVJWeNVgY39DuHNAO3mqczwhw02jq02fHzLx2/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\nJ0mv0B4CKXaWvrO4+fjtB943aObiqdKewUyOaXQEfkAchsL6m53un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SayLEFp6PuW0Jh5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P/fif5nv/qe/hg5//WQaLoKBJUC/M5oqGjBNHPybMpDJyq/KCK9VpXB1/gAkinmQjZXKY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0SuRppFZSBX+fZeq7gDcq1kdNEVCCp37RkBXEYwj0ZjdY7Gk475QiF5ewd/yux031FlX/md2FGy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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f318eee8ad0>"
+        "<matplotlib.figure.Figure at 0x10a00e310>"
        ]
       }
      ],
@@ -323,7 +334,9 @@
      "source": [
       "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
       "\n",
-      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself."
+      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+      "\n",
+      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
      ]
     },
     {

From 99bea71ce48eba5a4cd30d7f6b68b30fc9a97f92 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 24 Aug 2014 15:36:52 -0700
Subject: [PATCH 0525/2053] [example] add fully-convolutional efficiency note +
 confidence map

- spell out fully-convolutional efficiency
- add confidence map
- fix input size: 451 x 451 is correct for an 8 x 8 output map by the
  equation input size = 227 + 32(d-1) for output map dimension of d
---
 examples/imagenet/imagenet_full_conv.prototxt |  4 +-
 examples/net_surgery.ipynb                    | 57 ++++++++++++-------
 2 files changed, 37 insertions(+), 24 deletions(-)

diff --git a/examples/imagenet/imagenet_full_conv.prototxt b/examples/imagenet/imagenet_full_conv.prototxt
index 6473c1f78fc..570efae5901 100644
--- a/examples/imagenet/imagenet_full_conv.prototxt
+++ b/examples/imagenet/imagenet_full_conv.prototxt
@@ -2,8 +2,8 @@ name: "CaffeNetConv"
 input: "data"
 input_dim: 1
 input_dim: 3
-input_dim: 454
-input_dim: 454
+input_dim: 451
+input_dim: 451
 layers {
   name: "conv1"
   type: CONVOLUTION
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 4710236c6e9..2d8bbb106be 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:742fe1bbeabccae59725a5fd6e97a0a0b2b90842d1dd5953aecd353b2d87cab3"
+  "signature": "sha256:10c551b31a64c2210f6094dbb603f26c206a7b72cd99032f475cb5023edcdc43"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -14,11 +14,11 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Net Surgery\n",
+      "## Net Surgery for a Fully-Convolutional Model\n",
       "\n",
-      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we translate the inner product classifier layers of the Caffe Reference ImageNet model into convolutional layers. This yields a fully-convolutional model that generates a classification map for any given input size instead of a single classification. In particular, a classification will be made for every 6 $\\times$ 6 region of the `pool5` layer, giving a 8 $\\times$ 8 classification map for our example 454 $\\times$ 454 input dimensions.\n",
+      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully-convolutional model for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional neural network (CNN) structure by dynamic programming in the forward pass from shallow to deep layers.\n",
       "\n",
-      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (While planned, this is currently an exercise for the reader.)\n",
+      "To do so we translate the inner product classifier layers of CaffeNet into convolutional layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding.\n",
       "\n",
       "Roll up your sleeves for net surgery with pycaffe!"
      ]
@@ -45,8 +45,8 @@
         "---\r\n",
         "> input_dim: 10\r\n",
         "5,6c5,6\r\n",
-        "< input_dim: 454\r\n",
-        "< input_dim: 454\r\n",
+        "< input_dim: 451\r\n",
+        "< input_dim: 451\r\n",
         "---\r\n",
         "> input_dim: 227\r\n",
         "> input_dim: 227\r\n",
@@ -266,12 +266,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "To conclude, let's make a classification map from the example cat image. This gives an 8-by-8 labeling of overlapping image regions."
+      "To conclude, let's make a classification map from the example cat image and visualize the confidence as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
      ]
     },
     {
      "cell_type": "code",
-     "collapsed": false,
+     "collapsed": true,
      "input": [
       "import numpy as np\n",
       "import matplotlib.pyplot as plt\n",
@@ -279,39 +279,50 @@
       "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
-      "plt.imshow(im)\n",
       "net_full_conv.set_phase_test()\n",
       "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
       "net_full_conv.set_raw_scale('data', 255.0)\n",
-      "# make classification map by forward pass and show top prediction index per location\n",
+      "# make classification map by forward and print prediction indices at each location\n",
       "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
-      "out['prob'][0].argmax(axis=0)"
+      "print out['prob'][0].argmax(axis=0)\n",
+      "# show net input and confidence map (probability of the top prediction at each location)\n",
+      "plt.subplot(1, 2, 1)\n",
+      "plt.imshow(net_full_conv.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+      "plt.subplot(1, 2, 2)\n",
+      "plt.imshow(out['prob'][0].max(axis=0))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[282 282 281 281 281 281 277 282]\n",
+        " [281 283 281 281 281 281 281 282]\n",
+        " [283 283 283 283 283 283 281 282]\n",
+        " [283 283 283 281 283 283 283 259]\n",
+        " [283 283 283 283 283 283 283 259]\n",
+        " [283 283 283 283 283 283 259 259]\n",
+        " [283 283 283 283 259 259 259 277]\n",
+        " [335 335 283 283 263 263 263 277]]\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "array([[282, 281, 281, 281, 281, 281, 281, 282],\n",
-        "       [283, 283, 283, 281, 281, 281, 281, 282],\n",
-        "       [283, 283, 283, 283, 283, 283, 287, 282],\n",
-        "       [283, 283, 283, 281, 283, 283, 283, 283],\n",
-        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
-        "       [283, 283, 283, 283, 283, 283, 283, 259],\n",
-        "       [283, 283, 283, 283, 283, 259, 259, 852],\n",
-        "       [335, 335, 283, 283, 283, 263, 263, 331]])"
+        "<matplotlib.image.AxesImage at 0x14c8050d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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BmckRMNVnRDBxBTrzVeatK6qMSf7/38r0EThOl+QrXOK59LoKD8RgzITpreM+MJOJaozD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WwYM2AwCBbvDetVI9mh20w+V5QtGBtoJlJVJWeNVgY39DuHNAO3mqczwhw02jq02fHzLx2/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\nJ0mv0B4CKXaWvrO4+fjtB943aObiqdKewUyOaXQEfkAchsL6m53un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SayLEFp6PuW0Jh5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P/fif5nv/qe/hg5//WQaLoKBJUC/M5oqGjBNHPybMpDJyq/KCK9VpXB1/gAkinmQjZXKY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0SuRppFZSBX+fZeq7gDcq1kdNEVCCp37RkBXEYwj0ZjdY7Gk475QiF5ewd/yux031FlX/md2FGy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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f318eee8ad0>"
+        "<matplotlib.figure.Figure at 0x10a00e310>"
        ]
       }
      ],
@@ -323,7 +334,9 @@
      "source": [
       "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
       "\n",
-      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself."
+      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+      "\n",
+      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
      ]
     },
     {

From fab0c2867594cea4fa7f0c3aafa32704e4e3704e Mon Sep 17 00:00:00 2001
From: alfredtofu <alfredtofu@gmail.com>
Date: Mon, 25 Aug 2014 16:22:52 +0800
Subject: [PATCH 0526/2053] fix bug for resizing images.

---
 src/caffe/util/io.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 4f200474459..07c0b05ac58 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -74,7 +74,7 @@ bool ReadImageToDatum(const string& filename, const int label,
     CV_LOAD_IMAGE_GRAYSCALE);
   if (height > 0 && width > 0) {
     cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
-    cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
   } else {
     cv_img = cv::imread(filename, cv_read_flag);
   }

From e0c855063ecfad63d1f21c8b276a99e609d41b5f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ozan=20=C3=87a=C4=9Flayan?= <ozancag@gmail.com>
Date: Mon, 25 Aug 2014 15:16:34 +0300
Subject: [PATCH 0527/2053] [docs] Update installation docs to include Fedora

- All the instructions mentioned for CentOS/RHEL are also valid for Fedora Linux so add it.
- The package name for atlas is atlas-devel not libatlas-devel for CentOS/RHEL/Fedora so fix it.
- Add 'sudo' prefix to missing places to be consistent with the rest of the document.
- Add instructions for installing python headers in Fedora into the python support section.
- Update Linux installation section to reflect Fedora, refactor a bit.
---
 docs/installation.md | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 6306f6338e4..1ac6c08b6ca 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -39,7 +39,7 @@ The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
     + Ubuntu: `sudo apt-get install libatlas-base-dev`
-    + CentOS/RHEL: `sudo yum install libatlas-devel`
+    + CentOS/RHEL/Fedora: `sudo yum install atlas-devel`
     + OS X: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html).
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
@@ -60,7 +60,9 @@ You can install the dependencies with
 
 but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
-For **Ubuntu**, if you use the default Python you will need to `apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
+For **Ubuntu**, if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
+
+For **Fedora**, if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the wrapper.
 
 For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
 
@@ -82,15 +84,21 @@ On **Ubuntu**, most of the dependencies can be installed with
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
 
-And on **CentOS / RHEL**, you can install via yum with
-
-    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
-
 and for **Ubuntu 14.04** the rest of the dependencies can be installed with
 
     sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
 
-For **Ubuntu 12.04 and CentOS / RHEL** the only exceptions to package installation are the Google flags library, Google logging library, and LMDB. To install these, do:
+Keep reading to find out how to manually build and install the Google flags library, Google logging library and LMDB on **Ubuntu 12.04**.
+
+On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
+
+    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
+    
+The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
+
+    sudo yum install gflags-devel glog-devel lmdb
+
+**Finally** in case you couldn't find those 3 libraries in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora**
 
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz

From 870f13a3ba0d60a6e8df1c138c1bf8f3f0a35b58 Mon Sep 17 00:00:00 2001
From: alfredtofu <alfredtofu@gmail.com>
Date: Mon, 25 Aug 2014 16:22:52 +0800
Subject: [PATCH 0528/2053] fix bug for resizing images.

---
 src/caffe/util/io.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index fd7454d4f98..7758557a950 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -72,7 +72,7 @@ bool ReadImageToDatum(const string& filename, const int label,
     CV_LOAD_IMAGE_GRAYSCALE);
   if (height > 0 && width > 0) {
     cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
-    cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
   } else {
     cv_img = cv::imread(filename, cv_read_flag);
   }

From b6161e3469436f5906237ff8aebb0e01bfdd76f8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 25 Aug 2014 11:59:20 -0700
Subject: [PATCH 0529/2053] 'caffe test' prints all scores and their names

---
 tools/caffe.cpp | 45 +++++++++++++++++++++++++++++++++++++++------
 1 file changed, 39 insertions(+), 6 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 77e031ef3c1..5b3ad0b4691 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -136,14 +136,47 @@ int test() {
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
-  double test_score = 0;
+  vector<Blob<float>* > bottom_vec;
+  vector<int> test_score_output_id;
+  vector<float> test_score;
+  float loss = 0;
   for (int i = 0; i < FLAGS_iterations; ++i) {
-    const vector<Blob<float>*>& result = caffe_net.ForwardPrefilled();
-    test_score += result[0]->cpu_data()[0];
-    LOG(INFO) << "Batch " << i << ", score: " << result[0]->cpu_data()[0];
+    float iter_loss;
+    const vector<Blob<float>*>& result =
+        caffe_net.Forward(bottom_vec, &iter_loss);
+    loss += iter_loss;
+    int idx = 0;
+    for (int j = 0; j < result.size(); ++j) {
+      const float* result_vec = result[j]->cpu_data();
+      for (int k = 0; k < result[j]->count(); ++k, ++idx) {
+        const float score = result_vec[k];
+        if (i == 0) {
+          test_score.push_back(score);
+          test_score_output_id.push_back(j);
+        } else {
+          test_score[idx] += score;
+        }
+        const std::string& output_name = caffe_net.blob_names()[
+            caffe_net.output_blob_indices()[j]];
+        LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score;
+      }
+    }
+  }
+  loss /= FLAGS_iterations;
+  LOG(INFO) << "Loss: " << loss;
+  for (int i = 0; i < test_score.size(); ++i) {
+    const std::string& output_name = caffe_net.blob_names()[
+        caffe_net.output_blob_indices()[test_score_output_id[i]]];
+    const float loss_weight =
+        caffe_net.blob_loss_weights()[caffe_net.output_blob_indices()[i]];
+    std::ostringstream loss_msg_stream;
+    const float mean_score = test_score[i] / FLAGS_iterations;
+    if (loss_weight) {
+      loss_msg_stream << " (* " << loss_weight
+                      << " = " << loss_weight * mean_score << " loss)";
+    }
+    LOG(INFO) << output_name << " = " << mean_score << loss_msg_stream.str();
   }
-  test_score /= FLAGS_iterations;
-  LOG(INFO) << "Score: " << test_score;
 
   return 0;
 }

From 0cc2e94a7f13116757a529ce173b52398cc17c94 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 25 Aug 2014 12:47:50 -0700
Subject: [PATCH 0530/2053] fix memory_used_ by computing after SetUp

---
 src/caffe/net.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 1ee60bfe5e3..a3e7122c2c4 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -108,6 +108,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       if (layer->loss(top_id)) {
         LOG(INFO) << "    with loss weight " << layer->loss(top_id);
       }
+      memory_used_ += top_vecs_[layer_id][top_id]->count();
     }
     DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
     const int blobs_lr_size = layer_param.blobs_lr_size();
@@ -374,7 +375,6 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
       top_id_vecs_[layer_id].push_back(blob_id);
       top_vecs_[layer_id].push_back(blob_pointer.get());
     }
-    memory_used_ += blob_pointer->count();
   }
   if (available_blobs) { available_blobs->insert(blob_name); }
 }

From 910db9796d936b40f245ce9a008531da8d38e570 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 25 Aug 2014 16:10:22 -0700
Subject: [PATCH 0531/2053] Add "stable_prod_grad" option (on by default) to
 ELTWISE layer to compute the eltwise product gradient using a slower but
 stabler formula.

---
 include/caffe/common_layers.hpp       |  2 ++
 src/caffe/layers/eltwise_layer.cpp    | 17 ++++++++++++++++-
 src/caffe/layers/eltwise_layer.cu     | 16 +++++++++++++++-
 src/caffe/proto/caffe.proto           |  4 ++++
 src/caffe/test/test_eltwise_layer.cpp | 15 ++++++++++++++-
 5 files changed, 51 insertions(+), 3 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index ed47dbc2620..1b50e9563ca 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -115,6 +115,8 @@ class EltwiseLayer : public Layer<Dtype> {
 
   EltwiseParameter_EltwiseOp op_;
   vector<Dtype> coeffs_;
+
+  bool stable_prod_grad_;
 };
 
 /* FlattenLayer
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index ec6a46ff8f5..56a1920e32f 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -35,6 +35,7 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       coeffs_[i] = this->layer_param().eltwise_param().coeff(i);
     }
   }
+  stable_prod_grad_ = this->layer_param_.eltwise_param().stable_prod_grad();
 }
 
 template <typename Dtype>
@@ -73,7 +74,21 @@ void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
       switch (op_) {
       case EltwiseParameter_EltwiseOp_PROD:
-        caffe_div(count, top_data, bottom_data, bottom_diff);
+        if (stable_prod_grad_) {
+          bool initialized = false;
+          for (int j = 0; j < bottom->size(); ++j) {
+            if (i == j) { continue; }
+            if (!initialized) {
+              caffe_copy(count, (*bottom)[j]->cpu_data(), bottom_diff);
+              initialized = true;
+            } else {
+              caffe_mul(count, (*bottom)[j]->cpu_data(), bottom_diff,
+                        bottom_diff);
+            }
+          }
+        } else {
+          caffe_div(count, top_data, bottom_data, bottom_diff);
+        }
         caffe_mul(count, bottom_diff, top_diff, bottom_diff);
         break;
       case EltwiseParameter_EltwiseOp_SUM:
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 4b38949da09..e005cb91122 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -43,7 +43,21 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
       switch (op_) {
       case EltwiseParameter_EltwiseOp_PROD:
-        caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+        if (stable_prod_grad_) {
+          bool initialized = false;
+          for (int j = 0; j < bottom->size(); ++j) {
+            if (i == j) { continue; }
+            if (!initialized) {
+              caffe_copy(count, (*bottom)[j]->gpu_data(), bottom_diff);
+              initialized = true;
+            } else {
+              caffe_gpu_mul(count, (*bottom)[j]->gpu_data(), bottom_diff,
+                            bottom_diff);
+            }
+          }
+        } else {
+          caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+        }
         caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
         break;
       case EltwiseParameter_EltwiseOp_SUM:
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9c0825284d8..49a6e142fcd 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -420,6 +420,10 @@ message EltwiseParameter {
   }
   optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
   repeated float coeff = 2; // blob-wise coefficient for SUM operation
+
+  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
+  // of computing the gradient for the PROD operation. (No effect for SUM op.)
+  optional bool stable_prod_grad = 3 [default = true];
 }
 
 // Message that stores parameters used by ThresholdLayer
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 4c17dfd341c..da5e3538131 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -124,11 +124,24 @@ TYPED_TEST(EltwiseLayerTest, TestSumCoeff) {
   }
 }
 
-TYPED_TEST(EltwiseLayerTest, TestProdGradient) {
+TYPED_TEST(EltwiseLayerTest, TestStableProdGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  eltwise_param->set_stable_prod_grad(true);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(EltwiseLayerTest, TestUnstableProdGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  eltwise_param->set_stable_prod_grad(false);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),

From 0063028c8371ceba477f4278041a326eb32d94cc Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 25 Aug 2014 16:12:33 -0700
Subject: [PATCH 0532/2053] remove warning about LRN layers in CPU mode

---
 examples/cifar10/cifar10_full_solver.prototxt | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt
index 49de3f58803..d8e0bb27c44 100644
--- a/examples/cifar10/cifar10_full_solver.prototxt
+++ b/examples/cifar10/cifar10_full_solver.prototxt
@@ -23,9 +23,4 @@ max_iter: 60000
 snapshot: 10000
 snapshot_prefix: "cifar10_full"
 # solver mode: CPU or GPU
-# Note: there seems to be a bug with CPU computation in the pooling layers,
-# and changing to solver_mode: CPU may result in NaNs on this example.
-# If you want to train a variant of this architecture on the
-# CPU, try changing the pooling regions from WITHIN_CHANNEL to ACROSS_CHANNELS
-# in both cifar_full_train.prototxt and cifar_full_test.prototxt.
 solver_mode: GPU

From 7014bfb976524e95b6e13eb44cf62401568bff1a Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 26 Aug 2014 00:44:56 -0700
Subject: [PATCH 0533/2053] FIX web_demo upload was not processing grayscale
 correctly

---
 examples/web_demo/exifutil.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/examples/web_demo/exifutil.py b/examples/web_demo/exifutil.py
index 8c07aa887b2..01918b2a491 100644
--- a/examples/web_demo/exifutil.py
+++ b/examples/web_demo/exifutil.py
@@ -23,7 +23,13 @@ def open_oriented_im(im_path):
         if exif is not None and 274 in exif:
             orientation = exif[274]
             im = apply_orientation(im, orientation)
-    return np.asarray(im).astype(np.float32) / 255.
+    img = np.asarray(im).astype(np.float32) / 255.
+    if img.ndim == 2:
+        img = img[:, :, np.newaxis]
+        img = np.tile(img, (1, 1, 3))
+    elif img.shape[2] == 4:
+        img = img[:, :, :3]
+    return img
 
 
 def apply_orientation(im, orientation):

From 9516115f722fb3f95882553d8077bf1ab4a670ef Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 26 Aug 2014 00:44:56 -0700
Subject: [PATCH 0534/2053] FIX web_demo upload was not processing grayscale
 correctly

---
 examples/web_demo/exifutil.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/examples/web_demo/exifutil.py b/examples/web_demo/exifutil.py
index 8c07aa887b2..01918b2a491 100644
--- a/examples/web_demo/exifutil.py
+++ b/examples/web_demo/exifutil.py
@@ -23,7 +23,13 @@ def open_oriented_im(im_path):
         if exif is not None and 274 in exif:
             orientation = exif[274]
             im = apply_orientation(im, orientation)
-    return np.asarray(im).astype(np.float32) / 255.
+    img = np.asarray(im).astype(np.float32) / 255.
+    if img.ndim == 2:
+        img = img[:, :, np.newaxis]
+        img = np.tile(img, (1, 1, 3))
+    elif img.shape[2] == 4:
+        img = img[:, :, :3]
+    return img
 
 
 def apply_orientation(im, orientation):

From f6536b50d2ba1fb69b8f3a61f9079923b282771f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 26 Aug 2014 09:51:19 -0700
Subject: [PATCH 0535/2053] clarify project origin

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index 27ca28d2753..67b424ceefd 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,7 +4,7 @@ layout: default
 # Caffe
 
 Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
-It was created by [Yangqing Jia](http://daggerfs.com), and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+It was created by [Yangqing Jia](http://daggerfs.com) during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
 Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!

From 8880a7a4158cf0a80eb9650bd4fe77dffb518177 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 26 Aug 2014 14:01:20 -0700
Subject: [PATCH 0536/2053] FIX drop obsolete CURAND reset for CUDA 6.5
 compatibility

Drop the legacy CURAND initialization steps; these are unnecessary and
cause dramatic slowdowns for CUDA 6.5. This does no harm for K20 usage
counter to the note at least for CUDA 6.5 and 5.0.
---
 src/caffe/common.cpp | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index e6c0a4f56a3..a900d6f0198 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -108,15 +108,11 @@ Caffe::~Caffe() {
 
 void Caffe::set_random_seed(const unsigned int seed) {
   // Curand seed
-  // Yangqing's note: simply setting the generator seed does not seem to
-  // work on the tesla K20s, so I wrote the ugly reset thing below.
   static bool g_curand_availability_logged = false;
   if (Get().curand_generator_) {
-    CURAND_CHECK(curandDestroyGenerator(curand_generator()));
-    CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
-        CURAND_RNG_PSEUDO_DEFAULT));
     CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(curand_generator(),
         seed));
+    CURAND_CHECK(curandSetGeneratorOffset(curand_generator(), 0));
   } else {
     if (!g_curand_availability_logged) {
         LOG(ERROR) <<

From 4947c91f7353d7aff3d0d08bc0a4ef4e4fbb1212 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 26 Aug 2014 14:32:14 -0700
Subject: [PATCH 0537/2053] default ilsvrc solving to GPU

---
 examples/imagenet/alexnet_solver.prototxt  | 1 +
 examples/imagenet/imagenet_solver.prototxt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/examples/imagenet/alexnet_solver.prototxt b/examples/imagenet/alexnet_solver.prototxt
index 8581e99c6a7..a3aeae07dc7 100644
--- a/examples/imagenet/alexnet_solver.prototxt
+++ b/examples/imagenet/alexnet_solver.prototxt
@@ -11,3 +11,4 @@ momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "caffe_alexnet_train"
+solver_mode: GPU
diff --git a/examples/imagenet/imagenet_solver.prototxt b/examples/imagenet/imagenet_solver.prototxt
index aedec4104a6..1ad40fe71e7 100644
--- a/examples/imagenet/imagenet_solver.prototxt
+++ b/examples/imagenet/imagenet_solver.prototxt
@@ -11,3 +11,4 @@ momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "caffe_imagenet_train"
+solver_mode: GPU

From c349b3c173838ad841c92602992f404783181b88 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 26 Aug 2014 14:32:14 -0700
Subject: [PATCH 0538/2053] default ilsvrc solving to GPU

---
 examples/imagenet/alexnet_solver.prototxt  | 1 +
 examples/imagenet/imagenet_solver.prototxt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/examples/imagenet/alexnet_solver.prototxt b/examples/imagenet/alexnet_solver.prototxt
index 8581e99c6a7..a3aeae07dc7 100644
--- a/examples/imagenet/alexnet_solver.prototxt
+++ b/examples/imagenet/alexnet_solver.prototxt
@@ -11,3 +11,4 @@ momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "caffe_alexnet_train"
+solver_mode: GPU
diff --git a/examples/imagenet/imagenet_solver.prototxt b/examples/imagenet/imagenet_solver.prototxt
index aedec4104a6..1ad40fe71e7 100644
--- a/examples/imagenet/imagenet_solver.prototxt
+++ b/examples/imagenet/imagenet_solver.prototxt
@@ -11,3 +11,4 @@ momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "caffe_imagenet_train"
+solver_mode: GPU

From cd40ec5974194f21197621a8f26fd6996be56fcf Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Wed, 27 Aug 2014 15:40:14 -0700
Subject: [PATCH 0539/2053] fix layer_factory.cpp bug: there should be no
 ifdefs

---
 src/caffe/layer_factory.cpp | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index d18d24627a9..1b34702c0c4 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,6 +1,3 @@
-#ifndef CAFFE_LAYER_FACTORY_HPP_
-#define CAFFE_LAYER_FACTORY_HPP_
-
 #include <string>
 
 #include "caffe/layer.hpp"
@@ -98,5 +95,3 @@ template Layer<float>* GetLayer(const LayerParameter& param);
 template Layer<double>* GetLayer(const LayerParameter& param);
 
 }  // namespace caffe
-
-#endif  // CAFFE_LAYER_FACTORY_HPP_

From df7fe6f581d5de66dc5196fef53852eb9f20ff37 Mon Sep 17 00:00:00 2001
From: netheril96 <netheril96@gmail.com>
Date: Thu, 28 Aug 2014 11:39:28 +0800
Subject: [PATCH 0540/2053] create_imagenet.sh updated to new syntax

---
 examples/imagenet/create_imagenet.sh | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index 24f961bee4c..79cb17c4692 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -36,17 +36,21 @@ fi
 echo "Creating train leveldb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
+    --resize_height=$RESIZE_HEIGHT \
+    --resize_width=$RESIZE_WIDTH \
+    --shuffle \
     $TRAIN_DATA_ROOT \
     $DATA/train.txt \
-    ilsvrc12_train_leveldb 1
-    $RESIZE_HEIGHT $RESIZE_WIDTH
+    ilsvrc12_train_leveldb
 
 echo "Creating val leveldb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
+    --resize_height=$RESIZE_HEIGHT \
+    --resize_width=$RESIZE_WIDTH \
+    --shuffle \
     $VAL_DATA_ROOT \
     $DATA/val.txt \
-    ilsvrc12_val_leveldb 1
-    $RESIZE_HEIGHT $RESIZE_WIDTH
+    ilsvrc12_val_leveldb
 
 echo "Done."

From 6249d3e2c51578821131ef4bc683640f6d3390f8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 26 Aug 2014 09:51:19 -0700
Subject: [PATCH 0541/2053] clarify project origin

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index e7a819824de..c86116d9f54 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,7 +4,7 @@ layout: default
 # Caffe
 
 Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
-It was created by [Yangqing Jia](http://daggerfs.com), and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+It was created by [Yangqing Jia](http://daggerfs.com) during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
 Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!

From c01f07a9362a8726046267fe22ad97fbe5fae5a0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ozan=20=C3=87a=C4=9Flayan?= <ozancag@gmail.com>
Date: Mon, 25 Aug 2014 15:16:34 +0300
Subject: [PATCH 0542/2053] [docs] Update installation docs to include Fedora

- All the instructions mentioned for CentOS/RHEL are also valid for Fedora Linux so add it.
- The package name for atlas is atlas-devel not libatlas-devel for CentOS/RHEL/Fedora so fix it.
- Add 'sudo' prefix to missing places to be consistent with the rest of the document.
- Add instructions for installing python headers in Fedora into the python support section.
- Update Linux installation section to reflect Fedora, refactor a bit.
---
 docs/installation.md | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index f9731f8fb7f..b9360a9c631 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -39,7 +39,7 @@ The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
     + Ubuntu: `sudo apt-get install libatlas-base-dev`
-    + CentOS/RHEL: `sudo yum install libatlas-devel`
+    + CentOS/RHEL/Fedora: `sudo yum install atlas-devel`
     + OS X: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html).
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
@@ -60,7 +60,9 @@ You can install the dependencies with
 
 but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
-For **Ubuntu**, if you use the default Python you will need to `apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
+For **Ubuntu**, if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
+
+For **Fedora**, if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the wrapper.
 
 For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
 
@@ -82,15 +84,21 @@ On **Ubuntu**, most of the dependencies can be installed with
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
 
-And on **CentOS / RHEL**, you can install via yum with
-
-    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
-
 and for **Ubuntu 14.04** the rest of the dependencies can be installed with
 
     sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
 
-For **Ubuntu 12.04 and CentOS / RHEL** the only exceptions to package installation are the Google flags library, Google logging library, and LMDB. To install these, do:
+Keep reading to find out how to manually build and install the Google flags library, Google logging library and LMDB on **Ubuntu 12.04**.
+
+On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
+
+    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
+    
+The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
+
+    sudo yum install gflags-devel glog-devel lmdb
+
+**Finally** in case you couldn't find those 3 libraries in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora**
 
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz

From 46cbb649b498bc49cc1fc73aab75ae4c9ba10419 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 19:17:07 +0800
Subject: [PATCH 0543/2053] Replace pthread with boost::thread

---
 Makefile                                |  2 +-
 include/caffe/internal_thread.hpp       | 32 +++++++++-------------
 src/caffe/CMakeLists.txt                |  2 +-
 src/caffe/internal_thread.cpp           | 36 +++++++++++++++++++++++++
 src/caffe/layers/data_layer.cpp         |  4 +--
 src/caffe/layers/image_data_layer.cpp   |  4 +--
 src/caffe/layers/window_data_layer.cpp  |  4 +--
 src/caffe/test/test_internal_thread.cpp | 23 ++++++++++++++++
 8 files changed, 80 insertions(+), 27 deletions(-)
 create mode 100644 src/caffe/internal_thread.cpp
 create mode 100644 src/caffe/test/test_internal_thread.cpp

diff --git a/Makefile b/Makefile
index 2c3ce5415bd..9c420249887 100644
--- a/Makefile
+++ b/Makefile
@@ -161,7 +161,7 @@ endif
 LIBRARIES += \
 	glog gflags pthread protobuf leveldb snappy \
 	lmdb \
-	boost_system \
+	boost_system boost_thread \
 	hdf5_hl hdf5 \
 	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index bb0f932566d..9989936e519 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -1,42 +1,36 @@
 #ifndef CAFFE_INTERNAL_THREAD_HPP_
 #define CAFFE_INTERNAL_THREAD_HPP_
 
-#include <pthread.h>
+#include <boost/thread.hpp>
+
+#include "caffe/common.hpp"
 
 namespace caffe {
 
 /**
- * Virutal class encapsulate pthread for use in base class
- * The child class will acquire the ability to run a single pthread,
+ * Virutal class encapsulate boost::thread for use in base class
+ * The child class will acquire the ability to run a single thread,
  * by reimplementing the virutal function InternalThreadEntry.
  */
 class InternalThread {
  public:
-  InternalThread() {}
-  virtual ~InternalThread() {}
+  InternalThread() : thread_(NULL) {}
+  virtual ~InternalThread();
 
   /** Returns true if the thread was successfully started. **/
-  bool StartInternalThread() {
-    return (pthread_create(&_thread, NULL, InternalThreadEntryFunc, this) == 0);
-  }
+  bool StartInternalThread();
 
   /** Will not return until the internal thread has exited. */
-  bool WaitForInternalThreadToExit() {
-    return pthread_join(_thread, NULL);
-  }
+  bool WaitForInternalThreadToExit();
+
+  bool is_started() const { return thread_ != NULL && thread_->joinable(); }
 
  protected:
   /* Implement this method in your subclass
       with the code you want your thread to run. */
-  virtual void InternalThreadEntry() = 0;
-
- private:
-  static void * InternalThreadEntryFunc(void * This) {
-    reinterpret_cast<InternalThread *>(This)->InternalThreadEntry();
-    return NULL;
-  }
+  virtual void InternalThreadEntry() {}
 
-  pthread_t _thread;
+  boost::thread* thread_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index ec756bede71..fa30eb5870f 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -57,7 +57,7 @@ find_package(LMDB REQUIRED)
 include_directories(${LMDB_INCLUDE_DIR})
 
 #    Boost
-find_package(Boost 1.46 COMPONENTS system REQUIRED)
+find_package(Boost 1.46 COMPONENTS system thread REQUIRED)
 include_directories( ${Boost_INCLUDE_DIR} )
 link_directories( ${Boost_LIBRARY_DIRS} )
 
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
new file mode 100644
index 00000000000..99342090882
--- /dev/null
+++ b/src/caffe/internal_thread.cpp
@@ -0,0 +1,36 @@
+#include <iostream>
+
+#include "caffe/internal_thread.hpp"
+
+namespace caffe {
+using namespace std;
+
+InternalThread::~InternalThread() {
+  WaitForInternalThreadToExit();
+  if (thread_ != NULL) {
+    delete thread_;
+  }
+}
+
+bool InternalThread::StartInternalThread() {
+  try {
+    thread_ = new boost::thread(&InternalThread::InternalThreadEntry, this);
+  } catch (...) {
+    return false;
+  }
+  return true;
+}
+
+/** Will not return until the internal thread has exited. */
+bool InternalThread::WaitForInternalThreadToExit() {
+  if (is_started()) {
+    try {
+      thread_->join();
+    } catch (...) {
+      return false;
+    }
+  }
+  return true;
+}
+
+}  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 0cc2769bfc9..a09eb34e1cf 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -244,12 +244,12 @@ void DataLayer<Dtype>::CreatePrefetchThread() {
 
   data_transformer_.InitRand();
 
-  CHECK(StartInternalThread()) << "Pthread execution failed";
+  CHECK(StartInternalThread()) << "Thread execution failed";
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
+  CHECK(!WaitForInternalThreadToExit()) << "Thread joining failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index dc39e694bbb..8810a381f7f 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -159,7 +159,7 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
   data_transformer_.InitRand();
 
   // Create the thread.
-  CHECK(StartInternalThread()) << "Pthread execution failed";
+  CHECK(StartInternalThread()) << "Thread execution failed";
 }
 
 template <typename Dtype>
@@ -172,7 +172,7 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 
 template <typename Dtype>
 void ImageDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
+  CHECK(!WaitForInternalThreadToExit()) << "Thread joining failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index bed03d8e8d2..ebd855d48b7 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -408,12 +408,12 @@ void WindowDataLayer<Dtype>::CreatePrefetchThread() {
     prefetch_rng_.reset();
   }
   // Create the thread.
-  CHECK(StartInternalThread()) << "Pthread execution failed.";
+  CHECK(StartInternalThread()) << "Thread execution failed.";
 }
 
 template <typename Dtype>
 void WindowDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Thread joining failed.";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_internal_thread.cpp b/src/caffe/test/test_internal_thread.cpp
new file mode 100644
index 00000000000..31882b6db1d
--- /dev/null
+++ b/src/caffe/test/test_internal_thread.cpp
@@ -0,0 +1,23 @@
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+#include "caffe/internal_thread.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+
+class InternalThreadTest : public ::testing::Test {};
+
+TEST_F(InternalThreadTest, TestStartAndExit) {
+  InternalThread thread;
+  EXPECT_FALSE(thread.is_started());
+  EXPECT_TRUE(thread.StartInternalThread());
+  EXPECT_TRUE(thread.is_started());
+  EXPECT_TRUE(thread.WaitForInternalThreadToExit());
+  EXPECT_FALSE(thread.is_started());
+}
+
+}  // namespace caffe
+

From 425f69ff5bf8c87c7a52bbee2904e9cf6b22d39e Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 00:05:49 +0800
Subject: [PATCH 0544/2053] Add boost thread in the travis install script

---
 scripts/travis/travis_install.sh | 2 +-
 src/caffe/CMakeLists.txt         | 9 +++++----
 src/caffe/internal_thread.cpp    | 3 ---
 3 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index f6a357b5c57..753bae879af 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -6,7 +6,7 @@ sudo apt-get install \
     wget git curl \
     python-dev python-numpy \
     libleveldb-dev libsnappy-dev libopencv-dev \
-    libboost-dev libboost-system-dev libboost-python-dev \
+    libboost-dev libboost-system-dev libboost-python-dev libboost-thread-dev \
     libprotobuf-dev protobuf-compiler \
     libatlas-dev libatlas-base-dev \
     libhdf5-serial-dev \
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index fa30eb5870f..139185b52dc 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -99,14 +99,15 @@ if(NOT CPU_ONLY)
 endif()
 
 target_link_libraries(caffe proto
-        ${GLOG_LIBRARIES}
+        ${BLAS_LIBRARIES}
+        ${Boost_LIBRARIES}
+        ${CMAKE_THREAD_LIBS_INIT}
         ${GFLAGS_LIBRARIES}
+        ${GLOG_LIBRARIES}
         ${HDF5_LIBRARIES}
-        ${OpenCV_LIBS}
         ${LEVELDB_LIBS}
         ${LMDB_LIBRARIES}
-        ${BLAS_LIBRARIES}
-        ${CMAKE_THREAD_LIBS_INIT}
+        ${OpenCV_LIBS}
 )
 
 #set output directory
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 99342090882..b89aa2ef061 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,9 +1,6 @@
-#include <iostream>
-
 #include "caffe/internal_thread.hpp"
 
 namespace caffe {
-using namespace std;
 
 InternalThread::~InternalThread() {
   WaitForInternalThreadToExit();

From f2a291c0e92fbde0e1b29d01ffc62b0a35e1fbea Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 00:16:13 +0800
Subject: [PATCH 0545/2053] The return value of WaitForInternalThreadToExit has
 reversed

---
 src/caffe/layers/data_layer.cpp        | 2 +-
 src/caffe/layers/image_data_layer.cpp  | 2 +-
 src/caffe/layers/window_data_layer.cpp | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index a09eb34e1cf..a815c4a6a16 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -249,7 +249,7 @@ void DataLayer<Dtype>::CreatePrefetchThread() {
 
 template <typename Dtype>
 void DataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Thread joining failed";
+  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 8810a381f7f..ce8338bc028 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -172,7 +172,7 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 
 template <typename Dtype>
 void ImageDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Thread joining failed";
+  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index ebd855d48b7..82bee0ecd19 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -413,7 +413,7 @@ void WindowDataLayer<Dtype>::CreatePrefetchThread() {
 
 template <typename Dtype>
 void WindowDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Thread joining failed.";
+  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed.";
 }
 
 template <typename Dtype>

From eafe24961d9178ce5a4df4042b7a7a4b8476e524 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 28 Aug 2014 16:12:01 -0700
Subject: [PATCH 0546/2053] ignore caffe generated files and stop ignoring
 examples

- ignore models and snapshots, means, and input databases
- excluding exceptions to commits is easier than including exhaustively
---
 .gitignore | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index d162cb42bb0..07eebe34591 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,11 +47,16 @@ python/caffe/proto/
 # User's build configuration
 Makefile.config
 
-# Data and examples are either
+# Data and models are either
 # 1. reference, and not casually committed
 # 2. custom, and live on their own unless they're deliberated contributed
 data/*
-examples/*
+*model
+*_iter_*
+*.solverstate
+*.binaryproto
+*leveldb
+*lmdb
 
 # Generated documentation
 docs/_site

From e79380631f036722c890430eef4eb6ebafb2a477 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 28 Aug 2014 16:34:57 -0700
Subject: [PATCH 0547/2053] set examples paths relative to root

---
 examples/cifar10/cifar10_full_solver.prototxt     |  4 ++--
 examples/cifar10/cifar10_full_solver_lr1.prototxt |  4 ++--
 examples/cifar10/cifar10_full_solver_lr2.prototxt |  4 ++--
 examples/cifar10/cifar10_full_train_test.prototxt |  8 ++++----
 examples/cifar10/cifar10_quick_solver.prototxt    |  4 ++--
 .../cifar10/cifar10_quick_solver_lr1.prototxt     |  4 ++--
 .../cifar10/cifar10_quick_train_test.prototxt     |  8 ++++----
 examples/cifar10/convert_cifar_data.cpp           |  4 ++--
 examples/cifar10/create_cifar10.sh                | 13 ++++++-------
 .../pascal_finetune_solver.prototxt               | 14 ++++++++++++++
 .../pascal_finetune_train.prototxt                |  4 ++--
 .../pascal_finetune_val.prototxt                  |  4 ++--
 examples/imagenet/alexnet_solver.prototxt         |  4 ++--
 examples/imagenet/alexnet_train_val.prototxt      |  8 ++++----
 examples/imagenet/create_imagenet.sh              | 13 +++++++------
 examples/imagenet/get_caffe_alexnet_model.sh      |  2 +-
 .../imagenet/get_caffe_rcnn_imagenet_model.sh     |  3 ++-
 .../get_caffe_reference_imagenet_model.sh         |  2 +-
 examples/imagenet/imagenet_solver.prototxt        |  4 ++--
 examples/imagenet/imagenet_train_val.prototxt     |  8 ++++----
 examples/imagenet/make_imagenet_mean.sh           |  6 ++----
 examples/imagenet/resume_training.sh              |  8 +++-----
 examples/imagenet/time_imagenet.sh                |  6 +++---
 examples/imagenet/train_alexnet.sh                |  4 +---
 examples/imagenet/train_imagenet.sh               |  4 +---
 examples/mnist/create_mnist.sh                    | 15 +++++++++------
 examples/mnist/lenet_consolidated_solver.prototxt |  8 ++++----
 examples/mnist/lenet_solver.prototxt              |  4 ++--
 examples/mnist/lenet_train_test.prototxt          |  4 ++--
 examples/mnist/mnist_autoencoder.prototxt         |  4 ++--
 examples/mnist/mnist_autoencoder_solver.prototxt  |  4 ++--
 examples/mnist/train_lenet.sh                     |  4 +---
 examples/mnist/train_lenet_consolidated.sh        |  5 ++---
 examples/mnist/train_mnist_autoencoder.sh         |  6 +++---
 .../pascal_finetune_solver.prototxt               | 14 --------------
 35 files changed, 104 insertions(+), 111 deletions(-)
 create mode 100644 examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
 rename examples/{pascal-finetuning => finetune_pascal_detection}/pascal_finetune_train.prototxt (97%)
 rename examples/{pascal-finetuning => finetune_pascal_detection}/pascal_finetune_val.prototxt (97%)
 delete mode 100644 examples/pascal-finetuning/pascal_finetune_solver.prototxt

diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt
index d8e0bb27c44..f30b3986142 100644
--- a/examples/cifar10/cifar10_full_solver.prototxt
+++ b/examples/cifar10/cifar10_full_solver.prototxt
@@ -2,7 +2,7 @@
 # then another factor of 10 after 10 more epochs (5000 iters)
 
 # The train/test net protocol buffer definition
-net: "cifar10_full_train_test.prototxt"
+net: "examples/cifar10/cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -21,6 +21,6 @@ display: 200
 max_iter: 60000
 # snapshot intermediate results
 snapshot: 10000
-snapshot_prefix: "cifar10_full"
+snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_solver_lr1.prototxt b/examples/cifar10/cifar10_full_solver_lr1.prototxt
index 746f4fba15a..59bc5721f4c 100644
--- a/examples/cifar10/cifar10_full_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr1.prototxt
@@ -2,7 +2,7 @@
 # then another factor of 10 after 10 more epochs (5000 iters)
 
 # The train/test net protocol buffer definition
-net: "cifar10_full_train_test.prototxt"
+net: "examples/cifar10/cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -21,6 +21,6 @@ display: 200
 max_iter: 65000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "cifar10_full"
+snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_solver_lr2.prototxt b/examples/cifar10/cifar10_full_solver_lr2.prototxt
index 5a549ffc96d..d4ed5d8e041 100644
--- a/examples/cifar10/cifar10_full_solver_lr2.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr2.prototxt
@@ -2,7 +2,7 @@
 # then another factor of 10 after 10 more epochs (5000 iters)
 
 # The train/test net protocol buffer definition
-net: "cifar10_full_train_test.prototxt"
+net: "examples/cifar10/cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -21,6 +21,6 @@ display: 200
 max_iter: 70000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "cifar10_full"
+snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index c01fef46ac0..ffecceb2a39 100644
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -5,10 +5,10 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "cifar10-leveldb/cifar-train-leveldb"
+    source: "examples/cifar10/cifar10_train_leveldb"
     batch_size: 100
     transform_param {
-      mean_file: "mean.binaryproto"
+      mean_file: "examples/cifar10/mean.binaryproto"
     }
   }
   include: { phase: TRAIN }
@@ -19,10 +19,10 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "cifar10-leveldb/cifar-test-leveldb"
+    source: "examples/cifar10/cifar10_test_leveldb"
     batch_size: 100
     transform_param {
-      mean_file: "mean.binaryproto"
+      mean_file: "examples/cifar10/mean.binaryproto"
     }
   }
   include: { phase: TEST }
diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index cdd0722b3a0..14b4401ba16 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -1,7 +1,7 @@
 # reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
 
 # The train/test net protocol buffer definition
-net: "cifar10_quick_train_test.prototxt"
+net: "examples/cifar10/cifar10_quick_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -20,6 +20,6 @@ display: 100
 max_iter: 4000
 # snapshot intermediate results
 snapshot: 4000
-snapshot_prefix: "cifar10_quick"
+snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_quick_solver_lr1.prototxt b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
index 2ed54ad980f..d3af70c05e7 100644
--- a/examples/cifar10/cifar10_quick_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
@@ -1,7 +1,7 @@
 # reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
 
 # The train/test net protocol buffer definition
-net: "cifar10_quick_train_test.prototxt"
+net: "examples/cifar10/cifar10_quick_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -20,6 +20,6 @@ display: 100
 max_iter: 5000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "cifar10_quick"
+snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index 779dacf6758..f708798c71c 100644
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -5,10 +5,10 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "cifar10-leveldb/cifar-train-leveldb"
+    source: "examples/cifar10/cifar10_train_leveldb"
     batch_size: 100
     transform_param {
-      mean_file: "mean.binaryproto"
+      mean_file: "examples/cifar10/mean.binaryproto"
     }
   }
   include: { phase: TRAIN }
@@ -19,10 +19,10 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "cifar10-leveldb/cifar-test-leveldb"
+    source: "examples/cifar10/cifar10_test_leveldb"
     batch_size: 100
     transform_param {
-      mean_file: "mean.binaryproto"
+      mean_file: "examples/cifar10/mean.binaryproto"
     }
   }
   include: { phase: TEST }
diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 2d5589bd30a..90ecb6d9a88 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -48,7 +48,7 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
   LOG(INFO) << "Writing Training data";
   leveldb::DB* train_db;
   leveldb::Status status;
-  status = leveldb::DB::Open(options, output_folder + "/cifar-train-leveldb",
+  status = leveldb::DB::Open(options, output_folder + "/cifar10_train_leveldb",
       &train_db);
   CHECK(status.ok()) << "Failed to open leveldb.";
   for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
@@ -71,7 +71,7 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
 
   LOG(INFO) << "Writing Testing data";
   leveldb::DB* test_db;
-  CHECK(leveldb::DB::Open(options, output_folder + "/cifar-test-leveldb",
+  CHECK(leveldb::DB::Open(options, output_folder + "/cifar10_test_leveldb",
       &test_db).ok()) << "Failed to open leveldb.";
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index 85757f345ef..ad5038e0c3e 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -1,19 +1,18 @@
 #!/usr/bin/env sh
 # This script converts the cifar data into leveldb format.
 
-EXAMPLES=../../build/examples/cifar10
-DATA=../../data/cifar10
-TOOLS=../../build/tools
+EXAMPLE=examples/cifar10
+DATA=data/cifar10
 
 echo "Creating leveldb..."
 
-rm -rf cifar10-leveldb
-mkdir cifar10-leveldb
+rm -rf $EXAMPLE/cifar10_train_leveldb $EXAMPLE/cifar10_test_leveldb
 
-$EXAMPLES/convert_cifar_data.bin $DATA ./cifar10-leveldb
+./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE
 
 echo "Computing image mean..."
 
-$TOOLS/compute_image_mean.bin ./cifar10-leveldb/cifar-train-leveldb mean.binaryproto
+./build/tools/compute_image_mean $EXAMPLE/cifar10_train_leveldb \
+  $EXAMPLE/mean.binaryproto
 
 echo "Done."
diff --git a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
new file mode 100644
index 00000000000..54ba6350448
--- /dev/null
+++ b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
@@ -0,0 +1,14 @@
+train_net: "examples/finetuning_pascal_detection/pascal_finetune_train.prototxt"
+test_net: "examples/finetuning_pascal_detection/pascal_finetune_val.prototxt"
+test_iter: 100
+test_interval: 1000
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+stepsize: 20000
+display: 20
+max_iter: 100000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/finetuning_pascal_detection/pascal_det_finetune"
diff --git a/examples/pascal-finetuning/pascal_finetune_train.prototxt b/examples/finetune_pascal_detection/pascal_finetune_train.prototxt
similarity index 97%
rename from examples/pascal-finetuning/pascal_finetune_train.prototxt
rename to examples/finetune_pascal_detection/pascal_finetune_train.prototxt
index dfc60fe4b8a..ae7ee4ccf88 100644
--- a/examples/pascal-finetuning/pascal_finetune_train.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_train.prototxt
@@ -5,8 +5,8 @@ layers {
   top: "data"
   top: "label"
   window_data_param {
-    source: "window_file_2007_trainval.txt"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 128
     crop_size: 227
     mirror: true
diff --git a/examples/pascal-finetuning/pascal_finetune_val.prototxt b/examples/finetune_pascal_detection/pascal_finetune_val.prototxt
similarity index 97%
rename from examples/pascal-finetuning/pascal_finetune_val.prototxt
rename to examples/finetune_pascal_detection/pascal_finetune_val.prototxt
index 91ded585d85..30dd164c4fa 100644
--- a/examples/pascal-finetuning/pascal_finetune_val.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_val.prototxt
@@ -5,8 +5,8 @@ layers {
   top: "data"
   top: "label"
   window_data_param {
-    source: "window_file_2007_test.txt"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
     batch_size: 128
     crop_size: 227
     mirror: true
diff --git a/examples/imagenet/alexnet_solver.prototxt b/examples/imagenet/alexnet_solver.prototxt
index a3aeae07dc7..94bda7f36a5 100644
--- a/examples/imagenet/alexnet_solver.prototxt
+++ b/examples/imagenet/alexnet_solver.prototxt
@@ -1,4 +1,4 @@
-net: "alexnet_train_val.prototxt"
+net: "examples/imagenet/alexnet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
@@ -10,5 +10,5 @@ max_iter: 450000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "caffe_alexnet_train"
+snapshot_prefix: "examples/imagenet/caffe_alexnet"
 solver_mode: GPU
diff --git a/examples/imagenet/alexnet_train_val.prototxt b/examples/imagenet/alexnet_train_val.prototxt
index 8ffdb7a8f69..b0f5815e007 100644
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -5,11 +5,11 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "ilsvrc12_train_leveldb"
+    source: "examples/imagenet/ilsvrc12_train_leveldb"
     batch_size: 256
     transform_param {
       crop_size: 227
-      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
       mirror: true
     }
   }
@@ -21,11 +21,11 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "ilsvrc12_val_leveldb"
+    source: "examples/imagenet/ilsvrc12_val_leveldb"
     batch_size: 50
     transform_param {
       crop_size: 227
-      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
       mirror: false
     }
   }
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index 79cb17c4692..a286b8fe74c 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -2,8 +2,9 @@
 # Create the imagenet leveldb inputs
 # N.B. set the path to the imagenet train + val data dirs
 
-TOOLS=../../build/tools
-DATA=../../data/ilsvrc12
+EXAMPLE=examples/imagenet
+DATA=data/ilsvrc12
+TOOLS=build/tools
 
 TRAIN_DATA_ROOT=/path/to/imagenet/train/
 VAL_DATA_ROOT=/path/to/imagenet/val/
@@ -35,22 +36,22 @@ fi
 
 echo "Creating train leveldb..."
 
-GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
+GLOG_logtostderr=1 $TOOLS/convert_imageset \
     --resize_height=$RESIZE_HEIGHT \
     --resize_width=$RESIZE_WIDTH \
     --shuffle \
     $TRAIN_DATA_ROOT \
     $DATA/train.txt \
-    ilsvrc12_train_leveldb
+    $EXAMPLE/ilsvrc12_train_leveldb
 
 echo "Creating val leveldb..."
 
-GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
+GLOG_logtostderr=1 $TOOLS/convert_imageset \
     --resize_height=$RESIZE_HEIGHT \
     --resize_width=$RESIZE_WIDTH \
     --shuffle \
     $VAL_DATA_ROOT \
     $DATA/val.txt \
-    ilsvrc12_val_leveldb
+    $EXAMPLE/ilsvrc12_val_leveldb
 
 echo "Done."
diff --git a/examples/imagenet/get_caffe_alexnet_model.sh b/examples/imagenet/get_caffe_alexnet_model.sh
index b680a32d1cc..7312ed93070 100755
--- a/examples/imagenet/get_caffe_alexnet_model.sh
+++ b/examples/imagenet/get_caffe_alexnet_model.sh
@@ -23,6 +23,6 @@ fi
 
 echo "Downloading..."
 
-wget http://dl.caffe.berkeleyvision.org/caffe_alexnet_model
+wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
 
 echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
index c7f36edcbf0..9a8d0a155a0 100755
--- a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
+++ b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
@@ -23,5 +23,6 @@ fi
 
 echo "Downloading..."
 
-wget http://dl.caffe.berkeleyvision.org/$MODEL
+wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
+
 echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/get_caffe_reference_imagenet_model.sh b/examples/imagenet/get_caffe_reference_imagenet_model.sh
index 85007994051..f687ebfa79e 100755
--- a/examples/imagenet/get_caffe_reference_imagenet_model.sh
+++ b/examples/imagenet/get_caffe_reference_imagenet_model.sh
@@ -23,6 +23,6 @@ fi
 
 echo "Downloading..."
 
-wget http://dl.caffe.berkeleyvision.org/$MODEL
+wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
 
 echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/imagenet_solver.prototxt b/examples/imagenet/imagenet_solver.prototxt
index 1ad40fe71e7..5b5be4bb8a9 100644
--- a/examples/imagenet/imagenet_solver.prototxt
+++ b/examples/imagenet/imagenet_solver.prototxt
@@ -1,4 +1,4 @@
-net: "imagenet_train_val.prototxt"
+net: "examples/imagenet/imagenet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
@@ -10,5 +10,5 @@ max_iter: 450000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "caffe_imagenet_train"
+snapshot_prefix: "examples/imagenet/caffe_imagenet"
 solver_mode: GPU
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/examples/imagenet/imagenet_train_val.prototxt
index 5f9c83949f5..766d546a385 100644
--- a/examples/imagenet/imagenet_train_val.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -5,11 +5,11 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "ilsvrc12_train_leveldb"
+    source: "examples/imagenet/ilsvrc12_train_leveldb"
     batch_size: 256
     transform_param {
       crop_size: 227
-      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
       mirror: true
     }
   }
@@ -21,11 +21,11 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "ilsvrc12_val_leveldb"
+    source: "examples/imagenet/ilsvrc12_val_leveldb"
     batch_size: 50
     transform_param {
       crop_size: 227
-      mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
       mirror: false
     }
   }
diff --git a/examples/imagenet/make_imagenet_mean.sh b/examples/imagenet/make_imagenet_mean.sh
index f3d69cf2f64..d3d0c9af5d2 100755
--- a/examples/imagenet/make_imagenet_mean.sh
+++ b/examples/imagenet/make_imagenet_mean.sh
@@ -2,9 +2,7 @@
 # Compute the mean image from the imagenet training leveldb
 # N.B. this is available in data/ilsvrc12
 
-TOOLS=../../build/tools
-DATA=../../data/ilsvrc12
-
-$TOOLS/compute_image_mean.bin ilsvrc12_train_leveldb $DATA/imagenet_mean.binaryproto
+./build/tools/compute_image_mean examples/imagenet/ilsvrc12_train_leveldb \
+  data/ilsvrc12/imagenet_mean.binaryproto
 
 echo "Done."
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index 9e00d93c469..3c964b56ffc 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -1,9 +1,7 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
-
-$TOOLS/caffe train\
-    --solver=imagenet_solver.prototxt \
-    --snapshot=caffe_imagenet_train_10000.solverstate
+./build/tools/caffe train \
+    --solver=examples/imagenet/imagenet_solver.prototxt \
+    --snapshot=examples/imagenet/caffe_imagenet_10000.solverstate
 
 echo "Done."
diff --git a/examples/imagenet/time_imagenet.sh b/examples/imagenet/time_imagenet.sh
index c448b4977ce..3f46e0e0f97 100755
--- a/examples/imagenet/time_imagenet.sh
+++ b/examples/imagenet/time_imagenet.sh
@@ -1,7 +1,5 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
-
 if [ -z "$1" ]; then
   echo "Using CPU! To time GPU mode, use:"
   echo "    ./time_imagenet.sh <device ID>"
@@ -12,6 +10,8 @@ else
   GPU="--gpu=$1"
 fi
 
-$TOOLS/caffe time --model=imagenet_train_val.prototxt ${GPU}
+./build/tools/caffe time \
+  --model=examples/imagenet/imagenet_train_val.prototxt \
+  ${GPU}
 
 echo "Done."
diff --git a/examples/imagenet/train_alexnet.sh b/examples/imagenet/train_alexnet.sh
index 6a7c0577413..1ddcbeee4b0 100755
--- a/examples/imagenet/train_alexnet.sh
+++ b/examples/imagenet/train_alexnet.sh
@@ -1,7 +1,5 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
-
-$TOOLS/caffe train --solver=alexnet_solver.prototxt
+./build/tools/caffe train --solver=examples/imagenet/alexnet_solver.prototxt
 
 echo "Done."
diff --git a/examples/imagenet/train_imagenet.sh b/examples/imagenet/train_imagenet.sh
index 008b96c01a1..cba2ad59581 100755
--- a/examples/imagenet/train_imagenet.sh
+++ b/examples/imagenet/train_imagenet.sh
@@ -1,7 +1,5 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
-
-$TOOLS/caffe train --solver=imagenet_solver.prototxt
+./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt
 
 echo "Done."
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index ae75bec2ab2..ed6e36903c2 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -1,15 +1,18 @@
 #!/usr/bin/env sh
 # This script converts the mnist data into leveldb format.
 
-EXAMPLES=../../build/examples/mnist
-DATA=../../data/mnist
+EXAMPLE=examples/mnist
+DATA=data/mnist
+BUILD=build/examples/mnist
 
 echo "Creating leveldb..."
 
-rm -rf mnist-train-leveldb
-rm -rf mnist-test-leveldb
+rm -rf mnist_train_leveldb
+rm -rf mnist_test_leveldb
 
-$EXAMPLES/convert_mnist_data.bin $DATA/train-images-idx3-ubyte $DATA/train-labels-idx1-ubyte mnist-train-leveldb
-$EXAMPLES/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte $DATA/t10k-labels-idx1-ubyte mnist-test-leveldb
+$BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \
+  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_leveldb
+$BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \
+  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_leveldb
 
 echo "Done."
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 2e69589c67f..8ff593f2781 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -22,7 +22,7 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "lenet"
+snapshot_prefix: "examples/mnist/lenet"
 # Set a random_seed for repeatable results.
 # (For results that vary due to random initialization, comment out the below
 # line, or set to a negative integer -- e.g. "random_seed: -1")
@@ -50,7 +50,7 @@ net_param {
     top: "data"
     top: "label"
     data_param {
-      source: "mnist-train-leveldb"
+      source: "examples/mnist/mnist_train_leveldb"
       batch_size: 64
       transform_param {
         scale: 0.00390625
@@ -64,7 +64,7 @@ net_param {
     top: "data"
     top: "label"
     data_param {
-      source: "mnist-test-leveldb"
+      source: "examples/mnist/mnist_test_leveldb"
       batch_size: 100
       transform_param {
         scale: 0.00390625
@@ -81,7 +81,7 @@ net_param {
     top: "data"
     top: "label"
     data_param {
-      source: "mnist-train-leveldb"
+      source: "examples/mnist/mnist_train_leveldb"
       batch_size: 100
       transform_param {
         scale: 0.00390625
diff --git a/examples/mnist/lenet_solver.prototxt b/examples/mnist/lenet_solver.prototxt
index a3b33090472..2dfbc834f41 100644
--- a/examples/mnist/lenet_solver.prototxt
+++ b/examples/mnist/lenet_solver.prototxt
@@ -1,5 +1,5 @@
 # The train/test net protocol buffer definition
-net: "lenet_train_test.prototxt"
+net: "examples/mnist/lenet_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -20,6 +20,6 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "lenet"
+snapshot_prefix: "examples/mnist/lenet"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index 578641d5b2d..8369af1daa0 100644
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -5,7 +5,7 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "mnist-train-leveldb"
+    source: "examples/mnist/mnist_train_leveldb"
     batch_size: 64
     transform_param {
       scale: 0.00390625
@@ -19,7 +19,7 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "mnist-test-leveldb"
+    source: "examples/mnist/mnist_test_leveldb"
     batch_size: 100
     transform_param {
       scale: 0.00390625
diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index 3af6b7afcb3..d0abc685f0b 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -4,7 +4,7 @@ layers {
   name: "data"
   type: DATA
   data_param {
-    source: "mnist-train-leveldb"
+    source: "examples/mnist/mnist_train_leveldb"
     batch_size: 100
     transform_param {
       scale: 0.0039215684
@@ -17,7 +17,7 @@ layers {
   name: "data"
   type: DATA
   data_param {
-    source: "mnist-test-leveldb"
+    source: "examples/mnist/mnist_test_leveldb"
     batch_size: 100
     transform_param {
       scale: 0.0039215684
diff --git a/examples/mnist/mnist_autoencoder_solver.prototxt b/examples/mnist/mnist_autoencoder_solver.prototxt
index ae1ddebccd2..af1202fc1fd 100644
--- a/examples/mnist/mnist_autoencoder_solver.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver.prototxt
@@ -1,4 +1,4 @@
-net: "mnist_autoencoder.prototxt"
+net: "examples/mnist/mnist_autoencoder.prototxt"
 test_iter: 50
 test_interval: 100
 test_compute_loss: true
@@ -8,7 +8,7 @@ display: 20
 max_iter: 4000000
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "mnist_autoencoder_train"
+snapshot_prefix: "examples/mnist/mnist_autoencoder"
 momentum: 0.9
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index b93e48fb629..1b6bf7d978d 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -1,5 +1,3 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
-
-$TOOLS/caffe train --solver=lenet_solver.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
index 83fe895ba0b..c855467897e 100755
--- a/examples/mnist/train_lenet_consolidated.sh
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -1,5 +1,4 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
-
-$TOOLS/caffe train --solver=lenet_consolidated_solver.prototxt
+./build/tools/caffe train \
+  --solver=examples/mnist/lenet_consolidated_solver.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index 628c74b969a..cfd67e82fda 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
-TOOLS=../../build/tools
+#!/usr/bin/env sh
 
-$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver.prototxt
+./build/tools/caffe train \
+  --solver=examples/mnist/mnist_autoencoder_solver.prototxt
diff --git a/examples/pascal-finetuning/pascal_finetune_solver.prototxt b/examples/pascal-finetuning/pascal_finetune_solver.prototxt
deleted file mode 100644
index f2b80111e2f..00000000000
--- a/examples/pascal-finetuning/pascal_finetune_solver.prototxt
+++ /dev/null
@@ -1,14 +0,0 @@
-train_net: "pascal_finetune_train.prototxt"
-test_net: "pascal_finetune_val.prototxt"
-test_iter: 100
-test_interval: 1000
-base_lr: 0.001
-lr_policy: "step"
-gamma: 0.1
-stepsize: 20000
-display: 20
-max_iter: 100000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "pascal_finetune_train"

From 400411bf4893c97ca5371c26ecdaccc278fcf3a4 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Fri, 22 Aug 2014 19:30:07 -0700
Subject: [PATCH 0548/2053] [example] finetuning CaffeNet on Flickr Style data

---
 .../assemble_data.py                          |  89 +++++
 .../flickr_style.csv.gz                       | Bin 0 -> 2178982 bytes
 .../models/.gitignore                         |   0
 examples/finetuning_on_flickr_style/readme.md | 100 +++++
 .../solver.prototxt                           |  15 +
 .../train_val.prototxt                        | 349 ++++++++++++++++++
 6 files changed, 553 insertions(+)
 create mode 100644 examples/finetuning_on_flickr_style/assemble_data.py
 create mode 100644 examples/finetuning_on_flickr_style/flickr_style.csv.gz
 create mode 100644 examples/finetuning_on_flickr_style/models/.gitignore
 create mode 100644 examples/finetuning_on_flickr_style/readme.md
 create mode 100644 examples/finetuning_on_flickr_style/solver.prototxt
 create mode 100644 examples/finetuning_on_flickr_style/train_val.prototxt

diff --git a/examples/finetuning_on_flickr_style/assemble_data.py b/examples/finetuning_on_flickr_style/assemble_data.py
new file mode 100644
index 00000000000..d8770e92914
--- /dev/null
+++ b/examples/finetuning_on_flickr_style/assemble_data.py
@@ -0,0 +1,89 @@
+"""
+Form a subset of the Flickr Style data, download images to dirname, and write
+Caffe ImagesDataLayer training file.
+"""
+import os
+import urllib
+import hashlib
+import argparse
+import numpy as np
+import pandas as pd
+import multiprocessing
+
+# Flickr returns a special image if the request is unavailable.
+MISSING_IMAGE_SHA1 = '6a92790b1c2a301c6e7ddef645dca1f53ea97ac2'
+
+example_dirname = os.path.abspath(os.path.dirname(__file__))
+caffe_dirname = os.path.abspath(os.path.join(example_dirname, '../..'))
+training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')
+
+
+def download_image(args_tuple):
+    "For use with multiprocessing map. Returns filename on fail."
+    try:
+        url, filename = args_tuple
+        if not os.path.exists(filename):
+            urllib.urlretrieve(url, filename)
+        with open(filename) as f:
+            assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+        return True
+    except KeyboardInterrupt:
+        raise Exception()  # multiprocessing doesn't catch keyboard exceptions
+    except:
+        return False
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Download a subset of Flickr Style to a directory')
+    parser.add_argument(
+        '-s', '--seed', type=int, default=0,
+        help="random seed")
+    parser.add_argument(
+        '-i', '--images', type=int, default=-1,
+        help="number of images to use (-1 for all [default])",
+    )
+    parser.add_argument(
+        '-w', '--workers', type=int, default=-1,
+        help="num workers used to download images. -x uses (all - x) cores [-1 default]."
+    )
+
+    args = parser.parse_args()
+    np.random.seed(args.seed)
+
+    # Read data, shuffle order, and subsample.
+    csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
+    df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
+    df = df.iloc[np.random.permutation(df.shape[0])]
+    if args.images > 0 and args.images < df.shape[0]:
+        df = df.iloc[:args.images]
+
+    # Make directory for images and get local filenames.
+    if training_dirname is None:
+        training_dirname = os.path.join(caffe_dirname, 'data/flickr_style')
+    images_dirname = os.path.join(training_dirname, 'images')
+    if not os.path.exists(images_dirname):
+        os.makedirs(images_dirname)
+    df['image_filename'] = [
+        os.path.join(images_dirname, _.split('/')[-1]) for _ in df['image_url']
+    ]
+
+    # Download images.
+    num_workers = args.workers
+    if num_workers <= 0:
+        num_workers = multiprocessing.cpu_count() + num_workers
+    print('Downloading {} images with {} workers...'.format(
+        df.shape[0], num_workers))
+    pool = multiprocessing.Pool(processes=num_workers)
+    map_args = zip(df['image_url'], df['image_filename'])
+    results = pool.map(download_image, map_args)
+
+    # Only keep rows with valid images, and write out training file lists.
+    df = df[results]
+    for split in ['train', 'test']:
+        split_df = df[df['_split'] == split]
+        filename = os.path.join(training_dirname, '{}.txt'.format(split))
+        split_df[['image_filename', 'label']].to_csv(
+            filename, sep=' ', header=None, index=None)
+    print('Writing train/val for {} successfully downloaded images.'.format(
+        df.shape[0]))
diff --git a/examples/finetuning_on_flickr_style/flickr_style.csv.gz b/examples/finetuning_on_flickr_style/flickr_style.csv.gz
new file mode 100644
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diff --git a/examples/finetuning_on_flickr_style/models/.gitignore b/examples/finetuning_on_flickr_style/models/.gitignore
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/examples/finetuning_on_flickr_style/readme.md b/examples/finetuning_on_flickr_style/readme.md
new file mode 100644
index 00000000000..4a164e5f16a
--- /dev/null
+++ b/examples/finetuning_on_flickr_style/readme.md
@@ -0,0 +1,100 @@
+---
+title: Fine-tuning CaffeNet on "Flickr Style" data
+description: We fine-tune the ImageNet-trained CaffeNet on another dataset.
+category: example
+include_in_docs: true
+layout: default
+priority: 5
+---
+
+# Fine-tuning CaffeNet on "Flickr Style" data
+
+This example shows how to fine-tune the BVLC-distributed CaffeNet model on a different dataset: [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), which has style category labels.
+
+## Explanation
+
+The Flickr-sourced data of the Style dataset is visually very similar to the ImageNet dataset, on which the `caffe_reference_imagenet_model` was trained.
+Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
+We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
+If we give provide the `model` parameter to the `caffe train` command, the trained weights will be loaded into our model, matching layers by name.
+
+Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
+Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
+Since there is no layer named that in the `caffe_reference_imagenet_model`, that layer will begin training with random weights.
+
+We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
+The idea is to have the rest of the model change very slowly with new data, but the new layer needs to learn fast.
+Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
+Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
+
+## Procedure
+
+All steps are to be done from the root caffe directory.
+
+The dataset is distributed as a list of URLs with corresponding labels.
+Using a script, we will download a small subset of the data and split it into train and val sets.
+
+    caffe % python examples/finetuning_on_flickr_style/assemble_data.py -h
+    usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
+
+    Download a subset of Flickr Style to a directory
+
+    optional arguments:
+      -h, --help            show this help message and exit
+      -s SEED, --seed SEED  random seed
+      -i IMAGES, --images IMAGES
+                            number of images to use (-1 for all)
+      -w WORKERS, --workers WORKERS
+                            num workers used to download images. -x uses (all - x)
+                            cores.
+
+    caffe % python examples/finetuning_on_flickr_style/assemble_data.py --workers=-1 --images=200
+    Downloading 200 images with 7 workers...
+    Writing train/val for 190 successfully downloaded images.
+
+This script downloads images and writes train/val file lists into `data/flickr_style`.
+The prototxt's in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).
+
+We'll also need the ImageNet-trained model, which you can obtain by running `get_caffe_reference_imagenet_model.sh` from `examples/imagenet`.
+
+Now we can train!
+
+    caffe % ./build/tools/caffe train -solver examples/finetuning_on_flickr_style/solver.prototxt -weights examples/imagenet/caffe_reference_imagenet_model
+    I0827 19:41:52.455621 2129298192 caffe.cpp:90] Starting Optimization
+    I0827 19:41:52.456883 2129298192 solver.cpp:32] Initializing solver from parameters:
+
+    [...]
+
+    I0827 19:41:55.520205 2129298192 solver.cpp:46] Solver scaffolding done.
+    I0827 19:41:55.520211 2129298192 caffe.cpp:99] Use CPU.
+    I0827 19:41:55.520217 2129298192 caffe.cpp:107] Finetuning from examples/imagenet/caffe_reference_imagenet_model
+    I0827 19:41:57.433044 2129298192 solver.cpp:165] Solving CaffeNet
+    I0827 19:41:57.433104 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)
+    I0827 19:44:44.145447 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.004
+    I0827 19:44:48.774271 2129298192 solver.cpp:195] Iteration 0, loss = 3.46922
+    I0827 19:44:48.774333 2129298192 solver.cpp:397] Iteration 0, lr = 0.001
+    I0827 19:46:15.107447 2129298192 solver.cpp:195] Iteration 20, loss = 0.0147678
+    I0827 19:46:15.107511 2129298192 solver.cpp:397] Iteration 20, lr = 0.001
+    I0827 19:47:41.941119 2129298192 solver.cpp:195] Iteration 40, loss = 0.00455839
+    I0827 19:47:41.941181 2129298192 solver.cpp:397] Iteration 40, lr = 0.001
+
+Note how rapidly the loss went down.
+For comparison, here is how the loss goes down when we do not start with a pre-trained model:
+
+    I0827 18:57:08.496208 2129298192 solver.cpp:46] Solver scaffolding done.
+    I0827 18:57:08.496227 2129298192 caffe.cpp:99] Use CPU.
+    I0827 18:57:08.496235 2129298192 solver.cpp:165] Solving CaffeNet
+    I0827 18:57:08.496271 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)
+    I0827 19:00:00.894336 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.075
+    I0827 19:00:05.825129 2129298192 solver.cpp:195] Iteration 0, loss = 3.51759
+    I0827 19:00:05.825187 2129298192 solver.cpp:397] Iteration 0, lr = 0.001
+    I0827 19:01:36.090224 2129298192 solver.cpp:195] Iteration 20, loss = 3.32227
+    I0827 19:01:36.091948 2129298192 solver.cpp:397] Iteration 20, lr = 0.001
+    I0827 19:03:08.522105 2129298192 solver.cpp:195] Iteration 40, loss = 2.97031
+    I0827 19:03:08.522176 2129298192 solver.cpp:397] Iteration 40, lr = 0.001
+
+## License
+
+The Flickr Style dataset as distributed here contains only URLs to images.
+Some of the images may have copyright.
+Training a category-recognition model for research/non-commercial use may constitute fair use of this data.
diff --git a/examples/finetuning_on_flickr_style/solver.prototxt b/examples/finetuning_on_flickr_style/solver.prototxt
new file mode 100644
index 00000000000..ed1548c0aac
--- /dev/null
+++ b/examples/finetuning_on_flickr_style/solver.prototxt
@@ -0,0 +1,15 @@
+net: "examples/finetuning_on_flickr_style/train_val.prototxt"
+test_iter: 100
+test_interval: 1000
+# lr for fine-tuning should be lower than when starting from scratch
+base_lr: 0.001
+lr_policy: "step"
+gamma: 0.1
+# stepsize should also be lower, as we're closer to being done
+stepsize: 20000
+display: 20
+max_iter: 100000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/finetuning_on_flickr_style/models/finetuning"
diff --git a/examples/finetuning_on_flickr_style/train_val.prototxt b/examples/finetuning_on_flickr_style/train_val.prototxt
new file mode 100644
index 00000000000..bcb1e1ce1e2
--- /dev/null
+++ b/examples/finetuning_on_flickr_style/train_val.prototxt
@@ -0,0 +1,349 @@
+name: "FlickrStyleCaffeNet"
+layers {
+  name: "data"
+  type: IMAGE_DATA
+  top: "data"
+  top: "label"
+  image_data_param {
+    source: "data/flickr_style/train.txt"
+    batch_size: 50
+    transform_param {
+      crop_size: 227
+      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: true
+    }
+    new_height: 256
+    new_width: 256
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: IMAGE_DATA
+  top: "data"
+  top: "label"
+  image_data_param {
+    source: "data/flickr_style/train.txt"
+    batch_size: 50
+    transform_param {
+      crop_size: 227
+      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+      mirror: true
+    }
+    new_height: 256
+    new_width: 256
+  }
+  include: { phase: TEST }
+}
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8_flickr"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8_flickr"
+  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  blobs_lr: 10
+  blobs_lr: 20
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 20
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8_flickr"
+  bottom: "label"
+}
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc8_flickr"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}

From 23a50260846f3fd4f45f81a70ab5c837a4ed0b40 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 28 Aug 2014 21:45:46 -0700
Subject: [PATCH 0549/2053] [example] edit fine-tuning and train on ~2000
 images, 1557 / 382 split

- further detail merits of fine-tuning: less starving for itme and data
- set random seed for reproducing the tutorial
- 1557 train / 382 test split is more indicative of training quality
  than splits of 200 images
---
 .../assemble_data.py                          |   1 +
 .../flickr_style.csv.gz                       | Bin
 .../flickr_style_solver.prototxt}             |   4 +-
 .../flickr_style_train_val.prototxt}          |   0
 examples/finetune_flickr_style/readme.md      | 159 ++++++++++++++++++
 .../models/.gitignore                         |   0
 examples/finetuning_on_flickr_style/readme.md | 100 -----------
 7 files changed, 162 insertions(+), 102 deletions(-)
 rename examples/{finetuning_on_flickr_style => finetune_flickr_style}/assemble_data.py (99%)
 mode change 100644 => 100755
 rename examples/{finetuning_on_flickr_style => finetune_flickr_style}/flickr_style.csv.gz (100%)
 rename examples/{finetuning_on_flickr_style/solver.prototxt => finetune_flickr_style/flickr_style_solver.prototxt} (69%)
 rename examples/{finetuning_on_flickr_style/train_val.prototxt => finetune_flickr_style/flickr_style_train_val.prototxt} (100%)
 create mode 100644 examples/finetune_flickr_style/readme.md
 delete mode 100644 examples/finetuning_on_flickr_style/models/.gitignore
 delete mode 100644 examples/finetuning_on_flickr_style/readme.md

diff --git a/examples/finetuning_on_flickr_style/assemble_data.py b/examples/finetune_flickr_style/assemble_data.py
old mode 100644
new mode 100755
similarity index 99%
rename from examples/finetuning_on_flickr_style/assemble_data.py
rename to examples/finetune_flickr_style/assemble_data.py
index d8770e92914..b4c995e8eae
--- a/examples/finetuning_on_flickr_style/assemble_data.py
+++ b/examples/finetune_flickr_style/assemble_data.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 """
 Form a subset of the Flickr Style data, download images to dirname, and write
 Caffe ImagesDataLayer training file.
diff --git a/examples/finetuning_on_flickr_style/flickr_style.csv.gz b/examples/finetune_flickr_style/flickr_style.csv.gz
similarity index 100%
rename from examples/finetuning_on_flickr_style/flickr_style.csv.gz
rename to examples/finetune_flickr_style/flickr_style.csv.gz
diff --git a/examples/finetuning_on_flickr_style/solver.prototxt b/examples/finetune_flickr_style/flickr_style_solver.prototxt
similarity index 69%
rename from examples/finetuning_on_flickr_style/solver.prototxt
rename to examples/finetune_flickr_style/flickr_style_solver.prototxt
index ed1548c0aac..740ec39fe6a 100644
--- a/examples/finetuning_on_flickr_style/solver.prototxt
+++ b/examples/finetune_flickr_style/flickr_style_solver.prototxt
@@ -1,4 +1,4 @@
-net: "examples/finetuning_on_flickr_style/train_val.prototxt"
+net: "examples/finetune_flickr_style/flickr_style_train_val.prototxt"
 test_iter: 100
 test_interval: 1000
 # lr for fine-tuning should be lower than when starting from scratch
@@ -12,4 +12,4 @@ max_iter: 100000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/finetuning_on_flickr_style/models/finetuning"
+snapshot_prefix: "examples/finetune_flickr_style/flickr_style"
diff --git a/examples/finetuning_on_flickr_style/train_val.prototxt b/examples/finetune_flickr_style/flickr_style_train_val.prototxt
similarity index 100%
rename from examples/finetuning_on_flickr_style/train_val.prototxt
rename to examples/finetune_flickr_style/flickr_style_train_val.prototxt
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
new file mode 100644
index 00000000000..822499829e6
--- /dev/null
+++ b/examples/finetune_flickr_style/readme.md
@@ -0,0 +1,159 @@
+---
+title: Fine-tuning for style recognition
+description: Fine-tune the ImageNet-trained CaffeNet on the "Flickr Style" dataset.
+category: example
+include_in_docs: true
+layout: default
+priority: 5
+---
+
+# Fine-tuning CaffeNet for Style Recognition on "Flickr Style" Data
+
+Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights.
+Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), to predict image style instead of object category.
+
+## Explanation
+
+The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `caffe_reference_imagenet_model` was trained.
+Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
+We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
+If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
+
+Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
+Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
+Since there is no layer named that in the `caffe_reference_imagenet_model`, that layer will begin training with random weights.
+
+We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
+The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.
+Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
+Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
+
+## Procedure
+
+All steps are to be done from the caffe root directory.
+
+The dataset is distributed as a list of URLs with corresponding labels.
+Using a script, we will download a small subset of the data and split it into train and val sets.
+
+    caffe % ./examples/finetune_flickr_style/assemble_data.py -h
+    usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
+
+    Download a subset of Flickr Style to a directory
+
+    optional arguments:
+      -h, --help            show this help message and exit
+      -s SEED, --seed SEED  random seed
+      -i IMAGES, --images IMAGES
+                            number of images to use (-1 for all)
+      -w WORKERS, --workers WORKERS
+                            num workers used to download images. -x uses (all - x)
+                            cores.
+
+    caffe % python examples/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
+    Downloading 2000 images with 7 workers...
+    Writing train/val for 1939 successfully downloaded images.
+
+This script downloads images and writes train/val file lists into `data/flickr_style`.
+With this random seed there are 1,557 train images and 382 test images.
+The prototxts in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).
+
+We'll also need the ImageNet-trained model, which you can obtain by running `get_caffe_reference_imagenet_model.sh` from `examples/imagenet`.
+
+Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
+
+    caffe % ./build/tools/caffe train -solver examples/finetune_flickr_style/flickr_style_solver.prototxt -weights examples/imagenet/caffe_reference_imagenet_model -gpu 0
+
+    [...]
+
+    I0828 22:10:04.025378  9718 solver.cpp:46] Solver scaffolding done.
+    I0828 22:10:04.025388  9718 caffe.cpp:95] Use GPU with device ID 0
+    I0828 22:10:04.192004  9718 caffe.cpp:107] Finetuning from examples/imagenet/caffe_reference_imagenet_model
+
+    [...]
+
+    I0828 22:17:48.338963 11510 solver.cpp:165] Solving FlickrStyleCaffeNet
+    I0828 22:17:48.339010 11510 solver.cpp:251] Iteration 0, Testing net (#0)
+    I0828 22:18:14.313817 11510 solver.cpp:302]     Test net output #0: accuracy = 0.0416
+    I0828 22:18:14.476822 11510 solver.cpp:195] Iteration 0, loss = 3.75717
+    I0828 22:18:14.476878 11510 solver.cpp:397] Iteration 0, lr = 0.001
+    I0828 22:18:19.700408 11510 solver.cpp:195] Iteration 20, loss = 3.1689
+    I0828 22:18:19.700461 11510 solver.cpp:397] Iteration 20, lr = 0.001
+    I0828 22:18:24.924685 11510 solver.cpp:195] Iteration 40, loss = 2.3549
+    I0828 22:18:24.924741 11510 solver.cpp:397] Iteration 40, lr = 0.001
+    I0828 22:18:30.114858 11510 solver.cpp:195] Iteration 60, loss = 2.74191
+    I0828 22:18:30.114910 11510 solver.cpp:397] Iteration 60, lr = 0.001
+    I0828 22:18:35.328071 11510 solver.cpp:195] Iteration 80, loss = 1.9147
+    I0828 22:18:35.328127 11510 solver.cpp:397] Iteration 80, lr = 0.001
+    I0828 22:18:40.588317 11510 solver.cpp:195] Iteration 100, loss = 1.81419
+    I0828 22:18:40.588373 11510 solver.cpp:397] Iteration 100, lr = 0.001
+    I0828 22:18:46.171576 11510 solver.cpp:195] Iteration 120, loss = 2.02105
+    I0828 22:18:46.171669 11510 solver.cpp:397] Iteration 120, lr = 0.001
+    I0828 22:18:51.757809 11510 solver.cpp:195] Iteration 140, loss = 1.49083
+    I0828 22:18:51.757863 11510 solver.cpp:397] Iteration 140, lr = 0.001
+    I0828 22:18:57.345080 11510 solver.cpp:195] Iteration 160, loss = 1.35319
+    I0828 22:18:57.345135 11510 solver.cpp:397] Iteration 160, lr = 0.001
+    I0828 22:19:02.928794 11510 solver.cpp:195] Iteration 180, loss = 1.11658
+    I0828 22:19:02.928850 11510 solver.cpp:397] Iteration 180, lr = 0.001
+    I0828 22:19:08.514497 11510 solver.cpp:195] Iteration 200, loss = 1.08851
+    I0828 22:19:08.514552 11510 solver.cpp:397] Iteration 200, lr = 0.001
+
+    [...]
+
+    I0828 22:22:40.789010 11510 solver.cpp:195] Iteration 960, loss = 0.0844627
+    I0828 22:22:40.789175 11510 solver.cpp:397] Iteration 960, lr = 0.001
+    I0828 22:22:46.376626 11510 solver.cpp:195] Iteration 980, loss = 0.0110937
+    I0828 22:22:46.376682 11510 solver.cpp:397] Iteration 980, lr = 0.001
+    I0828 22:22:51.687258 11510 solver.cpp:251] Iteration 1000, Testing net (#0)
+    I0828 22:23:17.438894 11510 solver.cpp:302]     Test net output #0: accuracy = 1
+
+Note how rapidly the loss went down. Although the 100% accuracy is optimistic, it is evidence the model is learning quickly and well.
+
+For comparison, here is how the loss goes down when we do not start with a pre-trained model:
+
+    I0828 22:24:18.624004 12919 solver.cpp:165] Solving FlickrStyleCaffeNet
+    I0828 22:24:18.624099 12919 solver.cpp:251] Iteration 0, Testing net (#0)
+    I0828 22:24:44.520992 12919 solver.cpp:302]     Test net output #0: accuracy = 0.045
+    I0828 22:24:44.676905 12919 solver.cpp:195] Iteration 0, loss = 3.33111
+    I0828 22:24:44.677120 12919 solver.cpp:397] Iteration 0, lr = 0.001
+    I0828 22:24:50.152454 12919 solver.cpp:195] Iteration 20, loss = 2.98133
+    I0828 22:24:50.152509 12919 solver.cpp:397] Iteration 20, lr = 0.001
+    I0828 22:24:55.736256 12919 solver.cpp:195] Iteration 40, loss = 3.02124
+    I0828 22:24:55.736311 12919 solver.cpp:397] Iteration 40, lr = 0.001
+    I0828 22:25:01.316514 12919 solver.cpp:195] Iteration 60, loss = 2.99509
+    I0828 22:25:01.316567 12919 solver.cpp:397] Iteration 60, lr = 0.001
+    I0828 22:25:06.899554 12919 solver.cpp:195] Iteration 80, loss = 2.9928
+    I0828 22:25:06.899610 12919 solver.cpp:397] Iteration 80, lr = 0.001
+    I0828 22:25:12.484624 12919 solver.cpp:195] Iteration 100, loss = 2.99072
+    I0828 22:25:12.484678 12919 solver.cpp:397] Iteration 100, lr = 0.001
+    I0828 22:25:18.069056 12919 solver.cpp:195] Iteration 120, loss = 3.01816
+    I0828 22:25:18.069149 12919 solver.cpp:397] Iteration 120, lr = 0.001
+    I0828 22:25:23.650928 12919 solver.cpp:195] Iteration 140, loss = 2.9694
+    I0828 22:25:23.650984 12919 solver.cpp:397] Iteration 140, lr = 0.001
+    I0828 22:25:29.235535 12919 solver.cpp:195] Iteration 160, loss = 3.00383
+    I0828 22:25:29.235589 12919 solver.cpp:397] Iteration 160, lr = 0.001
+    I0828 22:25:34.816898 12919 solver.cpp:195] Iteration 180, loss = 2.99802
+    I0828 22:25:34.816953 12919 solver.cpp:397] Iteration 180, lr = 0.001
+    I0828 22:25:40.396656 12919 solver.cpp:195] Iteration 200, loss = 2.99769
+    I0828 22:25:40.396711 12919 solver.cpp:397] Iteration 200, lr = 0.001
+
+    [...]
+
+    I0828 22:29:12.539094 12919 solver.cpp:195] Iteration 960, loss = 2.99314
+    I0828 22:29:12.539258 12919 solver.cpp:397] Iteration 960, lr = 0.001
+    I0828 22:29:18.123092 12919 solver.cpp:195] Iteration 980, loss = 2.99503
+    I0828 22:29:18.123147 12919 solver.cpp:397] Iteration 980, lr = 0.001
+    I0828 22:29:23.432059 12919 solver.cpp:251] Iteration 1000, Testing net (#0)
+    I0828 22:29:49.409044 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0624
+
+This model is only beginning to learn.
+
+Fine-tuning can be feasible when training from scratch would not be for lack of time or data.
+Even in CPU mode each pass through the training set takes ~100 s. GPU fine-tuning is of course faster still and can learn a useful model in minutes or hours instead of days or weeks.
+Furthermore, note that the model has only trained on < 2,000 instances. Transfer learning a new task like style recognition from the ImageNet pretraining can require much less data than training from scratch.
+Now try fine-tuning to your own tasks and data!
+
+## License
+
+The Flickr Style dataset as distributed here contains only URLs to images.
+Some of the images may have copyright.
+Training a category-recognition model for research/non-commercial use may constitute fair use of this data.
diff --git a/examples/finetuning_on_flickr_style/models/.gitignore b/examples/finetuning_on_flickr_style/models/.gitignore
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/examples/finetuning_on_flickr_style/readme.md b/examples/finetuning_on_flickr_style/readme.md
deleted file mode 100644
index 4a164e5f16a..00000000000
--- a/examples/finetuning_on_flickr_style/readme.md
+++ /dev/null
@@ -1,100 +0,0 @@
----
-title: Fine-tuning CaffeNet on "Flickr Style" data
-description: We fine-tune the ImageNet-trained CaffeNet on another dataset.
-category: example
-include_in_docs: true
-layout: default
-priority: 5
----
-
-# Fine-tuning CaffeNet on "Flickr Style" data
-
-This example shows how to fine-tune the BVLC-distributed CaffeNet model on a different dataset: [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), which has style category labels.
-
-## Explanation
-
-The Flickr-sourced data of the Style dataset is visually very similar to the ImageNet dataset, on which the `caffe_reference_imagenet_model` was trained.
-Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
-We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
-If we give provide the `model` parameter to the `caffe train` command, the trained weights will be loaded into our model, matching layers by name.
-
-Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
-Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
-Since there is no layer named that in the `caffe_reference_imagenet_model`, that layer will begin training with random weights.
-
-We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
-The idea is to have the rest of the model change very slowly with new data, but the new layer needs to learn fast.
-Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
-Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
-
-## Procedure
-
-All steps are to be done from the root caffe directory.
-
-The dataset is distributed as a list of URLs with corresponding labels.
-Using a script, we will download a small subset of the data and split it into train and val sets.
-
-    caffe % python examples/finetuning_on_flickr_style/assemble_data.py -h
-    usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
-
-    Download a subset of Flickr Style to a directory
-
-    optional arguments:
-      -h, --help            show this help message and exit
-      -s SEED, --seed SEED  random seed
-      -i IMAGES, --images IMAGES
-                            number of images to use (-1 for all)
-      -w WORKERS, --workers WORKERS
-                            num workers used to download images. -x uses (all - x)
-                            cores.
-
-    caffe % python examples/finetuning_on_flickr_style/assemble_data.py --workers=-1 --images=200
-    Downloading 200 images with 7 workers...
-    Writing train/val for 190 successfully downloaded images.
-
-This script downloads images and writes train/val file lists into `data/flickr_style`.
-The prototxt's in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).
-
-We'll also need the ImageNet-trained model, which you can obtain by running `get_caffe_reference_imagenet_model.sh` from `examples/imagenet`.
-
-Now we can train!
-
-    caffe % ./build/tools/caffe train -solver examples/finetuning_on_flickr_style/solver.prototxt -weights examples/imagenet/caffe_reference_imagenet_model
-    I0827 19:41:52.455621 2129298192 caffe.cpp:90] Starting Optimization
-    I0827 19:41:52.456883 2129298192 solver.cpp:32] Initializing solver from parameters:
-
-    [...]
-
-    I0827 19:41:55.520205 2129298192 solver.cpp:46] Solver scaffolding done.
-    I0827 19:41:55.520211 2129298192 caffe.cpp:99] Use CPU.
-    I0827 19:41:55.520217 2129298192 caffe.cpp:107] Finetuning from examples/imagenet/caffe_reference_imagenet_model
-    I0827 19:41:57.433044 2129298192 solver.cpp:165] Solving CaffeNet
-    I0827 19:41:57.433104 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)
-    I0827 19:44:44.145447 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.004
-    I0827 19:44:48.774271 2129298192 solver.cpp:195] Iteration 0, loss = 3.46922
-    I0827 19:44:48.774333 2129298192 solver.cpp:397] Iteration 0, lr = 0.001
-    I0827 19:46:15.107447 2129298192 solver.cpp:195] Iteration 20, loss = 0.0147678
-    I0827 19:46:15.107511 2129298192 solver.cpp:397] Iteration 20, lr = 0.001
-    I0827 19:47:41.941119 2129298192 solver.cpp:195] Iteration 40, loss = 0.00455839
-    I0827 19:47:41.941181 2129298192 solver.cpp:397] Iteration 40, lr = 0.001
-
-Note how rapidly the loss went down.
-For comparison, here is how the loss goes down when we do not start with a pre-trained model:
-
-    I0827 18:57:08.496208 2129298192 solver.cpp:46] Solver scaffolding done.
-    I0827 18:57:08.496227 2129298192 caffe.cpp:99] Use CPU.
-    I0827 18:57:08.496235 2129298192 solver.cpp:165] Solving CaffeNet
-    I0827 18:57:08.496271 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)
-    I0827 19:00:00.894336 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.075
-    I0827 19:00:05.825129 2129298192 solver.cpp:195] Iteration 0, loss = 3.51759
-    I0827 19:00:05.825187 2129298192 solver.cpp:397] Iteration 0, lr = 0.001
-    I0827 19:01:36.090224 2129298192 solver.cpp:195] Iteration 20, loss = 3.32227
-    I0827 19:01:36.091948 2129298192 solver.cpp:397] Iteration 20, lr = 0.001
-    I0827 19:03:08.522105 2129298192 solver.cpp:195] Iteration 40, loss = 2.97031
-    I0827 19:03:08.522176 2129298192 solver.cpp:397] Iteration 40, lr = 0.001
-
-## License
-
-The Flickr Style dataset as distributed here contains only URLs to images.
-Some of the images may have copyright.
-Training a category-recognition model for research/non-commercial use may constitute fair use of this data.

From 5ea91dd1522a011c0f2fefa1a8102ca6f058f60b Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 14:14:32 +0800
Subject: [PATCH 0550/2053] Ignore LevelDB files

---
 .gitignore | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/.gitignore b/.gitignore
index 07eebe34591..c349a3e1179 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,6 +58,13 @@ data/*
 *leveldb
 *lmdb
 
+# LevelDB files
+*.ldb
+LOCK
+LOG*
+CURRENT
+MANIFEST-*
+
 # Generated documentation
 docs/_site
 docs/gathered

From 1c49d630c771f8cb99d4fcc9cecbfc87799112a4 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 27 Aug 2014 09:25:10 -0700
Subject: [PATCH 0551/2053] Explain how to fix homebrew to allow updates after
 editing formulae

---
 docs/installation.md | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/docs/installation.md b/docs/installation.md
index 1ac6c08b6ca..8e5078ab36a 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -193,6 +193,38 @@ If you're not using Anaconda, include `hdf5` in the list above.
 **Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
 
     brew install --build-from-source --with-python --fresh -vd boost
+    
+**Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
+
+    $ brew update
+    error: Your local changes to the following files would be overwritten by merge:
+      Library/Formula/lmdb.rb
+    Please, commit your changes or stash them before you can merge.
+    Aborting
+    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
+    
+One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master, as follows:
+
+    cd /usr/local
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies to use libstdc++"
+    git checkout master
+    brew update
+    git rebase master caffe
+    # Resolve any merge conflicts here
+    git checkout caffe
+    
+At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place. You may still get the following error:
+
+    $ brew update
+    error: Your local changes to the following files would be overwritten by merge:
+	opencv.rb
+    Please, commit your changes or stash them before you can merge.
+    Aborting
+    Error: Failed to update tap: homebrew/science
+
+but non-OpenCV packages will still update as expected.
 
 #### Windows
 

From ea04cf82686af668a5da8827443a2c9f7e6daf2a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ozan=20=C3=87a=C4=9Flayan?= <ozancag@gmail.com>
Date: Thu, 28 Aug 2014 14:05:09 +0300
Subject: [PATCH 0552/2053] [docs] Several documentation fixes

- Fix a typo and a path in MNIST tutorial
- Rework instructions about Fedora installation
---
 docs/installation.md     | 6 +++---
 examples/mnist/readme.md | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 8e5078ab36a..898425a8b04 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -96,9 +96,9 @@ On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
     
 The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
 
-    sudo yum install gflags-devel glog-devel lmdb
+    sudo yum install gflags-devel glog-devel lmdb-devel
 
-**Finally** in case you couldn't find those 3 libraries in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora**
+**Finally** in case you couldn't find those extra libraries mentioned above in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora** (or practically on any Linux distribution)
 
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
@@ -232,7 +232,7 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup.
+Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup (you should especially uncomment and set `BLAS_LIB` accordingly on distributions like **CentOS / RHEL / Fedora** where ATLAS is installed under `/usr/lib[64]/atlas`)
 The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 266ff4c9689..975a77e5128 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -26,13 +26,13 @@ If it complains that `wget` or `gunzip` are not installed, you need to install t
 
 Before we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.
 
-The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `CAFFE_ROOT/data/lenet.prototxt`.
+The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `CAFFE_ROOT/examples/lenet.prototxt`.
 
 ## Define the MNIST Network
 
 This section explains the prototxt file `lenet_train.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at [src/caffe/proto/caffe.proto](https://github.com/Yangqing/caffe/blob/master/src/caffe/proto/caffe.proto).
 
-Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protubuf. We will start by giving the network a name:
+Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:
 
     name: "LeNet"
 

From 190c43e06adb92c112b122198b093b55706862ec Mon Sep 17 00:00:00 2001
From: Yung Siang Liau <liauys@gmail.com>
Date: Sun, 31 Aug 2014 01:22:04 +0800
Subject: [PATCH 0553/2053] Fix norm_region param in cifar10 deployment net

---
 examples/cifar10/cifar10_full.prototxt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 9e7016362fc..8bbd30004fd 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -75,7 +75,6 @@ layers {
   bottom: "conv2"
   top: "pool2"
   pooling_param {
-    norm_region: WITHIN_CHANNEL
     pool: AVE
     kernel_size: 3
     stride: 2
@@ -87,6 +86,7 @@ layers {
   bottom: "pool2"
   top: "norm2"
   lrn_param {
+    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75

From a888de30b85bc89307c22b33da7377417bfd2b02 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 30 Aug 2014 10:28:15 -0700
Subject: [PATCH 0554/2053] patch MacOS NVCC boost::thread issue

---
 include/caffe/internal_thread.hpp     | 18 +++++++++++++++---
 include/caffe/util/thread_wrapper.hpp | 25 +++++++++++++++++++++++++
 src/caffe/internal_thread.cpp         |  5 ++++-
 3 files changed, 44 insertions(+), 4 deletions(-)
 create mode 100644 include/caffe/util/thread_wrapper.hpp

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 9989936e519..48ee113878d 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -1,12 +1,24 @@
 #ifndef CAFFE_INTERNAL_THREAD_HPP_
 #define CAFFE_INTERNAL_THREAD_HPP_
 
-#include <boost/thread.hpp>
-
 #include "caffe/common.hpp"
 
 namespace caffe {
 
+/**
+ * A minimal wrapper for boost::thread to force host compilation for boost
+ * Defined in caffe/util/thread_wrapper.cpp
+ */
+class ThreadWrapper {
+ public:
+  template<typename Callable, class A1>
+  ThreadWrapper(Callable func, A1 a1);
+  void join();
+  bool joinable();
+ private:
+  void* thread_;
+};
+
 /**
  * Virutal class encapsulate boost::thread for use in base class
  * The child class will acquire the ability to run a single thread,
@@ -30,7 +42,7 @@ class InternalThread {
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
-  boost::thread* thread_;
+  caffe::ThreadWrapper* thread_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/util/thread_wrapper.hpp b/include/caffe/util/thread_wrapper.hpp
new file mode 100644
index 00000000000..5842d9a27fc
--- /dev/null
+++ b/include/caffe/util/thread_wrapper.hpp
@@ -0,0 +1,25 @@
+#ifndef CAFFE_THREAD_WRAPPER_CPP_HPP_
+#define CAFFE_THREAD_WRAPPER_CPP_HPP_
+
+#include <boost/thread.hpp>
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+
+namespace caffe {
+
+template<typename Callable, class A1>
+ThreadWrapper::ThreadWrapper(Callable func, A1 a1) {
+  this->thread_ = new boost::thread(func, a1);
+}
+
+void ThreadWrapper::join() {
+  static_cast<boost::thread*>(this->thread_)->join();
+}
+
+bool ThreadWrapper::joinable() {
+  return static_cast<boost::thread*>(this->thread_)->joinable();
+}
+
+}  // namespace caffe
+
+#endif
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index b89aa2ef061..89c4147f2bb 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,5 +1,7 @@
 #include "caffe/internal_thread.hpp"
 
+#include "caffe/util/thread_wrapper.hpp"
+
 namespace caffe {
 
 InternalThread::~InternalThread() {
@@ -11,7 +13,8 @@ InternalThread::~InternalThread() {
 
 bool InternalThread::StartInternalThread() {
   try {
-    thread_ = new boost::thread(&InternalThread::InternalThreadEntry, this);
+    thread_ = new caffe::ThreadWrapper
+        (&InternalThread::InternalThreadEntry, this);
   } catch (...) {
     return false;
   }

From 1a71867e676173c7f45366b4088b08b854a6f34e Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 30 Aug 2014 10:38:33 -0700
Subject: [PATCH 0555/2053] xcode compiler complaints (warnings)...

---
 include/caffe/util/io.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 8ff8b0c25ae..546c3d6af80 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -14,7 +14,7 @@
 
 namespace leveldb {
 // Forward declaration for leveldb::Options to be used in GetlevelDBOptions().
-class Options;
+struct Options;
 }
 
 namespace caffe {

From 8742fe665787efe2e32664b0f9ff2bc5b72dfaeb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 29 Aug 2014 03:31:33 -0700
Subject: [PATCH 0556/2053] fix data_transformer param_name

---
 include/caffe/data_transformer.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 302bf70e606..5d5134f5db5 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -31,7 +31,7 @@ class DataTransformer {
    * @param datum
    *    Datum containing the data to be transformed.
    * @param mean
-   * @param top_data
+   * @param transformed_data
    *    This is meant to be the top blob's data. The transformed data will be
    *    written at the appropriate place within the blob's data.
    */

From c9f938b338cd6ae200847d3fcb40649b330106ab Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 30 Aug 2014 16:06:17 -0700
Subject: [PATCH 0557/2053] [docs] new PR policy: send master fixes + docs to
 master

To address fixes and documentation updates to master between official
releases from `dev` to `master` PRs will be accepted to `master`.

All new development must still branch off and PR to `dev`.
---
 docs/development.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/development.md b/docs/development.md
index d586a2b1c30..822d88023f5 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -44,7 +44,7 @@ Use this if you want more stability.
 
 ### Issues & Pull Request Protocol
 
-Use Github Issues to report [bugs], propose features, and ask [questions].
+Use Github Issues to report [bugs], propose features, and ask development [questions].
 Large-scale development work is guided by [milestones], which are sets of Issues selected for concurrent release (integration from `dev` to `master`).
 
 Please note that since the core developers are largely researchers, we may work on a feature in isolation for some time before releasing it to the community, so as to claim honest academic contribution.
@@ -52,12 +52,14 @@ We do release things as soon as a reasonable technical report may be written, an
 
 When you are ready to start developing your feature or fixing a bug, follow this protocol:
 
-- Do new development in [feature branches] with descriptive names.
-- Bring your work up-to-date by [rebasing] onto the latest `dev`.
+- Develop in [feature branches] with descriptive names.
+    - For new development branch off `dev`.
+    - For documentation and fixes for `master` branch off `master`.
+- Bring your work up-to-date by [rebasing] onto the latest `dev` / `master`.
 (Polish your changes by [interactive rebase], if you'd like.)
-- [Pull request] your contribution to `BVLC/caffe`'s `dev` branch for discussion and review.
+- [Pull request] your contribution to `BVLC/caffe`'s `dev` / `master` branch for discussion and review.
   - Make PRs *as soon as development begins*, to let discussion guide development.
-  - A PR is only ready for merge review when it is a fast-forward merge to dev, and all code is documented, linted, and tested -- that means your PR must include tests!
+  - A PR is only ready for merge review when it is a fast-forward merge, and all code is documented, linted, and tested -- that means your PR must include tests!
 - When the PR satisfies the above properties, use comments to request maintainer review.
 
 Below is a poetic presentation of the protocol in code form.

From b036c1df7741808d3e65195c532d7b273e1e6783 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 30 Aug 2014 16:31:33 -0700
Subject: [PATCH 0558/2053] renaming && typo fix

---
 include/caffe/internal_thread.hpp                     |  8 ++++----
 include/caffe/util/{thread_wrapper.hpp => thread.hpp} | 10 +++++-----
 src/caffe/internal_thread.cpp                         |  2 +-
 3 files changed, 10 insertions(+), 10 deletions(-)
 rename include/caffe/util/{thread_wrapper.hpp => thread.hpp} (65%)

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 48ee113878d..245ffa080e6 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -7,12 +7,12 @@ namespace caffe {
 
 /**
  * A minimal wrapper for boost::thread to force host compilation for boost
- * Defined in caffe/util/thread_wrapper.cpp
+ * Defined in caffe/util/thread.hpp
  */
-class ThreadWrapper {
+class Thread {
  public:
   template<typename Callable, class A1>
-  ThreadWrapper(Callable func, A1 a1);
+  Thread(Callable func, A1 a1);
   void join();
   bool joinable();
  private:
@@ -20,7 +20,7 @@ class ThreadWrapper {
 };
 
 /**
- * Virutal class encapsulate boost::thread for use in base class
+ * Virtual class encapsulate boost::thread for use in base class
  * The child class will acquire the ability to run a single thread,
  * by reimplementing the virutal function InternalThreadEntry.
  */
diff --git a/include/caffe/util/thread_wrapper.hpp b/include/caffe/util/thread.hpp
similarity index 65%
rename from include/caffe/util/thread_wrapper.hpp
rename to include/caffe/util/thread.hpp
index 5842d9a27fc..7251402cf1a 100644
--- a/include/caffe/util/thread_wrapper.hpp
+++ b/include/caffe/util/thread.hpp
@@ -1,5 +1,5 @@
-#ifndef CAFFE_THREAD_WRAPPER_CPP_HPP_
-#define CAFFE_THREAD_WRAPPER_CPP_HPP_
+#ifndef CAFFE_THREAD_CPP_HPP_
+#define CAFFE_THREAD_CPP_HPP_
 
 #include <boost/thread.hpp>
 #include "caffe/common.hpp"
@@ -8,15 +8,15 @@
 namespace caffe {
 
 template<typename Callable, class A1>
-ThreadWrapper::ThreadWrapper(Callable func, A1 a1) {
+Thread::Thread(Callable func, A1 a1) {
   this->thread_ = new boost::thread(func, a1);
 }
 
-void ThreadWrapper::join() {
+void Thread::join() {
   static_cast<boost::thread*>(this->thread_)->join();
 }
 
-bool ThreadWrapper::joinable() {
+bool Thread::joinable() {
   return static_cast<boost::thread*>(this->thread_)->joinable();
 }
 
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 89c4147f2bb..1e2b3366f9b 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,6 +1,6 @@
 #include "caffe/internal_thread.hpp"
 
-#include "caffe/util/thread_wrapper.hpp"
+#include "caffe/util/thread.hpp"
 
 namespace caffe {
 

From 39acda0ccd45a64476fe146b40723ca259663c1d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 30 Aug 2014 16:37:51 -0700
Subject: [PATCH 0559/2053] fix up renaming

---
 include/caffe/internal_thread.hpp | 2 +-
 src/caffe/internal_thread.cpp     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 245ffa080e6..6a106e6eefa 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -42,7 +42,7 @@ class InternalThread {
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
-  caffe::ThreadWrapper* thread_;
+  caffe::Thread* thread_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 1e2b3366f9b..e8cfc861932 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -13,7 +13,7 @@ InternalThread::~InternalThread() {
 
 bool InternalThread::StartInternalThread() {
   try {
-    thread_ = new caffe::ThreadWrapper
+    thread_ = new caffe::Thread
         (&InternalThread::InternalThreadEntry, this);
   } catch (...) {
     return false;

From 1193b25c1795da321cdc2de3a9131557a4c8daca Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 30 Aug 2014 16:41:01 -0700
Subject: [PATCH 0560/2053] Makefile: fix boost::thread linking, drop pthread,
 sort

---
 Makefile | 30 +++++++++++++++++-------------
 1 file changed, 17 insertions(+), 13 deletions(-)

diff --git a/Makefile b/Makefile
index 9c420249887..731e6fd1f35 100644
--- a/Makefile
+++ b/Makefile
@@ -159,9 +159,9 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 LIBRARIES += \
-	glog gflags pthread protobuf leveldb snappy \
+	glog gflags protobuf leveldb snappy \
 	lmdb \
-	boost_system boost_thread \
+	boost_system \
 	hdf5_hl hdf5 \
 	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
@@ -205,16 +205,8 @@ ifeq ($(LINUX), 1)
 	ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
 		WARNINGS += -Wno-uninitialized
 	endif
-endif
-
-# CPU-only configuration
-ifeq ($(CPU_ONLY), 1)
-	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
-	TEST_OBJS := $(TEST_CXX_OBJS)
-	TEST_BINS := $(TEST_CXX_BINS)
-	ALL_WARNS := $(ALL_CXX_WARNS)
-	TEST_FILTER := --gtest_filter="-*GPU*"
-	COMMON_FLAGS += -DCPU_ONLY
+	# boost::thread is reasonably called boost_thread (compare OS X)
+	LIBRARIES += boost_thread
 endif
 
 # OS X:
@@ -228,6 +220,8 @@ ifeq ($(OSX), 1)
 		CXXFLAGS += -stdlib=libstdc++
 		LINKFLAGS += -stdlib=libstdc++
 	endif
+	# boost::thread is called boost_thread-mt to mark multithreading on OS X
+	LIBRARIES += boost_thread-mt
 endif
 
 # Custom compiler
@@ -242,6 +236,16 @@ else
 	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
+# CPU-only configuration
+ifeq ($(CPU_ONLY), 1)
+	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
+	TEST_OBJS := $(TEST_CXX_OBJS)
+	TEST_BINS := $(TEST_CXX_BINS)
+	ALL_WARNS := $(ALL_CXX_WARNS)
+	TEST_FILTER := --gtest_filter="-*GPU*"
+	COMMON_FLAGS += -DCPU_ONLY
+endif
+
 # BLAS configuration (default = ATLAS)
 BLAS ?= atlas
 ifeq ($(BLAS), mkl)
@@ -273,7 +277,7 @@ LIBRARY_DIRS += $(BLAS_LIB)
 
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
-CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
+CXXFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized

From 67ae8dec664d6f098b8582af5e601fdcd475fd13 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 30 Aug 2014 16:59:02 -0700
Subject: [PATCH 0561/2053] fix up leveldb ignore

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index c349a3e1179..810c178f672 100644
--- a/.gitignore
+++ b/.gitignore
@@ -59,7 +59,7 @@ data/*
 *lmdb
 
 # LevelDB files
-*.ldb
+*.sst
 LOCK
 LOG*
 CURRENT

From 45a80fa588be78e7a7ef8bbc28ba5893b28190ac Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 30 Aug 2014 21:32:21 -0700
Subject: [PATCH 0562/2053] ignore leveldb extension: ldb

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 810c178f672..62d96f8e9dd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -60,6 +60,7 @@ data/*
 
 # LevelDB files
 *.sst
+*.ldb
 LOCK
 LOG*
 CURRENT

From 4dcb96e42dfa212d07c351e4a75c158a5ec95b9b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 30 Aug 2014 21:37:12 -0700
Subject: [PATCH 0563/2053] remove residual pthread references, but restore in
 build for gtest

gtest depends on pthread in Ubuntu 14.04
---
 Makefile                               | 4 ++--
 include/caffe/loss_layers.hpp          | 1 -
 include/caffe/neuron_layers.hpp        | 1 -
 src/caffe/layers/data_layer.cpp        | 2 +-
 src/caffe/layers/data_layer.cu         | 1 -
 src/caffe/layers/image_data_layer.cpp  | 2 +-
 src/caffe/layers/window_data_layer.cpp | 3 ---
 src/caffe/layers/window_data_layer.cu  | 4 ----
 8 files changed, 4 insertions(+), 14 deletions(-)

diff --git a/Makefile b/Makefile
index 731e6fd1f35..b6e04444fb0 100644
--- a/Makefile
+++ b/Makefile
@@ -158,7 +158,7 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARY_DIRS += $(CUDA_LIB_DIR)
 	LIBRARIES := cudart cublas curand
 endif
-LIBRARIES += \
+LIBRARIES += pthread \
 	glog gflags protobuf leveldb snappy \
 	lmdb \
 	boost_system \
@@ -277,7 +277,7 @@ LIBRARY_DIRS += $(BLAS_LIB)
 
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
-CXXFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
+CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index f24b1b749f3..df887b8f856 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -8,7 +8,6 @@
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
 #include "leveldb/db.h"
-#include "pthread.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index d17120fa73a..b5bbc5b6482 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -7,7 +7,6 @@
 
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
-#include "pthread.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index a815c4a6a16..519b666e35b 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -13,7 +13,7 @@
 
 namespace caffe {
 
-// This function is used to create a pthread that prefetches the data.
+// This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 467b146fa1f..28ca2c1082a 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -2,7 +2,6 @@
 #include <vector>
 
 #include "leveldb/db.h"
-#include "pthread.h"
 #include "stdint.h"
 
 #include "caffe/layer.hpp"
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index ce8338bc028..5039ad03b08 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -12,7 +12,7 @@
 
 namespace caffe {
 
-// This function is used to create a pthread that prefetches the data.
+// This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 82bee0ecd19..ab12c61a08a 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,6 +1,3 @@
-//
-// Based on data_layer.cpp by Yangqing Jia.
-
 #include <stdint.h>
 
 #include <algorithm>
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index 475ec265a6b..947f2a39e7e 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -1,7 +1,3 @@
-//
-// Based on data_layer.cpp by Yangqing Jia.
-
-#include <pthread.h>
 #include <stdint.h>
 
 #include <string>

From e7f712eb977287ef89c63385aaa291c5672ca3b1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 30 Aug 2014 23:48:41 -0700
Subject: [PATCH 0564/2053] [pycaffe] use class names of the form Py* instead
 of Caffe*

---
 python/caffe/_caffe.cpp | 140 ++++++++++++++++++++--------------------
 1 file changed, 70 insertions(+), 70 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 3103d0267be..0014c581bc9 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -47,9 +47,9 @@ static void CheckFile(const string& filename) {
 
 // wrap shared_ptr<Blob<float> > in a class that we construct in C++ and pass
 // to Python
-class CaffeBlob {
+class PyBlob {
  public:
-  CaffeBlob(const shared_ptr<Blob<float> > &blob, const string& name)
+  PyBlob(const shared_ptr<Blob<float> > &blob, const string& name)
       : blob_(blob), name_(name) {}
 
   string name() const { return name_; }
@@ -60,7 +60,7 @@ class CaffeBlob {
   int count() const { return blob_->count(); }
 
   // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const CaffeBlob &other) {
+  bool operator == (const PyBlob &other) {
       return this->blob_ == other.blob_;
   }
 
@@ -73,10 +73,10 @@ class CaffeBlob {
 // We need another wrapper (used as boost::python's HeldType) that receives a
 // self PyObject * which we can use as ndarray.base, so that data/diff memory
 // is not freed while still being used in Python.
-class CaffeBlobWrap : public CaffeBlob {
+class PyBlobWrap : public PyBlob {
  public:
-  CaffeBlobWrap(PyObject *p, const CaffeBlob &blob)
-      : CaffeBlob(blob), self_(p) {}
+  PyBlobWrap(PyObject *p, const PyBlob &blob)
+      : PyBlob(blob), self_(p) {}
 
   object get_data() {
       npy_intp dims[] = {num(), channels(), height(), width()};
@@ -107,22 +107,22 @@ class CaffeBlobWrap : public CaffeBlob {
 };
 
 
-class CaffeLayer {
+class PyLayer {
  public:
-  CaffeLayer(const shared_ptr<Layer<float> > &layer, const string &name)
+  PyLayer(const shared_ptr<Layer<float> > &layer, const string &name)
     : layer_(layer), name_(name) {}
 
   string name() const { return name_; }
-  vector<CaffeBlob> blobs() {
-    vector<CaffeBlob> result;
+  vector<PyBlob> blobs() {
+    vector<PyBlob> result;
     for (int i = 0; i < layer_->blobs().size(); ++i) {
-      result.push_back(CaffeBlob(layer_->blobs()[i], name_));
+      result.push_back(PyBlob(layer_->blobs()[i], name_));
     }
     return result;
   }
 
   // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const CaffeLayer &other) {
+  bool operator == (const PyLayer &other) {
       return this->layer_ == other.layer_;
   }
 
@@ -132,22 +132,22 @@ class CaffeLayer {
 };
 
 
-// A simple wrapper over CaffeNet that runs the forward process.
-struct CaffeNet {
+// A simple wrapper over PyNet that runs the forward process.
+struct PyNet {
   // For cases where parameters will be determined later by the Python user,
   // create a Net with unallocated parameters (which will not be zero-filled
   // when accessed).
-  explicit CaffeNet(string param_file) {
+  explicit PyNet(string param_file) {
     Init(param_file);
   }
 
-  CaffeNet(string param_file, string pretrained_param_file) {
+  PyNet(string param_file, string pretrained_param_file) {
     Init(param_file);
     CheckFile(pretrained_param_file);
     net_->CopyTrainedLayersFrom(pretrained_param_file);
   }
 
-  explicit CaffeNet(shared_ptr<Net<float> > net)
+  explicit PyNet(shared_ptr<Net<float> > net)
       : net_(net) {}
 
   void Init(string param_file) {
@@ -156,7 +156,7 @@ struct CaffeNet {
   }
 
 
-  virtual ~CaffeNet() {}
+  virtual ~PyNet() {}
 
   // Generate Python exceptions for badly shaped or discontiguous arrays.
   inline void check_contiguous_array(PyArrayObject* arr, string name,
@@ -238,18 +238,18 @@ struct CaffeNet {
   void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
   void set_device(int device_id) { Caffe::SetDevice(device_id); }
 
-  vector<CaffeBlob> blobs() {
-    vector<CaffeBlob> result;
+  vector<PyBlob> blobs() {
+    vector<PyBlob> result;
     for (int i = 0; i < net_->blobs().size(); ++i) {
-      result.push_back(CaffeBlob(net_->blobs()[i], net_->blob_names()[i]));
+      result.push_back(PyBlob(net_->blobs()[i], net_->blob_names()[i]));
     }
     return result;
   }
 
-  vector<CaffeLayer> layers() {
-    vector<CaffeLayer> result;
+  vector<PyLayer> layers() {
+    vector<PyLayer> result;
     for (int i = 0; i < net_->layers().size(); ++i) {
-      result.push_back(CaffeLayer(net_->layers()[i], net_->layer_names()[i]));
+      result.push_back(PyLayer(net_->layers()[i], net_->layer_names()[i]));
     }
     return result;
   }
@@ -284,19 +284,19 @@ struct CaffeNet {
   object input_labels_;
 };
 
-class CaffeSGDSolver {
+class PySGDSolver {
  public:
-  explicit CaffeSGDSolver(const string& param_file) {
-    // as in CaffeNet, (as a convenience, not a guarantee), create a Python
+  explicit PySGDSolver(const string& param_file) {
+    // as in PyNet, (as a convenience, not a guarantee), create a Python
     // exception if param_file can't be opened
     CheckFile(param_file);
     solver_.reset(new SGDSolver<float>(param_file));
     // we need to explicitly store the net wrapper, rather than constructing
     // it on the fly, so that it can hold references to Python objects
-    net_.reset(new CaffeNet(solver_->net()));
+    net_.reset(new PyNet(solver_->net()));
   }
 
-  shared_ptr<CaffeNet> net() { return net_; }
+  shared_ptr<PyNet> net() { return net_; }
   void Solve() { return solver_->Solve(); }
   void SolveResume(const string& resume_file) {
     CheckFile(resume_file);
@@ -304,7 +304,7 @@ class CaffeSGDSolver {
   }
 
  protected:
-  shared_ptr<CaffeNet> net_;
+  shared_ptr<PyNet> net_;
   shared_ptr<SGDSolver<float> > solver_;
 };
 
@@ -313,54 +313,54 @@ class CaffeSGDSolver {
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
-  boost::python::class_<CaffeNet, shared_ptr<CaffeNet> >(
+  boost::python::class_<PyNet, shared_ptr<PyNet> >(
       "Net", boost::python::init<string, string>())
       .def(boost::python::init<string>())
-      .def("_forward",              &CaffeNet::Forward)
-      .def("_backward",             &CaffeNet::Backward)
-      .def("set_mode_cpu",          &CaffeNet::set_mode_cpu)
-      .def("set_mode_gpu",          &CaffeNet::set_mode_gpu)
-      .def("set_phase_train",       &CaffeNet::set_phase_train)
-      .def("set_phase_test",        &CaffeNet::set_phase_test)
-      .def("set_device",            &CaffeNet::set_device)
-      .add_property("_blobs",       &CaffeNet::blobs)
-      .add_property("layers",       &CaffeNet::layers)
-      .add_property("inputs",       &CaffeNet::inputs)
-      .add_property("outputs",      &CaffeNet::outputs)
-      .add_property("mean",         &CaffeNet::mean_)
-      .add_property("input_scale",  &CaffeNet::input_scale_)
-      .add_property("raw_scale",    &CaffeNet::raw_scale_)
-      .add_property("channel_swap", &CaffeNet::channel_swap_)
-      .def("_set_input_arrays",     &CaffeNet::set_input_arrays)
-      .def("save",                  &CaffeNet::save);
-
-  boost::python::class_<CaffeBlob, CaffeBlobWrap>(
+      .def("_forward",              &PyNet::Forward)
+      .def("_backward",             &PyNet::Backward)
+      .def("set_mode_cpu",          &PyNet::set_mode_cpu)
+      .def("set_mode_gpu",          &PyNet::set_mode_gpu)
+      .def("set_phase_train",       &PyNet::set_phase_train)
+      .def("set_phase_test",        &PyNet::set_phase_test)
+      .def("set_device",            &PyNet::set_device)
+      .add_property("_blobs",       &PyNet::blobs)
+      .add_property("layers",       &PyNet::layers)
+      .add_property("inputs",       &PyNet::inputs)
+      .add_property("outputs",      &PyNet::outputs)
+      .add_property("mean",         &PyNet::mean_)
+      .add_property("input_scale",  &PyNet::input_scale_)
+      .add_property("raw_scale",    &PyNet::raw_scale_)
+      .add_property("channel_swap", &PyNet::channel_swap_)
+      .def("_set_input_arrays",     &PyNet::set_input_arrays)
+      .def("save",                  &PyNet::save);
+
+  boost::python::class_<PyBlob, PyBlobWrap>(
       "Blob", boost::python::no_init)
-      .add_property("name",     &CaffeBlob::name)
-      .add_property("num",      &CaffeBlob::num)
-      .add_property("channels", &CaffeBlob::channels)
-      .add_property("height",   &CaffeBlob::height)
-      .add_property("width",    &CaffeBlob::width)
-      .add_property("count",    &CaffeBlob::count)
-      .add_property("data",     &CaffeBlobWrap::get_data)
-      .add_property("diff",     &CaffeBlobWrap::get_diff);
-
-  boost::python::class_<CaffeLayer>(
+      .add_property("name",     &PyBlob::name)
+      .add_property("num",      &PyBlob::num)
+      .add_property("channels", &PyBlob::channels)
+      .add_property("height",   &PyBlob::height)
+      .add_property("width",    &PyBlob::width)
+      .add_property("count",    &PyBlob::count)
+      .add_property("data",     &PyBlobWrap::get_data)
+      .add_property("diff",     &PyBlobWrap::get_diff);
+
+  boost::python::class_<PyLayer>(
       "Layer", boost::python::no_init)
-      .add_property("name",  &CaffeLayer::name)
-      .add_property("blobs", &CaffeLayer::blobs);
+      .add_property("name",  &PyLayer::name)
+      .add_property("blobs", &PyLayer::blobs);
 
-  boost::python::class_<CaffeSGDSolver, boost::noncopyable>(
+  boost::python::class_<PySGDSolver, boost::noncopyable>(
       "SGDSolver", boost::python::init<string>())
-      .add_property("net", &CaffeSGDSolver::net)
-      .def("solve",        &CaffeSGDSolver::Solve)
-      .def("solve",        &CaffeSGDSolver::SolveResume);
+      .add_property("net", &PySGDSolver::net)
+      .def("solve",        &PySGDSolver::Solve)
+      .def("solve",        &PySGDSolver::SolveResume);
 
-  boost::python::class_<vector<CaffeBlob> >("BlobVec")
-      .def(vector_indexing_suite<vector<CaffeBlob>, true>());
+  boost::python::class_<vector<PyBlob> >("BlobVec")
+      .def(vector_indexing_suite<vector<PyBlob>, true>());
 
-  boost::python::class_<vector<CaffeLayer> >("LayerVec")
-      .def(vector_indexing_suite<vector<CaffeLayer>, true>());
+  boost::python::class_<vector<PyLayer> >("LayerVec")
+      .def(vector_indexing_suite<vector<PyLayer>, true>());
 
   import_array();
 }

From 9d541499a992d89347602c198560f440fabae43e Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 30 Aug 2014 23:49:44 -0700
Subject: [PATCH 0565/2053] [pycaffe] live in caffe namespace instead of
 opening it

---
 python/caffe/_caffe.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 0014c581bc9..dd4062c25f4 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -22,8 +22,6 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
-
-using namespace caffe;  // NOLINT(build/namespaces)
 using boost::python::dict;
 using boost::python::extract;
 using boost::python::len;
@@ -32,6 +30,8 @@ using boost::python::object;
 using boost::python::handle;
 using boost::python::vector_indexing_suite;
 
+namespace caffe {
+
 // for convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
@@ -364,3 +364,5 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   import_array();
 }
+
+}  // namespace caffe

From 128be26dad4877d6414cbfffb9ce07b4d91bdbc3 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 30 Aug 2014 23:55:57 -0700
Subject: [PATCH 0566/2053] [pycaffe] use a namespace alias instead of using
 directives for boost::python

---
 python/caffe/_caffe.cpp | 68 +++++++++++++++++++----------------------
 1 file changed, 31 insertions(+), 37 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index dd4062c25f4..4810cbf2f4a 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -22,13 +22,7 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
-using boost::python::dict;
-using boost::python::extract;
-using boost::python::len;
-using boost::python::list;
-using boost::python::object;
-using boost::python::handle;
-using boost::python::vector_indexing_suite;
+namespace bp = boost::python;
 
 namespace caffe {
 
@@ -78,28 +72,28 @@ class PyBlobWrap : public PyBlob {
   PyBlobWrap(PyObject *p, const PyBlob &blob)
       : PyBlob(blob), self_(p) {}
 
-  object get_data() {
+  bp::object get_data() {
       npy_intp dims[] = {num(), channels(), height(), width()};
 
       PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
                                                 blob_->mutable_cpu_data());
       PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
       Py_INCREF(self_);
-      handle<> h(obj);
+      bp::handle<> h(obj);
 
-      return object(h);
+      return bp::object(h);
   }
 
-  object get_diff() {
+  bp::object get_diff() {
       npy_intp dims[] = {num(), channels(), height(), width()};
 
       PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
                                                 blob_->mutable_cpu_diff());
       PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
       Py_INCREF(self_);
-      handle<> h(obj);
+      bp::handle<> h(obj);
 
-      return object(h);
+      return bp::object(h);
   }
 
  private:
@@ -189,7 +183,7 @@ struct PyNet {
     net_->BackwardFromTo(start, end);
   }
 
-  void set_input_arrays(object data_obj, object labels_obj) {
+  void set_input_arrays(bp::object data_obj, bp::object labels_obj) {
     // check that this network has an input MemoryDataLayer
     shared_ptr<MemoryDataLayer<float> > md_layer =
       boost::dynamic_pointer_cast<MemoryDataLayer<float> >(net_->layers()[0]);
@@ -254,8 +248,8 @@ struct PyNet {
     return result;
   }
 
-  list inputs() {
-    list input_blob_names;
+  bp::list inputs() {
+    bp::list input_blob_names;
     for (int i = 0; i < net_->input_blob_indices().size(); ++i) {
       input_blob_names.append(
           net_->blob_names()[net_->input_blob_indices()[i]]);
@@ -263,8 +257,8 @@ struct PyNet {
     return input_blob_names;
   }
 
-  list outputs() {
-    list output_blob_names;
+  bp::list outputs() {
+    bp::list output_blob_names;
     for (int i = 0; i < net_->output_blob_indices().size(); ++i) {
       output_blob_names.append(
           net_->blob_names()[net_->output_blob_indices()[i]]);
@@ -275,13 +269,13 @@ struct PyNet {
   // The pointer to the internal caffe::Net instant.
   shared_ptr<Net<float> > net_;
   // Input preprocessing configuration attributes.
-  dict mean_;
-  dict input_scale_;
-  dict raw_scale_;
-  dict channel_swap_;
+  bp::dict mean_;
+  bp::dict input_scale_;
+  bp::dict raw_scale_;
+  bp::dict channel_swap_;
   // if taking input from an ndarray, we need to hold references
-  object input_data_;
-  object input_labels_;
+  bp::object input_data_;
+  bp::object input_labels_;
 };
 
 class PySGDSolver {
@@ -313,9 +307,9 @@ class PySGDSolver {
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
-  boost::python::class_<PyNet, shared_ptr<PyNet> >(
-      "Net", boost::python::init<string, string>())
-      .def(boost::python::init<string>())
+  bp::class_<PyNet, shared_ptr<PyNet> >(
+      "Net", bp::init<string, string>())
+      .def(bp::init<string>())
       .def("_forward",              &PyNet::Forward)
       .def("_backward",             &PyNet::Backward)
       .def("set_mode_cpu",          &PyNet::set_mode_cpu)
@@ -334,8 +328,8 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def("_set_input_arrays",     &PyNet::set_input_arrays)
       .def("save",                  &PyNet::save);
 
-  boost::python::class_<PyBlob, PyBlobWrap>(
-      "Blob", boost::python::no_init)
+  bp::class_<PyBlob, PyBlobWrap>(
+      "Blob", bp::no_init)
       .add_property("name",     &PyBlob::name)
       .add_property("num",      &PyBlob::num)
       .add_property("channels", &PyBlob::channels)
@@ -345,22 +339,22 @@ BOOST_PYTHON_MODULE(_caffe) {
       .add_property("data",     &PyBlobWrap::get_data)
       .add_property("diff",     &PyBlobWrap::get_diff);
 
-  boost::python::class_<PyLayer>(
-      "Layer", boost::python::no_init)
+  bp::class_<PyLayer>(
+      "Layer", bp::no_init)
       .add_property("name",  &PyLayer::name)
       .add_property("blobs", &PyLayer::blobs);
 
-  boost::python::class_<PySGDSolver, boost::noncopyable>(
-      "SGDSolver", boost::python::init<string>())
+  bp::class_<PySGDSolver, boost::noncopyable>(
+      "SGDSolver", bp::init<string>())
       .add_property("net", &PySGDSolver::net)
       .def("solve",        &PySGDSolver::Solve)
       .def("solve",        &PySGDSolver::SolveResume);
 
-  boost::python::class_<vector<PyBlob> >("BlobVec")
-      .def(vector_indexing_suite<vector<PyBlob>, true>());
+  bp::class_<vector<PyBlob> >("BlobVec")
+      .def(bp::vector_indexing_suite<vector<PyBlob>, true>());
 
-  boost::python::class_<vector<PyLayer> >("LayerVec")
-      .def(vector_indexing_suite<vector<PyLayer>, true>());
+  bp::class_<vector<PyLayer> >("LayerVec")
+      .def(bp::vector_indexing_suite<vector<PyLayer>, true>());
 
   import_array();
 }

From 3535b4731a9334ac35796f39064a066900852cea Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 31 Aug 2014 00:01:20 -0700
Subject: [PATCH 0567/2053] [pycaffe] make PyNet a class, not a struct

---
 python/caffe/_caffe.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 4810cbf2f4a..773f51f1aaf 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -127,7 +127,8 @@ class PyLayer {
 
 
 // A simple wrapper over PyNet that runs the forward process.
-struct PyNet {
+class PyNet {
+ public:
   // For cases where parameters will be determined later by the Python user,
   // create a Net with unallocated parameters (which will not be zero-filled
   // when accessed).
@@ -266,13 +267,16 @@ struct PyNet {
     return output_blob_names;
   }
 
-  // The pointer to the internal caffe::Net instant.
-  shared_ptr<Net<float> > net_;
-  // Input preprocessing configuration attributes.
+  // Input preprocessing configuration attributes. These are public for
+  // direct access from Python.
   bp::dict mean_;
   bp::dict input_scale_;
   bp::dict raw_scale_;
   bp::dict channel_swap_;
+
+ protected:
+  // The pointer to the internal caffe::Net instant.
+  shared_ptr<Net<float> > net_;
   // if taking input from an ndarray, we need to hold references
   bp::object input_data_;
   bp::object input_labels_;

From 6be5b6ed7a74d4e00e6805f632b14eb3f2f116f1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 31 Aug 2014 00:07:43 -0700
Subject: [PATCH 0568/2053] [pycaffe] make PyBlob a template over Dtype

We will not actually support double nets in Python with this commit, but
this change will be necessary to create layers that embed Python.
---
 python/caffe/_caffe.cpp | 43 +++++++++++++++++++++--------------------
 1 file changed, 22 insertions(+), 21 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 773f51f1aaf..49e28624db2 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -39,11 +39,12 @@ static void CheckFile(const string& filename) {
     f.close();
 }
 
-// wrap shared_ptr<Blob<float> > in a class that we construct in C++ and pass
+// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
 // to Python
+template <typename Dtype>
 class PyBlob {
  public:
-  PyBlob(const shared_ptr<Blob<float> > &blob, const string& name)
+  PyBlob(const shared_ptr<Blob<Dtype> > &blob, const string& name)
       : blob_(blob), name_(name) {}
 
   string name() const { return name_; }
@@ -59,7 +60,7 @@ class PyBlob {
   }
 
  protected:
-  shared_ptr<Blob<float> > blob_;
+  shared_ptr<Blob<Dtype> > blob_;
   string name_;
 };
 
@@ -67,10 +68,10 @@ class PyBlob {
 // We need another wrapper (used as boost::python's HeldType) that receives a
 // self PyObject * which we can use as ndarray.base, so that data/diff memory
 // is not freed while still being used in Python.
-class PyBlobWrap : public PyBlob {
+class PyBlobWrap : public PyBlob<float> {
  public:
-  PyBlobWrap(PyObject *p, const PyBlob &blob)
-      : PyBlob(blob), self_(p) {}
+  PyBlobWrap(PyObject *p, const PyBlob<float> &blob)
+      : PyBlob<float>(blob), self_(p) {}
 
   bp::object get_data() {
       npy_intp dims[] = {num(), channels(), height(), width()};
@@ -107,10 +108,10 @@ class PyLayer {
     : layer_(layer), name_(name) {}
 
   string name() const { return name_; }
-  vector<PyBlob> blobs() {
-    vector<PyBlob> result;
+  vector<PyBlob<float> > blobs() {
+    vector<PyBlob<float> > result;
     for (int i = 0; i < layer_->blobs().size(); ++i) {
-      result.push_back(PyBlob(layer_->blobs()[i], name_));
+      result.push_back(PyBlob<float>(layer_->blobs()[i], name_));
     }
     return result;
   }
@@ -233,10 +234,10 @@ class PyNet {
   void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
   void set_device(int device_id) { Caffe::SetDevice(device_id); }
 
-  vector<PyBlob> blobs() {
-    vector<PyBlob> result;
+  vector<PyBlob<float> > blobs() {
+    vector<PyBlob<float> > result;
     for (int i = 0; i < net_->blobs().size(); ++i) {
-      result.push_back(PyBlob(net_->blobs()[i], net_->blob_names()[i]));
+      result.push_back(PyBlob<float>(net_->blobs()[i], net_->blob_names()[i]));
     }
     return result;
   }
@@ -332,14 +333,14 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def("_set_input_arrays",     &PyNet::set_input_arrays)
       .def("save",                  &PyNet::save);
 
-  bp::class_<PyBlob, PyBlobWrap>(
+  bp::class_<PyBlob<float>, PyBlobWrap>(
       "Blob", bp::no_init)
-      .add_property("name",     &PyBlob::name)
-      .add_property("num",      &PyBlob::num)
-      .add_property("channels", &PyBlob::channels)
-      .add_property("height",   &PyBlob::height)
-      .add_property("width",    &PyBlob::width)
-      .add_property("count",    &PyBlob::count)
+      .add_property("name",     &PyBlob<float>::name)
+      .add_property("num",      &PyBlob<float>::num)
+      .add_property("channels", &PyBlob<float>::channels)
+      .add_property("height",   &PyBlob<float>::height)
+      .add_property("width",    &PyBlob<float>::width)
+      .add_property("count",    &PyBlob<float>::count)
       .add_property("data",     &PyBlobWrap::get_data)
       .add_property("diff",     &PyBlobWrap::get_diff);
 
@@ -354,8 +355,8 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def("solve",        &PySGDSolver::Solve)
       .def("solve",        &PySGDSolver::SolveResume);
 
-  bp::class_<vector<PyBlob> >("BlobVec")
-      .def(bp::vector_indexing_suite<vector<PyBlob>, true>());
+  bp::class_<vector<PyBlob<float> > >("BlobVec")
+      .def(bp::vector_indexing_suite<vector<PyBlob<float> >, true>());
 
   bp::class_<vector<PyLayer> >("LayerVec")
       .def(bp::vector_indexing_suite<vector<PyLayer>, true>());

From f4f508e5f2d4959cec70317552ed092ff748830b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 31 Aug 2014 00:26:12 -0700
Subject: [PATCH 0569/2053] [pycaffe] split _caffe into source and header files

This brings pycaffe more in line with usual C++ style, and will allow
the wrapper classes to be used within caffe when embedding Python.
---
 Makefile                |   3 +-
 python/caffe/_caffe.cpp | 350 +++++++++++-----------------------------
 python/caffe/_caffe.hpp | 186 +++++++++++++++++++++
 3 files changed, 279 insertions(+), 260 deletions(-)
 create mode 100644 python/caffe/_caffe.hpp

diff --git a/Makefile b/Makefile
index b6e04444fb0..dd9ba5c8376 100644
--- a/Makefile
+++ b/Makefile
@@ -70,6 +70,7 @@ EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
 NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
+PY$(PROJECT)_HXX_SRC := python/$(PROJECT)/_$(PROJECT).hpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
 # MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
 MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
@@ -345,7 +346,7 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC)
+$(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
 	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
 		$(STATIC_NAME) $(LINKFLAGS) $(PYTHON_LDFLAGS)
 	@ echo
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 49e28624db2..7c26c7f79f9 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -2,17 +2,14 @@
 // caffe::Caffe functions so that one could easily call it from Python.
 // Note that for Python, we will simply use float as the data type.
 
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-#include "boost/python.hpp"
-#include "boost/python/suite/indexing/vector_indexing_suite.hpp"
-#include "numpy/arrayobject.h"
+#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
 
 // these need to be included after boost on OS X
 #include <string>  // NOLINT(build/include_order)
 #include <vector>  // NOLINT(build/include_order)
 #include <fstream>  // NOLINT
 
+#include "_caffe.hpp"
 #include "caffe/caffe.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
@@ -22,8 +19,6 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
-namespace bp = boost::python;
-
 namespace caffe {
 
 // for convenience, check that input files can be opened, and raise an
@@ -39,276 +34,113 @@ static void CheckFile(const string& filename) {
     f.close();
 }
 
-// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
-// to Python
-template <typename Dtype>
-class PyBlob {
- public:
-  PyBlob(const shared_ptr<Blob<Dtype> > &blob, const string& name)
-      : blob_(blob), name_(name) {}
-
-  string name() const { return name_; }
-  int num() const { return blob_->num(); }
-  int channels() const { return blob_->channels(); }
-  int height() const { return blob_->height(); }
-  int width() const { return blob_->width(); }
-  int count() const { return blob_->count(); }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyBlob &other) {
-      return this->blob_ == other.blob_;
-  }
-
- protected:
-  shared_ptr<Blob<Dtype> > blob_;
-  string name_;
-};
-
-
-// We need another wrapper (used as boost::python's HeldType) that receives a
-// self PyObject * which we can use as ndarray.base, so that data/diff memory
-// is not freed while still being used in Python.
-class PyBlobWrap : public PyBlob<float> {
- public:
-  PyBlobWrap(PyObject *p, const PyBlob<float> &blob)
-      : PyBlob<float>(blob), self_(p) {}
-
-  bp::object get_data() {
-      npy_intp dims[] = {num(), channels(), height(), width()};
-
-      PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                                blob_->mutable_cpu_data());
-      PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-      Py_INCREF(self_);
-      bp::handle<> h(obj);
-
-      return bp::object(h);
-  }
-
-  bp::object get_diff() {
-      npy_intp dims[] = {num(), channels(), height(), width()};
-
-      PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                                blob_->mutable_cpu_diff());
-      PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-      Py_INCREF(self_);
-      bp::handle<> h(obj);
-
-      return bp::object(h);
-  }
-
- private:
-  PyObject *self_;
-};
-
+bp::object PyBlobWrap::get_data() {
+  npy_intp dims[] = {num(), channels(), height(), width()};
 
-class PyLayer {
- public:
-  PyLayer(const shared_ptr<Layer<float> > &layer, const string &name)
-    : layer_(layer), name_(name) {}
+  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
+                                            blob_->mutable_cpu_data());
+  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
+  Py_INCREF(self_);
+  bp::handle<> h(obj);
 
-  string name() const { return name_; }
-  vector<PyBlob<float> > blobs() {
-    vector<PyBlob<float> > result;
-    for (int i = 0; i < layer_->blobs().size(); ++i) {
-      result.push_back(PyBlob<float>(layer_->blobs()[i], name_));
-    }
-    return result;
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyLayer &other) {
-      return this->layer_ == other.layer_;
-  }
-
- protected:
-  shared_ptr<Layer<float> > layer_;
-  string name_;
-};
-
-
-// A simple wrapper over PyNet that runs the forward process.
-class PyNet {
- public:
-  // For cases where parameters will be determined later by the Python user,
-  // create a Net with unallocated parameters (which will not be zero-filled
-  // when accessed).
-  explicit PyNet(string param_file) {
-    Init(param_file);
-  }
-
-  PyNet(string param_file, string pretrained_param_file) {
-    Init(param_file);
-    CheckFile(pretrained_param_file);
-    net_->CopyTrainedLayersFrom(pretrained_param_file);
-  }
-
-  explicit PyNet(shared_ptr<Net<float> > net)
-      : net_(net) {}
-
-  void Init(string param_file) {
-    CheckFile(param_file);
-    net_.reset(new Net<float>(param_file));
-  }
-
-
-  virtual ~PyNet() {}
-
-  // Generate Python exceptions for badly shaped or discontiguous arrays.
-  inline void check_contiguous_array(PyArrayObject* arr, string name,
-      int channels, int height, int width) {
-    if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
-      throw std::runtime_error(name + " must be C contiguous");
-    }
-    if (PyArray_NDIM(arr) != 4) {
-      throw std::runtime_error(name + " must be 4-d");
-    }
-    if (PyArray_TYPE(arr) != NPY_FLOAT32) {
-      throw std::runtime_error(name + " must be float32");
-    }
-    if (PyArray_DIMS(arr)[1] != channels) {
-      throw std::runtime_error(name + " has wrong number of channels");
-    }
-    if (PyArray_DIMS(arr)[2] != height) {
-      throw std::runtime_error(name + " has wrong height");
-    }
-    if (PyArray_DIMS(arr)[3] != width) {
-      throw std::runtime_error(name + " has wrong width");
-    }
-  }
+  return bp::object(h);
+}
 
-  void Forward(int start, int end) {
-    net_->ForwardFromTo(start, end);
-  }
+bp::object PyBlobWrap::get_diff() {
+  npy_intp dims[] = {num(), channels(), height(), width()};
 
-  void Backward(int start, int end) {
-    net_->BackwardFromTo(start, end);
-  }
+  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
+                                            blob_->mutable_cpu_diff());
+  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
+  Py_INCREF(self_);
+  bp::handle<> h(obj);
 
-  void set_input_arrays(bp::object data_obj, bp::object labels_obj) {
-    // check that this network has an input MemoryDataLayer
-    shared_ptr<MemoryDataLayer<float> > md_layer =
-      boost::dynamic_pointer_cast<MemoryDataLayer<float> >(net_->layers()[0]);
-    if (!md_layer) {
-      throw std::runtime_error("set_input_arrays may only be called if the"
-          " first layer is a MemoryDataLayer");
-    }
-
-    // check that we were passed appropriately-sized contiguous memory
-    PyArrayObject* data_arr =
-        reinterpret_cast<PyArrayObject*>(data_obj.ptr());
-    PyArrayObject* labels_arr =
-        reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
-    check_contiguous_array(data_arr, "data array", md_layer->datum_channels(),
-        md_layer->datum_height(), md_layer->datum_width());
-    check_contiguous_array(labels_arr, "labels array", 1, 1, 1);
-    if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
-      throw std::runtime_error("data and labels must have the same first"
-          " dimension");
-    }
-    if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
-      throw std::runtime_error("first dimensions of input arrays must be a"
-          " multiple of batch size");
-    }
+  return bp::object(h);
+}
 
-    // hold references
-    input_data_ = data_obj;
-    input_labels_ = labels_obj;
+PyNet::PyNet(string param_file, string pretrained_param_file) {
+  Init(param_file);
+  CheckFile(pretrained_param_file);
+  net_->CopyTrainedLayersFrom(pretrained_param_file);
+}
 
-    md_layer->Reset(static_cast<float*>(PyArray_DATA(data_arr)),
-        static_cast<float*>(PyArray_DATA(labels_arr)),
-        PyArray_DIMS(data_arr)[0]);
-  }
+void PyNet::Init(string param_file) {
+  CheckFile(param_file);
+  net_.reset(new Net<float>(param_file));
+}
 
-  // save the network weights to binary proto for net surgeries.
-  void save(string filename) {
-    NetParameter net_param;
-    net_->ToProto(&net_param, false);
-    WriteProtoToBinaryFile(net_param, filename.c_str());
+void PyNet::check_contiguous_array(PyArrayObject* arr, string name,
+    int channels, int height, int width) {
+  if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
+    throw std::runtime_error(name + " must be C contiguous");
   }
-
-  // The caffe::Caffe utility functions.
-  void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
-  void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
-  void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
-  void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
-  void set_device(int device_id) { Caffe::SetDevice(device_id); }
-
-  vector<PyBlob<float> > blobs() {
-    vector<PyBlob<float> > result;
-    for (int i = 0; i < net_->blobs().size(); ++i) {
-      result.push_back(PyBlob<float>(net_->blobs()[i], net_->blob_names()[i]));
-    }
-    return result;
+  if (PyArray_NDIM(arr) != 4) {
+    throw std::runtime_error(name + " must be 4-d");
   }
-
-  vector<PyLayer> layers() {
-    vector<PyLayer> result;
-    for (int i = 0; i < net_->layers().size(); ++i) {
-      result.push_back(PyLayer(net_->layers()[i], net_->layer_names()[i]));
-    }
-    return result;
+  if (PyArray_TYPE(arr) != NPY_FLOAT32) {
+    throw std::runtime_error(name + " must be float32");
   }
-
-  bp::list inputs() {
-    bp::list input_blob_names;
-    for (int i = 0; i < net_->input_blob_indices().size(); ++i) {
-      input_blob_names.append(
-          net_->blob_names()[net_->input_blob_indices()[i]]);
-    }
-    return input_blob_names;
+  if (PyArray_DIMS(arr)[1] != channels) {
+    throw std::runtime_error(name + " has wrong number of channels");
   }
-
-  bp::list outputs() {
-    bp::list output_blob_names;
-    for (int i = 0; i < net_->output_blob_indices().size(); ++i) {
-      output_blob_names.append(
-          net_->blob_names()[net_->output_blob_indices()[i]]);
-    }
-    return output_blob_names;
+  if (PyArray_DIMS(arr)[2] != height) {
+    throw std::runtime_error(name + " has wrong height");
   }
-
-  // Input preprocessing configuration attributes. These are public for
-  // direct access from Python.
-  bp::dict mean_;
-  bp::dict input_scale_;
-  bp::dict raw_scale_;
-  bp::dict channel_swap_;
-
- protected:
-  // The pointer to the internal caffe::Net instant.
-  shared_ptr<Net<float> > net_;
-  // if taking input from an ndarray, we need to hold references
-  bp::object input_data_;
-  bp::object input_labels_;
-};
-
-class PySGDSolver {
- public:
-  explicit PySGDSolver(const string& param_file) {
-    // as in PyNet, (as a convenience, not a guarantee), create a Python
-    // exception if param_file can't be opened
-    CheckFile(param_file);
-    solver_.reset(new SGDSolver<float>(param_file));
-    // we need to explicitly store the net wrapper, rather than constructing
-    // it on the fly, so that it can hold references to Python objects
-    net_.reset(new PyNet(solver_->net()));
+  if (PyArray_DIMS(arr)[3] != width) {
+    throw std::runtime_error(name + " has wrong width");
   }
+}
 
-  shared_ptr<PyNet> net() { return net_; }
-  void Solve() { return solver_->Solve(); }
-  void SolveResume(const string& resume_file) {
-    CheckFile(resume_file);
-    return solver_->Solve(resume_file);
-  }
+void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
+  // check that this network has an input MemoryDataLayer
+  shared_ptr<MemoryDataLayer<float> > md_layer =
+    boost::dynamic_pointer_cast<MemoryDataLayer<float> >(net_->layers()[0]);
+  if (!md_layer) {
+    throw std::runtime_error("set_input_arrays may only be called if the"
+        " first layer is a MemoryDataLayer");
+  }
+
+  // check that we were passed appropriately-sized contiguous memory
+  PyArrayObject* data_arr =
+      reinterpret_cast<PyArrayObject*>(data_obj.ptr());
+  PyArrayObject* labels_arr =
+      reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
+  check_contiguous_array(data_arr, "data array", md_layer->datum_channels(),
+      md_layer->datum_height(), md_layer->datum_width());
+  check_contiguous_array(labels_arr, "labels array", 1, 1, 1);
+  if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
+    throw std::runtime_error("data and labels must have the same first"
+        " dimension");
+  }
+  if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
+    throw std::runtime_error("first dimensions of input arrays must be a"
+        " multiple of batch size");
+  }
+
+  // hold references
+  input_data_ = data_obj;
+  input_labels_ = labels_obj;
+
+  md_layer->Reset(static_cast<float*>(PyArray_DATA(data_arr)),
+      static_cast<float*>(PyArray_DATA(labels_arr)),
+      PyArray_DIMS(data_arr)[0]);
+}
 
- protected:
-  shared_ptr<PyNet> net_;
-  shared_ptr<SGDSolver<float> > solver_;
-};
+PySGDSolver::PySGDSolver(const string& param_file) {
+  // as in PyNet, (as a convenience, not a guarantee), create a Python
+  // exception if param_file can't be opened
+  CheckFile(param_file);
+  solver_.reset(new SGDSolver<float>(param_file));
+  // we need to explicitly store the net wrapper, rather than constructing
+  // it on the fly, so that it can hold references to Python objects
+  net_.reset(new PyNet(solver_->net()));
+}
 
+void PySGDSolver::SolveResume(const string& resume_file) {
+  CheckFile(resume_file);
+  return solver_->Solve(resume_file);
+}
 
-// The boost_python module definition.
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
new file mode 100644
index 00000000000..20f9908bfe1
--- /dev/null
+++ b/python/caffe/_caffe.hpp
@@ -0,0 +1,186 @@
+#ifndef PYTHON_CAFFE__CAFFE_HPP_
+#define PYTHON_CAFFE__CAFFE_HPP_
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <boost/python.hpp>
+#include <boost/shared_ptr.hpp>
+#include <numpy/arrayobject.h>
+
+// these need to be included after boost on OS X
+#include <string>  // NOLINT(build/include_order)
+#include <vector>  // NOLINT(build/include_order)
+
+#include "caffe/caffe.hpp"
+
+namespace bp = boost::python;
+using boost::shared_ptr;
+
+namespace caffe {
+
+// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
+// to Python
+template <typename Dtype>
+class PyBlob {
+ public:
+  PyBlob(const shared_ptr<Blob<Dtype> > &blob, const string& name)
+      : blob_(blob), name_(name) {}
+
+  string name() const { return name_; }
+  int num() const { return blob_->num(); }
+  int channels() const { return blob_->channels(); }
+  int height() const { return blob_->height(); }
+  int width() const { return blob_->width(); }
+  int count() const { return blob_->count(); }
+
+  // this is here only to satisfy boost's vector_indexing_suite
+  bool operator == (const PyBlob &other) {
+      return this->blob_ == other.blob_;
+  }
+
+ protected:
+  shared_ptr<Blob<Dtype> > blob_;
+  string name_;
+};
+
+// We need another wrapper (used as boost::python's HeldType) that receives a
+// self PyObject * which we can use as ndarray.base, so that data/diff memory
+// is not freed while still being used in Python.
+class PyBlobWrap : public PyBlob<float> {
+ public:
+  PyBlobWrap(PyObject *p, const PyBlob<float> &blob)
+      : PyBlob<float>(blob), self_(p) {}
+
+  bp::object get_data();
+  bp::object get_diff();
+
+ private:
+  PyObject *self_;
+};
+
+class PyLayer {
+ public:
+  PyLayer(const shared_ptr<Layer<float> > &layer, const string &name)
+    : layer_(layer), name_(name) {}
+
+  string name() const { return name_; }
+  vector<PyBlob<float> > blobs() {
+    vector<PyBlob<float> > result;
+    for (int i = 0; i < layer_->blobs().size(); ++i) {
+      result.push_back(PyBlob<float>(layer_->blobs()[i], name_));
+    }
+    return result;
+  }
+
+  // this is here only to satisfy boost's vector_indexing_suite
+  bool operator == (const PyLayer &other) {
+      return this->layer_ == other.layer_;
+  }
+
+ protected:
+  shared_ptr<Layer<float> > layer_;
+  string name_;
+};
+
+class PyNet {
+ public:
+  // For cases where parameters will be determined later by the Python user,
+  // create a Net with unallocated parameters (which will not be zero-filled
+  // when accessed).
+  explicit PyNet(string param_file) { Init(param_file); }
+  PyNet(string param_file, string pretrained_param_file);
+  explicit PyNet(shared_ptr<Net<float> > net)
+      : net_(net) {}
+  virtual ~PyNet() {}
+
+  void Init(string param_file);
+
+
+  // Generate Python exceptions for badly shaped or discontiguous arrays.
+  inline void check_contiguous_array(PyArrayObject* arr, string name,
+      int channels, int height, int width);
+
+  void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
+  void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
+
+  void set_input_arrays(bp::object data_obj, bp::object labels_obj);
+
+  // Save the network weights to binary proto for net surgeries.
+  void save(string filename) {
+    NetParameter net_param;
+    net_->ToProto(&net_param, false);
+    WriteProtoToBinaryFile(net_param, filename.c_str());
+  }
+
+  // The caffe::Caffe utility functions.
+  void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
+  void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
+  void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
+  void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
+  void set_device(int device_id) { Caffe::SetDevice(device_id); }
+
+  vector<PyBlob<float> > blobs() {
+    vector<PyBlob<float> > result;
+    for (int i = 0; i < net_->blobs().size(); ++i) {
+      result.push_back(PyBlob<float>(net_->blobs()[i], net_->blob_names()[i]));
+    }
+    return result;
+  }
+
+  vector<PyLayer> layers() {
+    vector<PyLayer> result;
+    for (int i = 0; i < net_->layers().size(); ++i) {
+      result.push_back(PyLayer(net_->layers()[i], net_->layer_names()[i]));
+    }
+    return result;
+  }
+
+  bp::list inputs() {
+    bp::list input_blob_names;
+    for (int i = 0; i < net_->input_blob_indices().size(); ++i) {
+      input_blob_names.append(
+          net_->blob_names()[net_->input_blob_indices()[i]]);
+    }
+    return input_blob_names;
+  }
+
+  bp::list outputs() {
+    bp::list output_blob_names;
+    for (int i = 0; i < net_->output_blob_indices().size(); ++i) {
+      output_blob_names.append(
+          net_->blob_names()[net_->output_blob_indices()[i]]);
+    }
+    return output_blob_names;
+  }
+
+  // Input preprocessing configuration attributes. These are public for
+  // direct access from Python.
+  bp::dict mean_;
+  bp::dict input_scale_;
+  bp::dict raw_scale_;
+  bp::dict channel_swap_;
+
+ protected:
+  // The pointer to the internal caffe::Net instant.
+  shared_ptr<Net<float> > net_;
+  // if taking input from an ndarray, we need to hold references
+  bp::object input_data_;
+  bp::object input_labels_;
+};
+
+class PySGDSolver {
+ public:
+  explicit PySGDSolver(const string& param_file);
+
+  shared_ptr<PyNet> net() { return net_; }
+  void Solve() { return solver_->Solve(); }
+  void SolveResume(const string& resume_file);
+
+ protected:
+  shared_ptr<PyNet> net_;
+  shared_ptr<SGDSolver<float> > solver_;
+};
+
+}  // namespace caffe
+
+#endif

From 7907a56b86956403b6941e3a8af8076ea71c0d9d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 31 Aug 2014 00:36:26 -0700
Subject: [PATCH 0570/2053] [pycaffe] declare the _caffe module init function

---
 python/caffe/_caffe.hpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 20f9908bfe1..5500f6dd3aa 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -181,6 +181,10 @@ class PySGDSolver {
   shared_ptr<SGDSolver<float> > solver_;
 };
 
+// Declare the module init function created by boost::python, so that we can
+// use this module from C++ when embedding Python.
+PyMODINIT_FUNC init_caffe(void);
+
 }  // namespace caffe
 
 #endif

From 88221c9a45120bd892180444c4d6ee9c41841079 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 31 Aug 2014 00:45:06 -0700
Subject: [PATCH 0571/2053] [pycaffe] remove name property from PyBlob and
 PyLayer

Attaching names to pycaffe Blobs and Layers isn't necessary anymore,
since they are now accessed by name. This change brings PyBlob and
PyLayer closer to their C++ counterparts, and makes it easier to pass
around blobs without knowing their names, e.g., when embedding Python
in a layer.
---
 python/caffe/_caffe.cpp |  2 --
 python/caffe/_caffe.hpp | 31 ++++++++-----------------------
 2 files changed, 8 insertions(+), 25 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 7c26c7f79f9..5f18ff63b4e 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -167,7 +167,6 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   bp::class_<PyBlob<float>, PyBlobWrap>(
       "Blob", bp::no_init)
-      .add_property("name",     &PyBlob<float>::name)
       .add_property("num",      &PyBlob<float>::num)
       .add_property("channels", &PyBlob<float>::channels)
       .add_property("height",   &PyBlob<float>::height)
@@ -178,7 +177,6 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   bp::class_<PyLayer>(
       "Layer", bp::no_init)
-      .add_property("name",  &PyLayer::name)
       .add_property("blobs", &PyLayer::blobs);
 
   bp::class_<PySGDSolver, boost::noncopyable>(
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 5500f6dd3aa..cd567b665c2 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -23,10 +23,9 @@ namespace caffe {
 template <typename Dtype>
 class PyBlob {
  public:
-  PyBlob(const shared_ptr<Blob<Dtype> > &blob, const string& name)
-      : blob_(blob), name_(name) {}
+  explicit PyBlob(const shared_ptr<Blob<Dtype> > &blob)
+      : blob_(blob) {}
 
-  string name() const { return name_; }
   int num() const { return blob_->num(); }
   int channels() const { return blob_->channels(); }
   int height() const { return blob_->height(); }
@@ -40,7 +39,6 @@ class PyBlob {
 
  protected:
   shared_ptr<Blob<Dtype> > blob_;
-  string name_;
 };
 
 // We need another wrapper (used as boost::python's HeldType) that receives a
@@ -60,16 +58,12 @@ class PyBlobWrap : public PyBlob<float> {
 
 class PyLayer {
  public:
-  PyLayer(const shared_ptr<Layer<float> > &layer, const string &name)
-    : layer_(layer), name_(name) {}
+  explicit PyLayer(const shared_ptr<Layer<float> > &layer)
+    : layer_(layer) {}
 
-  string name() const { return name_; }
   vector<PyBlob<float> > blobs() {
-    vector<PyBlob<float> > result;
-    for (int i = 0; i < layer_->blobs().size(); ++i) {
-      result.push_back(PyBlob<float>(layer_->blobs()[i], name_));
-    }
-    return result;
+    return vector<PyBlob<float> >(layer_->blobs().begin(),
+        layer_->blobs().end());
   }
 
   // this is here only to satisfy boost's vector_indexing_suite
@@ -79,7 +73,6 @@ class PyLayer {
 
  protected:
   shared_ptr<Layer<float> > layer_;
-  string name_;
 };
 
 class PyNet {
@@ -120,19 +113,11 @@ class PyNet {
   void set_device(int device_id) { Caffe::SetDevice(device_id); }
 
   vector<PyBlob<float> > blobs() {
-    vector<PyBlob<float> > result;
-    for (int i = 0; i < net_->blobs().size(); ++i) {
-      result.push_back(PyBlob<float>(net_->blobs()[i], net_->blob_names()[i]));
-    }
-    return result;
+    return vector<PyBlob<float> >(net_->blobs().begin(), net_->blobs().end());
   }
 
   vector<PyLayer> layers() {
-    vector<PyLayer> result;
-    for (int i = 0; i < net_->layers().size(); ++i) {
-      result.push_back(PyLayer(net_->layers()[i], net_->layer_names()[i]));
-    }
-    return result;
+    return vector<PyLayer>(net_->layers().begin(), net_->layers().end());
   }
 
   bp::list inputs() {

From 3e924969357da0e0c680a557375c69a8bbd05800 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 31 Aug 2014 01:14:58 -0700
Subject: [PATCH 0572/2053] [pycaffe] expose Blob.Reshape

While this function should never be called in the current interface, it
will be necessary to have when embedding Python.
---
 python/caffe/_caffe.cpp | 1 +
 python/caffe/_caffe.hpp | 3 +++
 2 files changed, 4 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5f18ff63b4e..9c61ea869b9 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -172,6 +172,7 @@ BOOST_PYTHON_MODULE(_caffe) {
       .add_property("height",   &PyBlob<float>::height)
       .add_property("width",    &PyBlob<float>::width)
       .add_property("count",    &PyBlob<float>::count)
+      .def("reshape",           &PyBlob<float>::Reshape)
       .add_property("data",     &PyBlobWrap::get_data)
       .add_property("diff",     &PyBlobWrap::get_diff);
 
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index cd567b665c2..2df47c7cec5 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -31,6 +31,9 @@ class PyBlob {
   int height() const { return blob_->height(); }
   int width() const { return blob_->width(); }
   int count() const { return blob_->count(); }
+  void Reshape(const int n, const int c, const int h, const int w) {
+    return blob_->Reshape(n, c, h, w);
+  }
 
   // this is here only to satisfy boost's vector_indexing_suite
   bool operator == (const PyBlob &other) {

From 750f92ea4e677cd181d4b907f34988d8dc0730f5 Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Fri, 29 Aug 2014 16:04:24 +0200
Subject: [PATCH 0573/2053] mnist demo now works with lmdb and leveldb (set
 parameter in create_mnist.sh), switched around includes

---
 examples/mnist/convert_mnist_data.cpp         | 109 ++++++++++++++----
 examples/mnist/create_mnist.sh                |  14 ++-
 .../mnist/lenet_consolidated_solver.prototxt  |   9 +-
 3 files changed, 101 insertions(+), 31 deletions(-)

diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index a97a9285a19..671ca0d4c5b 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -6,23 +6,28 @@
 // The MNIST dataset could be downloaded at
 //    http://yann.lecun.com/exdb/mnist/
 
+#include <gflags/gflags.h>  // GFLAGS
+#include <glog/logging.h>
+#include <google/protobuf/text_format.h>
+#include <leveldb/db.h>
+#include <lmdb.h>
+#include <stdint.h>
+#include <sys/stat.h>
+
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
-#include "glog/logging.h"
-#include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
-#include "stdint.h"
-
 #include "caffe/proto/caffe.pb.h"
 
+DEFINE_string(backend, "leveldb", "The backend for storing the result");
+
 uint32_t swap_endian(uint32_t val) {
     val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
     return (val << 16) | (val >> 16);
 }
 
 void convert_dataset(const char* image_filename, const char* label_filename,
-        const char* db_filename) {
+        const char* db_filename, const std::string& db_backend) {
   // Open files
   std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
   std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
@@ -51,15 +56,38 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   image_file.read(reinterpret_cast<char*>(&cols), 4);
   cols = swap_endian(cols);
 
-  // Open leveldb
+  // lmdb
+  MDB_env *mdb_env;
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_data;
+  MDB_txn *mdb_txn;
+  // leveldb
   leveldb::DB* db;
   leveldb::Options options;
   options.create_if_missing = true;
   options.error_if_exists = true;
-  leveldb::Status status = leveldb::DB::Open(
-      options, db_filename, &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << db_filename
-      << ". Is it already existing?";
+
+  // Open new db
+  if (db_backend == "leveldb") {  // leveldb
+    leveldb::Status status = leveldb::DB::Open(
+        options, db_filename, &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << db_filename
+        << ". Is it already existing?";
+  } else if (db_backend == "lmdb") {  // lmdb
+    CHECK_EQ(mkdir(db_filename, 0744), 0)
+        << "mkdir " << db_filename << "failed";
+    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
+        << "mdb_env_set_mapsize failed";
+    CHECK_EQ(mdb_env_open(mdb_env, db_filename, 0, 0664), MDB_SUCCESS)
+        << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
+        << "mdb_open failed";
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
 
   char label;
   char* pixels = new char[rows * cols];
@@ -80,26 +108,63 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     datum.set_label(label);
     datum.SerializeToString(&value);
     snprintf(key, kMaxKeyLength, "%08d", itemid);
-    db->Put(leveldb::WriteOptions(), std::string(key), value);
+    std::string keystr(key);
+
+    // Put in db
+    if (db_backend == "leveldb") {  // leveldb
+      db->Put(leveldb::WriteOptions(), keystr, value);
+    } else if (db_backend == "lmdb") {  // lmdb
+      mdb_data.mv_size = value.size();
+      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
+      mdb_key.mv_size = keystr.size();
+      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
+          << "mdb_put failed";
+      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
+          << "mdb_txn_commit failed";
+      CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+          << "mdb_txn_begin failed";
+    } else {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
+  }
+
+  if (db_backend == "leveldb") {  // leveldb
+    delete db;
+  } else if (db_backend == "lmdb") {  // lmdb
+    mdb_close(mdb_env, mdb_dbi);
+    mdb_env_close(mdb_env);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
   }
 
-  delete db;
   delete pixels;
 }
 
 int main(int argc, char** argv) {
+#ifndef GFLAGS_GFLAGS_H_
+  namespace gflags = google;
+#endif
+
+  gflags::SetUsageMessage("This script converts the MNIST dataset to\n"
+        "the leveldb/lmdb format used by Caffe to perform classification.\n"
+        "Usage:\n"
+        "    convert_mnist_data [FLAGS] input_image_file input_label_file "
+        "output_db_file\n"
+        "The MNIST dataset could be downloaded at\n"
+        "    http://yann.lecun.com/exdb/mnist/\n"
+        "You should gunzip them after downloading,"
+        "or directly use data/mnist/get_mnist.sh\n");
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  const std::string& db_backend = FLAGS_backend;
+
   if (argc != 4) {
-    printf("This script converts the MNIST dataset to the leveldb format used\n"
-           "by caffe to perform classification.\n"
-           "Usage:\n"
-           "    convert_mnist_data input_image_file input_label_file "
-           "output_db_file\n"
-           "The MNIST dataset could be downloaded at\n"
-           "    http://yann.lecun.com/exdb/mnist/\n"
-           "You should gunzip them after downloading.\n");
+    gflags::ShowUsageWithFlagsRestrict(argv[0],
+        "examples/mnist/convert_mnist_data");
   } else {
     google::InitGoogleLogging(argv[0]);
-    convert_dataset(argv[1], argv[2], argv[3]);
+    convert_dataset(argv[1], argv[2], argv[3], db_backend);
   }
   return 0;
 }
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index ed6e36903c2..29f625e6329 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -1,18 +1,20 @@
 #!/usr/bin/env sh
-# This script converts the mnist data into leveldb format.
+# This script converts the mnist data into leveldb/lmdb format,
+# depending on the value assigned to $BACKEND.
 
 EXAMPLE=examples/mnist
 DATA=data/mnist
 BUILD=build/examples/mnist
+BACKEND="lmdb"
 
-echo "Creating leveldb..."
+echo "Creating ${BACKEND}..."
 
-rm -rf mnist_train_leveldb
-rm -rf mnist_test_leveldb
+rm -rf $EXAMPLE/mnist_train_${BACKEND}
+rm -rf $EXAMPLE/mnist_test_${BACKEND}
 
 $BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \
-  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_leveldb
+  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} -backend ${BACKEND}
 $BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \
-  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_leveldb
+  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} -backend ${BACKEND}
 
 echo "Done."
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index 8ff593f2781..f57d086c671 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -50,7 +50,8 @@ net_param {
     top: "data"
     top: "label"
     data_param {
-      source: "examples/mnist/mnist_train_leveldb"
+      source: "examples/mnist/mnist_train_lmdb"
+      backend: LMDB
       batch_size: 64
       transform_param {
         scale: 0.00390625
@@ -64,7 +65,8 @@ net_param {
     top: "data"
     top: "label"
     data_param {
-      source: "examples/mnist/mnist_test_leveldb"
+      source: "examples/mnist/mnist_test_lmdb"
+      backend: LMDB
       batch_size: 100
       transform_param {
         scale: 0.00390625
@@ -81,7 +83,8 @@ net_param {
     top: "data"
     top: "label"
     data_param {
-      source: "examples/mnist/mnist_train_leveldb"
+      source: "examples/mnist/mnist_train_lmdb"
+      backend: LMDB
       batch_size: 100
       transform_param {
         scale: 0.00390625

From 4670c320b3539e9e55e60d6555fd1cf8d200e3e4 Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Fri, 29 Aug 2014 17:04:25 +0200
Subject: [PATCH 0574/2053] data now written to backend in batches

---
 examples/mnist/convert_mnist_data.cpp | 52 +++++++++++++++++++--------
 examples/mnist/create_mnist.sh        |  1 +
 2 files changed, 38 insertions(+), 15 deletions(-)

diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 671ca0d4c5b..6fbd2943d2d 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -6,10 +6,11 @@
 // The MNIST dataset could be downloaded at
 //    http://yann.lecun.com/exdb/mnist/
 
-#include <gflags/gflags.h>  // GFLAGS
+#include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <google/protobuf/text_format.h>
 #include <leveldb/db.h>
+#include <leveldb/write_batch.h>
 #include <lmdb.h>
 #include <stdint.h>
 #include <sys/stat.h>
@@ -66,6 +67,8 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   leveldb::Options options;
   options.create_if_missing = true;
   options.error_if_exists = true;
+  options.write_buffer_size = 268435456;
+  leveldb::WriteBatch* batch = NULL;
 
   // Open new db
   if (db_backend == "leveldb") {  // leveldb
@@ -73,6 +76,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
         options, db_filename, &db);
     CHECK(status.ok()) << "Failed to open leveldb " << db_filename
         << ". Is it already existing?";
+    batch = new leveldb::WriteBatch();
   } else if (db_backend == "lmdb") {  // lmdb
     CHECK_EQ(mkdir(db_filename, 0744), 0)
         << "mkdir " << db_filename << "failed";
@@ -94,6 +98,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   const int kMaxKeyLength = 10;
   char key[kMaxKeyLength];
   std::string value;
+  int count = 0;
 
   caffe::Datum datum;
   datum.set_channels(1);
@@ -112,7 +117,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
 
     // Put in db
     if (db_backend == "leveldb") {  // leveldb
-      db->Put(leveldb::WriteOptions(), keystr, value);
+      batch->Put(keystr, value);
     } else if (db_backend == "lmdb") {  // lmdb
       mdb_data.mv_size = value.size();
       mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
@@ -120,24 +125,41 @@ void convert_dataset(const char* image_filename, const char* label_filename,
       mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
       CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
           << "mdb_put failed";
-      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
-          << "mdb_txn_commit failed";
-      CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-          << "mdb_txn_begin failed";
     } else {
       LOG(FATAL) << "Unknown db backend " << db_backend;
     }
-  }
 
-  if (db_backend == "leveldb") {  // leveldb
-    delete db;
-  } else if (db_backend == "lmdb") {  // lmdb
-    mdb_close(mdb_env, mdb_dbi);
-    mdb_env_close(mdb_env);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
+    if (++count % 1000 == 0) {
+      // Commit txn
+      if (db_backend == "leveldb") {  // leveldb
+        db->Write(leveldb::WriteOptions(), batch);
+        delete batch;
+        batch = new leveldb::WriteBatch();
+      } else if (db_backend == "lmdb") {  // lmdb
+        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
+            << "mdb_txn_commit failed";
+        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+            << "mdb_txn_begin failed";
+      } else {
+        LOG(FATAL) << "Unknown db backend " << db_backend;
+      }
+    }
+  }
+  // write the last batch
+  if (count % 1000 != 0) {
+    if (db_backend == "leveldb") {  // leveldb
+      db->Write(leveldb::WriteOptions(), batch);
+      delete batch;
+      delete db;
+    } else if (db_backend == "lmdb") {  // lmdb
+      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
+      mdb_close(mdb_env, mdb_dbi);
+      mdb_env_close(mdb_env);
+    } else {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
+    LOG(ERROR) << "Processed " << count << " files.";
   }
-
   delete pixels;
 }
 
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index 29f625e6329..ae9469c0f20 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -5,6 +5,7 @@
 EXAMPLE=examples/mnist
 DATA=data/mnist
 BUILD=build/examples/mnist
+
 BACKEND="lmdb"
 
 echo "Creating ${BACKEND}..."

From c98ed3b8b2da44182551ed8c996c7102b9fec113 Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Fri, 29 Aug 2014 17:16:41 +0200
Subject: [PATCH 0575/2053] minor changes to variable names and error messages
 + set default backed in convert_mnist_data.cpp to lmdb

---
 examples/mnist/convert_mnist_data.cpp | 45 ++++++++++++++++-----------
 tools/convert_imageset.cpp            |  5 +--
 2 files changed, 29 insertions(+), 21 deletions(-)

diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 6fbd2943d2d..0d3e91ec3ad 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -2,7 +2,8 @@
 // This script converts the MNIST dataset to the leveldb format used
 // by caffe to perform classification.
 // Usage:
-//    convert_mnist_data input_image_file input_label_file output_db_file
+//    convert_mnist_data [FLAGS] input_image_file input_label_file
+//                        output_db_file
 // The MNIST dataset could be downloaded at
 //    http://yann.lecun.com/exdb/mnist/
 
@@ -20,7 +21,10 @@
 
 #include "caffe/proto/caffe.pb.h"
 
-DEFINE_string(backend, "leveldb", "The backend for storing the result");
+using namespace caffe;  // NOLINT(build/namespaces)
+using std::string;
+
+DEFINE_string(backend, "lmdb", "The backend for storing the result");
 
 uint32_t swap_endian(uint32_t val) {
     val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
@@ -28,7 +32,7 @@ uint32_t swap_endian(uint32_t val) {
 }
 
 void convert_dataset(const char* image_filename, const char* label_filename,
-        const char* db_filename, const std::string& db_backend) {
+        const char* db_path, const string& db_backend) {
   // Open files
   std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
   std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
@@ -65,55 +69,58 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   // leveldb
   leveldb::DB* db;
   leveldb::Options options;
-  options.create_if_missing = true;
   options.error_if_exists = true;
+  options.create_if_missing = true;
   options.write_buffer_size = 268435456;
   leveldb::WriteBatch* batch = NULL;
 
-  // Open new db
+  // Open db
   if (db_backend == "leveldb") {  // leveldb
+    LOG(INFO) << "Opening leveldb " << db_path;
     leveldb::Status status = leveldb::DB::Open(
-        options, db_filename, &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << db_filename
+        options, db_path, &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << db_path
         << ". Is it already existing?";
     batch = new leveldb::WriteBatch();
   } else if (db_backend == "lmdb") {  // lmdb
-    CHECK_EQ(mkdir(db_filename, 0744), 0)
-        << "mkdir " << db_filename << "failed";
+    LOG(INFO) << "Opening lmdb " << db_path;
+    CHECK_EQ(mkdir(db_path, 0744), 0)
+        << "mkdir " << db_path << "failed";
     CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
     CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
         << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(mdb_env, db_filename, 0, 0664), MDB_SUCCESS)
+    CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
         << "mdb_env_open failed";
     CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
         << "mdb_txn_begin failed";
     CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed";
+        << "mdb_open failed. Does the lmdb already exist? ";
   } else {
     LOG(FATAL) << "Unknown db backend " << db_backend;
   }
 
+  // Storing to db
   char label;
   char* pixels = new char[rows * cols];
-  const int kMaxKeyLength = 10;
-  char key[kMaxKeyLength];
-  std::string value;
   int count = 0;
+  const int kMaxKeyLength = 10;
+  char key_cstr[kMaxKeyLength];
+  string value;
 
-  caffe::Datum datum;
+  Datum datum;
   datum.set_channels(1);
   datum.set_height(rows);
   datum.set_width(cols);
   LOG(INFO) << "A total of " << num_items << " items.";
   LOG(INFO) << "Rows: " << rows << " Cols: " << cols;
-  for (int itemid = 0; itemid < num_items; ++itemid) {
+  for (int item_id = 0; item_id < num_items; ++item_id) {
     image_file.read(pixels, rows * cols);
     label_file.read(&label, 1);
     datum.set_data(pixels, rows*cols);
     datum.set_label(label);
+    snprintf(key_cstr, kMaxKeyLength, "%08d", item_id);
     datum.SerializeToString(&value);
-    snprintf(key, kMaxKeyLength, "%08d", itemid);
-    std::string keystr(key);
+    string keystr(key_cstr);
 
     // Put in db
     if (db_backend == "leveldb") {  // leveldb
@@ -179,7 +186,7 @@ int main(int argc, char** argv) {
         "or directly use data/mnist/get_mnist.sh\n");
   gflags::ParseCommandLineFlags(&argc, &argv, true);
 
-  const std::string& db_backend = FLAGS_backend;
+  const string& db_backend = FLAGS_backend;
 
   if (argc != 4) {
     gflags::ShowUsageWithFlagsRestrict(argv[0],
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 416a1460912..1c58f3de33f 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -102,7 +102,8 @@ int main(int argc, char** argv) {
     LOG(INFO) << "Opening leveldb " << db_path;
     leveldb::Status status = leveldb::DB::Open(
         options, db_path, &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << db_path;
+    CHECK(status.ok()) << "Failed to open leveldb " << db_path
+        << ". Is it already existing?";
     batch = new leveldb::WriteBatch();
   } else if (db_backend == "lmdb") {  // lmdb
     LOG(INFO) << "Opening lmdb " << db_path;
@@ -116,7 +117,7 @@ int main(int argc, char** argv) {
     CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
         << "mdb_txn_begin failed";
     CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed";
+        << "mdb_open failed. Does the lmdb already exist? ";
   } else {
     LOG(FATAL) << "Unknown db backend " << db_backend;
   }

From 5615bf6c17a10464c23e11c370a4241a910870e2 Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Sun, 31 Aug 2014 14:27:33 +0200
Subject: [PATCH 0576/2053] updated lenet_train_test.prototxt + minor
 correction to create_mnist.sh

---
 examples/mnist/create_mnist.sh           | 4 ++--
 examples/mnist/lenet_train_test.prototxt | 6 ++++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index ae9469c0f20..8c43dc3391f 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -14,8 +14,8 @@ rm -rf $EXAMPLE/mnist_train_${BACKEND}
 rm -rf $EXAMPLE/mnist_test_${BACKEND}
 
 $BUILD/convert_mnist_data.bin $DATA/train-images-idx3-ubyte \
-  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} -backend ${BACKEND}
+  $DATA/train-labels-idx1-ubyte $EXAMPLE/mnist_train_${BACKEND} --backend=${BACKEND}
 $BUILD/convert_mnist_data.bin $DATA/t10k-images-idx3-ubyte \
-  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} -backend ${BACKEND}
+  $DATA/t10k-labels-idx1-ubyte $EXAMPLE/mnist_test_${BACKEND} --backend=${BACKEND}
 
 echo "Done."
diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index 8369af1daa0..e4d65892f37 100644
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -5,7 +5,8 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/mnist/mnist_train_leveldb"
+    source: "examples/mnist/mnist_train_lmdb"
+    backend: LMDB
     batch_size: 64
     transform_param {
       scale: 0.00390625
@@ -19,7 +20,8 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/mnist/mnist_test_leveldb"
+    source: "examples/mnist/mnist_test_lmdb"
+    backend: LMDB
     batch_size: 100
     transform_param {
       scale: 0.00390625

From aae8545427685a7ecd8560ba8f606ded2892123e Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Sun, 31 Aug 2014 15:00:09 +0200
Subject: [PATCH 0577/2053] parse_log.sh adapted to new training log format +
 fixed typos and updated description

---
 tools/extra/parse_log.sh | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/tools/extra/parse_log.sh b/tools/extra/parse_log.sh
index 01ea6f493a6..98ef0a05002 100755
--- a/tools/extra/parse_log.sh
+++ b/tools/extra/parse_log.sh
@@ -1,7 +1,9 @@
 #!/bin/bash
 # Usage parse_log.sh caffe.log
-# It creates two files one caffe.log.test that contains the loss and test accuracy of the test and
-# another one caffe.log.loss that contains the loss computed during the training
+# It creates the following two text files, each containing a table:
+#     caffe.log.test (columns: '#Iters Seconds TestAccuracy TestLoss')
+#     caffe.log.train (columns: '#Iters Seconds TrainingLoss LearningRate')
+
 
 # get the dirname of the script
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
@@ -14,31 +16,31 @@ fi
 LOG=`basename $1`
 grep -B 1 'Test ' $1 > aux.txt
 grep 'Iteration ' aux.txt | sed  's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
-grep 'Test score #0' aux.txt | awk '{print $8}' > aux1.txt
-grep 'Test score #1' aux.txt | awk '{print $8}' > aux2.txt
+grep 'Test net output #0' aux.txt | awk '{print $11}' > aux1.txt
+grep 'Test net output #1' aux.txt | awk '{print $11}' > aux2.txt
 
-# Extracting elpased seconds
-# For extraction of time since this line constains the start time
+# Extracting elapsed seconds
+# For extraction of time since this line contains the start time
 grep '] Solving ' $1 > aux3.txt
 grep 'Testing net' $1 >> aux3.txt
 $DIR/extract_seconds.py aux3.txt aux4.txt
 
 # Generating
-echo '# Iters Seconds TestAccuracy TestLoss'> $LOG.test
+echo '#Iters Seconds TestAccuracy TestLoss'> $LOG.test
 paste aux0.txt aux4.txt aux1.txt aux2.txt | column -t >> $LOG.test
 rm aux.txt aux0.txt aux1.txt aux2.txt aux3.txt aux4.txt
 
-# For extraction of time since this line constains the start time
+# For extraction of time since this line contains the start time
 grep '] Solving ' $1 > aux.txt
 grep ', loss = ' $1 >> aux.txt
 grep 'Iteration ' aux.txt | sed  's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
 grep ', loss = ' $1 | awk '{print $9}' > aux1.txt
 grep ', lr = ' $1 | awk '{print $9}' > aux2.txt
 
-# Extracting elpased seconds
+# Extracting elapsed seconds
 $DIR/extract_seconds.py aux.txt aux3.txt
 
 # Generating
-echo '# Iters Seconds TrainingLoss LearningRate'> $LOG.train
+echo '#Iters Seconds TrainingLoss LearningRate'> $LOG.train
 paste aux0.txt aux3.txt aux1.txt aux2.txt | column -t >> $LOG.train
 rm aux.txt aux0.txt aux1.txt aux2.txt  aux3.txt

From fe9502467814b13cf363c5470e7dd5047bfd5a0c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 31 Aug 2014 23:25:31 -0700
Subject: [PATCH 0578/2053] include comment on CPU mode fine-tuning for Flickr
 example

---
 examples/finetune_flickr_style/flickr_style_solver.prototxt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/finetune_flickr_style/flickr_style_solver.prototxt b/examples/finetune_flickr_style/flickr_style_solver.prototxt
index 740ec39fe6a..756e162ba1e 100644
--- a/examples/finetune_flickr_style/flickr_style_solver.prototxt
+++ b/examples/finetune_flickr_style/flickr_style_solver.prototxt
@@ -13,3 +13,5 @@ momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "examples/finetune_flickr_style/flickr_style"
+# uncomment the following to default to CPU mode solving
+# solver_mode: CPU

From f7458b5cd2e687ad467a28ae25c7aed1f4683011 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 31 Aug 2014 23:28:28 -0700
Subject: [PATCH 0579/2053] make no GPU error in CPU-only mode a little clearer

---
 include/caffe/util/device_alternate.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 4141c3d60a3..aa359e007cb 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -7,7 +7,7 @@
 
 // Stub out GPU calls as unavailable.
 
-#define NO_GPU LOG(FATAL) << "CPU-only Mode"
+#define NO_GPU LOG(FATAL) << "CPU-only Mode: cannot make GPU call."
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \

From 5f350aa24654337555ef07f790a10c10a188bef7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 11:32:12 -0700
Subject: [PATCH 0580/2053] use LMDB in mnist autoencoder examples

---
 examples/mnist/mnist_autoencoder.prototxt | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index d0abc685f0b..b6009fb50a0 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -4,7 +4,8 @@ layers {
   name: "data"
   type: DATA
   data_param {
-    source: "examples/mnist/mnist_train_leveldb"
+    source: "examples/mnist/mnist_train_lmdb"
+    backend: LMDB
     batch_size: 100
     transform_param {
       scale: 0.0039215684
@@ -17,7 +18,8 @@ layers {
   name: "data"
   type: DATA
   data_param {
-    source: "examples/mnist/mnist_test_leveldb"
+    source: "examples/mnist/mnist_test_lmdb"
+    backend: LMDB
     batch_size: 100
     transform_param {
       scale: 0.0039215684

From 9d10569a79d4dc1a0aa3f42a0bd5214a085ab7c9 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 19 Jul 2014 14:14:21 -0700
Subject: [PATCH 0581/2053] Solver switching support & implementation of
 Nesterov's accelerated gradient and AdaGrad

---
 include/caffe/solver.hpp |  53 ++++++++++-
 src/caffe/solver.cpp     | 189 +++++++++++++++++++++++++++++++++++++++
 tools/train_net.cpp      |   3 +
 3 files changed, 241 insertions(+), 4 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 9012c5d50d9..87c6563eae4 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -66,17 +66,62 @@ class SGDSolver : public Solver<Dtype> {
       : Solver<Dtype>(param_file) {}
 
  protected:
-  virtual void PreSolve();
+  void PreSolve();
   Dtype GetLearningRate();
   virtual void ComputeUpdateValue();
-  virtual void SnapshotSolverState(SolverState * state);
-  virtual void RestoreSolverState(const SolverState& state);
+  void SnapshotSolverState(SolverState * state);
+  void RestoreSolverState(const SolverState& state);
   // history maintains the historical momentum data.
-  vector<shared_ptr<Blob<Dtype> > > history_;
+  // update maintains update related data and is not needed in snapshots.
+  vector<shared_ptr<Blob<Dtype> > > history_, update_;
 
   DISABLE_COPY_AND_ASSIGN(SGDSolver);
 };
 
+template <typename Dtype>
+class NesterovSolver : public SGDSolver<Dtype> {
+ public:
+  explicit NesterovSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) {}
+  explicit NesterovSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) {}
+
+ protected:
+  virtual void ComputeUpdateValue();
+
+  DISABLE_COPY_AND_ASSIGN(NesterovSolver);
+};
+
+template <typename Dtype>
+class AdaGradSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaGradSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) {}
+  explicit AdaGradSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) {}
+
+ protected:
+  virtual void ComputeUpdateValue();
+
+  DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
+};
+
+template <typename Dtype>
+Solver<Dtype>* GetSolver(const SolverParameter& param) {
+  SolverParameter_SolverType type = param.solver_type();
+
+  switch (type) {
+  case SolverParameter_SolverType_SGD:
+      return new SGDSolver<Dtype>(param);
+  case SolverParameter_SolverType_NESTEROV:
+      return new NesterovSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADAGRAD:
+      return new AdaGradSolver<Dtype>(param);
+  default:
+      LOG(FATAL) << "Unknown SolverType: " << type;
+  }
+}
+
 
 }  // namespace caffe
 
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 80582b312fe..5632f249e33 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -377,11 +377,15 @@ void SGDSolver<Dtype>::PreSolve() {
   // Initialize the history
   vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   history_.clear();
+  update_.clear();
   for (int i = 0; i < net_params.size(); ++i) {
     const Blob<Dtype>* net_param = net_params[i].get();
     history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
         net_param->num(), net_param->channels(), net_param->height(),
         net_param->width())));
+    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
   }
 }
 
@@ -470,7 +474,192 @@ void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
   }
 }
 
+template <typename Dtype>
+void NesterovSolver<Dtype>::ComputeUpdateValue() {
+  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  vector<float>& net_params_lr = this->net_->params_lr();
+  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  // get the learning rate
+  Dtype rate = this->GetLearningRate();
+  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
+    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+  }
+  Dtype momentum = this->param_.momentum();
+  Dtype weight_decay = this->param_.weight_decay();
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+      // save history momentum for stepping back
+      caffe_copy(net_params[param_id]->count(),
+          this->history_[param_id]->cpu_data(),
+          this->update_[param_id]->mutable_cpu_data());
+
+      Dtype local_rate = rate * net_params_lr[param_id];
+      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+          net_params[param_id]->cpu_diff(), momentum,
+          this->history_[param_id]->mutable_cpu_data());
+      if (local_decay) {
+        // add weight decay
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay * local_rate,
+            net_params[param_id]->cpu_data(),
+            this->history_[param_id]->mutable_cpu_data());
+      }
+      // compute udpate: step back then over step
+      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+          this->history_[param_id]->cpu_data(), -momentum,
+          this->update_[param_id]->mutable_cpu_data());
+
+      // copy
+      caffe_copy(net_params[param_id]->count(),
+          this->update_[param_id]->cpu_data(),
+          net_params[param_id]->mutable_cpu_diff());
+    }
+    break;
+  case Caffe::GPU:
+#ifndef CPU_ONLY
+    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+      // save history momentum for stepping back
+      caffe_copy(net_params[param_id]->count(),
+          this->history_[param_id]->gpu_data(),
+          this->update_[param_id]->mutable_gpu_data());
+
+      Dtype local_rate = rate * net_params_lr[param_id];
+      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+          net_params[param_id]->gpu_diff(), momentum,
+          this->history_[param_id]->mutable_gpu_data());
+      if (local_decay) {
+        // add weight decay
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay * local_rate,
+            net_params[param_id]->gpu_data(),
+            this->history_[param_id]->mutable_gpu_data());
+      }
+      // compute udpate: step back then over step
+      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+          this->history_[param_id]->gpu_data(), -momentum,
+          this->update_[param_id]->mutable_gpu_data());
+
+      // copy
+      caffe_copy(net_params[param_id]->count(),
+          this->update_[param_id]->gpu_data(),
+          net_params[param_id]->mutable_gpu_diff());
+    }
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void AdaGradSolver<Dtype>::ComputeUpdateValue() {
+  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  vector<float>& net_params_lr = this->net_->params_lr();
+  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  // get the learning rate
+  Dtype rate = this->GetLearningRate();
+  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
+    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+  }
+  Dtype weight_decay = this->param_.weight_decay();
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+      Dtype local_rate = rate * net_params_lr[param_id];
+      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+
+      if (local_decay) {
+        // add weight decay
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay * local_rate,
+            net_params[param_id]->cpu_data(),
+            this->history_[param_id]->mutable_cpu_data());
+      }
+
+      // compute square of gradient in update
+      caffe_powx(net_params[param_id]->count(),
+          net_params[param_id]->cpu_data(), Dtype(2),
+          this->update_[param_id]->mutable_cpu_data());
+
+      // update history
+      caffe_add(net_params[param_id]->count(),
+          this->update_[param_id]->cpu_data(),
+          this->history_[param_id]->cpu_data(),
+          this->history_[param_id]->mutable_cpu_data());
+
+      // prepare update
+      caffe_powx(net_params[param_id]->count(),
+                this->history_[param_id]->cpu_data(), Dtype(-0.5),
+                this->update_[param_id]->mutable_cpu_data());
+
+      caffe_mul(net_params[param_id]->count(),
+                net_params[param_id]->cpu_diff(),
+                this->update_[param_id]->cpu_data(),
+                this->update_[param_id]->mutable_cpu_data());
+
+      // scale and copy
+      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+          this->update_[param_id]->cpu_data(), Dtype(0),
+          net_params[param_id]->mutable_cpu_diff());
+    }
+    break;
+  case Caffe::GPU:
+#ifndef CPU_ONLY
+    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+      Dtype local_rate = rate * net_params_lr[param_id];
+      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+
+      if (local_decay) {
+        // add weight decay
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay * local_rate,
+            net_params[param_id]->gpu_data(),
+            this->history_[param_id]->mutable_gpu_data());
+      }
+
+      // compute square of gradient in update
+      caffe_gpu_powx(net_params[param_id]->count(),
+          net_params[param_id]->gpu_data(), Dtype(2),
+          this->update_[param_id]->mutable_gpu_data());
+
+      // update history
+      caffe_gpu_add(net_params[param_id]->count(),
+          this->update_[param_id]->gpu_data(),
+          this->history_[param_id]->gpu_data(),
+          this->history_[param_id]->mutable_gpu_data());
+
+      // prepare update
+      caffe_gpu_powx(net_params[param_id]->count(),
+                this->history_[param_id]->gpu_data(), Dtype(-0.5),
+                this->update_[param_id]->mutable_gpu_data());
+
+      caffe_gpu_mul(net_params[param_id]->count(),
+                net_params[param_id]->gpu_diff(),
+                this->update_[param_id]->gpu_data(),
+                this->update_[param_id]->mutable_gpu_data());
+
+      // scale and copy
+      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+          this->update_[param_id]->gpu_data(), Dtype(0),
+          net_params[param_id]->mutable_gpu_diff());
+    }
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 INSTANTIATE_CLASS(Solver);
 INSTANTIATE_CLASS(SGDSolver);
+INSTANTIATE_CLASS(NesterovSolver);
+INSTANTIATE_CLASS(AdaGradSolver);
 
 }  // namespace caffe
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 622bca311c8..11767591991 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,6 +1,9 @@
 #include "caffe/caffe.hpp"
 
+using namespace caffe;  // NOLINT(build/namespaces)
+
 int main(int argc, char** argv) {
+
   LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
                 "[--snapshot=...] instead.";
   return 0;

From 8a9c268bd53767365fa0760c167bdcd0158a56f3 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sat, 19 Jul 2014 17:33:35 -0700
Subject: [PATCH 0582/2053] restored vituals in solver.hpp

---
 include/caffe/solver.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 87c6563eae4..03c655800a7 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -66,11 +66,11 @@ class SGDSolver : public Solver<Dtype> {
       : Solver<Dtype>(param_file) {}
 
  protected:
-  void PreSolve();
+  virtual void PreSolve();
   Dtype GetLearningRate();
   virtual void ComputeUpdateValue();
-  void SnapshotSolverState(SolverState * state);
-  void RestoreSolverState(const SolverState& state);
+  virtual void SnapshotSolverState(SolverState * state);
+  virtual void RestoreSolverState(const SolverState& state);
   // history maintains the historical momentum data.
   // update maintains update related data and is not needed in snapshots.
   vector<shared_ptr<Blob<Dtype> > > history_, update_;

From ed8b1da57fbbadb611d98372671fafd77d863234 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Sun, 20 Jul 2014 08:57:33 -0700
Subject: [PATCH 0583/2053] converted pointers to shared_ptr

---
 include/caffe/solver.hpp |  3 +++
 tools/train_net.cpp      | 30 ++++++++++++++++++++++++++++--
 2 files changed, 31 insertions(+), 2 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 03c655800a7..9d5481cc368 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -120,8 +120,11 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
   default:
       LOG(FATAL) << "Unknown SolverType: " << type;
   }
+  return (Solver<Dtype>*) NULL;
 }
 
+template Solver<float>* GetSolver(const SolverParameter& param);
+template Solver<double>* GetSolver(const SolverParameter& param);
 
 }  // namespace caffe
 
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 11767591991..2a2a522dfb9 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,10 +1,36 @@
+// Copyright 2014 BVLC and contributors.
+//
+// This is a simple script that allows one to quickly train a network whose
+// parameters are specified by text format protocol buffers.
+// Usage:
+//    train_net net_proto_file solver_proto_file [resume_point_file]
+
+#include <cstring>
+
 #include "caffe/caffe.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  ::google::InitGoogleLogging(argv[0]);
+  if (argc < 2 || argc > 3) {
+    LOG(ERROR) << "Usage: train_net solver_proto_file [resume_point_file]";
+    return 1;
+  }
+
+  SolverParameter solver_param;
+  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
+
+  LOG(INFO) << "Starting Optimization";
+  shared_ptr<Solver<float> > solver =
+      (shared_ptr<Solver<float> >) GetSolver<float>(solver_param);
+  if (argc == 3) {
+    LOG(INFO) << "Resuming from " << argv[2];
+    solver->Solve(argv[2]);
+  } else {
+    solver->Solve();
+  }
+  LOG(INFO) << "Optimization Done.";
 
-  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
-                "[--snapshot=...] instead.";
   return 0;
 }

From 8b3dde08fc664fa2ba19694c918bc1d411adbd56 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 21 Jul 2014 10:47:55 -0700
Subject: [PATCH 0584/2053] fixed solver constructor in train_net.cpp

---
 tools/train_net.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 2a2a522dfb9..3652182d606 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -22,8 +22,7 @@ int main(int argc, char** argv) {
   ReadProtoFromTextFileOrDie(argv[1], &solver_param);
 
   LOG(INFO) << "Starting Optimization";
-  shared_ptr<Solver<float> > solver =
-      (shared_ptr<Solver<float> >) GetSolver<float>(solver_param);
+  shared_ptr<Solver<float> > solver(GetSolver<float>(solver_param));
   if (argc == 3) {
     LOG(INFO) << "Resuming from " << argv[2];
     solver->Solve(argv[2]);

From 0144de68e6f98d5158f8c0f94cd31c9bb6f79db6 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 22 Jul 2014 21:17:19 -0700
Subject: [PATCH 0585/2053] improved numerical stability for AdaGrad

---
 src/caffe/solver.cpp | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 5632f249e33..abcbe5e6a20 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -481,6 +481,7 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
   vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
   // get the learning rate
   Dtype rate = this->GetLearningRate();
+  Dtype delta = this->param_.delta();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
@@ -594,10 +595,13 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
 
       // prepare update
       caffe_powx(net_params[param_id]->count(),
-                this->history_[param_id]->cpu_data(), Dtype(-0.5),
+                this->history_[param_id]->cpu_data(), Dtype(0.5),
                 this->update_[param_id]->mutable_cpu_data());
 
-      caffe_mul(net_params[param_id]->count(),
+      caffe_add_scalar(net_params[param_id]->count(),
+                delta, this->update_[param_id]->mutable_cpu_data());
+
+      caffe_div(net_params[param_id]->count(),
                 net_params[param_id]->cpu_diff(),
                 this->update_[param_id]->cpu_data(),
                 this->update_[param_id]->mutable_cpu_data());
@@ -635,10 +639,13 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
 
       // prepare update
       caffe_gpu_powx(net_params[param_id]->count(),
-                this->history_[param_id]->gpu_data(), Dtype(-0.5),
+                this->history_[param_id]->gpu_data(), Dtype(0.5),
                 this->update_[param_id]->mutable_gpu_data());
 
-      caffe_gpu_mul(net_params[param_id]->count(),
+      caffe_gpu_add_scalar(net_params[param_id]->count(),
+                delta, this->update_[param_id]->mutable_gpu_data());
+
+      caffe_gpu_div(net_params[param_id]->count(),
                 net_params[param_id]->gpu_diff(),
                 this->update_[param_id]->gpu_data(),
                 this->update_[param_id]->mutable_gpu_data());

From 76ef2ca1f6e326622090aa7d57a10e80d5831350 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Wed, 23 Jul 2014 10:25:44 -0700
Subject: [PATCH 0586/2053] bugfixes for AdaGrad

---
 src/caffe/solver.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index abcbe5e6a20..8928c7b29ae 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -577,14 +577,14 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
       if (local_decay) {
         // add weight decay
         caffe_axpy(net_params[param_id]->count(),
-            local_decay * local_rate,
+            local_decay,
             net_params[param_id]->cpu_data(),
-            this->history_[param_id]->mutable_cpu_data());
+            net_params[param_id]->mutable_cpu_diff());
       }
 
       // compute square of gradient in update
       caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_data(), Dtype(2),
+          net_params[param_id]->cpu_diff(), Dtype(2),
           this->update_[param_id]->mutable_cpu_data());
 
       // update history
@@ -621,14 +621,14 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
       if (local_decay) {
         // add weight decay
         caffe_gpu_axpy(net_params[param_id]->count(),
-            local_decay * local_rate,
+            local_decay,
             net_params[param_id]->gpu_data(),
-            this->history_[param_id]->mutable_gpu_data());
+            net_params[param_id]->mutable_gpu_diff());
       }
 
       // compute square of gradient in update
       caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_data(), Dtype(2),
+          net_params[param_id]->gpu_diff(), Dtype(2),
           this->update_[param_id]->mutable_gpu_data());
 
       // update history

From a683c40d63b79dcb4e2407d59335fb7f8129c82f Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Thu, 24 Jul 2014 13:09:28 -0700
Subject: [PATCH 0587/2053] Added L1 regularization support for the weights

---
 include/caffe/solver.hpp    |   4 +-
 src/caffe/proto/caffe.proto |   3 +
 src/caffe/solver.cpp        | 139 ++++++++++++++++++++++++++++--------
 3 files changed, 115 insertions(+), 31 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 9d5481cc368..4bf50d413c8 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -73,7 +73,9 @@ class SGDSolver : public Solver<Dtype> {
   virtual void RestoreSolverState(const SolverState& state);
   // history maintains the historical momentum data.
   // update maintains update related data and is not needed in snapshots.
-  vector<shared_ptr<Blob<Dtype> > > history_, update_;
+  // temp maintains other information that might be needed in computation
+  //   of gradients/updates and is not needed in snapshots
+  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
 
   DISABLE_COPY_AND_ASSIGN(SGDSolver);
 };
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 49a6e142fcd..0bb5d11cfbe 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -116,6 +116,9 @@ message SolverParameter {
   optional float power = 10; // The parameter to compute the learning rate.
   optional float momentum = 11; // The momentum value.
   optional float weight_decay = 12; // The weight decay.
+  // regularization types supported: L1 and L2
+  // controled by weight_decay
+  optional string regularization_type = 25 [default = "L2"];
   optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 8928c7b29ae..223194bdb09 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -378,6 +378,7 @@ void SGDSolver<Dtype>::PreSolve() {
   vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   history_.clear();
   update_.clear();
+  temp_.clear();
   for (int i = 0; i < net_params.size(); ++i) {
     const Blob<Dtype>* net_param = net_params[i].get();
     history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
@@ -386,6 +387,9 @@ void SGDSolver<Dtype>::PreSolve() {
     update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
         net_param->num(), net_param->channels(), net_param->height(),
         net_param->width())));
+    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
   }
 }
 
@@ -402,6 +406,7 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
   }
   Dtype momentum = this->param_.momentum();
   Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {
   case Caffe::CPU:
     for (int param_id = 0; param_id < net_params.size(); ++param_id) {
@@ -412,11 +417,23 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
           net_params[param_id]->cpu_diff(), momentum,
           history_[param_id]->mutable_cpu_data());
       if (local_decay) {
-        // add weight decay
-        caffe_axpy(net_params[param_id]->count(),
-            local_decay * local_rate,
-            net_params[param_id]->cpu_data(),
-            history_[param_id]->mutable_cpu_data());
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              net_params[param_id]->cpu_data(),
+              history_[param_id]->mutable_cpu_data());
+        } else if (regularization_type == "L1") {
+          caffe_cpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->cpu_data(),
+              temp_[param_id]->mutable_cpu_data());
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              temp_[param_id]->cpu_data(),
+              history_[param_id]->mutable_cpu_data());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
       }
       // copy
       caffe_copy(net_params[param_id]->count(),
@@ -434,11 +451,23 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
           net_params[param_id]->gpu_diff(), momentum,
           history_[param_id]->mutable_gpu_data());
       if (local_decay) {
-        // add weight decay
-        caffe_gpu_axpy(net_params[param_id]->count(),
-            local_decay * local_rate,
-            net_params[param_id]->gpu_data(),
-            history_[param_id]->mutable_gpu_data());
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              net_params[param_id]->gpu_data(),
+              history_[param_id]->mutable_gpu_data());
+        } else if (regularization_type == "L1") {
+          caffe_gpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->gpu_data(),
+              temp_[param_id]->mutable_gpu_data());
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              temp_[param_id]->gpu_data(),
+              history_[param_id]->mutable_gpu_data());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
       }
       // copy
       caffe_copy(net_params[param_id]->count(),
@@ -487,6 +516,7 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
   }
   Dtype momentum = this->param_.momentum();
   Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {
   case Caffe::CPU:
     for (int param_id = 0; param_id < net_params.size(); ++param_id) {
@@ -501,11 +531,23 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
           net_params[param_id]->cpu_diff(), momentum,
           this->history_[param_id]->mutable_cpu_data());
       if (local_decay) {
-        // add weight decay
-        caffe_axpy(net_params[param_id]->count(),
-            local_decay * local_rate,
-            net_params[param_id]->cpu_data(),
-            this->history_[param_id]->mutable_cpu_data());
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              net_params[param_id]->cpu_data(),
+              this->history_[param_id]->mutable_cpu_data());
+        } else if (regularization_type == "L1") {
+          caffe_cpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->cpu_data(),
+              this->temp_[param_id]->mutable_cpu_data());
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              this->temp_[param_id]->cpu_data(),
+              this->history_[param_id]->mutable_cpu_data());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
       }
       // compute udpate: step back then over step
       caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
@@ -532,11 +574,23 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
           net_params[param_id]->gpu_diff(), momentum,
           this->history_[param_id]->mutable_gpu_data());
       if (local_decay) {
-        // add weight decay
-        caffe_gpu_axpy(net_params[param_id]->count(),
-            local_decay * local_rate,
-            net_params[param_id]->gpu_data(),
-            this->history_[param_id]->mutable_gpu_data());
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              net_params[param_id]->gpu_data(),
+              this->history_[param_id]->mutable_gpu_data());
+        } else if (regularization_type == "L1") {
+          caffe_gpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->gpu_data(),
+              this->temp_[param_id]->mutable_gpu_data());
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay * local_rate,
+              this->temp_[param_id]->gpu_data(),
+              this->history_[param_id]->mutable_gpu_data());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
       }
       // compute udpate: step back then over step
       caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
@@ -568,6 +622,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
   Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {
   case Caffe::CPU:
     for (int param_id = 0; param_id < net_params.size(); ++param_id) {
@@ -575,11 +630,23 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
       Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
 
       if (local_decay) {
-        // add weight decay
-        caffe_axpy(net_params[param_id]->count(),
-            local_decay,
-            net_params[param_id]->cpu_data(),
-            net_params[param_id]->mutable_cpu_diff());
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay,
+              net_params[param_id]->cpu_data(),
+              this->history_[param_id]->mutable_cpu_data());
+        } else if (regularization_type == "L1") {
+          caffe_cpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->cpu_data(),
+              this->temp_[param_id]->mutable_cpu_data());
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay,
+              this->temp_[param_id]->cpu_data(),
+              this->history_[param_id]->mutable_cpu_data());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
       }
 
       // compute square of gradient in update
@@ -619,11 +686,23 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
       Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
 
       if (local_decay) {
-        // add weight decay
-        caffe_gpu_axpy(net_params[param_id]->count(),
-            local_decay,
-            net_params[param_id]->gpu_data(),
-            net_params[param_id]->mutable_gpu_diff());
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay,
+              net_params[param_id]->gpu_data(),
+              this->history_[param_id]->mutable_gpu_data());
+        } else if (regularization_type == "L1") {
+          caffe_gpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->gpu_data(),
+              this->temp_[param_id]->mutable_gpu_data());
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay,
+              this->temp_[param_id]->gpu_data(),
+              this->history_[param_id]->mutable_gpu_data());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
       }
 
       // compute square of gradient in update

From b0ec5314f407f47bd2b593c609f88cf23ffa32cf Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 29 Jul 2014 10:06:32 -0700
Subject: [PATCH 0588/2053] fixed caffe.proto after a mistaken rebase

---
 src/caffe/proto/caffe.proto | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 0bb5d11cfbe..73ae89d326a 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -137,6 +137,16 @@ message SolverParameter {
   // random number generator -- useful for reproducible results. Otherwise,
   // (and by default) initialize using a seed derived from the system clock.
   optional int64 random_seed = 20 [default = -1];
+  
+  // Solver type
+  enum SolverType {
+    SGD = 0;
+    NESTEROV = 1;
+    ADAGRAD = 2;
+  }
+  optional SolverType solver_type = 26 [default = SGD];
+  // numerical stability for AdaGrad
+  optional float delta = 27 [default = 1e-8];
 
   // If true, print information about the state of the net that may help with
   // debugging learning problems.

From 3f7a910e05c1ee6e9d09ac201831592357fc18ee Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 29 Jul 2014 19:46:37 -0700
Subject: [PATCH 0589/2053] Addressed Yangqing's comments

---
 include/caffe/solver.hpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 4bf50d413c8..2f51268f295 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -125,9 +125,6 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
   return (Solver<Dtype>*) NULL;
 }
 
-template Solver<float>* GetSolver(const SolverParameter& param);
-template Solver<double>* GetSolver(const SolverParameter& param);
-
 }  // namespace caffe
 
 #endif  // CAFFE_OPTIMIZATION_SOLVER_HPP_

From 23d44308692e469bd7b587d1697ac68068986608 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 29 Jul 2014 19:56:36 -0700
Subject: [PATCH 0590/2053] fixes after rebase

---
 src/caffe/proto/caffe.proto | 8 ++++----
 src/caffe/solver.cpp        | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 73ae89d326a..9afe8e83a8e 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 27 (last added: test_state)
+// SolverParameter next available ID: 31 (last added: delta)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -118,7 +118,7 @@ message SolverParameter {
   optional float weight_decay = 12; // The weight decay.
   // regularization types supported: L1 and L2
   // controled by weight_decay
-  optional string regularization_type = 25 [default = "L2"];
+  optional string regularization_type = 28 [default = "L2"];
   optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
@@ -144,9 +144,9 @@ message SolverParameter {
     NESTEROV = 1;
     ADAGRAD = 2;
   }
-  optional SolverType solver_type = 26 [default = SGD];
+  optional SolverType solver_type = 29 [default = SGD];
   // numerical stability for AdaGrad
-  optional float delta = 27 [default = 1e-8];
+  optional float delta = 30 [default = 1e-8];
 
   // If true, print information about the state of the net that may help with
   // debugging learning problems.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 223194bdb09..52fd6523e95 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -510,7 +510,6 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
   vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
   // get the learning rate
   Dtype rate = this->GetLearningRate();
-  Dtype delta = this->param_.delta();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
@@ -618,6 +617,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
   vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
   // get the learning rate
   Dtype rate = this->GetLearningRate();
+  Dtype delta = this->param_.delta();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }

From 29b3b2442dc37d7c7481e96a3db747447181b1b8 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Wed, 20 Aug 2014 15:19:30 -0700
Subject: [PATCH 0591/2053] proto conflit, lint, and math_functions (compiler
 complaint)

---
 src/caffe/proto/caffe.proto | 8 ++++----
 tools/train_net.cpp         | 9 ---------
 2 files changed, 4 insertions(+), 13 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9afe8e83a8e..bb14b838f9b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 31 (last added: delta)
+// SolverParameter next available ID: 32 (last added: delta)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -118,7 +118,7 @@ message SolverParameter {
   optional float weight_decay = 12; // The weight decay.
   // regularization types supported: L1 and L2
   // controled by weight_decay
-  optional string regularization_type = 28 [default = "L2"];
+  optional string regularization_type = 29 [default = "L2"];
   optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
@@ -144,9 +144,9 @@ message SolverParameter {
     NESTEROV = 1;
     ADAGRAD = 2;
   }
-  optional SolverType solver_type = 29 [default = SGD];
+  optional SolverType solver_type = 30 [default = SGD];
   // numerical stability for AdaGrad
-  optional float delta = 30 [default = 1e-8];
+  optional float delta = 31 [default = 1e-8];
 
   // If true, print information about the state of the net that may help with
   // debugging learning problems.
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 3652182d606..9429fe892e9 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,12 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly train a network whose
-// parameters are specified by text format protocol buffers.
-// Usage:
-//    train_net net_proto_file solver_proto_file [resume_point_file]
-
-#include <cstring>
-
 #include "caffe/caffe.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)

From 7f2e66e6cc0133e18a659611eaffbadb7b0edf2c Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Wed, 20 Aug 2014 20:07:53 -0700
Subject: [PATCH 0592/2053] added unit test for solvers and fixed solver bugs

---
 src/caffe/solver.cpp                    |  76 ++---
 src/caffe/test/test_adagrad_solver.cpp  | 351 ++++++++++++++++++++++++
 src/caffe/test/test_nesterov_solver.cpp | 351 ++++++++++++++++++++++++
 3 files changed, 746 insertions(+), 32 deletions(-)
 create mode 100644 src/caffe/test/test_adagrad_solver.cpp
 create mode 100644 src/caffe/test/test_nesterov_solver.cpp

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 52fd6523e95..dcac4c1537c 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -413,28 +413,30 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
       // Compute the value to history, and then copy them to the blob's diff.
       Dtype local_rate = rate * net_params_lr[param_id];
       Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-          net_params[param_id]->cpu_diff(), momentum,
-          history_[param_id]->mutable_cpu_data());
+
       if (local_decay) {
         if (regularization_type == "L2") {
           // add weight decay
           caffe_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               net_params[param_id]->cpu_data(),
-              history_[param_id]->mutable_cpu_data());
+              net_params[param_id]->mutable_cpu_diff());
         } else if (regularization_type == "L1") {
           caffe_cpu_sign(net_params[param_id]->count(),
               net_params[param_id]->cpu_data(),
               temp_[param_id]->mutable_cpu_data());
           caffe_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               temp_[param_id]->cpu_data(),
-              history_[param_id]->mutable_cpu_data());
+              net_params[param_id]->mutable_cpu_diff());
         } else {
           LOG(FATAL) << "Unknown regularization type: " << regularization_type;
         }
       }
+
+      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+                net_params[param_id]->cpu_diff(), momentum,
+                history_[param_id]->mutable_cpu_data());
       // copy
       caffe_copy(net_params[param_id]->count(),
           history_[param_id]->cpu_data(),
@@ -447,28 +449,30 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
       // Compute the value to history, and then copy them to the blob's diff.
       Dtype local_rate = rate * net_params_lr[param_id];
       Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-          net_params[param_id]->gpu_diff(), momentum,
-          history_[param_id]->mutable_gpu_data());
+
       if (local_decay) {
         if (regularization_type == "L2") {
           // add weight decay
           caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               net_params[param_id]->gpu_data(),
-              history_[param_id]->mutable_gpu_data());
+              net_params[param_id]->mutable_gpu_diff());
         } else if (regularization_type == "L1") {
           caffe_gpu_sign(net_params[param_id]->count(),
               net_params[param_id]->gpu_data(),
               temp_[param_id]->mutable_gpu_data());
           caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               temp_[param_id]->gpu_data(),
-              history_[param_id]->mutable_gpu_data());
+              net_params[param_id]->mutable_gpu_diff());
         } else {
           LOG(FATAL) << "Unknown regularization type: " << regularization_type;
         }
       }
+
+      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+                net_params[param_id]->gpu_diff(), momentum,
+                history_[param_id]->mutable_gpu_data());
       // copy
       caffe_copy(net_params[param_id]->count(),
           history_[param_id]->gpu_data(),
@@ -526,28 +530,32 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
 
       Dtype local_rate = rate * net_params_lr[param_id];
       Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-          net_params[param_id]->cpu_diff(), momentum,
-          this->history_[param_id]->mutable_cpu_data());
+
       if (local_decay) {
         if (regularization_type == "L2") {
           // add weight decay
           caffe_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               net_params[param_id]->cpu_data(),
-              this->history_[param_id]->mutable_cpu_data());
+              net_params[param_id]->mutable_cpu_diff());
         } else if (regularization_type == "L1") {
           caffe_cpu_sign(net_params[param_id]->count(),
               net_params[param_id]->cpu_data(),
               this->temp_[param_id]->mutable_cpu_data());
           caffe_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               this->temp_[param_id]->cpu_data(),
-              this->history_[param_id]->mutable_cpu_data());
+              net_params[param_id]->mutable_cpu_diff());
         } else {
           LOG(FATAL) << "Unknown regularization type: " << regularization_type;
         }
       }
+
+      // update history
+      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+                net_params[param_id]->cpu_diff(), momentum,
+                this->history_[param_id]->mutable_cpu_data());
+
       // compute udpate: step back then over step
       caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
           this->history_[param_id]->cpu_data(), -momentum,
@@ -569,28 +577,32 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
 
       Dtype local_rate = rate * net_params_lr[param_id];
       Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-          net_params[param_id]->gpu_diff(), momentum,
-          this->history_[param_id]->mutable_gpu_data());
+
       if (local_decay) {
         if (regularization_type == "L2") {
           // add weight decay
           caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               net_params[param_id]->gpu_data(),
-              this->history_[param_id]->mutable_gpu_data());
+              net_params[param_id]->mutable_gpu_diff());
         } else if (regularization_type == "L1") {
           caffe_gpu_sign(net_params[param_id]->count(),
               net_params[param_id]->gpu_data(),
               this->temp_[param_id]->mutable_gpu_data());
           caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay * local_rate,
+              local_decay,
               this->temp_[param_id]->gpu_data(),
-              this->history_[param_id]->mutable_gpu_data());
+              net_params[param_id]->mutable_gpu_diff());
         } else {
           LOG(FATAL) << "Unknown regularization type: " << regularization_type;
         }
       }
+
+      // update history
+      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+                net_params[param_id]->gpu_diff(), momentum,
+                this->history_[param_id]->mutable_gpu_data());
+
       // compute udpate: step back then over step
       caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
           this->history_[param_id]->gpu_data(), -momentum,
@@ -635,7 +647,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
           caffe_axpy(net_params[param_id]->count(),
               local_decay,
               net_params[param_id]->cpu_data(),
-              this->history_[param_id]->mutable_cpu_data());
+              net_params[param_id]->mutable_cpu_diff());
         } else if (regularization_type == "L1") {
           caffe_cpu_sign(net_params[param_id]->count(),
               net_params[param_id]->cpu_data(),
@@ -643,7 +655,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
           caffe_axpy(net_params[param_id]->count(),
               local_decay,
               this->temp_[param_id]->cpu_data(),
-              this->history_[param_id]->mutable_cpu_data());
+              net_params[param_id]->mutable_cpu_diff());
         } else {
           LOG(FATAL) << "Unknown regularization type: " << regularization_type;
         }
@@ -691,7 +703,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
           caffe_gpu_axpy(net_params[param_id]->count(),
               local_decay,
               net_params[param_id]->gpu_data(),
-              this->history_[param_id]->mutable_gpu_data());
+              net_params[param_id]->mutable_gpu_diff());
         } else if (regularization_type == "L1") {
           caffe_gpu_sign(net_params[param_id]->count(),
               net_params[param_id]->gpu_data(),
@@ -699,7 +711,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
           caffe_gpu_axpy(net_params[param_id]->count(),
               local_decay,
               this->temp_[param_id]->gpu_data(),
-              this->history_[param_id]->mutable_gpu_data());
+              net_params[param_id]->mutable_gpu_diff());
         } else {
           LOG(FATAL) << "Unknown regularization type: " << regularization_type;
         }
diff --git a/src/caffe/test/test_adagrad_solver.cpp b/src/caffe/test/test_adagrad_solver.cpp
new file mode 100644
index 00000000000..45cf2002e33
--- /dev/null
+++ b/src/caffe/test/test_adagrad_solver.cpp
@@ -0,0 +1,351 @@
+#include <algorithm>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::ostringstream;
+
+namespace caffe {
+
+template <typename TypeParam>
+class AdaGradSolverTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  AdaGradSolverTest() :
+      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+
+  // MockAdaGradSolver: an AdaGradSolver with public history.
+  class MockAdaGradSolver : public AdaGradSolver<Dtype> {
+   public:
+    explicit MockAdaGradSolver(const SolverParameter& param) :
+        AdaGradSolver<Dtype>(param) {}
+    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
+    Dtype delta() { return this->param_.delta(); }
+  };
+
+  shared_ptr<MockAdaGradSolver> solver_;
+  int seed_;
+  int num_, channels_, height_, width_;
+
+  virtual void InitSolverFromProtoString(const string& proto) {
+    SolverParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    // Disable saving a final snapshot so the tests don't pollute the user's
+    // working directory with useless snapshots.
+    param.set_snapshot_after_train(false);
+    // Set the solver_mode according to current Caffe::mode.
+    switch (Caffe::mode()) {
+      case Caffe::CPU:
+        param.set_solver_mode(SolverParameter_SolverMode_CPU);
+        break;
+      case Caffe::GPU:
+        param.set_solver_mode(SolverParameter_SolverMode_GPU);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
+    }
+    solver_.reset(new MockAdaGradSolver(param));
+  }
+
+  void RunLeastSquaresSolver(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+    ostringstream proto;
+    proto <<
+       "max_iter: " << num_iters << " "
+       "base_lr: " << learning_rate << " "
+       "lr_policy: 'fixed' "
+       "net_param { "
+       "  name: 'TestNetwork' "
+       "  layers: { "
+       "    name: 'data' "
+       "    type: DUMMY_DATA "
+       "    dummy_data_param { "
+       "      num: " << num_ << " "
+       "      channels: " << channels_ << " "
+       "      height: " << height_ << " "
+       "      width: " << width_ << " "
+       "      channels: 1 "
+       "      height: 1 "
+       "      width: 1 "
+       "      data_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    top: 'data' "
+       "    top: 'targets' "
+       "  } "
+       "  layers: { "
+       "    name: 'innerprod' "
+       "    type: INNER_PRODUCT "
+       "    inner_product_param { "
+       "      num_output: 1 "
+       "      weight_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "      bias_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    bottom: 'data' "
+       "    top: 'innerprod' "
+       "  } "
+       "  layers: { "
+       "    name: 'loss' "
+       "    type: EUCLIDEAN_LOSS "
+       "    bottom: 'innerprod' "
+       "    bottom: 'targets' "
+       "  } "
+       "} ";
+    if (weight_decay != 0) {
+      proto << "weight_decay: " << weight_decay << " ";
+    }
+    if (momentum != 0) {
+      proto << "momentum: " << momentum << " ";
+    }
+    Caffe::set_random_seed(this->seed_);
+    this->InitSolverFromProtoString(proto.str());
+    this->solver_->Solve();
+  }
+
+  // Compute an update value given the current state of the train net,
+  // using the analytical formula for the least squares gradient.
+  // updated_params will store the updated weight and bias results,
+  // using the blobs' diffs to hold the update values themselves.
+  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum,
+      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
+    const int N = num_;
+    const int D = channels_ * height_ * width_;
+
+    // Run a forward pass, and manually compute the update values from the
+    // result.
+    Net<Dtype>& net = *this->solver_->net();
+    vector<Blob<Dtype>*> empty_bottom_vec;
+    net.Forward(empty_bottom_vec);
+    ASSERT_TRUE(net.has_blob("data"));
+    const Blob<Dtype>& data = *net.blob_by_name("data");
+    ASSERT_TRUE(net.has_blob("targets"));
+    const Blob<Dtype>& targets = *net.blob_by_name("targets");
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    const int num_param_blobs = 2;
+    ASSERT_EQ(num_param_blobs, param_blobs.size());
+    const Blob<Dtype>& weights = *param_blobs[0];
+    const Blob<Dtype>& bias = *param_blobs[1];
+    ASSERT_EQ(D * N, data.count());
+    ASSERT_EQ(N, targets.count());
+    ASSERT_EQ(D, weights.count());
+    ASSERT_EQ(1, bias.count());
+
+    updated_params->clear();
+    updated_params->resize(num_param_blobs);
+    for (int i = 0; i < num_param_blobs; ++i) {
+      (*updated_params)[i].reset(new Blob<Dtype>());
+    }
+    Blob<Dtype>& updated_weights = *(*updated_params)[0];
+    updated_weights.ReshapeLike(weights);
+    Blob<Dtype>& updated_bias = *(*updated_params)[1];
+    updated_bias.ReshapeLike(bias);
+
+    for (int i = 0; i <= D; ++i) {
+      // Compute the derivative with respect to the ith weight (i.e., the ith
+      // element of the gradient).
+      Dtype grad = 0;
+      for (int j = 0; j <= D; ++j) {
+        // Compute element (i, j) of X^T * X.
+        Dtype element = 0;
+        for (int k = 0; k < N; ++k) {
+          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
+          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
+          element += element_i * element_j;
+        }
+        if (j == D) {
+          grad += element * bias.cpu_data()[0];
+        } else {
+          grad += element * weights.cpu_data()[j];
+        }
+      }
+      for (int k = 0; k < N; ++k) {
+        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+        grad -= element_i * targets.cpu_data()[k];
+      }
+      // Scale the gradient over the N samples.
+      grad /= N;
+      // Add the weight decay to the gradient.
+      grad += weight_decay *
+          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
+      // Finally, compute update
+      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+      Dtype delta = solver_->delta();
+      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      Dtype update_value, temp;
+      if (i == D) {
+        temp = history[1]->cpu_data()[0];
+        temp += grad * grad;
+        update_value = learning_rate * grad / (std::sqrt(temp) + delta);
+        updated_bias.mutable_cpu_diff()[0] = update_value;
+        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
+      } else {
+        temp = history[0]->cpu_data()[i];
+        temp += grad * grad;
+        update_value = learning_rate * grad / (std::sqrt(temp) + delta);
+        updated_weights.mutable_cpu_diff()[i] = update_value;
+        updated_weights.mutable_cpu_data()[i] =
+            weights.cpu_data()[i] - update_value;
+      }
+    }
+  }
+
+  void CheckLeastSquaresUpdate(
+      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
+    const int D = channels_ * height_ * width_;
+
+    const Blob<Dtype>& updated_weights = *updated_params[0];
+    const Blob<Dtype>& updated_bias = *updated_params[1];
+
+    Net<Dtype>& net = *this->solver_->net();
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    ASSERT_EQ(2, param_blobs.size());
+    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
+    ASSERT_EQ(D, solver_updated_weights.count());
+    const double kPrecision = 1e-3;
+    const double kMinPrecision = 1e-7;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
+      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
+      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
+    }
+    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
+    ASSERT_EQ(1, solver_updated_bias_blob.count());
+    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
+    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
+    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
+
+    // Check the solver's history -- should contain the previous update value.
+//    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
+//    ASSERT_EQ(2, history.size());
+//    for (int i = 0; i < D; ++i) {
+//      const Dtype expected_history = updated_weights.cpu_diff()[i];
+//      const Dtype solver_history = history[0]->cpu_data()[i];
+//      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+//          std::min(fabs(expected_history), fabs(solver_history)));
+//      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+//    }
+//    const Dtype expected_history = updated_bias.cpu_diff()[0];
+//    const Dtype solver_history = history[1]->cpu_data()[0];
+//    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+//        std::min(fabs(expected_history), fabs(solver_history)));
+//    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+  }
+
+  // Test that the correct update is computed for a regularized least squares
+  // problem:
+  //
+  //            E = (1/(2n)) || X w - y ||^2 + (lambda / 2) || w ||^2
+  //   \nabla_w E = (1/n) (X^T X w - X^T y) + lambda * w
+  //
+  // X \in R^{n x (d+1)} (each example is a row, (d+1)th element is always 1)
+  // w \in R^{(d+1) x 1} ((d+1)th element is the bias)
+  // y \in R^{n x 1}
+  // lambda is weight_decay
+  //
+  // TestLeastSquaresUpdate works "inductively", assuming that the solver
+  // correctly updates the net K (= iter_to_check) times, then given the history
+  // from the Kth update, we compute the (K+1)th update and check that it
+  // matches the solver's (K+1)th update.
+  void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
+      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+      const int iter_to_check = 0) {
+    // Initialize the solver and run K (= iter_to_check) solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+
+    // Compute the (K+1)th update using the analytic least squares gradient.
+    vector<shared_ptr<Blob<Dtype> > > updated_params;
+    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+                              &updated_params);
+
+    // Reinitialize the solver and run K+1 solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                          iter_to_check + 1);
+
+    // Check that the solver's solution matches ours.
+    CheckLeastSquaresUpdate(updated_params);
+  }
+};
+
+TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->TestLeastSquaresUpdate();
+}
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+/*
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+*/
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_nesterov_solver.cpp b/src/caffe/test/test_nesterov_solver.cpp
new file mode 100644
index 00000000000..f2fcba30b29
--- /dev/null
+++ b/src/caffe/test/test_nesterov_solver.cpp
@@ -0,0 +1,351 @@
+#include <algorithm>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::ostringstream;
+
+namespace caffe {
+
+template <typename TypeParam>
+class NesterovSolverTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  NesterovSolverTest() :
+      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+
+  // MockNesterovSolver: an NesterovSolver with public history.
+  class MockNesterovSolver : public NesterovSolver<Dtype> {
+   public:
+    explicit MockNesterovSolver(const SolverParameter& param) :
+        NesterovSolver<Dtype>(param) {}
+    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
+  };
+
+  shared_ptr<MockNesterovSolver> solver_;
+  int seed_;
+  int num_, channels_, height_, width_;
+
+  virtual void InitSolverFromProtoString(const string& proto) {
+    SolverParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    // Disable saving a final snapshot so the tests don't pollute the user's
+    // working directory with useless snapshots.
+    param.set_snapshot_after_train(false);
+    // Set the solver_mode according to current Caffe::mode.
+    switch (Caffe::mode()) {
+      case Caffe::CPU:
+        param.set_solver_mode(SolverParameter_SolverMode_CPU);
+        break;
+      case Caffe::GPU:
+        param.set_solver_mode(SolverParameter_SolverMode_GPU);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
+    }
+    solver_.reset(new MockNesterovSolver(param));
+  }
+
+  void RunLeastSquaresSolver(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+    ostringstream proto;
+    proto <<
+       "max_iter: " << num_iters << " "
+       "base_lr: " << learning_rate << " "
+       "lr_policy: 'fixed' "
+       "net_param { "
+       "  name: 'TestNetwork' "
+       "  layers: { "
+       "    name: 'data' "
+       "    type: DUMMY_DATA "
+       "    dummy_data_param { "
+       "      num: " << num_ << " "
+       "      channels: " << channels_ << " "
+       "      height: " << height_ << " "
+       "      width: " << width_ << " "
+       "      channels: 1 "
+       "      height: 1 "
+       "      width: 1 "
+       "      data_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    top: 'data' "
+       "    top: 'targets' "
+       "  } "
+       "  layers: { "
+       "    name: 'innerprod' "
+       "    type: INNER_PRODUCT "
+       "    inner_product_param { "
+       "      num_output: 1 "
+       "      weight_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "      bias_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    bottom: 'data' "
+       "    top: 'innerprod' "
+       "  } "
+       "  layers: { "
+       "    name: 'loss' "
+       "    type: EUCLIDEAN_LOSS "
+       "    bottom: 'innerprod' "
+       "    bottom: 'targets' "
+       "  } "
+       "} ";
+    if (weight_decay != 0) {
+      proto << "weight_decay: " << weight_decay << " ";
+    }
+    if (momentum != 0) {
+      proto << "momentum: " << momentum << " ";
+    }
+    Caffe::set_random_seed(this->seed_);
+    this->InitSolverFromProtoString(proto.str());
+    this->solver_->Solve();
+  }
+
+  // Compute an update value given the current state of the train net,
+  // using the analytical formula for the least squares gradient.
+  // updated_params will store the updated weight and bias results,
+  // using the blobs' diffs to hold the update values themselves.
+  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum,
+      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
+    const int N = num_;
+    const int D = channels_ * height_ * width_;
+
+    // Run a forward pass, and manually compute the update values from the
+    // result.
+    Net<Dtype>& net = *this->solver_->net();
+    vector<Blob<Dtype>*> empty_bottom_vec;
+    net.Forward(empty_bottom_vec);
+    ASSERT_TRUE(net.has_blob("data"));
+    const Blob<Dtype>& data = *net.blob_by_name("data");
+    ASSERT_TRUE(net.has_blob("targets"));
+    const Blob<Dtype>& targets = *net.blob_by_name("targets");
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    const int num_param_blobs = 2;
+    ASSERT_EQ(num_param_blobs, param_blobs.size());
+    const Blob<Dtype>& weights = *param_blobs[0];
+    const Blob<Dtype>& bias = *param_blobs[1];
+    ASSERT_EQ(D * N, data.count());
+    ASSERT_EQ(N, targets.count());
+    ASSERT_EQ(D, weights.count());
+    ASSERT_EQ(1, bias.count());
+
+    updated_params->clear();
+    updated_params->resize(num_param_blobs);
+    for (int i = 0; i < num_param_blobs; ++i) {
+      (*updated_params)[i].reset(new Blob<Dtype>());
+    }
+    Blob<Dtype>& updated_weights = *(*updated_params)[0];
+    updated_weights.ReshapeLike(weights);
+    Blob<Dtype>& updated_bias = *(*updated_params)[1];
+    updated_bias.ReshapeLike(bias);
+
+    for (int i = 0; i <= D; ++i) {
+      // Compute the derivative with respect to the ith weight (i.e., the ith
+      // element of the gradient).
+      Dtype grad = 0;
+      for (int j = 0; j <= D; ++j) {
+        // Compute element (i, j) of X^T * X.
+        Dtype element = 0;
+        for (int k = 0; k < N; ++k) {
+          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
+          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
+          element += element_i * element_j;
+        }
+        if (j == D) {
+          grad += element * bias.cpu_data()[0];
+        } else {
+          grad += element * weights.cpu_data()[j];
+        }
+      }
+      for (int k = 0; k < N; ++k) {
+        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+        grad -= element_i * targets.cpu_data()[k];
+      }
+      // Scale the gradient over the N samples.
+      grad /= N;
+      // Add the weight decay to the gradient.
+      grad += weight_decay *
+          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
+      // Finally, add any momentum.
+      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      Dtype update_value = learning_rate * grad, temp;
+      if (i == D) {
+        temp = history[1]->cpu_data()[0] * momentum;
+        update_value += temp;  // update history
+        // step back then over-step
+        update_value = (1 + momentum) * update_value - temp;
+        updated_bias.mutable_cpu_diff()[0] = update_value;
+        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
+      } else {
+        temp = history[0]->cpu_data()[i] * momentum;
+        update_value += temp;  // update history
+        // step back then over-step
+        update_value = (1 + momentum) * update_value - temp;
+        updated_weights.mutable_cpu_diff()[i] = update_value;
+        updated_weights.mutable_cpu_data()[i] =
+            weights.cpu_data()[i] - update_value;
+      }
+    }
+  }
+
+  void CheckLeastSquaresUpdate(
+      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
+    const int D = channels_ * height_ * width_;
+
+    const Blob<Dtype>& updated_weights = *updated_params[0];
+    const Blob<Dtype>& updated_bias = *updated_params[1];
+
+    Net<Dtype>& net = *this->solver_->net();
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    ASSERT_EQ(2, param_blobs.size());
+    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
+    ASSERT_EQ(D, solver_updated_weights.count());
+    const double kPrecision = 1e-3;
+    const double kMinPrecision = 1e-7;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
+      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
+      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
+    }
+    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
+    ASSERT_EQ(1, solver_updated_bias_blob.count());
+    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
+    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
+    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
+
+    // Check the solver's history -- should contain the previous update value.
+//    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
+//    ASSERT_EQ(2, history.size());
+//    for (int i = 0; i < D; ++i) {
+//      const Dtype expected_history = updated_weights.cpu_diff()[i];
+//      const Dtype solver_history = history[0]->cpu_data()[i];
+//      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+//          std::min(fabs(expected_history), fabs(solver_history)));
+//      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+//    }
+//    const Dtype expected_history = updated_bias.cpu_diff()[0];
+//    const Dtype solver_history = history[1]->cpu_data()[0];
+//    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+//        std::min(fabs(expected_history), fabs(solver_history)));
+//    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+  }
+
+  // Test that the correct update is computed for a regularized least squares
+  // problem:
+  //
+  //            E = (1/(2n)) || X w - y ||^2 + (lambda / 2) || w ||^2
+  //   \nabla_w E = (1/n) (X^T X w - X^T y) + lambda * w
+  //
+  // X \in R^{n x (d+1)} (each example is a row, (d+1)th element is always 1)
+  // w \in R^{(d+1) x 1} ((d+1)th element is the bias)
+  // y \in R^{n x 1}
+  // lambda is weight_decay
+  //
+  // TestLeastSquaresUpdate works "inductively", assuming that the solver
+  // correctly updates the net K (= iter_to_check) times, then given the history
+  // from the Kth update, we compute the (K+1)th update and check that it
+  // matches the solver's (K+1)th update.
+  void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
+      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+      const int iter_to_check = 0) {
+    // Initialize the solver and run K (= iter_to_check) solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+
+    // Compute the (K+1)th update using the analytic least squares gradient.
+    vector<shared_ptr<Blob<Dtype> > > updated_params;
+    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+                              &updated_params);
+
+    // Reinitialize the solver and run K+1 solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                          iter_to_check + 1);
+
+    // Check that the solver's solution matches ours.
+    CheckLeastSquaresUpdate(updated_params);
+  }
+};
+
+TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->TestLeastSquaresUpdate();
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+}  // namespace caffe

From dbb9296050054095d8d2979992e969534b9b1430 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 22 Aug 2014 11:51:29 -0700
Subject: [PATCH 0593/2053] cleanup caffe.proto

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index bb14b838f9b..ff18779f62d 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -137,7 +137,7 @@ message SolverParameter {
   // random number generator -- useful for reproducible results. Otherwise,
   // (and by default) initialize using a seed derived from the system clock.
   optional int64 random_seed = 20 [default = -1];
-  
+
   // Solver type
   enum SolverType {
     SGD = 0;

From f206c6479fd355f17bd821ceb6931fd0663c1e70 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 22 Aug 2014 11:51:16 -0700
Subject: [PATCH 0594/2053] Merge Test{SGD,AdaGrad,Nesterov}Solver; they become
 subclasses of TestGradientBasedSolver

---
 include/caffe/solver.hpp                      |   2 +
 src/caffe/test/test_adagrad_solver.cpp        | 351 ------------------
 ...ver.cpp => test_gradient_based_solver.cpp} | 193 ++++++++--
 src/caffe/test/test_nesterov_solver.cpp       | 351 ------------------
 4 files changed, 170 insertions(+), 727 deletions(-)
 delete mode 100644 src/caffe/test/test_adagrad_solver.cpp
 rename src/caffe/test/{test_sgd_solver.cpp => test_gradient_based_solver.cpp} (66%)
 delete mode 100644 src/caffe/test/test_nesterov_solver.cpp

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 2f51268f295..b3c7f6f35d6 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -65,6 +65,8 @@ class SGDSolver : public Solver<Dtype> {
   explicit SGDSolver(const string& param_file)
       : Solver<Dtype>(param_file) {}
 
+  const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
+
  protected:
   virtual void PreSolve();
   Dtype GetLearningRate();
diff --git a/src/caffe/test/test_adagrad_solver.cpp b/src/caffe/test/test_adagrad_solver.cpp
deleted file mode 100644
index 45cf2002e33..00000000000
--- a/src/caffe/test/test_adagrad_solver.cpp
+++ /dev/null
@@ -1,351 +0,0 @@
-#include <algorithm>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "google/protobuf/text_format.h"
-
-#include "gtest/gtest.h"
-
-#include "caffe/common.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-using std::ostringstream;
-
-namespace caffe {
-
-template <typename TypeParam>
-class AdaGradSolverTest : public MultiDeviceTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  AdaGradSolverTest() :
-      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
-
-  // MockAdaGradSolver: an AdaGradSolver with public history.
-  class MockAdaGradSolver : public AdaGradSolver<Dtype> {
-   public:
-    explicit MockAdaGradSolver(const SolverParameter& param) :
-        AdaGradSolver<Dtype>(param) {}
-    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
-    Dtype delta() { return this->param_.delta(); }
-  };
-
-  shared_ptr<MockAdaGradSolver> solver_;
-  int seed_;
-  int num_, channels_, height_, width_;
-
-  virtual void InitSolverFromProtoString(const string& proto) {
-    SolverParameter param;
-    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
-    // Disable saving a final snapshot so the tests don't pollute the user's
-    // working directory with useless snapshots.
-    param.set_snapshot_after_train(false);
-    // Set the solver_mode according to current Caffe::mode.
-    switch (Caffe::mode()) {
-      case Caffe::CPU:
-        param.set_solver_mode(SolverParameter_SolverMode_CPU);
-        break;
-      case Caffe::GPU:
-        param.set_solver_mode(SolverParameter_SolverMode_GPU);
-        break;
-      default:
-        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
-    }
-    solver_.reset(new MockAdaGradSolver(param));
-  }
-
-  void RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
-    ostringstream proto;
-    proto <<
-       "max_iter: " << num_iters << " "
-       "base_lr: " << learning_rate << " "
-       "lr_policy: 'fixed' "
-       "net_param { "
-       "  name: 'TestNetwork' "
-       "  layers: { "
-       "    name: 'data' "
-       "    type: DUMMY_DATA "
-       "    dummy_data_param { "
-       "      num: " << num_ << " "
-       "      channels: " << channels_ << " "
-       "      height: " << height_ << " "
-       "      width: " << width_ << " "
-       "      channels: 1 "
-       "      height: 1 "
-       "      width: 1 "
-       "      data_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
-       "    } "
-       "    top: 'data' "
-       "    top: 'targets' "
-       "  } "
-       "  layers: { "
-       "    name: 'innerprod' "
-       "    type: INNER_PRODUCT "
-       "    inner_product_param { "
-       "      num_output: 1 "
-       "      weight_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
-       "      bias_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
-       "    } "
-       "    bottom: 'data' "
-       "    top: 'innerprod' "
-       "  } "
-       "  layers: { "
-       "    name: 'loss' "
-       "    type: EUCLIDEAN_LOSS "
-       "    bottom: 'innerprod' "
-       "    bottom: 'targets' "
-       "  } "
-       "} ";
-    if (weight_decay != 0) {
-      proto << "weight_decay: " << weight_decay << " ";
-    }
-    if (momentum != 0) {
-      proto << "momentum: " << momentum << " ";
-    }
-    Caffe::set_random_seed(this->seed_);
-    this->InitSolverFromProtoString(proto.str());
-    this->solver_->Solve();
-  }
-
-  // Compute an update value given the current state of the train net,
-  // using the analytical formula for the least squares gradient.
-  // updated_params will store the updated weight and bias results,
-  // using the blobs' diffs to hold the update values themselves.
-  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum,
-      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
-    const int N = num_;
-    const int D = channels_ * height_ * width_;
-
-    // Run a forward pass, and manually compute the update values from the
-    // result.
-    Net<Dtype>& net = *this->solver_->net();
-    vector<Blob<Dtype>*> empty_bottom_vec;
-    net.Forward(empty_bottom_vec);
-    ASSERT_TRUE(net.has_blob("data"));
-    const Blob<Dtype>& data = *net.blob_by_name("data");
-    ASSERT_TRUE(net.has_blob("targets"));
-    const Blob<Dtype>& targets = *net.blob_by_name("targets");
-    ASSERT_TRUE(net.has_layer("innerprod"));
-    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
-        net.layer_by_name("innerprod")->blobs();
-    const int num_param_blobs = 2;
-    ASSERT_EQ(num_param_blobs, param_blobs.size());
-    const Blob<Dtype>& weights = *param_blobs[0];
-    const Blob<Dtype>& bias = *param_blobs[1];
-    ASSERT_EQ(D * N, data.count());
-    ASSERT_EQ(N, targets.count());
-    ASSERT_EQ(D, weights.count());
-    ASSERT_EQ(1, bias.count());
-
-    updated_params->clear();
-    updated_params->resize(num_param_blobs);
-    for (int i = 0; i < num_param_blobs; ++i) {
-      (*updated_params)[i].reset(new Blob<Dtype>());
-    }
-    Blob<Dtype>& updated_weights = *(*updated_params)[0];
-    updated_weights.ReshapeLike(weights);
-    Blob<Dtype>& updated_bias = *(*updated_params)[1];
-    updated_bias.ReshapeLike(bias);
-
-    for (int i = 0; i <= D; ++i) {
-      // Compute the derivative with respect to the ith weight (i.e., the ith
-      // element of the gradient).
-      Dtype grad = 0;
-      for (int j = 0; j <= D; ++j) {
-        // Compute element (i, j) of X^T * X.
-        Dtype element = 0;
-        for (int k = 0; k < N; ++k) {
-          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
-          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
-          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
-          element += element_i * element_j;
-        }
-        if (j == D) {
-          grad += element * bias.cpu_data()[0];
-        } else {
-          grad += element * weights.cpu_data()[j];
-        }
-      }
-      for (int k = 0; k < N; ++k) {
-        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
-        grad -= element_i * targets.cpu_data()[k];
-      }
-      // Scale the gradient over the N samples.
-      grad /= N;
-      // Add the weight decay to the gradient.
-      grad += weight_decay *
-          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
-      // Finally, compute update
-      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
-      Dtype delta = solver_->delta();
-      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
-      Dtype update_value, temp;
-      if (i == D) {
-        temp = history[1]->cpu_data()[0];
-        temp += grad * grad;
-        update_value = learning_rate * grad / (std::sqrt(temp) + delta);
-        updated_bias.mutable_cpu_diff()[0] = update_value;
-        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
-      } else {
-        temp = history[0]->cpu_data()[i];
-        temp += grad * grad;
-        update_value = learning_rate * grad / (std::sqrt(temp) + delta);
-        updated_weights.mutable_cpu_diff()[i] = update_value;
-        updated_weights.mutable_cpu_data()[i] =
-            weights.cpu_data()[i] - update_value;
-      }
-    }
-  }
-
-  void CheckLeastSquaresUpdate(
-      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
-    const int D = channels_ * height_ * width_;
-
-    const Blob<Dtype>& updated_weights = *updated_params[0];
-    const Blob<Dtype>& updated_bias = *updated_params[1];
-
-    Net<Dtype>& net = *this->solver_->net();
-    ASSERT_TRUE(net.has_layer("innerprod"));
-    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
-        net.layer_by_name("innerprod")->blobs();
-    ASSERT_EQ(2, param_blobs.size());
-    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
-    ASSERT_EQ(D, solver_updated_weights.count());
-    const double kPrecision = 1e-3;
-    const double kMinPrecision = 1e-7;
-    for (int i = 0; i < D; ++i) {
-      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
-      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
-      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
-          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
-      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
-    }
-    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
-    ASSERT_EQ(1, solver_updated_bias_blob.count());
-    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
-    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
-    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
-          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
-    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
-
-    // Check the solver's history -- should contain the previous update value.
-//    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
-//    ASSERT_EQ(2, history.size());
-//    for (int i = 0; i < D; ++i) {
-//      const Dtype expected_history = updated_weights.cpu_diff()[i];
-//      const Dtype solver_history = history[0]->cpu_data()[i];
-//      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
-//          std::min(fabs(expected_history), fabs(solver_history)));
-//      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
-//    }
-//    const Dtype expected_history = updated_bias.cpu_diff()[0];
-//    const Dtype solver_history = history[1]->cpu_data()[0];
-//    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
-//        std::min(fabs(expected_history), fabs(solver_history)));
-//    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
-  }
-
-  // Test that the correct update is computed for a regularized least squares
-  // problem:
-  //
-  //            E = (1/(2n)) || X w - y ||^2 + (lambda / 2) || w ||^2
-  //   \nabla_w E = (1/n) (X^T X w - X^T y) + lambda * w
-  //
-  // X \in R^{n x (d+1)} (each example is a row, (d+1)th element is always 1)
-  // w \in R^{(d+1) x 1} ((d+1)th element is the bias)
-  // y \in R^{n x 1}
-  // lambda is weight_decay
-  //
-  // TestLeastSquaresUpdate works "inductively", assuming that the solver
-  // correctly updates the net K (= iter_to_check) times, then given the history
-  // from the Kth update, we compute the (K+1)th update and check that it
-  // matches the solver's (K+1)th update.
-  void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
-      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
-      const int iter_to_check = 0) {
-    // Initialize the solver and run K (= iter_to_check) solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
-
-    // Compute the (K+1)th update using the analytic least squares gradient.
-    vector<shared_ptr<Blob<Dtype> > > updated_params;
-    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-                              &updated_params);
-
-    // Reinitialize the solver and run K+1 solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-                          iter_to_check + 1);
-
-    // Check that the solver's solution matches ours.
-    CheckLeastSquaresUpdate(updated_params);
-  }
-};
-
-TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
-
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
-  this->TestLeastSquaresUpdate();
-}
-
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
-  this->TestLeastSquaresUpdate(kLearningRate);
-}
-
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.5;
-  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
-}
-/*
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithMomentum) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.5;
-  const int kNumIters = 1;
-  for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.5;
-  const int kNumIters = 5;
-  for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-*/
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.0;
-  const int kNumIters = 5;
-  for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-}  // namespace caffe
diff --git a/src/caffe/test/test_sgd_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
similarity index 66%
rename from src/caffe/test/test_sgd_solver.cpp
rename to src/caffe/test/test_gradient_based_solver.cpp
index 1ec24b134c6..9c21499ea5e 100644
--- a/src/caffe/test/test_sgd_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -18,24 +18,20 @@ using std::ostringstream;
 namespace caffe {
 
 template <typename TypeParam>
-class SGDSolverTest : public MultiDeviceTest<TypeParam> {
+class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
 
  protected:
-  SGDSolverTest() :
+  GradientBasedSolverTest() :
       seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
 
-  // MockSGDSolver: an SGDSolver with public history.
-  class MockSGDSolver : public SGDSolver<Dtype> {
-   public:
-    explicit MockSGDSolver(const SolverParameter& param) :
-        SGDSolver<Dtype>(param) {}
-    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
-  };
-
-  shared_ptr<MockSGDSolver> solver_;
+  shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
   int num_, channels_, height_, width_;
+  Dtype delta_;  // Stability constant for AdaGrad.
+
+  virtual SolverParameter_SolverType solver_type() = 0;
+  virtual void InitSolver(const SolverParameter& param) = 0;
 
   virtual void InitSolverFromProtoString(const string& proto) {
     SolverParameter param;
@@ -54,7 +50,9 @@ class SGDSolverTest : public MultiDeviceTest<TypeParam> {
       default:
         LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
     }
-    solver_.reset(new MockSGDSolver(param));
+    InitSolver(param);
+    delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD) ?
+         param.delta() : 0;
   }
 
   void RunLeastSquaresSolver(const Dtype learning_rate,
@@ -189,16 +187,32 @@ class SGDSolverTest : public MultiDeviceTest<TypeParam> {
       // Add the weight decay to the gradient.
       grad += weight_decay *
           ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
-      // Finally, add any momentum.
+      // Finally, compute update.
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
       ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
       Dtype update_value = learning_rate * grad;
+      const Dtype history_value = (i == D) ?
+            history[1]->cpu_data()[0] : history[0]->cpu_data()[i];
+      const Dtype temp = momentum * history_value;
+      switch (solver_type()) {
+      case SolverParameter_SolverType_SGD:
+        update_value += temp;
+        break;
+      case SolverParameter_SolverType_NESTEROV:
+        update_value += temp;
+        // step back then over-step
+        update_value = (1 + momentum) * update_value - temp;
+        break;
+      case SolverParameter_SolverType_ADAGRAD:
+        update_value /= std::sqrt(history_value + grad * grad) + delta_;
+        break;
+      default:
+        LOG(FATAL) << "Unknown solver type: " << solver_type();
+      }
       if (i == D) {
-        update_value += momentum * history[1]->cpu_data()[0];
         updated_bias.mutable_cpu_diff()[0] = update_value;
         updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
       } else {
-        update_value += momentum * history[0]->cpu_data()[i];
         updated_weights.mutable_cpu_diff()[i] = update_value;
         updated_weights.mutable_cpu_data()[i] =
             weights.cpu_data()[i] - update_value;
@@ -238,20 +252,22 @@ class SGDSolverTest : public MultiDeviceTest<TypeParam> {
     EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
 
     // Check the solver's history -- should contain the previous update value.
-    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
-    ASSERT_EQ(2, history.size());
-    for (int i = 0; i < D; ++i) {
-      const Dtype expected_history = updated_weights.cpu_diff()[i];
-      const Dtype solver_history = history[0]->cpu_data()[i];
+    if (solver_type() == SolverParameter_SolverType_SGD) {
+      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+      ASSERT_EQ(2, history.size());
+      for (int i = 0; i < D; ++i) {
+        const Dtype expected_history = updated_weights.cpu_diff()[i];
+        const Dtype solver_history = history[0]->cpu_data()[i];
+        const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+            std::min(fabs(expected_history), fabs(solver_history)));
+        EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+      }
+      const Dtype expected_history = updated_bias.cpu_diff()[0];
+      const Dtype solver_history = history[1]->cpu_data()[0];
       const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
           std::min(fabs(expected_history), fabs(solver_history)));
       EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
     }
-    const Dtype expected_history = updated_bias.cpu_diff()[0];
-    const Dtype solver_history = history[1]->cpu_data()[0];
-    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
-        std::min(fabs(expected_history), fabs(solver_history)));
-    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
   }
 
   // Test that the correct update is computed for a regularized least squares
@@ -289,6 +305,21 @@ class SGDSolverTest : public MultiDeviceTest<TypeParam> {
   }
 };
 
+
+template <typename TypeParam>
+class SGDSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new SGDSolver<Dtype>(param));
+  }
+
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_SGD;
+  }
+};
+
 TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
@@ -342,4 +373,116 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   }
 }
 
+
+template <typename TypeParam>
+class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new AdaGradSolver<Dtype>(param));
+  }
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_ADAGRAD;
+  }
+};
+
+TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->TestLeastSquaresUpdate();
+}
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+
+template <typename TypeParam>
+class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new NesterovSolver<Dtype>(param));
+  }
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_NESTEROV;
+  }
+};
+
+TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->TestLeastSquaresUpdate();
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_nesterov_solver.cpp b/src/caffe/test/test_nesterov_solver.cpp
deleted file mode 100644
index f2fcba30b29..00000000000
--- a/src/caffe/test/test_nesterov_solver.cpp
+++ /dev/null
@@ -1,351 +0,0 @@
-#include <algorithm>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "google/protobuf/text_format.h"
-
-#include "gtest/gtest.h"
-
-#include "caffe/common.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-using std::ostringstream;
-
-namespace caffe {
-
-template <typename TypeParam>
-class NesterovSolverTest : public MultiDeviceTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  NesterovSolverTest() :
-      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
-
-  // MockNesterovSolver: an NesterovSolver with public history.
-  class MockNesterovSolver : public NesterovSolver<Dtype> {
-   public:
-    explicit MockNesterovSolver(const SolverParameter& param) :
-        NesterovSolver<Dtype>(param) {}
-    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
-  };
-
-  shared_ptr<MockNesterovSolver> solver_;
-  int seed_;
-  int num_, channels_, height_, width_;
-
-  virtual void InitSolverFromProtoString(const string& proto) {
-    SolverParameter param;
-    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
-    // Disable saving a final snapshot so the tests don't pollute the user's
-    // working directory with useless snapshots.
-    param.set_snapshot_after_train(false);
-    // Set the solver_mode according to current Caffe::mode.
-    switch (Caffe::mode()) {
-      case Caffe::CPU:
-        param.set_solver_mode(SolverParameter_SolverMode_CPU);
-        break;
-      case Caffe::GPU:
-        param.set_solver_mode(SolverParameter_SolverMode_GPU);
-        break;
-      default:
-        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
-    }
-    solver_.reset(new MockNesterovSolver(param));
-  }
-
-  void RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
-    ostringstream proto;
-    proto <<
-       "max_iter: " << num_iters << " "
-       "base_lr: " << learning_rate << " "
-       "lr_policy: 'fixed' "
-       "net_param { "
-       "  name: 'TestNetwork' "
-       "  layers: { "
-       "    name: 'data' "
-       "    type: DUMMY_DATA "
-       "    dummy_data_param { "
-       "      num: " << num_ << " "
-       "      channels: " << channels_ << " "
-       "      height: " << height_ << " "
-       "      width: " << width_ << " "
-       "      channels: 1 "
-       "      height: 1 "
-       "      width: 1 "
-       "      data_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
-       "    } "
-       "    top: 'data' "
-       "    top: 'targets' "
-       "  } "
-       "  layers: { "
-       "    name: 'innerprod' "
-       "    type: INNER_PRODUCT "
-       "    inner_product_param { "
-       "      num_output: 1 "
-       "      weight_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
-       "      bias_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
-       "    } "
-       "    bottom: 'data' "
-       "    top: 'innerprod' "
-       "  } "
-       "  layers: { "
-       "    name: 'loss' "
-       "    type: EUCLIDEAN_LOSS "
-       "    bottom: 'innerprod' "
-       "    bottom: 'targets' "
-       "  } "
-       "} ";
-    if (weight_decay != 0) {
-      proto << "weight_decay: " << weight_decay << " ";
-    }
-    if (momentum != 0) {
-      proto << "momentum: " << momentum << " ";
-    }
-    Caffe::set_random_seed(this->seed_);
-    this->InitSolverFromProtoString(proto.str());
-    this->solver_->Solve();
-  }
-
-  // Compute an update value given the current state of the train net,
-  // using the analytical formula for the least squares gradient.
-  // updated_params will store the updated weight and bias results,
-  // using the blobs' diffs to hold the update values themselves.
-  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum,
-      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
-    const int N = num_;
-    const int D = channels_ * height_ * width_;
-
-    // Run a forward pass, and manually compute the update values from the
-    // result.
-    Net<Dtype>& net = *this->solver_->net();
-    vector<Blob<Dtype>*> empty_bottom_vec;
-    net.Forward(empty_bottom_vec);
-    ASSERT_TRUE(net.has_blob("data"));
-    const Blob<Dtype>& data = *net.blob_by_name("data");
-    ASSERT_TRUE(net.has_blob("targets"));
-    const Blob<Dtype>& targets = *net.blob_by_name("targets");
-    ASSERT_TRUE(net.has_layer("innerprod"));
-    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
-        net.layer_by_name("innerprod")->blobs();
-    const int num_param_blobs = 2;
-    ASSERT_EQ(num_param_blobs, param_blobs.size());
-    const Blob<Dtype>& weights = *param_blobs[0];
-    const Blob<Dtype>& bias = *param_blobs[1];
-    ASSERT_EQ(D * N, data.count());
-    ASSERT_EQ(N, targets.count());
-    ASSERT_EQ(D, weights.count());
-    ASSERT_EQ(1, bias.count());
-
-    updated_params->clear();
-    updated_params->resize(num_param_blobs);
-    for (int i = 0; i < num_param_blobs; ++i) {
-      (*updated_params)[i].reset(new Blob<Dtype>());
-    }
-    Blob<Dtype>& updated_weights = *(*updated_params)[0];
-    updated_weights.ReshapeLike(weights);
-    Blob<Dtype>& updated_bias = *(*updated_params)[1];
-    updated_bias.ReshapeLike(bias);
-
-    for (int i = 0; i <= D; ++i) {
-      // Compute the derivative with respect to the ith weight (i.e., the ith
-      // element of the gradient).
-      Dtype grad = 0;
-      for (int j = 0; j <= D; ++j) {
-        // Compute element (i, j) of X^T * X.
-        Dtype element = 0;
-        for (int k = 0; k < N; ++k) {
-          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
-          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
-          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
-          element += element_i * element_j;
-        }
-        if (j == D) {
-          grad += element * bias.cpu_data()[0];
-        } else {
-          grad += element * weights.cpu_data()[j];
-        }
-      }
-      for (int k = 0; k < N; ++k) {
-        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
-        grad -= element_i * targets.cpu_data()[k];
-      }
-      // Scale the gradient over the N samples.
-      grad /= N;
-      // Add the weight decay to the gradient.
-      grad += weight_decay *
-          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
-      // Finally, add any momentum.
-      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
-      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
-      Dtype update_value = learning_rate * grad, temp;
-      if (i == D) {
-        temp = history[1]->cpu_data()[0] * momentum;
-        update_value += temp;  // update history
-        // step back then over-step
-        update_value = (1 + momentum) * update_value - temp;
-        updated_bias.mutable_cpu_diff()[0] = update_value;
-        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
-      } else {
-        temp = history[0]->cpu_data()[i] * momentum;
-        update_value += temp;  // update history
-        // step back then over-step
-        update_value = (1 + momentum) * update_value - temp;
-        updated_weights.mutable_cpu_diff()[i] = update_value;
-        updated_weights.mutable_cpu_data()[i] =
-            weights.cpu_data()[i] - update_value;
-      }
-    }
-  }
-
-  void CheckLeastSquaresUpdate(
-      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
-    const int D = channels_ * height_ * width_;
-
-    const Blob<Dtype>& updated_weights = *updated_params[0];
-    const Blob<Dtype>& updated_bias = *updated_params[1];
-
-    Net<Dtype>& net = *this->solver_->net();
-    ASSERT_TRUE(net.has_layer("innerprod"));
-    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
-        net.layer_by_name("innerprod")->blobs();
-    ASSERT_EQ(2, param_blobs.size());
-    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
-    ASSERT_EQ(D, solver_updated_weights.count());
-    const double kPrecision = 1e-3;
-    const double kMinPrecision = 1e-7;
-    for (int i = 0; i < D; ++i) {
-      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
-      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
-      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
-          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
-      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
-    }
-    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
-    ASSERT_EQ(1, solver_updated_bias_blob.count());
-    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
-    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
-    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
-          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
-    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
-
-    // Check the solver's history -- should contain the previous update value.
-//    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
-//    ASSERT_EQ(2, history.size());
-//    for (int i = 0; i < D; ++i) {
-//      const Dtype expected_history = updated_weights.cpu_diff()[i];
-//      const Dtype solver_history = history[0]->cpu_data()[i];
-//      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
-//          std::min(fabs(expected_history), fabs(solver_history)));
-//      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
-//    }
-//    const Dtype expected_history = updated_bias.cpu_diff()[0];
-//    const Dtype solver_history = history[1]->cpu_data()[0];
-//    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
-//        std::min(fabs(expected_history), fabs(solver_history)));
-//    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
-  }
-
-  // Test that the correct update is computed for a regularized least squares
-  // problem:
-  //
-  //            E = (1/(2n)) || X w - y ||^2 + (lambda / 2) || w ||^2
-  //   \nabla_w E = (1/n) (X^T X w - X^T y) + lambda * w
-  //
-  // X \in R^{n x (d+1)} (each example is a row, (d+1)th element is always 1)
-  // w \in R^{(d+1) x 1} ((d+1)th element is the bias)
-  // y \in R^{n x 1}
-  // lambda is weight_decay
-  //
-  // TestLeastSquaresUpdate works "inductively", assuming that the solver
-  // correctly updates the net K (= iter_to_check) times, then given the history
-  // from the Kth update, we compute the (K+1)th update and check that it
-  // matches the solver's (K+1)th update.
-  void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
-      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
-      const int iter_to_check = 0) {
-    // Initialize the solver and run K (= iter_to_check) solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
-
-    // Compute the (K+1)th update using the analytic least squares gradient.
-    vector<shared_ptr<Blob<Dtype> > > updated_params;
-    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-                              &updated_params);
-
-    // Reinitialize the solver and run K+1 solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-                          iter_to_check + 1);
-
-    // Check that the solver's solution matches ours.
-    CheckLeastSquaresUpdate(updated_params);
-  }
-};
-
-TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
-
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
-  this->TestLeastSquaresUpdate();
-}
-
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
-  this->TestLeastSquaresUpdate(kLearningRate);
-}
-
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.5;
-  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
-}
-
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.5;
-  const int kNumIters = 1;
-  for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.5;
-  const int kNumIters = 5;
-  for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.9;
-  const int kNumIters = 5;
-  for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-}  // namespace caffe

From c1ff97c947af1a6cea0d9f47b397e87b74334d87 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 25 Aug 2014 23:02:56 -0700
Subject: [PATCH 0595/2053] Added sanity check for AdaGradSolver; added MNIST
 examples for solvers

---
 .../mnist_autoencoder_solver_adagrad.prototxt     | 15 +++++++++++++++
 .../mnist_autoencoder_solver_nesterov.prototxt    | 15 +++++++++++++++
 examples/mnist/train_mnist_autoencoder_adagrad.sh |  4 ++++
 .../mnist/train_mnist_autoencoder_nesterov.sh     |  4 ++++
 include/caffe/solver.hpp                          |  7 +++++--
 5 files changed, 43 insertions(+), 2 deletions(-)
 create mode 100644 examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
 create mode 100644 examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
 create mode 100755 examples/mnist/train_mnist_autoencoder_adagrad.sh
 create mode 100755 examples/mnist/train_mnist_autoencoder_nesterov.sh

diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
new file mode 100644
index 00000000000..6193351fcd2
--- /dev/null
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -0,0 +1,15 @@
+net: "mnist_autoencoder.prototxt"
+test_iter: 50
+test_interval: 100
+test_compute_loss: true
+base_lr: 0.01
+lr_policy: "fixed"
+display: 20
+max_iter: 4000000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "mnist_autoencoder_train"
+momentum: 0.9
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: ADAGRAD
diff --git a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
new file mode 100644
index 00000000000..17487301554
--- /dev/null
+++ b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
@@ -0,0 +1,15 @@
+net: "mnist_autoencoder.prototxt"
+test_iter: 50
+test_interval: 100
+test_compute_loss: true
+base_lr: 0.0001
+lr_policy: "fixed"
+display: 20
+max_iter: 4000000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "mnist_autoencoder_train"
+momentum: 0.95
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: NESTEROV
diff --git a/examples/mnist/train_mnist_autoencoder_adagrad.sh b/examples/mnist/train_mnist_autoencoder_adagrad.sh
new file mode 100755
index 00000000000..628c74b969a
--- /dev/null
+++ b/examples/mnist/train_mnist_autoencoder_adagrad.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+TOOLS=../../build/tools
+
+$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder_nesterov.sh b/examples/mnist/train_mnist_autoencoder_nesterov.sh
new file mode 100755
index 00000000000..8f004c4635d
--- /dev/null
+++ b/examples/mnist/train_mnist_autoencoder_nesterov.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+TOOLS=../../build/tools
+
+$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver_nesterov.prototxt
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index b3c7f6f35d6..a73ba2ccd9f 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -100,12 +100,15 @@ template <typename Dtype>
 class AdaGradSolver : public SGDSolver<Dtype> {
  public:
   explicit AdaGradSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) {}
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
   explicit AdaGradSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) {}
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
 
  protected:
   virtual void ComputeUpdateValue();
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.momentum()) << "Momentum cannot be used with AdaGrad.";
+  }
 
   DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
 };

From 06f335f04b16321d73eeb667c39afe65798fef93 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Mon, 25 Aug 2014 23:17:51 -0700
Subject: [PATCH 0596/2053] lint

---
 include/caffe/solver.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index a73ba2ccd9f..5a7f31be017 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -107,7 +107,8 @@ class AdaGradSolver : public SGDSolver<Dtype> {
  protected:
   virtual void ComputeUpdateValue();
   void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.momentum()) << "Momentum cannot be used with AdaGrad.";
+    CHECK_EQ(0, this->param_.momentum())
+        << "Momentum cannot be used with AdaGrad.";
   }
 
   DISABLE_COPY_AND_ASSIGN(AdaGradSolver);

From a464df45a782e2cd45412744de9c0abbd671df6a Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 26 Aug 2014 12:01:26 -0700
Subject: [PATCH 0597/2053] Re-added solver switch into the new caffe main
 excutable; fixed AdaGrad MNIST example

---
 .../mnist/mnist_autoencoder_solver_adagrad.prototxt    |  1 -
 examples/mnist/train_mnist_autoencoder_adagrad.sh      |  2 +-
 tools/caffe.cpp                                        | 10 +++++-----
 3 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
index 6193351fcd2..fa7d65cd70c 100644
--- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -9,7 +9,6 @@ max_iter: 4000000
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "mnist_autoencoder_train"
-momentum: 0.9
 # solver mode: CPU or GPU
 solver_mode: GPU
 solver_type: ADAGRAD
diff --git a/examples/mnist/train_mnist_autoencoder_adagrad.sh b/examples/mnist/train_mnist_autoencoder_adagrad.sh
index 628c74b969a..25a48c3c442 100755
--- a/examples/mnist/train_mnist_autoencoder_adagrad.sh
+++ b/examples/mnist/train_mnist_autoencoder_adagrad.sh
@@ -1,4 +1,4 @@
 #!/bin/bash
 TOOLS=../../build/tools
 
-$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver.prototxt
+$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver_adagrad.prototxt
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 5b3ad0b4691..9958ac3701a 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -88,7 +88,7 @@ int train() {
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
   LOG(INFO) << "Starting Optimization";
-  caffe::SGDSolver<float> solver(solver_param);
+  shared_ptr<caffe::Solver<float>> solver(caffe::GetSolver<float>(solver_param));
 
   // Set device id and mode
   if (FLAGS_gpu >= 0) {
@@ -102,13 +102,13 @@ int train() {
 
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
-    solver.Solve(FLAGS_snapshot);
+    solver->Solve(FLAGS_snapshot);
   } else if (FLAGS_weights.size()) {
     LOG(INFO) << "Finetuning from " << FLAGS_weights;
-    solver.net()->CopyTrainedLayersFrom(FLAGS_weights);
-    solver.Solve();
+    solver->net()->CopyTrainedLayersFrom(FLAGS_weights);
+    solver->Solve();
   } else {
-    solver.Solve();
+    solver->Solve();
   }
   LOG(INFO) << "Optimization Done.";
   return 0;

From 36f9de4303ddb0166d34a2104ec70abe0493d924 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 26 Aug 2014 12:02:20 -0700
Subject: [PATCH 0598/2053] lint

---
 tools/caffe.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 9958ac3701a..9c49a0a9d15 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -88,7 +88,8 @@ int train() {
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
   LOG(INFO) << "Starting Optimization";
-  shared_ptr<caffe::Solver<float>> solver(caffe::GetSolver<float>(solver_param));
+  shared_ptr<caffe::Solver<float>>
+    solver(caffe::GetSolver<float>(solver_param));
 
   // Set device id and mode
   if (FLAGS_gpu >= 0) {

From 972264946cf8896689facfbec2f683347b1a8f7e Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 26 Aug 2014 12:21:06 -0700
Subject: [PATCH 0599/2053] hot fix for warning

---
 tools/caffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 9c49a0a9d15..10ba70d152e 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -88,7 +88,7 @@ int train() {
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
   LOG(INFO) << "Starting Optimization";
-  shared_ptr<caffe::Solver<float>>
+  shared_ptr<caffe::Solver<float> >
     solver(caffe::GetSolver<float>(solver_param));
 
   // Set device id and mode

From 5894f039a454c249ba65141b9c20f9edc9b71627 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 11:39:39 -0700
Subject: [PATCH 0600/2053] mnist_autoencoder: always compute both
 cross-entropy loss and L2 (euclidean) error

---
 examples/mnist/mnist_autoencoder.prototxt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index b6009fb50a0..b2bce47b008 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -248,22 +248,22 @@ layers {
 layers {
   bottom: "decode1"
   bottom: "flatdata"
+  top: "cross_entropy_loss"
   name: "loss"
   type: SIGMOID_CROSS_ENTROPY_LOSS
-  include: { phase: TRAIN }
+  loss_weight: 1
 }
 layers {
   bottom: "decode1"
   top: "decode1neuron"
   name: "decode1neuron"
   type: SIGMOID
-  include: { phase: TEST }
 }
 layers {
   bottom: "decode1neuron"
   bottom: "flatdata"
+  top: "l2_error"
   name: "loss"
   type: EUCLIDEAN_LOSS
-  top: "loss"
-  include: { phase: TEST }
+  loss_weight: 0
 }

From b49b2d3e8e0f5ebe453404c79ac9528f426ab2f0 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 12:04:31 -0700
Subject: [PATCH 0601/2053] Add "test-on-train" stage to test accuracy on the
 training data; correct test_iter (should be 100 instead of 50)

---
 examples/mnist/mnist_autoencoder.prototxt     | 22 ++++++++++++++++++-
 .../mnist/mnist_autoencoder_solver.prototxt   |  7 ++++--
 .../mnist_autoencoder_solver_adagrad.prototxt |  9 +++++---
 ...mnist_autoencoder_solver_nesterov.prototxt | 11 ++++++----
 4 files changed, 39 insertions(+), 10 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index b2bce47b008..45d0802339e 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -13,6 +13,23 @@ layers {
   }
   include: { phase: TRAIN }
 }
+layers {
+  top: "data"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    backend: LMDB
+    batch_size: 100
+    transform_param {
+      scale: 0.0039215684
+    }
+  }
+  include: {
+    phase: TEST
+    stage: 'test-on-train'
+  }
+}
 layers {
   top: "data"
   name: "data"
@@ -25,7 +42,10 @@ layers {
       scale: 0.0039215684
     }
   }
-  include: { phase: TEST }
+  include: {
+    phase: TEST
+    stage: 'test-on-test'
+  }
 }
 layers {
   bottom: "data"
diff --git a/examples/mnist/mnist_autoencoder_solver.prototxt b/examples/mnist/mnist_autoencoder_solver.prototxt
index af1202fc1fd..be0939d92db 100644
--- a/examples/mnist/mnist_autoencoder_solver.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver.prototxt
@@ -1,6 +1,9 @@
 net: "examples/mnist/mnist_autoencoder.prototxt"
-test_iter: 50
-test_interval: 100
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
 test_compute_loss: true
 base_lr: 0.0001
 lr_policy: "fixed"
diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
index fa7d65cd70c..641ce8a001c 100644
--- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -1,6 +1,9 @@
-net: "mnist_autoencoder.prototxt"
-test_iter: 50
-test_interval: 100
+net: "examples/mnist/mnist_autoencoder.prototxt"
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
 test_compute_loss: true
 base_lr: 0.01
 lr_policy: "fixed"
diff --git a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
index 17487301554..254dceecf9b 100644
--- a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
@@ -1,6 +1,9 @@
-net: "mnist_autoencoder.prototxt"
-test_iter: 50
-test_interval: 100
+net: "examples/mnist/mnist_autoencoder.prototxt"
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
 test_compute_loss: true
 base_lr: 0.0001
 lr_policy: "fixed"
@@ -8,7 +11,7 @@ display: 20
 max_iter: 4000000
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "mnist_autoencoder_train"
+snapshot_prefix: "examples/mnist/mnist_autoencoder_nesterov_train"
 momentum: 0.95
 # solver mode: CPU or GPU
 solver_mode: GPU

From eaf28fe5758164c4af73f9a721e41ee7952488b0 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 12:32:54 -0700
Subject: [PATCH 0602/2053] make adagrad/nesterov train scripts follow new
 "run-from-root" convention

---
 examples/mnist/train_mnist_autoencoder_adagrad.sh  | 4 ++--
 examples/mnist/train_mnist_autoencoder_nesterov.sh | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/mnist/train_mnist_autoencoder_adagrad.sh b/examples/mnist/train_mnist_autoencoder_adagrad.sh
index 25a48c3c442..95fe1b17bd5 100755
--- a/examples/mnist/train_mnist_autoencoder_adagrad.sh
+++ b/examples/mnist/train_mnist_autoencoder_adagrad.sh
@@ -1,4 +1,4 @@
 #!/bin/bash
-TOOLS=../../build/tools
 
-$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver_adagrad.prototxt
+./build/tools/caffe train \
+  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder_nesterov.sh b/examples/mnist/train_mnist_autoencoder_nesterov.sh
index 8f004c4635d..cf19ea749b3 100755
--- a/examples/mnist/train_mnist_autoencoder_nesterov.sh
+++ b/examples/mnist/train_mnist_autoencoder_nesterov.sh
@@ -1,4 +1,4 @@
 #!/bin/bash
-TOOLS=../../build/tools
 
-$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver_nesterov.prototxt
+./build/tools/caffe train \
+  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt

From b0f97fda761b8687503a36f4545bedd6497f3c92 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 12:35:03 -0700
Subject: [PATCH 0603/2053] make MNIST autoencoder solvers start from base_lr
 0.01 and step (much better performance) and terminate at iter 65K

---
 examples/mnist/mnist_autoencoder_solver.prototxt       | 10 ++++++----
 .../mnist/mnist_autoencoder_solver_adagrad.prototxt    |  4 ++--
 .../mnist/mnist_autoencoder_solver_nesterov.prototxt   | 10 ++++++----
 3 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder_solver.prototxt b/examples/mnist/mnist_autoencoder_solver.prototxt
index be0939d92db..6e35cb6a32d 100644
--- a/examples/mnist/mnist_autoencoder_solver.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver.prototxt
@@ -5,10 +5,12 @@ test_state: { stage: 'test-on-test' }
 test_iter: 100
 test_interval: 500
 test_compute_loss: true
-base_lr: 0.0001
-lr_policy: "fixed"
-display: 20
-max_iter: 4000000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 10000
+display: 100
+max_iter: 65000
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "examples/mnist/mnist_autoencoder"
diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
index 641ce8a001c..b18a0cf5f27 100644
--- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -7,8 +7,8 @@ test_interval: 500
 test_compute_loss: true
 base_lr: 0.01
 lr_policy: "fixed"
-display: 20
-max_iter: 4000000
+display: 100
+max_iter: 65000
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "mnist_autoencoder_train"
diff --git a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
index 254dceecf9b..2a59fd45c8d 100644
--- a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
@@ -5,10 +5,12 @@ test_state: { stage: 'test-on-test' }
 test_iter: 100
 test_interval: 500
 test_compute_loss: true
-base_lr: 0.0001
-lr_policy: "fixed"
-display: 20
-max_iter: 4000000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 10000
+display: 100
+max_iter: 65000
 weight_decay: 0.0005
 snapshot: 10000
 snapshot_prefix: "examples/mnist/mnist_autoencoder_nesterov_train"

From 77d66618af116b6faddc9e8b6035ce1b0f099149 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 14:51:59 -0700
Subject: [PATCH 0604/2053] revert tools/train_net.cpp to previous, depecated
 version

---
 tools/train_net.cpp | 23 ++---------------------
 1 file changed, 2 insertions(+), 21 deletions(-)

diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 9429fe892e9..622bca311c8 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,26 +1,7 @@
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  ::google::InitGoogleLogging(argv[0]);
-  if (argc < 2 || argc > 3) {
-    LOG(ERROR) << "Usage: train_net solver_proto_file [resume_point_file]";
-    return 1;
-  }
-
-  SolverParameter solver_param;
-  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
-
-  LOG(INFO) << "Starting Optimization";
-  shared_ptr<Solver<float> > solver(GetSolver<float>(solver_param));
-  if (argc == 3) {
-    LOG(INFO) << "Resuming from " << argv[2];
-    solver->Solve(argv[2]);
-  } else {
-    solver->Solve();
-  }
-  LOG(INFO) << "Optimization Done.";
-
+  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
+                "[--snapshot=...] instead.";
   return 0;
 }

From 396e4afd632183175723225c2b764782db2e1055 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 1 Sep 2014 16:45:20 -0700
Subject: [PATCH 0605/2053] add CUDA 6.5 error CUBLAS_STATUS_LICENSE_ERROR to
 cublasGetErrorString enum

---
 src/caffe/common.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index a900d6f0198..94fdf924f44 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -223,6 +223,10 @@ const char* cublasGetErrorString(cublasStatus_t error) {
 #if CUDA_VERSION >= 6000
   case CUBLAS_STATUS_NOT_SUPPORTED:
     return "CUBLAS_STATUS_NOT_SUPPORTED";
+#endif
+#if CUDA_VERSION >= 6050
+  case CUBLAS_STATUS_LICENSE_ERROR:
+    return "CUBLAS_STATUS_LICENSE_ERROR";
 #endif
   }
   return "Unknown cublas status";

From ed13a6199058798bc8631299f3bd086ae06c94da Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 1 Sep 2014 19:36:49 -0700
Subject: [PATCH 0606/2053] [pycaffe] add converter for vector<string> used by
 _*_names

---
 python/caffe/_caffe.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 9c61ea869b9..7bf5ab2ecb8 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -192,6 +192,9 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<vector<PyLayer> >("LayerVec")
       .def(bp::vector_indexing_suite<vector<PyLayer>, true>());
 
+  bp::class_<vector<string> >("StringVec")
+      .def(bp::vector_indexing_suite<vector<string> >());
+
   import_array();
 }
 

From afd8f370b47291e9944695dee470626ede8b44fe Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 1 Sep 2014 19:26:02 -0700
Subject: [PATCH 0607/2053] [pycaffe] expose Net.blob_names and Net.layer_names

---
 python/caffe/_caffe.cpp | 2 ++
 python/caffe/_caffe.hpp | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 7bf5ab2ecb8..39e5d09077c 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -156,6 +156,8 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def("set_device",            &PyNet::set_device)
       .add_property("_blobs",       &PyNet::blobs)
       .add_property("layers",       &PyNet::layers)
+      .add_property("_blob_names",  &PyNet::blob_names)
+      .add_property("_layer_names", &PyNet::layer_names)
       .add_property("inputs",       &PyNet::inputs)
       .add_property("outputs",      &PyNet::outputs)
       .add_property("mean",         &PyNet::mean_)
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 2df47c7cec5..5884900fcfe 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -123,6 +123,9 @@ class PyNet {
     return vector<PyLayer>(net_->layers().begin(), net_->layers().end());
   }
 
+  vector<string> blob_names() { return net_->blob_names(); }
+  vector<string> layer_names() { return net_->layer_names(); }
+
   bp::list inputs() {
     bp::list input_blob_names;
     for (int i = 0; i < net_->input_blob_indices().size(); ++i) {

From 3e12d49324793d4798ee10bb6ef6a1c1b7633baf Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 1 Sep 2014 19:32:24 -0700
Subject: [PATCH 0608/2053] [pycaffe] use _blob_names, _layer_names instead of
 removed .name

---
 python/caffe/pycaffe.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 9a230a72b3c..31dc1f9b001 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -21,7 +21,7 @@ def _Net_blobs(self):
     An OrderedDict (bottom to top, i.e., input to output) of network
     blobs indexed by name
     """
-    return OrderedDict([(bl.name, bl) for bl in self._blobs])
+    return OrderedDict(zip(self._blob_names, self._blobs))
 
 
 @property
@@ -31,7 +31,8 @@ def _Net_params(self):
     parameters indexed by name; each is a list of multiple blobs (e.g.,
     weights and biases)
     """
-    return OrderedDict([(lr.name, lr.blobs) for lr in self.layers
+    return OrderedDict([(name, lr.blobs)
+                        for name, lr in zip(self._layer_names, self.layers)
                         if len(lr.blobs) > 0])
 
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
@@ -53,12 +54,12 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
         blobs = []
 
     if start is not None:
-        start_ind = [lr.name for lr in self.layers].index(start)
+        start_ind = list(self._layer_names).index(start)
     else:
         start_ind = 0
 
     if end is not None:
-        end_ind = [lr.name for lr in self.layers].index(end)
+        end_ind = list(self._layer_names).index(end)
         outputs = set([end] + blobs)
     else:
         end_ind = len(self.layers) - 1
@@ -100,12 +101,12 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
         diffs = []
 
     if start is not None:
-        start_ind = [lr.name for lr in self.layers].index(start)
+        start_ind = list(self._layer_names).index(start)
     else:
         start_ind = len(self.layers) - 1
 
     if end is not None:
-        end_ind = [lr.name for lr in self.layers].index(end)
+        end_ind = list(self._layer_names).index(end)
         outputs = set([end] + diffs)
     else:
         end_ind = 0

From d3c6eb62073cdef5e33278f8f707fb153da3c3e5 Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Tue, 2 Sep 2014 13:01:13 +0200
Subject: [PATCH 0609/2053] fixed relative path and prefix for
 adagrad-optimised autoencoder snapshots

---
 examples/mnist/mnist_autoencoder_solver_adagrad.prototxt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
index b18a0cf5f27..cc0ed9e310a 100644
--- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -11,7 +11,7 @@ display: 100
 max_iter: 65000
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "mnist_autoencoder_train"
+snapshot_prefix: "examples/mnist/mnist_autoencoder_adagrad_train"
 # solver mode: CPU or GPU
 solver_mode: GPU
 solver_type: ADAGRAD

From fea9f0c16dacf5522b0a2d68c40d72de1d66c696 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 15:29:25 +0800
Subject: [PATCH 0610/2053] Create base data layer and base prefetching data
 layer

---
 include/caffe/data_layers.hpp        | 48 ++++++++++++++++++++++++++--
 src/caffe/layers/base_data_layer.cpp | 19 +++++++++++
 2 files changed, 65 insertions(+), 2 deletions(-)
 create mode 100644 src/caffe/layers/base_data_layer.cpp

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index f972965b45b..b33d1eef022 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -23,8 +23,52 @@ namespace caffe {
 #define HDF5_DATA_DATASET_NAME "data"
 #define HDF5_DATA_LABEL_NAME "label"
 
-// TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
-// same basic structure and a lot of duplicated code.
+template <typename Dtype>
+class BaseDataLayer : public Layer<Dtype> {
+ public:
+  explicit BaseDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param),
+        data_transformer_(param.data_param().transform_param()) {}
+  virtual ~BaseDataLayer() {}
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) = 0;
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) = 0;
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+
+ protected:
+  DataTransformer<Dtype> data_transformer_;
+  int datum_channels_;
+  int datum_height_;
+  int datum_width_;
+  int datum_size_;
+  Blob<Dtype> data_mean_;
+  Caffe::Phase phase_;
+};
+
+template <typename Dtype>
+class BasePrefetchingDataLayer :
+    public BaseDataLayer<Dtype>, public InternalThread {
+ public:
+  explicit BasePrefetchingDataLayer(const LayerParameter& param)
+      : BaseDataLayer<Dtype>(param) {}
+  virtual ~BasePrefetchingDataLayer() {}
+
+  virtual void CreatePrefetchThread();
+  virtual void JoinPrefetchThread();
+  // The thread's function
+  virtual void InternalThreadEntry() = 0;
+
+ protected:
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
+};
+
 template <typename Dtype>
 class DataLayer : public Layer<Dtype>, public InternalThread {
  public:
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
new file mode 100644
index 00000000000..2a22c8c39ac
--- /dev/null
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -0,0 +1,19 @@
+#include "caffe/data_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
+  this->phase_ = Caffe::phase();
+
+  this->data_transformer_.InitRand();
+
+  CHECK(StartInternalThread()) << "Pthread execution failed";
+}
+
+template <typename Dtype>
+void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
+}
+
+}  // namespace caffe

From ee65a97ea8458f00b12d1f4524b3e1abeaaf904e Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 16:10:47 +0800
Subject: [PATCH 0611/2053] Extract common data layer functionalities out of
 the DataLayer

---
 include/caffe/data_layers.hpp        |  55 +++++----------
 src/caffe/layers/base_data_layer.cpp |  36 +++++++++-
 src/caffe/layers/data_layer.cpp      | 100 +++++++++------------------
 3 files changed, 84 insertions(+), 107 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index b33d1eef022..bc207c0146a 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -30,16 +30,18 @@ class BaseDataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param),
         data_transformer_(param.data_param().transform_param()) {}
   virtual ~BaseDataLayer() {}
+  // LayerSetUp: implements common data layer setup functionality, and calls
+  // DataLayerSetUp to do special data layer setup for individual layer types.
+  // This method may not be overridden.
+  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
 
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
  protected:
   DataTransformer<Dtype> data_transformer_;
@@ -49,6 +51,7 @@ class BaseDataLayer : public Layer<Dtype> {
   int datum_size_;
   Blob<Dtype> data_mean_;
   Caffe::Phase phase_;
+  bool output_labels_;
 };
 
 template <typename Dtype>
@@ -59,10 +62,15 @@ class BasePrefetchingDataLayer :
       : BaseDataLayer<Dtype>(param) {}
   virtual ~BasePrefetchingDataLayer() {}
 
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
   // The thread's function
-  virtual void InternalThreadEntry() = 0;
+  virtual void InternalThreadEntry() {}
 
  protected:
   Blob<Dtype> prefetch_data_;
@@ -70,13 +78,12 @@ class BasePrefetchingDataLayer :
 };
 
 template <typename Dtype>
-class DataLayer : public Layer<Dtype>, public InternalThread {
+class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  public:
   explicit DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param),
-        data_transformer_(param.data_param().transform_param()) {}
+      : BasePrefetchingDataLayer<Dtype>(param) {}
   virtual ~DataLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -87,22 +94,8 @@ class DataLayer : public Layer<Dtype>, public InternalThread {
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
-  // The thread's function
   virtual void InternalThreadEntry();
 
-  DataTransformer<Dtype> data_transformer_;
-
   // LEVELDB
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::Iterator> iter_;
@@ -112,16 +105,6 @@ class DataLayer : public Layer<Dtype>, public InternalThread {
   MDB_txn* mdb_txn_;
   MDB_cursor* mdb_cursor_;
   MDB_val mdb_key_, mdb_value_;
-
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  Blob<Dtype> prefetch_data_;
-  Blob<Dtype> prefetch_label_;
-  Blob<Dtype> data_mean_;
-  bool output_labels_;
-  Caffe::Phase phase_;
 };
 
 template <typename Dtype>
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 2a22c8c39ac..f8f8ad02ed7 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -2,12 +2,21 @@
 
 namespace caffe {
 
+template <typename Dtype>
+void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  if (top->size() == 1) {
+    output_labels_ = false;
+  } else {
+    output_labels_ = true;
+  }
+  DataLayerSetUp(bottom, top);
+}
+
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
   this->phase_ = Caffe::phase();
-
   this->data_transformer_.InitRand();
-
   CHECK(StartInternalThread()) << "Pthread execution failed";
 }
 
@@ -16,4 +25,27 @@ void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
   CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
+template <typename Dtype>
+void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  // First, join the thread
+  JoinPrefetchThread();
+  // Copy the data
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+             (*top)[0]->mutable_cpu_data());
+  if (this->output_labels_) {
+    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+               (*top)[1]->mutable_cpu_data());
+  }
+  // Start a new prefetch thread
+  CreatePrefetchThread();
+}
+
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(BasePrefetchingDataLayer, Forward);
+#endif
+
+INSTANTIATE_CLASS(BaseDataLayer);
+INSTANTIATE_CLASS(BasePrefetchingDataLayer);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 519b666e35b..0d3054f8f7f 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -17,15 +17,15 @@ namespace caffe {
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
-  CHECK(prefetch_data_.count());
-  Dtype* top_data = prefetch_data_.mutable_cpu_data();
+  CHECK(this->prefetch_data_.count());
+  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
-  if (output_labels_) {
-    top_label = prefetch_label_.mutable_cpu_data();
+  if (this->output_labels_) {
+    top_label = this->prefetch_label_.mutable_cpu_data();
   }
   const int batch_size = this->layer_param_.data_param().batch_size();
 
-  const Dtype* mean = data_mean_.cpu_data();
+  const Dtype* mean = this->data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
     switch (this->layer_param_.data_param().backend()) {
@@ -45,9 +45,9 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // Apply data transformations (mirror, scale, crop...)
-    data_transformer_.Transform(item_id, datum, mean, top_data);
+    this->data_transformer_.Transform(item_id, datum, mean, top_data);
 
-    if (output_labels_) {
+    if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
 
@@ -78,7 +78,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
-  JoinPrefetchThread();
+  this->JoinPrefetchThread();
   // clean up the database resources
   switch (this->layer_param_.data_param().backend()) {
   case DataParameter_DB_LEVELDB:
@@ -95,13 +95,8 @@ DataLayer<Dtype>::~DataLayer<Dtype>() {
 }
 
 template <typename Dtype>
-void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  if (top->size() == 1) {
-    output_labels_ = false;
-  } else {
-    output_labels_ = true;
-  }
   // Initialize DB
   switch (this->layer_param_.data_param().backend()) {
   case DataParameter_DB_LEVELDB:
@@ -183,31 +178,31 @@ void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   if (crop_size > 0) {
     (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
                        datum.channels(), crop_size, crop_size);
-    prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
+    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), crop_size, crop_size);
   } else {
     (*top)[0]->Reshape(
         this->layer_param_.data_param().batch_size(), datum.channels(),
         datum.height(), datum.width());
-    prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
+    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), datum.height(), datum.width());
   }
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
   // label
-  if (output_labels_) {
+  if (this->output_labels_) {
     (*top)[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
-    prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
+    this->prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
         1, 1, 1);
   }
   // datum size
-  datum_channels_ = datum.channels();
-  datum_height_ = datum.height();
-  datum_width_ = datum.width();
-  datum_size_ = datum.channels() * datum.height() * datum.width();
-  CHECK_GT(datum_height_, crop_size);
-  CHECK_GT(datum_width_, crop_size);
+  this->datum_channels_ = datum.channels();
+  this->datum_height_ = datum.height();
+  this->datum_width_ = datum.width();
+  this->datum_size_ = datum.channels() * datum.height() * datum.width();
+  CHECK_GT(this->datum_height_, crop_size);
+  CHECK_GT(this->datum_width_, crop_size);
   // check if we want to have mean
   if (this->layer_param_.data_param().transform_param().has_mean_file()) {
     const string& mean_file =
@@ -215,63 +210,30 @@ void DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
-    data_mean_.FromProto(blob_proto);
-    CHECK_EQ(data_mean_.num(), 1);
-    CHECK_EQ(data_mean_.channels(), datum_channels_);
-    CHECK_EQ(data_mean_.height(), datum_height_);
-    CHECK_EQ(data_mean_.width(), datum_width_);
+    this->data_mean_.FromProto(blob_proto);
+    CHECK_EQ(this->data_mean_.num(), 1);
+    CHECK_EQ(this->data_mean_.channels(), this->datum_channels_);
+    CHECK_EQ(this->data_mean_.height(), this->datum_height_);
+    CHECK_EQ(this->data_mean_.width(), this->datum_width_);
   } else {
     // Simply initialize an all-empty mean.
-    data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
+    this->data_mean_.Reshape(1, this->datum_channels_, this->datum_height_,
+                             this->datum_width_);
   }
   // Now, start the prefetch thread. Before calling prefetch, we make two
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_.mutable_cpu_data();
-  if (output_labels_) {
-    prefetch_label_.mutable_cpu_data();
+  this->prefetch_data_.mutable_cpu_data();
+  if (this->output_labels_) {
+    this->prefetch_label_.mutable_cpu_data();
   }
-  data_mean_.cpu_data();
+  this->data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
-  CreatePrefetchThread();
+  this->CreatePrefetchThread();
   DLOG(INFO) << "Prefetch initialized.";
 }
 
-template <typename Dtype>
-void DataLayer<Dtype>::CreatePrefetchThread() {
-  phase_ = Caffe::phase();
-
-  data_transformer_.InitRand();
-
-  CHECK(StartInternalThread()) << "Thread execution failed";
-}
-
-template <typename Dtype>
-void DataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
-}
-
-template <typename Dtype>
-void DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-             (*top)[0]->mutable_cpu_data());
-  if (output_labels_) {
-    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-               (*top)[1]->mutable_cpu_data());
-  }
-  // Start a new prefetch thread
-  CreatePrefetchThread();
-}
-
-#ifdef CPU_ONLY
-STUB_GPU_FORWARD(DataLayer, Forward);
-#endif
-
 INSTANTIATE_CLASS(DataLayer);
 
 }  // namespace caffe

From 6833dc0b7acf51a9bd30986df27e4c00fdfac741 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 16:22:41 +0800
Subject: [PATCH 0612/2053] Remove duplicate codes from the ImageDataLayer

---
 include/caffe/data_layers.hpp         | 35 ++---------
 src/caffe/layers/base_data_layer.cpp  |  5 ++
 src/caffe/layers/data_layer.cpp       |  1 -
 src/caffe/layers/image_data_layer.cpp | 90 ++++++++-------------------
 4 files changed, 37 insertions(+), 94 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index bc207c0146a..499ee7f6cdc 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -60,7 +60,7 @@ class BasePrefetchingDataLayer :
  public:
   explicit BasePrefetchingDataLayer(const LayerParameter& param)
       : BaseDataLayer<Dtype>(param) {}
-  virtual ~BasePrefetchingDataLayer() {}
+  virtual ~BasePrefetchingDataLayer();
 
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -202,13 +202,12 @@ class HDF5OutputLayer : public Layer<Dtype> {
 };
 
 template <typename Dtype>
-class ImageDataLayer : public Layer<Dtype>, public InternalThread {
+class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
  public:
   explicit ImageDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param),
-        data_transformer_(param.image_data_param().transform_param()) {}
-  virtual ~ImageDataLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      : BasePrefetchingDataLayer<Dtype>(param) {}
+  virtual ~ImageDataLayer() {}
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -218,34 +217,12 @@ class ImageDataLayer : public Layer<Dtype>, public InternalThread {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
  protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-
+  shared_ptr<Caffe::RNG> prefetch_rng_;
   virtual void ShuffleImages();
-
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
   virtual void InternalThreadEntry();
 
-  DataTransformer<Dtype> data_transformer_;
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-
   vector<std::pair<std::string, int> > lines_;
   int lines_id_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  Blob<Dtype> prefetch_data_;
-  Blob<Dtype> prefetch_label_;
-  Blob<Dtype> data_mean_;
-  Caffe::Phase phase_;
 };
 
 /* MemoryDataLayer
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index f8f8ad02ed7..957a771af13 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -13,6 +13,11 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   DataLayerSetUp(bottom, top);
 }
 
+template <typename Dtype>
+BasePrefetchingDataLayer<Dtype>::~BasePrefetchingDataLayer<Dtype>() {
+  JoinPrefetchThread();
+}
+
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
   this->phase_ = Caffe::phase();
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 0d3054f8f7f..1b3acbdf54c 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -78,7 +78,6 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
-  this->JoinPrefetchThread();
   // clean up the database resources
   switch (this->layer_param_.data_param().backend()) {
   case DataParameter_DB_LEVELDB:
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 5039ad03b08..e4805ca851e 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -16,9 +16,9 @@ namespace caffe {
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
-  CHECK(prefetch_data_.count());
-  Dtype* top_data = prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = prefetch_label_.mutable_cpu_data();
+  CHECK(this->prefetch_data_.count());
+  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
@@ -26,7 +26,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
 
   // datum scales
   const int lines_size = lines_.size();
-  const Dtype* mean = data_mean_.cpu_data();
+  const Dtype* mean = this->data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
     CHECK_GT(lines_size, lines_id_);
@@ -37,7 +37,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // Apply transformations (mirror, crop...) to the data
-    data_transformer_.Transform(item_id, datum, mean, top_data);
+    this->data_transformer_.Transform(item_id, datum, mean, top_data);
 
     top_label[item_id] = datum.label();
     // go to the next iter
@@ -54,12 +54,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
 }
 
 template <typename Dtype>
-ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
-  JoinPrefetchThread();
-}
-
-template <typename Dtype>
-void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const int new_height = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_width();
@@ -106,11 +101,11 @@ void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       .transform_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
+    this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
   } else {
     (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
                        datum.width());
-    prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
+    this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
         datum.width());
   }
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
@@ -118,50 +113,41 @@ void ImageDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  prefetch_label_.Reshape(batch_size, 1, 1, 1);
+  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
   // datum size
-  datum_channels_ = datum.channels();
-  datum_height_ = datum.height();
-  datum_width_ = datum.width();
-  datum_size_ = datum.channels() * datum.height() * datum.width();
-  CHECK_GT(datum_height_, crop_size);
-  CHECK_GT(datum_width_, crop_size);
+  this->datum_channels_ = datum.channels();
+  this->datum_height_ = datum.height();
+  this->datum_width_ = datum.width();
+  this->datum_size_ = datum.channels() * datum.height() * datum.width();
+  CHECK_GT(this->datum_height_, crop_size);
+  CHECK_GT(this->datum_width_, crop_size);
   // check if we want to have mean
   if (this->layer_param_.image_data_param().transform_param().has_mean_file()) {
     BlobProto blob_proto;
     LOG(INFO) << "Loading mean file from" << mean_file;
     ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
-    data_mean_.FromProto(blob_proto);
-    CHECK_EQ(data_mean_.num(), 1);
-    CHECK_EQ(data_mean_.channels(), datum_channels_);
-    CHECK_EQ(data_mean_.height(), datum_height_);
-    CHECK_EQ(data_mean_.width(), datum_width_);
+    this->data_mean_.FromProto(blob_proto);
+    CHECK_EQ(this->data_mean_.num(), 1);
+    CHECK_EQ(this->data_mean_.channels(), this->datum_channels_);
+    CHECK_EQ(this->data_mean_.height(), this->datum_height_);
+    CHECK_EQ(this->data_mean_.width(), this->datum_width_);
   } else {
     // Simply initialize an all-empty mean.
-    data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
+    this->data_mean_.Reshape(1, this->datum_channels_, this->datum_height_,
+                             this->datum_width_);
   }
   // Now, start the prefetch thread. Before calling prefetch, we make two
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_.mutable_cpu_data();
-  prefetch_label_.mutable_cpu_data();
-  data_mean_.cpu_data();
+  this->prefetch_data_.mutable_cpu_data();
+  this->prefetch_label_.mutable_cpu_data();
+  this->data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
-  CreatePrefetchThread();
+  this->CreatePrefetchThread();
   DLOG(INFO) << "Prefetch initialized.";
 }
 
-template <typename Dtype>
-void ImageDataLayer<Dtype>::CreatePrefetchThread() {
-  phase_ = Caffe::phase();
-
-  data_transformer_.InitRand();
-
-  // Create the thread.
-  CHECK(StartInternalThread()) << "Thread execution failed";
-}
-
 template <typename Dtype>
 void ImageDataLayer<Dtype>::ShuffleImages() {
   caffe::rng_t* prefetch_rng =
@@ -169,30 +155,6 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
   shuffle(lines_.begin(), lines_.end(), prefetch_rng);
 }
 
-
-template <typename Dtype>
-void ImageDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
-}
-
-template <typename Dtype>
-void ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-             (*top)[0]->mutable_cpu_data());
-  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-             (*top)[1]->mutable_cpu_data());
-  // Start a new prefetch thread
-  CreatePrefetchThread();
-}
-
-#ifdef CPU_ONLY
-STUB_GPU_FORWARD(ImageDataLayer, Forward);
-#endif
-
 INSTANTIATE_CLASS(ImageDataLayer);
 
 }  // namespace caffe

From b794cf9433233ec002dc454fa40286bbd3f4ecfe Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 16:55:56 +0800
Subject: [PATCH 0613/2053] Simplify the WindowDataLayer using the base class

---
 include/caffe/common.hpp                  |  6 ++
 include/caffe/data_layers.hpp             | 22 +-----
 src/caffe/layers/base_data_layer.cpp      |  2 +
 src/caffe/layers/data_layer.cpp           |  3 +-
 src/caffe/layers/image_data_layer.cpp     |  5 +-
 src/caffe/layers/window_data_layer.cpp    | 95 ++++++++---------------
 src/caffe/test/test_data_layer.cpp        |  6 +-
 src/caffe/test/test_image_data_layer.cpp  |  5 --
 src/caffe/test/test_window_data_layer.cpp | 49 ++++++++++++
 9 files changed, 98 insertions(+), 95 deletions(-)
 create mode 100644 src/caffe/test/test_window_data_layer.cpp

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 9008eae02bf..683d1d68f1d 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -6,9 +6,14 @@
 #include <glog/logging.h>
 
 #include <cmath>
+#include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 #include <map>
 #include <set>
+#include <sstream>
 #include <string>
+#include <utility>  // pair
+#include <vector>
 
 #include "caffe/util/device_alternate.hpp"
 
@@ -88,6 +93,7 @@ using std::ostringstream;
 using std::pair;
 using std::set;
 using std::string;
+using std::stringstream;
 using std::vector;
 
 // A global initialization function that you should call in your main function.
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 499ee7f6cdc..8651ece4d5f 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -269,12 +269,12 @@ class MemoryDataLayer : public Layer<Dtype> {
 };
 
 template <typename Dtype>
-class WindowDataLayer : public Layer<Dtype>, public InternalThread {
+class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
  public:
   explicit WindowDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~WindowDataLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      : BasePrefetchingDataLayer<Dtype>(param) {}
+  virtual ~WindowDataLayer() {}
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -284,24 +284,10 @@ class WindowDataLayer : public Layer<Dtype>, public InternalThread {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
  protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
   virtual unsigned int PrefetchRand();
   virtual void InternalThreadEntry();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
-  Blob<Dtype> prefetch_data_;
-  Blob<Dtype> prefetch_label_;
-  Blob<Dtype> data_mean_;
   vector<std::pair<std::string, vector<int> > > image_database_;
   enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
   vector<vector<float> > fg_windows_;
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 957a771af13..8310710dbc1 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <vector>
+
 #include "caffe/data_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 1b3acbdf54c..fbf2d91c7ac 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -4,12 +4,13 @@
 #include <string>
 #include <vector>
 
+#include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e4805ca851e..687beb8efa3 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -4,11 +4,11 @@
 #include <utility>
 #include <vector>
 
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -101,7 +101,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       .transform_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
+    this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
+                                 crop_size);
   } else {
     (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
                        datum.width());
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index ab12c61a08a..f4349825aba 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,7 +1,6 @@
 #include <stdint.h>
 
 #include <algorithm>
-#include <fstream>  // NOLINT(readability/streams)
 #include <map>
 #include <string>
 #include <utility>
@@ -11,11 +10,12 @@
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"
 
+#include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
-#include "caffe/vision_layers.hpp"
 
 // caffe.proto > LayerParameter > WindowDataParameter
 //   'source' field specifies the window_file
@@ -29,8 +29,8 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
 
-  Dtype* top_data = prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = prefetch_label_.mutable_cpu_data();
+  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   const int crop_size = this->layer_param_.window_data_param().crop_size();
@@ -38,17 +38,17 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   const bool mirror = this->layer_param_.window_data_param().mirror();
   const float fg_fraction =
       this->layer_param_.window_data_param().fg_fraction();
-  const Dtype* mean = data_mean_.cpu_data();
-  const int mean_off = (data_mean_.width() - crop_size) / 2;
-  const int mean_width = data_mean_.width();
-  const int mean_height = data_mean_.height();
+  const Dtype* mean = this->data_mean_.cpu_data();
+  const int mean_off = (this->data_mean_.width() - crop_size) / 2;
+  const int mean_width = this->data_mean_.width();
+  const int mean_height = this->data_mean_.height();
   cv::Size cv_crop_size(crop_size, crop_size);
   const string& crop_mode = this->layer_param_.window_data_param().crop_mode();
 
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  caffe_set(prefetch_data_.count(), Dtype(0), top_data);
+  caffe_set(this->prefetch_data_.count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
@@ -238,12 +238,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
 }
 
 template <typename Dtype>
-WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
-  JoinPrefetchThread();
-}
-
-template <typename Dtype>
-void WindowDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   // LayerSetUp runs through the window_file and creates two structures
   // that hold windows: one for foreground (object) windows and one
@@ -268,6 +263,16 @@ void WindowDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << "  foreground sampling fraction: "
       << this->layer_param_.window_data_param().fg_fraction();
 
+  const bool prefetch_needs_rand =
+      this->layer_param_.window_data_param().mirror() ||
+      this->layer_param_.window_data_param().crop_size();
+  if (prefetch_needs_rand) {
+    const unsigned int prefetch_rng_seed = caffe_rng_rand();
+    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
+  } else {
+    prefetch_rng_.reset();
+  }
+
   std::ifstream infile(this->layer_param_.window_data_param().source().c_str());
   CHECK(infile.good()) << "Failed to open window file "
       << this->layer_param_.window_data_param().source() << std::endl;
@@ -357,14 +362,14 @@ void WindowDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
+  this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  prefetch_label_.Reshape(batch_size, 1, 1, 1);
+  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
 
   // check if we want to have mean
   if (this->layer_param_.window_data_param().has_mean_file()) {
@@ -373,46 +378,26 @@ void WindowDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file, &blob_proto);
-    data_mean_.FromProto(blob_proto);
-    CHECK_EQ(data_mean_.num(), 1);
-    CHECK_EQ(data_mean_.width(), data_mean_.height());
-    CHECK_EQ(data_mean_.channels(), channels);
+    this->data_mean_.FromProto(blob_proto);
+    CHECK_EQ(this->data_mean_.num(), 1);
+    CHECK_EQ(this->data_mean_.width(), this->data_mean_.height());
+    CHECK_EQ(this->data_mean_.channels(), channels);
   } else {
     // Simply initialize an all-empty mean.
-    data_mean_.Reshape(1, channels, crop_size, crop_size);
+    this->data_mean_.Reshape(1, channels, crop_size, crop_size);
   }
   // Now, start the prefetch thread. Before calling prefetch, we make two
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_.mutable_cpu_data();
-  prefetch_label_.mutable_cpu_data();
-  data_mean_.cpu_data();
+  this->prefetch_data_.mutable_cpu_data();
+  this->prefetch_label_.mutable_cpu_data();
+  this->data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
-  CreatePrefetchThread();
+  this->CreatePrefetchThread();
   DLOG(INFO) << "Prefetch initialized.";
 }
 
-template <typename Dtype>
-void WindowDataLayer<Dtype>::CreatePrefetchThread() {
-  const bool prefetch_needs_rand =
-      this->layer_param_.window_data_param().mirror() ||
-      this->layer_param_.window_data_param().crop_size();
-  if (prefetch_needs_rand) {
-    const unsigned int prefetch_rng_seed = caffe_rng_rand();
-    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
-  } else {
-    prefetch_rng_.reset();
-  }
-  // Create the thread.
-  CHECK(StartInternalThread()) << "Thread execution failed.";
-}
-
-template <typename Dtype>
-void WindowDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed.";
-}
-
 template <typename Dtype>
 unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
   CHECK(prefetch_rng_);
@@ -421,24 +406,6 @@ unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
   return (*prefetch_rng)();
 }
 
-template <typename Dtype>
-void WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-             (*top)[0]->mutable_cpu_data());
-  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-             (*top)[1]->mutable_cpu_data());
-  // Start a new prefetch thread
-  CreatePrefetchThread();
-}
-
-#ifdef CPU_ONLY
-STUB_GPU_FORWARD(WindowDataLayer, Forward);
-#endif
-
 INSTANTIATE_CLASS(WindowDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 5c21f2e745d..52cc2c082a9 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,9 +1,8 @@
 #include <string>
 #include <vector>
 
-#include "leveldb/db.h"
-
 #include "gtest/gtest.h"
+#include "leveldb/db.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -13,9 +12,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::stringstream;
-
 namespace caffe {
 
 template <typename TypeParam>
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 73be5da7b23..ab2365c6556 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,5 +1,3 @@
-#include <fstream>  // NOLINT(readability/streams)
-#include <iostream>  // NOLINT(readability/streams)
 #include <map>
 #include <string>
 #include <vector>
@@ -14,9 +12,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::map;
-using std::string;
-
 namespace caffe {
 
 template <typename TypeParam>
diff --git a/src/caffe/test/test_window_data_layer.cpp b/src/caffe/test/test_window_data_layer.cpp
new file mode 100644
index 00000000000..47591b60154
--- /dev/null
+++ b/src/caffe/test/test_window_data_layer.cpp
@@ -0,0 +1,49 @@
+#include <string>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class WindowDataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  WindowDataLayerTest()
+      : seed_(1701),
+        filename_(new string(tmpnam(NULL))),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    blob_top_vec_.push_back(blob_top_data_);
+    blob_top_vec_.push_back(blob_top_label_);
+    Caffe::set_random_seed(seed_);
+  }
+
+  virtual ~WindowDataLayerTest() {
+    delete blob_top_data_;
+    delete blob_top_label_;
+  }
+
+  int seed_;
+  shared_ptr<string> filename_;
+  Blob<Dtype>* const blob_top_data_;
+  Blob<Dtype>* const blob_top_label_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(WindowDataLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(WindowDataLayerTest, TestNothing) {
+}
+
+}  // namespace caffe

From 5af0d240cd22adb60bb6fd7eadd3249a46369faf Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 17:32:46 +0800
Subject: [PATCH 0614/2053] The BasePrefetchingDataLayer shouldn't join the
 thread

---
 include/caffe/data_layers.hpp          | 6 +++---
 src/caffe/layers/base_data_layer.cpp   | 5 -----
 src/caffe/layers/data_layer.cpp        | 1 +
 src/caffe/layers/image_data_layer.cpp  | 5 +++++
 src/caffe/layers/window_data_layer.cpp | 5 +++++
 5 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 8651ece4d5f..4f599e4a306 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -60,7 +60,7 @@ class BasePrefetchingDataLayer :
  public:
   explicit BasePrefetchingDataLayer(const LayerParameter& param)
       : BaseDataLayer<Dtype>(param) {}
-  virtual ~BasePrefetchingDataLayer();
+  virtual ~BasePrefetchingDataLayer() {}
 
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -206,7 +206,7 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
  public:
   explicit ImageDataLayer(const LayerParameter& param)
       : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~ImageDataLayer() {}
+  virtual ~ImageDataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -273,7 +273,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
  public:
   explicit WindowDataLayer(const LayerParameter& param)
       : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~WindowDataLayer() {}
+  virtual ~WindowDataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 8310710dbc1..b25f0873f1d 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -15,11 +15,6 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   DataLayerSetUp(bottom, top);
 }
 
-template <typename Dtype>
-BasePrefetchingDataLayer<Dtype>::~BasePrefetchingDataLayer<Dtype>() {
-  JoinPrefetchThread();
-}
-
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
   this->phase_ = Caffe::phase();
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index fbf2d91c7ac..b57ca587749 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -79,6 +79,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
+  this->JoinPrefetchThread();
   // clean up the database resources
   switch (this->layer_param_.data_param().backend()) {
   case DataParameter_DB_LEVELDB:
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 687beb8efa3..72b14832358 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -53,6 +53,11 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   }
 }
 
+template <typename Dtype>
+ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
+  this->JoinPrefetchThread();
+}
+
 template <typename Dtype>
 void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index f4349825aba..f7e8186a477 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -237,6 +237,11 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   }
 }
 
+template <typename Dtype>
+WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
+  this->JoinPrefetchThread();
+}
+
 template <typename Dtype>
 void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {

From 4f7c9b487d4e0eed5f93f61bf271d94c7dc6e99a Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Thu, 28 Aug 2014 17:45:39 +0800
Subject: [PATCH 0615/2053] Implement Forward_gpu in the base prefetching data
 layer

---
 .../{data_layer.cu => base_data_layer.cu}     | 16 +++-------
 src/caffe/layers/image_data_layer.cu          | 27 ----------------
 src/caffe/layers/window_data_layer.cu         | 32 -------------------
 3 files changed, 5 insertions(+), 70 deletions(-)
 rename src/caffe/layers/{data_layer.cu => base_data_layer.cu} (58%)
 delete mode 100644 src/caffe/layers/image_data_layer.cu
 delete mode 100644 src/caffe/layers/window_data_layer.cu

diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/base_data_layer.cu
similarity index 58%
rename from src/caffe/layers/data_layer.cu
rename to src/caffe/layers/base_data_layer.cu
index 28ca2c1082a..8189c79c9d4 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -1,24 +1,18 @@
-#include <string>
 #include <vector>
 
-#include "leveldb/db.h"
-#include "stdint.h"
-
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/data_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-void DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       (*top)[0]->mutable_gpu_data());
-  if (output_labels_) {
+  if (this->output_labels_) {
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
         (*top)[1]->mutable_gpu_data());
   }
@@ -26,6 +20,6 @@ void DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   CreatePrefetchThread();
 }
 
-INSTANTIATE_CLASS(DataLayer);
+INSTANTIATE_CLASS(BasePrefetchingDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
deleted file mode 100644
index 30a22100dc3..00000000000
--- a/src/caffe/layers/image_data_layer.cu
+++ /dev/null
@@ -1,27 +0,0 @@
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-      (*top)[0]->mutable_gpu_data());
-  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-      (*top)[1]->mutable_gpu_data());
-  // Start a new prefetch thread
-  CreatePrefetchThread();
-}
-
-INSTANTIATE_CLASS(ImageDataLayer);
-
-}  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
deleted file mode 100644
index 947f2a39e7e..00000000000
--- a/src/caffe/layers/window_data_layer.cu
+++ /dev/null
@@ -1,32 +0,0 @@
-#include <stdint.h>
-
-#include <string>
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
-
-// caffe.proto > LayerParameter > WindowDataParameter
-//   'source' field specifies the window_file
-//   'crop_size' indicates the desired warped size
-
-namespace caffe {
-
-template <typename Dtype>
-void WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-      (*top)[0]->mutable_gpu_data());
-  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-      (*top)[1]->mutable_gpu_data());
-  // Start a new prefetch thread
-  CreatePrefetchThread();
-}
-
-INSTANTIATE_CLASS(WindowDataLayer);
-
-}  // namespace caffe

From 4c35ad2dbc1e31c8bb347a67ff750a37336a7363 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 03:02:10 +0800
Subject: [PATCH 0616/2053] Add transformer to the memory data layer

---
 include/caffe/data_layers.hpp          | 26 +++++++-------
 src/caffe/layers/memory_data_layer.cpp | 47 +++++++++++++++++++++-----
 src/caffe/proto/caffe.proto            |  1 +
 3 files changed, 52 insertions(+), 22 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 4f599e4a306..952fcb71832 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -9,6 +9,7 @@
 #include "hdf5.h"
 #include "leveldb/db.h"
 #include "lmdb.h"
+#include <opencv2/opencv.hpp>
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -43,6 +44,11 @@ class BaseDataLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
+  int datum_channels() const { return datum_channels_; }
+  int datum_height() const { return datum_height_; }
+  int datum_width() const { return datum_width_; }
+  int datum_size() const { return datum_size_; }
+
  protected:
   DataTransformer<Dtype> data_transformer_;
   int datum_channels_;
@@ -228,10 +234,10 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
 /* MemoryDataLayer
 */
 template <typename Dtype>
-class MemoryDataLayer : public Layer<Dtype> {
+class MemoryDataLayer : public BaseDataLayer<Dtype> {
  public:
   explicit MemoryDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+      : BaseDataLayer<Dtype>(param), is_data_set_up_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -244,28 +250,22 @@ class MemoryDataLayer : public Layer<Dtype> {
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
-  int datum_channels() { return datum_channels_; }
-  int datum_height() { return datum_height_; }
-  int datum_width() { return datum_width_; }
+
+  virtual void AddImagesAndLabels(const vector<cv::Mat>& images,
+                                  const vector<int>& labels);
+
   int batch_size() { return batch_size_; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   Dtype* data_;
   Dtype* labels_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
   int batch_size_;
   int n_;
   int pos_;
+  bool is_data_set_up_;
 };
 
 template <typename Dtype>
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index fda92976c49..d482df52d00 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,7 +1,7 @@
 #include <vector>
 
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -9,13 +9,16 @@ template <typename Dtype>
 void MemoryDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
-  datum_channels_ = this->layer_param_.memory_data_param().channels();
-  datum_height_ = this->layer_param_.memory_data_param().height();
-  datum_width_ = this->layer_param_.memory_data_param().width();
-  datum_size_ = datum_channels_ * datum_height_ * datum_width_;
-  CHECK_GT(batch_size_ * datum_size_, 0) << "batch_size, channels, height,"
-    " and width must be specified and positive in memory_data_param";
-  (*top)[0]->Reshape(batch_size_, datum_channels_, datum_height_, datum_width_);
+  this->datum_channels_ = this->layer_param_.memory_data_param().channels();
+  this->datum_height_ = this->layer_param_.memory_data_param().height();
+  this->datum_width_ = this->layer_param_.memory_data_param().width();
+  this->datum_size_ = this->datum_channels_ * this->datum_height_ *
+      this->datum_width_;
+  CHECK_GT(batch_size_ * this->datum_size_, 0) <<
+      "batch_size, channels, height, and width must be specified and"
+      " positive in memory_data_param";
+  (*top)[0]->Reshape(batch_size_, this->datum_channels_, this->datum_height_,
+                     this->datum_width_);
   (*top)[1]->Reshape(batch_size_, 1, 1, 1);
   data_ = NULL;
   labels_ = NULL;
@@ -36,11 +39,37 @@ template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
-  (*top)[0]->set_cpu_data(data_ + pos_ * datum_size_);
+  (*top)[0]->set_cpu_data(data_ + pos_ * this->datum_size_);
   (*top)[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
 }
 
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::AddImagesAndLabels(
+    const vector<cv::Mat>& images, const vector<int>& labels) {
+  size_t num_images = images.size();
+  CHECK_GT(num_images, 0) << "There is no image to add";
+  CHECK_LE(num_images, batch_size_)<<
+      "The number of added images " << images.size() <<
+      " must be no greater than the batch size " << batch_size;
+  CHECK_LE(num_images, labels.size()) <<
+      "The number of images " << images.size() <<
+      " must be no greater than the number of labels " << labels.size();
+
+  Datum datum;
+  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+  // Apply data transformations (mirror, scale, crop...)
+  this->data_transformer_.Transform(item_id, datum, mean, top_data);
+  int image_id;
+  for (int item_id = 0; item_id < batch_size; ++item_id) {
+    image_id = item_id % num_images;
+    OpenCVImageToDatum(images[image_id], labels[image_id], new_height,
+                       new_width, &datum);
+    this->data_transformer_.Transform(item_id, datum, mean, top_data);
+  }
+}
+
 INSTANTIATE_CLASS(MemoryDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index ff18779f62d..5acc776fb99 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -529,6 +529,7 @@ message MemoryDataParameter {
   optional uint32 channels = 2;
   optional uint32 height = 3;
   optional uint32 width = 4;
+  optional TransformationParameter transform_param = 5;
 }
 
 // Message that stores parameters used by MVNLayer

From 156a5a29878c6ded8482b9084853628daff9e188 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 10:12:57 +0800
Subject: [PATCH 0617/2053] Remove pthread which has been replaced with boost
 thread

---
 include/caffe/data_layers.hpp        | 3 ++-
 include/caffe/loss_layers.hpp        | 4 ----
 include/caffe/neuron_layers.hpp      | 3 ---
 src/caffe/internal_thread.cpp        | 3 +++
 src/caffe/layers/base_data_layer.cpp | 4 ++--
 5 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 952fcb71832..1f33748ac99 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -1,6 +1,8 @@
 #ifndef CAFFE_DATA_LAYERS_HPP_
 #define CAFFE_DATA_LAYERS_HPP_
 
+#include <opencv2/opencv.hpp>
+
 #include <string>
 #include <utility>
 #include <vector>
@@ -9,7 +11,6 @@
 #include "hdf5.h"
 #include "leveldb/db.h"
 #include "lmdb.h"
-#include <opencv2/opencv.hpp>
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index df887b8f856..a50488ff5ef 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -5,10 +5,6 @@
 #include <utility>
 #include <vector>
 
-#include "boost/scoped_ptr.hpp"
-#include "hdf5.h"
-#include "leveldb/db.h"
-
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index b5bbc5b6482..f062f0e95c4 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -5,9 +5,6 @@
 #include <utility>
 #include <vector>
 
-#include "boost/scoped_ptr.hpp"
-#include "hdf5.h"
-
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index e8cfc861932..d7b6ae206cf 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -12,6 +12,9 @@ InternalThread::~InternalThread() {
 }
 
 bool InternalThread::StartInternalThread() {
+  if (!WaitForInternalThreadToExit()) {
+    return false;
+  }
   try {
     thread_ = new caffe::Thread
         (&InternalThread::InternalThreadEntry, this);
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index b25f0873f1d..3062cff4741 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -19,12 +19,12 @@ template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
   this->phase_ = Caffe::phase();
   this->data_transformer_.InitRand();
-  CHECK(StartInternalThread()) << "Pthread execution failed";
+  CHECK(StartInternalThread()) << "Thread execution failed";
 }
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
+  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
 }
 
 template <typename Dtype>

From 3c9a13c5cd289aeb3b6524fca1e34bfe8ccf990e Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 11:45:37 +0800
Subject: [PATCH 0618/2053] Move transform param one level up in the proto to
 reduce redundancy

---
 .../cifar10/cifar10_full_train_test.prototxt  |  12 +-
 .../cifar10/cifar10_quick_train_test.prototxt |  12 +-
 .../feature_extraction/imagenet_val.prototxt  |  10 +-
 examples/imagenet/alexnet_train_val.prototxt  |  20 +--
 examples/imagenet/imagenet_train_val.prototxt |  20 +--
 .../mnist/lenet_consolidated_solver.prototxt  |  18 +--
 examples/mnist/lenet_train_test.prototxt      |  12 +-
 examples/mnist/mnist_autoencoder.prototxt     |  12 +-
 include/caffe/data_layers.hpp                 |  17 ++-
 src/caffe/layers/data_layer.cpp               |   6 +-
 src/caffe/layers/image_data_layer.cpp         |   8 +-
 src/caffe/layers/memory_data_layer.cpp        |  42 +++---
 src/caffe/proto/caffe.proto                   |  50 ++++---
 src/caffe/test/test_data_layer.cpp            |   8 +-
 src/caffe/test/test_net.cpp                   |  24 ++--
 src/caffe/test/test_split_layer.cpp           |  10 +-
 src/caffe/test/test_upgrade_proto.cpp         |  52 +++----
 src/caffe/util/upgrade_proto.cpp              | 129 +++++-------------
 18 files changed, 207 insertions(+), 255 deletions(-)

diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index ffecceb2a39..8fde19f046e 100644
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -7,9 +7,9 @@ layers {
   data_param {
     source: "examples/cifar10/cifar10_train_leveldb"
     batch_size: 100
-    transform_param {
-      mean_file: "examples/cifar10/mean.binaryproto"
-    }
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
   }
   include: { phase: TRAIN }
 }
@@ -21,9 +21,9 @@ layers {
   data_param {
     source: "examples/cifar10/cifar10_test_leveldb"
     batch_size: 100
-    transform_param {
-      mean_file: "examples/cifar10/mean.binaryproto"
-    }
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
   }
   include: { phase: TEST }
 }
diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index f708798c71c..409cfe809f4 100644
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -7,9 +7,9 @@ layers {
   data_param {
     source: "examples/cifar10/cifar10_train_leveldb"
     batch_size: 100
-    transform_param {
-      mean_file: "examples/cifar10/mean.binaryproto"
-    }
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
   }
   include: { phase: TRAIN }
 }
@@ -21,9 +21,9 @@ layers {
   data_param {
     source: "examples/cifar10/cifar10_test_leveldb"
     batch_size: 100
-    transform_param {
-      mean_file: "examples/cifar10/mean.binaryproto"
-    }
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
   }
   include: { phase: TEST }
 }
diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 8d5b07626b5..32310904a66 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -9,11 +9,11 @@ layers {
     batch_size: 50
     new_height: 256
     new_width: 256
-    transform_param {
-      crop_size: 227
-      mean_file: "$CAFFE_DIR/data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: false
-    }
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "$CAFFE_DIR/data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: false
   }
 }
 layers {
diff --git a/examples/imagenet/alexnet_train_val.prototxt b/examples/imagenet/alexnet_train_val.prototxt
index b0f5815e007..3fa46773403 100644
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -7,11 +7,11 @@ layers {
   data_param {
     source: "examples/imagenet/ilsvrc12_train_leveldb"
     batch_size: 256
-    transform_param {
-      crop_size: 227
-      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: true
-    }
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: true
   }
   include: { phase: TRAIN }
 }
@@ -23,11 +23,11 @@ layers {
   data_param {
     source: "examples/imagenet/ilsvrc12_val_leveldb"
     batch_size: 50
-    transform_param {
-      crop_size: 227
-      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: false
-    }
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: false
   }
   include: { phase: TEST }
 }
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/examples/imagenet/imagenet_train_val.prototxt
index 766d546a385..d6d64073619 100644
--- a/examples/imagenet/imagenet_train_val.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -7,11 +7,11 @@ layers {
   data_param {
     source: "examples/imagenet/ilsvrc12_train_leveldb"
     batch_size: 256
-    transform_param {
-      crop_size: 227
-      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: true
-    }
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: true
   }
   include: { phase: TRAIN }
 }
@@ -23,11 +23,11 @@ layers {
   data_param {
     source: "examples/imagenet/ilsvrc12_val_leveldb"
     batch_size: 50
-    transform_param {
-      crop_size: 227
-      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: false
-    }
+  }
+  transform_param {
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: false
   }
   include: { phase: TEST }
 }
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
index f57d086c671..b81f30f2c10 100644
--- a/examples/mnist/lenet_consolidated_solver.prototxt
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -53,9 +53,9 @@ net_param {
       source: "examples/mnist/mnist_train_lmdb"
       backend: LMDB
       batch_size: 64
-      transform_param {
-        scale: 0.00390625
-      }
+    }
+    transform_param {
+      scale: 0.00390625
     }
     include: { phase: TRAIN }
   }
@@ -68,9 +68,9 @@ net_param {
       source: "examples/mnist/mnist_test_lmdb"
       backend: LMDB
       batch_size: 100
-      transform_param {
-        scale: 0.00390625
-      }
+    }
+    transform_param {
+      scale: 0.00390625
     }
     include: {
       phase: TEST
@@ -86,9 +86,9 @@ net_param {
       source: "examples/mnist/mnist_train_lmdb"
       backend: LMDB
       batch_size: 100
-      transform_param {
-        scale: 0.00390625
-      }
+    }
+    transform_param {
+      scale: 0.00390625
     }
     include: {
       phase: TEST
diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index e4d65892f37..2bd960b56aa 100644
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -8,9 +8,9 @@ layers {
     source: "examples/mnist/mnist_train_lmdb"
     backend: LMDB
     batch_size: 64
-    transform_param {
-      scale: 0.00390625
-    }
+  }
+  transform_param {
+    scale: 0.00390625
   }
   include: { phase: TRAIN }
 }
@@ -23,9 +23,9 @@ layers {
     source: "examples/mnist/mnist_test_lmdb"
     backend: LMDB
     batch_size: 100
-    transform_param {
-      scale: 0.00390625
-    }
+  }
+  transform_param {
+    scale: 0.00390625
   }
   include: { phase: TEST }
 }
diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index 45d0802339e..b4345e71ce4 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -7,9 +7,9 @@ layers {
     source: "examples/mnist/mnist_train_lmdb"
     backend: LMDB
     batch_size: 100
-    transform_param {
-      scale: 0.0039215684
-    }
+  }
+  transform_param {
+    scale: 0.0039215684
   }
   include: { phase: TRAIN }
 }
@@ -21,9 +21,9 @@ layers {
     source: "examples/mnist/mnist_train_lmdb"
     backend: LMDB
     batch_size: 100
-    transform_param {
-      scale: 0.0039215684
-    }
+  }
+  transform_param {
+    scale: 0.0039215684
   }
   include: {
     phase: TEST
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 1f33748ac99..4002a00e11c 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -30,7 +30,8 @@ class BaseDataLayer : public Layer<Dtype> {
  public:
   explicit BaseDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param),
-        data_transformer_(param.data_param().transform_param()) {}
+        transform_param_(param.transform_param()),
+        data_transformer_(transform_param_) {}
   virtual ~BaseDataLayer() {}
   // LayerSetUp: implements common data layer setup functionality, and calls
   // DataLayerSetUp to do special data layer setup for individual layer types.
@@ -51,6 +52,7 @@ class BaseDataLayer : public Layer<Dtype> {
   int datum_size() const { return datum_size_; }
 
  protected:
+  TransformationParameter transform_param_;
   DataTransformer<Dtype> data_transformer_;
   int datum_channels_;
   int datum_height_;
@@ -238,8 +240,8 @@ template <typename Dtype>
 class MemoryDataLayer : public BaseDataLayer<Dtype> {
  public:
   explicit MemoryDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param), is_data_set_up_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -253,7 +255,8 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   void Reset(Dtype* data, Dtype* label, int n);
 
   virtual void AddImagesAndLabels(const vector<cv::Mat>& images,
-                                  const vector<int>& labels);
+                                  const vector<int>& labels,
+                                  const bool is_color_images = true);
 
   int batch_size() { return batch_size_; }
 
@@ -261,12 +264,14 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  int batch_size_;
   Dtype* data_;
   Dtype* labels_;
-  int batch_size_;
   int n_;
   int pos_;
-  bool is_data_set_up_;
+  Blob<Dtype> added_data_;
+  Blob<Dtype> added_label_;
+  bool has_new_data_;
 };
 
 template <typename Dtype>
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index b57ca587749..715960be302 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -175,7 +175,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
 
   // image
-  int crop_size = this->layer_param_.data_param().transform_param().crop_size();
+  int crop_size = this->layer_param_.transform_param().crop_size();
   if (crop_size > 0) {
     (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
                        datum.channels(), crop_size, crop_size);
@@ -205,9 +205,9 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(this->datum_height_, crop_size);
   CHECK_GT(this->datum_width_, crop_size);
   // check if we want to have mean
-  if (this->layer_param_.data_param().transform_param().has_mean_file()) {
+  if (this->transform_param_.has_mean_file()) {
     const string& mean_file =
-        this->layer_param_.data_param().transform_param().mean_file();
+        this->transform_param_.mean_file();
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 72b14832358..4d4f62eb361 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -99,11 +99,9 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK(ReadImageToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
                          new_height, new_width, &datum));
   // image
-  const int crop_size = this->layer_param_.image_data_param()
-      .transform_param().crop_size();
+  const int crop_size = this->layer_param_.transform_param().crop_size();
   const int batch_size = this->layer_param_.image_data_param().batch_size();
-  const string& mean_file = this->layer_param_.image_data_param()
-      .transform_param().mean_file();
+  const string& mean_file = this->layer_param_.transform_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
@@ -128,7 +126,7 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(this->datum_height_, crop_size);
   CHECK_GT(this->datum_width_, crop_size);
   // check if we want to have mean
-  if (this->layer_param_.image_data_param().transform_param().has_mean_file()) {
+  if (this->layer_param_.transform_param().has_mean_file()) {
     BlobProto blob_proto;
     LOG(INFO) << "Loading mean file from" << mean_file;
     ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index d482df52d00..60dc65868b7 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -2,11 +2,12 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/io.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-void MemoryDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
   this->datum_channels_ = this->layer_param_.memory_data_param().channels();
@@ -42,32 +43,37 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   (*top)[0]->set_cpu_data(data_ + pos_ * this->datum_size_);
   (*top)[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
+  has_new_data_ = false;
 }
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddImagesAndLabels(
-    const vector<cv::Mat>& images, const vector<int>& labels) {
+    const vector<cv::Mat>& images, const vector<int>& labels,
+    const bool is_color_images) {
+  CHECK(!has_new_data_) <<
+      "There are pending images that haven't been consumed";
   size_t num_images = images.size();
   CHECK_GT(num_images, 0) << "There is no image to add";
-  CHECK_LE(num_images, batch_size_)<<
-      "The number of added images " << images.size() <<
-      " must be no greater than the batch size " << batch_size;
-  CHECK_LE(num_images, labels.size()) <<
-      "The number of images " << images.size() <<
-      " must be no greater than the number of labels " << labels.size();
+  CHECK_EQ(num_images, batch_size_) <<
+      "The number of added images must be equal to the batch size";
+  CHECK_EQ(num_images, labels.size()) <<
+      "The number of images must be equal to the number of labels";
 
   Datum datum;
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
-  // Apply data transformations (mirror, scale, crop...)
-  this->data_transformer_.Transform(item_id, datum, mean, top_data);
-  int image_id;
-  for (int item_id = 0; item_id < batch_size; ++item_id) {
-    image_id = item_id % num_images;
-    OpenCVImageToDatum(images[image_id], labels[image_id], new_height,
-                       new_width, &datum);
-    this->data_transformer_.Transform(item_id, datum, mean, top_data);
+  Dtype* top_data = added_data_.mutable_cpu_data();
+  Dtype* top_label = added_label_.mutable_cpu_data();
+  const Dtype* mean = this->data_mean_.cpu_data();
+  for (int batch_item_id = 0; batch_item_id < num_images; ++batch_item_id) {
+    OpenCVImageToDatum(
+        images[batch_item_id], labels[batch_item_id], this->datum_height_,
+        this->datum_width_, &datum);
+    // Apply data transformations (mirror, scale, crop...)
+    this->data_transformer_.Transform(batch_item_id, datum, mean, top_data);
+    top_label[batch_item_id] = labels[batch_item_id];
   }
+  // num_images == batch_size_
+  Reset(top_data, top_label, batch_size_);
+  has_new_data_ = true;
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5acc776fb99..74c2bab73eb 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -195,7 +195,7 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 36 (last added: loss_weight)
+// LayerParameter next available ID: 37 (last added: transform_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -307,6 +307,9 @@ message LayerParameter {
   optional HingeLossParameter hinge_loss_param = 29;
   optional SliceParameter slice_param = 31;
   optional MVNParameter mvn_param = 34;
+  
+  // Parameters for data pre-processing.
+  optional TransformationParameter transform_param = 36;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -314,6 +317,20 @@ message LayerParameter {
   optional V0LayerParameter layer = 1;
 }
 
+// Message that stores parameters used to apply transformation
+// to the data layer's data
+message TransformationParameter {
+  // For data pre-processing, we can do simple scaling and subtracting the
+  // data mean, if provided. Note that the mean subtraction is always carried
+  // out before scaling.
+  optional float scale = 1 [default = 1];
+  // Specify if we want to randomly mirror data.
+  optional bool mirror = 2 [default = false];
+  // Specify if we would like to randomly crop an image.
+  optional uint32 crop_size = 3 [default = 0];
+  optional string mean_file = 4;
+}
+
 // Message that stores parameters used by AccuracyLayer
 message AccuracyParameter {
   // When computing accuracy, count as correct by comparing the true label to
@@ -357,20 +374,6 @@ message ConvolutionParameter {
   optional FillerParameter bias_filler = 8; // The filler for the bias
 }
 
-// Message that stores parameters used to apply transformation
-// to the data layer's data
-message TransformationParameter {
-  // For data pre-processing, we can do simple scaling and subtracting the
-  // data mean, if provided. Note that the mean subtraction is always carried
-  // out before scaling.
-  optional float scale = 1 [default = 1];
-  // Specify if we want to randomly mirror data.
-  optional bool mirror = 2 [default = false];
-  // Specify if we would like to randomly crop an image.
-  optional uint32 crop_size = 3 [default = 0];
-  optional string mean_file = 4;
-}
-
 // Message that stores parameters used by DataLayer
 message DataParameter {
   enum DB {
@@ -387,17 +390,15 @@ message DataParameter {
   // be larger than the number of keys in the leveldb.
   optional uint32 rand_skip = 7 [default = 0];
   optional DB backend = 8 [default = LEVELDB];
-  // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 9;
-  // DEPRECATED. See TransformParameter. For data pre-processing, we can do
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
   // simple scaling and subtracting the data mean, if provided. Note that the
   // mean subtraction is always carried out before scaling.
   optional float scale = 2 [default = 1];
   optional string mean_file = 3;
-  // DEPRECATED. See TransformParameter. Specify if we would like to randomly
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
   // crop an image.
   optional uint32 crop_size = 5 [default = 0];
-  // DEPRECATED. See TransformParameter. Specify if we want to randomly mirror
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
   // data.
   optional bool mirror = 6 [default = false];
 }
@@ -482,17 +483,15 @@ message ImageDataParameter {
   // It will also resize images if new_height or new_width are not zero.
   optional uint32 new_height = 9 [default = 0];
   optional uint32 new_width = 10 [default = 0];
-  // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 11;
-  // DEPRECATED. See TransformParameter. For data pre-processing, we can do
+  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
   // simple scaling and subtracting the data mean, if provided. Note that the
   // mean subtraction is always carried out before scaling.
   optional float scale = 2 [default = 1];
   optional string mean_file = 3;
-  // DEPRECATED. See TransformParameter. Specify if we would like to randomly
+  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
   // crop an image.
   optional uint32 crop_size = 5 [default = 0];
-  // DEPRECATED. See TransformParameter. Specify if we want to randomly mirror
+  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
   // data.
   optional bool mirror = 6 [default = false];
 }
@@ -529,7 +528,6 @@ message MemoryDataParameter {
   optional uint32 channels = 2;
   optional uint32 height = 3;
   optional uint32 width = 4;
-  optional TransformationParameter transform_param = 5;
 }
 
 // Message that stores parameters used by MVNLayer
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 52cc2c082a9..e01932b7f1f 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -118,7 +118,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     data_param->set_backend(backend_);
 
     TransformationParameter* transform_param =
-        data_param->mutable_transform_param();
+        param.mutable_transform_param();
     transform_param->set_scale(scale);
 
     DataLayer<Dtype> layer(param);
@@ -157,7 +157,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     data_param->set_backend(backend_);
 
     TransformationParameter* transform_param =
-        data_param->mutable_transform_param();
+        param.mutable_transform_param();
     transform_param->set_scale(scale);
     transform_param->set_crop_size(1);
 
@@ -207,7 +207,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     data_param->set_backend(backend_);
 
     TransformationParameter* transform_param =
-        data_param->mutable_transform_param();
+        param.mutable_transform_param();
     transform_param->set_crop_size(1);
     transform_param->set_mirror(true);
 
@@ -261,7 +261,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     data_param->set_backend(backend_);
 
     TransformationParameter* transform_param =
-        data_param->mutable_transform_param();
+        param.mutable_transform_param();
     transform_param->set_crop_size(1);
     transform_param->set_mirror(true);
 
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index f0a368245a5..24f72aef3af 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1177,11 +1177,11 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-train-leveldb' "
-      "    transform_param { "
-      "      scale: 0.00390625 "
-      "    } "
       "    batch_size: 64 "
       "  } "
+      "  transform_param { "
+      "    scale: 0.00390625 "
+      "  } "
       "  include: { phase: TRAIN } "
       "} "
       "layers { "
@@ -1191,11 +1191,11 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-test-leveldb' "
-      "    transform_param { "
-      "      scale: 0.00390625 "
-      "    } "
       "    batch_size: 100 "
       "  } "
+      "  transform_param { "
+      "    scale: 0.00390625 "
+      "  } "
       "  include: { phase: TEST } "
       "} "
       "layers { "
@@ -1260,11 +1260,11 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-train-leveldb' "
-      "    transform_param { "
-      "      scale: 0.00390625 "
-      "    } "
       "    batch_size: 64 "
       "  } "
+      "  transform_param { "
+      "    scale: 0.00390625 "
+      "  } "
       "  include: { phase: TRAIN } "
       "} "
       "layers { "
@@ -1319,11 +1319,11 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'label' "
       "  data_param { "
       "    source: 'mnist-test-leveldb' "
-      "    transform_param { "
-      "      scale: 0.00390625 "
-      "    } "
       "    batch_size: 100 "
       "  } "
+      "  transform_param { "
+      "    scale: 0.00390625 "
+      "  } "
       "  include: { phase: TEST } "
       "} "
       "layers { "
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index bee73f64568..e9b942c5c51 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -181,11 +181,11 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
-      "    transform_param { "
-      "      crop_size: 227 "
-      "      mirror: true "
-      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
-      "    } "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "  } "
       "  top: 'data' "
       "  top: 'label' "
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 9d0cd58d84c..f46a0e823c1 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1190,11 +1190,11 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
-      "    transform_param { "
-      "      crop_size: 227 "
-      "      mirror: true "
-      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
-      "    } "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "  } "
       "  top: 'data' "
       "  top: 'label' "
@@ -1526,14 +1526,14 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
-      "    transform_param { "
-      "      crop_size: 227 "
-      "      mirror: true "
-      "      scale: 0.25 "
-      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
-      "    } "
       "    rand_skip: 73 "
       "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    scale: 0.25 "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "  } "
       "  top: 'data' "
       "  top: 'label' "
       "} "
@@ -1543,17 +1543,17 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "  image_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-images' "
       "    batch_size: 256 "
-      "    transform_param {"
-      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
-      "      crop_size: 227 "
-      "      mirror: true "
-      "      scale: 0.25 "
-      "    } "
       "    rand_skip: 73 "
       "    shuffle: true "
       "    new_height: 40 "
       "    new_width: 30 "
       "  } "
+      "  transform_param {"
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    scale: 0.25 "
+      "  } "
       "  top: 'images_data' "
       "  top: 'images_label' "
       "} "
@@ -1562,16 +1562,18 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "  type: WINDOW_DATA "
       "  window_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
-      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "    batch_size: 256 "
-      "    crop_size: 227 "
-      "    mirror: true "
       "    fg_threshold: 0.25 "
       "    bg_threshold: 0.75 "
       "    fg_fraction: 0.5 "
       "    context_pad: 16 "
       "    crop_mode: 'square' "
       "  } "
+      "  transform_param { "
+      "    mirror: true "
+      "    crop_size: 227 "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "  }"
       "  top: 'window_data' "
       "  top: 'window_label' "
       "} "
@@ -2126,11 +2128,11 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
-      "    transform_param { "
-      "      crop_size: 227 "
-      "      mirror: true "
-      "      mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
-      "    } "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
       "  } "
       "  top: 'data' "
       "  top: 'label' "
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 9045abbe17d..c3d854c0595 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -306,31 +306,12 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
       }
     }
     if (v0_layer_param.has_scale()) {
-      if (type == "data") {
-        layer_param->mutable_data_param()->mutable_transform_param()->
-            set_scale(v0_layer_param.scale());
-      } else if (type == "images") {
-        layer_param->mutable_image_data_param()->mutable_transform_param()->
-            set_scale(v0_layer_param.scale());
-      } else {
-        LOG(ERROR) << "Unknown parameter scale for layer type " << type;
-        is_fully_compatible = false;
-      }
+      layer_param->mutable_transform_param()->
+          set_scale(v0_layer_param.scale());
     }
     if (v0_layer_param.has_meanfile()) {
-      if (type == "data") {
-        layer_param->mutable_data_param()->mutable_transform_param()->
-            set_mean_file(v0_layer_param.meanfile());
-      } else if (type == "images") {
-        layer_param->mutable_image_data_param()->mutable_transform_param()->
-            set_mean_file(v0_layer_param.meanfile());
-      } else if (type == "window_data") {
-        layer_param->mutable_window_data_param()->set_mean_file(
-            v0_layer_param.meanfile());
-      } else {
-        LOG(ERROR) << "Unknown parameter meanfile for layer type " << type;
-        is_fully_compatible = false;
-      }
+      layer_param->mutable_transform_param()->
+          set_mean_file(v0_layer_param.meanfile());
     }
     if (v0_layer_param.has_batchsize()) {
       if (type == "data") {
@@ -351,34 +332,12 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
       }
     }
     if (v0_layer_param.has_cropsize()) {
-      if (type == "data") {
-        layer_param->mutable_data_param()->mutable_transform_param()->
-            set_crop_size(v0_layer_param.cropsize());
-      } else if (type == "images") {
-        layer_param->mutable_image_data_param()->mutable_transform_param()->
-            set_crop_size(v0_layer_param.cropsize());
-      } else if (type == "window_data") {
-        layer_param->mutable_window_data_param()->set_crop_size(
-            v0_layer_param.cropsize());
-      } else {
-        LOG(ERROR) << "Unknown parameter cropsize for layer type " << type;
-        is_fully_compatible = false;
-      }
+      layer_param->mutable_transform_param()->
+          set_crop_size(v0_layer_param.cropsize());
     }
     if (v0_layer_param.has_mirror()) {
-      if (type == "data") {
-        layer_param->mutable_data_param()->mutable_transform_param()->
-            set_mirror(v0_layer_param.mirror());
-      } else if (type == "images") {
-        layer_param->mutable_image_data_param()->mutable_transform_param()->
-            set_mirror(v0_layer_param.mirror());
-      } else if (type == "window_data") {
-        layer_param->mutable_window_data_param()->set_mirror(
-            v0_layer_param.mirror());
-      } else {
-        LOG(ERROR) << "Unknown parameter mirror for layer type " << type;
-        is_fully_compatible = false;
-      }
+      layer_param->mutable_transform_param()->
+          set_mirror(v0_layer_param.mirror());
     }
     if (v0_layer_param.has_rand_skip()) {
       if (type == "data") {
@@ -567,52 +526,36 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   return false;
 }
 
+#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \
+  do { \
+    if (net_param->layers(i).type() == LayerParameter_LayerType_##TYPE) { \
+      Name##Parameter* layer_param = \
+          net_param->mutable_layers(i)->mutable_##param_name##_param(); \
+      TransformationParameter* transform_param = \
+          net_param->mutable_layers(i)->mutable_transform_param(); \
+      if (layer_param->has_scale()) { \
+        transform_param->set_scale(layer_param->scale()); \
+        layer_param->clear_scale(); \
+      } \
+      if (layer_param->has_mean_file()) { \
+        transform_param->set_mean_file(layer_param->mean_file()); \
+        layer_param->clear_mean_file(); \
+      } \
+      if (layer_param->has_crop_size()) { \
+        transform_param->set_crop_size(layer_param->crop_size()); \
+        layer_param->clear_crop_size(); \
+      } \
+      if (layer_param->has_mirror()) { \
+        transform_param->set_mirror(layer_param->mirror()); \
+        layer_param->clear_mirror(); \
+      } \
+    } \
+  } while (0)
+
 void UpgradeNetDataTransformation(NetParameter* net_param) {
   for (int i = 0; i < net_param->layers_size(); ++i) {
-    if (net_param->layers(i).type() == LayerParameter_LayerType_DATA) {
-      DataParameter* layer_param =
-          net_param->mutable_layers(i)->mutable_data_param();
-      TransformationParameter* transform_param =
-          layer_param->mutable_transform_param();
-      if (layer_param->has_scale()) {
-        transform_param->set_scale(layer_param->scale());
-        layer_param->clear_scale();
-      }
-      if (layer_param->has_mean_file()) {
-        transform_param->set_mean_file(layer_param->mean_file());
-        layer_param->clear_mean_file();
-      }
-      if (layer_param->has_crop_size()) {
-        transform_param->set_crop_size(layer_param->crop_size());
-        layer_param->clear_crop_size();
-      }
-      if (layer_param->has_mirror()) {
-        transform_param->set_mirror(layer_param->mirror());
-        layer_param->clear_mirror();
-      }
-    }
-    if (net_param->layers(i).type() == LayerParameter_LayerType_IMAGE_DATA) {
-      ImageDataParameter* layer_param =
-          net_param->mutable_layers(i)->mutable_image_data_param();
-      TransformationParameter* transform_param =
-          layer_param->mutable_transform_param();
-      if (layer_param->has_scale()) {
-        transform_param->set_scale(layer_param->scale());
-        layer_param->clear_scale();
-      }
-      if (layer_param->has_mean_file()) {
-        transform_param->set_mean_file(layer_param->mean_file());
-        layer_param->clear_mean_file();
-      }
-      if (layer_param->has_crop_size()) {
-        transform_param->set_crop_size(layer_param->crop_size());
-        layer_param->clear_crop_size();
-      }
-      if (layer_param->has_mirror()) {
-        transform_param->set_mirror(layer_param->mirror());
-        layer_param->clear_mirror();
-      }
-    }
+    CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data);
+    CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data);
   }
 }
 

From 05ade8171c8fff8c73a4febe4ecd64322444cba2 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 16:36:04 +0800
Subject: [PATCH 0619/2053] Test adding images w/o resizing to the memory data
 layer

---
 include/caffe/data_layers.hpp             |  2 +
 src/caffe/layers/memory_data_layer.cpp    |  7 ++
 src/caffe/test/test_memory_data_layer.cpp | 96 ++++++++++++++++++++---
 3 files changed, 95 insertions(+), 10 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 4002a00e11c..cdd1b89ecd3 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -254,6 +254,8 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
 
+  virtual void AddImages(const vector<cv::Mat>& images,
+                         const bool is_color_images = true);
   virtual void AddImagesAndLabels(const vector<cv::Mat>& images,
                                   const vector<int>& labels,
                                   const bool is_color_images = true);
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 60dc65868b7..1d48a49b841 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -46,6 +46,13 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   has_new_data_ = false;
 }
 
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::AddImages(
+    const vector<cv::Mat>& images, const bool is_color_images) {
+  const vector<int> fake_labels(images.size(), 0);
+  AddImagesAndLabels(images, fake_labels, is_color_images);
+}
+
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddImagesAndLabels(
     const vector<cv::Mat>& images, const vector<int>& labels,
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index f401bb0f4f2..39b97d863ae 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,20 +1,25 @@
+#include <opencv2/opencv.hpp>
+
 #include <vector>
 
+#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
-template <typename Dtype>
-class MemoryDataLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class MemoryDataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   MemoryDataLayerTest()
     : data_(new Blob<Dtype>()),
       labels_(new Blob<Dtype>()),
       data_blob_(new Blob<Dtype>()),
-      label_blob_(new Blob<Dtype>()) {}
+      label_blob_(new Blob<Dtype>()),
+      image_(cv::imread("examples/images/cat.jpg", CV_LOAD_IMAGE_COLOR)) {}
   virtual void SetUp() {
     batch_size_ = 8;
     batches_ = 12;
@@ -30,6 +35,9 @@ class MemoryDataLayerTest : public ::testing::Test {
     labels_->Reshape(batches_ * batch_size_, 1, 1, 1);
     filler.Fill(this->data_);
     filler.Fill(this->labels_);
+    for (int i = 0; i < batch_size_; ++i) {
+      image_labels_.push_back(i);
+    }
   }
 
   virtual ~MemoryDataLayerTest() {
@@ -52,19 +60,23 @@ class MemoryDataLayerTest : public ::testing::Test {
   Blob<Dtype>* const label_blob_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  const cv::Mat image_;
+  vector<int> image_labels_;
 };
 
-TYPED_TEST_CASE(MemoryDataLayerTest, TestDtypes);
+TYPED_TEST_CASE(MemoryDataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(MemoryDataLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+
   LayerParameter layer_param;
   MemoryDataParameter* md_param = layer_param.mutable_memory_data_param();
   md_param->set_batch_size(this->batch_size_);
   md_param->set_channels(this->channels_);
   md_param->set_height(this->height_);
   md_param->set_width(this->width_);
-  shared_ptr<Layer<TypeParam> > layer(
-      new MemoryDataLayer<TypeParam>(layer_param));
+  shared_ptr<Layer<Dtype> > layer(
+      new MemoryDataLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->data_blob_->num(), this->batch_size_);
   EXPECT_EQ(this->data_blob_->channels(), this->channels_);
@@ -78,15 +90,17 @@ TYPED_TEST(MemoryDataLayerTest, TestSetup) {
 
 // run through a few batches and check that the right data appears
 TYPED_TEST(MemoryDataLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+
   LayerParameter layer_param;
   MemoryDataParameter* md_param = layer_param.mutable_memory_data_param();
   md_param->set_batch_size(this->batch_size_);
   md_param->set_channels(this->channels_);
   md_param->set_height(this->height_);
   md_param->set_width(this->width_);
-  shared_ptr<MemoryDataLayer<TypeParam> > layer(
-      new MemoryDataLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  shared_ptr<MemoryDataLayer<Dtype> > layer(
+      new MemoryDataLayer<Dtype>(layer_param));
+  layer->DataLayerSetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Reset(this->data_->mutable_cpu_data(),
       this->labels_->mutable_cpu_data(), this->data_->num());
   for (int i = 0; i < this->batches_ * 6; ++i) {
@@ -104,4 +118,66 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   }
 }
 
+TYPED_TEST(MemoryDataLayerTest, TestAddImagesAndLabels) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->image_.channels());
+  memory_data_param->set_height(this->image_.rows);
+  memory_data_param->set_width(this->image_.cols);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  cv::Mat image = this->image_;
+  vector<cv::Mat> images(this->batch_size_, image);
+  layer.AddImagesAndLabels(images, this->image_labels_);
+  // Go through the data 5 times
+  for (int iter = 0; iter < 5; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0, index = 0; i < this->batch_size_; ++i) {
+      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
+      for (int c = 0; c < image.channels(); ++c) {
+        for (int h = 0; h < image.rows; ++h) {
+          for (int w = 0; w < image.cols; ++w) {
+            EXPECT_EQ(static_cast<uint8_t>(image.at<cv::Vec3b>(h, w)[c]),
+                      data[index++]);
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(MemoryDataLayerTest, TestAddImagesAndLabelsResize) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  const int resize_height = 256;
+  const int resize_width = 256;
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->image_.channels());
+  memory_data_param->set_height(resize_height);
+  memory_data_param->set_width(resize_width);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->data_blob_->num(), this->batch_size_);
+  EXPECT_EQ(this->data_blob_->channels(), this->image_.channels());
+  EXPECT_EQ(this->data_blob_->height(), resize_height);
+  EXPECT_EQ(this->data_blob_->width(), resize_width);
+  cv::Mat image = this->image_;
+  vector<cv::Mat> images(this->batch_size_, image);
+  layer.AddImagesAndLabels(images, this->image_labels_);
+  // Go through the data 5 times
+  for (int iter = 0; iter < 5; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    const Dtype* label_data = this->label_blob_->cpu_data();
+    for (int i = 0; i < this->batch_size_; ++i) {
+      EXPECT_EQ(i, label_data[i]);
+    }
+  }
+}
+
 }  // namespace caffe

From 20c59928431abafbdb4c02c8e012234d8c53a3f5 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Fri, 29 Aug 2014 16:37:59 +0800
Subject: [PATCH 0620/2053] Move the rest duplicate codes of the data layers
 into their base class

---
 include/caffe/data_layers.hpp          | 15 ++++----
 src/caffe/layers/base_data_layer.cpp   | 48 ++++++++++++++++++++++++++
 src/caffe/layers/data_layer.cpp        | 34 +-----------------
 src/caffe/layers/image_data_layer.cpp  | 31 +----------------
 src/caffe/layers/memory_data_layer.cpp |  9 +++--
 src/caffe/layers/window_data_layer.cpp | 26 --------------
 6 files changed, 66 insertions(+), 97 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index cdd1b89ecd3..8c1b3f90e27 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -28,15 +28,12 @@ namespace caffe {
 template <typename Dtype>
 class BaseDataLayer : public Layer<Dtype> {
  public:
-  explicit BaseDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param),
-        transform_param_(param.transform_param()),
-        data_transformer_(transform_param_) {}
+  explicit BaseDataLayer(const LayerParameter& param);
   virtual ~BaseDataLayer() {}
   // LayerSetUp: implements common data layer setup functionality, and calls
   // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden.
-  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  // This method may not be overridden except by the BasePrefetchingDataLayer.
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {}
@@ -59,6 +56,7 @@ class BaseDataLayer : public Layer<Dtype> {
   int datum_width_;
   int datum_size_;
   Blob<Dtype> data_mean_;
+  const Dtype* mean_;
   Caffe::Phase phase_;
   bool output_labels_;
 };
@@ -70,6 +68,11 @@ class BasePrefetchingDataLayer :
   explicit BasePrefetchingDataLayer(const LayerParameter& param)
       : BaseDataLayer<Dtype>(param) {}
   virtual ~BasePrefetchingDataLayer() {}
+  // LayerSetUp: implements common data layer setup functionality, and calls
+  // DataLayerSetUp to do special data layer setup for individual layer types.
+  // This method may not be overridden.
+  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 3062cff4741..c31081c3540 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -1,9 +1,18 @@
+#include <string>
 #include <vector>
 
 #include "caffe/data_layers.hpp"
+#include "caffe/util/io.hpp"
 
 namespace caffe {
 
+template <typename Dtype>
+BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)
+    : Layer<Dtype>(param),
+      transform_param_(param.transform_param()),
+      data_transformer_(transform_param_) {
+}
+
 template <typename Dtype>
 void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
@@ -13,6 +22,45 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     output_labels_ = true;
   }
   DataLayerSetUp(bottom, top);
+  // The subclasses should setup the datum channels, height and width
+  CHECK_GT(datum_channels_, 0);
+  CHECK_GT(datum_height_, 0);
+  CHECK_GT(datum_width_, 0);
+  CHECK_GT(datum_height_, transform_param_.crop_size());
+  CHECK_GT(datum_width_, transform_param_.crop_size());
+  // check if we want to have mean
+  if (transform_param_.has_mean_file()) {
+    const string& mean_file = transform_param_.mean_file();
+    LOG(INFO) << "Loading mean file from" << mean_file;
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+    data_mean_.FromProto(blob_proto);
+    CHECK_EQ(data_mean_.num(), 1);
+    CHECK_EQ(data_mean_.channels(), datum_channels_);
+    CHECK_EQ(data_mean_.height(), datum_height_);
+    CHECK_EQ(data_mean_.width(), datum_width_);
+  } else {
+    // Simply initialize an all-empty mean.
+    data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
+  }
+  mean_ = data_mean_.cpu_data();
+}
+
+template <typename Dtype>
+void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
+  // Now, start the prefetch thread. Before calling prefetch, we make two
+  // cpu_data calls so that the prefetch thread does not accidentally make
+  // simultaneous cudaMalloc calls when the main thread is running. In some
+  // GPUs this seems to cause failures if we do not so.
+  this->prefetch_data_.mutable_cpu_data();
+  if (this->output_labels_) {
+    this->prefetch_label_.mutable_cpu_data();
+  }
+  DLOG(INFO) << "Initializing prefetch";
+  this->CreatePrefetchThread();
+  DLOG(INFO) << "Prefetch initialized.";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 715960be302..7a46a599069 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -26,7 +26,6 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   }
   const int batch_size = this->layer_param_.data_param().batch_size();
 
-  const Dtype* mean = this->data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
     switch (this->layer_param_.data_param().backend()) {
@@ -46,7 +45,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(item_id, datum, mean, top_data);
+    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
 
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
@@ -202,37 +201,6 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   this->datum_height_ = datum.height();
   this->datum_width_ = datum.width();
   this->datum_size_ = datum.channels() * datum.height() * datum.width();
-  CHECK_GT(this->datum_height_, crop_size);
-  CHECK_GT(this->datum_width_, crop_size);
-  // check if we want to have mean
-  if (this->transform_param_.has_mean_file()) {
-    const string& mean_file =
-        this->transform_param_.mean_file();
-    LOG(INFO) << "Loading mean file from" << mean_file;
-    BlobProto blob_proto;
-    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
-    this->data_mean_.FromProto(blob_proto);
-    CHECK_EQ(this->data_mean_.num(), 1);
-    CHECK_EQ(this->data_mean_.channels(), this->datum_channels_);
-    CHECK_EQ(this->data_mean_.height(), this->datum_height_);
-    CHECK_EQ(this->data_mean_.width(), this->datum_width_);
-  } else {
-    // Simply initialize an all-empty mean.
-    this->data_mean_.Reshape(1, this->datum_channels_, this->datum_height_,
-                             this->datum_width_);
-  }
-  // Now, start the prefetch thread. Before calling prefetch, we make two
-  // cpu_data calls so that the prefetch thread does not accidentally make
-  // simultaneous cudaMalloc calls when the main thread is running. In some
-  // GPUs this seems to cause failures if we do not so.
-  this->prefetch_data_.mutable_cpu_data();
-  if (this->output_labels_) {
-    this->prefetch_label_.mutable_cpu_data();
-  }
-  this->data_mean_.cpu_data();
-  DLOG(INFO) << "Initializing prefetch";
-  this->CreatePrefetchThread();
-  DLOG(INFO) << "Prefetch initialized.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 4d4f62eb361..9ff1b8e7eb0 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -26,7 +26,6 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
 
   // datum scales
   const int lines_size = lines_.size();
-  const Dtype* mean = this->data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
     CHECK_GT(lines_size, lines_id_);
@@ -37,7 +36,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // Apply transformations (mirror, crop...) to the data
-    this->data_transformer_.Transform(item_id, datum, mean, top_data);
+    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
 
     top_label[item_id] = datum.label();
     // go to the next iter
@@ -101,7 +100,6 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // image
   const int crop_size = this->layer_param_.transform_param().crop_size();
   const int batch_size = this->layer_param_.image_data_param().batch_size();
-  const string& mean_file = this->layer_param_.transform_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
@@ -123,33 +121,6 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   this->datum_height_ = datum.height();
   this->datum_width_ = datum.width();
   this->datum_size_ = datum.channels() * datum.height() * datum.width();
-  CHECK_GT(this->datum_height_, crop_size);
-  CHECK_GT(this->datum_width_, crop_size);
-  // check if we want to have mean
-  if (this->layer_param_.transform_param().has_mean_file()) {
-    BlobProto blob_proto;
-    LOG(INFO) << "Loading mean file from" << mean_file;
-    ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
-    this->data_mean_.FromProto(blob_proto);
-    CHECK_EQ(this->data_mean_.num(), 1);
-    CHECK_EQ(this->data_mean_.channels(), this->datum_channels_);
-    CHECK_EQ(this->data_mean_.height(), this->datum_height_);
-    CHECK_EQ(this->data_mean_.width(), this->datum_width_);
-  } else {
-    // Simply initialize an all-empty mean.
-    this->data_mean_.Reshape(1, this->datum_channels_, this->datum_height_,
-                             this->datum_width_);
-  }
-  // Now, start the prefetch thread. Before calling prefetch, we make two
-  // cpu_data calls so that the prefetch thread does not accidentally make
-  // simultaneous cudaMalloc calls when the main thread is running. In some
-  // GPUs this seems to cause failures if we do not so.
-  this->prefetch_data_.mutable_cpu_data();
-  this->prefetch_label_.mutable_cpu_data();
-  this->data_mean_.cpu_data();
-  DLOG(INFO) << "Initializing prefetch";
-  this->CreatePrefetchThread();
-  DLOG(INFO) << "Prefetch initialized.";
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 1d48a49b841..693c4d51b5f 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -21,8 +21,13 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   (*top)[0]->Reshape(batch_size_, this->datum_channels_, this->datum_height_,
                      this->datum_width_);
   (*top)[1]->Reshape(batch_size_, 1, 1, 1);
+  added_data_.Reshape(batch_size_, this->datum_channels_, this->datum_height_,
+                      this->datum_width_);
+  added_label_.Reshape(batch_size_, 1, 1, 1);
   data_ = NULL;
   labels_ = NULL;
+  added_data_.cpu_data();
+  added_label_.cpu_data();
 }
 
 template <typename Dtype>
@@ -69,13 +74,13 @@ void MemoryDataLayer<Dtype>::AddImagesAndLabels(
   Datum datum;
   Dtype* top_data = added_data_.mutable_cpu_data();
   Dtype* top_label = added_label_.mutable_cpu_data();
-  const Dtype* mean = this->data_mean_.cpu_data();
   for (int batch_item_id = 0; batch_item_id < num_images; ++batch_item_id) {
     OpenCVImageToDatum(
         images[batch_item_id], labels[batch_item_id], this->datum_height_,
         this->datum_width_, &datum);
     // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(batch_item_id, datum, mean, top_data);
+    this->data_transformer_.Transform(batch_item_id, datum, this->mean_,
+                                      top_data);
     top_label[batch_item_id] = labels[batch_item_id];
   }
   // num_images == batch_size_
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index f7e8186a477..d8b693d3116 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -375,32 +375,6 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
-
-  // check if we want to have mean
-  if (this->layer_param_.window_data_param().has_mean_file()) {
-    const string& mean_file =
-        this->layer_param_.window_data_param().mean_file();
-    LOG(INFO) << "Loading mean file from" << mean_file;
-    BlobProto blob_proto;
-    ReadProtoFromBinaryFileOrDie(mean_file, &blob_proto);
-    this->data_mean_.FromProto(blob_proto);
-    CHECK_EQ(this->data_mean_.num(), 1);
-    CHECK_EQ(this->data_mean_.width(), this->data_mean_.height());
-    CHECK_EQ(this->data_mean_.channels(), channels);
-  } else {
-    // Simply initialize an all-empty mean.
-    this->data_mean_.Reshape(1, channels, crop_size, crop_size);
-  }
-  // Now, start the prefetch thread. Before calling prefetch, we make two
-  // cpu_data calls so that the prefetch thread does not accidentally make
-  // simultaneous cudaMalloc calls when the main thread is running. In some
-  // GPUs this seems to cause failures if we do not so.
-  this->prefetch_data_.mutable_cpu_data();
-  this->prefetch_label_.mutable_cpu_data();
-  this->data_mean_.cpu_data();
-  DLOG(INFO) << "Initializing prefetch";
-  this->CreatePrefetchThread();
-  DLOG(INFO) << "Prefetch initialized.";
 }
 
 template <typename Dtype>

From 09cfe1ca4b1410fb41b3969ca4e50c34bef9a8ba Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sun, 31 Aug 2014 00:16:31 +0800
Subject: [PATCH 0621/2053] Fix conflict between nvcc and boost for cmake

---
 src/caffe/CMakeLists.txt | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 139185b52dc..86c7c7eb4a3 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -86,6 +86,16 @@ if(NOT CPU_ONLY)
             -gencode arch=compute_35,code=sm_35
     )
     
+# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
+    # work-arounds
+if(Boost_VERSION EQUAL 105500)
+    # see https://svn.boost.org/trac/boost/ticket/9392
+    message(STATUS "Boost: Applying noinline work around")
+    # avoid warning for CMake >= 2.8.12
+    set(CUDA_NVCC_FLAGS
+      "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
+endif(Boost_VERSION EQUAL 105500)
+
     #    cuda sources
     file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
     file(GLOB_RECURSE TEST_CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cu)

From 74fa8797739440ea254a59d2bfe801a2bfe870f2 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sun, 31 Aug 2014 17:47:58 +0800
Subject: [PATCH 0622/2053] Add lint rule for caffe data layer setup

---
 scripts/cpp_lint.py | 41 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 61b8b0cd320..1b7c6c0536c 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -155,6 +155,7 @@
   'build/printf_format',
   'build/storage_class',
   'caffe/alt_fn',
+  'caffe/data_layer_setup',
   'caffe/random_fn',
   'legal/copyright',
   'readability/alt_tokens',
@@ -1591,6 +1592,45 @@ def CheckCaffeAlternatives(filename, clean_lines, linenum, error):
                 (' or '.join(disp_alts), function))
 
 
+def CheckCaffeDataLayerSetUp(filename, clean_lines, linenum, error):
+  """Except the base classes, Caffe DataLayer should define DataLayerSetUp
+     instead of LayerSetUp.
+     
+  The base DataLayers define common SetUp steps, the subclasses should
+  not override them.
+  
+  Args:
+    filename: The name of the current file.
+    clean_lines: A CleansedLines instance containing the file.
+    linenum: The number of the line to check.
+    error: The function to call with any errors found.
+  """
+  line = clean_lines.elided[linenum]
+  ix = line.find('DataLayer<Dtype>::LayerSetUp')
+  if ix >= 0 and (
+       line.find('void DataLayer<Dtype>::LayerSetUp') != -1 or
+       line.find('void ImageDataLayer<Dtype>::LayerSetUp') != -1 or
+       line.find('void MemoryDataLayer<Dtype>::LayerSetUp') != -1 or
+       line.find('void WindowDataLayer<Dtype>::LayerSetUp') != -1):
+      error(filename, linenum, 'caffe/data_layer_setup', 2,
+            'Except the base classes, Caffe DataLayer should define'
+            + ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+            + ' define common SetUp steps, the subclasses should'
+            + ' not override them.')
+  ix = line.find('DataLayer<Dtype>::DataLayerSetUp')
+  if ix >= 0 and (
+       line.find('void Base') == -1 and
+       line.find('void DataLayer<Dtype>::DataLayerSetUp') == -1 and
+       line.find('void ImageDataLayer<Dtype>::DataLayerSetUp') == -1 and
+       line.find('void MemoryDataLayer<Dtype>::DataLayerSetUp') == -1 and
+       line.find('void WindowDataLayer<Dtype>::DataLayerSetUp') == -1):
+      error(filename, linenum, 'caffe/data_layer_setup', 2,
+            'Except the base classes, Caffe DataLayer should define'
+            + ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+            + ' define common SetUp steps, the subclasses should'
+            + ' not override them.')
+
+
 c_random_function_list = (
     'rand(',
     'rand_r(',
@@ -4593,6 +4633,7 @@ def ProcessLine(filename, file_extension, clean_lines, line,
                                 nesting_state, error)
   CheckVlogArguments(filename, clean_lines, line, error)
   CheckCaffeAlternatives(filename, clean_lines, line, error)
+  CheckCaffeDataLayerSetUp(filename, clean_lines, line, error)
   CheckCaffeRandom(filename, clean_lines, line, error)
   CheckPosixThreading(filename, clean_lines, line, error)
   CheckInvalidIncrement(filename, clean_lines, line, error)

From 725e98d32d2fe5caf4c065cd1241d3622092b809 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 2 Sep 2014 11:50:08 +0800
Subject: [PATCH 0623/2053] Remove OpenCV stuffs from the memory data layer and
 io utils

---
 include/caffe/data_layers.hpp             |  8 ---
 src/caffe/layers/memory_data_layer.cpp    | 37 ------------
 src/caffe/test/test_memory_data_layer.cpp | 72 +----------------------
 src/caffe/util/io.cpp                     |  1 -
 4 files changed, 1 insertion(+), 117 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 8c1b3f90e27..0d435dda05b 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -1,8 +1,6 @@
 #ifndef CAFFE_DATA_LAYERS_HPP_
 #define CAFFE_DATA_LAYERS_HPP_
 
-#include <opencv2/opencv.hpp>
-
 #include <string>
 #include <utility>
 #include <vector>
@@ -257,12 +255,6 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
 
-  virtual void AddImages(const vector<cv::Mat>& images,
-                         const bool is_color_images = true);
-  virtual void AddImagesAndLabels(const vector<cv::Mat>& images,
-                                  const vector<int>& labels,
-                                  const bool is_color_images = true);
-
   int batch_size() { return batch_size_; }
 
  protected:
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 693c4d51b5f..4a778064cf0 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -51,43 +51,6 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   has_new_data_ = false;
 }
 
-template <typename Dtype>
-void MemoryDataLayer<Dtype>::AddImages(
-    const vector<cv::Mat>& images, const bool is_color_images) {
-  const vector<int> fake_labels(images.size(), 0);
-  AddImagesAndLabels(images, fake_labels, is_color_images);
-}
-
-template <typename Dtype>
-void MemoryDataLayer<Dtype>::AddImagesAndLabels(
-    const vector<cv::Mat>& images, const vector<int>& labels,
-    const bool is_color_images) {
-  CHECK(!has_new_data_) <<
-      "There are pending images that haven't been consumed";
-  size_t num_images = images.size();
-  CHECK_GT(num_images, 0) << "There is no image to add";
-  CHECK_EQ(num_images, batch_size_) <<
-      "The number of added images must be equal to the batch size";
-  CHECK_EQ(num_images, labels.size()) <<
-      "The number of images must be equal to the number of labels";
-
-  Datum datum;
-  Dtype* top_data = added_data_.mutable_cpu_data();
-  Dtype* top_label = added_label_.mutable_cpu_data();
-  for (int batch_item_id = 0; batch_item_id < num_images; ++batch_item_id) {
-    OpenCVImageToDatum(
-        images[batch_item_id], labels[batch_item_id], this->datum_height_,
-        this->datum_width_, &datum);
-    // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(batch_item_id, datum, this->mean_,
-                                      top_data);
-    top_label[batch_item_id] = labels[batch_item_id];
-  }
-  // num_images == batch_size_
-  Reset(top_data, top_label, batch_size_);
-  has_new_data_ = true;
-}
-
 INSTANTIATE_CLASS(MemoryDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 39b97d863ae..748a6a96230 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,5 +1,3 @@
-#include <opencv2/opencv.hpp>
-
 #include <vector>
 
 #include "caffe/data_layers.hpp"
@@ -18,8 +16,7 @@ class MemoryDataLayerTest : public MultiDeviceTest<TypeParam> {
     : data_(new Blob<Dtype>()),
       labels_(new Blob<Dtype>()),
       data_blob_(new Blob<Dtype>()),
-      label_blob_(new Blob<Dtype>()),
-      image_(cv::imread("examples/images/cat.jpg", CV_LOAD_IMAGE_COLOR)) {}
+      label_blob_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     batch_size_ = 8;
     batches_ = 12;
@@ -35,9 +32,6 @@ class MemoryDataLayerTest : public MultiDeviceTest<TypeParam> {
     labels_->Reshape(batches_ * batch_size_, 1, 1, 1);
     filler.Fill(this->data_);
     filler.Fill(this->labels_);
-    for (int i = 0; i < batch_size_; ++i) {
-      image_labels_.push_back(i);
-    }
   }
 
   virtual ~MemoryDataLayerTest() {
@@ -60,8 +54,6 @@ class MemoryDataLayerTest : public MultiDeviceTest<TypeParam> {
   Blob<Dtype>* const label_blob_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
-  const cv::Mat image_;
-  vector<int> image_labels_;
 };
 
 TYPED_TEST_CASE(MemoryDataLayerTest, TestDtypesAndDevices);
@@ -118,66 +110,4 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   }
 }
 
-TYPED_TEST(MemoryDataLayerTest, TestAddImagesAndLabels) {
-  typedef typename TypeParam::Dtype Dtype;
-
-  LayerParameter param;
-  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
-  memory_data_param->set_batch_size(this->batch_size_);
-  memory_data_param->set_channels(this->image_.channels());
-  memory_data_param->set_height(this->image_.rows);
-  memory_data_param->set_width(this->image_.cols);
-  MemoryDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  cv::Mat image = this->image_;
-  vector<cv::Mat> images(this->batch_size_, image);
-  layer.AddImagesAndLabels(images, this->image_labels_);
-  // Go through the data 5 times
-  for (int iter = 0; iter < 5; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    const Dtype* data = this->data_blob_->cpu_data();
-    for (int i = 0, index = 0; i < this->batch_size_; ++i) {
-      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
-      for (int c = 0; c < image.channels(); ++c) {
-        for (int h = 0; h < image.rows; ++h) {
-          for (int w = 0; w < image.cols; ++w) {
-            EXPECT_EQ(static_cast<uint8_t>(image.at<cv::Vec3b>(h, w)[c]),
-                      data[index++]);
-          }
-        }
-      }
-    }
-  }
-}
-
-TYPED_TEST(MemoryDataLayerTest, TestAddImagesAndLabelsResize) {
-  typedef typename TypeParam::Dtype Dtype;
-
-  const int resize_height = 256;
-  const int resize_width = 256;
-  LayerParameter param;
-  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
-  memory_data_param->set_batch_size(this->batch_size_);
-  memory_data_param->set_channels(this->image_.channels());
-  memory_data_param->set_height(resize_height);
-  memory_data_param->set_width(resize_width);
-  MemoryDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->data_blob_->num(), this->batch_size_);
-  EXPECT_EQ(this->data_blob_->channels(), this->image_.channels());
-  EXPECT_EQ(this->data_blob_->height(), resize_height);
-  EXPECT_EQ(this->data_blob_->width(), resize_width);
-  cv::Mat image = this->image_;
-  vector<cv::Mat> images(this->batch_size_, image);
-  layer.AddImagesAndLabels(images, this->image_labels_);
-  // Go through the data 5 times
-  for (int iter = 0; iter < 5; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    const Dtype* label_data = this->label_blob_->cpu_data();
-    for (int i = 0; i < this->batch_size_; ++i) {
-      EXPECT_EQ(i, label_data[i]);
-    }
-  }
-}
-
 }  // namespace caffe
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 7b6c67cf633..0dd36a33bbb 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,7 +2,6 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>

From ab1f9b5bf53c7eb76a4ee1eaf4b176e4dc1ea0dc Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 2 Sep 2014 13:44:59 +0800
Subject: [PATCH 0624/2053] Add leveldb header back to util/io.cpp

---
 src/caffe/util/io.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 0dd36a33bbb..7b6c67cf633 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,6 +2,7 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
+#include <leveldb/db.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>

From 25ce7f51dbf28b3a87e83409c66ba27d4170f0fd Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 2 Sep 2014 13:53:18 +0800
Subject: [PATCH 0625/2053] Place InternalThreadEntry lower in the
 {,Image,Window}DataLayer.cpp

---
 src/caffe/layers/data_layer.cpp        | 124 +++++------
 src/caffe/layers/image_data_layer.cpp  |  80 +++----
 src/caffe/layers/window_data_layer.cpp | 296 ++++++++++++-------------
 3 files changed, 250 insertions(+), 250 deletions(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 7a46a599069..d2071e2fa4f 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -14,68 +14,6 @@
 
 namespace caffe {
 
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void DataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
-  CHECK(this->prefetch_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
-  if (this->output_labels_) {
-    top_label = this->prefetch_label_.mutable_cpu_data();
-  }
-  const int batch_size = this->layer_param_.data_param().batch_size();
-
-  for (int item_id = 0; item_id < batch_size; ++item_id) {
-    // get a blob
-    switch (this->layer_param_.data_param().backend()) {
-    case DataParameter_DB_LEVELDB:
-      CHECK(iter_);
-      CHECK(iter_->Valid());
-      datum.ParseFromString(iter_->value().ToString());
-      break;
-    case DataParameter_DB_LMDB:
-      CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
-              &mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
-      datum.ParseFromArray(mdb_value_.mv_data,
-          mdb_value_.mv_size);
-      break;
-    default:
-      LOG(FATAL) << "Unknown database backend";
-    }
-
-    // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
-
-    if (this->output_labels_) {
-      top_label[item_id] = datum.label();
-    }
-
-    // go to the next iter
-    switch (this->layer_param_.data_param().backend()) {
-    case DataParameter_DB_LEVELDB:
-      iter_->Next();
-      if (!iter_->Valid()) {
-        // We have reached the end. Restart from the first.
-        DLOG(INFO) << "Restarting data prefetching from start.";
-        iter_->SeekToFirst();
-      }
-      break;
-    case DataParameter_DB_LMDB:
-      if (mdb_cursor_get(mdb_cursor_, &mdb_key_,
-              &mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
-        // We have reached the end. Restart from the first.
-        DLOG(INFO) << "Restarting data prefetching from start.";
-        CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
-                &mdb_value_, MDB_FIRST), MDB_SUCCESS);
-      }
-      break;
-    default:
-      LOG(FATAL) << "Unknown database backend";
-    }
-  }
-}
-
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
@@ -203,6 +141,68 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   this->datum_size_ = datum.channels() * datum.height() * datum.width();
 }
 
+// This function is used to create a thread that prefetches the data.
+template <typename Dtype>
+void DataLayer<Dtype>::InternalThreadEntry() {
+  Datum datum;
+  CHECK(this->prefetch_data_.count());
+  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
+  if (this->output_labels_) {
+    top_label = this->prefetch_label_.mutable_cpu_data();
+  }
+  const int batch_size = this->layer_param_.data_param().batch_size();
+
+  for (int item_id = 0; item_id < batch_size; ++item_id) {
+    // get a blob
+    switch (this->layer_param_.data_param().backend()) {
+    case DataParameter_DB_LEVELDB:
+      CHECK(iter_);
+      CHECK(iter_->Valid());
+      datum.ParseFromString(iter_->value().ToString());
+      break;
+    case DataParameter_DB_LMDB:
+      CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
+              &mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
+      datum.ParseFromArray(mdb_value_.mv_data,
+          mdb_value_.mv_size);
+      break;
+    default:
+      LOG(FATAL) << "Unknown database backend";
+    }
+
+    // Apply data transformations (mirror, scale, crop...)
+    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
+
+    if (this->output_labels_) {
+      top_label[item_id] = datum.label();
+    }
+
+    // go to the next iter
+    switch (this->layer_param_.data_param().backend()) {
+    case DataParameter_DB_LEVELDB:
+      iter_->Next();
+      if (!iter_->Valid()) {
+        // We have reached the end. Restart from the first.
+        DLOG(INFO) << "Restarting data prefetching from start.";
+        iter_->SeekToFirst();
+      }
+      break;
+    case DataParameter_DB_LMDB:
+      if (mdb_cursor_get(mdb_cursor_, &mdb_key_,
+              &mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
+        // We have reached the end. Restart from the first.
+        DLOG(INFO) << "Restarting data prefetching from start.";
+        CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
+                &mdb_value_, MDB_FIRST), MDB_SUCCESS);
+      }
+      break;
+    default:
+      LOG(FATAL) << "Unknown database backend";
+    }
+  }
+}
+
 INSTANTIATE_CLASS(DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 9ff1b8e7eb0..e4a575bec93 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -12,46 +12,6 @@
 
 namespace caffe {
 
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void ImageDataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
-  CHECK(this->prefetch_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
-  ImageDataParameter image_data_param = this->layer_param_.image_data_param();
-  const int batch_size = image_data_param.batch_size();
-  const int new_height = image_data_param.new_height();
-  const int new_width = image_data_param.new_width();
-
-  // datum scales
-  const int lines_size = lines_.size();
-  for (int item_id = 0; item_id < batch_size; ++item_id) {
-    // get a blob
-    CHECK_GT(lines_size, lines_id_);
-    if (!ReadImageToDatum(lines_[lines_id_].first,
-          lines_[lines_id_].second,
-          new_height, new_width, &datum)) {
-      continue;
-    }
-
-    // Apply transformations (mirror, crop...) to the data
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
-
-    top_label[item_id] = datum.label();
-    // go to the next iter
-    lines_id_++;
-    if (lines_id_ >= lines_size) {
-      // We have reached the end. Restart from the first.
-      DLOG(INFO) << "Restarting data prefetching from start.";
-      lines_id_ = 0;
-      if (this->layer_param_.image_data_param().shuffle()) {
-        ShuffleImages();
-      }
-    }
-  }
-}
-
 template <typename Dtype>
 ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
   this->JoinPrefetchThread();
@@ -130,6 +90,46 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
   shuffle(lines_.begin(), lines_.end(), prefetch_rng);
 }
 
+// This function is used to create a thread that prefetches the data.
+template <typename Dtype>
+void ImageDataLayer<Dtype>::InternalThreadEntry() {
+  Datum datum;
+  CHECK(this->prefetch_data_.count());
+  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+  ImageDataParameter image_data_param = this->layer_param_.image_data_param();
+  const int batch_size = image_data_param.batch_size();
+  const int new_height = image_data_param.new_height();
+  const int new_width = image_data_param.new_width();
+
+  // datum scales
+  const int lines_size = lines_.size();
+  for (int item_id = 0; item_id < batch_size; ++item_id) {
+    // get a blob
+    CHECK_GT(lines_size, lines_id_);
+    if (!ReadImageToDatum(lines_[lines_id_].first,
+          lines_[lines_id_].second,
+          new_height, new_width, &datum)) {
+      continue;
+    }
+
+    // Apply transformations (mirror, crop...) to the data
+    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
+
+    top_label[item_id] = datum.label();
+    // go to the next iter
+    lines_id_++;
+    if (lines_id_ >= lines_size) {
+      // We have reached the end. Restart from the first.
+      DLOG(INFO) << "Restarting data prefetching from start.";
+      lines_id_ = 0;
+      if (this->layer_param_.image_data_param().shuffle()) {
+        ShuffleImages();
+      }
+    }
+  }
+}
+
 INSTANTIATE_CLASS(ImageDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index d8b693d3116..4284c327681 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -23,6 +23,154 @@
 
 namespace caffe {
 
+template <typename Dtype>
+WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
+  this->JoinPrefetchThread();
+}
+
+template <typename Dtype>
+void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  // LayerSetUp runs through the window_file and creates two structures
+  // that hold windows: one for foreground (object) windows and one
+  // for background (non-object) windows. We use an overlap threshold
+  // to decide which is which.
+
+  // window_file format
+  // repeated:
+  //    # image_index
+  //    img_path (abs path)
+  //    channels
+  //    height
+  //    width
+  //    num_windows
+  //    class_index overlap x1 y1 x2 y2
+
+  LOG(INFO) << "Window data layer:" << std::endl
+      << "  foreground (object) overlap threshold: "
+      << this->layer_param_.window_data_param().fg_threshold() << std::endl
+      << "  background (non-object) overlap threshold: "
+      << this->layer_param_.window_data_param().bg_threshold() << std::endl
+      << "  foreground sampling fraction: "
+      << this->layer_param_.window_data_param().fg_fraction();
+
+  const bool prefetch_needs_rand =
+      this->layer_param_.window_data_param().mirror() ||
+      this->layer_param_.window_data_param().crop_size();
+  if (prefetch_needs_rand) {
+    const unsigned int prefetch_rng_seed = caffe_rng_rand();
+    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
+  } else {
+    prefetch_rng_.reset();
+  }
+
+  std::ifstream infile(this->layer_param_.window_data_param().source().c_str());
+  CHECK(infile.good()) << "Failed to open window file "
+      << this->layer_param_.window_data_param().source() << std::endl;
+
+  map<int, int> label_hist;
+  label_hist.insert(std::make_pair(0, 0));
+
+  string hashtag;
+  int image_index, channels;
+  if (!(infile >> hashtag >> image_index)) {
+    LOG(FATAL) << "Window file is empty";
+  }
+  do {
+    CHECK_EQ(hashtag, "#");
+    // read image path
+    string image_path;
+    infile >> image_path;
+    // read image dimensions
+    vector<int> image_size(3);
+    infile >> image_size[0] >> image_size[1] >> image_size[2];
+    channels = image_size[0];
+    image_database_.push_back(std::make_pair(image_path, image_size));
+
+    // read each box
+    int num_windows;
+    infile >> num_windows;
+    const float fg_threshold =
+        this->layer_param_.window_data_param().fg_threshold();
+    const float bg_threshold =
+        this->layer_param_.window_data_param().bg_threshold();
+    for (int i = 0; i < num_windows; ++i) {
+      int label, x1, y1, x2, y2;
+      float overlap;
+      infile >> label >> overlap >> x1 >> y1 >> x2 >> y2;
+
+      vector<float> window(WindowDataLayer::NUM);
+      window[WindowDataLayer::IMAGE_INDEX] = image_index;
+      window[WindowDataLayer::LABEL] = label;
+      window[WindowDataLayer::OVERLAP] = overlap;
+      window[WindowDataLayer::X1] = x1;
+      window[WindowDataLayer::Y1] = y1;
+      window[WindowDataLayer::X2] = x2;
+      window[WindowDataLayer::Y2] = y2;
+
+      // add window to foreground list or background list
+      if (overlap >= fg_threshold) {
+        int label = window[WindowDataLayer::LABEL];
+        CHECK_GT(label, 0);
+        fg_windows_.push_back(window);
+        label_hist.insert(std::make_pair(label, 0));
+        label_hist[label]++;
+      } else if (overlap < bg_threshold) {
+        // background window, force label and overlap to 0
+        window[WindowDataLayer::LABEL] = 0;
+        window[WindowDataLayer::OVERLAP] = 0;
+        bg_windows_.push_back(window);
+        label_hist[0]++;
+      }
+    }
+
+    if (image_index % 100 == 0) {
+      LOG(INFO) << "num: " << image_index << " "
+          << image_path << " "
+          << image_size[0] << " "
+          << image_size[1] << " "
+          << image_size[2] << " "
+          << "windows to process: " << num_windows;
+    }
+  } while (infile >> hashtag >> image_index);
+
+  LOG(INFO) << "Number of images: " << image_index+1;
+
+  for (map<int, int>::iterator it = label_hist.begin();
+      it != label_hist.end(); ++it) {
+    LOG(INFO) << "class " << it->first << " has " << label_hist[it->first]
+              << " samples";
+  }
+
+  LOG(INFO) << "Amount of context padding: "
+      << this->layer_param_.window_data_param().context_pad();
+
+  LOG(INFO) << "Crop mode: "
+      << this->layer_param_.window_data_param().crop_mode();
+
+  // image
+  int crop_size = this->layer_param_.window_data_param().crop_size();
+  CHECK_GT(crop_size, 0);
+  const int batch_size = this->layer_param_.window_data_param().batch_size();
+  (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
+  this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
+
+  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
+      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
+      << (*top)[0]->width();
+  // label
+  (*top)[1]->Reshape(batch_size, 1, 1, 1);
+  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+}
+
+template <typename Dtype>
+unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
+  CHECK(prefetch_rng_);
+  caffe::rng_t* prefetch_rng =
+      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
+  return (*prefetch_rng)();
+}
+
 // Thread fetching the data
 template <typename Dtype>
 void WindowDataLayer<Dtype>::InternalThreadEntry() {
@@ -237,154 +385,6 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   }
 }
 
-template <typename Dtype>
-WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
-  this->JoinPrefetchThread();
-}
-
-template <typename Dtype>
-void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // LayerSetUp runs through the window_file and creates two structures
-  // that hold windows: one for foreground (object) windows and one
-  // for background (non-object) windows. We use an overlap threshold
-  // to decide which is which.
-
-  // window_file format
-  // repeated:
-  //    # image_index
-  //    img_path (abs path)
-  //    channels
-  //    height
-  //    width
-  //    num_windows
-  //    class_index overlap x1 y1 x2 y2
-
-  LOG(INFO) << "Window data layer:" << std::endl
-      << "  foreground (object) overlap threshold: "
-      << this->layer_param_.window_data_param().fg_threshold() << std::endl
-      << "  background (non-object) overlap threshold: "
-      << this->layer_param_.window_data_param().bg_threshold() << std::endl
-      << "  foreground sampling fraction: "
-      << this->layer_param_.window_data_param().fg_fraction();
-
-  const bool prefetch_needs_rand =
-      this->layer_param_.window_data_param().mirror() ||
-      this->layer_param_.window_data_param().crop_size();
-  if (prefetch_needs_rand) {
-    const unsigned int prefetch_rng_seed = caffe_rng_rand();
-    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
-  } else {
-    prefetch_rng_.reset();
-  }
-
-  std::ifstream infile(this->layer_param_.window_data_param().source().c_str());
-  CHECK(infile.good()) << "Failed to open window file "
-      << this->layer_param_.window_data_param().source() << std::endl;
-
-  map<int, int> label_hist;
-  label_hist.insert(std::make_pair(0, 0));
-
-  string hashtag;
-  int image_index, channels;
-  if (!(infile >> hashtag >> image_index)) {
-    LOG(FATAL) << "Window file is empty";
-  }
-  do {
-    CHECK_EQ(hashtag, "#");
-    // read image path
-    string image_path;
-    infile >> image_path;
-    // read image dimensions
-    vector<int> image_size(3);
-    infile >> image_size[0] >> image_size[1] >> image_size[2];
-    channels = image_size[0];
-    image_database_.push_back(std::make_pair(image_path, image_size));
-
-    // read each box
-    int num_windows;
-    infile >> num_windows;
-    const float fg_threshold =
-        this->layer_param_.window_data_param().fg_threshold();
-    const float bg_threshold =
-        this->layer_param_.window_data_param().bg_threshold();
-    for (int i = 0; i < num_windows; ++i) {
-      int label, x1, y1, x2, y2;
-      float overlap;
-      infile >> label >> overlap >> x1 >> y1 >> x2 >> y2;
-
-      vector<float> window(WindowDataLayer::NUM);
-      window[WindowDataLayer::IMAGE_INDEX] = image_index;
-      window[WindowDataLayer::LABEL] = label;
-      window[WindowDataLayer::OVERLAP] = overlap;
-      window[WindowDataLayer::X1] = x1;
-      window[WindowDataLayer::Y1] = y1;
-      window[WindowDataLayer::X2] = x2;
-      window[WindowDataLayer::Y2] = y2;
-
-      // add window to foreground list or background list
-      if (overlap >= fg_threshold) {
-        int label = window[WindowDataLayer::LABEL];
-        CHECK_GT(label, 0);
-        fg_windows_.push_back(window);
-        label_hist.insert(std::make_pair(label, 0));
-        label_hist[label]++;
-      } else if (overlap < bg_threshold) {
-        // background window, force label and overlap to 0
-        window[WindowDataLayer::LABEL] = 0;
-        window[WindowDataLayer::OVERLAP] = 0;
-        bg_windows_.push_back(window);
-        label_hist[0]++;
-      }
-    }
-
-    if (image_index % 100 == 0) {
-      LOG(INFO) << "num: " << image_index << " "
-          << image_path << " "
-          << image_size[0] << " "
-          << image_size[1] << " "
-          << image_size[2] << " "
-          << "windows to process: " << num_windows;
-    }
-  } while (infile >> hashtag >> image_index);
-
-  LOG(INFO) << "Number of images: " << image_index+1;
-
-  for (map<int, int>::iterator it = label_hist.begin();
-      it != label_hist.end(); ++it) {
-    LOG(INFO) << "class " << it->first << " has " << label_hist[it->first]
-              << " samples";
-  }
-
-  LOG(INFO) << "Amount of context padding: "
-      << this->layer_param_.window_data_param().context_pad();
-
-  LOG(INFO) << "Crop mode: "
-      << this->layer_param_.window_data_param().crop_mode();
-
-  // image
-  int crop_size = this->layer_param_.window_data_param().crop_size();
-  CHECK_GT(crop_size, 0);
-  const int batch_size = this->layer_param_.window_data_param().batch_size();
-  (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
-
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
-  // label
-  (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
-}
-
-template <typename Dtype>
-unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
-  CHECK(prefetch_rng_);
-  caffe::rng_t* prefetch_rng =
-      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
-  return (*prefetch_rng)();
-}
-
 INSTANTIATE_CLASS(WindowDataLayer);
 
 }  // namespace caffe

From 4761072392f923203dde85dcd4cc69998494c98c Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Tue, 2 Sep 2014 14:43:58 +0800
Subject: [PATCH 0626/2053] Add and transform Datum vector in the
 MemeoryDataLayer

---
 include/caffe/data_layers.hpp             |  2 +
 src/caffe/layers/memory_data_layer.cpp    | 23 ++++++++++
 src/caffe/test/test_memory_data_layer.cpp | 54 +++++++++++++++++++++++
 3 files changed, 79 insertions(+)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 0d435dda05b..e6a1410007f 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -251,6 +251,8 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
+  virtual void AddDatumVector(const vector<Datum>& datum_vector);
+
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 4a778064cf0..03b3e37a626 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -30,6 +30,29 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   added_label_.cpu_data();
 }
 
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
+  CHECK(!has_new_data_) <<
+      "Can't add Datum when earlier ones haven't been consumed"
+      << " by the upper layers";
+  size_t num = datum_vector.size();
+  CHECK_GT(num, 0) << "There is no datum to add";
+  CHECK_EQ(num, batch_size_) <<
+      "The number of added datum must be equal to the batch size";
+
+  Dtype* top_data = added_data_.mutable_cpu_data();
+  Dtype* top_label = added_label_.mutable_cpu_data();
+  for (int batch_item_id = 0; batch_item_id < num; ++batch_item_id) {
+    // Apply data transformations (mirror, scale, crop...)
+    this->data_transformer_.Transform(
+        batch_item_id, datum_vector[batch_item_id], this->mean_, top_data);
+    top_label[batch_item_id] = datum_vector[batch_item_id].label();
+  }
+  // num_images == batch_size_
+  Reset(top_data, top_label, batch_size_);
+  has_new_data_ = true;
+}
+
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   CHECK(data);
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 748a6a96230..3dc00345743 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,3 +1,4 @@
+#include <string>
 #include <vector>
 
 #include "caffe/data_layers.hpp"
@@ -110,4 +111,57 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   }
 }
 
+TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->channels_);
+  memory_data_param->set_height(this->height_);
+  memory_data_param->set_width(this->width_);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+
+  vector<Datum> datum_vector(this->batch_size_);
+  const size_t count = this->channels_ * this->height_ * this->width_;
+  size_t pixel_index = 0;
+  for (int i = 0; i < this->batch_size_; ++i) {
+    LOG(ERROR) << "i " << i;
+    datum_vector[i].set_channels(this->channels_);
+    datum_vector[i].set_height(this->height_);
+    datum_vector[i].set_width(this->width_);
+    datum_vector[i].set_label(i);
+    vector<char> pixels(count);
+    for (int j = 0; j < count; ++j) {
+      pixels[j] = pixel_index++ % 256;
+    }
+    datum_vector[i].set_data(&(pixels[0]), count);
+  }
+
+  layer.AddDatumVector(datum_vector);
+
+  int data_index;
+  // Go through the data 5 times
+  for (int iter = 0; iter < 5; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    const Dtype* data = this->data_blob_->cpu_data();
+    size_t index = 0;
+    for (int i = 0; i < this->batch_size_; ++i) {
+      const string& data_string = datum_vector[i].data();
+      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
+      for (int c = 0; c < this->channels_; ++c) {
+        for (int h = 0; h < this->height_; ++h) {
+          for (int w = 0; w < this->width_; ++w) {
+            data_index = (c * this->height_ + h) * this->width_ + w;
+            EXPECT_EQ(static_cast<Dtype>(
+                static_cast<uint8_t>(data_string[data_index])),
+                      data[index++]);
+          }
+        }
+      }
+    }
+  }
+}
+
 }  // namespace caffe

From 858ad41f0272f7acff354e81e1e84bec34374b1c Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Wed, 3 Sep 2014 14:48:02 +0800
Subject: [PATCH 0627/2053] Correct the datum size checking conditions of the
 data layers

---
 src/caffe/data_transformer.cpp         |  2 --
 src/caffe/layers/base_data_layer.cpp   | 14 ++++++++------
 src/caffe/layers/memory_data_layer.cpp |  4 ++--
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index bdde028435b..7150fd99c18 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -21,8 +21,6 @@ void DataTransformer<Dtype>::Transform(const int batch_item_id,
   const bool mirror = param_.mirror();
   const Dtype scale = param_.scale();
 
-
-
   if (mirror && crop_size == 0) {
     LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
                << "set at the same time.";
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index c31081c3540..058e361ac59 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -26,8 +26,10 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(datum_channels_, 0);
   CHECK_GT(datum_height_, 0);
   CHECK_GT(datum_width_, 0);
-  CHECK_GT(datum_height_, transform_param_.crop_size());
-  CHECK_GT(datum_width_, transform_param_.crop_size());
+  if (transform_param_.crop_size() > 0) {
+    CHECK_GE(datum_height_, transform_param_.crop_size());
+    CHECK_GE(datum_width_, transform_param_.crop_size());
+  }
   // check if we want to have mean
   if (transform_param_.has_mean_file()) {
     const string& mean_file = transform_param_.mean_file();
@@ -35,10 +37,10 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
-    CHECK_EQ(data_mean_.num(), 1);
-    CHECK_EQ(data_mean_.channels(), datum_channels_);
-    CHECK_EQ(data_mean_.height(), datum_height_);
-    CHECK_EQ(data_mean_.width(), datum_width_);
+    CHECK_GE(data_mean_.num(), 1);
+    CHECK_GE(data_mean_.channels(), datum_channels_);
+    CHECK_GE(data_mean_.height(), datum_height_);
+    CHECK_GE(data_mean_.width(), datum_width_);
   } else {
     // Simply initialize an all-empty mean.
     data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 03b3e37a626..ab631a884fb 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -37,8 +37,8 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
       << " by the upper layers";
   size_t num = datum_vector.size();
   CHECK_GT(num, 0) << "There is no datum to add";
-  CHECK_EQ(num, batch_size_) <<
-      "The number of added datum must be equal to the batch size";
+  CHECK_LE(num, batch_size_) <<
+      "The number of added datum must be no greater than the batch size";
 
   Dtype* top_data = added_data_.mutable_cpu_data();
   Dtype* top_label = added_label_.mutable_cpu_data();

From a08f111ca7d1a4d0d92a009ce59cbcd6eef83394 Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Wed, 3 Sep 2014 15:13:26 +0800
Subject: [PATCH 0628/2053] Initialize the transformer rng in the base data
 layer

---
 src/caffe/layers/base_data_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 058e361ac59..9b1d55831a7 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -46,6 +46,7 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
   }
   mean_ = data_mean_.cpu_data();
+  data_transformer_.InitRand();
 }
 
 template <typename Dtype>

From 5d8c93c2015d252278e0653adca987fd515a723f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 23 Aug 2014 18:19:10 -0700
Subject: [PATCH 0629/2053] [docs] skeleton documentation subjects

---
 docs/tutorial/convolution.md      | 4 ++++
 docs/tutorial/data.md             | 4 ++++
 docs/tutorial/forward_backward.md | 4 ++++
 docs/tutorial/layers.md           | 4 ++++
 docs/tutorial/loss.md             | 4 ++++
 docs/tutorial/net_layer_blob.md   | 4 ++++
 docs/tutorial/solver.md           | 4 ++++
 7 files changed, 28 insertions(+)
 create mode 100644 docs/tutorial/convolution.md
 create mode 100644 docs/tutorial/data.md
 create mode 100644 docs/tutorial/forward_backward.md
 create mode 100644 docs/tutorial/layers.md
 create mode 100644 docs/tutorial/loss.md
 create mode 100644 docs/tutorial/net_layer_blob.md
 create mode 100644 docs/tutorial/solver.md

diff --git a/docs/tutorial/convolution.md b/docs/tutorial/convolution.md
new file mode 100644
index 00000000000..a8bd0e656ec
--- /dev/null
+++ b/docs/tutorial/convolution.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# Caffeinated Convolution
diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
new file mode 100644
index 00000000000..de2d5526dcb
--- /dev/null
+++ b/docs/tutorial/data.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# Data: Ins and Outs
diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
new file mode 100644
index 00000000000..52376e6aa82
--- /dev/null
+++ b/docs/tutorial/forward_backward.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# The Forward / Backward Passes
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
new file mode 100644
index 00000000000..de2d5526dcb
--- /dev/null
+++ b/docs/tutorial/layers.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# Data: Ins and Outs
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
new file mode 100644
index 00000000000..59c626dc318
--- /dev/null
+++ b/docs/tutorial/loss.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# Loss
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
new file mode 100644
index 00000000000..0d8cc4b4d72
--- /dev/null
+++ b/docs/tutorial/net_layer_blob.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# Nets, Layers, and Blobs: anatomy of a Caffe model
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
new file mode 100644
index 00000000000..d6a95b42d20
--- /dev/null
+++ b/docs/tutorial/solver.md
@@ -0,0 +1,4 @@
+---
+layout: default
+---
+# Solving

From b256a7601a7789817cf1342e179f436be4698d63 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 23 Aug 2014 19:22:09 -0700
Subject: [PATCH 0630/2053] [docs] draft tutorial subjects

---
 docs/tutorial/fig/.gitignore           |   0
 docs/tutorial/fig/backward.jpg         | Bin 0 -> 105017 bytes
 docs/tutorial/fig/forward.jpg          | Bin 0 -> 71957 bytes
 docs/tutorial/fig/forward_backward.png | Bin 0 -> 57267 bytes
 docs/tutorial/fig/layer.jpg            | Bin 0 -> 54757 bytes
 docs/tutorial/fig/logreg.jpg           | Bin 0 -> 42966 bytes
 docs/tutorial/forward_backward.md      |  35 ++++-
 docs/tutorial/index.md                 |  44 ++++++
 docs/tutorial/interfaces.md            |  68 ++++++++++
 docs/tutorial/layers.md                | 148 ++++++++++++++++++++-
 docs/tutorial/loss.md                  |  53 ++++++++
 docs/tutorial/net_layer_blob.md        | 133 +++++++++++++++++++
 docs/tutorial/solver.md                | 177 ++++++++++++++++++++++++-
 13 files changed, 655 insertions(+), 3 deletions(-)
 create mode 100644 docs/tutorial/fig/.gitignore
 create mode 100644 docs/tutorial/fig/backward.jpg
 create mode 100644 docs/tutorial/fig/forward.jpg
 create mode 100644 docs/tutorial/fig/forward_backward.png
 create mode 100644 docs/tutorial/fig/layer.jpg
 create mode 100644 docs/tutorial/fig/logreg.jpg
 create mode 100644 docs/tutorial/index.md
 create mode 100644 docs/tutorial/interfaces.md

diff --git a/docs/tutorial/fig/.gitignore b/docs/tutorial/fig/.gitignore
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/docs/tutorial/fig/backward.jpg b/docs/tutorial/fig/backward.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..906b080c975881dbb5bd598b3f0824c7a63014aa
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diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index 52376e6aa82..8e740262817 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -1,4 +1,37 @@
 ---
 layout: default
 ---
-# The Forward / Backward Passes
+# Forward and Backward
+
+The forward and backward passes are the essential computations of a [Net](net_layer_blob.html).
+
+<img src="fig/forward_backward.png" alt="Forward and Backward" width="480">
+
+Let's consider a simple logistic regression classifier.
+
+The **forward** pass computes the output given the input for inference.
+In forward Caffe composes the computation of each layer to compute the "function" represented by the model.
+This pass goes from bottom to top.
+
+<img src="fig/forward.jpg" alt="Forward pass" width="320">
+
+The data $x$ is passed through an inner product layer for $g(x)$ then through a softmax for $h(g(x))$ and softmax loss to give $f_W(x)$.
+
+The **backward** pass computes the gradient given the loss for learning.
+In backward Caffe reverse-composes the gradient of each layer to compute the gradient of the whole model by automatic differentiation.
+This is back-propagation.
+This pass goes from top to bottom.
+
+<img src="fig/backward.jpg" alt="Backward pass" width="320">
+
+The backward pass begins with the loss and computes the gradient with respect to the output $\frac{\partial f_W}{\partial h}$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $\frac{\partial f_W}{\partial W_{\text{ip}}}$ during the backward step.
+
+These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
+
+- The `Net::Forward()` and `Net::Backward()` methods carry out the respective passes while `Layer::Forward()` and `Layer::Backward()` compute each step.
+- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
+
+The [Solver](solver.html) optimizes a model by first calling forward to yield the output and loss, then calling backward to generate the gradient of the model, and then incorporating the gradient into a weight update that attempts to minimize the loss. Division of labor between the Solver, Net, and Layer keep Caffe modular and open to development.
+
+For the details of the forward and backward steps of Caffe's layer types, refer to the [layer catalogue](layers.html).
+
diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
new file mode 100644
index 00000000000..e9f593e48d0
--- /dev/null
+++ b/docs/tutorial/index.md
@@ -0,0 +1,44 @@
+---
+layout: default
+---
+# Caffe Tutorial
+
+Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage.
+This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here.
+While explanations will be given where possible, a background in machine learning and neural networks is helpful.
+
+## Philosophy
+
+In one sip, Caffe is brewed for
+
+- Expression: models and optimizations are defined as plaintext schemas instead of code.
+- Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
+- Modularity: new tasks and settings require flexibility and extension.
+- Openness: scientific and applied progress call for common code, reference models, and reproducibility.
+- Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.
+
+and these principles direct the project.
+
+## Tour
+
+- [Nets, Layers, and Blobs](net_layer_blob.html): the anatomy of a Caffe model.
+- [Forward / Backward](forward_backward.html): the essential computations of layered compositional models.
+- [Loss](loss.html): the task to be learned is defined by the loss.
+- [Solver Optimization](solver.html): the solver coordinates model optimization.
+- [Layer Catalogue](layers.html): the layer is the fundamental unit of modeling and computation -- Caffe's catalogue includes layers for state-of-the-art models.
+- [Interfaces](interfaces.html): command line, Python, and MATLAB Caffe.
+
+## Deeper Learning
+
+There are helpful references freely online for deep learning that complement our hands-on tutorial.
+These cover introductory and advanced material, background and history, and the latest advances.
+
+A broad introduction is given in the free online draft of [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) by Michael Nielsen. In particular the chapters on using neural nets and how backpropagation works are helpful if you are new to the subject.
+
+These recent academic tutorials explain deep learning for researchers in machine learning and vision:
+
+- [Deep Learning Tutorial](http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf) by Yann LeCun (NYU, Facebook) and Marc'Aurelio Ranzato (Facebook). ICML 2013 tutorial.
+- [Large-Scale Visual Recognition: Deep Learning Tutorial](https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxsc3ZydHV0b3JpYWxjdnByMTR8Z3g6Njg5MmZkZTM1MDhhZWNmZA) by Marc'Aurelio Ranzato (Facebook). CPVR 2014 tutorial.
+- [LISA Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by the LISA Lab directed by Yoshua Bengio (U. Montréal).
+
+For an exposition of neural networks in circuits and code, check out [Understanding Neural Networks from a Programmer's Perspective](http://karpathy.github.io/neuralnets/) by Andrej Karpathy (Stanford).
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
new file mode 100644
index 00000000000..33323410a16
--- /dev/null
+++ b/docs/tutorial/interfaces.md
@@ -0,0 +1,68 @@
+---
+layout: default
+---
+# Interfaces
+
+Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping. While Caffe is a C++ library at heart and it exposes a modular interface for development, not every occasion calls for custom compilation. The cmdcaffe, pycaffe, and matcaffe interfaces are here for you.
+
+## Command Line
+
+The command line interface -- cmdcaffe -- is the `caffe` tool for model training, scoring, and diagnostics. Run `caffe` without any arguments for help. This tool and others are found in caffe/build/tools. (The following example calls require completing the LeNet / MNIST example first.)
+
+**Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks. All training requires a solver configuration through the `-solver solver.prototxt` argument. Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
+
+    # train LeNet
+    caffe train -solver examples/mnist/lenet_solver.prototxt
+    # train on GPU 2
+    caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 2
+    # resume training from the half-way point snapshot
+    caffe train -solver examples/mnist/lenet_solver.prototxt -snapshot examples/mnist/lenet_iter_5000.solverstate
+
+For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but the training call alone is
+
+    # fine-tune CaffeNet model weights for style recognition
+    caffe train -solver examples/finetuning_on_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
+
+**Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
+
+    #
+    # score the learned LeNet model on the validation set as defined in the model architeture lenet_train_test.prototxt
+    caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000 -gpu 0 -iterations 100
+
+**Benchmarking**: `caffe time` benchmarks model execution layer-by-layer through timing and synchronization. This is useful to check system performance and measure relative execution times for models.
+
+    # (These example calls require you complete the LeNet / MNIST example first.)
+    # time LeNet training on CPU for 10 iterations
+    caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10
+    # time a model architecture with the given weights on the first GPU for 10 iterations
+    # time LeNet training on GPU for the default 50 iterations
+    caffe time -model examples/mnist/lenet_train_test.prototxt -gpu 0
+
+**Diagnostics**: `caffe device_query` reports GPU details for reference and checking device ordinals for running on a given device in multi-GPU machines.
+
+    # query the first device
+    caffe device_query -gpu 0
+
+## Python
+
+The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.
+
+- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classsifier` and `caffe.Detector` provide convenience interfaces for common tasks.
+- `caffe.SGDSolver` exposes the solving interface.
+- `caffe.io` handles input / output with preprocessing and protocol buffers.
+- `caffe.draw` visualizes network architectures.
+- Caffe blobs are exposed as numpy ndarrays for ease-of-use and efficiency.
+
+Tutorial IPython notebooks are found in caffe/examples: do `ipython notebook caffe/examples` to try them. For developer reference docstrings can be found throughout the code.
+
+Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be installed in your PYTHONPATH for `import caffe`.
+
+## MATLAB
+
+The MATLAB interface -- matcaffe -- is the `caffe` mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix.
+
+A MATLAB demo is in caffe/matlab/caffe/matcaffe_demo.m
+
+Note that MATLAB matrices and memory are in column-major layout counter to Caffe's row-major layout! Double-check your work accordingly.
+
+Compile matcaffe by `make matcaffe`.
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index de2d5526dcb..74b2675755e 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -1,4 +1,150 @@
 ---
 layout: default
 ---
-# Data: Ins and Outs
+# Layers
+
+To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
+
+Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The latest definitions are in the [dev caffe.proto](https://github.com/BVLC/caffe/blob/dev/src/caffe/proto/caffe.proto).
+
+TODO complete list of layers linking to headings
+
+### Vision Layers
+
+#### Convolution
+
+`CONVOLUTION`
+
+#### Pooling**
+
+`POOLING`
+
+#### Local Response Normalization
+
+`LRN`
+
+#### im2col
+
+`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about.
+
+### Loss Layers
+
+Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.
+
+#### Softmax
+
+`SOFTMAX_LOSS`
+
+#### Sum-of-Squares / Euclidean
+
+`EUCLIDEAN_LOSS`
+
+#### Hinge / Margin
+
+`HINGE_LOSS`
+
+#### Sigmoid Cross-Entropy
+
+`SIGMOID_CROSS_ENTROPY_LOSS`
+
+#### Infogain
+
+`INFOGAIN_LOSS`
+
+#### Accuracy and Top-k
+
+`ACCURACY` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+
+### Activation / Neuron Layers
+
+#### ReLU / Rectified-Linear and Leaky ReLU
+
+`RELU`
+
+#### Sigmoid
+
+`SIGMOID`
+
+#### TanH / Hyperbolic Tangent
+
+`TANH`
+
+#### Absolute Value
+
+`ABSVAL`
+
+#### Power
+
+`POWER`
+
+#### BNLL
+
+`BNLL`
+
+### Data Layers
+
+#### Database
+
+`DATA`
+
+#### In-Memory
+
+`MEMORY_DATA`
+
+#### HDF5 Input
+
+`HDF5_DATA`
+
+#### HDF5 Output
+
+`HDF5_OUTPUT`
+
+#### Images
+
+`IMAGE_DATA`
+
+#### Windows
+
+`WINDOW_DATA`
+
+#### Dummy
+
+`DUMMY_DATA` is for development and debugging. See `DummyDataParameter`.
+
+### Common Layers
+
+#### Inner Product
+
+`INNER_PRODUCT`
+
+#### Splitting
+
+`SPLIT`
+
+#### Flattening
+
+`FLATTEN`
+
+#### Concatenation
+
+`CONCAT`
+
+#### Slicing
+
+`SLICE`
+
+#### Elementwise Operations
+
+`ELTWISE`
+
+#### Argmax
+
+`ARGMAX`
+
+#### Softmax
+
+`SOFTMAX`
+
+#### Mean-Variance Normalization
+
+`MVN`
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index 59c626dc318..b470eaf943e 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -2,3 +2,56 @@
 layout: default
 ---
 # Loss
+
+In Caffe, as in most of machine learning, learning is driven by a **loss** function (also known as an **error**, **cost**, or **objective** function).
+A loss function specifies the goal of learning by mapping parameter settings (i.e., the current network weights) to a scalar value specifying the  "badness" of these parameter settings.
+Hence, the goal of learning is to find a setting of the weights that *minimizes* the loss function.
+
+The loss in Caffe is computed by the Forward pass of the network.
+Each layer takes a set of input (`bottom`) blobs and produces a set of output (`top`) blobs.
+Some of these layers' outputs may be used in the loss function.
+A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example:
+
+```
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "pred"
+  bottom: "label"
+  top: "loss"
+}
+```
+
+In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $1 \times 1 \times 1 \times 1$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
+
+### Loss weights
+
+For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SOFTMAX_LOSS` layer and reconstructs it using a `EUCLIDEAN_LOSS` layer), *loss weights* can be used to specify their relative importance.
+
+By convention, Caffe layer types with the suffix `_LOSS` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
+However, any layer can be used as a loss by adding a field `loss_weight: <float>` to a layer definition for each `top` blob produced by the layer.
+Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
+So, the above `SOFTMAX_LOSS` layer could be equivalently written as:
+
+```
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "pred"
+  bottom: "label"
+  top: "loss"
+  loss_weight: 1
+}
+```
+
+However, *any* layer able to backpropagate may be given a non-zero `loss_weight`, allowing one to, for example, regularize the activations produced by some intermediate layer(s) of the network if desired.
+For non-singleton outputs with an associated non-zero loss, the loss is computed simply by summing over all entries of the blob.
+
+The final loss in Caffe, then, is computed by summing the total weighted loss over the network, as in the following pseudo-code:
+
+```
+loss := 0
+for layer in layers:
+  for top, loss_weight in layer.tops, layer.loss_weights:
+    loss += loss_weight * sum(top)
+```
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 0d8cc4b4d72..21de96c8035 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -2,3 +2,136 @@
 layout: default
 ---
 # Nets, Layers, and Blobs: anatomy of a Caffe model
+
+Deep networks are compositional models that are naturally represented as a collection of inter-connected layers. Caffe defines a net layer-by-layer in its own model schema. The network defines the entire model bottom-to-top from input data to loss. As data and derivatives flow through the network in the [forward and backward passes](forward_backward.html) Caffe stores, communicates, and manipulates the information as *blobs*: the blob is the standard array and unified memory interface for the framework.
+
+[Solving](solver.html) is configured separately to decouple modeling and optimization.
+
+The layer comes first as the foundation of both model and computation. The net follows as the collection and connection of layers. The details of blob describe how information is stored and communicated in and across layers and nets.
+
+## Layer computation and connections
+
+The layer is the essence of a model and the fundamental unit of computation. Layers convolve filters, pool, take inner products, apply nonlinearities like rectified-linear and sigmoid and other elementwise transformations, normalize, load data, and compute losses like softmax and hinge. [See the layer catalogue](layers.html) for all operations. All the types needed for state-of-the-art deep learning tasks are there.
+
+<img src="fig/layer.jpg" alt="A layer with bottom and top blob." width="256">
+
+A layer takes input through *bottom* connections and makes output through *top* connections.
+
+Each layer type defines three critical computations: *setup*, *forward*, and *backward*.
+
+- Setup: initialize the layer and its connections once at model initialization.
+- Forward: given input from bottom compute the output and send to the top.
+- Backward: given the gradient w.r.t. the top output compute the gradient w.r.t. to the input and send to the bottom. A layer with parameters computes the gradient w.r.t. to its parameters and stores it internally.
+
+Layers have two key responsibilities for the operation of the network as a whole: a *forward pass* that takes the inputs and produces the outputs, and a *backward pass* that takes the gradient with respect to the output, and computes the gradients with respect to the parameters and to the inputs, which are in turn back-propagated to earlier layers. These passes are simply the composition of each layer's forward and backward.
+
+Developing custom layers requires minimal effort by the compositionality of the network and modularity of the code. Define the setup, forward, and backward for the layer and it is ready for inclusion in a net.
+
+## Net definition and operation
+
+The net jointly defines a function and its gradient by composition and auto-differentiation. The composition of every layer's output computes the function to do a given task, and the composition of every layer's backward computes the gradient from the loss to learn the task. Caffe models are end-to-end machine learning engines.
+
+The net is a set of layers connected in a computation graph -- a DAG / directed acyclic graph to be exact. Caffe does all the bookkeeping for any DAG of layers to ensure correctness of the forward and backward passes. A typical net begins with a data layer that loads from disk and ends with a loss layer that computes the objective for a task such as classification or reconstruction.
+
+The net is defined as a set of layers and their connections in a plaintext modeling language.
+A simple logistic regression classifier
+
+<img src="fig/logreg.jpg" alt="Softmax Regression" width="256">
+
+is defined by
+
+    name: "LogReg"
+    layers {
+      name: "mnist"
+      type: DATA
+      top: "data"
+      top: "label"
+      data_param {
+        source: "input_leveldb"
+        batch_size: 64
+      }
+    }
+    layers {
+      name: "ip"
+      type: INNER_PRODUCT
+      bottom: "data"
+      top: "ip"
+      inner_product_param {
+        num_output: 2
+      }
+    }
+    layers {
+      name: "loss"
+      type: SOFTMAX_LOSS
+      bottom: "ip"
+      bottom: "label"
+      top: "loss"
+    }
+
+The Net explains its initialization as it goes:
+
+    I0902 22:52:17.931977 2079114000 net.cpp:39] Initializing net from parameters:
+    name: "LogReg"
+    [...model prototxt printout...]
+    # construct the network layer-by-layer
+    I0902 22:52:17.932152 2079114000 net.cpp:67] Creating Layer mnist
+    I0902 22:52:17.932165 2079114000 net.cpp:356] mnist -> data
+    I0902 22:52:17.932188 2079114000 net.cpp:356] mnist -> label
+    I0902 22:52:17.932200 2079114000 net.cpp:96] Setting up mnist
+    I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb
+    I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28
+    I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176)
+    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 1 1 1 (64)
+    I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip
+    I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data
+    I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip
+    I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip
+    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 1 1 (128)
+    I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss
+    I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip
+    I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label
+    I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss
+    # set up the loss and configure the backward pass
+    I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss
+    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: 1 1 1 1 (1)
+    I0902 22:52:17.941329 2079114000 net.cpp:109]     with loss weight 1
+    I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation.
+    I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation.
+    I0902 22:52:17.941794 2079114000 net.cpp:172] mnist does not need backward computation.
+    # determine outputs
+    I0902 22:52:17.941800 2079114000 net.cpp:208] This network produces output loss
+    # finish initialization and report memory usage
+    I0902 22:52:17.941810 2079114000 net.cpp:467] Collecting Learning Rate and Weight Decay.
+    I0902 22:52:17.941818 2079114000 net.cpp:219] Network initialization done.
+    I0902 22:52:17.941824 2079114000 net.cpp:220] Memory required for data: 201476
+
+Model initialization is handled by `Net::Init()`.
+
+The network is run on CPU or GPU by setting a single switch. Layers come with corresponding CPU and GPU routines that produce identical results (with tests to prove it). The CPU / GPU switch is seamless and independent of the model definition. For research and deployment alike it is best to divide model and implementation.
+
+## Blob storage and communication
+
+Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
+
+Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. In practice, one loads data from the disk to a blob in CPU code, calls a device kernel to do GPU computation, and ferries the blob off to the next layer, ignoring low-level details while maintaining a high level of performance.
+
+Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
+
+The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest.
+
+- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
+- Channel / K is the feature dimension e.g. for RGB images K = 3.
+
+Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
+
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 4096.
+
+For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
+
+### Model format
+
+The models are defined in plaintext protocol buffer schema (prototxt) while the learned models are serialized as binary protocol buffer (binaryproto) .caffemodel files.
+
+The model format is defined by the protobuf schema in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
+
+Caffe speaks [Google Protocol Buffer](https://code.google.com/p/protobuf/) for the following strengths: minimal-size binary strings when serialized, efficient serialization, a human-readable text format compatible with the binary version, and efficient interface implementations in multiple languages, most notably C++ and Python. This all contributes to the flexibility and extensibility of modeling in Caffe.
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index d6a95b42d20..992869f88a6 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -1,4 +1,179 @@
 ---
 layout: default
 ---
-# Solving
+# Solver Optimization
+
+The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
+The responsibilities of learning are divided between the solver for overseeing the optimization and generating parameter updates and the network for yielding loss and gradients.
+
+The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NAG).
+
+The solver
+
+1. scaffolds the optimization bookkeeping and creates the training network for learning and test network(s) for evaluation.
+2. iteratively optimizes by calling forward / backward and updating parameters
+3. (periodically) evaluates the test networks
+4. snapshots the model and solver state throughout the optimization
+
+where each iteration
+
+1. calls network forward to make the output and loss
+2. calls network backward to make the gradients
+3. incorporates the gradients into parameter updates according to the solver method
+4. updates the solver state according to learning rate, history, and method
+
+to take the weights all the way from initialization to learned model.
+
+Like Caffe models, Caffe solvers run in CPU / GPU modes.
+
+## Methods
+
+The solver methods address the general optimization problem of loss minimization.
+The optimization objective is the average loss over instances
+
+$L(W) = \frac{1}{N} \sum_i f_W^{(i)}\left(X^{(i)}\right) + \lambda r(W)$
+
+where $f_W^{(i)}$ is the loss on instance $i$ with data $X^{(i)}$ in a mini-batch of size $N$ and $r(W)$ is a regularization term with weight $\lambda$.
+
+The model computes $f_W$ in the forward pass and the gradient $\nabla f_W$ in the backward pass.
+
+The gradient of the loss $\nabla L(W)$ is formed by the solver from the model gradient $\nabla f_W$, the regularlization gradient $r(W)$, and other particulars to each method.
+The method then computes the parameter update $\Delta W$ to update the weights and iterate.
+
+### SGD
+
+Stochastic gradient descent (SGD)
+
+TODO Bottou pointer
+
+### ADAGRAD
+
+The adaptive gradient (ADAGRAD)
+
+TODO cite Duchi
+
+### NAG
+
+Nesterov's accelerated gradient (NAG)
+
+TODO cite ???
+
+## Scaffolding
+
+The solver scaffolding prepares the optimization method and initializes the model to be learned in `Solver::Presolve()`.
+
+    > caffe train -solver examples/mnist/lenet_solver.prototxt
+    I0902 13:35:56.474978 16020 caffe.cpp:90] Starting Optimization
+    I0902 13:35:56.475190 16020 solver.cpp:32] Initializing solver from parameters:
+    test_iter: 100
+    test_interval: 500
+    base_lr: 0.01
+    display: 100
+    max_iter: 10000
+    lr_policy: "inv"
+    gamma: 0.0001
+    power: 0.75
+    momentum: 0.9
+    weight_decay: 0.0005
+    snapshot: 5000
+    snapshot_prefix: "examples/mnist/lenet"
+    solver_mode: GPU
+    net: "examples/mnist/lenet_train_test.prototxt"
+
+Net initialization
+
+    I0902 13:35:56.655681 16020 solver.cpp:72] Creating training net from net file: examples/mnist/lenet_train_test.prototxt
+    [...]
+    I0902 13:35:56.656740 16020 net.cpp:56] Memory required for data: 0
+    I0902 13:35:56.656791 16020 net.cpp:67] Creating Layer mnist
+    I0902 13:35:56.656811 16020 net.cpp:356] mnist -> data
+    I0902 13:35:56.656846 16020 net.cpp:356] mnist -> label
+    I0902 13:35:56.656874 16020 net.cpp:96] Setting up mnist
+    I0902 13:35:56.694052 16020 data_layer.cpp:135] Opening lmdb examples/mnist/mnist_train_lmdb
+    I0902 13:35:56.701062 16020 data_layer.cpp:195] output data size: 64,1,28,28
+    I0902 13:35:56.701146 16020 data_layer.cpp:236] Initializing prefetch
+    I0902 13:35:56.701196 16020 data_layer.cpp:238] Prefetch initialized.
+    I0902 13:35:56.701212 16020 net.cpp:103] Top shape: 64 1 28 28 (50176)
+    I0902 13:35:56.701230 16020 net.cpp:103] Top shape: 64 1 1 1 (64)
+    [...]
+    I0902 13:35:56.703737 16020 net.cpp:67] Creating Layer ip1
+    I0902 13:35:56.703753 16020 net.cpp:394] ip1 <- pool2
+    I0902 13:35:56.703778 16020 net.cpp:356] ip1 -> ip1
+    I0902 13:35:56.703797 16020 net.cpp:96] Setting up ip1
+    I0902 13:35:56.728127 16020 net.cpp:103] Top shape: 64 500 1 1 (32000)
+    I0902 13:35:56.728142 16020 net.cpp:113] Memory required for data: 5039360
+    I0902 13:35:56.728175 16020 net.cpp:67] Creating Layer relu1
+    I0902 13:35:56.728194 16020 net.cpp:394] relu1 <- ip1
+    I0902 13:35:56.728219 16020 net.cpp:345] relu1 -> ip1 (in-place)
+    I0902 13:35:56.728240 16020 net.cpp:96] Setting up relu1
+    I0902 13:35:56.728256 16020 net.cpp:103] Top shape: 64 500 1 1 (32000)
+    I0902 13:35:56.728270 16020 net.cpp:113] Memory required for data: 5167360
+    I0902 13:35:56.728287 16020 net.cpp:67] Creating Layer ip2
+    I0902 13:35:56.728304 16020 net.cpp:394] ip2 <- ip1
+    I0902 13:35:56.728333 16020 net.cpp:356] ip2 -> ip2
+    I0902 13:35:56.728356 16020 net.cpp:96] Setting up ip2
+    I0902 13:35:56.728690 16020 net.cpp:103] Top shape: 64 10 1 1 (640)
+    I0902 13:35:56.728705 16020 net.cpp:113] Memory required for data: 5169920
+    I0902 13:35:56.728734 16020 net.cpp:67] Creating Layer loss
+    I0902 13:35:56.728747 16020 net.cpp:394] loss <- ip2
+    I0902 13:35:56.728767 16020 net.cpp:394] loss <- label
+    I0902 13:35:56.728786 16020 net.cpp:356] loss -> loss
+    I0902 13:35:56.728811 16020 net.cpp:96] Setting up loss
+    I0902 13:35:56.728837 16020 net.cpp:103] Top shape: 1 1 1 1 (1)
+    I0902 13:35:56.728849 16020 net.cpp:109]     with loss weight 1
+    I0902 13:35:56.728878 16020 net.cpp:113] Memory required for data: 5169924
+
+Loss
+
+    I0902 13:35:56.728893 16020 net.cpp:170] loss needs backward computation.
+    I0902 13:35:56.728909 16020 net.cpp:170] ip2 needs backward computation.
+    I0902 13:35:56.728924 16020 net.cpp:170] relu1 needs backward computation.
+    I0902 13:35:56.728938 16020 net.cpp:170] ip1 needs backward computation.
+    I0902 13:35:56.728953 16020 net.cpp:170] pool2 needs backward computation.
+    I0902 13:35:56.728970 16020 net.cpp:170] conv2 needs backward computation.
+    I0902 13:35:56.728984 16020 net.cpp:170] pool1 needs backward computation.
+    I0902 13:35:56.728998 16020 net.cpp:170] conv1 needs backward computation.
+    I0902 13:35:56.729014 16020 net.cpp:172] mnist does not need backward computation.
+    I0902 13:35:56.729027 16020 net.cpp:208] This network produces output loss
+    I0902 13:35:56.729053 16020 net.cpp:467] Collecting Learning Rate and Weight Decay.
+    I0902 13:35:56.729071 16020 net.cpp:219] Network initialization done.
+    I0902 13:35:56.729085 16020 net.cpp:220] Memory required for data: 5169924
+    I0902 13:35:56.729277 16020 solver.cpp:156] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt
+
+Completion
+
+    I0902 13:35:56.806970 16020 solver.cpp:46] Solver scaffolding done.
+    I0902 13:35:56.806984 16020 solver.cpp:165] Solving LeNet
+
+
+## Updating Parameters
+
+The actual weight update is made by the solver then applied to the net parameters in `Solver::ComputeUpdateValue()`.
+
+TODO
+
+## Snapshotting and Resuming
+
+The solver snapshots the weights and its own state during training in `Solver::Snapshot()` and `Solver::SnapshotSolverState()`.
+The weight snapshots export the learned model while the solver snapshots allow training to be resumed from a given point.
+Training is resumed by `Solver::Restore()` and `Solver::RestoreSolverState()`.
+
+Weights are saved without extension while solver states are saved with `.solverstate` extension.
+Both files will have an `_iter_N` suffix for the snapshot iteration number.
+
+Snapshotting is configured by:
+
+    # The snapshot interval in iterations.
+    snapshot: 5000
+    # File path prefix for snapshotting model weights and solver state.
+    # Note: this is relative to the invocation of the `caffe` utility, not the
+    # solver definition file.
+    snapshot_prefix: /path/to/model
+    # Snapshot the diff along with the weights. This can help debugging training
+    # but takes more storage.
+    snapshot_diff: false
+    # A final snapshot is saved at the end of training unless
+    # this flag is set to false. The default is true.
+    snapshot_after_train: true
+
+in the solver definition prototxt.

From 17252db7a0d89eb66b76e5eec0df6e36cb6417a4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 10:58:25 -0700
Subject: [PATCH 0631/2053] [docs] add note on Caffe convolution

---
 docs/tutorial/convolution.md | 9 +++++++++
 docs/tutorial/index.md       | 4 ++++
 2 files changed, 13 insertions(+)

diff --git a/docs/tutorial/convolution.md b/docs/tutorial/convolution.md
index a8bd0e656ec..2119d640e71 100644
--- a/docs/tutorial/convolution.md
+++ b/docs/tutorial/convolution.md
@@ -2,3 +2,12 @@
 layout: default
 ---
 # Caffeinated Convolution
+
+The Caffe strategy for convolution is to reduce the problem to matrix-matrix multiplication.
+This linear algebra computation is highly-tuned in BLAS libraries and efficiently computed on GPU devices.
+
+For more details read Yangqing's [Convolution in Caffe: a memo](https://github.com/Yangqing/caffe/wiki/Convolution-in-Caffe:-a-memo).
+
+As it turns out, this same reduction was independently explored in the context of conv. nets by
+
+> K. Chellapilla, S. Puri, P. Simard, et al. High performance convolutional neural networks for document processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.
diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
index e9f593e48d0..5638e41f09d 100644
--- a/docs/tutorial/index.md
+++ b/docs/tutorial/index.md
@@ -28,6 +28,10 @@ and these principles direct the project.
 - [Layer Catalogue](layers.html): the layer is the fundamental unit of modeling and computation -- Caffe's catalogue includes layers for state-of-the-art models.
 - [Interfaces](interfaces.html): command line, Python, and MATLAB Caffe.
 
+For a closer look at a few details:
+
+- [Caffeinated Convolution](convolution.html): how Caffe computes convolutions.
+
 ## Deeper Learning
 
 There are helpful references freely online for deep learning that complement our hands-on tutorial.

From d15405a4b0df53aaf296c77d5f1d81b4d2ffa9bf Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 27 Aug 2014 21:16:18 -0700
Subject: [PATCH 0632/2053] use kramdown for markdown syntax; add mathjax

---
 docs/_config.yml           | 1 +
 docs/_layouts/default.html | 4 ++++
 2 files changed, 5 insertions(+)
 create mode 100644 docs/_config.yml

diff --git a/docs/_config.yml b/docs/_config.yml
new file mode 100644
index 00000000000..10e6731ad27
--- /dev/null
+++ b/docs/_config.yml
@@ -0,0 +1 @@
+markdown: kramdown
diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index 756e8ebae1d..73c6d5873d3 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -1,6 +1,10 @@
 <!doctype html>
 <html>
   <head>
+    <!-- MathJax -->
+    <script type="text/javascript"
+      src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
+    </script>
     <meta charset="utf-8">
     <meta http-equiv="X-UA-Compatible" content="chrome=1">
     <title>

From 59eaba19acb873034e007a5fe2f2a008bf078179 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 27 Aug 2014 19:38:13 -0700
Subject: [PATCH 0633/2053] [wip] vision layers, start convolution

---
 docs/tutorial/layers.md | 53 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 52 insertions(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 74b2675755e..f316e826bab 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -11,9 +11,60 @@ TODO complete list of layers linking to headings
 
 ### Vision Layers
 
+* Header: `./include/caffe/vision_layers.hpp`
+
+Vision layers usually take *images* as input and produce other *images* as output.
+A typical "image" in the real-world may have one color channel ($c = 1$), as in a grayscale image, or three color channels ($c = 3$) as in an RGB (red, green, blue) image.
+But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $h > 1$ and width $w > 1$.
+This 2D geometry naturally lends itself to certain decisions about how to process the input.
+In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
+In contrast, other layers (with few exceptions) ignore the spatial structure of the input, treating it as "one big vector" with dimension $$ c h w $$.
+
+
 #### Convolution
 
-`CONVOLUTION`
+* LayerType: `CONVOLUTION`
+* CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
+* Options (`ConvolutionParameter convolution_param`)
+    - Required | `num_output` ($c_o$), the number of filters
+    - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
+    - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
+    - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
+    - Optional (default 0): `pad` or (`pad_h`, `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
+    - Optional (default 1): `stride` or (`stride_h`, `stride_w`), specifies the intervals at which to apply the filters to the input
+    - Optional (default 1): `group` ($g$) if $>1$, restricts the connectivity of each filter to a subset of the input.  In particular, the input to the $i^{th}$ group of $n_f / g$ filters is the $i^{th}$ group of $c_i / g$ input channels.
+* Input
+    - $n \times c_i \times h_i \times w_i$ (repeated $K \ge 1$ times)
+* Output
+    - $n \times c_o \times h_o \times w_o$ (repeated $K$ times)
+* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+
+        layers {
+          name: "conv1"
+          type: CONVOLUTION
+          bottom: "data"
+          top: "conv1"
+          blobs_lr: 1          # learning rate multiplier for the filters
+          blobs_lr: 2          # learning rate multiplier for the biases
+          weight_decay: 1      # weight decay multiplier for the filters
+          weight_decay: 0      # weight decay multiplier for the biases
+          convolution_param {
+            num_output: 96     # learn 96 filters
+            kernel_size: 11    # each filter is 11x11
+            stride: 4          # step 4 pixels between each filter application
+            weight_filler {
+              type: "gaussian" # initialize the filters from a Gaussian
+              std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+            }
+            bias_filler {
+              type: "constant" # initialize the biases to zero (0)
+              value: 0
+            }
+          }
+        }
+
+The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 
 #### Pooling**
 

From eebf2e29aedca09ffc77526ea82d7a76d585e912 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 27 Aug 2014 22:21:49 -0700
Subject: [PATCH 0634/2053] add .Doxyfile: the default Doxygen config file from
 `doxygen -g`

---
 .Doxyfile | 1781 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1781 insertions(+)
 create mode 100644 .Doxyfile

diff --git a/.Doxyfile b/.Doxyfile
new file mode 100644
index 00000000000..552a39cf933
--- /dev/null
+++ b/.Doxyfile
@@ -0,0 +1,1781 @@
+# Doxyfile 1.7.6.1
+
+# This file describes the settings to be used by the documentation system
+# doxygen (www.doxygen.org) for a project.
+#
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+# The format is:
+#       TAG = value [value, ...]
+# For lists items can also be appended using:
+#       TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (" ").
+
+#---------------------------------------------------------------------------
+# Project related configuration options
+#---------------------------------------------------------------------------
+
+# This tag specifies the encoding used for all characters in the config file
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+# http://www.gnu.org/software/libiconv for the list of possible encodings.
+
+DOXYFILE_ENCODING      = UTF-8
+
+# The PROJECT_NAME tag is a single word (or sequence of words) that should
+# identify the project. Note that if you do not use Doxywizard you need
+# to put quotes around the project name if it contains spaces.
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+PROJECT_NAME           = "My Project"
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+
+PROJECT_NUMBER         =
+
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+# for a project that appears at the top of each page and should give viewer
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+PROJECT_BRIEF          =
+
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+PROJECT_LOGO           =
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+OUTPUT_DIRECTORY       =
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+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip sources only.
+# Doxygen will parse them like normal C++ but will assume all classes use public
+# instead of private inheritance when no explicit protection keyword is present.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate getter
+# and setter methods for a property. Setting this option to YES (the default)
+# will make doxygen replace the get and set methods by a property in the
+# documentation. This will only work if the methods are indeed getting or
+# setting a simple type. If this is not the case, or you want to show the
+# methods anyway, you should set this option to NO.
+
+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES, then doxygen will reuse the documentation of the first
+# member in the group (if any) for the other members of the group. By default
+# all members of a group must be documented explicitly.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# Set the SUBGROUPING tag to YES (the default) to allow class member groups of
+# the same type (for instance a group of public functions) to be put as a
+# subgroup of that type (e.g. under the Public Functions section). Set it to
+# NO to prevent subgrouping. Alternatively, this can be done per class using
+# the \nosubgrouping command.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and
+# unions are shown inside the group in which they are included (e.g. using
+# @ingroup) instead of on a separate page (for HTML and Man pages) or
+# section (for LaTeX and RTF).
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and
+# unions with only public data fields will be shown inline in the documentation
+# of the scope in which they are defined (i.e. file, namespace, or group
+# documentation), provided this scope is documented. If set to NO (the default),
+# structs, classes, and unions are shown on a separate page (for HTML and Man
+# pages) or section (for LaTeX and RTF).
+
+INLINE_SIMPLE_STRUCTS  = NO
+
+# When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum
+# is documented as struct, union, or enum with the name of the typedef. So
+# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
+# with name TypeT. When disabled the typedef will appear as a member of a file,
+# namespace, or class. And the struct will be named TypeS. This can typically
+# be useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The SYMBOL_CACHE_SIZE determines the size of the internal cache use to
+# determine which symbols to keep in memory and which to flush to disk.
+# When the cache is full, less often used symbols will be written to disk.
+# For small to medium size projects (<1000 input files) the default value is
+# probably good enough. For larger projects a too small cache size can cause
+# doxygen to be busy swapping symbols to and from disk most of the time
+# causing a significant performance penalty.
+# If the system has enough physical memory increasing the cache will improve the
+# performance by keeping more symbols in memory. Note that the value works on
+# a logarithmic scale so increasing the size by one will roughly double the
+# memory usage. The cache size is given by this formula:
+# 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0,
+# corresponding to a cache size of 2^16 = 65536 symbols.
+
+SYMBOL_CACHE_SIZE      = 0
+
+# Similar to the SYMBOL_CACHE_SIZE the size of the symbol lookup cache can be
+# set using LOOKUP_CACHE_SIZE. This cache is used to resolve symbols given
+# their name and scope. Since this can be an expensive process and often the
+# same symbol appear multiple times in the code, doxygen keeps a cache of
+# pre-resolved symbols. If the cache is too small doxygen will become slower.
+# If the cache is too large, memory is wasted. The cache size is given by this
+# formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range is 0..9, the default is 0,
+# corresponding to a cache size of 2^16 = 65536 symbols.
+
+LOOKUP_CACHE_SIZE      = 0
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
+# documentation are documented, even if no documentation was available.
+# Private class members and static file members will be hidden unless
+# the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES
+
+EXTRACT_ALL            = NO
+
+# If the EXTRACT_PRIVATE tag is set to YES all private members of a class
+# will be included in the documentation.
+
+EXTRACT_PRIVATE        = NO
+
+# If the EXTRACT_STATIC tag is set to YES all static members of a file
+# will be included in the documentation.
+
+EXTRACT_STATIC         = NO
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs)
+# defined locally in source files will be included in the documentation.
+# If set to NO only classes defined in header files are included.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. When set to YES local
+# methods, which are defined in the implementation section but not in
+# the interface are included in the documentation.
+# If set to NO (the default) only methods in the interface are included.
+
+EXTRACT_LOCAL_METHODS  = NO
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base
+# name of the file that contains the anonymous namespace. By default
+# anonymous namespaces are hidden.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all
+# undocumented members of documented classes, files or namespaces.
+# If set to NO (the default) these members will be included in the
+# various overviews, but no documentation section is generated.
+# This option has no effect if EXTRACT_ALL is enabled.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy.
+# If set to NO (the default) these classes will be included in the various
+# overviews. This option has no effect if EXTRACT_ALL is enabled.
+
+HIDE_UNDOC_CLASSES     = NO
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all
+# friend (class|struct|union) declarations.
+# If set to NO (the default) these declarations will be included in the
+# documentation.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any
+# documentation blocks found inside the body of a function.
+# If set to NO (the default) these blocks will be appended to the
+# function's detailed documentation block.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation
+# that is typed after a \internal command is included. If the tag is set
+# to NO (the default) then the documentation will be excluded.
+# Set it to YES to include the internal documentation.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate
+# file names in lower-case letters. If set to YES upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# and Mac users are advised to set this option to NO.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen
+# will show members with their full class and namespace scopes in the
+# documentation. If set to YES the scope will be hidden.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen
+# will put a list of the files that are included by a file in the documentation
+# of that file.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen
+# will list include files with double quotes in the documentation
+# rather than with sharp brackets.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES (the default) then a tag [inline]
+# is inserted in the documentation for inline members.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen
+# will sort the (detailed) documentation of file and class members
+# alphabetically by member name. If set to NO the members will appear in
+# declaration order.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the
+# brief documentation of file, namespace and class members alphabetically
+# by member name. If set to NO (the default) the members will appear in
+# declaration order.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen
+# will sort the (brief and detailed) documentation of class members so that
+# constructors and destructors are listed first. If set to NO (the default)
+# the constructors will appear in the respective orders defined by
+# SORT_MEMBER_DOCS and SORT_BRIEF_DOCS.
+# This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO
+# and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the
+# hierarchy of group names into alphabetical order. If set to NO (the default)
+# the group names will appear in their defined order.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be
+# sorted by fully-qualified names, including namespaces. If set to
+# NO (the default), the class list will be sorted only by class name,
+# not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the
+# alphabetical list.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to
+# do proper type resolution of all parameters of a function it will reject a
+# match between the prototype and the implementation of a member function even
+# if there is only one candidate or it is obvious which candidate to choose
+# by doing a simple string match. By disabling STRICT_PROTO_MATCHING doxygen
+# will still accept a match between prototype and implementation in such cases.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable (YES) or
+# disable (NO) the todo list. This list is created by putting \todo
+# commands in the documentation.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable (YES) or
+# disable (NO) the test list. This list is created by putting \test
+# commands in the documentation.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable (YES) or
+# disable (NO) the bug list. This list is created by putting \bug
+# commands in the documentation.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or
+# disable (NO) the deprecated list. This list is created by putting
+# \deprecated commands in the documentation.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional
+# documentation sections, marked by \if sectionname ... \endif.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines
+# the initial value of a variable or macro consists of for it to appear in
+# the documentation. If the initializer consists of more lines than specified
+# here it will be hidden. Use a value of 0 to hide initializers completely.
+# The appearance of the initializer of individual variables and macros in the
+# documentation can be controlled using \showinitializer or \hideinitializer
+# command in the documentation regardless of this setting.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated
+# at the bottom of the documentation of classes and structs. If set to YES the
+# list will mention the files that were used to generate the documentation.
+
+SHOW_USED_FILES        = YES
+
+# If the sources in your project are distributed over multiple directories
+# then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy
+# in the documentation. The default is NO.
+
+SHOW_DIRECTORIES       = NO
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page.
+# This will remove the Files entry from the Quick Index and from the
+# Folder Tree View (if specified). The default is YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the
+# Namespaces page.
+# This will remove the Namespaces entry from the Quick Index
+# and from the Folder Tree View (if specified). The default is YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command <command> <input-file>, where <command> is the value of
+# the FILE_VERSION_FILTER tag, and <input-file> is the name of an input file
+# provided by doxygen. Whatever the program writes to standard output
+# is used as the file version. See the manual for examples.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. The create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option.
+# You can optionally specify a file name after the option, if omitted
+# DoxygenLayout.xml will be used as the name of the layout file.
+
+LAYOUT_FILE            =
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files
+# containing the references data. This must be a list of .bib files. The
+# .bib extension is automatically appended if omitted. Using this command
+# requires the bibtex tool to be installed. See also
+# http://en.wikipedia.org/wiki/BibTeX for more info. For LaTeX the style
+# of the bibliography can be controlled using LATEX_BIB_STYLE. To use this
+# feature you need bibtex and perl available in the search path.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated
+# by doxygen. Possible values are YES and NO. If left blank NO is used.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated by doxygen. Possible values are YES and NO. If left blank
+# NO is used.
+
+WARNINGS               = YES
+
+# If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings
+# for undocumented members. If EXTRACT_ALL is set to YES then this flag will
+# automatically be disabled.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some
+# parameters in a documented function, or documenting parameters that
+# don't exist or using markup commands wrongly.
+
+WARN_IF_DOC_ERROR      = YES
+
+# The WARN_NO_PARAMDOC option can be enabled to get warnings for
+# functions that are documented, but have no documentation for their parameters
+# or return value. If set to NO (the default) doxygen will only warn about
+# wrong or incomplete parameter documentation, but not about the absence of
+# documentation.
+
+WARN_NO_PARAMDOC       = NO
+
+# The WARN_FORMAT tag determines the format of the warning messages that
+# doxygen can produce. The string should contain the $file, $line, and $text
+# tags, which will be replaced by the file and line number from which the
+# warning originated and the warning text. Optionally the format may contain
+# $version, which will be replaced by the version of the file (if it could
+# be obtained via FILE_VERSION_FILTER)
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning
+# and error messages should be written. If left blank the output is written
+# to stderr.
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag can be used to specify the files and/or directories that contain
+# documented source files. You may enter file names like "myfile.cpp" or
+# directories like "/usr/src/myproject". Separate the files or directories
+# with spaces.
+
+INPUT                  =
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is
+# also the default input encoding. Doxygen uses libiconv (or the iconv built
+# into libc) for the transcoding. See http://www.gnu.org/software/libiconv for
+# the list of possible encodings.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp
+# and *.h) to filter out the source-files in the directories. If left
+# blank the following patterns are tested:
+# *.c *.cc *.cxx *.cpp *.c++ *.d *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh
+# *.hxx *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.dox *.py
+# *.f90 *.f *.for *.vhd *.vhdl
+
+FILE_PATTERNS          =
+
+# The RECURSIVE tag can be used to turn specify whether or not subdirectories
+# should be searched for input files as well. Possible values are YES and NO.
+# If left blank NO is used.
+
+RECURSIVE              = NO
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories. Note that the wildcards are matched
+# against the file with absolute path, so to exclude all test directories
+# for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       =
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+
+EXCLUDE_SYMBOLS        =
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or
+# directories that contain example code fragments that are included (see
+# the \include command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp
+# and *.h) to filter out the source-files in the directories. If left
+# blank all files are included.
+
+EXAMPLE_PATTERNS       =
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude
+# commands irrespective of the value of the RECURSIVE tag.
+# Possible values are YES and NO. If left blank NO is used.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or
+# directories that contain image that are included in the documentation (see
+# the \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command <filter> <input-file>, where <filter>
+# is the value of the INPUT_FILTER tag, and <input-file> is the name of an
+# input file. Doxygen will then use the output that the filter program writes
+# to standard output.
+# If FILTER_PATTERNS is specified, this tag will be
+# ignored.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis.
+# Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match.
+# The filters are a list of the form:
+# pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further
+# info on how filters are used. If FILTER_PATTERNS is empty or if
+# non of the patterns match the file name, INPUT_FILTER is applied.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER) will be used to filter the input files when producing source
+# files to browse (i.e. when SOURCE_BROWSER is set to YES).
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any)
+# and it is also possible to disable source filtering for a specific pattern
+# using *.ext= (so without naming a filter). This option only has effect when
+# FILTER_SOURCE_FILES is enabled.
+
+FILTER_SOURCE_PATTERNS =
+
+#---------------------------------------------------------------------------
+# configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will
+# be generated. Documented entities will be cross-referenced with these sources.
+# Note: To get rid of all source code in the generated output, make sure also
+# VERBATIM_HEADERS is set to NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body
+# of functions and classes directly in the documentation.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct
+# doxygen to hide any special comment blocks from generated source code
+# fragments. Normal C and C++ comments will always remain visible.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES
+# then for each documented function all documented
+# functions referencing it will be listed.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES
+# then for each documented function all documented entities
+# called/used by that function will be listed.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES (the default)
+# and SOURCE_BROWSER tag is set to YES, then the hyperlinks from
+# functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will
+# link to the source code.
+# Otherwise they will link to the documentation.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code
+# will point to the HTML generated by the htags(1) tool instead of doxygen
+# built-in source browser. The htags tool is part of GNU's global source
+# tagging system (see http://www.gnu.org/software/global/global.html). You
+# will need version 4.8.6 or higher.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen
+# will generate a verbatim copy of the header file for each class for
+# which an include is specified. Set to NO to disable this.
+
+VERBATIM_HEADERS       = YES
+
+#---------------------------------------------------------------------------
+# configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index
+# of all compounds will be generated. Enable this if the project
+# contains a lot of classes, structs, unions or interfaces.
+
+ALPHABETICAL_INDEX     = YES
+
+# If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then
+# the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns
+# in which this list will be split (can be a number in the range [1..20])
+
+COLS_IN_ALPHA_INDEX    = 5
+
+# In case all classes in a project start with a common prefix, all
+# classes will be put under the same header in the alphabetical index.
+# The IGNORE_PREFIX tag can be used to specify one or more prefixes that
+# should be ignored while generating the index headers.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES (the default) Doxygen will
+# generate HTML output.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be
+# put in front of it. If left blank `html' will be used as the default path.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for
+# each generated HTML page (for example: .htm,.php,.asp). If it is left blank
+# doxygen will generate files with .html extension.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a personal HTML header for
+# each generated HTML page. If it is left blank doxygen will generate a
+# standard header. Note that when using a custom header you are responsible
+#  for the proper inclusion of any scripts and style sheets that doxygen
+# needs, which is dependent on the configuration options used.
+# It is advised to generate a default header using "doxygen -w html
+# header.html footer.html stylesheet.css YourConfigFile" and then modify
+# that header. Note that the header is subject to change so you typically
+# have to redo this when upgrading to a newer version of doxygen or when
+# changing the value of configuration settings such as GENERATE_TREEVIEW!
+
+HTML_HEADER            =
+
+# The HTML_FOOTER tag can be used to specify a personal HTML footer for
+# each generated HTML page. If it is left blank doxygen will generate a
+# standard footer.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading
+# style sheet that is used by each HTML page. It can be used to
+# fine-tune the look of the HTML output. If the tag is left blank doxygen
+# will generate a default style sheet. Note that doxygen will try to copy
+# the style sheet file to the HTML output directory, so don't put your own
+# style sheet in the HTML output directory as well, or it will be erased!
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath$ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that
+# the files will be copied as-is; there are no commands or markers available.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output.
+# Doxygen will adjust the colors in the style sheet and background images
+# according to this color. Hue is specified as an angle on a colorwheel,
+# see http://en.wikipedia.org/wiki/Hue for more information.
+# For instance the value 0 represents red, 60 is yellow, 120 is green,
+# 180 is cyan, 240 is blue, 300 purple, and 360 is red again.
+# The allowed range is 0 to 359.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of
+# the colors in the HTML output. For a value of 0 the output will use
+# grayscales only. A value of 255 will produce the most vivid colors.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to
+# the luminance component of the colors in the HTML output. Values below
+# 100 gradually make the output lighter, whereas values above 100 make
+# the output darker. The value divided by 100 is the actual gamma applied,
+# so 80 represents a gamma of 0.8, The value 220 represents a gamma of 2.2,
+# and 100 does not change the gamma.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting
+# this to NO can help when comparing the output of multiple runs.
+
+HTML_TIMESTAMP         = YES
+
+# If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes,
+# files or namespaces will be aligned in HTML using tables. If set to
+# NO a bullet list will be used.
+
+HTML_ALIGN_MEMBERS     = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded. For this to work a browser that supports
+# JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox
+# Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari).
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files
+# will be generated that can be used as input for Apple's Xcode 3
+# integrated development environment, introduced with OSX 10.5 (Leopard).
+# To create a documentation set, doxygen will generate a Makefile in the
+# HTML output directory. Running make will produce the docset in that
+# directory and running "make install" will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find
+# it at startup.
+# See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
+# for more information.
+
+GENERATE_DOCSET        = NO
+
+# When GENERATE_DOCSET tag is set to YES, this tag determines the name of the
+# feed. A documentation feed provides an umbrella under which multiple
+# documentation sets from a single provider (such as a company or product suite)
+# can be grouped.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# When GENERATE_DOCSET tag is set to YES, this tag specifies a string that
+# should uniquely identify the documentation set bundle. This should be a
+# reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen
+# will append .docset to the name.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# When GENERATE_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The GENERATE_PUBLISHER_NAME tag identifies the documentation publisher.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES, additional index files
+# will be generated that can be used as input for tools like the
+# Microsoft HTML help workshop to generate a compiled HTML help file (.chm)
+# of the generated HTML documentation.
+
+GENERATE_HTMLHELP      = NO
+
+# If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can
+# be used to specify the file name of the resulting .chm file. You
+# can add a path in front of the file if the result should not be
+# written to the html output directory.
+
+CHM_FILE               =
+
+# If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can
+# be used to specify the location (absolute path including file name) of
+# the HTML help compiler (hhc.exe). If non-empty doxygen will try to run
+# the HTML help compiler on the generated index.hhp.
+
+HHC_LOCATION           =
+
+# If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag
+# controls if a separate .chi index file is generated (YES) or that
+# it should be included in the master .chm file (NO).
+
+GENERATE_CHI           = NO
+
+# If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING
+# is used to encode HtmlHelp index (hhk), content (hhc) and project file
+# content.
+
+CHM_INDEX_ENCODING     =
+
+# If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag
+# controls whether a binary table of contents is generated (YES) or a
+# normal table of contents (NO) in the .chm file.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members
+# to the contents of the HTML help documentation and to the tree view.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated
+# that can be used as input for Qt's qhelpgenerator to generate a
+# Qt Compressed Help (.qch) of the generated HTML documentation.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can
+# be used to specify the file name of the resulting .qch file.
+# The path specified is relative to the HTML output folder.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating
+# Qt Help Project output. For more information please see
+# http://doc.trolltech.com/qthelpproject.html#namespace
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating
+# Qt Help Project output. For more information please see
+# http://doc.trolltech.com/qthelpproject.html#virtual-folders
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to
+# add. For more information please see
+# http://doc.trolltech.com/qthelpproject.html#custom-filters
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see
+# <a href="http://doc.trolltech.com/qthelpproject.html#custom-filters">
+# Qt Help Project / Custom Filters</a>.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's
+# filter section matches.
+# <a href="http://doc.trolltech.com/qthelpproject.html#filter-attributes">
+# Qt Help Project / Filter Attributes</a>.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can
+# be used to specify the location of Qt's qhelpgenerator.
+# If non-empty doxygen will try to run qhelpgenerator on the generated
+# .qhp file.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files
+#  will be generated, which together with the HTML files, form an Eclipse help
+# plugin. To install this plugin and make it available under the help contents
+# menu in Eclipse, the contents of the directory containing the HTML and XML
+# files needs to be copied into the plugins directory of eclipse. The name of
+# the directory within the plugins directory should be the same as
+# the ECLIPSE_DOC_ID value. After copying Eclipse needs to be restarted before
+# the help appears.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have
+# this name.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# The DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs)
+# at top of each HTML page. The value NO (the default) enables the index and
+# the value YES disables it. Since the tabs have the same information as the
+# navigation tree you can set this option to NO if you already set
+# GENERATE_TREEVIEW to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information.
+# If the tag value is set to YES, a side panel will be generated
+# containing a tree-like index structure (just like the one that
+# is generated for HTML Help). For this to work a browser that supports
+# JavaScript, DHTML, CSS and frames is required (i.e. any modern browser).
+# Windows users are probably better off using the HTML help feature.
+# Since the tree basically has the same information as the tab index you
+# could consider to set DISABLE_INDEX to NO when enabling this option.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values
+# (range [0,1..20]) that doxygen will group on one line in the generated HTML
+# documentation. Note that a value of 0 will completely suppress the enum
+# values from appearing in the overview section.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# By enabling USE_INLINE_TREES, doxygen will generate the Groups, Directories,
+# and Class Hierarchy pages using a tree view instead of an ordered list.
+
+USE_INLINE_TREES       = NO
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be
+# used to set the initial width (in pixels) of the frame in which the tree
+# is shown.
+
+TREEVIEW_WIDTH         = 250
+
+# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open
+# links to external symbols imported via tag files in a separate window.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of Latex formulas included
+# as images in the HTML documentation. The default is 10. Note that
+# when you change the font size after a successful doxygen run you need
+# to manually remove any form_*.png images from the HTML output directory
+# to force them to be regenerated.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are
+# not supported properly for IE 6.0, but are supported on all modern browsers.
+# Note that when changing this option you need to delete any form_*.png files
+# in the HTML output before the changes have effect.
+
+FORMULA_TRANSPARENT    = YES
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax
+# (see http://www.mathjax.org) which uses client side Javascript for the
+# rendering instead of using prerendered bitmaps. Use this if you do not
+# have LaTeX installed or if you want to formulas look prettier in the HTML
+# output. When enabled you also need to install MathJax separately and
+# configure the path to it using the MATHJAX_RELPATH option.
+
+USE_MATHJAX            = NO
+
+# When MathJax is enabled you need to specify the location relative to the
+# HTML output directory using the MATHJAX_RELPATH option. The destination
+# directory should contain the MathJax.js script. For instance, if the mathjax
+# directory is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the
+# mathjax.org site, so you can quickly see the result without installing
+# MathJax, but it is strongly recommended to install a local copy of MathJax
+# before deployment.
+
+MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or MathJax extension
+# names that should be enabled during MathJax rendering.
+
+MATHJAX_EXTENSIONS     =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box
+# for the HTML output. The underlying search engine uses javascript
+# and DHTML and should work on any modern browser. Note that when using
+# HTML help (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets
+# (GENERATE_DOCSET) there is already a search function so this one should
+# typically be disabled. For large projects the javascript based search engine
+# can be slow, then enabling SERVER_BASED_SEARCH may provide a better solution.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a PHP enabled web server instead of at the web client
+# using Javascript. Doxygen will generate the search PHP script and index
+# file to put on the web server. The advantage of the server
+# based approach is that it scales better to large projects and allows
+# full text search. The disadvantages are that it is more difficult to setup
+# and does not have live searching capabilities.
+
+SERVER_BASED_SEARCH    = NO
+
+#---------------------------------------------------------------------------
+# configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES (the default) Doxygen will
+# generate Latex output.
+
+GENERATE_LATEX         = YES
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be
+# put in front of it. If left blank `latex' will be used as the default path.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked. If left blank `latex' will be used as the default command name.
+# Note that when enabling USE_PDFLATEX this option is only used for
+# generating bitmaps for formulas in the HTML output, but not in the
+# Makefile that is written to the output directory.
+
+LATEX_CMD_NAME         = latex
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to
+# generate index for LaTeX. If left blank `makeindex' will be used as the
+# default command name.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# If the COMPACT_LATEX tag is set to YES Doxygen generates more compact
+# LaTeX documents. This may be useful for small projects and may help to
+# save some trees in general.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used
+# by the printer. Possible values are: a4, letter, legal and
+# executive. If left blank a4wide will be used.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX
+# packages that should be included in the LaTeX output.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for
+# the generated latex document. The header should contain everything until
+# the first chapter. If it is left blank doxygen will generate a
+# standard header. Notice: only use this tag if you know what you are doing!
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for
+# the generated latex document. The footer should contain everything after
+# the last chapter. If it is left blank doxygen will generate a
+# standard footer. Notice: only use this tag if you know what you are doing!
+
+LATEX_FOOTER           =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated
+# is prepared for conversion to pdf (using ps2pdf). The pdf file will
+# contain links (just like the HTML output) instead of page references
+# This makes the output suitable for online browsing using a pdf viewer.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of
+# plain latex in the generated Makefile. Set this option to YES to get a
+# higher quality PDF documentation.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode.
+# command to the generated LaTeX files. This will instruct LaTeX to keep
+# running if errors occur, instead of asking the user for help.
+# This option is also used when generating formulas in HTML.
+
+LATEX_BATCHMODE        = NO
+
+# If LATEX_HIDE_INDICES is set to YES then doxygen will not
+# include the index chapters (such as File Index, Compound Index, etc.)
+# in the output.
+
+LATEX_HIDE_INDICES     = NO
+
+# If LATEX_SOURCE_CODE is set to YES then doxygen will include
+# source code with syntax highlighting in the LaTeX output.
+# Note that which sources are shown also depends on other settings
+# such as SOURCE_BROWSER.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. The default style is "plain". See
+# http://en.wikipedia.org/wiki/BibTeX for more info.
+
+LATEX_BIB_STYLE        = plain
+
+#---------------------------------------------------------------------------
+# configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output
+# The RTF output is optimized for Word 97 and may not look very pretty with
+# other RTF readers or editors.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be
+# put in front of it. If left blank `rtf' will be used as the default path.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES Doxygen generates more compact
+# RTF documents. This may be useful for small projects and may help to
+# save some trees in general.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated
+# will contain hyperlink fields. The RTF file will
+# contain links (just like the HTML output) instead of page references.
+# This makes the output suitable for online browsing using WORD or other
+# programs which support those fields.
+# Note: wordpad (write) and others do not support links.
+
+RTF_HYPERLINKS         = NO
+
+# Load style sheet definitions from file. Syntax is similar to doxygen's
+# config file, i.e. a series of assignments. You only have to provide
+# replacements, missing definitions are set to their default value.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an rtf document.
+# Syntax is similar to doxygen's config file.
+
+RTF_EXTENSIONS_FILE    =
+
+#---------------------------------------------------------------------------
+# configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES (the default) Doxygen will
+# generate man pages
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be
+# put in front of it. If left blank `man' will be used as the default path.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to
+# the generated man pages (default is the subroutine's section .3)
+
+MAN_EXTENSION          = .3
+
+# If the MAN_LINKS tag is set to YES and Doxygen generates man output,
+# then it will generate one additional man file for each entity
+# documented in the real man page(s). These additional files
+# only source the real man page, but without them the man command
+# would be unable to find the correct page. The default is NO.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES Doxygen will
+# generate an XML file that captures the structure of
+# the code including all documentation.
+
+GENERATE_XML           = NO
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be
+# put in front of it. If left blank `xml' will be used as the default path.
+
+XML_OUTPUT             = xml
+
+# The XML_SCHEMA tag can be used to specify an XML schema,
+# which can be used by a validating XML parser to check the
+# syntax of the XML files.
+
+XML_SCHEMA             =
+
+# The XML_DTD tag can be used to specify an XML DTD,
+# which can be used by a validating XML parser to check the
+# syntax of the XML files.
+
+XML_DTD                =
+
+# If the XML_PROGRAMLISTING tag is set to YES Doxygen will
+# dump the program listings (including syntax highlighting
+# and cross-referencing information) to the XML output. Note that
+# enabling this will significantly increase the size of the XML output.
+
+XML_PROGRAMLISTING     = YES
+
+#---------------------------------------------------------------------------
+# configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will
+# generate an AutoGen Definitions (see autogen.sf.net) file
+# that captures the structure of the code including all
+# documentation. Note that this feature is still experimental
+# and incomplete at the moment.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES Doxygen will
+# generate a Perl module file that captures the structure of
+# the code including all documentation. Note that this
+# feature is still experimental and incomplete at the
+# moment.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES Doxygen will generate
+# the necessary Makefile rules, Perl scripts and LaTeX code to be able
+# to generate PDF and DVI output from the Perl module output.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be
+# nicely formatted so it can be parsed by a human reader.
+# This is useful
+# if you want to understand what is going on.
+# On the other hand, if this
+# tag is set to NO the size of the Perl module output will be much smaller
+# and Perl will parse it just the same.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file
+# are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX.
+# This is useful so different doxyrules.make files included by the same
+# Makefile don't overwrite each other's variables.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will
+# evaluate all C-preprocessor directives found in the sources and include
+# files.
+
+ENABLE_PREPROCESSING   = YES
+
+# If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro
+# names in the source code. If set to NO (the default) only conditional
+# compilation will be performed. Macro expansion can be done in a controlled
+# way by setting EXPAND_ONLY_PREDEF to YES.
+
+MACRO_EXPANSION        = NO
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES
+# then the macro expansion is limited to the macros specified with the
+# PREDEFINED and EXPAND_AS_DEFINED tags.
+
+EXPAND_ONLY_PREDEF     = NO
+
+# If the SEARCH_INCLUDES tag is set to YES (the default) the includes files
+# pointed to by INCLUDE_PATH will be searched when a #include is found.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by
+# the preprocessor.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will
+# be used.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that
+# are defined before the preprocessor is started (similar to the -D option of
+# gcc). The argument of the tag is a list of macros of the form: name
+# or name=definition (no spaces). If the definition and the = are
+# omitted =1 is assumed. To prevent a macro definition from being
+# undefined via #undef or recursively expanded use the := operator
+# instead of the = operator.
+
+PREDEFINED             =
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then
+# this tag can be used to specify a list of macro names that should be expanded.
+# The macro definition that is found in the sources will be used.
+# Use the PREDEFINED tag if you want to use a different macro definition that
+# overrules the definition found in the source code.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then
+# doxygen's preprocessor will remove all references to function-like macros
+# that are alone on a line, have an all uppercase name, and do not end with a
+# semicolon, because these will confuse the parser if not removed.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration::additions related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES option can be used to specify one or more tagfiles.
+# Optionally an initial location of the external documentation
+# can be added for each tagfile. The format of a tag file without
+# this location is as follows:
+#
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+#
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where "loc1" and "loc2" can be relative or absolute paths or
+# URLs. If a location is present for each tag, the installdox tool
+# does not have to be run to correct the links.
+# Note that each tag file must have a unique name
+# (where the name does NOT include the path)
+# If a tag file is not located in the directory in which doxygen
+# is run, you must also specify the path to the tagfile here.
+
+TAGFILES               =
+
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create
+# a tag file that is based on the input files it reads.
+
+GENERATE_TAGFILE       =
+
+# If the ALLEXTERNALS tag is set to YES all external classes will be listed
+# in the class index. If set to NO only the inherited external classes
+# will be listed.
+
+ALLEXTERNALS           = NO
+
+# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed
+# in the modules index. If set to NO, only the current project's groups will
+# be listed.
+
+EXTERNAL_GROUPS        = YES
+
+# The PERL_PATH should be the absolute path and name of the perl script
+# interpreter (i.e. the result of `which perl').
+
+PERL_PATH              = /usr/bin/perl
+
+#---------------------------------------------------------------------------
+# Configuration options related to the dot tool
+#---------------------------------------------------------------------------
+
+# If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will
+# generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base
+# or super classes. Setting the tag to NO turns the diagrams off. Note that
+# this option also works with HAVE_DOT disabled, but it is recommended to
+# install and use dot, since it yields more powerful graphs.
+
+CLASS_DIAGRAMS         = YES
+
+# You can define message sequence charts within doxygen comments using the \msc
+# command. Doxygen will then run the mscgen tool (see
+# http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the
+# documentation. The MSCGEN_PATH tag allows you to specify the directory where
+# the mscgen tool resides. If left empty the tool is assumed to be found in the
+# default search path.
+
+MSCGEN_PATH            =
+
+# If set to YES, the inheritance and collaboration graphs will hide
+# inheritance and usage relations if the target is undocumented
+# or is not a class.
+
+HIDE_UNDOC_RELATIONS   = YES
+
+# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# available from the path. This tool is part of Graphviz, a graph visualization
+# toolkit from AT&T and Lucent Bell Labs. The other options in this section
+# have no effect if this option is set to NO (the default)
+
+HAVE_DOT               = NO
+
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is
+# allowed to run in parallel. When set to 0 (the default) doxygen will
+# base this on the number of processors available in the system. You can set it
+# explicitly to a value larger than 0 to get control over the balance
+# between CPU load and processing speed.
+
+DOT_NUM_THREADS        = 0
+
+# By default doxygen will use the Helvetica font for all dot files that
+# doxygen generates. When you want a differently looking font you can specify
+# the font name using DOT_FONTNAME. You need to make sure dot is able to find
+# the font, which can be done by putting it in a standard location or by setting
+# the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the
+# directory containing the font.
+
+DOT_FONTNAME           = Helvetica
+
+# The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs.
+# The default size is 10pt.
+
+DOT_FONTSIZE           = 10
+
+# By default doxygen will tell dot to use the Helvetica font.
+# If you specify a different font using DOT_FONTNAME you can use DOT_FONTPATH to
+# set the path where dot can find it.
+
+DOT_FONTPATH           =
+
+# If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen
+# will generate a graph for each documented class showing the direct and
+# indirect inheritance relations. Setting this tag to YES will force the
+# CLASS_DIAGRAMS tag to NO.
+
+CLASS_GRAPH            = YES
+
+# If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen
+# will generate a graph for each documented class showing the direct and
+# indirect implementation dependencies (inheritance, containment, and
+# class references variables) of the class with other documented classes.
+
+COLLABORATION_GRAPH    = YES
+
+# If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen
+# will generate a graph for groups, showing the direct groups dependencies
+
+GROUP_GRAPHS           = YES
+
+# If the UML_LOOK tag is set to YES doxygen will generate inheritance and
+# collaboration diagrams in a style similar to the OMG's Unified Modeling
+# Language.
+
+UML_LOOK               = NO
+
+# If set to YES, the inheritance and collaboration graphs will show the
+# relations between templates and their instances.
+
+TEMPLATE_RELATIONS     = NO
+
+# If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT
+# tags are set to YES then doxygen will generate a graph for each documented
+# file showing the direct and indirect include dependencies of the file with
+# other documented files.
+
+INCLUDE_GRAPH          = YES
+
+# If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and
+# HAVE_DOT tags are set to YES then doxygen will generate a graph for each
+# documented header file showing the documented files that directly or
+# indirectly include this file.
+
+INCLUDED_BY_GRAPH      = YES
+
+# If the CALL_GRAPH and HAVE_DOT options are set to YES then
+# doxygen will generate a call dependency graph for every global function
+# or class method. Note that enabling this option will significantly increase
+# the time of a run. So in most cases it will be better to enable call graphs
+# for selected functions only using the \callgraph command.
+
+CALL_GRAPH             = NO
+
+# If the CALLER_GRAPH and HAVE_DOT tags are set to YES then
+# doxygen will generate a caller dependency graph for every global function
+# or class method. Note that enabling this option will significantly increase
+# the time of a run. So in most cases it will be better to enable caller
+# graphs for selected functions only using the \callergraph command.
+
+CALLER_GRAPH           = NO
+
+# If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen
+# will generate a graphical hierarchy of all classes instead of a textual one.
+
+GRAPHICAL_HIERARCHY    = YES
+
+# If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES
+# then doxygen will show the dependencies a directory has on other directories
+# in a graphical way. The dependency relations are determined by the #include
+# relations between the files in the directories.
+
+DIRECTORY_GRAPH        = YES
+
+# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
+# generated by dot. Possible values are svg, png, jpg, or gif.
+# If left blank png will be used. If you choose svg you need to set
+# HTML_FILE_EXTENSION to xhtml in order to make the SVG files
+# visible in IE 9+ (other browsers do not have this requirement).
+
+DOT_IMAGE_FORMAT       = png
+
+# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
+# enable generation of interactive SVG images that allow zooming and panning.
+# Note that this requires a modern browser other than Internet Explorer.
+# Tested and working are Firefox, Chrome, Safari, and Opera. For IE 9+ you
+# need to set HTML_FILE_EXTENSION to xhtml in order to make the SVG files
+# visible. Older versions of IE do not have SVG support.
+
+INTERACTIVE_SVG        = NO
+
+# The tag DOT_PATH can be used to specify the path where the dot tool can be
+# found. If left blank, it is assumed the dot tool can be found in the path.
+
+DOT_PATH               =
+
+# The DOTFILE_DIRS tag can be used to specify one or more directories that
+# contain dot files that are included in the documentation (see the
+# \dotfile command).
+
+DOTFILE_DIRS           =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the
+# \mscfile command).
+
+MSCFILE_DIRS           =
+
+# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of
+# nodes that will be shown in the graph. If the number of nodes in a graph
+# becomes larger than this value, doxygen will truncate the graph, which is
+# visualized by representing a node as a red box. Note that doxygen if the
+# number of direct children of the root node in a graph is already larger than
+# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note
+# that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+
+DOT_GRAPH_MAX_NODES    = 50
+
+# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the
+# graphs generated by dot. A depth value of 3 means that only nodes reachable
+# from the root by following a path via at most 3 edges will be shown. Nodes
+# that lay further from the root node will be omitted. Note that setting this
+# option to 1 or 2 may greatly reduce the computation time needed for large
+# code bases. Also note that the size of a graph can be further restricted by
+# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
+
+MAX_DOT_GRAPH_DEPTH    = 0
+
+# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
+# background. This is disabled by default, because dot on Windows does not
+# seem to support this out of the box. Warning: Depending on the platform used,
+# enabling this option may lead to badly anti-aliased labels on the edges of
+# a graph (i.e. they become hard to read).
+
+DOT_TRANSPARENT        = NO
+
+# Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output
+# files in one run (i.e. multiple -o and -T options on the command line). This
+# makes dot run faster, but since only newer versions of dot (>1.8.10)
+# support this, this feature is disabled by default.
+
+DOT_MULTI_TARGETS      = YES
+
+# If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will
+# generate a legend page explaining the meaning of the various boxes and
+# arrows in the dot generated graphs.
+
+GENERATE_LEGEND        = YES
+
+# If the DOT_CLEANUP tag is set to YES (the default) Doxygen will
+# remove the intermediate dot files that are used to generate
+# the various graphs.
+
+DOT_CLEANUP            = YES

From c34ed497ff35804048461445c85172217ba5ffd0 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 28 Aug 2014 01:44:02 -0700
Subject: [PATCH 0635/2053] add "make {docs,doxygen}" targets to build
 doxygen-generated docs

---
 Makefile | 26 +++++++++++++++++++++++++-
 1 file changed, 25 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index dd9ba5c8376..50e68acd84c 100644
--- a/Makefile
+++ b/Makefile
@@ -187,6 +187,25 @@ ALL_BUILD_DIRS := $(sort \
 		$(PROTO_BUILD_DIR) $(PROTO_BUILD_INCLUDE_DIR) $(PY_PROTO_BUILD_DIR) \
 		$(DISTRIBUTE_SUBDIRS))
 
+##############################
+# Set directory for Doxygen-generated documentation
+##############################
+DOXYGEN_CONFIG_FILE ?= ./.Doxyfile
+DOXYGEN_OUTPUT_DIR ?= ./doxygen # should be the same as OUTPUT_DIRECTORY in the .Doxyfile
+DOXYGEN_COMMAND ?= doxygen
+# All the files that might have Doxygen documentation.
+DOXYGEN_SOURCES := $(shell find \
+	src/$(PROJECT) \
+	include/$(PROJECT) \
+	python/ \
+	matlab/ \
+	examples \
+	tools \
+	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh" -or \
+        -name "*.py" -or -name "*.m")
+DOXYGEN_SOURCES += $(DOXYGEN_CONFIG_FILE)
+
+
 ##############################
 # Configure build
 ##############################
@@ -301,7 +320,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 ##############################
 # Define build targets
 ##############################
-.PHONY: all test clean linecount lint lintclean tools examples $(DIST_ALIASES) \
+.PHONY: all test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
@@ -317,6 +336,11 @@ lint: $(EMPTY_LINT_REPORT)
 lintclean:
 	@ $(RM) -r $(LINT_OUTPUT_DIR) $(EMPTY_LINT_REPORT) $(NONEMPTY_LINT_REPORT)
 
+docs: $(DOXYGEN_OUTPUT_DIR)
+
+$(DOXYGEN_OUTPUT_DIR): $(DOXYGEN_CONFIG_FILE) $(DOXYGEN_SOURCES)
+	$(DOXYGEN_COMMAND) $(DOXYGEN_CONFIG_FILE)
+
 $(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
 	@ cat $(LINT_OUTPUTS) > $@
 	@ if [ -s "$@" ]; then \

From 3223ca4c9865285863fcab888a3804891522c240 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 28 Aug 2014 01:48:49 -0700
Subject: [PATCH 0636/2053] .gitignore doxygen-generated documentation

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 62d96f8e9dd..9ba6de74561 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,6 +70,7 @@ MANIFEST-*
 docs/_site
 docs/gathered
 _site
+doxygen/
 
 # Sublime Text settings
 *.sublime-workspace

From ac9275bd6f6993a98dc4404db839d6bf6ccfb661 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 27 Aug 2014 22:39:51 -0700
Subject: [PATCH 0637/2053] .Doxyfile: modify to generate C++ docs, excluding
 tests

---
 .Doxyfile | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.Doxyfile b/.Doxyfile
index 552a39cf933..432d4eda007 100644
--- a/.Doxyfile
+++ b/.Doxyfile
@@ -26,7 +26,7 @@ DOXYFILE_ENCODING      = UTF-8
 # identify the project. Note that if you do not use Doxywizard you need
 # to put quotes around the project name if it contains spaces.
 
-PROJECT_NAME           = "My Project"
+PROJECT_NAME           = "Caffe"
 
 # The PROJECT_NUMBER tag can be used to enter a project or revision number.
 # This could be handy for archiving the generated documentation or
@@ -52,7 +52,7 @@ PROJECT_LOGO           =
 # If a relative path is entered, it will be relative to the location
 # where doxygen was started. If left blank the current directory will be used.
 
-OUTPUT_DIRECTORY       =
+OUTPUT_DIRECTORY       = ./doxygen/
 
 # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create
 # 4096 sub-directories (in 2 levels) under the output directory of each output
@@ -648,7 +648,7 @@ WARN_LOGFILE           =
 # directories like "/usr/src/myproject". Separate the files or directories
 # with spaces.
 
-INPUT                  =
+INPUT                  = ./include/caffe ./src/caffe
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is
@@ -672,7 +672,7 @@ FILE_PATTERNS          =
 # should be searched for input files as well. Possible values are YES and NO.
 # If left blank NO is used.
 
-RECURSIVE              = NO
+RECURSIVE              = YES
 
 # The EXCLUDE tag can be used to specify files and/or directories that should be
 # excluded from the INPUT source files. This way you can easily exclude a
@@ -680,7 +680,7 @@ RECURSIVE              = NO
 # Note that relative paths are relative to the directory from which doxygen is
 # run.
 
-EXCLUDE                =
+EXCLUDE                = ./src/caffe/test/ ./include/caffe/test/
 
 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
 # directories that are symbolic links (a Unix file system feature) are excluded
@@ -1247,7 +1247,7 @@ PAPER_TYPE             = a4
 # The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX
 # packages that should be included in the LaTeX output.
 
-EXTRA_PACKAGES         =
+EXTRA_PACKAGES         = amsmath amsfonts xr
 
 # The LATEX_HEADER tag can be used to specify a personal LaTeX header for
 # the generated latex document. The header should contain everything until

From afba4e6a38d49d0ca8d5324525b32e6de4a8a8c1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 29 Aug 2014 03:31:08 -0700
Subject: [PATCH 0638/2053] .Doxyfile: don't warn if undocumented (maybe
 someday...)

---
 .Doxyfile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.Doxyfile b/.Doxyfile
index 432d4eda007..e0b64bdd6a0 100644
--- a/.Doxyfile
+++ b/.Doxyfile
@@ -595,7 +595,7 @@ CITE_BIB_FILES         =
 # The QUIET tag can be used to turn on/off the messages that are generated
 # by doxygen. Possible values are YES and NO. If left blank NO is used.
 
-QUIET                  = NO
+QUIET                  = YES
 
 # The WARNINGS tag can be used to turn on/off the warning messages that are
 # generated by doxygen. Possible values are YES and NO. If left blank
@@ -607,7 +607,7 @@ WARNINGS               = YES
 # for undocumented members. If EXTRACT_ALL is set to YES then this flag will
 # automatically be disabled.
 
-WARN_IF_UNDOCUMENTED   = YES
+WARN_IF_UNDOCUMENTED   = NO
 
 # If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for
 # potential errors in the documentation, such as not documenting some

From 55eebfd511e6fb98f1b62ab327293b9eff19212f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 28 Aug 2014 00:24:12 -0700
Subject: [PATCH 0639/2053] layer.hpp: Doxygen-style documentation

---
 include/caffe/layer.hpp | 293 +++++++++++++++++++++++++++++++---------
 1 file changed, 226 insertions(+), 67 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 2881fe25807..59e6ccf4ef9 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -12,12 +12,24 @@
 
 namespace caffe {
 
+/**
+ * @brief An interface for the units of computation which can be composed into a
+ *        Net.
+ *
+ * Layer&s must implement a Forward function, in which they take their input
+ * (bottom) Blob&s (if any) and compute their output Blob&s (if any).
+ * They may also implement a Backward function, in which they compute the error
+ * gradients with respect to their input Blob&s, given the error gradients with
+ * their output Blob&s.
+ */
 template <typename Dtype>
 class Layer {
  public:
-  // You should not implement your own constructor. Any set up code should go
-  // to SetUp(), where the dimensions of the bottom blobs are provided to the
-  // layer.
+  /**
+   * You should not implement your own constructor. Any set up code should go
+   * to SetUp(), where the dimensions of the bottom blobs are provided to the
+   * layer.
+   */
   explicit Layer(const LayerParameter& param)
     : layer_param_(param) {
       // The only thing we do is to copy blobs if there are any.
@@ -30,113 +42,229 @@ class Layer {
       }
     }
   virtual ~Layer() {}
-  // SetUp: implements common layer setup functionality, and calls
-  // LayerSetUp to do special layer setup for individual layer types.
-  // This method may not be overridden.
+
+  /**
+   * @brief Implements common layer setup functionality.
+   *
+   * @param bottom the preshaped input blobs
+   * @param top
+   *     the allocated but unshaped output blobs, to be shaped by LayerSetUp
+   *
+   * Checks that the number of bottom and top blobs is correct.
+   * Calls LayerSetUp to do special layer setup for individual layer types.
+   * Sets up the loss weight multiplier blobs for any non-zero loss weights.
+   * This method may not be overridden.
+   */
   void SetUp(const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
     CheckBlobCounts(bottom, *top);
     LayerSetUp(bottom, top);
     SetLossWeights(top);
   }
-  // LayerSetUp: your layer should implement this.
+
+  /**
+   * @brief Does layer-specific setup: your layer should implement this.
+   *
+   * @param bottom
+   *     the preshaped input blobs, whose data fields store the input data for
+   *     this layer
+   * @param top
+   *     the allocated but unshaped output blobs, to be initialized by LayerSetUp
+   *
+   * This method should be used to do layer-specific setup. At a minimum, this
+   * includes reshaping the empty top blobs to the shape as dictated by the
+   * shapes of the bottom blobs and any relevant parameters from the
+   * <code>layer_param_</code>.
+   */
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) { NOT_IMPLEMENTED; }
 
-  // Forward and backward wrappers. You should implement the cpu and
-  // gpu specific implementations instead, and should not change these
-  // functions.
+  /**
+   * @brief Given the bottom blobs, compute the top blobs and the loss.
+   *
+   * @param bottom
+   *     the input blobs, whose data fields store the input data for this layer
+   * @param top
+   *     the preshaped output blobs, whose data fields will store this layers'
+   *     outputs
+   * \return The total loss from the layer.
+   *
+   * The Forward wrapper calls the relevant device wrapper function
+   * (Forward_cpu or Forward_gpu) to compute the top blob values given the
+   * bottom blobs.  If the layer has any non-zero loss_weights, the wrapper
+   * then computes and returns the loss.
+   *
+   * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
+   */
   inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Given the top blob error gradients, compute the bottom blob error
+   *        gradients.
+   *
+   * @param top
+   *     the output blobs, whose diff fields store the gradient of the error
+   *     with respect to themselves
+   * @param propagate_down
+   *     a vector with equal length to bottom, with each index indicating
+   *     whether to propagate the error gradients down to the bottom blob at
+   *     the corresponding index
+   * @param bottom
+   *     the input blobs, whose diff fields will store the gradient of the error
+   *     with respect to themselves after Backward is run
+   *
+   * The Backward wrapper calls the relevant device wrapper function
+   * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
+   * top blob diffs.
+   *
+   * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
+   */
   inline void Backward(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom);
 
-  // Returns the vector of blobs.
+  /**
+   * @brief Returns the vector of learnable parameter blobs.
+   */
   vector<shared_ptr<Blob<Dtype> > >& blobs() {
     return blobs_;
   }
 
-  // Returns the layer parameter
+  /**
+   * @brief Returns the layer parameter.
+   */
   const LayerParameter& layer_param() const { return layer_param_; }
-  // Writes the layer parameter to a protocol buffer
+
+  /**
+   * @brief Writes the layer parameter to a protocol buffer
+   */
   virtual void ToProto(LayerParameter* param, bool write_diff = false);
 
+  /**
+   * @brief Returns the scalar loss associated with a top blob at a given index.
+   */
   inline Dtype loss(const int top_index) const {
     return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
   }
+
+  /**
+   * @brief Sets the loss associated with a top blob at a given index.
+   */
   inline void set_loss(const int top_index, const Dtype value) {
     if (loss_.size() <= top_index) {
       loss_.resize(top_index + 1, Dtype(0));
     }
     loss_[top_index] = value;
   }
-  // Setup the weights associated with each top blob in the loss function.
-  // Store non-zero loss weights in the diff blob.
-  inline void SetLossWeights(vector<Blob<Dtype>*>* top) {
-    const int num_loss_weights = layer_param_.loss_weight_size();
-    if (num_loss_weights) {
-      CHECK_EQ(top->size(), num_loss_weights) << "loss_weight must be "
-          "unspecified or specified once per top blob.";
-      for (int top_id = 0; top_id < top->size(); ++top_id) {
-        const Dtype loss_weight = layer_param_.loss_weight(top_id);
-        if (loss_weight == Dtype(0)) { continue; }
-        this->set_loss(top_id, loss_weight);
-        const int count = (*top)[top_id]->count();
-        Dtype* loss_multiplier = (*top)[top_id]->mutable_cpu_diff();
-        caffe_set(count, loss_weight, loss_multiplier);
-      }
-    }
-  }
 
-  // Returns the layer type as an enum value.
+  /**
+   * @brief Returns the layer type as an enum value.
+   */
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_NONE;
   }
 
-  // Returns the layer type name.
+  /**
+   * @brief Returns the layer type name.
+   */
   virtual inline const string& type_name() const {
     return LayerParameter_LayerType_Name(type());
   }
 
-  // These methods can be overwritten to declare that this layer type expects
-  // a certain number of blobs as input and output.
-  //
-  // ExactNum{Bottom,Top}Blobs return a non-negative number to require an exact
-  // number of bottom/top blobs; the Min/Max versions return a non-negative
-  // number to require a minimum and/or maximum number of blobs.
-  // If Exact is specified, neither Min nor Max should be specified, and vice
-  // versa.  These methods may not rely on SetUp having been called.
+  /**
+   * @brief Returns the exact number of bottom blobs required by the layer,
+   *        or -1 if no exact number is required.
+   *
+   * This method should be overridden to return a non-negative value if your
+   * layer expects some exact number of bottom blobs.
+   */
   virtual inline int ExactNumBottomBlobs() const { return -1; }
+  /**
+   * @brief Returns the minimum number of bottom blobs required by the layer,
+   *        or -1 if no minimum number is required.
+   *
+   * This method should be overridden to return a non-negative value if your
+   * layer expects some minimum number of bottom blobs.
+   */
   virtual inline int MinBottomBlobs() const { return -1; }
+  /**
+   * @brief Returns the maximum number of bottom blobs required by the layer,
+   *        or -1 if no maximum number is required.
+   *
+   * This method should be overridden to return a non-negative value if your
+   * layer expects some maximum number of bottom blobs.
+   */
   virtual inline int MaxBottomBlobs() const { return -1; }
+  /**
+   * @brief Returns the exact number of top blobs required by the layer,
+   *        or -1 if no exact number is required.
+   *
+   * This method should be overridden to return a non-negative value if your
+   * layer expects some exact number of top blobs.
+   */
   virtual inline int ExactNumTopBlobs() const { return -1; }
+  /**
+   * @brief Returns the minimum number of top blobs required by the layer,
+   *        or -1 if no minimum number is required.
+   *
+   * This method should be overridden to return a non-negative value if your
+   * layer expects some minimum number of top blobs.
+   */
   virtual inline int MinTopBlobs() const { return -1; }
+  /**
+   * @brief Returns the maximum number of top blobs required by the layer,
+   *        or -1 if no maximum number is required.
+   *
+   * This method should be overridden to return a non-negative value if your
+   * layer expects some maximum number of top blobs.
+   */
   virtual inline int MaxTopBlobs() const { return -1; }
+  /**
+   * @brief Returns true if the layer requires an equal number of bottom and
+   *        top blobs.
+   *
+   * This method should be overridden to return true if your layer expects an
+   * equal number of bottom and top blobs.
+   */
+  virtual inline bool EqualNumBottomTopBlobs() const { return false; }
 
-  // AutoTopBlobs may be overridden with a positive integer to automatically
-  // create enough "anonymous" top blobs to fulfill the requirement specified
-  // by ExactNumTopBlobs() or MinTopBlobs().
+  /**
+   * @brief Return whether "anonymous" top blobs are created automatically
+   *        by the layer.
+   *
+   * If this method returns true, Net::Init will create enough "anonymous" top
+   * blobs to fulfill the requirement specified by ExactNumTopBlobs() or
+   * MinTopBlobs().
+   */
   virtual inline bool AutoTopBlobs() const { return false; }
 
-  // EqualNumBottomTopBlobs should return true for layers requiring an equal
-  // number of bottom and top blobs.
-  virtual inline bool EqualNumBottomTopBlobs() const { return false; }
-
-  // Declare for each bottom blob whether to allow force_backward -- that is,
-  // if AllowForceBackward(i) == false, we will ignore the force_backward
-  // setting and backpropagate to blob i only if it needs gradient information
-  // (as is done when force_backward == false).
+  /**
+   * @brief Return whether to allow force_backward for a given bottom blob
+   *        index.
+   *
+   * If AllowForceBackward(i) == false, we will ignore the force_backward
+   * setting and backpropagate to blob i only if it needs gradient information
+   * (as is done when force_backward == false).
+   */
   virtual inline bool AllowForceBackward(const int bottom_index) const {
     return true;
   }
 
-  // param_propagate_down specifies whether the layer should compute gradients
-  // in Backward.  You can safely ignore false and always compute gradients
-  // for all parameters, but possibly with wasteful computation.
+  /**
+   * @brief Specifies whether the layer should compute gradients w.r.t. a
+   *        parameter at a particular index given by param_id.
+   *
+   * You can safely ignore false values and always compute gradients
+   * for all parameters, but possibly with wasteful computation.
+   */
   inline bool param_propagate_down(const int param_id) {
     return (param_propagate_down_.size() > param_id) ?
         param_propagate_down_[param_id] : false;
   }
+  /**
+   * @brief Sets whether the layer should compute gradients w.r.t. a
+   *        parameter at a particular index given by param_id.
+   */
   inline void set_param_propagate_down(const int param_id, const bool value) {
     if (param_propagate_down_.size() <= param_id) {
       param_propagate_down_.resize(param_id + 1, true);
@@ -146,33 +274,42 @@ class Layer {
 
 
  protected:
-  // The protobuf that stores the layer parameters
+  /** The protobuf that stores the layer parameters */
   LayerParameter layer_param_;
-  // The vector that stores the parameters as a set of blobs.
+  /** The vector that stores the learnable parameters as a set of blobs. */
   vector<shared_ptr<Blob<Dtype> > > blobs_;
-  // Vector indicating whether to compute the diff of each param blob.
+  /** Vector indicating whether to compute the diff of each param blob. */
   vector<bool> param_propagate_down_;
 
-  // The vector that indicates whether each top blob has a non-zero weight in
-  // the objective function.
+  /** The vector that indicates whether each top blob has a non-zero weight in
+   *  the objective function. */
   vector<Dtype> loss_;
 
-  // Forward functions: compute the layer output
-  // (and loss layers return the loss; other layers return the dummy value 0.)
+  /** @brief Using the CPU device, compute the layer output. */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) = 0;
-  // If no gpu code is provided, we will simply use cpu code.
+  /**
+   * @brief Using the GPU device, compute the layer output.
+   *        Fall back to Forward_cpu() if unavailable.
+   */
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
     // LOG(WARNING) << "Using CPU code as backup.";
     return Forward_cpu(bottom, top);
   }
 
-  // Backward functions: compute the gradients for any parameters and
-  // for the bottom blobs if propagate_down is true.
+  /**
+   * @brief Using the CPU device, compute the gradients for any parameters and
+   *        for the bottom blobs if propagate_down is true.
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom) = 0;
+  /**
+   * @brief Using the GPU device, compute the gradients for any parameters and
+   *        for the bottom blobs if propagate_down is true.
+   *        Fall back to Backward_cpu() if unavailable.
+   */
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom) {
@@ -180,9 +317,11 @@ class Layer {
     Backward_cpu(top, propagate_down, bottom);
   }
 
-  // CheckBlobCounts: called by the parent Layer's SetUp to check that the
-  // number of bottom and top Blobs provided as input match the expected
-  // numbers specified by the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
+  /**
+   * Called by the parent Layer's SetUp to check that the number of bottom
+   * and top Blobs provided as input match the expected numbers specified by
+   * the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
+   */
   virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
                                const vector<Blob<Dtype>*>& top) {
     if (ExactNumBottomBlobs() >= 0) {
@@ -222,6 +361,26 @@ class Layer {
     }
   }
 
+  /**
+   * Called by SetUp to initialize the weights associated with any top blobs in
+   * the loss function. Store non-zero loss weights in the diff blob.
+   */
+  inline void SetLossWeights(vector<Blob<Dtype>*>* top) {
+    const int num_loss_weights = layer_param_.loss_weight_size();
+    if (num_loss_weights) {
+      CHECK_EQ(top->size(), num_loss_weights) << "loss_weight must be "
+          "unspecified or specified once per top blob.";
+      for (int top_id = 0; top_id < top->size(); ++top_id) {
+        const Dtype loss_weight = layer_param_.loss_weight(top_id);
+        if (loss_weight == Dtype(0)) { continue; }
+        this->set_loss(top_id, loss_weight);
+        const int count = (*top)[top_id]->count();
+        Dtype* loss_multiplier = (*top)[top_id]->mutable_cpu_diff();
+        caffe_set(count, loss_weight, loss_multiplier);
+      }
+    }
+  }
+
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 

From 9f5857459d1f85f4fadbe9ad7f4c03d29a87ed5b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 28 Aug 2014 04:17:43 -0700
Subject: [PATCH 0640/2053] loss_layers.hpp: Doxygen-style documentation

---
 include/caffe/loss_layers.hpp | 490 +++++++++++++++++++++++++++++++---
 1 file changed, 448 insertions(+), 42 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index df887b8f856..1a93e4d8060 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -19,13 +19,21 @@ namespace caffe {
 
 const float kLOG_THRESHOLD = 1e-20;
 
-/* AccuracyLayer
-  Note: not an actual loss layer! Does not implement backwards step.
-  Computes the accuracy of argmax(a) with respect to b.
-*/
+/**
+ * @brief Computes the classification accuracy for a one-of-many
+ *        classification task.
+ */
 template <typename Dtype>
 class AccuracyLayer : public Layer<Dtype> {
  public:
+  /**
+   * @param param provides AccuracyParameter accuracy_param,
+   *     with AccuracyLayer options:
+   *   - top_k (\b optional, default 1).
+   *     Sets the maximum rank @f$ k @f$ at which a prediction is considered
+   *     correct.  For example, if @f$ k = 5 @f$, a prediction is counted
+   *     correct if the correct label is among the top 5 predicted labels.
+   */
   explicit AccuracyLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -39,8 +47,35 @@ class AccuracyLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ x @f$, a Blob with values in
+   *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+   *      the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
+   *      label @f$ \hat{l}_n @f$ given by its maximal index:
+   *      @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels @f$ l @f$, an integer-valued Blob with values
+   *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+   *      indicating the correct class label among the @f$ K @f$ classes
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      the computed accuracy: @f$
+   *        \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
+   *      @f$, where @f$
+   *      \delta\{\mathrm{condition}\} = \left\{
+   *         \begin{array}{lr}
+   *            1 & \mbox{if condition} \\
+   *            0 & \mbox{otherwise}
+   *         \end{array} \right.
+   *      @f$
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+
+  /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     for (int i = 0; i < propagate_down.size(); ++i) {
@@ -51,10 +86,14 @@ class AccuracyLayer : public Layer<Dtype> {
   int top_k_;
 };
 
-/* LossLayer
-  Takes two inputs of same num (a and b), and has no output.
-  The gradient is propagated to a.
-*/
+/**
+ * @brief An interface for Layer%s that take two Blob%s as input -- usually
+ *        (1) predictions and (2) ground-truth labels -- and output a
+ *        singleton Blob representing the loss.
+ *
+ * LossLayers are typically only capable of backpropagating to their first input
+ * -- the predictions.
+ */
 template <typename Dtype>
 class LossLayer : public Layer<Dtype> {
  public:
@@ -64,24 +103,50 @@ class LossLayer : public Layer<Dtype> {
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
-  // For convenience and backwards compatibility, LossLayer's automatically
-  // allocate a singleton top blob into which they output their singleton loss,
-  // (even if the user didn't specify one in the prototxt, etc.).
+
+  /**
+   * @brief For convenience and backwards compatibility, instruct the Net to
+   *        automatically allocate a single top Blob for LossLayers, into which
+   *        they output their singleton loss, (even if the user didn't specify
+   *        one in the prototxt, etc.).
+   */
   virtual inline bool AutoTopBlobs() const { return true; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
-  // We usually cannot backpropagate to the labels; ignore force_backward for
-  // these inputs.
+  /**
+   * We usually cannot backpropagate to the labels; ignore force_backward for
+   * these inputs.
+   */
   virtual inline bool AllowForceBackward(const int bottom_index) const {
     return bottom_index != 1;
   }
 };
 
-/* EuclideanLossLayer
-  Compute the L_2 distance between the two inputs.
-
-  loss = (1/2 \sum_i (a_i - b_i)^2)
-  a' = 1/I (a - b)
-*/
+/**
+ * @brief Computes the Euclidean (L2) loss @f$
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ *        \right| \right|_2^2 @f$ for real-valued regression tasks.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the targets @f$ y \in [-\infty, +\infty]@f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed Euclidean loss: @f$ E =
+ *          \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ *        \right| \right|_2^2 @f$
+ *
+ * This can be used for least-squares regression tasks.  An InnerProductLayer
+ * input to a EuclideanLossLayer exactly formulates a linear least squares
+ * regression problem. With non-zero weight decay the problem becomes one of
+ * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
+ * example wherein we check that the gradients computed for a Net with exactly
+ * this structure match hand-computed gradient formulas for ridge regression.
+ *
+ * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
+ * linear least squares problems! We use it only as an instructive example.)
+ */
 template <typename Dtype>
 class EuclideanLossLayer : public LossLayer<Dtype> {
  public:
@@ -93,17 +158,55 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_EUCLIDEAN_LOSS;
   }
-  // Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
-  // to both inputs.
+
+  /**
+   * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+   * to both inputs -- override to return true and always allow force_backward.
+   */
   virtual inline bool AllowForceBackward(const int bottom_index) const {
     return true;
   }
 
  protected:
+  /// @copydoc EuclideanLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the Euclidean error gradient w.r.t. the inputs.
+   *
+   * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
+   * gradients with respect to the label inputs bottom[1] (but still only will
+   * if propagate_down[1] is set, due to being produced by learnable parameters
+   * or if force_backward is set). In fact, this layer is "commutative" -- the
+   * result is the same regardless of the order of the two bottoms.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$\hat{y}@f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial \hat{y}} =
+   *            \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
+   *      @f$ if propagate_down[0]
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the targets @f$y@f$; Backward fills their diff with gradients
+   *      @f$ \frac{\partial E}{\partial y} =
+   *          \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
+   *      @f$ if propagate_down[1]
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -112,8 +215,49 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   Blob<Dtype> diff_;
 };
 
-/* HingeLossLayer
-*/
+/**
+ * @brief Computes the hinge loss for a one-of-many classification task.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ t @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
+ *      taking the inner product @f$ X^T W @f$ of the D-dimensional features
+ *      @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
+ *      parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
+ *      an InnerProductLayer (with num_output = D) providing predictions to a
+ *      HingeLossLayer and no other learnable parameters or losses is
+ *      equivalent to an SVM.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed hinge loss: @f$ E =
+ *        \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
+ *        [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
+ *      @f$, for the @f$ L^p @f$ norm
+ *      (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
+ *      is also available), and @f$
+ *      \delta\{\mathrm{condition}\} = \left\{
+ *         \begin{array}{lr}
+ *            1 & \mbox{if condition} \\
+ *           -1 & \mbox{otherwise}
+ *         \end{array} \right.
+ *      @f$
+ *
+ * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
+ * the inner product @f$ X^T W @f$ of the features
+ * @f$ X \in \mathcal{R}^{D \times N} @f$
+ * and the learned hyperplane parameters
+ * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
+ * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
+ * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
+ * outside the InnerProductLayer and no other losses outside the
+ * HingeLossLayer).
+ */
 template <typename Dtype>
 class HingeLossLayer : public LossLayer<Dtype> {
  public:
@@ -125,14 +269,73 @@ class HingeLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
+  /// @copydoc HingeLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the hinge loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$t@f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial t} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* InfogainLossLayer
-*/
+/**
+ * @brief A generalization of MultinomialLogisticLossLayer that takes an
+ *        "information gain" (infogain) matrix specifying the "value" of all label
+ *        pairs.
+ *
+ * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
+ * identity.
+ *
+ * @param bottom input Blob vector (length 2-3)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ *   -# @f$ (1 \times 1 \times K \times K) @f$
+ *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
+ *      the third bottom blob input if not provided as the infogain_mat in the
+ *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
+ *      MultinomialLogisticLossLayer.
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed infogain multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
+ *        \log(\hat{p}_{n,k})
+ *      @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
+ */
 template <typename Dtype>
 class InfogainLossLayer : public LossLayer<Dtype> {
  public:
@@ -141,7 +344,7 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
-  // InfogainLossLayer takes 2-3 bottom blobs; if there are 3 the third should
+  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
   // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
   // file specified by LayerParameter.)
   virtual inline int ExactNumBottomBlobs() const { return -1; }
@@ -153,16 +356,77 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
+  /// @copydoc InfogainLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the infogain loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set. (The same applies to the infogain matrix, if
+   * provided as bottom[2] rather than in the layer_param.)
+   *
+   * @param top output Blob vector (length 1), providing the error gradient
+   *      with respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels (similarly for propagate_down[2] and the
+   *      infogain matrix, if provided as bottom[2])
+   * @param bottom input Blob vector (length 2-3)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   *   -# @f$ (1 \times 1 \times K \times K) @f$
+   *      (\b optional) the information gain matrix -- ignored as its error
+   *      gradient computation is not implemented.
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Blob<Dtype> infogain_;
 };
 
-/* MultinomialLogisticLossLayer
-*/
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, directly taking a predicted probability
+ *        distribution as input.
+ *
+ * When predictions are not already a probability distribution, you should
+ * instead use the SoftmaxWithLossLayer, which maps predictions to a
+ * distribution using the SoftmaxLayer, before computing the multinomial
+ * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$
+ */
 template <typename Dtype>
 class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
  public:
@@ -176,14 +440,71 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
+  /// @copydoc MultinomialLogisticLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the multinomial logistic loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* SigmoidCrossEntropyLossLayer
-*/
+/**
+ * @brief Computes the cross-entropy (logistic) loss @f$
+ *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ *                  p_n \log \hat{p}_n +
+ *                  (1 - p_n) \log(1 - \hat{p}_n)
+ *              \right]
+ *        @f$, often used for predicting targets interpreted as probabilities.
+ *
+ * This layer is implemented rather than separate
+ * SigmoidLayer + CrossEntropyLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SigmoidLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the scores @f$ x \in [-\infty, +\infty]@f$,
+ *      which this layer maps to probability predictions
+ *      @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
+ *      using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the targets @f$ y \in [0, 1] @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed cross-entropy loss: @f$
+ *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ *                  p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
+ *              \right]
+ *      @f$
+ */
 template <typename Dtype>
 class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
  public:
@@ -199,34 +520,88 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   }
 
  protected:
+  /// @copydoc SigmoidCrossEntropyLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the target inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as gradient computation with respect
+   *      to the targets is not implemented.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$x@f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} =
+   *          \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
+   *      @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  /// The internal SigmoidLayer used to map predictions to probabilities.
   shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
-  // sigmoid_output stores the output of the sigmoid layer.
+  /// sigmoid_output stores the output of the SigmoidLayer.
   shared_ptr<Blob<Dtype> > sigmoid_output_;
-  // Vector holders to call the underlying sigmoid layer forward and backward.
+  /// bottom vector holder to call the underlying SigmoidLayer::Forward
   vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+  /// top vector holder to call the underlying SigmoidLayer::Forward
   vector<Blob<Dtype>*> sigmoid_top_vec_;
 };
 
 // Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
 template <typename Dtype> class SoftmaxLayer;
 
-/* SoftmaxWithLossLayer
-  Implements softmax and computes the loss.
-
-  It is preferred over separate softmax + multinomiallogisticloss
-  layers due to more numerically stable gradients.
-
-  In test, this layer could be replaced by simple softmax layer.
-*/
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, passing real-valued predictions through a
+ *        softmax to get a probability distribution over classes.
+ *
+ * This layer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SoftmaxLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ x @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
+ *      probability distribution over classes using the softmax function
+ *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
+ *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed cross-entropy classification loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$, for softmax output class probabilites @f$ \hat{p} @f$
+ */
 template <typename Dtype>
 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
@@ -247,20 +622,51 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
+  /// @copydoc SoftmaxWithLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the softmax loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ x @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  /// The internal SoftmaxLayer used to map predictions to a distribution.
   shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
-  // prob stores the output probability of the layer.
+  /// prob stores the output probability predictions from the SoftmaxLayer.
   Blob<Dtype> prob_;
-  // Vector holders to call the underlying softmax layer forward and backward.
+  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
   vector<Blob<Dtype>*> softmax_bottom_vec_;
+  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
   vector<Blob<Dtype>*> softmax_top_vec_;
 };
 

From c5d53088b896ed93fb94b76e398524feb523ffbf Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 28 Aug 2014 02:36:35 -0700
Subject: [PATCH 0641/2053] neuron_layers.hpp: Doxygen-style documentation

---
 include/caffe/neuron_layers.hpp | 372 ++++++++++++++++++++++++++------
 1 file changed, 302 insertions(+), 70 deletions(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index b5bbc5b6482..370f851a9ee 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -18,10 +18,12 @@
 
 namespace caffe {
 
-/* NeuronLayer
-  An interface for layers that take one blob as input (x),
-  and produce one blob as output (y).
-*/
+/**
+ * @brief An interface for layers that take one blob as input (@f$ x @f$)
+ *        and produce one equally-sized blob as output (@f$ y @f$), where
+ *        each element of the output depends only on the corresponding input
+ *        element.
+ */
 template <typename Dtype>
 class NeuronLayer : public Layer<Dtype> {
  public:
@@ -37,12 +39,16 @@ class NeuronLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 };
 
-/* AbsVal Layer
-  y = |x|
-
-  y' = 1    if x > 0
-     = -1   if x < 0
-*/
+/**
+ * @brief Computes @f$ y = |x| @f$
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$ y = |x| @f$
+ */
 template <typename Dtype>
 class AbsValLayer : public NeuronLayer<Dtype> {
  public:
@@ -56,24 +62,54 @@ class AbsValLayer : public NeuronLayer<Dtype> {
   }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
+  /// @copydoc AbsValLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the absolute value inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \mathrm{sign}(x) \frac{\partial E}{\partial y}
+   *      @f$ if propagate_down[0]
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* BNLLLayer
-
-  y = x + log(1 + exp(-x))  if x > 0
-  y = log(1 + exp(x))       if x <= 0
-
-  y' = exp(x) / (exp(x) + 1)
-*/
+/**
+ * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
+ *        @f$ y = \log(1 + \exp(x)) @f$ otherwise.
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$
+ *      y = \left\{
+ *         \begin{array}{ll}
+ *            x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
+ *            \log(1 + \exp(x)) & \mbox{otherwise}
+ *         \end{array} \right.
+ *      @f$
+ */
 template <typename Dtype>
 class BNLLLayer : public NeuronLayer<Dtype> {
  public:
@@ -85,29 +121,54 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /// @copydoc BNLLLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the BNLL inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *      @f$ if propagate_down[0]
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* DropoutLayer
-  During training only, sets some portion of x to 0, adjusting the
-  vector magnitude accordingly.
-
-  mask = bernoulli(1 - threshold)
-  scale = 1 / (1 - threshold)
-  y = x * mask * scale
-
-  y' = mask * scale
-*/
+/**
+ * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
+ *        the rest of the vector magnitude accordingly.
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$ y = |x| @f$
+ */
 template <typename Dtype>
 class DropoutLayer : public NeuronLayer<Dtype> {
  public:
+  /**
+   * @param param provides DropoutParameter dropout_param,
+   *     with DropoutLayer options:
+   *   - dropout_ratio (\b optional, default 0.5).
+   *     Sets the probability @f$ p @f$ that any given unit is dropped.
+   */
   explicit DropoutLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -118,6 +179,22 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs. At training time, we have @f$
+   *      y_{\mbox{train}} = \left\{
+   *         \begin{array}{ll}
+   *            \frac{x}{1 - p} & \mbox{if } u > p \\
+   *            0 & \mbox{otherwise}
+   *         \end{array} \right.
+   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
+   *      input at each iteration. At test time, we simply have
+   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
@@ -127,21 +204,30 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
   Blob<unsigned int> rand_vec_;
+  /// the probability @f$ p @f$ of dropping any input
   Dtype threshold_;
+  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
   Dtype scale_;
   unsigned int uint_thres_;
 };
 
-/* PowerLayer
-  y = (shift + scale * x) ^ power
-
-  y' = scale * power * (shift + scale * x) ^ (power - 1)
-     = scale * power * y / (shift + scale * x)
-*/
+/**
+ * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and power @f$ \gamma @f$.
+ */
 template <typename Dtype>
 class PowerLayer : public NeuronLayer<Dtype> {
  public:
+  /**
+   * @param param provides PowerParameter power_param,
+   *     with PowerLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - power (\b optional, default 1) the power @f$ \gamma @f$
+   */
   explicit PowerLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -152,33 +238,69 @@ class PowerLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = (\alpha x + \beta) ^ \gamma
+   *      @f$
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the power inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y}
+   *            \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
+   *            \frac{\partial E}{\partial y}
+   *            \frac{\alpha \gamma y}{\alpha x + \beta}
+   *      @f$ if propagate_down[0]
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  /// @brief @f$ \gamma @f$ from layer_param_.power_param()
   Dtype power_;
+  /// @brief @f$ \alpha @f$ from layer_param_.power_param()
   Dtype scale_;
+  /// @brief @f$ \beta @f$ from layer_param_.power_param()
   Dtype shift_;
+  /// @brief Result of @f$ \alpha \gamma @f$
   Dtype diff_scale_;
 };
 
-/* ReLULayer
-  Rectified Linear Unit non-linearity.
-  The simple max is fast to compute, and the function does not saturate.
-
-  y = max(0, x).
-
-  y' = 0  if x < 0
-  y' = 1 if x > 0
-*/
+/**
+ * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
+ *        The simple max is fast to compute, and the function does not saturate.
+ */
 template <typename Dtype>
 class ReLULayer : public NeuronLayer<Dtype> {
  public:
+  /**
+   * @param param provides ReLUParameter relu_param,
+   *     with ReLULayer options:
+   *   - negative_slope (\b optional, default 0).
+   *     the value @f$ \nu @f$ by which negative values are multiplied.
+   */
   explicit ReLULayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
@@ -187,28 +309,64 @@ class ReLULayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \max(0, x)
+   *      @f$ by default.  If a non-zero negative_slope @f$ \nu @f$ is provided,
+   *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  /**
+   * @brief Computes the error gradient w.r.t. the ReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            0 & \mathrm{if} \; x \le 0 \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+   *        \end{array} \right.
+   *      @f$ if propagate_down[0], by default.
+   *      If a non-zero negative_slope @f$ \nu @f$ is provided,
+   *      the computed gradients are @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* SigmoidLayer
-  Sigmoid function non-linearity, a classic choice in neural networks.
-  Note that the gradient vanishes as the values move away from 0.
-  The ReLULayer is often a better choice for this reason.
-
-  y = 1. / (1 + exp(-x))
-
-  y ' = exp(x) / (1 + exp(x))^2
-  or
-  y' = y * (1 - y)
-*/
+/**
+ * @brief Sigmoid function non-linearity @f$
+ *         y = (1 + \exp(-x))^{-1}
+ *     @f$, a classic choice in neural networks.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
 template <typename Dtype>
 class SigmoidLayer : public NeuronLayer<Dtype> {
  public:
@@ -220,23 +378,52 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = (1 + \exp(-x))^{-1}
+   *      @f$
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y} y (1 - y)
+   *      @f$ if propagate_down[0]
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* TanHLayer
-  Hyperbolic tangent non-linearity, popular in auto-encoders.
-
-  y = 1. * (exp(2x) - 1) / (exp(2x) + 1)
-
-  y' = 1 - ( (exp(2x) - 1) / (exp(2x) + 1) ) ^ 2
-*/
+/**
+ * @brief TanH hyperbolic tangent non-linearity @f$
+ *         y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+ *     @f$, popular in auto-encoders.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
 template <typename Dtype>
 class TanHLayer : public NeuronLayer<Dtype> {
  public:
@@ -248,29 +435,59 @@ class TanHLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+   *      @f$
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y}
+   *              \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
+   *            = \frac{\partial E}{\partial y} (1 - y^2)
+   *      @f$ if propagate_down[0]
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* ThresholdLayer
-  Outputs 1 if value in input is above threshold, 0 otherwise.
-  The defult threshold = 0, which means positive values would become 1 and
-  negative or 0, would become 0
-
-  y = 1 if x > threshold
-  y = 0 if x <= threshold
-
-  y' = don't differenciable
-*/
+/**
+ * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
+ *        above threshold; 0 otherwise.
+ */
 template <typename Dtype>
 class ThresholdLayer : public NeuronLayer<Dtype> {
  public:
+  /**
+   * @param param provides ThresholdParameter threshold_param,
+   *     with ThresholdLayer options:
+   *   - threshold (\b optional, default 0).
+   *     the threshold value @f$ t @f$ to which the input values are compared.
+   */
   explicit ThresholdLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -281,10 +498,25 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *       y = \left\{
+   *       \begin{array}{lr}
+   *         0 & \mathrm{if} \; x \le t \\
+   *         1 & \mathrm{if} \; x > t
+   *       \end{array} \right.
+   *      @f$
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  /// @brief Not implemented (non-differentiable function)
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;

From 57171b507aff14f16773b4791b53d19aee1e7be8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 29 Aug 2014 21:53:12 -0700
Subject: [PATCH 0642/2053] common_layers.hpp: Doxygen \brief & TODO stubs.

---
 include/caffe/common_layers.hpp | 169 ++++++++++++++++++++++++++------
 1 file changed, 137 insertions(+), 32 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 1b50e9563ca..e32dac71332 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -15,18 +15,27 @@
 
 namespace caffe {
 
-/* ArgmaxLayer
-  Compute the index of the max value across all (channels x height x width).
-  [In the future, can take specific dimension.]
-  Intended for use after a classification layer to produce prediction.
-  If parameter out_max_val is set to true, then output is a vector of pairs
-  (max_ind, max_val) for each image.
-
-  NOTE: does not implement Backwards operation.
-*/
+/**
+ * @brief Compute the index of the @f$ K @f$ max values for each datum across
+ *        all dimensions @f$ (C \times H \times W) @f$.
+ *
+ * Intended for use after a classification layer to produce a prediction.
+ * If parameter out_max_val is set to true, output is a vector of pairs
+ * (max_ind, max_val) for each image.
+ *
+ * NOTE: does not implement Backwards operation.
+ */
 template <typename Dtype>
 class ArgMaxLayer : public Layer<Dtype> {
  public:
+  /**
+   * @param param provides ArgMaxParameter argmax_param,
+   *     with ArgMaxLayer options:
+   *   - top_k (\b optional uint, default 1).
+   *     the number @f$ K @f$ of maximal items to output.
+   *   - out_max_val (\b optional bool, default false).
+   *     if set, output a vector of pairs (max_ind, max_val) for each image.
+   */
   explicit ArgMaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -39,8 +48,20 @@ class ArgMaxLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val
+   *      @f$ (N \times 2 \times K \times 1) @f$
+   *      the computed outputs @f$
+   *       y_n = \arg\max\limits_i x_{ni}
+   *      @f$ (for @f$ K = 1 @f$).
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  /// @brief Not implemented (non-differentiable function)
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
     NOT_IMPLEMENTED;
@@ -49,10 +70,10 @@ class ArgMaxLayer : public Layer<Dtype> {
   size_t top_k_;
 };
 
-/* ConcatLayer
-  Takes at least two blobs and concatenates them along either num or
-  channel dim, outputting the result.
-*/
+/**
+ * @brief Takes at least two Blob%s and concatenates them along either the num
+ *        or channel dimension, outputting the result.
+ */
 template <typename Dtype>
 class ConcatLayer : public Layer<Dtype> {
  public:
@@ -68,10 +89,49 @@ class ConcatLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_1 @f$
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_2 @f$
+   *   -# ...
+   *   - K @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_K @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (KN \times C \times H \times W) @f$ if concat_dim == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if concat_dim == 1:
+   *      the concatenated output @f$
+   *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
+   *      @f$
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the concatenate inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *        respect to the outputs
+   *   -# @f$ (KN \times C \times H \times W) @f$ if concat_dim == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if concat_dim == 1:
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to concatenated outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length K), into which the top gradient
+   *        @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
+   *        inputs @f$
+   *        \left[ \begin{array}{cccc}
+   *          \frac{\partial E}{\partial x_1} &
+   *          \frac{\partial E}{\partial x_2} &
+   *          ... &
+   *          \frac{\partial E}{\partial x_K}
+   *        \end{array} \right] =
+   *        \frac{\partial E}{\partial y}
+   *        @f$
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -86,9 +146,12 @@ class ConcatLayer : public Layer<Dtype> {
   int concat_dim_;
 };
 
-/* EltwiseLayer
-  Compute elementwise operations like product or sum.
-*/
+/**
+ * @brief Compute elementwise operations, such as product and sum,
+ *        along multiple input Blobs.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class EltwiseLayer : public Layer<Dtype> {
  public:
@@ -119,8 +182,16 @@ class EltwiseLayer : public Layer<Dtype> {
   bool stable_prod_grad_;
 };
 
-/* FlattenLayer
-*/
+/**
+ * @brief Reshapes the input Blob into flat vectors.
+ *
+ * Note: because this layer does not change the input values -- merely the
+ * dimensions -- it can simply copy the input. The copy happens "virtually"
+ * (thus taking effectively 0 real time) by setting, in Forward, the data
+ * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
+ * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
+ * (see Blob::ShareDiff).
+ */
 template <typename Dtype>
 class FlattenLayer : public Layer<Dtype> {
  public:
@@ -136,10 +207,28 @@ class FlattenLayer : public Layer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times CHW \times 1 \times 1) @f$
+   *      the outputs -- i.e., the (virtually) copied, flattened inputs
+   */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the concatenate inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length K), into which the top error
+   *        gradient is (virtually) copied
+   */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -148,8 +237,12 @@ class FlattenLayer : public Layer<Dtype> {
   int count_;
 };
 
-/* InnerProductLayer
-*/
+/**
+ * @brief Also known as a "fully-connected" layer, computes an inner product
+ *        with a set of learned weights, and (optionally) adds biases.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class InnerProductLayer : public Layer<Dtype> {
  public:
@@ -181,7 +274,10 @@ class InnerProductLayer : public Layer<Dtype> {
   Blob<Dtype> bias_multiplier_;
 };
 
-/* MVNLayer
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
  */
 template <typename Dtype>
 class MVNLayer : public Layer<Dtype> {
@@ -209,12 +305,15 @@ class MVNLayer : public Layer<Dtype> {
 
   Blob<Dtype> mean_, variance_, temp_;
 
-  // sum_multiplier is just used to carry out sum using blas
+  /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
 };
 
-/* SoftmaxLayer
-*/
+/**
+ * @brief Computes the softmax function.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class SoftmaxLayer : public Layer<Dtype> {
  public:
@@ -239,14 +338,18 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  // sum_multiplier is just used to carry out sum using blas
+  /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
-  // scale is an intermediate blob to hold temporary results.
+  /// scale is an intermediate Blob to hold temporary results.
   Blob<Dtype> scale_;
 };
 
-/* SplitLayer
-*/
+/**
+ * @brief Creates a "split" path in the network by copying the bottom Blob
+ *        into multiple top Blob%s to be used by multiple consuming layers.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class SplitLayer : public Layer<Dtype> {
  public:
@@ -274,10 +377,12 @@ class SplitLayer : public Layer<Dtype> {
   int count_;
 };
 
-/* SliceLayer
-  Takes one blobs and slices it along either num or channel dim,
-  outputting the result into as many top blobs as needed.
-*/
+/**
+ * @brief Takes a Blob and slices it along either the num or channel dimension,
+ *        outputting multiple sliced Blob results.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class SliceLayer : public Layer<Dtype> {
  public:

From c3151bb9aed7deb8b7483f46e7e36bff80efca87 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 29 Aug 2014 22:48:11 -0700
Subject: [PATCH 0643/2053] data_layers: Doxygen \brief & TODO stubs.

---
 include/caffe/data_layers.hpp | 43 +++++++++++++++++++++++++++++++----
 1 file changed, 39 insertions(+), 4 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index f972965b45b..bebbab10c03 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -23,8 +23,14 @@ namespace caffe {
 #define HDF5_DATA_DATASET_NAME "data"
 #define HDF5_DATA_LABEL_NAME "label"
 
-// TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
-// same basic structure and a lot of duplicated code.
+/**
+ * @brief Provides data to the Net from a LevelDB or LMDB.
+ *
+ * TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
+ * same basic structure and a lot of duplicated code.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class DataLayer : public Layer<Dtype>, public InternalThread {
  public:
@@ -80,6 +86,11 @@ class DataLayer : public Layer<Dtype>, public InternalThread {
   Caffe::Phase phase_;
 };
 
+/**
+ * @brief Provides data to the Net generated by a Filler.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class DummyDataLayer : public Layer<Dtype> {
  public:
@@ -106,6 +117,11 @@ class DummyDataLayer : public Layer<Dtype> {
   vector<bool> refill_;
 };
 
+/**
+ * @brief Provides data to the Net from HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class HDF5DataLayer : public Layer<Dtype> {
  public:
@@ -140,6 +156,11 @@ class HDF5DataLayer : public Layer<Dtype> {
   Blob<Dtype> label_blob_;
 };
 
+/**
+ * @brief Write blobs to disk as HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class HDF5OutputLayer : public Layer<Dtype> {
  public:
@@ -174,6 +195,11 @@ class HDF5OutputLayer : public Layer<Dtype> {
   Blob<Dtype> label_blob_;
 };
 
+/**
+ * @brief Provides data to the Net from image files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class ImageDataLayer : public Layer<Dtype>, public InternalThread {
  public:
@@ -221,8 +247,11 @@ class ImageDataLayer : public Layer<Dtype>, public InternalThread {
   Caffe::Phase phase_;
 };
 
-/* MemoryDataLayer
-*/
+/**
+ * @brief Provides data to the Net from memory.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class MemoryDataLayer : public Layer<Dtype> {
  public:
@@ -264,6 +293,12 @@ class MemoryDataLayer : public Layer<Dtype> {
   int pos_;
 };
 
+/**
+ * @brief Provides data to the Net from windows of images files, specified
+ *        by a window data file.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
 template <typename Dtype>
 class WindowDataLayer : public Layer<Dtype>, public InternalThread {
  public:

From f2f73cfda455d941ace64294024042c602806639 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 29 Aug 2014 22:57:27 -0700
Subject: [PATCH 0644/2053] vision_layers.hpp: Doxygen \brief & TODO stubs.

---
 include/caffe/vision_layers.hpp | 32 +++++++++++++++++++++++---------
 1 file changed, 23 insertions(+), 9 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 02f9e43b6d9..86a54ceddaf 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -16,8 +16,12 @@
 
 namespace caffe {
 
-/* ConvolutionLayer
-*/
+/**
+ * @brief Convolves the input image with a bank of learned filters,
+ *        and (optionally) adds biases.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class ConvolutionLayer : public Layer<Dtype> {
  public:
@@ -60,8 +64,13 @@ class ConvolutionLayer : public Layer<Dtype> {
   int N_;
 };
 
-/* Im2colLayer
-*/
+/**
+ * @brief A helper for image operations that rearranges image regions into
+ *        column vectors.  Used by ConvolutionLayer to perform convolution
+ *        by matrix multiplication.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class Im2colLayer : public Layer<Dtype> {
  public:
@@ -98,9 +107,11 @@ class Im2colLayer : public Layer<Dtype> {
 template <typename Dtype> class PoolingLayer;
 template <typename Dtype> class SplitLayer;
 
-/* LRNLayer
-  Local Response Normalization
-*/
+/**
+ * @brief Normalize the input in a local region across or within feature maps.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class LRNLayer : public Layer<Dtype> {
  public:
@@ -171,8 +182,11 @@ class LRNLayer : public Layer<Dtype> {
 };
 
 
-/* PoolingLayer
-*/
+/**
+ * @brief Pools the input image by taking the max, average, etc. within regions.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class PoolingLayer : public Layer<Dtype> {
  public:

From 81eb2ebf0e8ab407c01d4702e6aaa17fc51108eb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 30 Aug 2014 14:46:13 -0700
Subject: [PATCH 0645/2053] filler.hpp: add brief filler descriptions

---
 include/caffe/filler.hpp | 39 ++++++++++++++++++++++++++++-----------
 1 file changed, 28 insertions(+), 11 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index a8366801132..136ce958aed 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -15,6 +15,7 @@
 
 namespace caffe {
 
+/// @brief Fills a Blob with constant or randomly-generated data.
 template <typename Dtype>
 class Filler {
  public:
@@ -26,6 +27,7 @@ class Filler {
 };  // class Filler
 
 
+/// @brief Fills a Blob with constant values @f$ x = 0 @f$.
 template <typename Dtype>
 class ConstantFiller : public Filler<Dtype> {
  public:
@@ -44,6 +46,7 @@ class ConstantFiller : public Filler<Dtype> {
   }
 };
 
+/// @brief Fills a Blob with uniformly distributed values @f$ x\sim U(a, b) @f$.
 template <typename Dtype>
 class UniformFiller : public Filler<Dtype> {
  public:
@@ -58,6 +61,7 @@ class UniformFiller : public Filler<Dtype> {
   }
 };
 
+/// @brief Fills a Blob with Gaussian-distributed values @f$ x = a @f$.
 template <typename Dtype>
 class GaussianFiller : public Filler<Dtype> {
  public:
@@ -92,6 +96,9 @@ class GaussianFiller : public Filler<Dtype> {
   shared_ptr<SyncedMemory> rand_vec_;
 };
 
+/** @brief Fills a Blob with values @f$ x \in [0, 1] @f$
+ *         such that @f$ \forall i \sum_j x_{ij} = 1 @f$.
+ */
 template <typename Dtype>
 class PositiveUnitballFiller : public Filler<Dtype> {
  public:
@@ -119,14 +126,21 @@ class PositiveUnitballFiller : public Filler<Dtype> {
   }
 };
 
-// A filler based on the paper [Bengio and Glorot 2010]: Understanding
-// the difficulty of training deep feedforward neuralnetworks, but does not
-// use the fan_out value.
-//
-// It fills the incoming matrix by randomly sampling uniform data from
-// [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
-// of input nodes. You should make sure the input blob has shape (num, a, b, c)
-// where a * b * c = fan_in.
+/**
+ * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$
+ *        is set inversely proportional to the number of incoming nodes.
+ *
+ * A Filler based on the paper [Bengio and Glorot 2010]: Understanding
+ * the difficulty of training deep feedforward neuralnetworks, but does not
+ * use the fan_out value.
+ *
+ * It fills the incoming matrix by randomly sampling uniform data from
+ * [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
+ * of input nodes. You should make sure the input blob has shape (num, a, b, c)
+ * where a * b * c = fan_in.
+ *
+ * TODO(dox): make notation in above comment consistent with rest & use LaTeX.
+ */
 template <typename Dtype>
 class XavierFiller : public Filler<Dtype> {
  public:
@@ -144,9 +158,12 @@ class XavierFiller : public Filler<Dtype> {
 };
 
 
-// A function to get a specific filler from the specification given in
-// FillerParameter. Ideally this would be replaced by a factory pattern,
-// but we will leave it this way for now.
+/**
+ * @brief Get a specific filler from the specification given in FillerParameter.
+ *
+ * Ideally this would be replaced by a factory pattern, but we will leave it
+ * this way for now.
+ */
 template <typename Dtype>
 Filler<Dtype>* GetFiller(const FillerParameter& param) {
   const std::string& type = param.type();

From c84908a6052e3e773d776048a7658ac85efa366b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 30 Aug 2014 15:02:22 -0700
Subject: [PATCH 0646/2053] blob.hpp: a little Doxygen-style documentation

---
 include/caffe/blob.hpp | 42 ++++++++++++++++++++++++++++++++++--------
 1 file changed, 34 insertions(+), 8 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 69600290858..b2a5c2f697d 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -8,6 +8,13 @@
 
 namespace caffe {
 
+/**
+ * @brief A wrapper around SyncedMemory holders serving as the basic
+ *        computational unit through which Layer%s, Net%s, and Solver%s
+ *        interact.
+ *
+ * TODO(dox): more thorough description.
+ */
 template <typename Dtype>
 class Blob {
  public:
@@ -36,8 +43,15 @@ class Blob {
     CHECK_LE(w, width_);
     return ((n * channels_ + c) * height_ + h) * width_ + w;
   }
-  // Copy from source. If copy_diff is false, we copy the data; if copy_diff
-  // is true, we copy the diff.
+  /**
+   * @brief Copy from a source Blob.
+   *
+   * @param source the Blob to copy from
+   * @param copy_diff if false, copy the data; if true, copy the diff
+   * @param reshape if false, require this Blob to be pre-shaped to the shape
+   *        of other (and die otherwise); if true, Reshape this Blob to other's
+   *        shape if necessary
+   */
   void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
       bool reshape = false);
 
@@ -74,16 +88,28 @@ class Blob {
   void FromProto(const BlobProto& proto);
   void ToProto(BlobProto* proto, bool write_diff = false) const;
 
-  // Compute the sum of absolute values (L1 norm) of the data or diff.
+  /// @brief Compute the sum of absolute values (L1 norm) of the data.
   Dtype asum_data() const;
+  /// @brief Compute the sum of absolute values (L1 norm) of the diff.
   Dtype asum_diff() const;
 
-  // Set the data_/diff_ shared_ptr to point to the SyncedMemory holding the
-  // data_/diff_ of Blob other -- useful in layers which simply perform a copy
-  // in their forward or backward pass.
-  // This deallocates the SyncedMemory holding this blob's data/diff, as
-  // shared_ptr calls its destructor when reset with the = operator.
+  /**
+   * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
+   *        data_ of Blob other -- useful in Layer&s which simply perform a copy
+   *        in their Forward pass.
+   *
+   * This deallocates the SyncedMemory holding this Blob's data_, as
+   * shared_ptr calls its destructor when reset with the "=" operator.
+   */
   void ShareData(const Blob& other);
+  /**
+   * @brief Set the diff_ shared_ptr to point to the SyncedMemory holding the
+   *        diff_ of Blob other -- useful in Layer&s which simply perform a copy
+   *        in their Forward pass.
+   *
+   * This deallocates the SyncedMemory holding this Blob's diff_, as
+   * shared_ptr calls its destructor when reset with the "=" operator.
+   */
   void ShareDiff(const Blob& other);
 
  protected:

From 19cf38504856ea36780d8f039b9b0627536c85aa Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 30 Aug 2014 15:06:16 -0700
Subject: [PATCH 0647/2053] syncedmem.hpp: \brief and todo

---
 include/caffe/syncedmem.hpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index b2fc287b23e..db8d0e80e12 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -31,6 +31,12 @@ inline void CaffeFreeHost(void* ptr) {
 }
 
 
+/**
+ * @brief Manages memory allocation and synchronization between the host (CPU)
+ *        and device (GPU).
+ *
+ * TODO(dox): more thorough description.
+ */
 class SyncedMemory {
  public:
   SyncedMemory()

From 4572663839ee7132cb7b38004800367e4dc89c22 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 30 Aug 2014 15:12:39 -0700
Subject: [PATCH 0648/2053] solver.hpp: add \briefs

---
 include/caffe/solver.hpp | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 5a7f31be017..6fd159d0b98 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -8,6 +8,12 @@
 
 namespace caffe {
 
+/**
+ * @brief An interface for classes that perform optimization on Net%s.
+ *
+ * Requires implementation of ComputeUpdateValue to compute a parameter update
+ * given the current state of the Net parameters.
+ */
 template <typename Dtype>
 class Solver {
  public:
@@ -57,6 +63,10 @@ class Solver {
 };
 
 
+/**
+ * @brief Optimizes the parameters of a Net using
+ *        stochastic gradient descent (SGD) with momentum.
+ */
 template <typename Dtype>
 class SGDSolver : public Solver<Dtype> {
  public:

From 9c31482e04ca6f215b079c04cf3784123373bf37 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 30 Aug 2014 15:30:48 -0700
Subject: [PATCH 0649/2053] net.hpp: Doxygen-format docs

---
 include/caffe/net.hpp | 137 +++++++++++++++++++++++++-----------------
 1 file changed, 82 insertions(+), 55 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index cc238e721b6..3dcd0d5d598 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -14,6 +14,12 @@
 
 namespace caffe {
 
+/**
+ * @brief Connects Layer%s together into a directed acyclic graph (DAG)
+ *        specified by a NetParameter.
+ *
+ * TODO(dox): more thorough description.
+ */
 template <typename Dtype>
 class Net {
  public:
@@ -21,32 +27,41 @@ class Net {
   explicit Net(const string& param_file);
   virtual ~Net() {}
 
-  // Initialize a network with the network parameter.
+  /// @brief Initialize a network with a NetParameter.
   void Init(const NetParameter& param);
 
-  // Run forward with the input blobs already fed separately. You can get the
-  // input blobs using input_blobs().
+  /**
+   * @brief Run Forward with the input Blob%s already fed separately.
+   *
+   * You can get the input blobs using input_blobs().
+   */
   const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
 
-  // The From and To variants of Forward and Backward operate on the
-  // (topological) ordering by which the net is specified. For general DAG
-  // networks, note that (1) computing from one layer to another might entail
-  // extra computation on unrelated branches, and (2) computation starting in
-  // the middle may be incorrect if all of the layers of a fan-in are not
-  // included.
+  /**
+   * The From and To variants of Forward and Backward operate on the
+   * (topological) ordering by which the net is specified. For general DAG
+   * networks, note that (1) computing from one layer to another might entail
+   * extra computation on unrelated branches, and (2) computation starting in
+   * the middle may be incorrect if all of the layers of a fan-in are not
+   * included.
+   */
   Dtype ForwardFromTo(int start, int end);
   Dtype ForwardFrom(int start);
   Dtype ForwardTo(int end);
-  // Run forward using a set of bottom blobs, and return the result.
+  /// @brief Run forward using a set of bottom blobs, and return the result.
   const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
       Dtype* loss = NULL);
-  // Run forward using a serialized BlobProtoVector and return the result
-  // as a serialized BlobProtoVector
+  /**
+   * @brief Run forward using a serialized BlobProtoVector and return the
+   *        result as a serialized BlobProtoVector
+   */
   string Forward(const string& input_blob_protos, Dtype* loss = NULL);
 
-  // The network backward should take no input and output, since it solely
-  // computes the gradient w.r.t the parameters, and the data has already
-  // been provided during the forward pass.
+  /**
+   * The network backward should take no input and output, since it solely
+   * computes the gradient w.r.t the parameters, and the data has already been
+   * provided during the forward pass.
+   */
   void Backward();
   void BackwardFromTo(int start, int end);
   void BackwardFrom(int start);
@@ -59,33 +74,44 @@ class Net {
     return loss;
   }
 
-  // Updates the network weights based on the diff values computed.
+  /// @brief Updates the network weights based on the diff values computed.
   void Update();
 
-  // For an already initialized net, ShareTrainedLayersWith() implicitly copies
-  // (i.e., using no additional memory) the already trained layers from another
-  // Net.
+  /**
+   * @brief For an already initialized net, implicitly copies (i.e., using no
+   *        additional memory) the pre-trained layers from another Net.
+   */
   void ShareTrainedLayersWith(Net* other);
   // For an already initialized net, CopyTrainedLayersFrom() copies the already
   // trained layers from another net parameter instance.
+  /**
+   * @brief For an already initialized net, copies the pre-trained layers from
+   *        another Net.
+   */
   void CopyTrainedLayersFrom(const NetParameter& param);
   void CopyTrainedLayersFrom(const string trained_filename);
-  // Writes the net to a proto.
+  /// @brief Writes the net to a proto.
   void ToProto(NetParameter* param, bool write_diff = false);
 
-  // returns the network name.
+  /// @brief returns the network name.
   inline const string& name() { return name_; }
-  // returns the layer names
+  /// @brief returns the layer names
   inline const vector<string>& layer_names() { return layer_names_; }
-  // returns the blob names
+  /// @brief returns the blob names
   inline const vector<string>& blob_names() { return blob_names_; }
-  // returns the blobs
+  /// @brief returns the blobs
   inline const vector<shared_ptr<Blob<Dtype> > >& blobs() { return blobs_; }
-  // returns the layers
+  /// @brief returns the layers
   inline const vector<shared_ptr<Layer<Dtype> > >& layers() { return layers_; }
-  // returns the bottom and top vecs for each layer - usually you won't need
-  // this unless you do per-layer checks such as gradients.
+  /**
+   * @brief returns the bottom vecs for each layer -- usually you won't
+   *        need this unless you do per-layer checks such as gradients.
+   */
   inline vector<vector<Blob<Dtype>*> >& bottom_vecs() { return bottom_vecs_; }
+  /**
+   * @brief returns the top vecs for each layer -- usually you won't
+   *        need this unless you do per-layer checks such as gradients.
+   */
   inline vector<vector<Blob<Dtype>*> >& top_vecs() { return top_vecs_; }
   inline vector<vector<bool> >& bottom_need_backward() {
     return bottom_need_backward_;
@@ -93,22 +119,19 @@ class Net {
   inline vector<Dtype>& blob_loss_weights() {
     return blob_loss_weights_;
   }
-  // returns the parameters
+  /// @brief returns the parameters
   inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
-  // returns the parameter learning rate multipliers
+  /// @brief returns the parameter learning rate multipliers
   inline vector<float>& params_lr() { return params_lr_; }
   inline vector<float>& params_weight_decay() { return params_weight_decay_; }
   const map<string, int>& param_names_index() { return param_names_index_; }
-  // Input and output blob numbers
+  /// @brief Input and output blob numbers
   inline int num_inputs() { return net_input_blobs_.size(); }
   inline int num_outputs() { return net_output_blobs_.size(); }
   inline vector<Blob<Dtype>*>& input_blobs() { return net_input_blobs_; }
   inline vector<Blob<Dtype>*>& output_blobs() { return net_output_blobs_; }
   inline vector<int>& input_blob_indices() { return net_input_blob_indices_; }
   inline vector<int>& output_blob_indices() { return net_output_blob_indices_; }
-  // has_blob and blob_by_name are inspired by
-  // https://github.com/kencoken/caffe/commit/f36e71569455c9fbb4bf8a63c2d53224e32a4e7b
-  // Access intermediary computation layers, testing with centre image only
   bool has_blob(const string& blob_name);
   const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name);
   bool has_layer(const string& layer_name);
@@ -117,77 +140,81 @@ class Net {
   void set_debug_info(const bool value) { debug_info_ = value; }
 
   // Helpers for Init.
-  // Remove layers that the user specified should be excluded given the current
-  // phase, level, and stage.
+  /**
+   * @brief Remove layers that the user specified should be excluded given the current
+   *        phase, level, and stage.
+   */
   static void FilterNet(const NetParameter& param,
       NetParameter* param_filtered);
+  /// @brief return whether NetState state meets NetStateRule rule
   static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
       const string& layer_name);
 
  protected:
   // Helpers for Init.
-  // Append a new input or top blob to the net.
+  /// @brief Append a new input or top blob to the net.
   void AppendTop(const NetParameter& param, const int layer_id,
                  const int top_id, set<string>* available_blobs,
                  map<string, int>* blob_name_to_idx);
-  // Append a new bottom blob to the net.
+  /// @brief Append a new bottom blob to the net.
   int AppendBottom(const NetParameter& param, const int layer_id,
                    const int bottom_id, set<string>* available_blobs,
                    map<string, int>* blob_name_to_idx);
+  /// @brief Append a new parameter blob to the net.
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
 
-  // Helpers for displaying debug info.
+  /// @brief Helper for displaying debug info in Forward.
   void ForwardDebugInfo(const int layer_id);
+  /// @brief Helper for displaying debug info in Backward.
   void BackwardDebugInfo(const int layer_id);
+  /// @brief Helper for displaying debug info in Update.
   void UpdateDebugInfo(const int param_id);
 
-  // Function to get misc parameters, e.g. the learning rate multiplier and
-  // weight decay.
+  /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
   void GetLearningRateAndWeightDecay();
 
-  // Individual layers in the net
+  /// @brief Individual layers in the net
   vector<shared_ptr<Layer<Dtype> > > layers_;
   vector<string> layer_names_;
   map<string, int> layer_names_index_;
   vector<bool> layer_need_backward_;
-  // blobs stores the blobs that store intermediate results between the
-  // layers.
+  /// @brief the blobs storing intermediate results between the layer.
   vector<shared_ptr<Blob<Dtype> > > blobs_;
   vector<string> blob_names_;
   map<string, int> blob_names_index_;
   vector<bool> blob_need_backward_;
-  // bottom_vecs stores the vectors containing the input for each layer.
-  // They don't actually host the blobs (blobs_ does), so we simply store
-  // pointers.
+  /// bottom_vecs stores the vectors containing the input for each layer.
+  /// They don't actually host the blobs (blobs_ does), so we simply store
+  /// pointers.
   vector<vector<Blob<Dtype>*> > bottom_vecs_;
   vector<vector<int> > bottom_id_vecs_;
   vector<vector<bool> > bottom_need_backward_;
-  // top_vecs stores the vectors containing the output for each layer
+  /// top_vecs stores the vectors containing the output for each layer
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
-  // Vector of weight in the loss (or objective) function of each net blob,
-  // indexed by blob_id.
+  /// Vector of weight in the loss (or objective) function of each net blob,
+  /// indexed by blob_id.
   vector<Dtype> blob_loss_weights_;
   vector<int> param_owners_;
   vector<string> param_display_names_;
   vector<pair<int, int> > param_layer_indices_;
   map<string, int> param_names_index_;
-  // blob indices for the input and the output of the net
+  /// blob indices for the input and the output of the net
   vector<int> net_input_blob_indices_;
   vector<int> net_output_blob_indices_;
   vector<Blob<Dtype>*> net_input_blobs_;
   vector<Blob<Dtype>*> net_output_blobs_;
   string name_;
-  // The parameters in the network.
+  /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
-  // the learning rate multipliers
+  /// the learning rate multipliers
   vector<float> params_lr_;
-  // the weight decay multipliers
+  /// the weight decay multipliers
   vector<float> params_weight_decay_;
-  // The bytes of memory used by this net
+  /// The bytes of memory used by this net
   size_t memory_used_;
-  // Whether to compute and display debug info for the net.
+  /// Whether to compute and display debug info for the net.
   bool debug_info_;
 
   DISABLE_COPY_AND_ASSIGN(Net);

From 134e2408bef8017b22f9af23559c17a2e4c01731 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 3 Sep 2014 00:44:23 -0700
Subject: [PATCH 0650/2053] wrap up solver.md -- add update info for all
 solvers with citations; rules of thumb for SGD

---
 docs/tutorial/index.md  |   2 +-
 docs/tutorial/solver.md | 132 ++++++++++++++++++++++++++++++++++------
 2 files changed, 113 insertions(+), 21 deletions(-)

diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
index 5638e41f09d..d499806d7f4 100644
--- a/docs/tutorial/index.md
+++ b/docs/tutorial/index.md
@@ -24,7 +24,7 @@ and these principles direct the project.
 - [Nets, Layers, and Blobs](net_layer_blob.html): the anatomy of a Caffe model.
 - [Forward / Backward](forward_backward.html): the essential computations of layered compositional models.
 - [Loss](loss.html): the task to be learned is defined by the loss.
-- [Solver Optimization](solver.html): the solver coordinates model optimization.
+- [Solver](solver.html): the solver coordinates model optimization.
 - [Layer Catalogue](layers.html): the layer is the fundamental unit of modeling and computation -- Caffe's catalogue includes layers for state-of-the-art models.
 - [Interfaces](interfaces.html): command line, Python, and MATLAB Caffe.
 
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index 992869f88a6..3780fc3ab8c 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -1,10 +1,10 @@
 ---
 layout: default
 ---
-# Solver Optimization
+# Solver
 
 The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
-The responsibilities of learning are divided between the solver for overseeing the optimization and generating parameter updates and the network for yielding loss and gradients.
+The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.
 
 The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NAG).
 
@@ -17,8 +17,8 @@ The solver
 
 where each iteration
 
-1. calls network forward to make the output and loss
-2. calls network backward to make the gradients
+1. calls network forward to compute the output and loss
+2. calls network backward to compute the gradients
 3. incorporates the gradients into parameter updates according to the solver method
 4. updates the solver state according to learning rate, history, and method
 
@@ -29,34 +29,125 @@ Like Caffe models, Caffe solvers run in CPU / GPU modes.
 ## Methods
 
 The solver methods address the general optimization problem of loss minimization.
-The optimization objective is the average loss over instances
+For dataset $$D$$, the optimization objective is the average loss over all $$|D|$$ data instances throughout the dataset
 
-$L(W) = \frac{1}{N} \sum_i f_W^{(i)}\left(X^{(i)}\right) + \lambda r(W)$
+$$L(W) = \frac{1}{|D|} \sum_i^{|D|} f_W\left(X^{(i)}\right) + \lambda r(W)$$
 
-where $f_W^{(i)}$ is the loss on instance $i$ with data $X^{(i)}$ in a mini-batch of size $N$ and $r(W)$ is a regularization term with weight $\lambda$.
+where $$f_W\left(X^{(i)}\right)$$ is the loss on data instance $$X^{(i)}$$ and $$r(W)$$ is a regularization term with weight $$\lambda$$.
+$$|D|$$ can be very large, so in practice, in each solver iteration we use a stochastic approximation of this objective, drawing a mini-batch of $$N << |D|$$ instances:
 
-The model computes $f_W$ in the forward pass and the gradient $\nabla f_W$ in the backward pass.
+$$L(W) \approx \frac{1}{N} \sum_i^N f_W\left(X^{(i)}\right) + \lambda r(W)$$
 
-The gradient of the loss $\nabla L(W)$ is formed by the solver from the model gradient $\nabla f_W$, the regularlization gradient $r(W)$, and other particulars to each method.
-The method then computes the parameter update $\Delta W$ to update the weights and iterate.
+The model computes $$f_W$$ in the forward pass and the gradient $$\nabla f_W$$ in the backward pass.
+
+The parameter update $$\Delta W$$ is formed by the solver from the error gradient $$\nabla f_W$$, the regularization gradient $$r(W)$$, and other particulars to each method.
 
 ### SGD
 
-Stochastic gradient descent (SGD)
+**Stochastic gradient descent** (`solver_type: SGD`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
+The **learning rate** $$ \alpha $$ is the weight of the negative gradient.
+The **momentum** $$ \mu $$ is the weight of the previous update.
+
+Formally, we have the following formulas to compute the update value $$ V_{t+1} $$ and the updated weights $$ W_{t+1} $$ at iteration $$ t+1 $$, given the previous weight update $$ V_t $$ and current weights $$ W_t $$:
+
+$$
+V_{t+1} = \mu V_t - \alpha \nabla L(W_t)
+$$
+
+$$
+W_{t+1} = W_t + V_{t+1}
+$$
+
+The learning "hyperparameters" ($$\alpha$$ and $$\mu$$) might require a bit of tuning for best results.
+If you're not sure where to start, take a look at the "Rules of thumb" below, and for further information you might refer to Leon Bottou's [Stochastic Gradient Descent Tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf) [1].
+
+[1] L. Bottou.
+    [Stochastic Gradient Descent Tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf).
+    *Neural Networks: Tricks of the Trade*: Springer, 2012.
+
+#### Rules of thumb for setting the learning rate $$ \alpha $$ and momentum $$ \mu $$
+
+A good strategy for deep learning with SGD is to initialize the learning rate $$ \alpha $$ to a value around $$ \alpha \approx 0.01 = 10^{-2} $$, and dropping it by a constant factor (e.g., 10) throughout training when the loss begins to reach an apparent "plateau", repeating this several times.
+Generally, you probably want to use a momentum $$ \mu = 0.9 $$ or similar value.
+By smoothing the weight updates across iterations, momentum tends to make deep learning with SGD both stabler and faster.
 
-TODO Bottou pointer
+This was the strategy used by Krizhevsky et al. [1] in their famously winning CNN entry to the ILSVRC-2012 competition, and Caffe makes this strategy easy to implement in a `SolverParameter`, as in our reproduction of [1] at `./examples/imagenet/alexnet_solver.prototxt`.
 
-### ADAGRAD
+To use a learning rate policy like this, you can put the following lines somewhere in your solver prototxt file:
+
+    base_lr: 0.01     # begin training at a learning rate of 0.01 = 1e-2
+
+    lr_policy: "step" # learning rate policy: drop the learning rate in "steps"
+                      # by a factor of gamma every stepsize iterations
+
+    gamma: 0.1        # drop the learning rate by a factor of 10
+                      # (i.e., multiply it by a factor of gamma = 0.1)
+
+    stepsize: 100000  # drop the learning rate every 100K iterations
+
+    max_iter: 350000  # train for 350K iterations total
+
+    momentum: 0.9
 
-The adaptive gradient (ADAGRAD)
+Under the above settings, we'll always use `momentum` $$ \mu = 0.9 $$.
+We'll begin training at a `base_lr` of $$ \alpha = 0.01 = 10^{-2} $$ for the first 100,000 iterations, then multiply the learning rate by `gamma` ($$ \gamma $$) and train at $$ \alpha' = \alpha \gamma = (0.01) (0.1) = 0.001 = 10^{-3} $$ for iterations 100K-200K, then at $$ \alpha'' = 10^{-4} $$ for iterations 200K-300K, and finally train until iteration 350K (since we have `max_iter: 350000`) at $$ \alpha''' = 10^{-5} $$.
 
-TODO cite Duchi
+Note that the momentum setting $$ \mu $$ effectively multiplies the size of your updates by a factor of $$ \frac{1}{1 - \mu} $$ after many iterations of training, so if you increase $$ \mu $$, it may be a good idea to **decrease** $$ \alpha $$ accordingly (and vice versa).
+
+For example, with $$ \mu = 0.9 $$, we have an effective update size multiplier of $$ \frac{1}{1 - 0.9} = 10 $$.
+If we increased the momentum to $$ \mu = 0.99 $$, we've increased our update size multiplier to 100, so we should drop $$ \alpha $$ (`base_lr`) by a factor of 10.
+
+Note also that the above settings are merely guidelines, and they're definitely not guaranteed to be optimal (or even work at all!) in every situation.
+If learning diverges (e.g., you start to see very large or `NaN` or `inf` loss values or outputs), try dropping the `base_lr` (e.g., `base_lr: 0.001`) and re-training, repeating this until you find a `base_lr` value that works.
+
+[1] A. Krizhevsky, I. Sutskever, and G. Hinton.
+    [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
+    *Advances in Neural Information Processing Systems*, 2012.
+
+### AdaGrad
+
+The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
+Given the update information from all previous iterations $$ \left( \nabla L(W) \right)_{t'} $$ for $$ t' \in \{1, 2, ..., t\} $$,
+the update formulas proposed by [1] are as follows, specified for each component $$i$$ of the weights $$W$$:
+
+$$
+(W_{t+1})_i =
+(W_t)_i +
+\frac{\alpha}{
+    \sqrt{\sum_{t'=1}^{t} \left( \nabla L(W) \right)_{t',i}^2}
+}
+$$
+
+Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementations (including the one in Caffe) use only $$ \mathcal{O}(d) $$ extra storage for the historical gradient information (rather than the $$ \mathcal{O}(dt) $$ storage that would be necessary to store each historical gradient individually).
+
+[1] J. Duchi, E. Hazan, and Y. Singer.
+    [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf).
+    *The Journal of Machine Learning Research*, 2011.
 
 ### NAG
 
-Nesterov's accelerated gradient (NAG)
+**Nesterov's accelerated gradient** (`solver_type: NAG`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
+Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].
+
+The weight update formulas look very similar to the SGD updates given above:
+
+$$
+V_{t+1} = \mu V_t - \alpha \nabla L(W_t + \mu V_t)
+$$
+
+$$
+W_{t+1} = W_t + V_{t+1}
+$$
 
-TODO cite ???
+What distinguishes the method from SGD is the weight setting $$ W $$ on which we compute the error gradient $$ \nabla L(W) $$ -- in NAG we take the gradient on weights with added momentum $$ \nabla L(W_t + \mu V_t) $$; in SGD we simply take the gradient $$ \nabla L(W_t) $$ on the current weights themselves.
+
+[1] Y. Nesterov.
+    A Method of Solving a Convex Programming Problem with Convergence Rate $$\mathcal{O}(1/\sqrt{k})$$.
+    *Soviet Mathematics Doklady*, 1983.
+
+[2] I. Sutskever, J. Martens, G. Dahl, and G. Hinton.
+    [On the Importance of Initialization and Momentum in Deep Learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf).
+    *Proceedings of the 30th International Conference on Machine Learning*, 2013.
 
 ## Scaffolding
 
@@ -149,8 +240,9 @@ Completion
 ## Updating Parameters
 
 The actual weight update is made by the solver then applied to the net parameters in `Solver::ComputeUpdateValue()`.
-
-TODO
+The `ComputeUpdateValue` method incorporates any weight decay $$ r(W) $$ into the weight gradients (which currently just contain the error gradients) to get the final gradient with respect to each network weight.
+Then these gradients are scaled by the learning rate $$ \alpha $$ and the update to subtract is stored in each parameter Blob's `diff` field.
+Finally, the `Blob::Update` method is called on each parameter blob, which performs the final update (subtracting the Blob's `diff` from its `data`).
 
 ## Snapshotting and Resuming
 
@@ -168,7 +260,7 @@ Snapshotting is configured by:
     # File path prefix for snapshotting model weights and solver state.
     # Note: this is relative to the invocation of the `caffe` utility, not the
     # solver definition file.
-    snapshot_prefix: /path/to/model
+    snapshot_prefix: "/path/to/model"
     # Snapshot the diff along with the weights. This can help debugging training
     # but takes more storage.
     snapshot_diff: false

From 9f1903074eda2d60758867943b0974d8b1ce4de4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 12:25:52 -0700
Subject: [PATCH 0651/2053] [docs] draft data

---
 docs/tutorial/data.md  | 74 ++++++++++++++++++++++++++++++++++++++++++
 docs/tutorial/index.md |  1 +
 2 files changed, 75 insertions(+)

diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index de2d5526dcb..644ba9bef8b 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -2,3 +2,77 @@
 layout: default
 ---
 # Data: Ins and Outs
+
+Data flows through Caffe as [Blobs](net_layer_blob.html#blob-storage-and-communication).
+Data layers load input and save output by converting to and from Blob to other formats.
+Common transformations like mean-subtraction and feature-scaling are done by data layer configuration.
+New input types are supported by developing a new data layer -- the rest of the Net follows by the modularity of the Caffe layer catalogue.
+
+This data layer definition
+
+    layers {
+      name: "mnist"
+      # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
+      type: DATA
+      # the 1st top is the data itself: the name is only convention
+      top: "data"
+      # the 2nd top is the ground truth: the name is only convention
+      top: "label"
+      # the DATA layer configuration
+      data_param {
+        # path to the DB
+        source: "examples/mnist/mnist_train_lmdb"
+        # type of DB: LEVELDB or LMDB (LMDB supports concurrent reads)
+        backend: LMDB
+        # batch processing improves efficiency.
+        batch_size: 64
+      }
+      # common data transformations
+      transform_param {
+        # feature scaling coefficient: this maps the [0, 255] MNIST data to [0, 1]
+        scale: 0.00390625
+      }
+    }
+
+loads the MNIST digits.
+
+**Tops and Bottoms**: A data layer makes **top** blobs to output data to the model.
+It does not have **bottom** blobs since it takes no input.
+
+**Data and Label**: a data layer has at least one top canonically named **data**.
+For ground truth a second top can be defined that is canonically named **label**.
+Both tops simply produce blobs and there is nothing inherently special about these names.
+The (data, label) pairing is a convenience for classification models.
+
+**Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
+
+    layers {
+      name: "data"
+      type: DATA
+      [...]
+      transform_param {
+        scale: 0.1
+        mean_file_size: mean.binaryproto
+        # for images in particular horizontal mirroring and random cropping
+        # can be done as simple data augmentations.
+        mirror: 1  # 1 = on, 0 = off
+        # crop a `crop_size` x `crop_size` patch:
+        # - at random during training
+        # - from the center during testing
+        crop_size: 227
+      }
+    }
+
+**Prefetching**: for throughput data layers fetch the next batch of data and prepare it in the background while the Net computes the current batch.
+
+**Multiple Inputs**: a Net can have multiple inputs of any number and type. Define as many data layers as needed giving each a unique name and top. Multiple inputs are useful for non-trivial ground truth: one data layer loads the actual data and the other data layer loads the ground truth in lock-step. In this arrangement both data and label can be any 4D array. Further applications of multiple inputs are found in multi-modal and sequence models. In these cases you may need to implement your own data preparation routines or a special data layer.
+
+*Improvements to data processing to add formats, generality, or helper utilities are welcome!*
+
+## Formats
+
+Refer to the layer catalogue of [data layers](layers.html#data-layers) for close-ups on each type of data Caffe understands.
+
+## Deployment Input
+
+For on-the-fly computation deployment Nets define their inputs by `input` fields: these Nets then accept direct assignment of data for online or interactive computation.
diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
index d499806d7f4..d47ecad95c6 100644
--- a/docs/tutorial/index.md
+++ b/docs/tutorial/index.md
@@ -27,6 +27,7 @@ and these principles direct the project.
 - [Solver](solver.html): the solver coordinates model optimization.
 - [Layer Catalogue](layers.html): the layer is the fundamental unit of modeling and computation -- Caffe's catalogue includes layers for state-of-the-art models.
 - [Interfaces](interfaces.html): command line, Python, and MATLAB Caffe.
+- [Data](data.html): how to caffeinate data for model input.
 
 For a closer look at a few details:
 

From b3673174f8301d9d3fa8203323f5ad8d7b3f65fd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 12:46:47 -0700
Subject: [PATCH 0652/2053] [docs] suggest the CVPR14 deep learning tutorial
 for nice contrast

---
 docs/tutorial/index.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
index d47ecad95c6..0c048111c55 100644
--- a/docs/tutorial/index.md
+++ b/docs/tutorial/index.md
@@ -38,12 +38,14 @@ For a closer look at a few details:
 There are helpful references freely online for deep learning that complement our hands-on tutorial.
 These cover introductory and advanced material, background and history, and the latest advances.
 
+The [Tutorial on Deep Learning for Vision](https://sites.google.com/site/deeplearningcvpr2014/) from CVPR '14 is a good companion tutorial for researchers.
+Once you have the framework and practice foundations from the Caffe tutorial, explore the fundamental ideas and advanced research directions in the CVPR '14 tutorial.
+
 A broad introduction is given in the free online draft of [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) by Michael Nielsen. In particular the chapters on using neural nets and how backpropagation works are helpful if you are new to the subject.
 
-These recent academic tutorials explain deep learning for researchers in machine learning and vision:
+These recent academic tutorials cover deep learning for researchers in machine learning and vision:
 
 - [Deep Learning Tutorial](http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf) by Yann LeCun (NYU, Facebook) and Marc'Aurelio Ranzato (Facebook). ICML 2013 tutorial.
-- [Large-Scale Visual Recognition: Deep Learning Tutorial](https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxsc3ZydHV0b3JpYWxjdnByMTR8Z3g6Njg5MmZkZTM1MDhhZWNmZA) by Marc'Aurelio Ranzato (Facebook). CPVR 2014 tutorial.
 - [LISA Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by the LISA Lab directed by Yoshua Bengio (U. Montréal).
 
 For an exposition of neural networks in circuits and code, check out [Understanding Neural Networks from a Programmer's Perspective](http://karpathy.github.io/neuralnets/) by Andrej Karpathy (Stanford).

From 09a1ce79ca6e97df2a48555f217545ce2dd971c3 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 12:59:55 -0700
Subject: [PATCH 0653/2053] update doxygen config to stop warnings

---
 .Doxyfile | 2406 ++++++++++++++++++++++++++++++++---------------------
 1 file changed, 1480 insertions(+), 926 deletions(-)

diff --git a/.Doxyfile b/.Doxyfile
index e0b64bdd6a0..432c9c62f1c 100644
--- a/.Doxyfile
+++ b/.Doxyfile
@@ -1,110 +1,129 @@
-# Doxyfile 1.7.6.1
+# Doxyfile 1.8.8
 
 # This file describes the settings to be used by the documentation system
 # doxygen (www.doxygen.org) for a project.
 #
-# All text after a hash (#) is considered a comment and will be ignored.
+# All text after a double hash (##) is considered a comment and is placed in
+# front of the TAG it is preceding.
+#
+# All text after a single hash (#) is considered a comment and will be ignored.
 # The format is:
-#       TAG = value [value, ...]
-# For lists items can also be appended using:
-#       TAG += value [value, ...]
-# Values that contain spaces should be placed between quotes (" ").
+# TAG = value [value, ...]
+# For lists, items can also be appended using:
+# TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (\" \").
 
 #---------------------------------------------------------------------------
 # Project related configuration options
 #---------------------------------------------------------------------------
 
 # This tag specifies the encoding used for all characters in the config file
-# that follow. The default is UTF-8 which is also the encoding used for all
-# text before the first occurrence of this tag. Doxygen uses libiconv (or the
-# iconv built into libc) for the transcoding. See
-# http://www.gnu.org/software/libiconv for the list of possible encodings.
+# that follow. The default is UTF-8 which is also the encoding used for all text
+# before the first occurrence of this tag. Doxygen uses libiconv (or the iconv
+# built into libc) for the transcoding. See http://www.gnu.org/software/libiconv
+# for the list of possible encodings.
+# The default value is: UTF-8.
 
 DOXYFILE_ENCODING      = UTF-8
 
-# The PROJECT_NAME tag is a single word (or sequence of words) that should
-# identify the project. Note that if you do not use Doxywizard you need
-# to put quotes around the project name if it contains spaces.
+# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
+# double-quotes, unless you are using Doxywizard) that should identify the
+# project for which the documentation is generated. This name is used in the
+# title of most generated pages and in a few other places.
+# The default value is: My Project.
 
 PROJECT_NAME           = "Caffe"
 
-# The PROJECT_NUMBER tag can be used to enter a project or revision number.
-# This could be handy for archiving the generated documentation or
-# if some version control system is used.
+# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
+# could be handy for archiving the generated documentation or if some version
+# control system is used.
 
 PROJECT_NUMBER         =
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
-# for a project that appears at the top of each page and should give viewer
-# a quick idea about the purpose of the project. Keep the description short.
+# for a project that appears at the top of each page and should give viewer a
+# quick idea about the purpose of the project. Keep the description short.
 
 PROJECT_BRIEF          =
 
-# With the PROJECT_LOGO tag one can specify an logo or icon that is
-# included in the documentation. The maximum height of the logo should not
-# exceed 55 pixels and the maximum width should not exceed 200 pixels.
-# Doxygen will copy the logo to the output directory.
+# With the PROJECT_LOGO tag one can specify an logo or icon that is included in
+# the documentation. The maximum height of the logo should not exceed 55 pixels
+# and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
+# to the output directory.
 
 PROJECT_LOGO           =
 
-# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute)
-# base path where the generated documentation will be put.
-# If a relative path is entered, it will be relative to the location
-# where doxygen was started. If left blank the current directory will be used.
+# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
+# into which the generated documentation will be written. If a relative path is
+# entered, it will be relative to the location where doxygen was started. If
+# left blank the current directory will be used.
 
 OUTPUT_DIRECTORY       = ./doxygen/
 
-# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create
-# 4096 sub-directories (in 2 levels) under the output directory of each output
-# format and will distribute the generated files over these directories.
-# Enabling this option can be useful when feeding doxygen a huge amount of
-# source files, where putting all generated files in the same directory would
-# otherwise cause performance problems for the file system.
+# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
+# directories (in 2 levels) under the output directory of each output format and
+# will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise causes
+# performance problems for the file system.
+# The default value is: NO.
 
 CREATE_SUBDIRS         = NO
 
+# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# characters to appear in the names of generated files. If set to NO, non-ASCII
+# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
+# U+3044.
+# The default value is: NO.
+
+ALLOW_UNICODE_NAMES    = NO
+
 # The OUTPUT_LANGUAGE tag is used to specify the language in which all
 # documentation generated by doxygen is written. Doxygen will use this
 # information to generate all constant output in the proper language.
-# The default language is English, other supported languages are:
-# Afrikaans, Arabic, Brazilian, Catalan, Chinese, Chinese-Traditional,
-# Croatian, Czech, Danish, Dutch, Esperanto, Farsi, Finnish, French, German,
-# Greek, Hungarian, Italian, Japanese, Japanese-en (Japanese with English
-# messages), Korean, Korean-en, Lithuanian, Norwegian, Macedonian, Persian,
-# Polish, Portuguese, Romanian, Russian, Serbian, Serbian-Cyrillic, Slovak,
-# Slovene, Spanish, Swedish, Ukrainian, and Vietnamese.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
+# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
+# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
+# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
+# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
+# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
+# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
+# Ukrainian and Vietnamese.
+# The default value is: English.
 
 OUTPUT_LANGUAGE        = English
 
-# If the BRIEF_MEMBER_DESC tag is set to YES (the default) Doxygen will
-# include brief member descriptions after the members that are listed in
-# the file and class documentation (similar to JavaDoc).
-# Set to NO to disable this.
+# If the BRIEF_MEMBER_DESC tag is set to YES doxygen will include brief member
+# descriptions after the members that are listed in the file and class
+# documentation (similar to Javadoc). Set to NO to disable this.
+# The default value is: YES.
 
 BRIEF_MEMBER_DESC      = YES
 
-# If the REPEAT_BRIEF tag is set to YES (the default) Doxygen will prepend
-# the brief description of a member or function before the detailed description.
-# Note: if both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
+# If the REPEAT_BRIEF tag is set to YES doxygen will prepend the brief
+# description of a member or function before the detailed description
+#
+# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
 # brief descriptions will be completely suppressed.
+# The default value is: YES.
 
 REPEAT_BRIEF           = YES
 
-# This tag implements a quasi-intelligent brief description abbreviator
-# that is used to form the text in various listings. Each string
-# in this list, if found as the leading text of the brief description, will be
-# stripped from the text and the result after processing the whole list, is
-# used as the annotated text. Otherwise, the brief description is used as-is.
-# If left blank, the following values are used ("$name" is automatically
-# replaced with the name of the entity): "The $name class" "The $name widget"
-# "The $name file" "is" "provides" "specifies" "contains"
-# "represents" "a" "an" "the"
+# This tag implements a quasi-intelligent brief description abbreviator that is
+# used to form the text in various listings. Each string in this list, if found
+# as the leading text of the brief description, will be stripped from the text
+# and the result, after processing the whole list, is used as the annotated
+# text. Otherwise, the brief description is used as-is. If left blank, the
+# following values are used ($name is automatically replaced with the name of
+# the entity):The $name class, The $name widget, The $name file, is, provides,
+# specifies, contains, represents, a, an and the.
 
 ABBREVIATE_BRIEF       =
 
 # If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
-# Doxygen will generate a detailed section even if there is only a brief
+# doxygen will generate a detailed section even if there is only a brief
 # description.
+# The default value is: NO.
 
 ALWAYS_DETAILED_SEC    = NO
 
@@ -112,160 +131,207 @@ ALWAYS_DETAILED_SEC    = NO
 # inherited members of a class in the documentation of that class as if those
 # members were ordinary class members. Constructors, destructors and assignment
 # operators of the base classes will not be shown.
+# The default value is: NO.
 
 INLINE_INHERITED_MEMB  = NO
 
-# If the FULL_PATH_NAMES tag is set to YES then Doxygen will prepend the full
-# path before files name in the file list and in the header files. If set
-# to NO the shortest path that makes the file name unique will be used.
+# If the FULL_PATH_NAMES tag is set to YES doxygen will prepend the full path
+# before files name in the file list and in the header files. If set to NO the
+# shortest path that makes the file name unique will be used
+# The default value is: YES.
 
 FULL_PATH_NAMES        = YES
 
-# If the FULL_PATH_NAMES tag is set to YES then the STRIP_FROM_PATH tag
-# can be used to strip a user-defined part of the path. Stripping is
-# only done if one of the specified strings matches the left-hand part of
-# the path. The tag can be used to show relative paths in the file list.
-# If left blank the directory from which doxygen is run is used as the
-# path to strip.
+# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
+# Stripping is only done if one of the specified strings matches the left-hand
+# part of the path. The tag can be used to show relative paths in the file list.
+# If left blank the directory from which doxygen is run is used as the path to
+# strip.
+#
+# Note that you can specify absolute paths here, but also relative paths, which
+# will be relative from the directory where doxygen is started.
+# This tag requires that the tag FULL_PATH_NAMES is set to YES.
 
 STRIP_FROM_PATH        =
 
-# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of
-# the path mentioned in the documentation of a class, which tells
-# the reader which header file to include in order to use a class.
-# If left blank only the name of the header file containing the class
-# definition is used. Otherwise one should specify the include paths that
-# are normally passed to the compiler using the -I flag.
+# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
+# path mentioned in the documentation of a class, which tells the reader which
+# header file to include in order to use a class. If left blank only the name of
+# the header file containing the class definition is used. Otherwise one should
+# specify the list of include paths that are normally passed to the compiler
+# using the -I flag.
 
 STRIP_FROM_INC_PATH    =
 
-# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter
-# (but less readable) file names. This can be useful if your file system
-# doesn't support long names like on DOS, Mac, or CD-ROM.
+# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
+# less readable) file names. This can be useful is your file systems doesn't
+# support long names like on DOS, Mac, or CD-ROM.
+# The default value is: NO.
 
 SHORT_NAMES            = NO
 
-# If the JAVADOC_AUTOBRIEF tag is set to YES then Doxygen
-# will interpret the first line (until the first dot) of a JavaDoc-style
-# comment as the brief description. If set to NO, the JavaDoc
-# comments will behave just like regular Qt-style comments
-# (thus requiring an explicit @brief command for a brief description.)
+# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
+# first line (until the first dot) of a Javadoc-style comment as the brief
+# description. If set to NO, the Javadoc-style will behave just like regular Qt-
+# style comments (thus requiring an explicit @brief command for a brief
+# description.)
+# The default value is: NO.
 
 JAVADOC_AUTOBRIEF      = NO
 
-# If the QT_AUTOBRIEF tag is set to YES then Doxygen will
-# interpret the first line (until the first dot) of a Qt-style
-# comment as the brief description. If set to NO, the comments
-# will behave just like regular Qt-style comments (thus requiring
-# an explicit \brief command for a brief description.)
+# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
+# line (until the first dot) of a Qt-style comment as the brief description. If
+# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
+# requiring an explicit \brief command for a brief description.)
+# The default value is: NO.
 
 QT_AUTOBRIEF           = NO
 
-# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen
-# treat a multi-line C++ special comment block (i.e. a block of //! or ///
-# comments) as a brief description. This used to be the default behaviour.
-# The new default is to treat a multi-line C++ comment block as a detailed
-# description. Set this tag to YES if you prefer the old behaviour instead.
+# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
+# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
+# a brief description. This used to be the default behavior. The new default is
+# to treat a multi-line C++ comment block as a detailed description. Set this
+# tag to YES if you prefer the old behavior instead.
+#
+# Note that setting this tag to YES also means that rational rose comments are
+# not recognized any more.
+# The default value is: NO.
 
 MULTILINE_CPP_IS_BRIEF = NO
 
-# If the INHERIT_DOCS tag is set to YES (the default) then an undocumented
-# member inherits the documentation from any documented member that it
-# re-implements.
+# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
+# documentation from any documented member that it re-implements.
+# The default value is: YES.
 
 INHERIT_DOCS           = YES
 
-# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce
-# a new page for each member. If set to NO, the documentation of a member will
-# be part of the file/class/namespace that contains it.
+# If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce a
+# new page for each member. If set to NO, the documentation of a member will be
+# part of the file/class/namespace that contains it.
+# The default value is: NO.
 
 SEPARATE_MEMBER_PAGES  = NO
 
-# The TAB_SIZE tag can be used to set the number of spaces in a tab.
-# Doxygen uses this value to replace tabs by spaces in code fragments.
+# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
+# uses this value to replace tabs by spaces in code fragments.
+# Minimum value: 1, maximum value: 16, default value: 4.
 
 TAB_SIZE               = 8
 
-# This tag can be used to specify a number of aliases that acts
-# as commands in the documentation. An alias has the form "name=value".
-# For example adding "sideeffect=\par Side Effects:\n" will allow you to
-# put the command \sideeffect (or @sideeffect) in the documentation, which
-# will result in a user-defined paragraph with heading "Side Effects:".
-# You can put \n's in the value part of an alias to insert newlines.
+# This tag can be used to specify a number of aliases that act as commands in
+# the documentation. An alias has the form:
+# name=value
+# For example adding
+# "sideeffect=@par Side Effects:\n"
+# will allow you to put the command \sideeffect (or @sideeffect) in the
+# documentation, which will result in a user-defined paragraph with heading
+# "Side Effects:". You can put \n's in the value part of an alias to insert
+# newlines.
 
 ALIASES                =
 
 # This tag can be used to specify a number of word-keyword mappings (TCL only).
-# A mapping has the form "name=value". For example adding
-# "class=itcl::class" will allow you to use the command class in the
-# itcl::class meaning.
+# A mapping has the form "name=value". For example adding "class=itcl::class"
+# will allow you to use the command class in the itcl::class meaning.
 
 TCL_SUBST              =
 
-# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C
-# sources only. Doxygen will then generate output that is more tailored for C.
-# For instance, some of the names that are used will be different. The list
-# of all members will be omitted, etc.
+# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
+# only. Doxygen will then generate output that is more tailored for C. For
+# instance, some of the names that are used will be different. The list of all
+# members will be omitted, etc.
+# The default value is: NO.
 
 OPTIMIZE_OUTPUT_FOR_C  = NO
 
-# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java
-# sources only. Doxygen will then generate output that is more tailored for
-# Java. For instance, namespaces will be presented as packages, qualified
-# scopes will look different, etc.
+# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
+# Python sources only. Doxygen will then generate output that is more tailored
+# for that language. For instance, namespaces will be presented as packages,
+# qualified scopes will look different, etc.
+# The default value is: NO.
 
 OPTIMIZE_OUTPUT_JAVA   = NO
 
 # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
-# sources only. Doxygen will then generate output that is more tailored for
-# Fortran.
+# sources. Doxygen will then generate output that is tailored for Fortran.
+# The default value is: NO.
 
 OPTIMIZE_FOR_FORTRAN   = NO
 
 # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
-# sources. Doxygen will then generate output that is tailored for
-# VHDL.
+# sources. Doxygen will then generate output that is tailored for VHDL.
+# The default value is: NO.
 
 OPTIMIZE_OUTPUT_VHDL   = NO
 
 # Doxygen selects the parser to use depending on the extension of the files it
-# parses. With this tag you can assign which parser to use for a given extension.
-# Doxygen has a built-in mapping, but you can override or extend it using this
-# tag. The format is ext=language, where ext is a file extension, and language
-# is one of the parsers supported by doxygen: IDL, Java, Javascript, CSharp, C,
-# C++, D, PHP, Objective-C, Python, Fortran, VHDL, C, C++. For instance to make
-# doxygen treat .inc files as Fortran files (default is PHP), and .f files as C
-# (default is Fortran), use: inc=Fortran f=C. Note that for custom extensions
-# you also need to set FILE_PATTERNS otherwise the files are not read by doxygen.
+# parses. With this tag you can assign which parser to use for a given
+# extension. Doxygen has a built-in mapping, but you can override or extend it
+# using this tag. The format is ext=language, where ext is a file extension, and
+# language is one of the parsers supported by doxygen: IDL, Java, Javascript,
+# C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
+# FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
+# Fortran. In the later case the parser tries to guess whether the code is fixed
+# or free formatted code, this is the default for Fortran type files), VHDL. For
+# instance to make doxygen treat .inc files as Fortran files (default is PHP),
+# and .f files as C (default is Fortran), use: inc=Fortran f=C.
+#
+# Note For files without extension you can use no_extension as a placeholder.
+#
+# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
+# the files are not read by doxygen.
 
 EXTENSION_MAPPING      =
 
+# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# according to the Markdown format, which allows for more readable
+# documentation. See http://daringfireball.net/projects/markdown/ for details.
+# The output of markdown processing is further processed by doxygen, so you can
+# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
+# case of backward compatibilities issues.
+# The default value is: YES.
+
+MARKDOWN_SUPPORT       = YES
+
+# When enabled doxygen tries to link words that correspond to documented
+# classes, or namespaces to their corresponding documentation. Such a link can
+# be prevented in individual cases by by putting a % sign in front of the word
+# or globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+AUTOLINK_SUPPORT       = YES
+
 # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
-# to include (a tag file for) the STL sources as input, then you should
-# set this tag to YES in order to let doxygen match functions declarations and
-# definitions whose arguments contain STL classes (e.g. func(std::string); v.s.
-# func(std::string) {}). This also makes the inheritance and collaboration
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
 # diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
 
 BUILTIN_STL_SUPPORT    = NO
 
 # If you use Microsoft's C++/CLI language, you should set this option to YES to
 # enable parsing support.
+# The default value is: NO.
 
 CPP_CLI_SUPPORT        = NO
 
-# Set the SIP_SUPPORT tag to YES if your project consists of sip sources only.
-# Doxygen will parse them like normal C++ but will assume all classes use public
-# instead of private inheritance when no explicit protection keyword is present.
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
 
 SIP_SUPPORT            = NO
 
-# For Microsoft's IDL there are propget and propput attributes to indicate getter
-# and setter methods for a property. Setting this option to YES (the default)
-# will make doxygen replace the get and set methods by a property in the
-# documentation. This will only work if the methods are indeed getting or
-# setting a simple type. If this is not the case, or you want to show the
-# methods anyway, you should set this option to NO.
+# For Microsoft's IDL there are propget and propput attributes to indicate
+# getter and setter methods for a property. Setting this option to YES will make
+# doxygen to replace the get and set methods by a property in the documentation.
+# This will only work if the methods are indeed getting or setting a simple
+# type. If this is not the case, or you want to show the methods anyway, you
+# should set this option to NO.
+# The default value is: YES.
 
 IDL_PROPERTY_SUPPORT   = YES
 
@@ -273,67 +339,61 @@ IDL_PROPERTY_SUPPORT   = YES
 # tag is set to YES, then doxygen will reuse the documentation of the first
 # member in the group (if any) for the other members of the group. By default
 # all members of a group must be documented explicitly.
+# The default value is: NO.
 
 DISTRIBUTE_GROUP_DOC   = NO
 
-# Set the SUBGROUPING tag to YES (the default) to allow class member groups of
-# the same type (for instance a group of public functions) to be put as a
-# subgroup of that type (e.g. under the Public Functions section). Set it to
-# NO to prevent subgrouping. Alternatively, this can be done per class using
-# the \nosubgrouping command.
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
+# subgrouping. Alternatively, this can be done per class using the
+# \nosubgrouping command.
+# The default value is: YES.
 
 SUBGROUPING            = YES
 
-# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and
-# unions are shown inside the group in which they are included (e.g. using
-# @ingroup) instead of on a separate page (for HTML and Man pages) or
-# section (for LaTeX and RTF).
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
 
 INLINE_GROUPED_CLASSES = NO
 
-# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and
-# unions with only public data fields will be shown inline in the documentation
-# of the scope in which they are defined (i.e. file, namespace, or group
-# documentation), provided this scope is documented. If set to NO (the default),
-# structs, classes, and unions are shown on a separate page (for HTML and Man
-# pages) or section (for LaTeX and RTF).
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
+# the documentation of the scope in which they are defined (i.e. file,
+# namespace, or group documentation), provided this scope is documented. If set
+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
 
 INLINE_SIMPLE_STRUCTS  = NO
 
-# When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum
-# is documented as struct, union, or enum with the name of the typedef. So
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
 # typedef struct TypeS {} TypeT, will appear in the documentation as a struct
 # with name TypeT. When disabled the typedef will appear as a member of a file,
-# namespace, or class. And the struct will be named TypeS. This can typically
-# be useful for C code in case the coding convention dictates that all compound
+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
 # types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
 
 TYPEDEF_HIDES_STRUCT   = NO
 
-# The SYMBOL_CACHE_SIZE determines the size of the internal cache use to
-# determine which symbols to keep in memory and which to flush to disk.
-# When the cache is full, less often used symbols will be written to disk.
-# For small to medium size projects (<1000 input files) the default value is
-# probably good enough. For larger projects a too small cache size can cause
-# doxygen to be busy swapping symbols to and from disk most of the time
-# causing a significant performance penalty.
-# If the system has enough physical memory increasing the cache will improve the
-# performance by keeping more symbols in memory. Note that the value works on
-# a logarithmic scale so increasing the size by one will roughly double the
-# memory usage. The cache size is given by this formula:
-# 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0,
-# corresponding to a cache size of 2^16 = 65536 symbols.
-
-SYMBOL_CACHE_SIZE      = 0
-
-# Similar to the SYMBOL_CACHE_SIZE the size of the symbol lookup cache can be
-# set using LOOKUP_CACHE_SIZE. This cache is used to resolve symbols given
-# their name and scope. Since this can be an expensive process and often the
-# same symbol appear multiple times in the code, doxygen keeps a cache of
-# pre-resolved symbols. If the cache is too small doxygen will become slower.
-# If the cache is too large, memory is wasted. The cache size is given by this
-# formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range is 0..9, the default is 0,
-# corresponding to a cache size of 2^16 = 65536 symbols.
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
 
 LOOKUP_CACHE_SIZE      = 0
 
@@ -342,357 +402,409 @@ LOOKUP_CACHE_SIZE      = 0
 #---------------------------------------------------------------------------
 
 # If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in
-# documentation are documented, even if no documentation was available.
-# Private class members and static file members will be hidden unless
-# the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
 
 EXTRACT_ALL            = NO
 
-# If the EXTRACT_PRIVATE tag is set to YES all private members of a class
-# will be included in the documentation.
+# If the EXTRACT_PRIVATE tag is set to YES all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
 
 EXTRACT_PRIVATE        = NO
 
-# If the EXTRACT_STATIC tag is set to YES all static members of a file
-# will be included in the documentation.
+# If the EXTRACT_PACKAGE tag is set to YES all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PACKAGE        = NO
+
+# If the EXTRACT_STATIC tag is set to YES all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
 
 EXTRACT_STATIC         = NO
 
-# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs)
-# defined locally in source files will be included in the documentation.
-# If set to NO only classes defined in header files are included.
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
 
 EXTRACT_LOCAL_CLASSES  = YES
 
-# This flag is only useful for Objective-C code. When set to YES local
-# methods, which are defined in the implementation section but not in
-# the interface are included in the documentation.
-# If set to NO (the default) only methods in the interface are included.
+# This flag is only useful for Objective-C code. When set to YES local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO only methods in the interface are
+# included.
+# The default value is: NO.
 
 EXTRACT_LOCAL_METHODS  = NO
 
 # If this flag is set to YES, the members of anonymous namespaces will be
 # extracted and appear in the documentation as a namespace called
-# 'anonymous_namespace{file}', where file will be replaced with the base
-# name of the file that contains the anonymous namespace. By default
-# anonymous namespaces are hidden.
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
 
 EXTRACT_ANON_NSPACES   = NO
 
-# If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all
-# undocumented members of documented classes, files or namespaces.
-# If set to NO (the default) these members will be included in the
-# various overviews, but no documentation section is generated.
-# This option has no effect if EXTRACT_ALL is enabled.
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
 
 HIDE_UNDOC_MEMBERS     = NO
 
-# If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all
-# undocumented classes that are normally visible in the class hierarchy.
-# If set to NO (the default) these classes will be included in the various
-# overviews. This option has no effect if EXTRACT_ALL is enabled.
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO these classes will be included in the various overviews. This option has
+# no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
 
 HIDE_UNDOC_CLASSES     = NO
 
-# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all
-# friend (class|struct|union) declarations.
-# If set to NO (the default) these declarations will be included in the
-# documentation.
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# (class|struct|union) declarations. If set to NO these declarations will be
+# included in the documentation.
+# The default value is: NO.
 
 HIDE_FRIEND_COMPOUNDS  = NO
 
-# If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any
-# documentation blocks found inside the body of a function.
-# If set to NO (the default) these blocks will be appended to the
-# function's detailed documentation block.
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
 
 HIDE_IN_BODY_DOCS      = NO
 
-# The INTERNAL_DOCS tag determines if documentation
-# that is typed after a \internal command is included. If the tag is set
-# to NO (the default) then the documentation will be excluded.
-# Set it to YES to include the internal documentation.
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
 
 INTERNAL_DOCS          = NO
 
-# If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate
-# file names in lower-case letters. If set to YES upper-case letters are also
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES upper-case letters are also
 # allowed. This is useful if you have classes or files whose names only differ
 # in case and if your file system supports case sensitive file names. Windows
 # and Mac users are advised to set this option to NO.
+# The default value is: system dependent.
 
 CASE_SENSE_NAMES       = YES
 
-# If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen
-# will show members with their full class and namespace scopes in the
-# documentation. If set to YES the scope will be hidden.
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES the
+# scope will be hidden.
+# The default value is: NO.
 
 HIDE_SCOPE_NAMES       = NO
 
-# If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen
-# will put a list of the files that are included by a file in the documentation
-# of that file.
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
 
 SHOW_INCLUDE_FILES     = YES
 
-# If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen
-# will list include files with double quotes in the documentation
-# rather than with sharp brackets.
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
 
 FORCE_LOCAL_INCLUDES   = NO
 
-# If the INLINE_INFO tag is set to YES (the default) then a tag [inline]
-# is inserted in the documentation for inline members.
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
 
 INLINE_INFO            = YES
 
-# If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen
-# will sort the (detailed) documentation of file and class members
-# alphabetically by member name. If set to NO the members will appear in
-# declaration order.
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO the members will appear in declaration order.
+# The default value is: YES.
 
 SORT_MEMBER_DOCS       = YES
 
-# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the
-# brief documentation of file, namespace and class members alphabetically
-# by member name. If set to NO (the default) the members will appear in
-# declaration order.
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
 
 SORT_BRIEF_DOCS        = NO
 
-# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen
-# will sort the (brief and detailed) documentation of class members so that
-# constructors and destructors are listed first. If set to NO (the default)
-# the constructors will appear in the respective orders defined by
-# SORT_MEMBER_DOCS and SORT_BRIEF_DOCS.
-# This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO
-# and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO.
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
 
 SORT_MEMBERS_CTORS_1ST = NO
 
-# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the
-# hierarchy of group names into alphabetical order. If set to NO (the default)
-# the group names will appear in their defined order.
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
 
 SORT_GROUP_NAMES       = NO
 
-# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be
-# sorted by fully-qualified names, including namespaces. If set to
-# NO (the default), the class list will be sorted only by class name,
-# not including the namespace part.
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
 # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
-# Note: This option applies only to the class list, not to the
-# alphabetical list.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
 
 SORT_BY_SCOPE_NAME     = NO
 
-# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to
-# do proper type resolution of all parameters of a function it will reject a
-# match between the prototype and the implementation of a member function even
-# if there is only one candidate or it is obvious which candidate to choose
-# by doing a simple string match. By disabling STRICT_PROTO_MATCHING doxygen
-# will still accept a match between prototype and implementation in such cases.
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
 
 STRICT_PROTO_MATCHING  = NO
 
-# The GENERATE_TODOLIST tag can be used to enable (YES) or
-# disable (NO) the todo list. This list is created by putting \todo
-# commands in the documentation.
+# The GENERATE_TODOLIST tag can be used to enable ( YES) or disable ( NO) the
+# todo list. This list is created by putting \todo commands in the
+# documentation.
+# The default value is: YES.
 
 GENERATE_TODOLIST      = YES
 
-# The GENERATE_TESTLIST tag can be used to enable (YES) or
-# disable (NO) the test list. This list is created by putting \test
-# commands in the documentation.
+# The GENERATE_TESTLIST tag can be used to enable ( YES) or disable ( NO) the
+# test list. This list is created by putting \test commands in the
+# documentation.
+# The default value is: YES.
 
 GENERATE_TESTLIST      = YES
 
-# The GENERATE_BUGLIST tag can be used to enable (YES) or
-# disable (NO) the bug list. This list is created by putting \bug
-# commands in the documentation.
+# The GENERATE_BUGLIST tag can be used to enable ( YES) or disable ( NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
 
 GENERATE_BUGLIST       = YES
 
-# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or
-# disable (NO) the deprecated list. This list is created by putting
-# \deprecated commands in the documentation.
+# The GENERATE_DEPRECATEDLIST tag can be used to enable ( YES) or disable ( NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
 
 GENERATE_DEPRECATEDLIST= YES
 
-# The ENABLED_SECTIONS tag can be used to enable conditional
-# documentation sections, marked by \if sectionname ... \endif.
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
 
 ENABLED_SECTIONS       =
 
-# The MAX_INITIALIZER_LINES tag determines the maximum number of lines
-# the initial value of a variable or macro consists of for it to appear in
-# the documentation. If the initializer consists of more lines than specified
-# here it will be hidden. Use a value of 0 to hide initializers completely.
-# The appearance of the initializer of individual variables and macros in the
-# documentation can be controlled using \showinitializer or \hideinitializer
-# command in the documentation regardless of this setting.
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
 
 MAX_INITIALIZER_LINES  = 30
 
-# Set the SHOW_USED_FILES tag to NO to disable the list of files generated
-# at the bottom of the documentation of classes and structs. If set to YES the
-# list will mention the files that were used to generate the documentation.
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES the list
+# will mention the files that were used to generate the documentation.
+# The default value is: YES.
 
 SHOW_USED_FILES        = YES
 
-# If the sources in your project are distributed over multiple directories
-# then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy
-# in the documentation. The default is NO.
-
-SHOW_DIRECTORIES       = NO
-
-# Set the SHOW_FILES tag to NO to disable the generation of the Files page.
-# This will remove the Files entry from the Quick Index and from the
-# Folder Tree View (if specified). The default is YES.
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
 
 SHOW_FILES             = YES
 
-# Set the SHOW_NAMESPACES tag to NO to disable the generation of the
-# Namespaces page.
-# This will remove the Namespaces entry from the Quick Index
-# and from the Folder Tree View (if specified). The default is YES.
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
 
 SHOW_NAMESPACES        = YES
 
 # The FILE_VERSION_FILTER tag can be used to specify a program or script that
 # doxygen should invoke to get the current version for each file (typically from
 # the version control system). Doxygen will invoke the program by executing (via
-# popen()) the command <command> <input-file>, where <command> is the value of
-# the FILE_VERSION_FILTER tag, and <input-file> is the name of an input file
-# provided by doxygen. Whatever the program writes to standard output
-# is used as the file version. See the manual for examples.
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
 
 FILE_VERSION_FILTER    =
 
 # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
 # by doxygen. The layout file controls the global structure of the generated
-# output files in an output format independent way. The create the layout file
-# that represents doxygen's defaults, run doxygen with the -l option.
-# You can optionally specify a file name after the option, if omitted
-# DoxygenLayout.xml will be used as the name of the layout file.
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
 
 LAYOUT_FILE            =
 
-# The CITE_BIB_FILES tag can be used to specify one or more bib files
-# containing the references data. This must be a list of .bib files. The
-# .bib extension is automatically appended if omitted. Using this command
-# requires the bibtex tool to be installed. See also
-# http://en.wikipedia.org/wiki/BibTeX for more info. For LaTeX the style
-# of the bibliography can be controlled using LATEX_BIB_STYLE. To use this
-# feature you need bibtex and perl available in the search path.
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
 
 CITE_BIB_FILES         =
 
 #---------------------------------------------------------------------------
-# configuration options related to warning and progress messages
+# Configuration options related to warning and progress messages
 #---------------------------------------------------------------------------
 
-# The QUIET tag can be used to turn on/off the messages that are generated
-# by doxygen. Possible values are YES and NO. If left blank NO is used.
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
 
 QUIET                  = YES
 
 # The WARNINGS tag can be used to turn on/off the warning messages that are
-# generated by doxygen. Possible values are YES and NO. If left blank
-# NO is used.
+# generated to standard error ( stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
 
 WARNINGS               = YES
 
-# If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings
-# for undocumented members. If EXTRACT_ALL is set to YES then this flag will
-# automatically be disabled.
+# If the WARN_IF_UNDOCUMENTED tag is set to YES, then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
 
 WARN_IF_UNDOCUMENTED   = NO
 
-# If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for
-# potential errors in the documentation, such as not documenting some
-# parameters in a documented function, or documenting parameters that
-# don't exist or using markup commands wrongly.
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
 
 WARN_IF_DOC_ERROR      = YES
 
-# The WARN_NO_PARAMDOC option can be enabled to get warnings for
-# functions that are documented, but have no documentation for their parameters
-# or return value. If set to NO (the default) doxygen will only warn about
-# wrong or incomplete parameter documentation, but not about the absence of
-# documentation.
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO doxygen will only warn about wrong or incomplete parameter
+# documentation, but not about the absence of documentation.
+# The default value is: NO.
 
 WARN_NO_PARAMDOC       = NO
 
-# The WARN_FORMAT tag determines the format of the warning messages that
-# doxygen can produce. The string should contain the $file, $line, and $text
-# tags, which will be replaced by the file and line number from which the
-# warning originated and the warning text. Optionally the format may contain
-# $version, which will be replaced by the version of the file (if it could
-# be obtained via FILE_VERSION_FILTER)
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
 
 WARN_FORMAT            = "$file:$line: $text"
 
-# The WARN_LOGFILE tag can be used to specify a file to which warning
-# and error messages should be written. If left blank the output is written
-# to stderr.
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
 
 WARN_LOGFILE           =
 
 #---------------------------------------------------------------------------
-# configuration options related to the input files
+# Configuration options related to the input files
 #---------------------------------------------------------------------------
 
-# The INPUT tag can be used to specify the files and/or directories that contain
-# documented source files. You may enter file names like "myfile.cpp" or
-# directories like "/usr/src/myproject". Separate the files or directories
-# with spaces.
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces.
+# Note: If this tag is empty the current directory is searched.
 
-INPUT                  = ./include/caffe ./src/caffe
+INPUT                  = ./include/caffe \
+                         ./src/caffe
 
 # This tag can be used to specify the character encoding of the source files
-# that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is
-# also the default input encoding. Doxygen uses libiconv (or the iconv built
-# into libc) for the transcoding. See http://www.gnu.org/software/libiconv for
-# the list of possible encodings.
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: http://www.gnu.org/software/libiconv) for the list of
+# possible encodings.
+# The default value is: UTF-8.
 
 INPUT_ENCODING         = UTF-8
 
 # If the value of the INPUT tag contains directories, you can use the
-# FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp
-# and *.h) to filter out the source-files in the directories. If left
-# blank the following patterns are tested:
-# *.c *.cc *.cxx *.cpp *.c++ *.d *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh
-# *.hxx *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.dox *.py
-# *.f90 *.f *.for *.vhd *.vhdl
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank the
+# following patterns are tested:*.c, *.cc, *.cxx, *.cpp, *.c++, *.java, *.ii,
+# *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp,
+# *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown,
+# *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf,
+# *.qsf, *.as and *.js.
 
 FILE_PATTERNS          =
 
-# The RECURSIVE tag can be used to turn specify whether or not subdirectories
-# should be searched for input files as well. Possible values are YES and NO.
-# If left blank NO is used.
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
 
 RECURSIVE              = YES
 
 # The EXCLUDE tag can be used to specify files and/or directories that should be
 # excluded from the INPUT source files. This way you can easily exclude a
 # subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
 # Note that relative paths are relative to the directory from which doxygen is
 # run.
 
-EXCLUDE                = ./src/caffe/test/ ./include/caffe/test/
+EXCLUDE                = ./src/caffe/test/ \
+                         ./include/caffe/test/
 
 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
 # directories that are symbolic links (a Unix file system feature) are excluded
 # from the input.
+# The default value is: NO.
 
 EXCLUDE_SYMLINKS       = NO
 
 # If the value of the INPUT tag contains directories, you can use the
 # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
-# certain files from those directories. Note that the wildcards are matched
-# against the file with absolute path, so to exclude all test directories
-# for example use the pattern */test/*
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
 
 EXCLUDE_PATTERNS       =
 
@@ -701,756 +813,1091 @@ EXCLUDE_PATTERNS       =
 # output. The symbol name can be a fully qualified name, a word, or if the
 # wildcard * is used, a substring. Examples: ANamespace, AClass,
 # AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
 
 EXCLUDE_SYMBOLS        =
 
-# The EXAMPLE_PATH tag can be used to specify one or more files or
-# directories that contain example code fragments that are included (see
-# the \include command).
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
 
 EXAMPLE_PATH           =
 
 # If the value of the EXAMPLE_PATH tag contains directories, you can use the
-# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp
-# and *.h) to filter out the source-files in the directories. If left
-# blank all files are included.
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
 
 EXAMPLE_PATTERNS       =
 
 # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
-# searched for input files to be used with the \include or \dontinclude
-# commands irrespective of the value of the RECURSIVE tag.
-# Possible values are YES and NO. If left blank NO is used.
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
 
 EXAMPLE_RECURSIVE      = NO
 
-# The IMAGE_PATH tag can be used to specify one or more files or
-# directories that contain image that are included in the documentation (see
-# the \image command).
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
 
 IMAGE_PATH             =
 
 # The INPUT_FILTER tag can be used to specify a program that doxygen should
 # invoke to filter for each input file. Doxygen will invoke the filter program
-# by executing (via popen()) the command <filter> <input-file>, where <filter>
-# is the value of the INPUT_FILTER tag, and <input-file> is the name of an
-# input file. Doxygen will then use the output that the filter program writes
-# to standard output.
-# If FILTER_PATTERNS is specified, this tag will be
-# ignored.
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
 
 INPUT_FILTER           =
 
 # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
-# basis.
-# Doxygen will compare the file name with each pattern and apply the
-# filter if there is a match.
-# The filters are a list of the form:
-# pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further
-# info on how filters are used. If FILTER_PATTERNS is empty or if
-# non of the patterns match the file name, INPUT_FILTER is applied.
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
 
 FILTER_PATTERNS        =
 
 # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
-# INPUT_FILTER) will be used to filter the input files when producing source
-# files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# INPUT_FILTER ) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
 
 FILTER_SOURCE_FILES    = NO
 
 # The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
-# pattern. A pattern will override the setting for FILTER_PATTERN (if any)
-# and it is also possible to disable source filtering for a specific pattern
-# using *.ext= (so without naming a filter). This option only has effect when
-# FILTER_SOURCE_FILES is enabled.
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
 
 FILTER_SOURCE_PATTERNS =
 
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE =
+
 #---------------------------------------------------------------------------
-# configuration options related to source browsing
+# Configuration options related to source browsing
 #---------------------------------------------------------------------------
 
-# If the SOURCE_BROWSER tag is set to YES then a list of source files will
-# be generated. Documented entities will be cross-referenced with these sources.
-# Note: To get rid of all source code in the generated output, make sure also
-# VERBATIM_HEADERS is set to NO.
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
 
 SOURCE_BROWSER         = NO
 
-# Setting the INLINE_SOURCES tag to YES will include the body
-# of functions and classes directly in the documentation.
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
 
 INLINE_SOURCES         = NO
 
-# Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct
-# doxygen to hide any special comment blocks from generated source code
-# fragments. Normal C and C++ comments will always remain visible.
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
 
 STRIP_CODE_COMMENTS    = YES
 
-# If the REFERENCED_BY_RELATION tag is set to YES
-# then for each documented function all documented
-# functions referencing it will be listed.
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# function all documented functions referencing it will be listed.
+# The default value is: NO.
 
 REFERENCED_BY_RELATION = NO
 
-# If the REFERENCES_RELATION tag is set to YES
-# then for each documented function all documented entities
-# called/used by that function will be listed.
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
 
 REFERENCES_RELATION    = NO
 
-# If the REFERENCES_LINK_SOURCE tag is set to YES (the default)
-# and SOURCE_BROWSER tag is set to YES, then the hyperlinks from
-# functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will
-# link to the source code.
-# Otherwise they will link to the documentation.
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES, then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
 
 REFERENCES_LINK_SOURCE = YES
 
-# If the USE_HTAGS tag is set to YES then the references to source code
-# will point to the HTML generated by the htags(1) tool instead of doxygen
-# built-in source browser. The htags tool is part of GNU's global source
-# tagging system (see http://www.gnu.org/software/global/global.html). You
-# will need version 4.8.6 or higher.
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see http://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the config file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
 
 USE_HTAGS              = NO
 
-# If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen
-# will generate a verbatim copy of the header file for each class for
-# which an include is specified. Set to NO to disable this.
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
 
 VERBATIM_HEADERS       = YES
 
 #---------------------------------------------------------------------------
-# configuration options related to the alphabetical class index
+# Configuration options related to the alphabetical class index
 #---------------------------------------------------------------------------
 
-# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index
-# of all compounds will be generated. Enable this if the project
-# contains a lot of classes, structs, unions or interfaces.
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
 
 ALPHABETICAL_INDEX     = YES
 
-# If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then
-# the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns
-# in which this list will be split (can be a number in the range [1..20])
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
 
 COLS_IN_ALPHA_INDEX    = 5
 
-# In case all classes in a project start with a common prefix, all
-# classes will be put under the same header in the alphabetical index.
-# The IGNORE_PREFIX tag can be used to specify one or more prefixes that
-# should be ignored while generating the index headers.
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
 
 IGNORE_PREFIX          =
 
 #---------------------------------------------------------------------------
-# configuration options related to the HTML output
+# Configuration options related to the HTML output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_HTML tag is set to YES (the default) Doxygen will
-# generate HTML output.
+# If the GENERATE_HTML tag is set to YES doxygen will generate HTML output
+# The default value is: YES.
 
 GENERATE_HTML          = YES
 
-# The HTML_OUTPUT tag is used to specify where the HTML docs will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be
-# put in front of it. If left blank `html' will be used as the default path.
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_OUTPUT            = html
 
-# The HTML_FILE_EXTENSION tag can be used to specify the file extension for
-# each generated HTML page (for example: .htm,.php,.asp). If it is left blank
-# doxygen will generate files with .html extension.
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_FILE_EXTENSION    = .html
 
-# The HTML_HEADER tag can be used to specify a personal HTML header for
-# each generated HTML page. If it is left blank doxygen will generate a
-# standard header. Note that when using a custom header you are responsible
-#  for the proper inclusion of any scripts and style sheets that doxygen
-# needs, which is dependent on the configuration options used.
-# It is advised to generate a default header using "doxygen -w html
-# header.html footer.html stylesheet.css YourConfigFile" and then modify
-# that header. Note that the header is subject to change so you typically
-# have to redo this when upgrading to a newer version of doxygen or when
-# changing the value of configuration settings such as GENERATE_TREEVIEW!
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_HEADER            =
 
-# The HTML_FOOTER tag can be used to specify a personal HTML footer for
-# each generated HTML page. If it is left blank doxygen will generate a
-# standard footer.
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_FOOTER            =
 
-# The HTML_STYLESHEET tag can be used to specify a user-defined cascading
-# style sheet that is used by each HTML page. It can be used to
-# fine-tune the look of the HTML output. If the tag is left blank doxygen
-# will generate a default style sheet. Note that doxygen will try to copy
-# the style sheet file to the HTML output directory, so don't put your own
-# style sheet in the HTML output directory as well, or it will be erased!
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_STYLESHEET        =
 
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefor more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra stylesheet files is of importance (e.g. the last
+# stylesheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  =
+
 # The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
 # other source files which should be copied to the HTML output directory. Note
 # that these files will be copied to the base HTML output directory. Use the
-# $relpath$ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
-# files. In the HTML_STYLESHEET file, use the file name only. Also note that
-# the files will be copied as-is; there are no commands or markers available.
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_EXTRA_FILES       =
 
-# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output.
-# Doxygen will adjust the colors in the style sheet and background images
-# according to this color. Hue is specified as an angle on a colorwheel,
-# see http://en.wikipedia.org/wiki/Hue for more information.
-# For instance the value 0 represents red, 60 is yellow, 120 is green,
-# 180 is cyan, 240 is blue, 300 purple, and 360 is red again.
-# The allowed range is 0 to 359.
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the stylesheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# http://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_COLORSTYLE_HUE    = 220
 
-# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of
-# the colors in the HTML output. For a value of 0 the output will use
-# grayscales only. A value of 255 will produce the most vivid colors.
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_COLORSTYLE_SAT    = 100
 
-# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to
-# the luminance component of the colors in the HTML output. Values below
-# 100 gradually make the output lighter, whereas values above 100 make
-# the output darker. The value divided by 100 is the actual gamma applied,
-# so 80 represents a gamma of 0.8, The value 220 represents a gamma of 2.2,
-# and 100 does not change the gamma.
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_COLORSTYLE_GAMMA  = 80
 
 # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
-# page will contain the date and time when the page was generated. Setting
-# this to NO can help when comparing the output of multiple runs.
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_TIMESTAMP         = YES
 
-# If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes,
-# files or namespaces will be aligned in HTML using tables. If set to
-# NO a bullet list will be used.
-
-HTML_ALIGN_MEMBERS     = YES
-
 # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
 # documentation will contain sections that can be hidden and shown after the
-# page has loaded. For this to work a browser that supports
-# JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox
-# Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari).
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 HTML_DYNAMIC_SECTIONS  = NO
 
-# If the GENERATE_DOCSET tag is set to YES, additional index files
-# will be generated that can be used as input for Apple's Xcode 3
-# integrated development environment, introduced with OSX 10.5 (Leopard).
-# To create a documentation set, doxygen will generate a Makefile in the
-# HTML output directory. Running make will produce the docset in that
-# directory and running "make install" will install the docset in
-# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find
-# it at startup.
-# See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: http://developer.apple.com/tools/xcode/), introduced with
+# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
 # for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_DOCSET        = NO
 
-# When GENERATE_DOCSET tag is set to YES, this tag determines the name of the
-# feed. A documentation feed provides an umbrella under which multiple
-# documentation sets from a single provider (such as a company or product suite)
-# can be grouped.
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
 
 DOCSET_FEEDNAME        = "Doxygen generated docs"
 
-# When GENERATE_DOCSET tag is set to YES, this tag specifies a string that
-# should uniquely identify the documentation set bundle. This should be a
-# reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen
-# will append .docset to the name.
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
 
 DOCSET_BUNDLE_ID       = org.doxygen.Project
 
-# When GENERATE_PUBLISHER_ID tag specifies a string that should uniquely identify
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
 # the documentation publisher. This should be a reverse domain-name style
 # string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
 
 DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
 
-# The GENERATE_PUBLISHER_NAME tag identifies the documentation publisher.
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
 
 DOCSET_PUBLISHER_NAME  = Publisher
 
-# If the GENERATE_HTMLHELP tag is set to YES, additional index files
-# will be generated that can be used as input for tools like the
-# Microsoft HTML help workshop to generate a compiled HTML help file (.chm)
-# of the generated HTML documentation.
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_HTMLHELP      = NO
 
-# If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can
-# be used to specify the file name of the resulting .chm file. You
-# can add a path in front of the file if the result should not be
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
 # written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 CHM_FILE               =
 
-# If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can
-# be used to specify the location (absolute path including file name) of
-# the HTML help compiler (hhc.exe). If non-empty doxygen will try to run
-# the HTML help compiler on the generated index.hhp.
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler ( hhc.exe). If non-empty
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 HHC_LOCATION           =
 
-# If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag
-# controls if a separate .chi index file is generated (YES) or that
-# it should be included in the master .chm file (NO).
+# The GENERATE_CHI flag controls if a separate .chi index file is generated (
+# YES) or that it should be included in the master .chm file ( NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 GENERATE_CHI           = NO
 
-# If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING
-# is used to encode HtmlHelp index (hhk), content (hhc) and project file
-# content.
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index ( hhk), content ( hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 CHM_INDEX_ENCODING     =
 
-# If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag
-# controls whether a binary table of contents is generated (YES) or a
-# normal table of contents (NO) in the .chm file.
+# The BINARY_TOC flag controls whether a binary table of contents is generated (
+# YES) or a normal table of contents ( NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 BINARY_TOC             = NO
 
-# The TOC_EXPAND flag can be set to YES to add extra items for group members
-# to the contents of the HTML help documentation and to the tree view.
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
 
 TOC_EXPAND             = NO
 
 # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
-# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated
-# that can be used as input for Qt's qhelpgenerator to generate a
-# Qt Compressed Help (.qch) of the generated HTML documentation.
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_QHP           = NO
 
-# If the QHG_LOCATION tag is specified, the QCH_FILE tag can
-# be used to specify the file name of the resulting .qch file.
-# The path specified is relative to the HTML output folder.
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QCH_FILE               =
 
-# The QHP_NAMESPACE tag specifies the namespace to use when generating
-# Qt Help Project output. For more information please see
-# http://doc.trolltech.com/qthelpproject.html#namespace
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_NAMESPACE          = org.doxygen.Project
 
-# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating
-# Qt Help Project output. For more information please see
-# http://doc.trolltech.com/qthelpproject.html#virtual-folders
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_VIRTUAL_FOLDER     = doc
 
-# If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to
-# add. For more information please see
-# http://doc.trolltech.com/qthelpproject.html#custom-filters
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_NAME   =
 
-# The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the
-# custom filter to add. For more information please see
-# <a href="http://doc.trolltech.com/qthelpproject.html#custom-filters">
-# Qt Help Project / Custom Filters</a>.
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_CUST_FILTER_ATTRS  =
 
 # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
-# project's
-# filter section matches.
-# <a href="http://doc.trolltech.com/qthelpproject.html#filter-attributes">
-# Qt Help Project / Filter Attributes</a>.
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHP_SECT_FILTER_ATTRS  =
 
-# If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can
-# be used to specify the location of Qt's qhelpgenerator.
-# If non-empty doxygen will try to run qhelpgenerator on the generated
-# .qhp file.
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
 
 QHG_LOCATION           =
 
-# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files
-#  will be generated, which together with the HTML files, form an Eclipse help
-# plugin. To install this plugin and make it available under the help contents
-# menu in Eclipse, the contents of the directory containing the HTML and XML
-# files needs to be copied into the plugins directory of eclipse. The name of
-# the directory within the plugins directory should be the same as
-# the ECLIPSE_DOC_ID value. After copying Eclipse needs to be restarted before
-# the help appears.
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_ECLIPSEHELP   = NO
 
-# A unique identifier for the eclipse help plugin. When installing the plugin
-# the directory name containing the HTML and XML files should also have
-# this name.
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
 
 ECLIPSE_DOC_ID         = org.doxygen.Project
 
-# The DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs)
-# at top of each HTML page. The value NO (the default) enables the index and
-# the value YES disables it. Since the tabs have the same information as the
-# navigation tree you can set this option to NO if you already set
-# GENERATE_TREEVIEW to YES.
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 DISABLE_INDEX          = NO
 
 # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
-# structure should be generated to display hierarchical information.
-# If the tag value is set to YES, a side panel will be generated
-# containing a tree-like index structure (just like the one that
-# is generated for HTML Help). For this to work a browser that supports
-# JavaScript, DHTML, CSS and frames is required (i.e. any modern browser).
-# Windows users are probably better off using the HTML help feature.
-# Since the tree basically has the same information as the tab index you
-# could consider to set DISABLE_INDEX to NO when enabling this option.
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom stylesheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 GENERATE_TREEVIEW      = NO
 
-# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values
-# (range [0,1..20]) that doxygen will group on one line in the generated HTML
-# documentation. Note that a value of 0 will completely suppress the enum
-# values from appearing in the overview section.
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 ENUM_VALUES_PER_LINE   = 4
 
-# By enabling USE_INLINE_TREES, doxygen will generate the Groups, Directories,
-# and Class Hierarchy pages using a tree view instead of an ordered list.
-
-USE_INLINE_TREES       = NO
-
-# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be
-# used to set the initial width (in pixels) of the frame in which the tree
-# is shown.
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 TREEVIEW_WIDTH         = 250
 
-# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open
-# links to external symbols imported via tag files in a separate window.
+# When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 EXT_LINKS_IN_WINDOW    = NO
 
-# Use this tag to change the font size of Latex formulas included
-# as images in the HTML documentation. The default is 10. Note that
-# when you change the font size after a successful doxygen run you need
-# to manually remove any form_*.png images from the HTML output directory
-# to force them to be regenerated.
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 FORMULA_FONTSIZE       = 10
 
 # Use the FORMULA_TRANPARENT tag to determine whether or not the images
-# generated for formulas are transparent PNGs. Transparent PNGs are
-# not supported properly for IE 6.0, but are supported on all modern browsers.
-# Note that when changing this option you need to delete any form_*.png files
-# in the HTML output before the changes have effect.
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 FORMULA_TRANSPARENT    = YES
 
-# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax
-# (see http://www.mathjax.org) which uses client side Javascript for the
-# rendering instead of using prerendered bitmaps. Use this if you do not
-# have LaTeX installed or if you want to formulas look prettier in the HTML
-# output. When enabled you also need to install MathJax separately and
-# configure the path to it using the MATHJAX_RELPATH option.
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# http://www.mathjax.org) which uses client side Javascript for the rendering
+# instead of using prerendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 USE_MATHJAX            = NO
 
-# When MathJax is enabled you need to specify the location relative to the
-# HTML output directory using the MATHJAX_RELPATH option. The destination
-# directory should contain the MathJax.js script. For instance, if the mathjax
-# directory is located at the same level as the HTML output directory, then
-# MATHJAX_RELPATH should be ../mathjax. The default value points to the
-# mathjax.org site, so you can quickly see the result without installing
-# MathJax, but it is strongly recommended to install a local copy of MathJax
-# before deployment.
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from http://www.mathjax.org before deployment.
+# The default value is: http://cdn.mathjax.org/mathjax/latest.
+# This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_RELPATH        = http://www.mathjax.org/mathjax
 
-# The MATHJAX_EXTENSIONS tag can be used to specify one or MathJax extension
-# names that should be enabled during MathJax rendering.
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
 
 MATHJAX_EXTENSIONS     =
 
-# When the SEARCHENGINE tag is enabled doxygen will generate a search box
-# for the HTML output. The underlying search engine uses javascript
-# and DHTML and should work on any modern browser. Note that when using
-# HTML help (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets
-# (GENERATE_DOCSET) there is already a search function so this one should
-# typically be disabled. For large projects the javascript based search engine
-# can be slow, then enabling SERVER_BASED_SEARCH may provide a better solution.
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
 
 SEARCHENGINE           = YES
 
 # When the SERVER_BASED_SEARCH tag is enabled the search engine will be
-# implemented using a PHP enabled web server instead of at the web client
-# using Javascript. Doxygen will generate the search PHP script and index
-# file to put on the web server. The advantage of the server
-# based approach is that it scales better to large projects and allows
-# full text search. The disadvantages are that it is more difficult to setup
-# and does not have live searching capabilities.
+# implemented using a web server instead of a web client using Javascript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
 
 SERVER_BASED_SEARCH    = NO
 
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer ( doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: http://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
 #---------------------------------------------------------------------------
-# configuration options related to the LaTeX output
+# Configuration options related to the LaTeX output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_LATEX tag is set to YES (the default) Doxygen will
-# generate Latex output.
+# If the GENERATE_LATEX tag is set to YES doxygen will generate LaTeX output.
+# The default value is: YES.
 
 GENERATE_LATEX         = YES
 
-# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be
-# put in front of it. If left blank `latex' will be used as the default path.
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_OUTPUT           = latex
 
 # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
-# invoked. If left blank `latex' will be used as the default command name.
-# Note that when enabling USE_PDFLATEX this option is only used for
-# generating bitmaps for formulas in the HTML output, but not in the
-# Makefile that is written to the output directory.
+# invoked.
+#
+# Note that when enabling USE_PDFLATEX this option is only used for generating
+# bitmaps for formulas in the HTML output, but not in the Makefile that is
+# written to the output directory.
+# The default file is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_CMD_NAME         = latex
 
-# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to
-# generate index for LaTeX. If left blank `makeindex' will be used as the
-# default command name.
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 MAKEINDEX_CMD_NAME     = makeindex
 
-# If the COMPACT_LATEX tag is set to YES Doxygen generates more compact
-# LaTeX documents. This may be useful for small projects and may help to
-# save some trees in general.
+# If the COMPACT_LATEX tag is set to YES doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 COMPACT_LATEX          = NO
 
-# The PAPER_TYPE tag can be used to set the paper type that is used
-# by the printer. Possible values are: a4, letter, legal and
-# executive. If left blank a4wide will be used.
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 PAPER_TYPE             = a4
 
-# The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX
-# packages that should be included in the LaTeX output.
-
-EXTRA_PACKAGES         = amsmath amsfonts xr
-
-# The LATEX_HEADER tag can be used to specify a personal LaTeX header for
-# the generated latex document. The header should contain everything until
-# the first chapter. If it is left blank doxygen will generate a
-# standard header. Notice: only use this tag if you know what you are doing!
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. To get the times font for
+# instance you can specify
+# EXTRA_PACKAGES=times
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         = amsmath \
+                         amsfonts \
+                         xr
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empy string,
+# for the replacement values of the other commands the user is refered to
+# HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_HEADER           =
 
-# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for
-# the generated latex document. The footer should contain everything after
-# the last chapter. If it is left blank doxygen will generate a
-# standard footer. Notice: only use this tag if you know what you are doing!
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_FOOTER           =
 
-# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated
-# is prepared for conversion to pdf (using ps2pdf). The pdf file will
-# contain links (just like the HTML output) instead of page references
-# This makes the output suitable for online browsing using a pdf viewer.
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 PDF_HYPERLINKS         = YES
 
-# If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of
-# plain latex in the generated Makefile. Set this option to YES to get a
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES to get a
 # higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 USE_PDFLATEX           = YES
 
-# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode.
-# command to the generated LaTeX files. This will instruct LaTeX to keep
-# running if errors occur, instead of asking the user for help.
-# This option is also used when generating formulas in HTML.
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_BATCHMODE        = NO
 
-# If LATEX_HIDE_INDICES is set to YES then doxygen will not
-# include the index chapters (such as File Index, Compound Index, etc.)
-# in the output.
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_HIDE_INDICES     = NO
 
-# If LATEX_SOURCE_CODE is set to YES then doxygen will include
-# source code with syntax highlighting in the LaTeX output.
-# Note that which sources are shown also depends on other settings
-# such as SOURCE_BROWSER.
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_SOURCE_CODE      = NO
 
 # The LATEX_BIB_STYLE tag can be used to specify the style to use for the
-# bibliography, e.g. plainnat, or ieeetr. The default style is "plain". See
-# http://en.wikipedia.org/wiki/BibTeX for more info.
+# bibliography, e.g. plainnat, or ieeetr. See
+# http://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
 
 LATEX_BIB_STYLE        = plain
 
 #---------------------------------------------------------------------------
-# configuration options related to the RTF output
+# Configuration options related to the RTF output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output
-# The RTF output is optimized for Word 97 and may not look very pretty with
-# other RTF readers or editors.
+# If the GENERATE_RTF tag is set to YES doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
 
 GENERATE_RTF           = NO
 
-# The RTF_OUTPUT tag is used to specify where the RTF docs will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be
-# put in front of it. If left blank `rtf' will be used as the default path.
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_OUTPUT             = rtf
 
-# If the COMPACT_RTF tag is set to YES Doxygen generates more compact
-# RTF documents. This may be useful for small projects and may help to
-# save some trees in general.
+# If the COMPACT_RTF tag is set to YES doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
 
 COMPACT_RTF            = NO
 
-# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated
-# will contain hyperlink fields. The RTF file will
-# contain links (just like the HTML output) instead of page references.
-# This makes the output suitable for online browsing using WORD or other
-# programs which support those fields.
-# Note: wordpad (write) and others do not support links.
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_HYPERLINKS         = NO
 
-# Load style sheet definitions from file. Syntax is similar to doxygen's
-# config file, i.e. a series of assignments. You only have to provide
-# replacements, missing definitions are set to their default value.
+# Load stylesheet definitions from file. Syntax is similar to doxygen's config
+# file, i.e. a series of assignments. You only have to provide replacements,
+# missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_STYLESHEET_FILE    =
 
-# Set optional variables used in the generation of an rtf document.
-# Syntax is similar to doxygen's config file.
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's config file. A template extensions file can be generated
+# using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
 
 RTF_EXTENSIONS_FILE    =
 
 #---------------------------------------------------------------------------
-# configuration options related to the man page output
+# Configuration options related to the man page output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_MAN tag is set to YES (the default) Doxygen will
-# generate man pages
+# If the GENERATE_MAN tag is set to YES doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
 
 GENERATE_MAN           = NO
 
-# The MAN_OUTPUT tag is used to specify where the man pages will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be
-# put in front of it. If left blank `man' will be used as the default path.
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
 
 MAN_OUTPUT             = man
 
-# The MAN_EXTENSION tag determines the extension that is added to
-# the generated man pages (default is the subroutine's section .3)
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
 
 MAN_EXTENSION          = .3
 
-# If the MAN_LINKS tag is set to YES and Doxygen generates man output,
-# then it will generate one additional man file for each entity
-# documented in the real man page(s). These additional files
-# only source the real man page, but without them the man command
-# would be unable to find the correct page. The default is NO.
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
 
 MAN_LINKS              = NO
 
 #---------------------------------------------------------------------------
-# configuration options related to the XML output
+# Configuration options related to the XML output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_XML tag is set to YES Doxygen will
-# generate an XML file that captures the structure of
-# the code including all documentation.
+# If the GENERATE_XML tag is set to YES doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
 
 GENERATE_XML           = NO
 
-# The XML_OUTPUT tag is used to specify where the XML pages will be put.
-# If a relative path is entered the value of OUTPUT_DIRECTORY will be
-# put in front of it. If left blank `xml' will be used as the default path.
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
 
 XML_OUTPUT             = xml
 
-# The XML_SCHEMA tag can be used to specify an XML schema,
-# which can be used by a validating XML parser to check the
-# syntax of the XML files.
+# If the XML_PROGRAMLISTING tag is set to YES doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
 
-XML_SCHEMA             =
+XML_PROGRAMLISTING     = YES
 
-# The XML_DTD tag can be used to specify an XML DTD,
-# which can be used by a validating XML parser to check the
-# syntax of the XML files.
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
 
-XML_DTD                =
+# If the GENERATE_DOCBOOK tag is set to YES doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
 
-# If the XML_PROGRAMLISTING tag is set to YES Doxygen will
-# dump the program listings (including syntax highlighting
-# and cross-referencing information) to the XML output. Note that
-# enabling this will significantly increase the size of the XML output.
+GENERATE_DOCBOOK       = NO
 
-XML_PROGRAMLISTING     = YES
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
 
 #---------------------------------------------------------------------------
-# configuration options for the AutoGen Definitions output
+# Configuration options for the AutoGen Definitions output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will
-# generate an AutoGen Definitions (see autogen.sf.net) file
-# that captures the structure of the code including all
-# documentation. Note that this feature is still experimental
-# and incomplete at the moment.
+# If the GENERATE_AUTOGEN_DEF tag is set to YES doxygen will generate an AutoGen
+# Definitions (see http://autogen.sf.net) file that captures the structure of
+# the code including all documentation. Note that this feature is still
+# experimental and incomplete at the moment.
+# The default value is: NO.
 
 GENERATE_AUTOGEN_DEF   = NO
 
 #---------------------------------------------------------------------------
-# configuration options related to the Perl module output
+# Configuration options related to the Perl module output
 #---------------------------------------------------------------------------
 
-# If the GENERATE_PERLMOD tag is set to YES Doxygen will
-# generate a Perl module file that captures the structure of
-# the code including all documentation. Note that this
-# feature is still experimental and incomplete at the
-# moment.
+# If the GENERATE_PERLMOD tag is set to YES doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
 
 GENERATE_PERLMOD       = NO
 
-# If the PERLMOD_LATEX tag is set to YES Doxygen will generate
-# the necessary Makefile rules, Perl scripts and LaTeX code to be able
-# to generate PDF and DVI output from the Perl module output.
+# If the PERLMOD_LATEX tag is set to YES doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
 
 PERLMOD_LATEX          = NO
 
-# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be
-# nicely formatted so it can be parsed by a human reader.
-# This is useful
-# if you want to understand what is going on.
-# On the other hand, if this
-# tag is set to NO the size of the Perl module output will be much smaller
-# and Perl will parse it just the same.
+# If the PERLMOD_PRETTY tag is set to YES the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
 
 PERLMOD_PRETTY         = YES
 
-# The names of the make variables in the generated doxyrules.make file
-# are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX.
-# This is useful so different doxyrules.make files included by the same
-# Makefile don't overwrite each other's variables.
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
 
 PERLMOD_MAKEVAR_PREFIX =
 
@@ -1458,110 +1905,128 @@ PERLMOD_MAKEVAR_PREFIX =
 # Configuration options related to the preprocessor
 #---------------------------------------------------------------------------
 
-# If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will
-# evaluate all C-preprocessor directives found in the sources and include
-# files.
+# If the ENABLE_PREPROCESSING tag is set to YES doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
 
 ENABLE_PREPROCESSING   = YES
 
-# If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro
-# names in the source code. If set to NO (the default) only conditional
-# compilation will be performed. Macro expansion can be done in a controlled
-# way by setting EXPAND_ONLY_PREDEF to YES.
+# If the MACRO_EXPANSION tag is set to YES doxygen will expand all macro names
+# in the source code. If set to NO only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 MACRO_EXPANSION        = NO
 
-# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES
-# then the macro expansion is limited to the macros specified with the
-# PREDEFINED and EXPAND_AS_DEFINED tags.
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 EXPAND_ONLY_PREDEF     = NO
 
-# If the SEARCH_INCLUDES tag is set to YES (the default) the includes files
-# pointed to by INCLUDE_PATH will be searched when a #include is found.
+# If the SEARCH_INCLUDES tag is set to YES the includes files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 SEARCH_INCLUDES        = YES
 
 # The INCLUDE_PATH tag can be used to specify one or more directories that
-# contain include files that are not input files but should be processed by
-# the preprocessor.
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
 
 INCLUDE_PATH           =
 
 # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
 # patterns (like *.h and *.hpp) to filter out the header-files in the
-# directories. If left blank, the patterns specified with FILE_PATTERNS will
-# be used.
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 INCLUDE_FILE_PATTERNS  =
 
-# The PREDEFINED tag can be used to specify one or more macro names that
-# are defined before the preprocessor is started (similar to the -D option of
-# gcc). The argument of the tag is a list of macros of the form: name
-# or name=definition (no spaces). If the definition and the = are
-# omitted =1 is assumed. To prevent a macro definition from being
-# undefined via #undef or recursively expanded use the := operator
-# instead of the = operator.
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 PREDEFINED             =
 
-# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then
-# this tag can be used to specify a list of macro names that should be expanded.
-# The macro definition that is found in the sources will be used.
-# Use the PREDEFINED tag if you want to use a different macro definition that
-# overrules the definition found in the source code.
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 EXPAND_AS_DEFINED      =
 
-# If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then
-# doxygen's preprocessor will remove all references to function-like macros
-# that are alone on a line, have an all uppercase name, and do not end with a
-# semicolon, because these will confuse the parser if not removed.
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
 SKIP_FUNCTION_MACROS   = YES
 
 #---------------------------------------------------------------------------
-# Configuration::additions related to external references
+# Configuration options related to external references
 #---------------------------------------------------------------------------
 
-# The TAGFILES option can be used to specify one or more tagfiles.
-# Optionally an initial location of the external documentation
-# can be added for each tagfile. The format of a tag file without
-# this location is as follows:
-#
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
 # TAGFILES = file1 file2 ...
 # Adding location for the tag files is done as follows:
-#
 # TAGFILES = file1=loc1 "file2 = loc2" ...
-# where "loc1" and "loc2" can be relative or absolute paths or
-# URLs. If a location is present for each tag, the installdox tool
-# does not have to be run to correct the links.
-# Note that each tag file must have a unique name
-# (where the name does NOT include the path)
-# If a tag file is not located in the directory in which doxygen
-# is run, you must also specify the path to the tagfile here.
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
+# the path). If a tag file is not located in the directory in which doxygen is
+# run, you must also specify the path to the tagfile here.
 
 TAGFILES               =
 
-# When a file name is specified after GENERATE_TAGFILE, doxygen will create
-# a tag file that is based on the input files it reads.
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# tag file that is based on the input files it reads. See section "Linking to
+# external documentation" for more information about the usage of tag files.
 
 GENERATE_TAGFILE       =
 
-# If the ALLEXTERNALS tag is set to YES all external classes will be listed
-# in the class index. If set to NO only the inherited external classes
-# will be listed.
+# If the ALLEXTERNALS tag is set to YES all external class will be listed in the
+# class index. If set to NO only the inherited external classes will be listed.
+# The default value is: NO.
 
 ALLEXTERNALS           = NO
 
-# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed
-# in the modules index. If set to NO, only the current project's groups will
-# be listed.
+# If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed in
+# the modules index. If set to NO, only the current project's groups will be
+# listed.
+# The default value is: YES.
 
 EXTERNAL_GROUPS        = YES
 
+# If the EXTERNAL_PAGES tag is set to YES all external pages will be listed in
+# the related pages index. If set to NO, only the current project's pages will
+# be listed.
+# The default value is: YES.
+
+EXTERNAL_PAGES         = YES
+
 # The PERL_PATH should be the absolute path and name of the perl script
-# interpreter (i.e. the result of `which perl').
+# interpreter (i.e. the result of 'which perl').
+# The default file (with absolute path) is: /usr/bin/perl.
 
 PERL_PATH              = /usr/bin/perl
 
@@ -1569,213 +2034,302 @@ PERL_PATH              = /usr/bin/perl
 # Configuration options related to the dot tool
 #---------------------------------------------------------------------------
 
-# If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will
-# generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base
-# or super classes. Setting the tag to NO turns the diagrams off. Note that
-# this option also works with HAVE_DOT disabled, but it is recommended to
-# install and use dot, since it yields more powerful graphs.
+# If the CLASS_DIAGRAMS tag is set to YES doxygen will generate a class diagram
+# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
+# NO turns the diagrams off. Note that this option also works with HAVE_DOT
+# disabled, but it is recommended to install and use dot, since it yields more
+# powerful graphs.
+# The default value is: YES.
 
 CLASS_DIAGRAMS         = YES
 
 # You can define message sequence charts within doxygen comments using the \msc
-# command. Doxygen will then run the mscgen tool (see
-# http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the
+# command. Doxygen will then run the mscgen tool (see:
+# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
 # documentation. The MSCGEN_PATH tag allows you to specify the directory where
 # the mscgen tool resides. If left empty the tool is assumed to be found in the
 # default search path.
 
 MSCGEN_PATH            =
 
-# If set to YES, the inheritance and collaboration graphs will hide
-# inheritance and usage relations if the target is undocumented
-# or is not a class.
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
+
+DIA_PATH               =
+
+# If set to YES, the inheritance and collaboration graphs will hide inheritance
+# and usage relations if the target is undocumented or is not a class.
+# The default value is: YES.
 
 HIDE_UNDOC_RELATIONS   = YES
 
 # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
-# available from the path. This tool is part of Graphviz, a graph visualization
-# toolkit from AT&T and Lucent Bell Labs. The other options in this section
-# have no effect if this option is set to NO (the default)
+# available from the path. This tool is part of Graphviz (see:
+# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# Bell Labs. The other options in this section have no effect if this option is
+# set to NO
+# The default value is: NO.
 
 HAVE_DOT               = NO
 
-# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is
-# allowed to run in parallel. When set to 0 (the default) doxygen will
-# base this on the number of processors available in the system. You can set it
-# explicitly to a value larger than 0 to get control over the balance
-# between CPU load and processing speed.
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
+# to run in parallel. When set to 0 doxygen will base this on the number of
+# processors available in the system. You can set it explicitly to a value
+# larger than 0 to get control over the balance between CPU load and processing
+# speed.
+# Minimum value: 0, maximum value: 32, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_NUM_THREADS        = 0
 
-# By default doxygen will use the Helvetica font for all dot files that
-# doxygen generates. When you want a differently looking font you can specify
-# the font name using DOT_FONTNAME. You need to make sure dot is able to find
-# the font, which can be done by putting it in a standard location or by setting
-# the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the
-# directory containing the font.
+# When you want a differently looking font in the dot files that doxygen
+# generates you can specify the font name using DOT_FONTNAME. You need to make
+# sure dot is able to find the font, which can be done by putting it in a
+# standard location or by setting the DOTFONTPATH environment variable or by
+# setting DOT_FONTPATH to the directory containing the font.
+# The default value is: Helvetica.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_FONTNAME           = Helvetica
 
-# The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs.
-# The default size is 10pt.
+# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
+# dot graphs.
+# Minimum value: 4, maximum value: 24, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_FONTSIZE           = 10
 
-# By default doxygen will tell dot to use the Helvetica font.
-# If you specify a different font using DOT_FONTNAME you can use DOT_FONTPATH to
-# set the path where dot can find it.
+# By default doxygen will tell dot to use the default font as specified with
+# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
+# the path where dot can find it using this tag.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_FONTPATH           =
 
-# If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen
-# will generate a graph for each documented class showing the direct and
-# indirect inheritance relations. Setting this tag to YES will force the
-# CLASS_DIAGRAMS tag to NO.
+# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
+# each documented class showing the direct and indirect inheritance relations.
+# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 CLASS_GRAPH            = YES
 
-# If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen
-# will generate a graph for each documented class showing the direct and
-# indirect implementation dependencies (inheritance, containment, and
-# class references variables) of the class with other documented classes.
+# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
+# graph for each documented class showing the direct and indirect implementation
+# dependencies (inheritance, containment, and class references variables) of the
+# class with other documented classes.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 COLLABORATION_GRAPH    = YES
 
-# If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen
-# will generate a graph for groups, showing the direct groups dependencies
+# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
+# groups, showing the direct groups dependencies.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 GROUP_GRAPHS           = YES
 
 # If the UML_LOOK tag is set to YES doxygen will generate inheritance and
 # collaboration diagrams in a style similar to the OMG's Unified Modeling
 # Language.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 UML_LOOK               = NO
 
-# If set to YES, the inheritance and collaboration graphs will show the
-# relations between templates and their instances.
+# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
+# class node. If there are many fields or methods and many nodes the graph may
+# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
+# number of items for each type to make the size more manageable. Set this to 0
+# for no limit. Note that the threshold may be exceeded by 50% before the limit
+# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
+# but if the number exceeds 15, the total amount of fields shown is limited to
+# 10.
+# Minimum value: 0, maximum value: 100, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LIMIT_NUM_FIELDS   = 10
+
+# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
+# collaboration graphs will show the relations between templates and their
+# instances.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 TEMPLATE_RELATIONS     = NO
 
-# If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT
-# tags are set to YES then doxygen will generate a graph for each documented
-# file showing the direct and indirect include dependencies of the file with
-# other documented files.
+# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
+# YES then doxygen will generate a graph for each documented file showing the
+# direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 INCLUDE_GRAPH          = YES
 
-# If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and
-# HAVE_DOT tags are set to YES then doxygen will generate a graph for each
-# documented header file showing the documented files that directly or
-# indirectly include this file.
+# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
+# set to YES then doxygen will generate a graph for each documented file showing
+# the direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 INCLUDED_BY_GRAPH      = YES
 
-# If the CALL_GRAPH and HAVE_DOT options are set to YES then
-# doxygen will generate a call dependency graph for every global function
-# or class method. Note that enabling this option will significantly increase
-# the time of a run. So in most cases it will be better to enable call graphs
-# for selected functions only using the \callgraph command.
+# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 CALL_GRAPH             = NO
 
-# If the CALLER_GRAPH and HAVE_DOT tags are set to YES then
-# doxygen will generate a caller dependency graph for every global function
-# or class method. Note that enabling this option will significantly increase
-# the time of a run. So in most cases it will be better to enable caller
-# graphs for selected functions only using the \callergraph command.
+# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable caller graphs for selected
+# functions only using the \callergraph command.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 CALLER_GRAPH           = NO
 
-# If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen
-# will generate a graphical hierarchy of all classes instead of a textual one.
+# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# hierarchy of all classes instead of a textual one.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 GRAPHICAL_HIERARCHY    = YES
 
-# If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES
-# then doxygen will show the dependencies a directory has on other directories
-# in a graphical way. The dependency relations are determined by the #include
-# relations between the files in the directories.
+# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
+# dependencies a directory has on other directories in a graphical way. The
+# dependency relations are determined by the #include relations between the
+# files in the directories.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DIRECTORY_GRAPH        = YES
 
 # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
-# generated by dot. Possible values are svg, png, jpg, or gif.
-# If left blank png will be used. If you choose svg you need to set
-# HTML_FILE_EXTENSION to xhtml in order to make the SVG files
-# visible in IE 9+ (other browsers do not have this requirement).
+# generated by dot.
+# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
+# to make the SVG files visible in IE 9+ (other browsers do not have this
+# requirement).
+# Possible values are: png, jpg, gif and svg.
+# The default value is: png.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_IMAGE_FORMAT       = png
 
 # If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
 # enable generation of interactive SVG images that allow zooming and panning.
-# Note that this requires a modern browser other than Internet Explorer.
-# Tested and working are Firefox, Chrome, Safari, and Opera. For IE 9+ you
-# need to set HTML_FILE_EXTENSION to xhtml in order to make the SVG files
-# visible. Older versions of IE do not have SVG support.
+#
+# Note that this requires a modern browser other than Internet Explorer. Tested
+# and working are Firefox, Chrome, Safari, and Opera.
+# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
+# the SVG files visible. Older versions of IE do not have SVG support.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 INTERACTIVE_SVG        = NO
 
-# The tag DOT_PATH can be used to specify the path where the dot tool can be
+# The DOT_PATH tag can be used to specify the path where the dot tool can be
 # found. If left blank, it is assumed the dot tool can be found in the path.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_PATH               =
 
 # The DOTFILE_DIRS tag can be used to specify one or more directories that
-# contain dot files that are included in the documentation (see the
-# \dotfile command).
+# contain dot files that are included in the documentation (see the \dotfile
+# command).
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOTFILE_DIRS           =
 
 # The MSCFILE_DIRS tag can be used to specify one or more directories that
-# contain msc files that are included in the documentation (see the
-# \mscfile command).
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
 
 MSCFILE_DIRS           =
 
-# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of
-# nodes that will be shown in the graph. If the number of nodes in a graph
-# becomes larger than this value, doxygen will truncate the graph, which is
-# visualized by representing a node as a red box. Note that doxygen if the
-# number of direct children of the root node in a graph is already larger than
-# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note
-# that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+# The DIAFILE_DIRS tag can be used to specify one or more directories that
+# contain dia files that are included in the documentation (see the \diafile
+# command).
+
+DIAFILE_DIRS           =
+
+# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
+# path where java can find the plantuml.jar file. If left blank, it is assumed
+# PlantUML is not used or called during a preprocessing step. Doxygen will
+# generate a warning when it encounters a \startuml command in this case and
+# will not generate output for the diagram.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+PLANTUML_JAR_PATH      =
+
+# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
+# that will be shown in the graph. If the number of nodes in a graph becomes
+# larger than this value, doxygen will truncate the graph, which is visualized
+# by representing a node as a red box. Note that doxygen if the number of direct
+# children of the root node in a graph is already larger than
+# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
+# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+# Minimum value: 0, maximum value: 10000, default value: 50.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_GRAPH_MAX_NODES    = 50
 
-# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the
-# graphs generated by dot. A depth value of 3 means that only nodes reachable
-# from the root by following a path via at most 3 edges will be shown. Nodes
-# that lay further from the root node will be omitted. Note that setting this
-# option to 1 or 2 may greatly reduce the computation time needed for large
-# code bases. Also note that the size of a graph can be further restricted by
+# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
+# generated by dot. A depth value of 3 means that only nodes reachable from the
+# root by following a path via at most 3 edges will be shown. Nodes that lay
+# further from the root node will be omitted. Note that setting this option to 1
+# or 2 may greatly reduce the computation time needed for large code bases. Also
+# note that the size of a graph can be further restricted by
 # DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
+# Minimum value: 0, maximum value: 1000, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 MAX_DOT_GRAPH_DEPTH    = 0
 
 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
-# background. This is disabled by default, because dot on Windows does not
-# seem to support this out of the box. Warning: Depending on the platform used,
-# enabling this option may lead to badly anti-aliased labels on the edges of
-# a graph (i.e. they become hard to read).
+# background. This is disabled by default, because dot on Windows does not seem
+# to support this out of the box.
+#
+# Warning: Depending on the platform used, enabling this option may lead to
+# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
+# read).
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_TRANSPARENT        = NO
 
 # Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output
 # files in one run (i.e. multiple -o and -T options on the command line). This
-# makes dot run faster, but since only newer versions of dot (>1.8.10)
-# support this, this feature is disabled by default.
+# makes dot run faster, but since only newer versions of dot (>1.8.10) support
+# this, this feature is disabled by default.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_MULTI_TARGETS      = YES
 
-# If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will
-# generate a legend page explaining the meaning of the various boxes and
-# arrows in the dot generated graphs.
+# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
+# explaining the meaning of the various boxes and arrows in the dot generated
+# graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 GENERATE_LEGEND        = YES
 
-# If the DOT_CLEANUP tag is set to YES (the default) Doxygen will
-# remove the intermediate dot files that are used to generate
-# the various graphs.
+# If the DOT_CLEANUP tag is set to YES doxygen will remove the intermediate dot
+# files that are used to generate the various graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
 
 DOT_CLEANUP            = YES

From c4b9ec58fe6e1d7fd325b1c131ac7a2334238ebc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 13:22:09 -0700
Subject: [PATCH 0654/2053] [docs] configure doxygen + docs script for
 docs/doxygen site output

---
 .gitignore            | 3 ++-
 Makefile              | 4 +++-
 docs/index.md         | 2 ++
 scripts/build_docs.sh | 3 +++
 4 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9ba6de74561..2ac7a0096eb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,7 +70,8 @@ MANIFEST-*
 docs/_site
 docs/gathered
 _site
-doxygen/
+doxygen
+docs/dev
 
 # Sublime Text settings
 *.sublime-workspace
diff --git a/Makefile b/Makefile
index 50e68acd84c..546de931f7e 100644
--- a/Makefile
+++ b/Makefile
@@ -191,7 +191,8 @@ ALL_BUILD_DIRS := $(sort \
 # Set directory for Doxygen-generated documentation
 ##############################
 DOXYGEN_CONFIG_FILE ?= ./.Doxyfile
-DOXYGEN_OUTPUT_DIR ?= ./doxygen # should be the same as OUTPUT_DIRECTORY in the .Doxyfile
+# should be the same as OUTPUT_DIRECTORY in the .Doxyfile
+DOXYGEN_OUTPUT_DIR ?= ./doxygen
 DOXYGEN_COMMAND ?= doxygen
 # All the files that might have Doxygen documentation.
 DOXYGEN_SOURCES := $(shell find \
@@ -337,6 +338,7 @@ lintclean:
 	@ $(RM) -r $(LINT_OUTPUT_DIR) $(EMPTY_LINT_REPORT) $(NONEMPTY_LINT_REPORT)
 
 docs: $(DOXYGEN_OUTPUT_DIR)
+	@ cd ./docs ; ln -sfn ../$(DOXYGEN_OUTPUT_DIR)/html doxygen
 
 $(DOXYGEN_OUTPUT_DIR): $(DOXYGEN_CONFIG_FILE) $(DOXYGEN_SOURCES)
 	$(DOXYGEN_COMMAND) $(DOXYGEN_CONFIG_FILE)
diff --git a/docs/index.md b/docs/index.md
index 67b424ceefd..94227c25249 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -43,6 +43,8 @@ Tested on Ubuntu, Red Hat, OS X.
 BVLC provides ready-to-use models for non-commercial use.
 * [Developing & Contributing](/development.html)<br />
 Guidelines for development and contributing to Caffe.
+* [API Documentation](/doxygen/)<br />
+Developer documentation automagically generated from code comments.
 
 ### Examples
 
diff --git a/scripts/build_docs.sh b/scripts/build_docs.sh
index c5680151416..f8ace0ead43 100755
--- a/scripts/build_docs.sh
+++ b/scripts/build_docs.sh
@@ -12,6 +12,9 @@ cd $ROOT_DIR
 # Gather docs.
 scripts/gather_examples.sh
 
+# Generate developer docs.
+make docs
+
 # Display docs using web server.
 cd docs
 jekyll serve -w -s . -d _site --port=$PORT

From 9302b1de5a1549a40e3797414b1d2f37167a319b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 13:58:00 -0700
Subject: [PATCH 0655/2053] [example] upgrade fine-tuning example to new
 transformation param

---
 .../flickr_style_train_val.prototxt           | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/examples/finetune_flickr_style/flickr_style_train_val.prototxt b/examples/finetune_flickr_style/flickr_style_train_val.prototxt
index bcb1e1ce1e2..46a198a8d89 100644
--- a/examples/finetune_flickr_style/flickr_style_train_val.prototxt
+++ b/examples/finetune_flickr_style/flickr_style_train_val.prototxt
@@ -7,14 +7,14 @@ layers {
   image_data_param {
     source: "data/flickr_style/train.txt"
     batch_size: 50
-    transform_param {
-      crop_size: 227
-      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: true
-    }
     new_height: 256
     new_width: 256
   }
+  transform_param {
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: true
+  }
   include: { phase: TRAIN }
 }
 layers {
@@ -25,14 +25,14 @@ layers {
   image_data_param {
     source: "data/flickr_style/train.txt"
     batch_size: 50
-    transform_param {
-      crop_size: 227
-      mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-      mirror: true
-    }
     new_height: 256
     new_width: 256
   }
+  transform_param {
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    mirror: false
+  }
   include: { phase: TEST }
 }
 layers {

From 66acf92e0ed310eb20245305ded56ad95550af36 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 3 Sep 2014 13:00:58 -0700
Subject: [PATCH 0656/2053] Update paths

---
 examples/mnist/readme.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 975a77e5128..074923d2a68 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -26,7 +26,7 @@ If it complains that `wget` or `gunzip` are not installed, you need to install t
 
 Before we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.
 
-The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `CAFFE_ROOT/examples/lenet.prototxt`.
+The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `$CAFFE_ROOT/examples/mnist/lenet.prototxt`.
 
 ## Define the MNIST Network
 
@@ -102,7 +102,7 @@ Phew. Pooling layers are actually much easier to define:
 
 This says we will perform max pooling with a pool kernel size 2 and a stride of 2 (so no overlapping between neighboring pooling regions).
 
-Similarly, you can write up the second convolution and pooling layers. Check `data/lenet.prototxt` for details.
+Similarly, you can write up the second convolution and pooling layers. Check `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt` for details.
 
 ### Writing the Fully Connected Layer
 
@@ -176,7 +176,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 ## Define the MNIST Solver
 
-Check out the comments explaining each line in the prototxt:
+Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt`:
 
     # The train/test net protocol buffer definition
     net: "lenet_train_test.prototxt"

From be9a912b430dc5066517dc21022d08526ac3777c Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 3 Sep 2014 13:01:29 -0700
Subject: [PATCH 0657/2053] Point to local file, not github file

This makes the readme file more consistent
---
 examples/mnist/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 074923d2a68..a51fc1c0d3b 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -30,7 +30,7 @@ The design of LeNet contains the essence of CNNs that are still used in larger m
 
 ## Define the MNIST Network
 
-This section explains the prototxt file `lenet_train.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at [src/caffe/proto/caffe.proto](https://github.com/Yangqing/caffe/blob/master/src/caffe/proto/caffe.proto).
+This section explains the prototxt file `lenet_train.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.
 
 Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:
 

From f9a67784b02a62a41aa68fc485b0f13d505d3c17 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 3 Sep 2014 13:18:48 -0700
Subject: [PATCH 0658/2053] Correct reference to lenet_train_test.prototxt

Not lenet.prototxt
---
 examples/mnist/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index a51fc1c0d3b..13354916ab8 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -26,7 +26,7 @@ If it complains that `wget` or `gunzip` are not installed, you need to install t
 
 Before we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.
 
-The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `$CAFFE_ROOT/examples/mnist/lenet.prototxt`.
+The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt`.
 
 ## Define the MNIST Network
 

From a865a235764fdadc9df7029e67b5e31c1a70a892 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 3 Sep 2014 14:13:18 -0700
Subject: [PATCH 0659/2053] Inline latest lenet_solver.prototxt

---
 examples/mnist/readme.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 13354916ab8..ae294cff2c1 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -179,7 +179,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt`:
 
     # The train/test net protocol buffer definition
-    net: "lenet_train_test.prototxt"
+    net: "examples/mnist/lenet_train_test.prototxt"
     # test_iter specifies how many forward passes the test should carry out.
     # In the case of MNIST, we have test batch size 100 and 100 test iterations,
     # covering the full 10,000 testing images.
@@ -200,9 +200,9 @@ Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/example
     max_iter: 10000
     # snapshot intermediate results
     snapshot: 5000
-    snapshot_prefix: "lenet"
-    # solver mode: 0 for CPU and 1 for GPU
-    solver_mode: 1
+    snapshot_prefix: "examples/mnist/lenet"
+    # solver mode: CPU or GPU
+    solver_mode: GPU
 
 ## Training and Testing the Model
 

From 0766cd97f8a4d97ad1f1bdd62a8b310121cb0859 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 15:18:21 -0700
Subject: [PATCH 0660/2053] [example] drop stale mentions of glog env var

---
 examples/cifar10/readme.md | 2 +-
 examples/mnist/readme.md   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index d0b0b766217..bf64ce89f0e 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -43,7 +43,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/cifar10
     ./train_quick.sh
 
-`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with the `train` action, and the solver protobuf text file as its argument.
+`train_quick.sh` is a simple script, so have a look inside. The main tool for training is `caffe` with the `train` action, and the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index ae294cff2c1..d4fb62d901b 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -211,7 +211,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh
 
-`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with action `train`, with the solver protobuf text file as its argument.
+`train_lenet.sh` is a simple script, but here is a quick explanation: the main tool for training is `caffe` with action `train` and the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 

From b136da595be1a0f176a48df989b7b91ba5ac6819 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Wed, 3 Sep 2014 16:30:34 -0700
Subject: [PATCH 0661/2053] [example] convert mnist name fix (crashes xcode
 compiler)

---
 examples/mnist/convert_mnist_data.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 0d3e91ec3ad..19040153c86 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -37,7 +37,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
   std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
   CHECK(image_file) << "Unable to open file " << image_filename;
-  CHECK(label_file) << "Unable to open file " << label_file;
+  CHECK(label_file) << "Unable to open file " << label_filename;
   // Read the magic and the meta data
   uint32_t magic;
   uint32_t num_items;

From 59fafb1f0d7d57cedd849d8ccd3cf4d179b54144 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 01:44:28 +0100
Subject: [PATCH 0662/2053] [docs] default setting for layout

---
 docs/_config.yml                         | 8 +++++++-
 docs/development.md                      | 1 -
 docs/index.md                            | 1 -
 docs/installation.md                     | 3 +--
 docs/performance_hardware.md             | 3 +--
 docs/tutorial/convolution.md             | 1 -
 docs/tutorial/data.md                    | 1 -
 docs/tutorial/forward_backward.md        | 1 -
 docs/tutorial/index.md                   | 1 -
 docs/tutorial/interfaces.md              | 1 -
 docs/tutorial/layers.md                  | 1 -
 docs/tutorial/loss.md                    | 1 -
 docs/tutorial/net_layer_blob.md          | 1 -
 docs/tutorial/solver.md                  | 1 -
 examples/cifar10/readme.md               | 1 -
 examples/feature_extraction/readme.md    | 1 -
 examples/finetune_flickr_style/readme.md | 1 -
 examples/imagenet/readme.md              | 1 -
 examples/mnist/readme.md                 | 1 -
 examples/web_demo/readme.md              | 1 -
 20 files changed, 9 insertions(+), 22 deletions(-)

diff --git a/docs/_config.yml b/docs/_config.yml
index 10e6731ad27..95aec12babe 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -1 +1,7 @@
-markdown: kramdown
+defaults:
+  -
+    scope:
+      path: "" # an empty string here means all files in the project
+    values:
+      layout: "default"
+
diff --git a/docs/development.md b/docs/development.md
index 822d88023f5..d65c126cf14 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 title: Development
 ---
 # Development
diff --git a/docs/index.md b/docs/index.md
index 94227c25249..47191ba8646 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Caffe
 
diff --git a/docs/installation.md b/docs/installation.md
index c2382c92906..82a22d5cde3 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -1,6 +1,5 @@
 ---
-layout: default
-title: Caffe
+title: Installation
 ---
 
 # Installation
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
index a39e954d160..62385431751 100644
--- a/docs/performance_hardware.md
+++ b/docs/performance_hardware.md
@@ -1,6 +1,5 @@
 ---
-layout: default
-title: Caffe
+title: Performance and Hardware Configuration
 ---
 
 # Performance and Hardware Configuration
diff --git a/docs/tutorial/convolution.md b/docs/tutorial/convolution.md
index 2119d640e71..6a86e3afcfd 100644
--- a/docs/tutorial/convolution.md
+++ b/docs/tutorial/convolution.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Caffeinated Convolution
 
diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index 644ba9bef8b..87954e1da63 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Data: Ins and Outs
 
diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index 8e740262817..699a26c8953 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Forward and Backward
 
diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
index 0c048111c55..fd21a440cee 100644
--- a/docs/tutorial/index.md
+++ b/docs/tutorial/index.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Caffe Tutorial
 
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 33323410a16..334d27e8137 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Interfaces
 
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index f316e826bab..c6040255dc5 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Layers
 
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index b470eaf943e..8c053ed60ab 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Loss
 
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 21de96c8035..21b8ddece64 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Nets, Layers, and Blobs: anatomy of a Caffe model
 
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index 3780fc3ab8c..f5156ee0aa9 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -1,5 +1,4 @@
 ---
-layout: default
 ---
 # Solver
 
diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index bf64ce89f0e..86fd5ea2967 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -3,7 +3,6 @@ title: CIFAR-10 tutorial
 category: example
 description: Train and test Caffe on CIFAR-10 data.
 include_in_docs: true
-layout: default
 priority: 5
 ---
 
diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index 4aab26c941e..083908ebe54 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -3,7 +3,6 @@ title: Feature extraction with Caffe C++ code.
 description: Extract CaffeNet / AlexNet features using the Caffe utility.
 category: example
 include_in_docs: true
-layout: default
 priority: 10
 ---
 
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 822499829e6..da584f0088a 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -3,7 +3,6 @@ title: Fine-tuning for style recognition
 description: Fine-tune the ImageNet-trained CaffeNet on the "Flickr Style" dataset.
 category: example
 include_in_docs: true
-layout: default
 priority: 5
 ---
 
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 4c716d9ce0b..b4a3110ecb6 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -3,7 +3,6 @@ title: ImageNet tutorial
 description: Train and test "CaffeNet" on ImageNet challenge data.
 category: example
 include_in_docs: true
-layout: default
 priority: 1
 ---
 
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index d4fb62d901b..5a8e8cab3f5 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -3,7 +3,6 @@ title: MNIST Tutorial
 description: Train and test "LeNet" on MNIST data.
 category: example
 include_in_docs: true
-layout: default
 priority: 1
 ---
 
diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index b0c14e379b2..3c8fdc068e7 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -2,7 +2,6 @@
 title: Web demo
 description: Image classification demo running as a Flask web server.
 category: example
-layout: default
 include_in_docs: true
 priority: 10
 ---

From e553573e2c4800e11050d6b83f0579766ebf4648 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 12 Aug 2014 17:29:25 -0700
Subject: [PATCH 0663/2053] [models] adding zoo readme; caffenet, alexnet, and
 rcnn models in zoo format

---
 .gitignore                                    |  4 +-
 docs/getting_pretrained_models.md             | 26 +------
 docs/model_zoo.md                             | 31 ++++++++
 examples/imagenet/get_caffe_alexnet_model.sh  | 28 -------
 .../imagenet/get_caffe_rcnn_imagenet_model.sh | 28 -------
 .../get_caffe_reference_imagenet_model.sh     | 28 -------
 examples/imagenet/readme.md                   | 22 +++---
 .../bvlc_alexnet}/alexnet_deploy.prototxt     |  0
 .../bvlc_alexnet}/alexnet_solver.prototxt     |  4 +-
 .../bvlc_alexnet}/alexnet_train_val.prototxt  | 60 +++++++--------
 models/bvlc_alexnet/readme.md                 | 25 ++++++
 .../caffenet_deploy.prototxt                  |  0
 .../caffenet_solver.prototxt                  |  4 +-
 .../caffenet_train_val.prototxt               |  0
 models/bvlc_reference_caffenet/readme.md      | 26 +++++++
 .../rcnn_imagenet_deploy.prototxt             |  0
 models/bvlc_reference_rcnn_ilsvrc13/readme.md | 20 +++++
 scripts/download_model_binary.py              | 76 +++++++++++++++++++
 18 files changed, 229 insertions(+), 153 deletions(-)
 create mode 100644 docs/model_zoo.md
 delete mode 100755 examples/imagenet/get_caffe_alexnet_model.sh
 delete mode 100755 examples/imagenet/get_caffe_rcnn_imagenet_model.sh
 delete mode 100755 examples/imagenet/get_caffe_reference_imagenet_model.sh
 rename {examples/imagenet => models/bvlc_alexnet}/alexnet_deploy.prototxt (100%)
 rename {examples/imagenet => models/bvlc_alexnet}/alexnet_solver.prototxt (63%)
 rename {examples/imagenet => models/bvlc_alexnet}/alexnet_train_val.prototxt (100%)
 create mode 100644 models/bvlc_alexnet/readme.md
 rename examples/imagenet/imagenet_deploy.prototxt => models/bvlc_reference_caffenet/caffenet_deploy.prototxt (100%)
 rename examples/imagenet/imagenet_solver.prototxt => models/bvlc_reference_caffenet/caffenet_solver.prototxt (59%)
 rename examples/imagenet/imagenet_train_val.prototxt => models/bvlc_reference_caffenet/caffenet_train_val.prototxt (100%)
 create mode 100644 models/bvlc_reference_caffenet/readme.md
 rename {examples/imagenet => models/bvlc_reference_rcnn_ilsvrc13}/rcnn_imagenet_deploy.prototxt (100%)
 create mode 100644 models/bvlc_reference_rcnn_ilsvrc13/readme.md
 create mode 100755 scripts/download_model_binary.py

diff --git a/.gitignore b/.gitignore
index 2ac7a0096eb..fa279f9071b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -51,8 +51,8 @@ Makefile.config
 # 1. reference, and not casually committed
 # 2. custom, and live on their own unless they're deliberated contributed
 data/*
-*model
-*_iter_*
+models/*
+*.caffemodel
 *.solverstate
 *.binaryproto
 *leveldb
diff --git a/docs/getting_pretrained_models.md b/docs/getting_pretrained_models.md
index 5df2bd4dc2d..70d8f7bde68 100644
--- a/docs/getting_pretrained_models.md
+++ b/docs/getting_pretrained_models.md
@@ -5,30 +5,12 @@ layout: default
 # Pre-trained models
 
 [BVLC](http://bvlc.eecs.berkeley.edu) aims to provide a variety of high quality pre-trained models.
-Note that unlike Caffe itself, these models are licensed for **academic research / non-commercial use only**.
-If you have any questions, please get in touch with us.
+Note that unlike Caffe itself, these models usually have licenses **academic research / non-commercial use only**.
 
-*UPDATE* July 2014: we are actively working on a service for hosting user-uploaded model definition and trained weight files.
-Soon, the community will be able to easily contribute different architectures!
+## TODO
 
-### ImageNet
+Write something about the model zoo.
 
-**Caffe Reference ImageNet Model**: Our reference implementation of an ImageNet model trained on ILSVRC-2012 can be downloaded (232.6MB) by running `examples/imagenet/get_caffe_reference_imagenet_model.sh` from the Caffe root directory.
-
-- The bundled model is the iteration 310,000 snapshot.
-- The best validation performance during training was iteration 313,000 with
-  validation accuracy 57.412% and loss 1.82328.
-- This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy)
-
-**AlexNet**: Our training of the Krizhevsky architecture, which differs from the paper's methodology by (1) not training with the relighting data-augmentation and (2) initializing non-zero biases to 0.1 instead of 1. (2) was found necessary for training, as initialization to 1 gave flat loss. Download the model (243.9MB) by running `examples/imagenet/get_caffe_alexnet_model.sh` from the Caffe root directory.
-
-- The bundled model is the iteration 360,000 snapshot.
-- The best validation performance during training was iteration 358,000 with
-  validation accuracy 57.258% and loss 1.83948.
-- This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy)
-
-**R-CNN (ILSVRC13)**: The pure Caffe instantiation of the [R-CNN](https://github.com/rbgirshick/rcnn) model for ILSVRC13 detection. Download the model (230.8MB) by running `examples/imagenet/get_caffe_rcnn_imagenet_model.sh` from the Caffe root directory. This model was made by transplanting the R-CNN SVM classifiers into a `fc-rcnn` classification layer, provided here as an off-the-shelf Caffe detector. Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb) to see it in action. For the full details, refer to the R-CNN site. *N.B. For research purposes, make use of the official R-CNN package and not this example.*
-
-### Auxiliary Data
+## Auxiliary Data
 
 Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
new file mode 100644
index 00000000000..b4cbeb1d467
--- /dev/null
+++ b/docs/model_zoo.md
@@ -0,0 +1,31 @@
+# Caffe Model Zoo
+
+A caffe model is distributed as a directory containing:
+- solver/model prototxt(s)
+- model binary file, with .caffemodel extension
+- readme.md, containing:
+  - YAML header:
+    - model file URL or (torrent magnet link) and MD5 hash
+    - Caffe commit hash use to train this model
+    - [optional] github gist id
+    - license type or text
+  - main body: free-form description/details
+- helpful scripts
+
+It is up to the user where to host the model file.
+Dropbox or their own server are both fine.
+
+We provide scripts:
+
+- publish_model_as_gist.sh: uploads non-binary files in the model directory as a Github Gist and returns the id. If gist id is already part of the readme, then updates existing gist.
+- download_model_from_gist.sh <gist_id>: downloads the non-binary files from a Gist.
+- download_model_binary.py: downloads the .caffemodel from the URL specified in readme.
+
+The Gist is a good format for distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
+
+The existing models distributed with Caffe can stay bundled with Caffe, so I am re-working them all into this format.
+All relevant examples will be updated to start with `cd models/model_of_interest && ../scripts/download_model_binary.sh`.
+
+## Tasks
+
+- get the imagenet example to work with the new prototxt location
diff --git a/examples/imagenet/get_caffe_alexnet_model.sh b/examples/imagenet/get_caffe_alexnet_model.sh
deleted file mode 100755
index 7312ed93070..00000000000
--- a/examples/imagenet/get_caffe_alexnet_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the caffe reference imagenet model
-# for ilsvrc image classification and deep feature extraction
-
-MODEL=caffe_alexnet_model
-CHECKSUM=29eb495b11613825c1900382f5286963
-
-if [ -f $MODEL ]; then
-  echo "Model already exists. Checking md5..."
-  os=`uname -s`
-  if [ "$os" = "Linux" ]; then
-    checksum=`md5sum $MODEL | awk '{ print $1 }'`
-  elif [ "$os" = "Darwin" ]; then
-    checksum=`cat $MODEL | md5`
-  fi
-  if [ "$checksum" = "$CHECKSUM" ]; then
-    echo "Model checksum is correct. No need to download."
-    exit 0
-  else
-    echo "Model checksum is incorrect. Need to download again."
-  fi
-fi
-
-echo "Downloading..."
-
-wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
-
-echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
deleted file mode 100755
index 9a8d0a155a0..00000000000
--- a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the Caffe R-CNN ImageNet
-# for ILSVRC13 detection.
-
-MODEL=caffe_rcnn_imagenet_model
-CHECKSUM=42c1556d2d47a9128c4a90e0a9c5341c
-
-if [ -f $MODEL ]; then
-  echo "Model already exists. Checking md5..."
-  os=`uname -s`
-  if [ "$os" = "Linux" ]; then
-    checksum=`md5sum $MODEL | awk '{ print $1 }'`
-  elif [ "$os" = "Darwin" ]; then
-    checksum=`cat $MODEL | md5`
-  fi
-  if [ "$checksum" = "$CHECKSUM" ]; then
-    echo "Model checksum is correct. No need to download."
-    exit 0
-  else
-    echo "Model checksum is incorrect. Need to download again."
-  fi
-fi
-
-echo "Downloading..."
-
-wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
-
-echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/get_caffe_reference_imagenet_model.sh b/examples/imagenet/get_caffe_reference_imagenet_model.sh
deleted file mode 100755
index f687ebfa79e..00000000000
--- a/examples/imagenet/get_caffe_reference_imagenet_model.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env sh
-# This scripts downloads the caffe reference imagenet model
-# for ilsvrc image classification and deep feature extraction
-
-MODEL=caffe_reference_imagenet_model
-CHECKSUM=af678f0bd3cdd2437e35679d88665170
-
-if [ -f $MODEL ]; then
-  echo "Model already exists. Checking md5..."
-  os=`uname -s`
-  if [ "$os" = "Linux" ]; then
-    checksum=`md5sum $MODEL | awk '{ print $1 }'`
-  elif [ "$os" = "Darwin" ]; then
-    checksum=`cat $MODEL | md5`
-  fi
-  if [ "$checksum" = "$CHECKSUM" ]; then
-    echo "Model checksum is correct. No need to download."
-    exit 0
-  else
-    echo "Model checksum is incorrect. Need to download again."
-  fi
-fi
-
-echo "Downloading..."
-
-wget http://dl.caffe.berkeleyvision.org/$MODEL examples/imagenet/$MODEL
-
-echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index b4a3110ecb6..6b18407dd44 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -6,18 +6,16 @@ include_in_docs: true
 priority: 1
 ---
 
-Yangqing's Recipe on Brewing ImageNet
-=====================================
+Brewing ImageNet
+================
 
-    "All your braincells are belong to us."
-        - Caffeine
-
-We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf). Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming. If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page. *Note that the pretrained model is for academic research / non-commercial use only*.
+We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf).
+Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming.
+If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page.
+*Note that the pretrained model is for academic research / non-commercial use only*.
 
 To clarify, by ImageNet we actually mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.
 
-(If you don't get the quote, visit [Yann LeCun's fun page](http://yann.lecun.com/ex/fun/).
-
 Data Preparation
 ----------------
 
@@ -100,11 +98,13 @@ We all experience times when the power goes out, or we feel like rewarding ourse
 
     ./resume_training.sh
 
-where in the script `caffe_imagenet_train_1000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `imagenet_train_1000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------
 
-Hope you liked this recipe! Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network. The recent ILSVRC 2013 challenge suggests that there are quite some room for improvement. **Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
+Hope you liked this recipe!
+Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network.
+**Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it: [Running Pretrained ImageNet](../../getting_pretrained_models.html). This time we will use Python, but if you have wrappers for other languages, please kindly send a pull request!
+And since now you have a trained network, check out how to use it with the Python interface: [Running Pretrained ImageNet](../../getting_pretrained_models.html).
diff --git a/examples/imagenet/alexnet_deploy.prototxt b/models/bvlc_alexnet/alexnet_deploy.prototxt
similarity index 100%
rename from examples/imagenet/alexnet_deploy.prototxt
rename to models/bvlc_alexnet/alexnet_deploy.prototxt
diff --git a/examples/imagenet/alexnet_solver.prototxt b/models/bvlc_alexnet/alexnet_solver.prototxt
similarity index 63%
rename from examples/imagenet/alexnet_solver.prototxt
rename to models/bvlc_alexnet/alexnet_solver.prototxt
index 94bda7f36a5..0d61fed4692 100644
--- a/examples/imagenet/alexnet_solver.prototxt
+++ b/models/bvlc_alexnet/alexnet_solver.prototxt
@@ -1,4 +1,4 @@
-net: "examples/imagenet/alexnet_train_val.prototxt"
+net: "models/bvlc_alexnet/alexnet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
@@ -10,5 +10,5 @@ max_iter: 450000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/imagenet/caffe_alexnet"
+snapshot_prefix: "models/bvlc_alexnet/caffe_alexnet_train"
 solver_mode: GPU
diff --git a/examples/imagenet/alexnet_train_val.prototxt b/models/bvlc_alexnet/alexnet_train_val.prototxt
similarity index 100%
rename from examples/imagenet/alexnet_train_val.prototxt
rename to models/bvlc_alexnet/alexnet_train_val.prototxt
index 3fa46773403..69b8916d769 100644
--- a/examples/imagenet/alexnet_train_val.prototxt
+++ b/models/bvlc_alexnet/alexnet_train_val.prototxt
@@ -34,6 +34,8 @@ layers {
 layers {
   name: "conv1"
   type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -51,8 +53,6 @@ layers {
       value: 0
     }
   }
-  bottom: "data"
-  top: "conv1"
 }
 layers {
   name: "relu1"
@@ -63,28 +63,30 @@ layers {
 layers {
   name: "norm1"
   type: LRN
+  bottom: "conv1"
+  top: "norm1"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv1"
-  top: "norm1"
 }
 layers {
   name: "pool1"
   type: POOLING
+  bottom: "norm1"
+  top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm1"
-  top: "pool1"
 }
 layers {
   name: "conv2"
   type: CONVOLUTION
+  bottom: "pool1"
+  top: "conv2"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -103,8 +105,6 @@ layers {
       value: 0.1
     }
   }
-  bottom: "pool1"
-  top: "conv2"
 }
 layers {
   name: "relu2"
@@ -115,28 +115,30 @@ layers {
 layers {
   name: "norm2"
   type: LRN
+  bottom: "conv2"
+  top: "norm2"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv2"
-  top: "norm2"
 }
 layers {
   name: "pool2"
   type: POOLING
+  bottom: "norm2"
+  top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm2"
-  top: "pool2"
 }
 layers {
   name: "conv3"
   type: CONVOLUTION
+  bottom: "pool2"
+  top: "conv3"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -154,8 +156,6 @@ layers {
       value: 0
     }
   }
-  bottom: "pool2"
-  top: "conv3"
 }
 layers {
   name: "relu3"
@@ -166,6 +166,8 @@ layers {
 layers {
   name: "conv4"
   type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -184,8 +186,6 @@ layers {
       value: 0.1
     }
   }
-  bottom: "conv3"
-  top: "conv4"
 }
 layers {
   name: "relu4"
@@ -196,6 +196,8 @@ layers {
 layers {
   name: "conv5"
   type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -214,8 +216,6 @@ layers {
       value: 0.1
     }
   }
-  bottom: "conv4"
-  top: "conv5"
 }
 layers {
   name: "relu5"
@@ -226,17 +226,19 @@ layers {
 layers {
   name: "pool5"
   type: POOLING
+  bottom: "conv5"
+  top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "conv5"
-  top: "pool5"
 }
 layers {
   name: "fc6"
   type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -252,8 +254,6 @@ layers {
       value: 0.1
     }
   }
-  bottom: "pool5"
-  top: "fc6"
 }
 layers {
   name: "relu6"
@@ -264,15 +264,17 @@ layers {
 layers {
   name: "drop6"
   type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc6"
-  top: "fc6"
 }
 layers {
   name: "fc7"
   type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -288,8 +290,6 @@ layers {
       value: 0.1
     }
   }
-  bottom: "fc6"
-  top: "fc7"
 }
 layers {
   name: "relu7"
@@ -300,15 +300,17 @@ layers {
 layers {
   name: "drop7"
   type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc7"
-  top: "fc7"
 }
 layers {
   name: "fc8"
   type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
   blobs_lr: 1
   blobs_lr: 2
   weight_decay: 1
@@ -324,8 +326,6 @@ layers {
       value: 0
     }
   }
-  bottom: "fc7"
-  top: "fc8"
 }
 layers {
   name: "accuracy"
diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
new file mode 100644
index 00000000000..20c393ff26b
--- /dev/null
+++ b/models/bvlc_alexnet/readme.md
@@ -0,0 +1,25 @@
+---
+name: BVLC AlexNet Model
+caffemodel: bvlc_alexnet.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
+license: non-commercial
+sha1: 9116a64c0fbe4459d18f4bb6b56d647b63920377
+caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
+---
+
+This model is a replication of the model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication.
+
+Differences:
+- not training with the relighting data-augmentation;
+- initializing non-zero biases to 0.1 instead of 1 (found necessary for training, as initialization to 1 gave flat loss).
+
+The bundled model is the iteration 360,000 snapshot.
+The best validation performance during training was iteration 358,000 with validation accuracy 57.258% and loss 1.83948.
+This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop.
+(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
+
+## License
+
+The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
+"Researcher shall use the Database only for non-commercial research and educational purposes."
+Accordingly, this model is distributed under a non-commercial license.
diff --git a/examples/imagenet/imagenet_deploy.prototxt b/models/bvlc_reference_caffenet/caffenet_deploy.prototxt
similarity index 100%
rename from examples/imagenet/imagenet_deploy.prototxt
rename to models/bvlc_reference_caffenet/caffenet_deploy.prototxt
diff --git a/examples/imagenet/imagenet_solver.prototxt b/models/bvlc_reference_caffenet/caffenet_solver.prototxt
similarity index 59%
rename from examples/imagenet/imagenet_solver.prototxt
rename to models/bvlc_reference_caffenet/caffenet_solver.prototxt
index 5b5be4bb8a9..d52e11a56f9 100644
--- a/examples/imagenet/imagenet_solver.prototxt
+++ b/models/bvlc_reference_caffenet/caffenet_solver.prototxt
@@ -1,4 +1,4 @@
-net: "examples/imagenet/imagenet_train_val.prototxt"
+net: "models/bvlc_reference_caffenet/caffenet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
@@ -10,5 +10,5 @@ max_iter: 450000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/imagenet/caffe_imagenet"
+snapshot_prefix: "models/bvlc_reference_caffenet/caffenet_train"
 solver_mode: GPU
diff --git a/examples/imagenet/imagenet_train_val.prototxt b/models/bvlc_reference_caffenet/caffenet_train_val.prototxt
similarity index 100%
rename from examples/imagenet/imagenet_train_val.prototxt
rename to models/bvlc_reference_caffenet/caffenet_train_val.prototxt
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
new file mode 100644
index 00000000000..1fbdbe12c3f
--- /dev/null
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -0,0 +1,26 @@
+---
+name: BVLC CaffeNet Model
+caffemodel: bvlc_reference_caffenet.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
+license: non-commercial
+sha1: 4c8d77deb20ea792f84eb5e6d0a11ca0a8660a46
+caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
+---
+
+This model is the result of following the Caffe [instructions](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html) on training an ImageNet model.
+This model is a replication of the model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication with some differences:
+
+- not training with the relighting data-augmentation;
+- the order of pooling and normalization layers is switched (in CaffeNet, pooling is done before normalization).
+
+
+This model is snapshot of iteration 310,000.
+The best validation performance during training was iteration 313,000 with validation accuracy 57.412% and loss 1.82328.
+This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.
+(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)
+
+## License
+
+The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
+"Researcher shall use the Database only for non-commercial research and educational purposes."
+Accordingly, this model is distributed under a non-commercial license.
diff --git a/examples/imagenet/rcnn_imagenet_deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/rcnn_imagenet_deploy.prototxt
similarity index 100%
rename from examples/imagenet/rcnn_imagenet_deploy.prototxt
rename to models/bvlc_reference_rcnn_ilsvrc13/rcnn_imagenet_deploy.prototxt
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
new file mode 100644
index 00000000000..fb8f26d15df
--- /dev/null
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -0,0 +1,20 @@
+---
+name: BVLC Reference RCNN ILSVRC13 Model
+caffemodel: bvlc_reference_rcnn_ilsvrc13.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel
+license: non-commercial
+sha1: bdd8abb885819cba5e2fe1eb36235f2319477e64
+caffe_commit: a7e397abbda52c0b90323c23ab95bdeabee90a98
+---
+
+The pure Caffe instantiation of the [R-CNN](https://github.com/rbgirshick/rcnn) model for ILSVRC13 detection.
+This model was made by transplanting the R-CNN SVM classifiers into a `fc-rcnn` classification layer, provided here as an off-the-shelf Caffe detector.
+Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb) to see it in action.
+
+*N.B. For research purposes, make use of the official R-CNN package and not this example.*
+
+## License
+
+The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
+"Researcher shall use the Database only for non-commercial research and educational purposes."
+Accordingly, this model is distributed under a non-commercial license.
diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
new file mode 100755
index 00000000000..48e9015fd26
--- /dev/null
+++ b/scripts/download_model_binary.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+import os
+import sys
+import time
+import yaml
+import urllib
+import hashlib
+import argparse
+
+required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
+
+
+def reporthook(count, block_size, total_size):
+    """
+    From http://blog.moleculea.com/2012/10/04/urlretrieve-progres-indicator/
+    """
+    global start_time
+    if count == 0:
+        start_time = time.time()
+        return
+    duration = time.time() - start_time
+    progress_size = int(count * block_size)
+    speed = int(progress_size / (1024 * duration))
+    percent = int(count * block_size * 100 / total_size)
+    sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
+                    (percent, progress_size / (1024 * 1024), speed, duration))
+    sys.stdout.flush()
+
+
+def parse_readme_frontmatter(dirname):
+    readme_filename = os.path.join(dirname, 'readme.md')
+    with open(readme_filename) as f:
+        lines = [line.strip() for line in f.readlines()]
+    top = lines.index('---')
+    bottom = lines[top + 1:].index('---')
+    frontmatter = yaml.load('\n'.join(lines[top + 1:bottom]))
+    assert all(key in frontmatter for key in required_keys)
+    return dirname, frontmatter
+
+
+def valid_dirname(dirname):
+    try:
+        return parse_readme_frontmatter(dirname)
+    except Exception as e:
+        print('ERROR: {}'.format(e))
+        raise argparse.ArgumentTypeError(
+            'Must be valid Caffe model directory with a correct readme.md')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Download trained model binary.')
+    parser.add_argument('dirname', type=valid_dirname)
+    args = parser.parse_args()
+
+    # A tiny hack: the dirname validator also returns readme YAML frontmatter.
+    dirname = args.dirname[0]
+    frontmatter = args.dirname[1]
+    model_filename = os.path.join(dirname, frontmatter['caffemodel'])
+
+    # Closure-d function for checking SHA1.
+    def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
+        with open(filename, 'r') as f:
+            return hashlib.sha1(f.read()).hexdigest() == sha1
+
+    # Check if model exists.
+    if os.path.exists(model_filename) and model_checks_out():
+        print("Model already exists.")
+        sys.exit(0)
+
+    # Download and verify model.
+    urllib.urlretrieve(
+        frontmatter['caffemodel_url'], model_filename, reporthook)
+    if not model_checks_out():
+        print('ERROR: model did not download correctly! Run this again.')
+        sys.exit(1)

From bcc12ef597f5eec04c582fe16e65dbb12a3b84f8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 28 Aug 2014 15:47:05 -0700
Subject: [PATCH 0664/2053] snapshot model with caffemodel extension

---
 src/caffe/solver.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index dcac4c1537c..d8517c64f85 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -316,6 +316,7 @@ void Solver<Dtype>::Snapshot() {
   char iter_str_buffer[kBufferSize];
   snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_);
   filename += iter_str_buffer;
+  filename += ".caffemodel";
   LOG(INFO) << "Snapshotting to " << filename;
   WriteProtoToBinaryFile(net_param, filename.c_str());
   SolverState state;

From 39f7a4d327d6ca044114db600c2de1324fb43c1e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 28 Aug 2014 16:28:51 -0700
Subject: [PATCH 0665/2053] proofread model zoo

---
 examples/imagenet/readme.md              | 6 +++---
 models/bvlc_reference_caffenet/readme.md | 5 ++---
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 6b18407dd44..1cd467803a8 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -104,7 +104,7 @@ Parting Words
 -------------
 
 Hope you liked this recipe!
-Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network.
-**Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
+Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks.
+**Caffe lets you explore different network choices more easily by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it with the Python interface: [Running Pretrained ImageNet](../../getting_pretrained_models.html).
+And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb).
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index 1fbdbe12c3f..d1c6269ae73 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -7,13 +7,12 @@ sha1: 4c8d77deb20ea792f84eb5e6d0a11ca0a8660a46
 caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
 ---
 
-This model is the result of following the Caffe [instructions](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html) on training an ImageNet model.
-This model is a replication of the model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication with some differences:
+This model is the result of following the Caffe [ImageNet model training instructions](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html).
+It is a replication of the model described in the [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) publication with some differences:
 
 - not training with the relighting data-augmentation;
 - the order of pooling and normalization layers is switched (in CaffeNet, pooling is done before normalization).
 
-
 This model is snapshot of iteration 310,000.
 The best validation performance during training was iteration 313,000 with validation accuracy 57.412% and loss 1.82328.
 This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.

From 84917d6ccae60fc4063a5d1bb68b86e658e80545 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Wed, 3 Sep 2014 15:42:46 +0100
Subject: [PATCH 0666/2053] removing unneeded scripts from imagenet example

---
 examples/imagenet/readme.md          | 10 +++++++---
 examples/imagenet/resume_training.sh |  7 -------
 examples/imagenet/time_imagenet.sh   | 17 -----------------
 examples/imagenet/train_alexnet.sh   |  5 -----
 examples/imagenet/train_imagenet.sh  |  5 -----
 5 files changed, 7 insertions(+), 37 deletions(-)
 delete mode 100755 examples/imagenet/resume_training.sh
 delete mode 100755 examples/imagenet/time_imagenet.sh
 delete mode 100755 examples/imagenet/train_alexnet.sh
 delete mode 100755 examples/imagenet/train_imagenet.sh

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 1cd467803a8..8ce3644942a 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -87,16 +87,20 @@ Training ImageNet
 
 Ready? Let's train.
 
-    ./train_imagenet.sh
+    ./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt
 
-Sit back and enjoy! On my K20 machine, every 20 iterations take about 36 seconds to run, so effectively about 7 ms per image for the full forward-backward pass. About 2.5 ms of this is on forward, and the rest is backward. If you are interested in dissecting the computation time, you can look at `examples/net_speed_benchmark.cpp`, but it was written purely for debugging purpose, so you may need to figure a few things out yourself.
+Sit back and enjoy!
+
+On my K20 machine, every 20 iterations take about 36 seconds to run, so effectively about 7 ms per image for the full forward-backward pass. About 2.5 ms of this is on forward, and the rest is backward. If you are interested in dissecting the computation time, you can run
+
+    ./build/tools/caffe time --model=models/bvlc_reference_caffenet/train_val.prototxt
 
 Resume Training?
 ----------------
 
 We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does someone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
 
-    ./resume_training.sh
+    ./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt --snapshot=examples/imagenet/caffe_imagenet_10000.solverstate
 
 where in the script `imagenet_train_1000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
deleted file mode 100755
index 3c964b56ffc..00000000000
--- a/examples/imagenet/resume_training.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env sh
-
-./build/tools/caffe train \
-    --solver=examples/imagenet/imagenet_solver.prototxt \
-    --snapshot=examples/imagenet/caffe_imagenet_10000.solverstate
-
-echo "Done."
diff --git a/examples/imagenet/time_imagenet.sh b/examples/imagenet/time_imagenet.sh
deleted file mode 100755
index 3f46e0e0f97..00000000000
--- a/examples/imagenet/time_imagenet.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env sh
-
-if [ -z "$1" ]; then
-  echo "Using CPU! To time GPU mode, use:"
-  echo "    ./time_imagenet.sh <device ID>"
-  echo "(Try ./time_imagenet.sh 0 if you have just one GPU.)"
-  sleep 3  # Let the user read
-  GPU=""
-else
-  GPU="--gpu=$1"
-fi
-
-./build/tools/caffe time \
-  --model=examples/imagenet/imagenet_train_val.prototxt \
-  ${GPU}
-
-echo "Done."
diff --git a/examples/imagenet/train_alexnet.sh b/examples/imagenet/train_alexnet.sh
deleted file mode 100755
index 1ddcbeee4b0..00000000000
--- a/examples/imagenet/train_alexnet.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env sh
-
-./build/tools/caffe train --solver=examples/imagenet/alexnet_solver.prototxt
-
-echo "Done."
diff --git a/examples/imagenet/train_imagenet.sh b/examples/imagenet/train_imagenet.sh
deleted file mode 100755
index cba2ad59581..00000000000
--- a/examples/imagenet/train_imagenet.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env sh
-
-./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt
-
-echo "Done."

From a66100181be17f843bac411cf52ff24aeace6306 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 01:24:57 +0100
Subject: [PATCH 0667/2053] Renaming CaffeNet model prototxts and unignoring
 models/*

---
 .gitignore                                                      | 1 -
 .../bvlc_alexnet/{alexnet_deploy.prototxt => deploy.prototxt}   | 0
 .../bvlc_alexnet/{alexnet_solver.prototxt => solver.prototxt}   | 2 +-
 .../{alexnet_train_val.prototxt => train_val.prototxt}          | 0
 .../{caffenet_deploy.prototxt => deploy.prototxt}               | 0
 .../{caffenet_solver.prototxt => solver.prototxt}               | 2 +-
 .../{caffenet_train_val.prototxt => train_val.prototxt}         | 0
 .../{rcnn_imagenet_deploy.prototxt => deploy.prototxt}          | 0
 8 files changed, 2 insertions(+), 3 deletions(-)
 rename models/bvlc_alexnet/{alexnet_deploy.prototxt => deploy.prototxt} (100%)
 rename models/bvlc_alexnet/{alexnet_solver.prototxt => solver.prototxt} (82%)
 rename models/bvlc_alexnet/{alexnet_train_val.prototxt => train_val.prototxt} (100%)
 rename models/bvlc_reference_caffenet/{caffenet_deploy.prototxt => deploy.prototxt} (100%)
 rename models/bvlc_reference_caffenet/{caffenet_solver.prototxt => solver.prototxt} (79%)
 rename models/bvlc_reference_caffenet/{caffenet_train_val.prototxt => train_val.prototxt} (100%)
 rename models/bvlc_reference_rcnn_ilsvrc13/{rcnn_imagenet_deploy.prototxt => deploy.prototxt} (100%)

diff --git a/.gitignore b/.gitignore
index fa279f9071b..cf92e5fd4db 100644
--- a/.gitignore
+++ b/.gitignore
@@ -51,7 +51,6 @@ Makefile.config
 # 1. reference, and not casually committed
 # 2. custom, and live on their own unless they're deliberated contributed
 data/*
-models/*
 *.caffemodel
 *.solverstate
 *.binaryproto
diff --git a/models/bvlc_alexnet/alexnet_deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
similarity index 100%
rename from models/bvlc_alexnet/alexnet_deploy.prototxt
rename to models/bvlc_alexnet/deploy.prototxt
diff --git a/models/bvlc_alexnet/alexnet_solver.prototxt b/models/bvlc_alexnet/solver.prototxt
similarity index 82%
rename from models/bvlc_alexnet/alexnet_solver.prototxt
rename to models/bvlc_alexnet/solver.prototxt
index 0d61fed4692..129265e679b 100644
--- a/models/bvlc_alexnet/alexnet_solver.prototxt
+++ b/models/bvlc_alexnet/solver.prototxt
@@ -1,4 +1,4 @@
-net: "models/bvlc_alexnet/alexnet_train_val.prototxt"
+net: "models/bvlc_alexnet/train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
diff --git a/models/bvlc_alexnet/alexnet_train_val.prototxt b/models/bvlc_alexnet/train_val.prototxt
similarity index 100%
rename from models/bvlc_alexnet/alexnet_train_val.prototxt
rename to models/bvlc_alexnet/train_val.prototxt
diff --git a/models/bvlc_reference_caffenet/caffenet_deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
similarity index 100%
rename from models/bvlc_reference_caffenet/caffenet_deploy.prototxt
rename to models/bvlc_reference_caffenet/deploy.prototxt
diff --git a/models/bvlc_reference_caffenet/caffenet_solver.prototxt b/models/bvlc_reference_caffenet/solver.prototxt
similarity index 79%
rename from models/bvlc_reference_caffenet/caffenet_solver.prototxt
rename to models/bvlc_reference_caffenet/solver.prototxt
index d52e11a56f9..af1315ba2ac 100644
--- a/models/bvlc_reference_caffenet/caffenet_solver.prototxt
+++ b/models/bvlc_reference_caffenet/solver.prototxt
@@ -1,4 +1,4 @@
-net: "models/bvlc_reference_caffenet/caffenet_train_val.prototxt"
+net: "models/bvlc_reference_caffenet/train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
diff --git a/models/bvlc_reference_caffenet/caffenet_train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
similarity index 100%
rename from models/bvlc_reference_caffenet/caffenet_train_val.prototxt
rename to models/bvlc_reference_caffenet/train_val.prototxt
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/rcnn_imagenet_deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
similarity index 100%
rename from models/bvlc_reference_rcnn_ilsvrc13/rcnn_imagenet_deploy.prototxt
rename to models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt

From d5e9739e5261de5f30832c06d849da5265bc95c6 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 01:13:29 +0100
Subject: [PATCH 0668/2053] updating feature extraction example

---
 examples/feature_extraction/imagenet_val.prototxt |  4 ++--
 examples/feature_extraction/readme.md             | 10 +++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 32310904a66..83fe8c1a08d 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -5,14 +5,14 @@ layers {
   top: "data"
   top: "label"
   image_data_param {
-    source: "$CAFFE_DIR/examples/_temp/file_list.txt"
+    source: "examples/_temp/file_list.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
   transform_param {
     crop_size: 227
-    mean_file: "$CAFFE_DIR/data/ilsvrc12/imagenet_mean.binaryproto"
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
     mirror: false
   }
 }
diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index 083908ebe54..c325ed482e5 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -10,7 +10,9 @@ Extracting Features
 ===================
 
 In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
-Follow instructions for [installing Caffe](../../installation.html) and for [downloading the reference model](../../getting_pretrained_models.html) for ImageNet.
+Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+
+Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.
 If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
 
 Select data to run on
@@ -35,7 +37,7 @@ Define the Feature Extraction Network Architecture
 In practice, subtracting the mean image from a dataset significantly improves classification accuracies.
 Download the mean image of the ILSVRC dataset.
 
-    data/ilsvrc12/get_ilsvrc_aux.sh
+    ./data/ilsvrc12/get_ilsvrc_aux.sh
 
 We will use `data/ilsvrc212/imagenet_mean.binaryproto` in the network definition prototxt.
 
@@ -44,14 +46,12 @@ We'll be using the `ImageDataLayer`, which will load and resize images for us.
 
     cp examples/feature_extraction/imagenet_val.prototxt examples/_temp
 
-Edit `examples/_temp/imagenet_val.prototxt` to use correct path for your setup (replace `$CAFFE_DIR`)
-
 Extract Features
 ----------------
 
 Now everything necessary is in place.
 
-    build/tools/extract_features.bin examples/imagenet/caffe_reference_imagenet_model examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10
+    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10
 
 The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
 We can use any other layer, as well, such as `conv5` or `pool3`.

From da715ea07af8ea42487d333e3441d138320da36a Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 01:08:19 +0100
Subject: [PATCH 0669/2053] removed mention of getting_pretrained_models page
 and old paths

---
 docs/getting_pretrained_models.md             | 16 -----
 docs/index.md                                 |  4 +-
 docs/model_zoo.md                             | 68 +++++++++++++------
 examples/classification.ipynb                 |  9 ++-
 examples/detection.ipynb                      |  5 +-
 examples/filter_visualization.ipynb           |  9 ++-
 examples/finetune_flickr_style/readme.md      |  4 +-
 examples/imagenet/imagenet_full_conv.prototxt |  1 +
 examples/imagenet/readme.md                   | 55 +++++++--------
 examples/net_surgery.ipynb                    |  9 ++-
 examples/web_demo/app.py                      |  4 +-
 examples/web_demo/readme.md                   |  6 +-
 matlab/caffe/matcaffe_batch.m                 |  3 +-
 matlab/caffe/matcaffe_init.m                  |  4 +-
 python/classify.py                            |  4 +-
 python/detect.py                              |  4 +-
 16 files changed, 104 insertions(+), 101 deletions(-)
 delete mode 100644 docs/getting_pretrained_models.md

diff --git a/docs/getting_pretrained_models.md b/docs/getting_pretrained_models.md
deleted file mode 100644
index 70d8f7bde68..00000000000
--- a/docs/getting_pretrained_models.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-layout: default
----
-
-# Pre-trained models
-
-[BVLC](http://bvlc.eecs.berkeley.edu) aims to provide a variety of high quality pre-trained models.
-Note that unlike Caffe itself, these models usually have licenses **academic research / non-commercial use only**.
-
-## TODO
-
-Write something about the model zoo.
-
-## Auxiliary Data
-
-Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.
diff --git a/docs/index.md b/docs/index.md
index 47191ba8646..83ba236bcf1 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -38,8 +38,8 @@ Slides about the Caffe architecture, *updated 03/14*.
 A 4-page report for the ACM Multimedia Open Source competition.
 - [Installation instructions](/installation.html)<br />
 Tested on Ubuntu, Red Hat, OS X.
-* [Pre-trained models](/getting_pretrained_models.html)<br />
-BVLC provides ready-to-use models for non-commercial use.
+* [Model Zoo](/model_zoo.html)<br />
+BVLC suggests a standard distribution format for Caffe models, and provides trained models for non-commercial use.
 * [Developing & Contributing](/development.html)<br />
 Guidelines for development and contributing to Caffe.
 * [API Documentation](/doxygen/)<br />
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index b4cbeb1d467..490bb68f808 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -1,31 +1,59 @@
 # Caffe Model Zoo
 
+Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
+
+To lower the friction of sharing these models, we introduce the model zoo framework:
+
+- A standard format for packaging Caffe model info.
+- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
+- A central wiki page for sharing model info Gists.
+
+## Where to get trained models
+
+First of all, we provide some trained models out of the box.
+Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
+
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper.
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012.
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn).
+
+User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+
+## Model info format
+
 A caffe model is distributed as a directory containing:
-- solver/model prototxt(s)
-- model binary file, with .caffemodel extension
-- readme.md, containing:
-  - YAML header:
-    - model file URL or (torrent magnet link) and MD5 hash
-    - Caffe commit hash use to train this model
-    - [optional] github gist id
-    - license type or text
-  - main body: free-form description/details
-- helpful scripts
 
-It is up to the user where to host the model file.
-Dropbox or their own server are both fine.
+- Solver/model prototxt(s)
+- Readme.md containing
+    - YAML frontmatter
+        - Caffe version used to train this model (tagged release or commit hash).
+        - [optional] file URL and SHA1 of the trained `.caffemodel`.
+        - [optional] github gist id.
+    - Information about what data the model was trained on, explanation of modeling choices, etc.
+    - License information.
+- [optional] Other helpful scripts.
+
+## Hosting model info
+
+Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
+
+- `scripts/download_model_from_gist.sh <gist_id>`: downloads the non-binary files from a Gist into `<dirname>`
+- `scripts/upload_model_to_gist.sh <dirname>`: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
 
-We provide scripts:
+### Hosting trained models
 
-- publish_model_as_gist.sh: uploads non-binary files in the model directory as a Github Gist and returns the id. If gist id is already part of the readme, then updates existing gist.
-- download_model_from_gist.sh <gist_id>: downloads the non-binary files from a Gist.
-- download_model_binary.py: downloads the .caffemodel from the URL specified in readme.
+It is up to the user where to host the `.caffemodel` file.
+We host our BVLC-provided models on our own server.
+Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
-The Gist is a good format for distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
+- `scripts/download_model_binary.py <dirname>`: downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
 
-The existing models distributed with Caffe can stay bundled with Caffe, so I am re-working them all into this format.
-All relevant examples will be updated to start with `cd models/model_of_interest && ../scripts/download_model_binary.sh`.
 
 ## Tasks
 
-- get the imagenet example to work with the new prototxt location
+x get the imagenet example to work with the new prototxt location
+x make wiki page for user-submitted models
+- add flickr model to the user-submitted models wiki page
+x make docs section listing bvlc-distributed models
+- write the publish_model_as_gist script
+- write the download_model_from_gist script
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 4d4a738a9f3..7c01b9e235e 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -3,7 +3,6 @@
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
   "example_name": "ImageNet classification",
   "include_in_docs": true,
-  "signature": "sha256:4f8d4c079c30d20ef4b6818e9672b1741fd1377354e5b83e291710736cecd24f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,7 +18,7 @@
       "\n",
       "Caffe provides a general Python interface for models with `caffe.Net` in `python/caffe/pycaffe.py`, but to make off-the-shelf classification easy we provide a `caffe.Classifier` class and `classify.py` script. Both Python and MATLAB wrappers are provided. However, the Python wrapper has more features so we will describe it here. For MATLAB, refer to `matlab/caffe/matcaffe_demo.m`.\n",
       "\n",
-      "Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained ImageNet model, an ILSVRC12 image classifier. You can download it (232.57MB) by running `examples/imagenet/get_caffe_reference_imagenet_model.sh`. Note that this pre-trained model is licensed for academic research / non-commercial use only.\n",
+      "Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`. Note that this pre-trained model is licensed for academic research / non-commercial use only.\n",
       "\n",
       "Ready? Let's start."
      ]
@@ -41,8 +40,8 @@
       "\n",
       "# Set the right path to your model definition file, pretrained model weights,\n",
       "# and the image you would like to classify.\n",
-      "MODEL_FILE = 'imagenet/imagenet_deploy.prototxt'\n",
-      "PRETRAINED = 'imagenet/caffe_reference_imagenet_model'\n",
+      "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
+      "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
       "IMAGE_FILE = 'images/cat.jpg'"
      ],
      "language": "python",
@@ -404,4 +403,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index beae000a11b..e1620417af1 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -3,7 +3,6 @@
   "description": "Run a pretrained model as a detector in Python.",
   "example_name": "R-CNN detection",
   "include_in_docs": true,
-  "signature": "sha256:8a744fbbb9ed80acab471247eaf50c27dcbd652105404df9feca599939f0c0ee"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -26,7 +25,7 @@
       "\n",
       "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
       "\n",
-      "- Follow the [model instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) to get the Caffe R-CNN ImageNet model.\n",
+      "-Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the Caffe R-CNN ImageNet model.\n",
       "\n",
       "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
      ]
@@ -37,7 +36,7 @@
      "input": [
       "!mkdir -p _temp\n",
       "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 5fdcbe25fb4..4d69dc0f64e 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -3,7 +3,6 @@
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
   "example_name": "Filter visualization",
   "include_in_docs": true,
-  "signature": "sha256:b1b0457e2b10110aca847a718a3fe631ebcfce63a61cbc33653244f52b1ff4af"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -54,15 +53,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Follow the [instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) for getting the pretrained models, load the net, specify test phase and CPU mode, and configure input preprocessing."
+      "Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model, load the net, specify test phase and CPU mode, and configure input preprocessing."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "net = caffe.Classifier(caffe_root + 'examples/imagenet/imagenet_deploy.prototxt',\n",
-      "                       caffe_root + 'examples/imagenet/caffe_reference_imagenet_model')\n",
+      "net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
+      "                       caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "net.set_phase_test()\n",
       "net.set_mode_cpu()\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
@@ -598,4 +597,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index da584f0088a..68b2778cafa 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -60,13 +60,13 @@ We'll also need the ImageNet-trained model, which you can obtain by running `get
 
 Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
 
-    caffe % ./build/tools/caffe train -solver examples/finetune_flickr_style/flickr_style_solver.prototxt -weights examples/imagenet/caffe_reference_imagenet_model -gpu 0
+    caffe % ./build/tools/caffe train -solver examples/finetune_flickr_style/flickr_style_solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
 
     [...]
 
     I0828 22:10:04.025378  9718 solver.cpp:46] Solver scaffolding done.
     I0828 22:10:04.025388  9718 caffe.cpp:95] Use GPU with device ID 0
-    I0828 22:10:04.192004  9718 caffe.cpp:107] Finetuning from examples/imagenet/caffe_reference_imagenet_model
+    I0828 22:10:04.192004  9718 caffe.cpp:107] Finetuning from models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
 
     [...]
 
diff --git a/examples/imagenet/imagenet_full_conv.prototxt b/examples/imagenet/imagenet_full_conv.prototxt
index 570efae5901..395b0f0162f 100644
--- a/examples/imagenet/imagenet_full_conv.prototxt
+++ b/examples/imagenet/imagenet_full_conv.prototxt
@@ -1,3 +1,4 @@
+# This file is for the net_surgery.ipynb example notebook.
 name: "CaffeNetConv"
 input: "data"
 input_dim: 1
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 8ce3644942a..97a68f306d8 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -1,6 +1,6 @@
 ---
 title: ImageNet tutorial
-description: Train and test "CaffeNet" on ImageNet challenge data.
+description: Train and test "CaffeNet" on ImageNet data.
 category: example
 include_in_docs: true
 priority: 1
@@ -9,16 +9,16 @@ priority: 1
 Brewing ImageNet
 ================
 
-We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf).
-Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming.
-If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page.
-*Note that the pretrained model is for academic research / non-commercial use only*.
-
-To clarify, by ImageNet we actually mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.
+This guide is meant to get you ready to train your own model on your own data.
+If you just want an ImageNet-trained network, then note that since training takes a lot of energy and we hate global warming, we provide the CaffeNet model trained as described below in the [model zoo](/model_zoo.html).
 
 Data Preparation
 ----------------
 
+*The guide specifies all paths and assumes all commands are executed from the root caffe directory.*
+
+*By "ImageNet" we here mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.*
+
 We assume that you already have downloaded the ImageNet training data and validation data, and they are stored on your disk like:
 
     /path/to/imagenet/train/n01440764/n01440764_10026.JPEG
@@ -26,44 +26,39 @@ We assume that you already have downloaded the ImageNet training data and valida
 
 You will first need to prepare some auxiliary data for training. This data can be downloaded by:
 
-    cd $CAFFE_ROOT/data/ilsvrc12/
-    ./get_ilsvrc_aux.sh
+    ./data/get_ilsvrc_aux.sh
 
 The training and validation input are described in `train.txt` and `val.txt` as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See `synset_words.txt` for the synset/name mapping.
 
-You may want to resize the images to 256x256 in advance. By default, we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightedweighted [mincepie](https://github.com/Yangqing/mincepie) package to do mapreduce on the Berkeley cluster. If you would things to be rather simple and straightforward, you can also use shell commands, something like:
+You may want to resize the images to 256x256 in advance. By default, we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightweight [mincepie](https://github.com/Yangqing/mincepie) package. If you prefer things to be simpler, you can also use shell commands, something like:
 
     for name in /path/to/imagenet/val/*.JPEG; do
         convert -resize 256x256\! $name $name
     done
 
-Go to `$CAFFE_ROOT/examples/imagenet/` for the rest of this guide.
-
-Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set "RESIZE=true" to resize all images to 256x256 if you haven't resized the images in advance.
-Now simply create the leveldbs with `./create_imagenet.sh`. Note that `ilsvrc12_train_leveldb` and `ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
+Take a look at `examples/imagenet/create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set "RESIZE=true" to resize all images to 256x256 if you haven't resized the images in advance.
+Now simply create the leveldbs with `examples/imagenet/create_imagenet.sh`. Note that `examples/imagenet/ilsvrc12_train_leveldb` and `examples/imagenet/ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
 
 Compute Image Mean
 ------------------
 
 The model requires us to subtract the image mean from each image, so we have to compute the mean. `tools/compute_image_mean.cpp` implements that - it is also a good example to familiarize yourself on how to manipulate the multiple components, such as protocol buffers, leveldbs, and logging, if you are not familiar with them. Anyway, the mean computation can be carried out as:
 
-    ./make_imagenet_mean.sh
+    ./examples/imagenet/make_imagenet_mean.sh
 
 which will make `data/ilsvrc12/imagenet_mean.binaryproto`.
 
-Network Definition
-------------------
-
-The network definition follows strictly the one in Krizhevsky et al. You can find the detailed definition at `examples/imagenet/imagenet_train_val.prototxt`. Note the paths in the data layer --- if you have not followed the exact paths in this guide you will need to change the following lines:
+Model Definition
+----------------
 
-    source: "ilvsrc12_train_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf).
 
-to point to your own leveldb and image mean.
+The network definition (`models/bvlc_reference_caffenet/train_val.prototxt`) follows the one in Krizhevsky et al.
+Note that if you deviated from file paths suggested in this guide, you'll need to adjust the relevant paths in the `.prototxt` files.
 
-If you look carefully at `imagenet_train_val.prototxt`, you will notice several `include` sections specifying either `phase: TRAIN` or `phase: TEST`. These sections allow us to define two closely related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for those marked with `include { phase: TRAIN }` or `include { phase: TEST }`. In this case, only the input layers and one output layer are different.
+If you look carefully at `models/bvlc_reference_caffenet/train_val.prototxt`, you will notice several `include` sections specifying either `phase: TRAIN` or `phase: TEST`. These sections allow us to define two closely related networks in one file: the network used for training and the network used for testing. These two networks are almost identical, sharing all layers except for those marked with `include { phase: TRAIN }` or `include { phase: TEST }`. In this case, only the input layers and one output layer are different.
 
-**Input layer differences:** The training network's `data` input layer draws its data from `ilsvrc12_train_leveldb` and randomly mirrors the input image. The testing network's `data` layer takes data from `ilsvrc12_val_leveldb` and does not perform random mirroring.
+**Input layer differences:** The training network's `data` input layer draws its data from `examples/imagenet/ilsvrc12_train_leveldb` and randomly mirrors the input image. The testing network's `data` layer takes data from `examples/imagenet/ilsvrc12_val_leveldb` and does not perform random mirroring.
 
 **Output layer differences:** Both networks output the `softmax_loss` layer, which in training is used to compute the loss function and to initialize the backpropagation, while in validation this loss is simply reported. The testing network also has a second output layer, `accuracy`, which is used to report the accuracy on the test set. In the process of training, the test network will occasionally be instantiated and tested on the test set, producing lines like `Test score #0: xxx` and `Test score #1: xxx`. In this case score 0 is the accuracy (which will start around 1/1000 = 0.001 for an untrained network) and score 1 is the loss (which will start around 7 for an untrained network).
 
@@ -76,18 +71,14 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 
-Sound good? This is implemented in `examples/imagenet/imagenet_solver.prototxt`. Again, you will need to change the first line:
-
-    net: "imagenet_train_val.prototxt"
-
-to point to the actual path if you have changed it.
+Sound good? This is implemented in `models/bvlc_reference_caffenet/solver.prototxt`.
 
 Training ImageNet
 -----------------
 
 Ready? Let's train.
 
-    ./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt
+    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt
 
 Sit back and enjoy!
 
@@ -98,11 +89,11 @@ On my K20 machine, every 20 iterations take about 36 seconds to run, so effectiv
 Resume Training?
 ----------------
 
-We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does someone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
+We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
 
     ./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt --snapshot=examples/imagenet/caffe_imagenet_10000.solverstate
 
-where in the script `imagenet_train_1000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `imagenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 2d8bbb106be..336c8457c2c 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,7 +3,6 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
-  "signature": "sha256:10c551b31a64c2210f6094dbb603f26c206a7b72cd99032f475cb5023edcdc43"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -27,7 +26,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "!diff imagenet/imagenet_full_conv.prototxt imagenet/imagenet_deploy.prototxt"
+      "!diff imagenet/imagenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
      ],
      "language": "python",
      "metadata": {},
@@ -144,7 +143,7 @@
       "import caffe\n",
       "\n",
       "# Load the original network and extract the fully-connected layers' parameters.\n",
-      "net = caffe.Net('imagenet/imagenet_deploy.prototxt', 'imagenet/caffe_reference_imagenet_model')\n",
+      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "params = ['fc6', 'fc7', 'fc8']\n",
       "# fc_params = {name: (weights, biases)}\n",
       "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
@@ -179,7 +178,7 @@
      "collapsed": false,
      "input": [
       "# Load the fully-convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('imagenet/imagenet_full_conv.prototxt', 'imagenet/caffe_reference_imagenet_model')\n",
+      "net_full_conv = caffe.Net('imagenet/imagenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
       "# conv_params = {name: (weights, biases)}\n",
       "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
@@ -350,4 +349,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index f7f46ce6c02..d33fc92f078 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -98,9 +98,9 @@ def allowed_file(filename):
 class ImagenetClassifier(object):
     default_args = {
         'model_def_file': (
-            '{}/examples/imagenet/imagenet_deploy.prototxt'.format(REPO_DIRNAME)),
+            '{}/models/bvlc_reference_caffenet/deploy.prototxt'.format(REPO_DIRNAME)),
         'pretrained_model_file': (
-            '{}/examples/imagenet/caffe_reference_imagenet_model'.format(REPO_DIRNAME)),
+            '{}/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'.format(REPO_DIRNAME)),
         'mean_file': (
             '{}/python/caffe/imagenet/ilsvrc_2012_mean.npy'.format(REPO_DIRNAME)),
         'class_labels_file': (
diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index 3c8fdc068e7..fe74b9ef7d3 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -13,7 +13,11 @@ priority: 10
 The demo server requires Python with some dependencies.
 To make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](/installation.html)).
 
-Make sure that you have obtained the Caffe Reference ImageNet Model and the ImageNet Auxiliary Data ([instructions](/getting_pretrained_models.html)).
+Make sure that you have obtained the Reference CaffeNet Model and the ImageNet Auxiliary Data:
+
+    ./scripts/download_model_binary.py models/bvlc_reference_caffenet
+    ./data/ilsvrc12/get_ilsvrc_aux.sh
+
 NOTE: if you run into trouble, try re-downloading the auxiliary files.
 
 ## Run
diff --git a/matlab/caffe/matcaffe_batch.m b/matlab/caffe/matcaffe_batch.m
index 3cb7f1445fb..f6d1aa83b84 100644
--- a/matlab/caffe/matcaffe_batch.m
+++ b/matlab/caffe/matcaffe_batch.m
@@ -27,9 +27,8 @@
     filename = list_im;
     list_im = read_cell(filename);
 end
-% Adjust the batch size to match with imagenet_deploy.prototxt
+% Adjust the batch size and dim to match with models/bvlc_reference_caffenet/deploy.prototxt
 batch_size = 10;
-% Adjust dim to the output size of imagenet_deploy.prototxt
 dim = 1000;
 disp(list_im)
 if mod(length(list_im),batch_size)
diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m
index 4e4ef8bff4a..7cc6935758e 100644
--- a/matlab/caffe/matcaffe_init.m
+++ b/matlab/caffe/matcaffe_init.m
@@ -8,11 +8,11 @@ function  matcaffe_init(use_gpu, model_def_file, model_file)
 end
 if nargin < 2 || isempty(model_def_file)
   % By default use imagenet_deploy
-  model_def_file = '../../examples/imagenet/imagenet_deploy.prototxt';
+  model_def_file = '../../models/bvlc_reference_caffenet/deploy.prototxt';
 end
 if nargin < 3 || isempty(model_file)
   % By default use caffe reference model
-  model_file = '../../examples/imagenet/caffe_reference_imagenet_model';
+  model_file = '../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
 end
 
 
diff --git a/python/classify.py b/python/classify.py
index ddc5429feac..873b5e38f19 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -31,13 +31,13 @@ def main(argv):
     parser.add_argument(
         "--model_def",
         default=os.path.join(pycaffe_dir,
-                "../examples/imagenet/imagenet_deploy.prototxt"),
+                "../models/bvlc_reference_caffenet/deploy.prototxt"),
         help="Model definition file."
     )
     parser.add_argument(
         "--pretrained_model",
         default=os.path.join(pycaffe_dir,
-                "../examples/imagenet/caffe_reference_imagenet_model"),
+                "../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
         help="Trained model weights file."
     )
     parser.add_argument(
diff --git a/python/detect.py b/python/detect.py
index 4598fc7a327..bc8c0703646 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -46,13 +46,13 @@ def main(argv):
     parser.add_argument(
         "--model_def",
         default=os.path.join(pycaffe_dir,
-                "../examples/imagenet/imagenet_deploy.prototxt"),
+                "../models/bvlc_reference_caffenet/deploy.prototxt.prototxt"),
         help="Model definition file."
     )
     parser.add_argument(
         "--pretrained_model",
         default=os.path.join(pycaffe_dir,
-                "../examples/imagenet/caffe_reference_imagenet_model"),
+                "../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
         help="Trained model weights file."
     )
     parser.add_argument(

From bc601e9060cc71b95f0250b9efacaffcbb5d8c0e Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 01:43:35 +0100
Subject: [PATCH 0670/2053] minor fixes to docs

---
 docs/index.md                       | 2 +-
 docs/model_zoo.md                   | 2 ++
 examples/classification.ipynb       | 2 +-
 examples/detection.ipynb            | 2 +-
 examples/filter_visualization.ipynb | 2 +-
 examples/net_surgery.ipynb          | 2 +-
 6 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 83ba236bcf1..af4b50c323b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -39,7 +39,7 @@ A 4-page report for the ACM Multimedia Open Source competition.
 - [Installation instructions](/installation.html)<br />
 Tested on Ubuntu, Red Hat, OS X.
 * [Model Zoo](/model_zoo.html)<br />
-BVLC suggests a standard distribution format for Caffe models, and provides trained models for non-commercial use.
+BVLC suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br />
 Guidelines for development and contributing to Caffe.
 * [API Documentation](/doxygen/)<br />
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 490bb68f808..5fd3ecf4ffe 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -1,3 +1,5 @@
+---
+---
 # Caffe Model Zoo
 
 Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 7c01b9e235e..9c9d247a0cf 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -2,7 +2,7 @@
  "metadata": {
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
   "example_name": "ImageNet classification",
-  "include_in_docs": true,
+  "include_in_docs": true
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index e1620417af1..62263b66809 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -2,7 +2,7 @@
  "metadata": {
   "description": "Run a pretrained model as a detector in Python.",
   "example_name": "R-CNN detection",
-  "include_in_docs": true,
+  "include_in_docs": true
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 4d69dc0f64e..abf4c0dd796 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -2,7 +2,7 @@
  "metadata": {
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
   "example_name": "Filter visualization",
-  "include_in_docs": true,
+  "include_in_docs": true
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 336c8457c2c..0854018af4a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -2,7 +2,7 @@
  "metadata": {
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
-  "include_in_docs": true,
+  "include_in_docs": true
  },
  "nbformat": 3,
  "nbformat_minor": 0,

From c6827bf3fcd8bf6cafb2aeb7a1ce4d3a958b7f19 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 02:50:53 +0100
Subject: [PATCH 0671/2053] flickr style fine-tuning model (separated from
 example read me)

---
 examples/finetune_flickr_style/readme.md      | 16 ++++++++++-----
 models/finetune_flickr_style/readme.md        | 20 +++++++++++++++++++
 .../finetune_flickr_style/solver.prototxt     |  4 ++--
 .../finetune_flickr_style/val.prototxt        |  0
 4 files changed, 33 insertions(+), 7 deletions(-)
 create mode 100644 models/finetune_flickr_style/readme.md
 rename examples/finetune_flickr_style/flickr_style_solver.prototxt => models/finetune_flickr_style/solver.prototxt (74%)
 rename examples/finetune_flickr_style/flickr_style_train_val.prototxt => models/finetune_flickr_style/val.prototxt (100%)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 68b2778cafa..2c9ee0a8a94 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -34,7 +34,7 @@ All steps are to be done from the caffe root directory.
 The dataset is distributed as a list of URLs with corresponding labels.
 Using a script, we will download a small subset of the data and split it into train and val sets.
 
-    caffe % ./examples/finetune_flickr_style/assemble_data.py -h
+    caffe % ./models/finetune_flickr_style/assemble_data.py -h
     usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
 
     Download a subset of Flickr Style to a directory
@@ -48,7 +48,7 @@ Using a script, we will download a small subset of the data and split it into tr
                             num workers used to download images. -x uses (all - x)
                             cores.
 
-    caffe % python examples/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
+    caffe % python models/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
     Downloading 2000 images with 7 workers...
     Writing train/val for 1939 successfully downloaded images.
 
@@ -56,11 +56,11 @@ This script downloads images and writes train/val file lists into `data/flickr_s
 With this random seed there are 1,557 train images and 382 test images.
 The prototxts in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).
 
-We'll also need the ImageNet-trained model, which you can obtain by running `get_caffe_reference_imagenet_model.sh` from `examples/imagenet`.
+We'll also need the ImageNet-trained model, which you can obtain by running `get_caffe_reference_imagenet_model.sh` from `models/imagenet`.
 
 Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
 
-    caffe % ./build/tools/caffe train -solver examples/finetune_flickr_style/flickr_style_solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
+    caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/flickr_style_solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
 
     [...]
 
@@ -149,10 +149,16 @@ This model is only beginning to learn.
 Fine-tuning can be feasible when training from scratch would not be for lack of time or data.
 Even in CPU mode each pass through the training set takes ~100 s. GPU fine-tuning is of course faster still and can learn a useful model in minutes or hours instead of days or weeks.
 Furthermore, note that the model has only trained on < 2,000 instances. Transfer learning a new task like style recognition from the ImageNet pretraining can require much less data than training from scratch.
+
 Now try fine-tuning to your own tasks and data!
 
+## Trained model
+
+We provide a model trained on all 80K images, with final accuracy of 98%.
+Simply do `./scripts/download_model_binary.py models/finetune_flickr_style` to obtain it.
+
 ## License
 
 The Flickr Style dataset as distributed here contains only URLs to images.
 Some of the images may have copyright.
-Training a category-recognition model for research/non-commercial use may constitute fair use of this data.
+Training a category-recognition model for research/non-commercial use may constitute fair use of this data, but the result should not be used for commercial purposes.
diff --git a/models/finetune_flickr_style/readme.md b/models/finetune_flickr_style/readme.md
new file mode 100644
index 00000000000..f73fd5ce1ee
--- /dev/null
+++ b/models/finetune_flickr_style/readme.md
@@ -0,0 +1,20 @@
+---
+name: Finetuning CaffeNet on Flickr Style
+caffemodel: finetune_flickr_style.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/finetune_flickr_style.caffemodel
+license: non-commercial
+sha1: 443ad95a61fb0b5cd3cee55951bcc1f299186b5e
+caffe_commit: 41751046f18499b84dbaf529f64c0e664e2a09fe
+---
+
+This model is trained exactly as described in `docs/finetune_flickr_style/readme.md`, using all 80000 images.
+The final performance on the test set:
+
+    I0903 18:40:59.211707 11585 caffe.cpp:167] Loss: 0.407405
+    I0903 18:40:59.211717 11585 caffe.cpp:179] accuracy = 0.9164
+
+## License
+
+The Flickr Style dataset contains only URLs to images.
+Some of the images may have copyright.
+Training a category-recognition model for research/non-commercial use may constitute fair use of this data, but the result should not be used for commercial purposes.
diff --git a/examples/finetune_flickr_style/flickr_style_solver.prototxt b/models/finetune_flickr_style/solver.prototxt
similarity index 74%
rename from examples/finetune_flickr_style/flickr_style_solver.prototxt
rename to models/finetune_flickr_style/solver.prototxt
index 756e162ba1e..5e189bc93c0 100644
--- a/examples/finetune_flickr_style/flickr_style_solver.prototxt
+++ b/models/finetune_flickr_style/solver.prototxt
@@ -1,4 +1,4 @@
-net: "examples/finetune_flickr_style/flickr_style_train_val.prototxt"
+net: "models/finetune_flickr_style/train_val.prototxt"
 test_iter: 100
 test_interval: 1000
 # lr for fine-tuning should be lower than when starting from scratch
@@ -12,6 +12,6 @@ max_iter: 100000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/finetune_flickr_style/flickr_style"
+snapshot_prefix: "models/finetune_flickr_style/finetune_flickr_style"
 # uncomment the following to default to CPU mode solving
 # solver_mode: CPU
diff --git a/examples/finetune_flickr_style/flickr_style_train_val.prototxt b/models/finetune_flickr_style/val.prototxt
similarity index 100%
rename from examples/finetune_flickr_style/flickr_style_train_val.prototxt
rename to models/finetune_flickr_style/val.prototxt

From 51c4e6ed1ae91468ee560edd56c98538bfac0df0 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 03:50:20 +0100
Subject: [PATCH 0672/2053] script to upload/update model info as gist

---
 docs/model_zoo.md                      | 20 ++++----------
 examples/imagenet/readme.md            |  4 +--
 models/finetune_flickr_style/readme.md |  1 +
 scripts/upload_model_to_gist.sh        | 38 ++++++++++++++++++++++++++
 4 files changed, 47 insertions(+), 16 deletions(-)
 create mode 100755 scripts/upload_model_to_gist.sh

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 5fd3ecf4ffe..0b7daa0bf17 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -3,7 +3,6 @@
 # Caffe Model Zoo
 
 Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
-
 To lower the friction of sharing these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
@@ -26,12 +25,12 @@ User-provided models are posted to a public-editable [wiki page](https://github.
 A caffe model is distributed as a directory containing:
 
 - Solver/model prototxt(s)
-- Readme.md containing
+- `readme.md` containing
     - YAML frontmatter
         - Caffe version used to train this model (tagged release or commit hash).
         - [optional] file URL and SHA1 of the trained `.caffemodel`.
         - [optional] github gist id.
-    - Information about what data the model was trained on, explanation of modeling choices, etc.
+    - Information about what data the model was trained on, modeling choices, etc.
     - License information.
 - [optional] Other helpful scripts.
 
@@ -39,9 +38,12 @@ A caffe model is distributed as a directory containing:
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 
-- `scripts/download_model_from_gist.sh <gist_id>`: downloads the non-binary files from a Gist into `<dirname>`
 - `scripts/upload_model_to_gist.sh <dirname>`: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
 
+Try doing `scripts/upload_model_to_gist.sh models/bvlc_alexnet` to test the uploading (don't forget to delete the uploaded gist afterward).
+
+Downloading models is not yet supported as a script (there is no good commandline tool for this right now), so simply go to the Gist URL and click "Download Gist" for now.
+
 ### Hosting trained models
 
 It is up to the user where to host the `.caffemodel` file.
@@ -49,13 +51,3 @@ We host our BVLC-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
 - `scripts/download_model_binary.py <dirname>`: downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
-
-
-## Tasks
-
-x get the imagenet example to work with the new prototxt location
-x make wiki page for user-submitted models
-- add flickr model to the user-submitted models wiki page
-x make docs section listing bvlc-distributed models
-- write the publish_model_as_gist script
-- write the download_model_from_gist script
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 97a68f306d8..39a7fad8fda 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -91,9 +91,9 @@ Resume Training?
 
 We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
 
-    ./build/tools/caffe train --solver=examples/imagenet/imagenet_solver.prototxt --snapshot=examples/imagenet/caffe_imagenet_10000.solverstate
+    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
 
-where in the script `imagenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `caffenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------
diff --git a/models/finetune_flickr_style/readme.md b/models/finetune_flickr_style/readme.md
index f73fd5ce1ee..c08485f7fd3 100644
--- a/models/finetune_flickr_style/readme.md
+++ b/models/finetune_flickr_style/readme.md
@@ -5,6 +5,7 @@ caffemodel_url: http://dl.caffe.berkeleyvision.org/finetune_flickr_style.caffemo
 license: non-commercial
 sha1: 443ad95a61fb0b5cd3cee55951bcc1f299186b5e
 caffe_commit: 41751046f18499b84dbaf529f64c0e664e2a09fe
+gist_id: 034c6ac3865563b69e60
 ---
 
 This model is trained exactly as described in `docs/finetune_flickr_style/readme.md`, using all 80000 images.
diff --git a/scripts/upload_model_to_gist.sh b/scripts/upload_model_to_gist.sh
new file mode 100755
index 00000000000..2dfbabd72a3
--- /dev/null
+++ b/scripts/upload_model_to_gist.sh
@@ -0,0 +1,38 @@
+#!/bin/bash
+
+# Check for valid directory
+DIRNAME=$1
+if [ ! -f $DIRNAME/readme.md ]; then
+    echo "usage: upload_model_to_gist.sh <dirname>"
+    echo "  <dirname>/readme.md must exist"
+fi
+cd $DIRNAME
+FILES=`find . -type f -maxdepth 1 ! -name "*.caffemodel*" | xargs echo`
+
+# Check for gist tool.
+gist -v >/dev/null 2>&1 || { echo >&2 "I require 'gist' but it's not installed. Do 'gem install gist'."; exit 1; }
+
+NAME=`sed -n 's/^name:[[:space:]]*//p' readme.md`
+if [ -z "$NAME" ]; then
+    echo "  <dirname>/readme.md must contain name field in the front-matter."
+fi
+
+GIST=`sed -n 's/^gist_id:[[:space:]]*//p' readme.md`
+if [ -z "$GIST" ]; then
+    echo "Uploading new Gist"
+    gist -p -d "$NAME" $FILES
+else
+    echo "Updating existing Gist, id $GIST"
+    gist -u $GIST -d "$NAME" $FILES
+fi
+
+RESULT=$?
+if [ $RESULT -eq 0 ]; then
+    echo "You've uploaded your model!"
+    echo "Don't forget to add the gist_id field to your <dirname>/readme.md now!"
+    echo "Run the command again after you do that, to make sure the Gist id propagates."
+    echo ""
+    echo "And do share your model over at https://github.com/BVLC/caffe/wiki/Model-Zoo"
+else
+    echo "Something went wrong!"
+fi

From 2bdf516440139ce59f9a45f3cd6c84540e2cb040 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 3 Sep 2014 20:55:00 -0700
Subject: [PATCH 0673/2053] add test_initialization option to allow skipping
 initial test

---
 src/caffe/proto/caffe.proto | 3 +++
 src/caffe/solver.cpp        | 3 ++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 74c2bab73eb..364cde6cbe0 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -106,6 +106,9 @@ message SolverParameter {
   // The number of iterations between two testing phases.
   optional int32 test_interval = 4 [default = 0];
   optional bool test_compute_loss = 19 [default = false];
+  // If true, run an initial test pass before the first iteration,
+  // ensuring memory availability and printing the starting value of the loss.
+  optional bool test_initialization = 32 [default = true];
   optional float base_lr = 5; // The base learning rate
   // the number of iterations between displaying info. If display = 0, no info
   // will be displayed.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index dcac4c1537c..d68de5fa930 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -184,7 +184,8 @@ void Solver<Dtype>::Solve(const char* resume_file) {
       Snapshot();
     }
 
-    if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
+    if (param_.test_interval() && iter_ % param_.test_interval() == 0
+        && (iter_ > 0 || param_.test_initialization())) {
       TestAll();
     }
 

From d8f56fb2c21058e9ea01cae65f1f9a0cc5371cb8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 3 Jul 2014 21:53:31 -0700
Subject: [PATCH 0674/2053] add SILENCE layer -- takes one or more inputs and
 produces no output

This is useful for suppressing undesired outputs.
---
 include/caffe/common_layers.hpp    | 31 ++++++++++++++++++++++++++++++
 src/caffe/layer_factory.cpp        |  2 ++
 src/caffe/layers/silence_layer.cpp | 26 +++++++++++++++++++++++++
 src/caffe/layers/silence_layer.cu  | 28 +++++++++++++++++++++++++++
 src/caffe/proto/caffe.proto        |  3 ++-
 5 files changed, 89 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/silence_layer.cpp
 create mode 100644 src/caffe/layers/silence_layer.cu

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index e32dac71332..37535920992 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -309,6 +309,37 @@ class MVNLayer : public Layer<Dtype> {
   Blob<Dtype> sum_multiplier_;
 };
 
+/**
+ * @brief Ignores bottom blobs while producing no top blobs. (This is useful
+ *        to suppress outputs during testing.)
+ */
+template <typename Dtype>
+class SilenceLayer : public Layer<Dtype> {
+ public:
+  explicit SilenceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SILENCE;
+  }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
+  // We can't define Forward_gpu here, since STUB_GPU will provide
+  // its own definition for CPU_ONLY mode.
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+};
+
 /**
  * @brief Computes the softmax function.
  *
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 1b34702c0c4..4bb7cd7cd7f 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -66,6 +66,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new PowerLayer<Dtype>(param);
   case LayerParameter_LayerType_RELU:
     return new ReLULayer<Dtype>(param);
+  case LayerParameter_LayerType_SILENCE:
+    return new SilenceLayer<Dtype>(param);
   case LayerParameter_LayerType_SIGMOID:
     return new SigmoidLayer<Dtype>(param);
   case LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
new file mode 100644
index 00000000000..75dbbf31f0d
--- /dev/null
+++ b/src/caffe/layers/silence_layer.cpp
@@ -0,0 +1,26 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SilenceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < bottom->size(); ++i) {
+    if (propagate_down[i]) {
+      caffe_set((*bottom)[i]->count(), Dtype(0),
+                (*bottom)[i]->mutable_cpu_data());
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(SilenceLayer);
+#endif
+
+INSTANTIATE_CLASS(SilenceLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
new file mode 100644
index 00000000000..735abe61eaf
--- /dev/null
+++ b/src/caffe/layers/silence_layer.cu
@@ -0,0 +1,28 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SilenceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  // Do nothing.
+}
+
+template <typename Dtype>
+void SilenceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < bottom->size(); ++i) {
+    if (propagate_down[i]) {
+      caffe_gpu_set((*bottom)[i]->count(), Dtype(0),
+                    (*bottom)[i]->mutable_gpu_data());
+    }
+  }
+}
+
+INSTANTIATE_CLASS(SilenceLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 74c2bab73eb..dd650be68ed 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -220,7 +220,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 36 (last added: ABSVAL)
+  // LayerType next available ID: 37 (last added: SILENCE)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -254,6 +254,7 @@ message LayerParameter {
     RELU = 18;
     SIGMOID = 19;
     SIGMOID_CROSS_ENTROPY_LOSS = 27;
+    SILENCE = 36;
     SOFTMAX = 20;
     SOFTMAX_LOSS = 21;
     SPLIT = 22;

From f7baf2b486c3ff196f408c61790bd1b235208e98 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 4 Sep 2014 02:33:19 -0700
Subject: [PATCH 0675/2053] fix model download advice and prototxt name for
 fine-tuning

---
 examples/finetune_flickr_style/readme.md                        | 2 +-
 .../finetune_flickr_style/{val.prototxt => train_val.prototxt}  | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename models/finetune_flickr_style/{val.prototxt => train_val.prototxt} (100%)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 2c9ee0a8a94..dad45aeb560 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -56,7 +56,7 @@ This script downloads images and writes train/val file lists into `data/flickr_s
 With this random seed there are 1,557 train images and 382 test images.
 The prototxts in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).
 
-We'll also need the ImageNet-trained model, which you can obtain by running `get_caffe_reference_imagenet_model.sh` from `models/imagenet`.
+We'll also need the ImageNet-trained model, which you can obtain by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.
 
 Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
 
diff --git a/models/finetune_flickr_style/val.prototxt b/models/finetune_flickr_style/train_val.prototxt
similarity index 100%
rename from models/finetune_flickr_style/val.prototxt
rename to models/finetune_flickr_style/train_val.prototxt

From d46f3cd65255f088c3ac35654427549509d71b05 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 4 Sep 2014 02:37:04 -0700
Subject: [PATCH 0676/2053] [example] update ImageNet timing for K40

---
 examples/imagenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 39a7fad8fda..41384f9475b 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -82,7 +82,7 @@ Ready? Let's train.
 
 Sit back and enjoy!
 
-On my K20 machine, every 20 iterations take about 36 seconds to run, so effectively about 7 ms per image for the full forward-backward pass. About 2.5 ms of this is on forward, and the rest is backward. If you are interested in dissecting the computation time, you can run
+On a K40 machine, every 20 iterations take about 26.5 seconds to run (while a on a K20 this takes 36 seconds), so effectively about 5.2 ms per image for the full forward-backward pass. About 2 ms of this is on forward, and the rest is backward. If you are interested in dissecting the computation time, you can run
 
     ./build/tools/caffe time --model=models/bvlc_reference_caffenet/train_val.prototxt
 

From e23ac45004130889f9b1c1d23903f9a9d6cb81d3 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 4 Sep 2014 18:06:31 +0100
Subject: [PATCH 0677/2053] HDF5 classification example

---
 examples/hdf5_classification.ipynb            | 505 ++++++++++++++++++
 examples/hdf5_classification/solver.prototxt  |  14 +
 .../hdf5_classification/train_val.prototxt    |  59 ++
 3 files changed, 578 insertions(+)
 create mode 100644 examples/hdf5_classification.ipynb
 create mode 100644 examples/hdf5_classification/solver.prototxt
 create mode 100644 examples/hdf5_classification/train_val.prototxt

diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
new file mode 100644
index 00000000000..9232cb18455
--- /dev/null
+++ b/examples/hdf5_classification.ipynb
@@ -0,0 +1,505 @@
+{
+ "metadata": {
+  "example_name": "Classification with HDF5 data",
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "include_in_docs": true
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Classification with HDF5 data\n",
+      "\n",
+      "In this example we'll use Caffe to do simple logistic regression on a simple binary dataset, showcasing HDF5DataLayer functionality."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import os\n",
+      "import h5py\n",
+      "import shutil\n",
+      "import sklearn\n",
+      "import tempfile\n",
+      "import numpy as np\n",
+      "import pandas as pd\n",
+      "import sklearn.datasets\n",
+      "import sklearn.linear_model\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "X, y = sklearn.datasets.make_classification(\n",
+      "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+      "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+      ")\n",
+      "\n",
+      "# Split into train and test\n",
+      "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Visualize sample of the data\n",
+      "ind = np.random.permutation(X.shape[0])[:1000]\n",
+      "df = pd.DataFrame(X[ind])\n",
+      "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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l6oxG2nZs53O/uIOensaCqlKpceFCjNmpFFq9jr5+Dzt3thMOF7l2LYEoygSD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\nWyx4BwdRWi38Bw7gG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mZvE5qaRKrV8D99ltm8h/iVDcqFKnq7nXy6RGBkkK31y4yFLOzm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pDHYnXhCPpZfnsRm9PI3uE+41/7XSyBAOVCgUDQTvQgh8upIxuNY3fqCLjVbP74XRqxPQYe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m2Lo1RDJZJperEgrZqFZV6nWVxdkykx/MMj+RJRp1sLRYxOm0oNVlAi4DWruNyekcM9N1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\nfNjjrVYTgpDD64WOjuiav59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ryMpKnpGRGJJYoybVyWfKnDoT54H7ulGWp/D092N7TXv1\ny1Gvy4yNJZmcTFKvq7S22tm2LYjTeWun8W42VFVlcjLF6GgCpwVU1YmhksJq0aHV6/ENDWH1+6/L\nWIpFiaNHV8hmq0CjfH10NIHPZ6G/30utUqGwsoJYKGD2eLC3tr6l9e11LuXrX/86+/fv573vfe+b\nfq7BYOADH/gAjz32GF/4wheuwejWuVLyeZGRkSgrKwUcDgP1usrKSh5BaCwwRCJFTp4Mc+hgF97+\nflJTU6iyjEYQGtf2lpZL9inXahQiESqpFAabDXtrK4bbtI3FejByHYnFSkhSnez8PMVIBLlWpxiL\nkd/bztDBg9jb2q5IvDozk+H48ZVma+l8XkSSZA4d6kGrvZE1ybcXS0s5Dh9eolZTSGkgGBjGbq4S\najFi83uxtbS8bfHqlZLNVikUxFXbFEVlZaVAV7uFxWefJbe0BKqKRhDwDQ7Sfuedt62Y+mqxsrLC\nt7/9bUZGRt7yPh5++GG++MUvrgcja4iqqpw4EWZ6Og2AwaDl5MkIBoNAKPSKuDiZLFMqy7Tt3Yuz\nuxspn8fgcGBraUFnWJ3BVhWF8MmTJMfGGvoSjQZ7ayvdBw68qQz5rcK6ZuQ6YjJoKcZipCcnEYtF\nNFoNVpcTrVKjXq1ecVnv9HS6GYi8TCRSIJ2+vvqDdd6YpaV8s/OyqkI0IXEhqodANyaXi+z8PPHR\nUQrR6DXXb+j12staxFutekrRaDMQgcb6dnpq6qqIa293/uZv/oZPfepTtLe3v+V9HDx4kPn5eWZn\nZ6/iyNZ5M2QyFSKRQvO+ooDJpCMeX+12rNcLGAxadAYDrs5OAps34+rsvCQQASglEqQmJppCV1SV\nwspK47t4kXIqRXJ8nNTkJJXs67Z1uSVYn/ZcJ+qShKkax6oVyYfDaDQajA4HAwd3YFGLiLnVbaKl\nYpHU1BT55WWMDgfegQHsoRDAZbMfGo2GN1N6XqnUkGV1XW9yDbnc+VAUFU2tzNzTxylGo6CqCEYj\noTvuILhlC9AQtqZnZsjMzaE3m/H09+Pq7LyiY4piHVFseI682ovA57PQ1eViairV1NHa7Qa6u12U\n58cuEdfKkkStXH5rL3wdoKEV+fd//3dOnz79tvaj0+n40Ic+xGOPPcaf/dmfXaXRrXMlyLJCqdRw\nOtbrXwnmCwWRwUEfuVy1uU0QNAwO+rBYruyaWq9WkUXxku3VTKPtSHZhgaUXX2wWNRhdLrruuQf7\nZZZ73gyqqpKdnyc9NYVcq+Hp68PV23vZgOl6sh6MXCeK0SiliRHueccGnOpOEtEsoVYHPR0gpROY\nt25qPlau1wmfPEl6ehpVlilGIuSXl+m7/34Eh5uWFlvTKrhYlKhU6rS1XVkJsijWOX8+zsxMBkVR\n6ehwsG1by3pQcg3o7HQxNZVeZd/c0WFHTqxQfFWZnyxJZJbDVC1B0BmoJ8NUZkYRL86E8svLdB24\nF1OwFZNJd1nDI1VVmZhIMTaWQBTr+P0Wtm8P4fNZgEY78jvvbMPvt7CyUsBuN9DX58Hvt5IpeBuR\n06sCEsFovC1TxVeT7373uxw4cIDOKwwk34iHH36YRx99dD0YuY40NH5RcjkRi0WP32+lVlOoVOo4\nnUZsNgMPPTRMLiei02np7nbR1eVCFOtoNPxGYbjeYkFnNjcr6jSCgGAwYPH5kGs1YiMjq6orxWyW\n+Llz2ILBt2V6lpmdZeG551BqjSCrGIkglUq07tz5lvd5NVgPRq4Dcq1GJZXCaLejp8LWjTaSyhKl\n8AwVTRsd73gHjotp3LokkZqcJDs3h9HhQCsIlFMpaqUSkeUUk/MrJON5UkUt2UKd3bvbGBy0MjTk\nu6IP6MREilOnIs3fndHRBLKscs89Xc3HpNMVKpUaNpthXRT7Nmhvd3DXXZ2MjiaoVGq0tNjYtq2F\n1PG5VY8z+EOcvFAhP3GBaqmCmk+wZ08HLT1uxFyOcqXO3Kkxau4iislOf78X0CCKdRwOIzarnuWp\nZRYnkiiygWJRolCQKJfrvOtdfRiNja+52axn06YAmzYFVh3f3taGp7+f7OwsSr2OYDQS2Lz5uolr\nb1X+9V//la997WtXZV9333038XiciYkJNm7ceFX2uc7rk8tVefHFRbLZRuaiUJBIpcps3hwkn68y\nOpqgWJTw+ay0t9vZvDmA12vh7NkYMzNptFoNfX0ehoZ8ze/fa7H4fAQ2byZ+/jwGmw2lXkcrCGi0\nWqRiEalYxOhyNZbvL17ba+UysiShMxovjksknxcxGgW8Xsuq3wC5XqeayaDV6TC73UBj0pIcH28G\nItDQrqSnpvAODGBcQ3O9tQxG7gT+jkaDvOPA59dwLNeMSjbL8ksvUUilSWfqyEaV0MZ+2r1+qukk\n/uFhvAMD6AwG5HqdlaNHWT5yhPi5c6DRNP7e348ka1g4eZ7jPz9LuViltbeFjdt3EYnk2bUrhM12\naUtpWVaaSm9oNFGbmUlf0nxpZSVPPl/FZjMyMhJlbCxBtVrHYtGzdWvwprYfXisURW1ekLq6XNRq\nctOUrOLzkZ1rBCSC0Ui8pGd6OorFqyE9PY2JKjN+B5VOD15/gMyZ48R+8TwEe2jffQfPLOUoFBsX\nk752E9bcDNGpBcLhIv6eDjo2bGYpXieZLJNKlWltfeOSQp3RSOf+/Xj6+qhXKg3BXSBw3cS1tyJz\nc3PMz89z3333XZX9CYLAb//2b/P973+fL3/5y1dln+s0Jl7JZBmdTksgYG1miJPJcjMQeRlB0OLz\nmVBVFYNBwO9vVL2srBQQRZn+fg8nT4ab19d0egWA7dsvv6yi0Who2bYNq9/P/LPPkl9exmC3U8lk\n8A4M4OrtJXLyJInRUWqVChafj74HHqBYkcmE06RSVcbHk4hiHYNBoL/fw86drej1AuVkkpVjxygn\nk2gEAVd3N6E77kAwGKhfZmlIkeVXtCtrxFoGI/PAQUACvgtsBs5f64OKYh1BuLyY71qQOH+eaqnK\nVMrCyV+doRBP4u/vZfc7t7NjUx++jRubJZSlWIz09DQajQaT2029UiFx4QLe/n4UWWXkV0eJL5cw\nmXScPTyObyWPbcddnDgRZmDAy8CAD2h8kcbHk8RiRTweM8PDfoJBGxoNrxtUaDQaIpECZ85Em6LL\nQkHi1KkIfr+FQGA9ZX8lFAoi4+NJFhdz2GwGhoZ8dHa6Vn3e3L295JeXKUaj6EwmoitlbMEWCuEw\nvpCbus7C//yfZ+ntcaMpJOgc6mT//Q9QSqYplWXOnJvC7vfT3e0iOzHK2Mh5/H4L5WyBhdNj9BqN\n2H2DVCq1Kw4iBb0e57qPxVXjscce44Mf/OAb9p55szz88MN85jOf4c///M/XJwdXgfn5LIcPL1Es\nSmg0DV3V3Xd34fNZmhq8lwMLr9dMtVrn9OkYs7MZSqUawaC1OdlLpyssL+dXTfRUFaanU2+YHdFo\ntUilElKphM5oRKnVUHU6MjMz+DdtInb2LLVSQyRbzeWIjE+zKLeSrWh58cUlyuUaGza4sdmMnD+f\nIBCw0d1pZ+XYMfLLy83jJMbGMDocBLdswd3dTSWZXDUOayCAyem8iu/um2ctg5FX+57XgGsalhWL\nIufPJ1haymE06ti40Ut/vxettpHuzmSq6PVaPB7zVfui10WRYjRKQeNi7MwFBJsDu8EEehNLKQ07\nAn2rvByq2SyJCxeoptPUKhW0goDZ50NrMFDPZRAMBgShjCjWyeVEanMR7j2kQZIUTp+OEgrZ0Wg0\nPPfcAslkQ3yYSlVIJEo88EAfLpeZ/n4PyWQZRXnlW9Pd7cJuNzI7m2kGIi9TqTTem/Vg5DdTrysc\nObLM3FxD65FKVYjFSrzzndpV2QmT00nPoUMUo1GUep1ci4b0yQhyrYarJcRPH5+kUJTQIlMpV4gl\nqiwtZDDGJ8llQMppKFncCLJIdmkFUayj1wvo9FrqNYXU/CJmZzc2mwmX69KM2TrXnh/84Ad84xvf\nuKr73LdvH9VqlVOnTrFzjdf3b3ZEsc6ZMxGKRQloBA6JRJnR0TibN/sxGARcLhOZTBWzWUexKPHS\nS8vs2dOGwSBw7lwcRVGbndyNRm1TsC4IGrTahinaGxXJqYqCRqulFI+TmphoFjGYfT6c7e1IpRK+\nwUGkQqOKR7DYmJpM0dleQpZtXLiQpFAQqVbr9PS48PmsRCIFgg6Zcir1moOpZGZnCW7ZgnfjRsRC\ngfzSEqqiYPH7CV2FHllvlxtBM7IV8APj1+oAiqJy7NgrNeIAqVQZg0HAYBA4dmylKULq7XWxc2fr\n60aybwatTofeZiMfkajXZIxWK+6eVgRFQs0nic4uE/DqmyY3UrFRVVOvVtGZTFh6NqJ4u5CCg+iV\nObzeRTJZCVlnoaW3DavTQs8GH9F8nWq13hSzplKrqyCyWZFIpIjLZWZgwHvRjCuNLCv09LgZHm5k\nVMxm/Wt1jGi1GozGt95Z9nYimSyzslJYta1arTM3l71kqcRgteLu7QVgOFBhJSKSnjSiMxlJpyoM\nbOvFYNeitTkpF4rEElVay2VEoUB/yIlGmaE8rUVTSOK1qdhsejZs8BAJF9AIOvRGPSaLgeeeW2TP\nnjbc7tcXN8u1GrIoorda12fcV4FYLMb09DT33HPPVd2vRqPhE5/4BN/5znfWg5G3ycu6qldTKjWM\nAfP5RmO6tjYHVqseQdBy5kyUnh4XsqzQ3e1iYSFHLFYkGLReXM5uQVXVRqZEVahUZRRFpbfXfclv\nSTWXI3nhAvmVFYwOB4LRiFxr2DsAVFIpfIODWHw+HO3tiBft5fP5KrKqRUZLNlulUqkhijKJRAmj\nUUBVG6X6gk532W7guov9kAxWK1333EMlnUZVFMxu9w3hJ7TWI/AA/wj89uX++Oijj+JyNdqhDw4O\nsnfvXrq7uwGYn58HuKL76XSZ6ekZRFHGaGz88BaLcc6fl6jVHCSTZUSxkbYaHa3h8VgwmYpXvP/L\n3Z+bm6NWLuPr7yddj2B0QWevnfrUFKVkEmN3C4WEkcipDG179zF6bpz01Aytu3YRPnECqXeQY3MV\nyuNh2uvthHwKuoCVd27uJTE+zkp0GZ83hN9QQPEEmF9OkEpFMBi8qCrN1/Py6w2HlzCbS3R3d7N5\ncxCTqYiiwMBAa3P89bpEIGAlFis1n9/d3opDLzF14QKCwUDAZkOp1YgVCujN5rf8/rze/ZsZWVZW\nZZxe5rXZJqVeJz093XRodG/YwKF7QnT476SYq3DgXZtYnM9y7lwCk8VIfHqWvfsPISwsMNDvZfyl\nsywtZsHiYNOmINLcWQy9XrSaOj5zleAd3agemE+JlEp1piYTbN3oQGexXFK+l56eJnbuHOVkEovf\nT/uePdhbW6/p+3Sr89RTT3Ho0CH018DB9uMf/zh79uzhb//2bzGscSnmzYzFosdi0VOpXGxmp6os\nL+dpa3NQLtcoFCQmJ1Ps3Bmip8dFJlMlnxdR1YaB4KFD3UhiHa/LQFevl45OF4mFMLPPHGfm7BxW\nv4/WbZvRaDzkclVkWcHlMqPUJJZefLG5hJJfXqaSTNK5fz8zTz2FyeXCu3EjxUiEmSeeQDCbcff0\nUE4mMRjrtG8ZIFHSkUoVOXRvJ+PnlgiGbBh0CjZDjVDAhMnlwtXb29AdXpxZCgYDvlcJnzUaDRav\n9/q/8W/AWk6DdMBPgL+gIWB9LerVMoJKJEo88cQ01erqlaD+fg/z89lLfiy6u1088EDfqm21SgWN\nIFxRLXYlmyV25gyFSASNVou9t5/FlIbxH/2I2aMjGCwmWju89PW5adl1J2VPH7GsSuKFX2Ix6xjY\n2sl8Wse5M2H0Nhu2QACLWeCOTQ4qZ55l6YUXkQUTlZqGUrHKnZ/5BO6tu9g4FCSdrvDkk9PN9COA\n2azjgQf6CAZ/81JLNltlbi5DMlnCShmnkqIWXcTW2kqtUEAsFFDqdYxOJ+179uC8CmWLL3MjtxSP\nRApMT6cplWp0dDjo7XVf0iW3Wq3zxBNTJBKvZKYEQcPBgz309rqb2xJjYyy99BKqLKPIMoVwGO/A\nABafD5M/wKl5Ld/5zgj5koqBKj1BLYPdJoZ6TBiVMlPPHkW2ekhl6/Rv6ybUYkURyyQW4wiuAHVF\nJRXJsOHg3eitVirT57BripidDtrvvLN5UcovLzP5+OMkxsaoXiwj9g8Ps+WjH8Xe2nrZ2dW14EY+\n72+Fj33sY9x99938wR/8wTXZ/4EDB3j00Uf5wAc+cE32f71Y6/M+OZni6NFlKpVGZjkeL7JrVxuJ\nREOjYbcbCbh17LurmzNnE5w7Fwca19OQpYyxsILPKaDV6fBv2sTIk88zcniSWl1BowFPawDHjv1Y\nPG5SqTJms47BVhj76eM47Xq8HguyJBI7d46uAwew+v1odTou/PCH5JaWMNntqEBoxw467roLs8tF\nFie/fiGMUEmjzcex+P2UC1XsVoH2Nhs+cw3PwAC2YJD09DSZ2Vl0JhPe/n6cXV1rnvm8ePzLDmIt\nMyO/DewC/q+L9/87cORaHMjjMRMMWllYeMVYTBA0+P1WlpfzlwQjFssrPzJSsUj8/Hlyi4todTo8\nAwP4Bgdf1y1VkWXCx483qyUAyseP0LVrD3FzHd2mVixWI3opT3L8AvpgJ6qxgxOns7S72jjy1HNo\ndVrGYwaq1RqCQSJx4QKKVCOg78GUKeDqaKOYyiBVylj1CuLSJKG772i+1n372hkZiVIoSFgserZs\nCV5RIALgcpnYsSNEIRJh/tfHqRYK6EwmSpEIC88/j394GIPVSjWdJnziBBa/H/11bPa2FkSjBZ5+\neq5pfrS0lCOVqnD33Z2rvtwmk459e9t46fkZkrE8ZoeVLdvb6Ox8RRgm1+skJyaaTbSqmQy5hQVk\nUaR9717SF8ZwOzbw8O9sJ54QsVk0VMJLLI/OMLxxELWYQxXLmAMhPIKeSjrNRCLL1nt3IKU0nH1x\nBrVeo6/PjZpLUJo6xeKR43jsGgRBS2JsjF1/8Ae4e3rILS+TnZujcnF9uV6tMv/MMzi7u7G3tODq\n7sbV1cU6V46iKDz11FN8/etfv2bH+OQnP8l3vvOdmz4YWWsGBrw4nUZisRIaDUSjxeYya0dAh7Q8\nQXoiymyhha7NOxAHPITDRUIuhcLpU1jsGsqSrtkULzW3AID+olh97sISfa0xLB4X8/NZopE8vne6\noZQhldOgKj78ARsWnw8pn8caDJKanCRy8iTWQAA0GjRAZn6eDe95D3qbjYBG4b3v7uTF//ckuWSW\ngWEXU6d+RWRsnKLdRMfuHaiAxeslsGkTgU2bXufV33isZTDyHxdv1xxB0DaFR9FoEb1ey8CAj40b\nPWQyFcbHk02dhMdjpqPDzvx8Fp1OgxKeIfGqdFclk0HQ6fANDq46hlyvU4xEqGYyJMfH0V5ct5NK\nJdLT0xgdDqyaCtXMAsX5HKosU68rBKolzBYj2WyF0MYODnzqg1jUAoMhE5PTOaqxCKrcCJb0gpZS\nNIxeq6JFwWrVI0oySl1GKr/iBNjT48bnEsilitidZpx+N2+W/MpKUzilM5tJT01Rr1Sol8tNjUs1\nl6Oazd5SwUitUqEYiVAXRcxuN9ZgsKmefxlVbXTAHRryNcv7Xn6uOD3CoDFLuU2H0Vyj3elbVUmj\nKkqzhE6WFcRyBa1Oh1Kvo6oqsiShlUokV8rYPG7SyRK5vA5ToAWLEaxmN53bBllJytjsBixOG6pW\nTzFbJDq9hEmvksiKaLRaLCYNU2dOo1NEZFlPpSRRLM4RHTmLq7u70RwvEmmOpxRvzPzEbBadXk9+\naQmdyXRFzRvXaTAyMoLb7W4uP14LHnroIb7whS+wsrJCW1vbNTvO7UAwaGtO1DQaWFzM4XaZKE+d\nYeXcBfo2eKimU0QP/5rt993H9u39FOaniC29Yj6o0WioVSro5QpSTUUSZQSdFg0qHX4txnqcAZ+I\nU5aIhnO4OruJnhslt1TF7e7Hu3EjLdu2IZZKqIqCNRBo+n3IkkQ1myUfiZOejKFqNPgCNlrd4Gvp\nYfGFF5j51TOYjDoUnZvM9DRmt5vgli03XcO9tdaMXDfcbjP33ttNoSCi1wvNFPuuXa14vWYWF/NN\nV72XXmqImBSxilVKsHVDO/VYo1+AKsukpqZwbegnkShTqdSwW3WUJ8+SnprE5HKRnplB0Otx9fRQ\niscRczlURcEeClGMRiknk+gsVmyBFkytPSyPTjE83MbCfAZji4rBrNIb1OJ2Bhk5UiCTyGN3WQi0\neciHAmTn56nVVaLhAhhMhOxBpuaLKI4igYCN5Pg40TNnqJXLZEwmfENDBLdufVNp95fFTtDQOBjs\ndjRaLZpX7UMwGJrmO7cCYj7PwvPPUwiHQVXRmUy07NiBolxa8larKZdk1HILCyTHx5uBqwgs55KY\nvd7mxUVnMODo7GRmdIF4rEQ1V8ZhtNE2tJlyscLiTApTyESwbZjDx2IszyWoVascur8fc2cAa3mZ\n9m1DtKoN74nkQhjBE8Do9GB1O1DUHIKgxWrVYXdZMepAMOoIrxSa442H09gvzKAqF0u4w2EMdjuy\nJOEbGmoGJ7VymUIksh6MvAmef/557r333mt6DLvdzkc+8hG+9a1v8dWvfvWaHut2YmjIj9msJ7MS\nZWksycBGL15Pw8FYrddJjo+z4V1dyBbjqoyoUq+jM5qwetzU5pNEokW8XjNdIT1KMcOxn79IoSAy\nfN9+EpKJlVwA1aEy0G3C2RvEaDYjFYtYvF7a9uwhPTVFMZ4gm8giViS67trP2fE8R585j8FipX2g\nnS3dQYIeA/lTJdwuE1qtBkHf+DkvxePcjIuet00wAo0I1uFY7ShqNusZHg4wPBygUqnx+OOT5PMN\nU5h6XWFqLIzX3U27xdLs1aHUZUZH45w+HaNWkxkIKmRPHqM1ZEGRZVzd3URPncLs9SIWChjsdqze\nRpfWaqGAua0bjdWJd/M2ipKWcq3O089Os7tfy4vffRa7uaFb2bYtwH0Hh0kmyxikPOLYEfybN+Pu\n6WHi8BlcPV5ad25H39JFOCegnY6jK8YbNvKqilKvIxWLRM+cwer3N11erwRHWxtmj4dKOo1ULOLs\n6GiIqC7+qGq0WjwbNmD2eK7S2Vl7MnNzFFZWmvfr1Srxc+do23EXF15TZeRymXC7TUjFIpm5OcR8\nnuzCAlKxuGpG8nKF1KudDauODjT+HrTZWfQOLVqnnZq/j6VTI4QXc/S2dtPa6WFTScHr0uP3GikX\nqzzx5Czvf+8GDNplTDYb4RMnUMQa4y+cxeUyM7irn/OHx/B6YXBHD+1bBkkNbWDkV8epSY3Aw9ES\nQDU7GD+3TIetyvBDDzH5+OPUSiXcvb2Nc+p2v9KX5hbSclwPjhw5wv3336/Atp8AACAASURBVH/N\nj/PII49w8OBBvvjFL2K8hSYEa4kgaOnqcmFUSqTMeqiL1OoKBsPFCdjF74I1EMDocjXbNQCURRn3\n7rsZ1p6nM5PD57Pi9Dk5f2aRxcUcgd4OfvyfI7jaOzBaDRSiJVo2tFGIZyiJS9TKZcrpNO7eXvrf\n8x6mfvkMRXGe3nu3Qc9Ovvd3T9La4cEbsJGO55jU2QkErThaQ6ScDmqVSqN9g0aDo6PjhhOnXgm3\nVTDym2iUdL2SjtdbzJhcLlaWC3QPm6iVy2i0WvShLk6eiyNJMoJWJR8Jc+HYBbSbW3DlCljau/Df\noUfQqgQ2b8Zot1PJ5XD39qLffA9LY0uki1r04yKBdj/hfIkd24NUx1/CbdeiKCrlUg2NqpI/exQD\nkBofb6whajRs+p3fRe3dSU2SKVQhKepxkCf2/GFEMtTyWQKbNjXETYkESq1GKZHA1tJCPhymkkyi\nt1qxt7a+rv2v2e2m+957iV2YoJBI4ezpIrRjB4VolHqlgr2tDectpicov8YICBpLL04LbNrkZ3Y2\nS60m43Sa2LOnDb1WYf75w+Tm59FbLNSqVVITE/iGhppLV4LRiPCqHwtJqnN2LEvdt5FgqBcNKulc\nnROjcQZ6+ghu244+0M7YhRSlUo19O5zERiepLidQZT1Tp1X6+72kTpygGA6jNRjo8Khkshmq2RwH\nP/MhLCYt3rYgVo+Lvve8l3RRQ3phCbPbTWDLFkTBztxYlNA2A1qNhq0f/SiVbBYxn0dnNoMsU81m\n0ZnNONaXAd4UR48e5Utf+tI1P87Q0BBbt27lscce43d/93ev+fFuNSqVGqlUoyeM32/BaNShKCon\nT4aJRgrUsLMyHsbjMbFhgwej2Yi7r1HUYLrYsC5+7hzVTAaT240m2MPR0Qq2ob1sdMjYtFWmfv5z\n5GKVQNBC+2AP58bPIOYyVBUfFqMWo5RneXye1pCdxIULFCMRYmfPsue//Tc677oL3/adaK0Onns+\nzId//50IUpFqsYq7I4RscWNudxOSZeqShJTLodFosLW10fvOd15xB/gbiZtvxNcQk0mH0SggSTKV\nSq2hE/C00rbRis6SwAR4+/vJ6oOUy40ZdGZxGZdLRCpVECWZyfEYQkzF1dpC545N6NQaiWd+Sm5h\nAd87DnFyxUJ4MkFoYx9PPz2P05PA6Pbhceqw1iQ8HjNabSODI0s1tPU67Xv3YnI6MXs86C0WSrEI\n8YqlKcht9WvIjZxAWpqkVkmRX14iMTbGHZ/5TLMRk8FuJ3L6NPFz55ppeFtLC9333ovR4UBV1dV9\nDWSF6bDMQtYHeh/RmMCWgIf2O7uQa7VGR8kXXkAwGnH39iI4vSwvF8hmq/h8Ftra7FfFq+V6YvF6\nyczMrNqmN5sx223s2+dm40YfkiTjdpsxmXTklpbIX2z3XSuXsQaDmFwuqpkMerMZjVaLu7cXi8/X\n3J+qNnxvcjmRV8mpEdwBdJ0+MgWRI4/PkEiU2dULoz8+jbZa4Myvz+HrCGLd1cL0C8exa0oUU2ny\n6RLo9FhsJoxygaWFDItpLd6Awo4dEBgaouv/EPBGo6gaAdngYGIshtdjRuNwE45WKMXztHUH6Lnn\nDjITFygnkzja23H39ZFdWCB29iy2UAhXd/dNtw59PUkkEqRSKQZfoye7Vnz2s5/lq1/9Kh/72MfW\nvEriZiKVKvP88wvNYCQQsLJ/fwe1msL4eBJJkunYuJUuk5Hs4jJ1g53eO3c0SmzLEslkBTAT2n8A\ng6AiGAwsL+cpl2ewmvTMvXQSs16lmC0hVySMGi1oNGSyVVxdDjAaaG/zIei0oNVTyWZJz8xQKxQw\nezzEzp3n1GM/Jrj9DgweP1u27eDsk8/z0v8+jtFioGOgnfs/8wHcnZto3dhNYPNmMrOzjayszUa9\nWqWay61yVK1W66ys5EmlKrjdJtra7FfcXfh6cXP9WlxjnE4Tg4M+nn56lvHxFNVqnVDIhsXXjXd4\nAJtNTyJdIzKVJp2u0N5qQXAaUMwmNr3rbnQalZwiE16q0O5yokRqlOJRNm/e3tBgWF0Uc1mGDu5j\naSlHpqhBcJro7XSzMJ9lwOlGk61i0As4nUYMejA4/UiVCo62NjQvCx1rNYaHfU3jHqsgUUysIGfi\nqMZGdKw1WUnNr+C7YxeaYgadwUDk5MmG0MrSsDsuRCJEz51DrlaRCgVcPT14BwbQm80sLeU5cSJM\nvf6KLqJQkHjwwQ2kz48QGxl5RXOQKTJT8RPLqqhqQwi2YYOHu+7qRK+/eQzTXD095JaWKEajoDYu\nMv7h4eZSlNfbWD+WazXkWg1VURq9Hi523ayk07Tv24eg12NwOLC3tODoXF1xYzTq6Olxk8lE0Go1\n2GwGtFoNPp8Zg0Hg7JkU+VSOTzw8wOLPfsjM2ZO4fHbe+Z7NFIs1yvOT1M1O9C4zF355mnwsjd5i\nwux249xqRypVyITLyOh54YXFxkXW4EJodVCtNpx72ze00RmAp586T2Q+ARpwzZe4193Brvvuo1Yu\nUxNFFn7962alTWZ2lkI4TNeBA2veavxG5ejRo+zevRvtdXKyfPDBB/n85z/P4cOH2b9//3U55o1C\nKlVmYSFHuSwRCtnp6HC8YZfcXK7K1FSalZU8hYKI3W5EELRIkkwkUuTcuTidnc5mh+2leB1HcBhP\n20ZcvR6CmzuIx4u8+OJS0926pcXGO97RgderIRi0Nsr95TzJllb0VhPt/W08/b9+ybkT82wLdLFh\nxwCxipGAx4al1YGj00wxGyWXiKPxhHAM7cbXGcIW8OBqD2E2C8SmpnF09zLy4hjuVj993TaMaoXY\n878i0uWidXgAs8dDamKCSirVnBxlgkF67rsPjVaLqhE4eizC5GSqeX3u7HRyzz1dl9gTrCW3VTBS\nTGXIJTJojSZ87cHL/lAODvqolioMdxnQ6A0IFhsLC1msVj1Wq54jR1Zoa7Pj8Zj51S9nKcdj9Pb7\neO9DB0lEc4y+9BKy1sryuTLPPXuM971vEMWibbR9dpgxm4sIKJSrCoqgZ3Exx669XQwN+wk5vGhW\nzqNm43iNVRRRwdHfj8HhIL+8TG5hAaO/BVvvRoz5MBstEeoOG66AG9EKFbsRx9A2FlJ65udSlIsO\nzHU9rmAH+Yttol9emxR0ukb1TzZLYMsW6qLIyrFjSKUSne94B6lUGVleLdDMZKqkIynSU1PNQEQj\nCKTLAqPHJ7B29JJOVyiXaySTZbq6XKv8NW5kikUJGSPdBw9SikYpZAqIWjNh2U55NkN7ux0tCpmZ\nGcrJZOMHu1wGVcUaDFKMxZpeHb0PPIDzDZY3Nm3yIwhQDK+QX7iAzaxhYNNmyrKOxIVxtm4IEjt5\nirHnTpIOx4ktxqFaom97P5pKlg1378HgDbClpJJPZslly1jdDur2FkJBC8uJGrKscupUFFUFrRZi\nsRKZTAWPU09Pp526zoEo2PB26vB3tyMJRk6fidDeYScUcpCZn28GIi+TX1qinEisL928DkePHmXv\n3r3X7XiCIPDoo4/yd3/3d7dVMJJMlnn66dlmI7vx8RSbNvnZu7f9shmiSqXGCy8ssrJSoFKpc+5c\nDK+3YYEQjzcCi1xOxITEQEilUteSEw3kciJ5DQxttaCqKmfPxkgkytjtBtp8ArJU5rlnpnF6bHR2\nOrnjjhBHXihzYqyAXldi/6GN9D34f2LdMIfZ46FlXyfHTkRxOIz42ry4O904dNuopLs49ZMTlGa1\ntNt99NgsdOzZhau9Ff92LTWpzv0f3o/ZoKU4N0FhOUFuKkUpvI9orYKrq4vM7GzzmoxGQ10UmX7i\nCZLj42gcASqubuz2VvJ5CVWFmZk0gYCNUMiKz2f9jb3apFKJerXacIzVN7I51XQajSBgDQSuSkXl\nbROMLJwc4ehPniMZTmOxW9nwjjvY8s69OF2WVY/LLq+w8uyvyMdTCAY9/o0bsTk6mZlJ43abkWWF\ncrnGxEQSi92E0xoimqzywx+N4/VZSUpmDCY96XQNbUVlbiHPnuEAZUBfzbJtRxuTSyIBn4Webjte\nlwExEcPbHcDfFWLoYC/lmVGUSsN4JzU1hSxJGOx28qY28lkLU9/93xRjcRz+hjPghh39BHbuJn7+\nPKemZU4+dw6jSUe2ZqSEjV1dMqlTR4iPnAKNhs59+4iNjlIIh3F2dzP9xBO0792LJGs49+wZFkpO\nIhmVQMBKOl1pVmEIggbkRlbgZQS9nkJepK5omZpKkck0SowjkQJTU6k3XK4plyXK5RpWq2HNInRR\nrHP+fJzp6TSKohIK2RkYCHL2Qp1IpASU0Go1DA/72BiQWDl2jNziIrO/+AUGmw1PXx8aQcC3aROC\nyYTZ46Ecj2Mwm1eJe0WxTipVQattNOTqdolMHh/BVRcx1fTMPPEzrKE2hjYFsFr1nPzJGCaHHV08\nidWsQ6fTkJyYwBjqZOa5w3Tt3Eo+liI2s9ioorFv4Nnnl3nwPRbavVomszXsdj0tAQvLKwVOngzj\ns8pI80myU3oWuzvp7Q+Qr5s4eiKMKMoMDHiYmEihKCpquXzJe6XU68iSdMn2dRocOXKEz33uc9f1\nmJ/85Cf5y7/8S2ZmZujr6/vNT7gFmJ3NrOqoqygq09Np+vs9+HyNZcRiUUSSZBwOI/F4iUik4ait\n02nQ6bSsrBQoFCQMBgG9Xotfmyb+0imyExEyeZng5mH0ni4cTjNtbXYikQITEymsZi0+aYmJn40y\nP5vE2xrANbyNp55S2b27jXSyTDStUMzmSRfG6d7go2RoQWe24dMW+cj7u0jkFOqKll8+Pc873tHL\n1PIymu5tWKtFxn99lIm6yLs//k5WpjN4A05qS3Nkj59A1+rG6XYTOR7B6vEQPXOaotuBxetdZR5n\ndDiIjYyQX1lBKwiUNCnm5o9wxyd+h5LQ6E02N5fFYtEzPq7D77eyd297s2Pxq1FkmcTYGImxMWRJ\nwuL342hrIzE6ipjPo9FqsQYCdN59N2b325t43hbBSCEW59iPnyE8lwBAqkic/fkLOENBtux/xRSm\nVqkQO3mc1HzDlU+R68QmF+g/dBcGTzvplTwmo4VstkoqVcFq1ZMvqyQiKfLZAoFAP4tLRQa3tKLP\nphFMBmxOC7p6uWGMozfj8bhw5zJ0buzAZpQ59+J5JiMp0rNuBnsOsHx2gYkXz9DS5sbv1qG/6PHR\n96GP8MsfT9IWFBl58gh1Bby9nXR2ukjMLtK19T7MWjcLx3+Nt6cTh9eFp81HR4uZyWeepDw1gs1u\nJj03x8RPfkLXvfdicruRq1WqmQzR86OI7h4iK3m0A1UWFqoUChJ33BEiHm8ERq2tdnxtAYpud2Mp\ng0YdvMdjRjZoyMZf8TqxWPRUq3VisdIq06+XmZxMcvZsjHK5jtWqZ9u2y7fZvtZMT6c5dSrSLBop\nl9MUi43yb4OhoR9SFJXJiQT2ao1iNEr4+HFkUaRSq5FbWsLV04PRaqWcTjPz1FMAtO/bR/eBAzg7\nOkgmy7z00lKzVXlfnwtnYhStXEOrhcXFLJmFZaSXxtnyOx8lW5CZOjvL3e/ahqDWsGgkCuEI7i0b\nMAeCKLLM8//3v6DYAxx9fgapWiPYs0D3e96HqOqpxmbwh7YxN77M7KjM2GSWgFuPFItQrBTRCzYK\nBZFyVeY/HzvBxHy1uV7e1uYkna5w91Zf08zpZQw225p39rxRURSF48ePc+edd17X41qtVn7v936P\nf/iHf+Dv//7vr+ux14qXqx1fjSTJiKJMsShy5MgyZ8/GcDiM9Pe7cbutzTYNer1Aa6ud2dnMxfYg\nAkGHgjQ/isGg0BayYLeISLEZdu7aQGiwi+npNLOzGfJ5EbdS4MJLv2ZqPIKq1TF3YYWhfBHLwD6e\nfnoOUBFMJuLpDC2desplmeEhL+LsGGrNwLmVLKVCBVVnJhEVea6QZ3o2i89toBZLUxdFbF29nLhQ\nYnuvlRf/7THaNrQhmExkZ6YppLIMPPAARrOJ/Nw01ZiF7kOHGtrAZrVnneSFC1iDwYs6Eg2yKJEc\nPY9l2yEWFnJIUkOIL4p1YrEiU1MpduwIXfpeLy83qvYuTkBrpRITP/kJRocDndGIqigUo1GS4+N0\n7Nv3ts7rbRGM5GNJkpHMqm1SRSSxEEbctRFB0JBMVpCzcWq5LKFWOwvzWUrJLNVshmp0mW07Bxg9\nG8bnqZMXBZzaEmazi2pVR9tAF6pcx+WzE+zwUMyV6Oqw4nBZGNrUQsVhJNTTT3I+zMrIBVr8fjz6\nEqHaHK5tNoS9foxGHeEjR9i2fwiDmCE1lcR1xxC2QAC9zUYpk8OtzdIW6mHSoqdYUTG5vWy4cxix\npmF2qYq9tRX/tp0YKaHVm5maz+MwhclHSgQcLsRaheCOXUiFLP6hIQrxOJHjx9HqdBSSOWSjhLu9\nhbJqxOlszDhMJoFAwEJrq4PBQR9Gi5HW3btZPnKEajaLVhBo7XSzRdYTTs1RLtew2w3s2tVKtVq/\nxIIfIBYrcvToSrMvRLVa5+jR5Used61R1caMSlUbWZ+WFhtufYXU9Cg6oU63N0he8BBNSIhVmUqh\n2jCyKzZmWQpa0gtL2Lt6SE7PUC+XEPN5jHY7pWi0UVLd0sLp0xFKpRo9PS40GnCbVSLnL5C+cJZK\nWUJwBTAadSxOZwnOR+jetYX3fPQAC2Nz7Prw+/EEvRSXZ9HWRE4+dZSuwU4KiQxGrRlfe5BUIo+g\n09HbakItZujs9ZE31Lh7UIPGrgE0TE0mcBgE2oN+FLGA1awjla5QyJQwGg3YbAb8fiuHDy+ye3cb\nJSGAtXeQ8sIMqlzDYLMR2rHjlirlvpqMj4/j9Xrx+/3X/diPPPIImzdv5itf+Uqzl9etTGurnbm5\nzKqqc5vNgMtl4he/mOX06TBOp4lotEStprBzpwGTSaBabQTWwaAVu93A8LAPQdAQMhVJRBrZX61W\ni8dvR2+1YtJKVKs1FhayOBwGDh3s5vwP/ot8Ik21LKJQx9/ixu600j3g4JxOxGLRs6HLRrdLRKdk\n2bO7g1CLjZmCnaTs5shLs2hR6WqTcdsNxBej6FQBbbVCNZPG3tqKwRtAJ2hRqiWqpSqptIjT8v+z\n96bBkdznmecvK+vKuu+7CijcQKNx9c3uJrupZvOQREnWYWrHkmWPHTExsRsh2eP9sBGr8Ne1Qw7v\nF48jNCPZY48te20dS9GURDaPPth342qgcReAKgB13/e5H0BCpEjJ1tmkvE8EApGFTOQ//lmZ+fzf\n93mf10QgaCe7tYV/bJi1l16imEwjOT105CqcY+MkFu4jUygQDDY0A4dpV4qIihryVhl/0IJAh1qt\njSAInDnjw6WrEw0vIyoU1FIdWk07MlF8R6orv7NzQEQAEASKe3vIRPEdHlPFvb13FUH8tPh3QUZU\nGjUKlZza21+MgoCk11KrNbl+PcLeXhG/pUV0PYvPJTHQb2KXAjKnk94BB6pynF5rjTe+/QOcR06g\n6pQopTrkczJ2YxUeecRPoMvMYFBHNpWjd8iFQa+i02wQ3Uix+loIjc1JLNtG7ZCRvX+P+b/9W9LR\nNHK5SEeh4un/80uEr99g+/Lr1BodyptrDD12DI3NTmx9i82b0zgsKsZPDZDXBdmKNfnBrRrlfJ7u\nAREiG9RQ0jXo5f5MBLNZh9ahIaPSkciXUDSbVIoy7APHKGncyEwCxkCA1PIyjuFxSioViq5+otn9\nULzZLOHzGTl0yI4gCOTzNWZno2QyVdyDJ/Cpm0g6NRqrFdFdRK6U02i0EUWBWq1FtdrEaHy3B8K+\nWdw7ScrbHU5/VRAEYT/1xH7qpJZO8uI3XyAeTmK1SgQCJvxTo5htwzSbbcwOObv3a2idTuLLa1Tr\nNfQWEwgyZCqJfChEs1SiViigsdupFwoUUlnK5QYWi5r79+NEtjP42tuY6kU2766j14qUF9cZuvg4\nxz/3GV6/mebG7BtMnDnMiakj3L6ygnKngE7rIuiXcAwkEeQg6dTIJTU2txa9WQeVAspOBUWlRKkl\n0WlFENVqXvjmNF3jA0znauQ7bTRmI26bAX+Pg+2NOONTPnZS+700stkq1apEOl1h/n6Ket2MyztF\nt1+DzWv/sWXg/z/29SK/6qjIW/B4PHzkIx/hq1/9Kn/0R3/0UMbwq0QwaCIeL7K5maPRaKHXqzh2\nzEOp1GBnJ0+p1OTVV5ep1VoHFZJHjnhYXU1RLDZQq+U88oifQ4ccyGQCuXCY5JtRQKXdTUXjIpxs\nEN4U8JFHkuQsLibp79Lg9hophhT4AhbMPb1kGmrmtyqsNCJ0DbgQOg1y87do7UYRXV2sLO6xFzMj\nWftQlqpkkyVK+SLhtSgXPnmS00MOEpEE9y5No5LtL5IlAU6c7SMzfwdRLqcQi5PKZXGeG6ASj5PZ\n3KSYyiI321EHR1jZ7WDz2um++DR37+6SyVbJNw3sLWwS6DajbRVw6BUMfuQ0LbsPr1ePuhzl7jde\nollr0KzXKSzP4ZCl6LRa2IaGMPr9wH4a/u0QBGHfz+RHRNpqs/nnruj6d0FGjF43h04Mc+fSDK3W\nPp12dHuw9/q5fz9+UCKbravQuLxsbW3Q36NH9GrR2mw4ewPUikWSt6/R7ZJjkIp89DcmePmlVZpt\n8PocdNotXnklxKkTbvzdo0R28rxxOcSd2w3ODrbQGS3MzMdxaeu0og0ii3cZOj1JS6WnUBOh06Sd\njlNvtuk9f452o468XSW5vMzgxz7O3vcuU8ukuX9lmkf/429y5ZsPKDfkbG/vUq5BTWnCZmkRDNop\nlZvcnUtSLTco16xMHD1J/sEsVrNItKRle09JK7GHqlXm7IlTDI+OYh4Z4/LVHfZuh1BbbXRaLXRm\nPTaLGkEQKJXqXL68ye7uflRgZQW8Xj3nz7uQiSIOhw6XS8fKSopKpYlGs596cTjeXQqqULxbLPWw\nKhMHBqwUCnUqlSbJhWVSuynM5n1jvFAojcawQVdXH65eHz6HSGnVj0qrpVSqI2QLDH38GUw+H41y\niemv3UCtUWHs6qJWKCCXJNR6PRZLifv341y7FmG8V05ydomGSYOuf5RqbBtB2UY/PMZcXE14p0C7\nUqJvopcbs3n0Ohdb8TKR5S10Yo0nL4xi96tpFfPI9XrK62lktRo9jx3BOTbO8t1VthfXaHYUeI5M\nYQl4ufHKfU4c6ydX2Rds9ww52ZhbJ10RUWnU1LbiJDN1tHoNgYABtXq/m+l+OhIyNQVP9Wl+0jT+\nm1Eo1A7SfjabBqNR/a8c8cHAr1q8+qP40pe+xLPPPssXv/jFX0q34PcTJEnBmTNdDA3tRz6MRhUG\ng5qdnX3icOvWzkFVTLXa5NVXN3n00QAf/vAA+fx+9EKv/+EiSetwYPD5qObyRNs2Lr+4yW60QE3U\n02g94MKFXv7xHxfpdsp4+qQRmnV6gjYyegvXvjmLwetFU4OV5SQfe8pPbQ3GLxxlea3Mzdsx9nbX\nKLZVnH00yIc/PcUbL05TyeZIbu1ydrwfKVtGemKUeFGkkC3hsim5eXkJd6CHsY8KvPH1f8DkstBq\ntvCdOUtbpcU5PknHYMc9NUWyVCOdru4LU8P7v73HTyMTRbK7O7iGBwiePonv6BiiQoHQqHH9f9ym\nWWvQbrVoFrKoak327tzBFAyy9frr9DzxBDqn82Cx+pYJYr1YxH/mDMVYDN5M4aqMRuzDwz/3df13\nQUYUksTkxy9i6fazu7KNTGdE4+1iO9rk6tXtg6Z5tXob9+gkZp8Ti6mDwmAilyqwOL2J/1AvLVFC\no5WhVcKtH1yhtrpLV/8gJbnE4loGk0WL94Kb/OI00a0qoqAik66Q3CuTrSmwqyus/uBVWmO9VEIR\nrKfOsbRWYPvBJs4+P/0n3ERXN8nsRJHJZOhcTsy+fvLRJLnQKsMTXRSaSpZXc5i6g5hEObvpdUSh\nwez0HmfP+EnG8qyG6+RyDURRYCdep14VOH/qERQ0mfnmAhVBAElJwKFhLavh/CPdVHa3GRx2k0ut\nkd1YwWQzMTiooh1do+0YY2+veCACewu7uwWi0RI9PUoUCpFjx7wEgyYqlX0diMUikc1WEQQBk+mH\nLx23W4/VKh3U+cP+i+lhoLfXglwucuXKJtSrWK0SKqWcfL6KIBNQKsDjkugbsCIIAiOf/CTZrS30\nh4+hVMkpbm2wffUKSq2W4Y8/y+rzz5Pd2MA+PIzj0CHUGhUmk4ROp6Knx0h/r5J7d9oktvdw+B24\ne4NYzWqqKhvLCyGqGieDU1ZqgsTC9DLebjuhlRgqnZ5CS0lVbmQj0eDcf/p94vPzWHwZZBYP5p4u\nbn/vCon1LRwOA22FnHs31unuNtNsCUxfX+Xoh46Qy1aoN1osPogzPNVHPNNgctxGqVhjdKobh8tA\noVB/R14+k6m+6R/z83mMxGJFrlzZIpPZf2CaTCpOn/7FdX1+mLhx4wa/8zu/89DOPzk5SX9/P//0\nT//EZz/72Yc2jl8mWq02ori/kJHLZbhc74zUWSwSCoX4jipAmWy/JL9abSFJivcUystVKgJnzhDb\nzbA2k6Qj6TB6dFhtWqano9y8GWFszMHy/V1e+H6cz3zsw3TySSKbDY48eYKGykih3KLbLpJ9MI8i\nu4OgU6PX6lhbDFPrqCm12nzrH6b54h+eQVFOkEjmKaW0CE0fd/7lKo7hIcpFBaVskZX1PCqHl81Y\nlI9/4ginP99AauUx242UFWYQFVi65Oyk2ly7tIhB1aL31BSJpudgsb2TV2I8egFjq8TQ8QBiKcna\niy/uG6N5PIyM+1hrl+k066jNOsRqjmpOg0wUqZbL5CMRdE4nOqeT7nPnSDx4QC2fx+DzYRsepl4o\nUIzFEBUK9B7PL8Tx9WGSETfwAjAMaIH2T97954PaYGDw0RPYRw9z/XqElaUCZrOEIEA8XmK8Xw2Z\nCJlYmYFjw1R317h/J8TO2g71UplceAR7zwCqRoZSLEatBNHNKE1rDWn8ogAAIABJREFUkFh+D4/d\nhCRkqe9tsXj5LnNzUTyDQfp6Rjl0bpS2TEUx5WQiEEDIxbH0dHHp5RBynR5z/yCu4QBNmZJEaJut\njRTJRBFRvsb5//A0xql+NBYTlVwKm8UFKhnqmoBMrURSdOioRZKFJghQrrRpNdskU2U6HbA7NCyt\nlzl1boCt3SwLYSjkUljcYLF6CIUrdMdBjwWXts64PYt6vAuNJKdT2iM2G0bncFCtKt/lDN7p8A5N\niEwm4HDsN50qFGpcvrxFJFKg3W7T02NmctKNVqvEYFDx6KNdLC+nSCRKOJ06Bgcfjn2xKMro6TGT\nTpdJKYYo72yzuRalki9hMKppt30sh0ponAW8XgNKrRbHyAjx8gbTL7yIIrFO5PJVaoUCwUfP8Mgf\n/RdqhRKWoSGKKgff+7t5YrEiLpeOYrHBa7eKyFpq1mdW6cpWGegzk1mv4DAPICg1KDsK8tkyNosa\nk1lib7fATiSLXF5ALSkQ9UYKuRypQpOWo5dItUwjXUGx8DKX/vY1arkcFqeR3gEnxy5+kpmZPUYO\nOUkX2uxES/T2mtGajdi7vdy5scGJs/1MHe9ib2OPgUEdGouJjVAWmUxAoRAPru+BJfbPiLdKI9Pp\nH4qcs9kas7Oxn+v/vh9QKpVYXV1lYmLioY7jD/7gD/jjP/5jnnvuuV8rE7SdnTxLS0my2Soej56h\nIRtm87tLSSVJwcSEi+Fh20H33e5uE/39FoxGNfV682BeqtUGiUSFTqeDSiWyu1skl6sRidXYjFS4\nfz9Bp9Ph+HEPkiTH49Fx40YHs8tIsiDD6/Ihq8hYvxdlayfMQFBLNrpGpZpGW08QjyTJNjQcOTbE\n7ZkkBpWGXKrF+mqC4KCHerXKI+cGWViIU681qbVEXv7mTZrNNv3DLvyWEuZWkUKlQ2B0kHw8QabZ\noZGvoDBref0H21y/GUYtqXjm2UOkQ5uYD1spFGrkclWazQ4GgxKv10AxvEVh5YfNXouxGFq7nZGJ\nAOVEgr2ZEK1WC2MgQLO6f3+23yZcN/h8GHw+Ou02wpvpGbXB8Asv8X+YZCQNPA5861d50kikcPBF\nLRRqDA3ZKMXjbLz0Mu1KCYVKgc/SYffOXUSFGVvAQzFfIZEoMXTOw95ikc37e3jGjhCv60g1lCzN\nrnNotI7aJBK+v4bGbOToh/so12WMPNbPTqbBi9+ZYen6HA6bmo99aoKJ0wM0ZwSiiSItuUS3yc52\nKITv+AlC4ddAXsfZ70MXCLKd6OB74qNkFucQ5TJMQy6S0xm0NiNdA162NuIcDpop5MqMHfeQLCT5\n/OfHMWrlNGtlKrkifhu0KvsVLkq1Ep1ejc2upV4qU6u2icVLdBpyEjsppFITo0l94NBXKxSw2Xyo\nVOJB+BNApRKxWN47zH7r2jqvvrzK7m6RjijnvstMpwNnz+5byNvt2nd0vH3Y6O21UMp58U8ME9mM\nozdITJweRN/dx/LdVTzdLrxew8H+JkUFsV6kWamisZhoV4rE52exnXqM69NFtJk8hr1NIpE8q6sp\njEY1kUiefL7GycNHeaLPgq5TpGdqCNHiRe10sVPRcvtmBKWowKST4w1YyS+nESUNjWqdgEPLTiiK\n3axAKcqYXU5x80aYU5NWYmthOoKATKmkUmmRSZXwF/c4eqKLTK6BDQmny0S5XGd9LYOvx0n/sJt7\n0zEGh8poK7ts39gmlFGTLQsUOzoUKjmTk25GRmwYDGqa1Sr1YhGFVvtT+wpUq03S6cq7Ps/lqu+x\n9wcLd+7c4fDhww+9R8wzzzzDH/7hH3LlyhUeffTRhzqWXxQSiRKvv75FsbivY0ulKqRSZS5c6EWt\nfvfra3DQynPPjTI/v9+B2mBQ0dNjplCo8dprm6TTZQYHbTx4kDyItESjBfx+E0tLSe7c2SUYNNHb\na2Z6eo+ZmSj/+T8fw2hUMzGR3Y8QDvbT1aXh/tYCtWIRGQJdliY3v7HAE0/2YpJZUZkMzLy+RX9g\nEFGjR2g26Rl0oTVqqOdNHLt4nK4eK//1/7rF6VPHQBTRm/VUK3WCx8fxubV0RDk6oxZnr5NqE5rJ\nFF2H+5hfqzA3H2NvO41cIXLjsoqPfmoSpdBAoEMxvk+kwutNutwDrN2aR9kuHxA4QRCoFQpYenvp\nNJv43zRq1NhslGIxRKUSg8fzrrkVfslmfg+TjNTe/PmV4q0Vnl6vRKUSsVolOtUyIWUTW48Xo99P\nRymSzdVpqysUim1MFi2dToe9ZJNa4AgutQ2FKDB2YYj5u9uMTgaYmHTTjm8TXo8yevEs//SNaUrF\nJqGyGaEDj53rRqiX2FgIc/lyiMFjQ6RyTSqCRLkiIxavUtrbw+ky0HfxCZRKGYlYnoW7G6j8Miw9\nctqiAtPgKO1cnA999BhbOxV0OiXHTgfp7jazFcqgUAocm3Ry88Y295a3EZsVTj4SoLW1SJday6c+\nPcrCgxRur5HZqwuMTQWodJQ0NVYWNxNsLZVoNXMMDdro67OgNUgodTr0Dh0TEy4WFhJUKg0kScHh\nw46D9ttvR2wjwvSNdeZvrtOoN0EmUMiWuWqROHLEhUbz/mvsZTZLTB3vQlK00Xu8SCqBdLLE7GyE\nWqMNrXf6a7SaLaqVJghKaqhoqg1I/iG2N7PEkjWMLhWXv7vMyIgDr9dAOl1BqRTp7jZidhmwy9qo\ndDquzMVJRG7hHOrD1Rfg9KO9hEJZlDoNz37Cg+blDXLpIkavhvEpL9GdFGceGWV3O4VKaOF1aRBl\nHarVJoPjQTKJAgqxjUzWQiEHp6nF4EQ/y6EKr7++yd27u7icWhxWFR95dhibTWLzxh0qWytYDo3z\n8nfu7psy9feR7Ohwu3VcvNhDemOD2MwM9VIJhUaDc2wMa3//v3l+lUoRg0FFLvfOW16r/eC7uT5M\n8erbIZPJ+NKXvsSf/dmf/dqQkUgkf0BE3kIsViKRKOH3v7vMXKmU8+ijXfT372vBDAYl+Xydq1e3\n2d7Oo1LJuHYtTDpdQaUSUankvPHGDocPNzAYlCiVIvPzMZ58so9btyK0Wp2DEtgzZ/y43Xq6uoxk\nd/ao5bOcOuFjNVLF7RI4ctSFXCGgNNrJZkocGveAUo5MCWpJyWC/hYBDQWgjws1Lq2h1H8VgNTK/\n3eHQpIfzn7WwvZXD4PVy6fVFBLWOyZaOxG6W2NIuGzNL+CdauF1aegecFLMl9EYJlUpkL1pC721w\nYsKM39xkfiGJzSinnU8RDiVo12uMjNjR6ZS0Gg0q2SxKjYZiLEan3cbc04NMLsfg92Pu6UEuSbRb\nrZ+q0/vPi38XmpG3w+XSEQya2NjIsLqaxuHQMiCmGT3WQwobr92ME/RJJIsyLOoOVqOcaqOF0aIn\nmmmzvbOFWC+TC28jWvMMjjjpcspx+4yshKt0nz3D917boyHXc+iUm6WlGEszW+hko7j1TYwneihW\nWhTLLSZO9HLt+i5t2mzu1jnj91FJbPH3/3MOk0mFxyVx9Jkz5NoKjFYjhY0OZpeFjMLDxnaRLo+E\nxt1BiK+x+4NvM/n0p8mki1xdiCEUUxgkUMqURO6v0K00o0zf4hPPfYGRQ3YW7oZwn3CjEOu8dmkF\nhd6E3azBPzbI4rU51tbT2Ow6/JOjaJ1OZDKB8XEXgYCRUqmBWgF6rexd4eBOp0M5ESefr+0TEYB2\nh1ouR7lco1Covy/JCIBer8Iqy7EYzbJ2P3yQlnIE7Hjc7yRdFUFDsS5Sz3eQa41YrVZSTS2S0YE0\n0M3SRolotIxSmeaJJ3r2DZYUAkK9jEsF6VCUmaUK1VKRWkNGS4wQTVSZOhog4FbSQSC6l8dmUfJb\nnx1mcyOFpISRc/2srcVZnIshk8s499QYrXIBjcOBU98io22RiaWxuwwMTfZQbshRarXcv79FPl3k\nxLiJbHiH8J0Em3546rFuXvubbeoVaCZrlMsNJLWcRjaJ1m0mFMqSTuQp37lOo7QvPG1WKkRu3EBl\nNKJzOCglk5Tj8X0DJKfzPc2PRFHGyIid7e0cuVwNnU6BwaBmbMz5S7+uv2zcvHmTT33qUw9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OSQq9Ugl1Ovlzh5/jB6lwNrt5VOPknpzVRremWF4BNPYB0cpF4o7Gu/3G5K0Six6el9kbokIchk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bukxw+Pp6oq8/Mp5uaSbG4WcTgMVKstRkfdOBwG0qky1UIFm9hmwNZE3o5gCwYx2O04hoep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+EokKsizTbst88Ytjt5JaM5TL3SyM555b5PjxMK1Whz17fMzM7HD4cIiVlTylUgOLxcnk\n5PsrrZPJChcuxHddSI1Gi/Pn49hsepzOz7ZC/ufBbjdgt7/73bLZKqlUt7GeWt3NaKnX22QyNdrt\nDsVig3y+zvb8GtnNbXRaJRuJLdL3htnY3EZEgzvo5OZilgsXk/T3O7BaNej1as6ejdGsSyjaIqLU\nYOpAGJvPQa0mUSg36RRahMNWBgddeL0mzGYtJp1M8nyMslAimaiQLrSQzrzNwQceoKbpksRGpY5/\n1Emr1W2GmEpVkZoR9n3pac5/71nUUgWtRkHPgQl6J4eQt6rUmx36TxxDY5xh4eo6dr+DSO8ERque\nX/7lcfL5BtWqhM9nQqtVodN1tUQGQ5Xe3l5arffvV6JQKDh4MIgkddjZqdDpyLhcBvbvD3xiOqLB\nQSeC0GRtrbjriDp0KEi1KrK2VkAQROJxAaEsYdW12VjewSw3MOoVyOU2SpUKuSPj95v54hfHWFjI\nEI12tUGCIDI66uLEiSh+v5m33xZweB2UamWUCitf+z+/QGFlGanexByOstkwsXFhh0yuTThsoy9i\nxBlyYbAYmBi0cO47f4vd68A+McXl8xsoNWr2T/uw2UUOHAig1WmR5Q5ra0XUaiVXr+50dUcNN+li\nHanWRNUso1CoaWssSI0O43t8/PB/XkOrbLNvugerVceew8OMDkXQa5WsXbpGtVKksH6TarYKHQm9\n3szIniA1qVt2EWodAtPTxM+do1kuI7U6VJRWWtjx+7UsLSnZ2CjuipPDYSu5XFcPNTDQvQ4rFfGO\ndO/+uPDDH/6Qhx56COfH1HH6TuGJJ57gD//wD/mDP/iDOz2UD427ev/dZNJit+t3a9c/DbP5/YVv\nPwu1Votv717cY2MAtBoNNs+cYXFdYubSPE6vDaGupyjpyScrmN1OsvEMNrOS7NYOkeNH8E8Mcfo/\n/TlD0yN4p/ZjcLqQlDrSmQrFjgqtJODXi4QHPFydE/jRmRiGTpSAX4Wr34PgcFIvCfT31Pj8lyaR\n5a4v32BQc/nsMvsPhUBr4i/+4grlcpOBAScvvrjK9etpenvtJJMChw+HkeUOer0ap7OE1arjyJEQ\notji8uVtRLFDpSJiMjWoVEQqFZGHH+5ncTHH1as7fOUrBvr77djtBvR65W3z+dMsNp2u7hKRdyAI\nIrlc7WMjI3eLZuTnQadTEY3aMRi6BEKS3Gg03QhsSergchko5ipUsnk6bRllp41aq0JrNKHWNRka\nGeD//fMrbG8LuLw2FAoYHXURj5e4MZNArhSoFAQiAz7iWwWGhhx84ZlRZmdT5HK13TTdl19eI5Op\nMTjgYG84ROl8isWlPDabnkatSWY7izmo48GHBli+uszSmauYIv2EQlYGBpz8f//1Gg8/1MuT/9e3\n0SpEOmodReys3NzBbxJpVRrMbbTIyn3sWK0oXQ5eeDOHy9W1766u5nE6jYRCFvbu9XHsWBiDQYPT\nGeD8+TjxeBmjsVsa7O29vfRitep46KE+stn6Lhn5aWfWB52bj3rudDo1R46EGR310Gp1sNv1qNVK\nCoU6hUIdUexgMnkoFAQy+Sb+kIsesxl9M09bq4XgKK++EadYbKDRKPH7LdTrIg880EsgYCEctu3u\nJIpii5mZFGfPpqjXt7n//gi+CQ+jUQtXrqb5wX/7CcqOhN3npJzO4TD1U69J/M0LaX7tq8PY/U6a\nKitn31xlYNCD3qRDazBitRsQJZmm2OD06RhKpYJUqoJaCQemvVjMGjTtOjOLWXp9aqrZLE1lhfGj\n42xtlnH57VTLda5cTeINulGag+w/6uH65U12djqE/b00FlawG8EYtuLtD2EbHmMjWdsVbtujUfR2\nO41CAaEikVluEEtL6HQSJ0/uI5GoYLFomJ7uYX29wM5OBWBXSPzzEnV/UXxaz4Pvfve7/N7v/d4n\nPpZP+jgnT55kcXGRZDKJ3++/4+P5MPg0yIgP+J8/829J4Fc+6IPf+ta3MJutrKzk8XojDA3tQ6dz\no1Ip0Okqt20HXbkyTzxeRq12Eg7b0GrL6PWa3b/H4nGgO1G6vr0svvkCsr6NUG0xfXyIt68tkYwV\nUKgtnPjq5xjsl6mld3CMjZFZmKfn/sPENrbIvnqJ8MH97GAgfI+O7OIsp398AW+vBafbiD8yhFJh\n4eK1dRp1kWbdyG9+4xAGTY6pAxEuXdrmypUdvF6Jel1idCDMlXPrqC3gsNdxOR3IcgeDoQK00Gqd\nlEoNkskY5XKTV17Jo9WqCQbb9PS0mZwcviU2TKDVtgELpVKTQmGHtbUy9boBj8eEIKSZny+hUDh4\n6qkRXn/9Knq9hsnJEUIhK5ubmwBoNN301Gaz65zQ6dwoFJDNJtBohN353NjY2J3Pj/P13QCjsZtq\n++yzi7z11hYKBYyMuJie9qPXqwkELAyPeigs6amVqxitBk6eHMNCCWVhm4VtAUM1TcjvZGGtQG+v\nnUajjSTJaJDIFcpY7UYUWh1rm2Xa/2uG9VSH3uFuwFw6XeXy5W1mrqZQaxTodSqCPUGij3hoOm6i\nUKsxWgy88fwsufwsv/RbDzLzSp6aUMOqVjI15UOh6DrQ/vGfVrHZ96LTqdnczPPkQxqqN6+yLjYo\nF2tsbRbY/+QDVGpWmmKbmzezPPboAG++scFOskpfXweNRkm1Gsdu1zM66uLcuRibm10XRT5fJ5ut\noVYr3+O+UqtVn3pvIrv9dit5rSYyPR3gwoU4VquO6WkjQ4MODh0Not2ZRarI2Pcd4sJNiZLQJU6x\nWIlqNc5jjw3SbLaQZRmTSUOlIrK9XebGjQz/+I9LVCoSW1slrl7d4Vd/dS+Y1SwtpGhWa5hNGkrp\nPJlMHZ2yzcPPHEKSOlRqLfbce5hUosR8LE56p8xmvEIqJ5HOSzz99AhTU35AZv+0j8WZGtdX1kjm\nFBhqfiKDPgIuDZJQInNznrGjk5w4EuDFV7eoVFtk0hXatSpai42VlRyLizn+4r/NYVVUMKsaPP35\np9G2BApCG9EaZDmtAToMDr5rg9aYTOgsFmrJCtniajcAsiZRrYrIsozXa6bTkbHb9buxAQAqlWJ3\nh+SziM3NTWZnZ3n88cfv9FB+YWg0Gh599FGee+45vvnNb97p4XwofBpkJAU88C/54J/8yZ8AkE5X\nuHJlh1yujkajZGzMg8lU3f0hSyYr3LzZolrVAAKJhMDEhIfDh0O7x7qNremM2MJ7MftEVBoN5brM\nsUPDhH1eVO0GUlNk6dIy1UyWiMdF6fpVSlKTQDCMKuhEazTgUFhxGOy88PILtKU2tXQDnQRr6Vn6\n9x/nCk60GhGjsTvFHdFEpmQhmUwzMeGh1ZLxehXki3VOPjzCwnKZQk4gFFZRFjLcvNnGbJY4dEjH\ngw/2c+bMFn19Xp55xntrK7rN0lKbSKTJ5KSX554rUSzWGRvT4vEYcbmCxGIlksluEzCvN8jcnITR\n2OHy5QR+v4lcTiadXmNgQMnU1Cg7OwKtVguVSoFW69q1UHu9JiYn+29b9fws+/0or9+vpvizr+9E\nL4p3oFIpEYQmgiDS12dHoxHodCAWK1OrSZhMWg7dEyLqPsHO0iYmA7jIsf7KG4jqHnRaE9pOhUDQ\njzfkwenUYzbX8Pt7qJZrNJttDDYLS4sZHvzcMMVMHKPewdlz24SjDhIJAUmSCUesWK16Ll5K8Nbp\nLR5/3IPDM8j6egGbUMdkUlMsAqUUj38ugj0SItWw0Jbh0qUELpcBn8+MXq/C4TDQ1+eg01ijqXdw\nYzmN1erGPeRk5uXzHP/K48TWMxjtCTQlPcp8jKGQn1JVZGOjSKvV1dPUahKLi6u4XAE6HZl6vUWj\n0WJzs/iRrOAfl2bkg/CO7uKRRwYoFJLY7X6USrB7bPhG7wcgmRUpV1YBKBYbxOPl3bhzk0lLNruD\n0ajhzJnYrji81eouGFyu7g94s9HCpIPIUIDsqhc6LQRBRF+RsLislFsa9u3zce5Cks1lGYdNx77D\n/SzMpZm9kUJS6nF7u0L9YnGTQMCIWcqhaxbIpUp4I17EjpL1rTInHxlH2W6ge2qSyvIsbm2FqekA\nN2YSjIx6qTXaqHU6/P42uVwNi0XL6poSp9XCc2erfPlXpglbTLz55gahkJq9e72MjLgQq1Wy8/OU\ntrZQ6fW4Jia7Orh4V+C8vb1FsagnGLRw4UIclUrB8LCbeLyEWq1keNj9iZGRT+N58Nd//dd8+ctf\nRqf75y3Jd1pb8WGP89RTT/G9733vQ5ORO/297mSZ5gDwfwN7gJeAJ+E9CdAAeL1mHnlkgHK5iVar\nwmTS3raS3tgo7CZ/ms1avIYGpbkZVuobBEb6uy3NFe9mkxiNGkwmLfVbtqeaUGc7U8CgVXDppQsY\nQxESSztMHxmkXSmhdzhQ2q34x/eh9EVZX84w/tgRluZ30GqUNG85X8bGXCSTVUIBE1/52oHdjsD9\nvVbMFj0bm2XW1orUal3L4+Skn/FxHz6/DYNJTzbXYHE5TzpTI5/vcOhQD2fOxJDlbo7CtWsp9Ho1\nDzzQy+pqnrm5DH6/GY/HwIkTYVKpKjabnkcfHaDRaGOxaPH7LXi9JkSxjcnUFaLt2eNFrVYhSa2u\ndTNT4/nnlymVuvPr85loNFoolcpdO+sntf16t6Hd7iCKbZRKJePjHjqdDs2mCo2mW0PulsK0RCJ2\nhHIdX6VAdWudhpTDFghQ20wxPBAhPhxheSWFyqkgmarwzDN+3G4jjz65hwuvzxId8BCKOAn3GLhw\nvUTbYSGXbxCPCwwOOkmnq/T1+fn7v7/J9rYAdEO9BKFENGrn3Ok8e4b6Of6wgeL8NWStgbZmiKbQ\nIRSyMTnpZ2zMg99rJOjXs7KUoVrSIZXyPP+9GwhCE4tVR++Ahz1DLsrZIor0GouvncXrtyIJJQqC\ngG1whGpTgV6vwm7X33JUqCiXm6jVSrxeE4LQfF9n1t2AUMjG/HyWTKaGJEm02zUCATMejwntLZeY\nQiGhUCgQxRbpdIVMplu2AAWy3CVc6+tFlpZyjI46KaXzKJtVGlIZvcZFJGDA0MzS3swTdHpIj/VR\nyWTRGEUsoSDHP38PxUqbmbcXUagUhII91KU28Z0GY1MRrs6VWFop4PebaTYlWq0OAwMOYleu4jRb\n+MJvPsCFC1ukiuB2dqjXRS784GWKmQL3PzJOs1IjEPQwOObnxo0UZosOm1OL2azC4dDx5S9PkEgI\nt4L6cgg1GVnZ5MiREMeOhW8lrXZYf+0tCquru3NXz+WYvvdhnE4Dq6t5zGYd+/b1UqlIyDK0WjKt\nVocnnhi5Jc797DpoZFnmu9/9Ln/1V391p4fyseGxxx7jt3/7tz8zXXzvJBm5Anzobj4qlXJ3GxFu\nX0m/E6Kk0SjxaMqsvvQ6jWodbd5DPbZG+NgxXMPDu+93u43s2ePl+vVuXHwtuUOP38BwoIPrq0ep\niQrSxhL17BaX5zIM3XeURKzAylUJVD5CU0NcXW3R43NhtRtQKLqJpWazjuOPDlMw2nj52WVA5vjx\nMCaTttt6vNwkHi/f0iG0OXNmi54eM1VJwetnEoyMeckXRZzOIY4e1aNWK/jhDxcIBi0cOxbmxz9e\n7m71VkSCQSsPPdSP02kgkShz6tQAm5tllpayvPHGFnv2eLj//ijnz8eZn89y6tQgg4NOzGaBUMi6\nG81tNmspFEAQBEwmLaLYZmenQk+PhZMno5jNH39w0d2qGdncLDI7m6ZSEZGkNv39dtbWilgsXjwe\nE52OvKsbsFp1TO/zsVxPohs4RHFhjpsvv0F8bp1wzwiRkJ1aVUTtMODv8+P1dnt0WBwGHnxoEJfX\nyvXrKRavrmEJBVnPdMscPp+Zel2i3e7Qbsv09Jjx+804nQZcLhOrqwXuOdTD8KATf9hCdK8Hf8TF\n1RsFOioT5XKJl15apdXqsHePl/5+G/H1DDdmUxw/0c/cbAeQqVREooMeenq9hKYHsXusGIUYwYCR\n9I0bHD62h3On1zEqm2DSs29fAINBTbEocuZMGVnurpadTgMPPdT3vq6aX+TcfFz1a7fbyAMP9LKw\nkKFQMBAMWhkddd1mV3e7jbhcBs6ejSEIIvl8HY/HCMiUy91O4M1mG51ORUBfYSa5jUqqImQ6hEMi\nPRY4cGgcZTWHWSEwEDVTHQqj1unwes0IgohKBb39DgwGDWsbZaoVEbtdRzDiYM+kj6ERD8lkBVAQ\njVoxmzW0Ux3owObCOnvHPRSFNuuz6yzObHLf0SFW//Y1tvNQaJv58/90iZP3RbDZdICC0bFujkm5\nLFEo1JmZSaJQKPjqV8fxeEzU6xLj4x4qFYkbN9KommVyM8uYdfItIgbtZpPS8jyT951kcNDBmTMa\nstn6rnUaum6bd+6JTxKf9PPg8uXLiKLIsWPHPpWxfBrHsdvt3HPPPbz88ss888wzd3w8H4S7WsD6\nYREOd2PTrVYtubnL1IUaJrMGo0lDWxRJz89j6+3djZJWKBRMTXVDlxLxPBlDnlJsi7WKkrrOTV2o\nUYnFyKbKhKYnePZv38bhNiNaNFx8a4mDD0zi7/VTqeqZfPAgqblFqkINqaPANTHB9cs1+vpsjI52\nE0+ff36FX/qlcWKxEiMjLt58cxOVqhsstrpaQKNR4XabyeVq2Gw6YrESCoWMRqPCZtMRDFpRKhVM\nTflYXy/i8ZiYmuqugroBTRZef30Tk0mLJMm02x0uXUqQyzUYHLTTanUFhG63kSNHQrsN+KDbYyKd\nlm7rLyLLkMnUqFSkT4SM3I1Ipyu89dYWtVv9NxYXc5RKDfr67CwsZOnpsfDrv77vth2idq1CO76I\nqNejdvgoxJOYrQYaosxrf/Mye+8/gDOoptaSePnlNU6dGkBn0NNUeDDZtBw4YuTSJR3zKwKDg3Z8\nPhOS1KbTUdDb60Cr7V4jjYbE4mKWnZ1udkw6U6UnaGZhdof19QKnHorw+ad8nHu7wLlzMRqNNuGw\nlfPnt7h6aZNwxI7ZbGB9LU++oSM8GMDqbhAaiXLlRoa4EGd4xMP+oItDR6KszMXxGQS+8VsHUTp9\ntPR2Oh0QxW6JLxi07moIms0W1ar4kcnIp4lAwEIg0E1Zfb/0ZoNBw969Pra3BWy2Ol6vCZfLyOxs\nmkDAwuCgE7fbSF9Qz+rp0wTsJupBHR63joefnKKQKfP8j65SyZXoiXo49tAYIZeXsxd22NgoYTRq\n6OmxoFYrUShgfj6DTqtiK1bi8ceHaTTa6PVq3G4DnY5MNOrAbNbSd2I/21tZMme2yBdF1jYraHUa\nOrJMra0lvGcE2erj7Zs1btxIY7PqKOTrdDptMtk6fr+Z//JfruJ0GjhyJMT2dpnz57e5996uK6hS\nEXn11TWKxSZRd4f1lSw2k5LBQSetVod4rIyuamS1vUEgYKbZ7NxGRIDd9hyfdfzlX/4l3/jGNz7W\nCPu7AU8//TTPPvvshyIjdxqfWTLy03WpaNTO5GSdYqZMul7DbNHS22vfDehpNRo0SyXymQy1dBq9\nw4G9txen08z6YoOd67MUdrKIKgPZTp1cusjB/XtxDNQoilpURjPeySiL18s4PVayxTb7I06uXN7B\ne3SAfV8cRINEW61jM69jY3MHt9vE7GyapaU8arUSUWxTKDTQ6VScOBFleTmP3a6jWhV57rlF+vsd\nHD0aZnY2QyIRQ6t1kcvVUakUBIMW3npri2DQgtNpYGDAyXPPLbK6WiSdrmI0qjl5spdcro7FokOp\nVKBUKslmqxw8GOCLX+xhcNCJWq2kWGxQLDZIparUaiJKpYJoVCaTuf0mNJk0WCyfzIrnbsoZeQc7\nO5VdIlKtihSLdWo1iXDYhiTlMZtNu/1r3oHGYECt19NptWjpzIw8foqFNy9idVmJTAyRK3VIXVlj\nq+kk3KsmFhO4eTPDwkIWv9/E8eMRjtw3iDdUpK/PTiZTJRYTuHRpG71excGDQVKpCkajhmazTaWS\nZnx8CI/HxBuvb7C0UMKoK6FXSjx4aox6o3MrE0PL0lIei0lFLlXEaNJwbSbJb/6r/WxsxYlOTTDl\n1PPj5xbRGO24vTaqQpUzZ+KcnPCzty3gCGvQWwTMY3s5e71Co9HG7TbQbrdxuUR6e/3U6xIajfJD\n9y/6KOfm46pf/zQ2Nzd/7jElqY3b3XUOWSw6trfLyLLM9LSfw4dD1GoSlXSGJbHJ6turmFx2IiM+\ndKoOr/3t6xSENjabDrGj4rVzGbZT2/zTi90wsBMnojz++ABmkxaVWonTYWB+IcOxoyEWbmZRKhVM\nT/tJp7cJBiP4/WZmZ9M0qmpCPT2oTOsYDGoG9/aSLbWQxDYap4eKuY0tHOLCxR2Uyq6YeGkpy/T+\nnlti08Kt3ZEmMzNJnn56hOtvx0hupiA+R1Vp+//Ze68guc7zXPdZnXNO092Tc8DMAIMMECTAJFIS\nZW9ucpPSkSXTKqfap+yyL1x22T6usn2jsrlVR8d1LNd2SS7VsbZFypIcJFGkKJAgAolEpMFgMLkn\ndM55dfc6F2vQ4DACJIABtPXekN3o/te/1ur51/d/3/u9b1OXpYgBe6CF2OwCPp+cHQpHSwxsa2M5\nWiAaLeByVREEE+l0GZVKgd9vprf3zrTA3s71IJfL8cILL3D58uU7Npc7Nc4TTzzBX/7lX1Kv11Eq\nP7zlerPP654NRt4JtVrJzp2yuNQyg+QXZlG9o9fd2trK2unTss/FOjKLi9i27mE5WsPZ00UmmkSr\nEOnqsLFwNcxcXI3N3YbNb8Ec10Ajj7/dTQUtNamOVCkh0KDWEFjJaTl/PkU4HFlX6BQJBlWcPLlK\nOl3GZtOSy1UYH2/h+PEQDoeeZLKIKNbZvt3Pa68tri/oEpFInk99qpfJSTlb8cgjXczNpcjnq6RS\nZX7t10a5dClKKJRFo1HQ1mZZrwdn8HiMKBQyq72314HHY2TnTj9u9/WOBrtdz/33t3PxYpRksowk\nNZAkBTablkymgiTJnJqxMe//NjwRkGvG11CrNajVGqhUCoxGZtgc+gAAIABJREFUNS6XHo1G+55g\nRGMy4RkZYfXUKTTUkRxBGr4MSr0BvcfLhZNX8fV2ko7X2eqyMD0dp1aT6O11oNOp+fu/P0Vvr5Ny\nucaePa2Mjno4ciREoyEhSQKvvjrPtm0tiGKd9nYbdnsFo9FCJlPhwsUoKqUSjVFLWWEgnKrj95sZ\nH/eSSJRYWcnS0Cvo6nFRKYsE2+w43SZ27QqSzWjIlxrsua8Ls9VAOl1Cq9VhD4yi79NgNigweTx4\nRkaw93Sjd+fWgyIVpUyO+csXSMZyGN1uNEY7fr/5rvch+ihcM5rMZitEo0X0ehVtbVb6+pwYjRqM\nRg1bd/dgzA6h1wiEFpNoEZm+HMVgtVCs5mhIAjaPjddPxLC4bdTrcifS1FSM//pkP/v2tZHPV5Ea\ncOBAOwajhhdfvEShICstJ5M5XnvtAs89txWzWcOrry6wZ0+QkUO7mZ2O0Rawkzy7Sme7FVHQoLfb\nqDdkgq6A/HdvNGno7nbg85mIx1d58skhFhZSLC1lEStV1NUcYi5LMjrN7EwS29BWKjonyVSF1i3b\nUGk1KHQVaioFPQfGyCodiKL8u0+nyzz++AjFokxyd7uN97SmyDV85zvf4dChQzfcAnsvob29Hb/f\nz7Fjx+56B+J7Nhh5v8jLatWh3j1BqF6mEIuBJKF3OtHZ7cSnpjZ8Nh+JoI+HqVQknF19bDfC2ttv\nY7IVefZ3H2JmDcSaREevm0pDiaJWRhMOo9HY0VuMtPs0jA+00VCqmZwvU6nUkSSZAHlNTdVoVFMu\n1xgYcHHpUpzOTrl1b2kpg9s9iNmswWbT88UvjqLVKjEatSwuptm5MwAkMZnUnDixwvCwmwce6KCv\nz0GjIXdKRKMFSqUaRqO6STD1eIzUag0EQYHPZ2T37iBut4lKpcbKSpZUqoxCIaBSya6zyXVDNKVS\nTV+fmYkJP/W6hN2uw+2+fYSnu5Ez0tJiRq9XUSrV0OtV6HQq7HYdCoWARuPCbNYQCJgJh/NYrdom\nWc89NARmJ7FEBYfZR3tVIHRuit3bOtn74CAnJ4vYOhoMD/v45jffxuHQc/BgBz/8wWUsRiVIEvl8\nhVyugtdrpLPTSjZbpr3dwsJChosXo/T3Oxkb8zI/l0SlFDDolCiVCgxGDd29LuLJMqdOhXnuuTGm\nphJIkszlCATMDPfbiK+mOXCwlUS8iFjV0dNtxWLT8+qrCxw/cZVkukqhUGVszMvI2HZ6P/cUNocJ\n1XpXQSBgIRCwED5/nj57noRCQywcRcymaX1g+8dS3LxTnJEPGrNUEkkkigiCgMtlwOs1MT7uIxTK\nUq83mhlJr1cO5CuVGvFUlZKzj7b9dgbvK4FKy7klBWVBh2F6nlqpiNagw2A1otPLpZlKpUZHh5V8\nQeSlH0+zY7sPnV5JoShiNGmQJNi1K8DkZJRsVuLcuQjnzkXo7bVjsWg4dy7M3r1BRoZdGLSw7Qv9\nOFqcVKp1JifjnDixTGurFbtdz9XpOE89NUI8XuT7379MsVhjbS3H/v3t9PYqUUk1toz5sOmqVEQR\no04gNjmJbeJ+wvEaSzEIjuxieIsV6XKKlWiVQrKCVqvEbNZgMlkxGjU4nXeeDHk714N/+Id/4K//\n+q/v6Fzu5DhPPvkkL7744kcGI5t9XndzgUx65271ZlArlynG40iShMHlIhsKsXD48Hs+1zKxncWq\ni8z0ZcIXJzEYtQhKBT2DfjzjE6jMVmw2LYuLWd589TzR6QV8bU5GRzzMvPRjZt86R+v2CdSBblKG\nTl784RyhUIYvfnELer0alUrJpUsRMhnZ56Veb3DwYAfpdIWeHjvxeJHvfe8yBoMaj8fIffe1IYp1\nlpayLCykmZ1NsWWLh0ZDYm0tz3PPbWV+PkU8XuT06TVCoQy5XJXeXgeHDnWyfXsLbreJUCiN2azD\n75flrCcn40SjeU6eXCWVKmEwqNm3rw29Xk0yWQJkAu5jj/Xgcm0e61oQBD7uPb8VmJ9PceFClEKh\nilIpYLFoiUQKWCxaGg1p3bJefn/79hba2mysruZ4661lFhdlbsDIiAeVUGdpMc1bZ2KoVArm5lLo\n9Wra262srGT5zOM9vPrqPEqFQHunjVSiwGqkyMREAJfLwM9+Ns/8fBq9XsXcXIrhYTcOu47LF1b5\nr0+NsHXCz4njS1yYlPlG2awcyPze7+0ikykzO5smFMqgUMjKmm2tFl56aYbwcgKnVcvWUSdb9/Tw\n//y/p5majJHK1tDpVOzaFeCpp4bp7XWwa5csqBeJFCgURAxaieLkW5RjMQSrixIGFEi4PUY69+3+\nRNf9Tt73fL5KKJRheTlLoSBnG51OA/fd18bKSo5jx5aIx2X36u3bA2zb1gJIHDsWYno6SaMhIZYr\naDVK/AEjhbLAT38yhZIGqbUYE3t6iKXrFEp1zpwJk0oWmZhooafXQbVSZ3pylYagplqDZ54ZWVeN\nTaBWq1CpFGSzFbZubUEUGzgcOpZDGRKz84z0W1DWiogKHTOLRdRuPx2dDtRqpazqW5GD6NFRHy+/\nPMvKSpZEooTFIrtwf/H/GMVhauDUVTCk56nmctTEOtEsaEf2EYrK1g87dvjp6nJw7lyYkydXMZs1\nNBoSU1Nx7HZZRXfLFs+GZoKPi83+ewc4ceIEn//855mZmbkpU7l7CVNTUzz44IOEQqFNP8d1Ts77\nxh33bGbkw+pSKp0OS/C6xojWZkOl11MrlZrvKdRqjB43g1oTb721gsGgRqWUMGmq1BNrlBcu0/3Q\nQwD09TlJL9QZdbsw2QzM/fT71FeWaQ3aaO310tAoUCnlkkdnpx1JEtZl7M08/HA3S0sZIpECdruO\nU6fWWFvLkUqVGB/3YjCoCQQsqNUK/vVfp3jmmWHs9jK9vd10dSVQq+VW5r17WzGb1Zw4sczBgx1E\nIgUUCrmTaP/+dkZHPajVSl5/fQGNRsnS0jJOp45g0MbqapYf/3iGXE6kq8tOPl/l+PEQDz8sy80X\nizGMxtsn1f1u3I2cEYDOTjutrRZKpRoGg5piUSSTKTM9PcvMzPVFMx4vcvy4LKJ18WKETKbC668v\nsrKSw+s18swzIwwN+RDUmvX2bgMmU4HR0W5KhSo/+bcLXD4fplatcf6tOo89sYVqoYTfL3OCvF4T\nFy5EkCSJAwfa6e6yo1IJtAbrHDt8hdnLy7i9VnbuDHDxYgS328CePUEsFh0tLWY0Gjmrs7ycxe02\nkM/LwYaiLlIpJDj9Zp5gbwCv2wiDAqvhAi6XAbVaSaUiks9XWFvLEQplmZqKUy7XEMQyfoOKdoOB\nleV5XOtZkzLu23Jvbgdn5OTJSywswPHjy1QqNbZs8dLTY2dhIcOlSzEWFtKA0AzIZQKrCaVSYHY2\n1XQozpcanDob5b77rNTKWoY7tMTTdUZHBulqN4NSw8+PrrBtWwu1SpWHHu7m2JF5UpkqBpOeZLKE\npFASjebp73cSieQ4fHgRvT7P3r1bUKkEVlfzRKN5TDqBBw60Eb90nuWKme+/8BYulwn3sIp//48Z\nfu/3dmE0qjl3LkJnp5VEQtaxeeSRHiBFsSh3Zw0OuXEJSdKXLlJdXwdVaiW9o61YtrYz1JAdi81m\n+b4ODsrikqFQlpdemsVs1iBJKaam1BQKVR5+uAuV6s6VaG7XevDVr36VP/iDP7iph/RmcytudpyB\ngQGcTifHjh1j//79mz6fD8I9G4zcDIxuN77xcaIXLiAWiyi1WlyDg5hbWihnc5jau1krRREU4O20\no20kqGazG9j3erMRMbRIVeGgEI1i8riROrdycU1JvljGGazwm785wblzERYWUmzZ4mVyMsaWLW7c\nbiP/+Z9XWVrKoNEosVp1LC9nGBnx4PEYsFq1VCo1bDYtqVSZbLbC2lqE8XEfg4Mu2tttZLNlzp+P\nMjTkZnJS3nGPjflwOPT09Nj5+c8X2bs3yMpKbt1gsERfn5Mf/nCKgwc7Safluu/0dILxcS+lUo14\nvIher6ZUgo4O2y3Z7dyLkCSpaTlgt+uaC7LZLC/OZ8+KvPtPJZutyP4votxhsraWZ8sWL3a7jp/+\ndJZsVvYZGR52k8tVOHLkPCsrOSLLCbKhJTpaTFy8kKKh1HL5Soqnnxnj+FsreDxyIHLNB+XkWyv8\nx8VVHnm0lwun5rh8OkltyIdNV6NkMvKrvzpItVrH7TZitWoRBIH+fhednTZOnlzl7Nk1rlxJ4PWZ\ncTu6uHz2bYrlOtlMGatVy+paAYNB3bQnsNv1nDy5yoULMXK5CiMjHur1BpW6mstTSby7Xcg6hjJs\nt6GccjsginWmpxMkElqcTj0qlYLz5yMUClUGB11Uq3UymfIGDkSt1iAaLeBw6LBadQiC7Iu1tJRp\nttgXEiUu/vw0Lq+N9tEhQj97Bf/2rQx1GSlKOsqZPIlIgsmpOJKgksuojTrJbAmVSkmhIFKvSwQC\nFrq7reRyVX7841kcDtnZu1rMMnU+SafbyuHDMfKZMioBPA0RUWzwyitzfPrTvVitWnp7ZRXVbLbC\n2bNhFIo0VqtMjrVYtHhcAarhJfKRCLVqjXxDT1EV4NyREF6vaT0LJEOjUTEy4iUWK9Lf70SpVDRV\nmSORAvH1bp17GdPT07zxxht8+9vf3uyp3HY8/fTTfPe73/3QYGSzcc8GIzcTeQmCgHfLFizBINVC\nAZVWS10UWTl1iqWkkjeOrlBM56iVSsxPR7n/wV76O1wb2ryGduwgrFJRiEbRWq0ou7fy+okYtYYC\nFBDOqmhT2jGbNYRCDc6cWWPHDj/1OiQSRebm0iSTJer1BiMjWnw+M5lMGaNRw+xskkSihCRJ7NvX\nxtKSwO7dDkwmDaLYwGBQMzOTpFSqcd99bYTDeUKhLPl8FZ1OxdGjsjz3tm0txONFTCYNDocOUZRd\nfnftknf4Wq2qWXJobbXg9RpRKhVs2TJGX5/zNtyl98fdxBkpl2ucPLnSVBm1WnXs2hUgELiuJur3\nt7K2trbheyqV3EWiUAhNIzWlUuBf//UyFouWarVOo9HA5zMRDFp47LHtrK3leSWaxWJSUSwV2Lq7\ni3SuhtGk5sp0gosXY3z2s3YuX47xyitzHDrUSS5XIZutEV7LkkspsVi09Pa5UAki+VwJTTWDNr6I\n09JCOaVFv27wdf58lG984xT5vMjKSha1WsG2bX5c7UMUMiXMNgMHH3KRzFwmqFbQ1mZlZMSzrnKs\nJBotsLSUoVqts3VrC3Gxgcbto67V47VYEAQBS2vrBv2eW3lvbnVWJJut0GhYcThkbYwf/vAK9brE\n0lKGQibP/fcFKa7Mo9SZ0DudKNUyJ6hSqfPGGyGOHg2h0SgZHfUSCJiJRgsMDHSzupREd2iMdDRF\nPp0nk8ziSGdIlQQuzafJpIooqbNzdzurq3lOHF+iu9fJyHiAeLxAOJzD6zWhUikxGtW88MJZarUG\n27a1EAqluW9vALGiQmcUUKuUqDVKNFoVlWqDkREPJpMGu13P9u0Bzp4N4/PVOHMmzNJSGrNZSzSa\npK/Pid9vQm/U0vnQQ5TiccLhLGsLIvGVGrWaSCZTodGQOHiwY8O6Jwg0M6Za7Tu5QXe2vHI71oO/\n/du/5Xd+53duWhBss7kVH2ecp556ioMHD/K1r33tA7NAm31e92ww8nGgt9vR2+0kZ2dZOnIEpdnO\n2TM5Uour6B0OlBoNolhnflVk5+M9G76rVKkI7NhBKZ1G0Bs4cjTCQiiPgICn1YmnzcfJk6uMjfmw\n2bQYDGrUaiUnT67S1mahu1tOtQuCQC4nK516PCZOnlylWKyRzVYYH/chinX6+10sLcndMZOTshuv\nzydrUEQiBRYWMkSjeapVuR2xUqmzdasPrVZ+WEmSRKVSp1SS1RxjsQI7dwZ47bVFDAYVfr+JkREP\nBw50NHfT8XiRqak4lUqNlhYzwaDlnu+QuBHMzaWYmoo3Wxzj8SJvvrnC448bmsJYbW1WpqcT5HLV\n5vdaW60Eg+b1ThQlfr/cfi3rRRjw+UwolQpmZhI4nTrOnFkjlSqBoELt9qPOl7g0lSKVKtHV62X6\nappSSWRmJkl7u42VlRxLSxl6ehw4HHqCAQsnX8nQN+jDZlETTYJRStKIFtAXI8TOR8ivhOh88EEE\nnZHXX1+kWKxRLIq43Ubm51OEwznuu68dSYK9+9qZm0vy+c+P8PrrSywspFldzaFWyw/cYrGKIMDq\naq65YzY7bQRGOrCq21GoVBicToR7pM5+jZSsVMol1GuwWVRELl9l1lKhr9vOW4cnMeVyOLp7sNq0\nZLNlikURl8vA6mqOY8dCPP54L5/5TC9nzoQ5c2aV1EqYwQEnCpWG1bUcQ6o6O+/rZi0zx/x8mmpV\npL2nwc5dAQaH3KTTZS5ciOD1mhgb81Gt1ggGLZw+vYZOp8JgUGO1aimVVKxGyjx+aABx4SL797eR\nzxZQW50ozBZ0Bg1bt/pYWsowO5siHM4zPZ2QBe+2eNFolASDFsxmDbmciNGoRaXRYGpp4cqZPIWK\nHFRfU89dW8uRTpc3ZEjb223Mz6c3GGte02G5lxEOh3nhhRe4cuXKZk/ljqC/vx+3283Ro0fv2q6a\nezYYuZG6lCRJlMuyfPW1B2ujViN26RIIAlqLhZ4OAb9bS6qooKS20kCBzu8iUxSIpdMYjWqcTkNT\no6ChNhDXdqILaHF0FFBpNagsdrIlAYVCwO02MDubRKtV4ffL2Y9kssz27XKLZjRaoKfHwfCwB7Va\nwWOP9XD1agqzWU29LrPpR0Y0LCzUUKlkJ81stozXa8Th0PHtb58nlSrLUt8+PZFIns9+tp9wOIfT\nqWdiws+FCzKPIZ0u85nP9HH8eIgtW7z81m9tR6USePjhLoaHPU1TsTNnLnPlSq35sA2FsmQyFQIB\nM1ar7H56p+/dneKMhEIZ3s2hy2TKpFIlWlrMABQKMQ4d6uTq1QSZjEwWdbkMzM+nEATYv7+VcLiA\nyaSmq8uOw6HH77/2XZGFhQznzl1Bp3Ph85koFr0UltKsRKO0dzhp63CwtLyIzabjyJEldu70MzLi\nxmXX0uZq0NVuxeC0YbNtp5CAcKxKQ6nAbiixPL1MR6tJbrtNJMguL6Nq6aLRaOByGVAqBUSxzvCw\nB6tVS0cH7Nw5jNcrk5u/850LLC1lcDoNpNNlzp2LYjJp6Oqyk0iUNpig9fU58fmtLC9n6Ghp4ZPg\nTnNGDAYNLleVbFaPWi0b+AkCtHrUVFZKVPN5hiZsmLSDxBJlurbYcXjtvP76IoIg0NpqwWbTUanU\nCATMRCJ5EolV3G4jLmcXSqHKcqLOE//n02idLiwOE5///Ba6u+0kkyXW1nKcOrXKxESA2dkkbW02\nhodddHc7OHVqdV1kMIpOp8LjMVGvSzQaDSwWLSPbu7DsDZJPFzF3dHPmYoparYFWq6Svw0ByJUrd\nIWI2mFmNVFhYSJPNVggEamSz2vXA+Hq2o1QSkSSJcDiP3a7H4zGSTJYQBHkNeyfa223s2dPK5GSM\ndDpMZ2cHY2O+O97Se6vXg69//es8++yzuN03z3nabG7Fxx3nqaee4oUXXvjAYGSzz2szg5HfBH59\n/f//b+A7t3LwSCTPhQsREokSVquO0VEvJnJkl5eJXbmC0eUidPgVFmZSRNYyBLcM0nnwUZZiclry\nxIllYjFZb0D2cikTieQRxTrprIjGYiUp2SgmawQNSlJLWfr7nZw+vcapU3JKPxzO8cUvjnP+fJhE\nokQgYKatzcrgoItwOMvQkIdMpozZrGZ2NkWpVEOhEKjV5NKMIMi7kEgkz+RkDINBTV+fk0DAjCSx\nTjiUzbBisQLnzkUYHHTzxBP91GoNFhdlH5yHH+4mmy3T12enr8/JxIS/KQgHsLycIZeT09IOh55U\nqsx3vnOBri47waCF7dv9t7XVdzNhMLzXT0OlUjQlsa/B6zXh9ZqIxwu8+eYKZ8+GmZ5OoFIp2LrV\ny/i4l2i0wOJiBrNZw/JylvZ2Kx0dNiKRPCaTGoNB5qLs2hWku9tBe4edtbUcb74ZorfXyfJylpWV\nnMw32WKhsXCRl1+4gEJo8NCntxDcNshk0YTFW2fHFgvZ6UmKoo8riQa2uo5goIVcVc3k0RBqlcDc\nlTU8bgNaDVTXtS2Ghy3NdlWVSkGxKJNb0+kyRqMavV7F0lKWgQEXPT1OvF4jAwOupoqoUimQSBTJ\nZiPrDsamJsfmbkd7uw2t1kE4nMNu12G366nFlsnrVHR22CguL+BsNPD5DbR16kmKcneL7NqrwWLR\nIoqylcPp02tUq3V0OjUqlUA+L5AXTNSsfq5eTeNMSgwNuRkaclMoVPF6TWg0So4dWyKdLuNw6CmV\najidBgYGXFy9msRgcKLRyBYNmUwZn8/E0JCL8EqcVDWHphhlS6uLvoEe0nmJWjLK7E9+wltHpvG1\nmHG0Bbh/1x7m51PrBoYVyuUadruumckQxTonTqzw9tthrl6Vu4M6O21MTPhxuw1YrRtdjxUKgYEB\nF11dNmZnVQwMdNzzKqW5XI5vfOMbvPnmm5s9lTuKp59+mvvvv5/nn38eleruy0Ns5oxeAv5hfQ4n\nuMlg5MMir1yuwhtvLJFIyKzxbLaCsZFFFTqH2WbA2tbGzI9+RD4cxtvdj1g1EZtfwr8tTFtbPzWx\nxvJ8FIVGS6Za5wc/mGLnzgBnzsxgs+maNuP79rUxPZ1AkiR27QrQ0mJCEOL09jqIx4vrku/w4INd\nNBpy6aSlxYTNpkOhUNDdbSOfF5smf4IAIyMeSiWRkRETer2KZLLMwkKGEyeWeeSRbsxmLS+/PM+V\nK3HUatnU7r//952cOxcmk5G7IMbHfczNJbFa5Z12tVrH6dQTjxcZG/NtCETk49qBPBqNElGs88Yb\niygUAk6nAUmSdRcefbTnlu2G7ibOSE+Pg8XFDMlkiUgkTyxWYPfuVhYW0hQKIm1t1uY4tVqDt95a\nZWUlR6kkEo0WmJtLodUqcbkM2O16otECqVQZm02H12vGYtHy/e9PceVKCoUi3VTaPX58CbVaxfy8\nfJxEosTTTw8zPu5DrVaQmjzPq//fG5jNaoJBK6+9dImDKjU94/u4eDmL3qij7Anw8x+coVYqYjHr\nmA62MLjbzdpaETGfZduoiytXEnR22WjxGrhvb4COrut1f7VasZ7Cr6JWyyrBAwMufD4jXq8Rn89M\nX59jgyXAhQsRLl6sUqksA+DxGLj//o6bJj/fSc6IWCohFou0BQOoNBp+5VcGefPNFZLJEmWFgw6/\nFr+tTjW63mWi05EXVczMJKjXG0xOxnC7DU3p81qtgUIhkEhoqddlh1+HQ0+93mgKB0qSLKS2spLl\ntdcW+c53LuLxGNi3r43HHuulXm+wdauPUqlGMlmiUqkxNtZPd7fIK6/IBFabTcfKQoLD//xTLEYl\nYxOtKBLn6N7SRdt99/Ef3z3K2tUQVouGTKrE8aPHmcgqGR7uJ5cTsdkC6HQqWlutzYyHHDCncbmM\nSJLseB6Nys6+W7d+sOiXRqNicLD3lt2Tm8WtXA/+8R//kUOHDtHd3b1pc9mMcfr6+mhra+OVV17h\nU5/61KbP593YzGBkcf2/daB2KweOxYpN/QyQdxqJ2Sso4gk0ilqzA6BeqVBPxejtCqBy+LBrS5ja\n9Hz3Gy9TLZbQGI0UtW6WIxUGB93o9WoWF9P86EdXUamU1OsS27e38MgjXayu5vmXf7lEsVhjfNxL\nf7+LWq3OpUtxBgYcjI35mJ9PoVAI1OsSPp+B3l4nFouO6ekEwaAFu12P06mnVmuQSBRpNGRBrFde\nmSOdljMoS0tZjhxZxO02UqnUKRRELl2SywjxeAmnU48oyhmWUqnGrl1BFhZStLZa6e6WU++lkrjB\nYTMQsLC2Jou0Xb2apF6X0OvlXTLIjsHJZKm5o/5FQkuLmQMH2nj11QWWlzN0dztYWsqwuppjfNyL\nSiXg98tkVlmXQv5dKZWKpl+L7Ajb4NixZbZv9zE05MZm0+Nw6Dh/Xm7RVShkV9PTp9cQxTparQpB\nELj//vb1LisVRoOKjqCZlbU8cwuL+LwGLFa5RTebrRC4GKLVl8HrNfGjV5bJJbNYgwF8Nlibj1CQ\nDFyaStNih9XlZSx2J7t3+hgY9jEYAAsZQA5GstkKZ86s4febsdt1hEJZLBYtwaCOX/3VQUZHve/Z\nAWcyZS5ejFKp1JvvRaNFZmaS7NgRuDM37ENQLFZZXs6RycjaIYGAmdziXLOLTmux4Nu6FW9bG5/6\nVDepVBkandTXZolduoSgVKK1WPCMbePNC2nW1nJ0dztwuw2sreXp6pI9Y5aWMgwOysZ2hYJIqVTF\naFQzNOQml5MtFnp7najVCubm0iwvZzl0qJNkssSrry5QKIiYzRquXk3i85koFCrYbHqOHl0im63Q\n2WlncNDFT386x0xklej0GgG/hZNvLbNjxMbJV8/jGBggthwlk6nQ3m7l2LEQgYCJajxCw9GDyaRm\nZMTNkSNLhEIZ9uxpBaBarSOKsqib7PhtBGQDSIPhF19xWRRFnn/+eb73ve9t9lQ2BV/+8pf51re+\n9b7ByGbjbsjV/Dbwg5v90ofVpSRJ2sADUCgEauUSagmQJMRCAVt7OxqTCZPXi97lQqlSYbYbyYfX\nqGRySJJEXSOyEloBg5VqNYHZ7OHixZjseNpub4onTU7GWF2VvU0WF9PMzSV5+ulhnE59M7tRKIg4\nHAZ0OiVjY75mt0Zrq/U9ZlMLCwsMDLSte5XIOy+FQiC5bjE/Pu5DoRBwOPRkMmUuX46xb18rmYzc\nbtrb66CtzcaRI4vk8xUOHuxErVayspIlGi3S0+Ogvd3WPJ5Ol6O93Uo+X0WrVaLXq2hvtzYzIQqF\nsKHm/ElxN3FGQLZCr9dl+ffZ2RTFoohSKdDf72RhIU21mqSjowOtVoVer6ZQENFolLS0mJibS2I2\naxEEqNXqpNMVFhYyBAISBoOadLq8vuCnSSZVVKs18nnE8TIGAAAgAElEQVSRRx7p4rXXFpmZSRKP\nF3E6Dfzr/3qb0EKcP/q/HqZzMEg1HiGdrawr7WrQ+cxkiwIv/+dlXC4j8XgJlaKBdU+AvvvaWI5U\nKBZFHD1W8l4zlVKOTLyI0KaivJhAtI42r4nsm5TAatXyxBP9RCIFzGY1O3b46eh4f7+RYlGkXK5R\nqcQ3dFasreUolzeaLX7Se3Oz979YrPL660tNDpBCIdDWoqG1sUgpkQAgnE43gxK9zdYMriXfVqyt\nrdSrVXQ2G8mcRCx2FUmSMwk6nYrBQTder0leF+oSCoXE/v0WcjkDKpXAtm1+4vECarWSnh4HAwMu\nzp1bIxYrAjLvKxLJ02hc44XJgYtKpaC11cJrry2Sy0VIp/VMTsZZW8uj1SrJlKsUCyKNRoNcpoig\ndlEpVak3BAJtLsrZAoV8FatVJ6swt3qYKdQ4dWoRvb6AwWDYQDa1WLQYjepmNlapFFAqFXg8H12G\nvR3aLzeKW7UeHD16lO7ubnbs2LGpc9mscZ555hn++I//mFQqhd1u3/T5vBN3IhjxAv/rXe+tAZ8H\ndgGfAt7XUvD3f//3sdnkh+bAwAC7d+9unmQ4HAaup4QWFhaar2UBp+z6w9VFsShi91iRJLlPvloo\nUA8EyBeLuB0OlCoVGYUCQatFlY/R2e9jfjEEqhJqtYHOHhvFYgqQqNXqWCw6nM4KVquaQMDB+fNR\nGo0UgQBUq2bqdYlMJkx3dwtWq5yNUKkyxOMigmBnyxbvhvm+e/4AqVSYUkkmSzqdegyGAvH4Kjqd\ngZWVHGNjWkSxRDwuMDLiIRxeJp0u4ve3EgxaSSbX6OwEs9lFIlEil5O1IbRaF/W6tOF4BoOGrq46\n6bREb28XRqMaUUxSqRTRal20tVnJ5aLk88IHzvdmX8/Pz6/XzlvWH/DR9/sJ3BGUSiLxeJGVlVzT\nlbRel8nP1zoNAHQ6FUNDbk6cWKZalZ1xDx7sYHTUS6VSJ5erNtuDlUqBzk4rjYZEoSBis+mQJC0t\nLRYcDvnB8cQTfYRCOVZXc+RzZeJhAaPNxFtvLrN/5xjZ5WWSp+fx+Uz4/FZsnZ0cPh2lUBAZH7eg\n0ynl4Ge5SHu3B3NRoq3Nymwoy5XZEn6/id4xL363GinbwOB2UyqXKZdrLC9nAchkKnIZ06hBo1F9\nqNS3vHtWk8/Lr6vVOisrOQwGFS+/PIfPJ3dpvTPrdqewuprfQEZuNCQmzyxgHdahEgSu/UM1l6OU\nSKC3XQ/GBUHA+A4io7JYQKVSNDtIzGbNent+ErtdJsCq1QoSiTIOh53eXifbt/up1eSM0TUxsGpV\nPqbTqScUqjY3FsPDbiwWLfF4CY1GQSZTwW7XoVTq8HicRCJ5IpE8g4MuUiYzVrvcMecNmpEqJawe\nO6WGltEHd5JLZpBqIqWSiNlpxdDew+rR9Drfx0Brq4uuLrldPJ0u4XAY6O11cPlynGpVztD19joI\nBq+3sv+iQpIk/uZv/oa/+qu/2uypbBrsdjuPPvoo//Iv/8Jv//Zvb/Z0NuBOBCMR4OD7vB8A/gZ4\ngg9oWv/a1772gYPu3r1RgvqdkZjVquORRyaaPAqjUcO2sX1Iax5Ss7M0ajU6u7oY3b+faj6PUqNh\nIBikms8z/+qrbB9uoaNzC6l0hQMPt5DMS6SKLkwmPV6viM2mR6tVkMvJO0W1WsHPfiYvNL29dhwO\nA36/B5PJzosvTlKvSzideu6/v4N8vrK+G+r4wPlf5yjUKRZX+W//bZgXX7zE5KTIl77UgSjWefvt\nMKLY4KGHOnn88d51+3MLbW1WarU6HR0dqNUOFhbm1hcdeSdrsWjX69H29z22JElYLDqmpuKUSiId\nHTb6+53vSeG+O/K9mdfBYBtnz65x+XKWcnkWvV5+yH/Y9z8ObnQMh0OPw6HHYFA3O4oMBvV62cJK\nR8f1a9Xf78Rk0rCykkWplH1NCgWRZLJEMGhZJ4LK3SgdHTb0es26kJ2Zubko6XSZ4WE33/veZR54\noINcrkwsViAWzVMo1WmgIrRSxPRoN3uf/Qz9+9aoVkQqaitzcSWiGKery47LpcdoVFOrpQA5E+By\nGdFqlaxFipTQMTefxeO1YLOosfZOYPJ6MQsCtVodrfY6/0eSZJl0jUbxobwgs1nL2JiPkyfrlEo1\n1tby6PUqzGYta2t5wuE8CoXAxIT/E9+bm73/MtF043u1WoNyVcKsUCDV67JqrCAgfIR7qdNpoL3d\nypUrCUwmDbGYTFoeGHBRLst+L0NDHrq6uggEzE2Nnncrkno8BgYHXeTzVbq67KTTFXQ6FQcOtHPm\nzAoXL0bYvTtIoyGtu2234PUa1zO7shBaJGzHPtaHplFgYkcQcjFa9+xGYzLSNTqB1mLh6tmrtO3e\nRVawMLlQJRiUfaZaW60EAmbOnQtz+XKc6ekk2WyZz31uoBkQ2Wx63G7DDZFSNysrcquOPT8/T6VS\n4bHHHtv0uWzmOF/+8pf5i7/4i/cEI5t9XptJi/575CBldf31Y0D5Hf/+sb1prqFeb8i+GgZVc6Eo\nJpM0RBGdzdY0AruGSi7H3MsvU4zHUWo0KDUaWQCpc5yFFZnDYTJpuHo1QaEgMj+fYnDQTTJZ4n/+\nzzPodCoKBRGnU8+zz27Bbtdx+PDCOmtez/i4j23bfAwPe25YSnltLceVK3Hi8SLLy1lcLkNTN0Kp\nVLB9u59stkw0WmyqQno8Bh58sAuTScPUVFy2Iy/X1klqLRtKNB+GdyrQ3kosL2d5+eXZDdoFGo2S\nX//1rZviVdFoSJw/H+HEiRCXL8dQqZRMTPgZG5O5PzfS2vxh10rWLlkmHi+iUCi4dClKJJJnZMTD\nyIiHb3/7PPF4kYDfiNmsweXUs3XQzNlXz9C5e4ILl1NUKg30eiUjI17i8UKzJHJNo2LPniDhcIGT\nJ1dpNOSyk1QToVbl058bpr1rY7A3N5fkwoUo1Wq96Ua8a1eAkRHvR55rNFogGs2zsCDrT8hifvJ9\ns9t1PPFE/7oL9Y3hVniULC6meeWVueY8ABT1ChMddRqh69bwBo+H7ocfRvMRQlelktgUIzx+fBmF\nQmi2wgO43QY++9m+D/07FsU6Fy/K9zqfF9FqFdTrEqdPr3LhQhS328i+fUGMRi3/9m9XqNUaDA+7\nqdUkjEYV+/a1kUiUUCgE/B4NpXSOYl2F3S0L1Gm1Kqam4szMJNDplOTzculFo1EyMuKhtdXKuXNh\nXn99iTfeWGJxUdZXcTr1fO5z/YyN+di9O/iB87/duNPeNI8//jhPPvkkv/Ebv3HHjnk3olar0dXV\nxQ9+8AO2bdt2R499t3rTfKIc0Y3UpZRKBRbLxoDD4Hj/ejiA1mym7b77iE1OUozH0TsceIaHMXo8\nCJoFOjo6ARgYcJFOl1lezja7Kp58cogjRxYxGtUcONCBRqNgYSHN0JAbo1FDNFpgejqB223A6TR+\naFr0nefW0mLGZtMxN5dCFBtcvZrkJz+ZoaXFxL597USjeRYXsxu+H4sViUYLmM1aBgfdtLdbKRbl\nYOT9HhIfdC1vVwvflSsziOLGRaharW94fSc5IwqFwPi4b70cJT+YnU59s4vkRsb5sGvlchnIZsOA\nhVJJJBi04HIZqNcl5uZSzMwkicUKTF+J88AD7TxwoJXl5TiewX5KorDuoeKgWIwxMdHH7GxynWjc\nIBBwMT4uy33PzaWb/ilKpQKUWhR6HUrN9azWwsICTmcLiUSJeLxIvd6gp8dJV5fsy3MjKBZjdHcH\nuHw5LpNAN1yHGxrilnNG/H4zAwMupqcTiKKswTEy0kFPq5qYVqScSpHTaGjfu/cjAxEAvV7NyIiX\nXK7C6mpug+AdyAHs4uLCh3ZkqNVKtm5tIZMpU6vVyeerXLwYZ2DAzd69bXg8Ro4cWeSNN5aZmPAT\nDofIZKrs2uVndNRHuVzD7Tbi95ub7dcqlWJD+60kSayu5t9z7N5eJ4uLi6RSEul0mXD4+meyWZlb\nFApl1sUSb+wxcC9zRi5dukQkEuELX/jCps9ls8dRqVT87u/+Ll//+tf55je/uenzac7rEx/5LkMp\nmaRaKKA2GDA4b17i3Oh2Y7z/fhr1OooPSOdarbp1MTCB6ekkZrOcth8YcKPVKlGrBbRaFQsLGQ4c\naGN5WTYc83pNrK7mSacXeOihrhv2dtDr1bS1WQmFsmzZ4mFoyI1SKVAqiWg07921S9LGh7vBcHcx\n5fV6NYJQfQ/JeLNxrVxzO2C16llelu+JRqNEo1GSyZRZWcnh85loa5NJzOlMhVyxgbvVRyZTWe98\nUDA05CaRqGGxaNm6tYWBARe1WmODxkcwaCEUyqBSKZAkmXBqNGqa5yRJEqV0hsOnUyyF8qgNBkBY\n/+06b0pxV69X09lpJ51ea95HQYDubsdNZUVuFdRqJbt2BenqslMsiphMGtxuIwqFgPnQIRr1Okuh\n0E2vCWazlmDQwuXL8eZ7ggBdXXaUysp7Pl+pyJ1sKqVAIR6nXq2iNZtJ5SQOH15sagY1GhIHDnSQ\nzVaoVGrU6w0GB92YTB46Omzva8/wfqqnXq+p2Z59DTabFrfbSDqde98OOJfLQLlcQ5KkOy5etll4\n/vnn+ZVf+RV0Ot1Hf/h/A3zlK1+ht7eXr371qx9L+O12YPOfAB+MmyrTSJJE9MIFIuttfGq9Htfg\nIL7x8Q8MKj4JlpbSXLgQZWkpg9GoRqEQ+PnPF7DZdExM+HG5DPz85/OMjXn52c/mUasV9PTIfAOA\n8XEvO3feXIr0woUIp06tUqs1kCSwWrVMTLRw/PgypdL17miDQc2jj3bftUJlpZLI4cMLhEJyRkcQ\noL3dyqOP9m66pfjtQjpd4o03lgiHC+scAQ0ej5F/+7cpJicTzYyMWq3gj/5oH/V6g0ikiFqtYGLC\nz+joR5dPSiWRI0eWOHFimUajweioj717g/h8ZmrVKmunTxONl/nxf15BrElY29ow+WRtiZERebd+\nMyiXa0xOxpidTTYDkaEh900HI3eDlfyHIZ+vcOZMmJWVLIIg0NVlY3TU17QLAPnaT07GmJtLQaNB\ni7mCsx6hFFnD7PdzOecmnFFQrzeYmUkRjeYJBCx4PAbm5zP09V0P4rq77Tz4YNcNz29lJcuFC1Ey\nGVnfZnTU+w714Co/+9k8L744ycxMEodDz759rRSLIs88M8LwsOfWXqybwJ267+FwmKGhIa5evYrz\nY2xQf1Hx3HPP0dPTw5/8yZ/csWN+WJnmFyYYyUcizL70ErXy9bSxQq2m++GHsQRvXV00kykzP59i\ndjaFUimQzVYpFOSW2PZ2G5mM3MZ7zYzO4dDz0kuzGAzqDYv08LCbfftubvFvNCRWV3OsreXQamXf\nCYfDwJUrcc6fj6zvhNWMjvruqPHdx0GhIHcXJJOyNkprqwWjUXtXP5SuoVisEokUqFTq2O06PB7j\nDZW0rpX06vUGDofsrvrd715iejoh28pLsHt3kGefHSGVKlEu1/H5TAQClhvKHJ08KStrVqt1arUG\ner2aXbsCjI35SM3NMf/qq9Sc7fzoP64gVmqodFrcw8NojEb6+pw88EDHx7oelYocCH/cjMjdHoxc\nQy5XQaEQMBrfm2V8883ldU0ZyEeipGevsv9QH14hhlqv5605JQWtG7VeL6s4p8soFAKDgy5CoWzT\ndNHp1NPWZsVo1GCzafF6TTf026rXG83umHf/VqrV2jq3JEk+X6VabTA66mHHjsCmqqneqfv+p3/6\np6RSKf7u7/7uth/rXsLbb7/NZz7zGWZnZ9G+iz95u3C3ckY+Ed5dlyqnUhsCEYCGKFJMJG5JMLKw\nsIBO5+KVV2Y5ezbM/Hya1lYLBw60NxcCo1FNNFoA5FTo8LDsqjkzkySZvD43lUpxw5yRd0KhEAgG\nLe/5bn+/i2DQ0gxGbrYkc6drwdeONzDg+sjP3IrjfFK8c5xstsKRI4usruaQJNmAbdu2lo/cYV4b\n453kYbNZy8MPd+PzmcjlqrjdBvbsaSUQsBAMWj9wjFyuQipVanbQ6HQqisUqc3Oppk3AtfT7zExS\n7uiIRJAaDfK5KB6/nZX5GLVyhVq5jN5yvUx0s9cDPl4Qcqs5I7fimB+Fd8veXxsvn68yP59ulqsq\n2Qz1WoOZqwkCW01Usxnagn4uLZdBL7cGu90yb2xiogW1epVksoRWmyOdFohGCwiCgFarZHzcx9jY\nByujXoNSqUCv31hmuzY/jUbF6KiP3l5n0+m7XK6xuJjBYFDfcDfNvcgZKRQKfOMb3+D48eObzom4\n28YZHx9ny5YtfOtb3+K3fuu3Nn0+92ww8m6odDoEhQKpcb1DA0FYr4t/ckiSxNGjSxw7ttwktaVS\nZaxWHWNjXpJJ2VhtaMhNtdrAZtM2mfZ797Zx+vQq6XQZjUbJ4KDMLTl8eJ5MpkIwaKGvz/mJPD6M\nRs377th+iVuL2dkkKyu55utSqcb58xHcbgOrq3mWltIYjRr6+pzvEbN7N5RKBWNjPrq67FQqMh/k\n3VL978bKSpZjx0LNnbXXa2TfvrYPrP03GrIAoMYkcwfqhSw7J3p4W6MiHsvjcJsZnfDfVDDyS2zE\ntVbca7jWpddogCQJ1KtVfM4amp09hNYq1OsSLpeB7dvlcu6jj3aTyVQ4fXqSdLrRDAwqlTpnzsic\nnMXFNGazhr4+18fWBNHrZSfxM2dWmZpKUC7Xmm314+O+X0iX7n/6p39i//799PT0NLWOfonr+LM/\n+zO+8IUv8Nxzz232VH5xyjS1cpnFI0fILi/TEOUWN5PfT+fBgzfEnv8o5HIV/sf/OM7Vq0lMJg2T\nkzHKZdnL44kn+gB49NGeJifk3RBFuYXy2q7kpZdmyeevk87a260cOtR5Swhl0Wieubk0+XyVYNBC\nR4dtQ337bsXdnK6/RjY9cmRxvcXa2JTLVyhktdZ3khzdbgP79rXi8dw6Cf1arc6PfzzD2trG7omR\nEQ9797Zy/HiICxeui8cJAkxM+Nm2rYVSOs3Cq69SjMdRqFRo7E7UvnZ8Q/1YrLeHtHujuJvv+43i\n6NElLl2KAVAtFElOT7FjZ5CAOoFYKuHs76dt3z7yBZF4vEQmUyadLuP3m+nosKHVqnjttQWuXEk0\nx2w0JK5eTbB/f9u6kquciXvooa4mJ+Rm8UFt9Y8+2v2xx/y4uN33vdFo0N/fzze/+U32799/245z\nr+PQoUN86Utf4ktf+tJtP9YvZJnm3ShWBaotI9Q0fsxqEadDh8XvvyWBCMjBhMGgQZJAFBv09ztZ\nXc3jcGjxeIz09jo+MBABOXV+jQ0/NRXfEIgArK7mSCSKFAoi09MJqtU6XV2yy+7NpMFjsQI/+9l8\nk10/P58iFiuwb1/bXdGxci8il6tw+PACuVwVpVLB/HyadLpCf78TjUaJJElkMjIB9ZoQ2upqjn/+\n5wuMj/sYGHDfcOfUhyGbrTSP806sruao1RpNkuvSkqwn0dVlb4rJ6W02Og4eJL24SLnSoCCYKakN\n5PIiJrPul7+Nm0C1Kgu+5XJVLBYNLS1mxsd9CAIsLWURrFomth+i3S5STRgwt7RgbWtDoVRSLBY5\nfjzUlGOfmUmSSpUYHJQF1c6eXcNgUOPzyVyRa63a11Aq1ZifT3/swCGZLG0IROTzkTksdzoYud34\n93//d+x2O/v27dvsqdzV+PM//3O+8pWv8Oyzz6LRbF52/Z7Ny70z5RaLFXj55VmOHFvl7JUyp+YU\nZFQetJZbJ3EciawwPOzG6zVSLteoVhts2eLhv/yXIfbubcXnu/E/5GuEv3dCEOSHyuuvLzIzM0ck\nUuDEiWXefjt8U/Ocm0ttaPOTJJifTxOPFz/wO3c6fXkjx7sVc7pV53Xu3BTRaIFiUcTtNtDdbSeb\nLZPNVtBqlQwPuykW5Wtut+s5dy7C0aMhFhczTE8nOXx4gXPnpj7xPGKxVXS692bOrFYtKpUCo1HD\nnj2tfPrTfXz2s33s2BHYkBHT2+2kNAYuJ8wcP5flzZNhXn557qZ/Y3fi3tyO3+StGLNarXHs2DKv\nvDLHz39+lpdfnuP48WU0GiV797bxmc/I137Pfd34RwbouP9+nH19qNZbSmdnU81ABOS/z0SixMsv\nzxOLreD3m4nFily5kkCSGk2rgXfi/daPGz1fk0nzHi0YpVLAYPhoCf/NLHN8nGM///zz/OEf/mGz\n7HWr5v+LNs4DDzxAb28vX//61zd1Pr8QmZHLl2MkEtddeotFkXPnwgQC5lvmk6HXq2lvN/DAAx1N\n6WmnU09/v3N9l1TBZNLQ0mL6SHVVv9/C1FRig++J329haSm7YdciSXJwMTDg2iB09GF450J3DaJY\nRxTr7/PpX+JGUC7Xm+TEeLzI+LjszOvxGBkYcGG365rEZUGQ79k1I0OQMxqJRPV9xxbFOuFwnny+\nitmsxeczfuDvR6tVMTRk5a23Vpo6MkajmsHBjToBH5ahi0QKLC9f/41Vq3UmJ2O0t1vfV8fil9iI\ntbU8s7PJDR5GMzNJOjtttLZaP/TaA+/JiF4bIxTKoNFUGBlppb/fRakkMjAg+2pdvZpsflahED6S\niyRJEpFIfj1DlsLnMzeD0pYWE62t1mb2TBCgrc16SzJ3dxNOnDjBwsICTz755GZP5Z7AV7/6VX7t\n137t/2/vTGPbuq4E/HEVqV2yZO2rN0m2Fe/7lrie2kmTYtCiQYHUdprMpJkiMRpjGjRj1AiCIM2P\nTtKiy7hTBGgTNEGRDJoBpnYnLRI5iONMvW+x5FW2rMUSKZG0SFHc5scTadKSJZJ6XCSdDxBEUeK5\n571z79V59557Dk899VSoHlyySaUzshN4CsgAfgO8FcuHg9G6gUAgtJ8ajtPpYXDQg9lswGmx4Bkc\nRG82k1lUFNdxttra2lANiZ6eOxgMStXWtjYLbW1WvF6l6uv8+YWsXl05buxHVVUuy5aVcfFiH263\nl7y8DJqbZ3P06C2AiIqoPp+S2jtaKipyuHLFGpFQTKlBcX9nJtkR8tG0p4ZOal1XY+NcOjqUGj8+\nn9LfgiXai4uVSXzNmspQKnaDQUtxsVLgMEhe3ugTEcPDXr48fpX+2wO4/TocPhNlZTmsXl0xpkNS\nW1s7Ujsog85Oe+hUVixxKVptAWCJeM/l8uB0eog2BUMybJOIPqmGTLvdHXJEMjKK8AwN4XI66evM\no6IsC61+/Cm1qiqXGzdsEeMzmATPYJhFb68zVElXo4GmJqW678DAEAaDlvnzZ1FbO74zcvp0D6dP\nd+N2+2hru0ZlZS4bN1aTlWXEbDawcWM1N2/a6e93UVioHKuPZis4VSdp4ml7//79vPTSS+jD7KGW\n/tNRzuLFi1m2bBmvvfYar7/+ekr0SaUz8gfg9yhbRf9HjM5IEI1GQ3FxVsTKCARPlxjoPn2a22fP\n4nG50JtMFDU0ULZsWVyJ0IJPJcEnk5s3bSFHBJTCXG1tVmpq8sd9etHptCxZUkp9fT5ut4/c3Awy\nMvTU1eXT2zsYMVHNnp1Jfn70AYa1tfn09TlD6eNzc42sXFkhJ20mQTAm4MKFXlwuD5mZBpqbSygq\nuhuPVFqaw/btc+nqctDf72J4ODJA8N4nz4Dfz7XPj/H3/2ph0OrAnJdN5ZJF9Gjq6O4evO+JCY1m\n7OPd0VJUlBkRJAlKkrxolukFxbHX6TT4fAEG+/oYuHYdTcCDp1FP+6ftVKxejXGcE3x1dQX09Tm5\nft2G16uMz4aGWdhsQ6F4IJ8vgFYboKwsh5KSbLZvV07bGI26CVdILRYn58/fDm3t+P0Bbtyw0d5u\nC8UPZWUZxz1WP9U5cuQIra2tPPnkk6lWZUrxyiuv8MADD7B7924aGxuT3n4qY0aCG58ZwGCsHw7f\nl2pqKqaoKDO0F5qVZeCBB0rw2a10nzqFx+mEQACvy8Xtc+e409UVs7Jj7YPZ7e6IrRZQHJKxggzH\nIjfXRHFxVuipZP78WSxcWIxWa8Ns1lNbm8eKFRUxBRdmZOhZt66K7dvn8tWvzmHHjnkTFsaTmJHx\nuXGjnSVLStmxYy7bt8/l4YfnsXDh7FErbAaDjurqfB58sI7ZszMxmfQUFJhYtaqC4eFIB+BOTw9X\nPz/GHYudQCCAc8DB9S9OkKsdDBWuS8T1aDRKfpxgn8rI0NHUVBzTFs1MjhkpK8tm3rxC8HuxdbWh\n8XtYtKSaTE8/1kuX6L96ddzPm80GNmyoYccOZXw+8sh8amsLWLu2iszMO5hMevLyMli5spyKCsXh\nNBr1FBdnRbVV63Qqq1wAbvfd013jxYxFy1SIGQkEArz00kvs27dvVDBmusRopKuc4eFh9u/fzzPP\nPIPfH/1qvFr6pDpm5MfAPwH7JiOkqEg5Rmm1OjEa9eTlmSgqyqTv4kV87siJXc1EaNnZxtBTUhCd\nTkNOTnyrEGazgXXrqsnNHaKsrJL8fFNcZ/+Dq0XC5Bge9uJwDONyKZN7QYGZgoKJV6lqavJHEpm5\nMZsNZGUZuX490t8eGhhA5/ei0WoIjJyYGHa58d2xkZubuFWszEwjW7ZU0N19B5fLS0GBacz6JcLY\nGI161q6toqxQxxl9GZWF+WT67bityj9+x61blCxaFPGZgYEh/H4/BQVmNBoNWu3o8VldnceqVZUU\nFpaG+kw8mM16ZWvaGRk7Fr5lOJ159913sdls7N69O9WqTEmeffZZ3n77bd566y2efvrppLadjPN8\nJcB797zXDXx75LUR+BuwAwhPoBDYs2dPKJimoaGBNWvWhPajgt5XeXklp0/3cOZMK4ODHgoKSlm8\neDY+Xz9Gjx3P+fMEfD76RpySIpOJ2i1bcBiUZel75d37c2GhUrCst7eTvLwM5sypD/3e4/Fx65aO\nK1esOJ29aLUaFi6cz7p1lXR2dkQlX36++3NdXV3a5Jvo6LBz/HhnKDdMU1MxTU3FodUQi8VJV5cD\nj8dPSUk2ZWXRpe0O0n/9OpcO/oXLly309joJ+LtK/F8AAA8GSURBVANo9To27vw6izYumXS+Gadz\nGKt1aOQfX2ZaF0SbinlGXFYrVz76CLfdTvi+6uzFi6lauxZQbHDyZDft7TYCAWXbZfnysjFXOIaH\nvfT1OfH7FcchPPDe5/PT2eng9u1BMjMNVFTkjqpGHiQQCHDiRBdnzvTg8fjRaJSKxps21UwqqWIi\nUNvuFouF5uZm3n//fdaO2ECInTNnzrB161aOHj06blXqeEjX2jRGYHhEh0+ArwGOsN9HlfTs4sU+\nPv20nYGBIVpblayCDQ1FNDfPZnFTIb6rp7CFLRvlVFRQu2VLVPlHrlyx8ve/d+JwKPu1dXX5rFpV\nGXFcMphzwGZzk5ubQVlZdkqqlk4H0uWfksPh5s9/vhSx3WYwaNm2bQ6Vlbl0d9+hpeV66PcZGUrF\n2Fj24b1DQ1xvacFy+So2mxvXkJfyhnoWfm0bmbmTy/fQ1eXg8887Qinjy8qyWbu2KuoTWckmXewe\nKx1ffMHts2dDWZ+NubnUb91K1kgV1OPHOzl+PHJLeP78WWzeXBPhuPb3uzhy5CY9PYMEAgEKC82s\nW1cVWrE6fryT06d7QlvCxcXKqb77rdL5fH66u+9gsbjIzDRQVpadljFjatrd7/fz2GOPsWDBAn76\n05+qInMm87Of/Yx33nmHzz77TNXcI+M5I6mMGfkR8DHwGfABkY7IhASfrIP1OLq6HAwNKWEoHR12\nvN4AJ073UrpyDdUbN1LU2EjV+vXUbNoUlSPicLg5dqwzdIzXbu+htdVCe/tAxN8ZjXpqavJpbi4J\nZVKcLOkYw5Hs9lIZM2KxuCLiNtzuPjweP11dShf98sveCEfF7fZx7lxPKNdINLroTSaqN2yg/sHN\nLNi4nLWP76D50fEdkWiux+tVUoj39Tnx+QJ4PH5u3LBz8WJf1DImYibHjITjnjWLuoceoqipibLl\ny5nzla+EHBGPx8e1awOjPtPV5QhVaA5y/nwvt245GBy8HTqtdfJkNz6fH6vVxcWLfRGxab29SoD6\n/dDptCOrJy7mzi1UzRFJ15iRQCDAD3/4Q/r7+/nJT34Slwy1dJkucp5//nlKS0t54YUXYnYYp2LM\nyMsjX5PCaNTh9wdwOu8mAtLrtYDyntuvpziOyGCbzR2RPAyU1djOTgcLFkzfSHRBifvRaiNjgUDp\nV4FAAKvVNeozLpcXl8sbU5FCY1YWxU1Nk9Y3HLvdTX//0Kj3OzrsrFgRf1CaMBqdXk9BbS0F9fWj\nfqfRaDAYRj/rabWaiDgwZWV19HOY1eoKxSsFH7LCGcvGMxGPx8MLL7zA4cOH+fjjjzEY5FSYGmg0\nGt5++23Wr1/Pm2++yQ9+8IOEtzllM7AGYw3mzSvEZNKH9lA1Gpg3bxY+n5KPId5g0uDZ/yDB3B/J\n2HdNx7wfyW4vlXlGiouzKCm5u3qWkVFEdraRyspcNBrNmGmzc3KM4ya8Stb1ZGToI/qt2awnO9tI\nXp4SDJ0u+Vumap6RaOXp9VoWLChCp7u7Iq3RMGqlQq/XkZWl/AMNzy+UkaHDZFJsN9ax6/D+GY9+\n8ZBueUaOHTvG+vXruXLlCp988gmFhYUxy1BLl+koJz8/n4MHD/LGG2/wy1/+MuH6TPnghurqPDZv\nriE310hbm4WSkmwKCswMDXlZurR0wiqo9yOY9vvChd5QfFp+fgZz5uTT0WGno8OGTqelqio3plTw\nQvpjMunZsKGaixct3LplJz/fRGNjcegERENDEX19Tnp67hAIKI7I0qVlaRErlJVlpLGxiNOne8jJ\nMWKxuBgcHKapqRiv1zdhdmBBPebNK0SrhdZWC35/gLlzC5VjwWFotRqammbT1+cKrYAYDFqamoox\nmfSYTHqWLi3j2LFOLBYndrub8vKcUMHNqVAAU008Hg+HDh3iF7/4BWfPnuXVV19l165daLVT9rk6\nramurqalpYVt27bR2dnJyy+/HJFITk3SuTrWuAGs169fH+WBuVzD9PU58Xj8FBZmjpt1NBrcbi83\nbthGjkH2sXJlIz09To4e7Qil4zab9WzeXEN1tXopdMe6tkSSLu2FB7SpoZMaMpSkUe2j5Didw/T2\nOvH5/BQWmidMTJfM6/H5/Fy+bOXDD1tDWTYLC800N5dQXOxJi/saLmOsQMZE9Em1ZUYrL3ht4522\n6ux0cOrUl+TllVJVlUtVVV7E39+8aePMmR7cbh+BQID+fhfz5s1i/frqkW3p+PWLFrXlRRvA6nA4\n+OCDD/joo484ePAgDQ0NfO973+Nb3/oWJlP0c7xa+s9EOT09PXznO9/B6XRy4MABFi5cGJecdA1g\nnRRHjx4d9Z7ZbKSqKp/6+sJJOyKgLHnPmzeLjRtrsFovYTYbuXChN+SIgBIrcOFC36jqmpNhrGtL\nJOnYnho6qSFDq9WMKScz00hNTbCvTZzDIZnXo9Np6e8fIj/fRF1dAXl5Jny+AG1tFv72t8NJ02My\nMhLRJ9WWGa08jUYz4bHv8vIc7ParrF9fTXV1/qi/t9vddHXdoa/PicXiwu9Xgvd7e++fLzJV16sG\n7e3t/OpXv+Lhhx+moqKC3/3ud2zatIlz585x5MgRdu7cGZMjAurpPxPllJSUcOjQIR5//HEeeugh\nnnjiCVpaWsZMjhavPlPWGbl4cfJVUGNtz+32hhJgheNwuPF61StEl4prS7f21NBJretKF11ikWGz\njQ5wVIrifZlUPeKVkYg+qbbMZMpzOIZHPfB4PP5RFX2jlRcPiZwnvF4vhw8f5sUXX2TRokWsWLGC\no0ePsmvXLjo6Oti8eTPPPPMM5eXlcbeRTvPBVJSj1Wp57rnnaGtrY+nSpXz/+9+npKSEb37zm7z6\n6qv88Y9/5OTJk5w6dSqu9mfWhuMkyc42UlBgHlUZt6wsJ+7YFEFIBOXlObS32yLey842pkVcixA7\ns2dnodVqIhySrCxD3AH66YLf72fnzp0cPHiQmpoaHnnkEX7729+ycuVKdHHUDxMST15eHnv37mXv\n3r3cvHmTlpYWzp49y3vvvcelS5dobW3lwIED1NXVUV9fz6OPPsquXbsmlDtlZ6aBgdFn+BPdnk6n\nZdmyUoaGvFitLjQaJap94cLiiQXE2FYyScf21NBJretKF11ikVFfX8Dt24PcuKEUZMvJyWD58nI+\n+yymdD6T1iNeGYnok2rLTKa8ysocGhuLuHzZitvtIyvLwJIlpePWFEr36wXlafsb3/gGr7/+OhUV\nFQltO53mg+kip6qqiieeeCLivT179rBv3z6uXbvGtWvXKCqKLhVGOgewfgJsTrUSQlKxAePXRxem\nI2L3mYnYfebRAmxJtRKCIAiCIAiCIAiCIAiCIAiCIAiCIIxiVaoVEJKC2HnmILaeGUwLO6dzAGs0\nmIBkVYzKANwT/lV8rADWAvnAAPA5cCxBbY2VW0YD/AX4SgLaWwR4gfBD7GuAiTLjbAQ+nUS72SjX\nNfnjI+r0s1j7z2T7hFp2jtd+97IU5TquAdsAI3AQGKt6n5q2C0ft+WIyc4KaYz4RY1otuweJxf7h\nqN0X1OoD8dg+XcY0pGZcj8tUcUa+DexFuXl/Al4HAsDHwINJ0uF/gX9IgNw3UQz4V+5Gl29FudY9\nCWjPxdgd7gFg/EpTsfPvwGzAAxQD3wVuM9pur6DYM7w/fhv4A/DjKNv6LvAvwCDwFvA0yoD4APh5\nlDIS2c9i6T9q9Ak17Byt/Sbi1yiTtxnlH4EDsAOVwG7UsV04yZov4p0T1B7zao9pteweZCL7h6NW\nX0h0H4jV9ukypiF543pa8jlKThQN8CzwIVCAcvPU5tP7fPUnoC2A++Xnnnze7rE5geKZ38tfE9BW\n+MpGM8qxrpWMttuHKN7911GOc28BviC2o91HUZ4czMBNlIGvAY7EIEONfqZG/1GjT6hh52jtNxHh\nep8Ne90y8l0N24Wj9nyh9pyg9phXe0yrZfcgE9k/HLX6glp9QC3bp8uYhuSN65iYSknPvCPff41i\nlP9G8e7UpgjF0xy+5/2PEtAWwHHgNyietgPIRfGYTySovUdQPOx72Z6AtrQok8kwcAb4R+Ad4N4q\nS19HGRTPoXjoPwesxNap3ShPUC7gP7lrv1iXUifbz9ToP2r0CTXsHK39JiI8lea/hb0OphNVy3bh\nqDlfqD0nqD3m1R7Tatk9yET2D0fNvqBGH1DL9ukypiF543pa8s9AzT3vVQD/kYC2djC297k8AW0F\nWYbivf8IZYlyaQLbSiargZJ73tOjLKHejwbgAHAoxrZ2Mtq5NgL7Y5ChRj9Tq/+kQ5+Ix35jsZCx\nbfPYyGs1bBeO2vNFIuaEdLDv/VDL7kEmsn84avUFtfqAmrZPF5sna1zPCP6QxLbeTWJbgoIa91wN\nGWr0M+k/saP2PVN7vhCbJg+17rVafUBsnyCmatXesiS2VZrEtgQFNe65GjLU6GfSf2JH7Xum9nwh\nNk0eat1rtfqA2D5BTFVnRBAEQRCEaYI4I4IgCIIgpBRxRgRBEARBEOLg3kjg6dKWoKDGPZ9OMmYa\nat+zdJcn3B+17nW6yREEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQUgrtqOUdL4EvJhiXYTk8BbQ\nQ2ShKWF6U4VSlOw8cA54PrXqCEnChFIA9BRwAXgtteoIwtjogMtALWBA6bCNqVRISAobUWpUiDMy\ncygFloy8zgZakbE+U8gc+a5HqVC8IYW6pBWSZyR9WIXijFwHPMB7KNVshenNZErRC1OTbpSHDYA7\nwJdAeerUEZKIc+S7EeUB1JpCXdIKcUbShwrgZtjPHSPvCYIwfalFWRn7IsV6CMlBi+KI9qBs1V1I\nrTrpgzgj6UMg1QoIgpBUsoH3gT0oKyTC9MePskVXCWwCtqRUmzRCnJH04RZKYFuQKpTVEUEQph8G\n4APgHeBPKdZFSD424H+AFalWRBDuRQ9cQVm2NSIBrDOJWiSAdSahAX4PvJFqRYSkUgTkj7w2A4eB\nralTRxDuzw6UyPrLwI9SrIuQHN4FOgE3SszQk6lVR0gCG1CW608BJ0e+tqdUIyEZLAZOoNj9DPCv\nqVVHEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARB\nEARhBvH/79EO0YiEt/4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1154b4310>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Train and test the scikit-learn SGD logistic regression.\n",
+      "clf = sklearn.linear_model.SGDClassifier(\n",
+      "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
+      "\n",
+      "clf.fit(X, y)\n",
+      "yt_pred = clf.predict(Xt)\n",
+      "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Accuracy: 0.764\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Write out the data to HDF5 files in a temp directory.\n",
+      "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+      "dirname = os.path.abspath('./hdf5_classification/data')\n",
+      "if not os.path.exists(dirname):\n",
+      "    os.makedirs(dirname)\n",
+      "\n",
+      "train_filename = os.path.join(dirname, 'train.h5')\n",
+      "test_filename = os.path.join(dirname, 'test.h5')\n",
+      "\n",
+      "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+      "# To show this off, we'll list the same data file twice.\n",
+      "with h5py.File(train_filename, 'w') as f:\n",
+      "    f['data'] = X\n",
+      "    f['label'] = y.astype(np.float32)\n",
+      "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+      "    f.write(train_filename + '\\n')\n",
+      "    f.write(train_filename + '\\n')\n",
+      "    \n",
+      "# HDF5 is pretty efficient, but can be further compressed.\n",
+      "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+      "with h5py.File(test_filename, 'w') as f:\n",
+      "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+      "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+      "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+      "    f.write(test_filename + '\\n')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Run caffe. Scroll down in the output to see the final\n",
+      "# test accuracy, which should be about the same as above.\n",
+      "!cd .. && ./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0904 17:54:31.724112 2129298192 caffe.cpp:90] Starting Optimization\r\n",
+        "I0904 17:54:31.725520 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
+        "test_iter: 1000\r\n",
+        "test_interval: 1000\r\n",
+        "base_lr: 0.01\r\n",
+        "display: 1000\r\n",
+        "max_iter: 10000\r\n",
+        "lr_policy: \"step\"\r\n",
+        "gamma: 0.1\r\n",
+        "momentum: 0.9\r\n",
+        "weight_decay: 0.0005\r\n",
+        "stepsize: 5000\r\n",
+        "snapshot: 10000\r\n",
+        "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
+        "solver_mode: CPU\r\n",
+        "net: \"examples/hdf5_classification/train_val.prototxt\"\r\n",
+        "I0904 17:54:31.725751 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val.prototxt\r\n",
+        "I0904 17:54:31.726464 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
+        "I0904 17:54:31.726560 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
+        "I0904 17:54:31.726586 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "name: \"LogisticRegressionNet\"\r\n",
+        "layers {\r\n",
+        "  top: \"data\"\r\n",
+        "  top: \"label\"\r\n",
+        "  name: \"data\"\r\n",
+        "  type: HDF5_DATA\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"examples/hdf5_classification/data/train.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
+        "  include {\r\n",
+        "    phase: TRAIN\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"data\"\r\n",
+        "  top: \"fc1\"\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  blobs_lr: 1\r\n",
+        "  blobs_lr: 2\r\n",
+        "  weight_decay: 1\r\n",
+        "  weight_decay: 0\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 2\r\n",
+        "    weight_filler {\r\n",
+        "      type: \"gaussian\"\r\n",
+        "      std: 0.01\r\n",
+        "    }\r\n",
+        "    bias_filler {\r\n",
+        "      type: \"constant\"\r\n",
+        "      value: 0\r\n",
+        "    }\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  bottom: \"label\"\r\n",
+        "  top: \"loss\"\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: SOFTMAX_LOSS\r\n",
+        "}\r\n",
+        "state {\r\n",
+        "  phase: TRAIN\r\n",
+        "}\r\n",
+        "I0904 17:54:31.727036 2129298192 net.cpp:67] Creating Layer data\r\n",
+        "I0904 17:54:31.727066 2129298192 net.cpp:356] data -> data\r\n",
+        "I0904 17:54:31.727125 2129298192 net.cpp:356] data -> label\r\n",
+        "I0904 17:54:31.727144 2129298192 net.cpp:96] Setting up data\r\n",
+        "I0904 17:54:31.727429 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
+        "I0904 17:54:31.727666 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
+        "I0904 17:54:31.727689 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.731825 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.731884 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
+        "I0904 17:54:31.731904 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
+        "I0904 17:54:31.731912 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0904 17:54:31.731935 2129298192 net.cpp:67] Creating Layer fc1\r\n",
+        "I0904 17:54:31.731942 2129298192 net.cpp:394] fc1 <- data\r\n",
+        "I0904 17:54:31.731964 2129298192 net.cpp:356] fc1 -> fc1\r\n",
+        "I0904 17:54:31.731992 2129298192 net.cpp:96] Setting up fc1\r\n",
+        "I0904 17:54:31.738070 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0904 17:54:31.738148 2129298192 net.cpp:67] Creating Layer loss\r\n",
+        "I0904 17:54:31.738158 2129298192 net.cpp:394] loss <- fc1\r\n",
+        "I0904 17:54:31.738172 2129298192 net.cpp:394] loss <- label\r\n",
+        "I0904 17:54:31.738185 2129298192 net.cpp:356] loss -> loss\r\n",
+        "I0904 17:54:31.738201 2129298192 net.cpp:96] Setting up loss\r\n",
+        "I0904 17:54:31.738327 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0904 17:54:31.738343 2129298192 net.cpp:109]     with loss weight 1\r\n",
+        "I0904 17:54:31.738368 2129298192 net.cpp:170] loss needs backward computation.\r\n",
+        "I0904 17:54:31.738378 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
+        "I0904 17:54:31.738386 2129298192 net.cpp:172] data does not need backward computation.\r\n",
+        "I0904 17:54:31.738392 2129298192 net.cpp:208] This network produces output loss\r\n",
+        "I0904 17:54:31.738402 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0904 17:54:31.738410 2129298192 net.cpp:219] Network initialization done.\r\n",
+        "I0904 17:54:31.738415 2129298192 net.cpp:220] Memory required for data: 284\r\n",
+        "I0904 17:54:31.738785 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val.prototxt\r\n",
+        "I0904 17:54:31.738836 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
+        "I0904 17:54:31.738858 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "name: \"LogisticRegressionNet\"\r\n",
+        "layers {\r\n",
+        "  top: \"data\"\r\n",
+        "  top: \"label\"\r\n",
+        "  name: \"data\"\r\n",
+        "  type: HDF5_DATA\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"examples/hdf5_classification/data/test.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
+        "  include {\r\n",
+        "    phase: TEST\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"data\"\r\n",
+        "  top: \"fc1\"\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  blobs_lr: 1\r\n",
+        "  blobs_lr: 2\r\n",
+        "  weight_decay: 1\r\n",
+        "  weight_decay: 0\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 2\r\n",
+        "    weight_filler {\r\n",
+        "      type: \"gaussian\"\r\n",
+        "      std: 0.01\r\n",
+        "    }\r\n",
+        "    bias_filler {\r\n",
+        "      type: \"constant\"\r\n",
+        "      value: 0\r\n",
+        "    }\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  bottom: \"label\"\r\n",
+        "  top: \"loss\"\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: SOFTMAX_LOSS\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  bottom: \"label\"\r\n",
+        "  top: \"accuracy\"\r\n",
+        "  name: \"accuracy\"\r\n",
+        "  type: ACCURACY\r\n",
+        "  include {\r\n",
+        "    phase: TEST\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "state {\r\n",
+        "  phase: TEST\r\n",
+        "}\r\n",
+        "I0904 17:54:31.739138 2129298192 net.cpp:67] Creating Layer data\r\n",
+        "I0904 17:54:31.739156 2129298192 net.cpp:356] data -> data\r\n",
+        "I0904 17:54:31.739235 2129298192 net.cpp:356] data -> label\r\n",
+        "I0904 17:54:31.739249 2129298192 net.cpp:96] Setting up data\r\n",
+        "I0904 17:54:31.739256 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
+        "I0904 17:54:31.739305 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n",
+        "I0904 17:54:31.739315 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
+        "I0904 17:54:31.740720 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
+        "I0904 17:54:31.740757 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
+        "I0904 17:54:31.740768 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
+        "I0904 17:54:31.740774 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0904 17:54:31.740795 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
+        "I0904 17:54:31.740803 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
+        "I0904 17:54:31.740813 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
+        "I0904 17:54:31.740826 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
+        "I0904 17:54:31.740835 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
+        "I0904 17:54:31.740842 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0904 17:54:31.740849 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0904 17:54:31.740859 2129298192 net.cpp:67] Creating Layer fc1\r\n",
+        "I0904 17:54:31.740865 2129298192 net.cpp:394] fc1 <- data\r\n",
+        "I0904 17:54:31.740877 2129298192 net.cpp:356] fc1 -> fc1\r\n",
+        "I0904 17:54:31.740887 2129298192 net.cpp:96] Setting up fc1\r\n",
+        "I0904 17:54:31.740906 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0904 17:54:31.740921 2129298192 net.cpp:67] Creating Layer fc1_fc1_0_split\r\n",
+        "I0904 17:54:31.740926 2129298192 net.cpp:394] fc1_fc1_0_split <- fc1\r\n",
+        "I0904 17:54:31.740934 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
+        "I0904 17:54:31.740943 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
+        "I0904 17:54:31.741047 2129298192 net.cpp:96] Setting up fc1_fc1_0_split\r\n",
+        "I0904 17:54:31.741065 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0904 17:54:31.741070 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0904 17:54:31.741081 2129298192 net.cpp:67] Creating Layer loss\r\n",
+        "I0904 17:54:31.741087 2129298192 net.cpp:394] loss <- fc1_fc1_0_split_0\r\n",
+        "I0904 17:54:31.741094 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
+        "I0904 17:54:31.741102 2129298192 net.cpp:356] loss -> loss\r\n",
+        "I0904 17:54:31.741111 2129298192 net.cpp:96] Setting up loss\r\n",
+        "I0904 17:54:31.741128 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0904 17:54:31.741139 2129298192 net.cpp:109]     with loss weight 1\r\n",
+        "I0904 17:54:31.741159 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
+        "I0904 17:54:31.741166 2129298192 net.cpp:394] accuracy <- fc1_fc1_0_split_1\r\n",
+        "I0904 17:54:31.741173 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
+        "I0904 17:54:31.741181 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
+        "I0904 17:54:31.741190 2129298192 net.cpp:96] Setting up accuracy\r\n",
+        "I0904 17:54:31.741197 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0904 17:54:31.741204 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
+        "I0904 17:54:31.741209 2129298192 net.cpp:170] loss needs backward computation.\r\n",
+        "I0904 17:54:31.741245 2129298192 net.cpp:170] fc1_fc1_0_split needs backward computation.\r\n",
+        "I0904 17:54:31.741252 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
+        "I0904 17:54:31.741257 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
+        "I0904 17:54:31.741263 2129298192 net.cpp:172] data does not need backward computation.\r\n",
+        "I0904 17:54:31.741269 2129298192 net.cpp:208] This network produces output accuracy\r\n",
+        "I0904 17:54:31.741336 2129298192 net.cpp:208] This network produces output loss\r\n",
+        "I0904 17:54:31.741353 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0904 17:54:31.741360 2129298192 net.cpp:219] Network initialization done.\r\n",
+        "I0904 17:54:31.741366 2129298192 net.cpp:220] Memory required for data: 528\r\n",
+        "I0904 17:54:31.741401 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
+        "I0904 17:54:31.741408 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
+        "I0904 17:54:31.741426 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
+        "I0904 17:54:31.747434 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.366799\r\n",
+        "I0904 17:54:31.747484 2129298192 solver.cpp:302]     Test net output #1: loss = 0.700084 (* 1 = 0.700084 loss)\r\n",
+        "I0904 17:54:31.747532 2129298192 solver.cpp:195] Iteration 0, loss = 0.701716\r\n",
+        "I0904 17:54:31.747545 2129298192 solver.cpp:210]     Train net output #0: loss = 0.701716 (* 1 = 0.701716 loss)\r\n",
+        "I0904 17:54:31.747561 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
+        "I0904 17:54:31.750849 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.752188 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.753466 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
+        "I0904 17:54:31.760200 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.5632\r\n",
+        "I0904 17:54:31.760282 2129298192 solver.cpp:302]     Test net output #1: loss = 0.619953 (* 1 = 0.619953 loss)\r\n",
+        "I0904 17:54:31.760310 2129298192 solver.cpp:195] Iteration 1000, loss = 0.520827\r\n",
+        "I0904 17:54:31.760323 2129298192 solver.cpp:210]     Train net output #0: loss = 0.520827 (* 1 = 0.520827 loss)\r\n",
+        "I0904 17:54:31.760334 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
+        "I0904 17:54:31.762578 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0904 17:54:31.762634 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.763407 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.765583 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
+        "I0904 17:54:31.772900 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
+        "I0904 17:54:31.773041 2129298192 solver.cpp:302]     Test net output #1: loss = 0.596873 (* 1 = 0.596873 loss)\r\n",
+        "I0904 17:54:31.773085 2129298192 solver.cpp:195] Iteration 2000, loss = 0.483448\r\n",
+        "I0904 17:54:31.773109 2129298192 solver.cpp:210]     Train net output #0: loss = 0.483448 (* 1 = 0.483448 loss)\r\n",
+        "I0904 17:54:31.773128 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
+        "I0904 17:54:31.774942 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.776607 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0904 17:54:31.781949 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n",
+        "I0904 17:54:31.788913 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7376\r\n",
+        "I0904 17:54:31.788986 2129298192 solver.cpp:302]     Test net output #1: loss = 0.596472 (* 1 = 0.596472 loss)\r\n",
+        "I0904 17:54:31.789000 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0904 17:54:31.789005 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.789858 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.789932 2129298192 solver.cpp:195] Iteration 3000, loss = 0.42153\r\n",
+        "I0904 17:54:31.789959 2129298192 solver.cpp:210]     Train net output #0: loss = 0.42153 (* 1 = 0.42153 loss)\r\n",
+        "I0904 17:54:31.790071 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
+        "I0904 17:54:31.793167 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.794184 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.795362 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
+        "I0904 17:54:31.802089 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.5632\r\n",
+        "I0904 17:54:31.802183 2129298192 solver.cpp:302]     Test net output #1: loss = 0.619953 (* 1 = 0.619953 loss)\r\n",
+        "I0904 17:54:31.802232 2129298192 solver.cpp:195] Iteration 4000, loss = 0.520827\r\n",
+        "I0904 17:54:31.802254 2129298192 solver.cpp:210]     Train net output #0: loss = 0.520827 (* 1 = 0.520827 loss)\r\n",
+        "I0904 17:54:31.802275 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
+        "I0904 17:54:31.805856 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0904 17:54:31.805949 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.807231 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.809342 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
+        "I0904 17:54:31.815631 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
+        "I0904 17:54:31.815783 2129298192 solver.cpp:302]     Test net output #1: loss = 0.596873 (* 1 = 0.596873 loss)\r\n",
+        "I0904 17:54:31.815820 2129298192 solver.cpp:195] Iteration 5000, loss = 0.483448\r\n",
+        "I0904 17:54:31.815835 2129298192 solver.cpp:210]     Train net output #0: loss = 0.483448 (* 1 = 0.483448 loss)\r\n",
+        "I0904 17:54:31.815853 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
+        "I0904 17:54:31.816906 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.818512 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.823189 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0904 17:54:31.833202 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7716\r\n",
+        "I0904 17:54:31.833345 2129298192 solver.cpp:302]     Test net output #1: loss = 0.591513 (* 1 = 0.591513 loss)\r\n",
+        "I0904 17:54:31.833375 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0904 17:54:31.833389 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.835263 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.835388 2129298192 solver.cpp:195] Iteration 6000, loss = 0.444828\r\n",
+        "I0904 17:54:31.835438 2129298192 solver.cpp:210]     Train net output #0: loss = 0.444828 (* 1 = 0.444828 loss)\r\n",
+        "I0904 17:54:31.835461 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
+        "I0904 17:54:31.840736 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.842892 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.844861 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
+        "I0904 17:54:31.852855 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7468\r\n",
+        "I0904 17:54:31.853005 2129298192 solver.cpp:302]     Test net output #1: loss = 0.593044 (* 1 = 0.593044 loss)\r\n",
+        "I0904 17:54:31.853076 2129298192 solver.cpp:195] Iteration 7000, loss = 0.489475\r\n",
+        "I0904 17:54:31.853099 2129298192 solver.cpp:210]     Train net output #0: loss = 0.489475 (* 1 = 0.489475 loss)\r\n",
+        "I0904 17:54:31.853118 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n",
+        "I0904 17:54:31.855607 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0904 17:54:31.855667 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.856359 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.858321 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
+        "I0904 17:54:31.864265 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7632\r\n",
+        "I0904 17:54:31.864409 2129298192 solver.cpp:302]     Test net output #1: loss = 0.592058 (* 1 = 0.592058 loss)\r\n",
+        "I0904 17:54:31.864442 2129298192 solver.cpp:195] Iteration 8000, loss = 0.525746\r\n",
+        "I0904 17:54:31.864454 2129298192 solver.cpp:210]     Train net output #0: loss = 0.525746 (* 1 = 0.525746 loss)\r\n",
+        "I0904 17:54:31.864466 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
+        "I0904 17:54:31.865531 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.866392 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.869691 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
+        "I0904 17:54:31.876518 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7708\r\n",
+        "I0904 17:54:31.876603 2129298192 solver.cpp:302]     Test net output #1: loss = 0.591526 (* 1 = 0.591526 loss)\r\n",
+        "I0904 17:54:31.876616 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0904 17:54:31.876624 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0904 17:54:31.877727 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.877826 2129298192 solver.cpp:195] Iteration 9000, loss = 0.445997\r\n",
+        "I0904 17:54:31.877867 2129298192 solver.cpp:210]     Train net output #0: loss = 0.445997 (* 1 = 0.445997 loss)\r\n",
+        "I0904 17:54:31.877882 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
+        "I0904 17:54:31.881119 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0904 17:54:31.882143 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0904 17:54:31.883280 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
+        "I0904 17:54:31.883776 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
+        "I0904 17:54:31.884004 2129298192 solver.cpp:232] Iteration 10000, loss = 0.489453\r\n",
+        "I0904 17:54:31.884037 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
+        "I0904 17:54:31.890312 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7468\r\n",
+        "I0904 17:54:31.890403 2129298192 solver.cpp:302]     Test net output #1: loss = 0.593055 (* 1 = 0.593055 loss)\r\n",
+        "I0904 17:54:31.890415 2129298192 solver.cpp:237] Optimization Done.\r\n",
+        "I0904 17:54:31.890422 2129298192 caffe.cpp:114] Optimization Done.\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+      "shutil.rmtree(dirname)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
new file mode 100644
index 00000000000..040162076b8
--- /dev/null
+++ b/examples/hdf5_classification/solver.prototxt
@@ -0,0 +1,14 @@
+net: "examples/hdf5_classification/train_val.prototxt"
+test_iter: 1000
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 5000
+display: 1000
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/hdf5_classification/data/train"
+solver_mode: CPU
diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
new file mode 100644
index 00000000000..b55b6644b17
--- /dev/null
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -0,0 +1,59 @@
+name: "LogisticRegressionNet"
+layers {
+  name: "data"
+  type: HDF5_DATA
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: HDF5_DATA
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+  include: { phase: TEST }
+}
+layers {
+  name: "fc1"
+  type: INNER_PRODUCT
+  bottom: "data"
+  top: "fc1"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc1"
+  bottom: "label"
+  top: "loss"
+}
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc1"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}

From a8570017758a29da1c8a2187d9561790d97c6425 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 4 Sep 2014 11:43:41 -0700
Subject: [PATCH 0678/2053] fix fine-tuning example: paths, test acc., and
 total fine-tuning time

---
 examples/finetune_flickr_style/readme.md      | 65 ++++++++++---------
 .../finetune_flickr_style/train_val.prototxt  |  2 +-
 2 files changed, 34 insertions(+), 33 deletions(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index dad45aeb560..4220102e7f7 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -60,7 +60,7 @@ We'll also need the ImageNet-trained model, which you can obtain by running `./s
 
 Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
 
-    caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/flickr_style_solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
+    caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
 
     [...]
 
@@ -72,77 +72,78 @@ Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.
 
     I0828 22:17:48.338963 11510 solver.cpp:165] Solving FlickrStyleCaffeNet
     I0828 22:17:48.339010 11510 solver.cpp:251] Iteration 0, Testing net (#0)
-    I0828 22:18:14.313817 11510 solver.cpp:302]     Test net output #0: accuracy = 0.0416
-    I0828 22:18:14.476822 11510 solver.cpp:195] Iteration 0, loss = 3.75717
+    I0828 22:18:14.313817 11510 solver.cpp:302]     Test net output #0: accuracy = 0.0308
+    I0828 22:18:14.476822 11510 solver.cpp:195] Iteration 0, loss = 3.78589
     I0828 22:18:14.476878 11510 solver.cpp:397] Iteration 0, lr = 0.001
-    I0828 22:18:19.700408 11510 solver.cpp:195] Iteration 20, loss = 3.1689
+    I0828 22:18:19.700408 11510 solver.cpp:195] Iteration 20, loss = 3.25728
     I0828 22:18:19.700461 11510 solver.cpp:397] Iteration 20, lr = 0.001
-    I0828 22:18:24.924685 11510 solver.cpp:195] Iteration 40, loss = 2.3549
+    I0828 22:18:24.924685 11510 solver.cpp:195] Iteration 40, loss = 2.18531
     I0828 22:18:24.924741 11510 solver.cpp:397] Iteration 40, lr = 0.001
-    I0828 22:18:30.114858 11510 solver.cpp:195] Iteration 60, loss = 2.74191
+    I0828 22:18:30.114858 11510 solver.cpp:195] Iteration 60, loss = 2.4915
     I0828 22:18:30.114910 11510 solver.cpp:397] Iteration 60, lr = 0.001
-    I0828 22:18:35.328071 11510 solver.cpp:195] Iteration 80, loss = 1.9147
+    I0828 22:18:35.328071 11510 solver.cpp:195] Iteration 80, loss = 2.04539
     I0828 22:18:35.328127 11510 solver.cpp:397] Iteration 80, lr = 0.001
-    I0828 22:18:40.588317 11510 solver.cpp:195] Iteration 100, loss = 1.81419
+    I0828 22:18:40.588317 11510 solver.cpp:195] Iteration 100, loss = 2.1924
     I0828 22:18:40.588373 11510 solver.cpp:397] Iteration 100, lr = 0.001
-    I0828 22:18:46.171576 11510 solver.cpp:195] Iteration 120, loss = 2.02105
+    I0828 22:18:46.171576 11510 solver.cpp:195] Iteration 120, loss = 2.25107
     I0828 22:18:46.171669 11510 solver.cpp:397] Iteration 120, lr = 0.001
-    I0828 22:18:51.757809 11510 solver.cpp:195] Iteration 140, loss = 1.49083
+    I0828 22:18:51.757809 11510 solver.cpp:195] Iteration 140, loss = 1.355
     I0828 22:18:51.757863 11510 solver.cpp:397] Iteration 140, lr = 0.001
-    I0828 22:18:57.345080 11510 solver.cpp:195] Iteration 160, loss = 1.35319
+    I0828 22:18:57.345080 11510 solver.cpp:195] Iteration 160, loss = 1.40815
     I0828 22:18:57.345135 11510 solver.cpp:397] Iteration 160, lr = 0.001
-    I0828 22:19:02.928794 11510 solver.cpp:195] Iteration 180, loss = 1.11658
+    I0828 22:19:02.928794 11510 solver.cpp:195] Iteration 180, loss = 1.6558
     I0828 22:19:02.928850 11510 solver.cpp:397] Iteration 180, lr = 0.001
-    I0828 22:19:08.514497 11510 solver.cpp:195] Iteration 200, loss = 1.08851
+    I0828 22:19:08.514497 11510 solver.cpp:195] Iteration 200, loss = 0.88126
     I0828 22:19:08.514552 11510 solver.cpp:397] Iteration 200, lr = 0.001
 
     [...]
 
-    I0828 22:22:40.789010 11510 solver.cpp:195] Iteration 960, loss = 0.0844627
+    I0828 22:22:40.789010 11510 solver.cpp:195] Iteration 960, loss = 0.112586
     I0828 22:22:40.789175 11510 solver.cpp:397] Iteration 960, lr = 0.001
-    I0828 22:22:46.376626 11510 solver.cpp:195] Iteration 980, loss = 0.0110937
+    I0828 22:22:46.376626 11510 solver.cpp:195] Iteration 980, loss = 0.0959077
     I0828 22:22:46.376682 11510 solver.cpp:397] Iteration 980, lr = 0.001
     I0828 22:22:51.687258 11510 solver.cpp:251] Iteration 1000, Testing net (#0)
-    I0828 22:23:17.438894 11510 solver.cpp:302]     Test net output #0: accuracy = 1
+    I0828 22:23:17.438894 11510 solver.cpp:302]     Test net output #0: accuracy = 0.2356
 
-Note how rapidly the loss went down. Although the 100% accuracy is optimistic, it is evidence the model is learning quickly and well.
+Note how rapidly the loss went down. Although the 23.5% accuracy is only modest, it was achieved in only 1000, and evidence that the model is starting to learn quickly and well.
+Once the model is fully fine-tuned on the whole training set over 100,000 iterations the final validation accuracy is 91.64%. This takes ~7 hours in Caffe on a K40 GPU.
 
 For comparison, here is how the loss goes down when we do not start with a pre-trained model:
 
     I0828 22:24:18.624004 12919 solver.cpp:165] Solving FlickrStyleCaffeNet
     I0828 22:24:18.624099 12919 solver.cpp:251] Iteration 0, Testing net (#0)
-    I0828 22:24:44.520992 12919 solver.cpp:302]     Test net output #0: accuracy = 0.045
-    I0828 22:24:44.676905 12919 solver.cpp:195] Iteration 0, loss = 3.33111
+    I0828 22:24:44.520992 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0366
+    I0828 22:24:44.676905 12919 solver.cpp:195] Iteration 0, loss = 3.47942
     I0828 22:24:44.677120 12919 solver.cpp:397] Iteration 0, lr = 0.001
-    I0828 22:24:50.152454 12919 solver.cpp:195] Iteration 20, loss = 2.98133
+    I0828 22:24:50.152454 12919 solver.cpp:195] Iteration 20, loss = 2.99694
     I0828 22:24:50.152509 12919 solver.cpp:397] Iteration 20, lr = 0.001
-    I0828 22:24:55.736256 12919 solver.cpp:195] Iteration 40, loss = 3.02124
+    I0828 22:24:55.736256 12919 solver.cpp:195] Iteration 40, loss = 3.0498
     I0828 22:24:55.736311 12919 solver.cpp:397] Iteration 40, lr = 0.001
-    I0828 22:25:01.316514 12919 solver.cpp:195] Iteration 60, loss = 2.99509
+    I0828 22:25:01.316514 12919 solver.cpp:195] Iteration 60, loss = 2.99549
     I0828 22:25:01.316567 12919 solver.cpp:397] Iteration 60, lr = 0.001
-    I0828 22:25:06.899554 12919 solver.cpp:195] Iteration 80, loss = 2.9928
+    I0828 22:25:06.899554 12919 solver.cpp:195] Iteration 80, loss = 3.00573
     I0828 22:25:06.899610 12919 solver.cpp:397] Iteration 80, lr = 0.001
-    I0828 22:25:12.484624 12919 solver.cpp:195] Iteration 100, loss = 2.99072
+    I0828 22:25:12.484624 12919 solver.cpp:195] Iteration 100, loss = 2.99094
     I0828 22:25:12.484678 12919 solver.cpp:397] Iteration 100, lr = 0.001
-    I0828 22:25:18.069056 12919 solver.cpp:195] Iteration 120, loss = 3.01816
+    I0828 22:25:18.069056 12919 solver.cpp:195] Iteration 120, loss = 3.01616
     I0828 22:25:18.069149 12919 solver.cpp:397] Iteration 120, lr = 0.001
-    I0828 22:25:23.650928 12919 solver.cpp:195] Iteration 140, loss = 2.9694
+    I0828 22:25:23.650928 12919 solver.cpp:195] Iteration 140, loss = 2.98786
     I0828 22:25:23.650984 12919 solver.cpp:397] Iteration 140, lr = 0.001
-    I0828 22:25:29.235535 12919 solver.cpp:195] Iteration 160, loss = 3.00383
+    I0828 22:25:29.235535 12919 solver.cpp:195] Iteration 160, loss = 3.00724
     I0828 22:25:29.235589 12919 solver.cpp:397] Iteration 160, lr = 0.001
-    I0828 22:25:34.816898 12919 solver.cpp:195] Iteration 180, loss = 2.99802
+    I0828 22:25:34.816898 12919 solver.cpp:195] Iteration 180, loss = 3.00099
     I0828 22:25:34.816953 12919 solver.cpp:397] Iteration 180, lr = 0.001
-    I0828 22:25:40.396656 12919 solver.cpp:195] Iteration 200, loss = 2.99769
+    I0828 22:25:40.396656 12919 solver.cpp:195] Iteration 200, loss = 2.99848
     I0828 22:25:40.396711 12919 solver.cpp:397] Iteration 200, lr = 0.001
 
     [...]
 
-    I0828 22:29:12.539094 12919 solver.cpp:195] Iteration 960, loss = 2.99314
+    I0828 22:29:12.539094 12919 solver.cpp:195] Iteration 960, loss = 2.99203
     I0828 22:29:12.539258 12919 solver.cpp:397] Iteration 960, lr = 0.001
-    I0828 22:29:18.123092 12919 solver.cpp:195] Iteration 980, loss = 2.99503
+    I0828 22:29:18.123092 12919 solver.cpp:195] Iteration 980, loss = 2.99345
     I0828 22:29:18.123147 12919 solver.cpp:397] Iteration 980, lr = 0.001
     I0828 22:29:23.432059 12919 solver.cpp:251] Iteration 1000, Testing net (#0)
-    I0828 22:29:49.409044 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0624
+    I0828 22:29:49.409044 12919 solver.cpp:302]     Test net output #0: accuracy = 0.0572
 
 This model is only beginning to learn.
 
diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index 46a198a8d89..7155c492360 100644
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -23,7 +23,7 @@ layers {
   top: "data"
   top: "label"
   image_data_param {
-    source: "data/flickr_style/train.txt"
+    source: "data/flickr_style/test.txt"
     batch_size: 50
     new_height: 256
     new_width: 256

From f748aee3ec0892fe2b9a4e6aae07ea99f03f0baa Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 4 Sep 2014 12:54:48 -0700
Subject: [PATCH 0679/2053] [fix] stop cloc complaint about cu type

---
 caffe.cloc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/caffe.cloc b/caffe.cloc
index 37a98bf524c..a36ab619113 100644
--- a/caffe.cloc
+++ b/caffe.cloc
@@ -27,7 +27,7 @@ C/C++ Header
     extension h
     extension hh
     extension hpp
-Cuda
+CUDA
     filter remove_matches ^\s*//
     filter remove_inline //.*$
     filter call_regexp_common C
@@ -50,4 +50,4 @@ make
     filename Makefile
     filename gnumakefile
     filename makefile
-    script_exe make
\ No newline at end of file
+    script_exe make

From b5b02dc3be416459bf384978f5234f1826b6c7d0 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 4 Sep 2014 15:40:04 -0700
Subject: [PATCH 0680/2053] [docs] fix formatting and other errors in loss &
 solver

---
 docs/tutorial/loss.md   | 48 ++++++++++++++++++-----------------------
 docs/tutorial/solver.md |  8 +++----
 2 files changed, 25 insertions(+), 31 deletions(-)

diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index 8c053ed60ab..3591858ad31 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -11,17 +11,15 @@ Each layer takes a set of input (`bottom`) blobs and produces a set of output (`
 Some of these layers' outputs may be used in the loss function.
 A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example:
 
-```
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "pred"
-  bottom: "label"
-  top: "loss"
-}
-```
-
-In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $1 \times 1 \times 1 \times 1$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
+    layers {
+      name: "loss"
+      type: SOFTMAX_LOSS
+      bottom: "pred"
+      bottom: "label"
+      top: "loss"
+    }
+
+In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $$1 \times 1 \times 1 \times 1$$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
 
 ### Loss weights
 
@@ -32,25 +30,21 @@ However, any layer can be used as a loss by adding a field `loss_weight: <float>
 Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
 So, the above `SOFTMAX_LOSS` layer could be equivalently written as:
 
-```
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "pred"
-  bottom: "label"
-  top: "loss"
-  loss_weight: 1
-}
-```
+    layers {
+      name: "loss"
+      type: SOFTMAX_LOSS
+      bottom: "pred"
+      bottom: "label"
+      top: "loss"
+      loss_weight: 1
+    }
 
 However, *any* layer able to backpropagate may be given a non-zero `loss_weight`, allowing one to, for example, regularize the activations produced by some intermediate layer(s) of the network if desired.
 For non-singleton outputs with an associated non-zero loss, the loss is computed simply by summing over all entries of the blob.
 
 The final loss in Caffe, then, is computed by summing the total weighted loss over the network, as in the following pseudo-code:
 
-```
-loss := 0
-for layer in layers:
-  for top, loss_weight in layer.tops, layer.loss_weights:
-    loss += loss_weight * sum(top)
-```
+    loss := 0
+    for layer in layers:
+      for top, loss_weight in layer.tops, layer.loss_weights:
+        loss += loss_weight * sum(top)
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index f5156ee0aa9..d90470c467b 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -39,7 +39,7 @@ $$L(W) \approx \frac{1}{N} \sum_i^N f_W\left(X^{(i)}\right) + \lambda r(W)$$
 
 The model computes $$f_W$$ in the forward pass and the gradient $$\nabla f_W$$ in the backward pass.
 
-The parameter update $$\Delta W$$ is formed by the solver from the error gradient $$\nabla f_W$$, the regularization gradient $$r(W)$$, and other particulars to each method.
+The parameter update $$\Delta W$$ is formed by the solver from the error gradient $$\nabla f_W$$, the regularization gradient $$\nabla r(W)$$, and other particulars to each method.
 
 ### SGD
 
@@ -111,9 +111,9 @@ the update formulas proposed by [1] are as follows, specified for each component
 
 $$
 (W_{t+1})_i =
-(W_t)_i +
-\frac{\alpha}{
-    \sqrt{\sum_{t'=1}^{t} \left( \nabla L(W) \right)_{t',i}^2}
+(W_t)_i - \alpha
+\frac{\left( \nabla L(W_t) \right)_{i}}{
+    \sqrt{\sum_{t'=1}^{t} \left( \nabla L(W_{t'}) \right)_i^2}
 }
 $$
 

From 51dd4b2628cef4891992ddebf4728818425351dc Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Fri, 5 Sep 2014 01:08:01 +0100
Subject: [PATCH 0681/2053] added a two-layer network that gets higher accuracy

---
 examples/hdf5_classification.ipynb            | 847 +++++++++++++-----
 examples/hdf5_classification/solver2.prototxt |  14 +
 .../hdf5_classification/train_val2.prototxt   |  86 ++
 3 files changed, 744 insertions(+), 203 deletions(-)
 create mode 100644 examples/hdf5_classification/solver2.prototxt
 create mode 100644 examples/hdf5_classification/train_val2.prototxt

diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
index 9232cb18455..2059e6ff3a6 100644
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
-  "example_name": "Classification with HDF5 data",
   "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "include_in_docs": true
+  "example_name": "Classification with HDF5 data",
+  "include_in_docs": true,
+  "signature": "sha256:c3b84add3bb83e91137f396a48f46d46bf7921b242fc42c58390b30806e5a028"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -71,9 +72,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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l6oxG2nZs53O/uIOensaCqlKpceFCjNmpFFq9jr5+Dzt3thMOF7l2LYEoygSD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\nWyx4BwdRWi38Bw7gG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mZvE5qaRKrV8D99ltm8h/iVDcqFKnq7nXy6RGBkkK31y4yFLOzm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pDHYnXhCPpZfnsRm9PI3uE+41/7XSyBAOVCgUDQTvQgh8upIxuNY3fqCLjVbP74XRqxPQYe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m2Lo1RDJZJperEgrZqFZV6nWVxdkykx/MMj+RJRp1sLRYxOm0oNVlAi4DWruNyekcM9N1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\nfNjjrVYTgpDD64WOjuiav59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ryMpKnpGRGJJYoybVyWfKnDoT54H7ulGWp/D092N7TXv1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OSQq9Ugl1Ovlzh5/jB6lwNrt5VOPknpzVRremWF4BNPYB0cpF4o7Gu/3G5K0Six6el9kbokIchk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bukxw+Pp6oq8/Mp5uaSbG4WcTgMVKstRkfdOBwG0qky1UIFm9hmwNZE3o5gCwYx2O04hoep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+EokKsizTbst88Ytjt5JaM5TL3SyM555b5PjxMK1Whz17fMzM7HD4cIiVlTylUgOLxcnk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sdhObNgUaQYyyAZsGC8ePo9br2INBVFluVOV1OlE7zKhqY2rN4TBht5v44Q+v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h3spVisI7TqfOfUBEqVAml9gTd/dBOdRqSSzbORL+B269H1jjG8I0Cj0SC0EiG8nqBeM6E0ZxH1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6hUakweD5Ik01Xr6RTyOA+Noq7lUKlUiE4n9XQa6Z6Gqtto3AuMfPDHy782b4gfpDSGw1Zy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\nk+zV24cEuflBVv7uPl3Hj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s0mtVyOrhMnMHV1Eb5+nfT6OqVIBFGlwjhdRukcpmdugsTG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fIuVFOp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\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ofF0Zryw3GB2N4/FsnPDL5RqlkvyuOiOHDy+27MvlKhSLVR55pPstJdiOjSWQpBo6nYpM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\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8+Z//+YOexn3DR//u8R7wflv9ep1In0+mnkthsJrQyjKRy1Os/fRV/OMjJBaWsXidNCU9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\nALgAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1154b4310>"
+        "<matplotlib.figure.Figure at 0x1155cf1d0>"
        ]
       }
      ],
@@ -98,7 +99,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Accuracy: 0.764\n"
+        "Accuracy: 0.763\n"
        ]
       }
      ],
@@ -154,8 +155,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0904 17:54:31.724112 2129298192 caffe.cpp:90] Starting Optimization\r\n",
-        "I0904 17:54:31.725520 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
+        "I0905 01:07:27.099238 2129298192 caffe.cpp:90] Starting Optimization\r\n",
+        "I0905 01:07:27.100469 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
         "test_iter: 1000\r\n",
         "test_interval: 1000\r\n",
         "base_lr: 0.01\r\n",
@@ -170,10 +171,16 @@
         "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
         "solver_mode: CPU\r\n",
         "net: \"examples/hdf5_classification/train_val.prototxt\"\r\n",
-        "I0904 17:54:31.725751 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val.prototxt\r\n",
-        "I0904 17:54:31.726464 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0904 17:54:31.726560 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0904 17:54:31.726586 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "I0905 01:07:27.100630 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val.prototxt\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0905 01:07:27.100988 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
+        "I0905 01:07:27.101011 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
+        "I0905 01:07:27.101022 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
         "name: \"LogisticRegressionNet\"\r\n",
         "layers {\r\n",
         "  top: \"data\"\r\n",
@@ -219,39 +226,39 @@
         "state {\r\n",
         "  phase: TRAIN\r\n",
         "}\r\n",
-        "I0904 17:54:31.727036 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0904 17:54:31.727066 2129298192 net.cpp:356] data -> data\r\n",
-        "I0904 17:54:31.727125 2129298192 net.cpp:356] data -> label\r\n",
-        "I0904 17:54:31.727144 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0904 17:54:31.727429 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
-        "I0904 17:54:31.727666 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
-        "I0904 17:54:31.727689 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.731825 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.731884 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0904 17:54:31.731904 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0904 17:54:31.731912 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0904 17:54:31.731935 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0904 17:54:31.731942 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0904 17:54:31.731964 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0904 17:54:31.731992 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0904 17:54:31.738070 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0904 17:54:31.738148 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0904 17:54:31.738158 2129298192 net.cpp:394] loss <- fc1\r\n",
-        "I0904 17:54:31.738172 2129298192 net.cpp:394] loss <- label\r\n",
-        "I0904 17:54:31.738185 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0904 17:54:31.738201 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0904 17:54:31.738327 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0904 17:54:31.738343 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0904 17:54:31.738368 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0904 17:54:31.738378 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0904 17:54:31.738386 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0904 17:54:31.738392 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0904 17:54:31.738402 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0904 17:54:31.738410 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0904 17:54:31.738415 2129298192 net.cpp:220] Memory required for data: 284\r\n",
-        "I0904 17:54:31.738785 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val.prototxt\r\n",
-        "I0904 17:54:31.738836 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0904 17:54:31.738858 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "I0905 01:07:27.105614 2129298192 net.cpp:67] Creating Layer data\r\n",
+        "I0905 01:07:27.105664 2129298192 net.cpp:356] data -> data\r\n",
+        "I0905 01:07:27.105698 2129298192 net.cpp:356] data -> label\r\n",
+        "I0905 01:07:27.105710 2129298192 net.cpp:96] Setting up data\r\n",
+        "I0905 01:07:27.105717 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
+        "I0905 01:07:27.105813 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
+        "I0905 01:07:27.105828 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.109418 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.109501 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
+        "I0905 01:07:27.109522 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
+        "I0905 01:07:27.109531 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.109560 2129298192 net.cpp:67] Creating Layer fc1\r\n",
+        "I0905 01:07:27.109570 2129298192 net.cpp:394] fc1 <- data\r\n",
+        "I0905 01:07:27.109590 2129298192 net.cpp:356] fc1 -> fc1\r\n",
+        "I0905 01:07:27.109618 2129298192 net.cpp:96] Setting up fc1\r\n",
+        "I0905 01:07:27.115136 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.115190 2129298192 net.cpp:67] Creating Layer loss\r\n",
+        "I0905 01:07:27.115198 2129298192 net.cpp:394] loss <- fc1\r\n",
+        "I0905 01:07:27.115206 2129298192 net.cpp:394] loss <- label\r\n",
+        "I0905 01:07:27.115214 2129298192 net.cpp:356] loss -> loss\r\n",
+        "I0905 01:07:27.115224 2129298192 net.cpp:96] Setting up loss\r\n",
+        "I0905 01:07:27.115237 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0905 01:07:27.115244 2129298192 net.cpp:109]     with loss weight 1\r\n",
+        "I0905 01:07:27.115260 2129298192 net.cpp:170] loss needs backward computation.\r\n",
+        "I0905 01:07:27.115267 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
+        "I0905 01:07:27.115273 2129298192 net.cpp:172] data does not need backward computation.\r\n",
+        "I0905 01:07:27.115278 2129298192 net.cpp:208] This network produces output loss\r\n",
+        "I0905 01:07:27.115288 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0905 01:07:27.115295 2129298192 net.cpp:219] Network initialization done.\r\n",
+        "I0905 01:07:27.115301 2129298192 net.cpp:220] Memory required for data: 284\r\n",
+        "I0905 01:07:27.115622 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val.prototxt\r\n",
+        "I0905 01:07:27.115644 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
+        "I0905 01:07:27.115656 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
         "name: \"LogisticRegressionNet\"\r\n",
         "layers {\r\n",
         "  top: \"data\"\r\n",
@@ -307,184 +314,618 @@
         "state {\r\n",
         "  phase: TEST\r\n",
         "}\r\n",
-        "I0904 17:54:31.739138 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0904 17:54:31.739156 2129298192 net.cpp:356] data -> data\r\n",
-        "I0904 17:54:31.739235 2129298192 net.cpp:356] data -> label\r\n",
-        "I0904 17:54:31.739249 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0904 17:54:31.739256 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
-        "I0904 17:54:31.739305 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n",
-        "I0904 17:54:31.739315 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
-        "I0904 17:54:31.740720 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
-        "I0904 17:54:31.740757 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0904 17:54:31.740768 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0904 17:54:31.740774 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0904 17:54:31.740795 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
-        "I0904 17:54:31.740803 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
-        "I0904 17:54:31.740813 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0904 17:54:31.740826 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0904 17:54:31.740835 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
-        "I0904 17:54:31.740842 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0904 17:54:31.740849 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0904 17:54:31.740859 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0904 17:54:31.740865 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0904 17:54:31.740877 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0904 17:54:31.740887 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0904 17:54:31.740906 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0904 17:54:31.740921 2129298192 net.cpp:67] Creating Layer fc1_fc1_0_split\r\n",
-        "I0904 17:54:31.740926 2129298192 net.cpp:394] fc1_fc1_0_split <- fc1\r\n",
-        "I0904 17:54:31.740934 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
-        "I0904 17:54:31.740943 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
-        "I0904 17:54:31.741047 2129298192 net.cpp:96] Setting up fc1_fc1_0_split\r\n",
-        "I0904 17:54:31.741065 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0904 17:54:31.741070 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0904 17:54:31.741081 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0904 17:54:31.741087 2129298192 net.cpp:394] loss <- fc1_fc1_0_split_0\r\n",
-        "I0904 17:54:31.741094 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
-        "I0904 17:54:31.741102 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0904 17:54:31.741111 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0904 17:54:31.741128 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0904 17:54:31.741139 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0904 17:54:31.741159 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
-        "I0904 17:54:31.741166 2129298192 net.cpp:394] accuracy <- fc1_fc1_0_split_1\r\n",
-        "I0904 17:54:31.741173 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
-        "I0904 17:54:31.741181 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
-        "I0904 17:54:31.741190 2129298192 net.cpp:96] Setting up accuracy\r\n",
-        "I0904 17:54:31.741197 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0904 17:54:31.741204 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
-        "I0904 17:54:31.741209 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0904 17:54:31.741245 2129298192 net.cpp:170] fc1_fc1_0_split needs backward computation.\r\n",
-        "I0904 17:54:31.741252 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0904 17:54:31.741257 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
-        "I0904 17:54:31.741263 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0904 17:54:31.741269 2129298192 net.cpp:208] This network produces output accuracy\r\n",
-        "I0904 17:54:31.741336 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0904 17:54:31.741353 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0904 17:54:31.741360 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0904 17:54:31.741366 2129298192 net.cpp:220] Memory required for data: 528\r\n",
-        "I0904 17:54:31.741401 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
-        "I0904 17:54:31.741408 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
-        "I0904 17:54:31.741426 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
-        "I0904 17:54:31.747434 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.366799\r\n",
-        "I0904 17:54:31.747484 2129298192 solver.cpp:302]     Test net output #1: loss = 0.700084 (* 1 = 0.700084 loss)\r\n",
-        "I0904 17:54:31.747532 2129298192 solver.cpp:195] Iteration 0, loss = 0.701716\r\n",
-        "I0904 17:54:31.747545 2129298192 solver.cpp:210]     Train net output #0: loss = 0.701716 (* 1 = 0.701716 loss)\r\n",
-        "I0904 17:54:31.747561 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
-        "I0904 17:54:31.750849 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.752188 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.753466 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
-        "I0904 17:54:31.760200 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.5632\r\n",
-        "I0904 17:54:31.760282 2129298192 solver.cpp:302]     Test net output #1: loss = 0.619953 (* 1 = 0.619953 loss)\r\n",
-        "I0904 17:54:31.760310 2129298192 solver.cpp:195] Iteration 1000, loss = 0.520827\r\n",
-        "I0904 17:54:31.760323 2129298192 solver.cpp:210]     Train net output #0: loss = 0.520827 (* 1 = 0.520827 loss)\r\n",
-        "I0904 17:54:31.760334 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
-        "I0904 17:54:31.762578 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0904 17:54:31.762634 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.763407 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.765583 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
-        "I0904 17:54:31.772900 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
-        "I0904 17:54:31.773041 2129298192 solver.cpp:302]     Test net output #1: loss = 0.596873 (* 1 = 0.596873 loss)\r\n",
-        "I0904 17:54:31.773085 2129298192 solver.cpp:195] Iteration 2000, loss = 0.483448\r\n",
-        "I0904 17:54:31.773109 2129298192 solver.cpp:210]     Train net output #0: loss = 0.483448 (* 1 = 0.483448 loss)\r\n",
-        "I0904 17:54:31.773128 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
-        "I0904 17:54:31.774942 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.776607 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+        "I0905 01:07:27.115854 2129298192 net.cpp:67] Creating Layer data\r\n",
+        "I0905 01:07:27.115864 2129298192 net.cpp:356] data -> data\r\n",
+        "I0905 01:07:27.116004 2129298192 net.cpp:356] data -> label\r\n",
+        "I0905 01:07:27.116024 2129298192 net.cpp:96] Setting up data\r\n",
+        "I0905 01:07:27.116030 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
+        "I0905 01:07:27.116080 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n",
+        "I0905 01:07:27.116089 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
+        "I0905 01:07:27.117313 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
+        "I0905 01:07:27.117348 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
+        "I0905 01:07:27.117357 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
+        "I0905 01:07:27.117364 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.117377 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
+        "I0905 01:07:27.117384 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
+        "I0905 01:07:27.117393 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
+        "I0905 01:07:27.117409 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
+        "I0905 01:07:27.117419 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
+        "I0905 01:07:27.117427 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.117434 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.117444 2129298192 net.cpp:67] Creating Layer fc1\r\n",
+        "I0905 01:07:27.117449 2129298192 net.cpp:394] fc1 <- data\r\n",
+        "I0905 01:07:27.117470 2129298192 net.cpp:356] fc1 -> fc1\r\n",
+        "I0905 01:07:27.117478 2129298192 net.cpp:96] Setting up fc1\r\n",
+        "I0905 01:07:27.117506 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.117519 2129298192 net.cpp:67] Creating Layer fc1_fc1_0_split\r\n",
+        "I0905 01:07:27.117527 2129298192 net.cpp:394] fc1_fc1_0_split <- fc1\r\n",
+        "I0905 01:07:27.117534 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
+        "I0905 01:07:27.117543 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
+        "I0905 01:07:27.117640 2129298192 net.cpp:96] Setting up fc1_fc1_0_split\r\n",
+        "I0905 01:07:27.117655 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.117662 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.117673 2129298192 net.cpp:67] Creating Layer loss\r\n",
+        "I0905 01:07:27.117679 2129298192 net.cpp:394] loss <- fc1_fc1_0_split_0\r\n",
+        "I0905 01:07:27.117687 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
+        "I0905 01:07:27.117696 2129298192 net.cpp:356] loss -> loss\r\n",
+        "I0905 01:07:27.117704 2129298192 net.cpp:96] Setting up loss\r\n",
+        "I0905 01:07:27.117717 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0905 01:07:27.117723 2129298192 net.cpp:109]     with loss weight 1\r\n",
+        "I0905 01:07:27.117743 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
+        "I0905 01:07:27.117749 2129298192 net.cpp:394] accuracy <- fc1_fc1_0_split_1\r\n",
+        "I0905 01:07:27.117756 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
+        "I0905 01:07:27.117764 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
+        "I0905 01:07:27.117774 2129298192 net.cpp:96] Setting up accuracy\r\n",
+        "I0905 01:07:27.117781 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0905 01:07:27.117789 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
+        "I0905 01:07:27.117794 2129298192 net.cpp:170] loss needs backward computation.\r\n",
+        "I0905 01:07:27.117835 2129298192 net.cpp:170] fc1_fc1_0_split needs backward computation.\r\n",
+        "I0905 01:07:27.117842 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
+        "I0905 01:07:27.117848 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
+        "I0905 01:07:27.117854 2129298192 net.cpp:172] data does not need backward computation.\r\n",
+        "I0905 01:07:27.117861 2129298192 net.cpp:208] This network produces output accuracy\r\n",
+        "I0905 01:07:27.117866 2129298192 net.cpp:208] This network produces output loss\r\n",
+        "I0905 01:07:27.117877 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0905 01:07:27.117926 2129298192 net.cpp:219] Network initialization done.\r\n",
+        "I0905 01:07:27.117938 2129298192 net.cpp:220] Memory required for data: 528\r\n",
+        "I0905 01:07:27.117985 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
+        "I0905 01:07:27.117992 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
+        "I0905 01:07:27.118026 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
+        "I0905 01:07:27.123764 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.646801\r\n",
+        "I0905 01:07:27.123847 2129298192 solver.cpp:302]     Test net output #1: loss = 0.690777 (* 1 = 0.690777 loss)\r\n",
+        "I0905 01:07:27.123888 2129298192 solver.cpp:195] Iteration 0, loss = 0.689469\r\n",
+        "I0905 01:07:27.123898 2129298192 solver.cpp:210]     Train net output #0: loss = 0.689469 (* 1 = 0.689469 loss)\r\n",
+        "I0905 01:07:27.123915 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
+        "I0905 01:07:27.127096 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.128094 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.129258 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
+        "I0905 01:07:27.135226 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.745599\r\n",
+        "I0905 01:07:27.135296 2129298192 solver.cpp:302]     Test net output #1: loss = 0.573658 (* 1 = 0.573658 loss)\r\n",
+        "I0905 01:07:27.135315 2129298192 solver.cpp:195] Iteration 1000, loss = 0.49682\r\n",
+        "I0905 01:07:27.135325 2129298192 solver.cpp:210]     Train net output #0: loss = 0.49682 (* 1 = 0.49682 loss)\r\n",
+        "I0905 01:07:27.135334 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
+        "I0905 01:07:27.137315 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.137358 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.138335 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.140410 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
+        "I0905 01:07:27.147435 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.746399\r\n",
+        "I0905 01:07:27.147514 2129298192 solver.cpp:302]     Test net output #1: loss = 0.582127 (* 1 = 0.582127 loss)\r\n",
+        "I0905 01:07:27.147541 2129298192 solver.cpp:195] Iteration 2000, loss = 0.555272\r\n",
+        "I0905 01:07:27.147553 2129298192 solver.cpp:210]     Train net output #0: loss = 0.555272 (* 1 = 0.555272 loss)\r\n",
+        "I0905 01:07:27.147565 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
+        "I0905 01:07:27.148572 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.149441 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.152377 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0904 17:54:31.781949 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n",
-        "I0904 17:54:31.788913 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7376\r\n",
-        "I0904 17:54:31.788986 2129298192 solver.cpp:302]     Test net output #1: loss = 0.596472 (* 1 = 0.596472 loss)\r\n",
-        "I0904 17:54:31.789000 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0904 17:54:31.789005 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.789858 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.789932 2129298192 solver.cpp:195] Iteration 3000, loss = 0.42153\r\n",
-        "I0904 17:54:31.789959 2129298192 solver.cpp:210]     Train net output #0: loss = 0.42153 (* 1 = 0.42153 loss)\r\n",
-        "I0904 17:54:31.790071 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
-        "I0904 17:54:31.793167 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.794184 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.795362 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
-        "I0904 17:54:31.802089 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.5632\r\n",
-        "I0904 17:54:31.802183 2129298192 solver.cpp:302]     Test net output #1: loss = 0.619953 (* 1 = 0.619953 loss)\r\n",
-        "I0904 17:54:31.802232 2129298192 solver.cpp:195] Iteration 4000, loss = 0.520827\r\n",
-        "I0904 17:54:31.802254 2129298192 solver.cpp:210]     Train net output #0: loss = 0.520827 (* 1 = 0.520827 loss)\r\n",
-        "I0904 17:54:31.802275 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
-        "I0904 17:54:31.805856 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0904 17:54:31.805949 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.807231 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.809342 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
-        "I0904 17:54:31.815631 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
-        "I0904 17:54:31.815783 2129298192 solver.cpp:302]     Test net output #1: loss = 0.596873 (* 1 = 0.596873 loss)\r\n",
-        "I0904 17:54:31.815820 2129298192 solver.cpp:195] Iteration 5000, loss = 0.483448\r\n",
-        "I0904 17:54:31.815835 2129298192 solver.cpp:210]     Train net output #0: loss = 0.483448 (* 1 = 0.483448 loss)\r\n",
-        "I0904 17:54:31.815853 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
-        "I0904 17:54:31.816906 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.818512 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.823189 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n"
+        "I0905 01:07:27.158655 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.696\r\n",
+        "I0905 01:07:27.158746 2129298192 solver.cpp:302]     Test net output #1: loss = 0.580239 (* 1 = 0.580239 loss)\r\n",
+        "I0905 01:07:27.158761 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.158768 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.159765 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.159843 2129298192 solver.cpp:195] Iteration 3000, loss = 0.476517\r\n",
+        "I0905 01:07:27.159873 2129298192 solver.cpp:210]     Train net output #0: loss = 0.476517 (* 1 = 0.476517 loss)\r\n",
+        "I0905 01:07:27.159983 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
+        "I0905 01:07:27.163079 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.163602 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.164567 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
+        "I0905 01:07:27.170277 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.745599\r\n",
+        "I0905 01:07:27.170344 2129298192 solver.cpp:302]     Test net output #1: loss = 0.573658 (* 1 = 0.573658 loss)\r\n",
+        "I0905 01:07:27.170364 2129298192 solver.cpp:195] Iteration 4000, loss = 0.49682\r\n",
+        "I0905 01:07:27.170375 2129298192 solver.cpp:210]     Train net output #0: loss = 0.49682 (* 1 = 0.49682 loss)\r\n",
+        "I0905 01:07:27.170385 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
+        "I0905 01:07:27.172350 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.172374 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.173084 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.175192 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
+        "I0905 01:07:27.181659 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.746399\r\n",
+        "I0905 01:07:27.181710 2129298192 solver.cpp:302]     Test net output #1: loss = 0.582127 (* 1 = 0.582127 loss)\r\n",
+        "I0905 01:07:27.181730 2129298192 solver.cpp:195] Iteration 5000, loss = 0.555272\r\n",
+        "I0905 01:07:27.181740 2129298192 solver.cpp:210]     Train net output #0: loss = 0.555272 (* 1 = 0.555272 loss)\r\n",
+        "I0905 01:07:27.181748 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
+        "I0905 01:07:27.182734 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.183248 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.186180 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n",
+        "I0905 01:07:27.192646 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7684\r\n",
+        "I0905 01:07:27.192751 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574538 (* 1 = 0.574538 loss)\r\n",
+        "I0905 01:07:27.192766 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.192773 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.193936 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.194007 2129298192 solver.cpp:195] Iteration 6000, loss = 0.464052\r\n",
+        "I0905 01:07:27.194036 2129298192 solver.cpp:210]     Train net output #0: loss = 0.464052 (* 1 = 0.464052 loss)\r\n",
+        "I0905 01:07:27.194051 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
+        "I0905 01:07:27.197053 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.198092 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.199162 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
+        "I0905 01:07:27.205195 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7684\r\n",
+        "I0905 01:07:27.205298 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574549 (* 1 = 0.574549 loss)\r\n",
+        "I0905 01:07:27.205327 2129298192 solver.cpp:195] Iteration 7000, loss = 0.495483\r\n",
+        "I0905 01:07:27.205338 2129298192 solver.cpp:210]     Train net output #0: loss = 0.495483 (* 1 = 0.495483 loss)\r\n",
+        "I0905 01:07:27.205353 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0904 17:54:31.833202 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7716\r\n",
-        "I0904 17:54:31.833345 2129298192 solver.cpp:302]     Test net output #1: loss = 0.591513 (* 1 = 0.591513 loss)\r\n",
-        "I0904 17:54:31.833375 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0904 17:54:31.833389 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.835263 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.835388 2129298192 solver.cpp:195] Iteration 6000, loss = 0.444828\r\n",
-        "I0904 17:54:31.835438 2129298192 solver.cpp:210]     Train net output #0: loss = 0.444828 (* 1 = 0.444828 loss)\r\n",
-        "I0904 17:54:31.835461 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
-        "I0904 17:54:31.840736 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.842892 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.844861 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
-        "I0904 17:54:31.852855 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7468\r\n",
-        "I0904 17:54:31.853005 2129298192 solver.cpp:302]     Test net output #1: loss = 0.593044 (* 1 = 0.593044 loss)\r\n",
-        "I0904 17:54:31.853076 2129298192 solver.cpp:195] Iteration 7000, loss = 0.489475\r\n",
-        "I0904 17:54:31.853099 2129298192 solver.cpp:210]     Train net output #0: loss = 0.489475 (* 1 = 0.489475 loss)\r\n",
-        "I0904 17:54:31.853118 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n",
-        "I0904 17:54:31.855607 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0904 17:54:31.855667 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.856359 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.858321 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
-        "I0904 17:54:31.864265 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7632\r\n",
-        "I0904 17:54:31.864409 2129298192 solver.cpp:302]     Test net output #1: loss = 0.592058 (* 1 = 0.592058 loss)\r\n",
-        "I0904 17:54:31.864442 2129298192 solver.cpp:195] Iteration 8000, loss = 0.525746\r\n",
-        "I0904 17:54:31.864454 2129298192 solver.cpp:210]     Train net output #0: loss = 0.525746 (* 1 = 0.525746 loss)\r\n",
-        "I0904 17:54:31.864466 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
-        "I0904 17:54:31.865531 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.866392 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.869691 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
-        "I0904 17:54:31.876518 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7708\r\n",
-        "I0904 17:54:31.876603 2129298192 solver.cpp:302]     Test net output #1: loss = 0.591526 (* 1 = 0.591526 loss)\r\n",
-        "I0904 17:54:31.876616 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0904 17:54:31.876624 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0904 17:54:31.877727 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.877826 2129298192 solver.cpp:195] Iteration 9000, loss = 0.445997\r\n",
-        "I0904 17:54:31.877867 2129298192 solver.cpp:210]     Train net output #0: loss = 0.445997 (* 1 = 0.445997 loss)\r\n",
-        "I0904 17:54:31.877882 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
-        "I0904 17:54:31.881119 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n"
+        "I0905 01:07:27.207471 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.207489 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.208534 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.210860 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
+        "I0905 01:07:27.216624 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.762\r\n",
+        "I0905 01:07:27.216704 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574515 (* 1 = 0.574515 loss)\r\n",
+        "I0905 01:07:27.216723 2129298192 solver.cpp:195] Iteration 8000, loss = 0.524565\r\n",
+        "I0905 01:07:27.216733 2129298192 solver.cpp:210]     Train net output #0: loss = 0.524565 (* 1 = 0.524565 loss)\r\n",
+        "I0905 01:07:27.216743 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
+        "I0905 01:07:27.217738 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.218291 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.221294 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
+        "I0905 01:07:27.227104 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7688\r\n",
+        "I0905 01:07:27.227171 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574278 (* 1 = 0.574278 loss)\r\n",
+        "I0905 01:07:27.227183 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.227190 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.228143 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.228210 2129298192 solver.cpp:195] Iteration 9000, loss = 0.461831\r\n",
+        "I0905 01:07:27.228240 2129298192 solver.cpp:210]     Train net output #0: loss = 0.461831 (* 1 = 0.461831 loss)\r\n",
+        "I0905 01:07:27.228252 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
+        "I0905 01:07:27.231314 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.232293 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.233417 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
+        "I0905 01:07:27.233680 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
+        "I0905 01:07:27.233795 2129298192 solver.cpp:232] Iteration 10000, loss = 0.49554\r\n",
+        "I0905 01:07:27.233814 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
+        "I0905 01:07:27.240015 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
+        "I0905 01:07:27.240099 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574488 (* 1 = 0.574488 loss)\r\n",
+        "I0905 01:07:27.240110 2129298192 solver.cpp:237] Optimization Done.\r\n",
+        "I0905 01:07:27.240118 2129298192 caffe.cpp:114] Optimization Done.\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If you look at the `train_val.prototxt`, you'll see that it's simple logistic regression.\n",
+      "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer neural network.\n",
+      "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
+      "\n",
+      "The final accuracy of the network we'll train below should be higher than for the network above!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!cd .. && ./build/tools/caffe train -solver examples/hdf5_classification/solver2.prototxt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0905 01:07:27.466722 2129298192 caffe.cpp:90] Starting Optimization\r\n",
+        "I0905 01:07:27.468166 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
+        "test_iter: 1000\r\n",
+        "test_interval: 1000\r\n",
+        "base_lr: 0.01\r\n",
+        "display: 1000\r\n",
+        "max_iter: 10000\r\n",
+        "lr_policy: \"step\"\r\n",
+        "gamma: 0.1\r\n",
+        "momentum: 0.9\r\n",
+        "weight_decay: 0.0005\r\n",
+        "stepsize: 5000\r\n",
+        "snapshot: 10000\r\n",
+        "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
+        "solver_mode: CPU\r\n",
+        "net: \"examples/hdf5_classification/train_val2.prototxt\"\r\n",
+        "I0905 01:07:27.468351 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val2.prototxt\r\n",
+        "I0905 01:07:27.469081 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
+        "I0905 01:07:27.469100 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
+        "I0905 01:07:27.469110 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "name: \"LogisticRegressionNet\"\r\n",
+        "layers {\r\n",
+        "  top: \"data\"\r\n",
+        "  top: \"label\"\r\n",
+        "  name: \"data\"\r\n",
+        "  type: HDF5_DATA\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"examples/hdf5_classification/data/train.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
+        "  include {\r\n",
+        "    phase: TRAIN\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"data\"\r\n",
+        "  top: \"fc1\"\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  blobs_lr: 1\r\n",
+        "  blobs_lr: 2\r\n",
+        "  weight_decay: 1\r\n",
+        "  weight_decay: 0\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 40\r\n",
+        "    weight_filler {\r\n",
+        "      type: \"gaussian\"\r\n",
+        "      std: 0.01\r\n",
+        "    }\r\n",
+        "    bias_filler {\r\n",
+        "      type: \"constant\"\r\n",
+        "      value: 0\r\n",
+        "    }\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  top: \"fc1\"\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  top: \"fc2\"\r\n",
+        "  name: \"fc2\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  blobs_lr: 1\r\n",
+        "  blobs_lr: 2\r\n",
+        "  weight_decay: 1\r\n",
+        "  weight_decay: 0\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 2\r\n",
+        "    weight_filler {\r\n",
+        "      type: \"gaussian\"\r\n",
+        "      std: 0.01\r\n",
+        "    }\r\n",
+        "    bias_filler {\r\n",
+        "      type: \"constant\"\r\n",
+        "      value: 0\r\n",
+        "    }\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc2\"\r\n",
+        "  bottom: \"label\"\r\n",
+        "  top: \"loss\"\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: SOFTMAX_LOSS\r\n",
+        "}\r\n",
+        "state {\r\n",
+        "  phase: TRAIN\r\n",
+        "}\r\n",
+        "I0905 01:07:27.469447 2129298192 net.cpp:67] Creating Layer data\r\n",
+        "I0905 01:07:27.469467 2129298192 net.cpp:356] data -> data\r\n",
+        "I0905 01:07:27.469493 2129298192 net.cpp:356] data -> label\r\n",
+        "I0905 01:07:27.469503 2129298192 net.cpp:96] Setting up data\r\n",
+        "I0905 01:07:27.469511 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
+        "I0905 01:07:27.469558 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
+        "I0905 01:07:27.469569 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.471978 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.471997 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
+        "I0905 01:07:27.472008 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
+        "I0905 01:07:27.472015 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.472026 2129298192 net.cpp:67] Creating Layer fc1\r\n",
+        "I0905 01:07:27.472033 2129298192 net.cpp:394] fc1 <- data\r\n",
+        "I0905 01:07:27.472045 2129298192 net.cpp:356] fc1 -> fc1\r\n",
+        "I0905 01:07:27.472060 2129298192 net.cpp:96] Setting up fc1\r\n",
+        "I0905 01:07:27.476827 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
+        "I0905 01:07:27.476857 2129298192 net.cpp:67] Creating Layer relu1\r\n",
+        "I0905 01:07:27.476865 2129298192 net.cpp:394] relu1 <- fc1\r\n",
+        "I0905 01:07:27.476872 2129298192 net.cpp:345] relu1 -> fc1 (in-place)\r\n",
+        "I0905 01:07:27.476881 2129298192 net.cpp:96] Setting up relu1\r\n",
+        "I0905 01:07:27.476888 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
+        "I0905 01:07:27.476896 2129298192 net.cpp:67] Creating Layer fc2\r\n",
+        "I0905 01:07:27.476902 2129298192 net.cpp:394] fc2 <- fc1\r\n",
+        "I0905 01:07:27.476909 2129298192 net.cpp:356] fc2 -> fc2\r\n",
+        "I0905 01:07:27.476918 2129298192 net.cpp:96] Setting up fc2\r\n",
+        "I0905 01:07:27.476932 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.476955 2129298192 net.cpp:67] Creating Layer loss\r\n",
+        "I0905 01:07:27.476963 2129298192 net.cpp:394] loss <- fc2\r\n",
+        "I0905 01:07:27.476969 2129298192 net.cpp:394] loss <- label\r\n",
+        "I0905 01:07:27.476975 2129298192 net.cpp:356] loss -> loss\r\n",
+        "I0905 01:07:27.476984 2129298192 net.cpp:96] Setting up loss\r\n",
+        "I0905 01:07:27.477005 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0905 01:07:27.477040 2129298192 net.cpp:109]     with loss weight 1\r\n",
+        "I0905 01:07:27.477051 2129298192 net.cpp:170] loss needs backward computation.\r\n",
+        "I0905 01:07:27.477058 2129298192 net.cpp:170] fc2 needs backward computation.\r\n",
+        "I0905 01:07:27.477063 2129298192 net.cpp:170] relu1 needs backward computation.\r\n",
+        "I0905 01:07:27.477069 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
+        "I0905 01:07:27.477076 2129298192 net.cpp:172] data does not need backward computation.\r\n",
+        "I0905 01:07:27.477080 2129298192 net.cpp:208] This network produces output loss\r\n",
+        "I0905 01:07:27.477099 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0905 01:07:27.477105 2129298192 net.cpp:219] Network initialization done.\r\n",
+        "I0905 01:07:27.477112 2129298192 net.cpp:220] Memory required for data: 3484\r\n",
+        "I0905 01:07:27.477455 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val2.prototxt\r\n",
+        "I0905 01:07:27.477480 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
+        "I0905 01:07:27.477494 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "name: \"LogisticRegressionNet\"\r\n",
+        "layers {\r\n",
+        "  top: \"data\"\r\n",
+        "  top: \"label\"\r\n",
+        "  name: \"data\"\r\n",
+        "  type: HDF5_DATA\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"examples/hdf5_classification/data/test.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
+        "  include {\r\n",
+        "    phase: TEST\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"data\"\r\n",
+        "  top: \"fc1\"\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  blobs_lr: 1\r\n",
+        "  blobs_lr: 2\r\n",
+        "  weight_decay: 1\r\n",
+        "  weight_decay: 0\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 40\r\n",
+        "    weight_filler {\r\n",
+        "      type: \"gaussian\"\r\n",
+        "      std: 0.01\r\n",
+        "    }\r\n",
+        "    bias_filler {\r\n",
+        "      type: \"constant\"\r\n",
+        "      value: 0\r\n",
+        "    }\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  top: \"fc1\"\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc1\"\r\n",
+        "  top: \"fc2\"\r\n",
+        "  name: \"fc2\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  blobs_lr: 1\r\n",
+        "  blobs_lr: 2\r\n",
+        "  weight_decay: 1\r\n",
+        "  weight_decay: 0\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 2\r\n",
+        "    weight_filler {\r\n",
+        "      type: \"gaussian\"\r\n",
+        "      std: 0.01\r\n",
+        "    }\r\n",
+        "    bias_filler {\r\n",
+        "      type: \"constant\"\r\n",
+        "      value: 0\r\n",
+        "    }\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc2\"\r\n",
+        "  bottom: \"label\"\r\n",
+        "  top: \"loss\"\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: SOFTMAX_LOSS\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc2\"\r\n",
+        "  bottom: \"label\"\r\n",
+        "  top: \"accuracy\"\r\n",
+        "  name: \"accuracy\"\r\n",
+        "  type: ACCURACY\r\n",
+        "  include {\r\n",
+        "    phase: TEST\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "state {\r\n",
+        "  phase: TEST\r\n",
+        "}\r\n",
+        "I0905 01:07:27.477839 2129298192 net.cpp:67] Creating Layer data\r\n",
+        "I0905 01:07:27.477850 2129298192 net.cpp:356] data -> data\r\n",
+        "I0905 01:07:27.477861 2129298192 net.cpp:356] data -> label\r\n",
+        "I0905 01:07:27.477870 2129298192 net.cpp:96] Setting up data\r\n",
+        "I0905 01:07:27.477876 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
+        "I0905 01:07:27.477902 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0904 17:54:31.882143 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0904 17:54:31.883280 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
-        "I0904 17:54:31.883776 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0904 17:54:31.884004 2129298192 solver.cpp:232] Iteration 10000, loss = 0.489453\r\n",
-        "I0904 17:54:31.884037 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
-        "I0904 17:54:31.890312 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7468\r\n",
-        "I0904 17:54:31.890403 2129298192 solver.cpp:302]     Test net output #1: loss = 0.593055 (* 1 = 0.593055 loss)\r\n",
-        "I0904 17:54:31.890415 2129298192 solver.cpp:237] Optimization Done.\r\n",
-        "I0904 17:54:31.890422 2129298192 caffe.cpp:114] Optimization Done.\r\n"
+        "I0905 01:07:27.477910 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
+        "I0905 01:07:27.478999 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
+        "I0905 01:07:27.479014 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
+        "I0905 01:07:27.479022 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
+        "I0905 01:07:27.479028 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.479038 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
+        "I0905 01:07:27.479044 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
+        "I0905 01:07:27.479058 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
+        "I0905 01:07:27.479069 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
+        "I0905 01:07:27.479079 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
+        "I0905 01:07:27.479086 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.479092 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
+        "I0905 01:07:27.479100 2129298192 net.cpp:67] Creating Layer fc1\r\n",
+        "I0905 01:07:27.480850 2129298192 net.cpp:394] fc1 <- data\r\n",
+        "I0905 01:07:27.480871 2129298192 net.cpp:356] fc1 -> fc1\r\n",
+        "I0905 01:07:27.480887 2129298192 net.cpp:96] Setting up fc1\r\n",
+        "I0905 01:07:27.480908 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
+        "I0905 01:07:27.480978 2129298192 net.cpp:67] Creating Layer relu1\r\n",
+        "I0905 01:07:27.480986 2129298192 net.cpp:394] relu1 <- fc1\r\n",
+        "I0905 01:07:27.480994 2129298192 net.cpp:345] relu1 -> fc1 (in-place)\r\n",
+        "I0905 01:07:27.481003 2129298192 net.cpp:96] Setting up relu1\r\n",
+        "I0905 01:07:27.481009 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
+        "I0905 01:07:27.481017 2129298192 net.cpp:67] Creating Layer fc2\r\n",
+        "I0905 01:07:27.481024 2129298192 net.cpp:394] fc2 <- fc1\r\n",
+        "I0905 01:07:27.481031 2129298192 net.cpp:356] fc2 -> fc2\r\n",
+        "I0905 01:07:27.481041 2129298192 net.cpp:96] Setting up fc2\r\n",
+        "I0905 01:07:27.481055 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.481065 2129298192 net.cpp:67] Creating Layer fc2_fc2_0_split\r\n",
+        "I0905 01:07:27.481343 2129298192 net.cpp:394] fc2_fc2_0_split <- fc2\r\n",
+        "I0905 01:07:27.481360 2129298192 net.cpp:356] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
+        "I0905 01:07:27.481371 2129298192 net.cpp:356] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
+        "I0905 01:07:27.481379 2129298192 net.cpp:96] Setting up fc2_fc2_0_split\r\n",
+        "I0905 01:07:27.481387 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.481392 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
+        "I0905 01:07:27.481401 2129298192 net.cpp:67] Creating Layer loss\r\n",
+        "I0905 01:07:27.481407 2129298192 net.cpp:394] loss <- fc2_fc2_0_split_0\r\n",
+        "I0905 01:07:27.481413 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
+        "I0905 01:07:27.481421 2129298192 net.cpp:356] loss -> loss\r\n",
+        "I0905 01:07:27.481434 2129298192 net.cpp:96] Setting up loss\r\n",
+        "I0905 01:07:27.481446 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0905 01:07:27.481452 2129298192 net.cpp:109]     with loss weight 1\r\n",
+        "I0905 01:07:27.481466 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
+        "I0905 01:07:27.481472 2129298192 net.cpp:394] accuracy <- fc2_fc2_0_split_1\r\n",
+        "I0905 01:07:27.481504 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
+        "I0905 01:07:27.481513 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
+        "I0905 01:07:27.481521 2129298192 net.cpp:96] Setting up accuracy\r\n",
+        "I0905 01:07:27.481528 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
+        "I0905 01:07:27.481534 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
+        "I0905 01:07:27.481540 2129298192 net.cpp:170] loss needs backward computation.\r\n",
+        "I0905 01:07:27.481545 2129298192 net.cpp:170] fc2_fc2_0_split needs backward computation.\r\n",
+        "I0905 01:07:27.481551 2129298192 net.cpp:170] fc2 needs backward computation.\r\n",
+        "I0905 01:07:27.481557 2129298192 net.cpp:170] relu1 needs backward computation.\r\n",
+        "I0905 01:07:27.481562 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
+        "I0905 01:07:27.481569 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
+        "I0905 01:07:27.481575 2129298192 net.cpp:172] data does not need backward computation.\r\n",
+        "I0905 01:07:27.481730 2129298192 net.cpp:208] This network produces output accuracy\r\n",
+        "I0905 01:07:27.481742 2129298192 net.cpp:208] This network produces output loss\r\n",
+        "I0905 01:07:27.481758 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0905 01:07:27.481766 2129298192 net.cpp:219] Network initialization done.\r\n",
+        "I0905 01:07:27.481771 2129298192 net.cpp:220] Memory required for data: 3728\r\n",
+        "I0905 01:07:27.481814 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
+        "I0905 01:07:27.481822 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
+        "I0905 01:07:27.481844 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
+        "I0905 01:07:27.488900 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.4924\r\n",
+        "I0905 01:07:27.488932 2129298192 solver.cpp:302]     Test net output #1: loss = 0.693168 (* 1 = 0.693168 loss)\r\n",
+        "I0905 01:07:27.488962 2129298192 solver.cpp:195] Iteration 0, loss = 0.692972\r\n",
+        "I0905 01:07:27.488973 2129298192 solver.cpp:210]     Train net output #0: loss = 0.692972 (* 1 = 0.692972 loss)\r\n",
+        "I0905 01:07:27.488984 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
+        "I0905 01:07:27.495033 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.495604 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.497684 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
+        "I0905 01:07:27.504875 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7744\r\n",
+        "I0905 01:07:27.504930 2129298192 solver.cpp:302]     Test net output #1: loss = 0.486552 (* 1 = 0.486552 loss)\r\n",
+        "I0905 01:07:27.504955 2129298192 solver.cpp:195] Iteration 1000, loss = 0.660151\r\n",
+        "I0905 01:07:27.504966 2129298192 solver.cpp:210]     Train net output #0: loss = 0.660151 (* 1 = 0.660151 loss)\r\n",
+        "I0905 01:07:27.504976 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
+        "I0905 01:07:27.509419 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.509467 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.510288 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.514822 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
+        "I0905 01:07:27.522342 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8004\r\n",
+        "I0905 01:07:27.522444 2129298192 solver.cpp:302]     Test net output #1: loss = 0.447153 (* 1 = 0.447153 loss)\r\n",
+        "I0905 01:07:27.522483 2129298192 solver.cpp:195] Iteration 2000, loss = 0.505697\r\n",
+        "I0905 01:07:27.522495 2129298192 solver.cpp:210]     Train net output #0: loss = 0.505697 (* 1 = 0.505697 loss)\r\n",
+        "I0905 01:07:27.522507 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
+        "I0905 01:07:27.524762 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.525921 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0905 01:07:27.533335 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n",
+        "I0905 01:07:27.541055 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8144\r\n",
+        "I0905 01:07:27.541146 2129298192 solver.cpp:302]     Test net output #1: loss = 0.421441 (* 1 = 0.421441 loss)\r\n",
+        "I0905 01:07:27.541160 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.541167 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.542178 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.542261 2129298192 solver.cpp:195] Iteration 3000, loss = 0.242177\r\n",
+        "I0905 01:07:27.542284 2129298192 solver.cpp:210]     Train net output #0: loss = 0.242177 (* 1 = 0.242177 loss)\r\n",
+        "I0905 01:07:27.542310 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
+        "I0905 01:07:27.549348 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.550144 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.552340 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
+        "I0905 01:07:27.560089 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.784001\r\n",
+        "I0905 01:07:27.560227 2129298192 solver.cpp:302]     Test net output #1: loss = 0.4395 (* 1 = 0.4395 loss)\r\n",
+        "I0905 01:07:27.560286 2129298192 solver.cpp:195] Iteration 4000, loss = 1.01631\r\n",
+        "I0905 01:07:27.560302 2129298192 solver.cpp:210]     Train net output #0: loss = 1.01631 (* 1 = 1.01631 loss)\r\n",
+        "I0905 01:07:27.560315 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
+        "I0905 01:07:27.565016 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.565101 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.566145 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.570286 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
+        "I0905 01:07:27.577373 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.802\r\n",
+        "I0905 01:07:27.577426 2129298192 solver.cpp:302]     Test net output #1: loss = 0.463582 (* 1 = 0.463582 loss)\r\n",
+        "I0905 01:07:27.577452 2129298192 solver.cpp:195] Iteration 5000, loss = 0.632809\r\n",
+        "I0905 01:07:27.577463 2129298192 solver.cpp:210]     Train net output #0: loss = 0.632809 (* 1 = 0.632809 loss)\r\n",
+        "I0905 01:07:27.577564 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
+        "I0905 01:07:27.579649 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.580368 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0905 01:07:27.586956 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n",
+        "I0905 01:07:27.594288 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.822\r\n",
+        "I0905 01:07:27.594327 2129298192 solver.cpp:302]     Test net output #1: loss = 0.407026 (* 1 = 0.407026 loss)\r\n",
+        "I0905 01:07:27.594338 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.594344 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.594861 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.594897 2129298192 solver.cpp:195] Iteration 6000, loss = 0.214342\r\n",
+        "I0905 01:07:27.594910 2129298192 solver.cpp:210]     Train net output #0: loss = 0.214342 (* 1 = 0.214342 loss)\r\n",
+        "I0905 01:07:27.594919 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
+        "I0905 01:07:27.601003 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.601380 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.603358 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
+        "I0905 01:07:27.610307 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8264\r\n",
+        "I0905 01:07:27.610323 2129298192 solver.cpp:302]     Test net output #1: loss = 0.403283 (* 1 = 0.403283 loss)\r\n",
+        "I0905 01:07:27.610342 2129298192 solver.cpp:195] Iteration 7000, loss = 0.894732\r\n",
+        "I0905 01:07:27.610352 2129298192 solver.cpp:210]     Train net output #0: loss = 0.894732 (* 1 = 0.894732 loss)\r\n",
+        "I0905 01:07:27.610359 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n",
+        "I0905 01:07:27.614289 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.614297 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.614701 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.618602 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
+        "I0905 01:07:27.625637 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8216\r\n",
+        "I0905 01:07:27.625661 2129298192 solver.cpp:302]     Test net output #1: loss = 0.402446 (* 1 = 0.402446 loss)\r\n",
+        "I0905 01:07:27.625680 2129298192 solver.cpp:195] Iteration 8000, loss = 0.500503\r\n",
+        "I0905 01:07:27.625690 2129298192 solver.cpp:210]     Train net output #0: loss = 0.500503 (* 1 = 0.500503 loss)\r\n",
+        "I0905 01:07:27.625707 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
+        "I0905 01:07:27.627665 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.628075 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0905 01:07:27.634202 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
+        "I0905 01:07:27.641368 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8252\r\n",
+        "I0905 01:07:27.641412 2129298192 solver.cpp:302]     Test net output #1: loss = 0.404175 (* 1 = 0.404175 loss)\r\n",
+        "I0905 01:07:27.641422 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
+        "I0905 01:07:27.641428 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.641960 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.642004 2129298192 solver.cpp:195] Iteration 9000, loss = 0.201587\r\n",
+        "I0905 01:07:27.642016 2129298192 solver.cpp:210]     Train net output #0: loss = 0.201587 (* 1 = 0.201587 loss)\r\n",
+        "I0905 01:07:27.642026 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
+        "I0905 01:07:27.648680 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
+        "I0905 01:07:27.649211 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
+        "I0905 01:07:27.651327 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
+        "I0905 01:07:27.651476 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
+        "I0905 01:07:27.651564 2129298192 solver.cpp:232] Iteration 10000, loss = 0.935422\r\n",
+        "I0905 01:07:27.651582 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
+        "I0905 01:07:27.658738 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.826\r\n",
+        "I0905 01:07:27.658782 2129298192 solver.cpp:302]     Test net output #1: loss = 0.400826 (* 1 = 0.400826 loss)\r\n",
+        "I0905 01:07:27.658790 2129298192 solver.cpp:237] Optimization Done.\r\n",
+        "I0905 01:07:27.658797 2129298192 caffe.cpp:114] Optimization Done.\r\n"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -496,10 +937,10 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 8
     }
    ],
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/hdf5_classification/solver2.prototxt b/examples/hdf5_classification/solver2.prototxt
new file mode 100644
index 00000000000..32a3693b4a1
--- /dev/null
+++ b/examples/hdf5_classification/solver2.prototxt
@@ -0,0 +1,14 @@
+net: "examples/hdf5_classification/train_val2.prototxt"
+test_iter: 1000
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 5000
+display: 1000
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/hdf5_classification/data/train"
+solver_mode: CPU
diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/train_val2.prototxt
new file mode 100644
index 00000000000..b6a75650ad3
--- /dev/null
+++ b/examples/hdf5_classification/train_val2.prototxt
@@ -0,0 +1,86 @@
+name: "LogisticRegressionNet"
+layers {
+  name: "data"
+  type: HDF5_DATA
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: HDF5_DATA
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+  include: { phase: TEST }
+}
+layers {
+  name: "fc1"
+  type: INNER_PRODUCT
+  bottom: "data"
+  top: "fc1"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "fc1"
+  top: "fc1"
+}
+layers {
+  name: "fc2"
+  type: INNER_PRODUCT
+  bottom: "fc1"
+  top: "fc2"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc2"
+  bottom: "label"
+  top: "loss"
+}
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc2"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}

From 7c78cdbd47b3bbc25cbcbb1471f427a16d27a15e Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Thu, 4 Sep 2014 17:20:35 -0700
Subject: [PATCH 0682/2053] Gradient-based solver test fix

---
 src/caffe/test/test_gradient_based_solver.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 9c21499ea5e..d196e581230 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -356,7 +356,7 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 1.0;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.5;
-  const int kNumIters = 5;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
@@ -367,7 +367,7 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.9;
-  const int kNumIters = 5;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
@@ -412,7 +412,7 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.0;
-  const int kNumIters = 5;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
@@ -468,7 +468,7 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 1.0;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.5;
-  const int kNumIters = 5;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
@@ -479,7 +479,7 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.9;
-  const int kNumIters = 5;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }

From e82e72811cc24b198b6456fce12ada5335c95fca Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 10:40:39 -0700
Subject: [PATCH 0683/2053] [docs] add titles

---
 docs/development.md               | 2 +-
 docs/index.md                     | 2 ++
 docs/model_zoo.md                 | 1 +
 docs/tutorial/convolution.md      | 1 +
 docs/tutorial/data.md             | 1 +
 docs/tutorial/forward_backward.md | 1 +
 docs/tutorial/index.md            | 1 +
 docs/tutorial/interfaces.md       | 1 +
 docs/tutorial/layers.md           | 1 +
 docs/tutorial/loss.md             | 1 +
 docs/tutorial/net_layer_blob.md   | 1 +
 docs/tutorial/solver.md           | 1 +
 12 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/docs/development.md b/docs/development.md
index d65c126cf14..160e005dfb1 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -1,5 +1,5 @@
 ---
-title: Development
+title: Developing and Contributing
 ---
 # Development
 
diff --git a/docs/index.md b/docs/index.md
index af4b50c323b..f33bd4f9e2e 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,5 +1,7 @@
 ---
+title: Deep Learning Framework
 ---
+
 # Caffe
 
 Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 0b7daa0bf17..5603c62c6bb 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -1,4 +1,5 @@
 ---
+title: Model Zoo
 ---
 # Caffe Model Zoo
 
diff --git a/docs/tutorial/convolution.md b/docs/tutorial/convolution.md
index 6a86e3afcfd..a02fe4ef927 100644
--- a/docs/tutorial/convolution.md
+++ b/docs/tutorial/convolution.md
@@ -1,4 +1,5 @@
 ---
+title: Convolution
 ---
 # Caffeinated Convolution
 
diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index 87954e1da63..40605f7cd73 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -1,4 +1,5 @@
 ---
+title: Data
 ---
 # Data: Ins and Outs
 
diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index 699a26c8953..f58b9cac19e 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -1,4 +1,5 @@
 ---
+title: Forward and Backward for Inference and Learning
 ---
 # Forward and Backward
 
diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
index fd21a440cee..7d4e77b1d5f 100644
--- a/docs/tutorial/index.md
+++ b/docs/tutorial/index.md
@@ -1,4 +1,5 @@
 ---
+title: Caffe Tutorial
 ---
 # Caffe Tutorial
 
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 334d27e8137..6b0ec347dfe 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -1,4 +1,5 @@
 ---
+title: Interfaces
 ---
 # Interfaces
 
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index c6040255dc5..fdc0b4b4720 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -1,4 +1,5 @@
 ---
+title: Layer Catalogue
 ---
 # Layers
 
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index 3591858ad31..aac561774bb 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -1,4 +1,5 @@
 ---
+title: Loss
 ---
 # Loss
 
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 21b8ddece64..8b68ffada06 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -1,4 +1,5 @@
 ---
+title: Nets, Layers, and Blobs
 ---
 # Nets, Layers, and Blobs: anatomy of a Caffe model
 
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index d90470c467b..8884ea0e1e8 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -1,4 +1,5 @@
 ---
+title: Solver / Model Optimization
 ---
 # Solver
 

From 530ec45bdac01c1d48c81ffe4050ba16e73a860a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 10:41:20 -0700
Subject: [PATCH 0684/2053] [docs] link tutorial

---
 docs/index.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/index.md b/docs/index.md
index f33bd4f9e2e..b93606acdf0 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -38,6 +38,8 @@ Consult performance [details](/performance_hardware.html).
 Slides about the Caffe architecture, *updated 03/14*.
 - [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br />
 A 4-page report for the ACM Multimedia Open Source competition.
+- [Caffe Tutorial](/tutorial)<br />
+DIY deep learning with this hands-on tutorial to Caffe.
 - [Installation instructions](/installation.html)<br />
 Tested on Ubuntu, Red Hat, OS X.
 * [Model Zoo](/model_zoo.html)<br />

From ec861a5563d605379c23d1592247a17ce7dce509 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 10:42:45 -0700
Subject: [PATCH 0685/2053] [docs] fix br code

---
 docs/development.md |  2 +-
 docs/index.md       | 18 +++++++++---------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/development.md b/docs/development.md
index 160e005dfb1..dfed3308eeb 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -3,7 +3,7 @@ title: Developing and Contributing
 ---
 # Development
 
-Caffe is developed with active participation of the community.<br />
+Caffe is developed with active participation of the community.<br>
 The [BVLC](http://bvlc.eecs.berkeley.edu/) maintainers welcome all contributions!
 
 The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
diff --git a/docs/index.md b/docs/index.md
index b93606acdf0..f9072303d99 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -34,33 +34,33 @@ Consult performance [details](/performance_hardware.html).
 
 ## Documentation
 
-- [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf)<br />
+- [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf)<br>
 Slides about the Caffe architecture, *updated 03/14*.
-- [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br />
+- [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br>
 A 4-page report for the ACM Multimedia Open Source competition.
-- [Caffe Tutorial](/tutorial)<br />
+- [Caffe Tutorial](/tutorial)<br>
 DIY deep learning with this hands-on tutorial to Caffe.
-- [Installation instructions](/installation.html)<br />
+- [Installation instructions](/installation.html)<br>
 Tested on Ubuntu, Red Hat, OS X.
-* [Model Zoo](/model_zoo.html)<br />
+* [Model Zoo](/model_zoo.html)<br>
 BVLC suggests a standard distribution format for Caffe models, and provides trained models.
-* [Developing & Contributing](/development.html)<br />
+* [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
-* [API Documentation](/doxygen/)<br />
+* [API Documentation](/doxygen/)<br>
 Developer documentation automagically generated from code comments.
 
 ### Examples
 
 {% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
 {% for page in examples %}
-- <div><a href="{{page.url}}">{{page.title}}</a><br />{{page.description}}</div>
+- <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
 ### Notebook examples
 
 {% assign notebooks = site.pages | where:'category','notebook' %}
 {% for page in notebooks %}
-- <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br />{{page.description}}</div>
+- <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
 ## Citing Caffe

From ced3a37aeda1aed4d3e068ed445773a493c0b626 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Fri, 5 Sep 2014 11:06:26 -0700
Subject: [PATCH 0686/2053] relu,sigmoid,tanh

---
 docs/tutorial/layers.md | 56 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 52 insertions(+), 4 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index fdc0b4b4720..e3c84a37e33 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -108,17 +108,65 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 ### Activation / Neuron Layers
 
-#### ReLU / Rectified-Linear and Leaky ReLU
+#### ReLU / Rectified-Linear
 
-`RELU`
+* LayerType: `RELU`
+* CPU implementation: `./src/caffe/layers/relu_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
+* Input
+    - $n \times c \times h \times w$
+* Output
+    - $n \times c \times h \times w$
+* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+
+        layers {
+          name: "relu1"
+          type: RELU
+          bottom: "conv1"
+          top: "conv1"
+        }
+
+The `RELU` layer computes the element-wise max of 0 and the input. It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
 
 #### Sigmoid
 
-`SIGMOID`
+* LayerType: `SIGMOID`
+* CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
+* Input
+    - $n \times c \times h \times w$
+* Output
+    - $n \times c \times h \times w$
+* Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
+
+        layers {
+          name: "encode1neuron"
+          bottom: "encode1"
+          top: "encode1neuron"
+          type: SIGMOID
+        }
+
+The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
 
 #### TanH / Hyperbolic Tangent
 
-`TANH`
+* LayerType: `TANH`
+* CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
+* Input
+    - $n \times c \times h \times w$
+* Output
+    - $n \times c \times h \times w$
+* Sample
+
+        layers {
+          name: "encode1neuron"
+          bottom: "encode1"
+          top: "encode1neuron"
+          type: TANH
+        }
+
+The `TANH` layer computes the output as tanh(x) for each input element x.
 
 #### Absolute Value
 

From 4880a2b313b69899ade957ef62e8dabd189c4530 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Fri, 5 Sep 2014 12:58:28 -0700
Subject: [PATCH 0687/2053] more blob details

---
 docs/tutorial/net_layer_blob.md | 87 +++++++++++++++++++++++----------
 1 file changed, 61 insertions(+), 26 deletions(-)

diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 8b68ffada06..0c86c49745c 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -1,17 +1,69 @@
 ---
-title: Nets, Layers, and Blobs
+title: Blobs, Layers, and Nets
 ---
-# Nets, Layers, and Blobs: anatomy of a Caffe model
+# Blobs, Layers, and Nets: anatomy of a Caffe model
 
-Deep networks are compositional models that are naturally represented as a collection of inter-connected layers. Caffe defines a net layer-by-layer in its own model schema. The network defines the entire model bottom-to-top from input data to loss. As data and derivatives flow through the network in the [forward and backward passes](forward_backward.html) Caffe stores, communicates, and manipulates the information as *blobs*: the blob is the standard array and unified memory interface for the framework.
+Deep networks are compositional models that are naturally represented as a collection of inter-connected layers that work on chunks of data. Caffe defines a net layer-by-layer in its own model schema. The network defines the entire model bottom-to-top from input data to loss. As data and derivatives flow through the network in the [forward and backward passes](forward_backward.html) Caffe stores, communicates, and manipulates the information as *blobs*: the blob is the standard array and unified memory interface for the framework. The layer comes next as the foundation of both model and computation. The net follows as the collection and connection of layers. The details of blob describe how information is stored and communicated in and across layers and nets.
 
 [Solving](solver.html) is configured separately to decouple modeling and optimization.
 
-The layer comes first as the foundation of both model and computation. The net follows as the collection and connection of layers. The details of blob describe how information is stored and communicated in and across layers and nets.
+We will go over the details of these components in more detail.
+
+## Blob storage and communication
+
+A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is a 4-dimensional array that stores things in the order of (Num, Channels, Height and Width), from major to minor, and stored in a C-contiguous fashion.  The main reason for putting Num (the name is due to legacy reasons, and is equivalent to the notation of "batch" as in minibatch SGD).
+
+Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
+
+Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
+
+The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest. For example, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
+
+- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
+- Channel / K is the feature dimension e.g. for RGB images K = 3.
+
+Note that although we have designed blobs with its dimensions corresponding to image applications, they are named purely for notational purpose and it is totally valid for you to do non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use blobs of dimensions (Num, Channels, 1, 1) and call the InnerProductLayer (which we will cover soon).
+
+Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
+
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 4096.
+
+For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
+
+### Implementation Details
+
+As we are often interested in the values as well as the gradients of the blob, a Blob stores two chunks of memories, *data* and *diff*. The former is the normal data that we pass along, and the latter is the gradient computed by the network.
+
+Further, as the actual values could be stored either on the CPU and on the GPU, there are two different ways to access them: the const way, which does not change the values, and the mutable way, which changes the values:
+
+    const Dtype* cpu_data() const;
+    Dtype* mutable_cpu_data();
+
+(similarly for gpu and diff).
+
+The reason for such design is that, a Blob uses a SyncedMem class to synchronize values between the CPU and GPU in order to hide the synchronization details and to minimize data transfer. A rule of thumb is, always use the const call if you do not want to change the values, and never store the pointers in your own object. Every time you work on a blob, call the functions to get the pointers, as the SyncedMem will need this to figure out when to copy data.
+
+In practice when GPUs are present, one loads data from the disk to a blob in CPU code, calls a device kernel to do GPU computation, and ferries the blob off to the next layer, ignoring low-level details while maintaining a high level of performance. As long as all layers have GPU implementations, all the intermediate data and gradients will remain in the GPU.
+
+If you want to check out when a Blob will copy data, here is an illustrative example:
+
+    // Assuming that data are on the CPU initially, and we have a blob.
+    const Dtype* foo;
+    Dtype* bar;
+    foo = blob.gpu_data(); // data copied cpu->gpu.
+    foo = blob.cpu_data(); // no data copied since both have up-to-date contents.
+    bar = blob.mutable_gpu_data(); // no data copied.
+    // ... some operations ...
+    bar = blob.mutable_gpu_data(); // no data copied when we are still on GPU.
+    foo = blob.cpu_data(); // data copied gpu->cpu, since the gpu side has modified the data
+    foo = blob.gpu_data(); // no data copied since both have up-to-date contents
+    bar = blob.mutable_cpu_data(); // still no data copied.
+    bar = blob.mutable_gpu_data(); // data copied cpu->gpu.
+    bar = blob.mutable_cpu_data(); // data copied gpu->cpu.
 
 ## Layer computation and connections
 
-The layer is the essence of a model and the fundamental unit of computation. Layers convolve filters, pool, take inner products, apply nonlinearities like rectified-linear and sigmoid and other elementwise transformations, normalize, load data, and compute losses like softmax and hinge. [See the layer catalogue](layers.html) for all operations. All the types needed for state-of-the-art deep learning tasks are there.
+The layer is the essence of a model and the fundamental unit of computation. Layers convolve filters, pool, take inner products, apply nonlinearities like rectified-linear and sigmoid and other elementwise transformations, normalize, load data, and compute losses like softmax and hinge. [See the layer catalogue](layers.html) for all operations. Most of the types needed for state-of-the-art deep learning tasks are there.
 
 <img src="fig/layer.jpg" alt="A layer with bottom and top blob." width="256">
 
@@ -23,6 +75,8 @@ Each layer type defines three critical computations: *setup*, *forward*, and *ba
 - Forward: given input from bottom compute the output and send to the top.
 - Backward: given the gradient w.r.t. the top output compute the gradient w.r.t. to the input and send to the bottom. A layer with parameters computes the gradient w.r.t. to its parameters and stores it internally.
 
+More specifically, there will be two Forward and Backward functions implemented, one for CPU and one for GPU. If you do not implement a GPU version, the layer will fall back to the CPU functions as a backup option. This may come handy if you would like to do quick experiments, although it may come with additional data transfer cost (its inputs will be copied from GPU to CPU, and its outputs will be copied back from CPU to GPU).
+
 Layers have two key responsibilities for the operation of the network as a whole: a *forward pass* that takes the inputs and produces the outputs, and a *backward pass* that takes the gradient with respect to the output, and computes the gradients with respect to the parameters and to the inputs, which are in turn back-propagated to earlier layers. These passes are simply the composition of each layer's forward and backward.
 
 Developing custom layers requires minimal effort by the compositionality of the network and modularity of the code. Define the setup, forward, and backward for the layer and it is ready for inclusion in a net.
@@ -107,31 +161,12 @@ The Net explains its initialization as it goes:
 
 Model initialization is handled by `Net::Init()`.
 
-The network is run on CPU or GPU by setting a single switch. Layers come with corresponding CPU and GPU routines that produce identical results (with tests to prove it). The CPU / GPU switch is seamless and independent of the model definition. For research and deployment alike it is best to divide model and implementation.
-
-## Blob storage and communication
-
-Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
-
-Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. In practice, one loads data from the disk to a blob in CPU code, calls a device kernel to do GPU computation, and ferries the blob off to the next layer, ignoring low-level details while maintaining a high level of performance.
-
-Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
-
-The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest.
-
-- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
-- Channel / K is the feature dimension e.g. for RGB images K = 3.
-
-Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
-
-Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 4096.
-
-For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
+The network is run on CPU or GPU by setting a single switch. Layers come with corresponding CPU and GPU routines that produce identical results (up to numerical errors, and with tests to guard it). The CPU / GPU switch is seamless and independent of the model definition. For research and deployment alike it is best to divide model and implementation.
 
 ### Model format
 
 The models are defined in plaintext protocol buffer schema (prototxt) while the learned models are serialized as binary protocol buffer (binaryproto) .caffemodel files.
 
-The model format is defined by the protobuf schema in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
+The model format is defined by the protobuf schema in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The source file is mostly self-explanatory so one is encouraged to check it out.
 
 Caffe speaks [Google Protocol Buffer](https://code.google.com/p/protobuf/) for the following strengths: minimal-size binary strings when serialized, efficient serialization, a human-readable text format compatible with the binary version, and efficient interface implementations in multiple languages, most notably C++ and Python. This all contributes to the flexibility and extensibility of modeling in Caffe.

From 1f641483eb1778f75b1fe1758ade247dd316a076 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Fri, 5 Sep 2014 13:05:35 -0700
Subject: [PATCH 0688/2053] fix leaky relu

---
 docs/tutorial/layers.md | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index e3c84a37e33..d22e33428cd 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -27,7 +27,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Options (`ConvolutionParameter convolution_param`)
-    - Required | `num_output` ($c_o$), the number of filters
+    - Required: `num_output` ($c_o$), the number of filters
     - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
     - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
     - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
@@ -108,11 +108,13 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 ### Activation / Neuron Layers
 
-#### ReLU / Rectified-Linear
+#### ReLU / Rectified-Linear and Leaky-ReLU
 
 * LayerType: `RELU`
 * CPU implementation: `./src/caffe/layers/relu_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
+* Options (`ReLUParameter relu_param`)
+    - Optional (default 0): `negative_slope`, specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
 * Input
     - $n \times c \times h \times w$
 * Output
@@ -126,7 +128,7 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
           top: "conv1"
         }
 
-The `RELU` layer computes the element-wise max of 0 and the input. It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
 
 #### Sigmoid
 

From cb4ae5e875c2eb31c9b3dd1b68daa535ba910b97 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Fri, 5 Sep 2014 13:47:11 -0700
Subject: [PATCH 0689/2053] update net

---
 docs/tutorial/net_layer_blob.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 0c86c49745c..b500aaa64d5 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -122,7 +122,7 @@ is defined by
       top: "loss"
     }
 
-The Net explains its initialization as it goes:
+Model initialization is handled by `Net::Init()`. The initialization mainly does two things: scaffolding the overall DAG by creating the blobs and layers (for C++ geeks: the network will retain ownership of the blobs and layers during its lifetime), and calls the layers' `SetUp()` function. It also does a set of other bookkeeping things, such as validating the correctness of the overall network architecture. Also, during initialization the Net explains its initialization by logging to INFO as it goes:
 
     I0902 22:52:17.931977 2079114000 net.cpp:39] Initializing net from parameters:
     name: "LogReg"
@@ -159,9 +159,7 @@ The Net explains its initialization as it goes:
     I0902 22:52:17.941818 2079114000 net.cpp:219] Network initialization done.
     I0902 22:52:17.941824 2079114000 net.cpp:220] Memory required for data: 201476
 
-Model initialization is handled by `Net::Init()`.
-
-The network is run on CPU or GPU by setting a single switch. Layers come with corresponding CPU and GPU routines that produce identical results (up to numerical errors, and with tests to guard it). The CPU / GPU switch is seamless and independent of the model definition. For research and deployment alike it is best to divide model and implementation.
+Note that the construction of the network is device agnostic - recall our earlier explanation that blobs and layers hide implementation details from the model definition. After construction, the network is run on either CPU or GPU by setting a single switch defined in `Caffe::mode()` and set by `Caffe::set_mode()`. Layers come with corresponding CPU and GPU routines that produce identical results (up to numerical errors, and with tests to guard it). The CPU / GPU switch is seamless and independent of the model definition. For research and deployment alike it is best to divide model and implementation.
 
 ### Model format
 

From f12a74af9569042082cc117311b299a0b0867738 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Fri, 5 Sep 2014 16:44:06 -0700
Subject: [PATCH 0690/2053] neuron layers doc

---
 docs/tutorial/layers.md | 120 +++++++++++++++++++++++++++++++++-------
 1 file changed, 101 insertions(+), 19 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index d22e33428cd..72db52c81ce 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -72,7 +72,48 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 
 #### Local Response Normalization
 
-`LRN`
+* LayerType: `LRN`
+* CPU implementation: `./src/caffe/layers/lrn_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/lrn_layer.cu`
+* Options (`ConvolutionParameter convolution_param`)
+    - Required: `num_output` ($c_o$), the number of filters
+    - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
+    - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
+    - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
+    - Optional (default 0): `pad` or (`pad_h`, `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
+    - Optional (default 1): `stride` or (`stride_h`, `stride_w`), specifies the intervals at which to apply the filters to the input
+    - Optional (default 1): `group` ($g$) if $>1$, restricts the connectivity of each filter to a subset of the input.  In particular, the input to the $i^{th}$ group of $n_f / g$ filters is the $i^{th}$ group of $c_i / g$ input channels.
+* Input
+    - $n \times c_i \times h_i \times w_i$ (repeated $K \ge 1$ times)
+* Output
+    - $n \times c_o \times h_o \times w_o$ (repeated $K$ times)
+* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+
+        layers {
+          name: "conv1"
+          type: CONVOLUTION
+          bottom: "data"
+          top: "conv1"
+          blobs_lr: 1          # learning rate multiplier for the filters
+          blobs_lr: 2          # learning rate multiplier for the biases
+          weight_decay: 1      # weight decay multiplier for the filters
+          weight_decay: 0      # weight decay multiplier for the biases
+          convolution_param {
+            num_output: 96     # learn 96 filters
+            kernel_size: 11    # each filter is 11x11
+            stride: 4          # step 4 pixels between each filter application
+            weight_filler {
+              type: "gaussian" # initialize the filters from a Gaussian
+              std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+            }
+            bias_filler {
+              type: "constant" # initialize the biases to zero (0)
+              value: 0
+            }
+          }
+        }
+
+The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 
 #### im2col
 
@@ -108,6 +149,13 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 ### Activation / Neuron Layers
 
+In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size. In the layers below, we will ignore the input and out sizes as they are identical:
+
+* Input
+    - $n \times c \times h \times w$
+* Output
+    - $n \times c \times h \times w$
+
 #### ReLU / Rectified-Linear and Leaky-ReLU
 
 * LayerType: `RELU`
@@ -115,10 +163,6 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
 * Options (`ReLUParameter relu_param`)
     - Optional (default 0): `negative_slope`, specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
-* Input
-    - $n \times c \times h \times w$
-* Output
-    - $n \times c \times h \times w$
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
         layers {
@@ -135,10 +179,6 @@ Given an input value x, The `RELU` layer computes the output as x if x > 0 and n
 * LayerType: `SIGMOID`
 * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
-* Input
-    - $n \times c \times h \times w$
-* Output
-    - $n \times c \times h \times w$
 * Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
 
         layers {
@@ -155,16 +195,12 @@ The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
 * LayerType: `TANH`
 * CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
-* Input
-    - $n \times c \times h \times w$
-* Output
-    - $n \times c \times h \times w$
 * Sample
 
         layers {
-          name: "encode1neuron"
-          bottom: "encode1"
-          top: "encode1neuron"
+          name: "layer"
+          bottom: "in"
+          top: "out"
           type: TANH
         }
 
@@ -172,15 +208,61 @@ The `TANH` layer computes the output as tanh(x) for each input element x.
 
 #### Absolute Value
 
-`ABSVAL`
+* LayerType: `ABSVAL`
+* CPU implementation: `./src/caffe/layers/absval_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
+* Sample
+
+        layers {
+          name: "layer"
+          bottom: "in"
+          top: "out"
+          type: ABSVAL
+        }
+
+The `ABSVAL` layer computes the output as abs(x) for each input element x.
 
 #### Power
 
-`POWER`
+* LayerType: `POWER`
+* CPU implementation: `./src/caffe/layers/power_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
+* Options (`PowerParameter power_param`)
+    - Optional (default 1): `power`
+    - Optional (default 1): `scale`
+    - Optional (default 0): `shift`
+* Sample
+
+        layers {
+          name: "layer"
+          bottom: "in"
+          top: "out"
+          type: POWER
+          power_param {
+            power: 1
+            scale: 1
+            shift: 0
+          }
+        }
+
+The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x.
 
 #### BNLL
 
-`BNLL`
+* LayerType: `BNLL`
+* CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
+* Sample
+
+        layers {
+          name: "layer"
+          bottom: "in"
+          top: "out"
+          type: BNLL
+        }
+
+The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
+
 
 ### Data Layers
 

From f15fc364400d7d21d0a21030d4547ae00f7034bb Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Fri, 5 Sep 2014 17:03:29 -0700
Subject: [PATCH 0691/2053] conv and pooling

---
 docs/tutorial/layers.md | 77 ++++++++++++++++-------------------------
 1 file changed, 30 insertions(+), 47 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 72db52c81ce..d1eb83f2fd7 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -18,7 +18,7 @@ A typical "image" in the real-world may have one color channel ($c = 1$), as in
 But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $h > 1$ and width $w > 1$.
 This 2D geometry naturally lends itself to certain decisions about how to process the input.
 In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
-In contrast, other layers (with few exceptions) ignore the spatial structure of the input, treating it as "one big vector" with dimension $$ c h w $$.
+In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$ c h w $$.
 
 
 #### Convolution
@@ -27,17 +27,17 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Options (`ConvolutionParameter convolution_param`)
-    - Required: `num_output` ($c_o$), the number of filters
+    - Required: `num_output` (`c_o`), the number of filters
     - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
     - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
     - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
     - Optional (default 0): `pad` or (`pad_h`, `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
     - Optional (default 1): `stride` or (`stride_h`, `stride_w`), specifies the intervals at which to apply the filters to the input
-    - Optional (default 1): `group` ($g$) if $>1$, restricts the connectivity of each filter to a subset of the input.  In particular, the input to the $i^{th}$ group of $n_f / g$ filters is the $i^{th}$ group of $c_i / g$ input channels.
+    - Optional (default 1): `group` (g). If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated to g groups separately, and the i-th output group channels will be only connected to the i-th input group channels.
 * Input
-    - $n \times c_i \times h_i \times w_i$ (repeated $K \ge 1$ times)
+    - `n * c_i * h_i * w_i`
 * Output
-    - $n \times c_o \times h_o \times w_o$ (repeated $K$ times)
+    - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
         layers {
@@ -66,58 +66,41 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 
 The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 
-#### Pooling**
+#### Pooling
 
-`POOLING`
-
-#### Local Response Normalization
-
-* LayerType: `LRN`
-* CPU implementation: `./src/caffe/layers/lrn_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/lrn_layer.cu`
-* Options (`ConvolutionParameter convolution_param`)
-    - Required: `num_output` ($c_o$), the number of filters
+* LayerType: `POOLING`
+* CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
+* Options (`PoolingParameter pooling_param`)
+    - Optional (default MAX): `pool`, the pooling method. Currently MAX, AVE, or STOCHASTIC
     - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
-    - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
-    - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
     - Optional (default 0): `pad` or (`pad_h`, `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
     - Optional (default 1): `stride` or (`stride_h`, `stride_w`), specifies the intervals at which to apply the filters to the input
-    - Optional (default 1): `group` ($g$) if $>1$, restricts the connectivity of each filter to a subset of the input.  In particular, the input to the $i^{th}$ group of $n_f / g$ filters is the $i^{th}$ group of $c_i / g$ input channels.
 * Input
-    - $n \times c_i \times h_i \times w_i$ (repeated $K \ge 1$ times)
+    - `n * c * h_i * w_i`
 * Output
-    - $n \times c_o \times h_o \times w_o$ (repeated $K$ times)
+    - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-        layers {
-          name: "conv1"
-          type: CONVOLUTION
-          bottom: "data"
-          top: "conv1"
-          blobs_lr: 1          # learning rate multiplier for the filters
-          blobs_lr: 2          # learning rate multiplier for the biases
-          weight_decay: 1      # weight decay multiplier for the filters
-          weight_decay: 0      # weight decay multiplier for the biases
-          convolution_param {
-            num_output: 96     # learn 96 filters
-            kernel_size: 11    # each filter is 11x11
-            stride: 4          # step 4 pixels between each filter application
-            weight_filler {
-              type: "gaussian" # initialize the filters from a Gaussian
-              std: 0.01        # distribution with stdev 0.01 (default mean: 0)
-            }
-            bias_filler {
-              type: "constant" # initialize the biases to zero (0)
-              value: 0
-            }
-          }
-        }
+    layers {
+      name: "pool1"
+      type: POOLING
+      bottom: "conv1"
+      top: "pool1"
+      pooling_param {
+        pool: MAX
+        kernel_size: 3
+        stride: 2
+      }
+    }
 
-The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+#### Local Response Normalization (LRN)
+
+`LRN`
 
 #### im2col
 
-`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about.
+`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
 
 ### Loss Layers
 
@@ -152,9 +135,9 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size. In the layers below, we will ignore the input and out sizes as they are identical:
 
 * Input
-    - $n \times c \times h \times w$
+    - n * c * h * w
 * Output
-    - $n \times c \times h \times w$
+    - n * c * h * w
 
 #### ReLU / Rectified-Linear and Leaky-ReLU
 

From 5eb8dd3558474536f115d4e951e08683cf6be4d9 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 6 Sep 2014 09:35:13 -0700
Subject: [PATCH 0692/2053] more layers

---
 docs/tutorial/layers.md | 69 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 64 insertions(+), 5 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index d1eb83f2fd7..56fc338f972 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -281,23 +281,82 @@ The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 +
 
 #### Inner Product
 
-`INNER_PRODUCT`
+* LayerType: `INNER_PRODUCT`
+* CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
+* Options (`InnerProductParameter inner_product_param`)
+    - Required: `num_output` (`c_o`), the number of filters
+    - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
+    - Optional (default `type: 'constant' value: 0`): `bias_filler`
+    - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
+* Input
+    - `n * c_i * h_i * w_i`
+* Output
+    - `n * c_o * 1 * 1`
+* Sample
+
+    layers {
+      name: "fc8"
+      type: INNER_PRODUCT
+      blobs_lr: 1          # learning rate multiplier for the filters
+      blobs_lr: 2          # learning rate multiplier for the biases
+      weight_decay: 1      # weight decay multiplier for the filters
+      weight_decay: 0      # weight decay multiplier for the biases
+      inner_product_param {
+        num_output: 1000
+        weight_filler {
+          type: "gaussian"
+          std: 0.01
+        }
+        bias_filler {
+          type: "constant"
+          value: 0
+        }
+      }
+      bottom: "fc7"
+      top: "fc8"
+    }
+
+The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
 
 #### Splitting
 
-`SPLIT`
+The `SPLIT` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
 
 #### Flattening
 
-`FLATTEN`
+The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`.
 
 #### Concatenation
 
-`CONCAT`
+* LayerType: `CONCAT`
+* CPU implementation: `./src/caffe/layers/concat_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
+* Options (`ConcatParameter concat_param`)
+    - Optional (default 1): `concat_dim`, 0 for concatenation along num and 1 for channels.
+* Input
+    - `n_i * c_i * h * w` for each input blob i from 1 to K.
+* Output
+    - if `concat_dim = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+    - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+* Sample
+
+        layers {
+          name: "concat"
+          bottom: "in1"
+          bottom: "in2"
+          top: "out"
+          type: CONCAT
+          concat_param {
+            concat_dim: 1
+          }
+        }
+
+The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only.
 
 #### Slicing
 
-`SLICE`
+The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
 
 #### Elementwise Operations
 

From 3578d91da9ea45e52c049c5bfaec83aedea3f06b Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 6 Sep 2014 16:30:32 -0700
Subject: [PATCH 0693/2053] Added initial Hinge Loss

---
 docs/tutorial/layers.md | 32 +++++++++++++++++++++++++++++++-
 1 file changed, 31 insertions(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 56fc338f972..42ae5986fb6 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -116,7 +116,37 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 #### Hinge / Margin
 
-`HINGE_LOSS`
+* LayerType: `HINGE_LOSS`
+* CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
+* CUDA GPU implementation: `NOT_AVAILABLE`
+* Options (`HingeLossParameter hinge_loss_param`)
+    - Optional (default L1): `norm`, the norm used. Currently L1, L2
+* Inputs
+    - `n * c * h * w` Predictions
+    - `n * 1 * 1 * 1` Labels
+* Output
+    - `1 * 1 * 1 * 1` Computed Loss
+* Samples
+
+      # L1 Norm
+      layers {
+        name: "loss"
+        type: HINGE_LOSS
+        bottom: "pred"
+        bottom: "label"
+      }
+
+      # L2 Norm
+      layers {
+        name: "loss"
+        type: HINGE_LOSS
+        bottom: "pred"
+        bottom: "label"
+        top: "loss"
+        hinge_loss_param {
+          norm: L2
+        }
+      }
 
 #### Sigmoid Cross-Entropy
 

From 64fa7caf1e74ff47456c559c008e3e39156b3ca3 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 15:43:36 -0700
Subject: [PATCH 0694/2053] shift CUDA code out of common

---
 include/caffe/common.hpp                | 57 -------------------------
 include/caffe/util/device_alternate.hpp | 57 +++++++++++++++++++++++++
 2 files changed, 57 insertions(+), 57 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 683d1d68f1d..9c6eb4d6834 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -41,41 +41,6 @@ private:\
 // is executed we will see a fatal log.
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
 
-#ifndef CPU_ONLY
-
-// CUDA: various checks for different function calls.
-#define CUDA_CHECK(condition) \
-  /* Code block avoids redefinition of cudaError_t error */ \
-  do { \
-    cudaError_t error = condition; \
-    CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \
-  } while (0)
-
-#define CUBLAS_CHECK(condition) \
-  do { \
-    cublasStatus_t status = condition; \
-    CHECK_EQ(status, CUBLAS_STATUS_SUCCESS) << " " \
-      << caffe::cublasGetErrorString(status); \
-  } while (0)
-
-#define CURAND_CHECK(condition) \
-  do { \
-    curandStatus_t status = condition; \
-    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << " " \
-      << caffe::curandGetErrorString(status); \
-  } while (0)
-
-// CUDA: grid stride looping
-#define CUDA_KERNEL_LOOP(i, n) \
-  for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
-       i < (n); \
-       i += blockDim.x * gridDim.x)
-
-// CUDA: check for error after kernel execution and exit loudly if there is one.
-#define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
-
-#endif  // CPU_ONLY
-
 namespace caffe {
 
 // We will use the boost shared_ptr instead of the new C++11 one mainly
@@ -181,28 +146,6 @@ class Caffe {
   DISABLE_COPY_AND_ASSIGN(Caffe);
 };
 
-#ifndef CPU_ONLY
-
-// NVIDIA_CUDA-5.5_Samples/common/inc/helper_cuda.h
-const char* cublasGetErrorString(cublasStatus_t error);
-const char* curandGetErrorString(curandStatus_t error);
-
-// CUDA: thread number configuration.
-// Use 1024 threads per block, which requires cuda sm_2x or above,
-// or fall back to attempt compatibility (best of luck to you).
-#if __CUDA_ARCH__ >= 200
-    const int CAFFE_CUDA_NUM_THREADS = 1024;
-#else
-    const int CAFFE_CUDA_NUM_THREADS = 512;
-#endif
-
-// CUDA: number of blocks for threads.
-inline int CAFFE_GET_BLOCKS(const int N) {
-  return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS;
-}
-
-#endif  // CPU_ONLY
-
 }  // namespace caffe
 
 #endif  // CAFFE_COMMON_HPP_
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index aa359e007cb..bb3ac61648b 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -37,6 +37,63 @@ void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
 #include <curand.h>
 #include <driver_types.h>  // cuda driver types
 
+//
+// CUDA macros
+//
+
+// CUDA: various checks for different function calls.
+#define CUDA_CHECK(condition) \
+  /* Code block avoids redefinition of cudaError_t error */ \
+  do { \
+    cudaError_t error = condition; \
+    CHECK_EQ(error, cudaSuccess) << " " << cudaGetErrorString(error); \
+  } while (0)
+
+#define CUBLAS_CHECK(condition) \
+  do { \
+    cublasStatus_t status = condition; \
+    CHECK_EQ(status, CUBLAS_STATUS_SUCCESS) << " " \
+      << caffe::cublasGetErrorString(status); \
+  } while (0)
+
+#define CURAND_CHECK(condition) \
+  do { \
+    curandStatus_t status = condition; \
+    CHECK_EQ(status, CURAND_STATUS_SUCCESS) << " " \
+      << caffe::curandGetErrorString(status); \
+  } while (0)
+
+// CUDA: grid stride looping
+#define CUDA_KERNEL_LOOP(i, n) \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
+       i < (n); \
+       i += blockDim.x * gridDim.x)
+
+// CUDA: check for error after kernel execution and exit loudly if there is one.
+#define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
+
+namespace caffe {
+
+// CUDA: library error reporting.
+const char* cublasGetErrorString(cublasStatus_t error);
+const char* curandGetErrorString(curandStatus_t error);
+
+// CUDA: thread number configuration.
+// Use 1024 threads per block, which requires cuda sm_2x or above,
+// or fall back to attempt compatibility (best of luck to you).
+#if __CUDA_ARCH__ >= 200
+    const int CAFFE_CUDA_NUM_THREADS = 1024;
+#else
+    const int CAFFE_CUDA_NUM_THREADS = 512;
+#endif
+
+// CUDA: number of blocks for threads.
+inline int CAFFE_GET_BLOCKS(const int N) {
+  return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS;
+}
+
+}  // namespace caffe
+
 #endif  // CPU_ONLY
 
 #endif  // CAFFE_UTIL_DEVICE_ALTERNATE_H_

From cd52392d31e5f235d04cfccb722483c260c3cf1e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 16:54:01 -0700
Subject: [PATCH 0695/2053] groom proto: sort layer type parameters, put
 loss_weight after basics

---
 src/caffe/proto/caffe.proto | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 77f74a33cbf..38db12b6400 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -202,10 +202,6 @@ message NetStateRule {
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
-  // The amount of weight to assign each top blob in the objective.
-  // Each layer assigns a default value, usually of either 0 or 1,
-  // to each top blob.
-  repeated float loss_weight = 35;
   optional string name = 4; // the layer name
 
   // Rules controlling whether and when a layer is included in the network,
@@ -288,6 +284,11 @@ message LayerParameter {
   // The weight decay that is multiplied on the global weight decay.
   repeated float weight_decay = 8;
 
+  // The amount of weight to assign each top blob in the objective.
+  // Each layer assigns a default value, usually of either 0 or 1,
+  // to each top blob.
+  repeated float loss_weight = 35;
+
   optional AccuracyParameter accuracy_param = 27;
   optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
@@ -298,19 +299,19 @@ message LayerParameter {
   optional EltwiseParameter eltwise_param = 24;
   optional HDF5DataParameter hdf5_data_param = 13;
   optional HDF5OutputParameter hdf5_output_param = 14;
+  optional HingeLossParameter hinge_loss_param = 29;
   optional ImageDataParameter image_data_param = 15;
   optional InfogainLossParameter infogain_loss_param = 16;
   optional InnerProductParameter inner_product_param = 17;
   optional LRNParameter lrn_param = 18;
   optional MemoryDataParameter memory_data_param = 22;
+  optional MVNParameter mvn_param = 34;
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
   optional ReLUParameter relu_param = 30;
-  optional WindowDataParameter window_data_param = 20;
-  optional ThresholdParameter threshold_param = 25;
-  optional HingeLossParameter hinge_loss_param = 29;
   optional SliceParameter slice_param = 31;
-  optional MVNParameter mvn_param = 34;
+  optional ThresholdParameter threshold_param = 25;
+  optional WindowDataParameter window_data_param = 20;
   
   // Parameters for data pre-processing.
   optional TransformationParameter transform_param = 36;

From a3dcca2a2cc7bd891d13c2303372286e55596fd4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 17:15:33 -0700
Subject: [PATCH 0696/2053] add engine parameter for multiple computational
 strategies

add `engine` switch to layers for selecting a computational backend when
there is a choice. Currently the standard Caffe implementation is the
only backend.
---
 Makefile                           |  5 +++
 Makefile.config.example            |  3 ++
 src/caffe/layers/conv_layer.cpp    |  7 ++++
 src/caffe/layers/pooling_layer.cpp |  6 ++++
 src/caffe/layers/relu_layer.cpp    |  9 ++++-
 src/caffe/layers/sigmoid_layer.cpp |  7 ++++
 src/caffe/layers/softmax_layer.cpp |  7 ++++
 src/caffe/layers/tanh_layer.cpp    |  7 ++++
 src/caffe/proto/caffe.proto        | 54 ++++++++++++++++++++++++++++--
 9 files changed, 102 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 546de931f7e..89f7347b9f4 100644
--- a/Makefile
+++ b/Makefile
@@ -257,6 +257,11 @@ else
 	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
+# Computation engine configuration
+ifeq ($(ENGINE), caffe)
+	COMMON_FLAGS += -DCAFFE_ENGINE
+endif
+
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
diff --git a/Makefile.config.example b/Makefile.config.example
index 7c96d8a9356..12c44477c11 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -1,6 +1,9 @@
 ## Refer to http://caffe.berkeleyvision.org/installation.html
 # Contributions simplifying and improving our build system are welcome!
 
+# Computation engine switch: currently only the standard Caffe engine.
+ENGINE := caffe
+
 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 1
 
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 1a1248f3dda..43ec6dee9fa 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -12,6 +12,7 @@ template <typename Dtype>
 void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  // Check configuration.
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
       << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
@@ -112,6 +113,12 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
+  // Default computation engine.
+#ifdef CAFFE_ENGINE
+  if (conv_param.engine() == ConvolutionParameter_Engine_DEFAULT) {
+    conv_param.set_engine(ConvolutionParameter_Engine_CAFFE);
+  }
+#endif
 }
 
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 9e77fa28a45..fd13b1de4d9 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -96,6 +96,12 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     rand_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
   }
+  // Default computation engine.
+#ifdef CAFFE_ENGINE
+  if (pool_param.engine() == PoolingParameter_Engine_DEFAULT) {
+    pool_param.set_engine(PoolingParameter_Engine_CAFFE);
+  }
+#endif
 }
 
 // TODO(Yangqing): Is there a faster way to do pooling in the channel-first
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index b50352f8526..7b41f7a5236 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -9,14 +9,21 @@ namespace caffe {
 template <typename Dtype>
 void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
+  ReLUParameter relu_param = this->layer_param_.relu_param();
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
-  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
+  Dtype negative_slope = relu_param.negative_slope();
   for (int i = 0; i < count; ++i) {
     top_data[i] = std::max(bottom_data[i], Dtype(0))
         + negative_slope * std::min(bottom_data[i], Dtype(0));
   }
+  // Default computation engine.
+#ifdef CAFFE_ENGINE
+  if (relu_param.engine() == ReLUParameter_Engine_DEFAULT) {
+    relu_param.set_engine(ReLUParameter_Engine_CAFFE);
+  }
+#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index d7bba7fbfc3..e045b7ef860 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -15,12 +15,19 @@ inline Dtype sigmoid(Dtype x) {
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
+  SigmoidParameter sigmoid_param = this->layer_param_.sigmoid_param();
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = sigmoid(bottom_data[i]);
   }
+  // Default computation engine.
+#ifdef CAFFE_ENGINE
+  if (sigmoid_param.engine() == SigmoidParameter_Engine_DEFAULT) {
+    sigmoid_param.set_engine(SigmoidParameter_Engine_CAFFE);
+  }
+#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 29767ac775c..bfc41164eee 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -11,6 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
+  SoftmaxParameter softmax_param = this->layer_param_.softmax_param();
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
@@ -19,6 +20,12 @@ void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     multiplier_data[i] = 1.;
   }
   scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+  // Default computation engine.
+#ifdef CAFFE_ENGINE
+  if (softmax_param.engine() == SoftmaxParameter_Engine_DEFAULT) {
+    softmax_param.set_engine(SoftmaxParameter_Engine_CAFFE);
+  }
+#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 8dae0054aed..1ea00736ef0 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -12,6 +12,7 @@ namespace caffe {
 template <typename Dtype>
 void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
+  TanHParameter tanh_param = this->layer_param_.tanh_param();
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   Dtype exp2x;
@@ -20,6 +21,12 @@ void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     exp2x = exp(2 * bottom_data[i]);
     top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
+  // Default computation engine.
+#ifdef CAFFE_ENGINE
+  if (tanh_param.engine() == TanHParameter_Engine_DEFAULT) {
+    tanh_param.set_engine(TanHParameter_Engine_CAFFE);
+  }
+#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 38db12b6400..082feb831b0 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -198,7 +198,7 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 37 (last added: transform_param)
+// LayerParameter next available ID: 40 (last added: softmax_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -309,13 +309,21 @@ message LayerParameter {
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
   optional ReLUParameter relu_param = 30;
+  optional SigmoidParameter sigmoid_param = 38;
+  optional SoftmaxParameter softmax_param = 39;
   optional SliceParameter slice_param = 31;
+  optional TanHParameter tanh_param = 37;
   optional ThresholdParameter threshold_param = 25;
   optional WindowDataParameter window_data_param = 20;
-  
+
   // Parameters for data pre-processing.
   optional TransformationParameter transform_param = 36;
 
+  // Note: certain layers may have more than one computational engine
+  // for their implementation. These layers include an Engine type and
+  // engine parameter for selecting the implementation.
+  // The default for the engine is set by the ENGINE switch at compile-time.
+
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
   // LayerParameter specification.
@@ -377,6 +385,11 @@ message ConvolutionParameter {
   optional uint32 stride_w = 14; // The stride width
   optional FillerParameter weight_filler = 7; // The filler for the weight
   optional FillerParameter bias_filler = 8; // The filler for the bias
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+  }
+  optional Engine engine = 15 [default = DEFAULT];
 }
 
 // Message that stores parameters used by DataLayer
@@ -563,6 +576,11 @@ message PoolingParameter {
   optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
   optional uint32 stride_h = 7; // The stride height
   optional uint32 stride_w = 8; // The stride width
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+  }
+  optional Engine engine = 11 [default = DEFAULT];
 }
 
 // Message that stores parameters used by PowerLayer
@@ -581,6 +599,20 @@ message ReLUParameter {
   // improve neural network acoustic models. In ICML Workshop on Deep Learning
   // for Audio, Speech, and Language Processing.
   optional float negative_slope = 1 [default = 0];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+  }
+  optional Engine engine = 2 [default = DEFAULT];
+}
+
+// Message that stores parameters used by SigmoidLayer
+message SigmoidParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
 }
 
 // Message that stores parameters used by SliceLayer
@@ -593,6 +625,24 @@ message SliceParameter {
   repeated uint32 slice_point = 2;
 }
 
+// Message that stores parameters used by SoftmaxLayer, SoftMaxWithLossLayer
+message SoftmaxParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
+
+// Message that stores parameters used by SigmoidLayer
+message TanHParameter {
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+  }
+  optional Engine engine = 1 [default = DEFAULT];
+}
+
 // Message that stores parameters used by WindowDataLayer
 message WindowDataParameter {
   // Specify the data source.

From 237560cc34b5fec399e6cdd3ab380c75e9a64dd5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 17:44:50 -0700
Subject: [PATCH 0697/2053] ifdef engine default

---
 src/caffe/layers/conv_layer.cpp    | 7 -------
 src/caffe/layers/pooling_layer.cpp | 6 ------
 src/caffe/layers/relu_layer.cpp    | 9 +--------
 src/caffe/layers/sigmoid_layer.cpp | 7 -------
 src/caffe/layers/softmax_layer.cpp | 7 -------
 src/caffe/layers/tanh_layer.cpp    | 7 -------
 6 files changed, 1 insertion(+), 42 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 43ec6dee9fa..1a1248f3dda 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -12,7 +12,6 @@ template <typename Dtype>
 void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  // Check configuration.
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
       << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
@@ -113,12 +112,6 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
-  // Default computation engine.
-#ifdef CAFFE_ENGINE
-  if (conv_param.engine() == ConvolutionParameter_Engine_DEFAULT) {
-    conv_param.set_engine(ConvolutionParameter_Engine_CAFFE);
-  }
-#endif
 }
 
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index fd13b1de4d9..9e77fa28a45 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -96,12 +96,6 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     rand_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
   }
-  // Default computation engine.
-#ifdef CAFFE_ENGINE
-  if (pool_param.engine() == PoolingParameter_Engine_DEFAULT) {
-    pool_param.set_engine(PoolingParameter_Engine_CAFFE);
-  }
-#endif
 }
 
 // TODO(Yangqing): Is there a faster way to do pooling in the channel-first
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 7b41f7a5236..b50352f8526 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -9,21 +9,14 @@ namespace caffe {
 template <typename Dtype>
 void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
-  ReLUParameter relu_param = this->layer_param_.relu_param();
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
-  Dtype negative_slope = relu_param.negative_slope();
+  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   for (int i = 0; i < count; ++i) {
     top_data[i] = std::max(bottom_data[i], Dtype(0))
         + negative_slope * std::min(bottom_data[i], Dtype(0));
   }
-  // Default computation engine.
-#ifdef CAFFE_ENGINE
-  if (relu_param.engine() == ReLUParameter_Engine_DEFAULT) {
-    relu_param.set_engine(ReLUParameter_Engine_CAFFE);
-  }
-#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index e045b7ef860..d7bba7fbfc3 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -15,19 +15,12 @@ inline Dtype sigmoid(Dtype x) {
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
-  SigmoidParameter sigmoid_param = this->layer_param_.sigmoid_param();
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = sigmoid(bottom_data[i]);
   }
-  // Default computation engine.
-#ifdef CAFFE_ENGINE
-  if (sigmoid_param.engine() == SigmoidParameter_Engine_DEFAULT) {
-    sigmoid_param.set_engine(SigmoidParameter_Engine_CAFFE);
-  }
-#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index bfc41164eee..29767ac775c 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -11,7 +11,6 @@ namespace caffe {
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  SoftmaxParameter softmax_param = this->layer_param_.softmax_param();
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
@@ -20,12 +19,6 @@ void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     multiplier_data[i] = 1.;
   }
   scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
-  // Default computation engine.
-#ifdef CAFFE_ENGINE
-  if (softmax_param.engine() == SoftmaxParameter_Engine_DEFAULT) {
-    softmax_param.set_engine(SoftmaxParameter_Engine_CAFFE);
-  }
-#endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 1ea00736ef0..8dae0054aed 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -12,7 +12,6 @@ namespace caffe {
 template <typename Dtype>
 void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
-  TanHParameter tanh_param = this->layer_param_.tanh_param();
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   Dtype exp2x;
@@ -21,12 +20,6 @@ void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     exp2x = exp(2 * bottom_data[i]);
     top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
-  // Default computation engine.
-#ifdef CAFFE_ENGINE
-  if (tanh_param.engine() == TanHParameter_Engine_DEFAULT) {
-    tanh_param.set_engine(TanHParameter_Engine_CAFFE);
-  }
-#endif
 }
 
 template <typename Dtype>

From 98b4cd36ad3992f7ebc4fdf23ff10b653457687c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 18:56:08 -0700
Subject: [PATCH 0698/2053] strategize Caffe convolution

---
 include/caffe/vision_layers.hpp               |  41 ++++-
 src/caffe/layer_factory.cpp                   |  23 ++-
 src/caffe/layers/caffe_conv_layer.cpp         | 144 ++++++++++++++++++
 .../{conv_layer.cu => caffe_conv_layer.cu}    |  49 +++---
 src/caffe/layers/conv_layer.cpp               | 140 ++---------------
 src/caffe/test/test_convolution_layer.cpp     |  18 +--
 6 files changed, 245 insertions(+), 170 deletions(-)
 create mode 100644 src/caffe/layers/caffe_conv_layer.cpp
 rename src/caffe/layers/{conv_layer.cu => caffe_conv_layer.cu} (68%)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 86a54ceddaf..5bdd4f75f34 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -39,13 +39,13 @@ class ConvolutionLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -56,12 +56,41 @@ class ConvolutionLayer : public Layer<Dtype> {
   int width_;
   int num_output_;
   int group_;
-  Blob<Dtype> col_buffer_;
-  Blob<Dtype> bias_multiplier_;
   bool bias_term_;
+  int height_out_;
+  int width_out_;
+};
+
+template <typename Dtype>
+class CaffeConvolutionLayer : public ConvolutionLayer<Dtype> {
+ public:
+  explicit CaffeConvolutionLayer(const LayerParameter& param)
+      : ConvolutionLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONVOLUTION;
+  }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
   int M_;
   int K_;
   int N_;
+  Blob<Dtype> col_buffer_;
+  Blob<Dtype> bias_multiplier_;
 };
 
 /**
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 4bb7cd7cd7f..ae9ff3621de 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -6,6 +6,27 @@
 
 namespace caffe {
 
+// GetLayer() defines the overall layer factory. The Get*Layer() functions
+// define factories for layers with multiple computational engines.
+
+// Get convolution layer according to engine.
+template <typename Dtype>
+ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
+    const LayerParameter& param) {
+  ConvolutionParameter_Engine engine = param.convolution_param().engine();
+  if (engine == ConvolutionParameter_Engine_CAFFE) {
+    return new CaffeConvolutionLayer<Dtype>(param);
+  } else {
+    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+  }
+}
+
+template ConvolutionLayer<float>* GetConvolutionLayer(const string& name,
+    const LayerParameter& param);
+template ConvolutionLayer<double>* GetConvolutionLayer(const string& name,
+    const LayerParameter& param);
+
+
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
 // but we will leave it this way for now.
@@ -25,7 +46,7 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   case LayerParameter_LayerType_CONCAT:
     return new ConcatLayer<Dtype>(param);
   case LayerParameter_LayerType_CONVOLUTION:
-    return new ConvolutionLayer<Dtype>(param);
+    return GetConvolutionLayer<Dtype>(name, param);
   case LayerParameter_LayerType_DATA:
     return new DataLayer<Dtype>(param);
   case LayerParameter_LayerType_DROPOUT:
diff --git a/src/caffe/layers/caffe_conv_layer.cpp b/src/caffe/layers/caffe_conv_layer.cpp
new file mode 100644
index 00000000000..759a06ee9f0
--- /dev/null
+++ b/src/caffe/layers/caffe_conv_layer.cpp
@@ -0,0 +1,144 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CaffeConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>&
+    bottom, vector<Blob<Dtype>*>* top) {
+  ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
+  // Figure out the dimensions for individual gemms.
+  M_ = this->num_output_ / this->group_;
+  K_ = this->channels_ * this->kernel_h_ * this->kernel_w_ / this->group_;
+  N_ = this->height_out_ * this->width_out_;
+  // The im2col result buffer would only hold one image at a time to avoid
+  // overly large memory usage.
+  col_buffer_.Reshape(1, this->channels_ * this->kernel_h_ * this->kernel_w_,
+      this->height_out_, this->width_out_);
+  // Set up the all ones "bias multiplier" for adding bias using blas
+  if (this->bias_term_) {
+    bias_multiplier_.Reshape(1, 1, 1, N_);
+    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void CaffeConvolutionLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* top_data = (*top)[i]->mutable_cpu_data();
+    Dtype* col_data = col_buffer_.mutable_cpu_data();
+    const Dtype* weight = this->blobs_[0]->cpu_data();
+    int weight_offset = M_ * K_;
+    int col_offset = K_ * N_;
+    int top_offset = M_ * N_;
+    for (int n = 0; n < this->num_; ++n) {
+      // First, im2col
+      im2col_cpu(bottom_data + bottom[i]->offset(n), this->channels_,
+          this->height_, this->width_, this->kernel_h_, this->kernel_w_,
+          this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
+          col_data);
+      // Second, innerproduct with groups
+      for (int g = 0; g < this->group_; ++g) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
+          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+      }
+      // third, add bias
+      if (this->bias_term_) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, this->num_output_,
+            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
+            bias_multiplier_.cpu_data(),
+            (Dtype)1., top_data + (*top)[i]->offset(n));
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void CaffeConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->cpu_data();
+    weight_diff = this->blobs_[0]->mutable_cpu_diff();
+    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
+  Dtype* bias_diff = NULL;
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    bias_diff = this->blobs_[1]->mutable_cpu_diff();
+    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
+  }
+  const int weight_offset = M_ * K_;
+  const int col_offset = K_ * N_;
+  const int top_offset = M_ * N_;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = NULL;
+    // Bias gradient, if necessary.
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      top_diff = top[i]->cpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        caffe_cpu_gemv<Dtype>(CblasNoTrans, this->num_output_, N_,
+            1., top_diff + top[0]->offset(n),
+            bias_multiplier_.cpu_data(), 1.,
+            bias_diff);
+      }
+    }
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      if (!top_diff) {
+        top_diff = top[i]->cpu_diff();
+      }
+      Dtype* col_data = col_buffer_.mutable_cpu_data();
+      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
+      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        // Since we saved memory in the forward pass by not storing all col
+        // data, we will need to recompute them.
+        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), this->channels_,
+            this->height_, this->width_, this->kernel_h_, this->kernel_w_,
+            this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
+            col_data);
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          for (int g = 0; g < this->group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+                col_data + col_offset * g, (Dtype)1.,
+                weight_diff + weight_offset * g);
+          }
+        }
+        // gradient w.r.t. bottom data, if necessary
+        if (propagate_down[i]) {
+          for (int g = 0; g < this->group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+                (Dtype)1., weight + weight_offset * g,
+                top_diff + top[i]->offset(n) + top_offset * g,
+                (Dtype)0., col_diff + col_offset * g);
+          }
+          // col2im back to the data
+          col2im_cpu(col_diff, this->channels_, this->height_, this->width_,
+              this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
+              this->stride_h_, this->stride_w_, bottom_diff +
+              (*bottom)[i]->offset(n));
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(CaffeConvolutionLayer);
+#endif
+
+INSTANTIATE_CLASS(CaffeConvolutionLayer);
+
+}  // namespace caffe
+
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/caffe_conv_layer.cu
similarity index 68%
rename from src/caffe/layers/conv_layer.cu
rename to src/caffe/layers/caffe_conv_layer.cu
index f7f393badba..44a9f9737ab 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/caffe_conv_layer.cu
@@ -9,8 +9,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+void CaffeConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>&
+    bottom, vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = (*top)[i]->mutable_gpu_data();
@@ -19,20 +19,21 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     int weight_offset = M_ * K_;
     int col_offset = K_ * N_;
     int top_offset = M_ * N_;
-    for (int n = 0; n < num_; ++n) {
+    for (int n = 0; n < this->num_; ++n) {
       // First, im2col
-      im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+      im2col_gpu(bottom_data + bottom[i]->offset(n), this->channels_,
+          this->height_, this->width_, this->kernel_h_, this->kernel_w_,
+          this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
           col_data);
       // Second, innerproduct with groups
-      for (int g = 0; g < group_; ++g) {
+      for (int g = 0; g < this->group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
           (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
           (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
       }
       // third, add bias
-      if (bias_term_) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+      if (this->bias_term_) {
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, this->num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
             bias_multiplier_.gpu_data(),
             (Dtype)1., top_data + (*top)[i]->offset(n));
@@ -42,7 +43,7 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* weight = NULL;
   Dtype* weight_diff = NULL;
@@ -52,7 +53,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
-  if (bias_term_ && this->param_propagate_down_[1]) {
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
     caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
@@ -62,10 +63,10 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = NULL;
     // Bias gradient, if necessary.
-    if (bias_term_ && this->param_propagate_down_[1]) {
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
       top_diff = top[i]->gpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
+      for (int n = 0; n < this->num_; ++n) {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, this->num_output_, N_,
             1., top_diff + top[0]->offset(n),
             bias_multiplier_.gpu_data(), 1.,
             bias_diff);
@@ -79,15 +80,16 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       Dtype* col_diff = col_buffer_.mutable_gpu_diff();
       const Dtype* bottom_data = (*bottom)[i]->gpu_data();
       Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-      for (int n = 0; n < num_; ++n) {
+      for (int n = 0; n < this->num_; ++n) {
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
-        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                   stride_h_, stride_w_, col_data);
+        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), this->channels_,
+            this->height_, this->width_, this->kernel_h_, this->kernel_w_,
+            this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
+            col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < group_; ++g) {
+          for (int g = 0; g < this->group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
                 (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
                 col_data + col_offset * g, (Dtype)1.,
@@ -96,16 +98,17 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         }
         // gradient w.r.t. bottom data, if necessary
         if (propagate_down[i]) {
-          for (int g = 0; g < group_; ++g) {
+          for (int g = 0; g < this->group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
                 (Dtype)1., weight + weight_offset * g,
                 top_diff + top[i]->offset(n) + top_offset * g,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_gpu(col_diff, channels_, height_, width_,
-              kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-              bottom_diff + (*bottom)[i]->offset(n));
+          col2im_gpu(col_diff, this->channels_, this->height_, this->width_,
+              this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
+              this->stride_h_, this->stride_w_, bottom_diff +
+              (*bottom)[i]->offset(n));
         }
       }
     }
@@ -113,6 +116,6 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(ConvolutionLayer);
+INSTANTIATE_CLASS(CaffeConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 1a1248f3dda..a2ab13301e6 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -46,7 +46,7 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = conv_param.stride_h();
     stride_w_ = conv_param.stride_w();
   }
-  group_ = this->layer_param_.convolution_param().group();
+  group_ = conv_param.group();
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -61,26 +61,18 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(width_, bottom[bottom_id]->width())
         << "Inputs must have same width.";
   }
-  num_output_ = this->layer_param_.convolution_param().num_output();
+  num_output_ = conv_param.num_output();
   CHECK_GT(num_output_, 0);
   CHECK_EQ(channels_ % group_, 0);
-  // The im2col result buffer would only hold one image at a time to avoid
-  // overly large memory usage.
-  int height_out =
-      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
-  int width_out = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
-  col_buffer_.Reshape(
-      1, channels_ * kernel_h_ * kernel_w_, height_out, width_out);
+  // Calculate output dimensions.
+  height_out_ = (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
+  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
   // Set the parameters
   CHECK_EQ(num_output_ % group_, 0)
       << "Number of output should be multiples of group.";
-  bias_term_ = this->layer_param_.convolution_param().bias_term();
-  // Figure out the dimensions for individual gemms.
-  M_ = num_output_ / group_;
-  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
-  N_ = height_out * width_out;
+  bias_term_ = conv_param.bias_term();
   for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out, width_out);
+    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
   }
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
@@ -96,133 +88,19 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
         num_output_, channels_ / group_, kernel_h_, kernel_w_));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
-        this->layer_param_.convolution_param().weight_filler()));
+        conv_param.weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
     // If necessary, initialize and fill the bias term
     if (bias_term_) {
       this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
-          this->layer_param_.convolution_param().bias_filler()));
+          conv_param.bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
-  // Set up the all ones "bias multiplier" for adding bias using blas
-  if (bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, N_);
-    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
-  }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
-
-template <typename Dtype>
-void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < bottom.size(); ++i) {
-    const Dtype* bottom_data = bottom[i]->cpu_data();
-    Dtype* top_data = (*top)[i]->mutable_cpu_data();
-    Dtype* col_data = col_buffer_.mutable_cpu_data();
-    const Dtype* weight = this->blobs_[0]->cpu_data();
-    int weight_offset = M_ * K_;
-    int col_offset = K_ * N_;
-    int top_offset = M_ * N_;
-    for (int n = 0; n < num_; ++n) {
-      // First, im2col
-      im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-          col_data);
-      // Second, innerproduct with groups
-      for (int g = 0; g < group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
-      }
-      // third, add bias
-      if (bias_term_) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-            bias_multiplier_.cpu_data(),
-            (Dtype)1., top_data + (*top)[i]->offset(n));
-      }
-    }
-  }
-}
-
-template <typename Dtype>
-void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* weight = NULL;
-  Dtype* weight_diff = NULL;
-  if (this->param_propagate_down_[0]) {
-    weight = this->blobs_[0]->cpu_data();
-    weight_diff = this->blobs_[0]->mutable_cpu_diff();
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  Dtype* bias_diff = NULL;
-  if (bias_term_ && this->param_propagate_down_[1]) {
-    bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
-  }
-  const int weight_offset = M_ * K_;
-  const int col_offset = K_ * N_;
-  const int top_offset = M_ * N_;
-  for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = NULL;
-    // Bias gradient, if necessary.
-    if (bias_term_ && this->param_propagate_down_[1]) {
-      top_diff = top[i]->cpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-            1., top_diff + top[0]->offset(n),
-            bias_multiplier_.cpu_data(), 1.,
-            bias_diff);
-      }
-    }
-    if (this->param_propagate_down_[0] || propagate_down[i]) {
-      if (!top_diff) {
-        top_diff = top[i]->cpu_diff();
-      }
-      Dtype* col_data = col_buffer_.mutable_cpu_data();
-      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        // Since we saved memory in the forward pass by not storing all col
-        // data, we will need to recompute them.
-        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                   stride_h_, stride_w_, col_data);
-        // gradient w.r.t. weight. Note that we will accumulate diffs.
-        if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_data + col_offset * g, (Dtype)1.,
-                weight_diff + weight_offset * g);
-          }
-        }
-        // gradient w.r.t. bottom data, if necessary
-        if (propagate_down[i]) {
-          for (int g = 0; g < group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-                (Dtype)1., weight + weight_offset * g,
-                top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_diff + col_offset * g);
-          }
-          // col2im back to the data
-          col2im_cpu(col_diff, channels_, height_, width_,
-              kernel_h_, kernel_w_, pad_h_, pad_w_,
-              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
-        }
-      }
-    }
-  }
-}
-
-#ifdef CPU_ONLY
-STUB_GPU(ConvolutionLayer);
-#endif
-
 INSTANTIATE_CLASS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 5a7ea80467e..3489ead8123 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -62,7 +62,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<Dtype> > layer(
-      new ConvolutionLayer<Dtype>(layer_param));
+      new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
@@ -75,7 +75,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   // setting group should not change the shape
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
-  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer.reset(new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
@@ -111,7 +111,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
-      new ConvolutionLayer<Dtype>(layer_param));
+      new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 27.1
@@ -154,7 +154,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
-      new ConvolutionLayer<Dtype>(layer_param));
+      new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 9.1
@@ -192,7 +192,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
   shared_ptr<Layer<Dtype> > layer(
-      new ConvolutionLayer<Dtype>(layer_param));
+      new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
   layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 3));
   Dtype* weights = layer->blobs()[0]->mutable_cpu_data();
@@ -225,7 +225,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_stride_w(1);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
-  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer.reset(new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
   layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 1));
   Dtype* weights_1 = layer->blobs()[0]->mutable_cpu_data();
@@ -247,7 +247,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_stride_w(2);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
-  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer.reset(new CaffeConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
   layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
   Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
@@ -279,7 +279,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  ConvolutionLayer<Dtype> layer(layer_param);
+  CaffeConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -296,7 +296,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  ConvolutionLayer<Dtype> layer(layer_param);
+  CaffeConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));

From 63323765f5d3192cfa3e3dc06fba79a475374ab2 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 20:27:33 -0700
Subject: [PATCH 0699/2053] strategize pooling

Scaffold engine switching for pooling.

The Caffe pooling is instantiated without regard for engine in:
- LRNLayer
- PoolingLayer tests
- StochasticPoolingLayer tests
- MaxPoolingDropout tests
---
 include/caffe/vision_layers.hpp               |  44 +++-
 src/caffe/layer_factory.cpp                   |  18 +-
 src/caffe/layers/caffe_pooling_layer.cpp      | 230 ++++++++++++++++++
 ...ooling_layer.cu => caffe_pooling_layer.cu} |  55 +++--
 src/caffe/layers/lrn_layer.cpp                |   2 +-
 src/caffe/layers/pooling_layer.cpp            | 206 +---------------
 .../test/test_maxpool_dropout_layers.cpp      |   6 +-
 src/caffe/test/test_pooling_layer.cpp         |  22 +-
 src/caffe/test/test_stochastic_pooling.cpp    |   8 +-
 9 files changed, 337 insertions(+), 254 deletions(-)
 create mode 100644 src/caffe/layers/caffe_pooling_layer.cpp
 rename src/caffe/layers/{pooling_layer.cu => caffe_pooling_layer.cu} (86%)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 5bdd4f75f34..46df807fbd6 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -238,13 +238,13 @@ class PoolingLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -258,6 +258,42 @@ class PoolingLayer : public Layer<Dtype> {
   Blob<int> max_idx_;
 };
 
+/* PoolingLayer
+*/
+template <typename Dtype>
+class CaffePoolingLayer : public PoolingLayer<Dtype> {
+ public:
+  explicit CaffePoolingLayer(const LayerParameter& param)
+      : PoolingLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_POOLING;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  // MAX POOL layers can output an extra top blob for the mask;
+  // others can only output the pooled inputs.
+  virtual inline int MaxTopBlobs() const {
+    return (this->layer_param_.pooling_param().pool() ==
+            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> rand_idx_;
+  Blob<int> max_idx_;
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_VISION_LAYERS_HPP_
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index ae9ff3621de..2ffb7f8558a 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -26,6 +26,22 @@ template ConvolutionLayer<float>* GetConvolutionLayer(const string& name,
 template ConvolutionLayer<double>* GetConvolutionLayer(const string& name,
     const LayerParameter& param);
 
+// Get pooling layer according to engine.
+template <typename Dtype>
+PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
+    const LayerParameter& param) {
+  PoolingParameter_Engine engine = param.pooling_param().engine();
+  if (engine == PoolingParameter_Engine_CAFFE) {
+    return new CaffePoolingLayer<Dtype>(param);
+  } else {
+    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+  }
+}
+
+template PoolingLayer<float>* GetPoolingLayer(const string& name,
+    const LayerParameter& param);
+template PoolingLayer<double>* GetPoolingLayer(const string& name,
+    const LayerParameter& param);
 
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
@@ -82,7 +98,7 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
     return new MultinomialLogisticLossLayer<Dtype>(param);
   case LayerParameter_LayerType_POOLING:
-    return new PoolingLayer<Dtype>(param);
+    return GetPoolingLayer<Dtype>(name, param);
   case LayerParameter_LayerType_POWER:
     return new PowerLayer<Dtype>(param);
   case LayerParameter_LayerType_RELU:
diff --git a/src/caffe/layers/caffe_pooling_layer.cpp b/src/caffe/layers/caffe_pooling_layer.cpp
new file mode 100644
index 00000000000..5756b740159
--- /dev/null
+++ b/src/caffe/layers/caffe_pooling_layer.cpp
@@ -0,0 +1,230 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+using std::min;
+using std::max;
+
+template <typename Dtype>
+void CaffePoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  PoolingLayer<Dtype>::LayerSetUp(bottom, top);
+  PoolingParameter pool_param = this->layer_param_.pooling_param();
+  // If max pooling, we will initialize the vector index part.
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
+    max_idx_.Reshape(bottom[0]->num(), this->channels_, this->pooled_height_,
+        this->pooled_width_);
+  }
+  // If stochastic pooling, we will initialize the random index part.
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_STOCHASTIC) {
+    rand_idx_.Reshape(bottom[0]->num(), this->channels_, this->pooled_height_,
+      this->pooled_width_);
+  }
+}
+
+// TODO(Yangqing): Is there a faster way to do pooling in the channel-first
+// case?
+template <typename Dtype>
+void CaffePoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int top_count = (*top)[0]->count();
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top->size() > 1;
+  int* mask = NULL;  // suppress warnings about uninitalized variables
+  Dtype* top_mask = NULL;
+  // Different pooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more code.
+  switch (this->layer_param_.pooling_param().pool()) {
+  case PoolingParameter_PoolMethod_MAX:
+    // Initialize
+    if (use_top_mask) {
+      top_mask = (*top)[1]->mutable_cpu_data();
+      caffe_set(top_count, Dtype(-1), top_mask);
+    } else {
+      mask = max_idx_.mutable_cpu_data();
+      caffe_set(top_count, -1, mask);
+    }
+    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
+    // The main loop
+    for (int n = 0; n < bottom[0]->num(); ++n) {
+      for (int c = 0; c < this->channels_; ++c) {
+        for (int ph = 0; ph < this->pooled_height_; ++ph) {
+          for (int pw = 0; pw < this->pooled_width_; ++pw) {
+            int hstart = ph * this->stride_h_ - this->pad_h_;
+            int wstart = pw * this->stride_w_ - this->pad_w_;
+            int hend = min(hstart + this->kernel_h_, this->height_);
+            int wend = min(wstart + this->kernel_w_, this->width_);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            const int pool_index = ph * this->pooled_width_ + pw;
+            for (int h = hstart; h < hend; ++h) {
+              for (int w = wstart; w < wend; ++w) {
+                const int index = h * this->width_ + w;
+                if (bottom_data[index] > top_data[pool_index]) {
+                  top_data[pool_index] = bottom_data[index];
+                  if (use_top_mask) {
+                    top_mask[pool_index] = static_cast<Dtype>(index);
+                  } else {
+                    mask[pool_index] = index;
+                  }
+                }
+              }
+            }
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += (*top)[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += (*top)[0]->offset(0, 1);
+        } else {
+          mask += (*top)[0]->offset(0, 1);
+        }
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_AVE:
+    for (int i = 0; i < top_count; ++i) {
+      top_data[i] = 0;
+    }
+    // The main loop
+    for (int n = 0; n < bottom[0]->num(); ++n) {
+      for (int c = 0; c < this->channels_; ++c) {
+        for (int ph = 0; ph < this->pooled_height_; ++ph) {
+          for (int pw = 0; pw < this->pooled_width_; ++pw) {
+            int hstart = ph * this->stride_h_ - this->pad_h_;
+            int wstart = pw * this->stride_w_ - this->pad_w_;
+            int hend = min(hstart + this->kernel_h_,
+                this->height_ + this->pad_h_);
+            int wend = min(wstart + this->kernel_w_,
+                this->width_ + this->pad_w_);
+            int pool_size = (hend - hstart) * (wend - wstart);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            hend = min(hend, this->height_);
+            wend = min(wend, this->width_);
+            for (int h = hstart; h < hend; ++h) {
+              for (int w = wstart; w < wend; ++w) {
+                top_data[ph * this->pooled_width_ + pw] +=
+                    bottom_data[h * this->width_ + w];
+              }
+            }
+            top_data[ph * this->pooled_width_ + pw] /= pool_size;
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += (*top)[0]->offset(0, 1);
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_STOCHASTIC:
+    NOT_IMPLEMENTED;
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+}
+
+template <typename Dtype>
+void CaffePoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  // Different pooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more codes.
+  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top.size() > 1;
+  const int* mask = NULL;  // suppress warnings about uninitialized variables
+  const Dtype* top_mask = NULL;
+  switch (this->layer_param_.pooling_param().pool()) {
+  case PoolingParameter_PoolMethod_MAX:
+    // The main loop
+    if (use_top_mask) {
+      top_mask = top[1]->cpu_data();
+    } else {
+      mask = max_idx_.cpu_data();
+    }
+    for (int n = 0; n < top[0]->num(); ++n) {
+      for (int c = 0; c < this->channels_; ++c) {
+        for (int ph = 0; ph < this->pooled_height_; ++ph) {
+          for (int pw = 0; pw < this->pooled_width_; ++pw) {
+            const int index = ph * this->pooled_width_ + pw;
+            const int bottom_index =
+                use_top_mask ? top_mask[index] : mask[index];
+            bottom_diff[bottom_index] += top_diff[index];
+          }
+        }
+        bottom_diff += (*bottom)[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += top[0]->offset(0, 1);
+        } else {
+          mask += top[0]->offset(0, 1);
+        }
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_AVE:
+    // The main loop
+    for (int n = 0; n < top[0]->num(); ++n) {
+      for (int c = 0; c < this->channels_; ++c) {
+        for (int ph = 0; ph < this->pooled_height_; ++ph) {
+          for (int pw = 0; pw < this->pooled_width_; ++pw) {
+            int hstart = ph * this->stride_h_ - this->pad_h_;
+            int wstart = pw * this->stride_w_ - this->pad_w_;
+            int hend = min(hstart + this->kernel_h_,
+                this->height_ + this->pad_h_);
+            int wend = min(wstart + this->kernel_w_,
+                this->width_ + this->pad_w_);
+            int pool_size = (hend - hstart) * (wend - wstart);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            hend = min(hend, this->height_);
+            wend = min(wend, this->width_);
+            for (int h = hstart; h < hend; ++h) {
+              for (int w = wstart; w < wend; ++w) {
+                bottom_diff[h * this->width_ + w] +=
+                  top_diff[ph * this->pooled_width_ + pw] / pool_size;
+              }
+            }
+          }
+        }
+        // offset
+        bottom_diff += (*bottom)[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_STOCHASTIC:
+    NOT_IMPLEMENTED;
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(CaffePoolingLayer);
+#endif
+
+INSTANTIATE_CLASS(CaffePoolingLayer);
+
+}  // namespace caffe
+
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/caffe_pooling_layer.cu
similarity index 86%
rename from src/caffe/layers/pooling_layer.cu
rename to src/caffe/layers/caffe_pooling_layer.cu
index e64128b87f2..7a8c351e3a1 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/caffe_pooling_layer.cu
@@ -151,7 +151,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
 
 
 template <typename Dtype>
-void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void CaffePoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -169,17 +169,18 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_,
-        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data,
-        mask, top_mask);
+        count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
+        this->width_, this->pooled_height_, this->pooled_width_,
+        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+        this->pad_h_, this->pad_w_, top_data, mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_,
-        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
+        count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
+        this->width_, this->pooled_height_, this->pooled_width_,
+        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+        this->pad_h_, this->pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     if (Caffe::phase() == Caffe::TRAIN) {
@@ -189,17 +190,18 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
-          count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_h_,
-          kernel_w_, stride_h_, stride_w_,
+          count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
+          this->width_, this->pooled_height_, this->pooled_width_,
+          this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
-          count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_h_,
-          kernel_w_, stride_h_, stride_w_, top_data);
+          count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
+          this->width_, this->pooled_height_, this->pooled_width_,
+          this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+          top_data);
     }
     break;
   default:
@@ -324,7 +326,7 @@ __global__ void StoPoolBackward(const int nthreads,
 
 
 template <typename Dtype>
-void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void CaffePoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   if (!propagate_down[0]) {
     return;
@@ -346,24 +348,25 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, mask, top_mask, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_,
-        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_,
-        bottom_diff);
+        count, top_diff, mask, top_mask, top[0]->num(), this->channels_,
+        this->height_, this->width_, this->pooled_height_, this->pooled_width_,
+        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+        this->pad_h_, this->pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_,
-        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
+        count, top_diff, top[0]->num(), this->channels_, this->height_,
+        this->width_, this->pooled_height_, this->pooled_width_,
+        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+        this->pad_h_, this->pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     // NOLINT_NEXT_LINE(whitespace/operators)
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, rand_idx_.gpu_data(), top_diff,
-        top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_,
+        count, rand_idx_.gpu_data(), top_diff, top[0]->num(), this->channels_,
+        this->height_, this->width_, this->pooled_height_, this->pooled_width_,
+        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
         bottom_diff);
     break;
   default:
@@ -373,7 +376,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(PoolingLayer);
+INSTANTIATE_CLASS(CaffePoolingLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index e81a32ba84f..c792f65069f 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -53,7 +53,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
           PoolingParameter_PoolMethod_AVE);
       pool_param.mutable_pooling_param()->set_pad(pre_pad_);
       pool_param.mutable_pooling_param()->set_kernel_size(size_);
-      pool_layer_.reset(new PoolingLayer<Dtype>(pool_param));
+      pool_layer_.reset(new CaffePoolingLayer<Dtype>(pool_param));
       pool_layer_->SetUp(square_top_vec_, &pool_top_vec_);
       CHECK_EQ(pool_output_.num(), num_);
       CHECK_EQ(pool_output_.channels(), channels_);
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 9e77fa28a45..04f47762f9d 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -52,9 +52,9 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_w_ = pool_param.stride_w();
   }
   if (pad_h_ != 0 || pad_w_ != 0) {
-    CHECK(this->layer_param_.pooling_param().pool()
+    CHECK(pool_param.pool()
         == PoolingParameter_PoolMethod_AVE
-        || this->layer_param_.pooling_param().pool()
+        || pool_param.pool()
         == PoolingParameter_PoolMethod_MAX)
         << "Padding implemented only for average and max pooling.";
     CHECK_LT(pad_h_, kernel_h_);
@@ -84,210 +84,8 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   if (top->size() > 1) {
     (*top)[1]->ReshapeLike(*(*top)[0]);
   }
-  // If max pooling, we will initialize the vector index part.
-  if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
-    max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
-        pooled_width_);
-  }
-  // If stochastic pooling, we will initialize the random index part.
-  if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_STOCHASTIC) {
-    rand_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
-      pooled_width_);
-  }
-}
-
-// TODO(Yangqing): Is there a faster way to do pooling in the channel-first
-// case?
-template <typename Dtype>
-void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  const int top_count = (*top)[0]->count();
-  // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top->size() > 1;
-  int* mask = NULL;  // suppress warnings about uninitalized variables
-  Dtype* top_mask = NULL;
-  // Different pooling methods. We explicitly do the switch outside the for
-  // loop to save time, although this results in more code.
-  switch (this->layer_param_.pooling_param().pool()) {
-  case PoolingParameter_PoolMethod_MAX:
-    // Initialize
-    if (use_top_mask) {
-      top_mask = (*top)[1]->mutable_cpu_data();
-      caffe_set(top_count, Dtype(-1), top_mask);
-    } else {
-      mask = max_idx_.mutable_cpu_data();
-      caffe_set(top_count, -1, mask);
-    }
-    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
-    // The main loop
-    for (int n = 0; n < bottom[0]->num(); ++n) {
-      for (int c = 0; c < channels_; ++c) {
-        for (int ph = 0; ph < pooled_height_; ++ph) {
-          for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_h_ - pad_h_;
-            int wstart = pw * stride_w_ - pad_w_;
-            int hend = min(hstart + kernel_h_, height_);
-            int wend = min(wstart + kernel_w_, width_);
-            hstart = max(hstart, 0);
-            wstart = max(wstart, 0);
-            const int pool_index = ph * pooled_width_ + pw;
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                const int index = h * width_ + w;
-                if (bottom_data[index] > top_data[pool_index]) {
-                  top_data[pool_index] = bottom_data[index];
-                  if (use_top_mask) {
-                    top_mask[pool_index] = static_cast<Dtype>(index);
-                  } else {
-                    mask[pool_index] = index;
-                  }
-                }
-              }
-            }
-          }
-        }
-        // compute offset
-        bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
-        if (use_top_mask) {
-          top_mask += (*top)[0]->offset(0, 1);
-        } else {
-          mask += (*top)[0]->offset(0, 1);
-        }
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_AVE:
-    for (int i = 0; i < top_count; ++i) {
-      top_data[i] = 0;
-    }
-    // The main loop
-    for (int n = 0; n < bottom[0]->num(); ++n) {
-      for (int c = 0; c < channels_; ++c) {
-        for (int ph = 0; ph < pooled_height_; ++ph) {
-          for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_h_ - pad_h_;
-            int wstart = pw * stride_w_ - pad_w_;
-            int hend = min(hstart + kernel_h_, height_ + pad_h_);
-            int wend = min(wstart + kernel_w_, width_ + pad_w_);
-            int pool_size = (hend - hstart) * (wend - wstart);
-            hstart = max(hstart, 0);
-            wstart = max(wstart, 0);
-            hend = min(hend, height_);
-            wend = min(wend, width_);
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                top_data[ph * pooled_width_ + pw] +=
-                    bottom_data[h * width_ + w];
-              }
-            }
-            top_data[ph * pooled_width_ + pw] /= pool_size;
-          }
-        }
-        // compute offset
-        bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_STOCHASTIC:
-    NOT_IMPLEMENTED;
-    break;
-  default:
-    LOG(FATAL) << "Unknown pooling method.";
-  }
 }
 
-template <typename Dtype>
-void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (!propagate_down[0]) {
-    return;
-  }
-  const Dtype* top_diff = top[0]->cpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  // Different pooling methods. We explicitly do the switch outside the for
-  // loop to save time, although this results in more codes.
-  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
-  // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top.size() > 1;
-  const int* mask = NULL;  // suppress warnings about uninitialized variables
-  const Dtype* top_mask = NULL;
-  switch (this->layer_param_.pooling_param().pool()) {
-  case PoolingParameter_PoolMethod_MAX:
-    // The main loop
-    if (use_top_mask) {
-      top_mask = top[1]->cpu_data();
-    } else {
-      mask = max_idx_.cpu_data();
-    }
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < channels_; ++c) {
-        for (int ph = 0; ph < pooled_height_; ++ph) {
-          for (int pw = 0; pw < pooled_width_; ++pw) {
-            const int index = ph * pooled_width_ + pw;
-            const int bottom_index =
-                use_top_mask ? top_mask[index] : mask[index];
-            bottom_diff[bottom_index] += top_diff[index];
-          }
-        }
-        bottom_diff += (*bottom)[0]->offset(0, 1);
-        top_diff += top[0]->offset(0, 1);
-        if (use_top_mask) {
-          top_mask += top[0]->offset(0, 1);
-        } else {
-          mask += top[0]->offset(0, 1);
-        }
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_AVE:
-    // The main loop
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < channels_; ++c) {
-        for (int ph = 0; ph < pooled_height_; ++ph) {
-          for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_h_ - pad_h_;
-            int wstart = pw * stride_w_ - pad_w_;
-            int hend = min(hstart + kernel_h_, height_ + pad_h_);
-            int wend = min(wstart + kernel_w_, width_ + pad_w_);
-            int pool_size = (hend - hstart) * (wend - wstart);
-            hstart = max(hstart, 0);
-            wstart = max(wstart, 0);
-            hend = min(hend, height_);
-            wend = min(wend, width_);
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                bottom_diff[h * width_ + w] +=
-                  top_diff[ph * pooled_width_ + pw] / pool_size;
-              }
-            }
-          }
-        }
-        // offset
-        bottom_diff += (*bottom)[0]->offset(0, 1);
-        top_diff += top[0]->offset(0, 1);
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_STOCHASTIC:
-    NOT_IMPLEMENTED;
-    break;
-  default:
-    LOG(FATAL) << "Unknown pooling method.";
-  }
-}
-
-
-#ifdef CPU_ONLY
-STUB_GPU(PoolingLayer);
-#endif
-
 INSTANTIATE_CLASS(PoolingLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 311c7781be5..ffafbec4515 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -46,7 +46,7 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<Dtype> max_layer(layer_param);
+  CaffePoolingLayer<Dtype> max_layer(layer_param);
   max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   DropoutLayer<Dtype> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
@@ -63,7 +63,7 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<Dtype> layer(layer_param);
+  CaffePoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -93,7 +93,7 @@ TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<Dtype> layer(layer_param);
+  CaffePoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 5be01f77b32..4361949a897 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -72,7 +72,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 13] = 2;
       blob_bottom_->mutable_cpu_data()[i + 14] = 3;
     }
-    PoolingLayer<Dtype> layer(layer_param);
+    CaffePoolingLayer<Dtype> layer(layer_param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
@@ -170,7 +170,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 34] = 18;
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
-    PoolingLayer<Dtype> layer(layer_param);
+    CaffePoolingLayer<Dtype> layer(layer_param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
@@ -295,7 +295,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 34] = 18;
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
-    PoolingLayer<Dtype> layer(layer_param);
+    CaffePoolingLayer<Dtype> layer(layer_param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
@@ -376,7 +376,7 @@ TYPED_TEST(PoolingLayerTest, TestSetup) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<Dtype> layer(layer_param);
+  CaffePoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -392,7 +392,7 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
   pooling_param->set_stride(2);
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  PoolingLayer<Dtype> layer(layer_param);
+  CaffePoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -450,7 +450,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientMax) {
       pooling_param->set_stride(2);
       pooling_param->set_pad(1);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-      PoolingLayer<Dtype> layer(layer_param);
+      CaffePoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
@@ -480,7 +480,7 @@ TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
   this->blob_bottom_->mutable_cpu_data()[6] = 4;
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
-  PoolingLayer<Dtype> layer(layer_param);
+  CaffePoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -514,7 +514,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
       pooling_param->set_stride(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       this->blob_top_vec_.push_back(this->blob_top_mask_);
-      PoolingLayer<Dtype> layer(layer_param);
+      CaffePoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
@@ -536,7 +536,7 @@ TYPED_TEST(PoolingLayerTest, TestForwardAve) {
   filler_param.set_value(Dtype(2));
   ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
-  PoolingLayer<Dtype> layer(layer_param);
+  CaffePoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -565,7 +565,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAve) {
       pooling_param->set_kernel_w(kernel_w);
       pooling_param->set_stride(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      PoolingLayer<Dtype> layer(layer_param);
+      CaffePoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
@@ -584,7 +584,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
       pooling_param->set_stride(2);
       pooling_param->set_pad(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      PoolingLayer<Dtype> layer(layer_param);
+      CaffePoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 4f13981bd82..51edbb3d626 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -52,7 +52,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
+  CaffePoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -68,7 +68,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
-  PoolingLayer<TypeParam> layer(layer_param);
+  CaffePoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
 
@@ -112,7 +112,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
-  PoolingLayer<TypeParam> layer(layer_param);
+  CaffePoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
 
@@ -150,7 +150,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
-  PoolingLayer<TypeParam> layer(layer_param);
+  CaffePoolingLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-4, 1e-2);
   // it is too expensive to call curand multiple times, so we don't do an
   // exhaustive gradient check.

From 8e8872ddfd7d9eb865953e5019487b47740f3fd4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 21:01:11 -0700
Subject: [PATCH 0700/2053] strategize relu, sigmoid, tanh

---
 include/caffe/loss_layers.hpp                 |  4 +-
 include/caffe/neuron_layers.hpp               | 87 +++++++++++++++++--
 src/caffe/layer_factory.cpp                   | 57 +++++++++++-
 .../{relu_layer.cpp => caffe_relu_layer.cpp}  | 10 +--
 .../{relu_layer.cu => caffe_relu_layer.cu}    |  8 +-
 ...moid_layer.cpp => caffe_sigmoid_layer.cpp} |  8 +-
 ...igmoid_layer.cu => caffe_sigmoid_layer.cu} |  6 +-
 .../{tanh_layer.cpp => caffe_tanh_layer.cpp}  | 11 +--
 .../{tanh_layer.cu => caffe_tanh_layer.cu}    | 10 +--
 src/caffe/test/test_neuron_layer.cpp          | 44 ++++++++--
 src/caffe/test/test_tanh_layer.cpp            | 74 ----------------
 11 files changed, 196 insertions(+), 123 deletions(-)
 rename src/caffe/layers/{relu_layer.cpp => caffe_relu_layer.cpp} (84%)
 rename src/caffe/layers/{relu_layer.cu => caffe_relu_layer.cu} (91%)
 rename src/caffe/layers/{sigmoid_layer.cpp => caffe_sigmoid_layer.cpp} (82%)
 rename src/caffe/layers/{sigmoid_layer.cu => caffe_sigmoid_layer.cu} (90%)
 rename src/caffe/layers/{tanh_layer.cpp => caffe_tanh_layer.cpp} (77%)
 rename src/caffe/layers/{tanh_layer.cu => caffe_tanh_layer.cu} (85%)
 delete mode 100644 src/caffe/test/test_tanh_layer.cpp

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index a29c445d51e..555c06e4ed3 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -506,7 +506,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
  public:
   explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param),
-          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+          sigmoid_layer_(new CaffeSigmoidLayer<Dtype>(param)),
           sigmoid_output_(new Blob<Dtype>()) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -558,7 +558,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   /// The internal SigmoidLayer used to map predictions to probabilities.
-  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+  shared_ptr<CaffeSigmoidLayer<Dtype> > sigmoid_layer_;
   /// sigmoid_output stores the output of the SigmoidLayer.
   shared_ptr<Blob<Dtype> > sigmoid_output_;
   /// bottom vector holder to call the underlying SigmoidLayer::Forward
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 8c882eec125..f1d8f51e247 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -318,9 +318,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
 
   /**
    * @brief Computes the error gradient w.r.t. the ReLU inputs.
@@ -350,6 +350,31 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *        \end{array} \right.
    *      @f$.
    */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+};
+
+/**
+ * @brief standard Caffe implementation of ReLULayer.
+ */
+template <typename Dtype>
+class CaffeReLULayer : public ReLULayer<Dtype> {
+ public:
+  explicit CaffeReLULayer(const LayerParameter& param)
+      : ReLULayer<Dtype>(param) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_RELU;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -386,9 +411,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -407,6 +432,31 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *            = \frac{\partial E}{\partial y} y (1 - y)
    *      @f$ if propagate_down[0]
    */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+};
+
+/**
+ * @brief standard Caffe implementation of SigmoidLayer.
+ */
+template <typename Dtype>
+class CaffeSigmoidLayer : public SigmoidLayer<Dtype> {
+ public:
+  explicit CaffeSigmoidLayer(const LayerParameter& param)
+      : SigmoidLayer<Dtype>(param) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SIGMOID;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -443,9 +493,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) = 0;
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -466,6 +516,31 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *            = \frac{\partial E}{\partial y} (1 - y^2)
    *      @f$ if propagate_down[0]
    */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+};
+
+/**
+ * @brief standard Caffe implementation of TanHLayer.
+ */
+template <typename Dtype>
+class CaffeTanHLayer : public TanHLayer<Dtype> {
+ public:
+  explicit CaffeTanHLayer(const LayerParameter& param)
+      : TanHLayer<Dtype>(param) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_TANH;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 2ffb7f8558a..3a535f51ab1 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -43,6 +43,57 @@ template PoolingLayer<float>* GetPoolingLayer(const string& name,
 template PoolingLayer<double>* GetPoolingLayer(const string& name,
     const LayerParameter& param);
 
+// Get relu layer according to engine.
+template <typename Dtype>
+ReLULayer<Dtype>* GetReLULayer(const string& name,
+    const LayerParameter& param) {
+  ReLUParameter_Engine engine = param.relu_param().engine();
+  if (engine == ReLUParameter_Engine_CAFFE) {
+    return new CaffeReLULayer<Dtype>(param);
+  } else {
+    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+  }
+}
+
+template ReLULayer<float>* GetReLULayer(const string& name,
+    const LayerParameter& param);
+template ReLULayer<double>* GetReLULayer(const string& name,
+    const LayerParameter& param);
+
+// Get sigmoid layer according to engine.
+template <typename Dtype>
+SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
+    const LayerParameter& param) {
+  SigmoidParameter_Engine engine = param.sigmoid_param().engine();
+  if (engine == SigmoidParameter_Engine_CAFFE) {
+    return new CaffeSigmoidLayer<Dtype>(param);
+  } else {
+    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+  }
+}
+
+template SigmoidLayer<float>* GetSigmoidLayer(const string& name,
+    const LayerParameter& param);
+template SigmoidLayer<double>* GetSigmoidLayer(const string& name,
+    const LayerParameter& param);
+
+// Get tanh layer according to engine.
+template <typename Dtype>
+TanHLayer<Dtype>* GetTanHLayer(const string& name,
+    const LayerParameter& param) {
+  TanHParameter_Engine engine = param.tanh_param().engine();
+  if (engine == TanHParameter_Engine_CAFFE) {
+    return new CaffeTanHLayer<Dtype>(param);
+  } else {
+    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+  }
+}
+
+template TanHLayer<float>* GetTanHLayer(const string& name,
+    const LayerParameter& param);
+template TanHLayer<double>* GetTanHLayer(const string& name,
+    const LayerParameter& param);
+
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
 // but we will leave it this way for now.
@@ -102,11 +153,11 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   case LayerParameter_LayerType_POWER:
     return new PowerLayer<Dtype>(param);
   case LayerParameter_LayerType_RELU:
-    return new ReLULayer<Dtype>(param);
+    return GetReLULayer<Dtype>(name, param);
   case LayerParameter_LayerType_SILENCE:
     return new SilenceLayer<Dtype>(param);
   case LayerParameter_LayerType_SIGMOID:
-    return new SigmoidLayer<Dtype>(param);
+    return GetSigmoidLayer<Dtype>(name, param);
   case LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
     return new SigmoidCrossEntropyLossLayer<Dtype>(param);
   case LayerParameter_LayerType_SLICE:
@@ -118,7 +169,7 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   case LayerParameter_LayerType_SPLIT:
     return new SplitLayer<Dtype>(param);
   case LayerParameter_LayerType_TANH:
-    return new TanHLayer<Dtype>(param);
+    return GetTanHLayer<Dtype>(name, param);
   case LayerParameter_LayerType_WINDOW_DATA:
     return new WindowDataLayer<Dtype>(param);
   case LayerParameter_LayerType_NONE:
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/caffe_relu_layer.cpp
similarity index 84%
rename from src/caffe/layers/relu_layer.cpp
rename to src/caffe/layers/caffe_relu_layer.cpp
index b50352f8526..d708b3f3166 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/caffe_relu_layer.cpp
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -20,7 +20,7 @@ void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void CaffeReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -36,12 +36,10 @@ void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-
 #ifdef CPU_ONLY
-STUB_GPU(ReLULayer);
+STUB_GPU(CaffeReLULayer);
 #endif
 
-INSTANTIATE_CLASS(ReLULayer);
-
+INSTANTIATE_CLASS(CaffeReLULayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/caffe_relu_layer.cu
similarity index 91%
rename from src/caffe/layers/relu_layer.cu
rename to src/caffe/layers/caffe_relu_layer.cu
index def2bbcd7b9..ad89968d3a3 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/caffe_relu_layer.cu
@@ -15,7 +15,7 @@ __global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
 }
 
 template <typename Dtype>
-void ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -42,7 +42,7 @@ __global__ void ReLUBackward(const int n, const Dtype* in_diff,
 }
 
 template <typename Dtype>
-void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void CaffeReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -58,8 +58,6 @@ void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-
-INSTANTIATE_CLASS(ReLULayer);
-
+INSTANTIATE_CLASS(CaffeReLULayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/caffe_sigmoid_layer.cpp
similarity index 82%
rename from src/caffe/layers/sigmoid_layer.cpp
rename to src/caffe/layers/caffe_sigmoid_layer.cpp
index d7bba7fbfc3..b5bb0e3fd93 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/caffe_sigmoid_layer.cpp
@@ -13,7 +13,7 @@ inline Dtype sigmoid(Dtype x) {
 }
 
 template <typename Dtype>
-void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeSigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -24,7 +24,7 @@ void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void CaffeSigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -40,10 +40,10 @@ void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(SigmoidLayer);
+STUB_GPU(CaffeSigmoidLayer);
 #endif
 
-INSTANTIATE_CLASS(SigmoidLayer);
+INSTANTIATE_CLASS(CaffeSigmoidLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/caffe_sigmoid_layer.cu
similarity index 90%
rename from src/caffe/layers/sigmoid_layer.cu
rename to src/caffe/layers/caffe_sigmoid_layer.cu
index e1ebb1f6c41..030d34ed8c7 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/caffe_sigmoid_layer.cu
@@ -15,7 +15,7 @@ __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-void SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -41,7 +41,7 @@ __global__ void SigmoidBackward(const int n, const Dtype* in_diff,
 }
 
 template <typename Dtype>
-void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void CaffeSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -56,7 +56,7 @@ void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(SigmoidLayer);
+INSTANTIATE_CLASS(CaffeSigmoidLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/caffe_tanh_layer.cpp
similarity index 77%
rename from src/caffe/layers/tanh_layer.cpp
rename to src/caffe/layers/caffe_tanh_layer.cpp
index 8dae0054aed..a7433395dba 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/caffe_tanh_layer.cpp
@@ -1,6 +1,3 @@
-// TanH neuron activation function layer.
-// Adapted from ReLU layer code written by Yangqing Jia
-
 #include <algorithm>
 #include <vector>
 
@@ -10,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeTanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -23,7 +20,7 @@ void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void CaffeTanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -40,9 +37,9 @@ void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(TanHLayer);
+STUB_GPU(CaffeTanHLayer);
 #endif
 
-INSTANTIATE_CLASS(TanHLayer);
+INSTANTIATE_CLASS(CaffeTanHLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/caffe_tanh_layer.cu
similarity index 85%
rename from src/caffe/layers/tanh_layer.cu
rename to src/caffe/layers/caffe_tanh_layer.cu
index bdb7a94978e..f2096e68120 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/caffe_tanh_layer.cu
@@ -1,6 +1,3 @@
-// TanH neuron activation function layer.
-// Adapted from ReLU layer code written by Yangqing Jia
-
 #include <algorithm>
 #include <vector>
 
@@ -18,7 +15,7 @@ __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-void TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -39,7 +36,7 @@ __global__ void TanHBackward(const int n, const Dtype* in_diff,
 }
 
 template <typename Dtype>
-void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void CaffeTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -54,7 +51,6 @@ void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(TanHLayer);
-
+INSTANTIATE_CLASS(CaffeTanHLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 29dcec5d889..322e497b82d 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -96,7 +96,7 @@ TYPED_TEST(NeuronLayerTest, TestAbsGradient) {
 TYPED_TEST(NeuronLayerTest, TestReLU) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReLULayer<Dtype> layer(layer_param);
+  CaffeReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
@@ -111,7 +111,7 @@ TYPED_TEST(NeuronLayerTest, TestReLU) {
 TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReLULayer<Dtype> layer(layer_param);
+  CaffeReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -121,7 +121,7 @@ TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
-  ReLULayer<Dtype> layer(layer_param);
+  CaffeReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
@@ -137,7 +137,7 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
-  ReLULayer<Dtype> layer(layer_param);
+  CaffeReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -146,7 +146,7 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SigmoidLayer<Dtype> layer(layer_param);
+  CaffeSigmoidLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
@@ -163,12 +163,44 @@ TYPED_TEST(NeuronLayerTest, TestSigmoid) {
 TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SigmoidLayer<Dtype> layer(layer_param);
+  CaffeSigmoidLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(NeuronLayerTest, TestTanH) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CaffeTanHLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Test exact values
+  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
+    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
+      for (int k = 0; k < this->blob_bottom_->height(); ++k) {
+        for (int l = 0; l < this->blob_bottom_->width(); ++l) {
+          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
+          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestTanHGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CaffeTanHLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
   const float kDropoutRatio = 0.5;
   this->TestDropoutForward(kDropoutRatio);
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
deleted file mode 100644
index 9b8e745b8a7..00000000000
--- a/src/caffe/test/test_tanh_layer.cpp
+++ /dev/null
@@ -1,74 +0,0 @@
-// Adapted from other test files
-
-#include <cmath>
-#include <cstring>
-#include <vector>
-
-#include "gtest/gtest.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
-
-namespace caffe {
-
-template <typename TypeParam>
-class TanHLayerTest : public MultiDeviceTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
- protected:
-  TanHLayerTest()
-      : blob_bottom_(new Blob<Dtype>(2, 10, 1, 1)),
-        blob_top_(new Blob<Dtype>()) {
-    // fill the values
-    FillerParameter filler_param;
-    GaussianFiller<Dtype> filler(filler_param);
-    filler.Fill(this->blob_bottom_);
-    blob_bottom_vec_.push_back(blob_bottom_);
-    blob_top_vec_.push_back(blob_top_);
-  }
-  virtual ~TanHLayerTest() { delete blob_bottom_; delete blob_top_; }
-  Blob<Dtype>* const blob_bottom_;
-  Blob<Dtype>* const blob_top_;
-  vector<Blob<Dtype>*> blob_bottom_vec_;
-  vector<Blob<Dtype>*> blob_top_vec_;
-};
-
-TYPED_TEST_CASE(TanHLayerTest, TestDtypesAndDevices);
-
-TYPED_TEST(TanHLayerTest, TestForward) {
-  typedef typename TypeParam::Dtype Dtype;
-  LayerParameter layer_param;
-  TanHLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Test exact values
-  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
-      for (int k = 0; k < this->blob_bottom_->height(); ++k) {
-        for (int l = 0; l < this->blob_bottom_->width(); ++l) {
-          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
-          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
-        }
-      }
-    }
-  }
-}
-
-TYPED_TEST(TanHLayerTest, TestGradient) {
-  typedef typename TypeParam::Dtype Dtype;
-  LayerParameter layer_param;
-  TanHLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-}  // namespace caffe

From dd958e041078c3b66a95b46a01cb7203e9272bab Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 21:29:50 -0700
Subject: [PATCH 0701/2053] strategize softmax

---
 include/caffe/common_layers.hpp               | 25 +++++
 include/caffe/loss_layers.hpp                 |  8 +-
 src/caffe/layer_factory.cpp                   | 19 +++-
 src/caffe/layers/caffe_softmax_layer.cpp      | 97 +++++++++++++++++++
 ...oftmax_layer.cu => caffe_softmax_layer.cu} |  7 +-
 src/caffe/layers/softmax_layer.cpp            | 78 ---------------
 src/caffe/test/test_softmax_layer.cpp         |  4 +-
 7 files changed, 149 insertions(+), 89 deletions(-)
 create mode 100644 src/caffe/layers/caffe_softmax_layer.cpp
 rename src/caffe/layers/{softmax_layer.cu => caffe_softmax_layer.cu} (96%)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 37535920992..b3f698103ca 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -359,6 +359,31 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) = 0;
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) = 0;
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+};
+
+template <typename Dtype>
+class CaffeSoftmaxLayer : public SoftmaxLayer<Dtype> {
+ public:
+  explicit CaffeSoftmaxLayer(const LayerParameter& param)
+      : SoftmaxLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 555c06e4ed3..30a4c37ec83 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -567,8 +567,8 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   vector<Blob<Dtype>*> sigmoid_top_vec_;
 };
 
-// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
-template <typename Dtype> class SoftmaxLayer;
+// Forward declare CaffeSoftmaxLayer for use in SoftmaxWithLossLayer.
+template <typename Dtype> class CaffeSoftmaxLayer;
 
 /**
  * @brief Computes the multinomial logistic loss for a one-of-many
@@ -603,7 +603,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
   explicit SoftmaxWithLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param),
-        softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
+        softmax_layer_(new CaffeSoftmaxLayer<Dtype>(param)) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -657,7 +657,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
-  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
+  shared_ptr<CaffeSoftmaxLayer<Dtype> > softmax_layer_;
   /// prob stores the output probability predictions from the SoftmaxLayer.
   Blob<Dtype> prob_;
   /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 3a535f51ab1..afb530ae3a2 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -94,6 +94,23 @@ template TanHLayer<float>* GetTanHLayer(const string& name,
 template TanHLayer<double>* GetTanHLayer(const string& name,
     const LayerParameter& param);
 
+// Get softmax layer according to engine.
+template <typename Dtype>
+SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
+    const LayerParameter& param) {
+  SoftmaxParameter_Engine engine = param.softmax_param().engine();
+  if (engine == SoftmaxParameter_Engine_CAFFE) {
+    return new CaffeSoftmaxLayer<Dtype>(param);
+  } else {
+    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+  }
+}
+
+template SoftmaxLayer<float>* GetSoftmaxLayer(const string& name,
+    const LayerParameter& param);
+template SoftmaxLayer<double>* GetSoftmaxLayer(const string& name,
+    const LayerParameter& param);
+
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
 // but we will leave it this way for now.
@@ -163,7 +180,7 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   case LayerParameter_LayerType_SLICE:
     return new SliceLayer<Dtype>(param);
   case LayerParameter_LayerType_SOFTMAX:
-    return new SoftmaxLayer<Dtype>(param);
+    return GetSoftmaxLayer<Dtype>(name, param);
   case LayerParameter_LayerType_SOFTMAX_LOSS:
     return new SoftmaxWithLossLayer<Dtype>(param);
   case LayerParameter_LayerType_SPLIT:
diff --git a/src/caffe/layers/caffe_softmax_layer.cpp b/src/caffe/layers/caffe_softmax_layer.cpp
new file mode 100644
index 00000000000..64d027c8011
--- /dev/null
+++ b/src/caffe/layers/caffe_softmax_layer.cpp
@@ -0,0 +1,97 @@
+//
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CaffeSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
+  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
+  Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
+  for (int i = 0; i < sum_multiplier_.count(); ++i) {
+    multiplier_data[i] = 1.;
+  }
+  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+}
+
+template <typename Dtype>
+void CaffeSoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* scale_data = scale_.mutable_cpu_data();
+  int num = bottom[0]->num();
+  int channels = bottom[0]->channels();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  // We need to subtract the max to avoid numerical issues, compute the exp,
+  // and then normalize.
+  for (int i = 0; i < num; ++i) {
+    // initialize scale_data to the first plane
+    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
+    for (int j = 0; j < channels; j++) {
+      for (int k = 0; k < spatial_dim; k++) {
+        scale_data[k] = std::max(scale_data[k],
+            bottom_data[i * dim + j * spatial_dim + k]);
+      }
+    }
+    // subtraction
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
+    // exponentiation
+    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
+    // sum after exp
+    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
+        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
+    // division
+    for (int j = 0; j < channels; j++) {
+      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
+          top_data + (*top)[0]->offset(i, j));
+    }
+  }
+}
+
+template <typename Dtype>
+void CaffeSoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const Dtype* top_data = top[0]->cpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* scale_data = scale_.mutable_cpu_data();
+  int num = top[0]->num();
+  int channels = top[0]->channels();
+  int dim = top[0]->count() / top[0]->num();
+  int spatial_dim = top[0]->height() * top[0]->width();
+  caffe_copy(top[0]->count(), top_diff, bottom_diff);
+  for (int i = 0; i < num; ++i) {
+    // compute dot(top_diff, top_data) and subtract them from the bottom diff
+    for (int k = 0; k < spatial_dim; ++k) {
+      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
+          bottom_diff + i * dim + k, spatial_dim,
+          top_data + i * dim + k, spatial_dim);
+    }
+    // subtraction
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
+        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
+  }
+  // elementwise multiplication
+  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(CaffeSoftmaxLayer);
+#endif
+
+INSTANTIATE_CLASS(CaffeSoftmaxLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/caffe_softmax_layer.cu
similarity index 96%
rename from src/caffe/layers/softmax_layer.cu
rename to src/caffe/layers/caffe_softmax_layer.cu
index f97eafcedb0..74f6a7d16bc 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/caffe_softmax_layer.cu
@@ -86,7 +86,7 @@ __global__ void kernel_channel_dot(const int num, const int channels,
 }
 
 template <typename Dtype>
-void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void CaffeSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -125,7 +125,7 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void CaffeSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
@@ -148,7 +148,6 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }
 
-INSTANTIATE_CLASS(SoftmaxLayer);
-
+INSTANTIATE_CLASS(CaffeSoftmaxLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 29767ac775c..06b5e2b74a0 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -13,86 +13,8 @@ void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
-  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
-  Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
-  for (int i = 0; i < sum_multiplier_.count(); ++i) {
-    multiplier_data[i] = 1.;
-  }
-  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
 }
 
-template <typename Dtype>
-void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = bottom[0]->num();
-  int channels = bottom[0]->channels();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  int spatial_dim = bottom[0]->height() * bottom[0]->width();
-  caffe_copy(bottom[0]->count(), bottom_data, top_data);
-  // We need to subtract the max to avoid numerical issues, compute the exp,
-  // and then normalize.
-  for (int i = 0; i < num; ++i) {
-    // initialize scale_data to the first plane
-    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
-    for (int j = 0; j < channels; j++) {
-      for (int k = 0; k < spatial_dim; k++) {
-        scale_data[k] = std::max(scale_data[k],
-            bottom_data[i * dim + j * spatial_dim + k]);
-      }
-    }
-    // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
-        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
-    // exponentiation
-    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
-    // sum after exp
-    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
-        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
-    // division
-    for (int j = 0; j < channels; j++) {
-      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
-          top_data + (*top)[0]->offset(i, j));
-    }
-  }
-}
-
-template <typename Dtype>
-void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* top_data = top[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = top[0]->num();
-  int channels = top[0]->channels();
-  int dim = top[0]->count() / top[0]->num();
-  int spatial_dim = top[0]->height() * top[0]->width();
-  caffe_copy(top[0]->count(), top_diff, bottom_diff);
-  for (int i = 0; i < num; ++i) {
-    // compute dot(top_diff, top_data) and subtract them from the bottom diff
-    for (int k = 0; k < spatial_dim; ++k) {
-      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
-          bottom_diff + i * dim + k, spatial_dim,
-          top_data + i * dim + k, spatial_dim);
-    }
-    // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
-        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
-  }
-  // elementwise multiplication
-  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);
-}
-
-
-#ifdef CPU_ONLY
-STUB_GPU(SoftmaxLayer);
-#endif
-
 INSTANTIATE_CLASS(SoftmaxLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 9f45f762cdb..18c68b33915 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -40,7 +40,7 @@ TYPED_TEST_CASE(SoftmaxLayerTest, TestDtypesAndDevices);
 TYPED_TEST(SoftmaxLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SoftmaxLayer<Dtype> layer(layer_param);
+  CaffeSoftmaxLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test sum
@@ -74,7 +74,7 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
 TYPED_TEST(SoftmaxLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SoftmaxLayer<Dtype> layer(layer_param);
+  CaffeSoftmaxLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));

From 791243f4a0663690c3026d5062cb91ca2cf3408b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 22:21:33 -0700
Subject: [PATCH 0702/2053] grooming: drop pointless overrides, stub layer
 comments

---
 include/caffe/common_layers.hpp |  8 ++----
 include/caffe/neuron_layers.hpp | 12 ---------
 include/caffe/vision_layers.hpp | 45 ++++++++-------------------------
 3 files changed, 12 insertions(+), 53 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index b3f698103ca..3e3725bb4d3 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -370,6 +370,8 @@ class SoftmaxLayer : public Layer<Dtype> {
      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
 };
 
+/* CaffeSoftmaxLayer
+*/
 template <typename Dtype>
 class CaffeSoftmaxLayer : public SoftmaxLayer<Dtype> {
  public:
@@ -378,12 +380,6 @@ class CaffeSoftmaxLayer : public SoftmaxLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX;
-  }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index f1d8f51e247..883b122ec32 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -365,10 +365,6 @@ class CaffeReLULayer : public ReLULayer<Dtype> {
   explicit CaffeReLULayer(const LayerParameter& param)
       : ReLULayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_RELU;
-  }
-
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -447,10 +443,6 @@ class CaffeSigmoidLayer : public SigmoidLayer<Dtype> {
   explicit CaffeSigmoidLayer(const LayerParameter& param)
       : SigmoidLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SIGMOID;
-  }
-
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -531,10 +523,6 @@ class CaffeTanHLayer : public TanHLayer<Dtype> {
   explicit CaffeTanHLayer(const LayerParameter& param)
       : TanHLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_TANH;
-  }
-
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 46df807fbd6..3143c33d57b 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -52,15 +52,15 @@ class ConvolutionLayer : public Layer<Dtype> {
   int num_;
   int channels_;
   int pad_h_, pad_w_;
-  int height_;
-  int width_;
-  int num_output_;
+  int height_, width_;
   int group_;
+  int num_output_;
+  int height_out_, width_out_;
   bool bias_term_;
-  int height_out_;
-  int width_out_;
 };
 
+/* CaffeConvolutionLayer
+*/
 template <typename Dtype>
 class CaffeConvolutionLayer : public ConvolutionLayer<Dtype> {
  public:
@@ -69,13 +69,6 @@ class CaffeConvolutionLayer : public ConvolutionLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONVOLUTION;
-  }
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
-
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -86,9 +79,7 @@ class CaffeConvolutionLayer : public ConvolutionLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int M_;
-  int K_;
-  int N_;
+  int M_, K_, N_;
   Blob<Dtype> col_buffer_;
   Blob<Dtype> bias_multiplier_;
 };
@@ -127,8 +118,7 @@ class Im2colLayer : public Layer<Dtype> {
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
   int channels_;
-  int height_;
-  int width_;
+  int height_, width_;
   int pad_h_, pad_w_;
 };
 
@@ -227,14 +217,6 @@ class PoolingLayer : public Layer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_POOLING;
   }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  // MAX POOL layers can output an extra top blob for the mask;
-  // others can only output the pooled inputs.
-  virtual inline int MaxTopBlobs() const {
-    return (this->layer_param_.pooling_param().pool() ==
-            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
-  }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -250,15 +232,11 @@ class PoolingLayer : public Layer<Dtype> {
   int stride_h_, stride_w_;
   int pad_h_, pad_w_;
   int channels_;
-  int height_;
-  int width_;
-  int pooled_height_;
-  int pooled_width_;
-  Blob<Dtype> rand_idx_;
-  Blob<int> max_idx_;
+  int height_, width_;
+  int pooled_height_, pooled_width_;
 };
 
-/* PoolingLayer
+/* CaffePoolingLayer
 */
 template <typename Dtype>
 class CaffePoolingLayer : public PoolingLayer<Dtype> {
@@ -268,9 +246,6 @@ class CaffePoolingLayer : public PoolingLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_POOLING;
-  }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
   // MAX POOL layers can output an extra top blob for the mask;

From 347fdbd60999551286c75ac92d3b2d72bbec0d54 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 15:47:35 -0700
Subject: [PATCH 0703/2053] default engine to Caffe according to compile flag

---
 src/caffe/layer_factory.cpp | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index afb530ae3a2..587d9207a23 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -14,6 +14,11 @@ template <typename Dtype>
 ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
     const LayerParameter& param) {
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
+  if (engine == ConvolutionParameter_Engine_DEFAULT) {
+#ifdef CAFFE_ENGINE
+    engine = ConvolutionParameter_Engine_CAFFE;
+#endif
+  }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
     return new CaffeConvolutionLayer<Dtype>(param);
   } else {
@@ -31,6 +36,11 @@ template <typename Dtype>
 PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     const LayerParameter& param) {
   PoolingParameter_Engine engine = param.pooling_param().engine();
+  if (engine == PoolingParameter_Engine_DEFAULT) {
+#ifdef CAFFE_ENGINE
+    engine = PoolingParameter_Engine_CAFFE;
+#endif
+  }
   if (engine == PoolingParameter_Engine_CAFFE) {
     return new CaffePoolingLayer<Dtype>(param);
   } else {
@@ -48,6 +58,11 @@ template <typename Dtype>
 ReLULayer<Dtype>* GetReLULayer(const string& name,
     const LayerParameter& param) {
   ReLUParameter_Engine engine = param.relu_param().engine();
+  if (engine == ReLUParameter_Engine_DEFAULT) {
+#ifdef CAFFE_ENGINE
+    engine = ReLUParameter_Engine_CAFFE;
+#endif
+  }
   if (engine == ReLUParameter_Engine_CAFFE) {
     return new CaffeReLULayer<Dtype>(param);
   } else {
@@ -65,6 +80,11 @@ template <typename Dtype>
 SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
     const LayerParameter& param) {
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
+  if (engine == SigmoidParameter_Engine_DEFAULT) {
+#ifdef CAFFE_ENGINE
+    engine = SigmoidParameter_Engine_CAFFE;
+#endif
+  }
   if (engine == SigmoidParameter_Engine_CAFFE) {
     return new CaffeSigmoidLayer<Dtype>(param);
   } else {
@@ -82,6 +102,11 @@ template <typename Dtype>
 TanHLayer<Dtype>* GetTanHLayer(const string& name,
     const LayerParameter& param) {
   TanHParameter_Engine engine = param.tanh_param().engine();
+  if (engine == TanHParameter_Engine_DEFAULT) {
+#ifdef CAFFE_ENGINE
+    engine = TanHParameter_Engine_CAFFE;
+#endif
+  }
   if (engine == TanHParameter_Engine_CAFFE) {
     return new CaffeTanHLayer<Dtype>(param);
   } else {
@@ -99,6 +124,11 @@ template <typename Dtype>
 SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
     const LayerParameter& param) {
   SoftmaxParameter_Engine engine = param.softmax_param().engine();
+  if (engine == SoftmaxParameter_Engine_DEFAULT) {
+#ifdef CAFFE_ENGINE
+    engine = SoftmaxParameter_Engine_CAFFE;
+#endif
+  }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
     return new CaffeSoftmaxLayer<Dtype>(param);
   } else {

From d5605ec466e07fd9740a05a81877c82c02754da7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 17:14:59 -0700
Subject: [PATCH 0704/2053] default engine to Caffe in case config is missing

---
 Makefile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 89f7347b9f4..41793a94edd 100644
--- a/Makefile
+++ b/Makefile
@@ -257,7 +257,8 @@ else
 	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
-# Computation engine configuration
+# Computation engine configuration (default is standard Caffe)
+ENGINE ?= caffe
 ifeq ($(ENGINE), caffe)
 	COMMON_FLAGS += -DCAFFE_ENGINE
 endif

From e05428f1af54324415723a92b3cc72907378e35f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 19:16:55 -0700
Subject: [PATCH 0705/2053] revert engine switch for build to always include
 caffe engine

---
 Makefile                    |  6 ------
 Makefile.config.example     |  3 ---
 src/caffe/layer_factory.cpp | 12 ------------
 3 files changed, 21 deletions(-)

diff --git a/Makefile b/Makefile
index 41793a94edd..546de931f7e 100644
--- a/Makefile
+++ b/Makefile
@@ -257,12 +257,6 @@ else
 	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
-# Computation engine configuration (default is standard Caffe)
-ENGINE ?= caffe
-ifeq ($(ENGINE), caffe)
-	COMMON_FLAGS += -DCAFFE_ENGINE
-endif
-
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
diff --git a/Makefile.config.example b/Makefile.config.example
index 12c44477c11..7c96d8a9356 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -1,9 +1,6 @@
 ## Refer to http://caffe.berkeleyvision.org/installation.html
 # Contributions simplifying and improving our build system are welcome!
 
-# Computation engine switch: currently only the standard Caffe engine.
-ENGINE := caffe
-
 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 1
 
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 587d9207a23..24eff754ff6 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -15,9 +15,7 @@ ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
     const LayerParameter& param) {
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
-#ifdef CAFFE_ENGINE
     engine = ConvolutionParameter_Engine_CAFFE;
-#endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
     return new CaffeConvolutionLayer<Dtype>(param);
@@ -37,9 +35,7 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     const LayerParameter& param) {
   PoolingParameter_Engine engine = param.pooling_param().engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
-#ifdef CAFFE_ENGINE
     engine = PoolingParameter_Engine_CAFFE;
-#endif
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
     return new CaffePoolingLayer<Dtype>(param);
@@ -59,9 +55,7 @@ ReLULayer<Dtype>* GetReLULayer(const string& name,
     const LayerParameter& param) {
   ReLUParameter_Engine engine = param.relu_param().engine();
   if (engine == ReLUParameter_Engine_DEFAULT) {
-#ifdef CAFFE_ENGINE
     engine = ReLUParameter_Engine_CAFFE;
-#endif
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
     return new CaffeReLULayer<Dtype>(param);
@@ -81,9 +75,7 @@ SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
     const LayerParameter& param) {
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
   if (engine == SigmoidParameter_Engine_DEFAULT) {
-#ifdef CAFFE_ENGINE
     engine = SigmoidParameter_Engine_CAFFE;
-#endif
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
     return new CaffeSigmoidLayer<Dtype>(param);
@@ -103,9 +95,7 @@ TanHLayer<Dtype>* GetTanHLayer(const string& name,
     const LayerParameter& param) {
   TanHParameter_Engine engine = param.tanh_param().engine();
   if (engine == TanHParameter_Engine_DEFAULT) {
-#ifdef CAFFE_ENGINE
     engine = TanHParameter_Engine_CAFFE;
-#endif
   }
   if (engine == TanHParameter_Engine_CAFFE) {
     return new CaffeTanHLayer<Dtype>(param);
@@ -125,9 +115,7 @@ SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
     const LayerParameter& param) {
   SoftmaxParameter_Engine engine = param.softmax_param().engine();
   if (engine == SoftmaxParameter_Engine_DEFAULT) {
-#ifdef CAFFE_ENGINE
     engine = SoftmaxParameter_Engine_CAFFE;
-#endif
   }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
     return new CaffeSoftmaxLayer<Dtype>(param);

From e922d119b2101ba40c7ab5e129260a6b0d7c8fe4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 19:36:35 -0700
Subject: [PATCH 0706/2053] revert separate strategies: engines will extend the
 caffe standards

---
 include/caffe/common_layers.hpp               |  21 --
 include/caffe/loss_layers.hpp                 |  12 +-
 include/caffe/neuron_layers.hpp               |  75 +-----
 include/caffe/vision_layers.hpp               |  66 +----
 src/caffe/layer_factory.cpp                   |  12 +-
 src/caffe/layers/caffe_conv_layer.cpp         | 144 -----------
 src/caffe/layers/caffe_pooling_layer.cpp      | 230 ------------------
 src/caffe/layers/caffe_softmax_layer.cpp      |  97 --------
 src/caffe/layers/conv_layer.cpp               | 140 ++++++++++-
 .../{caffe_conv_layer.cu => conv_layer.cu}    |  49 ++--
 src/caffe/layers/lrn_layer.cpp                |   2 +-
 src/caffe/layers/pooling_layer.cpp            | 206 +++++++++++++++-
 ...affe_pooling_layer.cu => pooling_layer.cu} |  55 ++---
 .../{caffe_relu_layer.cpp => relu_layer.cpp}  |  10 +-
 .../{caffe_relu_layer.cu => relu_layer.cu}    |   8 +-
 ...fe_sigmoid_layer.cpp => sigmoid_layer.cpp} |   8 +-
 ...affe_sigmoid_layer.cu => sigmoid_layer.cu} |   6 +-
 src/caffe/layers/softmax_layer.cpp            |  78 ++++++
 ...affe_softmax_layer.cu => softmax_layer.cu} |   7 +-
 .../{caffe_tanh_layer.cpp => tanh_layer.cpp}  |  11 +-
 .../{caffe_tanh_layer.cu => tanh_layer.cu}    |  10 +-
 src/caffe/test/test_convolution_layer.cpp     |  18 +-
 .../test/test_maxpool_dropout_layers.cpp      |   6 +-
 src/caffe/test/test_neuron_layer.cpp          |  16 +-
 src/caffe/test/test_pooling_layer.cpp         |  22 +-
 src/caffe/test/test_softmax_layer.cpp         |   4 +-
 src/caffe/test/test_stochastic_pooling.cpp    |   8 +-
 27 files changed, 565 insertions(+), 756 deletions(-)
 delete mode 100644 src/caffe/layers/caffe_conv_layer.cpp
 delete mode 100644 src/caffe/layers/caffe_pooling_layer.cpp
 delete mode 100644 src/caffe/layers/caffe_softmax_layer.cpp
 rename src/caffe/layers/{caffe_conv_layer.cu => conv_layer.cu} (68%)
 rename src/caffe/layers/{caffe_pooling_layer.cu => pooling_layer.cu} (86%)
 rename src/caffe/layers/{caffe_relu_layer.cpp => relu_layer.cpp} (84%)
 rename src/caffe/layers/{caffe_relu_layer.cu => relu_layer.cu} (91%)
 rename src/caffe/layers/{caffe_sigmoid_layer.cpp => sigmoid_layer.cpp} (82%)
 rename src/caffe/layers/{caffe_sigmoid_layer.cu => sigmoid_layer.cu} (90%)
 rename src/caffe/layers/{caffe_softmax_layer.cu => softmax_layer.cu} (96%)
 rename src/caffe/layers/{caffe_tanh_layer.cpp => tanh_layer.cpp} (77%)
 rename src/caffe/layers/{caffe_tanh_layer.cu => tanh_layer.cu} (85%)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 3e3725bb4d3..37535920992 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -359,27 +359,6 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-};
-
-/* CaffeSoftmaxLayer
-*/
-template <typename Dtype>
-class CaffeSoftmaxLayer : public SoftmaxLayer<Dtype> {
- public:
-  explicit CaffeSoftmaxLayer(const LayerParameter& param)
-      : SoftmaxLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 30a4c37ec83..a29c445d51e 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -506,7 +506,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
  public:
   explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param),
-          sigmoid_layer_(new CaffeSigmoidLayer<Dtype>(param)),
+          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
           sigmoid_output_(new Blob<Dtype>()) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -558,7 +558,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   /// The internal SigmoidLayer used to map predictions to probabilities.
-  shared_ptr<CaffeSigmoidLayer<Dtype> > sigmoid_layer_;
+  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
   /// sigmoid_output stores the output of the SigmoidLayer.
   shared_ptr<Blob<Dtype> > sigmoid_output_;
   /// bottom vector holder to call the underlying SigmoidLayer::Forward
@@ -567,8 +567,8 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   vector<Blob<Dtype>*> sigmoid_top_vec_;
 };
 
-// Forward declare CaffeSoftmaxLayer for use in SoftmaxWithLossLayer.
-template <typename Dtype> class CaffeSoftmaxLayer;
+// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
+template <typename Dtype> class SoftmaxLayer;
 
 /**
  * @brief Computes the multinomial logistic loss for a one-of-many
@@ -603,7 +603,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
   explicit SoftmaxWithLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param),
-        softmax_layer_(new CaffeSoftmaxLayer<Dtype>(param)) {}
+        softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
@@ -657,7 +657,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
-  shared_ptr<CaffeSoftmaxLayer<Dtype> > softmax_layer_;
+  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
   /// prob stores the output probability predictions from the SoftmaxLayer.
   Blob<Dtype> prob_;
   /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 883b122ec32..8c882eec125 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -318,9 +318,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
 
   /**
    * @brief Computes the error gradient w.r.t. the ReLU inputs.
@@ -350,27 +350,6 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *        \end{array} \right.
    *      @f$.
    */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-};
-
-/**
- * @brief standard Caffe implementation of ReLULayer.
- */
-template <typename Dtype>
-class CaffeReLULayer : public ReLULayer<Dtype> {
- public:
-  explicit CaffeReLULayer(const LayerParameter& param)
-      : ReLULayer<Dtype>(param) {}
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -407,9 +386,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -428,27 +407,6 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *            = \frac{\partial E}{\partial y} y (1 - y)
    *      @f$ if propagate_down[0]
    */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-};
-
-/**
- * @brief standard Caffe implementation of SigmoidLayer.
- */
-template <typename Dtype>
-class CaffeSigmoidLayer : public SigmoidLayer<Dtype> {
- public:
-  explicit CaffeSigmoidLayer(const LayerParameter& param)
-      : SigmoidLayer<Dtype>(param) {}
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -485,9 +443,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -508,27 +466,6 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *            = \frac{\partial E}{\partial y} (1 - y^2)
    *      @f$ if propagate_down[0]
    */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-};
-
-/**
- * @brief standard Caffe implementation of TanHLayer.
- */
-template <typename Dtype>
-class CaffeTanHLayer : public TanHLayer<Dtype> {
- public:
-  explicit CaffeTanHLayer(const LayerParameter& param)
-      : TanHLayer<Dtype>(param) {}
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 3143c33d57b..2dca2bf73ed 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -39,13 +39,13 @@ class ConvolutionLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -57,28 +57,7 @@ class ConvolutionLayer : public Layer<Dtype> {
   int num_output_;
   int height_out_, width_out_;
   bool bias_term_;
-};
-
-/* CaffeConvolutionLayer
-*/
-template <typename Dtype>
-class CaffeConvolutionLayer : public ConvolutionLayer<Dtype> {
- public:
-  explicit CaffeConvolutionLayer(const LayerParameter& param)
-      : ConvolutionLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
-
+  // For the Caffe matrix multiplication convolution.
   int M_, K_, N_;
   Blob<Dtype> col_buffer_;
   Blob<Dtype> bias_multiplier_;
@@ -217,35 +196,6 @@ class PoolingLayer : public Layer<Dtype> {
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_POOLING;
   }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) = 0;
-
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int pad_h_, pad_w_;
-  int channels_;
-  int height_, width_;
-  int pooled_height_, pooled_width_;
-};
-
-/* CaffePoolingLayer
-*/
-template <typename Dtype>
-class CaffePoolingLayer : public PoolingLayer<Dtype> {
- public:
-  explicit CaffePoolingLayer(const LayerParameter& param)
-      : PoolingLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
   // MAX POOL layers can output an extra top blob for the mask;
@@ -265,6 +215,12 @@ class CaffePoolingLayer : public PoolingLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
+  int pad_h_, pad_w_;
+  int channels_;
+  int height_, width_;
+  int pooled_height_, pooled_width_;
   Blob<Dtype> rand_idx_;
   Blob<int> max_idx_;
 };
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 24eff754ff6..09b0f900d95 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -18,7 +18,7 @@ ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
     engine = ConvolutionParameter_Engine_CAFFE;
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
-    return new CaffeConvolutionLayer<Dtype>(param);
+    return new ConvolutionLayer<Dtype>(param);
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -38,7 +38,7 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     engine = PoolingParameter_Engine_CAFFE;
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
-    return new CaffePoolingLayer<Dtype>(param);
+    return new PoolingLayer<Dtype>(param);
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -58,7 +58,7 @@ ReLULayer<Dtype>* GetReLULayer(const string& name,
     engine = ReLUParameter_Engine_CAFFE;
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
-    return new CaffeReLULayer<Dtype>(param);
+    return new ReLULayer<Dtype>(param);
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -78,7 +78,7 @@ SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
     engine = SigmoidParameter_Engine_CAFFE;
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
-    return new CaffeSigmoidLayer<Dtype>(param);
+    return new SigmoidLayer<Dtype>(param);
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -98,7 +98,7 @@ TanHLayer<Dtype>* GetTanHLayer(const string& name,
     engine = TanHParameter_Engine_CAFFE;
   }
   if (engine == TanHParameter_Engine_CAFFE) {
-    return new CaffeTanHLayer<Dtype>(param);
+    return new TanHLayer<Dtype>(param);
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -118,7 +118,7 @@ SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
     engine = SoftmaxParameter_Engine_CAFFE;
   }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
-    return new CaffeSoftmaxLayer<Dtype>(param);
+    return new SoftmaxLayer<Dtype>(param);
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
diff --git a/src/caffe/layers/caffe_conv_layer.cpp b/src/caffe/layers/caffe_conv_layer.cpp
deleted file mode 100644
index 759a06ee9f0..00000000000
--- a/src/caffe/layers/caffe_conv_layer.cpp
+++ /dev/null
@@ -1,144 +0,0 @@
-#include <vector>
-
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void CaffeConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>&
-    bottom, vector<Blob<Dtype>*>* top) {
-  ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
-  // Figure out the dimensions for individual gemms.
-  M_ = this->num_output_ / this->group_;
-  K_ = this->channels_ * this->kernel_h_ * this->kernel_w_ / this->group_;
-  N_ = this->height_out_ * this->width_out_;
-  // The im2col result buffer would only hold one image at a time to avoid
-  // overly large memory usage.
-  col_buffer_.Reshape(1, this->channels_ * this->kernel_h_ * this->kernel_w_,
-      this->height_out_, this->width_out_);
-  // Set up the all ones "bias multiplier" for adding bias using blas
-  if (this->bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, N_);
-    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
-  }
-}
-
-template <typename Dtype>
-void CaffeConvolutionLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < bottom.size(); ++i) {
-    const Dtype* bottom_data = bottom[i]->cpu_data();
-    Dtype* top_data = (*top)[i]->mutable_cpu_data();
-    Dtype* col_data = col_buffer_.mutable_cpu_data();
-    const Dtype* weight = this->blobs_[0]->cpu_data();
-    int weight_offset = M_ * K_;
-    int col_offset = K_ * N_;
-    int top_offset = M_ * N_;
-    for (int n = 0; n < this->num_; ++n) {
-      // First, im2col
-      im2col_cpu(bottom_data + bottom[i]->offset(n), this->channels_,
-          this->height_, this->width_, this->kernel_h_, this->kernel_w_,
-          this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
-          col_data);
-      // Second, innerproduct with groups
-      for (int g = 0; g < this->group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
-      }
-      // third, add bias
-      if (this->bias_term_) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, this->num_output_,
-            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-            bias_multiplier_.cpu_data(),
-            (Dtype)1., top_data + (*top)[i]->offset(n));
-      }
-    }
-  }
-}
-
-template <typename Dtype>
-void CaffeConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* weight = NULL;
-  Dtype* weight_diff = NULL;
-  if (this->param_propagate_down_[0]) {
-    weight = this->blobs_[0]->cpu_data();
-    weight_diff = this->blobs_[0]->mutable_cpu_diff();
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  Dtype* bias_diff = NULL;
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
-  }
-  const int weight_offset = M_ * K_;
-  const int col_offset = K_ * N_;
-  const int top_offset = M_ * N_;
-  for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = NULL;
-    // Bias gradient, if necessary.
-    if (this->bias_term_ && this->param_propagate_down_[1]) {
-      top_diff = top[i]->cpu_diff();
-      for (int n = 0; n < this->num_; ++n) {
-        caffe_cpu_gemv<Dtype>(CblasNoTrans, this->num_output_, N_,
-            1., top_diff + top[0]->offset(n),
-            bias_multiplier_.cpu_data(), 1.,
-            bias_diff);
-      }
-    }
-    if (this->param_propagate_down_[0] || propagate_down[i]) {
-      if (!top_diff) {
-        top_diff = top[i]->cpu_diff();
-      }
-      Dtype* col_data = col_buffer_.mutable_cpu_data();
-      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
-      for (int n = 0; n < this->num_; ++n) {
-        // Since we saved memory in the forward pass by not storing all col
-        // data, we will need to recompute them.
-        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), this->channels_,
-            this->height_, this->width_, this->kernel_h_, this->kernel_w_,
-            this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
-            col_data);
-        // gradient w.r.t. weight. Note that we will accumulate diffs.
-        if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < this->group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_data + col_offset * g, (Dtype)1.,
-                weight_diff + weight_offset * g);
-          }
-        }
-        // gradient w.r.t. bottom data, if necessary
-        if (propagate_down[i]) {
-          for (int g = 0; g < this->group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-                (Dtype)1., weight + weight_offset * g,
-                top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_diff + col_offset * g);
-          }
-          // col2im back to the data
-          col2im_cpu(col_diff, this->channels_, this->height_, this->width_,
-              this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
-              this->stride_h_, this->stride_w_, bottom_diff +
-              (*bottom)[i]->offset(n));
-        }
-      }
-    }
-  }
-}
-
-#ifdef CPU_ONLY
-STUB_GPU(CaffeConvolutionLayer);
-#endif
-
-INSTANTIATE_CLASS(CaffeConvolutionLayer);
-
-}  // namespace caffe
-
diff --git a/src/caffe/layers/caffe_pooling_layer.cpp b/src/caffe/layers/caffe_pooling_layer.cpp
deleted file mode 100644
index 5756b740159..00000000000
--- a/src/caffe/layers/caffe_pooling_layer.cpp
+++ /dev/null
@@ -1,230 +0,0 @@
-#include <algorithm>
-#include <cfloat>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-using std::min;
-using std::max;
-
-template <typename Dtype>
-void CaffePoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  PoolingLayer<Dtype>::LayerSetUp(bottom, top);
-  PoolingParameter pool_param = this->layer_param_.pooling_param();
-  // If max pooling, we will initialize the vector index part.
-  if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
-    max_idx_.Reshape(bottom[0]->num(), this->channels_, this->pooled_height_,
-        this->pooled_width_);
-  }
-  // If stochastic pooling, we will initialize the random index part.
-  if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_STOCHASTIC) {
-    rand_idx_.Reshape(bottom[0]->num(), this->channels_, this->pooled_height_,
-      this->pooled_width_);
-  }
-}
-
-// TODO(Yangqing): Is there a faster way to do pooling in the channel-first
-// case?
-template <typename Dtype>
-void CaffePoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  const int top_count = (*top)[0]->count();
-  // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top->size() > 1;
-  int* mask = NULL;  // suppress warnings about uninitalized variables
-  Dtype* top_mask = NULL;
-  // Different pooling methods. We explicitly do the switch outside the for
-  // loop to save time, although this results in more code.
-  switch (this->layer_param_.pooling_param().pool()) {
-  case PoolingParameter_PoolMethod_MAX:
-    // Initialize
-    if (use_top_mask) {
-      top_mask = (*top)[1]->mutable_cpu_data();
-      caffe_set(top_count, Dtype(-1), top_mask);
-    } else {
-      mask = max_idx_.mutable_cpu_data();
-      caffe_set(top_count, -1, mask);
-    }
-    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
-    // The main loop
-    for (int n = 0; n < bottom[0]->num(); ++n) {
-      for (int c = 0; c < this->channels_; ++c) {
-        for (int ph = 0; ph < this->pooled_height_; ++ph) {
-          for (int pw = 0; pw < this->pooled_width_; ++pw) {
-            int hstart = ph * this->stride_h_ - this->pad_h_;
-            int wstart = pw * this->stride_w_ - this->pad_w_;
-            int hend = min(hstart + this->kernel_h_, this->height_);
-            int wend = min(wstart + this->kernel_w_, this->width_);
-            hstart = max(hstart, 0);
-            wstart = max(wstart, 0);
-            const int pool_index = ph * this->pooled_width_ + pw;
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                const int index = h * this->width_ + w;
-                if (bottom_data[index] > top_data[pool_index]) {
-                  top_data[pool_index] = bottom_data[index];
-                  if (use_top_mask) {
-                    top_mask[pool_index] = static_cast<Dtype>(index);
-                  } else {
-                    mask[pool_index] = index;
-                  }
-                }
-              }
-            }
-          }
-        }
-        // compute offset
-        bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
-        if (use_top_mask) {
-          top_mask += (*top)[0]->offset(0, 1);
-        } else {
-          mask += (*top)[0]->offset(0, 1);
-        }
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_AVE:
-    for (int i = 0; i < top_count; ++i) {
-      top_data[i] = 0;
-    }
-    // The main loop
-    for (int n = 0; n < bottom[0]->num(); ++n) {
-      for (int c = 0; c < this->channels_; ++c) {
-        for (int ph = 0; ph < this->pooled_height_; ++ph) {
-          for (int pw = 0; pw < this->pooled_width_; ++pw) {
-            int hstart = ph * this->stride_h_ - this->pad_h_;
-            int wstart = pw * this->stride_w_ - this->pad_w_;
-            int hend = min(hstart + this->kernel_h_,
-                this->height_ + this->pad_h_);
-            int wend = min(wstart + this->kernel_w_,
-                this->width_ + this->pad_w_);
-            int pool_size = (hend - hstart) * (wend - wstart);
-            hstart = max(hstart, 0);
-            wstart = max(wstart, 0);
-            hend = min(hend, this->height_);
-            wend = min(wend, this->width_);
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                top_data[ph * this->pooled_width_ + pw] +=
-                    bottom_data[h * this->width_ + w];
-              }
-            }
-            top_data[ph * this->pooled_width_ + pw] /= pool_size;
-          }
-        }
-        // compute offset
-        bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_STOCHASTIC:
-    NOT_IMPLEMENTED;
-    break;
-  default:
-    LOG(FATAL) << "Unknown pooling method.";
-  }
-}
-
-template <typename Dtype>
-void CaffePoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (!propagate_down[0]) {
-    return;
-  }
-  const Dtype* top_diff = top[0]->cpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  // Different pooling methods. We explicitly do the switch outside the for
-  // loop to save time, although this results in more codes.
-  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
-  // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top.size() > 1;
-  const int* mask = NULL;  // suppress warnings about uninitialized variables
-  const Dtype* top_mask = NULL;
-  switch (this->layer_param_.pooling_param().pool()) {
-  case PoolingParameter_PoolMethod_MAX:
-    // The main loop
-    if (use_top_mask) {
-      top_mask = top[1]->cpu_data();
-    } else {
-      mask = max_idx_.cpu_data();
-    }
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < this->channels_; ++c) {
-        for (int ph = 0; ph < this->pooled_height_; ++ph) {
-          for (int pw = 0; pw < this->pooled_width_; ++pw) {
-            const int index = ph * this->pooled_width_ + pw;
-            const int bottom_index =
-                use_top_mask ? top_mask[index] : mask[index];
-            bottom_diff[bottom_index] += top_diff[index];
-          }
-        }
-        bottom_diff += (*bottom)[0]->offset(0, 1);
-        top_diff += top[0]->offset(0, 1);
-        if (use_top_mask) {
-          top_mask += top[0]->offset(0, 1);
-        } else {
-          mask += top[0]->offset(0, 1);
-        }
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_AVE:
-    // The main loop
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < this->channels_; ++c) {
-        for (int ph = 0; ph < this->pooled_height_; ++ph) {
-          for (int pw = 0; pw < this->pooled_width_; ++pw) {
-            int hstart = ph * this->stride_h_ - this->pad_h_;
-            int wstart = pw * this->stride_w_ - this->pad_w_;
-            int hend = min(hstart + this->kernel_h_,
-                this->height_ + this->pad_h_);
-            int wend = min(wstart + this->kernel_w_,
-                this->width_ + this->pad_w_);
-            int pool_size = (hend - hstart) * (wend - wstart);
-            hstart = max(hstart, 0);
-            wstart = max(wstart, 0);
-            hend = min(hend, this->height_);
-            wend = min(wend, this->width_);
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                bottom_diff[h * this->width_ + w] +=
-                  top_diff[ph * this->pooled_width_ + pw] / pool_size;
-              }
-            }
-          }
-        }
-        // offset
-        bottom_diff += (*bottom)[0]->offset(0, 1);
-        top_diff += top[0]->offset(0, 1);
-      }
-    }
-    break;
-  case PoolingParameter_PoolMethod_STOCHASTIC:
-    NOT_IMPLEMENTED;
-    break;
-  default:
-    LOG(FATAL) << "Unknown pooling method.";
-  }
-}
-
-#ifdef CPU_ONLY
-STUB_GPU(CaffePoolingLayer);
-#endif
-
-INSTANTIATE_CLASS(CaffePoolingLayer);
-
-}  // namespace caffe
-
diff --git a/src/caffe/layers/caffe_softmax_layer.cpp b/src/caffe/layers/caffe_softmax_layer.cpp
deleted file mode 100644
index 64d027c8011..00000000000
--- a/src/caffe/layers/caffe_softmax_layer.cpp
+++ /dev/null
@@ -1,97 +0,0 @@
-//
-#include <algorithm>
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void CaffeSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
-  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
-  Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
-  for (int i = 0; i < sum_multiplier_.count(); ++i) {
-    multiplier_data[i] = 1.;
-  }
-  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
-}
-
-template <typename Dtype>
-void CaffeSoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = bottom[0]->num();
-  int channels = bottom[0]->channels();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  int spatial_dim = bottom[0]->height() * bottom[0]->width();
-  caffe_copy(bottom[0]->count(), bottom_data, top_data);
-  // We need to subtract the max to avoid numerical issues, compute the exp,
-  // and then normalize.
-  for (int i = 0; i < num; ++i) {
-    // initialize scale_data to the first plane
-    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
-    for (int j = 0; j < channels; j++) {
-      for (int k = 0; k < spatial_dim; k++) {
-        scale_data[k] = std::max(scale_data[k],
-            bottom_data[i * dim + j * spatial_dim + k]);
-      }
-    }
-    // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
-        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
-    // exponentiation
-    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
-    // sum after exp
-    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
-        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
-    // division
-    for (int j = 0; j < channels; j++) {
-      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
-          top_data + (*top)[0]->offset(i, j));
-    }
-  }
-}
-
-template <typename Dtype>
-void CaffeSoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* top_data = top[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = top[0]->num();
-  int channels = top[0]->channels();
-  int dim = top[0]->count() / top[0]->num();
-  int spatial_dim = top[0]->height() * top[0]->width();
-  caffe_copy(top[0]->count(), top_diff, bottom_diff);
-  for (int i = 0; i < num; ++i) {
-    // compute dot(top_diff, top_data) and subtract them from the bottom diff
-    for (int k = 0; k < spatial_dim; ++k) {
-      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
-          bottom_diff + i * dim + k, spatial_dim,
-          top_data + i * dim + k, spatial_dim);
-    }
-    // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
-        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
-  }
-  // elementwise multiplication
-  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);
-}
-
-
-#ifdef CPU_ONLY
-STUB_GPU(CaffeSoftmaxLayer);
-#endif
-
-INSTANTIATE_CLASS(CaffeSoftmaxLayer);
-
-
-}  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index a2ab13301e6..1a1248f3dda 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -46,7 +46,7 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = conv_param.stride_h();
     stride_w_ = conv_param.stride_w();
   }
-  group_ = conv_param.group();
+  group_ = this->layer_param_.convolution_param().group();
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -61,18 +61,26 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(width_, bottom[bottom_id]->width())
         << "Inputs must have same width.";
   }
-  num_output_ = conv_param.num_output();
+  num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
   CHECK_EQ(channels_ % group_, 0);
-  // Calculate output dimensions.
-  height_out_ = (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
-  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
+  // The im2col result buffer would only hold one image at a time to avoid
+  // overly large memory usage.
+  int height_out =
+      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
+  int width_out = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
+  col_buffer_.Reshape(
+      1, channels_ * kernel_h_ * kernel_w_, height_out, width_out);
   // Set the parameters
   CHECK_EQ(num_output_ % group_, 0)
       << "Number of output should be multiples of group.";
-  bias_term_ = conv_param.bias_term();
+  bias_term_ = this->layer_param_.convolution_param().bias_term();
+  // Figure out the dimensions for individual gemms.
+  M_ = num_output_ / group_;
+  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
+  N_ = height_out * width_out;
   for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+    (*top)[top_id]->Reshape(num_, num_output_, height_out, width_out);
   }
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
@@ -88,19 +96,133 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
         num_output_, channels_ / group_, kernel_h_, kernel_w_));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
-        conv_param.weight_filler()));
+        this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
     // If necessary, initialize and fill the bias term
     if (bias_term_) {
       this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
-          conv_param.bias_filler()));
+          this->layer_param_.convolution_param().bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
+  // Set up the all ones "bias multiplier" for adding bias using blas
+  if (bias_term_) {
+    bias_multiplier_.Reshape(1, 1, 1, N_);
+    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
+
+template <typename Dtype>
+void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* top_data = (*top)[i]->mutable_cpu_data();
+    Dtype* col_data = col_buffer_.mutable_cpu_data();
+    const Dtype* weight = this->blobs_[0]->cpu_data();
+    int weight_offset = M_ * K_;
+    int col_offset = K_ * N_;
+    int top_offset = M_ * N_;
+    for (int n = 0; n < num_; ++n) {
+      // First, im2col
+      im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+          col_data);
+      // Second, innerproduct with groups
+      for (int g = 0; g < group_; ++g) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
+          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+      }
+      // third, add bias
+      if (bias_term_) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
+            bias_multiplier_.cpu_data(),
+            (Dtype)1., top_data + (*top)[i]->offset(n));
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->cpu_data();
+    weight_diff = this->blobs_[0]->mutable_cpu_diff();
+    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
+  Dtype* bias_diff = NULL;
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    bias_diff = this->blobs_[1]->mutable_cpu_diff();
+    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
+  }
+  const int weight_offset = M_ * K_;
+  const int col_offset = K_ * N_;
+  const int top_offset = M_ * N_;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = NULL;
+    // Bias gradient, if necessary.
+    if (bias_term_ && this->param_propagate_down_[1]) {
+      top_diff = top[i]->cpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
+            1., top_diff + top[0]->offset(n),
+            bias_multiplier_.cpu_data(), 1.,
+            bias_diff);
+      }
+    }
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      if (!top_diff) {
+        top_diff = top[i]->cpu_diff();
+      }
+      Dtype* col_data = col_buffer_.mutable_cpu_data();
+      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
+      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        // Since we saved memory in the forward pass by not storing all col
+        // data, we will need to recompute them.
+        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                   stride_h_, stride_w_, col_data);
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+                col_data + col_offset * g, (Dtype)1.,
+                weight_diff + weight_offset * g);
+          }
+        }
+        // gradient w.r.t. bottom data, if necessary
+        if (propagate_down[i]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+                (Dtype)1., weight + weight_offset * g,
+                top_diff + top[i]->offset(n) + top_offset * g,
+                (Dtype)0., col_diff + col_offset * g);
+          }
+          // col2im back to the data
+          col2im_cpu(col_diff, channels_, height_, width_,
+              kernel_h_, kernel_w_, pad_h_, pad_w_,
+              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ConvolutionLayer);
+#endif
+
 INSTANTIATE_CLASS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_conv_layer.cu b/src/caffe/layers/conv_layer.cu
similarity index 68%
rename from src/caffe/layers/caffe_conv_layer.cu
rename to src/caffe/layers/conv_layer.cu
index 44a9f9737ab..f7f393badba 100644
--- a/src/caffe/layers/caffe_conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -9,8 +9,8 @@
 namespace caffe {
 
 template <typename Dtype>
-void CaffeConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>&
-    bottom, vector<Blob<Dtype>*>* top) {
+void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = (*top)[i]->mutable_gpu_data();
@@ -19,21 +19,20 @@ void CaffeConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>&
     int weight_offset = M_ * K_;
     int col_offset = K_ * N_;
     int top_offset = M_ * N_;
-    for (int n = 0; n < this->num_; ++n) {
+    for (int n = 0; n < num_; ++n) {
       // First, im2col
-      im2col_gpu(bottom_data + bottom[i]->offset(n), this->channels_,
-          this->height_, this->width_, this->kernel_h_, this->kernel_w_,
-          this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
+      im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
           col_data);
       // Second, innerproduct with groups
-      for (int g = 0; g < this->group_; ++g) {
+      for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
           (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
           (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
       }
       // third, add bias
-      if (this->bias_term_) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, this->num_output_,
+      if (bias_term_) {
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
             bias_multiplier_.gpu_data(),
             (Dtype)1., top_data + (*top)[i]->offset(n));
@@ -43,7 +42,7 @@ void CaffeConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>&
 }
 
 template <typename Dtype>
-void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* weight = NULL;
   Dtype* weight_diff = NULL;
@@ -53,7 +52,7 @@ void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
+  if (bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
     caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
@@ -63,10 +62,10 @@ void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = NULL;
     // Bias gradient, if necessary.
-    if (this->bias_term_ && this->param_propagate_down_[1]) {
+    if (bias_term_ && this->param_propagate_down_[1]) {
       top_diff = top[i]->gpu_diff();
-      for (int n = 0; n < this->num_; ++n) {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, this->num_output_, N_,
+      for (int n = 0; n < num_; ++n) {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
             1., top_diff + top[0]->offset(n),
             bias_multiplier_.gpu_data(), 1.,
             bias_diff);
@@ -80,16 +79,15 @@ void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       Dtype* col_diff = col_buffer_.mutable_gpu_diff();
       const Dtype* bottom_data = (*bottom)[i]->gpu_data();
       Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-      for (int n = 0; n < this->num_; ++n) {
+      for (int n = 0; n < num_; ++n) {
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
-        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), this->channels_,
-            this->height_, this->width_, this->kernel_h_, this->kernel_w_,
-            this->pad_h_, this->pad_w_, this->stride_h_, this->stride_w_,
-            col_data);
+        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                   stride_h_, stride_w_, col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < this->group_; ++g) {
+          for (int g = 0; g < group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
                 (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
                 col_data + col_offset * g, (Dtype)1.,
@@ -98,17 +96,16 @@ void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         }
         // gradient w.r.t. bottom data, if necessary
         if (propagate_down[i]) {
-          for (int g = 0; g < this->group_; ++g) {
+          for (int g = 0; g < group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
                 (Dtype)1., weight + weight_offset * g,
                 top_diff + top[i]->offset(n) + top_offset * g,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_gpu(col_diff, this->channels_, this->height_, this->width_,
-              this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
-              this->stride_h_, this->stride_w_, bottom_diff +
-              (*bottom)[i]->offset(n));
+          col2im_gpu(col_diff, channels_, height_, width_,
+              kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+              bottom_diff + (*bottom)[i]->offset(n));
         }
       }
     }
@@ -116,6 +113,6 @@ void CaffeConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(CaffeConvolutionLayer);
+INSTANTIATE_CLASS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index c792f65069f..e81a32ba84f 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -53,7 +53,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
           PoolingParameter_PoolMethod_AVE);
       pool_param.mutable_pooling_param()->set_pad(pre_pad_);
       pool_param.mutable_pooling_param()->set_kernel_size(size_);
-      pool_layer_.reset(new CaffePoolingLayer<Dtype>(pool_param));
+      pool_layer_.reset(new PoolingLayer<Dtype>(pool_param));
       pool_layer_->SetUp(square_top_vec_, &pool_top_vec_);
       CHECK_EQ(pool_output_.num(), num_);
       CHECK_EQ(pool_output_.channels(), channels_);
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 04f47762f9d..9e77fa28a45 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -52,9 +52,9 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_w_ = pool_param.stride_w();
   }
   if (pad_h_ != 0 || pad_w_ != 0) {
-    CHECK(pool_param.pool()
+    CHECK(this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_AVE
-        || pool_param.pool()
+        || this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_MAX)
         << "Padding implemented only for average and max pooling.";
     CHECK_LT(pad_h_, kernel_h_);
@@ -84,8 +84,210 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   if (top->size() > 1) {
     (*top)[1]->ReshapeLike(*(*top)[0]);
   }
+  // If max pooling, we will initialize the vector index part.
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
+    max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
+        pooled_width_);
+  }
+  // If stochastic pooling, we will initialize the random index part.
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_STOCHASTIC) {
+    rand_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
+      pooled_width_);
+  }
+}
+
+// TODO(Yangqing): Is there a faster way to do pooling in the channel-first
+// case?
+template <typename Dtype>
+void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int top_count = (*top)[0]->count();
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top->size() > 1;
+  int* mask = NULL;  // suppress warnings about uninitalized variables
+  Dtype* top_mask = NULL;
+  // Different pooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more code.
+  switch (this->layer_param_.pooling_param().pool()) {
+  case PoolingParameter_PoolMethod_MAX:
+    // Initialize
+    if (use_top_mask) {
+      top_mask = (*top)[1]->mutable_cpu_data();
+      caffe_set(top_count, Dtype(-1), top_mask);
+    } else {
+      mask = max_idx_.mutable_cpu_data();
+      caffe_set(top_count, -1, mask);
+    }
+    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
+    // The main loop
+    for (int n = 0; n < bottom[0]->num(); ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int ph = 0; ph < pooled_height_; ++ph) {
+          for (int pw = 0; pw < pooled_width_; ++pw) {
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_h_, height_);
+            int wend = min(wstart + kernel_w_, width_);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            const int pool_index = ph * pooled_width_ + pw;
+            for (int h = hstart; h < hend; ++h) {
+              for (int w = wstart; w < wend; ++w) {
+                const int index = h * width_ + w;
+                if (bottom_data[index] > top_data[pool_index]) {
+                  top_data[pool_index] = bottom_data[index];
+                  if (use_top_mask) {
+                    top_mask[pool_index] = static_cast<Dtype>(index);
+                  } else {
+                    mask[pool_index] = index;
+                  }
+                }
+              }
+            }
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += (*top)[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += (*top)[0]->offset(0, 1);
+        } else {
+          mask += (*top)[0]->offset(0, 1);
+        }
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_AVE:
+    for (int i = 0; i < top_count; ++i) {
+      top_data[i] = 0;
+    }
+    // The main loop
+    for (int n = 0; n < bottom[0]->num(); ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int ph = 0; ph < pooled_height_; ++ph) {
+          for (int pw = 0; pw < pooled_width_; ++pw) {
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_w_, width_ + pad_w_);
+            int pool_size = (hend - hstart) * (wend - wstart);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            hend = min(hend, height_);
+            wend = min(wend, width_);
+            for (int h = hstart; h < hend; ++h) {
+              for (int w = wstart; w < wend; ++w) {
+                top_data[ph * pooled_width_ + pw] +=
+                    bottom_data[h * width_ + w];
+              }
+            }
+            top_data[ph * pooled_width_ + pw] /= pool_size;
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += (*top)[0]->offset(0, 1);
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_STOCHASTIC:
+    NOT_IMPLEMENTED;
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
 }
 
+template <typename Dtype>
+void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  // Different pooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more codes.
+  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top.size() > 1;
+  const int* mask = NULL;  // suppress warnings about uninitialized variables
+  const Dtype* top_mask = NULL;
+  switch (this->layer_param_.pooling_param().pool()) {
+  case PoolingParameter_PoolMethod_MAX:
+    // The main loop
+    if (use_top_mask) {
+      top_mask = top[1]->cpu_data();
+    } else {
+      mask = max_idx_.cpu_data();
+    }
+    for (int n = 0; n < top[0]->num(); ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int ph = 0; ph < pooled_height_; ++ph) {
+          for (int pw = 0; pw < pooled_width_; ++pw) {
+            const int index = ph * pooled_width_ + pw;
+            const int bottom_index =
+                use_top_mask ? top_mask[index] : mask[index];
+            bottom_diff[bottom_index] += top_diff[index];
+          }
+        }
+        bottom_diff += (*bottom)[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += top[0]->offset(0, 1);
+        } else {
+          mask += top[0]->offset(0, 1);
+        }
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_AVE:
+    // The main loop
+    for (int n = 0; n < top[0]->num(); ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int ph = 0; ph < pooled_height_; ++ph) {
+          for (int pw = 0; pw < pooled_width_; ++pw) {
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_w_, width_ + pad_w_);
+            int pool_size = (hend - hstart) * (wend - wstart);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            hend = min(hend, height_);
+            wend = min(wend, width_);
+            for (int h = hstart; h < hend; ++h) {
+              for (int w = wstart; w < wend; ++w) {
+                bottom_diff[h * width_ + w] +=
+                  top_diff[ph * pooled_width_ + pw] / pool_size;
+              }
+            }
+          }
+        }
+        // offset
+        bottom_diff += (*bottom)[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  case PoolingParameter_PoolMethod_STOCHASTIC:
+    NOT_IMPLEMENTED;
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(PoolingLayer);
+#endif
+
 INSTANTIATE_CLASS(PoolingLayer);
 
+
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
similarity index 86%
rename from src/caffe/layers/caffe_pooling_layer.cu
rename to src/caffe/layers/pooling_layer.cu
index 7a8c351e3a1..e64128b87f2 100644
--- a/src/caffe/layers/caffe_pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -151,7 +151,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
 
 
 template <typename Dtype>
-void CaffePoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -169,18 +169,17 @@ void CaffePoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
-        this->width_, this->pooled_height_, this->pooled_width_,
-        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
-        this->pad_h_, this->pad_w_, top_data, mask, top_mask);
+        count, bottom_data, bottom[0]->num(), channels_,
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data,
+        mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
-        this->width_, this->pooled_height_, this->pooled_width_,
-        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
-        this->pad_h_, this->pad_w_, top_data);
+        count, bottom_data, bottom[0]->num(), channels_,
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     if (Caffe::phase() == Caffe::TRAIN) {
@@ -190,18 +189,17 @@ void CaffePoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
-          count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
-          this->width_, this->pooled_height_, this->pooled_width_,
-          this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+          count, bottom_data, bottom[0]->num(), channels_,
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
+          kernel_w_, stride_h_, stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
-          count, bottom_data, bottom[0]->num(), this->channels_, this->height_,
-          this->width_, this->pooled_height_, this->pooled_width_,
-          this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
-          top_data);
+          count, bottom_data, bottom[0]->num(), channels_,
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
+          kernel_w_, stride_h_, stride_w_, top_data);
     }
     break;
   default:
@@ -326,7 +324,7 @@ __global__ void StoPoolBackward(const int nthreads,
 
 
 template <typename Dtype>
-void CaffePoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   if (!propagate_down[0]) {
     return;
@@ -348,25 +346,24 @@ void CaffePoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, mask, top_mask, top[0]->num(), this->channels_,
-        this->height_, this->width_, this->pooled_height_, this->pooled_width_,
-        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
-        this->pad_h_, this->pad_w_, bottom_diff);
+        count, top_diff, mask, top_mask, top[0]->num(), channels_,
+        height_, width_, pooled_height_, pooled_width_,
+        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_,
+        bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, top[0]->num(), this->channels_, this->height_,
-        this->width_, this->pooled_height_, this->pooled_width_,
-        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
-        this->pad_h_, this->pad_w_, bottom_diff);
+        count, top_diff, top[0]->num(), channels_,
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     // NOLINT_NEXT_LINE(whitespace/operators)
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, rand_idx_.gpu_data(), top_diff, top[0]->num(), this->channels_,
-        this->height_, this->width_, this->pooled_height_, this->pooled_width_,
-        this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_,
+        count, rand_idx_.gpu_data(), top_diff,
+        top[0]->num(), channels_, height_, width_, pooled_height_,
+        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_,
         bottom_diff);
     break;
   default:
@@ -376,7 +373,7 @@ void CaffePoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(CaffePoolingLayer);
+INSTANTIATE_CLASS(PoolingLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
similarity index 84%
rename from src/caffe/layers/caffe_relu_layer.cpp
rename to src/caffe/layers/relu_layer.cpp
index d708b3f3166..b50352f8526 100644
--- a/src/caffe/layers/caffe_relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void CaffeReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -20,7 +20,7 @@ void CaffeReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void CaffeReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -36,10 +36,12 @@ void CaffeReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+
 #ifdef CPU_ONLY
-STUB_GPU(CaffeReLULayer);
+STUB_GPU(ReLULayer);
 #endif
 
-INSTANTIATE_CLASS(CaffeReLULayer);
+INSTANTIATE_CLASS(ReLULayer);
+
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_relu_layer.cu b/src/caffe/layers/relu_layer.cu
similarity index 91%
rename from src/caffe/layers/caffe_relu_layer.cu
rename to src/caffe/layers/relu_layer.cu
index ad89968d3a3..def2bbcd7b9 100644
--- a/src/caffe/layers/caffe_relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -15,7 +15,7 @@ __global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
 }
 
 template <typename Dtype>
-void CaffeReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -42,7 +42,7 @@ __global__ void ReLUBackward(const int n, const Dtype* in_diff,
 }
 
 template <typename Dtype>
-void CaffeReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -58,6 +58,8 @@ void CaffeReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(CaffeReLULayer);
+
+INSTANTIATE_CLASS(ReLULayer);
+
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
similarity index 82%
rename from src/caffe/layers/caffe_sigmoid_layer.cpp
rename to src/caffe/layers/sigmoid_layer.cpp
index b5bb0e3fd93..d7bba7fbfc3 100644
--- a/src/caffe/layers/caffe_sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -13,7 +13,7 @@ inline Dtype sigmoid(Dtype x) {
 }
 
 template <typename Dtype>
-void CaffeSigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -24,7 +24,7 @@ void CaffeSigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void CaffeSigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -40,10 +40,10 @@ void CaffeSigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(CaffeSigmoidLayer);
+STUB_GPU(SigmoidLayer);
 #endif
 
-INSTANTIATE_CLASS(CaffeSigmoidLayer);
+INSTANTIATE_CLASS(SigmoidLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
similarity index 90%
rename from src/caffe/layers/caffe_sigmoid_layer.cu
rename to src/caffe/layers/sigmoid_layer.cu
index 030d34ed8c7..e1ebb1f6c41 100644
--- a/src/caffe/layers/caffe_sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -15,7 +15,7 @@ __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-void CaffeSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -41,7 +41,7 @@ __global__ void SigmoidBackward(const int n, const Dtype* in_diff,
 }
 
 template <typename Dtype>
-void CaffeSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -56,7 +56,7 @@ void CaffeSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(CaffeSigmoidLayer);
+INSTANTIATE_CLASS(SigmoidLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 06b5e2b74a0..29767ac775c 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -13,8 +13,86 @@ void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
+  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
+  Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
+  for (int i = 0; i < sum_multiplier_.count(); ++i) {
+    multiplier_data[i] = 1.;
+  }
+  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
 }
 
+template <typename Dtype>
+void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* scale_data = scale_.mutable_cpu_data();
+  int num = bottom[0]->num();
+  int channels = bottom[0]->channels();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  // We need to subtract the max to avoid numerical issues, compute the exp,
+  // and then normalize.
+  for (int i = 0; i < num; ++i) {
+    // initialize scale_data to the first plane
+    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
+    for (int j = 0; j < channels; j++) {
+      for (int k = 0; k < spatial_dim; k++) {
+        scale_data[k] = std::max(scale_data[k],
+            bottom_data[i * dim + j * spatial_dim + k]);
+      }
+    }
+    // subtraction
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
+    // exponentiation
+    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
+    // sum after exp
+    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
+        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
+    // division
+    for (int j = 0; j < channels; j++) {
+      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
+          top_data + (*top)[0]->offset(i, j));
+    }
+  }
+}
+
+template <typename Dtype>
+void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const Dtype* top_data = top[0]->cpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* scale_data = scale_.mutable_cpu_data();
+  int num = top[0]->num();
+  int channels = top[0]->channels();
+  int dim = top[0]->count() / top[0]->num();
+  int spatial_dim = top[0]->height() * top[0]->width();
+  caffe_copy(top[0]->count(), top_diff, bottom_diff);
+  for (int i = 0; i < num; ++i) {
+    // compute dot(top_diff, top_data) and subtract them from the bottom diff
+    for (int k = 0; k < spatial_dim; ++k) {
+      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
+          bottom_diff + i * dim + k, spatial_dim,
+          top_data + i * dim + k, spatial_dim);
+    }
+    // subtraction
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
+        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
+  }
+  // elementwise multiplication
+  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(SoftmaxLayer);
+#endif
+
 INSTANTIATE_CLASS(SoftmaxLayer);
 
+
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
similarity index 96%
rename from src/caffe/layers/caffe_softmax_layer.cu
rename to src/caffe/layers/softmax_layer.cu
index 74f6a7d16bc..f97eafcedb0 100644
--- a/src/caffe/layers/caffe_softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -86,7 +86,7 @@ __global__ void kernel_channel_dot(const int num, const int channels,
 }
 
 template <typename Dtype>
-void CaffeSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -125,7 +125,7 @@ void CaffeSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void CaffeSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
@@ -148,6 +148,7 @@ void CaffeSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }
 
-INSTANTIATE_CLASS(CaffeSoftmaxLayer);
+INSTANTIATE_CLASS(SoftmaxLayer);
+
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
similarity index 77%
rename from src/caffe/layers/caffe_tanh_layer.cpp
rename to src/caffe/layers/tanh_layer.cpp
index a7433395dba..8dae0054aed 100644
--- a/src/caffe/layers/caffe_tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -1,3 +1,6 @@
+// TanH neuron activation function layer.
+// Adapted from ReLU layer code written by Yangqing Jia
+
 #include <algorithm>
 #include <vector>
 
@@ -7,7 +10,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void CaffeTanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
@@ -20,7 +23,7 @@ void CaffeTanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 template <typename Dtype>
-void CaffeTanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -37,9 +40,9 @@ void CaffeTanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(CaffeTanHLayer);
+STUB_GPU(TanHLayer);
 #endif
 
-INSTANTIATE_CLASS(CaffeTanHLayer);
+INSTANTIATE_CLASS(TanHLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/caffe_tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
similarity index 85%
rename from src/caffe/layers/caffe_tanh_layer.cu
rename to src/caffe/layers/tanh_layer.cu
index f2096e68120..bdb7a94978e 100644
--- a/src/caffe/layers/caffe_tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -1,3 +1,6 @@
+// TanH neuron activation function layer.
+// Adapted from ReLU layer code written by Yangqing Jia
+
 #include <algorithm>
 #include <vector>
 
@@ -15,7 +18,7 @@ __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
 }
 
 template <typename Dtype>
-void CaffeTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+void TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
@@ -36,7 +39,7 @@ __global__ void TanHBackward(const int n, const Dtype* in_diff,
 }
 
 template <typename Dtype>
-void CaffeTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   if (propagate_down[0]) {
@@ -51,6 +54,7 @@ void CaffeTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(CaffeTanHLayer);
+INSTANTIATE_CLASS(TanHLayer);
+
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 3489ead8123..5a7ea80467e 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -62,7 +62,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<Dtype> > layer(
-      new CaffeConvolutionLayer<Dtype>(layer_param));
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
@@ -75,7 +75,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   // setting group should not change the shape
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
-  layer.reset(new CaffeConvolutionLayer<Dtype>(layer_param));
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
@@ -111,7 +111,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
-      new CaffeConvolutionLayer<Dtype>(layer_param));
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 27.1
@@ -154,7 +154,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
-      new CaffeConvolutionLayer<Dtype>(layer_param));
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 9.1
@@ -192,7 +192,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
   shared_ptr<Layer<Dtype> > layer(
-      new CaffeConvolutionLayer<Dtype>(layer_param));
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
   layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 3));
   Dtype* weights = layer->blobs()[0]->mutable_cpu_data();
@@ -225,7 +225,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_stride_w(1);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
-  layer.reset(new CaffeConvolutionLayer<Dtype>(layer_param));
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
   layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 1));
   Dtype* weights_1 = layer->blobs()[0]->mutable_cpu_data();
@@ -247,7 +247,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_stride_w(2);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
-  layer.reset(new CaffeConvolutionLayer<Dtype>(layer_param));
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
   layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
   Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
@@ -279,7 +279,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  CaffeConvolutionLayer<Dtype> layer(layer_param);
+  ConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -296,7 +296,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  CaffeConvolutionLayer<Dtype> layer(layer_param);
+  ConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index ffafbec4515..311c7781be5 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -46,7 +46,7 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  CaffePoolingLayer<Dtype> max_layer(layer_param);
+  PoolingLayer<Dtype> max_layer(layer_param);
   max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   DropoutLayer<Dtype> dropout_layer(layer_param);
   dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
@@ -63,7 +63,7 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  CaffePoolingLayer<Dtype> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -93,7 +93,7 @@ TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  CaffePoolingLayer<Dtype> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 322e497b82d..2333c3a7dab 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -96,7 +96,7 @@ TYPED_TEST(NeuronLayerTest, TestAbsGradient) {
 TYPED_TEST(NeuronLayerTest, TestReLU) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeReLULayer<Dtype> layer(layer_param);
+  ReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
@@ -111,7 +111,7 @@ TYPED_TEST(NeuronLayerTest, TestReLU) {
 TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeReLULayer<Dtype> layer(layer_param);
+  ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -121,7 +121,7 @@ TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
-  CaffeReLULayer<Dtype> layer(layer_param);
+  ReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
@@ -137,7 +137,7 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
-  CaffeReLULayer<Dtype> layer(layer_param);
+  ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -146,7 +146,7 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeSigmoidLayer<Dtype> layer(layer_param);
+  SigmoidLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
@@ -163,7 +163,7 @@ TYPED_TEST(NeuronLayerTest, TestSigmoid) {
 TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeSigmoidLayer<Dtype> layer(layer_param);
+  SigmoidLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
@@ -172,7 +172,7 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
 TYPED_TEST(NeuronLayerTest, TestTanH) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeTanHLayer<Dtype> layer(layer_param);
+  TanHLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test exact values
@@ -195,7 +195,7 @@ TYPED_TEST(NeuronLayerTest, TestTanH) {
 TYPED_TEST(NeuronLayerTest, TestTanHGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeTanHLayer<Dtype> layer(layer_param);
+  TanHLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 4361949a897..5be01f77b32 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -72,7 +72,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 13] = 2;
       blob_bottom_->mutable_cpu_data()[i + 14] = 3;
     }
-    CaffePoolingLayer<Dtype> layer(layer_param);
+    PoolingLayer<Dtype> layer(layer_param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
@@ -170,7 +170,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 34] = 18;
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
-    CaffePoolingLayer<Dtype> layer(layer_param);
+    PoolingLayer<Dtype> layer(layer_param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
@@ -295,7 +295,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 34] = 18;
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
-    CaffePoolingLayer<Dtype> layer(layer_param);
+    PoolingLayer<Dtype> layer(layer_param);
     layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
@@ -376,7 +376,7 @@ TYPED_TEST(PoolingLayerTest, TestSetup) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  CaffePoolingLayer<Dtype> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -392,7 +392,7 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
   pooling_param->set_stride(2);
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  CaffePoolingLayer<Dtype> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -450,7 +450,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientMax) {
       pooling_param->set_stride(2);
       pooling_param->set_pad(1);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-      CaffePoolingLayer<Dtype> layer(layer_param);
+      PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
@@ -480,7 +480,7 @@ TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
   this->blob_bottom_->mutable_cpu_data()[6] = 4;
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
-  CaffePoolingLayer<Dtype> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -514,7 +514,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
       pooling_param->set_stride(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       this->blob_top_vec_.push_back(this->blob_top_mask_);
-      CaffePoolingLayer<Dtype> layer(layer_param);
+      PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
@@ -536,7 +536,7 @@ TYPED_TEST(PoolingLayerTest, TestForwardAve) {
   filler_param.set_value(Dtype(2));
   ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
-  CaffePoolingLayer<Dtype> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -565,7 +565,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAve) {
       pooling_param->set_kernel_w(kernel_w);
       pooling_param->set_stride(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      CaffePoolingLayer<Dtype> layer(layer_param);
+      PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
@@ -584,7 +584,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
       pooling_param->set_stride(2);
       pooling_param->set_pad(2);
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-      CaffePoolingLayer<Dtype> layer(layer_param);
+      PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
       checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
           &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 18c68b33915..9f45f762cdb 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -40,7 +40,7 @@ TYPED_TEST_CASE(SoftmaxLayerTest, TestDtypesAndDevices);
 TYPED_TEST(SoftmaxLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeSoftmaxLayer<Dtype> layer(layer_param);
+  SoftmaxLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test sum
@@ -74,7 +74,7 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
 TYPED_TEST(SoftmaxLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  CaffeSoftmaxLayer<Dtype> layer(layer_param);
+  SoftmaxLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 51edbb3d626..4f13981bd82 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -52,7 +52,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  CaffePoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -68,7 +68,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
-  CaffePoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
 
@@ -112,7 +112,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
-  CaffePoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
 
@@ -150,7 +150,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
-  CaffePoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-4, 1e-2);
   // it is too expensive to call curand multiple times, so we don't do an
   // exhaustive gradient check.

From 68849e4aab6747207394a42a5211829b4509464b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 20:14:28 -0700
Subject: [PATCH 0707/2053] [docs] fixup the MathJax notation in
 tutorial/layers

---
 docs/tutorial/layers.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 42ae5986fb6..4f0eae5cf3c 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -14,11 +14,11 @@ TODO complete list of layers linking to headings
 * Header: `./include/caffe/vision_layers.hpp`
 
 Vision layers usually take *images* as input and produce other *images* as output.
-A typical "image" in the real-world may have one color channel ($c = 1$), as in a grayscale image, or three color channels ($c = 3$) as in an RGB (red, green, blue) image.
-But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $h > 1$ and width $w > 1$.
+A typical "image" in the real-world may have one color channel ($$c = 1$$), as in a grayscale image, or three color channels ($$c = 3$$) as in an RGB (red, green, blue) image.
+But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $$h > 1$$ and width $$w > 1$$.
 This 2D geometry naturally lends itself to certain decisions about how to process the input.
 In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
-In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$ c h w $$.
+In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$chw$$.
 
 
 #### Convolution

From c099fd8b2e431b4b62a0ea3774b955f38c993b48 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 20:23:55 -0700
Subject: [PATCH 0708/2053] [doc] minor edits to convolution layer in tutorial

---
 docs/tutorial/layers.md | 50 ++++++++++++++++++++---------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 4f0eae5cf3c..4e65017dfd6 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -28,41 +28,41 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Options (`ConvolutionParameter convolution_param`)
     - Required: `num_output` (`c_o`), the number of filters
-    - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
+    - Required: `kernel_size` (or `kernel_h` and `kernel_w`), specifies height and width of each filter
     - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
     - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
-    - Optional (default 0): `pad` or (`pad_h`, `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
-    - Optional (default 1): `stride` or (`stride_h`, `stride_w`), specifies the intervals at which to apply the filters to the input
-    - Optional (default 1): `group` (g). If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated to g groups separately, and the i-th output group channels will be only connected to the i-th input group channels.
+    - Optional (default 0): `pad` (or `pad_h` and `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
+    - Optional (default 1): `stride` (or `stride_h` and `stride_w`), specifies the intervals at which to apply the filters to the input
+    - Optional (default 1): `group` (g). If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
 * Input
     - `n * c_i * h_i * w_i`
 * Output
     - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-        layers {
-          name: "conv1"
-          type: CONVOLUTION
-          bottom: "data"
-          top: "conv1"
-          blobs_lr: 1          # learning rate multiplier for the filters
-          blobs_lr: 2          # learning rate multiplier for the biases
-          weight_decay: 1      # weight decay multiplier for the filters
-          weight_decay: 0      # weight decay multiplier for the biases
-          convolution_param {
-            num_output: 96     # learn 96 filters
-            kernel_size: 11    # each filter is 11x11
-            stride: 4          # step 4 pixels between each filter application
-            weight_filler {
-              type: "gaussian" # initialize the filters from a Gaussian
-              std: 0.01        # distribution with stdev 0.01 (default mean: 0)
-            }
-            bias_filler {
-              type: "constant" # initialize the biases to zero (0)
-              value: 0
-            }
+      layers {
+        name: "conv1"
+        type: CONVOLUTION
+        bottom: "data"
+        top: "conv1"
+        blobs_lr: 1          # learning rate multiplier for the filters
+        blobs_lr: 2          # learning rate multiplier for the biases
+        weight_decay: 1      # weight decay multiplier for the filters
+        weight_decay: 0      # weight decay multiplier for the biases
+        convolution_param {
+          num_output: 96     # learn 96 filters
+          kernel_size: 11    # each filter is 11x11
+          stride: 4          # step 4 pixels between each filter application
+          weight_filler {
+            type: "gaussian" # initialize the filters from a Gaussian
+            std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+          }
+          bias_filler {
+            type: "constant" # initialize the biases to zero (0)
+            value: 0
           }
         }
+      }
 
 The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 

From 4f977d0514454a19f8ae98b770239c132ea361d2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 20:28:02 -0700
Subject: [PATCH 0709/2053] [docs] fix pooling markdown and add some comments
 in tutorial

---
 docs/tutorial/layers.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 4e65017dfd6..9ac3913da7e 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -82,17 +82,17 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
     - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-    layers {
-      name: "pool1"
-      type: POOLING
-      bottom: "conv1"
-      top: "pool1"
-      pooling_param {
-        pool: MAX
-        kernel_size: 3
-        stride: 2
+      layers {
+        name: "pool1"
+        type: POOLING
+        bottom: "conv1"
+        top: "pool1"
+        pooling_param {
+          pool: MAX
+          kernel_size: 3 # pool over a 3x3 region
+          stride: 2      # step two pixels (in the bottom blob) between pooling regions
+        }
       }
-    }
 
 #### Local Response Normalization (LRN)
 

From 85c93659fc668b0105d8458c6e9d7f0f86545df4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 21:05:56 -0700
Subject: [PATCH 0710/2053] [docs] add LRN layer to tutorial/layers

---
 docs/tutorial/layers.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 9ac3913da7e..fb6f26181dd 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -96,7 +96,16 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 
 #### Local Response Normalization (LRN)
 
-`LRN`
+* LayerType: `LRN`
+* CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
+* CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
+* Options (`LRNParameter lrn_param`)
+    - Optional (default 5): `local_size`, the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
+    - Optional (default 1): `alpha`, the scaling parameter (see below)
+    - Optional (default 5): `beta`, the exponent (see below)
+    - Optional (default `ACROSS_CHANNELS`): `norm_region`, whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
+
+The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
 
 #### im2col
 

From 853d65a8a5e0dce881c7b82389c61a4c4f32697f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 21:20:36 -0700
Subject: [PATCH 0711/2053] [docs] split layer params in required/optional

Also, make the parameter name come first. This makes it much easier to
find/scan parameters.
---
 docs/tutorial/layers.md | 63 +++++++++++++++++++++++++----------------
 1 file changed, 38 insertions(+), 25 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index fb6f26181dd..f77349b6934 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -27,13 +27,16 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Options (`ConvolutionParameter convolution_param`)
-    - Required: `num_output` (`c_o`), the number of filters
-    - Required: `kernel_size` (or `kernel_h` and `kernel_w`), specifies height and width of each filter
-    - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
-    - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
-    - Optional (default 0): `pad` (or `pad_h` and `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
-    - Optional (default 1): `stride` (or `stride_h` and `stride_w`), specifies the intervals at which to apply the filters to the input
-    - Optional (default 1): `group` (g). If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
+    - Required
+        - `num_output` (`c_o`): the number of filters
+        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
+    - Strongly Recommended
+        - `weight_filler` [default `type: 'constant' value: 0`]
+    - Optional
+        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
+        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
+        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
+        - `group` (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
 * Input
     - `n * c_i * h_i * w_i`
 * Output
@@ -72,10 +75,12 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 * CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
 * Options (`PoolingParameter pooling_param`)
-    - Optional (default MAX): `pool`, the pooling method. Currently MAX, AVE, or STOCHASTIC
-    - Required: `kernel_size` or (`kernel_h`, `kernel_w`), specifies height & width of each filter
-    - Optional (default 0): `pad` or (`pad_h`, `pad_w`), specifies the number of pixels to (implicitly) add to each side of the input
-    - Optional (default 1): `stride` or (`stride_h`, `stride_w`), specifies the intervals at which to apply the filters to the input
+    - Required
+        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
+    - Optional
+        - `pool` [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
+        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
+        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
 * Input
     - `n * c * h_i * w_i`
 * Output
@@ -100,10 +105,11 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 * CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
 * CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
 * Options (`LRNParameter lrn_param`)
-    - Optional (default 5): `local_size`, the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
-    - Optional (default 1): `alpha`, the scaling parameter (see below)
-    - Optional (default 5): `beta`, the exponent (see below)
-    - Optional (default `ACROSS_CHANNELS`): `norm_region`, whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
+    - Optional
+        - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
+        - `alpha` [default 1]: the scaling parameter (see below)
+        - `beta` [default 5]: the exponent (see below)
+        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
 
 The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
 
@@ -129,7 +135,8 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
 * CUDA GPU implementation: `NOT_AVAILABLE`
 * Options (`HingeLossParameter hinge_loss_param`)
-    - Optional (default L1): `norm`, the norm used. Currently L1, L2
+    - Optional
+        - `norm` [default L1]: the norm used. Currently L1, L2
 * Inputs
     - `n * c * h * w` Predictions
     - `n * 1 * 1 * 1` Labels
@@ -184,7 +191,8 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 * CPU implementation: `./src/caffe/layers/relu_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
 * Options (`ReLUParameter relu_param`)
-    - Optional (default 0): `negative_slope`, specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
+    - Optional
+        - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
         layers {
@@ -250,9 +258,10 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
 * CPU implementation: `./src/caffe/layers/power_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
 * Options (`PowerParameter power_param`)
-    - Optional (default 1): `power`
-    - Optional (default 1): `scale`
-    - Optional (default 0): `shift`
+    - Optional
+        - `power` [default 1]
+        - `scale` [default 1]
+        - `shift` [default 0]
 * Sample
 
         layers {
@@ -324,10 +333,13 @@ The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 +
 * CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
 * Options (`InnerProductParameter inner_product_param`)
-    - Required: `num_output` (`c_o`), the number of filters
-    - Strongly recommended (default `type: 'constant' value: 0`): `weight_filler`
-    - Optional (default `type: 'constant' value: 0`): `bias_filler`
-    - Optional (default `true`): `bias_term`, specifies whether to learn and apply a set of additive biases to the filter outputs
+    - Required
+        - `num_output` (`c_o`): the number of filters
+    - Strongly recommended
+        - `weight_filler` [default `type: 'constant' value: 0`]
+    - Optional
+        - `bias_filler` [default `type: 'constant' value: 0`]
+        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
 * Input
     - `n * c_i * h_i * w_i`
 * Output
@@ -372,7 +384,8 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c *
 * CPU implementation: `./src/caffe/layers/concat_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
 * Options (`ConcatParameter concat_param`)
-    - Optional (default 1): `concat_dim`, 0 for concatenation along num and 1 for channels.
+    - Optional
+        - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels.
 * Input
     - `n_i * c_i * h * w` for each input blob i from 1 to K.
 * Output

From 40fa5be9b11c17c289789bc14c37a6ce06f14451 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 21:22:23 -0700
Subject: [PATCH 0712/2053] [docs] in tutorial/layers, Options -> Parameters

It sounds funny to have optional options, and "parameters" is more in
line with the internal usage.
---
 docs/tutorial/layers.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index f77349b6934..11ca70e50b9 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -26,7 +26,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 * LayerType: `CONVOLUTION`
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
-* Options (`ConvolutionParameter convolution_param`)
+* Parameters (`ConvolutionParameter convolution_param`)
     - Required
         - `num_output` (`c_o`): the number of filters
         - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
@@ -74,7 +74,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 * LayerType: `POOLING`
 * CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
-* Options (`PoolingParameter pooling_param`)
+* Parameters (`PoolingParameter pooling_param`)
     - Required
         - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
     - Optional
@@ -104,7 +104,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 * LayerType: `LRN`
 * CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
 * CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
-* Options (`LRNParameter lrn_param`)
+* Parameters (`LRNParameter lrn_param`)
     - Optional
         - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
         - `alpha` [default 1]: the scaling parameter (see below)
@@ -134,7 +134,7 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 * LayerType: `HINGE_LOSS`
 * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
 * CUDA GPU implementation: `NOT_AVAILABLE`
-* Options (`HingeLossParameter hinge_loss_param`)
+* Parameters (`HingeLossParameter hinge_loss_param`)
     - Optional
         - `norm` [default L1]: the norm used. Currently L1, L2
 * Inputs
@@ -190,7 +190,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 * LayerType: `RELU`
 * CPU implementation: `./src/caffe/layers/relu_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
-* Options (`ReLUParameter relu_param`)
+* Parameters (`ReLUParameter relu_param`)
     - Optional
         - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
@@ -257,7 +257,7 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
 * LayerType: `POWER`
 * CPU implementation: `./src/caffe/layers/power_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
-* Options (`PowerParameter power_param`)
+* Parameters (`PowerParameter power_param`)
     - Optional
         - `power` [default 1]
         - `scale` [default 1]
@@ -332,7 +332,7 @@ The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 +
 * LayerType: `INNER_PRODUCT`
 * CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
-* Options (`InnerProductParameter inner_product_param`)
+* Parameters (`InnerProductParameter inner_product_param`)
     - Required
         - `num_output` (`c_o`): the number of filters
     - Strongly recommended
@@ -383,7 +383,7 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c *
 * LayerType: `CONCAT`
 * CPU implementation: `./src/caffe/layers/concat_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
-* Options (`ConcatParameter concat_param`)
+* Parameters (`ConcatParameter concat_param`)
     - Optional
         - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels.
 * Input

From 15456286da9378df6af796496ced5313ad7169f4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 21:39:56 -0700
Subject: [PATCH 0713/2053] [docs] tutorial/layers: brief descriptions of some
 loss layers

---
 docs/tutorial/layers.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 11ca70e50b9..036d9adc292 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -123,17 +123,21 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 #### Softmax
 
-`SOFTMAX_LOSS`
+* LayerType: `SOFTMAX_LOSS`
+
+The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
 
 #### Sum-of-Squares / Euclidean
 
-`EUCLIDEAN_LOSS`
+* LayerType: `EUCLIDEAN_LOSS`
+
+The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
 
 #### Hinge / Margin
 
 * LayerType: `HINGE_LOSS`
 * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
-* CUDA GPU implementation: `NOT_AVAILABLE`
+* CUDA GPU implementation: none yet
 * Parameters (`HingeLossParameter hinge_loss_param`)
     - Optional
         - `norm` [default L1]: the norm used. Currently L1, L2
@@ -164,6 +168,8 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
         }
       }
 
+The hinge loss layer computes a one-vs-all hinge or squared hinge loss.
+
 #### Sigmoid Cross-Entropy
 
 `SIGMOID_CROSS_ENTROPY_LOSS`

From bd13f32123dfe4f9a945367b94710a02172e3615 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 21:42:35 -0700
Subject: [PATCH 0714/2053] [docs] tutorial/layers: clean up sample markdown

---
 docs/tutorial/layers.md | 98 ++++++++++++++++++++---------------------
 1 file changed, 49 insertions(+), 49 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 036d9adc292..733ff96e523 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -201,12 +201,12 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
         - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-        layers {
-          name: "relu1"
-          type: RELU
-          bottom: "conv1"
-          top: "conv1"
-        }
+      layers {
+        name: "relu1"
+        type: RELU
+        bottom: "conv1"
+        top: "conv1"
+      }
 
 Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
 
@@ -217,12 +217,12 @@ Given an input value x, The `RELU` layer computes the output as x if x > 0 and n
 * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
 * Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
 
-        layers {
-          name: "encode1neuron"
-          bottom: "encode1"
-          top: "encode1neuron"
-          type: SIGMOID
-        }
+      layers {
+        name: "encode1neuron"
+        bottom: "encode1"
+        top: "encode1neuron"
+        type: SIGMOID
+      }
 
 The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
 
@@ -233,12 +233,12 @@ The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
 * CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
 * Sample
 
-        layers {
-          name: "layer"
-          bottom: "in"
-          top: "out"
-          type: TANH
-        }
+      layers {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: TANH
+      }
 
 The `TANH` layer computes the output as tanh(x) for each input element x.
 
@@ -249,12 +249,12 @@ The `TANH` layer computes the output as tanh(x) for each input element x.
 * CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
 * Sample
 
-        layers {
-          name: "layer"
-          bottom: "in"
-          top: "out"
-          type: ABSVAL
-        }
+      layers {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: ABSVAL
+      }
 
 The `ABSVAL` layer computes the output as abs(x) for each input element x.
 
@@ -270,17 +270,17 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         - `shift` [default 0]
 * Sample
 
-        layers {
-          name: "layer"
-          bottom: "in"
-          top: "out"
-          type: POWER
-          power_param {
-            power: 1
-            scale: 1
-            shift: 0
-          }
+      layers {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: POWER
+        power_param {
+          power: 1
+          scale: 1
+          shift: 0
         }
+      }
 
 The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x.
 
@@ -291,12 +291,12 @@ The `POWER` layer computes the output as (shift + scale * x) ^ power for each in
 * CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
 * Sample
 
-        layers {
-          name: "layer"
-          bottom: "in"
-          top: "out"
-          type: BNLL
-        }
+      layers {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: BNLL
+      }
 
 The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
 
@@ -399,16 +399,16 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c *
     - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
 * Sample
 
-        layers {
-          name: "concat"
-          bottom: "in1"
-          bottom: "in2"
-          top: "out"
-          type: CONCAT
-          concat_param {
-            concat_dim: 1
-          }
+      layers {
+        name: "concat"
+        bottom: "in1"
+        bottom: "in2"
+        top: "out"
+        type: CONCAT
+        concat_param {
+          concat_dim: 1
         }
+      }
 
 The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only.
 

From cbc50e1c6621dff7221247429db9569d7318cec0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 22:09:13 -0700
Subject: [PATCH 0715/2053] [docs] tutorial/layers: describe some more data
 layers

---
 docs/tutorial/layers.md | 45 ++++++++++++++++++++++++++++++++++++-----
 1 file changed, 40 insertions(+), 5 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 733ff96e523..266be934e82 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -303,25 +303,60 @@ The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 +
 
 ### Data Layers
 
+Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.
+
+Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying `TransformationParameter`s.
+
 #### Database
 
-`DATA`
+* LayerType: `DATA`
+* Parameters
+    - Required
+        - `source`: the name of the directory containing the database
+        - `batch_size`: the number of inputs to process at one time
+    - Optional
+        - `rand_skip`: skip up to this number of inputs at the beginning; useful for asynchronous sgd
+        - `backend` [default `LEVELDB`]: choose whether to use a `LEVELDB` or `LMDB`
+
+
 
 #### In-Memory
 
-`MEMORY_DATA`
+* LayerType: `MEMORY_DATA`
+* Parameters
+    - Required
+        - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
+
+The memory data layer reads data directly from memory, without copying it. In order to use it, one must call `MemoryDataLayer::Reset` (from C++) or `Net.set_input_arrays` (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
 
 #### HDF5 Input
 
-`HDF5_DATA`
+* LayerType: `HDF5_DATA`
+* Parameters
+    - Required
+        - `source`: the name of the file to read from
+        - `batch_size`
 
 #### HDF5 Output
 
-`HDF5_OUTPUT`
+* LayerType: `HDF5_OUTPUT`
+* Parameters
+    - Required
+        - `file_name`: name of file to write to
+
+The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.
 
 #### Images
 
-`IMAGE_DATA`
+* LayerType: `IMAGE_DATA`
+* Parameters
+    - Required
+        - `source`: name of a text file, with each line giving an image filename and label
+        - `batch_size`: number of images to batch together
+    - Optional
+        - `rand_skip`
+        - `shuffle` [default false]
+        - `new_height`, `new_width`: if provided, resize all images to this size
 
 #### Windows
 

From b37f4f9f68a6a3870dabfb71130f5a830652e24d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 6 Sep 2014 22:10:29 -0700
Subject: [PATCH 0716/2053] [docs] tutorial/layers: fix inner product sample

---
 docs/tutorial/layers.md | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 266be934e82..8dd67275f58 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -387,27 +387,27 @@ The HDF5 output layer performs the opposite function of the other layers in this
     - `n * c_o * 1 * 1`
 * Sample
 
-    layers {
-      name: "fc8"
-      type: INNER_PRODUCT
-      blobs_lr: 1          # learning rate multiplier for the filters
-      blobs_lr: 2          # learning rate multiplier for the biases
-      weight_decay: 1      # weight decay multiplier for the filters
-      weight_decay: 0      # weight decay multiplier for the biases
-      inner_product_param {
-        num_output: 1000
-        weight_filler {
-          type: "gaussian"
-          std: 0.01
-        }
-        bias_filler {
-          type: "constant"
-          value: 0
+      layers {
+        name: "fc8"
+        type: INNER_PRODUCT
+        blobs_lr: 1          # learning rate multiplier for the filters
+        blobs_lr: 2          # learning rate multiplier for the biases
+        weight_decay: 1      # weight decay multiplier for the filters
+        weight_decay: 0      # weight decay multiplier for the biases
+        inner_product_param {
+          num_output: 1000
+          weight_filler {
+            type: "gaussian"
+            std: 0.01
+          }
+          bias_filler {
+            type: "constant"
+            value: 0
+          }
         }
+        bottom: "fc7"
+        top: "fc8"
       }
-      bottom: "fc7"
-      top: "fc8"
-    }
 
 The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
 

From 3cf3df829eb9be6394203f257863469050b43ed4 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 7 Sep 2014 09:44:58 +0200
Subject: [PATCH 0717/2053] fix transform_param in mnist_autoencoder.prototxt

---
 examples/mnist/mnist_autoencoder.prototxt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index b4345e71ce4..0b33781a16f 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -38,9 +38,9 @@ layers {
     source: "examples/mnist/mnist_test_lmdb"
     backend: LMDB
     batch_size: 100
-    transform_param {
-      scale: 0.0039215684
-    }
+  }
+  transform_param {
+    scale: 0.0039215684
   }
   include: {
     phase: TEST

From fb0a3d0275107e56a0337cc009bcacd84997337c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 5 Sep 2014 05:01:46 -0700
Subject: [PATCH 0718/2053] remove uses of tmpnam

---
 include/caffe/util/io.hpp                 | 27 +++++++++++++
 src/caffe/test/test_data_layer.cpp        |  5 ++-
 src/caffe/test/test_hdf5_output_layer.cpp |  4 +-
 src/caffe/test/test_image_data_layer.cpp  | 15 +++----
 src/caffe/test/test_window_data_layer.cpp | 49 -----------------------
 5 files changed, 41 insertions(+), 59 deletions(-)
 delete mode 100644 src/caffe/test/test_window_data_layer.cpp

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 546c3d6af80..8dd338d2603 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,6 +1,7 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
+#include <unistd.h>
 #include <string>
 
 #include "google/protobuf/message.h"
@@ -21,6 +22,32 @@ namespace caffe {
 
 using ::google::protobuf::Message;
 
+inline void MakeTempFilename(string* temp_filename) {
+  temp_filename->clear();
+  *temp_filename = "/tmp/caffe_test.XXXXXX";
+  char* temp_filename_cstr = new char[temp_filename->size()];
+  // NOLINT_NEXT_LINE(runtime/printf)
+  strcpy(temp_filename_cstr, temp_filename->c_str());
+  int fd = mkstemp(temp_filename_cstr);
+  CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;
+  close(fd);
+  *temp_filename = temp_filename_cstr;
+  delete temp_filename_cstr;
+}
+
+inline void MakeTempDir(string* temp_dirname) {
+  temp_dirname->clear();
+  *temp_dirname = "/tmp/caffe_test.XXXXXX";
+  char* temp_dirname_cstr = new char[temp_dirname->size()];
+  // NOLINT_NEXT_LINE(runtime/printf)
+  strcpy(temp_dirname_cstr, temp_dirname->c_str());
+  char* mkdtemp_result = mkdtemp(temp_dirname_cstr);
+  CHECK(mkdtemp_result != NULL)
+      << "Failed to create a temporary directory at: " << *temp_dirname;
+  *temp_dirname = temp_dirname_cstr;
+  delete temp_dirname_cstr;
+}
+
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
 
 inline bool ReadProtoFromTextFile(const string& filename, Message* proto) {
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index e01932b7f1f..887124aa5bc 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -8,6 +8,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -21,11 +22,13 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   DataLayerTest()
       : backend_(DataParameter_DB_LEVELDB),
-        filename_(new string(tmpnam(NULL))),
         blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()),
         seed_(1701) {}
   virtual void SetUp() {
+    filename_.reset(new string());
+    MakeTempDir(filename_.get());
+    *filename_ += "/db";
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
   }
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index b2a511d1d09..eb09c8d1f3a 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -22,8 +22,7 @@ class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   HDF5OutputLayerTest()
-      : output_file_name_(tmpnam(NULL)),
-        input_file_name_(
+      : input_file_name_(
         CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data.h5"),
         blob_data_(new Blob<Dtype>()),
         blob_label_(new Blob<Dtype>()),
@@ -31,6 +30,7 @@ class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
         channels_(8),
         height_(5),
         width_(5) {
+    MakeTempFilename(&output_file_name_);
   }
 
   virtual ~HDF5OutputLayerTest() {
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index ab2365c6556..d098c765258 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -8,6 +8,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -21,16 +22,16 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   ImageDataLayerTest()
       : seed_(1701),
-        filename_(new string(tmpnam(NULL))),
         blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
+    MakeTempFilename(&filename_);
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
     Caffe::set_random_seed(seed_);
     // Create a Vector of files with labels
-    std::ofstream outfile(filename_->c_str(), std::ofstream::out);
-    LOG(INFO) << "Using temporary file " << *filename_;
+    std::ofstream outfile(filename_.c_str(), std::ofstream::out);
+    LOG(INFO) << "Using temporary file " << filename_;
     for (int i = 0; i < 5; ++i) {
       outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
     }
@@ -43,7 +44,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
   }
 
   int seed_;
-  shared_ptr<string> filename_;
+  string filename_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -57,7 +58,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
-  image_data_param->set_source(this->filename_->c_str());
+  image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_shuffle(false);
   ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
@@ -83,7 +84,7 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
-  image_data_param->set_source(this->filename_->c_str());
+  image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_new_height(256);
   image_data_param->set_new_width(256);
   image_data_param->set_shuffle(false);
@@ -111,7 +112,7 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
-  image_data_param->set_source(this->filename_->c_str());
+  image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_shuffle(true);
   ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
diff --git a/src/caffe/test/test_window_data_layer.cpp b/src/caffe/test/test_window_data_layer.cpp
deleted file mode 100644
index 47591b60154..00000000000
--- a/src/caffe/test/test_window_data_layer.cpp
+++ /dev/null
@@ -1,49 +0,0 @@
-#include <string>
-#include <vector>
-
-#include "gtest/gtest.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-namespace caffe {
-
-template <typename TypeParam>
-class WindowDataLayerTest : public MultiDeviceTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  WindowDataLayerTest()
-      : seed_(1701),
-        filename_(new string(tmpnam(NULL))),
-        blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()) {}
-  virtual void SetUp() {
-    blob_top_vec_.push_back(blob_top_data_);
-    blob_top_vec_.push_back(blob_top_label_);
-    Caffe::set_random_seed(seed_);
-  }
-
-  virtual ~WindowDataLayerTest() {
-    delete blob_top_data_;
-    delete blob_top_label_;
-  }
-
-  int seed_;
-  shared_ptr<string> filename_;
-  Blob<Dtype>* const blob_top_data_;
-  Blob<Dtype>* const blob_top_label_;
-  vector<Blob<Dtype>*> blob_bottom_vec_;
-  vector<Blob<Dtype>*> blob_top_vec_;
-};
-
-TYPED_TEST_CASE(WindowDataLayerTest, TestDtypesAndDevices);
-
-TYPED_TEST(WindowDataLayerTest, TestNothing) {
-}
-
-}  // namespace caffe

From 3182b1c330c8d1bc2abee0e1de537e0babb2ae85 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 7 Sep 2014 11:47:31 +0200
Subject: [PATCH 0719/2053] add <cuda>/lib64 only if exists to suppress linker
 warnings

---
 Makefile | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 546de931f7e..525e7e9ea8b 100644
--- a/Makefile
+++ b/Makefile
@@ -151,7 +151,13 @@ NONEMPTY_WARN_REPORT := $(BUILD_DIR)/$(WARNS_EXT)
 # Derive include and lib directories
 ##############################
 CUDA_INCLUDE_DIR := $(CUDA_DIR)/include
-CUDA_LIB_DIR := $(CUDA_DIR)/lib64 $(CUDA_DIR)/lib
+
+CUDA_LIB_DIR :=
+# add <cuda>/lib64 only if it exists
+ifneq ("$(wildcard $(CUDA_DIR)/lib64)","")
+	CUDA_LIB_DIR += $(CUDA_DIR)/lib64
+endif
+CUDA_LIB_DIR += $(CUDA_DIR)/lib
 
 INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./include
 ifneq ($(CPU_ONLY), 1)

From 1cb704025eb1f70dba5355fd374110f65ab7c365 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <kosiorek.adam@gmail.com>
Date: Sat, 6 Sep 2014 01:23:02 +0200
Subject: [PATCH 0720/2053] enabled object file reusing in test builds

---
 src/caffe/test/CMakeLists.txt | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index ffcc58f5463..8ff6390677f 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -10,6 +10,7 @@ set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are go
 set(TEST_EXT .testbin)           # test executable extension
 set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
 set(RUN_TEST runtest)            # dummy target for running tests
+set(TEST_MAIN_LIB test_main_lib) # name of a dummy lib containing main function
 
 #    Generate config files
 add_definitions(-DCMAKE_BUILD)    # definition needed in order to include CMake's generated files
@@ -42,20 +43,30 @@ if(NOT CPU_ONLY)
     set(TEST_CPP_SOURCES ${TEST_CPP_SOURCES} ${TEST_CU_SOURCES})
 endif()
 
+add_library(${TEST_MAIN_LIB} EXCLUDE_FROM_ALL ${TEST_MAIN})
+target_link_libraries(${TEST_MAIN_LIB} gtest caffe)
+
 #    Build each test separately
 foreach(source ${TEST_CPP_SOURCES})
     get_filename_component(name ${source} NAME_WE)
     set(TEST_NAME ${name}${TEST_EXT})
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_MAIN} ${source} ../blob.cpp)
-    target_link_libraries(${TEST_NAME} gtest caffe)
+    
+    add_library(${TEST_NAME}.lib OBJECT ${source})
+    set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.lib>)
+    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB}) 
+    
+    target_link_libraries(${TEST_NAME} ${TEST_MAIN_LIB})
     #    output dir
     set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+    
+    # Targets and object libs
     set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
+    set(TEST_OBJ_LIBS ${TEST_OBJ_LIBS} ${TEST_OBJ_LIB})
 endforeach()
 
 #    Build a compound test excluded from the ALL target
-add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_CPP_SOURCES} ${TEST_MAIN})
-target_link_libraries(${ALL_TEST} gtest caffe)
+add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} )
+target_link_libraries(${ALL_TEST} ${TEST_MAIN_LIB})
 add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 #    output dir
 set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})

From 37e55faef8566b3ae99cbf44805bd344135bffc1 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <kosiorek.adam@gmail.com>
Date: Sat, 6 Sep 2014 14:40:54 +0200
Subject: [PATCH 0721/2053] cpp and cu files processed separately in test build

---
 src/caffe/test/CMakeLists.txt | 58 ++++++++++++++++++++++++++---------
 1 file changed, 43 insertions(+), 15 deletions(-)

diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 8ff6390677f..7a2a5305b54 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -10,7 +10,7 @@ set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are go
 set(TEST_EXT .testbin)           # test executable extension
 set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
 set(RUN_TEST runtest)            # dummy target for running tests
-set(TEST_MAIN_LIB test_main_lib) # name of a dummy lib containing main function
+set(TEST_MAIN test_caffe_main.cpp)     # main test file (with main function)
 
 #    Generate config files
 add_definitions(-DCMAKE_BUILD)    # definition needed in order to include CMake's generated files
@@ -36,26 +36,28 @@ include_directories(
         ${CMAKE_CURRENT_SOURCE_DIR}
 )
 
-set(TEST_MAIN test_caffe_main.cpp)
+# Remove main from test sources and prepare an Object lib with main
+file(GLOB TEST_MAIN ${TEST_MAIN}) 
 list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
+add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN})
 
-if(NOT CPU_ONLY)
-    set(TEST_CPP_SOURCES ${TEST_CPP_SOURCES} ${TEST_CU_SOURCES})
-endif()
-
-add_library(${TEST_MAIN_LIB} EXCLUDE_FROM_ALL ${TEST_MAIN})
-target_link_libraries(${TEST_MAIN_LIB} gtest caffe)
+#if(NOT CPU_ONLY)
+#    set(TEST_CPP_SOURCES ${TEST_CPP_SOURCES} ${TEST_CU_SOURCES})
+#endif()
 
-#    Build each test separately
+#    Build each test separately from *.cpp files
 foreach(source ${TEST_CPP_SOURCES})
+    #
     get_filename_component(name ${source} NAME_WE)
     set(TEST_NAME ${name}${TEST_EXT})
     
-    add_library(${TEST_NAME}.lib OBJECT ${source})
-    set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.lib>)
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB}) 
+    #
+    add_library(${TEST_NAME}.obj OBJECT ${source})
+    set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
+    
+    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)     
+    target_link_libraries(${TEST_NAME} gtest caffe)
     
-    target_link_libraries(${TEST_NAME} ${TEST_MAIN_LIB})
     #    output dir
     set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
     
@@ -64,10 +66,36 @@ foreach(source ${TEST_CPP_SOURCES})
     set(TEST_OBJ_LIBS ${TEST_OBJ_LIBS} ${TEST_OBJ_LIB})
 endforeach()
 
+#    Build each test separately from *.cu files
+if(NOT CPU_ONLY)
+    foreach(source ${TEST_CU_SOURCES})
+        #
+        get_filename_component(name ${source} NAME_WE)
+        set(TEST_NAME ${name}${TEST_EXT})
+        
+        #
+        add_library(${TEST_NAME}.lib ${source})
+        
+        add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)     
+        target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest caffe)
+        
+        #    output dir
+        set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+        
+        # Targets and object libs
+        set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
+        set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
+    endforeach()
+endif()
+
 #    Build a compound test excluded from the ALL target
-add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} )
-target_link_libraries(${ALL_TEST} ${TEST_MAIN_LIB})
+add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:main_obj>)
+if(NOT CPU_ONLY)
+    target_link_libraries(${ALL_TEST} ${TEST_CU_LIBS})
+endif()
+target_link_libraries(${ALL_TEST} gtest caffe)
 add_dependencies(${ALL_TEST} ${TEST_TARGETS})
+
 #    output dir
 set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})
 #    Test command

From 9086df9e20fd45b001abd1d2ecd3082d8e1dc183 Mon Sep 17 00:00:00 2001
From: Adam Kosiorek <kosiorek.adam@gmail.com>
Date: Sun, 7 Sep 2014 19:03:44 +0200
Subject: [PATCH 0722/2053] added common.cpp explicitly to tests

---
 src/caffe/test/CMakeLists.txt | 58 +++++++++++++++++------------------
 1 file changed, 28 insertions(+), 30 deletions(-)

diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 7a2a5305b54..20a1d084d6a 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -19,6 +19,14 @@ set(GEN_EXT .gen.cmake)    # generated output file extension
 set(TEST_DEFINES_FILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp)
 set(TEST_DATA_FILE ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt)
 
+# Function prepares name of a test executable
+#    @output_name -  output variable's name
+#    @filename    -  test_*.cpp file path
+function(test_name output_name filename)
+    get_filename_component(name ${filename} NAME_WE)
+    set(${output_name} ${name}${TEST_EXT} PARENT_SCOPE)
+endfunction()
+
 set(IN_FILES             # generator input files
     ${TEST_DEFINES_FILE}
     ${TEST_DATA_FILE}
@@ -39,20 +47,14 @@ include_directories(
 # Remove main from test sources and prepare an Object lib with main
 file(GLOB TEST_MAIN ${TEST_MAIN}) 
 list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
-add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN})
-
-#if(NOT CPU_ONLY)
-#    set(TEST_CPP_SOURCES ${TEST_CPP_SOURCES} ${TEST_CU_SOURCES})
-#endif()
+add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN} ../common.cpp)
 
 #    Build each test separately from *.cpp files
 foreach(source ${TEST_CPP_SOURCES})
-    #
-    get_filename_component(name ${source} NAME_WE)
-    set(TEST_NAME ${name}${TEST_EXT})
+    test_name(TEST_NAME ${source})
     
     #
-    add_library(${TEST_NAME}.obj OBJECT ${source})
+    add_library(${TEST_NAME}.obj EXCLUDE_FROM_ALL OBJECT ${source})
     set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
     
     add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)     
@@ -67,26 +69,21 @@ foreach(source ${TEST_CPP_SOURCES})
 endforeach()
 
 #    Build each test separately from *.cu files
-if(NOT CPU_ONLY)
-    foreach(source ${TEST_CU_SOURCES})
-        #
-        get_filename_component(name ${source} NAME_WE)
-        set(TEST_NAME ${name}${TEST_EXT})
-        
-        #
-        add_library(${TEST_NAME}.lib ${source})
-        
-        add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)     
-        target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest caffe)
-        
-        #    output dir
-        set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-        
-        # Targets and object libs
-        set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-        set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
-    endforeach()
-endif()
+foreach(source ${TEST_CU_SOURCES})
+    test_name(TEST_NAME ${source})
+    
+    cuda_add_library(${TEST_NAME}.lib EXCLUDE_FROM_ALL ${source})
+    
+    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)     
+    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest caffe)
+    
+    #    output dir
+    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
+    
+    # Targets and object libs
+    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
+    set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
+endforeach()
 
 #    Build a compound test excluded from the ALL target
 add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:main_obj>)
@@ -96,8 +93,9 @@ endif()
 target_link_libraries(${ALL_TEST} gtest caffe)
 add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 
-#    output dir
+#    Output directory
 set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})
+
 #    Test command
 set(TEST_ARGS --gtest_shuffle)
 if(CPU_ONLY)

From 77d91242cd08b3671e2165769928f729e6ec4f17 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 14:51:24 -0700
Subject: [PATCH 0723/2053] add cuDNN to build

---
 Makefile                                |  9 ++-
 Makefile.config.example                 |  3 +
 include/caffe/util/cudnn.hpp            | 92 +++++++++++++++++++++++++
 include/caffe/util/device_alternate.hpp |  3 +
 src/caffe/layer_factory.cpp             | 18 +++++
 src/caffe/proto/caffe.proto             |  6 ++
 6 files changed, 130 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/util/cudnn.hpp

diff --git a/Makefile b/Makefile
index 546de931f7e..ab57088292f 100644
--- a/Makefile
+++ b/Makefile
@@ -253,10 +253,17 @@ endif
 # Debugging
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS += -DDEBUG -g -O0
+	NVCCFLAGS += -G
 else
 	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
+# cuDNN acceleration configuration.
+ifeq ($(USE_CUDNN), 1)
+	LIBRARIES += cudnn
+	COMMON_FLAGS += -DUSE_CUDNN
+endif
+
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
@@ -299,7 +306,7 @@ LIBRARY_DIRS += $(BLAS_LIB)
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
 CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
-NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
+NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
 LINKFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
diff --git a/Makefile.config.example b/Makefile.config.example
index 7c96d8a9356..5cb0b243aca 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -1,6 +1,9 @@
 ## Refer to http://caffe.berkeleyvision.org/installation.html
 # Contributions simplifying and improving our build system are welcome!
 
+# cuDNN acceleration switch (uncomment to build with cuDNN).
+# USE_CUDNN := 1
+
 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 1
 
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
new file mode 100644
index 00000000000..1fe72f1e8bc
--- /dev/null
+++ b/include/caffe/util/cudnn.hpp
@@ -0,0 +1,92 @@
+#ifndef CAFFE_UTIL_CUDNN_H_
+#define CAFFE_UTIL_CUDNN_H_
+#ifdef USE_CUDNN
+
+#include <cudnn.h>
+
+#include "caffe/proto/caffe.pb.h"
+
+#define CUDNN_CHECK(condition) \
+  do { \
+    cudnnStatus_t status = condition; \
+    CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " cuDNN error."; \
+  } while (0)
+
+namespace caffe {
+
+// TODO(cudnn): check existence, add to CUDN_CHECK
+// const char* cudnnGetErrorString(curandStatus_t error);
+//
+namespace cudnn {
+
+template <typename Dtype> class dataType;
+template<> class dataType<float>  {
+ public:
+  static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+};
+template<> class dataType<double> {
+ public:
+  static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+};
+
+template <typename Dtype>
+inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+    int n, int c, int h, int w,
+    int stride_n, int stride_c, int stride_h, int stride_w) {
+  CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
+  CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
+      n, c, h, w, stride_n, stride_c, stride_h, stride_w));
+}
+
+template <typename Dtype>
+inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+    int n, int c, int h, int w) {
+  const int stride_w = 1;
+  const int stride_h = w * stride_w;
+  const int stride_c = h * stride_h;
+  const int stride_n = c * stride_c;
+  createTensor4dDesc<Dtype>(desc, n, c, h, w,
+      stride_n, stride_c, stride_h, stride_w);
+}
+
+template <typename Dtype>
+inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
+    int n, int c, int h, int w) {
+  CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
+  CUDNN_CHECK(cudnnSetFilterDescriptor(*desc, dataType<Dtype>::type,
+      n, c, h, w));
+}
+
+template <typename Dtype>
+inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
+    cudnnTensor4dDescriptor_t bottom, cudnnFilterDescriptor_t filter,
+    int pad_h, int pad_w, int stride_h, int stride_w) {
+  CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
+  CUDNN_CHECK(cudnnSetConvolutionDescriptor(*conv, bottom, filter,
+      pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
+}
+
+template <typename Dtype>
+inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
+    PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
+    int h, int w, int stride_h, int stride_w) {
+  switch (poolmethod) {
+  case PoolingParameter_PoolMethod_MAX:
+    *mode = CUDNN_POOLING_MAX;
+    break;
+  case PoolingParameter_PoolMethod_AVE:
+    *mode = CUDNN_POOLING_AVERAGE;
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+  CUDNN_CHECK(cudnnCreatePoolingDescriptor(conv));
+  CUDNN_CHECK(cudnnSetPoolingDescriptor(*conv, *mode, h, w,
+        stride_h, stride_w));
+}
+
+}  // namespace cudnn
+}  // namespace caffe
+
+#endif  // USE_CUDNN
+#endif  // CAFFE_UTIL_CUDNN_H_
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index bb3ac61648b..3df28a49ac3 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -36,6 +36,9 @@ void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
 #include <cuda_runtime.h>
 #include <curand.h>
 #include <driver_types.h>  // cuda driver types
+#ifdef USE_CUDNN  // cuDNN acceleration library.
+#include "caffe/util/cudnn.hpp"
+#endif
 
 //
 // CUDA macros
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 09b0f900d95..b7b10988a3f 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -16,6 +16,9 @@ ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
     engine = ConvolutionParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = ConvolutionParameter_Engine_CUDNN;
+#endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
     return new ConvolutionLayer<Dtype>(param);
@@ -36,6 +39,9 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
   PoolingParameter_Engine engine = param.pooling_param().engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = PoolingParameter_Engine_CUDNN;
+#endif
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
     return new PoolingLayer<Dtype>(param);
@@ -56,6 +62,9 @@ ReLULayer<Dtype>* GetReLULayer(const string& name,
   ReLUParameter_Engine engine = param.relu_param().engine();
   if (engine == ReLUParameter_Engine_DEFAULT) {
     engine = ReLUParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = ReLUParameter_Engine_CUDNN;
+#endif
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
     return new ReLULayer<Dtype>(param);
@@ -76,6 +85,9 @@ SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
   if (engine == SigmoidParameter_Engine_DEFAULT) {
     engine = SigmoidParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = SigmoidParameter_Engine_CUDNN;
+#endif
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
     return new SigmoidLayer<Dtype>(param);
@@ -96,6 +108,9 @@ TanHLayer<Dtype>* GetTanHLayer(const string& name,
   TanHParameter_Engine engine = param.tanh_param().engine();
   if (engine == TanHParameter_Engine_DEFAULT) {
     engine = TanHParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = TanHParameter_Engine_CUDNN;
+#endif
   }
   if (engine == TanHParameter_Engine_CAFFE) {
     return new TanHLayer<Dtype>(param);
@@ -116,6 +131,9 @@ SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
   SoftmaxParameter_Engine engine = param.softmax_param().engine();
   if (engine == SoftmaxParameter_Engine_DEFAULT) {
     engine = SoftmaxParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = SoftmaxParameter_Engine_CUDNN;
+#endif
   }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
     return new SoftmaxLayer<Dtype>(param);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 082feb831b0..8cb82cebe22 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -388,6 +388,7 @@ message ConvolutionParameter {
   enum Engine {
     DEFAULT = 0;
     CAFFE = 1;
+    CUDNN = 2;
   }
   optional Engine engine = 15 [default = DEFAULT];
 }
@@ -579,6 +580,7 @@ message PoolingParameter {
   enum Engine {
     DEFAULT = 0;
     CAFFE = 1;
+    CUDNN = 2;
   }
   optional Engine engine = 11 [default = DEFAULT];
 }
@@ -602,6 +604,7 @@ message ReLUParameter {
   enum Engine {
     DEFAULT = 0;
     CAFFE = 1;
+    CUDNN = 2;
   }
   optional Engine engine = 2 [default = DEFAULT];
 }
@@ -611,6 +614,7 @@ message SigmoidParameter {
   enum Engine {
     DEFAULT = 0;
     CAFFE = 1;
+    CUDNN = 2;
   }
   optional Engine engine = 1 [default = DEFAULT];
 }
@@ -630,6 +634,7 @@ message SoftmaxParameter {
   enum Engine {
     DEFAULT = 0;
     CAFFE = 1;
+    CUDNN = 2;
   }
   optional Engine engine = 1 [default = DEFAULT];
 }
@@ -639,6 +644,7 @@ message TanHParameter {
   enum Engine {
     DEFAULT = 0;
     CAFFE = 1;
+    CUDNN = 2;
   }
   optional Engine engine = 1 [default = DEFAULT];
 }

From 8819f5953b903ec8b48e541271737e89a2cd24e6 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Sep 2014 17:45:14 -0700
Subject: [PATCH 0724/2053] call __signbit for CUDA >= 6.5 implementation

---
 src/caffe/util/math_functions.cu | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 4ae4bba6029..176da93161c 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -329,7 +329,12 @@ void caffe_gpu_powx<double>(const int N, const double* a,
 
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
+#if CUDA_VERSION >= 6050
+// __signbit to pick up the CUDA function.
+DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = __signbit(x[index]));
+#else
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
+#endif
 
 __global__ void popc_kernel(const int n, const float* a,
     const float* b, uint8_t* y) {

From d1b38ee630e3d83d70d5b8e51b416d5f94fb23cb Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 1 Sep 2014 22:05:43 -0700
Subject: [PATCH 0725/2053] strategize cuDNN convolution

---
 include/caffe/vision_layers.hpp           |  30 +++
 src/caffe/layer_factory.cpp               |   4 +
 src/caffe/layers/conv_layer.cpp           |  10 +-
 src/caffe/layers/cudnn_conv_layer.cpp     | 113 +++++++++
 src/caffe/layers/cudnn_conv_layer.cu      | 121 +++++++++
 src/caffe/test/test_convolution_layer.cpp | 291 ++++++++++++++++++++++
 6 files changed, 564 insertions(+), 5 deletions(-)
 create mode 100644 src/caffe/layers/cudnn_conv_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_conv_layer.cu

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 2dca2bf73ed..4269163afff 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -63,6 +63,36 @@ class ConvolutionLayer : public Layer<Dtype> {
   Blob<Dtype> bias_multiplier_;
 };
 
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of ConvolutionLayer.
+ *        Fallback to ConvolutionLayer for CPU mode.
+*/
+template <typename Dtype>
+class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
+ public:
+  explicit CuDNNConvolutionLayer(const LayerParameter& param)
+      : ConvolutionLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual ~CuDNNConvolutionLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  cudnnHandle_t* handle_;
+  cudaStream_t*  stream_;
+  vector<cudnnTensor4dDescriptor_t> bottom_descs_, top_descs_;
+  cudnnTensor4dDescriptor_t    bias_desc_;
+  cudnnFilterDescriptor_t      filter_desc_;
+  vector<cudnnConvolutionDescriptor_t> conv_descs_;
+  int bottom_offset_, top_offset_, weight_offset_, bias_offset_;
+};
+#endif
+
 /**
  * @brief A helper for image operations that rearranges image regions into
  *        column vectors.  Used by ConvolutionLayer to perform convolution
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index b7b10988a3f..ef1b7564518 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -22,6 +22,10 @@ ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
     return new ConvolutionLayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == ConvolutionParameter_Engine_CUDNN) {
+    return new CuDNNConvolutionLayer<Dtype>(param);
+#endif
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 1a1248f3dda..81ad4f968fb 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -66,11 +66,11 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_EQ(channels_ % group_, 0);
   // The im2col result buffer would only hold one image at a time to avoid
   // overly large memory usage.
-  int height_out =
+  height_out_ =
       (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
-  int width_out = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
+  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
   col_buffer_.Reshape(
-      1, channels_ * kernel_h_ * kernel_w_, height_out, width_out);
+      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
   // Set the parameters
   CHECK_EQ(num_output_ % group_, 0)
       << "Number of output should be multiples of group.";
@@ -78,9 +78,9 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Figure out the dimensions for individual gemms.
   M_ = num_output_ / group_;
   K_ = channels_ * kernel_h_ * kernel_w_ / group_;
-  N_ = height_out * width_out;
+  N_ = height_out_ * width_out_;
   for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out, width_out);
+    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
   }
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
new file mode 100644
index 00000000000..a27d19ce04a
--- /dev/null
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -0,0 +1,113 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+// Set to three for the benefit of the backward pass, which
+// can use separate streams for calculating the gradient w.r.t.
+// bias, filter weights, and bottom data for each group independently
+#define CUDNN_STREAMS_PER_GROUP 3
+
+/**
+ * TODO(dox) explain cuDNN interface
+ */
+template <typename Dtype>
+void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
+  // Initialize CUDA streams and cuNN.
+  stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+  handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
+    // TODO(cudnn) check
+    cudaError_t err = cudaStreamCreate(&stream_[g]);
+    CHECK_EQ(err, cudaSuccess) << "Error creating a CUDA stream.";
+
+    // TODO(cudnn) check
+    cudnnStatus_t stat;
+    stat = cudnnCreate(&handle_[g]);
+    CHECK_EQ(stat, CUDNN_STATUS_SUCCESS) << "Could not create a CUDNN handle.";
+    stat = cudnnSetStream(handle_[g], stream_[g]);
+    CHECK_EQ(stat, CUDNN_STATUS_SUCCESS) << "Could not set CUDNN stream.";
+  }
+
+  // Set the indexing parameters.
+  bottom_offset_ = (this->channels_ / this->group_)
+      * this->height_ * this->width_;
+  top_offset_ = (this->num_output_ / this->group_)
+      * this->height_out_ * this->width_out_;
+  weight_offset_ = (this->num_output_ / this->group_)
+      * (this->channels_ / this->group_) * this->kernel_h_ * this->kernel_w_;
+  bias_offset_ = (this->num_output_ / this->group_);
+
+  // Create filter descriptor.
+  cudnn::createFilterDesc<Dtype>(&filter_desc_,
+      this->num_output_ / this->group_, this->channels_ / this->group_,
+      this->kernel_h_, this->kernel_w_);
+
+  // Create tensor descriptor(s) for data and corresponding convolution(s).
+  for (int i = 0; i < bottom.size(); i++) {
+    cudnnTensor4dDescriptor_t bottom_desc;
+    cudnn::createTensor4dDesc<Dtype>(&bottom_desc,
+        this->num_,
+        this->channels_ / this->group_,
+        this->height_, this->width_,
+        this->channels_ * this->height_ * this->width_,
+        this->height_ * this->width_,
+        this->width_, 1);
+    bottom_descs_.push_back(bottom_desc);
+    cudnnTensor4dDescriptor_t top_desc;
+    cudnn::createTensor4dDesc<Dtype>(&top_desc,
+        this->num_,
+        this->num_output_ / this->group_,
+        this->height_out_, this->width_out_,
+        this->num_output_ * this->height_out_ * this->width_out_,
+        this->height_out_ * this->width_out_,
+        this->width_out_, 1);
+    top_descs_.push_back(top_desc);
+    cudnnConvolutionDescriptor_t conv_desc;
+    cudnn::createConvolutionDesc<Dtype>(&conv_desc, bottom_desc,
+        filter_desc_, this->pad_h_, this->pad_w_,
+        this->stride_h_, this->stride_w_);
+    conv_descs_.push_back(conv_desc);
+  }
+
+  // Tensor descriptor for bias.
+  if (this->bias_term_) {
+    cudnn::createTensor4dDesc<Dtype>(&bias_desc_,
+        1, this->num_output_ / this->group_, 1, 1);
+  }
+}
+
+template <typename Dtype>
+CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
+  for (int i = 0; i < bottom_descs_.size(); i++) {
+    cudnnDestroyTensor4dDescriptor(bottom_descs_[i]);
+    cudnnDestroyTensor4dDescriptor(top_descs_[i]);
+    cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
+  }
+  if (this->bias_term_) {
+    cudnnDestroyTensor4dDescriptor(bias_desc_);
+  }
+  cudnnDestroyFilterDescriptor(filter_desc_);
+
+  for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
+    cudaStreamDestroy(stream_[g]);
+    cudnnDestroy(handle_[g]);
+  }
+
+  delete [] stream_;
+  delete [] handle_;
+}
+
+INSTANTIATE_CLASS(CuDNNConvolutionLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
new file mode 100644
index 00000000000..9a654553b25
--- /dev/null
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -0,0 +1,121 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+__global__ void sync_conv_groups() { }
+
+template <typename Dtype>
+void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* top_data = (*top)[i]->mutable_gpu_data();
+    const Dtype* weight = this->blobs_[0]->gpu_data();
+
+    // Forward through cuDNN in parallel over groups.
+    for (int g = 0; g < this->group_; g++) {
+      cudnnStatus_t stat;
+
+      // Filters.
+      stat = cudnnConvolutionForward(handle_[g],
+          bottom_descs_[i], bottom_data + bottom_offset_ * g,
+          filter_desc_, weight + weight_offset_ * g,
+          conv_descs_[i],
+          top_descs_[i], top_data + top_offset_ * g,
+          CUDNN_RESULT_NO_ACCUMULATE);
+      CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnConvolutionForward";
+
+      // Bias.
+      if (this->bias_term_) {
+        const Dtype* bias_data = this->blobs_[1]->gpu_data();
+        Dtype alpha = 1.;
+        stat = cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
+                                bias_desc_, bias_data + bias_offset_ * g,
+                                top_descs_[i], top_data + top_offset_ * g);
+        CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnAddTensor4d";
+      }
+    }
+
+    // Synchronize the work across groups, each of which went into its own
+    // stream, by launching an empty kernel into the default (null) stream.
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    sync_conv_groups<<<1, 1>>>();
+  }
+}
+
+template <typename Dtype>
+void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->gpu_data();
+    weight_diff = this->blobs_[0]->mutable_gpu_diff();
+    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
+  Dtype* bias_diff = NULL;
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    bias_diff = this->blobs_[1]->mutable_gpu_diff();
+    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
+  }
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    // Backward through cuDNN in parallel over groups and gradients.
+    for (int g = 0; g < this->group_; g++) {
+      cudnnStatus_t stat;
+
+      // Gradient w.r.t. bias.
+      if (this->bias_term_ && this->param_propagate_down_[1]) {
+        stat = cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
+            top_descs_[i],  top_diff + top_offset_ * g,
+            bias_desc_, bias_diff + bias_offset_ * g,
+            CUDNN_RESULT_ACCUMULATE);
+        CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+            << "Error in cudnnConvolutionBackwardBias.";
+      }
+
+      // Gradient w.r.t. weights.
+      if (this->param_propagate_down_[0]) {
+        const Dtype* bottom_data = (*bottom)[i]->gpu_data();
+        stat = cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
+            bottom_descs_[i], bottom_data + bottom_offset_ * g,
+            top_descs_[i],    top_diff + top_offset_ * g,
+            conv_descs_[i],
+            filter_desc_, weight_diff + weight_offset_ * g,
+            CUDNN_RESULT_ACCUMULATE);
+      CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+          << "Error in cudnnConvolutionBackwardFilter.";
+      }
+
+      // Gradient w.r.t. bottom data.
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+        stat = cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
+            filter_desc_, weight + weight_offset_ * g,
+            top_descs_[i],    top_diff + top_offset_ * g,
+            conv_descs_[i],
+            bottom_descs_[i], bottom_diff + bottom_offset_ * g,
+            CUDNN_RESULT_NO_ACCUMULATE);
+        CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+            << "Error in cudnnConvolutionBackwardData.";
+      }
+    }
+
+    // Synchronize the work across groups, each of which went into its own
+    // stream, by launching an empty kernel into the default (null) stream.
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    sync_conv_groups<<<1, 1>>>();
+  }
+}
+
+INSTANTIATE_CLASS(CuDNNConvolutionLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 5a7ea80467e..0e7a8da567b 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -302,4 +302,295 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
       &(this->blob_top_vec_));
 }
 
+#ifdef USE_CUDNN
+
+template <typename Dtype>
+class CuDNNConvolutionLayerTest : public ::testing::Test {
+ protected:
+  CuDNNConvolutionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_2_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~CuDNNConvolutionLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_2_;
+    delete blob_top_;
+    delete blob_top_2_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  shared_ptr<Layer<TypeParam> > layer(
+      new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 4);
+  EXPECT_EQ(this->blob_top_->height(), 2);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 4);
+  EXPECT_EQ(this->blob_top_2_->height(), 2);
+  EXPECT_EQ(this->blob_top_2_->width(), 1);
+  // setting group should not change the shape
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 2);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 3);
+  EXPECT_EQ(this->blob_top_2_->height(), 2);
+  EXPECT_EQ(this->blob_top_2_->width(), 1);
+}
+
+TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
+  // We will simply see if the convolution layer carries out averaging well.
+  Caffe::set_mode(Caffe::GPU);
+  shared_ptr<ConstantFiller<TypeParam> > filler;
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  filler_param.set_value(2.);
+  filler.reset(new ConstantFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_2_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<TypeParam> > layer(
+      new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // After the convolution, the output should all have output values 27.1
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
+  }
+  top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
+  }
+}
+
+TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
+  // We will simply see if the convolution layer carries out averaging well.
+  Caffe::set_mode(Caffe::GPU);
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  ConstantFiller<TypeParam> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          bottom_data[this->blob_bottom_->offset(n, c, h, w)] = c;
+        }
+      }
+    }
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<TypeParam> > layer(
+      new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // After the convolution, the output should all have output values 9.1
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
+          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
+  // Test separable convolution by computing the Sobel operator
+  // as a single filter then comparing the result
+  // as the convolution of two rectangular filters.
+  Caffe::set_mode(Caffe::GPU);
+  // Fill bottoms with identical Gaussian noise.
+  shared_ptr<GaussianFiller<TypeParam> > filler;
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  filler.reset(new GaussianFiller<TypeParam>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  this->blob_bottom_2_->CopyFrom(*this->blob_bottom_);
+  // Compute Sobel G_x operator as 3 x 3 convolution.
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  shared_ptr<Layer<TypeParam> > layer(
+      new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<TypeParam>(1, 3, 3, 3));
+  TypeParam* weights = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 9;  // 3 x 3 filter
+    weights[i +  0] = -1;
+    weights[i +  1] =  0;
+    weights[i +  2] =  1;
+    weights[i +  3] = -2;
+    weights[i +  4] =  0;
+    weights[i +  5] =  2;
+    weights[i +  6] = -1;
+    weights[i +  7] =  0;
+    weights[i +  8] =  1;
+  }
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
+  // (1) the [1 2 1] column filter
+  vector<Blob<TypeParam>*> sep_blob_bottom_vec;
+  vector<Blob<TypeParam>*> sep_blob_top_vec;
+  shared_ptr<Blob<TypeParam> > blob_sep(new Blob<TypeParam>());
+  sep_blob_bottom_vec.push_back(this->blob_bottom_2_);
+  sep_blob_top_vec.push_back(this->blob_top_2_);
+  convolution_param->clear_kernel_size();
+  convolution_param->clear_stride();
+  convolution_param->set_kernel_h(3);
+  convolution_param->set_kernel_w(1);
+  convolution_param->set_stride_h(2);
+  convolution_param->set_stride_w(1);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<TypeParam>(1, 3, 3, 1));
+  TypeParam* weights_1 = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 3;  // 3 x 1 filter
+    weights_1[i +  0] = 1;
+    weights_1[i +  1] = 2;
+    weights_1[i +  2] = 1;
+  }
+  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  // (2) the [-1 0 1] row filter
+  blob_sep->CopyFrom(*this->blob_top_2_, false, true);
+  sep_blob_bottom_vec.clear();
+  sep_blob_bottom_vec.push_back(blob_sep.get());
+  convolution_param->set_kernel_h(1);
+  convolution_param->set_kernel_w(3);
+  convolution_param->set_stride_h(1);
+  convolution_param->set_stride_w(2);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<TypeParam>(1, 3, 1, 3));
+  TypeParam* weights_2 = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 3;  // 1 x 3 filter
+    weights_2[i +  0] = -1;
+    weights_2[i +  1] =  0;
+    weights_2[i +  2] =  1;
+  }
+  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  // Test equivalence of full and separable filters.
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const TypeParam* sep_top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], sep_top_data[i], 1e-4);
+  }
+}
+
+TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  CuDNNConvolutionLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  CuDNNConvolutionLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+#endif
+
 }  // namespace caffe

From 00f5fa6bf9287615724a99d7f423807bad576a39 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 21:47:56 -0700
Subject: [PATCH 0726/2053] strategize cuDNN pooling

---
 include/caffe/vision_layers.hpp          |  30 +-
 src/caffe/layer_factory.cpp              |   4 +
 src/caffe/layers/cudnn_pooling_layer.cpp |  40 ++
 src/caffe/layers/cudnn_pooling_layer.cu  |  55 +++
 src/caffe/test/test_pooling_layer.cpp    | 582 +++++++++++++++++++++++
 5 files changed, 710 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/cudnn_pooling_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_pooling_layer.cu

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 4269163afff..4dd2e9df99a 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -69,7 +69,8 @@ class ConvolutionLayer : public Layer<Dtype> {
  *        Fallback to ConvolutionLayer for CPU mode.
 */
 template <typename Dtype>
-class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
+class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype>
+{
  public:
   explicit CuDNNConvolutionLayer(const LayerParameter& param)
       : ConvolutionLayer<Dtype>(param) {}
@@ -255,6 +256,33 @@ class PoolingLayer : public Layer<Dtype> {
   Blob<int> max_idx_;
 };
 
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of PoolingLayer.
+ *        Fallback to PoolingLayer for CPU mode.
+*/
+template <typename Dtype>
+class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
+ public:
+  explicit CuDNNPoolingLayer(const LayerParameter& param)
+      : PoolingLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual ~CuDNNPoolingLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  cudnnHandle_t             handle_;
+  cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
+  cudnnPoolingDescriptor_t  pooling_desc_;
+  cudnnPoolingMode_t        mode_;
+};
+#endif
+
 }  // namespace caffe
 
 #endif  // CAFFE_VISION_LAYERS_HPP_
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index ef1b7564518..516fe87bdcd 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -49,6 +49,10 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
     return new PoolingLayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == PoolingParameter_Engine_CUDNN) {
+    return new CuDNNPoolingLayer<Dtype>(param);
+#endif
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
new file mode 100644
index 00000000000..c360b63b1f0
--- /dev/null
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -0,0 +1,40 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  PoolingLayer<Dtype>::LayerSetUp(bottom, top);
+
+  // TODO(cudnn) check
+  cudnnStatus_t stat = cudnnCreate(&handle_);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Could not create a CUDNN handle.";
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+      this->channels_, this->pooled_height_, this->pooled_width_);
+  cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
+      this->layer_param_.pooling_param().pool(), &mode_,
+      this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
+}
+
+template <typename Dtype>
+CuDNNPoolingLayer<Dtype>::~CuDNNPoolingLayer() {
+  cudnnDestroyTensor4dDescriptor(bottom_desc_);
+  cudnnDestroyTensor4dDescriptor(top_desc_);
+  cudnnDestroyPoolingDescriptor(pooling_desc_);
+  cudnnDestroy(handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNPoolingLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
new file mode 100644
index 00000000000..8f807ac811c
--- /dev/null
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -0,0 +1,55 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  // Fallback to Caffe for padded pooling, max top mask.
+  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || (*top).size() > 1) {
+    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
+    return PoolingLayer<Dtype>::Forward_gpu(bottom, top);
+  }
+
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  cudnnStatus_t stat = cudnnPoolingForward(handle_, pooling_desc_,
+      bottom_desc_, bottom_data, top_desc_, top_data);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnPoolingForward.";
+}
+
+template <typename Dtype>
+void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+
+  // Fallback to Caffe for padded pooling, max top mask.
+  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || top.size() > 1) {
+    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
+    return PoolingLayer<Dtype>::Backward_gpu(top, propagate_down, bottom);
+  }
+
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  cudnnStatus_t stat = cudnnPoolingBackward(handle_, pooling_desc_,
+      top_desc_, top_data, top_desc_, top_diff,
+      bottom_desc_, bottom_data, bottom_desc_, bottom_diff);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnPoolingBackward.";
+}
+
+INSTANTIATE_CLASS(CuDNNPoolingLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 5be01f77b32..ec23a682c2f 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -592,5 +592,587 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
   }
 }
 
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNPoolingLayerTest : public ::testing::Test {
+ protected:
+  CuDNNPoolingLayerTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_mask_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    blob_bottom_->Reshape(2, 3, 6, 5);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~CuDNNPoolingLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+    delete blob_top_mask_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_mask_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+  // Test for 2x 2 square pooling layer
+  void TestForwardSquare() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_size(2);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 3, 5);
+    // Input: 2x 2 channels of:
+    //     [1 2 5 2 3]
+    //     [9 4 1 4 8]
+    //     [1 2 5 2 3]
+    for (int i = 0; i < 15 * num * channels; i += 15) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 2;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 5;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 2;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 9;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 4;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 4;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 1;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 3;
+    }
+    CuDNNPoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 2);
+    EXPECT_EQ(blob_top_->width(), 4);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 2);
+      EXPECT_EQ(blob_top_mask_->width(), 4);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    //     [9 5 5 8]
+    //     [9 5 5 8]
+    for (int i = 0; i < 8 * num * channels; i += 8) {
+      EXPECT_EQ(blob_top_->cpu_data()[i + 0], 9);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 1], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 2], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 3], 8);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 4], 9);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 5], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 6], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 7], 8);
+    }
+    if (blob_top_vec_.size() > 1) {
+      // Expected mask output: 2x 2 channels of:
+      //     [5  2  2 9]
+      //     [5 12 12 9]
+      for (int i = 0; i < 8 * num * channels; i += 8) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0],  5);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1],  2);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2],  2);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3],  9);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4],  5);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7],  9);
+      }
+    }
+  }
+  // Test for 3x 2 rectangular pooling layer with kernel_h > kernel_w
+  void TestForwardRectHigh() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_h(3);
+    pooling_param->set_kernel_w(2);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 6, 6);
+    // Input: 2x 2 channels of:
+    // [35     1     6    26    19    24]
+    // [ 3    32     7    21    23    25]
+    // [31     9     2    22    27    20]
+    // [ 8    28    33    17    10    15]
+    // [30     5    34    12    14    16]
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
+    for (int i = 0; i < 36 * num * channels; i += 36) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 35;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 6;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 26;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 19;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 24;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 32;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 7;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 21;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 23;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 25;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 31;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 9;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 15] = 22;
+      blob_bottom_->mutable_cpu_data()[i + 16] = 27;
+      blob_bottom_->mutable_cpu_data()[i + 17] = 20;
+      blob_bottom_->mutable_cpu_data()[i + 18] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 19] = 28;
+      blob_bottom_->mutable_cpu_data()[i + 20] = 33;
+      blob_bottom_->mutable_cpu_data()[i + 21] = 17;
+      blob_bottom_->mutable_cpu_data()[i + 22] = 10;
+      blob_bottom_->mutable_cpu_data()[i + 23] = 15;
+      blob_bottom_->mutable_cpu_data()[i + 24] = 30;
+      blob_bottom_->mutable_cpu_data()[i + 25] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 26] = 34;
+      blob_bottom_->mutable_cpu_data()[i + 27] = 12;
+      blob_bottom_->mutable_cpu_data()[i + 28] = 14;
+      blob_bottom_->mutable_cpu_data()[i + 29] = 16;
+      blob_bottom_->mutable_cpu_data()[i + 30] = 4;
+      blob_bottom_->mutable_cpu_data()[i + 31] = 36;
+      blob_bottom_->mutable_cpu_data()[i + 32] = 29;
+      blob_bottom_->mutable_cpu_data()[i + 33] = 13;
+      blob_bottom_->mutable_cpu_data()[i + 34] = 18;
+      blob_bottom_->mutable_cpu_data()[i + 35] = 11;
+    }
+    CuDNNPoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 4);
+    EXPECT_EQ(blob_top_->width(), 5);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 4);
+      EXPECT_EQ(blob_top_mask_->width(), 5);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    // [35    32    26    27    27]
+    // [32    33    33    27    27]
+    // [31    34    34    27    27]
+    // [36    36    34    18    18]
+    for (int i = 0; i < 20 * num * channels; i += 20) {
+      EXPECT_EQ(blob_top_->cpu_data()[i +  0], 35);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  1], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  2], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  3], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  4], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  5], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  6], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  7], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  8], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  9], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 10], 31);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 11], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 13], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 14], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 15], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 17], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 18], 18);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
+    }
+    if (blob_top_vec_.size() > 1) {
+        // [ 1     8     4    17    17]
+        // [ 8    21    21    17    17]
+        // [13    27    27    17    17]
+        // [32    32    27    35    35]
+      for (int i = 0; i < 20 * num * channels; i += 20) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  0],  0);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  1],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  2],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  3], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  4], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  5],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  6], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  7], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  8], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  9], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 34);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
+      }
+    }
+  }
+  // Test for rectangular pooling layer with kernel_w > kernel_h
+  void TestForwardRectWide() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_h(2);
+    pooling_param->set_kernel_w(3);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 6, 6);
+    // Input: 2x 2 channels of:
+    // [35     1     6    26    19    24]
+    // [ 3    32     7    21    23    25]
+    // [31     9     2    22    27    20]
+    // [ 8    28    33    17    10    15]
+    // [30     5    34    12    14    16]
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
+    for (int i = 0; i < 36 * num * channels; i += 36) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 35;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 6;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 26;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 19;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 24;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 32;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 7;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 21;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 23;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 25;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 31;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 9;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 15] = 22;
+      blob_bottom_->mutable_cpu_data()[i + 16] = 27;
+      blob_bottom_->mutable_cpu_data()[i + 17] = 20;
+      blob_bottom_->mutable_cpu_data()[i + 18] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 19] = 28;
+      blob_bottom_->mutable_cpu_data()[i + 20] = 33;
+      blob_bottom_->mutable_cpu_data()[i + 21] = 17;
+      blob_bottom_->mutable_cpu_data()[i + 22] = 10;
+      blob_bottom_->mutable_cpu_data()[i + 23] = 15;
+      blob_bottom_->mutable_cpu_data()[i + 24] = 30;
+      blob_bottom_->mutable_cpu_data()[i + 25] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 26] = 34;
+      blob_bottom_->mutable_cpu_data()[i + 27] = 12;
+      blob_bottom_->mutable_cpu_data()[i + 28] = 14;
+      blob_bottom_->mutable_cpu_data()[i + 29] = 16;
+      blob_bottom_->mutable_cpu_data()[i + 30] = 4;
+      blob_bottom_->mutable_cpu_data()[i + 31] = 36;
+      blob_bottom_->mutable_cpu_data()[i + 32] = 29;
+      blob_bottom_->mutable_cpu_data()[i + 33] = 13;
+      blob_bottom_->mutable_cpu_data()[i + 34] = 18;
+      blob_bottom_->mutable_cpu_data()[i + 35] = 11;
+    }
+    CuDNNPoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 5);
+    EXPECT_EQ(blob_top_->width(), 4);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 5);
+      EXPECT_EQ(blob_top_mask_->width(), 4);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    // [35    32    26    26]
+    // [32    32    27    27]
+    // [33    33    33    27]
+    // [34    34    34    17]
+    // [36    36    34    18]
+    for (int i = 0; i < 20 * num * channels; i += 20) {
+      EXPECT_EQ(blob_top_->cpu_data()[i +  0], 35);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  1], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  2], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  3], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  4], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  5], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  6], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  7], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  8], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  9], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 10], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 11], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 13], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 14], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 15], 17);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 17], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 18], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
+    }
+    if (blob_top_vec_.size() > 1) {
+        // [ 1     8     4     4]
+        // [ 8     8    17    17]
+        // [21    21    21    17]
+        // [27    27    27    22]
+        // [32    32    27    35]
+      for (int i = 0; i < 20 * num * channels; i += 20) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  0],  0);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  1],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  2],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  3],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  4],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  5],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  6], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  7], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  8], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  9], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 21);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
+      }
+    }
+  }
+};
+
+TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  CuDNNPoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 2);
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  pooling_param->set_pad(1);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+  CuDNNPoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), 4);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+/*
+TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  layer_param.set_pool(LayerParameter_PoolMethod_MAX);
+  CuDNNPoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
+  }
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    cout << "top data " << i << " " << this->blob_top_->cpu_data()[i] << endl;
+  }
+
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = i;
+  }
+  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
+  }
+}
+*/
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  this->blob_top_vec_.push_back(this->blob_top_mask_);
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(1);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      CuDNNPoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  pooling_param->set_pad(2);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+  this->blob_bottom_->Reshape(1, 1, 3, 3);
+  // Input:
+  //     [ 1 2 4 ]
+  //     [ 2 3 2 ]
+  //     [ 4 2 1 ]
+  this->blob_bottom_->mutable_cpu_data()[0] = 1;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 4;
+  this->blob_bottom_->mutable_cpu_data()[3] = 2;
+  this->blob_bottom_->mutable_cpu_data()[4] = 3;
+  this->blob_bottom_->mutable_cpu_data()[5] = 2;
+  this->blob_bottom_->mutable_cpu_data()[6] = 4;
+  this->blob_bottom_->mutable_cpu_data()[7] = 2;
+  this->blob_bottom_->mutable_cpu_data()[8] = 1;
+  CuDNNPoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  TypeParam epsilon = 1e-8;
+  // Output:
+  //     [ 1 4 4 ]
+  //     [ 4 4 4 ]
+  //     [ 4 4 1 ]
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 1, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      this->blob_top_vec_.push_back(this->blob_top_mask_);
+      CuDNNPoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+      this->blob_top_vec_.pop_back();
+    }
+  }
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(1);
+  pooling_param->set_pad(1);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+  this->blob_bottom_->Reshape(1, 1, 3, 3);
+  FillerParameter filler_param;
+  filler_param.set_value(TypeParam(2));
+  ConstantFiller<TypeParam> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  CuDNNPoolingLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  TypeParam epsilon = 1e-5;
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4.0 / 3, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 2.0    , epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4.0 / 3, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 8.0 / 9, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4.0 / 3, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      CuDNNPoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
+}
+
+TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      CuDNNPoolingLayer<TypeParam> layer(layer_param);
+      GradientChecker<TypeParam> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
+}
+
+#endif
 
 }  // namespace caffe

From 14a9198ae1bf75f0de35409a9f14cbb8ece375a9 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 22:43:58 -0700
Subject: [PATCH 0727/2053] strategize cuDNN activations: ReLU, Sigmoid, TanH

---
 include/caffe/neuron_layers.hpp          |  75 +++++++++++++
 src/caffe/layer_factory.cpp              |  12 +++
 src/caffe/layers/cudnn_relu_layer.cpp    |  34 ++++++
 src/caffe/layers/cudnn_relu_layer.cu     |  55 ++++++++++
 src/caffe/layers/cudnn_sigmoid_layer.cpp |  34 ++++++
 src/caffe/layers/cudnn_sigmoid_layer.cu  |  45 ++++++++
 src/caffe/layers/cudnn_tanh_layer.cpp    |  34 ++++++
 src/caffe/layers/cudnn_tanh_layer.cu     |  45 ++++++++
 src/caffe/test/test_neuron_layer.cpp     | 132 +++++++++++++++++++++++
 9 files changed, 466 insertions(+)
 create mode 100644 src/caffe/layers/cudnn_relu_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_relu_layer.cu
 create mode 100644 src/caffe/layers/cudnn_sigmoid_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_sigmoid_layer.cu
 create mode 100644 src/caffe/layers/cudnn_tanh_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_tanh_layer.cu

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 8c882eec125..36acf96e5af 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -356,6 +356,31 @@ class ReLULayer : public NeuronLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of ReLULayer.
+ */
+template <typename Dtype>
+class CuDNNReLULayer : public ReLULayer<Dtype> {
+ public:
+  explicit CuDNNReLULayer(const LayerParameter& param)
+      : ReLULayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual ~CuDNNReLULayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  cudnnHandle_t             handle_;
+  cudnnTensor4dDescriptor_t bottom_desc_;
+  cudnnTensor4dDescriptor_t top_desc_;
+};
+#endif
+
 /**
  * @brief Sigmoid function non-linearity @f$
  *         y = (1 + \exp(-x))^{-1}
@@ -413,6 +438,31 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of SigmoidLayer.
+ */
+template <typename Dtype>
+class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
+ public:
+  explicit CuDNNSigmoidLayer(const LayerParameter& param)
+      : SigmoidLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual ~CuDNNSigmoidLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  cudnnHandle_t             handle_;
+  cudnnTensor4dDescriptor_t bottom_desc_;
+  cudnnTensor4dDescriptor_t top_desc_;
+};
+#endif
+
 /**
  * @brief TanH hyperbolic tangent non-linearity @f$
  *         y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
@@ -472,6 +522,31 @@ class TanHLayer : public NeuronLayer<Dtype> {
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of TanHLayer.
+ */
+template <typename Dtype>
+class CuDNNTanHLayer : public TanHLayer<Dtype> {
+ public:
+  explicit CuDNNTanHLayer(const LayerParameter& param)
+      : TanHLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual ~CuDNNTanHLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  cudnnHandle_t             handle_;
+  cudnnTensor4dDescriptor_t bottom_desc_;
+  cudnnTensor4dDescriptor_t top_desc_;
+};
+#endif
+
 /**
  * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
  *        above threshold; 0 otherwise.
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 516fe87bdcd..c5194853cbf 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -76,6 +76,10 @@ ReLULayer<Dtype>* GetReLULayer(const string& name,
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
     return new ReLULayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == ReLUParameter_Engine_CUDNN) {
+    return new CuDNNReLULayer<Dtype>(param);
+#endif
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -99,6 +103,10 @@ SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
     return new SigmoidLayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == SigmoidParameter_Engine_CUDNN) {
+    return new CuDNNSigmoidLayer<Dtype>(param);
+#endif
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
@@ -122,6 +130,10 @@ TanHLayer<Dtype>* GetTanHLayer(const string& name,
   }
   if (engine == TanHParameter_Engine_CAFFE) {
     return new TanHLayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == TanHParameter_Engine_CUDNN) {
+    return new CuDNNTanHLayer<Dtype>(param);
+#endif
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
new file mode 100644
index 00000000000..f8bf77f1844
--- /dev/null
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -0,0 +1,34 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  ReLULayer<Dtype>::LayerSetUp(bottom, top);
+  // initialize cuDNN
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  const int N = bottom[0]->num();
+  const int K = bottom[0]->channels();
+  const int H = bottom[0]->height();
+  const int W = bottom[0]->width();
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+}
+
+template <typename Dtype>
+CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
+  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
+  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  cudnnDestroy(this->handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNReLULayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
new file mode 100644
index 00000000000..8c8ca58140e
--- /dev/null
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -0,0 +1,55 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  // Fallback to standard Caffe for leaky ReLU.
+  if (ReLULayer<Dtype>::layer_param_.relu_param().negative_slope() != 0) {
+    return ReLULayer<Dtype>::Forward_gpu(bottom, top);
+  }
+
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  cudnnStatus_t stat = cudnnActivationForward(this->handle_,
+      CUDNN_ACTIVATION_RELU,
+      this->bottom_desc_, bottom_data, this->top_desc_, top_data);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnActivationForward.";
+}
+
+template <typename Dtype>
+void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+
+  // Fallback to standard Caffe for leaky ReLU.
+  if (ReLULayer<Dtype>::layer_param_.relu_param().negative_slope() != 0) {
+    return ReLULayer<Dtype>::Backward_gpu(top, propagate_down, bottom);
+  }
+
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  cudnnStatus_t stat = cudnnActivationBackward(this->handle_,
+      CUDNN_ACTIVATION_RELU,
+      this->top_desc_, top_data, this->top_desc_, top_diff,
+      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnActivationBackward.";
+}
+
+INSTANTIATE_CLASS(CuDNNReLULayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
new file mode 100644
index 00000000000..488c7545dba
--- /dev/null
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -0,0 +1,34 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  SigmoidLayer<Dtype>::LayerSetUp(bottom, top);
+  // initialize cuDNN
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  const int N = bottom[0]->num();
+  const int K = bottom[0]->channels();
+  const int H = bottom[0]->height();
+  const int W = bottom[0]->width();
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+}
+
+template <typename Dtype>
+CuDNNSigmoidLayer<Dtype>::~CuDNNSigmoidLayer() {
+  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
+  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  cudnnDestroy(this->handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNSigmoidLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
new file mode 100644
index 00000000000..c548a02dab3
--- /dev/null
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -0,0 +1,45 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  cudnnStatus_t stat = cudnnActivationForward(this->handle_,
+      CUDNN_ACTIVATION_SIGMOID,
+      this->bottom_desc_, bottom_data, this->top_desc_, top_data);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnActivationForward.";
+}
+
+template <typename Dtype>
+void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  cudnnStatus_t stat = cudnnActivationBackward(this->handle_,
+      CUDNN_ACTIVATION_SIGMOID,
+      this->top_desc_, top_data, this->top_desc_, top_diff,
+      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnActivationBackward.";
+}
+
+INSTANTIATE_CLASS(CuDNNSigmoidLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
new file mode 100644
index 00000000000..32b6611e40b
--- /dev/null
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -0,0 +1,34 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  TanHLayer<Dtype>::LayerSetUp(bottom, top);
+  // initialize cuDNN
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  const int N = bottom[0]->num();
+  const int K = bottom[0]->channels();
+  const int H = bottom[0]->height();
+  const int W = bottom[0]->width();
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+}
+
+template <typename Dtype>
+CuDNNTanHLayer<Dtype>::~CuDNNTanHLayer() {
+  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
+  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  cudnnDestroy(this->handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNTanHLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
new file mode 100644
index 00000000000..090b38b4ca7
--- /dev/null
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -0,0 +1,45 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  cudnnStatus_t stat = cudnnActivationForward(this->handle_,
+      CUDNN_ACTIVATION_TANH,
+      this->bottom_desc_, bottom_data, this->top_desc_, top_data);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnActivationForward.";
+}
+
+template <typename Dtype>
+void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  cudnnStatus_t stat = cudnnActivationBackward(this->handle_,
+      CUDNN_ACTIVATION_TANH,
+      this->top_desc_, top_data, this->top_desc_, top_diff,
+      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
+      << "Error in cudnnActivationBackward.";
+}
+
+INSTANTIATE_CLASS(CuDNNTanHLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 2333c3a7dab..4c19d3f9bae 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -272,5 +272,137 @@ TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
       &(this->blob_top_vec_));
 }
 
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNNeuronLayerTest : public ::testing::Test {
+ protected:
+  CuDNNNeuronLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~CuDNNNeuronLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNReLULayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+  }
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNReLULayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CuDNNReLULayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+  }
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CuDNNReLULayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNSigmoidLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_FLOAT_EQ(top_data[i], 1. / (1 + exp(-bottom_data[i])));
+    // check that we squashed the value between 0 and 1
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+  }
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNSigmoidLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNTanHLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Test exact values
+  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
+    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
+      for (int k = 0; k < this->blob_bottom_->height(); ++k) {
+        for (int l = 0; l < this->blob_bottom_->width(); ++l) {
+          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
+          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
+             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNTanHLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+#endif
 
 }  // namespace caffe

From 84bd1f5e98b9bd59be96f7af8c116123cee967f8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Sep 2014 23:53:04 -0700
Subject: [PATCH 0728/2053] strategize cuDNN softmax

---
 include/caffe/common_layers.hpp          | 26 +++++++++
 src/caffe/layer_factory.cpp              |  4 ++
 src/caffe/layers/cudnn_softmax_layer.cpp | 39 ++++++++++++++
 src/caffe/layers/cudnn_softmax_layer.cu  | 43 +++++++++++++++
 src/caffe/layers/softmax_layer.cpp       |  1 -
 src/caffe/test/test_softmax_layer.cpp    | 68 ++++++++++++++++++++++++
 6 files changed, 180 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/cudnn_softmax_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_softmax_layer.cu

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 37535920992..c170742308c 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -375,6 +375,32 @@ class SoftmaxLayer : public Layer<Dtype> {
   Blob<Dtype> scale_;
 };
 
+#ifdef USE_CUDNN
+/**
+ * @brief cuDNN implementation of SoftmaxLayer.
+ *        Fallback to SoftmaxLayer for CPU mode.
+ */
+template <typename Dtype>
+class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
+ public:
+  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
+      : SoftmaxLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual ~CuDNNSoftmaxLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  cudnnHandle_t             handle_;
+  cudnnTensor4dDescriptor_t bottom_desc_;
+  cudnnTensor4dDescriptor_t top_desc_;
+};
+#endif
+
 /**
  * @brief Creates a "split" path in the network by copying the bottom Blob
  *        into multiple top Blob%s to be used by multiple consuming layers.
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index c5194853cbf..41c547b8ad4 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -157,6 +157,10 @@ SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
   }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
     return new SoftmaxLayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == SoftmaxParameter_Engine_CUDNN) {
+    return new CuDNNSoftmaxLayer<Dtype>(param);
+#endif
   } else {
     LOG(FATAL) << "Layer " << name << " has unknown engine.";
   }
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
new file mode 100644
index 00000000000..58b3a783859
--- /dev/null
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -0,0 +1,39 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "thrust/device_vector.h"
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
+  // Initialize CUDNN.
+  cudnnStatus_t stat = cudnnCreate(&handle_);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Could not create a CUDNN handle.";
+  int N = bottom[0]->num();
+  int K = bottom[0]->channels();
+  int H = bottom[0]->height();
+  int W = bottom[0]->width();
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+}
+
+template <typename Dtype>
+CuDNNSoftmaxLayer<Dtype>::~CuDNNSoftmaxLayer() {
+  cudnnDestroyTensor4dDescriptor(bottom_desc_);
+  cudnnDestroyTensor4dDescriptor(top_desc_);
+  cudnnDestroy(handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNSoftmaxLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
new file mode 100644
index 00000000000..c333774458c
--- /dev/null
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -0,0 +1,43 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "thrust/device_vector.h"
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  cudnnStatus_t stat = cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
+      CUDNN_SOFTMAX_MODE_CHANNEL,
+      bottom_desc_, bottom_data, top_desc_, top_data);
+  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnSoftmaxBackward.";
+}
+
+template <typename Dtype>
+void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[0]) {
+    const Dtype* top_data = top[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    cudnnStatus_t stat = cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
+        CUDNN_SOFTMAX_MODE_CHANNEL,
+        top_desc_, top_data, top_desc_, top_diff, bottom_desc_, bottom_diff));
+    CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnSoftmaxBackward.";
+  }
+}
+
+INSTANTIATE_CLASS(CuDNNSoftmaxLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 29767ac775c..952db74a4b6 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -1,4 +1,3 @@
-//
 #include <algorithm>
 #include <vector>
 
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 9f45f762cdb..41f643f4e2a 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -80,4 +80,72 @@ TYPED_TEST(SoftmaxLayerTest, TestGradient) {
       &(this->blob_top_vec_));
 }
 
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNSoftmaxLayerTest : public ::testing::Test {
+ protected:
+  CuDNNSoftmaxLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 10, 2, 3)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~CuDNNSoftmaxLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(CuDNNSoftmaxLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Test sum
+  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
+    for (int k = 0; k < this->blob_bottom_->height(); ++k) {
+      for (int l = 0; l < this->blob_bottom_->width(); ++l) {
+        TypeParam sum = 0;
+        for (int j = 0; j < this->blob_top_->channels(); ++j) {
+          sum += this->blob_top_->data_at(i, j, k, l);
+        }
+        EXPECT_GE(sum, 0.999);
+        EXPECT_LE(sum, 1.001);
+        // Test exact values
+        TypeParam scale = 0;
+        for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
+          scale += exp(this->blob_bottom_->data_at(i, j, k, l));
+        }
+        for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
+          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
+              exp(this->blob_bottom_->data_at(i, j, k, l)) / scale)
+              << "debug: " << i << " " << j;
+          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
+              exp(this->blob_bottom_->data_at(i, j, k, l)) / scale)
+              << "debug: " << i << " " << j;
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) {
+  Caffe::set_mode(Caffe::GPU);
+  LayerParameter layer_param;
+  CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
+  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+#endif
+
 }  // namespace caffe

From 9e3d86f3b621ba2b7f45c67a0b2076a9953ddd6f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 6 Sep 2014 00:18:10 -0700
Subject: [PATCH 0729/2053] CUDNN_CHECK

---
 src/caffe/layers/cudnn_conv_layer.cpp    | 13 +++------
 src/caffe/layers/cudnn_conv_layer.cu     | 34 ++++++++----------------
 src/caffe/layers/cudnn_pooling_layer.cpp |  4 +--
 src/caffe/layers/cudnn_pooling_layer.cu  | 11 +++-----
 src/caffe/layers/cudnn_relu_layer.cu     | 12 +++------
 src/caffe/layers/cudnn_sigmoid_layer.cu  | 12 +++------
 src/caffe/layers/cudnn_softmax_layer.cpp |  3 +--
 src/caffe/layers/cudnn_softmax_layer.cu  |  8 +++---
 src/caffe/layers/cudnn_tanh_layer.cu     | 12 +++------
 9 files changed, 35 insertions(+), 74 deletions(-)

diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index a27d19ce04a..eaacddcb0cc 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -26,16 +26,9 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
 
   for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
-    // TODO(cudnn) check
-    cudaError_t err = cudaStreamCreate(&stream_[g]);
-    CHECK_EQ(err, cudaSuccess) << "Error creating a CUDA stream.";
-
-    // TODO(cudnn) check
-    cudnnStatus_t stat;
-    stat = cudnnCreate(&handle_[g]);
-    CHECK_EQ(stat, CUDNN_STATUS_SUCCESS) << "Could not create a CUDNN handle.";
-    stat = cudnnSetStream(handle_[g], stream_[g]);
-    CHECK_EQ(stat, CUDNN_STATUS_SUCCESS) << "Could not set CUDNN stream.";
+    CUDA_CHECK(cudaStreamCreate(&stream_[g]));
+    CUDNN_CHECK(cudnnCreate(&handle_[g]));
+    CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
   }
 
   // Set the indexing parameters.
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 9a654553b25..2af13309e18 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -21,25 +21,21 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
-      cudnnStatus_t stat;
-
       // Filters.
-      stat = cudnnConvolutionForward(handle_[g],
+      CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
           bottom_descs_[i], bottom_data + bottom_offset_ * g,
           filter_desc_, weight + weight_offset_ * g,
           conv_descs_[i],
           top_descs_[i], top_data + top_offset_ * g,
-          CUDNN_RESULT_NO_ACCUMULATE);
-      CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnConvolutionForward";
+          CUDNN_RESULT_NO_ACCUMULATE));
 
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
         Dtype alpha = 1.;
-        stat = cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
-                                bias_desc_, bias_data + bias_offset_ * g,
-                                top_descs_[i], top_data + top_offset_ * g);
-        CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnAddTensor4d";
+        CUDNN_CHECK(cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
+            bias_desc_, bias_data + bias_offset_ * g,
+            top_descs_[i], top_data + top_offset_ * g));
       }
     }
 
@@ -69,42 +65,34 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[i]->gpu_diff();
     // Backward through cuDNN in parallel over groups and gradients.
     for (int g = 0; g < this->group_; g++) {
-      cudnnStatus_t stat;
-
       // Gradient w.r.t. bias.
       if (this->bias_term_ && this->param_propagate_down_[1]) {
-        stat = cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
             top_descs_[i],  top_diff + top_offset_ * g,
             bias_desc_, bias_diff + bias_offset_ * g,
-            CUDNN_RESULT_ACCUMULATE);
-        CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-            << "Error in cudnnConvolutionBackwardBias.";
+            CUDNN_RESULT_ACCUMULATE));
       }
 
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-        stat = cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
             bottom_descs_[i], bottom_data + bottom_offset_ * g,
             top_descs_[i],    top_diff + top_offset_ * g,
             conv_descs_[i],
             filter_desc_, weight_diff + weight_offset_ * g,
-            CUDNN_RESULT_ACCUMULATE);
-      CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-          << "Error in cudnnConvolutionBackwardFilter.";
+            CUDNN_RESULT_ACCUMULATE));
       }
 
       // Gradient w.r.t. bottom data.
       if (propagate_down[i]) {
         Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-        stat = cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
             filter_desc_, weight + weight_offset_ * g,
             top_descs_[i],    top_diff + top_offset_ * g,
             conv_descs_[i],
             bottom_descs_[i], bottom_diff + bottom_offset_ * g,
-            CUDNN_RESULT_NO_ACCUMULATE);
-        CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-            << "Error in cudnnConvolutionBackwardData.";
+            CUDNN_RESULT_NO_ACCUMULATE));
       }
     }
 
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index c360b63b1f0..23c52012f1e 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -14,9 +14,7 @@ void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
 
-  // TODO(cudnn) check
-  cudnnStatus_t stat = cudnnCreate(&handle_);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Could not create a CUDNN handle.";
+  CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
       this->channels_, this->height_, this->width_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 8f807ac811c..99c409dcc96 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -20,10 +20,8 @@ void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  cudnnStatus_t stat = cudnnPoolingForward(handle_, pooling_desc_,
-      bottom_desc_, bottom_data, top_desc_, top_data);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnPoolingForward.";
+  CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
+      bottom_desc_, bottom_data, top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -43,10 +41,9 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* bottom_data = (*bottom)[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  cudnnStatus_t stat = cudnnPoolingBackward(handle_, pooling_desc_,
+  CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
       top_desc_, top_data, top_desc_, top_diff,
-      bottom_desc_, bottom_data, bottom_desc_, bottom_diff);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnPoolingBackward.";
+      bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_CLASS(CuDNNPoolingLayer);
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 8c8ca58140e..a8519f340cc 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -17,11 +17,9 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  cudnnStatus_t stat = cudnnActivationForward(this->handle_,
+  CUDNN_CHECK(cudnnActivationForward(this->handle_,
       CUDNN_ACTIVATION_RELU,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnActivationForward.";
+      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -41,12 +39,10 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = (*bottom)[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  cudnnStatus_t stat = cudnnActivationBackward(this->handle_,
+  CUDNN_CHECK(cudnnActivationBackward(this->handle_,
       CUDNN_ACTIVATION_RELU,
       this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnActivationBackward.";
+      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_CLASS(CuDNNReLULayer);
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index c548a02dab3..43019bd78ae 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -12,11 +12,9 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  cudnnStatus_t stat = cudnnActivationForward(this->handle_,
+  CUDNN_CHECK(cudnnActivationForward(this->handle_,
       CUDNN_ACTIVATION_SIGMOID,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnActivationForward.";
+      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -31,12 +29,10 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = (*bottom)[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  cudnnStatus_t stat = cudnnActivationBackward(this->handle_,
+  CUDNN_CHECK(cudnnActivationBackward(this->handle_,
       CUDNN_ACTIVATION_SIGMOID,
       this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnActivationBackward.";
+      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_CLASS(CuDNNSigmoidLayer);
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 58b3a783859..6dab2d6ac32 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -16,8 +16,7 @@ void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
   // Initialize CUDNN.
-  cudnnStatus_t stat = cudnnCreate(&handle_);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Could not create a CUDNN handle.";
+  CUDNN_CHECK(cudnnCreate(&handle_));
   int N = bottom[0]->num();
   int K = bottom[0]->channels();
   int H = bottom[0]->height();
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index c333774458c..300bdc496c8 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -16,10 +16,9 @@ void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  cudnnStatus_t stat = cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
+  CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
       CUDNN_SOFTMAX_MODE_CHANNEL,
-      bottom_desc_, bottom_data, top_desc_, top_data);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnSoftmaxBackward.";
+      bottom_desc_, bottom_data, top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -30,10 +29,9 @@ void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = (*bottom)[0]->gpu_data();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    cudnnStatus_t stat = cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
+    CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
         CUDNN_SOFTMAX_MODE_CHANNEL,
         top_desc_, top_data, top_desc_, top_diff, bottom_desc_, bottom_diff));
-    CHECK_EQ(stat,CUDNN_STATUS_SUCCESS) << "Error in cudnnSoftmaxBackward.";
   }
 }
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index 090b38b4ca7..c475b08c0ee 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -12,11 +12,9 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  cudnnStatus_t stat = cudnnActivationForward(this->handle_,
+  CUDNN_CHECK(cudnnActivationForward(this->handle_,
       CUDNN_ACTIVATION_TANH,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnActivationForward.";
+      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -31,12 +29,10 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = (*bottom)[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  cudnnStatus_t stat = cudnnActivationBackward(this->handle_,
+  CUDNN_CHECK(cudnnActivationBackward(this->handle_,
       CUDNN_ACTIVATION_TANH,
       this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff);
-  CHECK_EQ(stat,CUDNN_STATUS_SUCCESS)
-      << "Error in cudnnActivationBackward.";
+      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_CLASS(CuDNNTanHLayer);

From c65d5a035707b544cfa7a03ffc78a18ba81e921e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 6 Sep 2014 00:27:51 -0700
Subject: [PATCH 0730/2053] report cuDNN error string

---
 include/caffe/util/cudnn.hpp | 35 +++++++++++++++++++++++++++++++----
 1 file changed, 31 insertions(+), 4 deletions(-)

diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index 1fe72f1e8bc..e7ddea73e6b 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -9,14 +9,40 @@
 #define CUDNN_CHECK(condition) \
   do { \
     cudnnStatus_t status = condition; \
-    CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " cuDNN error."; \
+    CHECK_EQ(status, CUDNN_STATUS_SUCCESS) << " "\
+      << cudnnGetErrorString(status); \
   } while (0)
 
+inline const char* cudnnGetErrorString(cudnnStatus_t status) {
+  switch (status) {
+    case CUDNN_STATUS_SUCCESS:
+      return "CUDNN_STATUS_SUCCESS";
+    case CUDNN_STATUS_NOT_INITIALIZED:
+      return "CUDNN_STATUS_NOT_INITIALIZED";
+    case CUDNN_STATUS_ALLOC_FAILED:
+      return "CUDNN_STATUS_ALLOC_FAILED";
+    case CUDNN_STATUS_BAD_PARAM:
+      return "CUDNN_STATUS_BAD_PARAM";
+    case CUDNN_STATUS_INTERNAL_ERROR:
+      return "CUDNN_STATUS_INTERNAL_ERROR";
+    case CUDNN_STATUS_INVALID_VALUE:
+      return "CUDNN_STATUS_INVALID_VALUE";
+    case CUDNN_STATUS_ARCH_MISMATCH:
+      return "CUDNN_STATUS_ARCH_MISMATCH";
+    case CUDNN_STATUS_MAPPING_ERROR:
+      return "CUDNN_STATUS_MAPPING_ERROR";
+    case CUDNN_STATUS_EXECUTION_FAILED:
+      return "CUDNN_STATUS_EXECUTION_FAILED";
+    case CUDNN_STATUS_NOT_SUPPORTED:
+      return "CUDNN_STATUS_NOT_SUPPORTED";
+    case CUDNN_STATUS_LICENSE_ERROR:
+      return "CUDNN_STATUS_LICENSE_ERROR";
+  }
+  return "Unknown cudnn status";
+}
+
 namespace caffe {
 
-// TODO(cudnn): check existence, add to CUDN_CHECK
-// const char* cudnnGetErrorString(curandStatus_t error);
-//
 namespace cudnn {
 
 template <typename Dtype> class dataType;
@@ -86,6 +112,7 @@ inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
 }
 
 }  // namespace cudnn
+
 }  // namespace caffe
 
 #endif  // USE_CUDNN

From 359197b039f7dfb60fb9bc83f44f75b5477faf78 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 7 Sep 2014 18:59:40 +0200
Subject: [PATCH 0731/2053] [docs] include cuDNN in installation and
 performance reference

---
 docs/installation.md         | 18 ++++++++++++------
 docs/performance_hardware.md | 20 ++++++++++++++++----
 2 files changed, 28 insertions(+), 10 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 82a22d5cde3..dbf73d2c067 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -15,7 +15,7 @@ We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
 
 Caffe depends on several software packages.
 
-* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
+* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
 * [OpenCV](http://opencv.org/).
 * [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 is tested)
@@ -25,13 +25,17 @@ Caffe depends on several software packages.
 * For the MATLAB wrapper
     * MATLAB with the `mex` compiler.
 
-**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic.
+
+**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
 ### CUDA and BLAS
 
 Caffe requires the CUDA `nvcc` compiler to compile its GPU code and CUDA driver for GPU operation.
 To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cuda-downloads) and follow installation instructions there. Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date. **Warning!** The 331.* CUDA driver series has a critical performance issue: do not use it.
 
+For best performance, Caffe can be accelerated by [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). Register for free at the cuDNN site, install it, then continue with these installation instructions. To compile with cuDNN set the `USE_CUDNN := 1` flag set in your `Makefile.config`.
+
 Caffe requires BLAS as the backend of its matrix and vector computations.
 There are several implementations of this library.
 The choice is yours:
@@ -92,7 +96,7 @@ Keep reading to find out how to manually build and install the Google flags libr
 On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
 
     sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
-    
+
 The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
 
     sudo yum install gflags-devel glog-devel lmdb-devel
@@ -192,7 +196,7 @@ If you're not using Anaconda, include `hdf5` in the list above.
 **Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
 
     brew install --build-from-source --with-python --fresh -vd boost
-    
+
 **Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
 
     $ brew update
@@ -201,7 +205,7 @@ If you're not using Anaconda, include `hdf5` in the list above.
     Please, commit your changes or stash them before you can merge.
     Aborting
     Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
-    
+
 One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master, as follows:
 
     cd /usr/local
@@ -213,7 +217,7 @@ One solution is to commit your changes to a separate Homebrew branch, run `brew
     git rebase master caffe
     # Resolve any merge conflicts here
     git checkout caffe
-    
+
 At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place. You may still get the following error:
 
     $ brew update
@@ -240,6 +244,8 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
     make test
     make runtest
 
+To compile with cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
+
 If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
index 62385431751..b35246feabd 100644
--- a/docs/performance_hardware.md
+++ b/docs/performance_hardware.md
@@ -4,7 +4,7 @@ title: Performance and Hardware Configuration
 
 # Performance and Hardware Configuration
 
-To measure performance on different NVIDIA GPUs we use the Caffe reference ImageNet model.
+To measure performance on different NVIDIA GPUs we use CaffeNet, the Caffe reference ImageNet model.
 
 For training, each time point is 20 iterations/minibatches of 256 images for 5,120 images total. For testing, a 50,000 image validation set is classified.
 
@@ -14,11 +14,16 @@ For training, each time point is 20 iterations/minibatches of 256 images for 5,1
 
 Performance is best with ECC off and boost clock enabled. While ECC makes a negligible difference in speed, disabling it frees ~1 GB of GPU memory.
 
-Best settings with ECC off and maximum clock speed:
+Best settings with ECC off and maximum clock speed in standard Caffe:
 
 * Training is 26.5 secs / 20 iterations (5,120 images)
 * Testing is 100 secs / validation set (50,000 images)
 
+Best settings with Caffe + [cuDNN acceleration](http://nvidia.com/cudnn):
+
+* Training is 19.2 secs / 20 iterations (5,120 images)
+* Testing is 60.7 secs / validation set (50,000 images)
+
 Other settings:
 
 * ECC on, max speed: training 26.7 secs / 20 iterations, test 101 secs / validation set
@@ -50,12 +55,19 @@ but note that this configuration resets across driver reloading / rebooting. Inc
 Training: 26.26 secs / 20 iterations (5,120 images).
 Testing: 100 secs / validation set (50,000 images).
 
+cuDNN Training: 20.25 secs / 20 iterations (5,120 images).
+cuDNN Testing: 66.3 secs / validation set (50,000 images).
+
+
 ## NVIDIA K20
 
 Training: 36.0 secs / 20 iterations (5,120 images).
-Testing: 133 secs / validation set (50,000 images)
+Testing: 133 secs / validation set (50,000 images).
 
 ## NVIDIA GTX 770
 
 Training: 33.0 secs / 20 iterations (5,120 images).
-Testing: 129 secs / validation set (50,000 images)
+Testing: 129 secs / validation set (50,000 images).
+
+cuDNN Training: 24.3 secs / 20 iterations (5,120 images).
+cuDNN Testing: 104 secs / validation set (50,000 images).

From 396da71569124a2362efe034764fffd679f51f00 Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Sun, 7 Sep 2014 21:46:38 -0500
Subject: [PATCH 0732/2053] Repair crash in conv_layer due to weight pointer
 being NULL.

---
 src/caffe/layers/conv_layer.cpp | 3 +++
 src/caffe/layers/conv_layer.cu  | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 1a1248f3dda..be7a57402bc 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -203,6 +203,9 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         }
         // gradient w.r.t. bottom data, if necessary
         if (propagate_down[i]) {
+          if(weight==NULL) {
+            weight = this->blobs_[0]->cpu_data();
+          }
           for (int g = 0; g < group_; ++g) {
             caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
                 (Dtype)1., weight + weight_offset * g,
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index f7f393badba..02cfdb3b80c 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -96,6 +96,9 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         }
         // gradient w.r.t. bottom data, if necessary
         if (propagate_down[i]) {
+          if (weight == NULL) {
+            weight = this->blobs_[0]->gpu_data();
+          }
           for (int g = 0; g < group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
                 (Dtype)1., weight + weight_offset * g,

From a739cdadbbca18e5d7c4f97b144e9cda8503037a Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Sun, 7 Sep 2014 23:10:33 -0500
Subject: [PATCH 0733/2053] Fix more lint.

---
 src/caffe/layers/conv_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index be7a57402bc..1d3686b936a 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -203,7 +203,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         }
         // gradient w.r.t. bottom data, if necessary
         if (propagate_down[i]) {
-          if(weight==NULL) {
+          if (weight == NULL) {
             weight = this->blobs_[0]->cpu_data();
           }
           for (int g = 0; g < group_; ++g) {

From 68e26576d864406762c3ebf361c4227767cefbd8 Mon Sep 17 00:00:00 2001
From: ZhiHeng NIU <niuzhiheng@gmail.com>
Date: Mon, 8 Sep 2014 14:46:43 +0800
Subject: [PATCH 0734/2053] Fixed CMake script of FindOpenBLAS.

---
 CMakeScripts/FindOpenBLAS.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeScripts/FindOpenBLAS.cmake b/CMakeScripts/FindOpenBLAS.cmake
index 297a7b6bc43..b8434927a4d 100644
--- a/CMakeScripts/FindOpenBLAS.cmake
+++ b/CMakeScripts/FindOpenBLAS.cmake
@@ -26,7 +26,7 @@ SET(Open_BLAS_LIB_SEARCH_PATHS
         $ENV{OpenBLAS_HOME}/lib
  )
 
-FIND_PATH(OpenBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${ATLAS_POSSIBLE_INCLUDE_PATHS})
+FIND_PATH(OpenBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Open_BLAS_INCLUDE_SEARCH_PATHS})
 FIND_LIBRARY(OpenBLAS_LIB NAMES openblas PATHS ${Open_BLAS_LIB_SEARCH_PATHS})
 
 SET(OpenBLAS_FOUND ON)

From 99c4ed5bb7fbd961a19f564611166c4b702ca717 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 8 Sep 2014 10:03:55 +0200
Subject: [PATCH 0735/2053] [lint] cuDNN conv declaration

---
 include/caffe/vision_layers.hpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 4dd2e9df99a..8c2db8824ed 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -69,8 +69,7 @@ class ConvolutionLayer : public Layer<Dtype> {
  *        Fallback to ConvolutionLayer for CPU mode.
 */
 template <typename Dtype>
-class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype>
-{
+class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
  public:
   explicit CuDNNConvolutionLayer(const LayerParameter& param)
       : ConvolutionLayer<Dtype>(param) {}

From 2d881032159c0ce66f88dc28942f9cf19d037fb3 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 8 Sep 2014 10:42:55 +0200
Subject: [PATCH 0736/2053] linecount counts more dirs than just src/

---
 Makefile | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 546de931f7e..436a300bede 100644
--- a/Makefile
+++ b/Makefile
@@ -330,7 +330,9 @@ all: $(NAME) $(STATIC_NAME) tools examples
 everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
 
 linecount:
-	cloc --read-lang-def=$(PROJECT).cloc src/$(PROJECT)/
+	cloc --read-lang-def=$(PROJECT).cloc \
+		src/$(PROJECT) include/$(PROJECT) tools examples \
+		python matlab
 
 lint: $(EMPTY_LINT_REPORT)
 

From 63bad310c1c2c11e29326cf87b635c1600149a0a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 8 Sep 2014 11:02:31 +0200
Subject: [PATCH 0737/2053] Revert "call __signbit for CUDA >= 6.5
 implementation" -- doesn't compile on OSX w/ CUDA 6.5

This reverts commit 8819f5953b903ec8b48e541271737e89a2cd24e6.
---
 src/caffe/util/math_functions.cu | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 176da93161c..4ae4bba6029 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -329,12 +329,7 @@ void caffe_gpu_powx<double>(const int N, const double* a,
 
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
-#if CUDA_VERSION >= 6050
-// __signbit to pick up the CUDA function.
-DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = __signbit(x[index]));
-#else
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
-#endif
 
 __global__ void popc_kernel(const int n, const float* a,
     const float* b, uint8_t* y) {

From 64c8dcbd1dc6b5a66033a8f20e5497bdc6ff99e6 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 8 Sep 2014 12:44:21 +0200
Subject: [PATCH 0738/2053] [docs] replace intro slides with caffe tutorial

---
 docs/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 67b424ceefd..360f61a978f 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -33,8 +33,8 @@ Consult performance [details](/performance_hardware.html).
 
 ## Documentation
 
-- [Introductory slides](http://dl.caffe.berkeleyvision.org/caffe-presentation.pdf)<br />
-Slides about the Caffe architecture, *updated 03/14*.
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)<br />
+Caffe tutorial slides.
 - [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br />
 A 4-page report for the ACM Multimedia Open Source competition.
 - [Installation instructions](/installation.html)<br />

From 761c81505e86ef9e263820d38448cac5bf81f838 Mon Sep 17 00:00:00 2001
From: to3i <tobias.dencker@gmail.com>
Date: Fri, 11 Jul 2014 13:19:23 +0200
Subject: [PATCH 0739/2053] Implemented elementwise max layer

---
 include/caffe/common_layers.hpp       |  1 +
 src/caffe/layers/eltwise_layer.cpp    | 50 ++++++++++++++++++++
 src/caffe/layers/eltwise_layer.cu     | 67 ++++++++++++++++++++++++---
 src/caffe/proto/caffe.proto           |  1 +
 src/caffe/test/test_eltwise_layer.cpp | 32 +++++++++++++
 5 files changed, 145 insertions(+), 6 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index c170742308c..190b5c24b05 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -178,6 +178,7 @@ class EltwiseLayer : public Layer<Dtype> {
 
   EltwiseParameter_EltwiseOp op_;
   vector<Dtype> coeffs_;
+  shared_ptr<Blob<int> > max_idx_;
 
   bool stable_prod_grad_;
 };
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 56a1920e32f..b25611aa6c3 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -1,4 +1,5 @@
 #include <vector>
+#include <cfloat>
 
 #include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -36,11 +37,20 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     }
   }
   stable_prod_grad_ = this->layer_param_.eltwise_param().stable_prod_grad();
+  // If max operation, we will initialize the vector index part.
+  if (this->layer_param_.eltwise_param().operation() ==
+      EltwiseParameter_EltwiseOp_MAX && top->size() == 1) {
+    max_idx_.reset(new Blob<int>(bottom[0]->num(), channels,
+                                 height, width));
+  }
 }
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  int* mask = NULL;
+  const Dtype* bottom_data_a = NULL;
+  const Dtype* bottom_data_b = NULL;
   const int count = (*top)[0]->count();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   switch (op_) {
@@ -57,6 +67,35 @@ void EltwiseLayer<Dtype>::Forward_cpu(
       caffe_axpy(count, coeffs_[i], bottom[i]->cpu_data(), top_data);
     }
     break;
+  case EltwiseParameter_EltwiseOp_MAX:
+    // Initialize
+    mask = max_idx_->mutable_cpu_data();
+    caffe_set(count, -1, mask);
+    caffe_set(count, Dtype(-FLT_MAX), top_data);
+    // bottom 0 & 1
+    bottom_data_a = bottom[0]->cpu_data();
+    bottom_data_b = bottom[1]->cpu_data();
+    for (int idx = 0; idx < count; ++idx) {
+      if (bottom_data_a[idx] > bottom_data_b[idx]) {
+        top_data[idx] = bottom_data_a[idx]; // maxval
+        mask[idx] = 0; // maxid
+      } else {
+        top_data[idx] = bottom_data_b[idx]; // maxval
+        mask[idx] = 1; // maxid
+      }
+    }
+    // bottom 2++
+    bottom_data_a = top_data;
+    for (int blob_idx = 2; blob_idx < bottom.size(); ++blob_idx) {
+      bottom_data_b = bottom[blob_idx]->cpu_data();
+      for (int idx = 0; idx < count; ++idx) {
+        if (bottom_data_a[idx] < bottom_data_b[idx]) {
+          top_data[idx] = bottom_data_b[idx];  // maxval
+          mask[idx] = blob_idx;  // maxid
+        }
+      }
+    }
+    break;
   default:
     LOG(FATAL) << "Unknown elementwise operation.";
   }
@@ -65,6 +104,7 @@ void EltwiseLayer<Dtype>::Forward_cpu(
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int* mask = NULL;
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->cpu_data();
   const Dtype* top_diff = top[0]->cpu_diff();
@@ -98,6 +138,16 @@ void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           caffe_cpu_scale(count, coeffs_[i], top_diff, bottom_diff);
         }
         break;
+      case EltwiseParameter_EltwiseOp_MAX:
+        mask = max_idx_->cpu_data();
+        for (int index = 0; index < count; ++index) {
+          Dtype gradient = 0;
+          if (mask[index] == i) {
+            gradient += top_diff[index];
+          }
+          bottom_diff[index] = gradient;
+        }
+        break;
       default:
         LOG(FATAL) << "Unknown elementwise operation.";
       }
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index e005cb91122..78f911ba263 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -1,4 +1,5 @@
 #include <vector>
+#include <cfloat>
 
 #include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -7,14 +8,39 @@
 namespace caffe {
 
 template <typename Dtype>
-void EltwiseLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+__global__ void MaxForward(const int nthreads, const Dtype* bottom_data_a,
+    const Dtype* bottom_data_b, const int blob_idx, Dtype* top_data,
+    int* mask) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    Dtype maxval = -FLT_MAX;
+    int maxidx = -1;
+    if (bottom_data_a[index] > bottom_data_b[index]) {
+      // only update for very first bottom_data blob (blob_idx == 0)
+      if (blob_idx == 0) {
+        maxval = bottom_data_a[index];
+        top_data[index] = maxval;
+        maxidx = blob_idx;
+        mask[index] = maxidx;
+      }
+    } else {
+      maxval = bottom_data_b[index];
+      top_data[index] = maxval;
+      maxidx = blob_idx + 1;
+      mask[index] = maxidx;
+    }
+  }
+}
+
+template<typename Dtype>
+void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  int* mask = NULL;
   const int count = (*top)[0]->count();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   switch (op_) {
   case EltwiseParameter_EltwiseOp_PROD:
-    caffe_gpu_mul(count, bottom[0]->gpu_data(),
-        bottom[1]->gpu_data(), top_data);
+    caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(),
+        top_data);
     for (int i = 2; i < bottom.size(); ++i) {
       caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
     }
@@ -26,14 +52,38 @@ void EltwiseLayer<Dtype>::Forward_gpu(
       caffe_gpu_axpy(count, coeffs_[i], bottom[i]->gpu_data(), top_data);
     }
     break;
+  case EltwiseParameter_EltwiseOp_MAX:
+    mask = max_idx_->mutable_gpu_data();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    MaxForward<Dtype> <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), 0, top_data, mask);
+    for (int i = 2; i < bottom.size(); ++i) {
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      MaxForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+          count, top_data, bottom[i]->gpu_data(), i-1, top_data, mask);
+    }
+    break;
   default:
     LOG(FATAL) << "Unknown elementwise operation.";
   }
 }
 
-template <typename Dtype>
+template<typename Dtype>
+__global__ void MaxBackward(const int nthreads, const Dtype* top_diff,
+    const int blob_idx, const int* mask, Dtype* bottom_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    Dtype gradient = 0;
+    if (mask[index] == blob_idx) {
+      gradient += top_diff[index];
+    }
+    bottom_diff[index] = gradient;
+  }
+}
+
+template<typename Dtype>
 void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int* mask = NULL;
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
@@ -67,6 +117,12 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           caffe_gpu_scale(count, coeffs_[i], top_diff, bottom_diff);
         }
         break;
+      case EltwiseParameter_EltwiseOp_MAX:
+        mask = max_idx_->gpu_data();
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        MaxBackward<Dtype> <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+            count, top_diff, i, mask, bottom_diff);
+        break;
       default:
         LOG(FATAL) << "Unknown elementwise operation.";
       }
@@ -76,5 +132,4 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
 INSTANTIATE_CLASS(EltwiseLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8cb82cebe22..0e9ecf72728 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -450,6 +450,7 @@ message EltwiseParameter {
   enum EltwiseOp {
     PROD = 0;
     SUM = 1;
+    MAX = 2;
   }
   optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
   repeated float coeff = 2; // blob-wise coefficient for SUM operation
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index da5e3538131..3c617281504 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -173,4 +173,36 @@ TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
       &(this->blob_top_vec_));
 }
 
+TYPED_TEST(EltwiseLayerTest, TestMax) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_GE(data[i], in_data_a[i]);
+    EXPECT_GE(data[i], in_data_b[i]);
+    EXPECT_GE(data[i], in_data_c[i]);
+  }
+}
+
+TYPED_TEST(EltwiseLayerTest, TestMaxGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
 }  // namespace caffe

From 6bda40640bba5e3e4751ae5fa6161da7df941ebe Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 8 Sep 2014 17:49:42 +0200
Subject: [PATCH 0740/2053] lint & reduce gradient check stepsize to pass
 checks

---
 src/caffe/layers/eltwise_layer.cpp    | 13 ++++++-------
 src/caffe/layers/eltwise_layer.cu     | 12 ++++++------
 src/caffe/test/test_eltwise_layer.cpp |  9 +++++----
 3 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index b25611aa6c3..46034be4784 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -1,5 +1,5 @@
-#include <vector>
 #include <cfloat>
+#include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -77,19 +77,18 @@ void EltwiseLayer<Dtype>::Forward_cpu(
     bottom_data_b = bottom[1]->cpu_data();
     for (int idx = 0; idx < count; ++idx) {
       if (bottom_data_a[idx] > bottom_data_b[idx]) {
-        top_data[idx] = bottom_data_a[idx]; // maxval
-        mask[idx] = 0; // maxid
+        top_data[idx] = bottom_data_a[idx];  // maxval
+        mask[idx] = 0;  // maxid
       } else {
-        top_data[idx] = bottom_data_b[idx]; // maxval
-        mask[idx] = 1; // maxid
+        top_data[idx] = bottom_data_b[idx];  // maxval
+        mask[idx] = 1;  // maxid
       }
     }
     // bottom 2++
-    bottom_data_a = top_data;
     for (int blob_idx = 2; blob_idx < bottom.size(); ++blob_idx) {
       bottom_data_b = bottom[blob_idx]->cpu_data();
       for (int idx = 0; idx < count; ++idx) {
-        if (bottom_data_a[idx] < bottom_data_b[idx]) {
+        if (bottom_data_b[idx] > top_data[idx]) {
           top_data[idx] = bottom_data_b[idx];  // maxval
           mask[idx] = blob_idx;  // maxid
         }
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 78f911ba263..c0d47fd413b 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -1,5 +1,5 @@
-#include <vector>
 #include <cfloat>
+#include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -31,7 +31,7 @@ __global__ void MaxForward(const int nthreads, const Dtype* bottom_data_a,
   }
 }
 
-template<typename Dtype>
+template <typename Dtype>
 void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   int* mask = NULL;
@@ -68,7 +68,7 @@ void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
-template<typename Dtype>
+template <typename Dtype>
 __global__ void MaxBackward(const int nthreads, const Dtype* top_diff,
     const int blob_idx, const int* mask, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
@@ -80,7 +80,7 @@ __global__ void MaxBackward(const int nthreads, const Dtype* top_diff,
   }
 }
 
-template<typename Dtype>
+template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const int* mask = NULL;
@@ -119,8 +119,8 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         break;
       case EltwiseParameter_EltwiseOp_MAX:
         mask = max_idx_->gpu_data();
-        // NOLINT_NEXT_LINE(whitespace/operators)
-        MaxBackward<Dtype> <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        MaxBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+            <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
             count, top_diff, i, mask, bottom_diff);
         break;
       default:
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 3c617281504..d5cf08229ab 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -23,6 +24,7 @@ class EltwiseLayerTest : public MultiDeviceTest<TypeParam> {
         blob_bottom_c_(new Blob<Dtype>(2, 3, 4, 5)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
+    Caffe::set_random_seed(1701);
     FillerParameter filler_param;
     UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_a_);
@@ -188,9 +190,8 @@ TYPED_TEST(EltwiseLayerTest, TestMax) {
   const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
   const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], in_data_a[i]);
-    EXPECT_GE(data[i], in_data_b[i]);
-    EXPECT_GE(data[i], in_data_c[i]);
+    EXPECT_EQ(data[i],
+              std::max(in_data_a[i], std::max(in_data_b[i], in_data_c[i])));
   }
 }
 
@@ -200,7 +201,7 @@ TYPED_TEST(EltwiseLayerTest, TestMaxGradient) {
   EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
   EltwiseLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  GradientChecker<Dtype> checker(1e-4, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }

From d149c9a98d783eb64cefbbfeb02c6859dd81cfa6 Mon Sep 17 00:00:00 2001
From: Nick Carlevaris-Bianco <carlevar@umich.edu>
Date: Thu, 21 Aug 2014 09:32:10 -0400
Subject: [PATCH 0741/2053] Added contrastive loss layer, associated tests, and
 a siamese network example using shared weights and the contrastive loss.

---
 .../siamese/convert_mnist_siamese_data.cpp    | 123 +++++++
 examples/siamese/create_mnist_siamese.sh      |  21 ++
 examples/siamese/mnist_siamese.ipynb          | 169 ++++++++++
 examples/siamese/mnist_siamese.prototxt       |  95 ++++++
 .../siamese/mnist_siamese_solver.prototxt     |  25 ++
 .../siamese/mnist_siamese_train_test.prototxt | 313 ++++++++++++++++++
 examples/siamese/readme.md                    | 179 ++++++++++
 examples/siamese/train_mnist_siamese.sh       |   5 +
 include/caffe/loss_layers.hpp                 |  87 +++++
 src/caffe/layer_factory.cpp                   |   2 +
 src/caffe/layers/contrastive_loss_layer.cpp   | 101 ++++++
 src/caffe/layers/contrastive_loss_layer.cu    |  91 +++++
 src/caffe/proto/caffe.proto                   |  12 +-
 .../test/test_contrastive_loss_layer.cpp      | 102 ++++++
 14 files changed, 1323 insertions(+), 2 deletions(-)
 create mode 100644 examples/siamese/convert_mnist_siamese_data.cpp
 create mode 100755 examples/siamese/create_mnist_siamese.sh
 create mode 100644 examples/siamese/mnist_siamese.ipynb
 create mode 100644 examples/siamese/mnist_siamese.prototxt
 create mode 100644 examples/siamese/mnist_siamese_solver.prototxt
 create mode 100644 examples/siamese/mnist_siamese_train_test.prototxt
 create mode 100644 examples/siamese/readme.md
 create mode 100755 examples/siamese/train_mnist_siamese.sh
 create mode 100644 src/caffe/layers/contrastive_loss_layer.cpp
 create mode 100644 src/caffe/layers/contrastive_loss_layer.cu
 create mode 100644 src/caffe/test/test_contrastive_loss_layer.cpp

diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
new file mode 100644
index 00000000000..400d15a2705
--- /dev/null
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -0,0 +1,123 @@
+//
+// This script converts the MNIST dataset to the leveldb format used
+// by caffe to train siamese network.
+// Usage:
+//    convert_mnist_data input_image_file input_label_file output_db_file
+// The MNIST dataset could be downloaded at
+//    http://yann.lecun.com/exdb/mnist/
+#include <fstream>  // NOLINT(readability/streams)
+#include <string>
+
+#include "glog/logging.h"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "stdint.h"
+
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/math_functions.hpp"
+
+uint32_t swap_endian(uint32_t val) {
+    val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
+    return (val << 16) | (val >> 16);
+}
+
+void read_image(std::ifstream* image_file, std::ifstream* label_file,
+        uint32_t index, uint32_t rows, uint32_t cols,
+        char* pixels, char* label) {
+  image_file->seekg(index * rows * cols + 16);
+  image_file->read(pixels, rows * cols);
+  label_file->seekg(index + 8);
+  label_file->read(label, 1);
+}
+
+void convert_dataset(const char* image_filename, const char* label_filename,
+        const char* db_filename) {
+  // Open files
+  std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
+  std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
+  CHECK(image_file) << "Unable to open file " << image_filename;
+  CHECK(label_file) << "Unable to open file " << label_file;
+  // Read the magic and the meta data
+  uint32_t magic;
+  uint32_t num_items;
+  uint32_t num_labels;
+  uint32_t rows;
+  uint32_t cols;
+
+  image_file.read(reinterpret_cast<char*>(&magic), 4);
+  magic = swap_endian(magic);
+  CHECK_EQ(magic, 2051) << "Incorrect image file magic.";
+  label_file.read(reinterpret_cast<char*>(&magic), 4);
+  magic = swap_endian(magic);
+  CHECK_EQ(magic, 2049) << "Incorrect label file magic.";
+  image_file.read(reinterpret_cast<char*>(&num_items), 4);
+  num_items = swap_endian(num_items);
+  label_file.read(reinterpret_cast<char*>(&num_labels), 4);
+  num_labels = swap_endian(num_labels);
+  CHECK_EQ(num_items, num_labels);
+  image_file.read(reinterpret_cast<char*>(&rows), 4);
+  rows = swap_endian(rows);
+  image_file.read(reinterpret_cast<char*>(&cols), 4);
+  cols = swap_endian(cols);
+
+  // Open leveldb
+  leveldb::DB* db;
+  leveldb::Options options;
+  options.create_if_missing = true;
+  options.error_if_exists = true;
+  leveldb::Status status = leveldb::DB::Open(
+      options, db_filename, &db);
+  CHECK(status.ok()) << "Failed to open leveldb " << db_filename
+      << ". Is it already existing?";
+
+  char label_i;
+  char label_j;
+  char* pixels = new char[2 * rows * cols];
+  const int kMaxKeyLength = 10;
+  char key[kMaxKeyLength];
+  std::string value;
+
+  caffe::Datum datum;
+  datum.set_channels(2);  // one channel for each image in the pair
+  datum.set_height(rows);
+  datum.set_width(cols);
+  LOG(INFO) << "A total of " << num_items << " items.";
+  LOG(INFO) << "Rows: " << rows << " Cols: " << cols;
+  for (int itemid = 0; itemid < num_items; ++itemid) {
+    int i = caffe::caffe_rng_rand() % num_items;  // pick a random  pair
+    int j = caffe::caffe_rng_rand() % num_items;
+    read_image(&image_file, &label_file, i, rows, cols,
+        pixels, &label_i);
+    read_image(&image_file, &label_file, j, rows, cols,
+        pixels + (rows * cols), &label_j);
+    datum.set_data(pixels, 2*rows*cols);
+    if (label_i  == label_j) {
+      datum.set_label(1);
+    } else {
+      datum.set_label(0);
+    }
+    datum.SerializeToString(&value);
+    snprintf(key, kMaxKeyLength, "%08d", itemid);
+    db->Put(leveldb::WriteOptions(), std::string(key), value);
+  }
+
+  delete db;
+  delete pixels;
+}
+
+int main(int argc, char** argv) {
+  if (argc != 4) {
+    printf("This script converts the MNIST dataset to the leveldb format used\n"
+           "by caffe to train a siamese network.\n"
+           "Usage:\n"
+           "    convert_mnist_data input_image_file input_label_file "
+           "output_db_file\n"
+           "The MNIST dataset could be downloaded at\n"
+           "    http://yann.lecun.com/exdb/mnist/\n"
+           "You should gunzip them after downloading.\n");
+  } else {
+    google::InitGoogleLogging(argv[0]);
+    convert_dataset(argv[1], argv[2], argv[3]);
+  }
+  return 0;
+}
diff --git a/examples/siamese/create_mnist_siamese.sh b/examples/siamese/create_mnist_siamese.sh
new file mode 100755
index 00000000000..43ad6b184a7
--- /dev/null
+++ b/examples/siamese/create_mnist_siamese.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env sh
+# This script converts the mnist data into leveldb format.
+
+EXAMPLES=./build/examples/siamese
+DATA=./data/mnist
+
+echo "Creating leveldb..."
+
+rm -rf ./examples/siamese/mnist_siamese_train_leveldb
+rm -rf ./examples/siamese/mnist_siamese_test_leveldb
+
+$EXAMPLES/convert_mnist_siamese_data.bin \
+    $DATA/train-images-idx3-ubyte \
+    $DATA/train-labels-idx1-ubyte \
+    ./examples/siamese/mnist_siamese_train_leveldb
+$EXAMPLES/convert_mnist_siamese_data.bin \
+    $DATA/t10k-images-idx3-ubyte \
+    $DATA/t10k-labels-idx1-ubyte \
+    ./examples/siamese/mnist_siamese_test_leveldb
+
+echo "Done."
diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
new file mode 100644
index 00000000000..66776fe14bd
--- /dev/null
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -0,0 +1,169 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:ea70e3c13ccbae495d73f30e18b8e01e68ec8c7dc8bbb3635e6139b801c921a9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Setup and import caffe"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
+      "\n",
+      "# Make sure that caffe is on the python path:\n",
+      "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
+      "import caffe"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Load the trained net."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "MODEL_FILE = 'mnist_siamese.prototxt'\n",
+      "# decrease if you want to preview during training\n",
+      "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
+      "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE)\n",
+      "net.set_phase_test()\n",
+      "net.set_mode_cpu()\n",
+      "net.set_input_scale('data', 0.00390625)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Load some MNIST test data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
+      "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
+      "n = 10000\n",
+      "\n",
+      "with open(TEST_DATA_FILE, 'rb') as f:\n",
+      "    f.read(16) # skip the header\n",
+      "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
+      "\n",
+      "with open(TEST_LABEL_FILE, 'rb') as f:\n",
+      "    f.read(8) # skip the header\n",
+      "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Run the data through the net"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# reshape and preprocess\n",
+      "caffe_in = raw_data.reshape(n, 28, 28).transpose((1,2,0))\n",
+      "caffe_in = net.preprocess('data', caffe_in) \n",
+      "caffe_in = caffe_in.reshape((n,1,28,28))\n",
+      "# pass data through network\n",
+      "out = net.forward_all(data=caffe_in)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Plot the results"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "feat = out['feat']\n",
+      "f = plt.figure(figsize=(16,9))\n",
+      "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
+      "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
+      "for i in range(10):\n",
+      "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
+      "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
+      "plt.grid()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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vvyz37G3axPI/+f2yMpZP6nCI5+9yMRH34YfMI+rxsP9NTSzf1OlkOZbReDG5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ipWuXwuRdj48c8KYHSM3FeaVGpvihiJtGPGGSeNCPWS5jMap9zmuiJU6VJAmirJo9byPotErYfM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\nfjV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AM0Y4baXXS9+3v83yHTsYuGwtqVDj93POILJLy8u5fOUK8StXkvJWARaXlXFhfJxL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Ljd3mXj2ioIglActLjQW6PvL6GSG6VqZywDczr6rx1oJRYz2gW8ibJeahlw64CWhD6dwF8D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\nAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f31d5144c50>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
new file mode 100644
index 00000000000..8dd42e9c1b5
--- /dev/null
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -0,0 +1,95 @@
+name: "mnist_siamese"
+input: "data"
+input_dim: 10000
+input_dim: 1
+input_dim: 28
+input_dim: 28
+
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+  }
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "pool1"
+  top: "conv2"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 50
+    kernel_size: 5
+    stride: 1
+  }
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "ip1"
+  type: INNER_PRODUCT
+  bottom: "pool2"
+  top: "ip1"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 500
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "ip1"
+  top: "ip1"
+}
+layers {
+  name: "ip2"
+  type: INNER_PRODUCT
+  bottom: "ip1"
+  top: "ip2"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 10
+  }
+}
+
+layers {
+  name: "feat"
+  type: INNER_PRODUCT
+  bottom: "ip2"
+  top: "feat"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 2
+  }
+}
diff --git a/examples/siamese/mnist_siamese_solver.prototxt b/examples/siamese/mnist_siamese_solver.prototxt
new file mode 100644
index 00000000000..d4d994d1389
--- /dev/null
+++ b/examples/siamese/mnist_siamese_solver.prototxt
@@ -0,0 +1,25 @@
+# The train/test net protocol buffer definition
+net: "examples/siamese/mnist_siamese_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0000
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 50000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/siamese/mnist_siamese"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/siamese/mnist_siamese_train_test.prototxt b/examples/siamese/mnist_siamese_train_test.prototxt
new file mode 100644
index 00000000000..92361c31dc7
--- /dev/null
+++ b/examples/siamese/mnist_siamese_train_test.prototxt
@@ -0,0 +1,313 @@
+name: "mnist_siamese_train_test"
+layers {
+  name: "pair_data"
+  type: DATA
+  top: "pair_data"
+  top: "sim"
+  data_param {
+    source: "examples/siamese/mnist_siamese_train_leveldb"
+    scale: 0.00390625
+    batch_size: 64
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "pair_data"
+  type: DATA
+  top: "pair_data"
+  top: "sim"
+  data_param {
+    source: "examples/siamese/mnist_siamese_test_leveldb"
+    scale: 0.00390625
+    batch_size: 100
+  }
+  include: { phase: TEST }
+}
+layers {
+    name: "slice_pair"
+    type: SLICE
+    bottom: "pair_data"
+    top: "data"
+    top: "data_p"
+    slice_param {
+        slice_dim: 1
+        slice_point: 1
+    }
+}
+
+
+
+
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "conv1_w"
+  param: "conv1_b"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "pool1"
+  top: "conv2"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 50
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "conv2_w"
+  param: "conv2_b"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "ip1"
+  type: INNER_PRODUCT
+  bottom: "pool2"
+  top: "ip1"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "ip1_w"
+  param: "ip1_b"
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "ip1"
+  top: "ip1"
+}
+layers {
+  name: "ip2"
+  type: INNER_PRODUCT
+  bottom: "ip1"
+  top: "ip2"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "ip2_w"
+  param: "ip2_b"
+}
+
+layers {
+  name: "feat"
+  type: INNER_PRODUCT
+  bottom: "ip2"
+  top: "feat"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "feat_w"
+  param: "feat_b"
+}
+
+
+
+layers {
+  name: "conv1_p"
+  type: CONVOLUTION
+  bottom: "data_p"
+  top: "conv1_p"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "conv1_w"
+  param: "conv1_b"
+}
+layers {
+  name: "pool1_p"
+  type: POOLING
+  bottom: "conv1_p"
+  top: "pool1_p"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "conv2_p"
+  type: CONVOLUTION
+  bottom: "pool1_p"
+  top: "conv2_p"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 50
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "conv2_w"
+  param: "conv2_b"
+}
+layers {
+  name: "pool2_p"
+  type: POOLING
+  bottom: "conv2_p"
+  top: "pool2_p"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "ip1_p"
+  type: INNER_PRODUCT
+  bottom: "pool2_p"
+  top: "ip1_p"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "ip1_w"
+  param: "ip1_b"
+}
+layers {
+  name: "relu1_p"
+  type: RELU
+  bottom: "ip1_p"
+  top: "ip1_p"
+}
+layers {
+  name: "ip2_p"
+  type: INNER_PRODUCT
+  bottom: "ip1_p"
+  top: "ip2_p"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "ip2_w"
+  param: "ip2_b"
+}
+
+layers {
+  name: "feat_p"
+  type: INNER_PRODUCT
+  bottom: "ip2_p"
+  top: "feat_p"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param: "feat_w"
+  param: "feat_b"
+}
+
+
+
+layers {
+    name: "loss"
+    type: CONTRASTIVE_LOSS
+    contrastive_loss_param {
+        margin: 1.0
+    }
+    bottom: "feat"
+    bottom: "feat_p"
+    bottom: "sim"
+    top: "loss"
+}
diff --git a/examples/siamese/readme.md b/examples/siamese/readme.md
new file mode 100644
index 00000000000..ce98ec10819
--- /dev/null
+++ b/examples/siamese/readme.md
@@ -0,0 +1,179 @@
+---
+title: Siamese Network Tutorial
+description: Train and test a siamese network on MNIST data.
+category: example
+include_in_docs: true
+layout: default
+priority: 100
+---
+
+# Siamese Network Training with Caffe
+This example shows how you can use weight sharing and a contrastive loss
+function to learn a model using a siamese network in Caffe.
+
+We will assume that you have caffe successfully compiled. If not, please refer
+to the [Installation page](../../installation.html). This example builds on the
+[MNIST tutorial](mnist.html) so it would be a good idea to read that before
+continuing.
+
+*The guide specifies all paths and assumes all commands are executed from the
+root caffe directory*
+
+## Prepare Datasets
+
+You will first need to download and convert the data from the MNIST
+website. To do this, simply run the following commands:
+
+    ./data/mnist/get_mnist.sh
+    ./examples/siamese/create_mnist_siamese.sh
+
+After running the script there should be two datasets,
+`./examples/siamese/mnist_siamese_train_leveldb`, and
+`./examples/siamese/mnist_siamese_test_leveldb`.
+
+## The Model
+First, we will define the model that we want to train using the siamese network.
+We will use the convolutional net defined in
+`./examples/siamese/mnist_siamese.prototxt`. This model is almost
+exactly the same as the [LeNet model](mnist.html), the only difference is that
+we have replaced the top layers that produced probabilities over the 10 digit
+classes with a linear "feature" layer that produces a 2 dimensional vector.
+
+    layers {
+      name: "feat"
+      type: INNER_PRODUCT
+      bottom: "ip2"
+      top: "feat"
+      blobs_lr: 1
+      blobs_lr: 2
+      inner_product_param {
+        num_output: 2
+      }
+    }
+
+## Define the Siamese Network
+
+In this section we will define the siamese network used for training. The
+resulting network is defined in
+`./examples/siamese/mnist_siamese_train_test.prototxt`.
+
+### Reading in the Pair Data
+
+We start with a data layer that reads from the LevelDB database we created
+earlier. Each entry in this database contains the image data for a pair of
+images (`pair_data`) and a binary label saying if they belong to the same class
+or different classes (`sim`).
+
+    layers {
+      name: "pair_data"
+      type: DATA
+      top: "pair_data"
+      top: "sim"
+      data_param {
+        source: "examples/siamese/mnist-siamese-train-leveldb"
+        scale: 0.00390625
+        batch_size: 64
+      }
+      include: { phase: TRAIN }
+    }
+
+In order to pack a pair of images into the same blob in the database we pack one
+image per channel. We want to be able to work with these two images separately,
+so we add a slice layer after the data layer. This takes the `pair_data` and
+slices it along the channel dimension so that we have a single image in `data`
+and its paired image in `data_p.`
+
+    layers {
+        name: "slice_pair"
+        type: SLICE
+        bottom: "pair_data"
+        top: "data"
+        top: "data_p"
+        slice_param {
+            slice_dim: 1
+            slice_point: 1
+        }
+    }
+
+### Building the First Side of the Siamese Net
+
+Now we can specify the first side of the siamese net. This side operates on
+`data` and produces `feat`. Starting from the net in
+`./examples/siamese/mnist_siamese.prototxt` we add default weight fillers. Then
+we name the parameters of the convolutional and inner product layers. Naming the
+parameters allows Caffe to share the parameters between layers on both sides of
+the siamese net. In the definition this looks like:
+
+    ...
+    param: "conv1_w"
+    param: "conv1_b"
+    ...
+    param: "conv2_w"
+    param: "conv2_b"
+    ...
+    param: "ip1_w"
+    param: "ip1_b"
+    ...
+    param: "ip2_w"
+    param: "ip2_b"
+    ...
+
+### Building the Second Side of the Siamese Net
+
+Now we need to create the second path that operates on `data_p` and produces
+`feat_p`. This path is exactly the same as the first. So we can just copy and
+paste it. Then we change the name of each layer, input, and output by appending
+`_p` to differentiate the "paired" layers from the originals.
+
+### Adding the Contrastive Loss Function
+
+To train the network we will optimize a contrastive loss function proposed in:
+Raia Hadsell, Sumit Chopra, and Yann LeCun "Dimensionality Reduction by Learning
+an Invariant Mapping". This loss function encourages matching pairs to be close
+together in feature space while pushing non-matching pairs apart. This cost
+function is implemented with the `CONTRASTIVE_LOSS` layer:
+
+    layers {
+        name: "loss"
+        type: CONTRASTIVE_LOSS
+        contrastive_loss_param {
+            margin: 1.0
+        }
+        bottom: "feat"
+        bottom: "feat_p"
+        bottom: "sim"
+        top: "loss"
+    }
+
+## Define the Solver
+
+Nothing special needs to be done to the solver besides pointing it at the
+correct model file. The solver is defined in
+`./examples/siamese/mnist_siamese_solver.prototxt`.
+
+## Training and Testing the Model
+
+Training the model is simple after you have written the network definition
+protobuf and solver protobuf files. Simply run
+`./examples/siamese/train_mnist_siamese.sh`:
+
+    ./examples/siamese/train_mnist_siamese.sh
+
+# Plotting the results
+
+First, we can draw the model and siamese networks by running the following
+commands that draw the DAGs defined in the .prototxt files:
+
+    ./python/draw_net.py \
+        ./examples/siamese/mnist_siamese.prototxt \
+        ./examples/siamese/mnist_siamese.png
+
+    ./python/draw_net.py \
+        ./examples/siamese/mnist_siamese_train_test.prototxt \
+        ./examples/siamese/mnist_siamese_train_test.png
+
+Second, we can load the learned model and plot the features using the iPython
+notebook:
+
+    ipython notebook ./examples/siamese/mnist_siamese.ipynb
+
diff --git a/examples/siamese/train_mnist_siamese.sh b/examples/siamese/train_mnist_siamese.sh
new file mode 100755
index 00000000000..84a30a8ac44
--- /dev/null
+++ b/examples/siamese/train_mnist_siamese.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index a29c445d51e..c911d1a7312 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -117,6 +117,93 @@ class LossLayer : public Layer<Dtype> {
   }
 };
 
+/**
+ * @brief Computes the contrastive loss @f$
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
+ *              \left(1-y\right) \max \left(margin-d, 0\right)
+ *          @f$ where @f$
+ *          d = \left| \left| a_n - b_n \right| \right|_2^2 @f$. This can be
+ *          used to train siamese networks.
+ *
+ * @param bottom input Blob vector (length 3)
+ *   -# @f$ (N \times C \times 1 \times 1) @f$
+ *      the features @f$ a \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times C \times 1 \times 1) @f$
+ *      the features @f$ b \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the binary similarity @f$ s \in [0, 1]@f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed contrastive loss: @f$ E =
+ *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
+ *          \left(1-y\right) \max \left(margin-d, 0\right)
+ *          @f$ where @f$
+ *          d = \left| \left| a_n - b_n \right| \right|_2^2 @f$.
+ * This can be used to train siamese networks.
+ */
+template <typename Dtype>
+class ContrastiveLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit ContrastiveLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 3; }
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONTRASTIVE_LOSS;
+  }
+  /**
+   * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
+   * to the first two inputs.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 2;
+  }
+
+ protected:
+  /// @copydoc ContrastiveLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  /**
+   * @brief Computes the Contrastive error gradient w.r.t. the inputs.
+   * 
+   * Computes the gradients with respect to the two input vectors (bottom[0] and 
+   * bottom[1]), but not the similarity label (bottom[2]).
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times 1 \times 1) @f$
+   *      the features @f$a@f$; Backward fills their diff with
+   *      gradients if propagate_down[0]
+   *   -# @f$ (N \times C \times 1 \times 1) @f$
+   *      the features @f$b@f$; Backward fills their diff with gradients if 
+   *      propagate_down[1]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> diff_;  // cached for backward pass
+  Blob<Dtype> dist_sq_;  // cached for backward pass
+  Blob<Dtype> diff_sq_;  // tmp storage for gpu forward pass
+  Blob<Dtype> summer_vec_;  // tmp storage for gpu forward pass
+};
+
 /**
  * @brief Computes the Euclidean (L2) loss @f$
  *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 41c547b8ad4..b78167f21eb 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -189,6 +189,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new BNLLLayer<Dtype>(param);
   case LayerParameter_LayerType_CONCAT:
     return new ConcatLayer<Dtype>(param);
+  case LayerParameter_LayerType_CONTRASTIVE_LOSS:
+    return new ContrastiveLossLayer<Dtype>(param);
   case LayerParameter_LayerType_CONVOLUTION:
     return GetConvolutionLayer<Dtype>(name, param);
   case LayerParameter_LayerType_DATA:
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
new file mode 100644
index 00000000000..072a5a535be
--- /dev/null
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -0,0 +1,101 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/loss_layers.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ContrastiveLossLayer<Dtype>::LayerSetUp(
+  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::LayerSetUp(bottom, top);
+  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
+  CHECK_EQ(bottom[0]->height(), 1);
+  CHECK_EQ(bottom[0]->width(), 1);
+  CHECK_EQ(bottom[1]->height(), 1);
+  CHECK_EQ(bottom[1]->width(), 1);
+  CHECK_EQ(bottom[2]->channels(), 1);
+  CHECK_EQ(bottom[2]->height(), 1);
+  CHECK_EQ(bottom[2]->width(), 1);
+  diff_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1);
+  diff_sq_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1);
+  dist_sq_.Reshape(bottom[0]->num(), 1, 1, 1);
+  // vector of ones used to sum along channels
+  summer_vec_.Reshape(bottom[0]->channels(), 1, 1, 1);
+  for (int i = 0; i < bottom[0]->channels(); ++i)
+    summer_vec_.mutable_cpu_data()[i] = Dtype(1);
+}
+
+template <typename Dtype>
+void ContrastiveLossLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  int count = bottom[0]->count();
+  caffe_sub(
+      count,
+      bottom[0]->cpu_data(),  // a
+      bottom[1]->cpu_data(),  // b
+      diff_.mutable_cpu_data());  // a_i-b_i
+  const int channels = bottom[0]->channels();
+  Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  Dtype loss(0.0);
+  for (int i = 0; i < bottom[0]->num(); ++i) {
+    dist_sq_.mutable_cpu_data()[i] = caffe_cpu_dot(channels,
+        diff_.cpu_data() + (i*channels), diff_.cpu_data() + (i*channels));
+    if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
+      loss += dist_sq_.cpu_data()[i];
+    } else {  // dissimilar pairs
+      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+    }
+  }
+  loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
+  (*top)[0]->mutable_cpu_data()[0] = loss;
+}
+
+template <typename Dtype>
+void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const Dtype sign = (i == 0) ? 1 : -1;
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] /
+          static_cast<Dtype>((*bottom)[i]->num());
+      int num = (*bottom)[i]->num();
+      int channels = (*bottom)[i]->channels();
+      for (int j = 0; j < num; ++j) {
+        Dtype* bout = (*bottom)[i]->mutable_cpu_diff();
+        if (static_cast<int>((*bottom)[2]->cpu_data()[j])) {  // similar pairs
+          caffe_cpu_axpby(
+              channels,
+              alpha,
+              diff_.cpu_data() + (j*channels),
+              Dtype(0.0),
+              bout + (j*channels));
+        } else {  // dissimilar pairs
+          if ((margin-dist_sq_.cpu_data()[j]) > Dtype(0.0)) {
+            caffe_cpu_axpby(
+                channels,
+                -alpha,
+                diff_.cpu_data() + (j*channels),
+                Dtype(0.0),
+                bout + (j*channels));
+          } else {
+            caffe_set(channels, Dtype(0), bout + (j*channels));
+          }
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ContrastiveLossLayer);
+#endif
+
+INSTANTIATE_CLASS(ContrastiveLossLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
new file mode 100644
index 00000000000..672ad5bc2f8
--- /dev/null
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -0,0 +1,91 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ContrastiveLossLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const int count = bottom[0]->count();
+  caffe_gpu_sub(
+      count,
+      bottom[0]->gpu_data(),  // a
+      bottom[1]->gpu_data(),  // b
+      diff_.mutable_gpu_data());  // a_i-b_i
+  caffe_gpu_powx(
+      count,
+      diff_.mutable_gpu_data(),  // a_i-b_i
+      Dtype(2),
+      diff_sq_.mutable_gpu_data());  // (a_i-b_i)^2
+  caffe_gpu_gemv(
+      CblasNoTrans,
+      bottom[0]->num(),
+      bottom[0]->channels(),
+      Dtype(1.0),
+      diff_sq_.gpu_data(),  // (a_i-b_i)^2
+      summer_vec_.gpu_data(),
+      Dtype(0.0),
+      dist_sq_.mutable_gpu_data());  // \Sum (a_i-b_i)^2
+  Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  Dtype loss(0.0);
+  for (int i = 0; i < bottom[0]->num(); ++i) {
+    if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
+      loss += dist_sq_.cpu_data()[i];
+    } else {  // dissimilar pairs
+      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+    }
+  }
+  loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
+  (*top)[0]->mutable_cpu_data()[0] = loss;
+}
+
+template <typename Dtype>
+__global__ void CLLForward(const int count, const int channels,
+    const Dtype margin, const Dtype alpha,
+    const Dtype* y, const Dtype* diff, const Dtype* dist_sq,
+    Dtype *bottom_diff) {
+  CUDA_KERNEL_LOOP(i, count) {
+    int n = i / channels;  // the num index, to access y and dist_sq
+    if (static_cast<int>(y[n])) {  // similar pairs
+      bottom_diff[i] = alpha * diff[i];
+    } else {  // dissimilar pairs
+      if ((margin-dist_sq[n]) > 0.0) {
+        bottom_diff[i] = -alpha * diff[i];
+      } else {
+        bottom_diff[i] = 0;
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const int count = (*bottom)[0]->count();
+      const int channels = (*bottom)[0]->channels();
+      Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+      const Dtype sign = (i == 0) ? 1 : -1;
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] /
+          static_cast<Dtype>((*bottom)[0]->num());
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      CLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+          count, channels, margin, alpha,
+          (*bottom)[2]->gpu_data(),  // pair similarity 0 or 1
+          diff_.gpu_data(),  // the cached eltwise difference between a and b
+          dist_sq_.gpu_data(),  // the cached square distance between a and b
+          (*bottom)[i]->mutable_gpu_diff());
+      CUDA_POST_KERNEL_CHECK;
+    }
+  }
+}
+
+INSTANTIATE_CLASS(ContrastiveLossLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8cb82cebe22..37972e72b47 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -198,7 +198,7 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 40 (last added: softmax_param)
+// LayerParameter next available ID: 41 (last added: contrastive_loss_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -219,7 +219,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 37 (last added: SILENCE)
+  // LayerType next available ID: 38 (last added: CONTRASTIVE_LOSS)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -230,6 +230,7 @@ message LayerParameter {
     ARGMAX = 30;
     BNLL = 2;
     CONCAT = 3;
+    CONTRASTIVE_LOSS = 37;
     CONVOLUTION = 4;
     DATA = 5;
     DROPOUT = 6;
@@ -292,6 +293,7 @@ message LayerParameter {
   optional AccuracyParameter accuracy_param = 27;
   optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
+  optional ContrastiveLossParameter contrastive_loss_param = 40;
   optional ConvolutionParameter convolution_param = 10;
   optional DataParameter data_param = 11;
   optional DropoutParameter dropout_param = 12;
@@ -367,6 +369,12 @@ message ConcatParameter {
   optional uint32 concat_dim = 1 [default = 1];
 }
 
+// Message that stores parameters used by ContrastiveLossLayer
+message ContrastiveLossParameter {
+  //margin for dissimilar pair
+  optional float margin = 1 [default = 1.0];
+}
+
 // Message that stores parameters used by ConvolutionLayer
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
new file mode 100644
index 00000000000..a5bef4c9826
--- /dev/null
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -0,0 +1,102 @@
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ContrastiveLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ContrastiveLossLayerTest()
+      : blob_bottom_data_i_(new Blob<Dtype>(128, 10, 1, 1)),
+        blob_bottom_data_j_(new Blob<Dtype>(128, 10, 1, 1)),
+        blob_bottom_y_(new Blob<Dtype>(128, 1, 1, 1)),
+        blob_top_loss_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_mean(0.0);
+    filler_param.set_std(0.3);  // distances~=1.0 to test both sides of margin
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_data_i_);
+    blob_bottom_vec_.push_back(blob_bottom_data_i_);
+    filler.Fill(this->blob_bottom_data_j_);
+    blob_bottom_vec_.push_back(blob_bottom_data_j_);
+    for (int i = 0; i < blob_bottom_y_->count(); ++i) {
+      blob_bottom_y_->mutable_cpu_data()[i] = caffe_rng_rand() % 2;  // 0 or 1
+    }
+    blob_bottom_vec_.push_back(blob_bottom_y_);
+    blob_top_vec_.push_back(blob_top_loss_);
+  }
+  virtual ~ContrastiveLossLayerTest() {
+    delete blob_bottom_data_i_;
+    delete blob_bottom_data_j_;
+    delete blob_bottom_y_;
+    delete blob_top_loss_;
+  }
+
+  Blob<Dtype>* const blob_bottom_data_i_;
+  Blob<Dtype>* const blob_bottom_data_j_;
+  Blob<Dtype>* const blob_bottom_y_;
+  Blob<Dtype>* const blob_top_loss_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ContrastiveLossLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  // manually compute to compare
+  const Dtype margin = layer_param.contrastive_loss_param().margin();
+  const int num = this->blob_bottom_data_i_->num();
+  const int channels = this->blob_bottom_data_i_->channels();
+  Dtype loss(0);
+  for (int i = 0; i < num; ++i) {
+    Dtype dist_sq(0);
+    for (int j = 0; j < channels; ++j) {
+      Dtype diff = this->blob_bottom_data_i_->cpu_data()[i*channels+j] -
+          this->blob_bottom_data_j_->cpu_data()[i*channels+j];
+      dist_sq += diff*diff;
+    }
+    if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
+      loss += dist_sq;
+    } else {
+      loss += std::max(margin-dist_sq, Dtype(0));
+    }
+  }
+  loss /= static_cast<Dtype>(num) * Dtype(2);
+  EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6);
+}
+
+TYPED_TEST(ContrastiveLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  // check the gradient for the first two bottom layers
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 1);
+}
+
+}  // namespace caffe

From be9c5bd9327f862b2141535ca0ec36f7a28142f4 Mon Sep 17 00:00:00 2001
From: bhack <s.fabri@email.it>
Date: Thu, 11 Sep 2014 00:36:04 +0200
Subject: [PATCH 0742/2053] Fix lmbdb travis with openldap

---
 scripts/travis/travis_install.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 753bae879af..ef84e5dd9f4 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -67,12 +67,12 @@ if $WITH_CUDA; then
 fi
 
 # Install LMDB
-LMDB_URL=https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz
-LMDB_FILE=/tmp/mdb.tar.gz
+LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz
+LMDB_FILE=/tmp/openldap.tgz
 pushd .
 curl $LMDB_URL -o $LMDB_FILE && \
     tar -C /tmp -xzvf $LMDB_FILE && \
-    cd /tmp/mdb-mdb/libraries/liblmdb/ && \
+    cd /tmp/openldap*/libraries/liblmdb/ && \
     make && sudo make install || \
     (echo "LMDB install failed"; exit 1)
 popd

From 4ce6e43140819588d2e47c22ca13c05a8030f57d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 8 Sep 2014 11:11:41 +0200
Subject: [PATCH 0743/2053] restore "red X" build failures in Travis

---
 .travis.yml                                   |  9 +-
 scripts/travis/travis_build.sh                | 18 ----
 scripts/travis/travis_build_and_test.sh       | 35 +++++---
 scripts/travis/travis_install.sh              | 84 ++++++++++---------
 .../travis/travis_setup_makefile_config.sh    |  2 +
 5 files changed, 73 insertions(+), 75 deletions(-)
 delete mode 100755 scripts/travis/travis_build.sh

diff --git a/.travis.yml b/.travis.yml
index 46dfc117975..3deb45a2f0c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -16,19 +16,16 @@ compiler: gcc
 
 before_install:
   - export NUM_THREADS=4
-  - alias make="make --jobs=$NUM_THREADS"
   - export SCRIPTS=./scripts/travis
-  - set -e  # fail when a script fails
 
 install:
-  - source $SCRIPTS/travis_install.sh
+  - sudo -E $SCRIPTS/travis_install.sh
 
 before_script:
   - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
-  - if ! $WITH_CMAKE; then source $SCRIPTS/travis_setup_makefile_config.sh; fi
+  - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
 
-script:
-  - if $WITH_CUDA; then source $SCRIPTS/travis_build.sh; else source $SCRIPTS/travis_build_and_test.sh; fi
+script: $SCRIPTS/travis_build_and_test.sh
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
diff --git a/scripts/travis/travis_build.sh b/scripts/travis/travis_build.sh
deleted file mode 100755
index 06b4e24bb98..00000000000
--- a/scripts/travis/travis_build.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash
-# Script called by Travis to do a full build of Caffe,
-# including CUDA functionality.
-
-if $WITH_CMAKE; then
-  mkdir build
-  cd build
-  cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release ..
-  make --keep-going
-  cd -
-else
-  export CPU_ONLY=0
-  make --keep-going all test pycaffe warn
-  make all
-  make test
-  make pycaffe
-  make warn
-fi
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index 6b7c85e9d2d..dec4d097c17 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -1,22 +1,33 @@
 #!/bin/bash
 # Script called by Travis to do a CPU-only build of and test Caffe.
 
+set -e
+MAKE="make --jobs=$NUM_THREADS --keep-going"
+
 if $WITH_CMAKE; then
   mkdir build
   cd build
   cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
-  make --keep-going
-  make runtest
-  make lint
-  make clean
+  $MAKE
+  if ! $WITH_CUDA; then
+    $MAKE runtest
+    $MAKE lint
+  fi
+  $MAKE clean
   cd -
 else
-  export CPU_ONLY=1
-  make --keep-going all test pycaffe warn lint
-  make runtest
-  make all
-  make test
-  make pycaffe
-  make warn
-  make lint
+  if ! $WITH_CUDA; then
+    export CPU_ONLY=1
+  fi
+  $MAKE all test pycaffe warn lint || true
+  if ! $WITH_CUDA; then
+    $MAKE runtest
+  fi
+  $MAKE all
+  $MAKE test
+  $MAKE pycaffe
+  $MAKE warn
+  if ! $WITH_CUDA; then
+    $MAKE lint
+  fi
 fi
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index ef84e5dd9f4..15cafbe9b84 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -1,8 +1,13 @@
 #!/bin/bash
+# This script must be run with sudo.
+
+set -e
+
+MAKE="make --jobs=$NUM_THREADS"
 
 # Install apt packages where the Ubuntu 12.04 default works for Caffe
-sudo apt-get -y update
-sudo apt-get install \
+apt-get -y update
+apt-get install \
     wget git curl \
     python-dev python-numpy \
     libleveldb-dev libsnappy-dev libopencv-dev \
@@ -16,64 +21,65 @@ sudo apt-get install \
 # if needed.  By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
 # Caffe requires a minimum CMake version of 2.8.8.
 if $WITH_CMAKE; then
-    sudo add-apt-repository -y ppa:ubuntu-sdk-team/ppa
-    sudo apt-get -y update
-    sudo apt-get -y install cmake || (echo "CMake install failed"; exit 1)
+  add-apt-repository -y ppa:ubuntu-sdk-team/ppa
+  apt-get -y update
+  apt-get -y install cmake
 fi
 
 # Install glog
 GLOG_URL=https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
 GLOG_FILE=/tmp/glog-0.3.3.tar.gz
 pushd .
-wget $GLOG_URL -O $GLOG_FILE && \
-    tar -C /tmp -xzvf $GLOG_FILE && \
-    rm $GLOG_FILE && \
-    cd /tmp/glog-0.3.3 && \
-    ./configure && make && sudo make install || \
-    (echo "glog install failed"; exit 1)
+wget $GLOG_URL -O $GLOG_FILE
+tar -C /tmp -xzvf $GLOG_FILE
+rm $GLOG_FILE
+cd /tmp/glog-0.3.3
+./configure
+$MAKE
+$MAKE install
 popd
 
 # Install gflags
 GFLAGS_URL=https://github.com/schuhschuh/gflags/archive/master.zip
 GFLAGS_FILE=/tmp/gflags-master.zip
 pushd .
-wget $GFLAGS_URL -O $GFLAGS_FILE && \
-    cd /tmp/ && unzip gflags-master.zip && \
-    cd gflags-master && \
-    mkdir build && \
-    cd build && \
-    export CXXFLAGS="-fPIC" && \
-    cmake .. && make VERBOSE=1 && sudo make install || \
-    (echo "gflags install failed"; exit 1)
+wget $GFLAGS_URL -O $GFLAGS_FILE
+cd /tmp/
+unzip gflags-master.zip
+cd gflags-master
+mkdir build
+cd build
+export CXXFLAGS="-fPIC"
+cmake ..
+$MAKE VERBOSE=1
+$MAKE install
 popd
 
 # Install CUDA, if needed
-CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb
-CUDA_FILE=/tmp/cuda_install.deb
 if $WITH_CUDA; then
-    curl $CUDA_URL -o $CUDA_FILE && \
-        sudo dpkg -i $CUDA_FILE ||
-        (echo "CUDA install failed"; exit 1)
-    rm -f $CUDA_FILE
-    sudo apt-get -y update
-    # Install the minimal CUDA subpackages required to test Caffe build.
-    # For a full CUDA installation, add 'cuda' to the list of packages.
-    sudo apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
-    # Create CUDA symlink at /usr/local/cuda
-    # (This would normally be created by the CUDA installer, but we create it
-    # manually since we did a partial installation.)
-    sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda ||
-        (echo "CUDA symlink creation failed"; exit 1)
+  CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb
+  CUDA_FILE=/tmp/cuda_install.deb
+  curl $CUDA_URL -o $CUDA_FILE
+  dpkg -i $CUDA_FILE
+  rm -f $CUDA_FILE
+  apt-get -y update
+  # Install the minimal CUDA subpackages required to test Caffe build.
+  # For a full CUDA installation, add 'cuda' to the list of packages.
+  apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
+  # Create CUDA symlink at /usr/local/cuda
+  # (This would normally be created by the CUDA installer, but we create it
+  # manually since we did a partial installation.)
+  ln -s /usr/local/cuda-6.0 /usr/local/cuda
 fi
 
 # Install LMDB
 LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz
 LMDB_FILE=/tmp/openldap.tgz
 pushd .
-curl $LMDB_URL -o $LMDB_FILE && \
-    tar -C /tmp -xzvf $LMDB_FILE && \
-    cd /tmp/openldap*/libraries/liblmdb/ && \
-    make && sudo make install || \
-    (echo "LMDB install failed"; exit 1)
+curl $LMDB_URL -o $LMDB_FILE
+tar -C /tmp -xzvf $LMDB_FILE
+cd /tmp/openldap*/libraries/liblmdb/
+$MAKE
+$MAKE install
 popd
 rm -f $LMDB_FILE
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index fa86e92a131..a309bb3d6fe 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -1,5 +1,7 @@
 #!/bin/bash
 
+set -e
+
 mv Makefile.config.example Makefile.config
 
 if $WITH_CUDA; then

From f036ef4ded93e1164b640f210b16b63cac1a5d83 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 11 Sep 2014 16:08:12 +0200
Subject: [PATCH 0744/2053] add -fPIC flag to CMake build

---
 CMakeLists.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 64250eea0f2..3cb7d583504 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,9 +25,9 @@ endif()
 ###    Configuration    ###########################################################################
 #    Compiler Flags
 set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
-set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} )#set global flags here)
-set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG} )#set debug flags here)
-set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE} )#set release flags here)
+set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC) # set global flags
+set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) # set debug flags
+set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) # set release flags
 
 #    Global Definitions
 if(CPU_ONLY)

From 3a69e22b540d0dbd3f062e43e114ebfdf44ce68f Mon Sep 17 00:00:00 2001
From: bhack <s.fabri@email.it>
Date: Fri, 12 Sep 2014 19:50:15 +0200
Subject: [PATCH 0745/2053] Add ppa for gflag and glog

---
 scripts/travis/travis_install.sh | 32 ++------------------------------
 1 file changed, 2 insertions(+), 30 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 15cafbe9b84..36c63b9d699 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -6,6 +6,7 @@ set -e
 MAKE="make --jobs=$NUM_THREADS"
 
 # Install apt packages where the Ubuntu 12.04 default works for Caffe
+add-apt-repository -y ppa:tuleu/precise-backports
 apt-get -y update
 apt-get install \
     wget git curl \
@@ -14,7 +15,7 @@ apt-get install \
     libboost-dev libboost-system-dev libboost-python-dev libboost-thread-dev \
     libprotobuf-dev protobuf-compiler \
     libatlas-dev libatlas-base-dev \
-    libhdf5-serial-dev \
+    libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
     bc
 
 # Add a special apt-repository to install CMake 2.8.9 for CMake Caffe build,
@@ -26,35 +27,6 @@ if $WITH_CMAKE; then
   apt-get -y install cmake
 fi
 
-# Install glog
-GLOG_URL=https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-GLOG_FILE=/tmp/glog-0.3.3.tar.gz
-pushd .
-wget $GLOG_URL -O $GLOG_FILE
-tar -C /tmp -xzvf $GLOG_FILE
-rm $GLOG_FILE
-cd /tmp/glog-0.3.3
-./configure
-$MAKE
-$MAKE install
-popd
-
-# Install gflags
-GFLAGS_URL=https://github.com/schuhschuh/gflags/archive/master.zip
-GFLAGS_FILE=/tmp/gflags-master.zip
-pushd .
-wget $GFLAGS_URL -O $GFLAGS_FILE
-cd /tmp/
-unzip gflags-master.zip
-cd gflags-master
-mkdir build
-cd build
-export CXXFLAGS="-fPIC"
-cmake ..
-$MAKE VERBOSE=1
-$MAKE install
-popd
-
 # Install CUDA, if needed
 if $WITH_CUDA; then
   CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb

From 431a516f2c12ec5b7aa791155fde9f5eb149bd0d Mon Sep 17 00:00:00 2001
From: Kai Li <kaili_kloud@163.com>
Date: Sat, 13 Sep 2014 00:21:06 +0800
Subject: [PATCH 0746/2053] Update CUDA to version 6.5 in the Travis install
 script

---
 scripts/travis/travis_install.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 15cafbe9b84..c3bed8976ff 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -57,7 +57,7 @@ popd
 
 # Install CUDA, if needed
 if $WITH_CUDA; then
-  CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb
+  CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.5-14_amd64.deb
   CUDA_FILE=/tmp/cuda_install.deb
   curl $CUDA_URL -o $CUDA_FILE
   dpkg -i $CUDA_FILE
@@ -65,11 +65,11 @@ if $WITH_CUDA; then
   apt-get -y update
   # Install the minimal CUDA subpackages required to test Caffe build.
   # For a full CUDA installation, add 'cuda' to the list of packages.
-  apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
+  apt-get -y install cuda-core-6-5 cuda-cublas-6-5 cuda-cublas-dev-6-5 cuda-cudart-6-5 cuda-cudart-dev-6-5 cuda-curand-6-5 cuda-curand-dev-6-5
   # Create CUDA symlink at /usr/local/cuda
   # (This would normally be created by the CUDA installer, but we create it
   # manually since we did a partial installation.)
-  ln -s /usr/local/cuda-6.0 /usr/local/cuda
+  ln -s /usr/local/cuda-6.5 /usr/local/cuda
 fi
 
 # Install LMDB

From d54846c83efb974d8809b211c003bd63f89092e1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 13 Sep 2014 18:30:32 -0700
Subject: [PATCH 0747/2053] fix out-of-date next ID comment for SolverParameter

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 0e9ecf72728..896df03333a 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 32 (last added: delta)
+// SolverParameter next available ID: 33 (last added: test_initialization)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks

From e294f6a6e4f5dfc0cc260301a1b04f86c042f7c0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 13 Sep 2014 18:30:52 -0700
Subject: [PATCH 0748/2053] fix spelling error in caffe.proto

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 896df03333a..493bfa7ab17 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -120,7 +120,7 @@ message SolverParameter {
   optional float momentum = 11; // The momentum value.
   optional float weight_decay = 12; // The weight decay.
   // regularization types supported: L1 and L2
-  // controled by weight_decay
+  // controlled by weight_decay
   optional string regularization_type = 29 [default = "L2"];
   optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval

From 503ac0b7892faa03b5633999c729a2641461b690 Mon Sep 17 00:00:00 2001
From: bhack <bhack@users.noreply.github.com>
Date: Sun, 14 Sep 2014 21:16:38 +0200
Subject: [PATCH 0749/2053] Fix comments

---
 scripts/travis/travis_install.sh | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 36c63b9d699..f1dc5a66516 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -5,8 +5,10 @@ set -e
 
 MAKE="make --jobs=$NUM_THREADS"
 
-# Install apt packages where the Ubuntu 12.04 default works for Caffe
-add-apt-repository -y ppa:tuleu/precise-backports
+# Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
+
+#This ppa is for gflags and glog
+add-aptrepository -y ppa:tuleu/precise-backports
 apt-get -y update
 apt-get install \
     wget git curl \

From 8de9ab04e832491adf06386a81de0f54f3d62422 Mon Sep 17 00:00:00 2001
From: bhack <bhack@users.noreply.github.com>
Date: Sun, 14 Sep 2014 22:19:51 +0200
Subject: [PATCH 0750/2053] Fix a little typo

---
 scripts/travis/travis_install.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index f1dc5a66516..b1a59e09d95 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -7,8 +7,8 @@ MAKE="make --jobs=$NUM_THREADS"
 
 # Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
 
-#This ppa is for gflags and glog
-add-aptrepository -y ppa:tuleu/precise-backports
+# This ppa is for gflags and glog
+add-apt-repository -y ppa:tuleu/precise-backports
 apt-get -y update
 apt-get install \
     wget git curl \

From bbd166e07313a6b30c68f3a7557e81316adb3d14 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 15 Sep 2014 14:15:58 -0700
Subject: [PATCH 0751/2053] fix caffe train GPU initialization

Previously, the solver constructed nets before the caffe train tool read
the --gpu flag, which can cause errors due to LayerSetUp executing on
the wrong device (breaking cuDNN, for example).
---
 src/caffe/solver.cpp |  5 -----
 tools/caffe.cpp      | 15 +++++++++++----
 2 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 6b5570641e8..fc2576bc120 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -32,11 +32,6 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   LOG(INFO) << "Initializing solver from parameters: " << std::endl
             << param.DebugString();
   param_ = param;
-  if (param_.solver_mode() == SolverParameter_SolverMode_GPU &&
-      param_.has_device_id()) {
-    Caffe::SetDevice(param_.device_id());
-  }
-  Caffe::set_mode(Caffe::Brew(param_.solver_mode()));
   if (param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 10ba70d152e..fa27fdf2118 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -87,20 +87,27 @@ int train() {
   caffe::SolverParameter solver_param;
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
-  LOG(INFO) << "Starting Optimization";
-  shared_ptr<caffe::Solver<float> >
-    solver(caffe::GetSolver<float>(solver_param));
+  // If the gpu flag is not provided, allow the mode and device to be set
+  // in the solver prototxt.
+  if (FLAGS_gpu < 0
+      && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
+    FLAGS_gpu = solver_param.device_id();
+  }
 
   // Set device id and mode
   if (FLAGS_gpu >= 0) {
     LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
     Caffe::SetDevice(FLAGS_gpu);
     Caffe::set_mode(Caffe::GPU);
-  } else if (!solver_param.has_solver_mode()) {
+  } else {
     LOG(INFO) << "Use CPU.";
     Caffe::set_mode(Caffe::CPU);
   }
 
+  LOG(INFO) << "Starting Optimization";
+  shared_ptr<caffe::Solver<float> >
+    solver(caffe::GetSolver<float>(solver_param));
+
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
     solver->Solve(FLAGS_snapshot);

From 012047626efa78847d35a41b802796df20aa89aa Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 16 Sep 2014 08:13:17 -0700
Subject: [PATCH 0752/2053] [example] update paths in net surgery

---
 ...totxt => bvlc_caffenet_full_conv.prototxt} |  0
 examples/net_surgery.ipynb                    | 46 ++++++++++---------
 2 files changed, 24 insertions(+), 22 deletions(-)
 rename examples/imagenet/{imagenet_full_conv.prototxt => bvlc_caffenet_full_conv.prototxt} (100%)

diff --git a/examples/imagenet/imagenet_full_conv.prototxt b/examples/imagenet/bvlc_caffenet_full_conv.prototxt
similarity index 100%
rename from examples/imagenet/imagenet_full_conv.prototxt
rename to examples/imagenet/bvlc_caffenet_full_conv.prototxt
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 0854018af4a..e4f4f985992 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -2,7 +2,8 @@
  "metadata": {
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
-  "include_in_docs": true
+  "include_in_docs": true,
+  "signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -35,47 +36,48 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1c1\r\n",
+        "1,2c1\r\n",
+        "< # This file is for the net_surgery.ipynb example notebook.\r\n",
         "< name: \"CaffeNetConv\"\r\n",
         "---\r\n",
         "> name: \"CaffeNet\"\r\n",
-        "3c3\r\n",
+        "4c3\r\n",
         "< input_dim: 1\r\n",
         "---\r\n",
         "> input_dim: 10\r\n",
-        "5,6c5,6\r\n",
+        "6,7c5,6\r\n",
         "< input_dim: 451\r\n",
         "< input_dim: 451\r\n",
         "---\r\n",
         "> input_dim: 227\r\n",
         "> input_dim: 227\r\n",
-        "151,152c151,152\r\n",
+        "152,153c151,152\r\n",
         "<   name: \"fc6-conv\"\r\n",
         "<   type: CONVOLUTION\r\n",
         "---\r\n",
         ">   name: \"fc6\"\r\n",
         ">   type: INNER_PRODUCT\r\n",
-        "154,155c154,155\r\n",
+        "155,156c154,155\r\n",
         "<   top: \"fc6-conv\"\r\n",
         "<   convolution_param {\r\n",
         "---\r\n",
         ">   top: \"fc6\"\r\n",
         ">   inner_product_param {\r\n",
-        "157d156\r\n",
+        "158d156\r\n",
         "<     kernel_size: 6\r\n",
-        "163,164c162,163\r\n",
+        "164,165c162,163\r\n",
         "<   bottom: \"fc6-conv\"\r\n",
         "<   top: \"fc6-conv\"\r\n",
         "---\r\n",
         ">   bottom: \"fc6\"\r\n",
         ">   top: \"fc6\"\r\n",
-        "169,170c168,169\r\n",
+        "170,171c168,169\r\n",
         "<   bottom: \"fc6-conv\"\r\n",
         "<   top: \"fc6-conv\"\r\n",
         "---\r\n",
         ">   bottom: \"fc6\"\r\n",
         ">   top: \"fc6\"\r\n",
-        "176,180c175,179\r\n",
+        "177,181c175,179\r\n",
         "<   name: \"fc7-conv\"\r\n",
         "<   type: CONVOLUTION\r\n",
         "<   bottom: \"fc6-conv\"\r\n",
@@ -87,21 +89,21 @@
         ">   bottom: \"fc6\"\r\n",
         ">   top: \"fc7\"\r\n",
         ">   inner_product_param {\r\n",
-        "182d180\r\n",
+        "183d180\r\n",
         "<     kernel_size: 1\r\n",
-        "188,189c186,187\r\n",
+        "189,190c186,187\r\n",
         "<   bottom: \"fc7-conv\"\r\n",
         "<   top: \"fc7-conv\"\r\n",
         "---\r\n",
         ">   bottom: \"fc7\"\r\n",
         ">   top: \"fc7\"\r\n",
-        "194,195c192,193\r\n",
+        "195,196c192,193\r\n",
         "<   bottom: \"fc7-conv\"\r\n",
         "<   top: \"fc7-conv\"\r\n",
         "---\r\n",
         ">   bottom: \"fc7\"\r\n",
         ">   top: \"fc7\"\r\n",
-        "201,205c199,203\r\n",
+        "202,206c199,203\r\n",
         "<   name: \"fc8-conv\"\r\n",
         "<   type: CONVOLUTION\r\n",
         "<   bottom: \"fc7-conv\"\r\n",
@@ -113,9 +115,9 @@
         ">   bottom: \"fc7\"\r\n",
         ">   top: \"fc8\"\r\n",
         ">   inner_product_param {\r\n",
-        "207d204\r\n",
+        "208d204\r\n",
         "<     kernel_size: 1\r\n",
-        "213c210\r\n",
+        "214c210\r\n",
         "<   bottom: \"fc8-conv\"\r\n",
         "---\r\n",
         ">   bottom: \"fc8\"\r\n"
@@ -143,7 +145,7 @@
       "import caffe\n",
       "\n",
       "# Load the original network and extract the fully-connected layers' parameters.\n",
-      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "params = ['fc6', 'fc7', 'fc8']\n",
       "# fc_params = {name: (weights, biases)}\n",
       "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
@@ -178,7 +180,7 @@
      "collapsed": false,
      "input": [
       "# Load the fully-convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('imagenet/imagenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
       "# conv_params = {name: (weights, biases)}\n",
       "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
@@ -254,7 +256,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "net_full_conv.save('imagenet/caffe_imagenet_full_conv')"
+      "net_full_conv.save('imagenet/bvlc_caffenet_full_conv.caffemodel')"
      ],
      "language": "python",
      "metadata": {},
@@ -313,7 +315,7 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "<matplotlib.image.AxesImage at 0x14c8050d0>"
+        "<matplotlib.image.AxesImage at 0x7f283815ce50>"
        ]
       },
       {
@@ -321,7 +323,7 @@
        "output_type": "display_data",
        "png": 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P/fif5nv/qe/hg5//WQaLoKBJUC/M5oqGjBNHPybMpDJyq/KCK9VpXB1/gAkinmQjZXKY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0SuRppFZSBX+fZeq7gDcq1kdNEVCCp37RkBXEYwj0ZjdY7Gk475QiF5ewd/yux031FlX/md2FGy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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10a00e310>"
+        "<matplotlib.figure.Figure at 0x7f285ddfca50>"
        ]
       }
      ],
@@ -349,4 +351,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file

From 4e6d977e8b087a0107fc79618170f36785c38676 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 16 Sep 2014 08:19:02 -0700
Subject: [PATCH 0753/2053] [fix] snapshot model weights as .caffemodel, solver
 state as .solverstate

---
 src/caffe/solver.cpp | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index fc2576bc120..0ea4edcf9b8 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -308,20 +308,21 @@ void Solver<Dtype>::Snapshot() {
   // For intermediate results, we will also dump the gradient values.
   net_->ToProto(&net_param, param_.snapshot_diff());
   string filename(param_.snapshot_prefix());
+  string model_filename, snapshot_filename;
   const int kBufferSize = 20;
   char iter_str_buffer[kBufferSize];
   snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_);
   filename += iter_str_buffer;
-  filename += ".caffemodel";
-  LOG(INFO) << "Snapshotting to " << filename;
-  WriteProtoToBinaryFile(net_param, filename.c_str());
+  model_filename = filename + ".caffemodel";
+  LOG(INFO) << "Snapshotting to " << model_filename;
+  WriteProtoToBinaryFile(net_param, model_filename.c_str());
   SolverState state;
   SnapshotSolverState(&state);
   state.set_iter(iter_);
-  state.set_learned_net(filename);
-  filename += ".solverstate";
-  LOG(INFO) << "Snapshotting solver state to " << filename;
-  WriteProtoToBinaryFile(state, filename.c_str());
+  state.set_learned_net(model_filename);
+  snapshot_filename = filename + ".solverstate";
+  LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
+  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
 }
 
 template <typename Dtype>

From 06d7310aa8957d21cde86c3812325dfd9532c980 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Tue, 16 Sep 2014 09:56:11 -0700
Subject: [PATCH 0754/2053] set up datum size for WindowDataLayer

---
 src/caffe/layers/window_data_layer.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 4284c327681..a24d7de3daa 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -158,6 +158,12 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
+  // datum size
+  this->datum_channels_ = (*top)[0]->channels();
+  this->datum_height_ = (*top)[0]->height();
+  this->datum_width_ = (*top)[0]->width();
+  this->datum_size_ =
+      (*top)[0]->channels() * (*top)[0]->height() * (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);

From aecab61ff762eb4fb595b59948b25a5d10cd5505 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Tue, 9 Sep 2014 13:42:40 -0700
Subject: [PATCH 0755/2053] [Bugfix] Move error checking closer to file read

Previously, if (height > 0 && width > 0) was true, the
cv::resize() function would be called before cv_img_origin was
confirmed valid; if the image file/filename was not valid, this
caused an opencv assert error like this:

terminate called after throwing an instance of 'cv::Exception'
  what():  /tmp/A3p0_4200_32550/batserve/A3p0/glnxa64/OpenCV/modules/imgproc/src/imgwarp.cpp:1725: error: (-215)
  ssize.area() > 0 in function resize
---
 src/caffe/util/io.cpp | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 7b6c67cf633..ad835e35d66 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -71,16 +71,18 @@ bool ReadImageToDatum(const string& filename, const int label,
   cv::Mat cv_img;
   int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
     CV_LOAD_IMAGE_GRAYSCALE);
-  if (height > 0 && width > 0) {
-    cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
-    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
-  } else {
-    cv_img = cv::imread(filename, cv_read_flag);
-  }
-  if (!cv_img.data) {
+
+  cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
+  if (!cv_img_origin.data) {
     LOG(ERROR) << "Could not open or find file " << filename;
     return false;
   }
+  if (height > 0 && width > 0) {
+    cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
+  } else {
+    cv_img = cv_img_origin;
+  }
+
   int num_channels = (is_color ? 3 : 1);
   datum->set_channels(num_channels);
   datum->set_height(cv_img.rows);

From 03e0e01fce7361194b599c53c1f23d77cbb07086 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 16 Sep 2014 19:59:53 -0700
Subject: [PATCH 0756/2053] Display averaged loss over the last several
 iterations

---
 src/caffe/proto/caffe.proto |  4 +++-
 src/caffe/solver.cpp        | 18 +++++++++++++++++-
 2 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 493bfa7ab17..ba4c41cbf67 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 33 (last added: test_initialization)
+// SolverParameter next available ID: 34 (last added: average_loss)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -113,6 +113,8 @@ message SolverParameter {
   // the number of iterations between displaying info. If display = 0, no info
   // will be displayed.
   optional int32 display = 6;
+  // Display the cost averaged over the last average_cost iterations
+  optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
   optional string lr_policy = 8; // The learning rate decay policy.
   optional float gamma = 9; // The parameter to compute the learning rate.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 0ea4edcf9b8..0810f487fab 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -169,6 +169,13 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // resume_file above.
   const int start_iter = iter_;
 
+  int average_loss = this->param_.average_loss();
+
+  CHECK_GE(average_loss, 1) << "average_cost should be non-negative.";
+
+  vector<Dtype> losses;
+  Dtype smoothed_loss;
+
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.
   vector<Blob<Dtype>*> bottom_vec;
@@ -187,8 +194,17 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
     Dtype loss = net_->ForwardBackward(bottom_vec);
+    if (losses.size() < average_loss) {
+      losses.push_back(loss);
+      int size = losses.size();
+      smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
+    } else {
+      int idx = (iter_ - start_iter) % average_loss;
+      smoothed_loss += (loss - losses[idx]) / average_loss;
+      losses[idx] = loss;
+    }
     if (display) {
-      LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+      LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {

From 3fc22b35b5fe1b08fb42dd631492270bd1ee4060 Mon Sep 17 00:00:00 2001
From: Christos Nikolaou <christnn@auth.gr>
Date: Wed, 17 Sep 2014 21:28:51 +0300
Subject: [PATCH 0757/2053] Update readme.md files of cifar10 and mnist
 examples. Fixed broken links.

---
 examples/cifar10/readme.md |  6 +++---
 examples/mnist/readme.md   | 20 +++++++++++++++++---
 2 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 86fd5ea2967..a329b49c5ea 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -11,7 +11,7 @@ Alex's CIFAR-10 tutorial, Caffe style
 
 Alex Krizhevsky's [cuda-convnet](https://code.google.com/p/cuda-convnet/) details the model definitions, parameters, and training procedure for good performance on CIFAR-10. This example reproduces his results in Caffe.
 
-We will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.
+We will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.
 
 We thank @chyojn for the pull request that defined the model schemas and solver configurations.
 
@@ -32,12 +32,12 @@ If it complains that `wget` or `gunzip` are not installed, you need to install t
 The Model
 ---------
 
-The CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all. We have defined the model in the `CAFFE_ROOT/examples/cifar10` directory's `cifar10_quick_train.prototxt`.
+The CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all. We have defined the model in the `CAFFE_ROOT/examples/cifar10` directory's `cifar10_quick_train_test.prototxt`.
 
 Training and Testing the "Quick" Model
 --------------------------------------
 
-Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_quick.sh`, or the following command directly:
+Training the model is simple after you have written the network definition protobuf and solver protobuf files (refer to [MNIST Tutorial](../examples/mnist.html)). Simply run `train_quick.sh`, or the following command directly:
 
     cd $CAFFE_ROOT/examples/cifar10
     ./train_quick.sh
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 5a8e8cab3f5..2985240e2f9 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -8,7 +8,7 @@ priority: 1
 
 # Training MNIST with Caffe
 
-We will assume that you have caffe successfully compiled. If not, please refer to the [Installation page](installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.
+We will assume that you have caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.
 
 ## Prepare Datasets
 
@@ -29,7 +29,7 @@ The design of LeNet contains the essence of CNNs that are still used in larger m
 
 ## Define the MNIST Network
 
-This section explains the prototxt file `lenet_train.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.
+This section explains the prototxt file `lenet.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.
 
 Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:
 
@@ -203,9 +203,23 @@ Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/example
     # solver mode: CPU or GPU
     solver_mode: GPU
 
+## Additional Notes
+
+Note that the train and test protocol definition for the network is in the `lenet_train_test.prototxt` file which differs from the `lenet.prototxt` file. In `lenet_train_test.prototxt file`, layers may include one additional definition, like the one below:
+
+
+    layers {
+      
+      // Other layer definitions
+
+      include: { phase: TRAIN }
+    }
+
+This layer will be used only in training phase. If we change `TRAIN` with `TEST`, then this layer will be used only in test phase. Otherwise this layer will be used in both training and test phase. Thus `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for training and one for testing.
+
 ## Training and Testing the Model
 
-Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_mnist.sh`, or the following command directly:
+Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_lenet.sh`, or the following command directly:
 
     cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh

From 1096ddefbaff09d4af4147dabd7d4a448334b074 Mon Sep 17 00:00:00 2001
From: Christos Nikolaou <christnn@auth.gr>
Date: Thu, 18 Sep 2014 12:49:17 +0300
Subject: [PATCH 0758/2053] Updated mnist/readme.md file with additional
 information.

---
 examples/mnist/readme.md | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 2985240e2f9..fe6e36a12f8 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -173,6 +173,22 @@ Finally, we will write the loss!
 
 The `softmax_loss` layer implements both the softmax and the multinomial logistic loss (that saves time and improves numerical stability). It takes two blobs, the first one being the prediction and the second one being the `label` provided by the data layer (remember it?). It does not produce any outputs - all it does is to compute the loss function value, report it when backpropagation starts, and initiates the gradient with respect to `ip2`. This is where all magic starts.
 
+
+### Additional Notes: Writing Layer Rules
+
+The train and test protocol definition for the network is in the `lenet_train_test.prototxt` file, which differs from the `lenet.prototxt` file. In `lenet_train_test.prototxt` file, layers may include an additional definition, like the one below:
+
+
+    layers {
+      // Other layer definitions
+      include: { phase: TRAIN }
+    }
+
+This definition is a rule, which controls whether and when a layer is included in the network, based on current network's state. You can refeer to `$CAFFE_ROOT/src/caffe/proto/caffe.proto` for more information about layer's parameters.
+
+In the above example, this layer will be included only in `TRAIN` phase. If we change `TRAIN` with `TEST`, then this layer will be used only in test phase. Otherwise this layer will be used in both training and test phase. Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase. Also, there is an `ACCURACY` layer which is included only in `TEST` phase, because we want our network to report its accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
+
+
 ## Define the MNIST Solver
 
 Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt`:
@@ -203,19 +219,6 @@ Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/example
     # solver mode: CPU or GPU
     solver_mode: GPU
 
-## Additional Notes
-
-Note that the train and test protocol definition for the network is in the `lenet_train_test.prototxt` file which differs from the `lenet.prototxt` file. In `lenet_train_test.prototxt file`, layers may include one additional definition, like the one below:
-
-
-    layers {
-      
-      // Other layer definitions
-
-      include: { phase: TRAIN }
-    }
-
-This layer will be used only in training phase. If we change `TRAIN` with `TEST`, then this layer will be used only in test phase. Otherwise this layer will be used in both training and test phase. Thus `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for training and one for testing.
 
 ## Training and Testing the Model
 

From e4d48c560de0bb70be84054b098984293434a123 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 17 Sep 2014 15:10:18 -0700
Subject: [PATCH 0759/2053] test convolution against explicit reference
 implementation

To thoroughly check convolution, the output is compared against a
reference implementation by explicit looping. Simple and group
convolution by the Caffe and cuDNN engines are checked against the
reference.
---
 src/caffe/test/test_convolution_layer.cpp | 217 +++++++++++++++-------
 1 file changed, 149 insertions(+), 68 deletions(-)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 0e7a8da567b..0849a3a4591 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -13,6 +13,93 @@
 
 namespace caffe {
 
+// Reference convolution for checking results:
+// accumulate through explicit loops over input, output, and filters.
+template <typename Dtype>
+void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
+    const vector<shared_ptr<Blob<Dtype> > >& weights,
+    Blob<Dtype>* out) {
+  // Kernel size, stride, and pad
+  int kernel_h, kernel_w;
+  if (conv_param->has_kernel_size()) {
+    kernel_h = kernel_w = conv_param->kernel_size();
+  } else {
+    kernel_h = conv_param->kernel_h();
+    kernel_w = conv_param->kernel_w();
+  }
+  int pad_h, pad_w;
+  if (!conv_param->has_pad_h()) {
+    pad_h = pad_w = conv_param->pad();
+  } else {
+    pad_h = conv_param->pad_h();
+    pad_w = conv_param->pad_w();
+  }
+  int stride_h, stride_w;
+  if (!conv_param->has_stride_h()) {
+    stride_h = stride_w = conv_param->stride();
+  } else {
+    stride_h = conv_param->stride_h();
+    stride_w = conv_param->stride_w();
+  }
+  // Groups
+  int groups = conv_param->group();
+  int o_g = out->channels() / groups;
+  int k_g = in->channels() / groups;
+  int o_head, k_head;
+  // Convolution
+  const Dtype* in_data = in->cpu_data();
+  const Dtype* weight_data = weights[0]->cpu_data();
+  Dtype* out_data = out->mutable_cpu_data();
+  for (int n = 0; n < out->num(); n++) {
+    for (int g = 0; g < groups; g++) {
+      o_head = o_g * g;
+      k_head = k_g * g;
+      for (int o = 0; o < o_g; o++) {
+        for (int k = 0; k < k_g; k++) {
+          for (int y = 0; y < out->height(); y++) {
+            for (int x = 0; x < out->width(); x++) {
+              for (int p = 0; p < kernel_h; p++) {
+                for (int q = 0; q < kernel_w; q++) {
+                  int in_y = y * stride_h - pad_h + p;
+                  int in_x = x * stride_w - pad_w + q;
+                  if (in_y >= 0 && in_y < in->height()
+                    && in_x >= 0 && in_x < in->width()) {
+                    out_data[out->offset(n, o + o_head, y, x)] +=
+                        in_data[in->offset(n, k + k_head, in_y, in_x)]
+                        * weight_data[weights[0]->offset(o + o_head, k, p, q)];
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  // Bias
+  if (conv_param->bias_term()) {
+    const Dtype* bias_data = weights[1]->cpu_data();
+    for (int n = 0; n < out->num(); n++) {
+      for (int o = 0; o < out->channels(); o++) {
+        for (int y = 0; y < out->height(); y++) {
+          for (int x = 0; x < out->width(); x++) {
+            out_data[out->offset(n, o, y, x)] += bias_data[o];
+          }
+        }
+      }
+    }
+  }
+}
+
+template void caffe_conv(const Blob<float>* in,
+    ConvolutionParameter* conv_param,
+    const vector<shared_ptr<Blob<float> > >& weights,
+    Blob<float>* out);
+template void caffe_conv(const Blob<double>* in,
+    ConvolutionParameter* conv_param,
+    const vector<shared_ptr<Blob<double> > >& weights,
+    Blob<double>* out);
+
 template <typename TypeParam>
 class ConvolutionLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -41,10 +128,17 @@ class ConvolutionLayerTest : public MultiDeviceTest<TypeParam> {
     delete blob_top_2_;
   }
 
+  virtual Blob<Dtype>* MakeReferenceTop(Blob<Dtype>* top) {
+    this->ref_blob_top_.reset(new Blob<Dtype>());
+    this->ref_blob_top_->ReshapeLike(*top);
+    return this->ref_blob_top_.get();
+  }
+
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_bottom_2_;
   Blob<Dtype>* const blob_top_;
   Blob<Dtype>* const blob_top_2_;
+  shared_ptr<Blob<Dtype> > ref_blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -90,14 +184,6 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   // We will simply see if the convolution layer carries out averaging well.
   typedef typename TypeParam::Dtype Dtype;
-  shared_ptr<ConstantFiller<Dtype> > filler;
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  filler.reset(new ConstantFiller<Dtype>(filler_param));
-  filler->Fill(this->blob_bottom_);
-  filler_param.set_value(2.);
-  filler.reset(new ConstantFiller<Dtype>(filler_param));
-  filler->Fill(this->blob_bottom_2_);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
@@ -114,34 +200,28 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
       new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 27.1
-  const Dtype* top_data = this->blob_top_->cpu_data();
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
   }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_2_));
   top_data = this->blob_top_2_->cpu_data();
-  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
   }
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   // We will simply see if the convolution layer carries out averaging well.
   typedef typename TypeParam::Dtype Dtype;
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  ConstantFiller<Dtype> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
-  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
-      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
-        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
-          bottom_data[this->blob_bottom_->offset(n, c, h, w)] = c;
-        }
-      }
-    }
-  }
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -157,17 +237,15 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
       new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 9.1
-  const Dtype* top_data = this->blob_top_->cpu_data();
-  for (int n = 0; n < this->blob_top_->num(); ++n) {
-    for (int c = 0; c < this->blob_top_->channels(); ++c) {
-      for (int h = 0; h < this->blob_top_->height(); ++h) {
-        for (int w = 0; w < this->blob_top_->width(); ++w) {
-          Dtype data = top_data[this->blob_top_->offset(n, c, h, w)];
-          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
-        }
-      }
-    }
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
   }
 }
 
@@ -330,10 +408,17 @@ class CuDNNConvolutionLayerTest : public ::testing::Test {
     delete blob_top_2_;
   }
 
+  virtual Blob<Dtype>* MakeReferenceTop(Blob<Dtype>* top) {
+    this->ref_blob_top_.reset(new Blob<Dtype>());
+    this->ref_blob_top_->ReshapeLike(*top);
+    return this->ref_blob_top_.get();
+  }
+
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_bottom_2_;
   Blob<Dtype>* const blob_top_;
   Blob<Dtype>* const blob_top_2_;
+  shared_ptr<Blob<Dtype> > ref_blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -342,6 +427,8 @@ TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
   Caffe::set_mode(Caffe::GPU);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -379,6 +466,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
   // We will simply see if the convolution layer carries out averaging well.
   Caffe::set_mode(Caffe::GPU);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<ConstantFiller<TypeParam> > filler;
   FillerParameter filler_param;
   filler_param.set_value(1.);
@@ -403,34 +492,28 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 27.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  // Check against reference convolution.
+  const TypeParam* top_data;
+  const TypeParam* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
   }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_2_));
   top_data = this->blob_top_2_->cpu_data();
-  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
   }
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
   // We will simply see if the convolution layer carries out averaging well.
   Caffe::set_mode(Caffe::GPU);
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
-  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
-      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
-        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
-          bottom_data[this->blob_bottom_->offset(n, c, h, w)] = c;
-        }
-      }
-    }
-  }
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -446,17 +529,15 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 9.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int n = 0; n < this->blob_top_->num(); ++n) {
-    for (int c = 0; c < this->blob_top_->channels(); ++c) {
-      for (int h = 0; h < this->blob_top_->height(); ++h) {
-        for (int w = 0; w < this->blob_top_->width(); ++w) {
-          TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
-          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
-        }
-      }
-    }
+  // Check against reference convolution.
+  const TypeParam* top_data;
+  const TypeParam* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
   }
 }
 

From 355af161c2d04f474b5f9a26fef7c2822e3b88bc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 08:40:00 -0700
Subject: [PATCH 0760/2053] test convolution by random weights for robustness

---
 src/caffe/test/test_convolution_layer.cpp | 22 ++++------------------
 1 file changed, 4 insertions(+), 18 deletions(-)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 0849a3a4591..a38ad3fd1a8 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -192,8 +192,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(4);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
@@ -229,8 +228,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   convolution_param->set_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
@@ -468,24 +466,13 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  shared_ptr<ConstantFiller<TypeParam> > filler;
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  filler.reset(new ConstantFiller<TypeParam>(filler_param));
-  filler->Fill(this->blob_bottom_);
-  filler_param.set_value(2.);
-  filler.reset(new ConstantFiller<TypeParam>(filler_param));
-  filler->Fill(this->blob_bottom_2_);
-  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
-  this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(4);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<TypeParam> > layer(
@@ -521,8 +508,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
   convolution_param->set_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<TypeParam> > layer(

From 18ca3625c6accd021a4e05e6fdb77c8b6b06dc8e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 17 Sep 2014 23:38:31 -0700
Subject: [PATCH 0761/2053] [docs] comment ConvolutionLayer

---
 include/caffe/vision_layers.hpp | 62 +++++++++++++++++++++++++++++++--
 src/caffe/layers/conv_layer.cpp | 58 +++++++++++++++++-------------
 src/caffe/layers/conv_layer.cu  |  9 +++--
 3 files changed, 98 insertions(+), 31 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 8c2db8824ed..9c8656f1f2b 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -20,11 +20,49 @@ namespace caffe {
  * @brief Convolves the input image with a bank of learned filters,
  *        and (optionally) adds biases.
  *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ *   Caffe convolves by reduction to matrix multiplication. This achieves
+ *   high-throughput and generality of input and filter dimensions but comes at
+ *   the cost of memory for matrices. This makes use of efficiency in BLAS.
+ *
+ *   The input is "im2col" transformed to a channel K' x H x W data matrix
+ *   for multiplication with the N x K' x H x W filter matrix to yield a
+ *   N' x H x W output matrix that is then "col2im" restored. K' is the
+ *   input channel * kernel height * kernel width dimension of the unrolled
+ *   inputs so that the im2col matrix has a column for each input region to
+ *   be filtered. col2im restores the output spatial structure by rolling up
+ *   the output channel N' columns of the output matrix.
  */
 template <typename Dtype>
 class ConvolutionLayer : public Layer<Dtype> {
  public:
+  /**
+   * @param param provides ConvolutionParameter convolution_param,
+   *    with ConvolutionLayer options:
+   *  - num_output. The number of filters.
+   *  - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
+   *  kernel_size for square filters or kernel_h and kernel_w for rectangular
+   *  filters.
+   *  - stride / stride_h / stride_w (\b optional, default 1). The filter
+   *  stride, given by stride_size for equal dimensions or stride_h and stride_w
+   *  for different strides. By default the convolution is dense with stride 1.
+   *  - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
+   *  convolution, given by pad for equal dimensions or pad_h and pad_w for
+   *  different padding. Input padding is computed implicitly instead of
+   *  actually padding.
+   *  - group (\b optional, default 1). The number of filter groups. Group
+   *  convolution is a method for reducing parameterization by selectively
+   *  connecting input and output channels. The input and output channel dimensions must be divisible
+   *  by the number of groups. For group @f$ \geq 1 @f$, the
+   *  convolutional filters' input and output channels are separated s.t. each
+   *  group takes 1 / group of the input channels and makes 1 / group of the
+   *  output channels. Concretely 4 input channels, 8 output channels, and
+   *  2 groups separate input channels 1-2 and output channels 1-4 into the
+   *  first group and input channels 3-4 and output channels 5-8 into the second
+   *  group.
+   *  - bias_term (\b optional, default true). Whether to have a bias.
+   *  - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
+   *    kernels + stream parallelism) engines.
+   */
   explicit ConvolutionLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -57,8 +95,16 @@ class ConvolutionLayer : public Layer<Dtype> {
   int num_output_;
   int height_out_, width_out_;
   bool bias_term_;
-  // For the Caffe matrix multiplication convolution.
-  int M_, K_, N_;
+
+  /// M_ is the channel dimension of the output for a single group, which is the
+  /// leading dimension of the filter matrix.
+  int M_;
+  /// K_ is the dimension of an unrolled input for a single group, which is the
+  /// leading dimension of the data matrix.
+  int K_;
+  /// N_ is the spatial dimension of the output, the H x W, which are the last
+  /// dimensions of the data and filter matrices.
+  int N_;
   Blob<Dtype> col_buffer_;
   Blob<Dtype> bias_multiplier_;
 };
@@ -67,6 +113,16 @@ class ConvolutionLayer : public Layer<Dtype> {
 /*
  * @brief cuDNN implementation of ConvolutionLayer.
  *        Fallback to ConvolutionLayer for CPU mode.
+ *
+ * cuDNN accelerates convolution through forward kernels for filtering and bias
+ * plus backward kernels for the gradient w.r.t. the filters, biases, and
+ * inputs. Caffe + cuDNN further speeds up the computation through forward
+ * parallelism across groups and backward parallelism across gradients.
+ *
+ * The CUDNN engine does not have memory overhead for matrix buffers. For many
+ * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
+ * but for fully-convolutional models and large inputs the CAFFE engine can be
+ * faster as long as it fits in memory.
 */
 template <typename Dtype>
 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 16b6a688968..769dfa671f6 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -11,6 +11,7 @@ namespace caffe {
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
+  // Configure the kernel size, padding, stride, and inputs.
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
@@ -46,7 +47,6 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = conv_param.stride_h();
     stride_w_ = conv_param.stride_w();
   }
-  group_ = this->layer_param_.convolution_param().group();
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -61,28 +61,33 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(width_, bottom[bottom_id]->width())
         << "Inputs must have same width.";
   }
+  // Configure output channels, groups, and spatial dimensions.
   num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
+  group_ = this->layer_param_.convolution_param().group();
   CHECK_EQ(channels_ % group_, 0);
-  // The im2col result buffer would only hold one image at a time to avoid
-  // overly large memory usage.
+  CHECK_EQ(num_output_ % group_, 0)
+      << "Number of output should be multiples of group.";
   height_out_ =
       (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
   width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
-  col_buffer_.Reshape(
-      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
-  // Set the parameters
-  CHECK_EQ(num_output_ % group_, 0)
-      << "Number of output should be multiples of group.";
-  bias_term_ = this->layer_param_.convolution_param().bias_term();
-  // Figure out the dimensions for individual gemms.
-  M_ = num_output_ / group_;
-  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
-  N_ = height_out_ * width_out_;
   for (int top_id = 0; top_id < top->size(); ++top_id) {
     (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
   }
-  // Check if we need to set up the weights
+  // Prepare the matrix multiplication computation.
+  // Each input will be convolved as a single GEMM.
+  M_ = num_output_ / group_;
+  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
+  N_ = height_out_ * width_out_;
+  // The im2col result buffer holds one image at a time to avoid
+  // overly large memory usage.
+  col_buffer_.Reshape(
+      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
+  // Handle the parameters: weights and biases.
+  // - blobs_[0] holds the filter weights
+  // - blobs_[1] holds the biases (optional)
+  bias_term_ = this->layer_param_.convolution_param().bias_term();
+  // Check if we need to set up the weights.
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
   } else {
@@ -91,14 +96,15 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     } else {
       this->blobs_.resize(1);
     }
-    // Intialize the weight
+    // Initialize and fill the weights:
+    // output channels x input channels per-group x kernel height x kernel width
     this->blobs_[0].reset(new Blob<Dtype>(
         num_output_, channels_ / group_, kernel_h_, kernel_w_));
-    // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, initialize and fill the bias term
+    // If necessary, initialize and fill the biases:
+    // 1 x 1 x 1 x output channels
     if (bias_term_) {
       this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
@@ -106,11 +112,12 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
-  // Set up the all ones "bias multiplier" for adding bias using blas
+  // Set up the all ones "bias multiplier" for adding biases by BLAS
   if (bias_term_) {
     bias_multiplier_.Reshape(1, 1, 1, N_);
     caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
+  // Propagate gradients to the parameters (as directed by backward pass).
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
@@ -123,21 +130,22 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     Dtype* top_data = (*top)[i]->mutable_cpu_data();
     Dtype* col_data = col_buffer_.mutable_cpu_data();
     const Dtype* weight = this->blobs_[0]->cpu_data();
-    int weight_offset = M_ * K_;
-    int col_offset = K_ * N_;
-    int top_offset = M_ * N_;
+    int weight_offset = M_ * K_;  // number of filter parameters in a group
+    int col_offset = K_ * N_;  // number of values in an input region / column
+    int top_offset = M_ * N_;  // number of values in an output region / column
     for (int n = 0; n < num_; ++n) {
-      // First, im2col
+      // im2col transformation: unroll input regions for filtering
+      // into column matrix for multplication.
       im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
           width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
           col_data);
-      // Second, innerproduct with groups
+      // Take inner products for groups.
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
           (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
           (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
       }
-      // third, add bias
+      // Add bias.
       if (bias_term_) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
@@ -201,7 +209,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                 weight_diff + weight_offset * g);
           }
         }
-        // gradient w.r.t. bottom data, if necessary
+        // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
           if (weight == NULL) {
             weight = this->blobs_[0]->cpu_data();
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 02cfdb3b80c..43f76a2368f 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -8,6 +8,7 @@
 
 namespace caffe {
 
+/// @brief refer to CPU forward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
@@ -20,17 +21,18 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     int col_offset = K_ * N_;
     int top_offset = M_ * N_;
     for (int n = 0; n < num_; ++n) {
-      // First, im2col
+      // im2col transformation: unroll input regions for filtering
+      // into column matrix for multplication.
       im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
           width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
           col_data);
-      // Second, innerproduct with groups
+      // Take inner products for groups.
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
           (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
           (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
       }
-      // third, add bias
+      // Add bias.
       if (bias_term_) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
@@ -41,6 +43,7 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
+/// @brief refer to CPU backward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {

From 9a7f0a081d066fcc9f2b86288f75bd5b0f4cb2f6 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 09:41:19 -0700
Subject: [PATCH 0762/2053] [docs] lenet grooming

---
 examples/mnist/readme.md | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index fe6e36a12f8..ac1a0b7d7ea 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -8,7 +8,7 @@ priority: 1
 
 # Training MNIST with Caffe
 
-We will assume that you have caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.
+We will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your Caffe installation is located at `CAFFE_ROOT`.
 
 ## Prepare Datasets
 
@@ -29,7 +29,7 @@ The design of LeNet contains the essence of CNNs that are still used in larger m
 
 ## Define the MNIST Network
 
-This section explains the prototxt file `lenet.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.
+This section explains the `lenet_train_test.prototxt` model definition that specifies the LeNet model for MNIST handwritten digit classification. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.
 
 Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:
 
@@ -176,18 +176,21 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 ### Additional Notes: Writing Layer Rules
 
-The train and test protocol definition for the network is in the `lenet_train_test.prototxt` file, which differs from the `lenet.prototxt` file. In `lenet_train_test.prototxt` file, layers may include an additional definition, like the one below:
-
+Layer definitions can include rules for whether and when they are included in the network definition, like the one below:
 
     layers {
-      // Other layer definitions
+      // ...layer definition...
       include: { phase: TRAIN }
     }
 
-This definition is a rule, which controls whether and when a layer is included in the network, based on current network's state. You can refeer to `$CAFFE_ROOT/src/caffe/proto/caffe.proto` for more information about layer's parameters.
-
-In the above example, this layer will be included only in `TRAIN` phase. If we change `TRAIN` with `TEST`, then this layer will be used only in test phase. Otherwise this layer will be used in both training and test phase. Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase. Also, there is an `ACCURACY` layer which is included only in `TEST` phase, because we want our network to report its accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
+This is a rule, which controls layer inclusion in the network, based on current network's state.
+You can refer to `$CAFFE_ROOT/src/caffe/proto/caffe.proto` for more information about layer rules and model schema.
 
+In the above example, this layer will be included only in `TRAIN` phase.
+If we change `TRAIN` with `TEST`, then this layer will be used only in test phase.
+By default, that is without layer rules, a layer is always included in the network.
+Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.
+Also, there is an `ACCURACY` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
 
 ## Define the MNIST Solver
 

From 69bf6b54107306653b0be7f6cac5ae8a1f737ebd Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 22:06:15 -0700
Subject: [PATCH 0763/2053] use Blob directly instead of shared_ptr for
 EltwiseLayer::max_idx_

This is in keeping with #742.
---
 include/caffe/common_layers.hpp    | 2 +-
 src/caffe/layers/eltwise_layer.cpp | 7 +++----
 src/caffe/layers/eltwise_layer.cu  | 4 ++--
 3 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 190b5c24b05..deb4a572702 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -178,7 +178,7 @@ class EltwiseLayer : public Layer<Dtype> {
 
   EltwiseParameter_EltwiseOp op_;
   vector<Dtype> coeffs_;
-  shared_ptr<Blob<int> > max_idx_;
+  Blob<int> max_idx_;
 
   bool stable_prod_grad_;
 };
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 46034be4784..ca157a8212a 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -40,8 +40,7 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // If max operation, we will initialize the vector index part.
   if (this->layer_param_.eltwise_param().operation() ==
       EltwiseParameter_EltwiseOp_MAX && top->size() == 1) {
-    max_idx_.reset(new Blob<int>(bottom[0]->num(), channels,
-                                 height, width));
+    max_idx_.Reshape(bottom[0]->num(), channels, height, width);
   }
 }
 
@@ -69,7 +68,7 @@ void EltwiseLayer<Dtype>::Forward_cpu(
     break;
   case EltwiseParameter_EltwiseOp_MAX:
     // Initialize
-    mask = max_idx_->mutable_cpu_data();
+    mask = max_idx_.mutable_cpu_data();
     caffe_set(count, -1, mask);
     caffe_set(count, Dtype(-FLT_MAX), top_data);
     // bottom 0 & 1
@@ -138,7 +137,7 @@ void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         }
         break;
       case EltwiseParameter_EltwiseOp_MAX:
-        mask = max_idx_->cpu_data();
+        mask = max_idx_.cpu_data();
         for (int index = 0; index < count; ++index) {
           Dtype gradient = 0;
           if (mask[index] == i) {
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index c0d47fd413b..16cb6cc77e3 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -53,7 +53,7 @@ void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     }
     break;
   case EltwiseParameter_EltwiseOp_MAX:
-    mask = max_idx_->mutable_gpu_data();
+    mask = max_idx_.mutable_gpu_data();
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxForward<Dtype> <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), 0, top_data, mask);
@@ -118,7 +118,7 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         }
         break;
       case EltwiseParameter_EltwiseOp_MAX:
-        mask = max_idx_->gpu_data();
+        mask = max_idx_.gpu_data();
         MaxBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
             <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
             count, top_diff, i, mask, bottom_diff);

From 3194bb1ccf09efefbf9d2e47b080f723690191ad Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 14:43:11 -0700
Subject: [PATCH 0764/2053] add abstract Layer::Reshape, and document the new
 method protocol

---
 include/caffe/layer.hpp | 31 ++++++++++++++++++++++++-------
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 59e6ccf4ef9..9bd42e787b3 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -48,7 +48,7 @@ class Layer {
    *
    * @param bottom the preshaped input blobs
    * @param top
-   *     the allocated but unshaped output blobs, to be shaped by LayerSetUp
+   *     the allocated but unshaped output blobs, to be shaped by Reshape
    *
    * Checks that the number of bottom and top blobs is correct.
    * Calls LayerSetUp to do special layer setup for individual layer types.
@@ -62,22 +62,39 @@ class Layer {
   }
 
   /**
-   * @brief Does layer-specific setup: your layer should implement this.
+   * @brief Does layer-specific setup: your layer should implement this function
+   *        as well as Reshape.
    *
    * @param bottom
    *     the preshaped input blobs, whose data fields store the input data for
    *     this layer
    * @param top
-   *     the allocated but unshaped output blobs, to be initialized by LayerSetUp
+   *     the allocated but unshaped output blobs
    *
-   * This method should be used to do layer-specific setup. At a minimum, this
-   * includes reshaping the empty top blobs to the shape as dictated by the
-   * shapes of the bottom blobs and any relevant parameters from the
-   * <code>layer_param_</code>.
+   * This method should do one-time layer specific setup. This includes reading
+   * and processing relevent parameters from the <code>layer_param_</code>.
+   * Setting up the shapes of top blobs and internal buffers should be done in
+   * <code>Reshape</code>, which will be called before the forward pass to
+   * adjust the top blob sizes.
    */
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) { NOT_IMPLEMENTED; }
 
+  /**
+   * @brief Adjust the shapes of top blobs and internal buffers to accomodate
+   *        the shapes of the bottom blobs.
+   *
+   * @param bottom the input blobs, with the requested input shapes
+   * @param top the top blobs, which should be reshaped as needed
+   *
+   * This method should reshape top blobs as needed according to the shapes
+   * of the bottom (input) blobs, as well as reshaping any internal buffers
+   * and making any other necessary adjustments so that the layer can
+   * accomodate the bottom blobs.
+   */
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) = 0;
+
   /**
    * @brief Given the bottom blobs, compute the top blobs and the loss.
    *

From 4fff9663600d2384d07593dec8077627d5693933 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 2 Jul 2014 13:08:26 -0700
Subject: [PATCH 0765/2053] don't reallocate blobs when shrinking memory use

This allows nets to be reshaped very quickly (essentially for free) as
long as sufficient memory has been allocated. Calling Blob::Reshape in
order to free up memory becomes impossible; however, this is not a
normal use case (and deleting blobs does free memory).
---
 include/caffe/blob.hpp |  3 ++-
 src/caffe/blob.cpp     | 14 +++++++-------
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index b2a5c2f697d..c4f8b9e0f70 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -20,7 +20,7 @@ class Blob {
  public:
   Blob()
        : data_(), diff_(), num_(0), channels_(0), height_(0), width_(0),
-       count_(0) {}
+       count_(0), capacity_(0) {}
   explicit Blob(const int num, const int channels, const int height,
     const int width);
   void Reshape(const int num, const int channels, const int height,
@@ -120,6 +120,7 @@ class Blob {
   int height_;
   int width_;
   int count_;
+  int capacity_;
 
   DISABLE_COPY_AND_ASSIGN(Blob);
 };  // class Blob
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 9fd1232a9ad..cfffc379eb1 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -17,12 +17,10 @@ void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
   height_ = height;
   width_ = width;
   count_ = num_ * channels_ * height_ * width_;
-  if (count_) {
-    data_.reset(new SyncedMemory(count_ * sizeof(Dtype)));
-    diff_.reset(new SyncedMemory(count_ * sizeof(Dtype)));
-  } else {
-    data_.reset(reinterpret_cast<SyncedMemory*>(NULL));
-    diff_.reset(reinterpret_cast<SyncedMemory*>(NULL));
+  if (count_ > capacity_) {
+    capacity_ = count_;
+    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
+    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
   }
 }
 
@@ -33,7 +31,9 @@ void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
 
 template <typename Dtype>
 Blob<Dtype>::Blob(const int num, const int channels, const int height,
-    const int width) {
+    const int width)
+  // capacity_ must be initialized before calling Reshape
+  : capacity_(0) {
   Reshape(num, channels, height, width);
 }
 

From 87de5edec8208f8726700c4301081cae9559a7fc Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 14:51:58 -0700
Subject: [PATCH 0766/2053] enable reshaping in the forward pass

Note that calling Reshape when no reshape is necessary should be
effectively a no-op, so this is not a performance regression.
---
 src/caffe/net.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a3e7122c2c4..a8db4938a01 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -504,6 +504,7 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   Dtype loss = 0;
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
+    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }

From 5ce519c44c34e4c14193220d7aea5fdf760c5231 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 21:51:58 -0700
Subject: [PATCH 0767/2053] separate setTensor4dDesc from createTensor4dDesc

This will make it possible to add reshaping to cuDNN layers.
---
 include/caffe/util/cudnn.hpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index e7ddea73e6b..aef3e21676d 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -56,22 +56,26 @@ template<> class dataType<double> {
 };
 
 template <typename Dtype>
-inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc) {
+  CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
+}
+
+template <typename Dtype>
+inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
     int n, int c, int h, int w,
     int stride_n, int stride_c, int stride_h, int stride_w) {
-  CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
   CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
       n, c, h, w, stride_n, stride_c, stride_h, stride_w));
 }
 
 template <typename Dtype>
-inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
     int n, int c, int h, int w) {
   const int stride_w = 1;
   const int stride_h = w * stride_w;
   const int stride_c = h * stride_h;
   const int stride_n = c * stride_c;
-  createTensor4dDesc<Dtype>(desc, n, c, h, w,
+  setTensor4dDesc<Dtype>(desc, n, c, h, w,
       stride_n, stride_c, stride_h, stride_w);
 }
 

From d7e8f2a3a377cd3f5c07c59056df3c6c3315cab0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 23:15:22 -0700
Subject: [PATCH 0768/2053] separate setConvolutionDesc from
 createConvolutionDesc

---
 include/caffe/util/cudnn.hpp | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index aef3e21676d..aca5bd713fc 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -88,10 +88,14 @@ inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
 }
 
 template <typename Dtype>
-inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
+inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
+  CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
+}
+
+template <typename Dtype>
+inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
     cudnnTensor4dDescriptor_t bottom, cudnnFilterDescriptor_t filter,
     int pad_h, int pad_w, int stride_h, int stride_w) {
-  CUDNN_CHECK(cudnnCreateConvolutionDescriptor(conv));
   CUDNN_CHECK(cudnnSetConvolutionDescriptor(*conv, bottom, filter,
       pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
 }

From 4b34c72254a891a8e14c0bd9dcb0eeac23b80ce7 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 14:57:07 -0700
Subject: [PATCH 0769/2053] split off Reshape for data layers

---
 include/caffe/data_layers.hpp | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 15158029436..8e2637b0658 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -40,6 +40,9 @@ class BaseDataLayer : public Layer<Dtype> {
       vector<Blob<Dtype>*>* top);
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {}
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
 
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
@@ -134,6 +137,9 @@ class DummyDataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_DUMMY_DATA;
@@ -166,6 +172,9 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual ~HDF5DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_HDF5_DATA;
@@ -204,6 +213,9 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual ~HDF5OutputLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {}
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_HDF5_OUTPUT;

From 62bc0a8c1c9c8cdf4c6ff8638c465a9e739faf0f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 21:30:05 -0700
Subject: [PATCH 0770/2053] split off Reshape for loss layers

---
 include/caffe/loss_layers.hpp                    | 14 ++++++++++++--
 src/caffe/layers/accuracy_layer.cpp              |  5 +++++
 src/caffe/layers/euclidean_loss_layer.cpp        |  4 ++--
 src/caffe/layers/infogain_loss_layer.cpp         | 16 ++++++++++++----
 src/caffe/layers/loss_layer.cpp                  | 11 ++++++++---
 .../layers/multinomial_logistic_loss_layer.cpp   |  4 ++--
 .../layers/sigmoid_cross_entropy_loss_layer.cpp  | 11 +++++++++--
 src/caffe/layers/softmax_loss_layer.cpp          |  7 +++++++
 8 files changed, 57 insertions(+), 15 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index a29c445d51e..b95e919c25f 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -34,6 +34,8 @@ class AccuracyLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ACCURACY;
@@ -97,6 +99,8 @@ class LossLayer : public Layer<Dtype> {
      : Layer<Dtype>(param) {}
   virtual void LayerSetUp(
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
+  virtual void Reshape(
+      const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
 
@@ -148,7 +152,7 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
  public:
   explicit EuclideanLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), diff_() {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -339,6 +343,8 @@ class InfogainLossLayer : public LossLayer<Dtype> {
       : LossLayer<Dtype>(param), infogain_() {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
   // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
@@ -428,7 +434,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
  public:
   explicit MultinomialLogisticLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -510,6 +516,8 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
           sigmoid_output_(new Blob<Dtype>()) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
@@ -606,6 +614,8 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
         softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SOFTMAX_LOSS;
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 062e927183b..3e69bc84faa 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -14,6 +14,11 @@ template <typename Dtype>
 void AccuracyLayer<Dtype>::LayerSetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   top_k_ = this->layer_param_.accuracy_param().top_k();
+}
+
+template <typename Dtype>
+void AccuracyLayer<Dtype>::Reshape(
+  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index be83601f5a1..1b4a13d2ddc 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -8,9 +8,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void EuclideanLossLayer<Dtype>::LayerSetUp(
+void EuclideanLossLayer<Dtype>::Reshape(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  LossLayer<Dtype>::LayerSetUp(bottom, top);
+  LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
   CHECK_EQ(bottom[0]->height(), bottom[1]->height());
   CHECK_EQ(bottom[0]->width(), bottom[1]->width());
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 91dd89240e4..894cb69811a 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -14,10 +14,6 @@ template <typename Dtype>
 void InfogainLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  Blob<Dtype>* infogain = NULL;
   if (bottom.size() < 3) {
     CHECK(this->layer_param_.infogain_loss_param().has_source())
         << "Infogain matrix source must be specified.";
@@ -25,10 +21,22 @@ void InfogainLossLayer<Dtype>::LayerSetUp(
     ReadProtoFromBinaryFile(
       this->layer_param_.infogain_loss_param().source(), &blob_proto);
     infogain_.FromProto(blob_proto);
+  }
+}
+
+template <typename Dtype>
+void InfogainLossLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::Reshape(bottom, top);
+  Blob<Dtype>* infogain = NULL;
+  if (bottom.size() < 3) {
     infogain = &infogain_;
   } else {
     infogain = bottom[2];
   }
+  CHECK_EQ(bottom[1]->channels(), 1);
+  CHECK_EQ(bottom[1]->height(), 1);
+  CHECK_EQ(bottom[1]->width(), 1);
   const int num = bottom[0]->num();
   const int dim = bottom[0]->count() / num;
   CHECK_EQ(infogain->num(), 1);
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 89d8c91e342..9eb9dbd5c5b 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -13,15 +13,20 @@ namespace caffe {
 template <typename Dtype>
 void LossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  (*top)[0]->Reshape(1, 1, 1, 1);
   // LossLayers have a non-zero (1) loss by default.
   if (this->layer_param_.loss_weight_size() == 0) {
     this->layer_param_.add_loss_weight(Dtype(1));
   }
 }
 
+template <typename Dtype>
+void LossLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
+      << "The data and label should have the same number.";
+  (*top)[0]->Reshape(1, 1, 1, 1);
+}
+
 INSTANTIATE_CLASS(LossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index cf96bfe73ed..c0fe1966a4d 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -11,9 +11,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void MultinomialLogisticLossLayer<Dtype>::LayerSetUp(
+void MultinomialLogisticLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  LossLayer<Dtype>::LayerSetUp(bottom, top);
+  LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 6e440a820c4..6a48099ae8b 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -12,8 +12,6 @@ template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
-  CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
-      "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
   sigmoid_bottom_vec_.clear();
   sigmoid_bottom_vec_.push_back(bottom[0]);
   sigmoid_top_vec_.clear();
@@ -21,6 +19,15 @@ void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
   sigmoid_layer_->SetUp(sigmoid_bottom_vec_, &sigmoid_top_vec_);
 }
 
+template <typename Dtype>
+void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::Reshape(bottom, top);
+  CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
+      "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
+  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+}
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index e07c8dc9df9..55392c37ca0 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -17,6 +17,13 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   softmax_top_vec_.clear();
   softmax_top_vec_.push_back(&prob_);
   softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
+}
+
+template <typename Dtype>
+void SoftmaxWithLossLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  LossLayer<Dtype>::Reshape(bottom, top);
+  softmax_layer_->Reshape(softmax_bottom_vec_, &softmax_top_vec_);
   if (top->size() >= 2) {
     // softmax output
     (*top)[1]->ReshapeLike(*bottom[0]);

From 256209da4def9f54a6401c19802501bde5cbf1ba Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 21:48:51 -0700
Subject: [PATCH 0771/2053] split off Reshape for neuron layers

---
 include/caffe/neuron_layers.hpp          | 10 +++++++++-
 src/caffe/layers/cudnn_relu_layer.cpp    | 12 ++++++++++--
 src/caffe/layers/cudnn_sigmoid_layer.cpp | 12 ++++++++++--
 src/caffe/layers/cudnn_tanh_layer.cpp    | 12 ++++++++++--
 src/caffe/layers/dropout_layer.cpp       | 12 +++++++++---
 src/caffe/layers/neuron_layer.cpp        |  8 ++------
 6 files changed, 50 insertions(+), 16 deletions(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 36acf96e5af..0968a2007dc 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -26,7 +26,7 @@ class NeuronLayer : public Layer<Dtype> {
  public:
   explicit NeuronLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -170,6 +170,8 @@ class DropoutLayer : public NeuronLayer<Dtype> {
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_DROPOUT;
@@ -367,6 +369,8 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
       : ReLULayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual ~CuDNNReLULayer();
 
  protected:
@@ -449,6 +453,8 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
       : SigmoidLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual ~CuDNNSigmoidLayer();
 
  protected:
@@ -533,6 +539,8 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
       : TanHLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual ~CuDNNTanHLayer();
 
  protected:
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index f8bf77f1844..083868f572f 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -13,12 +13,20 @@ void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   ReLULayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+}
+
+template <typename Dtype>
+void CuDNNReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  ReLULayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
   const int H = bottom[0]->height();
   const int W = bottom[0]->width();
-  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
-  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 488c7545dba..3fe800db6f4 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -13,12 +13,20 @@ void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   SigmoidLayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+}
+
+template <typename Dtype>
+void CuDNNSigmoidLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  SigmoidLayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
   const int H = bottom[0]->height();
   const int W = bottom[0]->width();
-  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
-  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 32b6611e40b..7a5c06f6596 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -13,12 +13,20 @@ void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   TanHLayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+}
+
+template <typename Dtype>
+void CuDNNTanHLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  TanHLayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
   const int H = bottom[0]->height();
   const int W = bottom[0]->width();
-  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
-  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 52537d1aba9..47feb1d2543 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -14,9 +14,6 @@ template <typename Dtype>
 void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
-  // Set up the cache for random number generation
-  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
   threshold_ = this->layer_param_.dropout_param().dropout_ratio();
   DCHECK(threshold_ > 0.);
   DCHECK(threshold_ < 1.);
@@ -24,6 +21,15 @@ void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   uint_thres_ = static_cast<unsigned int>(UINT_MAX * threshold_);
 }
 
+template <typename Dtype>
+void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  NeuronLayer<Dtype>::Reshape(bottom, top);
+  // Set up the cache for random number generation
+  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
+}
+
 template <typename Dtype>
 void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index eff7948a616..c28e36ea23b 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -6,13 +6,9 @@
 namespace caffe {
 
 template <typename Dtype>
-void NeuronLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void NeuronLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  // NeuronLayer allows in-place computations. If the computation is not
-  // in-place, we will need to initialize the top blob.
-  if ((*top)[0] != bottom[0]) {
-    (*top)[0]->ReshapeLike(*bottom[0]);
-  }
+  (*top)[0]->ReshapeLike(*bottom[0]);
 }
 
 INSTANTIATE_CLASS(NeuronLayer);

From 07d6246adf02f8a9f674698b66ca3720c22e2443 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 22:42:45 -0700
Subject: [PATCH 0772/2053] split off Reshape for common layers

---
 include/caffe/common_layers.hpp          | 22 +++++++++++++++++-----
 src/caffe/layers/argmax_layer.cpp        |  5 +++++
 src/caffe/layers/concat_layer.cpp        |  4 ++++
 src/caffe/layers/cudnn_softmax_layer.cpp | 12 ++++++++++--
 src/caffe/layers/eltwise_layer.cpp       | 23 ++++++++++++++---------
 src/caffe/layers/flatten_layer.cpp       |  2 +-
 src/caffe/layers/inner_product_layer.cpp | 19 +++++++++++++------
 src/caffe/layers/mvn_layer.cpp           |  2 +-
 src/caffe/layers/slice_layer.cpp         |  6 +++++-
 src/caffe/layers/softmax_layer.cpp       |  2 +-
 src/caffe/layers/split_layer.cpp         |  2 +-
 11 files changed, 72 insertions(+), 27 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index deb4a572702..1f945ca34e9 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -40,6 +40,8 @@ class ArgMaxLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ARGMAX;
@@ -81,6 +83,8 @@ class ConcatLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_CONCAT;
@@ -159,6 +163,8 @@ class EltwiseLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ELTWISE;
@@ -198,7 +204,7 @@ class FlattenLayer : public Layer<Dtype> {
  public:
   explicit FlattenLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -251,6 +257,8 @@ class InnerProductLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_INNER_PRODUCT;
@@ -285,7 +293,7 @@ class MVNLayer : public Layer<Dtype> {
  public:
   explicit MVNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -319,7 +327,7 @@ class SilenceLayer : public Layer<Dtype> {
  public:
   explicit SilenceLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {}
 
   virtual inline LayerParameter_LayerType type() const {
@@ -351,7 +359,7 @@ class SoftmaxLayer : public Layer<Dtype> {
  public:
   explicit SoftmaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -388,6 +396,8 @@ class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
       : SoftmaxLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual ~CuDNNSoftmaxLayer();
 
  protected:
@@ -413,7 +423,7 @@ class SplitLayer : public Layer<Dtype> {
  public:
   explicit SplitLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
@@ -448,6 +458,8 @@ class SliceLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SLICE;
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 4b67f24cb9a..0d1a107257b 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -16,6 +16,11 @@ void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
   CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
       << "top_k must be less than or equal to the number of classes.";
+}
+
+template <typename Dtype>
+void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   if (out_max_val_) {
     // Produces max_ind and max_val
     (*top)[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 73d28b17850..10a11f1bb7c 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -14,7 +14,11 @@ void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     "concat_dim should be >= 0";
   CHECK_LE(concat_dim_, 1) <<
     "For now concat_dim <=1, it can only concat num and channels";
+}
 
+template <typename Dtype>
+void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   // Initialize with the first blob.
   count_ = bottom[0]->count();
   num_ = bottom[0]->num();
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 6dab2d6ac32..79ba5237ae3 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -17,12 +17,20 @@ void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
   // Initialize CUDNN.
   CUDNN_CHECK(cudnnCreate(&handle_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+}
+
+template <typename Dtype>
+void CuDNNSoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  SoftmaxLayer<Dtype>::Reshape(bottom, top);
   int N = bottom[0]->num();
   int K = bottom[0]->channels();
   int H = bottom[0]->height();
   int W = bottom[0]->width();
-  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
-  cudnn::createTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index ca157a8212a..569560f97d3 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -17,6 +17,20 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       == EltwiseParameter_EltwiseOp_PROD
       && this->layer_param().eltwise_param().coeff_size())) <<
       "Eltwise layer only takes coefficients for summation.";
+  op_ = this->layer_param_.eltwise_param().operation();
+  // Blob-wise coefficients for the elementwise operation.
+  coeffs_ = vector<Dtype>(bottom.size(), 1);
+  if (this->layer_param().eltwise_param().coeff_size()) {
+    for (int i = 0; i < bottom.size(); ++i) {
+      coeffs_[i] = this->layer_param().eltwise_param().coeff(i);
+    }
+  }
+  stable_prod_grad_ = this->layer_param_.eltwise_param().stable_prod_grad();
+}
+
+template <typename Dtype>
+void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   const int num = bottom[0]->num();
   const int channels = bottom[0]->channels();
   const int height = bottom[0]->height();
@@ -28,15 +42,6 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(width, bottom[i]->width());
   }
   (*top)[0]->Reshape(num, channels, height, width);
-  op_ = this->layer_param_.eltwise_param().operation();
-  // Blob-wise coefficients for the elementwise operation.
-  coeffs_ = vector<Dtype>(bottom.size(), 1);
-  if (this->layer_param().eltwise_param().coeff_size()) {
-    for (int i = 0; i < bottom.size(); ++i) {
-      coeffs_[i] = this->layer_param().eltwise_param().coeff(i);
-    }
-  }
-  stable_prod_grad_ = this->layer_param_.eltwise_param().stable_prod_grad();
   // If max operation, we will initialize the vector index part.
   if (this->layer_param_.eltwise_param().operation() ==
       EltwiseParameter_EltwiseOp_MAX && top->size() == 1) {
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 8c1fc74e159..65310cd1669 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void FlattenLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   int channels_out = bottom[0]->channels() * bottom[0]->height()
       * bottom[0]->width();
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 3ba0e1f29f6..ecd05a030db 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -14,11 +14,8 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
-  // Figure out the dimensions
-  M_ = bottom[0]->num();
-  K_ = bottom[0]->count() / bottom[0]->num();
   N_ = num_output;
-  (*top)[0]->Reshape(bottom[0]->num(), num_output, 1, 1);
+  K_ = bottom[0]->count() / bottom[0]->num();
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
@@ -42,12 +39,22 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       bias_filler->Fill(this->blobs_[1].get());
     }
   }  // parameter initialization
-  // Setting up the bias multiplier
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  // Figure out the dimensions
+  M_ = bottom[0]->num();
+  CHECK_EQ(bottom[0]->count() / bottom[0]->num(), K_) << "Input size "
+    "incompatible with inner product parameters.";
+  (*top)[0]->Reshape(bottom[0]->num(), N_, 1, 1);
+  // Set up the bias multiplier
   if (bias_term_) {
     bias_multiplier_.Reshape(1, 1, 1, M_);
     caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
-  this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 4d90702fd55..6a57b3ea7fc 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -8,7 +8,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void MVNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 9fa127528a0..ed679a9169e 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -18,6 +18,11 @@ void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   std::copy(slice_param.slice_point().begin(),
       slice_param.slice_point().end(),
       std::back_inserter(slice_point_));
+}
+
+template <typename Dtype>
+void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   count_ = 0;
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
@@ -50,7 +55,6 @@ void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
          count_ += (*top)[i]->count();
       }
     }
-
   } else {
     if (slice_dim_ == 0) {
       CHECK_EQ(num_ % top->size(), 0)
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 952db74a4b6..60668a3f8ce 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -8,7 +8,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void SoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void SoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 3bed347ea6c..40d3600ff17 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -7,7 +7,7 @@
 namespace caffe {
 
 template <typename Dtype>
-void SplitLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+void SplitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   count_ = bottom[0]->count();
   for (int i = 0; i < top->size(); ++i) {

From 6c63b8ce0835590caf58abe155ad3cbeca476576 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Sep 2014 23:31:33 -0700
Subject: [PATCH 0773/2053] split off Reshape for vision layers

Note that we are dropping some checks from LRN layer. However, these
checks are fairly redundant; something is very wrong if these layers
are producing top blobs that are different sizes than their inputs, and
tests are the right place to catch that. The thing that really should be
checked (that isn't) is that that local_size needs to be odd; this will
be added in a future commit.
---
 include/caffe/vision_layers.hpp          |  12 ++
 src/caffe/layers/conv_layer.cpp          |  75 +++++++------
 src/caffe/layers/cudnn_conv_layer.cpp    |  45 +++++---
 src/caffe/layers/cudnn_pooling_layer.cpp |  16 ++-
 src/caffe/layers/im2col_layer.cpp        |   5 +
 src/caffe/layers/lrn_layer.cpp           | 134 +++++++++++------------
 src/caffe/layers/pooling_layer.cpp       |   5 +
 7 files changed, 170 insertions(+), 122 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 9c8656f1f2b..1e7f3fcb297 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -67,6 +67,8 @@ class ConvolutionLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_CONVOLUTION;
@@ -131,6 +133,8 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
       : ConvolutionLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual ~CuDNNConvolutionLayer();
 
  protected:
@@ -163,6 +167,8 @@ class Im2colLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_IM2COL;
@@ -203,6 +209,8 @@ class LRNLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_LRN;
@@ -278,6 +286,8 @@ class PoolingLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_POOLING;
@@ -323,6 +333,8 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
       : PoolingLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual ~CuDNNPoolingLayer();
 
  protected:
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 769dfa671f6..58918fd4baf 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -47,47 +47,18 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = conv_param.stride_h();
     stride_w_ = conv_param.stride_w();
   }
-  num_ = bottom[0]->num();
+  // Configure output channels and groups.
   channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  // TODO: generalize to handle inputs of different shapes.
-  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
-    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
-    CHECK_EQ(channels_, bottom[bottom_id]->channels())
-        << "Inputs must have same channels.";
-    CHECK_EQ(height_, bottom[bottom_id]->height())
-        << "Inputs must have same height.";
-    CHECK_EQ(width_, bottom[bottom_id]->width())
-        << "Inputs must have same width.";
-  }
-  // Configure output channels, groups, and spatial dimensions.
   num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
   group_ = this->layer_param_.convolution_param().group();
   CHECK_EQ(channels_ % group_, 0);
   CHECK_EQ(num_output_ % group_, 0)
       << "Number of output should be multiples of group.";
-  height_out_ =
-      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
-  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
-  for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
-  }
-  // Prepare the matrix multiplication computation.
-  // Each input will be convolved as a single GEMM.
-  M_ = num_output_ / group_;
-  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
-  N_ = height_out_ * width_out_;
-  // The im2col result buffer holds one image at a time to avoid
-  // overly large memory usage.
-  col_buffer_.Reshape(
-      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
   // Handle the parameters: weights and biases.
   // - blobs_[0] holds the filter weights
   // - blobs_[1] holds the biases (optional)
   bias_term_ = this->layer_param_.convolution_param().bias_term();
-  // Check if we need to set up the weights.
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
   } else {
@@ -112,16 +83,54 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
+  // Propagate gradients to the parameters (as directed by backward pass).
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  num_ = bottom[0]->num();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
+    " convolution kernel.";
+  // TODO: generalize to handle inputs of different shapes.
+  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
+    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
+    CHECK_EQ(channels_, bottom[bottom_id]->channels())
+        << "Inputs must have same channels.";
+    CHECK_EQ(height_, bottom[bottom_id]->height())
+        << "Inputs must have same height.";
+    CHECK_EQ(width_, bottom[bottom_id]->width())
+        << "Inputs must have same width.";
+  }
+  // Shape the tops.
+  height_out_ =
+      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
+  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
+  for (int top_id = 0; top_id < top->size(); ++top_id) {
+    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+  }
+  // Prepare the matrix multiplication computation.
+  // Each input will be convolved as a single GEMM.
+  M_ = num_output_ / group_;
+  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
+  N_ = height_out_ * width_out_;
+  // The im2col result buffer will only hold one image at a time to avoid
+  // overly large memory usage.
+  col_buffer_.Reshape(
+      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
+  for (int top_id = 0; top_id < top->size(); ++top_id) {
+    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+  }
   // Set up the all ones "bias multiplier" for adding biases by BLAS
   if (bias_term_) {
     bias_multiplier_.Reshape(1, 1, 1, N_);
     caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
-  // Propagate gradients to the parameters (as directed by backward pass).
-  this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
-
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index eaacddcb0cc..137bbab1976 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -21,7 +21,7 @@ template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
-  // Initialize CUDA streams and cuNN.
+  // Initialize CUDA streams and cuDNN.
   stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
   handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
 
@@ -32,10 +32,6 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   }
 
   // Set the indexing parameters.
-  bottom_offset_ = (this->channels_ / this->group_)
-      * this->height_ * this->width_;
-  top_offset_ = (this->num_output_ / this->group_)
-      * this->height_out_ * this->width_out_;
   weight_offset_ = (this->num_output_ / this->group_)
       * (this->channels_ / this->group_) * this->kernel_h_ * this->kernel_w_;
   bias_offset_ = (this->num_output_ / this->group_);
@@ -48,33 +44,54 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   // Create tensor descriptor(s) for data and corresponding convolution(s).
   for (int i = 0; i < bottom.size(); i++) {
     cudnnTensor4dDescriptor_t bottom_desc;
-    cudnn::createTensor4dDesc<Dtype>(&bottom_desc,
+    cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
+    bottom_descs_.push_back(bottom_desc);
+    cudnnTensor4dDescriptor_t top_desc;
+    cudnn::createTensor4dDesc<Dtype>(&top_desc);
+    top_descs_.push_back(top_desc);
+    cudnnConvolutionDescriptor_t conv_desc;
+    cudnn::createConvolutionDesc<Dtype>(&conv_desc);
+    conv_descs_.push_back(conv_desc);
+  }
+
+  // Tensor descriptor for bias.
+  if (this->bias_term_) {
+    cudnn::createTensor4dDesc<Dtype>(&bias_desc_);
+  }
+}
+
+template <typename Dtype>
+void CuDNNConvolutionLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  ConvolutionLayer<Dtype>::Reshape(bottom, top);
+  bottom_offset_ = (this->channels_ / this->group_)
+      * this->height_ * this->width_;
+  top_offset_ = (this->num_output_ / this->group_)
+      * this->height_out_ * this->width_out_;
+
+  for (int i = 0; i < bottom.size(); i++) {
+    cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
         this->num_,
         this->channels_ / this->group_,
         this->height_, this->width_,
         this->channels_ * this->height_ * this->width_,
         this->height_ * this->width_,
         this->width_, 1);
-    bottom_descs_.push_back(bottom_desc);
-    cudnnTensor4dDescriptor_t top_desc;
-    cudnn::createTensor4dDesc<Dtype>(&top_desc,
+    cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
         this->num_,
         this->num_output_ / this->group_,
         this->height_out_, this->width_out_,
         this->num_output_ * this->height_out_ * this->width_out_,
         this->height_out_ * this->width_out_,
         this->width_out_, 1);
-    top_descs_.push_back(top_desc);
-    cudnnConvolutionDescriptor_t conv_desc;
-    cudnn::createConvolutionDesc<Dtype>(&conv_desc, bottom_desc,
+    cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
         filter_desc_, this->pad_h_, this->pad_w_,
         this->stride_h_, this->stride_w_);
-    conv_descs_.push_back(conv_desc);
   }
 
   // Tensor descriptor for bias.
   if (this->bias_term_) {
-    cudnn::createTensor4dDesc<Dtype>(&bias_desc_,
+    cudnn::setTensor4dDesc<Dtype>(&bias_desc_,
         1, this->num_output_ / this->group_, 1, 1);
   }
 }
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index 23c52012f1e..5aea0dc886e 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -15,15 +15,23 @@ void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
 
   CUDNN_CHECK(cudnnCreate(&handle_));
-  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
-      this->channels_, this->height_, this->width_);
-  cudnn::createTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
-      this->channels_, this->pooled_height_, this->pooled_width_);
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
   cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
       this->layer_param_.pooling_param().pool(), &mode_,
       this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
 }
 
+template <typename Dtype>
+void CuDNNPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  PoolingLayer<Dtype>::Reshape(bottom, top);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+      this->channels_, this->pooled_height_, this->pooled_width_);
+}
+
 template <typename Dtype>
 CuDNNPoolingLayer<Dtype>::~CuDNNPoolingLayer() {
   cudnnDestroyTensor4dDescriptor(bottom_desc_);
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 02f33f1cff4..870d5a9bde3 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -45,6 +45,11 @@ void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = conv_param.stride_h();
     stride_w_ = conv_param.stride_w();
   }
+}
+
+template <typename Dtype>
+void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index e81a32ba84f..55b7673a21f 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -9,88 +9,80 @@ namespace caffe {
 template <typename Dtype>
 void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  num_ = bottom[0]->num();
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
   size_ = this->layer_param_.lrn_param().local_size();
   pre_pad_ = (size_ - 1) / 2;
   alpha_ = this->layer_param_.lrn_param().alpha();
   beta_ = this->layer_param_.lrn_param().beta();
+  if (this->layer_param_.lrn_param().norm_region() ==
+      LRNParameter_NormRegion_WITHIN_CHANNEL) {
+    // Set up split_layer_ to use inputs in the numerator and denominator.
+    split_top_vec_.clear();
+    split_top_vec_.push_back(&product_input_);
+    split_top_vec_.push_back(&square_input_);
+    LayerParameter split_param;
+    split_layer_.reset(new SplitLayer<Dtype>(split_param));
+    split_layer_->SetUp(bottom, &split_top_vec_);
+    // Set up square_layer_ to square the inputs.
+    square_bottom_vec_.clear();
+    square_top_vec_.clear();
+    square_bottom_vec_.push_back(&square_input_);
+    square_top_vec_.push_back(&square_output_);
+    LayerParameter square_param;
+    square_param.mutable_power_param()->set_power(Dtype(2));
+    square_layer_.reset(new PowerLayer<Dtype>(square_param));
+    square_layer_->SetUp(square_bottom_vec_, &square_top_vec_);
+    // Set up pool_layer_ to sum over square neighborhoods of the input.
+    pool_top_vec_.clear();
+    pool_top_vec_.push_back(&pool_output_);
+    LayerParameter pool_param;
+    pool_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_AVE);
+    pool_param.mutable_pooling_param()->set_pad(pre_pad_);
+    pool_param.mutable_pooling_param()->set_kernel_size(size_);
+    pool_layer_.reset(new PoolingLayer<Dtype>(pool_param));
+    pool_layer_->SetUp(square_top_vec_, &pool_top_vec_);
+    // Set up power_layer_ to compute (1 + alpha_/N^2 s)^-beta_, where s is
+    // the sum of a squared neighborhood (the output of pool_layer_).
+    power_top_vec_.clear();
+    power_top_vec_.push_back(&power_output_);
+    LayerParameter power_param;
+    power_param.mutable_power_param()->set_power(-beta_);
+    power_param.mutable_power_param()->set_scale(alpha_);
+    power_param.mutable_power_param()->set_shift(Dtype(1));
+    power_layer_.reset(new PowerLayer<Dtype>(power_param));
+    power_layer_->SetUp(pool_top_vec_, &power_top_vec_);
+    // Set up a product_layer_ to compute outputs by multiplying inputs by the
+    // inverse demoninator computed by the power layer.
+    product_bottom_vec_.clear();
+    product_bottom_vec_.push_back(&product_input_);
+    product_bottom_vec_.push_back(&power_output_);
+    LayerParameter product_param;
+    EltwiseParameter* eltwise_param = product_param.mutable_eltwise_param();
+    eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+    product_layer_.reset(new EltwiseLayer<Dtype>(product_param));
+    product_layer_->SetUp(product_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void LRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     (*top)[0]->Reshape(num_, channels_, height_, width_);
     scale_.Reshape(num_, channels_, height_, width_);
     break;
   case LRNParameter_NormRegion_WITHIN_CHANNEL:
-    {
-      // Set up split_layer_ to use inputs in the numerator and denominator.
-      split_top_vec_.clear();
-      split_top_vec_.push_back(&product_input_);
-      split_top_vec_.push_back(&square_input_);
-      LayerParameter split_param;
-      split_layer_.reset(new SplitLayer<Dtype>(split_param));
-      split_layer_->SetUp(bottom, &split_top_vec_);
-      // Set up square_layer_ to square the inputs.
-      square_input_.Reshape(num_, channels_, height_, width_);
-      square_bottom_vec_.clear();
-      square_top_vec_.clear();
-      square_bottom_vec_.push_back(&square_input_);
-      square_top_vec_.push_back(&square_output_);
-      LayerParameter square_param;
-      square_param.mutable_power_param()->set_power(Dtype(2));
-      square_layer_.reset(new PowerLayer<Dtype>(square_param));
-      square_layer_->SetUp(square_bottom_vec_, &square_top_vec_);
-      CHECK_EQ(square_output_.num(), num_);
-      CHECK_EQ(square_output_.channels(), channels_);
-      CHECK_EQ(square_output_.height(), height_);
-      CHECK_EQ(square_output_.width(), width_);
-      // Set up pool_layer_ to sum over square neighborhoods of the input.
-      pool_top_vec_.clear();
-      pool_top_vec_.push_back(&pool_output_);
-      LayerParameter pool_param;
-      pool_param.mutable_pooling_param()->set_pool(
-          PoolingParameter_PoolMethod_AVE);
-      pool_param.mutable_pooling_param()->set_pad(pre_pad_);
-      pool_param.mutable_pooling_param()->set_kernel_size(size_);
-      pool_layer_.reset(new PoolingLayer<Dtype>(pool_param));
-      pool_layer_->SetUp(square_top_vec_, &pool_top_vec_);
-      CHECK_EQ(pool_output_.num(), num_);
-      CHECK_EQ(pool_output_.channels(), channels_);
-      CHECK_EQ(pool_output_.height(), height_);
-      CHECK_EQ(pool_output_.width(), width_);
-      // Set up power_layer_ to compute (1 + alpha_/N^2 s)^-beta_, where s is
-      // the sum of a squared neighborhood (the output of pool_layer_).
-      power_top_vec_.clear();
-      power_top_vec_.push_back(&power_output_);
-      LayerParameter power_param;
-      power_param.mutable_power_param()->set_power(-beta_);
-      power_param.mutable_power_param()->set_scale(alpha_);
-      power_param.mutable_power_param()->set_shift(Dtype(1));
-      power_layer_.reset(new PowerLayer<Dtype>(power_param));
-      power_layer_->SetUp(pool_top_vec_, &power_top_vec_);
-      CHECK_EQ(power_output_.num(), num_);
-      CHECK_EQ(power_output_.channels(), channels_);
-      CHECK_EQ(power_output_.height(), height_);
-      CHECK_EQ(power_output_.width(), width_);
-      // Set up a product_layer_ to compute outputs by multiplying inputs by the
-      // inverse demoninator computed by the power layer.
-      product_bottom_vec_.clear();
-      product_bottom_vec_.push_back(&product_input_);
-      product_bottom_vec_.push_back(&power_output_);
-      LayerParameter product_param;
-      EltwiseParameter* eltwise_param = product_param.mutable_eltwise_param();
-      eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
-      product_layer_.reset(new EltwiseLayer<Dtype>(product_param));
-      product_layer_->SetUp(product_bottom_vec_, top);
-      CHECK_EQ((*top)[0]->num(), num_);
-      CHECK_EQ((*top)[0]->channels(), channels_);
-      CHECK_EQ((*top)[0]->height(), height_);
-      CHECK_EQ((*top)[0]->width(), width_);
-    }
+    split_layer_->Reshape(bottom, &split_top_vec_);
+    square_layer_->Reshape(square_bottom_vec_, &square_top_vec_);
+    pool_layer_->Reshape(square_top_vec_, &pool_top_vec_);
+    power_layer_->Reshape(pool_top_vec_, &power_top_vec_);
+    product_layer_->Reshape(product_bottom_vec_, top);
     break;
-  default:
-    LOG(FATAL) << "Unknown normalization region.";
   }
 }
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 9e77fa28a45..8e8ffad66a0 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -60,6 +60,11 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_LT(pad_h_, kernel_h_);
     CHECK_LT(pad_w_, kernel_w_);
   }
+}
+
+template <typename Dtype>
+void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();

From d2de2ee54386500701527450145dd7cb7512a3e4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 13:56:38 -0700
Subject: [PATCH 0774/2053] call Reshape in Layer::SetUp

Strictly speaking, Reshape doesn't need to be called until the first
Forward call; however, much existing code (especially tests) assumes
that top blobs will be set up in SetUp, so we may as well do it there.
---
 include/caffe/layer.hpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 9bd42e787b3..b05c332f81b 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -51,13 +51,15 @@ class Layer {
    *     the allocated but unshaped output blobs, to be shaped by Reshape
    *
    * Checks that the number of bottom and top blobs is correct.
-   * Calls LayerSetUp to do special layer setup for individual layer types.
+   * Calls LayerSetUp to do special layer setup for individual layer types,
+   * followed by Reshape to set up sizes of top blobs and internal buffers.
    * Sets up the loss weight multiplier blobs for any non-zero loss weights.
    * This method may not be overridden.
    */
   void SetUp(const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
     CheckBlobCounts(bottom, *top);
     LayerSetUp(bottom, top);
+    Reshape(bottom, top);
     SetLossWeights(top);
   }
 

From 4f1b66857175ec12b11452da4f819145618b0a10 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 13:58:10 -0700
Subject: [PATCH 0775/2053] default LayerSetUp to no-op instead of
 NOT_IMPLEMENTED

Now that top blobs are set up in Layer::Reshape, it's Reshape that is
mandatory, and simple layers often don't need to implement LayerSetUp.
Reshape is (already) declared abstract, so not implementing it is a
compile-time error.
---
 include/caffe/layer.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index b05c332f81b..e160075b939 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -80,7 +80,7 @@ class Layer {
    * adjust the top blob sizes.
    */
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) { NOT_IMPLEMENTED; }
+      vector<Blob<Dtype>*>* top) {}
 
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accomodate

From db5bb1581f6d5b9422a1b608463aa94c84cc0d2c Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 2 Jul 2014 12:21:10 -0700
Subject: [PATCH 0776/2053] test net reshaping

---
 src/caffe/test/test_net.cpp | 121 ++++++++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 24f72aef3af..9b10d100afb 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -7,6 +7,7 @@
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
+#include "caffe/filler.hpp"
 #include "caffe/net.hpp"
 #include "caffe/util/math_functions.hpp"
 
@@ -533,6 +534,68 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitReshapableNet() {
+    const string& proto =
+        "name: 'ReshapableNetwork' "
+        "input: 'data' "
+        "input_dim: 1 "
+        "input_dim: 3 "
+        "input_dim: 100 "
+        "input_dim: 100 "
+        "layers: { "
+        "  name: 'conv1' "
+        "  type: CONVOLUTION "
+        "  bottom: 'data' "
+        "  top: 'conv1' "
+        "  convolution_param { "
+        "    num_output: 5 "
+        "    kernel_size: 3 "
+        "    stride: 2 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0.2 "
+        "    } "
+        "  } "
+        "} "
+        "layers: { "
+        "  name: 'relu1' "
+        "  type: RELU "
+        "  bottom: 'conv1' "
+        "  top: 'conv1' "
+        "} "
+        "layers: { "
+        "  name: 'pool1' "
+        "  type: POOLING "
+        "  bottom: 'conv1' "
+        "  top: 'pool1' "
+        "  pooling_param { "
+        "    pool: MAX "
+        "    kernel_size: 2 "
+        "    stride: 2 "
+        "  } "
+        "} "
+        "layers: { "
+        "  name: 'norm1' "
+        "  type: LRN "
+        "  bottom: 'pool1' "
+        "  top: 'norm1' "
+        "  lrn_param { "
+        "    local_size: 3 "
+        "  } "
+        "} "
+        "layers: { "
+        "  name: 'softmax' "
+        "  type: SOFTMAX "
+        "  bottom: 'norm1' "
+        "  top: 'softmax' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -2028,4 +2091,62 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   this->RunFilterNetTest(input_proto_test, output_proto_test);
 }
 
+TYPED_TEST(NetTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  // We set up bottom blobs of two different sizes, switch between
+  // them, and check that forward and backward both run and the results
+  // are the same.
+  Caffe::set_random_seed(this->seed_);
+  Caffe::set_mode(Caffe::CPU);
+  FillerParameter filler_param;
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  Blob<Dtype> blob1(4, 3, 9, 11);
+  Blob<Dtype> blob2(2, 3, 12, 10);
+  filler.Fill(&blob1);
+  filler.Fill(&blob2);
+
+  this->InitReshapableNet();
+  Blob<Dtype>* input_blob = this->net_->input_blobs()[0];
+  Blob<Dtype>* output_blob = this->net_->output_blobs()[0];
+  input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
+      blob1.width());
+  caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
+  this->net_->ForwardPrefilled();
+  // call backward just to make sure it runs
+  this->net_->Backward();
+  Blob<Dtype> output1(output_blob->num(), output_blob->channels(),
+      output_blob->height(), output_blob->width());
+  caffe_copy(output1.count(), output_blob->cpu_data(),
+      output1.mutable_cpu_data());
+
+  input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
+      blob2.width());
+  caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
+  this->net_->ForwardPrefilled();
+  this->net_->Backward();
+  Blob<Dtype> output2(output_blob->num(), output_blob->channels(),
+      output_blob->height(), output_blob->width());
+  caffe_copy(output2.count(), output_blob->cpu_data(),
+      output2.mutable_cpu_data());
+
+  input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
+      blob1.width());
+  caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
+  this->net_->ForwardPrefilled();
+  this->net_->Backward();
+  for (int i = 0; i < output1.count(); ++i) {
+    CHECK_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
+  }
+
+  input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
+      blob2.width());
+  caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
+  this->net_->ForwardPrefilled();
+  this->net_->Backward();
+  for (int i = 0; i < output2.count(); ++i) {
+    CHECK_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
+  }
+}
+
 }  // namespace caffe

From 24350a6f404b6f8988f742c91fbc07c1541480b7 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 14:34:16 -0700
Subject: [PATCH 0777/2053] include Reshape in caffe time

Since we are now calling Reshape in the Forward pass, it's only fair to
include it when timing. Reshape calls should normally be four or so
orders of magnitude faster than Forward calls; this change also makes it
easy to notice a mistake that causes something slow to happen in
Reshape.
---
 tools/caffe.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index fa27fdf2118..c8c8c1a6b4c 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -235,6 +235,9 @@ int time() {
     const caffe::string& layername = layers[i]->layer_param().name();
     timer.Start();
     for (int j = 0; j < FLAGS_iterations; ++j) {
+      // Although Reshape should be essentially free, we include it here
+      // so that we will notice Reshape performance bugs.
+      layers[i]->Reshape(bottom_vecs[i], &top_vecs[i]);
       layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
     }
     LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<

From 490077e372c92ac493611cfa2ee80fc1a09973fc Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 15:20:52 -0700
Subject: [PATCH 0778/2053] add Net::Reshape for only reshaping

Note that it is not normally necessary to call this function when using
reshapable nets, but sometimes it can be useful to compute the sizes of
intermediate layers without waiting for the forward pass.
---
 include/caffe/net.hpp | 8 ++++++++
 src/caffe/net.cpp     | 7 +++++++
 2 files changed, 15 insertions(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 3dcd0d5d598..1d06dc45533 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -67,6 +67,14 @@ class Net {
   void BackwardFrom(int start);
   void BackwardTo(int end);
 
+  /**
+   * @brief Reshape all layers from bottom to top.
+   *
+   * This is useful to propagate changes to layer sizes without running
+   * a forward pass, e.g. to compute output feature size.
+   */
+  void Reshape();
+
   Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
     Dtype loss;
     Forward(bottom, &loss);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a8db4938a01..6f4a651fb10 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -680,6 +680,13 @@ void Net<Dtype>::Backward() {
   BackwardFromTo(layers_.size() - 1, 0);
 }
 
+template <typename Dtype>
+void Net<Dtype>::Reshape() {
+  for (int i = 0; i < layers_.size(); ++i) {
+    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
+  }
+}
+
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
   int num_source_layers = param.layers_size();

From fdf2de1672fff44fd544a4dc925d41ffc8b16139 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 15:23:11 -0700
Subject: [PATCH 0779/2053] [pycaffe] expose Net::Reshape

---
 python/caffe/_caffe.cpp | 1 +
 python/caffe/_caffe.hpp | 1 +
 2 files changed, 2 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 39e5d09077c..5a81a42329b 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -149,6 +149,7 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def(bp::init<string>())
       .def("_forward",              &PyNet::Forward)
       .def("_backward",             &PyNet::Backward)
+      .def("reshape",               &PyNet::Reshape)
       .def("set_mode_cpu",          &PyNet::set_mode_cpu)
       .def("set_mode_gpu",          &PyNet::set_mode_gpu)
       .def("set_phase_train",       &PyNet::set_phase_train)
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 5884900fcfe..ba04d276351 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -98,6 +98,7 @@ class PyNet {
 
   void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
   void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
+  void Reshape() { net_->Reshape(); }
 
   void set_input_arrays(bp::object data_obj, bp::object labels_obj);
 

From 0b5e11d604851dde2f353e8ba207bcac601cbc47 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 15:33:49 -0700
Subject: [PATCH 0780/2053] [docs] clarify the use of Blob::Reshape a bit

---
 include/caffe/blob.hpp | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index c4f8b9e0f70..ef10aea53f0 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -23,6 +23,20 @@ class Blob {
        count_(0), capacity_(0) {}
   explicit Blob(const int num, const int channels, const int height,
     const int width);
+  /**
+   * @brief Change the dimensions of the blob, allocating new memory if
+   *        necessary.
+   *
+   * This function can be called both to create an initial allocation
+   * of memory, and to adjust the dimensions of a top blob during Layer::Reshape
+   * or Layer::Forward. When changing the size of blob, memory will only be
+   * reallocated if sufficient memory does not already exist, and excess memory
+   * will never be freed.
+   *
+   * Note that reshaping an input blob and immediately calling Net::Backward is
+   * an error; either Net::Forward or Net::Reshape need to be called to
+   * propagate the new input shape to higher layers.
+   */
   void Reshape(const int num, const int channels, const int height,
     const int width);
   void ReshapeLike(const Blob& other);

From d833ab3493c83a10f32f5c5012875d4aba807b56 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 12 Sep 2014 16:07:34 -0700
Subject: [PATCH 0781/2053] check that LRN's local_size is odd as the current
 implementation requires

---
 src/caffe/layers/lrn_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 55b7673a21f..d9e41e9c137 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -10,6 +10,7 @@ template <typename Dtype>
 void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   size_ = this->layer_param_.lrn_param().local_size();
+  CHECK_EQ(size_ % 2, 1) << "LRN only supports odd values for local_size";
   pre_pad_ = (size_ - 1) / 2;
   alpha_ = this->layer_param_.lrn_param().alpha();
   beta_ = this->layer_param_.lrn_param().beta();

From 7a688c70f04498ee7cb34cbede2176800c11544d Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Thu, 18 Sep 2014 15:13:44 -0700
Subject: [PATCH 0782/2053] fix warning

---
 src/caffe/solver.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 0810f487fab..cca32998d62 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -174,7 +174,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   CHECK_GE(average_loss, 1) << "average_cost should be non-negative.";
 
   vector<Dtype> losses;
-  Dtype smoothed_loss;
+  Dtype smoothed_loss = 0;
 
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.

From 08d7f8c6398cd9103cceb128aff8e3e7e3fd94cb Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Thu, 18 Sep 2014 16:11:07 -0700
Subject: [PATCH 0783/2053] [model zoo] download gist script

---
 docs/model_zoo.md                   |  2 +-
 scripts/download_model_from_gist.sh | 16 ++++++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)
 create mode 100755 scripts/download_model_from_gist.sh

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 5603c62c6bb..330850cbbd6 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -43,7 +43,7 @@ Github Gist is a good format for model info distribution because it can contain
 
 Try doing `scripts/upload_model_to_gist.sh models/bvlc_alexnet` to test the uploading (don't forget to delete the uploaded gist afterward).
 
-Downloading models is not yet supported as a script (there is no good commandline tool for this right now), so simply go to the Gist URL and click "Download Gist" for now.
+Downloading model info is done just as easily with `scripts/download_model_from_gist.sh <gist_id> <dirname>`.
 
 ### Hosting trained models
 
diff --git a/scripts/download_model_from_gist.sh b/scripts/download_model_from_gist.sh
new file mode 100755
index 00000000000..61d995fbe69
--- /dev/null
+++ b/scripts/download_model_from_gist.sh
@@ -0,0 +1,16 @@
+echo "usage: download_model_from_gist.sh <gist_id> <dirname>"
+
+GIST=$1
+DIRNAME=$2
+
+if [ -d "$DIRNAME/$GIST" ]; then
+    echo "$DIRNAME/$GIST already exists! Please make sure you're not overwriting anything important!"
+    exit
+fi
+
+echo "Downloading Caffe model info to $DIRNAME/$GIST ..."
+mkdir -p $DIRNAME/$GIST
+wget https://gist.github.com/$GIST/download -O $DIRNAME/$GIST/gist.tar.gz
+tar xzf $DIRNAME/$GIST/gist.tar.gz --directory=$DIRNAME/$GIST --strip-components=1
+rm $DIRNAME/$GIST/gist.tar.gz
+echo "Done"

From 4e02e06a6d7887539d2e96858d64c6905e883e3a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 20:06:24 -0700
Subject: [PATCH 0784/2053] [model zoo] download from gist grooming

- invoke by shell
- default download dir to models/
- save to flat dir of owner-gist instead of nested owner/gist
---
 scripts/download_model_from_gist.sh | 26 +++++++++++++++++---------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/scripts/download_model_from_gist.sh b/scripts/download_model_from_gist.sh
index 61d995fbe69..a1dccf78b5b 100755
--- a/scripts/download_model_from_gist.sh
+++ b/scripts/download_model_from_gist.sh
@@ -1,16 +1,24 @@
-echo "usage: download_model_from_gist.sh <gist_id> <dirname>"
+#!/usr/bin/env sh
 
 GIST=$1
-DIRNAME=$2
+DIRNAME=${2:-./models}
 
-if [ -d "$DIRNAME/$GIST" ]; then
-    echo "$DIRNAME/$GIST already exists! Please make sure you're not overwriting anything important!"
+if [ -z $GIST ]; then
+  echo "usage: download_model_from_gist.sh <gist_id> <dirname>"
+  exit
+fi
+
+GIST_DIR=$(echo $GIST | tr '/' '-')
+MODEL_DIR="$DIRNAME/$GIST_DIR"
+
+if [ -d $MODEL_DIR ]; then
+    echo "$MODEL_DIR already exists! Please make sure you're not overwriting anything important!"
     exit
 fi
 
-echo "Downloading Caffe model info to $DIRNAME/$GIST ..."
-mkdir -p $DIRNAME/$GIST
-wget https://gist.github.com/$GIST/download -O $DIRNAME/$GIST/gist.tar.gz
-tar xzf $DIRNAME/$GIST/gist.tar.gz --directory=$DIRNAME/$GIST --strip-components=1
-rm $DIRNAME/$GIST/gist.tar.gz
+echo "Downloading Caffe model info to $MODEL_DIR ..."
+mkdir -p $MODEL_DIR
+wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.tar.gz
+tar xzf $MODEL_DIR/gist.tar.gz --directory=$MODEL_DIR --strip-components=1
+rm $MODEL_DIR/gist.tar.gz
 echo "Done"

From 7a507d6c1d83965faf46857a01616288f1b5d7dc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 20:15:50 -0700
Subject: [PATCH 0785/2053] [model zoo] ignore models -- only for reference or
 zoo

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index cf92e5fd4db..fa279f9071b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -51,6 +51,7 @@ Makefile.config
 # 1. reference, and not casually committed
 # 2. custom, and live on their own unless they're deliberated contributed
 data/*
+models/*
 *.caffemodel
 *.solverstate
 *.binaryproto

From a920a14718fcb169f900ecab9197bbe17266d566 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 20:24:19 -0700
Subject: [PATCH 0786/2053] [example] resurrect imagenet training scripts

---
 examples/imagenet/resume_training.sh | 5 +++++
 examples/imagenet/train_caffenet.sh  | 4 ++++
 2 files changed, 9 insertions(+)
 create mode 100755 examples/imagenet/resume_training.sh
 create mode 100755 examples/imagenet/train_caffenet.sh

diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
new file mode 100755
index 00000000000..d1febff8d39
--- /dev/null
+++ b/examples/imagenet/resume_training.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env sh
+
+./build/tools/caffe train \
+    --solver=models/bvlc_reference_caffenet/solver.prototxt \
+    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
diff --git a/examples/imagenet/train_caffenet.sh b/examples/imagenet/train_caffenet.sh
new file mode 100755
index 00000000000..94558ec5466
--- /dev/null
+++ b/examples/imagenet/train_caffenet.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env sh
+
+./build/tools/caffe train \
+    --solver=models/bvlc_reference_caffenet/solver.prototxt

From e146423f2d5454a2229ad642706b57a7e7f9ed70 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 21:25:10 -0700
Subject: [PATCH 0787/2053] [docs] order ipython notebooks

---
 docs/index.md                       | 2 +-
 examples/classification.ipynb       | 3 ++-
 examples/detection.ipynb            | 3 ++-
 examples/filter_visualization.ipynb | 3 ++-
 examples/hdf5_classification.ipynb  | 5 +++--
 examples/net_surgery.ipynb          | 3 ++-
 6 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index e89d8428739..e90b06b4d22 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -58,7 +58,7 @@ Developer documentation automagically generated from code comments.
 
 ### Notebook examples
 
-{% assign notebooks = site.pages | where:'category','notebook' %}
+{% assign notebooks = site.pages | where:'category','notebook' | sort: 'priority' %}
 {% for page in notebooks %}
 - <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 9c9d247a0cf..fb44da8958f 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -2,7 +2,8 @@
  "metadata": {
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
   "example_name": "ImageNet classification",
-  "include_in_docs": true
+  "include_in_docs": true,
+  "priority": 1
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 62263b66809..d05c0c22052 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -2,7 +2,8 @@
  "metadata": {
   "description": "Run a pretrained model as a detector in Python.",
   "example_name": "R-CNN detection",
-  "include_in_docs": true
+  "include_in_docs": true,
+  "priority": 3
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index abf4c0dd796..09b416fb4ca 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -2,7 +2,8 @@
  "metadata": {
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
   "example_name": "Filter visualization",
-  "include_in_docs": true
+  "include_in_docs": true,
+  "priority": 2
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
index 2059e6ff3a6..51d854fa142 100644
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
@@ -1,8 +1,9 @@
 {
  "metadata": {
   "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Classification with HDF5 data",
+  "example_name": "Off-the-shelf SGD for classification",
   "include_in_docs": true,
+  "priority": 4,
   "signature": "sha256:c3b84add3bb83e91137f396a48f46d46bf7921b242fc42c58390b30806e5a028"
  },
  "nbformat": 3,
@@ -943,4 +944,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index e4f4f985992..64310016101 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -3,6 +3,7 @@
   "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
+  "priority": 5,
   "signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
  },
  "nbformat": 3,
@@ -351,4 +352,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}

From 403b56ba30ad3f2e3c640bbe8e486e0b1a8a634b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 20:46:49 -0700
Subject: [PATCH 0788/2053] [example] groom siamese notebook

---
 examples/siamese/mnist_siamese.ipynb | 37 ++++++++++------------------
 1 file changed, 13 insertions(+), 24 deletions(-)

diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
index 66776fe14bd..5abd0469ba6 100644
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -1,7 +1,9 @@
 {
  "metadata": {
-  "name": "",
-  "signature": "sha256:ea70e3c13ccbae495d73f30e18b8e01e68ec8c7dc8bbb3635e6139b801c921a9"
+  "description": "Extracting features and plotting the Siamese network embedding.",
+  "example_name": "Siamese network embedding",
+  "include_in_docs": true,
+  "priority": 6
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,7 +14,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Setup and import caffe"
+      "### Setup\n",
+      "\n",
+      "Import Caffe and the usual modules."
      ]
     },
     {
@@ -39,7 +43,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Load the trained net."
+      "### Load the trained net\n",
+      "\n",
+      "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
      ]
     },
     {
@@ -91,7 +97,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Run the data through the net"
+      "### Generate the Siamese features"
      ]
     },
     {
@@ -114,7 +120,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Plot the results"
+      "### Visualize the learned Siamese embedding"
      ]
     },
     {
@@ -144,26 +150,9 @@
       }
      ],
      "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}

From 89fd7da77983d5bbbb045f3802821c8372722c5f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Sep 2014 22:07:01 -0700
Subject: [PATCH 0789/2053] relax precision of gradient-based solver tests

---
 src/caffe/test/test_gradient_based_solver.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index d196e581230..3040eb134a4 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -234,7 +234,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     ASSERT_EQ(2, param_blobs.size());
     const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
     ASSERT_EQ(D, solver_updated_weights.count());
-    const double kPrecision = 1e-3;
+    const double kPrecision = 1e-2;
     const double kMinPrecision = 1e-7;
     for (int i = 0; i < D; ++i) {
       const Dtype expected_updated_weight = updated_weights.cpu_data()[i];

From 2077516b0fdfc6dd0517f5c05ddf2e166cacc432 Mon Sep 17 00:00:00 2001
From: rickardnorlander <rickardnorlander@gmail.com>
Date: Fri, 19 Sep 2014 20:47:44 +0200
Subject: [PATCH 0790/2053] Fix typo in LRN-expression in docs

This is more consistent with krizhevsky2012, and it seems to be what the code does.
---
 docs/tutorial/layers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 8dd67275f58..5f8f519cdc4 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -111,7 +111,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
         - `beta` [default 5]: the exponent (see below)
         - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
 
-The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
+The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i^2)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
 
 #### im2col
 

From 8fd17a32e45f4e9b01ea58e87514acefcde0b6f4 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 19 Sep 2014 12:38:39 -0700
Subject: [PATCH 0791/2053] fix cifar10 paths so they can be run from caffe
 root

---
 examples/cifar10/train_full.sh  | 13 +++++++------
 examples/cifar10/train_quick.sh | 13 +++++++------
 2 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 9dd9ad79822..4285a5d6468 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -1,15 +1,16 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
+TOOLS=./build/tools
 
-$TOOLS/caffe train --solver=cifar10_full_solver.prototxt
+$TOOLS/caffe train \
+    --solver=examples/cifar10/cifar10_full_solver.prototxt
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
-    --solver=cifar10_full_solver_lr1.prototxt \
-    --snapshot=cifar10_full_iter_60000.solverstate
+    --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
-    --solver=cifar10_full_solver_lr2.prototxt \
-    --snapshot=cifar10_full_iter_65000.solverstate
+    --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index e348e12fd94..2830c40945c 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -1,10 +1,11 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
+TOOLS=./build/tools
 
-$TOOLS/caffe.bin train --solver=cifar10_quick_solver.prototxt
+$TOOLS/caffe train \
+  --solver=examples/cifar10/cifar10_quick_solver.prototxt
 
-# reduce learning rate by fctor of 10 after 8 epochs
-$TOOLS/caffe.bin train \
-  --solver=cifar10_quick_solver_lr1.prototxt \
-  --snapshot=cifar10_quick_iter_4000.solverstate
+# reduce learning rate by factor of 10 after 8 epochs
+$TOOLS/caffe train \
+  --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate

From 31326a111bcb5ac5a9adeec13cfe6330e78ef31e Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 19 Sep 2014 16:04:37 -0700
Subject: [PATCH 0792/2053] fix types of (Layer)SetUp, Reshape, Forward, and
 Backward calls

Using the type vector<Blob<Dtype*>* for outputs allows modification of
the vector itself, while it is only okay to modify the blobs pointed to
by the elements of the vector. Switching the types to const
vector<Blob<Dtype>*>& makes them more correct.
---
 include/caffe/common_layers.hpp               | 114 +++++++++---------
 include/caffe/data_layers.hpp                 |  60 ++++-----
 include/caffe/layer.hpp                       |  51 ++++----
 include/caffe/loss_layers.hpp                 |  74 ++++++------
 include/caffe/neuron_layers.hpp               |  98 +++++++--------
 .../caffe/test/test_gradient_check_util.hpp   |  85 ++++++-------
 include/caffe/util/device_alternate.hpp       |   8 +-
 include/caffe/vision_layers.hpp               |  76 ++++++------
 src/caffe/layers/absval_layer.cpp             |  16 +--
 src/caffe/layers/absval_layer.cu              |  12 +-
 src/caffe/layers/accuracy_layer.cpp           |  10 +-
 src/caffe/layers/argmax_layer.cpp             |  16 +--
 src/caffe/layers/base_data_layer.cpp          |  12 +-
 src/caffe/layers/base_data_layer.cu           |   6 +-
 src/caffe/layers/bnll_layer.cpp               |  12 +-
 src/caffe/layers/bnll_layer.cu                |  12 +-
 src/caffe/layers/concat_layer.cpp             |  26 ++--
 src/caffe/layers/concat_layer.cu              |  18 +--
 src/caffe/layers/contrastive_loss_layer.cpp   |  18 +--
 src/caffe/layers/contrastive_loss_layer.cu    |  16 +--
 src/caffe/layers/conv_layer.cpp               |  30 ++---
 src/caffe/layers/conv_layer.cu                |  18 +--
 src/caffe/layers/cudnn_conv_layer.cpp         |   4 +-
 src/caffe/layers/cudnn_conv_layer.cu          |  10 +-
 src/caffe/layers/cudnn_pooling_layer.cpp      |   4 +-
 src/caffe/layers/cudnn_pooling_layer.cu       |  12 +-
 src/caffe/layers/cudnn_relu_layer.cpp         |   4 +-
 src/caffe/layers/cudnn_relu_layer.cu          |  10 +-
 src/caffe/layers/cudnn_sigmoid_layer.cpp      |   4 +-
 src/caffe/layers/cudnn_sigmoid_layer.cu       |  10 +-
 src/caffe/layers/cudnn_softmax_layer.cpp      |   4 +-
 src/caffe/layers/cudnn_softmax_layer.cu       |  10 +-
 src/caffe/layers/cudnn_tanh_layer.cpp         |   4 +-
 src/caffe/layers/cudnn_tanh_layer.cu          |  10 +-
 src/caffe/layers/data_layer.cpp               |  14 +--
 src/caffe/layers/dropout_layer.cpp            |  14 +--
 src/caffe/layers/dropout_layer.cu             |  10 +-
 src/caffe/layers/dummy_data_layer.cpp         |  12 +-
 src/caffe/layers/eltwise_layer.cpp            |  28 ++---
 src/caffe/layers/eltwise_layer.cu             |  20 +--
 src/caffe/layers/euclidean_loss_layer.cpp     |  14 +--
 src/caffe/layers/euclidean_loss_layer.cu      |  12 +-
 src/caffe/layers/flatten_layer.cpp            |  14 +--
 src/caffe/layers/flatten_layer.cu             |   8 +-
 src/caffe/layers/hdf5_data_layer.cpp          |  22 ++--
 src/caffe/layers/hdf5_data_layer.cu           |  10 +-
 src/caffe/layers/hdf5_output_layer.cpp        |   4 +-
 src/caffe/layers/hdf5_output_layer.cu         |   4 +-
 src/caffe/layers/hinge_loss_layer.cpp         |  14 +--
 src/caffe/layers/im2col_layer.cpp             |  18 +--
 src/caffe/layers/im2col_layer.cu              |  12 +-
 src/caffe/layers/image_data_layer.cpp         |  14 +--
 src/caffe/layers/infogain_loss_layer.cpp      |  24 ++--
 src/caffe/layers/inner_product_layer.cpp      |  16 +--
 src/caffe/layers/inner_product_layer.cu       |  10 +-
 src/caffe/layers/loss_layer.cpp               |   6 +-
 src/caffe/layers/lrn_layer.cpp                |  56 ++++-----
 src/caffe/layers/lrn_layer.cu                 |  14 +--
 src/caffe/layers/memory_data_layer.cpp        |  12 +-
 .../multinomial_logistic_loss_layer.cpp       |  20 +--
 src/caffe/layers/mvn_layer.cpp                |  20 +--
 src/caffe/layers/mvn_layer.cu                 |  16 +--
 src/caffe/layers/neuron_layer.cpp             |   4 +-
 src/caffe/layers/pooling_layer.cpp            |  40 +++---
 src/caffe/layers/pooling_layer.cu             |  16 +--
 src/caffe/layers/power_layer.cpp              |  14 +--
 src/caffe/layers/power_layer.cu               |  12 +-
 src/caffe/layers/relu_layer.cpp               |  12 +-
 src/caffe/layers/relu_layer.cu                |  12 +-
 .../sigmoid_cross_entropy_loss_layer.cpp      |  24 ++--
 .../sigmoid_cross_entropy_loss_layer.cu       |  16 +--
 src/caffe/layers/sigmoid_layer.cpp            |  10 +-
 src/caffe/layers/sigmoid_layer.cu             |  10 +-
 src/caffe/layers/silence_layer.cpp            |   8 +-
 src/caffe/layers/silence_layer.cu             |  10 +-
 src/caffe/layers/slice_layer.cpp              |  58 ++++-----
 src/caffe/layers/slice_layer.cu               |  18 +--
 src/caffe/layers/softmax_layer.cpp            |  16 +--
 src/caffe/layers/softmax_layer.cu             |   8 +-
 src/caffe/layers/softmax_loss_layer.cpp       |  28 ++---
 src/caffe/layers/softmax_loss_layer.cu        |   4 +-
 src/caffe/layers/split_layer.cpp              |  24 ++--
 src/caffe/layers/split_layer.cu               |  14 +--
 src/caffe/layers/tanh_layer.cpp               |  10 +-
 src/caffe/layers/tanh_layer.cu                |  10 +-
 src/caffe/layers/threshold_layer.cpp          |   6 +-
 src/caffe/layers/threshold_layer.cu           |   4 +-
 src/caffe/layers/window_data_layer.cpp        |  20 +--
 src/caffe/net.cpp                             |  10 +-
 src/caffe/test/test_accuracy_layer.cpp        |  12 +-
 src/caffe/test/test_argmax_layer.cpp          |  20 +--
 src/caffe/test/test_concat_layer.cpp          |  12 +-
 .../test/test_contrastive_loss_layer.cpp      |  14 +--
 src/caffe/test/test_convolution_layer.cpp     |  64 +++++-----
 src/caffe/test/test_data_layer.cpp            |  24 ++--
 src/caffe/test/test_dummy_data_layer.cpp      |  14 +--
 src/caffe/test/test_eltwise_layer.cpp         |  38 +++---
 src/caffe/test/test_euclidean_loss_layer.cpp  |  14 +--
 src/caffe/test/test_flatten_layer.cpp         |  10 +-
 src/caffe/test/test_hdf5_output_layer.cpp     |   4 +-
 src/caffe/test/test_hdf5data_layer.cpp        |   6 +-
 src/caffe/test/test_hinge_loss_layer.cpp      |   8 +-
 src/caffe/test/test_im2col_layer.cpp          |  18 +--
 src/caffe/test/test_image_data_layer.cpp      |  12 +-
 src/caffe/test/test_infogain_loss_layer.cpp   |   4 +-
 src/caffe/test/test_inner_product_layer.cpp   |  10 +-
 src/caffe/test/test_lrn_layer.cpp             |  30 ++---
 .../test/test_maxpool_dropout_layers.cpp      |  26 ++--
 src/caffe/test/test_memory_data_layer.cpp     |  10 +-
 .../test_multinomial_logistic_loss_layer.cpp  |   6 +-
 src/caffe/test/test_mvn_layer.cpp             |  24 ++--
 src/caffe/test/test_neuron_layer.cpp          |  96 +++++++--------
 src/caffe/test/test_pooling_layer.cpp         |  92 +++++++-------
 src/caffe/test/test_power_layer.cpp           |   8 +-
 .../test_sigmoid_cross_entropy_loss_layer.cpp |  10 +-
 src/caffe/test/test_slice_layer.cpp           |  20 +--
 src/caffe/test/test_softmax_layer.cpp         |  16 +--
 .../test/test_softmax_with_loss_layer.cpp     |   4 +-
 src/caffe/test/test_split_layer.cpp           |  10 +-
 src/caffe/test/test_stochastic_pooling.cpp    |  14 +--
 src/caffe/test/test_threshold_layer.cpp       |  10 +-
 tools/caffe.cpp                               |   6 +-
 122 files changed, 1199 insertions(+), 1197 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 1f945ca34e9..9718b825b14 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -39,9 +39,9 @@ class ArgMaxLayer : public Layer<Dtype> {
   explicit ArgMaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ARGMAX;
@@ -62,10 +62,10 @@ class ArgMaxLayer : public Layer<Dtype> {
    *      @f$ (for @f$ K = 1 @f$).
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   /// @brief Not implemented (non-differentiable function)
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     NOT_IMPLEMENTED;
   }
   bool out_max_val_;
@@ -82,9 +82,9 @@ class ConcatLayer : public Layer<Dtype> {
   explicit ConcatLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_CONCAT;
@@ -110,9 +110,9 @@ class ConcatLayer : public Layer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the concatenate inputs.
@@ -137,9 +137,9 @@ class ConcatLayer : public Layer<Dtype> {
    *        @f$
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> col_bob_;
   int count_;
@@ -162,9 +162,9 @@ class EltwiseLayer : public Layer<Dtype> {
   explicit EltwiseLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ELTWISE;
@@ -174,13 +174,13 @@ class EltwiseLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   EltwiseParameter_EltwiseOp op_;
   vector<Dtype> coeffs_;
@@ -205,7 +205,7 @@ class FlattenLayer : public Layer<Dtype> {
   explicit FlattenLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_FLATTEN;
@@ -223,9 +223,9 @@ class FlattenLayer : public Layer<Dtype> {
    *      the outputs -- i.e., the (virtually) copied, flattened inputs
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the concatenate inputs.
@@ -237,9 +237,9 @@ class FlattenLayer : public Layer<Dtype> {
    *        gradient is (virtually) copied
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int count_;
 };
@@ -256,9 +256,9 @@ class InnerProductLayer : public Layer<Dtype> {
   explicit InnerProductLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_INNER_PRODUCT;
@@ -268,13 +268,13 @@ class InnerProductLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int M_;
   int K_;
@@ -294,7 +294,7 @@ class MVNLayer : public Layer<Dtype> {
   explicit MVNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_MVN;
@@ -304,13 +304,13 @@ class MVNLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> mean_, variance_, temp_;
 
@@ -328,7 +328,7 @@ class SilenceLayer : public Layer<Dtype> {
   explicit SilenceLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SILENCE;
@@ -338,15 +338,15 @@ class SilenceLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
   // We can't define Forward_gpu here, since STUB_GPU will provide
   // its own definition for CPU_ONLY mode.
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -360,7 +360,7 @@ class SoftmaxLayer : public Layer<Dtype> {
   explicit SoftmaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SOFTMAX;
@@ -370,13 +370,13 @@ class SoftmaxLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
@@ -395,16 +395,16 @@ class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
   explicit CuDNNSoftmaxLayer(const LayerParameter& param)
       : SoftmaxLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNSoftmaxLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
@@ -424,7 +424,7 @@ class SplitLayer : public Layer<Dtype> {
   explicit SplitLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SPLIT;
@@ -434,13 +434,13 @@ class SplitLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int count_;
 };
@@ -457,9 +457,9 @@ class SliceLayer : public Layer<Dtype> {
   explicit SliceLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SLICE;
@@ -469,13 +469,13 @@ class SliceLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> col_bob_;
   int count_;
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 8e2637b0658..b3f93cea19e 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -37,17 +37,17 @@ class BaseDataLayer : public Layer<Dtype> {
   // DataLayerSetUp to do special data layer setup for individual layer types.
   // This method may not be overridden except by the BasePrefetchingDataLayer.
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
 
   int datum_channels() const { return datum_channels_; }
   int datum_height() const { return datum_height_; }
@@ -78,12 +78,12 @@ class BasePrefetchingDataLayer :
   // DataLayerSetUp to do special data layer setup for individual layer types.
   // This method may not be overridden.
   void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
@@ -102,7 +102,7 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
       : BasePrefetchingDataLayer<Dtype>(param) {}
   virtual ~DataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_DATA;
@@ -136,10 +136,10 @@ class DummyDataLayer : public Layer<Dtype> {
   explicit DummyDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_DUMMY_DATA;
@@ -149,11 +149,11 @@ class DummyDataLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
 
   vector<shared_ptr<Filler<Dtype> > > fillers_;
   vector<bool> refill_;
@@ -171,10 +171,10 @@ class HDF5DataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual ~HDF5DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_HDF5_DATA;
@@ -184,13 +184,13 @@ class HDF5DataLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
   virtual void LoadHDF5FileData(const char* filename);
 
   std::vector<std::string> hdf_filenames_;
@@ -212,10 +212,10 @@ class HDF5OutputLayer : public Layer<Dtype> {
   explicit HDF5OutputLayer(const LayerParameter& param);
   virtual ~HDF5OutputLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_HDF5_OUTPUT;
@@ -228,13 +228,13 @@ class HDF5OutputLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void SaveBlobs();
 
   std::string file_name_;
@@ -255,7 +255,7 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
       : BasePrefetchingDataLayer<Dtype>(param) {}
   virtual ~ImageDataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_IMAGE_DATA;
@@ -283,7 +283,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   explicit MemoryDataLayer(const LayerParameter& param)
       : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_MEMORY_DATA;
@@ -301,7 +301,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   int batch_size_;
   Dtype* data_;
@@ -326,7 +326,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
       : BasePrefetchingDataLayer<Dtype>(param) {}
   virtual ~WindowDataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_WINDOW_DATA;
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index e160075b939..18ff2743719 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -56,8 +56,9 @@ class Layer {
    * Sets up the loss weight multiplier blobs for any non-zero loss weights.
    * This method may not be overridden.
    */
-  void SetUp(const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-    CheckBlobCounts(bottom, *top);
+  void SetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    CheckBlobCounts(bottom, top);
     LayerSetUp(bottom, top);
     Reshape(bottom, top);
     SetLossWeights(top);
@@ -80,7 +81,7 @@ class Layer {
    * adjust the top blob sizes.
    */
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {}
+      const vector<Blob<Dtype>*>& top) {}
 
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accomodate
@@ -95,7 +96,7 @@ class Layer {
    * accomodate the bottom blobs.
    */
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      const vector<Blob<Dtype>*>& top) = 0;
 
   /**
    * @brief Given the bottom blobs, compute the top blobs and the loss.
@@ -115,7 +116,7 @@ class Layer {
    * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
    */
   inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Given the top blob error gradients, compute the bottom blob error
@@ -140,7 +141,7 @@ class Layer {
    */
   inline void Backward(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
-      vector<Blob<Dtype>*>* bottom);
+      const vector<Blob<Dtype>*>& bottom);
 
   /**
    * @brief Returns the vector of learnable parameter blobs.
@@ -306,13 +307,13 @@ class Layer {
 
   /** @brief Using the CPU device, compute the layer output. */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      const vector<Blob<Dtype>*>& top) = 0;
   /**
    * @brief Using the GPU device, compute the layer output.
    *        Fall back to Forward_cpu() if unavailable.
    */
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
     // LOG(WARNING) << "Using CPU code as backup.";
     return Forward_cpu(bottom, top);
   }
@@ -323,7 +324,7 @@ class Layer {
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
-      vector<Blob<Dtype>*>* bottom) = 0;
+      const vector<Blob<Dtype>*>& bottom) = 0;
   /**
    * @brief Using the GPU device, compute the gradients for any parameters and
    *        for the bottom blobs if propagate_down is true.
@@ -331,7 +332,7 @@ class Layer {
    */
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
-      vector<Blob<Dtype>*>* bottom) {
+      const vector<Blob<Dtype>*>& bottom) {
     // LOG(WARNING) << "Using CPU code as backup.";
     Backward_cpu(top, propagate_down, bottom);
   }
@@ -384,17 +385,17 @@ class Layer {
    * Called by SetUp to initialize the weights associated with any top blobs in
    * the loss function. Store non-zero loss weights in the diff blob.
    */
-  inline void SetLossWeights(vector<Blob<Dtype>*>* top) {
+  inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
     const int num_loss_weights = layer_param_.loss_weight_size();
     if (num_loss_weights) {
-      CHECK_EQ(top->size(), num_loss_weights) << "loss_weight must be "
+      CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
           "unspecified or specified once per top blob.";
-      for (int top_id = 0; top_id < top->size(); ++top_id) {
+      for (int top_id = 0; top_id < top.size(); ++top_id) {
         const Dtype loss_weight = layer_param_.loss_weight(top_id);
         if (loss_weight == Dtype(0)) { continue; }
         this->set_loss(top_id, loss_weight);
-        const int count = (*top)[top_id]->count();
-        Dtype* loss_multiplier = (*top)[top_id]->mutable_cpu_diff();
+        const int count = top[top_id]->count();
+        Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
         caffe_set(count, loss_weight, loss_multiplier);
       }
     }
@@ -408,27 +409,27 @@ class Layer {
 // functions.
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   Dtype loss = 0;
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Forward_cpu(bottom, top);
-    for (int top_id = 0; top_id < top->size(); ++top_id) {
+    for (int top_id = 0; top_id < top.size(); ++top_id) {
       if (!this->loss(top_id)) { continue; }
-      const int count = (*top)[top_id]->count();
-      const Dtype* data = (*top)[top_id]->cpu_data();
-      const Dtype* loss_weights = (*top)[top_id]->cpu_diff();
+      const int count = top[top_id]->count();
+      const Dtype* data = top[top_id]->cpu_data();
+      const Dtype* loss_weights = top[top_id]->cpu_diff();
       loss += caffe_cpu_dot(count, data, loss_weights);
     }
     break;
   case Caffe::GPU:
     Forward_gpu(bottom, top);
 #ifndef CPU_ONLY
-    for (int top_id = 0; top_id < top->size(); ++top_id) {
+    for (int top_id = 0; top_id < top.size(); ++top_id) {
       if (!this->loss(top_id)) { continue; }
-      const int count = (*top)[top_id]->count();
-      const Dtype* data = (*top)[top_id]->gpu_data();
-      const Dtype* loss_weights = (*top)[top_id]->gpu_diff();
+      const int count = top[top_id]->count();
+      const Dtype* data = top[top_id]->gpu_data();
+      const Dtype* loss_weights = top[top_id]->gpu_diff();
       Dtype blob_loss = 0;
       caffe_gpu_dot(count, data, loss_weights, &blob_loss);
       loss += blob_loss;
@@ -444,7 +445,7 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Backward_cpu(top, propagate_down, bottom);
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 08aa7752d4a..9fe58cd97bc 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -33,9 +33,9 @@ class AccuracyLayer : public Layer<Dtype> {
   explicit AccuracyLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ACCURACY;
@@ -70,12 +70,12 @@ class AccuracyLayer : public Layer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
 
   /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     for (int i = 0; i < propagate_down.size(); ++i) {
       if (propagate_down[i]) { NOT_IMPLEMENTED; }
     }
@@ -98,9 +98,9 @@ class LossLayer : public Layer<Dtype> {
   explicit LossLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
   virtual void LayerSetUp(
-      const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
   virtual void Reshape(
-      const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
 
   virtual inline int ExactNumBottomBlobs() const { return 2; }
 
@@ -151,7 +151,7 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
   explicit ContrastiveLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), diff_() {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline int ExactNumBottomBlobs() const { return 3; }
   virtual inline LayerParameter_LayerType type() const {
@@ -168,9 +168,9 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc ContrastiveLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the Contrastive error gradient w.r.t. the inputs.
@@ -198,9 +198,9 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
    *      propagate_down[1]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> diff_;  // cached for backward pass
   Blob<Dtype> dist_sq_;  // cached for backward pass
@@ -240,7 +240,7 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   explicit EuclideanLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), diff_() {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_EUCLIDEAN_LOSS;
@@ -257,9 +257,9 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc EuclideanLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the Euclidean error gradient w.r.t. the inputs.
@@ -295,9 +295,9 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
    *      @f$ if propagate_down[1]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> diff_;
 };
@@ -358,7 +358,7 @@ class HingeLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc HingeLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the hinge loss error gradient w.r.t. the predictions.
@@ -388,7 +388,7 @@ class HingeLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -429,9 +429,9 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   explicit InfogainLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param), infogain_() {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
   // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
@@ -447,7 +447,7 @@ class InfogainLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc InfogainLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the infogain loss error gradient w.r.t. the predictions.
@@ -482,7 +482,7 @@ class InfogainLossLayer : public LossLayer<Dtype> {
    *      gradient computation is not implemented.
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   Blob<Dtype> infogain_;
 };
@@ -522,7 +522,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   explicit MultinomialLogisticLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
@@ -531,7 +531,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc MultinomialLogisticLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the multinomial logistic loss error gradient w.r.t. the
@@ -562,7 +562,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -602,9 +602,9 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
           sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
           sigmoid_output_(new Blob<Dtype>()) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
@@ -613,9 +613,9 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc SigmoidCrossEntropyLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
@@ -648,9 +648,9 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// The internal SigmoidLayer used to map predictions to probabilities.
   shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
@@ -700,9 +700,9 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
       : LossLayer<Dtype>(param),
         softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_SOFTMAX_LOSS;
@@ -717,9 +717,9 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  protected:
   /// @copydoc SoftmaxWithLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the softmax loss error gradient w.r.t. the predictions.
@@ -749,9 +749,9 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
    *      the labels -- ignored as we can't compute their error gradients
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
   shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 0968a2007dc..64a1fd325ad 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -27,7 +27,7 @@ class NeuronLayer : public Layer<Dtype> {
   explicit NeuronLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_NONE;
@@ -52,7 +52,7 @@ class AbsValLayer : public NeuronLayer<Dtype> {
   explicit AbsValLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_ABSVAL;
@@ -63,9 +63,9 @@ class AbsValLayer : public NeuronLayer<Dtype> {
  protected:
   /// @copydoc AbsValLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the absolute value inputs.
@@ -85,9 +85,9 @@ class AbsValLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -120,9 +120,9 @@ class BNLLLayer : public NeuronLayer<Dtype> {
  protected:
   /// @copydoc BNLLLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the BNLL inputs.
@@ -141,9 +141,9 @@ class BNLLLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 /**
@@ -169,9 +169,9 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   explicit DropoutLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_DROPOUT;
@@ -195,13 +195,13 @@ class DropoutLayer : public NeuronLayer<Dtype> {
    *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
   Blob<unsigned int> rand_vec_;
@@ -230,7 +230,7 @@ class PowerLayer : public NeuronLayer<Dtype> {
   explicit PowerLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_POWER;
@@ -248,9 +248,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the power inputs.
@@ -273,9 +273,9 @@ class PowerLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// @brief @f$ \gamma @f$ from layer_param_.power_param()
   Dtype power_;
@@ -320,9 +320,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the ReLU inputs.
@@ -353,9 +353,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
    *      @f$.
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 #ifdef USE_CUDNN
@@ -368,16 +368,16 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
   explicit CuDNNReLULayer(const LayerParameter& param)
       : ReLULayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNReLULayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
@@ -415,9 +415,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -437,9 +437,9 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 #ifdef USE_CUDNN
@@ -452,16 +452,16 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
   explicit CuDNNSigmoidLayer(const LayerParameter& param)
       : SigmoidLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNSigmoidLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
@@ -499,9 +499,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the sigmoid inputs.
@@ -523,9 +523,9 @@ class TanHLayer : public NeuronLayer<Dtype> {
    *      @f$ if propagate_down[0]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 };
 
 #ifdef USE_CUDNN
@@ -538,16 +538,16 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
   explicit CuDNNTanHLayer(const LayerParameter& param)
       : TanHLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNTanHLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
@@ -571,7 +571,7 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   explicit ThresholdLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_THRESHOLD;
@@ -593,12 +593,12 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
    *      @f$
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   /// @brief Not implemented (non-differentiable function)
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     NOT_IMPLEMENTED;
   }
 
diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 5a8d382ff26..22937711b58 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -30,24 +30,24 @@ class GradientChecker {
   // layers.
   // Note that after the gradient check, we do not guarantee that the data
   // stored in the layer parameters and the blobs are unchanged.
-  void CheckGradient(Layer<Dtype>* layer, vector<Blob<Dtype>*>* bottom,
-      vector<Blob<Dtype>*>* top, int check_bottom = -1) {
-      layer->SetUp(*bottom, top);
+  void CheckGradient(Layer<Dtype>* layer, const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top, int check_bottom = -1) {
+      layer->SetUp(bottom, top);
       CheckGradientSingle(layer, bottom, top, check_bottom, -1, -1);
   }
   void CheckGradientExhaustive(Layer<Dtype>* layer,
-      vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top,
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
       int check_bottom = -1);
 
   // CheckGradientEltwise can be used to test layers that perform element-wise
   // computation only (e.g., neuron layers) -- where (d y_i) / (d x_j) = 0 when
   // i != j.
   void CheckGradientEltwise(Layer<Dtype>* layer,
-      vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
 
-  void CheckGradientSingle(Layer<Dtype>* layer, vector<Blob<Dtype>*>* bottom,
-      vector<Blob<Dtype>*>* top, int check_bottom, int top_id,
-      int top_data_id, bool element_wise = false);
+  void CheckGradientSingle(Layer<Dtype>* layer,
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
+      int check_bottom, int top_id, int top_data_id, bool element_wise = false);
 
   // Checks the gradient of a network. This network should not have any data
   // layers or loss layers, since the function does not explicitly deal with
@@ -57,8 +57,8 @@ class GradientChecker {
       const vector<Blob<Dtype>*>& input);
 
  protected:
-  Dtype GetObjAndGradient(const Layer<Dtype>& layer, vector<Blob<Dtype>*>* top,
-      int top_id = -1, int top_data_id = -1);
+  Dtype GetObjAndGradient(const Layer<Dtype>& layer,
+      const vector<Blob<Dtype>*>& top, int top_id = -1, int top_data_id = -1);
   Dtype stepsize_;
   Dtype threshold_;
   unsigned int seed_;
@@ -69,40 +69,40 @@ class GradientChecker {
 
 template <typename Dtype>
 void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
-    vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top,
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
     int check_bottom, int top_id, int top_data_id, bool element_wise) {
   if (element_wise) {
     CHECK_EQ(0, layer->blobs().size());
     CHECK_LE(0, top_id);
     CHECK_LE(0, top_data_id);
-    const int top_count = (*top)[top_id]->count();
-    for (int blob_id = 0; blob_id < bottom->size(); ++blob_id) {
-      CHECK_EQ(top_count, (*bottom)[blob_id]->count());
+    const int top_count = top[top_id]->count();
+    for (int blob_id = 0; blob_id < bottom.size(); ++blob_id) {
+      CHECK_EQ(top_count, bottom[blob_id]->count());
     }
   }
   // First, figure out what blobs we need to check against.
   vector<Blob<Dtype>*> blobs_to_check;
-  vector<bool> propagate_down(bottom->size(), check_bottom < 0);
+  vector<bool> propagate_down(bottom.size(), check_bottom < 0);
   for (int i = 0; i < layer->blobs().size(); ++i) {
     blobs_to_check.push_back(layer->blobs()[i].get());
   }
   if (check_bottom < 0) {
-    for (int i = 0; i < bottom->size(); ++i) {
-      blobs_to_check.push_back((*bottom)[i]);
+    for (int i = 0; i < bottom.size(); ++i) {
+      blobs_to_check.push_back(bottom[i]);
     }
   } else {
-    CHECK_LT(check_bottom, bottom->size());
-    blobs_to_check.push_back((*bottom)[check_bottom]);
+    CHECK_LT(check_bottom, bottom.size());
+    blobs_to_check.push_back(bottom[check_bottom]);
     propagate_down[check_bottom] = true;
   }
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
   // Ignore the loss from the layer (it's just the weighted sum of the losses
   // from the top blobs, whose gradients we may want to test individually).
-  layer->Forward(*bottom, top);
+  layer->Forward(bottom, top);
   // Get additional loss from the objective
   GetObjAndGradient(*layer, top, top_id, top_data_id);
-  layer->Backward(*top, propagate_down, bottom);
+  layer->Backward(top, propagate_down, bottom);
   // Store computed gradients for all checked blobs
   vector<shared_ptr<Blob<Dtype> > >
       computed_gradient_blobs(blobs_to_check.size());
@@ -127,8 +127,8 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
     //     << current_blob->count() << " parameters.";
     for (int feat_id = 0; feat_id < current_blob->count(); ++feat_id) {
       // For an element-wise layer, we only need to do finite differencing to
-      // compute the derivative of (*top)[top_id][top_data_id] w.r.t.
-      // (*bottom)[blob_id][i] only for i == top_data_id.  For any other
+      // compute the derivative of top[top_id][top_data_id] w.r.t.
+      // bottom[blob_id][i] only for i == top_data_id.  For any other
       // i != top_data_id, we know the derivative is 0 by definition, and simply
       // check that that's true.
       Dtype estimated_gradient = 0;
@@ -139,13 +139,13 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
         // Compute loss with stepsize_ added to input.
         current_blob->mutable_cpu_data()[feat_id] += stepsize_;
         Caffe::set_random_seed(seed_);
-        layer->Forward(*bottom, top);
+        layer->Forward(bottom, top);
         positive_objective =
             GetObjAndGradient(*layer, top, top_id, top_data_id);
         // Compute loss with stepsize_ subtracted from input.
         current_blob->mutable_cpu_data()[feat_id] -= stepsize_ * 2;
         Caffe::set_random_seed(seed_);
-        layer->Forward(*bottom, top);
+        layer->Forward(bottom, top);
         negative_objective =
             GetObjAndGradient(*layer, top, top_id, top_data_id);
         // Recover original input value.
@@ -179,13 +179,14 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
 
 template <typename Dtype>
 void GradientChecker<Dtype>::CheckGradientExhaustive(Layer<Dtype>* layer,
-    vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top, int check_bottom) {
-  layer->SetUp(*bottom, top);
-  CHECK_GT(top->size(), 0) << "Exhaustive mode requires at least one top blob.";
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
+    int check_bottom) {
+  layer->SetUp(bottom, top);
+  CHECK_GT(top.size(), 0) << "Exhaustive mode requires at least one top blob.";
   // LOG(ERROR) << "Exhaustive Mode.";
-  for (int i = 0; i < top->size(); ++i) {
+  for (int i = 0; i < top.size(); ++i) {
     // LOG(ERROR) << "Exhaustive: blob " << i << " size " << top[i]->count();
-    for (int j = 0; j < (*top)[i]->count(); ++j) {
+    for (int j = 0; j < top[i]->count(); ++j) {
       // LOG(ERROR) << "Exhaustive: blob " << i << " data " << j;
       CheckGradientSingle(layer, bottom, top, check_bottom, i, j);
     }
@@ -194,13 +195,13 @@ void GradientChecker<Dtype>::CheckGradientExhaustive(Layer<Dtype>* layer,
 
 template <typename Dtype>
 void GradientChecker<Dtype>::CheckGradientEltwise(Layer<Dtype>* layer,
-    vector<Blob<Dtype>*>* bottom, vector<Blob<Dtype>*>* top) {
-  layer->SetUp(*bottom, top);
-  CHECK_GT(top->size(), 0) << "Eltwise mode requires at least one top blob.";
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  layer->SetUp(bottom, top);
+  CHECK_GT(top.size(), 0) << "Eltwise mode requires at least one top blob.";
   const int check_bottom = -1;
   const bool element_wise = true;
-  for (int i = 0; i < top->size(); ++i) {
-    for (int j = 0; j < (*top)[i]->count(); ++j) {
+  for (int i = 0; i < top.size(); ++i) {
+    for (int j = 0; j < top[i]->count(); ++j) {
       CheckGradientSingle(layer, bottom, top, check_bottom, i, j, element_wise);
     }
   }
@@ -221,12 +222,12 @@ void GradientChecker<Dtype>::CheckGradientNet(
 
 template <typename Dtype>
 Dtype GradientChecker<Dtype>::GetObjAndGradient(const Layer<Dtype>& layer,
-    vector<Blob<Dtype>*>* top, int top_id, int top_data_id) {
+    const vector<Blob<Dtype>*>& top, int top_id, int top_data_id) {
   Dtype loss = 0;
   if (top_id < 0) {
     // the loss will be half of the sum of squares of all outputs
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* top_blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* top_blob = top[i];
       const Dtype* top_blob_data = top_blob->cpu_data();
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
       int count = top_blob->count();
@@ -239,14 +240,14 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(const Layer<Dtype>& layer,
     loss /= 2.;
   } else {
     // the loss will be the top_data_id-th element in the top_id-th blob.
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* top_blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* top_blob = top[i];
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
       caffe_set(top_blob->count(), Dtype(0), top_blob_diff);
     }
     const Dtype loss_weight = 2;
-    loss = (*top)[top_id]->cpu_data()[top_data_id] * loss_weight;
-    (*top)[top_id]->mutable_cpu_diff()[top_data_id] = loss_weight;
+    loss = top[top_id]->cpu_data()[top_data_id] * loss_weight;
+    top[top_id]->mutable_cpu_diff()[top_data_id] = loss_weight;
   }
   return loss;
 }
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 3df28a49ac3..5a45691bb17 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -12,22 +12,22 @@
 #define STUB_GPU(classname) \
 template <typename Dtype> \
 void classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
-    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& top) { NO_GPU; } \
 template <typename Dtype> \
 void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
     const vector<bool>& propagate_down, \
-    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& bottom) { NO_GPU; } \
 
 #define STUB_GPU_FORWARD(classname, funcname) \
 template <typename Dtype> \
 void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
-    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& top) { NO_GPU; } \
 
 #define STUB_GPU_BACKWARD(classname, funcname) \
 template <typename Dtype> \
 void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
     const vector<bool>& propagate_down, \
-    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+    const vector<Blob<Dtype>*>& bottom) { NO_GPU; } \
 
 #else  // Normal GPU + CPU Caffe.
 
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 1e7f3fcb297..0f1b2d9e529 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -66,9 +66,9 @@ class ConvolutionLayer : public Layer<Dtype> {
   explicit ConvolutionLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_CONVOLUTION;
@@ -79,13 +79,13 @@ class ConvolutionLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -132,16 +132,16 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
   explicit CuDNNConvolutionLayer(const LayerParameter& param)
       : ConvolutionLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNConvolutionLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   cudnnHandle_t* handle_;
   cudaStream_t*  stream_;
@@ -166,9 +166,9 @@ class Im2colLayer : public Layer<Dtype> {
   explicit Im2colLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_IM2COL;
@@ -178,13 +178,13 @@ class Im2colLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -208,9 +208,9 @@ class LRNLayer : public Layer<Dtype> {
   explicit LRNLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_LRN;
@@ -220,26 +220,26 @@ class LRNLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int size_;
   int pre_pad_;
@@ -285,9 +285,9 @@ class PoolingLayer : public Layer<Dtype> {
   explicit PoolingLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
 
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_POOLING;
@@ -303,13 +303,13 @@ class PoolingLayer : public Layer<Dtype> {
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int kernel_h_, kernel_w_;
   int stride_h_, stride_w_;
@@ -332,16 +332,16 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
   explicit CuDNNPoolingLayer(const LayerParameter& param)
       : PoolingLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNPoolingLayer();
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index ce9d05cc764..54e484b115d 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -8,29 +8,29 @@ namespace caffe {
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
-  CHECK_NE((*top)[0], bottom[0]) << this->type_name() << " Layer does not "
+  CHECK_NE(top[0], bottom[0]) << this->type_name() << " Layer does not "
     "allow in-place computation.";
 }
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   caffe_abs(count, bottom[0]->cpu_data(), top_data);
 }
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->cpu_data();
   const Dtype* top_diff = top[0]->cpu_diff();
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_div(count, top_data, bottom_data, bottom_diff);
     caffe_mul(count, bottom_diff, top_diff, bottom_diff);
   }
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index 46778aa79b1..48c766465fa 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -8,21 +8,21 @@ namespace caffe {
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   caffe_gpu_abs(count, bottom[0]->gpu_data(), top_data);
 }
 
 template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
     caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
   }
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 3e69bc84faa..399cf2a3585 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -12,13 +12,13 @@ namespace caffe {
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::LayerSetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   top_k_ = this->layer_param_.accuracy_param().top_k();
 }
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::Reshape(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
@@ -26,12 +26,12 @@ void AccuracyLayer<Dtype>::Reshape(
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
-  (*top)[0]->Reshape(1, 1, 1, 1);
+  top[0]->Reshape(1, 1, 1, 1);
 }
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   Dtype accuracy = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
@@ -59,7 +59,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
 
   // LOG(INFO) << "Accuracy: " << accuracy;
-  (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
+  top[0]->mutable_cpu_data()[0] = accuracy / num;
   // Accuracy layer should not be used as a loss function.
 }
 
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 0d1a107257b..dbe0a834aa5 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   out_max_val_ = this->layer_param_.argmax_param().out_max_val();
   top_k_ = this->layer_param_.argmax_param().top_k();
   CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
@@ -20,21 +20,21 @@ void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   if (out_max_val_) {
     // Produces max_ind and max_val
-    (*top)[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
+    top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
   } else {
     // Produces only max_ind
-    (*top)[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
+    top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
   }
 }
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
   for (int i = 0; i < num; ++i) {
@@ -47,11 +47,11 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
         bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
     for (int j = 0; j < top_k_; ++j) {
-      top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
+      top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
     }
     if (out_max_val_) {
       for (int j = 0; j < top_k_; ++j) {
-        top_data[(*top)[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+        top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
       }
     }
   }
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 9b1d55831a7..86abbae8f4c 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -15,8 +15,8 @@ BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)
 
 template <typename Dtype>
 void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  if (top->size() == 1) {
+      const vector<Blob<Dtype>*>& top) {
+  if (top.size() == 1) {
     output_labels_ = false;
   } else {
     output_labels_ = true;
@@ -51,7 +51,7 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
   // Now, start the prefetch thread. Before calling prefetch, we make two
   // cpu_data calls so that the prefetch thread does not accidentally make
@@ -80,15 +80,15 @@ void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-             (*top)[0]->mutable_cpu_data());
+             top[0]->mutable_cpu_data());
   if (this->output_labels_) {
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-               (*top)[1]->mutable_cpu_data());
+               top[1]->mutable_cpu_data());
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 8189c79c9d4..ff1510361b6 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -6,15 +6,15 @@ namespace caffe {
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
-      (*top)[0]->mutable_gpu_data());
+      top[0]->mutable_gpu_data());
   if (this->output_labels_) {
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-        (*top)[1]->mutable_gpu_data());
+        top[1]->mutable_gpu_data());
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index ef98326a23e..bae692509e3 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -10,9 +10,9 @@ const float kBNLL_THRESHOLD = 50.;
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = bottom_data[i] > 0 ?
@@ -24,12 +24,12 @@ void BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     Dtype expval;
     for (int i = 0; i < count; ++i) {
       expval = exp(std::min(bottom_data[i], Dtype(kBNLL_THRESHOLD)));
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index b940133b4b3..3993bccd9b5 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -19,9 +19,9 @@ __global__ void BNLLForward(const int n, const Dtype* in, Dtype* out) {
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   BNLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -41,12 +41,12 @@ __global__ void BNLLBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     BNLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, bottom_data, bottom_diff);
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 10a11f1bb7c..f8f73564a6f 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -8,7 +8,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   concat_dim_ = this->layer_param_.concat_param().concat_dim();
   CHECK_GE(concat_dim_, 0) <<
     "concat_dim should be >= 0";
@@ -18,7 +18,7 @@ void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Initialize with the first blob.
   count_ = bottom[0]->count();
   num_ = bottom[0]->num();
@@ -37,20 +37,20 @@ void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       width_ += bottom[i]->width();
     }
   }
-  (*top)[0]->Reshape(num_, channels_, height_, width_);
-  CHECK_EQ(count_, (*top)[0]->count());
+  top[0]->Reshape(num_, channels_, height_, width_);
+  CHECK_EQ(count_, top[0]->count());
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+      const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_cpu_data();
   if (concat_dim_== 0) {
     int offset_num = 0;
     for (int i = 0; i < bottom.size(); ++i) {
       const Dtype* bottom_data = bottom[i]->cpu_data();
       int num_elem = bottom[i]->count();
-      caffe_copy(num_elem, bottom_data, top_data+(*top)[0]->offset(offset_num));
+      caffe_copy(num_elem, bottom_data, top_data+top[0]->offset(offset_num));
       offset_num += bottom[i]->num();
     }
   } else if (concat_dim_ == 1) {
@@ -61,7 +61,7 @@ void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         bottom[i]->channels()*bottom[i]->height()*bottom[i]->width();
       for (int n = 0; n < num_; ++n) {
         caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
-          top_data+(*top)[0]->offset(n, offset_channel));
+          top_data+top[0]->offset(n, offset_channel));
       }
       offset_channel += bottom[i]->channels();
     }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
@@ -70,12 +70,12 @@ void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   if (concat_dim_ == 0) {
     int offset_num = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
+    for (int i = 0; i < bottom.size(); ++i) {
+      Blob<Dtype>* blob = bottom[i];
       if (propagate_down[i]) {
         Dtype* bottom_diff = blob->mutable_cpu_diff();
         caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
@@ -85,8 +85,8 @@ void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     }
   } else if (concat_dim_ == 1) {
     int offset_channel = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
+    for (int i = 0; i < bottom.size(); ++i) {
+      Blob<Dtype>* blob = bottom[i];
       if (propagate_down[i]) {
         Dtype* bottom_diff = blob->mutable_cpu_diff();
         int num_elem = blob->channels()*blob->height()*blob->width();
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 99c55da25cb..1b4e40aa415 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -8,14 +8,14 @@ namespace caffe {
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+      const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_gpu_data();
   if (concat_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < bottom.size(); ++i) {
       const Dtype* bottom_data = bottom[i]->gpu_data();
       caffe_copy(bottom[i]->count(), bottom_data,
-        top_data + (*top)[0]->offset(offset_num));
+        top_data + top[0]->offset(offset_num));
       offset_num += bottom[i]->num();
     }
   } else if (concat_dim_ == 1) {
@@ -26,7 +26,7 @@ void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         bottom[i]->channels() * bottom[i]->height() * bottom[i]->width();
       for (int n = 0; n < num_; ++n) {
         caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
-          top_data + (*top)[0]->offset(n, offset_channel));
+          top_data + top[0]->offset(n, offset_channel));
       }
       offset_channel += bottom[i]->channels();
     }
@@ -38,12 +38,12 @@ void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   if (concat_dim_ == 0) {
     int offset_num = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
+    for (int i = 0; i < bottom.size(); ++i) {
+      Blob<Dtype>* blob = bottom[i];
       if (propagate_down[i]) {
         Dtype* bottom_diff = blob->mutable_gpu_diff();
         caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
@@ -53,8 +53,8 @@ void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     }
   } else if (concat_dim_ == 1) {
     int offset_channel = 0;
-    for (int i = 0; i < bottom->size(); ++i) {
-      Blob<Dtype>* blob = (*bottom)[i];
+    for (int i = 0; i < bottom.size(); ++i) {
+      Blob<Dtype>* blob = bottom[i];
       if (propagate_down[i]) {
         Dtype* bottom_diff = blob->mutable_gpu_diff();
         int num_elem = blob->channels()*blob->height()*blob->width();
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 072a5a535be..d716d140ea0 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::LayerSetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
   CHECK_EQ(bottom[0]->height(), 1);
@@ -32,7 +32,7 @@ void ContrastiveLossLayer<Dtype>::LayerSetUp(
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int count = bottom[0]->count();
   caffe_sub(
       count,
@@ -52,23 +52,23 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
-          static_cast<Dtype>((*bottom)[i]->num());
-      int num = (*bottom)[i]->num();
-      int channels = (*bottom)[i]->channels();
+          static_cast<Dtype>(bottom[i]->num());
+      int num = bottom[i]->num();
+      int channels = bottom[i]->channels();
       for (int j = 0; j < num; ++j) {
-        Dtype* bout = (*bottom)[i]->mutable_cpu_diff();
-        if (static_cast<int>((*bottom)[2]->cpu_data()[j])) {  // similar pairs
+        Dtype* bout = bottom[i]->mutable_cpu_diff();
+        if (static_cast<int>(bottom[2]->cpu_data()[j])) {  // similar pairs
           caffe_cpu_axpby(
               channels,
               alpha,
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 672ad5bc2f8..14b55b37173 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const int count = bottom[0]->count();
   caffe_gpu_sub(
       count,
@@ -41,7 +41,7 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
@@ -65,22 +65,22 @@ __global__ void CLLForward(const int count, const int channels,
 
 template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
-      const int count = (*bottom)[0]->count();
-      const int channels = (*bottom)[0]->channels();
+      const int count = bottom[0]->count();
+      const int channels = bottom[0]->channels();
       Dtype margin = this->layer_param_.contrastive_loss_param().margin();
       const Dtype sign = (i == 0) ? 1 : -1;
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
-          static_cast<Dtype>((*bottom)[0]->num());
+          static_cast<Dtype>(bottom[0]->num());
       // NOLINT_NEXT_LINE(whitespace/operators)
       CLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
           count, channels, margin, alpha,
-          (*bottom)[2]->gpu_data(),  // pair similarity 0 or 1
+          bottom[2]->gpu_data(),  // pair similarity 0 or 1
           diff_.gpu_data(),  // the cached eltwise difference between a and b
           dist_sq_.gpu_data(),  // the cached square distance between a and b
-          (*bottom)[i]->mutable_gpu_diff());
+          bottom[i]->mutable_gpu_diff());
       CUDA_POST_KERNEL_CHECK;
     }
   }
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 58918fd4baf..8c08c100bd5 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -10,7 +10,7 @@ namespace caffe {
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Configure the kernel size, padding, stride, and inputs.
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
@@ -89,7 +89,7 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   num_ = bottom[0]->num();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
@@ -109,8 +109,8 @@ void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   height_out_ =
       (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
   width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
-  for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+  for (int top_id = 0; top_id < top.size(); ++top_id) {
+    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
   }
   // Prepare the matrix multiplication computation.
   // Each input will be convolved as a single GEMM.
@@ -121,8 +121,8 @@ void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   // overly large memory usage.
   col_buffer_.Reshape(
       1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
-  for (int top_id = 0; top_id < top->size(); ++top_id) {
-    (*top)[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+  for (int top_id = 0; top_id < top.size(); ++top_id) {
+    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
   }
   // Set up the all ones "bias multiplier" for adding biases by BLAS
   if (bias_term_) {
@@ -133,10 +133,10 @@ void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->cpu_data();
-    Dtype* top_data = (*top)[i]->mutable_cpu_data();
+    Dtype* top_data = top[i]->mutable_cpu_data();
     Dtype* col_data = col_buffer_.mutable_cpu_data();
     const Dtype* weight = this->blobs_[0]->cpu_data();
     int weight_offset = M_ * K_;  // number of filter parameters in a group
@@ -152,14 +152,14 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
           (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+          (Dtype)0., top_data + top[i]->offset(n) + top_offset * g);
       }
       // Add bias.
       if (bias_term_) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
             bias_multiplier_.cpu_data(),
-            (Dtype)1., top_data + (*top)[i]->offset(n));
+            (Dtype)1., top_data + top[i]->offset(n));
       }
     }
   }
@@ -167,7 +167,7 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = NULL;
   Dtype* weight_diff = NULL;
   if (this->param_propagate_down_[0]) {
@@ -201,12 +201,12 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       }
       Dtype* col_data = col_buffer_.mutable_cpu_data();
       Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      const Dtype* bottom_data = bottom[i]->cpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
       for (int n = 0; n < num_; ++n) {
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
-        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+        im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
                    width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
                    stride_h_, stride_w_, col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
@@ -232,7 +232,7 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           // col2im back to the data
           col2im_cpu(col_diff, channels_, height_, width_,
               kernel_h_, kernel_w_, pad_h_, pad_w_,
-              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
+              stride_h_, stride_w_, bottom_diff + bottom[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 43f76a2368f..908f5a65855 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -11,10 +11,10 @@ namespace caffe {
 /// @brief refer to CPU forward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
-    Dtype* top_data = (*top)[i]->mutable_gpu_data();
+    Dtype* top_data = top[i]->mutable_gpu_data();
     Dtype* col_data = col_buffer_.mutable_gpu_data();
     const Dtype* weight = this->blobs_[0]->gpu_data();
     int weight_offset = M_ * K_;
@@ -30,14 +30,14 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
           (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+          (Dtype)0., top_data + top[i]->offset(n) + top_offset * g);
       }
       // Add bias.
       if (bias_term_) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
             N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
             bias_multiplier_.gpu_data(),
-            (Dtype)1., top_data + (*top)[i]->offset(n));
+            (Dtype)1., top_data + top[i]->offset(n));
       }
     }
   }
@@ -46,7 +46,7 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 /// @brief refer to CPU backward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = NULL;
   Dtype* weight_diff = NULL;
   if (this->param_propagate_down_[0]) {
@@ -80,12 +80,12 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       }
       Dtype* col_data = col_buffer_.mutable_gpu_data();
       Dtype* col_diff = col_buffer_.mutable_gpu_diff();
-      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+      const Dtype* bottom_data = bottom[i]->gpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
       for (int n = 0; n < num_; ++n) {
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
-        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+        im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
                    width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
                    stride_h_, stride_w_, col_data);
         // gradient w.r.t. weight. Note that we will accumulate diffs.
@@ -111,7 +111,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           // col2im back to the data
           col2im_gpu(col_diff, channels_, height_, width_,
               kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-              bottom_diff + (*bottom)[i]->offset(n));
+              bottom_diff + bottom[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 137bbab1976..f74a3dbb02f 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -19,7 +19,7 @@ namespace caffe {
  */
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   ConvolutionLayer<Dtype>::LayerSetUp(bottom, top);
   // Initialize CUDA streams and cuDNN.
   stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
@@ -62,7 +62,7 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   ConvolutionLayer<Dtype>::Reshape(bottom, top);
   bottom_offset_ = (this->channels_ / this->group_)
       * this->height_ * this->width_;
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 2af13309e18..6961bbeaba5 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -13,10 +13,10 @@ __global__ void sync_conv_groups() { }
 
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
-    Dtype* top_data = (*top)[i]->mutable_gpu_data();
+    Dtype* top_data = top[i]->mutable_gpu_data();
     const Dtype* weight = this->blobs_[0]->gpu_data();
 
     // Forward through cuDNN in parallel over groups.
@@ -48,7 +48,7 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = NULL;
   Dtype* weight_diff = NULL;
   if (this->param_propagate_down_[0]) {
@@ -75,7 +75,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
-        const Dtype* bottom_data = (*bottom)[i]->gpu_data();
+        const Dtype* bottom_data = bottom[i]->gpu_data();
         CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
             bottom_descs_[i], bottom_data + bottom_offset_ * g,
             top_descs_[i],    top_diff + top_offset_ * g,
@@ -86,7 +86,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
       // Gradient w.r.t. bottom data.
       if (propagate_down[i]) {
-        Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+        Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
         CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
             filter_desc_, weight + weight_offset_ * g,
             top_descs_[i],    top_diff + top_offset_ * g,
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index 5aea0dc886e..2af9d21be26 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -11,7 +11,7 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
 
   CUDNN_CHECK(cudnnCreate(&handle_));
@@ -24,7 +24,7 @@ void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::Reshape(bottom, top);
   cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
       this->channels_, this->height_, this->width_);
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 99c409dcc96..9d270ed25d5 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -11,22 +11,22 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   // Fallback to Caffe for padded pooling, max top mask.
-  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || (*top).size() > 1) {
+  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || top.size() > 1) {
     LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
     return PoolingLayer<Dtype>::Forward_gpu(bottom, top);
   }
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
       bottom_desc_, bottom_data, top_desc_, top_data));
 }
 
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
@@ -39,8 +39,8 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
       top_desc_, top_data, top_desc_, top_diff,
       bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 083868f572f..20f486f6bcf 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   ReLULayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
@@ -19,7 +19,7 @@ void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   ReLULayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index a8519f340cc..e3900f37ab9 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -9,14 +9,14 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   // Fallback to standard Caffe for leaky ReLU.
   if (ReLULayer<Dtype>::layer_param_.relu_param().negative_slope() != 0) {
     return ReLULayer<Dtype>::Forward_gpu(bottom, top);
   }
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
       CUDNN_ACTIVATION_RELU,
       this->bottom_desc_, bottom_data, this->top_desc_, top_data));
@@ -25,7 +25,7 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
@@ -37,8 +37,8 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
       CUDNN_ACTIVATION_RELU,
       this->top_desc_, top_data, this->top_desc_, top_diff,
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 3fe800db6f4..a94c0040a28 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SigmoidLayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
@@ -19,7 +19,7 @@ void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SigmoidLayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 43019bd78ae..50bf5fcb9e0 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -9,9 +9,9 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
       CUDNN_ACTIVATION_SIGMOID,
       this->bottom_desc_, bottom_data, this->top_desc_, top_data));
@@ -20,15 +20,15 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
 
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
       CUDNN_ACTIVATION_SIGMOID,
       this->top_desc_, top_data, this->top_desc_, top_diff,
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 79ba5237ae3..1a0f406bf38 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -13,7 +13,7 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SoftmaxLayer<Dtype>::LayerSetUp(bottom, top);
   // Initialize CUDNN.
   CUDNN_CHECK(cudnnCreate(&handle_));
@@ -23,7 +23,7 @@ void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   SoftmaxLayer<Dtype>::Reshape(bottom, top);
   int N = bottom[0]->num();
   int K = bottom[0]->channels();
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index 300bdc496c8..aef66730bff 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -13,9 +13,9 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
       CUDNN_SOFTMAX_MODE_CHANNEL,
       bottom_desc_, bottom_data, top_desc_, top_data));
@@ -23,12 +23,12 @@ void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
         CUDNN_SOFTMAX_MODE_CHANNEL,
         top_desc_, top_data, top_desc_, top_diff, bottom_desc_, bottom_diff));
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 7a5c06f6596..39a3e14b3c8 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   TanHLayer<Dtype>::LayerSetUp(bottom, top);
   // initialize cuDNN
   CUDNN_CHECK(cudnnCreate(&handle_));
@@ -19,7 +19,7 @@ void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   TanHLayer<Dtype>::Reshape(bottom, top);
   const int N = bottom[0]->num();
   const int K = bottom[0]->channels();
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index c475b08c0ee..20f887de8d0 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -9,9 +9,9 @@ namespace caffe {
 
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
       CUDNN_ACTIVATION_TANH,
       this->bottom_desc_, bottom_data, this->top_desc_, top_data));
@@ -20,15 +20,15 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
 
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
       CUDNN_ACTIVATION_TANH,
       this->top_desc_, top_data, this->top_desc_, top_diff,
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index d2071e2fa4f..b3b6bf3d64f 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -34,7 +34,7 @@ DataLayer<Dtype>::~DataLayer<Dtype>() {
 
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Initialize DB
   switch (this->layer_param_.data_param().backend()) {
   case DataParameter_DB_LEVELDB:
@@ -114,23 +114,23 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // image
   int crop_size = this->layer_param_.transform_param().crop_size();
   if (crop_size > 0) {
-    (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
+    top[0]->Reshape(this->layer_param_.data_param().batch_size(),
                        datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), crop_size, crop_size);
   } else {
-    (*top)[0]->Reshape(
+    top[0]->Reshape(
         this->layer_param_.data_param().batch_size(), datum.channels(),
         datum.height(), datum.width());
     this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), datum.height(), datum.width());
   }
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
   // label
   if (this->output_labels_) {
-    (*top)[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
+    top[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
     this->prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
         1, 1, 1);
   }
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 47feb1d2543..f88027c7062 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   threshold_ = this->layer_param_.dropout_param().dropout_ratio();
   DCHECK(threshold_ > 0.);
@@ -23,7 +23,7 @@ void DropoutLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::Reshape(bottom, top);
   // Set up the cache for random number generation
   rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -32,9 +32,9 @@ void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   unsigned int* mask = rand_vec_.mutable_cpu_data();
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
@@ -51,13 +51,13 @@ void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     if (Caffe::phase() == Caffe::TRAIN) {
       const unsigned int* mask = rand_vec_.cpu_data();
-      const int count = (*bottom)[0]->count();
+      const int count = bottom[0]->count();
       for (int i = 0; i < count; ++i) {
         bottom_diff[i] = top_diff[i] * mask[i] * scale_;
       }
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 9756c862183..fa737b86a89 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -22,9 +22,9 @@ __global__ void DropoutForward(const int n, const Dtype* in,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
     unsigned int* mask =
@@ -52,14 +52,14 @@ __global__ void DropoutBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     if (Caffe::phase() == Caffe::TRAIN) {
       const unsigned int* mask =
           static_cast<const unsigned int*>(rand_vec_.gpu_data());
-      const int count = (*bottom)[0]->count();
+      const int count = bottom[0]->count();
       // NOLINT_NEXT_LINE(whitespace/operators)
       DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
         CAFFE_CUDA_NUM_THREADS>>>(
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 883f2528ef8..6aeba5487b8 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -8,8 +8,8 @@ namespace caffe {
 
 template <typename Dtype>
 void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  const int num_top = top->size();
+      const vector<Blob<Dtype>*>& top) {
+  const int num_top = top.size();
   const DummyDataParameter& param = this->layer_param_.dummy_data_param();
   const int num_data_filler = param.data_filler_size();
   CHECK(num_data_filler == 0 || num_data_filler == 1 ||
@@ -70,7 +70,7 @@ void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
         (param.height_size() == 1) ? param.height(0) : param.height(i);
     const int width =
         (param.width_size() == 1) ? param.width(0) : param.width(i);
-    (*top)[i]->Reshape(num, channels, height, width);
+    top[i]->Reshape(num, channels, height, width);
   }
   // Run Forward once, with refill_ inverted, to fill the constant Blobs.
   this->Forward(bottom, top);
@@ -83,11 +83,11 @@ void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < top->size(); ++i) {
+      const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < top.size(); ++i) {
     const int filler_id = (fillers_.size() > 1) ? i : 0;
     if (refill_[filler_id]) {
-      fillers_[filler_id]->Fill((*top)[i]);
+      fillers_[filler_id]->Fill(top[i]);
     }
   }
 }
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 569560f97d3..2830d6db195 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK(this->layer_param().eltwise_param().coeff_size() == 0
       || this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<
       "Eltwise Layer takes one coefficient per bottom blob.";
@@ -30,7 +30,7 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int num = bottom[0]->num();
   const int channels = bottom[0]->channels();
   const int height = bottom[0]->height();
@@ -41,22 +41,22 @@ void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(height, bottom[i]->height());
     CHECK_EQ(width, bottom[i]->width());
   }
-  (*top)[0]->Reshape(num, channels, height, width);
+  top[0]->Reshape(num, channels, height, width);
   // If max operation, we will initialize the vector index part.
   if (this->layer_param_.eltwise_param().operation() ==
-      EltwiseParameter_EltwiseOp_MAX && top->size() == 1) {
+      EltwiseParameter_EltwiseOp_MAX && top.size() == 1) {
     max_idx_.Reshape(bottom[0]->num(), channels, height, width);
   }
 }
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   int* mask = NULL;
   const Dtype* bottom_data_a = NULL;
   const Dtype* bottom_data_b = NULL;
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   switch (op_) {
   case EltwiseParameter_EltwiseOp_PROD:
     caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
@@ -106,26 +106,26 @@ void EltwiseLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int* mask = NULL;
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->cpu_data();
   const Dtype* top_diff = top[0]->cpu_diff();
-  for (int i = 0; i < bottom->size(); ++i) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      const Dtype* bottom_data = bottom[i]->cpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
       switch (op_) {
       case EltwiseParameter_EltwiseOp_PROD:
         if (stable_prod_grad_) {
           bool initialized = false;
-          for (int j = 0; j < bottom->size(); ++j) {
+          for (int j = 0; j < bottom.size(); ++j) {
             if (i == j) { continue; }
             if (!initialized) {
-              caffe_copy(count, (*bottom)[j]->cpu_data(), bottom_diff);
+              caffe_copy(count, bottom[j]->cpu_data(), bottom_diff);
               initialized = true;
             } else {
-              caffe_mul(count, (*bottom)[j]->cpu_data(), bottom_diff,
+              caffe_mul(count, bottom[j]->cpu_data(), bottom_diff,
                         bottom_diff);
             }
           }
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 16cb6cc77e3..9295cc6ad11 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -33,10 +33,10 @@ __global__ void MaxForward(const int nthreads, const Dtype* bottom_data_a,
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int* mask = NULL;
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  const int count = top[0]->count();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   switch (op_) {
   case EltwiseParameter_EltwiseOp_PROD:
     caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(),
@@ -82,26 +82,26 @@ __global__ void MaxBackward(const int nthreads, const Dtype* top_diff,
 
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int* mask = NULL;
   const int count = top[0]->count();
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
-  for (int i = 0; i < bottom->size(); ++i) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+      const Dtype* bottom_data = bottom[i]->gpu_data();
+      Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
       switch (op_) {
       case EltwiseParameter_EltwiseOp_PROD:
         if (stable_prod_grad_) {
           bool initialized = false;
-          for (int j = 0; j < bottom->size(); ++j) {
+          for (int j = 0; j < bottom.size(); ++j) {
             if (i == j) { continue; }
             if (!initialized) {
-              caffe_copy(count, (*bottom)[j]->gpu_data(), bottom_diff);
+              caffe_copy(count, bottom[j]->gpu_data(), bottom_diff);
               initialized = true;
             } else {
-              caffe_gpu_mul(count, (*bottom)[j]->gpu_data(), bottom_diff,
+              caffe_gpu_mul(count, bottom[j]->gpu_data(), bottom_diff,
                             bottom_diff);
             }
           }
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 1b4a13d2ddc..abc8610eef6 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Reshape(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
   CHECK_EQ(bottom[0]->height(), bottom[1]->height());
@@ -20,7 +20,7 @@ void EuclideanLossLayer<Dtype>::Reshape(
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int count = bottom[0]->count();
   caffe_sub(
       count,
@@ -29,22 +29,22 @@ void EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       diff_.mutable_cpu_data());
   Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data());
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
-      const Dtype alpha = sign * top[0]->cpu_diff()[0] / (*bottom)[i]->num();
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num();
       caffe_cpu_axpby(
-          (*bottom)[i]->count(),              // count
+          bottom[i]->count(),              // count
           alpha,                              // alpha
           diff_.cpu_data(),                   // a
           Dtype(0),                           // beta
-          (*bottom)[i]->mutable_cpu_diff());  // b
+          bottom[i]->mutable_cpu_diff());  // b
     }
   }
 }
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index 70b1b9ee9ea..0f0c1a86f9d 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   int count = bottom[0]->count();
   caffe_gpu_sub(
       count,
@@ -19,22 +19,22 @@ void EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype dot;
   caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
-  (*top)[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss;
 }
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
-      const Dtype alpha = sign * top[0]->cpu_diff()[0] / (*bottom)[i]->num();
+      const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num();
       caffe_gpu_axpby(
-          (*bottom)[i]->count(),              // count
+          bottom[i]->count(),              // count
           alpha,                              // alpha
           diff_.gpu_data(),                   // a
           Dtype(0),                           // beta
-          (*bottom)[i]->mutable_gpu_diff());  // b
+          bottom[i]->mutable_gpu_diff());  // b
     }
   }
 }
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 65310cd1669..44df96d8436 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -8,25 +8,25 @@ namespace caffe {
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   int channels_out = bottom[0]->channels() * bottom[0]->height()
       * bottom[0]->width();
-  (*top)[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
+  top[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
   count_ = bottom[0]->num() * channels_out;
   CHECK_EQ(count_, bottom[0]->count());
-  CHECK_EQ(count_, (*top)[0]->count());
+  CHECK_EQ(count_, top[0]->count());
 }
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ShareData(*bottom[0]);
 }
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  (*bottom)[0]->ShareDiff(*top[0]);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  bottom[0]->ShareDiff(*top[0]);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index ff23f523fee..ef89bab6ff4 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -8,14 +8,14 @@ namespace caffe {
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ShareData(*bottom[0]);
 }
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  (*bottom)[0]->ShareDiff(*top[0]);
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  bottom[0]->ShareDiff(*top[0]);
 }
 
 INSTANTIATE_CLASS(FlattenLayer);
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 1f2a83582f2..ff25cbb621f 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -51,7 +51,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
   LOG(INFO) << "Loading filename from " << source;
@@ -74,21 +74,21 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  (*top)[0]->Reshape(batch_size, data_blob_.channels(),
+  top[0]->Reshape(batch_size, data_blob_.channels(),
                      data_blob_.width(), data_blob_.height());
-  (*top)[1]->Reshape(batch_size, label_blob_.channels(),
+  top[1]->Reshape(batch_size, label_blob_.channels(),
                      label_blob_.width(), label_blob_.height());
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
 }
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  const int data_count = (*top)[0]->count() / (*top)[0]->num();
-  const int label_data_count = (*top)[1]->count() / (*top)[1]->num();
+  const int data_count = top[0]->count() / top[0]->num();
+  const int label_data_count = top[1]->count() / top[1]->num();
 
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
     if (current_row_ == data_blob_.num()) {
@@ -103,10 +103,10 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       current_row_ = 0;
     }
     caffe_copy(data_count, &data_blob_.cpu_data()[current_row_ * data_count],
-               &(*top)[0]->mutable_cpu_data()[i * data_count]);
+               &top[0]->mutable_cpu_data()[i * data_count]);
     caffe_copy(label_data_count,
                &label_blob_.cpu_data()[current_row_ * label_data_count],
-               &(*top)[1]->mutable_cpu_data()[i * label_data_count]);
+               &top[1]->mutable_cpu_data()[i * label_data_count]);
   }
 }
 
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 79cc536eb28..f671ea12d76 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -18,10 +18,10 @@ namespace caffe {
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  const int data_count = (*top)[0]->count() / (*top)[0]->num();
-  const int label_data_count = (*top)[1]->count() / (*top)[1]->num();
+  const int data_count = top[0]->count() / top[0]->num();
+  const int label_data_count = top[1]->count() / top[1]->num();
 
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
     if (current_row_ == data_blob_.num()) {
@@ -39,10 +39,10 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     }
     caffe_copy(data_count,
         &data_blob_.cpu_data()[current_row_ * data_count],
-        &(*top)[0]->mutable_gpu_data()[i * data_count]);
+        &top[0]->mutable_gpu_data()[i * data_count]);
     caffe_copy(label_data_count,
         &label_blob_.cpu_data()[current_row_ * label_data_count],
-        &(*top)[1]->mutable_gpu_data()[i * label_data_count]);
+        &top[1]->mutable_gpu_data()[i * label_data_count]);
   }
 }
 
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 3cdbbb31a6a..b41d387a786 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -40,7 +40,7 @@ void HDF5OutputLayer<Dtype>::SaveBlobs() {
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK_GE(bottom.size(), 2);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num());
   data_blob_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -61,7 +61,7 @@ void HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   return;
 }
 
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 0813c02a440..929c1ad8e22 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -13,7 +13,7 @@ namespace caffe {
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK_GE(bottom.size(), 2);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num());
   data_blob_.Reshape(bottom[0]->num(), bottom[0]->channels(),
@@ -34,7 +34,7 @@ void HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   return;
 }
 
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 8022aae279c..f09916e2556 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   const Dtype* label = bottom[1]->cpu_data();
@@ -30,7 +30,7 @@ void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
-  Dtype* loss = (*top)[0]->mutable_cpu_data();
+  Dtype* loss = top[0]->mutable_cpu_data();
   switch (this->layer_param_.hinge_loss_param().norm()) {
   case HingeLossParameter_Norm_L1:
     loss[0] = caffe_cpu_asum(count, bottom_diff) / num;
@@ -45,16 +45,16 @@ void HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const Dtype* label = (*bottom)[1]->cpu_data();
-    int num = (*bottom)[0]->num();
-    int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const Dtype* label = bottom[1]->cpu_data();
+    int num = bottom[0]->num();
+    int count = bottom[0]->count();
     int dim = count / num;
 
     for (int i = 0; i < num; ++i) {
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 870d5a9bde3..10588cebe7f 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
       !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
@@ -49,11 +49,11 @@ void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
-  (*top)[0]->Reshape(
+  top[0]->Reshape(
       bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
       (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
       (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
@@ -61,25 +61,25 @@ void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
         width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
+        stride_h_, stride_w_, top_data + top[0]->offset(n));
   }
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
+        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
   }
 }
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 8df061d88e1..bad789c17fe 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -9,25 +9,25 @@ namespace caffe {
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
         width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
+        stride_h_, stride_w_, top_data + top[0]->offset(n));
   }
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
+        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
   }
 }
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e4a575bec93..dc972f13799 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -19,7 +19,7 @@ ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
 
 template <typename Dtype>
 void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int new_height = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_width();
   CHECK((new_height == 0 && new_width == 0) ||
@@ -61,20 +61,20 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int crop_size = this->layer_param_.transform_param().crop_size();
   const int batch_size = this->layer_param_.image_data_param().batch_size();
   if (crop_size > 0) {
-    (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
+    top[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
                                  crop_size);
   } else {
-    (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
+    top[0]->Reshape(batch_size, datum.channels(), datum.height(),
                        datum.width());
     this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
         datum.width());
   }
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
   // label
-  (*top)[1]->Reshape(batch_size, 1, 1, 1);
+  top[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
   // datum size
   this->datum_channels_ = datum.channels();
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 894cb69811a..c4dc1b4513a 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   if (bottom.size() < 3) {
     CHECK(this->layer_param_.infogain_loss_param().has_source())
@@ -26,7 +26,7 @@ void InfogainLossLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   Blob<Dtype>* infogain = NULL;
   if (bottom.size() < 3) {
@@ -48,7 +48,7 @@ void InfogainLossLayer<Dtype>::Reshape(
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   const Dtype* infogain_mat = NULL;
@@ -67,13 +67,13 @@ void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
@@ -83,17 +83,17 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                << " Layer cannot backpropagate to infogain inputs.";
   }
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* bottom_label = bottom[1]->cpu_data();
     const Dtype* infogain_mat = NULL;
-    if (bottom->size() < 3) {
+    if (bottom.size() < 3) {
       infogain_mat = infogain_.cpu_data();
     } else {
-      infogain_mat = (*bottom)[2]->cpu_data();
+      infogain_mat = bottom[2]->cpu_data();
     }
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    int num = (*bottom)[0]->num();
-    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    int num = bottom[0]->num();
+    int dim = bottom[0]->count() / bottom[0]->num();
     const Dtype scale = - top[0]->cpu_diff()[0] / num;
     for (int i = 0; i < num; ++i) {
       const int label = static_cast<int>(bottom_label[i]);
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index ecd05a030db..ffd872c5a70 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -11,7 +11,7 @@ namespace caffe {
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
   N_ = num_output;
@@ -44,12 +44,12 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Figure out the dimensions
   M_ = bottom[0]->num();
   CHECK_EQ(bottom[0]->count() / bottom[0]->num(), K_) << "Input size "
     "incompatible with inner product parameters.";
-  (*top)[0]->Reshape(bottom[0]->num(), N_, 1, 1);
+  top[0]->Reshape(bottom[0]->num(), N_, 1, 1);
   // Set up the bias multiplier
   if (bias_term_) {
     bias_multiplier_.Reshape(1, 1, 1, M_);
@@ -59,9 +59,9 @@ void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const Dtype* weight = this->blobs_[0]->cpu_data();
   caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
@@ -75,10 +75,10 @@ void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (this->param_propagate_down_[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
     caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
         top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
@@ -95,7 +95,7 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     // Gradient with respect to bottom data
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
-        (*bottom)[0]->mutable_cpu_diff());
+        bottom[0]->mutable_cpu_diff());
   }
 }
 
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 3a0d4388352..2164b4d4f16 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -11,9 +11,9 @@ namespace caffe {
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const Dtype* weight = this->blobs_[0]->gpu_data();
   caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
@@ -27,10 +27,10 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (this->param_propagate_down_[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
     caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
         top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
@@ -47,7 +47,7 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     // Gradient with respect to bottom data
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
-        (*bottom)[0]->mutable_gpu_diff());
+        bottom[0]->mutable_gpu_diff());
   }
 }
 
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 9eb9dbd5c5b..a5b6d11b065 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void LossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // LossLayers have a non-zero (1) loss by default.
   if (this->layer_param_.loss_weight_size() == 0) {
     this->layer_param_.add_loss_weight(Dtype(1));
@@ -21,10 +21,10 @@ void LossLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void LossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
-  (*top)[0]->Reshape(1, 1, 1, 1);
+  top[0]->Reshape(1, 1, 1, 1);
 }
 
 INSTANTIATE_CLASS(LossLayer);
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index d9e41e9c137..fb74b03dc88 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -8,7 +8,7 @@ namespace caffe {
 
 template <typename Dtype>
 void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   size_ = this->layer_param_.lrn_param().local_size();
   CHECK_EQ(size_ % 2, 1) << "LRN only supports odd values for local_size";
   pre_pad_ = (size_ - 1) / 2;
@@ -22,7 +22,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     split_top_vec_.push_back(&square_input_);
     LayerParameter split_param;
     split_layer_.reset(new SplitLayer<Dtype>(split_param));
-    split_layer_->SetUp(bottom, &split_top_vec_);
+    split_layer_->SetUp(bottom, split_top_vec_);
     // Set up square_layer_ to square the inputs.
     square_bottom_vec_.clear();
     square_top_vec_.clear();
@@ -31,7 +31,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     LayerParameter square_param;
     square_param.mutable_power_param()->set_power(Dtype(2));
     square_layer_.reset(new PowerLayer<Dtype>(square_param));
-    square_layer_->SetUp(square_bottom_vec_, &square_top_vec_);
+    square_layer_->SetUp(square_bottom_vec_, square_top_vec_);
     // Set up pool_layer_ to sum over square neighborhoods of the input.
     pool_top_vec_.clear();
     pool_top_vec_.push_back(&pool_output_);
@@ -41,7 +41,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     pool_param.mutable_pooling_param()->set_pad(pre_pad_);
     pool_param.mutable_pooling_param()->set_kernel_size(size_);
     pool_layer_.reset(new PoolingLayer<Dtype>(pool_param));
-    pool_layer_->SetUp(square_top_vec_, &pool_top_vec_);
+    pool_layer_->SetUp(square_top_vec_, pool_top_vec_);
     // Set up power_layer_ to compute (1 + alpha_/N^2 s)^-beta_, where s is
     // the sum of a squared neighborhood (the output of pool_layer_).
     power_top_vec_.clear();
@@ -51,7 +51,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     power_param.mutable_power_param()->set_scale(alpha_);
     power_param.mutable_power_param()->set_shift(Dtype(1));
     power_layer_.reset(new PowerLayer<Dtype>(power_param));
-    power_layer_->SetUp(pool_top_vec_, &power_top_vec_);
+    power_layer_->SetUp(pool_top_vec_, power_top_vec_);
     // Set up a product_layer_ to compute outputs by multiplying inputs by the
     // inverse demoninator computed by the power layer.
     product_bottom_vec_.clear();
@@ -67,21 +67,21 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
-    (*top)[0]->Reshape(num_, channels_, height_, width_);
+    top[0]->Reshape(num_, channels_, height_, width_);
     scale_.Reshape(num_, channels_, height_, width_);
     break;
   case LRNParameter_NormRegion_WITHIN_CHANNEL:
-    split_layer_->Reshape(bottom, &split_top_vec_);
-    square_layer_->Reshape(square_bottom_vec_, &square_top_vec_);
-    pool_layer_->Reshape(square_top_vec_, &pool_top_vec_);
-    power_layer_->Reshape(pool_top_vec_, &power_top_vec_);
+    split_layer_->Reshape(bottom, split_top_vec_);
+    square_layer_->Reshape(square_bottom_vec_, square_top_vec_);
+    pool_layer_->Reshape(square_top_vec_, pool_top_vec_);
+    power_layer_->Reshape(pool_top_vec_, power_top_vec_);
     product_layer_->Reshape(product_bottom_vec_, top);
     break;
   }
@@ -89,7 +89,7 @@ void LRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelForward_cpu(bottom, top);
@@ -104,9 +104,9 @@ void LRNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelForward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   Dtype* scale_data = scale_.mutable_cpu_data();
   // start with the constant value
   for (int i = 0; i < scale_.count(); ++i) {
@@ -151,17 +151,17 @@ void LRNLayer<Dtype>::CrossChannelForward_cpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::WithinChannelForward(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  split_layer_->Forward(bottom, &split_top_vec_);
-  square_layer_->Forward(square_bottom_vec_, &square_top_vec_);
-  pool_layer_->Forward(square_top_vec_, &pool_top_vec_);
-  power_layer_->Forward(pool_top_vec_, &power_top_vec_);
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  split_layer_->Forward(bottom, split_top_vec_);
+  square_layer_->Forward(square_bottom_vec_, square_top_vec_);
+  pool_layer_->Forward(square_top_vec_, pool_top_vec_);
+  power_layer_->Forward(pool_top_vec_, power_top_vec_);
   product_layer_->Forward(product_bottom_vec_, top);
 }
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_cpu(top, propagate_down, bottom);
@@ -177,12 +177,12 @@ void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* scale_data = scale_.cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   Blob<Dtype> padded_ratio(1, channels_ + size_ - 1, height_, width_);
   Blob<Dtype> accum_ratio(1, 1, height_, width_);
   Dtype* padded_ratio_data = padded_ratio.mutable_cpu_data();
@@ -232,14 +232,14 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
 template <typename Dtype>
 void LRNLayer<Dtype>::WithinChannelBackward(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     vector<bool> product_propagate_down(2, true);
-    product_layer_->Backward(top, product_propagate_down, &product_bottom_vec_);
-    power_layer_->Backward(power_top_vec_, propagate_down, &pool_top_vec_);
-    pool_layer_->Backward(pool_top_vec_, propagate_down, &square_top_vec_);
+    product_layer_->Backward(top, product_propagate_down, product_bottom_vec_);
+    power_layer_->Backward(power_top_vec_, propagate_down, pool_top_vec_);
+    pool_layer_->Backward(pool_top_vec_, propagate_down, square_top_vec_);
     square_layer_->Backward(square_top_vec_, propagate_down,
-                            &square_bottom_vec_);
+                            square_bottom_vec_);
     split_layer_->Backward(split_top_vec_, propagate_down, bottom);
   }
 }
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index d6cb23bf245..ee5e359ff0b 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -55,7 +55,7 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelForward_gpu(bottom, top);
@@ -79,10 +79,10 @@ __global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelForward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, compute scale
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
   // We will launch one kernel for each pixel location, and have the kernel
   // go through all the channels.
@@ -102,7 +102,7 @@ void LRNLayer<Dtype>::CrossChannelForward_gpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_gpu(top, propagate_down, bottom);
@@ -179,14 +179,14 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   int n_threads = num_ * height_ * width_;
   // NOLINT_NEXT_LINE(whitespace/operators)
   LRNComputeDiff<<<CAFFE_GET_BLOCKS(n_threads), CAFFE_CUDA_NUM_THREADS>>>(
-      n_threads, (*bottom)[0]->gpu_data(), top[0]->gpu_data(),
+      n_threads, bottom[0]->gpu_data(), top[0]->gpu_data(),
       scale_.gpu_data(), top[0]->gpu_diff(), num_, channels_, height_, width_,
       size_, -beta_, Dtype(2. * alpha_ * beta_ / size_),
-      (*bottom)[0]->mutable_gpu_diff());
+      bottom[0]->mutable_gpu_diff());
 }
 
 
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index ab631a884fb..d254da34322 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -8,7 +8,7 @@ namespace caffe {
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-     vector<Blob<Dtype>*>* top) {
+     const vector<Blob<Dtype>*>& top) {
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
   this->datum_channels_ = this->layer_param_.memory_data_param().channels();
   this->datum_height_ = this->layer_param_.memory_data_param().height();
@@ -18,9 +18,9 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(batch_size_ * this->datum_size_, 0) <<
       "batch_size, channels, height, and width must be specified and"
       " positive in memory_data_param";
-  (*top)[0]->Reshape(batch_size_, this->datum_channels_, this->datum_height_,
+  top[0]->Reshape(batch_size_, this->datum_channels_, this->datum_height_,
                      this->datum_width_);
-  (*top)[1]->Reshape(batch_size_, 1, 1, 1);
+  top[1]->Reshape(batch_size_, 1, 1, 1);
   added_data_.Reshape(batch_size_, this->datum_channels_, this->datum_height_,
                       this->datum_width_);
   added_label_.Reshape(batch_size_, 1, 1, 1);
@@ -66,10 +66,10 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
-  (*top)[0]->set_cpu_data(data_ + pos_ * this->datum_size_);
-  (*top)[1]->set_cpu_data(labels_ + pos_);
+  top[0]->set_cpu_data(data_ + pos_ * this->datum_size_);
+  top[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
   has_new_data_ = false;
 }
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index c0fe1966a4d..66f9ba813aa 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
@@ -21,7 +21,7 @@ void MultinomialLogisticLossLayer<Dtype>::Reshape(
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   int num = bottom[0]->num();
@@ -33,24 +33,24 @@ void MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
         bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    int num = (*bottom)[0]->num();
-    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-    caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* bottom_label = bottom[1]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    int num = bottom[0]->num();
+    int dim = bottom[0]->count() / bottom[0]->num();
+    caffe_set(bottom[0]->count(), Dtype(0), bottom_diff);
     const Dtype scale = - top[0]->cpu_diff()[0] / num;
     for (int i = 0; i < num; ++i) {
       int label = static_cast<int>(bottom_label[i]);
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 6a57b3ea7fc..419f170ad7c 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -9,8 +9,8 @@ namespace caffe {
 
 template <typename Dtype>
 void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   mean_.Reshape(bottom[0]->num(), bottom[0]->channels(),
       1, 1);
@@ -26,9 +26,9 @@ void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   int num;
   if (this->layer_param_.mvn_param().across_channels())
     num = bottom[0]->num();
@@ -89,19 +89,19 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
 
   int num;
   if (this->layer_param_.mvn_param().across_channels())
-    num = (*bottom)[0]->num();
+    num = bottom[0]->num();
   else
-    num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+    num = bottom[0]->num() * bottom[0]->channels();
 
-  int dim = (*bottom)[0]->count() / num;
+  int dim = bottom[0]->count() / num;
   Dtype eps = 1e-10;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 2c02dfe1ddc..c2a241f093f 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -9,9 +9,9 @@ namespace caffe {
 
 template <typename Dtype>
 void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   int num;
   if (this->layer_param_.mvn_param().across_channels())
     num = bottom[0]->num();
@@ -73,19 +73,19 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
 
   int num;
   if (this->layer_param_.mvn_param().across_channels())
-    num = (*bottom)[0]->num();
+    num = bottom[0]->num();
   else
-    num = (*bottom)[0]->num() * (*bottom)[0]->channels();
+    num = bottom[0]->num() * bottom[0]->channels();
 
-  int dim = (*bottom)[0]->count() / num;
+  int dim = bottom[0]->count() / num;
 
   Dtype eps = 1e-10;
 
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index c28e36ea23b..ba67b43878e 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -7,8 +7,8 @@ namespace caffe {
 
 template <typename Dtype>
 void NeuronLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->ReshapeLike(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ReshapeLike(*bottom[0]);
 }
 
 INSTANTIATE_CLASS(NeuronLayer);
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 8e8ffad66a0..26c92c13b63 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -15,7 +15,7 @@ using std::max;
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   PoolingParameter pool_param = this->layer_param_.pooling_param();
   CHECK(!pool_param.has_kernel_size() !=
       !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
@@ -64,7 +64,7 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
@@ -84,14 +84,14 @@ void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     CHECK_LT((pooled_height_ - 1) * stride_h_, height_ + pad_h_);
     CHECK_LT((pooled_width_ - 1) * stride_w_, width_ + pad_w_);
   }
-  (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
+  top[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
-  if (top->size() > 1) {
-    (*top)[1]->ReshapeLike(*(*top)[0]);
+  if (top.size() > 1) {
+    top[1]->ReshapeLike(*top[0]);
   }
   // If max pooling, we will initialize the vector index part.
   if (this->layer_param_.pooling_param().pool() ==
-      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
+      PoolingParameter_PoolMethod_MAX && top.size() == 1) {
     max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
         pooled_width_);
   }
@@ -107,12 +107,12 @@ void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 // case?
 template <typename Dtype>
 void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  const int top_count = (*top)[0]->count();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int top_count = top[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top->size() > 1;
+  const bool use_top_mask = top.size() > 1;
   int* mask = NULL;  // suppress warnings about uninitalized variables
   Dtype* top_mask = NULL;
   // Different pooling methods. We explicitly do the switch outside the for
@@ -121,7 +121,7 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   case PoolingParameter_PoolMethod_MAX:
     // Initialize
     if (use_top_mask) {
-      top_mask = (*top)[1]->mutable_cpu_data();
+      top_mask = top[1]->mutable_cpu_data();
       caffe_set(top_count, Dtype(-1), top_mask);
     } else {
       mask = max_idx_.mutable_cpu_data();
@@ -157,11 +157,11 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         }
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
         if (use_top_mask) {
-          top_mask += (*top)[0]->offset(0, 1);
+          top_mask += top[0]->offset(0, 1);
         } else {
-          mask += (*top)[0]->offset(0, 1);
+          mask += top[0]->offset(0, 1);
         }
       }
     }
@@ -195,7 +195,7 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         }
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
-        top_data += (*top)[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
       }
     }
     break;
@@ -209,15 +209,15 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->cpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
-  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+  caffe_set(bottom[0]->count(), Dtype(0), bottom_diff);
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
   const int* mask = NULL;  // suppress warnings about uninitialized variables
@@ -240,7 +240,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
             bottom_diff[bottom_index] += top_diff[index];
           }
         }
-        bottom_diff += (*bottom)[0]->offset(0, 1);
+        bottom_diff += bottom[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
         if (use_top_mask) {
           top_mask += top[0]->offset(0, 1);
@@ -274,7 +274,7 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           }
         }
         // offset
-        bottom_diff += (*bottom)[0]->offset(0, 1);
+        bottom_diff += bottom[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
       }
     }
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index e64128b87f2..aec985a7864 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -152,18 +152,18 @@ __global__ void StoPoolForwardTest(const int nthreads,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  int count = (*top)[0]->count();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  int count = top[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
-  const bool use_top_mask = top->size() > 1;
+  const bool use_top_mask = top.size() > 1;
   int* mask = NULL;
   Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     if (use_top_mask) {
-      top_mask = (*top)[1]->mutable_gpu_data();
+      top_mask = top[1]->mutable_gpu_data();
     } else {
       mask = max_idx_.mutable_gpu_data();
     }
@@ -325,13 +325,13 @@ __global__ void StoPoolBackward(const int nthreads,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->gpu_diff();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  const int count = (*bottom)[0]->count();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int count = bottom[0]->count();
   caffe_gpu_set(count, Dtype(0.), bottom_diff);
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index bf61955d065..04c612296aa 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void PowerLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   power_ = this->layer_param_.power_param().power();
   scale_ = this->layer_param_.power_param().scale();
@@ -20,8 +20,8 @@ void PowerLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // Compute y = (shift + scale * x)^power
 template <typename Dtype>
 void PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+    const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   // Special case where we can ignore the input: scale or power is 0.
   if (diff_scale_ == Dtype(0)) {
@@ -45,15 +45,15 @@ void PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     const Dtype* top_diff = top[0]->cpu_diff();
     if (diff_scale_ == Dtype(0) || power_ == Dtype(1)) {
       caffe_set(count, diff_scale_, bottom_diff);
     } else {
-      const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+      const Dtype* bottom_data = bottom[0]->cpu_data();
       // Compute dy/dx = scale * power * (shift + scale * x)^(power - 1)
       //               = diff_scale * y / (shift + scale * x)
       if (power_ == Dtype(2)) {
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index a40bc75829d..367320a9b1f 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -9,8 +9,8 @@ namespace caffe {
 
 template <typename Dtype>
 void PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+    const vector<Blob<Dtype>*>& top) {
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // Special case where we can ignore the input: scale or power is 0.
   if (diff_scale_ == Dtype(0)) {
@@ -34,15 +34,15 @@ void PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     const Dtype* top_diff = top[0]->gpu_diff();
     if (diff_scale_ == Dtype(0) || power_ == Dtype(1)) {
       caffe_gpu_set(count, diff_scale_, bottom_diff);
     } else {
-      const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+      const Dtype* bottom_data = bottom[0]->gpu_data();
       // Compute dy/dx = scale * power * (shift + scale * x)^(power - 1)
       //               = diff_scale * y / (shift + scale * x)
       if (power_ == Dtype(2)) {
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index b50352f8526..7d5e6034589 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -8,9 +8,9 @@ namespace caffe {
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   for (int i = 0; i < count; ++i) {
@@ -22,12 +22,12 @@ void ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     for (int i = 0; i < count; ++i) {
       bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index def2bbcd7b9..22d5f4b5c5e 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -16,9 +16,9 @@ __global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   // NOLINT_NEXT_LINE(whitespace/operators)
@@ -44,12 +44,12 @@ __global__ void ReLUBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     // NOLINT_NEXT_LINE(whitespace/operators)
     ReLUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 6a48099ae8b..11850ac68e3 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -10,30 +10,30 @@ namespace caffe {
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   sigmoid_bottom_vec_.clear();
   sigmoid_bottom_vec_.push_back(bottom[0]);
   sigmoid_top_vec_.clear();
   sigmoid_top_vec_.push_back(sigmoid_output_.get());
-  sigmoid_layer_->SetUp(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
       "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
-  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // The forward pass computes the sigmoid outputs.
   sigmoid_bottom_vec_[0] = bottom[0];
-  sigmoid_layer_->Forward(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
   // Compute the loss (negative log likelihood)
   const int count = bottom[0]->count();
   const int num = bottom[0]->num();
@@ -45,24 +45,24 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
     // First, compute the diff
-    const int count = (*bottom)[0]->count();
-    const int num = (*bottom)[0]->num();
+    const int count = bottom[0]->count();
+    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
-    const Dtype* target = (*bottom)[1]->cpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* target = bottom[1]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_sub(count, sigmoid_output_data, target, bottom_diff);
     // Scale down gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 8d0fdc6fac4..175f6f86574 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -10,10 +10,10 @@ namespace caffe {
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // The forward pass computes the sigmoid outputs.
   sigmoid_bottom_vec_[0] = bottom[0];
-  sigmoid_layer_->Forward(sigmoid_bottom_vec_, &sigmoid_top_vec_);
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
   // Compute the loss (negative log likelihood)
   const int count = bottom[0]->count();
   const int num = bottom[0]->num();
@@ -25,24 +25,24 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / num;
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
     // First, compute the diff
-    const int count = (*bottom)[0]->count();
-    const int num = (*bottom)[0]->num();
+    const int count = bottom[0]->count();
+    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
-    const Dtype* target = (*bottom)[1]->gpu_data();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    const Dtype* target = bottom[1]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_copy(count, sigmoid_output_data, bottom_diff);
     caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
     // Scale down gradient
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index d7bba7fbfc3..48c384905bf 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -14,9 +14,9 @@ inline Dtype sigmoid(Dtype x) {
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = sigmoid(bottom_data[i]);
@@ -26,12 +26,12 @@ void SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     for (int i = 0; i < count; ++i) {
       const Dtype sigmoid_x = top_data[i];
       bottom_diff[i] = top_diff[i] * sigmoid_x * (1. - sigmoid_x);
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index e1ebb1f6c41..6879ff7f570 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -16,9 +16,9 @@ __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   SigmoidForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -43,12 +43,12 @@ __global__ void SigmoidBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     SigmoidBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top_data, bottom_diff);
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 75dbbf31f0d..0fd88580eaf 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -8,11 +8,11 @@ namespace caffe {
 
 template <typename Dtype>
 void SilenceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  for (int i = 0; i < bottom->size(); ++i) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      caffe_set((*bottom)[i]->count(), Dtype(0),
-                (*bottom)[i]->mutable_cpu_data());
+      caffe_set(bottom[i]->count(), Dtype(0),
+                bottom[i]->mutable_cpu_data());
     }
   }
 }
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index 735abe61eaf..b350b1950bd 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -8,17 +8,17 @@ namespace caffe {
 
 template <typename Dtype>
 void SilenceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // Do nothing.
 }
 
 template <typename Dtype>
 void SilenceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
-  for (int i = 0; i < bottom->size(); ++i) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
-      caffe_gpu_set((*bottom)[i]->count(), Dtype(0),
-                    (*bottom)[i]->mutable_gpu_data());
+      caffe_gpu_set(bottom[i]->count(), Dtype(0),
+                    bottom[i]->mutable_gpu_data());
     }
   }
 }
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index ed679a9169e..17144c1a280 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 template <typename Dtype>
 void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const SliceParameter& slice_param = this->layer_param_.slice_param();
   slice_dim_ = slice_param.slice_dim();
   CHECK_GE(slice_dim_, 0);
@@ -22,18 +22,18 @@ void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   count_ = 0;
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   if (slice_point_.size() != 0) {
-    CHECK_EQ(slice_point_.size(), top->size() - 1);
+    CHECK_EQ(slice_point_.size(), top.size() - 1);
     if (slice_dim_ == 0) {
-      CHECK_LE(top->size(), num_);
+      CHECK_LE(top.size(), num_);
     } else {
-      CHECK_LE(top->size(), channels_);
+      CHECK_LE(top.size(), channels_);
     }
     int prev = 0;
     vector<int> slices;
@@ -44,32 +44,32 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     }
     if (slice_dim_ == 0) {
       slices.push_back(num_ - prev);
-      for (int i = 0; i < top->size(); ++i) {
-        (*top)[i]->Reshape(slices[i], channels_, height_, width_);
-         count_ += (*top)[i]->count();
+      for (int i = 0; i < top.size(); ++i) {
+        top[i]->Reshape(slices[i], channels_, height_, width_);
+         count_ += top[i]->count();
       }
     } else {
       slices.push_back(channels_ - prev);
-      for (int i = 0; i < top->size(); ++i) {
-        (*top)[i]->Reshape(num_, slices[i], height_, width_);
-         count_ += (*top)[i]->count();
+      for (int i = 0; i < top.size(); ++i) {
+        top[i]->Reshape(num_, slices[i], height_, width_);
+         count_ += top[i]->count();
       }
     }
   } else {
     if (slice_dim_ == 0) {
-      CHECK_EQ(num_ % top->size(), 0)
-          << "Number of top blobs (" << top->size() << ") "
+      CHECK_EQ(num_ % top.size(), 0)
+          << "Number of top blobs (" << top.size() << ") "
           << "should evenly divide input num ( " << num_ << ")";
-      num_ = num_ / top->size();
+      num_ = num_ / top.size();
     } else {
-      CHECK_EQ(channels_ % top->size(), 0)
-          << "Number of top blobs (" << top->size() << ") "
+      CHECK_EQ(channels_ % top.size(), 0)
+          << "Number of top blobs (" << top.size() << ") "
           << "should evenly divide input channels ( " << channels_ << ")";
-      channels_ = channels_ / top->size();
+      channels_ = channels_ / top.size();
     }
-    for (int i = 0; i < top->size(); ++i) {
-      (*top)[i]->Reshape(num_, channels_, height_, width_);
-      count_ += (*top)[i]->count();
+    for (int i = 0; i < top.size(); ++i) {
+      top[i]->Reshape(num_, channels_, height_, width_);
+      count_ += top[i]->count();
     }
   }
   CHECK_EQ(count_, bottom[0]->count());
@@ -77,12 +77,12 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->mutable_cpu_data();
   if (slice_dim_ == 0) {
     int offset_num = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
       Dtype* top_data = blob->mutable_cpu_data();
       caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
                  top_data);
@@ -90,8 +90,8 @@ void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     }
   } else if (slice_dim_ == 1) {
     int offset_channel = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
       Dtype* top_data = blob->mutable_cpu_data();
       const int num_elem = blob->channels() * blob->height() * blob->width();
       for (int n = 0; n < num_; ++n) {
@@ -105,16 +105,16 @@ void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   if (slice_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < top.size(); ++i) {
       Blob<Dtype>* blob = top[i];
       const Dtype* top_diff = blob->cpu_diff();
       caffe_copy(blob->count(), top_diff,
-                 bottom_diff + (*bottom)[0]->offset(offset_num));
+                 bottom_diff + bottom[0]->offset(offset_num));
       offset_num += blob->num();
     }
   } else if (slice_dim_ == 1) {
@@ -125,7 +125,7 @@ void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const int num_elem = blob->channels() * blob->height() * blob->width();
       for (int n = 0; n < num_; ++n) {
         caffe_copy(num_elem, top_diff + blob->offset(n),
-                   bottom_diff + (*bottom)[0]->offset(n, offset_channel));
+                   bottom_diff + bottom[0]->offset(n, offset_channel));
       }
       offset_channel += blob->channels();
     }
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index f64e5754890..657840809f6 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -8,12 +8,12 @@ namespace caffe {
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
   if (slice_dim_ == 0) {
     int offset_num = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
       Dtype* top_data = blob->mutable_gpu_data();
       caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
                  top_data);
@@ -21,8 +21,8 @@ void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     }
   } else if (slice_dim_ == 1) {
     int offset_channel = 0;
-    for (int i = 0; i < top->size(); ++i) {
-      Blob<Dtype>* blob = (*top)[i];
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
       Dtype* top_data = blob->mutable_gpu_data();
       const int num_elem = blob->channels() * blob->height() * blob->width();
       for (int n = 0; n < num_; ++n) {
@@ -36,16 +36,16 @@ void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   if (slice_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < top.size(); ++i) {
       Blob<Dtype>* blob = top[i];
       const Dtype* top_diff = blob->gpu_diff();
       caffe_copy(blob->count(), top_diff,
-                 bottom_diff + (*bottom)[0]->offset(offset_num));
+                 bottom_diff + bottom[0]->offset(offset_num));
       offset_num += blob->num();
     }
   } else if (slice_dim_ == 1) {
@@ -56,7 +56,7 @@ void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const int num_elem = blob->channels() * blob->height() * blob->width();
       for (int n = 0; n < num_; ++n) {
         caffe_copy(num_elem, top_diff + blob->offset(n),
-                   bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
+                   bottom_diff +  bottom[0]->offset(n, offset_channel));
       }
       offset_channel += blob->channels();
     }
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 60668a3f8ce..04e8c4f35fc 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -9,8 +9,8 @@ namespace caffe {
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
@@ -22,9 +22,9 @@ void SoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = bottom[0]->num();
   int channels = bottom[0]->channels();
@@ -52,8 +52,8 @@ void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
     // division
     for (int j = 0; j < channels; j++) {
-      caffe_div(spatial_dim, top_data + (*top)[0]->offset(i, j), scale_data,
-          top_data + (*top)[0]->offset(i, j));
+      caffe_div(spatial_dim, top_data + top[0]->offset(i, j), scale_data,
+          top_data + top[0]->offset(i, j));
     }
   }
 }
@@ -61,10 +61,10 @@ void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = top[0]->num();
   int channels = top[0]->channels();
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index f97eafcedb0..8ba31d75951 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -87,9 +87,9 @@ __global__ void kernel_channel_dot(const int num, const int channels,
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
   int num = bottom[0]->num();
   int channels = bottom[0]->channels();
@@ -126,10 +126,10 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   Dtype* scale_data = scale_.mutable_gpu_data();
   int num = top[0]->num();
   int channels = top[0]->channels();
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 55392c37ca0..651320c77f8 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -10,31 +10,31 @@ namespace caffe {
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.clear();
   softmax_top_vec_.push_back(&prob_);
-  softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
+  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
-  softmax_layer_->Reshape(softmax_bottom_vec_, &softmax_top_vec_);
-  if (top->size() >= 2) {
+  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
+  if (top.size() >= 2) {
     // softmax output
-    (*top)[1]->ReshapeLike(*bottom[0]);
+    top[1]->ReshapeLike(*bottom[0]);
   }
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // The forward pass computes the softmax prob values.
-  softmax_layer_->Forward(softmax_bottom_vec_, &softmax_top_vec_);
+  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
   const Dtype* prob_data = prob_.cpu_data();
   const Dtype* label = bottom[1]->cpu_data();
   int num = prob_.num();
@@ -48,25 +48,25 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
                            Dtype(FLT_MIN)));
     }
   }
-  (*top)[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
-  if (top->size() == 2) {
-    (*top)[1]->ShareData(prob_);
+  top[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
+  if (top.size() == 2) {
+    top[1]->ShareData(prob_);
   }
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     const Dtype* prob_data = prob_.cpu_data();
     caffe_copy(prob_.count(), prob_data, bottom_diff);
-    const Dtype* label = (*bottom)[1]->cpu_data();
+    const Dtype* label = bottom[1]->cpu_data();
     int num = prob_.num();
     int dim = prob_.count() / num;
     int spatial_dim = prob_.height() * prob_.width();
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 9ef8dd23615..060dc24d06e 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -10,13 +10,13 @@ namespace caffe {
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   Forward_cpu(bottom, top);
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   // TODO(Yangqing): implement the GPU version of softmax.
   Backward_cpu(top, propagate_down, bottom);
 }
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 40d3600ff17..ee6b5a91d3c 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -8,44 +8,44 @@ namespace caffe {
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   count_ = bottom[0]->count();
-  for (int i = 0; i < top->size(); ++i) {
+  for (int i = 0; i < top.size(); ++i) {
     // Do not allow in-place computation in the SplitLayer.  Instead, share data
     // by reference in the forward pass, and keep separate diff allocations in
     // the backward pass.  (Technically, it should be possible to share the diff
     // blob of the first split output with the input, but this seems to cause
     // some strange effects in practice...)
-    CHECK_NE((*top)[i], bottom[0]) << this->type_name() << " Layer does not "
+    CHECK_NE(top[i], bottom[0]) << this->type_name() << " Layer does not "
         "allow in-place computation.";
-    (*top)[i]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+    top[i]->Reshape(bottom[0]->num(), bottom[0]->channels(),
                        bottom[0]->height(), bottom[0]->width());
-    CHECK_EQ(count_, (*top)[i]->count());
+    CHECK_EQ(count_, top[i]->count());
   }
 }
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < top->size(); ++i) {
-    (*top)[i]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < top.size(); ++i) {
+    top[i]->ShareData(*bottom[0]);
   }
 }
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
   if (top.size() == 1) {
-    caffe_copy(count_, top[0]->cpu_diff(), (*bottom)[0]->mutable_cpu_diff());
+    caffe_copy(count_, top[0]->cpu_diff(), bottom[0]->mutable_cpu_diff());
     return;
   }
   caffe_add(count_, top[0]->cpu_diff(), top[1]->cpu_diff(),
-            (*bottom)[0]->mutable_cpu_diff());
+            bottom[0]->mutable_cpu_diff());
   // Add remaining top blob diffs.
   for (int i = 2; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_axpy(count_, Dtype(1.), top_diff, bottom_diff);
   }
 }
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index fcc0917e67d..0513b204d91 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -8,26 +8,26 @@ namespace caffe {
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  for (int i = 0; i < top->size(); ++i) {
-    (*top)[i]->ShareData(*bottom[0]);
+      const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < top.size(); ++i) {
+    top[i]->ShareData(*bottom[0]);
   }
 }
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
   if (top.size() == 1) {
-    caffe_copy(count_, top[0]->gpu_diff(), (*bottom)[0]->mutable_gpu_diff());
+    caffe_copy(count_, top[0]->gpu_diff(), bottom[0]->mutable_gpu_diff());
     return;
   }
   caffe_gpu_add(count_, top[0]->gpu_diff(), top[1]->gpu_diff(),
-                (*bottom)[0]->mutable_gpu_diff());
+                bottom[0]->mutable_gpu_diff());
   // Add remaining top blob diffs.
   for (int i = 2; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_gpu_axpy(count_, Dtype(1.), top_diff, bottom_diff);
   }
 }
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 8dae0054aed..18413d9b181 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -11,9 +11,9 @@ namespace caffe {
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   Dtype exp2x;
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
@@ -25,12 +25,12 @@ void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
     Dtype tanhx;
     for (int i = 0; i < count; ++i) {
       tanhx = top_data[i];
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index bdb7a94978e..a141f8e872e 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -19,9 +19,9 @@ __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   TanHForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
@@ -41,12 +41,12 @@ __global__ void TanHBackward(const int n, const Dtype* in_diff,
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
+    const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
-    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const int count = (*bottom)[0]->count();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     TanHBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top_data, bottom_diff);
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index 180ea6a322b..b4b2df51bc9 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -8,16 +8,16 @@ namespace caffe {
 
 template <typename Dtype>
 void ThresholdLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
   threshold_ = this->layer_param_.threshold_param().threshold();
 }
 
 template <typename Dtype>
 void ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
     top_data[i] = (bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 93430815900..7e651290897 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -16,9 +16,9 @@ __global__ void ThresholdForward(const int n, const Dtype threshold,
 
 template <typename Dtype>
 void ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
+    const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
   ThresholdForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index a24d7de3daa..cf1f386e454 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -30,7 +30,7 @@ WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
 
 template <typename Dtype>
 void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
   // LayerSetUp runs through the window_file and creates two structures
   // that hold windows: one for foreground (object) windows and one
   // for background (non-object) windows. We use an overlap threshold
@@ -152,20 +152,20 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   int crop_size = this->layer_param_.window_data_param().crop_size();
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
-  (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
+  top[0]->Reshape(batch_size, channels, crop_size, crop_size);
   this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
 
-  LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
-      << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
-      << (*top)[0]->width();
+  LOG(INFO) << "output data size: " << top[0]->num() << ","
+      << top[0]->channels() << "," << top[0]->height() << ","
+      << top[0]->width();
   // datum size
-  this->datum_channels_ = (*top)[0]->channels();
-  this->datum_height_ = (*top)[0]->height();
-  this->datum_width_ = (*top)[0]->width();
+  this->datum_channels_ = top[0]->channels();
+  this->datum_height_ = top[0]->height();
+  this->datum_width_ = top[0]->width();
   this->datum_size_ =
-      (*top)[0]->channels() * (*top)[0]->height() * (*top)[0]->width();
+      top[0]->channels() * top[0]->height() * top[0]->width();
   // label
-  (*top)[1]->Reshape(batch_size, 1, 1, 1);
+  top[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
 }
 
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 6f4a651fb10..21ab15fd31b 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -94,7 +94,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
     // After this layer is connected, set it up.
     LOG(INFO) << "Setting up " << layer_names_[layer_id];
-    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
+    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
@@ -504,8 +504,8 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   Dtype loss = 0;
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
-    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
-    Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
+    layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
+    Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
   }
@@ -570,7 +570,7 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
   for (int i = start; i >= end; --i) {
     if (layer_need_backward_[i]) {
       layers_[i]->Backward(
-          top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
+          top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);
       if (debug_info_) { BackwardDebugInfo(i); }
     }
   }
@@ -683,7 +683,7 @@ void Net<Dtype>::Backward() {
 template <typename Dtype>
 void Net<Dtype>::Reshape() {
   for (int i = 0; i < layers_.size(); ++i) {
-    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
+    layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
   }
 }
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index e11e3f2a981..fa59fab1e8a 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -59,7 +59,7 @@ TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
 TYPED_TEST(AccuracyLayerTest, TestSetup) {
   LayerParameter layer_param;
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -72,7 +72,7 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
       layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(5);
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -83,8 +83,8 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   TypeParam max_value;
   int max_id;
@@ -111,8 +111,8 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(this->top_k_);
   AccuracyLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   TypeParam current_value;
   int current_rank;
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index fb3951c3098..3487d42f21e 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -41,7 +41,7 @@ TYPED_TEST_CASE(ArgMaxLayerTest, TestDtypes);
 TYPED_TEST(ArgMaxLayerTest, TestSetup) {
   LayerParameter layer_param;
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 1);
 }
@@ -51,7 +51,7 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), 2);
 }
@@ -59,8 +59,8 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
 TYPED_TEST(ArgMaxLayerTest, TestCPU) {
   LayerParameter layer_param;
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -84,8 +84,8 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_out_max_val(true);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -110,8 +110,8 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
   argmax_param->set_top_k(this->top_k_);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   int max_ind;
   TypeParam max_val;
@@ -140,8 +140,8 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   argmax_param->set_out_max_val(true);
   argmax_param->set_top_k(this->top_k_);
   ArgMaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   int max_ind;
   TypeParam max_val;
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index c60b7f744cc..f14f1d2fa4f 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -63,7 +63,7 @@ TYPED_TEST(ConcatLayerTest, TestSetupNum) {
   LayerParameter layer_param;
   layer_param.mutable_concat_param()->set_concat_dim(0);
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_1, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_1, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(),
     this->blob_bottom_0->num() + this->blob_bottom_2->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_0->channels());
@@ -75,7 +75,7 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_0, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0->num());
   EXPECT_EQ(this->blob_top_->channels(),
     this->blob_bottom_0->channels()+this->blob_bottom_1->channels());
@@ -88,8 +88,8 @@ TYPED_TEST(ConcatLayerTest, TestNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_0, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_0, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0, this->blob_top_vec_);
   for (int n = 0; n < this->blob_top_->num(); ++n) {
     for (int c = 0; c < this->blob_bottom_0->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
@@ -115,8 +115,8 @@ TYPED_TEST(ConcatLayerTest, TestGradient) {
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
-    &(this->blob_top_vec_));
+  checker.CheckGradient(&layer, this->blob_bottom_vec_0,
+    this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index a5bef4c9826..d269fbc26f2 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -62,8 +62,8 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ContrastiveLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // manually compute to compare
   const Dtype margin = layer_param.contrastive_loss_param().margin();
   const int num = this->blob_bottom_data_i_->num();
@@ -90,13 +90,13 @@ TYPED_TEST(ContrastiveLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ContrastiveLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   // check the gradient for the first two bottom layers
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 1);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 1);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index a38ad3fd1a8..aef9092d8fe 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -157,7 +157,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<Dtype> > layer(
       new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -170,7 +170,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   layer.reset(new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -197,8 +197,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
       new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const Dtype* top_data;
   const Dtype* ref_top_data;
@@ -233,8 +233,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<Dtype> > layer(
       new ConvolutionLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const Dtype* top_data;
   const Dtype* ref_top_data;
@@ -284,8 +284,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
     weights[i +  7] =  0;
     weights[i +  8] =  1;
   }
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
   // (1) the [1 2 1] column filter
   vector<Blob<Dtype>*> sep_blob_bottom_vec;
@@ -311,8 +311,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
     weights_1[i +  1] = 2;
     weights_1[i +  2] = 1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // (2) the [-1 0 1] row filter
   blob_sep->CopyFrom(*this->blob_top_2_, false, true);
   sep_blob_bottom_vec.clear();
@@ -333,8 +333,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
     weights_2[i +  1] =  0;
     weights_2[i +  2] =  1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
   const Dtype* top_data = this->blob_top_->cpu_data();
   const Dtype* sep_top_data = this->blob_top_2_->cpu_data();
@@ -357,8 +357,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   ConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
@@ -374,8 +374,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   ConvolutionLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #ifdef USE_CUDNN
@@ -437,7 +437,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
   this->blob_top_vec_.push_back(this->blob_top_2_);
   shared_ptr<Layer<TypeParam> > layer(
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -450,7 +450,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -477,8 +477,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<TypeParam> > layer(
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const TypeParam* top_data;
   const TypeParam* ref_top_data;
@@ -513,8 +513,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
   convolution_param->mutable_bias_filler()->set_value(0.1);
   shared_ptr<Layer<TypeParam> > layer(
       new CuDNNConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Check against reference convolution.
   const TypeParam* top_data;
   const TypeParam* ref_top_data;
@@ -564,8 +564,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
     weights[i +  7] =  0;
     weights[i +  8] =  1;
   }
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
   // (1) the [1 2 1] column filter
   vector<Blob<TypeParam>*> sep_blob_bottom_vec;
@@ -591,8 +591,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
     weights_1[i +  1] = 2;
     weights_1[i +  2] = 1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // (2) the [-1 0 1] row filter
   blob_sep->CopyFrom(*this->blob_top_2_, false, true);
   sep_blob_bottom_vec.clear();
@@ -613,8 +613,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
     weights_2[i +  1] =  0;
     weights_2[i +  2] =  1;
   }
-  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
-  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
+  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
   const TypeParam* top_data = this->blob_top_->cpu_data();
   const TypeParam* sep_top_data = this->blob_top_2_->cpu_data();
@@ -637,8 +637,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   CuDNNConvolutionLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
@@ -654,8 +654,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
   convolution_param->mutable_bias_filler()->set_type("gaussian");
   CuDNNConvolutionLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #endif
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 887124aa5bc..657ffdea68c 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -125,7 +125,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     transform_param->set_scale(scale);
 
     DataLayer<Dtype> layer(param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
     EXPECT_EQ(blob_top_data_->channels(), 2);
     EXPECT_EQ(blob_top_data_->height(), 3);
@@ -136,7 +136,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     EXPECT_EQ(blob_top_label_->width(), 1);
 
     for (int iter = 0; iter < 100; ++iter) {
-      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -165,7 +165,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     transform_param->set_crop_size(1);
 
     DataLayer<Dtype> layer(param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_data_->num(), 5);
     EXPECT_EQ(blob_top_data_->channels(), 2);
     EXPECT_EQ(blob_top_data_->height(), 1);
@@ -176,7 +176,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     EXPECT_EQ(blob_top_label_->width(), 1);
 
     for (int iter = 0; iter < 2; ++iter) {
-      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -219,9 +219,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     vector<vector<Dtype> > crop_sequence;
     {
       DataLayer<Dtype> layer1(param);
-      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      layer1.SetUp(blob_bottom_vec_, blob_top_vec_);
       for (int iter = 0; iter < 2; ++iter) {
-        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        layer1.Forward(blob_bottom_vec_, blob_top_vec_);
         for (int i = 0; i < 5; ++i) {
           EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
         }
@@ -240,9 +240,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     // Check that the sequence is the same as the original.
     Caffe::set_random_seed(seed_);
     DataLayer<Dtype> layer2(param);
-    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer2.SetUp(blob_bottom_vec_, blob_top_vec_);
     for (int iter = 0; iter < 2; ++iter) {
-      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer2.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
@@ -274,9 +274,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     vector<vector<Dtype> > crop_sequence;
     {
       DataLayer<Dtype> layer1(param);
-      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      layer1.SetUp(blob_bottom_vec_, blob_top_vec_);
       for (int iter = 0; iter < 2; ++iter) {
-        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        layer1.Forward(blob_bottom_vec_, blob_top_vec_);
         for (int i = 0; i < 5; ++i) {
           EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
         }
@@ -295,9 +295,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     // srand with 1701. Check that the sequence differs from the original.
     srand(seed_);
     DataLayer<Dtype> layer2(param);
-    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer2.SetUp(blob_bottom_vec_, blob_top_vec_);
     for (int iter = 0; iter < 2; ++iter) {
-      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
+      layer2.Forward(blob_bottom_vec_, blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
         EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 4188bb68c9e..da121fa5d99 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -56,7 +56,7 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
   dummy_data_param->add_width(4);
   this->blob_top_vec_.resize(1);
   DummyDataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 5);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 2);
@@ -68,7 +68,7 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
       EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
     }
   }
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
     for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
       EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
@@ -92,7 +92,7 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
   data_filler_param->set_value(7);
   this->blob_top_vec_.resize(2);
   DummyDataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 5);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 2);
@@ -107,7 +107,7 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
       EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
     }
   }
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
     for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
       EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
@@ -134,7 +134,7 @@ TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
   FillerParameter* data_filler_param_c = dummy_data_param->add_data_filler();
   data_filler_param_c->set_value(9);
   DummyDataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 5);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 2);
@@ -160,7 +160,7 @@ TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
   }
 
   // Do a Forward pass to fill in Blob b with Gaussian data.
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_a_->count(); ++i) {
     EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
   }
@@ -180,7 +180,7 @@ TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
 
   // Do another Forward pass to fill in Blob b with Gaussian data again,
   // checking that we get different values.
-  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_a_->count(); ++i) {
     EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
   }
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index d5cf08229ab..be0c1347709 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -58,7 +58,7 @@ TYPED_TEST(EltwiseLayerTest, TestSetUp) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 4);
@@ -72,8 +72,8 @@ TYPED_TEST(EltwiseLayerTest, TestProd) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -91,8 +91,8 @@ TYPED_TEST(EltwiseLayerTest, TestSum) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -113,8 +113,8 @@ TYPED_TEST(EltwiseLayerTest, TestSumCoeff) {
   eltwise_param->add_coeff(2);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -134,8 +134,8 @@ TYPED_TEST(EltwiseLayerTest, TestStableProdGradient) {
   eltwise_param->set_stable_prod_grad(true);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestUnstableProdGradient) {
@@ -146,8 +146,8 @@ TYPED_TEST(EltwiseLayerTest, TestUnstableProdGradient) {
   eltwise_param->set_stable_prod_grad(false);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestSumGradient) {
@@ -157,8 +157,8 @@ TYPED_TEST(EltwiseLayerTest, TestSumGradient) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
@@ -171,8 +171,8 @@ TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
   eltwise_param->add_coeff(2);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(EltwiseLayerTest, TestMax) {
@@ -182,8 +182,8 @@ TYPED_TEST(EltwiseLayerTest, TestMax) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
   shared_ptr<EltwiseLayer<Dtype> > layer(
       new EltwiseLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* data = this->blob_top_->cpu_data();
   const int count = this->blob_top_->count();
   const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
@@ -202,8 +202,8 @@ TYPED_TEST(EltwiseLayerTest, TestMaxGradient) {
   eltwise_param->set_operation(EltwiseParameter_EltwiseOp_MAX);
   EltwiseLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-4, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index d7d2de7e9d4..1949742bbcb 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -44,18 +44,18 @@ class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
     // equivalent to explicitly specifiying a weight of 1.
     LayerParameter layer_param;
     EuclideanLossLayer<Dtype> layer_weight_1(layer_param);
-    layer_weight_1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer_weight_1.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype loss_weight_1 =
-        layer_weight_1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+        layer_weight_1.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
     // Get the loss again with a different objective weight; check that it is
     // scaled appropriately.
     const Dtype kLossWeight = 3.7;
     layer_param.add_loss_weight(kLossWeight);
     EuclideanLossLayer<Dtype> layer_weight_2(layer_param);
-    layer_weight_2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer_weight_2.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype loss_weight_2 =
-        layer_weight_2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+        layer_weight_2.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype kErrorMargin = 1e-5;
     EXPECT_NEAR(loss_weight_1 * kLossWeight, loss_weight_2, kErrorMargin);
     // Make sure the loss is non-trivial.
@@ -82,10 +82,10 @@ TYPED_TEST(EuclideanLossLayerTest, TestGradient) {
   const Dtype kLossWeight = 3.7;
   layer_param.add_loss_weight(kLossWeight);
   EuclideanLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index cbd01f245f2..3042d293cf7 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -41,7 +41,7 @@ TYPED_TEST(FlattenLayerTest, TestSetup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -52,8 +52,8 @@ TYPED_TEST(FlattenLayerTest, Test) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int c = 0; c < 3 * 6 * 5; ++c) {
     EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
         this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
@@ -67,8 +67,8 @@ TYPED_TEST(FlattenLayerTest, TestGradient) {
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index eb09c8d1f3a..c8282233e5b 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -96,8 +96,8 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
   {
     HDF5OutputLayer<Dtype> layer(param);
     EXPECT_EQ(layer.file_name(), this->output_file_name_);
-    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   }
   file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
                           H5P_DEFAULT);
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index acca75b19b1..db9068b32ed 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -67,7 +67,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
 
   // Test that the layer setup got the correct parameters.
   HDF5DataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), batch_size);
   EXPECT_EQ(this->blob_top_data_->channels(), num_cols);
   EXPECT_EQ(this->blob_top_data_->height(), height);
@@ -78,12 +78,12 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_label_->height(), 1);
   EXPECT_EQ(this->blob_top_label_->width(), 1);
 
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Go through the data 10 times (5 batches).
   const int data_size = num_cols * height * width;
   for (int iter = 0; iter < 10; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
     // On even iterations, we're reading the first half of the data.
     // On odd iterations, we're reading the second half of the data.
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 3c11b9ac491..b6a99022905 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -57,8 +57,8 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL1) {
   LayerParameter layer_param;
   HingeLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 2e-3, 1701, 1, 0.01);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
@@ -69,8 +69,8 @@ TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
   hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
   HingeLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 32cf6369361..f50abe103f8 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -44,7 +44,7 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 27);
   EXPECT_EQ(this->blob_top_->height(), 2);
@@ -59,8 +59,8 @@ TYPED_TEST(Im2colLayerTest, TestForward) {
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // We are lazy and will only check the top left block
   for (int c = 0; c < 27; ++c) {
     EXPECT_EQ(this->blob_bottom_->data_at(0, (c / 9), (c / 3) % 3, c % 3),
@@ -77,8 +77,8 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
@@ -91,8 +91,8 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
   convolution_param->set_kernel_w(3);
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // We are lazy and will only check the top left block
   for (int c = 0; c < 45; ++c) {
     EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
@@ -111,8 +111,8 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) {
   convolution_param->set_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index d098c765258..77523ef8c18 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -61,7 +61,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
   image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_shuffle(false);
   ImageDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
   EXPECT_EQ(this->blob_top_data_->height(), 360);
@@ -72,7 +72,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_label_->width(), 1);
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     for (int i = 0; i < 5; ++i) {
       EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
     }
@@ -89,7 +89,7 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   image_data_param->set_new_width(256);
   image_data_param->set_shuffle(false);
   ImageDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
   EXPECT_EQ(this->blob_top_data_->height(), 256);
@@ -100,7 +100,7 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   EXPECT_EQ(this->blob_top_label_->width(), 1);
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     for (int i = 0; i < 5; ++i) {
       EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
     }
@@ -115,7 +115,7 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   image_data_param->set_source(this->filename_.c_str());
   image_data_param->set_shuffle(true);
   ImageDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
   EXPECT_EQ(this->blob_top_data_->height(), 360);
@@ -126,7 +126,7 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   EXPECT_EQ(this->blob_top_label_->width(), 1);
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     map<Dtype, int> values_to_indices;
     int num_in_order = 0;
     for (int i = 0; i < 5; ++i) {
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index de2f901af31..7ec2f8073c1 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -63,8 +63,8 @@ TYPED_TEST(InfogainLossLayerTest, TestGradient) {
   LayerParameter layer_param;
   InfogainLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index 5f9729c4f90..c03df17383a 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -48,7 +48,7 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   inner_product_param->set_num_output(10);
   shared_ptr<InnerProductLayer<Dtype> > layer(
       new InnerProductLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->height(), 1);
   EXPECT_EQ(this->blob_top_->width(), 1);
@@ -73,8 +73,8 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
     inner_product_param->mutable_bias_filler()->set_max(2);
     shared_ptr<InnerProductLayer<Dtype> > layer(
         new InnerProductLayer<Dtype>(layer_param));
-    layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype* data = this->blob_top_->cpu_data();
     const int count = this->blob_top_->count();
     for (int i = 0; i < count; ++i) {
@@ -103,8 +103,8 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) {
     inner_product_param->mutable_bias_filler()->set_max(2);
     InnerProductLayer<Dtype> layer(layer_param);
     GradientChecker<Dtype> checker(1e-2, 1e-3);
-    checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
   } else {
     LOG(ERROR) << "Skipping test due to old architecture.";
   }
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 3bd62fd9e18..07425df9b3a 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -116,7 +116,7 @@ TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -127,8 +127,8 @@ TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
@@ -143,20 +143,20 @@ TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
   LayerParameter layer_param;
   LRNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
   vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
   layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
+                 this->blob_bottom_vec_);
   // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
   //   std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
   //       << std::endl;
   // }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
@@ -166,7 +166,7 @@ TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -180,8 +180,8 @@ TYPED_TEST(LRNLayerTest, TestForwardWithinChannel) {
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
   LRNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
@@ -199,13 +199,13 @@ TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
   layer_param.mutable_lrn_param()->set_local_size(3);
   LRNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 311c7781be5..b1f4e4eac9a 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -47,9 +47,9 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> max_layer(layer_param);
-  max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  max_layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   DropoutLayer<Dtype> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.SetUp(this->blob_top_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -64,8 +64,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* top_data = this->blob_top_->cpu_data();
   Dtype sum = 0.;
   for (int i = 0; i < this->blob_top_->count(); ++i) {
@@ -74,8 +74,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
   DropoutLayer<Dtype> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.SetUp(this->blob_top_vec_, this->blob_top_vec_);
+  dropout_layer.Forward(this->blob_top_vec_, this->blob_top_vec_);
   sum = 0.;
   Dtype scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
   top_data = this->blob_top_->cpu_data();
@@ -94,14 +94,14 @@ TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
   vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
   layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
+                 this->blob_bottom_vec_);
   const Dtype* bottom_diff = this->blob_bottom_->cpu_diff();
   Dtype sum = 0.;
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -110,12 +110,12 @@ TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   EXPECT_EQ(sum, this->blob_top_->count());
   // Dropout in-place
   DropoutLayer<Dtype> dropout_layer(layer_param);
-  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
-  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.SetUp(this->blob_top_vec_, this->blob_top_vec_);
+  dropout_layer.Forward(this->blob_top_vec_, this->blob_top_vec_);
   dropout_layer.Backward(this->blob_top_vec_, propagate_down,
-                         &(this->blob_top_vec_));
+                         this->blob_top_vec_);
   layer.Backward(this->blob_top_vec_, propagate_down,
-                 &(this->blob_bottom_vec_));
+                 this->blob_bottom_vec_);
   Dtype sum_with_dropout = 0.;
   bottom_diff = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 3dc00345743..497ab0d15e7 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -70,7 +70,7 @@ TYPED_TEST(MemoryDataLayerTest, TestSetup) {
   md_param->set_width(this->width_);
   shared_ptr<Layer<Dtype> > layer(
       new MemoryDataLayer<Dtype>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->data_blob_->num(), this->batch_size_);
   EXPECT_EQ(this->data_blob_->channels(), this->channels_);
   EXPECT_EQ(this->data_blob_->height(), this->height_);
@@ -93,12 +93,12 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   md_param->set_width(this->width_);
   shared_ptr<MemoryDataLayer<Dtype> > layer(
       new MemoryDataLayer<Dtype>(layer_param));
-  layer->DataLayerSetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->DataLayerSetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer->Reset(this->data_->mutable_cpu_data(),
       this->labels_->mutable_cpu_data(), this->data_->num());
   for (int i = 0; i < this->batches_ * 6; ++i) {
     int batch_num = i % this->batches_;
-    layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     for (int j = 0; j < this->data_blob_->count(); ++j) {
       EXPECT_EQ(this->data_blob_->cpu_data()[j],
           this->data_->cpu_data()[
@@ -121,7 +121,7 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   memory_data_param->set_height(this->height_);
   memory_data_param->set_width(this->width_);
   MemoryDataLayer<Dtype> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
 
   vector<Datum> datum_vector(this->batch_size_);
   const size_t count = this->channels_ * this->height_ * this->width_;
@@ -144,7 +144,7 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   int data_index;
   // Go through the data 5 times
   for (int iter = 0; iter < 5; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype* data = this->data_blob_->cpu_data();
     size_t index = 0;
     for (int i = 0; i < this->batch_size_; ++i) {
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 1fc4c42f0f4..9038017e3e2 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -53,10 +53,10 @@ TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
   LayerParameter layer_param;
   Caffe::set_mode(Caffe::CPU);
   MultinomialLogisticLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<TypeParam> checker(1e-2, 2*1e-2, 1701, 0, 0.05);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index d3d8012f09f..933b4326417 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -40,8 +40,8 @@ TYPED_TEST(MVNLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   MVNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test mean
   int num = this->blob_bottom_->num();
   int channels = this->blob_bottom_->channels();
@@ -75,8 +75,8 @@ TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
   LayerParameter layer_param;
   layer_param.ParseFromString("mvn_param{normalize_variance: false}");
   MVNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test mean
   int num = this->blob_bottom_->num();
   int channels = this->blob_bottom_->channels();
@@ -107,8 +107,8 @@ TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) {
   LayerParameter layer_param;
   layer_param.ParseFromString("mvn_param{across_channels: true}");
   MVNLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test mean
   int num = this->blob_bottom_->num();
   int channels = this->blob_bottom_->channels();
@@ -142,8 +142,8 @@ TYPED_TEST(MVNLayerTest, TestGradient) {
   LayerParameter layer_param;
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
@@ -152,8 +152,8 @@ TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
   layer_param.ParseFromString("mvn_param{normalize_variance: false}");
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
@@ -162,8 +162,8 @@ TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
   layer_param.ParseFromString("mvn_param{across_channels: true}");
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 4c19d3f9bae..fdbb63cb3e5 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -44,8 +44,8 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     }
     Caffe::set_phase(Caffe::TRAIN);
     DropoutLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     // Now, check values
     const Dtype* bottom_data = this->blob_bottom_->cpu_data();
     const Dtype* top_data = this->blob_top_->cpu_data();
@@ -74,8 +74,8 @@ TYPED_TEST(NeuronLayerTest, TestAbsVal) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   AbsValLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data    = this->blob_top_->cpu_data();
   const int count = this->blob_bottom_->count();
@@ -89,16 +89,16 @@ TYPED_TEST(NeuronLayerTest, TestAbsGradient) {
   LayerParameter layer_param;
   AbsValLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLU) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ReLULayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -113,8 +113,8 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
   LayerParameter layer_param;
   ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
@@ -122,8 +122,8 @@ TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
   LayerParameter layer_param;
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
   ReLULayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -139,16 +139,16 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
   ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SigmoidLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -165,16 +165,16 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
   LayerParameter layer_param;
   SigmoidLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestTanH) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   TanHLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test exact values
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
@@ -197,8 +197,8 @@ TYPED_TEST(NeuronLayerTest, TestTanHGradient) {
   LayerParameter layer_param;
   TanHLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
@@ -216,8 +216,8 @@ TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
   LayerParameter layer_param;
   Caffe::set_phase(Caffe::TEST);
   DropoutLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -234,8 +234,8 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
   Caffe::set_phase(Caffe::TRAIN);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
@@ -244,16 +244,16 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
   Caffe::set_phase(Caffe::TEST);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestBNLL) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   BNLLLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -268,8 +268,8 @@ TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
   LayerParameter layer_param;
   BNLLLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #ifdef USE_CUDNN
@@ -300,8 +300,8 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -316,8 +316,8 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
@@ -325,8 +325,8 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
   LayerParameter layer_param;
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
   CuDNNReLULayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -342,16 +342,16 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
   layer_param.ParseFromString("relu_param{negative_slope:0.01}");
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
@@ -368,16 +368,16 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test exact values
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
@@ -400,8 +400,8 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 #endif
 
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index ec23a682c2f..e2980331dc0 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -73,7 +73,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 14] = 3;
     }
     PoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 2);
@@ -84,7 +84,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       EXPECT_EQ(blob_top_mask_->height(), 2);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     //     [9 5 5 8]
     //     [9 5 5 8]
@@ -171,7 +171,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     PoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 4);
@@ -182,7 +182,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       EXPECT_EQ(blob_top_mask_->height(), 4);
       EXPECT_EQ(blob_top_mask_->width(), 5);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    27    27]
     // [32    33    33    27    27]
@@ -296,7 +296,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     PoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 5);
@@ -307,7 +307,7 @@ class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
       EXPECT_EQ(blob_top_mask_->height(), 5);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    26]
     // [32    32    27    27]
@@ -377,7 +377,7 @@ TYPED_TEST(PoolingLayerTest, TestSetup) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -393,7 +393,7 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 4);
@@ -407,8 +407,8 @@ TYPED_TEST(PoolingLayerTest, PrintBackward) {
   layer_param.set_stride(2);
   layer_param.set_pool(LayerParameter_PoolMethod_MAX);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
   }
@@ -419,7 +419,7 @@ TYPED_TEST(PoolingLayerTest, PrintBackward) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = i;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  layer.Backward(this->blob_top_vec_, true, this->blob_bottom_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
   }
@@ -452,8 +452,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientMax) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -481,12 +481,12 @@ TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Dtype epsilon = 1e-8;
   // Output:
   //     [ 1 4 4 ]
@@ -516,8 +516,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
       this->blob_top_vec_.push_back(this->blob_top_mask_);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
       this->blob_top_vec_.pop_back();
     }
   }
@@ -537,12 +537,12 @@ TYPED_TEST(PoolingLayerTest, TestForwardAve) {
   ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
   PoolingLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   Dtype epsilon = 1e-5;
   EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
@@ -567,8 +567,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientAve) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -586,8 +586,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       PoolingLayer<Dtype> layer(layer_param);
       GradientChecker<Dtype> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -651,7 +651,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       blob_bottom_->mutable_cpu_data()[i + 14] = 3;
     }
     CuDNNPoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 2);
@@ -662,7 +662,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       EXPECT_EQ(blob_top_mask_->height(), 2);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     //     [9 5 5 8]
     //     [9 5 5 8]
@@ -749,7 +749,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     CuDNNPoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 4);
@@ -760,7 +760,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       EXPECT_EQ(blob_top_mask_->height(), 4);
       EXPECT_EQ(blob_top_mask_->width(), 5);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    27    27]
     // [32    33    33    27    27]
@@ -874,7 +874,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       blob_bottom_->mutable_cpu_data()[i + 35] = 11;
     }
     CuDNNPoolingLayer<Dtype> layer(layer_param);
-    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
     EXPECT_EQ(blob_top_->num(), num);
     EXPECT_EQ(blob_top_->channels(), channels);
     EXPECT_EQ(blob_top_->height(), 5);
@@ -885,7 +885,7 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
       EXPECT_EQ(blob_top_mask_->height(), 5);
       EXPECT_EQ(blob_top_mask_->width(), 4);
     }
-    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
     // Expected output: 2x 2 channels of:
     // [35    32    26    26]
     // [32    32    27    27]
@@ -955,7 +955,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -971,7 +971,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 4);
@@ -986,8 +986,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
   layer_param.set_stride(2);
   layer_param.set_pool(LayerParameter_PoolMethod_MAX);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
   }
@@ -998,7 +998,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = i;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  layer.Backward(this->blob_top_vec_, true, this->blob_bottom_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
   }
@@ -1033,8 +1033,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -1062,12 +1062,12 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   this->blob_bottom_->mutable_cpu_data()[7] = 2;
   this->blob_bottom_->mutable_cpu_data()[8] = 1;
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   TypeParam epsilon = 1e-8;
   // Output:
   //     [ 1 4 4 ]
@@ -1097,8 +1097,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
       this->blob_top_vec_.push_back(this->blob_top_mask_);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-4, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
       this->blob_top_vec_.pop_back();
     }
   }
@@ -1118,12 +1118,12 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
   ConstantFiller<TypeParam> filler(filler_param);
   filler.Fill(this->blob_bottom_);
   CuDNNPoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   TypeParam epsilon = 1e-5;
   EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
@@ -1148,8 +1148,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
@@ -1167,8 +1167,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
       pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-2, 1e-2);
-      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-          &(this->blob_top_vec_));
+      checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+          this->blob_top_vec_);
     }
   }
 }
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 0c104c20b7c..0d52fa1c8e2 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -37,8 +37,8 @@ class PowerLayerTest : public MultiDeviceTest<TypeParam> {
     layer_param.mutable_power_param()->set_scale(scale);
     layer_param.mutable_power_param()->set_shift(shift);
     PowerLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     // Now, check values
     const Dtype* bottom_data = this->blob_bottom_->cpu_data();
     const Dtype* top_data = this->blob_top_->cpu_data();
@@ -75,8 +75,8 @@ class PowerLayerTest : public MultiDeviceTest<TypeParam> {
       }
     }
     GradientChecker<Dtype> checker(1e-2, 1e-2, 1701, 0., 0.01);
-    checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
+    checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
   }
 
   Blob<Dtype>* const blob_bottom_;
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 47ccdea1538..e5737e43f6e 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -79,9 +79,9 @@ class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
       // Fill the targets vector
       targets_filler.Fill(this->blob_bottom_targets_);
       SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
-      layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+      layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
       Dtype layer_loss =
-          layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+          layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
       const int count = this->blob_bottom_data_->count();
       const int num = this->blob_bottom_data_->num();
       const Dtype* blob_bottom_data = this->blob_bottom_data_->cpu_data();
@@ -112,10 +112,10 @@ TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
   const Dtype kLossWeight = 3.7;
   layer_param.add_loss_weight(kLossWeight);
   SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index ee8818781f5..395be280089 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -64,7 +64,7 @@ TYPED_TEST(SliceLayerTest, TestSetupNum) {
   LayerParameter layer_param;
   layer_param.mutable_slice_param()->set_slice_dim(0);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_1_);
   EXPECT_EQ(this->blob_bottom_->num(), 3 * this->blob_top_0_->num());
   EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_1_->num());
   EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_2_->num());
@@ -78,7 +78,7 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) {
   LayerParameter layer_param;
   layer_param.mutable_slice_param()->add_slice_point(3);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
   EXPECT_EQ(this->blob_top_0_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_0_->channels(), 3);
   EXPECT_EQ(this->blob_top_1_->channels(), 9);
@@ -93,11 +93,11 @@ TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
   LayerParameter layer_param;
   layer_param.mutable_slice_param()->set_slice_dim(0);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
   const int top_num = this->blob_bottom_->num() / 2;
   ASSERT_EQ(top_num, this->blob_top_0_->num());
   ASSERT_EQ(top_num, this->blob_top_1_->num());
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_0_);
   for (int n = 0; n < top_num; ++n) {
     for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
@@ -127,12 +127,12 @@ TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) {
   layer_param.mutable_slice_param()->add_slice_point(kSlicePoint0);
   layer_param.mutable_slice_param()->add_slice_point(kSlicePoint1);
   SliceLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_1_);
   ASSERT_EQ(kSlicePoint0, this->blob_top_0_->channels());
   ASSERT_EQ(kSlicePoint1 - kSlicePoint0, this->blob_top_1_->channels());
   ASSERT_EQ(this->blob_bottom_->channels() - kSlicePoint1,
             this->blob_top_2_->channels());
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_1_);
   for (int n = 0; n < this->blob_bottom_->num(); ++n) {
     for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
@@ -169,8 +169,8 @@ TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
   layer_param.mutable_slice_param()->set_slice_dim(0);
   SliceLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-    &(this->blob_top_vec_0_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+    this->blob_top_vec_0_);
 }
 
 TYPED_TEST(SliceLayerTest, TestGradientAcrossChannels) {
@@ -182,8 +182,8 @@ TYPED_TEST(SliceLayerTest, TestGradientAcrossChannels) {
   layer_param.mutable_slice_param()->add_slice_point(kSlicePoint);
   SliceLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-    &(this->blob_top_vec_0_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+    this->blob_top_vec_0_);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 41f643f4e2a..f6674422e56 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -41,8 +41,8 @@ TYPED_TEST(SoftmaxLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SoftmaxLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int k = 0; k < this->blob_bottom_->height(); ++k) {
@@ -76,8 +76,8 @@ TYPED_TEST(SoftmaxLayerTest, TestGradient) {
   LayerParameter layer_param;
   SoftmaxLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #ifdef USE_CUDNN
@@ -107,8 +107,8 @@ TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
     for (int k = 0; k < this->blob_bottom_->height(); ++k) {
@@ -142,8 +142,8 @@ TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) {
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 #endif
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 246d64e116a..badda3b5503 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -57,8 +57,8 @@ TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
   layer_param.add_loss_weight(3);
   SoftmaxWithLossLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index e9b942c5c51..38e762195a2 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -52,7 +52,7 @@ TYPED_TEST(SplitLayerTest, TestSetup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SplitLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_a_->num(), 2);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
   EXPECT_EQ(this->blob_top_a_->height(), 6);
@@ -67,8 +67,8 @@ TYPED_TEST(SplitLayerTest, Test) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   SplitLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
     EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
@@ -81,8 +81,8 @@ TYPED_TEST(SplitLayerTest, TestGradient) {
   LayerParameter layer_param;
   SplitLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 4f13981bd82..ad5151007c2 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -53,7 +53,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), 3);
@@ -69,8 +69,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Check if the output is correct - it should do random sampling
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
@@ -113,8 +113,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   pooling_param->set_stride(2);
   pooling_param->set_pool(PoolingParameter_PoolMethod_STOCHASTIC);
   PoolingLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Check if the output is correct - it should do random sampling
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
@@ -154,8 +154,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
   GradientChecker<TypeParam> checker(1e-4, 1e-2);
   // it is too expensive to call curand multiple times, so we don't do an
   // exhaustive gradient check.
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  checker.CheckGradient(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
 }
 
 
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 32dfbeeac92..05ce82120e6 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -40,7 +40,7 @@ TYPED_TEST(ThresholdLayerTest, TestSetup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ThresholdLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
   EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_->height());
@@ -51,8 +51,8 @@ TYPED_TEST(ThresholdLayerTest, Test) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ThresholdLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
@@ -76,8 +76,8 @@ TYPED_TEST(ThresholdLayerTest, Test2) {
     layer_param.mutable_threshold_param();
   threshold_param->set_threshold(0.5);
   ThresholdLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index c8c8c1a6b4c..bfcd9f19cec 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -237,8 +237,8 @@ int time() {
     for (int j = 0; j < FLAGS_iterations; ++j) {
       // Although Reshape should be essentially free, we include it here
       // so that we will notice Reshape performance bugs.
-      layers[i]->Reshape(bottom_vecs[i], &top_vecs[i]);
-      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
+      layers[i]->Reshape(bottom_vecs[i], top_vecs[i]);
+      layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
     }
     LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
         " milliseconds.";
@@ -252,7 +252,7 @@ int time() {
     timer.Start();
     for (int j = 0; j < FLAGS_iterations; ++j) {
       layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
-                          &bottom_vecs[i]);
+                          bottom_vecs[i]);
     }
     LOG(INFO) << layername << "\tbackward: "
         << timer.MilliSeconds() << " milliseconds.";

From 7cfc3627c7e65251d4a559ca8bfe963f43398ae8 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 20 Sep 2014 07:55:32 +0800
Subject: [PATCH 0793/2053] fix directory in finetune pascal example

---
 .../pascal_finetune_solver.prototxt                         | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
index 54ba6350448..83f5abdf891 100644
--- a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
@@ -1,5 +1,5 @@
-train_net: "examples/finetuning_pascal_detection/pascal_finetune_train.prototxt"
-test_net: "examples/finetuning_pascal_detection/pascal_finetune_val.prototxt"
+train_net: "examples/finetune_pascal_detection/pascal_finetune_train.prototxt"
+test_net: "examples/finetune_pascal_detection/pascal_finetune_val.prototxt"
 test_iter: 100
 test_interval: 1000
 base_lr: 0.001
@@ -11,4 +11,4 @@ max_iter: 100000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/finetuning_pascal_detection/pascal_det_finetune"
+snapshot_prefix: "examples/finetune_pascal_detection/pascal_det_finetune"

From 2f45b2109b1fb6080abe035d1a8e0684cda13bb5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 19 Sep 2014 21:32:16 -0700
Subject: [PATCH 0794/2053] drop out-of-date conv test comments

---
 src/caffe/test/test_convolution_layer.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index aef9092d8fe..c2e0a86271a 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -182,7 +182,6 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
-  // We will simply see if the convolution layer carries out averaging well.
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -219,7 +218,6 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
 }
 
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
-  // We will simply see if the convolution layer carries out averaging well.
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
@@ -462,7 +460,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
-  // We will simply see if the convolution layer carries out averaging well.
   Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -499,7 +496,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
-  // We will simply see if the convolution layer carries out averaging well.
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =

From 8109a6e56ed18e1f40b48e494a30e290019eefb4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 19 Sep 2014 18:59:38 -0700
Subject: [PATCH 0795/2053] optimize 1x1 convolution for Network-in-Network
 style layers

1x1 convolution with stride 1 is a special case of Caffe matrix
multiplication convolution for which im2col / col2im transformations are
actually the identity. For this special case the memory and
transformation are skipped.
---
 include/caffe/vision_layers.hpp           |  1 +
 src/caffe/layers/conv_layer.cpp           | 46 +++++++++++++++++------
 src/caffe/layers/conv_layer.cu            | 42 +++++++++++++++------
 src/caffe/test/test_convolution_layer.cpp | 45 ++++++++++++++++++++++
 4 files changed, 110 insertions(+), 24 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 0f1b2d9e529..fa516144d25 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -97,6 +97,7 @@ class ConvolutionLayer : public Layer<Dtype> {
   int num_output_;
   int height_out_, width_out_;
   bool bias_term_;
+  bool is_1x1_;
 
   /// M_ is the channel dimension of the output for a single group, which is the
   /// leading dimension of the filter matrix.
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 8c08c100bd5..d33aa05e961 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -47,6 +47,10 @@ void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = conv_param.stride_h();
     stride_w_ = conv_param.stride_w();
   }
+  // Special case: im2col is the identity for 1x1 convolution with stride 1
+  // and no padding, so flag for skipping the buffer and transformation.
+  is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
+      && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
   // Configure output channels and groups.
   channels_ = bottom[0]->channels();
   num_output_ = this->layer_param_.convolution_param().num_output();
@@ -137,7 +141,10 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
-    Dtype* col_data = col_buffer_.mutable_cpu_data();
+    Dtype* col_data = NULL;
+    if (!is_1x1_) {
+      col_data = col_buffer_.mutable_cpu_data();
+    }
     const Dtype* weight = this->blobs_[0]->cpu_data();
     int weight_offset = M_ * K_;  // number of filter parameters in a group
     int col_offset = K_ * N_;  // number of values in an input region / column
@@ -145,9 +152,13 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     for (int n = 0; n < num_; ++n) {
       // im2col transformation: unroll input regions for filtering
       // into column matrix for multplication.
-      im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-          col_data);
+      if (!is_1x1_) {
+        im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+            width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+            col_data);
+      } else {  // special case for 1x1 convolution
+        col_data = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
+      }
       // Take inner products for groups.
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
@@ -199,16 +210,25 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       if (!top_diff) {
         top_diff = top[i]->cpu_diff();
       }
-      Dtype* col_data = col_buffer_.mutable_cpu_data();
-      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
+      Dtype* col_data = NULL;
+      Dtype* col_diff = NULL;
+      if (!is_1x1_) {
+        col_data = col_buffer_.mutable_cpu_data();
+        col_diff = col_buffer_.mutable_cpu_diff();
+      }
       const Dtype* bottom_data = bottom[i]->cpu_data();
       Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
       for (int n = 0; n < num_; ++n) {
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
-        im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                   stride_h_, stride_w_, col_data);
+        if (!is_1x1_) {
+          im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+                    width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                    stride_h_, stride_w_, col_data);
+        } else {
+          col_data = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
+          col_diff = bottom[i]->mutable_cpu_diff() + bottom[i]->offset(n);
+        }
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
@@ -230,9 +250,11 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_cpu(col_diff, channels_, height_, width_,
-              kernel_h_, kernel_w_, pad_h_, pad_w_,
-              stride_h_, stride_w_, bottom_diff + bottom[i]->offset(n));
+          if (!is_1x1_) {
+            col2im_cpu(col_diff, channels_, height_, width_,
+                kernel_h_, kernel_w_, pad_h_, pad_w_,
+                stride_h_, stride_w_, bottom_diff + bottom[i]->offset(n));
+          }
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 908f5a65855..885d90a47d3 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -15,7 +15,10 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
-    Dtype* col_data = col_buffer_.mutable_gpu_data();
+    Dtype* col_data = NULL;
+    if (!is_1x1_) {
+      col_data = col_buffer_.mutable_gpu_data();
+    }
     const Dtype* weight = this->blobs_[0]->gpu_data();
     int weight_offset = M_ * K_;
     int col_offset = K_ * N_;
@@ -23,9 +26,13 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     for (int n = 0; n < num_; ++n) {
       // im2col transformation: unroll input regions for filtering
       // into column matrix for multplication.
-      im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-          col_data);
+      if (!is_1x1_) {
+        im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+            width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+            col_data);
+      } else {
+        col_data = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
+      }
       // Take inner products for groups.
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
@@ -78,16 +85,25 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       if (!top_diff) {
         top_diff = top[i]->gpu_diff();
       }
-      Dtype* col_data = col_buffer_.mutable_gpu_data();
-      Dtype* col_diff = col_buffer_.mutable_gpu_diff();
+      Dtype* col_data = NULL;
+      Dtype* col_diff = NULL;
+      if (!is_1x1_) {
+        col_data = col_buffer_.mutable_gpu_data();
+        col_diff = col_buffer_.mutable_gpu_diff();
+      }
       const Dtype* bottom_data = bottom[i]->gpu_data();
       Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
       for (int n = 0; n < num_; ++n) {
         // Since we saved memory in the forward pass by not storing all col
         // data, we will need to recompute them.
-        im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                   stride_h_, stride_w_, col_data);
+        if (!is_1x1_) {
+          im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+                    width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                    stride_h_, stride_w_, col_data);
+        } else {
+          col_data = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
+          col_diff = bottom[i]->mutable_gpu_diff() + bottom[i]->offset(n);
+        }
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
@@ -109,9 +125,11 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
                 (Dtype)0., col_diff + col_offset * g);
           }
           // col2im back to the data
-          col2im_gpu(col_diff, channels_, height_, width_,
-              kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-              bottom_diff + bottom[i]->offset(n));
+          if (!is_1x1_) {
+            col2im_gpu(col_diff, channels_, height_, width_,
+                kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+                bottom_diff + bottom[i]->offset(n));
+          }
         }
       }
     }
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index c2e0a86271a..c1fe3b58c58 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -217,6 +217,33 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(1);
+  convolution_param->set_stride(1);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -359,6 +386,24 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(ConvolutionLayerTest, Test1x1Gradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  convolution_param->set_kernel_size(1);
+  convolution_param->set_stride(1);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From e8ba4c83902196c44f1390f77cbe9c5fc74b711d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 19 Sep 2014 23:12:12 -0700
Subject: [PATCH 0796/2053] combine col_{data,diff} into single col_buff to
 halve memory usage

conv forward / backward only need one of the im2col data and diff
at-a-time so consolidating the two saves a lazy allocation.
---
 src/caffe/layers/conv_layer.cpp | 33 +++++++++++++++++----------------
 src/caffe/layers/conv_layer.cu  | 30 +++++++++++++++---------------
 2 files changed, 32 insertions(+), 31 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index d33aa05e961..e6d6588096b 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -122,7 +122,8 @@ void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   K_ = channels_ * kernel_h_ * kernel_w_ / group_;
   N_ = height_out_ * width_out_;
   // The im2col result buffer will only hold one image at a time to avoid
-  // overly large memory usage.
+  // overly large memory usage. In the special case of 1x1 convolution
+  // it goes lazily unused to save memory.
   col_buffer_.Reshape(
       1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
   for (int top_id = 0; top_id < top.size(); ++top_id) {
@@ -141,9 +142,9 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
-    Dtype* col_data = NULL;
+    Dtype* col_buff = NULL;
     if (!is_1x1_) {
-      col_data = col_buffer_.mutable_cpu_data();
+      col_buff = col_buffer_.mutable_cpu_data();
     }
     const Dtype* weight = this->blobs_[0]->cpu_data();
     int weight_offset = M_ * K_;  // number of filter parameters in a group
@@ -155,14 +156,14 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (!is_1x1_) {
         im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
             width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-            col_data);
+            col_buff);
       } else {  // special case for 1x1 convolution
-        col_data = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
+        col_buff = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
       }
       // Take inner products for groups.
       for (int g = 0; g < group_; ++g) {
         caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)1., weight + weight_offset * g, col_buff + col_offset * g,
           (Dtype)0., top_data + top[i]->offset(n) + top_offset * g);
       }
       // Add bias.
@@ -210,11 +211,9 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       if (!top_diff) {
         top_diff = top[i]->cpu_diff();
       }
-      Dtype* col_data = NULL;
-      Dtype* col_diff = NULL;
+      Dtype* col_buff = NULL;
       if (!is_1x1_) {
-        col_data = col_buffer_.mutable_cpu_data();
-        col_diff = col_buffer_.mutable_cpu_diff();
+        col_buff = col_buffer_.mutable_cpu_data();
       }
       const Dtype* bottom_data = bottom[i]->cpu_data();
       Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
@@ -224,17 +223,16 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
         if (!is_1x1_) {
           im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
                     width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                    stride_h_, stride_w_, col_data);
+                    stride_h_, stride_w_, col_buff);
         } else {
-          col_data = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
-          col_diff = bottom[i]->mutable_cpu_diff() + bottom[i]->offset(n);
+          col_buff = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
         }
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
             caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
                 (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_data + col_offset * g, (Dtype)1.,
+                col_buff + col_offset * g, (Dtype)1.,
                 weight_diff + weight_offset * g);
           }
         }
@@ -243,15 +241,18 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           if (weight == NULL) {
             weight = this->blobs_[0]->cpu_data();
           }
+          if (is_1x1_) {
+            col_buff = bottom[i]->mutable_cpu_diff() + bottom[i]->offset(n);
+          }
           for (int g = 0; g < group_; ++g) {
             caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
                 (Dtype)1., weight + weight_offset * g,
                 top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_diff + col_offset * g);
+                (Dtype)0., col_buff + col_offset * g);
           }
           // col2im back to the data
           if (!is_1x1_) {
-            col2im_cpu(col_diff, channels_, height_, width_,
+            col2im_cpu(col_buff, channels_, height_, width_,
                 kernel_h_, kernel_w_, pad_h_, pad_w_,
                 stride_h_, stride_w_, bottom_diff + bottom[i]->offset(n));
           }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 885d90a47d3..27d6500d6ef 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -15,9 +15,9 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
-    Dtype* col_data = NULL;
+    Dtype* col_buff = NULL;
     if (!is_1x1_) {
-      col_data = col_buffer_.mutable_gpu_data();
+      col_buff = col_buffer_.mutable_gpu_data();
     }
     const Dtype* weight = this->blobs_[0]->gpu_data();
     int weight_offset = M_ * K_;
@@ -29,14 +29,14 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       if (!is_1x1_) {
         im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
             width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-            col_data);
+            col_buff);
       } else {
-        col_data = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
+        col_buff = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
       }
       // Take inner products for groups.
       for (int g = 0; g < group_; ++g) {
         caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)1., weight + weight_offset * g, col_buff + col_offset * g,
           (Dtype)0., top_data + top[i]->offset(n) + top_offset * g);
       }
       // Add bias.
@@ -85,11 +85,9 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       if (!top_diff) {
         top_diff = top[i]->gpu_diff();
       }
-      Dtype* col_data = NULL;
-      Dtype* col_diff = NULL;
+      Dtype* col_buff = NULL;
       if (!is_1x1_) {
-        col_data = col_buffer_.mutable_gpu_data();
-        col_diff = col_buffer_.mutable_gpu_diff();
+        col_buff = col_buffer_.mutable_gpu_data();
       }
       const Dtype* bottom_data = bottom[i]->gpu_data();
       Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
@@ -99,17 +97,16 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         if (!is_1x1_) {
           im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
                     width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                    stride_h_, stride_w_, col_data);
+                    stride_h_, stride_w_, col_buff);
         } else {
-          col_data = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
-          col_diff = bottom[i]->mutable_gpu_diff() + bottom[i]->offset(n);
+          col_buff = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
         }
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
           for (int g = 0; g < group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
                 (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_data + col_offset * g, (Dtype)1.,
+                col_buff + col_offset * g, (Dtype)1.,
                 weight_diff + weight_offset * g);
           }
         }
@@ -118,15 +115,18 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           if (weight == NULL) {
             weight = this->blobs_[0]->gpu_data();
           }
+          if (is_1x1_) {
+            col_buff = bottom[i]->mutable_gpu_diff() + bottom[i]->offset(n);
+          }
           for (int g = 0; g < group_; ++g) {
             caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
                 (Dtype)1., weight + weight_offset * g,
                 top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_diff + col_offset * g);
+                (Dtype)0., col_buff + col_offset * g);
           }
           // col2im back to the data
           if (!is_1x1_) {
-            col2im_gpu(col_diff, channels_, height_, width_,
+            col2im_gpu(col_buff, channels_, height_, width_,
                 kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
                 bottom_diff + bottom[i]->offset(n));
           }

From d3126f2f061c7a36461e58df8eed550877a1ecc4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 21 Sep 2014 09:52:51 -0700
Subject: [PATCH 0797/2053] include WindowDataLayer in data param upgrade

---
 src/caffe/util/upgrade_proto.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index c3d854c0595..c69c58eb340 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -522,6 +522,13 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
+    if (net_param.layers(i).type() == LayerParameter_LayerType_WINDOW_DATA) {
+      WindowDataParameter layer_param = net_param.layers(i).window_data_param();
+      if (layer_param.has_scale()) { return true; }
+      if (layer_param.has_mean_file()) { return true; }
+      if (layer_param.has_crop_size()) { return true; }
+      if (layer_param.has_mirror()) { return true; }
+    }
   }
   return false;
 }
@@ -556,6 +563,7 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
   for (int i = 0; i < net_param->layers_size(); ++i) {
     CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data);
     CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data);
+    CONVERT_LAYER_TRANSFORM_PARAM(WINDOW_DATA, WindowData, window_data);
   }
 }
 

From 6435b7e103e4bfe9fb683bb81390ed40fa85a418 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 21 Sep 2014 10:29:39 -0700
Subject: [PATCH 0798/2053] load transform params in window data layer

---
 src/caffe/layers/window_data_layer.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index a24d7de3daa..afb430468cc 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -55,8 +55,8 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << this->layer_param_.window_data_param().fg_fraction();
 
   const bool prefetch_needs_rand =
-      this->layer_param_.window_data_param().mirror() ||
-      this->layer_param_.window_data_param().crop_size();
+      this->transform_param_.mirror() ||
+      this->transform_param_.crop_size();
   if (prefetch_needs_rand) {
     const unsigned int prefetch_rng_seed = caffe_rng_rand();
     prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
@@ -149,7 +149,7 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << this->layer_param_.window_data_param().crop_mode();
 
   // image
-  int crop_size = this->layer_param_.window_data_param().crop_size();
+  const int crop_size = this->transform_param_.crop_size();
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
@@ -187,9 +187,9 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
   const int batch_size = this->layer_param_.window_data_param().batch_size();
-  const int crop_size = this->layer_param_.window_data_param().crop_size();
   const int context_pad = this->layer_param_.window_data_param().context_pad();
-  const bool mirror = this->layer_param_.window_data_param().mirror();
+  const int crop_size = this->transform_param_.crop_size();
+  const bool mirror = this->transform_param_.mirror();
   const float fg_fraction =
       this->layer_param_.window_data_param().fg_fraction();
   const Dtype* mean = this->data_mean_.cpu_data();

From 28800ef7789a5bdbb5cd7ee38a38c7b233ef5961 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 21 Sep 2014 11:38:20 -0700
Subject: [PATCH 0799/2053] define up-to-date all-in-one model for pascal
 finetuning

---
 .../pascal_finetune_solver.prototxt           |   3 +-
 .../pascal_finetune_train.prototxt            | 321 ------------------
 ...=> pascal_finetune_trainval_test.prototxt} |  43 ++-
 3 files changed, 34 insertions(+), 333 deletions(-)
 delete mode 100644 examples/finetune_pascal_detection/pascal_finetune_train.prototxt
 rename examples/finetune_pascal_detection/{pascal_finetune_val.prototxt => pascal_finetune_trainval_test.prototxt} (89%)

diff --git a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
index 83f5abdf891..829b2005921 100644
--- a/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "examples/finetune_pascal_detection/pascal_finetune_train.prototxt"
-test_net: "examples/finetune_pascal_detection/pascal_finetune_val.prototxt"
+net: "examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt"
 test_iter: 100
 test_interval: 1000
 base_lr: 0.001
diff --git a/examples/finetune_pascal_detection/pascal_finetune_train.prototxt b/examples/finetune_pascal_detection/pascal_finetune_train.prototxt
deleted file mode 100644
index ae7ee4ccf88..00000000000
--- a/examples/finetune_pascal_detection/pascal_finetune_train.prototxt
+++ /dev/null
@@ -1,321 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: WINDOW_DATA
-  top: "data"
-  top: "label"
-  window_data_param {
-    source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 128
-    crop_size: 227
-    mirror: true
-    fg_threshold: 0.5
-    bg_threshold: 0.5
-    fg_fraction: 0.25
-    context_pad: 16
-    crop_mode: "warp"
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8_pascal"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8_pascal"
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 21
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8_pascal"
-  bottom: "label"
-}
diff --git a/examples/finetune_pascal_detection/pascal_finetune_val.prototxt b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
similarity index 89%
rename from examples/finetune_pascal_detection/pascal_finetune_val.prototxt
rename to examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
index 30dd164c4fa..5cd605bbf11 100644
--- a/examples/finetune_pascal_detection/pascal_finetune_val.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
@@ -5,17 +5,41 @@ layers {
   top: "data"
   top: "label"
   window_data_param {
-    source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
     batch_size: 128
-    crop_size: 227
+    fg_threshold: 0.5
+    bg_threshold: 0.5
+    fg_fraction: 0.25
+    context_pad: 16
+    crop_mode: "warp"
+  }
+  transform_param {
     mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: WINDOW_DATA
+  top: "data"
+  top: "label"
+  window_data_param {
+    source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
+    batch_size: 128
     fg_threshold: 0.5
     bg_threshold: 0.5
     fg_fraction: 0.25
     context_pad: 16
     crop_mode: "warp"
   }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -314,17 +338,16 @@ layers {
   }
 }
 layers {
-  name: "accuracy"
-  type: ACCURACY
+  name: "loss"
+  type: SOFTMAX_LOSS
   bottom: "fc8_pascal"
   bottom: "label"
-  top: "accuracy"
 }
 layers {
-  name: "prob"
-  type: SOFTMAX_LOSS
+  name: "accuracy"
+  type: ACCURACY
   bottom: "fc8_pascal"
   bottom: "label"
-  top: "loss"
+  top: "accuracy"
+  include { phase: TEST }
 }
-

From a6693464787b63d805f077961cb39eb96698807f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 21 Sep 2014 15:20:47 -0700
Subject: [PATCH 0800/2053] default backend to lmdb for image conversion and
 mean computation

lmdb is 10-15% faster than leveldb although it takes ~1.1x the storage.
This is usually irrelevant in prefetching since both are fast enough,
but more important lmdb allows multiple, concurrent reads for training
and evaluation several models on the same data.
---
 tools/compute_image_mean.cpp |  4 ++--
 tools/convert_imageset.cpp   | 15 +++++----------
 2 files changed, 7 insertions(+), 12 deletions(-)

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index fe3497fa87d..20f1ff81f1c 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -17,12 +17,12 @@ using std::max;
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
   if (argc < 3 || argc > 4) {
-    LOG(ERROR) << "Usage: compute_image_mean input_leveldb output_file"
+    LOG(ERROR) << "Usage: compute_image_mean input_db output_file"
                << " db_backend[leveldb or lmdb]";
     return 1;
   }
 
-  string db_backend = "leveldb";
+  string db_backend = "lmdb";
   if (argc == 4) {
     db_backend = string(argv[3]);
   }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 1c58f3de33f..7e1e83d9d0c 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -1,17 +1,12 @@
-// This program converts a set of images to a leveldb by storing them as Datum
-// proto buffers.
+// This program converts a set of images to a lmdb/leveldb by storing them
+// as Datum proto buffers.
 // Usage:
-//   convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1]
-//                     [resize_height] [resize_width]
+//   convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE DB_NAME
+//
 // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
 // should be a list of files as well as their labels, in the format as
 //   subfolder1/file1.JPEG 7
 //   ....
-// if RANDOM_SHUFFLE is 1, a random shuffle will be carried out before we
-// process the file lines.
-// Optional flag -g indicates the images should be read as
-// single-channel grayscale. If omitted, grayscale images will be
-// converted to color.
 
 #include <gflags/gflags.h>
 #include <glog/logging.h>
@@ -38,7 +33,7 @@ DEFINE_bool(gray, false,
     "When this option is on, treat images as grayscale ones");
 DEFINE_bool(shuffle, false,
     "Randomly shuffle the order of images and their labels");
-DEFINE_string(backend, "leveldb", "The backend for storing the result");
+DEFINE_string(backend, "lmdb", "The backend for storing the result");
 DEFINE_int32(resize_width, 0, "Width images are resized to");
 DEFINE_int32(resize_height, 0, "Height images are resized to");
 

From 315ce1d706e6bb7e891862c0ac16087c46a1e406 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 19 Sep 2014 12:38:39 -0700
Subject: [PATCH 0801/2053] fix cifar10 paths so they can be run from caffe
 root

---
 examples/cifar10/train_full.sh  | 13 +++++++------
 examples/cifar10/train_quick.sh | 13 +++++++------
 2 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 9dd9ad79822..4285a5d6468 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -1,15 +1,16 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
+TOOLS=./build/tools
 
-$TOOLS/caffe train --solver=cifar10_full_solver.prototxt
+$TOOLS/caffe train \
+    --solver=examples/cifar10/cifar10_full_solver.prototxt
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
-    --solver=cifar10_full_solver_lr1.prototxt \
-    --snapshot=cifar10_full_iter_60000.solverstate
+    --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
-    --solver=cifar10_full_solver_lr2.prototxt \
-    --snapshot=cifar10_full_iter_65000.solverstate
+    --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index e348e12fd94..2830c40945c 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -1,10 +1,11 @@
 #!/usr/bin/env sh
 
-TOOLS=../../build/tools
+TOOLS=./build/tools
 
-$TOOLS/caffe.bin train --solver=cifar10_quick_solver.prototxt
+$TOOLS/caffe train \
+  --solver=examples/cifar10/cifar10_quick_solver.prototxt
 
-# reduce learning rate by fctor of 10 after 8 epochs
-$TOOLS/caffe.bin train \
-  --solver=cifar10_quick_solver_lr1.prototxt \
-  --snapshot=cifar10_quick_iter_4000.solverstate
+# reduce learning rate by factor of 10 after 8 epochs
+$TOOLS/caffe train \
+  --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate

From 73f1de4b9c6b21b894639f75fb4438fa2e61a052 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 21 Sep 2014 15:32:03 -0700
Subject: [PATCH 0802/2053] switch examples to lmdb (except for custom data
 loaders)

---
 examples/cifar10/create_cifar10.sh                |  2 +-
 examples/imagenet/create_imagenet.sh              | 10 +++++-----
 examples/mnist/convert_mnist_data.cpp             |  7 +++----
 examples/mnist/create_mnist.sh                    |  2 +-
 examples/mnist/readme.md                          |  9 +++++----
 models/bvlc_alexnet/train_val.prototxt            |  6 ++++--
 models/bvlc_reference_caffenet/train_val.prototxt |  6 ++++--
 7 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index ad5038e0c3e..dfba7cca48a 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -13,6 +13,6 @@ rm -rf $EXAMPLE/cifar10_train_leveldb $EXAMPLE/cifar10_test_leveldb
 echo "Computing image mean..."
 
 ./build/tools/compute_image_mean $EXAMPLE/cifar10_train_leveldb \
-  $EXAMPLE/mean.binaryproto
+  $EXAMPLE/mean.binaryproto leveldb
 
 echo "Done."
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index a286b8fe74c..e912ac43cd7 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -1,5 +1,5 @@
 #!/usr/bin/env sh
-# Create the imagenet leveldb inputs
+# Create the imagenet lmdb inputs
 # N.B. set the path to the imagenet train + val data dirs
 
 EXAMPLE=examples/imagenet
@@ -34,7 +34,7 @@ if [ ! -d "$VAL_DATA_ROOT" ]; then
   exit 1
 fi
 
-echo "Creating train leveldb..."
+echo "Creating train lmdb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset \
     --resize_height=$RESIZE_HEIGHT \
@@ -42,9 +42,9 @@ GLOG_logtostderr=1 $TOOLS/convert_imageset \
     --shuffle \
     $TRAIN_DATA_ROOT \
     $DATA/train.txt \
-    $EXAMPLE/ilsvrc12_train_leveldb
+    $EXAMPLE/ilsvrc12_train_lmdb
 
-echo "Creating val leveldb..."
+echo "Creating val lmdb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset \
     --resize_height=$RESIZE_HEIGHT \
@@ -52,6 +52,6 @@ GLOG_logtostderr=1 $TOOLS/convert_imageset \
     --shuffle \
     $VAL_DATA_ROOT \
     $DATA/val.txt \
-    $EXAMPLE/ilsvrc12_val_leveldb
+    $EXAMPLE/ilsvrc12_val_lmdb
 
 echo "Done."
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 19040153c86..2749e4521b6 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -1,6 +1,5 @@
-//
-// This script converts the MNIST dataset to the leveldb format used
-// by caffe to perform classification.
+// This script converts the MNIST dataset to a lmdb (default) or
+// leveldb (--backend=leveldb) format used by caffe to load data.
 // Usage:
 //    convert_mnist_data [FLAGS] input_image_file input_label_file
 //                        output_db_file
@@ -176,7 +175,7 @@ int main(int argc, char** argv) {
 #endif
 
   gflags::SetUsageMessage("This script converts the MNIST dataset to\n"
-        "the leveldb/lmdb format used by Caffe to perform classification.\n"
+        "the lmdb/leveldb format used by Caffe to load data.\n"
         "Usage:\n"
         "    convert_mnist_data [FLAGS] input_image_file input_label_file "
         "output_db_file\n"
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index 8c43dc3391f..06ecc27de63 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -1,5 +1,5 @@
 #!/usr/bin/env sh
-# This script converts the mnist data into leveldb/lmdb format,
+# This script converts the mnist data into lmdb/leveldb format,
 # depending on the value assigned to $BACKEND.
 
 EXAMPLE=examples/mnist
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index ac1a0b7d7ea..44e0091f420 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -19,7 +19,7 @@ You will first need to download and convert the data format from the MNIST websi
     cd $CAFFE_ROOT/examples/mnist
     ./create_mnist.sh
 
-If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist-train-leveldb`, and `mnist-test-leveldb`.
+If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist_train_lmdb`, and `mnist_test_lmdb`.
 
 ## LeNet: the MNIST Classification Model
 
@@ -37,13 +37,14 @@ Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.
 
 ### Writing the Data Layer
 
-Currently, we will read the MNIST data from the leveldb we created earlier in the demo. This is defined by a data layer:
+Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:
 
     layers {
       name: "mnist"
       type: DATA
       data_param {
-        source: "mnist-train-leveldb"
+        source: "mnist_train_lmdb"
+        backend: LMDB
         batch_size: 64
         scale: 0.00390625
       }
@@ -51,7 +52,7 @@ Currently, we will read the MNIST data from the leveldb we created earlier in th
       top: "label"
     }
 
-Specifically, this layer has name `mnist`, type `data`, and it reads the data from the given leveldb source. We will use a batch size of 64, and scale the incoming pixels so that they are in the range \[0,1\). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the `data` blob, and one is the `label` blob.
+Specifically, this layer has name `mnist`, type `data`, and it reads the data from the given lmdb source. We will use a batch size of 64, and scale the incoming pixels so that they are in the range \[0,1\). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the `data` blob, and one is the `label` blob.
 
 ### Writing the Convolution Layer
 
diff --git a/models/bvlc_alexnet/train_val.prototxt b/models/bvlc_alexnet/train_val.prototxt
index 69b8916d769..717b6fa447c 100644
--- a/models/bvlc_alexnet/train_val.prototxt
+++ b/models/bvlc_alexnet/train_val.prototxt
@@ -5,7 +5,8 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/imagenet/ilsvrc12_train_leveldb"
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    backend: LMDB
     batch_size: 256
   }
   transform_param {
@@ -21,7 +22,8 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/imagenet/ilsvrc12_val_leveldb"
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    backend: LMDB
     batch_size: 50
   }
   transform_param {
diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index d6d64073619..073d8aeff4a 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -5,7 +5,8 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/imagenet/ilsvrc12_train_leveldb"
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    backend: LMDB
     batch_size: 256
   }
   transform_param {
@@ -21,7 +22,8 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/imagenet/ilsvrc12_val_leveldb"
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    backend: LMDB
     batch_size: 50
   }
   transform_param {

From 2c8d946b67d5024ec56d6abc85ea53ed33df8fa4 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Sun, 21 Sep 2014 22:53:30 -0700
Subject: [PATCH 0803/2053] web demo fix, closes #1002

---
 examples/web_demo/app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index d33fc92f078..e456526fa55 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -112,7 +112,7 @@ class ImagenetClassifier(object):
         if not os.path.exists(val):
             raise Exception(
                 "File for {} is missing. Should be at: {}".format(key, val))
-    default_args['image_dim'] = 227
+    default_args['image_dim'] = 256
     default_args['raw_scale'] = 255.
     default_args['gpu_mode'] = False
 

From 502141dd43500cd38fd78a4bc1f3262a45d95db7 Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Thu, 18 Sep 2014 01:08:56 +0100
Subject: [PATCH 0804/2053] adds a parameter to the LRN layer (denoted as "k"
 in  [Krizhevsky et al., NIPS 2012])

---
 include/caffe/vision_layers.hpp |  1 +
 src/caffe/layers/lrn_layer.cpp  |  3 ++-
 src/caffe/layers/lrn_layer.cu   | 10 +++++-----
 src/caffe/proto/caffe.proto     |  1 +
 4 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index fa516144d25..a3c8bc0a31f 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -246,6 +246,7 @@ class LRNLayer : public Layer<Dtype> {
   int pre_pad_;
   Dtype alpha_;
   Dtype beta_;
+  Dtype k_;
   int num_;
   int channels_;
   int height_;
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index fb74b03dc88..a09a47940d2 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -14,6 +14,7 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   pre_pad_ = (size_ - 1) / 2;
   alpha_ = this->layer_param_.lrn_param().alpha();
   beta_ = this->layer_param_.lrn_param().beta();
+  k_ = this->layer_param_.lrn_param().k();
   if (this->layer_param_.lrn_param().norm_region() ==
       LRNParameter_NormRegion_WITHIN_CHANNEL) {
     // Set up split_layer_ to use inputs in the numerator and denominator.
@@ -110,7 +111,7 @@ void LRNLayer<Dtype>::CrossChannelForward_cpu(
   Dtype* scale_data = scale_.mutable_cpu_data();
   // start with the constant value
   for (int i = 0; i < scale_.count(); ++i) {
-    scale_data[i] = 1.;
+    scale_data[i] = k_;
   }
   Blob<Dtype> padded_square(1, channels_ + size_ - 1, height_, width_);
   Dtype* padded_square_data = padded_square.mutable_cpu_data();
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index ee5e359ff0b..47b003bc4aa 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -10,7 +10,7 @@ template <typename Dtype>
 __global__ void LRNFillScale(const int nthreads, const Dtype* in,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype alpha_over_size,
-    Dtype* scale) {
+    const Dtype k, Dtype* scale) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
     int w = index % width;
@@ -33,20 +33,20 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
     // until we reach size, nothing needs to be subtracted
     while (head < size) {
       accum_scale += in[head * step] * in[head * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha_over_size;
+      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
     // both add and subtract
     while (head < channels) {
       accum_scale += in[head * step] * in[head * step];
       accum_scale -= in[(head - size) * step] * in[(head - size) * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha_over_size;
+      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
       accum_scale -= in[(head - size) * step] * in[(head - size) * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha_over_size;
+      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
   }
@@ -90,7 +90,7 @@ void LRNLayer<Dtype>::CrossChannelForward_gpu(
   // NOLINT_NEXT_LINE(whitespace/operators)
   LRNFillScale<<<CAFFE_GET_BLOCKS(n_threads), CAFFE_CUDA_NUM_THREADS>>>(
       n_threads, bottom_data, num_, channels_, height_, width_, size_,
-      alpha_ / size_, scale_data);
+      alpha_ / size_, k_, scale_data);
   CUDA_POST_KERNEL_CHECK;
   n_threads = bottom[0]->count();
   // NOLINT_NEXT_LINE(whitespace/operators)
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9395c38f3e9..b9712fa7414 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -548,6 +548,7 @@ message LRNParameter {
     WITHIN_CHANNEL = 1;
   }
   optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
+  optional float k = 5 [default = 1.];
 }
 
 // Message that stores parameters used by MemoryDataLayer

From aeb0e984a168030769c107e278d223cae5f8c0ea Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Sun, 21 Sep 2014 17:58:41 +0100
Subject: [PATCH 0805/2053] added support for "k" LRN parameter to
 upgrade_proto

---
 src/caffe/proto/caffe.proto      | 1 +
 src/caffe/util/upgrade_proto.cpp | 8 ++++++++
 2 files changed, 9 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b9712fa7414..01a516ee3b9 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -716,6 +716,7 @@ message V0LayerParameter {
   optional uint32 local_size = 13 [default = 5]; // for local response norm
   optional float alpha = 14 [default = 1.]; // for local response norm
   optional float beta = 15 [default = 0.75]; // for local response norm
+  optional float k = 22 [default = 1.];
 
   // For data layers, specify the data source
   optional string source = 16;
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index c69c58eb340..cbd6003c948 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -285,6 +285,14 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
         is_fully_compatible = false;
       }
     }
+    if (v0_layer_param.has_k()) {
+      if (type == "lrn") {
+        layer_param->mutable_lrn_param()->set_k(v0_layer_param.k());
+      } else {
+        LOG(ERROR) << "Unknown parameter k for layer type " << type;
+        is_fully_compatible = false;
+      }
+    }
     if (v0_layer_param.has_source()) {
       if (type == "data") {
         layer_param->mutable_data_param()->set_source(v0_layer_param.source());

From 90584bd86fda189a1b5597bbeb7803dd5546f769 Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Sun, 21 Sep 2014 17:59:25 +0100
Subject: [PATCH 0806/2053] added matcaffe_demo for the VGG models (RGB input)

---
 matlab/caffe/matcaffe_demo_vgg.m | 109 +++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 matlab/caffe/matcaffe_demo_vgg.m

diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m
new file mode 100644
index 00000000000..698c26b9a79
--- /dev/null
+++ b/matlab/caffe/matcaffe_demo_vgg.m
@@ -0,0 +1,109 @@
+function [scores, maxlabel] = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
+% scores = matcaffe_demo(im, use_gpu)
+%
+% Demo of the matlab wrapper using the ILSVRC network.
+%
+% input
+%   im       color image as uint8 HxWx3
+%   use_gpu  1 to use the GPU, 0 to use the CPU
+%
+% output
+%   scores   1000-dimensional ILSVRC score vector
+%
+% You may need to do the following before you start matlab:
+%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
+%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+% Or the equivalent based on where things are installed on your system
+%
+% Usage:
+%  im = imread('../../examples/images/cat.jpg');
+%  scores = matcaffe_demo(im, 1);
+%  [score, class] = max(scores);
+% Five things to be aware of:
+%   caffe uses row-major order
+%   matlab uses column-major order
+%   caffe uses BGR color channel order
+%   matlab uses RGB color channel order
+%   images need to have the data mean subtracted
+
+% Data coming in from matlab needs to be in the order 
+%   [width, height, channels, images]
+% where width is the fastest dimension.
+% Here is the rough matlab for putting image data into the correct
+% format:
+%   % convert from uint8 to single
+%   im = single(im);
+%   % reshape to a fixed size (e.g., 227x227)
+%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+%   % permute from RGB to BGR and subtract the data mean (already in BGR)
+%   im = im(:,:,[3 2 1]) - data_mean;
+%   % flip width and height to make width the fastest dimension
+%   im = permute(im, [2 1 3]);
+
+% If you have multiple images, cat them with cat(4, ...)
+
+% The actual forward function. It takes in a cell array of 4-D arrays as
+% input and outputs a cell array. 
+
+
+% init caffe network (spews logging info)
+matcaffe_init(use_gpu, model_def_file, model_file);
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im, mean_file)};
+toc;
+
+% do forward pass to get scores
+% scores are now Width x Height x Channels x Num
+tic;
+scores = caffe('forward', input_data);
+toc;
+
+scores = scores{1};
+% size(scores)
+scores = squeeze(scores);
+% scores = mean(scores,2);
+
+% [~,maxlabel] = max(scores);
+
+% ------------------------------------------------------------------------
+function images = prepare_image(im, mean_file)
+% ------------------------------------------------------------------------
+IMAGE_DIM = 256;
+CROPPED_DIM = 224;
+
+d = load(mean_file);
+IMAGE_MEAN = d.image_mean;
+
+% resize to fixed input size
+im = single(im);
+
+if size(im, 1) < size(im, 2)
+    im = imresize(im, [IMAGE_DIM NaN]);
+else
+    im = imresize(im, [NaN IMAGE_DIM]);
+end
+
+% oversample (4 corners, center, and their x-axis flips)
+images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+
+indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
+indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
+center_y = floor(indices_y(2) / 2)+1;
+center_x = floor(indices_x(2) / 2)+1;
+
+curr = 1;
+for i = indices_y
+  for j = indices_x
+    images(:, :, :, curr) = ...
+        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :)-IMAGE_MEAN, [2 1 3]);
+    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
+    curr = curr + 1;
+  end
+end
+images(:,:,:,5) = ...
+    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:)-IMAGE_MEAN, ...
+        [2 1 3]);
+images(:,:,:,10) = images(end:-1:1, :, :, curr);

From 536335939d2b35da7ca76509969eda90d9220278 Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Mon, 22 Sep 2014 20:09:35 +0100
Subject: [PATCH 0807/2053] added comments to the Matlab demo script

---
 matlab/caffe/matcaffe_demo_vgg.m | 47 ++++++++------------------------
 1 file changed, 12 insertions(+), 35 deletions(-)

diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m
index 698c26b9a79..3e25618164a 100644
--- a/matlab/caffe/matcaffe_demo_vgg.m
+++ b/matlab/caffe/matcaffe_demo_vgg.m
@@ -1,11 +1,15 @@
-function [scores, maxlabel] = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-% scores = matcaffe_demo(im, use_gpu)
+function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
+% s = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
 %
-% Demo of the matlab wrapper using the ILSVRC network.
+% Demo of the matlab wrapper using the VGG networks: https://gist.github.com/ksimonyan/5c9129cfb8f0359eaf67
+% described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
 %
 % input
-%   im       color image as uint8 HxWx3
-%   use_gpu  1 to use the GPU, 0 to use the CPU
+%   im - color image as uint8 HxWx3
+%   use_gpu - 1 to use the GPU, 0 to use the CPU
+%   model_def_file - network configuration (.prototxt file)
+%   model_file - network weights (.caffemodel file)
+%   mean_file - mean RGB image as uint8 HxWx3 (.mat file)
 %
 % output
 %   scores   1000-dimensional ILSVRC score vector
@@ -15,36 +19,9 @@
 %  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
 % Or the equivalent based on where things are installed on your system
 %
-% Usage:
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo(im, 1);
-%  [score, class] = max(scores);
-% Five things to be aware of:
-%   caffe uses row-major order
-%   matlab uses column-major order
-%   caffe uses BGR color channel order
-%   matlab uses RGB color channel order
-%   images need to have the data mean subtracted
-
-% Data coming in from matlab needs to be in the order 
-%   [width, height, channels, images]
-% where width is the fastest dimension.
-% Here is the rough matlab for putting image data into the correct
-% format:
-%   % convert from uint8 to single
-%   im = single(im);
-%   % reshape to a fixed size (e.g., 227x227)
-%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-%   % permute from RGB to BGR and subtract the data mean (already in BGR)
-%   im = im(:,:,[3 2 1]) - data_mean;
-%   % flip width and height to make width the fastest dimension
-%   im = permute(im, [2 1 3]);
-
-% If you have multiple images, cat them with cat(4, ...)
-
-% The actual forward function. It takes in a cell array of 4-D arrays as
-% input and outputs a cell array. 
-
+% NOTES
+%  the network is trained on RGB images and expects an RGB input
+%  the image crops are prepared as described in the paper (the aspect ratio is preserved)
 
 % init caffe network (spews logging info)
 matcaffe_init(use_gpu, model_def_file, model_file);

From 5d2a9399ae03d9862c4962c770237393dbcafeb6 Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Mon, 22 Sep 2014 20:39:37 +0100
Subject: [PATCH 0808/2053] added example usage to the Matlab script

---
 matlab/caffe/matcaffe_demo_vgg.m | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m
index 3e25618164a..d396f50bb84 100644
--- a/matlab/caffe/matcaffe_demo_vgg.m
+++ b/matlab/caffe/matcaffe_demo_vgg.m
@@ -1,27 +1,36 @@
 function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-% s = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
+% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
 %
 % Demo of the matlab wrapper using the VGG networks: https://gist.github.com/ksimonyan/5c9129cfb8f0359eaf67
 % described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
 %
-% input
+% INPUT
 %   im - color image as uint8 HxWx3
 %   use_gpu - 1 to use the GPU, 0 to use the CPU
 %   model_def_file - network configuration (.prototxt file)
 %   model_file - network weights (.caffemodel file)
 %   mean_file - mean RGB image as uint8 HxWx3 (.mat file)
 %
-% output
+% OUTPUT
 %   scores   1000-dimensional ILSVRC score vector
 %
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
+% EXAMPLE USAGE
+%  model_def_file = 'zoo/VGG_CNN_F_deploy.prototxt';
+%  model_file = 'zoo/VGG_CNN_F.caffemodel';
+%  mean_file = 'zoo/VGG_mean.mat';
+%  use_gpu = true;
+%  im = imread('../../examples/images/cat.jpg');
+%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file);
+% 
 % NOTES
 %  the network is trained on RGB images and expects an RGB input
 %  the image crops are prepared as described in the paper (the aspect ratio is preserved)
+%
+% PREREQUISITES
+%  You may need to do the following before you start matlab:
+%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+%   $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+%  Or the equivalent based on where things are installed on your system
 
 % init caffe network (spews logging info)
 matcaffe_init(use_gpu, model_def_file, model_file);

From 60d47bf5344cde5c376cbcb354260e6366a4da1e Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Tue, 23 Sep 2014 16:39:08 +0100
Subject: [PATCH 0809/2053] RGB -> BGR in the matlab demo

---
 matlab/caffe/matcaffe_demo_vgg.m | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m
index d396f50bb84..4e5a98eb5f4 100644
--- a/matlab/caffe/matcaffe_demo_vgg.m
+++ b/matlab/caffe/matcaffe_demo_vgg.m
@@ -1,15 +1,14 @@
 function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
 % scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
 %
-% Demo of the matlab wrapper using the VGG networks: https://gist.github.com/ksimonyan/5c9129cfb8f0359eaf67
-% described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
+% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
 %
 % INPUT
 %   im - color image as uint8 HxWx3
 %   use_gpu - 1 to use the GPU, 0 to use the CPU
 %   model_def_file - network configuration (.prototxt file)
 %   model_file - network weights (.caffemodel file)
-%   mean_file - mean RGB image as uint8 HxWx3 (.mat file)
+%   mean_file - mean BGR image as uint8 HxWx3 (.mat file)
 %
 % OUTPUT
 %   scores   1000-dimensional ILSVRC score vector
@@ -23,7 +22,6 @@
 %  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file);
 % 
 % NOTES
-%  the network is trained on RGB images and expects an RGB input
 %  the image crops are prepared as described in the paper (the aspect ratio is preserved)
 %
 % PREREQUISITES
@@ -72,6 +70,9 @@
     im = imresize(im, [NaN IMAGE_DIM]);
 end
 
+% RGB -> BGR
+im = im(:, :, [3 2 1]);
+
 % oversample (4 corners, center, and their x-axis flips)
 images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
 

From cc96b0b80965cc2d8d3b94a9e52347aa5917cc9f Mon Sep 17 00:00:00 2001
From: savy-91 <savy91@gmail.com>
Date: Tue, 23 Sep 2014 21:57:16 +0200
Subject: [PATCH 0810/2053] Changed "blas" to "openblas"

The change was made because openblas has to be linked as "-lopenblas" and not "-lblas". This caused an error when compiling.
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 5020b4109d6..e85f338ed23 100644
--- a/Makefile
+++ b/Makefile
@@ -291,7 +291,7 @@ ifeq ($(BLAS), mkl)
 	BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
 else ifeq ($(BLAS), open)
 	# OpenBLAS
-	LIBRARIES += blas
+	LIBRARIES += openblas
 else
 	# ATLAS
 	ifeq ($(LINUX), 1)

From 9007d96a2d6258b233fe201102f610cd2ed73586 Mon Sep 17 00:00:00 2001
From: Karen Simonyan <karen@robots.ox.ac.uk>
Date: Wed, 24 Sep 2014 22:08:46 +0100
Subject: [PATCH 0811/2053] added a Matlab demo with mean BGR pixel subtraction
 instead of the mean image subtraction

---
 matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 102 ++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 matlab/caffe/matcaffe_demo_vgg_mean_pix.m

diff --git a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m b/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
new file mode 100644
index 00000000000..5f7898a7029
--- /dev/null
+++ b/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
@@ -0,0 +1,102 @@
+function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
+% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
+%
+% Demo of the matlab wrapper based on the networks used for the "VGG" entry
+% in the ILSVRC-2014 competition and described in the tech. report 
+% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
+% http://arxiv.org/abs/1409.1556/
+%
+% INPUT
+%   im - color image as uint8 HxWx3
+%   use_gpu - 1 to use the GPU, 0 to use the CPU
+%   model_def_file - network configuration (.prototxt file)
+%   model_file - network weights (.caffemodel file)
+%
+% OUTPUT
+%   scores   1000-dimensional ILSVRC score vector
+%
+% EXAMPLE USAGE
+%  model_def_file = 'zoo/deploy.prototxt';
+%  model_file = 'zoo/model.caffemodel';
+%  use_gpu = true;
+%  im = imread('../../examples/images/cat.jpg');
+%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
+% 
+% NOTES
+%  mean pixel subtraction is used instead of the mean image subtraction
+%
+% PREREQUISITES
+%  You may need to do the following before you start matlab:
+%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+%   $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+%  Or the equivalent based on where things are installed on your system
+
+% init caffe network (spews logging info)
+matcaffe_init(use_gpu, model_def_file, model_file);
+
+% mean BGR pixel
+mean_pix = [103.939, 116.779, 123.68];
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im, mean_pix)};
+toc;
+
+% do forward pass to get scores
+% scores are now Width x Height x Channels x Num
+tic;
+scores = caffe('forward', input_data);
+toc;
+
+scores = scores{1};
+% size(scores)
+scores = squeeze(scores);
+% scores = mean(scores,2);
+
+% [~,maxlabel] = max(scores);
+
+% ------------------------------------------------------------------------
+function images = prepare_image(im, mean_pix)
+% ------------------------------------------------------------------------
+IMAGE_DIM = 256;
+CROPPED_DIM = 224;
+
+% resize to fixed input size
+im = single(im);
+
+if size(im, 1) < size(im, 2)
+    im = imresize(im, [IMAGE_DIM NaN]);
+else
+    im = imresize(im, [NaN IMAGE_DIM]);
+end
+
+% RGB -> BGR
+im = im(:, :, [3 2 1]);
+
+% oversample (4 corners, center, and their x-axis flips)
+images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+
+indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
+indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
+center_y = floor(indices_y(2) / 2)+1;
+center_x = floor(indices_x(2) / 2)+1;
+
+curr = 1;
+for i = indices_y
+  for j = indices_x
+    images(:, :, :, curr) = ...
+        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
+    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
+    curr = curr + 1;
+  end
+end
+images(:,:,:,5) = ...
+    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
+        [2 1 3]);
+images(:,:,:,10) = images(end:-1:1, :, :, curr);
+
+% mean BGR pixel subtraction
+for c = 1:3
+    images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
+end

From 71e731e1c6b564ca58f7f469882500ae93f1add3 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 24 Sep 2014 14:27:57 -0700
Subject: [PATCH 0812/2053] Fixed param order of cv::Size in cv::resize

---
 src/caffe/util/io.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index ad835e35d66..4a22e18725d 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -78,7 +78,7 @@ bool ReadImageToDatum(const string& filename, const int label,
     return false;
   }
   if (height > 0 && width > 0) {
-    cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
   } else {
     cv_img = cv_img_origin;
   }

From 98db2dc31e5fec4ae7eb4c026348a4b69d50a670 Mon Sep 17 00:00:00 2001
From: "D.Mishkin" <ducha.aiki@gmail.com>
Date: Thu, 25 Sep 2014 17:30:48 +0300
Subject: [PATCH 0813/2053] Removed unnecessary "mutable"

---
 tools/extract_features.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 49e8f98971c..9b0288a4d5a 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -148,14 +148,14 @@ int feature_extraction_pipeline(int argc, char** argv) {
           ->blob_by_name(blob_names[i]);
       int batch_size = feature_blob->num();
       int dim_features = feature_blob->count() / batch_size;
-      Dtype* feature_blob_data;
+      const Dtype* feature_blob_data;
       for (int n = 0; n < batch_size; ++n) {
         datum.set_height(dim_features);
         datum.set_width(1);
         datum.set_channels(1);
         datum.clear_data();
         datum.clear_float_data();
-        feature_blob_data = feature_blob->mutable_cpu_data() +
+        feature_blob_data = feature_blob->cpu_data() +
             feature_blob->offset(n);
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);

From f1a1426580a1279282e2d16998cb46d9755351c8 Mon Sep 17 00:00:00 2001
From: Alireza Shafaei <alireza@shafaei.net>
Date: Tue, 23 Sep 2014 22:35:00 -0700
Subject: [PATCH 0814/2053] Random crop bugfix and abstracting random number
 generation inside data_transformer

---
 include/caffe/data_transformer.hpp | 10 +++++++++-
 src/caffe/data_transformer.cpp     | 11 ++++++-----
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 5d5134f5db5..3ca99e7b62c 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -39,7 +39,15 @@ class DataTransformer {
                  const Dtype* mean, Dtype* transformed_data);
 
  protected:
-  virtual unsigned int Rand();
+   /**
+   * @brief Generates a random integer from Uniform({0, 1, ..., n-1}).
+   * 
+   * @param n
+   *    The upperbound (exclusive) value of the random number.
+   * @return 
+   *    A uniformly random integer value from ({0, 1, ..., n-1}).
+   */
+  virtual int Rand(int n);
 
   // Tranformation parameters
   TransformationParameter param_;
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 7150fd99c18..e52c3b6a55d 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -31,13 +31,13 @@ void DataTransformer<Dtype>::Transform(const int batch_item_id,
     int h_off, w_off;
     // We only do random crop when we do training.
     if (phase_ == Caffe::TRAIN) {
-      h_off = Rand() % (height - crop_size);
-      w_off = Rand() % (width - crop_size);
+      h_off = Rand(height - crop_size + 1);
+      w_off = Rand(width - crop_size + 1);
     } else {
       h_off = (height - crop_size) / 2;
       w_off = (width - crop_size) / 2;
     }
-    if (mirror && Rand() % 2) {
+    if (mirror && (Rand(2) == 1)) {
       // Copy mirrored version
       for (int c = 0; c < channels; ++c) {
         for (int h = 0; h < crop_size; ++h) {
@@ -99,11 +99,12 @@ void DataTransformer<Dtype>::InitRand() {
 }
 
 template <typename Dtype>
-unsigned int DataTransformer<Dtype>::Rand() {
+int DataTransformer<Dtype>::Rand(int n) {
   CHECK(rng_);
+  CHECK_GT(n, 0);
   caffe::rng_t* rng =
       static_cast<caffe::rng_t*>(rng_->generator());
-  return (*rng)();
+  return ((*rng)() % n);
 }
 
 INSTANTIATE_CLASS(DataTransformer);

From 3ea823c461884c259c07c441d07c99c68cf91298 Mon Sep 17 00:00:00 2001
From: jjkjkj <jjkjkj42@gmail.com>
Date: Fri, 26 Sep 2014 12:39:49 +0300
Subject: [PATCH 0815/2053] Update layer_factory.cpp

---
 src/caffe/layer_factory.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index b78167f21eb..69c8c9ed70b 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -251,6 +251,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return GetTanHLayer<Dtype>(name, param);
   case LayerParameter_LayerType_WINDOW_DATA:
     return new WindowDataLayer<Dtype>(param);
+  case LayerParameter_LayerType_THRESHOLD:
+    return new ThresholdLayer<Dtype>(param);
   case LayerParameter_LayerType_NONE:
     LOG(FATAL) << "Layer " << name << " has unspecified type.";
   default:

From e31d0ac1e033cff4b6bd42e9b0f9fb3060508073 Mon Sep 17 00:00:00 2001
From: Chiyuan Zhang <pluskid@gmail.com>
Date: Fri, 26 Sep 2014 13:18:38 -0400
Subject: [PATCH 0816/2053] tweak test case to expose bug.

---
 src/caffe/test/test_data/generate_sample_data.py | 2 +-
 src/caffe/test/test_hdf5data_layer.cpp           | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 0516eff9158..2ab238611d8 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -7,7 +7,7 @@
 
 num_cols = 8
 num_rows = 10
-height = 5
+height = 6
 width = 5
 total_size = num_cols * num_rows * height * width
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index acca75b19b1..78a45223b90 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -62,7 +62,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   hdf5_data_param->set_batch_size(batch_size);
   hdf5_data_param->set_source(*(this->filename));
   int num_cols = 8;
-  int height = 5;
+  int height = 6;
   int width = 5;
 
   // Test that the layer setup got the correct parameters.

From c85b14b02c425937a3937080d83a49da9e59fece Mon Sep 17 00:00:00 2001
From: Chiyuan Zhang <pluskid@gmail.com>
Date: Fri, 26 Sep 2014 15:21:45 -0400
Subject: [PATCH 0817/2053] update HDF5 layer test data.

---
 src/caffe/test/test_data/sample_data.h5       | Bin 10184 -> 11784 bytes
 .../test/test_data/sample_data_2_gzip.h5      | Bin 9992 -> 10548 bytes
 src/caffe/test/test_hdf5data_layer.cpp        |   4 ++--
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_data/sample_data.h5 b/src/caffe/test/test_data/sample_data.h5
index cb327573cae5de98cb1008eb38a3372c229a499c..90eaaa56ec773b5de2303100a5611786c8d78766 100644
GIT binary patch
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zY@K;yD_!<A_#}&Q=7D){(z^1tz9{<Gyv^%V_ridJ6ZVk8xSsIY2V>K!FK+Lx-J=+E
zs^L5{L@`5GdzY|VU%qsmeZZc^=jofa=ieW?*y_GFCT;Qxo5%h|J!|Je+yZej!Ve(M
zfY@{oVm-nrw-sTOI}u@&+YWIv${lSA-aS5XRM9nS)y$P++9TfSeGV=tYZ)0oyzs^|
z?|E?$*C4zI;*AKKAnt~^9_1c{un~IaBCLlv4&iu+Z$n&i$QYP#eA=wcTPYha%(=e&
zWlr$a9kHbm2PRF=x*fEo4B=N0pNF^&d7nlYd0&Az5_y**jJ#_gu0!6Z5Qg5ta4~FX
zgA36E;?9Q<haubw@fd_3K|Bv(lv{vUhjIrXjB<M;40AVa4$O%!zW;J?Nba5Xx7w)k
z(1NC66L#G-S3mX(%kO%Ma52OvwdZTF7@S9;;wGrL^Bly@@Oe2a5JujYAr430Xc>nC
z?;BM+J+r@F*3N1e?JSCR5@8T6$ax9k4amD3VdPy4aVPRVgD~>0hggTaivgaK*scK&
z+;J2>(VOsrJ4zrvhA>(#FAzq{Wk14YFnAlnr4ZL4d<^3E5ceMW8hajoh(5*~sER^i
znK)1t`63Lfs3#I(RKcDQ=YwbTp((3Z=M=BL%@9_b*H)&P&FLi2e?58P8oh;N>&TSM
zAWbImnm9uwj;RA8<gYs4?oW(U?4q(kp#HDUwhQD<06IXu{(buo%D#q1)ZFuvXC$Yl
xX@9Wq5Uq^*#q*E$Cqnv(yBB@0#JchQhkgX)m@B{);0kaBxB^@OuE1ZWz-OO4{r&&|

diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 78a45223b90..29e70c9417f 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -93,8 +93,8 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
 
     // Every two iterations we are reading the second file,
     // which has the same labels, but data is offset by total data size,
-    // which is 2000 (see generate_sample_data).
-    int file_offset = (iter % 4 < 2) ? 0 : 2000;
+    // which is 2400 (see generate_sample_data).
+    int file_offset = (iter % 4 < 2) ? 0 : 2400;
 
     for (int i = 0; i < batch_size; ++i) {
       EXPECT_EQ(

From e8589c6cdf7bbe8becea08ee209779317dca744a Mon Sep 17 00:00:00 2001
From: Chiyuan Zhang <pluskid@gmail.com>
Date: Fri, 26 Sep 2014 15:21:57 -0400
Subject: [PATCH 0818/2053] fix hdf5 data layer bug

---
 src/caffe/layers/hdf5_data_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 1f2a83582f2..3f1396a9acc 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -75,9 +75,9 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
   (*top)[0]->Reshape(batch_size, data_blob_.channels(),
-                     data_blob_.width(), data_blob_.height());
+                     data_blob_.height(), data_blob_.width());
   (*top)[1]->Reshape(batch_size, label_blob_.channels(),
-                     label_blob_.width(), label_blob_.height());
+                     label_blob_.height(), label_blob_.width());
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();

From 6d80a633f43f41f2188aee9e05917b7e0dc6363c Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 27 Sep 2014 20:10:13 -0700
Subject: [PATCH 0819/2053] cudnn pooling fallback option

---
 src/caffe/layer_factory.cpp              |  6 +++++-
 src/caffe/layers/cudnn_pooling_layer.cpp |  5 ++++-
 src/caffe/layers/cudnn_pooling_layer.cu  | 13 -------------
 3 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index b78167f21eb..19e111cbcc8 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -40,7 +40,8 @@ template ConvolutionLayer<double>* GetConvolutionLayer(const string& name,
 template <typename Dtype>
 PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     const LayerParameter& param) {
-  PoolingParameter_Engine engine = param.pooling_param().engine();
+  const PoolingParameter& p_param = param.pooling_param();
+  const PoolingParameter_Engine& engine = p_param.engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
 #ifdef USE_CUDNN
@@ -51,6 +52,9 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     return new PoolingLayer<Dtype>(param);
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
+    if (p_param.pad_h() || p_param.pad_w() || param.top_size() > 1) {
+      return new PoolingLayer<DType>(param);
+    }
     return new CuDNNPoolingLayer<Dtype>(param);
 #endif
   } else {
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index 2af9d21be26..b61ed2a7ede 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -13,7 +13,10 @@ template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
-
+  // Sanity check: CUDNN currently only supports pad=0 and top.size()=1 cases.
+  CHECK_EQ(pad_h_, 0);
+  CHECK_EQ(pad_w_, 0);
+  CHECK_EQ(top.size(), 1);
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 9d270ed25d5..8b9e7f1bf10 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -12,12 +12,6 @@ namespace caffe {
 template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // Fallback to Caffe for padded pooling, max top mask.
-  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || top.size() > 1) {
-    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
-    return PoolingLayer<Dtype>::Forward_gpu(bottom, top);
-  }
-
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
@@ -30,13 +24,6 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (!propagate_down[0]) {
     return;
   }
-
-  // Fallback to Caffe for padded pooling, max top mask.
-  if ((this->pad_h_ > 0 || this->pad_w_ > 0) || top.size() > 1) {
-    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
-    return PoolingLayer<Dtype>::Backward_gpu(top, propagate_down, bottom);
-  }
-
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* bottom_data = bottom[0]->gpu_data();

From 560bd7ee646da7659471d142b4156a8e86e0e8b8 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 27 Sep 2014 20:22:10 -0700
Subject: [PATCH 0820/2053] const fix

---
 src/caffe/layer_factory.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 19e111cbcc8..3f15825bd88 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -41,7 +41,7 @@ template <typename Dtype>
 PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     const LayerParameter& param) {
   const PoolingParameter& p_param = param.pooling_param();
-  const PoolingParameter_Engine& engine = p_param.engine();
+  PoolingParameter_Engine engine = p_param.engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
 #ifdef USE_CUDNN

From 7accbeeed51c76bfc94d65366b857aa1b81bd4f4 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sun, 28 Sep 2014 15:33:52 -0700
Subject: [PATCH 0821/2053] message

---
 src/caffe/layer_factory.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 3f15825bd88..9e62e9528fb 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -53,6 +53,8 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
     if (p_param.pad_h() || p_param.pad_w() || param.top_size() > 1) {
+      LOG(INFO) << "CUDNN does not support padding or multiple tops. "
+                << "Using Caffe's own pooling layer.";
       return new PoolingLayer<DType>(param);
     }
     return new CuDNNPoolingLayer<Dtype>(param);

From d316628f8dfffb6910fe8010b9c50085ba7196a8 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 26 Sep 2014 13:58:45 -0700
Subject: [PATCH 0822/2053] running factory.

---
 Makefile                        |  38 +++---
 include/caffe/layer.hpp         |   5 +-
 include/caffe/layer_factory.hpp |  85 +++++++++++++
 src/caffe/layer_factory.cpp     | 205 ++++++++++----------------------
 4 files changed, 165 insertions(+), 168 deletions(-)
 create mode 100644 include/caffe/layer_factory.hpp

diff --git a/Makefile b/Makefile
index 5020b4109d6..243650b2ad1 100644
--- a/Makefile
+++ b/Makefile
@@ -20,7 +20,6 @@ endif
 # The target shared library and static library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
 NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
-STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
 
 ##############################
 # Get all source files
@@ -338,7 +337,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-all: $(NAME) $(STATIC_NAME) tools examples
+all: $(NAME) tools examples
 
 everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
 
@@ -387,16 +386,16 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
+$(PY$(PROJECT)_SO): $(OBJS) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
 	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(STATIC_NAME) $(LINKFLAGS) $(PYTHON_LDFLAGS)
+		$(OBJS) $(LINKFLAGS) $(PYTHON_LDFLAGS)
 	@ echo
 
 mat$(PROJECT): mat
 
 mat: $(MAT$(PROJECT)_SO)
 
-$(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
+$(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(OBJS)
 	@ if [ -z "$(MATLAB_DIR)" ]; then \
 		echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \
 			"to build mat$(PROJECT)."; \
@@ -405,7 +404,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
-			CXXLIBS="\$$CXXLIBS $(STATIC_NAME) $(LDFLAGS)" -output $@
+			CXXLIBS="\$$CXXLIBS $(OBJS) $(LDFLAGS)" -output $@
 	@ echo
 
 runtest: $(TEST_ALL_BIN)
@@ -443,14 +442,10 @@ $(BUILD_DIR)/.linked:
 $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
-$(NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
+$(NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
-	ar rcs $@ $(PROTO_OBJS) $(OBJS)
-	@ echo
-
 $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HXX_SRCS) \
 		| $(TEST_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
@@ -465,21 +460,21 @@ $(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HXX_SRCS) \
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
-$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
+$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(OBJS) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
+	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(OBJS) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
+$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(OBJS) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
+	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(OBJS) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
+$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(OBJS) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
+	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(OBJS) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
@@ -488,12 +483,12 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
 	@ ln -s $(abspath $<) $@
 
-$(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
+$(TOOL_BINS): %.bin : %.o $(OBJS)
+	$(CXX) $< $(OBJS) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
+$(EXAMPLE_BINS): %.bin : %.o $(OBJS)
+	$(CXX) $< $(OBJS) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
@@ -612,6 +607,5 @@ $(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
 	# add libraries
 	cp $(NAME) $(DISTRIBUTE_DIR)/lib
-	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 18ff2743719..8a8330bca57 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -7,6 +7,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/device_alternate.hpp"
 
@@ -469,10 +470,6 @@ void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
   }
 }
 
-// The layer factory function
-template <typename Dtype>
-Layer<Dtype>* GetLayer(const LayerParameter& param);
-
 }  // namespace caffe
 
 #endif  // CAFFE_LAYER_H_
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
new file mode 100644
index 00000000000..08e3e6d3ad8
--- /dev/null
+++ b/include/caffe/layer_factory.hpp
@@ -0,0 +1,85 @@
+#ifndef CAFFE_LAYER_FACTORY_H_
+#define CAFFE_LAYER_FACTORY_H_
+
+#include <map>
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype>
+class Layer;
+
+template <typename Dtype>
+class LayerRegistry {
+ public:
+  typedef Layer<Dtype>* (*Creator)(const LayerParameter&);
+  typedef std::map<LayerParameter_LayerType, Creator> CreatorRegistry;
+
+  // Adds a creator.
+  static void AddCreator(const LayerParameter_LayerType& type,
+                         Creator creator) {
+    CHECK_EQ(registry_.count(type), 0)
+        << "Layer type " << type << " already registered.";
+    registry_[type] = creator;
+  }
+
+  // Get a layer using a LayerParameter.
+  static Layer<Dtype>* CreateLayer(const LayerParameter& param) {
+    LOG(INFO) << "Creating layer " << param.name();
+    const LayerParameter_LayerType& type = param.type();
+    CHECK_EQ(registry_.count(type), 1);
+    return registry_[type](param);
+  }
+
+ private:
+  // Layer registry should never be instantiated - everything is done with its
+  // static variables.
+  LayerRegistry() {}
+  static CreatorRegistry registry_;
+};
+
+// Static variables for the templated layer factory registry.
+template <typename Dtype>
+typename LayerRegistry<Dtype>::CreatorRegistry LayerRegistry<Dtype>::registry_;
+
+template <typename Dtype>
+class LayerRegisterer {
+ public:
+  LayerRegisterer(const LayerParameter_LayerType& type,
+                  Layer<Dtype>* (*creator)(const LayerParameter&)) {
+    LOG(INFO) << "Registering layer type: " << type;
+    LayerRegistry<Dtype>::AddCreator(type, creator);
+  }
+};
+
+
+#define REGISTER_LAYER_CREATOR(type, creator, classname)                       \
+  static LayerRegisterer<float> g_creator_f_##classname(type, creator<float>); \
+  static LayerRegisterer<double> g_creator_d_##classname(type, creator<double>)
+
+#define REGISTER_LAYER_CLASS(type, clsname)                                    \
+  template <typename Dtype>                                                    \
+  Layer<Dtype>* Creator_##clsname(const LayerParameter& param) {               \
+    return new clsname<Dtype>(param);                                          \
+  }                                                                            \
+  static LayerRegisterer<float> g_creator_f_##clsname(                         \
+      type, Creator_##clsname<float>);                                         \
+  static LayerRegisterer<double> g_creator_d_##clsname(                        \
+      type, Creator_##clsname<double>)
+
+// A function to get a specific layer from the specification given in
+// LayerParameter. Ideally this would be replaced by a factory pattern,
+// but we will leave it this way for now.
+// Yangqing's note: With LayerRegistry, we no longer need this thin wrapper any
+// more. It is provided here for backward compatibility and should be removed in
+// the future.
+template <typename Dtype>
+Layer<Dtype>* GetLayer(const LayerParameter& param) {
+  return LayerRegistry<Dtype>::CreateLayer(param);
+}
+
+}  // namespace caffe
+
+#endif  // CAFFE_LAYER_FACTORY_H_
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 566162911ef..79a732b965b 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,6 +1,7 @@
 #include <string>
 
 #include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/vision_layers.hpp"
 
@@ -11,7 +12,7 @@ namespace caffe {
 
 // Get convolution layer according to engine.
 template <typename Dtype>
-ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
+Layer<Dtype>* GetConvolutionLayer(
     const LayerParameter& param) {
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
@@ -27,21 +28,14 @@ ConvolutionLayer<Dtype>* GetConvolutionLayer(const string& name,
     return new CuDNNConvolutionLayer<Dtype>(param);
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template ConvolutionLayer<float>* GetConvolutionLayer(const string& name,
-    const LayerParameter& param);
-template ConvolutionLayer<double>* GetConvolutionLayer(const string& name,
-    const LayerParameter& param);
-
 // Get pooling layer according to engine.
 template <typename Dtype>
-PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
-    const LayerParameter& param) {
-  const PoolingParameter& p_param = param.pooling_param();
-  PoolingParameter_Engine engine = p_param.engine();
+Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
+  PoolingParameter_Engine engine = param.pooling_param().engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
 #ifdef USE_CUDNN
@@ -60,19 +54,13 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     return new CuDNNPoolingLayer<Dtype>(param);
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template PoolingLayer<float>* GetPoolingLayer(const string& name,
-    const LayerParameter& param);
-template PoolingLayer<double>* GetPoolingLayer(const string& name,
-    const LayerParameter& param);
-
 // Get relu layer according to engine.
 template <typename Dtype>
-ReLULayer<Dtype>* GetReLULayer(const string& name,
-    const LayerParameter& param) {
+Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
   ReLUParameter_Engine engine = param.relu_param().engine();
   if (engine == ReLUParameter_Engine_DEFAULT) {
     engine = ReLUParameter_Engine_CAFFE;
@@ -87,19 +75,13 @@ ReLULayer<Dtype>* GetReLULayer(const string& name,
     return new CuDNNReLULayer<Dtype>(param);
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template ReLULayer<float>* GetReLULayer(const string& name,
-    const LayerParameter& param);
-template ReLULayer<double>* GetReLULayer(const string& name,
-    const LayerParameter& param);
-
 // Get sigmoid layer according to engine.
 template <typename Dtype>
-SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
-    const LayerParameter& param) {
+Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
   if (engine == SigmoidParameter_Engine_DEFAULT) {
     engine = SigmoidParameter_Engine_CAFFE;
@@ -114,19 +96,13 @@ SigmoidLayer<Dtype>* GetSigmoidLayer(const string& name,
     return new CuDNNSigmoidLayer<Dtype>(param);
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template SigmoidLayer<float>* GetSigmoidLayer(const string& name,
-    const LayerParameter& param);
-template SigmoidLayer<double>* GetSigmoidLayer(const string& name,
-    const LayerParameter& param);
-
 // Get tanh layer according to engine.
 template <typename Dtype>
-TanHLayer<Dtype>* GetTanHLayer(const string& name,
-    const LayerParameter& param) {
+Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
   TanHParameter_Engine engine = param.tanh_param().engine();
   if (engine == TanHParameter_Engine_DEFAULT) {
     engine = TanHParameter_Engine_CAFFE;
@@ -141,19 +117,13 @@ TanHLayer<Dtype>* GetTanHLayer(const string& name,
     return new CuDNNTanHLayer<Dtype>(param);
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
 }
 
-template TanHLayer<float>* GetTanHLayer(const string& name,
-    const LayerParameter& param);
-template TanHLayer<double>* GetTanHLayer(const string& name,
-    const LayerParameter& param);
-
 // Get softmax layer according to engine.
 template <typename Dtype>
-SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
-    const LayerParameter& param) {
+Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
   SoftmaxParameter_Engine engine = param.softmax_param().engine();
   if (engine == SoftmaxParameter_Engine_DEFAULT) {
     engine = SoftmaxParameter_Engine_CAFFE;
@@ -168,107 +138,58 @@ SoftmaxLayer<Dtype>* GetSoftmaxLayer(const string& name,
     return new CuDNNSoftmaxLayer<Dtype>(param);
 #endif
   } else {
-    LOG(FATAL) << "Layer " << name << " has unknown engine.";
-  }
-}
-
-template SoftmaxLayer<float>* GetSoftmaxLayer(const string& name,
-    const LayerParameter& param);
-template SoftmaxLayer<double>* GetSoftmaxLayer(const string& name,
-    const LayerParameter& param);
-
-// A function to get a specific layer from the specification given in
-// LayerParameter. Ideally this would be replaced by a factory pattern,
-// but we will leave it this way for now.
-template <typename Dtype>
-Layer<Dtype>* GetLayer(const LayerParameter& param) {
-  const string& name = param.name();
-  const LayerParameter_LayerType& type = param.type();
-  switch (type) {
-  case LayerParameter_LayerType_ACCURACY:
-    return new AccuracyLayer<Dtype>(param);
-  case LayerParameter_LayerType_ABSVAL:
-    return new AbsValLayer<Dtype>(param);
-  case LayerParameter_LayerType_ARGMAX:
-    return new ArgMaxLayer<Dtype>(param);
-  case LayerParameter_LayerType_BNLL:
-    return new BNLLLayer<Dtype>(param);
-  case LayerParameter_LayerType_CONCAT:
-    return new ConcatLayer<Dtype>(param);
-  case LayerParameter_LayerType_CONTRASTIVE_LOSS:
-    return new ContrastiveLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_CONVOLUTION:
-    return GetConvolutionLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_DATA:
-    return new DataLayer<Dtype>(param);
-  case LayerParameter_LayerType_DROPOUT:
-    return new DropoutLayer<Dtype>(param);
-  case LayerParameter_LayerType_DUMMY_DATA:
-    return new DummyDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_EUCLIDEAN_LOSS:
-    return new EuclideanLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_ELTWISE:
-    return new EltwiseLayer<Dtype>(param);
-  case LayerParameter_LayerType_FLATTEN:
-    return new FlattenLayer<Dtype>(param);
-  case LayerParameter_LayerType_HDF5_DATA:
-    return new HDF5DataLayer<Dtype>(param);
-  case LayerParameter_LayerType_HDF5_OUTPUT:
-    return new HDF5OutputLayer<Dtype>(param);
-  case LayerParameter_LayerType_HINGE_LOSS:
-    return new HingeLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_IMAGE_DATA:
-    return new ImageDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_IM2COL:
-    return new Im2colLayer<Dtype>(param);
-  case LayerParameter_LayerType_INFOGAIN_LOSS:
-    return new InfogainLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_INNER_PRODUCT:
-    return new InnerProductLayer<Dtype>(param);
-  case LayerParameter_LayerType_LRN:
-    return new LRNLayer<Dtype>(param);
-  case LayerParameter_LayerType_MEMORY_DATA:
-    return new MemoryDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_MVN:
-    return new MVNLayer<Dtype>(param);
-  case LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
-    return new MultinomialLogisticLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_POOLING:
-    return GetPoolingLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_POWER:
-    return new PowerLayer<Dtype>(param);
-  case LayerParameter_LayerType_RELU:
-    return GetReLULayer<Dtype>(name, param);
-  case LayerParameter_LayerType_SILENCE:
-    return new SilenceLayer<Dtype>(param);
-  case LayerParameter_LayerType_SIGMOID:
-    return GetSigmoidLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
-    return new SigmoidCrossEntropyLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_SLICE:
-    return new SliceLayer<Dtype>(param);
-  case LayerParameter_LayerType_SOFTMAX:
-    return GetSoftmaxLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_SOFTMAX_LOSS:
-    return new SoftmaxWithLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_SPLIT:
-    return new SplitLayer<Dtype>(param);
-  case LayerParameter_LayerType_TANH:
-    return GetTanHLayer<Dtype>(name, param);
-  case LayerParameter_LayerType_WINDOW_DATA:
-    return new WindowDataLayer<Dtype>(param);
-  case LayerParameter_LayerType_THRESHOLD:
-    return new ThresholdLayer<Dtype>(param);
-  case LayerParameter_LayerType_NONE:
-    LOG(FATAL) << "Layer " << name << " has unspecified type.";
-  default:
-    LOG(FATAL) << "Layer " << name << " has unknown type " << type;
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
   }
-  // just to suppress old compiler warnings.
-  return (Layer<Dtype>*)(NULL);
 }
 
-template Layer<float>* GetLayer(const LayerParameter& param);
-template Layer<double>* GetLayer(const LayerParameter& param);
+// Layers that have a specific creator function.
+REGISTER_LAYER_CREATOR(LayerParameter_LayerType_CONVOLUTION,
+                       GetConvolutionLayer, ConvolutionLayer);
+REGISTER_LAYER_CREATOR(LayerParameter_LayerType_POOLING,
+                       GetPoolingLayer, PoolingLayer);
+REGISTER_LAYER_CREATOR(LayerParameter_LayerType_RELU,
+                       GetReLULayer, ReLULayer);
+REGISTER_LAYER_CREATOR(LayerParameter_LayerType_SIGMOID,
+                       GetSigmoidLayer, SigmoidLayer);
+REGISTER_LAYER_CREATOR(LayerParameter_LayerType_SOFTMAX,
+                       GetSoftmaxLayer, SoftmaxLayer);
+REGISTER_LAYER_CREATOR(LayerParameter_LayerType_TANH,
+                       GetTanHLayer, TanHLayer);
+// Layers that use their constructor as their default creator.
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_ACCURACY, AccuracyLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_ABSVAL, AbsValLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_ARGMAX, ArgMaxLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_BNLL, BNLLLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_CONCAT, ConcatLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_CONTRASTIVE_LOSS,
+                     ContrastiveLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_DATA, DataLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_DROPOUT, DropoutLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_DUMMY_DATA, DummyDataLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_EUCLIDEAN_LOSS,
+                     EuclideanLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_ELTWISE, EltwiseLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_FLATTEN, FlattenLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_HDF5_DATA, HDF5DataLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_HDF5_OUTPUT, HDF5OutputLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_HINGE_LOSS, HingeLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_IMAGE_DATA, ImageDataLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_IM2COL, Im2colLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_INFOGAIN_LOSS, InfogainLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_INNER_PRODUCT, InnerProductLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_LRN, LRNLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_MEMORY_DATA, MemoryDataLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_MVN, MVNLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS,
+                     MultinomialLogisticLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_POWER, PowerLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_SILENCE, SilenceLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS,
+                     SigmoidCrossEntropyLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_SLICE, SliceLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_SOFTMAX_LOSS,
+                     SoftmaxWithLossLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_SPLIT, SplitLayer);
+REGISTER_LAYER_CLASS(LayerParameter_LayerType_WINDOW_DATA, WindowDataLayer);
 
 }  // namespace caffe

From 076980625023d99f8fa20ac32a2b19c6ae0a1ffb Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 26 Sep 2014 16:17:04 -0700
Subject: [PATCH 0823/2053] more docs

---
 include/caffe/layer_factory.hpp | 50 ++++++++++++++++++--
 src/caffe/layer_factory.cpp     | 84 +++++++++++++++------------------
 2 files changed, 82 insertions(+), 52 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 08e3e6d3ad8..a2c4ab56a89 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -1,3 +1,41 @@
+// This file defines a layer factory that allows one to register layers. During
+// runtime, registered layers could be called by passing a LayerParameter:
+//     LayerRegistry<Dtype>::CreateLayer(param);
+//
+// There are two ways to register a layer. Assuming that we have a layer like:
+//
+//   template <typename Dtype>
+//   class MyAwesomeLayer : public Layer<Dtype> {
+//     // your implementations
+//   };
+//
+// and its type is defined in the protobuffer as
+//
+//   enum LayerType {
+//     // other definitions
+//     AWESOME = 46,
+//   }
+//
+// If the layer is going to be created simply by its constructor, in your c++
+// file, add the following line:
+//
+//    REGISTER_LAYER_CLASS(AWESOME, MyAwesomeLayer);
+//
+// Or, if the layer is going to be created by another creator function, in the
+// format of:
+//
+//    template <typename Dtype>
+//    Layer<Dtype*> GetMyAwesomeLayer(const LayerParameter& param) {
+//      // your implementation
+//    }
+//
+// (for example, when your layer has multiple backends, see GetConvolutionLayer
+// for a use case), then you can register the creator function instead, like
+//
+// REGISTER_LAYER_CREATOR(AWESOME, GetMyAwesomeLayer)
+//
+// Note that each layer type should only be registered once.
+
 #ifndef CAFFE_LAYER_FACTORY_H_
 #define CAFFE_LAYER_FACTORY_H_
 
@@ -55,9 +93,11 @@ class LayerRegisterer {
 };
 
 
-#define REGISTER_LAYER_CREATOR(type, creator, classname)                       \
-  static LayerRegisterer<float> g_creator_f_##classname(type, creator<float>); \
-  static LayerRegisterer<double> g_creator_d_##classname(type, creator<double>)
+#define REGISTER_LAYER_CREATOR(type, creator)                                  \
+  static LayerRegisterer<float> g_creator_f_##type(                            \
+      LayerParameter_LayerType_##type, creator<float>);                        \
+  static LayerRegisterer<double> g_creator_d_##type(                           \
+      LayerParameter_LayerType_##type, creator<double>)
 
 #define REGISTER_LAYER_CLASS(type, clsname)                                    \
   template <typename Dtype>                                                    \
@@ -65,9 +105,9 @@ class LayerRegisterer {
     return new clsname<Dtype>(param);                                          \
   }                                                                            \
   static LayerRegisterer<float> g_creator_f_##clsname(                         \
-      type, Creator_##clsname<float>);                                         \
+      LayerParameter_LayerType_##type, Creator_##clsname<float>);                \
   static LayerRegisterer<double> g_creator_d_##clsname(                        \
-      type, Creator_##clsname<double>)
+      LayerParameter_LayerType_##type, Creator_##clsname<double>)
 
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 79a732b965b..b60b3b6fdb7 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -143,53 +143,43 @@ Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
 }
 
 // Layers that have a specific creator function.
-REGISTER_LAYER_CREATOR(LayerParameter_LayerType_CONVOLUTION,
-                       GetConvolutionLayer, ConvolutionLayer);
-REGISTER_LAYER_CREATOR(LayerParameter_LayerType_POOLING,
-                       GetPoolingLayer, PoolingLayer);
-REGISTER_LAYER_CREATOR(LayerParameter_LayerType_RELU,
-                       GetReLULayer, ReLULayer);
-REGISTER_LAYER_CREATOR(LayerParameter_LayerType_SIGMOID,
-                       GetSigmoidLayer, SigmoidLayer);
-REGISTER_LAYER_CREATOR(LayerParameter_LayerType_SOFTMAX,
-                       GetSoftmaxLayer, SoftmaxLayer);
-REGISTER_LAYER_CREATOR(LayerParameter_LayerType_TANH,
-                       GetTanHLayer, TanHLayer);
+REGISTER_LAYER_CREATOR(CONVOLUTION, GetConvolutionLayer);
+REGISTER_LAYER_CREATOR(POOLING, GetPoolingLayer);
+REGISTER_LAYER_CREATOR(RELU, GetReLULayer);
+REGISTER_LAYER_CREATOR(SIGMOID, GetSigmoidLayer);
+REGISTER_LAYER_CREATOR(SOFTMAX, GetSoftmaxLayer);
+REGISTER_LAYER_CREATOR(TANH, GetTanHLayer);
+
 // Layers that use their constructor as their default creator.
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_ACCURACY, AccuracyLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_ABSVAL, AbsValLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_ARGMAX, ArgMaxLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_BNLL, BNLLLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_CONCAT, ConcatLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_CONTRASTIVE_LOSS,
-                     ContrastiveLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_DATA, DataLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_DROPOUT, DropoutLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_DUMMY_DATA, DummyDataLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_EUCLIDEAN_LOSS,
-                     EuclideanLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_ELTWISE, EltwiseLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_FLATTEN, FlattenLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_HDF5_DATA, HDF5DataLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_HDF5_OUTPUT, HDF5OutputLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_HINGE_LOSS, HingeLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_IMAGE_DATA, ImageDataLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_IM2COL, Im2colLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_INFOGAIN_LOSS, InfogainLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_INNER_PRODUCT, InnerProductLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_LRN, LRNLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_MEMORY_DATA, MemoryDataLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_MVN, MVNLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS,
-                     MultinomialLogisticLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_POWER, PowerLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_SILENCE, SilenceLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS,
-                     SigmoidCrossEntropyLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_SLICE, SliceLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_SOFTMAX_LOSS,
-                     SoftmaxWithLossLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_SPLIT, SplitLayer);
-REGISTER_LAYER_CLASS(LayerParameter_LayerType_WINDOW_DATA, WindowDataLayer);
+REGISTER_LAYER_CLASS(ACCURACY, AccuracyLayer);
+REGISTER_LAYER_CLASS(ABSVAL, AbsValLayer);
+REGISTER_LAYER_CLASS(ARGMAX, ArgMaxLayer);
+REGISTER_LAYER_CLASS(BNLL, BNLLLayer);
+REGISTER_LAYER_CLASS(CONCAT, ConcatLayer);
+REGISTER_LAYER_CLASS(CONTRASTIVE_LOSS, ContrastiveLossLayer);
+REGISTER_LAYER_CLASS(DATA, DataLayer);
+REGISTER_LAYER_CLASS(DROPOUT, DropoutLayer);
+REGISTER_LAYER_CLASS(DUMMY_DATA, DummyDataLayer);
+REGISTER_LAYER_CLASS(EUCLIDEAN_LOSS, EuclideanLossLayer);
+REGISTER_LAYER_CLASS(ELTWISE, EltwiseLayer);
+REGISTER_LAYER_CLASS(FLATTEN, FlattenLayer);
+REGISTER_LAYER_CLASS(HDF5_DATA, HDF5DataLayer);
+REGISTER_LAYER_CLASS(HDF5_OUTPUT, HDF5OutputLayer);
+REGISTER_LAYER_CLASS(HINGE_LOSS, HingeLossLayer);
+REGISTER_LAYER_CLASS(IMAGE_DATA, ImageDataLayer);
+REGISTER_LAYER_CLASS(IM2COL, Im2colLayer);
+REGISTER_LAYER_CLASS(INFOGAIN_LOSS, InfogainLossLayer);
+REGISTER_LAYER_CLASS(INNER_PRODUCT, InnerProductLayer);
+REGISTER_LAYER_CLASS(LRN, LRNLayer);
+REGISTER_LAYER_CLASS(MEMORY_DATA, MemoryDataLayer);
+REGISTER_LAYER_CLASS(MVN, MVNLayer);
+REGISTER_LAYER_CLASS(MULTINOMIAL_LOGISTIC_LOSS, MultinomialLogisticLossLayer);
+REGISTER_LAYER_CLASS(POWER, PowerLayer);
+REGISTER_LAYER_CLASS(SILENCE, SilenceLayer);
+REGISTER_LAYER_CLASS(SIGMOID_CROSS_ENTROPY_LOSS, SigmoidCrossEntropyLossLayer);
+REGISTER_LAYER_CLASS(SLICE, SliceLayer);
+REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer);
+REGISTER_LAYER_CLASS(SPLIT, SplitLayer);
+REGISTER_LAYER_CLASS(WINDOW_DATA, WindowDataLayer);
 
 }  // namespace caffe

From 6f15c7a8b6acbc239a9fbf8104d56d9a7301c8f5 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 27 Sep 2014 12:06:10 -0700
Subject: [PATCH 0824/2053] Pre-lunch fun: add a dynamic library guard test.

---
 Makefile | 30 +++++++++++++++++++++---------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/Makefile b/Makefile
index 243650b2ad1..72fca4655b6 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,3 @@
-# The makefile for caffe. Pretty hacky.
 PROJECT := caffe
 
 CONFIG_FILE := Makefile.config
@@ -17,9 +16,9 @@ else
 	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
 endif
 
-# The target shared library and static library name
+# The target shared library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
-NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
+LIBRARY_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
 
 ##############################
 # Get all source files
@@ -127,7 +126,10 @@ TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
 TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
+# TEST_ALL_BIN is the test binary that links caffe source objects directly.
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
+# TEST_ALL_DYNINK_BIN is the test binary that links caffe as a dynamic library.
+TEST_ALL_DYNLINK_BIN := $(TEST_BIN_DIR)/test_all_dynamic_link.testbin
 
 ##############################
 # Derive compiler warning dump locations
@@ -308,6 +310,8 @@ endif
 INCLUDE_DIRS += $(BLAS_INCLUDE)
 LIBRARY_DIRS += $(BLAS_LIB)
 
+LIBRARY_DIRS += $(LIB_BUILD_DIR)
+
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
 CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
@@ -337,7 +341,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-all: $(NAME) tools examples
+all: $(LIBRARY_NAME) tools examples
 
 everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
 
@@ -376,7 +380,7 @@ $(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % $(LINT_SCRIPT) | $(LINT_OUTPU
 		> $@ \
 		|| true
 
-test: $(TEST_ALL_BIN) $(TEST_BINS)
+test: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN) $(TEST_BINS)
 
 tools: $(TOOL_BINS) $(TOOL_BIN_LINKS)
 
@@ -407,8 +411,9 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(OBJS)
 			CXXLIBS="\$$CXXLIBS $(OBJS) $(LDFLAGS)" -output $@
 	@ echo
 
-runtest: $(TEST_ALL_BIN)
-	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
+runtest: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN)
+	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER) && \
+	$(TEST_ALL_DYNLINK_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
 warn: $(EMPTY_WARN_REPORT)
 
@@ -442,7 +447,7 @@ $(BUILD_DIR)/.linked:
 $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
-$(NAME): $(OBJS) | $(LIB_BUILD_DIR)
+$(LIBRARY_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
@@ -466,6 +471,13 @@ $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(OBJS) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
+$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(LIBRARY_NAME) \
+		| $(TEST_BIN_DIR)
+	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(LIB_BUILD_DIR)
+	@ echo
+
+
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(OBJS) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(OBJS) \
@@ -606,6 +618,6 @@ $(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
 	cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
 	# add libraries
-	cp $(NAME) $(DISTRIBUTE_DIR)/lib
+	cp $(LIBRARY_NAME) $(DISTRIBUTE_DIR)/lib
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python

From f0fdb8cf7432cf11d10f9bfce7e03e45cff9b1cf Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 27 Sep 2014 13:17:33 -0700
Subject: [PATCH 0825/2053] Add back static library. Using -Wl,--whole-archive
 will allow us to preserve all symbols.

---
 Makefile | 47 +++++++++++++++++++++++++++--------------------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/Makefile b/Makefile
index 72fca4655b6..ad87e9b4ca2 100644
--- a/Makefile
+++ b/Makefile
@@ -18,7 +18,9 @@ endif
 
 # The target shared library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
-LIBRARY_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
+STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
+DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
+STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
 
 ##############################
 # Get all source files
@@ -126,7 +128,7 @@ TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
 TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
-# TEST_ALL_BIN is the test binary that links caffe source objects directly.
+# TEST_ALL_BIN is the test binary that links caffe statically.
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 # TEST_ALL_DYNINK_BIN is the test binary that links caffe as a dynamic library.
 TEST_ALL_DYNLINK_BIN := $(TEST_BIN_DIR)/test_all_dynamic_link.testbin
@@ -341,7 +343,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-all: $(LIBRARY_NAME) tools examples
+all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples
 
 everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
 
@@ -390,16 +392,16 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(OBJS) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
+$(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
 	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(OBJS) $(LINKFLAGS) $(PYTHON_LDFLAGS)
+		$(STATIC_LINK_COMMAND) $(LINKFLAGS) $(PYTHON_LDFLAGS)
 	@ echo
 
 mat$(PROJECT): mat
 
 mat: $(MAT$(PROJECT)_SO)
 
-$(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(OBJS)
+$(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 	@ if [ -z "$(MATLAB_DIR)" ]; then \
 		echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \
 			"to build mat$(PROJECT)."; \
@@ -408,7 +410,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(OBJS)
 	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
-			CXXLIBS="\$$CXXLIBS $(OBJS) $(LDFLAGS)" -output $@
+			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
 	@ echo
 
 runtest: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN)
@@ -447,10 +449,14 @@ $(BUILD_DIR)/.linked:
 $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
-$(LIBRARY_NAME): $(OBJS) | $(LIB_BUILD_DIR)
+$(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
+$(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
+	ar rcs $@ $(OBJS)
+	@ echo
+
 $(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HXX_SRCS) \
 		| $(TEST_BUILD_DIR)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
@@ -465,28 +471,28 @@ $(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HXX_SRCS) \
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
-$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(OBJS) \
+$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(OBJS) \
+	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(LIBRARY_NAME) \
+$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(LIB_BUILD_DIR)
 	@ echo
 
 
-$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(OBJS) \
+$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(OBJS) \
+	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(OBJS) \
+$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(OBJS) \
+	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
@@ -495,12 +501,12 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
 	@ ln -s $(abspath $<) $@
 
-$(TOOL_BINS): %.bin : %.o $(OBJS)
-	$(CXX) $< $(OBJS) -o $@ $(LINKFLAGS) $(LDFLAGS)
+$(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
+	$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(EXAMPLE_BINS): %.bin : %.o $(OBJS)
-	$(CXX) $< $(OBJS) -o $@ $(LINKFLAGS) $(LDFLAGS)
+$(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
+	$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
@@ -618,6 +624,7 @@ $(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
 	cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
 	# add libraries
-	cp $(LIBRARY_NAME) $(DISTRIBUTE_DIR)/lib
+	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
+	cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python

From 319a95f06a469de1c6bf4ebcd4cf5ff17c6126fc Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 27 Sep 2014 13:18:27 -0700
Subject: [PATCH 0826/2053] Mute factory registration message

---
 include/caffe/layer_factory.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index a2c4ab56a89..24b427643d5 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -87,7 +87,7 @@ class LayerRegisterer {
  public:
   LayerRegisterer(const LayerParameter_LayerType& type,
                   Layer<Dtype>* (*creator)(const LayerParameter&)) {
-    LOG(INFO) << "Registering layer type: " << type;
+    // LOG(INFO) << "Registering layer type: " << type;
     LayerRegistry<Dtype>::AddCreator(type, creator);
   }
 };
@@ -105,7 +105,7 @@ class LayerRegisterer {
     return new clsname<Dtype>(param);                                          \
   }                                                                            \
   static LayerRegisterer<float> g_creator_f_##clsname(                         \
-      LayerParameter_LayerType_##type, Creator_##clsname<float>);                \
+      LayerParameter_LayerType_##type, Creator_##clsname<float>);              \
   static LayerRegisterer<double> g_creator_d_##clsname(                        \
       LayerParameter_LayerType_##type, Creator_##clsname<double>)
 

From c9fb1f4a0ae92593b4e82983285921037f1db823 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sun, 28 Sep 2014 00:11:05 -0700
Subject: [PATCH 0827/2053] cmake.

---
 python/CMakeLists.txt         |  3 ++-
 src/caffe/test/CMakeLists.txt | 10 ++++++----
 tools/CMakeLists.txt          |  4 +++-
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 8642c39367a..5024dbfea5d 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -10,6 +10,7 @@ include_directories(${Boost_INCLUDE_DIRS})
 
 file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
+set(CAFFE_WHOLE_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
 add_library(pycaffe SHARED ${Python_SOURCES})
 target_link_libraries(pycaffe caffe ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
 
@@ -17,4 +18,4 @@ target_link_libraries(pycaffe caffe ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
 
 install(DIRECTORY caffe DESTINATION python)
 install(FILES requirements.txt DESTINATION python)
-install(TARGETS pycaffe DESTINATION python/caffe)
\ No newline at end of file
+install(TARGETS pycaffe DESTINATION python/caffe)
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 20a1d084d6a..7b308bff168 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -47,7 +47,9 @@ include_directories(
 # Remove main from test sources and prepare an Object lib with main
 file(GLOB TEST_MAIN ${TEST_MAIN}) 
 list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
-add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN} ../common.cpp)
+add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN})
+
+set(CAFFE_WHOLE_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
 
 #    Build each test separately from *.cpp files
 foreach(source ${TEST_CPP_SOURCES})
@@ -58,7 +60,7 @@ foreach(source ${TEST_CPP_SOURCES})
     set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
     
     add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)     
-    target_link_libraries(${TEST_NAME} gtest caffe)
+    target_link_libraries(${TEST_NAME} gtest ${CAFFE_WHOLE_LINK})
     
     #    output dir
     set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
@@ -75,7 +77,7 @@ foreach(source ${TEST_CU_SOURCES})
     cuda_add_library(${TEST_NAME}.lib EXCLUDE_FROM_ALL ${source})
     
     add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)     
-    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest caffe)
+    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest ${CAFFE_WHOLE_LINK})
     
     #    output dir
     set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
@@ -90,7 +92,7 @@ add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:ma
 if(NOT CPU_ONLY)
     target_link_libraries(${ALL_TEST} ${TEST_CU_LIBS})
 endif()
-target_link_libraries(${ALL_TEST} gtest caffe)
+target_link_libraries(${ALL_TEST} gtest ${CAFFE_WHOLE_LINK})
 add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 
 #    Output directory
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 0da19fbadc8..118443a86a0 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -3,12 +3,14 @@ project( Tools )
 #    Find all source files
 file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
+set (CAFFE_WHOLE_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+
 #    Build each source file independently
 foreach(source ${TOOLS_SOURCES})
     get_filename_component(name ${source} NAME_WE)
     add_executable(${name}.bin ${source})
     set_target_properties(${name}.bin PROPERTIES OUTPUT_NAME ${name})
-    target_link_libraries(${name}.bin caffe)
+    target_link_libraries(${name}.bin ${CAFFE_WHOLE_LINK})
 
 ###    Install    #################################################################################
 

From 20b79f69a1dd9291fd1a8b0b1c7389d426b2cb4b Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sun, 28 Sep 2014 15:46:03 -0700
Subject: [PATCH 0828/2053] consolidate duplicate code

---
 include/caffe/layer_factory.hpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 24b427643d5..5f44d97ca27 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -104,10 +104,7 @@ class LayerRegisterer {
   Layer<Dtype>* Creator_##clsname(const LayerParameter& param) {               \
     return new clsname<Dtype>(param);                                          \
   }                                                                            \
-  static LayerRegisterer<float> g_creator_f_##clsname(                         \
-      LayerParameter_LayerType_##type, Creator_##clsname<float>);              \
-  static LayerRegisterer<double> g_creator_d_##clsname(                        \
-      LayerParameter_LayerType_##type, Creator_##clsname<double>)
+  REGISTER_LAYER_CREATOR(type, Creator_##clsname)
 
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,

From 1dacdd819544bd491a1c7e00349ea1acae5bc66c Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sun, 28 Sep 2014 16:04:53 -0700
Subject: [PATCH 0829/2053] add long-ignored threshold layer

---
 src/caffe/layer_factory.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index b60b3b6fdb7..d0bbd3f27d4 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -180,6 +180,7 @@ REGISTER_LAYER_CLASS(SIGMOID_CROSS_ENTROPY_LOSS, SigmoidCrossEntropyLossLayer);
 REGISTER_LAYER_CLASS(SLICE, SliceLayer);
 REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer);
 REGISTER_LAYER_CLASS(SPLIT, SplitLayer);
+REGISTER_LAYER_CLASS(THRESHOLD, ThresholdLayer);
 REGISTER_LAYER_CLASS(WINDOW_DATA, WindowDataLayer);
 
 }  // namespace caffe

From 57a7ae1d539ffdbf30ca13f6ab0d4214f8f2aa2d Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sun, 28 Sep 2014 18:11:35 -0700
Subject: [PATCH 0830/2053] add explicit declaration - does that help the
 flakyness?

---
 src/caffe/layer_factory.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index d0bbd3f27d4..f37d10511ee 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -7,6 +7,11 @@
 
 namespace caffe {
 
+template <>
+LayerRegistry<float>::CreatorRegistry LayerRegistry<float>::registry_;
+template <>
+LayerRegistry<double>::CreatorRegistry LayerRegistry<double>::registry_;
+
 // GetLayer() defines the overall layer factory. The Get*Layer() functions
 // define factories for layers with multiple computational engines.
 

From 4d54cade5a6d7aca4e98eeb20984aa9925509a76 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sun, 28 Sep 2014 19:16:01 -0700
Subject: [PATCH 0831/2053] static initialization order fiasco

---
 include/caffe/layer_factory.hpp | 19 +++++++++++--------
 src/caffe/layer_factory.cpp     |  5 -----
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 5f44d97ca27..dd896453f41 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -55,32 +55,35 @@ class LayerRegistry {
   typedef Layer<Dtype>* (*Creator)(const LayerParameter&);
   typedef std::map<LayerParameter_LayerType, Creator> CreatorRegistry;
 
+  static CreatorRegistry& Registry() {
+    static CreatorRegistry* g_registry_ = new CreatorRegistry();
+    return *g_registry_;
+  }
+
   // Adds a creator.
   static void AddCreator(const LayerParameter_LayerType& type,
                          Creator creator) {
-    CHECK_EQ(registry_.count(type), 0)
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 0)
         << "Layer type " << type << " already registered.";
-    registry_[type] = creator;
+    registry[type] = creator;
   }
 
   // Get a layer using a LayerParameter.
   static Layer<Dtype>* CreateLayer(const LayerParameter& param) {
     LOG(INFO) << "Creating layer " << param.name();
     const LayerParameter_LayerType& type = param.type();
-    CHECK_EQ(registry_.count(type), 1);
-    return registry_[type](param);
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 1);
+    return registry[type](param);
   }
 
  private:
   // Layer registry should never be instantiated - everything is done with its
   // static variables.
   LayerRegistry() {}
-  static CreatorRegistry registry_;
 };
 
-// Static variables for the templated layer factory registry.
-template <typename Dtype>
-typename LayerRegistry<Dtype>::CreatorRegistry LayerRegistry<Dtype>::registry_;
 
 template <typename Dtype>
 class LayerRegisterer {
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index f37d10511ee..d0bbd3f27d4 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -7,11 +7,6 @@
 
 namespace caffe {
 
-template <>
-LayerRegistry<float>::CreatorRegistry LayerRegistry<float>::registry_;
-template <>
-LayerRegistry<double>::CreatorRegistry LayerRegistry<double>::registry_;
-
 // GetLayer() defines the overall layer factory. The Get*Layer() functions
 // define factories for layers with multiple computational engines.
 

From c68f9ce858c98e94dc0aa18f07bb838af18192a0 Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Mon, 29 Sep 2014 02:55:22 -0700
Subject: [PATCH 0832/2053] [examples] adding class names and deploy version of
 Flickr Style net

---
 .../finetune_flickr_style/style_names.txt     |  20 ++
 models/finetune_flickr_style/deploy.prototxt  | 310 ++++++++++++++++++
 2 files changed, 330 insertions(+)
 create mode 100644 examples/finetune_flickr_style/style_names.txt
 create mode 100644 models/finetune_flickr_style/deploy.prototxt

diff --git a/examples/finetune_flickr_style/style_names.txt b/examples/finetune_flickr_style/style_names.txt
new file mode 100644
index 00000000000..73090c97821
--- /dev/null
+++ b/examples/finetune_flickr_style/style_names.txt
@@ -0,0 +1,20 @@
+Detailed
+Pastel
+Melancholy
+Noir
+HDR
+Vintage
+Long Exposure
+Horror
+Sunny
+Bright
+Hazy
+Bokeh
+Serene
+Texture
+Ethereal
+Macro
+Depth of Field
+Geometric Composition
+Minimal
+Romantic
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
new file mode 100644
index 00000000000..aa2ad961874
--- /dev/null
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -0,0 +1,310 @@
+name: "FlickrStyleCaffeNet"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8_flickr"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8_flickr"
+  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  blobs_lr: 10
+  blobs_lr: 20
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 20
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8_flickr"
+  top: "prob"
+}

From db67d52dcc3567ce636e472fa4554ce43878c84c Mon Sep 17 00:00:00 2001
From: Dmytro Mishkin <ducha.aiki@gmail.com>
Date: Mon, 29 Sep 2014 16:10:04 +0300
Subject: [PATCH 0833/2053] Changed linking order: -pthread -> back. Otherwise
 error: /usr/bin/ld: /usr/local/lib/libgflags.a(gflags.cc.o): undefined
 reference to symbol 'pthread_rwlock_wrlock@@GLIBC_2.2.5'
 /lib/x86_64-linux-gnu/libpthread.so.0: error adding symbols: DSO missing from
 command line collect2: error: ld returned 1 exit status

---
 Makefile | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 5020b4109d6..929f8d5cea6 100644
--- a/Makefile
+++ b/Makefile
@@ -165,12 +165,11 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARY_DIRS += $(CUDA_LIB_DIR)
 	LIBRARIES := cudart cublas curand
 endif
-LIBRARIES += pthread \
-	glog gflags protobuf leveldb snappy \
+LIBRARIES += glog gflags protobuf leveldb snappy \
 	lmdb \
 	boost_system \
 	hdf5_hl hdf5 \
-	opencv_core opencv_highgui opencv_imgproc
+	opencv_core opencv_highgui opencv_imgproc pthread
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 

From f13461801c8912bf2029367554d229f77020bef7 Mon Sep 17 00:00:00 2001
From: Alireza Shafaei <alireza@shafaei.net>
Date: Mon, 29 Sep 2014 20:24:00 -0700
Subject: [PATCH 0834/2053] Fixed some errors in layer_factory and
 cudnn_pooling

---
 src/caffe/layer_factory.cpp              | 2 +-
 src/caffe/layers/cudnn_pooling_layer.cpp | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 566162911ef..539ef84d03e 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -55,7 +55,7 @@ PoolingLayer<Dtype>* GetPoolingLayer(const string& name,
     if (p_param.pad_h() || p_param.pad_w() || param.top_size() > 1) {
       LOG(INFO) << "CUDNN does not support padding or multiple tops. "
                 << "Using Caffe's own pooling layer.";
-      return new PoolingLayer<DType>(param);
+      return new PoolingLayer<Dtype>(param);
     }
     return new CuDNNPoolingLayer<Dtype>(param);
 #endif
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index b61ed2a7ede..4d7573c7f12 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -14,8 +14,8 @@ void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
   // Sanity check: CUDNN currently only supports pad=0 and top.size()=1 cases.
-  CHECK_EQ(pad_h_, 0);
-  CHECK_EQ(pad_w_, 0);
+  CHECK_EQ(this->pad_h_, 0);
+  CHECK_EQ(this->pad_w_, 0);
   CHECK_EQ(top.size(), 1);
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);

From c5e404dc2988532906b08eb90b226be91b778ac0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Sep 2014 21:20:37 -0700
Subject: [PATCH 0835/2053] use method overrides for CuDNNPoolingLayer top blob
 checking

---
 include/caffe/vision_layers.hpp          | 3 +++
 src/caffe/layers/cudnn_pooling_layer.cpp | 3 +--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index a3c8bc0a31f..58ac5344da4 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -338,6 +338,9 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNPoolingLayer();
+  // Currently, cuDNN does not support the extra top blob.
+  virtual inline int MinTopBlobs() const { return -1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index 4d7573c7f12..fc0222ac0e3 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -13,10 +13,9 @@ template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
-  // Sanity check: CUDNN currently only supports pad=0 and top.size()=1 cases.
+  // Sanity check: CUDNN currently only supports pad == 0.
   CHECK_EQ(this->pad_h_, 0);
   CHECK_EQ(this->pad_w_, 0);
-  CHECK_EQ(top.size(), 1);
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);

From 618b92934b4d1449c10b6611c87d56be9a4d75d2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Sep 2014 21:21:03 -0700
Subject: [PATCH 0836/2053] cuDNN pooling layer tests know that nonzero padding
 is not supported

After #1172, some cuDNN pooling layer tests were failing due to use of
padding. (Before #1172, these tests were actually testing PoolingLayer
instead of CuDNNPoolingLayer via the fallback.) This commit disables
many tests via commenting, so that they can be easily readded when cuDNN
gains pooling padding support.
---
 src/caffe/test/test_pooling_layer.cpp | 35 +++++++++++++++++----------
 1 file changed, 22 insertions(+), 13 deletions(-)

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index e2980331dc0..f52073c4e6f 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -962,6 +962,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
+// This test and all following cuDNN pooling tests with padding are commented
+// for now, since cuDNN pooling does not currently support padding.
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
@@ -977,6 +980,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
   EXPECT_EQ(this->blob_top_->height(), 4);
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
+*/
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
@@ -1012,6 +1016,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
   this->TestForwardRectWide();
 }
 
+// Currently, cuDNN does not support a top mask, so we comment this and
+// the corresponding backward test.
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   this->blob_top_vec_.push_back(this->blob_top_mask_);
@@ -1019,6 +1026,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
 }
+*/
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
   Caffe::set_mode(Caffe::GPU);
@@ -1029,7 +1037,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
       pooling_param->set_kernel_h(kernel_h);
       pooling_param->set_kernel_w(kernel_w);
       pooling_param->set_stride(2);
-      pooling_param->set_pad(1);
+      // currenty, cuDNN pooling does not support padding
+      pooling_param->set_pad(0);
       pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
       CuDNNPoolingLayer<TypeParam> layer(layer_param);
       GradientChecker<TypeParam> checker(1e-4, 1e-2);
@@ -1039,6 +1048,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
   }
 }
 
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
@@ -1083,7 +1093,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
 }
+*/
 
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
@@ -1103,6 +1115,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
     }
   }
 }
+*/
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
   Caffe::set_mode(Caffe::GPU);
@@ -1110,7 +1123,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(1);
-  pooling_param->set_pad(1);
+  // Currently, cuDNN pooling does not support padding, so we use
+  // a simplified version of this test.
+  pooling_param->set_pad(0);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
   this->blob_bottom_->Reshape(1, 1, 3, 3);
   FillerParameter filler_param;
@@ -1121,19 +1136,11 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
-  EXPECT_EQ(this->blob_top_->height(), 3);
-  EXPECT_EQ(this->blob_top_->width(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   TypeParam epsilon = 1e-5;
-  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 2.0    , epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 2.0, epsilon);
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
@@ -1154,6 +1161,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
   }
 }
 
+/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
@@ -1172,6 +1180,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
     }
   }
 }
+*/
 
 #endif
 

From b2c26469703741007a4953a778a0b92a0d3dee57 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Tue, 30 Sep 2014 10:20:36 -0700
Subject: [PATCH 0837/2053] doxygen

---
 include/caffe/layer_factory.hpp | 78 +++++++++++++++++----------------
 1 file changed, 41 insertions(+), 37 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index dd896453f41..b86d53346d9 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -1,40 +1,44 @@
-// This file defines a layer factory that allows one to register layers. During
-// runtime, registered layers could be called by passing a LayerParameter:
-//     LayerRegistry<Dtype>::CreateLayer(param);
-//
-// There are two ways to register a layer. Assuming that we have a layer like:
-//
-//   template <typename Dtype>
-//   class MyAwesomeLayer : public Layer<Dtype> {
-//     // your implementations
-//   };
-//
-// and its type is defined in the protobuffer as
-//
-//   enum LayerType {
-//     // other definitions
-//     AWESOME = 46,
-//   }
-//
-// If the layer is going to be created simply by its constructor, in your c++
-// file, add the following line:
-//
-//    REGISTER_LAYER_CLASS(AWESOME, MyAwesomeLayer);
-//
-// Or, if the layer is going to be created by another creator function, in the
-// format of:
-//
-//    template <typename Dtype>
-//    Layer<Dtype*> GetMyAwesomeLayer(const LayerParameter& param) {
-//      // your implementation
-//    }
-//
-// (for example, when your layer has multiple backends, see GetConvolutionLayer
-// for a use case), then you can register the creator function instead, like
-//
-// REGISTER_LAYER_CREATOR(AWESOME, GetMyAwesomeLayer)
-//
-// Note that each layer type should only be registered once.
+/**
+ * @brief A layer factory that allows one to register layers.
+ * During runtime, registered layers could be called by passing a LayerParameter
+ * protobuffer to the CreateLayer function:
+ *
+ *     LayerRegistry<Dtype>::CreateLayer(param);
+ *
+ * There are two ways to register a layer. Assuming that we have a layer like:
+ *
+ *   template <typename Dtype>
+ *   class MyAwesomeLayer : public Layer<Dtype> {
+ *     // your implementations
+ *   };
+ *
+ * and its type is defined in the protobuffer as
+ *
+ *   enum LayerType {
+ *     // other definitions
+ *     AWESOME = 46,
+ *   }
+ *
+ * If the layer is going to be created simply by its constructor, in your c++
+ * file, add the following line:
+ *
+ *    REGISTER_LAYER_CLASS(AWESOME, MyAwesomeLayer);
+ *
+ * Or, if the layer is going to be created by another creator function, in the
+ * format of:
+ *
+ *    template <typename Dtype>
+ *    Layer<Dtype*> GetMyAwesomeLayer(const LayerParameter& param) {
+ *      // your implementation
+ *    }
+ *
+ * (for example, when your layer has multiple backends, see GetConvolutionLayer
+ * for a use case), then you can register the creator function instead, like
+ *
+ * REGISTER_LAYER_CREATOR(AWESOME, GetMyAwesomeLayer)
+ *
+ * Note that each layer type should only be registered once.
+ */
 
 #ifndef CAFFE_LAYER_FACTORY_H_
 #define CAFFE_LAYER_FACTORY_H_

From 10ee219999f53ba0bfc304daf2222c2090481b02 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Sep 2014 11:00:37 -0700
Subject: [PATCH 0838/2053] test_gradient_based_solver.cpp: removed unused
 typedefs

---
 src/caffe/test/test_gradient_based_solver.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 3040eb134a4..65de52aae4a 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -323,7 +323,6 @@ class SGDSolverTest : public GradientBasedSolverTest<TypeParam> {
 TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
   this->TestLeastSquaresUpdate();
 }
 
@@ -390,7 +389,6 @@ class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
 TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
   this->TestLeastSquaresUpdate();
 }
 
@@ -435,7 +433,6 @@ class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
 TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
   this->TestLeastSquaresUpdate();
 }
 

From a7bce254c32a25f2b1260afb62493dbb4f96dc46 Mon Sep 17 00:00:00 2001
From: Chiyuan Zhang <pluskid@gmail.com>
Date: Fri, 26 Sep 2014 11:21:59 -0400
Subject: [PATCH 0839/2053] make HDF5 layer support multiple data output

---
 include/caffe/data_layers.hpp          |  8 +---
 src/caffe/layers/hdf5_data_layer.cpp   | 55 ++++++++++++++------------
 src/caffe/layers/hdf5_data_layer.cu    | 19 ++++-----
 src/caffe/test/test_hdf5data_layer.cpp |  3 ++
 4 files changed, 41 insertions(+), 44 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index b3f93cea19e..c1720b7f8ad 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -20,9 +20,6 @@
 
 namespace caffe {
 
-#define HDF5_DATA_DATASET_NAME "data"
-#define HDF5_DATA_LABEL_NAME "label"
-
 /**
  * @brief Provides base for data layers that feed blobs to the Net.
  *
@@ -180,7 +177,7 @@ class HDF5DataLayer : public Layer<Dtype> {
     return LayerParameter_LayerType_HDF5_DATA;
   }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
+  virtual inline int MinTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -197,8 +194,7 @@ class HDF5DataLayer : public Layer<Dtype> {
   unsigned int num_files_;
   unsigned int current_file_;
   hsize_t current_row_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
+  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
 };
 
 /**
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 8fec61789e9..bf771fd093a 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -33,20 +33,27 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
     return;
   }
 
-  const int MIN_DATA_DIM = 2;
+  int top_size = this->layer_param_.top_size();
+  hdf_blobs_.resize(top_size);
+
+  const int MIN_DATA_DIM = 1;
   const int MAX_DATA_DIM = 4;
-  hdf5_load_nd_dataset(
-    file_id, "data",  MIN_DATA_DIM, MAX_DATA_DIM, &data_blob_);
 
-  const int MIN_LABEL_DIM = 1;
-  const int MAX_LABEL_DIM = 2;
-  hdf5_load_nd_dataset(
-    file_id, "label", MIN_LABEL_DIM, MAX_LABEL_DIM, &label_blob_);
+  for (int i = 0; i < top_size; ++i) {
+      hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
+      hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
+        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());
+  }
 
   herr_t status = H5Fclose(file_id);
   CHECK_GE(status, 0) << "Failed to close HDF5 file " << filename;
-  CHECK_EQ(data_blob_.num(), label_blob_.num());
-  LOG(INFO) << "Successully loaded " << data_blob_.num() << " rows";
+
+  // MinTopBlobs==1 guarantees at least one top blob
+  int num = hdf_blobs_[0]->num();
+  for (int i = 1; i < top_size; ++i) {
+      CHECK_EQ(hdf_blobs_[i]->num(), num);
+  }
+  LOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
 }
 
 template <typename Dtype>
@@ -74,26 +81,21 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  top[0]->Reshape(batch_size, data_blob_.channels(),
-                  data_blob_.height(), data_blob_.width());
-  top[1]->Reshape(batch_size, label_blob_.channels(),
-                  label_blob_.height(), label_blob_.width());
-  LOG(INFO) << "output data size: " << top[0]->num() << ","
-      << top[0]->channels() << "," << top[0]->height() << ","
-      << top[0]->width();
+  const int top_size = this->layer_param_.top_size();
+  for (int i = 0; i < top_size; ++i) {
+    top[i]->Reshape(batch_size, hdf_blobs_[i]->channels(),
+                    hdf_blobs_[i]->height(), hdf_blobs_[i]->width());
+  }
 }
 
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  const int data_count = top[0]->count() / top[0]->num();
-  const int label_data_count = top[1]->count() / top[1]->num();
-
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == data_blob_.num()) {
+    if (current_row_ == hdf_blobs_[0]->num()) {
       if (num_files_ > 1) {
-        current_file_ += 1;
+        ++current_file_;
         if (current_file_ == num_files_) {
           current_file_ = 0;
           LOG(INFO) << "looping around to first file";
@@ -102,11 +104,12 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       }
       current_row_ = 0;
     }
-    caffe_copy(data_count, &data_blob_.cpu_data()[current_row_ * data_count],
-               &top[0]->mutable_cpu_data()[i * data_count]);
-    caffe_copy(label_data_count,
-               &label_blob_.cpu_data()[current_row_ * label_data_count],
-               &top[1]->mutable_cpu_data()[i * label_data_count]);
+    for (int j = 0; j < this->layer_param_.top_size(); ++j) {
+      int data_dim = top[j]->count() / top[j]->num();
+      caffe_copy(data_dim,
+          &hdf_blobs_[j]->cpu_data()[current_row_ * data_dim],
+          &top[j]->mutable_cpu_data()[i * data_dim]);
+    }
   }
 }
 
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index f671ea12d76..02ec6733d49 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -20,29 +20,24 @@ template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  const int data_count = top[0]->count() / top[0]->num();
-  const int label_data_count = top[1]->count() / top[1]->num();
-
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == data_blob_.num()) {
+    if (current_row_ == hdf_blobs_[0]->num()) {
       if (num_files_ > 1) {
         current_file_ += 1;
-
         if (current_file_ == num_files_) {
           current_file_ = 0;
           LOG(INFO) << "looping around to first file";
         }
-
         LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
       }
       current_row_ = 0;
     }
-    caffe_copy(data_count,
-        &data_blob_.cpu_data()[current_row_ * data_count],
-        &top[0]->mutable_gpu_data()[i * data_count]);
-    caffe_copy(label_data_count,
-        &label_blob_.cpu_data()[current_row_ * label_data_count],
-        &top[1]->mutable_gpu_data()[i * label_data_count]);
+    for (int j = 0; j < this->layer_param_.top_size(); ++j) {
+      int data_dim = top[j]->count() / top[j]->num();
+      caffe_copy(data_dim,
+          &hdf_blobs_[j]->cpu_data()[current_row_ * data_dim],
+          &top[j]->mutable_gpu_data()[i * data_dim]);
+    }
   }
 }
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 9026a91ad85..769c1323ec7 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -57,6 +57,9 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   // The data file we are reading has 10 rows and 8 columns,
   // with values from 0 to 10*8 reshaped in row-major order.
   LayerParameter param;
+  param.add_top("data");
+  param.add_top("label");
+
   HDF5DataParameter* hdf5_data_param = param.mutable_hdf5_data_param();
   int batch_size = 5;
   hdf5_data_param->set_batch_size(batch_size);

From 7e8aefb2d76cef014f2576efcb08d79526040544 Mon Sep 17 00:00:00 2001
From: Chiyuan Zhang <pluskid@gmail.com>
Date: Fri, 26 Sep 2014 16:20:48 -0400
Subject: [PATCH 0840/2053] added test case to cover new HDF5 behavior

---
 .../test/test_data/generate_sample_data.py    |   9 +++++++++
 src/caffe/test/test_data/sample_data.h5       | Bin 11784 -> 11824 bytes
 .../test/test_data/sample_data_2_gzip.h5      | Bin 10548 -> 15446 bytes
 src/caffe/test/test_hdf5data_layer.cpp        |  16 +++++++++++++++-
 4 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 2ab238611d8..e5dbc3406d8 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -20,12 +20,17 @@
 label = 1 + np.arange(num_rows)[:, np.newaxis]
 label = label.astype('float32')
 
+# We add an extra label2 dataset to test HDF5 layer's ability
+# to handle arbitrary number of output ("top") Blobs.
+label2 = label + 1
+
 print data
 print label
 
 with h5py.File(os.path.dirname(__file__) + '/sample_data.h5', 'w') as f:
     f['data'] = data
     f['label'] = label
+    f['label2'] = label2
 
 with h5py.File(os.path.dirname(__file__) + '/sample_data_2_gzip.h5', 'w') as f:
     f.create_dataset(
@@ -36,6 +41,10 @@
         'label', data=label,
         compression='gzip', compression_opts=1
     )
+    f.create_dataset(
+        'label2', data=label2,
+        compression='gzip', compression_opts=1
+    )
 
 with open(os.path.dirname(__file__) + '/sample_data_list.txt', 'w') as f:
     f.write(os.path.dirname(__file__) + '/sample_data.h5\n')
diff --git a/src/caffe/test/test_data/sample_data.h5 b/src/caffe/test/test_data/sample_data.h5
index 90eaaa56ec773b5de2303100a5611786c8d78766..236e66b0d122523107aac97430a3dfb5bf30ec7c 100644
GIT binary patch
delta 127
zcmeB(*$^{9gVA83W;>(A#*OSuj0%%?GN}vZBqpWi7%?y~GC+XE<d00RnGPJBe2+PT
zk$JK?OCXm7RH}n*;>Kr_6_|x4cd>S{A2_HQ!oZL*abqr2D1(VZZ}Jyb1(?jnjn<5t
N(>NBXPR`)a0009dB}f1O

delta 84
zcmdlG(-AX4gOOvRW;>(6#*OSuj1rS~GO2SiGC+XC#Es^Y6`1!hJy|jN9&-dE(`0j&
lz=;Pw0L6qRcd>S{KUtv~!oZNRapQN!i3dV9r*SM&1pp~c8@>Pl

diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5
index ff49db4ea742f207cbea04de3027087f4994215a..a138e0367be3d4b4ce4b51dcf0d7895056018883 100644
GIT binary patch
delta 170
zcmdlIbgg2722+^LM6I^TLM(C{58Ps$yn;!9QDO2<CUwD_#H7?5BL)UW1_-d&{E<nT
zk?Fv}&3r5ce2mPK>xBZjB%qRfh7&hF+k8Oy6c<QAkK76_l?uk>goFYHC51eON30Gt
z3?1?ehRw>%%?twC3>zDj87f;?pKjjBvW<PR0<+NM8?qCb<cua~%gF-`nA{_m#HccH
NqdcR@W`6nqoB+w_Ga&!~

delta 71
zcmcasu_b7N29t^AM6EVPfsGs4nHVJ|?_^TvWMqH<hlv}_Co3?&VS2J+GapL<A0yM`
WdZECH2R>{*Abg4oB-$glf(rlzSr?rE

diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 769c1323ec7..ed6fed83708 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -25,10 +25,12 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
   HDF5DataLayerTest()
       : filename(NULL),
         blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()) {}
+        blob_top_label_(new Blob<Dtype>()),
+        blob_top_label2_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
+    blob_top_vec_.push_back(blob_top_label2_);
 
     // Check out generate_sample_data.py in the same directory.
     filename = new string(
@@ -39,12 +41,14 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
   virtual ~HDF5DataLayerTest() {
     delete blob_top_data_;
     delete blob_top_label_;
+    delete blob_top_label2_;
     delete filename;
   }
 
   string* filename;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
+  Blob<Dtype>* const blob_top_label2_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
@@ -59,6 +63,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   LayerParameter param;
   param.add_top("data");
   param.add_top("label");
+  param.add_top("label2");
 
   HDF5DataParameter* hdf5_data_param = param.mutable_hdf5_data_param();
   int batch_size = 5;
@@ -81,6 +86,11 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_label_->height(), 1);
   EXPECT_EQ(this->blob_top_label_->width(), 1);
 
+  EXPECT_EQ(this->blob_top_label2_->num(), batch_size);
+  EXPECT_EQ(this->blob_top_label2_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label2_->height(), 1);
+  EXPECT_EQ(this->blob_top_label2_->width(), 1);
+
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
 
   // Go through the data 10 times (5 batches).
@@ -92,6 +102,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
     // On odd iterations, we're reading the second half of the data.
     // NB: label is 1-indexed
     int label_offset = 1 + ((iter % 2 == 0) ? 0 : batch_size);
+    int label2_offset = 1 + label_offset;
     int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
 
     // Every two iterations we are reading the second file,
@@ -103,6 +114,9 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
       EXPECT_EQ(
         label_offset + i,
         this->blob_top_label_->cpu_data()[i]);
+      EXPECT_EQ(
+        label2_offset + i,
+        this->blob_top_label2_->cpu_data()[i]);
     }
     for (int i = 0; i < batch_size; ++i) {
       for (int j = 0; j < num_cols; ++j) {

From 3fde283606b604a8a25becc7958c7a1268fb2249 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Sep 2014 11:37:55 -0700
Subject: [PATCH 0841/2053] hdf5_data_layer.cpp: fix indentation

---
 src/caffe/layers/hdf5_data_layer.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index bf771fd093a..a2c806699b8 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -40,8 +40,8 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   const int MAX_DATA_DIM = 4;
 
   for (int i = 0; i < top_size; ++i) {
-      hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
-      hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
+    hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
+    hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
         MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());
   }
 
@@ -51,7 +51,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   // MinTopBlobs==1 guarantees at least one top blob
   int num = hdf_blobs_[0]->num();
   for (int i = 1; i < top_size; ++i) {
-      CHECK_EQ(hdf_blobs_[i]->num(), num);
+    CHECK_EQ(hdf_blobs_[i]->num(), num);
   }
   LOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
 }

From db50f0749a9ee491ecf3daad783827e4c215bf0e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Sep 2014 11:13:59 -0700
Subject: [PATCH 0842/2053] Cleanup HDF5DataLayer log messages.

-Only use "DLOG" during Forward/Backward for less noise during
training/testing
-Cleanup spacing, capitalization, etc.
---
 src/caffe/layers/hdf5_data_layer.cpp | 14 +++++++-------
 src/caffe/layers/hdf5_data_layer.cu  |  2 +-
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index a2c806699b8..d4173844667 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -26,10 +26,10 @@ HDF5DataLayer<Dtype>::~HDF5DataLayer<Dtype>() { }
 // Load data and label from HDF5 filename into the class property blobs.
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
-  LOG(INFO) << "Loading HDF5 file" << filename;
+  DLOG(INFO) << "Loading HDF5 file: " << filename;
   hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);
   if (file_id < 0) {
-    LOG(ERROR) << "Failed opening HDF5 file" << filename;
+    LOG(ERROR) << "Failed opening HDF5 file: " << filename;
     return;
   }
 
@@ -46,14 +46,14 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   }
 
   herr_t status = H5Fclose(file_id);
-  CHECK_GE(status, 0) << "Failed to close HDF5 file " << filename;
+  CHECK_GE(status, 0) << "Failed to close HDF5 file: " << filename;
 
   // MinTopBlobs==1 guarantees at least one top blob
   int num = hdf_blobs_[0]->num();
   for (int i = 1; i < top_size; ++i) {
     CHECK_EQ(hdf_blobs_[i]->num(), num);
   }
-  LOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
+  DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
 }
 
 template <typename Dtype>
@@ -61,7 +61,7 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
-  LOG(INFO) << "Loading filename from " << source;
+  LOG(INFO) << "Loading list of HDF5 filenames from: " << source;
   hdf_filenames_.clear();
   std::ifstream source_file(source.c_str());
   if (source_file.is_open()) {
@@ -73,7 +73,7 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   source_file.close();
   num_files_ = hdf_filenames_.size();
   current_file_ = 0;
-  LOG(INFO) << "Number of files: " << num_files_;
+  LOG(INFO) << "Number of HDF5 files: " << num_files_;
 
   // Load the first HDF5 file and initialize the line counter.
   LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
@@ -98,7 +98,7 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         ++current_file_;
         if (current_file_ == num_files_) {
           current_file_ = 0;
-          LOG(INFO) << "looping around to first file";
+          DLOG(INFO) << "Looping around to first file.";
         }
         LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
       }
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 02ec6733d49..9f5daf230e7 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -26,7 +26,7 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         current_file_ += 1;
         if (current_file_ == num_files_) {
           current_file_ = 0;
-          LOG(INFO) << "looping around to first file";
+          DLOG(INFO) << "Looping around to first file.";
         }
         LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
       }

From fce99474e7803767dbb5ea25696e23fa8002cb50 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Sep 2014 11:15:21 -0700
Subject: [PATCH 0843/2053] Die on failure to load HDF5 data file.

---
 src/caffe/layers/hdf5_data_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index d4173844667..601fd345f3b 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -29,8 +29,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   DLOG(INFO) << "Loading HDF5 file: " << filename;
   hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);
   if (file_id < 0) {
-    LOG(ERROR) << "Failed opening HDF5 file: " << filename;
-    return;
+    LOG(FATAL) << "Failed opening HDF5 file: " << filename;
   }
 
   int top_size = this->layer_param_.top_size();

From 7a223ec3198f3d004155e54cab4790bee19972c7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Sep 2014 12:27:09 -0700
Subject: [PATCH 0844/2053] HDF5DataLayer: die on failure to open source file

---
 src/caffe/layers/hdf5_data_layer.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 601fd345f3b..61aa293d2ac 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -68,6 +68,8 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     while (source_file >> line) {
       hdf_filenames_.push_back(line);
     }
+  } else {
+    LOG(FATAL) << "Failed to open source file: " << source;
   }
   source_file.close();
   num_files_ = hdf_filenames_.size();

From e43c079fc33a197942d73a57250d09a26cbb2fab Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Sep 2014 12:32:34 -0700
Subject: [PATCH 0845/2053] hdf5_load_nd_dataset_helper: check that dataset
 exists first

---
 src/caffe/util/io.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 4a22e18725d..43e5c01e36a 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -124,6 +124,9 @@ template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
     Blob<Dtype>* blob) {
+  // Verify that the dataset exists.
+  CHECK(H5LTfind_dataset(file_id, dataset_name_))
+      << "Failed to find HDF5 dataset " << dataset_name_;
   // Verify that the number of dimensions is in the accepted range.
   herr_t status;
   int ndims;

From a36cceb3bdbaeb10e1057307b9aa69037de0f031 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 15:28:36 -0700
Subject: [PATCH 0846/2053] fix cuDNN build by readding line deleted in #1167

---
 src/caffe/layer_factory.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 96481dd72a4..9b77ca14677 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -46,6 +46,7 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
     return new PoolingLayer<Dtype>(param);
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
+    PoolingParameter p_param = param.pooling_param();
     if (p_param.pad_h() || p_param.pad_w() || param.top_size() > 1) {
       LOG(INFO) << "CUDNN does not support padding or multiple tops. "
                 << "Using Caffe's own pooling layer.";

From 4f463bdaa14c8906b03ade524aab154bec712958 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Tue, 30 Sep 2014 15:57:38 -0700
Subject: [PATCH 0847/2053] remove a wrong space in common.hpp

---
 include/caffe/common.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 9c6eb4d6834..811b45e2c15 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -19,7 +19,7 @@
 
 // gflags 2.1 issue: namespace google was changed to gflags without warning.
 // Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
-// 2.1. If yes , we will add a temporary solution to redirect the namespace.
+// 2.1. If yes, we will add a temporary solution to redirect the namespace.
 // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
 // remove the following hack.
 #ifndef GFLAGS_GFLAGS_H_

From a02ec65ce8f07896dfa05d52263dbe8acfbb02cd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 30 Sep 2014 17:24:26 -0700
Subject: [PATCH 0848/2053] [fix] check and load weight for backward w.r.t.
 data

fix NULL pointer when back-propagating to the data for fixed i.e. not
back-propagated weights. same issue as #1048
---
 src/caffe/layers/cudnn_conv_layer.cu | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 6961bbeaba5..cf8607e1b78 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -86,6 +86,9 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
       // Gradient w.r.t. bottom data.
       if (propagate_down[i]) {
+        if (weight == NULL) {
+          weight = this->blobs_[0]->gpu_data();
+        }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
         CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
             filter_desc_, weight + weight_offset_ * g,

From 08ccc947f128ef88fcd24c3d5807bc7c4338efda Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 19:16:56 -0700
Subject: [PATCH 0849/2053] [pycaffe] fix comment typo

---
 python/caffe/_caffe.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index ba04d276351..622e887ac78 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -153,7 +153,7 @@ class PyNet {
   bp::dict channel_swap_;
 
  protected:
-  // The pointer to the internal caffe::Net instant.
+  // The pointer to the internal caffe::Net instance.
   shared_ptr<Net<float> > net_;
   // if taking input from an ndarray, we need to hold references
   bp::object input_data_;

From 2f29ec4672dca2ab4ecafb1ec75d7c597a09cfca Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 16:20:27 -0700
Subject: [PATCH 0850/2053] [pycaffe] add converter for vectors of Nets

---
 python/caffe/_caffe.cpp | 3 +++
 python/caffe/_caffe.hpp | 5 +++++
 2 files changed, 8 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5a81a42329b..ce7b726f955 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -189,6 +189,9 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def("solve",        &PySGDSolver::Solve)
       .def("solve",        &PySGDSolver::SolveResume);
 
+  bp::class_<vector<shared_ptr<PyNet> > >("NetVec")
+      .def(bp::vector_indexing_suite<vector<shared_ptr<PyNet> >, true>());
+
   bp::class_<vector<PyBlob<float> > >("BlobVec")
       .def(bp::vector_indexing_suite<vector<PyBlob<float> >, true>());
 
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 622e887ac78..0ed0974842e 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -152,6 +152,11 @@ class PyNet {
   bp::dict raw_scale_;
   bp::dict channel_swap_;
 
+  // this is here only to satisfy boost's vector_indexing_suite
+  bool operator == (const PyNet &other) {
+      return this->net_ == other.net_;
+  }
+
  protected:
   // The pointer to the internal caffe::Net instance.
   shared_ptr<Net<float> > net_;

From 34e661b27ee0d4554b4377d3fbe5793e858f37c7 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 16:21:18 -0700
Subject: [PATCH 0851/2053] [pycaffe] expose SGDSolver.test_nets

---
 python/caffe/_caffe.cpp | 11 ++++++++---
 python/caffe/_caffe.hpp |  2 ++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index ce7b726f955..1e2dd079ad0 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -2,6 +2,7 @@
 // caffe::Caffe functions so that one could easily call it from Python.
 // Note that for Python, we will simply use float as the data type.
 
+#include <boost/make_shared.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
 
 // these need to be included after boost on OS X
@@ -134,6 +135,9 @@ PySGDSolver::PySGDSolver(const string& param_file) {
   // we need to explicitly store the net wrapper, rather than constructing
   // it on the fly, so that it can hold references to Python objects
   net_.reset(new PyNet(solver_->net()));
+  for (int i = 0; i < solver_->test_nets().size(); ++i) {
+    test_nets_.push_back(boost::make_shared<PyNet>(solver_->test_nets()[i]));
+  }
 }
 
 void PySGDSolver::SolveResume(const string& resume_file) {
@@ -185,9 +189,10 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   bp::class_<PySGDSolver, boost::noncopyable>(
       "SGDSolver", bp::init<string>())
-      .add_property("net", &PySGDSolver::net)
-      .def("solve",        &PySGDSolver::Solve)
-      .def("solve",        &PySGDSolver::SolveResume);
+      .add_property("net",       &PySGDSolver::net)
+      .add_property("test_nets", &PySGDSolver::test_nets)
+      .def("solve",              &PySGDSolver::Solve)
+      .def("solve",              &PySGDSolver::SolveResume);
 
   bp::class_<vector<shared_ptr<PyNet> > >("NetVec")
       .def(bp::vector_indexing_suite<vector<shared_ptr<PyNet> >, true>());
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 0ed0974842e..bef90e1b359 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -170,11 +170,13 @@ class PySGDSolver {
   explicit PySGDSolver(const string& param_file);
 
   shared_ptr<PyNet> net() { return net_; }
+  vector<shared_ptr<PyNet> > test_nets() { return test_nets_; }
   void Solve() { return solver_->Solve(); }
   void SolveResume(const string& resume_file);
 
  protected:
   shared_ptr<PyNet> net_;
+  vector<shared_ptr<PyNet> > test_nets_;
   shared_ptr<SGDSolver<float> > solver_;
 };
 

From e222c5776506591715769df087c4b980a391c588 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 20:24:47 -0700
Subject: [PATCH 0852/2053] add accessor for Solver::iter_

This will be useful in code controlling the solver (from pycaffe, for
example).
---
 include/caffe/solver.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 6fd159d0b98..2bad0e2fe1e 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -31,6 +31,7 @@ class Solver {
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
     return test_nets_;
   }
+  int iter() { return iter_; }
 
  protected:
   // PreSolve is run before any solving iteration starts, allowing one to

From 5be4d7681c489d187a2c8fa3cdc746bd37d57ce2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 20:26:25 -0700
Subject: [PATCH 0853/2053] [pycaffe] expose SGDSolver.iter

---
 python/caffe/_caffe.cpp | 1 +
 python/caffe/_caffe.hpp | 1 +
 2 files changed, 2 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 1e2dd079ad0..5a8d99d3f22 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -191,6 +191,7 @@ BOOST_PYTHON_MODULE(_caffe) {
       "SGDSolver", bp::init<string>())
       .add_property("net",       &PySGDSolver::net)
       .add_property("test_nets", &PySGDSolver::test_nets)
+      .add_property("iter",      &PySGDSolver::iter)
       .def("solve",              &PySGDSolver::Solve)
       .def("solve",              &PySGDSolver::SolveResume);
 
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index bef90e1b359..fc7be49af1b 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -171,6 +171,7 @@ class PySGDSolver {
 
   shared_ptr<PyNet> net() { return net_; }
   vector<shared_ptr<PyNet> > test_nets() { return test_nets_; }
+  int iter() { return solver_->iter(); }
   void Solve() { return solver_->Solve(); }
   void SolveResume(const string& resume_file);
 

From ac8a03d297265efd33a73bfd95f16913ea7b3099 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 1 Oct 2014 14:19:30 -0700
Subject: [PATCH 0854/2053] [fix] solver indexing of output blobs was incorrect
 for non-singleton outputs

---
 src/caffe/solver.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 0ea4edcf9b8..8d187ea638b 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -285,10 +285,10 @@ void Solver<Dtype>::Test(const int test_net_id) {
     LOG(INFO) << "Test loss: " << loss;
   }
   for (int i = 0; i < test_score.size(); ++i) {
-    const string& output_name = test_net->blob_names()[
-        test_net->output_blob_indices()[test_score_output_id[i]]];
-    const Dtype loss_weight =
-        test_net->blob_loss_weights()[test_net->output_blob_indices()[i]];
+    const int output_blob_index =
+        test_net->output_blob_indices()[test_score_output_id[i]];
+    const string& output_name = test_net->blob_names()[output_blob_index];
+    const Dtype loss_weight = test_net->blob_loss_weights()[output_blob_index];
     ostringstream loss_msg_stream;
     const Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);
     if (loss_weight) {

From e6ba910ed9e6270e989cb7b634b206034c39f2cd Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 1 Oct 2014 20:53:31 -0700
Subject: [PATCH 0855/2053] caffe.proto: do some minor cleanup (fix comments,
 alphabetization)

---
 src/caffe/proto/caffe.proto | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 1f26fd346d5..2dea5a09ee1 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -470,11 +470,6 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
-// Message that stores parameters used by ThresholdLayer
-message ThresholdParameter {
-  optional float threshold = 1 [default = 0]; // Strictly Positive values
-}
-
 // Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
@@ -641,7 +636,7 @@ message SliceParameter {
   repeated uint32 slice_point = 2;
 }
 
-// Message that stores parameters used by SoftmaxLayer, SoftMaxWithLossLayer
+// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
 message SoftmaxParameter {
   enum Engine {
     DEFAULT = 0;
@@ -651,7 +646,7 @@ message SoftmaxParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
-// Message that stores parameters used by SigmoidLayer
+// Message that stores parameters used by TanHLayer
 message TanHParameter {
   enum Engine {
     DEFAULT = 0;
@@ -661,6 +656,11 @@ message TanHParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
+// Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+  optional float threshold = 1 [default = 0]; // Strictly positive values
+}
+
 // Message that stores parameters used by WindowDataLayer
 message WindowDataParameter {
   // Specify the data source.

From 22515af9a964840a1ab7082eddf27d7733a9956f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 2 Oct 2014 11:19:05 -0700
Subject: [PATCH 0856/2053] [fix] lint causing travis failures

---
 matlab/caffe/matcaffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index db8896da6ae..fc047585cfe 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -372,7 +372,7 @@ static handler_registry handlers[] = {
  ** matlab entry point: caffe(api_command, arg1, arg2, ...)
  **/
 void mexFunction(MEX_ARGS) {
-  mexLock(); // Avoid clearing the mex file.
+  mexLock();  // Avoid clearing the mex file.
   if (nrhs == 0) {
     LOG(ERROR) << "No API command given";
     mexErrMsgTxt("An API command is requires");

From 4e0868d091718e99f2f3696a61f88240f33503b2 Mon Sep 17 00:00:00 2001
From: Bert <db.bert@gmail.com>
Date: Wed, 1 Oct 2014 15:29:24 +0200
Subject: [PATCH 0857/2053] Update detect.py

This generator causes an error in detect_windows because it is run twice there.
Better store it as a data structure instead.
---
 python/detect.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/detect.py b/python/detect.py
index bc8c0703646..b67b500aafd 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -130,10 +130,10 @@ def main(argv):
     # Detect.
     if args.crop_mode == 'list':
         # Unpack sequence of (image filename, windows).
-        images_windows = (
+        images_windows = [
             (ix, inputs.iloc[np.where(inputs.index == ix)][COORD_COLS].values)
             for ix in inputs.index.unique()
-        )
+        ]
         detections = detector.detect_windows(images_windows)
     else:
         detections = detector.detect_selective_search(inputs)

From 4b004162622963d2b857ad3adaa76f143278011d Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Thu, 2 Oct 2014 12:11:45 -0700
Subject: [PATCH 0858/2053] add factory header to caffe hpp

---
 include/caffe/caffe.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index 0af9ef04c43..3c829f2f9b0 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -8,6 +8,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"

From ab5e86e2babf22892a241ae06a93b3f389a23db1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Sep 2014 19:06:18 -0700
Subject: [PATCH 0859/2053] [pycaffe] expose Net::CopyTrainedLayersFrom as
 Net.copy_from

This allows finetuning from Python, for example.
---
 python/caffe/_caffe.cpp | 1 +
 python/caffe/_caffe.hpp | 3 +++
 2 files changed, 4 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5a81a42329b..5696f2625f6 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -147,6 +147,7 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<PyNet, shared_ptr<PyNet> >(
       "Net", bp::init<string, string>())
       .def(bp::init<string>())
+      .def("copy_from",             &PyNet::CopyTrainedLayersFrom)
       .def("_forward",              &PyNet::Forward)
       .def("_backward",             &PyNet::Backward)
       .def("reshape",               &PyNet::Reshape)
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index 622e887ac78..a15144ba999 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -96,6 +96,9 @@ class PyNet {
   inline void check_contiguous_array(PyArrayObject* arr, string name,
       int channels, int height, int width);
 
+  void CopyTrainedLayersFrom(const string filename) {
+    net_->CopyTrainedLayersFrom(filename);
+  }
   void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
   void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
   void Reshape() { net_->Reshape(); }

From 0dfda37c7f463b19f1504a08dc026b38bdddf7c6 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 2 Oct 2014 12:50:32 -0700
Subject: [PATCH 0860/2053] [pycaffe] expose Net::SharedTrainedLayersWith as
 Net.share_with

---
 python/caffe/_caffe.cpp | 1 +
 python/caffe/_caffe.hpp | 3 +++
 2 files changed, 4 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5696f2625f6..28027bed0a4 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -148,6 +148,7 @@ BOOST_PYTHON_MODULE(_caffe) {
       "Net", bp::init<string, string>())
       .def(bp::init<string>())
       .def("copy_from",             &PyNet::CopyTrainedLayersFrom)
+      .def("share_with",            &PyNet::ShareTrainedLayersWith)
       .def("_forward",              &PyNet::Forward)
       .def("_backward",             &PyNet::Backward)
       .def("reshape",               &PyNet::Reshape)
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index a15144ba999..780f82e5666 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -99,6 +99,9 @@ class PyNet {
   void CopyTrainedLayersFrom(const string filename) {
     net_->CopyTrainedLayersFrom(filename);
   }
+  void ShareTrainedLayersWith(PyNet* other) {
+    net_->ShareTrainedLayersWith(other->net_.get());
+  }
   void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
   void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
   void Reshape() { net_->Reshape(); }

From 0aae4a509862f7c42b32d7653b5c068f948b462c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 1 Oct 2014 23:11:41 -0700
Subject: [PATCH 0861/2053] Add caffe_gpu_exp math function

---
 include/caffe/util/math_functions.hpp |  3 +++
 src/caffe/util/math_functions.cu      | 21 +++++++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 62467fb5c52..a78eb5bc978 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -205,6 +205,9 @@ void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 4ae4bba6029..43e65eb9a69 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -303,6 +303,27 @@ void caffe_gpu_abs<double>(const int N, const double* a, double* y) {
 }
 
 
+template <typename Dtype>
+__global__ void exp_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = exp(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_exp<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  exp_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_exp<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  exp_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
 template <typename Dtype>
 __global__ void powx_kernel(const int n, const Dtype* a,
     const Dtype alpha, Dtype* y) {

From 7995a38cc14de2560de7611b1e845c4946bcd3dd Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 1 Oct 2014 23:12:06 -0700
Subject: [PATCH 0862/2053] Add ExpLayer to calculate y = base ^ (scale * x +
 shift)

---
 include/caffe/neuron_layers.hpp      |  66 ++++++++++++++++
 src/caffe/layer_factory.cpp          |   1 +
 src/caffe/layers/exp_layer.cpp       |  69 ++++++++++++++++
 src/caffe/layers/exp_layer.cu        |  44 +++++++++++
 src/caffe/proto/caffe.proto          |  16 +++-
 src/caffe/test/test_neuron_layer.cpp | 114 +++++++++++++++++++++++++++
 6 files changed, 308 insertions(+), 2 deletions(-)
 create mode 100644 src/caffe/layers/exp_layer.cpp
 create mode 100644 src/caffe/layers/exp_layer.cu

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 64a1fd325ad..5daeeefe7ae 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -212,6 +212,72 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   unsigned int uint_thres_;
 };
 
+/**
+ * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class ExpLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ExpParameter exp_param,
+   *     with ExpLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit ExpLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_EXP;
+  }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \gamma ^ {\alpha x + \beta}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype inner_scale_, outer_scale_;
+};
+
 /**
  * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
  *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 9b77ca14677..8815ddd021d 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -163,6 +163,7 @@ REGISTER_LAYER_CLASS(DROPOUT, DropoutLayer);
 REGISTER_LAYER_CLASS(DUMMY_DATA, DummyDataLayer);
 REGISTER_LAYER_CLASS(EUCLIDEAN_LOSS, EuclideanLossLayer);
 REGISTER_LAYER_CLASS(ELTWISE, EltwiseLayer);
+REGISTER_LAYER_CLASS(EXP, ExpLayer);
 REGISTER_LAYER_CLASS(FLATTEN, FlattenLayer);
 REGISTER_LAYER_CLASS(HDF5_DATA, HDF5DataLayer);
 REGISTER_LAYER_CLASS(HDF5_OUTPUT, HDF5OutputLayer);
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
new file mode 100644
index 00000000000..25da1201c10
--- /dev/null
+++ b/src/caffe/layers/exp_layer.cpp
@@ -0,0 +1,69 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ExpLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  const Dtype base = this->layer_param_.exp_param().base();
+  if (base != Dtype(-1)) {
+    CHECK_GT(base, 0) << "base must be strictly positive.";
+  }
+  // If base == -1, interpret the base as e and set log_base = 1 exactly.
+  // Otherwise, calculate its log explicitly.
+  const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
+  CHECK(!std::isnan(log_base))
+      << "NaN result: log(base) = log(" << base << ") = " << log_base;
+  CHECK(!std::isinf(log_base))
+      << "Inf result: log(base) = log(" << base << ") = " << log_base;
+  const Dtype input_scale = this->layer_param_.exp_param().scale();
+  const Dtype input_shift = this->layer_param_.exp_param().shift();
+  inner_scale_ = log_base * input_scale;
+  outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) : pow(base, input_shift);
+}
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (inner_scale_ == Dtype(1)) {
+    caffe_exp(count, bottom_data, top_data);
+  } else {
+    caffe_cpu_scale(count, inner_scale_, bottom_data, top_data);
+    caffe_exp(count, top_data, top_data);
+  }
+  if (outer_scale_ != Dtype(1)) {
+    caffe_scal(count, outer_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  caffe_mul(count, top_data, top_diff, bottom_diff);
+  if (inner_scale_ != Dtype(1)) {
+    caffe_scal(count, inner_scale_, bottom_diff);
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ExpLayer);
+#endif
+
+INSTANTIATE_CLASS(ExpLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu
new file mode 100644
index 00000000000..a0fd672a25a
--- /dev/null
+++ b/src/caffe/layers/exp_layer.cu
@@ -0,0 +1,44 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (inner_scale_ == Dtype(1)) {
+    caffe_gpu_exp(count, bottom_data, top_data);
+  } else {
+    caffe_gpu_scale(count, inner_scale_, bottom_data, top_data);
+    caffe_gpu_exp(count, top_data, top_data);
+  }
+  if (outer_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, outer_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ExpLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  caffe_gpu_mul(count, top_data, top_diff, bottom_diff);
+  if (inner_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, inner_scale_, bottom_diff);
+  }
+}
+
+INSTANTIATE_CLASS(ExpLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 2dea5a09ee1..6944ae847ff 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -200,7 +200,7 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 41 (last added: contrastive_loss_param)
+// LayerParameter next available ID: 42 (last added: exp_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -221,7 +221,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 38 (last added: CONTRASTIVE_LOSS)
+  // LayerType next available ID: 39 (last added: EXP)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -239,6 +239,7 @@ message LayerParameter {
     DUMMY_DATA = 32;
     EUCLIDEAN_LOSS = 7;
     ELTWISE = 25;
+    EXP = 38;
     FLATTEN = 8;
     HDF5_DATA = 9;
     HDF5_OUTPUT = 10;
@@ -301,6 +302,7 @@ message LayerParameter {
   optional DropoutParameter dropout_param = 12;
   optional DummyDataParameter dummy_data_param = 26;
   optional EltwiseParameter eltwise_param = 24;
+  optional ExpParameter exp_param = 41;
   optional HDF5DataParameter hdf5_data_param = 13;
   optional HDF5OutputParameter hdf5_output_param = 14;
   optional HingeLossParameter hinge_loss_param = 29;
@@ -470,6 +472,16 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
+// Message that stores parameters used by ExpLayer
+message ExpParameter {
+  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = exp(shift + scale * x).
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
+
 // Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index fdbb63cb3e5..34955c3af63 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -66,6 +66,38 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     const Dtype empirical_dropout_ratio = 1 - num_kept / Dtype(count);
     EXPECT_NEAR(empirical_dropout_ratio, dropout_ratio, 1.96 * std_error);
   }
+
+  void TestExpForward(const float base, const float scale, const float shift) {
+    LayerParameter layer_param;
+    layer_param.mutable_exp_param()->set_base(base);
+    layer_param.mutable_exp_param()->set_scale(scale);
+    layer_param.mutable_exp_param()->set_shift(shift);
+    ExpLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    const Dtype kDelta = 2e-4;
+    const Dtype* bottom_data = blob_bottom_->cpu_data();
+    const Dtype* top_data = blob_top_->cpu_data();
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      const Dtype bottom_val = bottom_data[i];
+      const Dtype top_val = top_data[i];
+      if (base == -1) {
+        EXPECT_NEAR(top_val, exp(shift + scale * bottom_val), kDelta);
+      } else {
+        EXPECT_NEAR(top_val, pow(base, shift + scale * bottom_val), kDelta);
+      }
+    }
+  }
+
+  void TestExpGradient(const float base, const float scale, const float shift) {
+    LayerParameter layer_param;
+    layer_param.mutable_exp_param()->set_base(base);
+    layer_param.mutable_exp_param()->set_scale(scale);
+    layer_param.mutable_exp_param()->set_shift(shift);
+    ExpLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
+  }
 };
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
@@ -201,6 +233,88 @@ TYPED_TEST(NeuronLayerTest, TestTanHGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(NeuronLayerTest, TestExpLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
 TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
   const float kDropoutRatio = 0.5;
   this->TestDropoutForward(kDropoutRatio);

From 159a422aab94d465e73b02a5d377e695c3cefc51 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 2 Oct 2014 16:20:04 -0700
Subject: [PATCH 0863/2053] [fix] comment typo

---
 src/caffe/test/test_net.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 9b10d100afb..d48a60ece46 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -993,7 +993,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // Check that data blobs of shared weights share the same location in memory.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   // Check that diff blobs of shared weights are at different locations in
-  // locations.  (The diffs should be accumulated at update time.)
+  // memory.  (The diffs should be accumulated at update time.)
   EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
   this->net_->Forward(bottom);
   this->net_->Backward();

From 4dc5bd0953aee7771487a1d8f989bbef6aecb72d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 2 Oct 2014 17:11:38 -0700
Subject: [PATCH 0864/2053] save/restore shared weights unit test

---
 src/caffe/test/test_net.cpp | 48 +++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index d48a60ece46..930486dd6c5 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1068,6 +1068,54 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   }
 }
 
+TYPED_TEST(NetTest, TestSharedWeightsResume) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  // Create a net with weight sharing; Update it once.
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataSharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  // Check that diff blobs of shared weights are at different locations in
+  // memory.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->ForwardBackward(bottom);
+  this->net_->Update();
+  Blob<Dtype> shared_params;
+  const bool kReshape = true;
+  const bool kCopyDiff = false;
+  shared_params.CopyFrom(*ip1_weights, kCopyDiff, kReshape);
+  const int count = ip1_weights->count();
+
+  // Write the net to a NetParameter, as in Solver::Snapshot.
+  NetParameter net_param;
+  this->net_->ToProto(&net_param);
+
+  // Reinitialize the net and copy parameters from net_param, as in
+  // Solver::Restore.
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataSharedWeightsNet();
+  this->net_->CopyTrainedLayersFrom(net_param);
+  ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  ASSERT_FALSE(NULL == ip1_weights);
+  ASSERT_FALSE(NULL == ip2_weights);
+  EXPECT_NE(ip1_weights, ip2_weights);
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  for (int i = 0; i < count; ++i) {
+    EXPECT_FLOAT_EQ(shared_params.cpu_data()[i], ip1_weights->cpu_data()[i]);
+  }
+  // Check that diff blobs of shared weights are at different locations in
+  // memory.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+}
+
 TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
   vector<Blob<Dtype>*> bottom;

From 7182bfb831c3c4dc37817af7695e6247cb9eae5c Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Fri, 19 Sep 2014 17:10:59 -0700
Subject: [PATCH 0865/2053] Move data_mean into data_transformer remove datum_*
 from BaseData

Conflicts:

	src/caffe/layers/memory_data_layer.cpp
---
 include/caffe/data_layers.hpp          | 14 ++------------
 include/caffe/data_transformer.hpp     | 16 ++++++++++++++--
 src/caffe/data_transformer.cpp         | 18 ++++++++++++++++++
 src/caffe/layers/base_data_layer.cpp   | 25 +------------------------
 src/caffe/layers/data_layer.cpp        |  8 ++------
 src/caffe/layers/image_data_layer.cpp  |  7 +------
 src/caffe/layers/memory_data_layer.cpp | 21 +++++++++------------
 src/caffe/layers/window_data_layer.cpp | 12 ++++++++++++
 8 files changed, 59 insertions(+), 62 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index c1720b7f8ad..bf06865c7d7 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -46,20 +46,9 @@ class BaseDataLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
 
-  int datum_channels() const { return datum_channels_; }
-  int datum_height() const { return datum_height_; }
-  int datum_width() const { return datum_width_; }
-  int datum_size() const { return datum_size_; }
-
  protected:
   TransformationParameter transform_param_;
   DataTransformer<Dtype> data_transformer_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  Blob<Dtype> data_mean_;
-  const Dtype* mean_;
   Caffe::Phase phase_;
   bool output_labels_;
 };
@@ -299,7 +288,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  int batch_size_;
+  int batch_size_, channels_, height_, width_, size_;
   Dtype* data_;
   Dtype* labels_;
   int n_;
@@ -339,6 +328,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
   enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
   vector<vector<float> > fg_windows_;
   vector<vector<float> > bg_windows_;
+  Blob<Dtype> data_mean_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 3ca99e7b62c..33afa72359b 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -3,6 +3,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
 
 namespace caffe {
 
@@ -16,6 +17,14 @@ class DataTransformer {
   explicit DataTransformer(const TransformationParameter& param)
     : param_(param) {
     phase_ = Caffe::phase();
+      // check if we want to have mean
+    if (param_.has_mean_file()) {
+      const string& mean_file = param.mean_file();
+      LOG(INFO) << "Loading mean file from" << mean_file;
+      BlobProto blob_proto;
+      ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+      data_mean_.FromProto(blob_proto);
+    }
   }
   virtual ~DataTransformer() {}
 
@@ -30,13 +39,13 @@ class DataTransformer {
    *    writing position in the top blob's data
    * @param datum
    *    Datum containing the data to be transformed.
-   * @param mean
    * @param transformed_data
    *    This is meant to be the top blob's data. The transformed data will be
    *    written at the appropriate place within the blob's data.
    */
+
   void Transform(const int batch_item_id, const Datum& datum,
-                 const Dtype* mean, Dtype* transformed_data);
+                 Dtype* transformed_data);
 
  protected:
    /**
@@ -49,12 +58,15 @@ class DataTransformer {
    */
   virtual int Rand(int n);
 
+  void Transform(const int batch_item_id, const Datum& datum,
+                 const Dtype* mean, Dtype* transformed_data);
   // Tranformation parameters
   TransformationParameter param_;
 
 
   shared_ptr<Caffe::RNG> rng_;
   Caffe::Phase phase_;
+  Blob<Dtype> data_mean_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index e52c3b6a55d..81cb17b5cd0 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -6,6 +6,24 @@
 
 namespace caffe {
 
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const int batch_item_id,
+                                       const Datum& datum,
+                                       Dtype* transformed_data) {
+
+  CHECK_GT(datum.channels(), 0);
+  CHECK_GE(datum.height(), param_.crop_size());
+  CHECK_GE(datum.height(), param_.crop_size());
+  const int size = datum.channels() * datum.height() * datum.width();
+  if (data_mean_.count() < size) {
+    data_mean_.Reshape(1, datum.channels(), datum.height(), datum.width());
+    LOG(INFO) << "Transform without mean";
+  }
+  const Dtype* mean = data_mean_.cpu_data();
+  Transform(batch_item_id, datum, mean, transformed_data);
+}
+
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const int batch_item_id,
                                        const Datum& datum,
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 86abbae8f4c..5038d532ee1 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -21,31 +21,8 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   } else {
     output_labels_ = true;
   }
+  // The subclasses should setup the size of bottom and top
   DataLayerSetUp(bottom, top);
-  // The subclasses should setup the datum channels, height and width
-  CHECK_GT(datum_channels_, 0);
-  CHECK_GT(datum_height_, 0);
-  CHECK_GT(datum_width_, 0);
-  if (transform_param_.crop_size() > 0) {
-    CHECK_GE(datum_height_, transform_param_.crop_size());
-    CHECK_GE(datum_width_, transform_param_.crop_size());
-  }
-  // check if we want to have mean
-  if (transform_param_.has_mean_file()) {
-    const string& mean_file = transform_param_.mean_file();
-    LOG(INFO) << "Loading mean file from" << mean_file;
-    BlobProto blob_proto;
-    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
-    data_mean_.FromProto(blob_proto);
-    CHECK_GE(data_mean_.num(), 1);
-    CHECK_GE(data_mean_.channels(), datum_channels_);
-    CHECK_GE(data_mean_.height(), datum_height_);
-    CHECK_GE(data_mean_.width(), datum_width_);
-  } else {
-    // Simply initialize an all-empty mean.
-    data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
-  }
-  mean_ = data_mean_.cpu_data();
   data_transformer_.InitRand();
 }
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index b3b6bf3d64f..3867a87f8b0 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -134,11 +134,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     this->prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
         1, 1, 1);
   }
-  // datum size
-  this->datum_channels_ = datum.channels();
-  this->datum_height_ = datum.height();
-  this->datum_width_ = datum.width();
-  this->datum_size_ = datum.channels() * datum.height() * datum.width();
+
 }
 
 // This function is used to create a thread that prefetches the data.
@@ -172,7 +168,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
+    this->data_transformer_.Transform(item_id, datum, top_data);
 
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index dc972f13799..a5d46fd0c63 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -76,11 +76,6 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   top[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
-  // datum size
-  this->datum_channels_ = datum.channels();
-  this->datum_height_ = datum.height();
-  this->datum_width_ = datum.width();
-  this->datum_size_ = datum.channels() * datum.height() * datum.width();
 }
 
 template <typename Dtype>
@@ -114,7 +109,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // Apply transformations (mirror, crop...) to the data
-    this->data_transformer_.Transform(item_id, datum, this->mean_, top_data);
+    this->data_transformer_.Transform(item_id, datum, top_data);
 
     top_label[item_id] = datum.label();
     // go to the next iter
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index d254da34322..269a2679777 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -10,19 +10,16 @@ template <typename Dtype>
 void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
-  this->datum_channels_ = this->layer_param_.memory_data_param().channels();
-  this->datum_height_ = this->layer_param_.memory_data_param().height();
-  this->datum_width_ = this->layer_param_.memory_data_param().width();
-  this->datum_size_ = this->datum_channels_ * this->datum_height_ *
-      this->datum_width_;
-  CHECK_GT(batch_size_ * this->datum_size_, 0) <<
+  channels_ = this->layer_param_.memory_data_param().channels();
+  height_ = this->layer_param_.memory_data_param().height();
+  width_ = this->layer_param_.memory_data_param().width();
+  size_ = channels_ * height_ * width_;
+  CHECK_GT(batch_size_ * size_, 0) <<
       "batch_size, channels, height, and width must be specified and"
       " positive in memory_data_param";
-  top[0]->Reshape(batch_size_, this->datum_channels_, this->datum_height_,
-                     this->datum_width_);
+  top[0]->Reshape(batch_size_, channels_, height_, width_);
   top[1]->Reshape(batch_size_, 1, 1, 1);
-  added_data_.Reshape(batch_size_, this->datum_channels_, this->datum_height_,
-                      this->datum_width_);
+  added_data_.Reshape(batch_size_, channels_, height_, width_);
   added_label_.Reshape(batch_size_, 1, 1, 1);
   data_ = NULL;
   labels_ = NULL;
@@ -45,7 +42,7 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   for (int batch_item_id = 0; batch_item_id < num; ++batch_item_id) {
     // Apply data transformations (mirror, scale, crop...)
     this->data_transformer_.Transform(
-        batch_item_id, datum_vector[batch_item_id], this->mean_, top_data);
+        batch_item_id, datum_vector[batch_item_id], top_data);
     top_label[batch_item_id] = datum_vector[batch_item_id].label();
   }
   // num_images == batch_size_
@@ -68,7 +65,7 @@ template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
-  top[0]->set_cpu_data(data_ + pos_ * this->datum_size_);
+  top[0]->set_cpu_data(data_ + pos_ * size_);
   top[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
   has_new_data_ = false;
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 33a473d64bf..88afe829537 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -167,6 +167,18 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   top[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+
+  // data mean
+  if (this->layer_param_.window_data_param().has_mean_file()) {
+    const string& mean_file = 
+          this->layer_param_.window_data_param().mean_file();
+    LOG(INFO) << "Loading mean file from" << mean_file;
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+    data_mean_.FromProto(blob_proto);
+  } else {
+    data_mean_.Reshape(1, channels, crop_size, crop_size);
+  }
 }
 
 template <typename Dtype>

From e9c08e5a8c39508ecff052ef36df63bf883d2dad Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Thu, 11 Sep 2014 15:19:57 -0700
Subject: [PATCH 0866/2053] Fixed MemoryDataLayer to make it work with pycaffe

---
 include/caffe/data_layers.hpp          |  3 +++
 include/caffe/data_transformer.hpp     | 13 +------------
 python/caffe/_caffe.cpp                |  4 ++--
 src/caffe/data_transformer.cpp         | 14 +++++++++++++-
 src/caffe/layers/data_layer.cpp        |  1 -
 src/caffe/layers/window_data_layer.cpp |  2 +-
 6 files changed, 20 insertions(+), 17 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index bf06865c7d7..e598a71db0a 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -283,6 +283,9 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   void Reset(Dtype* data, Dtype* label, int n);
 
   int batch_size() { return batch_size_; }
+  int channels() { return channels_; }
+  int height() { return height_; }
+  int width() { return width_; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 33afa72359b..fcd101165cf 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -14,18 +14,7 @@ namespace caffe {
 template <typename Dtype>
 class DataTransformer {
  public:
-  explicit DataTransformer(const TransformationParameter& param)
-    : param_(param) {
-    phase_ = Caffe::phase();
-      // check if we want to have mean
-    if (param_.has_mean_file()) {
-      const string& mean_file = param.mean_file();
-      LOG(INFO) << "Loading mean file from" << mean_file;
-      BlobProto blob_proto;
-      ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
-      data_mean_.FromProto(blob_proto);
-    }
-  }
+  explicit DataTransformer(const TransformationParameter& param);
   virtual ~DataTransformer() {}
 
   void InitRand();
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5a8d99d3f22..33e68fe40fa 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -106,8 +106,8 @@ void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
       reinterpret_cast<PyArrayObject*>(data_obj.ptr());
   PyArrayObject* labels_arr =
       reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
-  check_contiguous_array(data_arr, "data array", md_layer->datum_channels(),
-      md_layer->datum_height(), md_layer->datum_width());
+  check_contiguous_array(data_arr, "data array", md_layer->channels(),
+      md_layer->height(), md_layer->width());
   check_contiguous_array(labels_arr, "labels array", 1, 1, 1);
   if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
     throw std::runtime_error("data and labels must have the same first"
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 81cb17b5cd0..553717a50da 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -6,12 +6,24 @@
 
 namespace caffe {
 
+template<typename Dtype>
+DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
+    : param_(param) {
+  phase_ = Caffe::phase();
+    // check if we want to have mean
+  if (param_.has_mean_file()) {
+    const string& mean_file = param.mean_file();
+    LOG(INFO) << "Loading mean file from" << mean_file;
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+    data_mean_.FromProto(blob_proto);
+  }
+}
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const int batch_item_id,
                                        const Datum& datum,
                                        Dtype* transformed_data) {
-
   CHECK_GT(datum.channels(), 0);
   CHECK_GE(datum.height(), param_.crop_size());
   CHECK_GE(datum.height(), param_.crop_size());
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 3867a87f8b0..40c48735b41 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -134,7 +134,6 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     this->prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
         1, 1, 1);
   }
-
 }
 
 // This function is used to create a thread that prefetches the data.
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 88afe829537..6b70aa3cf22 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -170,7 +170,7 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
   // data mean
   if (this->layer_param_.window_data_param().has_mean_file()) {
-    const string& mean_file = 
+    const string& mean_file =
           this->layer_param_.window_data_param().mean_file();
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;

From 4602439900b616c590b9873f7bebdf3f9ecf716c Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 25 Sep 2014 19:58:06 -0700
Subject: [PATCH 0867/2053] Initial cv::Mat transformation

Added cv::Mat transformation to ImageDataLayer

Conflicts:

	src/caffe/layers/image_data_layer.cpp

Added transform Datum to Blob

Conflicts:

	src/caffe/layers/base_data_layer.cpp
	src/caffe/layers/base_data_layer.cu

Added transform cv::Mat to Blob

Added transform Vector<Datum> to Blob

Conflicts:
	src/caffe/data_transformer.cpp
---
 include/caffe/data_layers.hpp          |   3 +
 include/caffe/data_transformer.hpp     |  17 +-
 include/caffe/util/benchmark.hpp       |   2 +
 include/caffe/util/io.hpp              |  18 ++
 src/caffe/data_transformer.cpp         | 393 +++++++++++++++++++++----
 src/caffe/layers/base_data_layer.cpp   |   7 +
 src/caffe/layers/base_data_layer.cu    |   2 +
 src/caffe/layers/data_layer.cpp        |  17 +-
 src/caffe/layers/image_data_layer.cpp  |  49 +--
 src/caffe/layers/memory_data_layer.cpp |  12 +-
 src/caffe/proto/caffe.proto            |   2 +
 src/caffe/util/benchmark.cpp           |  22 ++
 src/caffe/util/io.cpp                  |  17 ++
 13 files changed, 461 insertions(+), 100 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e598a71db0a..e3ba2d13ccf 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -17,6 +17,7 @@
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/benchmark.hpp"
 
 namespace caffe {
 
@@ -51,6 +52,7 @@ class BaseDataLayer : public Layer<Dtype> {
   DataTransformer<Dtype> data_transformer_;
   Caffe::Phase phase_;
   bool output_labels_;
+  Timer timer_forward_;
 };
 
 template <typename Dtype>
@@ -79,6 +81,7 @@ class BasePrefetchingDataLayer :
  protected:
   Blob<Dtype> prefetch_data_;
   Blob<Dtype> prefetch_label_;
+  Blob<Dtype> transformed_data_;
 };
 
 template <typename Dtype>
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index fcd101165cf..23594217d91 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -1,9 +1,11 @@
 #ifndef CAFFE_DATA_TRANSFORMER_HPP
 #define CAFFE_DATA_TRANSFORMER_HPP
 
+#include <opencv2/core/core.hpp>
+
+#include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/io.hpp"
 
 namespace caffe {
 
@@ -33,8 +35,15 @@ class DataTransformer {
    *    written at the appropriate place within the blob's data.
    */
 
-  void Transform(const int batch_item_id, const Datum& datum,
-                 Dtype* transformed_data);
+  void Transform(const Datum& datum, Dtype* transformed_data);
+
+  void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
+
+  void Transform(const vector<Datum> & datum_vector, Blob<Dtype>* transformed_blob);
+
+  void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
+
+  void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
 
  protected:
    /**
@@ -47,8 +56,6 @@ class DataTransformer {
    */
   virtual int Rand(int n);
 
-  void Transform(const int batch_item_id, const Datum& datum,
-                 const Dtype* mean, Dtype* transformed_data);
   // Tranformation parameters
   TransformationParameter param_;
 
diff --git a/include/caffe/util/benchmark.hpp b/include/caffe/util/benchmark.hpp
index f7ef8eaf3ee..6c01ac4ee18 100644
--- a/include/caffe/util/benchmark.hpp
+++ b/include/caffe/util/benchmark.hpp
@@ -14,6 +14,7 @@ class Timer {
   void Start();
   void Stop();
   float MilliSeconds();
+  float MicroSeconds();
   float Seconds();
 
   inline bool initted() { return initted_; }
@@ -33,6 +34,7 @@ class Timer {
   boost::posix_time::ptime start_cpu_;
   boost::posix_time::ptime stop_cpu_;
   float elapsed_milliseconds_;
+  float elapsed_microseconds_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 8dd338d2603..88037c86035 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -7,6 +7,7 @@
 #include "google/protobuf/message.h"
 #include "hdf5.h"
 #include "hdf5_hl.h"
+#include <opencv2/core/core.hpp>
 
 #include "caffe/blob.hpp"
 #include "caffe/proto/caffe.pb.h"
@@ -102,6 +103,23 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, datum);
 }
 
+cv::Mat ReadImageToCVMat(const string& filename, 
+    const int height, const int width, const bool is_color);
+
+inline cv::Mat ReadImageToCVMat(const string& filename, 
+    const int height, const int width) {
+  return ReadImageToCVMat(filename, height, width, true);
+}
+
+inline cv::Mat ReadImageToCVMat(const string& filename,
+    const bool is_color) {
+  return ReadImageToCVMat(filename, 0, 0, is_color);
+}
+
+inline cv::Mat ReadImageToCVMat(const string& filename) {
+  return ReadImageToCVMat(filename, 0, 0, true);
+}
+
 leveldb::Options GetLevelDBOptions();
 
 template <typename Dtype>
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 553717a50da..836bf3b05c4 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,9 +1,12 @@
 #include <string>
 
 #include "caffe/data_transformer.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
 
+#include <opencv2/core/core.hpp>
+
 namespace caffe {
 
 template<typename Dtype>
@@ -21,105 +24,365 @@ DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
 }
 
 template<typename Dtype>
-void DataTransformer<Dtype>::Transform(const int batch_item_id,
-                                       const Datum& datum,
-                                       Dtype* transformed_data) {
-  CHECK_GT(datum.channels(), 0);
-  CHECK_GE(datum.height(), param_.crop_size());
-  CHECK_GE(datum.height(), param_.crop_size());
-  const int size = datum.channels() * datum.height() * datum.width();
-  if (data_mean_.count() < size) {
-    data_mean_.Reshape(1, datum.channels(), datum.height(), datum.width());
-    LOG(INFO) << "Transform without mean";
-  }
-  const Dtype* mean = data_mean_.cpu_data();
-  Transform(batch_item_id, datum, mean, transformed_data);
-}
-
-template<typename Dtype>
-void DataTransformer<Dtype>::Transform(const int batch_item_id,
-                                       const Datum& datum,
-                                       const Dtype* mean,
+void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Dtype* transformed_data) {
   const string& data = datum.data();
-  const int channels = datum.channels();
-  const int height = datum.height();
-  const int width = datum.width();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
   const int size = datum.channels() * datum.height() * datum.width();
 
   const int crop_size = param_.crop_size();
-  const bool mirror = param_.mirror();
   const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool has_mean_file = param_.has_mean_file();
+  const bool has_unit8 = data.size() > 0;
+
+  CHECK_GT(datum_channels, 0);
+  CHECK_GE(datum_height, crop_size);
+  CHECK_GE(datum_width, crop_size);
 
-  if (mirror && crop_size == 0) {
-    LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
-               << "set at the same time.";
+  Dtype* mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(datum_channels, data_mean_.channels());
+    CHECK_EQ(datum_height, data_mean_.height());
+    CHECK_EQ(datum_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
   }
 
+  int h_off = 0;
+  int w_off = 0;
+  Dtype datum_element;
   if (crop_size) {
-    CHECK(data.size()) << "Image cropping only support uint8 data";
-    int h_off, w_off;
     // We only do random crop when we do training.
     if (phase_ == Caffe::TRAIN) {
       h_off = Rand(height - crop_size + 1);
       w_off = Rand(width - crop_size + 1);
     } else {
-      h_off = (height - crop_size) / 2;
-      w_off = (width - crop_size) / 2;
+      h_off = (datum_height - crop_size) / 2;
+      w_off = (datum_width - crop_size) / 2;
     }
-    if (mirror && (Rand(2) == 1)) {
-      // Copy mirrored version
-      for (int c = 0; c < channels; ++c) {
-        for (int h = 0; h < crop_size; ++h) {
-          for (int w = 0; w < crop_size; ++w) {
-            int data_index = (c * height + h + h_off) * width + w + w_off;
-            int top_index = ((batch_item_id * channels + c) * crop_size + h)
-                * crop_size + (crop_size - 1 - w);
-            Dtype datum_element =
-                static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+
+    int top_index, data_index;
+    for (int c = 0; c < datum_channels; ++c) {
+      int top_index_c = c * crop_size;
+      int data_index_c = c * datum_height + h_off;
+      for (int h = 0; h < crop_size; ++h) {
+        int top_index_h = (top_index_c + h) * crop_size;
+        int data_index_h = (data_index_c + h) * datum_width + w_off;
+        for (int w = 0; w < crop_size; ++w) {
+          data_index = data_index_h + w;
+          if (do_mirror) {
+            top_index = top_index_h + (crop_size - 1 - w);
+          } else {
+            top_index = top_index_h + w;
+          }
+          if (has_unit8) {
+            datum_element =
+              static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+          } else {
+            datum_element = datum.float_data(data_index);
+          }
+          if (has_mean_file) {
             transformed_data[top_index] =
-                (datum_element - mean[data_index]) * scale;
+              (datum_element - mean[data_index]) * scale;
+          } else {
+            transformed_data[top_index] = datum_element * scale;
           }
         }
       }
+    }
+  } else {
+    for (int j = 0; j < size; ++j) {
+      if (has_unit8) {
+          datum_element =
+            static_cast<Dtype>(static_cast<uint8_t>(data[j]));
+      } else {
+        datum_element = datum.float_data(j);
+      }
+      if (has_mean_file) {
+        transformed_data[j] =
+          (datum_element - mean[j]) * scale;
+      } else {
+        transformed_data[j] = datum_element * scale;
+      }
+    }
+  }
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const Datum& datum,
+                                       Blob<Dtype>* transformed_blob) {
+  const string& data = datum.data();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
+   
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_EQ(datum_channels, channels);
+  CHECK_GE(datum_height, height);
+  CHECK_GE(datum_width, width);
+
+  CHECK_EQ(transformed_blob->num(), 1) <<
+    "transformed_blob should have num() = 1";
+
+  const int crop_size = param_.crop_size();
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool has_mean_file = param_.has_mean_file();
+  const bool has_unit8 = data.size()>0;
+
+  int h_off = 0;
+  int w_off = 0;
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    // We only do random crop when we do training.
+    if (phase_ == Caffe::TRAIN) {
+      h_off = Rand() % (datum_height - crop_size);
+      w_off = Rand() % (datum_width - crop_size);
     } else {
-      // Normal copy
-      for (int c = 0; c < channels; ++c) {
-        for (int h = 0; h < crop_size; ++h) {
-          for (int w = 0; w < crop_size; ++w) {
-            int top_index = ((batch_item_id * channels + c) * crop_size + h)
-                * crop_size + w;
-            int data_index = (c * height + h + h_off) * width + w + w_off;
-            Dtype datum_element =
-                static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-            transformed_data[top_index] =
-                (datum_element - mean[data_index]) * scale;
-          }
+      h_off = (datum_height - crop_size) / 2;
+      w_off = (datum_width - crop_size) / 2;
+    }
+  } else {
+    CHECK_EQ(datum_height, height);
+    CHECK_EQ(datum_width, width);
+  }
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+  
+  Dtype* mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(datum_channels, data_mean_.channels());
+    CHECK_EQ(datum_height, data_mean_.height());
+    CHECK_EQ(datum_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
+  }
+
+  Dtype datum_element;
+  int top_index, data_index;
+  for (int c = 0; c < channels; ++c) {
+    int top_index_c = c * height;
+    int data_index_c = c * datum_height + h_off;
+    for (int h = 0; h < height; ++h) {
+      int top_index_h = (top_index_c + h) * width;
+      int data_index_h = (data_index_c + h) * datum_width + w_off;
+      for (int w = 0; w < width; ++w) {
+        data_index = data_index_h + w;
+        if (do_mirror) {
+          top_index = top_index_h + (width - 1 - w);
+        } else {
+          top_index = top_index_h + w;
+        }
+        if (has_unit8) {
+          datum_element =
+            static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+        } else {
+          datum_element = datum.float_data(data_index);
+        }
+        if (has_mean_file) {
+          transformed_data[top_index] =
+            (datum_element - mean[data_index]) * scale;
+        } else {
+          transformed_data[top_index] = datum_element * scale;
         }
       }
     }
+  }
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
+                                       Blob<Dtype>* transformed_blob) {
+  const int datum_num = datum_vector.size();
+  const int num = transformed_blob->num();
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_GT(datum_num, 0) << "There is no datum to add";
+  CHECK_LE(datum_num, num) <<
+    "The size of datum_vector must be smaller than transformed_blob->num()";
+  Blob<Dtype> uni_blob(1, channels, height, width);
+  for (int item_id = 0; item_id < datum_num; ++item_id) {
+    int offset = transformed_blob->offset(item_id);
+    uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);
+    Transform(datum_vector[item_id], &uni_blob);
+  }
+}
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
+                                       Blob<Dtype>* transformed_blob) {
+
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_EQ(img_channels, channels);
+  CHECK_GE(img_height, height);
+  CHECK_GE(img_width, width);
+
+  CHECK_EQ(transformed_blob->num(), 1) <<
+    "transformed_blob should have num() = 1";
+
+  const int crop_size = param_.crop_size();
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool has_mean_file = param_.has_mean_file();
+  
+  int h_off = 0;
+  int w_off = 0;
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    // We only do random crop when we do training.
+    if (phase_ == Caffe::TRAIN) {
+      h_off = Rand() % (img_height - crop_size);
+      w_off = Rand() % (img_width - crop_size);
+    } else {
+      h_off = (img_height - crop_size) / 2;
+      w_off = (img_width - crop_size) / 2;
+    }
   } else {
-    // we will prefer to use data() first, and then try float_data()
-    if (data.size()) {
-      for (int j = 0; j < size; ++j) {
-        Dtype datum_element =
-            static_cast<Dtype>(static_cast<uint8_t>(data[j]));
-        transformed_data[j + batch_item_id * size] =
-            (datum_element - mean[j]) * scale;
+    CHECK_EQ(img_height, height);
+    CHECK_EQ(img_width, width);
+  }
+
+  Dtype* mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(img_channels, data_mean_.channels());
+    CHECK_EQ(img_height, data_mean_.height());
+    CHECK_EQ(img_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
+  }
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+  Dtype pixel;
+  int top_index;
+  for (int c = 0; c < channels; ++c) {
+    int top_index_c = c * height;
+    int mean_index_c = c * img_height + h_off;
+    for (int h = 0; h < height; ++h) {
+      int top_index_h = (top_index_c + h) * width;
+      int mean_index_h = (mean_index_c + h) * img_width + w_off;
+      for (int w = 0; w < width; ++w) {
+        if (do_mirror) {
+          top_index = top_index_h + (width - 1 - w);
+        } else {
+          top_index = top_index_h + w;
+        }
+        pixel = static_cast<Dtype>(
+              cv_img.at<cv::Vec3b>(h + h_off, w + w_off)[c]);
+        if (has_mean_file) {
+          int mean_index = mean_index_h + w;
+          transformed_data[top_index] = (pixel - mean[mean_index]) * scale;
+        } else {
+          transformed_data[top_index] = pixel * scale;
+        }
       }
+    }
+  }
+}
+
+
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
+                                       Blob<Dtype>* transformed_blob) {
+
+  const int input_num = input_blob->num();
+  const int input_channels = input_blob->channels();
+  const int input_height = input_blob->height();
+  const int input_width = input_blob->width();
+ 
+  const int num = transformed_blob->num();
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+  const int size = transformed_blob->count();
+
+  CHECK_LE(input_num, num);
+  CHECK_EQ(input_channels, channels);
+  CHECK_GE(input_height, height);
+  CHECK_GE(input_width, width);
+
+  const int crop_size = param_.crop_size();
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool has_mean_file = param_.has_mean_file();
+
+  int h_off = 0;
+  int w_off = 0;
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    // We only do random crop when we do training.
+    if (phase_ == Caffe::TRAIN) {
+      h_off = Rand() % (input_height - crop_size);
+      w_off = Rand() % (input_width - crop_size);
     } else {
-      for (int j = 0; j < size; ++j) {
-        transformed_data[j + batch_item_id * size] =
-            (datum.float_data(j) - mean[j]) * scale;
+      h_off = (input_height - crop_size) / 2;
+      w_off = (input_width - crop_size) / 2;
+    }
+  } else {
+    CHECK_EQ(input_height, height);
+    CHECK_EQ(input_width, width);
+  }
+
+  Dtype* input_data = input_blob->mutable_cpu_data();
+  if (has_mean_file) {
+    CHECK_EQ(input_channels, data_mean_.channels());
+    CHECK_EQ(input_height, data_mean_.height());
+    CHECK_EQ(input_width, data_mean_.width());
+    for (int n = 0; n < input_num; ++n) {
+      int offset = input_blob->offset(n);
+      caffe_sub(data_mean_.count(), input_data + offset,
+            data_mean_.cpu_data(), input_data + offset);
+    } 
+  }
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+
+  for (int n = 0; n < input_num; ++n) {
+    int top_index_n = n * channels;
+    int data_index_n = n * channels;
+    for (int c = 0; c < channels; ++c) {
+      int top_index_c = (top_index_n + c) * height;
+      int data_index_c = (data_index_n + c) * input_height + h_off;
+      for (int h = 0; h < height; ++h) {
+        int top_index_h = (top_index_c + h) * width;
+        int data_index_h = (data_index_c + h) * input_width + w_off;
+        if (do_mirror) {
+          int top_index_w = top_index_h + width - 1;
+          for (int w = 0; w < width; ++w) {
+            transformed_data[top_index_w-w] = input_data[data_index_h + w];
+          }
+        } else {
+          for (int w = 0; w < width; ++w) {
+            transformed_data[top_index_h + w] = input_data[data_index_h + w];
+          }
+        }
       }
     }
   }
+  if (scale!=Dtype(1)) {
+    DLOG(INFO) << "Scale: " << scale;
+    caffe_scal(size, scale, transformed_data);
+  }
 }
 
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
-  const bool needs_rand = (phase_ == Caffe::TRAIN) &&
-      (param_.mirror() || param_.crop_size());
+  const bool needs_rand = param_.mirror() ||
+      (phase_ == Caffe::TRAIN && param_.crop_size());
   if (needs_rand) {
     const unsigned int rng_seed = caffe_rng_rand();
     rng_.reset(new Caffe::RNG(rng_seed));
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 5038d532ee1..d7d475229a5 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -59,16 +59,23 @@ template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
+  if (this->timer_forward_.has_run_at_least_once()) {
+    DLOG(INFO) << "Proccessing: " << this->timer_forward_.MilliSeconds() << "ms.";
+  }
   JoinPrefetchThread();
+  DLOG(INFO) << "Thread joined";
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              top[0]->mutable_cpu_data());
+  DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
                top[1]->mutable_cpu_data());
   }
   // Start a new prefetch thread
+  DLOG(INFO) << "CreatePrefetchThread";
   CreatePrefetchThread();
+  this->timer_forward_.Start();
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index ff1510361b6..690858f09d3 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -7,6 +7,7 @@ namespace caffe {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  DLOG(INFO) << "Processing: " << this->timer_forward_.MilliSeconds() << "ms.";
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
@@ -18,6 +19,7 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
+  this->timer_forward_.Start();
 }
 
 INSTANTIATE_CLASS(BasePrefetchingDataLayer);
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 40c48735b41..b1d7ef93806 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -8,6 +8,7 @@
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -118,12 +119,15 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
                        datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), crop_size, crop_size);
+    this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
   } else {
     top[0]->Reshape(
         this->layer_param_.data_param().batch_size(), datum.channels(),
         datum.height(), datum.width());
     this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), datum.height(), datum.width());
+    this->transformed_data_.Reshape(1, datum.channels(),
+      datum.height(), datum.width());
   }
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
@@ -139,10 +143,13 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
+  Timer batch_timer;
+  batch_timer.Start();
   CHECK(this->prefetch_data_.count());
+  CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
+
   if (this->output_labels_) {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
@@ -150,6 +157,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
+    Datum datum;
     switch (this->layer_param_.data_param().backend()) {
     case DataParameter_DB_LEVELDB:
       CHECK(iter_);
@@ -165,10 +173,10 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     default:
       LOG(FATAL) << "Unknown database backend";
     }
-
     // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(item_id, datum, top_data);
-
+    int offset = this->prefetch_data_.offset(item_id);
+    this->transformed_data_.set_cpu_data(top_data + offset);
+    this->data_transformer_.Transform(datum, &(this->transformed_data_));    
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
@@ -196,6 +204,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
       LOG(FATAL) << "Unknown database backend";
     }
   }
+  DLOG(INFO) << "Prefetch: " << batch_timer.MilliSeconds() << " ms.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index a5d46fd0c63..e167f134f97 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -22,6 +22,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int new_height = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_width();
+  const bool is_color  = this->layer_param_.image_data_param().is_color();
+
   CHECK((new_height == 0 && new_width == 0) ||
       (new_height > 0 && new_width > 0)) << "Current implementation requires "
       "new_height and new_width to be set at the same time.";
@@ -53,22 +55,23 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_GT(lines_.size(), skip) << "Not enough points to skip";
     lines_id_ = skip;
   }
-  // Read a data point, and use it to initialize the top blob.
-  Datum datum;
-  CHECK(ReadImageToDatum(lines_[lines_id_].first, lines_[lines_id_].second,
-                         new_height, new_width, &datum));
+  // Read an image, and use it to initialize the top blob.
+  cv::Mat cv_img = ReadImageToCVMat(lines_[lines_id_].first,
+                                    new_height, new_width, is_color);
+  const int channels = cv_img.channels();
+  const int height = cv_img.rows;
+  const int width = cv_img.cols;
   // image
   const int crop_size = this->layer_param_.transform_param().crop_size();
   const int batch_size = this->layer_param_.image_data_param().batch_size();
   if (crop_size > 0) {
-    top[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), crop_size,
-                                 crop_size);
+    top[0]->Reshape(batch_size, channels, crop_size, crop_size);
+    this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
+    this->transformed_data_.Reshape(1, channels, crop_size, crop_size);
   } else {
-    top[0]->Reshape(batch_size, datum.channels(), datum.height(),
-                       datum.width());
-    this->prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
-        datum.width());
+    top[0]->Reshape(batch_size, channels, height, width);
+    this->prefetch_data_.Reshape(batch_size, channels, height, width);
+    this->transformed_data_.Reshape(1, channels, height, width);
   }
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
@@ -88,30 +91,35 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
-  Datum datum;
+  Timer batch_timer;
+  batch_timer.Start();
   CHECK(this->prefetch_data_.count());
+  CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
+  const bool is_color = image_data_param.is_color();
 
   // datum scales
   const int lines_size = lines_.size();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
     CHECK_GT(lines_size, lines_id_);
-    if (!ReadImageToDatum(lines_[lines_id_].first,
-          lines_[lines_id_].second,
-          new_height, new_width, &datum)) {
+    cv::Mat cv_img = ReadImageToCVMat(lines_[lines_id_].first,
+                                    new_height, new_width, is_color);
+    if (!cv_img.data) {
       continue;
     }
-
-    // Apply transformations (mirror, crop...) to the data
-    this->data_transformer_.Transform(item_id, datum, top_data);
-
-    top_label[item_id] = datum.label();
+    // Apply transformations (mirror, crop...) to the image
+    // this->data_transformer_.Transform(item_id, cv_img, top_data);
+    int offset = this->prefetch_data_.offset(item_id);
+    this->transformed_data_.set_cpu_data(top_data + offset);
+    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));    
+    
+    top_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
     lines_id_++;
     if (lines_id_ >= lines_size) {
@@ -123,6 +131,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       }
     }
   }
+  DLOG(INFO) << "Prefetch: " << batch_timer.MilliSeconds() << " ms.";
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 269a2679777..8f1c21f5424 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -37,15 +37,15 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   CHECK_LE(num, batch_size_) <<
       "The number of added datum must be no greater than the batch size";
 
-  Dtype* top_data = added_data_.mutable_cpu_data();
+  // Apply data transformations (mirror, scale, crop...)
+  this->data_transformer_.Transform(datum_vector, &added_data_);
+  // Copy Labels
   Dtype* top_label = added_label_.mutable_cpu_data();
-  for (int batch_item_id = 0; batch_item_id < num; ++batch_item_id) {
-    // Apply data transformations (mirror, scale, crop...)
-    this->data_transformer_.Transform(
-        batch_item_id, datum_vector[batch_item_id], top_data);
-    top_label[batch_item_id] = datum_vector[batch_item_id].label();
+  for (int item_id = 0; item_id < num; ++item_id) {
+    top_label[item_id] = datum_vector[item_id].label();
   }
   // num_images == batch_size_
+  Dtype* top_data = added_data_.mutable_cpu_data();
   Reset(top_data, top_label, batch_size_);
   has_new_data_ = true;
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6944ae847ff..8f88177944c 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -520,6 +520,8 @@ message ImageDataParameter {
   // It will also resize images if new_height or new_width are not zero.
   optional uint32 new_height = 9 [default = 0];
   optional uint32 new_width = 10 [default = 0];
+  // Specify if the images are color or gray
+  optional bool is_color = 11 [default = true];
   // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
   // simple scaling and subtracting the data mean, if provided. Note that the
   // mean subtraction is always carried out before scaling.
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 566d06a8f5f..76829f5b36c 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -55,6 +55,28 @@ void Timer::Stop() {
   }
 }
 
+
+float Timer::MicroSeconds() {
+  if (!has_run_at_least_once()) {
+    LOG(WARNING) << "Timer has never been run before reading time.";
+    return 0;
+  }
+  if (running()) {
+    Stop();
+  }
+  if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
+    CUDA_CHECK(cudaEventElapsedTime(&elapsed_microseconds_, start_gpu_,
+                                    stop_gpu_));
+#else
+      NO_GPU;
+#endif
+  } else {
+    elapsed_microseconds_ = (stop_cpu_ - start_cpu_).total_microseconds();
+  }
+  return elapsed_microseconds_;
+}
+
 float Timer::MilliSeconds() {
   if (!has_run_at_least_once()) {
     LOG(WARNING) << "Timer has never been run before reading time.";
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 43e5c01e36a..4c32979d82f 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -66,6 +66,23 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
   CHECK(proto.SerializeToOstream(&output));
 }
 
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width, const bool is_color) {
+  cv::Mat cv_img;
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
+  if (height > 0 && width > 0) {
+    cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
+  } else {
+    cv_img = cv::imread(filename, cv_read_flag);
+  }
+  if (!cv_img.data) {
+    LOG(ERROR) << "Could not open or find file " << filename;
+  }
+  return cv_img;
+}
+
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color, Datum* datum) {
   cv::Mat cv_img;

From b26e0b336c01dbccd05c0805871724d40af7ae7a Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Fri, 19 Sep 2014 17:23:39 -0700
Subject: [PATCH 0868/2053] Remove Datum from WindowDataLayer

Conflicts:

	src/caffe/layers/window_data_layer.cpp
---
 include/caffe/data_transformer.hpp     |  5 ++++-
 include/caffe/util/io.hpp              |  7 ++++---
 src/caffe/data_transformer.cpp         | 21 ++++++++++-----------
 src/caffe/layers/data_layer.cpp        |  2 +-
 src/caffe/layers/image_data_layer.cpp  |  4 ++--
 src/caffe/layers/window_data_layer.cpp |  6 ------
 6 files changed, 21 insertions(+), 24 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 23594217d91..25dee187e31 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -3,6 +3,8 @@
 
 #include <opencv2/core/core.hpp>
 
+#include <vector>
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
@@ -39,7 +41,8 @@ class DataTransformer {
 
   void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
 
-  void Transform(const vector<Datum> & datum_vector, Blob<Dtype>* transformed_blob);
+  void Transform(const vector<Datum> & datum_vector,
+                Blob<Dtype>* transformed_blob);
 
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 88037c86035..988305e26eb 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,13 +1,14 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
+#include <opencv2/core/core.hpp>
+
 #include <unistd.h>
 #include <string>
 
 #include "google/protobuf/message.h"
 #include "hdf5.h"
 #include "hdf5_hl.h"
-#include <opencv2/core/core.hpp>
 
 #include "caffe/blob.hpp"
 #include "caffe/proto/caffe.pb.h"
@@ -103,10 +104,10 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, datum);
 }
 
-cv::Mat ReadImageToCVMat(const string& filename, 
+cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
 
-inline cv::Mat ReadImageToCVMat(const string& filename, 
+inline cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width) {
   return ReadImageToCVMat(filename, height, width, true);
 }
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 836bf3b05c4..af1db3413bf 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,12 +1,13 @@
+#include <opencv2/core/core.hpp>
+
 #include <string>
+#include <vector>
 
 #include "caffe/data_transformer.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
 
-#include <opencv2/core/core.hpp>
-
 namespace caffe {
 
 template<typename Dtype>
@@ -117,7 +118,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
-   
+
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -133,7 +134,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand() % 2;
   const bool has_mean_file = param_.has_mean_file();
-  const bool has_unit8 = data.size()>0;
+  const bool has_unit8 = data.size() > 0;
 
   int h_off = 0;
   int w_off = 0;
@@ -154,7 +155,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   }
 
   Dtype* transformed_data = transformed_blob->mutable_cpu_data();
-  
+
   Dtype* mean = NULL;
   if (has_mean_file) {
     CHECK_EQ(datum_channels, data_mean_.channels());
@@ -218,7 +219,6 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
-
   const int img_channels = cv_img.channels();
   const int img_height = cv_img.rows;
   const int img_width = cv_img.cols;
@@ -238,7 +238,7 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand() % 2;
   const bool has_mean_file = param_.has_mean_file();
-  
+
   int h_off = 0;
   int w_off = 0;
   if (crop_size) {
@@ -297,12 +297,11 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
                                        Blob<Dtype>* transformed_blob) {
-
   const int input_num = input_blob->num();
   const int input_channels = input_blob->channels();
   const int input_height = input_blob->height();
   const int input_width = input_blob->width();
- 
+
   const int num = transformed_blob->num();
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
@@ -346,7 +345,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
       int offset = input_blob->offset(n);
       caffe_sub(data_mean_.count(), input_data + offset,
             data_mean_.cpu_data(), input_data + offset);
-    } 
+    }
   }
 
   Dtype* transformed_data = transformed_blob->mutable_cpu_data();
@@ -373,7 +372,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
       }
     }
   }
-  if (scale!=Dtype(1)) {
+  if (scale != Dtype(1)) {
     DLOG(INFO) << "Scale: " << scale;
     caffe_scal(size, scale, transformed_data);
   }
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index b1d7ef93806..980e4146fc9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -176,7 +176,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
-    this->data_transformer_.Transform(datum, &(this->transformed_data_));    
+    this->data_transformer_.Transform(datum, &(this->transformed_data_));
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e167f134f97..d93d56dead0 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -117,8 +117,8 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     // this->data_transformer_.Transform(item_id, cv_img, top_data);
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
-    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));    
-    
+    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+
     top_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
     lines_id_++;
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 6b70aa3cf22..c01b61bec9b 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -158,12 +158,6 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
-  // datum size
-  this->datum_channels_ = top[0]->channels();
-  this->datum_height_ = top[0]->height();
-  this->datum_width_ = top[0]->width();
-  this->datum_size_ =
-      top[0]->channels() * top[0]->height() * top[0]->width();
   // label
   top[1]->Reshape(batch_size, 1, 1, 1);
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);

From 28ee58eb13d7dcb7d31fb6910c025d8132ea6716 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Fri, 19 Sep 2014 08:57:00 -0700
Subject: [PATCH 0869/2053] Update description data_transformer.hpp

---
 include/caffe/data_transformer.hpp | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 25dee187e31..f2cbbd08586 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -27,14 +27,21 @@ class DataTransformer {
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to the data.
    *
-   * @param batch_item_id
-   *    Datum position within the batch. This is used to compute the
-   *    writing position in the top blob's data
    * @param datum
    *    Datum containing the data to be transformed.
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   * @param input_blob
+   *    A Blob containing the data to be transformed.
    * @param transformed_data
-   *    This is meant to be the top blob's data. The transformed data will be
-   *    written at the appropriate place within the blob's data.
+   *    This should point to the right place in memory where the 
+   *    transformed data would be written. Assumes there is enough space
+   * @param transformed_blob
+   *    This is destionation blob. It can be part of top blob's data. 
+   *    The transformed data will be  written at the appropriate place
+   *    within the blob's data.
    */
 
   void Transform(const Datum& datum, Dtype* transformed_data);

From d1ccbe325715646e21631829975412fc9b32a344 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 25 Sep 2014 19:58:52 -0700
Subject: [PATCH 0870/2053] Refactor common code

Make lint happy

Conflicts:
	src/caffe/data_transformer.cpp
---
 include/caffe/data_transformer.hpp   |   3 +-
 src/caffe/data_transformer.cpp       | 147 +++++++--------------------
 src/caffe/layers/base_data_layer.cpp |   3 +-
 3 files changed, 41 insertions(+), 112 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index f2cbbd08586..4a2afdaccce 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -44,8 +44,6 @@ class DataTransformer {
    *    within the blob's data.
    */
 
-  void Transform(const Datum& datum, Dtype* transformed_data);
-
   void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
 
   void Transform(const vector<Datum> & datum_vector,
@@ -66,6 +64,7 @@ class DataTransformer {
    */
   virtual int Rand(int n);
 
+  void Transform(const Datum& datum, Dtype* transformed_data);
   // Tranformation parameters
   TransformationParameter param_;
 
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index af1db3413bf..cdb6e10c8ab 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -31,7 +31,6 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
-  const int size = datum.channels() * datum.height() * datum.width();
 
   const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
@@ -51,10 +50,14 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     mean = data_mean_.mutable_cpu_data();
   }
 
+  int height = datum_height;
+  int width = datum_width;
+
   int h_off = 0;
   int w_off = 0;
-  Dtype datum_element;
   if (crop_size) {
+    height = crop_size;
+    width = crop_size;
     // We only do random crop when we do training.
     if (phase_ == Caffe::TRAIN) {
       h_off = Rand(height - crop_size + 1);
@@ -63,49 +66,31 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
       h_off = (datum_height - crop_size) / 2;
       w_off = (datum_width - crop_size) / 2;
     }
+  }
 
-    int top_index, data_index;
-    for (int c = 0; c < datum_channels; ++c) {
-      int top_index_c = c * crop_size;
-      int data_index_c = c * datum_height + h_off;
-      for (int h = 0; h < crop_size; ++h) {
-        int top_index_h = (top_index_c + h) * crop_size;
-        int data_index_h = (data_index_c + h) * datum_width + w_off;
-        for (int w = 0; w < crop_size; ++w) {
-          data_index = data_index_h + w;
-          if (do_mirror) {
-            top_index = top_index_h + (crop_size - 1 - w);
-          } else {
-            top_index = top_index_h + w;
-          }
-          if (has_unit8) {
-            datum_element =
-              static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-          } else {
-            datum_element = datum.float_data(data_index);
-          }
-          if (has_mean_file) {
-            transformed_data[top_index] =
-              (datum_element - mean[data_index]) * scale;
-          } else {
-            transformed_data[top_index] = datum_element * scale;
-          }
+  Dtype datum_element;
+  int top_index, data_index;
+  for (int c = 0; c < datum_channels; ++c) {
+    for (int h = 0; h < height; ++h) {
+      for (int w = 0; w < width; ++w) {
+        data_index = (c * datum_height + h_off + h) * datum_width + w_off + w;
+        if (do_mirror) {
+          top_index = (c * height + h) * width + (width - 1 - w);
+        } else {
+          top_index = (c * height + h) * width + w;
         }
-      }
-    }
-  } else {
-    for (int j = 0; j < size; ++j) {
-      if (has_unit8) {
+        if (has_unit8) {
           datum_element =
-            static_cast<Dtype>(static_cast<uint8_t>(data[j]));
-      } else {
-        datum_element = datum.float_data(j);
-      }
-      if (has_mean_file) {
-        transformed_data[j] =
-          (datum_element - mean[j]) * scale;
-      } else {
-        transformed_data[j] = datum_element * scale;
+            static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
+        } else {
+          datum_element = datum.float_data(data_index);
+        }
+        if (has_mean_file) {
+          transformed_data[top_index] =
+            (datum_element - mean[data_index]) * scale;
+        } else {
+          transformed_data[top_index] = datum_element * scale;
+        }
       }
     }
   }
@@ -114,7 +99,6 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
-  const string& data = datum.data();
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
@@ -122,78 +106,25 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
+  const int num = transformed_blob->num();
 
-  CHECK_EQ(datum_channels, channels);
-  CHECK_GE(datum_height, height);
-  CHECK_GE(datum_width, width);
-
-  CHECK_EQ(transformed_blob->num(), 1) <<
-    "transformed_blob should have num() = 1";
+  CHECK_EQ(channels, datum_channels);
+  CHECK_LE(height, datum_height);
+  CHECK_LE(width, datum_width);
+  CHECK_GE(num, 1);
 
   const int crop_size = param_.crop_size();
-  const Dtype scale = param_.scale();
-  const bool do_mirror = param_.mirror() && Rand() % 2;
-  const bool has_mean_file = param_.has_mean_file();
-  const bool has_unit8 = data.size() > 0;
 
-  int h_off = 0;
-  int w_off = 0;
   if (crop_size) {
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
-    // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
-      h_off = Rand() % (datum_height - crop_size);
-      w_off = Rand() % (datum_width - crop_size);
-    } else {
-      h_off = (datum_height - crop_size) / 2;
-      w_off = (datum_width - crop_size) / 2;
-    }
   } else {
     CHECK_EQ(datum_height, height);
     CHECK_EQ(datum_width, width);
   }
 
   Dtype* transformed_data = transformed_blob->mutable_cpu_data();
-
-  Dtype* mean = NULL;
-  if (has_mean_file) {
-    CHECK_EQ(datum_channels, data_mean_.channels());
-    CHECK_EQ(datum_height, data_mean_.height());
-    CHECK_EQ(datum_width, data_mean_.width());
-    mean = data_mean_.mutable_cpu_data();
-  }
-
-  Dtype datum_element;
-  int top_index, data_index;
-  for (int c = 0; c < channels; ++c) {
-    int top_index_c = c * height;
-    int data_index_c = c * datum_height + h_off;
-    for (int h = 0; h < height; ++h) {
-      int top_index_h = (top_index_c + h) * width;
-      int data_index_h = (data_index_c + h) * datum_width + w_off;
-      for (int w = 0; w < width; ++w) {
-        data_index = data_index_h + w;
-        if (do_mirror) {
-          top_index = top_index_h + (width - 1 - w);
-        } else {
-          top_index = top_index_h + w;
-        }
-        if (has_unit8) {
-          datum_element =
-            static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
-        } else {
-          datum_element = datum.float_data(data_index);
-        }
-        if (has_mean_file) {
-          transformed_data[top_index] =
-            (datum_element - mean[data_index]) * scale;
-        } else {
-          transformed_data[top_index] = datum_element * scale;
-        }
-      }
-    }
-  }
+  Transform(datum, transformed_data);
 }
 
 template<typename Dtype>
@@ -226,13 +157,12 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
+  const int num = transformed_blob->num();
 
-  CHECK_EQ(img_channels, channels);
-  CHECK_GE(img_height, height);
-  CHECK_GE(img_width, width);
-
-  CHECK_EQ(transformed_blob->num(), 1) <<
-    "transformed_blob should have num() = 1";
+  CHECK_EQ(channels, img_channels);
+  CHECK_LE(height, img_height);
+  CHECK_LE(width, img_width);
+  CHECK_GE(num, 1);
 
   const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
@@ -293,7 +223,6 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
   }
 }
 
-
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
                                        Blob<Dtype>* transformed_blob) {
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index d7d475229a5..5ce52f05a3c 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -60,7 +60,8 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   if (this->timer_forward_.has_run_at_least_once()) {
-    DLOG(INFO) << "Proccessing: " << this->timer_forward_.MilliSeconds() << "ms.";
+    DLOG(INFO) << "Proccessing: " <<
+                this->timer_forward_.MilliSeconds() << "ms.";
   }
   JoinPrefetchThread();
   DLOG(INFO) << "Thread joined";

From 497eb60b79db2922241c813945543df152145cac Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Mon, 22 Sep 2014 17:07:40 -0700
Subject: [PATCH 0871/2053] Add flag check_size=false to convert_imageset

---
 tools/convert_imageset.cpp | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 7e1e83d9d0c..4ab93fd0a0c 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -36,6 +36,8 @@ DEFINE_bool(shuffle, false,
 DEFINE_string(backend, "lmdb", "The backend for storing the result");
 DEFINE_int32(resize_width, 0, "Width images are resized to");
 DEFINE_int32(resize_height, 0, "Height images are resized to");
+DEFINE_bool(check_size, false,
+    "When this option is on, check that all the datum have the same size");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -58,6 +60,7 @@ int main(int argc, char** argv) {
   }
 
   bool is_color = !FLAGS_gray;
+  bool check_size = FLAGS_check_size;
   std::ifstream infile(argv[2]);
   std::vector<std::pair<string, int> > lines;
   string filename;
@@ -131,13 +134,15 @@ int main(int argc, char** argv) {
         lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
       continue;
     }
-    if (!data_size_initialized) {
-      data_size = datum.channels() * datum.height() * datum.width();
-      data_size_initialized = true;
-    } else {
-      const string& data = datum.data();
-      CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
-          << data.size();
+    if (check_size) {
+      if (!data_size_initialized) {
+        data_size = datum.channels() * datum.height() * datum.width();
+        data_size_initialized = true;
+      } else {
+        const string& data = datum.data();
+        CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
+            << data.size();
+      }
     }
     // sequential
     snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,

From 5527e8969567fa22d2cc7c9397585e93edcfa3d1 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 25 Sep 2014 19:59:37 -0700
Subject: [PATCH 0872/2053] Added test_io and faster cv::Mat processing

Conflicts:
	src/caffe/util/io.cpp
---
 include/caffe/util/io.hpp  |   7 +-
 src/caffe/test/test_io.cpp | 143 +++++++++++++++++++++++++++++++++++++
 src/caffe/util/io.cpp      |  48 +++++--------
 3 files changed, 166 insertions(+), 32 deletions(-)
 create mode 100644 src/caffe/test/test_io.cpp

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 988305e26eb..8b83534ed54 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -99,9 +99,14 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, height, width, true, datum);
 }
 
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const bool is_color, Datum* datum) {
+  return ReadImageToDatum(filename, label, 0, 0, is_color, datum);
+}
+
 inline bool ReadImageToDatum(const string& filename, const int label,
     Datum* datum) {
-  return ReadImageToDatum(filename, label, 0, 0, datum);
+  return ReadImageToDatum(filename, label, 0, 0, true, datum);
 }
 
 cv::Mat ReadImageToCVMat(const string& filename,
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
new file mode 100644
index 00000000000..a01b8524918
--- /dev/null
+++ b/src/caffe/test/test_io.cpp
@@ -0,0 +1,143 @@
+#include <string>
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/util/io.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+class IOTest : public ::testing::Test {};
+
+TEST_F(IOTest, TestReadImageToDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TEST_F(IOTest, TestReadImageToDatumContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, &datum);
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  EXPECT_EQ(datum.channels(), cv_img.channels());
+  EXPECT_EQ(datum.height(), cv_img.rows);
+  EXPECT_EQ(datum.width(), cv_img.cols);
+
+  const string& data = datum.data();
+  int index = 0;
+  for (int c = 0; c < datum.channels(); ++c) {
+    for (int h = 0; h < datum.height(); ++h) {
+      for (int w = 0; w < datum.width(); ++w) {
+        EXPECT_TRUE(data[index++] == static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+      }
+    }
+  }
+}
+
+TEST_F(IOTest, TestReadImageToDatumContentGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  const bool is_color = false;
+  ReadImageToDatum(filename, 0, is_color, &datum);
+  cv::Mat cv_img = ReadImageToCVMat(filename, is_color);
+  EXPECT_EQ(datum.channels(), cv_img.channels());
+  EXPECT_EQ(datum.height(), cv_img.rows);
+  EXPECT_EQ(datum.width(), cv_img.cols);
+
+  const string& data = datum.data();
+  int index = 0;
+  for (int h = 0; h < datum.height(); ++h) {
+    for (int w = 0; w < datum.width(); ++w) {
+      EXPECT_TRUE(data[index++] == static_cast<char>(cv_img.at<uchar>(h, w)));
+    }
+  }
+}
+
+TEST_F(IOTest, TestReadImageToDatumResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, 100, 200, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 100);
+  EXPECT_EQ(datum.width(), 200);
+}
+
+
+TEST_F(IOTest, TestReadImageToDatumResizedSquare) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  ReadImageToDatum(filename, 0, 256, 256, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 256);
+  EXPECT_EQ(datum.width(), 256);
+}
+
+TEST_F(IOTest, TestReadImageToDatumGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  const bool is_color = false;
+  ReadImageToDatum(filename, 0, is_color, &datum);
+  EXPECT_EQ(datum.channels(), 1);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TEST_F(IOTest, TestReadImageToDatumResizedGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  const bool is_color = false;
+  ReadImageToDatum(filename, 0, 256, 256, is_color, &datum);
+  EXPECT_EQ(datum.channels(), 1);
+  EXPECT_EQ(datum.height(), 256);
+  EXPECT_EQ(datum.width(), 256);
+}
+
+TEST_F(IOTest, TestReadImageToCVMat) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename, 100, 200);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 100);
+  EXPECT_EQ(cv_img.cols, 200);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatResizedSquare) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename, 256, 256);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 256);
+  EXPECT_EQ(cv_img.cols, 256);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  const bool is_color = false;
+  cv::Mat cv_img = ReadImageToCVMat(filename, is_color);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestReadImageToCVMatResizedGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  const bool is_color = false;
+  cv::Mat cv_img = ReadImageToCVMat(filename, 256, 256, is_color);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 256);
+  EXPECT_EQ(cv_img.cols, 256);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 4c32979d82f..a576a91cb4e 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -85,46 +85,32 @@ cv::Mat ReadImageToCVMat(const string& filename,
 
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color, Datum* datum) {
-  cv::Mat cv_img;
-  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
-    CV_LOAD_IMAGE_GRAYSCALE);
+  cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
 
-  cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
-  if (!cv_img_origin.data) {
-    LOG(ERROR) << "Could not open or find file " << filename;
-    return false;
-  }
-  if (height > 0 && width > 0) {
-    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
-  } else {
-    cv_img = cv_img_origin;
-  }
-
-  int num_channels = (is_color ? 3 : 1);
-  datum->set_channels(num_channels);
+  CHECK(cv_img.depth() == CV_8U) <<
+      "Image data type must be unsigned byte";
+  datum->set_channels(cv_img.channels());
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
   datum->set_label(label);
   datum->clear_data();
   datum->clear_float_data();
-  string* datum_string = datum->mutable_data();
-  if (is_color) {
-    for (int c = 0; c < num_channels; ++c) {
-      for (int h = 0; h < cv_img.rows; ++h) {
-        for (int w = 0; w < cv_img.cols; ++w) {
-          datum_string->push_back(
-            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
-        }
+  int datum_channels = datum->channels();
+  int datum_height = datum->height();
+  int datum_width = datum->width();
+  int datum_size = datum_channels * datum_height * datum_width;
+  std::string buffer(datum_size, ' ');
+  for (int h = 0; h < datum_height; ++h) {
+    const uchar* ptr = cv_img.ptr<uchar>(h);
+    int img_index = 0;
+    for (int w = 0; w < datum_width; ++w) {
+      for (int c = 0; c < datum_channels; ++c) {
+        int datum_index = (c * datum_height + h) * datum_width + w;
+        buffer[datum_index] = static_cast<char>(ptr[img_index++]);
       }
     }
-  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
-    for (int h = 0; h < cv_img.rows; ++h) {
-      for (int w = 0; w < cv_img.cols; ++w) {
-        datum_string->push_back(
-          static_cast<char>(cv_img.at<uchar>(h, w)));
-        }
-      }
   }
+  datum->set_data(buffer);
   return true;
 }
 

From 242546fa7cfdb26805202d03129340183e7e8ddb Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 24 Sep 2014 07:36:13 -0700
Subject: [PATCH 0873/2053] Add CVMatToDatum

---
 include/caffe/util/io.hpp      |  2 +
 src/caffe/data_transformer.cpp | 74 ++--------------------------------
 src/caffe/util/io.cpp          | 12 +++++-
 3 files changed, 15 insertions(+), 73 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 8b83534ed54..2032b1f3195 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -126,6 +126,8 @@ inline cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
 
+void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
+
 leveldb::Options GetLevelDBOptions();
 
 template <typename Dtype>
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index cdb6e10c8ab..f001f1caea3 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -150,77 +150,9 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
-  const int img_channels = cv_img.channels();
-  const int img_height = cv_img.rows;
-  const int img_width = cv_img.cols;
-
-  const int channels = transformed_blob->channels();
-  const int height = transformed_blob->height();
-  const int width = transformed_blob->width();
-  const int num = transformed_blob->num();
-
-  CHECK_EQ(channels, img_channels);
-  CHECK_LE(height, img_height);
-  CHECK_LE(width, img_width);
-  CHECK_GE(num, 1);
-
-  const int crop_size = param_.crop_size();
-  const Dtype scale = param_.scale();
-  const bool do_mirror = param_.mirror() && Rand() % 2;
-  const bool has_mean_file = param_.has_mean_file();
-
-  int h_off = 0;
-  int w_off = 0;
-  if (crop_size) {
-    CHECK_EQ(crop_size, height);
-    CHECK_EQ(crop_size, width);
-    // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
-      h_off = Rand() % (img_height - crop_size);
-      w_off = Rand() % (img_width - crop_size);
-    } else {
-      h_off = (img_height - crop_size) / 2;
-      w_off = (img_width - crop_size) / 2;
-    }
-  } else {
-    CHECK_EQ(img_height, height);
-    CHECK_EQ(img_width, width);
-  }
-
-  Dtype* mean = NULL;
-  if (has_mean_file) {
-    CHECK_EQ(img_channels, data_mean_.channels());
-    CHECK_EQ(img_height, data_mean_.height());
-    CHECK_EQ(img_width, data_mean_.width());
-    mean = data_mean_.mutable_cpu_data();
-  }
-
-  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
-  Dtype pixel;
-  int top_index;
-  for (int c = 0; c < channels; ++c) {
-    int top_index_c = c * height;
-    int mean_index_c = c * img_height + h_off;
-    for (int h = 0; h < height; ++h) {
-      int top_index_h = (top_index_c + h) * width;
-      int mean_index_h = (mean_index_c + h) * img_width + w_off;
-      for (int w = 0; w < width; ++w) {
-        if (do_mirror) {
-          top_index = top_index_h + (width - 1 - w);
-        } else {
-          top_index = top_index_h + w;
-        }
-        pixel = static_cast<Dtype>(
-              cv_img.at<cv::Vec3b>(h + h_off, w + w_off)[c]);
-        if (has_mean_file) {
-          int mean_index = mean_index_h + w;
-          transformed_data[top_index] = (pixel - mean[mean_index]) * scale;
-        } else {
-          transformed_data[top_index] = pixel * scale;
-        }
-      }
-    }
-  }
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  Transform(datum, transformed_blob);
 }
 
 template<typename Dtype>
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index a576a91cb4e..047b14014e4 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -86,13 +86,21 @@ cv::Mat ReadImageToCVMat(const string& filename,
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color, Datum* datum) {
   cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
+  if (cv_img.data) {
+    CVMatToDatum(cv_img, datum);
+    datum->set_label(label);
+    return true;
+  } else {
+    return false;
+  }
+}
 
+void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   CHECK(cv_img.depth() == CV_8U) <<
       "Image data type must be unsigned byte";
   datum->set_channels(cv_img.channels());
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
-  datum->set_label(label);
   datum->clear_data();
   datum->clear_float_data();
   int datum_channels = datum->channels();
@@ -111,9 +119,9 @@ bool ReadImageToDatum(const string& filename, const int label,
     }
   }
   datum->set_data(buffer);
-  return true;
 }
 
+
 leveldb::Options GetLevelDBOptions() {
   // In default, we will return the leveldb option and set the max open files
   // in order to avoid using up the operating system's limit.

From 760ffaa51475e95c9e64ec858a7d0c94fd7937ae Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 3 Oct 2014 08:19:40 -0700
Subject: [PATCH 0874/2053] Added global_pooling to set the kernel size equal
 to the bottom size

Added check for padding and stride with global_pooling
---
 include/caffe/vision_layers.hpp       |  1 +
 src/caffe/layers/pooling_layer.cpp    | 32 ++++++++++++++++++++++-----
 src/caffe/proto/caffe.proto           |  3 +++
 src/caffe/test/test_pooling_layer.cpp | 14 ++++++++++++
 4 files changed, 44 insertions(+), 6 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 58ac5344da4..c803cd72449 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -319,6 +319,7 @@ class PoolingLayer : public Layer<Dtype> {
   int channels_;
   int height_, width_;
   int pooled_height_, pooled_width_;
+  bool global_pooling_;
   Blob<Dtype> rand_idx_;
   Blob<int> max_idx_;
 };
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 26c92c13b63..2bfbb01fcec 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -17,12 +17,18 @@ template <typename Dtype>
 void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   PoolingParameter pool_param = this->layer_param_.pooling_param();
-  CHECK(!pool_param.has_kernel_size() !=
+  if (pool_param.global_pooling()) {
+    CHECK(!(pool_param.has_kernel_size() ||
+      pool_param.has_kernel_h() || pool_param.has_kernel_w()))
+      << "With Global_pooling: true Filter size cannot specified";
+  } else {
+    CHECK(!pool_param.has_kernel_size() !=
       !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(pool_param.has_kernel_size() ||
+    CHECK(pool_param.has_kernel_size() ||
       (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "For non-square filters both kernel_h and kernel_w are required.";
+  }
   CHECK((!pool_param.has_pad() && pool_param.has_pad_h()
       && pool_param.has_pad_w())
       || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
@@ -31,11 +37,17 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       && pool_param.has_stride_w())
       || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";
-  if (pool_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = pool_param.kernel_size();
+  global_pooling_ = pool_param.global_pooling();
+  if (global_pooling_) {
+    kernel_h_ = bottom[0]->height();
+    kernel_w_ = bottom[0]->width();
   } else {
-    kernel_h_ = pool_param.kernel_h();
-    kernel_w_ = pool_param.kernel_w();
+    if (pool_param.has_kernel_size()) {
+      kernel_h_ = kernel_w_ = pool_param.kernel_size();
+    } else {
+      kernel_h_ = pool_param.kernel_h();
+      kernel_w_ = pool_param.kernel_w();
+    }
   }
   CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
   CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
@@ -51,6 +63,10 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     stride_h_ = pool_param.stride_h();
     stride_w_ = pool_param.stride_w();
   }
+  if (global_pooling_) {
+    CHECK(pad_h_ == 0 && pad_w_ == 0 && stride_h_ == 1 && stride_w_ == 1)
+      << "With Global_pooling: true; only pad = 0 and stride = 1";
+  }
   if (pad_h_ != 0 || pad_w_ != 0) {
     CHECK(this->layer_param_.pooling_param().pool()
         == PoolingParameter_PoolMethod_AVE
@@ -68,6 +84,10 @@ void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
+  if (global_pooling_) {
+    kernel_h_ = bottom[0]->height();
+    kernel_w_ = bottom[0]->width();
+  }
   pooled_height_ = static_cast<int>(ceil(static_cast<float>(
       height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
   pooled_width_ = static_cast<int>(ceil(static_cast<float>(
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6944ae847ff..9374abe5b70 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -602,6 +602,9 @@ message PoolingParameter {
     CUDNN = 2;
   }
   optional Engine engine = 11 [default = DEFAULT];
+  // If global_pooling then it will pool over the size of the bottom by doing
+  // kernel_h = bottom->height and kernel_w = bottom->width
+  optional bool global_pooling = 12 [default = false];
 }
 
 // Message that stores parameters used by PowerLayer
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index f52073c4e6f..435caa8381e 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -400,6 +400,20 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
+TYPED_TEST(PoolingLayerTest, TestSetupGlobalPooling) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_global_pooling(true);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+  PoolingLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
 /*
 TYPED_TEST(PoolingLayerTest, PrintBackward) {
   LayerParameter layer_param;

From 2fcbc9d169d80c24e7acf3eedba90a94622f00ec Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 1 Oct 2014 11:50:42 -0700
Subject: [PATCH 0875/2053] [fix] translate g++ whole archive to force load for
 clang++ compilation

To build with the layer registry clang++ requires `-force_load` as
g++ requires `--whole-archive`.
---
 Makefile | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index b0b25150eb9..aac8205ccb0 100644
--- a/Makefile
+++ b/Makefile
@@ -20,7 +20,6 @@ endif
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
 DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
-STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
 
 ##############################
 # Get all source files
@@ -228,6 +227,7 @@ else ifeq ($(UNAME), Darwin)
 	OSX := 1
 endif
 
+# Linux
 ifeq ($(LINUX), 1)
 	CXX ?= /usr/bin/g++
 	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
@@ -259,6 +259,15 @@ ifdef CUSTOM_CXX
 	CXX := $(CUSTOM_CXX)
 endif
 
+# Static linking
+ifneq (,$(findstring clang++,$(CXX)))
+	STATIC_LINK_COMMAND := -Wl,-force_load $(STATIC_NAME)
+else ifneq (,$(findstring g++,$(CXX)))
+	STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
+else
+	$(error Cannot static link with the $(CXX) compiler.)
+endif
+
 # Debugging
 ifeq ($(DEBUG), 1)
 	COMMON_FLAGS += -DDEBUG -g -O0

From 1e9681f915e157b87aa1bdc0197b001078122405 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 3 Oct 2014 13:30:21 -0700
Subject: [PATCH 0876/2053] [fix] set cmake static link command for clang++ and
 g++ globally

---
 CMakeLists.txt                |  9 ++++++++-
 python/CMakeLists.txt         |  1 -
 src/caffe/test/CMakeLists.txt | 36 +++++++++++++++++------------------
 tools/CMakeLists.txt          |  3 +--
 4 files changed, 26 insertions(+), 23 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3cb7d583504..6de6b102981 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -29,6 +29,13 @@ set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC) # set global flags
 set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) # set debug flags
 set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) # set release flags
 
+#    Link Flags
+if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")  # clang++
+  set(CAFFE_STATIC_LINK -Wl,-force_load $(STATIC_NAME))
+elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")  # g++
+  set(CAFFE_STATIC_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+endif()
+
 #    Global Definitions
 if(CPU_ONLY)
     add_definitions(-DCPU_ONLY)
@@ -77,7 +84,7 @@ endif()
 set(LINT_TARGET lint)
 set(LINT_SCRIPT ${CMAKE_SCRIPT_DIR}/lint.cmake)
 add_custom_target(
-    ${LINT_TARGET} 
+    ${LINT_TARGET}
     COMMAND ${CMAKE_COMMAND} -P ${LINT_SCRIPT}
 )
 
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 5024dbfea5d..343c863adf5 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -10,7 +10,6 @@ include_directories(${Boost_INCLUDE_DIRS})
 
 file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
-set(CAFFE_WHOLE_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
 add_library(pycaffe SHARED ${Python_SOURCES})
 target_link_libraries(pycaffe caffe ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
 
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 7b308bff168..ce0aa4c5148 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -32,12 +32,12 @@ set(IN_FILES             # generator input files
     ${TEST_DATA_FILE}
 )
 
-foreach(in_file ${IN_FILES})    
+foreach(in_file ${IN_FILES})
     configure_file(
         ${in_file}${IN_EXT}
         ${in_file}${GEN_EXT}
-    )    
-endforeach()       
+    )
+endforeach()
 
 include_directories(
         ${Caffe_SOURCE_DIR}
@@ -45,26 +45,24 @@ include_directories(
 )
 
 # Remove main from test sources and prepare an Object lib with main
-file(GLOB TEST_MAIN ${TEST_MAIN}) 
+file(GLOB TEST_MAIN ${TEST_MAIN})
 list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
 add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN})
 
-set(CAFFE_WHOLE_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
-
 #    Build each test separately from *.cpp files
 foreach(source ${TEST_CPP_SOURCES})
     test_name(TEST_NAME ${source})
-    
+
     #
     add_library(${TEST_NAME}.obj EXCLUDE_FROM_ALL OBJECT ${source})
     set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
-    
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)     
-    target_link_libraries(${TEST_NAME} gtest ${CAFFE_WHOLE_LINK})
-    
+
+    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)
+    target_link_libraries(${TEST_NAME} gtest ${CAFFE_STATIC_LINK})
+
     #    output dir
     set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-    
+
     # Targets and object libs
     set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
     set(TEST_OBJ_LIBS ${TEST_OBJ_LIBS} ${TEST_OBJ_LIB})
@@ -73,15 +71,15 @@ endforeach()
 #    Build each test separately from *.cu files
 foreach(source ${TEST_CU_SOURCES})
     test_name(TEST_NAME ${source})
-    
+
     cuda_add_library(${TEST_NAME}.lib EXCLUDE_FROM_ALL ${source})
-    
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)     
-    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest ${CAFFE_WHOLE_LINK})
-    
+
+    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)
+    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest ${CAFFE_STATIC_LINK})
+
     #    output dir
     set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-    
+
     # Targets and object libs
     set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
     set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
@@ -92,7 +90,7 @@ add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:ma
 if(NOT CPU_ONLY)
     target_link_libraries(${ALL_TEST} ${TEST_CU_LIBS})
 endif()
-target_link_libraries(${ALL_TEST} gtest ${CAFFE_WHOLE_LINK})
+target_link_libraries(${ALL_TEST} gtest ${CAFFE_STATIC_LINK})
 add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 
 #    Output directory
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 118443a86a0..110f368b2c8 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -3,14 +3,13 @@ project( Tools )
 #    Find all source files
 file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
-set (CAFFE_WHOLE_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
 
 #    Build each source file independently
 foreach(source ${TOOLS_SOURCES})
     get_filename_component(name ${source} NAME_WE)
     add_executable(${name}.bin ${source})
     set_target_properties(${name}.bin PROPERTIES OUTPUT_NAME ${name})
-    target_link_libraries(${name}.bin ${CAFFE_WHOLE_LINK})
+    target_link_libraries(${name}.bin ${CAFFE_STATIC_LINK})
 
 ###    Install    #################################################################################
 

From 562ecba3642116afd5cda4590b9a40bd5fb90364 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 3 Oct 2014 13:44:36 -0700
Subject: [PATCH 0877/2053] [fix] bend pycaffe to the whims of OS X header
 order

To build pycaffe on OS X the boost headers need to be in this order.
OS X is a bewildering platform.
---
 python/caffe/_caffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5a8d99d3f22..5af4c94cbaf 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -2,8 +2,8 @@
 // caffe::Caffe functions so that one could easily call it from Python.
 // Note that for Python, we will simply use float as the data type.
 
-#include <boost/make_shared.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
+#include <boost/make_shared.hpp>  // NOLINT(build/include_alpha)
 
 // these need to be included after boost on OS X
 #include <string>  // NOLINT(build/include_order)

From aee2efb0019b07cf9323a96e8f0fba469bead369 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 3 Oct 2014 13:53:52 -0700
Subject: [PATCH 0878/2053] [fix] include Python.h instead of re-ordering for
 pycaffe build on OS X

---
 python/caffe/_caffe.cpp | 4 +++-
 python/caffe/_caffe.hpp | 2 ++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5af4c94cbaf..133eb98b5d2 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -2,8 +2,10 @@
 // caffe::Caffe functions so that one could easily call it from Python.
 // Note that for Python, we will simply use float as the data type.
 
+#include <Python.h>  // NOLINT(build/include_alpha)
+
+#include <boost/make_shared.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
-#include <boost/make_shared.hpp>  // NOLINT(build/include_alpha)
 
 // these need to be included after boost on OS X
 #include <string>  // NOLINT(build/include_order)
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index fc7be49af1b..a4e0557c590 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -3,6 +3,8 @@
 
 #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
 
+#include <Python.h>  // NOLINT(build/include_alpha)
+
 #include <boost/python.hpp>
 #include <boost/shared_ptr.hpp>
 #include <numpy/arrayobject.h>

From 408c258ccd870c108d4a5326193c79b9e0f8c744 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 24 Sep 2014 12:39:59 -0700
Subject: [PATCH 0879/2053] Add ReadImageToDatumReference to test_io

---
 src/caffe/data_transformer.cpp | 79 ++++++++++++++++++++++++++++--
 src/caffe/test/test_io.cpp     | 87 ++++++++++++++++++++++++++++++++++
 src/caffe/util/io.cpp          | 11 +++--
 3 files changed, 169 insertions(+), 8 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index f001f1caea3..d3be1d8c696 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -150,9 +150,82 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
-  Datum datum;
-  CVMatToDatum(cv_img, &datum);
-  Transform(datum, transformed_blob);
+  // Datum datum;
+  // CVMatToDatum(cv_img, &datum);
+  // Transform(datum, transformed_blob);
+
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_EQ(img_channels, channels);
+  CHECK_GE(img_height, height);
+  CHECK_GE(img_width, width);
+
+  CHECK_EQ(transformed_blob->num(), 1) <<
+    "transformed_blob should have num() = 1";
+
+  const int crop_size = param_.crop_size();
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool has_mean_file = param_.has_mean_file();
+  
+  int h_off = 0;
+  int w_off = 0;
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    // We only do random crop when we do training.
+    if (phase_ == Caffe::TRAIN) {
+      h_off = Rand() % (img_height - crop_size);
+      w_off = Rand() % (img_width - crop_size);
+    } else {
+      h_off = (img_height - crop_size) / 2;
+      w_off = (img_width - crop_size) / 2;
+    }
+  } else {
+    CHECK_EQ(img_height, height);
+    CHECK_EQ(img_width, width);
+  }
+
+  Dtype* mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(img_channels, data_mean_.channels());
+    CHECK_EQ(img_height, data_mean_.height());
+    CHECK_EQ(img_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
+  }
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+  Dtype pixel;
+  int top_index;
+  for (int c = 0; c < channels; ++c) {
+    int top_index_c = c * height;
+    int mean_index_c = c * img_height + h_off;
+    for (int h = 0; h < height; ++h) {
+      int top_index_h = (top_index_c + h) * width;
+      int mean_index_h = (mean_index_c + h) * img_width + w_off;
+      for (int w = 0; w < width; ++w) {
+        if (do_mirror) {
+          top_index = top_index_h + (width - 1 - w);
+        } else {
+          top_index = top_index_h + w;
+        }
+        pixel = static_cast<Dtype>(
+              cv_img.at<cv::Vec3b>(h + h_off, w + w_off)[c]);
+        if (has_mean_file) {
+          int mean_index = mean_index_h + w;
+          transformed_data[top_index] = (pixel - mean[mean_index]) * scale;
+        } else {
+          transformed_data[top_index] = pixel * scale;
+        }
+      }
+    }
+  }
 }
 
 template<typename Dtype>
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index a01b8524918..5e85af75654 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -1,3 +1,8 @@
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/highgui/highgui_c.h>
+#include <opencv2/imgproc/imgproc.hpp>
+
 #include <string>
 
 #include "gtest/gtest.h"
@@ -11,6 +16,51 @@ namespace caffe {
 
 class IOTest : public ::testing::Test {};
 
+bool ReadImageToDatumReference(const string& filename, const int label,
+    const int height, const int width, const bool is_color, Datum* datum) {
+  cv::Mat cv_img;
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
+
+  cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
+  if (!cv_img_origin.data) {
+    LOG(ERROR) << "Could not open or find file " << filename;
+    return false;
+  }
+  if (height > 0 && width > 0) {
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
+  } else {
+    cv_img = cv_img_origin;
+  }
+
+  int num_channels = (is_color ? 3 : 1);
+  datum->set_channels(num_channels);
+  datum->set_height(cv_img.rows);
+  datum->set_width(cv_img.cols);
+  datum->set_label(label);
+  datum->clear_data();
+  datum->clear_float_data();
+  string* datum_string = datum->mutable_data();
+  if (is_color) {
+    for (int c = 0; c < num_channels; ++c) {
+      for (int h = 0; h < cv_img.rows; ++h) {
+        for (int w = 0; w < cv_img.cols; ++w) {
+          datum_string->push_back(
+            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+        }
+      }
+    }
+  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
+    for (int h = 0; h < cv_img.rows; ++h) {
+      for (int w = 0; w < cv_img.cols; ++w) {
+        datum_string->push_back(
+          static_cast<char>(cv_img.at<uchar>(h, w)));
+        }
+      }
+  }
+  return true;
+}
+
 TEST_F(IOTest, TestReadImageToDatum) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
@@ -20,6 +70,43 @@ TEST_F(IOTest, TestReadImageToDatum) {
   EXPECT_EQ(datum.width(), 480);
 }
 
+TEST_F(IOTest, TestReadImageToDatumReference) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum, datum_ref;
+  ReadImageToDatum(filename, 0, 0, 0, true, &datum);
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum.data();
+
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+
+TEST_F(IOTest, TestReadImageToDatumReferenceResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum, datum_ref;
+  ReadImageToDatum(filename, 0, 100, 200, true, &datum);
+  ReadImageToDatumReference(filename, 0, 100, 200, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum.data();
+
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
 TEST_F(IOTest, TestReadImageToDatumContent) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 047b14014e4..36510d61d40 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -71,14 +71,15 @@ cv::Mat ReadImageToCVMat(const string& filename,
   cv::Mat cv_img;
   int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
     CV_LOAD_IMAGE_GRAYSCALE);
+  cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
+  if (!cv_img_origin.data) {
+    LOG(ERROR) << "Could not open or find file " << filename;
+    return cv_img_origin;
+  }
   if (height > 0 && width > 0) {
-    cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
     cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
   } else {
-    cv_img = cv::imread(filename, cv_read_flag);
-  }
-  if (!cv_img.data) {
-    LOG(ERROR) << "Could not open or find file " << filename;
+    cv_img = cv_img_origin;
   }
   return cv_img;
 }

From 0a96030ba66e4476cc6b153863e409200989883d Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 24 Sep 2014 13:41:11 -0700
Subject: [PATCH 0880/2053] Added more tests to test_io for CVMatToDatum

---
 src/caffe/data_transformer.cpp | 79 ++--------------------------------
 src/caffe/test/test_io.cpp     | 48 +++++++++++++++++++++
 2 files changed, 51 insertions(+), 76 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index d3be1d8c696..f001f1caea3 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -150,82 +150,9 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
-  // Datum datum;
-  // CVMatToDatum(cv_img, &datum);
-  // Transform(datum, transformed_blob);
-
-  const int img_channels = cv_img.channels();
-  const int img_height = cv_img.rows;
-  const int img_width = cv_img.cols;
-
-  const int channels = transformed_blob->channels();
-  const int height = transformed_blob->height();
-  const int width = transformed_blob->width();
-
-  CHECK_EQ(img_channels, channels);
-  CHECK_GE(img_height, height);
-  CHECK_GE(img_width, width);
-
-  CHECK_EQ(transformed_blob->num(), 1) <<
-    "transformed_blob should have num() = 1";
-
-  const int crop_size = param_.crop_size();
-  const Dtype scale = param_.scale();
-  const bool do_mirror = param_.mirror() && Rand() % 2;
-  const bool has_mean_file = param_.has_mean_file();
-  
-  int h_off = 0;
-  int w_off = 0;
-  if (crop_size) {
-    CHECK_EQ(crop_size, height);
-    CHECK_EQ(crop_size, width);
-    // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
-      h_off = Rand() % (img_height - crop_size);
-      w_off = Rand() % (img_width - crop_size);
-    } else {
-      h_off = (img_height - crop_size) / 2;
-      w_off = (img_width - crop_size) / 2;
-    }
-  } else {
-    CHECK_EQ(img_height, height);
-    CHECK_EQ(img_width, width);
-  }
-
-  Dtype* mean = NULL;
-  if (has_mean_file) {
-    CHECK_EQ(img_channels, data_mean_.channels());
-    CHECK_EQ(img_height, data_mean_.height());
-    CHECK_EQ(img_width, data_mean_.width());
-    mean = data_mean_.mutable_cpu_data();
-  }
-
-  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
-  Dtype pixel;
-  int top_index;
-  for (int c = 0; c < channels; ++c) {
-    int top_index_c = c * height;
-    int mean_index_c = c * img_height + h_off;
-    for (int h = 0; h < height; ++h) {
-      int top_index_h = (top_index_c + h) * width;
-      int mean_index_h = (mean_index_c + h) * img_width + w_off;
-      for (int w = 0; w < width; ++w) {
-        if (do_mirror) {
-          top_index = top_index_h + (width - 1 - w);
-        } else {
-          top_index = top_index_h + w;
-        }
-        pixel = static_cast<Dtype>(
-              cv_img.at<cv::Vec3b>(h + h_off, w + w_off)[c]);
-        if (has_mean_file) {
-          int mean_index = mean_index_h + w;
-          transformed_data[top_index] = (pixel - mean[mean_index]) * scale;
-        } else {
-          transformed_data[top_index] = pixel * scale;
-        }
-      }
-    }
-  }
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  Transform(datum, transformed_blob);
 }
 
 template<typename Dtype>
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 5e85af75654..affbfd88f8e 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -227,4 +227,52 @@ TEST_F(IOTest, TestReadImageToCVMatResizedGray) {
   EXPECT_EQ(cv_img.cols, 256);
 }
 
+TEST_F(IOTest, TestCVMatToDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TEST_F(IOTest, TestCVMatToDatumContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  Datum datum_ref;
+  ReadImageToDatum(filename, 0, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestCVMatToDatumReference) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  cv::Mat cv_img = ReadImageToCVMat(filename);
+  Datum datum;
+  CVMatToDatum(cv_img, &datum);
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
 }  // namespace caffe

From a9572b1bbe8dca87ec3e31804fee49301e066a94 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 24 Sep 2014 14:58:11 -0700
Subject: [PATCH 0881/2053] Added mean_value to specify mean channel
 substraction Added example of use to
 models/bvlc_reference_caffenet/train_val_mean_value.prototxt

---
 include/caffe/data_transformer.hpp            |   1 +
 .../train_val_mean_value.prototxt             | 348 ++++++++++++++++++
 src/caffe/data_transformer.cpp                |  44 ++-
 src/caffe/proto/caffe.proto                   |   5 +
 src/caffe/test/test_io.cpp                    |   3 +-
 5 files changed, 399 insertions(+), 2 deletions(-)
 create mode 100644 models/bvlc_reference_caffenet/train_val_mean_value.prototxt

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 4a2afdaccce..735f1d586c9 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -72,6 +72,7 @@ class DataTransformer {
   shared_ptr<Caffe::RNG> rng_;
   Caffe::Phase phase_;
   Blob<Dtype> data_mean_;
+  vector<Dtype> mean_values_;
 };
 
 }  // namespace caffe
diff --git a/models/bvlc_reference_caffenet/train_val_mean_value.prototxt b/models/bvlc_reference_caffenet/train_val_mean_value.prototxt
new file mode 100644
index 00000000000..c2fbb7118ca
--- /dev/null
+++ b/models/bvlc_reference_caffenet/train_val_mean_value.prototxt
@@ -0,0 +1,348 @@
+name: "CaffeNet"
+layers {
+  name: "data"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_leveldb"
+    batch_size: 256
+  }
+  transform_param {
+    crop_size: 227
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+    mirror: true
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_leveldb"
+    batch_size: 50
+  }
+  transform_param {
+    crop_size: 227
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+    mirror: false
+  }
+  include: { phase: TEST }
+}
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 11
+    stride: 4
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu3"
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "conv4"
+  type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu4"
+  type: RELU
+  bottom: "conv4"
+  top: "conv4"
+}
+layers {
+  name: "conv5"
+  type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    group: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu5"
+  type: RELU
+  bottom: "conv5"
+  top: "conv5"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv5"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 4096
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index f001f1caea3..abc6ec2b590 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -16,12 +16,21 @@ DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
   phase_ = Caffe::phase();
     // check if we want to have mean
   if (param_.has_mean_file()) {
+    CHECK_EQ(param_.mean_value_size(), 0) <<
+      "Cannot specify mean_file and mean_value at the same time";
     const string& mean_file = param.mean_file();
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
   }
+  if (param_.mean_value_size() > 0) {
+    CHECK(param_.has_mean_file() == false) <<
+      "Cannot specify mean_file and mean_value at the same time";
+    for (int c = 0; c < param_.mean_value_size(); ++c) {
+      mean_values_.push_back(param_.mean_value(c));
+    }
+  }
 }
 
 template<typename Dtype>
@@ -37,6 +46,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   const bool do_mirror = param_.mirror() && Rand() % 2;
   const bool has_mean_file = param_.has_mean_file();
   const bool has_unit8 = data.size() > 0;
+  const bool has_mean_values = mean_values_.size() > 0;
 
   CHECK_GT(datum_channels, 0);
   CHECK_GE(datum_height, crop_size);
@@ -49,6 +59,16 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     CHECK_EQ(datum_width, data_mean_.width());
     mean = data_mean_.mutable_cpu_data();
   }
+  if (has_mean_values) {
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<
+     "Specify either 1 mean_value or as many as channels: " << datum_channels;
+    if (datum_channels > 1 && mean_values_.size() == 1) {
+      // Replicate the mean_value for simplicity
+      for (int c = 1; c < datum_channels; ++c) {
+        mean_values_.push_back(mean_values_[0]);
+      }
+    }
+  }
 
   int height = datum_height;
   int width = datum_width;
@@ -89,7 +109,12 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
           transformed_data[top_index] =
             (datum_element - mean[data_index]) * scale;
         } else {
-          transformed_data[top_index] = datum_element * scale;
+          if (has_mean_values) {
+            transformed_data[top_index] =
+              (datum_element - mean_values_[c]) * scale;
+          } else {
+            transformed_data[top_index] = datum_element * scale;
+          }
         }
       }
     }
@@ -178,6 +203,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand() % 2;
   const bool has_mean_file = param_.has_mean_file();
+  const bool has_mean_values = mean_values_.size() > 0;
 
   int h_off = 0;
   int w_off = 0;
@@ -209,6 +235,22 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
     }
   }
 
+  if (has_mean_values) {
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == input_channels) <<
+     "Specify either 1 mean_value or as many as channels: " << input_channels;
+    if (mean_values_.size() == 1) {
+      caffe_add_scalar(input_blob->count(), -(mean_values_[0]), input_data);
+    } else {
+      for (int n = 0; n < input_num; ++n) {
+        for (int c = 0; c < input_channels; ++c) {
+          int offset = input_blob->offset(n, c);
+          caffe_add_scalar(input_height * input_width, -(mean_values_[c]),
+            input_data + offset);
+        }
+      }
+    }
+  }
+
   Dtype* transformed_data = transformed_blob->mutable_cpu_data();
 
   for (int n = 0; n < input_num; ++n) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8f88177944c..85418f0adaf 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -347,7 +347,12 @@ message TransformationParameter {
   optional bool mirror = 2 [default = false];
   // Specify if we would like to randomly crop an image.
   optional uint32 crop_size = 3 [default = 0];
+  // mean_file and mean_value cannot be specified at the same time
   optional string mean_file = 4;
+  // if specified can be repeated once (would substract it from all the channels)
+  // or can be repeated the same number of times as channels
+  // (would subtract them from the corresponding channel)
+  repeated float mean_value = 5;
 }
 
 // Message that stores parameters used by AccuracyLayer
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index affbfd88f8e..6994afda0e3 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -121,7 +121,8 @@ TEST_F(IOTest, TestReadImageToDatumContent) {
   for (int c = 0; c < datum.channels(); ++c) {
     for (int h = 0; h < datum.height(); ++h) {
       for (int w = 0; w < datum.width(); ++w) {
-        EXPECT_TRUE(data[index++] == static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+        EXPECT_TRUE(data[index++] ==
+          static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
       }
     }
   }

From 8cd863e7cb5181cbbdfcf0dd75a78c80c56c1281 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 25 Sep 2014 19:53:33 -0700
Subject: [PATCH 0882/2053] Fix OSX compilation for nvcc with opencv

---
 Makefile                                          |  1 +
 include/caffe/data_transformer.hpp                |  4 ++++
 include/caffe/util/io.hpp                         |  4 ++++
 models/bvlc_reference_caffenet/train_val.prototxt | 13 +++++++++++--
 src/caffe/data_transformer.cpp                    |  4 ++++
 5 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index b0b25150eb9..7dea267e4c3 100644
--- a/Makefile
+++ b/Makefile
@@ -252,6 +252,7 @@ ifeq ($(OSX), 1)
 	endif
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
+        NVCCFLAGS += -DOSX
 endif
 
 # Custom compiler
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 735f1d586c9..7e48df339e2 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -1,7 +1,9 @@
 #ifndef CAFFE_DATA_TRANSFORMER_HPP
 #define CAFFE_DATA_TRANSFORMER_HPP
 
+#ifndef OSX
 #include <opencv2/core/core.hpp>
+#endif
 
 #include <vector>
 
@@ -49,7 +51,9 @@ class DataTransformer {
   void Transform(const vector<Datum> & datum_vector,
                 Blob<Dtype>* transformed_blob);
 
+#ifndef OSX
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
+#endif
 
   void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 2032b1f3195..e518979a75b 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,7 +1,9 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
+#ifndef OSX
 #include <opencv2/core/core.hpp>
+#endif
 
 #include <unistd.h>
 #include <string>
@@ -109,6 +111,7 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, true, datum);
 }
 
+#ifndef OSX
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
 
@@ -127,6 +130,7 @@ inline cv::Mat ReadImageToCVMat(const string& filename) {
 }
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
+#endif
 
 leveldb::Options GetLevelDBOptions();
 
diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index 073d8aeff4a..6cfeab10e1b 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -330,11 +330,20 @@ layers {
   }
 }
 layers {
-  name: "accuracy"
+  name: "top-1"
   type: ACCURACY
   bottom: "fc8"
   bottom: "label"
-  top: "accuracy"
+  top: "top-1"
+  include: { phase: TEST }
+}
+layers {
+  name: "top-5"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "top-5"
+  accuracy_param: { top_k: 5 }
   include: { phase: TEST }
 }
 layers {
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index abc6ec2b590..a195c5d2ad6 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,4 +1,6 @@
+#ifndef OSX
 #include <opencv2/core/core.hpp>
+#endif
 
 #include <string>
 #include <vector>
@@ -172,6 +174,7 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
+#ifndef OSX
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
@@ -179,6 +182,7 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
   CVMatToDatum(cv_img, &datum);
   Transform(datum, transformed_blob);
 }
+#endif
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,

From 8a6d348bed4e25161a6146c394499f0f9ba7054d Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 25 Sep 2014 20:07:01 -0700
Subject: [PATCH 0883/2053] Fix calls to Rand() and test_data_layer error

Remove benchmarking code, fixed data_transformer doxygen docs
---
 include/caffe/data_layers.hpp         |  2 -
 include/caffe/data_transformer.hpp    | 54 ++++++++++++++++++++-------
 src/caffe/data_transformer.cpp        | 12 +++---
 src/caffe/layers/base_data_layer.cpp  |  5 ---
 src/caffe/layers/base_data_layer.cu   |  2 -
 src/caffe/layers/data_layer.cpp       |  4 --
 src/caffe/layers/image_data_layer.cpp |  3 --
 7 files changed, 46 insertions(+), 36 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e3ba2d13ccf..1ed26c17178 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -17,7 +17,6 @@
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/benchmark.hpp"
 
 namespace caffe {
 
@@ -52,7 +51,6 @@ class BaseDataLayer : public Layer<Dtype> {
   DataTransformer<Dtype> data_transformer_;
   Caffe::Phase phase_;
   bool output_labels_;
-  Timer timer_forward_;
 };
 
 template <typename Dtype>
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 7e48df339e2..db2cac5e4cf 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -23,6 +23,12 @@ class DataTransformer {
   explicit DataTransformer(const TransformationParameter& param);
   virtual ~DataTransformer() {}
 
+  /**
+   * @brief Initialize the Random number generations if needed by the
+   *    transformation. Currently:
+   *    needs_rand = param_.mirror() ||
+   *      (phase_ == Caffe::TRAIN && param_.crop_size());
+   */
   void InitRand();
 
   /**
@@ -31,30 +37,50 @@ class DataTransformer {
    *
    * @param datum
    *    Datum containing the data to be transformed.
-   * @param datum_vector
-   *    A vector of Datum containing the data to be transformed.
-   * @param cv_img
-   *    cv::Mat containing the data to be transformed.
-   * @param input_blob
-   *    A Blob containing the data to be transformed.
-   * @param transformed_data
-   *    This should point to the right place in memory where the 
-   *    transformed data would be written. Assumes there is enough space
    * @param transformed_blob
-   *    This is destionation blob. It can be part of top blob's data. 
-   *    The transformed data will be  written at the appropriate place
-   *    within the blob's data.
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used See data_layer.cpp for an example.
    */
-
   void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
 
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to a vector of Datum.
+   *
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used See memory_layer.cpp for an example.
+   */
   void Transform(const vector<Datum> & datum_vector,
                 Blob<Dtype>* transformed_blob);
 
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to a cv::Mat
+   *
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used See image_data_layer.cpp for an example.
+   */
 #ifndef OSX
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
 #endif
 
+  /**
+   * @brief Applies the same transformation defined in the data layer's
+   * transform_param block to all the num images in a input_blob.
+   *
+   * @param input_blob
+   *    A Blob containing the data to be transformed. It applies the same
+   *    transformation to all the num images in the blob.
+   * @param transformed_blob
+   *    This is destination blob, it will contain as many images as the
+   *    input blob. It can be part of top blob's data.
+   */
   void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
 
  protected:
@@ -63,7 +89,7 @@ class DataTransformer {
    * 
    * @param n
    *    The upperbound (exclusive) value of the random number.
-   * @return 
+   * @return
    *    A uniformly random integer value from ({0, 1, ..., n-1}).
    */
   virtual int Rand(int n);
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index a195c5d2ad6..c7d1ecaf56f 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -45,7 +45,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
 
   const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
-  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
   const bool has_unit8 = data.size() > 0;
   const bool has_mean_values = mean_values_.size() > 0;
@@ -82,8 +82,8 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     width = crop_size;
     // We only do random crop when we do training.
     if (phase_ == Caffe::TRAIN) {
-      h_off = Rand(height - crop_size + 1);
-      w_off = Rand(width - crop_size + 1);
+      h_off = Rand(datum_height - crop_size + 1);
+      w_off = Rand(datum_width - crop_size + 1);
     } else {
       h_off = (datum_height - crop_size) / 2;
       w_off = (datum_width - crop_size) / 2;
@@ -205,7 +205,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
 
   const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
-  const bool do_mirror = param_.mirror() && Rand() % 2;
+  const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
   const bool has_mean_values = mean_values_.size() > 0;
 
@@ -216,8 +216,8 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
     CHECK_EQ(crop_size, width);
     // We only do random crop when we do training.
     if (phase_ == Caffe::TRAIN) {
-      h_off = Rand() % (input_height - crop_size);
-      w_off = Rand() % (input_width - crop_size);
+      h_off = Rand(height - crop_size + 1);
+      w_off = Rand(width - crop_size + 1);
     } else {
       h_off = (input_height - crop_size) / 2;
       w_off = (input_width - crop_size) / 2;
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 5ce52f05a3c..eb0aaf82120 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -59,10 +59,6 @@ template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
-  if (this->timer_forward_.has_run_at_least_once()) {
-    DLOG(INFO) << "Proccessing: " <<
-                this->timer_forward_.MilliSeconds() << "ms.";
-  }
   JoinPrefetchThread();
   DLOG(INFO) << "Thread joined";
   // Copy the data
@@ -76,7 +72,6 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
   // Start a new prefetch thread
   DLOG(INFO) << "CreatePrefetchThread";
   CreatePrefetchThread();
-  this->timer_forward_.Start();
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 690858f09d3..ff1510361b6 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -7,7 +7,6 @@ namespace caffe {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  DLOG(INFO) << "Processing: " << this->timer_forward_.MilliSeconds() << "ms.";
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
@@ -19,7 +18,6 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
-  this->timer_forward_.Start();
 }
 
 INSTANTIATE_CLASS(BasePrefetchingDataLayer);
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 980e4146fc9..283a3a52f59 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -8,7 +8,6 @@
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -143,8 +142,6 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
-  Timer batch_timer;
-  batch_timer.Start();
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -204,7 +201,6 @@ void DataLayer<Dtype>::InternalThreadEntry() {
       LOG(FATAL) << "Unknown database backend";
     }
   }
-  DLOG(INFO) << "Prefetch: " << batch_timer.MilliSeconds() << " ms.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index d93d56dead0..b3e5d445ed1 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -91,8 +91,6 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
-  Timer batch_timer;
-  batch_timer.Start();
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -131,7 +129,6 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       }
     }
   }
-  DLOG(INFO) << "Prefetch: " << batch_timer.MilliSeconds() << " ms.";
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);

From b07a54fc36a4751c7181ba9a8a155be77cc35596 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Fri, 26 Sep 2014 16:35:21 -0700
Subject: [PATCH 0884/2053] Fixed crop error and add test_data_transformer

Make lint happy

Fix Name conventions
---
 include/caffe/data_transformer.hpp            |   4 +-
 .../train_val.prototxt                        |  13 +-
 src/caffe/data_transformer.cpp                |   7 +-
 src/caffe/layers/image_data_layer.cpp         |   1 -
 src/caffe/layers/window_data_layer.cpp        |   4 +-
 src/caffe/test/test_data_transformer.cpp      | 360 ++++++++++++++++++
 src/caffe/util/benchmark.cpp                  |   4 +-
 7 files changed, 372 insertions(+), 21 deletions(-)
 create mode 100644 src/caffe/test/test_data_transformer.cpp

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index db2cac5e4cf..5f08335d4a3 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -25,9 +25,7 @@ class DataTransformer {
 
   /**
    * @brief Initialize the Random number generations if needed by the
-   *    transformation. Currently:
-   *    needs_rand = param_.mirror() ||
-   *      (phase_ == Caffe::TRAIN && param_.crop_size());
+   *    transformation.
    */
   void InitRand();
 
diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index 6cfeab10e1b..073d8aeff4a 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -330,20 +330,11 @@ layers {
   }
 }
 layers {
-  name: "top-1"
+  name: "accuracy"
   type: ACCURACY
   bottom: "fc8"
   bottom: "label"
-  top: "top-1"
-  include: { phase: TEST }
-}
-layers {
-  name: "top-5"
-  type: ACCURACY
-  bottom: "fc8"
-  bottom: "label"
-  top: "top-5"
-  accuracy_param: { top_k: 5 }
+  top: "accuracy"
   include: { phase: TEST }
 }
 layers {
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index c7d1ecaf56f..78112b9b8c0 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -16,7 +16,7 @@ template<typename Dtype>
 DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
     : param_(param) {
   phase_ = Caffe::phase();
-    // check if we want to have mean
+  // check if we want to use mean_file
   if (param_.has_mean_file()) {
     CHECK_EQ(param_.mean_value_size(), 0) <<
       "Cannot specify mean_file and mean_value at the same time";
@@ -26,6 +26,7 @@ DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
   }
+  // check if we want to use mean_value
   if (param_.mean_value_size() > 0) {
     CHECK(param_.has_mean_file() == false) <<
       "Cannot specify mean_file and mean_value at the same time";
@@ -216,8 +217,8 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
     CHECK_EQ(crop_size, width);
     // We only do random crop when we do training.
     if (phase_ == Caffe::TRAIN) {
-      h_off = Rand(height - crop_size + 1);
-      w_off = Rand(width - crop_size + 1);
+      h_off = Rand(input_height - crop_size + 1);
+      w_off = Rand(input_width - crop_size + 1);
     } else {
       h_off = (input_height - crop_size) / 2;
       w_off = (input_width - crop_size) / 2;
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index b3e5d445ed1..19ad2dc2126 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -112,7 +112,6 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       continue;
     }
     // Apply transformations (mirror, crop...) to the image
-    // this->data_transformer_.Transform(item_id, cv_img, top_data);
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index c01b61bec9b..009bf97c612 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -163,9 +163,9 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
 
   // data mean
-  if (this->layer_param_.window_data_param().has_mean_file()) {
+  if (this->transform_param_.has_mean_file()) {
     const string& mean_file =
-          this->layer_param_.window_data_param().mean_file();
+          this->transform_param_.mean_file();
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
new file mode 100644
index 00000000000..28c7241050b
--- /dev/null
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -0,0 +1,360 @@
+#include <string>
+#include <vector>
+
+#include "gtest/gtest.h"
+#include "leveldb/db.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+void FillDatum(const int label, const int channels, const int height,
+  const int width, const bool unique_pixels, Datum * datum) {
+  datum->set_label(label);
+  datum->set_channels(channels);
+  datum->set_height(height);
+  datum->set_width(width);
+  int size = channels * height * width;
+  std::string* data = datum->mutable_data();
+  for (int j = 0; j < size; ++j) {
+    int datum = unique_pixels ? j : label;
+    data->push_back(static_cast<uint8_t>(datum));
+  }
+}
+
+template <typename Dtype>
+class DataTransformTest : public ::testing::Test {
+ protected:
+  DataTransformTest()
+      : seed_(1701),
+      num_iter_(10) {}
+
+  int NumSequenceMatches(const TransformationParameter transform_param,
+      const Datum& datum) {
+    // Get crop sequence with Caffe seed 1701.
+    DataTransformer<Dtype>* transformer =
+        new DataTransformer<Dtype>(transform_param);
+    const int crop_size = transform_param.crop_size();
+    Caffe::set_random_seed(seed_);
+    transformer->InitRand();
+    Blob<Dtype>* blob =
+        new Blob<Dtype>(1, datum.channels(), datum.height(), datum.width());
+    if (transform_param.crop_size() > 0) {
+      blob->Reshape(1, datum.channels(), crop_size, crop_size);
+    }
+
+    vector<vector<Dtype> > crop_sequence;
+    for (int iter = 0; iter < this->num_iter_; ++iter) {
+      vector<Dtype> iter_crop_sequence;
+      transformer->Transform(datum, blob);
+      for (int j = 0; j < blob->count(); ++j) {
+        iter_crop_sequence.push_back(blob->cpu_data()[j]);
+      }
+      crop_sequence.push_back(iter_crop_sequence);
+    }
+    // Check if the sequence differs from the previous
+    int num_sequence_matches = 0;
+    for (int iter = 0; iter < this->num_iter_; ++iter) {
+      vector<Dtype> iter_crop_sequence = crop_sequence[iter];
+      transformer->Transform(datum, blob);
+      for (int j = 0; j < blob->count(); ++j) {
+        num_sequence_matches +=
+            (crop_sequence[iter][j] == blob->cpu_data()[j]);
+      }
+    }
+    return num_sequence_matches;
+  }
+
+  virtual ~DataTransformTest() { }
+
+  int seed_;
+  int num_iter_;
+};
+
+TYPED_TEST_CASE(DataTransformTest, TestDtypes);
+
+TYPED_TEST(DataTransformTest, TestEmptyTransform) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // all pixels the same equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  EXPECT_EQ(blob->num(), 1);
+  EXPECT_EQ(blob->channels(), datum.channels());
+  EXPECT_EQ(blob->height(), datum.height());
+  EXPECT_EQ(blob->width(), datum.width());
+  for (int j = 0; j < blob->count(); ++j) {
+    EXPECT_EQ(blob->cpu_data()[j], label);
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  EXPECT_EQ(blob->num(), 1);
+  EXPECT_EQ(blob->channels(), datum.channels());
+  EXPECT_EQ(blob->height(), datum.height());
+  EXPECT_EQ(blob->width(), datum.width());
+  for (int j = 0; j < blob->count(); ++j) {
+    EXPECT_EQ(blob->cpu_data()[j], j);
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestCropSize) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // all pixels the same equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+
+  transform_param.set_crop_size(crop_size);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param);
+  transformer->InitRand();
+  Blob<TypeParam>* blob =
+      new Blob<TypeParam>(1, channels, crop_size, crop_size);
+  for (int iter = 0; iter < this->num_iter_; ++iter) {
+    transformer->Transform(datum, blob);
+    EXPECT_EQ(blob->num(), 1);
+    EXPECT_EQ(blob->channels(), datum.channels());
+    EXPECT_EQ(blob->height(), crop_size);
+    EXPECT_EQ(blob->width(), crop_size);
+    for (int j = 0; j < blob->count(); ++j) {
+      EXPECT_EQ(blob->cpu_data()[j], label);
+    }
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestCropTrain) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+  const int size = channels * crop_size * crop_size;
+
+  transform_param.set_crop_size(crop_size);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Caffe::set_phase(Caffe::TRAIN);
+  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  EXPECT_LT(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestCropTest) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+  const int size = channels * crop_size * crop_size;
+
+  transform_param.set_crop_size(crop_size);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Caffe::set_phase(Caffe::TEST);
+  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  EXPECT_EQ(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestMirrorTrain) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int size = channels * height * width;
+
+  transform_param.set_mirror(true);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Caffe::set_phase(Caffe::TRAIN);
+  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  EXPECT_LT(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestMirrorTest) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int size = channels * height * width;
+
+  transform_param.set_mirror(true);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Caffe::set_phase(Caffe::TEST);
+  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  EXPECT_LT(num_matches, size * this->num_iter_);
+}
+
+TYPED_TEST(DataTransformTest, TestCropMirrorTrain) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  transform_param.set_crop_size(crop_size);
+  Caffe::set_phase(Caffe::TRAIN);
+  int num_matches_crop = this->NumSequenceMatches(transform_param, datum);
+
+  transform_param.set_mirror(true);
+  int num_matches_crop_mirror =
+      this->NumSequenceMatches(transform_param, datum);
+  // When doing crop and mirror we expect less num_matches than just crop
+  EXPECT_LE(num_matches_crop_mirror, num_matches_crop);
+}
+
+TYPED_TEST(DataTransformTest, TestCropMirrorTest) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int crop_size = 2;
+
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  transform_param.set_crop_size(crop_size);
+  Caffe::set_phase(Caffe::TEST);
+  int num_matches_crop = this->NumSequenceMatches(transform_param, datum);
+
+  transform_param.set_mirror(true);
+  int num_matches_crop_mirror =
+      this->NumSequenceMatches(transform_param, datum);
+  // When doing crop and mirror we expect less num_matches than just crop
+  EXPECT_LT(num_matches_crop_mirror, num_matches_crop);
+}
+
+
+TYPED_TEST(DataTransformTest, TestMeanValue) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // pixels are equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int mean_value = 2;
+
+  transform_param.add_mean_value(mean_value);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  for (int j = 0; j < blob->count(); ++j) {
+    EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestMeanValues) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = false;  // pixels are equal to label
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+
+  transform_param.add_mean_value(0);
+  transform_param.add_mean_value(1);
+  transform_param.add_mean_value(2);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  for (int c = 0; c < channels; ++c) {
+    for (int j = 0; j < height * width; ++j) {
+      EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
+    }
+  }
+}
+
+TYPED_TEST(DataTransformTest, TestMeanFile) {
+  TransformationParameter transform_param;
+  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
+  const int label = 0;
+  const int channels = 3;
+  const int height = 4;
+  const int width = 5;
+  const int size = channels * height * width;
+
+  // Create a mean file
+  string* mean_file = new string();
+  MakeTempFilename(mean_file);
+  BlobProto blob_mean;
+  blob_mean.set_num(1);
+  blob_mean.set_channels(channels);
+  blob_mean.set_height(height);
+  blob_mean.set_width(width);
+
+  for (int j = 0; j < size; ++j) {
+      blob_mean.add_data(j);
+  }
+
+  LOG(INFO) << "Using temporary mean_file " << *mean_file;
+  WriteProtoToBinaryFile(blob_mean, *mean_file);
+
+  transform_param.set_mean_file(*mean_file);
+  Datum datum;
+  FillDatum(label, channels, height, width, unique_pixels, &datum);
+  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
+  DataTransformer<TypeParam>* transformer =
+      new DataTransformer<TypeParam>(transform_param);
+  transformer->InitRand();
+  transformer->Transform(datum, blob);
+  for (int j = 0; j < blob->count(); ++j) {
+      EXPECT_EQ(blob->cpu_data()[j], 0);
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 76829f5b36c..2edc9a53ef0 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -66,8 +66,10 @@ float Timer::MicroSeconds() {
   }
   if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
-    CUDA_CHECK(cudaEventElapsedTime(&elapsed_microseconds_, start_gpu_,
+    CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
+    // Cuda only measure milliseconds
+    elapsed_microseconds_ = elapsed_milliseconds_ / 1000;
 #else
       NO_GPU;
 #endif

From 3115b207239b53744a600debc8b4aea146d2a79b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 3 Oct 2014 18:02:32 -0700
Subject: [PATCH 0885/2053] bundle pixel mean into CaffeNet as comments

---
 .../train_val.prototxt                        |  16 +
 .../train_val_mean_value.prototxt             | 348 ------------------
 2 files changed, 16 insertions(+), 348 deletions(-)
 delete mode 100644 models/bvlc_reference_caffenet/train_val_mean_value.prototxt

diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index 073d8aeff4a..00fcc080261 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -14,6 +14,14 @@ layers {
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
     mirror: true
   }
+# mean pixel / channel-wise mean instead of mean image
+#  transform_param {
+#    crop_size: 227
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: true
+#  }
   include: { phase: TRAIN }
 }
 layers {
@@ -31,6 +39,14 @@ layers {
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
     mirror: false
   }
+# mean pixel / channel-wise mean instead of mean image
+#  transform_param {
+#    crop_size: 227
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: true
+#  }
   include: { phase: TEST }
 }
 layers {
diff --git a/models/bvlc_reference_caffenet/train_val_mean_value.prototxt b/models/bvlc_reference_caffenet/train_val_mean_value.prototxt
deleted file mode 100644
index c2fbb7118ca..00000000000
--- a/models/bvlc_reference_caffenet/train_val_mean_value.prototxt
+++ /dev/null
@@ -1,348 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_leveldb"
-    batch_size: 256
-  }
-  transform_param {
-    crop_size: 227
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    mirror: true
-  }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_leveldb"
-    batch_size: 50
-  }
-  transform_param {
-    crop_size: 227
-    mean_value: 104
-    mean_value: 117
-    mean_value: 123
-    mirror: false
-  }
-  include: { phase: TEST }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 4096
-    weight_filler {
-      type: "gaussian"
-      std: 0.005
-    }
-    bias_filler {
-      type: "constant"
-      value: 1
-    }
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-      value: 0
-    }
-  }
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "fc8"
-  bottom: "label"
-  top: "accuracy"
-  include: { phase: TEST }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "fc8"
-  bottom: "label"
-  top: "loss"
-}

From e8b48b41e0f6e1c38cf1bef144e6907ea30d6a7d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 3 Oct 2014 18:03:02 -0700
Subject: [PATCH 0886/2053] uin8 spell check

---
 src/caffe/data_transformer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 78112b9b8c0..dffaba545b5 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -48,7 +48,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
-  const bool has_unit8 = data.size() > 0;
+  const bool has_uint8 = data.size() > 0;
   const bool has_mean_values = mean_values_.size() > 0;
 
   CHECK_GT(datum_channels, 0);
@@ -102,7 +102,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
         } else {
           top_index = (c * height + h) * width + w;
         }
-        if (has_unit8) {
+        if (has_uint8) {
           datum_element =
             static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));
         } else {

From 1d3bbe2e7bf896379aecf8faa3fcdd5cf5bad522 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 5 Oct 2014 14:34:55 -0700
Subject: [PATCH 0887/2053] Missing param.pad condition for CUDNN pooling

---
 src/caffe/layer_factory.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 8815ddd021d..69863543c30 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -47,7 +47,8 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
     PoolingParameter p_param = param.pooling_param();
-    if (p_param.pad_h() || p_param.pad_w() || param.top_size() > 1) {
+    if (p_param.pad() || p_param.pad_h() || p_param.pad_w() ||
+        param.top_size() > 1) {
       LOG(INFO) << "CUDNN does not support padding or multiple tops. "
                 << "Using Caffe's own pooling layer.";
       return new PoolingLayer<Dtype>(param);

From c20cf21ab430e50f0d3e8d93cd42d2e930d41dd2 Mon Sep 17 00:00:00 2001
From: Dmytro Mishkin <ducha.aiki@gmail.com>
Date: Mon, 6 Oct 2014 10:32:47 +0300
Subject: [PATCH 0888/2053] change -lpthread to -pthread in linking

Compilation flags is already there
---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 929f8d5cea6..f49ea5649a4 100644
--- a/Makefile
+++ b/Makefile
@@ -169,7 +169,7 @@ LIBRARIES += glog gflags protobuf leveldb snappy \
 	lmdb \
 	boost_system \
 	hdf5_hl hdf5 \
-	opencv_core opencv_highgui opencv_imgproc pthread
+	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
@@ -314,7 +314,7 @@ CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
-LINKFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
+LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))

From 6893ea722505c889fd40c9f692d7a87eb750c752 Mon Sep 17 00:00:00 2001
From: Martin Baeuml <baeuml@kit.edu>
Date: Mon, 6 Oct 2014 13:48:29 +0200
Subject: [PATCH 0889/2053] change linking order such that pthread comes in the
 back

This resolves an error with the static gflags library which would
other wise not link properly with an undefined reference to symbol 'pthread_rwlock_wrlock@@GLIBC_2.2.5'
error under Ubuntu 14.04.
---
 src/caffe/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 86c7c7eb4a3..0ff0412230c 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -111,13 +111,13 @@ endif()
 target_link_libraries(caffe proto
         ${BLAS_LIBRARIES}
         ${Boost_LIBRARIES}
-        ${CMAKE_THREAD_LIBS_INIT}
         ${GFLAGS_LIBRARIES}
         ${GLOG_LIBRARIES}
         ${HDF5_LIBRARIES}
         ${LEVELDB_LIBS}
         ${LMDB_LIBRARIES}
         ${OpenCV_LIBS}
+        ${CMAKE_THREAD_LIBS_INIT}
 )
 
 #set output directory

From aaccb48bd29fef703e0260bbc47685960797d7a1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 6 Oct 2014 12:34:18 -0700
Subject: [PATCH 0890/2053] [fix] check solver prototxt parsing

---
 src/caffe/solver.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 8d187ea638b..ba262920a9a 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -23,7 +23,7 @@ template <typename Dtype>
 Solver<Dtype>::Solver(const string& param_file)
     : net_() {
   SolverParameter param;
-  ReadProtoFromTextFile(param_file, &param);
+  ReadProtoFromTextFileOrDie(param_file, &param);
   Init(param);
 }
 

From a1a48563d3cbb37ffa510d0ffae718183835e2dc Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 6 Oct 2014 13:27:10 -0700
Subject: [PATCH 0891/2053] use glog to provide a backtrace on crash

---
 src/caffe/common.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 94fdf924f44..834d5694aad 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -35,6 +35,8 @@ void GlobalInit(int* pargc, char*** pargv) {
   ::gflags::ParseCommandLineFlags(pargc, pargv, true);
   // Google logging.
   ::google::InitGoogleLogging(*(pargv)[0]);
+  // Provide a backtrace on segfault.
+  ::google::InstallFailureSignalHandler();
 }
 
 #ifdef CPU_ONLY  // CPU-only Caffe.

From cf270600c588c77f0c5d0242d833fe5f615a4662 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 4 Oct 2014 10:00:59 -0700
Subject: [PATCH 0892/2053] SliceLayer: fix whitespace

---
 src/caffe/layers/slice_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 17144c1a280..76522c3e456 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -46,13 +46,13 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       slices.push_back(num_ - prev);
       for (int i = 0; i < top.size(); ++i) {
         top[i]->Reshape(slices[i], channels_, height_, width_);
-         count_ += top[i]->count();
+        count_ += top[i]->count();
       }
     } else {
       slices.push_back(channels_ - prev);
       for (int i = 0; i < top.size(); ++i) {
         top[i]->Reshape(num_, slices[i], height_, width_);
-         count_ += top[i]->count();
+        count_ += top[i]->count();
       }
     }
   } else {

From 914da95ed1059c05d39d9b6fbe0f797970aaa376 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 6 Oct 2014 16:35:33 -0700
Subject: [PATCH 0893/2053] correct naming in comment and message about
 average_loss

---
 src/caffe/proto/caffe.proto | 2 +-
 src/caffe/solver.cpp        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 751b68232cd..f0404a09b90 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -113,7 +113,7 @@ message SolverParameter {
   // the number of iterations between displaying info. If display = 0, no info
   // will be displayed.
   optional int32 display = 6;
-  // Display the cost averaged over the last average_cost iterations
+  // Display the loss averaged over the last average_loss iterations
   optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
   optional string lr_policy = 8; // The learning rate decay policy.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 221fa07e464..995df06ddc7 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -171,7 +171,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
 
   int average_loss = this->param_.average_loss();
 
-  CHECK_GE(average_loss, 1) << "average_cost should be non-negative.";
+  CHECK_GE(average_loss, 1) << "average_loss should be non-negative.";
 
   vector<Dtype> losses;
   Dtype smoothed_loss = 0;

From 84a5b8bb9aeb2b1ed965af598c63b1d3f57c2197 Mon Sep 17 00:00:00 2001
From: Martin Baeuml <baeuml@kit.edu>
Date: Tue, 7 Oct 2014 10:35:00 +0200
Subject: [PATCH 0894/2053] [cmake] move dependency finding to root
 CMakeLists.txt

In this way, custom include directories for the dependencies are
also available for tools/ and the bindings.  This is necessary for
example when someone is using custom built dependencies which are
not installed in the system's default directories (e.g. OpenCV includes
are required in tools/ through io.hpp)
---
 CMakeLists.txt           | 84 +++++++++++++++++++++++++++++++++++++++-
 src/caffe/CMakeLists.txt | 80 +-------------------------------------
 2 files changed, 83 insertions(+), 81 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6de6b102981..b97eb565f45 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -49,15 +49,95 @@ include_directories(${CMAKE_SOURCE_DIR}/src)
 #    CMake Scripts dir
 set(CMAKE_SCRIPT_DIR ${CMAKE_SOURCE_DIR}/CMakeScripts)
 
-#	CMake module path for custom module finding
+#	 CMake module path for custom module finding
 set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SCRIPT_DIR})
 
-#    CUDA is required globally
+###    Dependencies    ##########################################################################
+
+#    Boost
+find_package(Boost 1.46 COMPONENTS system thread REQUIRED)
+include_directories(${Boost_INCLUDE_DIR})
+link_directories(${Boost_LIBRARY_DIRS})
+
+#    CUDA
 if(NOT CPU_ONLY)
     find_package(CUDA 5.5 REQUIRED)
     include_directories(${CUDA_INCLUDE_DIRS})
+
+    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
+            -gencode arch=compute_20,code=sm_20
+            -gencode arch=compute_20,code=sm_21
+            -gencode arch=compute_30,code=sm_30
+            -gencode arch=compute_35,code=sm_35
+    )
+
+    # https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
+    # work-arounds
+    if(Boost_VERSION EQUAL 105500)
+        # see https://svn.boost.org/trac/boost/ticket/9392
+        message(STATUS "Boost: Applying noinline work around")
+        # avoid warning for CMake >= 2.8.12
+        set(CUDA_NVCC_FLAGS
+          "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
+    endif(Boost_VERSION EQUAL 105500)
+endif()
+
+#    Threads
+find_package(Threads REQUIRED)
+
+#	 Google-glog
+find_package(Glog REQUIRED)
+include_directories(${GLOG_INCLUDE_DIRS})
+
+#    Google-gflags
+find_package(GFlags REQUIRED)
+include_directories(${GFLAGS_INCLUDE_DIRS})
+
+#    BLAS
+if(BLAS STREQUAL "atlas")
+
+    find_package(Atlas REQUIRED)
+    include_directories(${Atlas_INCLUDE_DIR})
+    set(BLAS_LIBRARIES ${Atlas_LIBRARIES})
+
+elseif(BLAS STREQUAL "open")
+
+    find_package(OpenBLAS REQUIRED)
+    include_directories(${OpenBLAS_INCLUDE_DIR})
+    set(BLAS_LIBRARIES ${OpenBLAS_LIB})
+
+elseif(BLAS STREQUAL "mkl")
+
+    find_package(MKL REQUIRED)
+    include_directories(${MKL_INCLUDE_DIR})
+    set(BLAS_LIBRARIES ${MKL_LIBRARIES})
+
 endif()
 
+#    HDF5
+find_package(HDF5 COMPONENTS HL REQUIRED)
+include_directories(${HDF5_INCLUDE_DIRS})
+
+#    LevelDB
+find_package(LevelDB REQUIRED)
+include_directories(${LEVELDB_INCLUDE})
+if(LEVELDB_FOUND)
+    find_package(Snappy REQUIRED)
+    include_directories(${SNAPPY_INCLUDE_DIR})
+    set(LEVELDB_LIBS
+        ${LEVELDB_LIBS}
+        ${SNAPPY_LIBS}
+    )
+endif()
+
+#    LMDB
+find_package(LMDB REQUIRED)
+include_directories(${LMDB_INCLUDE_DIR})
+
+#    OpenCV
+find_package(OpenCV REQUIRED core highgui imgproc)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
 ###    Subdirectories    ##########################################################################
 
 add_subdirectory(src/gtest)
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 0ff0412230c..dda072688f8 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -1,65 +1,5 @@
 project( CaffeSrc )
 
-#    Threads
-find_package(Threads REQUIRED)
-
-#	Google-glog
-find_package(Glog REQUIRED)
-include_directories(${GLOG_INCLUDE_DIRS})
-
-#   Google-gflags
-find_package(GFlags REQUIRED)
-include_directories(${GFLAGS_INCLUDE_DIRS})
-
-#   BLAS
-if(BLAS STREQUAL "atlas")
-
-    find_package(Atlas REQUIRED)
-    include_directories(${Atlas_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${Atlas_LIBRARIES})
-
-elseif(BLAS STREQUAL "open")
-
-    find_package(OpenBLAS REQUIRED)
-    include_directories(${OpenBLAS_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${OpenBLAS_LIB})
-
-elseif(BLAS STREQUAL "mkl")
-
-    find_package(MKL REQUIRED)
-    include_directories(${MKL_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${MKL_LIBRARIES})
-
-endif()
-
-#    HDF5
-find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(${HDF5_INCLUDE_DIRS})
-
-#    OpenCV
-find_package(OpenCV REQUIRED core highgui imgproc)
-include_directories(${OpenCV_INCLUDE_DIRS})
-
-#    LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(${LEVELDB_INCLUDE})
-if(LEVELDB_FOUND)
-    find_package(Snappy REQUIRED)
-    include_directories(${SNAPPY_INCLUDE_DIR})
-    set(LEVELDB_LIBS
-        ${LEVELDB_LIBS}
-        ${SNAPPY_LIBS}
-    )
-endif()
-
-#    LMDB
-find_package(LMDB REQUIRED)
-include_directories(${LMDB_INCLUDE_DIR})
-
-#    Boost
-find_package(Boost 1.46 COMPONENTS system thread REQUIRED)
-include_directories( ${Boost_INCLUDE_DIR} )
-link_directories( ${Boost_LIBRARY_DIRS} )
 
 add_subdirectory(proto)
 
@@ -77,26 +17,8 @@ add_library(caffe ${CPP_SOURCES})
 # both depend on proto
 add_dependencies(caffe proto)
 
-#    CUDA
+#    cuda sources
 if(NOT CPU_ONLY)
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
-            -gencode arch=compute_20,code=sm_20
-            -gencode arch=compute_20,code=sm_21
-            -gencode arch=compute_30,code=sm_30
-            -gencode arch=compute_35,code=sm_35
-    )
-    
-# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
-    # work-arounds
-if(Boost_VERSION EQUAL 105500)
-    # see https://svn.boost.org/trac/boost/ticket/9392
-    message(STATUS "Boost: Applying noinline work around")
-    # avoid warning for CMake >= 2.8.12
-    set(CUDA_NVCC_FLAGS
-      "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
-endif(Boost_VERSION EQUAL 105500)
-
-    #    cuda sources
     file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
     file(GLOB_RECURSE TEST_CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cu)
     list(REMOVE_ITEM CU_SOURCES ${TEST_CU_SOURCES})

From b9c1eec5a6821e21d12fa67fca98558c2dd3c5f4 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Sat, 4 Oct 2014 11:46:38 +0000
Subject: [PATCH 0895/2053] Fix CMake build of pycaffe - generate right shared
 library name - fix CMake linking with the new layer factory - find numpy

Fix finding protobuf.
---
 CMakeScripts/FindNumPy.cmake    | 103 ++++++++++++++++++++++
 CMakeScripts/FindProtobuf.cmake | 152 ++++++++++++++++++++++++++++++++
 python/CMakeLists.txt           |  48 ++++++++--
 src/caffe/proto/CMakeLists.txt  |  11 ++-
 4 files changed, 308 insertions(+), 6 deletions(-)
 create mode 100644 CMakeScripts/FindNumPy.cmake
 create mode 100644 CMakeScripts/FindProtobuf.cmake

diff --git a/CMakeScripts/FindNumPy.cmake b/CMakeScripts/FindNumPy.cmake
new file mode 100644
index 00000000000..baf21541e63
--- /dev/null
+++ b/CMakeScripts/FindNumPy.cmake
@@ -0,0 +1,103 @@
+# - Find the NumPy libraries
+# This module finds if NumPy is installed, and sets the following variables
+# indicating where it is.
+#
+# TODO: Update to provide the libraries and paths for linking npymath lib.
+#
+#  NUMPY_FOUND               - was NumPy found
+#  NUMPY_VERSION             - the version of NumPy found as a string
+#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
+#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
+#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
+#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
+#  NUMPY_INCLUDE_DIRS        - path to the NumPy include files
+
+#============================================================================
+# Copyright 2012 Continuum Analytics, Inc.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files
+# (the "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+# OTHER DEALINGS IN THE SOFTWARE.
+#
+#============================================================================
+
+# Finding NumPy involves calling the Python interpreter
+if(NumPy_FIND_REQUIRED)
+    find_package(PythonInterp REQUIRED)
+else()
+    find_package(PythonInterp)
+endif()
+
+if(NOT PYTHONINTERP_FOUND)
+    set(NUMPY_FOUND FALSE)
+    return()
+endif()
+
+execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
+    "import numpy as n; print(n.__version__); print(n.get_include());"
+    RESULT_VARIABLE _NUMPY_SEARCH_SUCCESS
+    OUTPUT_VARIABLE _NUMPY_VALUES_OUTPUT
+    ERROR_VARIABLE _NUMPY_ERROR_VALUE
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+if(NOT _NUMPY_SEARCH_SUCCESS MATCHES 0)
+    if(NumPy_FIND_REQUIRED)
+        message(FATAL_ERROR
+            "NumPy import failure:\n${_NUMPY_ERROR_VALUE}")
+    endif()
+    set(NUMPY_FOUND FALSE)
+    return()
+endif()
+
+# Convert the process output into a list
+string(REGEX REPLACE ";" "\\\\;" _NUMPY_VALUES ${_NUMPY_VALUES_OUTPUT})
+string(REGEX REPLACE "\n" ";" _NUMPY_VALUES ${_NUMPY_VALUES})
+# Just in case there is unexpected output from the Python command.
+list(GET _NUMPY_VALUES -2 NUMPY_VERSION)
+list(GET _NUMPY_VALUES -1 NUMPY_INCLUDE_DIRS)
+
+string(REGEX MATCH "^[0-9]+\\.[0-9]+\\.[0-9]+" _VER_CHECK "${NUMPY_VERSION}")
+if("${_VER_CHECK}" STREQUAL "")
+    # The output from Python was unexpected. Raise an error always
+    # here, because we found NumPy, but it appears to be corrupted somehow.
+    message(FATAL_ERROR
+        "Requested version and include path from NumPy, got instead:\n${_NUMPY_VALUES_OUTPUT}\n")
+    return()
+endif()
+
+# Make sure all directory separators are '/'
+string(REGEX REPLACE "\\\\" "/" NUMPY_INCLUDE_DIRS ${NUMPY_INCLUDE_DIRS})
+
+# Get the major and minor version numbers
+string(REGEX REPLACE "\\." ";" _NUMPY_VERSION_LIST ${NUMPY_VERSION})
+list(GET _NUMPY_VERSION_LIST 0 NUMPY_VERSION_MAJOR)
+list(GET _NUMPY_VERSION_LIST 1 NUMPY_VERSION_MINOR)
+list(GET _NUMPY_VERSION_LIST 2 NUMPY_VERSION_PATCH)
+string(REGEX MATCH "[0-9]*" NUMPY_VERSION_PATCH ${NUMPY_VERSION_PATCH})
+math(EXPR NUMPY_VERSION_DECIMAL
+    "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
+
+find_package_message(NUMPY
+    "Found NumPy: version \"${NUMPY_VERSION}\" ${NUMPY_INCLUDE_DIRS}"
+    "${NUMPY_INCLUDE_DIRS}${NUMPY_VERSION}")
+
+set(NUMPY_FOUND TRUE)
+
+
diff --git a/CMakeScripts/FindProtobuf.cmake b/CMakeScripts/FindProtobuf.cmake
new file mode 100644
index 00000000000..0f94f498197
--- /dev/null
+++ b/CMakeScripts/FindProtobuf.cmake
@@ -0,0 +1,152 @@
+# Locate and configure the Google Protocol Buffers library.
+# Defines the following variables:
+#
+#   PROTOBUF_FOUND - Found the Google Protocol Buffers library
+#   PROTOBUF_INCLUDE_DIRS - Include directories for Google Protocol Buffers
+#   PROTOBUF_LIBRARIES - The protobuf library
+#
+# The following cache variables are also defined:
+#   PROTOBUF_LIBRARY - The protobuf library
+#   PROTOBUF_PROTOC_LIBRARY   - The protoc library
+#   PROTOBUF_INCLUDE_DIR - The include directory for protocol buffers
+#   PROTOBUF_PROTOC_EXECUTABLE - The protoc compiler
+#
+#  ====================================================================
+#  Example:
+#
+#   find_package(Protobuf REQUIRED)
+#   include_directories(${PROTOBUF_INCLUDE_DIRS})
+#
+#   include_directories(${CMAKE_CURRENT_BINARY_DIR})
+#   PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS foo.proto)
+#   add_executable(bar bar.cc ${PROTO_SRCS} ${PROTO_HDRS})
+#   target_link_libraries(bar ${PROTOBUF_LIBRARY})
+#
+# NOTE: You may need to link against pthreads, depending
+# on the platform.
+#  ====================================================================
+#
+# PROTOBUF_GENERATE_CPP (public function)
+#   SRCS = Variable to define with autogenerated
+#          source files
+#   HDRS = Variable to define with autogenerated
+#          header files
+#   ARGN = proto files
+#
+#  ====================================================================
+
+
+#=============================================================================
+# Copyright 2009 Kitware, Inc.
+# Copyright 2009 Philip Lowman <philip@yhbt.com>
+# Copyright 2008 Esben Mose Hansen, Ange Optimization ApS
+#
+# Distributed under the OSI-approved BSD License (the "License");
+# see accompanying file Copyright.txt for details.
+#
+# This software is distributed WITHOUT ANY WARRANTY; without even the
+# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the License for more information.
+#=============================================================================
+# (To distributed this file outside of CMake, substitute the full
+#  License text for the above reference.)
+
+function(PROTOBUF_GENERATE_PYTHON SRCS)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: PROTOBUF_GENERATE_PYTHON() called without any proto files")
+    return()
+  endif(NOT ARGN)
+
+  set(${SRCS})
+  foreach(FIL ${ARGN})
+    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
+    get_filename_component(FIL_WE ${FIL} NAME_WE)
+    
+    
+    list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}_pb2.py")
+
+    add_custom_command(
+      OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}_pb2.py"
+      COMMAND  ${PROTOBUF_PROTOC_EXECUTABLE}
+      ARGS --python_out  ${CMAKE_CURRENT_BINARY_DIR} --proto_path ${CMAKE_CURRENT_SOURCE_DIR} 
+${ABS_FIL}
+      DEPENDS ${ABS_FIL}
+      COMMENT "Running Python protocol buffer compiler on ${FIL}"
+      VERBATIM )
+  endforeach()
+
+
+  set_source_files_properties(${${SRCS}} PROPERTIES GENERATED TRUE)
+  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
+endfunction()
+
+
+function(PROTOBUF_GENERATE_CPP SRCS HDRS)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: PROTOBUF_GENERATE_CPP() called without any proto files")
+    return()
+  endif(NOT ARGN)
+
+  set(${SRCS})
+  set(${HDRS})
+  foreach(FIL ${ARGN})
+    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
+    get_filename_component(FIL_WE ${FIL} NAME_WE)
+    
+    list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc")
+    list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h")
+
+    add_custom_command(
+      OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc"
+             "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h"
+      COMMAND  ${PROTOBUF_PROTOC_EXECUTABLE}
+      ARGS --cpp_out  ${CMAKE_CURRENT_BINARY_DIR} --proto_path ${CMAKE_CURRENT_SOURCE_DIR} 
+${ABS_FIL}
+      DEPENDS ${ABS_FIL}
+      COMMENT "Running C++ protocol buffer compiler on ${FIL}"
+      VERBATIM )
+  endforeach()
+
+  set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE)
+  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
+  set(${HDRS} ${${HDRS}} PARENT_SCOPE)
+endfunction()
+
+
+find_path(PROTOBUF_INCLUDE_DIR google/protobuf/service.h)
+
+# Google's provided vcproj files generate libraries with a "lib"
+# prefix on Windows
+if(WIN32)
+    set(PROTOBUF_ORIG_FIND_LIBRARY_PREFIXES "${CMAKE_FIND_LIBRARY_PREFIXES}")
+    set(CMAKE_FIND_LIBRARY_PREFIXES "lib" "")
+endif()
+
+find_library(PROTOBUF_LIBRARY NAMES protobuf
+             DOC "The Google Protocol Buffers Library"
+)
+find_library(PROTOBUF_PROTOC_LIBRARY NAMES protoc
+             DOC "The Google Protocol Buffers Compiler Library"
+)
+find_program(PROTOBUF_PROTOC_EXECUTABLE NAMES protoc
+             DOC "The Google Protocol Buffers Compiler"
+)
+
+mark_as_advanced(PROTOBUF_INCLUDE_DIR
+                 PROTOBUF_LIBRARY
+                 PROTOBUF_PROTOC_LIBRARY
+                 PROTOBUF_PROTOC_EXECUTABLE)
+
+# Restore original find library prefixes
+if(WIN32)
+    set(CMAKE_FIND_LIBRARY_PREFIXES "${PROTOBUF_ORIG_FIND_LIBRARY_PREFIXES}")
+endif()
+
+include(FindPackageHandleStandardArgs)
+FIND_PACKAGE_HANDLE_STANDARD_ARGS(PROTOBUF DEFAULT_MSG
+    PROTOBUF_LIBRARY PROTOBUF_INCLUDE_DIR)
+
+if(PROTOBUF_FOUND)
+    set(PROTOBUF_INCLUDE_DIRS ${PROTOBUF_INCLUDE_DIR})
+    set(PROTOBUF_LIBRARIES    ${PROTOBUF_LIBRARY})
+endif()
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 343c863adf5..6470517d213 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -1,20 +1,58 @@
 project( Python )
 
 #    Python
-find_package(PythonLibs REQUIRED)
-include_directories(${PYTHON_INCLUDE_DIRS})
+find_package(PythonLibs 2.7 REQUIRED)
+
+#   Numpy
+find_package(NumPy REQUIRED)
 
 #    Boost.Python
 find_package(Boost 1.46 COMPONENTS python REQUIRED)
-include_directories(${Boost_INCLUDE_DIRS})
+
+
+
+#In case you have both python2 and python3 installed the quickest way to
+#compile pycaffe with cmake is to replace the following hardcoded paths.
+#Althernativley the Find* scripts could be rewritten to support choice of
+#of python version.
+#if(${PYTHONLIBS_VERSION_STRING} MATCHES "^[3-9]+\\.[0-9]+(\\.[0-9]+.*)?$") 
+#    
+#    set( PYTHON_INCLUDE_DIRS "/usr/include/python2.7")
+#    set( PYTHON_LIBRARIES    "/usr/lib64/libpython2.7.so")
+#    set( NUMPY_INCLUDE_DIRS  "/usr/lib64/python2.7/site-packages/numpy/core/include/")
+#    set( PYTHON_LIBRARIES    "/usr/lib64/python2.7/site-packages/numpy/lib/")
+#    set(Boost_LIBRARIES      "/usr/lib64/libboost_python-2.7-mt.so")
+#
+#    message( "Warning: cmake found python3 by default, switching to hardcoded paths")
+#
+#    message( "PYTHON_INCLUDE_DIRS =/usr/include/python2.7")
+#    message( "PYTHON_LIBRARIES    =/usr/lib64/libpython2.7.so")
+#    message( "NUMPY_INCLUDE_DIRS  =/usr/lib64/python2.7/site-packages/numpy/core/include/")
+#    message( "PYTHON_LIBRARIES    =/usr/lib64/python2.7/site-packages/numpy/lib/")
+#    message( "Boost_LIBRARIES     =/usr/lib64/libboost_python-2.7-mt.so")
+#endif()
+
+
+include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIRS}
+    ${Boost_INCLUDE_DIRS})
 
 file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 
 add_library(pycaffe SHARED ${Python_SOURCES})
-target_link_libraries(pycaffe caffe ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
 
-###    Install    #################################################################################
+add_dependencies(pycaffe protoPy)
+
+target_link_libraries(pycaffe ${CAFFE_STATIC_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+
+set_target_properties(pycaffe PROPERTIES PREFIX "")
+set_target_properties(pycaffe PROPERTIES OUTPUT_NAME "_caffe")
+
+###    Install    #############################################################
 
 install(DIRECTORY caffe DESTINATION python)
 install(FILES requirements.txt DESTINATION python)
+
+#This installs a library named "libpycaffe.so"
 install(TARGETS pycaffe DESTINATION python/caffe)
+
+
diff --git a/src/caffe/proto/CMakeLists.txt b/src/caffe/proto/CMakeLists.txt
index 186a856509b..12e7ce0a326 100644
--- a/src/caffe/proto/CMakeLists.txt
+++ b/src/caffe/proto/CMakeLists.txt
@@ -14,11 +14,15 @@ endif()
 include_directories(${PROTOBUF_INCLUDE_DIR})
 file(GLOB ProtoFiles "${CMAKE_CURRENT_SOURCE_DIR}/*.proto")
 PROTOBUF_GENERATE_CPP(ProtoSources ProtoHeaders ${ProtoFiles})
+PROTOBUF_GENERATE_PYTHON(ProtoSourcesPy ${ProtoFiles})
+
+add_custom_target(protoPy DEPENDS ${ProtoSourcesPy})
 
 add_library(proto
         ${ProtoSources}
         ${ProtoHeaders}
-)
+        )
+   
 
 target_link_libraries(proto ${PROTOBUF_LIBRARIES})
 
@@ -35,3 +39,8 @@ foreach(header ${ProtoHeaders})
 )
 
 endforeach(header)
+
+file(WRITE __init__.py)
+install(PROGRAMS __init__.py       DESTINATION python/caffe/proto)
+install(PROGRAMS ${ProtoSourcesPy} DESTINATION python/caffe/proto)
+

From 57d14637d6b337d4a1bb854dcb402c56a6eb44d5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 8 Oct 2014 19:56:07 -0700
Subject: [PATCH 0896/2053] [example] fix data script paths for flickr
 fine-tuning

---
 examples/finetune_flickr_style/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 4220102e7f7..e0183510bef 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -34,7 +34,7 @@ All steps are to be done from the caffe root directory.
 The dataset is distributed as a list of URLs with corresponding labels.
 Using a script, we will download a small subset of the data and split it into train and val sets.
 
-    caffe % ./models/finetune_flickr_style/assemble_data.py -h
+    caffe % ./examples/finetune_flickr_style/assemble_data.py -h
     usage: assemble_data.py [-h] [-s SEED] [-i IMAGES] [-w WORKERS]
 
     Download a subset of Flickr Style to a directory
@@ -48,7 +48,7 @@ Using a script, we will download a small subset of the data and split it into tr
                             num workers used to download images. -x uses (all - x)
                             cores.
 
-    caffe % python models/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
+    caffe % python examples/finetune_flickr_style/assemble_data.py --workers=-1 --images=2000 --seed 831486
     Downloading 2000 images with 7 workers...
     Writing train/val for 1939 successfully downloaded images.
 

From 3ff2201f2706f8aa1522b635a2d46f4003abc032 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 9 Oct 2014 21:00:03 -0700
Subject: [PATCH 0897/2053] [example] add LeNet to MNIST title, fix paths to be
 from root

---
 examples/mnist/readme.md | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 44e0091f420..2b8f0d782d4 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -1,6 +1,6 @@
 ---
-title: MNIST Tutorial
-description: Train and test "LeNet" on MNIST data.
+title: LeNet MNIST Tutorial
+description: Train and test "LeNet" on the MNIST handwritten digit data.
 category: example
 include_in_docs: true
 priority: 1
@@ -14,10 +14,9 @@ We will assume that you have Caffe successfully compiled. If not, please refer t
 
 You will first need to download and convert the data format from the MNIST website. To do this, simply run the following commands:
 
-    cd $CAFFE_ROOT/data/mnist
-    ./get_mnist.sh
-    cd $CAFFE_ROOT/examples/mnist
-    ./create_mnist.sh
+    cd $CAFFE_ROOT
+    ./data/mnist/get_mnist.sh
+    ./examples/mnist/create_mnist.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist_train_lmdb`, and `mnist_test_lmdb`.
 
@@ -228,8 +227,8 @@ Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/example
 
 Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_lenet.sh`, or the following command directly:
 
-    cd $CAFFE_ROOT/examples/mnist
-    ./train_lenet.sh
+    cd $CAFFE_ROOT
+    ./examples/mnist/train_lenet.sh
 
 `train_lenet.sh` is a simple script, but here is a quick explanation: the main tool for training is `caffe` with action `train` and the solver protobuf text file as its argument.
 

From 5d2fb984347339c0ca2988a21215a2647bb90781 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 9 Oct 2014 21:01:41 -0700
Subject: [PATCH 0898/2053] [example] re-title LeNet / MNIST heading too

---
 examples/mnist/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 2b8f0d782d4..33ed371b4a1 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -6,7 +6,7 @@ include_in_docs: true
 priority: 1
 ---
 
-# Training MNIST with Caffe
+# Training LeNet on MNIST with Caffe
 
 We will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](/installation.html). In this tutorial, we will assume that your Caffe installation is located at `CAFFE_ROOT`.
 

From cb60a41c246c36680431ad7cf5535395d61d5cb7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 9 Oct 2014 22:43:36 -0700
Subject: [PATCH 0899/2053] [docs] update homebrew instructions for boost and
 boost-python split

---
 docs/installation.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index dbf73d2c067..7cfa04eca1f 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -18,7 +18,7 @@ Caffe depends on several software packages.
 * [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
 * [OpenCV](http://opencv.org/).
-* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 is tested)
+* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
 * `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
 * For the Python wrapper
     * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
@@ -145,7 +145,7 @@ In other `ENV` settings, things may not work as expected.
 
 Simply run the following:
 
-    brew install --build-from-source --with-python boost
+    brew install --build-from-source boost boost-python
     brew install --with-python protobuf
     for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
 
@@ -186,16 +186,16 @@ After this, run
 
     for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
     brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --with-python --fresh -vd boost
+    brew install --build-from-source --fresh -vd boost boost-python
 
 **Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
 
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
 
-**Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
+**Note** that in order to build the Caffe Python wrappers you must install `boost` and `boost-python`:
 
-    brew install --build-from-source --with-python --fresh -vd boost
+    brew install --build-from-source --fresh -vd boost boost-python
 
 **Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
 

From e6deb5dfaccec378dd3e1481dadc69b7ddd08502 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 9 Oct 2014 23:59:10 -0700
Subject: [PATCH 0900/2053] [docs] note boost 1.56 an CUDA conflict on OS X

---
 docs/installation.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/installation.md b/docs/installation.md
index 7cfa04eca1f..7eaf31c6d45 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -141,6 +141,8 @@ Do `brew edit opencv` and change the lines that look like the two lines below to
 **NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
 In other `ENV` settings, things may not work as expected.
 
+**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55.
+
 #### 10.8-specific Instructions
 
 Simply run the following:

From 44bc1552781eea958c228981a8638ddfcf80e3d9 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Thu, 9 Oct 2014 21:41:11 -0700
Subject: [PATCH 0901/2053] fix instantiation

---
 include/caffe/common.hpp                      | 23 +++++++++++++++++++
 src/caffe/layers/absval_layer.cu              |  2 +-
 src/caffe/layers/base_data_layer.cu           |  2 +-
 src/caffe/layers/bnll_layer.cu                |  2 +-
 src/caffe/layers/concat_layer.cu              |  2 +-
 src/caffe/layers/contrastive_loss_layer.cu    |  2 +-
 src/caffe/layers/conv_layer.cu                |  2 +-
 src/caffe/layers/cudnn_conv_layer.cu          |  2 +-
 src/caffe/layers/cudnn_pooling_layer.cu       |  2 +-
 src/caffe/layers/cudnn_relu_layer.cu          |  2 +-
 src/caffe/layers/cudnn_sigmoid_layer.cu       |  2 +-
 src/caffe/layers/cudnn_softmax_layer.cu       |  2 +-
 src/caffe/layers/cudnn_tanh_layer.cu          |  2 +-
 src/caffe/layers/dropout_layer.cu             |  2 +-
 src/caffe/layers/eltwise_layer.cu             |  2 +-
 src/caffe/layers/euclidean_loss_layer.cu      |  2 +-
 src/caffe/layers/exp_layer.cu                 |  2 +-
 src/caffe/layers/flatten_layer.cu             |  2 +-
 src/caffe/layers/hdf5_data_layer.cu           |  2 +-
 src/caffe/layers/hdf5_output_layer.cu         |  2 +-
 src/caffe/layers/im2col_layer.cu              |  2 +-
 src/caffe/layers/inner_product_layer.cu       |  2 +-
 src/caffe/layers/lrn_layer.cu                 | 13 ++++++++++-
 src/caffe/layers/mvn_layer.cu                 |  2 +-
 src/caffe/layers/pooling_layer.cu             |  2 +-
 src/caffe/layers/power_layer.cu               |  2 +-
 src/caffe/layers/relu_layer.cu                |  2 +-
 .../sigmoid_cross_entropy_loss_layer.cu       |  2 +-
 src/caffe/layers/sigmoid_layer.cu             |  2 +-
 src/caffe/layers/silence_layer.cu             |  2 +-
 src/caffe/layers/slice_layer.cu               |  2 +-
 src/caffe/layers/softmax_layer.cu             |  2 +-
 src/caffe/layers/softmax_loss_layer.cu        |  2 +-
 src/caffe/layers/split_layer.cu               |  2 +-
 src/caffe/layers/tanh_layer.cu                |  2 +-
 src/caffe/layers/threshold_layer.cu           |  2 +-
 36 files changed, 69 insertions(+), 35 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 811b45e2c15..e7776ba5d7b 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -34,9 +34,32 @@ private:\
 
 // Instantiate a class with float and double specifications.
 #define INSTANTIATE_CLASS(classname) \
+  char gInstantiationGuard##classname; \
   template class classname<float>; \
   template class classname<double>
 
+#define INSTANTIATE_LAYER_GPU_FORWARD(classname) \
+  template void classname<float>::Forward_gpu( \
+      const std::vector<Blob<float>*>& bottom, \
+      const std::vector<Blob<float>*>& top); \
+  template void classname<double>::Forward_gpu( \
+      const std::vector<Blob<double>*>& bottom, \
+      const std::vector<Blob<double>*>& top);
+
+#define INSTANTIATE_LAYER_GPU_BACKWARD(classname) \
+  template void classname<float>::Backward_gpu( \
+      const std::vector<Blob<float>*>& top, \
+      const std::vector<bool>& propagate_down, \
+      const std::vector<Blob<float>*>& bottom); \
+  template void classname<double>::Backward_gpu( \
+      const std::vector<Blob<double>*>& top, \
+      const std::vector<bool>& propagate_down, \
+      const std::vector<Blob<double>*>& bottom)
+
+#define INSTANTIATE_LAYER_GPU_FUNCS(classname) \
+  INSTANTIATE_LAYER_GPU_FORWARD(classname); \
+  INSTANTIATE_LAYER_GPU_BACKWARD(classname)
+
 // A simple macro to mark codes that are not implemented, so that when the code
 // is executed we will see a fatal log.
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index 48c766465fa..732d4c6252b 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -28,7 +28,7 @@ void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(AbsValLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(AbsValLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index ff1510361b6..204a16d260a 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -20,6 +20,6 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
   CreatePrefetchThread();
 }
 
-INSTANTIATE_CLASS(BasePrefetchingDataLayer);
+INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 3993bccd9b5..d963d0687d2 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -54,7 +54,7 @@ void BNLLLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(BNLLLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(BNLLLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 1b4e40aa415..88fc090025f 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -71,6 +71,6 @@ void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(ConcatLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ConcatLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 14b55b37173..78a55995a0a 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -86,6 +86,6 @@ void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(ContrastiveLossLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ContrastiveLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 27d6500d6ef..af14facb523 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -137,6 +137,6 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(ConvolutionLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index cf8607e1b78..071014e1b48 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -106,7 +106,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(CuDNNConvolutionLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNConvolutionLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 8b9e7f1bf10..1c113aad75f 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -33,7 +33,7 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
 }
 
-INSTANTIATE_CLASS(CuDNNPoolingLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNPoolingLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index e3900f37ab9..862508707a0 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -45,7 +45,7 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
 }
 
-INSTANTIATE_CLASS(CuDNNReLULayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 50bf5fcb9e0..31b094e25d4 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -35,7 +35,7 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
 }
 
-INSTANTIATE_CLASS(CuDNNSigmoidLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index aef66730bff..f328afdd831 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -35,7 +35,7 @@ void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(CuDNNSoftmaxLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSoftmaxLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index 20f887de8d0..bf9ec7cfac4 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -35,7 +35,7 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
 }
 
-INSTANTIATE_CLASS(CuDNNTanHLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);
 
 }  // namespace caffe
 #endif
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index fa737b86a89..df13d8ecb23 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -71,7 +71,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(DropoutLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(DropoutLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 9295cc6ad11..2247870d97f 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -130,6 +130,6 @@ void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(EltwiseLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(EltwiseLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index 0f0c1a86f9d..5b1de3ad2d9 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -39,6 +39,6 @@ void EuclideanLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(EuclideanLossLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(EuclideanLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu
index a0fd672a25a..2d75d8dd6c7 100644
--- a/src/caffe/layers/exp_layer.cu
+++ b/src/caffe/layers/exp_layer.cu
@@ -38,7 +38,7 @@ void ExpLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(ExpLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ExpLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index ef89bab6ff4..42abdad4499 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -18,6 +18,6 @@ void FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   bottom[0]->ShareDiff(*top[0]);
 }
 
-INSTANTIATE_CLASS(FlattenLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(FlattenLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 9f5daf230e7..02e3821d104 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -41,6 +41,6 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
-INSTANTIATE_CLASS(HDF5DataLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(HDF5DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index 929c1ad8e22..ae497c34fc2 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -38,6 +38,6 @@ void HDF5OutputLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   return;
 }
 
-INSTANTIATE_CLASS(HDF5OutputLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(HDF5OutputLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index bad789c17fe..9c338b14cb7 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -32,6 +32,6 @@ void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(Im2colLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(Im2colLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index 2164b4d4f16..a9e1784a205 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -51,6 +51,6 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(InnerProductLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(InnerProductLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 47b003bc4aa..58c39926c72 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -98,6 +98,10 @@ void LRNLayer<Dtype>::CrossChannelForward_gpu(
       n_threads, bottom_data, scale_data, -beta_, top_data);
   CUDA_POST_KERNEL_CHECK;
 }
+template void LRNLayer<float>::CrossChannelForward_gpu(
+    const vector<Blob<float>*>& bottom, const vector<Blob<float>*>& top);
+template void LRNLayer<double>::CrossChannelForward_gpu(
+    const vector<Blob<double>*>& bottom, const vector<Blob<double>*>& top);
 
 
 template <typename Dtype>
@@ -188,8 +192,15 @@ void LRNLayer<Dtype>::CrossChannelBackward_gpu(
       size_, -beta_, Dtype(2. * alpha_ * beta_ / size_),
       bottom[0]->mutable_gpu_diff());
 }
+template void LRNLayer<float>::CrossChannelBackward_gpu(
+    const vector<Blob<float>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<float>*>& bottom);
+template void LRNLayer<double>::CrossChannelBackward_gpu(
+    const vector<Blob<double>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<double>*>& bottom);
 
 
-INSTANTIATE_CLASS(LRNLayer);
+
+INSTANTIATE_LAYER_GPU_FUNCS(LRNLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index c2a241f093f..0667f50380f 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -139,7 +139,7 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(MVNLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(MVNLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index aec985a7864..0d3f2183e71 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -373,7 +373,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(PoolingLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(PoolingLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 367320a9b1f..90d944059b6 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -81,7 +81,7 @@ void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(PowerLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(PowerLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index 22d5f4b5c5e..b8924c855e5 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -59,7 +59,7 @@ void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(ReLULayer);
+INSTANTIATE_LAYER_GPU_FUNCS(ReLULayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 175f6f86574..d9db4af64e6 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -51,7 +51,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   }
 }
 
-INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index 6879ff7f570..e1af0657ec1 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -56,7 +56,7 @@ void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(SigmoidLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SigmoidLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index b350b1950bd..8d044ee7307 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -23,6 +23,6 @@ void SilenceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(SilenceLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SilenceLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index 657840809f6..b5c5e61533f 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -63,6 +63,6 @@ void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
 }
 
-INSTANTIATE_CLASS(SliceLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SliceLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 8ba31d75951..292ad2b396a 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -148,7 +148,7 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }
 
-INSTANTIATE_CLASS(SoftmaxLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SoftmaxLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 060dc24d06e..4cdaf51b165 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -21,7 +21,7 @@ void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Backward_cpu(top, propagate_down, bottom);
 }
 
-INSTANTIATE_CLASS(SoftmaxWithLossLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SoftmaxWithLossLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index 0513b204d91..a4f5df26452 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -33,6 +33,6 @@ void SplitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 
-INSTANTIATE_CLASS(SplitLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(SplitLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index a141f8e872e..f1a0fc88bfd 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -54,7 +54,7 @@ void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-INSTANTIATE_CLASS(TanHLayer);
+INSTANTIATE_LAYER_GPU_FUNCS(TanHLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 7e651290897..bfa7f159460 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -27,7 +27,7 @@ void ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 
-INSTANTIATE_CLASS(ThresholdLayer);
+INSTANTIATE_LAYER_GPU_FORWARD(ThresholdLayer);
 
 
 }  // namespace caffe

From b025da73e397b50e7f4136ad7546961d60ea934b Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 3 Oct 2014 17:14:20 -0700
Subject: [PATCH 0902/2053] Added Multistep, Poly and Sigmoid learning rate
 decay policies

Conflicts:
	include/caffe/solver.hpp
	src/caffe/proto/caffe.proto
	src/caffe/solver.cpp
---
 .../lenet/lenet_multistep_solver.prototxt     | 33 +++++++++++++++++
 .../lenet/lenet_stepearly_solver.prototxt     | 28 ++++++++++++++
 include/caffe/solver.hpp                      |  1 +
 src/caffe/proto/caffe.proto                   |  8 +++-
 src/caffe/solver.cpp                          | 37 ++++++++++++++++---
 5 files changed, 99 insertions(+), 8 deletions(-)
 create mode 100644 examples/lenet/lenet_multistep_solver.prototxt
 create mode 100644 examples/lenet/lenet_stepearly_solver.prototxt

diff --git a/examples/lenet/lenet_multistep_solver.prototxt b/examples/lenet/lenet_multistep_solver.prototxt
new file mode 100644
index 00000000000..fadd7c904d7
--- /dev/null
+++ b/examples/lenet/lenet_multistep_solver.prototxt
@@ -0,0 +1,33 @@
+# The training protocol buffer definition
+train_net: "lenet_train.prototxt"
+# The testing protocol buffer definition
+test_net: "lenet_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "multistep"
+gamma: 0.9
+stepvalue: 1000
+stepvalue: 2000
+stepvalue: 2500
+stepvalue: 3000
+stepvalue: 3500
+stepvalue: 4000
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# solver mode: 0 for CPU and 1 for GPU
+solver_mode: 1
+device_id: 1
diff --git a/examples/lenet/lenet_stepearly_solver.prototxt b/examples/lenet/lenet_stepearly_solver.prototxt
new file mode 100644
index 00000000000..efc6a335d8f
--- /dev/null
+++ b/examples/lenet/lenet_stepearly_solver.prototxt
@@ -0,0 +1,28 @@
+# The training protocol buffer definition
+train_net: "lenet_train.prototxt"
+# The testing protocol buffer definition
+test_net: "lenet_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "stepearly"
+gamma: 0.9
+stepearly: 1
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# solver mode: 0 for CPU and 1 for GPU
+solver_mode: 1
+device_id: 1
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 2bad0e2fe1e..b20912606f8 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -57,6 +57,7 @@ class Solver {
 
   SolverParameter param_;
   int iter_;
+  int current_step_;
   shared_ptr<Net<Dtype> > net_;
   vector<shared_ptr<Net<Dtype> > > test_nets_;
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f0404a09b90..949bafecdb2 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 34 (last added: average_loss)
+// SolverParameter next available ID: 35 (last added: stepvalue)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -124,7 +124,10 @@ message SolverParameter {
   // regularization types supported: L1 and L2
   // controlled by weight_decay
   optional string regularization_type = 29 [default = "L2"];
-  optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
+  // the stepsize for learning rate policy "step"
+  optional int32 stepsize = 13;
+  // the stepsize for learning rate policy "multistep"
+  repeated int32 stepvalue = 34;
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
   // whether to snapshot diff in the results or not. Snapshotting diff will help
@@ -166,6 +169,7 @@ message SolverState {
   optional int32 iter = 1; // The current iteration
   optional string learned_net = 2; // The file that stores the learned net.
   repeated BlobProto history = 3; // The history for sgd solvers
+  optional int32 current_step = 4 [default = 0]; // The current step for learning rate
 }
 
 enum Phase {
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 995df06ddc7..a13bca8b44a 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -158,6 +158,7 @@ template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
   Caffe::set_phase(Caffe::TRAIN);
   LOG(INFO) << "Solving " << net_->name();
+  LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
   PreSolve();
 
   iter_ = 0;
@@ -257,7 +258,6 @@ void Solver<Dtype>::TestAll() {
   }
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
   LOG(INFO) << "Iteration " << iter_
@@ -336,6 +336,7 @@ void Solver<Dtype>::Snapshot() {
   SnapshotSolverState(&state);
   state.set_iter(iter_);
   state.set_learned_net(model_filename);
+  state.set_current_step(current_step_);
   snapshot_filename = filename + ".solverstate";
   LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
   WriteProtoToBinaryFile(state, snapshot_filename.c_str());
@@ -351,6 +352,7 @@ void Solver<Dtype>::Restore(const char* state_file) {
     net_->CopyTrainedLayersFrom(net_param);
   }
   iter_ = state.iter();
+  current_step_ = state.current_step();
   RestoreSolverState(state);
 }
 
@@ -361,8 +363,15 @@ void Solver<Dtype>::Restore(const char* state_file) {
 //    - step: return base_lr * gamma ^ (floor(iter / step))
 //    - exp: return base_lr * gamma ^ iter
 //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
-// where base_lr, gamma, step and power are defined in the solver parameter
-// protocol buffer, and iter is the current iteration.
+//    - multistep: similar to step but it allows non uniform steps defined by
+//      stepvalue
+//    - poly: the effective learning rate follows a polynomial decay, to be
+//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+//    - sigmoid: the effective learning rate follows a sigmod decay
+//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+//
+// where base_lr, max_iter, gamma, step, stepvalue and power are defined
+// in the solver parameter protocol buffer, and iter is the current iteration.
 template <typename Dtype>
 Dtype SGDSolver<Dtype>::GetLearningRate() {
   Dtype rate;
@@ -370,22 +379,38 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
   if (lr_policy == "fixed") {
     rate = this->param_.base_lr();
   } else if (lr_policy == "step") {
-    int current_step = this->iter_ / this->param_.stepsize();
+    this->current_step_ = this->iter_ / this->param_.stepsize();
     rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), current_step);
+        pow(this->param_.gamma(), this->current_step_);
   } else if (lr_policy == "exp") {
     rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
   } else if (lr_policy == "inv") {
     rate = this->param_.base_lr() *
         pow(Dtype(1) + this->param_.gamma() * this->iter_,
             - this->param_.power());
+  } else if (lr_policy == "multistep") {
+    if (this->current_step_ < this->param_.stepvalue_size() &&
+          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
+      this->current_step_++;
+      LOG(INFO) << "MultiStep Status: Iteration " <<
+      this->iter_ << ", step = " << this->current_step_;
+    }
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "poly") {
+    rate = this->param_.base_lr() * pow(Dtype(1.) -
+        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
+        this->param_.power());
+  } else if (lr_policy == "sigmoid") {
+    rate = this->param_.base_lr() * (Dtype(1.) /
+        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
+          Dtype(this->param_.stepsize())))));
   } else {
     LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
   }
   return rate;
 }
 
-
 template <typename Dtype>
 void SGDSolver<Dtype>::PreSolve() {
   // Initialize the history

From 0598fe93ac7bdc3f51c413c081283fdbc918c056 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 11 Oct 2014 05:01:28 +0000
Subject: [PATCH 0903/2053] adding missing libraries - lm and lstdc++

---
 Makefile | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/Makefile b/Makefile
index 5fb597c0d60..35c37af50fb 100644
--- a/Makefile
+++ b/Makefile
@@ -168,9 +168,7 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 LIBRARIES += glog gflags protobuf leveldb snappy \
-	lmdb \
-	boost_system \
-	hdf5_hl hdf5 \
+	lmdb boost_system hdf5_hl hdf5 m \
 	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
@@ -235,7 +233,8 @@ ifeq ($(LINUX), 1)
 		WARNINGS += -Wno-uninitialized
 	endif
 	# boost::thread is reasonably called boost_thread (compare OS X)
-	LIBRARIES += boost_thread
+	# We will also explicitly add stdc++ to the link target.
+	LIBRARIES += boost_thread stdc++
 endif
 
 # OS X:

From b992de2afefe2324dc9f0804b031d3dddfb24299 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 11 Oct 2014 00:05:57 -0700
Subject: [PATCH 0904/2053] [docs] pip install packages in order for
 dependencies

---
 docs/installation.md    | 2 +-
 python/requirements.txt | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 7eaf31c6d45..6d80aaef635 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -59,7 +59,7 @@ The main requirements are `numpy` and `boost.python` (provided by boost). `panda
 
 You can install the dependencies with
 
-    pip install -r /path/to/caffe/python/requirements.txt
+    cat caffe/python/requirements.txt | xargs pip install
 
 but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
diff --git a/python/requirements.txt b/python/requirements.txt
index 5c076b668df..6d42c0ed3fb 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -8,6 +8,7 @@ nose>=1.3.0
 numpy>=1.7.1
 pandas>=0.12.0
 protobuf>=2.5.0
+python-dateutil>=1.4,<2
 python-gflags>=2.0
 scikit-image>=0.9.3
 scikit-learn>=0.14.1

From 5fb32067e059ee1a2c631b2283e17458cee64331 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 11 Oct 2014 00:25:27 -0700
Subject: [PATCH 0905/2053] [docs] pip install harder

---
 docs/installation.md    |  2 +-
 python/requirements.txt | 14 +++++++-------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 6d80aaef635..d8fb1c019e2 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -59,7 +59,7 @@ The main requirements are `numpy` and `boost.python` (provided by boost). `panda
 
 You can install the dependencies with
 
-    cat caffe/python/requirements.txt | xargs pip install
+    for req in $(cat requirements.txt); do sudo pip install $req; done
 
 but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
diff --git a/python/requirements.txt b/python/requirements.txt
index 6d42c0ed3fb..4c35dcb0b6f 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -1,15 +1,15 @@
 Cython>=0.19.2
-h5py>=2.2.0
+numpy>=1.7.1
+scipy>=0.13.2
+scikit-image>=0.9.3
+scikit-learn>=0.14.1
+matplotlib>=1.3.1
 ipython>=1.1.0
+h5py>=2.2.0
 leveldb>=0.191
-matplotlib>=1.3.1
 networkx>=1.8.1
 nose>=1.3.0
-numpy>=1.7.1
 pandas>=0.12.0
-protobuf>=2.5.0
 python-dateutil>=1.4,<2
+protobuf>=2.5.0
 python-gflags>=2.0
-scikit-image>=0.9.3
-scikit-learn>=0.14.1
-scipy>=0.13.2

From 802ee6cbd2925a6db55f9c56d939a233403bd556 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 23 Sep 2014 10:47:40 -0400
Subject: [PATCH 0906/2053] Minor fixes.  (1) cudnn.hpp uses CHECK_EQ
 internally, so it needs to include glog at some point.  Including
 caffe/common.hpp.  (2) I often misconfigure some layer and softmax breaks
 when the dimensionality is too small for the input layers.  Check and fail
 fast.  (3) CV_LOAD_IMAGE_COLOR is deprecated and has been removed in OpenCV
 3.0.  Replaced with cv::IMREAD_COLOR.

---
 include/caffe/util/cudnn.hpp            | 1 +
 src/caffe/layers/softmax_loss_layer.cpp | 4 +++-
 src/caffe/layers/window_data_layer.cpp  | 2 +-
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index aca5bd713fc..eaed7333df8 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -4,6 +4,7 @@
 
 #include <cudnn.h>
 
+#include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #define CUDNN_CHECK(condition) \
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 651320c77f8..d39429cee5d 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -43,8 +43,10 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
     for (int j = 0; j < spatial_dim; j++) {
+      const int label_value = static_cast<int>(label[i * spatial_dim + j]);
+      CHECK_GT(dim, label_value * spatial_dim);
       loss -= log(std::max(prob_data[i * dim +
-          static_cast<int>(label[i * spatial_dim + j]) * spatial_dim + j],
+          label_value * spatial_dim + j],
                            Dtype(FLT_MIN)));
     }
   }
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 009bf97c612..c6d0be0f509 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -233,7 +233,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       pair<std::string, vector<int> > image =
           image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
 
-      cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
+      cv::Mat cv_img = cv::imread(image.first, cv::IMREAD_COLOR);
       if (!cv_img.data) {
         LOG(ERROR) << "Could not open or find file " << image.first;
         return;

From 4587b2f9043996fed36fc989c00329ec829ecae9 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 23 Sep 2014 11:29:31 -0400
Subject: [PATCH 0907/2053] OpenCV should be compiled using pkg-config options.

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 35c37af50fb..c8226cb6872 100644
--- a/Makefile
+++ b/Makefile
@@ -168,8 +168,7 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 LIBRARIES += glog gflags protobuf leveldb snappy \
-	lmdb boost_system hdf5_hl hdf5 m \
-	opencv_core opencv_highgui opencv_imgproc
+	lmdb boost_system hdf5_hl hdf5 m
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
@@ -330,6 +329,7 @@ NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
 LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
+		`pkg-config opencv --libs` \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 

From 82b614b29ba48cc4be81aa41dd688dfba82a08e1 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Mon, 29 Sep 2014 12:45:25 -0400
Subject: [PATCH 0908/2053] Added version dependent test for IMREAD_COLOR.

---
 src/caffe/layers/window_data_layer.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index c6d0be0f509..62ca872a32b 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -21,6 +21,10 @@
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
 
+#if CV_VERSION_MAJOR == 3
+const int CV_LOAD_IMAGE_COLOR = cv::IMREAD_COLOR;
+#endif
+
 namespace caffe {
 
 template <typename Dtype>
@@ -233,7 +237,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       pair<std::string, vector<int> > image =
           image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
 
-      cv::Mat cv_img = cv::imread(image.first, cv::IMREAD_COLOR);
+      cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
       if (!cv_img.data) {
         LOG(ERROR) << "Could not open or find file " << image.first;
         return;

From 94394a059c5b0440590f561a594b84c271bc3154 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Fri, 10 Oct 2014 18:42:27 -0400
Subject: [PATCH 0909/2053] Fixed CMakeList to work with OpenCV 3.

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index b97eb565f45..d6b08470a7f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -135,7 +135,7 @@ find_package(LMDB REQUIRED)
 include_directories(${LMDB_INCLUDE_DIR})
 
 #    OpenCV
-find_package(OpenCV REQUIRED core highgui imgproc)
+find_package(OpenCV REQUIRED)
 include_directories(${OpenCV_INCLUDE_DIRS})
 
 ###    Subdirectories    ##########################################################################

From 0160a0bc16055948e79dd214a61c33041994d163 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 11 Oct 2014 19:35:06 +0000
Subject: [PATCH 0910/2053] fix flaky test EXPECT_EQ code, using
 EXPECT_FLOAT_NEAR per Jeff

---
 src/caffe/test/test_net.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 930486dd6c5..611d11bd832 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1155,7 +1155,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   for (int i = 0; i < num_params; ++i) {
     const Dtype param_asum =
        caffe_cpu_asum(params2[i]->count(), params2[i]->cpu_diff());
-    EXPECT_EQ(param_asum, param_asums[i]);
+    EXPECT_FLOAT_EQ(param_asum, param_asums[i]);
   }
 
   // Change a subset of the learning rates to zero; check that we see zero
@@ -1172,9 +1172,9 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
     const Dtype param_asum =
        caffe_cpu_asum(params3[i]->count(), params3[i]->cpu_diff());
     if (i == 1 || i == 2) {
-      EXPECT_EQ(0, param_asum);
+      EXPECT_FLOAT_EQ(0, param_asum);
     } else {
-      EXPECT_EQ(param_asum, param_asums[i]);
+      EXPECT_FLOAT_EQ(param_asum, param_asums[i]);
     }
   }
 
@@ -1191,9 +1191,9 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
     const Dtype param_asum =
        caffe_cpu_asum(params4[i]->count(), params4[i]->cpu_diff());
     if (i == 0 || i == 3) {
-      EXPECT_EQ(0, param_asum);
+      EXPECT_FLOAT_EQ(0, param_asum);
     } else {
-      EXPECT_EQ(param_asum, param_asums[i]);
+      EXPECT_FLOAT_EQ(param_asum, param_asums[i]);
     }
   }
 }

From fc5ba940744b7294846a63ee5f427b934af9cc6d Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Sat, 11 Oct 2014 19:35:40 +0000
Subject: [PATCH 0911/2053] fix flaky math functions, remove unnecessary
 instantiations.

---
 include/caffe/util/math_functions.hpp | 11 +++--------
 src/caffe/util/math_functions.cpp     |  3 ---
 2 files changed, 3 insertions(+), 11 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index a78eb5bc978..12823f8da0c 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -108,7 +108,7 @@ Dtype caffe_cpu_asum(const int n, const Dtype* x);
 // the branchless, type-safe version from
 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
 template<typename Dtype>
-inline char caffe_sign(Dtype val) {
+inline int8_t caffe_sign(Dtype val) {
   return (Dtype(0) < val) - (val < Dtype(0));
 }
 
@@ -127,12 +127,6 @@ inline char caffe_sign(Dtype val) {
     } \
   }
 
-#define INSTANTIATE_CAFFE_CPU_UNARY_FUNC(name) \
-  template <> \
-  void caffe_cpu_##name<float>(const int n, const float* x, float* y); \
-  template <> \
-  void caffe_cpu_##name<double>(const int n, const double* x, double* y)
-
 // output is 1 for the positives, 0 for zero, and -1 for the negatives
 DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
 
@@ -140,7 +134,8 @@ DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
 // and we don't want that to expand here when CUDA headers are also included.
-DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = (std::signbit)(x[i]));
+DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
+    y[i] = static_cast<bool>((std::signbit)(x[i])));
 
 DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
 
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index c3afd2ffbc5..13e17be582b 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -370,9 +370,6 @@ double caffe_cpu_asum<double>(const int n, const double* x) {
   return cblas_dasum(n, x, 1);
 }
 
-INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sign);
-INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sgnbit);
-
 template <>
 void caffe_cpu_scale<float>(const int n, const float alpha, const float *x,
                             float* y) {

From 94f00c076f973627cd552b4b0fde6dd534039f3a Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Mon, 13 Oct 2014 13:48:27 -0700
Subject: [PATCH 0912/2053] some namespace simplification

---
 include/caffe/common.hpp       | 1 +
 src/caffe/layers/exp_layer.cpp | 4 ++--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index e7776ba5d7b..81b2e9ae101 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -74,6 +74,7 @@ using boost::shared_ptr;
 using std::fstream;
 using std::ios;
 using std::isnan;
+using std::isinf;
 using std::iterator;
 using std::make_pair;
 using std::map;
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index 25da1201c10..2c99bfcff93 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -18,9 +18,9 @@ void ExpLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // If base == -1, interpret the base as e and set log_base = 1 exactly.
   // Otherwise, calculate its log explicitly.
   const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
-  CHECK(!std::isnan(log_base))
+  CHECK(!isnan(log_base))
       << "NaN result: log(base) = log(" << base << ") = " << log_base;
-  CHECK(!std::isinf(log_base))
+  CHECK(!isinf(log_base))
       << "Inf result: log(base) = log(" << base << ") = " << log_base;
   const Dtype input_scale = this->layer_param_.exp_param().scale();
   const Dtype input_shift = this->layer_param_.exp_param().shift();

From 2f88daba0a1187ed2828ddc58349cd71c13f11a5 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Mon, 13 Oct 2014 15:25:41 -0700
Subject: [PATCH 0913/2053] move registration code to corresponding cpp files.

---
 src/caffe/layer_factory.cpp                   | 93 +++++++------------
 src/caffe/layers/absval_layer.cpp             |  3 +-
 src/caffe/layers/accuracy_layer.cpp           |  2 +-
 src/caffe/layers/argmax_layer.cpp             |  1 +
 src/caffe/layers/bnll_layer.cpp               |  3 +-
 src/caffe/layers/concat_layer.cpp             |  2 +-
 src/caffe/layers/contrastive_loss_layer.cpp   |  2 +-
 src/caffe/layers/conv_layer.cpp               |  1 -
 src/caffe/layers/data_layer.cpp               |  2 +-
 src/caffe/layers/dropout_layer.cpp            |  3 +-
 src/caffe/layers/dummy_data_layer.cpp         |  2 +-
 src/caffe/layers/eltwise_layer.cpp            |  3 +-
 src/caffe/layers/euclidean_loss_layer.cpp     |  2 +-
 src/caffe/layers/exp_layer.cpp                |  3 +-
 src/caffe/layers/flatten_layer.cpp            |  2 +-
 src/caffe/layers/hdf5_data_layer.cpp          |  2 +-
 src/caffe/layers/hdf5_output_layer.cpp        |  2 +-
 src/caffe/layers/hinge_loss_layer.cpp         |  2 +-
 src/caffe/layers/im2col_layer.cpp             |  2 +-
 src/caffe/layers/image_data_layer.cpp         |  2 +-
 src/caffe/layers/infogain_loss_layer.cpp      |  2 +-
 src/caffe/layers/inner_product_layer.cpp      |  2 +-
 src/caffe/layers/lrn_layer.cpp                |  3 +-
 src/caffe/layers/memory_data_layer.cpp        |  2 +-
 .../multinomial_logistic_loss_layer.cpp       |  2 +-
 src/caffe/layers/mvn_layer.cpp                |  3 +-
 src/caffe/layers/power_layer.cpp              |  3 +-
 .../sigmoid_cross_entropy_loss_layer.cpp      |  3 +-
 src/caffe/layers/silence_layer.cpp            |  2 +-
 src/caffe/layers/slice_layer.cpp              |  2 +-
 src/caffe/layers/softmax_layer.cpp            |  2 -
 src/caffe/layers/softmax_loss_layer.cpp       |  3 +-
 src/caffe/layers/split_layer.cpp              |  2 +-
 src/caffe/layers/threshold_layer.cpp          |  2 +-
 src/caffe/layers/window_data_layer.cpp        |  2 +-
 35 files changed, 63 insertions(+), 106 deletions(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 69863543c30..5a286cd4691 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -7,9 +7,6 @@
 
 namespace caffe {
 
-// GetLayer() defines the overall layer factory. The Get*Layer() functions
-// define factories for layers with multiple computational engines.
-
 // Get convolution layer according to engine.
 template <typename Dtype>
 Layer<Dtype>* GetConvolutionLayer(
@@ -32,6 +29,8 @@ Layer<Dtype>* GetConvolutionLayer(
   }
 }
 
+REGISTER_LAYER_CREATOR(CONVOLUTION, GetConvolutionLayer);
+
 // Get pooling layer according to engine.
 template <typename Dtype>
 Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
@@ -60,6 +59,8 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
   }
 }
 
+REGISTER_LAYER_CREATOR(POOLING, GetPoolingLayer);
+
 // Get relu layer according to engine.
 template <typename Dtype>
 Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
@@ -81,6 +82,8 @@ Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
   }
 }
 
+REGISTER_LAYER_CREATOR(RELU, GetReLULayer);
+
 // Get sigmoid layer according to engine.
 template <typename Dtype>
 Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
@@ -102,26 +105,7 @@ Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
   }
 }
 
-// Get tanh layer according to engine.
-template <typename Dtype>
-Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
-  TanHParameter_Engine engine = param.tanh_param().engine();
-  if (engine == TanHParameter_Engine_DEFAULT) {
-    engine = TanHParameter_Engine_CAFFE;
-#ifdef USE_CUDNN
-    engine = TanHParameter_Engine_CUDNN;
-#endif
-  }
-  if (engine == TanHParameter_Engine_CAFFE) {
-    return new TanHLayer<Dtype>(param);
-#ifdef USE_CUDNN
-  } else if (engine == TanHParameter_Engine_CUDNN) {
-    return new CuDNNTanHLayer<Dtype>(param);
-#endif
-  } else {
-    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
-  }
-}
+REGISTER_LAYER_CREATOR(SIGMOID, GetSigmoidLayer);
 
 // Get softmax layer according to engine.
 template <typename Dtype>
@@ -144,46 +128,31 @@ Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
   }
 }
 
-// Layers that have a specific creator function.
-REGISTER_LAYER_CREATOR(CONVOLUTION, GetConvolutionLayer);
-REGISTER_LAYER_CREATOR(POOLING, GetPoolingLayer);
-REGISTER_LAYER_CREATOR(RELU, GetReLULayer);
-REGISTER_LAYER_CREATOR(SIGMOID, GetSigmoidLayer);
 REGISTER_LAYER_CREATOR(SOFTMAX, GetSoftmaxLayer);
-REGISTER_LAYER_CREATOR(TANH, GetTanHLayer);
 
-// Layers that use their constructor as their default creator.
-REGISTER_LAYER_CLASS(ACCURACY, AccuracyLayer);
-REGISTER_LAYER_CLASS(ABSVAL, AbsValLayer);
-REGISTER_LAYER_CLASS(ARGMAX, ArgMaxLayer);
-REGISTER_LAYER_CLASS(BNLL, BNLLLayer);
-REGISTER_LAYER_CLASS(CONCAT, ConcatLayer);
-REGISTER_LAYER_CLASS(CONTRASTIVE_LOSS, ContrastiveLossLayer);
-REGISTER_LAYER_CLASS(DATA, DataLayer);
-REGISTER_LAYER_CLASS(DROPOUT, DropoutLayer);
-REGISTER_LAYER_CLASS(DUMMY_DATA, DummyDataLayer);
-REGISTER_LAYER_CLASS(EUCLIDEAN_LOSS, EuclideanLossLayer);
-REGISTER_LAYER_CLASS(ELTWISE, EltwiseLayer);
-REGISTER_LAYER_CLASS(EXP, ExpLayer);
-REGISTER_LAYER_CLASS(FLATTEN, FlattenLayer);
-REGISTER_LAYER_CLASS(HDF5_DATA, HDF5DataLayer);
-REGISTER_LAYER_CLASS(HDF5_OUTPUT, HDF5OutputLayer);
-REGISTER_LAYER_CLASS(HINGE_LOSS, HingeLossLayer);
-REGISTER_LAYER_CLASS(IMAGE_DATA, ImageDataLayer);
-REGISTER_LAYER_CLASS(IM2COL, Im2colLayer);
-REGISTER_LAYER_CLASS(INFOGAIN_LOSS, InfogainLossLayer);
-REGISTER_LAYER_CLASS(INNER_PRODUCT, InnerProductLayer);
-REGISTER_LAYER_CLASS(LRN, LRNLayer);
-REGISTER_LAYER_CLASS(MEMORY_DATA, MemoryDataLayer);
-REGISTER_LAYER_CLASS(MVN, MVNLayer);
-REGISTER_LAYER_CLASS(MULTINOMIAL_LOGISTIC_LOSS, MultinomialLogisticLossLayer);
-REGISTER_LAYER_CLASS(POWER, PowerLayer);
-REGISTER_LAYER_CLASS(SILENCE, SilenceLayer);
-REGISTER_LAYER_CLASS(SIGMOID_CROSS_ENTROPY_LOSS, SigmoidCrossEntropyLossLayer);
-REGISTER_LAYER_CLASS(SLICE, SliceLayer);
-REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer);
-REGISTER_LAYER_CLASS(SPLIT, SplitLayer);
-REGISTER_LAYER_CLASS(THRESHOLD, ThresholdLayer);
-REGISTER_LAYER_CLASS(WINDOW_DATA, WindowDataLayer);
+// Get tanh layer according to engine.
+template <typename Dtype>
+Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
+  TanHParameter_Engine engine = param.tanh_param().engine();
+  if (engine == TanHParameter_Engine_DEFAULT) {
+    engine = TanHParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    engine = TanHParameter_Engine_CUDNN;
+#endif
+  }
+  if (engine == TanHParameter_Engine_CAFFE) {
+    return new TanHLayer<Dtype>(param);
+#ifdef USE_CUDNN
+  } else if (engine == TanHParameter_Engine_CUDNN) {
+    return new CuDNNTanHLayer<Dtype>(param);
+#endif
+  } else {
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+  }
+}
+
+REGISTER_LAYER_CREATOR(TANH, GetTanHLayer);
 
+// Layers that use their constructor as their default creator should be
+// registered in their corresponding cpp files. Do not registere them here.
 }  // namespace caffe
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index 54e484b115d..70e0b583e61 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -41,6 +41,5 @@ STUB_GPU(AbsValLayer);
 #endif
 
 INSTANTIATE_CLASS(AbsValLayer);
-
-
+REGISTER_LAYER_CLASS(ABSVAL, AbsValLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 399cf2a3585..800b848f3bc 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -64,5 +64,5 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(AccuracyLayer);
-
+REGISTER_LAYER_CLASS(ACCURACY, AccuracyLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index dbe0a834aa5..15e199eb600 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -58,5 +58,6 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(ArgMaxLayer);
+REGISTER_LAYER_CLASS(ARGMAX, ArgMaxLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index bae692509e3..cb3583ae8be 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -43,6 +43,5 @@ STUB_GPU(BNLLLayer);
 #endif
 
 INSTANTIATE_CLASS(BNLLLayer);
-
-
+REGISTER_LAYER_CLASS(BNLL, BNLLLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index f8f73564a6f..420821952b0 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -105,5 +105,5 @@ STUB_GPU(ConcatLayer);
 #endif
 
 INSTANTIATE_CLASS(ConcatLayer);
-
+REGISTER_LAYER_CLASS(CONCAT, ConcatLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index d716d140ea0..0d0b443b66b 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -97,5 +97,5 @@ STUB_GPU(ContrastiveLossLayer);
 #endif
 
 INSTANTIATE_CLASS(ContrastiveLossLayer);
-
+REGISTER_LAYER_CLASS(CONTRASTIVE_LOSS, ContrastiveLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index e6d6588096b..65ff9167128 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -267,5 +267,4 @@ STUB_GPU(ConvolutionLayer);
 #endif
 
 INSTANTIATE_CLASS(ConvolutionLayer);
-
 }  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 283a3a52f59..daa3289b9c2 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -204,5 +204,5 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 }
 
 INSTANTIATE_CLASS(DataLayer);
-
+REGISTER_LAYER_CLASS(DATA, DataLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index f88027c7062..8c8936a711e 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -73,6 +73,5 @@ STUB_GPU(DropoutLayer);
 #endif
 
 INSTANTIATE_CLASS(DropoutLayer);
-
-
+REGISTER_LAYER_CLASS(DROPOUT, DropoutLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 6aeba5487b8..15cf5a58ac6 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -93,5 +93,5 @@ void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(DummyDataLayer);
-
+REGISTER_LAYER_CLASS(DUMMY_DATA, DummyDataLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 2830d6db195..0c239f4ac11 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -163,6 +163,5 @@ STUB_GPU(EltwiseLayer);
 #endif
 
 INSTANTIATE_CLASS(EltwiseLayer);
-
-
+REGISTER_LAYER_CLASS(ELTWISE, EltwiseLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index abc8610eef6..d965027f712 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -54,5 +54,5 @@ STUB_GPU(EuclideanLossLayer);
 #endif
 
 INSTANTIATE_CLASS(EuclideanLossLayer);
-
+REGISTER_LAYER_CLASS(EUCLIDEAN_LOSS, EuclideanLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index 2c99bfcff93..92cb5deb1e6 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -64,6 +64,5 @@ STUB_GPU(ExpLayer);
 #endif
 
 INSTANTIATE_CLASS(ExpLayer);
-
-
+REGISTER_LAYER_CLASS(EXP, ExpLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 44df96d8436..ec43cababca 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -34,5 +34,5 @@ STUB_GPU(FlattenLayer);
 #endif
 
 INSTANTIATE_CLASS(FlattenLayer);
-
+REGISTER_LAYER_CLASS(FLATTEN, FlattenLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 61aa293d2ac..f6865efe78e 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -119,5 +119,5 @@ STUB_GPU_FORWARD(HDF5DataLayer, Forward);
 #endif
 
 INSTANTIATE_CLASS(HDF5DataLayer);
-
+REGISTER_LAYER_CLASS(HDF5_DATA, HDF5DataLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index b41d387a786..b8210e2ee2a 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -70,5 +70,5 @@ STUB_GPU(HDF5OutputLayer);
 #endif
 
 INSTANTIATE_CLASS(HDF5OutputLayer);
-
+REGISTER_LAYER_CLASS(HDF5_OUTPUT, HDF5OutputLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index f09916e2556..4dfafcc8648 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -77,5 +77,5 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(HingeLossLayer);
-
+REGISTER_LAYER_CLASS(HINGE_LOSS, HingeLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 10588cebe7f..2c4bb9029e0 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -88,5 +88,5 @@ STUB_GPU(Im2colLayer);
 #endif
 
 INSTANTIATE_CLASS(Im2colLayer);
-
+REGISTER_LAYER_CLASS(IM2COL, Im2colLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 19ad2dc2126..2085df2760a 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -131,5 +131,5 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
-
+REGISTER_LAYER_CLASS(IMAGE_DATA, ImageDataLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index c4dc1b4513a..8910431d42b 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -106,5 +106,5 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(InfogainLossLayer);
-
+REGISTER_LAYER_CLASS(INFOGAIN_LOSS, InfogainLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index ffd872c5a70..ecb849ba4d8 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -104,5 +104,5 @@ STUB_GPU(InnerProductLayer);
 #endif
 
 INSTANTIATE_CLASS(InnerProductLayer);
-
+REGISTER_LAYER_CLASS(INNER_PRODUCT, InnerProductLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index a09a47940d2..a878cf847b3 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -252,6 +252,5 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
 #endif
 
 INSTANTIATE_CLASS(LRNLayer);
-
-
+REGISTER_LAYER_CLASS(LRN, LRNLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 8f1c21f5424..613ca2d4c76 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -72,5 +72,5 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);
-
+REGISTER_LAYER_CLASS(MEMORY_DATA, MemoryDataLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 66f9ba813aa..78a1f60fca9 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -62,5 +62,5 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
 }
 
 INSTANTIATE_CLASS(MultinomialLogisticLossLayer);
-
+REGISTER_LAYER_CLASS(MULTINOMIAL_LOGISTIC_LOSS, MultinomialLogisticLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 419f170ad7c..104ad95c7ed 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -159,6 +159,5 @@ STUB_GPU(MVNLayer);
 #endif
 
 INSTANTIATE_CLASS(MVNLayer);
-
-
+REGISTER_LAYER_CLASS(MVN, MVNLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 04c612296aa..69bd120e3dc 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -99,6 +99,5 @@ STUB_GPU(PowerLayer);
 #endif
 
 INSTANTIATE_CLASS(PowerLayer);
-
-
+REGISTER_LAYER_CLASS(POWER, PowerLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 11850ac68e3..d1e327a54c9 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -75,6 +75,5 @@ STUB_GPU(SigmoidCrossEntropyLossLayer);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
-
-
+REGISTER_LAYER_CLASS(SIGMOID_CROSS_ENTROPY_LOSS, SigmoidCrossEntropyLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 0fd88580eaf..9bd20574816 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -22,5 +22,5 @@ STUB_GPU(SilenceLayer);
 #endif
 
 INSTANTIATE_CLASS(SilenceLayer);
-
+REGISTER_LAYER_CLASS(SILENCE, SilenceLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 76522c3e456..60a5ecfa416 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -137,5 +137,5 @@ STUB_GPU(SliceLayer);
 #endif
 
 INSTANTIATE_CLASS(SliceLayer);
-
+REGISTER_LAYER_CLASS(SLICE, SliceLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 04e8c4f35fc..c7b09fffd7a 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -92,6 +92,4 @@ STUB_GPU(SoftmaxLayer);
 #endif
 
 INSTANTIATE_CLASS(SoftmaxLayer);
-
-
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index d39429cee5d..35033b67c21 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -90,6 +90,5 @@ STUB_GPU(SoftmaxWithLossLayer);
 #endif
 
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
-
-
+REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index ee6b5a91d3c..51ac61f4cae 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -56,5 +56,5 @@ STUB_GPU(SplitLayer);
 #endif
 
 INSTANTIATE_CLASS(SplitLayer);
-
+REGISTER_LAYER_CLASS(SPLIT, SplitLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index b4b2df51bc9..9e68c32d348 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -29,5 +29,5 @@ STUB_GPU_FORWARD(ThresholdLayer, Forward);
 #endif
 
 INSTANTIATE_CLASS(ThresholdLayer);
-
+REGISTER_LAYER_CLASS(THRESHOLD, ThresholdLayer);
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 62ca872a32b..529c3b47bd9 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -402,5 +402,5 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
-
+REGISTER_LAYER_CLASS(WINDOW_DATA, WindowDataLayer);
 }  // namespace caffe

From 4302304ddc678723d5ea308f637fcd4d45252c76 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 14 Oct 2014 10:18:20 -0700
Subject: [PATCH 0914/2053] [examples] fix reference model name for flickr
 fine-tuning

---
 examples/finetune_flickr_style/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index e0183510bef..c4aafbc66fd 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -13,14 +13,14 @@ Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Fli
 
 ## Explanation
 
-The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `caffe_reference_imagenet_model` was trained.
+The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained.
 Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
 We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
 If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
 
 Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
 Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
-Since there is no layer named that in the `caffe_reference_imagenet_model`, that layer will begin training with random weights.
+Since there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights.
 
 We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
 The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.

From d6dbfc84db42d813747c4a4b038faa31a6b9503d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 14 Oct 2014 11:41:40 -0700
Subject: [PATCH 0915/2053] Revert "OpenCV should be compiled using pkg-config
 options." -- breaks compilation on working systems

This reverts commit 4587b2f9043996fed36fc989c00329ec829ecae9.
---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index c8226cb6872..35c37af50fb 100644
--- a/Makefile
+++ b/Makefile
@@ -168,7 +168,8 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 LIBRARIES += glog gflags protobuf leveldb snappy \
-	lmdb boost_system hdf5_hl hdf5 m
+	lmdb boost_system hdf5_hl hdf5 m \
+	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
@@ -329,7 +330,6 @@ NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
 LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
-		`pkg-config opencv --libs` \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 

From 370217bf7206150901133ac9399bcf3a87462d90 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Mon, 13 Oct 2014 16:01:07 -0700
Subject: [PATCH 0916/2053] some namespace cleaning.

---
 src/caffe/test/test_caffe_main.cpp        |  3 ++-
 src/caffe/test/test_dummy_data_layer.cpp  |  3 ---
 src/caffe/test/test_hdf5_output_layer.cpp |  3 ---
 src/caffe/test/test_hdf5data_layer.cpp    |  2 --
 src/caffe/test/test_upgrade_proto.cpp     |  2 --
 tools/compute_image_mean.cpp              |  9 +++----
 tools/convert_imageset.cpp                | 15 ++++++------
 tools/dump_network.cpp                    | 15 ++++++++----
 tools/extract_features.cpp                | 30 +++++++++++++----------
 9 files changed, 40 insertions(+), 42 deletions(-)

diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index bff0c4ed20e..c8caf5ac58e 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,6 +1,7 @@
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 
+#include "caffe/caffe.hpp"
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
@@ -15,7 +16,7 @@ using caffe::CAFFE_TEST_CUDA_PROP;
 
 int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
-  ::google::InitGoogleLogging(argv[0]);
+  caffe::GlobalInit(&argc, &argv);
 #ifndef CPU_ONLY
   // Before starting testing, let's first print out a few cuda defice info.
   int device;
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index da121fa5d99..99548352746 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -10,9 +10,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::stringstream;
-
 namespace caffe {
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index c8282233e5b..2e8f096980a 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -11,9 +11,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::vector;
-
 namespace caffe {
 
 template<typename TypeParam>
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index ed6fed83708..41a3a83dd9f 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -13,8 +13,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename TypeParam>
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index f46a0e823c1..52e7f1f99f5 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -11,8 +11,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-
 namespace caffe {
 
 class PaddingLayerUpgradeTest : public ::testing::Test {
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 20f1ff81f1c..6adde8b1d1c 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -11,7 +11,6 @@
 
 using caffe::Datum;
 using caffe::BlobProto;
-using std::string;
 using std::max;
 
 int main(int argc, char** argv) {
@@ -22,9 +21,9 @@ int main(int argc, char** argv) {
     return 1;
   }
 
-  string db_backend = "lmdb";
+  std::string db_backend = "lmdb";
   if (argc == 4) {
-    db_backend = string(argv[3]);
+    db_backend = std::string(argv[3]);
   }
 
   // leveldb
@@ -94,7 +93,7 @@ int main(int argc, char** argv) {
     for (it->SeekToFirst(); it->Valid(); it->Next()) {
       // just a dummy operation
       datum.ParseFromString(it->value().ToString());
-      const string& data = datum.data();
+      const std::string& data = datum.data();
       size_in_datum = std::max<int>(datum.data().size(),
           datum.float_data_size());
       CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
@@ -120,7 +119,7 @@ int main(int argc, char** argv) {
     do {
       // just a dummy operation
       datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
-      const string& data = datum.data();
+      const std::string& data = datum.data();
       size_in_datum = std::max<int>(datum.data().size(),
           datum.float_data_size());
       CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 4ab93fd0a0c..7c8c1dab3fb 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -27,7 +27,6 @@
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
-using std::string;
 
 DEFINE_bool(gray, false,
     "When this option is on, treat images as grayscale ones");
@@ -62,8 +61,8 @@ int main(int argc, char** argv) {
   bool is_color = !FLAGS_gray;
   bool check_size = FLAGS_check_size;
   std::ifstream infile(argv[2]);
-  std::vector<std::pair<string, int> > lines;
-  string filename;
+  std::vector<std::pair<std::string, int> > lines;
+  std::string filename;
   int label;
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
@@ -75,7 +74,7 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  const string& db_backend = FLAGS_backend;
+  const std::string& db_backend = FLAGS_backend;
   const char* db_path = argv[3];
 
   int resize_height = std::max<int>(0, FLAGS_resize_height);
@@ -121,7 +120,7 @@ int main(int argc, char** argv) {
   }
 
   // Storing to db
-  string root_folder(argv[1]);
+  std::string root_folder(argv[1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;
@@ -139,7 +138,7 @@ int main(int argc, char** argv) {
         data_size = datum.channels() * datum.height() * datum.width();
         data_size_initialized = true;
       } else {
-        const string& data = datum.data();
+        const std::string& data = datum.data();
         CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
             << data.size();
       }
@@ -147,9 +146,9 @@ int main(int argc, char** argv) {
     // sequential
     snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
-    string value;
+    std::string value;
     datum.SerializeToString(&value);
-    string keystr(key_cstr);
+    std::string keystr(key_cstr);
 
     // Put in db
     if (db_backend == "leveldb") {  // leveldb
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index 90895fdc969..9cb996ef612 100644
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -22,7 +22,12 @@
 #include "caffe/solver.hpp"
 #include "caffe/util/io.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
+using boost::shared_ptr;
+using caffe::Blob;
+using caffe::BlobProto;
+using caffe::Caffe;
+using caffe::Net;
+using caffe::NetParameter;
 
 int main(int argc, char** argv) {
   Caffe::set_mode(Caffe::GPU);
@@ -37,7 +42,7 @@ int main(int argc, char** argv) {
   }
   caffe_net->CopyTrainedLayersFrom(argv[2]);
 
-  vector<Blob<float>* > input_vec;
+  std::vector<Blob<float>* > input_vec;
   shared_ptr<Blob<float> > input_blob(new Blob<float>());
   if (strcmp(argv[3], "none") != 0) {
     BlobProto input_blob_proto;
@@ -46,7 +51,7 @@ int main(int argc, char** argv) {
     input_vec.push_back(input_blob.get());
   }
 
-  string output_prefix(argv[4]);
+  std::string output_prefix(argv[4]);
   // Run the network without training.
   LOG(ERROR) << "Performing Forward";
   caffe_net->Forward(input_vec);
@@ -62,8 +67,8 @@ int main(int argc, char** argv) {
   }
   // Now, let's dump all the layers
 
-  const vector<string>& blob_names = caffe_net->blob_names();
-  const vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
+  const std::vector<std::string>& blob_names = caffe_net->blob_names();
+  const std::vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
   for (int blobid = 0; blobid < caffe_net->blobs().size(); ++blobid) {
     // Serialize blob
     LOG(ERROR) << "Dumping " << blob_names[blobid];
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 9b0288a4d5a..299b3118760 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -14,7 +14,11 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
+using boost::shared_ptr;
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Datum;
+using caffe::Net;
 
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv);
@@ -62,7 +66,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   Caffe::set_phase(Caffe::TEST);
 
   arg_pos = 0;  // the name of the executable
-  string pretrained_binary_proto(argv[++arg_pos]);
+  std::string pretrained_binary_proto(argv[++arg_pos]);
 
   // Expected prototxt contains at least one data layer such as
   //  the layer data_layer_name and one feature blob such as the
@@ -91,17 +95,17 @@ int feature_extraction_pipeline(int argc, char** argv) {
      top: "fc7"
    }
    */
-  string feature_extraction_proto(argv[++arg_pos]);
+  std::string feature_extraction_proto(argv[++arg_pos]);
   shared_ptr<Net<Dtype> > feature_extraction_net(
       new Net<Dtype>(feature_extraction_proto));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
-  string extract_feature_blob_names(argv[++arg_pos]);
-  vector<string> blob_names;
+  std::string extract_feature_blob_names(argv[++arg_pos]);
+  std::vector<std::string> blob_names;
   boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
 
-  string save_feature_leveldb_names(argv[++arg_pos]);
-  vector<string> leveldb_names;
+  std::string save_feature_leveldb_names(argv[++arg_pos]);
+  std::vector<std::string> leveldb_names;
   boost::split(leveldb_names, save_feature_leveldb_names,
                boost::is_any_of(","));
   CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
@@ -118,7 +122,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  vector<shared_ptr<leveldb::DB> > feature_dbs;
+  std::vector<shared_ptr<leveldb::DB> > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
     leveldb::DB* db;
@@ -134,13 +138,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
+  std::vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
       num_features,
       shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
-  vector<Blob<float>*> input_vec;
-  vector<int> image_indices(num_features, 0);
+  std::vector<Blob<float>*> input_vec;
+  std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
     for (int i = 0; i < num_features; ++i) {
@@ -160,10 +164,10 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        string value;
+        std::string value;
         datum.SerializeToString(&value);
         snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
-        feature_batches[i]->Put(string(key_str), value);
+        feature_batches[i]->Put(std::string(key_str), value);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           feature_dbs[i]->Write(leveldb::WriteOptions(),

From 6ad4f951133535f8fd43a8b6d4ece85f33465aca Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 7 Oct 2014 15:37:35 -0400
Subject: [PATCH 0917/2053] Refactored leveldb and lmdb code.

---
 include/caffe/data_layers.hpp          |  14 +--
 include/caffe/database.hpp             | 103 +++++++++++++++++
 include/caffe/database_factory.hpp     |  16 +++
 include/caffe/leveldb_database.hpp     |  52 +++++++++
 include/caffe/lmdb_database.hpp        |  54 +++++++++
 include/caffe/util/io.hpp              |   7 --
 src/caffe/database_factory.cpp         |  33 ++++++
 src/caffe/layers/data_layer.cpp        | 129 +++------------------
 src/caffe/leveldb_database.cpp         | 151 ++++++++++++++++++++++++
 src/caffe/lmdb_database.cpp            | 154 +++++++++++++++++++++++++
 src/caffe/proto/caffe.proto            |   6 +-
 src/caffe/test/test_data_layer.cpp     |  93 ++++-----------
 src/caffe/test/test_hdf5data_layer.cpp |   2 -
 src/caffe/util/io.cpp                  |  10 --
 tools/compute_image_mean.cpp           | 132 +++++----------------
 tools/convert_imageset.cpp             |  81 ++-----------
 tools/extract_features.cpp             |  50 ++++----
 17 files changed, 660 insertions(+), 427 deletions(-)
 create mode 100644 include/caffe/database.hpp
 create mode 100644 include/caffe/database_factory.hpp
 create mode 100644 include/caffe/leveldb_database.hpp
 create mode 100644 include/caffe/lmdb_database.hpp
 create mode 100644 src/caffe/database_factory.cpp
 create mode 100644 src/caffe/leveldb_database.cpp
 create mode 100644 src/caffe/lmdb_database.cpp

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 1ed26c17178..810f2bb833d 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -7,12 +7,11 @@
 
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
-#include "leveldb/db.h"
-#include "lmdb.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/data_transformer.hpp"
+#include "caffe/database.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
@@ -101,15 +100,8 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  protected:
   virtual void InternalThreadEntry();
 
-  // LEVELDB
-  shared_ptr<leveldb::DB> db_;
-  shared_ptr<leveldb::Iterator> iter_;
-  // LMDB
-  MDB_env* mdb_env_;
-  MDB_dbi mdb_dbi_;
-  MDB_txn* mdb_txn_;
-  MDB_cursor* mdb_cursor_;
-  MDB_val mdb_key_, mdb_value_;
+  shared_ptr<Database> database_;
+  Database::const_iterator iter_;
 };
 
 /**
diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
new file mode 100644
index 00000000000..4a1a25efd12
--- /dev/null
+++ b/include/caffe/database.hpp
@@ -0,0 +1,103 @@
+#ifndef CAFFE_DATABASE_H_
+#define CAFFE_DATABASE_H_
+
+#include <algorithm>
+#include <iterator>
+#include <string>
+#include <utility>
+
+#include "caffe/common.hpp"
+
+namespace caffe {
+
+class Database {
+ public:
+  enum Mode {
+    New,
+    ReadWrite,
+    ReadOnly
+  };
+
+  virtual void open(const string& filename, Mode mode) = 0;
+  virtual void put(const string& key, const string& value) = 0;
+  virtual void commit() = 0;
+  virtual void close() = 0;
+
+  Database() { }
+  virtual ~Database() { }
+
+  class iterator;
+  typedef iterator const_iterator;
+
+  virtual const_iterator begin() const = 0;
+  virtual const_iterator cbegin() const = 0;
+  virtual const_iterator end() const = 0;
+  virtual const_iterator cend() const = 0;
+
+ protected:
+  class DatabaseState;
+
+ public:
+  class iterator :
+      public std::iterator<std::forward_iterator_tag, pair<string, string> > {
+   public:
+    typedef pair<string, string> T;
+    typedef T value_type;
+    typedef T& reference_type;
+    typedef T* pointer_type;
+
+    iterator()
+        : parent_(NULL) { }
+
+    iterator(const Database* parent, shared_ptr<DatabaseState> state)
+        : parent_(parent),
+          state_(state) { }
+    ~iterator() { }
+
+    bool operator==(const iterator& other) const {
+      return parent_->equal(state_, other.state_);
+    }
+
+    bool operator!=(const iterator& other) const {
+      return !(*this == other);
+    }
+
+    iterator& operator++() {
+      parent_->increment(state_);
+      return *this;
+    }
+    iterator operator++(int) {
+      iterator copy(*this);
+      parent_->increment(state_);
+      return copy;
+    }
+
+    reference_type operator*() const {
+      return parent_->dereference(state_);
+    }
+
+    pointer_type operator->() const {
+      return &parent_->dereference(state_);
+    }
+
+   protected:
+    const Database* parent_;
+    shared_ptr<DatabaseState> state_;
+  };
+
+ protected:
+  class DatabaseState {
+   public:
+    virtual ~DatabaseState() { }
+  };
+
+  virtual bool equal(shared_ptr<DatabaseState> state1,
+      shared_ptr<DatabaseState> state2) const = 0;
+  virtual void increment(shared_ptr<DatabaseState> state) const = 0;
+  virtual pair<string, string>& dereference(
+      shared_ptr<DatabaseState> state) const = 0;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATABASE_H_
diff --git a/include/caffe/database_factory.hpp b/include/caffe/database_factory.hpp
new file mode 100644
index 00000000000..a6e39e76188
--- /dev/null
+++ b/include/caffe/database_factory.hpp
@@ -0,0 +1,16 @@
+#ifndef CAFFE_DATABASE_FACTORY_H_
+#define CAFFE_DATABASE_FACTORY_H_
+
+#include <string>
+
+#include "caffe/common.hpp"
+#include "caffe/database.hpp"
+
+namespace caffe {
+
+shared_ptr<Database> DatabaseFactory(const DataParameter_DB& type);
+shared_ptr<Database> DatabaseFactory(const string& type);
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATABASE_FACTORY_H_
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
new file mode 100644
index 00000000000..5daf0e51e12
--- /dev/null
+++ b/include/caffe/leveldb_database.hpp
@@ -0,0 +1,52 @@
+#ifndef CAFFE_LEVELDB_DATABASE_H_
+#define CAFFE_LEVELDB_DATABASE_H_
+
+#include <leveldb/db.h>
+#include <leveldb/write_batch.h>
+
+#include <string>
+#include <utility>
+
+#include "caffe/common.hpp"
+#include "caffe/database.hpp"
+
+namespace caffe {
+
+class LeveldbDatabase : public Database {
+ public:
+  void open(const string& filename, Mode mode);
+  void put(const string& key, const string& value);
+  void commit();
+  void close();
+
+  ~LeveldbDatabase() { this->close(); }
+
+ protected:
+  class LeveldbState : public Database::DatabaseState {
+   public:
+    explicit LeveldbState(shared_ptr<leveldb::Iterator> iter)
+        : Database::DatabaseState(),
+          iter_(iter) { }
+
+    shared_ptr<leveldb::Iterator> iter_;
+    pair<string, string> kv_pair_;
+  };
+
+  bool equal(shared_ptr<DatabaseState> state1,
+      shared_ptr<DatabaseState> state2) const;
+  void increment(shared_ptr<DatabaseState> state) const;
+  pair<string, string>& dereference(shared_ptr<DatabaseState> state) const;
+
+  const_iterator begin() const;
+  const_iterator cbegin() const;
+  const_iterator end() const;
+  const_iterator cend() const;
+
+ protected:
+  shared_ptr<leveldb::DB> db_;
+  shared_ptr<leveldb::WriteBatch> batch_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LEVELDB_DATABASE_H_
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
new file mode 100644
index 00000000000..f275cb48f5c
--- /dev/null
+++ b/include/caffe/lmdb_database.hpp
@@ -0,0 +1,54 @@
+#ifndef CAFFE_LMDB_DATABASE_H_
+#define CAFFE_LMDB_DATABASE_H_
+
+#include <string>
+#include <utility>
+
+#include "lmdb.h"
+
+#include "caffe/common.hpp"
+#include "caffe/database.hpp"
+
+namespace caffe {
+
+class LmdbDatabase : public Database {
+ public:
+  LmdbDatabase()
+      : dbi_(0) { }
+  ~LmdbDatabase() { this->close(); }
+
+  void open(const string& filename, Mode mode);
+  void put(const string& key, const string& value);
+  void commit();
+  void close();
+
+ protected:
+  class LmdbState : public Database::DatabaseState {
+   public:
+    explicit LmdbState(MDB_cursor* cursor)
+        : Database::DatabaseState(),
+          cursor_(cursor) { }
+
+    MDB_cursor* cursor_;
+    pair<string, string> kv_pair_;
+  };
+
+  bool equal(shared_ptr<DatabaseState> state1,
+      shared_ptr<DatabaseState> state2) const;
+  void increment(shared_ptr<DatabaseState> state) const;
+  pair<string, string>& dereference(shared_ptr<DatabaseState> state) const;
+
+ protected:
+  const_iterator begin() const;
+  const_iterator cbegin() const;
+  const_iterator end() const;
+  const_iterator cend() const;
+
+  MDB_env *env_ = NULL;
+  MDB_dbi dbi_;
+  MDB_txn *txn_ = NULL;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LMDB_DATABASE_H_
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index e518979a75b..1124bef179a 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -17,11 +17,6 @@
 
 #define HDF5_NUM_DIMS 4
 
-namespace leveldb {
-// Forward declaration for leveldb::Options to be used in GetlevelDBOptions().
-struct Options;
-}
-
 namespace caffe {
 
 using ::google::protobuf::Message;
@@ -132,8 +127,6 @@ inline cv::Mat ReadImageToCVMat(const string& filename) {
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 #endif
 
-leveldb::Options GetLevelDBOptions();
-
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
   hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
diff --git a/src/caffe/database_factory.cpp b/src/caffe/database_factory.cpp
new file mode 100644
index 00000000000..393635b3208
--- /dev/null
+++ b/src/caffe/database_factory.cpp
@@ -0,0 +1,33 @@
+#include <string>
+#include <utility>
+
+#include "caffe/database_factory.hpp"
+#include "caffe/leveldb_database.hpp"
+#include "caffe/lmdb_database.hpp"
+
+namespace caffe {
+
+shared_ptr<Database> DatabaseFactory(const DataParameter_DB& type) {
+  switch (type) {
+  case DataParameter_DB_LEVELDB:
+    return shared_ptr<Database>(new LeveldbDatabase());
+  case DataParameter_DB_LMDB:
+    return shared_ptr<Database>(new LmdbDatabase());
+  default:
+    LOG(FATAL) << "Unknown database type " << type;
+  }
+}
+
+shared_ptr<Database> DatabaseFactory(const string& type) {
+  if ("leveldb" == type) {
+    return DatabaseFactory(DataParameter_DB_LEVELDB);
+  } else if ("lmdb" == type) {
+    return DatabaseFactory(DataParameter_DB_LMDB);
+  } else {
+    LOG(FATAL) << "Unknown database type " << type;
+  }
+}
+
+}  // namespace caffe
+
+
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index daa3289b9c2..b746bc8a37d 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,4 +1,3 @@
-#include <leveldb/db.h>
 #include <stdint.h>
 
 #include <string>
@@ -6,6 +5,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
+#include "caffe/database_factory.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
@@ -18,60 +18,18 @@ template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
   // clean up the database resources
-  switch (this->layer_param_.data_param().backend()) {
-  case DataParameter_DB_LEVELDB:
-    break;  // do nothing
-  case DataParameter_DB_LMDB:
-    mdb_cursor_close(mdb_cursor_);
-    mdb_close(mdb_env_, mdb_dbi_);
-    mdb_txn_abort(mdb_txn_);
-    mdb_env_close(mdb_env_);
-    break;
-  default:
-    LOG(FATAL) << "Unknown database backend";
-  }
+  iter_ = database_->end();
+  database_->close();
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Initialize DB
-  switch (this->layer_param_.data_param().backend()) {
-  case DataParameter_DB_LEVELDB:
-    {
-    leveldb::DB* db_temp;
-    leveldb::Options options = GetLevelDBOptions();
-    options.create_if_missing = false;
-    LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
-    leveldb::Status status = leveldb::DB::Open(
-        options, this->layer_param_.data_param().source(), &db_temp);
-    CHECK(status.ok()) << "Failed to open leveldb "
-                       << this->layer_param_.data_param().source() << std::endl
-                       << status.ToString();
-    db_.reset(db_temp);
-    iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
-    iter_->SeekToFirst();
-    }
-    break;
-  case DataParameter_DB_LMDB:
-    CHECK_EQ(mdb_env_create(&mdb_env_), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env_, 1099511627776), MDB_SUCCESS);  // 1TB
-    CHECK_EQ(mdb_env_open(mdb_env_,
-             this->layer_param_.data_param().source().c_str(),
-             MDB_RDONLY|MDB_NOTLS, 0664), MDB_SUCCESS) << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn_), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn_, NULL, 0, &mdb_dbi_), MDB_SUCCESS)
-        << "mdb_open failed";
-    CHECK_EQ(mdb_cursor_open(mdb_txn_, mdb_dbi_, &mdb_cursor_), MDB_SUCCESS)
-        << "mdb_cursor_open failed";
-    LOG(INFO) << "Opening lmdb " << this->layer_param_.data_param().source();
-    CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_FIRST),
-        MDB_SUCCESS) << "mdb_cursor_get failed";
-    break;
-  default:
-    LOG(FATAL) << "Unknown database backend";
-  }
+  database_ = DatabaseFactory(this->layer_param_.data_param().backend());
+  LOG(INFO) << "Opening database " << this->layer_param_.data_param().source();
+  database_->open(this->layer_param_.data_param().source(), Database::ReadOnly);
+  iter_ = database_->begin();
 
   // Check if we would need to randomly skip a few data points
   if (this->layer_param_.data_param().rand_skip()) {
@@ -79,37 +37,16 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      switch (this->layer_param_.data_param().backend()) {
-      case DataParameter_DB_LEVELDB:
-        iter_->Next();
-        if (!iter_->Valid()) {
-          iter_->SeekToFirst();
-        }
-        break;
-      case DataParameter_DB_LMDB:
-        if (mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_NEXT)
-            != MDB_SUCCESS) {
-          CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_,
-                   MDB_FIRST), MDB_SUCCESS);
-        }
-        break;
-      default:
-        LOG(FATAL) << "Unknown database backend";
+      LOG(INFO) << iter_->first;
+      if (++iter_ == database_->end()) {
+        iter_ = database_->begin();
       }
     }
   }
   // Read a data point, and use it to initialize the top blob.
+  CHECK(iter_ != database_->end());
   Datum datum;
-  switch (this->layer_param_.data_param().backend()) {
-  case DataParameter_DB_LEVELDB:
-    datum.ParseFromString(iter_->value().ToString());
-    break;
-  case DataParameter_DB_LMDB:
-    datum.ParseFromArray(mdb_value_.mv_data, mdb_value_.mv_size);
-    break;
-  default:
-    LOG(FATAL) << "Unknown database backend";
-  }
+  datum.ParseFromString(iter_->second);
 
   // image
   int crop_size = this->layer_param_.transform_param().crop_size();
@@ -153,23 +90,10 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   const int batch_size = this->layer_param_.data_param().batch_size();
 
   for (int item_id = 0; item_id < batch_size; ++item_id) {
-    // get a blob
     Datum datum;
-    switch (this->layer_param_.data_param().backend()) {
-    case DataParameter_DB_LEVELDB:
-      CHECK(iter_);
-      CHECK(iter_->Valid());
-      datum.ParseFromString(iter_->value().ToString());
-      break;
-    case DataParameter_DB_LMDB:
-      CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
-              &mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
-      datum.ParseFromArray(mdb_value_.mv_data,
-          mdb_value_.mv_size);
-      break;
-    default:
-      LOG(FATAL) << "Unknown database backend";
-    }
+    CHECK(iter_ != database_->end());
+    datum.ParseFromString(iter_->second);
+
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
@@ -179,26 +103,9 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     }
 
     // go to the next iter
-    switch (this->layer_param_.data_param().backend()) {
-    case DataParameter_DB_LEVELDB:
-      iter_->Next();
-      if (!iter_->Valid()) {
-        // We have reached the end. Restart from the first.
-        DLOG(INFO) << "Restarting data prefetching from start.";
-        iter_->SeekToFirst();
-      }
-      break;
-    case DataParameter_DB_LMDB:
-      if (mdb_cursor_get(mdb_cursor_, &mdb_key_,
-              &mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
-        // We have reached the end. Restart from the first.
-        DLOG(INFO) << "Restarting data prefetching from start.";
-        CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
-                &mdb_value_, MDB_FIRST), MDB_SUCCESS);
-      }
-      break;
-    default:
-      LOG(FATAL) << "Unknown database backend";
+    ++iter_;
+    if (iter_ == database_->end()) {
+      iter_ = database_->begin();
     }
   }
 }
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
new file mode 100644
index 00000000000..be7ac7f141c
--- /dev/null
+++ b/src/caffe/leveldb_database.cpp
@@ -0,0 +1,151 @@
+#include <string>
+#include <utility>
+
+#include "caffe/leveldb_database.hpp"
+
+namespace caffe {
+
+void LeveldbDatabase::open(const string& filename, Mode mode) {
+  LOG(INFO) << "LevelDB: Open " << filename;
+
+  leveldb::Options options;
+  switch (mode) {
+  case New:
+    LOG(INFO) << " mode NEW";
+    options.error_if_exists = true;
+    options.create_if_missing = true;
+    break;
+  case ReadWrite:
+    LOG(INFO) << " mode RW";
+    options.error_if_exists = false;
+    options.create_if_missing = true;
+    break;
+  case ReadOnly:
+    LOG(INFO) << " mode RO";
+    options.error_if_exists = false;
+    options.create_if_missing = false;
+    break;
+  default:
+    LOG(FATAL) << "unknown mode " << mode;
+  }
+  options.write_buffer_size = 268435456;
+  options.max_open_files = 100;
+
+  leveldb::DB* db;
+
+  LOG(INFO) << "Opening leveldb " << filename;
+  leveldb::Status status = leveldb::DB::Open(
+      options, filename, &db);
+  db_.reset(db);
+  CHECK(status.ok()) << "Failed to open leveldb " << filename
+      << ". Is it already existing?";
+  batch_.reset(new leveldb::WriteBatch());
+}
+
+void LeveldbDatabase::put(const string& key, const string& value) {
+  LOG(INFO) << "LevelDB: Put " << key;
+
+  CHECK_NOTNULL(batch_.get());
+  batch_->Put(key, value);
+}
+
+void LeveldbDatabase::commit() {
+  LOG(INFO) << "LevelDB: Commit";
+
+  CHECK_NOTNULL(db_.get());
+  CHECK_NOTNULL(batch_.get());
+
+  db_->Write(leveldb::WriteOptions(), batch_.get());
+  batch_.reset(new leveldb::WriteBatch());
+}
+
+void LeveldbDatabase::close() {
+  LOG(INFO) << "LevelDB: Close";
+
+  if (batch_ && db_) {
+    this->commit();
+  }
+  batch_.reset();
+  db_.reset();
+}
+
+LeveldbDatabase::const_iterator LeveldbDatabase::begin() const {
+  CHECK_NOTNULL(db_.get());
+  shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
+  iter->SeekToFirst();
+  if (!iter->Valid()) {
+    iter.reset();
+  }
+  shared_ptr<DatabaseState> state(new LeveldbState(iter));
+  return const_iterator(this, state);
+}
+
+LeveldbDatabase::const_iterator LeveldbDatabase::end() const {
+  shared_ptr<leveldb::Iterator> iter;
+  shared_ptr<DatabaseState> state(new LeveldbState(iter));
+  return const_iterator(this, state);
+}
+
+LeveldbDatabase::const_iterator LeveldbDatabase::cbegin() const {
+  return begin();
+}
+
+LeveldbDatabase::const_iterator LeveldbDatabase::cend() const { return end(); }
+
+bool LeveldbDatabase::equal(shared_ptr<DatabaseState> state1,
+    shared_ptr<DatabaseState> state2) const {
+  shared_ptr<LeveldbState> leveldb_state1 =
+      boost::dynamic_pointer_cast<LeveldbState>(state1);
+
+  CHECK_NOTNULL(leveldb_state1.get());
+
+  shared_ptr<LeveldbState> leveldb_state2 =
+      boost::dynamic_pointer_cast<LeveldbState>(state2);
+
+  CHECK_NOTNULL(leveldb_state2.get());
+
+  CHECK(!leveldb_state1->iter_ || leveldb_state1->iter_->Valid());
+  CHECK(!leveldb_state2->iter_ || leveldb_state2->iter_->Valid());
+
+  // The KV store doesn't really have any sort of ordering,
+  // so while we can do a sequential scan over the collection,
+  // we can't really use subranges.
+  return !leveldb_state1->iter_ && !leveldb_state2->iter_;
+}
+
+void LeveldbDatabase::increment(shared_ptr<DatabaseState> state) const {
+  shared_ptr<LeveldbState> leveldb_state =
+      boost::dynamic_pointer_cast<LeveldbState>(state);
+
+  CHECK_NOTNULL(leveldb_state.get());
+
+  shared_ptr<leveldb::Iterator>& iter = leveldb_state->iter_;
+
+  CHECK_NOTNULL(iter.get());
+  CHECK(iter->Valid());
+
+  iter->Next();
+  if (!iter->Valid()) {
+    iter.reset();
+  }
+}
+
+pair<string, string>& LeveldbDatabase::dereference(
+    shared_ptr<DatabaseState> state) const {
+  shared_ptr<LeveldbState> leveldb_state =
+      boost::dynamic_pointer_cast<LeveldbState>(state);
+
+  CHECK_NOTNULL(leveldb_state.get());
+
+  shared_ptr<leveldb::Iterator>& iter = leveldb_state->iter_;
+
+  CHECK_NOTNULL(iter.get());
+
+  CHECK(iter->Valid());
+
+  leveldb_state->kv_pair_ = make_pair(iter->key().ToString(),
+      iter->value().ToString());
+  return leveldb_state->kv_pair_;
+}
+
+}  // namespace caffe
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
new file mode 100644
index 00000000000..796bbc9d4ac
--- /dev/null
+++ b/src/caffe/lmdb_database.cpp
@@ -0,0 +1,154 @@
+#include <sys/stat.h>
+
+#include <string>
+#include <utility>
+
+#include "caffe/lmdb_database.hpp"
+
+namespace caffe {
+
+void LmdbDatabase::open(const string& filename, Mode mode) {
+  LOG(INFO) << "LMDB: Open " << filename;
+
+  CHECK(NULL == env_);
+  CHECK(NULL == txn_);
+  CHECK_EQ(0, dbi_);
+
+  if (mode != ReadOnly) {
+    CHECK_EQ(mkdir(filename.c_str(), 0744), 0) << "mkdir " << filename
+                                                << "failed";
+  }
+
+  CHECK_EQ(mdb_env_create(&env_), MDB_SUCCESS) << "mdb_env_create failed";
+  CHECK_EQ(mdb_env_set_mapsize(env_, 1099511627776), MDB_SUCCESS)  // 1TB
+      << "mdb_env_set_mapsize failed";
+
+  int flag1 = 0;
+  int flag2 = 0;
+  if (mode == ReadOnly) {
+    flag1 = MDB_RDONLY | MDB_NOTLS;
+    flag2 = MDB_RDONLY;
+  }
+
+  CHECK_EQ(mdb_env_open(env_, filename.c_str(), flag1, 0664), MDB_SUCCESS)
+      << "mdb_env_open failed";
+  CHECK_EQ(mdb_txn_begin(env_, NULL, flag2, &txn_), MDB_SUCCESS)
+      << "mdb_txn_begin failed";
+  CHECK_EQ(mdb_open(txn_, NULL, 0, &dbi_), MDB_SUCCESS) << "mdb_open failed";
+}
+
+void LmdbDatabase::put(const string& key, const string& value) {
+  LOG(INFO) << "LMDB: Put " << key;
+
+  MDB_val mdbkey, mdbdata;
+  mdbdata.mv_size = value.size();
+  mdbdata.mv_data = const_cast<char*>(&value[0]);
+  mdbkey.mv_size = key.size();
+  mdbkey.mv_data = const_cast<char*>(&key[0]);
+
+  CHECK_NOTNULL(txn_);
+  CHECK_NE(0, dbi_);
+
+  CHECK_EQ(mdb_put(txn_, dbi_, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
+      << "mdb_put failed";
+}
+
+void LmdbDatabase::commit() {
+  LOG(INFO) << "LMDB: Commit";
+
+  CHECK_NOTNULL(txn_);
+
+  CHECK_EQ(mdb_txn_commit(txn_), MDB_SUCCESS) << "mdb_txn_commit failed";
+}
+
+void LmdbDatabase::close() {
+  LOG(INFO) << "LMDB: Close";
+
+  if (env_ && dbi_ && txn_) {
+    this->commit();
+  }
+
+  if (env_ && dbi_) {
+    mdb_close(env_, dbi_);
+    mdb_env_close(env_);
+    env_ = NULL;
+    dbi_ = 0;
+    txn_ = NULL;
+  }
+}
+
+LmdbDatabase::const_iterator LmdbDatabase::begin() const {
+  MDB_cursor* cursor;
+  CHECK_EQ(mdb_cursor_open(txn_, dbi_, &cursor), MDB_SUCCESS);
+  MDB_val key;
+  MDB_val val;
+  CHECK_EQ(mdb_cursor_get(cursor, &key, &val, MDB_FIRST), MDB_SUCCESS);
+
+  shared_ptr<DatabaseState> state(new LmdbState(cursor));
+  return const_iterator(this, state);
+}
+
+LmdbDatabase::const_iterator LmdbDatabase::end() const {
+  shared_ptr<DatabaseState> state(new LmdbState(NULL));
+  return const_iterator(this, state);
+}
+
+LmdbDatabase::const_iterator LmdbDatabase::cbegin() const { return begin(); }
+LmdbDatabase::const_iterator LmdbDatabase::cend() const { return end(); }
+
+bool LmdbDatabase::equal(shared_ptr<DatabaseState> state1,
+    shared_ptr<DatabaseState> state2) const {
+  shared_ptr<LmdbState> lmdb_state1 =
+      boost::dynamic_pointer_cast<LmdbState>(state1);
+
+  CHECK_NOTNULL(lmdb_state1.get());
+
+  shared_ptr<LmdbState> lmdb_state2 =
+      boost::dynamic_pointer_cast<LmdbState>(state2);
+
+  CHECK_NOTNULL(lmdb_state2.get());
+
+  // The KV store doesn't really have any sort of ordering,
+  // so while we can do a sequential scan over the collection,
+  // we can't really use subranges.
+  return !lmdb_state1->cursor_ && !lmdb_state2->cursor_;
+}
+
+void LmdbDatabase::increment(shared_ptr<DatabaseState> state) const {
+  shared_ptr<LmdbState> lmdb_state =
+      boost::dynamic_pointer_cast<LmdbState>(state);
+
+  CHECK_NOTNULL(lmdb_state.get());
+
+  MDB_cursor*& cursor = lmdb_state->cursor_;
+
+  MDB_val key;
+  MDB_val val;
+  if (MDB_SUCCESS != mdb_cursor_get(cursor, &key, &val, MDB_NEXT)) {
+    mdb_cursor_close(cursor);
+    cursor = NULL;
+  }
+}
+
+pair<string, string>& LmdbDatabase::dereference(
+    shared_ptr<DatabaseState> state) const {
+  shared_ptr<LmdbState> lmdb_state =
+      boost::dynamic_pointer_cast<LmdbState>(state);
+
+  CHECK_NOTNULL(lmdb_state.get());
+
+  MDB_cursor*& cursor = lmdb_state->cursor_;
+
+  MDB_val mdb_key;
+  MDB_val mdb_val;
+  CHECK_EQ(mdb_cursor_get(cursor, &mdb_key, &mdb_val, MDB_GET_CURRENT),
+      MDB_SUCCESS);
+
+  lmdb_state->kv_pair_ = make_pair(
+    string(reinterpret_cast<char*>(mdb_key.mv_data), mdb_key.mv_size),
+    string(reinterpret_cast<char*>(mdb_val.mv_data), mdb_val.mv_size));
+
+  return lmdb_state->kv_pair_;
+}
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f0404a09b90..f0dba097d05 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -423,7 +423,7 @@ message DataParameter {
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the leveldb.
+  // be larger than the number of keys in the database.
   optional uint32 rand_skip = 7 [default = 0];
   optional DB backend = 8 [default = LEVELDB];
   // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
@@ -518,7 +518,7 @@ message ImageDataParameter {
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the leveldb.
+  // be larger than the number of keys in the database.
   optional uint32 rand_skip = 7 [default = 0];
   // Whether or not ImageLayer should shuffle the list of files at every epoch.
   optional bool shuffle = 8 [default = false];
@@ -767,7 +767,7 @@ message V0LayerParameter {
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
-  // be larger than the number of keys in the leveldb.
+  // be larger than the number of keys in the database.
   optional uint32 rand_skip = 53 [default = 0];
 
   // Fields related to detection (det_*)
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 657ffdea68c..d99b5e3a0d3 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -2,10 +2,10 @@
 #include <vector>
 
 #include "gtest/gtest.h"
-#include "leveldb/db.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/database_factory.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
@@ -36,16 +36,11 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
   // Fill the LevelDB with data: if unique_pixels, each pixel is unique but
   // all images are the same; else each image is unique but all pixels within
   // an image are the same.
-  void FillLevelDB(const bool unique_pixels) {
-    backend_ = DataParameter_DB_LEVELDB;
-    LOG(INFO) << "Using temporary leveldb " << *filename_;
-    leveldb::DB* db;
-    leveldb::Options options;
-    options.error_if_exists = true;
-    options.create_if_missing = true;
-    leveldb::Status status =
-        leveldb::DB::Open(options, filename_->c_str(), &db);
-    CHECK(status.ok());
+  void Fill(const bool unique_pixels, DataParameter_DB backend) {
+    backend_ = backend;
+    LOG(INFO) << "Using temporary database " << *filename_;
+    shared_ptr<Database> database = DatabaseFactory(backend_);
+    database->open(*filename_, Database::New);
     for (int i = 0; i < 5; ++i) {
       Datum datum;
       datum.set_label(i);
@@ -59,57 +54,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       }
       stringstream ss;
       ss << i;
-      db->Put(leveldb::WriteOptions(), ss.str(), datum.SerializeAsString());
+      database->put(ss.str(), datum.SerializeAsString());
     }
-    delete db;
-  }
-
-  // Fill the LMDB with data: unique_pixels has same meaning as in FillLevelDB.
-  void FillLMDB(const bool unique_pixels) {
-    backend_ = DataParameter_DB_LMDB;
-    LOG(INFO) << "Using temporary lmdb " << *filename_;
-    CHECK_EQ(mkdir(filename_->c_str(), 0744), 0) << "mkdir " << filename_
-                                                 << "failed";
-    MDB_env *env;
-    MDB_dbi dbi;
-    MDB_val mdbkey, mdbdata;
-    MDB_txn *txn;
-    CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(env, NULL, 0, &txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(txn, NULL, 0, &dbi), MDB_SUCCESS) << "mdb_open failed";
-
-    for (int i = 0; i < 5; ++i) {
-      Datum datum;
-      datum.set_label(i);
-      datum.set_channels(2);
-      datum.set_height(3);
-      datum.set_width(4);
-      std::string* data = datum.mutable_data();
-      for (int j = 0; j < 24; ++j) {
-        int datum = unique_pixels ? j : i;
-        data->push_back(static_cast<uint8_t>(datum));
-      }
-      stringstream ss;
-      ss << i;
-
-      string value;
-      datum.SerializeToString(&value);
-      mdbdata.mv_size = value.size();
-      mdbdata.mv_data = reinterpret_cast<void*>(&value[0]);
-      string keystr = ss.str();
-      mdbkey.mv_size = keystr.size();
-      mdbkey.mv_data = reinterpret_cast<void*>(&keystr[0]);
-      CHECK_EQ(mdb_put(txn, dbi, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
-          << "mdb_put failed";
-    }
-    CHECK_EQ(mdb_txn_commit(txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-    mdb_close(env, dbi);
-    mdb_env_close(env);
+    database->close();
   }
 
   void TestRead() {
@@ -234,7 +181,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the leveldb
+    }  // destroy 1st data layer and unlock the database
 
     // Get crop sequence after reseeding Caffe with 1701.
     // Check that the sequence is the same as the original.
@@ -289,7 +236,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the leveldb
+    }  // destroy 1st data layer and unlock the database
 
     // Get crop sequence continuing from previous Caffe RNG state; reseed
     // srand with 1701. Check that the sequence differs from the original.
@@ -327,14 +274,14 @@ TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestRead();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCrop();
 }
 
@@ -343,7 +290,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceSeeded();
 }
 
@@ -352,27 +299,27 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
   Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCrop();
 }
 
 TYPED_TEST(DataLayerTest, TestReadLMDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestRead();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCrop();
 }
 
@@ -381,7 +328,7 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceSeeded();
 }
 
@@ -390,14 +337,14 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
   Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLMDB(unique_pixels);
+  this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCrop();
 }
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 41a3a83dd9f..8d3b3d1e987 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,8 +1,6 @@
 #include <string>
 #include <vector>
 
-#include "leveldb/db.h"
-
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 36510d61d40..a4a6627b277 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,7 +2,6 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
@@ -122,15 +121,6 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   datum->set_data(buffer);
 }
 
-
-leveldb::Options GetLevelDBOptions() {
-  // In default, we will return the leveldb option and set the max open files
-  // in order to avoid using up the operating system's limit.
-  leveldb::Options options;
-  options.max_open_files = 100;
-  return options;
-}
-
 // Verifies format of data stored in HDF5 file and reshapes blob accordingly.
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 6adde8b1d1c..e59bbf19f0d 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -1,14 +1,14 @@
 #include <glog/logging.h>
-#include <leveldb/db.h>
-#include <lmdb.h>
 #include <stdint.h>
 
 #include <algorithm>
 #include <string>
 
+#include "caffe/database_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 
+using caffe::Database;
 using caffe::Datum;
 using caffe::BlobProto;
 using std::max;
@@ -26,57 +26,16 @@ int main(int argc, char** argv) {
     db_backend = std::string(argv[3]);
   }
 
-  // leveldb
-  leveldb::DB* db;
-  leveldb::Options options;
-  options.create_if_missing = false;
-  leveldb::Iterator* it = NULL;
-  // lmdb
-  MDB_env* mdb_env;
-  MDB_dbi mdb_dbi;
-  MDB_val mdb_key, mdb_value;
-  MDB_txn* mdb_txn;
-  MDB_cursor* mdb_cursor;
+  caffe::shared_ptr<Database> database = caffe::DatabaseFactory(db_backend);
 
   // Open db
-  if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << argv[1];
-    leveldb::Status status = leveldb::DB::Open(
-        options, argv[1], &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
-    leveldb::ReadOptions read_options;
-    read_options.fill_cache = false;
-    it = db->NewIterator(read_options);
-    it->SeekToFirst();
-  } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << argv[1];
-    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS);  // 1TB
-    CHECK_EQ(mdb_env_open(mdb_env, argv[1], MDB_RDONLY, 0664),
-        MDB_SUCCESS) << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, MDB_RDONLY, &mdb_txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed";
-    CHECK_EQ(mdb_cursor_open(mdb_txn, mdb_dbi, &mdb_cursor), MDB_SUCCESS)
-        << "mdb_cursor_open failed";
-    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
-        MDB_SUCCESS);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+  database->open(argv[1], Database::ReadOnly);
 
   Datum datum;
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  if (db_backend == "leveldb") {
-    datum.ParseFromString(it->value().ToString());
-  } else if (db_backend == "lmdb") {
-    datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+  datum.ParseFromString(database->begin()->second);
 
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());
@@ -89,59 +48,29 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  if (db_backend == "leveldb") {  // leveldb
-    for (it->SeekToFirst(); it->Valid(); it->Next()) {
-      // just a dummy operation
-      datum.ParseFromString(it->value().ToString());
-      const std::string& data = datum.data();
-      size_in_datum = std::max<int>(datum.data().size(),
-          datum.float_data_size());
-      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
-          size_in_datum;
-      if (data.size() != 0) {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
-        }
-      } else {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) +
-              static_cast<float>(datum.float_data(i)));
-        }
+  for (Database::const_iterator iter = database->begin();
+      iter != database->end(); ++iter) {
+    // just a dummy operation
+    datum.ParseFromString(iter->second);
+    const std::string& data = datum.data();
+    size_in_datum = std::max<int>(datum.data().size(),
+        datum.float_data_size());
+    CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
+        size_in_datum;
+    if (data.size() != 0) {
+      for (int i = 0; i < size_in_datum; ++i) {
+        sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
       }
-      ++count;
-      if (count % 10000 == 0) {
-        LOG(ERROR) << "Processed " << count << " files.";
+    } else {
+      for (int i = 0; i < size_in_datum; ++i) {
+        sum_blob.set_data(i, sum_blob.data(i) +
+            static_cast<float>(datum.float_data(i)));
       }
     }
-  } else if (db_backend == "lmdb") {  // lmdb
-    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
-        MDB_SUCCESS);
-    do {
-      // just a dummy operation
-      datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
-      const std::string& data = datum.data();
-      size_in_datum = std::max<int>(datum.data().size(),
-          datum.float_data_size());
-      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
-          size_in_datum;
-      if (data.size() != 0) {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
-        }
-      } else {
-        for (int i = 0; i < size_in_datum; ++i) {
-          sum_blob.set_data(i, sum_blob.data(i) +
-              static_cast<float>(datum.float_data(i)));
-        }
-      }
-      ++count;
-      if (count % 10000 == 0) {
-        LOG(ERROR) << "Processed " << count << " files.";
-      }
-    } while (mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_NEXT)
-        == MDB_SUCCESS);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
+    ++count;
+    if (count % 10000 == 0) {
+      LOG(ERROR) << "Processed " << count << " files.";
+    }
   }
 
   if (count % 10000 != 0) {
@@ -155,15 +84,6 @@ int main(int argc, char** argv) {
   WriteProtoToBinaryFile(sum_blob, argv[2]);
 
   // Clean up
-  if (db_backend == "leveldb") {
-    delete db;
-  } else if (db_backend == "lmdb") {
-    mdb_cursor_close(mdb_cursor);
-    mdb_close(mdb_env, mdb_dbi);
-    mdb_txn_abort(mdb_txn);
-    mdb_env_close(mdb_env);
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+  database->close();
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 7c8c1dab3fb..6f03a9d9d49 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -10,10 +10,6 @@
 
 #include <gflags/gflags.h>
 #include <glog/logging.h>
-#include <leveldb/db.h>
-#include <leveldb/write_batch.h>
-#include <lmdb.h>
-#include <sys/stat.h>
 
 #include <algorithm>
 #include <fstream>  // NOLINT(readability/streams)
@@ -21,6 +17,7 @@
 #include <utility>
 #include <vector>
 
+#include "caffe/database_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/rng.hpp"
@@ -81,43 +78,10 @@ int main(int argc, char** argv) {
   int resize_width = std::max<int>(0, FLAGS_resize_width);
 
   // Open new db
-  // lmdb
-  MDB_env *mdb_env;
-  MDB_dbi mdb_dbi;
-  MDB_val mdb_key, mdb_data;
-  MDB_txn *mdb_txn;
-  // leveldb
-  leveldb::DB* db;
-  leveldb::Options options;
-  options.error_if_exists = true;
-  options.create_if_missing = true;
-  options.write_buffer_size = 268435456;
-  leveldb::WriteBatch* batch = NULL;
+  shared_ptr<Database> database = DatabaseFactory(db_backend);
 
   // Open db
-  if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << db_path;
-    leveldb::Status status = leveldb::DB::Open(
-        options, db_path, &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << db_path
-        << ". Is it already existing?";
-    batch = new leveldb::WriteBatch();
-  } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << db_path;
-    CHECK_EQ(mkdir(db_path, 0744), 0)
-        << "mkdir " << db_path << "failed";
-    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed. Does the lmdb already exist? ";
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+  database->open(db_path, Database::New);
 
   // Storing to db
   std::string root_folder(argv[1]);
@@ -151,50 +115,19 @@ int main(int argc, char** argv) {
     std::string keystr(key_cstr);
 
     // Put in db
-    if (db_backend == "leveldb") {  // leveldb
-      batch->Put(keystr, value);
-    } else if (db_backend == "lmdb") {  // lmdb
-      mdb_data.mv_size = value.size();
-      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
-      mdb_key.mv_size = keystr.size();
-      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
-      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
-          << "mdb_put failed";
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
+    database->put(keystr, value);
 
     if (++count % 1000 == 0) {
       // Commit txn
-      if (db_backend == "leveldb") {  // leveldb
-        db->Write(leveldb::WriteOptions(), batch);
-        delete batch;
-        batch = new leveldb::WriteBatch();
-      } else if (db_backend == "lmdb") {  // lmdb
-        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_commit failed";
-        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_begin failed";
-      } else {
-        LOG(FATAL) << "Unknown db backend " << db_backend;
-      }
+      database->commit();
       LOG(ERROR) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    if (db_backend == "leveldb") {  // leveldb
-      db->Write(leveldb::WriteOptions(), batch);
-      delete batch;
-      delete db;
-    } else if (db_backend == "lmdb") {  // lmdb
-      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-      mdb_close(mdb_env, mdb_dbi);
-      mdb_env_close(mdb_env);
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
+    database->commit();
     LOG(ERROR) << "Processed " << count << " files.";
   }
+  database->close();
   return 0;
 }
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 299b3118760..b3ad8e60622 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -4,11 +4,10 @@
 
 #include "boost/algorithm/string.hpp"
 #include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
-#include "leveldb/write_batch.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/database_factory.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
@@ -17,6 +16,8 @@
 using boost::shared_ptr;
 using caffe::Blob;
 using caffe::Caffe;
+using caffe::Database;
+using caffe::DatabaseFactory;
 using caffe::Datum;
 using caffe::Net;
 
@@ -38,12 +39,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
     " extract features of the input data produced by the net.\n"
     "Usage: extract_features  pretrained_net_param"
     "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
-    "  save_feature_leveldb_name1[,name2,...]  num_mini_batches  [CPU/GPU]"
+    "  save_feature_database_name1[,name2,...]  num_mini_batches  [CPU/GPU]"
     "  [DEVICE_ID=0]\n"
     "Note: you can extract multiple features in one pass by specifying"
-    " multiple feature blob names and leveldb names seperated by ','."
+    " multiple feature blob names and database names seperated by ','."
     " The names cannot contain white space characters and the number of blobs"
-    " and leveldbs must be equal.";
+    " and databases must be equal.";
     return 1;
   }
   int arg_pos = num_required_args;
@@ -104,12 +105,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
   std::vector<std::string> blob_names;
   boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
 
-  std::string save_feature_leveldb_names(argv[++arg_pos]);
-  std::vector<std::string> leveldb_names;
-  boost::split(leveldb_names, save_feature_leveldb_names,
+  std::string save_feature_database_names(argv[++arg_pos]);
+  std::vector<std::string> database_names;
+  boost::split(database_names, save_feature_database_names,
                boost::is_any_of(","));
-  CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
-      " the number of blob names and leveldb names must be equal";
+  CHECK_EQ(blob_names.size(), database_names.size()) <<
+      " the number of blob names and database names must be equal";
   size_t num_features = blob_names.size();
 
   for (size_t i = 0; i < num_features; i++) {
@@ -118,19 +119,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
         << " in the network " << feature_extraction_proto;
   }
 
-  leveldb::Options options;
-  options.error_if_exists = true;
-  options.create_if_missing = true;
-  options.write_buffer_size = 268435456;
-  std::vector<shared_ptr<leveldb::DB> > feature_dbs;
+  std::vector<shared_ptr<Database> > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
-    LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
-    leveldb::DB* db;
-    leveldb::Status status = leveldb::DB::Open(options,
-                                               leveldb_names[i].c_str(),
-                                               &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << leveldb_names[i];
-    feature_dbs.push_back(shared_ptr<leveldb::DB>(db));
+    LOG(INFO)<< "Opening database " << database_names[i];
+    shared_ptr<Database> database = DatabaseFactory("leveldb");
+    database->open(database_names.at(i), Database::New);
+    feature_dbs.push_back(database);
   }
 
   int num_mini_batches = atoi(argv[++arg_pos]);
@@ -138,9 +132,6 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  std::vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
-      num_features,
-      shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
   std::vector<Blob<float>*> input_vec;
@@ -167,14 +158,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
         std::string value;
         datum.SerializeToString(&value);
         snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
-        feature_batches[i]->Put(std::string(key_str), value);
+        feature_dbs.at(i)->put(std::string(key_str), value);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
-          feature_dbs[i]->Write(leveldb::WriteOptions(),
-                                feature_batches[i].get());
+          feature_dbs.at(i)->commit();
           LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
               " query images for feature blob " << blob_names[i];
-          feature_batches[i].reset(new leveldb::WriteBatch());
         }
       }  // for (int n = 0; n < batch_size; ++n)
     }  // for (int i = 0; i < num_features; ++i)
@@ -182,10 +171,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
   // write the last batch
   for (int i = 0; i < num_features; ++i) {
     if (image_indices[i] % 1000 != 0) {
-      feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i].get());
+      feature_dbs.at(i)->commit();
     }
     LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
         " query images for feature blob " << blob_names[i];
+    feature_dbs.at(i)->close();
   }
 
   LOG(ERROR)<< "Successfully extracted the features!";

From 329e4481263d396b22a6e8ca6b00b19d57860ab9 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 7 Oct 2014 16:14:29 -0400
Subject: [PATCH 0918/2053] Some cleanup to make travis happy.

---
 include/caffe/database_factory.hpp |  1 +
 include/caffe/leveldb_database.hpp | 11 +++++------
 include/caffe/lmdb_database.hpp    | 19 ++++++++++---------
 src/caffe/database_factory.cpp     |  2 ++
 src/caffe/layers/data_layer.cpp    |  3 +++
 5 files changed, 21 insertions(+), 15 deletions(-)

diff --git a/include/caffe/database_factory.hpp b/include/caffe/database_factory.hpp
index a6e39e76188..91185e99b89 100644
--- a/include/caffe/database_factory.hpp
+++ b/include/caffe/database_factory.hpp
@@ -5,6 +5,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/database.hpp"
+#include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 5daf0e51e12..ee8c5f2eed8 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -19,6 +19,11 @@ class LeveldbDatabase : public Database {
   void commit();
   void close();
 
+  const_iterator begin() const;
+  const_iterator cbegin() const;
+  const_iterator end() const;
+  const_iterator cend() const;
+
   ~LeveldbDatabase() { this->close(); }
 
  protected:
@@ -37,12 +42,6 @@ class LeveldbDatabase : public Database {
   void increment(shared_ptr<DatabaseState> state) const;
   pair<string, string>& dereference(shared_ptr<DatabaseState> state) const;
 
-  const_iterator begin() const;
-  const_iterator cbegin() const;
-  const_iterator end() const;
-  const_iterator cend() const;
-
- protected:
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::WriteBatch> batch_;
 };
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index f275cb48f5c..7387afd24d0 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -14,7 +14,9 @@ namespace caffe {
 class LmdbDatabase : public Database {
  public:
   LmdbDatabase()
-      : dbi_(0) { }
+      : env_(NULL),
+        dbi_(0),
+        txn_(NULL) { }
   ~LmdbDatabase() { this->close(); }
 
   void open(const string& filename, Mode mode);
@@ -22,6 +24,11 @@ class LmdbDatabase : public Database {
   void commit();
   void close();
 
+  const_iterator begin() const;
+  const_iterator cbegin() const;
+  const_iterator end() const;
+  const_iterator cend() const;
+
  protected:
   class LmdbState : public Database::DatabaseState {
    public:
@@ -38,15 +45,9 @@ class LmdbDatabase : public Database {
   void increment(shared_ptr<DatabaseState> state) const;
   pair<string, string>& dereference(shared_ptr<DatabaseState> state) const;
 
- protected:
-  const_iterator begin() const;
-  const_iterator cbegin() const;
-  const_iterator end() const;
-  const_iterator cend() const;
-
-  MDB_env *env_ = NULL;
+  MDB_env *env_;
   MDB_dbi dbi_;
-  MDB_txn *txn_ = NULL;
+  MDB_txn *txn_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/database_factory.cpp b/src/caffe/database_factory.cpp
index 393635b3208..062de8c02e3 100644
--- a/src/caffe/database_factory.cpp
+++ b/src/caffe/database_factory.cpp
@@ -15,6 +15,7 @@ shared_ptr<Database> DatabaseFactory(const DataParameter_DB& type) {
     return shared_ptr<Database>(new LmdbDatabase());
   default:
     LOG(FATAL) << "Unknown database type " << type;
+    return shared_ptr<Database>();
   }
 }
 
@@ -25,6 +26,7 @@ shared_ptr<Database> DatabaseFactory(const string& type) {
     return DatabaseFactory(DataParameter_DB_LMDB);
   } else {
     LOG(FATAL) << "Unknown database type " << type;
+    return shared_ptr<Database>();
   }
 }
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index b746bc8a37d..1d37170bdde 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -18,6 +18,9 @@ template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
   // clean up the database resources
+
+  // Very important to invalidate iterators before closing the database.
+  // TODO(kmatzen): Figure out a better design to avoid this.
   iter_ = database_->end();
   database_->close();
 }

From 7e504c0612b23449aa83fd7aac8d49c56b03fd62 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 7 Oct 2014 18:03:02 -0400
Subject: [PATCH 0919/2053] Updated interface to make fewer string copies.

---
 include/caffe/database.hpp         | 13 ++++--
 include/caffe/leveldb_database.hpp |  6 +--
 include/caffe/lmdb_database.hpp    |  6 +--
 include/caffe/util/io.hpp          |  8 ++--
 src/caffe/layers/data_layer.cpp    |  5 +-
 src/caffe/leveldb_database.cpp     | 18 +++++---
 src/caffe/lmdb_database.cpp        | 73 ++++++++++++++++++++----------
 src/caffe/test/test_data_layer.cpp |  7 ++-
 tools/compute_image_mean.cpp       |  6 ++-
 tools/convert_imageset.cpp         | 11 +++--
 tools/extract_features.cpp         | 11 +++--
 11 files changed, 103 insertions(+), 61 deletions(-)

diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 4a1a25efd12..23036a8e075 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -5,6 +5,7 @@
 #include <iterator>
 #include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/common.hpp"
 
@@ -18,8 +19,10 @@ class Database {
     ReadOnly
   };
 
+  typedef vector<char> buffer_t;
+
   virtual void open(const string& filename, Mode mode) = 0;
-  virtual void put(const string& key, const string& value) = 0;
+  virtual void put(buffer_t* key, buffer_t* value) = 0;
   virtual void commit() = 0;
   virtual void close() = 0;
 
@@ -38,10 +41,10 @@ class Database {
   class DatabaseState;
 
  public:
-  class iterator :
-      public std::iterator<std::forward_iterator_tag, pair<string, string> > {
+  class iterator : public std::iterator<
+      std::forward_iterator_tag, pair<buffer_t, buffer_t> > {
    public:
-    typedef pair<string, string> T;
+    typedef pair<buffer_t, buffer_t> T;
     typedef T value_type;
     typedef T& reference_type;
     typedef T* pointer_type;
@@ -94,7 +97,7 @@ class Database {
   virtual bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const = 0;
   virtual void increment(shared_ptr<DatabaseState> state) const = 0;
-  virtual pair<string, string>& dereference(
+  virtual pair<buffer_t, buffer_t>& dereference(
       shared_ptr<DatabaseState> state) const = 0;
 };
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index ee8c5f2eed8..f30273d6fe7 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -15,7 +15,7 @@ namespace caffe {
 class LeveldbDatabase : public Database {
  public:
   void open(const string& filename, Mode mode);
-  void put(const string& key, const string& value);
+  void put(buffer_t* key, buffer_t* value);
   void commit();
   void close();
 
@@ -34,13 +34,13 @@ class LeveldbDatabase : public Database {
           iter_(iter) { }
 
     shared_ptr<leveldb::Iterator> iter_;
-    pair<string, string> kv_pair_;
+    pair<buffer_t, buffer_t> kv_pair_;
   };
 
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
   void increment(shared_ptr<DatabaseState> state) const;
-  pair<string, string>& dereference(shared_ptr<DatabaseState> state) const;
+  pair<buffer_t, buffer_t>& dereference(shared_ptr<DatabaseState> state) const;
 
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::WriteBatch> batch_;
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 7387afd24d0..ee3806ddf36 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -20,7 +20,7 @@ class LmdbDatabase : public Database {
   ~LmdbDatabase() { this->close(); }
 
   void open(const string& filename, Mode mode);
-  void put(const string& key, const string& value);
+  void put(buffer_t* key, buffer_t* value);
   void commit();
   void close();
 
@@ -37,13 +37,13 @@ class LmdbDatabase : public Database {
           cursor_(cursor) { }
 
     MDB_cursor* cursor_;
-    pair<string, string> kv_pair_;
+    pair<buffer_t, buffer_t> kv_pair_;
   };
 
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
   void increment(shared_ptr<DatabaseState> state) const;
-  pair<string, string>& dereference(shared_ptr<DatabaseState> state) const;
+  pair<buffer_t, buffer_t>& dereference(shared_ptr<DatabaseState> state) const;
 
   MDB_env *env_;
   MDB_dbi dbi_;
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 1124bef179a..b64c821a86e 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -24,27 +24,27 @@ using ::google::protobuf::Message;
 inline void MakeTempFilename(string* temp_filename) {
   temp_filename->clear();
   *temp_filename = "/tmp/caffe_test.XXXXXX";
-  char* temp_filename_cstr = new char[temp_filename->size()];
+  char* temp_filename_cstr = new char[temp_filename->size() + 1];
   // NOLINT_NEXT_LINE(runtime/printf)
   strcpy(temp_filename_cstr, temp_filename->c_str());
   int fd = mkstemp(temp_filename_cstr);
   CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;
   close(fd);
   *temp_filename = temp_filename_cstr;
-  delete temp_filename_cstr;
+  delete[] temp_filename_cstr;
 }
 
 inline void MakeTempDir(string* temp_dirname) {
   temp_dirname->clear();
   *temp_dirname = "/tmp/caffe_test.XXXXXX";
-  char* temp_dirname_cstr = new char[temp_dirname->size()];
+  char* temp_dirname_cstr = new char[temp_dirname->size() + 1];
   // NOLINT_NEXT_LINE(runtime/printf)
   strcpy(temp_dirname_cstr, temp_dirname->c_str());
   char* mkdtemp_result = mkdtemp(temp_dirname_cstr);
   CHECK(mkdtemp_result != NULL)
       << "Failed to create a temporary directory at: " << *temp_dirname;
   *temp_dirname = temp_dirname_cstr;
-  delete temp_dirname_cstr;
+  delete[] temp_dirname_cstr;
 }
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 1d37170bdde..4d36b8ede42 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -40,7 +40,6 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      LOG(INFO) << iter_->first;
       if (++iter_ == database_->end()) {
         iter_ = database_->begin();
       }
@@ -49,7 +48,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Read a data point, and use it to initialize the top blob.
   CHECK(iter_ != database_->end());
   Datum datum;
-  datum.ParseFromString(iter_->second);
+  datum.ParseFromArray(iter_->second.data(), iter_->second.size());
 
   // image
   int crop_size = this->layer_param_.transform_param().crop_size();
@@ -95,7 +94,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     Datum datum;
     CHECK(iter_ != database_->end());
-    datum.ParseFromString(iter_->second);
+    datum.ParseFromArray(iter_->second.data(), iter_->second.size());
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index be7ac7f141c..a8fe02a3157 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -42,11 +42,15 @@ void LeveldbDatabase::open(const string& filename, Mode mode) {
   batch_.reset(new leveldb::WriteBatch());
 }
 
-void LeveldbDatabase::put(const string& key, const string& value) {
-  LOG(INFO) << "LevelDB: Put " << key;
+void LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
+  LOG(INFO) << "LevelDB: Put";
 
   CHECK_NOTNULL(batch_.get());
-  batch_->Put(key, value);
+
+  leveldb::Slice key_slice(key->data(), key->size());
+  leveldb::Slice value_slice(value->data(), value->size());
+
+  batch_->Put(key_slice, value_slice);
 }
 
 void LeveldbDatabase::commit() {
@@ -130,7 +134,7 @@ void LeveldbDatabase::increment(shared_ptr<DatabaseState> state) const {
   }
 }
 
-pair<string, string>& LeveldbDatabase::dereference(
+pair<Database::buffer_t, Database::buffer_t>& LeveldbDatabase::dereference(
     shared_ptr<DatabaseState> state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(state);
@@ -143,8 +147,10 @@ pair<string, string>& LeveldbDatabase::dereference(
 
   CHECK(iter->Valid());
 
-  leveldb_state->kv_pair_ = make_pair(iter->key().ToString(),
-      iter->value().ToString());
+  leveldb_state->kv_pair_ = make_pair(
+      buffer_t(iter->key().data(), iter->key().data() + iter->key().size()),
+      buffer_t(iter->value().data(),
+          iter->value().data() + iter->value().size()));
   return leveldb_state->kv_pair_;
 }
 
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index 796bbc9d4ac..7197a4775dc 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -19,9 +19,13 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
                                                 << "failed";
   }
 
-  CHECK_EQ(mdb_env_create(&env_), MDB_SUCCESS) << "mdb_env_create failed";
-  CHECK_EQ(mdb_env_set_mapsize(env_, 1099511627776), MDB_SUCCESS)  // 1TB
-      << "mdb_env_set_mapsize failed";
+  int retval;
+  retval = mdb_env_create(&env_);
+  CHECK_EQ(retval, MDB_SUCCESS) << "mdb_env_create failed "
+      << mdb_strerror(retval);
+  retval = mdb_env_set_mapsize(env_, 1099511627776);
+  CHECK_EQ(retval, MDB_SUCCESS)  // 1TB
+      << "mdb_env_set_mapsize failed " << mdb_strerror(retval);
 
   int flag1 = 0;
   int flag2 = 0;
@@ -30,27 +34,31 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
     flag2 = MDB_RDONLY;
   }
 
-  CHECK_EQ(mdb_env_open(env_, filename.c_str(), flag1, 0664), MDB_SUCCESS)
-      << "mdb_env_open failed";
-  CHECK_EQ(mdb_txn_begin(env_, NULL, flag2, &txn_), MDB_SUCCESS)
-      << "mdb_txn_begin failed";
-  CHECK_EQ(mdb_open(txn_, NULL, 0, &dbi_), MDB_SUCCESS) << "mdb_open failed";
+  retval = mdb_env_open(env_, filename.c_str(), flag1, 0664);
+  CHECK_EQ(retval, MDB_SUCCESS)
+      << "mdb_env_open failed " << mdb_strerror(retval);
+  retval = mdb_txn_begin(env_, NULL, flag2, &txn_);
+  CHECK_EQ(retval, MDB_SUCCESS)
+      << "mdb_txn_begin failed " << mdb_strerror(retval);
+  retval = mdb_open(txn_, NULL, 0, &dbi_);
+  CHECK_EQ(retval, MDB_SUCCESS) << "mdb_open failed" << mdb_strerror(retval);
 }
 
-void LmdbDatabase::put(const string& key, const string& value) {
-  LOG(INFO) << "LMDB: Put " << key;
+void LmdbDatabase::put(buffer_t* key, buffer_t* value) {
+  LOG(INFO) << "LMDB: Put";
 
   MDB_val mdbkey, mdbdata;
-  mdbdata.mv_size = value.size();
-  mdbdata.mv_data = const_cast<char*>(&value[0]);
-  mdbkey.mv_size = key.size();
-  mdbkey.mv_data = const_cast<char*>(&key[0]);
+  mdbdata.mv_size = value->size();
+  mdbdata.mv_data = value->data();
+  mdbkey.mv_size = key->size();
+  mdbkey.mv_data = key->data();
 
   CHECK_NOTNULL(txn_);
   CHECK_NE(0, dbi_);
 
-  CHECK_EQ(mdb_put(txn_, dbi_, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
-      << "mdb_put failed";
+  int retval = mdb_put(txn_, dbi_, &mdbkey, &mdbdata, 0);
+  CHECK_EQ(retval, MDB_SUCCESS)
+      << "mdb_put failed " << mdb_strerror(retval);
 }
 
 void LmdbDatabase::commit() {
@@ -58,7 +66,9 @@ void LmdbDatabase::commit() {
 
   CHECK_NOTNULL(txn_);
 
-  CHECK_EQ(mdb_txn_commit(txn_), MDB_SUCCESS) << "mdb_txn_commit failed";
+  int retval = mdb_txn_commit(txn_);
+  CHECK_EQ(retval, MDB_SUCCESS) << "mdb_txn_commit failed "
+      << mdb_strerror(retval);
 }
 
 void LmdbDatabase::close() {
@@ -79,10 +89,13 @@ void LmdbDatabase::close() {
 
 LmdbDatabase::const_iterator LmdbDatabase::begin() const {
   MDB_cursor* cursor;
-  CHECK_EQ(mdb_cursor_open(txn_, dbi_, &cursor), MDB_SUCCESS);
+  int retval;
+  retval = mdb_cursor_open(txn_, dbi_, &cursor);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
   MDB_val key;
   MDB_val val;
-  CHECK_EQ(mdb_cursor_get(cursor, &key, &val, MDB_FIRST), MDB_SUCCESS);
+  retval = mdb_cursor_get(cursor, &key, &val, MDB_FIRST);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
   shared_ptr<DatabaseState> state(new LmdbState(cursor));
   return const_iterator(this, state);
@@ -122,15 +135,20 @@ void LmdbDatabase::increment(shared_ptr<DatabaseState> state) const {
 
   MDB_cursor*& cursor = lmdb_state->cursor_;
 
+  CHECK_NOTNULL(cursor);
+
   MDB_val key;
   MDB_val val;
-  if (MDB_SUCCESS != mdb_cursor_get(cursor, &key, &val, MDB_NEXT)) {
+  int retval = mdb_cursor_get(cursor, &key, &val, MDB_NEXT);
+  if (MDB_NOTFOUND == retval) {
     mdb_cursor_close(cursor);
     cursor = NULL;
+  } else {
+    CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
   }
 }
 
-pair<string, string>& LmdbDatabase::dereference(
+pair<Database::buffer_t, Database::buffer_t>& LmdbDatabase::dereference(
     shared_ptr<DatabaseState> state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(state);
@@ -139,14 +157,19 @@ pair<string, string>& LmdbDatabase::dereference(
 
   MDB_cursor*& cursor = lmdb_state->cursor_;
 
+  CHECK_NOTNULL(cursor);
+
   MDB_val mdb_key;
   MDB_val mdb_val;
-  CHECK_EQ(mdb_cursor_get(cursor, &mdb_key, &mdb_val, MDB_GET_CURRENT),
-      MDB_SUCCESS);
+  int retval = mdb_cursor_get(cursor, &mdb_key, &mdb_val, MDB_GET_CURRENT);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+
+  char* key_data = reinterpret_cast<char*>(mdb_key.mv_data);
+  char* value_data = reinterpret_cast<char*>(mdb_val.mv_data);
 
   lmdb_state->kv_pair_ = make_pair(
-    string(reinterpret_cast<char*>(mdb_key.mv_data), mdb_key.mv_size),
-    string(reinterpret_cast<char*>(mdb_val.mv_data), mdb_val.mv_size));
+    buffer_t(key_data, key_data + mdb_key.mv_size),
+    buffer_t(value_data, value_data + mdb_val.mv_size));
 
   return lmdb_state->kv_pair_;
 }
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index d99b5e3a0d3..c17f7295c3c 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -54,7 +54,12 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       }
       stringstream ss;
       ss << i;
-      database->put(ss.str(), datum.SerializeAsString());
+      string key_str = ss.str();
+      Database::buffer_t key(key_str.c_str(), key_str.c_str() + key_str.size());
+      Database::buffer_t value(datum.ByteSize());
+      datum.SerializeWithCachedSizesToArray(
+          reinterpret_cast<unsigned char*>(value.data()));
+      database->put(&key, &value);
     }
     database->close();
   }
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index e59bbf19f0d..f981af40fdf 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -35,7 +35,8 @@ int main(int argc, char** argv) {
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  datum.ParseFromString(database->begin()->second);
+  const Database::buffer_t& first_blob = database->begin()->second;
+  datum.ParseFromArray(first_blob.data(), first_blob.size());
 
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());
@@ -51,7 +52,8 @@ int main(int argc, char** argv) {
   for (Database::const_iterator iter = database->begin();
       iter != database->end(); ++iter) {
     // just a dummy operation
-    datum.ParseFromString(iter->second);
+    const Database::buffer_t& blob = iter->second;
+    datum.ParseFromArray(blob.data(), blob.size());
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
         datum.float_data_size());
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 6f03a9d9d49..19c87e53ff0 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -108,14 +108,15 @@ int main(int argc, char** argv) {
       }
     }
     // sequential
-    snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
+    int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
-    std::string value;
-    datum.SerializeToString(&value);
-    std::string keystr(key_cstr);
+    Database::buffer_t value(datum.ByteSize());
+    datum.SerializeWithCachedSizesToArray(
+        reinterpret_cast<unsigned char*>(value.data()));
+    Database::buffer_t keystr(key_cstr, key_cstr + length);
 
     // Put in db
-    database->put(keystr, value);
+    database->put(&keystr, &value);
 
     if (++count % 1000 == 0) {
       // Commit txn
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index b3ad8e60622..1065d449a42 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -155,10 +155,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        std::string value;
-        datum.SerializeToString(&value);
-        snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
-        feature_dbs.at(i)->put(std::string(key_str), value);
+        Database::buffer_t value(datum.ByteSize());
+        datum.SerializeWithCachedSizesToArray(
+            reinterpret_cast<unsigned char*>(value.data()));
+        int length = snprintf(key_str, kMaxKeyStrLength, "%d",
+            image_indices[i]);
+        Database::buffer_t key(key_str, key_str + length);
+        feature_dbs.at(i)->put(&key, &value);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           feature_dbs.at(i)->commit();

From edff676ea8482d48f4b7bc794288696afce782ca Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 7 Oct 2014 21:46:15 -0400
Subject: [PATCH 0920/2053] Don't autocommit on close for the databases.  If
 they were read-only, then they might fail.

---
 examples/cifar10/convert_cifar_data.cpp | 59 ++++++++++++++-----------
 include/caffe/leveldb_database.hpp      |  2 -
 include/caffe/lmdb_database.hpp         |  1 -
 src/caffe/leveldb_database.cpp          |  3 --
 src/caffe/lmdb_database.cpp             | 15 ++++---
 tools/compute_image_mean.cpp            |  4 +-
 6 files changed, 43 insertions(+), 41 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 90ecb6d9a88..c4930878405 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -11,13 +11,17 @@
 
 #include "glog/logging.h"
 #include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
 #include "stdint.h"
 
+#include "caffe/database_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 using std::string;
 
+using caffe::Database;
+using caffe::DatabaseFactory;
+using caffe::shared_ptr;
+
 const int kCIFARSize = 32;
 const int kCIFARImageNBytes = 3072;
 const int kCIFARBatchSize = 10000;
@@ -31,26 +35,20 @@ void read_image(std::ifstream* file, int* label, char* buffer) {
   return;
 }
 
-void convert_dataset(const string& input_folder, const string& output_folder) {
-  // Leveldb options
-  leveldb::Options options;
-  options.create_if_missing = true;
-  options.error_if_exists = true;
+void convert_dataset(const string& input_folder, const string& output_folder,
+    const string& db_type) {
+  shared_ptr<Database> train_database = DatabaseFactory(db_type);
+  train_database->open(output_folder + "/cifar10_train_" + db_type,
+      Database::New);
   // Data buffer
   int label;
   char str_buffer[kCIFARImageNBytes];
-  string value;
   caffe::Datum datum;
   datum.set_channels(3);
   datum.set_height(kCIFARSize);
   datum.set_width(kCIFARSize);
 
   LOG(INFO) << "Writing Training data";
-  leveldb::DB* train_db;
-  leveldb::Status status;
-  status = leveldb::DB::Open(options, output_folder + "/cifar10_train_leveldb",
-      &train_db);
-  CHECK(status.ok()) << "Failed to open leveldb.";
   for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
     // Open files
     LOG(INFO) << "Training Batch " << fileid + 1;
@@ -62,17 +60,22 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
       read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
       datum.set_data(str_buffer, kCIFARImageNBytes);
-      datum.SerializeToString(&value);
-      snprintf(str_buffer, kCIFARImageNBytes, "%05d",
+      Database::buffer_t value(datum.ByteSize());
+      datum.SerializeWithCachedSizesToArray(
+          reinterpret_cast<unsigned char*>(value.data()));
+      int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
-      train_db->Put(leveldb::WriteOptions(), string(str_buffer), value);
+      Database::buffer_t key(str_buffer, str_buffer + length);
+      train_database->put(&key, &value);
     }
   }
+  train_database->commit();
+  train_database->close();
 
   LOG(INFO) << "Writing Testing data";
-  leveldb::DB* test_db;
-  CHECK(leveldb::DB::Open(options, output_folder + "/cifar10_test_leveldb",
-      &test_db).ok()) << "Failed to open leveldb.";
+  shared_ptr<Database> test_database = DatabaseFactory(db_type);
+  test_database->open(output_folder + "/cifar10_test_" + db_type,
+      Database::New);
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
@@ -81,28 +84,30 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
     read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
-    datum.SerializeToString(&value);
-    snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
-    test_db->Put(leveldb::WriteOptions(), string(str_buffer), value);
+    Database::buffer_t value(datum.ByteSize());
+    datum.SerializeWithCachedSizesToArray(
+        reinterpret_cast<unsigned char*>(value.data()));
+    int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
+    Database::buffer_t key(str_buffer, str_buffer + length);
+    test_database->put(&key, &value);
   }
-
-  delete train_db;
-  delete test_db;
+  test_database->commit();
+  test_database->close();
 }
 
 int main(int argc, char** argv) {
-  if (argc != 3) {
+  if (argc != 4) {
     printf("This script converts the CIFAR dataset to the leveldb format used\n"
            "by caffe to perform classification.\n"
            "Usage:\n"
-           "    convert_cifar_data input_folder output_folder\n"
+           "    convert_cifar_data input_folder output_folder db_type\n"
            "Where the input folder should contain the binary batch files.\n"
            "The CIFAR dataset could be downloaded at\n"
            "    http://www.cs.toronto.edu/~kriz/cifar.html\n"
            "You should gunzip them after downloading.\n");
   } else {
     google::InitGoogleLogging(argv[0]);
-    convert_dataset(string(argv[1]), string(argv[2]));
+    convert_dataset(string(argv[1]), string(argv[2]), string(argv[3]));
   }
   return 0;
 }
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index f30273d6fe7..dda669704bd 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -24,8 +24,6 @@ class LeveldbDatabase : public Database {
   const_iterator end() const;
   const_iterator cend() const;
 
-  ~LeveldbDatabase() { this->close(); }
-
  protected:
   class LeveldbState : public Database::DatabaseState {
    public:
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index ee3806ddf36..96542227c9a 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -17,7 +17,6 @@ class LmdbDatabase : public Database {
       : env_(NULL),
         dbi_(0),
         txn_(NULL) { }
-  ~LmdbDatabase() { this->close(); }
 
   void open(const string& filename, Mode mode);
   void put(buffer_t* key, buffer_t* value);
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index a8fe02a3157..a5cdaa3b7f1 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -66,9 +66,6 @@ void LeveldbDatabase::commit() {
 void LeveldbDatabase::close() {
   LOG(INFO) << "LevelDB: Close";
 
-  if (batch_ && db_) {
-    this->commit();
-  }
   batch_.reset();
   db_.reset();
 }
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index 7197a4775dc..08607789bb1 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -14,9 +14,9 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
   CHECK(NULL == txn_);
   CHECK_EQ(0, dbi_);
 
-  if (mode != ReadOnly) {
+  if (mode == New) {
     CHECK_EQ(mkdir(filename.c_str(), 0744), 0) << "mkdir " << filename
-                                                << "failed";
+                                                << " failed";
   }
 
   int retval;
@@ -66,18 +66,19 @@ void LmdbDatabase::commit() {
 
   CHECK_NOTNULL(txn_);
 
-  int retval = mdb_txn_commit(txn_);
+  int retval;
+  retval = mdb_txn_commit(txn_);
   CHECK_EQ(retval, MDB_SUCCESS) << "mdb_txn_commit failed "
       << mdb_strerror(retval);
+
+  retval = mdb_txn_begin(env_, NULL, 0, &txn_);
+  CHECK_EQ(retval, MDB_SUCCESS)
+      << "mdb_txn_begin failed " << mdb_strerror(retval);
 }
 
 void LmdbDatabase::close() {
   LOG(INFO) << "LMDB: Close";
 
-  if (env_ && dbi_ && txn_) {
-    this->commit();
-  }
-
   if (env_ && dbi_) {
     mdb_close(env_, dbi_);
     mdb_env_close(env_);
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index f981af40fdf..11f6fb820e6 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -35,8 +35,10 @@ int main(int argc, char** argv) {
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  const Database::buffer_t& first_blob = database->begin()->second;
+  Database::const_iterator iter = database->begin();
+  const Database::buffer_t& first_blob = iter->second;
   datum.ParseFromArray(first_blob.data(), first_blob.size());
+  iter = database->end();
 
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());

From c7a5d8a4fbb3c6bf17130e75305fb5dabdc273f3 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 7 Oct 2014 22:10:20 -0400
Subject: [PATCH 0921/2053] data layer test was relying on the autocommit on
 close db behavior that was recently removed.

---
 src/caffe/test/test_data_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index c17f7295c3c..98ef1b96f24 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -61,6 +61,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
           reinterpret_cast<unsigned char*>(value.data()));
       database->put(&key, &value);
     }
+    database->commit();
     database->close();
   }
 

From b1150c9ad9c6091348618a714ad6d36b715b893f Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Wed, 8 Oct 2014 16:15:23 +0000
Subject: [PATCH 0922/2053] Updated cifar10 build script to specify db backend.

---
 examples/cifar10/create_cifar10.sh | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index dfba7cca48a..b82fd3e3a06 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -3,16 +3,17 @@
 
 EXAMPLE=examples/cifar10
 DATA=data/cifar10
+DBTYPE=leveldb
 
-echo "Creating leveldb..."
+echo "Creating $DBTYPE..."
 
-rm -rf $EXAMPLE/cifar10_train_leveldb $EXAMPLE/cifar10_test_leveldb
+rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE
 
-./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE
+./build/examples/cifar10/convert_cifar_data.bin $DATA $EXAMPLE $DBTYPE
 
 echo "Computing image mean..."
 
-./build/tools/compute_image_mean $EXAMPLE/cifar10_train_leveldb \
-  $EXAMPLE/mean.binaryproto leveldb
+./build/tools/compute_image_mean $EXAMPLE/cifar10_train_$DBTYPE \
+  $EXAMPLE/mean.binaryproto $DBTYPE
 
 echo "Done."

From 5a559f55c38330f462c3e4258450e898deee016e Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Fri, 10 Oct 2014 19:47:50 -0400
Subject: [PATCH 0923/2053] Switched create_cifar10.sh output from leveldb to
 lmdb.

---
 examples/cifar10/create_cifar10.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index b82fd3e3a06..ebfca46de87 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -3,7 +3,7 @@
 
 EXAMPLE=examples/cifar10
 DATA=data/cifar10
-DBTYPE=leveldb
+DBTYPE=lmdb
 
 echo "Creating $DBTYPE..."
 

From a1ea5ba4b2a6e1af9ef360aefd0c8ab777039a5b Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 14:15:17 -0400
Subject: [PATCH 0924/2053] Updated extract_features to take a leveldb/lmdb
 config option.

---
 examples/feature_extraction/readme.md |  2 +-
 tools/extract_features.cpp            | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index c325ed482e5..6c8917e27e1 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -51,7 +51,7 @@ Extract Features
 
 Now everything necessary is in place.
 
-    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10
+    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb
 
 The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
 We can use any other layer, as well, such as `conv5` or `pool3`.
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 1065d449a42..c4d1a39eb5b 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -32,15 +32,15 @@ int main(int argc, char** argv) {
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  const int num_required_args = 6;
+  const int num_required_args = 7;
   if (argc < num_required_args) {
     LOG(ERROR)<<
     "This program takes in a trained network and an input data layer, and then"
     " extract features of the input data produced by the net.\n"
     "Usage: extract_features  pretrained_net_param"
     "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
-    "  save_feature_database_name1[,name2,...]  num_mini_batches  [CPU/GPU]"
-    "  [DEVICE_ID=0]\n"
+    "  save_feature_database_name1[,name2,...]  num_mini_batches  db_type"
+    "  [CPU/GPU] [DEVICE_ID=0]\n"
     "Note: you can extract multiple features in one pass by specifying"
     " multiple feature blob names and database names seperated by ','."
     " The names cannot contain white space characters and the number of blobs"
@@ -119,16 +119,16 @@ int feature_extraction_pipeline(int argc, char** argv) {
         << " in the network " << feature_extraction_proto;
   }
 
+  int num_mini_batches = atoi(argv[++arg_pos]);
+
   std::vector<shared_ptr<Database> > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening database " << database_names[i];
-    shared_ptr<Database> database = DatabaseFactory("leveldb");
+    shared_ptr<Database> database = DatabaseFactory(argv[++arg_pos]);
     database->open(database_names.at(i), Database::New);
     feature_dbs.push_back(database);
   }
 
-  int num_mini_batches = atoi(argv[++arg_pos]);
-
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;

From 8fef285e536a67a6b2c72b54734335aeb3069d59 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 14:39:31 -0400
Subject: [PATCH 0925/2053] Updated Database interface to use custom KV type
 rather than std::pair.  Removed two buffer copies in dereference operation
 for DB iterators.

---
 include/caffe/database.hpp         | 12 ++++++++----
 include/caffe/leveldb_database.hpp |  4 ++--
 include/caffe/lmdb_database.hpp    |  4 ++--
 src/caffe/layers/data_layer.cpp    |  4 ++--
 src/caffe/leveldb_database.cpp     | 14 +++++++++-----
 src/caffe/lmdb_database.cpp        | 13 ++++++++-----
 tools/compute_image_mean.cpp       |  4 ++--
 7 files changed, 33 insertions(+), 22 deletions(-)

diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 23036a8e075..953b58cfa2e 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -21,6 +21,11 @@ class Database {
 
   typedef vector<char> buffer_t;
 
+  struct KV {
+    buffer_t key;
+    buffer_t value;
+  };
+
   virtual void open(const string& filename, Mode mode) = 0;
   virtual void put(buffer_t* key, buffer_t* value) = 0;
   virtual void commit() = 0;
@@ -41,10 +46,9 @@ class Database {
   class DatabaseState;
 
  public:
-  class iterator : public std::iterator<
-      std::forward_iterator_tag, pair<buffer_t, buffer_t> > {
+  class iterator : public std::iterator<std::forward_iterator_tag, KV> {
    public:
-    typedef pair<buffer_t, buffer_t> T;
+    typedef KV T;
     typedef T value_type;
     typedef T& reference_type;
     typedef T* pointer_type;
@@ -97,7 +101,7 @@ class Database {
   virtual bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const = 0;
   virtual void increment(shared_ptr<DatabaseState> state) const = 0;
-  virtual pair<buffer_t, buffer_t>& dereference(
+  virtual KV& dereference(
       shared_ptr<DatabaseState> state) const = 0;
 };
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index dda669704bd..1c084cb070d 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -32,13 +32,13 @@ class LeveldbDatabase : public Database {
           iter_(iter) { }
 
     shared_ptr<leveldb::Iterator> iter_;
-    pair<buffer_t, buffer_t> kv_pair_;
+    KV kv_pair_;
   };
 
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
   void increment(shared_ptr<DatabaseState> state) const;
-  pair<buffer_t, buffer_t>& dereference(shared_ptr<DatabaseState> state) const;
+  Database::KV& dereference(shared_ptr<DatabaseState> state) const;
 
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::WriteBatch> batch_;
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 96542227c9a..d72be3d4c66 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -36,13 +36,13 @@ class LmdbDatabase : public Database {
           cursor_(cursor) { }
 
     MDB_cursor* cursor_;
-    pair<buffer_t, buffer_t> kv_pair_;
+    KV kv_pair_;
   };
 
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
   void increment(shared_ptr<DatabaseState> state) const;
-  pair<buffer_t, buffer_t>& dereference(shared_ptr<DatabaseState> state) const;
+  Database::KV& dereference(shared_ptr<DatabaseState> state) const;
 
   MDB_env *env_;
   MDB_dbi dbi_;
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 4d36b8ede42..998c00c3561 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -48,7 +48,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Read a data point, and use it to initialize the top blob.
   CHECK(iter_ != database_->end());
   Datum datum;
-  datum.ParseFromArray(iter_->second.data(), iter_->second.size());
+  datum.ParseFromArray(iter_->value.data(), iter_->value.size());
 
   // image
   int crop_size = this->layer_param_.transform_param().crop_size();
@@ -94,7 +94,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     Datum datum;
     CHECK(iter_ != database_->end());
-    datum.ParseFromArray(iter_->second.data(), iter_->second.size());
+    datum.ParseFromArray(iter_->value.data(), iter_->value.size());
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index a5cdaa3b7f1..8084a6c4d8d 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -131,7 +131,7 @@ void LeveldbDatabase::increment(shared_ptr<DatabaseState> state) const {
   }
 }
 
-pair<Database::buffer_t, Database::buffer_t>& LeveldbDatabase::dereference(
+Database::KV& LeveldbDatabase::dereference(
     shared_ptr<DatabaseState> state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(state);
@@ -144,10 +144,14 @@ pair<Database::buffer_t, Database::buffer_t>& LeveldbDatabase::dereference(
 
   CHECK(iter->Valid());
 
-  leveldb_state->kv_pair_ = make_pair(
-      buffer_t(iter->key().data(), iter->key().data() + iter->key().size()),
-      buffer_t(iter->value().data(),
-          iter->value().data() + iter->value().size()));
+  Database::buffer_t temp_key(buffer_t(iter->key().data(),
+      iter->key().data() + iter->key().size()));
+
+  Database::buffer_t temp_value(buffer_t(iter->value().data(),
+      iter->value().data() + iter->value().size()));
+
+  leveldb_state->kv_pair_.key.swap(temp_key);
+  leveldb_state->kv_pair_.value.swap(temp_value);
   return leveldb_state->kv_pair_;
 }
 
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index 08607789bb1..54d67d5442f 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -149,8 +149,7 @@ void LmdbDatabase::increment(shared_ptr<DatabaseState> state) const {
   }
 }
 
-pair<Database::buffer_t, Database::buffer_t>& LmdbDatabase::dereference(
-    shared_ptr<DatabaseState> state) const {
+Database::KV& LmdbDatabase::dereference(shared_ptr<DatabaseState> state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(state);
 
@@ -168,9 +167,13 @@ pair<Database::buffer_t, Database::buffer_t>& LmdbDatabase::dereference(
   char* key_data = reinterpret_cast<char*>(mdb_key.mv_data);
   char* value_data = reinterpret_cast<char*>(mdb_val.mv_data);
 
-  lmdb_state->kv_pair_ = make_pair(
-    buffer_t(key_data, key_data + mdb_key.mv_size),
-    buffer_t(value_data, value_data + mdb_val.mv_size));
+  Database::buffer_t temp_key(key_data, key_data + mdb_key.mv_size);
+
+  Database::buffer_t temp_value(value_data,
+      value_data + mdb_val.mv_size);
+
+  lmdb_state->kv_pair_.key.swap(temp_key);
+  lmdb_state->kv_pair_.value.swap(temp_value);
 
   return lmdb_state->kv_pair_;
 }
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 11f6fb820e6..01e16c179b6 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -36,7 +36,7 @@ int main(int argc, char** argv) {
   int count = 0;
   // load first datum
   Database::const_iterator iter = database->begin();
-  const Database::buffer_t& first_blob = iter->second;
+  const Database::buffer_t& first_blob = iter->value;
   datum.ParseFromArray(first_blob.data(), first_blob.size());
   iter = database->end();
 
@@ -54,7 +54,7 @@ int main(int argc, char** argv) {
   for (Database::const_iterator iter = database->begin();
       iter != database->end(); ++iter) {
     // just a dummy operation
-    const Database::buffer_t& blob = iter->second;
+    const Database::buffer_t& blob = iter->value;
     datum.ParseFromArray(blob.data(), blob.size());
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),

From be2df844221476a2f8ee5c1bb03258eadecac047 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 14:47:04 -0400
Subject: [PATCH 0926/2053] Added a couple of sanity checks to make sure the
 datum buffer sizes matched what we expected.

---
 tools/compute_image_mean.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 01e16c179b6..aaa324a02b3 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -62,10 +62,12 @@ int main(int argc, char** argv) {
     CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
         size_in_datum;
     if (data.size() != 0) {
+      CHECK_EQ(data.size(), size_in_datum);
       for (int i = 0; i < size_in_datum; ++i) {
         sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
       }
     } else {
+      CHECK_EQ(datum.float_data_size(), size_in_datum);
       for (int i = 0; i < size_in_datum; ++i) {
         sum_blob.set_data(i, sum_blob.data(i) +
             static_cast<float>(datum.float_data(i)));

From c31e4446884262ff3c68e5d2adf7afd5050771e4 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 16:31:17 -0400
Subject: [PATCH 0927/2053] Added get interface to Database.  Added test cases
 for Database.  Fixed a few bugs related to ReadOnly mode in Database in order
 to pass test cases.

---
 include/caffe/database.hpp         |   1 +
 include/caffe/leveldb_database.hpp |   2 +
 include/caffe/lmdb_database.hpp    |   5 +-
 src/caffe/leveldb_database.cpp     |  22 ++
 src/caffe/lmdb_database.cpp        |  44 ++-
 src/caffe/test/test_database.cpp   | 590 +++++++++++++++++++++++++++++
 6 files changed, 658 insertions(+), 6 deletions(-)
 create mode 100644 src/caffe/test/test_database.cpp

diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 953b58cfa2e..c61ab51f06c 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -28,6 +28,7 @@ class Database {
 
   virtual void open(const string& filename, Mode mode) = 0;
   virtual void put(buffer_t* key, buffer_t* value) = 0;
+  virtual void get(buffer_t* key, buffer_t* value) = 0;
   virtual void commit() = 0;
   virtual void close() = 0;
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 1c084cb070d..42f73f98f2a 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -16,6 +16,7 @@ class LeveldbDatabase : public Database {
  public:
   void open(const string& filename, Mode mode);
   void put(buffer_t* key, buffer_t* value);
+  void get(buffer_t* key, buffer_t* value);
   void commit();
   void close();
 
@@ -42,6 +43,7 @@ class LeveldbDatabase : public Database {
 
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::WriteBatch> batch_;
+  bool read_only_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index d72be3d4c66..4796b4dc73b 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -20,6 +20,7 @@ class LmdbDatabase : public Database {
 
   void open(const string& filename, Mode mode);
   void put(buffer_t* key, buffer_t* value);
+  void get(buffer_t* key, buffer_t* value);
   void commit();
   void close();
 
@@ -44,9 +45,9 @@ class LmdbDatabase : public Database {
   void increment(shared_ptr<DatabaseState> state) const;
   Database::KV& dereference(shared_ptr<DatabaseState> state) const;
 
-  MDB_env *env_;
+  MDB_env* env_;
   MDB_dbi dbi_;
-  MDB_txn *txn_;
+  MDB_txn* txn_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index 8084a6c4d8d..d2b37e6aa15 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -14,16 +14,19 @@ void LeveldbDatabase::open(const string& filename, Mode mode) {
     LOG(INFO) << " mode NEW";
     options.error_if_exists = true;
     options.create_if_missing = true;
+    read_only_ = false;
     break;
   case ReadWrite:
     LOG(INFO) << " mode RW";
     options.error_if_exists = false;
     options.create_if_missing = true;
+    read_only_ = false;
     break;
   case ReadOnly:
     LOG(INFO) << " mode RO";
     options.error_if_exists = false;
     options.create_if_missing = false;
+    read_only_ = true;
     break;
   default:
     LOG(FATAL) << "unknown mode " << mode;
@@ -45,6 +48,8 @@ void LeveldbDatabase::open(const string& filename, Mode mode) {
 void LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
   LOG(INFO) << "LevelDB: Put";
 
+  CHECK(!read_only_);
+
   CHECK_NOTNULL(batch_.get());
 
   leveldb::Slice key_slice(key->data(), key->size());
@@ -53,9 +58,26 @@ void LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
   batch_->Put(key_slice, value_slice);
 }
 
+void LeveldbDatabase::get(buffer_t* key, buffer_t* value) {
+  LOG(INFO) << "LevelDB: Get";
+
+  leveldb::Slice key_slice(key->data(), key->size());
+
+  string value_string;
+  leveldb::Status status =
+      db_->Get(leveldb::ReadOptions(), key_slice, &value_string);
+  CHECK(status.ok()) << "leveldb get failed";
+
+  Database::buffer_t temp_value(value_string.data(),
+      value_string.data() + value_string.size());
+  value->swap(temp_value);
+}
+
 void LeveldbDatabase::commit() {
   LOG(INFO) << "LevelDB: Commit";
 
+  CHECK(!read_only_);
+
   CHECK_NOTNULL(db_.get());
   CHECK_NOTNULL(batch_.get());
 
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index 54d67d5442f..952e95a62d8 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -14,12 +14,24 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
   CHECK(NULL == txn_);
   CHECK_EQ(0, dbi_);
 
-  if (mode == New) {
-    CHECK_EQ(mkdir(filename.c_str(), 0744), 0) << "mkdir " << filename
-                                                << " failed";
+  int retval;
+  if (mode != ReadOnly) {
+    retval = mkdir(filename.c_str(), 0744);
+    switch (mode) {
+    case New:
+      CHECK_EQ(0, retval) << "mkdir " << filename << " failed";
+      break;
+    case ReadWrite:
+      if (-1 == retval) {
+        CHECK_EQ(EEXIST, errno) << "mkdir " << filename << " failed ("
+            << strerror(errno) << ")";
+      }
+      break;
+    default:
+      LOG(FATAL) << "Invalid mode " << mode;
+    }
   }
 
-  int retval;
   retval = mdb_env_create(&env_);
   CHECK_EQ(retval, MDB_SUCCESS) << "mdb_env_create failed "
       << mdb_strerror(retval);
@@ -61,6 +73,30 @@ void LmdbDatabase::put(buffer_t* key, buffer_t* value) {
       << "mdb_put failed " << mdb_strerror(retval);
 }
 
+void LmdbDatabase::get(buffer_t* key, buffer_t* value) {
+  LOG(INFO) << "LMDB: Get";
+
+  MDB_val mdbkey, mdbdata;
+  mdbkey.mv_data = key->data();
+  mdbkey.mv_size = key->size();
+
+  int retval;
+  MDB_txn* get_txn;
+  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &get_txn);
+  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
+      << mdb_strerror(retval);
+
+  retval = mdb_get(get_txn, dbi_, &mdbkey, &mdbdata);
+  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_get failed " << mdb_strerror(retval);
+
+  mdb_txn_abort(get_txn);
+
+  Database::buffer_t temp_value(reinterpret_cast<char*>(mdbdata.mv_data),
+      reinterpret_cast<char*>(mdbdata.mv_data) + mdbdata.mv_size);
+
+  value->swap(temp_value);
+}
+
 void LmdbDatabase::commit() {
   LOG(INFO) << "LMDB: Commit";
 
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
new file mode 100644
index 00000000000..dce92d88b5d
--- /dev/null
+++ b/src/caffe/test/test_database.cpp
@@ -0,0 +1,590 @@
+#include <string>
+#include <vector>
+
+#include "caffe/util/io.hpp"
+
+#include "gtest/gtest.h"
+
+#include "caffe/database_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class DatabaseTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  string DBName() {
+    string filename;
+    MakeTempDir(&filename);
+    filename += "/db";
+    return filename;
+  }
+
+  Database::buffer_t TestKey() {
+    const char* kKey = "hello";
+    Database::buffer_t key(kKey, kKey + 5);
+    return key;
+  }
+
+  Database::buffer_t TestValue() {
+    const char* kValue = "world";
+    Database::buffer_t value(kValue, kValue + 5);
+    return value;
+  }
+};
+
+TYPED_TEST_CASE(DatabaseTest, TestDtypesAndDevices);
+
+TYPED_TEST(DatabaseTest, TestNewDoesntExistLevelDBPasses) {
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(this->DBName(), Database::New);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewExistsFailsLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+  database->close();
+
+  EXPECT_DEATH(database->open(name, Database::New), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyExistsLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFailsLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  EXPECT_DEATH(database->open(name, Database::ReadOnly), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteExistsLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadWrite);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::ReadWrite);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  const int kNumExamples = 4;
+  for (int i = 0; i < kNumExamples; ++i) {
+    stringstream ss;
+    ss << i;
+    string key = ss.str();
+    ss << " here be data";
+    string value = ss.str();
+    Database::buffer_t key_buf(key.data(), key.data() + key.size());
+    Database::buffer_t val_buf(value.data(), value.data() + value.size());
+    database->put(&key_buf, &val_buf);
+  }
+  database->commit();
+
+  int count = 0;
+  for (Database::const_iterator iter = database->begin();
+      iter != database->end(); ++iter) {
+    (void)iter;
+    ++count;
+  }
+
+  EXPECT_EQ(kNumExamples, count);
+}
+
+TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewCommitLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  Database::buffer_t new_val;
+
+  database->get(&key, &new_val);
+
+  EXPECT_EQ(val.size(), new_val.size());
+  for (size_t i = 0; i < val.size(); ++i) {
+    EXPECT_EQ(val.at(i), new_val.at(i));
+  }
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewGetNoCommitLevelDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  Database::buffer_t new_val;
+
+  EXPECT_DEATH(database->get(&key, &new_val), "");
+}
+
+
+TYPED_TEST(DatabaseTest, TestReadWritePutLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::ReadWrite);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteCommitLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::ReadWrite);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  Database::buffer_t new_val;
+
+  database->get(&key, &new_val);
+
+  EXPECT_EQ(val.size(), new_val.size());
+  for (size_t i = 0; i < val.size(); ++i) {
+    EXPECT_EQ(val.at(i), new_val.at(i));
+  }
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLevelDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  Database::buffer_t new_val;
+
+  EXPECT_DEATH(database->get(&key, &new_val), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyPutLevelDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  EXPECT_DEATH(database->put(&key, &val), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyCommitLevelDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  EXPECT_DEATH(database->commit(), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  Database::buffer_t new_val;
+
+  database->get(&key, &new_val);
+
+  EXPECT_EQ(val.size(), new_val.size());
+  for (size_t i = 0; i < val.size(); ++i) {
+    EXPECT_EQ(val.at(i), new_val.at(i));
+  }
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  Database::buffer_t new_val;
+
+  EXPECT_DEATH(database->get(&key, &new_val), "");
+}
+
+TYPED_TEST(DatabaseTest, TestNewDoesntExistLMDBPasses) {
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(this->DBName(), Database::New);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewExistsFailsLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+  database->close();
+
+  EXPECT_DEATH(database->open(name, Database::New), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyExistsLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFailsLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  EXPECT_DEATH(database->open(name, Database::ReadOnly), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteExistsLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadWrite);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::ReadWrite);
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  const int kNumExamples = 4;
+  for (int i = 0; i < kNumExamples; ++i) {
+    stringstream ss;
+    ss << i;
+    string key = ss.str();
+    ss << " here be data";
+    string value = ss.str();
+    Database::buffer_t key_buf(key.data(), key.data() + key.size());
+    Database::buffer_t val_buf(value.data(), value.data() + value.size());
+    database->put(&key_buf, &val_buf);
+  }
+  database->commit();
+
+  int count = 0;
+  for (Database::const_iterator iter = database->begin();
+      iter != database->end(); ++iter) {
+    (void)iter;
+    ++count;
+  }
+
+  EXPECT_EQ(kNumExamples, count);
+}
+
+TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewCommitLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  Database::buffer_t new_val;
+
+  database->get(&key, &new_val);
+
+  EXPECT_EQ(val.size(), new_val.size());
+  for (size_t i = 0; i < val.size(); ++i) {
+    EXPECT_EQ(val.at(i), new_val.at(i));
+  }
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestNewGetNoCommitLMDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  Database::buffer_t new_val;
+
+  EXPECT_DEATH(database->get(&key, &new_val), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::ReadWrite);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteCommitLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::ReadWrite);
+
+  database->commit();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  Database::buffer_t new_val;
+
+  database->get(&key, &new_val);
+
+  EXPECT_EQ(val.size(), new_val.size());
+  for (size_t i = 0; i < val.size(); ++i) {
+    EXPECT_EQ(val.at(i), new_val.at(i));
+  }
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLMDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  Database::buffer_t new_val;
+
+  EXPECT_DEATH(database->get(&key, &new_val), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyPutLMDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  EXPECT_DEATH(database->put(&key, &val), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyCommitLMDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  EXPECT_DEATH(database->commit(), "");
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->commit();
+
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  Database::buffer_t new_val;
+
+  database->get(&key, &new_val);
+
+  EXPECT_EQ(val.size(), new_val.size());
+  for (size_t i = 0; i < val.size(); ++i) {
+    EXPECT_EQ(val.at(i), new_val.at(i));
+  }
+}
+
+TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key = this->TestKey();
+  Database::buffer_t val = this->TestValue();
+
+  database->put(&key, &val);
+
+  database->close();
+
+  database->open(name, Database::ReadOnly);
+
+  Database::buffer_t new_val;
+
+  EXPECT_DEATH(database->get(&key, &new_val), "");
+}
+
+}  // namespace caffe

From c86ed418b9e9a89a98de9c5d339a595ac7ebc3c2 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 18:43:44 -0400
Subject: [PATCH 0928/2053] Added some tests for the Database iterator
 interface.  Updated the post-increment operator so that it forks off a copy
 of the LevelDB or LMDB iterator/cursor when necessary.  Neither of these APIs
 allow you to directly copy an iterator or cursor, so I create a new iterator
 and seek to the key that the previous one was currently on.  This means the
 pre-increment operator can be much cheaper than the post-increment operator.

---
 include/caffe/database.hpp         |  15 ++
 include/caffe/leveldb_database.hpp |  18 ++-
 include/caffe/lmdb_database.hpp    |  28 +++-
 src/caffe/leveldb_database.cpp     |   4 +-
 src/caffe/lmdb_database.cpp        |   4 +-
 src/caffe/test/test_database.cpp   | 218 +++++++++++++++++++++++++----
 6 files changed, 256 insertions(+), 31 deletions(-)

diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index c61ab51f06c..08baf2fea92 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -62,6 +62,20 @@ class Database {
           state_(state) { }
     ~iterator() { }
 
+    iterator(const iterator& other)
+        : parent_(other.parent_),
+          state_(other.state_->clone()) { }
+
+    iterator& operator=(iterator copy) {
+      copy.swap(*this);
+      return *this;
+    }
+
+    void swap(iterator& other) throw() {
+      std::swap(this->parent_, other.parent_);
+      std::swap(this->state_, other.state_);
+    }
+
     bool operator==(const iterator& other) const {
       return parent_->equal(state_, other.state_);
     }
@@ -97,6 +111,7 @@ class Database {
   class DatabaseState {
    public:
     virtual ~DatabaseState() { }
+    virtual shared_ptr<DatabaseState> clone() = 0;
   };
 
   virtual bool equal(shared_ptr<DatabaseState> state1,
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 42f73f98f2a..03bfd38a437 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -28,10 +28,26 @@ class LeveldbDatabase : public Database {
  protected:
   class LeveldbState : public Database::DatabaseState {
    public:
-    explicit LeveldbState(shared_ptr<leveldb::Iterator> iter)
+    explicit LeveldbState(shared_ptr<leveldb::DB> db,
+        shared_ptr<leveldb::Iterator> iter)
         : Database::DatabaseState(),
+          db_(db),
           iter_(iter) { }
 
+    shared_ptr<DatabaseState> clone() {
+      shared_ptr<leveldb::Iterator> new_iter;
+
+      if (iter_.get()) {
+        new_iter.reset(db_->NewIterator(leveldb::ReadOptions()));
+        CHECK(iter_->Valid());
+        new_iter->Seek(iter_->key());
+        CHECK(new_iter->Valid());
+      }
+
+      return shared_ptr<DatabaseState>(new LeveldbState(db_, new_iter));
+    }
+
+    shared_ptr<leveldb::DB> db_;
     shared_ptr<leveldb::Iterator> iter_;
     KV kv_pair_;
   };
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 4796b4dc73b..7b532f8161a 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -32,11 +32,35 @@ class LmdbDatabase : public Database {
  protected:
   class LmdbState : public Database::DatabaseState {
    public:
-    explicit LmdbState(MDB_cursor* cursor)
+    explicit LmdbState(MDB_cursor* cursor, MDB_txn* txn, const MDB_dbi* dbi)
         : Database::DatabaseState(),
-          cursor_(cursor) { }
+          cursor_(cursor),
+          txn_(txn),
+          dbi_(dbi) { }
+
+    shared_ptr<DatabaseState> clone() {
+      MDB_cursor* new_cursor;
+
+      if (cursor_) {
+        int retval;
+        retval = mdb_cursor_open(txn_, *dbi_, &new_cursor);
+        CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+        MDB_val key;
+        MDB_val val;
+        retval = mdb_cursor_get(cursor_, &key, &val, MDB_GET_CURRENT);
+        CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+        retval = mdb_cursor_get(new_cursor, &key, &val, MDB_SET);
+        CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
+      } else {
+        new_cursor = cursor_;
+      }
+
+      return shared_ptr<DatabaseState>(new LmdbState(new_cursor, txn_, dbi_));
+    }
 
     MDB_cursor* cursor_;
+    MDB_txn* txn_;
+    const MDB_dbi* dbi_;
     KV kv_pair_;
   };
 
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index d2b37e6aa15..51d50cce5c0 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -99,13 +99,13 @@ LeveldbDatabase::const_iterator LeveldbDatabase::begin() const {
   if (!iter->Valid()) {
     iter.reset();
   }
-  shared_ptr<DatabaseState> state(new LeveldbState(iter));
+  shared_ptr<DatabaseState> state(new LeveldbState(db_, iter));
   return const_iterator(this, state);
 }
 
 LeveldbDatabase::const_iterator LeveldbDatabase::end() const {
   shared_ptr<leveldb::Iterator> iter;
-  shared_ptr<DatabaseState> state(new LeveldbState(iter));
+  shared_ptr<DatabaseState> state(new LeveldbState(db_, iter));
   return const_iterator(this, state);
 }
 
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index 952e95a62d8..a546c8c5259 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -134,12 +134,12 @@ LmdbDatabase::const_iterator LmdbDatabase::begin() const {
   retval = mdb_cursor_get(cursor, &key, &val, MDB_FIRST);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
-  shared_ptr<DatabaseState> state(new LmdbState(cursor));
+  shared_ptr<DatabaseState> state(new LmdbState(cursor, txn_, &dbi_));
   return const_iterator(this, state);
 }
 
 LmdbDatabase::const_iterator LmdbDatabase::end() const {
-  shared_ptr<DatabaseState> state(new LmdbState(NULL));
+  shared_ptr<DatabaseState> state(new LmdbState(NULL, txn_, &dbi_));
   return const_iterator(this, state);
 }
 
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index dce92d88b5d..5d5f4ea79c4 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -34,6 +34,32 @@ class DatabaseTest : public MultiDeviceTest<TypeParam> {
     Database::buffer_t value(kValue, kValue + 5);
     return value;
   }
+
+  Database::buffer_t TestAltKey() {
+    const char* kKey = "foo";
+    Database::buffer_t key(kKey, kKey + 3);
+    return key;
+  }
+
+  Database::buffer_t TestAltValue() {
+    const char* kValue = "bar";
+    Database::buffer_t value(kValue, kValue + 3);
+    return value;
+  }
+
+  bool BufferEq(const Database::buffer_t& buf1,
+      const Database::buffer_t& buf2) {
+    if (buf1.size() != buf2.size()) {
+      return false;
+    }
+    for (size_t i = 0; i < buf1.size(); ++i) {
+      if (buf1.at(i) != buf2.at(i)) {
+        return false;
+      }
+    }
+
+    return true;
+  }
 };
 
 TYPED_TEST_CASE(DatabaseTest, TestDtypesAndDevices);
@@ -114,6 +140,87 @@ TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
   EXPECT_EQ(kNumExamples, count);
 }
 
+TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key1 = this->TestAltKey();
+  Database::buffer_t value1 = this->TestAltValue();
+
+  Database::buffer_t key2 = this->TestKey();
+  Database::buffer_t value2 = this->TestValue();
+
+  database->put(&key1, &value1);
+  database->put(&key2, &value2);
+  database->commit();
+
+  Database::const_iterator iter1 = database->begin();
+
+  EXPECT_FALSE(database->end() == iter1);
+
+  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
+
+  Database::const_iterator iter2 = ++iter1;
+
+  EXPECT_FALSE(database->end() == iter1);
+  EXPECT_FALSE(database->end() == iter2);
+
+  EXPECT_TRUE(this->BufferEq(iter2->key, key2));
+
+  Database::const_iterator iter3 = ++iter2;
+
+  EXPECT_TRUE(database->end() == iter3);
+
+  iter1 = database->end();
+  iter2 = database->end();
+  iter3 = database->end();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key1 = this->TestAltKey();
+  Database::buffer_t value1 = this->TestAltValue();
+
+  Database::buffer_t key2 = this->TestKey();
+  Database::buffer_t value2 = this->TestValue();
+
+  database->put(&key1, &value1);
+  database->put(&key2, &value2);
+  database->commit();
+
+  Database::const_iterator iter1 = database->begin();
+
+  EXPECT_FALSE(database->end() == iter1);
+
+  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
+
+  Database::const_iterator iter2 = iter1++;
+
+  EXPECT_FALSE(database->end() == iter1);
+  EXPECT_FALSE(database->end() == iter2);
+
+  EXPECT_TRUE(this->BufferEq(iter2->key, key1));
+  EXPECT_TRUE(this->BufferEq(iter1->key, key2));
+
+  Database::const_iterator iter3 = iter1++;
+
+  EXPECT_FALSE(database->end() == iter3);
+  EXPECT_TRUE(this->BufferEq(iter3->key, key2));
+  EXPECT_TRUE(database->end() == iter1);
+
+  iter1 = database->end();
+  iter2 = database->end();
+  iter3 = database->end();
+
+  database->close();
+}
+
 TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
@@ -155,10 +262,7 @@ TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
 
   database->get(&key, &new_val);
 
-  EXPECT_EQ(val.size(), new_val.size());
-  for (size_t i = 0; i < val.size(); ++i) {
-    EXPECT_EQ(val.at(i), new_val.at(i));
-  }
+  EXPECT_TRUE(this->BufferEq(val, new_val));
 
   database->close();
 }
@@ -220,10 +324,7 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
 
   database->get(&key, &new_val);
 
-  EXPECT_EQ(val.size(), new_val.size());
-  for (size_t i = 0; i < val.size(); ++i) {
-    EXPECT_EQ(val.at(i), new_val.at(i));
-  }
+  EXPECT_TRUE(this->BufferEq(val, new_val));
 
   database->close();
 }
@@ -288,10 +389,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
 
   database->get(&key, &new_val);
 
-  EXPECT_EQ(val.size(), new_val.size());
-  for (size_t i = 0; i < val.size(); ++i) {
-    EXPECT_EQ(val.at(i), new_val.at(i));
-  }
+  EXPECT_TRUE(this->BufferEq(val, new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
@@ -389,6 +487,87 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
   EXPECT_EQ(kNumExamples, count);
 }
 
+TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key1 = this->TestAltKey();
+  Database::buffer_t value1 = this->TestAltValue();
+
+  Database::buffer_t key2 = this->TestKey();
+  Database::buffer_t value2 = this->TestValue();
+
+  database->put(&key1, &value1);
+  database->put(&key2, &value2);
+  database->commit();
+
+  Database::const_iterator iter1 = database->begin();
+
+  EXPECT_FALSE(database->end() == iter1);
+
+  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
+
+  Database::const_iterator iter2 = ++iter1;
+
+  EXPECT_FALSE(database->end() == iter1);
+  EXPECT_FALSE(database->end() == iter2);
+
+  EXPECT_TRUE(this->BufferEq(iter2->key, key2));
+
+  Database::const_iterator iter3 = ++iter2;
+
+  EXPECT_TRUE(database->end() == iter3);
+
+  iter1 = database->end();
+  iter2 = database->end();
+  iter3 = database->end();
+
+  database->close();
+}
+
+TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  database->open(name, Database::New);
+
+  Database::buffer_t key1 = this->TestAltKey();
+  Database::buffer_t value1 = this->TestAltValue();
+
+  Database::buffer_t key2 = this->TestKey();
+  Database::buffer_t value2 = this->TestValue();
+
+  database->put(&key1, &value1);
+  database->put(&key2, &value2);
+  database->commit();
+
+  Database::const_iterator iter1 = database->begin();
+
+  EXPECT_FALSE(database->end() == iter1);
+
+  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
+
+  Database::const_iterator iter2 = iter1++;
+
+  EXPECT_FALSE(database->end() == iter1);
+  EXPECT_FALSE(database->end() == iter2);
+
+  EXPECT_TRUE(this->BufferEq(iter2->key, key1));
+  EXPECT_TRUE(this->BufferEq(iter1->key, key2));
+
+  Database::const_iterator iter3 = iter1++;
+
+  EXPECT_FALSE(database->end() == iter3);
+  EXPECT_TRUE(this->BufferEq(iter3->key, key2));
+  EXPECT_TRUE(database->end() == iter1);
+
+  iter1 = database->end();
+  iter2 = database->end();
+  iter3 = database->end();
+
+  database->close();
+}
+
 TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
@@ -430,10 +609,7 @@ TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
 
   database->get(&key, &new_val);
 
-  EXPECT_EQ(val.size(), new_val.size());
-  for (size_t i = 0; i < val.size(); ++i) {
-    EXPECT_EQ(val.at(i), new_val.at(i));
-  }
+  EXPECT_TRUE(this->BufferEq(val, new_val));
 
   database->close();
 }
@@ -494,10 +670,7 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
 
   database->get(&key, &new_val);
 
-  EXPECT_EQ(val.size(), new_val.size());
-  for (size_t i = 0; i < val.size(); ++i) {
-    EXPECT_EQ(val.at(i), new_val.at(i));
-  }
+  EXPECT_TRUE(this->BufferEq(val, new_val));
 
   database->close();
 }
@@ -562,10 +735,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
 
   database->get(&key, &new_val);
 
-  EXPECT_EQ(val.size(), new_val.size());
-  for (size_t i = 0; i < val.size(); ++i) {
-    EXPECT_EQ(val.at(i), new_val.at(i));
-  }
+  EXPECT_TRUE(this->BufferEq(val, new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {

From 73b16e43e4376207ddc30b5772ed4a3f5dba88d9 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 20:20:31 -0400
Subject: [PATCH 0929/2053] Updated Database interface so that rather than
 CHECKing for certain conditions inside open, put, get, and commit, these
 functions return a bool indicating whether or not the operation was
 successful or a failure.  This means the caller is now responsible for error
 checking.

---
 examples/cifar10/convert_cifar_data.cpp |  16 +-
 include/caffe/database.hpp              |   8 +-
 include/caffe/leveldb_database.hpp      |   8 +-
 include/caffe/lmdb_database.hpp         |   8 +-
 src/caffe/layers/data_layer.cpp         |   5 +-
 src/caffe/leveldb_database.cpp          |  43 ++++-
 src/caffe/lmdb_database.cpp             |  88 ++++++---
 src/caffe/test/test_data_layer.cpp      |   6 +-
 src/caffe/test/test_database.cpp        | 236 ++++++++++++------------
 tools/compute_image_mean.cpp            |   2 +-
 tools/convert_imageset.cpp              |   8 +-
 tools/extract_features.cpp              |   8 +-
 12 files changed, 249 insertions(+), 187 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index c4930878405..b29e4121bf5 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -38,8 +38,8 @@ void read_image(std::ifstream* file, int* label, char* buffer) {
 void convert_dataset(const string& input_folder, const string& output_folder,
     const string& db_type) {
   shared_ptr<Database> train_database = DatabaseFactory(db_type);
-  train_database->open(output_folder + "/cifar10_train_" + db_type,
-      Database::New);
+  CHECK(train_database->open(output_folder + "/cifar10_train_" + db_type,
+      Database::New));
   // Data buffer
   int label;
   char str_buffer[kCIFARImageNBytes];
@@ -66,16 +66,16 @@ void convert_dataset(const string& input_folder, const string& output_folder,
       int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
       Database::buffer_t key(str_buffer, str_buffer + length);
-      train_database->put(&key, &value);
+      CHECK(train_database->put(&key, &value));
     }
   }
-  train_database->commit();
+  CHECK(train_database->commit());
   train_database->close();
 
   LOG(INFO) << "Writing Testing data";
   shared_ptr<Database> test_database = DatabaseFactory(db_type);
-  test_database->open(output_folder + "/cifar10_test_" + db_type,
-      Database::New);
+  CHECK(test_database->open(output_folder + "/cifar10_test_" + db_type,
+      Database::New));
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
@@ -89,9 +89,9 @@ void convert_dataset(const string& input_folder, const string& output_folder,
         reinterpret_cast<unsigned char*>(value.data()));
     int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
     Database::buffer_t key(str_buffer, str_buffer + length);
-    test_database->put(&key, &value);
+    CHECK(test_database->put(&key, &value));
   }
-  test_database->commit();
+  CHECK(test_database->commit());
   test_database->close();
 }
 
diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 08baf2fea92..148b1ed7f38 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -26,10 +26,10 @@ class Database {
     buffer_t value;
   };
 
-  virtual void open(const string& filename, Mode mode) = 0;
-  virtual void put(buffer_t* key, buffer_t* value) = 0;
-  virtual void get(buffer_t* key, buffer_t* value) = 0;
-  virtual void commit() = 0;
+  virtual bool open(const string& filename, Mode mode) = 0;
+  virtual bool put(buffer_t* key, buffer_t* value) = 0;
+  virtual bool get(buffer_t* key, buffer_t* value) = 0;
+  virtual bool commit() = 0;
   virtual void close() = 0;
 
   Database() { }
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 03bfd38a437..64bfa7cebee 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -14,10 +14,10 @@ namespace caffe {
 
 class LeveldbDatabase : public Database {
  public:
-  void open(const string& filename, Mode mode);
-  void put(buffer_t* key, buffer_t* value);
-  void get(buffer_t* key, buffer_t* value);
-  void commit();
+  bool open(const string& filename, Mode mode);
+  bool put(buffer_t* key, buffer_t* value);
+  bool get(buffer_t* key, buffer_t* value);
+  bool commit();
   void close();
 
   const_iterator begin() const;
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 7b532f8161a..69e3ce0f03d 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -18,10 +18,10 @@ class LmdbDatabase : public Database {
         dbi_(0),
         txn_(NULL) { }
 
-  void open(const string& filename, Mode mode);
-  void put(buffer_t* key, buffer_t* value);
-  void get(buffer_t* key, buffer_t* value);
-  void commit();
+  bool open(const string& filename, Mode mode);
+  bool put(buffer_t* key, buffer_t* value);
+  bool get(buffer_t* key, buffer_t* value);
+  bool commit();
   void close();
 
   const_iterator begin() const;
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 998c00c3561..dcba10fabec 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -30,8 +30,9 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Initialize DB
   database_ = DatabaseFactory(this->layer_param_.data_param().backend());
-  LOG(INFO) << "Opening database " << this->layer_param_.data_param().source();
-  database_->open(this->layer_param_.data_param().source(), Database::ReadOnly);
+  const string& source = this->layer_param_.data_param().source();
+  LOG(INFO) << "Opening database " << source;
+  CHECK(database_->open(source, Database::ReadOnly));
   iter_ = database_->begin();
 
   // Check if we would need to randomly skip a few data points
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index 51d50cce5c0..d7506edf74e 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -5,7 +5,7 @@
 
 namespace caffe {
 
-void LeveldbDatabase::open(const string& filename, Mode mode) {
+bool LeveldbDatabase::open(const string& filename, Mode mode) {
   LOG(INFO) << "LevelDB: Open " << filename;
 
   leveldb::Options options;
@@ -40,15 +40,24 @@ void LeveldbDatabase::open(const string& filename, Mode mode) {
   leveldb::Status status = leveldb::DB::Open(
       options, filename, &db);
   db_.reset(db);
-  CHECK(status.ok()) << "Failed to open leveldb " << filename
-      << ". Is it already existing?";
+
+  if (!status.ok()) {
+    LOG(ERROR) << "Failed to open leveldb " << filename
+        << ". Is it already existing?";
+    return false;
+  }
+
   batch_.reset(new leveldb::WriteBatch());
+  return true;
 }
 
-void LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
+bool LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
   LOG(INFO) << "LevelDB: Put";
 
-  CHECK(!read_only_);
+  if (read_only_) {
+    LOG(ERROR) << "put can not be used on a database in ReadOnly mode";
+    return false;
+  }
 
   CHECK_NOTNULL(batch_.get());
 
@@ -56,9 +65,11 @@ void LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
   leveldb::Slice value_slice(value->data(), value->size());
 
   batch_->Put(key_slice, value_slice);
+
+  return true;
 }
 
-void LeveldbDatabase::get(buffer_t* key, buffer_t* value) {
+bool LeveldbDatabase::get(buffer_t* key, buffer_t* value) {
   LOG(INFO) << "LevelDB: Get";
 
   leveldb::Slice key_slice(key->data(), key->size());
@@ -66,23 +77,35 @@ void LeveldbDatabase::get(buffer_t* key, buffer_t* value) {
   string value_string;
   leveldb::Status status =
       db_->Get(leveldb::ReadOptions(), key_slice, &value_string);
-  CHECK(status.ok()) << "leveldb get failed";
+
+  if (!status.ok()) {
+    LOG(ERROR) << "leveldb get failed";
+    return false;
+  }
 
   Database::buffer_t temp_value(value_string.data(),
       value_string.data() + value_string.size());
   value->swap(temp_value);
+
+  return true;
 }
 
-void LeveldbDatabase::commit() {
+bool LeveldbDatabase::commit() {
   LOG(INFO) << "LevelDB: Commit";
 
-  CHECK(!read_only_);
+  if (read_only_) {
+    LOG(ERROR) << "commit can not be used on a database in ReadOnly mode";
+    return false;
+  }
 
   CHECK_NOTNULL(db_.get());
   CHECK_NOTNULL(batch_.get());
 
-  db_->Write(leveldb::WriteOptions(), batch_.get());
+  leveldb::Status status = db_->Write(leveldb::WriteOptions(), batch_.get());
+
   batch_.reset(new leveldb::WriteBatch());
+
+  return status.ok();
 }
 
 void LeveldbDatabase::close() {
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index a546c8c5259..d71513a55ee 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -7,7 +7,7 @@
 
 namespace caffe {
 
-void LmdbDatabase::open(const string& filename, Mode mode) {
+bool LmdbDatabase::open(const string& filename, Mode mode) {
   LOG(INFO) << "LMDB: Open " << filename;
 
   CHECK(NULL == env_);
@@ -19,12 +19,16 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
     retval = mkdir(filename.c_str(), 0744);
     switch (mode) {
     case New:
-      CHECK_EQ(0, retval) << "mkdir " << filename << " failed";
+      if (0 != retval) {
+        LOG(ERROR) << "mkdir " << filename << " failed";
+        return false;
+      }
       break;
     case ReadWrite:
-      if (-1 == retval) {
-        CHECK_EQ(EEXIST, errno) << "mkdir " << filename << " failed ("
+      if (-1 == retval && EEXIST != errno) {
+        LOG(ERROR) << "mkdir " << filename << " failed ("
             << strerror(errno) << ")";
+        return false;
       }
       break;
     default:
@@ -33,11 +37,17 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
   }
 
   retval = mdb_env_create(&env_);
-  CHECK_EQ(retval, MDB_SUCCESS) << "mdb_env_create failed "
-      << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_env_create failed "
+        << mdb_strerror(retval);
+    return false;
+  }
+
   retval = mdb_env_set_mapsize(env_, 1099511627776);
-  CHECK_EQ(retval, MDB_SUCCESS)  // 1TB
-      << "mdb_env_set_mapsize failed " << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_env_set_mapsize failed " << mdb_strerror(retval);
+    return false;
+  }
 
   int flag1 = 0;
   int flag2 = 0;
@@ -47,16 +57,27 @@ void LmdbDatabase::open(const string& filename, Mode mode) {
   }
 
   retval = mdb_env_open(env_, filename.c_str(), flag1, 0664);
-  CHECK_EQ(retval, MDB_SUCCESS)
-      << "mdb_env_open failed " << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_env_open failed " << mdb_strerror(retval);
+    return false;
+  }
+
   retval = mdb_txn_begin(env_, NULL, flag2, &txn_);
-  CHECK_EQ(retval, MDB_SUCCESS)
-      << "mdb_txn_begin failed " << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
+    return false;
+  }
+
   retval = mdb_open(txn_, NULL, 0, &dbi_);
-  CHECK_EQ(retval, MDB_SUCCESS) << "mdb_open failed" << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_open failed" << mdb_strerror(retval);
+    return false;
+  }
+
+  return true;
 }
 
-void LmdbDatabase::put(buffer_t* key, buffer_t* value) {
+bool LmdbDatabase::put(buffer_t* key, buffer_t* value) {
   LOG(INFO) << "LMDB: Put";
 
   MDB_val mdbkey, mdbdata;
@@ -69,11 +90,15 @@ void LmdbDatabase::put(buffer_t* key, buffer_t* value) {
   CHECK_NE(0, dbi_);
 
   int retval = mdb_put(txn_, dbi_, &mdbkey, &mdbdata, 0);
-  CHECK_EQ(retval, MDB_SUCCESS)
-      << "mdb_put failed " << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_put failed " << mdb_strerror(retval);
+    return false;
+  }
+
+  return true;
 }
 
-void LmdbDatabase::get(buffer_t* key, buffer_t* value) {
+bool LmdbDatabase::get(buffer_t* key, buffer_t* value) {
   LOG(INFO) << "LMDB: Get";
 
   MDB_val mdbkey, mdbdata;
@@ -83,11 +108,16 @@ void LmdbDatabase::get(buffer_t* key, buffer_t* value) {
   int retval;
   MDB_txn* get_txn;
   retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &get_txn);
-  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
-      << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
+    return false;
+  }
 
   retval = mdb_get(get_txn, dbi_, &mdbkey, &mdbdata);
-  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_get failed " << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_get failed " << mdb_strerror(retval);
+    return false;
+  }
 
   mdb_txn_abort(get_txn);
 
@@ -95,21 +125,29 @@ void LmdbDatabase::get(buffer_t* key, buffer_t* value) {
       reinterpret_cast<char*>(mdbdata.mv_data) + mdbdata.mv_size);
 
   value->swap(temp_value);
+
+  return true;
 }
 
-void LmdbDatabase::commit() {
+bool LmdbDatabase::commit() {
   LOG(INFO) << "LMDB: Commit";
 
   CHECK_NOTNULL(txn_);
 
   int retval;
   retval = mdb_txn_commit(txn_);
-  CHECK_EQ(retval, MDB_SUCCESS) << "mdb_txn_commit failed "
-      << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_txn_commit failed " << mdb_strerror(retval);
+    return false;
+  }
 
   retval = mdb_txn_begin(env_, NULL, 0, &txn_);
-  CHECK_EQ(retval, MDB_SUCCESS)
-      << "mdb_txn_begin failed " << mdb_strerror(retval);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
+    return false;
+  }
+
+  return true;
 }
 
 void LmdbDatabase::close() {
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 98ef1b96f24..cc9ad20487c 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -40,7 +40,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     backend_ = backend;
     LOG(INFO) << "Using temporary database " << *filename_;
     shared_ptr<Database> database = DatabaseFactory(backend_);
-    database->open(*filename_, Database::New);
+    CHECK(database->open(*filename_, Database::New));
     for (int i = 0; i < 5; ++i) {
       Datum datum;
       datum.set_label(i);
@@ -59,9 +59,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       Database::buffer_t value(datum.ByteSize());
       datum.SerializeWithCachedSizesToArray(
           reinterpret_cast<unsigned char*>(value.data()));
-      database->put(&key, &value);
+      CHECK(database->put(&key, &value));
     }
-    database->commit();
+    CHECK(database->commit());
     database->close();
   }
 
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index 5d5f4ea79c4..f65865014fa 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -66,56 +66,56 @@ TYPED_TEST_CASE(DatabaseTest, TestDtypesAndDevices);
 
 TYPED_TEST(DatabaseTest, TestNewDoesntExistLevelDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(this->DBName(), Database::New);
+  EXPECT_TRUE(database->open(this->DBName(), Database::New));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestNewExistsFailsLevelDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  EXPECT_DEATH(database->open(name, Database::New), "");
+  EXPECT_FALSE(database->open(name, Database::New));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyExistsLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFailsLevelDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_DEATH(database->open(name, Database::ReadOnly), "");
+  EXPECT_FALSE(database->open(name, Database::ReadOnly));
 }
 
 TYPED_TEST(DatabaseTest, TestReadWriteExistsLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   const int kNumExamples = 4;
   for (int i = 0; i < kNumExamples; ++i) {
@@ -126,9 +126,9 @@ TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
     string value = ss.str();
     Database::buffer_t key_buf(key.data(), key.data() + key.size());
     Database::buffer_t val_buf(value.data(), value.data() + value.size());
-    database->put(&key_buf, &val_buf);
+    EXPECT_TRUE(database->put(&key_buf, &val_buf));
   }
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   int count = 0;
   for (Database::const_iterator iter = database->begin();
@@ -143,7 +143,7 @@ TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
 TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key1 = this->TestAltKey();
   Database::buffer_t value1 = this->TestAltValue();
@@ -151,9 +151,9 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  database->put(&key1, &value1);
-  database->put(&key2, &value2);
-  database->commit();
+  EXPECT_TRUE(database->put(&key1, &value1));
+  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
 
@@ -182,7 +182,7 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
 TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key1 = this->TestAltKey();
   Database::buffer_t value1 = this->TestAltValue();
@@ -190,9 +190,9 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  database->put(&key1, &value1);
-  database->put(&key2, &value2);
-  database->commit();
+  EXPECT_TRUE(database->put(&key1, &value1));
+  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
 
@@ -224,14 +224,14 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
 TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -239,9 +239,9 @@ TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestNewCommitLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -249,18 +249,18 @@ TYPED_TEST(DatabaseTest, TestNewCommitLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  database->get(&key, &new_val);
+  EXPECT_TRUE(database->get(&key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -270,30 +270,30 @@ TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestNewGetNoCommitLevelDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
   Database::buffer_t new_val;
 
-  EXPECT_DEATH(database->get(&key, &new_val), "");
+  EXPECT_FALSE(database->get(&key, &new_val));
 }
 
 
 TYPED_TEST(DatabaseTest, TestReadWritePutLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -301,9 +301,9 @@ TYPED_TEST(DatabaseTest, TestReadWritePutLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadWriteCommitLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -311,18 +311,18 @@ TYPED_TEST(DatabaseTest, TestReadWriteCommitLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  database->get(&key, &new_val);
+  EXPECT_TRUE(database->get(&key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -332,62 +332,62 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLevelDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
   Database::buffer_t new_val;
 
-  EXPECT_DEATH(database->get(&key, &new_val), "");
+  EXPECT_FALSE(database->get(&key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyPutLevelDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_DEATH(database->put(&key, &val), "");
+  EXPECT_FALSE(database->put(&key, &val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyCommitLevelDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  EXPECT_DEATH(database->commit(), "");
+  EXPECT_FALSE(database->commit());
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
   Database::buffer_t new_val;
 
-  database->get(&key, &new_val);
+  EXPECT_TRUE(database->get(&key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 }
@@ -395,74 +395,74 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
   Database::buffer_t new_val;
 
-  EXPECT_DEATH(database->get(&key, &new_val), "");
+  EXPECT_FALSE(database->get(&key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestNewDoesntExistLMDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(this->DBName(), Database::New);
+  EXPECT_TRUE(database->open(this->DBName(), Database::New));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestNewExistsFailsLMDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  EXPECT_DEATH(database->open(name, Database::New), "");
+  EXPECT_FALSE(database->open(name, Database::New));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyExistsLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFailsLMDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_DEATH(database->open(name, Database::ReadOnly), "");
+  EXPECT_FALSE(database->open(name, Database::ReadOnly));
 }
 
 TYPED_TEST(DatabaseTest, TestReadWriteExistsLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
   database->close();
 }
 
 TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   const int kNumExamples = 4;
   for (int i = 0; i < kNumExamples; ++i) {
@@ -473,9 +473,9 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
     string value = ss.str();
     Database::buffer_t key_buf(key.data(), key.data() + key.size());
     Database::buffer_t val_buf(value.data(), value.data() + value.size());
-    database->put(&key_buf, &val_buf);
+    EXPECT_TRUE(database->put(&key_buf, &val_buf));
   }
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   int count = 0;
   for (Database::const_iterator iter = database->begin();
@@ -490,7 +490,7 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
 TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key1 = this->TestAltKey();
   Database::buffer_t value1 = this->TestAltValue();
@@ -498,9 +498,9 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  database->put(&key1, &value1);
-  database->put(&key2, &value2);
-  database->commit();
+  EXPECT_TRUE(database->put(&key1, &value1));
+  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
 
@@ -529,7 +529,7 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
 TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key1 = this->TestAltKey();
   Database::buffer_t value1 = this->TestAltValue();
@@ -537,9 +537,9 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  database->put(&key1, &value1);
-  database->put(&key2, &value2);
-  database->commit();
+  EXPECT_TRUE(database->put(&key1, &value1));
+  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
 
@@ -571,14 +571,14 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
 TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -586,9 +586,9 @@ TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestNewCommitLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -596,18 +596,18 @@ TYPED_TEST(DatabaseTest, TestNewCommitLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  database->get(&key, &new_val);
+  EXPECT_TRUE(database->get(&key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -617,29 +617,29 @@ TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestNewGetNoCommitLMDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
   Database::buffer_t new_val;
 
-  EXPECT_DEATH(database->get(&key, &new_val), "");
+  EXPECT_FALSE(database->get(&key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -647,9 +647,9 @@ TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadWriteCommitLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::ReadWrite);
+  EXPECT_TRUE(database->open(name, Database::ReadWrite));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 }
@@ -657,18 +657,18 @@ TYPED_TEST(DatabaseTest, TestReadWriteCommitLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  database->get(&key, &new_val);
+  EXPECT_TRUE(database->get(&key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -678,62 +678,62 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLMDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
   Database::buffer_t new_val;
 
-  EXPECT_DEATH(database->get(&key, &new_val), "");
+  EXPECT_FALSE(database->get(&key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyPutLMDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_DEATH(database->put(&key, &val), "");
+  EXPECT_FALSE(database->put(&key, &val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyCommitLMDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  EXPECT_DEATH(database->commit(), "");
+  EXPECT_FALSE(database->commit());
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
-  database->commit();
+  EXPECT_TRUE(database->commit());
 
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
   Database::buffer_t new_val;
 
-  database->get(&key, &new_val);
+  EXPECT_TRUE(database->get(&key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 }
@@ -741,20 +741,20 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
 TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");
-  database->open(name, Database::New);
+  EXPECT_TRUE(database->open(name, Database::New));
 
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  database->put(&key, &val);
+  EXPECT_TRUE(database->put(&key, &val));
 
   database->close();
 
-  database->open(name, Database::ReadOnly);
+  EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
   Database::buffer_t new_val;
 
-  EXPECT_DEATH(database->get(&key, &new_val), "");
+  EXPECT_FALSE(database->get(&key, &new_val));
 }
 
 }  // namespace caffe
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index aaa324a02b3..b973c5277ad 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -29,7 +29,7 @@ int main(int argc, char** argv) {
   caffe::shared_ptr<Database> database = caffe::DatabaseFactory(db_backend);
 
   // Open db
-  database->open(argv[1], Database::ReadOnly);
+  CHECK(database->open(argv[1], Database::ReadOnly));
 
   Datum datum;
   BlobProto sum_blob;
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 19c87e53ff0..3345c9c7e26 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -81,7 +81,7 @@ int main(int argc, char** argv) {
   shared_ptr<Database> database = DatabaseFactory(db_backend);
 
   // Open db
-  database->open(db_path, Database::New);
+  CHECK(database->open(db_path, Database::New));
 
   // Storing to db
   std::string root_folder(argv[1]);
@@ -116,17 +116,17 @@ int main(int argc, char** argv) {
     Database::buffer_t keystr(key_cstr, key_cstr + length);
 
     // Put in db
-    database->put(&keystr, &value);
+    CHECK(database->put(&keystr, &value));
 
     if (++count % 1000 == 0) {
       // Commit txn
-      database->commit();
+      CHECK(database->commit());
       LOG(ERROR) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    database->commit();
+    CHECK(database->commit());
     LOG(ERROR) << "Processed " << count << " files.";
   }
   database->close();
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index c4d1a39eb5b..1560ef60a7c 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -125,7 +125,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening database " << database_names[i];
     shared_ptr<Database> database = DatabaseFactory(argv[++arg_pos]);
-    database->open(database_names.at(i), Database::New);
+    CHECK(database->open(database_names.at(i), Database::New));
     feature_dbs.push_back(database);
   }
 
@@ -161,10 +161,10 @@ int feature_extraction_pipeline(int argc, char** argv) {
         int length = snprintf(key_str, kMaxKeyStrLength, "%d",
             image_indices[i]);
         Database::buffer_t key(key_str, key_str + length);
-        feature_dbs.at(i)->put(&key, &value);
+        CHECK(feature_dbs.at(i)->put(&key, &value));
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
-          feature_dbs.at(i)->commit();
+          CHECK(feature_dbs.at(i)->commit());
           LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
               " query images for feature blob " << blob_names[i];
         }
@@ -174,7 +174,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   // write the last batch
   for (int i = 0; i < num_features; ++i) {
     if (image_indices[i] % 1000 != 0) {
-      feature_dbs.at(i)->commit();
+      CHECK(feature_dbs.at(i)->commit());
     }
     LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
         " query images for feature blob " << blob_names[i];

From e0b572dd1a63d5dda000ee4cc803be04e51597a1 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 20:30:29 -0400
Subject: [PATCH 0930/2053] Updated Database interface to take key and value by
 const reference for put and key by const reference for put.  Additional
 copies are made for get and put in the LMDB implementation.

---
 examples/cifar10/convert_cifar_data.cpp |  4 +-
 include/caffe/database.hpp              |  4 +-
 include/caffe/leveldb_database.hpp      |  4 +-
 include/caffe/lmdb_database.hpp         |  4 +-
 src/caffe/leveldb_database.cpp          | 10 ++--
 src/caffe/lmdb_database.cpp             | 22 ++++---
 src/caffe/test/test_data_layer.cpp      |  2 +-
 src/caffe/test/test_database.cpp        | 80 ++++++++++++-------------
 tools/convert_imageset.cpp              |  2 +-
 tools/extract_features.cpp              |  2 +-
 10 files changed, 70 insertions(+), 64 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index b29e4121bf5..af845eadfb6 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -66,7 +66,7 @@ void convert_dataset(const string& input_folder, const string& output_folder,
       int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
       Database::buffer_t key(str_buffer, str_buffer + length);
-      CHECK(train_database->put(&key, &value));
+      CHECK(train_database->put(key, value));
     }
   }
   CHECK(train_database->commit());
@@ -89,7 +89,7 @@ void convert_dataset(const string& input_folder, const string& output_folder,
         reinterpret_cast<unsigned char*>(value.data()));
     int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
     Database::buffer_t key(str_buffer, str_buffer + length);
-    CHECK(test_database->put(&key, &value));
+    CHECK(test_database->put(key, value));
   }
   CHECK(test_database->commit());
   test_database->close();
diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 148b1ed7f38..3f3970d77a7 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -27,8 +27,8 @@ class Database {
   };
 
   virtual bool open(const string& filename, Mode mode) = 0;
-  virtual bool put(buffer_t* key, buffer_t* value) = 0;
-  virtual bool get(buffer_t* key, buffer_t* value) = 0;
+  virtual bool put(const buffer_t& key, const buffer_t& value) = 0;
+  virtual bool get(const buffer_t& key, buffer_t* value) = 0;
   virtual bool commit() = 0;
   virtual void close() = 0;
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 64bfa7cebee..e2558ff477f 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -15,8 +15,8 @@ namespace caffe {
 class LeveldbDatabase : public Database {
  public:
   bool open(const string& filename, Mode mode);
-  bool put(buffer_t* key, buffer_t* value);
-  bool get(buffer_t* key, buffer_t* value);
+  bool put(const buffer_t& key, const buffer_t& value);
+  bool get(const buffer_t& key, buffer_t* value);
   bool commit();
   void close();
 
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 69e3ce0f03d..4a0f3183bd5 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -19,8 +19,8 @@ class LmdbDatabase : public Database {
         txn_(NULL) { }
 
   bool open(const string& filename, Mode mode);
-  bool put(buffer_t* key, buffer_t* value);
-  bool get(buffer_t* key, buffer_t* value);
+  bool put(const buffer_t& key, const buffer_t& value);
+  bool get(const buffer_t& key, buffer_t* value);
   bool commit();
   void close();
 
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index d7506edf74e..c09112f668b 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -51,7 +51,7 @@ bool LeveldbDatabase::open(const string& filename, Mode mode) {
   return true;
 }
 
-bool LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
+bool LeveldbDatabase::put(const buffer_t& key, const buffer_t& value) {
   LOG(INFO) << "LevelDB: Put";
 
   if (read_only_) {
@@ -61,18 +61,18 @@ bool LeveldbDatabase::put(buffer_t* key, buffer_t* value) {
 
   CHECK_NOTNULL(batch_.get());
 
-  leveldb::Slice key_slice(key->data(), key->size());
-  leveldb::Slice value_slice(value->data(), value->size());
+  leveldb::Slice key_slice(key.data(), key.size());
+  leveldb::Slice value_slice(value.data(), value.size());
 
   batch_->Put(key_slice, value_slice);
 
   return true;
 }
 
-bool LeveldbDatabase::get(buffer_t* key, buffer_t* value) {
+bool LeveldbDatabase::get(const buffer_t& key, buffer_t* value) {
   LOG(INFO) << "LevelDB: Get";
 
-  leveldb::Slice key_slice(key->data(), key->size());
+  leveldb::Slice key_slice(key.data(), key.size());
 
   string value_string;
   leveldb::Status status =
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index d71513a55ee..2cb699b4234 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -77,14 +77,18 @@ bool LmdbDatabase::open(const string& filename, Mode mode) {
   return true;
 }
 
-bool LmdbDatabase::put(buffer_t* key, buffer_t* value) {
+bool LmdbDatabase::put(const buffer_t& key, const buffer_t& value) {
   LOG(INFO) << "LMDB: Put";
 
+  // MDB_val::mv_size is not const, so we need to make a local copy.
+  buffer_t local_key = key;
+  buffer_t local_value = value;
+
   MDB_val mdbkey, mdbdata;
-  mdbdata.mv_size = value->size();
-  mdbdata.mv_data = value->data();
-  mdbkey.mv_size = key->size();
-  mdbkey.mv_data = key->data();
+  mdbdata.mv_size = local_value.size();
+  mdbdata.mv_data = local_value.data();
+  mdbkey.mv_size = local_key.size();
+  mdbkey.mv_data = local_key.data();
 
   CHECK_NOTNULL(txn_);
   CHECK_NE(0, dbi_);
@@ -98,12 +102,14 @@ bool LmdbDatabase::put(buffer_t* key, buffer_t* value) {
   return true;
 }
 
-bool LmdbDatabase::get(buffer_t* key, buffer_t* value) {
+bool LmdbDatabase::get(const buffer_t& key, buffer_t* value) {
   LOG(INFO) << "LMDB: Get";
 
+  buffer_t local_key = key;
+
   MDB_val mdbkey, mdbdata;
-  mdbkey.mv_data = key->data();
-  mdbkey.mv_size = key->size();
+  mdbkey.mv_data = local_key.data();
+  mdbkey.mv_size = local_key.size();
 
   int retval;
   MDB_txn* get_txn;
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index cc9ad20487c..8ae8f2b8da8 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -59,7 +59,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       Database::buffer_t value(datum.ByteSize());
       datum.SerializeWithCachedSizesToArray(
           reinterpret_cast<unsigned char*>(value.data()));
-      CHECK(database->put(&key, &value));
+      CHECK(database->put(key, value));
     }
     CHECK(database->commit());
     database->close();
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index f65865014fa..70e1d962619 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -126,7 +126,7 @@ TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
     string value = ss.str();
     Database::buffer_t key_buf(key.data(), key.data() + key.size());
     Database::buffer_t val_buf(value.data(), value.data() + value.size());
-    EXPECT_TRUE(database->put(&key_buf, &val_buf));
+    EXPECT_TRUE(database->put(key_buf, val_buf));
   }
   EXPECT_TRUE(database->commit());
 
@@ -151,8 +151,8 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key1, &value1));
-  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->put(key1, value1));
+  EXPECT_TRUE(database->put(key2, value2));
   EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
@@ -190,8 +190,8 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key1, &value1));
-  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->put(key1, value1));
+  EXPECT_TRUE(database->put(key2, value2));
   EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
@@ -229,7 +229,7 @@ TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
@@ -254,13 +254,13 @@ TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  EXPECT_TRUE(database->get(&key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -275,11 +275,11 @@ TYPED_TEST(DatabaseTest, TestNewGetNoCommitLevelDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   Database::buffer_t new_val;
 
-  EXPECT_FALSE(database->get(&key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_val));
 }
 
 
@@ -291,7 +291,7 @@ TYPED_TEST(DatabaseTest, TestReadWritePutLevelDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
@@ -316,13 +316,13 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  EXPECT_TRUE(database->get(&key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -337,11 +337,11 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLevelDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   Database::buffer_t new_val;
 
-  EXPECT_FALSE(database->get(&key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyPutLevelDBFails) {
@@ -355,7 +355,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyPutLevelDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_FALSE(database->put(&key, &val));
+  EXPECT_FALSE(database->put(key, val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyCommitLevelDBFails) {
@@ -377,7 +377,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
@@ -387,7 +387,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
 
   Database::buffer_t new_val;
 
-  EXPECT_TRUE(database->get(&key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 }
@@ -400,7 +400,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   database->close();
 
@@ -408,7 +408,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
 
   Database::buffer_t new_val;
 
-  EXPECT_FALSE(database->get(&key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestNewDoesntExistLMDBPasses) {
@@ -473,7 +473,7 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
     string value = ss.str();
     Database::buffer_t key_buf(key.data(), key.data() + key.size());
     Database::buffer_t val_buf(value.data(), value.data() + value.size());
-    EXPECT_TRUE(database->put(&key_buf, &val_buf));
+    EXPECT_TRUE(database->put(key_buf, val_buf));
   }
   EXPECT_TRUE(database->commit());
 
@@ -498,8 +498,8 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key1, &value1));
-  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->put(key1, value1));
+  EXPECT_TRUE(database->put(key2, value2));
   EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
@@ -537,8 +537,8 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
   Database::buffer_t key2 = this->TestKey();
   Database::buffer_t value2 = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key1, &value1));
-  EXPECT_TRUE(database->put(&key2, &value2));
+  EXPECT_TRUE(database->put(key1, value1));
+  EXPECT_TRUE(database->put(key2, value2));
   EXPECT_TRUE(database->commit());
 
   Database::const_iterator iter1 = database->begin();
@@ -576,7 +576,7 @@ TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
@@ -601,13 +601,13 @@ TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  EXPECT_TRUE(database->get(&key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -622,11 +622,11 @@ TYPED_TEST(DatabaseTest, TestNewGetNoCommitLMDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   Database::buffer_t new_val;
 
-  EXPECT_FALSE(database->get(&key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
@@ -637,7 +637,7 @@ TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
@@ -662,13 +662,13 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
   Database::buffer_t new_val;
 
-  EXPECT_TRUE(database->get(&key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 
@@ -683,11 +683,11 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLMDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   Database::buffer_t new_val;
 
-  EXPECT_FALSE(database->get(&key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyPutLMDBFails) {
@@ -701,7 +701,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyPutLMDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_FALSE(database->put(&key, &val));
+  EXPECT_FALSE(database->put(key, val));
 }
 
 TYPED_TEST(DatabaseTest, TestReadOnlyCommitLMDBFails) {
@@ -723,7 +723,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
@@ -733,7 +733,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
 
   Database::buffer_t new_val;
 
-  EXPECT_TRUE(database->get(&key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_val));
 
   EXPECT_TRUE(this->BufferEq(val, new_val));
 }
@@ -746,7 +746,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
   Database::buffer_t key = this->TestKey();
   Database::buffer_t val = this->TestValue();
 
-  EXPECT_TRUE(database->put(&key, &val));
+  EXPECT_TRUE(database->put(key, val));
 
   database->close();
 
@@ -754,7 +754,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
 
   Database::buffer_t new_val;
 
-  EXPECT_FALSE(database->get(&key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_val));
 }
 
 }  // namespace caffe
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 3345c9c7e26..5ad2c0b4d5a 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -116,7 +116,7 @@ int main(int argc, char** argv) {
     Database::buffer_t keystr(key_cstr, key_cstr + length);
 
     // Put in db
-    CHECK(database->put(&keystr, &value));
+    CHECK(database->put(keystr, value));
 
     if (++count % 1000 == 0) {
       // Commit txn
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 1560ef60a7c..0c7660d5d49 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -161,7 +161,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
         int length = snprintf(key_str, kMaxKeyStrLength, "%d",
             image_indices[i]);
         Database::buffer_t key(key_str, key_str + length);
-        CHECK(feature_dbs.at(i)->put(&key, &value));
+        CHECK(feature_dbs.at(i)->put(key, value));
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           CHECK(feature_dbs.at(i)->commit());

From 2200a7a4b1f7fa7ac5fac7a2463552e8fe0ca9c2 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 20:43:01 -0400
Subject: [PATCH 0931/2053] The LevelDB iterator/DB deallocation order bug is
 pretty much fixed by having each iterator hold a shared pointer to the DB.  I
 manually specified a deconstructor for the LeveldbState to make it clear what
 order these two things need to be deallocated in.

---
 include/caffe/leveldb_database.hpp |  8 ++++++++
 src/caffe/layers/data_layer.cpp    |  4 ----
 src/caffe/test/test_database.cpp   | 16 ----------------
 tools/compute_image_mean.cpp       |  1 -
 4 files changed, 8 insertions(+), 21 deletions(-)

diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index e2558ff477f..2a610a370a4 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -34,6 +34,14 @@ class LeveldbDatabase : public Database {
           db_(db),
           iter_(iter) { }
 
+    ~LeveldbState() {
+      // This order is very important.
+      // Iterators must be destroyed before their associated DB
+      // is destroyed.
+      iter_.reset();
+      db_.reset();
+    }
+
     shared_ptr<DatabaseState> clone() {
       shared_ptr<leveldb::Iterator> new_iter;
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index dcba10fabec..c379d78d8f3 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -18,10 +18,6 @@ template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
   // clean up the database resources
-
-  // Very important to invalidate iterators before closing the database.
-  // TODO(kmatzen): Figure out a better design to avoid this.
-  iter_ = database_->end();
   database_->close();
 }
 
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index 70e1d962619..3dd200673ff 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -172,10 +172,6 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
 
   EXPECT_TRUE(database->end() == iter3);
 
-  iter1 = database->end();
-  iter2 = database->end();
-  iter3 = database->end();
-
   database->close();
 }
 
@@ -214,10 +210,6 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
   EXPECT_TRUE(this->BufferEq(iter3->key, key2));
   EXPECT_TRUE(database->end() == iter1);
 
-  iter1 = database->end();
-  iter2 = database->end();
-  iter3 = database->end();
-
   database->close();
 }
 
@@ -519,10 +511,6 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
 
   EXPECT_TRUE(database->end() == iter3);
 
-  iter1 = database->end();
-  iter2 = database->end();
-  iter3 = database->end();
-
   database->close();
 }
 
@@ -561,10 +549,6 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
   EXPECT_TRUE(this->BufferEq(iter3->key, key2));
   EXPECT_TRUE(database->end() == iter1);
 
-  iter1 = database->end();
-  iter2 = database->end();
-  iter3 = database->end();
-
   database->close();
 }
 
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index b973c5277ad..5f6ea6eecc5 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -38,7 +38,6 @@ int main(int argc, char** argv) {
   Database::const_iterator iter = database->begin();
   const Database::buffer_t& first_blob = iter->value;
   datum.ParseFromArray(first_blob.data(), first_blob.size());
-  iter = database->end();
 
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());

From 80a99b939bfad88d918e82b41fd2c28c454abb50 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 20:54:39 -0400
Subject: [PATCH 0932/2053] Changed Database::buffer_t to Database::key_type
 and Database::value_type

---
 examples/cifar10/convert_cifar_data.cpp |   8 +-
 include/caffe/database.hpp              |  11 +-
 include/caffe/leveldb_database.hpp      |   4 +-
 include/caffe/lmdb_database.hpp         |   4 +-
 src/caffe/leveldb_database.cpp          |  10 +-
 src/caffe/lmdb_database.cpp             |  16 +--
 src/caffe/test/test_data_layer.cpp      |   4 +-
 src/caffe/test/test_database.cpp        | 156 ++++++++++++------------
 tools/compute_image_mean.cpp            |   4 +-
 tools/convert_imageset.cpp              |   4 +-
 tools/extract_features.cpp              |   4 +-
 11 files changed, 113 insertions(+), 112 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index af845eadfb6..46ab1558623 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -60,12 +60,12 @@ void convert_dataset(const string& input_folder, const string& output_folder,
       read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
       datum.set_data(str_buffer, kCIFARImageNBytes);
-      Database::buffer_t value(datum.ByteSize());
+      Database::value_type value(datum.ByteSize());
       datum.SerializeWithCachedSizesToArray(
           reinterpret_cast<unsigned char*>(value.data()));
       int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
-      Database::buffer_t key(str_buffer, str_buffer + length);
+      Database::key_type key(str_buffer, str_buffer + length);
       CHECK(train_database->put(key, value));
     }
   }
@@ -84,11 +84,11 @@ void convert_dataset(const string& input_folder, const string& output_folder,
     read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
-    Database::buffer_t value(datum.ByteSize());
+    Database::value_type value(datum.ByteSize());
     datum.SerializeWithCachedSizesToArray(
         reinterpret_cast<unsigned char*>(value.data()));
     int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
-    Database::buffer_t key(str_buffer, str_buffer + length);
+    Database::key_type key(str_buffer, str_buffer + length);
     CHECK(test_database->put(key, value));
   }
   CHECK(test_database->commit());
diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 3f3970d77a7..2341149ce59 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -19,16 +19,17 @@ class Database {
     ReadOnly
   };
 
-  typedef vector<char> buffer_t;
+  typedef vector<char> key_type;
+  typedef vector<char> value_type;
 
   struct KV {
-    buffer_t key;
-    buffer_t value;
+    key_type key;
+    value_type value;
   };
 
   virtual bool open(const string& filename, Mode mode) = 0;
-  virtual bool put(const buffer_t& key, const buffer_t& value) = 0;
-  virtual bool get(const buffer_t& key, buffer_t* value) = 0;
+  virtual bool put(const key_type& key, const value_type& value) = 0;
+  virtual bool get(const key_type& key, value_type* value) = 0;
   virtual bool commit() = 0;
   virtual void close() = 0;
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 2a610a370a4..9c7f70ec053 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -15,8 +15,8 @@ namespace caffe {
 class LeveldbDatabase : public Database {
  public:
   bool open(const string& filename, Mode mode);
-  bool put(const buffer_t& key, const buffer_t& value);
-  bool get(const buffer_t& key, buffer_t* value);
+  bool put(const key_type& key, const value_type& value);
+  bool get(const key_type& key, value_type* value);
   bool commit();
   void close();
 
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 4a0f3183bd5..107d936da83 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -19,8 +19,8 @@ class LmdbDatabase : public Database {
         txn_(NULL) { }
 
   bool open(const string& filename, Mode mode);
-  bool put(const buffer_t& key, const buffer_t& value);
-  bool get(const buffer_t& key, buffer_t* value);
+  bool put(const key_type& key, const value_type& value);
+  bool get(const key_type& key, value_type* value);
   bool commit();
   void close();
 
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index c09112f668b..2061b8ea65e 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -51,7 +51,7 @@ bool LeveldbDatabase::open(const string& filename, Mode mode) {
   return true;
 }
 
-bool LeveldbDatabase::put(const buffer_t& key, const buffer_t& value) {
+bool LeveldbDatabase::put(const key_type& key, const value_type& value) {
   LOG(INFO) << "LevelDB: Put";
 
   if (read_only_) {
@@ -69,7 +69,7 @@ bool LeveldbDatabase::put(const buffer_t& key, const buffer_t& value) {
   return true;
 }
 
-bool LeveldbDatabase::get(const buffer_t& key, buffer_t* value) {
+bool LeveldbDatabase::get(const key_type& key, value_type* value) {
   LOG(INFO) << "LevelDB: Get";
 
   leveldb::Slice key_slice(key.data(), key.size());
@@ -83,7 +83,7 @@ bool LeveldbDatabase::get(const buffer_t& key, buffer_t* value) {
     return false;
   }
 
-  Database::buffer_t temp_value(value_string.data(),
+  Database::value_type temp_value(value_string.data(),
       value_string.data() + value_string.size());
   value->swap(temp_value);
 
@@ -189,10 +189,10 @@ Database::KV& LeveldbDatabase::dereference(
 
   CHECK(iter->Valid());
 
-  Database::buffer_t temp_key(buffer_t(iter->key().data(),
+  Database::key_type temp_key(key_type(iter->key().data(),
       iter->key().data() + iter->key().size()));
 
-  Database::buffer_t temp_value(buffer_t(iter->value().data(),
+  Database::value_type temp_value(value_type(iter->value().data(),
       iter->value().data() + iter->value().size()));
 
   leveldb_state->kv_pair_.key.swap(temp_key);
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index 2cb699b4234..eb22d15f28d 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -77,12 +77,12 @@ bool LmdbDatabase::open(const string& filename, Mode mode) {
   return true;
 }
 
-bool LmdbDatabase::put(const buffer_t& key, const buffer_t& value) {
+bool LmdbDatabase::put(const key_type& key, const value_type& value) {
   LOG(INFO) << "LMDB: Put";
 
   // MDB_val::mv_size is not const, so we need to make a local copy.
-  buffer_t local_key = key;
-  buffer_t local_value = value;
+  key_type local_key = key;
+  value_type local_value = value;
 
   MDB_val mdbkey, mdbdata;
   mdbdata.mv_size = local_value.size();
@@ -102,10 +102,10 @@ bool LmdbDatabase::put(const buffer_t& key, const buffer_t& value) {
   return true;
 }
 
-bool LmdbDatabase::get(const buffer_t& key, buffer_t* value) {
+bool LmdbDatabase::get(const key_type& key, value_type* value) {
   LOG(INFO) << "LMDB: Get";
 
-  buffer_t local_key = key;
+  key_type local_key = key;
 
   MDB_val mdbkey, mdbdata;
   mdbkey.mv_data = local_key.data();
@@ -127,7 +127,7 @@ bool LmdbDatabase::get(const buffer_t& key, buffer_t* value) {
 
   mdb_txn_abort(get_txn);
 
-  Database::buffer_t temp_value(reinterpret_cast<char*>(mdbdata.mv_data),
+  Database::value_type temp_value(reinterpret_cast<char*>(mdbdata.mv_data),
       reinterpret_cast<char*>(mdbdata.mv_data) + mdbdata.mv_size);
 
   value->swap(temp_value);
@@ -247,9 +247,9 @@ Database::KV& LmdbDatabase::dereference(shared_ptr<DatabaseState> state) const {
   char* key_data = reinterpret_cast<char*>(mdb_key.mv_data);
   char* value_data = reinterpret_cast<char*>(mdb_val.mv_data);
 
-  Database::buffer_t temp_key(key_data, key_data + mdb_key.mv_size);
+  Database::key_type temp_key(key_data, key_data + mdb_key.mv_size);
 
-  Database::buffer_t temp_value(value_data,
+  Database::value_type temp_value(value_data,
       value_data + mdb_val.mv_size);
 
   lmdb_state->kv_pair_.key.swap(temp_key);
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 8ae8f2b8da8..5f982063cc7 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -55,8 +55,8 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       stringstream ss;
       ss << i;
       string key_str = ss.str();
-      Database::buffer_t key(key_str.c_str(), key_str.c_str() + key_str.size());
-      Database::buffer_t value(datum.ByteSize());
+      Database::key_type key(key_str.c_str(), key_str.c_str() + key_str.size());
+      Database::value_type value(datum.ByteSize());
       datum.SerializeWithCachedSizesToArray(
           reinterpret_cast<unsigned char*>(value.data()));
       CHECK(database->put(key, value));
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index 3dd200673ff..ad1c786465a 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -23,32 +23,32 @@ class DatabaseTest : public MultiDeviceTest<TypeParam> {
     return filename;
   }
 
-  Database::buffer_t TestKey() {
+  Database::key_type TestKey() {
     const char* kKey = "hello";
-    Database::buffer_t key(kKey, kKey + 5);
+    Database::key_type key(kKey, kKey + 5);
     return key;
   }
 
-  Database::buffer_t TestValue() {
+  Database::value_type TestValue() {
     const char* kValue = "world";
-    Database::buffer_t value(kValue, kValue + 5);
+    Database::value_type value(kValue, kValue + 5);
     return value;
   }
 
-  Database::buffer_t TestAltKey() {
+  Database::key_type TestAltKey() {
     const char* kKey = "foo";
-    Database::buffer_t key(kKey, kKey + 3);
+    Database::key_type key(kKey, kKey + 3);
     return key;
   }
 
-  Database::buffer_t TestAltValue() {
+  Database::value_type TestAltValue() {
     const char* kValue = "bar";
-    Database::buffer_t value(kValue, kValue + 3);
+    Database::value_type value(kValue, kValue + 3);
     return value;
   }
 
-  bool BufferEq(const Database::buffer_t& buf1,
-      const Database::buffer_t& buf2) {
+  template <typename T>
+  bool BufferEq(const T& buf1, const T& buf2) {
     if (buf1.size() != buf2.size()) {
       return false;
     }
@@ -124,8 +124,8 @@ TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
     string key = ss.str();
     ss << " here be data";
     string value = ss.str();
-    Database::buffer_t key_buf(key.data(), key.data() + key.size());
-    Database::buffer_t val_buf(value.data(), value.data() + value.size());
+    Database::key_type key_buf(key.data(), key.data() + key.size());
+    Database::value_type val_buf(value.data(), value.data() + value.size());
     EXPECT_TRUE(database->put(key_buf, val_buf));
   }
   EXPECT_TRUE(database->commit());
@@ -145,11 +145,11 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key1 = this->TestAltKey();
-  Database::buffer_t value1 = this->TestAltValue();
+  Database::key_type key1 = this->TestAltKey();
+  Database::value_type value1 = this->TestAltValue();
 
-  Database::buffer_t key2 = this->TestKey();
-  Database::buffer_t value2 = this->TestValue();
+  Database::key_type key2 = this->TestKey();
+  Database::value_type value2 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
   EXPECT_TRUE(database->put(key2, value2));
@@ -180,11 +180,11 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key1 = this->TestAltKey();
-  Database::buffer_t value1 = this->TestAltValue();
+  Database::key_type key1 = this->TestAltKey();
+  Database::value_type value1 = this->TestAltValue();
 
-  Database::buffer_t key2 = this->TestKey();
-  Database::buffer_t value2 = this->TestValue();
+  Database::key_type key2 = this->TestKey();
+  Database::value_type value2 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
   EXPECT_TRUE(database->put(key2, value2));
@@ -218,8 +218,8 @@ TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -243,14 +243,14 @@ TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_TRUE(database->get(key, &new_val));
 
@@ -264,12 +264,12 @@ TYPED_TEST(DatabaseTest, TestNewGetNoCommitLevelDBFails) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_FALSE(database->get(key, &new_val));
 }
@@ -280,8 +280,8 @@ TYPED_TEST(DatabaseTest, TestReadWritePutLevelDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::ReadWrite));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -305,14 +305,14 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_TRUE(database->get(key, &new_val));
 
@@ -326,12 +326,12 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLevelDBFails) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_FALSE(database->get(key, &new_val));
 }
@@ -344,8 +344,8 @@ TYPED_TEST(DatabaseTest, TestReadOnlyPutLevelDBFails) {
 
   EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_FALSE(database->put(key, val));
 }
@@ -366,8 +366,8 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -377,7 +377,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
 
   EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_TRUE(database->get(key, &new_val));
 
@@ -389,8 +389,8 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
   shared_ptr<Database> database = DatabaseFactory("leveldb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -398,7 +398,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
 
   EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_FALSE(database->get(key, &new_val));
 }
@@ -463,8 +463,8 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
     string key = ss.str();
     ss << " here be data";
     string value = ss.str();
-    Database::buffer_t key_buf(key.data(), key.data() + key.size());
-    Database::buffer_t val_buf(value.data(), value.data() + value.size());
+    Database::key_type key_buf(key.data(), key.data() + key.size());
+    Database::value_type val_buf(value.data(), value.data() + value.size());
     EXPECT_TRUE(database->put(key_buf, val_buf));
   }
   EXPECT_TRUE(database->commit());
@@ -484,11 +484,11 @@ TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key1 = this->TestAltKey();
-  Database::buffer_t value1 = this->TestAltValue();
+  Database::key_type key1 = this->TestAltKey();
+  Database::value_type value1 = this->TestAltValue();
 
-  Database::buffer_t key2 = this->TestKey();
-  Database::buffer_t value2 = this->TestValue();
+  Database::key_type key2 = this->TestKey();
+  Database::value_type value2 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
   EXPECT_TRUE(database->put(key2, value2));
@@ -519,11 +519,11 @@ TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key1 = this->TestAltKey();
-  Database::buffer_t value1 = this->TestAltValue();
+  Database::key_type key1 = this->TestAltKey();
+  Database::value_type value1 = this->TestAltValue();
 
-  Database::buffer_t key2 = this->TestKey();
-  Database::buffer_t value2 = this->TestValue();
+  Database::key_type key2 = this->TestKey();
+  Database::value_type value2 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
   EXPECT_TRUE(database->put(key2, value2));
@@ -557,8 +557,8 @@ TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -582,14 +582,14 @@ TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_TRUE(database->get(key, &new_val));
 
@@ -603,12 +603,12 @@ TYPED_TEST(DatabaseTest, TestNewGetNoCommitLMDBFails) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_FALSE(database->get(key, &new_val));
 }
@@ -618,8 +618,8 @@ TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::ReadWrite));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -643,14 +643,14 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
   EXPECT_TRUE(database->commit());
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_TRUE(database->get(key, &new_val));
 
@@ -664,12 +664,12 @@ TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLMDBFails) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_FALSE(database->get(key, &new_val));
 }
@@ -682,8 +682,8 @@ TYPED_TEST(DatabaseTest, TestReadOnlyPutLMDBFails) {
 
   EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_FALSE(database->put(key, val));
 }
@@ -704,8 +704,8 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -715,7 +715,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
 
   EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_TRUE(database->get(key, &new_val));
 
@@ -727,8 +727,8 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
   shared_ptr<Database> database = DatabaseFactory("lmdb");
   EXPECT_TRUE(database->open(name, Database::New));
 
-  Database::buffer_t key = this->TestKey();
-  Database::buffer_t val = this->TestValue();
+  Database::key_type key = this->TestKey();
+  Database::value_type val = this->TestValue();
 
   EXPECT_TRUE(database->put(key, val));
 
@@ -736,7 +736,7 @@ TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
 
   EXPECT_TRUE(database->open(name, Database::ReadOnly));
 
-  Database::buffer_t new_val;
+  Database::value_type new_val;
 
   EXPECT_FALSE(database->get(key, &new_val));
 }
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 5f6ea6eecc5..d13c4a0f833 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -36,7 +36,7 @@ int main(int argc, char** argv) {
   int count = 0;
   // load first datum
   Database::const_iterator iter = database->begin();
-  const Database::buffer_t& first_blob = iter->value;
+  const Database::value_type& first_blob = iter->value;
   datum.ParseFromArray(first_blob.data(), first_blob.size());
 
   sum_blob.set_num(1);
@@ -53,7 +53,7 @@ int main(int argc, char** argv) {
   for (Database::const_iterator iter = database->begin();
       iter != database->end(); ++iter) {
     // just a dummy operation
-    const Database::buffer_t& blob = iter->value;
+    const Database::value_type& blob = iter->value;
     datum.ParseFromArray(blob.data(), blob.size());
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 5ad2c0b4d5a..1cdca7e015a 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -110,10 +110,10 @@ int main(int argc, char** argv) {
     // sequential
     int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
-    Database::buffer_t value(datum.ByteSize());
+    Database::value_type value(datum.ByteSize());
     datum.SerializeWithCachedSizesToArray(
         reinterpret_cast<unsigned char*>(value.data()));
-    Database::buffer_t keystr(key_cstr, key_cstr + length);
+    Database::key_type keystr(key_cstr, key_cstr + length);
 
     // Put in db
     CHECK(database->put(keystr, value));
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 0c7660d5d49..1340192cbe4 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -155,12 +155,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        Database::buffer_t value(datum.ByteSize());
+        Database::value_type value(datum.ByteSize());
         datum.SerializeWithCachedSizesToArray(
             reinterpret_cast<unsigned char*>(value.data()));
         int length = snprintf(key_str, kMaxKeyStrLength, "%d",
             image_indices[i]);
-        Database::buffer_t key(key_str, key_str + length);
+        Database::key_type key(key_str, key_str + length);
         CHECK(feature_dbs.at(i)->put(key, value));
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {

From 08e2cdedbbe4d7178597d6dc1479eccfcce3b0d5 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 21:36:59 -0400
Subject: [PATCH 0933/2053] Added function to Database interface to retrieve
 keys.  Exposed a bug with LMDB iterators.  Fix the bug and updated how
 invalid iterators are represented.

---
 include/caffe/database.hpp         |  11 ++--
 include/caffe/leveldb_database.hpp |   5 +-
 include/caffe/lmdb_database.hpp    |   5 +-
 src/caffe/leveldb_database.cpp     |  35 ++++++----
 src/caffe/lmdb_database.cpp        |  44 +++++++++----
 src/caffe/test/test_database.cpp   | 101 +++++++++++++++++++++++++++++
 6 files changed, 169 insertions(+), 32 deletions(-)

diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 2341149ce59..8469e84a1dd 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -33,6 +33,8 @@ class Database {
   virtual bool commit() = 0;
   virtual void close() = 0;
 
+  virtual void keys(vector<key_type>* keys) = 0;
+
   Database() { }
   virtual ~Database() { }
 
@@ -65,7 +67,8 @@ class Database {
 
     iterator(const iterator& other)
         : parent_(other.parent_),
-          state_(other.state_->clone()) { }
+          state_(other.state_ ? other.state_->clone()
+              : shared_ptr<DatabaseState>()) { }
 
     iterator& operator=(iterator copy) {
       copy.swap(*this);
@@ -86,12 +89,12 @@ class Database {
     }
 
     iterator& operator++() {
-      parent_->increment(state_);
+      parent_->increment(&state_);
       return *this;
     }
     iterator operator++(int) {
       iterator copy(*this);
-      parent_->increment(state_);
+      parent_->increment(&state_);
       return copy;
     }
 
@@ -117,7 +120,7 @@ class Database {
 
   virtual bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const = 0;
-  virtual void increment(shared_ptr<DatabaseState> state) const = 0;
+  virtual void increment(shared_ptr<DatabaseState>* state) const = 0;
   virtual KV& dereference(
       shared_ptr<DatabaseState> state) const = 0;
 };
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 9c7f70ec053..48cf11e06d4 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -6,6 +6,7 @@
 
 #include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/common.hpp"
 #include "caffe/database.hpp"
@@ -20,6 +21,8 @@ class LeveldbDatabase : public Database {
   bool commit();
   void close();
 
+  void keys(vector<key_type>* keys);
+
   const_iterator begin() const;
   const_iterator cbegin() const;
   const_iterator end() const;
@@ -62,7 +65,7 @@ class LeveldbDatabase : public Database {
 
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
-  void increment(shared_ptr<DatabaseState> state) const;
+  void increment(shared_ptr<DatabaseState>* state) const;
   Database::KV& dereference(shared_ptr<DatabaseState> state) const;
 
   shared_ptr<leveldb::DB> db_;
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index 107d936da83..a8ce60dada5 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -3,6 +3,7 @@
 
 #include <string>
 #include <utility>
+#include <vector>
 
 #include "lmdb.h"
 
@@ -24,6 +25,8 @@ class LmdbDatabase : public Database {
   bool commit();
   void close();
 
+  void keys(vector<key_type>* keys);
+
   const_iterator begin() const;
   const_iterator cbegin() const;
   const_iterator end() const;
@@ -66,7 +69,7 @@ class LmdbDatabase : public Database {
 
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
-  void increment(shared_ptr<DatabaseState> state) const;
+  void increment(shared_ptr<DatabaseState>* state) const;
   Database::KV& dereference(shared_ptr<DatabaseState> state) const;
 
   MDB_env* env_;
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index 2061b8ea65e..ad98f9e2d4f 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -1,5 +1,6 @@
 #include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/leveldb_database.hpp"
 
@@ -115,6 +116,16 @@ void LeveldbDatabase::close() {
   db_.reset();
 }
 
+void LeveldbDatabase::keys(vector<key_type>* keys) {
+  LOG(INFO) << "LevelDB: Keys";
+
+  keys->clear();
+  for (Database::const_iterator iter = begin(); iter != end(); ++iter) {
+    LOG(INFO) << "KEY";
+    keys->push_back(iter->key);
+  }
+}
+
 LeveldbDatabase::const_iterator LeveldbDatabase::begin() const {
   CHECK_NOTNULL(db_.get());
   shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
@@ -122,13 +133,16 @@ LeveldbDatabase::const_iterator LeveldbDatabase::begin() const {
   if (!iter->Valid()) {
     iter.reset();
   }
-  shared_ptr<DatabaseState> state(new LeveldbState(db_, iter));
+
+  shared_ptr<DatabaseState> state;
+  if (iter) {
+    state.reset(new LeveldbState(db_, iter));
+  }
   return const_iterator(this, state);
 }
 
 LeveldbDatabase::const_iterator LeveldbDatabase::end() const {
-  shared_ptr<leveldb::Iterator> iter;
-  shared_ptr<DatabaseState> state(new LeveldbState(db_, iter));
+  shared_ptr<DatabaseState> state;
   return const_iterator(this, state);
 }
 
@@ -143,25 +157,20 @@ bool LeveldbDatabase::equal(shared_ptr<DatabaseState> state1,
   shared_ptr<LeveldbState> leveldb_state1 =
       boost::dynamic_pointer_cast<LeveldbState>(state1);
 
-  CHECK_NOTNULL(leveldb_state1.get());
-
   shared_ptr<LeveldbState> leveldb_state2 =
       boost::dynamic_pointer_cast<LeveldbState>(state2);
 
-  CHECK_NOTNULL(leveldb_state2.get());
-
-  CHECK(!leveldb_state1->iter_ || leveldb_state1->iter_->Valid());
-  CHECK(!leveldb_state2->iter_ || leveldb_state2->iter_->Valid());
+  LOG(INFO) << leveldb_state1 << " " << leveldb_state2;
 
   // The KV store doesn't really have any sort of ordering,
   // so while we can do a sequential scan over the collection,
   // we can't really use subranges.
-  return !leveldb_state1->iter_ && !leveldb_state2->iter_;
+  return !leveldb_state1 && !leveldb_state2;
 }
 
-void LeveldbDatabase::increment(shared_ptr<DatabaseState> state) const {
+void LeveldbDatabase::increment(shared_ptr<DatabaseState>* state) const {
   shared_ptr<LeveldbState> leveldb_state =
-      boost::dynamic_pointer_cast<LeveldbState>(state);
+      boost::dynamic_pointer_cast<LeveldbState>(*state);
 
   CHECK_NOTNULL(leveldb_state.get());
 
@@ -172,7 +181,7 @@ void LeveldbDatabase::increment(shared_ptr<DatabaseState> state) const {
 
   iter->Next();
   if (!iter->Valid()) {
-    iter.reset();
+    state->reset();
   }
 }
 
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index eb22d15f28d..aabb733b635 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -2,6 +2,7 @@
 
 #include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/lmdb_database.hpp"
 
@@ -168,22 +169,43 @@ void LmdbDatabase::close() {
   }
 }
 
+void LmdbDatabase::keys(vector<key_type>* keys) {
+  LOG(INFO) << "LMDB: Keys";
+
+  keys->clear();
+  for (Database::const_iterator iter = begin(); iter != end(); ++iter) {
+    keys->push_back(iter->key);
+  }
+}
+
 LmdbDatabase::const_iterator LmdbDatabase::begin() const {
-  MDB_cursor* cursor;
   int retval;
-  retval = mdb_cursor_open(txn_, dbi_, &cursor);
+
+  MDB_txn* iter_txn;
+
+  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &iter_txn);
+  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
+      << mdb_strerror(retval);
+
+  MDB_cursor* cursor;
+  retval = mdb_cursor_open(iter_txn, dbi_, &cursor);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
   MDB_val key;
   MDB_val val;
   retval = mdb_cursor_get(cursor, &key, &val, MDB_FIRST);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
-  shared_ptr<DatabaseState> state(new LmdbState(cursor, txn_, &dbi_));
+  CHECK(MDB_SUCCESS == retval || MDB_NOTFOUND == retval)
+      << mdb_strerror(retval);
+
+  shared_ptr<DatabaseState> state;
+  if (MDB_SUCCESS == retval) {
+    state.reset(new LmdbState(cursor, iter_txn, &dbi_));
+  }
   return const_iterator(this, state);
 }
 
 LmdbDatabase::const_iterator LmdbDatabase::end() const {
-  shared_ptr<DatabaseState> state(new LmdbState(NULL, txn_, &dbi_));
+  shared_ptr<DatabaseState> state;
   return const_iterator(this, state);
 }
 
@@ -195,22 +217,18 @@ bool LmdbDatabase::equal(shared_ptr<DatabaseState> state1,
   shared_ptr<LmdbState> lmdb_state1 =
       boost::dynamic_pointer_cast<LmdbState>(state1);
 
-  CHECK_NOTNULL(lmdb_state1.get());
-
   shared_ptr<LmdbState> lmdb_state2 =
       boost::dynamic_pointer_cast<LmdbState>(state2);
 
-  CHECK_NOTNULL(lmdb_state2.get());
-
   // The KV store doesn't really have any sort of ordering,
   // so while we can do a sequential scan over the collection,
   // we can't really use subranges.
-  return !lmdb_state1->cursor_ && !lmdb_state2->cursor_;
+  return !lmdb_state1 && !lmdb_state2;
 }
 
-void LmdbDatabase::increment(shared_ptr<DatabaseState> state) const {
+void LmdbDatabase::increment(shared_ptr<DatabaseState>* state) const {
   shared_ptr<LmdbState> lmdb_state =
-      boost::dynamic_pointer_cast<LmdbState>(state);
+      boost::dynamic_pointer_cast<LmdbState>(*state);
 
   CHECK_NOTNULL(lmdb_state.get());
 
@@ -223,7 +241,7 @@ void LmdbDatabase::increment(shared_ptr<DatabaseState> state) const {
   int retval = mdb_cursor_get(cursor, &key, &val, MDB_NEXT);
   if (MDB_NOTFOUND == retval) {
     mdb_cursor_close(cursor);
-    cursor = NULL;
+    state->reset();
   } else {
     CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
   }
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index ad1c786465a..9c569106f10 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -112,6 +112,56 @@ TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLevelDBPasses) {
   database->close();
 }
 
+TYPED_TEST(DatabaseTest, TestKeysLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  EXPECT_TRUE(database->open(name, Database::New));
+
+  Database::key_type key1 = this->TestKey();
+  Database::value_type value1 = this->TestValue();
+
+  EXPECT_TRUE(database->put(key1, value1));
+
+  Database::key_type key2 = this->TestAltKey();
+  Database::value_type value2 = this->TestAltValue();
+
+  EXPECT_TRUE(database->put(key2, value2));
+
+  EXPECT_TRUE(database->commit());
+
+  vector<Database::key_type> keys;
+  database->keys(&keys);
+
+  EXPECT_EQ(2, keys.size());
+
+  EXPECT_TRUE(this->BufferEq(keys.at(0), key1) ||
+      this->BufferEq(keys.at(0), key2));
+  EXPECT_TRUE(this->BufferEq(keys.at(1), key1) ||
+      this->BufferEq(keys.at(2), key2));
+  EXPECT_FALSE(this->BufferEq(keys.at(0), keys.at(1)));
+}
+
+TYPED_TEST(DatabaseTest, TestKeysNoCommitLevelDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("leveldb");
+  EXPECT_TRUE(database->open(name, Database::New));
+
+  Database::key_type key1 = this->TestKey();
+  Database::value_type value1 = this->TestValue();
+
+  EXPECT_TRUE(database->put(key1, value1));
+
+  Database::key_type key2 = this->TestAltKey();
+  Database::value_type value2 = this->TestAltValue();
+
+  EXPECT_TRUE(database->put(key2, value2));
+
+  vector<Database::key_type> keys;
+  database->keys(&keys);
+
+  EXPECT_EQ(0, keys.size());
+}
+
 TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("leveldb");
@@ -451,6 +501,57 @@ TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLMDBPasses) {
   database->close();
 }
 
+TYPED_TEST(DatabaseTest, TestKeysLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  EXPECT_TRUE(database->open(name, Database::New));
+
+  Database::key_type key1 = this->TestKey();
+  Database::value_type value1 = this->TestValue();
+
+  EXPECT_TRUE(database->put(key1, value1));
+
+  Database::key_type key2 = this->TestAltKey();
+  Database::value_type value2 = this->TestAltValue();
+
+  EXPECT_TRUE(database->put(key2, value2));
+
+  EXPECT_TRUE(database->commit());
+
+  vector<Database::key_type> keys;
+  database->keys(&keys);
+
+  EXPECT_EQ(2, keys.size());
+
+  EXPECT_TRUE(this->BufferEq(keys.at(0), key1) ||
+      this->BufferEq(keys.at(0), key2));
+  EXPECT_TRUE(this->BufferEq(keys.at(1), key1) ||
+      this->BufferEq(keys.at(2), key2));
+  EXPECT_FALSE(this->BufferEq(keys.at(0), keys.at(1)));
+}
+
+TYPED_TEST(DatabaseTest, TestKeysNoCommitLMDB) {
+  string name = this->DBName();
+  shared_ptr<Database> database = DatabaseFactory("lmdb");
+  EXPECT_TRUE(database->open(name, Database::New));
+
+  Database::key_type key1 = this->TestKey();
+  Database::value_type value1 = this->TestValue();
+
+  EXPECT_TRUE(database->put(key1, value1));
+
+  Database::key_type key2 = this->TestAltKey();
+  Database::value_type value2 = this->TestAltValue();
+
+  EXPECT_TRUE(database->put(key2, value2));
+
+  vector<Database::key_type> keys;
+  database->keys(&keys);
+
+  EXPECT_EQ(0, keys.size());
+}
+
+
 TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
   string name = this->DBName();
   shared_ptr<Database> database = DatabaseFactory("lmdb");

From 0987c72c9d11b94524e1dc91150daf273c5b2537 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sun, 12 Oct 2014 21:39:33 -0400
Subject: [PATCH 0934/2053] Switched some Database logging statements from LOG
 to DLOG.

---
 src/caffe/leveldb_database.cpp | 23 ++++++++++-------------
 src/caffe/lmdb_database.cpp    | 12 ++++++------
 2 files changed, 16 insertions(+), 19 deletions(-)

diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index ad98f9e2d4f..a163abc09af 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -7,30 +7,30 @@
 namespace caffe {
 
 bool LeveldbDatabase::open(const string& filename, Mode mode) {
-  LOG(INFO) << "LevelDB: Open " << filename;
+  DLOG(INFO) << "LevelDB: Open " << filename;
 
   leveldb::Options options;
   switch (mode) {
   case New:
-    LOG(INFO) << " mode NEW";
+    DLOG(INFO) << " mode NEW";
     options.error_if_exists = true;
     options.create_if_missing = true;
     read_only_ = false;
     break;
   case ReadWrite:
-    LOG(INFO) << " mode RW";
+    DLOG(INFO) << " mode RW";
     options.error_if_exists = false;
     options.create_if_missing = true;
     read_only_ = false;
     break;
   case ReadOnly:
-    LOG(INFO) << " mode RO";
+    DLOG(INFO) << " mode RO";
     options.error_if_exists = false;
     options.create_if_missing = false;
     read_only_ = true;
     break;
   default:
-    LOG(FATAL) << "unknown mode " << mode;
+    DLOG(FATAL) << "unknown mode " << mode;
   }
   options.write_buffer_size = 268435456;
   options.max_open_files = 100;
@@ -53,7 +53,7 @@ bool LeveldbDatabase::open(const string& filename, Mode mode) {
 }
 
 bool LeveldbDatabase::put(const key_type& key, const value_type& value) {
-  LOG(INFO) << "LevelDB: Put";
+  DLOG(INFO) << "LevelDB: Put";
 
   if (read_only_) {
     LOG(ERROR) << "put can not be used on a database in ReadOnly mode";
@@ -71,7 +71,7 @@ bool LeveldbDatabase::put(const key_type& key, const value_type& value) {
 }
 
 bool LeveldbDatabase::get(const key_type& key, value_type* value) {
-  LOG(INFO) << "LevelDB: Get";
+  DLOG(INFO) << "LevelDB: Get";
 
   leveldb::Slice key_slice(key.data(), key.size());
 
@@ -92,7 +92,7 @@ bool LeveldbDatabase::get(const key_type& key, value_type* value) {
 }
 
 bool LeveldbDatabase::commit() {
-  LOG(INFO) << "LevelDB: Commit";
+  DLOG(INFO) << "LevelDB: Commit";
 
   if (read_only_) {
     LOG(ERROR) << "commit can not be used on a database in ReadOnly mode";
@@ -110,18 +110,17 @@ bool LeveldbDatabase::commit() {
 }
 
 void LeveldbDatabase::close() {
-  LOG(INFO) << "LevelDB: Close";
+  DLOG(INFO) << "LevelDB: Close";
 
   batch_.reset();
   db_.reset();
 }
 
 void LeveldbDatabase::keys(vector<key_type>* keys) {
-  LOG(INFO) << "LevelDB: Keys";
+  DLOG(INFO) << "LevelDB: Keys";
 
   keys->clear();
   for (Database::const_iterator iter = begin(); iter != end(); ++iter) {
-    LOG(INFO) << "KEY";
     keys->push_back(iter->key);
   }
 }
@@ -160,8 +159,6 @@ bool LeveldbDatabase::equal(shared_ptr<DatabaseState> state1,
   shared_ptr<LeveldbState> leveldb_state2 =
       boost::dynamic_pointer_cast<LeveldbState>(state2);
 
-  LOG(INFO) << leveldb_state1 << " " << leveldb_state2;
-
   // The KV store doesn't really have any sort of ordering,
   // so while we can do a sequential scan over the collection,
   // we can't really use subranges.
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index aabb733b635..de94391ae7d 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -9,7 +9,7 @@
 namespace caffe {
 
 bool LmdbDatabase::open(const string& filename, Mode mode) {
-  LOG(INFO) << "LMDB: Open " << filename;
+  DLOG(INFO) << "LMDB: Open " << filename;
 
   CHECK(NULL == env_);
   CHECK(NULL == txn_);
@@ -79,7 +79,7 @@ bool LmdbDatabase::open(const string& filename, Mode mode) {
 }
 
 bool LmdbDatabase::put(const key_type& key, const value_type& value) {
-  LOG(INFO) << "LMDB: Put";
+  DLOG(INFO) << "LMDB: Put";
 
   // MDB_val::mv_size is not const, so we need to make a local copy.
   key_type local_key = key;
@@ -104,7 +104,7 @@ bool LmdbDatabase::put(const key_type& key, const value_type& value) {
 }
 
 bool LmdbDatabase::get(const key_type& key, value_type* value) {
-  LOG(INFO) << "LMDB: Get";
+  DLOG(INFO) << "LMDB: Get";
 
   key_type local_key = key;
 
@@ -137,7 +137,7 @@ bool LmdbDatabase::get(const key_type& key, value_type* value) {
 }
 
 bool LmdbDatabase::commit() {
-  LOG(INFO) << "LMDB: Commit";
+  DLOG(INFO) << "LMDB: Commit";
 
   CHECK_NOTNULL(txn_);
 
@@ -158,7 +158,7 @@ bool LmdbDatabase::commit() {
 }
 
 void LmdbDatabase::close() {
-  LOG(INFO) << "LMDB: Close";
+  DLOG(INFO) << "LMDB: Close";
 
   if (env_ && dbi_) {
     mdb_close(env_, dbi_);
@@ -170,7 +170,7 @@ void LmdbDatabase::close() {
 }
 
 void LmdbDatabase::keys(vector<key_type>* keys) {
-  LOG(INFO) << "LMDB: Keys";
+  DLOG(INFO) << "LMDB: Keys";
 
   keys->clear();
   for (Database::const_iterator iter = begin(); iter != end(); ++iter) {

From 08b971feae8551ca5c7ce31a938a1f232ee56af2 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Mon, 13 Oct 2014 13:16:04 -0400
Subject: [PATCH 0935/2053] Templated the key and value types for the Database
 interface.  The Database is now responsible for serialization.  Refactored
 the tests so that they reuse the same code for each value type and backend
 configuration.

---
 examples/cifar10/convert_cifar_data.cpp |  25 +-
 include/caffe/data_layers.hpp           |   4 +-
 include/caffe/database.hpp              | 116 ++-
 include/caffe/database_factory.hpp      |   7 +-
 include/caffe/leveldb_database.hpp      |  23 +-
 include/caffe/lmdb_database.hpp         |  23 +-
 src/caffe/database_factory.cpp          |  31 +-
 src/caffe/layers/data_layer.cpp         |  11 +-
 src/caffe/leveldb_database.cpp          |  95 ++-
 src/caffe/lmdb_database.cpp             | 113 +--
 src/caffe/test/test_data_layer.cpp      |  12 +-
 src/caffe/test/test_database.cpp        | 901 +++++++++---------------
 tools/compute_image_mean.cpp            |  16 +-
 tools/convert_imageset.cpp              |  11 +-
 tools/extract_features.cpp              |  14 +-
 15 files changed, 689 insertions(+), 713 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 46ab1558623..f86f3936a35 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -20,6 +20,7 @@ using std::string;
 
 using caffe::Database;
 using caffe::DatabaseFactory;
+using caffe::Datum;
 using caffe::shared_ptr;
 
 const int kCIFARSize = 32;
@@ -37,13 +38,14 @@ void read_image(std::ifstream* file, int* label, char* buffer) {
 
 void convert_dataset(const string& input_folder, const string& output_folder,
     const string& db_type) {
-  shared_ptr<Database> train_database = DatabaseFactory(db_type);
+  shared_ptr<Database<string, Datum> > train_database =
+      DatabaseFactory<string, Datum>(db_type);
   CHECK(train_database->open(output_folder + "/cifar10_train_" + db_type,
-      Database::New));
+      Database<string, Datum>::New));
   // Data buffer
   int label;
   char str_buffer[kCIFARImageNBytes];
-  caffe::Datum datum;
+  Datum datum;
   datum.set_channels(3);
   datum.set_height(kCIFARSize);
   datum.set_width(kCIFARSize);
@@ -60,22 +62,19 @@ void convert_dataset(const string& input_folder, const string& output_folder,
       read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
       datum.set_data(str_buffer, kCIFARImageNBytes);
-      Database::value_type value(datum.ByteSize());
-      datum.SerializeWithCachedSizesToArray(
-          reinterpret_cast<unsigned char*>(value.data()));
       int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
-      Database::key_type key(str_buffer, str_buffer + length);
-      CHECK(train_database->put(key, value));
+      CHECK(train_database->put(string(str_buffer, length), datum));
     }
   }
   CHECK(train_database->commit());
   train_database->close();
 
   LOG(INFO) << "Writing Testing data";
-  shared_ptr<Database> test_database = DatabaseFactory(db_type);
+  shared_ptr<Database<string, Datum> > test_database =
+      DatabaseFactory<string, Datum>(db_type);
   CHECK(test_database->open(output_folder + "/cifar10_test_" + db_type,
-      Database::New));
+      Database<string, Datum>::New));
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
@@ -84,12 +83,8 @@ void convert_dataset(const string& input_folder, const string& output_folder,
     read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
-    Database::value_type value(datum.ByteSize());
-    datum.SerializeWithCachedSizesToArray(
-        reinterpret_cast<unsigned char*>(value.data()));
     int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
-    Database::key_type key(str_buffer, str_buffer + length);
-    CHECK(test_database->put(key, value));
+    CHECK(test_database->put(string(str_buffer, length), datum));
   }
   CHECK(test_database->commit());
   test_database->close();
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 810f2bb833d..13ba4e6f8b3 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -100,8 +100,8 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  protected:
   virtual void InternalThreadEntry();
 
-  shared_ptr<Database> database_;
-  Database::const_iterator iter_;
+  shared_ptr<Database<string, Datum> > database_;
+  Database<string, Datum>::const_iterator iter_;
 };
 
 /**
diff --git a/include/caffe/database.hpp b/include/caffe/database.hpp
index 8469e84a1dd..711ebdd3fa0 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/database.hpp
@@ -11,6 +11,83 @@
 
 namespace caffe {
 
+namespace database_internal {
+
+template <typename T>
+struct Coder {
+  static bool serialize(const T& obj, string* serialized) {
+    return obj.SerializeToString(serialized);
+  }
+
+  static bool serialize(const T& obj, vector<char>* serialized) {
+    serialized->resize(obj.ByteSize());
+    return obj.SerializeWithCachedSizesToArray(
+        reinterpret_cast<unsigned char*>(serialized->data()));
+  }
+
+  static bool deserialize(const string& serialized, T* obj) {
+    return obj->ParseFromString(serialized);
+  }
+
+  static bool deserialize(const char* data, size_t size, T* obj) {
+    return obj->ParseFromArray(data, size);
+  }
+};
+
+template <>
+struct Coder<string> {
+  static bool serialize(string obj, string* serialized) {
+    *serialized = obj;
+    return true;
+  }
+
+  static bool serialize(const string& obj, vector<char>* serialized) {
+    vector<char> temp(obj.data(), obj.data() + obj.size());
+    serialized->swap(temp);
+    return true;
+  }
+
+  static bool deserialize(const string& serialized, string* obj) {
+    *obj = serialized;
+    return true;
+  }
+
+  static bool deserialize(const char* data, size_t size, string* obj) {
+    string temp_string(data, size);
+    obj->swap(temp_string);
+    return true;
+  }
+};
+
+template <>
+struct Coder<vector<char> > {
+  static bool serialize(vector<char> obj, string* serialized) {
+    string tmp(obj.data(), obj.size());
+    serialized->swap(tmp);
+    return true;
+  }
+
+  static bool serialize(const vector<char>& obj, vector<char>* serialized) {
+    *serialized = obj;
+    return true;
+  }
+
+  static bool deserialize(const string& serialized, vector<char>* obj) {
+    vector<char> tmp(serialized.data(), serialized.data() + serialized.size());
+    obj->swap(tmp);
+    return true;
+  }
+
+  static bool deserialize(const char* data, size_t size, vector<char>* obj) {
+    vector<char> tmp(data, data + size);
+    obj->swap(tmp);
+    return true;
+  }
+};
+
+}  // namespace database_internal
+
+template <typename K, typename V>
 class Database {
  public:
   enum Mode {
@@ -19,21 +96,21 @@ class Database {
     ReadOnly
   };
 
-  typedef vector<char> key_type;
-  typedef vector<char> value_type;
+  typedef K key_type;
+  typedef V value_type;
 
   struct KV {
-    key_type key;
-    value_type value;
+    K key;
+    V value;
   };
 
   virtual bool open(const string& filename, Mode mode) = 0;
-  virtual bool put(const key_type& key, const value_type& value) = 0;
-  virtual bool get(const key_type& key, value_type* value) = 0;
+  virtual bool put(const K& key, const V& value) = 0;
+  virtual bool get(const K& key, V* value) = 0;
   virtual bool commit() = 0;
   virtual void close() = 0;
 
-  virtual void keys(vector<key_type>* keys) = 0;
+  virtual void keys(vector<K>* keys) = 0;
 
   Database() { }
   virtual ~Database() { }
@@ -123,8 +200,33 @@ class Database {
   virtual void increment(shared_ptr<DatabaseState>* state) const = 0;
   virtual KV& dereference(
       shared_ptr<DatabaseState> state) const = 0;
+
+  template <typename T>
+  static bool serialize(const T& obj, string* serialized) {
+    return database_internal::Coder<T>::serialize(obj, serialized);
+  }
+
+  template <typename T>
+  static bool serialize(const T& obj, vector<char>* serialized) {
+    return database_internal::Coder<T>::serialize(obj, serialized);
+  }
+
+  template <typename T>
+  static bool deserialize(const string& serialized, T* obj) {
+    return database_internal::Coder<T>::deserialize(serialized, obj);
+  }
+
+  template <typename T>
+  static bool deserialize(const char* data, size_t size, T* obj) {
+    return database_internal::Coder<T>::deserialize(data, size, obj);
+  }
 };
 
 }  // namespace caffe
 
+#define INSTANTIATE_DATABASE(type) \
+  template class type<string, string>; \
+  template class type<string, vector<char> >; \
+  template class type<string, Datum>;
+
 #endif  // CAFFE_DATABASE_H_
diff --git a/include/caffe/database_factory.hpp b/include/caffe/database_factory.hpp
index 91185e99b89..30f191e76b4 100644
--- a/include/caffe/database_factory.hpp
+++ b/include/caffe/database_factory.hpp
@@ -9,8 +9,11 @@
 
 namespace caffe {
 
-shared_ptr<Database> DatabaseFactory(const DataParameter_DB& type);
-shared_ptr<Database> DatabaseFactory(const string& type);
+template <typename K, typename V>
+shared_ptr<Database<K, V> > DatabaseFactory(const DataParameter_DB& type);
+
+template <typename K, typename V>
+shared_ptr<Database<K, V> > DatabaseFactory(const string& type);
 
 }  // namespace caffe
 
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_database.hpp
index 48cf11e06d4..cd966c440cc 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_database.hpp
@@ -13,15 +13,24 @@
 
 namespace caffe {
 
-class LeveldbDatabase : public Database {
+template <typename K, typename V>
+class LeveldbDatabase : public Database<K, V> {
  public:
+  typedef Database<K, V> Base;
+  typedef typename Base::key_type key_type;
+  typedef typename Base::value_type value_type;
+  typedef typename Base::DatabaseState DatabaseState;
+  typedef typename Base::Mode Mode;
+  typedef typename Base::const_iterator const_iterator;
+  typedef typename Base::KV KV;
+
   bool open(const string& filename, Mode mode);
-  bool put(const key_type& key, const value_type& value);
-  bool get(const key_type& key, value_type* value);
+  bool put(const K& key, const V& value);
+  bool get(const K& key, V* value);
   bool commit();
   void close();
 
-  void keys(vector<key_type>* keys);
+  void keys(vector<K>* keys);
 
   const_iterator begin() const;
   const_iterator cbegin() const;
@@ -29,11 +38,11 @@ class LeveldbDatabase : public Database {
   const_iterator cend() const;
 
  protected:
-  class LeveldbState : public Database::DatabaseState {
+  class LeveldbState : public DatabaseState {
    public:
     explicit LeveldbState(shared_ptr<leveldb::DB> db,
         shared_ptr<leveldb::Iterator> iter)
-        : Database::DatabaseState(),
+        : DatabaseState(),
           db_(db),
           iter_(iter) { }
 
@@ -66,7 +75,7 @@ class LeveldbDatabase : public Database {
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
   void increment(shared_ptr<DatabaseState>* state) const;
-  Database::KV& dereference(shared_ptr<DatabaseState> state) const;
+  KV& dereference(shared_ptr<DatabaseState> state) const;
 
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::WriteBatch> batch_;
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_database.hpp
index a8ce60dada5..b5a02ca46a7 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_database.hpp
@@ -12,20 +12,29 @@
 
 namespace caffe {
 
-class LmdbDatabase : public Database {
+template <typename K, typename V>
+class LmdbDatabase : public Database<K, V> {
  public:
+  typedef Database<K, V> Base;
+  typedef typename Base::key_type key_type;
+  typedef typename Base::value_type value_type;
+  typedef typename Base::DatabaseState DatabaseState;
+  typedef typename Base::Mode Mode;
+  typedef typename Base::const_iterator const_iterator;
+  typedef typename Base::KV KV;
+
   LmdbDatabase()
       : env_(NULL),
         dbi_(0),
         txn_(NULL) { }
 
   bool open(const string& filename, Mode mode);
-  bool put(const key_type& key, const value_type& value);
-  bool get(const key_type& key, value_type* value);
+  bool put(const K& key, const V& value);
+  bool get(const K& key, V* value);
   bool commit();
   void close();
 
-  void keys(vector<key_type>* keys);
+  void keys(vector<K>* keys);
 
   const_iterator begin() const;
   const_iterator cbegin() const;
@@ -33,10 +42,10 @@ class LmdbDatabase : public Database {
   const_iterator cend() const;
 
  protected:
-  class LmdbState : public Database::DatabaseState {
+  class LmdbState : public DatabaseState {
    public:
     explicit LmdbState(MDB_cursor* cursor, MDB_txn* txn, const MDB_dbi* dbi)
-        : Database::DatabaseState(),
+        : DatabaseState(),
           cursor_(cursor),
           txn_(txn),
           dbi_(dbi) { }
@@ -70,7 +79,7 @@ class LmdbDatabase : public Database {
   bool equal(shared_ptr<DatabaseState> state1,
       shared_ptr<DatabaseState> state2) const;
   void increment(shared_ptr<DatabaseState>* state) const;
-  Database::KV& dereference(shared_ptr<DatabaseState> state) const;
+  KV& dereference(shared_ptr<DatabaseState> state) const;
 
   MDB_env* env_;
   MDB_dbi dbi_;
diff --git a/src/caffe/database_factory.cpp b/src/caffe/database_factory.cpp
index 062de8c02e3..4ccd429ef97 100644
--- a/src/caffe/database_factory.cpp
+++ b/src/caffe/database_factory.cpp
@@ -1,5 +1,6 @@
 #include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/database_factory.hpp"
 #include "caffe/leveldb_database.hpp"
@@ -7,29 +8,43 @@
 
 namespace caffe {
 
-shared_ptr<Database> DatabaseFactory(const DataParameter_DB& type) {
+template <typename K, typename V>
+shared_ptr<Database<K, V> > DatabaseFactory(const DataParameter_DB& type) {
   switch (type) {
   case DataParameter_DB_LEVELDB:
-    return shared_ptr<Database>(new LeveldbDatabase());
+    return shared_ptr<Database<K, V> >(new LeveldbDatabase<K, V>());
   case DataParameter_DB_LMDB:
-    return shared_ptr<Database>(new LmdbDatabase());
+    return shared_ptr<Database<K, V> >(new LmdbDatabase<K, V>());
   default:
     LOG(FATAL) << "Unknown database type " << type;
-    return shared_ptr<Database>();
+    return shared_ptr<Database<K, V> >();
   }
 }
 
-shared_ptr<Database> DatabaseFactory(const string& type) {
+template <typename K, typename V>
+shared_ptr<Database<K, V> > DatabaseFactory(const string& type) {
   if ("leveldb" == type) {
-    return DatabaseFactory(DataParameter_DB_LEVELDB);
+    return DatabaseFactory<K, V>(DataParameter_DB_LEVELDB);
   } else if ("lmdb" == type) {
-    return DatabaseFactory(DataParameter_DB_LMDB);
+    return DatabaseFactory<K, V>(DataParameter_DB_LMDB);
   } else {
     LOG(FATAL) << "Unknown database type " << type;
-    return shared_ptr<Database>();
+    return shared_ptr<Database<K, V> >();
   }
 }
 
+#define REGISTER_DATABASE(key_type, value_type) \
+  template shared_ptr<Database<key_type, value_type> > \
+      DatabaseFactory(const string& type); \
+  template shared_ptr<Database<key_type, value_type> > \
+      DatabaseFactory(const DataParameter_DB& type); \
+
+REGISTER_DATABASE(string, string);
+REGISTER_DATABASE(string, vector<char>);
+REGISTER_DATABASE(string, Datum);
+
+#undef REGISTER_DATABASE
+
 }  // namespace caffe
 
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index c379d78d8f3..6296335276e 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -25,10 +25,11 @@ template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Initialize DB
-  database_ = DatabaseFactory(this->layer_param_.data_param().backend());
+  database_ = DatabaseFactory<string, Datum>(
+      this->layer_param_.data_param().backend());
   const string& source = this->layer_param_.data_param().source();
   LOG(INFO) << "Opening database " << source;
-  CHECK(database_->open(source, Database::ReadOnly));
+  CHECK(database_->open(source, Database<string, Datum>::ReadOnly));
   iter_ = database_->begin();
 
   // Check if we would need to randomly skip a few data points
@@ -44,8 +45,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // Read a data point, and use it to initialize the top blob.
   CHECK(iter_ != database_->end());
-  Datum datum;
-  datum.ParseFromArray(iter_->value.data(), iter_->value.size());
+  const Datum& datum = iter_->value;
 
   // image
   int crop_size = this->layer_param_.transform_param().crop_size();
@@ -89,9 +89,8 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   const int batch_size = this->layer_param_.data_param().batch_size();
 
   for (int item_id = 0; item_id < batch_size; ++item_id) {
-    Datum datum;
     CHECK(iter_ != database_->end());
-    datum.ParseFromArray(iter_->value.data(), iter_->value.size());
+    const Datum& datum = iter_->value;
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_database.cpp
index a163abc09af..267ddf07330 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_database.cpp
@@ -2,28 +2,30 @@
 #include <utility>
 #include <vector>
 
+#include "caffe/caffe.hpp"
 #include "caffe/leveldb_database.hpp"
 
 namespace caffe {
 
-bool LeveldbDatabase::open(const string& filename, Mode mode) {
+template <typename K, typename V>
+bool LeveldbDatabase<K, V>::open(const string& filename, Mode mode) {
   DLOG(INFO) << "LevelDB: Open " << filename;
 
   leveldb::Options options;
   switch (mode) {
-  case New:
+  case Base::New:
     DLOG(INFO) << " mode NEW";
     options.error_if_exists = true;
     options.create_if_missing = true;
     read_only_ = false;
     break;
-  case ReadWrite:
+  case Base::ReadWrite:
     DLOG(INFO) << " mode RW";
     options.error_if_exists = false;
     options.create_if_missing = true;
     read_only_ = false;
     break;
-  case ReadOnly:
+  case Base::ReadOnly:
     DLOG(INFO) << " mode RO";
     options.error_if_exists = false;
     options.create_if_missing = false;
@@ -52,7 +54,8 @@ bool LeveldbDatabase::open(const string& filename, Mode mode) {
   return true;
 }
 
-bool LeveldbDatabase::put(const key_type& key, const value_type& value) {
+template <typename K, typename V>
+bool LeveldbDatabase<K, V>::put(const K& key, const V& value) {
   DLOG(INFO) << "LevelDB: Put";
 
   if (read_only_) {
@@ -62,36 +65,48 @@ bool LeveldbDatabase::put(const key_type& key, const value_type& value) {
 
   CHECK_NOTNULL(batch_.get());
 
-  leveldb::Slice key_slice(key.data(), key.size());
-  leveldb::Slice value_slice(value.data(), value.size());
+  string serialized_key;
+  if (!Base::serialize(key, &serialized_key)) {
+    return false;
+  }
+
+  string serialized_value;
+  if (!Base::serialize(value, &serialized_value)) {
+    return false;
+  }
 
-  batch_->Put(key_slice, value_slice);
+  batch_->Put(serialized_key, serialized_value);
 
   return true;
 }
 
-bool LeveldbDatabase::get(const key_type& key, value_type* value) {
+template <typename K, typename V>
+bool LeveldbDatabase<K, V>::get(const K& key, V* value) {
   DLOG(INFO) << "LevelDB: Get";
 
-  leveldb::Slice key_slice(key.data(), key.size());
+  string serialized_key;
+  if (!Base::serialize(key, &serialized_key)) {
+    return false;
+  }
 
-  string value_string;
+  string serialized_value;
   leveldb::Status status =
-      db_->Get(leveldb::ReadOptions(), key_slice, &value_string);
+      db_->Get(leveldb::ReadOptions(), serialized_key, &serialized_value);
 
   if (!status.ok()) {
     LOG(ERROR) << "leveldb get failed";
     return false;
   }
 
-  Database::value_type temp_value(value_string.data(),
-      value_string.data() + value_string.size());
-  value->swap(temp_value);
+  if (!Base::deserialize(serialized_value, value)) {
+    return false;
+  }
 
   return true;
 }
 
-bool LeveldbDatabase::commit() {
+template <typename K, typename V>
+bool LeveldbDatabase<K, V>::commit() {
   DLOG(INFO) << "LevelDB: Commit";
 
   if (read_only_) {
@@ -109,23 +124,27 @@ bool LeveldbDatabase::commit() {
   return status.ok();
 }
 
-void LeveldbDatabase::close() {
+template <typename K, typename V>
+void LeveldbDatabase<K, V>::close() {
   DLOG(INFO) << "LevelDB: Close";
 
   batch_.reset();
   db_.reset();
 }
 
-void LeveldbDatabase::keys(vector<key_type>* keys) {
+template <typename K, typename V>
+void LeveldbDatabase<K, V>::keys(vector<K>* keys) {
   DLOG(INFO) << "LevelDB: Keys";
 
   keys->clear();
-  for (Database::const_iterator iter = begin(); iter != end(); ++iter) {
+  for (const_iterator iter = begin(); iter != end(); ++iter) {
     keys->push_back(iter->key);
   }
 }
 
-LeveldbDatabase::const_iterator LeveldbDatabase::begin() const {
+template <typename K, typename V>
+typename LeveldbDatabase<K, V>::const_iterator
+    LeveldbDatabase<K, V>::begin() const {
   CHECK_NOTNULL(db_.get());
   shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
   iter->SeekToFirst();
@@ -140,18 +159,25 @@ LeveldbDatabase::const_iterator LeveldbDatabase::begin() const {
   return const_iterator(this, state);
 }
 
-LeveldbDatabase::const_iterator LeveldbDatabase::end() const {
+template <typename K, typename V>
+typename LeveldbDatabase<K, V>::const_iterator
+    LeveldbDatabase<K, V>::end() const {
   shared_ptr<DatabaseState> state;
   return const_iterator(this, state);
 }
 
-LeveldbDatabase::const_iterator LeveldbDatabase::cbegin() const {
+template <typename K, typename V>
+typename LeveldbDatabase<K, V>::const_iterator
+    LeveldbDatabase<K, V>::cbegin() const {
   return begin();
 }
 
-LeveldbDatabase::const_iterator LeveldbDatabase::cend() const { return end(); }
+template <typename K, typename V>
+typename LeveldbDatabase<K, V>::const_iterator
+    LeveldbDatabase<K, V>::cend() const { return end(); }
 
-bool LeveldbDatabase::equal(shared_ptr<DatabaseState> state1,
+template <typename K, typename V>
+bool LeveldbDatabase<K, V>::equal(shared_ptr<DatabaseState> state1,
     shared_ptr<DatabaseState> state2) const {
   shared_ptr<LeveldbState> leveldb_state1 =
       boost::dynamic_pointer_cast<LeveldbState>(state1);
@@ -165,7 +191,8 @@ bool LeveldbDatabase::equal(shared_ptr<DatabaseState> state1,
   return !leveldb_state1 && !leveldb_state2;
 }
 
-void LeveldbDatabase::increment(shared_ptr<DatabaseState>* state) const {
+template <typename K, typename V>
+void LeveldbDatabase<K, V>::increment(shared_ptr<DatabaseState>* state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(*state);
 
@@ -182,7 +209,8 @@ void LeveldbDatabase::increment(shared_ptr<DatabaseState>* state) const {
   }
 }
 
-Database::KV& LeveldbDatabase::dereference(
+template <typename K, typename V>
+typename Database<K, V>::KV& LeveldbDatabase<K, V>::dereference(
     shared_ptr<DatabaseState> state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(state);
@@ -195,15 +223,16 @@ Database::KV& LeveldbDatabase::dereference(
 
   CHECK(iter->Valid());
 
-  Database::key_type temp_key(key_type(iter->key().data(),
-      iter->key().data() + iter->key().size()));
+  const leveldb::Slice& key = iter->key();
+  const leveldb::Slice& value = iter->value();
+  CHECK(Base::deserialize(key.data(), key.size(),
+      &leveldb_state->kv_pair_.key));
+  CHECK(Base::deserialize(value.data(), value.size(),
+      &leveldb_state->kv_pair_.value));
 
-  Database::value_type temp_value(value_type(iter->value().data(),
-      iter->value().data() + iter->value().size()));
-
-  leveldb_state->kv_pair_.key.swap(temp_key);
-  leveldb_state->kv_pair_.value.swap(temp_value);
   return leveldb_state->kv_pair_;
 }
 
+INSTANTIATE_DATABASE(LeveldbDatabase);
+
 }  // namespace caffe
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_database.cpp
index de94391ae7d..22a08b2a580 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_database.cpp
@@ -4,11 +4,13 @@
 #include <utility>
 #include <vector>
 
+#include "caffe/caffe.hpp"
 #include "caffe/lmdb_database.hpp"
 
 namespace caffe {
 
-bool LmdbDatabase::open(const string& filename, Mode mode) {
+template <typename K, typename V>
+bool LmdbDatabase<K, V>::open(const string& filename, Mode mode) {
   DLOG(INFO) << "LMDB: Open " << filename;
 
   CHECK(NULL == env_);
@@ -16,16 +18,16 @@ bool LmdbDatabase::open(const string& filename, Mode mode) {
   CHECK_EQ(0, dbi_);
 
   int retval;
-  if (mode != ReadOnly) {
+  if (mode != Base::ReadOnly) {
     retval = mkdir(filename.c_str(), 0744);
     switch (mode) {
-    case New:
+    case Base::New:
       if (0 != retval) {
         LOG(ERROR) << "mkdir " << filename << " failed";
         return false;
       }
       break;
-    case ReadWrite:
+    case Base::ReadWrite:
       if (-1 == retval && EEXIST != errno) {
         LOG(ERROR) << "mkdir " << filename << " failed ("
             << strerror(errno) << ")";
@@ -52,7 +54,7 @@ bool LmdbDatabase::open(const string& filename, Mode mode) {
 
   int flag1 = 0;
   int flag2 = 0;
-  if (mode == ReadOnly) {
+  if (mode == Base::ReadOnly) {
     flag1 = MDB_RDONLY | MDB_NOTLS;
     flag2 = MDB_RDONLY;
   }
@@ -78,18 +80,27 @@ bool LmdbDatabase::open(const string& filename, Mode mode) {
   return true;
 }
 
-bool LmdbDatabase::put(const key_type& key, const value_type& value) {
+template <typename K, typename V>
+bool LmdbDatabase<K, V>::put(const K& key, const V& value) {
   DLOG(INFO) << "LMDB: Put";
 
-  // MDB_val::mv_size is not const, so we need to make a local copy.
-  key_type local_key = key;
-  value_type local_value = value;
+  vector<char> serialized_key;
+  if (!Base::serialize(key, &serialized_key)) {
+    LOG(ERROR) << "failed to serialize key";
+    return false;
+  }
+
+  vector<char> serialized_value;
+  if (!Base::serialize(value, &serialized_value)) {
+    LOG(ERROR) << "failed to serialized value";
+    return false;
+  }
 
   MDB_val mdbkey, mdbdata;
-  mdbdata.mv_size = local_value.size();
-  mdbdata.mv_data = local_value.data();
-  mdbkey.mv_size = local_key.size();
-  mdbkey.mv_data = local_key.data();
+  mdbdata.mv_size = serialized_value.size();
+  mdbdata.mv_data = serialized_value.data();
+  mdbkey.mv_size = serialized_key.size();
+  mdbkey.mv_data = serialized_key.data();
 
   CHECK_NOTNULL(txn_);
   CHECK_NE(0, dbi_);
@@ -103,14 +114,19 @@ bool LmdbDatabase::put(const key_type& key, const value_type& value) {
   return true;
 }
 
-bool LmdbDatabase::get(const key_type& key, value_type* value) {
+template <typename K, typename V>
+bool LmdbDatabase<K, V>::get(const K& key, V* value) {
   DLOG(INFO) << "LMDB: Get";
 
-  key_type local_key = key;
+  vector<char> serialized_key;
+  if (!Base::serialize(key, &serialized_key)) {
+    LOG(ERROR) << "failed to serialized key";
+    return false;
+  }
 
   MDB_val mdbkey, mdbdata;
-  mdbkey.mv_data = local_key.data();
-  mdbkey.mv_size = local_key.size();
+  mdbkey.mv_data = serialized_key.data();
+  mdbkey.mv_size = serialized_key.size();
 
   int retval;
   MDB_txn* get_txn;
@@ -128,15 +144,17 @@ bool LmdbDatabase::get(const key_type& key, value_type* value) {
 
   mdb_txn_abort(get_txn);
 
-  Database::value_type temp_value(reinterpret_cast<char*>(mdbdata.mv_data),
-      reinterpret_cast<char*>(mdbdata.mv_data) + mdbdata.mv_size);
-
-  value->swap(temp_value);
+  if (!Base::deserialize(reinterpret_cast<char*>(mdbdata.mv_data),
+      mdbdata.mv_size, value)) {
+    LOG(ERROR) << "failed to deserialize value";
+    return false;
+  }
 
   return true;
 }
 
-bool LmdbDatabase::commit() {
+template <typename K, typename V>
+bool LmdbDatabase<K, V>::commit() {
   DLOG(INFO) << "LMDB: Commit";
 
   CHECK_NOTNULL(txn_);
@@ -157,7 +175,8 @@ bool LmdbDatabase::commit() {
   return true;
 }
 
-void LmdbDatabase::close() {
+template <typename K, typename V>
+void LmdbDatabase<K, V>::close() {
   DLOG(INFO) << "LMDB: Close";
 
   if (env_ && dbi_) {
@@ -169,16 +188,19 @@ void LmdbDatabase::close() {
   }
 }
 
-void LmdbDatabase::keys(vector<key_type>* keys) {
+template <typename K, typename V>
+void LmdbDatabase<K, V>::keys(vector<K>* keys) {
   DLOG(INFO) << "LMDB: Keys";
 
   keys->clear();
-  for (Database::const_iterator iter = begin(); iter != end(); ++iter) {
+  for (const_iterator iter = begin(); iter != end(); ++iter) {
     keys->push_back(iter->key);
   }
 }
 
-LmdbDatabase::const_iterator LmdbDatabase::begin() const {
+template <typename K, typename V>
+typename LmdbDatabase<K, V>::const_iterator
+    LmdbDatabase<K, V>::begin() const {
   int retval;
 
   MDB_txn* iter_txn;
@@ -204,15 +226,23 @@ LmdbDatabase::const_iterator LmdbDatabase::begin() const {
   return const_iterator(this, state);
 }
 
-LmdbDatabase::const_iterator LmdbDatabase::end() const {
+template <typename K, typename V>
+typename LmdbDatabase<K, V>::const_iterator
+    LmdbDatabase<K, V>::end() const {
   shared_ptr<DatabaseState> state;
   return const_iterator(this, state);
 }
 
-LmdbDatabase::const_iterator LmdbDatabase::cbegin() const { return begin(); }
-LmdbDatabase::const_iterator LmdbDatabase::cend() const { return end(); }
+template <typename K, typename V>
+typename LmdbDatabase<K, V>::const_iterator
+    LmdbDatabase<K, V>::cbegin() const { return begin(); }
 
-bool LmdbDatabase::equal(shared_ptr<DatabaseState> state1,
+template <typename K, typename V>
+typename LmdbDatabase<K, V>::const_iterator
+    LmdbDatabase<K, V>::cend() const { return end(); }
+
+template <typename K, typename V>
+bool LmdbDatabase<K, V>::equal(shared_ptr<DatabaseState> state1,
     shared_ptr<DatabaseState> state2) const {
   shared_ptr<LmdbState> lmdb_state1 =
       boost::dynamic_pointer_cast<LmdbState>(state1);
@@ -226,7 +256,8 @@ bool LmdbDatabase::equal(shared_ptr<DatabaseState> state1,
   return !lmdb_state1 && !lmdb_state2;
 }
 
-void LmdbDatabase::increment(shared_ptr<DatabaseState>* state) const {
+template <typename K, typename V>
+void LmdbDatabase<K, V>::increment(shared_ptr<DatabaseState>* state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(*state);
 
@@ -247,7 +278,9 @@ void LmdbDatabase::increment(shared_ptr<DatabaseState>* state) const {
   }
 }
 
-Database::KV& LmdbDatabase::dereference(shared_ptr<DatabaseState> state) const {
+template <typename K, typename V>
+typename Database<K, V>::KV& LmdbDatabase<K, V>::dereference(
+    shared_ptr<DatabaseState> state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(state);
 
@@ -262,18 +295,14 @@ Database::KV& LmdbDatabase::dereference(shared_ptr<DatabaseState> state) const {
   int retval = mdb_cursor_get(cursor, &mdb_key, &mdb_val, MDB_GET_CURRENT);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
-  char* key_data = reinterpret_cast<char*>(mdb_key.mv_data);
-  char* value_data = reinterpret_cast<char*>(mdb_val.mv_data);
-
-  Database::key_type temp_key(key_data, key_data + mdb_key.mv_size);
-
-  Database::value_type temp_value(value_data,
-      value_data + mdb_val.mv_size);
-
-  lmdb_state->kv_pair_.key.swap(temp_key);
-  lmdb_state->kv_pair_.value.swap(temp_value);
+  CHECK(Base::deserialize(reinterpret_cast<char*>(mdb_key.mv_data),
+      mdb_key.mv_size, &lmdb_state->kv_pair_.key));
+  CHECK(Base::deserialize(reinterpret_cast<char*>(mdb_val.mv_data),
+      mdb_val.mv_size, &lmdb_state->kv_pair_.value));
 
   return lmdb_state->kv_pair_;
 }
 
+INSTANTIATE_DATABASE(LmdbDatabase);
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 5f982063cc7..1c3ec1fc576 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -39,8 +39,9 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
   void Fill(const bool unique_pixels, DataParameter_DB backend) {
     backend_ = backend;
     LOG(INFO) << "Using temporary database " << *filename_;
-    shared_ptr<Database> database = DatabaseFactory(backend_);
-    CHECK(database->open(*filename_, Database::New));
+    shared_ptr<Database<string, Datum> > database =
+        DatabaseFactory<string, Datum>(backend_);
+    CHECK(database->open(*filename_, Database<string, Datum>::New));
     for (int i = 0; i < 5; ++i) {
       Datum datum;
       datum.set_label(i);
@@ -54,12 +55,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       }
       stringstream ss;
       ss << i;
-      string key_str = ss.str();
-      Database::key_type key(key_str.c_str(), key_str.c_str() + key_str.size());
-      Database::value_type value(datum.ByteSize());
-      datum.SerializeWithCachedSizesToArray(
-          reinterpret_cast<unsigned char*>(value.data()));
-      CHECK(database->put(key, value));
+      CHECK(database->put(ss.str(), datum));
     }
     CHECK(database->commit());
     database->close();
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
index 9c569106f10..9a7d6dec8c7 100644
--- a/src/caffe/test/test_database.cpp
+++ b/src/caffe/test/test_database.cpp
@@ -11,551 +11,303 @@
 
 namespace caffe {
 
-template <typename TypeParam>
-class DatabaseTest : public MultiDeviceTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  string DBName() {
-    string filename;
-    MakeTempDir(&filename);
-    filename += "/db";
-    return filename;
-  }
-
-  Database::key_type TestKey() {
-    const char* kKey = "hello";
-    Database::key_type key(kKey, kKey + 5);
-    return key;
-  }
-
-  Database::value_type TestValue() {
-    const char* kValue = "world";
-    Database::value_type value(kValue, kValue + 5);
-    return value;
-  }
-
-  Database::key_type TestAltKey() {
-    const char* kKey = "foo";
-    Database::key_type key(kKey, kKey + 3);
-    return key;
-  }
-
-  Database::value_type TestAltValue() {
-    const char* kValue = "bar";
-    Database::value_type value(kValue, kValue + 3);
-    return value;
-  }
-
-  template <typename T>
-  bool BufferEq(const T& buf1, const T& buf2) {
-    if (buf1.size() != buf2.size()) {
-      return false;
-    }
-    for (size_t i = 0; i < buf1.size(); ++i) {
-      if (buf1.at(i) != buf2.at(i)) {
-        return false;
-      }
-    }
+namespace DatabaseTest_internal {
 
-    return true;
-  }
+template <typename T>
+struct TestData {
+  static T TestValue();
+  static T TestAltValue();
+  static bool equals(const T& a, const T& b);
 };
 
-TYPED_TEST_CASE(DatabaseTest, TestDtypesAndDevices);
-
-TYPED_TEST(DatabaseTest, TestNewDoesntExistLevelDBPasses) {
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(this->DBName(), Database::New));
-  database->close();
+template <>
+string TestData<string>::TestValue() {
+  return "world";
 }
 
-TYPED_TEST(DatabaseTest, TestNewExistsFailsLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-  database->close();
-
-  EXPECT_FALSE(database->open(name, Database::New));
+template <>
+string TestData<string>::TestAltValue() {
+  return "bar";
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyExistsLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
-  database->close();
+template <>
+bool TestData<string>::equals(const string& a, const string& b) {
+  return a == b;
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFailsLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_FALSE(database->open(name, Database::ReadOnly));
-}
-
-TYPED_TEST(DatabaseTest, TestReadWriteExistsLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
-  database->close();
+template <>
+vector<char> TestData<vector<char> >::TestValue() {
+  string str = "world";
+  vector<char> val(str.data(), str.data() + str.size());
+  return val;
 }
 
-TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
-  database->close();
+template <>
+vector<char> TestData<vector<char> >::TestAltValue() {
+  string str = "bar";
+  vector<char> val(str.data(), str.data() + str.size());
+  return val;
 }
 
-TYPED_TEST(DatabaseTest, TestKeysLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key1 = this->TestKey();
-  Database::value_type value1 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-
-  Database::key_type key2 = this->TestAltKey();
-  Database::value_type value2 = this->TestAltValue();
-
-  EXPECT_TRUE(database->put(key2, value2));
-
-  EXPECT_TRUE(database->commit());
-
-  vector<Database::key_type> keys;
-  database->keys(&keys);
-
-  EXPECT_EQ(2, keys.size());
-
-  EXPECT_TRUE(this->BufferEq(keys.at(0), key1) ||
-      this->BufferEq(keys.at(0), key2));
-  EXPECT_TRUE(this->BufferEq(keys.at(1), key1) ||
-      this->BufferEq(keys.at(2), key2));
-  EXPECT_FALSE(this->BufferEq(keys.at(0), keys.at(1)));
-}
-
-TYPED_TEST(DatabaseTest, TestKeysNoCommitLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key1 = this->TestKey();
-  Database::value_type value1 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-
-  Database::key_type key2 = this->TestAltKey();
-  Database::value_type value2 = this->TestAltValue();
-
-  EXPECT_TRUE(database->put(key2, value2));
-
-  vector<Database::key_type> keys;
-  database->keys(&keys);
-
-  EXPECT_EQ(0, keys.size());
-}
-
-TYPED_TEST(DatabaseTest, TestIteratorsLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  const int kNumExamples = 4;
-  for (int i = 0; i < kNumExamples; ++i) {
-    stringstream ss;
-    ss << i;
-    string key = ss.str();
-    ss << " here be data";
-    string value = ss.str();
-    Database::key_type key_buf(key.data(), key.data() + key.size());
-    Database::value_type val_buf(value.data(), value.data() + value.size());
-    EXPECT_TRUE(database->put(key_buf, val_buf));
+template <>
+bool TestData<vector<char> >::equals(const vector<char>& a,
+    const vector<char>& b) {
+  if (a.size() != b.size()) {
+    return false;
   }
-  EXPECT_TRUE(database->commit());
-
-  int count = 0;
-  for (Database::const_iterator iter = database->begin();
-      iter != database->end(); ++iter) {
-    (void)iter;
-    ++count;
+  for (size_t i = 0; i < a.size(); ++i) {
+    if (a.at(i) != b.at(i)) {
+      return false;
+    }
   }
 
-  EXPECT_EQ(kNumExamples, count);
+  return true;
 }
 
-TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key1 = this->TestAltKey();
-  Database::value_type value1 = this->TestAltValue();
-
-  Database::key_type key2 = this->TestKey();
-  Database::value_type value2 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-  EXPECT_TRUE(database->put(key2, value2));
-  EXPECT_TRUE(database->commit());
-
-  Database::const_iterator iter1 = database->begin();
-
-  EXPECT_FALSE(database->end() == iter1);
-
-  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
-
-  Database::const_iterator iter2 = ++iter1;
-
-  EXPECT_FALSE(database->end() == iter1);
-  EXPECT_FALSE(database->end() == iter2);
-
-  EXPECT_TRUE(this->BufferEq(iter2->key, key2));
-
-  Database::const_iterator iter3 = ++iter2;
-
-  EXPECT_TRUE(database->end() == iter3);
-
-  database->close();
+template <>
+Datum TestData<Datum>::TestValue() {
+  Datum datum;
+  datum.set_channels(3);
+  datum.set_height(32);
+  datum.set_width(32);
+  datum.set_data(string(32 * 32 * 3 * 4, ' '));
+  datum.set_label(0);
+  return datum;
 }
 
-TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLevelDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key1 = this->TestAltKey();
-  Database::value_type value1 = this->TestAltValue();
-
-  Database::key_type key2 = this->TestKey();
-  Database::value_type value2 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-  EXPECT_TRUE(database->put(key2, value2));
-  EXPECT_TRUE(database->commit());
-
-  Database::const_iterator iter1 = database->begin();
-
-  EXPECT_FALSE(database->end() == iter1);
-
-  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
-
-  Database::const_iterator iter2 = iter1++;
-
-  EXPECT_FALSE(database->end() == iter1);
-  EXPECT_FALSE(database->end() == iter2);
-
-  EXPECT_TRUE(this->BufferEq(iter2->key, key1));
-  EXPECT_TRUE(this->BufferEq(iter1->key, key2));
-
-  Database::const_iterator iter3 = iter1++;
-
-  EXPECT_FALSE(database->end() == iter3);
-  EXPECT_TRUE(this->BufferEq(iter3->key, key2));
-  EXPECT_TRUE(database->end() == iter1);
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewPutLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, val));
-
-  EXPECT_TRUE(database->commit());
-
-  database->close();
+template <>
+Datum TestData<Datum>::TestAltValue() {
+  Datum datum;
+  datum.set_channels(1);
+  datum.set_height(64);
+  datum.set_width(64);
+  datum.set_data(string(64 * 64 * 1 * 4, ' '));
+  datum.set_label(1);
+  return datum;
 }
 
-TYPED_TEST(DatabaseTest, TestNewCommitLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
+template <>
+bool TestData<Datum>::equals(const Datum& a, const Datum& b) {
+  string serialized_a;
+  a.SerializeToString(&serialized_a);
 
-  EXPECT_TRUE(database->commit());
+  string serialized_b;
+  b.SerializeToString(&serialized_b);
 
-  database->close();
+  return serialized_a == serialized_b;
 }
 
-TYPED_TEST(DatabaseTest, TestNewGetLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, val));
-
-  EXPECT_TRUE(database->commit());
-
-  Database::value_type new_val;
-
-  EXPECT_TRUE(database->get(key, &new_val));
-
-  EXPECT_TRUE(this->BufferEq(val, new_val));
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewGetNoCommitLevelDBFails) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, val));
+}  // namespace DatabaseTest_internal
 
-  Database::value_type new_val;
-
-  EXPECT_FALSE(database->get(key, &new_val));
-}
+#define UNPACK_TYPES \
+  typedef typename TypeParam::value_type value_type; \
+  const DataParameter_DB backend = TypeParam::backend;
 
+template <typename TypeParam>
+class DatabaseTest : public ::testing::Test {
+ protected:
+  typedef typename TypeParam::value_type value_type;
 
-TYPED_TEST(DatabaseTest, TestReadWritePutLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
+  string DBName() {
+    string filename;
+    MakeTempDir(&filename);
+    filename += "/db";
+    return filename;
+  }
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string TestKey() {
+    return "hello";
+  }
 
-  EXPECT_TRUE(database->put(key, val));
+  value_type TestValue() {
+    return DatabaseTest_internal::TestData<value_type>::TestValue();
+  }
 
-  EXPECT_TRUE(database->commit());
+  string TestAltKey() {
+    return "foo";
+  }
 
-  database->close();
-}
+  value_type TestAltValue() {
+    return DatabaseTest_internal::TestData<value_type>::TestAltValue();
+  }
 
-TYPED_TEST(DatabaseTest, TestReadWriteCommitLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
+  template <typename T>
+  bool equals(const T& a, const T& b) {
+    return DatabaseTest_internal::TestData<T>::equals(a, b);
+  }
+};
 
-  EXPECT_TRUE(database->commit());
+struct StringLeveldb {
+  typedef string value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB StringLeveldb::backend = DataParameter_DB_LEVELDB;
 
-  database->close();
-}
+struct StringLmdb {
+  typedef string value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB StringLmdb::backend = DataParameter_DB_LEVELDB;
 
-TYPED_TEST(DatabaseTest, TestReadWriteGetLevelDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
+struct VectorLeveldb {
+  typedef vector<char> value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB VectorLeveldb::backend = DataParameter_DB_LEVELDB;
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+struct VectorLmdb {
+  typedef vector<char> value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB VectorLmdb::backend = DataParameter_DB_LEVELDB;
 
-  EXPECT_TRUE(database->put(key, val));
+struct DatumLeveldb {
+  typedef Datum value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB DatumLeveldb::backend = DataParameter_DB_LEVELDB;
 
-  EXPECT_TRUE(database->commit());
+struct DatumLmdb {
+  typedef Datum value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB DatumLmdb::backend = DataParameter_DB_LEVELDB;
 
-  Database::value_type new_val;
+typedef ::testing::Types<StringLeveldb, StringLmdb, VectorLeveldb, VectorLmdb,
+    DatumLeveldb, DatumLmdb> TestTypes;
 
-  EXPECT_TRUE(database->get(key, &new_val));
+TYPED_TEST_CASE(DatabaseTest, TestTypes);
 
-  EXPECT_TRUE(this->BufferEq(val, new_val));
+TYPED_TEST(DatabaseTest, TestNewDoesntExistPasses) {
+  UNPACK_TYPES;
 
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(this->DBName(),
+      Database<string, value_type>::New));
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLevelDBFails) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, val));
-
-  Database::value_type new_val;
-
-  EXPECT_FALSE(database->get(key, &new_val));
-}
+TYPED_TEST(DatabaseTest, TestNewExistsFails) {
+  UNPACK_TYPES;
 
-TYPED_TEST(DatabaseTest, TestReadOnlyPutLevelDBFails) {
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_FALSE(database->put(key, val));
+  EXPECT_FALSE(database->open(name, Database<string, value_type>::New));
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyCommitLevelDBFails) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
-
-  EXPECT_FALSE(database->commit());
-}
+TYPED_TEST(DatabaseTest, TestReadOnlyExistsPasses) {
+  UNPACK_TYPES;
 
-TYPED_TEST(DatabaseTest, TestReadOnlyGetLevelDBPasses) {
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, val));
-
-  EXPECT_TRUE(database->commit());
-
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
-
-  Database::value_type new_val;
-
-  EXPECT_TRUE(database->get(key, &new_val));
-
-  EXPECT_TRUE(this->BufferEq(val, new_val));
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLevelDBFails) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("leveldb");
-  EXPECT_TRUE(database->open(name, Database::New));
-
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, val));
-
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
   database->close();
-
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
-
-  Database::value_type new_val;
-
-  EXPECT_FALSE(database->get(key, &new_val));
 }
 
-TYPED_TEST(DatabaseTest, TestNewDoesntExistLMDBPasses) {
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(this->DBName(), Database::New));
-  database->close();
-}
+TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFails) {
+  UNPACK_TYPES;
 
-TYPED_TEST(DatabaseTest, TestNewExistsFailsLMDB) {
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
-  database->close();
-
-  EXPECT_FALSE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_FALSE(database->open(name, Database<string, value_type>::ReadOnly));
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyExistsLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestReadWriteExistsPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFailsLMDB) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_FALSE(database->open(name, Database::ReadOnly));
-}
+TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistPasses) {
+  UNPACK_TYPES;
 
-TYPED_TEST(DatabaseTest, TestReadWriteExistsLMDBPasses) {
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistLMDBPasses) {
-  string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
-  database->close();
-}
+TYPED_TEST(DatabaseTest, TestKeys) {
+  UNPACK_TYPES;
 
-TYPED_TEST(DatabaseTest, TestKeysLMDB) {
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key1 = this->TestKey();
-  Database::value_type value1 = this->TestValue();
+  string key1 = this->TestKey();
+  value_type value1 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
 
-  Database::key_type key2 = this->TestAltKey();
-  Database::value_type value2 = this->TestAltValue();
+  string key2 = this->TestAltKey();
+  value_type value2 = this->TestAltValue();
 
   EXPECT_TRUE(database->put(key2, value2));
 
   EXPECT_TRUE(database->commit());
 
-  vector<Database::key_type> keys;
+  vector<string> keys;
   database->keys(&keys);
 
   EXPECT_EQ(2, keys.size());
 
-  EXPECT_TRUE(this->BufferEq(keys.at(0), key1) ||
-      this->BufferEq(keys.at(0), key2));
-  EXPECT_TRUE(this->BufferEq(keys.at(1), key1) ||
-      this->BufferEq(keys.at(2), key2));
-  EXPECT_FALSE(this->BufferEq(keys.at(0), keys.at(1)));
+  EXPECT_TRUE(this->equals(keys.at(0), key1) ||
+      this->equals(keys.at(0), key2));
+  EXPECT_TRUE(this->equals(keys.at(1), key1) ||
+      this->equals(keys.at(2), key2));
+  EXPECT_FALSE(this->equals(keys.at(0), keys.at(1)));
 }
 
-TYPED_TEST(DatabaseTest, TestKeysNoCommitLMDB) {
+TYPED_TEST(DatabaseTest, TestKeysNoCommit) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key1 = this->TestKey();
-  Database::value_type value1 = this->TestValue();
+  string key1 = this->TestKey();
+  value_type value1 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
 
-  Database::key_type key2 = this->TestAltKey();
-  Database::value_type value2 = this->TestAltValue();
+  string key2 = this->TestAltKey();
+  value_type value2 = this->TestAltValue();
 
   EXPECT_TRUE(database->put(key2, value2));
 
-  vector<Database::key_type> keys;
+  vector<string> keys;
   database->keys(&keys);
 
   EXPECT_EQ(0, keys.size());
 }
 
+TYPED_TEST(DatabaseTest, TestIterators) {
+  UNPACK_TYPES;
 
-TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
   const int kNumExamples = 4;
   for (int i = 0; i < kNumExamples; ++i) {
@@ -563,16 +315,14 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
     ss << i;
     string key = ss.str();
     ss << " here be data";
-    string value = ss.str();
-    Database::key_type key_buf(key.data(), key.data() + key.size());
-    Database::value_type val_buf(value.data(), value.data() + value.size());
-    EXPECT_TRUE(database->put(key_buf, val_buf));
+    value_type value = this->TestValue();
+    EXPECT_TRUE(database->put(key, value));
   }
   EXPECT_TRUE(database->commit());
 
   int count = 0;
-  for (Database::const_iterator iter = database->begin();
-      iter != database->end(); ++iter) {
+  typedef typename Database<string, value_type>::const_iterator Iter;
+  for (Iter iter = database->begin(); iter != database->end(); ++iter) {
     (void)iter;
     ++count;
   }
@@ -580,266 +330,313 @@ TYPED_TEST(DatabaseTest, TestIteratorsLMDB) {
   EXPECT_EQ(kNumExamples, count);
 }
 
-TYPED_TEST(DatabaseTest, TestIteratorsPreIncrementLMDB) {
+TYPED_TEST(DatabaseTest, TestIteratorsPreIncrement) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key1 = this->TestAltKey();
-  Database::value_type value1 = this->TestAltValue();
+  string key1 = this->TestAltKey();
+  value_type value1 = this->TestAltValue();
 
-  Database::key_type key2 = this->TestKey();
-  Database::value_type value2 = this->TestValue();
+  string key2 = this->TestKey();
+  value_type value2 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
   EXPECT_TRUE(database->put(key2, value2));
   EXPECT_TRUE(database->commit());
 
-  Database::const_iterator iter1 = database->begin();
+  typename Database<string, value_type>::const_iterator iter1 =
+      database->begin();
 
   EXPECT_FALSE(database->end() == iter1);
 
-  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
+  EXPECT_TRUE(this->equals(iter1->key, key1));
 
-  Database::const_iterator iter2 = ++iter1;
+  typename Database<string, value_type>::const_iterator iter2 = ++iter1;
 
   EXPECT_FALSE(database->end() == iter1);
   EXPECT_FALSE(database->end() == iter2);
 
-  EXPECT_TRUE(this->BufferEq(iter2->key, key2));
+  EXPECT_TRUE(this->equals(iter2->key, key2));
 
-  Database::const_iterator iter3 = ++iter2;
+  typename Database<string, value_type>::const_iterator iter3 = ++iter2;
 
   EXPECT_TRUE(database->end() == iter3);
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestIteratorsPostIncrementLMDB) {
+TYPED_TEST(DatabaseTest, TestIteratorsPostIncrement) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key1 = this->TestAltKey();
-  Database::value_type value1 = this->TestAltValue();
+  string key1 = this->TestAltKey();
+  value_type value1 = this->TestAltValue();
 
-  Database::key_type key2 = this->TestKey();
-  Database::value_type value2 = this->TestValue();
+  string key2 = this->TestKey();
+  value_type value2 = this->TestValue();
 
   EXPECT_TRUE(database->put(key1, value1));
   EXPECT_TRUE(database->put(key2, value2));
   EXPECT_TRUE(database->commit());
 
-  Database::const_iterator iter1 = database->begin();
+  typename Database<string, value_type>::const_iterator iter1 =
+      database->begin();
 
   EXPECT_FALSE(database->end() == iter1);
 
-  EXPECT_TRUE(this->BufferEq(iter1->key, key1));
+  EXPECT_TRUE(this->equals(iter1->key, key1));
 
-  Database::const_iterator iter2 = iter1++;
+  typename Database<string, value_type>::const_iterator iter2 = iter1++;
 
   EXPECT_FALSE(database->end() == iter1);
   EXPECT_FALSE(database->end() == iter2);
 
-  EXPECT_TRUE(this->BufferEq(iter2->key, key1));
-  EXPECT_TRUE(this->BufferEq(iter1->key, key2));
+  EXPECT_TRUE(this->equals(iter2->key, key1));
+  EXPECT_TRUE(this->equals(iter1->key, key2));
 
-  Database::const_iterator iter3 = iter1++;
+  typename Database<string, value_type>::const_iterator iter3 = iter1++;
 
   EXPECT_FALSE(database->end() == iter3);
-  EXPECT_TRUE(this->BufferEq(iter3->key, key2));
+  EXPECT_TRUE(this->equals(iter3->key, key2));
   EXPECT_TRUE(database->end() == iter1);
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestNewPutLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestNewPutPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
   EXPECT_TRUE(database->commit());
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestNewCommitLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestNewCommitPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
   EXPECT_TRUE(database->commit());
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestNewGetLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestNewGetPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
   EXPECT_TRUE(database->commit());
 
-  Database::value_type new_val;
+  value_type new_value;
 
-  EXPECT_TRUE(database->get(key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_value));
 
-  EXPECT_TRUE(this->BufferEq(val, new_val));
+  EXPECT_TRUE(this->equals(value, new_value));
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestNewGetNoCommitLMDBFails) {
+TYPED_TEST(DatabaseTest, TestNewGetNoCommitFails) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
-  Database::value_type new_val;
+  value_type new_value;
 
-  EXPECT_FALSE(database->get(key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_value));
 }
 
-TYPED_TEST(DatabaseTest, TestReadWritePutLMDBPasses) {
+
+TYPED_TEST(DatabaseTest, TestReadWritePutPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
   EXPECT_TRUE(database->commit());
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestReadWriteCommitLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestReadWriteCommitPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::ReadWrite));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
 
   EXPECT_TRUE(database->commit());
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestReadWriteGetLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestReadWriteGetPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
   EXPECT_TRUE(database->commit());
 
-  Database::value_type new_val;
+  value_type new_value;
 
-  EXPECT_TRUE(database->get(key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_value));
 
-  EXPECT_TRUE(this->BufferEq(val, new_val));
+  EXPECT_TRUE(this->equals(value, new_value));
 
   database->close();
 }
 
-TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitLMDBFails) {
+TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitFails) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
-  Database::value_type new_val;
+  value_type new_value;
 
-  EXPECT_FALSE(database->get(key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_value));
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyPutLMDBFails) {
+TYPED_TEST(DatabaseTest, TestReadOnlyPutFails) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_FALSE(database->put(key, val));
+  EXPECT_FALSE(database->put(key, value));
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyCommitLMDBFails) {
+TYPED_TEST(DatabaseTest, TestReadOnlyCommitFails) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
 
   EXPECT_FALSE(database->commit());
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyGetLMDBPasses) {
+TYPED_TEST(DatabaseTest, TestReadOnlyGetPasses) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
   EXPECT_TRUE(database->commit());
 
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
 
-  Database::value_type new_val;
+  value_type new_value;
 
-  EXPECT_TRUE(database->get(key, &new_val));
+  EXPECT_TRUE(database->get(key, &new_value));
 
-  EXPECT_TRUE(this->BufferEq(val, new_val));
+  EXPECT_TRUE(this->equals(value, new_value));
 }
 
-TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitLMDBFails) {
+TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitFails) {
+  UNPACK_TYPES;
+
   string name = this->DBName();
-  shared_ptr<Database> database = DatabaseFactory("lmdb");
-  EXPECT_TRUE(database->open(name, Database::New));
+  shared_ptr<Database<string, value_type> > database =
+      DatabaseFactory<string, value_type>(backend);
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
 
-  Database::key_type key = this->TestKey();
-  Database::value_type val = this->TestValue();
+  string key = this->TestKey();
+  value_type value = this->TestValue();
 
-  EXPECT_TRUE(database->put(key, val));
+  EXPECT_TRUE(database->put(key, value));
 
   database->close();
 
-  EXPECT_TRUE(database->open(name, Database::ReadOnly));
+  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
 
-  Database::value_type new_val;
+  value_type new_value;
 
-  EXPECT_FALSE(database->get(key, &new_val));
+  EXPECT_FALSE(database->get(key, &new_value));
 }
 
+#undef UNPACK_TYPES
+
 }  // namespace caffe
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index d13c4a0f833..f1a79679091 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -26,18 +26,17 @@ int main(int argc, char** argv) {
     db_backend = std::string(argv[3]);
   }
 
-  caffe::shared_ptr<Database> database = caffe::DatabaseFactory(db_backend);
+  caffe::shared_ptr<Database<std::string, Datum> > database =
+      caffe::DatabaseFactory<std::string, Datum>(db_backend);
 
   // Open db
-  CHECK(database->open(argv[1], Database::ReadOnly));
+  CHECK(database->open(argv[1], Database<std::string, Datum>::ReadOnly));
 
-  Datum datum;
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  Database::const_iterator iter = database->begin();
-  const Database::value_type& first_blob = iter->value;
-  datum.ParseFromArray(first_blob.data(), first_blob.size());
+  Database<std::string, Datum>::const_iterator iter = database->begin();
+  const Datum& datum = iter->value;
 
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());
@@ -50,11 +49,10 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  for (Database::const_iterator iter = database->begin();
+  for (Database<std::string, Datum>::const_iterator iter = database->begin();
       iter != database->end(); ++iter) {
     // just a dummy operation
-    const Database::value_type& blob = iter->value;
-    datum.ParseFromArray(blob.data(), blob.size());
+    const Datum& datum = iter->value;
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
         datum.float_data_size());
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 1cdca7e015a..2ba3e3c7250 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -78,10 +78,11 @@ int main(int argc, char** argv) {
   int resize_width = std::max<int>(0, FLAGS_resize_width);
 
   // Open new db
-  shared_ptr<Database> database = DatabaseFactory(db_backend);
+  shared_ptr<Database<string, Datum> > database =
+      DatabaseFactory<string, Datum>(db_backend);
 
   // Open db
-  CHECK(database->open(db_path, Database::New));
+  CHECK(database->open(db_path, Database<string, Datum>::New));
 
   // Storing to db
   std::string root_folder(argv[1]);
@@ -110,13 +111,9 @@ int main(int argc, char** argv) {
     // sequential
     int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
-    Database::value_type value(datum.ByteSize());
-    datum.SerializeWithCachedSizesToArray(
-        reinterpret_cast<unsigned char*>(value.data()));
-    Database::key_type keystr(key_cstr, key_cstr + length);
 
     // Put in db
-    CHECK(database->put(keystr, value));
+    CHECK(database->put(string(key_cstr, length), datum));
 
     if (++count % 1000 == 0) {
       // Commit txn
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 1340192cbe4..47565a83025 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -121,11 +121,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
-  std::vector<shared_ptr<Database> > feature_dbs;
+  std::vector<shared_ptr<Database<std::string, Datum> > > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening database " << database_names[i];
-    shared_ptr<Database> database = DatabaseFactory(argv[++arg_pos]);
-    CHECK(database->open(database_names.at(i), Database::New));
+    shared_ptr<Database<std::string, Datum> > database =
+        DatabaseFactory<std::string, Datum>(argv[++arg_pos]);
+    CHECK(database->open(database_names.at(i),
+        Database<std::string, Datum>::New));
     feature_dbs.push_back(database);
   }
 
@@ -155,13 +157,9 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        Database::value_type value(datum.ByteSize());
-        datum.SerializeWithCachedSizesToArray(
-            reinterpret_cast<unsigned char*>(value.data()));
         int length = snprintf(key_str, kMaxKeyStrLength, "%d",
             image_indices[i]);
-        Database::key_type key(key_str, key_str + length);
-        CHECK(feature_dbs.at(i)->put(key, value));
+        CHECK(feature_dbs.at(i)->put(std::string(key_str, length), datum));
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           CHECK(feature_dbs.at(i)->commit());

From d275f77332e1e4a1457a520ee3deaf4d126ddfdf Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Mon, 13 Oct 2014 14:54:48 -0400
Subject: [PATCH 0936/2053] Renamed Database interface to Dataset.

---
 examples/cifar10/convert_cifar_data.cpp       |  34 +-
 include/caffe/data_layers.hpp                 |   6 +-
 include/caffe/database_factory.hpp            |  20 -
 include/caffe/{database.hpp => dataset.hpp}   |  50 +-
 include/caffe/dataset_factory.hpp             |  20 +
 ...veldb_database.hpp => leveldb_dataset.hpp} |  30 +-
 .../{lmdb_database.hpp => lmdb_dataset.hpp}   |  32 +-
 src/caffe/database_factory.cpp                |  50 --
 src/caffe/dataset_factory.cpp                 |  50 ++
 src/caffe/layers/data_layer.cpp               |  26 +-
 ...veldb_database.cpp => leveldb_dataset.cpp} |  50 +-
 .../{lmdb_database.cpp => lmdb_dataset.cpp}   |  46 +-
 src/caffe/test/test_data_layer.cpp            |  20 +-
 src/caffe/test/test_database.cpp              | 642 ------------------
 src/caffe/test/test_dataset.cpp               | 642 ++++++++++++++++++
 tools/compute_image_mean.cpp                  |  18 +-
 tools/convert_imageset.cpp                    |  16 +-
 tools/extract_features.cpp                    |  35 +-
 18 files changed, 893 insertions(+), 894 deletions(-)
 delete mode 100644 include/caffe/database_factory.hpp
 rename include/caffe/{database.hpp => dataset.hpp} (81%)
 create mode 100644 include/caffe/dataset_factory.hpp
 rename include/caffe/{leveldb_database.hpp => leveldb_dataset.hpp} (70%)
 rename include/caffe/{lmdb_database.hpp => lmdb_dataset.hpp} (71%)
 delete mode 100644 src/caffe/database_factory.cpp
 create mode 100644 src/caffe/dataset_factory.cpp
 rename src/caffe/{leveldb_database.cpp => leveldb_dataset.cpp} (78%)
 rename src/caffe/{lmdb_database.cpp => lmdb_dataset.cpp} (85%)
 delete mode 100644 src/caffe/test/test_database.cpp
 create mode 100644 src/caffe/test/test_dataset.cpp

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index f86f3936a35..9eecc74c989 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -13,13 +13,13 @@
 #include "google/protobuf/text_format.h"
 #include "stdint.h"
 
-#include "caffe/database_factory.hpp"
+#include "caffe/dataset_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 using std::string;
 
-using caffe::Database;
-using caffe::DatabaseFactory;
+using caffe::Dataset;
+using caffe::DatasetFactory;
 using caffe::Datum;
 using caffe::shared_ptr;
 
@@ -38,10 +38,10 @@ void read_image(std::ifstream* file, int* label, char* buffer) {
 
 void convert_dataset(const string& input_folder, const string& output_folder,
     const string& db_type) {
-  shared_ptr<Database<string, Datum> > train_database =
-      DatabaseFactory<string, Datum>(db_type);
-  CHECK(train_database->open(output_folder + "/cifar10_train_" + db_type,
-      Database<string, Datum>::New));
+  shared_ptr<Dataset<string, Datum> > train_dataset =
+      DatasetFactory<string, Datum>(db_type);
+  CHECK(train_dataset->open(output_folder + "/cifar10_train_" + db_type,
+      Dataset<string, Datum>::New));
   // Data buffer
   int label;
   char str_buffer[kCIFARImageNBytes];
@@ -64,17 +64,17 @@ void convert_dataset(const string& input_folder, const string& output_folder,
       datum.set_data(str_buffer, kCIFARImageNBytes);
       int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
-      CHECK(train_database->put(string(str_buffer, length), datum));
+      CHECK(train_dataset->put(string(str_buffer, length), datum));
     }
   }
-  CHECK(train_database->commit());
-  train_database->close();
+  CHECK(train_dataset->commit());
+  train_dataset->close();
 
   LOG(INFO) << "Writing Testing data";
-  shared_ptr<Database<string, Datum> > test_database =
-      DatabaseFactory<string, Datum>(db_type);
-  CHECK(test_database->open(output_folder + "/cifar10_test_" + db_type,
-      Database<string, Datum>::New));
+  shared_ptr<Dataset<string, Datum> > test_dataset =
+      DatasetFactory<string, Datum>(db_type);
+  CHECK(test_dataset->open(output_folder + "/cifar10_test_" + db_type,
+      Dataset<string, Datum>::New));
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
@@ -84,10 +84,10 @@ void convert_dataset(const string& input_folder, const string& output_folder,
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
     int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
-    CHECK(test_database->put(string(str_buffer, length), datum));
+    CHECK(test_dataset->put(string(str_buffer, length), datum));
   }
-  CHECK(test_database->commit());
-  test_database->close();
+  CHECK(test_dataset->commit());
+  test_dataset->close();
 }
 
 int main(int argc, char** argv) {
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 13ba4e6f8b3..a2ea854e5ff 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -11,7 +11,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/data_transformer.hpp"
-#include "caffe/database.hpp"
+#include "caffe/dataset.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
@@ -100,8 +100,8 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  protected:
   virtual void InternalThreadEntry();
 
-  shared_ptr<Database<string, Datum> > database_;
-  Database<string, Datum>::const_iterator iter_;
+  shared_ptr<Dataset<string, Datum> > dataset_;
+  Dataset<string, Datum>::const_iterator iter_;
 };
 
 /**
diff --git a/include/caffe/database_factory.hpp b/include/caffe/database_factory.hpp
deleted file mode 100644
index 30f191e76b4..00000000000
--- a/include/caffe/database_factory.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-#ifndef CAFFE_DATABASE_FACTORY_H_
-#define CAFFE_DATABASE_FACTORY_H_
-
-#include <string>
-
-#include "caffe/common.hpp"
-#include "caffe/database.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-template <typename K, typename V>
-shared_ptr<Database<K, V> > DatabaseFactory(const DataParameter_DB& type);
-
-template <typename K, typename V>
-shared_ptr<Database<K, V> > DatabaseFactory(const string& type);
-
-}  // namespace caffe
-
-#endif  // CAFFE_DATABASE_FACTORY_H_
diff --git a/include/caffe/database.hpp b/include/caffe/dataset.hpp
similarity index 81%
rename from include/caffe/database.hpp
rename to include/caffe/dataset.hpp
index 711ebdd3fa0..1f07aa9d18d 100644
--- a/include/caffe/database.hpp
+++ b/include/caffe/dataset.hpp
@@ -1,5 +1,5 @@
-#ifndef CAFFE_DATABASE_H_
-#define CAFFE_DATABASE_H_
+#ifndef CAFFE_DATASET_H_
+#define CAFFE_DATASET_H_
 
 #include <algorithm>
 #include <iterator>
@@ -11,7 +11,7 @@
 
 namespace caffe {
 
-namespace database_internal {
+namespace dataset_internal {
 
 template <typename T>
 struct Coder {
@@ -85,10 +85,10 @@ struct Coder<vector<char> > {
   }
 };
 
-}  // namespace database_internal
+}  // namespace dataset_internal
 
 template <typename K, typename V>
-class Database {
+class Dataset {
  public:
   enum Mode {
     New,
@@ -112,8 +112,8 @@ class Database {
 
   virtual void keys(vector<K>* keys) = 0;
 
-  Database() { }
-  virtual ~Database() { }
+  Dataset() { }
+  virtual ~Dataset() { }
 
   class iterator;
   typedef iterator const_iterator;
@@ -124,7 +124,7 @@ class Database {
   virtual const_iterator cend() const = 0;
 
  protected:
-  class DatabaseState;
+  class DatasetState;
 
  public:
   class iterator : public std::iterator<std::forward_iterator_tag, KV> {
@@ -137,7 +137,7 @@ class Database {
     iterator()
         : parent_(NULL) { }
 
-    iterator(const Database* parent, shared_ptr<DatabaseState> state)
+    iterator(const Dataset* parent, shared_ptr<DatasetState> state)
         : parent_(parent),
           state_(state) { }
     ~iterator() { }
@@ -145,7 +145,7 @@ class Database {
     iterator(const iterator& other)
         : parent_(other.parent_),
           state_(other.state_ ? other.state_->clone()
-              : shared_ptr<DatabaseState>()) { }
+              : shared_ptr<DatasetState>()) { }
 
     iterator& operator=(iterator copy) {
       copy.swap(*this);
@@ -184,49 +184,49 @@ class Database {
     }
 
    protected:
-    const Database* parent_;
-    shared_ptr<DatabaseState> state_;
+    const Dataset* parent_;
+    shared_ptr<DatasetState> state_;
   };
 
  protected:
-  class DatabaseState {
+  class DatasetState {
    public:
-    virtual ~DatabaseState() { }
-    virtual shared_ptr<DatabaseState> clone() = 0;
+    virtual ~DatasetState() { }
+    virtual shared_ptr<DatasetState> clone() = 0;
   };
 
-  virtual bool equal(shared_ptr<DatabaseState> state1,
-      shared_ptr<DatabaseState> state2) const = 0;
-  virtual void increment(shared_ptr<DatabaseState>* state) const = 0;
+  virtual bool equal(shared_ptr<DatasetState> state1,
+      shared_ptr<DatasetState> state2) const = 0;
+  virtual void increment(shared_ptr<DatasetState>* state) const = 0;
   virtual KV& dereference(
-      shared_ptr<DatabaseState> state) const = 0;
+      shared_ptr<DatasetState> state) const = 0;
 
   template <typename T>
   static bool serialize(const T& obj, string* serialized) {
-    return database_internal::Coder<T>::serialize(obj, serialized);
+    return dataset_internal::Coder<T>::serialize(obj, serialized);
   }
 
   template <typename T>
   static bool serialize(const T& obj, vector<char>* serialized) {
-    return database_internal::Coder<T>::serialize(obj, serialized);
+    return dataset_internal::Coder<T>::serialize(obj, serialized);
   }
 
   template <typename T>
   static bool deserialize(const string& serialized, T* obj) {
-    return database_internal::Coder<T>::deserialize(serialized, obj);
+    return dataset_internal::Coder<T>::deserialize(serialized, obj);
   }
 
   template <typename T>
   static bool deserialize(const char* data, size_t size, T* obj) {
-    return database_internal::Coder<T>::deserialize(data, size, obj);
+    return dataset_internal::Coder<T>::deserialize(data, size, obj);
   }
 };
 
 }  // namespace caffe
 
-#define INSTANTIATE_DATABASE(type) \
+#define INSTANTIATE_DATASET(type) \
   template class type<string, string>; \
   template class type<string, vector<char> >; \
   template class type<string, Datum>;
 
-#endif  // CAFFE_DATABASE_H_
+#endif  // CAFFE_DATASET_H_
diff --git a/include/caffe/dataset_factory.hpp b/include/caffe/dataset_factory.hpp
new file mode 100644
index 00000000000..57db49bf524
--- /dev/null
+++ b/include/caffe/dataset_factory.hpp
@@ -0,0 +1,20 @@
+#ifndef CAFFE_DATASET_FACTORY_H_
+#define CAFFE_DATASET_FACTORY_H_
+
+#include <string>
+
+#include "caffe/common.hpp"
+#include "caffe/dataset.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename K, typename V>
+shared_ptr<Dataset<K, V> > DatasetFactory(const DataParameter_DB& type);
+
+template <typename K, typename V>
+shared_ptr<Dataset<K, V> > DatasetFactory(const string& type);
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATASET_FACTORY_H_
diff --git a/include/caffe/leveldb_database.hpp b/include/caffe/leveldb_dataset.hpp
similarity index 70%
rename from include/caffe/leveldb_database.hpp
rename to include/caffe/leveldb_dataset.hpp
index cd966c440cc..eb354d95db3 100644
--- a/include/caffe/leveldb_database.hpp
+++ b/include/caffe/leveldb_dataset.hpp
@@ -1,5 +1,5 @@
-#ifndef CAFFE_LEVELDB_DATABASE_H_
-#define CAFFE_LEVELDB_DATABASE_H_
+#ifndef CAFFE_LEVELDB_DATASET_H_
+#define CAFFE_LEVELDB_DATASET_H_
 
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
@@ -9,17 +9,17 @@
 #include <vector>
 
 #include "caffe/common.hpp"
-#include "caffe/database.hpp"
+#include "caffe/dataset.hpp"
 
 namespace caffe {
 
 template <typename K, typename V>
-class LeveldbDatabase : public Database<K, V> {
+class LeveldbDataset : public Dataset<K, V> {
  public:
-  typedef Database<K, V> Base;
+  typedef Dataset<K, V> Base;
   typedef typename Base::key_type key_type;
   typedef typename Base::value_type value_type;
-  typedef typename Base::DatabaseState DatabaseState;
+  typedef typename Base::DatasetState DatasetState;
   typedef typename Base::Mode Mode;
   typedef typename Base::const_iterator const_iterator;
   typedef typename Base::KV KV;
@@ -38,11 +38,11 @@ class LeveldbDatabase : public Database<K, V> {
   const_iterator cend() const;
 
  protected:
-  class LeveldbState : public DatabaseState {
+  class LeveldbState : public DatasetState {
    public:
     explicit LeveldbState(shared_ptr<leveldb::DB> db,
         shared_ptr<leveldb::Iterator> iter)
-        : DatabaseState(),
+        : DatasetState(),
           db_(db),
           iter_(iter) { }
 
@@ -54,7 +54,7 @@ class LeveldbDatabase : public Database<K, V> {
       db_.reset();
     }
 
-    shared_ptr<DatabaseState> clone() {
+    shared_ptr<DatasetState> clone() {
       shared_ptr<leveldb::Iterator> new_iter;
 
       if (iter_.get()) {
@@ -64,7 +64,7 @@ class LeveldbDatabase : public Database<K, V> {
         CHECK(new_iter->Valid());
       }
 
-      return shared_ptr<DatabaseState>(new LeveldbState(db_, new_iter));
+      return shared_ptr<DatasetState>(new LeveldbState(db_, new_iter));
     }
 
     shared_ptr<leveldb::DB> db_;
@@ -72,10 +72,10 @@ class LeveldbDatabase : public Database<K, V> {
     KV kv_pair_;
   };
 
-  bool equal(shared_ptr<DatabaseState> state1,
-      shared_ptr<DatabaseState> state2) const;
-  void increment(shared_ptr<DatabaseState>* state) const;
-  KV& dereference(shared_ptr<DatabaseState> state) const;
+  bool equal(shared_ptr<DatasetState> state1,
+      shared_ptr<DatasetState> state2) const;
+  void increment(shared_ptr<DatasetState>* state) const;
+  KV& dereference(shared_ptr<DatasetState> state) const;
 
   shared_ptr<leveldb::DB> db_;
   shared_ptr<leveldb::WriteBatch> batch_;
@@ -84,4 +84,4 @@ class LeveldbDatabase : public Database<K, V> {
 
 }  // namespace caffe
 
-#endif  // CAFFE_LEVELDB_DATABASE_H_
+#endif  // CAFFE_LEVELDB_DATASET_H_
diff --git a/include/caffe/lmdb_database.hpp b/include/caffe/lmdb_dataset.hpp
similarity index 71%
rename from include/caffe/lmdb_database.hpp
rename to include/caffe/lmdb_dataset.hpp
index b5a02ca46a7..57da5dea829 100644
--- a/include/caffe/lmdb_database.hpp
+++ b/include/caffe/lmdb_dataset.hpp
@@ -1,5 +1,5 @@
-#ifndef CAFFE_LMDB_DATABASE_H_
-#define CAFFE_LMDB_DATABASE_H_
+#ifndef CAFFE_LMDB_DATASET_H_
+#define CAFFE_LMDB_DATASET_H_
 
 #include <string>
 #include <utility>
@@ -8,22 +8,22 @@
 #include "lmdb.h"
 
 #include "caffe/common.hpp"
-#include "caffe/database.hpp"
+#include "caffe/dataset.hpp"
 
 namespace caffe {
 
 template <typename K, typename V>
-class LmdbDatabase : public Database<K, V> {
+class LmdbDataset : public Dataset<K, V> {
  public:
-  typedef Database<K, V> Base;
+  typedef Dataset<K, V> Base;
   typedef typename Base::key_type key_type;
   typedef typename Base::value_type value_type;
-  typedef typename Base::DatabaseState DatabaseState;
+  typedef typename Base::DatasetState DatasetState;
   typedef typename Base::Mode Mode;
   typedef typename Base::const_iterator const_iterator;
   typedef typename Base::KV KV;
 
-  LmdbDatabase()
+  LmdbDataset()
       : env_(NULL),
         dbi_(0),
         txn_(NULL) { }
@@ -42,15 +42,15 @@ class LmdbDatabase : public Database<K, V> {
   const_iterator cend() const;
 
  protected:
-  class LmdbState : public DatabaseState {
+  class LmdbState : public DatasetState {
    public:
     explicit LmdbState(MDB_cursor* cursor, MDB_txn* txn, const MDB_dbi* dbi)
-        : DatabaseState(),
+        : DatasetState(),
           cursor_(cursor),
           txn_(txn),
           dbi_(dbi) { }
 
-    shared_ptr<DatabaseState> clone() {
+    shared_ptr<DatasetState> clone() {
       MDB_cursor* new_cursor;
 
       if (cursor_) {
@@ -67,7 +67,7 @@ class LmdbDatabase : public Database<K, V> {
         new_cursor = cursor_;
       }
 
-      return shared_ptr<DatabaseState>(new LmdbState(new_cursor, txn_, dbi_));
+      return shared_ptr<DatasetState>(new LmdbState(new_cursor, txn_, dbi_));
     }
 
     MDB_cursor* cursor_;
@@ -76,10 +76,10 @@ class LmdbDatabase : public Database<K, V> {
     KV kv_pair_;
   };
 
-  bool equal(shared_ptr<DatabaseState> state1,
-      shared_ptr<DatabaseState> state2) const;
-  void increment(shared_ptr<DatabaseState>* state) const;
-  KV& dereference(shared_ptr<DatabaseState> state) const;
+  bool equal(shared_ptr<DatasetState> state1,
+      shared_ptr<DatasetState> state2) const;
+  void increment(shared_ptr<DatasetState>* state) const;
+  KV& dereference(shared_ptr<DatasetState> state) const;
 
   MDB_env* env_;
   MDB_dbi dbi_;
@@ -88,4 +88,4 @@ class LmdbDatabase : public Database<K, V> {
 
 }  // namespace caffe
 
-#endif  // CAFFE_LMDB_DATABASE_H_
+#endif  // CAFFE_LMDB_DATASET_H_
diff --git a/src/caffe/database_factory.cpp b/src/caffe/database_factory.cpp
deleted file mode 100644
index 4ccd429ef97..00000000000
--- a/src/caffe/database_factory.cpp
+++ /dev/null
@@ -1,50 +0,0 @@
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/database_factory.hpp"
-#include "caffe/leveldb_database.hpp"
-#include "caffe/lmdb_database.hpp"
-
-namespace caffe {
-
-template <typename K, typename V>
-shared_ptr<Database<K, V> > DatabaseFactory(const DataParameter_DB& type) {
-  switch (type) {
-  case DataParameter_DB_LEVELDB:
-    return shared_ptr<Database<K, V> >(new LeveldbDatabase<K, V>());
-  case DataParameter_DB_LMDB:
-    return shared_ptr<Database<K, V> >(new LmdbDatabase<K, V>());
-  default:
-    LOG(FATAL) << "Unknown database type " << type;
-    return shared_ptr<Database<K, V> >();
-  }
-}
-
-template <typename K, typename V>
-shared_ptr<Database<K, V> > DatabaseFactory(const string& type) {
-  if ("leveldb" == type) {
-    return DatabaseFactory<K, V>(DataParameter_DB_LEVELDB);
-  } else if ("lmdb" == type) {
-    return DatabaseFactory<K, V>(DataParameter_DB_LMDB);
-  } else {
-    LOG(FATAL) << "Unknown database type " << type;
-    return shared_ptr<Database<K, V> >();
-  }
-}
-
-#define REGISTER_DATABASE(key_type, value_type) \
-  template shared_ptr<Database<key_type, value_type> > \
-      DatabaseFactory(const string& type); \
-  template shared_ptr<Database<key_type, value_type> > \
-      DatabaseFactory(const DataParameter_DB& type); \
-
-REGISTER_DATABASE(string, string);
-REGISTER_DATABASE(string, vector<char>);
-REGISTER_DATABASE(string, Datum);
-
-#undef REGISTER_DATABASE
-
-}  // namespace caffe
-
-
diff --git a/src/caffe/dataset_factory.cpp b/src/caffe/dataset_factory.cpp
new file mode 100644
index 00000000000..3313de3c408
--- /dev/null
+++ b/src/caffe/dataset_factory.cpp
@@ -0,0 +1,50 @@
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/dataset_factory.hpp"
+#include "caffe/leveldb_dataset.hpp"
+#include "caffe/lmdb_dataset.hpp"
+
+namespace caffe {
+
+template <typename K, typename V>
+shared_ptr<Dataset<K, V> > DatasetFactory(const DataParameter_DB& type) {
+  switch (type) {
+  case DataParameter_DB_LEVELDB:
+    return shared_ptr<Dataset<K, V> >(new LeveldbDataset<K, V>());
+  case DataParameter_DB_LMDB:
+    return shared_ptr<Dataset<K, V> >(new LmdbDataset<K, V>());
+  default:
+    LOG(FATAL) << "Unknown dataset type " << type;
+    return shared_ptr<Dataset<K, V> >();
+  }
+}
+
+template <typename K, typename V>
+shared_ptr<Dataset<K, V> > DatasetFactory(const string& type) {
+  if ("leveldb" == type) {
+    return DatasetFactory<K, V>(DataParameter_DB_LEVELDB);
+  } else if ("lmdb" == type) {
+    return DatasetFactory<K, V>(DataParameter_DB_LMDB);
+  } else {
+    LOG(FATAL) << "Unknown dataset type " << type;
+    return shared_ptr<Dataset<K, V> >();
+  }
+}
+
+#define REGISTER_DATASET(key_type, value_type) \
+  template shared_ptr<Dataset<key_type, value_type> > \
+      DatasetFactory(const string& type); \
+  template shared_ptr<Dataset<key_type, value_type> > \
+      DatasetFactory(const DataParameter_DB& type); \
+
+REGISTER_DATASET(string, string);
+REGISTER_DATASET(string, vector<char>);
+REGISTER_DATASET(string, Datum);
+
+#undef REGISTER_DATASET
+
+}  // namespace caffe
+
+
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 6296335276e..fcf9bb20a57 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -5,7 +5,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
-#include "caffe/database_factory.hpp"
+#include "caffe/dataset_factory.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
@@ -17,20 +17,20 @@ namespace caffe {
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
-  // clean up the database resources
-  database_->close();
+  // clean up the dataset resources
+  dataset_->close();
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Initialize DB
-  database_ = DatabaseFactory<string, Datum>(
+  dataset_ = DatasetFactory<string, Datum>(
       this->layer_param_.data_param().backend());
   const string& source = this->layer_param_.data_param().source();
-  LOG(INFO) << "Opening database " << source;
-  CHECK(database_->open(source, Database<string, Datum>::ReadOnly));
-  iter_ = database_->begin();
+  LOG(INFO) << "Opening dataset " << source;
+  CHECK(dataset_->open(source, Dataset<string, Datum>::ReadOnly));
+  iter_ = dataset_->begin();
 
   // Check if we would need to randomly skip a few data points
   if (this->layer_param_.data_param().rand_skip()) {
@@ -38,13 +38,13 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      if (++iter_ == database_->end()) {
-        iter_ = database_->begin();
+      if (++iter_ == dataset_->end()) {
+        iter_ = dataset_->begin();
       }
     }
   }
   // Read a data point, and use it to initialize the top blob.
-  CHECK(iter_ != database_->end());
+  CHECK(iter_ != dataset_->end());
   const Datum& datum = iter_->value;
 
   // image
@@ -89,7 +89,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   const int batch_size = this->layer_param_.data_param().batch_size();
 
   for (int item_id = 0; item_id < batch_size; ++item_id) {
-    CHECK(iter_ != database_->end());
+    CHECK(iter_ != dataset_->end());
     const Datum& datum = iter_->value;
 
     // Apply data transformations (mirror, scale, crop...)
@@ -102,8 +102,8 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
     // go to the next iter
     ++iter_;
-    if (iter_ == database_->end()) {
-      iter_ = database_->begin();
+    if (iter_ == dataset_->end()) {
+      iter_ = dataset_->begin();
     }
   }
 }
diff --git a/src/caffe/leveldb_database.cpp b/src/caffe/leveldb_dataset.cpp
similarity index 78%
rename from src/caffe/leveldb_database.cpp
rename to src/caffe/leveldb_dataset.cpp
index 267ddf07330..41c71e44557 100644
--- a/src/caffe/leveldb_database.cpp
+++ b/src/caffe/leveldb_dataset.cpp
@@ -3,12 +3,12 @@
 #include <vector>
 
 #include "caffe/caffe.hpp"
-#include "caffe/leveldb_database.hpp"
+#include "caffe/leveldb_dataset.hpp"
 
 namespace caffe {
 
 template <typename K, typename V>
-bool LeveldbDatabase<K, V>::open(const string& filename, Mode mode) {
+bool LeveldbDataset<K, V>::open(const string& filename, Mode mode) {
   DLOG(INFO) << "LevelDB: Open " << filename;
 
   leveldb::Options options;
@@ -55,11 +55,11 @@ bool LeveldbDatabase<K, V>::open(const string& filename, Mode mode) {
 }
 
 template <typename K, typename V>
-bool LeveldbDatabase<K, V>::put(const K& key, const V& value) {
+bool LeveldbDataset<K, V>::put(const K& key, const V& value) {
   DLOG(INFO) << "LevelDB: Put";
 
   if (read_only_) {
-    LOG(ERROR) << "put can not be used on a database in ReadOnly mode";
+    LOG(ERROR) << "put can not be used on a dataset in ReadOnly mode";
     return false;
   }
 
@@ -81,7 +81,7 @@ bool LeveldbDatabase<K, V>::put(const K& key, const V& value) {
 }
 
 template <typename K, typename V>
-bool LeveldbDatabase<K, V>::get(const K& key, V* value) {
+bool LeveldbDataset<K, V>::get(const K& key, V* value) {
   DLOG(INFO) << "LevelDB: Get";
 
   string serialized_key;
@@ -106,11 +106,11 @@ bool LeveldbDatabase<K, V>::get(const K& key, V* value) {
 }
 
 template <typename K, typename V>
-bool LeveldbDatabase<K, V>::commit() {
+bool LeveldbDataset<K, V>::commit() {
   DLOG(INFO) << "LevelDB: Commit";
 
   if (read_only_) {
-    LOG(ERROR) << "commit can not be used on a database in ReadOnly mode";
+    LOG(ERROR) << "commit can not be used on a dataset in ReadOnly mode";
     return false;
   }
 
@@ -125,7 +125,7 @@ bool LeveldbDatabase<K, V>::commit() {
 }
 
 template <typename K, typename V>
-void LeveldbDatabase<K, V>::close() {
+void LeveldbDataset<K, V>::close() {
   DLOG(INFO) << "LevelDB: Close";
 
   batch_.reset();
@@ -133,7 +133,7 @@ void LeveldbDatabase<K, V>::close() {
 }
 
 template <typename K, typename V>
-void LeveldbDatabase<K, V>::keys(vector<K>* keys) {
+void LeveldbDataset<K, V>::keys(vector<K>* keys) {
   DLOG(INFO) << "LevelDB: Keys";
 
   keys->clear();
@@ -143,8 +143,8 @@ void LeveldbDatabase<K, V>::keys(vector<K>* keys) {
 }
 
 template <typename K, typename V>
-typename LeveldbDatabase<K, V>::const_iterator
-    LeveldbDatabase<K, V>::begin() const {
+typename LeveldbDataset<K, V>::const_iterator
+    LeveldbDataset<K, V>::begin() const {
   CHECK_NOTNULL(db_.get());
   shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
   iter->SeekToFirst();
@@ -152,7 +152,7 @@ typename LeveldbDatabase<K, V>::const_iterator
     iter.reset();
   }
 
-  shared_ptr<DatabaseState> state;
+  shared_ptr<DatasetState> state;
   if (iter) {
     state.reset(new LeveldbState(db_, iter));
   }
@@ -160,25 +160,25 @@ typename LeveldbDatabase<K, V>::const_iterator
 }
 
 template <typename K, typename V>
-typename LeveldbDatabase<K, V>::const_iterator
-    LeveldbDatabase<K, V>::end() const {
-  shared_ptr<DatabaseState> state;
+typename LeveldbDataset<K, V>::const_iterator
+    LeveldbDataset<K, V>::end() const {
+  shared_ptr<DatasetState> state;
   return const_iterator(this, state);
 }
 
 template <typename K, typename V>
-typename LeveldbDatabase<K, V>::const_iterator
-    LeveldbDatabase<K, V>::cbegin() const {
+typename LeveldbDataset<K, V>::const_iterator
+    LeveldbDataset<K, V>::cbegin() const {
   return begin();
 }
 
 template <typename K, typename V>
-typename LeveldbDatabase<K, V>::const_iterator
-    LeveldbDatabase<K, V>::cend() const { return end(); }
+typename LeveldbDataset<K, V>::const_iterator
+    LeveldbDataset<K, V>::cend() const { return end(); }
 
 template <typename K, typename V>
-bool LeveldbDatabase<K, V>::equal(shared_ptr<DatabaseState> state1,
-    shared_ptr<DatabaseState> state2) const {
+bool LeveldbDataset<K, V>::equal(shared_ptr<DatasetState> state1,
+    shared_ptr<DatasetState> state2) const {
   shared_ptr<LeveldbState> leveldb_state1 =
       boost::dynamic_pointer_cast<LeveldbState>(state1);
 
@@ -192,7 +192,7 @@ bool LeveldbDatabase<K, V>::equal(shared_ptr<DatabaseState> state1,
 }
 
 template <typename K, typename V>
-void LeveldbDatabase<K, V>::increment(shared_ptr<DatabaseState>* state) const {
+void LeveldbDataset<K, V>::increment(shared_ptr<DatasetState>* state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(*state);
 
@@ -210,8 +210,8 @@ void LeveldbDatabase<K, V>::increment(shared_ptr<DatabaseState>* state) const {
 }
 
 template <typename K, typename V>
-typename Database<K, V>::KV& LeveldbDatabase<K, V>::dereference(
-    shared_ptr<DatabaseState> state) const {
+typename Dataset<K, V>::KV& LeveldbDataset<K, V>::dereference(
+    shared_ptr<DatasetState> state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(state);
 
@@ -233,6 +233,6 @@ typename Database<K, V>::KV& LeveldbDatabase<K, V>::dereference(
   return leveldb_state->kv_pair_;
 }
 
-INSTANTIATE_DATABASE(LeveldbDatabase);
+INSTANTIATE_DATASET(LeveldbDataset);
 
 }  // namespace caffe
diff --git a/src/caffe/lmdb_database.cpp b/src/caffe/lmdb_dataset.cpp
similarity index 85%
rename from src/caffe/lmdb_database.cpp
rename to src/caffe/lmdb_dataset.cpp
index 22a08b2a580..d2b6fcd1c85 100644
--- a/src/caffe/lmdb_database.cpp
+++ b/src/caffe/lmdb_dataset.cpp
@@ -5,12 +5,12 @@
 #include <vector>
 
 #include "caffe/caffe.hpp"
-#include "caffe/lmdb_database.hpp"
+#include "caffe/lmdb_dataset.hpp"
 
 namespace caffe {
 
 template <typename K, typename V>
-bool LmdbDatabase<K, V>::open(const string& filename, Mode mode) {
+bool LmdbDataset<K, V>::open(const string& filename, Mode mode) {
   DLOG(INFO) << "LMDB: Open " << filename;
 
   CHECK(NULL == env_);
@@ -81,7 +81,7 @@ bool LmdbDatabase<K, V>::open(const string& filename, Mode mode) {
 }
 
 template <typename K, typename V>
-bool LmdbDatabase<K, V>::put(const K& key, const V& value) {
+bool LmdbDataset<K, V>::put(const K& key, const V& value) {
   DLOG(INFO) << "LMDB: Put";
 
   vector<char> serialized_key;
@@ -115,7 +115,7 @@ bool LmdbDatabase<K, V>::put(const K& key, const V& value) {
 }
 
 template <typename K, typename V>
-bool LmdbDatabase<K, V>::get(const K& key, V* value) {
+bool LmdbDataset<K, V>::get(const K& key, V* value) {
   DLOG(INFO) << "LMDB: Get";
 
   vector<char> serialized_key;
@@ -154,7 +154,7 @@ bool LmdbDatabase<K, V>::get(const K& key, V* value) {
 }
 
 template <typename K, typename V>
-bool LmdbDatabase<K, V>::commit() {
+bool LmdbDataset<K, V>::commit() {
   DLOG(INFO) << "LMDB: Commit";
 
   CHECK_NOTNULL(txn_);
@@ -176,7 +176,7 @@ bool LmdbDatabase<K, V>::commit() {
 }
 
 template <typename K, typename V>
-void LmdbDatabase<K, V>::close() {
+void LmdbDataset<K, V>::close() {
   DLOG(INFO) << "LMDB: Close";
 
   if (env_ && dbi_) {
@@ -189,7 +189,7 @@ void LmdbDatabase<K, V>::close() {
 }
 
 template <typename K, typename V>
-void LmdbDatabase<K, V>::keys(vector<K>* keys) {
+void LmdbDataset<K, V>::keys(vector<K>* keys) {
   DLOG(INFO) << "LMDB: Keys";
 
   keys->clear();
@@ -199,8 +199,8 @@ void LmdbDatabase<K, V>::keys(vector<K>* keys) {
 }
 
 template <typename K, typename V>
-typename LmdbDatabase<K, V>::const_iterator
-    LmdbDatabase<K, V>::begin() const {
+typename LmdbDataset<K, V>::const_iterator
+    LmdbDataset<K, V>::begin() const {
   int retval;
 
   MDB_txn* iter_txn;
@@ -219,7 +219,7 @@ typename LmdbDatabase<K, V>::const_iterator
   CHECK(MDB_SUCCESS == retval || MDB_NOTFOUND == retval)
       << mdb_strerror(retval);
 
-  shared_ptr<DatabaseState> state;
+  shared_ptr<DatasetState> state;
   if (MDB_SUCCESS == retval) {
     state.reset(new LmdbState(cursor, iter_txn, &dbi_));
   }
@@ -227,23 +227,23 @@ typename LmdbDatabase<K, V>::const_iterator
 }
 
 template <typename K, typename V>
-typename LmdbDatabase<K, V>::const_iterator
-    LmdbDatabase<K, V>::end() const {
-  shared_ptr<DatabaseState> state;
+typename LmdbDataset<K, V>::const_iterator
+    LmdbDataset<K, V>::end() const {
+  shared_ptr<DatasetState> state;
   return const_iterator(this, state);
 }
 
 template <typename K, typename V>
-typename LmdbDatabase<K, V>::const_iterator
-    LmdbDatabase<K, V>::cbegin() const { return begin(); }
+typename LmdbDataset<K, V>::const_iterator
+    LmdbDataset<K, V>::cbegin() const { return begin(); }
 
 template <typename K, typename V>
-typename LmdbDatabase<K, V>::const_iterator
-    LmdbDatabase<K, V>::cend() const { return end(); }
+typename LmdbDataset<K, V>::const_iterator
+    LmdbDataset<K, V>::cend() const { return end(); }
 
 template <typename K, typename V>
-bool LmdbDatabase<K, V>::equal(shared_ptr<DatabaseState> state1,
-    shared_ptr<DatabaseState> state2) const {
+bool LmdbDataset<K, V>::equal(shared_ptr<DatasetState> state1,
+    shared_ptr<DatasetState> state2) const {
   shared_ptr<LmdbState> lmdb_state1 =
       boost::dynamic_pointer_cast<LmdbState>(state1);
 
@@ -257,7 +257,7 @@ bool LmdbDatabase<K, V>::equal(shared_ptr<DatabaseState> state1,
 }
 
 template <typename K, typename V>
-void LmdbDatabase<K, V>::increment(shared_ptr<DatabaseState>* state) const {
+void LmdbDataset<K, V>::increment(shared_ptr<DatasetState>* state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(*state);
 
@@ -279,8 +279,8 @@ void LmdbDatabase<K, V>::increment(shared_ptr<DatabaseState>* state) const {
 }
 
 template <typename K, typename V>
-typename Database<K, V>::KV& LmdbDatabase<K, V>::dereference(
-    shared_ptr<DatabaseState> state) const {
+typename Dataset<K, V>::KV& LmdbDataset<K, V>::dereference(
+    shared_ptr<DatasetState> state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(state);
 
@@ -303,6 +303,6 @@ typename Database<K, V>::KV& LmdbDatabase<K, V>::dereference(
   return lmdb_state->kv_pair_;
 }
 
-INSTANTIATE_DATABASE(LmdbDatabase);
+INSTANTIATE_DATASET(LmdbDataset);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 1c3ec1fc576..32f5d41e2f2 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -5,7 +5,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/database_factory.hpp"
+#include "caffe/dataset_factory.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
@@ -38,10 +38,10 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
   // an image are the same.
   void Fill(const bool unique_pixels, DataParameter_DB backend) {
     backend_ = backend;
-    LOG(INFO) << "Using temporary database " << *filename_;
-    shared_ptr<Database<string, Datum> > database =
-        DatabaseFactory<string, Datum>(backend_);
-    CHECK(database->open(*filename_, Database<string, Datum>::New));
+    LOG(INFO) << "Using temporary dataset " << *filename_;
+    shared_ptr<Dataset<string, Datum> > dataset =
+        DatasetFactory<string, Datum>(backend_);
+    CHECK(dataset->open(*filename_, Dataset<string, Datum>::New));
     for (int i = 0; i < 5; ++i) {
       Datum datum;
       datum.set_label(i);
@@ -55,10 +55,10 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       }
       stringstream ss;
       ss << i;
-      CHECK(database->put(ss.str(), datum));
+      CHECK(dataset->put(ss.str(), datum));
     }
-    CHECK(database->commit());
-    database->close();
+    CHECK(dataset->commit());
+    dataset->close();
   }
 
   void TestRead() {
@@ -183,7 +183,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the database
+    }  // destroy 1st data layer and unlock the dataset
 
     // Get crop sequence after reseeding Caffe with 1701.
     // Check that the sequence is the same as the original.
@@ -238,7 +238,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the database
+    }  // destroy 1st data layer and unlock the dataset
 
     // Get crop sequence continuing from previous Caffe RNG state; reseed
     // srand with 1701. Check that the sequence differs from the original.
diff --git a/src/caffe/test/test_database.cpp b/src/caffe/test/test_database.cpp
deleted file mode 100644
index 9a7d6dec8c7..00000000000
--- a/src/caffe/test/test_database.cpp
+++ /dev/null
@@ -1,642 +0,0 @@
-#include <string>
-#include <vector>
-
-#include "caffe/util/io.hpp"
-
-#include "gtest/gtest.h"
-
-#include "caffe/database_factory.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-namespace caffe {
-
-namespace DatabaseTest_internal {
-
-template <typename T>
-struct TestData {
-  static T TestValue();
-  static T TestAltValue();
-  static bool equals(const T& a, const T& b);
-};
-
-template <>
-string TestData<string>::TestValue() {
-  return "world";
-}
-
-template <>
-string TestData<string>::TestAltValue() {
-  return "bar";
-}
-
-template <>
-bool TestData<string>::equals(const string& a, const string& b) {
-  return a == b;
-}
-
-template <>
-vector<char> TestData<vector<char> >::TestValue() {
-  string str = "world";
-  vector<char> val(str.data(), str.data() + str.size());
-  return val;
-}
-
-template <>
-vector<char> TestData<vector<char> >::TestAltValue() {
-  string str = "bar";
-  vector<char> val(str.data(), str.data() + str.size());
-  return val;
-}
-
-template <>
-bool TestData<vector<char> >::equals(const vector<char>& a,
-    const vector<char>& b) {
-  if (a.size() != b.size()) {
-    return false;
-  }
-  for (size_t i = 0; i < a.size(); ++i) {
-    if (a.at(i) != b.at(i)) {
-      return false;
-    }
-  }
-
-  return true;
-}
-
-template <>
-Datum TestData<Datum>::TestValue() {
-  Datum datum;
-  datum.set_channels(3);
-  datum.set_height(32);
-  datum.set_width(32);
-  datum.set_data(string(32 * 32 * 3 * 4, ' '));
-  datum.set_label(0);
-  return datum;
-}
-
-template <>
-Datum TestData<Datum>::TestAltValue() {
-  Datum datum;
-  datum.set_channels(1);
-  datum.set_height(64);
-  datum.set_width(64);
-  datum.set_data(string(64 * 64 * 1 * 4, ' '));
-  datum.set_label(1);
-  return datum;
-}
-
-template <>
-bool TestData<Datum>::equals(const Datum& a, const Datum& b) {
-  string serialized_a;
-  a.SerializeToString(&serialized_a);
-
-  string serialized_b;
-  b.SerializeToString(&serialized_b);
-
-  return serialized_a == serialized_b;
-}
-
-}  // namespace DatabaseTest_internal
-
-#define UNPACK_TYPES \
-  typedef typename TypeParam::value_type value_type; \
-  const DataParameter_DB backend = TypeParam::backend;
-
-template <typename TypeParam>
-class DatabaseTest : public ::testing::Test {
- protected:
-  typedef typename TypeParam::value_type value_type;
-
-  string DBName() {
-    string filename;
-    MakeTempDir(&filename);
-    filename += "/db";
-    return filename;
-  }
-
-  string TestKey() {
-    return "hello";
-  }
-
-  value_type TestValue() {
-    return DatabaseTest_internal::TestData<value_type>::TestValue();
-  }
-
-  string TestAltKey() {
-    return "foo";
-  }
-
-  value_type TestAltValue() {
-    return DatabaseTest_internal::TestData<value_type>::TestAltValue();
-  }
-
-  template <typename T>
-  bool equals(const T& a, const T& b) {
-    return DatabaseTest_internal::TestData<T>::equals(a, b);
-  }
-};
-
-struct StringLeveldb {
-  typedef string value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB StringLeveldb::backend = DataParameter_DB_LEVELDB;
-
-struct StringLmdb {
-  typedef string value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB StringLmdb::backend = DataParameter_DB_LEVELDB;
-
-struct VectorLeveldb {
-  typedef vector<char> value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB VectorLeveldb::backend = DataParameter_DB_LEVELDB;
-
-struct VectorLmdb {
-  typedef vector<char> value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB VectorLmdb::backend = DataParameter_DB_LEVELDB;
-
-struct DatumLeveldb {
-  typedef Datum value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB DatumLeveldb::backend = DataParameter_DB_LEVELDB;
-
-struct DatumLmdb {
-  typedef Datum value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB DatumLmdb::backend = DataParameter_DB_LEVELDB;
-
-typedef ::testing::Types<StringLeveldb, StringLmdb, VectorLeveldb, VectorLmdb,
-    DatumLeveldb, DatumLmdb> TestTypes;
-
-TYPED_TEST_CASE(DatabaseTest, TestTypes);
-
-TYPED_TEST(DatabaseTest, TestNewDoesntExistPasses) {
-  UNPACK_TYPES;
-
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(this->DBName(),
-      Database<string, value_type>::New));
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewExistsFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-  database->close();
-
-  EXPECT_FALSE(database->open(name, Database<string, value_type>::New));
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyExistsPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyDoesntExistFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_FALSE(database->open(name, Database<string, value_type>::ReadOnly));
-}
-
-TYPED_TEST(DatabaseTest, TestReadWriteExistsPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestReadWriteDoesntExistPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestKeys) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key1 = this->TestKey();
-  value_type value1 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-
-  string key2 = this->TestAltKey();
-  value_type value2 = this->TestAltValue();
-
-  EXPECT_TRUE(database->put(key2, value2));
-
-  EXPECT_TRUE(database->commit());
-
-  vector<string> keys;
-  database->keys(&keys);
-
-  EXPECT_EQ(2, keys.size());
-
-  EXPECT_TRUE(this->equals(keys.at(0), key1) ||
-      this->equals(keys.at(0), key2));
-  EXPECT_TRUE(this->equals(keys.at(1), key1) ||
-      this->equals(keys.at(2), key2));
-  EXPECT_FALSE(this->equals(keys.at(0), keys.at(1)));
-}
-
-TYPED_TEST(DatabaseTest, TestKeysNoCommit) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key1 = this->TestKey();
-  value_type value1 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-
-  string key2 = this->TestAltKey();
-  value_type value2 = this->TestAltValue();
-
-  EXPECT_TRUE(database->put(key2, value2));
-
-  vector<string> keys;
-  database->keys(&keys);
-
-  EXPECT_EQ(0, keys.size());
-}
-
-TYPED_TEST(DatabaseTest, TestIterators) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  const int kNumExamples = 4;
-  for (int i = 0; i < kNumExamples; ++i) {
-    stringstream ss;
-    ss << i;
-    string key = ss.str();
-    ss << " here be data";
-    value_type value = this->TestValue();
-    EXPECT_TRUE(database->put(key, value));
-  }
-  EXPECT_TRUE(database->commit());
-
-  int count = 0;
-  typedef typename Database<string, value_type>::const_iterator Iter;
-  for (Iter iter = database->begin(); iter != database->end(); ++iter) {
-    (void)iter;
-    ++count;
-  }
-
-  EXPECT_EQ(kNumExamples, count);
-}
-
-TYPED_TEST(DatabaseTest, TestIteratorsPreIncrement) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key1 = this->TestAltKey();
-  value_type value1 = this->TestAltValue();
-
-  string key2 = this->TestKey();
-  value_type value2 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-  EXPECT_TRUE(database->put(key2, value2));
-  EXPECT_TRUE(database->commit());
-
-  typename Database<string, value_type>::const_iterator iter1 =
-      database->begin();
-
-  EXPECT_FALSE(database->end() == iter1);
-
-  EXPECT_TRUE(this->equals(iter1->key, key1));
-
-  typename Database<string, value_type>::const_iterator iter2 = ++iter1;
-
-  EXPECT_FALSE(database->end() == iter1);
-  EXPECT_FALSE(database->end() == iter2);
-
-  EXPECT_TRUE(this->equals(iter2->key, key2));
-
-  typename Database<string, value_type>::const_iterator iter3 = ++iter2;
-
-  EXPECT_TRUE(database->end() == iter3);
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestIteratorsPostIncrement) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key1 = this->TestAltKey();
-  value_type value1 = this->TestAltValue();
-
-  string key2 = this->TestKey();
-  value_type value2 = this->TestValue();
-
-  EXPECT_TRUE(database->put(key1, value1));
-  EXPECT_TRUE(database->put(key2, value2));
-  EXPECT_TRUE(database->commit());
-
-  typename Database<string, value_type>::const_iterator iter1 =
-      database->begin();
-
-  EXPECT_FALSE(database->end() == iter1);
-
-  EXPECT_TRUE(this->equals(iter1->key, key1));
-
-  typename Database<string, value_type>::const_iterator iter2 = iter1++;
-
-  EXPECT_FALSE(database->end() == iter1);
-  EXPECT_FALSE(database->end() == iter2);
-
-  EXPECT_TRUE(this->equals(iter2->key, key1));
-  EXPECT_TRUE(this->equals(iter1->key, key2));
-
-  typename Database<string, value_type>::const_iterator iter3 = iter1++;
-
-  EXPECT_FALSE(database->end() == iter3);
-  EXPECT_TRUE(this->equals(iter3->key, key2));
-  EXPECT_TRUE(database->end() == iter1);
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewPutPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  EXPECT_TRUE(database->commit());
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewCommitPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  EXPECT_TRUE(database->commit());
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewGetPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  EXPECT_TRUE(database->commit());
-
-  value_type new_value;
-
-  EXPECT_TRUE(database->get(key, &new_value));
-
-  EXPECT_TRUE(this->equals(value, new_value));
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestNewGetNoCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  value_type new_value;
-
-  EXPECT_FALSE(database->get(key, &new_value));
-}
-
-
-TYPED_TEST(DatabaseTest, TestReadWritePutPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  EXPECT_TRUE(database->commit());
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestReadWriteCommitPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadWrite));
-
-  EXPECT_TRUE(database->commit());
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestReadWriteGetPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  EXPECT_TRUE(database->commit());
-
-  value_type new_value;
-
-  EXPECT_TRUE(database->get(key, &new_value));
-
-  EXPECT_TRUE(this->equals(value, new_value));
-
-  database->close();
-}
-
-TYPED_TEST(DatabaseTest, TestReadWriteGetNoCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  value_type new_value;
-
-  EXPECT_FALSE(database->get(key, &new_value));
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyPutFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_FALSE(database->put(key, value));
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
-
-  EXPECT_FALSE(database->commit());
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyGetPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  EXPECT_TRUE(database->commit());
-
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
-
-  value_type new_value;
-
-  EXPECT_TRUE(database->get(key, &new_value));
-
-  EXPECT_TRUE(this->equals(value, new_value));
-}
-
-TYPED_TEST(DatabaseTest, TestReadOnlyGetNoCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Database<string, value_type> > database =
-      DatabaseFactory<string, value_type>(backend);
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(database->put(key, value));
-
-  database->close();
-
-  EXPECT_TRUE(database->open(name, Database<string, value_type>::ReadOnly));
-
-  value_type new_value;
-
-  EXPECT_FALSE(database->get(key, &new_value));
-}
-
-#undef UNPACK_TYPES
-
-}  // namespace caffe
diff --git a/src/caffe/test/test_dataset.cpp b/src/caffe/test/test_dataset.cpp
new file mode 100644
index 00000000000..bb6cf4ce616
--- /dev/null
+++ b/src/caffe/test/test_dataset.cpp
@@ -0,0 +1,642 @@
+#include <string>
+#include <vector>
+
+#include "caffe/util/io.hpp"
+
+#include "gtest/gtest.h"
+
+#include "caffe/dataset_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+namespace DatasetTest_internal {
+
+template <typename T>
+struct TestData {
+  static T TestValue();
+  static T TestAltValue();
+  static bool equals(const T& a, const T& b);
+};
+
+template <>
+string TestData<string>::TestValue() {
+  return "world";
+}
+
+template <>
+string TestData<string>::TestAltValue() {
+  return "bar";
+}
+
+template <>
+bool TestData<string>::equals(const string& a, const string& b) {
+  return a == b;
+}
+
+template <>
+vector<char> TestData<vector<char> >::TestValue() {
+  string str = "world";
+  vector<char> val(str.data(), str.data() + str.size());
+  return val;
+}
+
+template <>
+vector<char> TestData<vector<char> >::TestAltValue() {
+  string str = "bar";
+  vector<char> val(str.data(), str.data() + str.size());
+  return val;
+}
+
+template <>
+bool TestData<vector<char> >::equals(const vector<char>& a,
+    const vector<char>& b) {
+  if (a.size() != b.size()) {
+    return false;
+  }
+  for (size_t i = 0; i < a.size(); ++i) {
+    if (a.at(i) != b.at(i)) {
+      return false;
+    }
+  }
+
+  return true;
+}
+
+template <>
+Datum TestData<Datum>::TestValue() {
+  Datum datum;
+  datum.set_channels(3);
+  datum.set_height(32);
+  datum.set_width(32);
+  datum.set_data(string(32 * 32 * 3 * 4, ' '));
+  datum.set_label(0);
+  return datum;
+}
+
+template <>
+Datum TestData<Datum>::TestAltValue() {
+  Datum datum;
+  datum.set_channels(1);
+  datum.set_height(64);
+  datum.set_width(64);
+  datum.set_data(string(64 * 64 * 1 * 4, ' '));
+  datum.set_label(1);
+  return datum;
+}
+
+template <>
+bool TestData<Datum>::equals(const Datum& a, const Datum& b) {
+  string serialized_a;
+  a.SerializeToString(&serialized_a);
+
+  string serialized_b;
+  b.SerializeToString(&serialized_b);
+
+  return serialized_a == serialized_b;
+}
+
+}  // namespace DatasetTest_internal
+
+#define UNPACK_TYPES \
+  typedef typename TypeParam::value_type value_type; \
+  const DataParameter_DB backend = TypeParam::backend;
+
+template <typename TypeParam>
+class DatasetTest : public ::testing::Test {
+ protected:
+  typedef typename TypeParam::value_type value_type;
+
+  string DBName() {
+    string filename;
+    MakeTempDir(&filename);
+    filename += "/db";
+    return filename;
+  }
+
+  string TestKey() {
+    return "hello";
+  }
+
+  value_type TestValue() {
+    return DatasetTest_internal::TestData<value_type>::TestValue();
+  }
+
+  string TestAltKey() {
+    return "foo";
+  }
+
+  value_type TestAltValue() {
+    return DatasetTest_internal::TestData<value_type>::TestAltValue();
+  }
+
+  template <typename T>
+  bool equals(const T& a, const T& b) {
+    return DatasetTest_internal::TestData<T>::equals(a, b);
+  }
+};
+
+struct StringLeveldb {
+  typedef string value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB StringLeveldb::backend = DataParameter_DB_LEVELDB;
+
+struct StringLmdb {
+  typedef string value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB StringLmdb::backend = DataParameter_DB_LEVELDB;
+
+struct VectorLeveldb {
+  typedef vector<char> value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB VectorLeveldb::backend = DataParameter_DB_LEVELDB;
+
+struct VectorLmdb {
+  typedef vector<char> value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB VectorLmdb::backend = DataParameter_DB_LEVELDB;
+
+struct DatumLeveldb {
+  typedef Datum value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB DatumLeveldb::backend = DataParameter_DB_LEVELDB;
+
+struct DatumLmdb {
+  typedef Datum value_type;
+  static const DataParameter_DB backend;
+};
+const DataParameter_DB DatumLmdb::backend = DataParameter_DB_LEVELDB;
+
+typedef ::testing::Types<StringLeveldb, StringLmdb, VectorLeveldb, VectorLmdb,
+    DatumLeveldb, DatumLmdb> TestTypes;
+
+TYPED_TEST_CASE(DatasetTest, TestTypes);
+
+TYPED_TEST(DatasetTest, TestNewDoesntExistPasses) {
+  UNPACK_TYPES;
+
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(this->DBName(),
+      Dataset<string, value_type>::New));
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestNewExistsFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+  dataset->close();
+
+  EXPECT_FALSE(dataset->open(name, Dataset<string, value_type>::New));
+}
+
+TYPED_TEST(DatasetTest, TestReadOnlyExistsPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+  dataset->close();
+
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestReadOnlyDoesntExistFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_FALSE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
+}
+
+TYPED_TEST(DatasetTest, TestReadWriteExistsPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+  dataset->close();
+
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestReadWriteDoesntExistPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestKeys) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key1 = this->TestKey();
+  value_type value1 = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key1, value1));
+
+  string key2 = this->TestAltKey();
+  value_type value2 = this->TestAltValue();
+
+  EXPECT_TRUE(dataset->put(key2, value2));
+
+  EXPECT_TRUE(dataset->commit());
+
+  vector<string> keys;
+  dataset->keys(&keys);
+
+  EXPECT_EQ(2, keys.size());
+
+  EXPECT_TRUE(this->equals(keys.at(0), key1) ||
+      this->equals(keys.at(0), key2));
+  EXPECT_TRUE(this->equals(keys.at(1), key1) ||
+      this->equals(keys.at(2), key2));
+  EXPECT_FALSE(this->equals(keys.at(0), keys.at(1)));
+}
+
+TYPED_TEST(DatasetTest, TestKeysNoCommit) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key1 = this->TestKey();
+  value_type value1 = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key1, value1));
+
+  string key2 = this->TestAltKey();
+  value_type value2 = this->TestAltValue();
+
+  EXPECT_TRUE(dataset->put(key2, value2));
+
+  vector<string> keys;
+  dataset->keys(&keys);
+
+  EXPECT_EQ(0, keys.size());
+}
+
+TYPED_TEST(DatasetTest, TestIterators) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  const int kNumExamples = 4;
+  for (int i = 0; i < kNumExamples; ++i) {
+    stringstream ss;
+    ss << i;
+    string key = ss.str();
+    ss << " here be data";
+    value_type value = this->TestValue();
+    EXPECT_TRUE(dataset->put(key, value));
+  }
+  EXPECT_TRUE(dataset->commit());
+
+  int count = 0;
+  typedef typename Dataset<string, value_type>::const_iterator Iter;
+  for (Iter iter = dataset->begin(); iter != dataset->end(); ++iter) {
+    (void)iter;
+    ++count;
+  }
+
+  EXPECT_EQ(kNumExamples, count);
+}
+
+TYPED_TEST(DatasetTest, TestIteratorsPreIncrement) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key1 = this->TestAltKey();
+  value_type value1 = this->TestAltValue();
+
+  string key2 = this->TestKey();
+  value_type value2 = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key1, value1));
+  EXPECT_TRUE(dataset->put(key2, value2));
+  EXPECT_TRUE(dataset->commit());
+
+  typename Dataset<string, value_type>::const_iterator iter1 =
+      dataset->begin();
+
+  EXPECT_FALSE(dataset->end() == iter1);
+
+  EXPECT_TRUE(this->equals(iter1->key, key1));
+
+  typename Dataset<string, value_type>::const_iterator iter2 = ++iter1;
+
+  EXPECT_FALSE(dataset->end() == iter1);
+  EXPECT_FALSE(dataset->end() == iter2);
+
+  EXPECT_TRUE(this->equals(iter2->key, key2));
+
+  typename Dataset<string, value_type>::const_iterator iter3 = ++iter2;
+
+  EXPECT_TRUE(dataset->end() == iter3);
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestIteratorsPostIncrement) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key1 = this->TestAltKey();
+  value_type value1 = this->TestAltValue();
+
+  string key2 = this->TestKey();
+  value_type value2 = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key1, value1));
+  EXPECT_TRUE(dataset->put(key2, value2));
+  EXPECT_TRUE(dataset->commit());
+
+  typename Dataset<string, value_type>::const_iterator iter1 =
+      dataset->begin();
+
+  EXPECT_FALSE(dataset->end() == iter1);
+
+  EXPECT_TRUE(this->equals(iter1->key, key1));
+
+  typename Dataset<string, value_type>::const_iterator iter2 = iter1++;
+
+  EXPECT_FALSE(dataset->end() == iter1);
+  EXPECT_FALSE(dataset->end() == iter2);
+
+  EXPECT_TRUE(this->equals(iter2->key, key1));
+  EXPECT_TRUE(this->equals(iter1->key, key2));
+
+  typename Dataset<string, value_type>::const_iterator iter3 = iter1++;
+
+  EXPECT_FALSE(dataset->end() == iter3);
+  EXPECT_TRUE(this->equals(iter3->key, key2));
+  EXPECT_TRUE(dataset->end() == iter1);
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestNewPutPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestNewCommitPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  EXPECT_TRUE(dataset->commit());
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestNewGetPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  value_type new_value;
+
+  EXPECT_TRUE(dataset->get(key, &new_value));
+
+  EXPECT_TRUE(this->equals(value, new_value));
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestNewGetNoCommitFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  value_type new_value;
+
+  EXPECT_FALSE(dataset->get(key, &new_value));
+}
+
+
+TYPED_TEST(DatasetTest, TestReadWritePutPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestReadWriteCommitPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
+
+  EXPECT_TRUE(dataset->commit());
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestReadWriteGetPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  value_type new_value;
+
+  EXPECT_TRUE(dataset->get(key, &new_value));
+
+  EXPECT_TRUE(this->equals(value, new_value));
+
+  dataset->close();
+}
+
+TYPED_TEST(DatasetTest, TestReadWriteGetNoCommitFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  value_type new_value;
+
+  EXPECT_FALSE(dataset->get(key, &new_value));
+}
+
+TYPED_TEST(DatasetTest, TestReadOnlyPutFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+  dataset->close();
+
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_FALSE(dataset->put(key, value));
+}
+
+TYPED_TEST(DatasetTest, TestReadOnlyCommitFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+  dataset->close();
+
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
+
+  EXPECT_FALSE(dataset->commit());
+}
+
+TYPED_TEST(DatasetTest, TestReadOnlyGetPasses) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  dataset->close();
+
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
+
+  value_type new_value;
+
+  EXPECT_TRUE(dataset->get(key, &new_value));
+
+  EXPECT_TRUE(this->equals(value, new_value));
+}
+
+TYPED_TEST(DatasetTest, TestReadOnlyGetNoCommitFails) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+
+  EXPECT_TRUE(dataset->put(key, value));
+
+  dataset->close();
+
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
+
+  value_type new_value;
+
+  EXPECT_FALSE(dataset->get(key, &new_value));
+}
+
+#undef UNPACK_TYPES
+
+}  // namespace caffe
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index f1a79679091..a720f162aa2 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -4,11 +4,11 @@
 #include <algorithm>
 #include <string>
 
-#include "caffe/database_factory.hpp"
+#include "caffe/dataset_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 
-using caffe::Database;
+using caffe::Dataset;
 using caffe::Datum;
 using caffe::BlobProto;
 using std::max;
@@ -26,16 +26,16 @@ int main(int argc, char** argv) {
     db_backend = std::string(argv[3]);
   }
 
-  caffe::shared_ptr<Database<std::string, Datum> > database =
-      caffe::DatabaseFactory<std::string, Datum>(db_backend);
+  caffe::shared_ptr<Dataset<std::string, Datum> > dataset =
+      caffe::DatasetFactory<std::string, Datum>(db_backend);
 
   // Open db
-  CHECK(database->open(argv[1], Database<std::string, Datum>::ReadOnly));
+  CHECK(dataset->open(argv[1], Dataset<std::string, Datum>::ReadOnly));
 
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  Database<std::string, Datum>::const_iterator iter = database->begin();
+  Dataset<std::string, Datum>::const_iterator iter = dataset->begin();
   const Datum& datum = iter->value;
 
   sum_blob.set_num(1);
@@ -49,8 +49,8 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  for (Database<std::string, Datum>::const_iterator iter = database->begin();
-      iter != database->end(); ++iter) {
+  for (Dataset<std::string, Datum>::const_iterator iter = dataset->begin();
+      iter != dataset->end(); ++iter) {
     // just a dummy operation
     const Datum& datum = iter->value;
     const std::string& data = datum.data();
@@ -87,6 +87,6 @@ int main(int argc, char** argv) {
   WriteProtoToBinaryFile(sum_blob, argv[2]);
 
   // Clean up
-  database->close();
+  dataset->close();
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 2ba3e3c7250..37efa5c13f7 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -17,7 +17,7 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/database_factory.hpp"
+#include "caffe/dataset_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/rng.hpp"
@@ -78,11 +78,11 @@ int main(int argc, char** argv) {
   int resize_width = std::max<int>(0, FLAGS_resize_width);
 
   // Open new db
-  shared_ptr<Database<string, Datum> > database =
-      DatabaseFactory<string, Datum>(db_backend);
+  shared_ptr<Dataset<string, Datum> > dataset =
+      DatasetFactory<string, Datum>(db_backend);
 
   // Open db
-  CHECK(database->open(db_path, Database<string, Datum>::New));
+  CHECK(dataset->open(db_path, Dataset<string, Datum>::New));
 
   // Storing to db
   std::string root_folder(argv[1]);
@@ -113,19 +113,19 @@ int main(int argc, char** argv) {
         lines[line_id].first.c_str());
 
     // Put in db
-    CHECK(database->put(string(key_cstr, length), datum));
+    CHECK(dataset->put(string(key_cstr, length), datum));
 
     if (++count % 1000 == 0) {
       // Commit txn
-      CHECK(database->commit());
+      CHECK(dataset->commit());
       LOG(ERROR) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    CHECK(database->commit());
+    CHECK(dataset->commit());
     LOG(ERROR) << "Processed " << count << " files.";
   }
-  database->close();
+  dataset->close();
   return 0;
 }
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 47565a83025..ddbce1075ed 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -7,7 +7,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/database_factory.hpp"
+#include "caffe/dataset_factory.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
@@ -16,8 +16,8 @@
 using boost::shared_ptr;
 using caffe::Blob;
 using caffe::Caffe;
-using caffe::Database;
-using caffe::DatabaseFactory;
+using caffe::Dataset;
+using caffe::DatasetFactory;
 using caffe::Datum;
 using caffe::Net;
 
@@ -39,12 +39,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
     " extract features of the input data produced by the net.\n"
     "Usage: extract_features  pretrained_net_param"
     "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
-    "  save_feature_database_name1[,name2,...]  num_mini_batches  db_type"
+    "  save_feature_dataset_name1[,name2,...]  num_mini_batches  db_type"
     "  [CPU/GPU] [DEVICE_ID=0]\n"
     "Note: you can extract multiple features in one pass by specifying"
-    " multiple feature blob names and database names seperated by ','."
+    " multiple feature blob names and dataset names seperated by ','."
     " The names cannot contain white space characters and the number of blobs"
-    " and databases must be equal.";
+    " and datasets must be equal.";
     return 1;
   }
   int arg_pos = num_required_args;
@@ -105,12 +105,12 @@ int feature_extraction_pipeline(int argc, char** argv) {
   std::vector<std::string> blob_names;
   boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
 
-  std::string save_feature_database_names(argv[++arg_pos]);
-  std::vector<std::string> database_names;
-  boost::split(database_names, save_feature_database_names,
+  std::string save_feature_dataset_names(argv[++arg_pos]);
+  std::vector<std::string> dataset_names;
+  boost::split(dataset_names, save_feature_dataset_names,
                boost::is_any_of(","));
-  CHECK_EQ(blob_names.size(), database_names.size()) <<
-      " the number of blob names and database names must be equal";
+  CHECK_EQ(blob_names.size(), dataset_names.size()) <<
+      " the number of blob names and dataset names must be equal";
   size_t num_features = blob_names.size();
 
   for (size_t i = 0; i < num_features; i++) {
@@ -121,14 +121,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
-  std::vector<shared_ptr<Database<std::string, Datum> > > feature_dbs;
+  std::vector<shared_ptr<Dataset<std::string, Datum> > > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
-    LOG(INFO)<< "Opening database " << database_names[i];
-    shared_ptr<Database<std::string, Datum> > database =
-        DatabaseFactory<std::string, Datum>(argv[++arg_pos]);
-    CHECK(database->open(database_names.at(i),
-        Database<std::string, Datum>::New));
-    feature_dbs.push_back(database);
+    LOG(INFO)<< "Opening dataset " << dataset_names[i];
+    shared_ptr<Dataset<std::string, Datum> > dataset =
+        DatasetFactory<std::string, Datum>(argv[++arg_pos]);
+    CHECK(dataset->open(dataset_names.at(i), Dataset<std::string, Datum>::New));
+    feature_dbs.push_back(dataset);
   }
 
   LOG(ERROR)<< "Extacting Features";

From 6fd72e106526e6cbdf8a8b867576816f87700681 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 14 Oct 2014 12:44:00 -0400
Subject: [PATCH 0937/2053] Had forgotten to set some of the Dataset test cases
 to LMDB backend.

---
 src/caffe/test/test_dataset.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/test/test_dataset.cpp b/src/caffe/test/test_dataset.cpp
index bb6cf4ce616..7d0f6b3d9bb 100644
--- a/src/caffe/test/test_dataset.cpp
+++ b/src/caffe/test/test_dataset.cpp
@@ -147,7 +147,7 @@ struct StringLmdb {
   typedef string value_type;
   static const DataParameter_DB backend;
 };
-const DataParameter_DB StringLmdb::backend = DataParameter_DB_LEVELDB;
+const DataParameter_DB StringLmdb::backend = DataParameter_DB_LMDB;
 
 struct VectorLeveldb {
   typedef vector<char> value_type;
@@ -159,7 +159,7 @@ struct VectorLmdb {
   typedef vector<char> value_type;
   static const DataParameter_DB backend;
 };
-const DataParameter_DB VectorLmdb::backend = DataParameter_DB_LEVELDB;
+const DataParameter_DB VectorLmdb::backend = DataParameter_DB_LMDB;
 
 struct DatumLeveldb {
   typedef Datum value_type;
@@ -171,7 +171,7 @@ struct DatumLmdb {
   typedef Datum value_type;
   static const DataParameter_DB backend;
 };
-const DataParameter_DB DatumLmdb::backend = DataParameter_DB_LEVELDB;
+const DataParameter_DB DatumLmdb::backend = DataParameter_DB_LMDB;
 
 typedef ::testing::Types<StringLeveldb, StringLmdb, VectorLeveldb, VectorLmdb,
     DatumLeveldb, DatumLmdb> TestTypes;

From ef518dc6e02b6d837a3281cf353a52a333a5a669 Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Tue, 14 Oct 2014 14:00:29 -0400
Subject: [PATCH 0938/2053] Reworked the Coder interface such that a Dataset
 now has both user-definable KCoder and VCoder which default to a set of
 DefaultCoder's based on types K and V.  Reworked the DefaultCoder's such that
 if none are available, a static assertion fails with a relevant message.

---
 include/caffe/dataset.hpp         | 66 ++++++++++++++------------
 include/caffe/leveldb_dataset.hpp |  8 ++--
 include/caffe/lmdb_dataset.hpp    |  8 ++--
 src/caffe/leveldb_dataset.cpp     | 77 ++++++++++++++++---------------
 src/caffe/lmdb_dataset.cpp        | 75 +++++++++++++++---------------
 5 files changed, 126 insertions(+), 108 deletions(-)

diff --git a/include/caffe/dataset.hpp b/include/caffe/dataset.hpp
index 1f07aa9d18d..efe3ffde582 100644
--- a/include/caffe/dataset.hpp
+++ b/include/caffe/dataset.hpp
@@ -1,6 +1,8 @@
 #ifndef CAFFE_DATASET_H_
 #define CAFFE_DATASET_H_
 
+#include <google/protobuf/message.h>
+
 #include <algorithm>
 #include <iterator>
 #include <string>
@@ -8,34 +10,58 @@
 #include <vector>
 
 #include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
 namespace dataset_internal {
 
+using google::protobuf::Message;
+
+template<bool condition>
+struct static_assertion {};
+template<>
+struct static_assertion<true> {
+  enum {
+    DEFAULT_CODER_NOT_AVAILABLE
+  };
+};
+
 template <typename T>
-struct Coder {
-  static bool serialize(const T& obj, string* serialized) {
+struct DefaultCoder {
+  using static_assertion<sizeof(T) == 0>::DEFAULT_CODER_NOT_AVAILABLE;
+  static bool serialize(const T& obj, string* serialized);
+  static bool serialize(const T& obj, vector<char>* serialized);
+  static bool deserialize(const string& serialized, T* obj);
+  static bool deserialize(const char* data, size_t size, T* obj);
+};
+
+template <>
+struct DefaultCoder<Message> {
+  static bool serialize(const Message& obj, string* serialized) {
     return obj.SerializeToString(serialized);
   }
 
-  static bool serialize(const T& obj, vector<char>* serialized) {
+  static bool serialize(const Message& obj, vector<char>* serialized) {
     serialized->resize(obj.ByteSize());
     return obj.SerializeWithCachedSizesToArray(
         reinterpret_cast<unsigned char*>(serialized->data()));
   }
 
-  static bool deserialize(const string& serialized, T* obj) {
+  static bool deserialize(const string& serialized, Message* obj) {
     return obj->ParseFromString(serialized);
   }
 
-  static bool deserialize(const char* data, size_t size, T* obj) {
+  static bool deserialize(const char* data, size_t size, Message* obj) {
     return obj->ParseFromArray(data, size);
   }
 };
 
 template <>
-struct Coder<string> {
+struct DefaultCoder<caffe::Datum> : public DefaultCoder<Message> { };
+
+template <>
+struct DefaultCoder<string> {
   static bool serialize(string obj, string* serialized) {
     *serialized = obj;
     return true;
@@ -60,7 +86,7 @@ struct Coder<string> {
 };
 
 template <>
-struct Coder<vector<char> > {
+struct DefaultCoder<vector<char> > {
   static bool serialize(vector<char> obj, string* serialized) {
     string tmp(obj.data(), obj.size());
     serialized->swap(tmp);
@@ -87,7 +113,9 @@ struct Coder<vector<char> > {
 
 }  // namespace dataset_internal
 
-template <typename K, typename V>
+template <typename K, typename V,
+          typename KCoder = dataset_internal::DefaultCoder<K>,
+          typename VCoder = dataset_internal::DefaultCoder<V> >
 class Dataset {
  public:
   enum Mode {
@@ -200,26 +228,6 @@ class Dataset {
   virtual void increment(shared_ptr<DatasetState>* state) const = 0;
   virtual KV& dereference(
       shared_ptr<DatasetState> state) const = 0;
-
-  template <typename T>
-  static bool serialize(const T& obj, string* serialized) {
-    return dataset_internal::Coder<T>::serialize(obj, serialized);
-  }
-
-  template <typename T>
-  static bool serialize(const T& obj, vector<char>* serialized) {
-    return dataset_internal::Coder<T>::serialize(obj, serialized);
-  }
-
-  template <typename T>
-  static bool deserialize(const string& serialized, T* obj) {
-    return dataset_internal::Coder<T>::deserialize(serialized, obj);
-  }
-
-  template <typename T>
-  static bool deserialize(const char* data, size_t size, T* obj) {
-    return dataset_internal::Coder<T>::deserialize(data, size, obj);
-  }
 };
 
 }  // namespace caffe
@@ -227,6 +235,6 @@ class Dataset {
 #define INSTANTIATE_DATASET(type) \
   template class type<string, string>; \
   template class type<string, vector<char> >; \
-  template class type<string, Datum>;
+  template class type<string, caffe::Datum>;
 
 #endif  // CAFFE_DATASET_H_
diff --git a/include/caffe/leveldb_dataset.hpp b/include/caffe/leveldb_dataset.hpp
index eb354d95db3..90f92d9336a 100644
--- a/include/caffe/leveldb_dataset.hpp
+++ b/include/caffe/leveldb_dataset.hpp
@@ -13,10 +13,12 @@
 
 namespace caffe {
 
-template <typename K, typename V>
-class LeveldbDataset : public Dataset<K, V> {
+template <typename K, typename V,
+          typename KCoder = dataset_internal::DefaultCoder<K>,
+          typename VCoder = dataset_internal::DefaultCoder<V> >
+class LeveldbDataset : public Dataset<K, V, KCoder, VCoder> {
  public:
-  typedef Dataset<K, V> Base;
+  typedef Dataset<K, V, KCoder, VCoder> Base;
   typedef typename Base::key_type key_type;
   typedef typename Base::value_type value_type;
   typedef typename Base::DatasetState DatasetState;
diff --git a/include/caffe/lmdb_dataset.hpp b/include/caffe/lmdb_dataset.hpp
index 57da5dea829..8817df7cade 100644
--- a/include/caffe/lmdb_dataset.hpp
+++ b/include/caffe/lmdb_dataset.hpp
@@ -12,10 +12,12 @@
 
 namespace caffe {
 
-template <typename K, typename V>
-class LmdbDataset : public Dataset<K, V> {
+template <typename K, typename V,
+          typename KCoder = dataset_internal::DefaultCoder<K>,
+          typename VCoder = dataset_internal::DefaultCoder<V> >
+class LmdbDataset : public Dataset<K, V, KCoder, VCoder> {
  public:
-  typedef Dataset<K, V> Base;
+  typedef Dataset<K, V, KCoder, VCoder> Base;
   typedef typename Base::key_type key_type;
   typedef typename Base::value_type value_type;
   typedef typename Base::DatasetState DatasetState;
diff --git a/src/caffe/leveldb_dataset.cpp b/src/caffe/leveldb_dataset.cpp
index 41c71e44557..af9560456d8 100644
--- a/src/caffe/leveldb_dataset.cpp
+++ b/src/caffe/leveldb_dataset.cpp
@@ -7,8 +7,9 @@
 
 namespace caffe {
 
-template <typename K, typename V>
-bool LeveldbDataset<K, V>::open(const string& filename, Mode mode) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::open(const string& filename,
+    Mode mode) {
   DLOG(INFO) << "LevelDB: Open " << filename;
 
   leveldb::Options options;
@@ -54,8 +55,8 @@ bool LeveldbDataset<K, V>::open(const string& filename, Mode mode) {
   return true;
 }
 
-template <typename K, typename V>
-bool LeveldbDataset<K, V>::put(const K& key, const V& value) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::put(const K& key, const V& value) {
   DLOG(INFO) << "LevelDB: Put";
 
   if (read_only_) {
@@ -66,12 +67,12 @@ bool LeveldbDataset<K, V>::put(const K& key, const V& value) {
   CHECK_NOTNULL(batch_.get());
 
   string serialized_key;
-  if (!Base::serialize(key, &serialized_key)) {
+  if (!KCoder::serialize(key, &serialized_key)) {
     return false;
   }
 
   string serialized_value;
-  if (!Base::serialize(value, &serialized_value)) {
+  if (!VCoder::serialize(value, &serialized_value)) {
     return false;
   }
 
@@ -80,12 +81,12 @@ bool LeveldbDataset<K, V>::put(const K& key, const V& value) {
   return true;
 }
 
-template <typename K, typename V>
-bool LeveldbDataset<K, V>::get(const K& key, V* value) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
   DLOG(INFO) << "LevelDB: Get";
 
   string serialized_key;
-  if (!Base::serialize(key, &serialized_key)) {
+  if (!KCoder::serialize(key, &serialized_key)) {
     return false;
   }
 
@@ -98,15 +99,15 @@ bool LeveldbDataset<K, V>::get(const K& key, V* value) {
     return false;
   }
 
-  if (!Base::deserialize(serialized_value, value)) {
+  if (!VCoder::deserialize(serialized_value, value)) {
     return false;
   }
 
   return true;
 }
 
-template <typename K, typename V>
-bool LeveldbDataset<K, V>::commit() {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::commit() {
   DLOG(INFO) << "LevelDB: Commit";
 
   if (read_only_) {
@@ -124,16 +125,16 @@ bool LeveldbDataset<K, V>::commit() {
   return status.ok();
 }
 
-template <typename K, typename V>
-void LeveldbDataset<K, V>::close() {
+template <typename K, typename V, typename KCoder, typename VCoder>
+void LeveldbDataset<K, V, KCoder, VCoder>::close() {
   DLOG(INFO) << "LevelDB: Close";
 
   batch_.reset();
   db_.reset();
 }
 
-template <typename K, typename V>
-void LeveldbDataset<K, V>::keys(vector<K>* keys) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+void LeveldbDataset<K, V, KCoder, VCoder>::keys(vector<K>* keys) {
   DLOG(INFO) << "LevelDB: Keys";
 
   keys->clear();
@@ -142,9 +143,9 @@ void LeveldbDataset<K, V>::keys(vector<K>* keys) {
   }
 }
 
-template <typename K, typename V>
-typename LeveldbDataset<K, V>::const_iterator
-    LeveldbDataset<K, V>::begin() const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
+    LeveldbDataset<K, V, KCoder, VCoder>::begin() const {
   CHECK_NOTNULL(db_.get());
   shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
   iter->SeekToFirst();
@@ -159,26 +160,26 @@ typename LeveldbDataset<K, V>::const_iterator
   return const_iterator(this, state);
 }
 
-template <typename K, typename V>
-typename LeveldbDataset<K, V>::const_iterator
-    LeveldbDataset<K, V>::end() const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
+    LeveldbDataset<K, V, KCoder, VCoder>::end() const {
   shared_ptr<DatasetState> state;
   return const_iterator(this, state);
 }
 
-template <typename K, typename V>
-typename LeveldbDataset<K, V>::const_iterator
-    LeveldbDataset<K, V>::cbegin() const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
+    LeveldbDataset<K, V, KCoder, VCoder>::cbegin() const {
   return begin();
 }
 
-template <typename K, typename V>
-typename LeveldbDataset<K, V>::const_iterator
-    LeveldbDataset<K, V>::cend() const { return end(); }
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
+    LeveldbDataset<K, V, KCoder, VCoder>::cend() const { return end(); }
 
-template <typename K, typename V>
-bool LeveldbDataset<K, V>::equal(shared_ptr<DatasetState> state1,
-    shared_ptr<DatasetState> state2) const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::equal(
+    shared_ptr<DatasetState> state1, shared_ptr<DatasetState> state2) const {
   shared_ptr<LeveldbState> leveldb_state1 =
       boost::dynamic_pointer_cast<LeveldbState>(state1);
 
@@ -191,8 +192,9 @@ bool LeveldbDataset<K, V>::equal(shared_ptr<DatasetState> state1,
   return !leveldb_state1 && !leveldb_state2;
 }
 
-template <typename K, typename V>
-void LeveldbDataset<K, V>::increment(shared_ptr<DatasetState>* state) const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+void LeveldbDataset<K, V, KCoder, VCoder>::increment(
+    shared_ptr<DatasetState>* state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(*state);
 
@@ -209,8 +211,9 @@ void LeveldbDataset<K, V>::increment(shared_ptr<DatasetState>* state) const {
   }
 }
 
-template <typename K, typename V>
-typename Dataset<K, V>::KV& LeveldbDataset<K, V>::dereference(
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename Dataset<K, V, KCoder, VCoder>::KV&
+    LeveldbDataset<K, V, KCoder, VCoder>::dereference(
     shared_ptr<DatasetState> state) const {
   shared_ptr<LeveldbState> leveldb_state =
       boost::dynamic_pointer_cast<LeveldbState>(state);
@@ -225,9 +228,9 @@ typename Dataset<K, V>::KV& LeveldbDataset<K, V>::dereference(
 
   const leveldb::Slice& key = iter->key();
   const leveldb::Slice& value = iter->value();
-  CHECK(Base::deserialize(key.data(), key.size(),
+  CHECK(KCoder::deserialize(key.data(), key.size(),
       &leveldb_state->kv_pair_.key));
-  CHECK(Base::deserialize(value.data(), value.size(),
+  CHECK(VCoder::deserialize(value.data(), value.size(),
       &leveldb_state->kv_pair_.value));
 
   return leveldb_state->kv_pair_;
diff --git a/src/caffe/lmdb_dataset.cpp b/src/caffe/lmdb_dataset.cpp
index d2b6fcd1c85..ca968432bc0 100644
--- a/src/caffe/lmdb_dataset.cpp
+++ b/src/caffe/lmdb_dataset.cpp
@@ -9,8 +9,9 @@
 
 namespace caffe {
 
-template <typename K, typename V>
-bool LmdbDataset<K, V>::open(const string& filename, Mode mode) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::open(const string& filename,
+    Mode mode) {
   DLOG(INFO) << "LMDB: Open " << filename;
 
   CHECK(NULL == env_);
@@ -80,18 +81,18 @@ bool LmdbDataset<K, V>::open(const string& filename, Mode mode) {
   return true;
 }
 
-template <typename K, typename V>
-bool LmdbDataset<K, V>::put(const K& key, const V& value) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::put(const K& key, const V& value) {
   DLOG(INFO) << "LMDB: Put";
 
   vector<char> serialized_key;
-  if (!Base::serialize(key, &serialized_key)) {
+  if (!KCoder::serialize(key, &serialized_key)) {
     LOG(ERROR) << "failed to serialize key";
     return false;
   }
 
   vector<char> serialized_value;
-  if (!Base::serialize(value, &serialized_value)) {
+  if (!VCoder::serialize(value, &serialized_value)) {
     LOG(ERROR) << "failed to serialized value";
     return false;
   }
@@ -114,12 +115,12 @@ bool LmdbDataset<K, V>::put(const K& key, const V& value) {
   return true;
 }
 
-template <typename K, typename V>
-bool LmdbDataset<K, V>::get(const K& key, V* value) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
   DLOG(INFO) << "LMDB: Get";
 
   vector<char> serialized_key;
-  if (!Base::serialize(key, &serialized_key)) {
+  if (!KCoder::serialize(key, &serialized_key)) {
     LOG(ERROR) << "failed to serialized key";
     return false;
   }
@@ -144,7 +145,7 @@ bool LmdbDataset<K, V>::get(const K& key, V* value) {
 
   mdb_txn_abort(get_txn);
 
-  if (!Base::deserialize(reinterpret_cast<char*>(mdbdata.mv_data),
+  if (!VCoder::deserialize(reinterpret_cast<char*>(mdbdata.mv_data),
       mdbdata.mv_size, value)) {
     LOG(ERROR) << "failed to deserialize value";
     return false;
@@ -153,8 +154,8 @@ bool LmdbDataset<K, V>::get(const K& key, V* value) {
   return true;
 }
 
-template <typename K, typename V>
-bool LmdbDataset<K, V>::commit() {
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::commit() {
   DLOG(INFO) << "LMDB: Commit";
 
   CHECK_NOTNULL(txn_);
@@ -175,8 +176,8 @@ bool LmdbDataset<K, V>::commit() {
   return true;
 }
 
-template <typename K, typename V>
-void LmdbDataset<K, V>::close() {
+template <typename K, typename V, typename KCoder, typename VCoder>
+void LmdbDataset<K, V, KCoder, VCoder>::close() {
   DLOG(INFO) << "LMDB: Close";
 
   if (env_ && dbi_) {
@@ -188,8 +189,8 @@ void LmdbDataset<K, V>::close() {
   }
 }
 
-template <typename K, typename V>
-void LmdbDataset<K, V>::keys(vector<K>* keys) {
+template <typename K, typename V, typename KCoder, typename VCoder>
+void LmdbDataset<K, V, KCoder, VCoder>::keys(vector<K>* keys) {
   DLOG(INFO) << "LMDB: Keys";
 
   keys->clear();
@@ -198,9 +199,9 @@ void LmdbDataset<K, V>::keys(vector<K>* keys) {
   }
 }
 
-template <typename K, typename V>
-typename LmdbDataset<K, V>::const_iterator
-    LmdbDataset<K, V>::begin() const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
+    LmdbDataset<K, V, KCoder, VCoder>::begin() const {
   int retval;
 
   MDB_txn* iter_txn;
@@ -226,23 +227,23 @@ typename LmdbDataset<K, V>::const_iterator
   return const_iterator(this, state);
 }
 
-template <typename K, typename V>
-typename LmdbDataset<K, V>::const_iterator
-    LmdbDataset<K, V>::end() const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
+    LmdbDataset<K, V, KCoder, VCoder>::end() const {
   shared_ptr<DatasetState> state;
   return const_iterator(this, state);
 }
 
-template <typename K, typename V>
-typename LmdbDataset<K, V>::const_iterator
-    LmdbDataset<K, V>::cbegin() const { return begin(); }
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
+    LmdbDataset<K, V, KCoder, VCoder>::cbegin() const { return begin(); }
 
-template <typename K, typename V>
-typename LmdbDataset<K, V>::const_iterator
-    LmdbDataset<K, V>::cend() const { return end(); }
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
+    LmdbDataset<K, V, KCoder, VCoder>::cend() const { return end(); }
 
-template <typename K, typename V>
-bool LmdbDataset<K, V>::equal(shared_ptr<DatasetState> state1,
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::equal(shared_ptr<DatasetState> state1,
     shared_ptr<DatasetState> state2) const {
   shared_ptr<LmdbState> lmdb_state1 =
       boost::dynamic_pointer_cast<LmdbState>(state1);
@@ -256,8 +257,9 @@ bool LmdbDataset<K, V>::equal(shared_ptr<DatasetState> state1,
   return !lmdb_state1 && !lmdb_state2;
 }
 
-template <typename K, typename V>
-void LmdbDataset<K, V>::increment(shared_ptr<DatasetState>* state) const {
+template <typename K, typename V, typename KCoder, typename VCoder>
+void LmdbDataset<K, V, KCoder, VCoder>::increment(
+    shared_ptr<DatasetState>* state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(*state);
 
@@ -278,8 +280,9 @@ void LmdbDataset<K, V>::increment(shared_ptr<DatasetState>* state) const {
   }
 }
 
-template <typename K, typename V>
-typename Dataset<K, V>::KV& LmdbDataset<K, V>::dereference(
+template <typename K, typename V, typename KCoder, typename VCoder>
+typename Dataset<K, V, KCoder, VCoder>::KV&
+    LmdbDataset<K, V, KCoder, VCoder>::dereference(
     shared_ptr<DatasetState> state) const {
   shared_ptr<LmdbState> lmdb_state =
       boost::dynamic_pointer_cast<LmdbState>(state);
@@ -295,9 +298,9 @@ typename Dataset<K, V>::KV& LmdbDataset<K, V>::dereference(
   int retval = mdb_cursor_get(cursor, &mdb_key, &mdb_val, MDB_GET_CURRENT);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
-  CHECK(Base::deserialize(reinterpret_cast<char*>(mdb_key.mv_data),
+  CHECK(KCoder::deserialize(reinterpret_cast<char*>(mdb_key.mv_data),
       mdb_key.mv_size, &lmdb_state->kv_pair_.key));
-  CHECK(Base::deserialize(reinterpret_cast<char*>(mdb_val.mv_data),
+  CHECK(VCoder::deserialize(reinterpret_cast<char*>(mdb_val.mv_data),
       mdb_val.mv_size, &lmdb_state->kv_pair_.value));
 
   return lmdb_state->kv_pair_;

From 8944b7addcff7be6947c255db7ee85881d1b586d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 14 Oct 2014 21:03:03 -0700
Subject: [PATCH 0939/2053] Reintroduce pkg-config with optional
 Makefile.config flag.

---
 Makefile                | 9 ++++++++-
 Makefile.config.example | 4 ++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 35c37af50fb..26d5964cacc 100644
--- a/Makefile
+++ b/Makefile
@@ -329,7 +329,14 @@ NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
 # mex may invoke an older gcc that is too liberal with -Wuninitalized
 MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
 LINKFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
-LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
+
+USE_PKG_CONFIG ?= 0
+ifeq ($(USE_PKG_CONFIG), 1)
+	PKG_CONFIG := $(shell pkg-config opencv --libs)
+else
+	PKG_CONFIG :=
+endif
+LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 
diff --git a/Makefile.config.example b/Makefile.config.example
index 5cb0b243aca..b6d95e31df8 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -60,6 +60,10 @@ PYTHON_LIB := /usr/lib
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 
+# Uncomment to use `pkg-config` to specify OpenCV library paths.
+# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
+# USE_PKG_CONFIG := 1
+
 BUILD_DIR := build
 DISTRIBUTE_DIR := distribute
 

From 5c4125da3ece192eeabd46dbb9e289071a7637b1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 14 Oct 2014 23:48:38 -0700
Subject: [PATCH 0940/2053] [docs] proofreading suggested by @cNikolaou

---
 docs/tutorial/net_layer_blob.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index b500aaa64d5..1f0966f88a4 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -26,7 +26,7 @@ Note that although we have designed blobs with its dimensions corresponding to i
 
 Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
 
-Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 4096.
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 1024.
 
 For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
 
@@ -85,7 +85,7 @@ Developing custom layers requires minimal effort by the compositionality of the
 
 The net jointly defines a function and its gradient by composition and auto-differentiation. The composition of every layer's output computes the function to do a given task, and the composition of every layer's backward computes the gradient from the loss to learn the task. Caffe models are end-to-end machine learning engines.
 
-The net is a set of layers connected in a computation graph -- a DAG / directed acyclic graph to be exact. Caffe does all the bookkeeping for any DAG of layers to ensure correctness of the forward and backward passes. A typical net begins with a data layer that loads from disk and ends with a loss layer that computes the objective for a task such as classification or reconstruction.
+The net is a set of layers connected in a computation graph -- a directed acyclic graph (DAG) to be exact. Caffe does all the bookkeeping for any DAG of layers to ensure correctness of the forward and backward passes. A typical net begins with a data layer that loads from disk and ends with a loss layer that computes the objective for a task such as classification or reconstruction.
 
 The net is defined as a set of layers and their connections in a plaintext modeling language.
 A simple logistic regression classifier

From 23af87fc49d7ffaa70584458cacb1528a5fb1f77 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 15 Oct 2014 09:45:09 -0700
Subject: [PATCH 0941/2053] Added first_key and last_key to dataset

---
 include/caffe/dataset.hpp         |   2 +
 include/caffe/leveldb_dataset.hpp |   2 +
 include/caffe/lmdb_dataset.hpp    |   2 +
 src/caffe/leveldb_dataset.cpp     |  24 +++++++
 src/caffe/lmdb_dataset.cpp        |  64 +++++++++++++++++
 src/caffe/test/test_dataset.cpp   | 114 ++++++++++++++++++++++++++++++
 6 files changed, 208 insertions(+)

diff --git a/include/caffe/dataset.hpp b/include/caffe/dataset.hpp
index efe3ffde582..90deb3160fa 100644
--- a/include/caffe/dataset.hpp
+++ b/include/caffe/dataset.hpp
@@ -135,6 +135,8 @@ class Dataset {
   virtual bool open(const string& filename, Mode mode) = 0;
   virtual bool put(const K& key, const V& value) = 0;
   virtual bool get(const K& key, V* value) = 0;
+  virtual bool first_key(K* key) = 0;
+  virtual bool last_key(K* key) = 0;
   virtual bool commit() = 0;
   virtual void close() = 0;
 
diff --git a/include/caffe/leveldb_dataset.hpp b/include/caffe/leveldb_dataset.hpp
index 90f92d9336a..f6c57fe65af 100644
--- a/include/caffe/leveldb_dataset.hpp
+++ b/include/caffe/leveldb_dataset.hpp
@@ -29,6 +29,8 @@ class LeveldbDataset : public Dataset<K, V, KCoder, VCoder> {
   bool open(const string& filename, Mode mode);
   bool put(const K& key, const V& value);
   bool get(const K& key, V* value);
+  bool first_key(K* key);
+  bool last_key(K* key);
   bool commit();
   void close();
 
diff --git a/include/caffe/lmdb_dataset.hpp b/include/caffe/lmdb_dataset.hpp
index 8817df7cade..71b322490c0 100644
--- a/include/caffe/lmdb_dataset.hpp
+++ b/include/caffe/lmdb_dataset.hpp
@@ -33,6 +33,8 @@ class LmdbDataset : public Dataset<K, V, KCoder, VCoder> {
   bool open(const string& filename, Mode mode);
   bool put(const K& key, const V& value);
   bool get(const K& key, V* value);
+  bool first_key(K* key);
+  bool last_key(K* key);
   bool commit();
   void close();
 
diff --git a/src/caffe/leveldb_dataset.cpp b/src/caffe/leveldb_dataset.cpp
index af9560456d8..53df985721c 100644
--- a/src/caffe/leveldb_dataset.cpp
+++ b/src/caffe/leveldb_dataset.cpp
@@ -106,6 +106,30 @@ bool LeveldbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
   return true;
 }
 
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::first_key(K* key) {
+  DLOG(INFO) << "LevelDB: First key";
+
+  CHECK_NOTNULL(db_.get());
+  shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
+  iter->SeekToFirst();
+  CHECK(iter->Valid());
+  const leveldb::Slice& key_slice = iter->key();
+  return KCoder::deserialize(key_slice.data(), key_slice.size(), key);
+}
+
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LeveldbDataset<K, V, KCoder, VCoder>::last_key(K* key) {
+  DLOG(INFO) << "LevelDB: Last key";
+
+  CHECK_NOTNULL(db_.get());
+  shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
+  iter->SeekToLast();
+  CHECK(iter->Valid());
+  const leveldb::Slice& key_slice = iter->key();
+  return KCoder::deserialize(key_slice.data(), key_slice.size(), key);
+}
+
 template <typename K, typename V, typename KCoder, typename VCoder>
 bool LeveldbDataset<K, V, KCoder, VCoder>::commit() {
   DLOG(INFO) << "LevelDB: Commit";
diff --git a/src/caffe/lmdb_dataset.cpp b/src/caffe/lmdb_dataset.cpp
index ca968432bc0..23852f0cd81 100644
--- a/src/caffe/lmdb_dataset.cpp
+++ b/src/caffe/lmdb_dataset.cpp
@@ -154,6 +154,70 @@ bool LmdbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
   return true;
 }
 
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::first_key(K* key) {
+  DLOG(INFO) << "LMDB: First key";
+
+  int retval;
+
+  MDB_txn* iter_txn;
+
+  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &iter_txn);
+  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
+      << mdb_strerror(retval);
+
+  MDB_cursor* cursor;
+  retval = mdb_cursor_open(iter_txn, dbi_, &cursor);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+  MDB_val mdbkey;
+  MDB_val mdbval;
+  retval = mdb_cursor_get(cursor, &mdbkey, &mdbval, MDB_FIRST);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+
+  mdb_cursor_close(cursor);
+  mdb_txn_abort(iter_txn);
+
+  if (!KCoder::deserialize(reinterpret_cast<char*>(mdbkey.mv_data),
+      mdbkey.mv_size, key)) {
+    LOG(ERROR) << "failed to deserialize key";
+    return false;
+  }
+
+  return true;
+}
+
+template <typename K, typename V, typename KCoder, typename VCoder>
+bool LmdbDataset<K, V, KCoder, VCoder>::last_key(K* key) {
+  DLOG(INFO) << "LMDB: Last key";
+
+  int retval;
+
+  MDB_txn* iter_txn;
+
+  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &iter_txn);
+  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
+      << mdb_strerror(retval);
+
+  MDB_cursor* cursor;
+  retval = mdb_cursor_open(iter_txn, dbi_, &cursor);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+  MDB_val mdbkey;
+  MDB_val mdbval;
+  retval = mdb_cursor_get(cursor, &mdbkey, &mdbval, MDB_LAST);
+  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+
+  mdb_cursor_close(cursor);
+  mdb_txn_abort(iter_txn);
+
+  if (!KCoder::deserialize(reinterpret_cast<char*>(mdbkey.mv_data),
+      mdbkey.mv_size, key)) {
+    LOG(ERROR) << "failed to deserialize key";
+    return false;
+  }
+
+  return true;
+}
+
 template <typename K, typename V, typename KCoder, typename VCoder>
 bool LmdbDataset<K, V, KCoder, VCoder>::commit() {
   DLOG(INFO) << "LMDB: Commit";
diff --git a/src/caffe/test/test_dataset.cpp b/src/caffe/test/test_dataset.cpp
index 7d0f6b3d9bb..e5bd1d2c40e 100644
--- a/src/caffe/test/test_dataset.cpp
+++ b/src/caffe/test/test_dataset.cpp
@@ -277,6 +277,120 @@ TYPED_TEST(DatasetTest, TestKeys) {
   EXPECT_FALSE(this->equals(keys.at(0), keys.at(1)));
 }
 
+TYPED_TEST(DatasetTest, TestFirstKey) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  value_type value = this->TestValue();
+
+  string key1 = "01";
+  EXPECT_TRUE(dataset->put(key1, value));
+
+  string key2 = "02";
+  EXPECT_TRUE(dataset->put(key2, value));
+
+  string key3 = "03";
+  EXPECT_TRUE(dataset->put(key3, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  string first_key;
+  dataset->first_key(&first_key);
+
+  EXPECT_TRUE(this->equals(first_key, key1));
+}
+
+TYPED_TEST(DatasetTest, TestLastKey) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  value_type value = this->TestValue();
+
+  string key1 = "01";
+  EXPECT_TRUE(dataset->put(key1, value));
+
+  string key2 = "02";
+  EXPECT_TRUE(dataset->put(key2, value));
+
+  string key3 = "03";
+  EXPECT_TRUE(dataset->put(key3, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  string last_key;
+  dataset->last_key(&last_key);
+
+  EXPECT_TRUE(this->equals(last_key, key3));
+}
+
+TYPED_TEST(DatasetTest, TestFirstLastKeys) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  value_type value = this->TestValue();
+
+  string key1 = "01";
+  EXPECT_TRUE(dataset->put(key1, value));
+
+  string key2 = "02";
+  EXPECT_TRUE(dataset->put(key2, value));
+
+  string key3 = "03";
+  EXPECT_TRUE(dataset->put(key3, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  string first_key;
+  dataset->first_key(&first_key);
+  string last_key;
+  dataset->last_key(&last_key);
+
+  EXPECT_TRUE(this->equals(first_key, key1));
+  EXPECT_TRUE(this->equals(last_key, key3));
+}
+
+TYPED_TEST(DatasetTest, TestFirstLastKeysUnOrdered) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  value_type value = this->TestValue();
+
+  string key3 = "03";
+  EXPECT_TRUE(dataset->put(key3, value));
+
+  string key1 = "01";
+  EXPECT_TRUE(dataset->put(key1, value));
+
+  string key2 = "02";
+  EXPECT_TRUE(dataset->put(key2, value));
+
+  EXPECT_TRUE(dataset->commit());
+
+  string first_key;
+  dataset->first_key(&first_key);
+  string last_key;
+  dataset->last_key(&last_key);
+
+  EXPECT_TRUE(this->equals(first_key, key1));
+  EXPECT_TRUE(this->equals(last_key, key3));
+}
+
 TYPED_TEST(DatasetTest, TestKeysNoCommit) {
   UNPACK_TYPES;
 

From ae88994128858ffc66095c2d927a825ed04f5ed3 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Mon, 6 Oct 2014 18:05:11 -0700
Subject: [PATCH 0942/2053] Added encoded option and check_size to
 convert_imageset

Conflicts:
	tools/convert_imageset.cpp
---
 tools/convert_imageset.cpp | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 37efa5c13f7..c554ed389ab 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -34,6 +34,8 @@ DEFINE_int32(resize_width, 0, "Width images are resized to");
 DEFINE_int32(resize_height, 0, "Height images are resized to");
 DEFINE_bool(check_size, false,
     "When this option is on, check that all the datum have the same size");
+DEFINE_bool(encoded, false,
+    "When this option is on, the encoded image will be save in datum");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -55,8 +57,10 @@ int main(int argc, char** argv) {
     return 1;
   }
 
-  bool is_color = !FLAGS_gray;
-  bool check_size = FLAGS_check_size;
+  const bool is_color = !FLAGS_gray;
+  const bool check_size = FLAGS_check_size;
+  const bool encoded = FLAGS_encoded;
+
   std::ifstream infile(argv[2]);
   std::vector<std::pair<std::string, int> > lines;
   std::string filename;
@@ -74,6 +78,12 @@ int main(int argc, char** argv) {
   const std::string& db_backend = FLAGS_backend;
   const char* db_path = argv[3];
 
+  if (encoded) {
+    CHECK_EQ(FLAGS_resize_height, 0) << "With encoded don't resize images";
+    CHECK_EQ(FLAGS_resize_width, 0) << "With encoded don't resize images";
+    CHECK(!check_size) << "With encoded cannot check_size";
+  }
+
   int resize_height = std::max<int>(0, FLAGS_resize_height);
   int resize_width = std::max<int>(0, FLAGS_resize_width);
 
@@ -94,10 +104,15 @@ int main(int argc, char** argv) {
   bool data_size_initialized = false;
 
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
-    if (!ReadImageToDatum(root_folder + lines[line_id].first,
-        lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
-      continue;
+    bool status;
+    if (encoded) {
+      status = ReadFileToDatum(root_folder + lines[line_id].first,
+        lines[line_id].second, &datum);
+    } else {
+      status = ReadImageToDatum(root_folder + lines[line_id].first,
+          lines[line_id].second, resize_height, resize_width, is_color, &datum);
     }
+    if (status == false) continue;
     if (check_size) {
       if (!data_size_initialized) {
         data_size = datum.channels() * datum.height() * datum.width();

From 9fc7f36e0445883249f6ef07d460d3806d4c9ab3 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Mon, 6 Oct 2014 18:25:28 -0700
Subject: [PATCH 0943/2053] Added encoded datum to io

---
 include/caffe/util/io.hpp   | 38 +++++++++++++++++++++++
 src/caffe/proto/caffe.proto |  2 ++
 src/caffe/util/io.cpp       | 61 +++++++++++++++++++++++++++++++++++--
 3 files changed, 98 insertions(+), 3 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index b64c821a86e..63afe6298ff 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -88,6 +88,12 @@ inline void WriteProtoToBinaryFile(
   WriteProtoToBinaryFile(proto, filename.c_str());
 }
 
+bool ReadFileToDatum(const string& filename, const int label, Datum* datum);
+
+inline bool ReadFileToDatum(const string& filename, Datum* datum) {
+  return ReadFileToDatum(filename, -1, datum);
+}
+
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color, Datum* datum);
 
@@ -106,6 +112,21 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, true, datum);
 }
 
+bool DecodeDatum(const int height, const int width, const bool is_color,
+  Datum& datum);
+
+inline bool DecodeDatum(const int height, const int width, Datum& datum) {
+  return DecodeDatum(height, width, true, datum);
+}
+
+inline bool DecodeDatum(const bool is_color, Datum& datum) {
+  return DecodeDatum(0, 0, is_color, datum);
+}
+
+inline bool DecodeDatum(Datum& datum) {
+  return DecodeDatum(0, 0, true, datum);
+}
+
 #ifndef OSX
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
@@ -124,6 +145,23 @@ inline cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
 
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width, const bool is_color);
+
+inline cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width) {
+  return DecodeDatumToCVMat(datum, height, width, true);
+}
+
+inline cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const bool is_color) {
+  return DecodeDatumToCVMat(datum, 0, 0, is_color);
+}
+
+inline cv::Mat DecodeDatumToCVMat(const Datum& datum) {
+  return DecodeDatumToCVMat(datum, 0, 0, true);
+}
+
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 #endif
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f0dba097d05..53db6f76945 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -26,6 +26,8 @@ message Datum {
   optional int32 label = 5;
   // Optionally, the datum could also hold float data.
   repeated float float_data = 6;
+  // If true data contains an encoded image that need to be decoded
+  optional bool encoded = 7 [default = false];
 }
 
 message FillerParameter {
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index a4a6627b277..6aa410a0feb 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -95,21 +95,76 @@ bool ReadImageToDatum(const string& filename, const int label,
   }
 }
 
+bool ReadFileToDatum(const string& filename, const int label,
+    Datum* datum) {
+  std::streampos size;
+
+  fstream file(filename.c_str(), ios::in|ios::binary|ios::ate);
+  if (file.is_open()) {
+    size = file.tellg();
+    std::string buffer(size, ' ');
+    file.seekg(0, ios::beg);
+    file.read(&buffer[0], size);
+    file.close();
+    datum->set_data(buffer);
+    datum->set_label(label);
+    datum->set_encoded(true);
+    return true;
+  } else {
+    return false;
+  }
+}
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width, const bool is_color) {
+  cv::Mat cv_img;
+  CHECK(datum.encoded()) << "Datum not encoded";
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
+  const string& data = datum.data();
+  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
+  if (height > 0 && width > 0) {
+    cv::Mat cv_img_origin = cv::imdecode(cv::Mat(vec_data), cv_read_flag);
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
+  } else {
+    cv_img = cv::imdecode(vec_data, cv_read_flag);
+  }
+  if (!cv_img.data) {
+    LOG(ERROR) << "Could not decode datum ";
+  }
+  return cv_img;
+}
+
+// If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
+// if height and width are set it will resize it
+// If Datum is not encoded will do nothing
+bool DecodeDatum(const int height, const int width, const bool is_color,
+                Datum& datum) {
+  if (datum.encoded()) {
+    cv::Mat cv_img = DecodeDatumToCVMat(datum, height, width, is_color);
+    CVMatToDatum(cv_img, &datum);
+    return true;
+  } else {
+    return false;
+  }
+}
+
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
-  CHECK(cv_img.depth() == CV_8U) <<
-      "Image data type must be unsigned byte";
+  CHECK(cv_img.depth() == CV_8U || cv_img.depth() == CV_8S) <<
+      "Image data type must be unsigned or signed byte";
   datum->set_channels(cv_img.channels());
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
   datum->clear_data();
   datum->clear_float_data();
+  datum->set_encoded(false);
   int datum_channels = datum->channels();
   int datum_height = datum->height();
   int datum_width = datum->width();
   int datum_size = datum_channels * datum_height * datum_width;
   std::string buffer(datum_size, ' ');
   for (int h = 0; h < datum_height; ++h) {
-    const uchar* ptr = cv_img.ptr<uchar>(h);
+    const char* ptr = cv_img.ptr<char>(h);
     int img_index = 0;
     for (int w = 0; w < datum_width; ++w) {
       for (int c = 0; c < datum_channels; ++c) {

From 1d586ff245447062cbd1c60603c485f99024753c Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Mon, 6 Oct 2014 23:27:40 -0700
Subject: [PATCH 0944/2053] Added test for encoded Datum to test_io.cpp

---
 include/caffe/util/io.hpp  |  8 ++--
 src/caffe/test/test_io.cpp | 80 ++++++++++++++++++++++++++++++++++++++
 src/caffe/util/io.cpp      |  8 ++--
 3 files changed, 88 insertions(+), 8 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 63afe6298ff..64df0155780 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -113,17 +113,17 @@ inline bool ReadImageToDatum(const string& filename, const int label,
 }
 
 bool DecodeDatum(const int height, const int width, const bool is_color,
-  Datum& datum);
+  Datum* datum);
 
-inline bool DecodeDatum(const int height, const int width, Datum& datum) {
+inline bool DecodeDatum(const int height, const int width, Datum* datum) {
   return DecodeDatum(height, width, true, datum);
 }
 
-inline bool DecodeDatum(const bool is_color, Datum& datum) {
+inline bool DecodeDatum(const bool is_color, Datum* datum) {
   return DecodeDatum(0, 0, is_color, datum);
 }
 
-inline bool DecodeDatum(Datum& datum) {
+inline bool DecodeDatum(Datum* datum) {
   return DecodeDatum(0, 0, true, datum);
 }
 
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 6994afda0e3..4d941fa8683 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -276,4 +276,84 @@ TEST_F(IOTest, TestCVMatToDatumReference) {
   }
 }
 
+TEST_F(IOTest, TestReadFileToDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(datum.encoded());
+  EXPECT_EQ(datum.label(), -1);
+  EXPECT_EQ(datum.data().size(), 140391);
+}
+
+TEST_F(IOTest, TestDecodeDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(DecodeDatum(&datum));
+  EXPECT_FALSE(DecodeDatum(&datum));
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMat) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, 100, 200);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 100);
+  EXPECT_EQ(cv_img.cols, 200);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatResizedGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  const bool is_color = false;
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, 200, 100, is_color);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 200);
+  EXPECT_EQ(cv_img.cols, 100);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum);
+  cv::Mat cv_img_ref = ReadImageToCVMat(filename);
+  EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
+  EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
+  EXPECT_EQ(cv_img_ref.cols, cv_img.cols);
+
+  for (int c = 0; c < datum.channels(); ++c) {
+    for (int h = 0; h < datum.height(); ++h) {
+      for (int w = 0; w < datum.width(); ++w) {
+        EXPECT_TRUE(cv_img.at<cv::Vec3b>(h, w)[c]==
+          cv_img_ref.at<cv::Vec3b>(h, w)[c]);
+      }
+    }
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 6aa410a0feb..06b9deb078a 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -139,10 +139,10 @@ cv::Mat DecodeDatumToCVMat(const Datum& datum,
 // if height and width are set it will resize it
 // If Datum is not encoded will do nothing
 bool DecodeDatum(const int height, const int width, const bool is_color,
-                Datum& datum) {
-  if (datum.encoded()) {
-    cv::Mat cv_img = DecodeDatumToCVMat(datum, height, width, is_color);
-    CVMatToDatum(cv_img, &datum);
+                Datum* datum) {
+  if (datum->encoded()) {
+    cv::Mat cv_img = DecodeDatumToCVMat((*datum), height, width, is_color);
+    CVMatToDatum(cv_img, datum);
     return true;
   } else {
     return false;

From 917e90a2e1be366fdc478a321accd96b79cb55f5 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 15 Oct 2014 15:35:01 -0700
Subject: [PATCH 0945/2053] Added timers for benchmarking

---
 src/caffe/layers/data_layer.cpp        | 22 +++++++++++++++++++---
 src/caffe/layers/image_data_layer.cpp  | 13 +++++++++++++
 src/caffe/layers/window_data_layer.cpp | 20 ++++++++++++++------
 src/caffe/net.cpp                      |  7 +++++++
 4 files changed, 53 insertions(+), 9 deletions(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index fcf9bb20a57..330381d2771 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -8,6 +8,7 @@
 #include "caffe/dataset_factory.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -47,6 +48,9 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK(iter_ != dataset_->end());
   const Datum& datum = iter_->value;
 
+  if (DecodeDatum(datum)) {
+    LOG(INFO) << "Decoding Datum";
+  }
   // image
   int crop_size = this->layer_param_.transform_param().crop_size();
   if (crop_size > 0) {
@@ -78,6 +82,8 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
+  Timer batch_timer;
+  batch_timer.Start();
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -87,25 +93,35 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
   const int batch_size = this->layer_param_.data_param().batch_size();
-
+  float read_time = 0;
+  float trans_time = 0;
+  Timer timer;
   for (int item_id = 0; item_id < batch_size; ++item_id) {
+    timer.Start();
+    // get a blob
     CHECK(iter_ != dataset_->end());
     const Datum& datum = iter_->value;
+    cv::Mat cv_img = DecodeDatumToCVMat(datum);
+    read_time += timer.MilliSeconds();
+    timer.Start();
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
-    this->data_transformer_.Transform(datum, &(this->transformed_data_));
+    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
-
+    trans_time += timer.MilliSeconds();
     // go to the next iter
     ++iter_;
     if (iter_ == dataset_->end()) {
       iter_ = dataset_->begin();
     }
   }
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  DLOG(INFO) << "Read time: " << read_time << "ms.";
+  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 2085df2760a..67493117b88 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -6,6 +6,7 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -91,6 +92,8 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
+  Timer batch_timer;
+  batch_timer.Start();
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -103,18 +106,25 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
 
   // datum scales
   const int lines_size = lines_.size();
+  float read_time = 0;
+  float trans_time = 0;
+  Timer timer;
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
+    timer.Start();
     CHECK_GT(lines_size, lines_id_);
     cv::Mat cv_img = ReadImageToCVMat(lines_[lines_id_].first,
                                     new_height, new_width, is_color);
     if (!cv_img.data) {
       continue;
     }
+    read_time += timer.MilliSeconds();
+    timer.Start();
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+    trans_time += timer.MilliSeconds();
 
     top_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
@@ -128,6 +138,9 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       }
     }
   }
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  DLOG(INFO) << "Read time: " << read_time << "ms.";
+  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 529c3b47bd9..47e0fb2a123 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -13,6 +13,7 @@
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -192,7 +193,8 @@ template <typename Dtype>
 void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
-
+  Timer batch_timer;
+  batch_timer.Start();
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
@@ -219,19 +221,20 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   const int num_samples[2] = { batch_size - num_fg, num_fg };
 
   int item_id = 0;
+  float read_time = 0;
+  float trans_time = 0;
+  Timer timer;
   // sample from bg set then fg set
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
       // sample a window
+      timer.Start();
       const unsigned int rand_index = PrefetchRand();
       vector<float> window = (is_fg) ?
           fg_windows_[rand_index % fg_windows_.size()] :
           bg_windows_[rand_index % bg_windows_.size()];
 
-      bool do_mirror = false;
-      if (mirror && PrefetchRand() % 2) {
-        do_mirror = true;
-      }
+      bool do_mirror = mirror && PrefetchRand() % 2;
 
       // load the image containing the window
       pair<std::string, vector<int> > image =
@@ -242,6 +245,8 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
         LOG(ERROR) << "Could not open or find file " << image.first;
         return;
       }
+      read_time += timer.MilliSeconds();
+      timer.Start();
       const int channels = cv_img.channels();
 
       // crop window out of image and warp it
@@ -359,7 +364,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
           }
         }
       }
-
+      trans_time += timer.MilliSeconds();
       // get window label
       top_label[item_id] = window[WindowDataLayer<Dtype>::LABEL];
 
@@ -399,6 +404,9 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       item_id++;
     }
   }
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  DLOG(INFO) << "Read time: " << read_time << "ms.";
+  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 21ab15fd31b..5b7152e6993 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -9,6 +9,7 @@
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/insert_splits.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -499,6 +500,8 @@ void Net<Dtype>::GetLearningRateAndWeightDecay() {
 
 template <typename Dtype>
 Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
+  Timer timer;
+  timer.Start();
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
@@ -509,6 +512,7 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
   }
+  LOG(INFO) << "Forward time: " << timer.MilliSeconds() << "ms.";
   return loss;
 }
 
@@ -565,6 +569,8 @@ string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
 
 template <typename Dtype>
 void Net<Dtype>::BackwardFromTo(int start, int end) {
+  Timer timer;
+  timer.Start();
   CHECK_GE(end, 0);
   CHECK_LT(start, layers_.size());
   for (int i = start; i >= end; --i) {
@@ -574,6 +580,7 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
       if (debug_info_) { BackwardDebugInfo(i); }
     }
   }
+  LOG(INFO) << "Backward time: " << timer.MilliSeconds() << "ms.";
 }
 
 template <typename Dtype>

From d337b044c25abf30f94dd9fb7d25aca940eda7c0 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 15 Oct 2014 15:35:26 -0700
Subject: [PATCH 0946/2053] Add fast code for transform(cv::Mat,Blob)

---
 src/caffe/data_transformer.cpp  | 103 +++++++++++++++++++++++++++++++-
 src/caffe/layers/data_layer.cpp |  12 +++-
 2 files changed, 110 insertions(+), 5 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index dffaba545b5..8f145994562 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -179,9 +179,106 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
-  Datum datum;
-  CVMatToDatum(cv_img, &datum);
-  Transform(datum, transformed_blob);
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+  const int num = transformed_blob->num();
+
+  CHECK_EQ(channels, img_channels);
+  CHECK_LE(height, img_height);
+  CHECK_LE(width, img_width);
+  CHECK_GE(num, 1);
+
+  CHECK(cv_img.depth() == CV_8U || cv_img.depth() == CV_8S) <<
+      "Image data type must be unsigned or signed byte";
+
+  const int crop_size = param_.crop_size();
+  const Dtype scale = param_.scale();
+  const bool do_mirror = param_.mirror() && Rand(2);
+  const bool has_mean_file = param_.has_mean_file();
+  const bool has_mean_values = mean_values_.size() > 0;
+
+  CHECK_GT(img_channels, 0);
+  CHECK_GE(img_height, crop_size);
+  CHECK_GE(img_width, crop_size);
+
+  Dtype* mean = NULL;
+  if (has_mean_file) {
+    CHECK_EQ(img_channels, data_mean_.channels());
+    CHECK_EQ(img_height, data_mean_.height());
+    CHECK_EQ(img_width, data_mean_.width());
+    mean = data_mean_.mutable_cpu_data();
+  }
+  if (has_mean_values) {
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<
+     "Specify either 1 mean_value or as many as channels: " << img_channels;
+    if (img_channels > 1 && mean_values_.size() == 1) {
+      // Replicate the mean_value for simplicity
+      for (int c = 1; c < img_channels; ++c) {
+        mean_values_.push_back(mean_values_[0]);
+      }
+    }
+  }
+
+  int h_off = 0;
+  int w_off = 0;
+  cv::Mat cv_cropped_img = cv_img;
+  if (crop_size) {
+    CHECK_EQ(crop_size, height);
+    CHECK_EQ(crop_size, width);
+    // We only do random crop when we do training.
+    if (phase_ == Caffe::TRAIN) {
+      h_off = Rand(img_height - crop_size + 1);
+      w_off = Rand(img_width - crop_size + 1);
+    } else {
+      h_off = (img_height - crop_size) / 2;
+      w_off = (img_width - crop_size) / 2;
+    }
+    cv::Rect roi(h_off, w_off, crop_size, crop_size);
+    cv_cropped_img = cv_img(roi);
+  } else {
+    CHECK_EQ(img_height, height);
+    CHECK_EQ(img_width, width);
+  }
+  
+  // if (do_mirror) {
+  //   cv::flip(cv_cropped_img, cv_cropped_img, 1);
+  // }
+  CHECK(cv_cropped_img.data);
+
+  Dtype* transformed_data = transformed_blob->mutable_cpu_data();
+  int top_index;
+  for (int h = 0; h < height; ++h) {
+    const char* ptr = cv_cropped_img.ptr<char>(h);
+    int img_index = 0;
+    for (int w = 0; w < width; ++w) {
+      for (int c = 0; c < img_channels; ++c) {
+        if (do_mirror) {
+          top_index = (c * height + h) * width + (width - 1 - w);
+        } else {
+          top_index = (c * height + h) * width + w;
+        }
+        // int top_index = (c * height + h) * width + w;
+        Dtype pixel = static_cast<Dtype>(ptr[img_index++]);
+        if (has_mean_file) {
+          int mean_index = (c * img_height + h_off + h) * img_width + w_off + w;
+          transformed_data[top_index] =
+            (pixel - mean[mean_index]) * scale;
+        } else {
+          if (has_mean_values) {
+            transformed_data[top_index] =
+              (pixel - mean_values_[c]) * scale;
+          } else {
+            transformed_data[top_index] = pixel * scale;
+          }
+        }
+      }
+    }
+  }
 }
 #endif
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 330381d2771..95c54279661 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -101,14 +101,22 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     // get a blob
     CHECK(iter_ != dataset_->end());
     const Datum& datum = iter_->value;
-    cv::Mat cv_img = DecodeDatumToCVMat(datum);
+    cv::Mat cv_img;
+    if (datum.encoded()) {
+       cv_img = DecodeDatumToCVMat(datum);
+    }
     read_time += timer.MilliSeconds();
     timer.Start();
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
-    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+    if (datum.encoded()) {
+      this->data_transformer_.Transform(cv_img, &(this->transformed_data_));  
+    } else {
+      this->data_transformer_.Transform(datum, &(this->transformed_data_));
+    }
+    
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }

From e9d6e5a0b22a9f4768b8c04c9031ee8adb822ece Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 7 Oct 2014 11:19:16 -0700
Subject: [PATCH 0947/2053] Add root_folder to ImageDataLayer

---
 Makefile                               |  6 ++++++
 include/caffe/net.hpp                  | 10 +++++++++
 src/caffe/data_transformer.cpp         | 12 ++++-------
 src/caffe/layers/data_layer.cpp        | 25 ++++++++++++++-------
 src/caffe/layers/image_data_layer.cpp  | 30 +++++++++++++++++++-------
 src/caffe/layers/window_data_layer.cpp | 24 +++++++++++++++------
 src/caffe/net.cpp                      | 10 +++++++++
 src/caffe/proto/caffe.proto            |  1 +
 src/caffe/util/io.cpp                  |  5 ++---
 9 files changed, 90 insertions(+), 33 deletions(-)

diff --git a/Makefile b/Makefile
index 26d5964cacc..393fe18a70d 100644
--- a/Makefile
+++ b/Makefile
@@ -281,6 +281,12 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
+TIMING ?= 0
+# Timing Flag
+ifneq ($(TIMING), 0)
+	COMMON_FLAGS += -DTIMING
+endif
+
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 1d06dc45533..879f474674d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -11,6 +11,9 @@
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#ifdef TIMING
+#include "caffe/util/benchmark.hpp"
+#endif
 
 namespace caffe {
 
@@ -76,9 +79,16 @@ class Net {
   void Reshape();
 
   Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
+    #ifdef TIMING
+    Timer timer;
+    timer.Start();
+    #endif
     Dtype loss;
     Forward(bottom, &loss);
     Backward();
+    #ifdef TIMING
+    LOG(INFO) << "ForwardBackward Time: " << timer.MilliSeconds() << "ms.";
+    #endif
     return loss;
   }
 
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 8f145994562..023396ce5e6 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -193,8 +193,7 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
   CHECK_LE(width, img_width);
   CHECK_GE(num, 1);
 
-  CHECK(cv_img.depth() == CV_8U || cv_img.depth() == CV_8S) <<
-      "Image data type must be unsigned or signed byte";
+  CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
 
   const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
@@ -238,22 +237,19 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
       h_off = (img_height - crop_size) / 2;
       w_off = (img_width - crop_size) / 2;
     }
-    cv::Rect roi(h_off, w_off, crop_size, crop_size);
+    cv::Rect roi(w_off, h_off, crop_size, crop_size);
     cv_cropped_img = cv_img(roi);
   } else {
     CHECK_EQ(img_height, height);
     CHECK_EQ(img_width, width);
   }
-  
-  // if (do_mirror) {
-  //   cv::flip(cv_cropped_img, cv_cropped_img, 1);
-  // }
+
   CHECK(cv_cropped_img.data);
 
   Dtype* transformed_data = transformed_blob->mutable_cpu_data();
   int top_index;
   for (int h = 0; h < height; ++h) {
-    const char* ptr = cv_cropped_img.ptr<char>(h);
+    const uchar* ptr = cv_cropped_img.ptr<uchar>(h);
     int img_index = 0;
     for (int w = 0; w < width; ++w) {
       for (int c = 0; c < img_channels; ++c) {
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 95c54279661..95604e5a1ac 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -8,7 +8,9 @@
 #include "caffe/dataset_factory.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
+#endif
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -82,8 +84,13 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
+  #ifdef TIMING
   Timer batch_timer;
   batch_timer.Start();
+  float read_time = 0;
+  float trans_time = 0;
+  Timer timer;
+  #endif
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -93,9 +100,6 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
   const int batch_size = this->layer_param_.data_param().batch_size();
-  float read_time = 0;
-  float trans_time = 0;
-  Timer timer;
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
     // get a blob
@@ -105,31 +109,36 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     if (datum.encoded()) {
        cv_img = DecodeDatumToCVMat(datum);
     }
+    #ifdef TIMING
     read_time += timer.MilliSeconds();
     timer.Start();
+    #endif
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     if (datum.encoded()) {
-      this->data_transformer_.Transform(cv_img, &(this->transformed_data_));  
+      this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
     } else {
       this->data_transformer_.Transform(datum, &(this->transformed_data_));
     }
-    
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
+    #ifdef TIMING
     trans_time += timer.MilliSeconds();
+    #endif
     // go to the next iter
     ++iter_;
     if (iter_ == dataset_->end()) {
       iter_ = dataset_->begin();
     }
   }
-  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  DLOG(INFO) << "Read time: " << read_time << "ms.";
-  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
+  #ifdef TIMING
+  LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  LOG(INFO) << "Read time: " << read_time << "ms.";
+  LOG(INFO) << "Transform time: " << trans_time << "ms.";
+  #endif
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 67493117b88..0abcd888b67 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -6,7 +6,9 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
+#endif
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -24,6 +26,7 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int new_height = this->layer_param_.image_data_param().new_height();
   const int new_width  = this->layer_param_.image_data_param().new_width();
   const bool is_color  = this->layer_param_.image_data_param().is_color();
+  string root_folder = this->layer_param_.image_data_param().root_folder();
 
   CHECK((new_height == 0 && new_width == 0) ||
       (new_height > 0 && new_width > 0)) << "Current implementation requires "
@@ -57,7 +60,7 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     lines_id_ = skip;
   }
   // Read an image, and use it to initialize the top blob.
-  cv::Mat cv_img = ReadImageToCVMat(lines_[lines_id_].first,
+  cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
   const int channels = cv_img.channels();
   const int height = cv_img.rows;
@@ -92,8 +95,13 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
+  #ifdef TIMING
   Timer batch_timer;
   batch_timer.Start();
+  float read_time = 0;
+  float trans_time = 0;
+  Timer timer;
+  #endif
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -103,28 +111,32 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
   const bool is_color = image_data_param.is_color();
+  string root_folder = image_data_param.root_folder();
 
   // datum scales
   const int lines_size = lines_.size();
-  float read_time = 0;
-  float trans_time = 0;
-  Timer timer;
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
+    #ifdef TIMING
     timer.Start();
+    #endif
     CHECK_GT(lines_size, lines_id_);
-    cv::Mat cv_img = ReadImageToCVMat(lines_[lines_id_].first,
+    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
     if (!cv_img.data) {
       continue;
     }
+    #ifdef TIMING
     read_time += timer.MilliSeconds();
     timer.Start();
+    #endif
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+    #ifdef TIMING
     trans_time += timer.MilliSeconds();
+    #endif
 
     top_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
@@ -138,9 +150,11 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       }
     }
   }
-  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  DLOG(INFO) << "Read time: " << read_time << "ms.";
-  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
+  #ifdef TIMING
+  LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  LOG(INFO) << "Read time: " << read_time << "ms.";
+  LOG(INFO) << "Transform time: " << trans_time << "ms.";
+  #endif
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 47e0fb2a123..8e65615507b 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -13,7 +13,9 @@
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
+#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
+#endif
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -193,8 +195,13 @@ template <typename Dtype>
 void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
+  #ifdef TIMING
   Timer batch_timer;
   batch_timer.Start();
+  float read_time = 0;
+  float trans_time = 0;
+  Timer timer;
+  #endif
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
@@ -221,14 +228,13 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   const int num_samples[2] = { batch_size - num_fg, num_fg };
 
   int item_id = 0;
-  float read_time = 0;
-  float trans_time = 0;
-  Timer timer;
   // sample from bg set then fg set
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
       // sample a window
+      #ifdef TIMING
       timer.Start();
+      #endif
       const unsigned int rand_index = PrefetchRand();
       vector<float> window = (is_fg) ?
           fg_windows_[rand_index % fg_windows_.size()] :
@@ -245,8 +251,10 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
         LOG(ERROR) << "Could not open or find file " << image.first;
         return;
       }
+      #ifdef TIMING
       read_time += timer.MilliSeconds();
       timer.Start();
+      #endif
       const int channels = cv_img.channels();
 
       // crop window out of image and warp it
@@ -364,7 +372,9 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
           }
         }
       }
+      #ifdef TIMING
       trans_time += timer.MilliSeconds();
+      #endif
       // get window label
       top_label[item_id] = window[WindowDataLayer<Dtype>::LABEL];
 
@@ -404,9 +414,11 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       item_id++;
     }
   }
-  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  DLOG(INFO) << "Read time: " << read_time << "ms.";
-  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
+  #ifdef TIMING
+  LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  LOG(INFO) << "Read time: " << read_time << "ms.";
+  LOG(INFO) << "Transform time: " << trans_time << "ms.";
+  #endif
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 5b7152e6993..c0e56920c83 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -9,7 +9,9 @@
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
+#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
+#endif
 #include "caffe/util/insert_splits.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -500,8 +502,10 @@ void Net<Dtype>::GetLearningRateAndWeightDecay() {
 
 template <typename Dtype>
 Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
+  #ifdef TIMING
   Timer timer;
   timer.Start();
+  #endif
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
@@ -512,7 +516,9 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
   }
+  #ifdef TIMING
   LOG(INFO) << "Forward time: " << timer.MilliSeconds() << "ms.";
+  #endif
   return loss;
 }
 
@@ -569,8 +575,10 @@ string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
 
 template <typename Dtype>
 void Net<Dtype>::BackwardFromTo(int start, int end) {
+  #ifdef TIMING
   Timer timer;
   timer.Start();
+  #endif
   CHECK_GE(end, 0);
   CHECK_LT(start, layers_.size());
   for (int i = start; i >= end; --i) {
@@ -580,7 +588,9 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
       if (debug_info_) { BackwardDebugInfo(i); }
     }
   }
+  #ifdef TIMING
   LOG(INFO) << "Backward time: " << timer.MilliSeconds() << "ms.";
+  #endif
 }
 
 template <typename Dtype>
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 53db6f76945..b602d0e4c7b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -540,6 +540,7 @@ message ImageDataParameter {
   // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
   // data.
   optional bool mirror = 6 [default = false];
+  optional string root_folder = 12 [default = ""];
 }
 
 // Message that stores parameters InfogainLossLayer
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 06b9deb078a..b136bc8a120 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -150,8 +150,7 @@ bool DecodeDatum(const int height, const int width, const bool is_color,
 }
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
-  CHECK(cv_img.depth() == CV_8U || cv_img.depth() == CV_8S) <<
-      "Image data type must be unsigned or signed byte";
+  CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
   datum->set_channels(cv_img.channels());
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
@@ -164,7 +163,7 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   int datum_size = datum_channels * datum_height * datum_width;
   std::string buffer(datum_size, ' ');
   for (int h = 0; h < datum_height; ++h) {
-    const char* ptr = cv_img.ptr<char>(h);
+    const uchar* ptr = cv_img.ptr<uchar>(h);
     int img_index = 0;
     for (int w = 0; w < datum_width; ++w) {
       for (int c = 0; c < datum_channels; ++c) {

From 3744598ed39d26d6219cd0369dd6923e2c747c99 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 7 Oct 2014 14:14:50 -0700
Subject: [PATCH 0948/2053] Speed up WindowDataLayer and add mean_values

---
 include/caffe/data_layers.hpp          |  3 ++
 src/caffe/layers/window_data_layer.cpp | 65 ++++++++++++++++++--------
 2 files changed, 49 insertions(+), 19 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index a2ea854e5ff..c4903ce0891 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -325,6 +325,9 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
   vector<vector<float> > fg_windows_;
   vector<vector<float> > bg_windows_;
   Blob<Dtype> data_mean_;
+  vector<Dtype> mean_values_;
+  bool has_mean_file_;
+  bool has_mean_values_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 8e65615507b..fc0ffc886bb 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -170,15 +170,30 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
 
   // data mean
-  if (this->transform_param_.has_mean_file()) {
+  has_mean_file_ = this->transform_param_.has_mean_file();
+  has_mean_values_ = this->transform_param_.mean_value_size() > 0;
+  if (has_mean_file_) {
     const string& mean_file =
           this->transform_param_.mean_file();
     LOG(INFO) << "Loading mean file from" << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
-  } else {
-    data_mean_.Reshape(1, channels, crop_size, crop_size);
+  }
+  if (has_mean_values_) {
+    CHECK(has_mean_file_ == false) <<
+      "Cannot specify mean_file and mean_value at the same time";
+    for (int c = 0; c < this->transform_param_.mean_value_size(); ++c) {
+      mean_values_.push_back(this->transform_param_.mean_value(c));
+    }
+    CHECK(mean_values_.size() == 1 || mean_values_.size() == channels) <<
+     "Specify either 1 mean_value or as many as channels: " << channels;
+    if (channels > 1 && mean_values_.size() == 1) {
+      // Replicate the mean_value for simplicity
+      for (int c = 1; c < channels; ++c) {
+        mean_values_.push_back(mean_values_[0]);
+      }
+    }
   }
 }
 
@@ -211,10 +226,14 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   const bool mirror = this->transform_param_.mirror();
   const float fg_fraction =
       this->layer_param_.window_data_param().fg_fraction();
-  const Dtype* mean = this->data_mean_.cpu_data();
-  const int mean_off = (this->data_mean_.width() - crop_size) / 2;
-  const int mean_width = this->data_mean_.width();
-  const int mean_height = this->data_mean_.height();
+  Dtype* mean = NULL;
+  int mean_off, mean_width, mean_height;
+  if (this->has_mean_file_) {
+    mean = this->data_mean_.mutable_cpu_data();
+    mean_off = (this->data_mean_.width() - crop_size) / 2;
+    mean_width = this->data_mean_.width();
+    mean_height = this->data_mean_.height();
+  }
   cv::Size cv_crop_size(crop_size, crop_size);
   const string& crop_mode = this->layer_param_.window_data_param().crop_mode();
 
@@ -357,18 +376,26 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       }
 
       // copy the warped window into top_data
-      for (int c = 0; c < channels; ++c) {
-        for (int h = 0; h < cv_cropped_img.rows; ++h) {
-          for (int w = 0; w < cv_cropped_img.cols; ++w) {
-            Dtype pixel =
-                static_cast<Dtype>(cv_cropped_img.at<cv::Vec3b>(h, w)[c]);
-
-            top_data[((item_id * channels + c) * crop_size + h + pad_h)
-                     * crop_size + w + pad_w]
-                = (pixel
-                    - mean[(c * mean_height + h + mean_off + pad_h)
-                           * mean_width + w + mean_off + pad_w])
-                  * scale;
+      for (int h = 0; h < cv_cropped_img.rows; ++h) {
+        const uchar* ptr = cv_cropped_img.ptr<uchar>(h);
+        int img_index = 0;
+        for (int w = 0; w < cv_cropped_img.cols; ++w) {
+          for (int c = 0; c < channels; ++c) {
+            int top_index = ((item_id * channels + c) * crop_size + h + pad_h)
+                     * crop_size + w + pad_w;
+            // int top_index = (c * height + h) * width + w;
+            Dtype pixel = static_cast<Dtype>(ptr[img_index++]);
+            if (this->has_mean_file_) {
+              int mean_index = (c * mean_height + h + mean_off + pad_h)
+                           * mean_width + w + mean_off + pad_w;
+              top_data[top_index] = (pixel - mean[mean_index]) * scale;
+            } else {
+              if (this->has_mean_values_) {
+                top_data[top_index] = (pixel - this->mean_values_[c]) * scale;
+              } else {
+                top_data[top_index] = pixel * scale;
+              }
+            }
           }
         }
       }

From 14f548d8e45573044ba20cdf52eeaccef2217ff5 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 7 Oct 2014 17:19:15 -0700
Subject: [PATCH 0949/2053] Added cache_images to WindowDataLayer Added
 root_folder to WindowDataLayer to locate images

---
 include/caffe/data_layers.hpp          |  2 ++
 src/caffe/layers/window_data_layer.cpp | 38 ++++++++++++++++++++++----
 src/caffe/proto/caffe.proto            |  4 +++
 3 files changed, 38 insertions(+), 6 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index c4903ce0891..34b9b30aa3e 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -328,6 +328,8 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
   vector<Dtype> mean_values_;
   bool has_mean_file_;
   bool has_mean_values_;
+  bool cache_images_;
+  vector<std::pair<std::string, Datum > > image_database_cache_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index fc0ffc886bb..8f75557d694 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -59,7 +59,14 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << "  background (non-object) overlap threshold: "
       << this->layer_param_.window_data_param().bg_threshold() << std::endl
       << "  foreground sampling fraction: "
-      << this->layer_param_.window_data_param().fg_fraction();
+      << this->layer_param_.window_data_param().fg_fraction() << std::endl
+      << "  cache_images: "
+      << this->layer_param_.window_data_param().cache_images() << std::endl
+      << "  root_folder: "
+      << this->layer_param_.window_data_param().root_folder();
+
+  cache_images_ = this->layer_param_.window_data_param().cache_images();
+  string root_folder = this->layer_param_.window_data_param().root_folder();
 
   const bool prefetch_needs_rand =
       this->transform_param_.mirror() ||
@@ -88,12 +95,21 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     // read image path
     string image_path;
     infile >> image_path;
+    image_path = root_folder + image_path;
     // read image dimensions
     vector<int> image_size(3);
     infile >> image_size[0] >> image_size[1] >> image_size[2];
     channels = image_size[0];
     image_database_.push_back(std::make_pair(image_path, image_size));
 
+    if (cache_images_) {
+      Datum datum;
+      if (!ReadFileToDatum(image_path, &datum)) {
+        LOG(ERROR) << "Could not open or find file " << image_path;
+        return;
+      }
+      image_database_cache_.push_back(std::make_pair(image_path, datum));
+    }
     // read each box
     int num_windows;
     infile >> num_windows;
@@ -227,7 +243,9 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   const float fg_fraction =
       this->layer_param_.window_data_param().fg_fraction();
   Dtype* mean = NULL;
-  int mean_off, mean_width, mean_height;
+  int mean_off = 0;
+  int mean_width = 0;
+  int mean_height = 0;
   if (this->has_mean_file_) {
     mean = this->data_mean_.mutable_cpu_data();
     mean_off = (this->data_mean_.width() - crop_size) / 2;
@@ -265,10 +283,17 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       pair<std::string, vector<int> > image =
           image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
 
-      cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
-      if (!cv_img.data) {
-        LOG(ERROR) << "Could not open or find file " << image.first;
-        return;
+      cv::Mat cv_img;
+      if (this->cache_images_) {
+        pair<std::string, Datum> image_cached =
+          image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
+        cv_img = DecodeDatumToCVMat(image_cached.second);
+      } else {
+        cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
+        if (!cv_img.data) {
+          LOG(ERROR) << "Could not open or find file " << image.first;
+          return;
+        }
       }
       #ifdef TIMING
       read_time += timer.MilliSeconds();
@@ -442,6 +467,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
     }
   }
   #ifdef TIMING
+  batch_timer.Stop();
   LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
   LOG(INFO) << "Read time: " << read_time << "ms.";
   LOG(INFO) << "Transform time: " << trans_time << "ms.";
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b602d0e4c7b..03d955fa7ac 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -714,6 +714,10 @@ message WindowDataParameter {
   // warp: cropped window is warped to a fixed size and aspect ratio
   // square: the tightest square around the window is cropped
   optional string crop_mode = 11 [default = "warp"];
+  // cache_images: will load all images in memory for faster access
+  optional bool cache_images = 12 [default = false];
+  // append root_folder to locate images
+  optional string root_folder = 13 [default = ""];
 }
 
 // DEPRECATED: V0LayerParameter is the old way of specifying layer parameters

From d6b71e58237968679cea44418b869721a5ea9654 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 15 Oct 2014 15:36:16 -0700
Subject: [PATCH 0950/2053] Change caffe time to do forward/backward and
 accumulate time per layer

---
 Makefile                               |  6 ----
 src/caffe/layers/data_layer.cpp        | 18 +++-------
 src/caffe/layers/image_data_layer.cpp  | 19 +++-------
 src/caffe/layers/window_data_layer.cpp | 18 ++--------
 src/caffe/net.cpp                      | 17 ---------
 tools/caffe.cpp                        | 49 ++++++++++++++++----------
 6 files changed, 42 insertions(+), 85 deletions(-)

diff --git a/Makefile b/Makefile
index 393fe18a70d..26d5964cacc 100644
--- a/Makefile
+++ b/Makefile
@@ -281,12 +281,6 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
-TIMING ?= 0
-# Timing Flag
-ifneq ($(TIMING), 0)
-	COMMON_FLAGS += -DTIMING
-endif
-
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 95604e5a1ac..7ddd4662e73 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -8,9 +8,7 @@
 #include "caffe/dataset_factory.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
-#endif
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -84,13 +82,11 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
-  #ifdef TIMING
   Timer batch_timer;
   batch_timer.Start();
   float read_time = 0;
   float trans_time = 0;
   Timer timer;
-  #endif
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -105,14 +101,13 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     // get a blob
     CHECK(iter_ != dataset_->end());
     const Datum& datum = iter_->value;
+
     cv::Mat cv_img;
     if (datum.encoded()) {
        cv_img = DecodeDatumToCVMat(datum);
     }
-    #ifdef TIMING
     read_time += timer.MilliSeconds();
     timer.Start();
-    #endif
 
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
@@ -125,20 +120,17 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
-    #ifdef TIMING
     trans_time += timer.MilliSeconds();
-    #endif
     // go to the next iter
     ++iter_;
     if (iter_ == dataset_->end()) {
       iter_ = dataset_->begin();
     }
   }
-  #ifdef TIMING
-  LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  LOG(INFO) << "Read time: " << read_time << "ms.";
-  LOG(INFO) << "Transform time: " << trans_time << "ms.";
-  #endif
+  batch_timer.Stop();
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  DLOG(INFO) << "Read time: " << read_time << "ms.";
+  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 0abcd888b67..e0778e5989f 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -6,9 +6,7 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
-#endif
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -95,13 +93,11 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
-  #ifdef TIMING
   Timer batch_timer;
   batch_timer.Start();
   float read_time = 0;
   float trans_time = 0;
   Timer timer;
-  #endif
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -117,26 +113,20 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   const int lines_size = lines_.size();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
-    #ifdef TIMING
     timer.Start();
-    #endif
     CHECK_GT(lines_size, lines_id_);
     cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
     if (!cv_img.data) {
       continue;
     }
-    #ifdef TIMING
     read_time += timer.MilliSeconds();
     timer.Start();
-    #endif
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
-    #ifdef TIMING
     trans_time += timer.MilliSeconds();
-    #endif
 
     top_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
@@ -150,11 +140,10 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
       }
     }
   }
-  #ifdef TIMING
-  LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  LOG(INFO) << "Read time: " << read_time << "ms.";
-  LOG(INFO) << "Transform time: " << trans_time << "ms.";
-  #endif
+  batch_timer.Stop();
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  DLOG(INFO) << "Read time: " << read_time << "ms.";
+  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 8f75557d694..14e8caad6c9 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -13,9 +13,7 @@
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#ifdef TIMING
 #include "caffe/util/benchmark.hpp"
-#endif
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
@@ -226,13 +224,11 @@ template <typename Dtype>
 void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
-  #ifdef TIMING
   Timer batch_timer;
   batch_timer.Start();
   float read_time = 0;
   float trans_time = 0;
   Timer timer;
-  #endif
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
@@ -269,9 +265,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
       // sample a window
-      #ifdef TIMING
       timer.Start();
-      #endif
       const unsigned int rand_index = PrefetchRand();
       vector<float> window = (is_fg) ?
           fg_windows_[rand_index % fg_windows_.size()] :
@@ -295,10 +289,8 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
           return;
         }
       }
-      #ifdef TIMING
       read_time += timer.MilliSeconds();
       timer.Start();
-      #endif
       const int channels = cv_img.channels();
 
       // crop window out of image and warp it
@@ -424,9 +416,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
           }
         }
       }
-      #ifdef TIMING
       trans_time += timer.MilliSeconds();
-      #endif
       // get window label
       top_label[item_id] = window[WindowDataLayer<Dtype>::LABEL];
 
@@ -466,12 +456,10 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       item_id++;
     }
   }
-  #ifdef TIMING
   batch_timer.Stop();
-  LOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  LOG(INFO) << "Read time: " << read_time << "ms.";
-  LOG(INFO) << "Transform time: " << trans_time << "ms.";
-  #endif
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
+  DLOG(INFO) << "Read time: " << read_time << "ms.";
+  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index c0e56920c83..21ab15fd31b 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -9,9 +9,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
-#ifdef TIMING
-#include "caffe/util/benchmark.hpp"
-#endif
 #include "caffe/util/insert_splits.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -502,10 +499,6 @@ void Net<Dtype>::GetLearningRateAndWeightDecay() {
 
 template <typename Dtype>
 Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
-  #ifdef TIMING
-  Timer timer;
-  timer.Start();
-  #endif
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
@@ -516,9 +509,6 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
   }
-  #ifdef TIMING
-  LOG(INFO) << "Forward time: " << timer.MilliSeconds() << "ms.";
-  #endif
   return loss;
 }
 
@@ -575,10 +565,6 @@ string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
 
 template <typename Dtype>
 void Net<Dtype>::BackwardFromTo(int start, int end) {
-  #ifdef TIMING
-  Timer timer;
-  timer.Start();
-  #endif
   CHECK_GE(end, 0);
   CHECK_LT(start, layers_.size());
   for (int i = start; i >= end; --i) {
@@ -588,9 +574,6 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
       if (debug_info_) { BackwardDebugInfo(i); }
     }
   }
-  #ifdef TIMING
-  LOG(INFO) << "Backward time: " << timer.MilliSeconds() << "ms.";
-  #endif
 }
 
 template <typename Dtype>
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index bfcd9f19cec..1c842a078c2 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -229,35 +229,46 @@ int time() {
   Timer total_timer;
   total_timer.Start();
   Timer forward_timer;
-  forward_timer.Start();
+  Timer backward_timer;
   Timer timer;
-  for (int i = 0; i < layers.size(); ++i) {
-    const caffe::string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < FLAGS_iterations; ++j) {
+  std::vector<float> forward_time_per_layer(layers.size(), 0.0);
+  std::vector<float> backward_time_per_layer(layers.size(), 0.0);
+  float forward_time = 0.0;
+  float backward_time = 0.0;
+  for (int j = 0; j < FLAGS_iterations; ++j) {
+    Timer iter_timer;
+    iter_timer.Start();
+    forward_timer.Start();
+    for (int i = 0; i < layers.size(); ++i) {
+      timer.Start();
       // Although Reshape should be essentially free, we include it here
       // so that we will notice Reshape performance bugs.
       layers[i]->Reshape(bottom_vecs[i], top_vecs[i]);
       layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
+      forward_time_per_layer[i] += timer.MilliSeconds();
     }
-    LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
-        " milliseconds.";
-  }
-  LOG(INFO) << "Forward pass: " << forward_timer.MilliSeconds() <<
-      " milliseconds.";
-  Timer backward_timer;
-  backward_timer.Start();
-  for (int i = layers.size() - 1; i >= 0; --i) {
-    const caffe::string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < FLAGS_iterations; ++j) {
+    forward_time += forward_timer.MilliSeconds();
+    backward_timer.Start();
+    for (int i = layers.size() - 1; i >= 0; --i) {
+      timer.Start();
       layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
                           bottom_vecs[i]);
+      backward_time_per_layer[i] += timer.MilliSeconds();
     }
-    LOG(INFO) << layername << "\tbackward: "
-        << timer.MilliSeconds() << " milliseconds.";
+    backward_time += backward_timer.MilliSeconds();
+    LOG(INFO) << "Iteration: " << j + 1 << " forward-backward time: "
+      << iter_timer.MilliSeconds() << " milliseconds.";
   }
-  LOG(INFO) << "Backward pass: " << backward_timer.MilliSeconds() <<
+  for (int i = 0; i < layers.size(); ++i) {
+    const caffe::string& layername = layers[i]->layer_param().name();
+    LOG(INFO) << std::setfill(' ') << std::setw(10) << layername <<
+      "\tforward: " << forward_time_per_layer[i] << " milliseconds.";
+    LOG(INFO) << std::setfill(' ') << std::setw(10) << layername  <<
+      "\tbackward: " << backward_time_per_layer[i] << " milliseconds.";
+  }
+  LOG(INFO) << "Forward pass: " << forward_time <<
+      " milliseconds.";
+  LOG(INFO) << "Backward pass: " << backward_time <<
       " milliseconds.";
   LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
       " milliseconds.";

From 65688bed17b3bc162fd5a0db9c97de11c9131258 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Wed, 15 Oct 2014 15:36:42 -0700
Subject: [PATCH 0951/2053] Upgrade compute_image_mean to use gflags, accept
 list_of_images, and print mean_values

---
 examples/cifar10/create_cifar10.sh |  4 +-
 src/caffe/layers/data_layer.cpp    |  4 +-
 tools/compute_image_mean.cpp       | 59 ++++++++++++++++++++++--------
 3 files changed, 48 insertions(+), 19 deletions(-)

diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index ebfca46de87..a42725cb610 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -13,7 +13,7 @@ rm -rf $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/cifar10_test_$DBTYPE
 
 echo "Computing image mean..."
 
-./build/tools/compute_image_mean $EXAMPLE/cifar10_train_$DBTYPE \
-  $EXAMPLE/mean.binaryproto $DBTYPE
+./build/tools/compute_image_mean -backend=$DBTYPE \
+  $EXAMPLE/cifar10_train_$DBTYPE $EXAMPLE/mean.binaryproto
 
 echo "Done."
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 7ddd4662e73..05f2e298ee7 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -46,9 +46,9 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // Read a data point, and use it to initialize the top blob.
   CHECK(iter_ != dataset_->end());
-  const Datum& datum = iter_->value;
+  Datum datum = iter_->value;
 
-  if (DecodeDatum(datum)) {
+  if (DecodeDatum(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
   // image
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index a720f162aa2..4c2c658bbb0 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -1,3 +1,4 @@
+#include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <stdint.h>
 
@@ -12,19 +13,31 @@ using caffe::Dataset;
 using caffe::Datum;
 using caffe::BlobProto;
 using std::max;
+using std::pair;
+
+
+DEFINE_string(backend, "lmdb", "The backend for containing the images");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 3 || argc > 4) {
-    LOG(ERROR) << "Usage: compute_image_mean input_db output_file"
-               << " db_backend[leveldb or lmdb]";
+
+#ifndef GFLAGS_GFLAGS_H_
+  namespace gflags = google;
+#endif
+
+  gflags::SetUsageMessage("Compute the mean_image of a set of images given by"
+        " a leveldb/lmdb or a list of images\n"
+        "Usage:\n"
+        "    compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n");
+
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  if (argc < 2 || argc > 3) {
+    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/compute_image_mean");
     return 1;
   }
 
-  std::string db_backend = "lmdb";
-  if (argc == 4) {
-    db_backend = std::string(argv[3]);
-  }
+  std::string db_backend = FLAGS_backend;
 
   caffe::shared_ptr<Dataset<std::string, Datum> > dataset =
       caffe::DatasetFactory<std::string, Datum>(db_backend);
@@ -36,7 +49,11 @@ int main(int argc, char** argv) {
   int count = 0;
   // load first datum
   Dataset<std::string, Datum>::const_iterator iter = dataset->begin();
-  const Datum& datum = iter->value;
+  Datum datum = iter->value;
+
+  if (DecodeDatum(&datum)) {
+    LOG(INFO) << "Decoding Datum";
+  }
 
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());
@@ -51,8 +68,9 @@ int main(int argc, char** argv) {
   LOG(INFO) << "Starting Iteration";
   for (Dataset<std::string, Datum>::const_iterator iter = dataset->begin();
       iter != dataset->end(); ++iter) {
-    // just a dummy operation
-    const Datum& datum = iter->value;
+    Datum datum = iter->value;
+    DecodeDatum(&datum);
+
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
         datum.float_data_size());
@@ -72,20 +90,31 @@ int main(int argc, char** argv) {
     }
     ++count;
     if (count % 10000 == 0) {
-      LOG(ERROR) << "Processed " << count << " files.";
+      LOG(INFO) << "Processed " << count << " files.";
     }
   }
 
   if (count % 10000 != 0) {
-    LOG(ERROR) << "Processed " << count << " files.";
+    LOG(INFO) << "Processed " << count << " files.";
   }
   for (int i = 0; i < sum_blob.data_size(); ++i) {
     sum_blob.set_data(i, sum_blob.data(i) / count);
   }
   // Write to disk
-  LOG(INFO) << "Write to " << argv[2];
-  WriteProtoToBinaryFile(sum_blob, argv[2]);
-
+  if (argc == 3) {
+    LOG(INFO) << "Write to " << argv[2];
+    WriteProtoToBinaryFile(sum_blob, argv[2]);
+  }
+  const int channels = sum_blob.channels();
+  const int dim = sum_blob.height() * sum_blob.width();
+  std::vector<float> mean_values(channels, 0.0);
+  LOG(INFO) << "Number of channels: " << channels;
+  for (int c = 0; c < channels; ++c) {
+    for (int i = 0; i < dim; ++i) {
+      mean_values[c] += sum_blob.data(dim * c + i);
+    }
+    LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
+  }
   // Clean up
   dataset->close();
   return 0;

From 8867a54599f1d35b36bb29ee6fea94d6f635d675 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 12 Oct 2014 23:16:59 -0700
Subject: [PATCH 0952/2053] Added CPUTimer Make timing more precise using
 double and microseconds

---
 include/caffe/util/benchmark.hpp       | 20 ++++++++---
 src/caffe/layers/data_layer.cpp        | 18 +++++-----
 src/caffe/layers/image_data_layer.cpp  | 18 +++++-----
 src/caffe/layers/window_data_layer.cpp | 18 +++++-----
 src/caffe/util/benchmark.cpp           | 49 +++++++++++++++++++++++++-
 tools/caffe.cpp                        | 39 +++++++++++---------
 tools/compute_image_mean.cpp           |  2 ++
 7 files changed, 114 insertions(+), 50 deletions(-)

diff --git a/include/caffe/util/benchmark.hpp b/include/caffe/util/benchmark.hpp
index 6c01ac4ee18..d63582776ee 100644
--- a/include/caffe/util/benchmark.hpp
+++ b/include/caffe/util/benchmark.hpp
@@ -11,11 +11,11 @@ class Timer {
  public:
   Timer();
   virtual ~Timer();
-  void Start();
-  void Stop();
-  float MilliSeconds();
-  float MicroSeconds();
-  float Seconds();
+  virtual void Start();
+  virtual void Stop();
+  virtual float MilliSeconds();
+  virtual float MicroSeconds();
+  virtual float Seconds();
 
   inline bool initted() { return initted_; }
   inline bool running() { return running_; }
@@ -37,6 +37,16 @@ class Timer {
   float elapsed_microseconds_;
 };
 
+class CPUTimer : public Timer {
+ public:
+  explicit CPUTimer();
+  virtual ~CPUTimer() {}
+  virtual void Start();
+  virtual void Stop();
+  virtual float MilliSeconds();
+  virtual float MicroSeconds();
+};
+
 }  // namespace caffe
 
 #endif   // CAFFE_UTIL_BENCHMARK_H_
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 05f2e298ee7..5d6f05802ce 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -82,11 +82,11 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void DataLayer<Dtype>::InternalThreadEntry() {
-  Timer batch_timer;
+  CPUTimer batch_timer;
   batch_timer.Start();
-  float read_time = 0;
-  float trans_time = 0;
-  Timer timer;
+  double read_time = 0;
+  double trans_time = 0;
+  CPUTimer timer;
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -106,7 +106,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     if (datum.encoded()) {
        cv_img = DecodeDatumToCVMat(datum);
     }
-    read_time += timer.MilliSeconds();
+    read_time += timer.MicroSeconds();
     timer.Start();
 
     // Apply data transformations (mirror, scale, crop...)
@@ -120,7 +120,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
-    trans_time += timer.MilliSeconds();
+    trans_time += timer.MicroSeconds();
     // go to the next iter
     ++iter_;
     if (iter_ == dataset_->end()) {
@@ -128,9 +128,9 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     }
   }
   batch_timer.Stop();
-  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  DLOG(INFO) << "Read time: " << read_time << "ms.";
-  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
+  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
+  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
 }
 
 INSTANTIATE_CLASS(DataLayer);
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e0778e5989f..50997a23bf9 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -93,11 +93,11 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
 // This function is used to create a thread that prefetches the data.
 template <typename Dtype>
 void ImageDataLayer<Dtype>::InternalThreadEntry() {
-  Timer batch_timer;
+  CPUTimer batch_timer;
   batch_timer.Start();
-  float read_time = 0;
-  float trans_time = 0;
-  Timer timer;
+  double read_time = 0;
+  double trans_time = 0;
+  CPUTimer timer;
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
@@ -120,13 +120,13 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     if (!cv_img.data) {
       continue;
     }
-    read_time += timer.MilliSeconds();
+    read_time += timer.MicroSeconds();
     timer.Start();
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
-    trans_time += timer.MilliSeconds();
+    trans_time += timer.MicroSeconds();
 
     top_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
@@ -141,9 +141,9 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     }
   }
   batch_timer.Stop();
-  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  DLOG(INFO) << "Read time: " << read_time << "ms.";
-  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
+  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
+  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 14e8caad6c9..6287b385dc5 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -224,11 +224,11 @@ template <typename Dtype>
 void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
-  Timer batch_timer;
+  CPUTimer batch_timer;
   batch_timer.Start();
-  float read_time = 0;
-  float trans_time = 0;
-  Timer timer;
+  double read_time = 0;
+  double trans_time = 0;
+  CPUTimer timer;
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
@@ -289,7 +289,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
           return;
         }
       }
-      read_time += timer.MilliSeconds();
+      read_time += timer.MicroSeconds();
       timer.Start();
       const int channels = cv_img.channels();
 
@@ -416,7 +416,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
           }
         }
       }
-      trans_time += timer.MilliSeconds();
+      trans_time += timer.MicroSeconds();
       // get window label
       top_label[item_id] = window[WindowDataLayer<Dtype>::LABEL];
 
@@ -457,9 +457,9 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
     }
   }
   batch_timer.Stop();
-  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << "ms.";
-  DLOG(INFO) << "Read time: " << read_time << "ms.";
-  DLOG(INFO) << "Transform time: " << trans_time << "ms.";
+  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
+  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
+  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 2edc9a53ef0..1d269c351c1 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -69,7 +69,7 @@ float Timer::MicroSeconds() {
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
     // Cuda only measure milliseconds
-    elapsed_microseconds_ = elapsed_milliseconds_ / 1000;
+    elapsed_microseconds_ = elapsed_milliseconds_ * 1000;
 #else
       NO_GPU;
 #endif
@@ -118,4 +118,51 @@ void Timer::Init() {
   }
 }
 
+CPUTimer::CPUTimer() {
+  this->initted_ = true;
+  this->running_ = false;
+  this->has_run_at_least_once_ = false;
+}
+
+void CPUTimer::Start() {
+  if (!running()) {
+    this->start_cpu_ = boost::posix_time::microsec_clock::local_time();
+    this->running_ = true;
+    this->has_run_at_least_once_ = true;
+  }
+}
+
+void CPUTimer::Stop() {
+  if (running()) {
+    this->stop_cpu_ = boost::posix_time::microsec_clock::local_time();
+    this->running_ = false;
+  }
+}
+
+float CPUTimer::MilliSeconds() {
+  if (!has_run_at_least_once()) {
+    LOG(WARNING) << "Timer has never been run before reading time.";
+    return 0;
+  }
+  if (running()) {
+    Stop();
+  }
+  this->elapsed_milliseconds_ = (this->stop_cpu_ -
+                                this->start_cpu_).total_milliseconds();
+  return this->elapsed_milliseconds_;
+}
+
+float CPUTimer::MicroSeconds() {
+  if (!has_run_at_least_once()) {
+    LOG(WARNING) << "Timer has never been run before reading time.";
+    return 0;
+  }
+  if (running()) {
+    Stop();
+  }
+  this->elapsed_microseconds_ = (this->stop_cpu_ -
+                                this->start_cpu_).total_microseconds();
+  return this->elapsed_microseconds_;
+}
+
 }  // namespace caffe
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 1c842a078c2..9f9d975dfba 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -231,10 +231,10 @@ int time() {
   Timer forward_timer;
   Timer backward_timer;
   Timer timer;
-  std::vector<float> forward_time_per_layer(layers.size(), 0.0);
-  std::vector<float> backward_time_per_layer(layers.size(), 0.0);
-  float forward_time = 0.0;
-  float backward_time = 0.0;
+  std::vector<double> forward_time_per_layer(layers.size(), 0.0);
+  std::vector<double> backward_time_per_layer(layers.size(), 0.0);
+  double forward_time = 0.0;
+  double backward_time = 0.0;
   for (int j = 0; j < FLAGS_iterations; ++j) {
     Timer iter_timer;
     iter_timer.Start();
@@ -245,33 +245,38 @@ int time() {
       // so that we will notice Reshape performance bugs.
       layers[i]->Reshape(bottom_vecs[i], top_vecs[i]);
       layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
-      forward_time_per_layer[i] += timer.MilliSeconds();
+      forward_time_per_layer[i] += timer.MicroSeconds();
     }
-    forward_time += forward_timer.MilliSeconds();
+    forward_time += forward_timer.MicroSeconds();
     backward_timer.Start();
     for (int i = layers.size() - 1; i >= 0; --i) {
       timer.Start();
       layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
                           bottom_vecs[i]);
-      backward_time_per_layer[i] += timer.MilliSeconds();
+      backward_time_per_layer[i] += timer.MicroSeconds();
     }
-    backward_time += backward_timer.MilliSeconds();
+    backward_time += backward_timer.MicroSeconds();
     LOG(INFO) << "Iteration: " << j + 1 << " forward-backward time: "
-      << iter_timer.MilliSeconds() << " milliseconds.";
+      << iter_timer.MilliSeconds() << " ms.";
   }
+  LOG(INFO) << "Average time per layer: ";
   for (int i = 0; i < layers.size(); ++i) {
     const caffe::string& layername = layers[i]->layer_param().name();
     LOG(INFO) << std::setfill(' ') << std::setw(10) << layername <<
-      "\tforward: " << forward_time_per_layer[i] << " milliseconds.";
+      "\tforward: " << forward_time_per_layer[i] / 1000 /
+      FLAGS_iterations << " ms.";
     LOG(INFO) << std::setfill(' ') << std::setw(10) << layername  <<
-      "\tbackward: " << backward_time_per_layer[i] << " milliseconds.";
+      "\tbackward: " << backward_time_per_layer[i] / 1000 /
+      FLAGS_iterations << " ms.";
   }
-  LOG(INFO) << "Forward pass: " << forward_time <<
-      " milliseconds.";
-  LOG(INFO) << "Backward pass: " << backward_time <<
-      " milliseconds.";
-  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
-      " milliseconds.";
+  total_timer.Stop();
+  LOG(INFO) << "Average Forward pass: " << forward_time / 1000 /
+    FLAGS_iterations << " ms.";
+  LOG(INFO) << "Average Backward pass: " << backward_time / 1000 /
+    FLAGS_iterations << " ms.";
+  LOG(INFO) << "Average Forward-Backward: " << total_timer.MilliSeconds() /
+    FLAGS_iterations << " ms.";
+  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() << " ms.";
   LOG(INFO) << "*** Benchmark ends ***";
   return 0;
 }
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 4c2c658bbb0..358f57e38d6 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -4,6 +4,8 @@
 
 #include <algorithm>
 #include <string>
+#include <utility>
+#include <vector>
 
 #include "caffe/dataset_factory.hpp"
 #include "caffe/proto/caffe.pb.h"

From a1f83100858121fd094df625ed40af8c5afb42e5 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 3 Oct 2014 17:14:20 -0700
Subject: [PATCH 0953/2053] Adapt lenet_multistep_solver.prototxt to current
 solvers

---
 .../lenet_multistep_solver.prototxt                 | 13 +++++--------
 examples/mnist/readme.md                            |  3 +++
 2 files changed, 8 insertions(+), 8 deletions(-)
 rename examples/{lenet => mnist}/lenet_multistep_solver.prototxt (74%)

diff --git a/examples/lenet/lenet_multistep_solver.prototxt b/examples/mnist/lenet_multistep_solver.prototxt
similarity index 74%
rename from examples/lenet/lenet_multistep_solver.prototxt
rename to examples/mnist/lenet_multistep_solver.prototxt
index fadd7c904d7..7ee9fb60e67 100644
--- a/examples/lenet/lenet_multistep_solver.prototxt
+++ b/examples/mnist/lenet_multistep_solver.prototxt
@@ -1,7 +1,5 @@
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -27,7 +25,6 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "lenet"
-# solver mode: 0 for CPU and 1 for GPU
-solver_mode: 1
-device_id: 1
+snapshot_prefix: "examples/mnist/lenet_multistep"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 44e0091f420..1f8a869630d 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -283,3 +283,6 @@ You just did! All the training was carried out on the GPU. In fact, if you would
 and you will be using CPU for training. Isn't that easy?
 
 MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.
+
+### How to reduce the learning rate a fixed steps?
+Look at lenet_multistep_solver.prototxt

From d7da420b4ce2b07dad7fca42535a576cff43ce96 Mon Sep 17 00:00:00 2001
From: Craig Quiter <cquiter@gmail.com>
Date: Wed, 15 Oct 2014 21:26:01 -0700
Subject: [PATCH 0954/2053] Sometimes anaconda is installed in root.

Helps fix #489.
---
 Makefile.config.example | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index b6d95e31df8..e11db51395d 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -48,13 +48,15 @@ BLAS := atlas
 PYTHON_INCLUDE := /usr/include/python2.7 \
 		/usr/lib/python2.7/dist-packages/numpy/core/include
 # Anaconda Python distribution is quite popular. Include path:
-# PYTHON_INCLUDE := $(HOME)/anaconda/include \
-		# $(HOME)/anaconda/include/python2.7 \
-		# $(HOME)/anaconda/lib/python2.7/site-packages/numpy/core/include
+# Verify anaconda location, sometimes it's in root.
+# ANACONDA_HOME := $(HOME)/anaconda
+# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
+		# $(ANACONDA_HOME)/include/python2.7 \
+		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
 
 # We need to be able to find libpythonX.X.so or .dylib.
 PYTHON_LIB := /usr/lib
-# PYTHON_LIB := $(HOME)/anaconda/lib
+# PYTHON_LIB := $(ANACONDA_HOME)/lib
 
 # Whatever else you find you need goes here.
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include

From 3d95ed3ae19f6cadcd3400c75970fdbeb10ad4c5 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 16 Oct 2014 11:17:24 -0700
Subject: [PATCH 0955/2053] Fix init current_step

---
 examples/mnist/lenet_multistep_solver.prototxt | 11 +++++------
 src/caffe/solver.cpp                           |  1 +
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/mnist/lenet_multistep_solver.prototxt b/examples/mnist/lenet_multistep_solver.prototxt
index 7ee9fb60e67..9b22b45ba6b 100644
--- a/examples/mnist/lenet_multistep_solver.prototxt
+++ b/examples/mnist/lenet_multistep_solver.prototxt
@@ -13,12 +13,11 @@ weight_decay: 0.0005
 # The learning rate policy
 lr_policy: "multistep"
 gamma: 0.9
-stepvalue: 1000
-stepvalue: 2000
-stepvalue: 2500
-stepvalue: 3000
-stepvalue: 3500
-stepvalue: 4000
+stepvalue: 5000
+stepvalue: 7000
+stepvalue: 8000
+stepvalue: 9000
+stepvalue: 9500
 # Display every 100 iterations
 display: 100
 # The maximum number of iterations
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a13bca8b44a..5b8f0795306 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -162,6 +162,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   PreSolve();
 
   iter_ = 0;
+  current_step_ = 0;
   if (resume_file) {
     LOG(INFO) << "Restoring previous solver status from " << resume_file;
     Restore(resume_file);

From 33a1752a4ea8b67804fd5ffccf2ef66737e7b44d Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Thu, 16 Oct 2014 12:55:53 -0700
Subject: [PATCH 0956/2053] Edit boost-python formula

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index d8fb1c019e2..207e17d1539 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -182,7 +182,7 @@ For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as
 
 To edit the formulae in turn, run
 
-    for x in snappy leveldb protobuf gflags glog szip boost lmdb homebrew/science/opencv; do brew edit $x; done
+    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
 
 After this, run
 

From f2f0a7a73d25cbbd010b1405dc0bfd922d869a9d Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Fri, 17 Oct 2014 11:34:25 -0700
Subject: [PATCH 0957/2053] Fixing finetune_flickr_style model reported
 accuracy.

---
 examples/finetune_flickr_style/readme.md |  6 +++---
 models/finetune_flickr_style/readme.md   | 11 ++++++-----
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index c4aafbc66fd..ecb9d3d2e6d 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -53,7 +53,6 @@ Using a script, we will download a small subset of the data and split it into tr
     Writing train/val for 1939 successfully downloaded images.
 
 This script downloads images and writes train/val file lists into `data/flickr_style`.
-With this random seed there are 1,557 train images and 382 test images.
 The prototxts in this example assume this, and also assume the presence of the ImageNet mean file (run `get_ilsvrc_aux.sh` from `data/ilsvrc12` to obtain this if you haven't yet).
 
 We'll also need the ImageNet-trained model, which you can obtain by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.
@@ -106,7 +105,8 @@ Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.
     I0828 22:23:17.438894 11510 solver.cpp:302]     Test net output #0: accuracy = 0.2356
 
 Note how rapidly the loss went down. Although the 23.5% accuracy is only modest, it was achieved in only 1000, and evidence that the model is starting to learn quickly and well.
-Once the model is fully fine-tuned on the whole training set over 100,000 iterations the final validation accuracy is 91.64%. This takes ~7 hours in Caffe on a K40 GPU.
+Once the model is fully fine-tuned on the whole training set over 100,000 iterations the final validation accuracy is 39.16%.
+This takes ~7 hours in Caffe on a K40 GPU.
 
 For comparison, here is how the loss goes down when we do not start with a pre-trained model:
 
@@ -155,7 +155,7 @@ Now try fine-tuning to your own tasks and data!
 
 ## Trained model
 
-We provide a model trained on all 80K images, with final accuracy of 98%.
+We provide a model trained on all 80K images, with final accuracy of 39%.
 Simply do `./scripts/download_model_binary.py models/finetune_flickr_style` to obtain it.
 
 ## License
diff --git a/models/finetune_flickr_style/readme.md b/models/finetune_flickr_style/readme.md
index c08485f7fd3..d2a8a95f669 100644
--- a/models/finetune_flickr_style/readme.md
+++ b/models/finetune_flickr_style/readme.md
@@ -3,16 +3,17 @@ name: Finetuning CaffeNet on Flickr Style
 caffemodel: finetune_flickr_style.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/finetune_flickr_style.caffemodel
 license: non-commercial
-sha1: 443ad95a61fb0b5cd3cee55951bcc1f299186b5e
-caffe_commit: 41751046f18499b84dbaf529f64c0e664e2a09fe
+sha1: b61b5cef7d771b53b0c488e78d35ccadc073e9cf
+caffe_commit: 737ea5e936821b5c69f9c3952d72693ae5843370
 gist_id: 034c6ac3865563b69e60
 ---
 
 This model is trained exactly as described in `docs/finetune_flickr_style/readme.md`, using all 80000 images.
-The final performance on the test set:
+The final performance:
 
-    I0903 18:40:59.211707 11585 caffe.cpp:167] Loss: 0.407405
-    I0903 18:40:59.211717 11585 caffe.cpp:179] accuracy = 0.9164
+    I1017 07:36:17.370688 31333 solver.cpp:228] Iteration 100000, loss = 0.757952
+    I1017 07:36:17.370730 31333 solver.cpp:247] Iteration 100000, Testing net (#0)
+    I1017 07:36:34.248730 31333 solver.cpp:298]     Test net output #0: accuracy = 0.3916
 
 ## License
 

From e41ddbd3d70eff49a7bab430bb085d3b89647b4d Mon Sep 17 00:00:00 2001
From: Kevin James Matzen <kmatzen@cs.cornell.edu>
Date: Sat, 18 Oct 2014 02:51:29 -0400
Subject: [PATCH 0958/2053] LMDB doesn't support many concurrent read-only
 transactions, so this preallocates one read-only transaction and reuses it. 
 It's very important that iterations are considered invalid after a commit has
 been performed.

---
 include/caffe/dataset.hpp         |  1 -
 include/caffe/leveldb_dataset.hpp | 11 ++---
 include/caffe/lmdb_dataset.hpp    | 30 ++++++------
 src/caffe/lmdb_dataset.cpp        | 77 ++++++++++++++-----------------
 src/caffe/test/test_dataset.cpp   | 38 +++++++++++++++
 5 files changed, 92 insertions(+), 65 deletions(-)

diff --git a/include/caffe/dataset.hpp b/include/caffe/dataset.hpp
index 90deb3160fa..1dd8458cd74 100644
--- a/include/caffe/dataset.hpp
+++ b/include/caffe/dataset.hpp
@@ -170,7 +170,6 @@ class Dataset {
     iterator(const Dataset* parent, shared_ptr<DatasetState> state)
         : parent_(parent),
           state_(state) { }
-    ~iterator() { }
 
     iterator(const iterator& other)
         : parent_(other.parent_),
diff --git a/include/caffe/leveldb_dataset.hpp b/include/caffe/leveldb_dataset.hpp
index f6c57fe65af..d58c181bb2b 100644
--- a/include/caffe/leveldb_dataset.hpp
+++ b/include/caffe/leveldb_dataset.hpp
@@ -61,12 +61,11 @@ class LeveldbDataset : public Dataset<K, V, KCoder, VCoder> {
     shared_ptr<DatasetState> clone() {
       shared_ptr<leveldb::Iterator> new_iter;
 
-      if (iter_.get()) {
-        new_iter.reset(db_->NewIterator(leveldb::ReadOptions()));
-        CHECK(iter_->Valid());
-        new_iter->Seek(iter_->key());
-        CHECK(new_iter->Valid());
-      }
+      CHECK(iter_.get());
+      new_iter.reset(db_->NewIterator(leveldb::ReadOptions()));
+      CHECK(iter_->Valid());
+      new_iter->Seek(iter_->key());
+      CHECK(new_iter->Valid());
 
       return shared_ptr<DatasetState>(new LeveldbState(db_, new_iter));
     }
diff --git a/include/caffe/lmdb_dataset.hpp b/include/caffe/lmdb_dataset.hpp
index 71b322490c0..ac1e5ee25dd 100644
--- a/include/caffe/lmdb_dataset.hpp
+++ b/include/caffe/lmdb_dataset.hpp
@@ -28,7 +28,8 @@ class LmdbDataset : public Dataset<K, V, KCoder, VCoder> {
   LmdbDataset()
       : env_(NULL),
         dbi_(0),
-        txn_(NULL) { }
+        write_txn_(NULL),
+        read_txn_(NULL) { }
 
   bool open(const string& filename, Mode mode);
   bool put(const K& key, const V& value);
@@ -55,21 +56,19 @@ class LmdbDataset : public Dataset<K, V, KCoder, VCoder> {
           dbi_(dbi) { }
 
     shared_ptr<DatasetState> clone() {
+      CHECK(cursor_);
+
       MDB_cursor* new_cursor;
+      int retval;
 
-      if (cursor_) {
-        int retval;
-        retval = mdb_cursor_open(txn_, *dbi_, &new_cursor);
-        CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-        MDB_val key;
-        MDB_val val;
-        retval = mdb_cursor_get(cursor_, &key, &val, MDB_GET_CURRENT);
-        CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-        retval = mdb_cursor_get(new_cursor, &key, &val, MDB_SET);
-        CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
-      } else {
-        new_cursor = cursor_;
-      }
+      retval = mdb_cursor_open(txn_, *dbi_, &new_cursor);
+      CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+      MDB_val key;
+      MDB_val val;
+      retval = mdb_cursor_get(cursor_, &key, &val, MDB_GET_CURRENT);
+      CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
+      retval = mdb_cursor_get(new_cursor, &key, &val, MDB_SET);
+      CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
 
       return shared_ptr<DatasetState>(new LmdbState(new_cursor, txn_, dbi_));
     }
@@ -87,7 +86,8 @@ class LmdbDataset : public Dataset<K, V, KCoder, VCoder> {
 
   MDB_env* env_;
   MDB_dbi dbi_;
-  MDB_txn* txn_;
+  MDB_txn* write_txn_;
+  MDB_txn* read_txn_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/lmdb_dataset.cpp b/src/caffe/lmdb_dataset.cpp
index 23852f0cd81..8f8e68e901e 100644
--- a/src/caffe/lmdb_dataset.cpp
+++ b/src/caffe/lmdb_dataset.cpp
@@ -15,7 +15,8 @@ bool LmdbDataset<K, V, KCoder, VCoder>::open(const string& filename,
   DLOG(INFO) << "LMDB: Open " << filename;
 
   CHECK(NULL == env_);
-  CHECK(NULL == txn_);
+  CHECK(NULL == write_txn_);
+  CHECK(NULL == read_txn_);
   CHECK_EQ(0, dbi_);
 
   int retval;
@@ -66,13 +67,19 @@ bool LmdbDataset<K, V, KCoder, VCoder>::open(const string& filename,
     return false;
   }
 
-  retval = mdb_txn_begin(env_, NULL, flag2, &txn_);
+  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &read_txn_);
   if (MDB_SUCCESS != retval) {
     LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
     return false;
   }
 
-  retval = mdb_open(txn_, NULL, 0, &dbi_);
+  retval = mdb_txn_begin(env_, NULL, flag2, &write_txn_);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
+    return false;
+  }
+
+  retval = mdb_open(write_txn_, NULL, 0, &dbi_);
   if (MDB_SUCCESS != retval) {
     LOG(ERROR) << "mdb_open failed" << mdb_strerror(retval);
     return false;
@@ -103,10 +110,10 @@ bool LmdbDataset<K, V, KCoder, VCoder>::put(const K& key, const V& value) {
   mdbkey.mv_size = serialized_key.size();
   mdbkey.mv_data = serialized_key.data();
 
-  CHECK_NOTNULL(txn_);
+  CHECK_NOTNULL(write_txn_);
   CHECK_NE(0, dbi_);
 
-  int retval = mdb_put(txn_, dbi_, &mdbkey, &mdbdata, 0);
+  int retval = mdb_put(write_txn_, dbi_, &mdbkey, &mdbdata, 0);
   if (MDB_SUCCESS != retval) {
     LOG(ERROR) << "mdb_put failed " << mdb_strerror(retval);
     return false;
@@ -130,21 +137,12 @@ bool LmdbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
   mdbkey.mv_size = serialized_key.size();
 
   int retval;
-  MDB_txn* get_txn;
-  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &get_txn);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  retval = mdb_get(get_txn, dbi_, &mdbkey, &mdbdata);
+  retval = mdb_get(read_txn_, dbi_, &mdbkey, &mdbdata);
   if (MDB_SUCCESS != retval) {
     LOG(ERROR) << "mdb_get failed " << mdb_strerror(retval);
     return false;
   }
 
-  mdb_txn_abort(get_txn);
-
   if (!VCoder::deserialize(reinterpret_cast<char*>(mdbdata.mv_data),
       mdbdata.mv_size, value)) {
     LOG(ERROR) << "failed to deserialize value";
@@ -160,14 +158,8 @@ bool LmdbDataset<K, V, KCoder, VCoder>::first_key(K* key) {
 
   int retval;
 
-  MDB_txn* iter_txn;
-
-  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &iter_txn);
-  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
-      << mdb_strerror(retval);
-
   MDB_cursor* cursor;
-  retval = mdb_cursor_open(iter_txn, dbi_, &cursor);
+  retval = mdb_cursor_open(read_txn_, dbi_, &cursor);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
   MDB_val mdbkey;
   MDB_val mdbval;
@@ -175,7 +167,6 @@ bool LmdbDataset<K, V, KCoder, VCoder>::first_key(K* key) {
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
   mdb_cursor_close(cursor);
-  mdb_txn_abort(iter_txn);
 
   if (!KCoder::deserialize(reinterpret_cast<char*>(mdbkey.mv_data),
       mdbkey.mv_size, key)) {
@@ -192,14 +183,8 @@ bool LmdbDataset<K, V, KCoder, VCoder>::last_key(K* key) {
 
   int retval;
 
-  MDB_txn* iter_txn;
-
-  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &iter_txn);
-  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
-      << mdb_strerror(retval);
-
   MDB_cursor* cursor;
-  retval = mdb_cursor_open(iter_txn, dbi_, &cursor);
+  retval = mdb_cursor_open(read_txn_, dbi_, &cursor);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
   MDB_val mdbkey;
   MDB_val mdbval;
@@ -207,7 +192,6 @@ bool LmdbDataset<K, V, KCoder, VCoder>::last_key(K* key) {
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
 
   mdb_cursor_close(cursor);
-  mdb_txn_abort(iter_txn);
 
   if (!KCoder::deserialize(reinterpret_cast<char*>(mdbkey.mv_data),
       mdbkey.mv_size, key)) {
@@ -222,16 +206,24 @@ template <typename K, typename V, typename KCoder, typename VCoder>
 bool LmdbDataset<K, V, KCoder, VCoder>::commit() {
   DLOG(INFO) << "LMDB: Commit";
 
-  CHECK_NOTNULL(txn_);
+  CHECK_NOTNULL(write_txn_);
 
   int retval;
-  retval = mdb_txn_commit(txn_);
+  retval = mdb_txn_commit(write_txn_);
   if (MDB_SUCCESS != retval) {
     LOG(ERROR) << "mdb_txn_commit failed " << mdb_strerror(retval);
     return false;
   }
 
-  retval = mdb_txn_begin(env_, NULL, 0, &txn_);
+  mdb_txn_abort(read_txn_);
+
+  retval = mdb_txn_begin(env_, NULL, 0, &write_txn_);
+  if (MDB_SUCCESS != retval) {
+    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
+    return false;
+  }
+
+  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &read_txn_);
   if (MDB_SUCCESS != retval) {
     LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
     return false;
@@ -245,11 +237,14 @@ void LmdbDataset<K, V, KCoder, VCoder>::close() {
   DLOG(INFO) << "LMDB: Close";
 
   if (env_ && dbi_) {
+    mdb_txn_abort(write_txn_);
+    mdb_txn_abort(read_txn_);
     mdb_close(env_, dbi_);
     mdb_env_close(env_);
     env_ = NULL;
     dbi_ = 0;
-    txn_ = NULL;
+    write_txn_ = NULL;
+    read_txn_ = NULL;
   }
 }
 
@@ -268,14 +263,8 @@ typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
     LmdbDataset<K, V, KCoder, VCoder>::begin() const {
   int retval;
 
-  MDB_txn* iter_txn;
-
-  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &iter_txn);
-  CHECK_EQ(MDB_SUCCESS, retval) << "mdb_txn_begin failed "
-      << mdb_strerror(retval);
-
   MDB_cursor* cursor;
-  retval = mdb_cursor_open(iter_txn, dbi_, &cursor);
+  retval = mdb_cursor_open(read_txn_, dbi_, &cursor);
   CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
   MDB_val key;
   MDB_val val;
@@ -286,7 +275,9 @@ typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
 
   shared_ptr<DatasetState> state;
   if (MDB_SUCCESS == retval) {
-    state.reset(new LmdbState(cursor, iter_txn, &dbi_));
+    state.reset(new LmdbState(cursor, read_txn_, &dbi_));
+  } else {
+    mdb_cursor_close(cursor);
   }
   return const_iterator(this, state);
 }
diff --git a/src/caffe/test/test_dataset.cpp b/src/caffe/test/test_dataset.cpp
index e5bd1d2c40e..6645ca228d2 100644
--- a/src/caffe/test/test_dataset.cpp
+++ b/src/caffe/test/test_dataset.cpp
@@ -751,6 +751,44 @@ TYPED_TEST(DatasetTest, TestReadOnlyGetNoCommitFails) {
   EXPECT_FALSE(dataset->get(key, &new_value));
 }
 
+TYPED_TEST(DatasetTest, TestCreateManyItersShortScope) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+  EXPECT_TRUE(dataset->put(key, value));
+  EXPECT_TRUE(dataset->commit());
+
+  for (int i = 0; i < 1000; ++i) {
+    typename Dataset<string, value_type>::const_iterator iter =
+        dataset->begin();
+  }
+}
+
+TYPED_TEST(DatasetTest, TestCreateManyItersLongScope) {
+  UNPACK_TYPES;
+
+  string name = this->DBName();
+  shared_ptr<Dataset<string, value_type> > dataset =
+      DatasetFactory<string, value_type>(backend);
+  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
+
+  string key = this->TestKey();
+  value_type value = this->TestValue();
+  EXPECT_TRUE(dataset->put(key, value));
+  EXPECT_TRUE(dataset->commit());
+
+  vector<typename Dataset<string, value_type>::const_iterator> iters;
+  for (int i = 0; i < 1000; ++i) {
+    iters.push_back(dataset->begin());
+  }
+}
+
 #undef UNPACK_TYPES
 
 }  // namespace caffe

From a723be04acf250a08b5a6f0fbb7e3fde7de429be Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 18 Oct 2014 07:59:53 -0700
Subject: [PATCH 0959/2053] Remove TIMING from ForwardBackward

---
 include/caffe/net.hpp | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 879f474674d..1d06dc45533 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -11,9 +11,6 @@
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#ifdef TIMING
-#include "caffe/util/benchmark.hpp"
-#endif
 
 namespace caffe {
 
@@ -79,16 +76,9 @@ class Net {
   void Reshape();
 
   Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
-    #ifdef TIMING
-    Timer timer;
-    timer.Start();
-    #endif
     Dtype loss;
     Forward(bottom, &loss);
     Backward();
-    #ifdef TIMING
-    LOG(INFO) << "ForwardBackward Time: " << timer.MilliSeconds() << "ms.";
-    #endif
     return loss;
   }
 

From 60554bc4d238f224ba7b4745715a9ae323cf3c4b Mon Sep 17 00:00:00 2001
From: Jack Culpepper <jackculpepper@gmail.com>
Date: Sun, 19 Oct 2014 08:31:29 -0700
Subject: [PATCH 0960/2053] define gradient at zero to be zero

---
 src/caffe/layers/absval_layer.cpp | 3 +--
 src/caffe/layers/absval_layer.cu  | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index 54e484b115d..c347d460c0c 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -26,12 +26,11 @@ template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
-  const Dtype* top_data = top[0]->cpu_data();
   const Dtype* top_diff = top[0]->cpu_diff();
   if (propagate_down[0]) {
     const Dtype* bottom_data = bottom[0]->cpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-    caffe_div(count, top_data, bottom_data, bottom_diff);
+    caffe_cpu_sign(count, bottom_data, bottom_diff);
     caffe_mul(count, bottom_diff, top_diff, bottom_diff);
   }
 }
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index 732d4c6252b..91f3c77fe9a 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -23,7 +23,7 @@ void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* bottom_data = bottom[0]->gpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-    caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+    caffe_gpu_sign(count, bottom_data, bottom_diff);
     caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
   }
 }

From c211fd01ed62c8279ffc810e4f4d1ab2f0b93366 Mon Sep 17 00:00:00 2001
From: Maksim Lapin <mlapin@mpi-inf.mpg.de>
Date: Tue, 7 Oct 2014 21:48:26 +0200
Subject: [PATCH 0961/2053] include opencv only in .cpp

---
 Makefile                              |  1 -
 include/caffe/common.hpp              |  3 +++
 include/caffe/data_transformer.hpp    |  6 -----
 include/caffe/util/io.hpp             | 39 ++++++++-------------------
 src/caffe/data_transformer.cpp        |  4 ---
 src/caffe/layers/data_layer.cpp       |  2 ++
 src/caffe/layers/image_data_layer.cpp |  2 ++
 src/caffe/util/io.cpp                 | 28 +++++++++++++++++++
 8 files changed, 46 insertions(+), 39 deletions(-)

diff --git a/Makefile b/Makefile
index 26d5964cacc..dcd7b2457b7 100644
--- a/Makefile
+++ b/Makefile
@@ -250,7 +250,6 @@ ifeq ($(OSX), 1)
 	endif
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
-        NVCCFLAGS += -DOSX
 endif
 
 # Custom compiler
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 81b2e9ae101..5fc4ed3a3e1 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -64,6 +64,9 @@ private:\
 // is executed we will see a fatal log.
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
 
+// See PR #1236
+namespace cv {class Mat;}
+
 namespace caffe {
 
 // We will use the boost shared_ptr instead of the new C++11 one mainly
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 5f08335d4a3..70e74368ca7 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -1,10 +1,6 @@
 #ifndef CAFFE_DATA_TRANSFORMER_HPP
 #define CAFFE_DATA_TRANSFORMER_HPP
 
-#ifndef OSX
-#include <opencv2/core/core.hpp>
-#endif
-
 #include <vector>
 
 #include "caffe/blob.hpp"
@@ -64,9 +60,7 @@ class DataTransformer {
    *    This is destination blob. It can be part of top blob's data if
    *    set_cpu_data() is used See image_data_layer.cpp for an example.
    */
-#ifndef OSX
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
-#endif
 
   /**
    * @brief Applies the same transformation defined in the data layer's
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 64df0155780..9c0b4872af6 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,10 +1,6 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
-#ifndef OSX
-#include <opencv2/core/core.hpp>
-#endif
-
 #include <unistd.h>
 #include <string>
 
@@ -13,6 +9,7 @@
 #include "hdf5_hl.h"
 
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #define HDF5_NUM_DIMS 4
@@ -127,43 +124,29 @@ inline bool DecodeDatum(Datum* datum) {
   return DecodeDatum(0, 0, true, datum);
 }
 
-#ifndef OSX
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
 
-inline cv::Mat ReadImageToCVMat(const string& filename,
-    const int height, const int width) {
-  return ReadImageToCVMat(filename, height, width, true);
-}
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width);
 
-inline cv::Mat ReadImageToCVMat(const string& filename,
-    const bool is_color) {
-  return ReadImageToCVMat(filename, 0, 0, is_color);
-}
+cv::Mat ReadImageToCVMat(const string& filename,
+    const bool is_color);
 
-inline cv::Mat ReadImageToCVMat(const string& filename) {
-  return ReadImageToCVMat(filename, 0, 0, true);
-}
+cv::Mat ReadImageToCVMat(const string& filename);
 
 cv::Mat DecodeDatumToCVMat(const Datum& datum,
     const int height, const int width, const bool is_color);
 
-inline cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width) {
-  return DecodeDatumToCVMat(datum, height, width, true);
-}
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width);
 
-inline cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const bool is_color) {
-  return DecodeDatumToCVMat(datum, 0, 0, is_color);
-}
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const bool is_color);
 
-inline cv::Mat DecodeDatumToCVMat(const Datum& datum) {
-  return DecodeDatumToCVMat(datum, 0, 0, true);
-}
+cv::Mat DecodeDatumToCVMat(const Datum& datum);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
-#endif
 
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 023396ce5e6..209dc0349ad 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,6 +1,4 @@
-#ifndef OSX
 #include <opencv2/core/core.hpp>
-#endif
 
 #include <string>
 #include <vector>
@@ -175,7 +173,6 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
-#ifndef OSX
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
@@ -276,7 +273,6 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
     }
   }
 }
-#endif
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 5d6f05802ce..a5030899cee 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <stdint.h>
 
 #include <string>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 50997a23bf9..ef6a342c30a 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
 #include <string>
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index b136bc8a120..ffdaeb12c4a 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -83,6 +83,20 @@ cv::Mat ReadImageToCVMat(const string& filename,
   return cv_img;
 }
 
+cv::Mat ReadImageToCVMat(const string& filename,
+    const int height, const int width) {
+  return ReadImageToCVMat(filename, height, width, true);
+}
+
+cv::Mat ReadImageToCVMat(const string& filename,
+    const bool is_color) {
+  return ReadImageToCVMat(filename, 0, 0, is_color);
+}
+
+cv::Mat ReadImageToCVMat(const string& filename) {
+  return ReadImageToCVMat(filename, 0, 0, true);
+}
+
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color, Datum* datum) {
   cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
@@ -135,6 +149,20 @@ cv::Mat DecodeDatumToCVMat(const Datum& datum,
   return cv_img;
 }
 
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width) {
+  return DecodeDatumToCVMat(datum, height, width, true);
+}
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const bool is_color) {
+  return DecodeDatumToCVMat(datum, 0, 0, is_color);
+}
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum) {
+  return DecodeDatumToCVMat(datum, 0, 0, true);
+}
+
 // If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
 // if height and width are set it will resize it
 // If Datum is not encoded will do nothing

From 293347196a81f68046b30984448cc4883d6c5aa5 Mon Sep 17 00:00:00 2001
From: Ryota Tomioka <tomioka@ttic.edu>
Date: Mon, 20 Oct 2014 09:35:20 -0500
Subject: [PATCH 0962/2053] Fixed a memory leak issue in InternalThread (and
 removed caffe::Thread calss).

---
 include/caffe/internal_thread.hpp | 19 +++----------------
 include/caffe/util/thread.hpp     | 25 -------------------------
 src/caffe/internal_thread.cpp     |  8 +-------
 3 files changed, 4 insertions(+), 48 deletions(-)
 delete mode 100644 include/caffe/util/thread.hpp

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 6a106e6eefa..9c24585b2cd 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -1,24 +1,11 @@
 #ifndef CAFFE_INTERNAL_THREAD_HPP_
 #define CAFFE_INTERNAL_THREAD_HPP_
 
+#include <boost/thread.hpp>
 #include "caffe/common.hpp"
 
 namespace caffe {
 
-/**
- * A minimal wrapper for boost::thread to force host compilation for boost
- * Defined in caffe/util/thread.hpp
- */
-class Thread {
- public:
-  template<typename Callable, class A1>
-  Thread(Callable func, A1 a1);
-  void join();
-  bool joinable();
- private:
-  void* thread_;
-};
-
 /**
  * Virtual class encapsulate boost::thread for use in base class
  * The child class will acquire the ability to run a single thread,
@@ -26,7 +13,7 @@ class Thread {
  */
 class InternalThread {
  public:
-  InternalThread() : thread_(NULL) {}
+  InternalThread() : thread_() {}
   virtual ~InternalThread();
 
   /** Returns true if the thread was successfully started. **/
@@ -42,7 +29,7 @@ class InternalThread {
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
-  caffe::Thread* thread_;
+  shared_ptr<boost::thread> thread_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/util/thread.hpp b/include/caffe/util/thread.hpp
deleted file mode 100644
index 7251402cf1a..00000000000
--- a/include/caffe/util/thread.hpp
+++ /dev/null
@@ -1,25 +0,0 @@
-#ifndef CAFFE_THREAD_CPP_HPP_
-#define CAFFE_THREAD_CPP_HPP_
-
-#include <boost/thread.hpp>
-#include "caffe/common.hpp"
-#include "caffe/internal_thread.hpp"
-
-namespace caffe {
-
-template<typename Callable, class A1>
-Thread::Thread(Callable func, A1 a1) {
-  this->thread_ = new boost::thread(func, a1);
-}
-
-void Thread::join() {
-  static_cast<boost::thread*>(this->thread_)->join();
-}
-
-bool Thread::joinable() {
-  return static_cast<boost::thread*>(this->thread_)->joinable();
-}
-
-}  // namespace caffe
-
-#endif
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index d7b6ae206cf..62a4c1faee2 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,14 +1,9 @@
 #include "caffe/internal_thread.hpp"
 
-#include "caffe/util/thread.hpp"
-
 namespace caffe {
 
 InternalThread::~InternalThread() {
   WaitForInternalThreadToExit();
-  if (thread_ != NULL) {
-    delete thread_;
-  }
 }
 
 bool InternalThread::StartInternalThread() {
@@ -16,8 +11,7 @@ bool InternalThread::StartInternalThread() {
     return false;
   }
   try {
-    thread_ = new caffe::Thread
-        (&InternalThread::InternalThreadEntry, this);
+    thread_.reset(new boost::thread(&InternalThread::InternalThreadEntry, this));
   } catch (...) {
     return false;
   }

From 686793e178b4282fbd3769c940e7188d1279bbc3 Mon Sep 17 00:00:00 2001
From: Traun Leyden <tleyden@couchbase.com>
Date: Mon, 20 Oct 2014 15:15:03 +0000
Subject: [PATCH 0963/2053] Fix build error caused by pthread lib order

https://github.com/BVLC/caffe/issues/1327
---
 Makefile | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index e85f338ed23..6a660f5e0f2 100644
--- a/Makefile
+++ b/Makefile
@@ -165,12 +165,11 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARY_DIRS += $(CUDA_LIB_DIR)
 	LIBRARIES := cudart cublas curand
 endif
-LIBRARIES += pthread \
-	glog gflags protobuf leveldb snappy \
+LIBRARIES += glog gflags protobuf leveldb snappy \
 	lmdb \
 	boost_system \
 	hdf5_hl hdf5 \
-	opencv_core opencv_highgui opencv_imgproc
+	opencv_core opencv_highgui opencv_imgproc pthread
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 

From f517cdc8dd4b78ba627308cceced9cb36de1fb81 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 20 Oct 2014 12:55:49 -0700
Subject: [PATCH 0964/2053] [docs] cite the arXiv paper

---
 docs/index.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index e90b06b4d22..cb187433e27 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -67,11 +67,11 @@ Developer documentation automagically generated from code comments.
 
 Please cite Caffe in your publications if it helps your research:
 
-    @misc{Jia13caffe,
-       Author = {Yangqing Jia},
-       Title = { {Caffe}: An Open Source Convolutional Architecture for Fast Feature Embedding},
-       Year  = {2013},
-       Howpublished = {\url{http://caffe.berkeleyvision.org/}}
+    @article{jia2014caffe,
+      Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
+      Journal = {arXiv preprint arXiv:1408.5093},
+      Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
+      Year = {2014}
     }
 
 If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).

From 6811463e898341a1fa5b819e8f7cf00f682dca9a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 20 Oct 2014 12:56:12 -0700
Subject: [PATCH 0965/2053] [docs] BVLC Caffe acknowledgements

---
 docs/index.md | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index cb187433e27..2ca487a591a 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -79,17 +79,19 @@ Citations are also tracked automatically by [Google Scholar](http://scholar.goog
 
 ## Acknowledgements
 
-Yangqing would like to thank the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
+The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
 
-A core set of BVLC members have contributed much new functionality and many fixes since the original release (alphabetical by first name):
-[Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/).
+The BVLC members who have contributed to Caffe are (alphabetical by first name):
+[Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
 
-Additionally, the open-source community plays a large and growing role in Caffe's development.
+The open-source community plays an important and growing role in Caffe's development.
 Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity, and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for a sorted list.
 
 We sincerely appreciate your interest and contributions!
 If you'd like to contribute, please read the [developing & contributing](development.html) guide.
 
+Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
+
 ## Contacting us
 
 All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).

From c18d22eb92488f02c0256a3fe4ac20a8ad827596 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 20 Oct 2014 12:57:18 -0700
Subject: [PATCH 0966/2053] [docs] re-title docs, count forks

---
 docs/index.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 2ca487a591a..ccc8f750eef 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -17,7 +17,7 @@ Networks are specified in simple config files, with no hard-coded parameters in
 Switching between CPU and GPU is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.
 
 **Readable & modifiable implementation** fosters active development.
-In Caffe's first six months, it has been forked by over 300 developers on Github, and many have pushed significant changes.
+In Caffe's first year, it has been forked by over 600 developers on Github, and many have pushed significant changes.
 
 **Speed** makes Caffe perfect for industry use.
 Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
@@ -35,11 +35,11 @@ Consult performance [details](/performance_hardware.html).
 ## Documentation
 
 - [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)<br>
-Caffe tutorial slides.
-- [ACM MM paper](http://ucb-icsi-vision-group.github.io/caffe-paper/caffe.pdf)<br>
-A 4-page report for the ACM Multimedia Open Source competition.
-- [Caffe Tutorial](/tutorial)<br>
-DIY deep learning with this hands-on tutorial to Caffe.
+Tutorial presentation.
+- [Tutorial Documentation](/tutorial)<br>
+Practical guide and framework reference.
+- [arXiv / ACM MM '14 paper](http://arxiv.org/abs/1408.5093)<br>
+A 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v1).
 - [Installation instructions](/installation.html)<br>
 Tested on Ubuntu, Red Hat, OS X.
 * [Model Zoo](/model_zoo.html)<br>

From ab94bea9ab54c1fea8c6d45d260c5973a388d745 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Mon, 20 Oct 2014 15:16:55 -0700
Subject: [PATCH 0967/2053] Update data_transformer.hpp

Minor grammar fix in comments.
---
 include/caffe/data_transformer.hpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 5f08335d4a3..84ebba28125 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -37,7 +37,7 @@ class DataTransformer {
    *    Datum containing the data to be transformed.
    * @param transformed_blob
    *    This is destination blob. It can be part of top blob's data if
-   *    set_cpu_data() is used See data_layer.cpp for an example.
+   *    set_cpu_data() is used. See data_layer.cpp for an example.
    */
   void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);
 
@@ -49,7 +49,7 @@ class DataTransformer {
    *    A vector of Datum containing the data to be transformed.
    * @param transformed_blob
    *    This is destination blob. It can be part of top blob's data if
-   *    set_cpu_data() is used See memory_layer.cpp for an example.
+   *    set_cpu_data() is used. See memory_layer.cpp for an example.
    */
   void Transform(const vector<Datum> & datum_vector,
                 Blob<Dtype>* transformed_blob);
@@ -62,7 +62,7 @@ class DataTransformer {
    *    cv::Mat containing the data to be transformed.
    * @param transformed_blob
    *    This is destination blob. It can be part of top blob's data if
-   *    set_cpu_data() is used See image_data_layer.cpp for an example.
+   *    set_cpu_data() is used. See image_data_layer.cpp for an example.
    */
 #ifndef OSX
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);

From 85477b75f7411efa3bf97c2c2cfec403205b7ce6 Mon Sep 17 00:00:00 2001
From: Ryota Tomioka <tomioka@ttic.edu>
Date: Tue, 21 Oct 2014 10:52:38 -0500
Subject: [PATCH 0968/2053] Forward declare boost::thread instead of including
 boost/thread.hpp in internal_thread.hpp.

---
 include/caffe/internal_thread.hpp | 11 ++++++++---
 src/caffe/internal_thread.cpp     |  6 ++++++
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 9c24585b2cd..fec22a47c86 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -1,9 +1,14 @@
 #ifndef CAFFE_INTERNAL_THREAD_HPP_
 #define CAFFE_INTERNAL_THREAD_HPP_
 
-#include <boost/thread.hpp>
 #include "caffe/common.hpp"
 
+/**
+ Forward declare boost::thread instead of including boost/thread.hpp
+ to avoid a boost/NVCC issues (#1009, #1010) on OSX.
+ */
+namespace boost { class thread; }
+
 namespace caffe {
 
 /**
@@ -22,7 +27,7 @@ class InternalThread {
   /** Will not return until the internal thread has exited. */
   bool WaitForInternalThreadToExit();
 
-  bool is_started() const { return thread_ != NULL && thread_->joinable(); }
+  bool is_started() const;
 
  protected:
   /* Implement this method in your subclass
@@ -34,4 +39,4 @@ class InternalThread {
 
 }  // namespace caffe
 
-#endif
+#endif // CAFFE_INTERNAL_THREAD_HPP_
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 62a4c1faee2..58772e36c23 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,3 +1,4 @@
+#include <boost/thread.hpp>
 #include "caffe/internal_thread.hpp"
 
 namespace caffe {
@@ -6,6 +7,11 @@ InternalThread::~InternalThread() {
   WaitForInternalThreadToExit();
 }
 
+bool InternalThread::is_started() const {
+  return thread_.get() != NULL && thread_->joinable();
+}
+
+
 bool InternalThread::StartInternalThread() {
   if (!WaitForInternalThreadToExit()) {
     return false;

From ce980cea21096f9961f2021b2992f2a46e93e552 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 21 Oct 2014 10:17:11 -0700
Subject: [PATCH 0969/2053] groom ignore

- re-group general and Caffe ignores
- ignore OS X files and Eclipse .settings as suggested by @cypof
---
 .gitignore | 56 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 30 insertions(+), 26 deletions(-)

diff --git a/.gitignore b/.gitignore
index fa279f9071b..c27c7a6a5f6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,21 +26,26 @@
 # Compiled MATLAB
 *.mex*
 
-# build, distribute, and bins
-build
-.build_debug/*
-.build_release/*
-distribute/*
-*.testbin
-*.bin
-python/caffe/proto/
+# IPython notebook checkpoints
+.ipynb_checkpoints
+
+# CMake generated files
+*.gen.cmake
 
 # Editor temporaries
 *.swp
 *~
 
-# IPython notebook checkpoints
-.ipynb_checkpoints
+# Sublime Text settings
+*.sublime-workspace
+*.sublime-project
+
+# Eclipse Project settings
+*.*project
+.settings
+
+# OSX dir files
+.DS_Store
 
 ## Caffe
 
@@ -58,13 +63,14 @@ models/*
 *leveldb
 *lmdb
 
-# LevelDB files
-*.sst
-*.ldb
-LOCK
-LOG*
-CURRENT
-MANIFEST-*
+# build, distribute, and bins (+ python proto bindings)
+build
+.build_debug/*
+.build_release/*
+distribute/*
+*.testbin
+*.bin
+python/caffe/proto/
 
 # Generated documentation
 docs/_site
@@ -73,12 +79,10 @@ _site
 doxygen
 docs/dev
 
-# Sublime Text settings
-*.sublime-workspace
-*.sublime-project
-
-# Eclipse Project settings
-*.*project
-
-# CMake generated files
-*.gen.cmake
+# LevelDB files
+*.sst
+*.ldb
+LOCK
+LOG*
+CURRENT
+MANIFEST-*

From 6d183710c2fbeb7b227cb0444d318f476cd18fd3 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 21 Oct 2014 10:19:56 -0700
Subject: [PATCH 0970/2053] make release, debug build dirs configurable in
 Makefile

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 26d5964cacc..1e70c2d5c6e 100644
--- a/Makefile
+++ b/Makefile
@@ -4,8 +4,8 @@ CONFIG_FILE := Makefile.config
 include $(CONFIG_FILE)
 
 BUILD_DIR_LINK := $(BUILD_DIR)
-RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
-DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+RELEASE_BUILD_DIR ?= .$(BUILD_DIR)_release
+DEBUG_BUILD_DIR ?= .$(BUILD_DIR)_debug
 
 DEBUG ?= 0
 ifeq ($(DEBUG), 1)

From 1ca3b01610d8431497671fbb5ab98a83777f909d Mon Sep 17 00:00:00 2001
From: Martin Baeuml <baeuml@kit.edu>
Date: Wed, 22 Oct 2014 10:19:44 +0200
Subject: [PATCH 0971/2053] Minor whitespace fix in logging message in HDF5
 output layer

---
 src/caffe/layers/hdf5_output_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index b8210e2ee2a..4a72a18aa12 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -30,7 +30,7 @@ HDF5OutputLayer<Dtype>::~HDF5OutputLayer<Dtype>() {
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::SaveBlobs() {
   // TODO: no limit on the number of blobs
-  LOG(INFO) << "Saving HDF5 file" << file_name_;
+  LOG(INFO) << "Saving HDF5 file " << file_name_;
   CHECK_EQ(data_blob_.num(), label_blob_.num()) <<
       "data blob and label blob must have the same batch size";
   hdf5_save_nd_dataset(file_id_, HDF5_DATA_DATASET_NAME, data_blob_);

From 0b6c663d9bcc7a77a4f63e7a5bb98ec18e18041f Mon Sep 17 00:00:00 2001
From: Rohit <rgirdhar@andrew.cmu.edu>
Date: Fri, 31 Oct 2014 21:43:44 -0400
Subject: [PATCH 0972/2053] documentation bug

---
 examples/cifar10/readme.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index a329b49c5ea..4a95cee9e8f 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -24,8 +24,8 @@ You will first need to download and convert the data format from the [CIFAR-10 w
 
     cd $CAFFE_ROOT/data/cifar10
     ./get_cifar10.sh
-    cd $CAFFE_ROOT/examples/cifar10
-    ./create_cifar10.sh
+    cd $CAFFE_ROOT
+    ./examples/cifar10/create_cifar10.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.
 
@@ -39,8 +39,8 @@ Training and Testing the "Quick" Model
 
 Training the model is simple after you have written the network definition protobuf and solver protobuf files (refer to [MNIST Tutorial](../examples/mnist.html)). Simply run `train_quick.sh`, or the following command directly:
 
-    cd $CAFFE_ROOT/examples/cifar10
-    ./train_quick.sh
+    cd $CAFFE_ROOT
+    ./examples/cifar10/train_quick.sh
 
 `train_quick.sh` is a simple script, so have a look inside. The main tool for training is `caffe` with the `train` action, and the solver protobuf text file as its argument.
 

From f7625e3632fd18ef0ebeafbe9c807990582618dc Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 5 Nov 2014 13:36:00 -0800
Subject: [PATCH 0973/2053] Prevent Matlab on OS X from crashing on error

Replace CHECK() and LOG(FATAL) with LOG(ERROR) and mexErrMsgTxt

A failed CHECK() or LOG(FATAL) causes Matlab to crash on OS X 10.9 with Matlab 2014a.
---
 matlab/caffe/matcaffe.cpp | 71 +++++++++++++++++++++++++--------------
 1 file changed, 46 insertions(+), 25 deletions(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index fc047585cfe..3de0f02e1eb 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -3,6 +3,7 @@
 // caffe::Caffe functions so that one could easily call it from matlab.
 // Note that for matlab, we will simply use float as the data type.
 
+#include <sstream>
 #include <string>
 #include <vector>
 
@@ -12,6 +13,12 @@
 
 #define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
 
+// Log and throw a Mex error
+inline void mex_error(const std::string &msg) {
+  LOG(ERROR) << msg;
+  mexErrMsgTxt(msg.c_str());
+}
+
 using namespace caffe;  // NOLINT(build/namespaces)
 
 // The pointer to the internal caffe::Net instance
@@ -46,14 +53,22 @@ static int init_key = -2;
 
 static mxArray* do_forward(const mxArray* const bottom) {
   vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  CHECK_EQ(static_cast<unsigned int>(mxGetDimensions(bottom)[0]),
-      input_blobs.size());
+  if (static_cast<unsigned int>(mxGetDimensions(bottom)[0]) !=
+      input_blobs.size()) {
+    mex_error("Invalid input size");
+  }
   for (unsigned int i = 0; i < input_blobs.size(); ++i) {
     const mxArray* const elem = mxGetCell(bottom, i);
-    CHECK(mxIsSingle(elem))
-        << "MatCaffe require single-precision float point data";
-    CHECK_EQ(mxGetNumberOfElements(elem), input_blobs[i]->count())
-        << "MatCaffe input size does not match the input size of the network";
+    if (!mxIsSingle(elem)) {
+      mex_error("MatCaffe require single-precision float point data");
+    }
+    if (mxGetNumberOfElements(elem) != input_blobs[i]->count()) {
+      std::string error_msg;
+      error_msg += "MatCaffe input size does not match the input size ";
+      error_msg += "of the network";
+      mex_error(error_msg);
+    }
+
     const float* const data_ptr =
         reinterpret_cast<const float* const>(mxGetPr(elem));
     switch (Caffe::mode()) {
@@ -66,7 +81,7 @@ static mxArray* do_forward(const mxArray* const bottom) {
           input_blobs[i]->mutable_gpu_data());
       break;
     default:
-      LOG(FATAL) << "Unknown Caffe mode.";
+      mex_error("Unknown Caffe mode");
     }  // switch (Caffe::mode())
   }
   const vector<Blob<float>*>& output_blobs = net_->ForwardPrefilled();
@@ -89,7 +104,7 @@ static mxArray* do_forward(const mxArray* const bottom) {
           data_ptr);
       break;
     default:
-      LOG(FATAL) << "Unknown Caffe mode.";
+      mex_error("Unknown Caffe mode");
     }  // switch (Caffe::mode())
   }
 
@@ -99,8 +114,10 @@ static mxArray* do_forward(const mxArray* const bottom) {
 static mxArray* do_backward(const mxArray* const top_diff) {
   vector<Blob<float>*>& output_blobs = net_->output_blobs();
   vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  CHECK_EQ(static_cast<unsigned int>(mxGetDimensions(top_diff)[0]),
-      output_blobs.size());
+  if (static_cast<unsigned int>(mxGetDimensions(top_diff)[0]) !=
+      output_blobs.size()) {
+    mex_error("Invalid input size");
+  }
   // First, copy the output diff
   for (unsigned int i = 0; i < output_blobs.size(); ++i) {
     const mxArray* const elem = mxGetCell(top_diff, i);
@@ -116,7 +133,7 @@ static mxArray* do_backward(const mxArray* const top_diff) {
           output_blobs[i]->mutable_gpu_diff());
       break;
     default:
-      LOG(FATAL) << "Unknown Caffe mode.";
+        mex_error("Unknown Caffe mode");
     }  // switch (Caffe::mode())
   }
   // LOG(INFO) << "Start";
@@ -139,7 +156,7 @@ static mxArray* do_backward(const mxArray* const top_diff) {
       caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
       break;
     default:
-      LOG(FATAL) << "Unknown Caffe mode.";
+        mex_error("Unknown Caffe mode");
     }  // switch (Caffe::mode())
   }
 
@@ -216,7 +233,7 @@ static mxArray* do_get_weights() {
               weights_ptr);
           break;
         default:
-          LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+          mex_error("Unknown Caffe mode");
         }
       }
     }
@@ -247,8 +264,9 @@ static void set_phase_test(MEX_ARGS) {
 
 static void set_device(MEX_ARGS) {
   if (nrhs != 1) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
+    ostringstream error_msg;
+    error_msg << "Expected 1 argument, got " << nrhs;
+    mex_error(error_msg.str());
   }
 
   int device_id = static_cast<int>(mxGetScalar(prhs[0]));
@@ -261,8 +279,9 @@ static void get_init_key(MEX_ARGS) {
 
 static void init(MEX_ARGS) {
   if (nrhs != 2) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
+    ostringstream error_msg;
+    error_msg << "Expected 2 arguments, got " << nrhs;
+    mex_error(error_msg.str());
   }
 
   char* param_file = mxArrayToString(prhs[0]);
@@ -291,8 +310,9 @@ static void reset(MEX_ARGS) {
 
 static void forward(MEX_ARGS) {
   if (nrhs != 1) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
+    ostringstream error_msg;
+    error_msg << "Expected 1 argument, got " << nrhs;
+    mex_error(error_msg.str());
   }
 
   plhs[0] = do_forward(prhs[0]);
@@ -300,8 +320,9 @@ static void forward(MEX_ARGS) {
 
 static void backward(MEX_ARGS) {
   if (nrhs != 1) {
-    LOG(ERROR) << "Only given " << nrhs << " arguments";
-    mexErrMsgTxt("Wrong number of arguments");
+    ostringstream error_msg;
+    error_msg << "Expected 1 argument, got " << nrhs;
+    mex_error(error_msg.str());
   }
 
   plhs[0] = do_backward(prhs[0]);
@@ -374,8 +395,7 @@ static handler_registry handlers[] = {
 void mexFunction(MEX_ARGS) {
   mexLock();  // Avoid clearing the mex file.
   if (nrhs == 0) {
-    LOG(ERROR) << "No API command given";
-    mexErrMsgTxt("An API command is requires");
+    mex_error("No API command given");
     return;
   }
 
@@ -391,8 +411,9 @@ void mexFunction(MEX_ARGS) {
       }
     }
     if (!dispatched) {
-      LOG(ERROR) << "Unknown command `" << cmd << "'";
-      mexErrMsgTxt("API command not recognized");
+      ostringstream error_msg;
+      error_msg << "Unknown command '" << cmd << "'";
+      mex_error(error_msg.str());
     }
     mxFree(cmd);
   }

From f29d4bd75423e4d9c91d7a26e092ab687ab87e97 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 7 Nov 2014 17:17:49 -0800
Subject: [PATCH 0974/2053] clean incorrect relu test code

---
 src/caffe/test/test_neuron_layer.cpp | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 34955c3af63..89db53a6979 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,6 +1,7 @@
 #include <cstring>
 #include <vector>
 
+#include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
@@ -152,7 +153,8 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
 TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   ReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -160,15 +162,19 @@ TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
   const Dtype* bottom_data = this->blob_bottom_->cpu_data();
   const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+    if (top_data[i] >= 0) {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+    } else {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] * 0.01);
+    }
   }
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   ReLULayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
@@ -437,7 +443,8 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   CuDNNReLULayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -453,7 +460,8 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
-  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "relu_param { negative_slope: 0.01 }", &layer_param));
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,

From 9ff22ec5283182198c2db6a4c1b5a5a9dceb1ead Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq@google.com>
Date: Fri, 7 Nov 2014 17:42:43 -0800
Subject: [PATCH 0975/2053] relax benchmark milliseconds threshold

---
 src/caffe/test/test_benchmark.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index dbbee08d667..43aaa639b3c 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -9,6 +9,8 @@
 
 namespace caffe {
 
+const float kMillisecondsThreshold = 30;
+
 template <typename TypeParam>
 class BenchmarkTest : public MultiDeviceTest<TypeParam> {};
 
@@ -63,8 +65,8 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.MilliSeconds(), 290);
-  EXPECT_LE(timer.MilliSeconds(), 310);
+  EXPECT_GE(timer.MilliSeconds(), 300 - kMillisecondsThreshold);
+  EXPECT_LE(timer.MilliSeconds(), 300 + kMillisecondsThreshold);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
@@ -78,8 +80,8 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  EXPECT_GE(timer.Seconds(), 0.290);
-  EXPECT_LE(timer.Seconds(), 0.310);
+  EXPECT_GE(timer.Seconds(), 0.3 - kMillisecondsThreshold / 1000.);
+  EXPECT_LE(timer.Seconds(), 0.3 + kMillisecondsThreshold / 1000.);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());

From 1fe4b81a4147d42b4a7042bb82b06f7b41fd2d99 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Thu, 13 Nov 2014 22:05:42 -0800
Subject: [PATCH 0976/2053] fix relu cudnn test bug

---
 src/caffe/test/test_neuron_layer.cpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 89db53a6979..b19a5abdcdf 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -452,8 +452,11 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   const TypeParam* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+    if (top_data[i] >= 0) {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+    } else {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] * 0.01);
+    }
   }
 }
 

From 51a09de81d10714d0d1a03e4eab028e994cb82e1 Mon Sep 17 00:00:00 2001
From: Pavel Campr <pavel@campr.cz>
Date: Fri, 14 Nov 2014 08:31:34 +0100
Subject: [PATCH 0977/2053] fixed filename in build_docs.sh

---
 scripts/build_docs.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/build_docs.sh b/scripts/build_docs.sh
index f8ace0ead43..0e28bd71631 100755
--- a/scripts/build_docs.sh
+++ b/scripts/build_docs.sh
@@ -3,7 +3,7 @@
 
 PORT=${1:-4000}
 
-echo "usage: build.sh [port]"
+echo "usage: build_docs.sh [port]"
 
 # Find the docs dir, no matter where the script is called
 ROOT_DIR="$( cd "$(dirname "$0")"/.. ; pwd -P )"

From 8271e6bd0cccd8aa8b878c1b9cafdd965a09ec9b Mon Sep 17 00:00:00 2001
From: Michele Pratusevich <michele.pratusevich@gmail.com>
Date: Tue, 18 Nov 2014 17:47:13 -0500
Subject: [PATCH 0978/2053] Update python requirements.txt

You need pyyaml to run the download scripts for getting the BVLC model references.
---
 python/requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/requirements.txt b/python/requirements.txt
index 4c35dcb0b6f..908373bf452 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -13,3 +13,4 @@ pandas>=0.12.0
 python-dateutil>=1.4,<2
 protobuf>=2.5.0
 python-gflags>=2.0
+pyyaml>=3.10

From dbb6a2054651cbd0a5058d45a27575b9de41b5a5 Mon Sep 17 00:00:00 2001
From: Sean Bell <sbell@cs.cornell.edu>
Date: Wed, 19 Nov 2014 17:25:08 -0500
Subject: [PATCH 0979/2053] fixed tanh to not return NaN for input values
 outside the range [-40, 40]

---
 src/caffe/layers/tanh_layer.cpp    |   4 +-
 src/caffe/layers/tanh_layer.cu     |   3 +-
 src/caffe/test/test_tanh_layer.cpp | 102 +++++++++++++++++++++++++++++
 3 files changed, 104 insertions(+), 5 deletions(-)
 create mode 100644 src/caffe/test/test_tanh_layer.cpp

diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 18413d9b181..ee5ed773c74 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -14,11 +14,9 @@ void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  Dtype exp2x;
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
-    exp2x = exp(2 * bottom_data[i]);
-    top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
+    top_data[i] = tanh(bottom_data[i]);
   }
 }
 
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index f1a0fc88bfd..ccd6e63ee7c 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -12,8 +12,7 @@ namespace caffe {
 template <typename Dtype>
 __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
-    Dtype exp2x = exp(2 * in[index]);
-    out[index] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
+    out[index] = tanh(in[index]);
   }
 }
 
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
new file mode 100644
index 00000000000..9a93e08e805
--- /dev/null
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -0,0 +1,102 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/common_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+double tanh_naive(double x) {
+  if (x < -40) {
+    // avoid negative overflow
+    return -1;
+  } else if (x > 40) {
+    // avoid positive overflow
+    return 1;
+  } else {
+    // exact expression for tanh, which is unstable for large x
+    double exp2x = exp(2 * x);
+    return (exp2x - 1.0) / (exp2x + 1.0);
+  }
+}
+
+template <typename TypeParam>
+class TanHLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  TanHLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~TanHLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  void TestForward(Dtype filler_std) {
+    FillerParameter filler_param;
+    filler_param.set_std(filler_std);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+
+    LayerParameter layer_param;
+    TanHLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    const Dtype min_precision = 1e-5;
+    for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+      Dtype expected_value = tanh_naive(bottom_data[i]);
+      Dtype precision = std::max(
+        Dtype(std::abs(expected_value * Dtype(1e-4))), min_precision);
+      EXPECT_NEAR(expected_value, top_data[i], precision);
+    }
+  }
+
+  void TestBackward(Dtype filler_std) {
+    FillerParameter filler_param;
+    filler_param.set_std(filler_std);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+
+    LayerParameter layer_param;
+    TanHLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+    checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(TanHLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(TanHLayerTest, TestTanH) {
+  this->TestForward(1.0);
+}
+
+TYPED_TEST(TanHLayerTest, TestTanHOverflow) {
+  // this will fail if tanh overflow is not properly handled
+  this->TestForward(10000.0);
+}
+
+TYPED_TEST(TanHLayerTest, TestTanHGradient) {
+  this->TestBackward(1.0);
+}
+
+}  // namespace caffe

From cb3eb31753cdd9ee2f9aa0f71c53bf2000ea5cfd Mon Sep 17 00:00:00 2001
From: Sean Bell <sbell@cs.cornell.edu>
Date: Wed, 19 Nov 2014 23:21:53 -0500
Subject: [PATCH 0980/2053] Fixed header order to satisfy linter

---
 src/caffe/test/test_tanh_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 9a93e08e805..5dc92832fc8 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -5,9 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"

From 9443c4417527d8a07f60410a52dc87d75f7304f5 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 22 Nov 2014 11:59:07 -0800
Subject: [PATCH 0981/2053] in SoftmaxLossLayer, check label >= 0 in addition
 to upper bound

---
 src/caffe/layers/softmax_loss_layer.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 35033b67c21..c4358731753 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -44,6 +44,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   for (int i = 0; i < num; ++i) {
     for (int j = 0; j < spatial_dim; j++) {
       const int label_value = static_cast<int>(label[i * spatial_dim + j]);
+      CHECK_GE(label_value, 0);
       CHECK_GT(dim, label_value * spatial_dim);
       loss -= log(std::max(prob_data[i * dim +
           label_value * spatial_dim + j],

From 33eb36e4034a0db92af8b08dc0a9217d46ba531b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 23 Nov 2014 17:58:24 -0800
Subject: [PATCH 0982/2053] use DCHECK in SoftmaxLossLayer so that bounds
 checking has no perf penalty without DEBUG

---
 src/caffe/layers/softmax_loss_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index c4358731753..82c62f6a1bd 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -44,8 +44,8 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   for (int i = 0; i < num; ++i) {
     for (int j = 0; j < spatial_dim; j++) {
       const int label_value = static_cast<int>(label[i * spatial_dim + j]);
-      CHECK_GE(label_value, 0);
-      CHECK_GT(dim, label_value * spatial_dim);
+      DCHECK_GE(label_value, 0);
+      DCHECK_GT(dim, label_value * spatial_dim);
       loss -= log(std::max(prob_data[i * dim +
           label_value * spatial_dim + j],
                            Dtype(FLT_MIN)));

From 8009436671ec01823fe465283b111141ef28e8f4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 30 Nov 2014 21:34:29 -0800
Subject: [PATCH 0983/2053] remove redundant code in ConvolutionLayer::Reshape

---
 src/caffe/layers/conv_layer.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 65ff9167128..0a032025bfb 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -126,9 +126,6 @@ void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   // it goes lazily unused to save memory.
   col_buffer_.Reshape(
       1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
-  for (int top_id = 0; top_id < top.size(); ++top_id) {
-    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
-  }
   // Set up the all ones "bias multiplier" for adding biases by BLAS
   if (bias_term_) {
     bias_multiplier_.Reshape(1, 1, 1, N_);

From 873a860ade3a4124bb5a9b34becf0858197a11f8 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Tue, 2 Dec 2014 11:20:58 -0800
Subject: [PATCH 0984/2053] Better instructions for updating Homebrew after
 modifying formulae

Previous instructions glossed over how to deal with OpenCV-related update problems
---
 docs/installation.md | 37 ++++++++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 13 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 207e17d1539..c667cd8ce01 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -208,28 +208,39 @@ If you're not using Anaconda, include `hdf5` in the list above.
     Aborting
     Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
 
-One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master, as follows:
+One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
 
     cd /usr/local
     git checkout -b caffe
     git add .
     git commit -m "Update Caffe dependencies to use libstdc++"
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies"
+
+Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
+
+    # Switch batch to homebrew master branches
+    cd /usr/local
+    git checkout master
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
     git checkout master
+    
+    # Update homebrew; hopefully this works without errors!
     brew update
+    
+    # Switch back to the caffe branches with the forumlae that you modified earlier
+    cd /usr/local
     git rebase master caffe
-    # Resolve any merge conflicts here
-    git checkout caffe
-
-At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place. You may still get the following error:
-
-    $ brew update
-    error: Your local changes to the following files would be overwritten by merge:
-	opencv.rb
-    Please, commit your changes or stash them before you can merge.
-    Aborting
-    Error: Failed to update tap: homebrew/science
+    # Fix any merge conflicts and commit to caffe branch
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+    
+    # Done!            
 
-but non-OpenCV packages will still update as expected.
+At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
 
 #### Windows
 

From a5b7b2d7ede8dd37f4e6bdcf87ca3e71f78633e0 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 3 Dec 2014 14:30:14 -0800
Subject: [PATCH 0985/2053] Update mean file help

Numpy array dimensions should be [Channels x Height x Width], NOT [Height x Width x Channels]

If an image mean file is supplied with channels as the last dimension, classify.py throws errors like:

ValueError: operands could not be broadcast together with shapes (3,227,227) (256,0,225) (3,227,227)
---
 python/classify.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/classify.py b/python/classify.py
index 873b5e38f19..d435a572266 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -60,8 +60,8 @@ def main(argv):
         "--mean_file",
         default=os.path.join(pycaffe_dir,
                              'caffe/imagenet/ilsvrc_2012_mean.npy'),
-        help="Data set image mean of H x W x K dimensions (numpy array). " +
-             "Set to '' for no mean subtraction."
+        help="Data set image mean of [Channels x Height x Width] dimensions " +
+             "(numpy array). Set to '' for no mean subtraction."
     )
     parser.add_argument(
         "--input_scale",

From e8dee350ade66a712144aebc8b5f4a8c989d43c0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 5 Dec 2014 08:06:40 -0800
Subject: [PATCH 0986/2053] compile for compute capability 5.0

---
 Makefile.config.example  | 9 ++++-----
 src/caffe/CMakeLists.txt | 4 +++-
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index 5cb0b243aca..18798ed2d06 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -17,15 +17,14 @@ CUDA_DIR := /usr/local/cuda
 # "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
 # CUDA_DIR := /usr
 
-# CUDA architecture setting: going with all of them (up to CUDA 5.5 compatible).
-# For the latest architecture, you need to install CUDA >= 6.0 and uncomment
-# the *_50 lines below.
+# CUDA architecture setting: going with all of them.
+# For CUDA < 6.0, comment the *_50 lines for compatibility.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
 		-gencode arch=compute_35,code=sm_35 \
-		#-gencode arch=compute_50,code=sm_50 \
-		#-gencode arch=compute_50,code=compute_50
+		-gencode arch=compute_50,code=sm_50 \
+		-gencode arch=compute_50,code=compute_50
 
 # BLAS choice:
 # atlas for ATLAS (default)
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 86c7c7eb4a3..dc6b5a2baac 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -84,8 +84,10 @@ if(NOT CPU_ONLY)
             -gencode arch=compute_20,code=sm_21
             -gencode arch=compute_30,code=sm_30
             -gencode arch=compute_35,code=sm_35
+            -gencode arch=compute_50,code=sm_50
+            -gencode arch=compute_50,code=compute_50
     )
-    
+
 # https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
     # work-arounds
 if(Boost_VERSION EQUAL 105500)

From fb298ddd4ef13375c953a201422293f33da44300 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 7 Dec 2014 03:06:35 -0800
Subject: [PATCH 0987/2053] clarify #endif comment

---
 include/caffe/util/math_functions.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 12823f8da0c..d3ecf5875fa 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -267,7 +267,7 @@ void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
       n, x, y); \
 }
 
-#endif  // CPU_ONLY
+#endif  // !CPU_ONLY
 
 }  // namespace caffe
 

From 9b78bcffbb515b39228f2d483dbfd596410d5f59 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Thu, 30 Oct 2014 15:51:14 -0700
Subject: [PATCH 0988/2053] Created parse_log.py, competitor to parse_log.sh

---
 tools/extra/extract_seconds.py |  33 ++++++--
 tools/extra/parse_log.py       | 140 +++++++++++++++++++++++++++++++++
 2 files changed, 167 insertions(+), 6 deletions(-)
 create mode 100755 tools/extra/parse_log.py

diff --git a/tools/extra/extract_seconds.py b/tools/extra/extract_seconds.py
index f791afa32a2..591a51f96bd 100755
--- a/tools/extra/extract_seconds.py
+++ b/tools/extra/extract_seconds.py
@@ -18,18 +18,39 @@ def extract_datetime_from_line(line, year):
     dt = datetime.datetime(year, month, day, hour, minute, second, microsecond)
     return dt
 
+
+def get_log_created_year(input_file):
+    """Get year from log file system timestamp
+    """
+
+    log_created_time = os.path.getctime(input_file)
+    log_created_year = datetime.datetime.fromtimestamp(log_created_time).year
+    return log_created_year
+
+
+def get_start_time(line_iterable, year):
+    """Find start time from group of lines
+    """
+
+    start_datetime = None
+    for line in line_iterable:
+        line = line.strip()
+        if line.find('Solving') != -1:
+            start_datetime = extract_datetime_from_line(line, year)
+            break
+    return start_datetime
+
+
 def extract_seconds(input_file, output_file):
     with open(input_file, 'r') as f:
         lines = f.readlines()
-    log_created_time = os.path.getctime(input_file)
-    log_created_year = datetime.datetime.fromtimestamp(log_created_time).year
-    start_time_found = False
+    log_created_year = get_log_created_year(input_file)
+    start_datetime = get_start_time(lines, log_created_year)
+    assert start_datetime, 'Start time not found'
+
     out = open(output_file, 'w')
     for line in lines:
         line = line.strip()
-        if not start_time_found and line.find('Solving') != -1:
-            start_time_found = True
-            start_datetime = extract_datetime_from_line(line, log_created_year)
         if line.find('Iteration') != -1:
             dt = extract_datetime_from_line(line, log_created_year)
             elapsed_seconds = (dt - start_datetime).total_seconds()
diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
new file mode 100755
index 00000000000..dca7999f1bc
--- /dev/null
+++ b/tools/extra/parse_log.py
@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+
+"""
+Parse training log
+
+Competitor to parse_log.sh
+"""
+
+import os
+import re
+import extract_seconds
+import argparse
+
+
+def get_line_type(line):
+    """Return either 'test' or 'train depending on line type
+    """
+
+    line_type = None
+    if line.find('Train') != -1:
+        line_type = 'train'
+    elif line.find('Test') != -1:
+        line_type = 'test'
+    return line_type
+
+
+def parse_log(path_to_log):
+    """Parse log file
+    """
+
+    re_iteration = re.compile('Iteration (\d+)')
+    re_accuracy = re.compile('output #\d+: accuracy = ([\.\d]+)')
+    re_loss = re.compile('output #\d+: loss = ([\.\d]+)')
+    re_lr = re.compile('lr = ([\.\d]+)')
+
+    # Pick out lines of interest
+    iteration = -1
+    test_accuracy = -1
+    learning_rate = float('NaN')
+    train_list = []
+    test_list = []
+
+    logfile_year = extract_seconds.get_log_created_year(path_to_log)
+    with open(path_to_log) as f:
+        start_time = extract_seconds.get_start_time(f, logfile_year)
+
+        for line in f:
+            iteration_match = re_iteration.search(line)
+            if iteration_match:
+                iteration = float(iteration_match.group(1))
+            if iteration == -1:
+                # Only look for other stuff if we've found the first iteration
+                continue
+
+            time = extract_seconds.extract_datetime_from_line(line,
+                                                              logfile_year)
+            seconds = (time - start_time).total_seconds()
+
+            lr_match = re_lr.search(line)
+            if lr_match:
+                learning_rate = float(lr_match.group(1))
+
+            accuracy_match = re_accuracy.search(line)
+            if accuracy_match:
+                test_accuracy = float(accuracy_match.group(1))
+
+            loss_match = re_loss.search(line)
+            if loss_match:
+                loss = float(loss_match.group(1))
+                line_type = get_line_type(line)
+                assert line_type, ('Failed to determine line type for line: ' +
+                                   line)
+                if line_type == 'test':
+                    # NOTE: we assume that accuracy always comes right before
+                    # loss for test data
+                    test_list.append((iteration, seconds, test_accuracy, loss))
+                elif line_type == 'train':
+                    train_list.append((iteration, seconds, loss, learning_rate))
+
+    return train_list, test_list
+
+
+def save_csv_files(logfile_path, output_dir, train_list, test_list,
+                   verbose=False):
+    """Save CSV files to output_dir
+
+    If the input log file is, e.g., caffe.INFO, the names will be
+    caffe.INFO.train and caffe.INFO.test
+    """
+
+    log_basename = os.path.basename(logfile_path)
+    train_filename = os.path.join(output_dir, log_basename + '.train')
+    write_csv(train_filename, train_list, '%d,%f,%f,%f',
+              'NumIters,Seconds,TrainingLoss,LearningRate', verbose)
+
+    test_filename = os.path.join(output_dir, log_basename + '.test')
+    write_csv(test_filename, test_list, '%d,%f,%f,%f',
+              'NumIters,Seconds,TestAccuracy,TestLoss', verbose)
+
+
+def write_csv(output_filename, list_of_tuples, format_string, header,
+              verbose=False):
+    """Write a CSV file
+    """
+    with open(output_filename, 'w') as f:
+        f.write(header + '\n')
+        for row in list_of_tuples:
+            line = format_string % row
+            f.write(line + '\n')
+    if verbose:
+        print 'Wrote %s' % output_filename
+
+
+def parse_args():
+    description = ('Parse a Caffe training log into two CSV files '
+                   'representing training and testing information')
+    parser = argparse.ArgumentParser(description=description)
+
+    parser.add_argument('logfile_path',
+                        help='Path to log file')
+
+    parser.add_argument('output_dir',
+                        help='Directory in which to place output CSV files')
+
+    parser.add_argument('--verbose',
+                        action='store_true',
+                        help='Print some extra info (e.g., output filenames)')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    train_list, test_list = parse_log(args.logfile_path)
+    save_csv_files(args.logfile_path, args.output_dir, train_list, test_list)
+
+
+if __name__ == '__main__':
+    main()

From f954c6bea18e7c69520ee4c962b9b263c18a9769 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Fri, 31 Oct 2014 10:24:07 -0700
Subject: [PATCH 0989/2053] Take train loss from `Iteration N, loss = X` lines

Was previously using `Train net output #M: loss = X` lines, but there may not be exactly one of those (e.g., for GoogLeNet, which has multiple loss layers); I believe that `Iteration N, loss = X` is the aggregated loss.

If there's only one loss layer, these two values will be equal and it won't matter. Otherwise, we presumably want to report the aggregated loss.
---
 tools/extra/parse_log.py | 35 +++++++++++++++++++----------------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index dca7999f1bc..41fe54dc8a0 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -13,7 +13,7 @@
 
 
 def get_line_type(line):
-    """Return either 'test' or 'train depending on line type
+    """Return either 'test' or 'train' depending on line type
     """
 
     line_type = None
@@ -30,7 +30,8 @@ def parse_log(path_to_log):
 
     re_iteration = re.compile('Iteration (\d+)')
     re_accuracy = re.compile('output #\d+: accuracy = ([\.\d]+)')
-    re_loss = re.compile('output #\d+: loss = ([\.\d]+)')
+    re_train_loss = re.compile('Iteration \d+, loss = ([\.\d]+)')
+    re_output_loss = re.compile('output #\d+: loss = ([\.\d]+)')
     re_lr = re.compile('lr = ([\.\d]+)')
 
     # Pick out lines of interest
@@ -61,21 +62,23 @@ def parse_log(path_to_log):
                 learning_rate = float(lr_match.group(1))
 
             accuracy_match = re_accuracy.search(line)
-            if accuracy_match:
+            if accuracy_match and get_line_type(line) == 'test':
                 test_accuracy = float(accuracy_match.group(1))
 
-            loss_match = re_loss.search(line)
-            if loss_match:
-                loss = float(loss_match.group(1))
-                line_type = get_line_type(line)
-                assert line_type, ('Failed to determine line type for line: ' +
-                                   line)
-                if line_type == 'test':
-                    # NOTE: we assume that accuracy always comes right before
-                    # loss for test data
-                    test_list.append((iteration, seconds, test_accuracy, loss))
-                elif line_type == 'train':
-                    train_list.append((iteration, seconds, loss, learning_rate))
+            train_loss_match = re_train_loss.search(line)
+            if train_loss_match:
+                train_loss = float(train_loss_match.group(1))
+                train_list.append((iteration, seconds, train_loss,
+                                   learning_rate))
+
+            output_loss_match = re_output_loss.search(line)
+            if output_loss_match and get_line_type(line) == 'test':
+                test_loss = float(output_loss_match.group(1))
+                # NOTE: we assume that (1) accuracy always comes right before
+                # loss for test data so the test_accuracy variable is already
+                # correctly populated and (2) there's one and only one output
+                # named "accuracy" for the test net
+                test_list.append((iteration, seconds, test_accuracy, test_loss))
 
     return train_list, test_list
 
@@ -113,7 +116,7 @@ def write_csv(output_filename, list_of_tuples, format_string, header,
 
 def parse_args():
     description = ('Parse a Caffe training log into two CSV files '
-                   'representing training and testing information')
+                   'containing training and testing information')
     parser = argparse.ArgumentParser(description=description)
 
     parser.add_argument('logfile_path',

From bae916f74b03793c323edd679eaa0e665da0ec0b Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Sun, 2 Nov 2014 21:02:07 -0800
Subject: [PATCH 0990/2053] Store data in lists of dicts and use csv package

Output format is unchanged (except that csv.DictWriter insists on writing ints as 0.0 instead of 0)
---
 tools/extra/parse_log.py | 54 +++++++++++++++++++++++++---------------
 1 file changed, 34 insertions(+), 20 deletions(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 41fe54dc8a0..16ba077aee6 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -10,6 +10,7 @@
 import re
 import extract_seconds
 import argparse
+import csv
 
 
 def get_line_type(line):
@@ -26,6 +27,13 @@ def get_line_type(line):
 
 def parse_log(path_to_log):
     """Parse log file
+    Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
+
+    train_dict_list and test_dict_list are lists of dicts that define the table
+    rows
+
+    train_dict_names and test_dict_names are ordered tuples of the column names
+    for the two dict_lists
     """
 
     re_iteration = re.compile('Iteration (\d+)')
@@ -38,8 +46,10 @@ def parse_log(path_to_log):
     iteration = -1
     test_accuracy = -1
     learning_rate = float('NaN')
-    train_list = []
-    test_list = []
+    train_dict_list = []
+    test_dict_list = []
+    train_dict_names = ('NumIters', 'Seconds', 'TrainingLoss', 'LearningRate')
+    test_dict_names = ('NumIters', 'Seconds', 'TestAccuracy', 'TestLoss')
 
     logfile_year = extract_seconds.get_log_created_year(path_to_log)
     with open(path_to_log) as f:
@@ -68,8 +78,10 @@ def parse_log(path_to_log):
             train_loss_match = re_train_loss.search(line)
             if train_loss_match:
                 train_loss = float(train_loss_match.group(1))
-                train_list.append((iteration, seconds, train_loss,
-                                   learning_rate))
+                train_dict_list.append({'NumIters': iteration,
+                                        'Seconds': seconds,
+                                        'TrainingLoss': train_loss,
+                                        'LearningRate': learning_rate})
 
             output_loss_match = re_output_loss.search(line)
             if output_loss_match and get_line_type(line) == 'test':
@@ -78,13 +90,16 @@ def parse_log(path_to_log):
                 # loss for test data so the test_accuracy variable is already
                 # correctly populated and (2) there's one and only one output
                 # named "accuracy" for the test net
-                test_list.append((iteration, seconds, test_accuracy, test_loss))
+                test_dict_list.append({'NumIters': iteration,
+                                       'Seconds': seconds,
+                                       'TestAccuracy': test_accuracy,
+                                       'TestLoss': test_loss})
 
-    return train_list, test_list
+    return train_dict_list, train_dict_names, test_dict_list, test_dict_names
 
 
-def save_csv_files(logfile_path, output_dir, train_list, test_list,
-                   verbose=False):
+def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names,
+                   test_dict_list, test_dict_names, verbose=False):
     """Save CSV files to output_dir
 
     If the input log file is, e.g., caffe.INFO, the names will be
@@ -93,23 +108,20 @@ def save_csv_files(logfile_path, output_dir, train_list, test_list,
 
     log_basename = os.path.basename(logfile_path)
     train_filename = os.path.join(output_dir, log_basename + '.train')
-    write_csv(train_filename, train_list, '%d,%f,%f,%f',
-              'NumIters,Seconds,TrainingLoss,LearningRate', verbose)
+    write_csv(train_filename, train_dict_list, train_dict_names, verbose)
 
     test_filename = os.path.join(output_dir, log_basename + '.test')
-    write_csv(test_filename, test_list, '%d,%f,%f,%f',
-              'NumIters,Seconds,TestAccuracy,TestLoss', verbose)
+    write_csv(test_filename, test_dict_list, test_dict_names, verbose)
 
 
-def write_csv(output_filename, list_of_tuples, format_string, header,
-              verbose=False):
+def write_csv(output_filename, dict_list, header_names, verbose=False):
     """Write a CSV file
     """
+
     with open(output_filename, 'w') as f:
-        f.write(header + '\n')
-        for row in list_of_tuples:
-            line = format_string % row
-            f.write(line + '\n')
+        dict_writer = csv.DictWriter(f, header_names)
+        dict_writer.writeheader()
+        dict_writer.writerows(dict_list)
     if verbose:
         print 'Wrote %s' % output_filename
 
@@ -135,8 +147,10 @@ def parse_args():
 
 def main():
     args = parse_args()
-    train_list, test_list = parse_log(args.logfile_path)
-    save_csv_files(args.logfile_path, args.output_dir, train_list, test_list)
+    train_dict_list, train_dict_names, test_dict_list, test_dict_names = \
+        parse_log(args.logfile_path)
+    save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
+                   train_dict_names, test_dict_list, test_dict_names)
 
 
 if __name__ == '__main__':

From 646a204c7d925d8c385b095d8a7a86563f54b706 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Wed, 10 Dec 2014 10:39:17 -0800
Subject: [PATCH 0991/2053] Improvements to network drawing

* Use ArgumentParser in draw_net.py to parse input arguments

* Plot network horizontally (left-right) by default; yes, this confuses the names "top" and "bottom", but since everyone's monitor is wider than it is tall, you'll be able to see over 50% more of the network on your screen at once. The top-bottom functionality is available via the "--rankdir BT" command line argument to draw_net.py

* Display number of outputs on edge for data, convolution and inner product layers

* In node names, add newline between layer name and layer type, to save horizontal space (only if graph rank direction is left-right or right-left)

* Include convolution and pooling parameters within node names

* Distinct colors for convolution, pooling and inner product nodes
---
 python/caffe/draw.py | 123 ++++++++++++++++++++++++++++++++++++++-----
 python/draw_net.py   |  45 +++++++++++-----
 2 files changed, 142 insertions(+), 26 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index f8631cfa09e..d95c193cc93 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -11,11 +11,11 @@
 import pydot
 
 # Internal layer and blob styles.
-LAYER_STYLE = {'shape': 'record', 'fillcolor': '#6495ED',
+LAYER_STYLE_DEFAULT = {'shape': 'record', 'fillcolor': '#6495ED',
          'style': 'filled'}
 NEURON_LAYER_STYLE = {'shape': 'record', 'fillcolor': '#90EE90',
          'style': 'filled'}
-BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#F0E68C',
+BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0',
         'style': 'filled'}
 def get_enum_name_by_value():
   desc = caffe_pb2.LayerParameter.LayerType.DESCRIPTOR
@@ -24,39 +24,136 @@ def get_enum_name_by_value():
     d[v.number] = k
   return d
 
-def get_pydot_graph(caffe_net):
-  pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir="BT")
+
+def get_pooling_types_dict():
+    """Get dictionary mapping pooling type number to type name
+    """
+    desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
+    d = {}
+    for k,v in desc.values_by_name.items():
+        d[v.number] = k
+    return d
+
+
+def determine_edge_label_by_layertype(layer, layertype):
+    """Define edge label based on layer type
+    """
+
+    if layertype == 'DATA':
+        edge_label = 'Batch ' + str(layer.data_param.batch_size)
+    elif layertype == 'CONVOLUTION':
+        edge_label = str(layer.convolution_param.num_output)
+    elif layertype == 'INNER_PRODUCT':
+        edge_label = str(layer.inner_product_param.num_output)
+    else:
+        edge_label = '""'
+
+    return edge_label
+
+
+def determine_node_label_by_layertype(layer, layertype, rankdir):
+    """Define node label based on layer type
+    """
+
+    if rankdir in ('TB', 'BT'):
+        # If graph orientation is vertical, horizontal space is free and
+        # vertical space is not; separate words with spaces
+        separator = ' '
+    else:
+        # If graph orientation is horizontal, vertical space is free and
+        # horizontal space is not; separate words with newlines
+        separator = '\n'
+
+    if layertype == 'CONVOLUTION':
+        # Outer double quotes needed or else colon characters don't parse
+        # properly
+        node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
+                     (layer.name,
+                      separator,
+                      layertype,
+                      separator,
+                      layer.convolution_param.kernel_size,
+                      separator,
+                      layer.convolution_param.stride,
+                      separator,
+                      layer.convolution_param.pad)
+    elif layertype == 'POOLING':
+        pooling_types_dict = get_pooling_types_dict()
+        node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
+                     (layer.name,
+                      separator,
+                      pooling_types_dict[layer.pooling_param.pool],
+                      layertype,
+                      separator,
+                      layer.pooling_param.kernel_size,
+                      separator,
+                      layer.pooling_param.stride,
+                      separator,
+                      layer.pooling_param.pad)
+    else:
+        node_label = '"%s%s(%s)"' % (layer.name, separator, layertype)
+    return node_label
+
+
+def choose_color_by_layertype(layertype):
+    """Define colors for nodes based on the layer type
+    """
+    color = '#6495ED'  # Default
+    if layertype == 'CONVOLUTION':
+        color = '#FF5050'
+    elif layertype == 'POOLING':
+        color = '#FF9900'
+    elif layertype == 'INNER_PRODUCT':
+        color = '#CC33FF'
+    return color
+
+
+def get_pydot_graph(caffe_net, rankdir, label_edges=True):
+  pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir)
   pydot_nodes = {}
   pydot_edges = []
   d = get_enum_name_by_value()
   for layer in caffe_net.layers:
     name = layer.name
     layertype = d[layer.type]
+    node_label = determine_node_label_by_layertype(layer, layertype, rankdir)
     if (len(layer.bottom) == 1 and len(layer.top) == 1 and
         layer.bottom[0] == layer.top[0]):
       # We have an in-place neuron layer.
       pydot_nodes[name + '_' + layertype] = pydot.Node(
-          '%s (%s)' % (name, layertype), **NEURON_LAYER_STYLE)
+          node_label, **NEURON_LAYER_STYLE)
     else:
+      layer_style = LAYER_STYLE_DEFAULT
+      layer_style['fillcolor'] = choose_color_by_layertype(layertype)
       pydot_nodes[name + '_' + layertype] = pydot.Node(
-          '%s (%s)' % (name, layertype), **LAYER_STYLE)
+          node_label, **layer_style)
     for bottom_blob in layer.bottom:
       pydot_nodes[bottom_blob + '_blob'] = pydot.Node(
         '%s' % (bottom_blob), **BLOB_STYLE)
-      pydot_edges.append((bottom_blob + '_blob', name + '_' + layertype))
+      edge_label = '""'
+      pydot_edges.append({'src': bottom_blob + '_blob',
+                          'dst': name + '_' + layertype,
+                          'label': edge_label})
     for top_blob in layer.top:
       pydot_nodes[top_blob + '_blob'] = pydot.Node(
         '%s' % (top_blob))
-      pydot_edges.append((name + '_' + layertype, top_blob + '_blob'))
+      if label_edges:
+        edge_label = determine_edge_label_by_layertype(layer, layertype)
+      else:
+        edge_label = '""'
+      pydot_edges.append({'src': name + '_' + layertype,
+                          'dst': top_blob + '_blob',
+                          'label': edge_label})
   # Now, add the nodes and edges to the graph.
   for node in pydot_nodes.values():
     pydot_graph.add_node(node)
   for edge in pydot_edges:
     pydot_graph.add_edge(
-        pydot.Edge(pydot_nodes[edge[0]], pydot_nodes[edge[1]]))
+        pydot.Edge(pydot_nodes[edge['src']], pydot_nodes[edge['dst']],
+                   label=edge['label']))
   return pydot_graph
 
-def draw_net(caffe_net, ext='png'):
+def draw_net(caffe_net, rankdir, ext='png'):
   """Draws a caffe net and returns the image string encoded using the given
   extension.
 
@@ -64,13 +161,13 @@ def draw_net(caffe_net, ext='png'):
     caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer.
     ext: the image extension. Default 'png'.
   """
-  return get_pydot_graph(caffe_net).create(format=ext)
+  return get_pydot_graph(caffe_net, rankdir).create(format=ext)
 
-def draw_net_to_file(caffe_net, filename):
+def draw_net_to_file(caffe_net, filename, rankdir='LR'):
   """Draws a caffe net, and saves it to file using the format given as the
   file extension. Use '.raw' to output raw text that you can manually feed
   to graphviz to draw graphs.
   """
   ext = filename[filename.rfind('.')+1:]
   with open(filename, 'wb') as fid:
-    fid.write(draw_net(caffe_net, ext))
+    fid.write(draw_net(caffe_net, rankdir, ext))
diff --git a/python/draw_net.py b/python/draw_net.py
index ba488294275..4457b793e86 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -2,24 +2,43 @@
 """
 Draw a graph of the net architecture.
 """
-import os
+import argparse
 from google.protobuf import text_format
 
-import caffe, caffe.draw
+import caffe
+import caffe.draw
 from caffe.proto import caffe_pb2
 
 
-def main(argv):
-    if len(argv) != 3:
-        print 'Usage: %s input_net_proto_file output_image_file' % \
-                os.path.basename(sys.argv[0])
-    else:
-        net = caffe_pb2.NetParameter()
-        text_format.Merge(open(sys.argv[1]).read(), net)
-        print 'Drawing net to %s' % sys.argv[2]
-        caffe.draw.draw_net_to_file(net, sys.argv[2])
+def parse_args():
+    """Parse input arguments
+    """
+
+    parser = argparse.ArgumentParser(description='Draw a network graph')
+
+    parser.add_argument('input_net_proto_file',
+                        help='Input network prototxt file')
+    parser.add_argument('output_image_file',
+                        help='Output image file')
+    parser.add_argument('--rankdir',
+                        help=('One of TB (top-bottom, i.e., vertical), '
+                              'RL (right-left, i.e., horizontal), or another'
+                              'valid dot option; see'
+                              'http://www.graphviz.org/doc/info/attrs.html#k:rankdir'
+                              '(default: LR)'),
+                        default='LR')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    net = caffe_pb2.NetParameter()
+    text_format.Merge(open(args.input_net_proto_file).read(), net)
+    print 'Drawing net to %s' % args.output_image_file
+    caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
 
 
 if __name__ == '__main__':
-    import sys
-    main(sys.argv)
+    main()

From 957318783d1aa3396a1b70755aac6de3639e6ab3 Mon Sep 17 00:00:00 2001
From: Jonathan Ho <hoj@cs.stanford.edu>
Date: Wed, 10 Dec 2014 18:27:56 -0800
Subject: [PATCH 0992/2053] Check input line count in HDF5 data layer

---
 src/caffe/layers/hdf5_data_layer.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index f6865efe78e..706c4a18319 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -75,6 +75,8 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   num_files_ = hdf_filenames_.size();
   current_file_ = 0;
   LOG(INFO) << "Number of HDF5 files: " << num_files_;
+  CHECK_GE(num_files_, 1) << "Must have at least 1 HDF5 filename listed in "
+    << source;
 
   // Load the first HDF5 file and initialize the line counter.
   LoadHDF5FileData(hdf_filenames_[current_file_].c_str());

From 0a56995d3df9b442d457f2187d842e3b8d5c2a45 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 16 Dec 2014 01:52:43 -0800
Subject: [PATCH 0993/2053] remove extra blank line

---
 Makefile | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Makefile b/Makefile
index 1e70c2d5c6e..b19555fe962 100644
--- a/Makefile
+++ b/Makefile
@@ -498,7 +498,6 @@ $(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NA
 		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(LIB_BUILD_DIR)
 	@ echo
 
-
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \

From f519338c5e38f29bb8f02d59b945cc3af73d828b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 23 Nov 2014 22:35:46 -0800
Subject: [PATCH 0994/2053] consolidate build rules for object files

---
 Makefile | 77 ++++++++------------------------------------------------
 1 file changed, 10 insertions(+), 67 deletions(-)

diff --git a/Makefile b/Makefile
index b19555fe962..da8a4d81921 100644
--- a/Makefile
+++ b/Makefile
@@ -472,15 +472,20 @@ $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	ar rcs $@ $(OBJS)
 	@ echo
 
-$(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HXX_SRCS) \
-		| $(TEST_BUILD_DIR)
+$(BUILD_DIR)/%.o: %.cpp $(HXX_SRCS) | $(ALL_BUILD_DIRS)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
-$(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HXX_SRCS) \
-		| $(TEST_BUILD_DIR)
+$(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
+		| $(PROTO_BUILD_DIR)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
+	@ echo
+
+$(BUILD_DIR)/%.cuo: %.cu $(HXX_SRCS) | $(ALL_BUILD_DIRS)
 	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
@@ -515,72 +520,10 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
 	@ ln -s $(abspath $<) $@
 
-$(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
+$(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
 	$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
-
-$(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
-		| $(LAYER_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
-		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
-		| $(LAYER_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
-		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
-$(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
-		|| (cat $@.$(WARNS_EXT); exit 1)
-	@ cat $@.$(WARNS_EXT)
-	@ echo
-
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
 $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \

From 89f7019acef532064f0897ceb45d2ed37a6bd608 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 24 Nov 2014 00:10:35 -0800
Subject: [PATCH 0995/2053] automatic discovery of source directories

---
 Makefile | 22 +++++++---------------
 1 file changed, 7 insertions(+), 15 deletions(-)

diff --git a/Makefile b/Makefile
index da8a4d81921..e5a4b5ee40c 100644
--- a/Makefile
+++ b/Makefile
@@ -16,6 +16,10 @@ else
 	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
 endif
 
+# All of the directories containing code.
+SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \
+	\( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
+
 # The target shared library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
@@ -98,9 +102,6 @@ PROTO_GEN_PY := $(foreach file,${PROTO_SRCS:.proto=_pb2.py}, \
 CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o})
 CU_OBJS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo})
 PROTO_OBJS := ${PROTO_GEN_CC:.cc=.o}
-OBJ_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)
-LAYER_BUILD_DIR := $(OBJ_BUILD_DIR)/layers
-UTIL_BUILD_DIR := $(OBJ_BUILD_DIR)/util
 OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
 # tool, example, and test objects
 TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
@@ -110,11 +111,7 @@ TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
 TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo})
 TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
 GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
-GTEST_BUILD_DIR := $(dir $(GTEST_OBJ))
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
-EXAMPLE_BUILD_DIR := $(BUILD_DIR)/examples
-EXAMPLE_BUILD_DIRS := $(EXAMPLE_BUILD_DIR)
-EXAMPLE_BUILD_DIRS += $(foreach obj,$(EXAMPLE_OBJS),$(dir $(obj)))
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
@@ -183,14 +180,9 @@ ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
 		DIST_ALIASES += distribute
 endif
 
-ALL_BUILD_DIRS := $(sort \
-		$(BUILD_DIR) $(LIB_BUILD_DIR) $(OBJ_BUILD_DIR) \
-		$(LAYER_BUILD_DIR) $(UTIL_BUILD_DIR) $(TOOL_BUILD_DIR) \
-		$(TEST_BUILD_DIR) $(TEST_BIN_DIR) $(GTEST_BUILD_DIR) \
-		$(EXAMPLE_BUILD_DIRS) \
-		$(LINT_OUTPUT_DIR) \
-		$(PROTO_BUILD_DIR) $(PROTO_BUILD_INCLUDE_DIR) $(PY_PROTO_BUILD_DIR) \
-		$(DISTRIBUTE_SUBDIRS))
+ALL_BUILD_DIRS := $(sort $(BUILD_DIR) $(addprefix $(BUILD_DIR)/, $(SRC_DIRS)) \
+	$(LIB_BUILD_DIR) $(TEST_BIN_DIR) $(PY_PROTO_BUILD_DIR) $(LINT_OUTPUT_DIR) \
+	$(DISTRIBUTE_SUBDIRS) $(PROTO_BUILD_INCLUDE_DIR))
 
 ##############################
 # Set directory for Doxygen-generated documentation

From 492f67c4084fd173f6592b69903491f7e47edbfb Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 10 Dec 2014 23:37:42 -0800
Subject: [PATCH 0996/2053] move cuda output from build/.../.cuo ->
 build/cuda/.../.o

This will allow nvcc's -M dependency generation option to work
harmoniously, since it assumes that output will have a .o extension.
---
 Makefile | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/Makefile b/Makefile
index e5a4b5ee40c..54d5636135d 100644
--- a/Makefile
+++ b/Makefile
@@ -100,15 +100,16 @@ PROTO_GEN_PY := $(foreach file,${PROTO_SRCS:.proto=_pb2.py}, \
 # These objects will be linked into the final shared library, so we
 # exclude the tool, example, and test objects.
 CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o})
-CU_OBJS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo})
+CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o})
 PROTO_OBJS := ${PROTO_GEN_CC:.cc=.o}
 OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
 # tool, example, and test objects
 TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
 TOOL_BUILD_DIR := $(BUILD_DIR)/tools
-TEST_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
+TEST_CXX_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
+TEST_CU_BUILD_DIR := $(BUILD_DIR)/cuda/src/$(PROJECT)/test
 TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
-TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo})
+TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o})
 TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
 GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
@@ -134,11 +135,11 @@ TEST_ALL_DYNLINK_BIN := $(TEST_BIN_DIR)/test_all_dynamic_link.testbin
 ##############################
 WARNS_EXT := warnings.txt
 CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.$(WARNS_EXT)})
-CU_WARNS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${CU_SRCS:.cu=.o.$(WARNS_EXT)})
 TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.$(WARNS_EXT)})
 EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.$(WARNS_EXT)})
 TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.$(WARNS_EXT)})
-TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o.$(WARNS_EXT)})
 ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
 ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
 ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)
@@ -181,6 +182,7 @@ ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
 endif
 
 ALL_BUILD_DIRS := $(sort $(BUILD_DIR) $(addprefix $(BUILD_DIR)/, $(SRC_DIRS)) \
+	$(addprefix $(BUILD_DIR)/cuda/, $(SRC_DIRS)) \
 	$(LIB_BUILD_DIR) $(TEST_BIN_DIR) $(PY_PROTO_BUILD_DIR) $(LINT_OUTPUT_DIR) \
 	$(DISTRIBUTE_SUBDIRS) $(PROTO_BUILD_INCLUDE_DIR))
 
@@ -437,9 +439,7 @@ $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
 	  $(RM) $(NONEMPTY_WARN_REPORT); \
 	  echo "No compiler warnings!";
 
-$(ALL_CXX_WARNS): %.o.$(WARNS_EXT) : %.o
-
-$(ALL_CU_WARNS): %.cuo.$(WARNS_EXT) : %.cuo
+$(ALL_WARNS): %.o.$(WARNS_EXT) : %.o
 
 $(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
 
@@ -495,14 +495,14 @@ $(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NA
 		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(LIB_BUILD_DIR)
 	@ echo
 
-$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
+$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
+	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
+$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
+	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo

From da18d318f4d99036a61fa6c3d843eb43827268b8 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 16 Dec 2014 20:18:53 -0800
Subject: [PATCH 0997/2053] check host malloc result

---
 include/caffe/syncedmem.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index db8d0e80e12..2564e0716ef 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -24,6 +24,7 @@ namespace caffe {
 
 inline void CaffeMallocHost(void** ptr, size_t size) {
   *ptr = malloc(size);
+  CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
 
 inline void CaffeFreeHost(void* ptr) {

From 8d3e46dda3783ba451942bf9708a6c1ef52ce9ff Mon Sep 17 00:00:00 2001
From: Thomas Schenker <mail.thomas.schenker@googlemail.com>
Date: Wed, 10 Dec 2014 16:16:34 +0100
Subject: [PATCH 0998/2053] Add CHECKs to prevent segfault for incorrect
 IMAGE_DATA layers.

Incomplete IMAGE_DATA layers cause a segmentation fault, e.g.:

layers {
  name: "mnist"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  image_data_param {
    batch_size: 100
  }
  include: { phase: TRAIN }
}

or

layers {
  name: "mnist"
  type: IMAGE_DATA
  top: "data"
  top: "label"
  data_param {
    source: "./data/train.txt"
    batch_size: 100
  }
  include: { phase: TRAIN }
}

Also empty image list files cause a segfault.
This is fixed by adding 3 CHECK instructions.
---
 src/caffe/layers/image_data_layer.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e4a575bec93..b5a34ff81aa 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -27,8 +27,11 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       "new_height and new_width to be set at the same time.";
   // Read the file with filenames and labels
   const string& source = this->layer_param_.image_data_param().source();
+  CHECK_GT(source.size(), 0);
   LOG(INFO) << "Opening file " << source;
   std::ifstream infile(source.c_str());
+  CHECK(infile.good())
+      << "Could not open image list (filename: \""+ source + "\")";
   string filename;
   int label;
   while (infile >> filename >> label) {
@@ -53,6 +56,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK_GT(lines_.size(), skip) << "Not enough points to skip";
     lines_id_ = skip;
   }
+  CHECK(!lines_.empty())
+      << "Image list is empty (filename: \"" + source + "\")";
   // Read a data point, and use it to initialize the top blob.
   Datum datum;
   CHECK(ReadImageToDatum(lines_[lines_id_].first, lines_[lines_id_].second,

From 555f4d3b7cf9d5db73b983c3757474deced0abfa Mon Sep 17 00:00:00 2001
From: Kyle Krafka <kyle.krafka@gmail.com>
Date: Sat, 20 Dec 2014 23:02:19 -0500
Subject: [PATCH 0999/2053] Use valid MathJax delimiters.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

MathJax does not support $ as a delimiter by default, so math was not
displayed properly on the “Forward and Backward” page.
---
 docs/tutorial/forward_backward.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index f58b9cac19e..a645f002f61 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -15,7 +15,7 @@ This pass goes from bottom to top.
 
 <img src="fig/forward.jpg" alt="Forward pass" width="320">
 
-The data $x$ is passed through an inner product layer for $g(x)$ then through a softmax for $h(g(x))$ and softmax loss to give $f_W(x)$.
+The data $$x$$ is passed through an inner product layer for $$g(x)$$ then through a softmax for $$h(g(x))$$ and softmax loss to give $$f_W(x)$$.
 
 The **backward** pass computes the gradient given the loss for learning.
 In backward Caffe reverse-composes the gradient of each layer to compute the gradient of the whole model by automatic differentiation.
@@ -24,7 +24,7 @@ This pass goes from top to bottom.
 
 <img src="fig/backward.jpg" alt="Backward pass" width="320">
 
-The backward pass begins with the loss and computes the gradient with respect to the output $\frac{\partial f_W}{\partial h}$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $\frac{\partial f_W}{\partial W_{\text{ip}}}$ during the backward step.
+The backward pass begins with the loss and computes the gradient with respect to the output $$\frac{\partial f_W}{\partial h}$$. The gradient with respect to the rest of the model is computed layer-by-layer through the chain rule. Layers with parameters, like the `INNER_PRODUCT` layer, compute the gradient with respect to their parameters $$\frac{\partial f_W}{\partial W_{\text{ip}}}$$ during the backward step.
 
 These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
 

From 3855ad574c89847552705cd1ceb70dd5e8687c4d Mon Sep 17 00:00:00 2001
From: Kyle Krafka <kyle.krafka@gmail.com>
Date: Sat, 20 Dec 2014 23:34:51 -0500
Subject: [PATCH 1000/2053] Fix minor typos in strings.

---
 tools/caffe.cpp            | 2 +-
 tools/convert_imageset.cpp | 2 +-
 tools/extract_features.cpp | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index c8c8c1a6b4c..046b38938e4 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -21,7 +21,7 @@ DEFINE_int32(gpu, -1,
 DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
-    "The model definition protocol buffer text file..");
+    "The model definition protocol buffer text file.");
 DEFINE_string(snapshot, "",
     "Optional; the snapshot solver state to resume training.");
 DEFINE_string(weights, "",
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 7e1e83d9d0c..09e84d6b3ef 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -112,7 +112,7 @@ int main(int argc, char** argv) {
     CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
         << "mdb_txn_begin failed";
     CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed. Does the lmdb already exist? ";
+        << "mdb_open failed. Does the lmdb already exist?";
   } else {
     LOG(FATAL) << "Unknown db backend " << db_backend;
   }
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 49e8f98971c..cb48c4796b9 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -131,7 +131,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
-  LOG(ERROR)<< "Extacting Features";
+  LOG(ERROR)<< "Extracting Features";
 
   Datum datum;
   vector<shared_ptr<leveldb::WriteBatch> > feature_batches(

From f15210cdc8f0e9b2e5866972ab81149ac68e96c6 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 2 Dec 2014 13:10:57 -0800
Subject: [PATCH 1001/2053] Added bvlc_googlenet prototxt and weights

---
 models/bvlc_googlenet/deploy.prototxt       | 1924 ++++++++++++++++
 models/bvlc_googlenet/quick_solver.prototxt |   15 +
 models/bvlc_googlenet/readme.md             |   33 +
 models/bvlc_googlenet/solver.prototxt       |   16 +
 models/bvlc_googlenet/train_val.prototxt    | 2240 +++++++++++++++++++
 5 files changed, 4228 insertions(+)
 create mode 100644 models/bvlc_googlenet/deploy.prototxt
 create mode 100644 models/bvlc_googlenet/quick_solver.prototxt
 create mode 100644 models/bvlc_googlenet/readme.md
 create mode 100644 models/bvlc_googlenet/solver.prototxt
 create mode 100644 models/bvlc_googlenet/train_val.prototxt

diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
new file mode 100644
index 00000000000..e31a4c9cd00
--- /dev/null
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -0,0 +1,1924 @@
+name: "GoogleNet"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layers {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+  name: "conv1/relu_7x7"
+  type: RELU
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  name: "pool1/norm1"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+  name: "conv2/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+  name: "conv2/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  name: "conv2/norm2"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+  name: "inception_3a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+  name: "inception_3a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  name: "inception_3a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+  name: "inception_3a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+  name: "inception_3b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+  name: "inception_3b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  name: "inception_3b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+  name: "inception_3b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  name: "pool3/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+  name: "inception_4a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+  name: "inception_4a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  name: "inception_4a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+  name: "inception_4a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+  name: "inception_4b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+  name: "inception_4b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  name: "inception_4b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+  name: "inception_4b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+  name: "inception_4c/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+  name: "inception_4c/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  name: "inception_4c/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+  name: "inception_4c/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+  name: "inception_4d/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+  name: "inception_4d/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  name: "inception_4d/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+  name: "inception_4d/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  name: "inception_4e/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+  name: "inception_4e/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+  name: "inception_4e/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  name: "inception_4e/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+  name: "inception_4e/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  name: "pool4/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+  name: "inception_5a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+  name: "inception_5a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  name: "inception_5a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+  name: "inception_5a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+  name: "inception_5b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+  name: "inception_5b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  name: "inception_5b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+  name: "inception_5b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  name: "pool5/drop_7x7_s1"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "loss3/classifier"
+  top: "prob"
+}
\ No newline at end of file
diff --git a/models/bvlc_googlenet/quick_solver.prototxt b/models/bvlc_googlenet/quick_solver.prototxt
new file mode 100644
index 00000000000..5d2f7ee70b9
--- /dev/null
+++ b/models/bvlc_googlenet/quick_solver.prototxt
@@ -0,0 +1,15 @@
+net: "models/bvlc_googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 4000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.01
+lr_policy: "poly"
+power: 0.5
+max_iter: 2400000
+momentum: 0.9
+weight_decay: 0.0002
+snapshot: 40000
+snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet_quick"
+solver_mode: GPU
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
new file mode 100644
index 00000000000..27022d3384a
--- /dev/null
+++ b/models/bvlc_googlenet/readme.md
@@ -0,0 +1,33 @@
+---
+name: BVLC GoogleNet Model
+caffemodel: bvlc_googlenet.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
+license: non-commercial
+sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
+caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
+---
+
+This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
+
+Differences:
+- not training with the relighting data-augmentation;
+- not training with the scale or aspect-ratio data-augmentation;
+- uses "xavier" to initialize the weights instead of "gaussian";
+- quick_solver.prototxt uses a different learning rate decay policy than the original solver.prototxt, that allows a much faster training (60 epochs vs 250 epochs);
+
+The bundled model is the iteration 2,400,000 snapshot (60 epochs) using quick_solver.prototxt
+
+This bundled model obtains a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error) on the validation set, using just the center crop.
+(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
+
+Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:
+ - Average Forward pass: 562.841 ms.
+ - Average Backward pass: 1123.84 ms.
+ - Average Forward-Backward: 1688.8 ms.
+
+
+## License
+
+The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
+"Researcher shall use the Database only for non-commercial research and educational purposes."
+Accordingly, this model is distributed under a non-commercial license.
diff --git a/models/bvlc_googlenet/solver.prototxt b/models/bvlc_googlenet/solver.prototxt
new file mode 100644
index 00000000000..d7d1788173c
--- /dev/null
+++ b/models/bvlc_googlenet/solver.prototxt
@@ -0,0 +1,16 @@
+net: "models/bvlc_googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 4000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.01
+lr_policy: "step"
+stepsize: 320000
+gamma: 0.96
+max_iter: 10000000
+momentum: 0.9
+weight_decay: 0.0002
+snapshot: 40000
+snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet"
+solver_mode: GPU
diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
new file mode 100644
index 00000000000..cd8f38abdc8
--- /dev/null
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -0,0 +1,2240 @@
+name: "GoogleNet"
+layers {
+  top: "data"
+  top: "label"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 32
+    backend: LMDB
+  }
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+  }
+}
+layers {
+  top: "data"
+  top: "label"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    mean_value: 104
+    mean_value: 117
+    mean_value: 123
+  }
+}
+layers {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+  name: "conv1/relu_7x7"
+  type: RELU
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  name: "pool1/norm1"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+  name: "conv2/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+  name: "conv2/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  name: "conv2/norm2"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+  name: "inception_3a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+  name: "inception_3a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  name: "inception_3a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+  name: "inception_3a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+  name: "inception_3b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+  name: "inception_3b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  name: "inception_3b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+  name: "inception_3b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  name: "pool3/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+  name: "inception_4a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+  name: "inception_4a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  name: "inception_4a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+  name: "inception_4a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "loss1/ave_pool"
+  name: "loss1/ave_pool"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layers {
+  bottom: "loss1/ave_pool"
+  top: "loss1/conv"
+  name: "loss1/conv"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.08
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss1/conv"
+  top: "loss1/conv"
+  name: "loss1/relu_conv"
+  type: RELU
+}
+layers {
+  bottom: "loss1/conv"
+  top: "loss1/fc"
+  name: "loss1/fc"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+      std: 0.02
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  name: "loss1/relu_fc"
+  type: RELU
+}
+layers {
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  name: "loss1/drop_fc"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layers {
+  bottom: "loss1/fc"
+  top: "loss1/classifier"
+  name: "loss1/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+      std: 0.0009765625
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss1"
+  name: "loss1/loss"
+  type: SOFTMAX_LOSS
+  loss_weight: 0.3
+}
+layers {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-1"
+  name: "loss1/top-1"
+  type: ACCURACY
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-5"
+  name: "loss1/top-5"
+  type: ACCURACY
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+  name: "inception_4b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+  name: "inception_4b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  name: "inception_4b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+  name: "inception_4b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+  name: "inception_4c/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+  name: "inception_4c/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  name: "inception_4c/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+  name: "inception_4c/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+  name: "inception_4d/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+  name: "inception_4d/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  name: "inception_4d/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+  name: "inception_4d/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "loss2/ave_pool"
+  name: "loss2/ave_pool"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layers {
+  bottom: "loss2/ave_pool"
+  top: "loss2/conv"
+  name: "loss2/conv"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.08
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss2/conv"
+  top: "loss2/conv"
+  name: "loss2/relu_conv"
+  type: RELU
+}
+layers {
+  bottom: "loss2/conv"
+  top: "loss2/fc"
+  name: "loss2/fc"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+      std: 0.02
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  name: "loss2/relu_fc"
+  type: RELU
+}
+layers {
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  name: "loss2/drop_fc"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layers {
+  bottom: "loss2/fc"
+  top: "loss2/classifier"
+  name: "loss2/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+      std: 0.0009765625
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss1"
+  name: "loss2/loss"
+  type: SOFTMAX_LOSS
+  loss_weight: 0.3
+}
+layers {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-1"
+  name: "loss2/top-1"
+  type: ACCURACY
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-5"
+  name: "loss2/top-5"
+  type: ACCURACY
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  name: "inception_4e/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+  name: "inception_4e/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+  name: "inception_4e/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  name: "inception_4e/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+  name: "inception_4e/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  name: "pool4/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+  name: "inception_5a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+  name: "inception_5a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  name: "inception_5a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+  name: "inception_5a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+  name: "inception_5b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+  name: "inception_5b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  name: "inception_5b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+  name: "inception_5b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  name: "pool5/drop_7x7_s1"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss3"
+  name: "loss3/loss3"
+  type: SOFTMAX_LOSS
+  loss_weight: 1
+}
+layers {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-1"
+  name: "loss3/top-1"
+  type: ACCURACY
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-5"
+  name: "loss3/top-5"
+  type: ACCURACY
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}

From f347355207601a602cc1081ea10f969f995138e9 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 20 Dec 2014 23:24:32 -0800
Subject: [PATCH 1002/2053] Added credits for training bvlc models

---
 models/bvlc_alexnet/readme.md                 | 2 ++
 models/bvlc_googlenet/readme.md               | 2 ++
 models/bvlc_reference_caffenet/readme.md      | 2 ++
 models/bvlc_reference_rcnn_ilsvrc13/readme.md | 2 ++
 models/finetune_flickr_style/readme.md        | 2 ++
 5 files changed, 10 insertions(+)

diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index 20c393ff26b..c25fd4f8018 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -18,6 +18,8 @@ The best validation performance during training was iteration 358,000 with valid
 This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop.
 (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
 
+This model was trained by Evan Shelhamer @shelhamer
+
 ## License
 
 The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 27022d3384a..8a3bbec4f8b 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -5,6 +5,7 @@ caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
 license: non-commercial
 sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
 caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
+gist_id: 866e2aa1fd707b89b913
 ---
 
 This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
@@ -25,6 +26,7 @@ Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:
  - Average Backward pass: 1123.84 ms.
  - Average Forward-Backward: 1688.8 ms.
 
+This model was trained by Sergio Guadarrama @sguada
 
 ## License
 
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index d1c6269ae73..b867e7386a3 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -18,6 +18,8 @@ The best validation performance during training was iteration 313,000 with valid
 This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.
 (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)
 
+This model was trained by Jeff Donahue @jeffdonahue
+
 ## License
 
 The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index fb8f26d15df..5d4bc5afe32 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -13,6 +13,8 @@ Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/m
 
 *N.B. For research purposes, make use of the official R-CNN package and not this example.*
 
+This model was trained by Ross Girshick @rbgirshick
+
 ## License
 
 The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
diff --git a/models/finetune_flickr_style/readme.md b/models/finetune_flickr_style/readme.md
index d2a8a95f669..aac7f7c96e8 100644
--- a/models/finetune_flickr_style/readme.md
+++ b/models/finetune_flickr_style/readme.md
@@ -15,6 +15,8 @@ The final performance:
     I1017 07:36:17.370730 31333 solver.cpp:247] Iteration 100000, Testing net (#0)
     I1017 07:36:34.248730 31333 solver.cpp:298]     Test net output #0: accuracy = 0.3916
 
+This model was trained by Sergey Karayev @sergeyk
+
 ## License
 
 The Flickr Style dataset contains only URLs to images.

From 396a7bd06838118b26fac7510b1cf28fbec1cd1e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 20 Dec 2014 23:28:19 -0800
Subject: [PATCH 1003/2053] Added credits and bvlc_googlenet to model_zoo.md

---
 docs/model_zoo.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 330850cbbd6..358bbb7fc91 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -15,9 +15,10 @@ To lower the friction of sharing these models, we introduce the model zoo framew
 First of all, we provide some trained models out of the box.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper.
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012.
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn).
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper. (Trained by Jeff Donahue @jeffdonahue)
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
+- **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
 
 User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
 

From 9e756bfd10bf6f3196905b8e721d9fb5abde2ad2 Mon Sep 17 00:00:00 2001
From: qipeng <pengrobertqi@163.com>
Date: Tue, 16 Sep 2014 19:59:53 -0700
Subject: [PATCH 1004/2053] Display averaged loss over the last several
 iterations

---
 src/caffe/proto/caffe.proto |  4 +++-
 src/caffe/solver.cpp        | 18 +++++++++++++++++-
 2 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 9395c38f3e9..a789aeefa25 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 33 (last added: test_initialization)
+// SolverParameter next available ID: 34 (last added: average_loss)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -113,6 +113,8 @@ message SolverParameter {
   // the number of iterations between displaying info. If display = 0, no info
   // will be displayed.
   optional int32 display = 6;
+  // Display the cost averaged over the last average_cost iterations
+  optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
   optional string lr_policy = 8; // The learning rate decay policy.
   optional float gamma = 9; // The parameter to compute the learning rate.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ba262920a9a..5c34e487cb6 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -169,6 +169,13 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // resume_file above.
   const int start_iter = iter_;
 
+  int average_loss = this->param_.average_loss();
+
+  CHECK_GE(average_loss, 1) << "average_cost should be non-negative.";
+
+  vector<Dtype> losses;
+  Dtype smoothed_loss = 0;
+
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.
   vector<Blob<Dtype>*> bottom_vec;
@@ -187,8 +194,17 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
     Dtype loss = net_->ForwardBackward(bottom_vec);
+    if (losses.size() < average_loss) {
+      losses.push_back(loss);
+      int size = losses.size();
+      smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
+    } else {
+      int idx = (iter_ - start_iter) % average_loss;
+      smoothed_loss += (loss - losses[idx]) / average_loss;
+      losses[idx] = loss;
+    }
     if (display) {
-      LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+      LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {

From 18749f8982e11eca91e1d97bc4317c6d64f9dca7 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 3 Oct 2014 17:14:20 -0700
Subject: [PATCH 1005/2053] Added Multistep, Poly and Sigmoid learning rate
 decay policies

Conflicts:
	include/caffe/solver.hpp
	src/caffe/proto/caffe.proto
	src/caffe/solver.cpp
---
 .../lenet/lenet_multistep_solver.prototxt     | 33 +++++++++++++++++
 .../lenet/lenet_stepearly_solver.prototxt     | 28 ++++++++++++++
 include/caffe/solver.hpp                      |  1 +
 src/caffe/proto/caffe.proto                   |  8 +++-
 src/caffe/solver.cpp                          | 37 ++++++++++++++++---
 5 files changed, 99 insertions(+), 8 deletions(-)
 create mode 100644 examples/lenet/lenet_multistep_solver.prototxt
 create mode 100644 examples/lenet/lenet_stepearly_solver.prototxt

diff --git a/examples/lenet/lenet_multistep_solver.prototxt b/examples/lenet/lenet_multistep_solver.prototxt
new file mode 100644
index 00000000000..fadd7c904d7
--- /dev/null
+++ b/examples/lenet/lenet_multistep_solver.prototxt
@@ -0,0 +1,33 @@
+# The training protocol buffer definition
+train_net: "lenet_train.prototxt"
+# The testing protocol buffer definition
+test_net: "lenet_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "multistep"
+gamma: 0.9
+stepvalue: 1000
+stepvalue: 2000
+stepvalue: 2500
+stepvalue: 3000
+stepvalue: 3500
+stepvalue: 4000
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# solver mode: 0 for CPU and 1 for GPU
+solver_mode: 1
+device_id: 1
diff --git a/examples/lenet/lenet_stepearly_solver.prototxt b/examples/lenet/lenet_stepearly_solver.prototxt
new file mode 100644
index 00000000000..efc6a335d8f
--- /dev/null
+++ b/examples/lenet/lenet_stepearly_solver.prototxt
@@ -0,0 +1,28 @@
+# The training protocol buffer definition
+train_net: "lenet_train.prototxt"
+# The testing protocol buffer definition
+test_net: "lenet_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "stepearly"
+gamma: 0.9
+stepearly: 1
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# solver mode: 0 for CPU and 1 for GPU
+solver_mode: 1
+device_id: 1
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 6fd159d0b98..51aebb323c5 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -56,6 +56,7 @@ class Solver {
 
   SolverParameter param_;
   int iter_;
+  int current_step_;
   shared_ptr<Net<Dtype> > net_;
   vector<shared_ptr<Net<Dtype> > > test_nets_;
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a789aeefa25..88f670fd497 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,7 +63,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 34 (last added: average_loss)
+// SolverParameter next available ID: 35 (last added: stepvalue)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -124,7 +124,10 @@ message SolverParameter {
   // regularization types supported: L1 and L2
   // controlled by weight_decay
   optional string regularization_type = 29 [default = "L2"];
-  optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
+  // the stepsize for learning rate policy "step"
+  optional int32 stepsize = 13;
+  // the stepsize for learning rate policy "multistep"
+  repeated int32 stepvalue = 34;
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
   // whether to snapshot diff in the results or not. Snapshotting diff will help
@@ -166,6 +169,7 @@ message SolverState {
   optional int32 iter = 1; // The current iteration
   optional string learned_net = 2; // The file that stores the learned net.
   repeated BlobProto history = 3; // The history for sgd solvers
+  optional int32 current_step = 4 [default = 0]; // The current step for learning rate
 }
 
 enum Phase {
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 5c34e487cb6..886c3cc6edc 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -158,6 +158,7 @@ template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
   Caffe::set_phase(Caffe::TRAIN);
   LOG(INFO) << "Solving " << net_->name();
+  LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
   PreSolve();
 
   iter_ = 0;
@@ -257,7 +258,6 @@ void Solver<Dtype>::TestAll() {
   }
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
   LOG(INFO) << "Iteration " << iter_
@@ -336,6 +336,7 @@ void Solver<Dtype>::Snapshot() {
   SnapshotSolverState(&state);
   state.set_iter(iter_);
   state.set_learned_net(model_filename);
+  state.set_current_step(current_step_);
   snapshot_filename = filename + ".solverstate";
   LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
   WriteProtoToBinaryFile(state, snapshot_filename.c_str());
@@ -351,6 +352,7 @@ void Solver<Dtype>::Restore(const char* state_file) {
     net_->CopyTrainedLayersFrom(net_param);
   }
   iter_ = state.iter();
+  current_step_ = state.current_step();
   RestoreSolverState(state);
 }
 
@@ -361,8 +363,15 @@ void Solver<Dtype>::Restore(const char* state_file) {
 //    - step: return base_lr * gamma ^ (floor(iter / step))
 //    - exp: return base_lr * gamma ^ iter
 //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
-// where base_lr, gamma, step and power are defined in the solver parameter
-// protocol buffer, and iter is the current iteration.
+//    - multistep: similar to step but it allows non uniform steps defined by
+//      stepvalue
+//    - poly: the effective learning rate follows a polynomial decay, to be
+//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+//    - sigmoid: the effective learning rate follows a sigmod decay
+//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+//
+// where base_lr, max_iter, gamma, step, stepvalue and power are defined
+// in the solver parameter protocol buffer, and iter is the current iteration.
 template <typename Dtype>
 Dtype SGDSolver<Dtype>::GetLearningRate() {
   Dtype rate;
@@ -370,22 +379,38 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
   if (lr_policy == "fixed") {
     rate = this->param_.base_lr();
   } else if (lr_policy == "step") {
-    int current_step = this->iter_ / this->param_.stepsize();
+    this->current_step_ = this->iter_ / this->param_.stepsize();
     rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), current_step);
+        pow(this->param_.gamma(), this->current_step_);
   } else if (lr_policy == "exp") {
     rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
   } else if (lr_policy == "inv") {
     rate = this->param_.base_lr() *
         pow(Dtype(1) + this->param_.gamma() * this->iter_,
             - this->param_.power());
+  } else if (lr_policy == "multistep") {
+    if (this->current_step_ < this->param_.stepvalue_size() &&
+          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
+      this->current_step_++;
+      LOG(INFO) << "MultiStep Status: Iteration " <<
+      this->iter_ << ", step = " << this->current_step_;
+    }
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "poly") {
+    rate = this->param_.base_lr() * pow(Dtype(1.) -
+        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
+        this->param_.power());
+  } else if (lr_policy == "sigmoid") {
+    rate = this->param_.base_lr() * (Dtype(1.) /
+        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
+          Dtype(this->param_.stepsize())))));
   } else {
     LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
   }
   return rate;
 }
 
-
 template <typename Dtype>
 void SGDSolver<Dtype>::PreSolve() {
   // Initialize the history

From 899dfde6f545faa73c0ffe5732ae641b10dfbc09 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 3 Oct 2014 17:14:20 -0700
Subject: [PATCH 1006/2053] Adapt lenet_multistep_solver.prototxt to current
 solvers

---
 .../lenet_multistep_solver.prototxt                 | 13 +++++--------
 examples/mnist/readme.md                            |  3 +++
 2 files changed, 8 insertions(+), 8 deletions(-)
 rename examples/{lenet => mnist}/lenet_multistep_solver.prototxt (74%)

diff --git a/examples/lenet/lenet_multistep_solver.prototxt b/examples/mnist/lenet_multistep_solver.prototxt
similarity index 74%
rename from examples/lenet/lenet_multistep_solver.prototxt
rename to examples/mnist/lenet_multistep_solver.prototxt
index fadd7c904d7..7ee9fb60e67 100644
--- a/examples/lenet/lenet_multistep_solver.prototxt
+++ b/examples/mnist/lenet_multistep_solver.prototxt
@@ -1,7 +1,5 @@
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -27,7 +25,6 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "lenet"
-# solver mode: 0 for CPU and 1 for GPU
-solver_mode: 1
-device_id: 1
+snapshot_prefix: "examples/mnist/lenet_multistep"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 33ed371b4a1..ef7f5da67d5 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -282,3 +282,6 @@ You just did! All the training was carried out on the GPU. In fact, if you would
 and you will be using CPU for training. Isn't that easy?
 
 MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.
+
+### How to reduce the learning rate a fixed steps?
+Look at lenet_multistep_solver.prototxt

From cb3dbee20a46e822c2e4306b03f4b127c1db1f5c Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Thu, 16 Oct 2014 11:17:24 -0700
Subject: [PATCH 1007/2053] Fix init current_step

---
 examples/mnist/lenet_multistep_solver.prototxt | 11 +++++------
 src/caffe/solver.cpp                           |  1 +
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/mnist/lenet_multistep_solver.prototxt b/examples/mnist/lenet_multistep_solver.prototxt
index 7ee9fb60e67..9b22b45ba6b 100644
--- a/examples/mnist/lenet_multistep_solver.prototxt
+++ b/examples/mnist/lenet_multistep_solver.prototxt
@@ -13,12 +13,11 @@ weight_decay: 0.0005
 # The learning rate policy
 lr_policy: "multistep"
 gamma: 0.9
-stepvalue: 1000
-stepvalue: 2000
-stepvalue: 2500
-stepvalue: 3000
-stepvalue: 3500
-stepvalue: 4000
+stepvalue: 5000
+stepvalue: 7000
+stepvalue: 8000
+stepvalue: 9000
+stepvalue: 9500
 # Display every 100 iterations
 display: 100
 # The maximum number of iterations
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 886c3cc6edc..0fd8d0f89dc 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -162,6 +162,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   PreSolve();
 
   iter_ = 0;
+  current_step_ = 0;
   if (resume_file) {
     LOG(INFO) << "Restoring previous solver status from " << resume_file;
     Restore(resume_file);

From 4ba6efc7ac939be59e5ff121c0289885a9a4263d Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Tue, 2 Dec 2014 13:10:57 -0800
Subject: [PATCH 1008/2053] Added bvlc_googlenet prototxt and weights

---
 models/bvlc_googlenet/deploy.prototxt       | 1924 ++++++++++++++++
 models/bvlc_googlenet/quick_solver.prototxt |   15 +
 models/bvlc_googlenet/readme.md             |   33 +
 models/bvlc_googlenet/solver.prototxt       |   16 +
 models/bvlc_googlenet/train_val.prototxt    | 2242 +++++++++++++++++++
 5 files changed, 4230 insertions(+)
 create mode 100644 models/bvlc_googlenet/deploy.prototxt
 create mode 100644 models/bvlc_googlenet/quick_solver.prototxt
 create mode 100644 models/bvlc_googlenet/readme.md
 create mode 100644 models/bvlc_googlenet/solver.prototxt
 create mode 100644 models/bvlc_googlenet/train_val.prototxt

diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
new file mode 100644
index 00000000000..e31a4c9cd00
--- /dev/null
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -0,0 +1,1924 @@
+name: "GoogleNet"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layers {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+  name: "conv1/relu_7x7"
+  type: RELU
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  name: "pool1/norm1"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+  name: "conv2/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+  name: "conv2/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  name: "conv2/norm2"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+  name: "inception_3a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+  name: "inception_3a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  name: "inception_3a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+  name: "inception_3a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+  name: "inception_3b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+  name: "inception_3b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  name: "inception_3b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+  name: "inception_3b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  name: "pool3/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+  name: "inception_4a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+  name: "inception_4a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  name: "inception_4a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+  name: "inception_4a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+  name: "inception_4b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+  name: "inception_4b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  name: "inception_4b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+  name: "inception_4b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+  name: "inception_4c/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+  name: "inception_4c/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  name: "inception_4c/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+  name: "inception_4c/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+  name: "inception_4d/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+  name: "inception_4d/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  name: "inception_4d/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+  name: "inception_4d/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  name: "inception_4e/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+  name: "inception_4e/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+  name: "inception_4e/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  name: "inception_4e/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+  name: "inception_4e/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  name: "pool4/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+  name: "inception_5a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+  name: "inception_5a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  name: "inception_5a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+  name: "inception_5a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+  name: "inception_5b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+  name: "inception_5b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  name: "inception_5b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+  name: "inception_5b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  name: "pool5/drop_7x7_s1"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "loss3/classifier"
+  top: "prob"
+}
\ No newline at end of file
diff --git a/models/bvlc_googlenet/quick_solver.prototxt b/models/bvlc_googlenet/quick_solver.prototxt
new file mode 100644
index 00000000000..5d2f7ee70b9
--- /dev/null
+++ b/models/bvlc_googlenet/quick_solver.prototxt
@@ -0,0 +1,15 @@
+net: "models/bvlc_googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 4000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.01
+lr_policy: "poly"
+power: 0.5
+max_iter: 2400000
+momentum: 0.9
+weight_decay: 0.0002
+snapshot: 40000
+snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet_quick"
+solver_mode: GPU
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
new file mode 100644
index 00000000000..27022d3384a
--- /dev/null
+++ b/models/bvlc_googlenet/readme.md
@@ -0,0 +1,33 @@
+---
+name: BVLC GoogleNet Model
+caffemodel: bvlc_googlenet.caffemodel
+caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
+license: non-commercial
+sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
+caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
+---
+
+This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
+
+Differences:
+- not training with the relighting data-augmentation;
+- not training with the scale or aspect-ratio data-augmentation;
+- uses "xavier" to initialize the weights instead of "gaussian";
+- quick_solver.prototxt uses a different learning rate decay policy than the original solver.prototxt, that allows a much faster training (60 epochs vs 250 epochs);
+
+The bundled model is the iteration 2,400,000 snapshot (60 epochs) using quick_solver.prototxt
+
+This bundled model obtains a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error) on the validation set, using just the center crop.
+(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
+
+Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:
+ - Average Forward pass: 562.841 ms.
+ - Average Backward pass: 1123.84 ms.
+ - Average Forward-Backward: 1688.8 ms.
+
+
+## License
+
+The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
+"Researcher shall use the Database only for non-commercial research and educational purposes."
+Accordingly, this model is distributed under a non-commercial license.
diff --git a/models/bvlc_googlenet/solver.prototxt b/models/bvlc_googlenet/solver.prototxt
new file mode 100644
index 00000000000..d7d1788173c
--- /dev/null
+++ b/models/bvlc_googlenet/solver.prototxt
@@ -0,0 +1,16 @@
+net: "models/bvlc_googlenet/train_val.prototxt"
+test_iter: 1000
+test_interval: 4000
+test_initialization: false
+display: 40
+average_loss: 40
+base_lr: 0.01
+lr_policy: "step"
+stepsize: 320000
+gamma: 0.96
+max_iter: 10000000
+momentum: 0.9
+weight_decay: 0.0002
+snapshot: 40000
+snapshot_prefix: "models/bvlc_googlenet/bvlc_googlenet"
+solver_mode: GPU
diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
new file mode 100644
index 00000000000..156d05fd254
--- /dev/null
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -0,0 +1,2242 @@
+name: "GoogleNet"
+layers {
+  top: "data"
+  top: "label"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 32
+    backend: LMDB
+  }
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    # mean_value: 104
+    # mean_value: 117
+    # mean_value: 123
+  }
+}
+layers {
+  top: "data"
+  top: "label"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+    # mean_value: 104
+    # mean_value: 117
+    # mean_value: 123
+  }
+}
+layers {
+  bottom: "data"
+  top: "conv1/7x7_s2"
+  name: "conv1/7x7_s2"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "conv1/7x7_s2"
+  name: "conv1/relu_7x7"
+  type: RELU
+}
+layers {
+  bottom: "conv1/7x7_s2"
+  top: "pool1/3x3_s2"
+  name: "pool1/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool1/3x3_s2"
+  top: "pool1/norm1"
+  name: "pool1/norm1"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "pool1/norm1"
+  top: "conv2/3x3_reduce"
+  name: "conv2/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3_reduce"
+  name: "conv2/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3_reduce"
+  top: "conv2/3x3"
+  name: "conv2/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/3x3"
+  name: "conv2/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "conv2/3x3"
+  top: "conv2/norm2"
+  name: "conv2/norm2"
+  type: LRN
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  bottom: "conv2/norm2"
+  top: "pool2/3x3_s2"
+  name: "pool2/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/1x1"
+  name: "inception_3a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/1x1"
+  top: "inception_3a/1x1"
+  name: "inception_3a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3_reduce"
+  name: "inception_3a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/3x3_reduce"
+  top: "inception_3a/3x3"
+  name: "inception_3a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/3x3"
+  top: "inception_3a/3x3"
+  name: "inception_3a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5_reduce"
+  name: "inception_3a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/5x5_reduce"
+  top: "inception_3a/5x5"
+  name: "inception_3a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/5x5"
+  top: "inception_3a/5x5"
+  name: "inception_3a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool2/3x3_s2"
+  top: "inception_3a/pool"
+  name: "inception_3a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3a/pool"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/pool_proj"
+  name: "inception_3a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/1x1"
+  bottom: "inception_3a/3x3"
+  bottom: "inception_3a/5x5"
+  bottom: "inception_3a/pool_proj"
+  top: "inception_3a/output"
+  name: "inception_3a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/1x1"
+  name: "inception_3b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/1x1"
+  top: "inception_3b/1x1"
+  name: "inception_3b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3_reduce"
+  name: "inception_3b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/3x3_reduce"
+  top: "inception_3b/3x3"
+  name: "inception_3b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/3x3"
+  top: "inception_3b/3x3"
+  name: "inception_3b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5_reduce"
+  name: "inception_3b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/5x5_reduce"
+  top: "inception_3b/5x5"
+  name: "inception_3b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/5x5"
+  top: "inception_3b/5x5"
+  name: "inception_3b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_3a/output"
+  top: "inception_3b/pool"
+  name: "inception_3b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_3b/pool"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/pool_proj"
+  name: "inception_3b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_3b/1x1"
+  bottom: "inception_3b/3x3"
+  bottom: "inception_3b/5x5"
+  bottom: "inception_3b/pool_proj"
+  top: "inception_3b/output"
+  name: "inception_3b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_3b/output"
+  top: "pool3/3x3_s2"
+  name: "pool3/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/1x1"
+  name: "inception_4a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/1x1"
+  top: "inception_4a/1x1"
+  name: "inception_4a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 96
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3_reduce"
+  name: "inception_4a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/3x3_reduce"
+  top: "inception_4a/3x3"
+  name: "inception_4a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 208
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/3x3"
+  top: "inception_4a/3x3"
+  name: "inception_4a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 16
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5_reduce"
+  name: "inception_4a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/5x5_reduce"
+  top: "inception_4a/5x5"
+  name: "inception_4a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/5x5"
+  top: "inception_4a/5x5"
+  name: "inception_4a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool3/3x3_s2"
+  top: "inception_4a/pool"
+  name: "inception_4a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4a/pool"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/pool_proj"
+  name: "inception_4a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/1x1"
+  bottom: "inception_4a/3x3"
+  bottom: "inception_4a/5x5"
+  bottom: "inception_4a/pool_proj"
+  top: "inception_4a/output"
+  name: "inception_4a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "loss1/ave_pool"
+  name: "loss1/ave_pool"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layers {
+  bottom: "loss1/ave_pool"
+  top: "loss1/conv"
+  name: "loss1/conv"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.08
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss1/conv"
+  top: "loss1/conv"
+  name: "loss1/relu_conv"
+  type: RELU
+}
+layers {
+  bottom: "loss1/conv"
+  top: "loss1/fc"
+  name: "loss1/fc"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+      std: 0.02
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  name: "loss1/relu_fc"
+  type: RELU
+}
+layers {
+  bottom: "loss1/fc"
+  top: "loss1/fc"
+  name: "loss1/drop_fc"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layers {
+  bottom: "loss1/fc"
+  top: "loss1/classifier"
+  name: "loss1/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+      std: 0.0009765625
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/loss1"
+  name: "loss1/loss"
+  type: SOFTMAX_LOSS
+  loss_weight: 0.3
+}
+layers {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-1"
+  name: "loss1/top-1"
+  type: ACCURACY
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "loss1/classifier"
+  bottom: "label"
+  top: "loss1/top-5"
+  name: "loss1/top-5"
+  type: ACCURACY
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/1x1"
+  name: "inception_4b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/1x1"
+  top: "inception_4b/1x1"
+  name: "inception_4b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3_reduce"
+  name: "inception_4b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/3x3_reduce"
+  top: "inception_4b/3x3"
+  name: "inception_4b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 224
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/3x3"
+  top: "inception_4b/3x3"
+  name: "inception_4b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5_reduce"
+  name: "inception_4b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/5x5_reduce"
+  top: "inception_4b/5x5"
+  name: "inception_4b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/5x5"
+  top: "inception_4b/5x5"
+  name: "inception_4b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4a/output"
+  top: "inception_4b/pool"
+  name: "inception_4b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4b/pool"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/pool_proj"
+  name: "inception_4b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/1x1"
+  bottom: "inception_4b/3x3"
+  bottom: "inception_4b/5x5"
+  bottom: "inception_4b/pool_proj"
+  top: "inception_4b/output"
+  name: "inception_4b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/1x1"
+  name: "inception_4c/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/1x1"
+  top: "inception_4c/1x1"
+  name: "inception_4c/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3_reduce"
+  name: "inception_4c/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/3x3_reduce"
+  top: "inception_4c/3x3"
+  name: "inception_4c/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/3x3"
+  top: "inception_4c/3x3"
+  name: "inception_4c/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 24
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5_reduce"
+  name: "inception_4c/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/5x5_reduce"
+  top: "inception_4c/5x5"
+  name: "inception_4c/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/5x5"
+  top: "inception_4c/5x5"
+  name: "inception_4c/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4b/output"
+  top: "inception_4c/pool"
+  name: "inception_4c/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4c/pool"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/pool_proj"
+  name: "inception_4c/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/1x1"
+  bottom: "inception_4c/3x3"
+  bottom: "inception_4c/5x5"
+  bottom: "inception_4c/pool_proj"
+  top: "inception_4c/output"
+  name: "inception_4c/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/1x1"
+  name: "inception_4d/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 112
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/1x1"
+  top: "inception_4d/1x1"
+  name: "inception_4d/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 144
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3_reduce"
+  name: "inception_4d/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/3x3_reduce"
+  top: "inception_4d/3x3"
+  name: "inception_4d/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 288
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/3x3"
+  top: "inception_4d/3x3"
+  name: "inception_4d/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5_reduce"
+  name: "inception_4d/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/5x5_reduce"
+  top: "inception_4d/5x5"
+  name: "inception_4d/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/5x5"
+  top: "inception_4d/5x5"
+  name: "inception_4d/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4c/output"
+  top: "inception_4d/pool"
+  name: "inception_4d/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4d/pool"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/pool_proj"
+  name: "inception_4d/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/1x1"
+  bottom: "inception_4d/3x3"
+  bottom: "inception_4d/5x5"
+  bottom: "inception_4d/pool_proj"
+  top: "inception_4d/output"
+  name: "inception_4d/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "loss2/ave_pool"
+  name: "loss2/ave_pool"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 5
+    stride: 3
+  }
+}
+layers {
+  bottom: "loss2/ave_pool"
+  top: "loss2/conv"
+  name: "loss2/conv"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.08
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss2/conv"
+  top: "loss2/conv"
+  name: "loss2/relu_conv"
+  type: RELU
+}
+layers {
+  bottom: "loss2/conv"
+  top: "loss2/fc"
+  name: "loss2/fc"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1024
+    weight_filler {
+      type: "xavier"
+      std: 0.02
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  name: "loss2/relu_fc"
+  type: RELU
+}
+layers {
+  bottom: "loss2/fc"
+  top: "loss2/fc"
+  name: "loss2/drop_fc"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.7
+  }
+}
+layers {
+  bottom: "loss2/fc"
+  top: "loss2/classifier"
+  name: "loss2/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+      std: 0.0009765625
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/loss1"
+  name: "loss2/loss"
+  type: SOFTMAX_LOSS
+  loss_weight: 0.3
+}
+layers {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-1"
+  name: "loss2/top-1"
+  type: ACCURACY
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "loss2/classifier"
+  bottom: "label"
+  top: "loss2/top-5"
+  name: "loss2/top-5"
+  type: ACCURACY
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/1x1"
+  name: "inception_4e/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/1x1"
+  top: "inception_4e/1x1"
+  name: "inception_4e/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3_reduce"
+  name: "inception_4e/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/3x3_reduce"
+  top: "inception_4e/3x3"
+  name: "inception_4e/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/3x3"
+  top: "inception_4e/3x3"
+  name: "inception_4e/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5_reduce"
+  name: "inception_4e/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/5x5_reduce"
+  top: "inception_4e/5x5"
+  name: "inception_4e/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/5x5"
+  top: "inception_4e/5x5"
+  name: "inception_4e/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_4d/output"
+  top: "inception_4e/pool"
+  name: "inception_4e/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_4e/pool"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/pool_proj"
+  name: "inception_4e/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_4e/1x1"
+  bottom: "inception_4e/3x3"
+  bottom: "inception_4e/5x5"
+  bottom: "inception_4e/pool_proj"
+  top: "inception_4e/output"
+  name: "inception_4e/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_4e/output"
+  top: "pool4/3x3_s2"
+  name: "pool4/3x3_s2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/1x1"
+  name: "inception_5a/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/1x1"
+  top: "inception_5a/1x1"
+  name: "inception_5a/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 160
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3_reduce"
+  name: "inception_5a/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/3x3_reduce"
+  top: "inception_5a/3x3"
+  name: "inception_5a/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 320
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/3x3"
+  top: "inception_5a/3x3"
+  name: "inception_5a/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 32
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5_reduce"
+  name: "inception_5a/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/5x5_reduce"
+  top: "inception_5a/5x5"
+  name: "inception_5a/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/5x5"
+  top: "inception_5a/5x5"
+  name: "inception_5a/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "pool4/3x3_s2"
+  top: "inception_5a/pool"
+  name: "inception_5a/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5a/pool"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/pool_proj"
+  name: "inception_5a/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/1x1"
+  bottom: "inception_5a/3x3"
+  bottom: "inception_5a/5x5"
+  bottom: "inception_5a/pool_proj"
+  top: "inception_5a/output"
+  name: "inception_5a/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/1x1"
+  name: "inception_5b/1x1"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/1x1"
+  top: "inception_5b/1x1"
+  name: "inception_5b/relu_1x1"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/3x3_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 192
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.09
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3_reduce"
+  name: "inception_5b/relu_3x3_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/3x3_reduce"
+  top: "inception_5b/3x3"
+  name: "inception_5b/3x3"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/3x3"
+  top: "inception_5b/3x3"
+  name: "inception_5b/relu_3x3"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/5x5_reduce"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 48
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.2
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5_reduce"
+  name: "inception_5b/relu_5x5_reduce"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/5x5_reduce"
+  top: "inception_5b/5x5"
+  name: "inception_5b/5x5"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    pad: 2
+    kernel_size: 5
+    weight_filler {
+      type: "xavier"
+      std: 0.03
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/5x5"
+  top: "inception_5b/5x5"
+  name: "inception_5b/relu_5x5"
+  type: RELU
+}
+layers {
+  bottom: "inception_5a/output"
+  top: "inception_5b/pool"
+  name: "inception_5b/pool"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layers {
+  bottom: "inception_5b/pool"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/pool_proj"
+  type: CONVOLUTION
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 1
+    weight_filler {
+      type: "xavier"
+      std: 0.1
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.2
+    }
+  }
+}
+layers {
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/pool_proj"
+  name: "inception_5b/relu_pool_proj"
+  type: RELU
+}
+layers {
+  bottom: "inception_5b/1x1"
+  bottom: "inception_5b/3x3"
+  bottom: "inception_5b/5x5"
+  bottom: "inception_5b/pool_proj"
+  top: "inception_5b/output"
+  name: "inception_5b/output"
+  type: CONCAT
+}
+layers {
+  bottom: "inception_5b/output"
+  top: "pool5/7x7_s1"
+  name: "pool5/7x7_s1"
+  type: POOLING
+  pooling_param {
+    pool: AVE
+    kernel_size: 7
+    stride: 1
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "pool5/7x7_s1"
+  name: "pool5/drop_7x7_s1"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.4
+  }
+}
+layers {
+  bottom: "pool5/7x7_s1"
+  top: "loss3/classifier"
+  name: "loss3/classifier"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/loss3"
+  name: "loss3/loss3"
+  type: SOFTMAX_LOSS
+  loss_weight: 1
+}
+layers {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-1"
+  name: "loss3/top-1"
+  type: ACCURACY
+  include {
+    phase: TEST
+  }
+}
+layers {
+  bottom: "loss3/classifier"
+  bottom: "label"
+  top: "loss3/top-5"
+  name: "loss3/top-5"
+  type: ACCURACY
+  accuracy_param {
+    top_k: 5
+  }
+  include {
+    phase: TEST
+  }
+}

From b99e6cdcaf10196b901cfa870d9ca5f7d9d886bf Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 20 Dec 2014 23:24:32 -0800
Subject: [PATCH 1009/2053] Added credits for training bvlc models

---
 models/bvlc_alexnet/readme.md                 | 2 ++
 models/bvlc_googlenet/readme.md               | 2 ++
 models/bvlc_reference_caffenet/readme.md      | 2 ++
 models/bvlc_reference_rcnn_ilsvrc13/readme.md | 2 ++
 models/finetune_flickr_style/readme.md        | 2 ++
 5 files changed, 10 insertions(+)

diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index 20c393ff26b..c25fd4f8018 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -18,6 +18,8 @@ The best validation performance during training was iteration 358,000 with valid
 This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop.
 (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
 
+This model was trained by Evan Shelhamer @shelhamer
+
 ## License
 
 The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 27022d3384a..8a3bbec4f8b 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -5,6 +5,7 @@ caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
 license: non-commercial
 sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
 caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
+gist_id: 866e2aa1fd707b89b913
 ---
 
 This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
@@ -25,6 +26,7 @@ Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:
  - Average Backward pass: 1123.84 ms.
  - Average Forward-Backward: 1688.8 ms.
 
+This model was trained by Sergio Guadarrama @sguada
 
 ## License
 
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index d1c6269ae73..b867e7386a3 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -18,6 +18,8 @@ The best validation performance during training was iteration 313,000 with valid
 This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.
 (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)
 
+This model was trained by Jeff Donahue @jeffdonahue
+
 ## License
 
 The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index fb8f26d15df..5d4bc5afe32 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -13,6 +13,8 @@ Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/m
 
 *N.B. For research purposes, make use of the official R-CNN package and not this example.*
 
+This model was trained by Ross Girshick @rbgirshick
+
 ## License
 
 The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
diff --git a/models/finetune_flickr_style/readme.md b/models/finetune_flickr_style/readme.md
index d2a8a95f669..aac7f7c96e8 100644
--- a/models/finetune_flickr_style/readme.md
+++ b/models/finetune_flickr_style/readme.md
@@ -15,6 +15,8 @@ The final performance:
     I1017 07:36:17.370730 31333 solver.cpp:247] Iteration 100000, Testing net (#0)
     I1017 07:36:34.248730 31333 solver.cpp:298]     Test net output #0: accuracy = 0.3916
 
+This model was trained by Sergey Karayev @sergeyk
+
 ## License
 
 The Flickr Style dataset contains only URLs to images.

From 43357d089484efdad843f79b4efcd83c7b74ed6e Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sat, 20 Dec 2014 23:28:19 -0800
Subject: [PATCH 1010/2053] Added credits and bvlc_googlenet to model_zoo.md

---
 docs/model_zoo.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 330850cbbd6..358bbb7fc91 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -15,9 +15,10 @@ To lower the friction of sharing these models, we introduce the model zoo framew
 First of all, we provide some trained models out of the box.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper.
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012.
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn).
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper. (Trained by Jeff Donahue @jeffdonahue)
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
+- **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
 
 User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
 

From d00c465a69541a1b82d6171d44f266b419c62b43 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Sun, 21 Dec 2014 14:03:49 -0800
Subject: [PATCH 1011/2053] Warning of fallback only the first time for
 cudnn_pooling_layer

---
 src/caffe/layers/cudnn_pooling_layer.cu | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 99c409dcc96..7cdc0afadcb 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -14,7 +14,8 @@ void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   // Fallback to Caffe for padded pooling, max top mask.
   if ((this->pad_h_ > 0 || this->pad_w_ > 0) || (*top).size() > 1) {
-    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
+    LOG_FIRST_N(WARNING, 1)
+        << "Falling back to standard Caffe for padded pooling.";
     return PoolingLayer<Dtype>::Forward_gpu(bottom, top);
   }
 
@@ -33,7 +34,8 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   // Fallback to Caffe for padded pooling, max top mask.
   if ((this->pad_h_ > 0 || this->pad_w_ > 0) || top.size() > 1) {
-    LOG(WARNING) << "Falling back to standard Caffe for padded pooling.";
+    LOG_FIRST_N(WARNING, 1)
+        << "Falling back to standard Caffe for padded pooling.";
     return PoolingLayer<Dtype>::Backward_gpu(top, propagate_down, bottom);
   }
 

From b00445630bef5db71dc4e9346b8da62cb4c2e2b5 Mon Sep 17 00:00:00 2001
From: "Peihao.Huang" <hao.1120@163.com>
Date: Tue, 23 Dec 2014 11:18:51 +0800
Subject: [PATCH 1012/2053] switch cifar10 example to lmdb

---
 examples/cifar10/cifar10_full_train_test.prototxt  | 6 ++++--
 examples/cifar10/cifar10_quick_train_test.prototxt | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index 8fde19f046e..38cc04f4a68 100644
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -5,8 +5,9 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/cifar10/cifar10_train_leveldb"
+    source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
+    backend: LMDB
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
@@ -19,8 +20,9 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/cifar10/cifar10_test_leveldb"
+    source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
+    backend: LMDB
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index 409cfe809f4..074bb0011e3 100644
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -5,8 +5,9 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/cifar10/cifar10_train_leveldb"
+    source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
+    backend: LMDB
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"
@@ -19,8 +20,9 @@ layers {
   top: "data"
   top: "label"
   data_param {
-    source: "examples/cifar10/cifar10_test_leveldb"
+    source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
+    backend: LMDB
   }
   transform_param {
     mean_file: "examples/cifar10/mean.binaryproto"

From 0d21a1f8826a04c18058449c0bd1a3c64b939c44 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 27 Dec 2014 01:47:59 -0800
Subject: [PATCH 1013/2053] update use of GetLayer ->
 LayerRegistry::CreateLayer

---
 src/caffe/net.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 21ab15fd31b..e4492cfd6e3 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -62,7 +62,8 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   bottom_need_backward_.resize(param.layers_size());
   for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
     const LayerParameter& layer_param = param.layers(layer_id);
-    layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
+    layers_.push_back(shared_ptr<Layer<Dtype> >(
+          LayerRegistry<Dtype>::CreateLayer(layer_param)));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
     bool need_backward = false;

From fba2471ab7e94fc6c6ae5984d58f643716dcec91 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 24 Nov 2014 13:37:06 -0800
Subject: [PATCH 1014/2053] automatic dependency generation

---
 Makefile | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/Makefile b/Makefile
index 54d5636135d..75619c01da0 100644
--- a/Makefile
+++ b/Makefile
@@ -30,8 +30,6 @@ DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
 ##############################
 # CXX_SRCS are the source files excluding the test ones.
 CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
-# HXX_SRCS are the header files
-HXX_SRCS := $(shell find include/$(PROJECT) ! -name "test_*.hpp" -name "*.hpp")
 # CU_SRCS are the cuda source files
 CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
 # TEST_SRCS are the test source files
@@ -40,8 +38,6 @@ TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
 TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
 TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
 GTEST_SRC := src/gtest/gtest-all.cpp
-# TEST_HXX_SRCS are the test header files
-TEST_HXX_SRCS := $(shell find include/$(PROJECT) -name "test_*.hpp")
 # TOOL_SRCS are the source files for the tool binaries
 TOOL_SRCS := $(shell find tools -name "*.cpp")
 # EXAMPLE_SRCS are the source files for the example binaries
@@ -90,7 +86,6 @@ PROTO_GEN_HEADER_SRCS := $(addprefix $(PROTO_BUILD_DIR)/, \
 		$(notdir ${PROTO_SRCS:.proto=.pb.h}))
 PROTO_GEN_HEADER := $(addprefix $(PROTO_BUILD_INCLUDE_DIR)/, \
 		$(notdir ${PROTO_SRCS:.proto=.pb.h}))
-HXX_SRCS += $(PROTO_GEN_HEADER)
 PROTO_GEN_CC := $(addprefix $(BUILD_DIR)/, ${PROTO_SRCS:.proto=.pb.cc})
 PY_PROTO_BUILD_DIR := python/$(PROJECT)/proto
 PY_PROTO_INIT := python/$(PROJECT)/proto/__init__.py
@@ -113,6 +108,9 @@ TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/cuda/, ${TEST_CU_SRCS:.cu=.o})
 TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
 GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
+# Output files for automatic dependency generation
+DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
+	${TEST_CU_OBJS:.o=.d}
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
@@ -316,6 +314,9 @@ LIBRARY_DIRS += $(BLAS_LIB)
 
 LIBRARY_DIRS += $(LIB_BUILD_DIR)
 
+# Automatic dependency generation (nvcc is handled separately)
+CXXFLAGS += -MMD -MP
+
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
 CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
@@ -464,7 +465,7 @@ $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	ar rcs $@ $(OBJS)
 	@ echo
 
-$(BUILD_DIR)/%.o: %.cpp $(HXX_SRCS) | $(ALL_BUILD_DIRS)
+$(BUILD_DIR)/%.o: %.cpp | $(ALL_BUILD_DIRS)
 	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
@@ -477,7 +478,9 @@ $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 	@ cat $@.$(WARNS_EXT)
 	@ echo
 
-$(BUILD_DIR)/%.cuo: %.cu $(HXX_SRCS) | $(ALL_BUILD_DIRS)
+$(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -M $< -o ${@:.o=.d} \
+		-odir $(@D)
 	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
@@ -564,7 +567,7 @@ superclean: clean supercleanfiles
 
 $(DIST_ALIASES): $(DISTRIBUTE_DIR)
 
-$(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
+$(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	# add include
 	cp -r include $(DISTRIBUTE_DIR)/
 	mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto
@@ -577,3 +580,5 @@ $(DISTRIBUTE_DIR): all py $(HXX_SRCS) | $(DISTRIBUTE_SUBDIRS)
 	cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python
+
+-include $(DEPS)

From b0f45a90029797d13e31e024605a1baa478bc7bd Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 11 Dec 2014 14:45:38 -0800
Subject: [PATCH 1015/2053] pretty the build with the Q variable

Unset Q (in the environment) to see full commands, or edit in
Makefile.config.
---
 Makefile                | 60 ++++++++++++++++++++---------------------
 Makefile.config.example |  3 +++
 2 files changed, 33 insertions(+), 30 deletions(-)

diff --git a/Makefile b/Makefile
index 75619c01da0..ea97392d8ef 100644
--- a/Makefile
+++ b/Makefile
@@ -402,10 +402,10 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC)
-	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
+$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(STATIC_NAME) $(PY$(PROJECT)_HXX_SRC)
+	@ echo CXX $<
+	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
 		$(STATIC_LINK_COMMAND) $(LINKFLAGS) $(PYTHON_LDFLAGS)
-	@ echo
 
 mat$(PROJECT): mat
 
@@ -417,11 +417,11 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			"to build mat$(PROJECT)."; \
 		exit 1; \
 	fi
-	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
+	@ echo MEX $<
+	$(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
-	@ echo
 
 runtest: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER) && \
@@ -458,57 +458,57 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
-	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
-	@ echo
+	@ echo LD $<
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
-	ar rcs $@ $(OBJS)
-	@ echo
+	@ echo AR $<
+	$(Q)ar rcs $@ $(OBJS)
 
 $(BUILD_DIR)/%.o: %.cpp | $(ALL_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+	@ echo CXX $<
+	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
-	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+	@ echo CXX $<
+	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
-	@ echo
 
 $(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -M $< -o ${@:.o=.d} \
+	@ echo NVCC $<
+	$(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -M $< -o ${@:.o=.d} \
 		-odir $(@D)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+	$(Q)$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
-	@ echo
 
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
+	@ echo CXX/LD -o $@ $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
 
 $(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NAME) \
 		| $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+	@ echo CXX/LD -o $@ $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(LIB_BUILD_DIR)
-	@ echo
 
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
+	@ echo LD $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
 
 $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
-	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
+	@ echo LD $<
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
 
 # Target for extension-less symlinks to tool binaries with extension '*.bin'.
 $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
@@ -516,20 +516,20 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ ln -s $(abspath $<) $@
 
 $(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
-	@ echo
+	@ echo LD $<
+	$(Q)$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
 $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
 		$(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
-	protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<
-	@ echo
+	@ echo PROTOC $<
+	$(Q)protoc --proto_path=$(PROTO_SRC_DIR) --cpp_out=$(PROTO_BUILD_DIR) $<
 
 $(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
 		$(PY_PROTO_INIT) | $(PY_PROTO_BUILD_DIR)
-	protoc --proto_path=$(PROTO_SRC_DIR) --python_out=$(PY_PROTO_BUILD_DIR) $<
-	@ echo
+	@ echo PROTOC \(python\) $<
+	$(Q)protoc --proto_path=$(PROTO_SRC_DIR) --python_out=$(PY_PROTO_BUILD_DIR) $<
 
 $(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
 	touch $(PY_PROTO_INIT)
diff --git a/Makefile.config.example b/Makefile.config.example
index e11db51395d..0c996038a7c 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -74,3 +74,6 @@ DISTRIBUTE_DIR := distribute
 
 # The ID of the GPU that 'make runtest' will use to run unit tests.
 TEST_GPUID := 0
+
+# enable pretty build (comment to see full commands)
+Q ?= @

From b91d0ab112720b3354314b58914d7c1054695251 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 28 Dec 2014 18:03:45 -0800
Subject: [PATCH 1016/2053] remove unused GetLayer function (replaced by
 LayerRegistry::CreateLayer)

---
 include/caffe/layer_factory.hpp | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index b86d53346d9..c1fd6aa0d43 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -113,17 +113,6 @@ class LayerRegisterer {
   }                                                                            \
   REGISTER_LAYER_CREATOR(type, Creator_##clsname)
 
-// A function to get a specific layer from the specification given in
-// LayerParameter. Ideally this would be replaced by a factory pattern,
-// but we will leave it this way for now.
-// Yangqing's note: With LayerRegistry, we no longer need this thin wrapper any
-// more. It is provided here for backward compatibility and should be removed in
-// the future.
-template <typename Dtype>
-Layer<Dtype>* GetLayer(const LayerParameter& param) {
-  return LayerRegistry<Dtype>::CreateLayer(param);
-}
-
 }  // namespace caffe
 
 #endif  // CAFFE_LAYER_FACTORY_H_

From ace55bbe1b9b97004d53ff29ea172c3df1142f25 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Tue, 2 Dec 2014 11:20:58 -0800
Subject: [PATCH 1017/2053] Better instructions for updating Homebrew after
 modifying formulae

Previous instructions glossed over how to deal with OpenCV-related update problems
---
 docs/installation.md | 37 ++++++++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 13 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 207e17d1539..c667cd8ce01 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -208,28 +208,39 @@ If you're not using Anaconda, include `hdf5` in the list above.
     Aborting
     Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
 
-One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master, as follows:
+One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
 
     cd /usr/local
     git checkout -b caffe
     git add .
     git commit -m "Update Caffe dependencies to use libstdc++"
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies"
+
+Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
+
+    # Switch batch to homebrew master branches
+    cd /usr/local
+    git checkout master
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
     git checkout master
+    
+    # Update homebrew; hopefully this works without errors!
     brew update
+    
+    # Switch back to the caffe branches with the forumlae that you modified earlier
+    cd /usr/local
     git rebase master caffe
-    # Resolve any merge conflicts here
-    git checkout caffe
-
-At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place. You may still get the following error:
-
-    $ brew update
-    error: Your local changes to the following files would be overwritten by merge:
-	opencv.rb
-    Please, commit your changes or stash them before you can merge.
-    Aborting
-    Error: Failed to update tap: homebrew/science
+    # Fix any merge conflicts and commit to caffe branch
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+    
+    # Done!            
 
-but non-OpenCV packages will still update as expected.
+At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
 
 #### Windows
 

From 150da0ebcfe96b236d68b5938d510dd18647bb26 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 28 Dec 2014 20:57:37 -0800
Subject: [PATCH 1018/2053] [scripts] fix find warnings in
 upload_model_to_gist.sh

---
 scripts/upload_model_to_gist.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/upload_model_to_gist.sh b/scripts/upload_model_to_gist.sh
index 2dfbabd72a3..3c4fd64e3fc 100755
--- a/scripts/upload_model_to_gist.sh
+++ b/scripts/upload_model_to_gist.sh
@@ -7,7 +7,7 @@ if [ ! -f $DIRNAME/readme.md ]; then
     echo "  <dirname>/readme.md must exist"
 fi
 cd $DIRNAME
-FILES=`find . -type f -maxdepth 1 ! -name "*.caffemodel*" | xargs echo`
+FILES=`find . -maxdepth 1 -type f ! -name "*.caffemodel*" | xargs echo`
 
 # Check for gist tool.
 gist -v >/dev/null 2>&1 || { echo >&2 "I require 'gist' but it's not installed. Do 'gem install gist'."; exit 1; }

From e348fdd73d67ff4615cb860886e9af21ed9d3d18 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Dec 2014 15:13:59 -0500
Subject: [PATCH 1019/2053] [docs] groom model zoo intro + list

---
 docs/model_zoo.md | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 358bbb7fc91..97025483c58 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -3,8 +3,10 @@ title: Model Zoo
 ---
 # Caffe Model Zoo
 
-Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
-To lower the friction of sharing these models, we introduce the model zoo framework:
+Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
+These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
+
+To help share these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
 - Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
@@ -15,10 +17,10 @@ To lower the friction of sharing these models, we introduce the model zoo framew
 First of all, we provide some trained models out of the box.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper. (Trained by Jeff Donahue @jeffdonahue)
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
-- **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
+- **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
 User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
 
@@ -36,11 +38,11 @@ A caffe model is distributed as a directory containing:
     - License information.
 - [optional] Other helpful scripts.
 
-## Hosting model info
+### Hosting model info
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 
-- `scripts/upload_model_to_gist.sh <dirname>`: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
+`scripts/upload_model_to_gist.sh <dirname>` uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
 
 Try doing `scripts/upload_model_to_gist.sh models/bvlc_alexnet` to test the uploading (don't forget to delete the uploaded gist afterward).
 
@@ -52,4 +54,4 @@ It is up to the user where to host the `.caffemodel` file.
 We host our BVLC-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
-- `scripts/download_model_binary.py <dirname>`: downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
+`scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.

From 6b842060a46fae212abd1b6b783730a2fc709c7f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Dec 2014 16:23:18 -0500
Subject: [PATCH 1020/2053] [docs] open release of BVLC models for unrestricted
 use

See BVLC model license details on the model zoo page.
---
 docs/model_zoo.md                             | 17 +++++++++++++++--
 examples/classification.ipynb                 |  2 +-
 models/bvlc_alexnet/readme.md                 |  4 +---
 models/bvlc_googlenet/readme.md               |  4 +---
 models/bvlc_reference_caffenet/readme.md      |  4 +---
 models/bvlc_reference_rcnn_ilsvrc13/readme.md |  4 +---
 6 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 97025483c58..06dc0a49ec7 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -14,7 +14,9 @@ To help share these models, we introduce the model zoo framework:
 
 ## Where to get trained models
 
-First of all, we provide some trained models out of the box.
+First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
+<br>
+See the [BVLC model license](#bvlc-model-license) for details.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
 - **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
@@ -22,7 +24,9 @@ Each one of these can be downloaded by running `scripts/download_model_binary.py
 - **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
 - **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
-User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+**Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+These models are subject to conditions of their respective authors such as citation and license.
+Thank you for sharing your models!
 
 ## Model info format
 
@@ -55,3 +59,12 @@ We host our BVLC-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
 `scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
+
+## BVLC model license
+
+The Caffe models bundled by the BVLC are released for unrestricted use.
+
+These models are trained on data from the [ImageNet project](http://www.image-net.org/) and training data includes internet photos that may be subject to copyright.
+
+Our present understanding as researchers is that there is no restriction placed on the open release of these learned model weights, since none of the original images are distributed in whole or in part.
+To the extent that the interpretation arises that weights are derivative works of the original copyright holder and they assert such a copyright, UC Berkeley makes no representations as to what use is allowed other than to consider our present release in the spirit of fair use in the academic mission of the university to disseminate knowledge and tools as broadly as possible without restriction.
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index fb44da8958f..1e2126fe652 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -19,7 +19,7 @@
       "\n",
       "Caffe provides a general Python interface for models with `caffe.Net` in `python/caffe/pycaffe.py`, but to make off-the-shelf classification easy we provide a `caffe.Classifier` class and `classify.py` script. Both Python and MATLAB wrappers are provided. However, the Python wrapper has more features so we will describe it here. For MATLAB, refer to `matlab/caffe/matcaffe_demo.m`.\n",
       "\n",
-      "Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`. Note that this pre-trained model is licensed for academic research / non-commercial use only.\n",
+      "Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.\n",
       "\n",
       "Ready? Let's start."
      ]
diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index c25fd4f8018..97b66bec8c2 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -22,6 +22,4 @@ This model was trained by Evan Shelhamer @shelhamer
 
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 8a3bbec4f8b..19f6b881f5c 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -30,6 +30,4 @@ This model was trained by Sergio Guadarrama @sguada
 
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index b867e7386a3..b6b6103429a 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -22,6 +22,4 @@ This model was trained by Jeff Donahue @jeffdonahue
 
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index 5d4bc5afe32..01996ee4d4a 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -17,6 +17,4 @@ This model was trained by Ross Girshick @rbgirshick
 
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.

From d8a415efd099980dc0d86f1739c5b0298a46a14e Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Dec 2014 17:36:08 -0800
Subject: [PATCH 1021/2053] move softmax loss GPU todo comment to header file

There's no need to have GPU stubs just for a comment, and the absence of
a GPU-specific implementation is made more obvious by the nonexistence
of the .cu file.
---
 include/caffe/loss_layers.hpp          |  5 +----
 src/caffe/layers/softmax_loss_layer.cu | 27 --------------------------
 2 files changed, 1 insertion(+), 31 deletions(-)
 delete mode 100644 src/caffe/layers/softmax_loss_layer.cu

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 9fe58cd97bc..13b108afd2d 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -718,8 +718,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   /// @copydoc SoftmaxWithLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
+  // TODO(Yangqing): implement the GPU version of softmax.
 
   /**
    * @brief Computes the softmax loss error gradient w.r.t. the predictions.
@@ -750,8 +749,6 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
   shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
deleted file mode 100644
index 4cdaf51b165..00000000000
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ /dev/null
@@ -1,27 +0,0 @@
-#include <algorithm>
-#include <cfloat>
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  Forward_cpu(bottom, top);
-}
-
-template <typename Dtype>
-void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  // TODO(Yangqing): implement the GPU version of softmax.
-  Backward_cpu(top, propagate_down, bottom);
-}
-
-INSTANTIATE_LAYER_GPU_FUNCS(SoftmaxWithLossLayer);
-
-
-}  // namespace caffe

From 11add5bca9f31dc08329aa220d8674b7832c513d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Dec 2014 17:59:07 -0800
Subject: [PATCH 1022/2053] remove SoftmaxLossLayer CPU_ONLY stubs, since there
 is no GPU version

---
 src/caffe/layers/softmax_loss_layer.cpp | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 82c62f6a1bd..db8dd8bb626 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -85,11 +85,7 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
-
-#ifdef CPU_ONLY
-STUB_GPU(SoftmaxWithLossLayer);
-#endif
-
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
 REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer);
+
 }  // namespace caffe

From 03f87a90a3ebb66a9a2e80e3864b004aa1da87d8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Dec 2014 01:31:46 -0800
Subject: [PATCH 1023/2053] only build matcaffe as part of 'make everything' if
 MATLAB_DIR is set

---
 Makefile | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index ea97392d8ef..6361305a991 100644
--- a/Makefile
+++ b/Makefile
@@ -346,6 +346,13 @@ PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 # default behavior of 'find'.
 SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 
+# Set the sub-targets of the 'everything' target.
+EVERYTHING_TARGETS := all py$(PROJECT) test warn lint runtest
+# Only build matcaffe as part of "everything" if MATLAB_DIR is specified.
+ifneq ($(MATLAB_DIR),)
+	EVERYTHING_TARGETS += mat$(PROJECT)
+endif
+
 ##############################
 # Define build targets
 ##############################
@@ -355,7 +362,7 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 
 all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples
 
-everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
+everything: $(EVERYTHING_TARGETS)
 
 linecount:
 	cloc --read-lang-def=$(PROJECT).cloc \

From dc1557d01c0bd43f6bc3f2d567343165c97f52e5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 30 Dec 2014 01:33:05 -0800
Subject: [PATCH 1024/2053] don't do runtest as part of 'make everything'

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 6361305a991..46bf7280919 100644
--- a/Makefile
+++ b/Makefile
@@ -347,7 +347,7 @@ PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
 SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 
 # Set the sub-targets of the 'everything' target.
-EVERYTHING_TARGETS := all py$(PROJECT) test warn lint runtest
+EVERYTHING_TARGETS := all py$(PROJECT) test warn lint
 # Only build matcaffe as part of "everything" if MATLAB_DIR is specified.
 ifneq ($(MATLAB_DIR),)
 	EVERYTHING_TARGETS += mat$(PROJECT)

From 2237540de0b0ba4ec7e457cd8efe05320ace48fe Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Dec 2014 12:52:53 -0800
Subject: [PATCH 1025/2053] [tests] don't use Gaussian labels in NetTest's
 TinyNet

Previously, labels were being nonsensically filled with a zero-mean
Gaussian. Now they are just set to constant zero. Note that this is not
necessary for TrickyNet, neither forward nor backward are ever called.
---
 src/caffe/test/test_net.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 611d11bd832..319958fee49 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -75,6 +75,10 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      type: 'gaussian' "
         "      std: 0.01 "
         "    } "
+        "    data_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
         "  } "
         "  top: 'data' "
         "  top: 'label' "

From 2326bed7a63ed08b8bc240a55d34e58e8794565e Mon Sep 17 00:00:00 2001
From: Sean Bell <sbell@cs.cornell.edu>
Date: Tue, 30 Dec 2014 20:06:31 -0500
Subject: [PATCH 1026/2053] fixed resize_image for the case of constant images

---
 python/caffe/io.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index aabcfddbbeb..a8354021b05 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -43,11 +43,17 @@ def resize_image(im, new_dims, interp_order=1):
     im: resized ndarray with shape (new_dims[0], new_dims[1], K)
     """
     if im.shape[-1] == 1 or im.shape[-1] == 3:
-        # skimage is fast but only understands {1,3} channel images in [0, 1].
         im_min, im_max = im.min(), im.max()
-        im_std = (im - im_min) / (im_max - im_min)
-        resized_std = resize(im_std, new_dims, order=interp_order)
-        resized_im = resized_std * (im_max - im_min) + im_min
+        if im_max > im_min:
+            # skimage is fast but only understands {1,3} channel images in [0, 1].
+            im_std = (im - im_min) / (im_max - im_min)
+            resized_std = resize(im_std, new_dims, order=interp_order)
+            resized_im = resized_std * (im_max - im_min) + im_min
+        else:
+            # the image is a constant -- avoid divide by 0
+            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]), dtype=np.float32)
+            ret.fill(im_min)
+            return ret
     else:
         # ndimage interpolates anything but more slowly.
         scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))

From 5843b52558491b636bebcc5cf4ba706b074a717b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Dec 2014 17:05:47 -0800
Subject: [PATCH 1027/2053] add missing value support to SoftmaxLossLayer

---
 include/caffe/loss_layers.hpp           |  4 ++++
 src/caffe/layers/softmax_loss_layer.cpp | 28 ++++++++++++++++++++-----
 src/caffe/proto/caffe.proto             | 11 +++++++++-
 3 files changed, 37 insertions(+), 6 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 13b108afd2d..3e4414ae6c1 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -758,6 +758,10 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   vector<Blob<Dtype>*> softmax_bottom_vec_;
   /// top vector holder used in call to the underlying SoftmaxLayer::Forward
   vector<Blob<Dtype>*> softmax_top_vec_;
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index db8dd8bb626..bc511cf603b 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -17,6 +17,12 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   softmax_top_vec_.clear();
   softmax_top_vec_.push_back(&prob_);
   softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
+
+  has_ignore_label_ =
+    this->layer_param_.loss_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.loss_param().ignore_label();
+  }
 }
 
 template <typename Dtype>
@@ -40,15 +46,19 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   int num = prob_.num();
   int dim = prob_.count() / num;
   int spatial_dim = prob_.height() * prob_.width();
+  int count = 0;
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
     for (int j = 0; j < spatial_dim; j++) {
       const int label_value = static_cast<int>(label[i * spatial_dim + j]);
+      if (has_ignore_label_ && label_value == ignore_label_) {
+        continue;
+      }
       DCHECK_GE(label_value, 0);
-      DCHECK_GT(dim, label_value * spatial_dim);
-      loss -= log(std::max(prob_data[i * dim +
-          label_value * spatial_dim + j],
+      DCHECK_LT(label_value, prob_.channels());
+      loss -= log(std::max(prob_data[i * dim + label_value * spatial_dim + j],
                            Dtype(FLT_MIN)));
+      ++count;
     }
   }
   top[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
@@ -73,10 +83,18 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     int num = prob_.num();
     int dim = prob_.count() / num;
     int spatial_dim = prob_.height() * prob_.width();
+    int count = 0;
     for (int i = 0; i < num; ++i) {
       for (int j = 0; j < spatial_dim; ++j) {
-        bottom_diff[i * dim + static_cast<int>(label[i * spatial_dim + j])
-            * spatial_dim + j] -= 1;
+        const int label_value = static_cast<int>(label[i * spatial_dim + j]);
+        if (has_ignore_label_ && label_value == ignore_label_) {
+          for (int c = 0; c < bottom[0]->channels(); ++c) {
+            bottom_diff[i * dim + c * spatial_dim + j] = 0;
+          }
+        } else {
+          bottom_diff[i * dim + label_value * spatial_dim + j] -= 1;
+          ++count;
+        }
       }
     }
     // Scale gradient
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8086ad66579..cfd5cca2518 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -206,7 +206,7 @@ message NetStateRule {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 42 (last added: exp_param)
+// LayerParameter next available ID: 43 (last added: loss_param)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
@@ -331,6 +331,9 @@ message LayerParameter {
   // Parameters for data pre-processing.
   optional TransformationParameter transform_param = 36;
 
+  // Parameters shared by loss layers.
+  optional LossParameter loss_param = 42;
+
   // Note: certain layers may have more than one computational engine
   // for their implementation. These layers include an Engine type and
   // engine parameter for selecting the implementation.
@@ -361,6 +364,12 @@ message TransformationParameter {
   repeated float mean_value = 5;
 }
 
+// Message that stores parameters shared by loss layers
+message LossParameter {
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 1;
+}
+
 // Message that stores parameters used by AccuracyLayer
 message AccuracyParameter {
   // When computing accuracy, count as correct by comparing the true label to

From 34321e4ecf26494ded5a05221010ccc9ea99c144 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Dec 2014 17:08:21 -0800
Subject: [PATCH 1028/2053] add spatial normalization option to
 SoftmaxLossLayer

With missing values (and batches of varying spatial dimension),
normalizing each batch across instances can inappropriately give
different instances different weights, so we give the option of simply
normalizing by the batch size instead.
---
 include/caffe/loss_layers.hpp           |  3 +++
 src/caffe/layers/softmax_loss_layer.cpp | 13 +++++++++++--
 src/caffe/proto/caffe.proto             |  3 +++
 3 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 3e4414ae6c1..321f7068273 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -762,6 +762,9 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   bool has_ignore_label_;
   /// The label indicating that an instance should be ignored.
   int ignore_label_;
+  /// Whether to normalize the loss by the total number of values present
+  /// (otherwise just by the batch size).
+  bool normalize_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index bc511cf603b..74893af7820 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -23,6 +23,7 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   if (has_ignore_label_) {
     ignore_label_ = this->layer_param_.loss_param().ignore_label();
   }
+  normalize_ = this->layer_param_.loss_param().normalize();
 }
 
 template <typename Dtype>
@@ -61,7 +62,11 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
       ++count;
     }
   }
-  top[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
+  if (normalize_) {
+    top[0]->mutable_cpu_data()[0] = loss / count;
+  } else {
+    top[0]->mutable_cpu_data()[0] = loss / num;
+  }
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
   }
@@ -99,7 +104,11 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     }
     // Scale gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_scal(prob_.count(), loss_weight / num / spatial_dim, bottom_diff);
+    if (normalize_) {
+      caffe_scal(prob_.count(), loss_weight / count, bottom_diff);
+    } else {
+      caffe_scal(prob_.count(), loss_weight / num, bottom_diff);
+    }
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index cfd5cca2518..2bfd7b36fa3 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -368,6 +368,9 @@ message TransformationParameter {
 message LossParameter {
   // If specified, ignore instances with the given label.
   optional int32 ignore_label = 1;
+  // If true, normalize each batch across all instances (including spatial
+  // dimesions, but not ignored instances); else, divide by batch size only.
+  optional bool normalize = 2 [default = true];
 }
 
 // Message that stores parameters used by AccuracyLayer

From c7f63da671d7dc6345d645b89d3641bcd8b7cc27 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Dec 2014 12:31:53 -0800
Subject: [PATCH 1029/2053] clean up formatting in SoftmaxLossLayer

---
 src/caffe/layers/softmax_loss_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 74893af7820..55eb0918ad2 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -74,8 +74,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down,
-    const vector<Blob<Dtype>*>& bottom) {
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
     LOG(FATAL) << this->type_name()
                << " Layer cannot backpropagate to label inputs.";

From 55d7ffde2907459f021dc72f56c36a61235e187a Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Sep 2014 16:47:06 -0700
Subject: [PATCH 1030/2053] break out Step from Solver

---
 include/caffe/solver.hpp | 10 +++---
 src/caffe/solver.cpp     | 73 +++++++++++++++++++++-------------------
 2 files changed, 42 insertions(+), 41 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index b20912606f8..fde66208207 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -26,6 +26,7 @@ class Solver {
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
+  void Step(int iters);
   virtual ~Solver() {}
   inline shared_ptr<Net<Dtype> > net() { return net_; }
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
@@ -34,9 +35,6 @@ class Solver {
   int iter() { return iter_; }
 
  protected:
-  // PreSolve is run before any solving iteration starts, allowing one to
-  // put up some scaffold.
-  virtual void PreSolve() {}
   // Get the update value for the current iteration.
   virtual void ComputeUpdateValue() = 0;
   // The Solver::Snapshot function implements the basic snapshotting utility
@@ -73,14 +71,14 @@ template <typename Dtype>
 class SGDSolver : public Solver<Dtype> {
  public:
   explicit SGDSolver(const SolverParameter& param)
-      : Solver<Dtype>(param) {}
+      : Solver<Dtype>(param) { PreSolve(); }
   explicit SGDSolver(const string& param_file)
-      : Solver<Dtype>(param_file) {}
+      : Solver<Dtype>(param_file) { PreSolve(); }
 
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
  protected:
-  virtual void PreSolve();
+  void PreSolve();
   Dtype GetLearningRate();
   virtual void ComputeUpdateValue();
   virtual void SnapshotSolverState(SolverState * state);
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 7f5d3d20f35..ab9c00eed8f 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -32,6 +32,7 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   LOG(INFO) << "Initializing solver from parameters: " << std::endl
             << param.DebugString();
   param_ = param;
+  CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
   if (param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
@@ -39,6 +40,8 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   InitTrainNet();
   InitTestNets();
   LOG(INFO) << "Solver scaffolding done.";
+  iter_ = 0;
+  current_step_ = 0;
 }
 
 template <typename Dtype>
@@ -155,39 +158,15 @@ void Solver<Dtype>::InitTestNets() {
 }
 
 template <typename Dtype>
-void Solver<Dtype>::Solve(const char* resume_file) {
-  Caffe::set_phase(Caffe::TRAIN);
-  LOG(INFO) << "Solving " << net_->name();
-  LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
-  PreSolve();
-
-  iter_ = 0;
-  current_step_ = 0;
-  if (resume_file) {
-    LOG(INFO) << "Restoring previous solver status from " << resume_file;
-    Restore(resume_file);
-  }
-  // Remember the initial iter_ value; will be non-zero if we loaded from a
-  // resume_file above.
+void Solver<Dtype>::Step(int iters) {
+  vector<Blob<Dtype>*> bottom_vec;
   const int start_iter = iter_;
-
+  const int stop_iter = iter_ + iters;
   int average_loss = this->param_.average_loss();
-
-  CHECK_GE(average_loss, 1) << "average_loss should be non-negative.";
-
   vector<Dtype> losses;
   Dtype smoothed_loss = 0;
 
-  // For a network that is trained by the solver, no bottom or top vecs
-  // should be given, and we will just provide dummy vecs.
-  vector<Blob<Dtype>*> bottom_vec;
-  for (; iter_ < param_.max_iter(); ++iter_) {
-    // Save a snapshot if needed.
-    if (param_.snapshot() && iter_ > start_iter &&
-        iter_ % param_.snapshot() == 0) {
-      Snapshot();
-    }
-
+  for (; iter_ < stop_iter; ++iter_) {
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())) {
       TestAll();
@@ -227,13 +206,36 @@ void Solver<Dtype>::Solve(const char* resume_file) {
         }
       }
     }
-
     ComputeUpdateValue();
     net_->Update();
+
+    // Save a snapshot if needed.
+    if (param_.snapshot() && (iter_ + 1) % param_.snapshot() == 0) {
+      Snapshot();
+    }
+  }
+}
+
+template <typename Dtype>
+void Solver<Dtype>::Solve(const char* resume_file) {
+  Caffe::set_phase(Caffe::TRAIN);
+  LOG(INFO) << "Solving " << net_->name();
+  LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
+
+  if (resume_file) {
+    LOG(INFO) << "Restoring previous solver status from " << resume_file;
+    Restore(resume_file);
+  }
+
+  // For a network that is trained by the solver, no bottom or top vecs
+  // should be given, and we will just provide dummy vecs.
+  Step(param_.max_iter() - iter_);
+  // If we haven't already, save a snapshot after optimization, unless
+  // overridden by setting snapshot_after_train := false
+  if (param_.snapshot_after_train()
+      && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
+    Snapshot();
   }
-  // Always save a snapshot after optimization, unless overridden by setting
-  // snapshot_after_train := false.
-  if (param_.snapshot_after_train()) { Snapshot(); }
   // After the optimization is done, run an additional train and test pass to
   // display the train and test loss/outputs if appropriate (based on the
   // display and test_interval settings, respectively).  Unlike in the rest of
@@ -242,7 +244,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // display the loss, which is computed in the forward pass.
   if (param_.display() && iter_ % param_.display() == 0) {
     Dtype loss;
-    net_->Forward(bottom_vec, &loss);
+    net_->ForwardPrefilled(&loss);
     LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
   }
   if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
@@ -328,14 +330,15 @@ void Solver<Dtype>::Snapshot() {
   string model_filename, snapshot_filename;
   const int kBufferSize = 20;
   char iter_str_buffer[kBufferSize];
-  snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_);
+  // Add one to iter_ to get the number of iterations that have completed.
+  snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_ + 1);
   filename += iter_str_buffer;
   model_filename = filename + ".caffemodel";
   LOG(INFO) << "Snapshotting to " << model_filename;
   WriteProtoToBinaryFile(net_param, model_filename.c_str());
   SolverState state;
   SnapshotSolverState(&state);
-  state.set_iter(iter_);
+  state.set_iter(iter_ + 1);
   state.set_learned_net(model_filename);
   state.set_current_step(current_step_);
   snapshot_filename = filename + ".solverstate";

From 10c23643bbf71628faad26c5ffbdc249b2c3fe37 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 29 Sep 2014 16:49:49 -0700
Subject: [PATCH 1031/2053] [pycaffe] expose SGDSolver.step

---
 python/caffe/_caffe.cpp | 3 ++-
 python/caffe/_caffe.hpp | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index f03dae3dce6..156b5187d58 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -197,7 +197,8 @@ BOOST_PYTHON_MODULE(_caffe) {
       .add_property("test_nets", &PySGDSolver::test_nets)
       .add_property("iter",      &PySGDSolver::iter)
       .def("solve",              &PySGDSolver::Solve)
-      .def("solve",              &PySGDSolver::SolveResume);
+      .def("solve",              &PySGDSolver::SolveResume)
+      .def("step",               &PySGDSolver::Step);
 
   bp::class_<vector<shared_ptr<PyNet> > >("NetVec")
       .def(bp::vector_indexing_suite<vector<shared_ptr<PyNet> >, true>());
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index bf1b7d168f8..a5cef74ad66 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -181,6 +181,7 @@ class PySGDSolver {
   vector<shared_ptr<PyNet> > test_nets() { return test_nets_; }
   int iter() { return solver_->iter(); }
   void Solve() { return solver_->Solve(); }
+  void Step(int iters) { solver_->Step(iters); }
   void SolveResume(const string& resume_file);
 
  protected:

From 9e9c21082d0ee038d1c789148521997bfd7b72fa Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 20 Nov 2014 20:36:56 -0800
Subject: [PATCH 1032/2053] add "make pytest" for running Python tests

---
 Makefile | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/Makefile b/Makefile
index 46bf7280919..d3bddea7bea 100644
--- a/Makefile
+++ b/Makefile
@@ -434,6 +434,9 @@ runtest: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN)
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER) && \
 	$(TEST_ALL_DYNLINK_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
+pytest: py
+	cd python; python -m unittest discover -s caffe/test
+
 warn: $(EMPTY_WARN_REPORT)
 
 $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)

From b26eaf78097d07575a4de7e89b2f91811ad587f2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 25 Nov 2014 11:07:52 -0800
Subject: [PATCH 1033/2053] [travis] remove unneeded Makefile.config sed
 hacking

Make allows variable redefinition, so sed brittleness can be avoided.
---
 scripts/travis/travis_setup_makefile_config.sh | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index a309bb3d6fe..5e4cd3ad7d5 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -5,9 +5,7 @@ set -e
 mv Makefile.config.example Makefile.config
 
 if $WITH_CUDA; then
-  # Remove default gencode set; only generate compute_50.
-  sed -i 's/-gencode arch=.*\\//' Makefile.config
-  sed -i 's/CUDA_ARCH :=//' Makefile.config
+  # Only generate compute_50.
   GENCODE="-gencode arch=compute_50,code=sm_50"
   GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
   echo "CUDA_ARCH := $GENCODE" >> Makefile.config

From 0c2a1bcf4feb84de91c7d56f231f100cf8ad0b4f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 20 Nov 2014 20:40:56 -0800
Subject: [PATCH 1034/2053] [travis] run pytest

---
 .travis.yml                                    |  3 ++-
 scripts/travis/travis_build_and_test.sh        |  1 +
 scripts/travis/travis_install.sh               | 11 +++++++++++
 scripts/travis/travis_setup_makefile_config.sh | 10 ++++++++++
 4 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 3deb45a2f0c..955aa8c3ba2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -22,7 +22,8 @@ install:
   - sudo -E $SCRIPTS/travis_install.sh
 
 before_script:
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64
+  - export PATH=/home/travis/miniconda/bin:$PATH
   - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
 
 script: $SCRIPTS/travis_build_and_test.sh
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index dec4d097c17..53c6c341101 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -26,6 +26,7 @@ else
   $MAKE all
   $MAKE test
   $MAKE pycaffe
+  $MAKE pytest
   $MAKE warn
   if ! $WITH_CUDA; then
     $MAKE lint
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index e17f253ecdc..82f386cf029 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -57,3 +57,14 @@ $MAKE
 $MAKE install
 popd
 rm -f $LMDB_FILE
+
+# Install the Python runtime dependencies via miniconda (this is much faster
+# than using pip for everything).
+wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+chmod +x miniconda.sh
+./miniconda.sh -b
+export PATH=/home/travis/miniconda/bin:$PATH
+conda update --yes conda
+conda install --yes numpy scipy matplotlib scikit-image pip
+pip install protobuf
+rm /home/travis/miniconda/lib/libm.*
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index 5e4cd3ad7d5..e8d85f9be78 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -10,3 +10,13 @@ if $WITH_CUDA; then
   GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
   echo "CUDA_ARCH := $GENCODE" >> Makefile.config
 fi
+
+cat << 'EOF' >> Makefile.config
+ANACONDA_HOME := $(HOME)/miniconda
+PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
+		$(ANACONDA_HOME)/include/python2.7 \
+		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
+PYTHON_LIB := $(ANACONDA_HOME)/lib
+INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
+LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
+EOF

From 3f6a85c23c8c3a016f6360baf9614b6720e5c1f4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 24 Nov 2014 18:53:14 -0800
Subject: [PATCH 1035/2053] [pycaffe] basic, partial testing of Net and
 SGDSolver

---
 python/caffe/test/test_net.py    | 77 ++++++++++++++++++++++++++++++++
 python/caffe/test/test_solver.py | 49 ++++++++++++++++++++
 2 files changed, 126 insertions(+)
 create mode 100644 python/caffe/test/test_net.py
 create mode 100644 python/caffe/test/test_solver.py

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
new file mode 100644
index 00000000000..f0e9deef19c
--- /dev/null
+++ b/python/caffe/test/test_net.py
@@ -0,0 +1,77 @@
+import unittest
+import tempfile
+import os
+import numpy as np
+
+import caffe
+
+def simple_net_file(num_output):
+    """Make a simple net prototxt, based on test_net.cpp, returning the name
+    of the (temporary) file."""
+
+    f = tempfile.NamedTemporaryFile(delete=False)
+    f.write("""name: 'testnet' force_backward: true
+    layers { type: DUMMY_DATA name: 'data' top: 'data' top: 'label'
+      dummy_data_param { num: 5 channels: 2 height: 3 width: 4
+        num: 5 channels: 1 height: 1 width: 1
+        data_filler { type: 'gaussian' std: 1 }
+        data_filler { type: 'constant' } } }
+    layers { type: CONVOLUTION name: 'conv' bottom: 'data' top: 'conv'
+      convolution_param { num_output: 11 kernel_size: 2 pad: 3
+        weight_filler { type: 'gaussian' std: 1 }
+        bias_filler { type: 'constant' value: 2 } }
+        weight_decay: 1 weight_decay: 0 }
+    layers { type: INNER_PRODUCT name: 'ip' bottom: 'conv' top: 'ip'
+      inner_product_param { num_output: """ + str(num_output) + """
+        weight_filler { type: 'gaussian' std: 2.5 }
+        bias_filler { type: 'constant' value: -3 } } }
+    layers { type: SOFTMAX_LOSS name: 'loss' bottom: 'ip' bottom: 'label'
+      top: 'loss' }""")
+    f.close()
+    return f.name
+
+class TestNet(unittest.TestCase):
+    def setUp(self):
+        self.num_output = 13
+        net_file = simple_net_file(self.num_output)
+        self.net = caffe.Net(net_file)
+        # fill in valid labels
+        self.net.blobs['label'].data[...] = \
+                np.random.randint(self.num_output,
+                    size=self.net.blobs['label'].data.shape)
+        os.remove(net_file)
+
+    def test_memory(self):
+        """Check that holding onto blob data beyond the life of a Net is OK"""
+
+        params = sum(map(list, self.net.params.itervalues()), [])
+        blobs = self.net.blobs.values()
+        del self.net
+
+        # now sum everything (forcing all memory to be read)
+        total = 0
+        for p in params:
+            total += p.data.sum() + p.diff.sum()
+        for bl in blobs:
+            total += bl.data.sum() + bl.diff.sum()
+
+    def test_forward_backward(self):
+        self.net.forward()
+        self.net.backward()
+
+    def test_inputs_outputs(self):
+        self.assertEqual(self.net.inputs, [])
+        self.assertEqual(self.net.outputs, ['loss'])
+
+    def test_save_and_read(self):
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.close()
+        self.net.save(f.name)
+        net_file = simple_net_file(self.num_output)
+        net2 = caffe.Net(net_file, f.name)
+        os.remove(net_file)
+        os.remove(f.name)
+        for name in self.net.params:
+            for i in range(len(self.net.params[name])):
+                self.assertEqual(abs(self.net.params[name][i].data
+                    - net2.params[name][i].data).sum(), 0)
diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
new file mode 100644
index 00000000000..b78c91f9978
--- /dev/null
+++ b/python/caffe/test/test_solver.py
@@ -0,0 +1,49 @@
+import unittest
+import tempfile
+import os
+import numpy as np
+
+import caffe
+from test_net import simple_net_file
+
+class TestSolver(unittest.TestCase):
+    def setUp(self):
+        self.num_output = 13
+        net_f = simple_net_file(self.num_output)
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.write("""net: '""" + net_f + """'
+        test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
+        weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
+        display: 100 max_iter: 100 snapshot_after_train: false""")
+        f.close()
+        self.solver = caffe.SGDSolver(f.name)
+        self.solver.net.set_mode_cpu()
+        # fill in valid labels
+        self.solver.net.blobs['label'].data[...] = \
+                np.random.randint(self.num_output,
+                    size=self.solver.net.blobs['label'].data.shape)
+        self.solver.test_nets[0].blobs['label'].data[...] = \
+                np.random.randint(self.num_output,
+                    size=self.solver.test_nets[0].blobs['label'].data.shape)
+        os.remove(f.name)
+        os.remove(net_f)
+
+    def test_solve(self):
+        self.assertEqual(self.solver.iter, 0)
+        self.solver.solve()
+        self.assertEqual(self.solver.iter, 100)
+
+    def test_net_memory(self):
+        """Check that nets survive after the solver is destroyed."""
+
+        nets = [self.solver.net] + list(self.solver.test_nets)
+        self.assertEqual(len(nets), 2)
+        del self.solver
+
+        total = 0
+        for net in nets:
+            for ps in net.params.itervalues():
+                for p in ps:
+                    total += p.data.sum() + p.diff.sum()
+            for bl in net.blobs.itervalues():
+                total += bl.data.sum() + bl.diff.sum()

From 7e465f288a4a7fc2d74b2a0a145f98a03891dcb8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 7 Jan 2015 23:00:27 -0500
Subject: [PATCH 1036/2053] BVLC models are for unrestricted use (follow-up to
 #1650)

---
 models/bvlc_alexnet/readme.md                 | 2 +-
 models/bvlc_googlenet/readme.md               | 2 +-
 models/bvlc_reference_caffenet/readme.md      | 2 +-
 models/bvlc_reference_rcnn_ilsvrc13/readme.md | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index 97b66bec8c2..008d690f7f4 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -2,7 +2,7 @@
 name: BVLC AlexNet Model
 caffemodel: bvlc_alexnet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: 9116a64c0fbe4459d18f4bb6b56d647b63920377
 caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
 ---
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 19f6b881f5c..2e22dfcf59a 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -2,7 +2,7 @@
 name: BVLC GoogleNet Model
 caffemodel: bvlc_googlenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
 caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
 gist_id: 866e2aa1fd707b89b913
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index b6b6103429a..671e47a5056 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -2,7 +2,7 @@
 name: BVLC CaffeNet Model
 caffemodel: bvlc_reference_caffenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: 4c8d77deb20ea792f84eb5e6d0a11ca0a8660a46
 caffe_commit: 709dc15af4a06bebda027c1eb2b3f3e3375d5077
 ---
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index 01996ee4d4a..9a11a24d8f8 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -2,7 +2,7 @@
 name: BVLC Reference RCNN ILSVRC13 Model
 caffemodel: bvlc_reference_rcnn_ilsvrc13.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: bdd8abb885819cba5e2fe1eb36235f2319477e64
 caffe_commit: a7e397abbda52c0b90323c23ab95bdeabee90a98
 ---

From 2377b68dcc3c20685ec388097dbe9e9c6b3b0e92 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 6 Jan 2015 20:25:53 -0800
Subject: [PATCH 1037/2053] improve const-ness of Net

---
 include/caffe/net.hpp | 72 ++++++++++++++++++++++++++++---------------
 src/caffe/net.cpp     | 16 +++++-----
 src/caffe/solver.cpp  | 23 ++++++++------
 tools/caffe.cpp       |  4 +--
 4 files changed, 70 insertions(+), 45 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 1d06dc45533..bbb220eb00d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -89,7 +89,7 @@ class Net {
    * @brief For an already initialized net, implicitly copies (i.e., using no
    *        additional memory) the pre-trained layers from another Net.
    */
-  void ShareTrainedLayersWith(Net* other);
+  void ShareTrainedLayersWith(const Net* other);
   // For an already initialized net, CopyTrainedLayersFrom() copies the already
   // trained layers from another net parameter instance.
   /**
@@ -99,51 +99,73 @@ class Net {
   void CopyTrainedLayersFrom(const NetParameter& param);
   void CopyTrainedLayersFrom(const string trained_filename);
   /// @brief Writes the net to a proto.
-  void ToProto(NetParameter* param, bool write_diff = false);
+  void ToProto(NetParameter* param, bool write_diff = false) const;
 
   /// @brief returns the network name.
-  inline const string& name() { return name_; }
+  inline const string& name() const { return name_; }
   /// @brief returns the layer names
-  inline const vector<string>& layer_names() { return layer_names_; }
+  inline const vector<string>& layer_names() const { return layer_names_; }
   /// @brief returns the blob names
-  inline const vector<string>& blob_names() { return blob_names_; }
+  inline const vector<string>& blob_names() const { return blob_names_; }
   /// @brief returns the blobs
-  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() { return blobs_; }
+  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const {
+    return blobs_;
+  }
   /// @brief returns the layers
-  inline const vector<shared_ptr<Layer<Dtype> > >& layers() { return layers_; }
+  inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
+    return layers_;
+  }
   /**
    * @brief returns the bottom vecs for each layer -- usually you won't
    *        need this unless you do per-layer checks such as gradients.
    */
-  inline vector<vector<Blob<Dtype>*> >& bottom_vecs() { return bottom_vecs_; }
+  inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const {
+    return bottom_vecs_;
+  }
   /**
    * @brief returns the top vecs for each layer -- usually you won't
    *        need this unless you do per-layer checks such as gradients.
    */
-  inline vector<vector<Blob<Dtype>*> >& top_vecs() { return top_vecs_; }
-  inline vector<vector<bool> >& bottom_need_backward() {
+  inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
+    return top_vecs_;
+  }
+  inline const vector<vector<bool> >& bottom_need_backward() const {
     return bottom_need_backward_;
   }
-  inline vector<Dtype>& blob_loss_weights() {
+  inline const vector<Dtype>& blob_loss_weights() const {
     return blob_loss_weights_;
   }
   /// @brief returns the parameters
-  inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
+  inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
+    return params_;
+  }
   /// @brief returns the parameter learning rate multipliers
-  inline vector<float>& params_lr() { return params_lr_; }
-  inline vector<float>& params_weight_decay() { return params_weight_decay_; }
-  const map<string, int>& param_names_index() { return param_names_index_; }
+  inline const vector<float>& params_lr() const { return params_lr_; }
+  inline const vector<float>& params_weight_decay() const {
+    return params_weight_decay_;
+  }
+  const map<string, int>& param_names_index() const {
+    return param_names_index_;
+  }
   /// @brief Input and output blob numbers
-  inline int num_inputs() { return net_input_blobs_.size(); }
-  inline int num_outputs() { return net_output_blobs_.size(); }
-  inline vector<Blob<Dtype>*>& input_blobs() { return net_input_blobs_; }
-  inline vector<Blob<Dtype>*>& output_blobs() { return net_output_blobs_; }
-  inline vector<int>& input_blob_indices() { return net_input_blob_indices_; }
-  inline vector<int>& output_blob_indices() { return net_output_blob_indices_; }
-  bool has_blob(const string& blob_name);
-  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name);
-  bool has_layer(const string& layer_name);
-  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name);
+  inline int num_inputs() const { return net_input_blobs_.size(); }
+  inline int num_outputs() const { return net_output_blobs_.size(); }
+  inline const vector<Blob<Dtype>*>& input_blobs() const {
+    return net_input_blobs_;
+  }
+  inline const vector<Blob<Dtype>*>& output_blobs() const {
+    return net_output_blobs_;
+  }
+  inline const vector<int>& input_blob_indices() const {
+    return net_input_blob_indices_;
+  }
+  inline const vector<int>& output_blob_indices() const {
+    return net_output_blob_indices_;
+  }
+  bool has_blob(const string& blob_name) const;
+  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
+  bool has_layer(const string& layer_name) const;
+  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;
 
   void set_debug_info(const bool value) { debug_info_ = value; }
 
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index e4492cfd6e3..47fc8446064 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -636,7 +636,7 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
 }
 
 template <typename Dtype>
-void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
+void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
   int num_source_layers = other->layers().size();
   for (int i = 0; i < num_source_layers; ++i) {
     Layer<Dtype>* source_layer = other->layers()[i].get();
@@ -726,7 +726,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
 }
 
 template <typename Dtype>
-void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) {
+void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   param->Clear();
   param->set_name(name_);
   // Add bottom and top
@@ -785,16 +785,16 @@ void Net<Dtype>::Update() {
 }
 
 template <typename Dtype>
-bool Net<Dtype>::has_blob(const string& blob_name) {
+bool Net<Dtype>::has_blob(const string& blob_name) const {
   return blob_names_index_.find(blob_name) != blob_names_index_.end();
 }
 
 template <typename Dtype>
 const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(
-    const string& blob_name) {
+    const string& blob_name) const {
   shared_ptr<Blob<Dtype> > blob_ptr;
   if (has_blob(blob_name)) {
-    blob_ptr = blobs_[blob_names_index_[blob_name]];
+    blob_ptr = blobs_[blob_names_index_.find(blob_name)->second];
   } else {
     blob_ptr.reset((Blob<Dtype>*)(NULL));
     LOG(WARNING) << "Unknown blob name " << blob_name;
@@ -803,16 +803,16 @@ const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(
 }
 
 template <typename Dtype>
-bool Net<Dtype>::has_layer(const string& layer_name) {
+bool Net<Dtype>::has_layer(const string& layer_name) const {
   return layer_names_index_.find(layer_name) != layer_names_index_.end();
 }
 
 template <typename Dtype>
 const shared_ptr<Layer<Dtype> > Net<Dtype>::layer_by_name(
-    const string& layer_name) {
+    const string& layer_name) const {
   shared_ptr<Layer<Dtype> > layer_ptr;
   if (has_layer(layer_name)) {
-    layer_ptr = layers_[layer_names_index_[layer_name]];
+    layer_ptr = layers_[layer_names_index_.find(layer_name)->second];
   } else {
     layer_ptr.reset((Layer<Dtype>*)(NULL));
     LOG(WARNING) << "Unknown layer name " << layer_name;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ab9c00eed8f..3fa0e2d14b7 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -418,7 +418,7 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
 template <typename Dtype>
 void SGDSolver<Dtype>::PreSolve() {
   // Initialize the history
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   history_.clear();
   update_.clear();
   temp_.clear();
@@ -439,9 +439,10 @@ void SGDSolver<Dtype>::PreSolve() {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_lr = this->net_->params_lr();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
   // get the learning rate
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
@@ -552,9 +553,10 @@ void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
 
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_lr = this->net_->params_lr();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
   // get the learning rate
   Dtype rate = this->GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
@@ -667,9 +669,10 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
 
 template <typename Dtype>
 void AdaGradSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_lr = this->net_->params_lr();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
   // get the learning rate
   Dtype rate = this->GetLearningRate();
   Dtype delta = this->param_.delta();
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 9f9d975dfba..ad54bc3b9eb 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -220,8 +220,8 @@ int time() {
   caffe_net.Backward();
 
   const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
-  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
-  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
+  const vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
+  const vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
   const vector<vector<bool> >& bottom_need_backward =
       caffe_net.bottom_need_backward();
   LOG(INFO) << "*** Benchmark begins ***";

From 25e37489294cabcc023b3583ad72d8753317e069 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 10 Jan 2015 01:16:12 -0800
Subject: [PATCH 1038/2053] fix typo in layer_factory.cpp

---
 src/caffe/layer_factory.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 5a286cd4691..80694834a33 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -154,5 +154,5 @@ Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
 REGISTER_LAYER_CREATOR(TANH, GetTanHLayer);
 
 // Layers that use their constructor as their default creator should be
-// registered in their corresponding cpp files. Do not registere them here.
+// registered in their corresponding cpp files. Do not register them here.
 }  // namespace caffe

From 9cdf40df6d0214bd50c21eee362f2c3e13df8dc7 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 10 Jan 2015 01:18:39 -0800
Subject: [PATCH 1039/2053] [build] more meaningful messages for link commands

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 46bf7280919..f6178162f34 100644
--- a/Makefile
+++ b/Makefile
@@ -465,11 +465,11 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
-	@ echo LD $<
+	@ echo LD -o $@
 	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
-	@ echo AR $<
+	@ echo AR -o $@
 	$(Q)ar rcs $@ $(OBJS)
 
 $(BUILD_DIR)/%.o: %.cpp | $(ALL_BUILD_DIRS)

From 3c5048c27e0579ca43e10186a0102b908b591a69 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 10 Jan 2015 01:42:23 -0800
Subject: [PATCH 1040/2053] [build] specify RPATH using $ORIGIN

Currently, when dynamically linking against libcaffe (right now, only
done for tests), RPATH is specified relative to the caffe source root.
This commit fixes RPATH using the special $ORIGIN variable to be
relative to the executable itself, so that there is no dependence on the
working directory.
---
 Makefile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 46bf7280919..c5f6addbfa0 100644
--- a/Makefile
+++ b/Makefile
@@ -334,6 +334,7 @@ endif
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
+DYNAMIC_LDFLAGS := -l$(PROJECT) -Wl,-rpath,\$$ORIGIN/../lib
 
 # 'superclean' target recursively* deletes all files ending with an extension
 # in $(SUPERCLEAN_EXTS) below.  This may be useful if you've built older
@@ -503,7 +504,7 @@ $(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NA
 		| $(TEST_BIN_DIR)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(LIB_BUILD_DIR)
+		-o $@ $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_LDFLAGS)
 
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)

From 8d2aebc2f4440e7d83b3031398d97230426e4196 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 21 Dec 2014 19:20:36 -0800
Subject: [PATCH 1041/2053] add BaseConvolutionLayer

This provides a common place for code used by ConvolutionLayer and
DeconvolutionLayer, simplifying the implementations of both.
---
 include/caffe/vision_layers.hpp      |  87 ++++++++
 src/caffe/layers/base_conv_layer.cpp | 289 +++++++++++++++++++++++++++
 2 files changed, 376 insertions(+)
 create mode 100644 src/caffe/layers/base_conv_layer.cpp

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index c803cd72449..e1bb7abf07c 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -16,6 +16,93 @@
 
 namespace caffe {
 
+template <typename Dtype>
+class BaseConvolutionLayer : public Layer<Dtype> {
+ public:
+  explicit BaseConvolutionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+ protected:
+  // Helper functions that abstract away the column buffer and gemm arguments.
+  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
+  // we just called weight_cpu_gemm with the same input.
+  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_cpu_bias(Dtype* output, const Dtype* bias);
+  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output);
+  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
+      weights);
+  void backward_cpu_bias(Dtype* bias, const Dtype* input);
+
+  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_gpu_bias(Dtype* output, const Dtype* bias);
+  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* col_output);
+  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
+      weights);
+  void backward_gpu_bias(Dtype* bias, const Dtype* input);
+
+  // reverse_dimensions should return true iff we are implementing deconv, so
+  // that conv helpers know which dimensions are which.
+  virtual bool reverse_dimensions() = 0;
+  // Compute height_out_ and width_out_ from other parameters.
+  virtual void compute_output_shape() = 0;
+
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
+  int num_;
+  int channels_;
+  int pad_h_, pad_w_;
+  int height_, width_;
+  int group_;
+  int num_output_;
+  int height_out_, width_out_;
+  bool bias_term_;
+  bool is_1x1_;
+
+ private:
+  // wrap im2col/col2im so we don't have to remember the (long) argument lists
+  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
+    im2col_cpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
+  }
+  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
+    col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
+  }
+  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
+    im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
+  }
+  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
+    col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
+        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
+  }
+
+  int conv_out_channels_;
+  int conv_in_channels_;
+  int conv_out_spatial_dim_;
+  int conv_in_height_;
+  int conv_in_width_;
+  int kernel_dim_;
+  int weight_offset_;
+  int col_offset_;
+  int output_offset_;
+
+  Blob<Dtype> col_buffer_;
+  Blob<Dtype> bias_multiplier_;
+};
+
 /**
  * @brief Convolves the input image with a bank of learned filters,
  *        and (optionally) adds biases.
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
new file mode 100644
index 00000000000..a44867a0cf3
--- /dev/null
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -0,0 +1,289 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // Configure the kernel size, padding, stride, and inputs.
+  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  CHECK(!conv_param.has_kernel_size() !=
+      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(conv_param.has_kernel_size() ||
+      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
+      && conv_param.has_pad_w())
+      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
+      && conv_param.has_stride_w())
+      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (conv_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  } else {
+    kernel_h_ = conv_param.kernel_h();
+    kernel_w_ = conv_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!conv_param.has_pad_h()) {
+    pad_h_ = pad_w_ = conv_param.pad();
+  } else {
+    pad_h_ = conv_param.pad_h();
+    pad_w_ = conv_param.pad_w();
+  }
+  if (!conv_param.has_stride_h()) {
+    stride_h_ = stride_w_ = conv_param.stride();
+  } else {
+    stride_h_ = conv_param.stride_h();
+    stride_w_ = conv_param.stride_w();
+  }
+  // Special case: im2col is the identity for 1x1 convolution with stride 1
+  // and no padding, so flag for skipping the buffer and transformation.
+  is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
+      && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
+  // Configure output channels and groups.
+  channels_ = bottom[0]->channels();
+  num_output_ = this->layer_param_.convolution_param().num_output();
+  CHECK_GT(num_output_, 0);
+  group_ = this->layer_param_.convolution_param().group();
+  CHECK_EQ(channels_ % group_, 0);
+  CHECK_EQ(num_output_ % group_, 0)
+      << "Number of output should be multiples of group.";
+  if (reverse_dimensions()) {
+    conv_out_channels_ = channels_;
+    conv_in_channels_ = num_output_;
+  } else {
+    conv_out_channels_ = num_output_;
+    conv_in_channels_ = channels_;
+  }
+  // Handle the parameters: weights and biases.
+  // - blobs_[0] holds the filter weights
+  // - blobs_[1] holds the biases (optional)
+  bias_term_ = this->layer_param_.convolution_param().bias_term();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    if (bias_term_) {
+      this->blobs_.resize(2);
+    } else {
+      this->blobs_.resize(1);
+    }
+    // Initialize and fill the weights:
+    // output channels x input channels per-group x kernel height x kernel width
+    this->blobs_[0].reset(new Blob<Dtype>(
+        conv_out_channels_, conv_in_channels_ / group_, kernel_h_, kernel_w_));
+    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
+        this->layer_param_.convolution_param().weight_filler()));
+    weight_filler->Fill(this->blobs_[0].get());
+    // If necessary, initialize and fill the biases:
+    // 1 x 1 x 1 x output channels
+    if (bias_term_) {
+      this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
+      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
+          this->layer_param_.convolution_param().bias_filler()));
+      bias_filler->Fill(this->blobs_[1].get());
+    }
+  }
+  // Propagate gradients to the parameters (as directed by backward pass).
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  num_ = bottom[0]->num();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
+    " convolution kernel.";
+  // TODO: generalize to handle inputs of different shapes.
+  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
+    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
+    CHECK_EQ(channels_, bottom[bottom_id]->channels())
+        << "Inputs must have same channels.";
+    CHECK_EQ(height_, bottom[bottom_id]->height())
+        << "Inputs must have same height.";
+    CHECK_EQ(width_, bottom[bottom_id]->width())
+        << "Inputs must have same width.";
+  }
+  // Shape the tops.
+  compute_output_shape();
+  for (int top_id = 0; top_id < top.size(); ++top_id) {
+    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+  }
+  if (reverse_dimensions()) {
+    conv_in_height_ = height_out_;
+    conv_in_width_ = width_out_;
+    conv_out_spatial_dim_ = height_ * width_;
+  } else {
+    conv_in_height_ = height_;
+    conv_in_width_ = width_;
+    conv_out_spatial_dim_ = height_out_ * width_out_;
+  }
+  kernel_dim_ = conv_in_channels_ * kernel_h_ * kernel_w_;
+  weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_;
+  col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_;
+  output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
+  // The im2col result buffer will only hold one image at a time to avoid
+  // overly large memory usage. In the special case of 1x1 convolution
+  // it goes lazily unused to save memory.
+  if (reverse_dimensions()) {
+    col_buffer_.Reshape(1, kernel_dim_, height_, width_);
+  } else {
+    col_buffer_.Reshape(1, kernel_dim_, height_out_, width_out_);
+  }
+  // Set up the all ones "bias multiplier" for adding biases by BLAS
+  if (bias_term_) {
+    bias_multiplier_.Reshape(1, 1, 1, height_out_ * width_out_);
+    caffe_set(bias_multiplier_.count(), Dtype(1),
+        bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_cpu_gemm(const Dtype* input,
+    const Dtype* weights, Dtype* output, bool skip_im2col) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    if (!skip_im2col) {
+      conv_im2col_cpu(input, col_buffer_.mutable_cpu_data());
+    }
+    col_buff = col_buffer_.cpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
+        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)0., output + output_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_cpu_bias(Dtype* output,
+    const Dtype* bias) {
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
+      (Dtype)1., output);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_cpu_gemm(const Dtype* output,
+    const Dtype* weights, Dtype* input) {
+  Dtype* col_buff = col_buffer_.mutable_cpu_data();
+  if (is_1x1_) {
+    col_buff = input;
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+        conv_out_spatial_dim_, conv_out_channels_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
+        (Dtype)0., col_buff + col_offset_ * g);
+  }
+  if (!is_1x1_) {
+    conv_col2im_cpu(col_buff, input);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::weight_cpu_gemm(const Dtype* input,
+    const Dtype* output, Dtype* weights) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    conv_im2col_cpu(input, col_buffer_.mutable_cpu_data());
+    col_buff = col_buffer_.cpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
+        kernel_dim_ / group_, conv_out_spatial_dim_,
+        (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)1., weights + weight_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_cpu_bias(Dtype* bias,
+    const Dtype* input) {
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+      input, bias_multiplier_.cpu_data(), 1., bias);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_gpu_gemm(const Dtype* input,
+    const Dtype* weights, Dtype* output, bool skip_im2col) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    if (!skip_im2col) {
+      conv_im2col_gpu(input, col_buffer_.mutable_gpu_data());
+    }
+    col_buff = col_buffer_.gpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
+        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)0., output + output_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::forward_gpu_bias(Dtype* output,
+    const Dtype* bias) {
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
+      (Dtype)1., output);
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_gpu_gemm(const Dtype* output,
+    const Dtype* weights, Dtype* input) {
+  Dtype* col_buff = col_buffer_.mutable_gpu_data();
+  if (is_1x1_) {
+    col_buff = input;
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+        conv_out_spatial_dim_, conv_out_channels_ / group_,
+        (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
+        (Dtype)0., col_buff + col_offset_ * g);
+  }
+  if (!is_1x1_) {
+    conv_col2im_gpu(col_buff, input);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::weight_gpu_gemm(const Dtype* input,
+    const Dtype* output, Dtype* weights) {
+  const Dtype* col_buff = input;
+  if (!is_1x1_) {
+    conv_im2col_gpu(input, col_buffer_.mutable_gpu_data());
+    col_buff = col_buffer_.gpu_data();
+  }
+  for (int g = 0; g < group_; ++g) {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
+        kernel_dim_ / group_, conv_out_spatial_dim_,
+        (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
+        (Dtype)1., weights + weight_offset_ * g);
+  }
+}
+
+template <typename Dtype>
+void BaseConvolutionLayer<Dtype>::backward_gpu_bias(Dtype* bias,
+    const Dtype* input) {
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+      input, bias_multiplier_.gpu_data(), 1., bias);
+}
+
+INSTANTIATE_CLASS(BaseConvolutionLayer);
+
+}  // namespace caffe

From a0e9db1347c325ff007166e79d1ca693e2e5de18 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 21 Dec 2014 23:33:35 -0800
Subject: [PATCH 1042/2053] add CPU_ONLY ifdef guards to BaseConvolutionLayer

---
 include/caffe/vision_layers.hpp      | 4 ++++
 src/caffe/layers/base_conv_layer.cpp | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index e1bb7abf07c..4cf7e8b0428 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -43,6 +43,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
       weights);
   void backward_cpu_bias(Dtype* bias, const Dtype* input);
 
+#ifndef CPU_ONLY
   void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
       Dtype* output, bool skip_im2col = false);
   void forward_gpu_bias(Dtype* output, const Dtype* bias);
@@ -51,6 +52,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
   void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
       weights);
   void backward_gpu_bias(Dtype* bias, const Dtype* input);
+#endif
 
   // reverse_dimensions should return true iff we are implementing deconv, so
   // that conv helpers know which dimensions are which.
@@ -80,6 +82,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
     col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
   }
+#ifndef CPU_ONLY
   inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
     im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
@@ -88,6 +91,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
     col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
         kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
   }
+#endif
 
   int conv_out_channels_;
   int conv_in_channels_;
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index a44867a0cf3..dccd5170c11 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -217,6 +217,8 @@ void BaseConvolutionLayer<Dtype>::backward_cpu_bias(Dtype* bias,
       input, bias_multiplier_.cpu_data(), 1., bias);
 }
 
+#ifndef CPU_ONLY
+
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::forward_gpu_gemm(const Dtype* input,
     const Dtype* weights, Dtype* output, bool skip_im2col) {
@@ -284,6 +286,8 @@ void BaseConvolutionLayer<Dtype>::backward_gpu_bias(Dtype* bias,
       input, bias_multiplier_.gpu_data(), 1., bias);
 }
 
+#endif  // !CPU_ONLY
+
 INSTANTIATE_CLASS(BaseConvolutionLayer);
 
 }  // namespace caffe

From e3e2f2d3139880f77355e6837e72ad6c2848b448 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 21 Dec 2014 19:42:29 -0800
Subject: [PATCH 1043/2053] rewrite ConvolutionLayer to use
 BaseConvolutionLayer helpers

---
 include/caffe/vision_layers.hpp |  36 +----
 src/caffe/layers/conv_layer.cpp | 243 ++++----------------------------
 src/caffe/layers/conv_layer.cu  | 117 +++------------
 3 files changed, 56 insertions(+), 340 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 4cf7e8b0428..4d93e6c10fc 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -124,7 +124,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
  *   the output channel N' columns of the output matrix.
  */
 template <typename Dtype>
-class ConvolutionLayer : public Layer<Dtype> {
+class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
  public:
   /**
    * @param param provides ConvolutionParameter convolution_param,
@@ -155,18 +155,10 @@ class ConvolutionLayer : public Layer<Dtype> {
    *    kernels + stream parallelism) engines.
    */
   explicit ConvolutionLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
+      : BaseConvolutionLayer<Dtype>(param) {}
   virtual inline LayerParameter_LayerType type() const {
     return LayerParameter_LayerType_CONVOLUTION;
   }
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -177,30 +169,10 @@ class ConvolutionLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return false; }
+  virtual void compute_output_shape();
 
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int num_;
-  int channels_;
-  int pad_h_, pad_w_;
-  int height_, width_;
-  int group_;
-  int num_output_;
-  int height_out_, width_out_;
-  bool bias_term_;
-  bool is_1x1_;
 
-  /// M_ is the channel dimension of the output for a single group, which is the
-  /// leading dimension of the filter matrix.
-  int M_;
-  /// K_ is the dimension of an unrolled input for a single group, which is the
-  /// leading dimension of the data matrix.
-  int K_;
-  /// N_ is the spatial dimension of the output, the H x W, which are the last
-  /// dimensions of the data and filter matrices.
-  int N_;
-  Blob<Dtype> col_buffer_;
-  Blob<Dtype> bias_multiplier_;
 };
 
 #ifdef USE_CUDNN
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 0a032025bfb..9fd2fc6a15f 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -9,166 +9,26 @@
 namespace caffe {
 
 template <typename Dtype>
-void ConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  // Configure the kernel size, padding, stride, and inputs.
-  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  CHECK(!conv_param.has_kernel_size() !=
-      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(conv_param.has_kernel_size() ||
-      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
-      && conv_param.has_pad_w())
-      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
-      << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
-      && conv_param.has_stride_w())
-      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
-      << "Stride is stride OR stride_h and stride_w are required.";
-  if (conv_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = conv_param.kernel_size();
-  } else {
-    kernel_h_ = conv_param.kernel_h();
-    kernel_w_ = conv_param.kernel_w();
-  }
-  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
-  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (!conv_param.has_pad_h()) {
-    pad_h_ = pad_w_ = conv_param.pad();
-  } else {
-    pad_h_ = conv_param.pad_h();
-    pad_w_ = conv_param.pad_w();
-  }
-  if (!conv_param.has_stride_h()) {
-    stride_h_ = stride_w_ = conv_param.stride();
-  } else {
-    stride_h_ = conv_param.stride_h();
-    stride_w_ = conv_param.stride_w();
-  }
-  // Special case: im2col is the identity for 1x1 convolution with stride 1
-  // and no padding, so flag for skipping the buffer and transformation.
-  is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
-      && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
-  // Configure output channels and groups.
-  channels_ = bottom[0]->channels();
-  num_output_ = this->layer_param_.convolution_param().num_output();
-  CHECK_GT(num_output_, 0);
-  group_ = this->layer_param_.convolution_param().group();
-  CHECK_EQ(channels_ % group_, 0);
-  CHECK_EQ(num_output_ % group_, 0)
-      << "Number of output should be multiples of group.";
-  // Handle the parameters: weights and biases.
-  // - blobs_[0] holds the filter weights
-  // - blobs_[1] holds the biases (optional)
-  bias_term_ = this->layer_param_.convolution_param().bias_term();
-  if (this->blobs_.size() > 0) {
-    LOG(INFO) << "Skipping parameter initialization";
-  } else {
-    if (bias_term_) {
-      this->blobs_.resize(2);
-    } else {
-      this->blobs_.resize(1);
-    }
-    // Initialize and fill the weights:
-    // output channels x input channels per-group x kernel height x kernel width
-    this->blobs_[0].reset(new Blob<Dtype>(
-        num_output_, channels_ / group_, kernel_h_, kernel_w_));
-    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
-        this->layer_param_.convolution_param().weight_filler()));
-    weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, initialize and fill the biases:
-    // 1 x 1 x 1 x output channels
-    if (bias_term_) {
-      this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
-      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
-          this->layer_param_.convolution_param().bias_filler()));
-      bias_filler->Fill(this->blobs_[1].get());
-    }
-  }
-  // Propagate gradients to the parameters (as directed by backward pass).
-  this->param_propagate_down_.resize(this->blobs_.size(), true);
-}
-
-template <typename Dtype>
-void ConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  num_ = bottom[0]->num();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
-    " convolution kernel.";
-  // TODO: generalize to handle inputs of different shapes.
-  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
-    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
-    CHECK_EQ(channels_, bottom[bottom_id]->channels())
-        << "Inputs must have same channels.";
-    CHECK_EQ(height_, bottom[bottom_id]->height())
-        << "Inputs must have same height.";
-    CHECK_EQ(width_, bottom[bottom_id]->width())
-        << "Inputs must have same width.";
-  }
-  // Shape the tops.
-  height_out_ =
-      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
-  width_out_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
-  for (int top_id = 0; top_id < top.size(); ++top_id) {
-    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
-  }
-  // Prepare the matrix multiplication computation.
-  // Each input will be convolved as a single GEMM.
-  M_ = num_output_ / group_;
-  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
-  N_ = height_out_ * width_out_;
-  // The im2col result buffer will only hold one image at a time to avoid
-  // overly large memory usage. In the special case of 1x1 convolution
-  // it goes lazily unused to save memory.
-  col_buffer_.Reshape(
-      1, channels_ * kernel_h_ * kernel_w_, height_out_, width_out_);
-  // Set up the all ones "bias multiplier" for adding biases by BLAS
-  if (bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, N_);
-    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
-  }
+void ConvolutionLayer<Dtype>::compute_output_shape() {
+  this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
+      / this->stride_h_ + 1;
+  this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
+      / this->stride_w_ + 1;
 }
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
-    Dtype* col_buff = NULL;
-    if (!is_1x1_) {
-      col_buff = col_buffer_.mutable_cpu_data();
-    }
-    const Dtype* weight = this->blobs_[0]->cpu_data();
-    int weight_offset = M_ * K_;  // number of filter parameters in a group
-    int col_offset = K_ * N_;  // number of values in an input region / column
-    int top_offset = M_ * N_;  // number of values in an output region / column
-    for (int n = 0; n < num_; ++n) {
-      // im2col transformation: unroll input regions for filtering
-      // into column matrix for multplication.
-      if (!is_1x1_) {
-        im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-            width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-            col_buff);
-      } else {  // special case for 1x1 convolution
-        col_buff = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
-      }
-      // Take inner products for groups.
-      for (int g = 0; g < group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_buff + col_offset * g,
-          (Dtype)0., top_data + top[i]->offset(n) + top_offset * g);
-      }
-      // Add bias.
-      if (bias_term_) {
-        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-            bias_multiplier_.cpu_data(),
-            (Dtype)1., top_data + top[i]->offset(n));
+    for (int n = 0; n < this->num_; ++n) {
+      this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->cpu_data();
+        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
       }
     }
   }
@@ -177,82 +37,37 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  const Dtype* weight = NULL;
-  Dtype* weight_diff = NULL;
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
   if (this->param_propagate_down_[0]) {
-    weight = this->blobs_[0]->cpu_data();
-    weight_diff = this->blobs_[0]->mutable_cpu_diff();
     caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
-  Dtype* bias_diff = NULL;
-  if (bias_term_ && this->param_propagate_down_[1]) {
-    bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    caffe_set(this->blobs_[1]->count(), Dtype(0),
+        this->blobs_[1]->mutable_cpu_diff());
   }
-  const int weight_offset = M_ * K_;
-  const int col_offset = K_ * N_;
-  const int top_offset = M_ * N_;
   for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = NULL;
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
     // Bias gradient, if necessary.
-    if (bias_term_ && this->param_propagate_down_[1]) {
-      top_diff = top[i]->cpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-            1., top_diff + top[0]->offset(n),
-            bias_multiplier_.cpu_data(), 1.,
-            bias_diff);
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
-      if (!top_diff) {
-        top_diff = top[i]->cpu_diff();
-      }
-      Dtype* col_buff = NULL;
-      if (!is_1x1_) {
-        col_buff = col_buffer_.mutable_cpu_data();
-      }
-      const Dtype* bottom_data = bottom[i]->cpu_data();
-      Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        // Since we saved memory in the forward pass by not storing all col
-        // data, we will need to recompute them.
-        if (!is_1x1_) {
-          im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                    width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                    stride_h_, stride_w_, col_buff);
-        } else {
-          col_buff = bottom[i]->mutable_cpu_data() + bottom[i]->offset(n);
-        }
+      for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_buff + col_offset * g, (Dtype)1.,
-                weight_diff + weight_offset * g);
-          }
+          this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),
+              top_diff + top[i]->offset(n), weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          if (weight == NULL) {
-            weight = this->blobs_[0]->cpu_data();
-          }
-          if (is_1x1_) {
-            col_buff = bottom[i]->mutable_cpu_diff() + bottom[i]->offset(n);
-          }
-          for (int g = 0; g < group_; ++g) {
-            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-                (Dtype)1., weight + weight_offset * g,
-                top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_buff + col_offset * g);
-          }
-          // col2im back to the data
-          if (!is_1x1_) {
-            col2im_cpu(col_buff, channels_, height_, width_,
-                kernel_h_, kernel_w_, pad_h_, pad_w_,
-                stride_h_, stride_w_, bottom_diff + bottom[i]->offset(n));
-          }
+          this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n));
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index af14facb523..3902fdf3930 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -8,135 +8,64 @@
 
 namespace caffe {
 
-/// @brief refer to CPU forward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
-    Dtype* col_buff = NULL;
-    if (!is_1x1_) {
-      col_buff = col_buffer_.mutable_gpu_data();
-    }
-    const Dtype* weight = this->blobs_[0]->gpu_data();
-    int weight_offset = M_ * K_;
-    int col_offset = K_ * N_;
-    int top_offset = M_ * N_;
-    for (int n = 0; n < num_; ++n) {
-      // im2col transformation: unroll input regions for filtering
-      // into column matrix for multplication.
-      if (!is_1x1_) {
-        im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-            width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-            col_buff);
-      } else {
-        col_buff = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
-      }
-      // Take inner products for groups.
-      for (int g = 0; g < group_; ++g) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-          (Dtype)1., weight + weight_offset * g, col_buff + col_offset * g,
-          (Dtype)0., top_data + top[i]->offset(n) + top_offset * g);
-      }
-      // Add bias.
-      if (bias_term_) {
-        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-            N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
-            bias_multiplier_.gpu_data(),
-            (Dtype)1., top_data + top[i]->offset(n));
+    for (int n = 0; n < this->num_; ++n) {
+      this->forward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->gpu_data();
+        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
       }
     }
   }
 }
 
-/// @brief refer to CPU backward -- the BLAS implementation is the same.
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  const Dtype* weight = NULL;
-  Dtype* weight_diff = NULL;
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
   if (this->param_propagate_down_[0]) {
-    weight = this->blobs_[0]->gpu_data();
-    weight_diff = this->blobs_[0]->mutable_gpu_diff();
     caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
-  Dtype* bias_diff = NULL;
-  if (bias_term_ && this->param_propagate_down_[1]) {
-    bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
+        this->blobs_[1]->mutable_gpu_diff());
   }
-  const int weight_offset = M_ * K_;
-  const int col_offset = K_ * N_;
-  const int top_offset = M_ * N_;
   for (int i = 0; i < top.size(); ++i) {
-    const Dtype* top_diff = NULL;
+    const Dtype* top_diff = top[i]->gpu_diff();
     // Bias gradient, if necessary.
-    if (bias_term_ && this->param_propagate_down_[1]) {
-      top_diff = top[i]->gpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-            1., top_diff + top[0]->offset(n),
-            bias_multiplier_.gpu_data(), 1.,
-            bias_diff);
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
-      if (!top_diff) {
-        top_diff = top[i]->gpu_diff();
-      }
-      Dtype* col_buff = NULL;
-      if (!is_1x1_) {
-        col_buff = col_buffer_.mutable_gpu_data();
-      }
       const Dtype* bottom_data = bottom[i]->gpu_data();
       Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
-      for (int n = 0; n < num_; ++n) {
-        // Since we saved memory in the forward pass by not storing all col
-        // data, we will need to recompute them.
-        if (!is_1x1_) {
-          im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
-                    width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-                    stride_h_, stride_w_, col_buff);
-        } else {
-          col_buff = bottom[i]->mutable_gpu_data() + bottom[i]->offset(n);
-        }
+      for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          for (int g = 0; g < group_; ++g) {
-            caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
-                col_buff + col_offset * g, (Dtype)1.,
-                weight_diff + weight_offset * g);
-          }
+          this->weight_gpu_gemm(bottom_data + bottom[i]->offset(n),
+              top_diff + top[i]->offset(n), weight_diff);
         }
-        // gradient w.r.t. bottom data, if necessary
+        // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          if (weight == NULL) {
-            weight = this->blobs_[0]->gpu_data();
-          }
-          if (is_1x1_) {
-            col_buff = bottom[i]->mutable_gpu_diff() + bottom[i]->offset(n);
-          }
-          for (int g = 0; g < group_; ++g) {
-            caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-                (Dtype)1., weight + weight_offset * g,
-                top_diff + top[i]->offset(n) + top_offset * g,
-                (Dtype)0., col_buff + col_offset * g);
-          }
-          // col2im back to the data
-          if (!is_1x1_) {
-            col2im_gpu(col_buff, channels_, height_, width_,
-                kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
-                bottom_diff + bottom[i]->offset(n));
-          }
+          this->backward_gpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n));
         }
       }
     }
   }
 }
 
-
 INSTANTIATE_LAYER_GPU_FUNCS(ConvolutionLayer);
 
 }  // namespace caffe

From 3617352124ab97154d37534e4ba30d7a2b9b6d2f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 21 Dec 2014 19:43:36 -0800
Subject: [PATCH 1044/2053] add DeconvolutionLayer, using BaseConvolutionLayer

---
 include/caffe/vision_layers.hpp   | 20 ++++++++
 src/caffe/layers/deconv_layer.cpp | 85 +++++++++++++++++++++++++++++++
 src/caffe/layers/deconv_layer.cu  | 71 ++++++++++++++++++++++++++
 src/caffe/proto/caffe.proto       |  3 +-
 4 files changed, 178 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/deconv_layer.cpp
 create mode 100644 src/caffe/layers/deconv_layer.cu

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 4d93e6c10fc..646378dea9f 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -171,8 +171,28 @@ class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual inline bool reverse_dimensions() { return false; }
   virtual void compute_output_shape();
+};
 
+template <typename Dtype>
+class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+  explicit DeconvolutionLayer(const LayerParameter& param)
+      : BaseConvolutionLayer<Dtype>(param) {}
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DECONVOLUTION;
+  }
 
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return true; }
+  virtual void compute_output_shape();
 };
 
 #ifdef USE_CUDNN
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
new file mode 100644
index 00000000000..59114f017bf
--- /dev/null
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -0,0 +1,85 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::compute_output_shape() {
+  this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_
+      - 2 * this->pad_h_;
+  this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_
+      - 2 * this->pad_w_;
+}
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* top_data = top[i]->mutable_cpu_data();
+    for (int n = 0; n < this->num_; ++n) {
+      this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->cpu_data();
+        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
+  if (this->param_propagate_down_[0]) {
+    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    caffe_set(this->blobs_[1]->count(), Dtype(0),
+        this->blobs_[1]->mutable_cpu_diff());
+  }
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+    // Bias gradient, if necessary.
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+      }
+    }
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      for (int n = 0; n < this->num_; ++n) {
+        // Gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          this->weight_cpu_gemm(top_diff + top[i]->offset(n),
+              bottom_data + bottom[i]->offset(n), weight_diff);
+        }
+        // Gradient w.r.t. bottom data, if necessary, reusing the column buffer
+        // we might have just computed above.
+        if (propagate_down[i]) {
+          this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n),
+              this->param_propagate_down_[0]);
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(DeconvolutionLayer);
+#endif
+
+INSTANTIATE_CLASS(DeconvolutionLayer);
+REGISTER_LAYER_CLASS(DECONVOLUTION, DeconvolutionLayer);
+}  // namespace caffe
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
new file mode 100644
index 00000000000..9198dd64c72
--- /dev/null
+++ b/src/caffe/layers/deconv_layer.cu
@@ -0,0 +1,71 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* top_data = top[i]->mutable_gpu_data();
+    for (int n = 0; n < this->num_; ++n) {
+      this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
+          top_data + top[i]->offset(n));
+      if (this->bias_term_) {
+        const Dtype* bias = this->blobs_[1]->gpu_data();
+        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
+  if (this->param_propagate_down_[0]) {
+    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
+        this->blobs_[1]->mutable_gpu_diff());
+  }
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+    // Bias gradient, if necessary.
+    if (this->bias_term_ && this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+      for (int n = 0; n < this->num_; ++n) {
+        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+      }
+    }
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      for (int n = 0; n < this->num_; ++n) {
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          this->weight_gpu_gemm(top_diff + top[i]->offset(n),
+              bottom_data + bottom[i]->offset(n), weight_diff);
+        }
+        // gradient w.r.t. bottom data, if necessary.
+        if (propagate_down[i]) {
+          this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight,
+              bottom_diff + bottom[i]->offset(n));
+        }
+      }
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(DeconvolutionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8086ad66579..e83eefa1622 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -227,7 +227,7 @@ message LayerParameter {
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 39 (last added: EXP)
+  // LayerType next available ID: 40 (last added: DECONVOLUTION)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
@@ -241,6 +241,7 @@ message LayerParameter {
     CONTRASTIVE_LOSS = 37;
     CONVOLUTION = 4;
     DATA = 5;
+    DECONVOLUTION = 39;
     DROPOUT = 6;
     DUMMY_DATA = 32;
     EUCLIDEAN_LOSS = 7;

From f2057680c7f633ec0d9d63d865d09fe1b958ebee Mon Sep 17 00:00:00 2001
From: Pannous <info@pannous.com>
Date: Wed, 14 Jan 2015 18:43:49 +0100
Subject: [PATCH 1045/2053] Message: Please ask usage questions and how to
 model different tasks on the caffe-users mailing list

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 0e3c833e8fb..9a9b3ee36eb 100644
--- a/README.md
+++ b/README.md
@@ -2,3 +2,7 @@
 
 Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.<br />
 Consult the [project website](http://caffe.berkeleyvision.org) for all documentation.
+
+
+Please ask usage questions and how to model different tasks on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
+

From 4041e4d0117e46bb213a6017e9e34806a8ccb286 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 14 Jan 2015 17:56:23 -0800
Subject: [PATCH 1046/2053] set mode, phase, device in pycaffe; fix #1700

Attach mode, phase, and device setters to caffe module itself
so that these can be set before making nets. This is needed to properly
initialize layers with the right device and phase configuration.

Update examples to new usage.
---
 examples/classification.ipynb       | 54 +++++++++++------------------
 examples/filter_visualization.ipynb |  9 ++---
 examples/net_surgery.ipynb          |  7 ++--
 python/caffe/__init__.py            |  2 ++
 python/caffe/_caffe.cpp             | 12 ++++---
 python/caffe/_caffe.hpp             | 13 ++++---
 python/caffe/classifier.py          |  6 ++--
 python/caffe/detector.py            |  6 ++--
 python/caffe/pycaffe.py             |  2 ++
 9 files changed, 52 insertions(+), 59 deletions(-)

diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 1e2126fe652..6b6c5488d36 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -3,7 +3,8 @@
   "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
   "example_name": "ImageNet classification",
   "include_in_docs": true,
-  "priority": 1
+  "priority": 1,
+  "signature": "sha256:2caae2c1fe3e282b8f836d380a45622351c91db18a1591e4f2fa67faba9ab72c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -54,13 +55,17 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
+      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
+      "\n",
+      "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "caffe.set_phase_test()\n",
+      "caffe.set_mode_cpu()\n",
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
       "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
       "                       channel_swap=(2,1,0),\n",
@@ -72,25 +77,6 @@
      "outputs": [],
      "prompt_number": 2
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net.set_phase_test()\n",
-      "net.set_mode_cpu()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -111,7 +97,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 3,
        "text": [
         "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
        ]
@@ -125,7 +111,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -163,7 +149,7 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -205,7 +191,7 @@
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -233,7 +219,7 @@
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -266,7 +252,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -279,12 +265,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "net.set_mode_gpu()"
+      "caffe.set_mode_gpu()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -314,7 +300,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 9,
        "text": [
         "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
        ]
@@ -328,7 +314,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -355,7 +341,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -375,7 +361,7 @@
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -404,4 +390,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 09b416fb4ca..8e068805d62 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -3,7 +3,8 @@
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
   "example_name": "Filter visualization",
   "include_in_docs": true,
-  "priority": 2
+  "priority": 2,
+  "signature": "sha256:526501b358e0f60c489eaf5799e8603a75019cc65401533baa307b5603fdefa1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -61,10 +62,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "caffe.set_phase_test()\n",
+      "caffe.set_mode_cpu()\n",
       "net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
       "                       caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-      "net.set_phase_test()\n",
-      "net.set_mode_cpu()\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
       "net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'))  # ImageNet mean\n",
       "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
@@ -598,4 +599,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 64310016101..a4fb4a66065 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Editing model parameters",
   "include_in_docs": true,
   "priority": 5,
-  "signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
+  "signature": "sha256:bf84bcbd78fe007310f86d71c5969ba4205b6e06f408029860eec94821844bee"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -279,9 +279,10 @@
       "import matplotlib.pyplot as plt\n",
       "%matplotlib inline\n",
       "\n",
+      "caffe.set_phase_test()\n",
+      "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
-      "net_full_conv.set_phase_test()\n",
       "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
       "net_full_conv.set_raw_scale('data', 255.0)\n",
@@ -352,4 +353,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 430bfce2ba5..8dd2464d1c4 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,4 +1,6 @@
 from .pycaffe import Net, SGDSolver
+from .pycaffe import set_mode_cpu, set_mode_gpu, set_phase_train, set_phase_test
+from .pycaffe import set_device
 from .classifier import Classifier
 from .detector import Detector
 import io
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5a81a42329b..2b279e8a6ce 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -142,6 +142,13 @@ void PySGDSolver::SolveResume(const string& resume_file) {
 }
 
 BOOST_PYTHON_MODULE(_caffe) {
+  // Caffe utility methods
+  bp::def("set_mode_cpu",          &set_mode_cpu);
+  bp::def("set_mode_gpu",          &set_mode_gpu);
+  bp::def("set_phase_train",       &set_phase_train);
+  bp::def("set_phase_test",        &set_phase_test);
+  bp::def("set_device",            &Caffe::SetDevice);
+
   // below, we prepend an underscore to methods that will be replaced
   // in Python
   bp::class_<PyNet, shared_ptr<PyNet> >(
@@ -150,11 +157,6 @@ BOOST_PYTHON_MODULE(_caffe) {
       .def("_forward",              &PyNet::Forward)
       .def("_backward",             &PyNet::Backward)
       .def("reshape",               &PyNet::Reshape)
-      .def("set_mode_cpu",          &PyNet::set_mode_cpu)
-      .def("set_mode_gpu",          &PyNet::set_mode_gpu)
-      .def("set_phase_train",       &PyNet::set_phase_train)
-      .def("set_phase_test",        &PyNet::set_phase_test)
-      .def("set_device",            &PyNet::set_device)
       .add_property("_blobs",       &PyNet::blobs)
       .add_property("layers",       &PyNet::layers)
       .add_property("_blob_names",  &PyNet::blob_names)
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
index ba04d276351..e2e1ca348ab 100644
--- a/python/caffe/_caffe.hpp
+++ b/python/caffe/_caffe.hpp
@@ -18,6 +18,12 @@ using boost::shared_ptr;
 
 namespace caffe {
 
+// Selecting mode and phase.
+void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
+void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
+void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
+void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
+
 // wrap shared_ptr<Blob> in a class that we construct in C++ and pass
 // to Python
 template <typename Dtype>
@@ -109,13 +115,6 @@ class PyNet {
     WriteProtoToBinaryFile(net_param, filename.c_str());
   }
 
-  // The caffe::Caffe utility functions.
-  void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
-  void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
-  void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
-  void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
-  void set_device(int device_id) { Caffe::SetDevice(device_id); }
-
   vector<PyBlob<float> > blobs() {
     return vector<PyBlob<float> >(net_->blobs().begin(), net_->blobs().end());
   }
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index fe471ca13b1..f9a13a39865 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -24,12 +24,12 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
             preprocessing options.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
-        self.set_phase_test()
+        caffe.set_phase_test()
 
         if gpu:
-            self.set_mode_gpu()
+            caffe.set_mode_gpu()
         else:
-            self.set_mode_cpu()
+            caffe.set_mode_cpu()
 
         if mean is not None:
             self.set_mean(self.inputs[0], mean)
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index f219b6105e1..b78abd12a89 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -36,12 +36,12 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
             R-CNN feature extraction.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
-        self.set_phase_test()
+        caffe.set_phase_test()
 
         if gpu:
-            self.set_mode_gpu()
+            caffe.set_mode_gpu()
         else:
-            self.set_mode_cpu()
+            caffe.set_mode_cpu()
 
         if mean is not None:
             self.set_mean(self.inputs[0], mean)
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 31dc1f9b001..8899309ddfd 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -8,6 +8,8 @@
 import numpy as np
 
 from ._caffe import Net, SGDSolver
+from ._caffe import set_mode_cpu, set_mode_gpu, set_phase_train, set_phase_test
+from ._caffe import set_device
 import caffe.io
 
 # We directly update methods from Net here (rather than using composition or

From ab839f5b2f5c93da34c2ab797ab2a64b62645976 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 15 Jan 2015 16:28:09 -0800
Subject: [PATCH 1047/2053] support OS X Yosemite / 10.10

- pick libstdc++ for OS X (regardless of version)
- make gtest rely on its own tuple to not conflict with clang
  (thanks @pluskid!)
- 10.10 has Accelerate while 10.9 has vecLib for BLAS
  (thanks @leonardt and @drdan14)
---
 Makefile             | 21 ++++++++++++++-------
 docs/installation.md | 14 +++++++-------
 2 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/Makefile b/Makefile
index 46bf7280919..2d82e058085 100644
--- a/Makefile
+++ b/Makefile
@@ -234,15 +234,15 @@ endif
 # libstdc++ instead of libc++ for CUDA compatibility on 10.9
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
+	CXXFLAGS += -stdlib=libstdc++
+	LINKFLAGS += -stdlib=libstdc++
 	# clang throws this warning for cuda headers
 	WARNINGS += -Wno-unneeded-internal-declaration
-	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)
-		CXXFLAGS += -stdlib=libstdc++
-		LINKFLAGS += -stdlib=libstdc++
-	endif
+	# gtest needs to use its own tuple to not conflict with clang
+	CXXFLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
-        NVCCFLAGS += -DOSX
+	NVCCFLAGS += -DOSX
 endif
 
 # Custom compiler
@@ -304,9 +304,16 @@ else
 		endif
 	else ifeq ($(OSX), 1)
 		# OS X packages atlas as the vecLib framework
-		BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/
 		LIBRARIES += cblas
-		LDFLAGS += -framework vecLib
+		# 10.10 has accelerate while 10.9 has veclib
+		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6')
+		ifneq (,$(findstring version: 6,$(XCODE_CLT_VER)))
+			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.10.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+			LDFLAGS += -framework Accelerate
+		else
+			BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/
+			LDFLAGS += -framework vecLib
+		endif
 	endif
 endif
 INCLUDE_DIRS += $(BLAS_INCLUDE)
diff --git a/docs/installation.md b/docs/installation.md
index c667cd8ce01..94068e503dc 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -5,7 +5,7 @@ title: Installation
 # Installation
 
 Prior to installing, it is best to read through this guide and take note of the details for your platform.
-We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
+We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.10, 10.9, and 10.8.
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
@@ -156,9 +156,9 @@ Building boost from source is needed to link against your local Python (exceptio
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
 
-#### 10.9-specific Instructions
+#### 10.10- and 10.9-specific Instructions
 
-In OS X 10.9, clang++ is the default C++ compiler and uses `libc++` as the standard library.
+In OS X 10.9+, clang++ is the default C++ compiler and uses `libc++` as the standard library.
 However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
 This makes it necessary to change the compilation settings for each of the dependencies.
 
@@ -226,10 +226,10 @@ Then, whenever you want to update homebrew, switch back to the master branches,
     git checkout master
     cd /usr/local/Library/Taps/homebrew/homebrew-science
     git checkout master
-    
+
     # Update homebrew; hopefully this works without errors!
     brew update
-    
+
     # Switch back to the caffe branches with the forumlae that you modified earlier
     cd /usr/local
     git rebase master caffe
@@ -237,8 +237,8 @@ Then, whenever you want to update homebrew, switch back to the master branches,
     cd /usr/local/Library/Taps/homebrew/homebrew-science
     git rebase master caffe
     # Fix any merge conflicts and commit to caffe branch
-    
-    # Done!            
+
+    # Done!
 
 At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
 

From c16493cfb2cdf57f9ee5f511826ea110686929cf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 15 Jan 2015 20:43:24 -0800
Subject: [PATCH 1048/2053] lint internal thread

---
 include/caffe/internal_thread.hpp | 2 +-
 src/caffe/internal_thread.cpp     | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index fec22a47c86..815ca54605e 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -39,4 +39,4 @@ class InternalThread {
 
 }  // namespace caffe
 
-#endif // CAFFE_INTERNAL_THREAD_HPP_
+#endif  // CAFFE_INTERNAL_THREAD_HPP_
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 58772e36c23..c2d19d433b4 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -17,7 +17,8 @@ bool InternalThread::StartInternalThread() {
     return false;
   }
   try {
-    thread_.reset(new boost::thread(&InternalThread::InternalThreadEntry, this));
+    thread_.reset(
+        new boost::thread(&InternalThread::InternalThreadEntry, this));
   } catch (...) {
     return false;
   }

From d612f4a35cf84d9d025757b3d1cd65b96e2e5850 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 15 Jan 2015 21:47:50 -0800
Subject: [PATCH 1049/2053] check for enough args to convert_imageset

(this might better be handled by making all args flags...)
---
 tools/convert_imageset.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index c554ed389ab..5d42e69c1ed 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -52,7 +52,7 @@ int main(int argc, char** argv) {
         "    http://www.image-net.org/download-images\n");
   gflags::ParseCommandLineFlags(&argc, &argv, true);
 
-  if (argc != 4) {
+  if (argc < 4) {
     gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/convert_imageset");
     return 1;
   }

From 6af39c503677fad31c8c3c7ba784080090a01399 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Jan 2015 13:41:34 -0800
Subject: [PATCH 1050/2053] [docs] OpenCV version >= 2.4

---
 docs/installation.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index c667cd8ce01..a42ef5b309c 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -17,7 +17,7 @@ Caffe depends on several software packages.
 
 * [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
-* [OpenCV](http://opencv.org/).
+* [OpenCV](http://opencv.org/) (>= 2.4)
 * [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
 * `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
 * For the Python wrapper
@@ -226,10 +226,10 @@ Then, whenever you want to update homebrew, switch back to the master branches,
     git checkout master
     cd /usr/local/Library/Taps/homebrew/homebrew-science
     git checkout master
-    
+
     # Update homebrew; hopefully this works without errors!
     brew update
-    
+
     # Switch back to the caffe branches with the forumlae that you modified earlier
     cd /usr/local
     git rebase master caffe
@@ -237,8 +237,8 @@ Then, whenever you want to update homebrew, switch back to the master branches,
     cd /usr/local/Library/Taps/homebrew/homebrew-science
     git rebase master caffe
     # Fix any merge conflicts and commit to caffe branch
-    
-    # Done!            
+
+    # Done!
 
 At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
 

From f476e9014830a502b0b01e18b3936021f7b2fb33 Mon Sep 17 00:00:00 2001
From: Christos Nikolaou <christnn@auth.gr>
Date: Thu, 30 Oct 2014 01:05:12 +0200
Subject: [PATCH 1051/2053] Update interfaces.md file

Proofread and update the /docs/tutorial/interfaces.md file.
---
 docs/tutorial/interfaces.md | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 6b0ec347dfe..17430b35c57 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -9,7 +9,13 @@ Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, inte
 
 The command line interface -- cmdcaffe -- is the `caffe` tool for model training, scoring, and diagnostics. Run `caffe` without any arguments for help. This tool and others are found in caffe/build/tools. (The following example calls require completing the LeNet / MNIST example first.)
 
-**Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks. All training requires a solver configuration through the `-solver solver.prototxt` argument. Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
+**Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:
+
+* All training requires a solver configuration through the `-solver solver.prototxt` argument. 
+* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. 
+* Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
+
+For example, you can run:
 
     # train LeNet
     caffe train -solver examples/mnist/lenet_solver.prototxt
@@ -26,17 +32,19 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
 **Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
 
     #
-    # score the learned LeNet model on the validation set as defined in the model architeture lenet_train_test.prototxt
-    caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000 -gpu 0 -iterations 100
+    # score the learned LeNet model on the validation set as defined in the 
+    # model architeture lenet_train_test.prototxt
+    caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
 
 **Benchmarking**: `caffe time` benchmarks model execution layer-by-layer through timing and synchronization. This is useful to check system performance and measure relative execution times for models.
 
     # (These example calls require you complete the LeNet / MNIST example first.)
     # time LeNet training on CPU for 10 iterations
     caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10
-    # time a model architecture with the given weights on the first GPU for 10 iterations
     # time LeNet training on GPU for the default 50 iterations
     caffe time -model examples/mnist/lenet_train_test.prototxt -gpu 0
+    # time a model architecture with the given weights on the first GPU for 10 iterations
+    caffe time -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 10
 
 **Diagnostics**: `caffe device_query` reports GPU details for reference and checking device ordinals for running on a given device in multi-GPU machines.
 

From 1304173d85d3d382d3ae2b2aa4f6c04b2c8c75ca Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 17 Jan 2015 15:41:44 -0800
Subject: [PATCH 1052/2053] Make comments for sparse GaussianFiller match
 actual behavior

(Fixes #1497 reported by @denizyuret)
---
 include/caffe/filler.hpp    | 6 +++---
 src/caffe/proto/caffe.proto | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index 136ce958aed..eebf565b1d5 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -76,13 +76,13 @@ class GaussianFiller : public Filler<Dtype> {
     CHECK_GE(sparse, -1);
     if (sparse >= 0) {
       // Sparse initialization is implemented for "weight" blobs; i.e. matrices.
-      // These have num == channels == 1; height is number of inputs; width is
+      // These have num == channels == 1; width is number of inputs; height is
       // number of outputs.  The 'sparse' variable specifies the mean number
       // of non-zero input weights for a given output.
       CHECK_EQ(blob->num(), 1);
       CHECK_EQ(blob->channels(), 1);
-      int num_inputs = blob->height();
-      Dtype non_zero_probability = Dtype(sparse) / Dtype(num_inputs);
+      int num_outputs = blob->height();
+      Dtype non_zero_probability = Dtype(sparse) / Dtype(num_outputs);
       rand_vec_.reset(new SyncedMemory(blob->count() * sizeof(int)));
       int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());
       caffe_rng_bernoulli(blob->count(), non_zero_probability, mask);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8086ad66579..d6eec02087b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -38,7 +38,7 @@ message FillerParameter {
   optional float max = 4 [default = 1]; // the max value in uniform filler
   optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
   optional float std = 6 [default = 1]; // the std value in Gaussian filler
-  // The expected number of non-zero input weights for a given output in
+  // The expected number of non-zero output weights for a given input in
   // Gaussian filler -- the default -1 means don't perform sparsification.
   optional int32 sparse = 7 [default = -1];
 }

From c795095fd26767152e3ee6a3183cda5edd75c39b Mon Sep 17 00:00:00 2001
From: Dinesh Jayaraman <dineshj@cs.utexas.edu>
Date: Sat, 17 Jan 2015 18:23:48 -0600
Subject: [PATCH 1053/2053] Matlab demo for Caffe-compatible HDF5 read/write

---
 matlab/caffe/hdf5creation/.gitignore   |  2 +
 matlab/caffe/hdf5creation/demo.m       | 64 ++++++++++++++++++++++++++
 matlab/caffe/hdf5creation/store2hdf5.m | 59 ++++++++++++++++++++++++
 3 files changed, 125 insertions(+)
 create mode 100644 matlab/caffe/hdf5creation/.gitignore
 create mode 100644 matlab/caffe/hdf5creation/demo.m
 create mode 100644 matlab/caffe/hdf5creation/store2hdf5.m

diff --git a/matlab/caffe/hdf5creation/.gitignore b/matlab/caffe/hdf5creation/.gitignore
new file mode 100644
index 00000000000..e2333dd1776
--- /dev/null
+++ b/matlab/caffe/hdf5creation/.gitignore
@@ -0,0 +1,2 @@
+*.h5
+list.txt
diff --git a/matlab/caffe/hdf5creation/demo.m b/matlab/caffe/hdf5creation/demo.m
new file mode 100644
index 00000000000..f554b87e5f6
--- /dev/null
+++ b/matlab/caffe/hdf5creation/demo.m
@@ -0,0 +1,64 @@
+%% WRITING TO HDF5
+filename='trial.h5';
+
+num_total_samples=10000;
+% to simulate data being read from disk / generated etc.
+data_disk=rand(5,5,1,num_total_samples); 
+label_disk=rand(10,num_total_samples); 
+
+chunksz=100;
+created_flag=false;
+totalct=0;
+for batchno=1:num_total_samples/chunksz
+  fprintf('batch no. %d\n', batchno);
+  last_read=(batchno-1)*chunksz;
+
+  % to simulate maximum data to be held in memory before dumping to hdf5 file 
+  batchdata=data_disk(:,:,1,last_read+1:last_read+chunksz); 
+  batchlabs=label_disk(:,last_read+1:last_read+chunksz);
+
+  % store to hdf5
+  startloc=struct('dat',[1,1,1,totalct+1], 'lab', [1,totalct+1]);
+  curr_dat_sz=store2hdf5(filename, batchdata, batchlabs, ~created_flag, startloc, chunksz); 
+  created_flag=true;% flag set so that file is created only once
+  totalct=curr_dat_sz(end);% updated dataset size (#samples)
+end
+
+% display structure of the stored HDF5 file
+h5disp(filename);
+
+%% READING FROM HDF5
+
+% Read data and labels for samples #1000 to 1999
+data_rd=h5read(filename, '/data', [1 1 1 1000], [5, 5, 1, 1000]);
+label_rd=h5read(filename, '/label', [1 1000], [10, 1000]);
+fprintf('Testing ...\n');
+try 
+  assert(isequal(data_rd, single(data_disk(:,:,:,1000:1999))), 'Data do not match');
+  assert(isequal(label_rd, single(label_disk(:,1000:1999))), 'Labels do not match');
+
+  fprintf('Success!\n');
+catch err
+  fprintf('Test failed ...\n');
+  getReport(err)
+end
+
+%delete(filename);
+
+% CREATE list.txt containing filename, to be used as source for HDF5_DATA_LAYER
+FILE=fopen('list.txt', 'w');
+fprintf(FILE, '%s', filename);
+fclose(FILE);
+fprintf('HDF5 filename listed in %s \n', 'list.txt');
+
+% NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: 
+% layers {
+%   name: "data"
+%   type: HDF5_DATA
+%   top: "data"
+%   top: "labelvec"
+%   hdf5_data_param {
+%     source: "/path/to/list.txt"
+%     batch_size: 64
+%   }
+% }
diff --git a/matlab/caffe/hdf5creation/store2hdf5.m b/matlab/caffe/hdf5creation/store2hdf5.m
new file mode 100644
index 00000000000..0a0016dca40
--- /dev/null
+++ b/matlab/caffe/hdf5creation/store2hdf5.m
@@ -0,0 +1,59 @@
+function [curr_dat_sz, curr_lab_sz] = store2hdf5(filename, data, labels, create, startloc, chunksz)  
+  % *data* is W*H*C*N matrix of images should be normalized (e.g. to lie between 0 and 1) beforehand
+  % *label* is D*N matrix of labels (D labels per sample) 
+  % *create* [0/1] specifies whether to create file newly or to append to previously created file, useful to store information in batches when a dataset is too big to be held in memory  (default: 1)
+  % *startloc* (point at which to start writing data). By default, 
+  % if create=1 (create mode), startloc.data=[1 1 1 1], and startloc.lab=[1 1]; 
+  % if create=0 (append mode), startloc.data=[1 1 1 K+1], and startloc.lab = [1 K+1]; where K is the current number of samples stored in the HDF
+  % chunksz (used only in create mode), specifies number of samples to be stored per chunk (see HDF5 documentation on chunking) for creating HDF5 files with unbounded maximum size - TLDR; higher chunk sizes allow faster read-write operations 
+
+  % verify that format is right
+  dat_dims=size(data);
+  lab_dims=size(labels);
+  num_samples=dat_dims(end);
+
+  assert(lab_dims(end)==num_samples, 'Number of samples should be matched between data and labels');
+
+  if ~exist('create','var')
+    create=true;
+  end
+
+  
+  if create
+    %fprintf('Creating dataset with %d samples\n', num_samples);
+    if ~exist('chunksz', 'var')
+      chunksz=1000;
+    end
+    if exist(filename, 'file')
+      fprintf('Warning: replacing existing file %s \n', filename);
+      delete(filename);
+    end      
+    h5create(filename, '/data', [dat_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [dat_dims(1:end-1) chunksz]); % width, height, channels, number 
+    h5create(filename, '/label', [lab_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [lab_dims(1:end-1) chunksz]); % width, height, channels, number 
+    if ~exist('startloc','var') 
+      startloc.dat=[ones(1,length(dat_dims)-1), 1];
+      startloc.lab=[ones(1,length(lab_dims)-1), 1];
+    end 
+  else  % append mode
+    if ~exist('startloc','var')
+      info=h5info(filename);
+      prev_dat_sz=info.Datasets(1).Dataspace.Size;
+      prev_lab_sz=info.Datasets(2).Dataspace.Size;
+      assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset');
+      assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset');
+      startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1];
+      startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1];
+    end
+  end
+
+  if ~isempty(data)
+    h5write(filename, '/data', single(data), startloc.dat, size(data));
+    h5write(filename, '/label', single(labels), startloc.lab, size(labels));  
+  end
+
+  if nargout
+    info=h5info(filename);
+    curr_dat_sz=info.Datasets(1).Dataspace.Size;
+    curr_lab_sz=info.Datasets(2).Dataspace.Size;
+  end
+end

From 21f1b5941df6faeed8bfc1d68f6b42559e2eb417 Mon Sep 17 00:00:00 2001
From: Jason Yosinski <jason@yosinski.com>
Date: Sat, 17 Jan 2015 23:07:36 -0500
Subject: [PATCH 1054/2053] Updated doc to suggest boost 1.57

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 94068e503dc..66f080eb781 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -141,7 +141,7 @@ Do `brew edit opencv` and change the lines that look like the two lines below to
 **NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
 In other `ENV` settings, things may not work as expected.
 
-**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55.
+**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55 or upgrading to 1.57.
 
 #### 10.8-specific Instructions
 

From 1856bb2d89ecbabe84cbf0a647de7a2ab6c29207 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Fri, 16 Jan 2015 13:18:50 -0800
Subject: [PATCH 1055/2053] add db wrappers

---
 include/caffe/util/db.hpp | 190 ++++++++++++++++++++++++++++++++++++++
 src/caffe/util/db.cpp     |  84 +++++++++++++++++
 2 files changed, 274 insertions(+)
 create mode 100644 include/caffe/util/db.hpp
 create mode 100644 src/caffe/util/db.cpp

diff --git a/include/caffe/util/db.hpp b/include/caffe/util/db.hpp
new file mode 100644
index 00000000000..afdb8d2c4f8
--- /dev/null
+++ b/include/caffe/util/db.hpp
@@ -0,0 +1,190 @@
+#ifndef CAFFE_UTIL_DB_HPP
+#define CAFFE_UTIL_DB_HPP
+
+#include <string>
+
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+#include "lmdb.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe { namespace db {
+
+enum Mode { READ, WRITE, NEW };
+
+class Cursor {
+ public:
+  Cursor() { }
+  virtual ~Cursor() { }
+  virtual void SeekToFirst() = 0;
+  virtual void Next() = 0;
+  virtual string key() = 0;
+  virtual string value() = 0;
+  virtual bool valid() = 0;
+
+  DISABLE_COPY_AND_ASSIGN(Cursor);
+};
+
+class Transaction {
+ public:
+  Transaction() { }
+  virtual ~Transaction() { }
+  virtual void Put(const string& key, const string& value) = 0;
+  virtual void Commit() = 0;
+
+  DISABLE_COPY_AND_ASSIGN(Transaction);
+};
+
+class DB {
+ public:
+  DB() { }
+  virtual ~DB() { }
+  virtual void Open(const string& source, Mode mode) = 0;
+  virtual void Close() = 0;
+  virtual Cursor* NewCursor() = 0;
+  virtual Transaction* NewTransaction() = 0;
+
+  DISABLE_COPY_AND_ASSIGN(DB);
+};
+
+class LevelDBCursor : public Cursor {
+ public:
+  explicit LevelDBCursor(leveldb::Iterator* iter)
+    : iter_(iter) { SeekToFirst(); }
+  ~LevelDBCursor() { delete iter_; }
+  virtual void SeekToFirst() { iter_->SeekToFirst(); }
+  virtual void Next() { iter_->Next(); }
+  virtual string key() { return iter_->key().ToString(); }
+  virtual string value() { return iter_->value().ToString(); }
+  virtual bool valid() { return iter_->Valid(); }
+
+ private:
+  leveldb::Iterator* iter_;
+};
+
+class LevelDBTransaction : public Transaction {
+ public:
+  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
+  virtual void Put(const string& key, const string& value) {
+    batch_.Put(key, value);
+  }
+  virtual void Commit() {
+    leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
+    CHECK(status.ok()) << "Failed to write batch to leveldb "
+                       << std::endl << status.ToString();
+  }
+
+ private:
+  leveldb::DB* db_;
+  leveldb::WriteBatch batch_;
+
+  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
+};
+
+class LevelDB : public DB {
+ public:
+  LevelDB() : db_(NULL) { }
+  virtual ~LevelDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (db_ != NULL) {
+      delete db_;
+      db_ = NULL;
+    }
+  }
+  virtual LevelDBCursor* NewCursor() {
+    return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
+  }
+  virtual LevelDBTransaction* NewTransaction() {
+    return new LevelDBTransaction(db_);
+  }
+
+ private:
+  leveldb::DB* db_;
+};
+
+inline void MDB_CHECK(int mdb_status) {
+  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
+}
+
+class LMDBCursor : public Cursor {
+ public:
+  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
+    : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
+    SeekToFirst();
+  }
+  virtual ~LMDBCursor() {
+    mdb_cursor_close(mdb_cursor_);
+    mdb_txn_abort(mdb_txn_);
+  }
+  virtual void SeekToFirst() { Seek(MDB_FIRST); }
+  virtual void Next() { Seek(MDB_NEXT); }
+  virtual string key() {
+    return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
+  }
+  virtual string value() {
+    return string(static_cast<const char*>(mdb_value_.mv_data),
+        mdb_value_.mv_size);
+  }
+  virtual bool valid() { return valid_; }
+
+ private:
+  void Seek(MDB_cursor_op op) {
+    int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
+    if (mdb_status == MDB_NOTFOUND) {
+      valid_ = false;
+    } else {
+      MDB_CHECK(mdb_status);
+      valid_ = true;
+    }
+  }
+
+  MDB_txn* mdb_txn_;
+  MDB_cursor* mdb_cursor_;
+  MDB_val mdb_key_, mdb_value_;
+  bool valid_;
+};
+
+class LMDBTransaction : public Transaction {
+ public:
+  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
+    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+  virtual void Put(const string& key, const string& value);
+  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+
+ private:
+  MDB_dbi* mdb_dbi_;
+  MDB_txn* mdb_txn_;
+
+  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
+};
+
+class LMDB : public DB {
+ public:
+  LMDB() : mdb_env_(NULL) { }
+  virtual ~LMDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (mdb_env_ != NULL) {
+      mdb_dbi_close(mdb_env_, mdb_dbi_);
+      mdb_env_close(mdb_env_);
+      mdb_env_ = NULL;
+    }
+  }
+  virtual LMDBCursor* NewCursor();
+  virtual LMDBTransaction* NewTransaction();
+
+ private:
+  MDB_env* mdb_env_;
+  MDB_dbi mdb_dbi_;
+};
+
+DB* GetDB(DataParameter::DB backend);
+DB* GetDB(const string& backend);
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_HPP
diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
new file mode 100644
index 00000000000..7f7018107ec
--- /dev/null
+++ b/src/caffe/util/db.cpp
@@ -0,0 +1,84 @@
+#include "caffe/util/db.hpp"
+
+#include <sys/stat.h>
+#include <string>
+
+namespace caffe { namespace db {
+
+const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
+
+void LevelDB::Open(const string& source, Mode mode) {
+  leveldb::Options options;
+  options.block_size = 65536;
+  options.write_buffer_size = 268435456;
+  options.max_open_files = 100;
+  options.error_if_exists = mode == NEW;
+  options.create_if_missing = mode != READ;
+  leveldb::Status status = leveldb::DB::Open(options, source, &db_);
+  CHECK(status.ok()) << "Failed to open leveldb " << source
+                     << std::endl << status.ToString();
+  LOG(INFO) << "Opened leveldb " << source;
+}
+
+void LMDB::Open(const string& source, Mode mode) {
+  MDB_CHECK(mdb_env_create(&mdb_env_));
+  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
+  if (mode == NEW) {
+    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+  }
+  int flags = 0;
+  if (mode == READ) {
+    flags = MDB_RDONLY | MDB_NOTLS;
+  }
+  MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+  LOG(INFO) << "Opened lmdb " << source;
+}
+
+LMDBCursor* LMDB::NewCursor() {
+  MDB_txn* mdb_txn;
+  MDB_cursor* mdb_cursor;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
+  return new LMDBCursor(mdb_txn, mdb_cursor);
+}
+
+LMDBTransaction* LMDB::NewTransaction() {
+  MDB_txn* mdb_txn;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+}
+
+void LMDBTransaction::Put(const string& key, const string& value) {
+  MDB_val mdb_key, mdb_value;
+  mdb_key.mv_data = const_cast<char*>(key.data());
+  mdb_key.mv_size = key.size();
+  mdb_value.mv_data = const_cast<char*>(value.data());
+  mdb_value.mv_size = value.size();
+  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+}
+
+DB* GetDB(DataParameter::DB backend) {
+  switch (backend) {
+  case DataParameter_DB_LEVELDB:
+    return new LevelDB();
+  case DataParameter_DB_LMDB:
+    return new LMDB();
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
+}
+
+DB* GetDB(const string& backend) {
+  if (backend == "leveldb") {
+    return new LevelDB();
+  } else if (backend == "lmdb") {
+    return new LMDB();
+  } else {
+    LOG(FATAL) << "Unknown database backend";
+  }
+}
+
+}  // namespace db
+}  // namespace caffe

From 7dfe23963c69759c3bdca1f0ee8ff9866c0fb5c2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 16 Jan 2015 13:20:15 -0800
Subject: [PATCH 1056/2053] use db wrappers

---
 examples/cifar10/convert_cifar_data.cpp | 41 ++++++++++++-----------
 include/caffe/data_layers.hpp           |  6 ++--
 src/caffe/layers/data_layer.cpp         | 34 ++++++++-----------
 src/caffe/test/test_data_layer.cpp      | 27 ++++++++-------
 tools/compute_image_mean.cpp            | 44 +++++++++++--------------
 tools/convert_imageset.cpp              | 38 ++++++++++-----------
 tools/extract_features.cpp              | 31 +++++++++--------
 7 files changed, 111 insertions(+), 110 deletions(-)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 9eecc74c989..f4c42e4d2e7 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -9,19 +9,18 @@
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "boost/scoped_ptr.hpp"
 #include "glog/logging.h"
 #include "google/protobuf/text_format.h"
 #include "stdint.h"
 
-#include "caffe/dataset_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 
-using std::string;
-
-using caffe::Dataset;
-using caffe::DatasetFactory;
 using caffe::Datum;
-using caffe::shared_ptr;
+using boost::scoped_ptr;
+using std::string;
+namespace db = caffe::db;
 
 const int kCIFARSize = 32;
 const int kCIFARImageNBytes = 3072;
@@ -38,10 +37,9 @@ void read_image(std::ifstream* file, int* label, char* buffer) {
 
 void convert_dataset(const string& input_folder, const string& output_folder,
     const string& db_type) {
-  shared_ptr<Dataset<string, Datum> > train_dataset =
-      DatasetFactory<string, Datum>(db_type);
-  CHECK(train_dataset->open(output_folder + "/cifar10_train_" + db_type,
-      Dataset<string, Datum>::New));
+  scoped_ptr<db::DB> train_db(db::GetDB(db_type));
+  train_db->Open(output_folder + "/cifar10_train_" + db_type, db::NEW);
+  scoped_ptr<db::Transaction> txn(train_db->NewTransaction());
   // Data buffer
   int label;
   char str_buffer[kCIFARImageNBytes];
@@ -64,17 +62,18 @@ void convert_dataset(const string& input_folder, const string& output_folder,
       datum.set_data(str_buffer, kCIFARImageNBytes);
       int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
           fileid * kCIFARBatchSize + itemid);
-      CHECK(train_dataset->put(string(str_buffer, length), datum));
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(string(str_buffer, length), out);
     }
   }
-  CHECK(train_dataset->commit());
-  train_dataset->close();
+  txn->Commit();
+  train_db->Close();
 
   LOG(INFO) << "Writing Testing data";
-  shared_ptr<Dataset<string, Datum> > test_dataset =
-      DatasetFactory<string, Datum>(db_type);
-  CHECK(test_dataset->open(output_folder + "/cifar10_test_" + db_type,
-      Dataset<string, Datum>::New));
+  scoped_ptr<db::DB> test_db(db::GetDB(db_type));
+  test_db->Open(output_folder + "/cifar10_test_" + db_type, db::NEW);
+  txn.reset(test_db->NewTransaction());
   // Open files
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
@@ -84,10 +83,12 @@ void convert_dataset(const string& input_folder, const string& output_folder,
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
     int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
-    CHECK(test_dataset->put(string(str_buffer, length), datum));
+    string out;
+    CHECK(datum.SerializeToString(&out));
+    txn->Put(string(str_buffer, length), out);
   }
-  CHECK(test_dataset->commit());
-  test_dataset->close();
+  txn->Commit();
+  test_db->Close();
 }
 
 int main(int argc, char** argv) {
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 34b9b30aa3e..e9c83856a99 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -11,11 +11,11 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/data_transformer.hpp"
-#include "caffe/dataset.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 
 namespace caffe {
 
@@ -100,8 +100,8 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  protected:
   virtual void InternalThreadEntry();
 
-  shared_ptr<Dataset<string, Datum> > dataset_;
-  Dataset<string, Datum>::const_iterator iter_;
+  shared_ptr<db::DB> db_;
+  shared_ptr<db::Cursor> cursor_;
 };
 
 /**
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index a5030899cee..96964566630 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -7,7 +7,6 @@
 
 #include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
-#include "caffe/dataset_factory.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/benchmark.hpp"
@@ -20,35 +19,28 @@ namespace caffe {
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   this->JoinPrefetchThread();
-  // clean up the dataset resources
-  dataset_->close();
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Initialize DB
-  dataset_ = DatasetFactory<string, Datum>(
-      this->layer_param_.data_param().backend());
-  const string& source = this->layer_param_.data_param().source();
-  LOG(INFO) << "Opening dataset " << source;
-  CHECK(dataset_->open(source, Dataset<string, Datum>::ReadOnly));
-  iter_ = dataset_->begin();
+  db_.reset(db::GetDB(this->layer_param_.data_param().backend()));
+  db_->Open(this->layer_param_.data_param().source(), db::READ);
+  cursor_.reset(db_->NewCursor());
 
-  // Check if we would need to randomly skip a few data points
+  // Check if we should randomly skip a few data points
   if (this->layer_param_.data_param().rand_skip()) {
     unsigned int skip = caffe_rng_rand() %
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      if (++iter_ == dataset_->end()) {
-        iter_ = dataset_->begin();
-      }
+      cursor_->Next();
     }
   }
   // Read a data point, and use it to initialize the top blob.
-  CHECK(iter_ != dataset_->end());
-  Datum datum = iter_->value;
+  Datum datum;
+  datum.ParseFromString(cursor_->value());
 
   if (DecodeDatum(&datum)) {
     LOG(INFO) << "Decoding Datum";
@@ -101,8 +93,8 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
     // get a blob
-    CHECK(iter_ != dataset_->end());
-    const Datum& datum = iter_->value;
+    Datum datum;
+    datum.ParseFromString(cursor_->value());
 
     cv::Mat cv_img;
     if (datum.encoded()) {
@@ -124,9 +116,10 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     }
     trans_time += timer.MicroSeconds();
     // go to the next iter
-    ++iter_;
-    if (iter_ == dataset_->end()) {
-      iter_ = dataset_->begin();
+    cursor_->Next();
+    if (!cursor_->valid()) {
+      DLOG(INFO) << "Restarting data prefetching from start.";
+      cursor_->SeekToFirst();
     }
   }
   batch_timer.Stop();
@@ -137,4 +130,5 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
 INSTANTIATE_CLASS(DataLayer);
 REGISTER_LAYER_CLASS(DATA, DataLayer);
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 32f5d41e2f2..8cc31b24167 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,20 +1,23 @@
 #include <string>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/dataset_factory.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
+using boost::scoped_ptr;
+
 template <typename TypeParam>
 class DataLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -33,15 +36,15 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     blob_top_vec_.push_back(blob_top_label_);
   }
 
-  // Fill the LevelDB with data: if unique_pixels, each pixel is unique but
+  // Fill the DB with data: if unique_pixels, each pixel is unique but
   // all images are the same; else each image is unique but all pixels within
   // an image are the same.
   void Fill(const bool unique_pixels, DataParameter_DB backend) {
     backend_ = backend;
     LOG(INFO) << "Using temporary dataset " << *filename_;
-    shared_ptr<Dataset<string, Datum> > dataset =
-        DatasetFactory<string, Datum>(backend_);
-    CHECK(dataset->open(*filename_, Dataset<string, Datum>::New));
+    scoped_ptr<db::DB> db(db::GetDB(backend));
+    db->Open(*filename_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
     for (int i = 0; i < 5; ++i) {
       Datum datum;
       datum.set_label(i);
@@ -55,10 +58,12 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       }
       stringstream ss;
       ss << i;
-      CHECK(dataset->put(ss.str(), datum));
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(ss.str(), out);
     }
-    CHECK(dataset->commit());
-    dataset->close();
+    txn->Commit();
+    db->Close();
   }
 
   void TestRead() {
@@ -183,7 +188,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the dataset
+    }  // destroy 1st data layer and unlock the db
 
     // Get crop sequence after reseeding Caffe with 1701.
     // Check that the sequence is the same as the original.
@@ -238,7 +243,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
         }
         crop_sequence.push_back(iter_crop_sequence);
       }
-    }  // destroy 1st data layer and unlock the dataset
+    }  // destroy 1st data layer and unlock the db
 
     // Get crop sequence continuing from previous Caffe RNG state; reseed
     // srand with 1701. Check that the sequence differs from the original.
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 358f57e38d6..dff63a09dca 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -1,24 +1,25 @@
-#include <gflags/gflags.h>
-#include <glog/logging.h>
 #include <stdint.h>
-
 #include <algorithm>
 #include <string>
 #include <utility>
 #include <vector>
 
-#include "caffe/dataset_factory.hpp"
+#include "boost/scoped_ptr.hpp"
+#include "gflags/gflags.h"
+#include "glog/logging.h"
+
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
 
-using caffe::Dataset;
-using caffe::Datum;
-using caffe::BlobProto;
+using namespace caffe;  // NOLINT(build/namespaces)
+
 using std::max;
 using std::pair;
+using boost::scoped_ptr;
 
-
-DEFINE_string(backend, "lmdb", "The backend for containing the images");
+DEFINE_string(backend, "lmdb",
+        "The backend {leveldb, lmdb} containing the images");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -28,7 +29,7 @@ int main(int argc, char** argv) {
 #endif
 
   gflags::SetUsageMessage("Compute the mean_image of a set of images given by"
-        " a leveldb/lmdb or a list of images\n"
+        " a leveldb/lmdb\n"
         "Usage:\n"
         "    compute_image_mean [FLAGS] INPUT_DB [OUTPUT_FILE]\n");
 
@@ -39,19 +40,15 @@ int main(int argc, char** argv) {
     return 1;
   }
 
-  std::string db_backend = FLAGS_backend;
-
-  caffe::shared_ptr<Dataset<std::string, Datum> > dataset =
-      caffe::DatasetFactory<std::string, Datum>(db_backend);
-
-  // Open db
-  CHECK(dataset->open(argv[1], Dataset<std::string, Datum>::ReadOnly));
+  scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
+  db->Open(argv[1], db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
 
   BlobProto sum_blob;
   int count = 0;
   // load first datum
-  Dataset<std::string, Datum>::const_iterator iter = dataset->begin();
-  Datum datum = iter->value;
+  Datum datum;
+  datum.ParseFromString(cursor->value());
 
   if (DecodeDatum(&datum)) {
     LOG(INFO) << "Decoding Datum";
@@ -68,9 +65,9 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  for (Dataset<std::string, Datum>::const_iterator iter = dataset->begin();
-      iter != dataset->end(); ++iter) {
-    Datum datum = iter->value;
+  while (cursor->valid()) {
+    Datum datum;
+    datum.ParseFromString(cursor->value());
     DecodeDatum(&datum);
 
     const std::string& data = datum.data();
@@ -94,6 +91,7 @@ int main(int argc, char** argv) {
     if (count % 10000 == 0) {
       LOG(INFO) << "Processed " << count << " files.";
     }
+    cursor->Next();
   }
 
   if (count % 10000 != 0) {
@@ -117,7 +115,5 @@ int main(int argc, char** argv) {
     }
     LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
   }
-  // Clean up
-  dataset->close();
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 5d42e69c1ed..7fbf5b0514c 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -8,28 +8,31 @@
 //   subfolder1/file1.JPEG 7
 //   ....
 
-#include <gflags/gflags.h>
-#include <glog/logging.h>
-
 #include <algorithm>
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 #include <utility>
 #include <vector>
 
-#include "caffe/dataset_factory.hpp"
+#include "boost/scoped_ptr.hpp"
+#include "gflags/gflags.h"
+#include "glog/logging.h"
+
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/rng.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
+using boost::scoped_ptr;
 
 DEFINE_bool(gray, false,
     "When this option is on, treat images as grayscale ones");
 DEFINE_bool(shuffle, false,
     "Randomly shuffle the order of images and their labels");
-DEFINE_string(backend, "lmdb", "The backend for storing the result");
+DEFINE_string(backend, "lmdb",
+        "The backend {lmdb, leveldb} for storing the result");
 DEFINE_int32(resize_width, 0, "Width images are resized to");
 DEFINE_int32(resize_height, 0, "Height images are resized to");
 DEFINE_bool(check_size, false,
@@ -75,9 +78,6 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  const std::string& db_backend = FLAGS_backend;
-  const char* db_path = argv[3];
-
   if (encoded) {
     CHECK_EQ(FLAGS_resize_height, 0) << "With encoded don't resize images";
     CHECK_EQ(FLAGS_resize_width, 0) << "With encoded don't resize images";
@@ -87,12 +87,10 @@ int main(int argc, char** argv) {
   int resize_height = std::max<int>(0, FLAGS_resize_height);
   int resize_width = std::max<int>(0, FLAGS_resize_width);
 
-  // Open new db
-  shared_ptr<Dataset<string, Datum> > dataset =
-      DatasetFactory<string, Datum>(db_backend);
-
-  // Open db
-  CHECK(dataset->open(db_path, Dataset<string, Datum>::New));
+  // Create new DB
+  scoped_ptr<db::DB> db(db::GetDB(FLAGS_backend));
+  db->Open(argv[3], db::NEW);
+  scoped_ptr<db::Transaction> txn(db->NewTransaction());
 
   // Storing to db
   std::string root_folder(argv[1]);
@@ -128,19 +126,21 @@ int main(int argc, char** argv) {
         lines[line_id].first.c_str());
 
     // Put in db
-    CHECK(dataset->put(string(key_cstr, length), datum));
+    string out;
+    CHECK(datum.SerializeToString(&out));
+    txn->Put(string(key_cstr, length), out);
 
     if (++count % 1000 == 0) {
-      // Commit txn
-      CHECK(dataset->commit());
+      // Commit db
+      txn->Commit();
+      txn.reset(db->NewTransaction());
       LOG(ERROR) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    CHECK(dataset->commit());
+    txn->Commit();
     LOG(ERROR) << "Processed " << count << " files.";
   }
-  dataset->close();
   return 0;
 }
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index ddbce1075ed..c17b88dd048 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -7,19 +7,19 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/dataset_factory.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using boost::shared_ptr;
 using caffe::Blob;
 using caffe::Caffe;
-using caffe::Dataset;
-using caffe::DatasetFactory;
 using caffe::Datum;
 using caffe::Net;
+using boost::shared_ptr;
+using std::string;
+namespace db = caffe::db;
 
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv);
@@ -121,13 +121,15 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
-  std::vector<shared_ptr<Dataset<std::string, Datum> > > feature_dbs;
+  std::vector<shared_ptr<db::DB> > feature_dbs;
+  std::vector<shared_ptr<db::Transaction> > txns;
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening dataset " << dataset_names[i];
-    shared_ptr<Dataset<std::string, Datum> > dataset =
-        DatasetFactory<std::string, Datum>(argv[++arg_pos]);
-    CHECK(dataset->open(dataset_names.at(i), Dataset<std::string, Datum>::New));
-    feature_dbs.push_back(dataset);
+    shared_ptr<db::DB> db(db::GetDB(argv[++arg_pos]));
+    db->Open(dataset_names.at(i), db::NEW);
+    feature_dbs.push_back(db);
+    shared_ptr<db::Transaction> txn(db->NewTransaction());
+    txns.push_back(txn);
   }
 
   LOG(ERROR)<< "Extacting Features";
@@ -158,10 +160,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
         }
         int length = snprintf(key_str, kMaxKeyStrLength, "%d",
             image_indices[i]);
-        CHECK(feature_dbs.at(i)->put(std::string(key_str, length), datum));
+        string out;
+        CHECK(datum.SerializeToString(&out));
+        txns.at(i)->Put(std::string(key_str, length), out);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
-          CHECK(feature_dbs.at(i)->commit());
+          txns.at(i)->Commit();
+          txns.at(i).reset(feature_dbs.at(i)->NewTransaction());
           LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
               " query images for feature blob " << blob_names[i];
         }
@@ -171,11 +176,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
   // write the last batch
   for (int i = 0; i < num_features; ++i) {
     if (image_indices[i] % 1000 != 0) {
-      CHECK(feature_dbs.at(i)->commit());
+      txns.at(i)->Commit();
     }
     LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
         " query images for feature blob " << blob_names[i];
-    feature_dbs.at(i)->close();
+    feature_dbs.at(i)->Close();
   }
 
   LOG(ERROR)<< "Successfully extracted the features!";

From 88e3bc88b4ca09d51dd551b62e64e3c149cd4e0c Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Fri, 16 Jan 2015 13:20:26 -0800
Subject: [PATCH 1057/2053] test db wrappers

---
 src/caffe/test/test_db.cpp | 134 +++++++++++++++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 src/caffe/test/test_db.cpp

diff --git a/src/caffe/test/test_db.cpp b/src/caffe/test/test_db.cpp
new file mode 100644
index 00000000000..5b2ac230a0b
--- /dev/null
+++ b/src/caffe/test/test_db.cpp
@@ -0,0 +1,134 @@
+#include <string>
+
+#include "boost/scoped_ptr.hpp"
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+using boost::scoped_ptr;
+
+template <typename TypeParam>
+class DBTest : public ::testing::Test {
+ protected:
+  DBTest()
+      : backend_(TypeParam::backend),
+      root_images_(string(EXAMPLES_SOURCE_DIR) + string("images/")) {}
+
+  virtual void SetUp() {
+    MakeTempDir(&source_);
+    source_ += "/db";
+    string keys[] = {"cat.jpg", "fish-bike.jpg"};
+    LOG(INFO) << "Using temporary db " << source_;
+    scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+    db->Open(this->source_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
+    for (int i = 0; i < 2; ++i) {
+      Datum datum;
+      ReadImageToDatum(root_images_ + keys[i], i, &datum);
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(keys[i], out);
+    }
+    txn->Commit();
+  }
+
+  virtual ~DBTest() { }
+
+  DataParameter_DB backend_;
+  string source_;
+  string root_images_;
+};
+
+struct TypeLevelDB {
+  static DataParameter_DB backend;
+};
+DataParameter_DB TypeLevelDB::backend = DataParameter_DB_LEVELDB;
+
+struct TypeLMDB {
+  static DataParameter_DB backend;
+};
+DataParameter_DB TypeLMDB::backend = DataParameter_DB_LMDB;
+
+// typedef ::testing::Types<TypeLmdb> TestTypes;
+typedef ::testing::Types<TypeLevelDB, TypeLMDB> TestTypes;
+
+TYPED_TEST_CASE(DBTest, TestTypes);
+
+TYPED_TEST(DBTest, TestGetDB) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+}
+
+TYPED_TEST(DBTest, TestNext) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+  EXPECT_TRUE(cursor->valid());
+  cursor->Next();
+  EXPECT_TRUE(cursor->valid());
+  cursor->Next();
+  EXPECT_FALSE(cursor->valid());
+}
+
+TYPED_TEST(DBTest, TestSeekToFirst) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+  cursor->Next();
+  cursor->SeekToFirst();
+  EXPECT_TRUE(cursor->valid());
+  string key = cursor->key();
+  Datum datum;
+  datum.ParseFromString(cursor->value());
+  EXPECT_EQ(key, "cat.jpg");
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+}
+
+TYPED_TEST(DBTest, TestKeyValue) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::READ);
+  scoped_ptr<db::Cursor> cursor(db->NewCursor());
+  EXPECT_TRUE(cursor->valid());
+  string key = cursor->key();
+  Datum datum;
+  datum.ParseFromString(cursor->value());
+  EXPECT_EQ(key, "cat.jpg");
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 360);
+  EXPECT_EQ(datum.width(), 480);
+  cursor->Next();
+  EXPECT_TRUE(cursor->valid());
+  key = cursor->key();
+  datum.ParseFromString(cursor->value());
+  EXPECT_EQ(key, "fish-bike.jpg");
+  EXPECT_EQ(datum.channels(), 3);
+  EXPECT_EQ(datum.height(), 323);
+  EXPECT_EQ(datum.width(), 481);
+  cursor->Next();
+  EXPECT_FALSE(cursor->valid());
+}
+
+TYPED_TEST(DBTest, TestWrite) {
+  scoped_ptr<db::DB> db(db::GetDB(TypeParam::backend));
+  db->Open(this->source_, db::WRITE);
+  scoped_ptr<db::Transaction> txn(db->NewTransaction());
+  Datum datum;
+  ReadFileToDatum(this->root_images_ + "cat.jpg", 0, &datum);
+  string out;
+  CHECK(datum.SerializeToString(&out));
+  txn->Put("cat.jpg", out);
+  ReadFileToDatum(this->root_images_ + "fish-bike.jpg", 1, &datum);
+  CHECK(datum.SerializeToString(&out));
+  txn->Put("fish-bike.jpg", out);
+  txn->Commit();
+}
+
+}  // namespace caffe

From 3b88e359f7f57a62608d42e2e6786a25eab4ecbb Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 16 Jan 2015 13:25:00 -0800
Subject: [PATCH 1058/2053] gut dataset wrappers

---
 include/caffe/dataset.hpp         | 241 ---------
 include/caffe/dataset_factory.hpp |  20 -
 include/caffe/leveldb_dataset.hpp |  90 ----
 include/caffe/lmdb_dataset.hpp    |  95 ----
 src/caffe/dataset_factory.cpp     |  50 --
 src/caffe/leveldb_dataset.cpp     | 265 ----------
 src/caffe/lmdb_dataset.cpp        | 366 --------------
 src/caffe/test/test_dataset.cpp   | 794 ------------------------------
 8 files changed, 1921 deletions(-)
 delete mode 100644 include/caffe/dataset.hpp
 delete mode 100644 include/caffe/dataset_factory.hpp
 delete mode 100644 include/caffe/leveldb_dataset.hpp
 delete mode 100644 include/caffe/lmdb_dataset.hpp
 delete mode 100644 src/caffe/dataset_factory.cpp
 delete mode 100644 src/caffe/leveldb_dataset.cpp
 delete mode 100644 src/caffe/lmdb_dataset.cpp
 delete mode 100644 src/caffe/test/test_dataset.cpp

diff --git a/include/caffe/dataset.hpp b/include/caffe/dataset.hpp
deleted file mode 100644
index 1dd8458cd74..00000000000
--- a/include/caffe/dataset.hpp
+++ /dev/null
@@ -1,241 +0,0 @@
-#ifndef CAFFE_DATASET_H_
-#define CAFFE_DATASET_H_
-
-#include <google/protobuf/message.h>
-
-#include <algorithm>
-#include <iterator>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-namespace dataset_internal {
-
-using google::protobuf::Message;
-
-template<bool condition>
-struct static_assertion {};
-template<>
-struct static_assertion<true> {
-  enum {
-    DEFAULT_CODER_NOT_AVAILABLE
-  };
-};
-
-template <typename T>
-struct DefaultCoder {
-  using static_assertion<sizeof(T) == 0>::DEFAULT_CODER_NOT_AVAILABLE;
-  static bool serialize(const T& obj, string* serialized);
-  static bool serialize(const T& obj, vector<char>* serialized);
-  static bool deserialize(const string& serialized, T* obj);
-  static bool deserialize(const char* data, size_t size, T* obj);
-};
-
-template <>
-struct DefaultCoder<Message> {
-  static bool serialize(const Message& obj, string* serialized) {
-    return obj.SerializeToString(serialized);
-  }
-
-  static bool serialize(const Message& obj, vector<char>* serialized) {
-    serialized->resize(obj.ByteSize());
-    return obj.SerializeWithCachedSizesToArray(
-        reinterpret_cast<unsigned char*>(serialized->data()));
-  }
-
-  static bool deserialize(const string& serialized, Message* obj) {
-    return obj->ParseFromString(serialized);
-  }
-
-  static bool deserialize(const char* data, size_t size, Message* obj) {
-    return obj->ParseFromArray(data, size);
-  }
-};
-
-template <>
-struct DefaultCoder<caffe::Datum> : public DefaultCoder<Message> { };
-
-template <>
-struct DefaultCoder<string> {
-  static bool serialize(string obj, string* serialized) {
-    *serialized = obj;
-    return true;
-  }
-
-  static bool serialize(const string& obj, vector<char>* serialized) {
-    vector<char> temp(obj.data(), obj.data() + obj.size());
-    serialized->swap(temp);
-    return true;
-  }
-
-  static bool deserialize(const string& serialized, string* obj) {
-    *obj = serialized;
-    return true;
-  }
-
-  static bool deserialize(const char* data, size_t size, string* obj) {
-    string temp_string(data, size);
-    obj->swap(temp_string);
-    return true;
-  }
-};
-
-template <>
-struct DefaultCoder<vector<char> > {
-  static bool serialize(vector<char> obj, string* serialized) {
-    string tmp(obj.data(), obj.size());
-    serialized->swap(tmp);
-    return true;
-  }
-
-  static bool serialize(const vector<char>& obj, vector<char>* serialized) {
-    *serialized = obj;
-    return true;
-  }
-
-  static bool deserialize(const string& serialized, vector<char>* obj) {
-    vector<char> tmp(serialized.data(), serialized.data() + serialized.size());
-    obj->swap(tmp);
-    return true;
-  }
-
-  static bool deserialize(const char* data, size_t size, vector<char>* obj) {
-    vector<char> tmp(data, data + size);
-    obj->swap(tmp);
-    return true;
-  }
-};
-
-}  // namespace dataset_internal
-
-template <typename K, typename V,
-          typename KCoder = dataset_internal::DefaultCoder<K>,
-          typename VCoder = dataset_internal::DefaultCoder<V> >
-class Dataset {
- public:
-  enum Mode {
-    New,
-    ReadWrite,
-    ReadOnly
-  };
-
-  typedef K key_type;
-  typedef V value_type;
-
-  struct KV {
-    K key;
-    V value;
-  };
-
-  virtual bool open(const string& filename, Mode mode) = 0;
-  virtual bool put(const K& key, const V& value) = 0;
-  virtual bool get(const K& key, V* value) = 0;
-  virtual bool first_key(K* key) = 0;
-  virtual bool last_key(K* key) = 0;
-  virtual bool commit() = 0;
-  virtual void close() = 0;
-
-  virtual void keys(vector<K>* keys) = 0;
-
-  Dataset() { }
-  virtual ~Dataset() { }
-
-  class iterator;
-  typedef iterator const_iterator;
-
-  virtual const_iterator begin() const = 0;
-  virtual const_iterator cbegin() const = 0;
-  virtual const_iterator end() const = 0;
-  virtual const_iterator cend() const = 0;
-
- protected:
-  class DatasetState;
-
- public:
-  class iterator : public std::iterator<std::forward_iterator_tag, KV> {
-   public:
-    typedef KV T;
-    typedef T value_type;
-    typedef T& reference_type;
-    typedef T* pointer_type;
-
-    iterator()
-        : parent_(NULL) { }
-
-    iterator(const Dataset* parent, shared_ptr<DatasetState> state)
-        : parent_(parent),
-          state_(state) { }
-
-    iterator(const iterator& other)
-        : parent_(other.parent_),
-          state_(other.state_ ? other.state_->clone()
-              : shared_ptr<DatasetState>()) { }
-
-    iterator& operator=(iterator copy) {
-      copy.swap(*this);
-      return *this;
-    }
-
-    void swap(iterator& other) throw() {
-      std::swap(this->parent_, other.parent_);
-      std::swap(this->state_, other.state_);
-    }
-
-    bool operator==(const iterator& other) const {
-      return parent_->equal(state_, other.state_);
-    }
-
-    bool operator!=(const iterator& other) const {
-      return !(*this == other);
-    }
-
-    iterator& operator++() {
-      parent_->increment(&state_);
-      return *this;
-    }
-    iterator operator++(int) {
-      iterator copy(*this);
-      parent_->increment(&state_);
-      return copy;
-    }
-
-    reference_type operator*() const {
-      return parent_->dereference(state_);
-    }
-
-    pointer_type operator->() const {
-      return &parent_->dereference(state_);
-    }
-
-   protected:
-    const Dataset* parent_;
-    shared_ptr<DatasetState> state_;
-  };
-
- protected:
-  class DatasetState {
-   public:
-    virtual ~DatasetState() { }
-    virtual shared_ptr<DatasetState> clone() = 0;
-  };
-
-  virtual bool equal(shared_ptr<DatasetState> state1,
-      shared_ptr<DatasetState> state2) const = 0;
-  virtual void increment(shared_ptr<DatasetState>* state) const = 0;
-  virtual KV& dereference(
-      shared_ptr<DatasetState> state) const = 0;
-};
-
-}  // namespace caffe
-
-#define INSTANTIATE_DATASET(type) \
-  template class type<string, string>; \
-  template class type<string, vector<char> >; \
-  template class type<string, caffe::Datum>;
-
-#endif  // CAFFE_DATASET_H_
diff --git a/include/caffe/dataset_factory.hpp b/include/caffe/dataset_factory.hpp
deleted file mode 100644
index 57db49bf524..00000000000
--- a/include/caffe/dataset_factory.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-#ifndef CAFFE_DATASET_FACTORY_H_
-#define CAFFE_DATASET_FACTORY_H_
-
-#include <string>
-
-#include "caffe/common.hpp"
-#include "caffe/dataset.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-template <typename K, typename V>
-shared_ptr<Dataset<K, V> > DatasetFactory(const DataParameter_DB& type);
-
-template <typename K, typename V>
-shared_ptr<Dataset<K, V> > DatasetFactory(const string& type);
-
-}  // namespace caffe
-
-#endif  // CAFFE_DATASET_FACTORY_H_
diff --git a/include/caffe/leveldb_dataset.hpp b/include/caffe/leveldb_dataset.hpp
deleted file mode 100644
index d58c181bb2b..00000000000
--- a/include/caffe/leveldb_dataset.hpp
+++ /dev/null
@@ -1,90 +0,0 @@
-#ifndef CAFFE_LEVELDB_DATASET_H_
-#define CAFFE_LEVELDB_DATASET_H_
-
-#include <leveldb/db.h>
-#include <leveldb/write_batch.h>
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/dataset.hpp"
-
-namespace caffe {
-
-template <typename K, typename V,
-          typename KCoder = dataset_internal::DefaultCoder<K>,
-          typename VCoder = dataset_internal::DefaultCoder<V> >
-class LeveldbDataset : public Dataset<K, V, KCoder, VCoder> {
- public:
-  typedef Dataset<K, V, KCoder, VCoder> Base;
-  typedef typename Base::key_type key_type;
-  typedef typename Base::value_type value_type;
-  typedef typename Base::DatasetState DatasetState;
-  typedef typename Base::Mode Mode;
-  typedef typename Base::const_iterator const_iterator;
-  typedef typename Base::KV KV;
-
-  bool open(const string& filename, Mode mode);
-  bool put(const K& key, const V& value);
-  bool get(const K& key, V* value);
-  bool first_key(K* key);
-  bool last_key(K* key);
-  bool commit();
-  void close();
-
-  void keys(vector<K>* keys);
-
-  const_iterator begin() const;
-  const_iterator cbegin() const;
-  const_iterator end() const;
-  const_iterator cend() const;
-
- protected:
-  class LeveldbState : public DatasetState {
-   public:
-    explicit LeveldbState(shared_ptr<leveldb::DB> db,
-        shared_ptr<leveldb::Iterator> iter)
-        : DatasetState(),
-          db_(db),
-          iter_(iter) { }
-
-    ~LeveldbState() {
-      // This order is very important.
-      // Iterators must be destroyed before their associated DB
-      // is destroyed.
-      iter_.reset();
-      db_.reset();
-    }
-
-    shared_ptr<DatasetState> clone() {
-      shared_ptr<leveldb::Iterator> new_iter;
-
-      CHECK(iter_.get());
-      new_iter.reset(db_->NewIterator(leveldb::ReadOptions()));
-      CHECK(iter_->Valid());
-      new_iter->Seek(iter_->key());
-      CHECK(new_iter->Valid());
-
-      return shared_ptr<DatasetState>(new LeveldbState(db_, new_iter));
-    }
-
-    shared_ptr<leveldb::DB> db_;
-    shared_ptr<leveldb::Iterator> iter_;
-    KV kv_pair_;
-  };
-
-  bool equal(shared_ptr<DatasetState> state1,
-      shared_ptr<DatasetState> state2) const;
-  void increment(shared_ptr<DatasetState>* state) const;
-  KV& dereference(shared_ptr<DatasetState> state) const;
-
-  shared_ptr<leveldb::DB> db_;
-  shared_ptr<leveldb::WriteBatch> batch_;
-  bool read_only_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_LEVELDB_DATASET_H_
diff --git a/include/caffe/lmdb_dataset.hpp b/include/caffe/lmdb_dataset.hpp
deleted file mode 100644
index ac1e5ee25dd..00000000000
--- a/include/caffe/lmdb_dataset.hpp
+++ /dev/null
@@ -1,95 +0,0 @@
-#ifndef CAFFE_LMDB_DATASET_H_
-#define CAFFE_LMDB_DATASET_H_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "lmdb.h"
-
-#include "caffe/common.hpp"
-#include "caffe/dataset.hpp"
-
-namespace caffe {
-
-template <typename K, typename V,
-          typename KCoder = dataset_internal::DefaultCoder<K>,
-          typename VCoder = dataset_internal::DefaultCoder<V> >
-class LmdbDataset : public Dataset<K, V, KCoder, VCoder> {
- public:
-  typedef Dataset<K, V, KCoder, VCoder> Base;
-  typedef typename Base::key_type key_type;
-  typedef typename Base::value_type value_type;
-  typedef typename Base::DatasetState DatasetState;
-  typedef typename Base::Mode Mode;
-  typedef typename Base::const_iterator const_iterator;
-  typedef typename Base::KV KV;
-
-  LmdbDataset()
-      : env_(NULL),
-        dbi_(0),
-        write_txn_(NULL),
-        read_txn_(NULL) { }
-
-  bool open(const string& filename, Mode mode);
-  bool put(const K& key, const V& value);
-  bool get(const K& key, V* value);
-  bool first_key(K* key);
-  bool last_key(K* key);
-  bool commit();
-  void close();
-
-  void keys(vector<K>* keys);
-
-  const_iterator begin() const;
-  const_iterator cbegin() const;
-  const_iterator end() const;
-  const_iterator cend() const;
-
- protected:
-  class LmdbState : public DatasetState {
-   public:
-    explicit LmdbState(MDB_cursor* cursor, MDB_txn* txn, const MDB_dbi* dbi)
-        : DatasetState(),
-          cursor_(cursor),
-          txn_(txn),
-          dbi_(dbi) { }
-
-    shared_ptr<DatasetState> clone() {
-      CHECK(cursor_);
-
-      MDB_cursor* new_cursor;
-      int retval;
-
-      retval = mdb_cursor_open(txn_, *dbi_, &new_cursor);
-      CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-      MDB_val key;
-      MDB_val val;
-      retval = mdb_cursor_get(cursor_, &key, &val, MDB_GET_CURRENT);
-      CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-      retval = mdb_cursor_get(new_cursor, &key, &val, MDB_SET);
-      CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
-
-      return shared_ptr<DatasetState>(new LmdbState(new_cursor, txn_, dbi_));
-    }
-
-    MDB_cursor* cursor_;
-    MDB_txn* txn_;
-    const MDB_dbi* dbi_;
-    KV kv_pair_;
-  };
-
-  bool equal(shared_ptr<DatasetState> state1,
-      shared_ptr<DatasetState> state2) const;
-  void increment(shared_ptr<DatasetState>* state) const;
-  KV& dereference(shared_ptr<DatasetState> state) const;
-
-  MDB_env* env_;
-  MDB_dbi dbi_;
-  MDB_txn* write_txn_;
-  MDB_txn* read_txn_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_LMDB_DATASET_H_
diff --git a/src/caffe/dataset_factory.cpp b/src/caffe/dataset_factory.cpp
deleted file mode 100644
index 3313de3c408..00000000000
--- a/src/caffe/dataset_factory.cpp
+++ /dev/null
@@ -1,50 +0,0 @@
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/dataset_factory.hpp"
-#include "caffe/leveldb_dataset.hpp"
-#include "caffe/lmdb_dataset.hpp"
-
-namespace caffe {
-
-template <typename K, typename V>
-shared_ptr<Dataset<K, V> > DatasetFactory(const DataParameter_DB& type) {
-  switch (type) {
-  case DataParameter_DB_LEVELDB:
-    return shared_ptr<Dataset<K, V> >(new LeveldbDataset<K, V>());
-  case DataParameter_DB_LMDB:
-    return shared_ptr<Dataset<K, V> >(new LmdbDataset<K, V>());
-  default:
-    LOG(FATAL) << "Unknown dataset type " << type;
-    return shared_ptr<Dataset<K, V> >();
-  }
-}
-
-template <typename K, typename V>
-shared_ptr<Dataset<K, V> > DatasetFactory(const string& type) {
-  if ("leveldb" == type) {
-    return DatasetFactory<K, V>(DataParameter_DB_LEVELDB);
-  } else if ("lmdb" == type) {
-    return DatasetFactory<K, V>(DataParameter_DB_LMDB);
-  } else {
-    LOG(FATAL) << "Unknown dataset type " << type;
-    return shared_ptr<Dataset<K, V> >();
-  }
-}
-
-#define REGISTER_DATASET(key_type, value_type) \
-  template shared_ptr<Dataset<key_type, value_type> > \
-      DatasetFactory(const string& type); \
-  template shared_ptr<Dataset<key_type, value_type> > \
-      DatasetFactory(const DataParameter_DB& type); \
-
-REGISTER_DATASET(string, string);
-REGISTER_DATASET(string, vector<char>);
-REGISTER_DATASET(string, Datum);
-
-#undef REGISTER_DATASET
-
-}  // namespace caffe
-
-
diff --git a/src/caffe/leveldb_dataset.cpp b/src/caffe/leveldb_dataset.cpp
deleted file mode 100644
index 53df985721c..00000000000
--- a/src/caffe/leveldb_dataset.cpp
+++ /dev/null
@@ -1,265 +0,0 @@
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/caffe.hpp"
-#include "caffe/leveldb_dataset.hpp"
-
-namespace caffe {
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::open(const string& filename,
-    Mode mode) {
-  DLOG(INFO) << "LevelDB: Open " << filename;
-
-  leveldb::Options options;
-  switch (mode) {
-  case Base::New:
-    DLOG(INFO) << " mode NEW";
-    options.error_if_exists = true;
-    options.create_if_missing = true;
-    read_only_ = false;
-    break;
-  case Base::ReadWrite:
-    DLOG(INFO) << " mode RW";
-    options.error_if_exists = false;
-    options.create_if_missing = true;
-    read_only_ = false;
-    break;
-  case Base::ReadOnly:
-    DLOG(INFO) << " mode RO";
-    options.error_if_exists = false;
-    options.create_if_missing = false;
-    read_only_ = true;
-    break;
-  default:
-    DLOG(FATAL) << "unknown mode " << mode;
-  }
-  options.write_buffer_size = 268435456;
-  options.max_open_files = 100;
-
-  leveldb::DB* db;
-
-  LOG(INFO) << "Opening leveldb " << filename;
-  leveldb::Status status = leveldb::DB::Open(
-      options, filename, &db);
-  db_.reset(db);
-
-  if (!status.ok()) {
-    LOG(ERROR) << "Failed to open leveldb " << filename
-        << ". Is it already existing?";
-    return false;
-  }
-
-  batch_.reset(new leveldb::WriteBatch());
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::put(const K& key, const V& value) {
-  DLOG(INFO) << "LevelDB: Put";
-
-  if (read_only_) {
-    LOG(ERROR) << "put can not be used on a dataset in ReadOnly mode";
-    return false;
-  }
-
-  CHECK_NOTNULL(batch_.get());
-
-  string serialized_key;
-  if (!KCoder::serialize(key, &serialized_key)) {
-    return false;
-  }
-
-  string serialized_value;
-  if (!VCoder::serialize(value, &serialized_value)) {
-    return false;
-  }
-
-  batch_->Put(serialized_key, serialized_value);
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
-  DLOG(INFO) << "LevelDB: Get";
-
-  string serialized_key;
-  if (!KCoder::serialize(key, &serialized_key)) {
-    return false;
-  }
-
-  string serialized_value;
-  leveldb::Status status =
-      db_->Get(leveldb::ReadOptions(), serialized_key, &serialized_value);
-
-  if (!status.ok()) {
-    LOG(ERROR) << "leveldb get failed";
-    return false;
-  }
-
-  if (!VCoder::deserialize(serialized_value, value)) {
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::first_key(K* key) {
-  DLOG(INFO) << "LevelDB: First key";
-
-  CHECK_NOTNULL(db_.get());
-  shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
-  iter->SeekToFirst();
-  CHECK(iter->Valid());
-  const leveldb::Slice& key_slice = iter->key();
-  return KCoder::deserialize(key_slice.data(), key_slice.size(), key);
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::last_key(K* key) {
-  DLOG(INFO) << "LevelDB: Last key";
-
-  CHECK_NOTNULL(db_.get());
-  shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
-  iter->SeekToLast();
-  CHECK(iter->Valid());
-  const leveldb::Slice& key_slice = iter->key();
-  return KCoder::deserialize(key_slice.data(), key_slice.size(), key);
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::commit() {
-  DLOG(INFO) << "LevelDB: Commit";
-
-  if (read_only_) {
-    LOG(ERROR) << "commit can not be used on a dataset in ReadOnly mode";
-    return false;
-  }
-
-  CHECK_NOTNULL(db_.get());
-  CHECK_NOTNULL(batch_.get());
-
-  leveldb::Status status = db_->Write(leveldb::WriteOptions(), batch_.get());
-
-  batch_.reset(new leveldb::WriteBatch());
-
-  return status.ok();
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-void LeveldbDataset<K, V, KCoder, VCoder>::close() {
-  DLOG(INFO) << "LevelDB: Close";
-
-  batch_.reset();
-  db_.reset();
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-void LeveldbDataset<K, V, KCoder, VCoder>::keys(vector<K>* keys) {
-  DLOG(INFO) << "LevelDB: Keys";
-
-  keys->clear();
-  for (const_iterator iter = begin(); iter != end(); ++iter) {
-    keys->push_back(iter->key);
-  }
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
-    LeveldbDataset<K, V, KCoder, VCoder>::begin() const {
-  CHECK_NOTNULL(db_.get());
-  shared_ptr<leveldb::Iterator> iter(db_->NewIterator(leveldb::ReadOptions()));
-  iter->SeekToFirst();
-  if (!iter->Valid()) {
-    iter.reset();
-  }
-
-  shared_ptr<DatasetState> state;
-  if (iter) {
-    state.reset(new LeveldbState(db_, iter));
-  }
-  return const_iterator(this, state);
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
-    LeveldbDataset<K, V, KCoder, VCoder>::end() const {
-  shared_ptr<DatasetState> state;
-  return const_iterator(this, state);
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
-    LeveldbDataset<K, V, KCoder, VCoder>::cbegin() const {
-  return begin();
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LeveldbDataset<K, V, KCoder, VCoder>::const_iterator
-    LeveldbDataset<K, V, KCoder, VCoder>::cend() const { return end(); }
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LeveldbDataset<K, V, KCoder, VCoder>::equal(
-    shared_ptr<DatasetState> state1, shared_ptr<DatasetState> state2) const {
-  shared_ptr<LeveldbState> leveldb_state1 =
-      boost::dynamic_pointer_cast<LeveldbState>(state1);
-
-  shared_ptr<LeveldbState> leveldb_state2 =
-      boost::dynamic_pointer_cast<LeveldbState>(state2);
-
-  // The KV store doesn't really have any sort of ordering,
-  // so while we can do a sequential scan over the collection,
-  // we can't really use subranges.
-  return !leveldb_state1 && !leveldb_state2;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-void LeveldbDataset<K, V, KCoder, VCoder>::increment(
-    shared_ptr<DatasetState>* state) const {
-  shared_ptr<LeveldbState> leveldb_state =
-      boost::dynamic_pointer_cast<LeveldbState>(*state);
-
-  CHECK_NOTNULL(leveldb_state.get());
-
-  shared_ptr<leveldb::Iterator>& iter = leveldb_state->iter_;
-
-  CHECK_NOTNULL(iter.get());
-  CHECK(iter->Valid());
-
-  iter->Next();
-  if (!iter->Valid()) {
-    state->reset();
-  }
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename Dataset<K, V, KCoder, VCoder>::KV&
-    LeveldbDataset<K, V, KCoder, VCoder>::dereference(
-    shared_ptr<DatasetState> state) const {
-  shared_ptr<LeveldbState> leveldb_state =
-      boost::dynamic_pointer_cast<LeveldbState>(state);
-
-  CHECK_NOTNULL(leveldb_state.get());
-
-  shared_ptr<leveldb::Iterator>& iter = leveldb_state->iter_;
-
-  CHECK_NOTNULL(iter.get());
-
-  CHECK(iter->Valid());
-
-  const leveldb::Slice& key = iter->key();
-  const leveldb::Slice& value = iter->value();
-  CHECK(KCoder::deserialize(key.data(), key.size(),
-      &leveldb_state->kv_pair_.key));
-  CHECK(VCoder::deserialize(value.data(), value.size(),
-      &leveldb_state->kv_pair_.value));
-
-  return leveldb_state->kv_pair_;
-}
-
-INSTANTIATE_DATASET(LeveldbDataset);
-
-}  // namespace caffe
diff --git a/src/caffe/lmdb_dataset.cpp b/src/caffe/lmdb_dataset.cpp
deleted file mode 100644
index 8f8e68e901e..00000000000
--- a/src/caffe/lmdb_dataset.cpp
+++ /dev/null
@@ -1,366 +0,0 @@
-#include <sys/stat.h>
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/caffe.hpp"
-#include "caffe/lmdb_dataset.hpp"
-
-namespace caffe {
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::open(const string& filename,
-    Mode mode) {
-  DLOG(INFO) << "LMDB: Open " << filename;
-
-  CHECK(NULL == env_);
-  CHECK(NULL == write_txn_);
-  CHECK(NULL == read_txn_);
-  CHECK_EQ(0, dbi_);
-
-  int retval;
-  if (mode != Base::ReadOnly) {
-    retval = mkdir(filename.c_str(), 0744);
-    switch (mode) {
-    case Base::New:
-      if (0 != retval) {
-        LOG(ERROR) << "mkdir " << filename << " failed";
-        return false;
-      }
-      break;
-    case Base::ReadWrite:
-      if (-1 == retval && EEXIST != errno) {
-        LOG(ERROR) << "mkdir " << filename << " failed ("
-            << strerror(errno) << ")";
-        return false;
-      }
-      break;
-    default:
-      LOG(FATAL) << "Invalid mode " << mode;
-    }
-  }
-
-  retval = mdb_env_create(&env_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_env_create failed "
-        << mdb_strerror(retval);
-    return false;
-  }
-
-  retval = mdb_env_set_mapsize(env_, 1099511627776);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_env_set_mapsize failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  int flag1 = 0;
-  int flag2 = 0;
-  if (mode == Base::ReadOnly) {
-    flag1 = MDB_RDONLY | MDB_NOTLS;
-    flag2 = MDB_RDONLY;
-  }
-
-  retval = mdb_env_open(env_, filename.c_str(), flag1, 0664);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_env_open failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &read_txn_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  retval = mdb_txn_begin(env_, NULL, flag2, &write_txn_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  retval = mdb_open(write_txn_, NULL, 0, &dbi_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_open failed" << mdb_strerror(retval);
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::put(const K& key, const V& value) {
-  DLOG(INFO) << "LMDB: Put";
-
-  vector<char> serialized_key;
-  if (!KCoder::serialize(key, &serialized_key)) {
-    LOG(ERROR) << "failed to serialize key";
-    return false;
-  }
-
-  vector<char> serialized_value;
-  if (!VCoder::serialize(value, &serialized_value)) {
-    LOG(ERROR) << "failed to serialized value";
-    return false;
-  }
-
-  MDB_val mdbkey, mdbdata;
-  mdbdata.mv_size = serialized_value.size();
-  mdbdata.mv_data = serialized_value.data();
-  mdbkey.mv_size = serialized_key.size();
-  mdbkey.mv_data = serialized_key.data();
-
-  CHECK_NOTNULL(write_txn_);
-  CHECK_NE(0, dbi_);
-
-  int retval = mdb_put(write_txn_, dbi_, &mdbkey, &mdbdata, 0);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_put failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::get(const K& key, V* value) {
-  DLOG(INFO) << "LMDB: Get";
-
-  vector<char> serialized_key;
-  if (!KCoder::serialize(key, &serialized_key)) {
-    LOG(ERROR) << "failed to serialized key";
-    return false;
-  }
-
-  MDB_val mdbkey, mdbdata;
-  mdbkey.mv_data = serialized_key.data();
-  mdbkey.mv_size = serialized_key.size();
-
-  int retval;
-  retval = mdb_get(read_txn_, dbi_, &mdbkey, &mdbdata);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_get failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  if (!VCoder::deserialize(reinterpret_cast<char*>(mdbdata.mv_data),
-      mdbdata.mv_size, value)) {
-    LOG(ERROR) << "failed to deserialize value";
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::first_key(K* key) {
-  DLOG(INFO) << "LMDB: First key";
-
-  int retval;
-
-  MDB_cursor* cursor;
-  retval = mdb_cursor_open(read_txn_, dbi_, &cursor);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-  MDB_val mdbkey;
-  MDB_val mdbval;
-  retval = mdb_cursor_get(cursor, &mdbkey, &mdbval, MDB_FIRST);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-
-  mdb_cursor_close(cursor);
-
-  if (!KCoder::deserialize(reinterpret_cast<char*>(mdbkey.mv_data),
-      mdbkey.mv_size, key)) {
-    LOG(ERROR) << "failed to deserialize key";
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::last_key(K* key) {
-  DLOG(INFO) << "LMDB: Last key";
-
-  int retval;
-
-  MDB_cursor* cursor;
-  retval = mdb_cursor_open(read_txn_, dbi_, &cursor);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-  MDB_val mdbkey;
-  MDB_val mdbval;
-  retval = mdb_cursor_get(cursor, &mdbkey, &mdbval, MDB_LAST);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-
-  mdb_cursor_close(cursor);
-
-  if (!KCoder::deserialize(reinterpret_cast<char*>(mdbkey.mv_data),
-      mdbkey.mv_size, key)) {
-    LOG(ERROR) << "failed to deserialize key";
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::commit() {
-  DLOG(INFO) << "LMDB: Commit";
-
-  CHECK_NOTNULL(write_txn_);
-
-  int retval;
-  retval = mdb_txn_commit(write_txn_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_txn_commit failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  mdb_txn_abort(read_txn_);
-
-  retval = mdb_txn_begin(env_, NULL, 0, &write_txn_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  retval = mdb_txn_begin(env_, NULL, MDB_RDONLY, &read_txn_);
-  if (MDB_SUCCESS != retval) {
-    LOG(ERROR) << "mdb_txn_begin failed " << mdb_strerror(retval);
-    return false;
-  }
-
-  return true;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-void LmdbDataset<K, V, KCoder, VCoder>::close() {
-  DLOG(INFO) << "LMDB: Close";
-
-  if (env_ && dbi_) {
-    mdb_txn_abort(write_txn_);
-    mdb_txn_abort(read_txn_);
-    mdb_close(env_, dbi_);
-    mdb_env_close(env_);
-    env_ = NULL;
-    dbi_ = 0;
-    write_txn_ = NULL;
-    read_txn_ = NULL;
-  }
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-void LmdbDataset<K, V, KCoder, VCoder>::keys(vector<K>* keys) {
-  DLOG(INFO) << "LMDB: Keys";
-
-  keys->clear();
-  for (const_iterator iter = begin(); iter != end(); ++iter) {
-    keys->push_back(iter->key);
-  }
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
-    LmdbDataset<K, V, KCoder, VCoder>::begin() const {
-  int retval;
-
-  MDB_cursor* cursor;
-  retval = mdb_cursor_open(read_txn_, dbi_, &cursor);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-  MDB_val key;
-  MDB_val val;
-  retval = mdb_cursor_get(cursor, &key, &val, MDB_FIRST);
-
-  CHECK(MDB_SUCCESS == retval || MDB_NOTFOUND == retval)
-      << mdb_strerror(retval);
-
-  shared_ptr<DatasetState> state;
-  if (MDB_SUCCESS == retval) {
-    state.reset(new LmdbState(cursor, read_txn_, &dbi_));
-  } else {
-    mdb_cursor_close(cursor);
-  }
-  return const_iterator(this, state);
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
-    LmdbDataset<K, V, KCoder, VCoder>::end() const {
-  shared_ptr<DatasetState> state;
-  return const_iterator(this, state);
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
-    LmdbDataset<K, V, KCoder, VCoder>::cbegin() const { return begin(); }
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename LmdbDataset<K, V, KCoder, VCoder>::const_iterator
-    LmdbDataset<K, V, KCoder, VCoder>::cend() const { return end(); }
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-bool LmdbDataset<K, V, KCoder, VCoder>::equal(shared_ptr<DatasetState> state1,
-    shared_ptr<DatasetState> state2) const {
-  shared_ptr<LmdbState> lmdb_state1 =
-      boost::dynamic_pointer_cast<LmdbState>(state1);
-
-  shared_ptr<LmdbState> lmdb_state2 =
-      boost::dynamic_pointer_cast<LmdbState>(state2);
-
-  // The KV store doesn't really have any sort of ordering,
-  // so while we can do a sequential scan over the collection,
-  // we can't really use subranges.
-  return !lmdb_state1 && !lmdb_state2;
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-void LmdbDataset<K, V, KCoder, VCoder>::increment(
-    shared_ptr<DatasetState>* state) const {
-  shared_ptr<LmdbState> lmdb_state =
-      boost::dynamic_pointer_cast<LmdbState>(*state);
-
-  CHECK_NOTNULL(lmdb_state.get());
-
-  MDB_cursor*& cursor = lmdb_state->cursor_;
-
-  CHECK_NOTNULL(cursor);
-
-  MDB_val key;
-  MDB_val val;
-  int retval = mdb_cursor_get(cursor, &key, &val, MDB_NEXT);
-  if (MDB_NOTFOUND == retval) {
-    mdb_cursor_close(cursor);
-    state->reset();
-  } else {
-    CHECK_EQ(MDB_SUCCESS, retval) << mdb_strerror(retval);
-  }
-}
-
-template <typename K, typename V, typename KCoder, typename VCoder>
-typename Dataset<K, V, KCoder, VCoder>::KV&
-    LmdbDataset<K, V, KCoder, VCoder>::dereference(
-    shared_ptr<DatasetState> state) const {
-  shared_ptr<LmdbState> lmdb_state =
-      boost::dynamic_pointer_cast<LmdbState>(state);
-
-  CHECK_NOTNULL(lmdb_state.get());
-
-  MDB_cursor*& cursor = lmdb_state->cursor_;
-
-  CHECK_NOTNULL(cursor);
-
-  MDB_val mdb_key;
-  MDB_val mdb_val;
-  int retval = mdb_cursor_get(cursor, &mdb_key, &mdb_val, MDB_GET_CURRENT);
-  CHECK_EQ(retval, MDB_SUCCESS) << mdb_strerror(retval);
-
-  CHECK(KCoder::deserialize(reinterpret_cast<char*>(mdb_key.mv_data),
-      mdb_key.mv_size, &lmdb_state->kv_pair_.key));
-  CHECK(VCoder::deserialize(reinterpret_cast<char*>(mdb_val.mv_data),
-      mdb_val.mv_size, &lmdb_state->kv_pair_.value));
-
-  return lmdb_state->kv_pair_;
-}
-
-INSTANTIATE_DATASET(LmdbDataset);
-
-}  // namespace caffe
diff --git a/src/caffe/test/test_dataset.cpp b/src/caffe/test/test_dataset.cpp
deleted file mode 100644
index 6645ca228d2..00000000000
--- a/src/caffe/test/test_dataset.cpp
+++ /dev/null
@@ -1,794 +0,0 @@
-#include <string>
-#include <vector>
-
-#include "caffe/util/io.hpp"
-
-#include "gtest/gtest.h"
-
-#include "caffe/dataset_factory.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-namespace caffe {
-
-namespace DatasetTest_internal {
-
-template <typename T>
-struct TestData {
-  static T TestValue();
-  static T TestAltValue();
-  static bool equals(const T& a, const T& b);
-};
-
-template <>
-string TestData<string>::TestValue() {
-  return "world";
-}
-
-template <>
-string TestData<string>::TestAltValue() {
-  return "bar";
-}
-
-template <>
-bool TestData<string>::equals(const string& a, const string& b) {
-  return a == b;
-}
-
-template <>
-vector<char> TestData<vector<char> >::TestValue() {
-  string str = "world";
-  vector<char> val(str.data(), str.data() + str.size());
-  return val;
-}
-
-template <>
-vector<char> TestData<vector<char> >::TestAltValue() {
-  string str = "bar";
-  vector<char> val(str.data(), str.data() + str.size());
-  return val;
-}
-
-template <>
-bool TestData<vector<char> >::equals(const vector<char>& a,
-    const vector<char>& b) {
-  if (a.size() != b.size()) {
-    return false;
-  }
-  for (size_t i = 0; i < a.size(); ++i) {
-    if (a.at(i) != b.at(i)) {
-      return false;
-    }
-  }
-
-  return true;
-}
-
-template <>
-Datum TestData<Datum>::TestValue() {
-  Datum datum;
-  datum.set_channels(3);
-  datum.set_height(32);
-  datum.set_width(32);
-  datum.set_data(string(32 * 32 * 3 * 4, ' '));
-  datum.set_label(0);
-  return datum;
-}
-
-template <>
-Datum TestData<Datum>::TestAltValue() {
-  Datum datum;
-  datum.set_channels(1);
-  datum.set_height(64);
-  datum.set_width(64);
-  datum.set_data(string(64 * 64 * 1 * 4, ' '));
-  datum.set_label(1);
-  return datum;
-}
-
-template <>
-bool TestData<Datum>::equals(const Datum& a, const Datum& b) {
-  string serialized_a;
-  a.SerializeToString(&serialized_a);
-
-  string serialized_b;
-  b.SerializeToString(&serialized_b);
-
-  return serialized_a == serialized_b;
-}
-
-}  // namespace DatasetTest_internal
-
-#define UNPACK_TYPES \
-  typedef typename TypeParam::value_type value_type; \
-  const DataParameter_DB backend = TypeParam::backend;
-
-template <typename TypeParam>
-class DatasetTest : public ::testing::Test {
- protected:
-  typedef typename TypeParam::value_type value_type;
-
-  string DBName() {
-    string filename;
-    MakeTempDir(&filename);
-    filename += "/db";
-    return filename;
-  }
-
-  string TestKey() {
-    return "hello";
-  }
-
-  value_type TestValue() {
-    return DatasetTest_internal::TestData<value_type>::TestValue();
-  }
-
-  string TestAltKey() {
-    return "foo";
-  }
-
-  value_type TestAltValue() {
-    return DatasetTest_internal::TestData<value_type>::TestAltValue();
-  }
-
-  template <typename T>
-  bool equals(const T& a, const T& b) {
-    return DatasetTest_internal::TestData<T>::equals(a, b);
-  }
-};
-
-struct StringLeveldb {
-  typedef string value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB StringLeveldb::backend = DataParameter_DB_LEVELDB;
-
-struct StringLmdb {
-  typedef string value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB StringLmdb::backend = DataParameter_DB_LMDB;
-
-struct VectorLeveldb {
-  typedef vector<char> value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB VectorLeveldb::backend = DataParameter_DB_LEVELDB;
-
-struct VectorLmdb {
-  typedef vector<char> value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB VectorLmdb::backend = DataParameter_DB_LMDB;
-
-struct DatumLeveldb {
-  typedef Datum value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB DatumLeveldb::backend = DataParameter_DB_LEVELDB;
-
-struct DatumLmdb {
-  typedef Datum value_type;
-  static const DataParameter_DB backend;
-};
-const DataParameter_DB DatumLmdb::backend = DataParameter_DB_LMDB;
-
-typedef ::testing::Types<StringLeveldb, StringLmdb, VectorLeveldb, VectorLmdb,
-    DatumLeveldb, DatumLmdb> TestTypes;
-
-TYPED_TEST_CASE(DatasetTest, TestTypes);
-
-TYPED_TEST(DatasetTest, TestNewDoesntExistPasses) {
-  UNPACK_TYPES;
-
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(this->DBName(),
-      Dataset<string, value_type>::New));
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestNewExistsFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-  dataset->close();
-
-  EXPECT_FALSE(dataset->open(name, Dataset<string, value_type>::New));
-}
-
-TYPED_TEST(DatasetTest, TestReadOnlyExistsPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-  dataset->close();
-
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestReadOnlyDoesntExistFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_FALSE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
-}
-
-TYPED_TEST(DatasetTest, TestReadWriteExistsPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-  dataset->close();
-
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestReadWriteDoesntExistPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestKeys) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key1 = this->TestKey();
-  value_type value1 = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key1, value1));
-
-  string key2 = this->TestAltKey();
-  value_type value2 = this->TestAltValue();
-
-  EXPECT_TRUE(dataset->put(key2, value2));
-
-  EXPECT_TRUE(dataset->commit());
-
-  vector<string> keys;
-  dataset->keys(&keys);
-
-  EXPECT_EQ(2, keys.size());
-
-  EXPECT_TRUE(this->equals(keys.at(0), key1) ||
-      this->equals(keys.at(0), key2));
-  EXPECT_TRUE(this->equals(keys.at(1), key1) ||
-      this->equals(keys.at(2), key2));
-  EXPECT_FALSE(this->equals(keys.at(0), keys.at(1)));
-}
-
-TYPED_TEST(DatasetTest, TestFirstKey) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  value_type value = this->TestValue();
-
-  string key1 = "01";
-  EXPECT_TRUE(dataset->put(key1, value));
-
-  string key2 = "02";
-  EXPECT_TRUE(dataset->put(key2, value));
-
-  string key3 = "03";
-  EXPECT_TRUE(dataset->put(key3, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  string first_key;
-  dataset->first_key(&first_key);
-
-  EXPECT_TRUE(this->equals(first_key, key1));
-}
-
-TYPED_TEST(DatasetTest, TestLastKey) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  value_type value = this->TestValue();
-
-  string key1 = "01";
-  EXPECT_TRUE(dataset->put(key1, value));
-
-  string key2 = "02";
-  EXPECT_TRUE(dataset->put(key2, value));
-
-  string key3 = "03";
-  EXPECT_TRUE(dataset->put(key3, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  string last_key;
-  dataset->last_key(&last_key);
-
-  EXPECT_TRUE(this->equals(last_key, key3));
-}
-
-TYPED_TEST(DatasetTest, TestFirstLastKeys) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  value_type value = this->TestValue();
-
-  string key1 = "01";
-  EXPECT_TRUE(dataset->put(key1, value));
-
-  string key2 = "02";
-  EXPECT_TRUE(dataset->put(key2, value));
-
-  string key3 = "03";
-  EXPECT_TRUE(dataset->put(key3, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  string first_key;
-  dataset->first_key(&first_key);
-  string last_key;
-  dataset->last_key(&last_key);
-
-  EXPECT_TRUE(this->equals(first_key, key1));
-  EXPECT_TRUE(this->equals(last_key, key3));
-}
-
-TYPED_TEST(DatasetTest, TestFirstLastKeysUnOrdered) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  value_type value = this->TestValue();
-
-  string key3 = "03";
-  EXPECT_TRUE(dataset->put(key3, value));
-
-  string key1 = "01";
-  EXPECT_TRUE(dataset->put(key1, value));
-
-  string key2 = "02";
-  EXPECT_TRUE(dataset->put(key2, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  string first_key;
-  dataset->first_key(&first_key);
-  string last_key;
-  dataset->last_key(&last_key);
-
-  EXPECT_TRUE(this->equals(first_key, key1));
-  EXPECT_TRUE(this->equals(last_key, key3));
-}
-
-TYPED_TEST(DatasetTest, TestKeysNoCommit) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key1 = this->TestKey();
-  value_type value1 = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key1, value1));
-
-  string key2 = this->TestAltKey();
-  value_type value2 = this->TestAltValue();
-
-  EXPECT_TRUE(dataset->put(key2, value2));
-
-  vector<string> keys;
-  dataset->keys(&keys);
-
-  EXPECT_EQ(0, keys.size());
-}
-
-TYPED_TEST(DatasetTest, TestIterators) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  const int kNumExamples = 4;
-  for (int i = 0; i < kNumExamples; ++i) {
-    stringstream ss;
-    ss << i;
-    string key = ss.str();
-    ss << " here be data";
-    value_type value = this->TestValue();
-    EXPECT_TRUE(dataset->put(key, value));
-  }
-  EXPECT_TRUE(dataset->commit());
-
-  int count = 0;
-  typedef typename Dataset<string, value_type>::const_iterator Iter;
-  for (Iter iter = dataset->begin(); iter != dataset->end(); ++iter) {
-    (void)iter;
-    ++count;
-  }
-
-  EXPECT_EQ(kNumExamples, count);
-}
-
-TYPED_TEST(DatasetTest, TestIteratorsPreIncrement) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key1 = this->TestAltKey();
-  value_type value1 = this->TestAltValue();
-
-  string key2 = this->TestKey();
-  value_type value2 = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key1, value1));
-  EXPECT_TRUE(dataset->put(key2, value2));
-  EXPECT_TRUE(dataset->commit());
-
-  typename Dataset<string, value_type>::const_iterator iter1 =
-      dataset->begin();
-
-  EXPECT_FALSE(dataset->end() == iter1);
-
-  EXPECT_TRUE(this->equals(iter1->key, key1));
-
-  typename Dataset<string, value_type>::const_iterator iter2 = ++iter1;
-
-  EXPECT_FALSE(dataset->end() == iter1);
-  EXPECT_FALSE(dataset->end() == iter2);
-
-  EXPECT_TRUE(this->equals(iter2->key, key2));
-
-  typename Dataset<string, value_type>::const_iterator iter3 = ++iter2;
-
-  EXPECT_TRUE(dataset->end() == iter3);
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestIteratorsPostIncrement) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key1 = this->TestAltKey();
-  value_type value1 = this->TestAltValue();
-
-  string key2 = this->TestKey();
-  value_type value2 = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key1, value1));
-  EXPECT_TRUE(dataset->put(key2, value2));
-  EXPECT_TRUE(dataset->commit());
-
-  typename Dataset<string, value_type>::const_iterator iter1 =
-      dataset->begin();
-
-  EXPECT_FALSE(dataset->end() == iter1);
-
-  EXPECT_TRUE(this->equals(iter1->key, key1));
-
-  typename Dataset<string, value_type>::const_iterator iter2 = iter1++;
-
-  EXPECT_FALSE(dataset->end() == iter1);
-  EXPECT_FALSE(dataset->end() == iter2);
-
-  EXPECT_TRUE(this->equals(iter2->key, key1));
-  EXPECT_TRUE(this->equals(iter1->key, key2));
-
-  typename Dataset<string, value_type>::const_iterator iter3 = iter1++;
-
-  EXPECT_FALSE(dataset->end() == iter3);
-  EXPECT_TRUE(this->equals(iter3->key, key2));
-  EXPECT_TRUE(dataset->end() == iter1);
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestNewPutPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestNewCommitPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  EXPECT_TRUE(dataset->commit());
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestNewGetPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  value_type new_value;
-
-  EXPECT_TRUE(dataset->get(key, &new_value));
-
-  EXPECT_TRUE(this->equals(value, new_value));
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestNewGetNoCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  value_type new_value;
-
-  EXPECT_FALSE(dataset->get(key, &new_value));
-}
-
-
-TYPED_TEST(DatasetTest, TestReadWritePutPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestReadWriteCommitPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadWrite));
-
-  EXPECT_TRUE(dataset->commit());
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestReadWriteGetPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  value_type new_value;
-
-  EXPECT_TRUE(dataset->get(key, &new_value));
-
-  EXPECT_TRUE(this->equals(value, new_value));
-
-  dataset->close();
-}
-
-TYPED_TEST(DatasetTest, TestReadWriteGetNoCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  value_type new_value;
-
-  EXPECT_FALSE(dataset->get(key, &new_value));
-}
-
-TYPED_TEST(DatasetTest, TestReadOnlyPutFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-  dataset->close();
-
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_FALSE(dataset->put(key, value));
-}
-
-TYPED_TEST(DatasetTest, TestReadOnlyCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-  dataset->close();
-
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
-
-  EXPECT_FALSE(dataset->commit());
-}
-
-TYPED_TEST(DatasetTest, TestReadOnlyGetPasses) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  EXPECT_TRUE(dataset->commit());
-
-  dataset->close();
-
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
-
-  value_type new_value;
-
-  EXPECT_TRUE(dataset->get(key, &new_value));
-
-  EXPECT_TRUE(this->equals(value, new_value));
-}
-
-TYPED_TEST(DatasetTest, TestReadOnlyGetNoCommitFails) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-
-  EXPECT_TRUE(dataset->put(key, value));
-
-  dataset->close();
-
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::ReadOnly));
-
-  value_type new_value;
-
-  EXPECT_FALSE(dataset->get(key, &new_value));
-}
-
-TYPED_TEST(DatasetTest, TestCreateManyItersShortScope) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-  EXPECT_TRUE(dataset->put(key, value));
-  EXPECT_TRUE(dataset->commit());
-
-  for (int i = 0; i < 1000; ++i) {
-    typename Dataset<string, value_type>::const_iterator iter =
-        dataset->begin();
-  }
-}
-
-TYPED_TEST(DatasetTest, TestCreateManyItersLongScope) {
-  UNPACK_TYPES;
-
-  string name = this->DBName();
-  shared_ptr<Dataset<string, value_type> > dataset =
-      DatasetFactory<string, value_type>(backend);
-  EXPECT_TRUE(dataset->open(name, Dataset<string, value_type>::New));
-
-  string key = this->TestKey();
-  value_type value = this->TestValue();
-  EXPECT_TRUE(dataset->put(key, value));
-  EXPECT_TRUE(dataset->commit());
-
-  vector<typename Dataset<string, value_type>::const_iterator> iters;
-  for (int i = 0; i < 1000; ++i) {
-    iters.push_back(dataset->begin());
-  }
-}
-
-#undef UNPACK_TYPES
-
-}  // namespace caffe

From 60f26d647ceccea5d0ab0bf85b35b492886bf451 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 16 Oct 2014 13:24:39 -0700
Subject: [PATCH 1059/2053] SetTotalBytesLimit to the max (2 GB minus 1 byte)

---
 src/caffe/util/io.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index ffdaeb12c4a..6fa6e68010e 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -17,6 +17,8 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 
+const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.
+
 namespace caffe {
 
 using google::protobuf::io::FileInputStream;
@@ -50,7 +52,7 @@ bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
   CHECK_NE(fd, -1) << "File not found: " << filename;
   ZeroCopyInputStream* raw_input = new FileInputStream(fd);
   CodedInputStream* coded_input = new CodedInputStream(raw_input);
-  coded_input->SetTotalBytesLimit(1073741824, 536870912);
+  coded_input->SetTotalBytesLimit(kProtoReadBytesLimit, 536870912);
 
   bool success = proto->ParseFromCodedStream(coded_input);
 

From a0ada4fc9e0075d8ba57f128a111fd5e214266c8 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 1 Nov 2014 21:22:11 -0700
Subject: [PATCH 1060/2053] Unroll kernels in SoftmaxLayer...from terrible
 performance to mediocre performance.

---
 src/caffe/layers/softmax_layer.cu | 48 +++++++++++++++----------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 292ad2b396a..6b8871a0b20 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -25,14 +25,13 @@ __global__ void kernel_channel_max(const int num, const int channels,
 }
 
 template <typename Dtype>
-__global__ void kernel_channel_subtract(const int num, const int channels,
-    const int spatial_dim, Dtype* data, const Dtype* channel_max) {
-  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
-    int n = index / spatial_dim;
+__global__ void kernel_channel_subtract(const int count,
+    const int num, const int channels,
+    const int spatial_dim, const Dtype* channel_max, Dtype* data) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / channels / spatial_dim;
     int s = index % spatial_dim;
-    for (int c = 0; c < channels; ++c) {
-      data[(n * channels + c) * spatial_dim + s] -= channel_max[index];
-    }
+    data[index] -= channel_max[n * spatial_dim + s];
   }
 }
 
@@ -58,14 +57,13 @@ __global__ void kernel_channel_sum(const int num, const int channels,
 }
 
 template <typename Dtype>
-__global__ void kernel_channel_div(const int num, const int channels,
-    const int spatial_dim, Dtype* data, const Dtype* channel_sum) {
-  CUDA_KERNEL_LOOP(index, num * spatial_dim) {
-    int n = index / spatial_dim;
+__global__ void kernel_channel_div(const int count,
+    const int num, const int channels,
+    const int spatial_dim, const Dtype* channel_sum, Dtype* data) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / channels / spatial_dim;
     int s = index % spatial_dim;
-    for (int c = 0; c < channels; ++c) {
-      data[(n * channels + c) * spatial_dim + s] /= channel_sum[index];
-    }
+    data[index] /= channel_sum[n * spatial_dim + s];
   }
 }
 
@@ -91,10 +89,11 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
+  int count = bottom[0]->count();
   int num = bottom[0]->num();
   int channels = bottom[0]->channels();
   int spatial_dim = bottom[0]->height() * bottom[0]->width();
-  caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  caffe_copy(count, bottom_data, top_data);
   // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   // compute max
@@ -104,9 +103,9 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       scale_data);
   // subtract
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
-      scale_data);
+  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, num, channels, spatial_dim,
+      scale_data, top_data);
   // exponentiate
   // NOLINT_NEXT_LINE(whitespace/operators)
   kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(num * channels * spatial_dim),
@@ -119,9 +118,9 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       scale_data);
   // divide
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_div<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
-      scale_data);
+  kernel_channel_div<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, num, channels, spatial_dim,
+      scale_data, top_data);
 }
 
 template <typename Dtype>
@@ -131,6 +130,7 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_data = top[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   Dtype* scale_data = scale_.mutable_gpu_data();
+  int count = top[0]->count();
   int num = top[0]->num();
   int channels = top[0]->channels();
   int spatial_dim = top[0]->height() * top[0]->width();
@@ -141,9 +141,9 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_diff, top_data,
       scale_data);
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, bottom_diff,
-      scale_data);
+  kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, num, channels, spatial_dim,
+      scale_data, bottom_diff);
   // elementwise multiplication
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
 }

From 89a92b9d7a4b1ecdef69fe5e52f73b362f0ce913 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 1 Nov 2014 21:37:05 -0700
Subject: [PATCH 1061/2053] SoftmaxWithLossLayer: use CreateLayer so that a
 CuDNNSoftmaxLayer is created if available

---
 include/caffe/loss_layers.hpp           | 5 ++---
 src/caffe/layers/softmax_loss_layer.cpp | 4 ++++
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 13b108afd2d..435013646a9 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -697,8 +697,7 @@ template <typename Dtype>
 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
   explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param),
-        softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
+      : LossLayer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -751,7 +750,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
-  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
+  shared_ptr<Layer<Dtype> > softmax_layer_;
   /// prob stores the output probability predictions from the SoftmaxLayer.
   Blob<Dtype> prob_;
   /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index db8dd8bb626..dfc41d2da15 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -3,6 +3,7 @@
 #include <vector>
 
 #include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
@@ -12,6 +13,9 @@ template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
+  LayerParameter softmax_param(this->layer_param_);
+  softmax_param.set_type(LayerParameter_LayerType_SOFTMAX);
+  softmax_layer_.reset(LayerRegistry<Dtype>::CreateLayer(softmax_param));
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.clear();

From daa4c5bd7bf02c21e82b1522ca44b2b6049010b5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 2 Nov 2014 01:56:19 -0800
Subject: [PATCH 1062/2053] hdf5_save_nd_dataset takes a const string& (instead
 of const string)

---
 include/caffe/util/io.hpp | 10 +++++-----
 src/caffe/util/io.cpp     |  4 ++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 9c0b4872af6..a01f917abf5 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -150,17 +150,17 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
-  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-  Blob<Dtype>* blob);
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
 
 template <typename Dtype>
 void hdf5_load_nd_dataset(
-  hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-  Blob<Dtype>* blob);
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
 
 template <typename Dtype>
 void hdf5_save_nd_dataset(
-  const hid_t file_id, const string dataset_name, const Blob<Dtype>& blob);
+    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob);
 
 }  // namespace caffe
 
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 6fa6e68010e..f738e053e86 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -256,7 +256,7 @@ void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
 
 template <>
 void hdf5_save_nd_dataset<float>(
-    const hid_t file_id, const string dataset_name, const Blob<float>& blob) {
+    const hid_t file_id, const string& dataset_name, const Blob<float>& blob) {
   hsize_t dims[HDF5_NUM_DIMS];
   dims[0] = blob.num();
   dims[1] = blob.channels();
@@ -269,7 +269,7 @@ void hdf5_save_nd_dataset<float>(
 
 template <>
 void hdf5_save_nd_dataset<double>(
-    const hid_t file_id, const string dataset_name, const Blob<double>& blob) {
+    const hid_t file_id, const string& dataset_name, const Blob<double>& blob) {
   hsize_t dims[HDF5_NUM_DIMS];
   dims[0] = blob.num();
   dims[1] = blob.channels();

From f4b386537e21ffa6fe128fd8ffd465b5eddc6dee Mon Sep 17 00:00:00 2001
From: Xianjie Chen <xianjiec@gmail.com>
Date: Thu, 22 Jan 2015 19:27:24 -0800
Subject: [PATCH 1063/2053] fix bugs by adding const

---
 matlab/caffe/matcaffe.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 3de0f02e1eb..0ab0b00eb23 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -52,7 +52,7 @@ static int init_key = -2;
 // input and outputs a cell array.
 
 static mxArray* do_forward(const mxArray* const bottom) {
-  vector<Blob<float>*>& input_blobs = net_->input_blobs();
+  const vector<Blob<float>*>& input_blobs = net_->input_blobs();
   if (static_cast<unsigned int>(mxGetDimensions(bottom)[0]) !=
       input_blobs.size()) {
     mex_error("Invalid input size");
@@ -112,8 +112,8 @@ static mxArray* do_forward(const mxArray* const bottom) {
 }
 
 static mxArray* do_backward(const mxArray* const top_diff) {
-  vector<Blob<float>*>& output_blobs = net_->output_blobs();
-  vector<Blob<float>*>& input_blobs = net_->input_blobs();
+  const vector<Blob<float>*>& output_blobs = net_->output_blobs();
+  const vector<Blob<float>*>& input_blobs = net_->input_blobs();
   if (static_cast<unsigned int>(mxGetDimensions(top_diff)[0]) !=
       output_blobs.size()) {
     mex_error("Invalid input size");

From c44bfb38fb207ff7d5814356c199651b2c18ab88 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 Jan 2015 19:54:57 -0800
Subject: [PATCH 1064/2053] [fix] align pytest for solver with #1728

---
 python/caffe/test/test_solver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index b78c91f9978..832b75c3219 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -17,7 +17,7 @@ def setUp(self):
         display: 100 max_iter: 100 snapshot_after_train: false""")
         f.close()
         self.solver = caffe.SGDSolver(f.name)
-        self.solver.net.set_mode_cpu()
+        caffe.set_mode_cpu()
         # fill in valid labels
         self.solver.net.blobs['label'].data[...] = \
                 np.random.randint(self.num_output,

From 47d7cb7b20a7fc4563423ae669c5237e20670206 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 22:46:53 -0800
Subject: [PATCH 1065/2053] drop dump_network tool

Nets are better serialized as a single binaryproto or saved however
desired through the Python and MATLAB interfaces.
---
 tools/dump_network.cpp | 82 ------------------------------------------
 1 file changed, 82 deletions(-)
 delete mode 100644 tools/dump_network.cpp

diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
deleted file mode 100644
index 9cb996ef612..00000000000
--- a/tools/dump_network.cpp
+++ /dev/null
@@ -1,82 +0,0 @@
-// This program takes in a trained network and an input blob, and then dumps
-// all the intermediate blobs produced by the net to individual binary
-// files stored in protobuffer binary formats.
-// Usage:
-//    dump_network input_net_param trained_net_param
-//        input_blob output_prefix 0/1
-// if input_net_param is 'none', we will directly load the network from
-// trained_net_param. If the last argv is 1, we will do a forward-backward pass
-// before dumping everyting, and also dump the who network.
-
-#include <string>
-#include <vector>
-
-#include "fcntl.h"
-#include "google/protobuf/text_format.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
-#include "caffe/util/io.hpp"
-
-using boost::shared_ptr;
-using caffe::Blob;
-using caffe::BlobProto;
-using caffe::Caffe;
-using caffe::Net;
-using caffe::NetParameter;
-
-int main(int argc, char** argv) {
-  Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TEST);
-
-  shared_ptr<Net<float> > caffe_net;
-  if (strcmp(argv[1], "none") == 0) {
-    // We directly load the net param from trained file
-    caffe_net.reset(new Net<float>(argv[2]));
-  } else {
-    caffe_net.reset(new Net<float>(argv[1]));
-  }
-  caffe_net->CopyTrainedLayersFrom(argv[2]);
-
-  std::vector<Blob<float>* > input_vec;
-  shared_ptr<Blob<float> > input_blob(new Blob<float>());
-  if (strcmp(argv[3], "none") != 0) {
-    BlobProto input_blob_proto;
-    ReadProtoFromBinaryFile(argv[3], &input_blob_proto);
-    input_blob->FromProto(input_blob_proto);
-    input_vec.push_back(input_blob.get());
-  }
-
-  std::string output_prefix(argv[4]);
-  // Run the network without training.
-  LOG(ERROR) << "Performing Forward";
-  caffe_net->Forward(input_vec);
-  if (argc > 5 && strcmp(argv[5], "1") == 0) {
-    LOG(ERROR) << "Performing Backward";
-    Caffe::set_phase(Caffe::TRAIN);
-    caffe_net->Backward();
-    // Dump the network
-    NetParameter output_net_param;
-    caffe_net->ToProto(&output_net_param, true);
-    WriteProtoToBinaryFile(output_net_param,
-        output_prefix + output_net_param.name());
-  }
-  // Now, let's dump all the layers
-
-  const std::vector<std::string>& blob_names = caffe_net->blob_names();
-  const std::vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
-  for (int blobid = 0; blobid < caffe_net->blobs().size(); ++blobid) {
-    // Serialize blob
-    LOG(ERROR) << "Dumping " << blob_names[blobid];
-    BlobProto output_blob_proto;
-    blobs[blobid]->ToProto(&output_blob_proto);
-    WriteProtoToBinaryFile(output_blob_proto,
-        output_prefix + blob_names[blobid]);
-  }
-
-  return 0;
-}

From 2f869e7426e47d5123ee399704ff386bfdee8387 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 24 Jan 2015 18:27:15 -0800
Subject: [PATCH 1066/2053] clarify draw_net.py usage: net prototxt, not
 caffemodel

---
 python/draw_net.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/draw_net.py b/python/draw_net.py
index ba488294275..abf701572a2 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -11,7 +11,7 @@
 
 def main(argv):
     if len(argv) != 3:
-        print 'Usage: %s input_net_proto_file output_image_file' % \
+        print 'Usage: %s input_net_prototxt output_image_file' % \
                 os.path.basename(sys.argv[0])
     else:
         net = caffe_pb2.NetParameter()

From 61c63f6d1ed4cd6a3c4b4d9229497fc89c5ef662 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 24 Jan 2015 18:28:46 -0800
Subject: [PATCH 1067/2053] [docs] ask install + hardware questions on
 caffe-users

---
 docs/installation.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index a42ef5b309c..16e319b4392 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -11,6 +11,8 @@ We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
 - [Compilation](#compilation)
 - [Hardware questions](#hardware_questions)
 
+Ask installation questions on the [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) mailing list.
+
 ## Prerequisites
 
 Caffe depends on several software packages.
@@ -299,4 +301,4 @@ As a workaround, if you are using Ubuntu 12.04 you can try the following steps t
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 
-Refer to the project's issue tracker for [hardware/compatibility](https://github.com/BVLC/caffe/issues?labels=hardware%2Fcompatibility&page=1&state=open).
+Ask hardware questions on the [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) mailing list.

From d33fffbd229ad29e06fa294356c4526ea2e82987 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 25 Jan 2015 16:04:51 -0800
Subject: [PATCH 1068/2053] [example] lenet early stopping -> mnist examples

---
 examples/{lenet => mnist}/lenet_stepearly_solver.prototxt | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/{lenet => mnist}/lenet_stepearly_solver.prototxt (100%)

diff --git a/examples/lenet/lenet_stepearly_solver.prototxt b/examples/mnist/lenet_stepearly_solver.prototxt
similarity index 100%
rename from examples/lenet/lenet_stepearly_solver.prototxt
rename to examples/mnist/lenet_stepearly_solver.prototxt

From 47e8831ac09c82fb210e8d032a2f770499619540 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 25 Jan 2015 20:37:52 -0800
Subject: [PATCH 1069/2053] [pycaffe] de-dupe imports

---
 python/caffe/__init__.py | 4 ++--
 python/caffe/pycaffe.py  | 2 --
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 8dd2464d1c4..acdfc071032 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,6 @@
 from .pycaffe import Net, SGDSolver
-from .pycaffe import set_mode_cpu, set_mode_gpu, set_phase_train, set_phase_test
-from .pycaffe import set_device
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, \
+    set_phase_train, set_phase_test
 from .classifier import Classifier
 from .detector import Detector
 import io
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 8899309ddfd..31dc1f9b001 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -8,8 +8,6 @@
 import numpy as np
 
 from ._caffe import Net, SGDSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_phase_train, set_phase_test
-from ._caffe import set_device
 import caffe.io
 
 # We directly update methods from Net here (rather than using composition or

From 408133ce4139a95960577e8d36daa16e9dd9e03a Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 27 Jan 2015 10:45:41 -0800
Subject: [PATCH 1070/2053] document DeconvolutionLayer

---
 include/caffe/vision_layers.hpp | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 646378dea9f..fc178e4c904 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -16,6 +16,10 @@
 
 namespace caffe {
 
+/**
+ * @brief Abstract base class that factors out the BLAS code common to
+ *        ConvolutionLayer and DeconvolutionLayer.
+ */
 template <typename Dtype>
 class BaseConvolutionLayer : public Layer<Dtype> {
  public:
@@ -173,6 +177,20 @@ class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
   virtual void compute_output_shape();
 };
 
+/**
+ * @brief Convolve the input with a bank of learned filters, and (optionally)
+ *        add biases, treating filters and convolution parameters in the
+ *        opposite sense as ConvolutionLayer.
+ *
+ *   ConvolutionLayer computes each output value by dotting an input window with
+ *   a filter; DeconvolutionLayer multiplies each input value by a filter
+ *   elementwise, and sums over the resulting output windows. In other words,
+ *   DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
+ *   reversed. DeconvolutionLayer reuses ConvolutionParameter for its
+ *   parameters, but they take the opposite sense as in ConvolutionLayer (so
+ *   padding is removed from the output rather than added to the input, and
+ *   stride results in upsampling rather than downsampling).
+ */
 template <typename Dtype>
 class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
  public:

From 25c2e3fdc7a27ec00893bd0335ac42e53ff2c7aa Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 27 Jan 2015 12:24:39 -0800
Subject: [PATCH 1071/2053] [test] simple test for DeconvolutionLayer

---
 src/caffe/test/test_deconvolution_layer.cpp | 158 ++++++++++++++++++++
 1 file changed, 158 insertions(+)
 create mode 100644 src/caffe/test/test_deconvolution_layer.cpp

diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
new file mode 100644
index 00000000000..fc63d5efbe3
--- /dev/null
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -0,0 +1,158 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+// Since ConvolutionLayerTest checks the shared conv/deconv code in detail,
+// we'll just do a simple forward test and a gradient check.
+template <typename TypeParam>
+class DeconvolutionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  DeconvolutionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_2_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~DeconvolutionLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_2_;
+    delete blob_top_;
+    delete blob_top_2_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(DeconvolutionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(DeconvolutionLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  shared_ptr<Layer<Dtype> > layer(
+      new DeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 4);
+  EXPECT_EQ(this->blob_top_->height(), 13);
+  EXPECT_EQ(this->blob_top_->width(), 9);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 4);
+  EXPECT_EQ(this->blob_top_2_->height(), 13);
+  EXPECT_EQ(this->blob_top_2_->width(), 9);
+  // setting group should not change the shape
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  layer.reset(new DeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 13);
+  EXPECT_EQ(this->blob_top_->width(), 9);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 3);
+  EXPECT_EQ(this->blob_top_2_->height(), 13);
+  EXPECT_EQ(this->blob_top_2_->width(), 9);
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestSimpleDeconvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new DeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // constant-fill the bottom blobs
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  ConstantFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  filler.Fill(this->blob_bottom_2_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // simply check that accumulation works with overlapping filters
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          Dtype expected = 3.1;
+          bool h_overlap = h % 2 == 0 && h > 0
+            && h < this->blob_top_->height() - 1;
+          bool w_overlap = w % 2 == 0 && w > 0
+            && w < this->blob_top_->width() - 1;
+          if (h_overlap && w_overlap) {
+            expected += 9;
+          } else if (h_overlap || w_overlap) {
+            expected += 3;
+          }
+          EXPECT_NEAR(top_data[this->blob_top_->offset(n, c, h, w)],
+              expected, 1e-4);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  convolution_param->set_kernel_size(2);
+  convolution_param->set_stride(1);
+  convolution_param->set_num_output(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  DeconvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe

From 41b754c4e2154f40952d123599f9fa04eda0c3e3 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 27 Jan 2015 13:09:32 -0800
Subject: [PATCH 1072/2053] document the loss_param options to
 SoftmaxWithLossLayer

---
 include/caffe/loss_layers.hpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 321f7068273..ca91579d51c 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -696,6 +696,14 @@ template <typename Dtype> class SoftmaxLayer;
 template <typename Dtype>
 class SoftmaxWithLossLayer : public LossLayer<Dtype> {
  public:
+   /**
+    * @param param provides LossParameter loss_param, with options:
+    *  - ignore_label (optional)
+    *    Specify a label value that should be ignored when computing the loss.
+    *  - normalize (optional, default true)
+    *    If true, the loss is normalized by the number of (nonignored) labels
+    *    present; otherwise the loss is simply summed over spatial locations.
+    */
   explicit SoftmaxWithLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param),
         softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}

From f1eada76b40729d379669480b6db60eb7e01f6ce Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 27 Jan 2015 13:27:48 -0800
Subject: [PATCH 1073/2053] [test] gradient checks for softmax ignore_label and
 normalize: false

---
 .../test/test_softmax_with_loss_layer.cpp     | 48 ++++++++++++++++++-
 1 file changed, 47 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index badda3b5503..1498d5c5ce1 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -3,6 +3,7 @@
 #include <cstring>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
@@ -13,6 +14,8 @@
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
 
+using boost::scoped_ptr;
+
 namespace caffe {
 
 template <typename TypeParam>
@@ -50,7 +53,6 @@ class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
 
 TYPED_TEST_CASE(SoftmaxWithLossLayerTest, TestDtypesAndDevices);
 
-
 TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -61,4 +63,48 @@ TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
       this->blob_top_vec_, 0);
 }
 
+TYPED_TEST(SoftmaxWithLossLayerTest, TestForwardIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_loss_param()->set_normalize(false);
+  // First, compute the loss with all labels
+  scoped_ptr<SoftmaxWithLossLayer<Dtype> > layer(
+      new SoftmaxWithLossLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Dtype full_loss = this->blob_top_loss_->cpu_data()[0];
+  // Now, accumulate the loss, ignoring each label in {0, ..., 4} in turn.
+  Dtype accum_loss = 0;
+  for (int label = 0; label < 5; ++label) {
+    layer_param.mutable_loss_param()->set_ignore_label(label);
+    layer.reset(new SoftmaxWithLossLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    accum_loss += this->blob_top_loss_->cpu_data()[0];
+  }
+  // Check that each label was included all but once.
+  EXPECT_NEAR(4 * full_loss, accum_loss, 1e-4);
+}
+
+TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // labels are in {0, ..., 4}, so we'll ignore about a fifth of them
+  layer_param.mutable_loss_param()->set_ignore_label(0);
+  SoftmaxWithLossLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientUnnormalized) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_loss_param()->set_normalize(false);
+  SoftmaxWithLossLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
 }  // namespace caffe

From 4cc8195d82f13391545b90c9c61baaca39b6df84 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 29 Jan 2015 09:26:12 -0800
Subject: [PATCH 1074/2053] [docs] send API link to class list

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index ccc8f750eef..bf1d9c3c78b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -46,7 +46,7 @@ Tested on Ubuntu, Red Hat, OS X.
 BVLC suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
-* [API Documentation](/doxygen/)<br>
+* [API Documentation](/doxygen/annotated.html)<br>
 Developer documentation automagically generated from code comments.
 
 ### Examples

From 1f7c3dea034ef19acd1addf5aa8f4c2b94bc358c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 29 Jan 2015 10:53:44 -0800
Subject: [PATCH 1075/2053] [docs] add check mode hint to CPU-only mode error

---
 include/caffe/util/device_alternate.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 3df28a49ac3..1a33b947991 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -7,7 +7,7 @@
 
 // Stub out GPU calls as unavailable.
 
-#define NO_GPU LOG(FATAL) << "CPU-only Mode: cannot make GPU call."
+#define NO_GPU LOG(FATAL) << "Cannot make GPU call in CPU-only mode: check mode setting."
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \

From 2a43133ce73678d633d68be4907a26a044e84176 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 24 Sep 2014 15:52:14 -0700
Subject: [PATCH 1076/2053] Enhancements for debug_info to display more
 information.

Now displays for:
-net inputs
-test nets
-params in ForwardDebugInfo
---
 include/caffe/net.hpp |  3 +++
 src/caffe/net.cpp     | 26 ++++++++++++++++++++++++++
 src/caffe/solver.cpp  |  1 +
 3 files changed, 30 insertions(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index bbb220eb00d..2709d87ec4e 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -194,6 +194,8 @@ class Net {
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
 
+  /// @brief Helper for displaying debug info in Forward about input Blobs.
+  void InputDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Forward.
   void ForwardDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Backward.
@@ -226,6 +228,7 @@ class Net {
   /// Vector of weight in the loss (or objective) function of each net blob,
   /// indexed by blob_id.
   vector<Dtype> blob_loss_weights_;
+  vector<vector<int> > param_id_vecs_;
   vector<int> param_owners_;
   vector<string> param_display_names_;
   vector<pair<int, int> > param_layer_indices_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 47fc8446064..692351fb7d2 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -58,6 +58,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   bottom_vecs_.resize(param.layers_size());
   top_vecs_.resize(param.layers_size());
   bottom_id_vecs_.resize(param.layers_size());
+  param_id_vecs_.resize(param.layers_size());
   top_id_vecs_.resize(param.layers_size());
   bottom_need_backward_.resize(param.layers_size());
   for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
@@ -416,6 +417,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
   }
   const int net_param_id = params_.size();
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
+  param_id_vecs_[layer_id].push_back(net_param_id);
   param_layer_indices_.push_back(make_pair(layer_id, param_id));
   if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
@@ -503,6 +505,11 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
+  if (debug_info_) {
+    for (int i = 0; i < net_input_blobs_.size(); ++i) {
+      InputDebugInfo(i);
+    }
+  }
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
     layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
@@ -577,6 +584,15 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::InputDebugInfo(const int input_id) {
+  const Blob<Dtype>& blob = *net_input_blobs_[input_id];
+  const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
+  const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
+  LOG(INFO) << "    [Forward] "
+     << "Input " << blob_name << " data: " << data_abs_val_mean;
+}
+
 template <typename Dtype>
 void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
@@ -587,6 +603,16 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
        << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
        << " data: " << data_abs_val_mean;
   }
+  for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
+       ++param_id) {
+    const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
+    const int net_param_id = param_id_vecs_[layer_id][param_id];
+    const string& blob_name = param_display_names_[net_param_id];
+    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
+    LOG(INFO) << "    [Forward] "
+       << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name
+       << " data: " << data_abs_val_mean;
+  }
 }
 
 template <typename Dtype>
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 3fa0e2d14b7..100cb357749 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -154,6 +154,7 @@ void Solver<Dtype>::InitTestNets() {
     LOG(INFO)
         << "Creating test net (#" << i << ") specified by " << sources[i];
     test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+    test_nets_[i]->set_debug_info(param_.debug_info());
   }
 }
 

From cfaadf61a5f082ee396354b51aa7fd8dda745750 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 6 Oct 2014 23:08:38 -0700
Subject: [PATCH 1077/2053] Blob: add sumsq_{data,diff} methods

---
 include/caffe/blob.hpp |  4 +++
 src/caffe/blob.cpp     | 74 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 78 insertions(+)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index ef10aea53f0..d425e3857e3 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -106,6 +106,10 @@ class Blob {
   Dtype asum_data() const;
   /// @brief Compute the sum of absolute values (L1 norm) of the diff.
   Dtype asum_diff() const;
+  /// @brief Compute the sum of squares (L2 norm squared) of the data.
+  Dtype sumsq_data() const;
+  /// @brief Compute the sum of squares (L2 norm squared) of the diff.
+  Dtype sumsq_diff() const;
 
   /**
    * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index cfffc379eb1..3b72ce0c8d0 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -205,6 +205,80 @@ Dtype Blob<Dtype>::asum_diff() const {
   return 0;
 }
 
+template <> unsigned int Blob<unsigned int>::sumsq_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::sumsq_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::sumsq_data() const {
+  Dtype sumsq;
+  const Dtype* data;
+  if (!data_) { return 0; }
+  switch (data_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    data = cpu_data();
+    sumsq = caffe_cpu_dot(count_, data, data);
+    break;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    data = gpu_data();
+    caffe_gpu_dot(count_, data, data, &sumsq);
+#else
+    NO_GPU;
+#endif
+    break;
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+  return sumsq;
+}
+
+template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::sumsq_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::sumsq_diff() const {
+  Dtype sumsq;
+  const Dtype* diff;
+  if (!diff_) { return 0; }
+  switch (diff_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    diff = cpu_diff();
+    sumsq = caffe_cpu_dot(count_, diff, diff);
+    break;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    diff = gpu_diff();
+    caffe_gpu_dot(count_, diff, diff, &sumsq);
+    break;
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+  return sumsq;
+}
+
 template <typename Dtype>
 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   if (num_ != source.num() || channels_ != source.channels() ||

From 705abfa69f242c4345372e06e3686c3887890dd3 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 29 Jan 2015 19:07:12 -0800
Subject: [PATCH 1078/2053] Add BlobMathTest with unit tests for sumsq and asum

---
 src/caffe/test/test_blob.cpp | 113 +++++++++++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)

diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index adf7a4d38e9..c619ad19f11 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -54,4 +54,117 @@ TYPED_TEST(BlobSimpleTest, TestReshape) {
   EXPECT_EQ(this->blob_->count(), 120);
 }
 
+template <typename TypeParam>
+class BlobMathTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  BlobMathTest()
+      : blob_(new Blob<Dtype>(2, 3, 4, 5)) {}
+  virtual ~BlobMathTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+};
+
+TYPED_TEST_CASE(BlobMathTest, TestDtypesAndDevices);
+
+TYPED_TEST(BlobMathTest, TestSumOfSquares) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  // Uninitialized Blob should have sum of squares == 0.
+  EXPECT_EQ(0, this->blob_->sumsq_data());
+  EXPECT_EQ(0, this->blob_->sumsq_diff());
+  FillerParameter filler_param;
+  filler_param.set_min(-3);
+  filler_param.set_max(3);
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_);
+  Dtype expected_sumsq = 0;
+  const Dtype* data = this->blob_->cpu_data();
+  for (int i = 0; i < this->blob_->count(); ++i) {
+    expected_sumsq += data[i] * data[i];
+  }
+  // Do a mutable access on the current device,
+  // so that the sumsq computation is done on that device.
+  // (Otherwise, this would only check the CPU sumsq implementation.)
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_data();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_FLOAT_EQ(expected_sumsq, this->blob_->sumsq_data());
+  EXPECT_EQ(0, this->blob_->sumsq_diff());
+
+  // Check sumsq_diff too.
+  const Dtype kDiffScaleFactor = 7;
+  caffe_cpu_scale(this->blob_->count(), kDiffScaleFactor, data,
+                  this->blob_->mutable_cpu_diff());
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_diff();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_diff();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_FLOAT_EQ(expected_sumsq, this->blob_->sumsq_data());
+  EXPECT_FLOAT_EQ(expected_sumsq * kDiffScaleFactor * kDiffScaleFactor,
+                  this->blob_->sumsq_diff());
+}
+
+TYPED_TEST(BlobMathTest, TestAsum) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  // Uninitialized Blob should have sum of squares == 0.
+  EXPECT_EQ(0, this->blob_->asum_data());
+  EXPECT_EQ(0, this->blob_->asum_diff());
+  FillerParameter filler_param;
+  filler_param.set_min(-3);
+  filler_param.set_max(3);
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_);
+  Dtype expected_asum = 0;
+  const Dtype* data = this->blob_->cpu_data();
+  for (int i = 0; i < this->blob_->count(); ++i) {
+    expected_asum += std::fabs(data[i]);
+  }
+  // Do a mutable access on the current device,
+  // so that the asum computation is done on that device.
+  // (Otherwise, this would only check the CPU asum implementation.)
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_data();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_FLOAT_EQ(expected_asum, this->blob_->asum_data());
+  EXPECT_EQ(0, this->blob_->asum_diff());
+
+  // Check asum_diff too.
+  const Dtype kDiffScaleFactor = 7;
+  caffe_cpu_scale(this->blob_->count(), kDiffScaleFactor, data,
+                  this->blob_->mutable_cpu_diff());
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_diff();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_diff();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  EXPECT_FLOAT_EQ(expected_asum, this->blob_->asum_data());
+  EXPECT_FLOAT_EQ(expected_asum * kDiffScaleFactor, this->blob_->asum_diff());
+}
+
 }  // namespace caffe

From 91ce7c0603fad74a391eab1e7a7cc3cfedda909c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 6 Oct 2014 23:46:05 -0700
Subject: [PATCH 1079/2053] debug_info: print param (and gradient) stats for
 whole Net after Backward

---
 src/caffe/net.cpp | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 692351fb7d2..e827a067ea1 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -705,6 +705,21 @@ void Net<Dtype>::BackwardTo(int end) {
 template <typename Dtype>
 void Net<Dtype>::Backward() {
   BackwardFromTo(layers_.size() - 1, 0);
+  if (debug_info_) {
+    Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;
+    for (int i = 0; i < params_.size(); ++i) {
+      if (param_owners_[i] >= 0) { continue; }
+      asum_data += params_[i]->asum_data();
+      asum_diff += params_[i]->asum_diff();
+      sumsq_data += params_[i]->sumsq_data();
+      sumsq_diff += params_[i]->sumsq_diff();
+    }
+    const Dtype l2norm_data = std::sqrt(sumsq_data);
+    const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+    LOG(ERROR) << "    [Backward] All net params (data, diff): "
+        << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
+        << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
+  }
 }
 
 template <typename Dtype>

From 3519d05283424730f300742d52f617c190313824 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 19 Jan 2015 11:52:04 -0800
Subject: [PATCH 1080/2053] debug_info in NetParameter so it can be enabled
 outside training

---
 src/caffe/net.cpp           | 3 +--
 src/caffe/proto/caffe.proto | 4 ++++
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index e827a067ea1..4350e056085 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -218,10 +218,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
   GetLearningRateAndWeightDecay();
+  debug_info_ = param.debug_info();
   LOG(INFO) << "Network initialization done.";
   LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-  // Don't display debug info by default.
-  debug_info_ = false;
 }
 
 template <typename Dtype>
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d6eec02087b..d65ef787a4c 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -60,6 +60,10 @@ message NetParameter {
   // Some layers may be included/excluded depending on this state and the states
   // specified in the layers' include and exclude fields.
   optional NetState state = 6;
+
+  // Print debugging information about results while running Net::Forward,
+  // Net::Backward, and Net::Update.
+  optional bool debug_info = 7 [default = false];
 }
 
 // NOTE

From 8b9647223346a2528804cb5bc8943f2065846d7d Mon Sep 17 00:00:00 2001
From: Andre Ambrosio Boechat <boechat107@gmail.com>
Date: Fri, 30 Jan 2015 13:57:31 -0200
Subject: [PATCH 1081/2053] Brief explanation of SLICE layer's attributes

* A sample code was added.
* `slice_dim` and `slice_point` attributes were explained.
---
 docs/tutorial/layers.md | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 5f8f519cdc4..34bb48050e8 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -451,6 +451,26 @@ The `CONCAT` layer is a utility layer that concatenates its multiple input blobs
 
 The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
 
+* Sample
+
+     layers {
+       name: "slicer_label"
+       type: SLICE
+       bottom: "label"
+       ## Example of label with a shape N x 3 x 1 x 1
+       top: "label1"
+       top: "label2"
+       top: "label3"
+       slice_param {
+           slice_dim: 1
+           slice_point: 1
+           slice_point: 2
+       }
+     }
+
+`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). 
+
+
 #### Elementwise Operations
 
 `ELTWISE`

From 75d0e16be912a8dd23eddd8756ee0d278c66d6ab Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 30 Jan 2015 11:25:31 -0800
Subject: [PATCH 1082/2053] lint 1f7c3de

---
 include/caffe/util/device_alternate.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 1a33b947991..4d731e26199 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -7,7 +7,7 @@
 
 // Stub out GPU calls as unavailable.
 
-#define NO_GPU LOG(FATAL) << "Cannot make GPU call in CPU-only mode: check mode setting."
+#define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \

From 33348217a7546ad63a6549f3469b2c49e09f4d31 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 2 Feb 2015 09:45:51 -0800
Subject: [PATCH 1083/2053] fix GoogLeNet license overwritten by back-merge
 (see #1650)

---
 models/bvlc_googlenet/readme.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 8a3bbec4f8b..2e22dfcf59a 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -2,7 +2,7 @@
 name: BVLC GoogleNet Model
 caffemodel: bvlc_googlenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
-license: non-commercial
+license: unrestricted
 sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
 caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
 gist_id: 866e2aa1fd707b89b913
@@ -30,6 +30,4 @@ This model was trained by Sergio Guadarrama @sguada
 
 ## License
 
-The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
-"Researcher shall use the Database only for non-commercial research and educational purposes."
-Accordingly, this model is distributed under a non-commercial license.
+This model is released for unrestricted use.

From 53924a76b66faad2831205c3694e640eccb15fb5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 2 Feb 2015 12:38:43 -0800
Subject: [PATCH 1084/2053] add space after "Loading mean file from"

---
 matlab/caffe/matcaffe.cpp              | 2 +-
 src/caffe/data_transformer.cpp         | 2 +-
 src/caffe/layers/window_data_layer.cpp | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 0ab0b00eb23..fd8397e7ff2 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -343,7 +343,7 @@ static void read_mean(MEX_ARGS) {
     }
     const string& mean_file = mxArrayToString(prhs[0]);
     Blob<float> data_mean;
-    LOG(INFO) << "Loading mean file from" << mean_file;
+    LOG(INFO) << "Loading mean file from: " << mean_file;
     BlobProto blob_proto;
     bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
     if (!result) {
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 209dc0349ad..3d2c78f8ec2 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -19,7 +19,7 @@ DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
     CHECK_EQ(param_.mean_value_size(), 0) <<
       "Cannot specify mean_file and mean_value at the same time";
     const string& mean_file = param.mean_file();
-    LOG(INFO) << "Loading mean file from" << mean_file;
+    LOG(INFO) << "Loading mean file from: " << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 6287b385dc5..83372e074f5 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -189,7 +189,7 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   if (has_mean_file_) {
     const string& mean_file =
           this->transform_param_.mean_file();
-    LOG(INFO) << "Loading mean file from" << mean_file;
+    LOG(INFO) << "Loading mean file from: " << mean_file;
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);

From b6f9dc8f864ebb9cd63398da4de83493e81b5b54 Mon Sep 17 00:00:00 2001
From: Pannous <info@pannous.com>
Date: Fri, 30 Jan 2015 13:38:48 +0100
Subject: [PATCH 1085/2053] fixed small bug: output label_file ->
 label_filename

---
 examples/siamese/convert_mnist_siamese_data.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
index 400d15a2705..71c56a0ae61 100644
--- a/examples/siamese/convert_mnist_siamese_data.cpp
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -36,7 +36,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   std::ifstream image_file(image_filename, std::ios::in | std::ios::binary);
   std::ifstream label_file(label_filename, std::ios::in | std::ios::binary);
   CHECK(image_file) << "Unable to open file " << image_filename;
-  CHECK(label_file) << "Unable to open file " << label_file;
+  CHECK(label_file) << "Unable to open file " << label_filename;
   // Read the magic and the meta data
   uint32_t magic;
   uint32_t num_items;

From 648aed72acf1c506009ddb33d8cace40b75e176e Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 4 Feb 2015 10:15:21 -0800
Subject: [PATCH 1086/2053] fix Nesterov typo found by @bamos

---
 docs/tutorial/solver.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index 8884ea0e1e8..17f793ef778 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -6,7 +6,7 @@ title: Solver / Model Optimization
 The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
 The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.
 
-The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NAG).
+The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NESTEROV).
 
 The solver
 
@@ -126,7 +126,7 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa
 
 ### NAG
 
-**Nesterov's accelerated gradient** (`solver_type: NAG`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
+**Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
 Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].
 
 The weight update formulas look very similar to the SGD updates given above:

From 3b13846486875be2911716bcd1947dca71a97817 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 7 Jan 2015 20:41:09 -0800
Subject: [PATCH 1087/2053] Layer type is a string

---
 include/caffe/common_layers.hpp               |  40 +--
 include/caffe/data_layers.hpp                 |  28 +-
 include/caffe/layer.hpp                       |  27 +-
 include/caffe/layer_factory.hpp               |  52 ++--
 include/caffe/loss_layers.hpp                 |  34 +--
 include/caffe/neuron_layers.hpp               |  39 +--
 include/caffe/util/upgrade_proto.hpp          |   2 +-
 include/caffe/vision_layers.hpp               |  22 +-
 src/caffe/layer_factory.cpp                   |  12 +-
 src/caffe/layers/absval_layer.cpp             |   5 +-
 src/caffe/layers/accuracy_layer.cpp           |   3 +-
 src/caffe/layers/argmax_layer.cpp             |   2 +-
 src/caffe/layers/bnll_layer.cpp               |   3 +-
 src/caffe/layers/concat_layer.cpp             |   3 +-
 src/caffe/layers/contrastive_loss_layer.cpp   |   3 +-
 src/caffe/layers/conv_layer.cpp               |   1 +
 src/caffe/layers/data_layer.cpp               |   2 +-
 src/caffe/layers/deconv_layer.cpp             |   3 +-
 src/caffe/layers/dropout_layer.cpp            |   3 +-
 src/caffe/layers/dummy_data_layer.cpp         |   3 +-
 src/caffe/layers/eltwise_layer.cpp            |   3 +-
 src/caffe/layers/euclidean_loss_layer.cpp     |   3 +-
 src/caffe/layers/exp_layer.cpp                |   3 +-
 src/caffe/layers/flatten_layer.cpp            |   3 +-
 src/caffe/layers/hdf5_data_layer.cpp          |   3 +-
 src/caffe/layers/hdf5_output_layer.cpp        |   3 +-
 src/caffe/layers/hinge_loss_layer.cpp         |   5 +-
 src/caffe/layers/im2col_layer.cpp             |   3 +-
 src/caffe/layers/image_data_layer.cpp         |   3 +-
 src/caffe/layers/infogain_loss_layer.cpp      |   6 +-
 src/caffe/layers/inner_product_layer.cpp      |   3 +-
 src/caffe/layers/lrn_layer.cpp                |   3 +-
 src/caffe/layers/memory_data_layer.cpp        |   3 +-
 .../multinomial_logistic_loss_layer.cpp       |   5 +-
 src/caffe/layers/mvn_layer.cpp                |   3 +-
 src/caffe/layers/pooling_layer.cpp            |   1 -
 src/caffe/layers/power_layer.cpp              |   3 +-
 src/caffe/layers/relu_layer.cpp               |   1 -
 .../sigmoid_cross_entropy_loss_layer.cpp      |   5 +-
 .../sigmoid_cross_entropy_loss_layer.cu       |   2 +-
 src/caffe/layers/silence_layer.cpp            |   3 +-
 src/caffe/layers/slice_layer.cpp              |   3 +-
 src/caffe/layers/softmax_layer.cpp            |   1 +
 src/caffe/layers/softmax_loss_layer.cpp       |   6 +-
 src/caffe/layers/split_layer.cpp              |   5 +-
 src/caffe/layers/threshold_layer.cpp          |   3 +-
 src/caffe/layers/window_data_layer.cpp        |   3 +-
 src/caffe/proto/caffe.proto                   |  54 +---
 src/caffe/test/test_gradient_based_solver.cpp |   6 +-
 src/caffe/test/test_net.cpp                   | 258 +++++++++---------
 src/caffe/test/test_protobuf.cpp              |   2 +-
 src/caffe/test/test_solver.cpp                |   8 +-
 src/caffe/test/test_split_layer.cpp           | 194 ++++++-------
 src/caffe/test/test_upgrade_proto.cpp         | 106 +++----
 src/caffe/util/insert_splits.cpp              |   2 +-
 src/caffe/util/upgrade_proto.cpp              |  74 ++---
 56 files changed, 487 insertions(+), 592 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 9718b825b14..3a5ccd2180e 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -43,9 +43,7 @@ class ArgMaxLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ARGMAX;
-  }
+  virtual inline const char* type() const { return "ArgMax"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -86,9 +84,7 @@ class ConcatLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONCAT;
-  }
+  virtual inline const char* type() const { return "Concat"; }
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -166,9 +162,7 @@ class EltwiseLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ELTWISE;
-  }
+  virtual inline const char* type() const { return "Eltwise"; }
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -207,9 +201,7 @@ class FlattenLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_FLATTEN;
-  }
+  virtual inline const char* type() const { return "Flatten"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -260,9 +252,7 @@ class InnerProductLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_INNER_PRODUCT;
-  }
+  virtual inline const char* type() const { return "InnerProduct"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -296,9 +286,7 @@ class MVNLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MVN;
-  }
+  virtual inline const char* type() const { return "MVN"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -330,9 +318,7 @@ class SilenceLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SILENCE;
-  }
+  virtual inline const char* type() const { return "Silence"; }
   virtual inline int MinBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 0; }
 
@@ -362,9 +348,7 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX;
-  }
+  virtual inline const char* type() const { return "Softmax"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -426,9 +410,7 @@ class SplitLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SPLIT;
-  }
+  virtual inline const char* type() const { return "Split"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
 
@@ -461,9 +443,7 @@ class SliceLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SLICE;
-  }
+  virtual inline const char* type() const { return "Slice"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 2; }
 
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e9c83856a99..e55a0bbbd7b 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -90,9 +90,7 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DATA;
-  }
+  virtual inline const char* type() const { return "Data"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int MinTopBlobs() const { return 1; }
   virtual inline int MaxTopBlobs() const { return 2; }
@@ -120,9 +118,7 @@ class DummyDataLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DUMMY_DATA;
-  }
+  virtual inline const char* type() const { return "DummyData"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int MinTopBlobs() const { return 1; }
 
@@ -155,9 +151,7 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HDF5_DATA;
-  }
+  virtual inline const char* type() const { return "HDF5Data"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int MinTopBlobs() const { return 1; }
 
@@ -195,9 +189,7 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HDF5_OUTPUT;
-  }
+  virtual inline const char* type() const { return "HDF5Output"; }
   // TODO: no limit on the number of blobs
   virtual inline int ExactNumBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 0; }
@@ -235,9 +227,7 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_IMAGE_DATA;
-  }
+  virtual inline const char* type() const { return "ImageData"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
@@ -263,9 +253,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MEMORY_DATA;
-  }
+  virtual inline const char* type() const { return "MemoryData"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
@@ -309,9 +297,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_WINDOW_DATA;
-  }
+  virtual inline const char* type() const { return "WindowData"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 8a8330bca57..c6461c19964 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -179,18 +179,9 @@ class Layer {
   }
 
   /**
-   * @brief Returns the layer type as an enum value.
+   * @brief Returns the layer type.
    */
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_NONE;
-  }
-
-  /**
-   * @brief Returns the layer type name.
-   */
-  virtual inline const string& type_name() const {
-    return LayerParameter_LayerType_Name(type());
-  }
+  virtual inline const char* type() const { return ""; }
 
   /**
    * @brief Returns the exact number of bottom blobs required by the layer,
@@ -347,37 +338,37 @@ class Layer {
                                const vector<Blob<Dtype>*>& top) {
     if (ExactNumBottomBlobs() >= 0) {
       CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
-          << type_name() << " Layer takes " << ExactNumBottomBlobs()
+          << type() << " Layer takes " << ExactNumBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (MinBottomBlobs() >= 0) {
       CHECK_LE(MinBottomBlobs(), bottom.size())
-          << type_name() << " Layer takes at least " << MinBottomBlobs()
+          << type() << " Layer takes at least " << MinBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (MaxBottomBlobs() >= 0) {
       CHECK_GE(MaxBottomBlobs(), bottom.size())
-          << type_name() << " Layer takes at most " << MaxBottomBlobs()
+          << type() << " Layer takes at most " << MaxBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (ExactNumTopBlobs() >= 0) {
       CHECK_EQ(ExactNumTopBlobs(), top.size())
-          << type_name() << " Layer produces " << ExactNumTopBlobs()
+          << type() << " Layer produces " << ExactNumTopBlobs()
           << " top blob(s) as output.";
     }
     if (MinTopBlobs() >= 0) {
       CHECK_LE(MinTopBlobs(), top.size())
-          << type_name() << " Layer produces at least " << MinTopBlobs()
+          << type() << " Layer produces at least " << MinTopBlobs()
           << " top blob(s) as output.";
     }
     if (MaxTopBlobs() >= 0) {
       CHECK_GE(MaxTopBlobs(), top.size())
-          << type_name() << " Layer produces at most " << MaxTopBlobs()
+          << type() << " Layer produces at most " << MaxTopBlobs()
           << " top blob(s) as output.";
     }
     if (EqualNumBottomTopBlobs()) {
       CHECK_EQ(bottom.size(), top.size())
-          << type_name() << " Layer produces one top blob as output for each "
+          << type() << " Layer produces one top blob as output for each "
           << "bottom blob input.";
     }
   }
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index c1fd6aa0d43..ede5d1fae3f 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -12,17 +12,13 @@
  *     // your implementations
  *   };
  *
- * and its type is defined in the protobuffer as
- *
- *   enum LayerType {
- *     // other definitions
- *     AWESOME = 46,
- *   }
+ * and its type is its C++ class name, but without the "Layer" at the end
+ * ("MyAwesomeLayer" -> "MyAwesome").
  *
  * If the layer is going to be created simply by its constructor, in your c++
  * file, add the following line:
  *
- *    REGISTER_LAYER_CLASS(AWESOME, MyAwesomeLayer);
+ *    REGISTER_LAYER_CLASS(MyAwesome);
  *
  * Or, if the layer is going to be created by another creator function, in the
  * format of:
@@ -35,7 +31,7 @@
  * (for example, when your layer has multiple backends, see GetConvolutionLayer
  * for a use case), then you can register the creator function instead, like
  *
- * REGISTER_LAYER_CREATOR(AWESOME, GetMyAwesomeLayer)
+ * REGISTER_LAYER_CREATOR(MyAwesome, GetMyAwesomeLayer)
  *
  * Note that each layer type should only be registered once.
  */
@@ -44,6 +40,7 @@
 #define CAFFE_LAYER_FACTORY_H_
 
 #include <map>
+#include <string>
 
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
@@ -57,7 +54,7 @@ template <typename Dtype>
 class LayerRegistry {
  public:
   typedef Layer<Dtype>* (*Creator)(const LayerParameter&);
-  typedef std::map<LayerParameter_LayerType, Creator> CreatorRegistry;
+  typedef std::map<string, Creator> CreatorRegistry;
 
   static CreatorRegistry& Registry() {
     static CreatorRegistry* g_registry_ = new CreatorRegistry();
@@ -65,8 +62,7 @@ class LayerRegistry {
   }
 
   // Adds a creator.
-  static void AddCreator(const LayerParameter_LayerType& type,
-                         Creator creator) {
+  static void AddCreator(const string& type, Creator creator) {
     CreatorRegistry& registry = Registry();
     CHECK_EQ(registry.count(type), 0)
         << "Layer type " << type << " already registered.";
@@ -76,9 +72,10 @@ class LayerRegistry {
   // Get a layer using a LayerParameter.
   static Layer<Dtype>* CreateLayer(const LayerParameter& param) {
     LOG(INFO) << "Creating layer " << param.name();
-    const LayerParameter_LayerType& type = param.type();
+    const string& type = param.type();
     CreatorRegistry& registry = Registry();
-    CHECK_EQ(registry.count(type), 1);
+    CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
+        << " (known types: " << LayerTypeList() << ")";
     return registry[type](param);
   }
 
@@ -86,13 +83,26 @@ class LayerRegistry {
   // Layer registry should never be instantiated - everything is done with its
   // static variables.
   LayerRegistry() {}
+
+  static string LayerTypeList() {
+    CreatorRegistry& registry = Registry();
+    string layer_types;
+    for (typename CreatorRegistry::iterator iter = registry.begin();
+         iter != registry.end(); ++iter) {
+      if (iter != registry.begin()) {
+        layer_types += ", ";
+      }
+      layer_types += iter->first;
+    }
+    return layer_types;
+  }
 };
 
 
 template <typename Dtype>
 class LayerRegisterer {
  public:
-  LayerRegisterer(const LayerParameter_LayerType& type,
+  LayerRegisterer(const string& type,
                   Layer<Dtype>* (*creator)(const LayerParameter&)) {
     // LOG(INFO) << "Registering layer type: " << type;
     LayerRegistry<Dtype>::AddCreator(type, creator);
@@ -101,17 +111,15 @@ class LayerRegisterer {
 
 
 #define REGISTER_LAYER_CREATOR(type, creator)                                  \
-  static LayerRegisterer<float> g_creator_f_##type(                            \
-      LayerParameter_LayerType_##type, creator<float>);                        \
-  static LayerRegisterer<double> g_creator_d_##type(                           \
-      LayerParameter_LayerType_##type, creator<double>)
+  static LayerRegisterer<float> g_creator_f_##type(#type, creator<float>);     \
+  static LayerRegisterer<double> g_creator_d_##type(#type, creator<double>)    \
 
-#define REGISTER_LAYER_CLASS(type, clsname)                                    \
+#define REGISTER_LAYER_CLASS(type)                                             \
   template <typename Dtype>                                                    \
-  Layer<Dtype>* Creator_##clsname(const LayerParameter& param) {               \
-    return new clsname<Dtype>(param);                                          \
+  Layer<Dtype>* Creator_##type##Layer(const LayerParameter& param) {           \
+    return new type##Layer<Dtype>(param);                                      \
   }                                                                            \
-  REGISTER_LAYER_CREATOR(type, Creator_##clsname)
+  REGISTER_LAYER_CREATOR(type, Creator_##type##Layer)
 
 }  // namespace caffe
 
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 600a13b8867..f14b9716a4a 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -37,10 +37,7 @@ class AccuracyLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ACCURACY;
-  }
-
+  virtual inline const char* type() const { return "Accuracy"; }
   virtual inline int ExactNumBottomBlobs() const { return 2; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -154,9 +151,7 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
       const vector<Blob<Dtype>*>& top);
 
   virtual inline int ExactNumBottomBlobs() const { return 3; }
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONTRASTIVE_LOSS;
-  }
+  virtual inline const char* type() const { return "ContrastiveLoss"; }
   /**
    * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
    * to the first two inputs.
@@ -242,10 +237,7 @@ class EuclideanLossLayer : public LossLayer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
-  }
-
+  virtual inline const char* type() const { return "EuclideanLoss"; }
   /**
    * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
    * to both inputs -- override to return true and always allow force_backward.
@@ -351,9 +343,7 @@ class HingeLossLayer : public LossLayer<Dtype> {
   explicit HingeLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_HINGE_LOSS;
-  }
+  virtual inline const char* type() const { return "HingeLoss"; }
 
  protected:
   /// @copydoc HingeLossLayer
@@ -440,9 +430,7 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int MaxBottomBlobs() const { return 3; }
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_INFOGAIN_LOSS;
-  }
+  virtual inline const char* type() const { return "InfogainLoss"; }
 
  protected:
   /// @copydoc InfogainLossLayer
@@ -524,9 +512,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
-  }
+  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
 
  protected:
   /// @copydoc MultinomialLogisticLossLayer
@@ -606,9 +592,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
-  }
+  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
 
  protected:
   /// @copydoc SigmoidCrossEntropyLossLayer
@@ -711,9 +695,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SOFTMAX_LOSS;
-  }
+  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
   virtual inline int ExactNumBottomBlobs() const { return -1; }
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int MaxBottomBlobs() const { return 3; }
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 5daeeefe7ae..b65d98435bc 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -29,9 +29,6 @@ class NeuronLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_NONE;
-  }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 };
@@ -54,9 +51,7 @@ class AbsValLayer : public NeuronLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_ABSVAL;
-  }
+  virtual inline const char* type() const { return "AbsVal"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -113,9 +108,7 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   explicit BNLLLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_BNLL;
-  }
+  virtual inline const char* type() const { return "BNLL"; }
 
  protected:
   /// @copydoc BNLLLayer
@@ -173,9 +166,7 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DROPOUT;
-  }
+  virtual inline const char* type() const { return "Dropout"; }
 
  protected:
   /**
@@ -233,9 +224,7 @@ class ExpLayer : public NeuronLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_EXP;
-  }
+  virtual inline const char* type() const { return "Exp"; }
 
  protected:
   /**
@@ -298,9 +287,7 @@ class PowerLayer : public NeuronLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_POWER;
-  }
+  virtual inline const char* type() const { return "Power"; }
 
  protected:
   /**
@@ -369,9 +356,7 @@ class ReLULayer : public NeuronLayer<Dtype> {
   explicit ReLULayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_RELU;
-  }
+  virtual inline const char* type() const { return "ReLU"; }
 
  protected:
   /**
@@ -465,9 +450,7 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   explicit SigmoidLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_SIGMOID;
-  }
+  virtual inline const char* type() const { return "Sigmoid"; }
 
  protected:
   /**
@@ -549,9 +532,7 @@ class TanHLayer : public NeuronLayer<Dtype> {
   explicit TanHLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_TANH;
-  }
+  virtual inline const char* type() const { return "TanH"; }
 
  protected:
   /**
@@ -639,9 +620,7 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_THRESHOLD;
-  }
+  virtual inline const char* type() const { return "Threshold"; }
 
  protected:
   /**
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 45483685133..0627afe694c 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -27,7 +27,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
 bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
                            LayerParameter* layer_param);
 
-LayerParameter_LayerType UpgradeV0LayerType(const string& type);
+const char* UpgradeV0LayerType(const string& type);
 
 // Return true iff any layer contains deprecated data transformation parameters.
 bool NetNeedsDataUpgrade(const NetParameter& net_param);
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index fc178e4c904..38e369c4092 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -160,9 +160,8 @@ class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
    */
   explicit ConvolutionLayer(const LayerParameter& param)
       : BaseConvolutionLayer<Dtype>(param) {}
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_CONVOLUTION;
-  }
+
+  virtual inline const char* type() const { return "Convolution"; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -196,9 +195,8 @@ class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
  public:
   explicit DeconvolutionLayer(const LayerParameter& param)
       : BaseConvolutionLayer<Dtype>(param) {}
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_DECONVOLUTION;
-  }
+
+  virtual inline const char* type() const { return "Deconvolution"; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -272,9 +270,7 @@ class Im2colLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_IM2COL;
-  }
+  virtual inline const char* type() const { return "Im2col"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -314,9 +310,7 @@ class LRNLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_LRN;
-  }
+  virtual inline const char* type() const { return "LRN"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -392,9 +386,7 @@ class PoolingLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline LayerParameter_LayerType type() const {
-    return LayerParameter_LayerType_POOLING;
-  }
+  virtual inline const char* type() const { return "Pooling"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int MinTopBlobs() const { return 1; }
   // MAX POOL layers can output an extra top blob for the mask;
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 80694834a33..c3fd1f30ff2 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -29,7 +29,7 @@ Layer<Dtype>* GetConvolutionLayer(
   }
 }
 
-REGISTER_LAYER_CREATOR(CONVOLUTION, GetConvolutionLayer);
+REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);
 
 // Get pooling layer according to engine.
 template <typename Dtype>
@@ -59,7 +59,7 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
   }
 }
 
-REGISTER_LAYER_CREATOR(POOLING, GetPoolingLayer);
+REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
 
 // Get relu layer according to engine.
 template <typename Dtype>
@@ -82,7 +82,7 @@ Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
   }
 }
 
-REGISTER_LAYER_CREATOR(RELU, GetReLULayer);
+REGISTER_LAYER_CREATOR(ReLU, GetReLULayer);
 
 // Get sigmoid layer according to engine.
 template <typename Dtype>
@@ -105,7 +105,7 @@ Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
   }
 }
 
-REGISTER_LAYER_CREATOR(SIGMOID, GetSigmoidLayer);
+REGISTER_LAYER_CREATOR(Sigmoid, GetSigmoidLayer);
 
 // Get softmax layer according to engine.
 template <typename Dtype>
@@ -128,7 +128,7 @@ Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
   }
 }
 
-REGISTER_LAYER_CREATOR(SOFTMAX, GetSoftmaxLayer);
+REGISTER_LAYER_CREATOR(Softmax, GetSoftmaxLayer);
 
 // Get tanh layer according to engine.
 template <typename Dtype>
@@ -151,7 +151,7 @@ Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
   }
 }
 
-REGISTER_LAYER_CREATOR(TANH, GetTanHLayer);
+REGISTER_LAYER_CREATOR(TanH, GetTanHLayer);
 
 // Layers that use their constructor as their default creator should be
 // registered in their corresponding cpp files. Do not register them here.
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index 0d054ee5665..5ce28c9e2b4 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -10,7 +10,7 @@ template <typename Dtype>
 void AbsValLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::LayerSetUp(bottom, top);
-  CHECK_NE(top[0], bottom[0]) << this->type_name() << " Layer does not "
+  CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
     "allow in-place computation.";
 }
 
@@ -40,5 +40,6 @@ STUB_GPU(AbsValLayer);
 #endif
 
 INSTANTIATE_CLASS(AbsValLayer);
-REGISTER_LAYER_CLASS(ABSVAL, AbsValLayer);
+REGISTER_LAYER_CLASS(AbsVal);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 800b848f3bc..3e8df34c0d6 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -64,5 +64,6 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(AccuracyLayer);
-REGISTER_LAYER_CLASS(ACCURACY, AccuracyLayer);
+REGISTER_LAYER_CLASS(Accuracy);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 15e199eb600..c4040cdcaaa 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -58,6 +58,6 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(ArgMaxLayer);
-REGISTER_LAYER_CLASS(ARGMAX, ArgMaxLayer);
+REGISTER_LAYER_CLASS(ArgMax);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index cb3583ae8be..9ba0ea9a715 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -43,5 +43,6 @@ STUB_GPU(BNLLLayer);
 #endif
 
 INSTANTIATE_CLASS(BNLLLayer);
-REGISTER_LAYER_CLASS(BNLL, BNLLLayer);
+REGISTER_LAYER_CLASS(BNLL);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 420821952b0..fc88433c42b 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -105,5 +105,6 @@ STUB_GPU(ConcatLayer);
 #endif
 
 INSTANTIATE_CLASS(ConcatLayer);
-REGISTER_LAYER_CLASS(CONCAT, ConcatLayer);
+REGISTER_LAYER_CLASS(Concat);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 0d0b443b66b..0692c11c257 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -97,5 +97,6 @@ STUB_GPU(ContrastiveLossLayer);
 #endif
 
 INSTANTIATE_CLASS(ContrastiveLossLayer);
-REGISTER_LAYER_CLASS(CONTRASTIVE_LOSS, ContrastiveLossLayer);
+REGISTER_LAYER_CLASS(ContrastiveLoss);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 9fd2fc6a15f..c0c9f6f3371 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -79,4 +79,5 @@ STUB_GPU(ConvolutionLayer);
 #endif
 
 INSTANTIATE_CLASS(ConvolutionLayer);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 96964566630..227db201759 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -129,6 +129,6 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 }
 
 INSTANTIATE_CLASS(DataLayer);
-REGISTER_LAYER_CLASS(DATA, DataLayer);
+REGISTER_LAYER_CLASS(Data);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index 59114f017bf..e6d65ab526b 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -81,5 +81,6 @@ STUB_GPU(DeconvolutionLayer);
 #endif
 
 INSTANTIATE_CLASS(DeconvolutionLayer);
-REGISTER_LAYER_CLASS(DECONVOLUTION, DeconvolutionLayer);
+REGISTER_LAYER_CLASS(Deconvolution);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 8c8936a711e..5f81cc1c692 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -73,5 +73,6 @@ STUB_GPU(DropoutLayer);
 #endif
 
 INSTANTIATE_CLASS(DropoutLayer);
-REGISTER_LAYER_CLASS(DROPOUT, DropoutLayer);
+REGISTER_LAYER_CLASS(Dropout);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 15cf5a58ac6..d254eb1f961 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -93,5 +93,6 @@ void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(DummyDataLayer);
-REGISTER_LAYER_CLASS(DUMMY_DATA, DummyDataLayer);
+REGISTER_LAYER_CLASS(DummyData);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 0c239f4ac11..bbc34449588 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -163,5 +163,6 @@ STUB_GPU(EltwiseLayer);
 #endif
 
 INSTANTIATE_CLASS(EltwiseLayer);
-REGISTER_LAYER_CLASS(ELTWISE, EltwiseLayer);
+REGISTER_LAYER_CLASS(Eltwise);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index d965027f712..b539d3487f5 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -54,5 +54,6 @@ STUB_GPU(EuclideanLossLayer);
 #endif
 
 INSTANTIATE_CLASS(EuclideanLossLayer);
-REGISTER_LAYER_CLASS(EUCLIDEAN_LOSS, EuclideanLossLayer);
+REGISTER_LAYER_CLASS(EuclideanLoss);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index 92cb5deb1e6..c7e7c60cfad 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -64,5 +64,6 @@ STUB_GPU(ExpLayer);
 #endif
 
 INSTANTIATE_CLASS(ExpLayer);
-REGISTER_LAYER_CLASS(EXP, ExpLayer);
+REGISTER_LAYER_CLASS(Exp);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index ec43cababca..eb7b42bc10b 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -34,5 +34,6 @@ STUB_GPU(FlattenLayer);
 #endif
 
 INSTANTIATE_CLASS(FlattenLayer);
-REGISTER_LAYER_CLASS(FLATTEN, FlattenLayer);
+REGISTER_LAYER_CLASS(Flatten);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 706c4a18319..77555211082 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -121,5 +121,6 @@ STUB_GPU_FORWARD(HDF5DataLayer, Forward);
 #endif
 
 INSTANTIATE_CLASS(HDF5DataLayer);
-REGISTER_LAYER_CLASS(HDF5_DATA, HDF5DataLayer);
+REGISTER_LAYER_CLASS(HDF5Data);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 4a72a18aa12..d2fdeffc53f 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -70,5 +70,6 @@ STUB_GPU(HDF5OutputLayer);
 #endif
 
 INSTANTIATE_CLASS(HDF5OutputLayer);
-REGISTER_LAYER_CLASS(HDF5_OUTPUT, HDF5OutputLayer);
+REGISTER_LAYER_CLASS(HDF5Output);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 4dfafcc8648..a2fb2a18309 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -47,7 +47,7 @@ template <typename Dtype>
 void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
@@ -77,5 +77,6 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(HingeLossLayer);
-REGISTER_LAYER_CLASS(HINGE_LOSS, HingeLossLayer);
+REGISTER_LAYER_CLASS(HingeLoss);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 2c4bb9029e0..112226116c8 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -88,5 +88,6 @@ STUB_GPU(Im2colLayer);
 #endif
 
 INSTANTIATE_CLASS(Im2colLayer);
-REGISTER_LAYER_CLASS(IM2COL, Im2colLayer);
+REGISTER_LAYER_CLASS(Im2col);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index ef6a342c30a..b96be6ad72d 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -149,5 +149,6 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
 }
 
 INSTANTIATE_CLASS(ImageDataLayer);
-REGISTER_LAYER_CLASS(IMAGE_DATA, ImageDataLayer);
+REGISTER_LAYER_CLASS(ImageData);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 8910431d42b..a1e0b40de0e 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -75,11 +75,11 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down.size() > 2 && propagate_down[2]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to infogain inputs.";
   }
   if (propagate_down[0]) {
@@ -106,5 +106,5 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(InfogainLossLayer);
-REGISTER_LAYER_CLASS(INFOGAIN_LOSS, InfogainLossLayer);
+REGISTER_LAYER_CLASS(InfogainLoss);
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index ecb849ba4d8..b1ec6cb25c0 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -104,5 +104,6 @@ STUB_GPU(InnerProductLayer);
 #endif
 
 INSTANTIATE_CLASS(InnerProductLayer);
-REGISTER_LAYER_CLASS(INNER_PRODUCT, InnerProductLayer);
+REGISTER_LAYER_CLASS(InnerProduct);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index a878cf847b3..5e3e7c429ef 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -252,5 +252,6 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
 #endif
 
 INSTANTIATE_CLASS(LRNLayer);
-REGISTER_LAYER_CLASS(LRN, LRNLayer);
+REGISTER_LAYER_CLASS(LRN);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 613ca2d4c76..3272b66ac55 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -72,5 +72,6 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);
-REGISTER_LAYER_CLASS(MEMORY_DATA, MemoryDataLayer);
+REGISTER_LAYER_CLASS(MemoryData);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 78a1f60fca9..4267a594a0f 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -41,7 +41,7 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
@@ -62,5 +62,6 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
 }
 
 INSTANTIATE_CLASS(MultinomialLogisticLossLayer);
-REGISTER_LAYER_CLASS(MULTINOMIAL_LOGISTIC_LOSS, MultinomialLogisticLossLayer);
+REGISTER_LAYER_CLASS(MultinomialLogisticLoss);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 104ad95c7ed..b74d7b4f300 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -159,5 +159,6 @@ STUB_GPU(MVNLayer);
 #endif
 
 INSTANTIATE_CLASS(MVNLayer);
-REGISTER_LAYER_CLASS(MVN, MVNLayer);
+REGISTER_LAYER_CLASS(MVN);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 2bfbb01fcec..6f4c69c861e 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -314,5 +314,4 @@ STUB_GPU(PoolingLayer);
 
 INSTANTIATE_CLASS(PoolingLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 69bd120e3dc..4fe34c49f32 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -99,5 +99,6 @@ STUB_GPU(PowerLayer);
 #endif
 
 INSTANTIATE_CLASS(PowerLayer);
-REGISTER_LAYER_CLASS(POWER, PowerLayer);
+REGISTER_LAYER_CLASS(Power);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 7d5e6034589..cc00319a578 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -43,5 +43,4 @@ STUB_GPU(ReLULayer);
 
 INSTANTIATE_CLASS(ReLULayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index d1e327a54c9..077d949981c 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -53,7 +53,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
@@ -75,5 +75,6 @@ STUB_GPU(SigmoidCrossEntropyLossLayer);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
-REGISTER_LAYER_CLASS(SIGMOID_CROSS_ENTROPY_LOSS, SigmoidCrossEntropyLossLayer);
+REGISTER_LAYER_CLASS(SigmoidCrossEntropyLoss);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index d9db4af64e6..08f7f492297 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -33,7 +33,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 9bd20574816..4abf9eff4a2 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -22,5 +22,6 @@ STUB_GPU(SilenceLayer);
 #endif
 
 INSTANTIATE_CLASS(SilenceLayer);
-REGISTER_LAYER_CLASS(SILENCE, SilenceLayer);
+REGISTER_LAYER_CLASS(Silence);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 60a5ecfa416..46c3acd6513 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -137,5 +137,6 @@ STUB_GPU(SliceLayer);
 #endif
 
 INSTANTIATE_CLASS(SliceLayer);
-REGISTER_LAYER_CLASS(SLICE, SliceLayer);
+REGISTER_LAYER_CLASS(Slice);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index c7b09fffd7a..25142fdec53 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -92,4 +92,5 @@ STUB_GPU(SoftmaxLayer);
 #endif
 
 INSTANTIATE_CLASS(SoftmaxLayer);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 14bd383718c..bf20b6056e4 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -14,7 +14,7 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   LayerParameter softmax_param(this->layer_param_);
-  softmax_param.set_type(LayerParameter_LayerType_SOFTMAX);
+  softmax_param.set_type("Softmax");
   softmax_layer_.reset(LayerRegistry<Dtype>::CreateLayer(softmax_param));
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
@@ -80,7 +80,7 @@ template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[1]) {
-    LOG(FATAL) << this->type_name()
+    LOG(FATAL) << this->type()
                << " Layer cannot backpropagate to label inputs.";
   }
   if (propagate_down[0]) {
@@ -116,6 +116,6 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
-REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer);
+REGISTER_LAYER_CLASS(SoftmaxWithLoss);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 51ac61f4cae..d6929b99683 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -16,7 +16,7 @@ void SplitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     // the backward pass.  (Technically, it should be possible to share the diff
     // blob of the first split output with the input, but this seems to cause
     // some strange effects in practice...)
-    CHECK_NE(top[i], bottom[0]) << this->type_name() << " Layer does not "
+    CHECK_NE(top[i], bottom[0]) << this->type() << " Layer does not "
         "allow in-place computation.";
     top[i]->Reshape(bottom[0]->num(), bottom[0]->channels(),
                        bottom[0]->height(), bottom[0]->width());
@@ -56,5 +56,6 @@ STUB_GPU(SplitLayer);
 #endif
 
 INSTANTIATE_CLASS(SplitLayer);
-REGISTER_LAYER_CLASS(SPLIT, SplitLayer);
+REGISTER_LAYER_CLASS(Split);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index 9e68c32d348..2365e7b9c72 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -29,5 +29,6 @@ STUB_GPU_FORWARD(ThresholdLayer, Forward);
 #endif
 
 INSTANTIATE_CLASS(ThresholdLayer);
-REGISTER_LAYER_CLASS(THRESHOLD, ThresholdLayer);
+REGISTER_LAYER_CLASS(Threshold);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 83372e074f5..23ec83d166b 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -463,5 +463,6 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
 }
 
 INSTANTIATE_CLASS(WindowDataLayer);
-REGISTER_LAYER_CLASS(WINDOW_DATA, WindowDataLayer);
+REGISTER_LAYER_CLASS(WindowData);
+
 }  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index dd2c1471c29..1fea3445415 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -225,59 +225,7 @@ message LayerParameter {
   repeated NetStateRule include = 32;
   repeated NetStateRule exclude = 33;
 
-  // NOTE
-  // Add new LayerTypes to the enum below in lexicographical order (other than
-  // starting with NONE), starting with the next available ID in the comment
-  // line above the enum. Update the next available ID when you add a new
-  // LayerType.
-  //
-  // LayerType next available ID: 40 (last added: DECONVOLUTION)
-  enum LayerType {
-    // "NONE" layer type is 0th enum element so that we don't cause confusion
-    // by defaulting to an existent LayerType (instead, should usually error if
-    // the type is unspecified).
-    NONE = 0;
-    ABSVAL = 35;
-    ACCURACY = 1;
-    ARGMAX = 30;
-    BNLL = 2;
-    CONCAT = 3;
-    CONTRASTIVE_LOSS = 37;
-    CONVOLUTION = 4;
-    DATA = 5;
-    DECONVOLUTION = 39;
-    DROPOUT = 6;
-    DUMMY_DATA = 32;
-    EUCLIDEAN_LOSS = 7;
-    ELTWISE = 25;
-    EXP = 38;
-    FLATTEN = 8;
-    HDF5_DATA = 9;
-    HDF5_OUTPUT = 10;
-    HINGE_LOSS = 28;
-    IM2COL = 11;
-    IMAGE_DATA = 12;
-    INFOGAIN_LOSS = 13;
-    INNER_PRODUCT = 14;
-    LRN = 15;
-    MEMORY_DATA = 29;
-    MULTINOMIAL_LOGISTIC_LOSS = 16;
-    MVN = 34;
-    POOLING = 17;
-    POWER = 26;
-    RELU = 18;
-    SIGMOID = 19;
-    SIGMOID_CROSS_ENTROPY_LOSS = 27;
-    SILENCE = 36;
-    SOFTMAX = 20;
-    SOFTMAX_LOSS = 21;
-    SPLIT = 22;
-    SLICE = 33;
-    TANH = 23;
-    WINDOW_DATA = 24;
-    THRESHOLD = 31;
-  }
-  optional LayerType type = 5; // the layer type from the enum above
+  optional string type = 5; // the layer type from the enum above
 
   // The blobs containing the numeric parameters of the layer
   repeated BlobProto blobs = 6;
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 65de52aae4a..1d8192aebbc 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -66,7 +66,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "  name: 'TestNetwork' "
        "  layers: { "
        "    name: 'data' "
-       "    type: DUMMY_DATA "
+       "    type: 'DummyData' "
        "    dummy_data_param { "
        "      num: " << num_ << " "
        "      channels: " << channels_ << " "
@@ -85,7 +85,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "  } "
        "  layers: { "
        "    name: 'innerprod' "
-       "    type: INNER_PRODUCT "
+       "    type: 'InnerProduct' "
        "    inner_product_param { "
        "      num_output: 1 "
        "      weight_filler { "
@@ -102,7 +102,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "  } "
        "  layers: { "
        "    name: 'loss' "
-       "    type: EUCLIDEAN_LOSS "
+       "    type: 'EuclideanLoss' "
        "    bottom: 'innerprod' "
        "    bottom: 'targets' "
        "  } "
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 319958fee49..a4e14f914c5 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -61,7 +61,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "name: 'TinyTestNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -85,7 +85,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1000 "
         "    weight_filler { "
@@ -106,7 +106,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: SOFTMAX_LOSS "
+        "  type: 'SoftmaxWithLoss' "
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
         "  top: 'top_loss' "
@@ -115,7 +115,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
       proto +=
           "layers: { "
           "  name: 'loss' "
-          "  type: ACCURACY "
+          "  type: 'Accuracy' "
           "  bottom: 'innerproduct' "
           "  bottom: 'label' "
           "  top: 'accuracy' "
@@ -132,7 +132,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "name: 'TinyTestEuclidLossNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -152,7 +152,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1 "
         "    weight_filler { "
@@ -173,7 +173,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
         "} ";
@@ -192,7 +192,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "name: 'TrickyTestNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -212,7 +212,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1000 "
         "    weight_filler { "
@@ -233,7 +233,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 1 "
         "    weight_filler { "
@@ -254,7 +254,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: SOFTMAX_LOSS " +
+        "  type: 'SoftmaxWithLoss' " +
         loss_weight_stream.str() +
         "  bottom: 'transformed_data' "
         "  bottom: 'transformed_label' "
@@ -262,8 +262,8 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
-  // loss_weight is the loss weight for the EUCLIDEAN_LOSS layer output.
-  // midnet_loss_weight is the loss weight for the first INNER_PRODUCT layer
+  // loss_weight is the loss weight for the 'EuclideanLoss' layer output.
+  // midnet_loss_weight is the loss weight for the first 'InnerProduct' layer
   // output.  Should both default to 0.0 if unspecified (i.e., if NULL is
   // passed to this function).
   virtual void InitUnsharedWeightsNet(const Dtype* loss_weight = NULL,
@@ -279,7 +279,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     proto <<
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -294,7 +294,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct1' "
-       "  type: INNER_PRODUCT "
+       "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: " << bias_term <<
@@ -322,7 +322,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: " << bias_term <<
@@ -346,7 +346,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS ";
+        "  type: 'EuclideanLoss' ";
     if (loss_weight) {
       proto << "  loss_weight: " << *loss_weight << " ";
     }
@@ -362,7 +362,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "name: 'SharedWeightsNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 5 "
         "    channels: 2 "
@@ -377,7 +377,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -392,7 +392,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -407,7 +407,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'innerproduct1' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -419,7 +419,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "name: 'DiffDataUnsharedWeightsNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 10 "
         "    channels: 10 "
@@ -439,7 +439,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -454,7 +454,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -469,7 +469,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'data2' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -481,7 +481,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "name: 'DiffDataSharedWeightsNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DUMMY_DATA "
+        "  type: 'DummyData' "
         "  dummy_data_param { "
         "    num: 10 "
         "    channels: 10 "
@@ -501,7 +501,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct1' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -516,7 +516,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'innerproduct2' "
-        "  type: INNER_PRODUCT "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: false "
@@ -531,7 +531,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'loss' "
-        "  type: EUCLIDEAN_LOSS "
+        "  type: 'EuclideanLoss' "
         "  bottom: 'data2' "
         "  bottom: 'innerproduct2' "
         "} ";
@@ -548,7 +548,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "input_dim: 100 "
         "layers: { "
         "  name: 'conv1' "
-        "  type: CONVOLUTION "
+        "  type: 'Convolution' "
         "  bottom: 'data' "
         "  top: 'conv1' "
         "  convolution_param { "
@@ -567,13 +567,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'relu1' "
-        "  type: RELU "
+        "  type: 'ReLU' "
         "  bottom: 'conv1' "
         "  top: 'conv1' "
         "} "
         "layers: { "
         "  name: 'pool1' "
-        "  type: POOLING "
+        "  type: 'Pooling' "
         "  bottom: 'conv1' "
         "  top: 'pool1' "
         "  pooling_param { "
@@ -584,7 +584,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'norm1' "
-        "  type: LRN "
+        "  type: 'LRN' "
         "  bottom: 'pool1' "
         "  top: 'norm1' "
         "  lrn_param { "
@@ -593,7 +593,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} "
         "layers: { "
         "  name: 'softmax' "
-        "  type: SOFTMAX "
+        "  type: 'Softmax' "
         "  bottom: 'norm1' "
         "  top: 'softmax' "
         "} ";
@@ -702,7 +702,7 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
 TYPED_TEST(NetTest, TestLossWeight) {
   typedef typename TypeParam::Dtype Dtype;
   // First, compute the loss and gradients with no loss_weight specified.
-  // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default
+  // In this case, the loss weight for the 'EuclideanLoss' layer should default
   // to 1.
   vector<Blob<Dtype>*> bottom;
   Caffe::set_random_seed(this->seed_);
@@ -796,8 +796,8 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   const bool kForceBackward = true;
   const Dtype kErrorMargin = 1e-4;
 
-  // Get the loss and gradients with EUCLIDEAN_LOSS weight 1,
-  // INNER_PRODUCT weight 1.
+  // Get the loss and gradients with 'EuclideanLoss' weight 1,
+  // 'InnerProduct' weight 1.
   loss_weight = 1;
   midnet_loss_weight = 1;
   Caffe::set_random_seed(this->seed_);
@@ -925,7 +925,7 @@ TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
   this->InitTinyNet(kForceBackward, kAccuracyLayer);
   EXPECT_TRUE(this->net_->has_blob("accuracy"));
   vector<Blob<Dtype>*> bottom;
-  // Test that we can do Backward even though we have an ACCURACY layer.
+  // Test that we can do Backward even though we have an 'Accuracy' layer.
   this->net_->ForwardBackward(bottom);
 }
 
@@ -1263,19 +1263,19 @@ TEST_F(FilterNetTest, TestNoFilter) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1287,7 +1287,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "name: 'LeNet' "
       "layers { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1301,7 +1301,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1315,7 +1315,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "  blobs_lr: 1 "
@@ -1334,7 +1334,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'ip1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
       "  blobs_lr: 1 "
@@ -1351,7 +1351,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'accuracy' "
-      "  type: ACCURACY "
+      "  type: 'Accuracy' "
       "  bottom: 'ip1' "
       "  bottom: 'label' "
       "  top: 'accuracy' "
@@ -1359,7 +1359,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
       "  bottom: 'label' "
       "  top: 'loss' "
@@ -1370,7 +1370,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "name: 'LeNet' "
       "layers { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1384,7 +1384,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "  blobs_lr: 1 "
@@ -1403,7 +1403,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'ip1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
       "  blobs_lr: 1 "
@@ -1420,7 +1420,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
       "  bottom: 'label' "
       "  top: 'loss' "
@@ -1429,7 +1429,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "name: 'LeNet' "
       "layers { "
       "  name: 'mnist' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  data_param { "
@@ -1443,7 +1443,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "  blobs_lr: 1 "
@@ -1462,7 +1462,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'ip1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
       "  blobs_lr: 1 "
@@ -1479,7 +1479,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'accuracy' "
-      "  type: ACCURACY "
+      "  type: 'Accuracy' "
       "  bottom: 'ip1' "
       "  bottom: 'label' "
       "  top: 'accuracy' "
@@ -1487,7 +1487,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
       "  bottom: 'label' "
       "  top: 'loss' "
@@ -1519,20 +1519,20 @@ TEST_F(FilterNetTest, TestFilterOutByStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  include: { stage: 'mystage' } "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1540,13 +1540,13 @@ TEST_F(FilterNetTest, TestFilterOutByStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1558,20 +1558,20 @@ TEST_F(FilterNetTest, TestFilterOutByStage2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1579,13 +1579,13 @@ TEST_F(FilterNetTest, TestFilterOutByStage2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1598,20 +1598,20 @@ TEST_F(FilterNetTest, TestFilterInByStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1623,20 +1623,20 @@ TEST_F(FilterNetTest, TestFilterInByStage2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { stage: 'mystage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1649,20 +1649,20 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' stage: 'myotherstage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1672,13 +1672,13 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1692,13 +1692,13 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'myotherstage' } "
@@ -1706,7 +1706,7 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1720,20 +1720,20 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' stage: 'myotherstage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { stage: 'mystage' } "
@@ -1747,20 +1747,20 @@ TEST_F(FilterNetTest, TestFilterInByNotStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { not_stage: 'myotherstage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { not_stage: 'myotherstage' } "
@@ -1774,20 +1774,20 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { not_stage: 'mystage' } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { not_stage: 'mystage' } "
@@ -1797,7 +1797,7 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} ";
@@ -1809,20 +1809,20 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 3 } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1830,13 +1830,13 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1848,20 +1848,20 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: -3 } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1869,13 +1869,13 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1887,20 +1887,20 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 0 } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1913,20 +1913,20 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 3 } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1938,20 +1938,20 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: 0 } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1964,20 +1964,20 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: -3 } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -1989,20 +1989,20 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { min_level: 2  phase: TEST } "
@@ -2016,13 +2016,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
@@ -2032,13 +2032,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { min_level: 2  phase: TEST } "
@@ -2052,13 +2052,13 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
@@ -2066,7 +2066,7 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  include: { min_level: 2  phase: TEST } "
@@ -2085,20 +2085,20 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { min_level: 2  phase: TRAIN } "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  exclude: { min_level: 2  phase: TEST } "
@@ -2112,13 +2112,13 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "  exclude: { min_level: 2  phase: TEST } "
@@ -2128,13 +2128,13 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { min_level: 2  phase: TRAIN } "
diff --git a/src/caffe/test/test_protobuf.cpp b/src/caffe/test/test_protobuf.cpp
index 0c502d6dd36..01de461afdf 100644
--- a/src/caffe/test/test_protobuf.cpp
+++ b/src/caffe/test/test_protobuf.cpp
@@ -16,7 +16,7 @@ class ProtoTest : public ::testing::Test {};
 TEST_F(ProtoTest, TestSerialization) {
   LayerParameter param;
   param.set_name("test");
-  param.set_type(LayerParameter_LayerType_NONE);
+  param.set_type("Test");
   std::cout << "Printing in binary format." << std::endl;
   std::cout << param.SerializeAsString() << std::endl;
   std::cout << "Printing in text format." << std::endl;
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index a7dbf77fd95..d3f646c5bb6 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -53,7 +53,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  name: 'TestNetwork' "
      "  layers: { "
      "    name: 'data' "
-     "    type: DUMMY_DATA "
+     "    type: 'DummyData' "
      "    dummy_data_param { "
      "      num: 5 "
      "      channels: 3 "
@@ -69,7 +69,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  } "
      "  layers: { "
      "    name: 'innerprod' "
-     "    type: INNER_PRODUCT "
+     "    type: 'InnerProduct' "
      "    inner_product_param { "
      "      num_output: 10 "
      "    } "
@@ -78,7 +78,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  } "
      "  layers: { "
      "    name: 'accuracy' "
-     "    type: ACCURACY "
+     "    type: 'Accuracy' "
      "    bottom: 'innerprod' "
      "    bottom: 'label' "
      "    top: 'accuracy' "
@@ -86,7 +86,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "  } "
      "  layers: { "
      "    name: 'loss' "
-     "    type: SOFTMAX_LOSS "
+     "    type: 'SoftmaxWithLoss' "
      "    bottom: 'innerprod' "
      "    bottom: 'label' "
      "    include: { phase: TRAIN } "
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 38e762195a2..584d0a4ab9c 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -116,19 +116,19 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion1) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -140,32 +140,32 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion2) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'data_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_split_0' "
       "  top: 'data_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_split_1' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} ";
@@ -177,7 +177,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -192,7 +192,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 96 "
       "    kernel_size: 11 "
@@ -215,13 +215,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv1' "
       "  top: 'conv1' "
       "} "
       "layers { "
       "  name: 'pool1' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -232,7 +232,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'norm1' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -243,7 +243,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'conv2' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -267,13 +267,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu2' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv2' "
       "  top: 'conv2' "
       "} "
       "layers { "
       "  name: 'pool2' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -284,7 +284,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'norm2' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -295,7 +295,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'conv3' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 384 "
       "    kernel_size: 3 "
@@ -318,13 +318,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu3' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv3' "
       "  top: 'conv3' "
       "} "
       "layers { "
       "  name: 'conv4' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 384 "
       "    group: 2 "
@@ -348,13 +348,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu4' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv4' "
       "  top: 'conv4' "
       "} "
       "layers { "
       "  name: 'conv5' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -378,13 +378,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu5' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv5' "
       "  top: 'conv5' "
       "} "
       "layers { "
       "  name: 'pool5' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    kernel_size: 3 "
       "    pool: MAX "
@@ -395,7 +395,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'fc6' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -416,13 +416,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu6' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
       "layers { "
       "  name: 'drop6' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
@@ -431,7 +431,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'fc7' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -452,13 +452,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'relu7' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
       "layers { "
       "  name: 'drop7' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
@@ -467,7 +467,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'fc8' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 1000 "
       "    weight_filler { "
@@ -488,7 +488,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
@@ -500,25 +500,25 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionWithInPlace) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
       "layers: { "
       "  name: 'relu' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'innerprod' "
       "  top: 'innerprod' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
       "  bottom: 'label' "
       "} ";
@@ -531,7 +531,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "force_backward: true "
       "layers: { "
       "  name: 'data' "
-      "  type: DUMMY_DATA "
+      "  type: 'DummyData' "
       "  dummy_data_param { "
       "    num: 5 "
       "    channels: 2 "
@@ -546,7 +546,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'innerproduct1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -562,7 +562,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'innerproduct2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -577,7 +577,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerproduct1' "
       "  bottom: 'innerproduct2' "
       "} ";
@@ -586,7 +586,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "force_backward: true "
       "layers: { "
       "  name: 'data' "
-      "  type: DUMMY_DATA "
+      "  type: 'DummyData' "
       "  dummy_data_param { "
       "    num: 5 "
       "    channels: 2 "
@@ -601,14 +601,14 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerproduct1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -623,7 +623,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'innerproduct1_innerproduct1_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'innerproduct1' "
       "  top: 'innerproduct1_innerproduct1_0_split_0' "
       "  top: 'innerproduct1_innerproduct1_0_split_1' "
@@ -632,7 +632,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'innerproduct2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 10 "
       "    bias_term: false "
@@ -647,7 +647,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerproduct1_innerproduct1_0_split_1' "
       "  bottom: 'innerproduct2' "
       "} ";
@@ -659,37 +659,37 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod3' "
       "} "
       "layers: { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'innerprod3' "
       "} ";
@@ -697,13 +697,13 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
@@ -711,38 +711,38 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'innerprod2_innerprod2_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'innerprod2' "
       "  top: 'innerprod2_innerprod2_0_split_0' "
       "  top: 'innerprod2_innerprod2_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_2' "
       "  top: 'innerprod3' "
       "} "
       "layers: { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2_innerprod2_0_split_0' "
       "} "
       "layers: { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2_innerprod2_0_split_1' "
       "  bottom: 'innerprod3' "
       "} ";
@@ -754,43 +754,43 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod3' "
       "} "
       "layers: { "
       "  name: 'innerprod4' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label' "
       "  top: 'innerprod4' "
       "} "
       "layers: { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod3' "
       "} "
       "layers: { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'innerprod4' "
       "} ";
@@ -798,57 +798,57 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
       "layers: { "
       "  name: 'label_data_1_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'label' "
       "  top: 'label_data_1_split_0' "
       "  top: 'label_data_1_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label_data_1_split_0' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'innerprod3' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerprod3' "
       "} "
       "layers: { "
       "  name: 'innerprod4' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'label_data_1_split_1' "
       "  top: 'innerprod4' "
       "} "
       "layers: { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod3' "
       "} "
       "layers: { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'innerprod4' "
       "} ";
@@ -865,19 +865,19 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "input_dim: 227 "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} ";
@@ -890,26 +890,26 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "input_dim: 227 "
       "layers: { "
       "  name: 'data_input_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_input_0_split_0' "
       "  top: 'data_input_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_input_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_input_0_split_1' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} ";
@@ -921,37 +921,37 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'label' "
       "} "
       "layers: { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'data' "
       "} ";
@@ -959,51 +959,51 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
       "name: 'TestNetwork' "
       "layers: { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
       "layers: { "
       "  name: 'data_data_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod1' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1' "
       "} "
       "layers: { "
       "  name: 'innerprod1_relu1_0_split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod1_relu1_0_split_1' "
       "} "
       "layers: { "
       "  name: 'innerprod2' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  bottom: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod2' "
       "} "
       "layers: { "
       "  name: 'loss1' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1_relu1_0_split_1' "
       "  bottom: 'label' "
       "} "
       "layers: { "
       "  name: 'loss2' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
       "  bottom: 'data_data_0_split_1' "
       "} ";
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 52e7f1f99f5..e520b85f291 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1184,7 +1184,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -1199,7 +1199,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 96 "
       "    kernel_size: 11 "
@@ -1223,7 +1223,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "} "
       "layers { "
       "  name: 'fc8' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 1000 "
       "    weight_filler { "
@@ -1244,7 +1244,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
@@ -1520,7 +1520,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "input_dim: 32 "
       "layers { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -1537,7 +1537,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'images' "
-      "  type: IMAGE_DATA "
+      "  type: 'ImageData' "
       "  image_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-images' "
       "    batch_size: 256 "
@@ -1557,7 +1557,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'window_data' "
-      "  type: WINDOW_DATA "
+      "  type: 'WindowData' "
       "  window_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -1577,7 +1577,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'hdf5data' "
-      "  type: HDF5_DATA "
+      "  type: 'HDF5Data' "
       "  hdf5_data_param { "
       "    source: '/my/hdf5/data' "
       "    batch_size: 256 "
@@ -1586,7 +1586,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 96 "
       "    bias_term: false "
@@ -1611,7 +1611,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'pool1ave' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: AVE "
       "    kernel_size: 3 "
@@ -1622,7 +1622,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'pool1stoch' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: STOCHASTIC "
       "    kernel_size: 4 "
@@ -1633,7 +1633,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'concat' "
-      "  type: CONCAT "
+      "  type: 'Concat' "
       "  concat_param { "
       "    concat_dim: 2 "
       "  } "
@@ -1643,7 +1643,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'norm1' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -1654,7 +1654,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'fc6' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    bias_term: false "
@@ -1676,13 +1676,13 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'relu6' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
       "layers { "
       "  name: 'drop6' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.2 "
       "  } "
@@ -1691,7 +1691,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: INFOGAIN_LOSS "
+      "  type: 'InfogainLoss' "
       "  infogain_loss_param { "
       "    source: '/my/infogain/matrix' "
       "  } "
@@ -1700,54 +1700,54 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'accuracy' "
-      "  type: ACCURACY "
+      "  type: 'Accuracy' "
       "} "
       "layers { "
       "  name: 'bnll' "
-      "  type: BNLL "
+      "  type: 'BNLL' "
       "} "
       "layers { "
       "  name: 'euclidean_loss' "
-      "  type: EUCLIDEAN_LOSS "
+      "  type: 'EuclideanLoss' "
       "} "
       "layers { "
       "  name: 'flatten' "
-      "  type: FLATTEN "
+      "  type: 'Flatten' "
       "} "
       "layers { "
       "  name: 'hdf5_output' "
-      "  type: HDF5_OUTPUT "
+      "  type: 'HDF5Output' "
       "  hdf5_output_param { "
       "    file_name: '/my/hdf5/output/file' "
       "  } "
       "} "
       "layers { "
       "  name: 'im2col' "
-      "  type: IM2COL "
+      "  type: 'Im2Col' "
       "} "
       "layers { "
       "  name: 'images' "
-      "  type: IMAGE_DATA "
+      "  type: 'ImageData' "
       "} "
       "layers { "
       "  name: 'multinomial_logistic_loss' "
-      "  type: MULTINOMIAL_LOGISTIC_LOSS "
+      "  type: 'MultinomialLogisticLoss' "
       "} "
       "layers { "
       "  name: 'sigmoid' "
-      "  type: SIGMOID "
+      "  type: 'Sigmoid' "
       "} "
       "layers { "
       "  name: 'softmax' "
-      "  type: SOFTMAX "
+      "  type: 'Softmax' "
       "} "
       "layers { "
       "  name: 'split' "
-      "  type: SPLIT "
+      "  type: 'Split' "
       "} "
       "layers { "
       "  name: 'tanh' "
-      "  type: TANH "
+      "  type: 'TanH' "
       "} ";
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
@@ -2122,7 +2122,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
-      "  type: DATA "
+      "  type: 'Data' "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -2137,7 +2137,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 96 "
       "    kernel_size: 11 "
@@ -2160,13 +2160,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu1' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv1' "
       "  top: 'conv1' "
       "} "
       "layers { "
       "  name: 'pool1' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -2177,7 +2177,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'norm1' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -2188,7 +2188,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'conv2' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -2212,13 +2212,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu2' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv2' "
       "  top: 'conv2' "
       "} "
       "layers { "
       "  name: 'pool2' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -2229,7 +2229,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'norm2' "
-      "  type: LRN "
+      "  type: 'LRN' "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -2240,7 +2240,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'conv3' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 384 "
       "    kernel_size: 3 "
@@ -2263,13 +2263,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu3' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv3' "
       "  top: 'conv3' "
       "} "
       "layers { "
       "  name: 'conv4' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 384 "
       "    group: 2 "
@@ -2293,13 +2293,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu4' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv4' "
       "  top: 'conv4' "
       "} "
       "layers { "
       "  name: 'conv5' "
-      "  type: CONVOLUTION "
+      "  type: 'Convolution' "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -2323,13 +2323,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu5' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'conv5' "
       "  top: 'conv5' "
       "} "
       "layers { "
       "  name: 'pool5' "
-      "  type: POOLING "
+      "  type: 'Pooling' "
       "  pooling_param { "
       "    kernel_size: 3 "
       "    pool: MAX "
@@ -2340,7 +2340,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'fc6' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -2361,13 +2361,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu6' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
       "layers { "
       "  name: 'drop6' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
@@ -2376,7 +2376,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'fc7' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -2397,13 +2397,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu7' "
-      "  type: RELU "
+      "  type: 'ReLU' "
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
       "layers { "
       "  name: 'drop7' "
-      "  type: DROPOUT "
+      "  type: 'Dropout' "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
@@ -2412,7 +2412,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'fc8' "
-      "  type: INNER_PRODUCT "
+      "  type: 'InnerProduct' "
       "  inner_product_param { "
       "    num_output: 1000 "
       "    weight_filler { "
@@ -2433,7 +2433,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: SOFTMAX_LOSS "
+      "  type: 'SoftmaxWithLoss' "
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index f20efdae8c6..8a0ad53ed2b 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -111,7 +111,7 @@ void ConfigureSplitLayer(const string& layer_name, const string& blob_name,
   split_layer_param->Clear();
   split_layer_param->add_bottom(blob_name);
   split_layer_param->set_name(SplitLayerName(layer_name, blob_name, blob_idx));
-  split_layer_param->set_type(LayerParameter_LayerType_SPLIT);
+  split_layer_param->set_type("Split");
   for (int k = 0; k < split_count; ++k) {
     split_layer_param->add_top(
         SplitBlobName(layer_name, blob_name, blob_idx, k));
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index cbd6003c948..31e7d4db298 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -459,78 +459,78 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
   return is_fully_compatible;
 }
 
-LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
+const char* UpgradeV0LayerType(const string& type) {
   if (type == "accuracy") {
-    return LayerParameter_LayerType_ACCURACY;
+    return "Accuracy";
   } else if (type == "bnll") {
-    return LayerParameter_LayerType_BNLL;
+    return "BNLL";
   } else if (type == "concat") {
-    return LayerParameter_LayerType_CONCAT;
+    return "Concat";
   } else if (type == "conv") {
-    return LayerParameter_LayerType_CONVOLUTION;
+    return "Convolution";
   } else if (type == "data") {
-    return LayerParameter_LayerType_DATA;
+    return "Data";
   } else if (type == "dropout") {
-    return LayerParameter_LayerType_DROPOUT;
+    return "Dropout";
   } else if (type == "euclidean_loss") {
-    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
+    return "EuclideanLoss";
   } else if (type == "flatten") {
-    return LayerParameter_LayerType_FLATTEN;
+    return "Flatten";
   } else if (type == "hdf5_data") {
-    return LayerParameter_LayerType_HDF5_DATA;
+    return "HDF5Data";
   } else if (type == "hdf5_output") {
-    return LayerParameter_LayerType_HDF5_OUTPUT;
+    return "HDF5Output";
   } else if (type == "im2col") {
-    return LayerParameter_LayerType_IM2COL;
+    return "Im2Col";
   } else if (type == "images") {
-    return LayerParameter_LayerType_IMAGE_DATA;
+    return "ImageData";
   } else if (type == "infogain_loss") {
-    return LayerParameter_LayerType_INFOGAIN_LOSS;
+    return "InfogainLoss";
   } else if (type == "innerproduct") {
-    return LayerParameter_LayerType_INNER_PRODUCT;
+    return "InnerProduct";
   } else if (type == "lrn") {
-    return LayerParameter_LayerType_LRN;
+    return "LRN";
   } else if (type == "multinomial_logistic_loss") {
-    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
+    return "MultinomialLogisticLoss";
   } else if (type == "pool") {
-    return LayerParameter_LayerType_POOLING;
+    return "Pooling";
   } else if (type == "relu") {
-    return LayerParameter_LayerType_RELU;
+    return "ReLU";
   } else if (type == "sigmoid") {
-    return LayerParameter_LayerType_SIGMOID;
+    return "Sigmoid";
   } else if (type == "softmax") {
-    return LayerParameter_LayerType_SOFTMAX;
+    return "Softmax";
   } else if (type == "softmax_loss") {
-    return LayerParameter_LayerType_SOFTMAX_LOSS;
+    return "SoftmaxWithLoss";
   } else if (type == "split") {
-    return LayerParameter_LayerType_SPLIT;
+    return "Split";
   } else if (type == "tanh") {
-    return LayerParameter_LayerType_TANH;
+    return "TanH";
   } else if (type == "window_data") {
-    return LayerParameter_LayerType_WINDOW_DATA;
+    return "WindowData";
   } else {
-    LOG(FATAL) << "Unknown layer name: " << type;
-    return LayerParameter_LayerType_NONE;
+    LOG(FATAL) << "Unknown layer type: " << type;
+    return "";
   }
 }
 
 bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   for (int i = 0; i < net_param.layers_size(); ++i) {
-    if (net_param.layers(i).type() == LayerParameter_LayerType_DATA) {
+    if (net_param.layers(i).type() == "Data") {
       DataParameter layer_param = net_param.layers(i).data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layers(i).type() == LayerParameter_LayerType_IMAGE_DATA) {
+    if (net_param.layers(i).type() == "ImageData") {
       ImageDataParameter layer_param = net_param.layers(i).image_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layers(i).type() == LayerParameter_LayerType_WINDOW_DATA) {
+    if (net_param.layers(i).type() == "WindowData") {
       WindowDataParameter layer_param = net_param.layers(i).window_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
@@ -541,11 +541,11 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   return false;
 }
 
-#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \
+#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE_NAME, PARAM_NAME) \
   do { \
-    if (net_param->layers(i).type() == LayerParameter_LayerType_##TYPE) { \
-      Name##Parameter* layer_param = \
-          net_param->mutable_layers(i)->mutable_##param_name##_param(); \
+    if (net_param->layers(i).type() == #TYPE_NAME) { \
+      TYPE_NAME##Parameter* layer_param = \
+          net_param->mutable_layers(i)->mutable_##PARAM_NAME##_param(); \
       TransformationParameter* transform_param = \
           net_param->mutable_layers(i)->mutable_transform_param(); \
       if (layer_param->has_scale()) { \
@@ -569,9 +569,9 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
 
 void UpgradeNetDataTransformation(NetParameter* net_param) {
   for (int i = 0; i < net_param->layers_size(); ++i) {
-    CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data);
-    CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data);
-    CONVERT_LAYER_TRANSFORM_PARAM(WINDOW_DATA, WindowData, window_data);
+    CONVERT_LAYER_TRANSFORM_PARAM(Data, data);
+    CONVERT_LAYER_TRANSFORM_PARAM(ImageData, image_data);
+    CONVERT_LAYER_TRANSFORM_PARAM(WindowData, window_data);
   }
 }
 

From 794cd7fbca367c27ab4303c2f6e05683eed3c117 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 12 Jan 2015 14:27:06 -0800
Subject: [PATCH 1088/2053] DataLayer and HDF5OutputLayer can be constructed
 and destroyed without errors

---
 include/caffe/data_layers.hpp             |  6 ++++--
 src/caffe/layers/hdf5_output_layer.cpp    | 14 ++++++++------
 src/caffe/test/test_hdf5_output_layer.cpp |  2 +-
 3 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e55a0bbbd7b..cdf2d18bb60 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -181,10 +181,11 @@ class HDF5DataLayer : public Layer<Dtype> {
 template <typename Dtype>
 class HDF5OutputLayer : public Layer<Dtype> {
  public:
-  explicit HDF5OutputLayer(const LayerParameter& param);
+  explicit HDF5OutputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), file_opened_(false) {}
   virtual ~HDF5OutputLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
+      const vector<Blob<Dtype>*>& top);
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
@@ -207,6 +208,7 @@ class HDF5OutputLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void SaveBlobs();
 
+  bool file_opened_;
   std::string file_name_;
   hid_t file_id_;
   Blob<Dtype> data_blob_;
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index d2fdeffc53f..f63375c3dc6 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -12,19 +12,21 @@
 namespace caffe {
 
 template <typename Dtype>
-HDF5OutputLayer<Dtype>::HDF5OutputLayer(const LayerParameter& param)
-    : Layer<Dtype>(param),
-      file_name_(param.hdf5_output_param().file_name()) {
-  /* create a HDF5 file */
+void HDF5OutputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  file_name_ = this->layer_param_.hdf5_output_param().file_name();
   file_id_ = H5Fcreate(file_name_.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
                        H5P_DEFAULT);
   CHECK_GE(file_id_, 0) << "Failed to open HDF5 file" << file_name_;
+  file_opened_ = true;
 }
 
 template <typename Dtype>
 HDF5OutputLayer<Dtype>::~HDF5OutputLayer<Dtype>() {
-  herr_t status = H5Fclose(file_id_);
-  CHECK_GE(status, 0) << "Failed to close HDF5 file " << file_name_;
+  if (file_opened_) {
+    herr_t status = H5Fclose(file_id_);
+    CHECK_GE(status, 0) << "Failed to close HDF5 file " << file_name_;
+  }
 }
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 2e8f096980a..a23034f284a 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -92,8 +92,8 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
   //   the output hdf5 file is closed.
   {
     HDF5OutputLayer<Dtype> layer(param);
-    EXPECT_EQ(layer.file_name(), this->output_file_name_);
     layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    EXPECT_EQ(layer.file_name(), this->output_file_name_);
     layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   }
   file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,

From c7b04c697bf923d13d139a8b162c5dd7d888396c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 12 Jan 2015 13:42:51 -0800
Subject: [PATCH 1089/2053] Add unit test for LayerRegistry::CreateLayer

---
 src/caffe/test/test_layer_factory.cpp | 33 +++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 src/caffe/test/test_layer_factory.cpp

diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
new file mode 100644
index 00000000000..3ad635a792a
--- /dev/null
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -0,0 +1,33 @@
+#include <map>
+#include <string>
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layer_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class LayerFactoryTest : public MultiDeviceTest<TypeParam> {};
+
+TYPED_TEST_CASE(LayerFactoryTest, TestDtypesAndDevices);
+
+TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  typename LayerRegistry<Dtype>::CreatorRegistry& registry =
+      LayerRegistry<Dtype>::Registry();
+  shared_ptr<Layer<Dtype> > layer;
+  LayerParameter layer_param;
+  for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
+       registry.begin(); iter != registry.end(); ++iter) {
+    layer_param.set_type(iter->first);
+    layer.reset(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    EXPECT_EQ(iter->first, layer->type());
+  }
+}
+
+}  // namespace caffe

From af37eac80f01694a4845607bfcede595f50f893f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 12 Jan 2015 17:02:15 -0800
Subject: [PATCH 1090/2053] 'layers' -> 'layer'

---
 src/caffe/net.cpp                        | 34 ++++++++--------
 src/caffe/proto/caffe.proto              |  8 +++-
 src/caffe/proto/caffe_pretty_print.proto |  2 +-
 src/caffe/util/insert_splits.cpp         | 20 ++++-----
 src/caffe/util/upgrade_proto.cpp         | 52 ++++++++++++------------
 5 files changed, 61 insertions(+), 55 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 4350e056085..db9ac7ddbb3 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -55,14 +55,14 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   }
   DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
   // For each layer, set up their input and output
-  bottom_vecs_.resize(param.layers_size());
-  top_vecs_.resize(param.layers_size());
-  bottom_id_vecs_.resize(param.layers_size());
-  param_id_vecs_.resize(param.layers_size());
-  top_id_vecs_.resize(param.layers_size());
-  bottom_need_backward_.resize(param.layers_size());
-  for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
-    const LayerParameter& layer_param = param.layers(layer_id);
+  bottom_vecs_.resize(param.layer_size());
+  top_vecs_.resize(param.layer_size());
+  bottom_id_vecs_.resize(param.layer_size());
+  param_id_vecs_.resize(param.layer_size());
+  top_id_vecs_.resize(param.layer_size());
+  bottom_need_backward_.resize(param.layer_size());
+  for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+    const LayerParameter& layer_param = param.layer(layer_id);
     layers_.push_back(shared_ptr<Layer<Dtype> >(
           LayerRegistry<Dtype>::CreateLayer(layer_param)));
     layer_names_.push_back(layer_param.name());
@@ -242,9 +242,9 @@ void Net<Dtype>::FilterNet(const NetParameter& param,
     }
   }
   param_filtered->CopyFrom(param);
-  param_filtered->clear_layers();
-  for (int i = 0; i < param.layers_size(); ++i) {
-    const LayerParameter& layer_param = param.layers(i);
+  param_filtered->clear_layer();
+  for (int i = 0; i < param.layer_size(); ++i) {
+    const LayerParameter& layer_param = param.layer(i);
     const string& layer_name = layer_param.name();
     CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)
           << "Specify either include rules or exclude rules; not both.";
@@ -262,7 +262,7 @@ void Net<Dtype>::FilterNet(const NetParameter& param,
       }
     }
     if (layer_included) {
-      param_filtered->add_layers()->CopyFrom(layer_param);
+      param_filtered->add_layer()->CopyFrom(layer_param);
     }
   }
 }
@@ -335,7 +335,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
                            const int top_id, set<string>* available_blobs,
                            map<string, int>* blob_name_to_idx) {
   shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
-    (new LayerParameter(param.layers(layer_id))) : NULL);
+    (new LayerParameter(param.layer(layer_id))) : NULL);
   const string& blob_name = layer_param ?
       (layer_param->top_size() > top_id ?
           layer_param->top(top_id) : "(automatic)") : param.input(top_id);
@@ -385,7 +385,7 @@ template <typename Dtype>
 int Net<Dtype>::AppendBottom(const NetParameter& param,
     const int layer_id, const int bottom_id,
     set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
-  const LayerParameter& layer_param = param.layers(layer_id);
+  const LayerParameter& layer_param = param.layer(layer_id);
   const string& blob_name = layer_param.bottom(bottom_id);
   if (available_blobs->find(blob_name) == available_blobs->end()) {
     LOG(FATAL) << "Unknown blob input " << blob_name
@@ -730,9 +730,9 @@ void Net<Dtype>::Reshape() {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
-  int num_source_layers = param.layers_size();
+  int num_source_layers = param.layer_size();
   for (int i = 0; i < num_source_layers; ++i) {
-    const LayerParameter& source_layer = param.layers(i);
+    const LayerParameter& source_layer = param.layer(i);
     const string& source_layer_name = source_layer.name();
     int target_layer_id = 0;
     while (target_layer_id != layer_names_.size() &&
@@ -775,7 +775,7 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   }
   DLOG(INFO) << "Serializing " << layers_.size() << " layers";
   for (int i = 0; i < layers_.size(); ++i) {
-    LayerParameter* layer_param = param->add_layers();
+    LayerParameter* layer_param = param->add_layer();
     for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
       layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);
     }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 1fea3445415..c62e7271c0f 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -45,7 +45,6 @@ message FillerParameter {
 
 message NetParameter {
   optional string name = 1; // consider giving the network a name
-  repeated LayerParameter layers = 2; // a bunch of layers.
   // The input blobs to the network.
   repeated string input = 3;
   // The dim of the input blobs. For each input blob there should be four
@@ -64,6 +63,13 @@ message NetParameter {
   // Print debugging information about results while running Net::Forward,
   // Net::Backward, and Net::Update.
   optional bool debug_info = 7 [default = false];
+
+  // The layers that make up the net.  Each of their configurations, including
+  // connectivity and behavior, is specified as a LayerParameter.
+  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.
+
+  // DEPRECATED: use 'layer' instead.
+  // repeated LayerParameter layers = 2; // a bunch of layers.
 }
 
 // NOTE
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
index 6f0a5f6b8df..056541f75f1 100644
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ b/src/caffe/proto/caffe_pretty_print.proto
@@ -14,5 +14,5 @@ message NetParameterPrettyPrint {
   optional bool force_backward = 2 [default = false];
   repeated string input = 3;
   repeated int32 input_dim = 4;
-  repeated LayerParameter layers = 5;
+  repeated LayerParameter layer = 5;
 }
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 8a0ad53ed2b..416f80ab3c2 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -12,7 +12,7 @@ namespace caffe {
 void InsertSplits(const NetParameter& param, NetParameter* param_split) {
   // Initialize by copying from the input NetParameter.
   param_split->CopyFrom(param);
-  param_split->clear_layers();
+  param_split->clear_layer();
   map<string, pair<int, int> > blob_name_to_last_top_idx;
   map<pair<int, int>, pair<int, int> > bottom_idx_to_source_top_idx;
   map<pair<int, int>, int> top_idx_to_bottom_count;
@@ -25,8 +25,8 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
     const string& blob_name = param.input(i);
     blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    const LayerParameter& layer_param = param.layers(i);
+  for (int i = 0; i < param.layer_size(); ++i) {
+    const LayerParameter& layer_param = param.layer(i);
     layer_idx_to_layer_name[i] = layer_param.name();
     for (int j = 0; j < layer_param.bottom_size(); ++j) {
       const string& blob_name = layer_param.bottom(j);
@@ -56,22 +56,22 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       }
     }
   }
-  // Create split layer for any input blobs used by other layers as bottom
+  // Create split layer for any input blobs used by other layer as bottom
   // blobs more than once.
   for (int i = 0; i < param.input_size(); ++i) {
     const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
     if (split_count > 1) {
       const string& layer_name = layer_idx_to_layer_name[-1];
       const string& blob_name = param.input(i);
-      LayerParameter* split_layer_param = param_split->add_layers();
+      LayerParameter* split_layer_param = param_split->add_layer();
       const float kZeroLossWeight = 0;
       ConfigureSplitLayer(layer_name, blob_name, i, split_count,
           kZeroLossWeight, split_layer_param);
     }
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    LayerParameter* layer_param = param_split->add_layers();
-    layer_param->CopyFrom(param.layers(i));
+  for (int i = 0; i < param.layer_size(); ++i) {
+    LayerParameter* layer_param = param_split->add_layer();
+    layer_param->CopyFrom(param.layer(i));
     // Replace any shared bottom blobs with split layer outputs.
     for (int j = 0; j < layer_param->bottom_size(); ++j) {
       const pair<int, int>& top_idx =
@@ -84,7 +84,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
             blob_name, top_idx.second, top_idx_to_bottom_split_idx[top_idx]++));
       }
     }
-    // Create split layer for any top blobs used by other layers as bottom
+    // Create split layer for any top blobs used by other layer as bottom
     // blobs more than once.
     for (int j = 0; j < layer_param->top_size(); ++j) {
       const pair<int, int>& top_idx = make_pair(i, j);
@@ -92,7 +92,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       if (split_count > 1) {
         const string& layer_name = layer_idx_to_layer_name[i];
         const string& blob_name = layer_param->top(j);
-        LayerParameter* split_layer_param = param_split->add_layers();
+        LayerParameter* split_layer_param = param_split->add_layer();
         const float loss_weight = top_idx_to_loss_weight[top_idx];
         ConfigureSplitLayer(layer_name, blob_name, j, split_count,
             loss_weight, split_layer_param);
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 31e7d4db298..352d08e90e3 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,8 +13,8 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
-  for (int i = 0; i < net_param.layers_size(); ++i) {
-    if (net_param.layers(i).has_layer()) {
+  for (int i = 0; i < net_param.layer_size(); ++i) {
+    if (net_param.layer(i).has_layer()) {
       return true;
     }
   }
@@ -32,9 +32,9 @@ bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
   if (v0_net_param.has_name()) {
     net_param->set_name(v0_net_param.name());
   }
-  for (int i = 0; i < v0_net_param.layers_size(); ++i) {
-    is_fully_compatible &= UpgradeLayerParameter(v0_net_param.layers(i),
-                                                 net_param->add_layers());
+  for (int i = 0; i < v0_net_param.layer_size(); ++i) {
+    is_fully_compatible &= UpgradeLayerParameter(v0_net_param.layer(i),
+                                                 net_param->add_layer());
   }
   for (int i = 0; i < v0_net_param.input_size(); ++i) {
     net_param->add_input(v0_net_param.input(i));
@@ -53,19 +53,19 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
   // Copy everything other than the layers from the original param.
   param_upgraded_pad->Clear();
   param_upgraded_pad->CopyFrom(param);
-  param_upgraded_pad->clear_layers();
+  param_upgraded_pad->clear_layer();
   // Figure out which layer each bottom blob comes from.
   map<string, int> blob_name_to_last_top_idx;
   for (int i = 0; i < param.input_size(); ++i) {
     const string& blob_name = param.input(i);
     blob_name_to_last_top_idx[blob_name] = -1;
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    const LayerParameter& layer_connection = param.layers(i);
+  for (int i = 0; i < param.layer_size(); ++i) {
+    const LayerParameter& layer_connection = param.layer(i);
     const V0LayerParameter& layer_param = layer_connection.layer();
     // Add the layer to the new net, unless it's a padding layer.
     if (layer_param.type() != "padding") {
-      param_upgraded_pad->add_layers()->CopyFrom(layer_connection);
+      param_upgraded_pad->add_layer()->CopyFrom(layer_connection);
     }
     for (int j = 0; j < layer_connection.bottom_size(); ++j) {
       const string& blob_name = layer_connection.bottom(j);
@@ -77,7 +77,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
       if (top_idx == -1) {
         continue;
       }
-      LayerParameter source_layer = param.layers(top_idx);
+      LayerParameter source_layer = param.layer(top_idx);
       if (source_layer.layer().type() == "padding") {
         // This layer has a padding layer as input -- check that it is a conv
         // layer or a pooling layer and takes only one input.  Also check that
@@ -93,10 +93,10 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
             << "Padding Layer takes a single blob as input.";
         CHECK_EQ(source_layer.top_size(), 1)
             << "Padding Layer produces a single blob as output.";
-        int layer_index = param_upgraded_pad->layers_size() - 1;
-        param_upgraded_pad->mutable_layers(layer_index)->mutable_layer()
+        int layer_index = param_upgraded_pad->layer_size() - 1;
+        param_upgraded_pad->mutable_layer(layer_index)->mutable_layer()
             ->set_pad(source_layer.layer().pad());
-        param_upgraded_pad->mutable_layers(layer_index)
+        param_upgraded_pad->mutable_layer(layer_index)
             ->set_bottom(j, source_layer.bottom(0));
       }
     }
@@ -515,23 +515,23 @@ const char* UpgradeV0LayerType(const string& type) {
 }
 
 bool NetNeedsDataUpgrade(const NetParameter& net_param) {
-  for (int i = 0; i < net_param.layers_size(); ++i) {
-    if (net_param.layers(i).type() == "Data") {
-      DataParameter layer_param = net_param.layers(i).data_param();
+  for (int i = 0; i < net_param.layer_size(); ++i) {
+    if (net_param.layer(i).type() == "Data") {
+      DataParameter layer_param = net_param.layer(i).data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layers(i).type() == "ImageData") {
-      ImageDataParameter layer_param = net_param.layers(i).image_data_param();
+    if (net_param.layer(i).type() == "ImageData") {
+      ImageDataParameter layer_param = net_param.layer(i).image_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layers(i).type() == "WindowData") {
-      WindowDataParameter layer_param = net_param.layers(i).window_data_param();
+    if (net_param.layer(i).type() == "WindowData") {
+      WindowDataParameter layer_param = net_param.layer(i).window_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
@@ -543,11 +543,11 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
 
 #define CONVERT_LAYER_TRANSFORM_PARAM(TYPE_NAME, PARAM_NAME) \
   do { \
-    if (net_param->layers(i).type() == #TYPE_NAME) { \
+    if (net_param->layer(i).type() == #TYPE_NAME) { \
       TYPE_NAME##Parameter* layer_param = \
-          net_param->mutable_layers(i)->mutable_##PARAM_NAME##_param(); \
+          net_param->mutable_layer(i)->mutable_##PARAM_NAME##_param(); \
       TransformationParameter* transform_param = \
-          net_param->mutable_layers(i)->mutable_transform_param(); \
+          net_param->mutable_layer(i)->mutable_transform_param(); \
       if (layer_param->has_scale()) { \
         transform_param->set_scale(layer_param->scale()); \
         layer_param->clear_scale(); \
@@ -568,7 +568,7 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   } while (0)
 
 void UpgradeNetDataTransformation(NetParameter* net_param) {
-  for (int i = 0; i < net_param->layers_size(); ++i) {
+  for (int i = 0; i < net_param->layer_size(); ++i) {
     CONVERT_LAYER_TRANSFORM_PARAM(Data, data);
     CONVERT_LAYER_TRANSFORM_PARAM(ImageData, image_data);
     CONVERT_LAYER_TRANSFORM_PARAM(WindowData, window_data);
@@ -590,8 +590,8 @@ void NetParameterToPrettyPrint(const NetParameter& param,
   for (int i = 0; i < param.input_dim_size(); ++i) {
     pretty_param->add_input_dim(param.input_dim(i));
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
-    pretty_param->add_layers()->CopyFrom(param.layers(i));
+  for (int i = 0; i < param.layer_size(); ++i) {
+    pretty_param->add_layer()->CopyFrom(param.layer(i));
   }
 }
 

From bb5ba1b7e025d4bb2a0fc3caedbe17e288b266c4 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 14 Jan 2015 15:07:20 -0800
Subject: [PATCH 1091/2053] restore upgrade_proto

---
 include/caffe/util/upgrade_proto.hpp |   6 +-
 src/caffe/proto/caffe.proto          |  98 ++++++++++++++++++++--
 src/caffe/util/upgrade_proto.cpp     | 118 +++++++++++++--------------
 3 files changed, 154 insertions(+), 68 deletions(-)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 0627afe694c..e89756ed055 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -24,10 +24,10 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
                             NetParameter* param_upgraded_pad);
 
 // Upgrade a single V0LayerConnection to the new LayerParameter format.
-bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
-                           LayerParameter* layer_param);
+bool UpgradeLayerParameter(const V1LayerParameter& v0_layer_connection,
+                           V1LayerParameter* layer_param);
 
-const char* UpgradeV0LayerType(const string& type);
+V1LayerParameter_LayerType UpgradeV0LayerType(const string& type);
 
 // Return true iff any layer contains deprecated data transformation parameters.
 bool NetNeedsDataUpgrade(const NetParameter& net_param);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index c62e7271c0f..d81acf309f4 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -69,7 +69,7 @@ message NetParameter {
   repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.
 
   // DEPRECATED: use 'layer' instead.
-  // repeated LayerParameter layers = 2; // a bunch of layers.
+  repeated V1LayerParameter layers = 2;
 }
 
 // NOTE
@@ -297,11 +297,6 @@ message LayerParameter {
   // for their implementation. These layers include an Engine type and
   // engine parameter for selecting the implementation.
   // The default for the engine is set by the ENGINE switch at compile-time.
-
-  // DEPRECATED: The layer parameters specified as a V0LayerParameter.
-  // This should never be used by any code except to upgrade to the new
-  // LayerParameter specification.
-  optional V0LayerParameter layer = 1;
 }
 
 // Message that stores parameters used to apply transformation
@@ -695,6 +690,97 @@ message WindowDataParameter {
   optional string root_folder = 13 [default = ""];
 }
 
+// DEPRECATED: use LayerParameter.
+message V1LayerParameter {
+  repeated string bottom = 2;
+  repeated string top = 3;
+  optional string name = 4;
+  repeated NetStateRule include = 32;
+  repeated NetStateRule exclude = 33;
+  enum LayerType {
+    NONE = 0;
+    ABSVAL = 35;
+    ACCURACY = 1;
+    ARGMAX = 30;
+    BNLL = 2;
+    CONCAT = 3;
+    CONTRASTIVE_LOSS = 37;
+    CONVOLUTION = 4;
+    DATA = 5;
+    DROPOUT = 6;
+    DUMMY_DATA = 32;
+    EUCLIDEAN_LOSS = 7;
+    ELTWISE = 25;
+    EXP = 38;
+    FLATTEN = 8;
+    HDF5_DATA = 9;
+    HDF5_OUTPUT = 10;
+    HINGE_LOSS = 28;
+    IM2COL = 11;
+    IMAGE_DATA = 12;
+    INFOGAIN_LOSS = 13;
+    INNER_PRODUCT = 14;
+    LRN = 15;
+    MEMORY_DATA = 29;
+    MULTINOMIAL_LOGISTIC_LOSS = 16;
+    MVN = 34;
+    POOLING = 17;
+    POWER = 26;
+    RELU = 18;
+    SIGMOID = 19;
+    SIGMOID_CROSS_ENTROPY_LOSS = 27;
+    SILENCE = 36;
+    SOFTMAX = 20;
+    SOFTMAX_LOSS = 21;
+    SPLIT = 22;
+    SLICE = 33;
+    TANH = 23;
+    WINDOW_DATA = 24;
+    THRESHOLD = 31;
+  }
+  optional LayerType type = 5;
+  repeated BlobProto blobs = 6;
+  repeated string param = 1001;
+  repeated DimCheckMode blob_share_mode = 1002;
+  enum DimCheckMode {
+    STRICT = 0;
+    PERMISSIVE = 1;
+  }
+  repeated float blobs_lr = 7;
+  repeated float weight_decay = 8;
+  repeated float loss_weight = 35;
+  optional AccuracyParameter accuracy_param = 27;
+  optional ArgMaxParameter argmax_param = 23;
+  optional ConcatParameter concat_param = 9;
+  optional ContrastiveLossParameter contrastive_loss_param = 40;
+  optional ConvolutionParameter convolution_param = 10;
+  optional DataParameter data_param = 11;
+  optional DropoutParameter dropout_param = 12;
+  optional DummyDataParameter dummy_data_param = 26;
+  optional EltwiseParameter eltwise_param = 24;
+  optional ExpParameter exp_param = 41;
+  optional HDF5DataParameter hdf5_data_param = 13;
+  optional HDF5OutputParameter hdf5_output_param = 14;
+  optional HingeLossParameter hinge_loss_param = 29;
+  optional ImageDataParameter image_data_param = 15;
+  optional InfogainLossParameter infogain_loss_param = 16;
+  optional InnerProductParameter inner_product_param = 17;
+  optional LRNParameter lrn_param = 18;
+  optional MemoryDataParameter memory_data_param = 22;
+  optional MVNParameter mvn_param = 34;
+  optional PoolingParameter pooling_param = 19;
+  optional PowerParameter power_param = 21;
+  optional ReLUParameter relu_param = 30;
+  optional SigmoidParameter sigmoid_param = 38;
+  optional SoftmaxParameter softmax_param = 39;
+  optional SliceParameter slice_param = 31;
+  optional TanHParameter tanh_param = 37;
+  optional ThresholdParameter threshold_param = 25;
+  optional WindowDataParameter window_data_param = 20;
+  optional TransformationParameter transform_param = 36;
+  optional V0LayerParameter layer = 1;
+}
+
 // DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
 // in Caffe.  We keep this message type around for legacy support.
 message V0LayerParameter {
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 352d08e90e3..16583b0f0ca 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,8 +13,8 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
-  for (int i = 0; i < net_param.layer_size(); ++i) {
-    if (net_param.layer(i).has_layer()) {
+  for (int i = 0; i < net_param.layers_size(); ++i) {
+    if (net_param.layers(i).has_layer()) {
       return true;
     }
   }
@@ -32,9 +32,9 @@ bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
   if (v0_net_param.has_name()) {
     net_param->set_name(v0_net_param.name());
   }
-  for (int i = 0; i < v0_net_param.layer_size(); ++i) {
-    is_fully_compatible &= UpgradeLayerParameter(v0_net_param.layer(i),
-                                                 net_param->add_layer());
+  for (int i = 0; i < v0_net_param.layers_size(); ++i) {
+    is_fully_compatible &= UpgradeLayerParameter(v0_net_param.layers(i),
+                                                 net_param->add_layers());
   }
   for (int i = 0; i < v0_net_param.input_size(); ++i) {
     net_param->add_input(v0_net_param.input(i));
@@ -53,19 +53,19 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
   // Copy everything other than the layers from the original param.
   param_upgraded_pad->Clear();
   param_upgraded_pad->CopyFrom(param);
-  param_upgraded_pad->clear_layer();
+  param_upgraded_pad->clear_layers();
   // Figure out which layer each bottom blob comes from.
   map<string, int> blob_name_to_last_top_idx;
   for (int i = 0; i < param.input_size(); ++i) {
     const string& blob_name = param.input(i);
     blob_name_to_last_top_idx[blob_name] = -1;
   }
-  for (int i = 0; i < param.layer_size(); ++i) {
-    const LayerParameter& layer_connection = param.layer(i);
+  for (int i = 0; i < param.layers_size(); ++i) {
+    const V1LayerParameter& layer_connection = param.layers(i);
     const V0LayerParameter& layer_param = layer_connection.layer();
     // Add the layer to the new net, unless it's a padding layer.
     if (layer_param.type() != "padding") {
-      param_upgraded_pad->add_layer()->CopyFrom(layer_connection);
+      param_upgraded_pad->add_layers()->CopyFrom(layer_connection);
     }
     for (int j = 0; j < layer_connection.bottom_size(); ++j) {
       const string& blob_name = layer_connection.bottom(j);
@@ -77,7 +77,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
       if (top_idx == -1) {
         continue;
       }
-      LayerParameter source_layer = param.layer(top_idx);
+      const V1LayerParameter& source_layer = param.layers(top_idx);
       if (source_layer.layer().type() == "padding") {
         // This layer has a padding layer as input -- check that it is a conv
         // layer or a pooling layer and takes only one input.  Also check that
@@ -93,10 +93,10 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
             << "Padding Layer takes a single blob as input.";
         CHECK_EQ(source_layer.top_size(), 1)
             << "Padding Layer produces a single blob as output.";
-        int layer_index = param_upgraded_pad->layer_size() - 1;
-        param_upgraded_pad->mutable_layer(layer_index)->mutable_layer()
+        int layer_index = param_upgraded_pad->layers_size() - 1;
+        param_upgraded_pad->mutable_layers(layer_index)->mutable_layer()
             ->set_pad(source_layer.layer().pad());
-        param_upgraded_pad->mutable_layer(layer_index)
+        param_upgraded_pad->mutable_layers(layer_index)
             ->set_bottom(j, source_layer.bottom(0));
       }
     }
@@ -107,8 +107,8 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
   }
 }
 
-bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
-                           LayerParameter* layer_param) {
+bool UpgradeLayerParameter(const V1LayerParameter& v0_layer_connection,
+                           V1LayerParameter* layer_param) {
   bool is_fully_compatible = true;
   layer_param->Clear();
   for (int i = 0; i < v0_layer_connection.bottom_size(); ++i) {
@@ -459,79 +459,79 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
   return is_fully_compatible;
 }
 
-const char* UpgradeV0LayerType(const string& type) {
+V1LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
   if (type == "accuracy") {
-    return "Accuracy";
+    return V1LayerParameter_LayerType_ACCURACY;
   } else if (type == "bnll") {
-    return "BNLL";
+    return V1LayerParameter_LayerType_BNLL;
   } else if (type == "concat") {
-    return "Concat";
+    return V1LayerParameter_LayerType_CONCAT;
   } else if (type == "conv") {
-    return "Convolution";
+    return V1LayerParameter_LayerType_CONVOLUTION;
   } else if (type == "data") {
-    return "Data";
+    return V1LayerParameter_LayerType_DATA;
   } else if (type == "dropout") {
-    return "Dropout";
+    return V1LayerParameter_LayerType_DROPOUT;
   } else if (type == "euclidean_loss") {
-    return "EuclideanLoss";
+    return V1LayerParameter_LayerType_EUCLIDEAN_LOSS;
   } else if (type == "flatten") {
-    return "Flatten";
+    return V1LayerParameter_LayerType_FLATTEN;
   } else if (type == "hdf5_data") {
-    return "HDF5Data";
+    return V1LayerParameter_LayerType_HDF5_DATA;
   } else if (type == "hdf5_output") {
-    return "HDF5Output";
+    return V1LayerParameter_LayerType_HDF5_OUTPUT;
   } else if (type == "im2col") {
-    return "Im2Col";
+    return V1LayerParameter_LayerType_IM2COL;
   } else if (type == "images") {
-    return "ImageData";
+    return V1LayerParameter_LayerType_IMAGE_DATA;
   } else if (type == "infogain_loss") {
-    return "InfogainLoss";
+    return V1LayerParameter_LayerType_INFOGAIN_LOSS;
   } else if (type == "innerproduct") {
-    return "InnerProduct";
+    return V1LayerParameter_LayerType_INNER_PRODUCT;
   } else if (type == "lrn") {
-    return "LRN";
+    return V1LayerParameter_LayerType_LRN;
   } else if (type == "multinomial_logistic_loss") {
-    return "MultinomialLogisticLoss";
+    return V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
   } else if (type == "pool") {
-    return "Pooling";
+    return V1LayerParameter_LayerType_POOLING;
   } else if (type == "relu") {
-    return "ReLU";
+    return V1LayerParameter_LayerType_RELU;
   } else if (type == "sigmoid") {
-    return "Sigmoid";
+    return V1LayerParameter_LayerType_SIGMOID;
   } else if (type == "softmax") {
-    return "Softmax";
+    return V1LayerParameter_LayerType_SOFTMAX;
   } else if (type == "softmax_loss") {
-    return "SoftmaxWithLoss";
+    return V1LayerParameter_LayerType_SOFTMAX_LOSS;
   } else if (type == "split") {
-    return "Split";
+    return V1LayerParameter_LayerType_SPLIT;
   } else if (type == "tanh") {
-    return "TanH";
+    return V1LayerParameter_LayerType_TANH;
   } else if (type == "window_data") {
-    return "WindowData";
+    return V1LayerParameter_LayerType_WINDOW_DATA;
   } else {
-    LOG(FATAL) << "Unknown layer type: " << type;
-    return "";
+    LOG(FATAL) << "Unknown layer name: " << type;
+    return V1LayerParameter_LayerType_NONE;
   }
 }
 
 bool NetNeedsDataUpgrade(const NetParameter& net_param) {
-  for (int i = 0; i < net_param.layer_size(); ++i) {
-    if (net_param.layer(i).type() == "Data") {
-      DataParameter layer_param = net_param.layer(i).data_param();
+  for (int i = 0; i < net_param.layers_size(); ++i) {
+    if (net_param.layers(i).type() == V1LayerParameter_LayerType_DATA) {
+      DataParameter layer_param = net_param.layers(i).data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layer(i).type() == "ImageData") {
-      ImageDataParameter layer_param = net_param.layer(i).image_data_param();
+    if (net_param.layers(i).type() == V1LayerParameter_LayerType_IMAGE_DATA) {
+      ImageDataParameter layer_param = net_param.layers(i).image_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
       if (layer_param.has_mirror()) { return true; }
     }
-    if (net_param.layer(i).type() == "WindowData") {
-      WindowDataParameter layer_param = net_param.layer(i).window_data_param();
+    if (net_param.layers(i).type() == V1LayerParameter_LayerType_WINDOW_DATA) {
+      WindowDataParameter layer_param = net_param.layers(i).window_data_param();
       if (layer_param.has_scale()) { return true; }
       if (layer_param.has_mean_file()) { return true; }
       if (layer_param.has_crop_size()) { return true; }
@@ -541,13 +541,13 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   return false;
 }
 
-#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE_NAME, PARAM_NAME) \
+#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \
   do { \
-    if (net_param->layer(i).type() == #TYPE_NAME) { \
-      TYPE_NAME##Parameter* layer_param = \
-          net_param->mutable_layer(i)->mutable_##PARAM_NAME##_param(); \
+    if (net_param->layers(i).type() == V1LayerParameter_LayerType_##TYPE) { \
+      Name##Parameter* layer_param = \
+          net_param->mutable_layers(i)->mutable_##param_name##_param(); \
       TransformationParameter* transform_param = \
-          net_param->mutable_layer(i)->mutable_transform_param(); \
+          net_param->mutable_layers(i)->mutable_transform_param(); \
       if (layer_param->has_scale()) { \
         transform_param->set_scale(layer_param->scale()); \
         layer_param->clear_scale(); \
@@ -568,10 +568,10 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) {
   } while (0)
 
 void UpgradeNetDataTransformation(NetParameter* net_param) {
-  for (int i = 0; i < net_param->layer_size(); ++i) {
-    CONVERT_LAYER_TRANSFORM_PARAM(Data, data);
-    CONVERT_LAYER_TRANSFORM_PARAM(ImageData, image_data);
-    CONVERT_LAYER_TRANSFORM_PARAM(WindowData, window_data);
+  for (int i = 0; i < net_param->layers_size(); ++i) {
+    CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data);
+    CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data);
+    CONVERT_LAYER_TRANSFORM_PARAM(WINDOW_DATA, WindowData, window_data);
   }
 }
 
@@ -590,7 +590,7 @@ void NetParameterToPrettyPrint(const NetParameter& param,
   for (int i = 0; i < param.input_dim_size(); ++i) {
     pretty_param->add_input_dim(param.input_dim(i));
   }
-  for (int i = 0; i < param.layer_size(); ++i) {
+  for (int i = 0; i < param.layers_size(); ++i) {
     pretty_param->add_layer()->CopyFrom(param.layer(i));
   }
 }

From 2e6a82c8445e588dbc72dabe7126830682a3c3e3 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 14 Jan 2015 16:55:14 -0800
Subject: [PATCH 1092/2053] automagic upgrade for v1->v2

---
 include/caffe/util/upgrade_proto.hpp |  25 ++-
 src/caffe/net.cpp                    |  13 +-
 src/caffe/proto/caffe.proto          | 105 ++++-----
 src/caffe/util/upgrade_proto.cpp     | 317 ++++++++++++++++++++++++++-
 tools/upgrade_net_proto_binary.cpp   |  13 +-
 tools/upgrade_net_proto_text.cpp     |  15 +-
 6 files changed, 410 insertions(+), 78 deletions(-)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index e89756ed055..0df3a8987f9 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -8,9 +8,12 @@
 
 namespace caffe {
 
+// Return true iff the net is not the current version.
+bool NetNeedsUpgrade(const NetParameter& net_param);
+
 // Return true iff any layer contains parameters specified using
 // deprecated V0LayerParameter.
-bool NetNeedsUpgrade(const NetParameter& net_param);
+bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);
 
 // Perform all necessary transformations to upgrade a V0NetParameter into a
 // NetParameter (including upgrading padding layers and LayerParameters).
@@ -23,9 +26,9 @@ bool UpgradeV0Net(const NetParameter& v0_net_param, NetParameter* net_param);
 void UpgradeV0PaddingLayers(const NetParameter& param,
                             NetParameter* param_upgraded_pad);
 
-// Upgrade a single V0LayerConnection to the new LayerParameter format.
-bool UpgradeLayerParameter(const V1LayerParameter& v0_layer_connection,
-                           V1LayerParameter* layer_param);
+// Upgrade a single V0LayerConnection to the V1LayerParameter format.
+bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
+                             V1LayerParameter* layer_param);
 
 V1LayerParameter_LayerType UpgradeV0LayerType(const string& type);
 
@@ -36,13 +39,25 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param);
 // into a TransformationParameter.
 void UpgradeNetDataTransformation(NetParameter* net_param);
 
+// Return true iff the Net contains any layers specified as V1LayerParameters.
+bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade a NetParameter with
+// deprecated V1LayerParameters.
+bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param);
+
+bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
+                             LayerParameter* layer_param);
+
+const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
+
 // Convert a NetParameter to NetParameterPrettyPrint used for dumping to
 // proto text files.
 void NetParameterToPrettyPrint(const NetParameter& param,
                                NetParameterPrettyPrint* pretty_param);
 
 // Check for deprecations and upgrade the NetParameter as needed.
-void UpgradeNetAsNeeded(NetParameter* param);
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
 
 // Read parameters from a file into a NetParameter proto message.
 void ReadNetParamsFromTextFileOrDie(const string& param_file,
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index db9ac7ddbb3..4461fa88e75 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -138,9 +138,10 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     CHECK(param_size == num_param_blobs || param_size == 0)
         << "Incorrect param size: should be either 0 or the same as "
            "the number of the layer's parameter blobs: " << num_param_blobs;
-    const int blob_share_mode_size = layer_param.blob_share_mode_size();
-    CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)
-        << "Incorrect blob_share_mode size: should be either 0 or the same as "
+    const int param_share_mode_size = layer_param.param_share_mode_size();
+    CHECK(param_share_mode_size == num_param_blobs ||
+          param_share_mode_size == 0)
+        << "Incorrect param_share_mode size: should be either 0 or the same as "
            "the number of the layer's parameter blobs: " << num_param_blobs;
     for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
       AppendParam(param, layer_id, param_id);
@@ -441,9 +442,9 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
     Blob<Dtype>* owner_blob =
         layers_[owner_layer_id]->blobs()[owner_param_id].get();
-    const int blob_share_mode_size = layer_param.blob_share_mode_size();
-    if (blob_share_mode_size > param_id &&
-        (layer_param.blob_share_mode(param_id) ==
+    const int param_share_mode_size = layer_param.param_share_mode_size();
+    if (param_share_mode_size > param_id &&
+        (layer_param.param_share_mode(param_id) ==
          LayerParameter_DimCheckMode_PERMISSIVE)) {
       // Permissive dimension checking -- only check counts are the same.
       CHECK_EQ(this_blob->count(), owner_blob->count())
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d81acf309f4..1178efe53a8 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -218,77 +218,80 @@ message NetStateRule {
 //
 // LayerParameter next available ID: 43 (last added: loss_param)
 message LayerParameter {
-  repeated string bottom = 2; // the name of the bottom blobs
-  repeated string top = 3; // the name of the top blobs
-  optional string name = 4; // the layer name
-
-  // Rules controlling whether and when a layer is included in the network,
-  // based on the current NetState.  You may specify a non-zero number of rules
-  // to include OR exclude, but not both.  If no include or exclude rules are
-  // specified, the layer is always included.  If the current NetState meets
-  // ANY (i.e., one or more) of the specified rules, the layer is
-  // included/excluded.
-  repeated NetStateRule include = 32;
-  repeated NetStateRule exclude = 33;
+  optional string name = 1; // the layer name
+  optional string type = 2; // the layer type
+  repeated string bottom = 3; // the name of each bottom blob
+  repeated string top = 4; // the name of each top blob
 
-  optional string type = 5; // the layer type from the enum above
+  // The amount of weight to assign each top blob in the objective.
+  // Each layer assigns a default value, usually of either 0 or 1,
+  // to each top blob.
+  repeated float loss_weight = 5;
 
   // The blobs containing the numeric parameters of the layer
   repeated BlobProto blobs = 6;
+
   // The names of the parameter blobs -- useful for sharing parameters among
   // layers (but never required).
-  repeated string param = 1001;
+  repeated string param = 7;
+
   // Whether to require shared weights to have the same shape, or just the same
   // count -- defaults to STRICT if unspecified.
-  repeated DimCheckMode blob_share_mode = 1002;
+  repeated DimCheckMode param_share_mode = 8;
   enum DimCheckMode {
     // STRICT (default) requires that num, channels, height, width each match.
     STRICT = 0;
     // PERMISSIVE requires only the count (num*channels*height*width) to match.
     PERMISSIVE = 1;
   }
+
   // The ratio that is multiplied on the global learning rate. If you want to
   // set the learning ratio for one blob, you need to set it for all blobs.
-  repeated float blobs_lr = 7;
-  // The weight decay that is multiplied on the global weight decay.
-  repeated float weight_decay = 8;
+  repeated float blobs_lr = 9;
 
-  // The amount of weight to assign each top blob in the objective.
-  // Each layer assigns a default value, usually of either 0 or 1,
-  // to each top blob.
-  repeated float loss_weight = 35;
+  // The weight decay that is multiplied on the global weight decay.
+  repeated float weight_decay = 10;
 
-  optional AccuracyParameter accuracy_param = 27;
-  optional ArgMaxParameter argmax_param = 23;
-  optional ConcatParameter concat_param = 9;
-  optional ContrastiveLossParameter contrastive_loss_param = 40;
-  optional ConvolutionParameter convolution_param = 10;
-  optional DataParameter data_param = 11;
-  optional DropoutParameter dropout_param = 12;
-  optional DummyDataParameter dummy_data_param = 26;
-  optional EltwiseParameter eltwise_param = 24;
-  optional ExpParameter exp_param = 41;
-  optional HDF5DataParameter hdf5_data_param = 13;
-  optional HDF5OutputParameter hdf5_output_param = 14;
-  optional HingeLossParameter hinge_loss_param = 29;
-  optional ImageDataParameter image_data_param = 15;
-  optional InfogainLossParameter infogain_loss_param = 16;
-  optional InnerProductParameter inner_product_param = 17;
-  optional LRNParameter lrn_param = 18;
-  optional MemoryDataParameter memory_data_param = 22;
-  optional MVNParameter mvn_param = 34;
-  optional PoolingParameter pooling_param = 19;
-  optional PowerParameter power_param = 21;
-  optional ReLUParameter relu_param = 30;
-  optional SigmoidParameter sigmoid_param = 38;
-  optional SoftmaxParameter softmax_param = 39;
-  optional SliceParameter slice_param = 31;
-  optional TanHParameter tanh_param = 37;
-  optional ThresholdParameter threshold_param = 25;
-  optional WindowDataParameter window_data_param = 20;
+  // Rules controlling whether and when a layer is included in the network,
+  // based on the current NetState.  You may specify a non-zero number of rules
+  // to include OR exclude, but not both.  If no include or exclude rules are
+  // specified, the layer is always included.  If the current NetState meets
+  // ANY (i.e., one or more) of the specified rules, the layer is
+  // included/excluded.
+  repeated NetStateRule include = 11;
+  repeated NetStateRule exclude = 12;
 
   // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 36;
+  optional TransformationParameter transform_param = 13;
+
+  optional AccuracyParameter accuracy_param = 14;
+  optional ArgMaxParameter argmax_param = 15;
+  optional ConcatParameter concat_param = 16;
+  optional ContrastiveLossParameter contrastive_loss_param = 17;
+  optional ConvolutionParameter convolution_param = 18;
+  optional DataParameter data_param = 19;
+  optional DropoutParameter dropout_param = 20;
+  optional DummyDataParameter dummy_data_param = 21;
+  optional EltwiseParameter eltwise_param = 22;
+  optional ExpParameter exp_param = 23;
+  optional HDF5DataParameter hdf5_data_param = 24;
+  optional HDF5OutputParameter hdf5_output_param = 25;
+  optional HingeLossParameter hinge_loss_param = 26;
+  optional ImageDataParameter image_data_param = 27;
+  optional InfogainLossParameter infogain_loss_param = 28;
+  optional InnerProductParameter inner_product_param = 29;
+  optional LRNParameter lrn_param = 30;
+  optional MemoryDataParameter memory_data_param = 31;
+  optional MVNParameter mvn_param = 32;
+  optional PoolingParameter pooling_param = 33;
+  optional PowerParameter power_param = 34;
+  optional ReLUParameter relu_param = 35;
+  optional SigmoidParameter sigmoid_param = 36;
+  optional SoftmaxParameter softmax_param = 37;
+  optional SliceParameter slice_param = 38;
+  optional TanHParameter tanh_param = 39;
+  optional ThresholdParameter threshold_param = 40;
+  optional WindowDataParameter window_data_param = 41;
 
   // Parameters shared by loss layers.
   optional LossParameter loss_param = 42;
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 16583b0f0ca..575d7a73fee 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,6 +13,10 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
+  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
+}
+
+bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
   for (int i = 0; i < net_param.layers_size(); ++i) {
     if (net_param.layers(i).has_layer()) {
       return true;
@@ -21,6 +25,10 @@ bool NetNeedsUpgrade(const NetParameter& net_param) {
   return false;
 }
 
+bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param) {
+  return net_param.layers_size() > 0;
+}
+
 bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
                   NetParameter* net_param) {
   // First upgrade padding layers to padded conv layers.
@@ -33,8 +41,8 @@ bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
     net_param->set_name(v0_net_param.name());
   }
   for (int i = 0; i < v0_net_param.layers_size(); ++i) {
-    is_fully_compatible &= UpgradeLayerParameter(v0_net_param.layers(i),
-                                                 net_param->add_layers());
+    is_fully_compatible &= UpgradeV0LayerParameter(v0_net_param.layers(i),
+                                                   net_param->add_layers());
   }
   for (int i = 0; i < v0_net_param.input_size(); ++i) {
     net_param->add_input(v0_net_param.input(i));
@@ -107,8 +115,8 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
   }
 }
 
-bool UpgradeLayerParameter(const V1LayerParameter& v0_layer_connection,
-                           V1LayerParameter* layer_param) {
+bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
+                             V1LayerParameter* layer_param) {
   bool is_fully_compatible = true;
   layer_param->Clear();
   for (int i = 0; i < v0_layer_connection.bottom_size(); ++i) {
@@ -590,19 +598,21 @@ void NetParameterToPrettyPrint(const NetParameter& param,
   for (int i = 0; i < param.input_dim_size(); ++i) {
     pretty_param->add_input_dim(param.input_dim(i));
   }
-  for (int i = 0; i < param.layers_size(); ++i) {
+  for (int i = 0; i < param.layer_size(); ++i) {
     pretty_param->add_layer()->CopyFrom(param.layer(i));
   }
 }
 
-void UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
-  if (NetNeedsUpgrade(*param)) {
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
+  bool success = true;
+  if (NetNeedsV0ToV1Upgrade(*param)) {
     // NetParameter was specified using the old style (V0LayerParameter); try to
     // upgrade it.
     LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
                << "V0LayerParameter: " << param_file;
     NetParameter original_param(*param);
     if (!UpgradeV0Net(original_param, param)) {
+      success = false;
       LOG(ERROR) << "Warning: had one or more problems upgrading "
           << "V0NetParameter to NetParameter (see above); continuing anyway.";
     } else {
@@ -624,6 +634,299 @@ void UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
     LOG(ERROR) << "Note that future Caffe releases will only support "
                << "transform_param messages for transformation fields.";
   }
+  if (NetNeedsV1ToV2Upgrade(*param)) {
+    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
+               << "V1LayerParameter: " << param_file;
+    NetParameter original_param(*param);
+    if (!UpgradeV1Net(original_param, param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+          << "V1LayerParameter (see above); continuing anyway.";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "V1LayerParameter";
+    }
+  }
+  return success;
+}
+
+bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
+  bool is_fully_compatible = true;
+  if (v1_net_param.layer_size() > 0) {
+    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
+               << "fields; these will be ignored for the upgrade.";
+    is_fully_compatible = false;
+  }
+  net_param->CopyFrom(v1_net_param);
+  net_param->clear_layers();
+  for (int i = 0; i < v1_net_param.layers_size(); ++i) {
+    if (!UpgradeV1LayerParameter(v1_net_param.layers(i),
+                                 net_param->add_layer())) {
+      LOG(ERROR) << "Upgrade of input layer " << i << " failed.";
+      is_fully_compatible = false;
+    }
+  }
+  return is_fully_compatible;
+}
+
+bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
+                             LayerParameter* layer_param) {
+  layer_param->Clear();
+  bool is_fully_compatible = true;
+  for (int i = 0; i < v1_layer_param.bottom_size(); ++i) {
+    layer_param->add_bottom(v1_layer_param.bottom(i));
+  }
+  for (int i = 0; i < v1_layer_param.top_size(); ++i) {
+    layer_param->add_top(v1_layer_param.top(i));
+  }
+  if (v1_layer_param.has_name()) {
+    layer_param->set_name(v1_layer_param.name());
+  }
+  for (int i = 0; i < v1_layer_param.include_size(); ++i) {
+    layer_param->add_include()->CopyFrom(v1_layer_param.include(i));
+  }
+  for (int i = 0; i < v1_layer_param.exclude_size(); ++i) {
+    layer_param->add_exclude()->CopyFrom(v1_layer_param.exclude(i));
+  }
+  if (v1_layer_param.has_type()) {
+    layer_param->set_type(UpgradeV1LayerType(v1_layer_param.type()));
+  }
+  for (int i = 0; i < v1_layer_param.blobs_size(); ++i) {
+    layer_param->add_blobs()->CopyFrom(v1_layer_param.blobs(i));
+  }
+  for (int i = 0; i < v1_layer_param.param_size(); ++i) {
+    layer_param->add_param(v1_layer_param.param(i));
+  }
+  for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
+    switch (v1_layer_param.blob_share_mode(i)) {
+    case V1LayerParameter_DimCheckMode_STRICT:
+      layer_param->add_param_share_mode(LayerParameter_DimCheckMode_STRICT);
+      break;
+    case V1LayerParameter_DimCheckMode_PERMISSIVE:
+      layer_param->add_param_share_mode(LayerParameter_DimCheckMode_PERMISSIVE);
+      break;
+    default:
+      LOG(FATAL) << "Unknown blob_share_mode: "
+                 << v1_layer_param.blob_share_mode(i);
+      break;
+    }
+  }
+  for (int i = 0; i < v1_layer_param.blobs_lr_size(); ++i) {
+    layer_param->add_blobs_lr(v1_layer_param.blobs_lr(i));
+  }
+  for (int i = 0; i < v1_layer_param.weight_decay_size(); ++i) {
+    layer_param->add_weight_decay(v1_layer_param.weight_decay(i));
+  }
+  for (int i = 0; i < v1_layer_param.loss_weight_size(); ++i) {
+    layer_param->add_loss_weight(v1_layer_param.loss_weight(i));
+  }
+  if (v1_layer_param.has_accuracy_param()) {
+    layer_param->mutable_accuracy_param()->CopyFrom(
+        v1_layer_param.accuracy_param());
+  }
+  if (v1_layer_param.has_argmax_param()) {
+    layer_param->mutable_argmax_param()->CopyFrom(
+        v1_layer_param.argmax_param());
+  }
+  if (v1_layer_param.has_concat_param()) {
+    layer_param->mutable_concat_param()->CopyFrom(
+        v1_layer_param.concat_param());
+  }
+  if (v1_layer_param.has_contrastive_loss_param()) {
+    layer_param->mutable_contrastive_loss_param()->CopyFrom(
+        v1_layer_param.contrastive_loss_param());
+  }
+  if (v1_layer_param.has_convolution_param()) {
+    layer_param->mutable_convolution_param()->CopyFrom(
+        v1_layer_param.convolution_param());
+  }
+  if (v1_layer_param.has_data_param()) {
+    layer_param->mutable_data_param()->CopyFrom(
+        v1_layer_param.data_param());
+  }
+  if (v1_layer_param.has_dropout_param()) {
+    layer_param->mutable_dropout_param()->CopyFrom(
+        v1_layer_param.dropout_param());
+  }
+  if (v1_layer_param.has_dummy_data_param()) {
+    layer_param->mutable_dummy_data_param()->CopyFrom(
+        v1_layer_param.dummy_data_param());
+  }
+  if (v1_layer_param.has_eltwise_param()) {
+    layer_param->mutable_eltwise_param()->CopyFrom(
+        v1_layer_param.eltwise_param());
+  }
+  if (v1_layer_param.has_exp_param()) {
+    layer_param->mutable_exp_param()->CopyFrom(
+        v1_layer_param.exp_param());
+  }
+  if (v1_layer_param.has_hdf5_data_param()) {
+    layer_param->mutable_hdf5_data_param()->CopyFrom(
+        v1_layer_param.hdf5_data_param());
+  }
+  if (v1_layer_param.has_hdf5_output_param()) {
+    layer_param->mutable_hdf5_output_param()->CopyFrom(
+        v1_layer_param.hdf5_output_param());
+  }
+  if (v1_layer_param.has_hinge_loss_param()) {
+    layer_param->mutable_hinge_loss_param()->CopyFrom(
+        v1_layer_param.hinge_loss_param());
+  }
+  if (v1_layer_param.has_image_data_param()) {
+    layer_param->mutable_image_data_param()->CopyFrom(
+        v1_layer_param.image_data_param());
+  }
+  if (v1_layer_param.has_infogain_loss_param()) {
+    layer_param->mutable_infogain_loss_param()->CopyFrom(
+        v1_layer_param.infogain_loss_param());
+  }
+  if (v1_layer_param.has_inner_product_param()) {
+    layer_param->mutable_inner_product_param()->CopyFrom(
+        v1_layer_param.inner_product_param());
+  }
+  if (v1_layer_param.has_lrn_param()) {
+    layer_param->mutable_lrn_param()->CopyFrom(
+        v1_layer_param.lrn_param());
+  }
+  if (v1_layer_param.has_memory_data_param()) {
+    layer_param->mutable_memory_data_param()->CopyFrom(
+        v1_layer_param.memory_data_param());
+  }
+  if (v1_layer_param.has_mvn_param()) {
+    layer_param->mutable_mvn_param()->CopyFrom(
+        v1_layer_param.mvn_param());
+  }
+  if (v1_layer_param.has_pooling_param()) {
+    layer_param->mutable_pooling_param()->CopyFrom(
+        v1_layer_param.pooling_param());
+  }
+  if (v1_layer_param.has_power_param()) {
+    layer_param->mutable_power_param()->CopyFrom(
+        v1_layer_param.power_param());
+  }
+  if (v1_layer_param.has_relu_param()) {
+    layer_param->mutable_relu_param()->CopyFrom(
+        v1_layer_param.relu_param());
+  }
+  if (v1_layer_param.has_sigmoid_param()) {
+    layer_param->mutable_sigmoid_param()->CopyFrom(
+        v1_layer_param.sigmoid_param());
+  }
+  if (v1_layer_param.has_softmax_param()) {
+    layer_param->mutable_softmax_param()->CopyFrom(
+        v1_layer_param.softmax_param());
+  }
+  if (v1_layer_param.has_slice_param()) {
+    layer_param->mutable_slice_param()->CopyFrom(
+        v1_layer_param.slice_param());
+  }
+  if (v1_layer_param.has_tanh_param()) {
+    layer_param->mutable_tanh_param()->CopyFrom(
+        v1_layer_param.tanh_param());
+  }
+  if (v1_layer_param.has_threshold_param()) {
+    layer_param->mutable_threshold_param()->CopyFrom(
+        v1_layer_param.threshold_param());
+  }
+  if (v1_layer_param.has_window_data_param()) {
+    layer_param->mutable_window_data_param()->CopyFrom(
+        v1_layer_param.window_data_param());
+  }
+  if (v1_layer_param.has_transform_param()) {
+    layer_param->mutable_transform_param()->CopyFrom(
+        v1_layer_param.transform_param());
+  }
+  if (v1_layer_param.has_layer()) {
+    LOG(ERROR) << "Input NetParameter has V0 layer -- ignoring.";
+    is_fully_compatible = false;
+  }
+  return is_fully_compatible;
+}
+
+const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
+  switch (type) {
+  case V1LayerParameter_LayerType_NONE:
+    return "";
+  case V1LayerParameter_LayerType_ABSVAL:
+    return "AbsVal";
+  case V1LayerParameter_LayerType_ACCURACY:
+    return "Accuracy";
+  case V1LayerParameter_LayerType_ARGMAX:
+    return "ArgMax";
+  case V1LayerParameter_LayerType_BNLL:
+    return "BNLL";
+  case V1LayerParameter_LayerType_CONCAT:
+    return "Concat";
+  case V1LayerParameter_LayerType_CONTRASTIVE_LOSS:
+    return "ContrastiveLoss";
+  case V1LayerParameter_LayerType_CONVOLUTION:
+    return "Convolution";
+  case V1LayerParameter_LayerType_DATA:
+    return "Data";
+  case V1LayerParameter_LayerType_DROPOUT:
+    return "Dropout";
+  case V1LayerParameter_LayerType_DUMMY_DATA:
+    return "DummyData";
+  case V1LayerParameter_LayerType_EUCLIDEAN_LOSS:
+    return "EuclideanLoss";
+  case V1LayerParameter_LayerType_ELTWISE:
+    return "Eltwise";
+  case V1LayerParameter_LayerType_EXP:
+    return "Exp";
+  case V1LayerParameter_LayerType_FLATTEN:
+    return "Flatten";
+  case V1LayerParameter_LayerType_HDF5_DATA:
+    return "HDF5Data";
+  case V1LayerParameter_LayerType_HDF5_OUTPUT:
+    return "HDF5Output";
+  case V1LayerParameter_LayerType_HINGE_LOSS:
+    return "HingeLoss";
+  case V1LayerParameter_LayerType_IM2COL:
+    return "Im2col";
+  case V1LayerParameter_LayerType_IMAGE_DATA:
+    return "ImageData";
+  case V1LayerParameter_LayerType_INFOGAIN_LOSS:
+    return "InfogainLoss";
+  case V1LayerParameter_LayerType_INNER_PRODUCT:
+    return "InnerProduct";
+  case V1LayerParameter_LayerType_LRN:
+    return "LRN";
+  case V1LayerParameter_LayerType_MEMORY_DATA:
+    return "MemoryData";
+  case V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
+    return "MultinomialLogisticLoss";
+  case V1LayerParameter_LayerType_MVN:
+    return "MVN";
+  case V1LayerParameter_LayerType_POOLING:
+    return "Pooling";
+  case V1LayerParameter_LayerType_POWER:
+    return "Power";
+  case V1LayerParameter_LayerType_RELU:
+    return "ReLU";
+  case V1LayerParameter_LayerType_SIGMOID:
+    return "Sigmoid";
+  case V1LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
+    return "SigmoidCrossEntropyLoss";
+  case V1LayerParameter_LayerType_SILENCE:
+    return "Silence";
+  case V1LayerParameter_LayerType_SOFTMAX:
+    return "Softmax";
+  case V1LayerParameter_LayerType_SOFTMAX_LOSS:
+    return "SoftmaxWithLoss";
+  case V1LayerParameter_LayerType_SPLIT:
+    return "Split";
+  case V1LayerParameter_LayerType_SLICE:
+    return "Slice";
+  case V1LayerParameter_LayerType_TANH:
+    return "TanH";
+  case V1LayerParameter_LayerType_WINDOW_DATA:
+    return "WindowData";
+  case V1LayerParameter_LayerType_THRESHOLD:
+    return "Threshold";
+  default:
+    LOG(FATAL) << "Unknown V1LayerParameter layer type: " << type;
+    return "";
+  }
 }
 
 void ReadNetParamsFromTextFileOrDie(const string& param_file,
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index d7a62e32441..8a0dd7af743 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -5,6 +5,7 @@
 #include <cstring>
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
+#include <string>
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"
@@ -23,16 +24,20 @@ int main(int argc, char** argv) {
   }
 
   NetParameter net_param;
-  if (!ReadProtoFromBinaryFile(argv[1], &net_param)) {
+  string input_filename(argv[1]);
+  if (!ReadProtoFromBinaryFile(input_filename, &net_param)) {
     LOG(ERROR) << "Failed to parse input binary file as NetParameter: "
-               << argv[1];
+               << input_filename;
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
-    NetParameter v0_net_param(net_param);
-    success = UpgradeV0Net(v0_net_param, &net_param);
+    success = UpgradeNetAsNeeded(input_filename, &net_param);
+    if (!success) {
+      LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+                 << "see details above.";
+    }
   } else {
     LOG(ERROR) << "File already in V1 proto format: " << argv[1];
   }
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 2f290fc5e14..0fe420a6d36 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -5,6 +5,7 @@
 #include <cstring>
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)
+#include <string>
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"
@@ -23,19 +24,23 @@ int main(int argc, char** argv) {
   }
 
   NetParameter net_param;
-  if (!ReadProtoFromTextFile(argv[1], &net_param)) {
+  string input_filename(argv[1]);
+  if (!ReadProtoFromTextFile(input_filename, &net_param)) {
     LOG(ERROR) << "Failed to parse input text file as NetParameter: "
-               << argv[1];
+               << input_filename;
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
   bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
-    NetParameter v0_net_param(net_param);
-    success = UpgradeV0Net(v0_net_param, &net_param);
+    success = UpgradeNetAsNeeded(input_filename, &net_param);
+    if (!success) {
+      LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+                 << "see details above.";
+    }
   } else {
-    LOG(ERROR) << "File already in V1 proto format: " << argv[1];
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
   if (need_data_upgrade) {

From cb08ffd9a3506e1c058ba5959ce05021b5f72338 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 14 Jan 2015 18:31:23 -0800
Subject: [PATCH 1093/2053] restore test_upgrade_proto to dev version

---
 src/caffe/test/test_upgrade_proto.cpp | 106 +++++++++++++-------------
 1 file changed, 53 insertions(+), 53 deletions(-)

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index e520b85f291..52e7f1f99f5 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1184,7 +1184,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
-      "  type: 'Data' "
+      "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -1199,7 +1199,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 96 "
       "    kernel_size: 11 "
@@ -1223,7 +1223,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "} "
       "layers { "
       "  name: 'fc8' "
-      "  type: 'InnerProduct' "
+      "  type: INNER_PRODUCT "
       "  inner_product_param { "
       "    num_output: 1000 "
       "    weight_filler { "
@@ -1244,7 +1244,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: 'SoftmaxWithLoss' "
+      "  type: SOFTMAX_LOSS "
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
@@ -1520,7 +1520,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "input_dim: 32 "
       "layers { "
       "  name: 'data' "
-      "  type: 'Data' "
+      "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -1537,7 +1537,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'images' "
-      "  type: 'ImageData' "
+      "  type: IMAGE_DATA "
       "  image_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-images' "
       "    batch_size: 256 "
@@ -1557,7 +1557,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'window_data' "
-      "  type: 'WindowData' "
+      "  type: WINDOW_DATA "
       "  window_data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -1577,7 +1577,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'hdf5data' "
-      "  type: 'HDF5Data' "
+      "  type: HDF5_DATA "
       "  hdf5_data_param { "
       "    source: '/my/hdf5/data' "
       "    batch_size: 256 "
@@ -1586,7 +1586,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 96 "
       "    bias_term: false "
@@ -1611,7 +1611,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'pool1ave' "
-      "  type: 'Pooling' "
+      "  type: POOLING "
       "  pooling_param { "
       "    pool: AVE "
       "    kernel_size: 3 "
@@ -1622,7 +1622,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'pool1stoch' "
-      "  type: 'Pooling' "
+      "  type: POOLING "
       "  pooling_param { "
       "    pool: STOCHASTIC "
       "    kernel_size: 4 "
@@ -1633,7 +1633,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'concat' "
-      "  type: 'Concat' "
+      "  type: CONCAT "
       "  concat_param { "
       "    concat_dim: 2 "
       "  } "
@@ -1643,7 +1643,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'norm1' "
-      "  type: 'LRN' "
+      "  type: LRN "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -1654,7 +1654,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'fc6' "
-      "  type: 'InnerProduct' "
+      "  type: INNER_PRODUCT "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    bias_term: false "
@@ -1676,13 +1676,13 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'relu6' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
       "layers { "
       "  name: 'drop6' "
-      "  type: 'Dropout' "
+      "  type: DROPOUT "
       "  dropout_param { "
       "    dropout_ratio: 0.2 "
       "  } "
@@ -1691,7 +1691,7 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: 'InfogainLoss' "
+      "  type: INFOGAIN_LOSS "
       "  infogain_loss_param { "
       "    source: '/my/infogain/matrix' "
       "  } "
@@ -1700,54 +1700,54 @@ TEST_F(V0UpgradeTest, TestAllParams) {
       "} "
       "layers { "
       "  name: 'accuracy' "
-      "  type: 'Accuracy' "
+      "  type: ACCURACY "
       "} "
       "layers { "
       "  name: 'bnll' "
-      "  type: 'BNLL' "
+      "  type: BNLL "
       "} "
       "layers { "
       "  name: 'euclidean_loss' "
-      "  type: 'EuclideanLoss' "
+      "  type: EUCLIDEAN_LOSS "
       "} "
       "layers { "
       "  name: 'flatten' "
-      "  type: 'Flatten' "
+      "  type: FLATTEN "
       "} "
       "layers { "
       "  name: 'hdf5_output' "
-      "  type: 'HDF5Output' "
+      "  type: HDF5_OUTPUT "
       "  hdf5_output_param { "
       "    file_name: '/my/hdf5/output/file' "
       "  } "
       "} "
       "layers { "
       "  name: 'im2col' "
-      "  type: 'Im2Col' "
+      "  type: IM2COL "
       "} "
       "layers { "
       "  name: 'images' "
-      "  type: 'ImageData' "
+      "  type: IMAGE_DATA "
       "} "
       "layers { "
       "  name: 'multinomial_logistic_loss' "
-      "  type: 'MultinomialLogisticLoss' "
+      "  type: MULTINOMIAL_LOGISTIC_LOSS "
       "} "
       "layers { "
       "  name: 'sigmoid' "
-      "  type: 'Sigmoid' "
+      "  type: SIGMOID "
       "} "
       "layers { "
       "  name: 'softmax' "
-      "  type: 'Softmax' "
+      "  type: SOFTMAX "
       "} "
       "layers { "
       "  name: 'split' "
-      "  type: 'Split' "
+      "  type: SPLIT "
       "} "
       "layers { "
       "  name: 'tanh' "
-      "  type: 'TanH' "
+      "  type: TANH "
       "} ";
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
@@ -2122,7 +2122,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
-      "  type: 'Data' "
+      "  type: DATA "
       "  data_param { "
       "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
       "    batch_size: 256 "
@@ -2137,7 +2137,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'conv1' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 96 "
       "    kernel_size: 11 "
@@ -2160,13 +2160,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu1' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'conv1' "
       "  top: 'conv1' "
       "} "
       "layers { "
       "  name: 'pool1' "
-      "  type: 'Pooling' "
+      "  type: POOLING "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -2177,7 +2177,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'norm1' "
-      "  type: 'LRN' "
+      "  type: LRN "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -2188,7 +2188,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'conv2' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -2212,13 +2212,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu2' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'conv2' "
       "  top: 'conv2' "
       "} "
       "layers { "
       "  name: 'pool2' "
-      "  type: 'Pooling' "
+      "  type: POOLING "
       "  pooling_param { "
       "    pool: MAX "
       "    kernel_size: 3 "
@@ -2229,7 +2229,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'norm2' "
-      "  type: 'LRN' "
+      "  type: LRN "
       "  lrn_param { "
       "    local_size: 5 "
       "    alpha: 0.0001 "
@@ -2240,7 +2240,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'conv3' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 384 "
       "    kernel_size: 3 "
@@ -2263,13 +2263,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu3' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'conv3' "
       "  top: 'conv3' "
       "} "
       "layers { "
       "  name: 'conv4' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 384 "
       "    group: 2 "
@@ -2293,13 +2293,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu4' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'conv4' "
       "  top: 'conv4' "
       "} "
       "layers { "
       "  name: 'conv5' "
-      "  type: 'Convolution' "
+      "  type: CONVOLUTION "
       "  convolution_param { "
       "    num_output: 256 "
       "    group: 2 "
@@ -2323,13 +2323,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu5' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'conv5' "
       "  top: 'conv5' "
       "} "
       "layers { "
       "  name: 'pool5' "
-      "  type: 'Pooling' "
+      "  type: POOLING "
       "  pooling_param { "
       "    kernel_size: 3 "
       "    pool: MAX "
@@ -2340,7 +2340,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'fc6' "
-      "  type: 'InnerProduct' "
+      "  type: INNER_PRODUCT "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -2361,13 +2361,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu6' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
       "layers { "
       "  name: 'drop6' "
-      "  type: 'Dropout' "
+      "  type: DROPOUT "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
@@ -2376,7 +2376,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'fc7' "
-      "  type: 'InnerProduct' "
+      "  type: INNER_PRODUCT "
       "  inner_product_param { "
       "    num_output: 4096 "
       "    weight_filler { "
@@ -2397,13 +2397,13 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'relu7' "
-      "  type: 'ReLU' "
+      "  type: RELU "
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
       "layers { "
       "  name: 'drop7' "
-      "  type: 'Dropout' "
+      "  type: DROPOUT "
       "  dropout_param { "
       "    dropout_ratio: 0.5 "
       "  } "
@@ -2412,7 +2412,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'fc8' "
-      "  type: 'InnerProduct' "
+      "  type: INNER_PRODUCT "
       "  inner_product_param { "
       "    num_output: 1000 "
       "    weight_filler { "
@@ -2433,7 +2433,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "} "
       "layers { "
       "  name: 'loss' "
-      "  type: 'SoftmaxWithLoss' "
+      "  type: SOFTMAX_LOSS "
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";

From 522d4644a2f54ce9bbc691e94d6ff93ab73d4868 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 14 Jan 2015 18:46:21 -0800
Subject: [PATCH 1094/2053] add v1 to v2 upgrade tests

---
 src/caffe/test/test_upgrade_proto.cpp | 432 +++++++++++++++++++++++++-
 1 file changed, 420 insertions(+), 12 deletions(-)

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 52e7f1f99f5..3a135a67174 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1083,7 +1083,7 @@ TEST_F(PaddingLayerUpgradeTest, TestImageNet) {
   this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
 }
 
-class V0UpgradeTest : public ::testing::Test {
+class NetUpgradeTest : public ::testing::Test {
  protected:
   void RunV0UpgradeTest(
       const string& input_param_string, const string& output_param_string) {
@@ -1101,10 +1101,27 @@ class V0UpgradeTest : public ::testing::Test {
     EXPECT_EQ(expected_output_param.DebugString(),
         actual_output_param.DebugString());
   }
+
+  void RunV1UpgradeTest(
+      const string& input_param_string, const string& output_param_string) {
+    // Test that UpgradeV0Net called on the NetParameter proto specified by
+    // input_param_string results in the NetParameter proto specified by
+    // output_param_string.
+    NetParameter input_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        input_param_string, &input_param));
+    NetParameter expected_output_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        output_param_string, &expected_output_param));
+    NetParameter actual_output_param;
+    UpgradeV1Net(input_param, &actual_output_param);
+    EXPECT_EQ(expected_output_param.DebugString(),
+        actual_output_param.DebugString());
+  }
 };
 
-TEST_F(V0UpgradeTest, TestSimple) {
-  const string& input_proto =
+TEST_F(NetUpgradeTest, TestSimple) {
+  const string& v0_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  layer { "
@@ -1180,7 +1197,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  const string& expected_output_proto =
+  const string& expected_v1_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
@@ -1248,11 +1265,81 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  this->RunV0UpgradeTest(input_proto, expected_output_proto);
+  this->RunV0UpgradeTest(v0_proto, expected_v1_proto);
+
+  const string& expected_v2_proto =
+      "name: 'CaffeNet' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'Data' "
+      "  data_param { "
+      "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
+      "    batch_size: 256 "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'conv1' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 96 "
+      "    kernel_size: 11 "
+      "    stride: 4 "
+      "    pad: 2 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "} "
+      "layer { "
+      "  name: 'fc8' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1000 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'conv1' "
+      "  top: 'fc8' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  type: 'SoftmaxWithLoss' "
+      "  bottom: 'fc8' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
 }
 
 // Test any layer or parameter upgrades not covered by other tests.
-TEST_F(V0UpgradeTest, TestAllParams) {
+TEST_F(NetUpgradeTest, TestAllParams) {
   const string& input_proto =
       "name: 'CaffeNet' "
       "input: 'input_data' "
@@ -1752,8 +1839,8 @@ TEST_F(V0UpgradeTest, TestAllParams) {
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
 
-TEST_F(V0UpgradeTest, TestImageNet) {
-  const string& input_proto =
+TEST_F(NetUpgradeTest, TestImageNet) {
+  const string& v0_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  layer { "
@@ -2118,7 +2205,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  const string& expected_output_proto =
+  const string& expected_v1_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
@@ -2437,7 +2524,328 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  this->RunV0UpgradeTest(input_proto, expected_output_proto);
-}
+  this->RunV0UpgradeTest(v0_proto, expected_v1_proto);
+
+  const string& expected_v2_proto =
+      "name: 'CaffeNet' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'Data' "
+      "  data_param { "
+      "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
+      "    batch_size: 256 "
+      "  } "
+      "  transform_param { "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'conv1' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 96 "
+      "    kernel_size: 11 "
+      "    stride: 4 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "} "
+      "layer { "
+      "  name: 'relu1' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv1' "
+      "  top: 'conv1' "
+      "} "
+      "layer { "
+      "  name: 'pool1' "
+      "  type: 'Pooling' "
+      "  pooling_param { "
+      "    pool: MAX "
+      "    kernel_size: 3 "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv1' "
+      "  top: 'pool1' "
+      "} "
+      "layer { "
+      "  name: 'norm1' "
+      "  type: 'LRN' "
+      "  lrn_param { "
+      "    local_size: 5 "
+      "    alpha: 0.0001 "
+      "    beta: 0.75 "
+      "  } "
+      "  bottom: 'pool1' "
+      "  top: 'norm1' "
+      "} "
+      "layer { "
+      "  name: 'conv2' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 256 "
+      "    group: 2 "
+      "    kernel_size: 5 "
+      "    pad: 2 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'norm1' "
+      "  top: 'conv2' "
+      "} "
+      "layer { "
+      "  name: 'relu2' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv2' "
+      "  top: 'conv2' "
+      "} "
+      "layer { "
+      "  name: 'pool2' "
+      "  type: 'Pooling' "
+      "  pooling_param { "
+      "    pool: MAX "
+      "    kernel_size: 3 "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv2' "
+      "  top: 'pool2' "
+      "} "
+      "layer { "
+      "  name: 'norm2' "
+      "  type: 'LRN' "
+      "  lrn_param { "
+      "    local_size: 5 "
+      "    alpha: 0.0001 "
+      "    beta: 0.75 "
+      "  } "
+      "  bottom: 'pool2' "
+      "  top: 'norm2' "
+      "} "
+      "layer { "
+      "  name: 'conv3' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 384 "
+      "    kernel_size: 3 "
+      "    pad: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'norm2' "
+      "  top: 'conv3' "
+      "} "
+      "layer { "
+      "  name: 'relu3' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv3' "
+      "  top: 'conv3' "
+      "} "
+      "layer { "
+      "  name: 'conv4' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 384 "
+      "    group: 2 "
+      "    kernel_size: 3 "
+      "    pad: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'conv3' "
+      "  top: 'conv4' "
+      "} "
+      "layer { "
+      "  name: 'relu4' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv4' "
+      "  top: 'conv4' "
+      "} "
+      "layer { "
+      "  name: 'conv5' "
+      "  type: 'Convolution' "
+      "  convolution_param { "
+      "    num_output: 256 "
+      "    group: 2 "
+      "    kernel_size: 3 "
+      "    pad: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'conv4' "
+      "  top: 'conv5' "
+      "} "
+      "layer { "
+      "  name: 'relu5' "
+      "  type: 'ReLU' "
+      "  bottom: 'conv5' "
+      "  top: 'conv5' "
+      "} "
+      "layer { "
+      "  name: 'pool5' "
+      "  type: 'Pooling' "
+      "  pooling_param { "
+      "    kernel_size: 3 "
+      "    pool: MAX "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv5' "
+      "  top: 'pool5' "
+      "} "
+      "layer { "
+      "  name: 'fc6' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 4096 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.005 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'pool5' "
+      "  top: 'fc6' "
+      "} "
+      "layer { "
+      "  name: 'relu6' "
+      "  type: 'ReLU' "
+      "  bottom: 'fc6' "
+      "  top: 'fc6' "
+      "} "
+      "layer { "
+      "  name: 'drop6' "
+      "  type: 'Dropout' "
+      "  dropout_param { "
+      "    dropout_ratio: 0.5 "
+      "  } "
+      "  bottom: 'fc6' "
+      "  top: 'fc6' "
+      "} "
+      "layer { "
+      "  name: 'fc7' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 4096 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.005 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 1. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'fc6' "
+      "  top: 'fc7' "
+      "} "
+      "layer { "
+      "  name: 'relu7' "
+      "  type: 'ReLU' "
+      "  bottom: 'fc7' "
+      "  top: 'fc7' "
+      "} "
+      "layer { "
+      "  name: 'drop7' "
+      "  type: 'Dropout' "
+      "  dropout_param { "
+      "    dropout_ratio: 0.5 "
+      "  } "
+      "  bottom: 'fc7' "
+      "  top: 'fc7' "
+      "} "
+      "layer { "
+      "  name: 'fc8' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1000 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 0. "
+      "  bottom: 'fc7' "
+      "  top: 'fc8' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  type: 'SoftmaxWithLoss' "
+      "  bottom: 'fc8' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
+}  // NOLINT(readability/fn_size)
 
-}  // namespace caffe
+}  // NOLINT(readability/fn_size)  // namespace caffe

From e64bf225b492d61f357cc52a7269ba5e7db0f00b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 14 Jan 2015 19:00:58 -0800
Subject: [PATCH 1095/2053] add test that all V1 layer type enum values upgrade
 to valid V2 string types

---
 src/caffe/test/test_upgrade_proto.cpp | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 3a135a67174..8cff961f9e8 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -7,6 +7,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layer.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -2848,4 +2849,21 @@ TEST_F(NetUpgradeTest, TestImageNet) {
   this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
 }  // NOLINT(readability/fn_size)
 
+TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
+  LayerParameter layer_param;
+  shared_ptr<Layer<float> > layer;
+  for (int i = 0; i < V1LayerParameter_LayerType_LayerType_ARRAYSIZE; ++i) {
+    ASSERT_TRUE(V1LayerParameter_LayerType_IsValid(i));
+    V1LayerParameter_LayerType v1_type = V1LayerParameter_LayerType(i);
+    string v2_layer_type(UpgradeV1LayerType(v1_type));
+    if (v2_layer_type == "") {
+      EXPECT_EQ(V1LayerParameter_LayerType_NONE, v1_type);
+      continue;  // Empty string isn't actually a valid layer type.
+    }
+    layer_param.set_type(v2_layer_type);
+    layer.reset(LayerRegistry<float>::CreateLayer(layer_param));
+    EXPECT_EQ(v2_layer_type, layer->type());
+  }
+}
+
 }  // NOLINT(readability/fn_size)  // namespace caffe

From 78b02e5fd3a43912556796bf29f118e0dea7b45e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 14 Jan 2015 20:17:26 -0800
Subject: [PATCH 1096/2053] add message ParamSpec to replace param name,
 blobs_lr, weight_decay, ...

---
 src/caffe/net.cpp                             |  82 ++--
 src/caffe/proto/caffe.proto                   |  52 ++-
 src/caffe/test/test_gradient_based_solver.cpp |   6 +-
 src/caffe/test/test_net.cpp                   | 380 ++++++++++--------
 src/caffe/test/test_solver.cpp                |   8 +-
 src/caffe/test/test_split_layer.cpp           | 298 ++++++++------
 src/caffe/test/test_upgrade_proto.cpp         | 120 ++++--
 src/caffe/util/upgrade_proto.cpp              |  18 +-
 8 files changed, 526 insertions(+), 438 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 4461fa88e75..b3e11943c1b 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -113,36 +113,19 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       memory_used_ += top_vecs_[layer_id][top_id]->count();
     }
     DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-    const int blobs_lr_size = layer_param.blobs_lr_size();
+    const int param_size = layer_param.param_size();
     const int num_param_blobs = layers_[layer_id]->blobs().size();
-    CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)
-        << "Incorrect blobs lr size: should be either 0 "
-        << "or the same as the number of the layer's parameter blobs.";
-    if (blobs_lr_size) {
-      // Check if this layer needs backward operation itself
-      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
-        const bool param_need_backward = layer_param.blobs_lr(param_id) > 0;
-        need_backward |= param_need_backward;
-        layers_[layer_id]->set_param_propagate_down(param_id,
-                                                    param_need_backward);
-      }
-    } else if (layers_[layer_id]->blobs().size()) {
-      // catch: if a layer param does not specify blobs_lr, we should assume the
-      // learning rate to be 1. Thus we will need to perform backward.
-      need_backward = true;
-      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
-        layers_[layer_id]->set_param_propagate_down(param_id, true);
-      }
+    CHECK_LE(param_size, num_param_blobs)
+        << "Too many params specified for layer " << layer_param.name();
+    ParamSpec default_param_spec;
+    for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
+      const ParamSpec* param_spec = (param_id < param_size) ?
+          &layer_param.param(param_id) : &default_param_spec;
+      const bool param_need_backward = param_spec->lr_mult() > 0;
+      need_backward |= param_need_backward;
+      layers_[layer_id]->set_param_propagate_down(param_id,
+                                                  param_need_backward);
     }
-    const int param_size = layer_param.param_size();
-    CHECK(param_size == num_param_blobs || param_size == 0)
-        << "Incorrect param size: should be either 0 or the same as "
-           "the number of the layer's parameter blobs: " << num_param_blobs;
-    const int param_share_mode_size = layer_param.param_share_mode_size();
-    CHECK(param_share_mode_size == num_param_blobs ||
-          param_share_mode_size == 0)
-        << "Incorrect param_share_mode size: should be either 0 or the same as "
-           "the number of the layer's parameter blobs: " << num_param_blobs;
     for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
       AppendParam(param, layer_id, param_id);
     }
@@ -407,7 +390,8 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                              const int param_id) {
   const LayerParameter& layer_param = layers_[layer_id]->layer_param();
   const int param_size = layer_param.param_size();
-  string param_name = param_size ? layer_param.param(param_id) : "";
+  string param_name =
+      (param_size > param_id) ? layer_param.param(param_id).name() : "";
   if (param_name.size()) {
     param_display_names_.push_back(param_name);
   } else {
@@ -442,10 +426,9 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
     Blob<Dtype>* owner_blob =
         layers_[owner_layer_id]->blobs()[owner_param_id].get();
-    const int param_share_mode_size = layer_param.param_share_mode_size();
-    if (param_share_mode_size > param_id &&
-        (layer_param.param_share_mode(param_id) ==
-         LayerParameter_DimCheckMode_PERMISSIVE)) {
+    const int param_size = layer_param.param_size();
+    if (param_size > param_id && (layer_param.param(param_id).share_mode() ==
+                                  ParamSpec_DimCheckMode_PERMISSIVE)) {
       // Permissive dimension checking -- only check counts are the same.
       CHECK_EQ(this_blob->count(), owner_blob->count())
           << "Shared parameter blobs must have the same count.";
@@ -468,34 +451,15 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
 template <typename Dtype>
 void Net<Dtype>::GetLearningRateAndWeightDecay() {
   LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
+  ParamSpec default_param_spec;
   for (int i = 0; i < layers_.size(); ++i) {
     vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
-    // push the learning rate mutlipliers
-    if (layers_[i]->layer_param().blobs_lr_size()) {
-      CHECK_EQ(layers_[i]->layer_param().blobs_lr_size(), layer_blobs.size());
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        float local_lr = layers_[i]->layer_param().blobs_lr(j);
-        CHECK_GE(local_lr, 0.);
-        params_lr_.push_back(local_lr);
-      }
-    } else {
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        params_lr_.push_back(1.);
-      }
-    }
-    // push the weight decay multipliers
-    if (layers_[i]->layer_param().weight_decay_size()) {
-      CHECK_EQ(layers_[i]->layer_param().weight_decay_size(),
-          layer_blobs.size());
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        float local_decay = layers_[i]->layer_param().weight_decay(j);
-        CHECK_GE(local_decay, 0.);
-        params_weight_decay_.push_back(local_decay);
-      }
-    } else {
-      for (int j = 0; j < layer_blobs.size(); ++j) {
-        params_weight_decay_.push_back(1.);
-      }
+    for (int j = 0; j < layer_blobs.size(); ++j) {
+      const ParamSpec* param_spec =
+          (layers_[i]->layer_param().param_size() > j) ?
+          &layers_[i]->layer_param().param(j) : &default_param_spec;
+      params_lr_.push_back(param_spec->lr_mult());
+      params_weight_decay_.push_back(param_spec->decay_mult());
     }
   }
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 1178efe53a8..453bf7ff285 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -213,6 +213,31 @@ message NetStateRule {
   repeated string not_stage = 5;
 }
 
+// Specifies training parameters (multipliers on global learning constants,
+// and the name and other settings used for weight sharing).
+message ParamSpec {
+  // The names of the parameter blobs -- useful for sharing parameters among
+  // layers, but never required otherwise.  To share a parameter between two
+  // layers, give it a (non-empty) name.
+  optional string name = 1;
+
+  // Whether to require shared weights to have the same shape, or just the same
+  // count -- defaults to STRICT if unspecified.
+  optional DimCheckMode share_mode = 2;
+  enum DimCheckMode {
+    // STRICT (default) requires that num, channels, height, width each match.
+    STRICT = 0;
+    // PERMISSIVE requires only the count (num*channels*height*width) to match.
+    PERMISSIVE = 1;
+  }
+
+  // The multiplier on the global learning rate for this parameter.
+  optional float lr_mult = 3 [default = 1.0];
+
+  // The multiplier on the global weight decay for this parameter.
+  optional float decay_mult = 4 [default = 1.0];
+}
+
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
@@ -228,29 +253,12 @@ message LayerParameter {
   // to each top blob.
   repeated float loss_weight = 5;
 
-  // The blobs containing the numeric parameters of the layer
-  repeated BlobProto blobs = 6;
+  // Specifies training parameters (multipliers on global learning constants,
+  // and the name and other settings used for weight sharing).
+  repeated ParamSpec param = 6;
 
-  // The names of the parameter blobs -- useful for sharing parameters among
-  // layers (but never required).
-  repeated string param = 7;
-
-  // Whether to require shared weights to have the same shape, or just the same
-  // count -- defaults to STRICT if unspecified.
-  repeated DimCheckMode param_share_mode = 8;
-  enum DimCheckMode {
-    // STRICT (default) requires that num, channels, height, width each match.
-    STRICT = 0;
-    // PERMISSIVE requires only the count (num*channels*height*width) to match.
-    PERMISSIVE = 1;
-  }
-
-  // The ratio that is multiplied on the global learning rate. If you want to
-  // set the learning ratio for one blob, you need to set it for all blobs.
-  repeated float blobs_lr = 9;
-
-  // The weight decay that is multiplied on the global weight decay.
-  repeated float weight_decay = 10;
+  // The blobs containing the numeric parameters of the layer.
+  repeated BlobProto blobs = 7;
 
   // Rules controlling whether and when a layer is included in the network,
   // based on the current NetState.  You may specify a non-zero number of rules
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 1d8192aebbc..eb2569c04f2 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -64,7 +64,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "lr_policy: 'fixed' "
        "net_param { "
        "  name: 'TestNetwork' "
-       "  layers: { "
+       "  layer { "
        "    name: 'data' "
        "    type: 'DummyData' "
        "    dummy_data_param { "
@@ -83,7 +83,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    top: 'data' "
        "    top: 'targets' "
        "  } "
-       "  layers: { "
+       "  layer { "
        "    name: 'innerprod' "
        "    type: 'InnerProduct' "
        "    inner_product_param { "
@@ -100,7 +100,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    bottom: 'data' "
        "    top: 'innerprod' "
        "  } "
-       "  layers: { "
+       "  layer { "
        "    name: 'loss' "
        "    type: 'EuclideanLoss' "
        "    bottom: 'innerprod' "
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index a4e14f914c5..bc0dae331b3 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -59,7 +59,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
                            const bool accuracy_layer = false) {
     string proto =
         "name: 'TinyTestNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -83,7 +83,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data' "
         "  top: 'label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -97,14 +97,18 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'data' "
         "  top: 'innerproduct' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'SoftmaxWithLoss' "
         "  bottom: 'innerproduct' "
@@ -113,7 +117,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "} ";
     if (accuracy_layer) {
       proto +=
-          "layers: { "
+          "layer { "
           "  name: 'loss' "
           "  type: 'Accuracy' "
           "  bottom: 'innerproduct' "
@@ -130,7 +134,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitTinyNetEuclidean(const bool force_backward = false) {
     string proto =
         "name: 'TinyTestEuclidLossNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -150,7 +154,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data' "
         "  top: 'label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -164,14 +168,18 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'data' "
         "  top: 'innerproduct' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'EuclideanLoss' "
         "  bottom: 'innerproduct' "
@@ -190,7 +198,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     }
     const string& proto =
         "name: 'TrickyTestNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -210,7 +218,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data' "
         "  top: 'label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -224,14 +232,18 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'data' "
         "  top: 'transformed_data' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -245,14 +257,18 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0 "
         "    } "
         "  } "
-        "  blobs_lr: 1. "
-        "  blobs_lr: 2. "
-        "  weight_decay: 1. "
-        "  weight_decay: 0. "
+        "  param { "
+        "    lr_mult: 1 "
+        "    decay_mult: 1 "
+        "  } "
+        "  param { "
+        "    lr_mult: 2 "
+        "    decay_mult: 0 "
+        "  } "
         "  bottom: 'label' "
         "  top: 'transformed_label' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'SoftmaxWithLoss' " +
         loss_weight_stream.str() +
@@ -277,7 +293,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
       proto << "force_backward: true ";
     }
     proto <<
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -292,9 +308,9 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  } "
         "  top: 'data' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
-       "  type: 'InnerProduct' "
+        "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
         "    bias_term: " << bias_term <<
@@ -303,14 +319,12 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'unsharedweights1' ";
+        "  param { "
+        "    name: 'unsharedweights1' "
+        "    lr_mult: " << blobs_lr_w1 <<
+        "  } ";
     if (bias_term) {
-      proto << "  param: '' ";
-    }
-    proto <<
-        "  blobs_lr: " << blobs_lr_w1;
-    if (bias_term) {
-      proto << "  blobs_lr: " << blobs_lr_b1;
+      proto << "  param { lr_mult: " << blobs_lr_b1 << " } ";
     }
     proto <<
         "  bottom: 'data' "
@@ -320,7 +334,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     }
     proto <<
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -331,20 +345,18 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'unsharedweights2' ";
+        "  param { "
+        "    name: 'unsharedweights2' "
+        "    lr_mult: " << blobs_lr_w2 <<
+        "  } ";
     if (bias_term) {
-      proto << "  param: '' ";
+      proto << "  param { lr_mult: " << blobs_lr_b2 << " } ";
     }
     proto <<
         "  bottom: 'data' "
-        "  blobs_lr: " << blobs_lr_w2;
-    if (bias_term) {
-      proto << "  blobs_lr: " << blobs_lr_b2;
-    }
-    proto <<
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'EuclideanLoss' ";
     if (loss_weight) {
@@ -360,7 +372,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitSharedWeightsNet() {
     const string& proto =
         "name: 'SharedWeightsNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -375,7 +387,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  } "
         "  top: 'data' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -386,11 +398,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'data' "
         "  top: 'innerproduct1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -401,11 +413,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      std: 10 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'data' "
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'EuclideanLoss' "
         "  bottom: 'innerproduct1' "
@@ -417,7 +429,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitDiffDataUnsharedWeightsNet() {
     const string& proto =
         "name: 'DiffDataUnsharedWeightsNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -437,7 +449,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data1' "
         "  top: 'data2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -448,11 +460,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'unsharedweights1' "
+        "  param { name: 'unsharedweights1' } "
         "  bottom: 'data1' "
         "  top: 'innerproduct1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -463,11 +475,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'unsharedweights2' "
+        "  param { name: 'unsharedweights2' } "
         "  bottom: 'innerproduct1' "
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'EuclideanLoss' "
         "  bottom: 'data2' "
@@ -479,7 +491,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitDiffDataSharedWeightsNet() {
     const string& proto =
         "name: 'DiffDataSharedWeightsNetwork' "
-        "layers: { "
+        "layer { "
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
@@ -499,7 +511,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  top: 'data1' "
         "  top: 'data2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct1' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -510,11 +522,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'data1' "
         "  top: 'innerproduct1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'innerproduct2' "
         "  type: 'InnerProduct' "
         "  inner_product_param { "
@@ -525,11 +537,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "      value: 0.5 "
         "    } "
         "  } "
-        "  param: 'sharedweights' "
+        "  param { name: 'sharedweights' } "
         "  bottom: 'innerproduct1' "
         "  top: 'innerproduct2' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'loss' "
         "  type: 'EuclideanLoss' "
         "  bottom: 'data2' "
@@ -546,7 +558,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "input_dim: 3 "
         "input_dim: 100 "
         "input_dim: 100 "
-        "layers: { "
+        "layer { "
         "  name: 'conv1' "
         "  type: 'Convolution' "
         "  bottom: 'data' "
@@ -565,13 +577,13 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "    } "
         "  } "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'relu1' "
         "  type: 'ReLU' "
         "  bottom: 'conv1' "
         "  top: 'conv1' "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'pool1' "
         "  type: 'Pooling' "
         "  bottom: 'conv1' "
@@ -582,7 +594,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "    stride: 2 "
         "  } "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'norm1' "
         "  type: 'LRN' "
         "  bottom: 'pool1' "
@@ -591,7 +603,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "    local_size: 3 "
         "  } "
         "} "
-        "layers: { "
+        "layer { "
         "  name: 'softmax' "
         "  type: 'Softmax' "
         "  bottom: 'norm1' "
@@ -1261,19 +1273,19 @@ class FilterNetTest : public ::testing::Test {
 TEST_F(FilterNetTest, TestNoFilter) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1285,7 +1297,7 @@ TEST_F(FilterNetTest, TestNoFilter) {
 TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
   const string& input_proto =
       "name: 'LeNet' "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
       "  type: 'Data' "
       "  top: 'data' "
@@ -1299,7 +1311,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TRAIN } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
       "  type: 'Data' "
       "  top: 'data' "
@@ -1313,13 +1325,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
       "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  convolution_param { "
       "    num_output: 20 "
       "    kernel_size: 5 "
@@ -1332,13 +1348,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'ip1' "
       "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  inner_product_param { "
       "    num_output: 10 "
       "    weight_filler { "
@@ -1349,7 +1369,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'accuracy' "
       "  type: 'Accuracy' "
       "  bottom: 'ip1' "
@@ -1357,7 +1377,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'accuracy' "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
@@ -1368,7 +1388,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
   const string input_proto_test = "state: { phase: TEST } " + input_proto;
   const string output_proto_train =
       "name: 'LeNet' "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
       "  type: 'Data' "
       "  top: 'data' "
@@ -1382,13 +1402,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TRAIN } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
       "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  convolution_param { "
       "    num_output: 20 "
       "    kernel_size: 5 "
@@ -1401,13 +1425,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'ip1' "
       "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  inner_product_param { "
       "    num_output: 10 "
       "    weight_filler { "
@@ -1418,7 +1446,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
@@ -1427,7 +1455,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "} ";
   const string& output_proto_test =
       "name: 'LeNet' "
-      "layers { "
+      "layer { "
       "  name: 'mnist' "
       "  type: 'Data' "
       "  top: 'data' "
@@ -1441,13 +1469,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  } "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
       "  type: 'Convolution' "
       "  bottom: 'data' "
       "  top: 'conv1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  convolution_param { "
       "    num_output: 20 "
       "    kernel_size: 5 "
@@ -1460,13 +1492,17 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'ip1' "
       "  type: 'InnerProduct' "
       "  bottom: 'conv1' "
       "  top: 'ip1' "
-      "  blobs_lr: 1 "
-      "  blobs_lr: 2 "
+      "  param { "
+      "    lr_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "  } "
       "  inner_product_param { "
       "    num_output: 10 "
       "    weight_filler { "
@@ -1477,7 +1513,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "    } "
       "  } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'accuracy' "
       "  type: 'Accuracy' "
       "  bottom: 'ip1' "
@@ -1485,7 +1521,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       "  top: 'accuracy' "
       "  include: { phase: TEST } "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'ip2' "
@@ -1517,20 +1553,20 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
 TEST_F(FilterNetTest, TestFilterOutByStage) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1538,13 +1574,13 @@ TEST_F(FilterNetTest, TestFilterOutByStage) {
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1556,20 +1592,20 @@ TEST_F(FilterNetTest, TestFilterOutByStage) {
 TEST_F(FilterNetTest, TestFilterOutByStage2) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1577,13 +1613,13 @@ TEST_F(FilterNetTest, TestFilterOutByStage2) {
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1596,20 +1632,20 @@ TEST_F(FilterNetTest, TestFilterInByStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1621,20 +1657,20 @@ TEST_F(FilterNetTest, TestFilterInByStage) {
 TEST_F(FilterNetTest, TestFilterInByStage2) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1647,20 +1683,20 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' stage: 'myotherstage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1670,13 +1706,13 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
   const string& output_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1690,13 +1726,13 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
@@ -1704,7 +1740,7 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
       "  include: { stage: 'myotherstage' } "
       "  include: { stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1718,20 +1754,20 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage2) {
   const string& input_proto =
       "state: { stage: 'mystage' stage: 'myotherstage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { stage: 'mystage' stage: 'myotherstage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1745,20 +1781,20 @@ TEST_F(FilterNetTest, TestFilterInByNotStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { not_stage: 'myotherstage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1772,20 +1808,20 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
   const string& input_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { not_stage: 'mystage' } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1795,7 +1831,7 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
   const string& output_proto =
       "state: { stage: 'mystage' } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
@@ -1807,20 +1843,20 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) {
 TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1828,13 +1864,13 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1846,20 +1882,20 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
 TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: -3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1867,13 +1903,13 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
       "} ";
   const string& output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1885,20 +1921,20 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
 TEST_F(FilterNetTest, TestFilterInByMinLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 0 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1911,20 +1947,20 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
   const string& input_proto =
       "state: { level: 7 } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1936,20 +1972,20 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
 TEST_F(FilterNetTest, TestFilterInByMaxLevel) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: 0 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1962,20 +1998,20 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
   const string& input_proto =
       "state: { level: -7 } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { max_level: -3 } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -1987,20 +2023,20 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
 TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  include: { min_level: 2  phase: TRAIN } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -2014,13 +2050,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
   const string& output_proto_train =
       "state: { level: 4  phase: TRAIN } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
@@ -2030,13 +2066,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
   const string& output_proto_test =
       "state: { level: 4  phase: TEST } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -2050,13 +2086,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
 TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
@@ -2064,7 +2100,7 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
       "  include: { min_level: 2  phase: TRAIN } "
       "  include: { phase: TEST } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -2083,20 +2119,20 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
 TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "  exclude: { min_level: 2  phase: TRAIN } "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -2110,13 +2146,13 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   const string& output_proto_train =
       "state: { level: 4  phase: TRAIN } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -2126,13 +2162,13 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
   const string& output_proto_test =
       "state: { level: 4  phase: TEST } "
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index d3f646c5bb6..1c2c9bbb740 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -51,7 +51,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "test_state: {}"
      "net_param { "
      "  name: 'TestNetwork' "
-     "  layers: { "
+     "  layer { "
      "    name: 'data' "
      "    type: 'DummyData' "
      "    dummy_data_param { "
@@ -67,7 +67,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "    top: 'data' "
      "    top: 'label' "
      "  } "
-     "  layers: { "
+     "  layer { "
      "    name: 'innerprod' "
      "    type: 'InnerProduct' "
      "    inner_product_param { "
@@ -76,7 +76,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "    bottom: 'data' "
      "    top: 'innerprod' "
      "  } "
-     "  layers: { "
+     "  layer { "
      "    name: 'accuracy' "
      "    type: 'Accuracy' "
      "    bottom: 'innerprod' "
@@ -84,7 +84,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "    top: 'accuracy' "
      "    exclude: { phase: TRAIN } "
      "  } "
-     "  layers: { "
+     "  layer { "
      "    name: 'loss' "
      "    type: 'SoftmaxWithLoss' "
      "    bottom: 'innerprod' "
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 584d0a4ab9c..be5204bfc3e 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -114,19 +114,19 @@ class SplitLayerInsertionTest : public ::testing::Test {
 TEST_F(SplitLayerInsertionTest, TestNoInsertion1) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -138,32 +138,32 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion1) {
 TEST_F(SplitLayerInsertionTest, TestNoInsertion2) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_split' "
       "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_split_0' "
       "  top: 'data_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_split_1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
@@ -175,7 +175,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion2) {
 TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
   const string& input_proto =
       "name: 'CaffeNet' "
-      "layers { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  data_param { "
@@ -190,7 +190,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv1' "
       "  type: 'Convolution' "
       "  convolution_param { "
@@ -206,20 +206,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu1' "
       "  type: 'ReLU' "
       "  bottom: 'conv1' "
       "  top: 'conv1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'pool1' "
       "  type: 'Pooling' "
       "  pooling_param { "
@@ -230,7 +234,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'conv1' "
       "  top: 'pool1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'norm1' "
       "  type: 'LRN' "
       "  lrn_param { "
@@ -241,7 +245,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'pool1' "
       "  top: 'norm1' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv2' "
       "  type: 'Convolution' "
       "  convolution_param { "
@@ -258,20 +262,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'norm1' "
       "  top: 'conv2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu2' "
       "  type: 'ReLU' "
       "  bottom: 'conv2' "
       "  top: 'conv2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'pool2' "
       "  type: 'Pooling' "
       "  pooling_param { "
@@ -282,7 +290,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'conv2' "
       "  top: 'pool2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'norm2' "
       "  type: 'LRN' "
       "  lrn_param { "
@@ -293,7 +301,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'pool2' "
       "  top: 'norm2' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv3' "
       "  type: 'Convolution' "
       "  convolution_param { "
@@ -309,20 +317,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'norm2' "
       "  top: 'conv3' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu3' "
       "  type: 'ReLU' "
       "  bottom: 'conv3' "
       "  top: 'conv3' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv4' "
       "  type: 'Convolution' "
       "  convolution_param { "
@@ -339,20 +351,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv3' "
       "  top: 'conv4' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu4' "
       "  type: 'ReLU' "
       "  bottom: 'conv4' "
       "  top: 'conv4' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'conv5' "
       "  type: 'Convolution' "
       "  convolution_param { "
@@ -369,20 +385,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv4' "
       "  top: 'conv5' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu5' "
       "  type: 'ReLU' "
       "  bottom: 'conv5' "
       "  top: 'conv5' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'pool5' "
       "  type: 'Pooling' "
       "  pooling_param { "
@@ -393,7 +413,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'conv5' "
       "  top: 'pool5' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'fc6' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -407,20 +427,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'pool5' "
       "  top: 'fc6' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu6' "
       "  type: 'ReLU' "
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'drop6' "
       "  type: 'Dropout' "
       "  dropout_param { "
@@ -429,7 +453,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'fc6' "
       "  top: 'fc6' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'fc7' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -443,20 +467,24 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'fc6' "
       "  top: 'fc7' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'relu7' "
       "  type: 'ReLU' "
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'drop7' "
       "  type: 'Dropout' "
       "  dropout_param { "
@@ -465,7 +493,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "  bottom: 'fc7' "
       "  top: 'fc7' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'fc8' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -479,14 +507,18 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
       "      value: 0 "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'fc7' "
       "  top: 'fc8' "
       "} "
-      "layers { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'fc8' "
@@ -498,25 +530,25 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) {
 TEST_F(SplitLayerInsertionTest, TestNoInsertionWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'relu' "
       "  type: 'ReLU' "
       "  bottom: 'innerprod' "
       "  top: 'innerprod' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'SoftmaxWithLoss' "
       "  bottom: 'innerprod' "
@@ -529,7 +561,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
   const string& input_proto =
       "name: 'UnsharedWeightsNetwork' "
       "force_backward: true "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'DummyData' "
       "  dummy_data_param { "
@@ -544,7 +576,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "  } "
       "  top: 'data' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct1' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -555,12 +587,12 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights1' "
+      "  param { name: 'unsharedweights1' } "
       "  bottom: 'data' "
       "  top: 'innerproduct1' "
       "  loss_weight: 2.5 "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct2' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -571,11 +603,11 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights2' "
+      "  param { name: 'unsharedweights2' } "
       "  bottom: 'data' "
       "  top: 'innerproduct2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerproduct1' "
@@ -584,7 +616,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
   const string& expected_output_proto =
       "name: 'UnsharedWeightsNetwork' "
       "force_backward: true "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'DummyData' "
       "  dummy_data_param { "
@@ -599,14 +631,14 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "  } "
       "  top: 'data' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
       "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct1' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -617,11 +649,11 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights1' "
+      "  param { name: 'unsharedweights1' } "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerproduct1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct1_innerproduct1_0_split' "
       "  type: 'Split' "
       "  bottom: 'innerproduct1' "
@@ -630,7 +662,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "  loss_weight: 2.5 "
       "  loss_weight: 0 "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerproduct2' "
       "  type: 'InnerProduct' "
       "  inner_product_param { "
@@ -641,11 +673,11 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
       "      std: 10 "
       "    } "
       "  } "
-      "  param: 'unsharedweights2' "
+      "  param { name: 'unsharedweights2' } "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerproduct2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerproduct1_innerproduct1_0_split_1' "
@@ -657,37 +689,37 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) {
 TEST_F(SplitLayerInsertionTest, TestInsertion) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
@@ -695,13 +727,13 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "} ";
   const string& expected_output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
       "  type: 'Split' "
       "  bottom: 'data' "
@@ -709,38 +741,38 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
       "  top: 'data_data_0_split_1' "
       "  top: 'data_data_0_split_2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2_innerprod2_0_split' "
       "  type: 'Split' "
       "  bottom: 'innerprod2' "
       "  top: 'innerprod2_innerprod2_0_split_0' "
       "  top: 'innerprod2_innerprod2_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_2' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod2_innerprod2_0_split_0' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2_innerprod2_0_split_1' "
@@ -752,43 +784,43 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) {
 TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'label' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod4' "
       "  type: 'InnerProduct' "
       "  bottom: 'label' "
       "  top: 'innerprod4' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
@@ -796,57 +828,57 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
       "} ";
   const string& expected_output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
       "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'label_data_1_split' "
       "  type: 'Split' "
       "  bottom: 'label' "
       "  top: 'label_data_1_split_0' "
       "  top: 'label_data_1_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'label_data_1_split_0' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod3' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_1' "
       "  top: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod4' "
       "  type: 'InnerProduct' "
       "  bottom: 'label_data_1_split_1' "
       "  top: 'innerprod4' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'innerprod3' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
@@ -863,19 +895,19 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "input_dim: 3 "
       "input_dim: 227 "
       "input_dim: 227 "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
@@ -888,26 +920,26 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
       "input_dim: 3 "
       "input_dim: 227 "
       "input_dim: 227 "
-      "layers: { "
+      "layer { "
       "  name: 'data_input_0_split' "
       "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_input_0_split_0' "
       "  top: 'data_input_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_input_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_input_0_split_1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
@@ -919,37 +951,37 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
 TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'relu1' "
       "  type: 'ReLU' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1' "
       "  bottom: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
@@ -957,51 +989,51 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
       "} ";
   const string& expected_output_proto =
       "name: 'TestNetwork' "
-      "layers: { "
+      "layer { "
       "  name: 'data' "
       "  type: 'Data' "
       "  top: 'data' "
       "  top: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'data_data_0_split' "
       "  type: 'Split' "
       "  bottom: 'data' "
       "  top: 'data_data_0_split_0' "
       "  top: 'data_data_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1' "
       "  type: 'InnerProduct' "
       "  bottom: 'data_data_0_split_0' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'relu1' "
       "  type: 'ReLU' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod1_relu1_0_split' "
       "  type: 'Split' "
       "  bottom: 'innerprod1' "
       "  top: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod1_relu1_0_split_1' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'innerprod2' "
       "  type: 'InnerProduct' "
       "  bottom: 'innerprod1_relu1_0_split_0' "
       "  top: 'innerprod2' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss1' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod1_relu1_0_split_1' "
       "  bottom: 'label' "
       "} "
-      "layers: { "
+      "layer { "
       "  name: 'loss2' "
       "  type: 'EuclideanLoss' "
       "  bottom: 'innerprod2' "
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 8cff961f9e8..7094055d4c8 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1302,10 +1302,14 @@ TEST_F(NetUpgradeTest, TestSimple) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "} "
@@ -1323,10 +1327,14 @@ TEST_F(NetUpgradeTest, TestSimple) {
       "      value: 0 "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv1' "
       "  top: 'fc8' "
       "} "
@@ -2560,10 +2568,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'data' "
       "  top: 'conv1' "
       "} "
@@ -2612,10 +2624,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'norm1' "
       "  top: 'conv2' "
       "} "
@@ -2663,10 +2679,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 0. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'norm2' "
       "  top: 'conv3' "
       "} "
@@ -2693,10 +2713,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv3' "
       "  top: 'conv4' "
       "} "
@@ -2723,10 +2747,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'conv4' "
       "  top: 'conv5' "
       "} "
@@ -2761,10 +2789,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'pool5' "
       "  top: 'fc6' "
       "} "
@@ -2797,10 +2829,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 1. "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'fc6' "
       "  top: 'fc7' "
       "} "
@@ -2833,10 +2869,14 @@ TEST_F(NetUpgradeTest, TestImageNet) {
       "      value: 0 "
       "    } "
       "  } "
-      "  blobs_lr: 1. "
-      "  blobs_lr: 2. "
-      "  weight_decay: 1. "
-      "  weight_decay: 0. "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
       "  bottom: 'fc7' "
       "  top: 'fc8' "
       "} "
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 575d7a73fee..6b401b537e0 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -659,6 +659,7 @@ bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
   }
   net_param->CopyFrom(v1_net_param);
   net_param->clear_layers();
+  net_param->clear_layer();
   for (int i = 0; i < v1_net_param.layers_size(); ++i) {
     if (!UpgradeV1LayerParameter(v1_net_param.layers(i),
                                  net_param->add_layer())) {
@@ -695,27 +696,34 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
     layer_param->add_blobs()->CopyFrom(v1_layer_param.blobs(i));
   }
   for (int i = 0; i < v1_layer_param.param_size(); ++i) {
-    layer_param->add_param(v1_layer_param.param(i));
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));
   }
+  ParamSpec_DimCheckMode mode;
   for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
     switch (v1_layer_param.blob_share_mode(i)) {
     case V1LayerParameter_DimCheckMode_STRICT:
-      layer_param->add_param_share_mode(LayerParameter_DimCheckMode_STRICT);
+      mode = ParamSpec_DimCheckMode_STRICT;
       break;
     case V1LayerParameter_DimCheckMode_PERMISSIVE:
-      layer_param->add_param_share_mode(LayerParameter_DimCheckMode_PERMISSIVE);
+      mode = ParamSpec_DimCheckMode_PERMISSIVE;
       break;
     default:
       LOG(FATAL) << "Unknown blob_share_mode: "
                  << v1_layer_param.blob_share_mode(i);
       break;
     }
+    layer_param->mutable_param(i)->set_share_mode(mode);
   }
   for (int i = 0; i < v1_layer_param.blobs_lr_size(); ++i) {
-    layer_param->add_blobs_lr(v1_layer_param.blobs_lr(i));
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    layer_param->mutable_param(i)->set_lr_mult(v1_layer_param.blobs_lr(i));
   }
   for (int i = 0; i < v1_layer_param.weight_decay_size(); ++i) {
-    layer_param->add_weight_decay(v1_layer_param.weight_decay(i));
+    while (layer_param->param_size() <= i) { layer_param->add_param(); }
+    layer_param->mutable_param(i)->set_decay_mult(
+        v1_layer_param.weight_decay(i));
   }
   for (int i = 0; i < v1_layer_param.loss_weight_size(); ++i) {
     layer_param->add_loss_weight(v1_layer_param.loss_weight(i));

From 62d1d3add909c86bf26e85203faa1ed64c88c597 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 15 Jan 2015 00:13:23 -0800
Subject: [PATCH 1097/2053] get rid of NetParameterPrettyPrint as layer is now
 after inputs (whoohoo)

---
 include/caffe/util/upgrade_proto.hpp     |  6 ------
 src/caffe/proto/caffe_pretty_print.proto | 18 ------------------
 src/caffe/util/upgrade_proto.cpp         | 20 --------------------
 tools/upgrade_net_proto_text.cpp         |  7 +------
 4 files changed, 1 insertion(+), 50 deletions(-)
 delete mode 100644 src/caffe/proto/caffe_pretty_print.proto

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 0df3a8987f9..c1f21a0d4d8 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -4,7 +4,6 @@
 #include <string>
 
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/proto/caffe_pretty_print.pb.h"
 
 namespace caffe {
 
@@ -51,11 +50,6 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
 
 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
-// Convert a NetParameter to NetParameterPrettyPrint used for dumping to
-// proto text files.
-void NetParameterToPrettyPrint(const NetParameter& param,
-                               NetParameterPrettyPrint* pretty_param);
-
 // Check for deprecations and upgrade the NetParameter as needed.
 bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
 
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
deleted file mode 100644
index 056541f75f1..00000000000
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ /dev/null
@@ -1,18 +0,0 @@
-syntax = "proto2";
-
-package caffe;
-
-import "caffe.proto";
-
-// A near-duplicate of NetParameter with fields re-numbered to beautify
-// automatic prototext dumps.  The main practical purpose is to print inputs
-// before layers, because having inputs at the end looks weird.
-// NetParameterPrettyPrint should never be used in code except for conversion
-// FROM NetParameter and subsequent dumping to proto text file.
-message NetParameterPrettyPrint {
-  optional string name = 1;
-  optional bool force_backward = 2 [default = false];
-  repeated string input = 3;
-  repeated int32 input_dim = 4;
-  repeated LayerParameter layer = 5;
-}
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 6b401b537e0..258d7d3ee7d 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -583,26 +583,6 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
   }
 }
 
-void NetParameterToPrettyPrint(const NetParameter& param,
-                               NetParameterPrettyPrint* pretty_param) {
-  pretty_param->Clear();
-  if (param.has_name()) {
-    pretty_param->set_name(param.name());
-  }
-  if (param.has_force_backward()) {
-    pretty_param->set_force_backward(param.force_backward());
-  }
-  for (int i = 0; i < param.input_size(); ++i) {
-    pretty_param->add_input(param.input(i));
-  }
-  for (int i = 0; i < param.input_dim_size(); ++i) {
-    pretty_param->add_input_dim(param.input_dim(i));
-  }
-  for (int i = 0; i < param.layer_size(); ++i) {
-    pretty_param->add_layer()->CopyFrom(param.layer(i));
-  }
-}
-
 bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
   bool success = true;
   if (NetNeedsV0ToV1Upgrade(*param)) {
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 0fe420a6d36..9200431bc27 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -47,13 +47,8 @@ int main(int argc, char** argv) {
     UpgradeNetDataTransformation(&net_param);
   }
 
-  // Convert to a NetParameterPrettyPrint to print fields in desired
-  // order.
-  NetParameterPrettyPrint net_param_pretty;
-  NetParameterToPrettyPrint(net_param, &net_param_pretty);
-
   // Save new format prototxt.
-  WriteProtoToTextFile(net_param_pretty, argv[2]);
+  WriteProtoToTextFile(net_param, argv[2]);
 
   LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2];
   return !success;

From 11a4c1655842a625a8fbb93f8c8ccd3629321a3f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 15 Jan 2015 01:43:23 -0800
Subject: [PATCH 1098/2053] start layer parameter field IDs at 100

(always want them printed at the end, and want to allow more fields to
be added in the future, so reserve fields 10-99 for that purpose)
---
 src/caffe/proto/caffe.proto | 69 +++++++++++++++++++------------------
 1 file changed, 35 insertions(+), 34 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 453bf7ff285..81fd2517dbf 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -241,7 +241,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 43 (last added: loss_param)
+// LayerParameter next available layer-specific ID: 129 (last added: window_data_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -266,48 +266,49 @@ message LayerParameter {
   // specified, the layer is always included.  If the current NetState meets
   // ANY (i.e., one or more) of the specified rules, the layer is
   // included/excluded.
-  repeated NetStateRule include = 11;
-  repeated NetStateRule exclude = 12;
+  repeated NetStateRule include = 8;
+  repeated NetStateRule exclude = 9;
 
   // Parameters for data pre-processing.
-  optional TransformationParameter transform_param = 13;
-
-  optional AccuracyParameter accuracy_param = 14;
-  optional ArgMaxParameter argmax_param = 15;
-  optional ConcatParameter concat_param = 16;
-  optional ContrastiveLossParameter contrastive_loss_param = 17;
-  optional ConvolutionParameter convolution_param = 18;
-  optional DataParameter data_param = 19;
-  optional DropoutParameter dropout_param = 20;
-  optional DummyDataParameter dummy_data_param = 21;
-  optional EltwiseParameter eltwise_param = 22;
-  optional ExpParameter exp_param = 23;
-  optional HDF5DataParameter hdf5_data_param = 24;
-  optional HDF5OutputParameter hdf5_output_param = 25;
-  optional HingeLossParameter hinge_loss_param = 26;
-  optional ImageDataParameter image_data_param = 27;
-  optional InfogainLossParameter infogain_loss_param = 28;
-  optional InnerProductParameter inner_product_param = 29;
-  optional LRNParameter lrn_param = 30;
-  optional MemoryDataParameter memory_data_param = 31;
-  optional MVNParameter mvn_param = 32;
-  optional PoolingParameter pooling_param = 33;
-  optional PowerParameter power_param = 34;
-  optional ReLUParameter relu_param = 35;
-  optional SigmoidParameter sigmoid_param = 36;
-  optional SoftmaxParameter softmax_param = 37;
-  optional SliceParameter slice_param = 38;
-  optional TanHParameter tanh_param = 39;
-  optional ThresholdParameter threshold_param = 40;
-  optional WindowDataParameter window_data_param = 41;
+  optional TransformationParameter transform_param = 100;
 
   // Parameters shared by loss layers.
-  optional LossParameter loss_param = 42;
+  optional LossParameter loss_param = 101;
 
+  // Layer type-specific parameters.
+  //
   // Note: certain layers may have more than one computational engine
   // for their implementation. These layers include an Engine type and
   // engine parameter for selecting the implementation.
   // The default for the engine is set by the ENGINE switch at compile-time.
+  optional AccuracyParameter accuracy_param = 102;
+  optional ArgMaxParameter argmax_param = 103;
+  optional ConcatParameter concat_param = 104;
+  optional ContrastiveLossParameter contrastive_loss_param = 105;
+  optional ConvolutionParameter convolution_param = 106;
+  optional DataParameter data_param = 107;
+  optional DropoutParameter dropout_param = 108;
+  optional DummyDataParameter dummy_data_param = 109;
+  optional EltwiseParameter eltwise_param = 110;
+  optional ExpParameter exp_param = 111;
+  optional HDF5DataParameter hdf5_data_param = 112;
+  optional HDF5OutputParameter hdf5_output_param = 113;
+  optional HingeLossParameter hinge_loss_param = 114;
+  optional ImageDataParameter image_data_param = 115;
+  optional InfogainLossParameter infogain_loss_param = 116;
+  optional InnerProductParameter inner_product_param = 117;
+  optional LRNParameter lrn_param = 118;
+  optional MemoryDataParameter memory_data_param = 119;
+  optional MVNParameter mvn_param = 120;
+  optional PoolingParameter pooling_param = 121;
+  optional PowerParameter power_param = 122;
+  optional ReLUParameter relu_param = 123;
+  optional SigmoidParameter sigmoid_param = 124;
+  optional SoftmaxParameter softmax_param = 125;
+  optional SliceParameter slice_param = 126;
+  optional TanHParameter tanh_param = 127;
+  optional ThresholdParameter threshold_param = 128;
+  optional WindowDataParameter window_data_param = 129;
 }
 
 // Message that stores parameters used to apply transformation

From e6c80dac40d4bb13390b56bc18294e6e91b00436 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 5 Feb 2015 15:17:24 -0800
Subject: [PATCH 1099/2053] Upgrade existing nets using upgrade_net_proto_text
 tool

Restore comments afterwards
---
 examples/cifar10/cifar10_full.prototxt        |   84 +-
 .../cifar10/cifar10_full_train_test.prototxt  |  120 +-
 examples/cifar10/cifar10_quick.prototxt       |   88 +-
 .../cifar10/cifar10_quick_train_test.prototxt |  124 +-
 .../feature_extraction/imagenet_val.prototxt  |  114 +-
 .../pascal_finetune_trainval_test.prototxt    |  230 ++-
 .../hdf5_classification/train_val.prototxt    |   44 +-
 .../hdf5_classification/train_val2.prototxt   |   64 +-
 .../imagenet/bvlc_caffenet_full_conv.prototxt |   92 +-
 examples/mnist/lenet.prototxt                 |   64 +-
 examples/mnist/lenet_train_test.prototxt      |  105 +-
 examples/mnist/mnist_autoencoder.prototxt     |  260 +--
 examples/siamese/mnist_siamese.prototxt       |   74 +-
 .../siamese/mnist_siamese_train_test.prototxt |  260 ++-
 models/bvlc_alexnet/deploy.prototxt           |  248 ++-
 models/bvlc_alexnet/train_val.prototxt        |  232 ++-
 models/bvlc_googlenet/deploy.prototxt         | 1548 ++++++++------
 models/bvlc_googlenet/train_val.prototxt      | 1802 ++++++++++-------
 .../bvlc_reference_caffenet/deploy.prototxt   |   92 +-
 .../train_val.prototxt                        |  232 ++-
 .../deploy.prototxt                           |   88 +-
 models/finetune_flickr_style/deploy.prototxt  |  192 +-
 .../finetune_flickr_style/train_val.prototxt  |  236 ++-
 23 files changed, 3661 insertions(+), 2732 deletions(-)

diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 8bbd30004fd..c16f7dca49f 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -6,13 +6,17 @@ input_dim: 1
 input_dim: 3
 input_dim: 32
 input_dim: 32
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -20,9 +24,9 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -31,31 +35,35 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -63,15 +71,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -80,21 +88,21 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -104,15 +112,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -121,22 +129,26 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 10
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip1"
   top: "prob"
 }
diff --git a/examples/cifar10/cifar10_full_train_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
index 38cc04f4a68..d45fc61e120 100644
--- a/examples/cifar10/cifar10_full_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -1,41 +1,49 @@
 name: "CIFAR10_full"
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -50,9 +58,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -61,31 +69,35 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -100,15 +112,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -117,21 +129,21 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
-    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
+    norm_region: WITHIN_CHANNEL
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -148,15 +160,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -165,15 +177,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -185,17 +201,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip1"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip1"
   bottom: "label"
   top: "loss"
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 505158f7a34..1ad190e185f 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -4,13 +4,17 @@ input_dim: 1
 input_dim: 3
 input_dim: 32
 input_dim: 32
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -18,9 +22,9 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -29,19 +33,23 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -49,15 +57,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -66,13 +74,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -80,15 +92,15 @@ layers {
     stride: 1
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -97,31 +109,39 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 64
   }
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip2"
   top: "prob"
 }
diff --git a/examples/cifar10/cifar10_quick_train_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
index 074bb0011e3..2317739353e 100644
--- a/examples/cifar10/cifar10_quick_train_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -1,41 +1,49 @@
 name: "CIFAR10_quick"
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_train_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "cifar"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
   data_param {
     source: "examples/cifar10/cifar10_test_lmdb"
     batch_size: 100
     backend: LMDB
   }
-  transform_param {
-    mean_file: "examples/cifar10/mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -50,9 +58,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -61,19 +69,23 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "pool1"
   top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -88,15 +100,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -105,13 +117,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -126,15 +142,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "pool3"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv3"
   top: "pool3"
   pooling_param {
@@ -143,13 +159,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool3"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 64
     weight_filler {
@@ -161,13 +181,17 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -179,17 +203,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip2"
   bottom: "label"
   top: "loss"
diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 83fe8c1a08d..b0a1cefa00e 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -1,24 +1,24 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "examples/_temp/file_list.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -27,15 +27,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -44,9 +44,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -55,9 +55,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -67,15 +67,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -84,9 +84,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -95,9 +95,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -106,15 +106,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -124,15 +124,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -142,15 +142,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -159,79 +159,79 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
   inner_product_param {
     num_output: 1000
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "prob"
   bottom: "label"
   top: "accuracy"
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"
diff --git a/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
index 5cd605bbf11..9dd2120acad 100644
--- a/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
+++ b/examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt
@@ -1,9 +1,17 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: WINDOW_DATA
+  type: "WindowData"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   window_data_param {
     source: "examples/finetune_pascal_detection/window_file_2007_trainval.txt"
     batch_size: 128
@@ -13,18 +21,20 @@ layers {
     context_pad: 16
     crop_mode: "warp"
   }
+}
+layer {
+  name: "data"
+  type: "WindowData"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
   transform_param {
     mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
   }
-  include: { phase: TRAIN }
-}
-layers {
-  name: "data"
-  type: WINDOW_DATA
-  top: "data"
-  top: "label"
   window_data_param {
     source: "examples/finetune_pascal_detection/window_file_2007_test.txt"
     batch_size: 128
@@ -34,22 +44,20 @@ layers {
     context_pad: 16
     crop_mode: "warp"
   }
-  transform_param {
-    mirror: true
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -64,15 +72,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -81,9 +89,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -92,15 +100,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -116,15 +128,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -133,9 +145,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -144,15 +156,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -167,21 +183,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -197,21 +217,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -227,15 +251,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -244,15 +268,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -265,30 +293,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -301,30 +333,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_pascal"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_pascal"
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 21
     weight_filler {
@@ -337,17 +373,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8_pascal"
   bottom: "label"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8_pascal"
   bottom: "label"
   top: "accuracy"
-  include { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
index b55b6644b17..b9ccc1a93ec 100644
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -1,35 +1,43 @@
 name: "LogisticRegressionNet"
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "fc1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "data"
   top: "fc1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -42,18 +50,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc1"
   bottom: "label"
   top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc1"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/train_val2.prototxt
index b6a75650ad3..f9ef731fff9 100644
--- a/examples/hdf5_classification/train_val2.prototxt
+++ b/examples/hdf5_classification/train_val2.prototxt
@@ -1,35 +1,43 @@
 name: "LogisticRegressionNet"
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: HDF5_DATA
+  type: "HDF5Data"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
   hdf5_data_param {
     source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "fc1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "data"
   top: "fc1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 40
     weight_filler {
@@ -42,21 +50,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "fc1"
   top: "fc1"
 }
-layers {
+layer {
   name: "fc2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc1"
   top: "fc2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -69,18 +81,20 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc2"
   bottom: "label"
   top: "loss"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
diff --git a/examples/imagenet/bvlc_caffenet_full_conv.prototxt b/examples/imagenet/bvlc_caffenet_full_conv.prototxt
index 395b0f0162f..7b22bfa1404 100644
--- a/examples/imagenet/bvlc_caffenet_full_conv.prototxt
+++ b/examples/imagenet/bvlc_caffenet_full_conv.prototxt
@@ -5,9 +5,9 @@ input_dim: 1
 input_dim: 3
 input_dim: 451
 input_dim: 451
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -16,15 +16,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -33,9 +33,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -44,9 +44,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -56,15 +56,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -73,9 +73,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -84,9 +84,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -95,15 +95,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -113,15 +113,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -131,15 +131,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -148,9 +148,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool5"
   top: "fc6-conv"
   convolution_param {
@@ -158,24 +158,24 @@ layers {
     kernel_size: 6
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6-conv"
   top: "fc6-conv"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6-conv"
   top: "fc6-conv"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "fc6-conv"
   top: "fc7-conv"
   convolution_param {
@@ -183,24 +183,24 @@ layers {
     kernel_size: 1
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7-conv"
   top: "fc7-conv"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7-conv"
   top: "fc7-conv"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8-conv"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "fc7-conv"
   top: "fc8-conv"
   convolution_param {
@@ -208,9 +208,9 @@ layers {
     kernel_size: 1
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8-conv"
   top: "prob"
 }
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index 491fad1b1c0..cb42610fe1e 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -4,13 +4,17 @@ input_dim: 64
 input_dim: 1
 input_dim: 28
 input_dim: 28
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -23,9 +27,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -34,13 +38,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -53,9 +61,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -64,13 +72,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -81,19 +93,23 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -104,9 +120,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "ip2"
   top: "prob"
 }
diff --git a/examples/mnist/lenet_train_test.prototxt b/examples/mnist/lenet_train_test.prototxt
index 2bd960b56aa..b18fc26cfd8 100644
--- a/examples/mnist/lenet_train_test.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -1,42 +1,49 @@
 name: "LeNet"
-layers {
+layer {
   name: "mnist"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
-    batch_size: 64
+  include {
+    phase: TRAIN
   }
   transform_param {
     scale: 0.00390625
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    batch_size: 64
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "mnist"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/mnist/mnist_test_lmdb"
-    backend: LMDB
-    batch_size: 100
+  include {
+    phase: TEST
   }
   transform_param {
     scale: 0.00390625
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
 }
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -49,9 +56,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -60,13 +67,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -79,9 +90,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -90,13 +101,17 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -107,19 +122,23 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -130,17 +149,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "ip2"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "ip2"
   bottom: "label"
   top: "loss"
diff --git a/examples/mnist/mnist_autoencoder.prototxt b/examples/mnist/mnist_autoencoder.prototxt
index 0b33781a16f..563c7c91e52 100644
--- a/examples/mnist/mnist_autoencoder.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -1,67 +1,73 @@
 name: "MNISTAutoencoder"
-layers {
-  top: "data"
+layer {
   name: "data"
-  type: DATA
-  data_param {
-    source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
-    batch_size: 100
+  type: "Data"
+  top: "data"
+  include {
+    phase: TRAIN
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: { phase: TRAIN }
-}
-layers {
-  top: "data"
-  name: "data"
-  type: DATA
   data_param {
     source: "examples/mnist/mnist_train_lmdb"
-    backend: LMDB
     batch_size: 100
+    backend: LMDB
+  }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  include {
+    phase: TEST
+    stage: "test-on-train"
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: {
-    phase: TEST
-    stage: 'test-on-train'
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    batch_size: 100
+    backend: LMDB
   }
 }
-layers {
-  top: "data"
+layer {
   name: "data"
-  type: DATA
-  data_param {
-    source: "examples/mnist/mnist_test_lmdb"
-    backend: LMDB
-    batch_size: 100
+  type: "Data"
+  top: "data"
+  include {
+    phase: TEST
+    stage: "test-on-test"
   }
   transform_param {
     scale: 0.0039215684
   }
-  include: {
-    phase: TEST
-    stage: 'test-on-test'
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    batch_size: 100
+    backend: LMDB
   }
 }
-layers {
+layer {
+  name: "flatdata"
+  type: "Flatten"
   bottom: "data"
   top: "flatdata"
-  name: "flatdata"
-  type: FLATTEN
 }
-layers {
+layer {
+  name: "encode1"
+  type: "InnerProduct"
   bottom: "data"
   top: "encode1"
-  name: "encode1"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -75,21 +81,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode1neuron"
+  type: "Sigmoid"
   bottom: "encode1"
   top: "encode1neuron"
-  name: "encode1neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode2"
+  type: "InnerProduct"
   bottom: "encode1neuron"
   top: "encode2"
-  name: "encode2"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -103,21 +113,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode2neuron"
+  type: "Sigmoid"
   bottom: "encode2"
   top: "encode2neuron"
-  name: "encode2neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode3"
+  type: "InnerProduct"
   bottom: "encode2neuron"
   top: "encode3"
-  name: "encode3"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 250
     weight_filler {
@@ -131,21 +145,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "encode3neuron"
+  type: "Sigmoid"
   bottom: "encode3"
   top: "encode3neuron"
-  name: "encode3neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "encode4"
+  type: "InnerProduct"
   bottom: "encode3neuron"
   top: "encode4"
-  name: "encode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 30
     weight_filler {
@@ -159,15 +177,19 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode4"
+  type: "InnerProduct"
   bottom: "encode4"
   top: "decode4"
-  name: "decode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 250
     weight_filler {
@@ -181,21 +203,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode4neuron"
+  type: "Sigmoid"
   bottom: "decode4"
   top: "decode4neuron"
-  name: "decode4neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode3"
+  type: "InnerProduct"
   bottom: "decode4neuron"
   top: "decode3"
-  name: "decode3"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -209,21 +235,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode3neuron"
+  type: "Sigmoid"
   bottom: "decode3"
   top: "decode3neuron"
-  name: "decode3neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode2"
+  type: "InnerProduct"
   bottom: "decode3neuron"
   top: "decode2"
-  name: "decode2"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -237,21 +267,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "decode2neuron"
+  type: "Sigmoid"
   bottom: "decode2"
   top: "decode2neuron"
-  name: "decode2neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "decode1"
+  type: "InnerProduct"
   bottom: "decode2neuron"
   top: "decode1"
-  name: "decode1"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 784
     weight_filler {
@@ -265,25 +299,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss"
+  type: "SigmoidCrossEntropyLoss"
   bottom: "decode1"
   bottom: "flatdata"
   top: "cross_entropy_loss"
-  name: "loss"
-  type: SIGMOID_CROSS_ENTROPY_LOSS
   loss_weight: 1
 }
-layers {
+layer {
+  name: "decode1neuron"
+  type: "Sigmoid"
   bottom: "decode1"
   top: "decode1neuron"
-  name: "decode1neuron"
-  type: SIGMOID
 }
-layers {
+layer {
+  name: "loss"
+  type: "EuclideanLoss"
   bottom: "decode1neuron"
   bottom: "flatdata"
   top: "l2_error"
-  name: "loss"
-  type: EUCLIDEAN_LOSS
   loss_weight: 0
 }
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 8dd42e9c1b5..0e903f85909 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -4,23 +4,26 @@ input_dim: 10000
 input_dim: 1
 input_dim: 28
 input_dim: 28
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -29,22 +32,26 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
     stride: 1
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -53,42 +60,53 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
   }
 }
-
-layers {
+layer {
   name: "feat"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2"
   top: "feat"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
   }
diff --git a/examples/siamese/mnist_siamese_train_test.prototxt b/examples/siamese/mnist_siamese_train_test.prototxt
index 92361c31dc7..8ff864f556f 100644
--- a/examples/siamese/mnist_siamese_train_test.prototxt
+++ b/examples/siamese/mnist_siamese_train_test.prototxt
@@ -1,50 +1,60 @@
 name: "mnist_siamese_train_test"
-layers {
+layer {
   name: "pair_data"
-  type: DATA
+  type: "Data"
   top: "pair_data"
   top: "sim"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    scale: 0.00390625
+  }
   data_param {
     source: "examples/siamese/mnist_siamese_train_leveldb"
-    scale: 0.00390625
     batch_size: 64
   }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "pair_data"
-  type: DATA
+  type: "Data"
   top: "pair_data"
   top: "sim"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    scale: 0.00390625
+  }
   data_param {
     source: "examples/siamese/mnist_siamese_test_leveldb"
-    scale: 0.00390625
     batch_size: 100
   }
-  include: { phase: TEST }
 }
-layers {
-    name: "slice_pair"
-    type: SLICE
-    bottom: "pair_data"
-    top: "data"
-    top: "data_p"
-    slice_param {
-        slice_dim: 1
-        slice_point: 1
-    }
+layer {
+  name: "slice_pair"
+  type: "Slice"
+  bottom: "pair_data"
+  top: "data"
+  top: "data_p"
+  slice_param {
+    slice_dim: 1
+    slice_point: 1
+  }
 }
-
-
-
-
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv1_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -56,12 +66,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv1_w"
-  param: "conv1_b"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -70,13 +78,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv2_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -88,12 +102,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv2_w"
-  param: "conv2_b"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -102,13 +114,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2"
   top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip1_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -118,22 +136,26 @@ layers {
       type: "constant"
     }
   }
-  param: "ip1_w"
-  param: "ip1_b"
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1"
   top: "ip1"
 }
-layers {
+layer {
   name: "ip2"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1"
   top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip2_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -143,17 +165,20 @@ layers {
       type: "constant"
     }
   }
-  param: "ip2_w"
-  param: "ip2_b"
 }
-
-layers {
+layer {
   name: "feat"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2"
   top: "feat"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -163,19 +188,20 @@ layers {
       type: "constant"
     }
   }
-  param: "feat_w"
-  param: "feat_b"
 }
-
-
-
-layers {
+layer {
   name: "conv1_p"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data_p"
   top: "conv1_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv1_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 20
     kernel_size: 5
@@ -187,12 +213,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv1_w"
-  param: "conv1_b"
 }
-layers {
+layer {
   name: "pool1_p"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1_p"
   top: "pool1_p"
   pooling_param {
@@ -201,13 +225,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2_p"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1_p"
   top: "conv2_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "conv2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "conv2_b"
+    lr_mult: 2
+  }
   convolution_param {
     num_output: 50
     kernel_size: 5
@@ -219,12 +249,10 @@ layers {
       type: "constant"
     }
   }
-  param: "conv2_w"
-  param: "conv2_b"
 }
-layers {
+layer {
   name: "pool2_p"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2_p"
   top: "pool2_p"
   pooling_param {
@@ -233,13 +261,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "ip1_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool2_p"
   top: "ip1_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip1_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip1_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 500
     weight_filler {
@@ -249,22 +283,26 @@ layers {
       type: "constant"
     }
   }
-  param: "ip1_w"
-  param: "ip1_b"
 }
-layers {
+layer {
   name: "relu1_p"
-  type: RELU
+  type: "ReLU"
   bottom: "ip1_p"
   top: "ip1_p"
 }
-layers {
+layer {
   name: "ip2_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip1_p"
   top: "ip2_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "ip2_w"
+    lr_mult: 1
+  }
+  param {
+    name: "ip2_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 10
     weight_filler {
@@ -274,17 +312,20 @@ layers {
       type: "constant"
     }
   }
-  param: "ip2_w"
-  param: "ip2_b"
 }
-
-layers {
+layer {
   name: "feat_p"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "ip2_p"
   top: "feat_p"
-  blobs_lr: 1
-  blobs_lr: 2
+  param {
+    name: "feat_w"
+    lr_mult: 1
+  }
+  param {
+    name: "feat_b"
+    lr_mult: 2
+  }
   inner_product_param {
     num_output: 2
     weight_filler {
@@ -294,20 +335,15 @@ layers {
       type: "constant"
     }
   }
-  param: "feat_w"
-  param: "feat_b"
 }
-
-
-
-layers {
-    name: "loss"
-    type: CONTRASTIVE_LOSS
-    contrastive_loss_param {
-        margin: 1.0
-    }
-    bottom: "feat"
-    bottom: "feat_p"
-    bottom: "sim"
-    top: "loss"
+layer {
+  name: "loss"
+  type: "ContrastiveLoss"
+  bottom: "feat"
+  bottom: "feat_p"
+  bottom: "sim"
+  top: "loss"
+  contrastive_loss_param {
+    margin: 1
+  }
 }
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index d010753f3fc..ced055b85d0 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -4,241 +4,273 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
     stride: 4
   }
-  bottom: "data"
-  top: "conv1"
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
+  bottom: "conv1"
+  top: "norm1"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv1"
-  top: "norm1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
+  bottom: "norm1"
+  top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm1"
-  top: "pool1"
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
     kernel_size: 5
     group: 2
   }
-  bottom: "pool1"
-  top: "conv2"
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
+  bottom: "conv2"
+  top: "norm2"
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
-  bottom: "conv2"
-  top: "norm2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
+  bottom: "norm2"
+  top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm2"
-  top: "pool2"
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
   }
-  bottom: "pool2"
-  top: "conv3"
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "conv3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv3"
-  top: "conv4"
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "Convolution"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv4"
-  top: "conv5"
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
+  bottom: "conv5"
+  top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "conv5"
-  top: "pool5"
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "pool5"
+  top: "fc6"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
   }
-  bottom: "pool5"
-  top: "fc6"
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
+  bottom: "fc6"
+  top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc6"
-  top: "fc6"
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "fc6"
+  top: "fc7"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
   }
-  bottom: "fc6"
-  top: "fc7"
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
+  bottom: "fc7"
+  top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc7"
-  top: "fc7"
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  type: "InnerProduct"
+  bottom: "fc7"
+  top: "fc8"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
   }
-  bottom: "fc7"
-  top: "fc8"
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
diff --git a/models/bvlc_alexnet/train_val.prototxt b/models/bvlc_alexnet/train_val.prototxt
index 717b6fa447c..588b4ea7cb5 100644
--- a/models/bvlc_alexnet/train_val.prototxt
+++ b/models/bvlc_alexnet/train_val.prototxt
@@ -1,47 +1,55 @@
 name: "AlexNet"
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    backend: LMDB
-    batch_size: 256
+  include {
+    phase: TRAIN
   }
   transform_param {
+    mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
   }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    backend: LMDB
-    batch_size: 50
+  include {
+    phase: TEST
   }
   transform_param {
+    mirror: false
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
   }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -56,15 +64,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "conv1"
   top: "norm1"
   lrn_param {
@@ -73,9 +81,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "norm1"
   top: "pool1"
   pooling_param {
@@ -84,15 +92,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -108,15 +120,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "conv2"
   top: "norm2"
   lrn_param {
@@ -125,9 +137,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "norm2"
   top: "pool2"
   pooling_param {
@@ -136,15 +148,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "pool2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -159,21 +175,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -189,21 +209,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -219,15 +243,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -236,15 +260,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -257,30 +285,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -293,30 +325,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -329,17 +365,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index e31a4c9cd00..4648bf26efc 100644
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -4,15 +4,19 @@ input_dim: 10
 input_dim: 3
 input_dim: 224
 input_dim: 224
-layers {
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
   bottom: "data"
   top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 3
@@ -28,43 +32,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
   bottom: "conv1/7x7_s2"
   top: "conv1/7x7_s2"
-  name: "conv1/relu_7x7"
-  type: RELU
 }
-layers {
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
   bottom: "conv1/7x7_s2"
   top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
   bottom: "pool1/3x3_s2"
   top: "pool1/norm1"
-  name: "pool1/norm1"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
   bottom: "pool1/norm1"
   top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -78,21 +86,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3_reduce"
-  name: "conv2/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -107,43 +119,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
   bottom: "conv2/3x3"
   top: "conv2/3x3"
-  name: "conv2/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
   bottom: "conv2/3x3"
   top: "conv2/norm2"
-  name: "conv2/norm2"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
   bottom: "conv2/norm2"
   top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -157,21 +173,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3a/1x1"
   top: "inception_3a/1x1"
-  name: "inception_3a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -185,21 +205,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 1
@@ -214,21 +238,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3a/3x3"
   top: "inception_3a/3x3"
-  name: "inception_3a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -242,21 +270,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5"
-  name: "inception_3a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -271,17 +303,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3a/5x5"
   top: "inception_3a/5x5"
-  name: "inception_3a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -289,15 +321,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
   bottom: "inception_3a/pool"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -311,30 +347,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
   bottom: "inception_3a/1x1"
   bottom: "inception_3a/3x3"
   bottom: "inception_3a/5x5"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -348,21 +388,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3b/1x1"
   top: "inception_3b/1x1"
-  name: "inception_3b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -376,21 +420,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -405,21 +453,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3b/3x3"
   top: "inception_3b/3x3"
-  name: "inception_3b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -433,21 +485,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5"
-  name: "inception_3b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     pad: 2
@@ -462,17 +518,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3b/5x5"
   top: "inception_3b/5x5"
-  name: "inception_3b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
   bottom: "inception_3a/output"
   top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -480,15 +536,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
   bottom: "inception_3b/pool"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -502,41 +562,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
   bottom: "inception_3b/1x1"
   bottom: "inception_3b/3x3"
   bottom: "inception_3b/5x5"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
   bottom: "inception_3b/output"
   top: "pool3/3x3_s2"
-  name: "pool3/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -550,21 +614,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4a/1x1"
   top: "inception_4a/1x1"
-  name: "inception_4a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -578,21 +646,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 208
     pad: 1
@@ -607,21 +679,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4a/3x3"
   top: "inception_4a/3x3"
-  name: "inception_4a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -635,21 +711,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5"
-  name: "inception_4a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     pad: 2
@@ -664,17 +744,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4a/5x5"
   top: "inception_4a/5x5"
-  name: "inception_4a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -682,15 +762,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
   bottom: "inception_4a/pool"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -704,30 +788,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
   bottom: "inception_4a/1x1"
   bottom: "inception_4a/3x3"
   bottom: "inception_4a/5x5"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -741,21 +829,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4b/1x1"
   top: "inception_4b/1x1"
-  name: "inception_4b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -769,21 +861,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 224
     pad: 1
@@ -798,21 +894,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4b/3x3"
   top: "inception_4b/3x3"
-  name: "inception_4b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -826,21 +926,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5"
-  name: "inception_4b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -855,17 +959,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4b/5x5"
   top: "inception_4b/5x5"
-  name: "inception_4b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
   bottom: "inception_4a/output"
   top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -873,15 +977,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
   bottom: "inception_4b/pool"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -895,30 +1003,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
   bottom: "inception_4b/1x1"
   bottom: "inception_4b/3x3"
   bottom: "inception_4b/5x5"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -932,21 +1044,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4c/1x1"
   top: "inception_4c/1x1"
-  name: "inception_4c/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -960,21 +1076,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -989,21 +1109,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4c/3x3"
   top: "inception_4c/3x3"
-  name: "inception_4c/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -1017,21 +1141,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5"
-  name: "inception_4c/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1046,17 +1174,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4c/5x5"
   top: "inception_4c/5x5"
-  name: "inception_4c/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
   bottom: "inception_4b/output"
   top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1064,15 +1192,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
   bottom: "inception_4c/pool"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1086,30 +1218,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
   bottom: "inception_4c/1x1"
   bottom: "inception_4c/3x3"
   bottom: "inception_4c/5x5"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -1123,21 +1259,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4d/1x1"
   top: "inception_4d/1x1"
-  name: "inception_4d/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 144
     kernel_size: 1
@@ -1151,21 +1291,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 288
     pad: 1
@@ -1180,21 +1324,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4d/3x3"
   top: "inception_4d/3x3"
-  name: "inception_4d/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1208,21 +1356,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5"
-  name: "inception_4d/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1237,17 +1389,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4d/5x5"
   top: "inception_4d/5x5"
-  name: "inception_4d/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
   bottom: "inception_4c/output"
   top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1255,15 +1407,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
   bottom: "inception_4d/pool"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1277,30 +1433,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
   bottom: "inception_4d/1x1"
   bottom: "inception_4d/3x3"
   bottom: "inception_4d/5x5"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/1x1"
-  name: "inception_4e/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1314,21 +1474,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4e/1x1"
   top: "inception_4e/1x1"
-  name: "inception_4e/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1342,21 +1506,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1371,21 +1539,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4e/3x3"
   top: "inception_4e/3x3"
-  name: "inception_4e/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1399,21 +1571,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5"
-  name: "inception_4e/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1428,17 +1604,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4e/5x5"
   top: "inception_4e/5x5"
-  name: "inception_4e/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
   bottom: "inception_4d/output"
   top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1446,15 +1622,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
   bottom: "inception_4e/pool"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1468,41 +1648,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
   bottom: "inception_4e/1x1"
   bottom: "inception_4e/3x3"
   bottom: "inception_4e/5x5"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
   bottom: "inception_4e/output"
   top: "pool4/3x3_s2"
-  name: "pool4/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1516,21 +1700,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5a/1x1"
   top: "inception_5a/1x1"
-  name: "inception_5a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1544,21 +1732,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1573,21 +1765,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5a/3x3"
   top: "inception_5a/3x3"
-  name: "inception_5a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1601,21 +1797,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5"
-  name: "inception_5a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1630,17 +1830,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5a/5x5"
   top: "inception_5a/5x5"
-  name: "inception_5a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1648,15 +1848,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
   bottom: "inception_5a/pool"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1670,30 +1874,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
   bottom: "inception_5a/1x1"
   bottom: "inception_5a/3x3"
   bottom: "inception_5a/5x5"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     kernel_size: 1
@@ -1707,21 +1915,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5b/1x1"
   top: "inception_5b/1x1"
-  name: "inception_5b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -1735,21 +1947,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -1764,21 +1980,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5b/3x3"
   top: "inception_5b/3x3"
-  name: "inception_5b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     kernel_size: 1
@@ -1792,21 +2012,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5"
-  name: "inception_5b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1821,17 +2045,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5b/5x5"
   top: "inception_5b/5x5"
-  name: "inception_5b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
   bottom: "inception_5a/output"
   top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1839,15 +2063,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
   bottom: "inception_5b/pool"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1861,50 +2089,54 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
   bottom: "inception_5b/1x1"
   bottom: "inception_5b/3x3"
   bottom: "inception_5b/5x5"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
   bottom: "inception_5b/output"
   top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 7
     stride: 1
   }
 }
-layers {
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
   bottom: "pool5/7x7_s1"
   top: "pool5/7x7_s1"
-  name: "pool5/drop_7x7_s1"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.4
   }
 }
-layers {
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
   bottom: "pool5/7x7_s1"
   top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -1916,9 +2148,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "loss3/classifier"
   top: "prob"
-}
\ No newline at end of file
+}
diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
index cd8f38abdc8..79ede2b9d9c 100644
--- a/models/bvlc_googlenet/train_val.prototxt
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -1,14 +1,9 @@
 name: "GoogleNet"
-layers {
+layer {
+  name: "data"
+  type: "Data"
   top: "data"
   top: "label"
-  name: "data"
-  type: DATA
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    batch_size: 32
-    backend: LMDB
-  }
   include {
     phase: TRAIN
   }
@@ -19,17 +14,17 @@ layers {
     mean_value: 117
     mean_value: 123
   }
-}
-layers {
-  top: "data"
-  top: "label"
-  name: "data"
-  type: DATA
   data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    batch_size: 50
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 32
     backend: LMDB
   }
+}
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
   include {
     phase: TEST
   }
@@ -40,16 +35,25 @@ layers {
     mean_value: 117
     mean_value: 123
   }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
+  name: "conv1/7x7_s2"
+  type: "Convolution"
   bottom: "data"
   top: "conv1/7x7_s2"
-  name: "conv1/7x7_s2"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 3
@@ -65,43 +69,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv1/relu_7x7"
+  type: "ReLU"
   bottom: "conv1/7x7_s2"
   top: "conv1/7x7_s2"
-  name: "conv1/relu_7x7"
-  type: RELU
 }
-layers {
+layer {
+  name: "pool1/3x3_s2"
+  type: "Pooling"
   bottom: "conv1/7x7_s2"
   top: "pool1/3x3_s2"
-  name: "pool1/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "pool1/norm1"
+  type: "LRN"
   bottom: "pool1/3x3_s2"
   top: "pool1/norm1"
-  name: "pool1/norm1"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "conv2/3x3_reduce"
+  type: "Convolution"
   bottom: "pool1/norm1"
   top: "conv2/3x3_reduce"
-  name: "conv2/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -115,21 +123,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3_reduce"
-  name: "conv2/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/3x3"
+  type: "Convolution"
   bottom: "conv2/3x3_reduce"
   top: "conv2/3x3"
-  name: "conv2/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -144,43 +156,47 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "conv2/relu_3x3"
+  type: "ReLU"
   bottom: "conv2/3x3"
   top: "conv2/3x3"
-  name: "conv2/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "conv2/norm2"
+  type: "LRN"
   bottom: "conv2/3x3"
   top: "conv2/norm2"
-  name: "conv2/norm2"
-  type: LRN
   lrn_param {
     local_size: 5
     alpha: 0.0001
     beta: 0.75
   }
 }
-layers {
+layer {
+  name: "pool2/3x3_s2"
+  type: "Pooling"
   bottom: "conv2/norm2"
   top: "pool2/3x3_s2"
-  name: "pool2/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_3a/1x1"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/1x1"
-  name: "inception_3a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -194,21 +210,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3a/1x1"
   top: "inception_3a/1x1"
-  name: "inception_3a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -222,21 +242,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3_reduce"
-  name: "inception_3a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/3x3"
+  type: "Convolution"
   bottom: "inception_3a/3x3_reduce"
   top: "inception_3a/3x3"
-  name: "inception_3a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 1
@@ -251,21 +275,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3a/3x3"
   top: "inception_3a/3x3"
-  name: "inception_3a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -279,21 +307,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5_reduce"
-  name: "inception_3a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/5x5"
+  type: "Convolution"
   bottom: "inception_3a/5x5_reduce"
   top: "inception_3a/5x5"
-  name: "inception_3a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     pad: 2
@@ -308,17 +340,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3a/5x5"
   top: "inception_3a/5x5"
-  name: "inception_3a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/pool"
+  type: "Pooling"
   bottom: "pool2/3x3_s2"
   top: "inception_3a/pool"
-  name: "inception_3a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -326,15 +358,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3a/pool_proj"
+  type: "Convolution"
   bottom: "inception_3a/pool"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -348,30 +384,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/pool_proj"
-  name: "inception_3a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3a/output"
+  type: "Concat"
   bottom: "inception_3a/1x1"
   bottom: "inception_3a/3x3"
   bottom: "inception_3a/5x5"
   bottom: "inception_3a/pool_proj"
   top: "inception_3a/output"
-  name: "inception_3a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_3b/1x1"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/1x1"
-  name: "inception_3b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -385,21 +425,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_3b/1x1"
   top: "inception_3b/1x1"
-  name: "inception_3b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -413,21 +457,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3_reduce"
-  name: "inception_3b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/3x3"
+  type: "Convolution"
   bottom: "inception_3b/3x3_reduce"
   top: "inception_3b/3x3"
-  name: "inception_3b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     pad: 1
@@ -442,21 +490,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_3b/3x3"
   top: "inception_3b/3x3"
-  name: "inception_3b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_3a/output"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -470,21 +522,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5_reduce"
-  name: "inception_3b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/5x5"
+  type: "Convolution"
   bottom: "inception_3b/5x5_reduce"
   top: "inception_3b/5x5"
-  name: "inception_3b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     pad: 2
@@ -499,17 +555,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_3b/5x5"
   top: "inception_3b/5x5"
-  name: "inception_3b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/pool"
+  type: "Pooling"
   bottom: "inception_3a/output"
   top: "inception_3b/pool"
-  name: "inception_3b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -517,15 +573,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_3b/pool_proj"
+  type: "Convolution"
   bottom: "inception_3b/pool"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -539,41 +599,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_3b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/pool_proj"
-  name: "inception_3b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_3b/output"
+  type: "Concat"
   bottom: "inception_3b/1x1"
   bottom: "inception_3b/3x3"
   bottom: "inception_3b/5x5"
   bottom: "inception_3b/pool_proj"
   top: "inception_3b/output"
-  name: "inception_3b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool3/3x3_s2"
+  type: "Pooling"
   bottom: "inception_3b/output"
   top: "pool3/3x3_s2"
-  name: "pool3/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_4a/1x1"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/1x1"
-  name: "inception_4a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -587,21 +651,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4a/1x1"
   top: "inception_4a/1x1"
-  name: "inception_4a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 1
@@ -615,21 +683,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3_reduce"
-  name: "inception_4a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/3x3"
+  type: "Convolution"
   bottom: "inception_4a/3x3_reduce"
   top: "inception_4a/3x3"
-  name: "inception_4a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 208
     pad: 1
@@ -644,21 +716,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4a/3x3"
   top: "inception_4a/3x3"
-  name: "inception_4a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 16
     kernel_size: 1
@@ -672,21 +748,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5_reduce"
-  name: "inception_4a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/5x5"
+  type: "Convolution"
   bottom: "inception_4a/5x5_reduce"
   top: "inception_4a/5x5"
-  name: "inception_4a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     pad: 2
@@ -701,17 +781,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4a/5x5"
   top: "inception_4a/5x5"
-  name: "inception_4a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/pool"
+  type: "Pooling"
   bottom: "pool3/3x3_s2"
   top: "inception_4a/pool"
-  name: "inception_4a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -719,15 +799,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4a/pool_proj"
+  type: "Convolution"
   bottom: "inception_4a/pool"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -741,41 +825,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/pool_proj"
-  name: "inception_4a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4a/output"
+  type: "Concat"
   bottom: "inception_4a/1x1"
   bottom: "inception_4a/3x3"
   bottom: "inception_4a/5x5"
   bottom: "inception_4a/pool_proj"
   top: "inception_4a/output"
-  name: "inception_4a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "loss1/ave_pool"
+  type: "Pooling"
   bottom: "inception_4a/output"
   top: "loss1/ave_pool"
-  name: "loss1/ave_pool"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 5
     stride: 3
   }
 }
-layers {
+layer {
+  name: "loss1/conv"
+  type: "Convolution"
   bottom: "loss1/ave_pool"
   top: "loss1/conv"
-  name: "loss1/conv"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -789,21 +877,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss1/relu_conv"
+  type: "ReLU"
   bottom: "loss1/conv"
   top: "loss1/conv"
-  name: "loss1/relu_conv"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss1/fc"
+  type: "InnerProduct"
   bottom: "loss1/conv"
   top: "loss1/fc"
-  name: "loss1/fc"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1024
     weight_filler {
@@ -816,30 +908,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss1/relu_fc"
+  type: "ReLU"
   bottom: "loss1/fc"
   top: "loss1/fc"
-  name: "loss1/relu_fc"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss1/drop_fc"
+  type: "Dropout"
   bottom: "loss1/fc"
   top: "loss1/fc"
-  name: "loss1/drop_fc"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.7
   }
 }
-layers {
+layer {
+  name: "loss1/classifier"
+  type: "InnerProduct"
   bottom: "loss1/fc"
   top: "loss1/classifier"
-  name: "loss1/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -852,46 +948,50 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss1/loss"
+  type: "SoftmaxWithLoss"
   bottom: "loss1/classifier"
   bottom: "label"
   top: "loss1/loss1"
-  name: "loss1/loss"
-  type: SOFTMAX_LOSS
   loss_weight: 0.3
 }
-layers {
+layer {
+  name: "loss1/top-1"
+  type: "Accuracy"
   bottom: "loss1/classifier"
   bottom: "label"
   top: "loss1/top-1"
-  name: "loss1/top-1"
-  type: ACCURACY
   include {
     phase: TEST
   }
 }
-layers {
+layer {
+  name: "loss1/top-5"
+  type: "Accuracy"
   bottom: "loss1/classifier"
   bottom: "label"
   top: "loss1/top-5"
-  name: "loss1/top-5"
-  type: ACCURACY
-  accuracy_param {
-    top_k: 5
-  }
   include {
     phase: TEST
   }
+  accuracy_param {
+    top_k: 5
+  }
 }
-layers {
+layer {
+  name: "inception_4b/1x1"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/1x1"
-  name: "inception_4b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -905,21 +1005,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4b/1x1"
   top: "inception_4b/1x1"
-  name: "inception_4b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -933,21 +1037,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3_reduce"
-  name: "inception_4b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/3x3"
+  type: "Convolution"
   bottom: "inception_4b/3x3_reduce"
   top: "inception_4b/3x3"
-  name: "inception_4b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 224
     pad: 1
@@ -962,21 +1070,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4b/3x3"
   top: "inception_4b/3x3"
-  name: "inception_4b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4a/output"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -990,21 +1102,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5_reduce"
-  name: "inception_4b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/5x5"
+  type: "Convolution"
   bottom: "inception_4b/5x5_reduce"
   top: "inception_4b/5x5"
-  name: "inception_4b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1019,17 +1135,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4b/5x5"
   top: "inception_4b/5x5"
-  name: "inception_4b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/pool"
+  type: "Pooling"
   bottom: "inception_4a/output"
   top: "inception_4b/pool"
-  name: "inception_4b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1037,15 +1153,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4b/pool_proj"
+  type: "Convolution"
   bottom: "inception_4b/pool"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1059,30 +1179,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/pool_proj"
-  name: "inception_4b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4b/output"
+  type: "Concat"
   bottom: "inception_4b/1x1"
   bottom: "inception_4b/3x3"
   bottom: "inception_4b/5x5"
   bottom: "inception_4b/pool_proj"
   top: "inception_4b/output"
-  name: "inception_4b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4c/1x1"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/1x1"
-  name: "inception_4c/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1096,21 +1220,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4c/1x1"
   top: "inception_4c/1x1"
-  name: "inception_4c/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1124,21 +1252,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3_reduce"
-  name: "inception_4c/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/3x3"
+  type: "Convolution"
   bottom: "inception_4c/3x3_reduce"
   top: "inception_4c/3x3"
-  name: "inception_4c/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -1153,21 +1285,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4c/3x3"
   top: "inception_4c/3x3"
-  name: "inception_4c/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4b/output"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 24
     kernel_size: 1
@@ -1181,21 +1317,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5_reduce"
-  name: "inception_4c/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/5x5"
+  type: "Convolution"
   bottom: "inception_4c/5x5_reduce"
   top: "inception_4c/5x5"
-  name: "inception_4c/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1210,17 +1350,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4c/5x5"
   top: "inception_4c/5x5"
-  name: "inception_4c/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/pool"
+  type: "Pooling"
   bottom: "inception_4b/output"
   top: "inception_4c/pool"
-  name: "inception_4c/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1228,15 +1368,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4c/pool_proj"
+  type: "Convolution"
   bottom: "inception_4c/pool"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1250,30 +1394,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4c/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/pool_proj"
-  name: "inception_4c/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4c/output"
+  type: "Concat"
   bottom: "inception_4c/1x1"
   bottom: "inception_4c/3x3"
   bottom: "inception_4c/5x5"
   bottom: "inception_4c/pool_proj"
   top: "inception_4c/output"
-  name: "inception_4c/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_4d/1x1"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/1x1"
-  name: "inception_4d/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 112
     kernel_size: 1
@@ -1287,21 +1435,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4d/1x1"
   top: "inception_4d/1x1"
-  name: "inception_4d/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 144
     kernel_size: 1
@@ -1315,21 +1467,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3_reduce"
-  name: "inception_4d/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/3x3"
+  type: "Convolution"
   bottom: "inception_4d/3x3_reduce"
   top: "inception_4d/3x3"
-  name: "inception_4d/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 288
     pad: 1
@@ -1344,21 +1500,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4d/3x3"
   top: "inception_4d/3x3"
-  name: "inception_4d/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4c/output"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1372,21 +1532,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5_reduce"
-  name: "inception_4d/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/5x5"
+  type: "Convolution"
   bottom: "inception_4d/5x5_reduce"
   top: "inception_4d/5x5"
-  name: "inception_4d/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     pad: 2
@@ -1401,17 +1565,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4d/5x5"
   top: "inception_4d/5x5"
-  name: "inception_4d/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/pool"
+  type: "Pooling"
   bottom: "inception_4c/output"
   top: "inception_4d/pool"
-  name: "inception_4d/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1419,15 +1583,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4d/pool_proj"
+  type: "Convolution"
   bottom: "inception_4d/pool"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 64
     kernel_size: 1
@@ -1441,41 +1609,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4d/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/pool_proj"
-  name: "inception_4d/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4d/output"
+  type: "Concat"
   bottom: "inception_4d/1x1"
   bottom: "inception_4d/3x3"
   bottom: "inception_4d/5x5"
   bottom: "inception_4d/pool_proj"
   top: "inception_4d/output"
-  name: "inception_4d/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "loss2/ave_pool"
+  type: "Pooling"
   bottom: "inception_4d/output"
   top: "loss2/ave_pool"
-  name: "loss2/ave_pool"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 5
     stride: 3
   }
 }
-layers {
+layer {
+  name: "loss2/conv"
+  type: "Convolution"
   bottom: "loss2/ave_pool"
   top: "loss2/conv"
-  name: "loss2/conv"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1489,21 +1661,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss2/relu_conv"
+  type: "ReLU"
   bottom: "loss2/conv"
   top: "loss2/conv"
-  name: "loss2/relu_conv"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss2/fc"
+  type: "InnerProduct"
   bottom: "loss2/conv"
   top: "loss2/fc"
-  name: "loss2/fc"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1024
     weight_filler {
@@ -1516,30 +1692,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss2/relu_fc"
+  type: "ReLU"
   bottom: "loss2/fc"
   top: "loss2/fc"
-  name: "loss2/relu_fc"
-  type: RELU
 }
-layers {
+layer {
+  name: "loss2/drop_fc"
+  type: "Dropout"
   bottom: "loss2/fc"
   top: "loss2/fc"
-  name: "loss2/drop_fc"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.7
   }
 }
-layers {
+layer {
+  name: "loss2/classifier"
+  type: "InnerProduct"
   bottom: "loss2/fc"
   top: "loss2/classifier"
-  name: "loss2/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -1552,46 +1732,50 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss2/loss"
+  type: "SoftmaxWithLoss"
   bottom: "loss2/classifier"
   bottom: "label"
   top: "loss2/loss1"
-  name: "loss2/loss"
-  type: SOFTMAX_LOSS
   loss_weight: 0.3
 }
-layers {
+layer {
+  name: "loss2/top-1"
+  type: "Accuracy"
   bottom: "loss2/classifier"
   bottom: "label"
   top: "loss2/top-1"
-  name: "loss2/top-1"
-  type: ACCURACY
   include {
     phase: TEST
   }
 }
-layers {
+layer {
+  name: "loss2/top-5"
+  type: "Accuracy"
   bottom: "loss2/classifier"
   bottom: "label"
   top: "loss2/top-5"
-  name: "loss2/top-5"
-  type: ACCURACY
-  accuracy_param {
-    top_k: 5
-  }
   include {
     phase: TEST
   }
+  accuracy_param {
+    top_k: 5
+  }
 }
-layers {
+layer {
+  name: "inception_4e/1x1"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/1x1"
-  name: "inception_4e/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1605,21 +1789,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_1x1"
+  type: "ReLU"
   bottom: "inception_4e/1x1"
   top: "inception_4e/1x1"
-  name: "inception_4e/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1633,21 +1821,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3_reduce"
-  name: "inception_4e/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/3x3"
+  type: "Convolution"
   bottom: "inception_4e/3x3_reduce"
   top: "inception_4e/3x3"
-  name: "inception_4e/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1662,21 +1854,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_3x3"
+  type: "ReLU"
   bottom: "inception_4e/3x3"
   top: "inception_4e/3x3"
-  name: "inception_4e/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_4d/output"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1690,21 +1886,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5_reduce"
-  name: "inception_4e/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/5x5"
+  type: "Convolution"
   bottom: "inception_4e/5x5_reduce"
   top: "inception_4e/5x5"
-  name: "inception_4e/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1719,17 +1919,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_5x5"
+  type: "ReLU"
   bottom: "inception_4e/5x5"
   top: "inception_4e/5x5"
-  name: "inception_4e/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/pool"
+  type: "Pooling"
   bottom: "inception_4d/output"
   top: "inception_4e/pool"
-  name: "inception_4e/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1737,15 +1937,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_4e/pool_proj"
+  type: "Convolution"
   bottom: "inception_4e/pool"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1759,41 +1963,45 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_4e/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/pool_proj"
-  name: "inception_4e/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_4e/output"
+  type: "Concat"
   bottom: "inception_4e/1x1"
   bottom: "inception_4e/3x3"
   bottom: "inception_4e/5x5"
   bottom: "inception_4e/pool_proj"
   top: "inception_4e/output"
-  name: "inception_4e/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool4/3x3_s2"
+  type: "Pooling"
   bottom: "inception_4e/output"
   top: "pool4/3x3_s2"
-  name: "pool4/3x3_s2"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
 }
-layers {
+layer {
+  name: "inception_5a/1x1"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/1x1"
-  name: "inception_5a/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     kernel_size: 1
@@ -1807,21 +2015,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5a/1x1"
   top: "inception_5a/1x1"
-  name: "inception_5a/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 160
     kernel_size: 1
@@ -1835,21 +2047,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3_reduce"
-  name: "inception_5a/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/3x3"
+  type: "Convolution"
   bottom: "inception_5a/3x3_reduce"
   top: "inception_5a/3x3"
-  name: "inception_5a/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 320
     pad: 1
@@ -1864,21 +2080,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5a/3x3"
   top: "inception_5a/3x3"
-  name: "inception_5a/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5_reduce"
+  type: "Convolution"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 32
     kernel_size: 1
@@ -1892,21 +2112,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5_reduce"
-  name: "inception_5a/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/5x5"
+  type: "Convolution"
   bottom: "inception_5a/5x5_reduce"
   top: "inception_5a/5x5"
-  name: "inception_5a/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -1921,17 +2145,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5a/5x5"
   top: "inception_5a/5x5"
-  name: "inception_5a/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/pool"
+  type: "Pooling"
   bottom: "pool4/3x3_s2"
   top: "inception_5a/pool"
-  name: "inception_5a/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -1939,15 +2163,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5a/pool_proj"
+  type: "Convolution"
   bottom: "inception_5a/pool"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -1961,30 +2189,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5a/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/pool_proj"
-  name: "inception_5a/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5a/output"
+  type: "Concat"
   bottom: "inception_5a/1x1"
   bottom: "inception_5a/3x3"
   bottom: "inception_5a/5x5"
   bottom: "inception_5a/pool_proj"
   top: "inception_5a/output"
-  name: "inception_5a/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "inception_5b/1x1"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/1x1"
-  name: "inception_5b/1x1"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     kernel_size: 1
@@ -1998,21 +2230,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_1x1"
+  type: "ReLU"
   bottom: "inception_5b/1x1"
   top: "inception_5b/1x1"
-  name: "inception_5b/relu_1x1"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/3x3_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 192
     kernel_size: 1
@@ -2026,21 +2262,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3_reduce"
+  type: "ReLU"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3_reduce"
-  name: "inception_5b/relu_3x3_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/3x3"
+  type: "Convolution"
   bottom: "inception_5b/3x3_reduce"
   top: "inception_5b/3x3"
-  name: "inception_5b/3x3"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -2055,21 +2295,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_3x3"
+  type: "ReLU"
   bottom: "inception_5b/3x3"
   top: "inception_5b/3x3"
-  name: "inception_5b/relu_3x3"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5_reduce"
+  type: "Convolution"
   bottom: "inception_5a/output"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/5x5_reduce"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 48
     kernel_size: 1
@@ -2083,21 +2327,25 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5_reduce"
+  type: "ReLU"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5_reduce"
-  name: "inception_5b/relu_5x5_reduce"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/5x5"
+  type: "Convolution"
   bottom: "inception_5b/5x5_reduce"
   top: "inception_5b/5x5"
-  name: "inception_5b/5x5"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     pad: 2
@@ -2112,17 +2360,17 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_5x5"
+  type: "ReLU"
   bottom: "inception_5b/5x5"
   top: "inception_5b/5x5"
-  name: "inception_5b/relu_5x5"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/pool"
+  type: "Pooling"
   bottom: "inception_5a/output"
   top: "inception_5b/pool"
-  name: "inception_5b/pool"
-  type: POOLING
   pooling_param {
     pool: MAX
     kernel_size: 3
@@ -2130,15 +2378,19 @@ layers {
     pad: 1
   }
 }
-layers {
+layer {
+  name: "inception_5b/pool_proj"
+  type: "Convolution"
   bottom: "inception_5b/pool"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/pool_proj"
-  type: CONVOLUTION
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 128
     kernel_size: 1
@@ -2152,50 +2404,54 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "inception_5b/relu_pool_proj"
+  type: "ReLU"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/pool_proj"
-  name: "inception_5b/relu_pool_proj"
-  type: RELU
 }
-layers {
+layer {
+  name: "inception_5b/output"
+  type: "Concat"
   bottom: "inception_5b/1x1"
   bottom: "inception_5b/3x3"
   bottom: "inception_5b/5x5"
   bottom: "inception_5b/pool_proj"
   top: "inception_5b/output"
-  name: "inception_5b/output"
-  type: CONCAT
 }
-layers {
+layer {
+  name: "pool5/7x7_s1"
+  type: "Pooling"
   bottom: "inception_5b/output"
   top: "pool5/7x7_s1"
-  name: "pool5/7x7_s1"
-  type: POOLING
   pooling_param {
     pool: AVE
     kernel_size: 7
     stride: 1
   }
 }
-layers {
+layer {
+  name: "pool5/drop_7x7_s1"
+  type: "Dropout"
   bottom: "pool5/7x7_s1"
   top: "pool5/7x7_s1"
-  name: "pool5/drop_7x7_s1"
-  type: DROPOUT
   dropout_param {
     dropout_ratio: 0.4
   }
 }
-layers {
+layer {
+  name: "loss3/classifier"
+  type: "InnerProduct"
   bottom: "pool5/7x7_s1"
   top: "loss3/classifier"
-  name: "loss3/classifier"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -2207,34 +2463,34 @@ layers {
     }
   }
 }
-layers {
+layer {
+  name: "loss3/loss3"
+  type: "SoftmaxWithLoss"
   bottom: "loss3/classifier"
   bottom: "label"
   top: "loss3/loss3"
-  name: "loss3/loss3"
-  type: SOFTMAX_LOSS
   loss_weight: 1
 }
-layers {
+layer {
+  name: "loss3/top-1"
+  type: "Accuracy"
   bottom: "loss3/classifier"
   bottom: "label"
   top: "loss3/top-1"
-  name: "loss3/top-1"
-  type: ACCURACY
   include {
     phase: TEST
   }
 }
-layers {
+layer {
+  name: "loss3/top-5"
+  type: "Accuracy"
   bottom: "loss3/classifier"
   bottom: "label"
   top: "loss3/top-5"
-  name: "loss3/top-5"
-  type: ACCURACY
-  accuracy_param {
-    top_k: 5
-  }
   include {
     phase: TEST
   }
+  accuracy_param {
+    top_k: 5
+  }
 }
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 4e494f420b5..29ccf1469f7 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -4,9 +4,9 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -15,15 +15,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -32,9 +32,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -43,9 +43,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -55,15 +55,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -72,9 +72,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -83,9 +83,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -94,15 +94,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -112,15 +112,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -130,15 +130,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -147,66 +147,66 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
   inner_product_param {
     num_output: 1000
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8"
   top: "prob"
 }
diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index 00fcc080261..c79472e09ab 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -1,18 +1,16 @@
 name: "CaffeNet"
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_train_lmdb"
-    backend: LMDB
-    batch_size: 256
+  include {
+    phase: TRAIN
   }
   transform_param {
+    mirror: true
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
   }
 # mean pixel / channel-wise mean instead of mean image
 #  transform_param {
@@ -22,22 +20,24 @@ layers {
 #    mean_value: 123
 #    mirror: true
 #  }
-  include: { phase: TRAIN }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_train_lmdb"
+    batch_size: 256
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "data"
-  type: DATA
+  type: "Data"
   top: "data"
   top: "label"
-  data_param {
-    source: "examples/imagenet/ilsvrc12_val_lmdb"
-    backend: LMDB
-    batch_size: 50
+  include {
+    phase: TEST
   }
   transform_param {
+    mirror: false
     crop_size: 227
     mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
   }
 # mean pixel / channel-wise mean instead of mean image
 #  transform_param {
@@ -47,17 +47,25 @@ layers {
 #    mean_value: 123
 #    mirror: true
 #  }
-  include: { phase: TEST }
+  data_param {
+    source: "examples/imagenet/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+  }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -72,15 +80,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -89,9 +97,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -100,15 +108,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -124,15 +136,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -141,9 +153,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -152,15 +164,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -175,21 +191,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -205,21 +225,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -235,15 +259,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -252,15 +276,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -273,30 +301,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -309,30 +341,34 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 1000
     weight_filler {
@@ -345,17 +381,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8"
   bottom: "label"
   top: "loss"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ef75a0a5e95..ea9cf98a926 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -4,9 +4,9 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
   convolution_param {
@@ -15,15 +15,15 @@ layers {
     stride: 4
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -32,9 +32,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -43,9 +43,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
   convolution_param {
@@ -55,15 +55,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -72,9 +72,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -83,9 +83,9 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
   convolution_param {
@@ -94,15 +94,15 @@ layers {
     kernel_size: 3
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
   convolution_param {
@@ -112,15 +112,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
   convolution_param {
@@ -130,15 +130,15 @@ layers {
     group: 2
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -147,48 +147,48 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
   inner_product_param {
     num_output: 4096
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
@@ -196,9 +196,9 @@ layers {
   }
 }
 # R-CNN classification layer made from R-CNN ILSVRC13 SVMs.
-layers {
+layer {
   name: "fc-rcnn"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc-rcnn"
   inner_product_param {
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index aa2ad961874..4a924f74927 100644
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -4,15 +4,19 @@ input_dim: 10
 input_dim: 3
 input_dim: 227
 input_dim: 227
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -27,15 +31,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -44,9 +48,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -55,15 +59,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -79,15 +87,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -96,9 +104,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -107,15 +115,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -130,21 +142,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -160,21 +176,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -190,15 +210,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -207,15 +227,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -228,31 +252,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -265,31 +293,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_flickr"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_flickr"
-  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 20
     weight_filler {
@@ -302,9 +334,9 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "prob"
-  type: SOFTMAX
+  type: "Softmax"
   bottom: "fc8_flickr"
   top: "prob"
 }
diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index 7155c492360..aa9c73e17ce 100644
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -1,49 +1,57 @@
 name: "FlickrStyleCaffeNet"
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mirror: true
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "data/flickr_style/train.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: true
-  }
-  include: { phase: TRAIN }
 }
-layers {
+layer {
   name: "data"
-  type: IMAGE_DATA
+  type: "ImageData"
   top: "data"
   top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 227
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
+  }
   image_data_param {
     source: "data/flickr_style/test.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
-  transform_param {
-    crop_size: 227
-    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
-    mirror: false
-  }
-  include: { phase: TEST }
 }
-layers {
+layer {
   name: "conv1"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -58,15 +66,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu1"
-  type: RELU
+  type: "ReLU"
   bottom: "conv1"
   top: "conv1"
 }
-layers {
+layer {
   name: "pool1"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv1"
   top: "pool1"
   pooling_param {
@@ -75,9 +83,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm1"
-  type: LRN
+  type: "LRN"
   bottom: "pool1"
   top: "norm1"
   lrn_param {
@@ -86,15 +94,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv2"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 2
@@ -110,15 +122,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu2"
-  type: RELU
+  type: "ReLU"
   bottom: "conv2"
   top: "conv2"
 }
-layers {
+layer {
   name: "pool2"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv2"
   top: "pool2"
   pooling_param {
@@ -127,9 +139,9 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "norm2"
-  type: LRN
+  type: "LRN"
   bottom: "pool2"
   top: "norm2"
   lrn_param {
@@ -138,15 +150,19 @@ layers {
     beta: 0.75
   }
 }
-layers {
+layer {
   name: "conv3"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -161,21 +177,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu3"
-  type: RELU
+  type: "ReLU"
   bottom: "conv3"
   top: "conv3"
 }
-layers {
+layer {
   name: "conv4"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 384
     pad: 1
@@ -191,21 +211,25 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu4"
-  type: RELU
+  type: "ReLU"
   bottom: "conv4"
   top: "conv4"
 }
-layers {
+layer {
   name: "conv5"
-  type: CONVOLUTION
+  type: "Convolution"
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   convolution_param {
     num_output: 256
     pad: 1
@@ -221,15 +245,15 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu5"
-  type: RELU
+  type: "ReLU"
   bottom: "conv5"
   top: "conv5"
 }
-layers {
+layer {
   name: "pool5"
-  type: POOLING
+  type: "Pooling"
   bottom: "conv5"
   top: "pool5"
   pooling_param {
@@ -238,15 +262,19 @@ layers {
     stride: 2
   }
 }
-layers {
+layer {
   name: "fc6"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -259,31 +287,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu6"
-  type: RELU
+  type: "ReLU"
   bottom: "fc6"
   top: "fc6"
 }
-layers {
+layer {
   name: "drop6"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc6"
   top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc7"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc6"
   top: "fc7"
-  # Note that blobs_lr can be set to 0 to disable any fine-tuning of this, and any other, layer
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
+  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 4096
     weight_filler {
@@ -296,31 +328,35 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "relu7"
-  type: RELU
+  type: "ReLU"
   bottom: "fc7"
   top: "fc7"
 }
-layers {
+layer {
   name: "drop7"
-  type: DROPOUT
+  type: "Dropout"
   bottom: "fc7"
   top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
 }
-layers {
+layer {
   name: "fc8_flickr"
-  type: INNER_PRODUCT
+  type: "InnerProduct"
   bottom: "fc7"
   top: "fc8_flickr"
-  # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
-  blobs_lr: 10
-  blobs_lr: 20
-  weight_decay: 1
-  weight_decay: 0
+  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
+  param {
+    lr_mult: 10
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 20
+    decay_mult: 0
+  }
   inner_product_param {
     num_output: 20
     weight_filler {
@@ -333,17 +369,19 @@ layers {
     }
   }
 }
-layers {
+layer {
   name: "loss"
-  type: SOFTMAX_LOSS
+  type: "SoftmaxWithLoss"
   bottom: "fc8_flickr"
   bottom: "label"
 }
-layers {
+layer {
   name: "accuracy"
-  type: ACCURACY
+  type: "Accuracy"
   bottom: "fc8_flickr"
   bottom: "label"
   top: "accuracy"
-  include: { phase: TEST }
+  include {
+    phase: TEST
+  }
 }

From e80b60fc1e64de7aacd717f70446afdd34ba6845 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 5 Feb 2015 16:41:46 -0800
Subject: [PATCH 1100/2053] die on inputs to IMAGE_DATA that fail to load

It's better to know than march silently on.
---
 src/caffe/layers/image_data_layer.cpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index ef6a342c30a..136694886d0 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -118,10 +118,8 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     timer.Start();
     CHECK_GT(lines_size, lines_id_);
     cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
-                                    new_height, new_width, is_color);
-    if (!cv_img.data) {
-      continue;
-    }
+        new_height, new_width, is_color);
+    CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
     read_time += timer.MicroSeconds();
     timer.Start();
     // Apply transformations (mirror, crop...) to the image

From 6a226970c1b2b365a165e5a8bf9aea7606815414 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 5 Feb 2015 19:28:42 -0800
Subject: [PATCH 1101/2053] fix for layer-type-str: loss_param and
 DECONVOLUTION type should have been included in V1LayerParameter, get
 upgraded

---
 src/caffe/proto/caffe.proto      | 2 ++
 src/caffe/util/upgrade_proto.cpp | 6 ++++++
 2 files changed, 8 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 81fd2517dbf..c2a39a5cadf 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -719,6 +719,7 @@ message V1LayerParameter {
     CONTRASTIVE_LOSS = 37;
     CONVOLUTION = 4;
     DATA = 5;
+    DECONVOLUTION = 39;
     DROPOUT = 6;
     DUMMY_DATA = 32;
     EUCLIDEAN_LOSS = 7;
@@ -790,6 +791,7 @@ message V1LayerParameter {
   optional ThresholdParameter threshold_param = 25;
   optional WindowDataParameter window_data_param = 20;
   optional TransformationParameter transform_param = 36;
+  optional LossParameter loss_param = 42;
   optional V0LayerParameter layer = 1;
 }
 
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 258d7d3ee7d..38a06026adf 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -824,6 +824,10 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
     layer_param->mutable_transform_param()->CopyFrom(
         v1_layer_param.transform_param());
   }
+  if (v1_layer_param.has_loss_param()) {
+    layer_param->mutable_loss_param()->CopyFrom(
+        v1_layer_param.loss_param());
+  }
   if (v1_layer_param.has_layer()) {
     LOG(ERROR) << "Input NetParameter has V0 layer -- ignoring.";
     is_fully_compatible = false;
@@ -849,6 +853,8 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
     return "ContrastiveLoss";
   case V1LayerParameter_LayerType_CONVOLUTION:
     return "Convolution";
+  case V1LayerParameter_LayerType_DECONVOLUTION:
+    return "Deconvolution";
   case V1LayerParameter_LayerType_DATA:
     return "Data";
   case V1LayerParameter_LayerType_DROPOUT:

From 33a56e08d9a2714c2492bb7999a350636ed142be Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Feb 2015 00:51:25 -0800
Subject: [PATCH 1102/2053] build with libc++ on Yosmite with CUDA 7

---
 Makefile | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/Makefile b/Makefile
index 53c16ea4a72..81c67efeaf4 100644
--- a/Makefile
+++ b/Makefile
@@ -231,15 +231,18 @@ endif
 
 # OS X:
 # clang++ instead of g++
-# libstdc++ instead of libc++ for CUDA compatibility on 10.9
+# libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
-	CXXFLAGS += -stdlib=libstdc++
-	LINKFLAGS += -stdlib=libstdc++
+	CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+	ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
+		CXXFLAGS += -stdlib=libstdc++
+		LINKFLAGS += -stdlib=libstdc++
+	endif
 	# clang throws this warning for cuda headers
 	WARNINGS += -Wno-unneeded-internal-declaration
 	# gtest needs to use its own tuple to not conflict with clang
-	CXXFLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
+	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
 endif

From 8ec095a6af69097330306eba2dcbed96c3e4b738 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Feb 2015 01:37:02 -0800
Subject: [PATCH 1103/2053] reduce step size in PowerLayer gradient checks: fix
 #1252

The gradient checker fails on certain elements of the PowerLayer checks,
but only 1-3 sometimes fail out of the 120 elements tested. This is not
due to any numerical issue in the PowerLayer, but the distribution of
the random inputs for the checks.

boost 1.56 switched the normal distribution RNG engine from Box-Muller
to Ziggurat.
---
 src/caffe/test/test_power_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 0d52fa1c8e2..76c9e857f36 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -74,7 +74,7 @@ class PowerLayerTest : public MultiDeviceTest<TypeParam> {
         }
       }
     }
-    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701, 0., 0.01);
+    GradientChecker<Dtype> checker(1e-3, 1e-2, 1701, 0., 0.01);
     checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
         this->blob_top_vec_);
   }

From 4c894d0b2d37c4f5e6a82a797dc8446fb1a9fc62 Mon Sep 17 00:00:00 2001
From: Sagan Bolliger <sagan@cs.toronto.edu>
Date: Fri, 6 Feb 2015 12:54:32 -0800
Subject: [PATCH 1104/2053] Added GPU implementation of SoftmaxWithLossLayer.

---
 include/caffe/loss_layers.hpp           |   7 +-
 src/caffe/layers/softmax_loss_layer.cpp |   4 +
 src/caffe/layers/softmax_loss_layer.cu  | 129 ++++++++++++++++++++++++
 3 files changed, 138 insertions(+), 2 deletions(-)
 create mode 100644 src/caffe/layers/softmax_loss_layer.cu

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index f14b9716a4a..09a5c24dde5 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -707,8 +707,8 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   /// @copydoc SoftmaxWithLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // TODO(Yangqing): implement the GPU version of softmax.
-
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
   /**
    * @brief Computes the softmax loss error gradient w.r.t. the predictions.
    *
@@ -738,6 +738,9 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
   shared_ptr<Layer<Dtype> > softmax_layer_;
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index bf20b6056e4..8f6a4ef7959 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -115,6 +115,10 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(SoftmaxWithLossLayer);
+#endif
+
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
 REGISTER_LAYER_CLASS(SoftmaxWithLoss);
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
new file mode 100644
index 00000000000..215d589ffee
--- /dev/null
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -0,0 +1,129 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void SoftmaxLossForwardGPU(const int nthreads,
+          const Dtype* prob_data, const Dtype* label, Dtype* loss,
+          const int num, const int dim, const int spatial_dim,
+          const bool has_ignore_label_, const int ignore_label_,
+          Dtype* counts) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      loss[index] = 0;
+      counts[index] = 0;
+    } else {
+      loss[index] = -log(max(prob_data[n * dim + label_value * spatial_dim + s],
+                      Dtype(FLT_MIN)));
+      counts[index] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
+  const Dtype* prob_data = prob_.gpu_data();
+  const Dtype* label = bottom[1]->gpu_data();
+  const int num = prob_.num();
+  const int dim = prob_.count() / num;
+  const int spatial_dim = prob_.height() * prob_.width();
+  const int nthreads = num * spatial_dim;
+  // Since this memory is not used for anything until it is overwritten
+  // on the backward pass, we use it here to avoid having to allocate new GPU
+  // memory to accumulate intermediate results in the kernel.
+  Dtype* loss_data = bottom[0]->mutable_gpu_diff();
+  // Similarly, this memory is never used elsewhere, and thus we can use it
+  // to avoid having to allocate additional GPU memory.
+  Dtype* counts = prob_.mutable_gpu_diff();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  SoftmaxLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+      CAFFE_CUDA_NUM_THREADS>>>(nthreads, prob_data, label, loss_data,
+      num, dim, spatial_dim, has_ignore_label_, ignore_label_, counts);
+  Dtype loss;
+  caffe_gpu_asum(nthreads, loss_data, &loss);
+  if (normalize_) {
+    Dtype count;
+    caffe_gpu_asum(nthreads, counts, &count);
+    loss /= count;
+  } else {
+    loss /= num;
+  }
+  top[0]->mutable_cpu_data()[0] = loss;
+  if (top.size() == 2) {
+    top[1]->ShareData(prob_);
+  }
+}
+
+template <typename Dtype>
+__global__ void SoftmaxLossBackwardGPU(const int nthreads, const Dtype* top,
+          const Dtype* label, Dtype* bottom_diff, const int num, const int dim,
+          const int spatial_dim, const bool has_ignore_label_,
+          const int ignore_label_, Dtype* counts) {
+  const int channels = dim / spatial_dim;
+
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      for (int c = 0; c < channels; ++c) {
+        bottom_diff[n * dim + c * spatial_dim + s] = 0;
+      }
+      counts[index] = 0;
+    } else {
+      bottom_diff[n * dim + label_value * spatial_dim + s] -= 1;
+      counts[index] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* prob_data = prob_.gpu_data();
+    const Dtype* top_data = top[0]->gpu_data();
+    caffe_gpu_memcpy(prob_.count() * sizeof(Dtype), prob_data, bottom_diff);
+    const Dtype* label = bottom[1]->gpu_data();
+    const int num = prob_.num();
+    const int dim = prob_.count() / num;
+    const int spatial_dim = prob_.height() * prob_.width();
+    const int nthreads = num * spatial_dim;
+    // Since this memory is never used for anything else,
+    // we use to to avoid allocating new GPU memory.
+    Dtype* counts = prob_.mutable_gpu_diff();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    SoftmaxLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+        CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, bottom_diff,
+        num, dim, spatial_dim, has_ignore_label_, ignore_label_, counts);
+    const Dtype loss_weight = top[0]->cpu_diff()[0];
+    if (normalize_) {
+      Dtype count;
+      caffe_gpu_asum(nthreads, counts, &count);
+      caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff);
+    } else {
+      caffe_gpu_scal(prob_.count(), loss_weight / num, bottom_diff);
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(SoftmaxWithLossLayer);
+
+}  // namespace caffe

From 67c727a70f1a17c0de80cf1b08ba434499583ac3 Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Fri, 7 Nov 2014 20:01:33 +0100
Subject: [PATCH 1105/2053] Added opencv vector<Mat> to memory data layer with
 tests

---
 include/caffe/data_layers.hpp             |  2 ++
 include/caffe/data_transformer.hpp        | 13 +++++++
 src/caffe/data_transformer.cpp            | 20 +++++++++++
 src/caffe/layers/memory_data_layer.cpp    | 24 +++++++++++++
 src/caffe/test/test_memory_data_layer.cpp | 43 ++++++++++++++++++++++-
 5 files changed, 101 insertions(+), 1 deletion(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index cdf2d18bb60..b5449b33034 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -260,6 +260,8 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
   virtual void AddDatumVector(const vector<Datum>& datum_vector);
+  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
+      const vector<int>& labels);
 
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 0cfb5dcc7a3..c5d7a229fd4 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -50,6 +50,19 @@ class DataTransformer {
   void Transform(const vector<Datum> & datum_vector,
                 Blob<Dtype>* transformed_blob);
 
+  /**
+   * @brief Applies the transformation defined in the data layer's
+   * transform_param block to a vector of Mat.
+   *
+   * @param mat_vector
+   *    A vector of Mat containing the data to be transformed.
+   * @param transformed_blob
+   *    This is destination blob. It can be part of top blob's data if
+   *    set_cpu_data() is used. See memory_layer.cpp for an example.
+   */
+  void Transform(const vector<cv::Mat> & mat_vector,
+                Blob<Dtype>* transformed_blob);
+
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a cv::Mat
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 3d2c78f8ec2..3b4fd120bff 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -173,6 +173,26 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
+template<typename Dtype>
+void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
+                                       Blob<Dtype>* transformed_blob) {
+  const int mat_num = mat_vector.size();
+  const int num = transformed_blob->num();
+  const int channels = transformed_blob->channels();
+  const int height = transformed_blob->height();
+  const int width = transformed_blob->width();
+
+  CHECK_GT(mat_num, 0) << "There is no MAT to add";
+  CHECK_LE(mat_num, num) <<
+    "The size of mat_vector must be smaller than transformed_blob->num()";
+  Blob<Dtype> uni_blob(1, channels, height, width);
+  for (int item_id = 0; item_id < mat_num; ++item_id) {
+    int offset = transformed_blob->offset(item_id);
+    uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);
+    Transform(mat_vector[item_id], &uni_blob);
+  }
+}
+
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 3272b66ac55..67990f3cb93 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -50,6 +50,30 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   has_new_data_ = true;
 }
 
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
+    const vector<int>& labels) {
+  CHECK(!has_new_data_) <<
+      "Can't add Mat when earlier ones haven't been consumed"
+      << " by the upper layers";
+  size_t num = mat_vector.size();
+  CHECK_GT(num, 0) << "There is no mat to add";
+  CHECK_LE(num, batch_size_) <<
+      "The number of added mat must be no greater than the batch size";
+
+  // Apply data transformations (mirror, scale, crop...)
+  this->data_transformer_.Transform(mat_vector, &added_data_);
+  // Copy Labels
+  Dtype* top_label = added_label_.mutable_cpu_data();
+  for (int item_id = 0; item_id < num; ++item_id) {
+    top_label[item_id] = labels[item_id];
+  }
+  // num_images == batch_size_
+  Dtype* top_data = added_data_.mutable_cpu_data();
+  Reset(top_data, top_label, batch_size_);
+  has_new_data_ = true;
+}
+
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   CHECK(data);
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 497ab0d15e7..72b2759f2c2 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -127,7 +127,6 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   const size_t count = this->channels_ * this->height_ * this->width_;
   size_t pixel_index = 0;
   for (int i = 0; i < this->batch_size_; ++i) {
-    LOG(ERROR) << "i " << i;
     datum_vector[i].set_channels(this->channels_);
     datum_vector[i].set_height(this->height_);
     datum_vector[i].set_width(this->width_);
@@ -164,4 +163,46 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   }
 }
 
+TYPED_TEST(MemoryDataLayerTest, AddMatVectorDefaultTransform) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->channels_);
+  memory_data_param->set_height(this->height_);
+  memory_data_param->set_width(this->width_);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<cv::Mat> mat_vector(this->batch_size_);
+  vector<int> label_vector(this->batch_size_);
+  for (int i = 0; i < this->batch_size_; ++i) {
+    mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
+    label_vector[i] = i;
+    cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
+  }
+  layer.AddMatVector(mat_vector, label_vector);
+  int data_index;
+  const size_t count = this->channels_ * this->height_ * this->width_;
+  for (int iter = 0; iter < 5; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0; i < this->batch_size_; ++i) {
+      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
+      for (int h = 0; h < this->height_; ++h) {
+        const unsigned char* ptr_mat = mat_vector[i].ptr<uchar>(h);
+        int index = 0;
+        for (int w = 0; w < this->width_; ++w) {
+          for (int c = 0; c < this->channels_; ++c) {
+            data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
+            Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
+            EXPECT_EQ(static_cast<int>(pixel),
+                      data[data_index]);
+          }
+        }
+      }
+    }
+  }
+}
+
 }  // namespace caffe

From cedefd77625db978623fcf00ecc32d899eed954b Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Mon, 10 Nov 2014 19:24:02 +0100
Subject: [PATCH 1106/2053] MemoryDataLayer now accepts dynamic batch_size

---
 include/caffe/data_layers.hpp          |  1 +
 src/caffe/layers/memory_data_layer.cpp | 25 +++++++++++++++++++++++++
 2 files changed, 26 insertions(+)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index b5449b33034..117b1b5bb9a 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -284,6 +284,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   Blob<Dtype> added_data_;
   Blob<Dtype> added_label_;
   bool has_new_data_;
+  bool needs_reshape_;
 };
 
 /**
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 67990f3cb93..f36eb6f85ed 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -32,7 +32,15 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   CHECK(!has_new_data_) <<
       "Can't add Datum when earlier ones haven't been consumed"
       << " by the upper layers";
+
   size_t num = datum_vector.size();
+  if (batch_size_ != num) {
+    needs_reshape_ = true;
+    batch_size_ = num;
+    added_data_.Reshape(batch_size_, channels_, height_, width_);
+    added_label_.Reshape(batch_size_, 1, 1, 1);
+  }
+
   CHECK_GT(num, 0) << "There is no datum to add";
   CHECK_LE(num, batch_size_) <<
       "The number of added datum must be no greater than the batch size";
@@ -53,10 +61,22 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
     const vector<int>& labels) {
+
   CHECK(!has_new_data_) <<
       "Can't add Mat when earlier ones haven't been consumed"
       << " by the upper layers";
+
+  CHECK_EQ(mat_vector.size(), labels.size()) <<
+      "vector of labels and vector of mats need to be of the same size";
+
   size_t num = mat_vector.size();
+  if (batch_size_ != num) {
+    needs_reshape_ = true;
+    batch_size_ = num;
+    added_data_.Reshape(batch_size_, channels_, height_, width_);
+    added_label_.Reshape(batch_size_, 1, 1, 1);
+  }
+
   CHECK_GT(num, 0) << "There is no mat to add";
   CHECK_LE(num, batch_size_) <<
       "The number of added mat must be no greater than the batch size";
@@ -89,10 +109,15 @@ template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
+  if (needs_reshape_) {
+    top[0]->Reshape(batch_size_, channels_, height_, width_);
+    top[1]->Reshape(batch_size_, 1, 1, 1);
+  }
   top[0]->set_cpu_data(data_ + pos_ * size_);
   top[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
   has_new_data_ = false;
+  needs_reshape_ = false;
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);

From eb4ca169b53e53b1b288920b2bae9db0c59b4ec2 Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Tue, 9 Dec 2014 15:12:28 +0100
Subject: [PATCH 1107/2053] MemoryDataLayer now correctly consumes batch_size
 elements

---
 include/caffe/data_layers.hpp             |   1 +
 include/caffe/data_transformer.hpp        |   3 +-
 src/caffe/data_transformer.cpp            |  10 +-
 src/caffe/layers/memory_data_layer.cpp    |  61 ++++++------
 src/caffe/test/test_memory_data_layer.cpp | 114 +++++++++++++++++++---
 5 files changed, 140 insertions(+), 49 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 117b1b5bb9a..e8c6eecf0ed 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -266,6 +266,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
+  void ChangeBatchSize(int new_size);
 
   int batch_size() { return batch_size_; }
   int channels() { return channels_; }
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index c5d7a229fd4..95062d4529d 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -60,9 +60,10 @@ class DataTransformer {
    *    This is destination blob. It can be part of top blob's data if
    *    set_cpu_data() is used. See memory_layer.cpp for an example.
    */
+#ifndef OSX
   void Transform(const vector<cv::Mat> & mat_vector,
                 Blob<Dtype>* transformed_blob);
-
+#endif
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a cv::Mat
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 3b4fd120bff..3e11085e928 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -163,8 +163,8 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   const int width = transformed_blob->width();
 
   CHECK_GT(datum_num, 0) << "There is no datum to add";
-  CHECK_LE(datum_num, num) <<
-    "The size of datum_vector must be smaller than transformed_blob->num()";
+  CHECK_EQ(datum_num, num) <<
+    "The size of datum_vector must be equals to transformed_blob->num()";
   Blob<Dtype> uni_blob(1, channels, height, width);
   for (int item_id = 0; item_id < datum_num; ++item_id) {
     int offset = transformed_blob->offset(item_id);
@@ -173,6 +173,7 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
+#ifndef OSX
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
                                        Blob<Dtype>* transformed_blob) {
@@ -183,8 +184,8 @@ void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
   const int width = transformed_blob->width();
 
   CHECK_GT(mat_num, 0) << "There is no MAT to add";
-  CHECK_LE(mat_num, num) <<
-    "The size of mat_vector must be smaller than transformed_blob->num()";
+  CHECK_EQ(mat_num, num) <<
+    "The size of mat_vector must be equals to transformed_blob->num()";
   Blob<Dtype> uni_blob(1, channels, height, width);
   for (int item_id = 0; item_id < mat_num; ++item_id) {
     int offset = transformed_blob->offset(item_id);
@@ -192,6 +193,7 @@ void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
     Transform(mat_vector[item_id], &uni_blob);
   }
 }
+#endif
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index f36eb6f85ed..214f9d865bc 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -23,6 +23,7 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   added_label_.Reshape(batch_size_, 1, 1, 1);
   data_ = NULL;
   labels_ = NULL;
+  needs_reshape_ = false;
   added_data_.cpu_data();
   added_label_.cpu_data();
 }
@@ -32,19 +33,14 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   CHECK(!has_new_data_) <<
       "Can't add Datum when earlier ones haven't been consumed"
       << " by the upper layers";
-
   size_t num = datum_vector.size();
-  if (batch_size_ != num) {
-    needs_reshape_ = true;
-    batch_size_ = num;
-    added_data_.Reshape(batch_size_, channels_, height_, width_);
-    added_label_.Reshape(batch_size_, 1, 1, 1);
-  }
-
   CHECK_GT(num, 0) << "There is no datum to add";
-  CHECK_LE(num, batch_size_) <<
-      "The number of added datum must be no greater than the batch size";
-
+  CHECK_LE(num % batch_size_, 0) <<
+      "The number of added datum must be multiple of the batch size";
+  if (num > batch_size_) {
+    added_data_.Reshape(num, channels_, height_, width_);
+    added_label_.Reshape(num, 1, 1, 1);
+  }
   // Apply data transformations (mirror, scale, crop...)
   this->data_transformer_.Transform(datum_vector, &added_data_);
   // Copy Labels
@@ -54,33 +50,24 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   }
   // num_images == batch_size_
   Dtype* top_data = added_data_.mutable_cpu_data();
-  Reset(top_data, top_label, batch_size_);
+  Reset(top_data, top_label, num);
   has_new_data_ = true;
 }
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
     const vector<int>& labels) {
-
+  size_t num = mat_vector.size();
   CHECK(!has_new_data_) <<
       "Can't add Mat when earlier ones haven't been consumed"
       << " by the upper layers";
-
-  CHECK_EQ(mat_vector.size(), labels.size()) <<
-      "vector of labels and vector of mats need to be of the same size";
-
-  size_t num = mat_vector.size();
-  if (batch_size_ != num) {
-    needs_reshape_ = true;
-    batch_size_ = num;
-    added_data_.Reshape(batch_size_, channels_, height_, width_);
-    added_label_.Reshape(batch_size_, 1, 1, 1);
-  }
-
+  CHECK_LE(num % batch_size_, 0) <<
+      "The number of added datum must be multiple of the batch size";
   CHECK_GT(num, 0) << "There is no mat to add";
-  CHECK_LE(num, batch_size_) <<
-      "The number of added mat must be no greater than the batch size";
-
+  if (num > batch_size_) {
+    added_data_.Reshape(num, channels_, height_, width_);
+    added_label_.Reshape(num, 1, 1, 1);
+  }
   // Apply data transformations (mirror, scale, crop...)
   this->data_transformer_.Transform(mat_vector, &added_data_);
   // Copy Labels
@@ -90,7 +77,7 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
   }
   // num_images == batch_size_
   Dtype* top_data = added_data_.mutable_cpu_data();
-  Reset(top_data, top_label, batch_size_);
+  Reset(top_data, top_label, num);
   has_new_data_ = true;
 }
 
@@ -105,6 +92,17 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   pos_ = 0;
 }
 
+template <typename Dtype>
+void MemoryDataLayer<Dtype>::ChangeBatchSize(int new_size) {
+  CHECK(!has_new_data_) <<
+      "Can't change batch_size before all data haven't been consumed"
+      << " by the upper layers";
+  batch_size_ = new_size;
+  added_data_.Reshape(batch_size_, channels_, height_, width_);
+  added_label_.Reshape(batch_size_, 1, 1, 1);
+  needs_reshape_ = true;
+}
+
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
@@ -112,12 +110,13 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   if (needs_reshape_) {
     top[0]->Reshape(batch_size_, channels_, height_, width_);
     top[1]->Reshape(batch_size_, 1, 1, 1);
+    needs_reshape_ = false;
   }
   top[0]->set_cpu_data(data_ + pos_ * size_);
   top[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
-  has_new_data_ = false;
-  needs_reshape_ = false;
+  if (pos_ == 0)
+    has_new_data_ = false;
 }
 
 INSTANTIATE_CLASS(MemoryDataLayer);
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 72b2759f2c2..bc6c9b43eda 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -122,11 +122,13 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   memory_data_param->set_width(this->width_);
   MemoryDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  vector<Datum> datum_vector(this->batch_size_);
+  // We add batch_size*num_iter items, then for each iteration
+  // we forward batch_size elements
+  int num_iter = 5;
+  vector<Datum> datum_vector(this->batch_size_ * num_iter);
   const size_t count = this->channels_ * this->height_ * this->width_;
   size_t pixel_index = 0;
-  for (int i = 0; i < this->batch_size_; ++i) {
+  for (int i = 0; i < this->batch_size_ * num_iter; ++i) {
     datum_vector[i].set_channels(this->channels_);
     datum_vector[i].set_height(this->height_);
     datum_vector[i].set_width(this->width_);
@@ -137,18 +139,18 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
     }
     datum_vector[i].set_data(&(pixels[0]), count);
   }
-
   layer.AddDatumVector(datum_vector);
 
   int data_index;
   // Go through the data 5 times
-  for (int iter = 0; iter < 5; ++iter) {
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = this->batch_size_ * iter;
     layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype* data = this->data_blob_->cpu_data();
     size_t index = 0;
     for (int i = 0; i < this->batch_size_; ++i) {
-      const string& data_string = datum_vector[i].data();
-      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
+      const string& data_string = datum_vector[offset + i].data();
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
       for (int c = 0; c < this->channels_; ++c) {
         for (int h = 0; h < this->height_; ++h) {
           for (int w = 0; w < this->width_; ++w) {
@@ -165,7 +167,52 @@ TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
 
 TYPED_TEST(MemoryDataLayerTest, AddMatVectorDefaultTransform) {
   typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
+  memory_data_param->set_batch_size(this->batch_size_);
+  memory_data_param->set_channels(this->channels_);
+  memory_data_param->set_height(this->height_);
+  memory_data_param->set_width(this->width_);
+  MemoryDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // We add batch_size*num_iter items, then for each iteration
+  // we forward batch_size elements
+  int num_iter = 5;
+  vector<cv::Mat> mat_vector(this->batch_size_ * num_iter);
+  vector<int> label_vector(this->batch_size_ * num_iter);
+  for (int i = 0; i < this->batch_size_*num_iter; ++i) {
+    mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
+    label_vector[i] = i;
+    cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
+  }
+  layer.AddMatVector(mat_vector, label_vector);
 
+  int data_index;
+  const size_t count = this->channels_ * this->height_ * this->width_;
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = this->batch_size_ * iter;
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0; i < this->batch_size_; ++i) {
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
+      for (int h = 0; h < this->height_; ++h) {
+        const unsigned char* ptr_mat = mat_vector[offset + i].ptr<uchar>(h);
+        int index = 0;
+        for (int w = 0; w < this->width_; ++w) {
+          for (int c = 0; c < this->channels_; ++c) {
+            data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
+            Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
+            EXPECT_EQ(static_cast<int>(pixel),
+                      data[data_index]);
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(MemoryDataLayerTest, TestChangeBatchSize) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
   memory_data_param->set_batch_size(this->batch_size_);
@@ -174,23 +221,64 @@ TYPED_TEST(MemoryDataLayerTest, AddMatVectorDefaultTransform) {
   memory_data_param->set_width(this->width_);
   MemoryDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  vector<cv::Mat> mat_vector(this->batch_size_);
-  vector<int> label_vector(this->batch_size_);
-  for (int i = 0; i < this->batch_size_; ++i) {
+  // first add data as usual
+  int num_iter = 5;
+  vector<cv::Mat> mat_vector(this->batch_size_ * num_iter);
+  vector<int> label_vector(this->batch_size_ * num_iter);
+  for (int i = 0; i < this->batch_size_*num_iter; ++i) {
     mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
     label_vector[i] = i;
     cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
   }
   layer.AddMatVector(mat_vector, label_vector);
+  // then consume the data
   int data_index;
   const size_t count = this->channels_ * this->height_ * this->width_;
-  for (int iter = 0; iter < 5; ++iter) {
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = this->batch_size_ * iter;
     layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     const Dtype* data = this->data_blob_->cpu_data();
     for (int i = 0; i < this->batch_size_; ++i) {
-      EXPECT_EQ(i, this->label_blob_->cpu_data()[i]);
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
+      for (int h = 0; h < this->height_; ++h) {
+        const unsigned char* ptr_mat = mat_vector[offset + i].ptr<uchar>(h);
+        int index = 0;
+        for (int w = 0; w < this->width_; ++w) {
+          for (int c = 0; c < this->channels_; ++c) {
+            data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
+            Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
+            EXPECT_EQ(static_cast<int>(pixel),
+                      data[data_index]);
+          }
+        }
+      }
+    }
+  }
+  // and then add new data with different batch_size
+  int new_batch_size = 16;
+  layer.ChangeBatchSize(new_batch_size);
+  mat_vector.clear();
+  mat_vector.resize(new_batch_size * num_iter);
+  label_vector.clear();
+  label_vector.resize(new_batch_size * num_iter);
+  for (int i = 0; i < new_batch_size*num_iter; ++i) {
+    mat_vector[i] = cv::Mat(this->height_, this->width_, CV_8UC4);
+    label_vector[i] = i;
+    cv::randu(mat_vector[i], cv::Scalar::all(0), cv::Scalar::all(255));
+  }
+  layer.AddMatVector(mat_vector, label_vector);
+
+  // finally consume new data and check if everything is fine
+  for (int iter = 0; iter < num_iter; ++iter) {
+    int offset = new_batch_size * iter;
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    EXPECT_EQ(new_batch_size, this->blob_top_vec_[0]->num());
+    EXPECT_EQ(new_batch_size, this->blob_top_vec_[1]->num());
+    const Dtype* data = this->data_blob_->cpu_data();
+    for (int i = 0; i < new_batch_size; ++i) {
+      EXPECT_EQ(offset + i, this->label_blob_->cpu_data()[i]);
       for (int h = 0; h < this->height_; ++h) {
-        const unsigned char* ptr_mat = mat_vector[i].ptr<uchar>(h);
+        const unsigned char* ptr_mat = mat_vector[offset + i].ptr<uchar>(h);
         int index = 0;
         for (int w = 0; w < this->width_; ++w) {
           for (int c = 0; c < this->channels_; ++c) {

From 4901f830c3b90f77f9bd699930f2b9830cbc3b11 Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Thu, 8 Jan 2015 19:26:20 +0100
Subject: [PATCH 1108/2053] small fixes

---
 src/caffe/layers/memory_data_layer.cpp    | 16 ++++++----------
 src/caffe/test/test_memory_data_layer.cpp |  6 ++----
 2 files changed, 8 insertions(+), 14 deletions(-)

diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 214f9d865bc..73b9f49115a 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -35,12 +35,10 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
       << " by the upper layers";
   size_t num = datum_vector.size();
   CHECK_GT(num, 0) << "There is no datum to add";
-  CHECK_LE(num % batch_size_, 0) <<
+  CHECK_EQ(num % batch_size_, 0) <<
       "The number of added datum must be multiple of the batch size";
-  if (num > batch_size_) {
-    added_data_.Reshape(num, channels_, height_, width_);
-    added_label_.Reshape(num, 1, 1, 1);
-  }
+  added_data_.Reshape(num, channels_, height_, width_);
+  added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
   this->data_transformer_.Transform(datum_vector, &added_data_);
   // Copy Labels
@@ -61,13 +59,11 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
   CHECK(!has_new_data_) <<
       "Can't add Mat when earlier ones haven't been consumed"
       << " by the upper layers";
-  CHECK_LE(num % batch_size_, 0) <<
+  CHECK_EQ(num % batch_size_, 0) <<
       "The number of added datum must be multiple of the batch size";
   CHECK_GT(num, 0) << "There is no mat to add";
-  if (num > batch_size_) {
-    added_data_.Reshape(num, channels_, height_, width_);
-    added_label_.Reshape(num, 1, 1, 1);
-  }
+  added_data_.Reshape(num, channels_, height_, width_);
+  added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
   this->data_transformer_.Transform(mat_vector, &added_data_);
   // Copy Labels
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index bc6c9b43eda..d4d57102516 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -247,8 +247,7 @@ TYPED_TEST(MemoryDataLayerTest, TestChangeBatchSize) {
           for (int c = 0; c < this->channels_; ++c) {
             data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
             Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
-            EXPECT_EQ(static_cast<int>(pixel),
-                      data[data_index]);
+            EXPECT_EQ(static_cast<int>(pixel), data[data_index]);
           }
         }
       }
@@ -284,8 +283,7 @@ TYPED_TEST(MemoryDataLayerTest, TestChangeBatchSize) {
           for (int c = 0; c < this->channels_; ++c) {
             data_index = (i*count) + (c * this->height_ + h) * this->width_ + w;
             Dtype pixel = static_cast<Dtype>(ptr_mat[index++]);
-            EXPECT_EQ(static_cast<int>(pixel),
-                      data[data_index]);
+            EXPECT_EQ(static_cast<int>(pixel), data[data_index]);
           }
         }
       }

From abe0fa2602ca1c185eff0580f4f317d970421631 Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Mon, 12 Jan 2015 12:22:49 +0100
Subject: [PATCH 1109/2053] removed needs_reshape_ and ChangeBatchSize is now
 set_batch_size

---
 include/caffe/data_layers.hpp             |  3 +--
 src/caffe/layers/memory_data_layer.cpp    | 11 +++--------
 src/caffe/test/test_memory_data_layer.cpp |  4 ++--
 3 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e8c6eecf0ed..1542af7c327 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -266,7 +266,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
   void Reset(Dtype* data, Dtype* label, int n);
-  void ChangeBatchSize(int new_size);
+  void set_batch_size(int new_size);
 
   int batch_size() { return batch_size_; }
   int channels() { return channels_; }
@@ -285,7 +285,6 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   Blob<Dtype> added_data_;
   Blob<Dtype> added_label_;
   bool has_new_data_;
-  bool needs_reshape_;
 };
 
 /**
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 73b9f49115a..1669db393e0 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -23,7 +23,6 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   added_label_.Reshape(batch_size_, 1, 1, 1);
   data_ = NULL;
   labels_ = NULL;
-  needs_reshape_ = false;
   added_data_.cpu_data();
   added_label_.cpu_data();
 }
@@ -89,25 +88,21 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
 }
 
 template <typename Dtype>
-void MemoryDataLayer<Dtype>::ChangeBatchSize(int new_size) {
+void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
   CHECK(!has_new_data_) <<
       "Can't change batch_size before all data haven't been consumed"
       << " by the upper layers";
   batch_size_ = new_size;
   added_data_.Reshape(batch_size_, channels_, height_, width_);
   added_label_.Reshape(batch_size_, 1, 1, 1);
-  needs_reshape_ = true;
 }
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
-  if (needs_reshape_) {
-    top[0]->Reshape(batch_size_, channels_, height_, width_);
-    top[1]->Reshape(batch_size_, 1, 1, 1);
-    needs_reshape_ = false;
-  }
+  top[0]->Reshape(batch_size_, channels_, height_, width_);
+  top[1]->Reshape(batch_size_, 1, 1, 1);
   top[0]->set_cpu_data(data_ + pos_ * size_);
   top[1]->set_cpu_data(labels_ + pos_);
   pos_ = (pos_ + batch_size_) % n_;
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index d4d57102516..6a7bf045d23 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -211,7 +211,7 @@ TYPED_TEST(MemoryDataLayerTest, AddMatVectorDefaultTransform) {
   }
 }
 
-TYPED_TEST(MemoryDataLayerTest, TestChangeBatchSize) {
+TYPED_TEST(MemoryDataLayerTest, TestSetBatchSize) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   MemoryDataParameter* memory_data_param = param.mutable_memory_data_param();
@@ -255,7 +255,7 @@ TYPED_TEST(MemoryDataLayerTest, TestChangeBatchSize) {
   }
   // and then add new data with different batch_size
   int new_batch_size = 16;
-  layer.ChangeBatchSize(new_batch_size);
+  layer.set_batch_size(new_batch_size);
   mat_vector.clear();
   mat_vector.resize(new_batch_size * num_iter);
   label_vector.clear();

From a2fbea3c5ae1b2e9f1fec77c8450c10c33d5b29b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Feb 2015 21:37:54 -0800
Subject: [PATCH 1110/2053] groom #1416

- keep current `DataTransformer` check so that datums can be transformed
  into a blob incrementally
- standardize check messages
- include opencv where needed, drop unneeded OS X guards

TODO these tests need to be de-duped
---
 include/caffe/data_transformer.hpp        |  4 +---
 src/caffe/data_transformer.cpp            |  6 ++----
 src/caffe/layers/memory_data_layer.cpp    | 19 +++++++++----------
 src/caffe/test/test_memory_data_layer.cpp |  2 ++
 4 files changed, 14 insertions(+), 17 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 95062d4529d..60696c96dcc 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -60,10 +60,8 @@ class DataTransformer {
    *    This is destination blob. It can be part of top blob's data if
    *    set_cpu_data() is used. See memory_layer.cpp for an example.
    */
-#ifndef OSX
   void Transform(const vector<cv::Mat> & mat_vector,
                 Blob<Dtype>* transformed_blob);
-#endif
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a cv::Mat
@@ -92,7 +90,7 @@ class DataTransformer {
  protected:
    /**
    * @brief Generates a random integer from Uniform({0, 1, ..., n-1}).
-   * 
+   *
    * @param n
    *    The upperbound (exclusive) value of the random number.
    * @return
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 3e11085e928..c0ad72ee67d 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -163,8 +163,8 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   const int width = transformed_blob->width();
 
   CHECK_GT(datum_num, 0) << "There is no datum to add";
-  CHECK_EQ(datum_num, num) <<
-    "The size of datum_vector must be equals to transformed_blob->num()";
+  CHECK_LE(datum_num, num) <<
+    "The size of datum_vector must be no greater than transformed_blob->num()";
   Blob<Dtype> uni_blob(1, channels, height, width);
   for (int item_id = 0; item_id < datum_num; ++item_id) {
     int offset = transformed_blob->offset(item_id);
@@ -173,7 +173,6 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
-#ifndef OSX
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
                                        Blob<Dtype>* transformed_blob) {
@@ -193,7 +192,6 @@ void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
     Transform(mat_vector[item_id], &uni_blob);
   }
 }
-#endif
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 1669db393e0..7cb5eafab02 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <vector>
 
 #include "caffe/data_layers.hpp"
@@ -30,12 +32,11 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   CHECK(!has_new_data_) <<
-      "Can't add Datum when earlier ones haven't been consumed"
-      << " by the upper layers";
+      "Can't add data until current data has been consumed.";
   size_t num = datum_vector.size();
-  CHECK_GT(num, 0) << "There is no datum to add";
+  CHECK_GT(num, 0) << "There is no datum to add.";
   CHECK_EQ(num % batch_size_, 0) <<
-      "The number of added datum must be multiple of the batch size";
+      "The added data must be a multiple of the batch size.";
   added_data_.Reshape(num, channels_, height_, width_);
   added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
@@ -56,11 +57,10 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
     const vector<int>& labels) {
   size_t num = mat_vector.size();
   CHECK(!has_new_data_) <<
-      "Can't add Mat when earlier ones haven't been consumed"
-      << " by the upper layers";
-  CHECK_EQ(num % batch_size_, 0) <<
-      "The number of added datum must be multiple of the batch size";
+      "Can't add mat until current data has been consumed.";
   CHECK_GT(num, 0) << "There is no mat to add";
+  CHECK_EQ(num % batch_size_, 0) <<
+      "The added data must be a multiple of the batch size.";
   added_data_.Reshape(num, channels_, height_, width_);
   added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
@@ -90,8 +90,7 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
   CHECK(!has_new_data_) <<
-      "Can't change batch_size before all data haven't been consumed"
-      << " by the upper layers";
+      "Can't change batch_size until current data has been consumed.";
   batch_size_ = new_size;
   added_data_.Reshape(batch_size_, channels_, height_, width_);
   added_label_.Reshape(batch_size_, 1, 1, 1);
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 6a7bf045d23..a79033f59f1 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,3 +1,5 @@
+#include <opencv2/core/core.hpp>
+
 #include <string>
 #include <vector>
 

From d12101edb4dcd2cdb38a2b7f7dbc011ff8887d02 Mon Sep 17 00:00:00 2001
From: Dmitry Ulyanov <dmitry.ulyanov.msu@gmail.com>
Date: Fri, 6 Feb 2015 10:56:47 +0300
Subject: [PATCH 1111/2053] Allow using arrays with n_ * size_ > 2^31

uint64_t

size_t
---
 include/caffe/data_layers.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 1542af7c327..e0d5a8aca46 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -281,7 +281,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   Dtype* data_;
   Dtype* labels_;
   int n_;
-  int pos_;
+  size_t pos_;
   Blob<Dtype> added_data_;
   Blob<Dtype> added_label_;
   bool has_new_data_;

From 56846e6dc2336c3856c98447dd89c20fc66d7faa Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Feb 2015 23:12:33 -0800
Subject: [PATCH 1112/2053] HDF5_DATA + MEMORY_DATA refuse loudly to transform

These layers are meant for generic inputs so they do not do
transformations. As `transform_param` was pulled out of data layers'
proto definitions for reuse, these layers silently ignored
transformation parameters until now instead of signalling their refusal.
---
 src/caffe/layers/hdf5_data_layer.cpp   | 3 +++
 src/caffe/layers/memory_data_layer.cpp | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 77555211082..3d856ec3001 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -58,6 +58,9 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  // Refuse transformation parameters since HDF5 is totally generic.
+  CHECK(!this->layer_param_.has_transform_param()) <<
+      this->type() << " does not transform data.";
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
   LOG(INFO) << "Loading list of HDF5 filenames from: " << source;
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 7cb5eafab02..6f584de32d0 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -81,6 +81,9 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   CHECK(data);
   CHECK(labels);
   CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
+  // Refuse transformation parameters since a memory array is totally generic.
+  CHECK(!this->layer_param_.has_transform_param()) <<
+      this->type() << " does not transform data.";
   data_ = data;
   labels_ = labels;
   n_ = n;

From 8821c28aa9af399454584e90c9addce75e38b6d2 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 9 Feb 2015 18:08:50 -0800
Subject: [PATCH 1113/2053] Fixes for CuDNN layers: only destroy handles if
 setup

---
 include/caffe/common_layers.hpp          | 3 ++-
 include/caffe/neuron_layers.hpp          | 9 ++++++---
 include/caffe/vision_layers.hpp          | 6 ++++--
 src/caffe/layers/cudnn_conv_layer.cpp    | 5 +++++
 src/caffe/layers/cudnn_pooling_layer.cpp | 4 ++++
 src/caffe/layers/cudnn_relu_layer.cpp    | 4 ++++
 src/caffe/layers/cudnn_sigmoid_layer.cpp | 4 ++++
 src/caffe/layers/cudnn_softmax_layer.cpp | 4 ++++
 src/caffe/layers/cudnn_tanh_layer.cpp    | 4 ++++
 9 files changed, 37 insertions(+), 6 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 3a5ccd2180e..c67822c3738 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -377,7 +377,7 @@ template <typename Dtype>
 class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
  public:
   explicit CuDNNSoftmaxLayer(const LayerParameter& param)
-      : SoftmaxLayer<Dtype>(param) {}
+      : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -390,6 +390,7 @@ class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
   cudnnTensor4dDescriptor_t top_desc_;
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index b65d98435bc..46c84171b9c 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -417,7 +417,7 @@ template <typename Dtype>
 class CuDNNReLULayer : public ReLULayer<Dtype> {
  public:
   explicit CuDNNReLULayer(const LayerParameter& param)
-      : ReLULayer<Dtype>(param) {}
+      : ReLULayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -430,6 +430,7 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
   cudnnTensor4dDescriptor_t top_desc_;
@@ -499,7 +500,7 @@ template <typename Dtype>
 class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
  public:
   explicit CuDNNSigmoidLayer(const LayerParameter& param)
-      : SigmoidLayer<Dtype>(param) {}
+      : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -512,6 +513,7 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
   cudnnTensor4dDescriptor_t top_desc_;
@@ -583,7 +585,7 @@ template <typename Dtype>
 class CuDNNTanHLayer : public TanHLayer<Dtype> {
  public:
   explicit CuDNNTanHLayer(const LayerParameter& param)
-      : TanHLayer<Dtype>(param) {}
+      : TanHLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -596,6 +598,7 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_;
   cudnnTensor4dDescriptor_t top_desc_;
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 38e369c4092..6cb507a5780 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -230,7 +230,7 @@ template <typename Dtype>
 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
  public:
   explicit CuDNNConvolutionLayer(const LayerParameter& param)
-      : ConvolutionLayer<Dtype>(param) {}
+      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -243,6 +243,7 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t* handle_;
   cudaStream_t*  stream_;
   vector<cudnnTensor4dDescriptor_t> bottom_descs_, top_descs_;
@@ -426,7 +427,7 @@ template <typename Dtype>
 class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
  public:
   explicit CuDNNPoolingLayer(const LayerParameter& param)
-      : PoolingLayer<Dtype>(param) {}
+      : PoolingLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -442,6 +443,7 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  bool handles_setup_;
   cudnnHandle_t             handle_;
   cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
   cudnnPoolingDescriptor_t  pooling_desc_;
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index f74a3dbb02f..4a69ca20d0a 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -58,6 +58,8 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   if (this->bias_term_) {
     cudnn::createTensor4dDesc<Dtype>(&bias_desc_);
   }
+
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
@@ -98,6 +100,9 @@ void CuDNNConvolutionLayer<Dtype>::Reshape(
 
 template <typename Dtype>
 CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   for (int i = 0; i < bottom_descs_.size(); i++) {
     cudnnDestroyTensor4dDescriptor(bottom_descs_[i]);
     cudnnDestroyTensor4dDescriptor(top_descs_[i]);
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index fc0222ac0e3..dd90195637b 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -22,6 +22,7 @@ void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
       this->layer_param_.pooling_param().pool(), &mode_,
       this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
@@ -36,6 +37,9 @@ void CuDNNPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNPoolingLayer<Dtype>::~CuDNNPoolingLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   cudnnDestroyTensor4dDescriptor(bottom_desc_);
   cudnnDestroyTensor4dDescriptor(top_desc_);
   cudnnDestroyPoolingDescriptor(pooling_desc_);
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 20f486f6bcf..0b8a6bc3248 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -15,6 +15,7 @@ void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
@@ -31,6 +32,9 @@ void CuDNNReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
   cudnnDestroyTensor4dDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index a94c0040a28..67bd9c373b0 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -15,6 +15,7 @@ void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
@@ -31,6 +32,9 @@ void CuDNNSigmoidLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNSigmoidLayer<Dtype>::~CuDNNSigmoidLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
   cudnnDestroyTensor4dDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 1a0f406bf38..83a5b69a626 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -19,6 +19,7 @@ void CuDNNSoftmaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
@@ -35,6 +36,9 @@ void CuDNNSoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNSoftmaxLayer<Dtype>::~CuDNNSoftmaxLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   cudnnDestroyTensor4dDescriptor(bottom_desc_);
   cudnnDestroyTensor4dDescriptor(top_desc_);
   cudnnDestroy(handle_);
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 39a3e14b3c8..b1d2b86384e 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -15,6 +15,7 @@ void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  handles_setup_ = true;
 }
 
 template <typename Dtype>
@@ -31,6 +32,9 @@ void CuDNNTanHLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 CuDNNTanHLayer<Dtype>::~CuDNNTanHLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
   cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
   cudnnDestroyTensor4dDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);

From 852a3c5355ba5cb7f48781f72f0e4baabf0afbea Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 5 Feb 2015 14:04:35 -0800
Subject: [PATCH 1114/2053] SoftmaxWithLossLayer fix: takes exactly 2 bottom
 blobs (inherited from LossLayer)

---
 include/caffe/loss_layers.hpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 09a5c24dde5..ea52f56e0f3 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -696,9 +696,6 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
       const vector<Blob<Dtype>*>& top);
 
   virtual inline const char* type() const { return "SoftmaxWithLoss"; }
-  virtual inline int ExactNumBottomBlobs() const { return -1; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int MaxBottomBlobs() const { return 3; }
   virtual inline int ExactNumTopBlobs() const { return -1; }
   virtual inline int MinTopBlobs() const { return 1; }
   virtual inline int MaxTopBlobs() const { return 2; }

From c15d1846969ab8338561489194d787676733caa7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 6 Oct 2014 23:45:43 -0700
Subject: [PATCH 1115/2053] Blob: add scale_{data,diff} methods and tests

---
 include/caffe/blob.hpp       |  5 +++
 src/caffe/blob.cpp           | 66 ++++++++++++++++++++++++++++++++++++
 src/caffe/test/test_blob.cpp | 60 +++++++++++++++++++++++++++++++-
 3 files changed, 130 insertions(+), 1 deletion(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index d425e3857e3..42e4420408c 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -111,6 +111,11 @@ class Blob {
   /// @brief Compute the sum of squares (L2 norm squared) of the diff.
   Dtype sumsq_diff() const;
 
+  /// @brief Scale the blob data by a constant factor.
+  void scale_data(Dtype scale_factor);
+  /// @brief Scale the blob diff by a constant factor.
+  void scale_diff(Dtype scale_factor);
+
   /**
    * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
    *        data_ of Blob other -- useful in Layer&s which simply perform a copy
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 3b72ce0c8d0..fbc1361a19d 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -279,6 +279,72 @@ Dtype Blob<Dtype>::sumsq_diff() const {
   return sumsq;
 }
 
+template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <> void Blob<int>::scale_data(int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <typename Dtype>
+void Blob<Dtype>::scale_data(Dtype scale_factor) {
+  Dtype* data;
+  if (!data_) { return; }
+  switch (data_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    data = mutable_cpu_data();
+    caffe_scal(count_, scale_factor, data);
+    return;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    data = mutable_gpu_data();
+    caffe_gpu_scal(count_, scale_factor, data);
+    return;
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+}
+
+template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <> void Blob<int>::scale_diff(int scale_factor) {
+  NOT_IMPLEMENTED;
+}
+
+template <typename Dtype>
+void Blob<Dtype>::scale_diff(Dtype scale_factor) {
+  Dtype* diff;
+  if (!diff_) { return; }
+  switch (diff_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    diff = mutable_cpu_diff();
+    caffe_scal(count_, scale_factor, diff);
+    return;
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+    diff = mutable_gpu_diff();
+    caffe_gpu_scal(count_, scale_factor, diff);
+    return;
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
+  }
+}
+
 template <typename Dtype>
 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   if (num_ != source.num() || channels_ != source.channels() ||
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index c619ad19f11..84d84e8f444 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -120,7 +120,7 @@ TYPED_TEST(BlobMathTest, TestSumOfSquares) {
 TYPED_TEST(BlobMathTest, TestAsum) {
   typedef typename TypeParam::Dtype Dtype;
 
-  // Uninitialized Blob should have sum of squares == 0.
+  // Uninitialized Blob should have asum == 0.
   EXPECT_EQ(0, this->blob_->asum_data());
   EXPECT_EQ(0, this->blob_->asum_diff());
   FillerParameter filler_param;
@@ -167,4 +167,62 @@ TYPED_TEST(BlobMathTest, TestAsum) {
   EXPECT_FLOAT_EQ(expected_asum * kDiffScaleFactor, this->blob_->asum_diff());
 }
 
+TYPED_TEST(BlobMathTest, TestScaleData) {
+  typedef typename TypeParam::Dtype Dtype;
+
+  EXPECT_EQ(0, this->blob_->asum_data());
+  EXPECT_EQ(0, this->blob_->asum_diff());
+  FillerParameter filler_param;
+  filler_param.set_min(-3);
+  filler_param.set_max(3);
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_);
+  const Dtype asum_before_scale = this->blob_->asum_data();
+  // Do a mutable access on the current device,
+  // so that the asum computation is done on that device.
+  // (Otherwise, this would only check the CPU asum implementation.)
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_data();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  const Dtype kDataScaleFactor = 3;
+  this->blob_->scale_data(kDataScaleFactor);
+  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor,
+                  this->blob_->asum_data());
+  EXPECT_EQ(0, this->blob_->asum_diff());
+
+  // Check scale_diff too.
+  const Dtype kDataToDiffScaleFactor = 7;
+  const Dtype* data = this->blob_->cpu_data();
+  caffe_cpu_scale(this->blob_->count(), kDataToDiffScaleFactor, data,
+                  this->blob_->mutable_cpu_diff());
+  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor,
+                  this->blob_->asum_data());
+  const Dtype diff_asum_before_scale = this->blob_->asum_diff();
+  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor * kDataToDiffScaleFactor,
+                  diff_asum_before_scale);
+  switch (TypeParam::device) {
+  case Caffe::CPU:
+    this->blob_->mutable_cpu_diff();
+    break;
+  case Caffe::GPU:
+    this->blob_->mutable_gpu_diff();
+    break;
+  default:
+    LOG(FATAL) << "Unknown device: " << TypeParam::device;
+  }
+  const Dtype kDiffScaleFactor = 3;
+  this->blob_->scale_diff(kDiffScaleFactor);
+  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor,
+                  this->blob_->asum_data());
+  EXPECT_FLOAT_EQ(diff_asum_before_scale * kDiffScaleFactor,
+                  this->blob_->asum_diff());
+}
+
 }  // namespace caffe

From e2358512dd36548c0080b0f381477418134b480f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 19 Jan 2015 14:49:39 -0800
Subject: [PATCH 1116/2053] add Net::param_owners accessor for param sharing
 info

---
 include/caffe/net.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 2709d87ec4e..23da5dbbf2d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -147,6 +147,7 @@ class Net {
   const map<string, int>& param_names_index() const {
     return param_names_index_;
   }
+  inline const vector<int>& param_owners() const { return param_owners_; }
   /// @brief Input and output blob numbers
   inline int num_inputs() const { return net_input_blobs_.size(); }
   inline int num_outputs() const { return net_output_blobs_.size(); }

From f38ddef614b386ec9074e5e9b64e96704cb3124f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 6 Oct 2014 23:46:48 -0700
Subject: [PATCH 1117/2053] Add gradient clipping -- limit L2 norm of parameter
 gradients

---
 include/caffe/solver.hpp    |  1 +
 src/caffe/proto/caffe.proto |  7 ++++++-
 src/caffe/solver.cpp        | 27 +++++++++++++++++++++++++++
 3 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index fde66208207..2510de748de 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -81,6 +81,7 @@ class SGDSolver : public Solver<Dtype> {
   void PreSolve();
   Dtype GetLearningRate();
   virtual void ComputeUpdateValue();
+  virtual void ClipGradients();
   virtual void SnapshotSolverState(SolverState * state);
   virtual void RestoreSolverState(const SolverState& state);
   // history maintains the historical momentum data.
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index c2a39a5cadf..8d937420eba 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -75,7 +75,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 35 (last added: stepvalue)
+// SolverParameter next available ID: 36 (last added: clip_gradients)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -140,6 +140,11 @@ message SolverParameter {
   optional int32 stepsize = 13;
   // the stepsize for learning rate policy "multistep"
   repeated int32 stepvalue = 34;
+
+  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
+  // whenever their actual L2 norm is larger.
+  optional float clip_gradients = 35 [default = -1];
+
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   optional string snapshot_prefix = 15; // The prefix for the snapshot.
   // whether to snapshot diff in the results or not. Snapshotting diff will help
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 100cb357749..9866d7cbc56 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -437,6 +437,30 @@ void SGDSolver<Dtype>::PreSolve() {
   }
 }
 
+template <typename Dtype>
+void SGDSolver<Dtype>::ClipGradients() {
+  const Dtype clip_gradients = this->param_.clip_gradients();
+  if (clip_gradients < 0) { return; }
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  Dtype sumsq_diff = 0;
+  for (int i = 0; i < net_params.size(); ++i) {
+    if (this->net_->param_owners()[i] < 0) {
+      sumsq_diff += net_params[i]->sumsq_diff();
+    }
+  }
+  const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+  if (l2norm_diff > clip_gradients) {
+    Dtype scale_factor = clip_gradients / l2norm_diff;
+    LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
+        << l2norm_diff << " > " << clip_gradients << ") "
+        << "by scale factor " << scale_factor;
+    for (int i = 0; i < net_params.size(); ++i) {
+      if (this->net_->param_owners()[i] < 0) {
+        net_params[i]->scale_diff(scale_factor);
+      }
+    }
+  }
+}
 
 template <typename Dtype>
 void SGDSolver<Dtype>::ComputeUpdateValue() {
@@ -449,6 +473,7 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
+  ClipGradients();
   Dtype momentum = this->param_.momentum();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
@@ -563,6 +588,7 @@ void NesterovSolver<Dtype>::ComputeUpdateValue() {
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
+  SGDSolver<Dtype>::ClipGradients();
   Dtype momentum = this->param_.momentum();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
@@ -680,6 +706,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue() {
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
+  SGDSolver<Dtype>::ClipGradients();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {

From 6a309c17fc386192df4780d85c92e3b36c2ce948 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 15 Feb 2015 17:05:10 -0800
Subject: [PATCH 1118/2053] BlobMathTest: fixes for numerical precision issues

---
 src/caffe/test/test_blob.cpp | 53 +++++++++++++++++++++++-------------
 1 file changed, 34 insertions(+), 19 deletions(-)

diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index 84d84e8f444..e0678061173 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -59,9 +59,12 @@ class BlobMathTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
  protected:
   BlobMathTest()
-      : blob_(new Blob<Dtype>(2, 3, 4, 5)) {}
+      : blob_(new Blob<Dtype>(2, 3, 4, 5)),
+        epsilon_(1e-6) {}
+
   virtual ~BlobMathTest() { delete blob_; }
   Blob<Dtype>* const blob_;
+  Dtype epsilon_;
 };
 
 TYPED_TEST_CASE(BlobMathTest, TestDtypesAndDevices);
@@ -95,7 +98,8 @@ TYPED_TEST(BlobMathTest, TestSumOfSquares) {
   default:
     LOG(FATAL) << "Unknown device: " << TypeParam::device;
   }
-  EXPECT_FLOAT_EQ(expected_sumsq, this->blob_->sumsq_data());
+  EXPECT_NEAR(expected_sumsq, this->blob_->sumsq_data(),
+              this->epsilon_ * expected_sumsq);
   EXPECT_EQ(0, this->blob_->sumsq_diff());
 
   // Check sumsq_diff too.
@@ -112,9 +116,12 @@ TYPED_TEST(BlobMathTest, TestSumOfSquares) {
   default:
     LOG(FATAL) << "Unknown device: " << TypeParam::device;
   }
-  EXPECT_FLOAT_EQ(expected_sumsq, this->blob_->sumsq_data());
-  EXPECT_FLOAT_EQ(expected_sumsq * kDiffScaleFactor * kDiffScaleFactor,
-                  this->blob_->sumsq_diff());
+  EXPECT_NEAR(expected_sumsq, this->blob_->sumsq_data(),
+              this->epsilon_ * expected_sumsq);
+  const Dtype expected_sumsq_diff =
+      expected_sumsq * kDiffScaleFactor * kDiffScaleFactor;
+  EXPECT_NEAR(expected_sumsq_diff, this->blob_->sumsq_diff(),
+              this->epsilon_ * expected_sumsq_diff);
 }
 
 TYPED_TEST(BlobMathTest, TestAsum) {
@@ -146,7 +153,8 @@ TYPED_TEST(BlobMathTest, TestAsum) {
   default:
     LOG(FATAL) << "Unknown device: " << TypeParam::device;
   }
-  EXPECT_FLOAT_EQ(expected_asum, this->blob_->asum_data());
+  EXPECT_NEAR(expected_asum, this->blob_->asum_data(),
+              this->epsilon_ * expected_asum);
   EXPECT_EQ(0, this->blob_->asum_diff());
 
   // Check asum_diff too.
@@ -163,8 +171,11 @@ TYPED_TEST(BlobMathTest, TestAsum) {
   default:
     LOG(FATAL) << "Unknown device: " << TypeParam::device;
   }
-  EXPECT_FLOAT_EQ(expected_asum, this->blob_->asum_data());
-  EXPECT_FLOAT_EQ(expected_asum * kDiffScaleFactor, this->blob_->asum_diff());
+  EXPECT_NEAR(expected_asum, this->blob_->asum_data(),
+              this->epsilon_ * expected_asum);
+  const Dtype expected_diff_asum = expected_asum * kDiffScaleFactor;
+  EXPECT_NEAR(expected_diff_asum, this->blob_->asum_diff(),
+              this->epsilon_ * expected_diff_asum);
 }
 
 TYPED_TEST(BlobMathTest, TestScaleData) {
@@ -193,8 +204,8 @@ TYPED_TEST(BlobMathTest, TestScaleData) {
   }
   const Dtype kDataScaleFactor = 3;
   this->blob_->scale_data(kDataScaleFactor);
-  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor,
-                  this->blob_->asum_data());
+  EXPECT_NEAR(asum_before_scale * kDataScaleFactor, this->blob_->asum_data(),
+              this->epsilon_ * asum_before_scale * kDataScaleFactor);
   EXPECT_EQ(0, this->blob_->asum_diff());
 
   // Check scale_diff too.
@@ -202,11 +213,13 @@ TYPED_TEST(BlobMathTest, TestScaleData) {
   const Dtype* data = this->blob_->cpu_data();
   caffe_cpu_scale(this->blob_->count(), kDataToDiffScaleFactor, data,
                   this->blob_->mutable_cpu_diff());
-  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor,
-                  this->blob_->asum_data());
-  const Dtype diff_asum_before_scale = this->blob_->asum_diff();
-  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor * kDataToDiffScaleFactor,
-                  diff_asum_before_scale);
+  const Dtype expected_asum_before_scale = asum_before_scale * kDataScaleFactor;
+  EXPECT_NEAR(expected_asum_before_scale, this->blob_->asum_data(),
+              this->epsilon_ * expected_asum_before_scale);
+  const Dtype expected_diff_asum_before_scale =
+      asum_before_scale * kDataScaleFactor * kDataToDiffScaleFactor;
+  EXPECT_NEAR(expected_diff_asum_before_scale, this->blob_->asum_diff(),
+              this->epsilon_ * expected_diff_asum_before_scale);
   switch (TypeParam::device) {
   case Caffe::CPU:
     this->blob_->mutable_cpu_diff();
@@ -219,10 +232,12 @@ TYPED_TEST(BlobMathTest, TestScaleData) {
   }
   const Dtype kDiffScaleFactor = 3;
   this->blob_->scale_diff(kDiffScaleFactor);
-  EXPECT_FLOAT_EQ(asum_before_scale * kDataScaleFactor,
-                  this->blob_->asum_data());
-  EXPECT_FLOAT_EQ(diff_asum_before_scale * kDiffScaleFactor,
-                  this->blob_->asum_diff());
+  EXPECT_NEAR(asum_before_scale * kDataScaleFactor, this->blob_->asum_data(),
+              this->epsilon_ * asum_before_scale * kDataScaleFactor);
+  const Dtype expected_diff_asum =
+      expected_diff_asum_before_scale * kDiffScaleFactor;
+  EXPECT_NEAR(expected_diff_asum, this->blob_->asum_diff(),
+              this->epsilon_ * expected_diff_asum);
 }
 
 }  // namespace caffe

From 1344d1bc51f781b593a52fa71ecd59a23dc031fe Mon Sep 17 00:00:00 2001
From: James Steven Supancic III <arrummzen@gmail.com>
Date: Thu, 12 Feb 2015 13:57:25 -0800
Subject: [PATCH 1119/2053] Fix  Draw Net Problem #1709 Introduced by  Layer
 type is a string #1694

---
 python/caffe/draw.py | 12 ++----------
 1 file changed, 2 insertions(+), 10 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index d95c193cc93..1871e7a08d7 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -17,13 +17,6 @@
          'style': 'filled'}
 BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0',
         'style': 'filled'}
-def get_enum_name_by_value():
-  desc = caffe_pb2.LayerParameter.LayerType.DESCRIPTOR
-  d = {}
-  for k,v in desc.values_by_name.items():
-    d[v.number] = k
-  return d
-
 
 def get_pooling_types_dict():
     """Get dictionary mapping pooling type number to type name
@@ -112,10 +105,9 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
   pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir)
   pydot_nodes = {}
   pydot_edges = []
-  d = get_enum_name_by_value()
-  for layer in caffe_net.layers:
+  for layer in caffe_net.layer:
     name = layer.name
-    layertype = d[layer.type]
+    layertype = layer.type
     node_label = determine_node_label_by_layertype(layer, layertype, rankdir)
     if (len(layer.bottom) == 1 and len(layer.top) == 1 and
         layer.bottom[0] == layer.top[0]):

From 1e0d49a39d1e38c8de2a4c24ab0bff3a71da21ff Mon Sep 17 00:00:00 2001
From: Brandon Amos <bdamos@vt.edu>
Date: Mon, 16 Feb 2015 15:09:24 -0500
Subject: [PATCH 1120/2053] Correct 'epochs' to 'iterations'

See https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/solver.prototxt
---
 examples/imagenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 41384f9475b..c2dd62ec963 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -67,7 +67,7 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
-* Information will be displayed every 20 epochs.
+* Information will be displayed every 20 iterations.
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 

From 02c99fb9c7386242a9480378195539232ad7f18f Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Mon, 16 Feb 2015 16:06:03 -0800
Subject: [PATCH 1121/2053] Fix `draw` to support new protobuf format

---
 python/caffe/draw.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 1871e7a08d7..6a4dbd47351 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -32,11 +32,11 @@ def determine_edge_label_by_layertype(layer, layertype):
     """Define edge label based on layer type
     """
 
-    if layertype == 'DATA':
+    if layertype == 'Data':
         edge_label = 'Batch ' + str(layer.data_param.batch_size)
-    elif layertype == 'CONVOLUTION':
+    elif layertype == 'Convolution':
         edge_label = str(layer.convolution_param.num_output)
-    elif layertype == 'INNER_PRODUCT':
+    elif layertype == 'InnerProduct':
         edge_label = str(layer.inner_product_param.num_output)
     else:
         edge_label = '""'
@@ -57,7 +57,7 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
         # horizontal space is not; separate words with newlines
         separator = '\n'
 
-    if layertype == 'CONVOLUTION':
+    if layertype == 'Convolution':
         # Outer double quotes needed or else colon characters don't parse
         # properly
         node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
@@ -70,7 +70,7 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
                       layer.convolution_param.stride,
                       separator,
                       layer.convolution_param.pad)
-    elif layertype == 'POOLING':
+    elif layertype == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
                      (layer.name,
@@ -92,11 +92,11 @@ def choose_color_by_layertype(layertype):
     """Define colors for nodes based on the layer type
     """
     color = '#6495ED'  # Default
-    if layertype == 'CONVOLUTION':
+    if layertype == 'Convolution':
         color = '#FF5050'
-    elif layertype == 'POOLING':
+    elif layertype == 'Pooling':
         color = '#FF9900'
-    elif layertype == 'INNER_PRODUCT':
+    elif layertype == 'InnerProduct':
         color = '#CC33FF'
     return color
 

From 82551adbf8c77a06fa7e8b2508e1fb24ff1b5b31 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 16 Oct 2014 22:52:30 -0700
Subject: [PATCH 1122/2053] reshape DATA + IMAGE_DATA for inputs of varying
 dimension

To feed inputs of varying dimension, the `DATA` and `IMAGE_DATA` layer
reshapes its prefetch and top blobs when the batch size is 1.

The `BasePrefetchingDataLayer` always reshapes on forward.
---
 src/caffe/layers/base_data_layer.cpp  |  3 +++
 src/caffe/layers/base_data_layer.cu   |  3 +++
 src/caffe/layers/data_layer.cpp       | 16 ++++++++++++++--
 src/caffe/layers/image_data_layer.cpp | 20 ++++++++++++++++----
 4 files changed, 36 insertions(+), 6 deletions(-)

diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index eb0aaf82120..c3b9bc421ba 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -61,6 +61,9 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
   // First, join the thread
   JoinPrefetchThread();
   DLOG(INFO) << "Thread joined";
+  // Reshape to loaded data.
+  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
+      this->prefetch_data_.height(), this->prefetch_data_.width());
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              top[0]->mutable_cpu_data());
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 204a16d260a..775f6c47f7e 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -9,6 +9,9 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // First, join the thread
   JoinPrefetchThread();
+  // Reshape to loaded data.
+  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
+      this->prefetch_data_.height(), this->prefetch_data_.width());
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       top[0]->mutable_gpu_data());
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 227db201759..98ce26e9f0a 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -49,7 +49,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   int crop_size = this->layer_param_.transform_param().crop_size();
   if (crop_size > 0) {
     top[0]->Reshape(this->layer_param_.data_param().batch_size(),
-                       datum.channels(), crop_size, crop_size);
+        datum.channels(), crop_size, crop_size);
     this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
         datum.channels(), crop_size, crop_size);
     this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
@@ -83,13 +83,25 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   CPUTimer timer;
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
+
+  // Reshape on single input batches for inputs of varying dimension.
+  const int batch_size = this->layer_param_.data_param().batch_size();
+  const int crop_size = this->layer_param_.transform_param().crop_size();
+  if (batch_size == 1 && crop_size == 0) {
+    Datum datum;
+    datum.ParseFromString(cursor_->value());
+    this->prefetch_data_.Reshape(1, datum.channels(),
+        datum.height(), datum.width());
+    this->transformed_data_.Reshape(1, datum.channels(),
+        datum.height(), datum.width());
+  }
+
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
 
   if (this->output_labels_) {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
-  const int batch_size = this->layer_param_.data_param().batch_size();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
     // get a blob
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index bd4b8a033c2..e98ff85a0ea 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -102,15 +102,27 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   CPUTimer timer;
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
+  const int crop_size = this->layer_param_.transform_param().crop_size();
   const bool is_color = image_data_param.is_color();
   string root_folder = image_data_param.root_folder();
 
+  // Reshape on single input batches for inputs of varying dimension.
+  if (batch_size == 1 && crop_size == 0 && new_height == 0 && new_width == 0) {
+    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+        0, 0, is_color);
+    this->prefetch_data_.Reshape(1, cv_img.channels(),
+        cv_img.rows, cv_img.cols);
+    this->transformed_data_.Reshape(1, cv_img.channels(),
+        cv_img.rows, cv_img.cols);
+  }
+
+  Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();
+
   // datum scales
   const int lines_size = lines_.size();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
@@ -124,11 +136,11 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     timer.Start();
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
-    this->transformed_data_.set_cpu_data(top_data + offset);
+    this->transformed_data_.set_cpu_data(prefetch_data + offset);
     this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
     trans_time += timer.MicroSeconds();
 
-    top_label[item_id] = lines_[lines_id_].second;
+    prefetch_label[item_id] = lines_[lines_id_].second;
     // go to the next iter
     lines_id_++;
     if (lines_id_ >= lines_size) {

From d3b20106c02ea17d198740a6e790d4f7901a3548 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 5 Feb 2015 16:31:52 -0800
Subject: [PATCH 1123/2053] test reshaping DATA and IMAGE_DATA

---
 src/caffe/test/test_data_layer.cpp       | 72 ++++++++++++++++++++++++
 src/caffe/test/test_image_data_layer.cpp | 39 ++++++++++++-
 2 files changed, 109 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 8cc31b24167..adc99dd71c9 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -103,6 +103,70 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void TestReshape(DataParameter_DB backend) {
+    const int num_inputs = 5;
+    // Save data of varying shapes.
+    LOG(INFO) << "Using temporary dataset " << *filename_;
+    scoped_ptr<db::DB> db(db::GetDB(backend));
+    db->Open(*filename_, db::NEW);
+    scoped_ptr<db::Transaction> txn(db->NewTransaction());
+    for (int i = 0; i < num_inputs; ++i) {
+      Datum datum;
+      datum.set_label(i);
+      datum.set_channels(2);
+      datum.set_height(i % 2 + 1);
+      datum.set_width(i % 4 + 1);
+      std::string* data = datum.mutable_data();
+      const int data_size = datum.channels() * datum.height() * datum.width();
+      for (int j = 0; j < data_size; ++j) {
+        data->push_back(static_cast<uint8_t>(j));
+      }
+      stringstream ss;
+      ss << i;
+      string out;
+      CHECK(datum.SerializeToString(&out));
+      txn->Put(ss.str(), out);
+    }
+    txn->Commit();
+    db->Close();
+
+    // Load and check data of various shapes.
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(1);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend);
+
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 1);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_label_->num(), 1);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
+    for (int iter = 0; iter < num_inputs; ++iter) {
+      layer.Forward(blob_bottom_vec_, blob_top_vec_);
+      EXPECT_EQ(blob_top_data_->height(), iter % 2 + 1);
+      EXPECT_EQ(blob_top_data_->width(), iter % 4 + 1);
+      EXPECT_EQ(iter, blob_top_label_->cpu_data()[0]);
+      const int channels = blob_top_data_->channels();
+      const int height = blob_top_data_->height();
+      const int width = blob_top_data_->width();
+      for (int c = 0; c < channels; ++c) {
+        for (int h = 0; h < height; ++h) {
+          for (int w = 0; w < width; ++w) {
+            const int idx = (c * height + h) * width + w;
+            EXPECT_EQ(idx, static_cast<int>(blob_top_data_->cpu_data()[idx]))
+                << "debug: iter " << iter << " c " << c
+                << " h " << h << " w " << w;
+          }
+        }
+      }
+    }
+  }
+
   void TestReadCrop() {
     const Dtype scale = 3;
     LayerParameter param;
@@ -285,6 +349,10 @@ TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestReshapeLevelDB) {
+  this->TestReshape(DataParameter_DB_LEVELDB);
+}
+
 TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
@@ -323,6 +391,10 @@ TYPED_TEST(DataLayerTest, TestReadLMDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestReshapeLMDB) {
+  this->TestReshape(DataParameter_DB_LMDB);
+}
+
 TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
   Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 77523ef8c18..931a5ebf137 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -25,17 +25,24 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
         blob_top_data_(new Blob<Dtype>()),
         blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
-    MakeTempFilename(&filename_);
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
     Caffe::set_random_seed(seed_);
-    // Create a Vector of files with labels
+    // Create test input file.
+    MakeTempFilename(&filename_);
     std::ofstream outfile(filename_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_;
     for (int i = 0; i < 5; ++i) {
       outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
     }
     outfile.close();
+    // Create test input file for images of distinct sizes.
+    MakeTempFilename(&filename_reshape_);
+    std::ofstream reshapefile(filename_reshape_.c_str(), std::ofstream::out);
+    LOG(INFO) << "Using temporary file " << filename_reshape_;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1;
+    reshapefile.close();
   }
 
   virtual ~ImageDataLayerTest() {
@@ -45,6 +52,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
 
   int seed_;
   string filename_;
+  string filename_reshape_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -107,6 +115,33 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   }
 }
 
+TYPED_TEST(ImageDataLayerTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  ImageDataParameter* image_data_param = param.mutable_image_data_param();
+  image_data_param->set_batch_size(1);
+  image_data_param->set_source(this->filename_reshape_.c_str());
+  image_data_param->set_shuffle(false);
+  ImageDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_label_->num(), 1);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+  // cat.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  // fish-bike.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 323);
+  EXPECT_EQ(this->blob_top_data_->width(), 481);
+}
+
 TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;

From 52465873d08cfd58a4c84ee8b239908814de4f39 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Mon, 16 Feb 2015 11:42:22 -0800
Subject: [PATCH 1124/2053] Cleaning up the encoded flag. Allowing any image
 (cropped or gray scale) to be encoded. Allowing for a change in encoded (jpg
 -> png vice versa) and cleaning up some unused functions.

---
 include/caffe/util/io.hpp              | 36 ++++++----------
 src/caffe/layers/data_layer.cpp        |  4 +-
 src/caffe/layers/window_data_layer.cpp |  2 +-
 src/caffe/test/test_io.cpp             | 31 +++----------
 src/caffe/util/io.cpp                  | 60 +++++++++++++-------------
 tools/compute_image_mean.cpp           |  4 +-
 tools/convert_imageset.cpp             | 30 +++++++------
 7 files changed, 72 insertions(+), 95 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index a01f917abf5..9d7540d62cc 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -92,7 +92,14 @@ inline bool ReadFileToDatum(const string& filename, Datum* datum) {
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum);
+    const int height, const int width, const bool is_color,
+    const std::string & encoding, Datum* datum);
+
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const int height, const int width, const bool is_color, Datum* datum) {
+  return ReadImageToDatum(filename, label, height, width, is_color,
+                          "", datum);
+}
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, Datum* datum) {
@@ -109,20 +116,12 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, true, datum);
 }
 
-bool DecodeDatum(const int height, const int width, const bool is_color,
-  Datum* datum);
-
-inline bool DecodeDatum(const int height, const int width, Datum* datum) {
-  return DecodeDatum(height, width, true, datum);
-}
-
-inline bool DecodeDatum(const bool is_color, Datum* datum) {
-  return DecodeDatum(0, 0, is_color, datum);
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const std::string & encoding, Datum* datum) {
+  return ReadImageToDatum(filename, label, 0, 0, true, encoding, datum);
 }
 
-inline bool DecodeDatum(Datum* datum) {
-  return DecodeDatum(0, 0, true, datum);
-}
+bool DecodeDatumNative(Datum* datum);
 
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
@@ -135,16 +134,7 @@ cv::Mat ReadImageToCVMat(const string& filename,
 
 cv::Mat ReadImageToCVMat(const string& filename);
 
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width, const bool is_color);
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width);
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const bool is_color);
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum);
+cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 227db201759..891d03911c2 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -42,7 +42,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   Datum datum;
   datum.ParseFromString(cursor_->value());
 
-  if (DecodeDatum(&datum)) {
+  if (DecodeDatumNative(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
   // image
@@ -98,7 +98,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
     cv::Mat cv_img;
     if (datum.encoded()) {
-       cv_img = DecodeDatumToCVMat(datum);
+       cv_img = DecodeDatumToCVMatNative(datum);
     }
     read_time += timer.MicroSeconds();
     timer.Start();
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 23ec83d166b..cceb4ffb1b5 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -281,7 +281,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       if (this->cache_images_) {
         pair<std::string, Datum> image_cached =
           image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
-        cv_img = DecodeDatumToCVMat(image_cached.second);
+        cv_img = DecodeDatumToCVMatNative(image_cached.second);
       } else {
         cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
         if (!cv_img.data) {
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 4d941fa8683..6b135efcf6e 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -289,8 +289,8 @@ TEST_F(IOTest, TestDecodeDatum) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  EXPECT_TRUE(DecodeDatum(&datum));
-  EXPECT_FALSE(DecodeDatum(&datum));
+  EXPECT_TRUE(DecodeDatumNative(&datum));
+  EXPECT_FALSE(DecodeDatumNative(&datum));
   Datum datum_ref;
   ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
   EXPECT_EQ(datum.channels(), datum_ref.channels());
@@ -309,38 +309,17 @@ TEST_F(IOTest, TestDecodeDatumToCVMat) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMat(datum);
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
   EXPECT_EQ(cv_img.channels(), 3);
   EXPECT_EQ(cv_img.rows, 360);
   EXPECT_EQ(cv_img.cols, 480);
 }
 
-TEST_F(IOTest, TestDecodeDatumToCVMatResized) {
-  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
-  Datum datum;
-  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMat(datum, 100, 200);
-  EXPECT_EQ(cv_img.channels(), 3);
-  EXPECT_EQ(cv_img.rows, 100);
-  EXPECT_EQ(cv_img.cols, 200);
-}
-
-TEST_F(IOTest, TestDecodeDatumToCVMatResizedGray) {
-  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
-  Datum datum;
-  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  const bool is_color = false;
-  cv::Mat cv_img = DecodeDatumToCVMat(datum, 200, 100, is_color);
-  EXPECT_EQ(cv_img.channels(), 1);
-  EXPECT_EQ(cv_img.rows, 200);
-  EXPECT_EQ(cv_img.cols, 100);
-}
-
 TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
-  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMat(datum);
+  EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
   cv::Mat cv_img_ref = ReadImageToCVMat(filename);
   EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
   EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index f738e053e86..65531687b63 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -98,11 +98,36 @@ cv::Mat ReadImageToCVMat(const string& filename,
 cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
-
+// Do the file extension and encoding match?
+static bool matchExt(const std::string & fn,
+                     std::string en) {
+  size_t p = fn.rfind('.');
+  std::string ext = p != fn.npos ? fn.substr(p) : fn;
+  std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
+  std::transform(en.begin(), en.end(), en.begin(), ::tolower);
+  if ( ext == en )
+    return true;
+  if ( en == "jpg" && ext == "jpeg" )
+    return true;
+  return false;
+}
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum) {
+    const int height, const int width, const bool is_color,
+    const std::string & encoding, Datum* datum) {
   cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
   if (cv_img.data) {
+    if (encoding.size()) {
+      if ( (cv_img.channels() == 3) == is_color && !height && !width &&
+          matchExt(filename, encoding) )
+        return ReadFileToDatum(filename, label, datum);
+      std::vector<uchar> buf;
+      cv::imencode("."+encoding, cv_img, buf);
+      datum->set_data(std::string(reinterpret_cast<char*>(&buf[0]),
+                      buf.size()));
+      datum->set_label(label);
+      datum->set_encoded(true);
+      return true;
+    }
     CVMatToDatum(cv_img, datum);
     datum->set_label(label);
     return true;
@@ -131,47 +156,24 @@ bool ReadFileToDatum(const string& filename, const int label,
   }
 }
 
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width, const bool is_color) {
+cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
   cv::Mat cv_img;
   CHECK(datum.encoded()) << "Datum not encoded";
-  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
-    CV_LOAD_IMAGE_GRAYSCALE);
   const string& data = datum.data();
   std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
-  if (height > 0 && width > 0) {
-    cv::Mat cv_img_origin = cv::imdecode(cv::Mat(vec_data), cv_read_flag);
-    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
-  } else {
-    cv_img = cv::imdecode(vec_data, cv_read_flag);
-  }
+  cv_img = cv::imdecode(vec_data, -1);
   if (!cv_img.data) {
     LOG(ERROR) << "Could not decode datum ";
   }
   return cv_img;
 }
 
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width) {
-  return DecodeDatumToCVMat(datum, height, width, true);
-}
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const bool is_color) {
-  return DecodeDatumToCVMat(datum, 0, 0, is_color);
-}
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum) {
-  return DecodeDatumToCVMat(datum, 0, 0, true);
-}
-
 // If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
 // if height and width are set it will resize it
 // If Datum is not encoded will do nothing
-bool DecodeDatum(const int height, const int width, const bool is_color,
-                Datum* datum) {
+bool DecodeDatumNative(Datum* datum) {
   if (datum->encoded()) {
-    cv::Mat cv_img = DecodeDatumToCVMat((*datum), height, width, is_color);
+    cv::Mat cv_img = DecodeDatumToCVMatNative((*datum));
     CVMatToDatum(cv_img, datum);
     return true;
   } else {
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index dff63a09dca..b1fc7cae38f 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -50,7 +50,7 @@ int main(int argc, char** argv) {
   Datum datum;
   datum.ParseFromString(cursor->value());
 
-  if (DecodeDatum(&datum)) {
+  if (DecodeDatumNative(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
 
@@ -68,7 +68,7 @@ int main(int argc, char** argv) {
   while (cursor->valid()) {
     Datum datum;
     datum.ParseFromString(cursor->value());
-    DecodeDatum(&datum);
+    DecodeDatumNative(&datum);
 
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 7fbf5b0514c..816a91f971b 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -39,6 +39,8 @@ DEFINE_bool(check_size, false,
     "When this option is on, check that all the datum have the same size");
 DEFINE_bool(encoded, false,
     "When this option is on, the encoded image will be save in datum");
+DEFINE_string(encode_type, "",
+    "Optional: What type should we encode the image as ('png','jpg',...).");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -63,6 +65,7 @@ int main(int argc, char** argv) {
   const bool is_color = !FLAGS_gray;
   const bool check_size = FLAGS_check_size;
   const bool encoded = FLAGS_encoded;
+  const string encode_type = FLAGS_encode_type;
 
   std::ifstream infile(argv[2]);
   std::vector<std::pair<std::string, int> > lines;
@@ -78,11 +81,8 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  if (encoded) {
-    CHECK_EQ(FLAGS_resize_height, 0) << "With encoded don't resize images";
-    CHECK_EQ(FLAGS_resize_width, 0) << "With encoded don't resize images";
-    CHECK(!check_size) << "With encoded cannot check_size";
-  }
+  if (encode_type.size() && !encoded)
+    LOG(INFO) << "encode_type specified, assuming encoded=true.";
 
   int resize_height = std::max<int>(0, FLAGS_resize_height);
   int resize_width = std::max<int>(0, FLAGS_resize_width);
@@ -98,18 +98,24 @@ int main(int argc, char** argv) {
   int count = 0;
   const int kMaxKeyLength = 256;
   char key_cstr[kMaxKeyLength];
-  int data_size;
+  int data_size = 0;
   bool data_size_initialized = false;
 
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     bool status;
-    if (encoded) {
-      status = ReadFileToDatum(root_folder + lines[line_id].first,
-        lines[line_id].second, &datum);
-    } else {
-      status = ReadImageToDatum(root_folder + lines[line_id].first,
-          lines[line_id].second, resize_height, resize_width, is_color, &datum);
+    std::string enc = encode_type;
+    if (encoded && !enc.size()) {
+      // Guess the encoding type from the file name
+      string fn = lines[line_id].first;
+      size_t p = fn.rfind('.');
+      if ( p == fn.npos )
+        LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
+      enc = fn.substr(p);
+      std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
     }
+    status = ReadImageToDatum(root_folder + lines[line_id].first,
+        lines[line_id].second, resize_height, resize_width, is_color,
+        enc, &datum);
     if (status == false) continue;
     if (check_size) {
       if (!data_size_initialized) {

From 32bf5c7ad804ad683aa5ea9382209e9284451e5f Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sat, 20 Dec 2014 18:22:47 +0300
Subject: [PATCH 1125/2053] ignore qtcreator files

---
 .gitignore | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.gitignore b/.gitignore
index c27c7a6a5f6..58adc5cb106 100644
--- a/.gitignore
+++ b/.gitignore
@@ -44,6 +44,9 @@
 *.*project
 .settings
 
+# QtCreator files
+*.user
+
 # OSX dir files
 .DS_Store
 
@@ -71,6 +74,8 @@ distribute/*
 *.testbin
 *.bin
 python/caffe/proto/
+cmake_build
+.cmake_build
 
 # Generated documentation
 docs/_site

From 9358247014eb8322160c7feaad46091412d75d3f Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Wed, 17 Dec 2014 12:09:14 +0300
Subject: [PATCH 1126/2053] improve CMake build

---
 .gitignore                                    |   3 -
 CMakeLists.txt                                | 199 +++-------
 CMakeScripts/FindLMDB.cmake                   |  28 --
 CMakeScripts/FindLevelDB.cmake                |  37 --
 CMakeScripts/FindNumPy.cmake                  | 103 -----
 CMakeScripts/FindProtobuf.cmake               | 152 --------
 CMakeScripts/FindSnappy.cmake                 |  33 --
 cmake/ConfigGen.cmake                         |  86 +++++
 cmake/Cuda.cmake                              | 243 ++++++++++++
 cmake/Dependencies.cmake                      | 124 ++++++
 cmake/Misc.cmake                              |  47 +++
 .../Modules}/FindAtlas.cmake                  |  25 +-
 .../Modules}/FindGFlags.cmake                 |   6 +-
 .../Modules}/FindGlog.cmake                   |  14 +-
 .../Modules}/FindLAPACK.cmake                 |   0
 cmake/Modules/FindLMDB.cmake                  |  28 ++
 cmake/Modules/FindLevelDB.cmake               |  44 +++
 {CMakeScripts => cmake/Modules}/FindMKL.cmake |   0
 cmake/Modules/FindMatlabMex.cmake             |  48 +++
 cmake/Modules/FindNumPy.cmake                 |  58 +++
 .../Modules}/FindOpenBLAS.cmake               |   0
 cmake/Modules/FindSnappy.cmake                |  28 ++
 cmake/Modules/FindvecLib.cmake                |  34 ++
 cmake/ProtoBuf.cmake                          |  90 +++++
 cmake/Summary.cmake                           | 166 ++++++++
 cmake/Targets.cmake                           | 170 ++++++++
 cmake/Templates/CaffeConfig.cmake.in          |  58 +++
 cmake/Templates/CaffeConfigVersion.cmake.in   |  11 +
 cmake/Templates/caffe_config.h.in             |  32 ++
 cmake/Utils.cmake                             | 365 ++++++++++++++++++
 {CMakeScripts => cmake}/lint.cmake            |   0
 docs/CMakeLists.txt                           | 106 +++++
 examples/CMakeLists.txt                       |  41 +-
 include/caffe/test/test_caffe_main.hpp        |   2 +-
 matlab/CMakeLists.txt                         |  73 +++-
 python/CMakeLists.txt                         |  77 ++--
 src/caffe/CMakeLists.txt                      |  75 ++--
 src/caffe/proto/CMakeLists.txt                |  46 ---
 src/caffe/test/CMakeLists.txt                 | 135 ++-----
 src/caffe/test/cmake_test_defines.hpp.in      |   4 -
 .../test/test_data/sample_data_list.txt.in    |   2 -
 src/gtest/CMakeLists.txt                      |   9 +-
 tools/CMakeLists.txt                          |  47 ++-
 43 files changed, 2017 insertions(+), 832 deletions(-)
 delete mode 100644 CMakeScripts/FindLMDB.cmake
 delete mode 100644 CMakeScripts/FindLevelDB.cmake
 delete mode 100644 CMakeScripts/FindNumPy.cmake
 delete mode 100644 CMakeScripts/FindProtobuf.cmake
 delete mode 100644 CMakeScripts/FindSnappy.cmake
 create mode 100644 cmake/ConfigGen.cmake
 create mode 100644 cmake/Cuda.cmake
 create mode 100644 cmake/Dependencies.cmake
 create mode 100644 cmake/Misc.cmake
 rename {CMakeScripts => cmake/Modules}/FindAtlas.cmake (63%)
 rename {CMakeScripts => cmake/Modules}/FindGFlags.cmake (79%)
 rename {CMakeScripts => cmake/Modules}/FindGlog.cmake (70%)
 rename {CMakeScripts => cmake/Modules}/FindLAPACK.cmake (100%)
 create mode 100644 cmake/Modules/FindLMDB.cmake
 create mode 100644 cmake/Modules/FindLevelDB.cmake
 rename {CMakeScripts => cmake/Modules}/FindMKL.cmake (100%)
 create mode 100644 cmake/Modules/FindMatlabMex.cmake
 create mode 100644 cmake/Modules/FindNumPy.cmake
 rename {CMakeScripts => cmake/Modules}/FindOpenBLAS.cmake (100%)
 create mode 100644 cmake/Modules/FindSnappy.cmake
 create mode 100644 cmake/Modules/FindvecLib.cmake
 create mode 100644 cmake/ProtoBuf.cmake
 create mode 100644 cmake/Summary.cmake
 create mode 100644 cmake/Targets.cmake
 create mode 100644 cmake/Templates/CaffeConfig.cmake.in
 create mode 100644 cmake/Templates/CaffeConfigVersion.cmake.in
 create mode 100644 cmake/Templates/caffe_config.h.in
 create mode 100644 cmake/Utils.cmake
 rename {CMakeScripts => cmake}/lint.cmake (100%)
 create mode 100644 docs/CMakeLists.txt
 delete mode 100644 src/caffe/proto/CMakeLists.txt
 delete mode 100644 src/caffe/test/cmake_test_defines.hpp.in
 delete mode 100644 src/caffe/test/test_data/sample_data_list.txt.in

diff --git a/.gitignore b/.gitignore
index 58adc5cb106..73bba6cb364 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,9 +29,6 @@
 # IPython notebook checkpoints
 .ipynb_checkpoints
 
-# CMake generated files
-*.gen.cmake
-
 # Editor temporaries
 *.swp
 *~
diff --git a/CMakeLists.txt b/CMakeLists.txt
index d6b08470a7f..d3d50da218b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,177 +1,64 @@
 cmake_minimum_required(VERSION 2.8.8)
-project( Caffe )
 
-###    Build Options     ##########################################################################
+# ---[ Caffe project
+project(Caffe C CXX)
 
-option(CPU_ONLY "Build Caffe without GPU support" OFF)
-option(BUILD_PYTHON "Build Python wrapper" OFF)
-option(BUILD_MATLAB "Build Matlab wrapper" OFF)
-option(BUILD_EXAMPLES "Build examples" ON)
-option(BUILD_SHARED_LIBS "Build SHARED libs if ON and STATIC otherwise" OFF)
+# ---[ Using cmake scripts and modules
+list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
 
-if(NOT BLAS)
-    set(BLAS atlas)
-endif()
+include(cmake/Utils.cmake)
+include(cmake/Targets.cmake)
+include(cmake/Misc.cmake)
+include(cmake/Summary.cmake)
+include(cmake/ConfigGen.cmake)
 
-if(NOT CUDA_TEST_DEVICE)
-    set(CUDA_TEST_DEVICE -1)
-endif()
+# ---[ Options
+caffe_option(CPU_ONLY  "Build Caffe wihtout CUDA support" OFF) # TODO: rename to USE_CUDA
+caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
+caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
+caffe_option(BUILD_python "Build Python wrapper" ON)
+caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
+caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
 
-#    Install Prefix
-if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
-    set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE )
-endif()
+# ---[ Dependencies
+include(cmake/Dependencies.cmake)
 
-###    Configuration    ###########################################################################
-#    Compiler Flags
-set(CMAKE_CXX_COMPILER_FLAGS ${CMAKE_CXX_COMPILER_FLAGS} -Wall)
-set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -fPIC) # set global flags
-set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) # set debug flags
-set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) # set release flags
-
-#    Link Flags
-if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")  # clang++
-  set(CAFFE_STATIC_LINK -Wl,-force_load $(STATIC_NAME))
-elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")  # g++
-  set(CAFFE_STATIC_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+# ---[ Flags
+if(UNIX OR APLE)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
-#    Global Definitions
-if(CPU_ONLY)
-    add_definitions(-DCPU_ONLY)
-endif()
-
-#    Include Directories
-set(${PROJECT_NAME}_INCLUDE_DIRS ${CMAKE_SOURCE_DIR}/include)
-include_directories(${${PROJECT_NAME}_INCLUDE_DIRS})
-include_directories(${CMAKE_SOURCE_DIR}/src)
-
-#    CMake Scripts dir
-set(CMAKE_SCRIPT_DIR ${CMAKE_SOURCE_DIR}/CMakeScripts)
-
-#	 CMake module path for custom module finding
-set( CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_SCRIPT_DIR})
-
-###    Dependencies    ##########################################################################
-
-#    Boost
-find_package(Boost 1.46 COMPONENTS system thread REQUIRED)
-include_directories(${Boost_INCLUDE_DIR})
-link_directories(${Boost_LIBRARY_DIRS})
-
-#    CUDA
-if(NOT CPU_ONLY)
-    find_package(CUDA 5.5 REQUIRED)
-    include_directories(${CUDA_INCLUDE_DIRS})
-
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS}
-            -gencode arch=compute_20,code=sm_20
-            -gencode arch=compute_20,code=sm_21
-            -gencode arch=compute_30,code=sm_30
-            -gencode arch=compute_35,code=sm_35
-    )
-
-    # https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
-    # work-arounds
-    if(Boost_VERSION EQUAL 105500)
-        # see https://svn.boost.org/trac/boost/ticket/9392
-        message(STATUS "Boost: Applying noinline work around")
-        # avoid warning for CMake >= 2.8.12
-        set(CUDA_NVCC_FLAGS
-          "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
-    endif(Boost_VERSION EQUAL 105500)
+if(APPLE)
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
 endif()
 
-#    Threads
-find_package(Threads REQUIRED)
-
-#	 Google-glog
-find_package(Glog REQUIRED)
-include_directories(${GLOG_INCLUDE_DIRS})
-
-#    Google-gflags
-find_package(GFlags REQUIRED)
-include_directories(${GFLAGS_INCLUDE_DIRS})
-
-#    BLAS
-if(BLAS STREQUAL "atlas")
-
-    find_package(Atlas REQUIRED)
-    include_directories(${Atlas_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${Atlas_LIBRARIES})
-
-elseif(BLAS STREQUAL "open")
+add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
 
-    find_package(OpenBLAS REQUIRED)
-    include_directories(${OpenBLAS_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${OpenBLAS_LIB})
+# ---[ Warnings
+caffe_warnings_disable(CMAKE_CXX_FLAGS -Wno-sign-compare -Wno-uninitialized)
 
-elseif(BLAS STREQUAL "mkl")
+# ---[ Config generation
+configure_file(cmake/Templates/caffe_config.h.in "${CMAKE_BINARY_DIR}/caffe_config.h")
 
-    find_package(MKL REQUIRED)
-    include_directories(${MKL_INCLUDE_DIR})
-    set(BLAS_LIBRARIES ${MKL_LIBRARIES})
-
-endif()
-
-#    HDF5
-find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(${HDF5_INCLUDE_DIRS})
-
-#    LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(${LEVELDB_INCLUDE})
-if(LEVELDB_FOUND)
-    find_package(Snappy REQUIRED)
-    include_directories(${SNAPPY_INCLUDE_DIR})
-    set(LEVELDB_LIBS
-        ${LEVELDB_LIBS}
-        ${SNAPPY_LIBS}
-    )
-endif()
-
-#    LMDB
-find_package(LMDB REQUIRED)
-include_directories(${LMDB_INCLUDE_DIR})
-
-#    OpenCV
-find_package(OpenCV REQUIRED)
-include_directories(${OpenCV_INCLUDE_DIRS})
-
-###    Subdirectories    ##########################################################################
+# ---[ Includes
+set(Caffe_INCLUDE_DIR ${CMAKE_SOURCE_DIR}/include)
+include_directories(${Caffe_INCLUDE_DIR} ${CMAKE_BINARY_DIR})
+include_directories(BEFORE src) # This is needed for gtest.
 
+# ---[ Subdirectories
 add_subdirectory(src/gtest)
 add_subdirectory(src/caffe)
 add_subdirectory(tools)
+add_subdirectory(examples)
+add_subdirectory(python)
+add_subdirectory(matlab)
+add_subdirectory(docs)
 
-if(BUILD_EXAMPLES)
-    message(STATUS "Examples enabled")
-    add_subdirectory(examples)
-endif()
-
-if(BUILD_PYTHON)
-    message(STATUS "Python enabled")
-    add_subdirectory(python)
-endif()
-
-if(BUILD_MATLAB)
-    message(STATUS "Matlab enabled")
-    add_subdirectory(matlab)
-endif()
-
-###    Lint Target Setup    ##########################################################################
-
-set(LINT_TARGET lint)
-set(LINT_SCRIPT ${CMAKE_SCRIPT_DIR}/lint.cmake)
-add_custom_target(
-    ${LINT_TARGET}
-    COMMAND ${CMAKE_COMMAND} -P ${LINT_SCRIPT}
-)
-
-###    Install    #################################################################################
-
-#    Install Includes
-file(GLOB folders ${${PROJECT_NAME}_INCLUDE_DIRS}/*)
-install(DIRECTORY ${folders} DESTINATION include)
+# ---[ Linter target
+add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${CMAKE_SOURCE_DIR}/cmake/lint.cmake)
 
+# ---[ Configuration summary
+caffe_print_configuration_summary()
 
+# ---[ Export configs generation
+caffe_generate_export_configs()
diff --git a/CMakeScripts/FindLMDB.cmake b/CMakeScripts/FindLMDB.cmake
deleted file mode 100644
index e615f542335..00000000000
--- a/CMakeScripts/FindLMDB.cmake
+++ /dev/null
@@ -1,28 +0,0 @@
-# Try to find the LMBD libraries and headers
-#  LMDB_FOUND - system has LMDB lib
-#  LMDB_INCLUDE_DIR - the LMDB include directory
-#  LMDB_LIBRARIES - Libraries needed to use LMDB
-
-# FindCWD based on FindGMP by:
-# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
-#
-# Redistribution and use is allowed according to the terms of the BSD license.
-
-# Adapted from FindCWD by:
-# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
-# Aug 31, 2013
-
-if (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
-  # Already in cache, be silent
-  set(LMDB_FIND_QUIETLY TRUE)
-endif (LMDB_INCLUDE_DIR AND LMDB_LIBRARIES)
-
-find_path(LMDB_INCLUDE_DIR NAMES "lmdb.h" HINTS "$ENV{LMDB_DIR}/include")
-find_library(LMDB_LIBRARIES NAMES lmdb HINTS $ENV{LMDB_DIR}/lib )
-MESSAGE(STATUS "LMDB lib: " ${LMDB_LIBRARIES} )
-MESSAGE(STATUS "LMDB include: " ${LMDB_INCLUDE} )
-
-include(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
-
-mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
diff --git a/CMakeScripts/FindLevelDB.cmake b/CMakeScripts/FindLevelDB.cmake
deleted file mode 100644
index f3386f26dbf..00000000000
--- a/CMakeScripts/FindLevelDB.cmake
+++ /dev/null
@@ -1,37 +0,0 @@
-# - Find LevelDB
-#
-#  LEVELDB_INCLUDE - Where to find leveldb/db.h
-#  LEVELDB_LIBS    - List of libraries when using LevelDB.
-#  LEVELDB_FOUND   - True if LevelDB found.
-
-get_filename_component(module_file_path ${CMAKE_CURRENT_LIST_FILE} PATH)
-
-# Look for the header file.
-find_path(LEVELDB_INCLUDE NAMES leveldb/db.h PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include DOC "Path in which the file leveldb/db.h is located." )
-mark_as_advanced(LEVELDB_INCLUDE)
-
-# Look for the library.
-# Does this work on UNIX systems? (LINUX)
-find_library(LEVELDB_LIBS NAMES leveldb PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib DOC "Path to leveldb library." )
-mark_as_advanced(LEVELDB_LIBS)
-
-# Copy the results to the output variables.
-if (LEVELDB_INCLUDE AND LEVELDB_LIBS)
-  message(STATUS "Found leveldb in ${LEVELDB_INCLUDE} ${LEVELDB_LIBS}")
-  set(LEVELDB_FOUND 1)
-  include(CheckCXXSourceCompiles)
-  set(CMAKE_REQUIRED_LIBRARY ${LEVELDB_LIBS} pthread)
-  set(CMAKE_REQUIRED_INCLUDES ${LEVELDB_INCLUDE})
- else ()
-   set(LEVELDB_FOUND 0)
- endif ()
-
- # Report the results.
- if (NOT LEVELDB_FOUND)
-   set(LEVELDB_DIR_MESSAGE "LEVELDB was not found. Make sure LEVELDB_LIBS and LEVELDB_INCLUDE are set.")
-   if (LEVELDB_FIND_REQUIRED)
-     message(FATAL_ERROR "${LEVELDB_DIR_MESSAGE}")
-   elseif (NOT LEVELDB_FIND_QUIETLY)
-     message(STATUS "${LEVELDB_DIR_MESSAGE}")
-   endif ()
- endif ()
\ No newline at end of file
diff --git a/CMakeScripts/FindNumPy.cmake b/CMakeScripts/FindNumPy.cmake
deleted file mode 100644
index baf21541e63..00000000000
--- a/CMakeScripts/FindNumPy.cmake
+++ /dev/null
@@ -1,103 +0,0 @@
-# - Find the NumPy libraries
-# This module finds if NumPy is installed, and sets the following variables
-# indicating where it is.
-#
-# TODO: Update to provide the libraries and paths for linking npymath lib.
-#
-#  NUMPY_FOUND               - was NumPy found
-#  NUMPY_VERSION             - the version of NumPy found as a string
-#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
-#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
-#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
-#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
-#  NUMPY_INCLUDE_DIRS        - path to the NumPy include files
-
-#============================================================================
-# Copyright 2012 Continuum Analytics, Inc.
-#
-# MIT License
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files
-# (the "Software"), to deal in the Software without restriction, including
-# without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and to permit
-# persons to whom the Software is furnished to do so, subject to
-# the following conditions:
-#
-# The above copyright notice and this permission notice shall be included
-# in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
-# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
-# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
-# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
-# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
-# OTHER DEALINGS IN THE SOFTWARE.
-#
-#============================================================================
-
-# Finding NumPy involves calling the Python interpreter
-if(NumPy_FIND_REQUIRED)
-    find_package(PythonInterp REQUIRED)
-else()
-    find_package(PythonInterp)
-endif()
-
-if(NOT PYTHONINTERP_FOUND)
-    set(NUMPY_FOUND FALSE)
-    return()
-endif()
-
-execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
-    "import numpy as n; print(n.__version__); print(n.get_include());"
-    RESULT_VARIABLE _NUMPY_SEARCH_SUCCESS
-    OUTPUT_VARIABLE _NUMPY_VALUES_OUTPUT
-    ERROR_VARIABLE _NUMPY_ERROR_VALUE
-    OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-if(NOT _NUMPY_SEARCH_SUCCESS MATCHES 0)
-    if(NumPy_FIND_REQUIRED)
-        message(FATAL_ERROR
-            "NumPy import failure:\n${_NUMPY_ERROR_VALUE}")
-    endif()
-    set(NUMPY_FOUND FALSE)
-    return()
-endif()
-
-# Convert the process output into a list
-string(REGEX REPLACE ";" "\\\\;" _NUMPY_VALUES ${_NUMPY_VALUES_OUTPUT})
-string(REGEX REPLACE "\n" ";" _NUMPY_VALUES ${_NUMPY_VALUES})
-# Just in case there is unexpected output from the Python command.
-list(GET _NUMPY_VALUES -2 NUMPY_VERSION)
-list(GET _NUMPY_VALUES -1 NUMPY_INCLUDE_DIRS)
-
-string(REGEX MATCH "^[0-9]+\\.[0-9]+\\.[0-9]+" _VER_CHECK "${NUMPY_VERSION}")
-if("${_VER_CHECK}" STREQUAL "")
-    # The output from Python was unexpected. Raise an error always
-    # here, because we found NumPy, but it appears to be corrupted somehow.
-    message(FATAL_ERROR
-        "Requested version and include path from NumPy, got instead:\n${_NUMPY_VALUES_OUTPUT}\n")
-    return()
-endif()
-
-# Make sure all directory separators are '/'
-string(REGEX REPLACE "\\\\" "/" NUMPY_INCLUDE_DIRS ${NUMPY_INCLUDE_DIRS})
-
-# Get the major and minor version numbers
-string(REGEX REPLACE "\\." ";" _NUMPY_VERSION_LIST ${NUMPY_VERSION})
-list(GET _NUMPY_VERSION_LIST 0 NUMPY_VERSION_MAJOR)
-list(GET _NUMPY_VERSION_LIST 1 NUMPY_VERSION_MINOR)
-list(GET _NUMPY_VERSION_LIST 2 NUMPY_VERSION_PATCH)
-string(REGEX MATCH "[0-9]*" NUMPY_VERSION_PATCH ${NUMPY_VERSION_PATCH})
-math(EXPR NUMPY_VERSION_DECIMAL
-    "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
-
-find_package_message(NUMPY
-    "Found NumPy: version \"${NUMPY_VERSION}\" ${NUMPY_INCLUDE_DIRS}"
-    "${NUMPY_INCLUDE_DIRS}${NUMPY_VERSION}")
-
-set(NUMPY_FOUND TRUE)
-
-
diff --git a/CMakeScripts/FindProtobuf.cmake b/CMakeScripts/FindProtobuf.cmake
deleted file mode 100644
index 0f94f498197..00000000000
--- a/CMakeScripts/FindProtobuf.cmake
+++ /dev/null
@@ -1,152 +0,0 @@
-# Locate and configure the Google Protocol Buffers library.
-# Defines the following variables:
-#
-#   PROTOBUF_FOUND - Found the Google Protocol Buffers library
-#   PROTOBUF_INCLUDE_DIRS - Include directories for Google Protocol Buffers
-#   PROTOBUF_LIBRARIES - The protobuf library
-#
-# The following cache variables are also defined:
-#   PROTOBUF_LIBRARY - The protobuf library
-#   PROTOBUF_PROTOC_LIBRARY   - The protoc library
-#   PROTOBUF_INCLUDE_DIR - The include directory for protocol buffers
-#   PROTOBUF_PROTOC_EXECUTABLE - The protoc compiler
-#
-#  ====================================================================
-#  Example:
-#
-#   find_package(Protobuf REQUIRED)
-#   include_directories(${PROTOBUF_INCLUDE_DIRS})
-#
-#   include_directories(${CMAKE_CURRENT_BINARY_DIR})
-#   PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS foo.proto)
-#   add_executable(bar bar.cc ${PROTO_SRCS} ${PROTO_HDRS})
-#   target_link_libraries(bar ${PROTOBUF_LIBRARY})
-#
-# NOTE: You may need to link against pthreads, depending
-# on the platform.
-#  ====================================================================
-#
-# PROTOBUF_GENERATE_CPP (public function)
-#   SRCS = Variable to define with autogenerated
-#          source files
-#   HDRS = Variable to define with autogenerated
-#          header files
-#   ARGN = proto files
-#
-#  ====================================================================
-
-
-#=============================================================================
-# Copyright 2009 Kitware, Inc.
-# Copyright 2009 Philip Lowman <philip@yhbt.com>
-# Copyright 2008 Esben Mose Hansen, Ange Optimization ApS
-#
-# Distributed under the OSI-approved BSD License (the "License");
-# see accompanying file Copyright.txt for details.
-#
-# This software is distributed WITHOUT ANY WARRANTY; without even the
-# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-# See the License for more information.
-#=============================================================================
-# (To distributed this file outside of CMake, substitute the full
-#  License text for the above reference.)
-
-function(PROTOBUF_GENERATE_PYTHON SRCS)
-  if(NOT ARGN)
-    message(SEND_ERROR "Error: PROTOBUF_GENERATE_PYTHON() called without any proto files")
-    return()
-  endif(NOT ARGN)
-
-  set(${SRCS})
-  foreach(FIL ${ARGN})
-    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
-    get_filename_component(FIL_WE ${FIL} NAME_WE)
-    
-    
-    list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}_pb2.py")
-
-    add_custom_command(
-      OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}_pb2.py"
-      COMMAND  ${PROTOBUF_PROTOC_EXECUTABLE}
-      ARGS --python_out  ${CMAKE_CURRENT_BINARY_DIR} --proto_path ${CMAKE_CURRENT_SOURCE_DIR} 
-${ABS_FIL}
-      DEPENDS ${ABS_FIL}
-      COMMENT "Running Python protocol buffer compiler on ${FIL}"
-      VERBATIM )
-  endforeach()
-
-
-  set_source_files_properties(${${SRCS}} PROPERTIES GENERATED TRUE)
-  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
-endfunction()
-
-
-function(PROTOBUF_GENERATE_CPP SRCS HDRS)
-  if(NOT ARGN)
-    message(SEND_ERROR "Error: PROTOBUF_GENERATE_CPP() called without any proto files")
-    return()
-  endif(NOT ARGN)
-
-  set(${SRCS})
-  set(${HDRS})
-  foreach(FIL ${ARGN})
-    get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
-    get_filename_component(FIL_WE ${FIL} NAME_WE)
-    
-    list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc")
-    list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h")
-
-    add_custom_command(
-      OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.cc"
-             "${CMAKE_CURRENT_BINARY_DIR}/${FIL_WE}.pb.h"
-      COMMAND  ${PROTOBUF_PROTOC_EXECUTABLE}
-      ARGS --cpp_out  ${CMAKE_CURRENT_BINARY_DIR} --proto_path ${CMAKE_CURRENT_SOURCE_DIR} 
-${ABS_FIL}
-      DEPENDS ${ABS_FIL}
-      COMMENT "Running C++ protocol buffer compiler on ${FIL}"
-      VERBATIM )
-  endforeach()
-
-  set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE)
-  set(${SRCS} ${${SRCS}} PARENT_SCOPE)
-  set(${HDRS} ${${HDRS}} PARENT_SCOPE)
-endfunction()
-
-
-find_path(PROTOBUF_INCLUDE_DIR google/protobuf/service.h)
-
-# Google's provided vcproj files generate libraries with a "lib"
-# prefix on Windows
-if(WIN32)
-    set(PROTOBUF_ORIG_FIND_LIBRARY_PREFIXES "${CMAKE_FIND_LIBRARY_PREFIXES}")
-    set(CMAKE_FIND_LIBRARY_PREFIXES "lib" "")
-endif()
-
-find_library(PROTOBUF_LIBRARY NAMES protobuf
-             DOC "The Google Protocol Buffers Library"
-)
-find_library(PROTOBUF_PROTOC_LIBRARY NAMES protoc
-             DOC "The Google Protocol Buffers Compiler Library"
-)
-find_program(PROTOBUF_PROTOC_EXECUTABLE NAMES protoc
-             DOC "The Google Protocol Buffers Compiler"
-)
-
-mark_as_advanced(PROTOBUF_INCLUDE_DIR
-                 PROTOBUF_LIBRARY
-                 PROTOBUF_PROTOC_LIBRARY
-                 PROTOBUF_PROTOC_EXECUTABLE)
-
-# Restore original find library prefixes
-if(WIN32)
-    set(CMAKE_FIND_LIBRARY_PREFIXES "${PROTOBUF_ORIG_FIND_LIBRARY_PREFIXES}")
-endif()
-
-include(FindPackageHandleStandardArgs)
-FIND_PACKAGE_HANDLE_STANDARD_ARGS(PROTOBUF DEFAULT_MSG
-    PROTOBUF_LIBRARY PROTOBUF_INCLUDE_DIR)
-
-if(PROTOBUF_FOUND)
-    set(PROTOBUF_INCLUDE_DIRS ${PROTOBUF_INCLUDE_DIR})
-    set(PROTOBUF_LIBRARIES    ${PROTOBUF_LIBRARY})
-endif()
diff --git a/CMakeScripts/FindSnappy.cmake b/CMakeScripts/FindSnappy.cmake
deleted file mode 100644
index d769b442812..00000000000
--- a/CMakeScripts/FindSnappy.cmake
+++ /dev/null
@@ -1,33 +0,0 @@
-# Find the Snappy libraries
-#
-# The following variables are optionally searched for defaults
-#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
-#
-# The following are set after configuration is done:
-#  Snappy_FOUND
-#  Snappy_INCLUDE_DIRS
-#  Snappy_LIBS
-
-find_path(SNAPPY_INCLUDE_DIR
-    NAMES snappy.h
-    HINTS ${SNAPPY_ROOT_DIR}
-          ${SNAPPY_ROOT_DIR}/include
-)
-
-find_library(SNAPPY_LIBS
-    NAMES snappy
-    HINTS ${SNAPPY_ROOT_DIR}
-          ${SNAPPY_ROOT_DIR}/lib
-)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(Snappy
-    DEFAULT_MSG
-    SNAPPY_LIBS
-    SNAPPY_INCLUDE_DIR
-)
-
-mark_as_advanced(
-    SNAPPY_LIBS
-    SNAPPY_INCLUDE_DIR
-)
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
new file mode 100644
index 00000000000..24f23e9c494
--- /dev/null
+++ b/cmake/ConfigGen.cmake
@@ -0,0 +1,86 @@
+
+################################################################################################
+# Helper function to fetch caffe includes which will be passed to dependent projects
+# Usage:
+#   caffe_get_current_includes(<includes_list_variable>)
+function(caffe_get_current_includes includes_variable)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  caffe_convert_absolute_paths(current_includes)
+
+  # remove at most one ${CMAKE_BINARY_DIR} include added for caffe_config.h
+  list(FIND current_includes ${CMAKE_BINARY_DIR} __index)
+  list(REMOVE_AT current_includes ${__index})
+
+  caffe_list_unique(current_includes)
+  set(${includes_variable} ${current_includes} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to get all list items that begin with given prefix
+# Usage:
+#   caffe_get_items_with_prefix(<prefix> <list_variable> <output_variable>)
+function(caffe_get_items_with_prefix prefix list_variable output_variable)
+  set(__result "")
+  foreach(__e ${${list_variable}})
+    if(__e MATCHES "^${prefix}.*")
+      list(APPEND __result ${__e})
+    endif()
+  endforeach()
+  set(${output_variable} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Function for generation Caffe build- and install- tree export config files
+# Usage:
+#  caffe_generate_export_configs()
+function(caffe_generate_export_configs)
+  set(install_cmake_suffix "share/Caffe")
+
+  # ---[ Configure build-tree CaffeConfig.cmake file ]---
+  caffe_get_current_includes(Caffe_INCLUDE_DIRS)
+  if(NOT HAVE_CUDA)
+    set(HAVE_CUDA FALSE)
+    set(Caffe_DEFINITIONS -DCPU_ONLY)
+  endif()
+  if(NOT HAVE_CUDNN)
+    set(HAVE_CUDNN FALSE)
+  else()
+    set(Caffe_DEFINITIONS -DUSE_CUDNN)
+  endif()
+
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${CMAKE_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
+
+  # Add targets to the build-tree export set
+  export(TARGETS caffe proto FILE "${CMAKE_BINARY_DIR}/CaffeTargets.cmake")
+  export(PACKAGE Caffe)
+
+  # ---[ Configure install-tree CaffeConfig.cmake file ]---
+
+  # remove source and build dir includes
+  caffe_get_items_with_prefix(${CMAKE_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
+  caffe_get_items_with_prefix(${CMAKE_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
+  list(REMOVE_ITEM Caffe_INCLUDE_DIRS ${__insource} ${__inbinary})
+
+  # add `install` include folder
+  set(lines
+     "get_filename_component(__caffe_include \"\${Caffe_CMAKE_DIR}/../../include\" ABSOLUTE)\n"
+     "list(APPEND Caffe_INCLUDE_DIRS \${__caffe_include})\n"
+     "unset(__caffe_include)\n")
+  string(REPLACE ";" "" Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND ${lines})
+
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${CMAKE_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
+
+  # Install the CaffeConfig.cmake and export set to use wuth install-tree
+  install(FILES "${CMAKE_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
+  install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
+
+  # ---[ Configure and install version file ]---
+
+  # TODO: Lines below are commented because Caffe does't declare its version in headers.
+  # When the declarations are added, modify `caffe_extract_caffe_version()` macro and uncomment
+
+  # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${CMAKE_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
+  # install(FILES "${CMAKE_BINARY_DIR}/CaffeConfigVersion.cmake" DESTINATION ${install_cmake_suffix})
+endfunction()
+
+
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
new file mode 100644
index 00000000000..42a94e5a295
--- /dev/null
+++ b/cmake/Cuda.cmake
@@ -0,0 +1,243 @@
+if(CPU_ONLY)
+  return()
+endif()
+
+# Known NVIDIA GPU achitectures Caffe can be compiled for.
+# This list will be used for CUDA_ARCH_NAME = All option
+set(Caffe_known_gpu_archs "20 21(20) 30 35 50")
+
+################################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   caffe_detect_installed_gpus(out_variable)
+function(caffe_detect_installed_gpus out_variable)
+  if(NOT CUDA_gpu_detect_output)
+    set(__cufile ${CMAKE_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${__cufile} ""
+      "#include <cstdio>\n"
+      "int main()\n"
+      "{\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device)\n"
+      "  {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "--run" "${__cufile}"
+                    WORKING_DIRECTORY "${CMAKE_BINARY_DIR}/CMakeFiles/"
+                    RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(__nvcc_res EQUAL 0)
+      string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
+      set(CUDA_gpu_detect_output ${__nvcc_out} CACHE INTERNAL "Returned GPU architetures from caffe_detect_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
+    set(${out_variable} ${Caffe_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   caffe_select_nvcc_arch_flags(out_variable)
+function(caffe_select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(__archs_names "Fermi" "Kepler" "Maxwell" "All" "Manual")
+  set(__archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND __archs_names "Auto")
+    set(__archs_name_default "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${__archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${__archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${__archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " __archs_names "${__archs_names}")
+    message(FATAL_ERROR "Only ${__archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${Caffe_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Fermi")
+    set(__cuda_arch_bin "20 21(20)")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(__cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(__cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(__cuda_arch_bin ${Caffe_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    caffe_detect_installed_gpus(__cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(__cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" __cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" __cuda_arch_bin "${__cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   __cuda_arch_ptx "${__cuda_arch_ptx}")
+  caffe_list_unique(__cuda_arch_bin __cuda_arch_ptx)
+
+  set(__nvcc_flags "")
+  set(__nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(__arch ${__cuda_arch_bin})
+    if(__arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND __nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=sm_${__arch})
+      list(APPEND __nvcc_archs_readable sm_${__arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(__arch ${__cuda_arch_ptx})
+    list(APPEND __nvcc_flags -gencode arch=compute_${__arch},code=compute_${__arch})
+    list(APPEND __nvcc_archs_readable compute_${__arch})
+  endforeach()
+
+  string(REPLACE ";" " " __nvcc_archs_readable "${__nvcc_archs_readable}")
+  set(${out_variable}          ${__nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${__nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for cuda comnpilation
+# Usage:
+#   caffe_cuda_compile(<objlist_variable> <cuda_files>)
+macro(caffe_cuda_compile objlist_variable)
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var}_backup_in_cuda_compile_ "${${var}}")
+
+    # we remove /EHa as it generates warnings under windows
+    string(REPLACE "/EHa" "" ${var} "${${var}}")
+
+  endforeach()
+
+  if(UNIX OR APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -fPIC)
+  endif()
+
+  if(APPLE)
+    list(APPEND CUDA_NVCC_FLAGS -Xcompiler -Wno-unused-function)
+  endif()
+
+  cuda_compile(cuda_objcs ${ARGN})
+
+  foreach(var CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS_DEBUG)
+    set(${var} "${${var}_backup_in_cuda_compile_}")
+    unset(${var}_backup_in_cuda_compile_)
+  endforeach()
+
+  set(${objlist_variable} ${cuda_objcs})
+endmacro()
+
+################################################################################################
+# Short command for cuDNN detection. Believe it soon will be a part of CUDA toolkit distribution.
+# That's why not FindcuDNN.cmake file, but just the macro
+# Usage:
+#   detect_cuDNN()
+function(detect_cuDNN)
+  set(CUDNN_ROOT "" CACHE PATH "CUDNN root folder")
+
+  find_path(CUDNN_INCLUDE cudnn.h
+            PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
+            DOC "Path to cuDNN include directory." )
+
+  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
+  find_library(CUDNN_LIBRARY NAMES libcudnn.so # libcudnn_static.a
+                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
+                             DOC "Path to cuDNN library.")
+
+  if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
+    set(HAVE_CUDNN  TRUE PARENT_SCOPE)
+    set(CUDNN_FOUND TRUE PARENT_SCOPE)
+
+    mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
+    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+  endif()
+endfunction()
+
+
+################################################################################################
+###  Non macro section
+################################################################################################
+
+find_package(CUDA 5.5 QUIET)
+
+if(NOT CUDA_FOUND)
+  return()
+endif()
+
+set(HAVE_CUDA TRUE)
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
+                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+
+# cudnn detection
+if(USE_CUDNN)
+  detect_cuDNN()
+  if(HAVE_CUDNN)
+    add_definitions(-DUSE_CUDNN)
+    include_directories(SYSTEM ${CUDNN_INCLUDE})
+    list(APPEND Caffe_LINKER_LIBS ${CUDNN_LIBRARY})
+  endif()
+endif()
+
+# setting nvcc arch flags
+caffe_select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+# Boost 1.55 workaround, see https://svn.boost.org/trac/boost/ticket/9392 or
+# https://github.com/ComputationalRadiationPhysics/picongpu/blob/master/src/picongpu/CMakeLists.txt
+if(Boost_VERSION EQUAL 105500)
+  message(STATUS "Cuda + Boost 1.55: Applying noinline work around")
+  # avoid warning for CMake >= 2.8.12
+  set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} \"-DBOOST_NOINLINE=__attribute__((noinline))\" ")
+endif()
+
+# disable some nvcc diagnostic that apears in boost, glog, glags, opencv, etc.
+foreach(diag cc_clobber_ignored integer_sign_change useless_using_declaration set_but_not_used)
+  list(APPEND CUDA_NVCC_FLAGS -Xcudafe --diag_suppress=${diag})
+endforeach()
+
+# setting default testing device
+if(NOT CUDA_TEST_DEVICE)
+  set(CUDA_TEST_DEVICE -1)
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
+
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
new file mode 100644
index 00000000000..5de435c350d
--- /dev/null
+++ b/cmake/Dependencies.cmake
@@ -0,0 +1,124 @@
+# This list is required for static linking and exported to CaffeConfig.cmake
+set(Caffe_LINKER_LIBS "")
+
+# ---[ Boost
+find_package(Boost 1.46 REQUIRED COMPONENTS system thread)
+include_directories(SYSTEM ${Boost_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
+
+# ---[ Threads
+find_package(Threads REQUIRED)
+list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
+
+# ---[ Google-glog
+find_package(Glog REQUIRED)
+include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
+
+# ---[ Google-gflags
+find_package(GFlags REQUIRED)
+include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
+
+# ---[ Google-protobuf
+include(cmake/ProtoBuf.cmake)
+
+# ---[ HDF5
+find_package(HDF5 COMPONENTS HL REQUIRED)
+include_directories(SYSTEM ${HDF5_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
+
+# ---[ LMDB
+find_package(LMDB REQUIRED)
+include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+
+# ---[ LevelDB
+find_package(LevelDB REQUIRED)
+include_directories(SYSTEM ${LEVELDB_INCLUDE})
+list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+
+# ---[ Snappy
+find_package(Snappy REQUIRED)
+include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+
+# ---[ CUDA
+include(cmake/Cuda.cmake)
+if(NOT HAVE_CUDA)
+  if(CPU_ONLY)
+    message("-- CUDA is disabled. Building without it...")
+  else()
+    message("-- CUDA is not detected by cmake. Building without it...")
+  endif()
+
+  # TODO: remove this not cross platform define in future. Use caffe_config.h instead.
+  add_definitions(-DCPU_ONLY)
+endif()
+
+# ---[ OpenCV
+find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
+if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
+  find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+endif()
+include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
+
+# ---[ BLAS
+if(NOT APPLE)
+  set(BLAS "Atlas" CACHE STRING "Selected BLAS library")
+  set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MLK")
+
+  if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
+    find_package(Atlas REQUIRED)
+    include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${Atlas_LIBRARIES})
+  elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
+    find_package(OpenBLAS REQUIRED)
+    include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${OpenBLAS_LIB})
+  elseif(BLAS STREQUAL "MLK" OR BLAS STREQUAL "mkl")
+    find_package(MKL REQUIRED)
+    include_directories(SYSTEM ${MKL_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS ${MKL_LIBRARIES})
+  endif()
+elseif(APPLE)
+  find_package(vecLib REQUIRED)
+  include_directories(SYSTEM ${vecLib_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${vecLib_LINKER_LIBS})
+endif()
+
+# ---[ Python
+if(BUILD_python)
+  # disable Python 3 search
+  find_package(PythonInterp 2.7)
+  find_package(PythonLibs 2.7)
+  find_package(NumPy 1.7.1)
+  find_package(Boost 1.46 COMPONENTS python)
+
+  if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
+    set(HAVE_PYTHON TRUE)
+  endif()
+endif()
+
+# ---[ Matlab
+if(BUILD_matlab)
+  find_package(MatlabMex)
+  if(MATLABMEX_FOUND)
+    set(HAVE_MATLAB TRUE)
+  endif()
+
+  # sudo apt-get install liboctave-dev
+  find_program(Octave_compiler NAMES mkoctfile DOC "Octave C++ compiler")
+
+  if(HAVE_MATLAB AND Octave_compiler)
+    set(Matlab_build_mex_using "Matlab" CACHE STRING "Select Matlab or Octave if both detected")
+    set_property(CACHE Matlab_build_mex_using PROPERTY STRINGS "Matlab;Octave")
+  endif()
+endif()
+
+# ---[ Doxygen
+if(BUILD_docs)
+  find_package(Doxygen)
+endif()
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
new file mode 100644
index 00000000000..68e8a662126
--- /dev/null
+++ b/cmake/Misc.cmake
@@ -0,0 +1,47 @@
+# ---[ Configurations types
+set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE)
+mark_as_advanced(CMAKE_CONFIGURATION_TYPES)
+
+if(DEFINED CMAKE_BUILD_TYPE)
+  set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS ${CMAKE_CONFIGURATION_TYPES})
+endif()
+
+# --[ If user doesn't specify build type then assume release
+if("${CMAKE_BUILD_TYPE}" STREQUAL "")
+  set(CMAKE_BUILD_TYPE Release)
+endif()
+
+if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+  set(CMAKE_COMPILER_IS_CLANGXX TRUE)
+endif()
+
+# ---[ Solution folders
+caffe_option(USE_PROJECT_FOLDERS "IDE Solution folders" (MSVC_IDE OR CMAKE_GENERATOR MATCHES Xcode) )
+
+if(USE_PROJECT_FOLDERS)
+  set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+  set_property(GLOBAL PROPERTY PREDEFINED_TARGETS_FOLDER "CMakeTargets")
+endif()
+
+# ---[ Install options
+if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
+  set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE)
+endif()
+
+# ---[ RPATH settings
+set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE CACHE BOOLEAN "Use link paths for shared library rpath")
+set(CMAKE_MACOSX_RPATH TRUE)
+
+# ---[ Funny target
+if(UNIX OR APPLE)
+  add_custom_target(simlink_to_build COMMAND "ln" "-sf" "${CMAKE_BINARY_DIR}" "${CMAKE_SOURCE_DIR}/build"
+                                     COMMENT "Adding simlink: <caffe_root>/build -> ${CMAKE_BINARY_DIR}" )
+endif()
+
+# ---[ Set debug postfix
+set(Caffe_DEBUG_POSTFIX "-d")
+
+set(CAffe_POSTFIX "")
+if(CMAKE_BUILD_TYPE MATCHES "Debug")
+  set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
+endif()
diff --git a/CMakeScripts/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
similarity index 63%
rename from CMakeScripts/FindAtlas.cmake
rename to cmake/Modules/FindAtlas.cmake
index 27657a6c7d7..6e1564351c7 100644
--- a/CMakeScripts/FindAtlas.cmake
+++ b/cmake/Modules/FindAtlas.cmake
@@ -23,14 +23,14 @@ set(Atlas_LIB_SEARCH_PATHS
   $ENV{Atlas_ROOT_DIR}/lib
 )
 
-find_path(Atlas_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
+find_path(Atlas_CBLAS_INCLUDE_DIR   NAMES cblas.h   PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
-find_library(Atlas_CBLAS_LIBRARY NAMES ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_BLAS_LIBRARY NAMES atlas_r atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
 
-set(LOOKED_FOR
+find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                 PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
 
+set(LOOKED_FOR
   Atlas_CBLAS_INCLUDE_DIR
   Atlas_CLAPACK_INCLUDE_DIR
 
@@ -43,19 +43,10 @@ include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(Atlas DEFAULT_MSG ${LOOKED_FOR})
 
 if(ATLAS_FOUND)
-
+  set(Atlas_INCLUDE_DIR ${Atlas_CBLAS_INCLUDE_DIR} ${Atlas_CLAPACK_INCLUDE_DIR})
+  set(Atlas_LIBRARIES ${Atlas_LAPACK_LIBRARY} ${Atlas_CBLAS_LIBRARY} ${Atlas_BLAS_LIBRARY})
   mark_as_advanced(${LOOKED_FOR})
 
-  set(Atlas_INCLUDE_DIR
-    ${Atlas_CBLAS_INCLUDE_DIR}
-    ${Atlas_CLAPACK_INCLUDE_DIR}
-  )
-
-  set(Atlas_LIBRARIES
-    ${Atlas_LAPACK_LIBRARY}
-    ${Atlas_CBLAS_LIBRARY}
-    ${Atlas_BLAS_LIBRARY}
-  )
-
+  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR}, library: ${Atlas_BLAS_LIBRARY})")
 endif(ATLAS_FOUND)
 
diff --git a/CMakeScripts/FindGFlags.cmake b/cmake/Modules/FindGFlags.cmake
similarity index 79%
rename from CMakeScripts/FindGFlags.cmake
rename to cmake/Modules/FindGFlags.cmake
index f93c57136a1..146e8455a50 100644
--- a/CMakeScripts/FindGFlags.cmake
+++ b/cmake/Modules/FindGFlags.cmake
@@ -38,11 +38,13 @@ else()
     find_library(GFLAGS_LIBRARY gflags)
 endif()
 
-find_package_handle_standard_args(GFLAGS DEFAULT_MSG
-    GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
 
 
 if(GFLAGS_FOUND)
     set(GFLAGS_INCLUDE_DIRS ${GFLAGS_INCLUDE_DIR})
     set(GFLAGS_LIBRARIES ${GFLAGS_LIBRARY})
+    message(STATUS "Found gflags  (include: ${GFLAGS_INCLUDE_DIR}, library: ${GFLAGS_LIBRARY})")
+    mark_as_advanced(GFLAGS_LIBRARY_DEBUG GFLAGS_LIBRARY_RELEASE
+                     GFLAGS_LIBRARY GFLAGS_INCLUDE_DIR GFLAGS_ROOT_DIR)
 endif()
diff --git a/CMakeScripts/FindGlog.cmake b/cmake/Modules/FindGlog.cmake
similarity index 70%
rename from CMakeScripts/FindGlog.cmake
rename to cmake/Modules/FindGlog.cmake
index 0dc30abdbf5..56c76434897 100644
--- a/CMakeScripts/FindGlog.cmake
+++ b/cmake/Modules/FindGlog.cmake
@@ -34,15 +34,15 @@ if(MSVC)
 else()
     find_library(GLOG_LIBRARY glog
         PATHS ${GLOG_ROOT_DIR}
-        PATH_SUFFIXES
-            lib
-            lib64)
+        PATH_SUFFIXES lib lib64)
 endif()
 
-find_package_handle_standard_args(GLOG DEFAULT_MSG
-    GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
 
 if(GLOG_FOUND)
-    set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
-    set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+  set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
+  set(GLOG_LIBRARIES ${GLOG_LIBRARY})
+  message(STATUS "Found glog    (include: ${GLOG_INCLUDE_DIR}, library: ${GLOG_LIBRARY})")
+  mark_as_advanced(GLOG_ROOT_DIR GLOG_LIBRARY_RELEASE GLOG_LIBRARY_DEBUG
+                                 GLOG_LIBRARY GLOG_INCLUDE_DIR)
 endif()
diff --git a/CMakeScripts/FindLAPACK.cmake b/cmake/Modules/FindLAPACK.cmake
similarity index 100%
rename from CMakeScripts/FindLAPACK.cmake
rename to cmake/Modules/FindLAPACK.cmake
diff --git a/cmake/Modules/FindLMDB.cmake b/cmake/Modules/FindLMDB.cmake
new file mode 100644
index 00000000000..8a817fd6f10
--- /dev/null
+++ b/cmake/Modules/FindLMDB.cmake
@@ -0,0 +1,28 @@
+# Try to find the LMBD libraries and headers
+#  LMDB_FOUND - system has LMDB lib
+#  LMDB_INCLUDE_DIR - the LMDB include directory
+#  LMDB_LIBRARIES - Libraries needed to use LMDB
+
+# FindCWD based on FindGMP by:
+# Copyright (c) 2006, Laurent Montel, <montel@kde.org>
+#
+# Redistribution and use is allowed according to the terms of the BSD license.
+
+# Adapted from FindCWD by:
+# Copyright 2013 Conrad Steenberg <conrad.steenberg@gmail.com>
+# Aug 31, 2013
+
+find_path(LMDB_INCLUDE_DIR NAMES  lmdb.h PATHS "$ENV{LMDB_DIR}/include")
+find_library(LMDB_LIBRARIES NAMES lmdb   PATHS "$ENV{LMDB_DIR}/lib" )
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(LMDB DEFAULT_MSG LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+if(LMDB_FOUND)
+  message(STATUS "Found lmdb    (include: ${LMDB_INCLUDE_DIR}, library: ${LMDB_LIBRARIES})")
+  mark_as_advanced(LMDB_INCLUDE_DIR LMDB_LIBRARIES)
+
+  caffe_parse_header(${LMDB_INCLUDE_DIR}/lmdb.h
+                     LMDB_VERSION_LINES MDB_VERSION_MAJOR MDB_VERSION_MINOR MDB_VERSION_PATCH)
+  set(LMDB_VERSION "${MDB_VERSION_MAJOR}.${MDB_VERSION_MINOR}.${MDB_VERSION_PATCH}")
+endif()
diff --git a/cmake/Modules/FindLevelDB.cmake b/cmake/Modules/FindLevelDB.cmake
new file mode 100644
index 00000000000..97f08ac9349
--- /dev/null
+++ b/cmake/Modules/FindLevelDB.cmake
@@ -0,0 +1,44 @@
+# - Find LevelDB
+#
+#  LevelDB_INCLUDES  - List of LevelDB includes
+#  LevelDB_LIBRARIES - List of libraries when using LevelDB.
+#  LevelDB_FOUND     - True if LevelDB found.
+
+# Look for the header file.
+find_path(LevelDB_INCLUDE NAMES leveldb/db.h
+                          PATHS $ENV{LEVELDB_ROOT}/include /opt/local/include /usr/local/include /usr/include
+                          DOC "Path in which the file leveldb/db.h is located." )
+
+# Look for the library.
+find_library(LevelDB_LIBRARY NAMES leveldb
+                             PATHS /usr/lib $ENV{LEVELDB_ROOT}/lib
+                             DOC "Path to leveldb library." )
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(LevelDB DEFAULT_MSG LevelDB_INCLUDE LevelDB_LIBRARY)
+
+if(LEVELDB_FOUND)
+  message(STATUS "Found LevelDB (include: ${LevelDB_INCLUDE}, library: ${LevelDB_LIBRARY})")
+  set(LevelDB_INCLUDES ${LevelDB_INCLUDE})
+  set(LevelDB_LIBRARIES ${LevelDB_LIBRARY})
+  mark_as_advanced(LevelDB_INCLUDE LevelDB_LIBRARY)
+
+  if(EXISTS "${LevelDB_INCLUDE}/leveldb/db.h")
+    file(STRINGS "${LevelDB_INCLUDE}/leveldb/db.h" __version_lines
+           REGEX "static const int k[^V]+Version[ \t]+=[ \t]+[0-9]+;")
+
+    foreach(__line ${__version_lines})
+      if(__line MATCHES "[^k]+kMajorVersion[ \t]+=[ \t]+([0-9]+);")
+        set(LEVELDB_VERSION_MAJOR ${CMAKE_MATCH_1})
+      elseif(__line MATCHES "[^k]+kMinorVersion[ \t]+=[ \t]+([0-9]+);")
+        set(LEVELDB_VERSION_MINOR ${CMAKE_MATCH_1})
+      endif()
+    endforeach()
+
+    if(LEVELDB_VERSION_MAJOR AND LEVELDB_VERSION_MINOR)
+      set(LEVELDB_VERSION "${LEVELDB_VERSION_MAJOR}.${LEVELDB_VERSION_MINOR}")
+    endif()
+
+    caffe_clear_vars(__line __version_lines)
+  endif()
+endif()
diff --git a/CMakeScripts/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
similarity index 100%
rename from CMakeScripts/FindMKL.cmake
rename to cmake/Modules/FindMKL.cmake
diff --git a/cmake/Modules/FindMatlabMex.cmake b/cmake/Modules/FindMatlabMex.cmake
new file mode 100644
index 00000000000..28ae65e7cbb
--- /dev/null
+++ b/cmake/Modules/FindMatlabMex.cmake
@@ -0,0 +1,48 @@
+# This module looks for MatlabMex compiler
+# Defines variables:
+#    Matlab_DIR    - Matlab root dir
+#    Matlab_mex    - path to mex compiler
+#    Matlab_mexext - path to mexext
+
+if(MSVC)
+  foreach(__ver "9.30" "7.14" "7.11" "7.10" "7.9" "7.8" "7.7")
+    get_filename_component(__matlab_root "[HKEY_LOCAL_MACHINE\\SOFTWARE\\MathWorks\\MATLAB\\${__ver};MATLABROOT]" ABSOLUTE)
+    if(__matlab_root)
+      break()
+    endif()
+  endforeach()
+endif()
+
+if(APPLE)
+  foreach(__ver "R2014b" "R2014a" "R2013b" "R2013a" "R2012b" "R2012a" "R2011b" "R2011a" "R2010b" "R2010a")
+    if(EXISTS /Applications/MATLAB_${__ver}.app)
+      set(__matlab_root /Applications/MATLAB_${__ver}.app)
+      break()
+    endif()
+  endforeach()
+endif()
+
+if(UNIX)
+   execute_process(COMMAND which matlab OUTPUT_STRIP_TRAILING_WHITESPACE
+                   OUTPUT_VARIABLE __out RESULT_VARIABLE __res)
+
+   if(__res MATCHES 0) # Suppress `readlink` warning if `which` returned nothing
+     execute_process(COMMAND which matlab  COMMAND xargs readlink
+                     COMMAND xargs dirname COMMAND xargs dirname COMMAND xargs echo -n
+                     OUTPUT_VARIABLE __matlab_root OUTPUT_STRIP_TRAILING_WHITESPACE)
+   endif()
+endif()
+
+
+find_path(Matlab_DIR NAMES bin/mex bin/mexext PATHS ${__matlab_root}
+                     DOC "Matlab directory" NO_DEFAULT_PATH)
+
+find_program(Matlab_mex    NAMES mex    mex.bat    HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH)
+find_program(Matlab_mexext NAMES mexext mexext.bat HINTS ${Matlab_DIR} PATH_SUFFIXES bin NO_DEFAULT_PATH)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MatlabMex DEFAULT_MSG Matlab_mex Matlab_mexext)
+
+if(MATLABMEX_FOUND)
+  mark_as_advanced(Matlab_mex Matlab_mexext)
+endif()
diff --git a/cmake/Modules/FindNumPy.cmake b/cmake/Modules/FindNumPy.cmake
new file mode 100644
index 00000000000..a671494caba
--- /dev/null
+++ b/cmake/Modules/FindNumPy.cmake
@@ -0,0 +1,58 @@
+# - Find the NumPy libraries
+# This module finds if NumPy is installed, and sets the following variables
+# indicating where it is.
+#
+# TODO: Update to provide the libraries and paths for linking npymath lib.
+#
+#  NUMPY_FOUND               - was NumPy found
+#  NUMPY_VERSION             - the version of NumPy found as a string
+#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
+#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
+#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
+#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
+#  NUMPY_INCLUDE_DIR         - path to the NumPy include files
+
+unset(NUMPY_VERSION)
+unset(NUMPY_INCLUDE_DIR)
+
+if(PYTHONINTERP_FOUND)
+  execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
+    "import numpy as n; print(n.__version__); print(n.get_include());"
+    RESULT_VARIABLE __result
+    OUTPUT_VARIABLE __output
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+  if(__result MATCHES 0)
+    string(REGEX REPLACE ";" "\\\\;" __values ${__output})
+    string(REGEX REPLACE "\r?\n" ";"    __values ${__values})
+    list(GET __values 0 NUMPY_VERSION)
+    list(GET __values 1 NUMPY_INCLUDE_DIR)
+
+    string(REGEX MATCH "^([0-9])+\\.([0-9])+\\.([0-9])+" __ver_check "${NUMPY_VERSION}")
+    if(NOT "${__ver_check}" STREQUAL "")
+      set(NUMPY_VERSION_MAJOR ${CMAKE_MATCH_1})
+      set(NUMPY_VERSION_MINOR ${CMAKE_MATCH_2})
+      set(NUMPY_VERSION_PATCH ${CMAKE_MATCH_3})
+      math(EXPR NUMPY_VERSION_DECIMAL
+        "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
+      string(REGEX REPLACE "\\\\" "/"  NUMPY_INCLUDE_DIR ${NUMPY_INCLUDE_DIR})
+    else()
+     unset(NUMPY_VERSION)
+     unset(NUMPY_INCLUDE_DIR)
+     message(STATUS "Requested NumPy version and include path, but got instead:\n${__output}\n")
+    endif()
+  endif()
+else()
+  message(STATUS "To find NumPy Python interpretator is required to be found.")
+endif()
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NumPy REQUIRED_VARS NUMPY_INCLUDE_DIR NUMPY_VERSION
+                                        VERSION_VAR   NUMPY_VERSION)
+
+if(NUMPY_FOUND)
+  message(STATUS "NumPy ver. ${NUMPY_VERSION} found (include: ${NUMPY_INCLUDE_DIR})")
+endif()
+
+caffe_clear_vars(__result __output __error_value __values __ver_check __error_value)
+
diff --git a/CMakeScripts/FindOpenBLAS.cmake b/cmake/Modules/FindOpenBLAS.cmake
similarity index 100%
rename from CMakeScripts/FindOpenBLAS.cmake
rename to cmake/Modules/FindOpenBLAS.cmake
diff --git a/cmake/Modules/FindSnappy.cmake b/cmake/Modules/FindSnappy.cmake
new file mode 100644
index 00000000000..eff2a864a7b
--- /dev/null
+++ b/cmake/Modules/FindSnappy.cmake
@@ -0,0 +1,28 @@
+# Find the Snappy libraries
+#
+# The following variables are optionally searched for defaults
+#  Snappy_ROOT_DIR:    Base directory where all Snappy components are found
+#
+# The following are set after configuration is done:
+#  SNAPPY_FOUND
+#  Snappy_INCLUDE_DIR
+#  Snappy_LIBRARIES
+
+find_path(Snappy_INCLUDE_DIR NAMES snappy.h
+                             PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/include)
+
+find_library(Snappy_LIBRARIES NAMES snappy
+                              PATHS ${SNAPPY_ROOT_DIR} ${SNAPPY_ROOT_DIR}/lib)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(Snappy DEFAULT_MSG Snappy_INCLUDE_DIR Snappy_LIBRARIES)
+
+if(SNAPPY_FOUND)
+  message(STATUS "Found Snappy  (include: ${Snappy_INCLUDE_DIR}, library: ${Snappy_LIBRARIES})")
+  mark_as_advanced(Snappy_INCLUDE_DIR Snappy_LIBRARIES)
+
+  caffe_parse_header(${Snappy_INCLUDE_DIR}/snappy-stubs-public.h
+                     SNAPPY_VERION_LINES SNAPPY_MAJOR SNAPPY_MINOR SNAPPY_PATCHLEVEL)
+  set(Snappy_VERSION "${SNAPPY_MAJOR}.${SNAPPY_MINOR}.${SNAPPY_PATCHLEVEL}")
+endif()
+
diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
new file mode 100644
index 00000000000..9600da43647
--- /dev/null
+++ b/cmake/Modules/FindvecLib.cmake
@@ -0,0 +1,34 @@
+# Find the vecLib libraries as part of Accelerate.framework or as standalon framework
+#
+# The following are set after configuration is done:
+#  VECLIB_FOUND
+#  vecLib_INCLUDE_DIR
+#  vecLib_LINKER_LIBS
+
+
+if(NOT APPLE)
+  return()
+endif()
+
+set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Headers")
+
+find_path(vecLib_INCLUDE_DIR vecLib.h
+          DOC "vecLib include directory"
+          PATHS /System/Library/${__veclib_include_suffix}
+                /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
+                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR)
+
+if(VECLIB_FOUND)
+  if(vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
+    set(vecLib_LINKER_LIBS -lcblas "-framework vecLib")
+    message(STATUS "Found standalone vecLib.framework")
+  else()
+    set(vecLib_LINKER_LIBS -lcblas "-framework Accelerate")
+    message(STATUS "Found vecLib as part of Accelerate.framework")
+  endif()
+
+  mark_as_advanced(vecLib_INCLUDE_DIR)
+endif()
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
new file mode 100644
index 00000000000..bb63c9ed736
--- /dev/null
+++ b/cmake/ProtoBuf.cmake
@@ -0,0 +1,90 @@
+# Finds Google Protocol Buffers library and compilers and extends
+# the standart cmake script with version and python generation support
+
+find_package( Protobuf REQUIRED )
+include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
+
+# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
+# and should be installed  separately as in: sudo apt-get install protobuf-compiler
+if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE})
+  message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
+else()
+  message(FATAL_ERROR "Could not find PROTOBUF Compiler")
+endif()
+
+if(PROTOBUF_FOUND)
+  # fetches protobuf version
+  caffe_parse_header(${PROTOBUF_INCLUDE_DIR}/google/protobuf/stubs/common.h VERION_LINE GOOGLE_PROTOBUF_VERSION)
+  string(REGEX MATCH "([0-9])00([0-9])00([0-9])" PROTOBUF_VERSION ${GOOGLE_PROTOBUF_VERSION})
+  set(PROTOBUF_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}")
+  unset(GOOGLE_PROTOBUF_VERSION)
+endif()
+
+# place where to generate protobuf sources
+set(proto_gen_folder "${CMAKE_BINARY_DIR}/include/caffe/proto")
+include_directories(SYSTEM "${CMAKE_BINARY_DIR}/include")
+
+set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
+
+################################################################################################
+# Modification of standard 'protobuf_generate_cpp()' with output dir parameter and python support
+# Usage:
+#   caffe_protobuf_generate_cpp_py(<output_dir> <srcs_var> <hdrs_var> <python_var> <proto_files>)
+function(caffe_protobuf_generate_cpp_py output_dir srcs_var hdrs_var python_var)
+  if(NOT ARGN)
+    message(SEND_ERROR "Error: caffe_protobuf_generate_cpp_py() called without any proto files")
+    return()
+  endif()
+
+  if(PROTOBUF_GENERATE_CPP_APPEND_PATH)
+    # Create an include path for each file specified
+    foreach(fil ${ARGN})
+      get_filename_component(abs_fil ${fil} ABSOLUTE)
+      get_filename_component(abs_path ${abs_fil} PATH)
+      list(FIND _protoc_include ${abs_path} _contains_already)
+      if(${_contains_already} EQUAL -1)
+        list(APPEND _protoc_include -I ${abs_path})
+      endif()
+    endforeach()
+  else()
+    set(_protoc_include -I ${CMAKE_CURRENT_SOURCE_DIR})
+  endif()
+
+  if(DEFINED PROTOBUF_IMPORT_DIRS)
+    foreach(dir ${PROTOBUF_IMPORT_DIRS})
+      get_filename_component(abs_path ${dir} ABSOLUTE)
+      list(FIND _protoc_include ${abs_path} _contains_already)
+      if(${_contains_already} EQUAL -1)
+        list(APPEND _protoc_include -I ${abs_path})
+      endif()
+    endforeach()
+  endif()
+
+  set(${srcs_var})
+  set(${hdrs_var})
+  set(${python_var})
+  foreach(fil ${ARGN})
+    get_filename_component(abs_fil ${fil} ABSOLUTE)
+    get_filename_component(fil_we ${fil} NAME_WE)
+
+    list(APPEND ${srcs_var} "${output_dir}/${fil_we}.pb.cc")
+    list(APPEND ${hdrs_var} "${output_dir}/${fil_we}.pb.h")
+    list(APPEND ${python_var} "${output_dir}/${fil_we}_pb2.py")
+
+    add_custom_command(
+      OUTPUT "${output_dir}/${fil_we}.pb.cc"
+             "${output_dir}/${fil_we}.pb.h"
+             "${output_dir}/${fil_we}_pb2.py"
+      COMMAND ${CMAKE_COMMAND} -E make_directory "${output_dir}"
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --cpp_out    ${output_dir} ${_protoc_include} ${abs_fil}
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --python_out ${output_dir} ${_protoc_include} ${abs_fil}
+      DEPENDS ${abs_fil}
+      COMMENT "Running C++/Python protocol buffer compiler on ${fil}" VERBATIM )
+  endforeach()
+
+  set_source_files_properties(${${srcs_var}} ${${hdrs_var}} ${${python_var}} PROPERTIES GENERATED TRUE)
+  set(${srcs_var} ${${srcs_var}} PARENT_SCOPE)
+  set(${hdrs_var} ${${hdrs_var}} PARENT_SCOPE)
+  set(${python_var} ${${python_var}} PARENT_SCOPE)
+endfunction()
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
new file mode 100644
index 00000000000..756a738c9ab
--- /dev/null
+++ b/cmake/Summary.cmake
@@ -0,0 +1,166 @@
+################################################################################################
+# Caffe status report function.
+# Automatically align right column and selects text based on condition.
+# Usage:
+#   caffe_status(<text>)
+#   caffe_status(<heading> <value1> [<value2> ...])
+#   caffe_status(<heading> <condition> THEN <text for TRUE> ELSE <text for FALSE> )
+function(caffe_status text)
+  set(status_cond)
+  set(status_then)
+  set(status_else)
+
+  set(status_current_name "cond")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "THEN")
+      set(status_current_name "then")
+    elseif(arg STREQUAL "ELSE")
+      set(status_current_name "else")
+    else()
+      list(APPEND status_${status_current_name} ${arg})
+    endif()
+  endforeach()
+
+  if(DEFINED status_cond)
+    set(status_placeholder_length 23)
+    string(RANDOM LENGTH ${status_placeholder_length} ALPHABET " " status_placeholder)
+    string(LENGTH "${text}" status_text_length)
+    if(status_text_length LESS status_placeholder_length)
+      string(SUBSTRING "${text}${status_placeholder}" 0 ${status_placeholder_length} status_text)
+    elseif(DEFINED status_then OR DEFINED status_else)
+      message(STATUS "${text}")
+      set(status_text "${status_placeholder}")
+    else()
+      set(status_text "${text}")
+    endif()
+
+    if(DEFINED status_then OR DEFINED status_else)
+      if(${status_cond})
+        string(REPLACE ";" " " status_then "${status_then}")
+        string(REGEX REPLACE "^[ \t]+" "" status_then "${status_then}")
+        message(STATUS "${status_text} ${status_then}")
+      else()
+        string(REPLACE ";" " " status_else "${status_else}")
+        string(REGEX REPLACE "^[ \t]+" "" status_else "${status_else}")
+        message(STATUS "${status_text} ${status_else}")
+      endif()
+    else()
+      string(REPLACE ";" " " status_cond "${status_cond}")
+      string(REGEX REPLACE "^[ \t]+" "" status_cond "${status_cond}")
+      message(STATUS "${status_text} ${status_cond}")
+    endif()
+  else()
+    message(STATUS "${text}")
+  endif()
+endfunction()
+
+
+################################################################################################
+# Function for fetching Caffe version from git and headers
+# Usage:
+#   caffe_extract_caffe_version()
+function(caffe_extract_caffe_version)
+  set(Caffe_GIT_VERSION "unknown")
+  find_package(Git)
+  if(GIT_FOUND)
+    execute_process(COMMAND ${GIT_EXECUTABLE} describe --tags --always --dirty
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE
+                    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+                    OUTPUT_VARIABLE Caffe_GIT_VERSION
+                    RESULT_VARIABLE __git_result)
+    if(NOT ${__git_result} EQUAL 0)
+      set(Caffe_GIT_VERSION "unknown")
+    endif()
+  endif()
+
+  set(Caffe_GIT_VERSION ${Caffe_GIT_VERSION} PARENT_SCOPE)
+  set(Caffe_VERSION "<TODO> (Caffe doesn't declare its version in headers)" PARENT_SCOPE)
+
+  # caffe_parse_header(${Caffe_INCLUDE_DIR}/caffe/version.hpp Caffe_VERSION_LINES CAFFE_MAJOR CAFFE_MINOR CAFFE_PATCH)
+  # set(Caffe_VERSION "${CAFFE_MAJOR}.${CAFFE_MINOR}.${CAFFE_PATCH}" PARENT_SCOPE)
+
+  # or for #define Caffe_VERSION "x.x.x"
+  # caffe_parse_header_single_define(Caffe ${Caffe_INCLUDE_DIR}/caffe/version.hpp Caffe_VERSION)
+  # set(Caffe_VERSION ${Caffe_VERSION_STRING} PARENT_SCOPE)
+
+endfunction()
+
+
+################################################################################################
+# Prints accumulatd caffe configuration summary
+# Usage:
+#   caffe_print_configuration_summary()
+
+function(caffe_print_configuration_summary)
+  caffe_extract_caffe_version()
+  set(Caffe_VERSION ${Caffe_VERSION} PARENT_SCOPE)
+
+  caffe_merge_flag_lists(__flags_rel CMAKE_CXX_FLAGS_RELEASE CMAKE_CXX_FLAGS)
+  caffe_merge_flag_lists(__flags_deb CMAKE_CXX_FLAGS_DEBUG   CMAKE_CXX_FLAGS)
+
+  caffe_status("")
+  caffe_status("******************* Caffe Configuration Summary *******************")
+  caffe_status("General:")
+  caffe_status("  Version           :   ${Caffe_VERSION}")
+  caffe_status("  Git               :   ${Caffe_GIT_VERSION}")
+  caffe_status("  System            :   ${CMAKE_SYSTEM_NAME}")
+  caffe_status("  C++ compiler      :   ${CMAKE_CXX_COMPILER}")
+  caffe_status("  Release CXX flags :   ${__flags_rel}")
+  caffe_status("  Debug CXX flags   :   ${__flags_deb}")
+  caffe_status("  BUILD_SHARED_LIBS :   ${BUILD_SHARED_LIBS}")
+  caffe_status("  Build type        :   ${CMAKE_BUILD_TYPE}")
+  caffe_status("  BUILD_python      :   ${BUILD_python}")
+  caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
+  caffe_status("  BUILD_docs        :   ${BUILD_docs}")
+  caffe_status("  CPU_ONLY          :   ${CPU_ONLY}")
+  caffe_status("")
+  caffe_status("Dependencies:")
+  caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
+  caffe_status("  glog              :   Yes")
+  caffe_status("  gflags            :   Yes")
+  caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
+  caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
+  caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
+  caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
+  caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  caffe_status("  CUDA              : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" )
+  caffe_status("")
+  if(HAVE_CUDA)
+    caffe_status("NVIDIA CUDA:")
+    caffe_status("  Target GPU(s)     :   ${CUDA_ARCH_NAME}" )
+    caffe_status("  GPU arch(s)       :   ${NVCC_FLAGS_EXTRA_readable}")
+    if(USE_CUDNN)
+      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes" ELSE "Not found")
+    else()
+      caffe_status("  cuDNN             :   Disabled")
+    endif()
+    caffe_status("")
+  endif()
+  if(HAVE_PYTHON)
+    caffe_status("Python:")
+    caffe_status("  Interpreter       :" PYTHON_EXECUTABLE THEN "${PYTHON_EXECUTABLE} (ver. ${PYTHON_VERSION_STRING})" ELSE "No")
+    caffe_status("  Libraries         :" PYTHONLIBS_FOUND  THEN "${PYTHON_LIBRARIES} (ver ${PYTHONLIBS_VERSION_STRING})" ELSE "No")
+    caffe_status("  NumPy             :" NUMPY_FOUND  THEN "${NUMPY_INCLUDE_DIR} (ver ${NUMPY_VERSION})" ELSE "No")
+    caffe_status("")
+  endif()
+  if(BUILD_matlab)
+    caffe_status("Matlab:")
+    caffe_status("  Matlab            :" HAVE_MATLAB THEN "Yes (${Matlab_mex}, ${Matlab_mexext}" ELSE "No")
+    caffe_status("  Octave            :" Octave_compiler THEN  "Yes (${Octave_compiler})" ELSE "No")
+    if(HAVE_MATLAB AND Octave_compiler)
+      caffe_status("  Build mex using   :   ${Matlab_build_mex_using}")
+    endif()
+    caffe_status("")
+  endif()
+  if(BUILD_docs)
+    caffe_status("Documentaion:")
+    caffe_status("  Doxygen           :" DOXYGEN_FOUND THEN "${DOXYGEN_EXECUTABLE} (${DOXYGEN_VERSION})" ELSE "No")
+    caffe_status("  config_file       :   ${DOXYGEN_config_file}")
+
+    caffe_status("")
+  endif()
+  caffe_status("Install:")
+  caffe_status("  Install path      :   ${CMAKE_INSTALL_PREFIX}")
+  caffe_status("")
+endfunction()
+
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
new file mode 100644
index 00000000000..84a2aaf58e9
--- /dev/null
+++ b/cmake/Targets.cmake
@@ -0,0 +1,170 @@
+################################################################################################
+# Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
+# some objects which are not addressed directly but are registered via static constructors
+if(BUILD_SHARED_LIBS)
+  set(Caffe_LINK caffe)
+else()
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(Caffe_LINK -Wl,-force_load caffe)
+  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+  endif()
+endif()
+
+################################################################################################
+# Convenient command to setup source group for IDEs that support this feature (VS, XCode)
+# Usage:
+#   caffe_source_group(<group> GLOB[_RECURSE] <globbing_expression>)
+function(caffe_source_group group)
+  cmake_parse_arguments(CAFFE_SOURCE_GROUP "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_SOURCE_GROUP_GLOB)
+    file(GLOB srcs1 ${CAFFE_SOURCE_GROUP_GLOB})
+    source_group(${group} FILES ${srcs1})
+  endif()
+
+  if(CAFFE_SOURCE_GROUP_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_SOURCE_GROUP_GLOB_RECURSE})
+    source_group(${group} FILES ${srcs2})
+  endif()
+endfunction()
+
+################################################################################################
+# Collecting sources from globbing and appending to output list variable
+# Usage:
+#   caffe_source_group(<output_variable> GLOB[_RECURSE] <globbing_expression>)
+function(caffe_collect_sources variable)
+  cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN})
+  if(CAFFE_COLLECT_SOURCES_GLOB)
+    file(GLOB srcs1 ${CAFFE_COLLECT_SOURCES_GLOB})
+    set(${variable} ${variable} ${srcs1})
+  endif()
+
+  if(CAFFE_COLLECT_SOURCES_GLOB_RECURSE)
+    file(GLOB_RECURSE srcs2 ${CAFFE_COLLECT_SOURCES_GLOB_RECURSE})
+    set(${variable} ${variable} ${srcs2})
+  endif()
+endfunction()
+
+################################################################################################
+# Short command getting caffe sources (assuming standard Caffe code tree)
+# Usage:
+#   caffe_pickup_caffe_sources(<root>)
+function(caffe_pickup_caffe_sources root)
+  # put all files in source groups (visible as subfolder in many IDEs)
+  caffe_source_group("Include"        GLOB "${root}/include/caffe/*.h*")
+  caffe_source_group("Include\\Util"  GLOB "${root}/include/caffe/util/*.h*")
+  caffe_source_group("Include"        GLOB "${CMAKE_BINARY_DIR}/caffe_config.h*")
+  caffe_source_group("Source"         GLOB "${root}/src/caffe/*.cpp")
+  caffe_source_group("Source\\Util"   GLOB "${root}/src/caffe/util/*.cpp")
+  caffe_source_group("Source\\Layers" GLOB "${root}/src/caffe/layers/*.cpp")
+  caffe_source_group("Source\\Cuda"   GLOB "${root}/src/caffe/layers/*.cu")
+  caffe_source_group("Source\\Cuda"   GLOB "${root}/src/caffe/util/*.cu")
+  caffe_source_group("Source\\Proto"  GLOB "${root}/src/caffe/proto/*.proto")
+
+  # source groups for test target
+  caffe_source_group("Include"      GLOB "${root}/include/caffe/test/test_*.h*")
+  caffe_source_group("Source"       GLOB "${root}/src/caffe/test/test_*.cpp")
+  caffe_source_group("Source\\Cuda" GLOB "${root}/src/caffe/test/test_*.cu")
+
+  # collect files
+  file(GLOB test_hdrs    ${root}/include/caffe/test/test_*.h*)
+  file(GLOB test_srcs    ${root}/src/caffe/test/test_*.cpp)
+  file(GLOB_RECURSE hdrs ${root}/include/caffe/*.h*)
+  file(GLOB_RECURSE srcs ${root}/src/caffe/*.cpp)
+  list(REMOVE_ITEM  hdrs ${test_hdrs})
+  list(REMOVE_ITEM  srcs ${test_srcs})
+
+  # adding headers to make the visible in some IDEs (Qt, VS, Xcode)
+  list(APPEND srcs ${hdrs} ${CMAKE_BINARY_DIR}/caffe_config.h)
+  list(APPEND test_srcs ${test_hdrs})
+
+  # collect cuda files
+  file(GLOB    test_cuda ${root}/src/caffe/test/test_*.cu)
+  file(GLOB_RECURSE cuda ${root}/src/caffe/*.cu)
+  list(REMOVE_ITEM  cuda ${test_cuda})
+
+  # add proto to make them editable in IDEs too
+  file(GLOB_RECURSE proto_files ${root}/src/caffe/*.proto)
+  list(APPEND srcs ${proto_files})
+
+  # convet to absolute paths
+  caffe_convert_absolute_paths(srcs)
+  caffe_convert_absolute_paths(cuda)
+  caffe_convert_absolute_paths(test_srcs)
+  caffe_convert_absolute_paths(test_cuda)
+
+  # propogate to parent scope
+  set(srcs ${srcs} PARENT_SCOPE)
+  set(cuda ${cuda} PARENT_SCOPE)
+  set(test_srcs ${test_srcs} PARENT_SCOPE)
+  set(test_cuda ${test_cuda} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Short command for setting defeault target properties
+# Usage:
+#   caffe_default_properties(<target>)
+function(caffe_default_properties target)
+  set_target_properties(${target} PROPERTIES
+    DEBUG_POSTFIX ${Caffe_DEBUG_POSTFIX}
+    ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib"
+    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib"
+    RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin")
+endfunction()
+
+################################################################################################
+# Short command for setting runtime directory for build target
+# Usage:
+#   caffe_set_runtime_directory(<target> <dir>)
+function(caffe_set_runtime_directory target dir)
+  set_target_properties(${target} PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${dir}")
+endfunction()
+
+################################################################################################
+# Short command for setting solution folder property for target
+# Usage:
+#   caffe_set_solution_folder(<target> <folder>)
+function(caffe_set_solution_folder target folder)
+  if(USE_PROJECT_FOLDERS)
+    set_target_properties(${target} PROPERTIES FOLDER "${folder}")
+  endif()
+endfunction()
+
+################################################################################################
+# Reads lines from input file, prepends source directory to each line and writes to output file
+# Usage:
+#   caffe_configure_testdatafile(<testdatafile>)
+function(caffe_configure_testdatafile file)
+  file(STRINGS ${file} __lines)
+  set(result "")
+  foreach(line ${__lines})
+    set(result "${result}${CMAKE_SOURCE_DIR}/${line}\n")
+  endforeach()
+  dmsg(${result})
+  file(WRITE ${file}.gen.cmake ${result})
+endfunction()
+
+################################################################################################
+# Filter outs all files that are not inlcuded in selected list
+# Usage:
+#   caffe_leave_only_selected_tests(<filelist_variable> <selected_list>)
+function(caffe_leave_only_selected_tests file_list)
+  if(NOT ARGN)
+    return() # blank list means leave all
+  endif()
+  string(REPLACE "," ";" __selected ${ARGN})
+  list(APPEND __selected caffe_main)
+
+  set(result "")
+  foreach(f ${${file_list}})
+    get_filename_component(name ${f} NAME_WE)
+    string(REGEX REPLACE "^test_" "" name ${name})
+    list(FIND __selected ${name} __index)
+    if(NOT __index EQUAL -1)
+      list(APPEND result ${f})
+    endif()
+  endforeach()
+  set(${file_list} ${result} PARENT_SCOPE)
+endfunction()
+
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
new file mode 100644
index 00000000000..a4b03d961e0
--- /dev/null
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -0,0 +1,58 @@
+# Config file for the Caffe package.
+#
+# Note:
+#   Caffe and this config file depends on opencv,
+#   so put `find_package(OpenCV)` before searching Caffe
+#   via `find_package(Caffe)`. All other lib/includes
+#   dependencies are hard coded int the file
+#
+# After successful configuration the following variables
+# will be defined:
+#
+#   Caffe_INCLUDE_DIRS - Caffe include directories
+#   Caffe_LIBRARIES    - libraries to link against
+#   Caffe_DEFINITIONS  - a list of definitions to pass to compiler
+#
+#   Caffe_HAVE_CUDA    - signals about CUDA support
+#   Caffe_HAVE_CUDNN   - signals about cuDNN support
+ 
+
+# OpenCV dependency
+
+if(NOT OpenCV_FOUND)
+  set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
+  if(Caffe_OpenCV_CONFIG_PATH)
+    get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+
+    if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
+      message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
+      include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+    endif()
+
+  else()
+    find_package(OpenCV REQUIRED)
+  endif()
+  unset(Caffe_OpenCV_CONFIG_PATH)
+endif()
+
+# Compute paths
+get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
+
+@Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
+ 
+# Our library dependencies
+if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
+  include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
+endif()
+
+# List of IMPORTED libs created by CaffeTargets.cmake
+set(Caffe_LIBRARIES caffe)
+
+# Definitions
+set(Caffe_DEFINITIONS "@Caffe_DEFINITIONS@")
+
+# Cuda support variables
+set(Caffe_CPU_ONLY @CPU_ONLY@)
+set(Caffe_HAVE_CUDA @HAVE_CUDA@)
+set(Caffe_HAVE_CUDNN @HAVE_CUDNN@)
diff --git a/cmake/Templates/CaffeConfigVersion.cmake.in b/cmake/Templates/CaffeConfigVersion.cmake.in
new file mode 100644
index 00000000000..cbfa514f1a6
--- /dev/null
+++ b/cmake/Templates/CaffeConfigVersion.cmake.in
@@ -0,0 +1,11 @@
+set(PACKAGE_VERSION "@Caffe_VERSION@")
+ 
+# Check whether the requested PACKAGE_FIND_VERSION is compatible
+if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+  set(PACKAGE_VERSION_COMPATIBLE TRUE)
+  if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
+    set(PACKAGE_VERSION_EXACT TRUE)
+  endif()
+endif()
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
new file mode 100644
index 00000000000..6aa22ed7b5c
--- /dev/null
+++ b/cmake/Templates/caffe_config.h.in
@@ -0,0 +1,32 @@
+/* Sources directory */
+#define SOURCE_FOLDER "${CMAKE_SOURCE_DIR}"
+
+/* Binaries directory */
+#define BINARY_FOLDER "${CMAKE_BINARY_DIR}"
+
+/* NVIDA Cuda */
+#cmakedefine HAVE_CUDA
+
+/* NVIDA cuDNN */
+#cmakedefine HAVE_CUDNN
+#cmakedefine USE_CUDNN
+
+/* NVIDA cuDNN */
+#cmakedefine CPU_ONLY
+
+/* Test device */
+#define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE}
+
+/* Temporary (TODO: remove) */
+#if 1
+  #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/"
+  #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/"
+  #define CMAKE_EXT ".gen.cmake"
+#else
+  #define CMAKE_SOURCE_DIR "src/"
+  #define EXAMPLES_SOURCE_DIR "examples/"
+  #define CMAKE_EXT ""
+#endif
+
+/* Matlab */
+#cmakedefine HAVE_MATLAB
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
new file mode 100644
index 00000000000..048123dbe10
--- /dev/null
+++ b/cmake/Utils.cmake
@@ -0,0 +1,365 @@
+################################################################################################
+# Command alias for debugging messages
+# Usage:
+#   dmgs(<message>)
+function(dmsg)
+  message(STATUS ${ARGN})
+endfunction()
+
+################################################################################################
+# Removes duplicates from list(s)
+# Usage:
+#   caffe_list_unique(<list_variable> [<list_variable>] [...])
+macro(caffe_list_unique)
+  foreach(__lst ${ARGN})
+    if(${__lst})
+      list(REMOVE_DUPLICATES ${__lst})
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Clears variables from lsit
+# Usage:
+#   caffe_list_unique(<variables_list>)
+macro(caffe_clear_vars)
+  foreach(_var ${ARGN})
+    unset(${_var})
+  endforeach()
+endmacro()
+
+################################################################################################
+# Removes duplicates from string
+# Usage:
+#   caffe_string_unique(<string_variable>)
+function(caffe_string_unique __string)
+  if(${__string})
+    set(__list ${${__string}})
+    separate_arguments(__list)
+    list(REMOVE_DUPLICATES __list)
+    foreach(__e ${__list})
+      set(__str "${__str} ${__e}")
+    endforeach()
+    set(${__string} ${__str} PARENT_SCOPE)
+  endif()
+endfunction()
+
+################################################################################################
+# Prints list element per line
+# Usage:
+#   caffe_print_list(<list>)
+function(caffe_print_list)
+  foreach(e ${ARGN})
+    message(STATUS ${e})
+  endforeach()
+endfunction()
+
+################################################################################################
+# Function merging lists of compiler flags to single string.
+# Usage:
+#   caffe_merge_flag_lists(out_variable <list1> [<list2>] [<list3>] ...)
+function(caffe_merge_flag_lists out_var)
+  set(__result "")
+  foreach(__list ${ARGN})
+    foreach(__flag ${${__list}})
+      string(STRIP ${__flag} __flag)
+      set(__result "${__result} ${__flag}")
+    endforeach()
+  endforeach()
+  string(STRIP ${__result} __result)
+  set(${out_var} ${__result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Converts all paths in list to absolute
+# Usage:
+#   caffe_convert_absolute_paths(<list_variable>)
+function(caffe_convert_absolute_paths variable)
+  set(__dlist "")
+  foreach(__s ${${variable}})
+    get_filename_component(__abspath ${__s} ABSOLUTE)
+    list(APPEND __list ${__abspath})
+  endforeach()
+  set(${variable} ${__list} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Reads set of version defines from the header file
+# Usage:
+#   caffe_parse_header(<file> <define1> <define2> <define3> ..)
+macro(caffe_parse_header FILENAME FILE_VAR)
+  set(vars_regex "")
+  set(__parnet_scope OFF)
+  set(__add_cache OFF)
+  foreach(name ${ARGN})
+    if("${name}" STREQUAL "PARENT_SCOPE")
+      set(__parnet_scope ON)
+    elseif("${name}" STREQUAL "CACHE")
+      set(__add_cache ON)
+    elseif(vars_regex)
+      set(vars_regex "${vars_regex}|${name}")
+    else()
+      set(vars_regex "${name}")
+    endif()
+  endforeach()
+  if(EXISTS "${FILENAME}")
+    file(STRINGS "${FILENAME}" ${FILE_VAR} REGEX "#define[ \t]+(${vars_regex})[ \t]+[0-9]+" )
+  else()
+    unset(${FILE_VAR})
+  endif()
+  foreach(name ${ARGN})
+    if(NOT "${name}" STREQUAL "PARENT_SCOPE" AND NOT "${name}" STREQUAL "CACHE")
+      if(${FILE_VAR})
+        if(${FILE_VAR} MATCHES ".+[ \t]${name}[ \t]+([0-9]+).*")
+          string(REGEX REPLACE ".+[ \t]${name}[ \t]+([0-9]+).*" "\\1" ${name} "${${FILE_VAR}}")
+        else()
+          set(${name} "")
+        endif()
+        if(__add_cache)
+          set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
+        elseif(__parnet_scope)
+          set(${name} "${${name}}" PARENT_SCOPE)          
+        endif()
+      else()
+        unset(${name} CACHE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+################################################################################################
+# Reads single version define from the header file and parses it
+# Usage:
+#   caffe_parse_header_single_define(<library_name> <file> <define_name>)
+function(caffe_parse_header_single_define LIBNAME HDR_PATH VARNAME)
+  set(${LIBNAME}_H "")
+  if(EXISTS "${HDR_PATH}")
+    file(STRINGS "${HDR_PATH}" ${LIBNAME}_H REGEX "^#define[ \t]+${VARNAME}[ \t]+\"[^\"]*\".*$" LIMIT_COUNT 1)
+  endif()
+
+  if(${LIBNAME}_H)
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MAJOR "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_MINOR  "${${LIBNAME}_H}")
+    string(REGEX REPLACE "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.([0-9]+).*$" "\\1" ${LIBNAME}_VERSION_PATCH "${${LIBNAME}_H}")
+    set(${LIBNAME}_VERSION_MAJOR ${${LIBNAME}_VERSION_MAJOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_MINOR ${${LIBNAME}_VERSION_MINOR} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_PATCH ${${LIBNAME}_VERSION_PATCH} ${ARGN} PARENT_SCOPE)
+    set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_MAJOR}.${${LIBNAME}_VERSION_MINOR}.${${LIBNAME}_VERSION_PATCH}" PARENT_SCOPE)
+
+    # append a TWEAK version if it exists:
+    set(${LIBNAME}_VERSION_TWEAK "")
+    if("${${LIBNAME}_H}" MATCHES "^.*[ \t]${VARNAME}[ \t]+\"[0-9]+\\.[0-9]+\\.[0-9]+\\.([0-9]+).*$")
+      set(${LIBNAME}_VERSION_TWEAK "${CMAKE_MATCH_1}" ${ARGN} PARENT_SCOPE)
+    endif()
+    if(${LIBNAME}_VERSION_TWEAK)
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}.${${LIBNAME}_VERSION_TWEAK}" ${ARGN} PARENT_SCOPE)
+    else()
+      set(${LIBNAME}_VERSION_STRING "${${LIBNAME}_VERSION_STRING}" ${ARGN} PARENT_SCOPE)
+    endif()
+  endif()
+endfunction()
+
+########################################################################################################
+# An option that the user can select. Can accept condition to control when option is available for user.
+# Usage:
+#   caffe_option(<option_variable> "doc string" <initial value or boolean expression> [IF <condition>])
+function(caffe_option variable description value)
+  set(__value ${value})
+  set(__condition "")
+  set(__varname "__value")
+  foreach(arg ${ARGN})
+    if(arg STREQUAL "IF" OR arg STREQUAL "if")
+      set(__varname "__condition")
+    else()
+      list(APPEND ${__varname} ${arg})
+    endif()
+  endforeach()
+  unset(__varname)
+  if("${__condition}" STREQUAL "")
+    set(__condition 2 GREATER 1)
+  endif()
+
+  if(${__condition})
+    if("${__value}" MATCHES ";")
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    elseif(DEFINED ${__value})
+      if(${__value})
+        option(${variable} "${description}" ON)
+      else()
+        option(${variable} "${description}" OFF)
+      endif()
+    else()
+      option(${variable} "${description}" ${__value})
+    endif()
+  else()
+    unset(${variable} CACHE)
+  endif()
+endfunction()
+
+################################################################################################
+# Utility macro for comparing two lists. Used for CMake debugging purposes
+# Usage:
+#   caffe_compare_lists(<list_variable> <list2_variable> [description])
+function(caffe_compare_lists list1 list2 desc)
+  set(__list1 ${${list1}})
+  set(__list2 ${${list2}})
+  list(SORT __list1)
+  list(SORT __list2)
+  list(LENGTH __list1 __len1)
+  list(LENGTH __list2 __len2)
+
+  if(NOT ${__len1} EQUAL ${__len2})
+    message(FATAL_ERROR "Lists are not equal. ${__len1} != ${__len2}. ${desc}")
+  endif()
+
+  foreach(__i RANGE 1 ${__len1})
+    math(EXPR __index "${__i}- 1")
+    list(GET __list1 ${__index} __item1)
+    list(GET __list2 ${__index} __item2)
+    if(NOT ${__item1} STREQUAL ${__item2})
+      message(FATAL_ERROR "Lists are not equal. Differ at element ${__index}. ${desc}")
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Command for disabling warnings for different platforms (see below for gcc and VisualStudio)
+# Usage:
+#   caffe_warnings_disable(<CMAKE_[C|CXX]_FLAGS[_CONFIGURATION]> -Wshadow /wd4996 ..,)
+macro(caffe_warnings_disable)
+  set(_flag_vars "")
+  set(_msvc_warnings "")
+  set(_gxx_warnings "")
+
+  foreach(arg ${ARGN})
+    if(arg MATCHES "^CMAKE_")
+      list(APPEND _flag_vars ${arg})
+    elseif(arg MATCHES "^/wd")
+      list(APPEND _msvc_warnings ${arg})
+    elseif(arg MATCHES "^-W")
+      list(APPEND _gxx_warnings ${arg})
+    endif()
+  endforeach()
+
+  if(NOT _flag_vars)
+    set(_flag_vars CMAKE_C_FLAGS CMAKE_CXX_FLAGS)
+  endif()
+
+  if(MSVC AND _msvc_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_msvc_warnings})
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  elseif((CMAKE_COMPILER_IS_GNUCXX OR CMAKE_COMPILER_IS_CLANGXX) AND _gxx_warnings)
+    foreach(var ${_flag_vars})
+      foreach(warning ${_gxx_warnings})
+        if(NOT warning MATCHES "^-Wno-")
+          string(REPLACE "${warning}" "" ${var} "${${var}}")
+          string(REPLACE "-W" "-Wno-" warning "${warning}")
+        endif()
+        set(${var} "${${var}} ${warning}")
+      endforeach()
+    endforeach()
+  endif()
+  caffe_clear_vars(_flag_vars _msvc_warnings _gxx_warnings)
+endmacro()
+
+################################################################################################
+# Helper function get current definitions
+# Usage:
+#   caffe_get_current_definitions(<definitions_variable>)
+function(caffe_get_current_definitions definitions_var)
+  get_property(current_definitions DIRECTORY PROPERTY COMPILE_DEFINITIONS)
+  set(result "")
+
+  foreach(d ${current_definitions})
+    list(APPEND result -D${d})
+  endforeach()
+
+  caffe_list_unique(result)
+  set(${definitions_var} ${result} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function get current includes/definitions
+# Usage:
+#   caffe_get_current_cflags(<cflagslist_variable>)
+function(caffe_get_current_cflags cflags_var)
+  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
+  caffe_convert_absolute_paths(current_includes)
+  caffe_get_current_definitions(cflags)
+
+  foreach(i ${current_includes})
+    list(APPEND cflags "-I${i}")
+  endforeach()
+
+  caffe_list_unique(cflags)
+  set(${cflags_var} ${cflags} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+# Helper function to parse current linker libs into link directoris, libflags and osx frameworks
+# Usage:
+#   caffe_parse_linker_libs(<Caffe_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
+function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var)
+
+  set(__unspec "")
+  set(__debug "")
+  set(__optimized "")
+  set(__framework "")
+  set(__varname "__unspec")
+
+  # split libs into debug, optimized, unspecified and frameworks
+  foreach(list_elem ${${Caffe_LINKER_LIBS_variable}})
+    if(list_elem STREQUAL "debug")
+      set(__varname "__debug")
+    elseif(list_elem STREQUAL "optimized")
+      set(__varname "__optimized")
+    elseif(list_elem MATCHES "^-framework[ \t]+([^ \t].*)")
+      list(APPEND __framework -framework ${CMAKE_MATCH_1})
+    else()
+      list(APPEND ${__varname} ${list_elem})
+      set(__varname "__unspec")
+    endif()
+  endforeach()
+
+  # attach debug or optimized libs to unspecified according to current configuration
+  if(CMAKE_BUILD_TYPE MATCHES "Debug")
+    set(__libs ${__unspec} ${__debug})
+  else()
+    set(__libs ${__unspec} ${__optimized})
+  endif()
+
+  set(libflags "")
+  set(folders "")
+
+  # convert linker libraries list to link flags
+  foreach(lib ${__libs})
+    if(TARGET ${lib})
+      list(APPEND folders $<TARGET_LINKER_FILE_DIR:${lib}>)
+      list(APPEND libflags -l${lib})
+    elseif(lib MATCHES "^-l.*")
+      list(APPEND libflags ${lib})
+    elseif(IS_ABSOLUTE ${lib})
+      get_filename_component(name_we ${lib} NAME_WE)
+      get_filename_component(folder  ${lib} PATH)
+
+      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      list(APPEND libflags -l${CMAKE_MATCH_1})
+      list(APPEND folders    ${folder})
+    else()
+      message(FATAL_ERROR "Logic error. Need to update cmake script")
+    endif()
+  endforeach()
+
+  caffe_list_unique(libflags folders)
+
+  set(${folders_var} ${folders} PARENT_SCOPE)
+  set(${flags_var} ${libflags} PARENT_SCOPE)
+  set(${frameworks_var} ${__framework} PARENT_SCOPE)
+endfunction()
diff --git a/CMakeScripts/lint.cmake b/cmake/lint.cmake
similarity index 100%
rename from CMakeScripts/lint.cmake
rename to cmake/lint.cmake
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
new file mode 100644
index 00000000000..927bf5bc731
--- /dev/null
+++ b/docs/CMakeLists.txt
@@ -0,0 +1,106 @@
+# Building docs script
+# Requirements:
+#   sudo apt-get install doxygen texlive ruby-dev
+#   sudo gem install jekyll execjs therubyracer
+
+if(NOT BUILD_docs OR NOT DOXYGEN_FOUND)
+  return()
+endif()
+
+#################################################################################################
+# Gather docs from <root>/examples/**/readme.md
+function(gather_readmes_as_prebuild_cmd target gathered_dir root)
+  set(full_gathered_dir ${root}/${gathered_dir})
+
+  file(GLOB_RECURSE readmes ${root}/examples/readme.md ${root}/examples/README.md)
+  foreach(file ${readmes})
+    # Only use file if it is to be included in docs.
+    file(STRINGS ${file} file_lines REGEX "include_in_docs: true")
+
+    if(file_lines)
+      # Since everything is called readme.md, rename it by its dirname.
+      file(RELATIVE_PATH file ${root} ${file})
+      get_filename_component(folder ${file} PATH)
+      set(new_filename ${full_gathered_dir}/${folder}.md)
+
+      # folder value might be like <subfolder>/readme.md. That's why make directory.
+      get_filename_component(new_folder ${new_filename} PATH)
+      add_custom_command(TARGET ${target} PRE_BUILD
+        COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
+        COMMAND ln -sf ${root}/${file} ${new_filename}
+        COMMENT "Creating simlink ${new_filename} -> ${root}/${file}"
+        WORKING_DIRECTORY ${root} VERBATIM)
+    endif()
+  endforeach()
+endfunction()
+
+################################################################################################
+# Gather docs from examples/*.ipynb and add YAML front-matter.
+function(gather_notebooks_as_prebuild_cmd target gathered_dir root)
+  set(full_gathered_dir ${root}/${gathered_dir})
+
+  if(NOT PYTHON_EXECUTABLE)
+    message(STATUS "Python interpeter is not found. Can't include *.ipynb files in docs. Skipping...")
+    return()
+  endif()
+
+  file(GLOB_RECURSE notebooks ${root}/examples/*.ipynb)
+  foreach(file ${notebooks})
+    file(RELATIVE_PATH file ${root} ${file})
+    set(new_filename ${full_gathered_dir}/${file})
+
+    get_filename_component(new_folder ${new_filename} PATH)
+    add_custom_command(TARGET ${target} PRE_BUILD
+      COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
+      COMMAND ${PYTHON_EXECUTABLE} scripts/copy_notebook.py ${file} ${new_filename}
+      COMMENT "Copying notebook ${file} to ${new_filename}"
+      WORKING_DIRECTORY ${root} VERBATIM)
+  endforeach()
+
+  set(${outputs_var} ${outputs} PARENT_SCOPE)
+endfunction()
+
+################################################################################################
+########################## [ Non macro part ] ##################################################
+
+# Gathering is done at each 'make doc'
+file(REMOVE_RECURSE ${CMAKE_SOURCE_DIR}/docs/gathered)
+
+# Doxygen config file path
+set(DOXYGEN_config_file ${CMAKE_SOURCE_DIR}/.Doxyfile CACHE FILEPATH "Doxygen config file")
+
+# Adding docs target
+add_custom_target(docs COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYGEN_config_file}
+                       WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+                       COMMENT "Launching doxygen..." VERBATIM)
+
+# Gathering examples into docs subfolder
+gather_notebooks_as_prebuild_cmd(docs docs/gathered ${CMAKE_SOURCE_DIR})
+gather_readmes_as_prebuild_cmd(docs docs/gathered  ${CMAKE_SOURCE_DIR})
+
+# Auto detect output directory
+file(STRINGS ${DOXYGEN_config_file} config_line REGEX "OUTPUT_DIRECTORY[ \t]+=[^=].*")
+if(config_line)
+  string(REGEX MATCH "OUTPUT_DIRECTORY[ \t]+=([^=].*)" __ver_check "${config_line}")
+  string(STRIP ${CMAKE_MATCH_1} output_dir)
+  message(STATUS "Detected Doxygen OUTPUT_DIRECTORY: ${output_dir}")
+else()
+  set(output_dir ./doxygen/)
+  message(STATUS "Can't find OUTPUT_DIRECTORY in doxygen config file. Try to use default: ${output_dir}")
+endif()
+
+if(NOT IS_ABSOLUTE ${output_dir})
+  set(output_dir ${CMAKE_SOURCE_DIR}/${output_dir})
+  get_filename_component(output_dir ${output_dir} ABSOLUTE)
+endif()
+
+# creates simlink in docs subfolder to code documentation built by doxygen
+add_custom_command(TARGET docs POST_BUILD VERBATIM
+                   COMMAND ln -sfn "${output_dir}/html" doxygen
+                   WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/docs
+                   COMMENT "Creating simlink ${CMAKE_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
+
+# for quick launch of jekyll
+add_custom_target(jekyll COMMAND jekyll serve -w -s . -d _site --port=4000
+                         WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/docs
+                         COMMENT "Launching jekyll..." VERBATIM)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 055f4ef0d35..4df14afbb98 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,16 +1,31 @@
-project( Examples )
+file(GLOB_RECURSE examples_srcs "${CMAKE_SOURCE_DIR}/examples/*.cpp")
 
-file(GLOB_RECURSE EXAMPLES_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-foreach(source ${EXAMPLES_SOURCES})
-    #    get file name
-    get_filename_component(name ${source} NAME_WE)  
+foreach(source_file ${examples_srcs})
+  # get file name
+  get_filename_component(name ${source_file} NAME_WE)
     
-    #get folder name
-    get_filename_component(path ${source} PATH)      
-    get_filename_component(folder ${path} NAME_WE)      
+  # get folder name
+  get_filename_component(path ${source_file} PATH)
+  get_filename_component(folder ${path} NAME_WE)
     
-    add_executable(${name} ${source})
-    target_link_libraries(${name} caffe)
-    set_target_properties(${name} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/${folder})
-endforeach(source)
+  add_executable(${name} ${source_file})
+  target_link_libraries(${name} ${Caffe_LINK})
+  caffe_default_properties(${name})
+
+  # set back RUNTIME_OUTPUT_DIRECTORY
+  set_target_properties(${name} PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/examples/${folder}")
+
+  caffe_set_solution_folder(${name} examples)
+
+  # install
+  install(TARGETS ${name} DESTINATION bin)
+
+  if(UNIX OR APPLE)
+    # Funny command to make tutorials work
+    # TODO: remove in future as soon as naming is standartaized everywhere
+    set(__outname ${CMAKE_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+    add_custom_command(TARGET ${name} POST_BUILD
+                       COMMAND ln -sf "${__outname}" "${__outname}.bin")
+  endif()
+endforeach()
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 438acf2bf17..bd5f31e063f 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -15,7 +15,7 @@ using std::cout;
 using std::endl;
 
 #ifdef CMAKE_BUILD
-  #include <cmake_test_defines.hpp.gen.cmake>
+  #include "caffe_config.h"
 #else
   #define CUDA_TEST_DEVICE -1
   #define CMAKE_SOURCE_DIR "src/"
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index f6a03ee4625..40d8729ae76 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -1 +1,72 @@
-project( Matlab )
\ No newline at end of file
+# Builds Matlab (or Octave) interface. In case of Matlab caffe must be
+# compield as shared library. Octave can link static or shared caffe library
+# To install octave run: sudo apt-get install liboctave-dev
+
+if(NOT BUILD_matlab)
+  return()
+endif()
+
+if(HAVE_MATLAB AND Octave_compiler)
+  set(build_using ${Matlab_build_mex_using})
+elseif(HAVE_MATLAB AND NOT Octave_compiler)
+  set(build_using "Matlab")
+elseif(NOT HAVE_MATLAB AND Octave_compiler)
+  set(build_using "Octave")
+else()
+  return()
+endif()
+
+if(NOT BUILD_SHARED_LIBS AND build_using MATCHES Matlab)
+  message(FATAL_ERROR "Matlab MEX interface (with default mex options file) can only be built if caffe is compiled as shared library. Please enable 'BUILD_SHARED_LIBS' in CMake. Aternativelly you can switch to Octave compiler.")
+endif()
+
+# helper function to set proper mex file extention
+function(caffe_fetch_and_set_proper_mexext mexfile_variable)
+  execute_process(COMMAND ${Matlab_mexext} OUTPUT_STRIP_TRAILING_WHITESPACE RESULT_VARIABLE res OUTPUT_VARIABLE ext)
+  if(res MATCHES 0)
+    get_filename_component(folder  ${${mexfile_variable}} PATH)
+    get_filename_component(name_we ${${mexfile_variable}} NAME_WE)
+    set(${mexfile_variable} ${folder}/${name_we}.${ext} PARENT_SCOPE)
+  endif()
+endfunction()
+
+# global settings
+file(GLOB Matlab_srcs caffe/matcaffe.cpp)
+set(Matlab_caffe_mex ${CMAKE_SOURCE_DIR}/matlab/caffe/caffe.mex)
+
+caffe_get_current_cflags(cflags)
+caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_framewoks)
+set(folders $<TARGET_LINKER_FILE_DIR:caffe> ${folders})
+
+# prepare linker flag lists
+string(REPLACE ";" ";-L" link_folders "-L${folders}")
+string(REPLACE ";" ":"  rpath_folders   "${folders}")
+
+if(build_using MATCHES "Matlab")
+  set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
+
+  caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex)
+  add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex}
+      ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags}
+      DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM)
+  add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+
+elseif(build_using MATCHES "Octave")
+
+  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+    set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
+  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+    set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
+  endif()
+
+  add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler}
+      ARGS --mex -o ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags} -Wl,-rpath,${rpath_folders}
+      DEPENDS caffe COMMENT "Building Octave interface: ${Matlab_caffe_mex}" VERBATIM)
+
+  add_custom_target(octave ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+endif()
+
+# ---[ Install
+file(GLOB mfiles caffe/*.m)
+install(FILES ${mfiles} ${Matlab_caffe_mex} DESTINATION matlab)
+
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 6470517d213..e943f691c49 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -1,58 +1,27 @@
-project( Python )
-
-#    Python
-find_package(PythonLibs 2.7 REQUIRED)
-
-#   Numpy
-find_package(NumPy REQUIRED)
-
-#    Boost.Python
-find_package(Boost 1.46 COMPONENTS python REQUIRED)
-
-
-
-#In case you have both python2 and python3 installed the quickest way to
-#compile pycaffe with cmake is to replace the following hardcoded paths.
-#Althernativley the Find* scripts could be rewritten to support choice of
-#of python version.
-#if(${PYTHONLIBS_VERSION_STRING} MATCHES "^[3-9]+\\.[0-9]+(\\.[0-9]+.*)?$") 
-#    
-#    set( PYTHON_INCLUDE_DIRS "/usr/include/python2.7")
-#    set( PYTHON_LIBRARIES    "/usr/lib64/libpython2.7.so")
-#    set( NUMPY_INCLUDE_DIRS  "/usr/lib64/python2.7/site-packages/numpy/core/include/")
-#    set( PYTHON_LIBRARIES    "/usr/lib64/python2.7/site-packages/numpy/lib/")
-#    set(Boost_LIBRARIES      "/usr/lib64/libboost_python-2.7-mt.so")
-#
-#    message( "Warning: cmake found python3 by default, switching to hardcoded paths")
-#
-#    message( "PYTHON_INCLUDE_DIRS =/usr/include/python2.7")
-#    message( "PYTHON_LIBRARIES    =/usr/lib64/libpython2.7.so")
-#    message( "NUMPY_INCLUDE_DIRS  =/usr/lib64/python2.7/site-packages/numpy/core/include/")
-#    message( "PYTHON_LIBRARIES    =/usr/lib64/python2.7/site-packages/numpy/lib/")
-#    message( "Boost_LIBRARIES     =/usr/lib64/libboost_python-2.7-mt.so")
-#endif()
-
-
-include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIRS}
-    ${Boost_INCLUDE_DIRS})
-
-file(GLOB_RECURSE Python_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-add_library(pycaffe SHARED ${Python_SOURCES})
-
-add_dependencies(pycaffe protoPy)
-
-target_link_libraries(pycaffe ${CAFFE_STATIC_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
-
-set_target_properties(pycaffe PROPERTIES PREFIX "")
-set_target_properties(pycaffe PROPERTIES OUTPUT_NAME "_caffe")
-
-###    Install    #############################################################
-
+if(NOT HAVE_PYTHON)
+  message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...")
+  return()
+endif()
+
+include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
+file(GLOB_RECURSE python_srcs ${CMAKE_SOURCE_DIR}/python/*.cpp)
+
+add_library(pycaffe SHARED ${python_srcs})
+target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
+caffe_default_properties(pycaffe)
+
+if(UNIX OR APPLE)
+    set(__linkname "${CMAKE_SOURCE_DIR}/python/_caffe.so")
+    add_custom_command(TARGET pycaffe POST_BUILD
+                       COMMAND ln -sf $<TARGET_LINKER_FILE:caffe> "${CMAKE_SOURCE_DIR}/python/_caffe.so"
+                       COMMENT "Creating simlink ${__linkname} -> ${CMAKE_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+endif()
+
+# ---[ Install
+file(GLOB files *.py requirements.txt)
+install(FILES  ${files} DESTINATION python)
 install(DIRECTORY caffe DESTINATION python)
-install(FILES requirements.txt DESTINATION python)
-
-#This installs a library named "libpycaffe.so"
 install(TARGETS pycaffe DESTINATION python/caffe)
 
 
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index dda072688f8..ef74e1f004b 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -1,55 +1,36 @@
-project( CaffeSrc )
+# generate protobuf sources
+file(GLOB proto_files proto/*.proto)
+caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_python ${proto_files})
 
+# include python files either to force generation
+add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
+set(Caffe_LINKER_LIBS proto ${Caffe_LINKER_LIBS}) # note, crucial to prepend!
+caffe_default_properties(proto)
 
-add_subdirectory(proto)
+# --[ Caffe library
 
-#    Recursively find source files
-##    test sources
-file(GLOB_RECURSE TEST_CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cpp)
+# creates 'test_srcs', 'srcs', 'test_cuda', 'cuda' lists
+caffe_pickup_caffe_sources(${CMAKE_SOURCE_DIR})
 
-##    all cpp sources
-file(GLOB_RECURSE CPP_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+if(HAVE_CUDA)
+  caffe_cuda_compile(cuda_objs ${cuda})
+  list(APPEND srcs ${cuda_objs} ${cuda})
+endif()
 
-##    remove test sources from cpp sources
-list(REMOVE_ITEM CPP_SOURCES ${TEST_CPP_SOURCES})
+add_library(caffe ${srcs})
+target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
+caffe_default_properties(caffe)
 
-add_library(caffe ${CPP_SOURCES})
-# both depend on proto
-add_dependencies(caffe proto)
+# ---[ Tests
+ add_subdirectory(test)
+
+# ---[ Install
+install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include)
+install(FILES ${proto_hdrs} DESTINATION include/caffe/proto)
+install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib)
+
+file(WRITE ${CMAKE_BINARY_DIR}/__init__.py)
+list(APPEND proto_python ${CMAKE_BINARY_DIR}/__init__.py)
+install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto)
 
-#    cuda sources
-if(NOT CPU_ONLY)
-    file(GLOB_RECURSE CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cu)
-    file(GLOB_RECURSE TEST_CU_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/test_*.cu)
-    list(REMOVE_ITEM CU_SOURCES ${TEST_CU_SOURCES})
-    cuda_add_library(caffe_cu ${CU_SOURCES})
-    add_dependencies(caffe_cu proto)
-    target_link_libraries(caffe caffe_cu
-        ${CUDA_CUBLAS_LIBRARIES}
-        ${CUDA_curand_LIBRARY}
-    )
-endif()
 
-target_link_libraries(caffe proto
-        ${BLAS_LIBRARIES}
-        ${Boost_LIBRARIES}
-        ${GFLAGS_LIBRARIES}
-        ${GLOG_LIBRARIES}
-        ${HDF5_LIBRARIES}
-        ${LEVELDB_LIBS}
-        ${LMDB_LIBRARIES}
-        ${OpenCV_LIBS}
-        ${CMAKE_THREAD_LIBS_INIT}
-)
-
-#set output directory
-set_target_properties(caffe PROPERTIES
-        ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
-        LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
-)
-
-add_subdirectory(test)
-
-###    Install    #################################################################################
-
-install(TARGETS caffe DESTINATION lib)
diff --git a/src/caffe/proto/CMakeLists.txt b/src/caffe/proto/CMakeLists.txt
deleted file mode 100644
index 12e7ce0a326..00000000000
--- a/src/caffe/proto/CMakeLists.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-project( Proto )
-
-#	Google Protocol Buffers
-find_package( Protobuf REQUIRED )
-
-# As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package and should be installed
-# separately as in: sudo apt-get install protobuf-compiler
-if(PROTOBUF_PROTOC_EXECUTABLE)
-	message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
-else()
-	message(FATAL_ERROR "Could not find PROTOBUF Compiler")
-endif()
-
-include_directories(${PROTOBUF_INCLUDE_DIR})
-file(GLOB ProtoFiles "${CMAKE_CURRENT_SOURCE_DIR}/*.proto")
-PROTOBUF_GENERATE_CPP(ProtoSources ProtoHeaders ${ProtoFiles})
-PROTOBUF_GENERATE_PYTHON(ProtoSourcesPy ${ProtoFiles})
-
-add_custom_target(protoPy DEPENDS ${ProtoSourcesPy})
-
-add_library(proto
-        ${ProtoSources}
-        ${ProtoHeaders}
-        )
-   
-
-target_link_libraries(proto ${PROTOBUF_LIBRARIES})
-
-# Create proto include directory
-file(MAKE_DIRECTORY ${CMAKE_SOURCE_DIR}/include/caffe/proto)
-
-# Copy proto headers to include/caffe/proto/
-foreach(header ${ProtoHeaders})
-
-    ADD_CUSTOM_COMMAND(TARGET proto
-        COMMAND cmake -E copy ${header}
-        ${Caffe_INCLUDE_DIRS}/caffe/proto/
-        DEPENDS ${header}
-)
-
-endforeach(header)
-
-file(WRITE __init__.py)
-install(PROGRAMS __init__.py       DESTINATION python/caffe/proto)
-install(PROGRAMS ${ProtoSourcesPy} DESTINATION python/caffe/proto)
-
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index ce0aa4c5148..ed374e8e110 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -1,105 +1,36 @@
-#
-#
-#    All test files' names must begin with a "test_" prefix
-#
-#
-project( Test )
-
-# Configuration
-set(TEST_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)    # test executables are going to be placed there
-set(TEST_EXT .testbin)           # test executable extension
-set(ALL_TEST test${TEST_EXT})    # name of an executable comprising of all tests
-set(RUN_TEST runtest)            # dummy target for running tests
-set(TEST_MAIN test_caffe_main.cpp)     # main test file (with main function)
-
-#    Generate config files
-add_definitions(-DCMAKE_BUILD)    # definition needed in order to include CMake's generated files
-set(IN_EXT .in)            # generator input file extension
-set(GEN_EXT .gen.cmake)    # generated output file extension
-set(TEST_DEFINES_FILE ${CMAKE_CURRENT_SOURCE_DIR}/cmake_test_defines.hpp)
-set(TEST_DATA_FILE ${CMAKE_CURRENT_SOURCE_DIR}/test_data/sample_data_list.txt)
-
-# Function prepares name of a test executable
-#    @output_name -  output variable's name
-#    @filename    -  test_*.cpp file path
-function(test_name output_name filename)
-    get_filename_component(name ${filename} NAME_WE)
-    set(${output_name} ${name}${TEST_EXT} PARENT_SCOPE)
-endfunction()
-
-set(IN_FILES             # generator input files
-    ${TEST_DEFINES_FILE}
-    ${TEST_DATA_FILE}
-)
-
-foreach(in_file ${IN_FILES})
-    configure_file(
-        ${in_file}${IN_EXT}
-        ${in_file}${GEN_EXT}
-    )
-endforeach()
-
-include_directories(
-        ${Caffe_SOURCE_DIR}
-        ${CMAKE_CURRENT_SOURCE_DIR}
-)
-
-# Remove main from test sources and prepare an Object lib with main
-file(GLOB TEST_MAIN ${TEST_MAIN})
-list(REMOVE_ITEM TEST_CPP_SOURCES ${TEST_MAIN})
-add_library(main_obj EXCLUDE_FROM_ALL OBJECT ${TEST_MAIN})
-
-#    Build each test separately from *.cpp files
-foreach(source ${TEST_CPP_SOURCES})
-    test_name(TEST_NAME ${source})
-
-    #
-    add_library(${TEST_NAME}.obj EXCLUDE_FROM_ALL OBJECT ${source})
-    set(TEST_OBJ_LIB $<TARGET_OBJECTS:${TEST_NAME}.obj>)
-
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${TEST_OBJ_LIB} $<TARGET_OBJECTS:main_obj>)
-    target_link_libraries(${TEST_NAME} gtest ${CAFFE_STATIC_LINK})
-
-    #    output dir
-    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-
-    # Targets and object libs
-    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-    set(TEST_OBJ_LIBS ${TEST_OBJ_LIBS} ${TEST_OBJ_LIB})
-endforeach()
-
-#    Build each test separately from *.cu files
-foreach(source ${TEST_CU_SOURCES})
-    test_name(TEST_NAME ${source})
-
-    cuda_add_library(${TEST_NAME}.lib EXCLUDE_FROM_ALL ${source})
-
-    add_executable(${TEST_NAME} EXCLUDE_FROM_ALL $<TARGET_OBJECTS:main_obj>)
-    target_link_libraries(${TEST_NAME} ${TEST_NAME}.lib gtest ${CAFFE_STATIC_LINK})
-
-    #    output dir
-    set_target_properties(${TEST_NAME} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/test)
-
-    # Targets and object libs
-    set(TEST_TARGETS ${TEST_TARGETS} ${TEST_NAME})
-    set(TEST_CU_LIBS ${TEST_CU_LIBS} ${TEST_NAME}.lib)
-endforeach()
-
-#    Build a compound test excluded from the ALL target
-add_executable(${ALL_TEST} EXCLUDE_FROM_ALL ${TEST_OBJ_LIBS} $<TARGET_OBJECTS:main_obj>)
-if(NOT CPU_ONLY)
-    target_link_libraries(${ALL_TEST} ${TEST_CU_LIBS})
+# The option allows to include in build only selected test files and exclude all others
+# Usage example:
+#  cmake -DBUILD_only_tests="common,net,blob,im2col_kernel"
+set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extention")
+caffe_leave_only_selected_tests(test_srcs ${BUILD_only_tests})
+caffe_leave_only_selected_tests(test_cuda ${BUILD_only_tests})
+
+# For 'make runtest' target we don't need to embed test data paths to
+# source files, because test target is executed in source directory
+# That's why the lines below are commented. TODO: remove them
+
+# definition needed to include CMake generated files
+#add_definitions(-DCMAKE_BUILD)
+
+# generates test_data/sample_data_list.txt.gen.cmake
+#caffe_configure_testdatafile(test_data/sample_data_list.txt)
+
+set(the_target test.testbin)
+set(test_args --gtest_shuffle)
+
+if(HAVE_CUDA)
+  caffe_cuda_compile(test_cuda_objs ${test_cuda})
+  list(APPEND test_srcs ${test_cuda_objs} ${test_cuda})
+else()
+  list(APPEND test_args --gtest_filter="-*GPU*")
 endif()
-target_link_libraries(${ALL_TEST} gtest ${CAFFE_STATIC_LINK})
-add_dependencies(${ALL_TEST} ${TEST_TARGETS})
 
-#    Output directory
-set_target_properties(${ALL_TEST} PROPERTIES RUNTIME_OUTPUT_DIRECTORY ${TEST_OUTPUT_DIRECTORY})
-
-#    Test command
-set(TEST_ARGS --gtest_shuffle)
-if(CPU_ONLY)
-    set(TEST_ARGS ${TEST_ARGS} --gtest_filter="-*GPU*")
-endif()
+# ---[ Adding test target
+add_executable(${the_target} EXCLUDE_FROM_ALL ${test_srcs})
+target_link_libraries(${the_target} gtest ${Caffe_LINK})
+caffe_default_properties(${the_target})
+caffe_set_runtime_directory(${the_target} "${CMAKE_BINARY_DIR}/test")
 
-add_custom_target(${RUN_TEST} COMMAND ${ALL_TEST} ${TEST_ARGS})
+# ---[ Adding runtest
+add_custom_target(runtest COMMAND ${the_target} ${test_args}
+                          WORKING_DIRECTORY ${CMAKE_SOURCE_DIR})
diff --git a/src/caffe/test/cmake_test_defines.hpp.in b/src/caffe/test/cmake_test_defines.hpp.in
deleted file mode 100644
index 870eaf5c26e..00000000000
--- a/src/caffe/test/cmake_test_defines.hpp.in
+++ /dev/null
@@ -1,4 +0,0 @@
-#define CUDA_TEST_DEVICE @CUDA_TEST_DEVICE@
-#define CMAKE_SOURCE_DIR "@CMAKE_SOURCE_DIR@/src/"
-#define EXAMPLES_SOURCE_DIR "@CMAKE_SOURCE_DIR@/examples/"
-#define CMAKE_EXT ".gen.cmake"
diff --git a/src/caffe/test/test_data/sample_data_list.txt.in b/src/caffe/test/test_data/sample_data_list.txt.in
deleted file mode 100644
index 9860ef583ab..00000000000
--- a/src/caffe/test/test_data/sample_data_list.txt.in
+++ /dev/null
@@ -1,2 +0,0 @@
-@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data.h5
-@CMAKE_SOURCE_DIR@/src/caffe/test/test_data/sample_data_2_gzip.h5
\ No newline at end of file
diff --git a/src/gtest/CMakeLists.txt b/src/gtest/CMakeLists.txt
index 82a4120ca3f..ef7ff7ed14b 100644
--- a/src/gtest/CMakeLists.txt
+++ b/src/gtest/CMakeLists.txt
@@ -1,6 +1,5 @@
-project(gtest CXX C)
-cmake_minimum_required(VERSION 2.6.2)
+add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp)
+caffe_default_properties(gtest)
 
-add_library(gtest gtest-all.cpp)
-add_library(gtest_main gtest_main.cc)
-target_link_libraries(gtest_main gtest)
\ No newline at end of file
+#add_library(gtest_main gtest_main.cc)
+#target_link_libraries(gtest_main gtest)
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 110f368b2c8..4e43ba2cf25 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -1,20 +1,29 @@
-project( Tools )
-
-#    Find all source files
-file(GLOB_RECURSE TOOLS_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-
-
-#    Build each source file independently
-foreach(source ${TOOLS_SOURCES})
-    get_filename_component(name ${source} NAME_WE)
-    add_executable(${name}.bin ${source})
-    set_target_properties(${name}.bin PROPERTIES OUTPUT_NAME ${name})
-    target_link_libraries(${name}.bin ${CAFFE_STATIC_LINK})
-
-###    Install    #################################################################################
-
-    install(TARGETS ${name}.bin DESTINATION tools)
-
-
+# Collect source files
+file(GLOB_RECURSE srcs ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+# Build each source file independently
+foreach(source ${srcs})
+  get_filename_component(name ${source} NAME_WE)
+
+  # caffe target already exits
+  if(name MATCHES "caffe")
+    set(name ${name}.bin)
+  endif()
+
+  # target
+  add_executable(${name} ${source})
+  target_link_libraries(${name} ${Caffe_LINK})
+  caffe_default_properties(${name})
+
+  # set back RUNTIME_OUTPUT_DIRECTORY
+  caffe_set_runtime_directory(${name} "${CMAKE_BINARY_DIR}/tools")
+  caffe_set_solution_folder(${name} tools)
+
+  # restore output name without suffix
+  if(name MATCHES "caffe.bin")
+    set_target_properties(${name} PROPERTIES OUTPUT_NAME caffe)
+  endif()
+
+  # Install
+  install(TARGETS ${name} DESTINATION bin)
 endforeach(source)
-

From a12dcba8c9417c5d8f8155e06bdfd6e75fd636ce Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 18 Jan 2015 22:55:26 +0300
Subject: [PATCH 1127/2053] opencv 3.0 compilation (replace #1714)

---
 cmake/lint.cmake                       | 4 +++-
 src/caffe/layers/window_data_layer.cpp | 5 +----
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/cmake/lint.cmake b/cmake/lint.cmake
index 04df3409e84..585babb3587 100644
--- a/cmake/lint.cmake
+++ b/cmake/lint.cmake
@@ -1,10 +1,12 @@
 
-set(CMAKE_SOURCE_DIR ../)
+set(CMAKE_SOURCE_DIR ..)
 set(LINT_COMMAND ${CMAKE_SOURCE_DIR}/scripts/cpp_lint.py)
 set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc)
 set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
 set(LINT_DIRS include src/caffe examples tools python matlab)
 
+cmake_policy(SET CMP0009 NEW)  # supress cmake warning
+
 # find all files of interest
 foreach(ext ${SRC_FILE_EXTENSIONS})
     foreach(dir ${LINT_DIRS})
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 23ec83d166b..3e9e9765ccf 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,3 +1,4 @@
+#include <opencv2/highgui/highgui_c.h>
 #include <stdint.h>
 
 #include <algorithm>
@@ -22,10 +23,6 @@
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
 
-#if CV_VERSION_MAJOR == 3
-const int CV_LOAD_IMAGE_COLOR = cv::IMREAD_COLOR;
-#endif
-
 namespace caffe {
 
 template <typename Dtype>

From c0ae656c0e80e721fec89b63e2cdc390e440d625 Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 18 Jan 2015 23:22:09 +0300
Subject: [PATCH 1128/2053] [travis] proper cmake params

---
 scripts/travis/travis_build_and_test.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index 53c6c341101..8ff63f31fdd 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -7,7 +7,7 @@ MAKE="make --jobs=$NUM_THREADS --keep-going"
 if $WITH_CMAKE; then
   mkdir build
   cd build
-  cmake -DBUILD_PYTHON=ON -DBUILD_EXAMPLES=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
   $MAKE
   if ! $WITH_CUDA; then
     $MAKE runtest

From aea5dd674adbccf9927869f5e9ba9e2dfc2cbf21 Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 1 Feb 2015 17:45:31 +0300
Subject: [PATCH 1129/2053] cmake 2.8.7. support

---
 CMakeLists.txt              |   5 +-
 cmake/ConfigGen.cmake       |  11 ++-
 cmake/Cuda.cmake            |  11 +++
 cmake/Dependencies.cmake    |   5 +-
 cmake/Misc.cmake            |   4 +-
 cmake/Modules/FindMKL.cmake | 155 ++++++++++++++++++------------------
 cmake/Targets.cmake         |   3 +-
 cmake/Utils.cmake           |  16 ++++
 docs/CMakeLists.txt         |   6 +-
 matlab/CMakeLists.txt       |   2 +-
 python/CMakeLists.txt       |   9 ++-
 11 files changed, 131 insertions(+), 96 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d3d50da218b..0cace5acfe3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 2.8.8)
+cmake_minimum_required(VERSION 2.8.7)
 
 # ---[ Caffe project
 project(Caffe C CXX)
@@ -28,8 +28,9 @@ if(UNIX OR APLE)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
-if(APPLE)
+if(USE_libstdcpp)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
+  message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
 endif()
 
 add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 24f23e9c494..b7cf9f4cc48 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -38,14 +38,21 @@ function(caffe_generate_export_configs)
 
   # ---[ Configure build-tree CaffeConfig.cmake file ]---
   caffe_get_current_includes(Caffe_INCLUDE_DIRS)
+
+  set(Caffe_DEFINITIONS "")
   if(NOT HAVE_CUDA)
     set(HAVE_CUDA FALSE)
-    set(Caffe_DEFINITIONS -DCPU_ONLY)
+    list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
   endif()
+
   if(NOT HAVE_CUDNN)
     set(HAVE_CUDNN FALSE)
   else()
-    set(Caffe_DEFINITIONS -DUSE_CUDNN)
+    list(APPEND DEFINITIONS -DUSE_CUDNN)
+  endif()
+
+  if(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
+    list(APPEND Caffe_DEFINITIONS -DUSE_MKL)
   endif()
 
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${CMAKE_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 42a94e5a295..07fa13789fa 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -194,6 +194,7 @@ endfunction()
 ################################################################################################
 
 find_package(CUDA 5.5 QUIET)
+find_cuda_helper_libs(curand)  # cmake 2.8.7 compartibility which doesn't search for curand
 
 if(NOT CUDA_FOUND)
   return()
@@ -241,3 +242,13 @@ endif()
 mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
 mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
 
+# Handle clang/libc++ issue
+if(APPLE)
+  caffe_detect_darwin_version(OSX_VERSION)
+
+  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
+  if(OSX_VERSION VERSION_GREATER 10.8)
+    # enabled by default if and only if CUDA version is less than 7.0
+    caffe_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
+  endif()
+endif()
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 5de435c350d..aa2dcbe1d0d 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -68,7 +68,7 @@ message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
 # ---[ BLAS
 if(NOT APPLE)
   set(BLAS "Atlas" CACHE STRING "Selected BLAS library")
-  set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MLK")
+  set_property(CACHE BLAS PROPERTY STRINGS "Atlas;Open;MKL")
 
   if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
     find_package(Atlas REQUIRED)
@@ -78,10 +78,11 @@ if(NOT APPLE)
     find_package(OpenBLAS REQUIRED)
     include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
     list(APPEND Caffe_LINKER_LIBS ${OpenBLAS_LIB})
-  elseif(BLAS STREQUAL "MLK" OR BLAS STREQUAL "mkl")
+  elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
     find_package(MKL REQUIRED)
     include_directories(SYSTEM ${MKL_INCLUDE_DIR})
     list(APPEND Caffe_LINKER_LIBS ${MKL_LIBRARIES})
+    add_definitions(-DUSE_MKL)
   endif()
 elseif(APPLE)
   find_package(vecLib REQUIRED)
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 68e8a662126..14eaeb0ab6e 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -34,8 +34,8 @@ set(CMAKE_MACOSX_RPATH TRUE)
 
 # ---[ Funny target
 if(UNIX OR APPLE)
-  add_custom_target(simlink_to_build COMMAND "ln" "-sf" "${CMAKE_BINARY_DIR}" "${CMAKE_SOURCE_DIR}/build"
-                                     COMMENT "Adding simlink: <caffe_root>/build -> ${CMAKE_BINARY_DIR}" )
+  add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${CMAKE_BINARY_DIR}" "${CMAKE_SOURCE_DIR}/build"
+                                     COMMENT "Adding symlink: <caffe_root>/build -> ${CMAKE_BINARY_DIR}" )
 endif()
 
 # ---[ Set debug postfix
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index eb2d9f8868b..d2012db579a 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -1,113 +1,110 @@
-# - Find Intel MKL
 # Find the MKL libraries
 #
 # Options:
 #
-#   MKL_STATAIC       :   use static linking
-#   MKL_MULTI_THREADED:   use multi-threading
-#   MKL_SDL           :   Single Dynamic Library interface
+#   MKL_USE_SINGLE_DYNAMIC_LIBRARY  : use single dynamic library interface
+#   MKL_USE_STATIC_LIBS             : use static libraries
+#   MKL_MULTI_THREADED              : use multi-threading
 #
 # This module defines the following variables:
 #
-#   MKL_FOUND            : True if MKL_INCLUDE_DIR are found
-#   MKL_INCLUDE_DIR      : where to find mkl.h, etc.
-#   MKL_INCLUDE_DIRS     : set when MKL_INCLUDE_DIR found
-#   MKL_LIBRARIES        : the library to link against.
+#   MKL_FOUND            : True mkl is found
+#   MKL_INCLUDE_DIR      : unclude directory
+#   MKL_LIBRARIES        : the libraries to link against.
 
 
-include(FindPackageHandleStandardArgs)
+# ---[ Options
+caffe_option(MKL_USE_SINGLE_DYNAMIC_LIBRARY "Use single dynamic library interface" ON)
+caffe_option(MKL_USE_STATIC_LIBS "Use static libraries" OFF IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+caffe_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
 
+# ---[ Root folders
 set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-set(MKL_ROOT ${INTEL_ROOT}/mkl CACHE PATH "Folder contains MKL")
-
-# Find include dir
-find_path(MKL_INCLUDE_DIR mkl.h
-    PATHS ${MKL_ROOT}/include)
-
-# Find include directory
-#  There is no include folder under linux
-if(WIN32)
-    find_path(INTEL_INCLUDE_DIR omp.h
-        PATHS ${INTEL_ROOT}/include)
-    set(MKL_INCLUDE_DIR ${MKL_INCLUDE_DIR} ${INTEL_INCLUDE_DIR})
-endif()
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+                                   DOC "Folder contains MKL")
 
-# Find libraries
+# ---[ Find include dir
+find_path(MKL_INCLUDE_DIR mkl.h PATHS ${MKL_ROOT} PATH_SUFFIXES include)
+set(__looked_for MKL_INCLUDE_DIR)
 
-# Handle suffix
-set(_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
+# ---[ Find libraries
+if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+  set(__path_suffixes lib lib/ia32)
+else()
+  set(__path_suffixes lib lib/intel64)
+endif()
 
-if(WIN32)
-    if(MKL_STATAIC)
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .lib)
-    else()
-        set(CMAKE_FIND_LIBRARY_SUFFIXES _dll.lib)
-    endif()
+set(__mkl_libs "")
+if(MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+  list(APPEND __mkl_libs rt)
 else()
-    if(MKL_STATAIC)
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .a)
+  if(CMAKE_SIZEOF_VOID_P EQUAL 4)
+    if(WIN32)
+      list(APPEND __mkl_libs intel_c)
     else()
-        set(CMAKE_FIND_LIBRARY_SUFFIXES .so)
+      list(APPEND __mkl_libs intel gf)
     endif()
+  else()
+    list(APPEND __mkl_libs intel_lp64 gf_lp64)
+  endif()
+
+  if(MKL_MULTI_THREADED)
+    list(APPEND __mkl_libs intel_thread)
+  else()
+     list(APPEND __mkl_libs sequential)
+  endif()
+
+  list(APPEND __mkl_libs core cdft_core)
 endif()
 
 
-# MKL is composed by four layers: Interface, Threading, Computational and RTL
+foreach (__lib ${__mkl_libs})
+  set(__mkl_lib "mkl_${__lib}")
+  string(TOUPPER ${__mkl_lib} __mkl_lib_upper)
 
-if(MKL_SDL)
-    find_library(MKL_LIBRARY mkl_rt
-        PATHS ${MKL_ROOT}/lib/ia32/)
+  if(MKL_USE_STATIC_LIBS)
+    set(__mkl_lib "lib${__mkl_lib}.a")
+  endif()
 
-    set(MKL_MINIMAL_LIBRARY ${MKL_LIBRARY})
-else()
-    ######################### Interface layer #######################
-    if(WIN32)
-        set(MKL_INTERFACE_LIBNAME mkl_intel_c)
-    else()
-        set(MKL_INTERFACE_LIBNAME mkl_intel)
-    endif()
+  find_library(${__mkl_lib_upper}_LIBRARY
+        NAMES ${__mkl_lib}
+        PATHS ${MKL_ROOT} "${MKL_INCLUDE_DIR}/.."
+        PATH_SUFFIXES ${__path_suffixes}
+        DOC "The path to Intel(R) MKL ${__mkl_lib} library")
+  mark_as_advanced(${__mkl_lib_upper}_LIBRARY)
 
-    find_library(MKL_INTERFACE_LIBRARY ${MKL_INTERFACE_LIBNAME}
-        PATHS ${MKL_ROOT}/lib/ia32/)
+  list(APPEND __looked_for ${__mkl_lib_upper}_LIBRARY)
+  list(APPEND MKL_LIBRARIES ${${__mkl_lib_upper}_LIBRARY})
+endforeach()
 
-    ######################## Threading layer ########################
-    if(MKL_MULTI_THREADED)
-        set(MKL_THREADING_LIBNAME mkl_intel_thread)
-    else()
-        set(MKL_THREADING_LIBNAME mkl_sequential)
-    endif()
 
-    find_library(MKL_THREADING_LIBRARY ${MKL_THREADING_LIBNAME}
-        PATHS ${MKL_ROOT}/lib/ia32/)
+if(NOT MKL_USE_SINGLE_DYNAMIC_LIBRARY)
+  if (MKL_USE_STATIC_LIBS)
+    set(__iomp5_libs iomp5 libiomp5mt.lib)
+  else()
+    set(__iomp5_libs iomp5 libiomp5md.lib)
+  endif()
 
-    ####################### Computational layer #####################
-    find_library(MKL_CORE_LIBRARY mkl_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-    find_library(MKL_FFT_LIBRARY mkl_cdft_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
-    find_library(MKL_SCALAPACK_LIBRARY mkl_scalapack_core
-        PATHS ${MKL_ROOT}/lib/ia32/)
+  if(WIN32)
+    find_path(INTEL_INCLUDE_DIR omp.h PATHS ${INTEL_ROOT} PATH_SUFFIXES include)
+    list(APPEND __looked_for INTEL_INCLUDE_DIR)
+  endif()
 
-    ############################ RTL layer ##########################
-    if(WIN32)
-        set(MKL_RTL_LIBNAME libiomp5md)
-    else()
-        set(MKL_RTL_LIBNAME libiomp5)
-    endif()
-    find_library(MKL_RTL_LIBRARY ${MKL_RTL_LIBNAME}
-        PATHS ${INTEL_RTL_ROOT}/lib)
+  find_library(MKL_RTL_LIBRARY ${__iomp5_libs}
+     PATHS ${INTEL_RTL_ROOT} ${INTEL_ROOT}/compiler ${MKL_ROOT}/.. ${MKL_ROOT}/../compiler
+     PATH_SUFFIXES ${__path_suffixes}
+     DOC "Path to Path to OpenMP runtime library")
 
-    set(MKL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_FFT_LIBRARY} ${MKL_SCALAPACK_LIBRARY} ${MKL_RTL_LIBRARY})
-    set(MKL_MINIMAL_LIBRARY ${MKL_INTERFACE_LIBRARY} ${MKL_THREADING_LIBRARY} ${MKL_CORE_LIBRARY} ${MKL_RTL_LIBRARY})
+  list(APPEND __looked_for MKL_RTL_LIBRARY)
+  list(APPEND MKL_LIBRARIES ${MKL_RTL_LIBRARY})
 endif()
 
-set(CMAKE_FIND_LIBRARY_SUFFIXES ${_MKL_ORIG_CMAKE_FIND_LIBRARY_SUFFIXES})
 
-find_package_handle_standard_args(MKL DEFAULT_MSG
-    MKL_INCLUDE_DIR MKL_LIBRARY MKL_MINIMAL_LIBRARY)
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(MKL DEFAULT_MSG ${__looked_for})
 
 if(MKL_FOUND)
-    set(MKL_INCLUDE_DIRS ${MKL_INCLUDE_DIR})
-    set(MKL_LIBRARIES ${MKL_LIBRARY})
-    set(MKL_MINIMAL_LIBRARIES ${MKL_LIBRARY})
+  message(STATUS "Found MKL (include: ${MKL_INCLUDE_DIR}, lib: ${MKL_LIBRARIES}")
 endif()
+
+caffe_clear_vars(__looked_for __mkl_libs __path_suffixes __lib_suffix __iomp5_libs)
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index 84a2aaf58e9..3b254ad37df 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -140,8 +140,7 @@ function(caffe_configure_testdatafile file)
   set(result "")
   foreach(line ${__lines})
     set(result "${result}${CMAKE_SOURCE_DIR}/${line}\n")
-  endforeach()
-  dmsg(${result})
+  endforeach()  
   file(WRITE ${file}.gen.cmake ${result})
 endfunction()
 
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index 048123dbe10..a56c7c300c0 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -363,3 +363,19 @@ function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_va
   set(${flags_var} ${libflags} PARENT_SCOPE)
   set(${frameworks_var} ${__framework} PARENT_SCOPE)
 endfunction()
+
+################################################################################################
+# Helper function to detect Darwin version, i.e. 10.8, 10.9, 10.10, ....
+# Usage:
+#   caffe_detect_darwin_version(<version_variable>)
+function(caffe_detect_darwin_version output_var)
+  if(APPLE)
+    execute_process(COMMAND /usr/bin/sw_vers -productVersion
+                    RESULT_VARIABLE __sw_vers OUTPUT_VARIABLE __sw_vers_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    set(${output_var} ${__sw_vers_out} PARENT_SCOPE)
+  else()
+    set(${output_var} "" PARENT_SCOPE)
+  endif()
+endfunction()
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 927bf5bc731..b1be683f907 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -28,7 +28,7 @@ function(gather_readmes_as_prebuild_cmd target gathered_dir root)
       add_custom_command(TARGET ${target} PRE_BUILD
         COMMAND ${CMAKE_COMMAND} -E make_directory ${new_folder}
         COMMAND ln -sf ${root}/${file} ${new_filename}
-        COMMENT "Creating simlink ${new_filename} -> ${root}/${file}"
+        COMMENT "Creating symlink ${new_filename} -> ${root}/${file}"
         WORKING_DIRECTORY ${root} VERBATIM)
     endif()
   endforeach()
@@ -94,11 +94,11 @@ if(NOT IS_ABSOLUTE ${output_dir})
   get_filename_component(output_dir ${output_dir} ABSOLUTE)
 endif()
 
-# creates simlink in docs subfolder to code documentation built by doxygen
+# creates symlink in docs subfolder to code documentation built by doxygen
 add_custom_command(TARGET docs POST_BUILD VERBATIM
                    COMMAND ln -sfn "${output_dir}/html" doxygen
                    WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/docs
-                   COMMENT "Creating simlink ${CMAKE_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
+                   COMMENT "Creating symlink ${CMAKE_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
 
 # for quick launch of jekyll
 add_custom_target(jekyll COMMAND jekyll serve -w -s . -d _site --port=4000
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index 40d8729ae76..51ab4fe274a 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -35,7 +35,7 @@ file(GLOB Matlab_srcs caffe/matcaffe.cpp)
 set(Matlab_caffe_mex ${CMAKE_SOURCE_DIR}/matlab/caffe/caffe.mex)
 
 caffe_get_current_cflags(cflags)
-caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_framewoks)
+caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
 set(folders $<TARGET_LINKER_FILE_DIR:caffe> ${folders})
 
 # prepare linker flag lists
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index e943f691c49..ee33a9e5285 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -12,10 +12,13 @@ set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
 caffe_default_properties(pycaffe)
 
 if(UNIX OR APPLE)
-    set(__linkname "${CMAKE_SOURCE_DIR}/python/_caffe.so")
+    set(__linkname "${CMAKE_SOURCE_DIR}/python/caffe/_caffe.so")
     add_custom_command(TARGET pycaffe POST_BUILD
-                       COMMAND ln -sf $<TARGET_LINKER_FILE:caffe> "${CMAKE_SOURCE_DIR}/python/_caffe.so"
-                       COMMENT "Creating simlink ${__linkname} -> ${CMAKE_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+                       COMMAND ln -sf $<TARGET_LINKER_FILE:pycaffe> "${__linkname}"
+                       COMMAND ${CMAKE_COMMAND} -E make_directory ${CMAKE_SOURCE_DIR}/python/caffe/proto
+                       COMMAND touch ${CMAKE_SOURCE_DIR}/python/caffe/proto/__init__.py
+                       COMMAND cp ${proto_gen_folder}/*.py ${CMAKE_SOURCE_DIR}/python/caffe/proto/
+                       COMMENT "Creating symlink ${__linkname} -> ${CMAKE_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
 endif()
 
 # ---[ Install

From 4ba3dfbcbd2eea13a4719c197309b67fead12480 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Feb 2015 21:00:04 -0800
Subject: [PATCH 1130/2053] [docs] note new CMake build

---
 docs/installation.md | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index c2b16a23244..73f6806c805 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -270,10 +270,10 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### Compilation using CMake (beta)
+### CMake Compilation
 
-In lieu of manually editing `Makefile.config` to tell Caffe where dependencies are located, Caffe also provides a CMake-based build system (currently in "beta").
-It requires CMake version >= 2.8.8.
+In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community.
+It requires CMake version >= 2.8.7.
 The basic installation steps are as follows:
 
     mkdir build
@@ -282,6 +282,8 @@ The basic installation steps are as follows:
     make all
     make runtest
 
+See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for details.
+
 #### Ubuntu 12.04
 
 Note that in Ubuntu 12.04, Aptitude will install version CMake 2.8.7 by default, which is not supported by Caffe's CMake build (requires at least 2.8.8).

From 79bc8645f9790398e5ddcb706495c6aca395989a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Feb 2015 21:41:24 -0800
Subject: [PATCH 1131/2053] [docs] README dispatch

---
 README.md | 30 +++++++++++++++++++++++++++---
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 9a9b3ee36eb..0a60095bdf1 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,32 @@
 # Caffe
 
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.<br />
-Consult the [project website](http://caffe.berkeleyvision.org) for all documentation.
+Caffe is a deep learning framework made with expression, speed, and modularity in mind.
+It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
 
+Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
 
-Please ask usage questions and how to model different tasks on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
+- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
+- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
+- [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
 
+and step-by-step examples.
+
+Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) to ask questions and talk about methods and models.
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Happy brewing!
+
+## License and Citation
+
+Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
+The BVLC reference models are released for unrestricted use.
+
+Please cite Caffe in your publications if it helps your research:
+
+    @article{jia2014caffe,
+      Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
+      Journal = {arXiv preprint arXiv:1408.5093},
+      Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
+      Year = {2014}
+    }

From 87b63358773902dd5217ec661386f8ef8abcf8b8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 7 Feb 2015 00:19:54 -0800
Subject: [PATCH 1132/2053] dynamic linking

---
 Makefile | 40 +++++++++++++++++++++++-----------------
 1 file changed, 23 insertions(+), 17 deletions(-)

diff --git a/Makefile b/Makefile
index 81c67efeaf4..0fcbb8c64cc 100644
--- a/Makefile
+++ b/Makefile
@@ -245,6 +245,11 @@ ifeq ($(OSX), 1)
 	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
+	# we need to explicitly ask for the rpath to be obeyed
+	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
+	ORIGIN := @loader_path
+else
+	ORIGIN := \$$ORIGIN
 endif
 
 # Custom compiler
@@ -343,7 +348,6 @@ endif
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
-DYNAMIC_LDFLAGS := -l$(PROJECT) -Wl,-rpath,\$$ORIGIN/../lib
 
 # 'superclean' target recursively* deletes all files ending with an extension
 # in $(SUPERCLEAN_EXTS) below.  This may be useful if you've built older
@@ -419,10 +423,11 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(STATIC_NAME) $(PY$(PROJECT)_HXX_SRC)
-	@ echo CXX $<
+$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC) | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(STATIC_LINK_COMMAND) $(LINKFLAGS) $(PYTHON_LDFLAGS)
+		-o $@ $(LINKFLAGS) $(PYTHON_LDFLAGS) -l$(PROJECT) \
+		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
 mat$(PROJECT): mat
 
@@ -479,7 +484,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo LD -o $@
-	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo AR -o $@
@@ -512,32 +517,33 @@ $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
 		-o $@ $(LINKFLAGS) $(LDFLAGS)
 
-$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(DYNAMIC_NAME) \
-		| $(TEST_BIN_DIR)
+$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_LDFLAGS)
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
-	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
+	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
-	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
-	$(GTEST_OBJ) $(STATIC_NAME) | $(TEST_BIN_DIR)
+	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
-	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
+	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
 
 # Target for extension-less symlinks to tool binaries with extension '*.bin'.
 $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
 	@ ln -s $(abspath $<) $@
 
-$(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	@ echo LD $<
-	$(Q)$(CXX) $< $(STATIC_LINK_COMMAND) -o $@ $(LINKFLAGS) $(LDFLAGS)
+$(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) \
+		-Wl,-rpath,$(ORIGIN)/../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 

From e4b7ceb05ddddbddb8809e22d86ac20a019959fd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 7 Feb 2015 00:33:15 -0800
Subject: [PATCH 1133/2053] only dynamically link the tests

No need to independently test static and dynamic linking.
---
 Makefile | 17 ++++-------------
 1 file changed, 4 insertions(+), 13 deletions(-)

diff --git a/Makefile b/Makefile
index 0fcbb8c64cc..f94867bdf31 100644
--- a/Makefile
+++ b/Makefile
@@ -123,10 +123,8 @@ TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
 		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
 TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
-# TEST_ALL_BIN is the test binary that links caffe statically.
+# TEST_ALL_BIN is the test binary that links caffe dynamically.
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
-# TEST_ALL_DYNINK_BIN is the test binary that links caffe as a dynamic library.
-TEST_ALL_DYNLINK_BIN := $(TEST_BIN_DIR)/test_all_dynamic_link.testbin
 
 ##############################
 # Derive compiler warning dump locations
@@ -445,9 +443,8 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
 
-runtest: $(TEST_ALL_BIN) $(TEST_ALL_DYNLINK_BIN)
-	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER) && \
-	$(TEST_ALL_DYNLINK_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
+runtest: $(TEST_ALL_BIN)
+	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
 pytest: py
 	cd python; python -m unittest discover -s caffe/test
@@ -511,13 +508,7 @@ $(BUILD_DIR)/cuda/%.o: %.cu | $(ALL_BUILD_DIRS)
 		|| (cat $@.$(WARNS_EXT); exit 1)
 	@ cat $@.$(WARNS_EXT)
 
-$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		| $(TEST_BIN_DIR)
-	@ echo CXX/LD -o $@ $<
-	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_LINK_COMMAND) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS)
-
-$(TEST_ALL_DYNLINK_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
+$(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
 		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \

From 3c629b1942ccd5c6ff2f37370767f662cab388c0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 20 Sep 2014 00:16:17 -0700
Subject: [PATCH 1134/2053] [docs] include caffeine favicon for site

---
 docs/_layouts/default.html    |   2 ++
 docs/images/caffeine-icon.png | Bin 0 -> 954 bytes
 2 files changed, 2 insertions(+)
 create mode 100644 docs/images/caffeine-icon.png

diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index 73c6d5873d3..433b0635f43 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -11,6 +11,8 @@
       Caffe {% if page contains 'title' %}| {{ page.title }}{% endif %}
     </title>
 
+    <link rel="icon" type="image/png" href="/images/caffeine-icon.png">
+
     <link rel="stylesheet" href="/stylesheets/reset.css">
     <link rel="stylesheet" href="/stylesheets/styles.css">
     <link rel="stylesheet" href="/stylesheets/pygment_trac.css">
diff --git a/docs/images/caffeine-icon.png b/docs/images/caffeine-icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..88b4a002bb000ad56a83d7bfb99a8b450f63fb98
GIT binary patch
literal 954
zcmeAS@N?(olHy`uVBq!ia0vp^3LwnE3?yBabR7dyk|nMYCBgY=CFO}lsSJ)O`AMk?
zp1FzXsX?iUDV2pMQ*9U+7|R2ELR|Ol+xO|yr(?&Coj!ef{`~oK=FFKob!vNi`}XbI
zA3S*Q{rmT)PoF+}_U!H3x0#ulH*eldNlCeN>(<$`XAc}W@aWN_J$v@ty?b}kq)F$`
zpRcd4U$SJ$^XJdQ!ouFae_vNuw`R?njT<+9`0(M+pFcZy?)>}r@3w8*A|oRY9y~aA
z?%W?ger(vV;mnyct5>gn_3G8_+qW-VxDXc?H*40c?(Xi6j*h&%yriV0#fukvdU{^F
zc1=Y^#m>&|<jIqkmX<X&H3bC)s;a6U9v<oG>1k<cg@uKVj*efxd<hN?zJLFIVq)UG
zd-s0*`V|@)+S%FJ)YP<e>C%}qXS%t$>FMeH{{8#hxpP;qUj6#@>+9FAdwYAAEnD{D
z#fzYzAb)@V&!0c%=jU5kSmfsBwzjrz-MTd^E9=UYD-$M6$jQmMc=4i+j!sNWOhZFM
zUteEyb8}^7<)K4|K7RZ-efso>h=}#;*T=`l@7=ri@ZrPG&d#%E&)&UzcUM<eMn*<>
zc=)_|^Zx(;|0*?E9vDxxB|(0{K<<D627#XyK!-2}c)B=-RNP8h;m8<wO=$&_DbI?S
z)>Sf&HC#<gT6&8FW72$igq?C~k{`(A^7^pOsX4>Zw=jUWj`fY$578H!pB)s@=xR$0
ze6YOdgX<zGdzT5fW7xIMo;a`4^29eX*jeCcvU=trF2RW&>AY^7?ALw-WEwMXoHZ+p
zF;5}f<np~a@}>*Ad?y__$g}rGO`&^bURhXf2<tne#tQ+{&W5oDUOJ|v!DSPA_^5)8
zo|u`Nz*@mZDLxq=(-mA<b1Xk<Yiu+<wA#VdsDXVw)7(o%BIX>CC!Hg^ih>*_T~`m4
z@@(YVJ*&aFOQ_>y{;PP!V6_tq&$V#0rzGa{Gj5a$Wd7=LZ;jW4pFBm=j%60G9^&*1
zd=OAFwP%_Uvvjbs(DX#c@C9oER<LgLJioL-`p~1Rm!3CjKhP|C+{*EK)8^NYe7~9U
q@R&BoH@2`avPv{8U}|7sX4q{$fBK~C>JnfiGkCiCxvX<aXaWFzzz-q-

literal 0
HcmV?d00001


From eb764bbf5d980e03dad558e3bb39471f49de0c85 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Feb 2015 21:47:20 -0800
Subject: [PATCH 1135/2053] [docs] Caffe is by the BVLC, created by Yangqing,
 and brewed by Evan

---
 docs/_layouts/default.html | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index 433b0635f43..b8efe60bc3b 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -36,8 +36,16 @@
       <header>
         <h1 class="header"><a href="/">Caffe</a></h1>
         <p class="header">
-          Deep learning framework developed by <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a> / <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
+          Deep learning framework by the <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
         </p>
+        <p class="header">
+          Created by
+          <br>
+          <a class="header name" href="http://daggerfs.com/">Yangqing Jia</a>
+          <br>
+          Lead Developer
+          <br>
+          <a class="header name" href="http://imaginarynumber.net/">Evan Shelhamer</a>
         <ul>
           <li>
             <a class="buttons github" href="https://github.com/BVLC/caffe">View On GitHub</a>

From 4e69bc9109f883b6cb35a06a766c61fb0f1f85c4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Feb 2015 22:22:49 -0800
Subject: [PATCH 1136/2053] [docs] groom index and zoo for highlights

---
 docs/index.md     | 56 ++++++++++++++++++++++++-----------------------
 docs/model_zoo.md | 12 ++++++----
 2 files changed, 37 insertions(+), 31 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index ccc8f750eef..7b87c24ce96 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -4,31 +4,33 @@ title: Deep Learning Framework
 
 # Caffe
 
-Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind.
-It was created by [Yangqing Jia](http://daggerfs.com) during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+Caffe is a deep learning framework made with expression, speed, and modularity in mind.
+It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+[Yangqing Jia](http://daggerfs.com) created the project during his PhD at UC Berkeley.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
 Check out our web image classification [demo](http://demo.caffe.berkeleyvision.org)!
 
-## Why use Caffe?
+## Why Caffe?
 
-**Clean architecture** enables rapid deployment.
-Networks are specified in simple config files, with no hard-coded parameters in the code.
-Switching between CPU and GPU is as simple as setting a flag -- so models can be trained on a GPU machine, and then used on commodity clusters.
+**Expressive architecture** encourages application and innovation.
+Models and optimization are defined by configuration without hard-coding.
+Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
 
-**Readable & modifiable implementation** fosters active development.
-In Caffe's first year, it has been forked by over 600 developers on Github, and many have pushed significant changes.
+**Extensible code** fosters active development.
+In Caffe's first year, it has been forked by over 1,000 developers and had many significant changes contributed back.
+Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
 
-**Speed** makes Caffe perfect for industry use.
-Caffe can process over **40M images per day** with a single NVIDIA K40 or Titan GPU\*.
-That's 5 ms/image in training, and 2 ms/image in test.
-We believe that Caffe is the fastest CNN implementation available.
+**Speed** makes Caffe perfect for research experiments and industry deployment.
+Caffe can process **over 60M images per day** with a single NVIDIA K40 GPU\*.
+That's 1 ms/image for inference and 4 ms/image for learning.
+We believe that Caffe is the fastest convnet implementation available.
 
 **Community**: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
-There is an active discussion and support community on [Github](https://github.com/BVLC/caffe/issues).
+Join our community of brewers on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) and [Github](https://github.com/BVLC/caffe/).
 
 <p class="footnote" markdown="1">
-\* When files are properly cached, and using the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model.
+\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and caching IO.
 Consult performance [details](/performance_hardware.html).
 </p>
 
@@ -56,7 +58,7 @@ Developer documentation automagically generated from code comments.
 - <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
-### Notebook examples
+### Notebook Examples
 
 {% assign notebooks = site.pages | where:'category','notebook' | sort: 'priority' %}
 {% for page in notebooks %}
@@ -77,6 +79,17 @@ Please cite Caffe in your publications if it helps your research:
 If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).
 Citations are also tracked automatically by [Google Scholar](http://scholar.google.com/scholar?oi=bibs&hl=en&cites=17333247995453974016).
 
+## Contacting Us
+
+Join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) to ask questions and discuss methods and models. This is where we talk about usage, installation, and applications.
+
+Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
+
+Contact [caffe-dev](mailto:caffe-dev@googlegroups.com) if you have a confidential proposal for the framework *and the ability to act on it*.
+Requests for features, explanations, or personal help will be ignored; post to [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) instead.
+
+The core Caffe developers offer [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
+
 ## Acknowledgements
 
 The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
@@ -85,20 +98,9 @@ The BVLC members who have contributed to Caffe are (alphabetical by first name):
 [Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
 
 The open-source community plays an important and growing role in Caffe's development.
-Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity, and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for a sorted list.
+Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for the full list.
 
 We sincerely appreciate your interest and contributions!
 If you'd like to contribute, please read the [developing & contributing](development.html) guide.
 
 Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
-
-## Contacting us
-
-All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
-
-All development discussion should be carried out at [GitHub Issues](https://github.com/BVLC/caffe/issues).
-
-If you have a proposal that may not be suited for public discussion *and an ability to act on it*, please email us [directly](mailto:caffe-dev@googlegroups.com).
-Requests for features, explanations, or personal help will be ignored; post such matters publicly as issues.
-
-The core Caffe developers may be able to provide [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 358bbb7fc91..ad30d0acd55 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -7,10 +7,10 @@ Lots of people have used Caffe to train models of different architectures and ap
 To lower the friction of sharing these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
-- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
+- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` parameters.
 - A central wiki page for sharing model info Gists.
 
-## Where to get trained models
+## BVLC Reference Models
 
 First of all, we provide some trained models out of the box.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
@@ -20,7 +20,11 @@ Each one of these can be downloaded by running `scripts/download_model_binary.py
 - **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
 - **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
 
-User-provided models are posted to a public-editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+
+## Community Models
+
+The publicly-editable [Caffe Model Zoo wiki](https://github.com/BVLC/caffe/wiki/Model-Zoo) catalogues user-made models.
+Refer to the model details for authorship and conditions -- please respect licenses and citations.
 
 ## Model info format
 
@@ -36,7 +40,7 @@ A caffe model is distributed as a directory containing:
     - License information.
 - [optional] Other helpful scripts.
 
-## Hosting model info
+### Hosting model info
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 

From d6df2e09eb16185b58b6f7ea4e317f4faab9fc8d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 5 Jan 2015 22:19:20 -0800
Subject: [PATCH 1137/2053] [pycaffe] enable numpy API deprecation warnings

---
 python/caffe/_caffe.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index e576eebcf49..7691d78deea 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -4,6 +4,9 @@
 
 #include <Python.h>  // NOLINT(build/include_alpha)
 
+// Produce deprecation warnings (needs to come before arrayobject.h inclusion).
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
 #include <boost/make_shared.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
 

From c2fe27b00270e76f9066bc0147091e5430451cd2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 5 Jan 2015 15:19:25 -0800
Subject: [PATCH 1138/2053] [pycaffe] strike down wrappers, momentarily gut all
 functionality

---
 Makefile                |   3 +-
 python/caffe/_caffe.cpp | 190 ++------------------------------------
 python/caffe/_caffe.hpp | 198 ----------------------------------------
 3 files changed, 8 insertions(+), 383 deletions(-)
 delete mode 100644 python/caffe/_caffe.hpp

diff --git a/Makefile b/Makefile
index f94867bdf31..88c0365ec62 100644
--- a/Makefile
+++ b/Makefile
@@ -69,7 +69,6 @@ EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
 NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
-PY$(PROJECT)_HXX_SRC := python/$(PROJECT)/_$(PROJECT).hpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
 # MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
 MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
@@ -421,7 +420,7 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX_SRC) | $(DYNAMIC_NAME)
+$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
 		-o $@ $(LINKFLAGS) $(PYTHON_LDFLAGS) -l$(PROJECT) \
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 7691d78deea..9a100468cc4 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -1,21 +1,18 @@
-// pycaffe provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from Python.
-// Note that for Python, we will simply use float as the data type.
-
 #include <Python.h>  // NOLINT(build/include_alpha)
 
 // Produce deprecation warnings (needs to come before arrayobject.h inclusion).
 #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
 
 #include <boost/make_shared.hpp>
+#include <boost/python.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
+#include <numpy/arrayobject.h>
 
 // these need to be included after boost on OS X
 #include <string>  // NOLINT(build/include_order)
 #include <vector>  // NOLINT(build/include_order)
 #include <fstream>  // NOLINT
 
-#include "_caffe.hpp"
 #include "caffe/caffe.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
@@ -27,10 +24,13 @@
 
 namespace caffe {
 
-// for convenience, check that input files can be opened, and raise an
+// For Python, for now, we'll just always use float as the type.
+typedef float Dtype;
+
+// For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
-// this saves frustration in most cases)
+// this saves frustration in most cases).
 static void CheckFile(const string& filename) {
     std::ifstream f(filename.c_str());
     if (!f.good()) {
@@ -40,183 +40,7 @@ static void CheckFile(const string& filename) {
     f.close();
 }
 
-bp::object PyBlobWrap::get_data() {
-  npy_intp dims[] = {num(), channels(), height(), width()};
-
-  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                            blob_->mutable_cpu_data());
-  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-  Py_INCREF(self_);
-  bp::handle<> h(obj);
-
-  return bp::object(h);
-}
-
-bp::object PyBlobWrap::get_diff() {
-  npy_intp dims[] = {num(), channels(), height(), width()};
-
-  PyObject *obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32,
-                                            blob_->mutable_cpu_diff());
-  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject *>(obj), self_);
-  Py_INCREF(self_);
-  bp::handle<> h(obj);
-
-  return bp::object(h);
-}
-
-PyNet::PyNet(string param_file, string pretrained_param_file) {
-  Init(param_file);
-  CheckFile(pretrained_param_file);
-  net_->CopyTrainedLayersFrom(pretrained_param_file);
-}
-
-void PyNet::Init(string param_file) {
-  CheckFile(param_file);
-  net_.reset(new Net<float>(param_file));
-}
-
-void PyNet::check_contiguous_array(PyArrayObject* arr, string name,
-    int channels, int height, int width) {
-  if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
-    throw std::runtime_error(name + " must be C contiguous");
-  }
-  if (PyArray_NDIM(arr) != 4) {
-    throw std::runtime_error(name + " must be 4-d");
-  }
-  if (PyArray_TYPE(arr) != NPY_FLOAT32) {
-    throw std::runtime_error(name + " must be float32");
-  }
-  if (PyArray_DIMS(arr)[1] != channels) {
-    throw std::runtime_error(name + " has wrong number of channels");
-  }
-  if (PyArray_DIMS(arr)[2] != height) {
-    throw std::runtime_error(name + " has wrong height");
-  }
-  if (PyArray_DIMS(arr)[3] != width) {
-    throw std::runtime_error(name + " has wrong width");
-  }
-}
-
-void PyNet::set_input_arrays(bp::object data_obj, bp::object labels_obj) {
-  // check that this network has an input MemoryDataLayer
-  shared_ptr<MemoryDataLayer<float> > md_layer =
-    boost::dynamic_pointer_cast<MemoryDataLayer<float> >(net_->layers()[0]);
-  if (!md_layer) {
-    throw std::runtime_error("set_input_arrays may only be called if the"
-        " first layer is a MemoryDataLayer");
-  }
-
-  // check that we were passed appropriately-sized contiguous memory
-  PyArrayObject* data_arr =
-      reinterpret_cast<PyArrayObject*>(data_obj.ptr());
-  PyArrayObject* labels_arr =
-      reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
-  check_contiguous_array(data_arr, "data array", md_layer->channels(),
-      md_layer->height(), md_layer->width());
-  check_contiguous_array(labels_arr, "labels array", 1, 1, 1);
-  if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
-    throw std::runtime_error("data and labels must have the same first"
-        " dimension");
-  }
-  if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
-    throw std::runtime_error("first dimensions of input arrays must be a"
-        " multiple of batch size");
-  }
-
-  // hold references
-  input_data_ = data_obj;
-  input_labels_ = labels_obj;
-
-  md_layer->Reset(static_cast<float*>(PyArray_DATA(data_arr)),
-      static_cast<float*>(PyArray_DATA(labels_arr)),
-      PyArray_DIMS(data_arr)[0]);
-}
-
-PySGDSolver::PySGDSolver(const string& param_file) {
-  // as in PyNet, (as a convenience, not a guarantee), create a Python
-  // exception if param_file can't be opened
-  CheckFile(param_file);
-  solver_.reset(new SGDSolver<float>(param_file));
-  // we need to explicitly store the net wrapper, rather than constructing
-  // it on the fly, so that it can hold references to Python objects
-  net_.reset(new PyNet(solver_->net()));
-  for (int i = 0; i < solver_->test_nets().size(); ++i) {
-    test_nets_.push_back(boost::make_shared<PyNet>(solver_->test_nets()[i]));
-  }
-}
-
-void PySGDSolver::SolveResume(const string& resume_file) {
-  CheckFile(resume_file);
-  return solver_->Solve(resume_file);
-}
-
 BOOST_PYTHON_MODULE(_caffe) {
-  // Caffe utility methods
-  bp::def("set_mode_cpu",          &set_mode_cpu);
-  bp::def("set_mode_gpu",          &set_mode_gpu);
-  bp::def("set_phase_train",       &set_phase_train);
-  bp::def("set_phase_test",        &set_phase_test);
-  bp::def("set_device",            &Caffe::SetDevice);
-
-  // below, we prepend an underscore to methods that will be replaced
-  // in Python
-  bp::class_<PyNet, shared_ptr<PyNet> >(
-      "Net", bp::init<string, string>())
-      .def(bp::init<string>())
-      .def("copy_from",             &PyNet::CopyTrainedLayersFrom)
-      .def("share_with",            &PyNet::ShareTrainedLayersWith)
-      .def("_forward",              &PyNet::Forward)
-      .def("_backward",             &PyNet::Backward)
-      .def("reshape",               &PyNet::Reshape)
-      .add_property("_blobs",       &PyNet::blobs)
-      .add_property("layers",       &PyNet::layers)
-      .add_property("_blob_names",  &PyNet::blob_names)
-      .add_property("_layer_names", &PyNet::layer_names)
-      .add_property("inputs",       &PyNet::inputs)
-      .add_property("outputs",      &PyNet::outputs)
-      .add_property("mean",         &PyNet::mean_)
-      .add_property("input_scale",  &PyNet::input_scale_)
-      .add_property("raw_scale",    &PyNet::raw_scale_)
-      .add_property("channel_swap", &PyNet::channel_swap_)
-      .def("_set_input_arrays",     &PyNet::set_input_arrays)
-      .def("save",                  &PyNet::save);
-
-  bp::class_<PyBlob<float>, PyBlobWrap>(
-      "Blob", bp::no_init)
-      .add_property("num",      &PyBlob<float>::num)
-      .add_property("channels", &PyBlob<float>::channels)
-      .add_property("height",   &PyBlob<float>::height)
-      .add_property("width",    &PyBlob<float>::width)
-      .add_property("count",    &PyBlob<float>::count)
-      .def("reshape",           &PyBlob<float>::Reshape)
-      .add_property("data",     &PyBlobWrap::get_data)
-      .add_property("diff",     &PyBlobWrap::get_diff);
-
-  bp::class_<PyLayer>(
-      "Layer", bp::no_init)
-      .add_property("blobs", &PyLayer::blobs);
-
-  bp::class_<PySGDSolver, boost::noncopyable>(
-      "SGDSolver", bp::init<string>())
-      .add_property("net",       &PySGDSolver::net)
-      .add_property("test_nets", &PySGDSolver::test_nets)
-      .add_property("iter",      &PySGDSolver::iter)
-      .def("solve",              &PySGDSolver::Solve)
-      .def("solve",              &PySGDSolver::SolveResume)
-      .def("step",               &PySGDSolver::Step);
-
-  bp::class_<vector<shared_ptr<PyNet> > >("NetVec")
-      .def(bp::vector_indexing_suite<vector<shared_ptr<PyNet> >, true>());
-
-  bp::class_<vector<PyBlob<float> > >("BlobVec")
-      .def(bp::vector_indexing_suite<vector<PyBlob<float> >, true>());
-
-  bp::class_<vector<PyLayer> >("LayerVec")
-      .def(bp::vector_indexing_suite<vector<PyLayer>, true>());
-
-  bp::class_<vector<string> >("StringVec")
-      .def(bp::vector_indexing_suite<vector<string> >());
-
   import_array();
 }
 
diff --git a/python/caffe/_caffe.hpp b/python/caffe/_caffe.hpp
deleted file mode 100644
index 78470c0fa1a..00000000000
--- a/python/caffe/_caffe.hpp
+++ /dev/null
@@ -1,198 +0,0 @@
-#ifndef PYTHON_CAFFE__CAFFE_HPP_
-#define PYTHON_CAFFE__CAFFE_HPP_
-
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-
-#include <Python.h>  // NOLINT(build/include_alpha)
-
-#include <boost/python.hpp>
-#include <boost/shared_ptr.hpp>
-#include <numpy/arrayobject.h>
-
-// these need to be included after boost on OS X
-#include <string>  // NOLINT(build/include_order)
-#include <vector>  // NOLINT(build/include_order)
-
-#include "caffe/caffe.hpp"
-
-namespace bp = boost::python;
-using boost::shared_ptr;
-
-namespace caffe {
-
-// Selecting mode and phase.
-void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
-void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
-void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
-void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
-
-// wrap shared_ptr<Blob> in a class that we construct in C++ and pass
-// to Python
-template <typename Dtype>
-class PyBlob {
- public:
-  explicit PyBlob(const shared_ptr<Blob<Dtype> > &blob)
-      : blob_(blob) {}
-
-  int num() const { return blob_->num(); }
-  int channels() const { return blob_->channels(); }
-  int height() const { return blob_->height(); }
-  int width() const { return blob_->width(); }
-  int count() const { return blob_->count(); }
-  void Reshape(const int n, const int c, const int h, const int w) {
-    return blob_->Reshape(n, c, h, w);
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyBlob &other) {
-      return this->blob_ == other.blob_;
-  }
-
- protected:
-  shared_ptr<Blob<Dtype> > blob_;
-};
-
-// We need another wrapper (used as boost::python's HeldType) that receives a
-// self PyObject * which we can use as ndarray.base, so that data/diff memory
-// is not freed while still being used in Python.
-class PyBlobWrap : public PyBlob<float> {
- public:
-  PyBlobWrap(PyObject *p, const PyBlob<float> &blob)
-      : PyBlob<float>(blob), self_(p) {}
-
-  bp::object get_data();
-  bp::object get_diff();
-
- private:
-  PyObject *self_;
-};
-
-class PyLayer {
- public:
-  explicit PyLayer(const shared_ptr<Layer<float> > &layer)
-    : layer_(layer) {}
-
-  vector<PyBlob<float> > blobs() {
-    return vector<PyBlob<float> >(layer_->blobs().begin(),
-        layer_->blobs().end());
-  }
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyLayer &other) {
-      return this->layer_ == other.layer_;
-  }
-
- protected:
-  shared_ptr<Layer<float> > layer_;
-};
-
-class PyNet {
- public:
-  // For cases where parameters will be determined later by the Python user,
-  // create a Net with unallocated parameters (which will not be zero-filled
-  // when accessed).
-  explicit PyNet(string param_file) { Init(param_file); }
-  PyNet(string param_file, string pretrained_param_file);
-  explicit PyNet(shared_ptr<Net<float> > net)
-      : net_(net) {}
-  virtual ~PyNet() {}
-
-  void Init(string param_file);
-
-
-  // Generate Python exceptions for badly shaped or discontiguous arrays.
-  inline void check_contiguous_array(PyArrayObject* arr, string name,
-      int channels, int height, int width);
-
-  void CopyTrainedLayersFrom(const string filename) {
-    net_->CopyTrainedLayersFrom(filename);
-  }
-  void ShareTrainedLayersWith(PyNet* other) {
-    net_->ShareTrainedLayersWith(other->net_.get());
-  }
-  void Forward(int start, int end) { net_->ForwardFromTo(start, end); }
-  void Backward(int start, int end) { net_->BackwardFromTo(start, end); }
-  void Reshape() { net_->Reshape(); }
-
-  void set_input_arrays(bp::object data_obj, bp::object labels_obj);
-
-  // Save the network weights to binary proto for net surgeries.
-  void save(string filename) {
-    NetParameter net_param;
-    net_->ToProto(&net_param, false);
-    WriteProtoToBinaryFile(net_param, filename.c_str());
-  }
-
-  vector<PyBlob<float> > blobs() {
-    return vector<PyBlob<float> >(net_->blobs().begin(), net_->blobs().end());
-  }
-
-  vector<PyLayer> layers() {
-    return vector<PyLayer>(net_->layers().begin(), net_->layers().end());
-  }
-
-  vector<string> blob_names() { return net_->blob_names(); }
-  vector<string> layer_names() { return net_->layer_names(); }
-
-  bp::list inputs() {
-    bp::list input_blob_names;
-    for (int i = 0; i < net_->input_blob_indices().size(); ++i) {
-      input_blob_names.append(
-          net_->blob_names()[net_->input_blob_indices()[i]]);
-    }
-    return input_blob_names;
-  }
-
-  bp::list outputs() {
-    bp::list output_blob_names;
-    for (int i = 0; i < net_->output_blob_indices().size(); ++i) {
-      output_blob_names.append(
-          net_->blob_names()[net_->output_blob_indices()[i]]);
-    }
-    return output_blob_names;
-  }
-
-  // Input preprocessing configuration attributes. These are public for
-  // direct access from Python.
-  bp::dict mean_;
-  bp::dict input_scale_;
-  bp::dict raw_scale_;
-  bp::dict channel_swap_;
-
-  // this is here only to satisfy boost's vector_indexing_suite
-  bool operator == (const PyNet &other) {
-      return this->net_ == other.net_;
-  }
-
- protected:
-  // The pointer to the internal caffe::Net instance.
-  shared_ptr<Net<float> > net_;
-  // if taking input from an ndarray, we need to hold references
-  bp::object input_data_;
-  bp::object input_labels_;
-};
-
-class PySGDSolver {
- public:
-  explicit PySGDSolver(const string& param_file);
-
-  shared_ptr<PyNet> net() { return net_; }
-  vector<shared_ptr<PyNet> > test_nets() { return test_nets_; }
-  int iter() { return solver_->iter(); }
-  void Solve() { return solver_->Solve(); }
-  void Step(int iters) { solver_->Step(iters); }
-  void SolveResume(const string& resume_file);
-
- protected:
-  shared_ptr<PyNet> net_;
-  vector<shared_ptr<PyNet> > test_nets_;
-  shared_ptr<SGDSolver<float> > solver_;
-};
-
-// Declare the module init function created by boost::python, so that we can
-// use this module from C++ when embedding Python.
-PyMODINIT_FUNC init_caffe(void);
-
-}  // namespace caffe
-
-#endif

From 798a65b442a798b7d20466e30e1f901c1aca8354 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 7 Jan 2015 16:47:52 -0800
Subject: [PATCH 1139/2053] [pycaffe] expose global ("Caffe::") functions

---
 python/caffe/_caffe.cpp | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 9a100468cc4..7caf785a1a2 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -22,11 +22,18 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
+namespace bp = boost::python;
+
 namespace caffe {
 
 // For Python, for now, we'll just always use float as the type.
 typedef float Dtype;
 
+void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
+void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
+void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
+void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
+
 // For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
@@ -41,6 +48,15 @@ static void CheckFile(const string& filename) {
 }
 
 BOOST_PYTHON_MODULE(_caffe) {
+  // below, we prepend an underscore to methods that will be replaced
+  // in Python
+  // Caffe utility functions
+  bp::def("set_mode_cpu", &set_mode_cpu);
+  bp::def("set_mode_gpu", &set_mode_gpu);
+  bp::def("set_phase_train", &set_phase_train);
+  bp::def("set_phase_test", &set_phase_test);
+  bp::def("set_device", &Caffe::SetDevice);
+
   import_array();
 }
 

From 74a584ca23fbf1b6b333689b3b77e61de4d582fe Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 5 Jan 2015 19:31:27 -0800
Subject: [PATCH 1140/2053] [pycaffe] re-expose Net

---
 python/caffe/_caffe.cpp | 108 ++++++++++++++++++++++++++++++++++++++++
 python/caffe/pycaffe.py |   9 ++++
 2 files changed, 117 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 7caf785a1a2..5fcd85328dc 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -47,6 +47,77 @@ static void CheckFile(const string& filename) {
     f.close();
 }
 
+void CheckContiguousArray(PyArrayObject* arr, string name,
+    int channels, int height, int width) {
+  if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
+    throw std::runtime_error(name + " must be C contiguous");
+  }
+  if (PyArray_NDIM(arr) != 4) {
+    throw std::runtime_error(name + " must be 4-d");
+  }
+  if (PyArray_TYPE(arr) != NPY_FLOAT32) {
+    throw std::runtime_error(name + " must be float32");
+  }
+  if (PyArray_DIMS(arr)[1] != channels) {
+    throw std::runtime_error(name + " has wrong number of channels");
+  }
+  if (PyArray_DIMS(arr)[2] != height) {
+    throw std::runtime_error(name + " has wrong height");
+  }
+  if (PyArray_DIMS(arr)[3] != width) {
+    throw std::runtime_error(name + " has wrong width");
+  }
+}
+
+// Net construct-and-load convenience constructor
+shared_ptr<Net<Dtype> > Net_Init(
+    string param_file, string pretrained_param_file) {
+  CheckFile(param_file);
+  CheckFile(pretrained_param_file);
+
+  shared_ptr<Net<Dtype> > net (new Net<Dtype>(param_file));
+  net->CopyTrainedLayersFrom(pretrained_param_file);
+  return net;
+}
+
+void Net_Save(const Net<Dtype>& net, string filename) {
+  NetParameter net_param;
+  net.ToProto(&net_param, false);
+  WriteProtoToBinaryFile(net_param, filename.c_str());
+}
+
+void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
+    bp::object labels_obj) {
+  // check that this network has an input MemoryDataLayer
+  shared_ptr<MemoryDataLayer<Dtype> > md_layer =
+    boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
+  if (!md_layer) {
+    throw std::runtime_error("set_input_arrays may only be called if the"
+        " first layer is a MemoryDataLayer");
+  }
+
+  // check that we were passed appropriately-sized contiguous memory
+  PyArrayObject* data_arr =
+      reinterpret_cast<PyArrayObject*>(data_obj.ptr());
+  PyArrayObject* labels_arr =
+      reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
+  CheckContiguousArray(data_arr, "data array", md_layer->channels(),
+      md_layer->height(), md_layer->width());
+  CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
+  if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
+    throw std::runtime_error("data and labels must have the same first"
+        " dimension");
+  }
+  if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
+    throw std::runtime_error("first dimensions of input arrays must be a"
+        " multiple of batch size");
+  }
+
+  md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
+      static_cast<Dtype*>(PyArray_DATA(labels_arr)),
+      PyArray_DIMS(data_arr)[0]);
+}
+
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
@@ -57,6 +128,43 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::def("set_phase_test", &set_phase_test);
   bp::def("set_device", &Caffe::SetDevice);
 
+  bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >(
+    "Net", bp::init<string>())
+    .def("__init__", bp::make_constructor(&Net_Init))
+    .def("_forward", &Net<Dtype>::ForwardFromTo)
+    .def("_backward", &Net<Dtype>::BackwardFromTo)
+    .def("reshape", &Net<Dtype>::Reshape)
+    // The cast is to select a particular overload.
+    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
+        &Net<Dtype>::CopyTrainedLayersFrom))
+    .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
+    .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
+        bp::return_internal_reference<>()))
+    .add_property("layers", bp::make_function(&Net<Dtype>::layers,
+        bp::return_internal_reference<>()))
+    .add_property("_blob_names", bp::make_function(&Net<Dtype>::blob_names,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_layer_names", bp::make_function(&Net<Dtype>::layer_names,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_inputs", bp::make_function(&Net<Dtype>::input_blob_indices,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .add_property("_outputs",
+        bp::make_function(&Net<Dtype>::output_blob_indices,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .def("_set_input_arrays", &Net_SetInputArrays,
+        bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
+    .def("save", &Net_Save);
+
+  // vector wrappers for all the vector types we use
+  bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+  bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
+  bp::class_<vector<string> >("StringVec")
+    .def(bp::vector_indexing_suite<vector<string> >());
+  bp::class_<vector<int> >("IntVec")
+    .def(bp::vector_indexing_suite<vector<int> >());
+
   import_array();
 }
 
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 31dc1f9b001..f6e8fbf1ba8 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -376,6 +376,13 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
+@property
+def _Net_inputs(self):
+    return [self.blobs.keys()[i] for i in self._inputs]
+
+@property
+def _Net_outputs(self):
+    return [self.blobs.keys()[i] for i in self._outputs]
 
 # Attach methods to Net.
 Net.blobs = _Net_blobs
@@ -392,3 +399,5 @@ def _Net_batch(self, blobs):
 Net.deprocess = _Net_deprocess
 Net.set_input_arrays = _Net_set_input_arrays
 Net._batch = _Net_batch
+Net.inputs = _Net_inputs
+Net.outputs = _Net_outputs

From 8a510f7f145999ea53c10d9871745a1cf314e50a Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 5 Jan 2015 21:02:04 -0800
Subject: [PATCH 1141/2053] [pycaffe] re-expose Blob

---
 python/caffe/_caffe.cpp | 52 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 5fcd85328dc..c545a745887 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -28,6 +28,7 @@ namespace caffe {
 
 // For Python, for now, we'll just always use float as the type.
 typedef float Dtype;
+const int NPY_DTYPE = NPY_FLOAT32;
 
 void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
 void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
@@ -118,6 +119,44 @@ void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
       PyArray_DIMS(data_arr)[0]);
 }
 
+struct NdarrayConverterGenerator {
+  template <typename T> struct apply;
+};
+
+template <>
+struct NdarrayConverterGenerator::apply<Dtype*> {
+  struct type {
+    PyObject* operator() (Dtype* data) const {
+      // Just store the data pointer, and add the shape information in postcall.
+      return PyArray_SimpleNewFromData(0, NULL, NPY_DTYPE, data);
+    }
+    const PyTypeObject* get_pytype() {
+      return &PyArray_Type;
+    }
+  };
+};
+
+struct NdarrayCallPolicies : public bp::default_call_policies {
+  typedef NdarrayConverterGenerator result_converter;
+  PyObject* postcall(PyObject* pyargs, PyObject* result) {
+    bp::object pyblob = bp::extract<bp::tuple>(pyargs)()[0];
+    shared_ptr<Blob<Dtype> > blob =
+      bp::extract<shared_ptr<Blob<Dtype> > >(pyblob);
+    // Free the temporary pointer-holding array, and construct a new one with
+    // the shape information from the blob.
+    void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
+    Py_DECREF(result);
+    npy_intp dims[] = {blob->num(), blob->channels(),
+                       blob->height(), blob->width()};
+    PyObject* arr_obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32, data);
+    // SetBaseObject steals a ref, so we need to INCREF.
+    Py_INCREF(pyblob.ptr());
+    PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
+        pyblob.ptr());
+    return arr_obj;
+  }
+};
+
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
@@ -155,6 +194,19 @@ BOOST_PYTHON_MODULE(_caffe) {
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
     .def("save", &Net_Save);
 
+  bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
+    "Blob", bp::no_init)
+    .add_property("num",      &Blob<Dtype>::num)
+    .add_property("channels", &Blob<Dtype>::channels)
+    .add_property("height",   &Blob<Dtype>::height)
+    .add_property("width",    &Blob<Dtype>::width)
+    .add_property("count",    &Blob<Dtype>::count)
+    .def("reshape",           &Blob<Dtype>::Reshape)
+    .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
+          NdarrayCallPolicies()))
+    .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
+          NdarrayCallPolicies()));
+
   // vector wrappers for all the vector types we use
   bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());

From 2660977b501ffe51fefac0e26d711dbfa9bc096a Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 5 Jan 2015 21:16:51 -0800
Subject: [PATCH 1142/2053] [pycaffe] re-expose Layer

---
 python/caffe/__init__.py | 2 +-
 python/caffe/_caffe.cpp  | 9 +++++++++
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index acdfc071032..9f0e12cb579 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,6 @@
 from .pycaffe import Net, SGDSolver
 from ._caffe import set_mode_cpu, set_mode_gpu, set_device, \
-    set_phase_train, set_phase_test
+    set_phase_train, set_phase_test, Layer
 from .classifier import Classifier
 from .detector import Detector
 import io
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index c545a745887..135178fe00f 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -207,6 +207,15 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
           NdarrayCallPolicies()));
 
+  bp::class_<Layer<Dtype>, shared_ptr<Layer<Dtype> >, boost::noncopyable>(
+    "Layer", bp::no_init)
+    .add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,
+          bp::return_internal_reference<>()))
+    .def("setup", &Layer<Dtype>::LayerSetUp)
+    .def("reshape", &Layer<Dtype>::Reshape)
+    .add_property("type_name", bp::make_function(&Layer<Dtype>::type_name,
+        bp::return_value_policy<bp::copy_const_reference>()));
+
   // vector wrappers for all the vector types we use
   bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());

From 86646f77be9c854a31318fb8d5330ecc2d82a8c7 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 5 Jan 2015 21:53:51 -0800
Subject: [PATCH 1143/2053] [pycaffe] re-expose SGDSolver, and expose other
 solvers

---
 python/caffe/__init__.py |  2 +-
 python/caffe/_caffe.cpp  | 33 +++++++++++++++++++++++++++++++++
 2 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 9f0e12cb579..49f8678054b 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,6 @@
 from .pycaffe import Net, SGDSolver
 from ._caffe import set_mode_cpu, set_mode_gpu, set_device, \
-    set_phase_train, set_phase_test, Layer
+    set_phase_train, set_phase_test, Layer, get_solver
 from .classifier import Classifier
 from .detector import Detector
 import io
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 135178fe00f..30e5c720124 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -119,6 +119,12 @@ void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
       PyArray_DIMS(data_arr)[0]);
 }
 
+Solver<Dtype>* GetSolverFromFile(const string& filename) {
+  SolverParameter param;
+  ReadProtoFromTextFileOrDie(filename, &param);
+  return GetSolver<Dtype>(param);
+}
+
 struct NdarrayConverterGenerator {
   template <typename T> struct apply;
 };
@@ -157,6 +163,8 @@ struct NdarrayCallPolicies : public bp::default_call_policies {
   }
 };
 
+BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
+
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
@@ -216,6 +224,29 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("type_name", bp::make_function(&Layer<Dtype>::type_name,
         bp::return_value_policy<bp::copy_const_reference>()));
 
+  bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
+    "Solver", bp::no_init)
+    .add_property("net", &Solver<Dtype>::net)
+    .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
+          bp::return_internal_reference<>()))
+    .add_property("iter", &Solver<Dtype>::iter)
+    .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
+          &Solver<Dtype>::Solve), SolveOverloads())
+    .def("step", &Solver<Dtype>::Step);
+
+  bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
+        "SGDSolver", bp::init<string>());
+  bp::class_<NesterovSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
+        "NesterovSolver", bp::init<string>());
+  bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
+        "AdaGradSolver", bp::init<string>());
+
+  bp::def("get_solver", &GetSolverFromFile,
+      bp::return_value_policy<bp::manage_new_object>());
+
   // vector wrappers for all the vector types we use
   bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
@@ -225,6 +256,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def(bp::vector_indexing_suite<vector<string> >());
   bp::class_<vector<int> >("IntVec")
     .def(bp::vector_indexing_suite<vector<int> >());
+  bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
 
   import_array();
 }

From 965df8f069481510d0993af203378cf8e604f466 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 16 Feb 2015 19:18:15 -0800
Subject: [PATCH 1144/2053] LayerRegistry uses shared_ptr instead of raw
 pointers

Sometimes it's useful for CreateLayer to return a Layer already in use
by another object (e.g., by a Python wrapper). With raw pointers, there
is no way to properly share owership.
---
 include/caffe/layer_factory.hpp         | 11 +++----
 src/caffe/layer_factory.cpp             | 38 ++++++++++++-------------
 src/caffe/layers/softmax_loss_layer.cpp |  2 +-
 src/caffe/net.cpp                       |  3 +-
 src/caffe/test/test_layer_factory.cpp   |  2 +-
 src/caffe/test/test_upgrade_proto.cpp   |  2 +-
 6 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index ede5d1fae3f..2fcd93869a0 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -53,7 +53,7 @@ class Layer;
 template <typename Dtype>
 class LayerRegistry {
  public:
-  typedef Layer<Dtype>* (*Creator)(const LayerParameter&);
+  typedef shared_ptr<Layer<Dtype> > (*Creator)(const LayerParameter&);
   typedef std::map<string, Creator> CreatorRegistry;
 
   static CreatorRegistry& Registry() {
@@ -70,7 +70,7 @@ class LayerRegistry {
   }
 
   // Get a layer using a LayerParameter.
-  static Layer<Dtype>* CreateLayer(const LayerParameter& param) {
+  static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
     LOG(INFO) << "Creating layer " << param.name();
     const string& type = param.type();
     CreatorRegistry& registry = Registry();
@@ -103,7 +103,7 @@ template <typename Dtype>
 class LayerRegisterer {
  public:
   LayerRegisterer(const string& type,
-                  Layer<Dtype>* (*creator)(const LayerParameter&)) {
+                  shared_ptr<Layer<Dtype> > (*creator)(const LayerParameter&)) {
     // LOG(INFO) << "Registering layer type: " << type;
     LayerRegistry<Dtype>::AddCreator(type, creator);
   }
@@ -116,8 +116,9 @@ class LayerRegisterer {
 
 #define REGISTER_LAYER_CLASS(type)                                             \
   template <typename Dtype>                                                    \
-  Layer<Dtype>* Creator_##type##Layer(const LayerParameter& param) {           \
-    return new type##Layer<Dtype>(param);                                      \
+  shared_ptr<Layer<Dtype> > Creator_##type##Layer(const LayerParameter& param) \
+  {                                                                            \
+    return shared_ptr<Layer<Dtype> >(new type##Layer<Dtype>(param));           \
   }                                                                            \
   REGISTER_LAYER_CREATOR(type, Creator_##type##Layer)
 
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index c3fd1f30ff2..a27c4cad3cf 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 
 // Get convolution layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetConvolutionLayer(
+shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     const LayerParameter& param) {
   ConvolutionParameter_Engine engine = param.convolution_param().engine();
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
@@ -19,10 +19,10 @@ Layer<Dtype>* GetConvolutionLayer(
 #endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
-    return new ConvolutionLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ConvolutionParameter_Engine_CUDNN) {
-    return new CuDNNConvolutionLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -33,7 +33,7 @@ REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);
 
 // Get pooling layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
   PoolingParameter_Engine engine = param.pooling_param().engine();
   if (engine == PoolingParameter_Engine_DEFAULT) {
     engine = PoolingParameter_Engine_CAFFE;
@@ -42,7 +42,7 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == PoolingParameter_Engine_CAFFE) {
-    return new PoolingLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
     PoolingParameter p_param = param.pooling_param();
@@ -50,9 +50,9 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) {
         param.top_size() > 1) {
       LOG(INFO) << "CUDNN does not support padding or multiple tops. "
                 << "Using Caffe's own pooling layer.";
-      return new PoolingLayer<Dtype>(param);
+      return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
     }
-    return new CuDNNPoolingLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -63,7 +63,7 @@ REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
 
 // Get relu layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
   ReLUParameter_Engine engine = param.relu_param().engine();
   if (engine == ReLUParameter_Engine_DEFAULT) {
     engine = ReLUParameter_Engine_CAFFE;
@@ -72,10 +72,10 @@ Layer<Dtype>* GetReLULayer(const LayerParameter& param) {
 #endif
   }
   if (engine == ReLUParameter_Engine_CAFFE) {
-    return new ReLULayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new ReLULayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ReLUParameter_Engine_CUDNN) {
-    return new CuDNNReLULayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNReLULayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -86,7 +86,7 @@ REGISTER_LAYER_CREATOR(ReLU, GetReLULayer);
 
 // Get sigmoid layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetSigmoidLayer(const LayerParameter& param) {
   SigmoidParameter_Engine engine = param.sigmoid_param().engine();
   if (engine == SigmoidParameter_Engine_DEFAULT) {
     engine = SigmoidParameter_Engine_CAFFE;
@@ -95,10 +95,10 @@ Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == SigmoidParameter_Engine_CAFFE) {
-    return new SigmoidLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new SigmoidLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == SigmoidParameter_Engine_CUDNN) {
-    return new CuDNNSigmoidLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNSigmoidLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -109,7 +109,7 @@ REGISTER_LAYER_CREATOR(Sigmoid, GetSigmoidLayer);
 
 // Get softmax layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetSoftmaxLayer(const LayerParameter& param) {
   SoftmaxParameter_Engine engine = param.softmax_param().engine();
   if (engine == SoftmaxParameter_Engine_DEFAULT) {
     engine = SoftmaxParameter_Engine_CAFFE;
@@ -118,10 +118,10 @@ Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == SoftmaxParameter_Engine_CAFFE) {
-    return new SoftmaxLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new SoftmaxLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == SoftmaxParameter_Engine_CUDNN) {
-    return new CuDNNSoftmaxLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNSoftmaxLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -132,7 +132,7 @@ REGISTER_LAYER_CREATOR(Softmax, GetSoftmaxLayer);
 
 // Get tanh layer according to engine.
 template <typename Dtype>
-Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
+shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
   TanHParameter_Engine engine = param.tanh_param().engine();
   if (engine == TanHParameter_Engine_DEFAULT) {
     engine = TanHParameter_Engine_CAFFE;
@@ -141,10 +141,10 @@ Layer<Dtype>* GetTanHLayer(const LayerParameter& param) {
 #endif
   }
   if (engine == TanHParameter_Engine_CAFFE) {
-    return new TanHLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new TanHLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == TanHParameter_Engine_CUDNN) {
-    return new CuDNNTanHLayer<Dtype>(param);
+    return shared_ptr<Layer<Dtype> >(new CuDNNTanHLayer<Dtype>(param));
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 8f6a4ef7959..0c9ba2c6626 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -15,7 +15,7 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   LossLayer<Dtype>::LayerSetUp(bottom, top);
   LayerParameter softmax_param(this->layer_param_);
   softmax_param.set_type("Softmax");
-  softmax_layer_.reset(LayerRegistry<Dtype>::CreateLayer(softmax_param));
+  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.clear();
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index b3e11943c1b..2e449119549 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -63,8 +63,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
     const LayerParameter& layer_param = param.layer(layer_id);
-    layers_.push_back(shared_ptr<Layer<Dtype> >(
-          LayerRegistry<Dtype>::CreateLayer(layer_param)));
+    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
     bool need_backward = false;
diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
index 3ad635a792a..7a6401c5ea3 100644
--- a/src/caffe/test/test_layer_factory.cpp
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -25,7 +25,7 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
   for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
        registry.begin(); iter != registry.end(); ++iter) {
     layer_param.set_type(iter->first);
-    layer.reset(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
     EXPECT_EQ(iter->first, layer->type());
   }
 }
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 7094055d4c8..eec627656ef 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2901,7 +2901,7 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
       continue;  // Empty string isn't actually a valid layer type.
     }
     layer_param.set_type(v2_layer_type);
-    layer.reset(LayerRegistry<float>::CreateLayer(layer_param));
+    layer = LayerRegistry<float>::CreateLayer(layer_param);
     EXPECT_EQ(v2_layer_type, layer->type());
   }
 }

From d94f107ad990f02aa544868250cc5d48fa269eb1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 7 Jan 2015 16:56:08 -0800
Subject: [PATCH 1145/2053] [pycaffe] allow Layer to be extended from Python

This is done by adding PythonLayer as a boost::python HeldType.
---
 Makefile                       |  3 +-
 include/caffe/python_layer.hpp | 68 ++++++++++++++++++++++++++++++++++
 python/caffe/_caffe.cpp        | 15 ++++++--
 src/caffe/layer_factory.cpp    | 21 +++++++++++
 src/caffe/proto/caffe.proto    |  9 ++++-
 5 files changed, 110 insertions(+), 6 deletions(-)
 create mode 100644 include/caffe/python_layer.hpp

diff --git a/Makefile b/Makefile
index 88c0365ec62..ba60ae97934 100644
--- a/Makefile
+++ b/Makefile
@@ -70,6 +70,7 @@ NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
+PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
 # MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
 MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
 ifneq ($(MATLAB_DIR),)
@@ -420,7 +421,7 @@ py$(PROJECT): py
 
 py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
-$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) | $(DYNAMIC_NAME)
+$(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
 		-o $@ $(LINKFLAGS) $(PYTHON_LDFLAGS) -l$(PROJECT) \
diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
new file mode 100644
index 00000000000..f1eab458466
--- /dev/null
+++ b/include/caffe/python_layer.hpp
@@ -0,0 +1,68 @@
+#ifndef CAFFE_PYTHON_LAYER_HPP_
+#define CAFFE_PYTHON_LAYER_HPP_
+
+#include <boost/python.hpp>
+#include <vector>
+
+#include "caffe/layer.hpp"
+
+namespace bp = boost::python;
+
+namespace caffe {
+
+template <typename Dtype>
+class PythonLayer : public Layer<Dtype> {
+ public:
+  PythonLayer(PyObject* self, const LayerParameter& param)
+      : Layer<Dtype>(param), self_(self) { }
+
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+    try {
+      bp::call_method<bp::object>(self_, "setup", bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    try {
+      bp::call_method<bp::object>(self_, "reshape", bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+
+  virtual inline const char* type() const { return "Python"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    try {
+      bp::call_method<bp::object>(self_, "forward", bottom, top);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    try {
+      bp::call_method<bp::object>(self_, "backward", top, propagate_down,
+          bottom);
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      throw;
+    }
+  }
+
+ private:
+  PyObject* self_;
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 30e5c720124..866272709a1 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -14,6 +14,7 @@
 #include <fstream>  // NOLINT
 
 #include "caffe/caffe.hpp"
+#include "caffe/python_layer.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
 // You're strongly advised to upgrade to >= 1.7.
@@ -215,14 +216,16 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
           NdarrayCallPolicies()));
 
-  bp::class_<Layer<Dtype>, shared_ptr<Layer<Dtype> >, boost::noncopyable>(
-    "Layer", bp::no_init)
+  bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
+    boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
     .add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,
           bp::return_internal_reference<>()))
     .def("setup", &Layer<Dtype>::LayerSetUp)
     .def("reshape", &Layer<Dtype>::Reshape)
-    .add_property("type_name", bp::make_function(&Layer<Dtype>::type_name,
-        bp::return_value_policy<bp::copy_const_reference>()));
+    .add_property("type", bp::make_function(&Layer<Dtype>::type));
+  bp::register_ptr_to_python<shared_ptr<Layer<Dtype> > >();
+
+  bp::class_<LayerParameter>("LayerParameter", bp::no_init);
 
   bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
     "Solver", bp::no_init)
@@ -250,6 +253,8 @@ BOOST_PYTHON_MODULE(_caffe) {
   // vector wrappers for all the vector types we use
   bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+  bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
+    .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
   bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
   bp::class_<vector<string> >("StringVec")
@@ -258,6 +263,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def(bp::vector_indexing_suite<vector<int> >());
   bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
+  bp::class_<vector<bool> >("BoolVec")
+    .def(bp::vector_indexing_suite<vector<bool> >());
 
   import_array();
 }
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index a27c4cad3cf..d6a1cac5090 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -5,6 +5,10 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/vision_layers.hpp"
 
+#ifdef WITH_PYTHON_LAYER
+#include "caffe/python_layer.hpp"
+#endif
+
 namespace caffe {
 
 // Get convolution layer according to engine.
@@ -153,6 +157,23 @@ shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
 
 REGISTER_LAYER_CREATOR(TanH, GetTanHLayer);
 
+#ifdef WITH_PYTHON_LAYER
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetPythonLayer(const LayerParameter& param) {
+  Py_Initialize();
+  try {
+    bp::object module = bp::import(param.python_param().module().c_str());
+    bp::object layer = module.attr(param.python_param().layer().c_str())(param);
+    return bp::extract<shared_ptr<PythonLayer<Dtype> > >(layer)();
+  } catch (bp::error_already_set) {
+    PyErr_Print();
+    throw;
+  }
+}
+
+REGISTER_LAYER_CREATOR(Python, GetPythonLayer);
+#endif
+
 // Layers that use their constructor as their default creator should be
 // registered in their corresponding cpp files. Do not register them here.
 }  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8d937420eba..61dd1862641 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -246,7 +246,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 129 (last added: window_data_param)
+// LayerParameter next available layer-specific ID: 131 (last added: python_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -307,6 +307,7 @@ message LayerParameter {
   optional MVNParameter mvn_param = 120;
   optional PoolingParameter pooling_param = 121;
   optional PowerParameter power_param = 122;
+  optional PythonParameter python_param = 130;
   optional ReLUParameter relu_param = 123;
   optional SigmoidParameter sigmoid_param = 124;
   optional SoftmaxParameter softmax_param = 125;
@@ -612,6 +613,12 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+// Message that stores parameters used by PythonLayer
+message PythonParameter {
+  optional string module = 1;
+  optional string layer = 2;
+}
+
 // Message that stores parameters used by ReLULayer
 message ReLUParameter {
   // Allow non-zero slope for negative inputs to speed up optimization

From da044c9b84d53611ddd32b364b02e97cbdd1a920 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 6 Jan 2015 21:42:46 -0800
Subject: [PATCH 1146/2053] [pytest] test that get_solver runs

---
 python/caffe/test/test_solver.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index 832b75c3219..d59f23d973a 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -17,6 +17,8 @@ def setUp(self):
         display: 100 max_iter: 100 snapshot_after_train: false""")
         f.close()
         self.solver = caffe.SGDSolver(f.name)
+        # also make sure get_solver runs
+        caffe.get_solver(f.name)
         caffe.set_mode_cpu()
         # fill in valid labels
         self.solver.net.blobs['label'].data[...] = \

From 3c94d96c08ab46b4c24dbcf7f6e90500859c211d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 7 Jan 2015 16:36:35 -0800
Subject: [PATCH 1147/2053] [pytest] basic test of Python layer

---
 python/caffe/test/test_python_layer.py | 62 ++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 python/caffe/test/test_python_layer.py

diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
new file mode 100644
index 00000000000..cfb1d834be3
--- /dev/null
+++ b/python/caffe/test/test_python_layer.py
@@ -0,0 +1,62 @@
+import unittest
+import tempfile
+import os
+
+import caffe
+
+class SimpleLayer(caffe.Layer):
+    """A layer that just multiplies by ten"""
+
+    def setup(self, bottom, top):
+        pass
+
+    def reshape(self, bottom, top):
+        top[0].reshape(bottom[0].num, bottom[0].channels, bottom[0].height,
+                bottom[0].width)
+
+    def forward(self, bottom, top):
+        top[0].data[...] = 10 * bottom[0].data
+
+    def backward(self, top, propagate_down, bottom):
+        bottom[0].diff[...] = 10 * top[0].diff
+
+def python_net_file():
+    f = tempfile.NamedTemporaryFile(delete=False)
+    f.write("""name: 'pythonnet' force_backward: true
+    input: 'data' input_dim: 10 input_dim: 9 input_dim: 8 input_dim: 7
+    layers { type: PYTHON name: 'one' bottom: 'data' top: 'one'
+      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+    layers { type: PYTHON name: 'two' bottom: 'one' top: 'two'
+      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+    layers { type: PYTHON name: 'three' bottom: 'two' top: 'three'
+      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
+    f.close()
+    return f.name
+
+class TestPythonLayer(unittest.TestCase):
+    def setUp(self):
+        net_file = python_net_file()
+        self.net = caffe.Net(net_file)
+        os.remove(net_file)
+
+    def test_forward(self):
+        x = 8
+        self.net.blobs['data'].data[...] = x
+        self.net.forward()
+        for y in self.net.blobs['three'].data.flat:
+            self.assertEqual(y, 10**3 * x)
+
+    def test_backward(self):
+        x = 7
+        self.net.blobs['three'].diff[...] = x
+        self.net.backward()
+        for y in self.net.blobs['data'].diff.flat:
+            self.assertEqual(y, 10**3 * x)
+
+    def test_reshape(self):
+        s = 4
+        self.net.blobs['data'].reshape(s, s, s, s)
+        self.net.forward()
+        for blob in self.net.blobs.itervalues():
+            for d in blob.data.shape:
+                self.assertEqual(s, d)

From 55e5aa2374574aff19ce28c1c6ed84df18eb139b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 9 Jan 2015 22:57:26 -0800
Subject: [PATCH 1148/2053] add WITH_PYTHON_LAYER build option to include
 Python layer

Currently, this will only work when running from pycaffe (but not the
tool binary), due to static linking conflicts with registration.
---
 Makefile                | 6 ++++++
 Makefile.config.example | 3 +++
 2 files changed, 9 insertions(+)

diff --git a/Makefile b/Makefile
index ba60ae97934..254f7fe9087 100644
--- a/Makefile
+++ b/Makefile
@@ -288,6 +288,12 @@ ifeq ($(CPU_ONLY), 1)
 	COMMON_FLAGS += -DCPU_ONLY
 endif
 
+# Python layer support
+ifeq ($(WITH_PYTHON_LAYER), 1)
+	COMMON_FLAGS += -DWITH_PYTHON_LAYER
+	LIBRARIES += $(PYTHON_LIBRARIES)
+endif
+
 # BLAS configuration (default = ATLAS)
 BLAS ?= atlas
 ifeq ($(BLAS), mkl)
diff --git a/Makefile.config.example b/Makefile.config.example
index 9d13b45715b..7a8aafd7c9f 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -57,6 +57,9 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(ANACONDA_HOME)/lib
 
+# Uncomment to support layers written in Python (will link against Python libs)
+# WITH_PYTHON_LAYER := 1
+
 # Whatever else you find you need goes here.
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

From 590d3967f2f63b81f88e27878f00d674f2cc8efa Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 14 Feb 2015 01:59:43 -0800
Subject: [PATCH 1149/2053] except PythonLayer from layer factory test

PythonLayer (rightfully) refuses to be instantiated when a module and
class aren't given. It's still covered by tests however through pytest.
---
 src/caffe/test/test_layer_factory.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
index 7a6401c5ea3..efb1b37ac42 100644
--- a/src/caffe/test/test_layer_factory.cpp
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -24,6 +24,8 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
   LayerParameter layer_param;
   for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
        registry.begin(); iter != registry.end(); ++iter) {
+    // Special case: PythonLayer is checked by pytest
+    if (iter->first == "Python") { continue; }
     layer_param.set_type(iter->first);
     layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
     EXPECT_EQ(iter->first, layer->type());

From 0396f9f7ef067771d315fd6404862fe5b16c8ff8 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 10 Jan 2015 01:51:39 -0800
Subject: [PATCH 1150/2053] [travis] enable Python layer for testing

---
 scripts/travis/travis_setup_makefile_config.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index e8d85f9be78..ba326262bf8 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -19,4 +19,5 @@ PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 PYTHON_LIB := $(ANACONDA_HOME)/lib
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
+WITH_PYTHON_LAYER := 1
 EOF

From 9b38744f22aa3eaddf22f8a3b4cd0a877a050817 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 10 Feb 2015 13:26:11 -0800
Subject: [PATCH 1151/2053] switch to V2 proto definitions for pytest

---
 python/caffe/test/test_net.py          | 11 ++++++-----
 python/caffe/test/test_python_layer.py |  6 +++---
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index f0e9deef19c..9381c72be6b 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -11,21 +11,22 @@ def simple_net_file(num_output):
 
     f = tempfile.NamedTemporaryFile(delete=False)
     f.write("""name: 'testnet' force_backward: true
-    layers { type: DUMMY_DATA name: 'data' top: 'data' top: 'label'
+    layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
       dummy_data_param { num: 5 channels: 2 height: 3 width: 4
         num: 5 channels: 1 height: 1 width: 1
         data_filler { type: 'gaussian' std: 1 }
         data_filler { type: 'constant' } } }
-    layers { type: CONVOLUTION name: 'conv' bottom: 'data' top: 'conv'
+    layer { type: 'Convolution' name: 'conv' bottom: 'data' top: 'conv'
       convolution_param { num_output: 11 kernel_size: 2 pad: 3
         weight_filler { type: 'gaussian' std: 1 }
         bias_filler { type: 'constant' value: 2 } }
-        weight_decay: 1 weight_decay: 0 }
-    layers { type: INNER_PRODUCT name: 'ip' bottom: 'conv' top: 'ip'
+        param { decay_mult: 1 } param { decay_mult: 0 }
+        }
+    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
       inner_product_param { num_output: """ + str(num_output) + """
         weight_filler { type: 'gaussian' std: 2.5 }
         bias_filler { type: 'constant' value: -3 } } }
-    layers { type: SOFTMAX_LOSS name: 'loss' bottom: 'ip' bottom: 'label'
+    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
       top: 'loss' }""")
     f.close()
     return f.name
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index cfb1d834be3..03f5834514e 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -24,11 +24,11 @@ def python_net_file():
     f = tempfile.NamedTemporaryFile(delete=False)
     f.write("""name: 'pythonnet' force_backward: true
     input: 'data' input_dim: 10 input_dim: 9 input_dim: 8 input_dim: 7
-    layers { type: PYTHON name: 'one' bottom: 'data' top: 'one'
+    layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
       python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
-    layers { type: PYTHON name: 'two' bottom: 'one' top: 'two'
+    layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
       python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
-    layers { type: PYTHON name: 'three' bottom: 'two' top: 'three'
+    layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
       python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
     f.close()
     return f.name

From 9c44810b930e951fe01f26f7cde78e02997af90a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 17 Feb 2015 00:46:14 -0800
Subject: [PATCH 1152/2053] [docs] update and split installation

---
 docs/install_apt.md  |  44 ++++++++
 docs/install_osx.md  | 128 ++++++++++++++++++++++
 docs/install_yum.md  |  45 ++++++++
 docs/installation.md | 253 +++++++------------------------------------
 4 files changed, 255 insertions(+), 215 deletions(-)
 create mode 100644 docs/install_apt.md
 create mode 100644 docs/install_osx.md
 create mode 100644 docs/install_yum.md

diff --git a/docs/install_apt.md b/docs/install_apt.md
new file mode 100644
index 00000000000..89bc9a00aef
--- /dev/null
+++ b/docs/install_apt.md
@@ -0,0 +1,44 @@
+---
+title: Installation: Ubuntu
+---
+
+# Ubuntu Installation
+
+**General dependencies**
+
+    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
+
+**Remaining dependencies, 14.04**
+
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+
+**Remaining dependencies, 12.04**
+
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
+    ./configure
+    make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
+
+**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+
+**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
+
+Continue with [compilation](installation.html#compilation).
diff --git a/docs/install_osx.md b/docs/install_osx.md
new file mode 100644
index 00000000000..55b098731fc
--- /dev/null
+++ b/docs/install_osx.md
@@ -0,0 +1,128 @@
+---
+title: Installation: OS X
+---
+
+# OS X Installation
+
+We highly recommend using the [Homebrew](http://brew.sh/) package manager.
+Ideally you could start from a clean `/usr/local` to avoid conflicts.
+In the following, we assume that you're using Anaconda Python and Homebrew.
+
+**CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
+
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+In other `ENV` settings, things may not work as expected.
+
+**General dependencies**
+
+    brew install --fresh -vd snappy leveldb gflags glog szip lmdb
+    # need the homebrew science source for OpenCV and hdf5
+    brew tap homebrew/science
+    hdf5 opencv
+
+If using Anaconda Python, a modification to the OpenCV formula might be needed
+Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
+
+      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
+      -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
+
+If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
+
+**Remaining dependencies, with / without Python**
+
+    # with Python pycaffe needs dependencies built from source
+    brew install --build-from-source --with-python --fresh -vd protobuf
+    brew install --build-from-source --fresh -vd boost
+    # without Python the usual installation suffices
+    brew install protobuf boost
+
+**BLAS**: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html). OpenBLAS and MKL are alternatives for faster CPU computation.
+
+**Python** (optional): Anaconda is the preferred Python.
+If you decide against it, please use Homebrew.
+Check that Caffe and dependencies are linking against the same, desired Python.
+
+Continue with [compilation](installation.html#compilation).
+
+## libstdc++ installation
+
+This route is not for the faint of heart.
+For OS X 10.10 and 10.9 you should install CUDA 7 and follow the instructions above.
+If that is not an option, take a deep breath and carry on.
+
+In OS X 10.9+, clang++ is the default C++ compiler and uses `libc++` as the standard library.
+However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
+This makes it necessary to change the compilation settings for each of the dependencies.
+
+We do this by modifying the Homebrew formulae before installing any packages.
+Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
+
+The prerequisite Homebrew formulae are
+
+    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
+
+For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
+
+      def install
+          # ADD THE FOLLOWING:
+          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
+          ENV.append "CFLAGS", "-stdlib=libstdc++"
+          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
+          # The following is necessary because libtool likes to strip LDFLAGS:
+          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
+          ...
+
+To edit the formulae in turn, run
+
+    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
+
+After this, run
+
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
+    brew install --build-from-source --fresh -vd boost boost-python
+
+If this is not done exactly right then linking errors will trouble you.
+
+**Homebrew versioning** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
+
+    $ brew update
+    error: Your local changes to the following files would be overwritten by merge:
+      Library/Formula/lmdb.rb
+    Please, commit your changes or stash them before you can merge.
+    Aborting
+    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
+
+One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
+
+    cd /usr/local
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies to use libstdc++"
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout -b caffe
+    git add .
+    git commit -m "Update Caffe dependencies"
+
+Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
+
+    # Switch batch to homebrew master branches
+    cd /usr/local
+    git checkout master
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git checkout master
+
+    # Update homebrew; hopefully this works without errors!
+    brew update
+
+    # Switch back to the caffe branches with the forumlae that you modified earlier
+    cd /usr/local
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+    cd /usr/local/Library/Taps/homebrew/homebrew-science
+    git rebase master caffe
+    # Fix any merge conflicts and commit to caffe branch
+
+    # Done!
+
+At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
diff --git a/docs/install_yum.md b/docs/install_yum.md
new file mode 100644
index 00000000000..478e7d952cc
--- /dev/null
+++ b/docs/install_yum.md
@@ -0,0 +1,45 @@
+---
+title: Installation: RHEL / Fedora / CentOS
+---
+
+# RHEL / Fedora / CentOS Installation
+
+**General dependencies**
+
+    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
+
+**Remaining dependencies, recent OS**
+
+    sudo yum install gflags-devel glog-devel lmdb-devel
+
+**Remaining dependencies, if not found**
+
+    # glog
+    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
+    ./configure
+    make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
+
+**CUDA**: Install via the NVIDIA package instead of `yum` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+    + CentOS/RHEL/Fedora:
+
+**BLAS**: install ATLAS by `sudo yum install atlas-devel` or install OpenBLAS or MKL for better CPU performance. For the Makefile build, uncomment and set `BLAS_LIB` accordingly as ATLAS is usually installed under `/usr/lib[64]/atlas`).
+
+**Python** (optional): if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the pycaffe wrapper.
+
+Continue with [compilation](installation.html#compilation).
diff --git a/docs/installation.md b/docs/installation.md
index 73f6806c805..16575b54029 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -4,28 +4,36 @@ title: Installation
 
 # Installation
 
-Prior to installing, it is best to read through this guide and take note of the details for your platform.
-We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.10, 10.9, and 10.8.
+Prior to installing, have a glance through this guide and take note of the details for your platform.
+We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
+The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
-- [Hardware questions](#hardware_questions)
+- [Hardware](#hardware)
+- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
+
+When updating Caffe, it's best to `make clean` before re-compiling.
 
 ## Prerequisites
 
-Caffe depends on several software packages.
+Caffe has several dependencies.
+
+* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
+    * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+    * 5.5, and 5.0 are compatible but considered legacy
+* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
+* [Boost](http://www.boost.org/) >= 1.55
+* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
+* `protobuf`, `glog`, `gflags`
+* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb`
 
-* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.5 (recommended), 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
-* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
-* [OpenCV](http://opencv.org/) (>= 2.4)
-* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 and 1.56 are tested)
-* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
-* For the Python wrapper
-    * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
-* For the MATLAB wrapper
-    * MATLAB with the `mex` compiler.
+Pycaffe and Matcaffe interfaces have their own natural needs.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic.
+* For Python Caffe:  `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
+* For MATLAB Caffe: MATLAB with the `mex` compiler.
+
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
@@ -37,13 +45,9 @@ To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cu
 For best performance, Caffe can be accelerated by [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). Register for free at the cuDNN site, install it, then continue with these installation instructions. To compile with cuDNN set the `USE_CUDNN := 1` flag set in your `Makefile.config`.
 
 Caffe requires BLAS as the backend of its matrix and vector computations.
-There are several implementations of this library.
-The choice is yours:
+There are several implementations of this library. The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
-    + Ubuntu: `sudo apt-get install libatlas-base-dev`
-    + CentOS/RHEL/Fedora: `sudo yum install atlas-devel`
-    + OS X: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html).
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
     2. Set `BLAS := mkl` in `Makefile.config`
@@ -51,7 +55,7 @@ The choice is yours:
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
 
-### Python and/or MATLAB wrappers (optional)
+### Python and/or MATLAB Caffe (optional)
 
 #### Python
 
@@ -59,15 +63,9 @@ The main requirements are `numpy` and `boost.python` (provided by boost). `panda
 
 You can install the dependencies with
 
-    for req in $(cat requirements.txt); do sudo pip install $req; done
-
-but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
+    for req in $(cat requirements.txt); do pip install $req; done
 
-For **Ubuntu**, if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
-
-For **Fedora**, if you use the default Python you will need to `sudo yum install` the `python-devel` package to have the Python headers for building the wrapper.
-
-For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
+but we suggest first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 
 To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
 
@@ -77,170 +75,7 @@ To import the `caffe` Python module after completing the installation, add the m
 
 Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
-*Caffe's MATLAB interface works with versions 2012b, 2013a/b, and 2014a.*
-
-### The rest of the dependencies
-
-#### Linux
-
-On **Ubuntu**, most of the dependencies can be installed with
-
-    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
-
-and for **Ubuntu 14.04** the rest of the dependencies can be installed with
-
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
-
-Keep reading to find out how to manually build and install the Google flags library, Google logging library and LMDB on **Ubuntu 12.04**.
-
-On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
-
-    sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
-
-The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
-
-    sudo yum install gflags-devel glog-devel lmdb-devel
-
-**Finally** in case you couldn't find those extra libraries mentioned above in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora** (or practically on any Linux distribution)
-
-    # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
-    cd glog-0.3.3
-    ./configure
-    make && make install
-    # gflags
-    wget https://github.com/schuhschuh/gflags/archive/master.zip
-    unzip master.zip
-    cd gflags-master
-    mkdir build && cd build
-    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
-    make && make install
-    # lmdb
-    git clone git://gitorious.org/mdb/mdb.git
-    cd mdb/libraries/liblmdb
-    make && make install
-
-Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
-
-#### OS X
-
-On **OS X**, we highly recommend using the [Homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
-In the following, we assume that you're using Anaconda Python and Homebrew.
-
-To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:
-
-    brew tap homebrew/science
-
-If using Anaconda Python, a modification is required to the OpenCV formula.
-Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
-
-      -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
-      -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
-
-**NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
-In other `ENV` settings, things may not work as expected.
-
-**NOTE**: There is currently a conflict between boost 1.56 and CUDA in some configurations. Check the [conflict description](https://github.com/BVLC/caffe/issues/1193#issuecomment-57491906) and try downgrading to 1.55 or upgrading to 1.57.
-
-#### 10.8-specific Instructions
-
-Simply run the following:
-
-    brew install --build-from-source boost boost-python
-    brew install --with-python protobuf
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
-
-Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
-
-**Note** that the HDF5 dependency is provided by Anaconda Python in this case.
-If you're not using Anaconda, include `hdf5` in the list above.
-
-#### 10.10- and 10.9-specific Instructions
-
-In OS X 10.9+, clang++ is the default C++ compiler and uses `libc++` as the standard library.
-However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
-This makes it necessary to change the compilation settings for each of the dependencies.
-
-We do this by modifying the Homebrew formulae before installing any packages.
-Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
-
-The prerequisite Homebrew formulae are
-
-    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
-
-For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
-
-      def install
-          # ADD THE FOLLOWING:
-          ENV.append "CXXFLAGS", "-stdlib=libstdc++"
-          ENV.append "CFLAGS", "-stdlib=libstdc++"
-          ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
-          # The following is necessary because libtool likes to strip LDFLAGS:
-          ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
-          ...
-
-To edit the formulae in turn, run
-
-    for x in snappy leveldb protobuf gflags glog szip boost boost-python lmdb homebrew/science/opencv; do brew edit $x; done
-
-After this, run
-
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
-    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
-
-**Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
-
-**Note** that the HDF5 dependency is provided by Anaconda Python in this case.
-If you're not using Anaconda, include `hdf5` in the list above.
-
-**Note** that in order to build the Caffe Python wrappers you must install `boost` and `boost-python`:
-
-    brew install --build-from-source --fresh -vd boost boost-python
-
-**Note** that Homebrew maintains itself as a separate git repository and making the above `brew edit FORMULA` changes will change files in your local copy of homebrew's master branch. By default, this will prevent you from updating Homebrew using `brew update`, as you will get an error message like the following:
-
-    $ brew update
-    error: Your local changes to the following files would be overwritten by merge:
-      Library/Formula/lmdb.rb
-    Please, commit your changes or stash them before you can merge.
-    Aborting
-    Error: Failure while executing: git pull -q origin refs/heads/master:refs/remotes/origin/master
-
-One solution is to commit your changes to a separate Homebrew branch, run `brew update`, and rebase your changes onto the updated master. You'll have to do this both for the main Homebrew repository in `/usr/local/` and the Homebrew science repository that contains OpenCV in  `/usr/local/Library/Taps/homebrew/homebrew-science`, as follows:
-
-    cd /usr/local
-    git checkout -b caffe
-    git add .
-    git commit -m "Update Caffe dependencies to use libstdc++"
-    cd /usr/local/Library/Taps/homebrew/homebrew-science
-    git checkout -b caffe
-    git add .
-    git commit -m "Update Caffe dependencies"
-
-Then, whenever you want to update homebrew, switch back to the master branches, do the update, rebase the caffe branches onto master and fix any conflicts:
-
-    # Switch batch to homebrew master branches
-    cd /usr/local
-    git checkout master
-    cd /usr/local/Library/Taps/homebrew/homebrew-science
-    git checkout master
-
-    # Update homebrew; hopefully this works without errors!
-    brew update
-
-    # Switch back to the caffe branches with the forumlae that you modified earlier
-    cd /usr/local
-    git rebase master caffe
-    # Fix any merge conflicts and commit to caffe branch
-    cd /usr/local/Library/Taps/homebrew/homebrew-science
-    git rebase master caffe
-    # Fix any merge conflicts and commit to caffe branch
-
-    # Done!
-
-At this point, you should be running the latest Homebrew packages and your Caffe-related modifications will remain in place.
+*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.*
 
 #### Windows
 
@@ -248,8 +83,7 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup (you should especially uncomment and set `BLAS_LIB` accordingly on distributions like **CentOS / RHEL / Fedora** where ATLAS is installed under `/usr/lib[64]/atlas`)
-The defaults should work, but uncomment the relevant lines if using Anaconda Python.
+Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config
     # Adjust Makefile.config (for example, if using Anaconda Python)
@@ -257,24 +91,22 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
     make test
     make runtest
 
-To compile with cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
-
-If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
+- For cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
+- For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
 Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
-*Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
+**Distribution**: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
 
-*Speed*: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
+**Speed**: for a faster build, compile in parallel by doing `make all -j8` where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
 ### CMake Compilation
 
-In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community.
-It requires CMake version >= 2.8.7.
-The basic installation steps are as follows:
+In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
+The basic steps are as follows:
 
     mkdir build
     cd build
@@ -282,23 +114,14 @@ The basic installation steps are as follows:
     make all
     make runtest
 
-See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for details.
-
-#### Ubuntu 12.04
-
-Note that in Ubuntu 12.04, Aptitude will install version CMake 2.8.7 by default, which is not supported by Caffe's CMake build (requires at least 2.8.8).
-As a workaround, if you are using Ubuntu 12.04 you can try the following steps to install (or upgrade to) CMake 2.8.9:
-
-    sudo add-apt-repository ppa:ubuntu-sdk-team/ppa -y
-    sudo apt-get -y update
-    sudo apt-get install cmake
+See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
-## Hardware Questions
+## Hardware
 
-**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
+**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
 **CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 
-Refer to the project's issue tracker for [hardware/compatibility](https://github.com/BVLC/caffe/issues?labels=hardware%2Fcompatibility&page=1&state=open).
+Ask hardware questions on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users).

From ca29af0995922700c2de314855992304081467ba Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 17 Feb 2015 01:07:05 -0800
Subject: [PATCH 1153/2053] [docs] explain one true branch and dev workflow

---
 docs/development.md | 72 ++++++++++++++++++++++-----------------------
 1 file changed, 35 insertions(+), 37 deletions(-)

diff --git a/docs/development.md b/docs/development.md
index dfed3308eeb..fe54864bd35 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -1,10 +1,10 @@
 ---
 title: Developing and Contributing
 ---
-# Development
+# Development and Contributing
 
 Caffe is developed with active participation of the community.<br>
-The [BVLC](http://bvlc.eecs.berkeley.edu/) maintainers welcome all contributions!
+The [BVLC](http://bvlc.eecs.berkeley.edu/) brewers welcome all contributions!
 
 The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
 This method is impartial and always up-to-date.
@@ -21,7 +21,7 @@ If a contributor wants to further mark their specific copyright on a particular
 
 ### Documentation
 
-This website, written with [Jekyll](http://jekyllrb.com/), functions as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
+This website, written with [Jekyll](http://jekyllrb.com/), acts as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
 
 We prefer tutorials and examples to be documented close to where they live, in `readme.md` files.
 The `build_docs.sh` script gathers all `examples/**/readme.md` and `examples/*.ipynb` files, and makes a table of contents.
@@ -33,79 +33,77 @@ Other docs, such as installation guides, are written in the `docs` directory and
 We strive to provide provide lots of usage examples, and to document all code in docstrings.
 We absolutely appreciate any contribution to this effort!
 
-### The release cycle
+### Versioning
 
-- The `dev` branch receives all new development, including community contributions.
-We aim to keep it in a functional state, but large changes do occur, and things do get broken every now and then.
-Use only if you want the "bleeding edge".
-- BVLC maintainers will periodically update the `master` branch with changes from `dev`, giving it a release tag ([releases so far](https://github.com/BVLC/caffe/releases)).
-Use this if you want more stability.
+The `master` branch receives all new development including community contributions.
+We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then.
+BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
 
-### Issues & Pull Request Protocol
+#### Issues & Pull Request Protocol
 
-Use Github Issues to report [bugs], propose features, and ask development [questions].
-Large-scale development work is guided by [milestones], which are sets of Issues selected for concurrent release (integration from `dev` to `master`).
+Post [Issues](https://github.com/BVLC/caffe/issues) to propose features, report [bugs], and discuss framework code.
+Large-scale development work is guided by [milestones], which are sets of Issues selected for bundling as releases.
 
 Please note that since the core developers are largely researchers, we may work on a feature in isolation for some time before releasing it to the community, so as to claim honest academic contribution.
 We do release things as soon as a reasonable technical report may be written, and we still aim to inform the community of ongoing development through Github Issues.
 
-When you are ready to start developing your feature or fixing a bug, follow this protocol:
+**When you are ready to develop a feature or fixing a bug, follow this protocol**:
 
-- Develop in [feature branches] with descriptive names.
-    - For new development branch off `dev`.
-    - For documentation and fixes for `master` branch off `master`.
-- Bring your work up-to-date by [rebasing] onto the latest `dev` / `master`.
-(Polish your changes by [interactive rebase], if you'd like.)
-- [Pull request] your contribution to `BVLC/caffe`'s `dev` / `master` branch for discussion and review.
+- Develop in [feature branches] with descriptive names. Branch off of the latest `master`.
+- Bring your work up-to-date by [rebasing] onto the latest `master` when done.
+(Groom your changes by [interactive rebase], if you'd like.)
+- [Pull request] your contribution to `BVLC/caffe`'s `master` branch for discussion and review.
   - Make PRs *as soon as development begins*, to let discussion guide development.
   - A PR is only ready for merge review when it is a fast-forward merge, and all code is documented, linted, and tested -- that means your PR must include tests!
 - When the PR satisfies the above properties, use comments to request maintainer review.
 
-Below is a poetic presentation of the protocol in code form.
+The following is a poetic presentation of the protocol in code form.
 
 #### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”
 
-Make the `feature` branch off of the latest `bvlc/dev`
+Make the `feature` branch off of the latest `bvlc/master`
 ```
-git checkout dev
-git pull upstream dev
+git checkout master
+git pull upstream master
 git checkout -b feature
 # do your work, make commits
 ```
 
-Prepare to merge by rebasing your branch on the latest `bvlc/dev`
+Prepare to merge by rebasing your branch on the latest `bvlc/master`
 ```
-# make sure dev is fresh
-git checkout dev
-git pull upstream dev
-# rebase your branch on the tip of dev
+# make sure master is fresh
+git checkout master
+git pull upstream master
+# rebase your branch on the tip of master
 git checkout feature
-git rebase dev
+git rebase master
 ```
 
-Push your branch to pull request it into `dev`
+Push your branch to pull request it into `BVLC/caffe:master`
 ```
 git push origin feature
-# ...make pull request to dev...
+# ...make pull request to master...
 ```
 
-Now make a pull request! You can do this from the command line (`git pull-request -b dev`) if you install [hub](https://github.com/github/hub).
+Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses.
 
-The pull request of `feature` into `dev` will be a clean merge. Applause.
+The pull request of `feature` into `master` will be a clean merge. Applause.
 
 [bugs]: https://github.com/BVLC/caffe/issues?labels=bug&page=1&state=open
-[questions]: https://github.com/BVLC/caffe/issues?labels=question&page=1&state=open
 [milestones]: https://github.com/BVLC/caffe/issues?milestone=1
 [Pull request]: https://help.github.com/articles/using-pull-requests
 [interactive rebase]: https://help.github.com/articles/interactive-rebase
 [rebasing]: http://git-scm.com/book/en/Git-Branching-Rebasing
 [feature branches]: https://www.atlassian.com/git/workflows#!workflow-feature-branch
 
+**Historical note**: Caffe once relied on a two branch `master` and `dev` workflow.
+PRs from this time are still open but these will be merged into `master` or closed.
+
 ### Testing
 
 Run `make runtest` to check the project tests. New code requires new tests. Pull requests that fail tests will not be accepted.
 
-The `googletest` framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is `--gtest_filter`, which allows you to filter tests by name:
+The `gtest` framework we use provides many additional options, which you can access by running the test binaries directly. One of the more useful options is `--gtest_filter`, which allows you to filter tests by name:
 
     # run all tests with CPU in the name
     build/test/test_all.testbin --gtest_filter='*CPU*'
@@ -119,7 +117,7 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 
 ### Style
 
-- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
+- **Run `make lint` to check C++ code.**
 - Wrap lines at 80 chars.
+- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
-- **Run `make lint` to check C++ code.**

From 91289b3d95e9baa0ee6f7083f1f74097e4475e93 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 17 Feb 2015 01:19:51 -0800
Subject: [PATCH 1154/2053] [make] link libcaffe.so before dependencies

---
 Makefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 254f7fe9087..b298d9d146e 100644
--- a/Makefile
+++ b/Makefile
@@ -430,7 +430,7 @@ py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		-o $@ $(LINKFLAGS) $(PYTHON_LDFLAGS) -l$(PROJECT) \
+		-o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
 mat$(PROJECT): mat
@@ -539,7 +539,7 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 
 $(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
-	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) \
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)

From 6eb0931aee72355a59eda649f83e33260fa88903 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 00:31:28 -0800
Subject: [PATCH 1155/2053] give phase to Net and Layer

Give the responsibility for phase to Net and Layer, making phase an
immutable choice at instantiation and dropping it from the Caffe
singleton.
---
 include/caffe/common.hpp                      |  7 ----
 include/caffe/data_layers.hpp                 |  1 +
 include/caffe/layer.hpp                       |  5 ++-
 include/caffe/loss_layers.hpp                 |  6 +--
 include/caffe/net.hpp                         |  7 +++-
 include/caffe/neuron_layers.hpp               |  1 +
 src/caffe/common.cpp                          |  4 +-
 src/caffe/layers/base_data_layer.cpp          |  2 +-
 src/caffe/layers/dropout_layer.cpp            |  4 +-
 src/caffe/layers/dropout_layer.cu             |  4 +-
 src/caffe/layers/pooling_layer.cu             |  2 +-
 src/caffe/net.cpp                             | 21 ++++------
 src/caffe/proto/caffe.proto                   |  3 ++
 src/caffe/test/test_common.cpp                |  7 ----
 src/caffe/test/test_data_layer.cpp            | 27 ++++++-------
 src/caffe/test/test_data_transformer.cpp      | 39 ++++++++-----------
 .../test/test_maxpool_dropout_layers.cpp      |  2 +-
 src/caffe/test/test_net.cpp                   | 14 -------
 src/caffe/test/test_neuron_layer.cpp          |  8 ++--
 src/caffe/test/test_stochastic_pooling.cpp    |  6 +--
 20 files changed, 70 insertions(+), 100 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 5fc4ed3a3e1..890673cd7e6 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -104,8 +104,6 @@ class Caffe {
     return *singleton_;
   }
   enum Brew { CPU, GPU };
-  enum Phase { TRAIN, TEST };
-
 
   // This random number generator facade hides boost and CUDA rng
   // implementation from one another (for cross-platform compatibility).
@@ -137,16 +135,12 @@ class Caffe {
 
   // Returns the mode: running on CPU or GPU.
   inline static Brew mode() { return Get().mode_; }
-  // Returns the phase: TRAIN or TEST.
-  inline static Phase phase() { return Get().phase_; }
   // The setters for the variables
   // Sets the mode. It is recommended that you don't change the mode halfway
   // into the program since that may cause allocation of pinned memory being
   // freed in a non-pinned way, which may cause problems - I haven't verified
   // it personally but better to note it here in the header file.
   inline static void set_mode(Brew mode) { Get().mode_ = mode; }
-  // Sets the phase.
-  inline static void set_phase(Phase phase) { Get().phase_ = phase; }
   // Sets the random seed of both boost and curand
   static void set_random_seed(const unsigned int seed);
   // Sets the device. Since we have cublas and curand stuff, set device also
@@ -163,7 +157,6 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
-  Phase phase_;
   static shared_ptr<Caffe> singleton_;
 
  private:
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index e0d5a8aca46..aa7e6967ead 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -14,6 +14,7 @@
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
 
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index c6461c19964..34e00d72c05 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -33,7 +33,8 @@ class Layer {
    */
   explicit Layer(const LayerParameter& param)
     : layer_param_(param) {
-      // The only thing we do is to copy blobs if there are any.
+      // Set phase and copy blobs (if there are any).
+      phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
         blobs_.resize(layer_param_.blobs_size());
         for (int i = 0; i < layer_param_.blobs_size(); ++i) {
@@ -288,6 +289,8 @@ class Layer {
  protected:
   /** The protobuf that stores the layer parameters */
   LayerParameter layer_param_;
+  /** The phase: TRAIN or TEST */
+  Phase phase_;
   /** The vector that stores the learnable parameters as a set of blobs. */
   vector<shared_ptr<Blob<Dtype> > > blobs_;
   /** Vector indicating whether to compute the diff of each param blob. */
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index ea52f56e0f3..36413ccd176 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -169,8 +169,8 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
 
   /**
    * @brief Computes the Contrastive error gradient w.r.t. the inputs.
-   * 
-   * Computes the gradients with respect to the two input vectors (bottom[0] and 
+   *
+   * Computes the gradients with respect to the two input vectors (bottom[0] and
    * bottom[1]), but not the similarity label (bottom[2]).
    *
    * @param top output Blob vector (length 1), providing the error gradient with
@@ -189,7 +189,7 @@ class ContrastiveLossLayer : public LossLayer<Dtype> {
    *      the features @f$a@f$; Backward fills their diff with
    *      gradients if propagate_down[0]
    *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$b@f$; Backward fills their diff with gradients if 
+   *      the features @f$b@f$; Backward fills their diff with gradients if
    *      propagate_down[1]
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 23da5dbbf2d..153b881fe9f 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -115,6 +115,8 @@ class Net {
   inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
     return layers_;
   }
+  /// @brief returns the phase: TRAIN or TEST
+  inline Phase phase() const { return phase_; }
   /**
    * @brief returns the bottom vecs for each layer -- usually you won't
    *        need this unless you do per-layer checks such as gradients.
@@ -207,6 +209,10 @@ class Net {
   /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
   void GetLearningRateAndWeightDecay();
 
+  /// @brief The network name
+  string name_;
+  /// @brief The phase: TRAIN or TEST
+  Phase phase_;
   /// @brief Individual layers in the net
   vector<shared_ptr<Layer<Dtype> > > layers_;
   vector<string> layer_names_;
@@ -239,7 +245,6 @@ class Net {
   vector<int> net_output_blob_indices_;
   vector<Blob<Dtype>*> net_input_blobs_;
   vector<Blob<Dtype>*> net_output_blobs_;
-  string name_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
   /// the learning rate multipliers
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 46c84171b9c..0c306fb41bf 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -8,6 +8,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #define HDF5_DATA_DATASET_NAME "data"
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 834d5694aad..af96cac40aa 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -42,7 +42,7 @@ void GlobalInit(int* pargc, char*** pargv) {
 #ifdef CPU_ONLY  // CPU-only Caffe.
 
 Caffe::Caffe()
-    : random_generator_(), mode_(Caffe::CPU), phase_(Caffe::TRAIN) { }
+    : random_generator_(), mode_(Caffe::CPU) { }
 
 Caffe::~Caffe() { }
 
@@ -86,7 +86,7 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU), phase_(Caffe::TRAIN) {
+    mode_(Caffe::CPU) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index c3b9bc421ba..d7e0b90801e 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -23,7 +23,7 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // The subclasses should setup the size of bottom and top
   DataLayerSetUp(bottom, top);
-  data_transformer_.InitRand();
+  data_transformer_->InitRand();
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 5f81cc1c692..ec1256fd2fa 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -37,7 +37,7 @@ void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = top[0]->mutable_cpu_data();
   unsigned int* mask = rand_vec_.mutable_cpu_data();
   const int count = bottom[0]->count();
-  if (Caffe::phase() == Caffe::TRAIN) {
+  if (this->phase_ == TRAIN) {
     // Create random numbers
     caffe_rng_bernoulli(count, 1. - threshold_, mask);
     for (int i = 0; i < count; ++i) {
@@ -55,7 +55,7 @@ void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       const unsigned int* mask = rand_vec_.cpu_data();
       const int count = bottom[0]->count();
       for (int i = 0; i < count; ++i) {
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index df13d8ecb23..f9ea04f4acf 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -26,7 +26,7 @@ void DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
-  if (Caffe::phase() == Caffe::TRAIN) {
+  if (this->phase_ == TRAIN) {
     unsigned int* mask =
         static_cast<unsigned int*>(rand_vec_.mutable_gpu_data());
     caffe_gpu_rng_uniform(count, mask);
@@ -56,7 +56,7 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       const unsigned int* mask =
           static_cast<const unsigned int*>(rand_vec_.gpu_data());
       const int count = bottom[0]->count();
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 0d3f2183e71..d1d48501af3 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -182,7 +182,7 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
-    if (Caffe::phase() == Caffe::TRAIN) {
+    if (this->phase_ == TRAIN) {
       // We need to create the random index as well.
       caffe_gpu_rng_uniform(count, Dtype(0), Dtype(1),
                             rand_idx_.mutable_gpu_data());
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 2e449119549..d1ae0bcf73b 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -32,6 +32,8 @@ Net<Dtype>::Net(const string& param_file) {
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  // Set phase from the state.
+  phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
   // the current NetState.
   NetParameter filtered_param;
@@ -62,6 +64,11 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_id_vecs_.resize(param.layer_size());
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+    // Inherit phase from net if unset.
+    if (!param.layer(layer_id).has_phase()) {
+      param.mutable_layer(layer_id)->set_phase(phase_);
+    }
+    // Setup layer.
     const LayerParameter& layer_param = param.layer(layer_id);
     layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
@@ -210,20 +217,6 @@ template <typename Dtype>
 void Net<Dtype>::FilterNet(const NetParameter& param,
     NetParameter* param_filtered) {
   NetState net_state(param.state());
-  // Let the phase of the net be the current global phase provided in the Caffe
-  // singleton, unless explicitly provided by the state.
-  if (!net_state.has_phase()) {
-    switch (Caffe::phase()) {
-      case Caffe::TRAIN:
-        net_state.set_phase(TRAIN);
-        break;
-      case Caffe::TEST:
-        net_state.set_phase(TEST);
-        break;
-      default:
-        LOG(FATAL) << "Unknown phase: " << Caffe::phase();
-    }
-  }
   param_filtered->CopyFrom(param);
   param_filtered->clear_layer();
   for (int i = 0; i < param.layer_size(); ++i) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 61dd1862641..6bdda33b463 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -253,6 +253,9 @@ message LayerParameter {
   repeated string bottom = 3; // the name of each bottom blob
   repeated string top = 4; // the name of each top blob
 
+  // The train / test phase for computation.
+  optional Phase phase = 10;
+
   // The amount of weight to assign each top blob in the objective.
   // Each layer assigns a default value, usually of either 0 or 1,
   // to each top blob.
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index 0b3639c7706..b3a61b0fd25 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -29,13 +29,6 @@ TEST_F(CommonTest, TestBrewMode) {
   EXPECT_EQ(Caffe::mode(), Caffe::GPU);
 }
 
-TEST_F(CommonTest, TestPhase) {
-  Caffe::set_phase(Caffe::TRAIN);
-  EXPECT_EQ(Caffe::phase(), Caffe::TRAIN);
-  Caffe::set_phase(Caffe::TEST);
-  EXPECT_EQ(Caffe::phase(), Caffe::TEST);
-}
-
 TEST_F(CommonTest, TestRandSeedCPU) {
   SyncedMemory data_a(10 * sizeof(int));
   SyncedMemory data_b(10 * sizeof(int));
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index adc99dd71c9..afe2a40d227 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -69,6 +69,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
   void TestRead() {
     const Dtype scale = 3;
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -132,6 +133,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
     // Load and check data of various shapes.
     LayerParameter param;
+    param.set_phase(TEST);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(1);
     data_param->set_source(filename_->c_str());
@@ -167,9 +169,10 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
-  void TestReadCrop() {
+  void TestReadCrop(Phase phase) {
     const Dtype scale = 3;
     LayerParameter param;
+    param.set_phase(phase);
     Caffe::set_random_seed(1701);
 
     DataParameter* data_param = param.mutable_data_param();
@@ -205,7 +208,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
           num_with_center_value +=
               (center_value == blob_top_data_->cpu_data()[i * 2 + j]);
           // At TEST time, check that we always get center value.
-          if (Caffe::phase() == Caffe::TEST) {
+          if (phase == caffe::TEST) {
             EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
                 << "debug: iter " << iter << " i " << i << " j " << j;
           }
@@ -214,7 +217,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
       // At TRAIN time, check that we did not get the center crop all 10 times.
       // (This check fails with probability 1-1/12^10 in a correct
       // implementation, so we call set_random_seed.)
-      if (Caffe::phase() == Caffe::TRAIN) {
+      if (phase == caffe::TRAIN) {
         EXPECT_LT(num_with_center_value, 10);
       }
     }
@@ -222,6 +225,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestReadCropTrainSequenceSeeded() {
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -276,6 +280,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestReadCropTrainSequenceUnseeded() {
     LayerParameter param;
+    param.set_phase(TRAIN);
     DataParameter* data_param = param.mutable_data_param();
     data_param->set_batch_size(5);
     data_param->set_source(filename_->c_str());
@@ -354,16 +359,14 @@ TYPED_TEST(DataLayerTest, TestReshapeLevelDB) {
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TRAIN);
 }
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceSeeded();
@@ -372,17 +375,15 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
-  Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TEST);
 }
 
 TYPED_TEST(DataLayerTest, TestReadLMDB) {
@@ -396,16 +397,14 @@ TYPED_TEST(DataLayerTest, TestReshapeLMDB) {
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TRAIN);
 }
 
 // Test that the sequence of random crops is consistent when using
 // Caffe::set_random_seed.
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceSeeded();
@@ -414,17 +413,15 @@ TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
 TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDB) {
-  Caffe::set_phase(Caffe::TRAIN);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
   this->TestReadCropTrainSequenceUnseeded();
 }
 
 TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
-  Caffe::set_phase(Caffe::TEST);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
-  this->TestReadCrop();
+  this->TestReadCrop(TEST);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 28c7241050b..16570e20356 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -37,10 +37,10 @@ class DataTransformTest : public ::testing::Test {
       num_iter_(10) {}
 
   int NumSequenceMatches(const TransformationParameter transform_param,
-      const Datum& datum) {
+      const Datum& datum, Phase phase) {
     // Get crop sequence with Caffe seed 1701.
     DataTransformer<Dtype>* transformer =
-        new DataTransformer<Dtype>(transform_param);
+        new DataTransformer<Dtype>(transform_param, phase);
     const int crop_size = transform_param.crop_size();
     Caffe::set_random_seed(seed_);
     transformer->InitRand();
@@ -92,7 +92,7 @@ TYPED_TEST(DataTransformTest, TestEmptyTransform) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   EXPECT_EQ(blob->num(), 1);
@@ -116,7 +116,7 @@ TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   EXPECT_EQ(blob->num(), 1);
@@ -141,7 +141,7 @@ TYPED_TEST(DataTransformTest, TestCropSize) {
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   Blob<TypeParam>* blob =
       new Blob<TypeParam>(1, channels, crop_size, crop_size);
@@ -170,8 +170,7 @@ TYPED_TEST(DataTransformTest, TestCropTrain) {
   transform_param.set_crop_size(crop_size);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TRAIN);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
   EXPECT_LT(num_matches, size * this->num_iter_);
 }
 
@@ -188,8 +187,7 @@ TYPED_TEST(DataTransformTest, TestCropTest) {
   transform_param.set_crop_size(crop_size);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TEST);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
   EXPECT_EQ(num_matches, size * this->num_iter_);
 }
 
@@ -205,8 +203,7 @@ TYPED_TEST(DataTransformTest, TestMirrorTrain) {
   transform_param.set_mirror(true);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TRAIN);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
   EXPECT_LT(num_matches, size * this->num_iter_);
 }
 
@@ -222,8 +219,7 @@ TYPED_TEST(DataTransformTest, TestMirrorTest) {
   transform_param.set_mirror(true);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Caffe::set_phase(Caffe::TEST);
-  int num_matches = this->NumSequenceMatches(transform_param, datum);
+  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
   EXPECT_LT(num_matches, size * this->num_iter_);
 }
 
@@ -239,12 +235,12 @@ TYPED_TEST(DataTransformTest, TestCropMirrorTrain) {
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   transform_param.set_crop_size(crop_size);
-  Caffe::set_phase(Caffe::TRAIN);
-  int num_matches_crop = this->NumSequenceMatches(transform_param, datum);
+  int num_matches_crop = this->NumSequenceMatches(
+      transform_param, datum, TRAIN);
 
   transform_param.set_mirror(true);
   int num_matches_crop_mirror =
-      this->NumSequenceMatches(transform_param, datum);
+      this->NumSequenceMatches(transform_param, datum, TRAIN);
   // When doing crop and mirror we expect less num_matches than just crop
   EXPECT_LE(num_matches_crop_mirror, num_matches_crop);
 }
@@ -261,12 +257,11 @@ TYPED_TEST(DataTransformTest, TestCropMirrorTest) {
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   transform_param.set_crop_size(crop_size);
-  Caffe::set_phase(Caffe::TEST);
-  int num_matches_crop = this->NumSequenceMatches(transform_param, datum);
+  int num_matches_crop = this->NumSequenceMatches(transform_param, datum, TEST);
 
   transform_param.set_mirror(true);
   int num_matches_crop_mirror =
-      this->NumSequenceMatches(transform_param, datum);
+      this->NumSequenceMatches(transform_param, datum, TEST);
   // When doing crop and mirror we expect less num_matches than just crop
   EXPECT_LT(num_matches_crop_mirror, num_matches_crop);
 }
@@ -286,7 +281,7 @@ TYPED_TEST(DataTransformTest, TestMeanValue) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   for (int j = 0; j < blob->count(); ++j) {
@@ -309,7 +304,7 @@ TYPED_TEST(DataTransformTest, TestMeanValues) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   for (int c = 0; c < channels; ++c) {
@@ -349,7 +344,7 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
   FillDatum(label, channels, height, width, unique_pixels, &datum);
   Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
   DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param);
+      new DataTransformer<TypeParam>(transform_param, TEST);
   transformer->InitRand();
   transformer->Transform(datum, blob);
   for (int j = 0; j < blob->count(); ++j) {
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index b1f4e4eac9a..611d9790863 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -88,8 +88,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
 
 TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
   typedef typename TypeParam::Dtype Dtype;
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index bc0dae331b3..1680a3f28d5 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1534,20 +1534,6 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
       output_proto_test + " state: { phase: TEST } ";
   this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
   this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
-
-  // Also check that nets are filtered according to the Caffe singleton phase,
-  // if not explicitly specified in the input proto.
-  Caffe::set_phase(Caffe::TRAIN);
-  this->RunFilterNetTest(input_proto, output_proto_train);
-  Caffe::set_phase(Caffe::TEST);
-  this->RunFilterNetTest(input_proto, output_proto_test);
-
-  // Finally, check that the current Caffe singleton phase is ignored if the
-  // phase is explicitly specified in the input proto.
-  Caffe::set_phase(Caffe::TEST);
-  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
-  Caffe::set_phase(Caffe::TRAIN);
-  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
 }
 
 TEST_F(FilterNetTest, TestFilterOutByStage) {
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index b19a5abdcdf..ad10720116d 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -43,8 +43,8 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     if (dropout_ratio != 0.5) {
       layer_param.mutable_dropout_param()->set_dropout_ratio(dropout_ratio);
     }
-    Caffe::set_phase(Caffe::TRAIN);
     DropoutLayer<Dtype> layer(layer_param);
+    layer_param.set_phase(TRAIN);
     layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
     layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
     // Now, check values
@@ -334,7 +334,7 @@ TYPED_TEST(NeuronLayerTest, TestDropoutThreeQuarters) {
 TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TEST);
+  layer_param.set_phase(TEST);
   DropoutLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -351,7 +351,7 @@ TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
 TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TRAIN);
+  layer_param.set_phase(TRAIN);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
@@ -361,7 +361,7 @@ TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
 TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_phase(Caffe::TEST);
+  layer_param.set_phase(TEST);
   DropoutLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index ad5151007c2..12962c65d85 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -62,8 +62,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
 
 TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
@@ -106,8 +106,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
 
 TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TEST);
   LayerParameter layer_param;
+  layer_param.set_phase(TEST);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
@@ -144,8 +144,8 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
 
 TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
   Caffe::set_mode(Caffe::GPU);
-  Caffe::set_phase(Caffe::TRAIN);
   LayerParameter layer_param;
+  layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);

From 02be9d7020ed2e3236f6dd553fc6690c77e9479c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 01:31:53 -0800
Subject: [PATCH 1156/2053] pass phase to transformer through layer

---
 include/caffe/data_layers.hpp          |  3 +--
 include/caffe/data_transformer.hpp     |  4 ++--
 src/caffe/data_transformer.cpp         | 14 +++++++-------
 src/caffe/layers/base_data_layer.cpp   |  9 +++++----
 src/caffe/layers/data_layer.cpp        |  4 ++--
 src/caffe/layers/image_data_layer.cpp  |  2 +-
 src/caffe/layers/memory_data_layer.cpp |  4 ++--
 7 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index aa7e6967ead..1f154408c27 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -48,8 +48,7 @@ class BaseDataLayer : public Layer<Dtype> {
 
  protected:
   TransformationParameter transform_param_;
-  DataTransformer<Dtype> data_transformer_;
-  Caffe::Phase phase_;
+  shared_ptr<DataTransformer<Dtype> > data_transformer_;
   bool output_labels_;
 };
 
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 60696c96dcc..880356601a4 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -16,7 +16,7 @@ namespace caffe {
 template <typename Dtype>
 class DataTransformer {
  public:
-  explicit DataTransformer(const TransformationParameter& param);
+  explicit DataTransformer(const TransformationParameter& param, Phase phase);
   virtual ~DataTransformer() {}
 
   /**
@@ -104,7 +104,7 @@ class DataTransformer {
 
 
   shared_ptr<Caffe::RNG> rng_;
-  Caffe::Phase phase_;
+  Phase phase_;
   Blob<Dtype> data_mean_;
   vector<Dtype> mean_values_;
 };
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index c0ad72ee67d..b0b98e478c1 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -11,9 +11,9 @@
 namespace caffe {
 
 template<typename Dtype>
-DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param)
-    : param_(param) {
-  phase_ = Caffe::phase();
+DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
+    Phase phase)
+    : param_(param), phase_(phase) {
   // check if we want to use mean_file
   if (param_.has_mean_file()) {
     CHECK_EQ(param_.mean_value_size(), 0) <<
@@ -80,7 +80,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     height = crop_size;
     width = crop_size;
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
+    if (phase_ == TRAIN) {
       h_off = Rand(datum_height - crop_size + 1);
       w_off = Rand(datum_width - crop_size + 1);
     } else {
@@ -247,7 +247,7 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
+    if (phase_ == TRAIN) {
       h_off = Rand(img_height - crop_size + 1);
       w_off = Rand(img_width - crop_size + 1);
     } else {
@@ -325,7 +325,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
     // We only do random crop when we do training.
-    if (phase_ == Caffe::TRAIN) {
+    if (phase_ == TRAIN) {
       h_off = Rand(input_height - crop_size + 1);
       w_off = Rand(input_width - crop_size + 1);
     } else {
@@ -398,7 +398,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
   const bool needs_rand = param_.mirror() ||
-      (phase_ == Caffe::TRAIN && param_.crop_size());
+      (phase_ == TRAIN && param_.crop_size());
   if (needs_rand) {
     const unsigned int rng_seed = caffe_rng_rand();
     rng_.reset(new Caffe::RNG(rng_seed));
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index d7e0b90801e..352200915d7 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -2,6 +2,7 @@
 #include <vector>
 
 #include "caffe/data_layers.hpp"
+#include "caffe/net.hpp"
 #include "caffe/util/io.hpp"
 
 namespace caffe {
@@ -9,8 +10,7 @@ namespace caffe {
 template <typename Dtype>
 BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)
     : Layer<Dtype>(param),
-      transform_param_(param.transform_param()),
-      data_transformer_(transform_param_) {
+      transform_param_(param.transform_param()) {
 }
 
 template <typename Dtype>
@@ -23,6 +23,8 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // The subclasses should setup the size of bottom and top
   DataLayerSetUp(bottom, top);
+  data_transformer_.reset(
+      new DataTransformer<Dtype>(transform_param_, this->phase_));
   data_transformer_->InitRand();
 }
 
@@ -45,8 +47,7 @@ void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
-  this->phase_ = Caffe::phase();
-  this->data_transformer_.InitRand();
+  this->data_transformer_->InitRand();
   CHECK(StartInternalThread()) << "Thread execution failed";
 }
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 7351ed03671..9a2dfac9b1f 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -119,9 +119,9 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     if (datum.encoded()) {
-      this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
     } else {
-      this->data_transformer_.Transform(datum, &(this->transformed_data_));
+      this->data_transformer_->Transform(datum, &(this->transformed_data_));
     }
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index e98ff85a0ea..f9046e1b3a1 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -137,7 +137,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     // Apply transformations (mirror, crop...) to the image
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(prefetch_data + offset);
-    this->data_transformer_.Transform(cv_img, &(this->transformed_data_));
+    this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
     trans_time += timer.MicroSeconds();
 
     prefetch_label[item_id] = lines_[lines_id_].second;
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 6f584de32d0..c7d73f0925d 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -40,7 +40,7 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   added_data_.Reshape(num, channels_, height_, width_);
   added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
-  this->data_transformer_.Transform(datum_vector, &added_data_);
+  this->data_transformer_->Transform(datum_vector, &added_data_);
   // Copy Labels
   Dtype* top_label = added_label_.mutable_cpu_data();
   for (int item_id = 0; item_id < num; ++item_id) {
@@ -64,7 +64,7 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
   added_data_.Reshape(num, channels_, height_, width_);
   added_label_.Reshape(num, 1, 1, 1);
   // Apply data transformations (mirror, scale, crop...)
-  this->data_transformer_.Transform(mat_vector, &added_data_);
+  this->data_transformer_->Transform(mat_vector, &added_data_);
   // Copy Labels
   Dtype* top_label = added_label_.mutable_cpu_data();
   for (int item_id = 0; item_id < num; ++item_id) {

From ab0cc510fca98807ffb7f9046e3bb08a7bdfe1bc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 01:33:09 -0800
Subject: [PATCH 1157/2053] no phase for the solver to orchestrate

---
 src/caffe/solver.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 9866d7cbc56..8ed8aec2fc8 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -219,7 +219,6 @@ void Solver<Dtype>::Step(int iters) {
 
 template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
-  Caffe::set_phase(Caffe::TRAIN);
   LOG(INFO) << "Solving " << net_->name();
   LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
 
@@ -266,8 +265,6 @@ template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
   LOG(INFO) << "Iteration " << iter_
             << ", Testing net (#" << test_net_id << ")";
-  // We need to set phase to test before running.
-  Caffe::set_phase(Caffe::TEST);
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
@@ -318,7 +315,6 @@ void Solver<Dtype>::Test(const int test_net_id) {
     LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
         << mean_score << loss_msg_stream.str();
   }
-  Caffe::set_phase(Caffe::TRAIN);
 }
 
 

From 6def482eadef12ecd548f8b235f6fb1482ee8a50 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 22:48:42 -0800
Subject: [PATCH 1158/2053] construct Net from file and phase

---
 include/caffe/net.hpp | 2 +-
 src/caffe/net.cpp     | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 153b881fe9f..075afebc9b0 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -24,7 +24,7 @@ template <typename Dtype>
 class Net {
  public:
   explicit Net(const NetParameter& param);
-  explicit Net(const string& param_file);
+  explicit Net(const string& param_file, Phase phase);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index d1ae0bcf73b..c359be9b575 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -24,9 +24,10 @@ Net<Dtype>::Net(const NetParameter& param) {
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file) {
+Net<Dtype>::Net(const string& param_file, Phase phase) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
+  param.mutable_state()->set_phase(phase);
   Init(param);
 }
 

From fe8fe5f3a1a725631ffa4a307ec03bc607c7d103 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 23 Jan 2015 22:49:38 -0800
Subject: [PATCH 1159/2053] tools make net with phase

---
 tools/caffe.cpp            | 6 ++----
 tools/extract_features.cpp | 3 +--
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ad54bc3b9eb..f04e28a3674 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -139,8 +139,7 @@ int test() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Caffe::set_phase(Caffe::TEST);
-  Net<float> caffe_net(FLAGS_model);
+  Net<float> caffe_net(FLAGS_model, caffe::TEST);
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
@@ -205,8 +204,7 @@ int time() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Caffe::set_phase(Caffe::TRAIN);
-  Net<float> caffe_net(FLAGS_model);
+  Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
 
   // Do a clean forward and backward pass, so that memory allocation are done
   // and future iterations will be more stable.
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index c17b88dd048..f86ff96ca82 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -64,7 +64,6 @@ int feature_extraction_pipeline(int argc, char** argv) {
     LOG(ERROR) << "Using CPU";
     Caffe::set_mode(Caffe::CPU);
   }
-  Caffe::set_phase(Caffe::TEST);
 
   arg_pos = 0;  // the name of the executable
   std::string pretrained_binary_proto(argv[++arg_pos]);
@@ -98,7 +97,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
    */
   std::string feature_extraction_proto(argv[++arg_pos]);
   shared_ptr<Net<Dtype> > feature_extraction_net(
-      new Net<Dtype>(feature_extraction_proto));
+      new Net<Dtype>(feature_extraction_proto, caffe::TEST));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
   std::string extract_feature_blob_names(argv[++arg_pos]);

From d2934aee4cee0805992a9c1690d0eedeed98ea51 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 25 Jan 2015 20:57:23 -0800
Subject: [PATCH 1160/2053] [pycaffe] give phase to Net

- expose `caffe.{TRAIN,TEST}` constants
- instantiate `caffe.Net`s with phase
- drop singleton phase interface `caffe.set_phase_{train,test}`
---
 python/caffe/__init__.py               |  3 ++-
 python/caffe/_caffe.cpp                | 27 +++++++++++++++++---------
 python/caffe/test/test_net.py          |  4 ++--
 python/caffe/test/test_python_layer.py |  2 +-
 4 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 49f8678054b..31502873749 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,7 @@
 from .pycaffe import Net, SGDSolver
 from ._caffe import set_mode_cpu, set_mode_gpu, set_device, \
-    set_phase_train, set_phase_test, Layer, get_solver
+    Layer, get_solver
+from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
 import io
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 866272709a1..a5d0e64605e 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -31,10 +31,9 @@ namespace caffe {
 typedef float Dtype;
 const int NPY_DTYPE = NPY_FLOAT32;
 
+// Selecting mode.
 void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
 void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
-void set_phase_train() { Caffe::set_phase(Caffe::TRAIN); }
-void set_phase_test() { Caffe::set_phase(Caffe::TEST); }
 
 // For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
@@ -71,13 +70,24 @@ void CheckContiguousArray(PyArrayObject* arr, string name,
   }
 }
 
-// Net construct-and-load convenience constructor
+// Net constructor for passing phase as int
 shared_ptr<Net<Dtype> > Net_Init(
-    string param_file, string pretrained_param_file) {
+    string param_file, int phase) {
+  CheckFile(param_file);
+
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
+      static_cast<Phase>(phase)));
+  return net;
+}
+
+// Net construct-and-load convenience constructor
+shared_ptr<Net<Dtype> > Net_Init_Load(
+    string param_file, string pretrained_param_file, int phase) {
   CheckFile(param_file);
   CheckFile(pretrained_param_file);
 
-  shared_ptr<Net<Dtype> > net (new Net<Dtype>(param_file));
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
+      static_cast<Phase>(phase)));
   net->CopyTrainedLayersFrom(pretrained_param_file);
   return net;
 }
@@ -172,13 +182,12 @@ BOOST_PYTHON_MODULE(_caffe) {
   // Caffe utility functions
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
-  bp::def("set_phase_train", &set_phase_train);
-  bp::def("set_phase_test", &set_phase_test);
   bp::def("set_device", &Caffe::SetDevice);
 
-  bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >(
-    "Net", bp::init<string>())
+  bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
+    bp::no_init)
     .def("__init__", bp::make_constructor(&Net_Init))
+    .def("__init__", bp::make_constructor(&Net_Init_Load))
     .def("_forward", &Net<Dtype>::ForwardFromTo)
     .def("_backward", &Net<Dtype>::BackwardFromTo)
     .def("reshape", &Net<Dtype>::Reshape)
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 9381c72be6b..62b407da8aa 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -35,7 +35,7 @@ class TestNet(unittest.TestCase):
     def setUp(self):
         self.num_output = 13
         net_file = simple_net_file(self.num_output)
-        self.net = caffe.Net(net_file)
+        self.net = caffe.Net(net_file, caffe.TRAIN)
         # fill in valid labels
         self.net.blobs['label'].data[...] = \
                 np.random.randint(self.num_output,
@@ -69,7 +69,7 @@ def test_save_and_read(self):
         f.close()
         self.net.save(f.name)
         net_file = simple_net_file(self.num_output)
-        net2 = caffe.Net(net_file, f.name)
+        net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
         os.remove(net_file)
         os.remove(f.name)
         for name in self.net.params:
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index 03f5834514e..383c283959d 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -36,7 +36,7 @@ def python_net_file():
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):
         net_file = python_net_file()
-        self.net = caffe.Net(net_file)
+        self.net = caffe.Net(net_file, caffe.TRAIN)
         os.remove(net_file)
 
     def test_forward(self):

From bb8b5d60eb47dff3977377a2a4db4fe9f2fbee62 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 25 Jan 2015 22:06:23 -0800
Subject: [PATCH 1161/2053] [matcaffe] give phase to Net

---
 matlab/caffe/matcaffe.cpp    | 25 +++++++++++++------------
 matlab/caffe/matcaffe_init.m |  7 ++-----
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index fd8397e7ff2..996d3d2149c 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -254,14 +254,6 @@ static void set_mode_gpu(MEX_ARGS) {
   Caffe::set_mode(Caffe::GPU);
 }
 
-static void set_phase_train(MEX_ARGS) {
-  Caffe::set_phase(Caffe::TRAIN);
-}
-
-static void set_phase_test(MEX_ARGS) {
-  Caffe::set_phase(Caffe::TEST);
-}
-
 static void set_device(MEX_ARGS) {
   if (nrhs != 1) {
     ostringstream error_msg;
@@ -278,7 +270,7 @@ static void get_init_key(MEX_ARGS) {
 }
 
 static void init(MEX_ARGS) {
-  if (nrhs != 2) {
+  if (nrhs != 3) {
     ostringstream error_msg;
     error_msg << "Expected 2 arguments, got " << nrhs;
     mex_error(error_msg.str());
@@ -286,12 +278,23 @@ static void init(MEX_ARGS) {
 
   char* param_file = mxArrayToString(prhs[0]);
   char* model_file = mxArrayToString(prhs[1]);
+  char* phase_name = mxArrayToString(prhs[2]);
+
+  Phase phase;
+  if (strcmp(phase_name, "train") == 0) {
+      phase = TRAIN;
+  } else if (strcmp(phase_name, "test") == 0) {
+      phase = TEST;
+  } else {
+    mex_error("Unknown phase.");
+  }
 
-  net_.reset(new Net<float>(string(param_file)));
+  net_.reset(new Net<float>(string(param_file), phase));
   net_->CopyTrainedLayersFrom(string(model_file));
 
   mxFree(param_file);
   mxFree(model_file);
+  mxFree(phase_name);
 
   init_key = random();  // NOLINT(caffe/random_fn)
 
@@ -377,8 +380,6 @@ static handler_registry handlers[] = {
   { "is_initialized",     is_initialized  },
   { "set_mode_cpu",       set_mode_cpu    },
   { "set_mode_gpu",       set_mode_gpu    },
-  { "set_phase_train",    set_phase_train },
-  { "set_phase_test",     set_phase_test  },
   { "set_device",         set_device      },
   { "get_weights",        get_weights     },
   { "get_init_key",       get_init_key    },
diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m
index 7cc6935758e..5d0a0a70bde 100644
--- a/matlab/caffe/matcaffe_init.m
+++ b/matlab/caffe/matcaffe_init.m
@@ -25,7 +25,8 @@ function  matcaffe_init(use_gpu, model_def_file, model_file)
     % NOTE: you'll have to get network definition
     error('You need the network prototxt definition');
   end
-  caffe('init', model_def_file, model_file)
+  % load network in TEST phase
+  caffe('init', model_def_file, model_file, 'test')
 end
 fprintf('Done with init\n');
 
@@ -38,7 +39,3 @@ function  matcaffe_init(use_gpu, model_def_file, model_file)
   caffe('set_mode_cpu');
 end
 fprintf('Done with set_mode\n');
-
-% put into test mode
-caffe('set_phase_test');
-fprintf('Done with set_phase_test\n');

From d9825e4e5af4290ab165f350632470761f531d01 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 17 Feb 2015 15:46:29 -0800
Subject: [PATCH 1162/2053] [pycaffe] fix bug in Python layer setup

Previously, setup did not correctly override LayerSetUp (and therefore
did not run) due to an incorrect type.
---
 include/caffe/python_layer.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
index f1eab458466..816ef453720 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/python_layer.hpp
@@ -17,7 +17,7 @@ class PythonLayer : public Layer<Dtype> {
       : Layer<Dtype>(param), self_(self) { }
 
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
+      const vector<Blob<Dtype>*>& top) {
     try {
       bp::call_method<bp::object>(self_, "setup", bottom, top);
     } catch (bp::error_already_set) {

From 30771911beae399cb9c61f3a9d45376a2a3a16ec Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 17 Feb 2015 18:23:32 -0800
Subject: [PATCH 1163/2053] comment fix: Decaf -> Caffe

---
 include/caffe/util/math_functions.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index d3ecf5875fa..f43036fcebc 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -12,7 +12,7 @@
 
 namespace caffe {
 
-// Decaf gemm provides a simpler interface to the gemm functions, with the
+// Caffe gemm provides a simpler interface to the gemm functions, with the
 // limitation that the data has to be contiguous in memory.
 template <typename Dtype>
 void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,

From d1238e14fa4ef7089d85e91cf3339699b89590e0 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Wed, 18 Feb 2015 08:38:33 -0800
Subject: [PATCH 1164/2053] Changing CMAKE_SOURCE/BINARY_DIR to
 PROJECT_SOURCE/BINARY_DIR

---
 CMakeLists.txt                    | 10 +++++-----
 cmake/ConfigGen.cmake             | 20 ++++++++++----------
 cmake/Cuda.cmake                  |  4 ++--
 cmake/Misc.cmake                  |  6 +++---
 cmake/ProtoBuf.cmake              |  4 ++--
 cmake/Summary.cmake               |  2 +-
 cmake/Targets.cmake               | 12 ++++++------
 cmake/Templates/caffe_config.h.in |  4 ++--
 docs/CMakeLists.txt               | 18 +++++++++---------
 examples/CMakeLists.txt           |  6 +++---
 matlab/CMakeLists.txt             |  2 +-
 python/CMakeLists.txt             | 12 ++++++------
 src/caffe/CMakeLists.txt          |  6 +++---
 src/caffe/test/CMakeLists.txt     |  4 ++--
 tools/CMakeLists.txt              |  2 +-
 15 files changed, 56 insertions(+), 56 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0cace5acfe3..adea37be565 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -4,7 +4,7 @@ cmake_minimum_required(VERSION 2.8.7)
 project(Caffe C CXX)
 
 # ---[ Using cmake scripts and modules
-list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
+list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
 include(cmake/Utils.cmake)
 include(cmake/Targets.cmake)
@@ -39,11 +39,11 @@ add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
 caffe_warnings_disable(CMAKE_CXX_FLAGS -Wno-sign-compare -Wno-uninitialized)
 
 # ---[ Config generation
-configure_file(cmake/Templates/caffe_config.h.in "${CMAKE_BINARY_DIR}/caffe_config.h")
+configure_file(cmake/Templates/caffe_config.h.in "${PROJECT_BINARY_DIR}/caffe_config.h")
 
 # ---[ Includes
-set(Caffe_INCLUDE_DIR ${CMAKE_SOURCE_DIR}/include)
-include_directories(${Caffe_INCLUDE_DIR} ${CMAKE_BINARY_DIR})
+set(Caffe_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/include)
+include_directories(${Caffe_INCLUDE_DIR} ${PROJECT_BINARY_DIR})
 include_directories(BEFORE src) # This is needed for gtest.
 
 # ---[ Subdirectories
@@ -56,7 +56,7 @@ add_subdirectory(matlab)
 add_subdirectory(docs)
 
 # ---[ Linter target
-add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${CMAKE_SOURCE_DIR}/cmake/lint.cmake)
+add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
 
 # ---[ Configuration summary
 caffe_print_configuration_summary()
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index b7cf9f4cc48..c82047dcc5f 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -7,8 +7,8 @@ function(caffe_get_current_includes includes_variable)
   get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
   caffe_convert_absolute_paths(current_includes)
 
-  # remove at most one ${CMAKE_BINARY_DIR} include added for caffe_config.h
-  list(FIND current_includes ${CMAKE_BINARY_DIR} __index)
+  # remove at most one ${PROJECT_BINARY_DIR} include added for caffe_config.h
+  list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
   list(REMOVE_AT current_includes ${__index})
 
   caffe_list_unique(current_includes)
@@ -55,17 +55,17 @@ function(caffe_generate_export_configs)
     list(APPEND Caffe_DEFINITIONS -DUSE_MKL)
   endif()
 
-  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${CMAKE_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
 
   # Add targets to the build-tree export set
-  export(TARGETS caffe proto FILE "${CMAKE_BINARY_DIR}/CaffeTargets.cmake")
+  export(TARGETS caffe proto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
   export(PACKAGE Caffe)
 
   # ---[ Configure install-tree CaffeConfig.cmake file ]---
 
   # remove source and build dir includes
-  caffe_get_items_with_prefix(${CMAKE_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
-  caffe_get_items_with_prefix(${CMAKE_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
+  caffe_get_items_with_prefix(${PROJECT_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
+  caffe_get_items_with_prefix(${PROJECT_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
   list(REMOVE_ITEM Caffe_INCLUDE_DIRS ${__insource} ${__inbinary})
 
   # add `install` include folder
@@ -75,10 +75,10 @@ function(caffe_generate_export_configs)
      "unset(__caffe_include)\n")
   string(REPLACE ";" "" Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND ${lines})
 
-  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${CMAKE_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
+  configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
 
   # Install the CaffeConfig.cmake and export set to use wuth install-tree
-  install(FILES "${CMAKE_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
+  install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
   install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
 
   # ---[ Configure and install version file ]---
@@ -86,8 +86,8 @@ function(caffe_generate_export_configs)
   # TODO: Lines below are commented because Caffe does't declare its version in headers.
   # When the declarations are added, modify `caffe_extract_caffe_version()` macro and uncomment
 
-  # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${CMAKE_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
-  # install(FILES "${CMAKE_BINARY_DIR}/CaffeConfigVersion.cmake" DESTINATION ${install_cmake_suffix})
+  # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
+  # install(FILES "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" DESTINATION ${install_cmake_suffix})
 endfunction()
 
 
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 07fa13789fa..ff58d31c166 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -12,7 +12,7 @@ set(Caffe_known_gpu_archs "20 21(20) 30 35 50")
 #   caffe_detect_installed_gpus(out_variable)
 function(caffe_detect_installed_gpus out_variable)
   if(NOT CUDA_gpu_detect_output)
-    set(__cufile ${CMAKE_BINARY_DIR}/detect_cuda_archs.cu)
+    set(__cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
 
     file(WRITE ${__cufile} ""
       "#include <cstdio>\n"
@@ -31,7 +31,7 @@ function(caffe_detect_installed_gpus out_variable)
       "}\n")
 
     execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "--run" "${__cufile}"
-                    WORKING_DIRECTORY "${CMAKE_BINARY_DIR}/CMakeFiles/"
+                    WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
                     RESULT_VARIABLE __nvcc_res OUTPUT_VARIABLE __nvcc_out
                     ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
 
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 14eaeb0ab6e..608a5f13a79 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -25,7 +25,7 @@ endif()
 
 # ---[ Install options
 if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
-  set(CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE)
+  set(CMAKE_INSTALL_PREFIX "${PROJECT_BINARY_DIR}/install" CACHE PATH "Default install path" FORCE)
 endif()
 
 # ---[ RPATH settings
@@ -34,8 +34,8 @@ set(CMAKE_MACOSX_RPATH TRUE)
 
 # ---[ Funny target
 if(UNIX OR APPLE)
-  add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${CMAKE_BINARY_DIR}" "${CMAKE_SOURCE_DIR}/build"
-                                     COMMENT "Adding symlink: <caffe_root>/build -> ${CMAKE_BINARY_DIR}" )
+  add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${PROJECT_BINARY_DIR}" "${PROJECT_SOURCE_DIR}/build"
+                                     COMMENT "Adding symlink: <caffe_root>/build -> ${PROJECT_BINARY_DIR}" )
 endif()
 
 # ---[ Set debug postfix
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index bb63c9ed736..8946d66c57b 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -22,8 +22,8 @@ if(PROTOBUF_FOUND)
 endif()
 
 # place where to generate protobuf sources
-set(proto_gen_folder "${CMAKE_BINARY_DIR}/include/caffe/proto")
-include_directories(SYSTEM "${CMAKE_BINARY_DIR}/include")
+set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
+include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
 
 set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
 
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 756a738c9ab..3f7dff6b6e0 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -65,7 +65,7 @@ function(caffe_extract_caffe_version)
   if(GIT_FOUND)
     execute_process(COMMAND ${GIT_EXECUTABLE} describe --tags --always --dirty
                     ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE
-                    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
+                    WORKING_DIRECTORY "${PROJECT_SOURCE_DIR}"
                     OUTPUT_VARIABLE Caffe_GIT_VERSION
                     RESULT_VARIABLE __git_result)
     if(NOT ${__git_result} EQUAL 0)
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index 3b254ad37df..e3ad872313b 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -53,7 +53,7 @@ function(caffe_pickup_caffe_sources root)
   # put all files in source groups (visible as subfolder in many IDEs)
   caffe_source_group("Include"        GLOB "${root}/include/caffe/*.h*")
   caffe_source_group("Include\\Util"  GLOB "${root}/include/caffe/util/*.h*")
-  caffe_source_group("Include"        GLOB "${CMAKE_BINARY_DIR}/caffe_config.h*")
+  caffe_source_group("Include"        GLOB "${PROJECT_BINARY_DIR}/caffe_config.h*")
   caffe_source_group("Source"         GLOB "${root}/src/caffe/*.cpp")
   caffe_source_group("Source\\Util"   GLOB "${root}/src/caffe/util/*.cpp")
   caffe_source_group("Source\\Layers" GLOB "${root}/src/caffe/layers/*.cpp")
@@ -75,7 +75,7 @@ function(caffe_pickup_caffe_sources root)
   list(REMOVE_ITEM  srcs ${test_srcs})
 
   # adding headers to make the visible in some IDEs (Qt, VS, Xcode)
-  list(APPEND srcs ${hdrs} ${CMAKE_BINARY_DIR}/caffe_config.h)
+  list(APPEND srcs ${hdrs} ${PROJECT_BINARY_DIR}/caffe_config.h)
   list(APPEND test_srcs ${test_hdrs})
 
   # collect cuda files
@@ -107,9 +107,9 @@ endfunction()
 function(caffe_default_properties target)
   set_target_properties(${target} PROPERTIES
     DEBUG_POSTFIX ${Caffe_DEBUG_POSTFIX}
-    ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib"
-    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib"
-    RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin")
+    ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
+    LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
+    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
 endfunction()
 
 ################################################################################################
@@ -139,7 +139,7 @@ function(caffe_configure_testdatafile file)
   file(STRINGS ${file} __lines)
   set(result "")
   foreach(line ${__lines})
-    set(result "${result}${CMAKE_SOURCE_DIR}/${line}\n")
+    set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n")
   endforeach()  
   file(WRITE ${file}.gen.cmake ${result})
 endfunction()
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 6aa22ed7b5c..6039e8f6b21 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -1,8 +1,8 @@
 /* Sources directory */
-#define SOURCE_FOLDER "${CMAKE_SOURCE_DIR}"
+#define SOURCE_FOLDER "${PROJECT_SOURCE_DIR}"
 
 /* Binaries directory */
-#define BINARY_FOLDER "${CMAKE_BINARY_DIR}"
+#define BINARY_FOLDER "${PROJECT_BINARY_DIR}"
 
 /* NVIDA Cuda */
 #cmakedefine HAVE_CUDA
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index b1be683f907..ae47e461736 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -64,19 +64,19 @@ endfunction()
 ########################## [ Non macro part ] ##################################################
 
 # Gathering is done at each 'make doc'
-file(REMOVE_RECURSE ${CMAKE_SOURCE_DIR}/docs/gathered)
+file(REMOVE_RECURSE ${PROJECT_SOURCE_DIR}/docs/gathered)
 
 # Doxygen config file path
-set(DOXYGEN_config_file ${CMAKE_SOURCE_DIR}/.Doxyfile CACHE FILEPATH "Doxygen config file")
+set(DOXYGEN_config_file ${PROJECT_SOURCE_DIR}/.Doxyfile CACHE FILEPATH "Doxygen config file")
 
 # Adding docs target
 add_custom_target(docs COMMAND ${DOXYGEN_EXECUTABLE} ${DOXYGEN_config_file}
-                       WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+                       WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
                        COMMENT "Launching doxygen..." VERBATIM)
 
 # Gathering examples into docs subfolder
-gather_notebooks_as_prebuild_cmd(docs docs/gathered ${CMAKE_SOURCE_DIR})
-gather_readmes_as_prebuild_cmd(docs docs/gathered  ${CMAKE_SOURCE_DIR})
+gather_notebooks_as_prebuild_cmd(docs docs/gathered ${PROJECT_SOURCE_DIR})
+gather_readmes_as_prebuild_cmd(docs docs/gathered  ${PROJECT_SOURCE_DIR})
 
 # Auto detect output directory
 file(STRINGS ${DOXYGEN_config_file} config_line REGEX "OUTPUT_DIRECTORY[ \t]+=[^=].*")
@@ -90,17 +90,17 @@ else()
 endif()
 
 if(NOT IS_ABSOLUTE ${output_dir})
-  set(output_dir ${CMAKE_SOURCE_DIR}/${output_dir})
+  set(output_dir ${PROJECT_SOURCE_DIR}/${output_dir})
   get_filename_component(output_dir ${output_dir} ABSOLUTE)
 endif()
 
 # creates symlink in docs subfolder to code documentation built by doxygen
 add_custom_command(TARGET docs POST_BUILD VERBATIM
                    COMMAND ln -sfn "${output_dir}/html" doxygen
-                   WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/docs
-                   COMMENT "Creating symlink ${CMAKE_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
+                   WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs
+                   COMMENT "Creating symlink ${PROJECT_SOURCE_DIR}/docs/doxygen -> ${output_dir}/html")
 
 # for quick launch of jekyll
 add_custom_target(jekyll COMMAND jekyll serve -w -s . -d _site --port=4000
-                         WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}/docs
+                         WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/docs
                          COMMENT "Launching jekyll..." VERBATIM)
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 4df14afbb98..f29fc7e5522 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,4 +1,4 @@
-file(GLOB_RECURSE examples_srcs "${CMAKE_SOURCE_DIR}/examples/*.cpp")
+file(GLOB_RECURSE examples_srcs "${PROJECT_SOURCE_DIR}/examples/*.cpp")
 
 foreach(source_file ${examples_srcs})
   # get file name
@@ -14,7 +14,7 @@ foreach(source_file ${examples_srcs})
 
   # set back RUNTIME_OUTPUT_DIRECTORY
   set_target_properties(${name} PROPERTIES
-    RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/examples/${folder}")
+    RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/examples/${folder}")
 
   caffe_set_solution_folder(${name} examples)
 
@@ -24,7 +24,7 @@ foreach(source_file ${examples_srcs})
   if(UNIX OR APPLE)
     # Funny command to make tutorials work
     # TODO: remove in future as soon as naming is standartaized everywhere
-    set(__outname ${CMAKE_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
     add_custom_command(TARGET ${name} POST_BUILD
                        COMMAND ln -sf "${__outname}" "${__outname}.bin")
   endif()
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index 51ab4fe274a..791a4e70f43 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -32,7 +32,7 @@ endfunction()
 
 # global settings
 file(GLOB Matlab_srcs caffe/matcaffe.cpp)
-set(Matlab_caffe_mex ${CMAKE_SOURCE_DIR}/matlab/caffe/caffe.mex)
+set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex)
 
 caffe_get_current_cflags(cflags)
 caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index ee33a9e5285..6afed4fa183 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -4,7 +4,7 @@ if(NOT HAVE_PYTHON)
 endif()
 
 include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
-file(GLOB_RECURSE python_srcs ${CMAKE_SOURCE_DIR}/python/*.cpp)
+file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
 
 add_library(pycaffe SHARED ${python_srcs})
 target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
@@ -12,13 +12,13 @@ set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
 caffe_default_properties(pycaffe)
 
 if(UNIX OR APPLE)
-    set(__linkname "${CMAKE_SOURCE_DIR}/python/caffe/_caffe.so")
+    set(__linkname "${PROJECT_SOURCE_DIR}/python/caffe/_caffe.so")
     add_custom_command(TARGET pycaffe POST_BUILD
                        COMMAND ln -sf $<TARGET_LINKER_FILE:pycaffe> "${__linkname}"
-                       COMMAND ${CMAKE_COMMAND} -E make_directory ${CMAKE_SOURCE_DIR}/python/caffe/proto
-                       COMMAND touch ${CMAKE_SOURCE_DIR}/python/caffe/proto/__init__.py
-                       COMMAND cp ${proto_gen_folder}/*.py ${CMAKE_SOURCE_DIR}/python/caffe/proto/
-                       COMMENT "Creating symlink ${__linkname} -> ${CMAKE_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+                       COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto
+                       COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py
+                       COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/
+                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
 endif()
 
 # ---[ Install
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index ef74e1f004b..40e6c11f5b0 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -10,7 +10,7 @@ caffe_default_properties(proto)
 # --[ Caffe library
 
 # creates 'test_srcs', 'srcs', 'test_cuda', 'cuda' lists
-caffe_pickup_caffe_sources(${CMAKE_SOURCE_DIR})
+caffe_pickup_caffe_sources(${PROJECT_SOURCE_DIR})
 
 if(HAVE_CUDA)
   caffe_cuda_compile(cuda_objs ${cuda})
@@ -29,8 +29,8 @@ install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include)
 install(FILES ${proto_hdrs} DESTINATION include/caffe/proto)
 install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib)
 
-file(WRITE ${CMAKE_BINARY_DIR}/__init__.py)
-list(APPEND proto_python ${CMAKE_BINARY_DIR}/__init__.py)
+file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
+list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)
 install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto)
 
 
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index ed374e8e110..35a803f2f41 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -29,8 +29,8 @@ endif()
 add_executable(${the_target} EXCLUDE_FROM_ALL ${test_srcs})
 target_link_libraries(${the_target} gtest ${Caffe_LINK})
 caffe_default_properties(${the_target})
-caffe_set_runtime_directory(${the_target} "${CMAKE_BINARY_DIR}/test")
+caffe_set_runtime_directory(${the_target} "${PROJECT_BINARY_DIR}/test")
 
 # ---[ Adding runtest
 add_custom_target(runtest COMMAND ${the_target} ${test_args}
-                          WORKING_DIRECTORY ${CMAKE_SOURCE_DIR})
+                          WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 4e43ba2cf25..02fbd5cadd8 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -16,7 +16,7 @@ foreach(source ${srcs})
   caffe_default_properties(${name})
 
   # set back RUNTIME_OUTPUT_DIRECTORY
-  caffe_set_runtime_directory(${name} "${CMAKE_BINARY_DIR}/tools")
+  caffe_set_runtime_directory(${name} "${PROJECT_BINARY_DIR}/tools")
   caffe_set_solution_folder(${name} tools)
 
   # restore output name without suffix

From cb6b09b46b6c0806834cc5fe8f690f96d4574b04 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 18 Feb 2015 20:46:41 -0800
Subject: [PATCH 1165/2053] [pycaffe] import newline cleanup

---
 python/caffe/__init__.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 31502873749..37e8956da4f 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,5 @@
 from .pycaffe import Net, SGDSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, \
-    Layer, get_solver
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector

From 74f171bcdb98d61ed83b3ab6c8ebabdefbb33116 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 18 Feb 2015 20:47:12 -0800
Subject: [PATCH 1166/2053] [pycaffe] take pre-processing from Net and give to
 Transformer

The pyreformation #1703 aligned pycaffe Net with the real Caffe Net.
This broke the pre-processing helpers that had been bolted on to
`caffe.Net`.

These responsibilities are gathered in `caffe.io.Transformer` instead.
This is only an intermediate step in a real solution to pycaffe IO that
should make use of the same data pipeline as the rest of the framework.

See #1245 for further thoughts on input processing.
---
 python/caffe/io.py      | 322 +++++++++++++++++++++++++++++++---------
 python/caffe/pycaffe.py | 157 ++------------------
 2 files changed, 260 insertions(+), 219 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index a8354021b05..0ce9ecfeeed 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -5,6 +5,255 @@
 
 from caffe.proto import caffe_pb2
 
+## proto / datum / ndarray conversion
+
+def blobproto_to_array(blob, return_diff=False):
+    """Convert a blob proto to an array. In default, we will just return the data,
+    unless return_diff is True, in which case we will return the diff.
+    """
+    if return_diff:
+        return np.array(blob.diff).reshape(
+            blob.num, blob.channels, blob.height, blob.width)
+    else:
+        return np.array(blob.data).reshape(
+            blob.num, blob.channels, blob.height, blob.width)
+
+
+def array_to_blobproto(arr, diff=None):
+    """Converts a 4-dimensional array to blob proto. If diff is given, also
+    convert the diff. You need to make sure that arr and diff have the same
+    shape, and this function does not do sanity check.
+    """
+    if arr.ndim != 4:
+        raise ValueError('Incorrect array shape.')
+    blob = caffe_pb2.BlobProto()
+    blob.num, blob.channels, blob.height, blob.width = arr.shape;
+    blob.data.extend(arr.astype(float).flat)
+    if diff is not None:
+        blob.diff.extend(diff.astype(float).flat)
+    return blob
+
+
+def arraylist_to_blobprotovecor_str(arraylist):
+    """Converts a list of arrays to a serialized blobprotovec, which could be
+    then passed to a network for processing.
+    """
+    vec = caffe_pb2.BlobProtoVector()
+    vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
+    return vec.SerializeToString()
+
+
+def blobprotovector_str_to_arraylist(str):
+    """Converts a serialized blobprotovec to a list of arrays.
+    """
+    vec = caffe_pb2.BlobProtoVector()
+    vec.ParseFromString(str)
+    return [blobproto_to_array(blob) for blob in vec.blobs]
+
+
+def array_to_datum(arr, label=0):
+    """Converts a 3-dimensional array to datum. If the array has dtype uint8,
+    the output data will be encoded as a string. Otherwise, the output data
+    will be stored in float format.
+    """
+    if arr.ndim != 3:
+        raise ValueError('Incorrect array shape.')
+    datum = caffe_pb2.Datum()
+    datum.channels, datum.height, datum.width = arr.shape
+    if arr.dtype == np.uint8:
+        datum.data = arr.tostring()
+    else:
+        datum.float_data.extend(arr.flat)
+    datum.label = label
+    return datum
+
+
+def datum_to_array(datum):
+    """Converts a datum to an array. Note that the label is not returned,
+    as one can easily get it by calling datum.label.
+    """
+    if len(datum.data):
+        return np.fromstring(datum.data, dtype = np.uint8).reshape(
+            datum.channels, datum.height, datum.width)
+    else:
+        return np.array(datum.float_data).astype(float).reshape(
+            datum.channels, datum.height, datum.width)
+
+
+## Pre-processing
+
+class Transformer:
+    """
+    Transform input for feeding into a Net.
+
+    Note: this is mostly for illustrative purposes and it is likely better
+    to define your own input preprocessing routine for your needs.
+
+    Take
+    net: a Net for which the input should be prepared
+    """
+    def __init__(self, inputs):
+        self.inputs = inputs
+        self.transpose = {}
+        self.channel_swap = {}
+        self.raw_scale = {}
+        self.mean = {}
+        self.input_scale = {}
+
+
+    def __check_input(self, in_):
+        if in_ not in self.inputs:
+            raise Exception('{} is not one of the net inputs: {}'.format(
+                in_, self.inputs))
+
+
+    def preprocess(self, in_, data):
+        """
+        Format input for Caffe:
+        - convert to single
+        - resize to input dimensions (preserving number of channels)
+        - transpose dimensions to K x H x W
+        - reorder channels (for instance color to BGR)
+        - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
+        - subtract mean
+        - scale feature
+
+        Take
+        in_: name of input blob to preprocess for
+        data: (H' x W' x K) ndarray
+
+        Give
+        caffe_in: (K x H x W) ndarray for input to a Net
+        """
+        self.__check_input(in_)
+        caffe_in = data.astype(np.float32, copy=False)
+        transpose = self.transpose.get(in_)
+        channel_swap = self.channel_swap.get(in_)
+        raw_scale = self.raw_scale.get(in_)
+        mean = self.mean.get(in_)
+        input_scale = self.input_scale.get(in_)
+        in_dims = self.inputs[in_][2:]
+        if caffe_in.shape[:2] != in_dims:
+            caffe_in = resize_image(caffe_in, in_dims)
+        if transpose is not None:
+            caffe_in = caffe_in.transpose(transpose)
+        if channel_swap is not None:
+            caffe_in = caffe_in[channel_swap, :, :]
+        if raw_scale is not None:
+            caffe_in *= raw_scale
+        if mean is not None:
+            caffe_in -= mean
+        if input_scale is not None:
+            caffe_in *= input_scale
+        return caffe_in
+
+
+    def deprocess(self, in_, data):
+        """
+        Invert Caffe formatting; see preprocess().
+        """
+        self.__check_input(in_)
+        decaf_in = data.copy().squeeze()
+        transpose = self.transpose.get(in_)
+        channel_swap = self.channel_swap.get(in_)
+        raw_scale = self.raw_scale.get(in_)
+        mean = self.mean.get(in_)
+        input_scale = self.input_scale.get(in_)
+        if input_scale is not None:
+            decaf_in /= input_scale
+        if mean is not None:
+            decaf_in += mean
+        if raw_scale is not None:
+            decaf_in /= raw_scale
+        if channel_swap is not None:
+            decaf_in = decaf_in[channel_swap, :, :]
+        if transpose is not None:
+            decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
+        return decaf_in
+
+
+    def set_transpose(self, in_, order):
+        """
+        Set the input channel order for e.g. RGB to BGR conversion
+        as needed for the reference ImageNet model.
+
+        Take
+        in_: which input to assign this channel order
+        order: the order to transpose the dimensions
+        """
+        self.__check_input(in_)
+        if len(order) != len(self.inputs[in_]) - 1:
+            raise Exception('Transpose order needs to have the same number of '
+                            'dimensions as the input.')
+        self.transpose[in_] = order
+
+
+    def set_channel_swap(self, in_, order):
+        """
+        Set the input channel order for e.g. RGB to BGR conversion
+        as needed for the reference ImageNet model.
+        N.B. this assumes the channels are the first dimension AFTER transpose.
+
+        Take
+        in_: which input to assign this channel order
+        order: the order to take the channels.
+            (2,1,0) maps RGB to BGR for example.
+        """
+        self.__check_input(in_)
+        if len(order) != self.inputs[in_][1]:
+            raise Exception('Channel swap needs to have the same number of '
+                            'dimensions as the input channels.')
+        self.channel_swap[in_] = order
+
+
+    def set_raw_scale(self, in_, scale):
+        """
+        Set the scale of raw features s.t. the input blob = input * scale.
+        While Python represents images in [0, 1], certain Caffe models
+        like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
+        of these models must be 255.
+
+        Take
+        in_: which input to assign this scale factor
+        scale: scale coefficient
+        """
+        self.__check_input(in_)
+        self.raw_scale[in_] = scale
+
+
+    def set_mean(self, in_, mean):
+        """
+        Set the mean to subtract for centering the data.
+
+        Take
+        in_: which input to assign this mean.
+        mean: mean ndarray (input dimensional or broadcastable)
+        """
+        self.__check_input(in_)
+        if mean.ndim == 1:
+            mean = mean[:, np.newaxis, np.newaxis]
+        mk, mh, mw = mean.shape
+        in_k, in_h, in_w = self.inputs[in_][1:]
+        #if mk != in_k or (mh, mw) != (in_h, in_w) and (mh, mw) != (1, 1):
+        #    raise Exception('Mean shape incompatible with input shape.')
+        self.mean[in_] = mean
+
+
+    def set_input_scale(self, in_, scale):
+        """
+        Set the scale of preprocessed inputs s.t. the blob = blob * scale.
+        N.B. input_scale is done AFTER mean subtraction and other preprocessing
+        while raw_scale is done BEFORE.
+
+        Take
+        in_: which input to assign this scale factor
+        scale: scale coefficient
+        """
+        self.__check_input(in_)
+        self.input_scale[in_] = scale
+
+
+## Image IO
 
 def load_image(filename, color=True):
     """
@@ -102,76 +351,3 @@ def oversample(images, crop_dims):
             ix += 1
         crops[ix-5:ix] = crops[ix-5:ix, :, ::-1, :]  # flip for mirrors
     return crops
-
-
-def blobproto_to_array(blob, return_diff=False):
-  """Convert a blob proto to an array. In default, we will just return the data,
-  unless return_diff is True, in which case we will return the diff.
-  """
-  if return_diff:
-    return np.array(blob.diff).reshape(
-        blob.num, blob.channels, blob.height, blob.width)
-  else:
-    return np.array(blob.data).reshape(
-        blob.num, blob.channels, blob.height, blob.width)
-
-
-def array_to_blobproto(arr, diff=None):
-  """Converts a 4-dimensional array to blob proto. If diff is given, also
-  convert the diff. You need to make sure that arr and diff have the same
-  shape, and this function does not do sanity check.
-  """
-  if arr.ndim != 4:
-    raise ValueError('Incorrect array shape.')
-  blob = caffe_pb2.BlobProto()
-  blob.num, blob.channels, blob.height, blob.width = arr.shape;
-  blob.data.extend(arr.astype(float).flat)
-  if diff is not None:
-    blob.diff.extend(diff.astype(float).flat)
-  return blob
-
-
-def arraylist_to_blobprotovecor_str(arraylist):
-  """Converts a list of arrays to a serialized blobprotovec, which could be
-  then passed to a network for processing.
-  """
-  vec = caffe_pb2.BlobProtoVector()
-  vec.blobs.extend([array_to_blobproto(arr) for arr in arraylist])
-  return vec.SerializeToString()
-
-
-def blobprotovector_str_to_arraylist(str):
-  """Converts a serialized blobprotovec to a list of arrays.
-  """
-  vec = caffe_pb2.BlobProtoVector()
-  vec.ParseFromString(str)
-  return [blobproto_to_array(blob) for blob in vec.blobs]
-
-
-def array_to_datum(arr, label=0):
-  """Converts a 3-dimensional array to datum. If the array has dtype uint8,
-  the output data will be encoded as a string. Otherwise, the output data
-  will be stored in float format.
-  """
-  if arr.ndim != 3:
-    raise ValueError('Incorrect array shape.')
-  datum = caffe_pb2.Datum()
-  datum.channels, datum.height, datum.width = arr.shape
-  if arr.dtype == np.uint8:
-    datum.data = arr.tostring()
-  else:
-    datum.float_data.extend(arr.flat)
-  datum.label = label
-  return datum
-
-
-def datum_to_array(datum):
-  """Converts a datum to an array. Note that the label is not returned,
-  as one can easily get it by calling datum.label.
-  """
-  if len(datum.data):
-    return np.fromstring(datum.data, dtype = np.uint8).reshape(
-        datum.channels, datum.height, datum.width)
-  else:
-    return np.array(datum.float_data).astype(float).reshape(
-        datum.channels, datum.height, datum.width)
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index f6e8fbf1ba8..31c145d77a5 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -35,6 +35,17 @@ def _Net_params(self):
                         for name, lr in zip(self._layer_names, self.layers)
                         if len(lr.blobs) > 0])
 
+
+@property
+def _Net_inputs(self):
+    return [self.blobs.keys()[i] for i in self._inputs]
+
+
+@property
+def _Net_outputs(self):
+    return [self.blobs.keys()[i] for i in self._outputs]
+
+
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
@@ -202,138 +213,6 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     return all_outs, all_diffs
 
 
-def _Net_set_mean(self, input_, mean, mode='elementwise'):
-    """
-    Set the mean to subtract for data centering.
-
-    Take
-    input_: which input to assign this mean.
-    mean: mean K x H x W ndarray (input dimensional or broadcastable)
-    mode: elementwise = use the whole mean (and check dimensions)
-          channel = channel constant (e.g. mean pixel instead of mean image)
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    in_shape = self.blobs[input_].data.shape
-    if mode == 'elementwise':
-        if mean.shape[1:] != in_shape[2:]:
-            # Resize mean (which requires H x W x K input).
-            mean = caffe.io.resize_image(mean.transpose((1,2,0)),
-                                         in_shape[2:]).transpose((2,0,1))
-        self.mean[input_] = mean
-    elif mode == 'channel':
-        self.mean[input_] = mean.mean(1).mean(1).reshape((in_shape[1], 1, 1))
-    else:
-        raise Exception('Mode not in {}'.format(['elementwise', 'channel']))
-
-
-def _Net_set_input_scale(self, input_, scale):
-    """
-    Set the scale of preprocessed inputs s.t. the blob = blob * scale.
-    N.B. input_scale is done AFTER mean subtraction and other preprocessing
-    while raw_scale is done BEFORE.
-
-    Take
-    input_: which input to assign this scale factor
-    scale: scale coefficient
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.input_scale[input_] = scale
-
-
-def _Net_set_raw_scale(self, input_, scale):
-    """
-    Set the scale of raw features s.t. the input blob = input * scale.
-    While Python represents images in [0, 1], certain Caffe models
-    like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
-    of these models must be 255.
-
-    Take
-    input_: which input to assign this scale factor
-    scale: scale coefficient
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.raw_scale[input_] = scale
-
-
-def _Net_set_channel_swap(self, input_, order):
-    """
-    Set the input channel order for e.g. RGB to BGR conversion
-    as needed for the reference ImageNet model.
-
-    Take
-    input_: which input to assign this channel order
-    order: the order to take the channels.
-           (2,1,0) maps RGB to BGR for example.
-    """
-    if input_ not in self.inputs:
-        raise Exception('Input not in {}'.format(self.inputs))
-    self.channel_swap[input_] = order
-
-
-def _Net_preprocess(self, input_name, input_):
-    """
-    Format input for Caffe:
-    - convert to single
-    - resize to input dimensions (preserving number of channels)
-    - reorder channels (for instance color to BGR)
-    - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
-    - transpose dimensions to K x H x W
-    - subtract mean
-    - scale feature
-
-    Take
-    input_name: name of input blob to preprocess for
-    input_: (H' x W' x K) ndarray
-
-    Give
-    caffe_inputs: (K x H x W) ndarray
-    """
-    caffe_in = input_.astype(np.float32, copy=False)
-    mean = self.mean.get(input_name)
-    input_scale = self.input_scale.get(input_name)
-    raw_scale = self.raw_scale.get(input_name)
-    channel_order = self.channel_swap.get(input_name)
-    in_size = self.blobs[input_name].data.shape[2:]
-    if caffe_in.shape[:2] != in_size:
-        caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if channel_order is not None:
-        caffe_in = caffe_in[:, :, channel_order]
-    caffe_in = caffe_in.transpose((2, 0, 1))
-    if raw_scale is not None:
-        caffe_in *= raw_scale
-    if mean is not None:
-        caffe_in -= mean
-    if input_scale is not None:
-        caffe_in *= input_scale
-    return caffe_in
-
-
-def _Net_deprocess(self, input_name, input_):
-    """
-    Invert Caffe formatting; see Net.preprocess().
-    """
-    decaf_in = input_.copy().squeeze()
-    mean = self.mean.get(input_name)
-    input_scale = self.input_scale.get(input_name)
-    raw_scale = self.raw_scale.get(input_name)
-    channel_order = self.channel_swap.get(input_name)
-    if input_scale is not None:
-        decaf_in /= input_scale
-    if mean is not None:
-        decaf_in += mean
-    if raw_scale is not None:
-        decaf_in /= raw_scale
-    decaf_in = decaf_in.transpose((1,2,0))
-    if channel_order is not None:
-        channel_order_inverse = [channel_order.index(i)
-                                 for i in range(decaf_in.shape[2])]
-        decaf_in = decaf_in[:, :, channel_order_inverse]
-    return decaf_in
-
-
 def _Net_set_input_arrays(self, data, labels):
     """
     Set input arrays of the in-memory MemoryDataLayer.
@@ -376,14 +255,6 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
-@property
-def _Net_inputs(self):
-    return [self.blobs.keys()[i] for i in self._inputs]
-
-@property
-def _Net_outputs(self):
-    return [self.blobs.keys()[i] for i in self._outputs]
-
 # Attach methods to Net.
 Net.blobs = _Net_blobs
 Net.params = _Net_params
@@ -391,12 +262,6 @@ def _Net_outputs(self):
 Net.backward = _Net_backward
 Net.forward_all = _Net_forward_all
 Net.forward_backward_all = _Net_forward_backward_all
-Net.set_mean = _Net_set_mean
-Net.set_input_scale = _Net_set_input_scale
-Net.set_raw_scale = _Net_set_raw_scale
-Net.set_channel_swap = _Net_set_channel_swap
-Net.preprocess = _Net_preprocess
-Net.deprocess = _Net_deprocess
 Net.set_input_arrays = _Net_set_input_arrays
 Net._batch = _Net_batch
 Net.inputs = _Net_inputs

From 26c16bf414dc0496628f7e34bd03e8f26f2c309d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 18 Feb 2015 20:53:22 -0800
Subject: [PATCH 1167/2053] [pycaffe] switch examples to Transformer

This is a quick translation of the examples to the caffe.io.Transformer
interface. The results are not strictly identical to the earlier
implementation! The models now use a mean pixel instead of a mean image
for simplicity. The output classifications and detections are preserved
but scores may differ.

Note: the examples will be rewritten to make use of `caffe.Net` alone
since it is the true interface, but not yet.
---
 examples/classification.ipynb       |   7 +-
 examples/detection.ipynb            | 604 ++++++++++++++++------------
 examples/filter_visualization.ipynb |  11 +-
 examples/net_surgery.ipynb          |  26 +-
 python/caffe/classifier.py          |  29 +-
 python/caffe/detector.py            |  55 +--
 python/detect.py                    |   2 +
 7 files changed, 417 insertions(+), 317 deletions(-)

diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 6b6c5488d36..6f8fa4252e6 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -4,7 +4,7 @@
   "example_name": "ImageNet classification",
   "include_in_docs": true,
   "priority": 1,
-  "signature": "sha256:2caae2c1fe3e282b8f836d380a45622351c91db18a1591e4f2fa67faba9ab72c"
+  "signature": "sha256:918b797b1b7d78125c8f1e3c84756b0679120cbe1071ce7fee7aeafef0fbae55"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -64,10 +64,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "caffe.set_phase_test()\n",
       "caffe.set_mode_cpu()\n",
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
+      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
       "                       channel_swap=(2,1,0),\n",
       "                       raw_scale=255,\n",
       "                       image_dims=(256, 256))"
@@ -237,7 +236,7 @@
       "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
       "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
       "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
-      "caffe_input = np.asarray([net.preprocess('data', in_) for in_ in input_oversampled])\n",
+      "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
       "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
       "%timeit net.forward(data=caffe_input)"
      ],
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index d05c0c22052..2ccf21f09eb 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -3,7 +3,8 @@
   "description": "Run a pretrained model as a detector in Python.",
   "example_name": "R-CNN detection",
   "include_in_docs": true,
-  "priority": 3
+  "priority": 3,
+  "signature": "sha256:5d53dc49c9b6b93c1a2714c99043a763029ec98aebfb44acfa8d9e61781c9499"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -37,7 +38,7 @@
      "input": [
       "!mkdir -p _temp\n",
       "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
@@ -47,52 +48,60 @@
        "stream": "stdout",
        "text": [
         "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0610 10:12:49.299607 25530 net.cpp:36] Initializing net from parameters: \r\n",
+        "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
         "name: \"R-CNN-ilsvrc13\"\r\n",
-        "layers {\r\n",
+        "input: \"data\"\r\n",
+        "input_dim: 10\r\n",
+        "input_dim: 3\r\n",
+        "input_dim: 227\r\n",
+        "input_dim: 227\r\n",
+        "state {\r\n",
+        "  phase: TEST\r\n",
+        "}\r\n",
+        "layer {\r\n",
+        "  name: \"conv1\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"data\"\r\n",
         "  top: \"conv1\"\r\n",
-        "  name: \"conv1\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 96\r\n",
         "    kernel_size: 11\r\n",
         "    stride: 4\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv1\"\r\n",
         "  top: \"conv1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"pool1\"\r\n",
+        "  type: \"Pooling\"\r\n",
         "  bottom: \"conv1\"\r\n",
         "  top: \"pool1\"\r\n",
-        "  name: \"pool1\"\r\n",
-        "  type: POOLING\r\n",
         "  pooling_param {\r\n",
         "    pool: MAX\r\n",
         "    kernel_size: 3\r\n",
         "    stride: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"norm1\"\r\n",
+        "  type: \"LRN\"\r\n",
         "  bottom: \"pool1\"\r\n",
         "  top: \"norm1\"\r\n",
-        "  name: \"norm1\"\r\n",
-        "  type: LRN\r\n",
         "  lrn_param {\r\n",
         "    local_size: 5\r\n",
         "    alpha: 0.0001\r\n",
         "    beta: 0.75\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv2\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"norm1\"\r\n",
         "  top: \"conv2\"\r\n",
-        "  name: \"conv2\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 256\r\n",
         "    pad: 2\r\n",
@@ -100,56 +109,57 @@
         "    group: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r",
+        "\r\n",
+        "  name: \"relu2\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv2\"\r\n",
         "  top: \"conv2\"\r\n",
-        "  name: \"relu2\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"pool2\"\r\n",
+        "  type: \"Pooling\"\r\n",
         "  bottom: \"conv2\"\r\n",
         "  top: \"pool2\"\r\n",
-        "  name: \"pool2\"\r\n",
-        "  type: POOLING\r\n",
         "  pooling_param {\r\n",
         "    pool: MAX\r\n",
         "    kernel_size: 3\r\n",
         "    stride: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"norm2\"\r\n",
+        "  type: \"LRN\"\r\n",
         "  bottom: \"pool2\"\r\n",
         "  top: \"norm2\"\r\n",
-        "  name: \"norm2\"\r\n",
-        "  type: LRN\r\n",
         "  lrn_param {\r\n",
         "    local_size: 5\r\n",
         "    alpha: 0.0001\r\n",
         "    beta: 0.75\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv3\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"norm2\"\r\n",
         "  top: \"conv3\"\r\n",
-        "  name: \"conv3\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 384\r\n",
         "    pad: 1\r\n",
         "    kernel_size: 3\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu3\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv3\"\r\n",
         "  top: \"conv3\"\r\n",
-        "  name: \"relu3\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv4\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"conv3\"\r\n",
         "  top: \"conv4\"\r\n",
-        "  name: \"conv4\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 384\r\n",
         "    pad: 1\r\n",
@@ -157,17 +167,17 @@
         "    group: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu4\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv4\"\r\n",
         "  top: \"conv4\"\r\n",
-        "  name: \"relu4\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"conv5\"\r\n",
+        "  type: \"Convolution\"\r\n",
         "  bottom: \"conv4\"\r\n",
         "  top: \"conv5\"\r\n",
-        "  name: \"conv5\"\r\n",
-        "  type: CONVOLUTION\r\n",
         "  convolution_param {\r\n",
         "    num_output: 256\r\n",
         "    pad: 1\r\n",
@@ -175,205 +185,265 @@
         "    group: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu5\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"conv5\"\r\n",
         "  top: \"conv5\"\r\n",
-        "  name: \"relu5\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"pool5\"\r\n",
+        "  type: \"Pooling\"\r\n",
         "  bottom: \"conv5\"\r\n",
         "  top: \"pool5\"\r\n",
-        "  name: \"pool5\"\r\n",
-        "  type: POOLING\r\n",
         "  pooling_param {\r\n",
         "    pool: MAX\r\n",
         "    kernel_size: 3\r\n",
         "    stride: 2\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc6\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"pool5\"\r\n",
         "  top: \"fc6\"\r\n",
-        "  name: \"fc6\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 4096\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu6\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"fc6\"\r\n",
         "  top: \"fc6\"\r\n",
-        "  name: \"relu6\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"drop6\"\r\n",
+        "  type: \"Dropout\"\r\n",
         "  bottom: \"fc6\"\r\n",
         "  top: \"fc6\"\r\n",
-        "  name: \"drop6\"\r\n",
-        "  type: DROPOUT\r\n",
         "  dropout_param {\r\n",
         "    dropout_ratio: 0.5\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc7\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"fc6\"\r\n",
         "  top: \"fc7\"\r\n",
-        "  name: \"fc7\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 4096\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu7\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"fc7\"\r\n",
         "  top: \"fc7\"\r\n",
-        "  name: \"relu7\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"drop7\"\r\n",
+        "  type: \"Dropout\"\r\n",
         "  bottom: \"fc7\"\r\n",
         "  top: \"fc7\"\r\n",
-        "  name: \"drop7\"\r\n",
-        "  type: DROPOUT\r\n",
         "  dropout_param {\r\n",
         "    dropout_ratio: 0.5\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc-rcnn\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"fc7\"\r\n",
         "  top: \"fc-rcnn\"\r\n",
-        "  name: \"fc-rcnn\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 200\r\n",
         "  }\r\n",
         "}\r\n",
-        "input: \"data\"\r\n",
-        "input_dim: 10\r\n",
-        "input_dim: 3\r\n",
-        "input_dim: 227\r\n",
-        "input_dim: 227\r\n",
-        "I0610 10:12:49.300204 25530 net.cpp:77] Creating Layer conv1\r\n",
-        "I0610 10:12:49.300214 25530 net.cpp:87] conv1 <- data\r\n",
-        "I0610 10:12:49.300220 25530 net.cpp:113] conv1 -> conv1\r\n",
-        "I0610 10:12:49.300283 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0610 10:12:49.300294 25530 net.cpp:154] conv1 needs backward computation.\r\n",
-        "I0610 10:12:49.300302 25530 net.cpp:77] Creating Layer relu1\r\n",
-        "I0610 10:12:49.300308 25530 net.cpp:87] relu1 <- conv1\r\n",
-        "I0610 10:12:49.300314 25530 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
-        "I0610 10:12:49.300323 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0610 10:12:49.300328 25530 net.cpp:154] relu1 needs backward computation.\r\n",
-        "I0610 10:12:49.300335 25530 net.cpp:77] Creating Layer pool1\r\n",
-        "I0610 10:12:49.300341 25530 net.cpp:87] pool1 <- conv1\r\n",
-        "I0610 10:12:49.300348 25530 net.cpp:113] pool1 -> pool1\r\n",
-        "I0610 10:12:49.300357 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0610 10:12:49.300365 25530 net.cpp:154] pool1 needs backward computation.\r\n",
-        "I0610 10:12:49.300372 25530 net.cpp:77] Creating Layer norm1\r\n",
-        "I0610 10:12:49.300379 25530 net.cpp:87] norm1 <- pool1\r\n",
-        "I0610 10:12:49.300384 25530 net.cpp:113] norm1 -> norm1\r\n",
-        "I0610 10:12:49.300393 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0610 10:12:49.300400 25530 net.cpp:154] norm1 needs backward computation.\r\n",
-        "I0610 10:12:49.300406 25530 net.cpp:77] Creating Layer conv2\r\n",
-        "I0610 10:12:49.300412 25530 net.cpp:87] conv2 <- norm1\r\n",
-        "I0610 10:12:49.300420 25530 net.cpp:113] conv2 -> conv2\r\n",
-        "I0610 10:12:49.300925 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0610 10:12:49.300935 25530 net.cpp:154] conv2 needs backward computation.\r\n",
-        "I0610 10:12:49.300941 25530 net.cpp:77] Creating Layer relu2\r\n",
-        "I0610 10:12:49.300947 25530 net.cpp:87] relu2 <- conv2\r\n",
-        "I0610 10:12:49.300954 25530 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
-        "I0610 10:12:49.300961 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0610 10:12:49.300967 25530 net.cpp:154] relu2 needs backward computation.\r\n",
-        "I0610 10:12:49.300974 25530 net.cpp:77] Creating Layer pool2\r\n",
-        "I0610 10:12:49.300981 25530 net.cpp:87] pool2 <- conv2\r\n",
-        "I0610 10:12:49.300987 25530 net.cpp:113] pool2 -> pool2\r\n",
-        "I0610 10:12:49.300994 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.301000 25530 net.cpp:154] pool2 needs backward computation.\r\n",
-        "I0610 10:12:49.301007 25530 net.cpp:77] Creating Layer norm2\r\n",
-        "I0610 10:12:49.301013 25530 net.cpp:87] norm2 <- pool2\r\n",
-        "I0610 10:12:49.301019 25530 net.cpp:113] norm2 -> norm2\r\n",
-        "I0610 10:12:49.301026 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.301033 25530 net.cpp:154] norm2 needs backward computation.\r\n",
-        "I0610 10:12:49.301041 25530 net.cpp:77] Creating Layer conv3\r\n",
-        "I0610 10:12:49.301048 25530 net.cpp:87] conv3 <- norm2\r\n",
-        "I0610 10:12:49.301054 25530 net.cpp:113] conv3 -> conv3\r\n",
-        "I0610 10:12:49.302455 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.302467 25530 net.cpp:154] conv3 needs backward computation.\r\n",
-        "I0610 10:12:49.302477 25530 net.cpp:77] Creating Layer relu3\r\n",
-        "I0610 10:12:49.302484 25530 net.cpp:87] relu3 <- conv3\r\n",
-        "I0610 10:12:49.302490 25530 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
-        "I0610 10:12:49.302496 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.302503 25530 net.cpp:154] relu3 needs backward computation.\r\n",
-        "I0610 10:12:49.302510 25530 net.cpp:77] Creating Layer conv4\r\n",
-        "I0610 10:12:49.302515 25530 net.cpp:87] conv4 <- conv3\r\n",
-        "I0610 10:12:49.302521 25530 net.cpp:113] conv4 -> conv4\r\n",
-        "I0610 10:12:49.303639 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.303650 25530 net.cpp:154] conv4 needs backward computation.\r\n",
-        "I0610 10:12:49.303658 25530 net.cpp:77] Creating Layer relu4\r\n",
-        "I0610 10:12:49.303663 25530 net.cpp:87] relu4 <- conv4\r\n",
-        "I0610 10:12:49.303670 25530 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
-        "I0610 10:12:49.303676 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0610 10:12:49.303683 25530 net.cpp:154] relu4 needs backward computation.\r\n",
-        "I0610 10:12:49.303691 25530 net.cpp:77] Creating Layer conv5\r\n",
-        "I0610 10:12:49.303697 25530 net.cpp:87] conv5 <- conv4\r\n",
-        "I0610 10:12:49.303704 25530 net.cpp:113] conv5 -> conv5\r\n",
-        "I0610 10:12:49.304410 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.304420 25530 net.cpp:154] conv5 needs backward computation.\r\n",
-        "I0610 10:12:49.304427 25530 net.cpp:77] Creating Layer relu5\r\n",
-        "I0610 10:12:49.304433 25530 net.cpp:87] relu5 <- conv5\r\n",
-        "I0610 10:12:49.304440 25530 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
-        "I0610 10:12:49.304446 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0610 10:12:49.304471 25530 net.cpp:154] relu5 needs backward computation.\r\n",
-        "I0610 10:12:49.304478 25530 net.cpp:77] Creating Layer pool5\r\n",
-        "I0610 10:12:49.304484 25530 net.cpp:87] pool5 <- conv5\r\n",
-        "I0610 10:12:49.304491 25530 net.cpp:113] pool5 -> pool5\r\n",
-        "I0610 10:12:49.304498 25530 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0610 10:12:49.304504 25530 net.cpp:154] pool5 needs backward computation.\r\n",
-        "I0610 10:12:49.304512 25530 net.cpp:77] Creating Layer fc6\r\n",
-        "I0610 10:12:49.304517 25530 net.cpp:87] fc6 <- pool5\r\n",
-        "I0610 10:12:49.304523 25530 net.cpp:113] fc6 -> fc6\r\n"
+        "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\r\n",
+        "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\r\n",
+        "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\r\n",
+        "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\r\n",
+        "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\r\n",
+        "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\r\n",
+        "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
+        "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\r\n",
+        "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\r\n",
+        "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\r\n",
+        "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\r\n",
+        "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\r\n",
+        "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\r\n",
+        "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\r\n",
+        "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\r\n",
+        "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\r\n",
+        "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\r\n",
+        "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\r\n",
+        "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\r\n",
+        "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\r\n",
+        "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\r\n",
+        "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\r\n",
+        "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\r\n",
+        "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\r\n",
+        "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\r\n",
+        "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\r\n",
+        "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\r\n",
+        "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\r\n",
+        "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\r\n",
+        "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\r\n",
+        "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\r\n",
+        "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\r\n",
+        "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\r\n",
+        "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\r\n",
+        "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\r\n",
+        "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\r\n",
+        "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\r\n",
+        "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\r\n",
+        "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\r\n",
+        "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\r\n",
+        "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\r\n",
+        "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\r\n",
+        "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\r\n",
+        "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\r\n",
+        "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\r\n",
+        "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\r\n",
+        "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\r\n",
+        "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\r\n",
+        "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\r\n",
+        "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\r\n",
+        "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\r\n",
+        "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\r\n",
+        "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\r\n",
+        "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\r\n",
+        "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\r\n",
+        "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\r\n",
+        "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\r\n",
+        "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\r\n",
+        "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\r\n",
+        "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\r\n",
+        "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\r\n",
+        "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\r\n",
+        "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\r\n",
+        "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\r\n",
+        "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\r\n",
+        "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\r\n",
+        "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\r\n",
+        "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\r\n",
+        "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\r\n",
+        "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\r\n",
+        "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\r\n",
+        "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\r\n",
+        "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\r\n",
+        "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\r\n",
+        "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\r\n",
+        "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\r\n",
+        "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\r\n",
+        "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\r\n",
+        "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\r\n",
+        "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\r\n",
+        "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\r\n",
+        "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\r\n",
+        "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\r\n",
+        "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\r\n",
+        "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\r\n",
+        "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\r\n",
+        "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\r\n",
+        "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\r\n",
+        "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\r\n",
+        "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\r\n",
+        "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\r\n",
+        "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\r\n",
+        "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\r\n",
+        "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\r\n",
+        "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\r\n",
+        "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\r\n",
+        "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\r\n",
+        "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\r\n",
+        "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\r\n",
+        "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\r\n",
+        "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\r\n",
+        "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\r\n",
+        "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\r\n",
+        "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\r\n",
+        "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\r\n",
+        "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\r\n",
+        "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\r\n",
+        "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\r\n",
+        "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\r\n",
+        "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\r\n",
+        "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\r\n",
+        "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\r\n",
+        "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\r\n",
+        "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\r\n",
+        "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\r\n",
+        "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\r\n",
+        "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\r\n",
+        "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\r\n",
+        "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\r\n",
+        "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\r\n",
+        "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\r\n",
+        "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\r\n",
+        "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\r\n",
+        "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\r\n",
+        "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\r\n",
+        "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\r\n",
+        "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\r\n",
+        "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\r\n",
+        "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\r\n",
+        "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\r\n",
+        "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\r\n",
+        "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\r\n",
+        "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0610 10:12:49.364333 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364372 25530 net.cpp:154] fc6 needs backward computation.\r\n",
-        "I0610 10:12:49.364387 25530 net.cpp:77] Creating Layer relu6\r\n",
-        "I0610 10:12:49.364420 25530 net.cpp:87] relu6 <- fc6\r\n",
-        "I0610 10:12:49.364429 25530 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
-        "I0610 10:12:49.364437 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364444 25530 net.cpp:154] relu6 needs backward computation.\r\n",
-        "I0610 10:12:49.364455 25530 net.cpp:77] Creating Layer drop6\r\n",
-        "I0610 10:12:49.364461 25530 net.cpp:87] drop6 <- fc6\r\n",
-        "I0610 10:12:49.364467 25530 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
-        "I0610 10:12:49.364480 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.364487 25530 net.cpp:154] drop6 needs backward computation.\r\n",
-        "I0610 10:12:49.364495 25530 net.cpp:77] Creating Layer fc7\r\n",
-        "I0610 10:12:49.364501 25530 net.cpp:87] fc7 <- fc6\r\n",
-        "I0610 10:12:49.364507 25530 net.cpp:113] fc7 -> fc7\r\n",
-        "I0610 10:12:49.391316 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391350 25530 net.cpp:154] fc7 needs backward computation.\r\n",
-        "I0610 10:12:49.391361 25530 net.cpp:77] Creating Layer relu7\r\n",
-        "I0610 10:12:49.391369 25530 net.cpp:87] relu7 <- fc7\r\n",
-        "I0610 10:12:49.391377 25530 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
-        "I0610 10:12:49.391384 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391391 25530 net.cpp:154] relu7 needs backward computation.\r\n",
-        "I0610 10:12:49.391398 25530 net.cpp:77] Creating Layer drop7\r\n",
-        "I0610 10:12:49.391427 25530 net.cpp:87] drop7 <- fc7\r\n",
-        "I0610 10:12:49.391433 25530 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
-        "I0610 10:12:49.391440 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0610 10:12:49.391446 25530 net.cpp:154] drop7 needs backward computation.\r\n",
-        "I0610 10:12:49.391454 25530 net.cpp:77] Creating Layer fc-rcnn\r\n",
-        "I0610 10:12:49.391459 25530 net.cpp:87] fc-rcnn <- fc7\r\n",
-        "I0610 10:12:49.391466 25530 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
-        "I0610 10:12:49.392812 25530 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0610 10:12:49.392823 25530 net.cpp:154] fc-rcnn needs backward computation.\r\n",
-        "I0610 10:12:49.392829 25530 net.cpp:165] This network produces output fc-rcnn\r\n",
-        "I0610 10:12:49.392850 25530 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0610 10:12:49.392868 25530 net.cpp:176] Network initialization done.\r\n",
-        "I0610 10:12:49.392875 25530 net.cpp:177] Memory required for Data 41950840\r\n"
+        "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\r\n"
        ]
       },
       {
@@ -381,21 +451,39 @@
        "stream": "stdout",
        "text": [
         "GPU mode\r\n",
-        "Loading input...\r\n",
-        "selective_search_rcnn({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpo7yOum.mat')\r\n"
+        "Loading input...\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Processed 1570 windows in 35.012 s.\r\n",
-        "/home/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
+        "Processed 1570 windows in 102.895 s.\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \r\n",
         "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
         "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
         "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n",
-        "Saved to _temp/det_output.h5 in 0.035 s.\r\n"
+        "  warnings.warn(ws, PerformanceWarning)\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Saved to _temp/det_output.h5 in 0.298 s.\r\n"
        ]
       }
      ],
@@ -432,12 +520,12 @@
        "stream": "stdout",
        "text": [
         "(1570, 5)\n",
-        "prediction    [-2.64547, -2.88455, -2.85903, -3.17038, -1.92...\n",
+        "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
         "ymin                                                     79.846\n",
         "xmin                                                       9.62\n",
         "ymax                                                     246.31\n",
         "xmax                                                    339.624\n",
-        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
+        "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
        ]
       }
      ],
@@ -481,37 +569,37 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "accordion      -2.645470\n",
-        "airplane       -2.884554\n",
-        "ant            -2.859026\n",
-        "antelope       -3.170383\n",
-        "apple          -1.924201\n",
-        "armadillo      -2.493925\n",
-        "artichoke      -2.235427\n",
-        "axe            -2.378177\n",
-        "baby bed       -2.757855\n",
-        "backpack       -2.160120\n",
-        "bagel          -2.715738\n",
-        "balance beam   -2.716172\n",
-        "banana         -2.418939\n",
-        "band aid       -1.604563\n",
-        "banjo          -2.329196\n",
+        "accordion      -2.622471\n",
+        "airplane       -2.845788\n",
+        "ant            -2.851219\n",
+        "antelope       -3.208377\n",
+        "apple          -1.949950\n",
+        "armadillo      -2.472935\n",
+        "artichoke      -2.201684\n",
+        "axe            -2.327404\n",
+        "baby bed       -2.737925\n",
+        "backpack       -2.176763\n",
+        "bagel          -2.681061\n",
+        "balance beam   -2.722538\n",
+        "banana         -2.390628\n",
+        "band aid       -1.598909\n",
+        "banjo          -2.298197\n",
         "...\n",
-        "trombone        -2.531519\n",
-        "trumpet         -2.382109\n",
-        "turtle          -2.378510\n",
-        "tv or monitor   -2.777433\n",
-        "unicycle        -2.263807\n",
-        "vacuum          -1.894700\n",
-        "violin          -2.797967\n",
-        "volleyball      -2.807812\n",
-        "waffle iron     -2.418155\n",
-        "washer          -2.429423\n",
-        "water bottle    -2.163465\n",
-        "watercraft      -2.803971\n",
-        "whale           -3.094172\n",
-        "wine bottle     -2.830827\n",
-        "zebra           -2.791829\n",
+        "trombone        -2.582361\n",
+        "trumpet         -2.352853\n",
+        "turtle          -2.360859\n",
+        "tv or monitor   -2.761043\n",
+        "unicycle        -2.218467\n",
+        "vacuum          -1.907717\n",
+        "violin          -2.757079\n",
+        "volleyball      -2.723689\n",
+        "waffle iron     -2.418540\n",
+        "washer          -2.408994\n",
+        "water bottle    -2.174899\n",
+        "watercraft      -2.837425\n",
+        "whale           -3.120338\n",
+        "wine bottle     -2.772960\n",
+        "zebra           -2.742913\n",
         "Name: 0, Length: 200, dtype: float32\n"
        ]
       }
@@ -542,22 +630,22 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x4e2c090>"
+        "<matplotlib.text.Text at 0x114f15f90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x4d008d0>"
+        "<matplotlib.figure.Figure at 0x114254b50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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lkkT/l19+GblcTuwRHA6HpDmDwUBYeYTfOp2O5NapVEq6sSz2AEiwaLfbiMVi\n0niyWq14/fXXce/evae+/+cqMjMS1Ot1OBwOZDIZ4UpQ/cGIBDwWuMZiMckpuQmCwSCi0SjeeOON\nJwq28Xj8BHnJaDTCZDLB4/FI9Op0OgAeC1xPK09OCwPm87moTRRFAQCJssSwFUVBNBqVYpbvez6f\nSxrDnNlmsyESieDKlSsi93c4HFhYWIDH44HVapV6gjYE9LNgwet2u4XBxyhtt9sRi8Wkc0fuczgc\nlhY78XKPx4N4PC6oTywWw8rKCgwGg5yUoVBIgo3D4RCJFa0GXlBAT61oNCryoXK5LEfmeDzGYDDA\n/v6+RKNwOIyvfe1r0vWjOcpgMEA+n0c8Hsfh4SHsdjsURUGxWBQdXKFQkIKFgllCT0xrKpUKarUa\nrly5ggcPHkjKQF4I3fh1Xcfe3h6SySRKpZL8rFwuQ9d19Pt9KIqC8XiMcrksPJDxeIxUKoVmswmP\nx4NKpSK8CvKnHz16BADodrtIJBLIZrOi8Gb3kMQgNlZY8G5ubuL4+FjqC/pwkJMNPOZlMEcmduxy\nuWA0GlGr1YSjwvcFQKRfXq8XN2/ehK7ruHPnDlZXV89sNXCuInOz2ZQ27mg0EvxW13W0Wi2srq4i\nHA6LKctrr72G27dvIxqN4uHDh5IzsmCkepoOQmazGScnJ4hGo+j1egiHw4hEIpKrApCiiBu70WgI\nmZ3NAxKWvF4vgsEgVlZWhIaqaRq63S76/T7m8zmi0Sjy+bwgEzxBiO96vV7Y7XY4nU5Uq1Vomiai\n2kQiIcXeYDDAfD4XDgaLtOFwKNgvCzWn0ymsQ5fLJTYBNJyZzWbI5XIyIYowKPFqIikULdjtdsTj\ncUFyGo0GWq2W9AKi0SisVut/OMbtv7PO1WZmIZdMJqV4IZsrEAgAgOCzFosFd+/eRTqdhtlsxuLi\noqgkgsEgAoEAdnZ2REW9srICh8OBZDIpHAoWlSsrK7Jh/X4/lpaWhHxOa7HpdCqDZ0KhEAwGgzD1\nVFUVTHl1dVVyYwDCU3Y4HOJ9sba2JgoRpgTj8Vge4uXlZUynU7TbbUFpAoEAEomEoAx8mE5zu5m+\n8GGjPtLlckkLngpzr9eLWq0mhbPVapXvkpve5/PB7XZDURRpBoXDYSwtLUkEp1yKOf5Z1rnazEQK\niDgoioLDw0MAEAhMVVUhvFMR3O/3USwW5XWKxaLIn1ipk8VWrVah67q0zEOhEHq9HtrtNqrVqmDC\ntKEi/8OMUZ1cAAAgAElEQVRkMknELpVK6Pf7cLlcaDQa0m5ut9toNBoS4cxmM8rlMur1OmazGer1\nOtrtNsrlMqxWq2xUXddxdHSE2WwGXdexu7uLwWCAer0uSALf93Q6FXomGxvEkPl5+f0cHx+j2+2i\n0+kI7fO0bwjZgoQ7+/0+jEajeJQAkCZMtVpFoVAQCJB+d7quw2g0CrPvLOu5ME78YZbBYMDnPvc5\n8XFj9KAagxW0zWZDuVwWwjuJMacbG8Djm0CIjvYALP5Go5FEeG5Ekn+oBqdEazqdSvQDIIaOnU5H\nIKzBYCCsvkAgIEoVFpCkXrLLxmKOKRGbRGazWQpVdiJpULO8vCzkqMlkgnq9Lg0XAJJe1Ot1BINB\nMaBstVoAIPg3BcONRkNUOoPBQIwi2e3b2dkRXxLaMRAJcjgcqFarSCaT2N3dRSgUkvf7hS984Xwb\nJ/6wi/mgoig4Pj7G6uoqTk5OcOHCBZhMJhweHiIcDqNUKsHn86FQKCCXy2F7exsOhwONRgOj0QiH\nh4fY3t7Go0ePcOXKFezu7mJtbQ1ms1nceIgCmEwmPHr0SJAONmtY4NntdtTrdfj9frFqrVQqIrZl\njsxCczwe48GDB+IzR45ytVpFsVhENBqF3++XwtPj8SCfz2MwGCAcDgsdc3V1FQ8fPoTRaMSdO3dg\ntVoxHA7l9yORiDg+kepJJQojbrFYhKIo8Pv9uHv3LpxOp1BP2+02FEXBlStXcHh4CF3X4ff7YTAY\nBHtmA8bn8yGfz8NisUjThdG9UCig2+2eWZkNnLPNPBgMkEwmUalUsLy8LL5vTqcTxWIRS0tL0oRo\ntVp49dVX8Vd/9VfCZV5fX0e9Xkc8HsdwOMTP//zPY2dnB0tLS+JvQQir3+9DVVUkk0ksLy8L4kEY\ni/RG5qP0gqPCYnFxEQ6HA16vF9PpVCJ6KBRCp9PB8vKyUEF5egSDQTnWqdbudrsCA7788su4efOm\nuBjduHFDZFjM/aPRKPb29jAcDkWPR44GVefMwy9evIhWq4WTkxPRAfp8Pmxvb+PevXuIxWJiacYO\nJV2WNjc34fV60Wg0MB6PEQ6H5bOymRMOh5FMJiUwnJ5B+DTrXG1mkl6Y/5G2SZnOyckJ3G63VPbv\nvfceVFVFu91GJBKRm0wl9b/+679KGlEqlYTgzrQAAOr1OprNJpaXlwVTHo1GkoPbbDa0Wi1pUpjN\nZjSbTWmtBwIBGAwG1Go1RKNR7O7uShoAPIbVdF0XPR4tuCaTCaxWqxRhzNnNZrM4kAIQV0/+new0\nysCYkrE7SlclniSFQkGomuvr68hmsygWi5IWKYqC+/fvSwFNPPrevXv44Ac/KOocAAIt9no98aym\nyps8jrOsc7WZI5EIXC6XGP1NJhMsLi4KcTwcDiMQCIhz5uXLl3H37l2hg7pcLlgsFtG1bW5uolAo\nYDqdwmq1iudFMpkUkg1b4DQRJOONPsbkIWQyGTSbTei6jmAwKG6iNHqhhwe7kjQgXFtbQzabhc/n\ng9lsFidS2l35/X5BO5iXn+Y/kMrpcDikqxiPx9HtdpFKpfDo0SNp1JDrTEYcH3I2iJjyaJqGer2O\n119/Hfl8HktLS4IEkaT0gQ98QHgbbOyQNRcOh0UowPcWCoUkP3/ada7QDEaYdrsNn8+HhYUF8S4m\nesEclagB8zqHw4FEIoFAICCtaao/yDyj8JOvx26c0+l8AkdlcUbdIY0LqXAeDAbSbQMgfIVgMCj5\nvc/nE3X2aUiPfm9EQeiUP5vN8PrrrwvzjSoPbi7gMYebHnXcwPTsIM+ZDxXhRxqbBwIB1Go1+P1+\nbG5uIhaLycNJSDKVSsnDAzwmfhEtIR+EhaLb7RajSKfT+cT38bTrXEVmGrSQrsno4vF4UCqVpLEw\nHo+RTqfx6NEjKdBIenE6nbh9+zaSySRu376NCxcuoF6vY3V1VaClCxcuoFwuI5vNIhAIYG9vT8hH\ns9lMult+v1+MVLrdrjj7mM1mVKtVrKysiDrEYrFIIcUUg25AOzs7UiCRm72/vy+ciGKxiKOjI4G6\n6By0v78PAEJ44sgFpiQrKysiGAgEAggGg0K8SqfT+Ld/+zdpzrAYLJVKUtyyLa+qKg4PD+UhpL6P\niAghxlAoJAJgVVVFWVMoFBAIBHBwcHCm+3+uoLlPfvKTWFhYEH3b+vo67t+/LyMQyIPIZrNwuVx4\n+eWX8Wd/9mfY3t6G3W5HOp1GpVIB8JgsdPHiRdy7dw+dTkf4C7S+Im2U7kfcoEQuqPsjUtDv98Ww\nm0QeNg5OFz4LCwtivcu2ONMkNlu4kQhDMof+8R//cbz99tuidAkGg6jX66jVakgmk8KZeOuttxCJ\nRGAymdBut6UwoxqH5KFYLIZGoyEKcnIxPvjBD+L27duIRCLiOx2JROSzqaoqCu1arSbsQ+bkLGDD\n4TB2d3fh8/lEhPuVr3zlqaG5c7WZP/e5z4lGj9xfikXJQzaZTDIOQlVVwYeLxaI4YbKT1u/3n3AM\nIiuONEwAEpFpNWC1WtHv9yVfPE2XNJvN0izpdrtyDFMdnsvlRLLEa9Ieljk5ha3Mj1nEUWXNY5y4\nsNFoFDYhZWRMVcgpMRgMsoHY/l9cXISqqqhUKiK3MplMqNfriEajAlEy/SAiw3rh8PAQ8/kcfr9f\nZqYwEtvtdsm1Hzx4gFQqJe/113/9119sZoPBgE9/+tMCV3W7XSwsLAg98+7du0Jg73Q6SCaTKBQK\n2N/fxyuvvALgMU4djUaxv78Pj8cj1ExuDDYrCM9pmia/xwbC7u6uMM/YCuYG5uuRokrP5Ha7LVgy\nPT14PLMwPW1d0O12BUqkWxIfQEJ8iqKgXC6LQ+nCwoJg4Sw0I5GIkOLb7TY2NzdRrVbhcrmQSCRE\nKtZut9Hv97G6uooHDx5gcXFRXEnZSeRDy1OCiAobRmww2e12FAoFOdF2d3cRj8eRTCZx69YtfPvb\n337qzXyuCkAa9fn9fil2AIgIk+1sGpLQ/40+bpqmIZvNCnRmtVpx9epVcRKKxWJIJpPCOTi9UakL\njMViWFxchNVqhc/nExIPmyn04WCqomma8EaI49KlH4CQgU6LZ5kiZLNZIc/TH4PfAf2YiU6QM0wJ\nFK8TCATQ7/dFrUIzdp4YFosFmUwGDodDDGkajQaazaa01dmtPO3Dp+s64vG4pC709KA7qcFgEM/p\nQCAgMrezrHNVAFI1fZoIRLRhMBhga2tL9Hej0Qgf+MAHcHh4KILMN954A41GA6urq5hMJnjf+96H\nw8NDeRD4pXNmn67ryGQyGI1GMlah2+1iOp1ic3NT3OYXFxfRaDSEgE+HTRoh8v9LpVKIxWI4OTkR\nDjId/xuNBlKplGDolCRpmgaz2YxQKIQ33ngDb731Fur1Onw+H1555RXUajVUq1XE43EZlUZFiNls\nRqfTwdbWljgf0aB9MpngjTfeQLVaRT6flwfObrfj/e9/P/b29kQuFo/HsbCwIKcJYUy2thmVGY0p\nA6NSh/fIarXiu9/97lPf/3OVZvziL/6iGJvE43GRGSUSCZjNZuzu7mJpaUlAe6YDwPfzYFIc19bW\n8E//9E/Y2tpCtVqVL/zk5ATpdFoMx9lsISpC5/tut4vj42OxxCUaMZvNYLfbkc1msby8jPl8LmQn\n5vd0NZpOp8K1LpVK4uHm9/uh6/oTNgaFQkEYf0RZCEeWy2WB6pjTMzrT9UhRFKk15vM5FhcXZcQD\nJVynR6jR9Z/ccVVVEQ6HATxuXtXrdeGeUC42n8+xvLwsn81sNsvpORwO8c477+Dv/u7vXqQZAKRa\ntlqtODg4kBzO5XKJApgiTrvdjvF4jKOjIzka6bTT6XRw7949gdXo3EMJFFUfmqahVqtJE+a0dzPx\nVvKQKRsajUbC0aChI8WyrOqB76uY4/H4EyMpPB6PWOJSAkVbWW4en8+HSCQiEiXaHhBloGCVAzx5\n6tjtdng8HqFw0t2ID3+z2ZQZJYQZ6T1HB1Cv14tkMomLFy9KuhGLxeB2uxGPxwXLJr+lWCzivffe\nQ6FQkI39tOtcbWYSVjiGgcQZCjoBCIuMzkH5fF7ojP1+H+12W3RvFGDu7e2JpRejpa7rYgnb6XSQ\nz+dRKBSEmH7//n1xv9/f38doNEI2m5Upp8wx5/O5yPLJYMvlcvjud7+Ler2Ohw8fotFooNfr4eHD\nhxLh2WqmUqRer6NarYq1Qa1WE8potVqF1+vFbDaTB42bs1KpYDAY4N1338VwOEShUBBvvv39fRSL\nRbEDoCnlwcGB/D7HDZfLZQAQxfo//uM/irqbLW6OgWbD6uTkRBpSs9nsTCkGcM5yZip+6dHGo5FE\nnGaziel0Kq1bjjOzWq2IRCJihghAzGJOowGcl8cpS8lkEslkEpqmSe69uroqptpUrLBgNBgMT6ig\nfT6fFJAcE0y3e9I3aStLb4xAIIBIJIKHDx8KVbPRaKBer+NDH/oQer0eyuUyIpGIpBos6Ng2pk0X\nCUitVkuaHZR00RycMN1pr410Oi2+IRQSsOAkkevatWuilo/FYjKFgDwN5vA8pVRVPbNz/rmLzBzr\ntbKyAlVVRRd42p2d0v5AIIALFy4ITEbz71dffVXmcwwGA1y+fBlOp1MomeyG0VetVqthcXERHo8H\n2WwWR0dHUrQBEAirXq9LDkrGGTFY+kVns1lx6PR6vdICNxqNuHbtmhDdORXr0aNHMBqNiMViMoCH\nLqNWqxWJREIIUizimHrQM89qtUpuqygK1tfXYTKZcOHCBWn3kzNC9CcWi8lUgFAoJK9D7jINy61W\nKyqVirTkKXgdj8c4OTlBvV6XqbixWOxM9/9cbWZugk6nI9wFGmCbzWYxvmZTguyuUCiE3d1dkfqT\nqwt8XylBz2U6ftZqNcm9AUjjhCQdCj/JLKPJNkckkC5psViwsrIiFEiSeZgWcCIUB9zQSKVWqwm9\nle1wuiNRlMA0hjk/8+vTm5tsuVQqJf555JFQb8huJUlKhOkI4ymKgkgkgnK5LNfv9/vSWInFYsLw\noydIvV4Xjjhd/vldPu06V5s5Go3C6/ViZWXlCaMRgvbMZzlc5/TEJI48o98xu3PEYwnPcWOQtA5A\nhqyz+CJpnr5ujMZEStgoIQRGf4xUKiVKECqoqWWkIsXn8yEQCAj7L5FICMmfpCJa0rrdblFQn74m\n549w3HClUkG9XpcIzROsUqkI6kNTQ7bY6eTPz8V0iUWp2+0WwSrb/QwQBoNBhMBEV1RVPTOf+Vxt\nZk7/JNREAgsraY/HI5wFXdfx+c9/HgCk6cH8mlTPzc1NmEwmpNNpHB4eih6OnAUA4s5P5IREH5Jt\nKJ4Nh8MyYJ4mMcSaiRzQoZ4uoNz8jLjj8VhMa65evSqUU13Xsbm5KTk029rA43b34uKi0FiJYhDS\nm0wmuHbtmjRzIpGIyL3C4TDS6bRQYtPptBTZuq4jnU7LJk6n009Yh3FqFsn4brcbyWQSNpsNqVQK\nABCLxcR2IBKJvEgzTi8y0AAIBkqZPAWmnPtntVrxO7/zO+JSxHSARaLX68Xdu3fh9XqRy+Ukysbj\ncbTbbSSTSSHoMzqRHMTjm6aCHKvATiBbwMRf3W63MNBoOkPTFLbE6Qr66NEjTCYTlEoliezA45mH\nRDc4BZZGLxwlzAeL7WxqBknqZ71BrJndPhZmJByR5sphmb1eD9lsVmx4AQhSQufQdruN/f198dkA\nHhtO2u128fujpdrTrnO1mQmtud1u7O/vo9PpYDweo9PpoNvtiiR/Mpmg0+mg0+ngzp07IlAtlUqo\nVqsYjUaiiqa9ldlsFsUFrb/G47FAfr1eTzaTpmnI5/NigBiPx9HpdOT1OLMvl8vBYrGIsrrT6Qj3\notlsCqRXqVSk/X6aqklv50ajITO/6VaUy+VwfHwsMi4O/aFiJp/PS3ODWjwA0sbWdR2lUgkWi0Xg\nS9Yf5XJZUpdWqyWsOOoGOeCdDzvHRYTDYcmL+ZDzoWeOf5Z1rqC5drstUZEypUqlApvNJmJVqrOj\n0aiQY2g/wP9yuRysVqvkjSTN8Pg1m82w2+14+PAhotEoTk5OhCBEWRXhLp/PhzfffBMXLlwQrJZR\nmYqSfr//xGy+TqcjQ4bocD+dTpHP57G6uipWBZzUSmopALFXsNvtQhUlFZWnDjkcNFPke+ZnZS1w\nfHyMRCIhAtdwOCzjKciem8/nAhOycK3X6xJ1aVxDG1s+LFSRc6ptrVbD7du3z3T/z1VkJkur1Wrh\n0qVL0tlKpVIynow8DPq03bhxA6PRCJcuXZKi8bXXXhP0wWq1YmNjAxcuXBAaJ+VQkUhEIKvV1VWh\nbA6HQ2xtbYk/xdraGhRFwXQ6FUYcSTj0k3Y6naIC8fl8osKmj5zL5cJrr70mkBt5HGTIEYNWFEVw\n4kAggEwmIxyIeDyOdDotci2z2fyESTgpm+wAbm9vA4DQOEkP3djYwPvf/37pGGYyGbhcLsTjccH2\nX3rpJZl/AjxO+5iDMzAYDAa0Wi3B8zc3N890/8/VZqYdVjKZxP7+PsbjMbrdLlqtlhQqp3O48XiM\n733ve/D5fIIN7+/v4/DwEB6PB4VCAcFgEKVSSYzGgccPDbuA5E1TmU2UI5fLwefzodPpIBKJSCvc\nYDCIiybHp5En3e/3xWwQgBCmOHqs2WzC7/cjk8mgXC5LC5yYMTuFGxsb0DRNuoMcBMTimH51TqdT\njFuA7xdk/96ilotQYC6XQy6Xe8JDhLxqkpUODw9lCi0NcEajkdiVjUYjpFIpQZF4/86yztVmZh63\nu7uL9fV1YZaxU3X//n30ej1Mp1OJcKdJ9fV6XcZ+EX8ltARAmgws9DjagBuGm4eUTd585u9s6hwd\nHaHVaiEWi0lDgbyR/f198Xkmjst2e7VaRalUQq1Wg81mQy6Xe0JHxwlU3W5XGiTM3ff29sStP5vN\nynB4TnVlusHUizkzYUNGZTZ4ut2uGC6y8cHJVNzURqMROzs7UtyVy2Vxe6IZei6Xk9e6f//+me7/\nucqZ2TywWq3C7vJ6vcKj3d7eFjNw3jxK5r1eLxKJhGjn+BCQhEQzE47ppY1tLpdDKBTCcDjE8vKy\ndN/IUpvP57h27RpyuZyMKF5eXsajR49wcnKCixcv4tGjR9KxpKv+yckJPB6P0ERp9cWZJiQWUWvH\nTRgOhxEOh/HgwQPhhtA0kmaJS0tL0qVrt9u4ePEiarWacEZsNhum0ykymQwSiYQ8BEzR5vO5eMz1\nej1ppPBz00XKZDJhYWFB0hG271nscgC8zWYTAcR3vvOdp77/5yoy9/t9qd7ZCSQkxnkb9LXg2AJW\n7pQesTrnzGd23yjcpA8d/z96NAMQNyBW90xDqtWq0FH5b1iwsSA7PDyEpmnycFFlzQYKI9npiVec\nLlWv1yWqlkolPHr0CLquo9lsykwRPrj0Ful2uyInI5+aBCV+jxzUw2uxQGa6RRoo+R+kkzJNImGJ\nUZ7jJnhfKNkiZKlp2pnu/7nazJTNk7DDaEKVAzcr29ykTtKtZzweiw0rPSQcDoegDYyS7IaxvczN\nAUDswZjK8D8aGJIhRi9kq9UKVVVlQ5BoT5sD3mh29KiUYTFLLzs6B/V6PWiaJja7hPNIkOfoB9JI\nAUh+zejOGStGoxHdblc6e0RbWOSyhT8cDuXz0oOPRR253XRMYoFILjU7sADE+fRp17nazEdHR9B1\nHQ8fPoTH40EwGJRZ11R0cPAMHY9449kSZnt4NBphbW1N9H2lUkncjxj1CaeVSiUZndbv91Eul+Fy\nuYTSSWdQVu67u7virUylRavVEliNuTvFqFarVeZQP3jwAA8ePBC1C+E1pje6ruPw8BCKoiCdTkPX\ndZmBTdSEtgtsVoxGI5nIdRqLX1lZkWh9fHyMUCiEZrOJpaUlgSknk4lEfgDSlKHlLv8MQGweWq0W\nyuUyzGYzjo6OZAgnHVufdp2rnHlpaQnT6VQQCE3TsLi4KKMKWP2z7T0ejxGJRCSCcIBNuVxGMBhE\ns9lEOp2WnNrtdosymcdrqVRCOByGpmnw+XwyspgDI00mE27cuIHDw0M4HA50u11kMhk8ePAAuq4L\n7+HSpUtyfV3XsbOzA5/Ph62tLeFNcOAk57KQjMSRxfTC+9CHPiRFJolVxIun06kM3CTNNBAIoNvt\nYnl5WaawsnGUTqdFh0ieRrFYFH4HNz0jLvFoOigBj5lyVMFwFgrrjlAoJLNj1tfX8eabbz71/T9X\nm5nGg4PBAKurq6LLo5CTnhd2ux3JZFK+WI45oI1UOp2WPJLzTNil4nheFpVUiVDbxz//e9stFjh2\nu126lMlkEpPJBAsLCwAgrWhFUXDjxg3JQTmU0m63YzAYSCEIQLDp07NLstmsDO3h3G9+D+FwWJAX\nmoszpaHPHP8NlSg0syFzLxgMSlrFa0ynUwAQVmAqlRJmHgtVjhbmFIHTFgxMo86yztVmBiAwFnHM\n4+NjbGxs4O7du2IRUKvVkMlk0G63kc1mcfnyZQDA3t6eCErdbjdKpZIINePxOKrVqkBqpDjSQ44b\nirkij+VYLIbDw0NJb6hazufzwh9hi3k4HIpRIvN0q9UKAALFRaNRDIdD1Go1GQPcaDQkh6Umz+fz\noVKpyGg14HFn8bTnss1mk4JW0zQsLCwIhOh0OvH222/j4sWLYjtLG2CSktjhI1WA1FD6YpCQxdY9\nEY/Dw0MEg0G8+eabQtx3Op24c+fOme79udrM5ONSWsRRtxzlRbEnu1KhUEh0dyaTCdFoFNVqFbFY\nTLyOOZ2JZtjEnuPxuAy75BgJyugVRcGFCxfEsDwcDsPtdiMWi4mzEmEws9ks4lu/3//EhFQqP4iW\nEB2xWq3IZDIS1QjFUfFMtbXf75eagJg7H9ZUKiXeHhzXRh894tvXr1+X1j3FCGxwkClIm10GCipP\naITIojCRSMgEAJq+ZDIZsSowmUxYWlp6Ac1xsVjil0xcmKJKWlxxSA95GeRIcP407QN8Pp9U3iQc\ncQoqq3wahdP6ljedG5USLaIaAITDTKdQyqn4u16vF6lUSmbk+Xw+IblzzshpAj/JS4yYTIHYRCHi\nwNFs169fB/B4bIbH43lC5e10OkUGxUKYSM7S0hJ8Ph+uXLkiCh4AggZR2EulOk0VT898YXNnNpth\neXkZpVJJnEJXVlbOdP/PVWQmdsxpooS76KXBiMP8jmOFk8mktHtNJpN4FxNjJgRFVOP0uAhekwUR\nEQWOXCA6QAyVTkLk+Z7WBBJy47853TmjioVFW6FQkNOBizxqblwe/QDkIcxms4IZE30h35iWt7RR\nIEmI81wqlcoT1gHEv0+PY6MJ5WmfZ+LmJBkxL+fYN5L7Cf097TpXm5ljCgwGg3AjCEPVajXJhXmz\nCbUNBgNomiabt1wuY3V1VXSAqqqKRpCK42AwiIODAzidToRCIWiaJl7HbC40Gg2k02ncu3cPGxsb\ngnJw0tTS0pJwqXVdF88P4HGKlM1mpZ3MXNfv98vQTgBCrKKIlYw2n88nimmLxYI7d+7IBCiy5Kgw\npwzL4/HAYrGgVqshHA7j4cOHMimLpuDlchkXL16ErutPzA/ndbxer7gdLS0t4d1335X5MURuWHgf\nHx8jm80ikUiIyeJZ1rlKM+gVx+OURx5hOcqBTlfOHO8wmUyEDH86j+YsEP4ej0qqs5m+2O12pFIp\nWCwWxGIxOJ1OhMNh9Pt9MSdnk4LYNRsXdADSNE0IO/1+X8zRbTabzAyhkUqz2ZQNRtNCYspUoMRi\nMXlAaBpJ2ibfO+sMohCc2krfD9oykE7q8XgEp+dcFaZpRG+YlvA7YR1BzSS1lqFQSOoQzvQ+yzpX\nm5m8Bgo5mcMyHwyHw7hy5QpcLhdUVcWv/dqv4cGDB0+0pEmFnE6nWFlZkebK/fv3hV5JaRJzXEVR\nxJSbhCFOuGJ3jqpkUk9pbM6HbDQayYRWHrskRZFc32g0hEh0/fr1J+iVqVQKq6urmM/nGA6H0igi\nNs1NRyP0RqMhtgsrKyuwWq0IBoNiAEmWWzwel5MglUqJIFZVVfzYj/2YcC0uX778hDNpOBzGeDyW\ncXYul0tQIApeE4mE1AR8f2e6/2fbPv+3Fj0qiPcC35fVM4c7ODhAv9+H0+nEl7/8ZeHsMic9PUnp\nzp07osh+6aWXROlMeI7YM2cMxuNxGQ1GTJnFKJ0ygcdqjpOTE5FQTadTEYCyYGOBykYP/emo0Lhz\n5w4mk4no7LLZLPb29qAoCkKhkDRL5vO5TNfqdrvSyGHRR+dSOjSRv0KiFQtkGraTLGWz2XDr1i3Z\ntNlsVk4e5tlEQfgQlctl8Z8DIKdIp9MR5c5Z1rnazOTDsnihqySLNOKhjMLEdKllI9WTSmZiv8Fg\nEHfu3BG0gwNlBoMBisWioBrtdltkVQBk3MJpUg1TntPWXcfHx+IwRH0iCTvtdlvI8yzQKHqdzWbY\n398XSJDzTnZ2dqCqqrgc0Zb3NA+E+Tu/N3o2c4Qa0ZjxeCwUUH6HXq9XLMBIciIPhmQmnoycf0Kf\nEk3T5KHI5XLil/0spk2dq818+oukixDlU1Q5e71ewWdTqRQ6nQ4ymYwQzZlyzGYztFotJJNJzGYz\nmUfNAtNiscjsaMJOJpNJBqezEcAWtdlsFqsDHr/0jkun03Kq0AyG5i+kd3KC62nTc9rVciAlR2Bw\nFjUtxTgCgigP0xCKFZgqMZKSQVipVMSPgyPf+FDRMy8cDotQlikaAOE2U/1DVIjdPmoaDQYDlpeX\nMRgMXhCNTi/mliaTCd/5znfEkovVfrvdxtHREU5OTjCbzXDr1i2Z18HRvOPxWIbCs3PFCELslPTQ\no6MjWK1WDAYD9Pt9aYj0+33hBI/HYyHwsOlBVGA0GqFarWJnZ0dooZxq2mg0sLe3h3w+L+OMiUmf\nJsYTftzb2xNzlXv37klXstVqif0sJ3CxUdFsNtFut8Ur7vj4GOVyWQj4ZNNRs7i7uyvFJJl8pAgw\n3wEBbMkAACAASURBVCfllScHXyuXy4l4IZfLQVVVea29vT25/lnWubK0/exnPyszPyaTCVKplBhp\n5/N54f9S4ElYi0Sh2WyGaDQqjvOlUgnxeFzGrtntdhlgw1yZ5CFuanbkOp2ObGqv1/vEJCrax7Kb\nRvzW5/Mhl8thdXVVUoxmsynNFeD7Q3uGwyE2NzdRLBaxsLAgm56Ee5/PJ6jB8fGx+M653W5Rxvh8\nPjQaDSkUeQ2DwQC/34/BYACbzSYNIjqoMvryhOADylQCgFiX8dSiJS/puHQmZXvc4XCg2Wzij//4\nj19Y2gKPqZaE1HiEkRhEsWetVhM/YaPRiHfeeUfMr1dWVjCZTOD1esWTmUf76YHtw+EQ1WpVNrHT\n6RQvtdFohHw+L0gKrXMp16f8iEw0ku05SyWVSqHdbovukN4YnEESCATEmahQKEik41Aetoyp2mba\npSgKEokElpaWnnCxZ0OEUZKWCJx/wpEaxICZljEH53dDM5vJZCIdQLblCWlyVAWDCc1oyPVmzfK0\n61xtZhZA0+kUi4uLGA6HT1AM6R+cSCRkRO7Kyop4INOlSNd1rK+vizaP8iAao9AQhoWazWZDoVAQ\nqIz8EBZ+w+EQq6ur4gTKxROBuDDTBqvVCq/XC4/HI2gF/TNo4OjxeBAOh5HJZOD1esUAMhaLCWOP\nNgupVEp0e81mU9ryRGRY1DocDrjdbhnMTvtdjrxg7ttsNlGv1+XEYWrEjctxy6xVqtUqIpGIYPt0\nTyVDEHjcoTxrznyuOoC0t1JVFXfv3kUqlZKuX7FYhKqqohThOAI2AIbDoTjTl0olUWeQGeZ2u4UI\nT4NyRvdmsyljyJj30kWID5GqqsL3IBZM5TYV5ACEGjkYDAS6ozKEpiwAUCwWxdjFaDSKWjocDguS\nw3FudEUtFApQVVUmP3Hj7ezsyJxrm80mRC2aoddqNUmDer2ecL0PDw/l9OLDkkwmxffj8uXLIs6l\nlzXzerqH0kaY3dizrHMVmckK8/v9T7i2EzOl+yYH9ui6jtXVVaiqiu3tbYG4GEXZQAgGg4hEItLV\nm81mSCQSglIAj+E/2l0Nh0Ox/QoEAhIFSeek0yfVLaVSCS6XSzY/PeZovRuPxxGJRHD16lXpBnIj\nUJyrKAqSyaScGOSkuFwuwaP5d6ZinE3IJg0bLbQaSyaTGA6HSCaTklLQnisej8Plckkn83SEJ1+c\n/GXmwKlUChsbG4hEIlhYWJAUhj7ZZy3fzlUB+Cu/8itYWFgQQSZVGdT50W+OfhTNZhPNZhMbGxtS\n9NEkfDAYCMeB2C9J+r1eT7pkJLVzQCVHrNF2imkENwnpjoPBAAsLC1I4zedz8Wtm/klLLKIsfHAI\ngZHsw5mFpx8uQnZ0KuKIiFQqJUJVFnWUbXGsHCmchN/YCvd6vcjn81hbW0M+n5efMYc3m83Smt7d\n3ZV6gi6phN/cbjdqtRpSqRS++93vYmFhQYrqL3zhC0+9qc9VmsHJTGwGFItFVCoVpNNpOcr8fj9O\nTk4wnU5RrVaRy+XgcrlgMplQKBQQCoVw584dbGxsoFAoiBKaapCHDx9icXERRqNRSDdHR0cSYYgo\nEMel8eJp3JfyJHpLEC9utVrCSKMtbC6Xk03GlCQajaLZbIoTva7ryGazIv9vtVqC67J1zNkkRqMR\n+XxesHJVVVEul0XuReMbk8mEw8NDrK2tCbRZr9fFX7nRaEgXsVQqIRaLySkBQN7vZDJBNpsVBl+5\nXBbhATWbTIv+5V/+5Uz3/1ylGRcuXBCSTr/fRzQaleOW1E7Ov7ZYLEilUrhw4YIQzYk8ABDSDDFf\nOsgHAgFRWbNlfHx8DF3XoSgKVlZWsLi4KA8VI2goFJL3eXh4iGazKRRKRjWv1yuKbUZcdi8BiCcF\nuSB0J6KkPx6PSwePCEKr1YKmaWKaTuRA13Uh/xMz52vRXJzKagBSHLOFP51OUSqVJO0hdkwc2+Fw\nyPdOtTprBtIG2EJn95QUhKdd52ozExfd3t4WV06y0MbjMdbW1hAMBpFOp0Xd/ODBA6GFKooifGUa\nYJPuydY0N7yqqiLB2tjYeMKngvP1qOZgXkkLhOXlZSQSCSkSWaSpqirMu2azKbnwaDSSKMhclQbp\nlUpF+BJ0MTUYDAKxUYhAnzcSfChQpYM/8Jh1mE6nYTKZhLdMCC8Wi2E6ncqpAEDMJx0Oh8B1tCrI\n5XJPFLNEasbjMYbDoYgdUqkUHA6H6BPPss5VmkFGG7kWwWBQOLzk6dLxiPgqbyQpngsLC0Jaj8fj\nQpghAYdOQBSWskIn/Me2M4n5RDdOD5MfjUbS5qZCnLk157Cw+eHxeGTuCaG606Qe5rMbGxvweDwy\n4y8cDgt5ZzKZSOq1tLQkjSLCaQ6HQ+A1qqpp+Ag8tvhi4dvr9XDjxg0cHR3J5iTCwogfjUZl8E8g\nEJBZimwOcS6K3+/H7u6u5POZ/39m4tOucxWZOUuk2+0+ocKgYSCPZWK3dPikyoIOPcPhEBsbGwJ1\nORwOKVA4SZVRaGFhQfBSRtLV1VXYbDZpeni9XplnQn412W1Op1O83UjNBICFhQUsLy/L6AkqrMvl\nskRIr9eLeDwuhKjBYCCngKZpMg6CeDC5JSwWTw+mpPLb6XSKkToJWZubm/D7/RJBCQOSnTibzQQZ\n4dgLUk3r9Tp2d3dRqVREjsVrTqdTLC8vCz03n8+f6f6fq8hMnzSTyYSDgwPEYjG0220kEgmUSqUn\n8NhoNCr8hmaziStXrggZx2Qy4b333hMMlBuRDvP7+/twOByCDZMyStYZldnT6RSBQECG+xwdHYk2\nkScFAOzu7koTwWg0ygZQFEWuSV8OdhE5aJKuTNzIw+EQpVIJkUgENptN1NA3btyQNnShUBBVeL/f\nF2NwkqpIrieuzW5nIBDAyckJFhb+P/beLTbSPL3r/5arylXlOp/P5VP7ONM7vTudmU0goNVqFy7Q\nKlKkSLlAueAKLlAuSBRFynVyESkiCC4QCFbiApCQYEECMayiCHazhJ3ZOXS7u30sl+vkOriq7LLL\ndvnAhfk8W0bZ/x+1RQBrXmml2e62XX7f3/v7Pc/3+R6KNjyJRqOGuFCSEFeBFAzUB5SiXq8rHo9r\nf3/fNhye0UOuRwXN/eZv/qaR4mHJoaqGNENSFHUwix2lxOnpqXlBYKgNOoAqA3+L8/NzU6lIdx4S\nJFmxuCTZ5I/dEx5COp22IQ4j5Xg8brs8BKbJVCcWKM0gHBFKKCA5GIJnZ2c6ODgwk0L8O1qtlhKJ\nhFFEUXNjAoNpO5AaGPX+/r59HQFHZIcDH0YiEX3++efyeDxKp9M2WOl0OorFYoZqFAoFvXjxwryz\nw+Gwfuu3futLboYk8xXGj5ma9uLiwngHNHZYQoHhThoewjGAs8zNHY1GVm8zoaO5mbR57XQ6ku4a\nKlJSSUIl5IfoMXYmzB6xJ6A+xZgbxQbw3eSOPIkiMG2UZIw2Yi+Oj4+NRw3V9OLiwhpKBiKMvOG1\n8MLx/zFKRxXj8XhUqVRMqS7JTpl2u22oCZNHdJSj0cjuC5vEQ65HVWaApeL/i48aGR5AXTRX1JCQ\nkvCIe/36te2C0t3OCse53W6bHB989OLiwrp27MGazaY1m5NwFcdwOp02fLnRaBhsBQknGAxaeA6j\ncDBs6c6FiJcsHo/bQl9bW9POzo6dQNTs/Kwf/OAHRryiiaQEq9frxi9BaJvJZExZTWjmp59+aosf\nBQ088ZOTE4vcCAaD6na7+qM/+iOTRU1a/iKmBdkYDocPev6PajGPx2P5/X4zFs9kMmbRitlhs9mU\nx+PR3Nyctra2LFSGkW2j0VClUlGhUDBWG2XA1NSU0um0arWaGQqm02nTwSGtJ4zm/PxcxWJRb968\nsZ0UBIW01pOTE21ubiqXy9lRz4I4OTmxn81iRiR7cHCghYUFM1wcDAZaXFxUvV7X1taWcUKurq40\nGAz04YcfajAYGDcEqdPx8bHVttxDIoIvLy9VLpdN5EDCFS5PwIacFIFAwHIW8b6TZIOnQqGgnZ0d\ni5KDyFStVm2Y9ZDrUZUZuExeXl6qUCgYHZHaLR6PGwx0eXmpbDZrSEIwGNTV1ZWcTqfW1taMk8ER\nDuQ0Gd5DCGQ4HNb8/LztvJQT/X5f9XrdKJ4EU0qy45hYBwg4RDYwVBgMBuat8eGHH+r09NR2MIYP\nvMT7+/u2mzIux7WJpo5YiW63a/wOlNk0zIFAwJrZcDis3d1dTU9PK5vN6vb21r4XfI/BYKBcLmel\nHSaNmO7EYjEb4AAlTuaRz8/Pq1gs6v3333/Q839UOzOYMdIcpnx+v1+tVstSV4Gsnj9/rt///d/X\nX/2rf1UnJyeamZlROp02lx3ssvA2BgtGgp9Op1UoFFStVq3hg2VHA8XnII4XjJWGB0uBSQXI7e2t\nksmkUVm73a5CoZCq1apWVlYsF+T29lbFYtHKJklGIMKWAIEs3htg8cfHx0omk6rX60qlUiYvY3rJ\nv3G5XJqfn9fZ2ZllsZDZ8vTpU9Xrdc3OzioWixkkNz09reXlZV1cXNiOjfsqpdJgMFAikbCMQV7G\nh1yPajFfXV1Z3XpycqJ8Pm/WsGC0+XxezWZTwWBQf/zHf2yZfhyLZGJL0p/8yZ9ocXHxnoGhz+dT\nrVazxhHHe1QUrVZLxWLRRuAQiOAiS3fQFAqSTCZjnF9UGePx2GIjhsOhfD6fut2ugsGgdnZ2bICS\nSCR0enpqpivYZAG7sfjIUsHOlgCio6Mjc7VnbI/TEvxuLGyPjo709OlTjUYjlUolbW5umr9du902\nQWypVNLt7a0+/vhjK8FAOra3t/XkyRPzZq5Wq9a0S/rSOX/yAnyPx+PGiQDKYlGgTsYK9itf+Yo1\nPZPkmYuLC4XDYTNGgSoJAYjIMXwpUKHAgZbuFv7kqBb2HKUOPA9ODZQpuCRJdzttq9WS2+1WKBRS\nMBjU4uKi6QHxnFtYWLAEWcS84NCSzMkIDJohTT6f1+3trU0qIfZHo1G1Wi3d3NyoWCyaMWQgENDn\nn39uv8vt7a1SqZRGo5EKhYJqtZp9XSQSUSKRsASA9957zzw8mDRGo1FrsL+cAE5cg8HAohQQjGKa\nDQbKkIQ6uNVqWQNHcGSv1zOLrePjY3W7XSsDgPmmpqbsayuVikmNCKHhZzAxY/cFUYH/gdP84eGh\niQVqtZq2trZMXeLz+XR1daWtrS1jqYEtQySq1WpWr8PIgzTfbDYtK5BME478g4MDS5FCuIqYFX5G\ntVo1sTCNL9HDZ2dn2tzcNC9pToder2efA2HrcDi0zy7J5FxsOD/+8Y8f9PwfVZnBtGp6elpPnjwx\n8SU1G1zfVCqlk5MTlUola8jYZYkWo+uHEx0MBq0hYvhB9O/t7a2Nugm8nJ2dtUENjkE0ZhgmQrXM\n5XLmk+dyubSysqJMJqNyuWz1rMPh0PLyshqNhiEvmUzGYDvkVHCIx+OxmabPzc3ZLk4pBCcEwlE8\nHjfLXU6hRCJhdl6Xl5c2mAkEAqaCDwQCFoEBud/tdmt1dVWxWMzSWMkjJ/9EkrHyvF6vUqmUlpaW\n9F/+y3956+f/qHZmvNDgIUxNTWl1ddXwTeiImJxAJPL7/WZ96/f79fTpU/l8Ps3NzandbluTcnFx\nYWGWPBRMtzk6JVlYDcE2+Kzxd9PT00qlUkbex0kTdcz+/r6Oj4+tnuZoBjO/vr7WysqKnTT4Zvj9\nfjkcDhuT93o9Q1hQkqDvg6NC2QNykkqlTFyQTqdtVC1Js7OzkmT9QTqdvsdXSSaTCoVCFuxJ+Dww\nHgaLMzMzpja5urqyyLovBa0TF1M78FySVPEN7vf7lmfSbrf14sULG4ZMOoPyP3w1WHCQ7jEEZyDC\ndIxSg52NgQLjZVQmjHYZkMRiMStNMFWMRCJmdUsQEPazp6en2t3dNdgQQr7b7Ta7A6BDVCzwMFBB\noy8E5Ugmk4ZGYDuL0TnTQhiDxB8DETI13NnZkSRTo3MfmbAeHx/by1WpVCTdvdydTkfn5+c2OX3b\n61EtZnYpqI/YZCHvHwwGlrW3sLCgTqejzz77zLR4MO6o6RgXS3cYNosTiI+xOTsZmPXOzo6azaZF\nAu/u7hrlE861JJPfMxKnYaTzp1YnCUq6c/u/vLy8Z7sFnr6zs2OSrJubG33yyScql8tWElDng9ww\nWnc4HNrY2DAOCajC4eGhxT/QxJ2enmpzc9Pw/JOTE4ukC4VCOj09NQopAmCcRImo6HQ6yuVy1kOQ\nrgUR6m2vR7WYaYYYkdLgTTaGeKYNBgMLRkfRTQQC7DscRCkNLi4uVK1Wtb+/r5OTEzkcDvn9fvMp\nvrq6MpI/DqDYbEky1Qb5fcBllDo0VDRi19fXRgll1E3eH+Yzh4eHtqOBi7PzIl6FhN/pdIy8hDyL\nE+Xw8NBM2Tm9mNz1+31rotm9+dxktVD7M0JHuQ7HmYxF+B1MOXu9npaWltRutx+8Mz8q1tzf/Jt/\n8x5XORQK2XDg8PDQXIQwII9Go6rVapqentbi4qI55wPo4xrqcrm0uLho5i84A2HKTUODeJZ6t1qt\nWt0cDAYtjy+fz5vukAWOBx0TuoODA62trenly5daXV1Vp9PR3NycNW/xeFyhUOget4S6nIYVEe4X\nX3yhUqmkaDQql8tlOYm5XE6VSsXw6kAgYIaRsPRguE2G8SACKBQKcjgcqlarWlxcVL/ft7KINFaa\nalzyMYFsNpt6/vy5/t2/+3daXV1VPB5XrVbTH/zBH7w1a+5RLea//bf/tvr9voWvr6ysaGtrS2tr\na0bQp4ZlNC3JFMUEPW5tbdl0a3Fx0VQicA0mIwuoQfFfG4/HymQydmSC4bpcLuP1zs7OamNjw4wI\nB4OBjcalO2QFCAsUBQkX37vZbCqbzWp6etoMDoHFKA0wbzw9PdXCwoJOT09t+NNsNrW0tKTXr1/b\nCQNVE4emzz//XOvr61aPM5on4hjHe4hFoVDInE7fvHmjcDhsxjBQbZvNpoV0zs/Pm4fGzMyMqtWq\n/u2//bdfLmaHw6Hf+I3fkM/nU7vdtrExJteVSsVgum63q0AgoGfPnum73/2ufv7nf96OTEk6ODiw\n6Rxw1NHR0b0x8OHhoVncwsBjZ0dXB456cXFhXyPJUqD8fr8duSAOTPAQrAaDwXtxDvhNEKGGfJ/G\nERYfnm/SnWo9m80a0YjskSdPnuj169fK5/NGvkcmlkwmLQaNkoeMwVKppEajoQ8//NAI9plMRs1m\n0zJKJksp7BuQo9FUJpNJM5/M5/N6/fq1/sk/+SdfWg1IsoXBsGN+fl6bm5uSZOhBOp02N8wf/OAH\nur6+NtPBqakpg+Omp6f14x//2F6EQqFgolMWxWR88c3NjSko5ufntb+/b3AZjkXwfV0ul168eKF3\n3nnHQi/BnLHzgs2HCAA5V6VS0e3trarVquG7e3t7Gg6Hmp2dNUyYhFpKGRAFpo9QRs/OzvTJJ5/Y\nyQFHIxAI6MWLF5qbm7NwHho8dIYff/yxYd3QZsnNLpfLWlxcNE72eDw2i956vW6cbsQH/X7fntXb\nXo+qAaTJw5aKDpqRNLEL7BIELYZCIX3wwQdmDNNsNm0iSKAlrDciw5DMY2fldDr19OlTeTwe7e/v\nKxaLGVbd7/cNrgPjRpUMHMiixiaA0gOxLG5MeD7TLE5NTSmZTFqCVCwWs5dKkpHecXYCp8b3GWd7\nj8ejbDarcDgsSWYJJv20VIJdCKQH3Al6go+0JL377rvy+/33Egagkp6fn2ttbc3gTU64L60GJi46\ndUlaWlqy/x4Oh0Zqh0+MGw+6NcxJyOwIBoOan583ExfySyZJ59SCk9klRC4gayqXyyaDwphbkpU7\nbrfbLMJarZbV3/Pz8/fi2mj0iCmbzN0GSjw6OrIMbORZTPomVeVQXcPh8D0eSafTsfvB1zOev7q6\nUr1et8Xb7/dN5gV6wqAI7SI7Mrs1UCUcZvD9yayTh1yPajGTMX1+fm6SIaAl8v/gUQwGA11eXtqw\nAFk/QxBI85IsP1uS7YjsXDc3Nza5witDupPnu91uUymTq4KqgloTzNjlcimXyxkP4+joyH6XyTEx\ntTW1MWoXPgeDCpfLZeUS3sxer9dKAVTatVrNeNGcNrwsg8HAxt2kEnA/2IFBLiBJ4frfbrd1cXFh\nuDYkMOr1s7Mz+Xw+S4VlKPOQ61HVzJeXlybzGY1GCofDKpVKKhQKpupAlQ0/o1qtamFhwXgXV1dX\nWlpaUqlUsqmf3+83qI96EYNuj8djzc7y8rJN6wjpwWbg6urK0ArGydls1hrEqakpq4G9Xq/V/Jg/\nYprOFHF9fd0cORkTU4YABz59+tTMcCgrUqmUKpWKMpmMPB6PMpmM3G63RVEwvPF6vXr27Jmmp6ft\n/sViMWs0pbsBTigUMuYbqndQFVQpKNdBWeLxuL2gcNCRXT0kbvhRLWa6bnZAajjMsnElglYZCoVU\nKBQ0NTWlWq1mIZbS3YsxGAwUj8eNuEMDRn1HQ9lqtSx4HX9j/NlwFIKvQZ3IiJtBBAqOarWq9fV1\npdNpGyrAsON0mZqa0ubmporFojltTpoholgBcoM7HIlEVKlUrNxA2/c/u/KzcGHX3dzcGKyGhzLl\nBAxDyiuSqLrd7j0LBHgsk5xlzM7z+bycTueDXUAfVZmBMTZ5doVCwdTVTOI6nY52d3et3j07O7Mj\nVrrjRL948cLcMKenp21owK6FbApMddKai+D5Wq2mhYUFWzgQaSYV5JNN3cLCguLxuJaXl3V2dqbB\nYGDj4MPDQys3AoGAisWivbDwgj0ej+bn561Uur29tZrZ5/NpeXnZJpxgutJdmdDr9TQajZRMJi0V\nFnx6MjeQETh4O6cEC5sGjs9cKBQUiUQUiUR0fX1tbp/j8dg8nwnJlGTUgbe9/twX88HBgb7xjW/o\nnXfe0bvvvqs//MM/lHTXvHzrW9/S8vKyvv3tb9+Tnf/u7/6ulpaWtLq6qv/4H//jz/ze5+fn1uhc\nXl4a2M9OSE345MkTTU9PmyIEZhv5HzMzM8ZnAKumLoaUz0LGZ8Lj8RgLDLiJYxsLK1CPYDCoWCym\ncrksv99vxBzkWrh/QtTHC4PPj5sQZQ9+du12W6enp7aoafS4D8Fg0Awik8mkoTpo+/x+v0qlkjKZ\njNXHDFJQumMoSSMsyZpFaKg+n0+RSESDwcDuK6mvIDE+n0+ZTEaSrJYvlUoPWlt/7ovZ7XbrD/7g\nD/Ty5Uv96Ec/0t//+39fr1690u/93u/pW9/6ljY3N/XNb35Tv/d7vydJ2tjY0L/4F/9CGxsb+g//\n4T/ob/2tv/UzCSk0dJi2MJ6lOyddCbx4Z2dHR0dHGg6HajabOjk5MbYamdD9ft94CmDYkuxYR16P\nNWylUrFdFNZbr9ezSAjI8wxEOMrD4bAuLy+1t7envb09Iz+hREEpjZNns9lUo9GQz+cz5TRihL29\nPfPhIICnWq1aWla1WrVhBtNE0AaUIjMzM8Y5IT0Wtl+tVtPNzY3trsi88AihoZ5sSmkGgTs9Ho/2\n9vYsEazZbOrly5cPWlt/7os5k8no2bNnku7qrrW1NdVqNX3ve9/Tr/3ar0mSfu3Xfk3/+l//a0nS\nv/k3/0a/+qu/Krfbrbm5OT158kR/+qd/+md+b2rkZDKpWq1mSg+igeH9Hh8fW14JuwT6PB44F7Uq\nxyzknEnKJC9MOp22qd4ktwHMGPdMmh3qx06no3q9bvgtHhaSzCh80jmIqeRoNLLPJcmQCmiWNzc3\nJhXDM4/UJxh8w+HQyhGaT0hVjOeZMnLP6DlomMH0+XpKKshehAxNTjkJJgKvhvf8kOv/aANYLpf1\nk5/8RB9++KEODw8tJDKdTtvot16v6+tf/7p9DTqzP+uKRCI23FhZWdHl5aVmZ2cVCARMQ4dXAxo1\n0kHBglFGYBPLg08mk/L7/drb2zO3+FQqpXg8bo5A1KqTKufhcKgPP/zQ4sykO2ppMBi0YzqXy5kH\nhcfjUalUMuRgfn7e9IWYJDqdTkWjUUUiEXuREAjgWgTllEWGVg/ob39/X6VSyULsJznUOButrq6a\nqQ0vDChILpdTIBCQx+PRO++8YxZiDKeWlpbMJZ8XFI4JSVX49xFB/Iu/+Iv64z/+47deT//HGsDh\ncKhf/uVf1t/9u3/XHioXsM7Pun7W3zH3B/f0eDzGBkNJzS7Jg1heXrbMwFarZSHtt7e3yuVy1tnj\n+0AZw2SLnRtUYDgcqlwuGyOPYQoWVZeXl/fKj+FwaDh4NBo1jR66PTL4GLUfHR1ZLcrvzOdjwYJx\nc8IQcImuEaU394adE14InGlwcvBjSbaDoydEP0gNPYmGgEHX63WNx2OFQiEb6JAy0Gw2rab/WZvU\n/+r1f2Qxj8dj/fIv/7L++l//6/qlX/olSbIwSUmmIJbuYrZgt0l3w4h8Pv9nft/PPvtMf/RHf6T/\n+l//q3Z3d43TAImIiR5wGKlUbrdb1WrViEW1Wk1Op9OoogwwUCxHo1HV63VrmiaP90AgYDtePp+3\nRYqZy6S3Bt7L0WjUOB4gA+jzcP68vr62cEr8Npi4dbtdK6NarZYKhYINZySZbIxRP58HJISBjCQT\nrE7aiiF7gtQPzAj1lN2Xe0pyFYseA/Xj42Pt7u5aYkAoFNL19bU2Njb0/e9/X7u7uw9aV3/ui/n2\n9lZ/42/8Da2vr+vXf/3X7c+/853v6Lvf/a4k6bvf/a4t8u985zv65//8n1tztLW1pQ8++ODP/N7f\n+MY39M1vflM///M/r/n5eXPNx+YWUxO/32/6tFwuZ/wIgnEoQYh5gPwjSYuLi5Jk8J8kU4NIuleH\nM0xh4bDjQwUl8mFSLSL91ESR3Z8xL/In1N1Op9O0fKFQyAY5l5eXFgyPobn0UxwZF1RwaKfTaXo+\n+CCSTKDA55j0gGYIxakK8y+Xy9nJh2SKvyNiTpIpgkqlkt599139pb/0l/Tuu+8+aG39udfM98xx\nVgAAIABJREFUP/jBD/TP/tk/01e+8hV99atflXQHvf3Wb/2WfuVXfkX/+B//Y83Nzelf/st/KUla\nX1/Xr/zKr2h9fV0ul0v/4B/8g59ZZlB3n5ycqN1ua2VlRbVaTZlMRtvb25LuOAjcVBAOGh6MWSDs\nN5tNhcNh9Xo9pVIpayrT6bR2d3fV7/f13nvv2SgWolMmk7GjM5lMqtVqKRAImAkiaAQE/XK5rFwu\np3K5bH5tFxcXJicieIiR+szMjPb29rS2tnZPU0gdyiiZzyTdyb7a7bZZffFZLy8vbThDmlQsFlMq\nldKLFy80Pz9v3BCSqlDUoLHEngFnJjyql5aWTHwAQjI9Pa16va5YLGZ+ITiZ/uQnP3nQ2vpzX8x/\n8S/+xZ8Jrf2n//Sf/sw//+3f/m399m//9v/v9wYeOzg4UDweN/PB8/Nz+Xw+NZtNc8HHBLBer5sa\n4vb21jga7Lrs3pPB6JFIROFwWO1226AmHOPPzs4Mbms0GlpfX7e/Y3zLFO/6+tq0c7u7u2YgHovF\nlEgkTOgZDoeNooq3HOQdyFTYF7AjMl7mPlB7w43gM5F9uLq6qt3dXcPUz87OzJGIxhBkKBaLmUcd\n9w31uNfrvWcZDFwo6Z7NwPHxsUnOyCWk1Hnb69GR86U7oejW1pbV3TMzM2YIyBEejUZ1dHSkg4MD\nra+vGywG5looFPTq1Ss9ffrUKJTsZHT8KJij0aiq1apNtOja8d6Ix+Pmst/tdvXOO+9oa2tLLpdL\npVLJTGKcTqeSyaRhtkwgWQCUFF6vV9vb25qbm7PPA+wXDAbl9/stww/H/g8++MC8n9k5V1ZWtLm5\nabX03Nyc9vb2zGwRXSAKd/IPIU1BjGICifYvnU7r5cuXisfjVqrt7u6qWCyapzNsucl0q36/r7/3\n9/7el2bjksy29uDgwDBSxKmMr8FfSXNip8NgnCRV7KZAAbrdrlE6JwcxTqfTeA/wQEhZYoBSrVaN\nCUeJ4fF4zDeZBXZ8fKytrS1JUjabtchjJFUOh0OtVsusaMfjsXkcSzLIEcoqglK86q6vr+17eDwe\ncxmiRoaiycidckKSNdMgMUw4pbsXlOELKAnsQoYoDodDnU7HDCgpLZrNpv0Ztf3bXo9qMYdCIYXD\nYZPGc3yxYBiasKvijomRCypiSPKgCRD6x+OxvvrVryoWiykQCCidTptKhKYREjxpVR6PR2tra0ac\nd7lcZmhYLpft8yHTyuVyCoVCajQatmsB3fHvCGZHvYGA9+rqSolEQn/lr/wVc//EcRTi0GQD7HA4\nFIlEzHi8VCqZs9Ht7a2ZxrBwQVXQUlJ2gUow6vd6vcb14EomkyoWi/aM3G63jefBoB96PSrWHEGR\n5+fnSqfTCgQCevnypZ4/f27cgnK5bH9PyhR+F0yn2LGZHCaTSZMLYUsLFgwkxctBWil469zcnDY2\nNgw/lqT9/X0j5YMobG1tGe8CUj7MNqRJBD+yUyOvYlqHl8dHH31kxpDU1S9evJDX67XfGYEuO/H1\n9bXq9brVuE+fPlW1WrWYNghRu7u7SqfT9nOZhHa7Xc3OzlqTyaCHewvsyk7sdrv1+vVrw8OPjo5U\nLpcf9Pwf1c6Mp9z09LSGw6Hxl6Fr0ojAXd7f37fuHosqvIlZ2Ni1EjdMHl4sFjMbqkni/M3NjdnX\nDodDU4LQMDLlI1YBdhzWACwaEmA7nY6hJfl8Xqenp+p2u/L5fFabM70kObXdbpt7E6E7i4uLxmCT\nfkpx5fNy9DPBu7y81PT0tPn1STJFTSaTsUxC+giITkxCZ2dnzV0KrorT6VQ+n5fD4bApIA0pJ+BD\nrke1mI+Pj1WpVGwChiczxib9ft8WErzeer2uy8tLffrpp6aQ6PV6xvCCfL+/v6/z83NtbW0pFArp\n4ODAamccfCATYZpCaqoksytgR0VmJN0Nhs7OztRoNAzPZaLI6LzVaqlcLuvs7MxsE0jTwqlpd3fX\nXDdRn8B33tnZUb/ft98PRARaKTEUnEacCmgSUaCj4sGxFC8RbLYYrrx580Yej0fNZtNIR8CDkqx/\nAMLkXj7kelSLmZiD1dVVVatV66ThyyYSCcNw4XEgcs1kMubzQEMYDocNAmMQEAgErHuX7rgjDofD\nsgAJuoxGo1pcXLR8P8jnCEcnr1qtZrtmLpczO1lGvpFIRPl8XvF43MxfICnd3t7K5XLp6upKq6ur\nkmSnEf0B1E64GsQjs2vCtKOxm8ThEfZi4ogNAtkxk4Y0jN0Z5kDqx6OZoRBcGVAjIEfu6dtej6pm\nxrMByRGRuaQ/jcdjlUolg72+/vWv6w//8A/11a9+1XBUsFOv16uf+7mfsx0Gd57p6Wnlcjl1Oh0V\nCgXl83kjRcFnoPmkXpyentbs7Kzp4ZiUgUKsr69bLBsRvZPh791u18QAwFvvvvuuwY3gy5OGMSAb\npAHApIvFYobt5nI5HR0dmQQrEokY3ZW0gUAgoMFgYJRa3EHr9bq+9rWvaXd3V06n01AgeNYIXeFe\n02ROxjcnEgnjj6fTab169epBz/9RLeatrS2rYcnaODo6ktvt1vb2tqEPqEv29va0v79v6VFEepFX\nsrW1ZXASNTQ6Nka7TNDYVSZJUtFo1HyXMQhkCkYiE6UOkiKv12tJVAQESTJtIVe73bZFj3xpd3dX\nsVjMyqnJ6SBjbKaCWHgNh0NVq1VrJoHeONFo7sbjsdkmlMtla3TPz8/VarWsiUulUiY2mJ2dNZoA\nFw6mWN6iiL+5uTFn0Le9HlWZQZoUOyQLR7o72rBZJbfuo48+0vr6ui4vLy1GgRGzz+fTxsaGmRTS\nwc/Pzxs1FC8JpFJAgQ6Hw0zCKTGYcMFjpjkklQq47fr6WplMxhyRcP5BnQKTjUaLF4rJHGIB8hAx\nRoRngdoEJUooFDJVD/cGm18YcvA4+v2+cUVKpZJN/Og1ZmZmbGcHGsR2gKFOIpEwYtLe3p4JIeDF\nPOR6VIsZm1V8MaampnR8fGwNIJgyYTKQc8bjsUmdGLOSPAr4L93xOvb39w26Y4entry8vNRoNDKm\nHWoReNQYc0NPBffFcf/09FSpVEqtVstqe8oNsGR8lWkWceJHcHt2dqZgMGg1M3Itdn2Hw2GjZ0mG\nNGBZe3Z2ZtNKaKtoAmHLsXtzz1nw3KPb21sbxU9PT5sR42AwULVa1fT09D0va+4PSMvbXo9qMVOf\nSTJpvcvluqeGRotGbEG1WjXnT3gK0CX7/b7Z4PI17MJnZ2fK5/Oam5tTOBw29humg5JsmAC8hZRK\n+inLjGgykJOTkxMjy5NqKslyAmkkM5mMfD6fYrGYiQkQ2lKSsFi4J6hIarWavahTU1Py+Xw2+JFk\npRqfAfYfrp+RSMTiNqh/OZFQYU82kIhiiU1jR4/H41bLTypx3vZ6VDUzXACOLbBMLGvL5bItMJCK\nXC6nbDZrvINJYhEO8SwOt9ttJCUUGwTXMOyQZE2Sw+Gw3D0GL5Mu/XhD0/1jjIJ4lawSun/qY2x0\nr66ulE6nbVCBE2ihULBJndPpNDjs5OTElN3s9FBZceWkFsYIBrIScB9Wv/l8XsPhUKVSSQsLC+ZI\nREOKz93V1ZXW19e1u7tr3h+UE263W3/6p3+qQqGgYDCod955R9///vff+vk/qsUMH4JFJd3hmeFw\nWJ1Ox4YTEGVQUjNcqFQqNhZmIAA/GXNvLF/hO9zc3NjOz1E+HA51eHiomZkZLSws6PDw0ILPKX/Q\nFbJjwzvGxJsXC2gQQ8N+v69EImFjZEk2GAmHw/L7/Zazh4H6ZJZItVpVtVpVqVSytNTNzU1TiV9f\nX+vVq1daW1vTF198oXw+b6y+QqFgdTwvMj7UDDzYpQ8PD5XNZs18nPsGdwVvaQYuV1dX+vjjjx/0\n/B9VmeFwOGyngc/g9/uNmklHz0AA00H8jTnSyQ2h1gwEAjo8PJTT6bR8PtztJ1XRxI/d3Nwon8/b\nsc7PAiVIp9M2SZSkzc1N+7zg2RzZpDERh5xKpUx2RPIszvXoFieDIoPBoDKZjL7yla9YaVEoFGzg\nwSKT7pIHSIa6vLzUkydP7CRJJpP2+xFSn8vlbBrJTo7gNRwOW6OJAxSnC82e1+s1uRq9xkOuR7WY\nyeBA2t7tdk2uhFKZkS3OlUBVMLuIQBiPx6rVasYFZlx8cHBgudbX19f3XDY5Xq+vr61Bo0FilM3R\njyAAZTjIBkwzCPPD4dBGzYVCwZo9sOKLiwub2qH1Y5cnkgIFdSAQMDjs6OjIhi2SrPmFZAUFgBQq\niEXcO/B4fufJ0gKeBzFrp6enurq6sgkk9NTJxX1xcWG9xttej2oxLyws3HO0n52dtRvOw0omk9YU\nMqVCb3d8fKxoNGqZealUSk+ePDF47PLy8l6oej6f18LCgnK5nGq1miEpkUjEoDz4DbOzs+Z7fHZ2\npkqlYpAXQ4f3339fMzMztpuGQiHFYjEb6FQqFQUCAWO2XV7ehdkzmcQxiJ0cmwFeROKF3W632fRO\nstZAOya1k6S+MvW7ublRNBq1DYC0rNFopGKxaF4ZKO0nJVrpdFrr6+tmNUwZx+/3ZdzwxDUZegNp\nnciuwWCgbrer0Whk9SxqbS4ankajYRZScAtAHohSmExK7fV6VhMjt0diBaUU2AxyETviZD5grVaz\nz068xGAwMPgN3u94PFar1TLSPAOZbrdraa8Qq4iJA0YkrKfRaJh6GjEA+DT00sFgYKcFEq7T01P1\nej2DEnEbHQwG2tvbs9+/Xq9bOM8k45B4OPDlRqNh4oD/52RT/zsvaJMOh8McNkOhkNk+kadBvsfK\nyso9kxJgorW1NXm9XoXDYcvtCIVCVndPWmzx8wjhQfKfSCQsnmFubk7D4VCpVMqcRTFKREZ1c3Nj\nLp+oVYDNgK04DTqdjpaXlw0mhMdxfX2tJ0+eGDttMiCIU4URtSR7OZ89e2blEZ7Nk+bpGKpfXl5q\nfn7e7gEDqXg8rlarZVZifr/fEBT4MuzcODnRZ2Bv4Ha79Y1vfMO0mm9zPaqdmbGoz+dTrVazDhvb\nKIgu7GgIPyVZcybJBhzspq9fv7Ydihp0MuYXzzT4wzRIEGkgEuGxcXNzo16vZx7IDBJ4SSbr+MvL\ny3tG4SjMDw8PTbh7fn6uWq1mpiqj0cjkXyhMwuGwDT9Qj1BOwaQDZWk0GmbjhbHi5uamms2mmZ7j\nYEpfQEj99fW11fP0KMPhUGdnZ2Y/wIgeshKe0g/lMz+qnZmj+Pb2Vn/5L/9lC44JBoPWeQcCAasB\nE4mE4aNYYlFPItn3+XwqFosmyR+Px0qlUja2lWQNITg3CwhsNpVK2Y57fn5+b0qH+9KkeoOTArXz\n1NSU+v3+PYEpuHSxWDSus8PhMKk/35sSaHLnrNfrKhaLNjqXpH6/r9XVVW1tbRnLjYWazWaNtIR9\nAzFxxWLR4jTYTEBvKLNwb8JxiTE3Jdj6+rq9zB999NFbP/9HtTPDlTg5OTFexcHBgS4uLky9jOKE\nmpDdkqgIgnEwHAT9wLVnNBqpVqsZvwJ5FU3WZOIUvAf0eMBv5GzzQgClUbLAIaYep85GaUK9SUgQ\nR3etVlOj0VCz2TRzwkKhIL/fr3Q6beaPbrdbBwcHJj4YDAZyu92mWKek4H50Oh2DCPn6SdU4vOrh\ncGhlFrtxJpOxxhNMGbOYmZkZS6liwvmQ61EtZq/Xa40NPAhMXViUUBlvbm6UzWZNIYK/A/Fq0CEZ\nBYfDYTMDBEuFHccg4ejoyB4ukRPQMqWfBggBTaXTaXu40p0glWQorMXgifB5kOujzaPhPTg4uKfd\no+GqVqtGxeRlhDyFNpIGjxev2WzaiJ96HlMbTr50Om0nSSQSUaPRsFoYKgCKaxpXdm4kZpPwIL/b\nQ65HtZibzabm5uas9uVBs3PykOn8e72ePv/8c3W7XTP+vrq6kt/vN09ixrCNRsM8jpEmnZ6emj1t\nsVhUoVAw5ANCOwQgJEXo9WKxmHZ3d63cQepEkiti16OjI4PLgL1AARqNhoX6TE9Pm20trp2FQkHR\naNRIVJMsQkb0k1g4CAuj+pWVFd3c3KharVr+tyRLgyUBttlsmms+34fhC3QApoLwNfb29ixeAr7I\n/5e/4P/K9agWcyKRMAl/vV63ulWScXOBqObm5rSzs6P333/fpm3S3UP+/PPPbfHQ1LAQWUzsQuPx\n2Pgae3t7mp2dNQopNXen07EEK06Gw8NDKxO++OILJRIJY7qBAWOHgMQJrSKuRzRwGK+Q54I/8s7O\njo6Pj80RaTAYaDweG1YOIw5+CD+HkHkomvl8XrFYzCRomNVMYtLoCWn4GKIwiTw/P1ckErFTrVQq\nWRIXyMeXO/PENamr48awgOFE9Ho9VSoV09DhqkmNyc7qdrvtuG21WtbBU6viN0EJgeMQwwe8MKhn\nB4OBTRclGf7NFBCp1tXVlZUdcCX4/ChY+GzAicPh0MIuEdlS0+JPgX+G1+s1G9tJ6imfn4gKegKX\ny2W0VxQnrVbLxvcQlkiE5ZTg5YevMllKwA5EgkYEHIqdt70e1WJmd52amrIJ1CQdEjwTEvvs7KxF\nhYF7hsNh6+QTiYSpoIH54Ftw1E4ubEhLxDNwtEPRnISyPB6P+Tuz21LvQrN0uVyGbMA7BivHHxll\ndjabtdH4xcXFPRdP0BscmPx+v2VWw6NA9QJzj8mgy+VSOp02yzCfz2ee1dgecGqAj/OCwwUBpuPe\nYSnG/Ybo9VAN4KNazGC78DH6/b6KxaLdMBYk+rh2u23H4LNnz6zBm5+fN4x3b2/PBhGSzMILZQm+\ncjMzM5aR8sMf/tBsrSgTrq6u1Gw2rYmanZ01ZAXVSbfbNWU5vwNGLARMut1u092BJ6P/o9kDeaB2\nxRcD1ctkzrYk2z2dTqdSqZSNymkSwbPZbRECSzJ+BQOi8/Nzk3QxLsfdn5IEZh2cFXD0Sbbj21yP\najFzDFOn5nI541zALSbQEToliunNzU1DMSqViu3mqVRKW1tbhpmenZ0ZlZGHBff4+PhYs7Ozeued\nd3RxcaFcLmcMMZfLpeXlZRN8Ap+BTjApw/wcLjC2XdLdsCeVSmlubk69Xs8I7pIMf6aWhXgP74EX\n3el0KpvNKhQKWWwyXBWwbmRW0t1JhOiAl2Q0Guno6MiI9nwtLxvQHgMSSqFQKGS+cqh+QqGQDbn+\nn0ub+t95YWfl8Xi0uLho/hckGYVCIRWLRRWLRZ2dnWlpacnqtdnZWUl3i+Jb3/qWUqmU1tbWjD98\ne3trx/Xt7V2GNLwHFm6pVLIHDYf69PTUNIDUopeXlyqVSkZMSqfT1pwBW5GRcnh4qFwuJ0n6hV/4\nBVUqFdVqNcvehr46Pz9vixVDG+IkWEyJRMJ8ONBBQrjHjuDm5sbM0nO5nNX7kszLGqMct9uteDyu\ndrttoZi83NJdzY+mEkyanqPX66lQKNimMz8/r/fff/9Bz/9RLWYUFnTbU1NT6nQ6cjgcBmPh7Qb5\npdvtKpFIaHt72wLc/9t/+29qNpva3t5WoVCwBTMYDGyh+v1+4xX0+31zFKW5Ojg4sMXJMIWy4fj4\nWF988YX5vSG7Z5R8cnJifIZEIqFXr16p3W6rXC7fsy04Pj62fwcywPE9HA715s0bG3dfX1+rWq0a\nj5nPRjMJ1jwcDrW/v69oNKpPPvnEdnawcax5W62WDg4ODNl5/fq1HA6H7e4Q9qvVqim8WeyHh4cK\nBAKWPtBqtcwQ8iHXo7K0/Z3f+R2b7GGtOhwONT8/r0qlYmEzdOFer9eGF5OOodhrgVcHg0FzuASt\n4GXgSGWyNxqNTBfHgIVAHlQjNzc3Oj4+ViwWk8/ns909FApZzkgoFFKlUjHUgd0RSFCSVldX1ev1\nFAwGjRmHvW42m7WJ4c3NjTwej7mTgqCgBj85OTHbAOmOY5JIJHR0dKRkMmncbGzLyDJB8IrMq9ls\nKpfL6fLy0hKoJFnkXDgcVjabNViScHlMJ6+urh5kafuouBmoJs7Pz1UqlYwbwTQQ+Q9mKNJdaYKJ\nIsT7ZrOppaUlbW1t2VELlxePCBbheDy2lFYaLemnFrDpdNrMGiclUpKMzYZhOTvr8fGx5bjA0UBa\nBFvt+PhYr169UjQa1cXFhQ4PDw3yYjDS7/etnmXhgpvf3NzY7+N0OrW7u6tcLmeQJTs201QEv/V6\nXTMzM+Z5nUwm1W631Wq1NDc3Z7DdaDQyXw+CMV0ul16/fm0TUkqW8Xhsp9tDrke1mBmtejweE50W\ni0VbbAwWYLYVi0V9+umndtRHo9F7HIMPPvhA/X5f/X7fLGopCYicIFiI5vPk5EStVkuzs7PGCYFW\niUoE0j2e0PF43PBjmj/gPa/Xa6UBkiqsdhmFT+ai0DAidgVCw14LohANWbVatR13MnEK83EGIn6/\nX4lEQoPBQMViUeVyWR9++KEx/tbW1iw2IxgMmrUYvxchPSBKJGO9evVKkUhEhUJBm5ubD3r+j6pm\npsm6ubkx96CNjQ1DJ3Z2dtTr9fTq1StNTU3po48+MgNEjLsxB7+4uNAPf/hDDYdD/eQnP7HS4uLi\nwnzZpqamdHl5qY8//ljn5+eq1+tqNpvGDut0OgY94d8xGo306tUrff/73zeDFiA7uBhXV1fa2toy\nj2Y0jfz76+trffzxx9rd3ZXL5dLBwYH29/eNrjmZmtpqtfTJJ5+oUqnY555UWbtcLpOHXV9fq1ar\nmUEjBpAOh0MvX77U3t6eyuWyuYb+4Ac/sLiKly9fGlZ/dXWlzc1Ni4dzOp3a3t7W559/rvF4bPyX\nwWCg3d1d+2ytVutBz/9R1cy//uu/rlgsZqmm4/FY9Xpd8/PzNkU7OztTrVbT4uKi+bjhu7axsaFI\nJGK0zWazKbfbrWKxeO8InFQaT07VqAfhb2DdShQb4eq5XE77+/taW1vTycmJarWa3G63lpeXrQFk\nEglFkzDOcDhsNT3xypMpUCAcpVJJGxsbNgVEXCrJvn5mZkaj0UjRaFSNRsN2V7fbrXQ6rdevXyuV\nSlnTB+uOwc/s7KxNTglE2tvbMyMYxMKSjOM8KYaAoQfv+eTkRP/wH/7DL2MgpJ/yL8bjsfb3920K\nhpjy4ODAFnX5f+RQf+9731Ov17MAytFoZJAbcquNjQ3T0rGbYeJyeXmpSqViR3ClUtHl5aW5AkGs\nGQwG1uhRd7KzRqNR41KgmGaQAWJwcXGhRqOh7e1ty+q7vb21v+/3+6pWqyqXy/azMpmMNZsIWZ1O\np/GwaQbL5bLG47GpxBlDk2PY6/VULpeNyYdb6nA4lM/nM3XI69evLUptc3NTnU7HUq94BtTHzWbT\n3Jvq9bqOjo4evDM/qpqZGhSa4vHxsYrFoiKRiNrttsUUwNVdWVnRysqK/fn19bXVqnCA6/W6kWbY\njSdH05D/GQPncjkjH+ECSjQFGYCpVMp2KAYx2WzW6uVJXzlJ9+y1QDey2axl7MHCm5yqMcyAxB8M\nBs1sBnYbQ5ZwOGxoCjstcOJk6CasNsbra2trZqs1KQm7vr7WO++8o1AoZDAhNAEQl4WFBWt4S6WS\nycge9PwfvIL+L7p4ANjKzszMaH9/X/F4XB6PR/v7+8rn88Y5/vTTT7W/v6+FhQWbTFUqFSP3bGxs\nKJVKWdMyGXbjdN5ldEPVZIhCGCWSf1hsQHfSHVUV1yXcgxqNhlkB3NzcaHt72xYD9TdxEIyNGURI\nss/BTn92dqZwOGxkJpw8cQzlM+KJR2APk0523kAgoF6vp2azaYFBxWJRzWZTm5ubSqVS9yRgz549\n09XVlV6+fGmUWCDHarVqiAcnB/l/8/PzDybnP6rF7Ha7lclkrN6cnp62aF78gmGVpdNpNRoNC4pn\nrMzRjM9aIBBQp9MxMjx51rDYaIDQ5mFtgGk5kzfGyaAPLArUJqlUyojt0h2GDOMOYS7qc8bGwHbt\ndtsimPmMeLs5nU7bpfGFply6uLhQOBw2G1xUH8iygA4LhYIptok3RkhLjHO321U6nTY+SKFQsBd4\nfn5ee3t7Zp+ALAs6LeboT548edDzf1Q1MwqHy8tLvXnzRkdHR+p0OsbHgO8A8I8QEyUGN5fc7amp\nKeMQYBrD4KVQKBgnmIcOOWdqakoHBwemmYODIckMCEmAxRYMNQeLr9FoaDQa6eDgwFAJRuMzMzOW\nIovcn4EMsb4gKJDlSb7a2dnR5eWl6vW6cZiPjo6sdDg9PTUWHBM5MkewYMDhk/IL/2dgwZOTE71+\n/VqSbBrImBzEZWdnR1NTU1paWrp3gj3kelSLGZ0eyg/MBanj8D2TZPXg3t6e+R2TwsSAg4UFbgps\nR8OFLwREofPzczWbTRMGtNttmw4yDr6+vrYcPiwEsCzo9XrGtKM5Y8EBC9JAQhllUcFKYyGCS5+e\nnsrj8Wh7e9t26na7babgHo/H+CCYlCNtonn1+/3a2dlRPp831TsbAyGh1MZAmDMzMzZo6vV6VtLA\nr4aRt7+/b8+EQdHbXo+qzEBRnclkzJoqn8/bokZBjMsQRz2uPMFg0JAJjl4svMLhsEUdoIubmZmR\nz+ez43pmZkaFQkGS7EHzs6Q7+ii84Xa7bbslFq+TZQp8EMoVSSoWi/Z74bzPSJkRM/a7DofDRtiJ\nRMJe6Egkonq9bj7RlEJoDXEEJUSe339hYcFU3efn51Z2MOLnZzPmzufzJiCmRKrX6+b4xMX9wpbh\nIdej2plJLsLfAWkR/AhJVtOxqCHbnJ2dqdVqKRKJmKqaf8cxSKIS5uXs3ghaSWACoUC9QaITlgTU\nkpPezZJs2khNivRKkiEhkqwMYcwMtRLyvCTD1MHNyT2hdALCJOEVwQHEfqy5oLD2+31Fo1FJUqlU\nksPhUDQaNd41/w7qwMnJiQ4PDw0axfoMNQ2carR/lGoPuR7VzhwOhyXJFMdut1uVSsV2bPgHNzc3\nCofDOjo60sbGhnkWgzqcnp5qbm5Oo9Honqlfo9GwIxmDRvgWzWZTnU7Hsjrq9bpcLpdiKAXoAAAg\nAElEQVSePHmier2unZ0dzczMmIEMuyEIAgSpZDKpWq2maDRqi5dRM7ESNIVY9J6dnWl/f1+lUume\nYACyP0QfFCz1ev3e6YVJTTqdNlHC7Oystra2jAuyublpbD0GHtTiFxcX+vTTT1UqlVSpVOT3+9Vo\nNLS0tGSqEl4OJpSxWMzMdYAMvxxnT1wzMzMKh8NmjsiCpenjQTNyHo/H+vDDD++hHIFAwLK3wVzx\nRuZ4pB4E/kN753a7VSqVFIlEFA6Htbi4KIfDocPDw3sWt6FQyIjpoBJ+v1+Li4sKBALmKIq3MmT2\n6+trFYtF09i5XC7F43HbgYkhw7EJs0NC1zFkpOnChoHSiUEPOkVOlWAwaFYHKF/wygDXT6VSFmsM\nMQvJGfU1xu9AnJlMRrOzs4bgPDQH8FHtzPgznJ+fa3V1VfV6XcvLy5bhXK1WVSgUjAvxcz/3c/pX\n/+pf6Rd/8RfNhJyjNBQKWZO0vr6ug4MDc+KPx+NGokHjxkPa399XoVBQLBYzPBoiEV54LpdL2WzW\n8vay2ay63a6Ojo5sobtcLuVyOYOwWJDQNDOZjDWBODKBVRcKBXk8HjslJqMYLi8vbTgTjUYtx4/6\nHg3j1NSUVlZWlM1mVa1WTbaFEbrT6VQul9NgMDAPO2py/KnhmvCyYdVA1iFYNlj7kydP9Omnn771\n839UOzNj05ubGyOLd7tddTodI/Ts7u6ao+WLFy9M5Nntdm0U3Ov1DD7CF4LmjfAd6mVGtEBbksyX\ngzodhAGvZUna29uTdDfoAf0gP4SRL9ZakmxwA0QIUZ40VU4gFCvcB8xdMIXB1RRvZ8xqMMLBV2Q4\nHGp3d9fkUaTaSrJ6/ZNPPjFkg1H5cDhUv9/X/v6+6QKx4eIzQRWgHMSX5EtL24kLe65EImGeFZIM\nPltYWFAqlTLhpMPhUKlU0tTUXUJoNpu1Bccuxr/FkRNkwOv12gCD8TZeb/i4YQoIgkFXT4YHY97J\nIKFSqWREJeifNKler1eJRMLIP5MJsrwINLVAbITgLC8vq1QqmWCV0HcoqBcXFyqVShqPxxbEg77w\n8PBQ8Xjc1Njcm+XlZZN44XrK702cWyKRUDwet5H3pBVEKBQyhCgajdrPe9vrUZUZQFzkzkGFxIuC\nXY0dAVwT21vCHXu9nkFQ9XrdYK1ms2lQFbmC6AndbrcpsCVZYwWTjqGK3+/X9va2WeDizO9yuQyH\nhZXGkIahTb/f19ramrlztlotOzHQBSIVw5UJu63d3V0roxCsjsdjK2/gp4zHY+3u7ur58+dGvvJ4\nPDb4gItBxgsvLH7S0F/z+bwhKuDsGxsb1r9gwkPMBNzyh1yPigL6d/7O39HMzIx6vZ5N43B+J9AS\ngebMzIyeP3+uf/SP/pF+4Rd+wUguNzc3Jt9fW1uzcqDf7ysWi5nQ8/Dw0GiVUCSJkeChSj/1jCZU\nh78D8iP1lYXLLk6di4ocZKVUKqnf79tuygs8Sc08Pj42LziMD0mHikajNvQpFova3t5WLpeznZ6R\neSqVUqfTkc/nM+PDYDCo7e1tzc/Pq9ls6v333zcedTKZVKfTsQg3dIaoVjjJ6BOAK0E/UqmUNjY2\n9E//6T/9kgIqySRJZ2dnWlxcNCSCiSD1HbjrxsaGWV0xVmYEjMUAgwM6dCxh8WA+ODiw/BAGFjgK\nwWMejUba2dkxh1Lq4kkzw/Pzc83OzprK+uTkRJVKRePx2JopCPyNRsOU3wwvyCPJZrNWDrDzdTod\n1et1I0KBJVcqFWs4qZ9x5QetYFIKPAhUmUgk1Ov15HK55PP59ObNG+VyOfscSMi4ny9fvjRlOv+7\nurqyxpDUgIdcj6rMAIQfj8d69eqVMpmMZWpsbW1pc3PTfIwPDg7sYdEcSbKBx9HRkRnAQBlttVq2\nK1JSkOfB+Bf7Kayout2uyfrp4FGMLCwsmOB0PB6rUqlYzASOoESdEQQ0aXQOD5rxM8y3i4sL2wEb\njYYikYgGg4ENfmjUsMHFFRVzG+kOqy+Xyxa4mUwmremEKjrZ9F1dXemLL76we7O3t2dCYl6uTqdj\nCzYSiWh3d9fuYTwef3Dc8KNazIPBwAIqoUhyHM/Nzdn0i6DJWq1mEBuOlcQxjMdjU0AcHR1Z5nM+\nnzd6Js0fDSQUTqRPMNqi0ahBdwhdmUCixRuNRsZ1vrq6MnSDIcmksz1iXHZPxKKIAvh5+XxeLpdL\n5XLZHEnff/99k095PB7FYjElk0kNh0PFYjG1Wi2zL0NpDeLD58aE0ePxKJvN6vT01E6rWCymUChk\nKVgLCwtaXV012wMaPrfbrVwuZ1NbTr2HXI+qzIALjKoBBUkoFNLe3p7tWqgp3G63ueBj64VcCKND\nFivj6NevX6tQKNiuztADjgNqlna7bdRR0BR2JYSn7NSgEaFQyNhx1OH9fl+fffaZarWajdBhsFFb\nkuI6PT1tglxU0jSU+Eu/fPnSIhkcDofC4bBqtZqku76g1WpZCVCtVuVwOEylQnQGKAhhlefn5/fi\n1ur1uk0xp6en9fHHH6vRaCifz1tawenpqQVf+nw+jUYjVavVBz3/R7WYOZoHg4GF16RSKfl8PiWT\nSYOjIpHIvVH1ZJ7J9va2Gf5RLxeLRcu0w3Ue3jCeyAwc5ubmrCyJxWLWoMGhQLkBAYmfS03KIIbd\n+ubmxrzfcFdipw0Gg0qlUpYKSwywx+MxLzsC7ff29gytoPm8vLzU/v6+KU76/b5ZmhF+yQuCABYG\nIZCfy+Wy04vTbDLcfjAY2IBn0lHV6bzLAN/b2zP4E1z+ba9HtZiJblhaWtJgMDAqZ7fb1enpqZ4/\nf650Om0uQwwkUGiAQDBeZgSLAQvk/3Q6baPamZkZLS0taWZmRtFoVF6vV5lMxsbFLFxGwNSf+E6w\ns0O3zGazkmTuRfi5hUIhRaNROZ1OraysGImqVqtZvSzd+YAsLi4aEsLv+ezZM/OIhlSEtx6up6ur\nq5qamlI+nzdIk6YQsQMQItERjLwXFhZs5O71eg1XjsfjdqJAA4BNt7y8rJWVFQUCASWTSZvUvu31\nqGpmyC9YabFTJ5NJ2617vZ6541PrFYtFjcdj089JsslYJpMxnnM0GjW/OKaG8XjcJobUlgg1CcTE\nGQi8mxwVHOoxHaS+ZhKG4vrk5MQaTUlWSvAzIfqAeVNi4GmHGQ6YNTwMiEUs3ElGYDKZVLlcNk0j\nglqGMqPRyLSN7PZAcSRoYYrYbrd1c3OjSqViWkJs02icr66urNx52+tR7cxI2Tc3NxUKhZRMJuX1\nei2r+sc//rHBc9jQptNpzczMWEkBrwG5EDttLBYzmI+XAekQfmtATTxo3IFAAqS7Lh7vCyiZ1OVY\nGsB7gG8cCARM2R0MBq3WDIfDNk2cm5tTPB7XysqKvF6vlpaWLOfb5XLpzZs36nQ6BovF43GLq5ie\nnjYVN3RUn89n08hUKqVoNKq5uTkzl8HKASwb1Tj8C4j53W5XmUxGDofDBinT09Mmb/vJT35iChoE\nvG97PaqdGW82aje0cSAB7733nuXsMTgBG8XeFRINEBUhNEz5IAGBv0KkmczvC4fDcrvd6nQ6ZnoO\nl/f4+NgaTyAuVCyYDnL8J5NJ7e7uKp1Oy+FwKJPJGKQo3fnOQXhnUkdq7GAwsAYPxiAeHEBwaBj9\nfr/5a7BzDodDk4BhqIMtGfeEuAqQCRh4eFyjxGm1Wrq+vrZkXOyCc7mcbm9vlcvl7KV+yPWoFnM4\nHLYHjb6u2WxqdnZWBwcH9/zhsBG4urpLblpbW9OrV68MBUGk6XQ6NTs7q9FoZDtwvV43ElM2mzXy\nuXRXtzcaDaXTaSMjMXaG4JROp027hxMpZQS0zI2NDROlgkO3Wi2FQiEzjaFhQpYE9txut22X29ra\nUrVa1QcffKBOp2PB7PBYTk9Ptbu7a6w18GgMGAkuarfbBslB70R+NRqNlMvlzP8C88ipqSmj4ZLa\ndXX102QswoRevHihfD6vjz/++EHP/1EtZvyOcRlCsXx9fa333ntPtVpNsVjMmiNSo+bm5vTmzRsj\nvoTDYZPmY/oXDoetJGH0S8zv5OgazsV4PFYikVCz2TSrA5o58GAW/JMnTzQajex7ERFxdHSkdDpt\n7DOfz6eTkxMtLCzYCQJzD4x2enpaT58+tdqWBhBk46tf/arC4bChK81mU9/85jd1cHBgoZvRaFTB\nYFALCws2EKL+dbvdWlhYMIWI1+s1pCgajZqKfWtry16ar33tayqXy2o0GvL7/VpbWzP/ux/96EfK\n5XJKp9P6zne+ox/96Edv/fwfVc1MyhRTN5oeXDPJh+52uza+jcVi6vV6ZlfV7Xb16aefmuDS6/Ua\n2R2bAZAO4DcuamnUFK1Wy7R0uCshnYL3K/1UiAvaQbmCjpCJYa/Xs4kf2SGUApMIA7UsYTt8H+mO\n0/3ZZ58ZujA1NaVKpWKnAjsrxCaPx2MjeQLgX7x4oVqtZjBbo9GwDYBhChsKk00UJXA/zs7OTMFD\nUNLOzs6Dnv+j2plPTk4Uj8d1fn6uP/mTP5Hf79eLFy/0ta99Tdvb22aTRaDl69evze0SRUan09EX\nX3xhBB1cd+jQccV0u92q1+taXV01HvDZ2Zk1bBzpgUBAn376qYWvY627sbFh+Oru7q5SqZSpV/r9\nvsUiTDaIn332mT744AMNBgO9evVKxWLRcv5qtZqeP3+uo6MjffHFF1pdXVW329X5+bn29/f17W9/\nWycnJ3bEMwA6OjrS8fGxIpGI+v2+ZmZm9Pr1a33wwQfa3d1VNBo1sxlMHPFSxmeDcEw899rttvkx\nj0YjNRoNO4k6nY55AVIuoVbf2tp60PN/VKy53/iN3zB+wu3trfL5vBn2MVXDzC+bzWowGKjb7Rr/\ngu6e5icUCqnT6diABdtYvDKOj4+Vy+VULpdtsZ6enqpYLKper5snG34QbrdbR0dHKhQKqlQqZsiC\nmQoNIzUmZHtJpnqe9GcOBoNKJBLqdrumhMEeAaSFupsxdCQSsaxrv99vsGO9XtfS0pL29vZMDwjl\nlQEI9zYSiejk5EQOh0PFYtFYiqjFccdnugqRCsnYJMUV/WEsFtNnn32m733ve1+ajUt341gWMITx\nRqOhubk5DQYDlctl9Xo9y6Arl8vGNCP/BI3gkydP9KMf/UgrKyumTIaKCQkddTPkII7lVqt1b6LG\nsQ83Y29vzwSefB3N3MnJier1ukFnpMd2u10tLi7a4oOKSS4fKVXST3FoRtm7u7vKZDJWInW7XbMI\nOD09NbHt3t6eqcwRKlAqHBwcmJsRgxSEwVdXV+aGz4s+mdrK7jtp08CEFaTD7XYbfPm216OqmZG3\ns4vV63XjELC7IlzFSKXdbqtSqVhZcXZ2ZoE2c3Nzlm6KGSMWAoeHh2bywm6ZSCRs1I0eDg825FjS\nHaRWKpVsbA5nGUMVpFqUCTR3ZLEcHh5qdXVVPp9POzs7crlcVlbgJYd3niQbkU/+HAYh9AJ4cWCv\ngKUZGkJG2vhroGLx+/3a2tpSsVi0+GJOGlyT4HM7HA6dnp5a8D2fKxwOW7jQQ65HtZhLpZKcTqfe\neecdbW9v26gVc5JMJmMYJ5IjBifQLkExpDuL1tnZWWsiI5GIjaoDgYA1fxDZDw8PlclkDMMlP7pY\nLCqTyWhubk5er1cLCwuWl0d2SCgUktfr1eLiojWNMNeQHxFF/OzZM1UqFZNRTU9Pq16vm4NpLpcz\n21hYfCsrK6YGX15etjq11+uZ5Gp+fl7T09M23scDhDE+ggfil1G7z8/P3+OZAOElk0lFIhELy8xk\nMorH44rH4za4mpubM0/A5eXlBz3/R7WYJZkUiZwSbAB46xuNhm5ubsyEhcUCzAZlVJKSyaRFfQH+\no5aWZHXrycmJ+b3hMIQhYSqVst0QDsVkqM0kJkx0wtXVlXlj8N/ESEgyLxAYdOyu6XTa+NJMMo+P\nj81sBmmXy+WyBgzuCeUHpxAvJFi5y+UyuRXKEUk2UcXchXr+yZMn5mMHfXTy++GJJ8nExl/6M09c\nwEsYdmO3enx8bEYp1I6DwUC1Wk3ValV/7a/9NaMyplIpvXz50soUTArfffddOZ1ObW5umiQJPSCq\n7MvLS8NSkUiBLdMIMiwpl8taXl62oz0QCOjVq1cmFiB3G2uC8/NzZTIZ4/+yM4dCIdM7lstlq/vR\n/s3MzBgtE84y/BIIRfi/gYszAdza2tLi4qIZqpMmUCwWbfg0Nzdno3Z8RZxOpzY2NrS0tGSwoyQ7\niThp0GrCKUGi9rbXo9qZsYWiRmNEPakQhrDvcrn07Nkz25FzuZx5L+dyOUWjUWWzWaVSKQuVRBJF\nUA6jbBhyZ2dnSiaTpvHr9/uanZ29l4eCcXg8HjduBrseKhhJppNjrM1LwZ9zVE9a3EajUc3OzprB\nCjVvLBYzEQAWWnwOScYzhunm8/nk8Xhsp8f3A4I/Lz3JVZRscFqkuyY0EAgoHA5rdnbWQjY5nWZm\nZszwhtxtpoRv/fwf9NX/l13sDrlcTm/evDF3TQIbGS4cHBwoGAzqhz/8oZaWliTJ3O79fr82Nzd1\nfX2t4XBoymp2VmwFWFzJZNKINIlEwiZlxWJRS0tLurq60u7urjn9uN1us7TCRguPOyT3BFzCbbi4\nuLDfBTk+QyAWACPz8XisN2/emNkjyMLy8rJxuWEAAvXRKGOJC7owGAzs53i9XlUqFeXzeVPIAP9R\nS/Mi3tzcaG5uzngb/7OIgWEUahvs076MG564UFOcnJxYeAy51fhdMAQ4PT3V+vq61W/cTESlbrdb\nkUhEpVLJYCRqaESg7DwQcZg4woy7uLhQq9XS6uqq8YjJ33O5XIav5nI5C9/h73O5nBkhgnIsLCyo\n2WwqnU4rFAoZfCfJMrd9Pp/y+by9cIlEQu+99568Xq9l7nm9XpXLZRufT55cKLudTqfm5uaUyWSU\nSCRUKBS0sLBgePXkIoU0hWl6OBzW7u6uNeCT/OlisWgxykxdvV6vCoWCnj59+qDn/6hq5k6nYzcV\nlhYdtiQbgrArsJC73a5BS9i9EvZTrVZtoMHkC8Xx2dmZXr16pdvbWy0uLpoXB+R+VN2np6emuwPl\nSKVSxmdAxtXpdBSPxzUajexEQD0CZHhzc6ODgwODy/b29mwMnUgkVKvV5Pf7je0GganZbNriOTo6\nsnvU6XR0c3NjRCVeSrwunE6nkZ2otSVZOXJ7e6t2u21+GcCIpNSikJ+amjKMH42hJNtwoKY+5HpU\nixmij8vlMghNkikpaHJGo5GZc9PEsIBRRRAImc1mtbm5aQ/gyZMnOjk5Mb4Fuj5I7h6Px1AUkJXz\n83OrO9HlYXcg6V4EBDs4uxcvD5ZcoBhAaKVSyRb6/v6+ZmdnbdHystIrYBk2Ho9tN8Va9vb2Vul0\n2lTXlA+gO3hQg7X3ej3F43EtLi6qVqup1+vd86uOx+OKxWJ2aiETm5ubU7fbNUEtZuaj0cimnW97\nPaoyA6MRp9NpMBNeZ/CQsQtg1yTInQeNrRVeFOPxWC6XS6VSSeFwWO122452mhxUJ1izsttSn+J+\niUELAk70ezRAOGLe3Nzo8PDQnI5Y+OygNG8kXKE6WVpast2S7GqQkP/ZgbNerxuZ6PDw0HgglAc0\ntIhVUbSw8H0+nzWANKOhUMjkaiRaQYaCFtrr9YyGyoBlNBrZ93rI9ah25tFoJL/fb/gpATbcTMSh\nw+HQygzYXQTuzMzMaH193dQipCXV63VbfMiGoIhODlDgJ6fTaZssoviASulwOJRKpXR2dqZAIGCl\nCIva6/UqGAwaq41BBRNDiD3T09NqNBoWsxaJRKw8mZmZMb8O/C2wS+j1epqbmzObXngnpA5Q466t\nrcntdpvXHMy3Uqmk/f19swVGOQK3BJSnXq/L5/OZXQJ1PEMXyE28ZF//+tf10UcfvfXzf1Q7M6pf\nHtDV1ZWJKqE8ptNpm6Z9+9vfNgtXFhCstXA4rA8++MDQBfgVZGcD6SHQhKc8NTVlx6zH47HhzCQV\nFGlRJBKxjt/r9SoQCJjaA4opnGeOfSZ+f+Ev/AVTugBrcYpIdx7S2WxW09PTpuAgfIf6lD9HC8jn\niEajVqYg9IXnjIr8+vrapqOTHiU4ROXzeaurQYpoDpFh0ZQiqfpyaDJxcSRKMh4uxBg4AhyfDodD\n//7f/3urB51Op6lRcKTf3d3V9PS0tre3LQMPOyysAOB4ELlbr9e1vr5uu//x8bHZzfL/+dper6d8\nPi+fz2dhNRjHwO7DYhbMfDLDkHB30rOOjo6Uz+etHBkOh8bY479JhOJlD4VCajQakmRGNN1u18wS\ngRdh5uGSdHNzo//8n/+zisWi+XTAxBsMBuZlx4uDuIBp6unpqdrttm0cpMs+5HpUi3lqaspGprVa\nTfPz8zYVPDk5sbBzeLuXl5fWpICf4qdGLARUUHRv5+fnuri40O3trZrN5r1ckuPjYzuOUX/Pzs6a\nGhvLWSJ4ybk+ODhQNpvVzMyMWXvxvRuNhi0KPju2BP1+X8fHx1bLZ7NZVSoVdbtdMwyXZMc7QlpM\nvpvNps7OzqzxYlfnFIJZyMYQDAbVarVMS+j1em2zQG1zfHxstTpK9KmpKTOWBO8PhUIWmXxycnIv\nv+Wtn/+Dvvr/sosmazAYWBCj3+9XoVAwl3hqx+PjY/NxLhQK1nxgWYWMPplMyuPxWIZdIBBQNps1\nW6vFxUUjz+fzeSWTSRs4gLGyC07aaaE9DAaDKhaL5qzPKD4QCCgUCundd9+1l4WmL5VKWenz7rvv\nWkkTjUa1vr6uYrGoZDKpYDCo8/NzORwOy/ALBALm70bDnEwmFY/HVSqVdHFxoUwmY7kklAc490ci\nEat58d2T7nZ+0myxDyCGLpvNyuv1KpfLGWwIUYnkqmw2+2B/5ke1mCETra+v2w52fn5ucp/V1VXb\nZf1+v/lcsFNy81E2c5xCLKIBhB+B+3w4HLZuPBQKKZ1O22JHmpVMJq3WXlpaUiAQ0N7enpmNHx8f\nKxAIaHFxUfl83sI4u92uJJmXxdzcnOkYFxYWLPwyHA6r0WiYIh30g9CelZUVFYtFnZ6eamVlxY59\nyEFMI4Hxbm9vDV25uLjQ6uqqcUF4qQqFginZcXKCChsIBMzM/eLiwvjPwWBQ6XTazM2BEh0OhxYX\nFx/0/B9VmcHO0+12tbe3p1KpZC6YEF5OTk7UbDb19a9/3bjAEMnZ/fb29pRIJAy6wvoVRES6q88Z\nSgD3cfn9fmOpQdKXZJZYnU5HOzs7NtFrtVom2z88PDTiEJZaEHi2t7e1tLRkv5/D4TDP6K2tLT1/\n/ty40xCYDg4OtL+/r1wup2azqVwup0qlYqXJ2dmZEZIIvtze3tbz58/VbrfvvbwMi/AYAaPGOwTv\nPKijnU7HfPVAeWAMApNOvqzETLzt9ah2ZmrBo6MjI7UwXKA2S6fTxgRj13A6nWo2m2o0GqYUZoqH\n3RQ4K0bdNGJMAqU7m4F2uy2n06mjoyMT1lLCwDSTZNgvRJ1AIKCrqyv7XqhQICnBY+j1ehYfgVfe\npIMp35tm8/b2VqlUymrpw8NDORwOtdttI97zu+FsBJmKRZzP53VxcaFCoWCmNjikUjZQVkHAx/0I\nX2nwfIYw1WrVsk2olR86NHlUOzPHJeqFSctWlMrwNDge4/G4mbYAjZEqxYME6iNZFUNGdhISpyDU\nQwyiccpkMma3Sz1dKpU0PT1t/mqj0UiFQkHVatWsCtitaS6j0agZmedyOZs08hmur69VKBSsaWQU\nzw4M/4KalslooVCwlFXSsaanp7W0tGT85WfPnpmkCrdVOM+4nF5cXJiTFFh3PB43/5KVlRWjBVCu\nABEmEgm5XK4HRUE8qsUMXyEYDGpjY8MGBrC/IPLgL3dwcGDOmjDBIJTTjXPsz87O2jGJfzJec0zx\nwFPZ5eLxuNxutz755BPN/Y+QTAYZWABsbW3ZSJfweJhqwGqgHJBzRqOR7XjAeiRItdttS4PixTg9\nPbX4CI59TGD4PZxOp+WN0/gdHBwYXRZYE8EvZo6SzPMDiij1tySzF769vdX29rZ53VGXk3RQLpcf\nHAT/qBZzv99XOp1Wv983+4BMJmO7T7VaNZzzv7P3Jr+xpmf997fK5bLLLtfkclW5qlyeh9Pd53RO\nhySgBAhhkFgQsQpigRASOxbABvEXQBBiwSYrWERsAmwIQ4SARRAZlJCk+wx9Bo/lcs2za/ZQ9m9h\nPheP8/7QKx2/LwSrH6nVffp4qKrnfu77ur7Xd/D5fLZ7k4NCzh7OPtSdYKc0NuCh7NhE7OKTDGUS\ncv3W1pbVifCanXl73HhCdqCKUrLAwoO8MzExYfg06nPss4bDoTWAW1tbmpqasuTTmZkZvfvuu8pm\ns2bciPsmY+ZKpWI01gcPHhhOTBnjdru1tbWlV69e6ezszHIHiaVIp9PmcMro/tGjR3r16pWKxaKd\nNsPhUJlMRt/5znfs9JOkv//7v3/j+3+vauZut2s1LkMCuv2joyMj0FDLttttHR4e6vT01DI+JOmb\n3/ymHZsQa+D+IllyJrRiSHh9fW0e0N1u1wIwKRPwUOYkwDyRYEqU3cfHxzo5OTG8/PDwUJVKRRMT\nE+aUVCqVbLxMWYKfB6YxfAaMlBkMwbrDkCWbzd7C1nEJhWnn9XpVrVatId7f3zc8mcEU08Z6va5c\nLmflyPT0tD744ANLmoVkxWsEjpubm7tz2tS92pmdcnVGo2RdU9Nms1nDN/EvZlrl9Nyo1Wrq9Xqm\n0YM4xAMhyaZ7Ho/HkpuGw6E1PkzEYLzhhMSCxFujWCyqVqup1WoZMZ+jGedP7LgoH0iOIgSIaSL0\nTeRe8/PzVoIQlzYzM2MLtVarWbCmJBsckX+Ckz9qGCcFFE8OMlToN4AUw+GwvQeMKtFU0guQdHBy\ncmK785te92oxu91uRSIRu3lOS1d0eeDMoVBIhUJBMzMztxpCSTZEAEtGUwenAFx0TBIAACAASURB\nVIISEzNomkQ54FYP7or0iJoTBAROCBM1mipMCClZCIyXZI3TcDi0EkSSecoFg0H7nXgn4/TE9M/r\n9RqvA7K9y+XS3NycarWamclQOp2dnenq6srkV6AgMBF5v04HVVCYeDxuAxJ6Gqdqh4kttsJ3uv93\n+u4fsQvJP0R1CDHgnK9fv7YG8fDwUFNTU8Z7xg+DXRcuRyQSMWf3s7MzY6Wxe+EzjBE4xuHk//Gz\n2FW5kYyYZ2ZmbCc+OzuzRUtaKqw1Gilgwfn5eblcLtMfwqvm/9PIMgz68MMPLbKChAEeqG63azsx\nnhn4fQwGAyWTSXuQ/X6/jakrlYqhN81m02DCer1uVIBKpWIPHwsZE8lSqWTUW5rXu1z3amfGfwI+\ncCqVUqfTUb/fV6fTUTqdtjIBW61CoaBPfepTZlqIY77H4zHTP3Zg2GkouWHRSbrFx4CQA5x3cnKi\nYDCobDZrmkEnX4MFNzU1ZZpFzAjhYcDNxu8ZvjJsOtCXk5MTm+TBx2Zkz7QTJh9KcOis0D+vr6/t\n/ROp5oxaQ1WCe+nExIQRtmgU/X6/pqen5fP5VC6X5fV6rbTB2oGFjQaRxNw3ve7VYl5YWDBkIJPJ\nWFBkKpVSOBzWkydPtLGxoePjY7ndbv3Kr/yKvvSlLxnrzUk+DwaD5qOGpg0t4Pr6uhH6I5GIwuGw\n+UAgFsUDA9RidnZW7777rqEkoVDIlNIYCrJoLy8vlclkTG+IETkUysvLSz148OCW/xswG/YH+G2w\nWPAPCYfDZpkFerO+vq5Go2EaR06dzc1NxWIxazbn5uaM2wInu1qt2p8J8KFphAqACj0YDJryhVKw\n0+loa2vL3uc3vvGNN77/96rMwEsYCVE8Htfe3p7K5bKePXsmj8ej/f19HRwc6Pz8XF//+tc1Pz+v\narWqbDarcrms6+trPX36VL1eTwcHB5qdndX3vvc9bWxsWBPFRA/i0OnpqY2lCYt//fq1KafJ/OD4\ndbvdOjg4kNvttvEv1gA0jHCjO52OyuWyiUexyf3Od75jkRTX19d2nENWIhoNfBjnT/DwbDZrLv5P\nnz61nXpvb89stgqFgpknkpzlnFKCGyPcZbI4GAz0/vvvm6WZ2+02wcTs7Kyy2awajYZBdeDmHylN\nHBcdMkT3SqWiBw8eWDwuUnhqt6urKyOdA+AjbnVKrnDOp2zApRN1BUGUTLuazabW1tYUDod1cXGh\n5eVl29n6/b4WFxc1GAys9EGZAayI+yfxDGgFXS6XNjc3bTfHow2yEEd+Op02GI3mkbH29fW1wuGw\nhsOhBXsuLS2ZoACrMqKMo9GoBeugHaRxDAQCJhsjqg0L3nQ6bV4alCWUcdFoVP1+X6lUSsVi0UIt\ng8Hgne7/vdqZ4SzDB5ZurKyA2nCsx8ETuyqaxl6vp06nY/wHJmwkvsIhYBGh9KYWhZ0nScViUd1u\nV51OR7lcznYpBhtM41h0p6enhmuHQiFrBpkE1ut1STeRD7Vazd7n6emp1cVYxwKTseNRVrAr4/BE\nuhPTRGpnScbtIJQIN3xiMvi9QHPs4PC+4XmMRiNrasH/W62WTVMZyoDl3+W6V4sZJ3vqOXgI1LHH\nx8eWeS3dEIMQWsIJZiIIv4AGEkiPhchYFt4vIY/Acel0WpKM80C8GmJTDFwQq+LRhlso08br62tz\nJnIqR3j98K2BGeFo8FCQzV2pVFQul43ws76+bhAa0WbYC2BxCwzo9/u1v79vMCCLv1QqGTccqq0k\nKy14gFH68DAwmUUIzEj93//93+90/+9dmQHygMhzfX3dLKo8Ho+JRHH9LBaLdtNpYphsRaNRJZNJ\nq+do2rDJ5c+Li4tmmYViAnwWg29nPR+JRCyjmzgEyPlOe1lwYSA4iEUEQiIEdbvdWl9ft0WMPRjY\nLva0TiEp5cBwODT3ISy5IBzxGV5eXuqdd94xTxFKMup7MHmsCYA8sURAxOucRgKfgvGHQiGtra3p\nX//1X9/4/t+rxYw6GBz1/PzcoLdcLmch56VSSeFwWNVq1XZCsjkWFxf1wQcfaGVlxZovduPhcKiT\nkxNTnRBjwM6GnwT+E0SgsVujMBmPxyqXy0bdhMhTr9dNxSzJGqrxeGyu/2DWWGXhlE/YUDgcNmPw\nXq9nyhJcnLD4ZSceDoeq1WqmSmHS2W63tbu7q+XlZY1GI73//vtKp9P68MMPtb29rWw2awsRnjMY\neqfTUTabVTwe1+XlpXFZmG5iz/X69WubCErSs2fP7nT/71WZwTQJ2iPUTAgvHNtQQkmkogz4YWUI\nf5ZuuNLASxi50HQB7Um6ZZ3FMAMvDGiOkUjE4oPhYFMaYX0FR5hcb2csXCQSMTU25uB8/9zcnFng\nEiXM70OuJMkmlohc4Xwz8aQXgCAFQYuFF4vFjO/BKYQ9GrZfDI/gtVCW0Y847Q/Oz88tavlNr3u1\nmCH1TE5OamlpyXR0ZFhLMjkPgwNQChYLE0JqTwSo8HOxu0LK7/f7LYePnzk7O2vH7Q8vBlCWSqVi\ngw4ciDB+xJs5GAxqcXHR4tMGg4EhL8FgUG6326T8sOfI8IOTTQoAE0o+l/X1dVOZQ/aJxWLGv56a\nmtL29rbJoHgwwZCB6LAroGkNBoPG2mM4sra2ZnYGExMT2traUiqVUiKR0OHhoYrForxer0Xdvel1\nrxbz2dmZ2u22er2estmsms2mEXb4wCYnJzU/P29yfuQ/TPN6vZ6KxaINNMBc2dGGw6Ht2CAas7Oz\nJvGnoy8UCre4z3yddDNJjEQilgPCzwJRIQgStGN/f99YZ8SvkXlYKBQ0NzdnZQRpADgNESMBDySX\ny6leryubzZpMDDIWPQelAE1lNps18hAlFwSoZrNp+DBK71qtZiGYnDAul0urq6s6PT1VpVLR0dGR\neXogHIZK8KbXvVrMGIsEg0GLXaAZw8CkXq9rb2/PJk7kY0Muos6Fg8AOTR2MSiMej2s8HttIl7B3\n8qYxmoE7EYlEjKI6HA4tHoILp02yVfCCw5oWIlG/31c4HLajHUvebrdrRz+wGSgMnnpXV1f2fsne\nhmONcxJeHezQ3W7X+MrBYNA+KyamvBZgTESv8Mk5HZGzRaNRLS0tmeKbMgqp1V2ue9UAEkLTaDQ0\nGAx0fn5uN7PZbKrZbJqpCVq+999/X7/4i79oqIIkVSoVVatV26lpjDAzYVc9Pz/X4eGh5ubm1Ov1\n9OzZMy0vL0u6Idpz5JZKJeNBgJiAL0v/SV0lfLLZbFoTRqPmHA03Gg0Tp4LzOqExdlsSnjqdjjWp\nnBgY0zidQJ88eWLvBRXMw4cPdXl5af0INTY+dziMQuUES9/f39ejR4/scwARcrvdKpfLNgpvtVpW\njn3knO+4iNCNxWK3zKup9YCsBoOBKR7Q5UE/ZBdHm4YaGSI+cFQ0GjX+A7smYk3MAqkngfGgObKD\nUn9i3A1ENTc3Z0gH5UQ4HLb6lp8N95jvY8JJeUPjR+3NMb6ysmK8Dv6Rbk6HyclJpdNpgy7RCobD\nYaupfT6fxSJjIxYOh+1EYxLLQ8kiphZHq4kGEhuy/7W+GePxWI8fP9Yv/dIvSbphvP38z/+8tra2\n9Au/8Au3pPt/+Id/qM3NTe3s7Oif/umf/sufeXV1ZS6UcJb7/b6urq6MrE88mjPzg5TR6elpiywD\nDoO/Oz09bU5IbrfbBjAQfLDDYmdD9EkQPCGQXKAHWBGwU9KUjUYjlUolY6Sx88IFgd6Kqz+vH/9m\nr9drJxT4NrFnTPSazaYNYZjYwRR0fpZEJDNNRVcpyewFnOodRtbkJZKBeH19rYuLCzUaDV1dXdnn\nRZ/Dyfim1/9YmfGnf/qneuutt+zD/OIXv6if//mf1+/93u/pj/7oj/TFL35RX/ziF/XixQv95V/+\npV68eKFCoaCf+7mf0+7urjVkzgsTlsvLSxt2pNNpRaNRKxOoXa+urvSJT3zChibwLi4uLrSzs2PO\n+zj/8L2kMmUyGRUKBfn9ftvVgbhAHjiOV1ZWzCgcKA/+A26c+EdjpOj3+w2nHY/H6nQ6FqiDITko\nCPJ/fudP/uRPqtls6tOf/rSFDEk30qRIJKIPP/zQeNy8XqizzvxAp8u9dLOjk6gKBTUQCGgwGJi1\nw+rqqo3ZETugrKGuXltbM2dRhi6cHne5/kd25nw+r6997Wv6zd/8TaNs/u3f/q1+/dd/XZL067/+\n6/qbv/kbSdJXv/pV/eqv/qomJye1srKijY0Nffe73/0vfzZcBDjF/BkuAiYwg8FAT58+tXoP7Zok\n7e7uqlQq2a6Jycvp6an29vYMNaHuZqDSbrfNC6JYLJqypVqtqlKpqFQqGecYE+56va5isWjSJepb\n+L1HR0cql8s6Pz/XycmJ8T/gV3S7XbVaLTWbTYXDYdP0jUYjffDBB2a0UqvV7NSBEcdnn8vldHx8\nbNKo/f19SbLxs5OfQk7iysqKKpWKvXd4HrASoaHW63VNTk6qXq8bPo8KHZ5Jp9NRt9v932kC87u/\n+7v64z/+41u7a6VSMeK5czcoFovGc5CkdDptH9wPXxiaXF5eWh14cHBgzYnX61U+nzdUAU/ler1u\n7jvUhFAbvV6vOd/DApudnVWr1dJwONTi4qLa7baVLZJu+Q63223bbRGkYnlF44PGDystHkK0fIhT\n3W634cWUIwwhsBqTZNZk19fXpuZGQY5BDONyXJogCMHfhu7qtL9l5x+NRmYmA0VAuulN6CMo52DY\nMWbnBOh2u4ZmAIM6y7A3uf7by4y///u/VywW0+PHj/X1r3/9//o1/29v7L/6Oxaz1+vV8vKyxuOx\nmf31+33Nzc0ZtZLp0+XlpRKJhOnfut2uZZ7gV7Gzs6Ner2c6wmazaePi169f20CGce1oNNLa2poZ\nuVB3wktwuVx66623zPLA5XLp4ODAeBhwR+A2fOxjHzNGXyKRkNvttmxr6QaSZCFhXLOwsKBSqWSL\nD641HIx0Om2LaG1tzUbom5ubJpsi0ZafwWvCbNHj8VhSQTgcNssCeCrEYMzMzFjNDgWAiGZ4J9TZ\nd7n+2xfzt771Lf3t3/6tvva1r9lx+Wu/9muKx+Mql8tKJBIqlUqKxWKSbjLqnHKafD7/X06Knjx5\nYg47wWBQH//4xzUxMWFhjnt7e4rH4yavOjk5UaPRsJraOa1Dp3Z9fa3Dw0Orey8uLmwRkduX/Y/k\nJmprpoWDwcDCJ0E62BlPTk7MPMbj8SiZTBpbDhk/i4aca0m2k2EvEIvFVCgUjN3GFBMR6XA4VLVa\n1fb2tsbjsZnfMHpmRO5yuUzixABjdnbWCFA0fNTpjKTX1tZMnkVZRWnFOB5hbj6f18LCgv2M4XCo\nf/iHf9DZ2Zk9aHe5/tvLjD/4gz+wWvArX/mKPve5z+kv/uIv9PnPf15f/vKXJUlf/vKX9cu//MuS\npM9//vP6yle+ovPzcx0dHWlvb0+f/OQn/68/+xd/8Rf1iU98Qu+++64k2ZHNhz8YDKyr7na7Wlxc\ntGxpJwSF9AgWm9frtQxBVMlOLzZyqcGBGfdOTEwomUzaTgcclslkLLQGIpDH4zGCO/CeJJtiMtSQ\nZDxlFgSvEygRJ1OgONhvoBBIvOCdgGK0Wi1DMdxut+HUPEhgzZFIxAJ4KKNg2aE6ubq6UjgcVqfT\n0XA4VK/X09LSkgKBgPUZ1WpV7777rj796U/r8ePHevDgwZ3W1v/40ISS4fd///f1hS98QX/+53+u\nlZUV/dVf/ZWkG3vaL3zhC3rrrbfk8Xj0pS996b8sMxhR93o9vfPOO/L5fEomk8YJJq4ArRrTNGTy\n8/Pzury81Mc//nE1Gg2z0KITB/VANIr+jt2cBdXv9+29FYtFJRIJ+zk0ifAmsNolLjgSiSidTt+q\n+8kqcblchoZgowtPhAWHYsSZvEUCQDqd1sp/BK9Xq9VbDyCTRlw5vV6vtre3dXZ2dosExQO6v79v\nXBNIRIS/U18TL7y2tmYMwGAwqHA4rG63q+3tbf3bv/2b0um09SR3WkvXd/0JPyKXy+XS7/zO79ii\n3N/fVzQatRIDhyF2WOysjo+P7Qh2Ouyvra3p8PBQ29vbqlQqymQyJuGH74Bg1efzmXLEieWy6xMA\n2e/3NRwOtb6+rpOTE52dnWl7e1u7u7u3wjfL5bK9Ruf0LRgMmjtoPp/Xyn/kVpPVJ91YcKHMZohU\nr9e1vb1tbkWTk5MqlUpaXV3VycmJNZEbGxvK5/OWaVKpVGy3Be/mdLq4uFA6nZbP51OhUFA0GjWO\nh9/v18HBgXFYcFldWlqymrvdbmtxcVHVatW42o1GQ3/yJ3/yxov6f3xn/v/yokFpNBqW+NRoNCxh\niZvSaDSUTqft5gIfIeuhlq/VakokEra7MqigDCFq4tWrV3bTz87OLIDeGcKOcpzwSupLTMWJaQBp\nmJmZMcdRyEWM4WdnZ/X69WtJsgELr5X4YKaeoApIw5aWlvT8+XMbAjWbTetVjo+PjexEiDsqEMoe\nhjHwOZLJpK6urpTP5+Xz+czgBg8NxvOSDAZFq0jd7Xa71ev19PLlyzvd/3s1zpZk2jkmdewEqC7Y\nLVlAlBhMCdfX120cTBA69S2kelATLGFx1cRzDtsq5w1nSudyuWzUvrCwYCUT/GMmloyZgQixFwN1\ngCssyXjGUFur1eqtiR5fw2dDk8kuSqY1mj6I9kBvxFSQ17K4uGijbz5fGHg4PR0eHkq6McUBDWHs\nDWJDNAaq7Y80gI6LnDxJ1vA4nXgYcweDQdOhSTcLA75BuVy2bD/waSZxoBTIoTgaOf5RM/d6PZMN\nOfkSZGGDtlCP4tkMz0GS6RLxmiaUEivYdDptzDc4FJjILC0tGVwIdLa/v28li8fjsXzws7MzKweA\nEhk7w42Gz+2UbwHNEa2GHZgkewAwEofADyqzuLiocDisyclJHR8f29Dqfx3O/P/nBai/tram7H+k\nJHm9XqttHz16ZKNhdjx2LsjlxK/hmUYq0sTEhIlcudGhUMiaymazqfn5eWuq4ENMTk4qHA6bypnM\nu1KppHq9rp2dHWOUTU5OmqFiuVw2PSBDDR6g6elpM7IBtQD6c7vdRtlk4RQKBX3605+22GIQFPB1\nPJ83NjaUy+VskojV19TUlJLJpHw+nzqdjnHEObnAvyEUEdS5sLBg7+vo6MjITpC9Njc3rQHH8+8u\n171azNINgQfoBx5FIpGwOo4PEossl8tl7C6UxMB4kGY6nY7t3qenpyZaPT09tZtJTQ6Zv1gs6tGj\nRyqXy7eI6ox5S6WSBbwPBgOFQiG1Wi1jzEHYxwjG7XZbGcAO1+l0DGrk505PT+vp06dW+lACMPp2\ncotRfIONk9/HZJZoMzDmRqNhWPZwODQ3f8ombL2wV+A0RBVzcXFhfQFkJxrAWCxmpcmbXveqzCCT\nDt4EdSPDkG63ax86NwP9HHUm08eJiQnbcSVZs4SKIpfLWRANuPBoNFK5XDZVyN7ennXmvB74CTRU\njKJPT08ttIYGEAk/Lp2tVstG1RcXF8ZRps6ltBoOh1YaVCoVDYdDdbtdG2TgwYH2jvjiQqFgR/7k\n5KRyudytfBSCNiXZ1I4H4eTkxBo57HahEBQKBRO5YrSD6xSB8HC+73Ldq8WM4bUk48tCP2y1WqpW\nq2o2m3Yzj46O7O/i8bh1+JisUMdic0WcAg0Li6jRaNjPRNwKVzmTydwKjwSD5bjlJnNywMJzTtU8\nHo+ur69tMWD8fX19bYlPzWZTsVhM4/HYcGVI9rVazXjSPp/P6nEWOicBwx18QWKxmDkxTU9Pa3d3\n1+wM4LHwcEFSYgLL+6/X60Z3pbyidCHfcHd3V1dXVzo4OLjT/b9XOPPv/u7vyu/3G2mIsWkymdTx\n8bGFlkOcSafT+ud//mfjSTBOzeVyRiSnG0cWBQRGeYAcC+MTLFqj0ajt2NfX15qbmzMoEEMZmisW\n92AwuEVKwmcC/i/IyGAwMF4JU0WgMPSB8/PztzjPiAiAC0ejkba2tvSDH/zA8k5CoZAGg4EF62BB\n0G63jfNycnKiVCqlRqOh9957TycnJ9bcUiahpuEhYWJKGQd8F41Glc/nzcTx4OBAf/7nf/7GOPO9\n2pmph+mcXS6XGfTBV8Ce6vz8XN/5zneUz+eNgkjdC+rx5MkTo5AyemY3pTzhpmFKCNxXqVSsbh0M\nBkZehyONKJTXjXfGxMSEiT6JZSBlleklmX2S7EHb29uz3BMgREQGWC1QWzO0AJ5rNBr2eprNppU3\nUGAZi4M38wAdHBwYXAhjD7ej3d1dq49nZ2dVrVYtKxt/Eco0hjB3NU68V4uZSRmuRPF43KAo6mjq\nOASbW1tbVvciiZJk42YwZjgW/BnFB/wI0A+I6Bz5wHtYZ7ndbuXz+f/HawdFubq6Mp8MJx7LUc4D\nB8pBwwnRCZf+hYUFxWIx+0yopZ0JAvy/4+NjY7Xh9Qw9lVE1E0nwdupqbG7r9bp53fH5bm9vmwIH\nKRUbCq+LB4GT7i7XvVrMTm4t0QWA9dRyU1NTGo1Gxv91GmO3222zwELZzJQOGT88ZhY6SASoA/a1\nyL6A9/h9yPn5e8oJIDxIT3jQ4TjPjshAg/fGKePEjUFiaM5AHHDSR/UdDoctxBObBD4fiEZg4tgZ\nsNgjkYg56zujhMHPz8/PrcanvIjFYsZ/icfjRot1u906Pz+/pVZ/o/t/t+Xzo3URmChJz58/1/Ly\nsjV0oBIcd4lEQh9++KFh0dy0/f19PX36VD/1Uz+lk5OTW9MxYCY6fmRTzWbTlNHpdNrMFn0+nyTp\nxYsXWltbsxuPyJOaF/SDnd7n8ymXy1nJBOehVqtpbW1N7XZbpVLJrHv7/b6ePXumnZ0dKzkYqV9e\nXqrRaOgTn/iEGcYwwSRrG0d7NHzIvmq1mpknTk5OmlUZP2NhYcHEAk71dSAQsLpfkpUUWCUALdKk\nSzdig29961t3uv/3amcmI6Rareozn/mMOezEYjGtra1pcXFR6XTa8klwwpybm9Pa2ppBRp/+9Kdv\n2Vu98847xiGAlwG5nwD3UCikBw8eWP1HyQFbrdfrmTIE1h4nCXFnq6urhlJgTDg/P2+U0UwmYwsS\nrZ10Qy5Kp9MWJ7G6uqqHDx9KkrHUGLWTle3kbD9+/Fher1dra2taWVmxv8OxicD4RCJh0Q7QA2ju\nJGl9fd3KokAgYJg56I/L5TIfZiaOTDUlaWNj4073/14tZqRAgUBA77//vnX2w+FQr169UqVSsey9\n4XCoYrFoPsoHBwc2IDk+Prba9PLyUs+fP7cINQYINHytVst24sPDQ2WzWVM1w6NG7UwMGc1SrVaz\n/+71enr//feNTASBvlAoWMIsmYbj8VgvXrzQ7OysDg8PDWsGR8Y5Hx4Ho22oqZh/w28+ODgwjNup\naURn6PRXbrfbqtfr2t3dNRZisVi01w8Z/9WrV6Zupw9BQ1goFExggIPU+fn5nQN67tVixuWHJoTj\nGt4totSXL1+amiMUClno5NnZmVZWVlSv1zU1NWXKj3w+r62tLUk3+DXEIXZ3Jov7+/uWTFUoFEyo\nSuKpJKtpDw8PTYPo8Xi0trZmMiWXy6WtrS2NRiN5PB69ePFCjUZDsVhMlUrFYoElaWtry1Jf4/G4\ncYX39vaML91qtRSLxW7lF+bzeds9GS5NT0/r5cuXNsigkUaBDb49Pz9vVFWQDY/HY5zxfr9vzRx2\nX7lczuBQQuShrZILyMP2pte9wpl/67d+S4lEwrzjUDVg2geqMBqNFIlE1Gg09Pz5c33iE59Qv9/X\n5OSkvF6v9vf3jYSERWwoFFIwGNTp6akNE0AFXC6X1b7FYlHvvfeeNW/UoRD0B4OBYrGY1eCEQLbb\nbR0fH2t+fl7dbldLS0tqNBrGvyDXkNt1dnamVCplEzwayvX1dUtvoiFst9va2tqyQCFOHExmGo2G\nQXcs7I2NDZ2cnGhzc9PyT0BNYPdNTk5aFDL4NEKGSqWiZDIpSaaMx0vDKcPCGcnlcqlcLusrX/nK\nR3xmSXZUgRqQP7KysmIxC9Vq9VZcGR06DDiO1svLy1vulETkssugvAZ+g1DDDaxUKrq6ulImk7H4\nsenpaUMy6O4xdWSR02QeHBzo4uLCav7BYKDj42Oz4D0/P7dUVYjzEHo6nY5p/VCXUxaAxdNoktAK\n/4SaH7ek3d1dE58iZqB/AM6ETReNRo2HAptwNLpJwkXalcvlbqnFMXl32ou96XWvygx2F0bYHNPO\nMoImDBU1u1S1WrVxL0cjEzEmfDRm7Bw0R85jGa0fGHCr1TK3IZo7Rr10+OCyNIOkpSKgPT09veVU\niggAwSn4NON1IiqgjXq9Xi0tLVk96wywZEIIc87j8SiTydh/w8YD5QiFQkokEjZ6l2SvBRN34Duw\ncvSKOEIx5aT8ajabpvC+y3WvdmakUFjNIrwkwMbv91uWNKmhRDkQ1YBknh07GAwaOwzJFbg0vw+v\nCMa3ZJvQ3DnDzqnpUT/zUMH1GI1GSiQSCofDqlQquri4MF8NcGOfz2fk/V6vZ4bd+XzeBhHOdCxK\nEDBd6mOaMHSMDEoYo/P1c3Nzdvqk02kb7UNsQjh8fX1tw6b19XVzP2KSCY8cEezMzIzy+bxmZmas\np7nLda925na7rWAwqGg0quXlZZO1u1wuPXz4UHNzc7Z7Li0tWSxZJBLRxcWFwuGwvF6vNjc3b+Gf\nExMTNnalaZNk5CDwVGee9szMjGKx2C3ZPzRQdn5IRYuLiwaH0SARNhSPx21Ak0gkFIlEbu2OTPJQ\n14RCIa2srCiRSBiUxm48NTWllZUVra+vm38HeSiwBOG0zM7OKhaL2fuLRCLmAfLo0SPzvMAIXfpP\nR/94PG6nkdvtNlNEHtx4PG6/j4fT7/fr4x//+J3u/73amb1er/7t3/7Njl7EqT/sgjQ/P6+joyNd\nXV1ZdDBZ1+12W8ViUe+8846azaZhoiwKUIB+v28+bk4xaKfT0cOHD1WrOcGQ3gAAIABJREFU1dRq\ntRQOh1UsFjU7O2s+xDj+QNvEfvby8tLiJ7797W+b0oNJG8gLItpQKGSwFpDd4uKiNZfD4dD0hk4v\njidPnli6QK/X0+HhoZUHo9HI1Off+MY39MlPftK8P7LZrGkYB4OB3G63tre39eLFC0k3aFK5XDYj\nxXQ6rd3dXePKoCckf5AELPJUPsrOdlw+n08f+9jHzGQlmUxa9+12u00gWq/X9fjxY+XzeTNXxPSa\nCd3CwoK53R8dHenx48e6vLxUKpWy0TLlACNupEDESiwuLsrj8RgRHg5HIBAw2zHc8dfW1lQqlUyg\nur6+bogB3OB+v6+FhQWzO5Ck5eVl9ft9HR8f2+/jwcSLo91uGzrB96DALpfLVmsTaQH0trOzY9YC\ntVrNyid0js7sF143LEX+G0gTCzN2/aWlpVvqc4/HYzYHb3rdqzKjVCopn8+b69Du7q76/b56vZ4p\nThiaABVNTk4aHAUUBrNOktWWKExgluHRBjONpqxWq6larapcLhtq0Ww2rTHFED0UCimXyxnpHkNH\nPD4KhYJN62DAwVsmWjgYDNqonjg2HPNpBDkJUGtzHR0d2YMEBQDHUZpTgjOpcaGikvA6NTVlu76z\nyc3n81ZSdDod+7zA0Tudjur1uoUXIfVaW1u70/2/VztzMBg0a9fNzU0tLCxYqtLi4qJKpZKSyaTa\n7bbm5+dNIb20tKRyuax4PC6Px6NHjx5Zguj09LRWV1dt/Lq0tKSjoyMj1MCDZiDx3nvvmacEbLV3\n333XmG69Xs923JWVFY3HYzOqkWSnw6c+9SmbJII0QC6am5vT8vKyNWnD4VCtVstqVwYkWC9AIZ2Z\nmdHm5qb5UmOsHolEjIcRiUTk9/sViUT0yU9+0sb1l5eXeuutt1QsFm1XBbLb3t62aWMqlbKHhr5g\nZ2dHr169UqvVMlvgubk5pVIp5fN5S7a6K9HoXg1NfuM3fsMgITrpdrutVCplNSP/JrPEmXI6MTGh\n8Xis169f65133lG5XLZaEoU2eDVm4pB2ut2u1c/hcNi80+jeqSN9Pp/i8bh5aUxPT6vRaMjj8dzC\nv7HgwpUUco7P51M4HDY/EElG3kGcSgxctVo1iihcCprPTqejZDJpPnWRSMTev9vtViwW0/7+vuHm\n7LSj0UiBQMDci3itPNjg0WShMMIGveHhaLVaZpDD9+VyOf31X//1R+R8SUokEqYoJlSHD5+aWZKO\nj4/ta9rttn2g4Lpo8tgV8URuNpvG8aWZA77j4QiFQnYMD4dDTU9PW5PGpMvpH4eUi2MaFQowH40d\nYtZ0Om38a4Y94OhYDeTzeSPZNxoNvXjxQt1u1zSDUD2JQ5uYmFClUjGrX6dD/3A41PX1tfnHYdsL\nn1q68cOjDAPu6/f7Oj09VT6f19LSkqngocrCg0YUUavV7hzQc68WM5M/3IjwBGZHddItkQDBTcDv\notvt6vT01PDjcrmsaDRqi1iShf/woKAKwSQFDR1kdPyY0dzBCeGhQODaarV0cXFhmdoMWYATEeJi\nGwuDb2FhwfByvo4dGGSDxcMCBslBKc3fORcdU0V8qmH78f0oaFBzHx8fW+Ir/h5zc3PmSwfGzVQQ\nu7J+v69IJGKlypte92oxX19fm/EKkQW46ESjUc3MzCiVSikYDGowGOhTn/qUqtWqZmZmrEYmw3l2\ndtZMBl0ul/b39y33LpVK2QQNqiaORblczpojLG3J4HNmazPJm5ycNPdP+CM4bTJxc+LI7OBOsjxD\nm5OTE3W7XYO6FhYWFI1GbRzPpBEEIZ1Oy+VyaXFx8VYNDE11bW3NyPoQ9p3DHszRiaHALwM3UkqW\nqakpLSwsmNVBNBq1gRVBQphc3uW6Vw0gShIIRoFAQJJsRyCOF78LcjjIzmNIgJXVzs6OTk5ObDrm\nHLmSpko9yw764MED+Xw+xWIxTU1NKRwO29SQZnF+ft4wY6c2kXE2pQDJWTh/zszMmGqF90tjB6SG\n7Ri7PwsRB9KpqSkzSoeMxOfEg8OYPJFI2EPBBA8YT7pJOMAgEimZJNNLwgDkveFiCo+bETfv7SN7\nLsdFkwOdEB+HZrNpx+NgMDB5/bNnz8wBk0EIu1YoFNL3v/99G9nSXNHZr66u2k5Nbcs0b2pqSnt7\ne/J6vVZj0th1u13L9oYx1mq1zBIX/BfRLNpFFj44NrU+DRgDH2zCkHhR13PM43rE109MTNj3g2cz\nOkesgDMSZRWT0CdPnpgkzSnn8nq9xmceDAYKBoPK5/O28yPCpQSivPiInO+4aOiI4cKWFRIO9Wyh\nUFC/3zfuA54R/X7frKuQB4GvUjs2m01ruLDLdbrhn56eKhgMmgr58vLS8kuwg6Uxi0Qi9tAxhOA1\nYlVQrVatURqNRtrf39d4PDZ2IIlWkqwX2N/ft4cMbNtp5gh2jPKjVquZTUKxWNTp6amx5tiJwYRx\ne2q1WqZc8fl81iDCgOMzQ56FJhG7tH6/r1qtpkgkYiXZXV1A71WZwW4FqYhjHWUFwxDYdSwYBiMM\nPeAis1MvLS2ZLF6STdewtuJ4JZ4XXzp4yejsQEyq1aoJO6WbxvXg4ECRSMQcjhhYBAIBJZNJcyKK\nx+NmPYa6GkhtYmLCIK/FxUVTgXc6Hb148UIej8dKjI2NDbOndYqA0+m0LXTG5Cx0HjboAtTRUAIW\nFhYM1YBc5fTNQCwxHo9NuIuz08XFhbklvel1rxazU91MvsiDBw+MI0wdWKvVjNWFOrvZbGpxcVHd\nblc7OzsW/4CY1WnHysKPRqO3GiRMxMfjsZULkkyizy6ayWRsJ7u6urIxdKlU0uLiolqtlnw+ny1u\nGjzG5CASOA1RU9dqNcv/vry8tIna4eGh3nnnHdvBMXfkNKjVakqlUlaLU3vzENHQgZi0222LloCo\nHwgEzDs6EonYZ4ASBV+7+fl5o8tCG0WTedccwHu1mFEKA4Mlk0m9fPlSb7/9tmXvMUjp9/s2qMjn\n84arFgoFm/Rls1llMhnDm3H9YVqI3wQ71LNnzzQ5OanNzU0dHh5a+KTTRObi4kLVatV8JpLJpI13\ngeq8Xq/29vbM4RNl9PT0tPk3Y0/AUV+tVu2Ih6yP716/31er1VKxWNT6+rqeP38uv9+vTCajw8ND\nI/WDYoCa5HI5hUIhvX79Wufn51peXrax+vvvv2+oz+npqXGUXS6XTk9PjZTEP9hxcX9Aghj3c+Lc\n5bpXixks2GkQnkwm7XgloPH6+toSlTBdYSS9srKiV69eWVopuzNTKgg0NDpwjVkEmBtCTAfqwuCc\nEogxu/SfSU8TExO3TLmZpqE4icfj9rs46lFw8L0oXhgpO1OzGH0zAYWsf319bdRS7HslWaOHj4iT\n3kp9DlIxMTFh9ADISWwchAZBbcVZlKabBC04Im98/+/03T9iVzAYtDoWwgxDAOnmeHXeqPPzc6M3\nohZhQOD1ei0/hCQqbioEeEhKOHhSv3Jjpf80QGdahq0rDkHD4VAul0uRSMQcSDkB+HsoqmdnZ2YF\n66yX8dYbj8cKhUJGueR3InIFAsNlyWmGzonU7XYN/4a0BKRG1NrV1ZXi8bi9b7fbbWgJECU9htfr\nNSNJamufz2exc7iGYmB5l+te7czZbNYM/05OTozVRWANGSQQb6rVqlZXV1Wr1bS6umrHKkdlv983\nK1cwWjp7mjR2bBYoDVEoFNL+/r52dnaMpMRujss8FlfFYlHRaFTValXhcFjj8VjZbNZq81gsZnRU\ntH9QTg8ODqz2DAaDKhaL1mDCECyVSnrw4IG9Xpo3vOAajYax93Dm/OGHBj7H3t6eNjc35Xa7FQwG\nlcvlrGllmolDESm11WpVZ2dnlguTz+dvqeXpA0gNeNPrXi1mCDCoPBBmut1uRSIRy/twJrDu7e3p\n4cOHqtfrt7BoRKTUnV6vV6FQSAcHBwqFQrq4uFAkErGIMxYBChWv16tYLKZer3erSUS10e12jdzD\nAwEpiJ2vWCwqHo8b/4NhiCQTqzKGZ1gEDCfJ0Bwc7sPhsKE7EJV4HXxWlGe8B7jgeIYsLy/b6eMc\njmBHQI1PCXd9fa1MJnPLR45NBgU8Cnd+75te96rM8Hg8ZkWL+SAoAolIcCJmZ2cNdsNqih2UehP+\nLTq2fr9vmXbYadGkQQxyRiEgzzo7O9Pc3JxNDPFYwzCQnZaFAeEJvSLyLmAykqwkWXwy0BcDHIS2\nUECB8qivmQ5STkjS1dWVZmZmrETD+gtSFt7S1P/s8KRZjcdjm5TOzMyYb5+z4aZMWVlZMddTNIhw\nyN/0uleLmRqYtFE6eD7sWCxmuGqz2TSZz9nZmcWNkSdydnamvb09U1gzASyXy3YDwHapv4G2QCMk\naW9vz0JqpBs/PKiowFbhcFjX19eq1WoG/xUKBbXbbZPn85poHMn45jTp9/vmiMQOx88/Pz9Xp9Ox\naWalUlE2m5Uks0/ga1GiSzIUhuRW5zQzl8upUqnYQyHdNLJMK1+/fm0nG5NSYibwxoNsBAR5V2ju\nXvGZf/u3f/sWJ5iyY3l5WfV6XY1Gw8gtBPS4XC7jLYAMEMD44Ycf6r333rNAn+npaXW7XRNsOlll\nNIVwh/E2hofhJOczBYNMVK/X1ev15PF41O/3TbZ0cnJiRzWoBuFAgUDAmjBUzljastChd15dXRnu\nfnFxYR7O1O3UqwxxvF6vIpGIxSmjsEHVzo4cDAat2WX3RzO5u7t7ayjV7XYVi8WMrMRDhztqMBhU\nqVTSn/3Zn33EZ5ZuJl2oiIF8Wq3WLe+Ls7MzlUolQx92d3fVaDTMofPy8ibkkpFus9m0bA8sAmq1\nmmWkQESHD4zbaLPZVCKRMIy41+vZz0X+z65OSQH/AgsxnIE6nY6KxaKVLzSEGIE3m03DmJGMOeMj\nYNLxkGOgzrgefvXl5aVyuZylcJHzUq/XValUDHFA4oULPg8o8RXQT2HY4ewEB5vgTJpK6LZoFN/0\nuleL2RlQg0cFGX2SbrG6wEpx49za2jKviZWVFVNkV6tVRaNRq7HBcweDgcFlksxX4/z8XIeHhzZp\ndDLgYKrhA91uty07BYonbkPsdtIN5Mjr5aZHo1ET51LnknzFbk1KFWUKPh80jBDxCc2Bvkm5AAkK\nSBBaJ7Itt9ttDTbG47gjYeELbg4hKRgM2u+kHCNqAz++N73uFZoxPz+viYkJ42BMT08bS4w8EI5V\nMjWkm4eg3W5rbW3NohRmZmZsAkiZATSGAUu9XlcikdD5+bk1VpFIRF6vV5eXl6YswfwklUpZw4by\nBLYcfs3RaNRsXkmTdbvdikajRjaKRCLWXKVSKStFwHW3t7fldrttV0wmk0qn06pWqwqFQtrd3bWh\nBwjKysqKoR9IsDAQbzQaCofDFpxZq9WM7+x2u43eWS6XTUoFIQlpFBmCo9FIm5ub5hD6+PFjFQoF\nY+Td5bpXO3O5XLbjrFar6eLiQgcHBwYLPX/+3EqJ0WikarVqXAoyrmdmZvTBBx+YJRY+EXhbcBMw\nR6FWzufzOjk5MbXI7u6uLdJqtaqjoyNls1nbbaFEQrDhgcO3gnKn0Wgol8upWCyq0WhYXfzhhx+a\npInXx7GPfwdK7vPzcx0fH6ter5sYwcmlxqqA9wbjbX9/31hzzWbTkAi8p4vFojEIi8WiUW/L5bK+\n973v2dAHu1086YrFovr9vpUrNIjktLzpda8WM4vBObFzog1gus4ywTmqpft2kotojiYnJ+2oJCca\nZUur1bJFxiSNoHN+Dg4+8CooV2Ddodm7uLgw6ysmbtINBIeDJ99HaCSlDLsgaAZkfupZThVJ5t/M\nZ8KJcnFxYVIzuMtOfST2Y0z1mJiCbTNq73a75nmHsoZTiEkh9TunB0y7N77/d/ruH7ELyyqsVaen\np40rgASJkS5KDwYDMOrOz88ND6ZMgZMMTspioSsnYZR6GwMYMgElWW2JzAnWGMer3++/paL2+/2K\nx+NmSghkB/ckmUyqUqkYIX5qasq4zyATzhE3/s1MMSORiDVqmBfCaYErAoGK18ygBLtgp0Kc+pqy\nBWMdeBsLCwvKZrM2LMEBlYFJrVb7SAPovGh82L38fr/tLkzG2K1pkFiMzikcuwcYLdxljlnAfjR7\nIBu9Xs9eC7gzuzuLkIHO1NSU8SJARTBsgUcN/wPeCDkhExMTVi7QzMI4Y/DBiNjv96tYLBoExtfw\nu3kdSKFo/oDUsOyF3wzygM6RJhPxLQvS+SCzU/OZ8jp4wFH2xGKxO93/e9UAMtGCTN5oNAx/PT09\ntQ8UHJabgxUrgP5oNLKIBGiR7KY0iNTNDFGwlkLxzNczfkZlgrMmOxuhldTf/C7sCQaDgVZXV42k\nMx6PzUz88vLS5Fl4X/BQQSWlZk2lUvaQJxIJFYvFW2iF1+tVrVa7hXRQKqFRnJycVKVSMZ8RTq5m\ns2khm+DOxWLRSglONiBJ8HZKH+Rld50A3qvFvLy8bE0bzjrhcFiJREITExPKZrN66623jJ6ZTqfN\n6IQ6lokW4+GlpSXjDXAkT01NKZFIWEOD7zCjcGiaEIWwzqLx4iQghgFhKnzlSCRisCBsOthooBKx\nWEyhUEiZTMbgOY733d1dK0OoQyH8MxiBujoxMaHl5WXL9gZNwf6XRcvOD22UoQcxakwmSe+iph6N\nRuYexbCJrEKszN5++21J+oho5LwKhYLZo2azWc3NzalarWphYUH7+/taXV01dx383MBsIQFNTk6q\nUCjYcZjL5XR5eWn84kqlYjAVpi/sfpD9cTbCgvb73/++FhcXlcvlNDs7e0tyRe52NBpVvV43Mxmn\n1wQN18nJiT0ouVzO4tnY9RmQXF1d2eiY6SEYM9M1hkBQMF0ul1Kp1C3TmefPn99y6icQKJPJqNls\namNjw04IeovT01MrTThparWavF6vXr58aW5MwJehUEiVSkV+v1/f/va373T/79Vi5kjDHd9ZB8di\nMauPaZjwPIPnzPejwi4WizZNpP4G02UQQCPFcU1uBwiHJC0tLVkNCXWTBQQVkiaVwQHWtCyQQCBg\nntLU7uTwzczM2KDGSehnvEy4DwMeamGnlzJNJ8bq7MCk1pJ9iC8eZRpax1gsZkYyPEypVMoYhrVa\nzbxLSKgCQaEfePfdd/XkyZM3vv/3qgEcj8eGYqBB++EPmZDG+fl5I+DPzc1pfn7e4DfIMSg74DIz\nBOBBYcckyTUUCtlAY3193bgHNIbEO5CyhMyfHTiRSBhDj+Pd5XJpfX3dalhKHuxgo9GoORrF43Ej\nHnk8HoPOGIVT2pAYxWDl+vraEB0eBsxZSHh1u93GLEQ4AJRH6cSDFolE9Pbbb1sJhYIEJfvp6anW\n19dtaMVDxr/f9LpXi9nj8SibzSqfz5v2DGNAgmnwkSDvDm4C+kEWwsXFhaWg4gJEOUFTBK8AIj3M\nuIuLCxt21Go1cxgaDoeqVCpmaM6pwRj++PjYGkry8+LxuA0l+v2+Tk5O7LXyu2ic8KkA9qvX69Y0\nlkolY84Vi8VbUzkQDIQFKMN5gCiHoGnWajXLAgTeOz4+1vHxsVFDsfqFf4HH3vT0tBYXF0393mq1\nDE25a3b2vSozIpGIKYIB/1Op1K1dGaqjy+XSwsKCisWiOe8Ae8HXWF5eNg4wbj/ssuye7XbbQuIx\nNSFmDZUyaAmCz3Q6bSUPI2InlEYNnc/njVa6tLRkpRDIB+iB3+836VW/3zf5WCwWU7/f1+LiomVV\nDwYDU6HTAEqyssEZUkR5dXFxoeXlZQ0GA7PQcqIZsPyA8HBXcjbFNI0scCaQ4XDY3osT2nyT617t\nzESfIV8CISBkhnoYzBfEASI79SrcCadAtlQq2SBiNBqZ0zsoBwaJHOdwqOFtOHdTNG+MsyWZZS3U\nUo/HY0MT59AG3JspIGXM7OysCoWCDTacahEI8pKsOQVrx7UTs0UQFwYnvHYCJ1GTYOMr3Zxm3W7X\njF4YvFxcXBjVFsgR6BRlNp/zzMzMrbyYN7nu1WJ2stjK5bLF7+KeQ6MxOztrhiNYvUqyDEBifTH6\nYyFRH2NYuLKyYg6bYKYscjICGf+CPLjdbtPnHR0dKRAIGCWSenx+fl6vXr0y/jVmLJIsopcRNCmw\nTkdRqK4c7yhkKGcIy2E4AqzHgAcoDWyZ8X4+n7efD+EJE/RAIGB+0xMTE9rb21Oz2TSjF4/Ho5WV\nFUk3lhC4Njk3EWroN73u1WJGXY3JH0fn0tKSwuGwfvCDH1ioJXRGaKLYd0Eyl2SWsfF43AYltVrN\nak58myEGgdVCH3W5XAbhkUHNgIXSRZIFoZ+fn6tararX6ymTyUiSKWIQoALrwbnY2toy40dJymQy\npozBV8/r9er73/++Go2G2ZAxBocLfXZ2ZhxqjF94fYlEQj6fT1tbW0b1BKXweDwaj8fa3d01YSuT\nS0l2gmApzPR0dnZWL1680KtXr2xxfzQ0cVzlctky+tghFhcXTVHy3nvvKRKJ2FQwGAxaHbm+vm6e\nEo1GQ4uLi1pdXVU2mzWkAcMXn89nTR2WXPw8PJTX1taUz+fNDyOTyej8/FylUumWXW6n07Gy5vr6\nWrFYTM1mU4VCQQ8ePNDTp0+1urpqNr0IAxCaHh8fa3Z2VhsbG9ZsoSBfWVmRz+fTkydPlMlkFI1G\n5XK5zLxmYWHBVB6SzCMPuC2RSBgrj9MHd6Pr62sbRp2enuqzn/2smZVL0ttvv63T01OLSPb7/ebD\nQdP9sz/7s/q7v/s7zc/PK5FI6PDw8E73/14tZjwjKBXW1tYs/iybzdqROxgMLE+DjD6i1Obn5/Xy\n5UuNRiMLXW+1Wmo0GlbzEeZ+dnZmMn0Yb6PRSKlUSsVi0RYAgxrq6omJCb3//vt68OCB8YBx/GdX\nD4fD2t/fVyQSMT0h0cAML8jTG4/HNuygFu90OibzB9vd29tTKBQyuJGG8+DgwGprdvh0Oq3vfve7\nWl9f1+TkpPr9vtXe9CPHx8c2oXzy5InlpFxcXKhcLmt9fd125vPzc0MtKpWKPB6PvvWtb2lhYUG5\nXO6WVe6bXveqzCAvr1wuazAYWCNITUfHDJxVq9WscVlaWjLlM9Kl0WhkfhTwGuDosiPh6MmRfnFx\nYY78pVLpFsPOaaEVi8XU7XZNbEoc8snJicF4+MeBdOBfUalUbkVKOLkQ4/HYalseMPKqoakysBmN\nRhadRvnBwzYcDrWwsGCZ4c7kKXZ+HhIeHEnG8yYg8+zsTK1Wy5AM0nLxtcba1+/3a3d39073/14t\nZo5YEAsAfXgRs7OzSiaTZu/62c9+VsVi0XYnSUbnDIVC2t7etocATBrz8YODA01NTVkTI8mOUhYD\nKnAiyuAuMH3z+/26urrS+vq6NWUMZ8B84Vaw+3JkwwtZWFjQxcWFVldXzQSSAQ6m4ZQnCAlgq1FO\nAI9tbW3dws+dD6LX69XCwoIR6WkAz8/PzZ0fAxv4K6BBIEaNRsMU3tBD8dVgJH+X614tZuplsFeO\nTgYooAkLCwvKZDLa3d21CAKw28nJSZVKJRN9ElqJ03swGFQymbR6kh0PyAvVNdM5nO0h8IADA7cx\nReTUgKG2vLxsfwfHYn5+/pbLJq8nFArZ6YCODzgMyiqSMiIrwNxXV1etlj08PDTbLuwQlpeXzfkU\n6G9hYUGSbCMYDAY6OjoybaPL5TJf7Onp6Vt52YuLi0okEobOQCOdmpp6Y1U2171azOPx2HBQpk0o\nlJHgNxoNNZtN1Wo1tdttKzngDCPFD4VChhXTqFEzFgoFG1Xn83mrM/FSxhAG3wkMxhF3OsPgEbjC\nQiND+/DwUC6Xy+A56mHsuTAlPzk5MQz5/Pzcck0kGXEKvrQz7KdcLsvtduvw8NBeH0JaZFGXl5c6\nOjqy5jQYDJrMCv4LnykPGCoc1N3kqFCqQc2tVCqW/4fiBfbcm173ajGzQ0IsYrHgZxwMBpVKpRSJ\nRKyUINMDPgIwGOpk/NFodGKxmDKZjPEcUqmU4vG4JFntyglBicIuDe+D3YqxM7sm07Hz83Otrq5q\nYmLC0A2cOznuY7GYIpGIUqmUAoGAISypVEoLCwtWIpD4xOkD1o411sbGhqlwmNZR97rdbm1tbdmu\niX0Dci1gzEQiYZNSTj+C5CFmEaURi8WUTqdN1QOv5erqyoxp3vS6V2gG0Qinp6eqVCp68OCBstms\npqentbe3p+FwaD7A8J4JQUf/1+12zU/uxYsX6vV6Ojk5USAQUDab1enpqeLxuHlxjMdjlctlzc7O\n6uTkRDMzM5bnQaY0TZ3P5zNX+oODA21tbalarZo/NLrEfr+v7373u2Y7QMwZ/ORAIKCDgwPrB4bD\noXK5nILBoEUwzM/Pq1QqmVeFJDuJjo+PLYHr4ODA7LxisZgqlYoGg4GhKEwWsa598uSJHjx4YCXR\nu+++qxcvXujy8tI+20AgoFwup3g8rg8++MC4LjSQKGqItUilUpqamvooBsJ50V2nUimL4l1dXVU4\nHNbGxobVZnT10s3UiV3z6OhILpdLS0tLFoS+urpqx2wikdDq6qplCUajUZ2enlpTSckQjUaVTqdt\nIhmLxeT3++X3+01l8vbbb1tjRf0NOy8WiykWi6lcLpt7kM/n09ramtXCmUzGzB2p57HLYvfEMpb/\nB/4ej8et3t7Y2LAyBYclpqXoGScmJqwGfvDggRkw+nw+BQIBLS8v22e7tramSqWixcVFzc3NaW1t\n7daInpr96uomg3ttbU3r6+uW9nUXRONelRkQfDBEpGGanZ3Vzs6OvF6vVldXTej66NEjtdtti0OL\nx+MWFcw/xBU7PSNIlBoOh9rZ2bnVWC4tLdn0Du8OKJuUGXAToH5CU4WbAIkJWiXunhDkPR6P3nnn\nHctlCQQCmpycVCaT0ezsrD3UXq/XkAL8OzCUpIzgZ8DNZpExnkYA7HK5lEwmzfaMhUqtv7y8bMgK\niArBooTwQPBKpVLyer1Kp9PGJYnH4x+ps50XTZbH49Hh4aEFRY6Dd9gQAAAgAElEQVRGI33ve9+z\ncEq4GM+ePbPdrdFoGHJAtNfh4aENAGKxmAkxsffqdrsqFArK5/NGK8UaC4sBxtq9Xk+tVssGGQcH\nB1YrnpycyO12K51OG0+Y2ps0Vmx4QQi+973vWeJVo9HQ8+fPjY/hZMJhxtJsNo0YRWwDiEo+n1e1\nWjVMGT85/DRarZaZ3mAXdnZ2ZjXu9PS0CoWCvUcweDgskKU6nY6lfLlcLiNiPXv27FZu45te92ox\nZzIZizc4OTkxbgSaNgxaaMoYPLDwxuOxHe8MDYgjlmQRC9xMFuvKyoqRgZLJpClGwFtBAqBcMgaW\nZJne7MA0hqFQSKenpzZsmJiYsIEFvhxOc8Tl5WWjmTYajVvca3ZVSUbZZIGhsmaMD5QJMhQOhw11\noAnNZDKan5/XwsKCDUIWFhYUj8cNa4c8BAlqOBxqfX3dmlMecmwfOp3OR2lTzgsTmE6no8ePH0uS\npTSFw2G9evVKkUjkljyKBCUcMuFbgN26XC49fPhQ0g35H0gJ77jR6CYgHuIP00PKiVqtZpFieEOj\nKYQ7vba2ZqYpWCSg4kDhjas+7zESiRjvAV70aDQy2mg8HlelUrH3f3R0pHA4bFM+ILrxeKz5+Xkz\nS2ehS1IymTSrMhan01DHaVFAhAP+HmQFwv0AMUKyNjExoXK5rGKxaGQphk9vfP/v9N0/Yhd0T7iy\nTjpoqVTS6uqqOd3jFB8KhUxpQr2JHzEm2zRikIkCgYDq9bod6dSxTnI8AlUnuUiSYbFOngdch0Kh\nYFAdJc319bVyuZxJrvL5vDWbnU5H+XzegjZRsEgySql0Axk+ePDAdn68kjkxqtWqgsGglpeXjRKA\nQeP19bUODg7UbreVy+XsZ7P7s2s73aRgItJQwldmZD4cDlWtVuX3+7W2tmYnGBksb3rdq52ZxeX1\nepXP521gwXTP7/ebTevc3Jzq9brJmpLJpHk787MYNlBauFwuW5g8LIxn0+m02dZSxqAjrFQqRjaa\nmppSOp22cTUPBiVDp9NRqVSy13B1daWlpSXzs5ibm9NwONRwOFQwGLSE12azaXEOYNVMGCWZ/zQP\ncL1eN4ortrsw8iSZsxNTULjaZP2Fw2FTkjNxJbDn/PxctVrNUJRWq3UrDoO8Rk4D6nSGPW963aud\nmXEoRCMmWkzC9vb2rJG5vr5WPp832ihezHCWybeDmO60nUXkyZDA5/OZtg+5EV7Gg8FAhULBDM77\n/b41UujeKCd4CAKBgI17GVYMh0O9fv3ahhCtVkuVSsWGOSwMYDQW3snJie2oTCvBtTkhMH9ZXV1V\nr9czpye4xqPRyIhMzlqcBxmTR+p6jF5Iq2Ii2Gw2NTk5qePjY4vQKJVKliR7fHx8p/t/r3Zmv98v\n6QYv3dzcNNXEzMyMeTxsbm4amWh+fl5f/epXTR83NzdnzQrTvUAgYIsO16PV1VV1Oh3zKcaIG0UJ\nOylaROp0sGBkWhzHm5ubxhmmVl5eXlapVLoVfJNIJOxY/+mf/mlj8TG2Pzs7UygU0vLysnw+nyW/\n4o7Kn0ljnZub0/n5ueLxuFqtlq6vrw2m9Pv9evjwoZU89Xpd4XBYFxcXCofDajabymQyJsgFPnQq\ntslGAaUgUTaRSKjdbmtjY0MffPCB3nrrLblcrjvHQNyrnRkcFooiRis+n0+1Wk1bW1s6PDw0S6nx\neKy3337bmiCOZ3Ys+AwLCwtmReB2u/X06VP72uPjY6NsOoMpOc5RqUiyxUd9iZEh6EkqlTLst1Qq\n2aJIJpOGRoCI7O3tGZwGrRKfDgg9cJBpOilJut2u+egxkMEoEesBXiNxaoQVwXkmGgJjR/gZlDcs\nWnIQJycnjYBVq9Xk8/nMh+P8/Fz1ev0jDaDzArKi2WD3wzVod3fXeAf4phUKBQ0GAz179sxomexy\nTOSePXtmvAm0bDgHIZjFZxmuszNOAniNXMHRaGRjXVTki4uLKpVKhgTEYjGjg2azWfNg5kRotVr2\nNVBDqcuxWJidnTWyjyQT8BJSCX6dzWY1HA51enpqpQM1LBpIvJhphBGvYg4D6YqpJvX/2dmZlWjg\n1TTftVpNjUbDzMsLhcKd7v+9KjPcbrd5WEBMpwkkmIcmB7wZji8OlEQe4MnGLtTpdBSPx1Uulw1v\n5sh3msfgIMTRDyMPHRxdPg8KcJzb7TYeBfIs7AIIAMLNHpEo0nw0hk4hKhwQSi+Yd/g4Oxs70B/q\nXAwX5+bmNB6P5XK5lEgkrGnETgFeNDFpIDCSzDUV11NwdewMyDjEiNKpiXzj+3+n7/4Ruy4vL7W+\nvm6+D9xwdplKpWI4KcMMJPO46dPo8P0sNkoFp86v0+nYIqXRwTcNzwlel9vttpuFw7wkW8T9ft84\nI3jGYc3F4KHRaBh1VPpP9GZ6elqDwcDkSfjRMew5OjoyCixfA7KAxAwPZmA3fPVoPpvN5q2v4ZTh\n8+OkQixMM8r0k+niaDQyeRilGg6nNIJvet2rxcwHgnwJkz6GBNTUkkxh3Wg0LKGJr4MOyTjciXSM\nRiMbPOCOxNHv5B0zWSQEB8wZM0a8OLCfbbVa9vM8Ho92d3dNouV2u3VycmKIwPHxsUGQ9XrdFtKr\nV69UKBTsOKcpI4YYj7h6vS5J5tiPoz99Ap8DE75+v69er2c7ORAnHBOck/DEI8oZ5AP+Bt4j9Xrd\nHnZOHSLj7nLdq8W8srJio1iOPCeXF0Eouxl8iHQ6bRJ+lCWQ9ZPJpDwej9LptC1MyDloCxOJhEaj\nkQqFgk322HFYUJlMxjyTw+GwLi8vlUwm7XgOhUK38NzPfe5zBs/VajXjO5OQFQqFlEwmtby8rPn5\neUUiES0tLendd981HJeaHX877BQI0On3+/bgUr6AcKBE8fv9SqfTWl5eNlGu3+/X6uqqpqam1O/3\nFYvF7JSgPGNKClzZbDa1uroqj+cmmnh7e1v5fF7tdtvEEHcVtN6rmnk4HFq8AF5tOA5NTU3p4cOH\n1gASbUbwInUbJHFqS0lG8A8EAlpdXZUkk/JfXl5aDQviQa3Jz4OPwECDQQYXYgKC4cneg2LJJJPs\nkEQioVwuZ4MSyhhnDAaLjiZ4Z2fHHnCPx2PoAbxkmIQkUo1GI+N90Bd0Oh1b/K1Wy2I1MBUnEmJq\nakrxeFzT09NaWlqykgZJG4OUlZUV852D6nqX617tzM5wRVwync1Js9k0my7gJ1QT8XjchiU//uM/\nbgMM+MA0ZcPh0IYA1H8MT5zeydPT07bDU4o44x5YvIhDqU8hABGLXC6XzdeCE+Dw8FDJZFKBQECF\nQkGBQEAzMzOq1+saDAYGP3IVCgVjD/LwJZNJ2+lRfmMYEwgE1O/3baqJDA2hALROxAx87tTMKMvR\n/aF0l2QhmPQVr169so3jI2jOcbHg8ITz+/0mxUcy1Gg0VCwWDT+t1+tGgxyPxxqPx/rHf/xHSTJb\n11wuZxa2RDdgUcWRXS6Xlc/n5ff7tbGxoaOjIyPdow6nISJ2LB6PWzjP4uKilpaWjBudyWSMQ/36\n9Ws1Gg0dHx/r6uomRP3w8FDD4VDz8/O2oOLxuILBoFl4gdqgnO71ekYxffHihXnIIcvCj6NYLCoa\njZp5OyaTaBIZxUOOmpyc1OnpqYkYJicn9fTpU/N+5nV4vV6TpR0dHanf7yuVStn9qlard7r/92ox\nj8djxeNxO3YlGbGcG+D3+xUOh21ahdcZBt3j8Vgf+9jHjGjkNAdHNYIgFTQkHA5rYWFB0WhUxWLR\niDvwmt1utzVy/DM/P2/6POCufD5vHT7oBqcMUzTqVlAQ3iPYODo+8GR2S1htoBlLS0vmu4eNQb/f\nVzQaNdzcmawFVOfz+Yy3wWsB9ZFkv9NJloI5V61WbRfmZMByDNjwLte9qpknJyd1cHCgfr+v7e1t\ny9Km6ULbRuPi8/ksf65QKFji6NTUlHZ2dvQv//IvVgdSTnQ6HRtn04FXq1WbzM3MzJj+DsRkaWnJ\nmjn8NKgTWdDNZtOIOzjOd7td5fN5c2ZiSJHL5WwqCEZdKpWs7IFh53a7FQwGLc8aByTMGCmxHj9+\nbOYwqVTKyqXV1VVdX18rmUzK5XKp3W4rEoloZmZG3W5X6XRakkzYwM7LNJLTDzwedff8/LxOT0+V\nyWQsr8Xn89nPe9PrXi3mXq+nhYUFdTodffOb39TOzo5OT0/NAqrRaJgf2vr6up4+fWpS/FQqpVqt\nJrfbbamuBMUjk8f+9eXLl4pGo3r9+rUdr5IMxkKqBdrxwQcf2EIlQpjJ2Hg81suXL5VOpw0rbrVa\nevnypTWy4/FYjUbDMrRxLmUXp/zhodnb29Pi4qLxI/L5vH7iJ35ClUrFqKnsoO12Wx9++KH5b8D2\nm5ub0+vXr03nmM1mFQ6HVSwWzcm/VqspEAiYYaQz5hkZWLPZtIc9EAio0+lYHvmrV68MeXr48OGd\nIiCke7aYk8mkhTOurKyYq47L5TKSEEd3r9fTzs6OcQqcaarpdNoMxLFhhZB+fHysWCwml8t1y5KV\niV0qlTKiv5PmGYlElE6nDbculUqanJy0rOtOp2MaPZfLpc3NTatlGdMPh0PDt1dWVszKAKUHpuXs\n9sViUVNTU9re3tbJyYnl8UFhpZxYWVmxsgyzdszLqWkjkYjK5bJFTQDncToxoME/AzpALpfTysqK\nuRV5PB4tLi4qHA7r+vpahUJB8Xhco9FIP/MzP6MPPvjgje//vaqZkfrPzc2ZBAeJOywxdmJkQvl8\n3jgSQGZ4o7FzHR0d2ULCUgp6JzDV2dnZrfIFrwxclVqtlg4PD41ID8pxfX1tkBfdPxES7XbbqJWI\nALxer6LRqPb3900gUKvVTFtHvne73TZosVQqaXl5WVNTU8aKAy3BrJzXkM1mbYSNyQy539BBa7Wa\nmcgwhEEpQp1MPY+QmF2b8uzFixfmlEStf1cX0Hu1mJ2Ok2CkZGu0222rFWnIwuGwwVnwaxGBki3t\ndKBHHAoTjIaTXadWq5lY1Bl8DmEdWRBOSTRP8BacU0N2YmIhMF1hcoa9ALo/VOCSDJVhYRHHIMkW\npCRLo+V38976/b4NaXAkYjzOVA/+h9vtls/nU7VaNdQCspUkq5tpTKG0Ou1z+TzvOs6+V2XG5uam\njo6OLI5LuuFSMNolx4MpIFwOYtXwao5EIkomk6acQAsHz7nX6xmhiNgwFlkikVCn09HCwoINFnq9\nnra2tkwXRyQZhucTExM6OzszI8VYLGYEKcbLCA9cLpdOTk40Pz9veSMsVgzW+/2+DYuwG0POBIJD\nDe80miFsUrp5IKijMUCkxk+n0+aYz4kEdTQYDJrSxxkhR7lHJAc7ebFYNB3m6uqqvv/977/x/b9X\ni/nw8NBgql6vp2w2ayA+dTNyKkkml8cjAjJOuVzW9PS0arWa3QgUx5VKRYFAQMfHx/b9mH03Gg29\nePHCFgRc5ouLC6OYXlxcKJFIKBgMmi8zO+Hp6ak1TXCFCfTh2J6cnFQoFFIulzNOhyRLr2o2m2q3\n2woGg2q1WopEIrdQHKC1UqmkaDR6i0iPZx4PLRYMDFMQFvCQAy86LXKdaV04SDFoQl/ozBsMhUIG\ngdJIv+l1r8oMMjWurq4UiUS0tramq6sr42bgiH9xcWECymg0qkgkYpO57e1tC4NcXFy0kTWLHcrj\n2tqaQqGQfuzHfszqxuvray0vL2tnZ8ceIHYu2HwE3tTrdVMlR6NRra2tmaFMJBLRxsaG+cbV63Ur\nOS4uLpROpy23L5PJaG5uThsbGzZsoZxZXV01hQuLmpOA5nNtbc2y+lCkk5+IRUMymTTrXczOKZWk\nG1UKOsBQKGQTV/oLqKLxeNw+X1iIzlgJNpk3ve7VzkwSKS6XEHkYDxObK8l2W3wiUGI/f/7cSELX\n19dW+75+/doEmgxMut2uNS1YWlWrVXU6HVu4U1NT+uY3v6nFxUWr49mdyuWywuGwKpWKKpWKuWVe\nXV3pww8/VDweV7Vatd27VCopmUzq+PjYBLWlUkmJRMJUJ2DR4XDYRt2MyxEFSDLMHPEsFguoRXq9\nno2w4WdTmnS7XfX7fT169EiXl5c6ODiwQRRDJEmmJQTSxCSGKGZMd+CP35Wbca8WMzvA1NSUPvax\nj2l2dlaLi4tyuVzmh8Z0DGokvhaSjMrZ7XbtCA0EAnr16pXt4M40Jqft1MLCgmWlrK+vW7h7p9Ox\nHD63261yuWy6QqeBS7/fVz6fN8Puz3zmM9rf37+VpIrPh3Qj2oXoA49kPB4bzAbG7axRXS6XiWWx\n9ULyhIMq4tipqSnrHSDrQ06an583mgCfCxeJAKlUSplMxkbU8LczmYydDk7yEXFwd7nuVZmByno0\nGunVq1dyu916/vy5oQv5fF6lUsl2tlqtZmgB+rhisWjkH7jR8CFAQuBzXFxcKBQKWWJpr9czUWa5\nXDblCQuNkoP4BeJ3yVN5++23b2WswAUGmcjn8wZtkUzFKHxiYkIrKysql8sWZcHX7u/v27HONRgM\n7HWjqG42m4ZUSDe7NbIrOCbg6TDp0FienZ0Zli/J4h+wK4Nc1O12VSwW1W63rVaHSvBRqKXjghTe\narX06NEjnZ2daeU/EpecimXGuplMRtlsVqFQSKlUym7c+vq6dfLo8wi6ASGYnp42PJuaMRAIWMOJ\nasXv9ysWi5mxDKw1iO80pihW2FWBzDhdGE0TDYxPHTIk8OL5+XkzLAcajMfj2t3dvaUKgY/i9/vN\nFRSXJEbMGLiQx0LD6HTPR4wAvAYvWpKRu6LRqP2dJJswspCJx0CZ86bXvVrMRDAg1bm6ujIVRrPZ\nVCwW0+npqYXNdDodpVIpnZ2dmRELmG2/3zeXfOpYckG4YbhrSjcIQCKRsCYL5AD8emJiwphslDLg\nvOxysVjMhLjOTDzYeZOTkyYaoOaHyUbjSz3r8/lMXYNavN/v225IQ/l/2Hv32Nbvu/7/6Th2nDi2\n47vj3HySnHPac+npadeuG2yDdR1iUtlg06bBHxPi2iGBBAihCST4a2X8gzaEhBBsaPwBk6ZdkABN\nk9gK67ZeTttz2iQnV8eJ704cO45jJ078+yN7vOoM9uuXEwoj2luatq7nxI79/rzfr9fz9bzw+ZTL\nZUNb0C/2vm8IUdwsmMdIsvwYBlJkmIC/M3DCRen4+NiErLxvSf/h9vivrnO1mTHbRhNHJgg46ebm\npjqdjhkXUotiHEj3jWg1FAoZVEbz19fXZzkoMNoYBaOqkGSsOzYGfN++vj5Tk4Av8yXevXvXwoB4\nbRJfkf5vbW3Z9S7J7GPJ7WOgwgSQUxMMe2JiwuwHeD+tVsuEBbVazW4QJqXxeNw2Kw0efGTEsAxd\nyDTh7+7u7p4S1dIkghhR5jAwOss6V5tZkkFEq6ur5r1WKBTMFbTb7ZrAc2NjQ4uLi6pWq0p/L7wy\nm82aPevdu3dVKpWMO8y1iHSK6SHE+1wuZyNqHPa3t7fNQHtgYMBC0pvNptXk5Kv4fD4NDw9rYGBA\n6+vr2tra0srKiilPwLQbjYZxmzc3N5XNZlWr1TQ3N6elpSVzVyoUClZ60Qin02nznSZmDhsAHPKZ\n2pXLZWWzWd29e1fdblfpdFp7e3uan5/XrVu3TBWDPQFmNR6PR/l83vgnUGFrtZqy2ayNxBnMSLJ+\n5izrXKEZxWLR6uFkMmk8DWTsNGDDw8M2zmaTclXifMSf9/l8CoVCZmwCr4JaFhYaWj9SoAYHB42Y\nfunSJRvvErrudrst/qDVapleDm+OYDBoo2RgtAcffND0h61WS5FIxNxMqZGZHOJyj9qGqx/7WGrV\n2dlZe/9DQ0MqlUqGiFy/ft2yR/CUg+ZaKBQMvyeaArsFJom8dyaHQ0NDmpycNEoqD+bQ0JCSyaQN\ngO51navNzBXOtKzX25iYBaZNuOpw3cMBpg6mc6feIzskFAqpVCoZcwwqJoQZVNOMzNvttk3AcBGC\nmJTP5y3EhsYMhTfQG4oZoDHoo9Vq1bSF2Cj0WhvQmJHdDUqBohs2HnAiVNbeJowHHCw9HA4bSb9a\nrVpvALIDxIb/B1M9PpN8Pi+fz6e9vT0rK3h/sPXOss7VZo5EIka0p970+Xzy+/0qFAoWaH5wcKDx\n8XHjO9DcHBwcaHJy0qy2wuGw0TI5lVBS0yhBO2XkC6c5GAyqUChodnbWvDBwwiQPhPiHvr4+Y9hR\nxxLBhlzp4sWLRkyivse0OxqN2lSN5oqfxeRPkt1E/Ozh4WFNTExYYwjvhNMcY5hLly6ZqAB/vJs3\nb9oAieEO4uBWq2W5MkwVO52OxsbGzEH06OhI+XzecHTey1nWuaqZO52OmRhub2/blccp63A4VC6X\nzToKqfvu7q6uXLkiSVYfw74DXvN6vTaizuVy5vQJrRGslOw8ygoaKWAxTF2i0aj5voHxkmAFXZLJ\nJdl7oAP0A0whqdsJ0WHaCSbea7OLYXqrdRKlXKvVrGaH/EMD16vtg2bKZ4uRDSFEDz74oOHizWbT\nHq7vZ+7BYabMKZfLKhaLp+yB73Wdu83MAISoMZhtRJNFIhHjZyQSCbsuv/Od71gkAqLV4+NjjY6O\nanl52bryWq1moe3Hx8fW/YdCISMLTU5OmsdcNpu1jTwxMWG3QCaTMcEoerqNjQ07sXE5SiQSFlTJ\nOBq3UMI2IdpTrkxOTp6KiYAsBY2UKR8IB9g0mkZQBSZ+hULB3jPcb5pLtIOLi4sGhxIXx4ia90LK\nK+UcY3pyE2dmZs70/f+vbOadnR196EMf0v33368rV67ou9/9rra3t/XEE0/o0qVLeu9732uTJEn6\n5Cc/qYsXL+q+++7T1772tR/4c/v7+63UcDqdikQi1gzB8S2Xy8pkMvL5fFpdXT3lEYGKY3Bw0ByM\nIMf3Go7v7e2ZSxHDi/n5eYuI4OFJJBK2sfFNhoDDQIZU1W63q4mJCTkcDm1ubpr6Gw9mr9drrDXq\n5P39feNo53I5K6FWV1fVap1k7HFDgGmTbwILDmU6vtS9zqDDw8P2OwE5As/RHONnh4IFc0f8NI6O\njizkh88W/ePS0pImJycN6VhYWDjTvvpf2cy/9Vu/pfe9732an5/X7du3dd999+npp5/WE088ocXF\nRT3++ON6+umnJUlzc3P6h3/4B83Nzelf/uVf9PGPf/wHqnghAKHD83g8isVip0B8pPyErddqNXm9\nXiszqBXR6sEvRq3M5iCBlDQoItcw+cYRnwkfwxXe5+HhoUGFyO/RHYIYMBpniANiQAPV64fHJkMn\n6PP5zE8ZnjZSrnw+b45DaPVQxsA5hgbKoSCdsBLByLk9esWqDH8YyUsncCSwIOR/SVa6lUolU82f\n1Tfjf7wBrNVq+rd/+zf97d/+7ckb+B7h56tf/aq++c1vSpI+9rGP6Sd+4if09NNP6ytf+Yo++tGP\nyuVyKZVKaXZ2Vs8995wee+yx//CzgcYwN4Hok0qlrItGITw4OGgBN/F43DzfCKShpuPkponDLhe+\nMJEKhO5wwlYqFcsIIYKChxDTGFwye8WnbHAQGK/Xa9c+uC5BP4ykA4GAwuGw0um0AoGAQXU0wYzb\nJdnYut1umwMShH20gzSj+FYTxJlIJMyOYGNjw0oTfj/8/fb395X+XjIuv6MkG4FjeRuLxYwiAK/m\nLOt//GReW1tTNBrVL/7iL+qhhx7Sr/zKr5h+jQxqckCkE9J5rwR9fHz8B/r4opiAWE7Ntre3Z1c9\nRoM4WB4dHalYLFpT15ukRF4g+Rx7e3tKp9M2iSNzBB9i/IlxO8J85vDw0AxTyOimCapWqzY5JOsP\njzjMDnd3d82egFMdN9CjoyOVSiUtLy8rGo1aXby9vW0DI1ySaFAhz9frdVUqFS0sLNhwhiEPFl54\nW7daLS0uLur4+Nj6DNyNBgcHDakhYQrxMDU2rwdyglaRz3FjY+PMQ5P/8c3c6XR069YtffzjH9et\nW7fk9XqtpGDh/fCD1g/6d88884xefvllPfvssyoUCjo8PNTExIQ1SgMDA/YlhEIhU0o7HA5dvXrV\nuMYYHxLvlUqlDKOdmJgwLwikVz6fT16v15TM2WzW4DsgNKiWQG9TU1Pmzwafg03DdQsuHQqFzHme\n0ByMyHuzB3vfO7cMjEHKD8owhjWQ6YeHh80wkV4DATCIBPwQPC54MPv7++1hR2953333yel0mu81\nvBdKKqzGarWa5ufn9eqrr1pS1r2u//EyA9fNRx55RJL0oQ99SJ/85CeVSCQMC2aYIEljY2Pa2Niw\nv7+5uamxsbH/9Gc/+uijCgaDWltb09WrV5XP543P++STT+q5555TMplUJpOR3++3LBFuBK51eBEO\nh8Oaye3tbSWTSRWLRcud7uvrM/dLLLh62XC91gatVkuPPPKIbVi+VAhGh4eHeutb36rj42Otrq6a\n9zO84G9/+9vGQIP9xzCi2Wya50UikTDbrmKxqOnpaRUKBaXTaYXDYSs7cEVyuVxKJpNG5CdUx+l0\nanZ21jBnmjnUJ0wbwcjX19f14IMPqlAo2IMC9BgIBLS5uWnfSTKZNF5MJBLRzZs3dXx8rGKxqPT3\nUl/vZf2Pn8yJREITExPGV/j617+uq1ev6sknn7Q6+m//9m/1gQ98QJL0Mz/zM/r7v/97HRwcaG1t\nTUtLS3r00Uf/05/NFG92dlYbGxs2BGg0Gnr22Wdt82G7ClsMHgYZ12CzrHQ6rWg0aoMRxKqE9gwM\nDFhJs7S0ZMw6SVbeDA0NKZ1Oq1Ao2BVO7c5p+OKLL5qFAMGajUZDL730kimx4UyDeEivIwyS7FSu\nVCoaGRmxgwE/D7B3Ruo+n08rKys2Wkb+xXSQ361erxtTj3+mpKGJzmaz5rxK0KckyzrM5/M2xCH3\nECjV6XSeYgrey/pfmQB+5jOf0S/8wi/o4OBAMzMz+uxnP6ujoyN9+MMf1l//9V8rlUrpC1/4giTp\nypUr+vCHP6wrV66ov79ff/EXf/EDywwEpxBwUD2QXoraAXCEUygAACAASURBVCssSfbh0jwSrRYI\nBNRoNBQKhewBAFcFHgP14AbI5XKamprS0NCQXn75ZV28eNFqVXgdKK2r1apNG3ErSiQSJnkCxiPr\nmuYIXjOkHtzoW62WkeMZV9NEcorC5KOevnbtmpVjCAlWV1ctRQrmHYcEmDJG7DykbrfbbAe4pXK5\nnOHdsPCwXADKnJqaMprq/1mvuRs3buj555//D///17/+9f/0z3/iE5/QJz7xiTf8uZubmzbQoAnC\nsHBnZ8dAenjB9Xpdd+7c0YMPPmjeDphwo5BGqZ1OpxWJRKwuPTo6Ui6Xs/Etcq1isWhGL9TTCwsL\n1pzxIPr9fm1vbysSiZg2DgsAGGzhcNiyB1utlo2SSWwlJAckYW1tzdyFsPHqzfo+ODiw9xYIBGz6\n53Q6LYAHLR8jcHgi2CVAONra2rK+APOdZrNpTSy8FXyqmQDu7u5aT0EDzqnfO1u4l+Xoclf9H18O\nh0O//uu/blzmw8NDUxXjXsQJSUyE3+9XqVRSKBQy8enAwIByuZwNBBjtojxeWVkx4SmxCvV63ZpM\nSeZ5jNiUWpMEUphzvZFqjJubzaYGBweNyNRoNAzRoGnqdrsql8u6cuWKstmsRkdHTVUNnZOsaq7v\naDRqDWE+n5fTeZLbDZzX+15cLpei0eip3G9KM2A0cG4eYAQH8D4whUFtQkIsSFG9XlcsFtPGxobi\n8bjdBH/5l3+pe92S54po1O12FYvFVCqVrBau1WqKxWJqtVqnogqSyaTxncluBrZjGIDRNycORi14\nQFAGcD2CSjBBpBlE0gQfmtM6lUpZRAInaTAYNHUKiVRer9c2JWHzXOU+n09bW1sqlUqWHYIvBZtp\nY2NDiUTCJoggGuDA6+vr8vv9Zi6OLCqbzRq5CkgQphuoBqoVmsHt7W17cH0+n01VmRo6HA6DUOE5\nF4tFswY7yzpX3AwcJ7nOEaNS18GhZcJ18eJFa/qQRMViMSUSCWv0OMEhAeXzeUub6nQ6xp/Gdxi+\nRrfbNbITJxuNYblcPqXsIGXV5XJZeZPP50+x6CC0k/KEWpsTHVcmcGpIQ91uV4lEQrlcTq1Wy1h9\noBTQTkulkoVscoJGIhG7xZrNpkZHR+3URHvI8AYFDqw7Dgh6An5XhLRwsmu1msbHx/9bBK3n6mSO\nRCKWMBWNRi0o0ul0amxszMbcjF+p3SDss2nHxsZMqTwyMmIjZq/XqwceeMBgLr6McDhspyiIA0E3\n/PtedTOEqEgkYuNzFNi46U9MTGhra8uc9cFgaehCoZAlA3DS4nscjUY1PDyso6OTEMt0Om0b7+bN\nm8rlcsrlcjo8PNTo6KjC4bBN96CkMk7H2JGpYDKZ1MzMjBYXF1Wv15VMJk0ZI50YJQ4NDem1114z\nDvcTTzyhu3fvnrJnaLfbGhsbM+QoGAzq8ccf17e+9a17/v7P1cnMtRkIBJROp83l8+DgwOLKcrmc\nmWMTD4bCoVAoaG9vT9/4xjcM0qOWg0SPHpATEiiv1TrJGmk0GvJ6vdrZ2dHU1JR5z5F9R4AmFmL4\nrYEZU9OWy2Wtra1pc3NTt2/ftgy/gYEBu5LZ7AhJQWOYEBYKBa2trUmSqV8WFxdVLBa1u7trZKnX\nXntNW1tb2tnZMcit2Wzq7t272tvbs+FMNpvVzs6OlpeXtbq6aiUIvUCn01EulzOt5fj4uBwOh779\n7W9rfX3dGnMkXdlsVmNjYzaUefbZZ8/0/Z+rzcyYFKYbzC1KiY2NDTNNwc1nYWFBDofDQnh8Pp9t\nRLRweGrgF3FwcKBkMqmxsTGFw2ErUeLxuGn4PB6PnnvuOWOIcbVDg8QPDiI/EzdKBQYmIA8gJvw7\nGkFsaXsDLcGaI5GIDTT4fQOBgLa3tzUxMaGpqSmFQiGr+allEeiGw2H7XbPZrA1BarWaMeUICeJ3\nY0gF3AdHBLwaw5xEIqF4PK719XVrmH+kzu5ZfNm7u7uamprS9va26ezwz4AvzKSPK44m5fj4WDdv\n3lS1WtX09LRarZZu3Lhh/Ak8mvP5vAqFgjKZjEZHR62zxw7X4XCYqgSOBz5wx8fHunLlipnKwIMG\ntqPkgNAE1k2qU6FQUCwWM50hY2Pse3no7ty5Y+aFh4eH8vl8KhQK8vl8JiogeYrTcXR01EhGNGs0\ni+Fw2PLFo9GolWO9qh0yvN1utxnvFAoFuVwuXbhwwZpu0giQfvX19RnN9J6//zP97R+yxQk5PDys\nO3fuyOl0KpPJqNPpaG5uzqZkvRg02DFK5W63q1wuZxgsigw8nEulkhlt8wUyGCHiYH9/38oaNqjT\n6dTt27dNwLm5uWlRahjM7O7umpsSnnWbm5va2tqyoUU2m5Xb7TY/PNQiDEZogOEkQ5jv7+9XoVAw\nlGR7e9vw5Gw2a0Oa3piz9fX1U45EtVrNslXy+bx2dnYMxwbTxteacgXWYbvd1vz8vCSpVCqZOj2f\nzxvZi4f+Xte5agCj0agNGHAlSqVSJuOJRqOWreH3+827mWtwfHxce3t7mpiYMMpjuVy2oB84znAh\nnE6n4vG4lpeXrcEaGhqyxFJqYLgkY2NjNhxB8NrtdjU6Omr+E7FYTNlsVj/2Yz+mvb0982oj6Yna\nF3U0w5deGiW8ZwJwIElNTk6aVAneMX/W6/VaMDvoColU0EzhQONRR83vdDo1MTFhinFCetjImONI\nsmQu6YSnwyQWt9OzrHN1MksnpzPqaZzuQQSwv6Kea7fbeuGFF2xKePv2bZVKJeMgk18SCASMTE+t\nKp1cqaurq4Y9FwoFG57gvxEOhy1gB9omQZk0fbu7uxYhhhq8WCxqeXlZkiwiAqfPbDZrDWRv/cqG\npnbG061UKmljY8PyCpvNpp3ikOixDwiFQma/wKCEkboke00U6TSewIXoBbklKaWazaaZsqMrpP/A\nbYlG8l7XuTqZUUrjsJlIJIwdhjaPqRgpTW95y1sMKrtx44bla4fDYePzOhwOxWIx9fX1mVqake7V\nq1e1tLSkSCRiQTMoNiSZSPPy5ctqNBqm4Dg4OLC6MZVK2dWOE1O329WFCxds2IBXHn8fRQg6Pxzr\nQUtcLpfV5ZVKxaahyWRSr732moVgcrUTIlQqlUztzWmMU2l/f79mZ2dNXdKrVgEjBoYkfwWvDJ/P\np3K5bO+zNwYaFc3/OaXJm7kQiBIXNjQ0ZDVmMBg0H2Hp5MsDb0U21MtTQNXh8Xi0sbFhJQknoM/n\ns6aL+hA6JiNcakXgPXI9GDmPjIxYXJrX69Xe3p4pYhj8oDGEzQb1E5I70CDj4aOjIyuDarWaqT56\noUhEtKil0TWiQOehQWjQ7XaN6QYng7oYJ1PIQqAyvdh2oVCwUgjaAAOhl156SeFwWB6PR6nvJXvd\n6zpXZcb+/r6JM2HOYRvAictwY3d314YcfAlAUr1Y6+HhocWBNZtNg9gwL4FZRieOlSyDEOAmypqD\ngwPF43EzPSHhislZNpu18HbMGTm1mRAymqfhAqlApd3bkDocDiPKDw0NaWlpSa1WyyKUW62WQZlY\n4dIQU68TqMP7AC+naWXQQgmBzVc0GjXFeKfTUTgc1tbWlmq1ml544QWVSiXrTxwOhw1e7nWdu808\nPj5+KgQecj2byeFwqFKpyOVy2WgV+RHOoGwwaJuFQsEeCmwBEGh+P5cY9QUntST75263a/U2XAdI\n7zxolArgssCFvSw8vOyOj4/tlK1Wq/afXoTE5XJpfn7emjEaN1TkRFVgdNhut1UqlYwQtLe3Z9NC\nmknkT/QACwsL9nm63W4TNoAYwaTDaRT7A6/Xa8aJ/x2suXO1mcExa7Wa8vm8hoaGLNgRfR+bB2Uz\nHzYbEnRgb2/PSgTwaZTOve4/IAKE2Uiy4Bwk95ubmyaP6nQ6qlar2t7eNm8OvuTx8XHTDNLg7e3t\n2c/d39+3phY3fKZz0gla4nA4bKPncjltbGzI5XJZKYLrKISiXvolZoqSLNICXL5X+zg5OWlUW/xH\nsKftpd9KJ4755XLZWH045BNgD+EfXP0s61xtZupGn8+nRx55RMPDw6bLgxWGKTibkpRVOvje4QC8\nZSaAlAxwMYCYHA6HqaJRhuMRd3BwYOaDfr/fvCOoWY+OjnTp0iWrqylTkDehnIbLzDgbjBojb5/P\nZwYr8XhcIyMjNliJxWJ2iwC7QXcNBoM2QMJ2gKlgJBKxNChUKZFIRHt7e/YwoLym0aYW9nq99v4x\nMccTGjNKpoYIX380AexZSHNo5LCw8vl8hmOSCcKmRVF9/fp1i00jN5CNMzU1ZTxj6UQK5ff7bdoG\n1AdzDBefarWqYDAoj8dj74VrnlPK5XKpVCopn8/bKJw6nlE4nGiMwbe2tkzrx8+CpE+4Ox7NPFCU\nALVazdTetVrNfkeSAMgvkWRCAaaW/Lt2uy2/33+q4WV8zgCFU5tcFUhGGL2j9gE5CgQCZodwz9//\n2bbPD9eiyRkZGTEpP7AbSVTUzcfHJ4GMo6OjarfbKhaLdnJLMkEpNrN8YQwtqHk5wXE9wpR7eHjY\nCE+gEHx54+PjZvPK6UZ5w0mFMgZu9PDwsFKplLxer/GOQ6HQKb7E1taW4chc+9T4V65cMSU2pziI\nB9RMShswcOr7RqOhiYkJY+TB/cZ8EQSDvoCpKnwSSadqYkbkbrfbRvKo28+yztVmxvr1+xUfPp9P\n0usJTTjaHxwcGJwH39nn85nm7ujoyBhpw8PDZqBN9BmQFcGQcJGxIiAYvt1uWxcvyVhl/BmIQgwP\nms2mhcYzdNje3tbGxobRSvm7JJ8yyUQ1g0EL7ymdTluzC9WUsoomFk7F9va2WWZBdIJpWKvVDOHB\n347xPMR8oirglNCAg/rAQqTeRx1EnX2v61xt5mKxaC5FkOnhEdN9Y/7XarVMgMp4F1MX6mR4GQD+\nx8fHmpqaMh9oqKPoB/GboMxBHwhRB2iLjECSTaGAAm9BZBocHLRN5Pf7LdSmN+ynWCwatAgOvLm5\nqXK5bHDh3t6exsbGjMzD59Fut3V0dGS6QCidlDmcuuDleC4zeqdxZhNjLlOr1Qwv93q92tjYMJwb\np3wotBhGgr+fZZ2rzYzvw/HxsR599FH19fWZQeHb3vY2+f1+S1Y9OjrS29/+dh0cHJwapHg8Hs3O\nzioUCunq1asKBoPy+/2WEVgsFq1GxdQEtAMKZCKRsORRrAaYyIFqoHgZGBjQlStXbKMzaYRGCcMP\nJGFwcFAOh0ORSEQej0fj4+Pq6+vT9PS08U+SyaT9TkxBK5WKfD6fTQ2Hh4eteZydnVUwGLTGjMnc\n2NiYmTlS16JGIbn16OhIFy5cMKd/GkzqZ5hxPETI2CRZ2YSR44+GJj0LE0O32607d+7o4OBAL7zw\ngiqVir75zW+a7Ak23O3bt608mJub08rKijwej1599VXVajXduXNH/f39mpub04ULF2xogtJ4f3/f\nrmD4GLlcTs1mU8vLy0bupwFaXl5WqVTSwcGBqb75d8lk0kQE5XLZTAb39vZ0+/ZttVotxWIxLS4u\namJiQisrK5JkZQmWXjDimHzmcjmFw2FDYJB7ZTIZK6c2NzclnaR13b59W5LMMZ/bB8hzcXHRlNcL\nCwvGeAM+JHD+1VdfNVhzZ2fHaAF4eRwcHGhhYcGGNJVKxX6ne13najOTkxcKhVSr1cySCo0egw7I\nPIg/6cQRufbGEgPPuVwu9ff3a2xszKiP0WjUhg1wci9fvmwZHcCCDFAY45JFgodEsVi0Ojwej8vr\n9UrSKVsA7LguXLhgUz5Gz5LMPQntXSQSsRE65QS3SK/HBRPFYrEot9tt0iuQIbjRsPkSiYRpHHs9\nnnuFvUjQiJcgF/vw8NA2NcQjegZ8QM6yztVmrtfr2t/fV6lUMogKwSnSe65wRsLYUV2+fNlGyIOD\ngwoGg5YbfeXKFROlYg/LZA6gnxqzUqlodXVVfX19WltbUygUMsMYoDVGvdLrNryVSkXb29vmlH9w\ncKDR0VGbOoI/SzJjQ3K/QR729/ft/UmyoQzvnQf56OhI8XhczWbT1CqSTPgLS3Bqasqa2Ww2azZi\n/H+oXCA6AU9iYcaUDwgQD2esuZBcbW9vn3qI73WdK6IRtESCYiRZfexyuRQOh62OBSLDTDuTyRhL\nDPJNIpGwsS9ezysrK1Y64AHdbrctwIf0JU5+am2cgbrdrqEmcIoJiYfgI8mC0vld8AHBWBFX+0wm\nY4pyFDOM8HkoSU/trX8lmXgAnBlkgeEPnxkKaiih1NXYbw0MDBhujZ+IJHMLhcLKKBu8OR6Pm9M/\ntfdZ1rk6mcl4BqLCoISmB6UEuChWtgwAisWiisWiEYX29vZsHAtejV9ErwsnXf7e3p6pUND2IaPa\n2dmx0xn5PeNj4DUMIikLlpeXjXFWr9eVyWSMDwFkiCMSHAlUJrhxVioVG7RAwiLqAciSEHoQBwxc\n8vm8eUPjQ9Kb2w0KRP0rydQ4xWLRTmNuFGBCWIlAf0wU5+bmzvT9n6uTOZlMmgYNmVAikVAkEtHx\n8bGi0agJOp1Op5566ik9/fTTpusbGBiwwUe329WDDz6o1dVVXbt2Tfl83sJ+ksmkZXrwd6FBEk3M\nyDwSiaivr8+4DEziQD/gKsBxCAQCNllMJpOmWMGZCcLQ9PS0NXMMJq5du6Z2u22cEuienOLAh4hW\nMS5HbS7JkBq3262LFy9aACdO/IuLi+bemUqlzNYBvrTL5VIkErHBTjQatdsITgi2D5Js8AK/5Zln\nnrnn7/9cncwMMHqd2Qm6wfgQ0vzh4aG+9KUvaXBw0CTy0skJRETBrVu3LNcDF85YLGZZIFzJvdwF\n8kfgfuBWhHoaJ85KpWLWspJsYxNoicYQaBCeBMaFkJa4FTBuZKACB4KcE24tfEDQBUqvZ3M7nU5z\ndGJ6CtTJVBUY0uVyWdqsdHLqEh5E2QaRKBwOq9PpaH19XZ1OR41Gw7yxwal7uS73us7VZuaKBZOl\nKYKRxanNqUAYIyQZGrR8Pm81cLVaNc81eM6ccGSGFItFC9OEN03OCN5r7XZbOzs7RrSH0wHZH4xW\nOnmgeG+Q3mnsoLWura3ZOByLLXBran943M1mU4uLi6rVaqaUBuWRZAE/BLsPDw/bYGRnZ0d+v1/V\natUiKRCk9ka04eEBpbNer1uTCSISDoctWSoWi1k2Ilzusya0nqvNzCmErInRKTwMalTwWHJLqtWq\ndnd3lc/nNT4+bjJ87Lx6Q3CYbjEixmtif39fxWJRDofDvIpBKXpV3JDauXoZiyMz2t3dtckZX/T8\n/LzhuOvr6yYcAMlgsoZYNJPJ2GYnjHJ2dtbKl06nozt37ti4HB3j7u6u1tfX7USFHirJqK74kHBi\ng2szwUMhUygUjFZbr9cNpQHXTqfTyuVyFioPMeos61xtZjBZXCbx0OCEIZ8DWRFRYdJJrRgOh/Xq\nq6+aLW2vlwYnPsR4TspeIaokQzeA00KhkPL5vNly9bpnYjDDiUmSE5AfzRQu/UtLSwqFQkbA72Xs\ngYYwPBkeHtba2pq2t7f1yiuv2OkPJs4QCCPwQqGgYrGoaDRqwaAcABCuMpmMjejB6sHR4Yhzw4XD\nYTPR6X2Y7969q52dHUvSQrGTy+W0tLR0pu//XG1mygsYZdhV+f1+xWIxw3z9fr86nY4NQLhWHQ6H\neb2NjIzYdQzdkmB3eLiYe1MioM3DrAX0A4kU7LqdnZ1TaVOcdJ1O51R2N++HUTPcjF7cnDE4gyEa\nP3BgtHXr6+v23kBWgBBh9GGnAGRHtDHGjfCyyYQByqMBBqqDH075xgOE1g+Mmd+fUfiPBK09C6PD\nVquldDqt0dFRG3wUi0XjSQD037lzR9JJjTo5OalyuWz1LDDa2tqa0TKRznPNb21tKRwOG2EIMtH6\n+ro1hryuJPNpBrsFmqrX6/L7/cpkMoYLA/FVq1WzDMPTGQuBVCplahFG+S6XS8ViUS6Xy36PfD6v\nt73tbadKrm63a+lQ5XLZWHCc9nh2QNfs6+tTqVSy0oImWZJxTebm5syAMRQK2XClXC6rr69Pd+7c\nsQRc9IOUFhMTEz8ygeldIyMjNuhAVgTmTMISdMOBgQE99NBD2tnZUTweV7Valcfj0fT0tCSZuXgk\nElE2m1UqlTKuhXRSBw4ODppos9lsWrMUDodVKBTk9/vNzgoGGsODra0tMy3nlAYZyGQySqVSpyIa\nUJB0Oh1dvnxZS0tLSiQSGhoaMpwagxsgstHRUe3v7xvZyO/3KxgMGmoRCAR06dIlG4NjE4DPSDwe\nVzKZtBMYeiev0dtwArmRELCwsKALFy5Y6cIBMjIyokAgYHbB0A+azaZSqZRee+21e/7+z1WZ0e12\nNTc3p3q9roWFBWWzWdVqNSPa9/pZVKtVHR4e2snElczf4Spls1WrVbvKMTzxeDzG4/3+IQQTNKaD\nlAf4RrCxiTajAd3f37eAIRAAh8NhURHo6xjXLyws2BgfYhI+0dJJU4z3Mo0x9X273dby8rJisZjd\nLDgUUZcTA1GtVg0RIb8Pwj3DG/xIJJk+kXKJh5Wmcm1tTWNjY9YjJBKJM08Az9XJ3N/fr5mZGXOj\nJwCzv7/fBKhIh65cuaJcLie32220Szp3TvZsNmsTM3BYvOxo5iYnJ9VoNIxYDjVzYGDATLhh2rEY\nIaMJZDSOPW02m7WTq9lsWs1+dHRktE2Qk0uXLqnVaqlarRpJnw2NxhDuM1wNBhTSydgczBu+h3Ri\nBxYIBFSv101lEolErJSrVqunJqE033BU0BSiMcxkMqfU6kjBaIRbrdaZy4xzdTKjk4PRBcFFOlE2\n00QFAgGr43qTXGmoGLqwiRkEsOm4WiVZRBhSo17MGsYaKVIE19CgkWqFJzOTSUbJbAxG4GweSh2M\n1VutlpLJpD0Y09PTxjHBOJzfAVmXJPtvVN0DAwP2+WA/i60tXGjqeEhJvWoVfjakKNyj+EwDgYB8\nPp/pJxEcUHr0JvHeyzpXJ7Mk81ZjtJ3NZm2kjP0sTRMbAyYbMBI6QcoAauJut6ulpSWz08L29s6d\nOxocHDTcloDJ+fl5jY+Pa3t724YYSIyIVUA1vbe3p2q1qkajoUgkopmZGdVqNYtBazabVhLx4PTa\nfbVaLUNfvvWtb9nrjo2Nye12G68DaqikUwMLyp3t7W2z7pJO6ne40C6XS+Vy2VJqsVLAw44+AyMY\nZFO8ViaTkSSTUfFQgLPD77jXda5O5kajYRDc8vKynXJHR0dWByOXgoOADKi/v9/k/PF43GxkMYZh\nGIPSA/UIJuMej0fxeNyi2er1ukZHR625wlClVCrZJJIBAzwMl8ulQCBguCuNV6/yw+fzGYEH2Auy\nE2br2IbBUtvd3TUSPA8mUBpkKKgAKK8xUkRXCd8aGwXwZyareNIxCAoEAkZYQl6FJQP1udvttmkn\n7/8s61xtZoYH1GTIjrAAoIaEp4ADJg0dNTM5KIybIaBDX4TqeXz8eig6sWQej0fb29tGGiKSAVIP\n9gK9UWVkk8A5ZrhBTdkLjxHKQ6IU2juufwJ1sO+l1IL9xqAkFAqZiye8kmAwaG7+BAoxAUXbyO/C\n6UrT26tx7OvrUzAY1MWLF62UA3umnGM0T0g95olnWedqM/OBUe+CGDCMcLlcNjQZHh62TpxTizq7\nVzlBChIK6lqtZpuE18hms2YQjnsRmC3c6IODA9s4eLoBu0myYYr0OvGHWh7bWGxfQRggA9XrdW1v\nb5uwgFMPFAGLBBQfNMMgEgxTGDuz8TFnodnjdIW8dXx8bA1nNpu1zUpEXKVSsb8D6Qtrr4GBATMu\nh3gEEnKv61zVzIFAwHzadnZ2FIlE7Fpng3W7XXMO4tRj+Xw+I/FIMmkUXhJwDmCz4YcBB6HT6ejm\nzZuKRqO6deuWpqenDfUgn7DT6Rgq0Bv6k0wmjfS+v79vHA+gPdARLGvBxWlW2byjo6O6deuWOSpJ\nr0cUg3F7vV6trq7qypUrZqfFJFKSPewkcaEw4Xfk5wK/TU5Omp6QQUo6ndajjz5qDv6FQkFTU1PW\ni+CkCkoUCATsc7/Xda5OZmrQarVqRivDw8Om/sCmi9hhn8+na9eumYwKDkI0GrVcPQYDvTIp8FdJ\nVodjooKBYCKR0N27d22AAayGEgP2GKNiiDdMA7lFDg4O7L0jz89ms/ZngAox/M5ms5JkPhioRtbX\n16006Ha7isfjBsONjIyYhzUTTSaK1NcgGyAyKKzx9KBpxoIB+BIhwuHhoYX8eDweOwQikYgmJias\nZznLOlebGT/lRCKh9fV1O4VguPX396tUKqlarVpEA2UENS6TNCZ4a2trBmlRj5dKJQ0MDCiTyWhu\nbs6kTMiwSqWS3G63RkdHjeqYzWbNvR5LLFANSqCjoyMlk0llMhlTedRqNcPJr127ZnxkqKPZbNZo\nl2wIRAQIbqmJ+/v7bXBE+QMldnd310qJXkyamn9jY8NeC7PwoaEhQ1SGh4ftgHC73Ya4cHjwXWxu\nbhp+zuh/eXlZ5XLZHpB7XedqM0Ne39zctBEvEWXBYNDqY5qd/v5+oz8CSzEooAGanZ01iA6fYgYv\nk5OTNiQplUpaXl62hpEaFxTD7Xar0WhYrV4sFjU6Omrke5/PpwsXLmhpacmmZYeHh5qYmDCV9ksv\nvWTNLernXi+MVCplnAnikmH2Ydm1ublpBuuS7H3x+2F+A5MO7jEEffBysg8pLYhKZvrJKJ6HhTg3\n7NDwqJNOJpGgJ2dZ52ozc2JEo1Hl83m79nrzM0hwarfbdiXTtJBbDZmc/0Zaj7ELwxFOWeRJqCoY\nHcPcA/IbHR01A3B4wFtbWyqXy4awpL6Xpw3ZBzPv3usduy8IPr35JgTXQ6iCN41lFnELTCwZg+OM\nz2tTwtB/SNLGxoYNYDj9KYcQJGDXRZwcdgSdTsd+JrZe3IzcdnC273WdqwYQ+iTxBHt7e3Yt07SB\ngQKJMXzweDw2SJmamrJxMHYFMMHm5uYUiUTsCwdzbXDe8gAAIABJREFUHRoaMlgLXgVaQrBuvkSU\n4fBEIpGIlRrUm5IsjhejRlyCOP28Xq+KxaLhxTRhvDZUTkSw+M/xeYDG8JD0Ev2JeGOTBgIB0wIW\nCgUNDQ3Zxm+326c8+di0oCs0qNiWMZgijxDrsLNOAM/VyYxwk26c8BoaLeyiUEagjK7VanbdA19B\niAHfTSaTkmQGLtgCwKSDaAOcxYZE+U2AJAQk/j51KtpAAtLz+bz29vZO1ewXL1604Q0RZvxchAi8\nJqNnsG7wdfBtBK2UCXhuICODw8F0EodVbBd6GX/cEJCpmCjCmeZW5IaA1zwwMGANcDQatanjva5z\ntZmJR0MlDV8X6idcW+rZ8fFxtdttTUxMmFlKqVQyUszu7q55OxMWKcn4BlyrONMDS0FiHxgYsDIA\nuRH1ZbPZVDgcthE0o3RwbJh6ly9ftkbq1q1b5r4knVz7+Li1Wi17EKXXU5/wb67X69YoEmpPTAQU\n1JGREQsKIoQH7jLEJKDDZrNp8RAgIKlUyghRWJc1Gg3jhXBb5nI5dTod1Wo1xeNx+Xw+y/A+yzp3\nmxlLrpWVFTuRkCfhbUbDk8lkzGuiVqsZaR2Jk3RicLK0tCSfz2e1N3J9jGLQGi4tLZnipFAoGOmJ\ncTiOoETuUrcDrzUaDfn9fuXzeTNl7HQ6Wl5eVi6XMxcm/DwoBYAMMUvkZOcWymQyNlGEBMX7cbvd\nSqfTRpRHyLC/v2/iXPyYOaFBXrD/4iHESBy7rd3dXZVKJUNl2u22TRQRAxNPwQj/LOtc1czNZtNK\nCupDyOVwZoeGhuwqj0aj+smf/MlTbpudTkfT09OmCMlms5Y9TaBMPp/XzMyMNjc3tbGxoW63q7Gx\nMUs4bbVampycVL1e19HRSVwZlrhwLJiihUIhraysGHTG6QgbbW1tTZcuXdLu7q4eeeQR3b5925Ky\nYrGYCoWCWRTkcjkTjYJ64HKKVIymlGFGrVY7FVMhyVQyExMTdoKSJEDeCggH1E1SaRmgeL1e+8/x\n8bGCwaCN0imDqMmZWr73ve/VK6+8cs/f/7k6mZEEcdIdHByYG73T6TQlNBsTmwBgK2rPzc1Nq/nG\nxsaMJtrpdDQ1NWWSoGQyqVQqZeNh0I1UKqVKpaJEImHC2JGRERt+SLKcPOKJuVFgukHhHB0dNfeg\nu3fvqr+/X+Pj42YKk0gkTBUSCASM7wwjsL+/3/jT4LuUCZQ4/f39SiaTxs5jc4Ofe71eK1sSiYTc\nbrdNGTGR6evrs0aZ0E5OaUqocDisWCxmRo38zi6XSyMjI5ZIe6/rXG1mTjN0bLDiwEapF2F3AX15\nPB6LHeO04GqEI01Xv7a2ZpuO4QkdPf4TXJcbGxuGZAC1ETSPz9zBwYFFQWBlxcQMtIC4CSC9XtNw\nTlkgSE58oMVe05V2u21MPOn1iSlm4js7O1a+MMnEtwOlTT6fV1/fSVItG55DBANzHhQayd3dXStj\noBR4vV4jLlHS0JPc6zpXmzkej8vtdhvZnKEDHAy4zIFAwGwAGFdfuHDBLFaZVvGlh8NhS2QCHuNU\n7jUE9Pv9euihhzQyMqLBwUHdf//9VpeiJ8T2iskkedMul0uhUEgzMzMaGxuzej8UChm3pL+/X5cu\nXbJSCYSCaxuWHeN4SadsxxAnUIpJJzgz4/1kMqnR0VGzNoDuiZuTdBLeDj/E4XBoaGhIQ0NDisfj\nNryhEeahS6VSGhsbM6kXaAZGjk6nU/F4XNevXz/T93+uNjPGful0WolEwk7gYDCowcFBa2jgzbrd\nbiOdcwKCsw4ODmpmZkaVSkUDAwNaWFgwpGRkZESFQsH4ujhukhBbKBQUCAQ0NzdnnAkaQfL52Ajb\n29vy+XxmasgwBOd88kMI39nZ2dHKysqpjHBQkt4atvfz8Pl8KpVK5lK0u7trECa+GdxcfX19KpfL\nBtvBW2m1WkbzdLvdlnhFX8BtQPxFKpUy05mVlRWLX+YU5wDgd+y1SLvXda4awP7+fgUCAYVCIS0s\nLOi+++5TqVQyrBi+ANAdcbfwgUdGRgwBaLfbpvool8tKpVJ2vXINg2QgT4IdJ50w7mZmZgy64iGB\n00CJg5cdqnLpRN0Bm488PoSmDGewsmKzbW9vKxaLGTlekiYnJ7W/v6/l5WXdvHnTItMgSdGAYjWA\nNpBbAAyYmtjpdFqd39sY9mYS4vdMBJ3b7VYikVChULAHFESESSoDFqRo97rO1ckMs2xlZcXGs71p\nRvw3Y26v12uWXXCbW62W0TtRdrtcLuNK4zTPQAGfNb58avVyuWzQFXUop1exWLTyBCcgeMm9qmdE\nBdS1TNmIOmMIg00XERTUrJKsDs1kMuauz+bDtAapP59h7+gZjnKvpS3Sqt7x89LSkn1WTEWlE94F\nFmW8L4QSuDhhb/Aje66e1Tu6RVHcC/wXCgWTOG1sbGh3d9e6eP4uJBrKBnwgsKMql8tGSMfWlgaJ\n6xnr2rW1tVPoCggEyANIAnnVWA4MDg6ae9Hy8rJFF9N0QmBiiknONQ9FJpNRoVBQJpOR1+tVIBAw\nbgZ8FFAWGljcOBm0cPKjfsHrgs8I/2tust4hS7FYNK+5VqtleSigG4h1pZPbg5KEB+pe17kqM9i0\nZHVghL2/v29Ggvif+f1+ra+vq1gs6urVq2aMQqAkzkbZbNbqQ042OLz4R5Dk5HA49N3vflc3b940\nM0AGKiAleHUcHBxoY2PDxu6Hh4dW6yPjovatVqs2GWT4wWuCNjB8uHjxokUfownk9+V0hzNNuA4k\nK4hYvTZiOHt2u11Vq1Wz1MVXBKEA/n2URATW06SOjo4qk8nY+6V/WFhYMHgRP497XefqZPZ6vRaN\ntr+/r8nJSZNQMbJl+saIGLEpkh1wVb54unPEpkBkvWE0kUjEUIOpqSmjhHo8HvsP76k3zwT7sG63\nq1gsduqfS6WSIQ54TWNszu0iyZrbcDhs0WwIFDitGV/jkYeiRZJ5d8B7hs8BTh8MBs37gtyRQCBg\naa8gG8lk0hQpbrfbGkvG5rlcTsPDwybyZbBDCCfB8GdZ52ozwx0Gp63X66cgMca5NGL1et1sCGZm\nZsweqzf0BgYeJCVOzmQyaeQl6JBwQHZ3dzU+Pm6nMV0+XnGdTkdDQ0NmAZvNZs3uCzYfaAeO+sB7\nRFtwAhLFgEqjN9GJAQ1/hoaM/x+lCWLedrtt6Vuw8rgR6vW69QD44fFAoQ7HdL13uAKRC4IRHGa4\nM6TSolU8yzpXm9nj8WhhYcFqOpCLg4MDhUIhIw2R4kTwJbFgND2oI+LxuC5cuGBRuU6nUwsLC9ao\nJBIJI8/Ameg1PsF0u1d1zMlLAwj2i2aPjRaNRs0FFJEuniCgMqAb6PPYnNgWUDJ4vV6l02mTTPFa\ncDpcLpfRMiEJIZkCxUCj+P0xdODRiGtxKGIIgsD36OjI+CU4MvFZMzH8URB8z+ILpuli44CPEnPG\nB0dHzciXJo+BSy/xXDo5gUZGRqwbR6lMvl5vbrZ08nAFg0ELocENlNE6o1yuYlAUQnXIJsEckUkh\n6ApOQvv7+3K73TY0Av7CeMbr9Wp4eNjeH1ERvD9M1dmUNH8MYlCtoKDhBO79mZQkfH6NRkPtdlvB\nYNBuung8blNNbkRJlszFP9/rOlcNYKfT0cWLF01FzXQPsWUkEjFpu8/n0/r6utEb4RYAN4VCIa2t\nrRliEAwGNT09rXw+L6/Xa6XM9PS0KcGhZjocDvNbo6nExxgHIuwKwFy73a7K5bKmpqYM6hseHrZy\niFpXkjV81OIul0tzc3NGwIf/wGbL5XLG/yCgiPdJT8Ht4XA4zE86mUyakXo4HNbS0pKSyaSq1arV\nxGNjY9re3tbExISq1arxQ65evWplE5BoNptVOBw2bsvo6KhJwrBPO8s6Vycz121vVBdOOXAdPB6P\notHoKUYXwH25XDZy/fr6ukZHRw3cB2OGgI7RYKlUMuej7e1tvfzyy9YAwYVAAcKpJsmaTKfTaUy2\ntbU1bW1tWXQDxCAopEzqXC6XuQtxMjL8wZ8axTiNrCQ7QSORiJ36QGx441F/9/f3W5IU+DIIBw8u\nXBD+bK/PHtO8gYEBNRoN89VjOAOFlNsS/P8s61ydzEzhJBlLjWYP3JkPGzeeqakpm3zBOHv7299u\n3g584bgM4QKEXQCgfygU0sjIiKampsxEBmUFEzQaSeCpVCp1SgI1Ojp6StKPF10kEjFrLcoLEBim\ncU6nU9Fo1OzBMGQ8Pj62Usrn8ymZTKpcLtuN0Ww2NT4+boOLfD5vJQQG7OFw2IzMd3Z27GeguQyF\nQmbLAPeDTO/h4eFTnzEoUH9/vy5fvqxbt24ZgvQjdXbPwn0yEokonU6brUC73VaxWJT0ulwI4Sou\n9NJJTbyxsaHbt2/L4XAYFZShAcQZkk8B/tHHYbGF/1uvzxod/+HhocbHx+V0OjU/Py+Px6NcLmf2\nB/F4XNvb23rttde0s7OjdrutQqFg/A6/3y+n02k4M272uAqRwMq1jqczp2E2m7XhBhRZWGv8PtgG\nkNeCLQGuSQsLC2aW3jtxRE/JxBN0ApULhHxuJLDpXuuwM33/Z/rbP2SrXq8rHA5rc3PTcFTifgml\nBE+OxWJaWloyCwLI5TgMMQGcn5+3EEuy/6ixYbOtr6+r3W4rHo8rnU6beLZYLCoWi1ksRK1W0+Dg\noFZXV00cUC6XrUkljRXrAU5xyPIQpZiuoSZBxAuR6IUXXlAkErENVywWLQ0qFAppZ2dHDodDlUpF\nBwcHeu2118xdiPi4SCRi42aUN/wO5Ati2wX5CPQE7w4mgDg0EdXcbDYVjUaVTqdPOa2++OKLZ/r+\nz9VmBnstFot6y1veYuPoUCik97///fr617+uyclJs8OCbBSNRk8lMQ0NDSkajWpsbEzHx8emyGBK\nBrLAxAy/DqfTaWVGKBRSLpfT0dGRhoaGNDg4qGvXrimXy1lyKVAg4+SHH35Yfr9f8/PzcrlcarVa\nuv/+++XxePSd73zHaJQul0v333+/YcfkUMNWazQaGh0dVbVa1dWrVw2yC4fDRhGlFNrd3dXNmzdt\noyPD8nq9evjhh+3UBxuHPsvnXSqVdPHiRa2srBixCTSlWq2aCXm5XNYDDzygdDotv99v9XO5XNb+\n/r5CoZCuXr36o4RWFqLR0dFRbW5uGtS0t7enb3zjG3K5XDbeJXxnbm7OcqWhg2IiePv2bdVqNa2v\nr5uPHVcqWOvs7Kz5KsP+8vl8Wl1dtVqQU291dVU7OztKJBJaXV01aM7pdCr1vQyT27dvq1gsKhQK\nKZFIKJfLaW5uThcuXDCS/8zMjJaXl625RAAwODioTCYjn8+nRqOhqakpy/2GFz0wMGDeHXhXLC4u\nWrwFpRkiArDsXi1lpVLR7u6uCX8zmYx5laBqx8wdbvfAwICWl5dP0QEWFxeNy1Eul3/kz9y7uHrJ\nw9vZ2dHy8rK5E+ELwUCDBg42Glf90tKS1cCSjFHHtIprl26cBi+dTqtcLiuXy6lUKqndbsvtdhv8\nBNaNxQCRvNlsVsvLy6bwRmXOkIShDAbnTAspL0qlkl5++WXl83lzLMrn8+ZRzc0iyWxwSaSq1WrW\nmOI6iqpmY2NDxWJRi4uLWlpa0ubmpv29tbU1azwhJg0ODhqakslkrKzL5XIqFot2QxwdHZlKRzq5\nMRBVnGWdqzKDjbO8vGxfSm8Ds7i4aLZXRKGhioC8zyABqqLT6dTIyIh9AegEoSyis4Noj8HM4OCg\n1tbWLPEJ80FqUFKueG3sq4gD9vl8RpaSpFKpZFPITCZjrqSIX8kKJF4CNh/2Y0BlnLI8GJiWk6+N\nNAt0ArQEx0/M2aempk6ZIvIgUr7gfgqRCEMa+gBunVwup2Qyac6oZ1nnajOjSJZkder4+LjlkNx3\n332SZKRySPgkqtI0kTLFNIu6GOd6SRYVhtM9XzADCJog/NXo2DFiIey910WeqV29Xj9lHcAkDlIQ\nmSOQ7WHnwQXBYAXyTjab1dWrV61XWFxc1NTUlNxutznmx+NxmyZirHjhwoVT9FeorFjzxmIxRSIR\n1Wo1xWIxc0L1eDwWHHTfffdZbd9rpN5ut5VMJm26SR1+lnWuNjPKDUSVe3t7p3KhqUWJOmMsDAca\n8g+wEv+bD7pUKsnv91uY5erqqsFuCGTBeBcXF5VMJlWpVFQul21IwwQS/zo82eASo4/DyQjlBogI\nNgmVSkUzMzPmT0E8MFM9QoR6kRdwXsLcJycnLWGWk59bhz8zMjJimxf9n8fjUSaTMQ0lbkogPdKJ\nmBebB8otPiuonpjfUK6cFZo7VzUzpwoTONKa4GhANsKbAlsq5Dts8N48PCT7GMfA6yDal+ELQwlS\nXPkZva6clD5wN6CTSidUzF4dH9c1r4UNLu9pbGzM6maYeBglAuXB0iOaGHIRUCBlChAjY3V+5+Hh\nYUMeut2uKbJ5PfB0gnpg1HFIoMHkfWN5gGCYxCrq+h9lmvQsiCw4B5EvglHgzs6O1tfXVa/XFQwG\nlcvlTCHc6/izurpqDY0kA/fJMIEExE3AcAMyPQLNvb09RSIRe2AkmVTJ6XTav0MUSgnDg4eP3OHh\noTW1vW5C8DPYwDdu3FAsFlMulzP/53K5rPn5eTu5j4+PNT4+btZlnNjYBBwfHyudThvEWavVLFs7\nnU5Lkjk5MS5HxSPJQi/xw+uNzwgGg4b8JBIJa6RRs0Bwutd1rsoM6XUa5NjYmEKhkK5cuaK+vj5N\nTk6aipmNOj4+biUD1yjQG2NwTi++NEkGyzHeprmBtM7tAMUS0j3lDgMMTitOM7ztaA59Pt+ppgiX\nTN4vfQE0zs3NTYVCIV27ds0ciSQZ4oAYFcSHZpjrPplMamtry9h2oVDI3EVR8Egym1qijDFHRHVN\nnIZ0whzkRtjZ2VEsFjPOM/izz+ezuLhbt27d83d/rk5mXIhCoZCWlpbUaDR0+/Ztdbtd5fN5VSoV\nlUolraysWKDOzs6OTcQ6nY4KhYJefvllNZtNm7Stra0pFotZkDqvBVri8/nU6XQMDcGnA5ppOp3W\n4eGhndDwk2u1miECNHM0g9vb22YtMDAwYB7GNE+8Z2rR5eVlOy1ffvllU9SUy2XTAlKKwJmGjET8\nBCY0GBvmcjmLbuh0OioWi8bg4+FgMkkWNxueYQvlWbFYVDAYVKlU0tLSkqE70GxJCzjLOleb2e12\na39/X5VKRfF43PR18HWp4fAKprbkei0UCpYjyClMbch1uLe3J0mWZhqPx7WysqKjoyNdunRJ7Xbb\nTAS5uillUH9XKhVVq1VTXgcCgVN8CkSqIBXU1clk0ozSqYNpsDhlfT6fpqenrYwhZapSqRizjdoY\nB9HeqSenLUYzhL6n02lzLWXDIqylROMzkl4vlbBvAMqcnJzU5cuXbayN/0cvv/le13+pzMDs46x5\nbW/WIs6r1Wrp8uXLKpVKmp2dNV7z4OCgLly4YJ7FlBMjIyPK5/NGh7x8+bLGx8e1tLRkTLZWq2W+\nb9PT00b9PDg40P33328Kjl7VdbFYtHIGsg+1/NbWlo6PjzU2NqZyuaytrS2jX87MzJi1LEmnfX19\nxvmtVquKRCKGLkxMTJilAfYH4OjBYNBYcKhDYrGYPWjBYFCjo6MWB3Hx4kWbKKZSKWs+n3zySRvQ\n4AoF2QqjHQY+2B8glqVpBWqUZOE84XDYXmN6evrNHWd/9KMfNUnQ9evXdf/99+tTn/rUPb/gm7ng\nQaC7GxkZsZoTX7Z8Pm9iTwYhnJLUneVyWZVKRZFIxBhtDAFQp0Ao39rasqYQh55arWY1tsvlMoU3\nPhwwy/B2Q08HglAoFGz61u12zRaAqIlkMqlms6lGo2EjZkoaoEaiLfL5vPm8FYtFDQ0NWZwcMRdr\na2tWwpD7B1eb0TO2umgMpdfNarABzufzlpnYi75gUinJiEXQDLLZrLa3t83A8izrDTfz3Nyc/H6/\nvvzlL+unf/qnlU6n9fnPf/5ML/pmLdhb1Jy98V8QXjihYIWtra3ZsAC+MB98sVi0qR30SFTW1I7g\n0ZBxEH1S/wIPAj0xVkc7B8ogyd5vr46Qh1OSnbxYWm1ubtpDxeu3223FYjFTs0SjUe3s7FjYOnX2\n4OCgeUIzBWWDQVUFN4aXTc2N8c33S78GBwftveNDQg8BcxGTHIfDYQ0uDMezlhlv+Lc5bb785S/r\nySeflMvlOrMi4M1cLpfLosYikYhJ2SF+DwwM2FU4NjamBx54wMbJKKJ//Md/3OymhoeHFQ6HjQ1G\nohJ1NHUg8ieuXr4gcgPBi9mMva5DYMWczKlUStFo1NTYCA2QgCHXunLliilPwGqZFkKUHx4eNiWM\ndGJvMDk5aWYuvF4vQgMWDL5MnMPk5KR8Pp8efvhhg0F5UI6OjkyyxeAH4cLMzIyhIegEeaB5QL1e\nr65cuXKm7/4Na+Zf+7VfUyqV0gMPPKB3vvOdSqfTZ/YEe7MWPIVOp6PV1VX5/X49//zzetvb3qa5\nuTk1Gg2zxnrssceUyWRsuAAhvVwu65//+Z/11re+1aQ+6+vreuyxx1Sv17WxsWGvUyqVlEgktLu7\nq/39fa2vr+vGjRvGhRgbG9Pk5KS++93vanZ21mpJ0AnwVUxfGErgNg+SQMY0lNCDgwPdvn1bwWBQ\nY2NjxneA5POd73xHV69eVSaTUX9/v/L5vMbGxlStVrW2tma4snSSA4P1Lg9KuVxWPB7Xv//7vxsF\ndWFhwWRTiG5jsZj29/cNf5ZOYDs43g888ICy2axeeuklra+va3JyUqVSSfV6XePj41peXlY4HLaJ\n7AsvvHCm79/RBZj9f1w8qZwqPyzL4XDod37nd8yPmJO5WCwqmUwql8upUChYQDx5I9lsVtFo1L5I\n0AEwXYYhXq/XSPput9tooJQoCD9JkiJUk4YMAxYGHVgcUBrxdxlJS7I6mQcHNIHJ3v7+voVger1e\nlUols8NFuMpAZmtrS36/X5cuXVI6nbYJKBg1g4zNzU07XTc2NszbIpPJGPtuenrafKqvXr1qUBuc\nFhQ7Fy9etGgIjMkpA4+Pj7W1taVcLqfx8XHF43Hlcjn91V/9lf6LW9LWG57MMzMzeuyxx/SOd7xD\n73jHO3T16tUfuo3MAsw/Pj7W/Py8kc5drpNIssPDQ+3u7qpSqVgcMdM0xquIM1GcoKqmsWKk7XK5\nLCuEL5xSBXULGXu9WYROp1OJROIUhRI+MI0o+DOaRTw0mG729fUZ/JhOp+VwOIxbgXKlVCpZrV2t\nVjU9PW2bm5QquM2YitPI5XI5+1nxeNw88jjI8vm8EomEjo+PtbGxIUn2+/Lw9ff3m5AX3grhPbVa\nzT4XpqBQV8+y3rBmfu211/Srv/qr2tra0u/+7u9qZmZGH/jAB870om/WAi8mc0OS5WUw8aIuxVOO\npFPUF1z/qD/wR0ZN0ltiXb58WRMTE2ZHwGtBGvJ4PCbklHTKJpapn3Qi4cJvjpH48fGxWXIxWWMj\ngkMPDg4qGo2aGTpZepyCjUbDNmqvVRfNK+QqGkIil1HegMbwfoh8QIeIsrzb7VqDyethryDJYtP4\nfJGf4TUyODhodNuzrDfczHSyFOz/HXltb9bClJATo1qtKpPJnBJdAke1Wi0FAgG98sor2tnZ0fz8\nvPkcc1rlcjlFIhFTRLBBwEbpzpkeZjIZO5UJXsdgkcaTAMh8Pm8Edf43ny/jbgwWK5WKKpWKlVDh\ncNgsFIARy+Wy6vW6VldXrTTCzvf4+NgU1kia8vm8CYDB4YkYZkMWCgWbig4NDenVV19VqVTS/v6+\nXn31VZs7QMDnBkJcAOGf5pcS7+joSHfv3jWcX5IxCM+y3rDM8Pv9un79un77t39bv/zLv2xGJz+M\ni6iGg4MDPfjgg0okEnZiYHg9NTVl6grq02g0qnq9bn/+ypUrBsc5HA6zvSXIBryWgQAnUCKRkM/n\nU7PZ1MzMjNm0TkxM2NgZc/CBgQFNTExof39fqVTKCO6E5jz66KMql8uGzKBWoVy5ceOGnE6ncZIR\nGoBkwKiTTjbK1taWvF6vHnjgAW1ubhpngt+Z8Tgeen6/X29961stwBP2XavV0vj4uHFQQqGQoRrd\nbleXL1/WwMCABX4ODQ1pdnZW6XRa6XRaY2NjNv2Lx+O6deuWEomEXC6XJicnz/T9O//oj/7oj/7/\n/sC1a9ckSV/5ylf0pS99ScvLyzo6OtL09PSZXvi/e/3xH/+xbty4YZG/hFkuLS1pZGREq6urxv0l\nbapcLptDPhO0oaEhvfTSS2bliuMR7kQ0Ooyrw+GwMpmM+Q9nMhmLNOPLBtZjoAJzDoI+JygowdHR\nkZ20DDXw8EBJvri4aJNBrmqQEowS6/W6ncCBQEC1Ws2CiIg3a7VaWl5eNlIVSu6hoSEtLCwY3Ieq\nhs+Mm4QoDX4e9rYcHKAd1WpV4XDYrIT7+/vNUYrJ5osvvqilpSW9wZb8gesNT+b3v//9ev/736+F\nhQX90z/9k/7sz/5Mn/rUp86cDPRmLNAAEAmn03kqBRW+L+Rwp/MkfPKd73ynfYkIUns9IGhs8Hou\nlUoGSyE36k0jZVCCpAo/Yyxeqe0ZTvBZEuPLa/aG6jC1C4fDduLCIx4cHLSNR2Qxm9DtdmtxcVH1\nel0ej0djY2Pa29vTzMyMnE6nyuWyNWHY1Eqy34lp3u7urk0cef88YH6/X9ls1iKZuYXoOUZGRsxT\npF6va3R01DD9paUlawbfdHuuD37wg5qZmdFv/uZvqtls6vOf//yZeadv1hocHLSBBugCSmROi3q9\nbiNZn8+nRCKhYrFodS9mK6RHMTHDJAWSPaPv3oAbSo5eCy5OfZo/lBycdpQJMMwgHnELbG9vG3EJ\n32VOW05wvOkQpGIcI8kyCJPJpAYGBmxjbmxsWNJr7/sAiQD/drvdWl5ePvWgQpqiac1kMpbCheaS\nQ8PpdKpUKpnUixgJRK6EZfLaZ1lvuJl///dxO4GpAAAgAElEQVR/X4uLi/ra176mP/iDP9C73vWu\nM/vovlkLAlGtVtPdu3fV7XatlgSfZTIFLlwoFE4RYGKxmLa2trS1tWVj2N6TmajearVqo3Gv12v8\nAjgfUCYh2SMLYkzNJut2uxZe4/f7VavVDBJDUoRLUzabtQeGjcLvRXY15cXR0ZEymYw1h2SD41IE\nSgFttlKpqFaraW9vzwhS5XLZxKe9WYbhcFhTU1O2YSHdY2K+u7tr/UStVlMkEtHGxoahOxiug9RQ\n8zOlvNf1hpv5xo0b+vM//3N98IMf1Ac/+EF95jOfMe7CD9vCcDASiWhyclLDw8N2ApKUJMk2HTRF\nVND1et1UyEBOvSbcmAiOjo7aKYtiQ5LV3DRUoA6w8YDlXK6T8HVIOrOzs8blCAQC5hhEHQw3mMxu\npPmSjGMBEgAuDK0yFosplUopHo8bXyMQCCibzdrwqNPpKBAIKJlM2meF5wU4cSqVMikYmx6kAq88\nporAnvBKekfl2AssLy/byD0YDFpDfpb1hpv5qaee0q1bt/Qbv/Eb+vjHP64XX3xRTz311Jle9M1a\nuA/BO+CEoi7FHRRMs1AoKJ1On3LcxFcOfzQyPNrttqrVqp26sN9o3jwej5LJpEqlkkFanIRYVnGV\n0zQVi0XbAM1m0wQFtVrNsG5sALiCKaW4IdhcBwcHxkCj1EEitri4aFAdn0U0GjXYjsayUCicSrfi\nz7ZaLbPSQgZF+iubkweIhwwPEsoKh8OhXC5nG3ZmZkbHx8fKZDImYDjrZn7DBvD555/X7du37Z8f\nf/xxPfDAA2d60TdrFYtFTUxMWDlAE8ipB3qAxJ1NjZweR31OYTp8n89nUQWQlGCEMTVEtQG7DLuB\n3kEJDZ7H49Hs7KyhBpubm6dSWNHlIcunaex1LgoGgzaNRA6GQQv9wNjYmDqdjiYnJzUyMmJKbdhv\nnMps/t7JLoQlyiNyR1DbkIjVGwZKI+dyucwRnxKs19KMSWCn07GhE6f5Wdb/09CkN6Ab87sfxoUF\nLOoMpmWYtHAtLi0tmYKbocHly5ftqstms8YjHhoa0u7urkWDSSefCTatjM8Rt2JowoQLA0SayVAo\npEajYeNnbg/eL3ZWlDC1Wk0jIyPWlFEqwfXw+/1yu91aXV3V5OSkneSgHh6PR3fv3j3lWOpyuTQ9\nPW0cD5q+kZER8+M4OjpSOBxWo9FQMpk0HLw37EeS1fngzjx8TBvZsEdHR8aBgaIK+Yox+puuNPnT\nP/1Tvfvd79aFCxckSel0Wp/97GfP9KJv1mJj7u3tGXSGdzDulq1WS4lEwlAIlMSQ9AcHB40ANDAw\noI2NDU1PT2tpacl+FtwJONCVSsViDF555RUTdeJ5weQNvgTSLMLVgdRg7UG0hxZaq9Wsvm21WiYO\n6DX5TqVS1tyhJAHTxnPu+PhYxWLRCPfcSvh2ELnGYcBDgedcPB43izGgO9zyj45OooWHh4fNRrdQ\nKMjj8WhpaclMFnv5Je12WysrK5qenrYk3LOsN3wUHn/8cS0uLurTn/60PvOZz2hxcVHvfve7z/Si\nb9ZicoVRy/HxsbLZrDUsjLr5Ire2tuxkxFycMW6j0dDq6qoNXGKxmNkV0PRgg4WbJtc8ximUFfAR\n4EUQiIljv8PhMLNBsPLerMHe99bpdKzBpPFsNBrKZDLmFVIoFLS1tWVC0/X19VPlBRwQHrJeiy1E\ntATqQEAqlUr2WYKFZ7PZU+oUaux2u20sPWwestmsiRNAc7a3t01pXiqVTlUA97J+4Mn8xS9+0Tr+\nXjI+L/hzP/dzZ3rhN2uBcV6/fl2Hh4dKpVKanJxUf3+/Ll26pLW1NRN/Xr9+XaOjo0ZG39zc1MDA\ngEZHRxWJRIzH8K53vcuamImJCbO5CoVCBv0hmsUxFMkQV6skM1j0er1KJBLy+/12IvNed3d3jTPh\ncrmUSqXMhw1/DG4XXE8hx0Pkd7lcmpiYMJ881ONut1t+v1/FYtG0jUNDQ4Zi4NgPaWpmZsbKn+np\naYPVJJ2iozLiT6VSKpVKkqTZ2VlLIsA6LB6P22nNZ464oNls6i1vecuZnEB/4Gb+x3/8R7t+n332\nWTuN//Vf/1Vvf/vbfyg3M6A+9eXo6KiNWmu1mjY3Ny1gBqUIxCPgOKwEaIo8Ho9WV1dNWYG/hCSb\ntuXzeRu0AK0x4IB4jrdzu90+FWFGPooko6L2JkU1m02trq7ag4GHxdDQkCYmJmziieaPaWe1WtXk\n5KTFuYXDYYMOh4aGzALA7XZraWlJY2NjGhoa0srKivn1ARXCC6GZdrvdyuVySqVSJkQYHh5WJpOx\nvmFubk6pVMpMJAcGBpTL5axhLpfLCgaDSqfTZkpz1gbwB27mz33uc5KkJ554QnNzc4Zj5vN5fexj\nHzvTi75Zq1gsGs7Klc2ww+PxqNFomDcEo+tqtWpBPJQhYM3QFnvTVnGMB4bC64Hatr+/X7lczho1\nTm7YZYxxiRaDqM7wQJKhH2x2oo6x4e10OkYcYgDicrmMhMQ0E+gNRTksNxpaamaIQSAovP9ms2lN\nKAQu4D30gWR6J5NJ+1mgMMVi0ZpuSF38Tnt7e9YYNhoNGy6dZb1hzbyxsWFPqnQCymcymTO96Ju1\nYLfRacM9oFFqNpumsqauY5TLCR0MBtVsNlWpVE5RPBlNr66uWiPGly/JNiNTOYxUOF3hHmA+AyGn\nNwcFzBn1M6duOp22n4f6mdqTWAimndweBwcHmp+fN8I7RjOohHgtSWZ8SLZ1L37daDQUCAS0sLBg\nnBYgOfIHYSRiUAOnhc1fKBS0u7trSa1bW1uanJzU8vKylS34epxlveFmfs973qOf+qmf0uc+9zl9\n9rOf1fve9z498cQTZ3rRN2stLS1Z8wYFEY4DJyAkpL29PZvk1et1swfgNMIIJpvNWj4J9S38DpCA\nra0ttVotUyCz4cBk4SJnMhmTBA0PD5sDEH4fjM6dTqeWl5fNUAYIsFgsWvopUCN6QYS8DGP6+vqM\ntI9fRy9fe2dnR319fWZFkE6ndenSJbuZyFLZ2dk5lRuICTq4OD7PTF4LhYINcThUdnZ25PGcxDBv\nbm7ardTtdlWr1UxJc9Yg+DfUAHa7XX3pS1/SM888I4fDoXe+85362Z/92TO96Cc/+Un93d/9nfr6\n+nT9+nV99rOf1d7enj7ykY9ofX1dqVRKX/jCFzQyMmJ//m/+5m/kdDr16U9/Wu9973v/4y/icOip\np54yOQ/XWD6fN0+2XC5nHyq1YKFQ0EMPPaRKpWK1HRPAXC5nmjacK9HD4clBbjZDBsoIUIDd3V3L\nL2E443K5VKlUdN999xlMxYQyEolYuA0cY8oSSEIDAwPmNIRNLOJYRuE075LMuqvVaunmzZsWBBQK\nhVQqlRSPx02GVSgU5Pf7FY/HT3lzcDuVSiWlUikTwZLAyvg6Go1qaGhIi4uLNua+cOGC1tfXlcvl\nFI/HTaA7MjKiZ599VhMTEzbA+sM//MN71gD+lwWtZ13pdFrvfve7NT8/r4GBAX3kIx/R+973Pr32\n2muKRCL6vd/7Pf3Jn/yJqtWqnn76ac3Nzennf/7n9fzzzyubzeo973mPFhcX/wPA7nA49Eu/9Eun\nMqHj8bjW19c1OzurcrlstR82VvV6XYVCQbOzszo8PNTOzo5CoZAWFxc1MzNjyaNLS0tmWg5lktOZ\n/BAyQSqVihl5gzZgGIiIk8YUyRa1cjqdtk0XCATs4UPXNzMzY6VQNpvV7OyshVqiSolEImo0Gobj\nHh0dmfCAVNSNjQ0FAgEbeICckDa1v7+vhx9+WC+99JIhDKApkkxRPTAwoNnZWa2vr5vhOFg8/s2S\n7EF1Op2amJgwE0VJ2tzc1IULF+RwODQ/P69nnnnmnjfzG5YZX/ziF3Xx4kWL84Ldda8Lsgv1a7PZ\nVDKZ1Fe/+lVrLD/2sY/py1/+sqQTUcBHP/pRg6lmZ2f13HPP/ac/Gy85/NwgCsH2gmqJSWAve47w\nShAMmIE0ldTaDofDxsWgFbgXQezhZETxgXcxSmg8kWkwQVDYYE6n0zDew8NDJf4/9t4kNtb0Ou9/\nWKxikTXPE4vF4nTn21dqd7flNowGkiiINoIRIAqchY0ESRAHCBLYgIdtsrCzSBxnoU0gCEK8iHeJ\nV44jxJtA7bbk7r6tvmzORbIG1jywilOxyPov6N9R0XbHwaWU6E/oAxrdzcvLKtb3fu97znOeIZXS\n3NycaeQoa/r9vvr9vnlPSDeG6wxdvF7vLVch3hOnisPhMCNxShUmfwxNiFcmFoOvs5uSrAqzjhG4\nJDOVRAFEJgy8DiaYDK4YzL3u9dcu5l/7tV/TH/zBH9gCAA143SsSiehXf/VXlcvllMlkFAqF9OUv\nf9kWjXTTZBJCiRSdK5vNqlwu/5U/Gw9lGGlEFUgy6VQ6nTajFpTULF5QAS6Sq+AxO51OPX361PJD\nvvCFL8jhcCiVSpm/2+LiojVCQHQvXrwwd6HZ2Vn5/X7T/uHXkUgkjLSOLo7Am8vLS/l8PvO7S6VS\n8vl8RvLhAYhGo8rn83rvvff+Eg4di8Xs9eFO8Jnl83kFAgHz+UgkErq8vNTq6qry+bzRAVjwKGJA\nTSjBGJUjruV3A1d+8OCBaUg9Ho+Wl5dtIoo5zF2uv/Zvp1IpPX78+E4vMnnt7u7qP/yH/2BmMn/v\n7/09/d7v/d6t72EH/Lzr8/6sXC6bo+XR0ZEJNB89emTj4EKhoOnpadPVHRwcKJFImNK43W6r3W6r\nXq9rfX3dPC1API6OjoxxB+SECQuxvjggoSD57ne/a/kldPmTKIrL5dLGxobZfXHUg4QgvUIKxuvj\ngVyv163m7XQ6pqv79NNPbZQ9CVliVxsKhSyXcHp62kTARCNvbm7axJRmkROa8TcKG8oIkrBOT0+t\nbDo7OzMSGCN3It6mpqasYf68Ter/9PprF/Nbb72lv//3/75+/ud/3mCoqamp1x6afO9739O7775r\nN+Lv/t2/q/fff9/gnVQqpaOjI9tR5+fnzZtBuqmxPi9i6+Dg4BbFEWFqKpVSo9HQq1evlM/ntbW1\npZ/5mZ9Rv9+3nZSskGQyqaOjI62tranb7SocDqvZbOrx48eWE8gUT7qhMpKIClMNBtjBwYGWlpaM\nhE/Zs7W1pV6vpy984QvWAGazWRuwMNiAnUd5FI/HbQrIqPzRo0fa2dkxkYHb7bbY4ZWVFaNiMkYn\nrzqfzxtvA0+9UCikcrlsChx0f4uLi/rss8/sYWE40+l0lMlktL29bZwOmu25uTnl/zwbnBNvf39f\na2trtntPTU3p5cuXkm5OIaaHr3v9tYuZiNw/+qM/uvX1113Mjx490r/5N//GeMDf/va39c4778jr\n9epb3/qWfv3Xf13f+ta3zJvjq1/9qv7BP/gH+pVf+RWVy2Vtb2/rnXfe+St/9nvvvadWq6Wrqyt7\n8BKJhMFG+D688cYbcjgcWlhY0OzsrLLZrJGGILVXq1XlcjlFo1HDqBkGXF1dWaPGpJHEqWq1qoWF\nBSPfLC0taXd3V1NTU+ZLR/oSPh7r6+tm8VoqlYxA3+l0zDkJGy5G4NBYCW6PxWJmj8trUzsnk0lD\nZbCPpdQCGSF2Gd9nOCf4beDPcXZ2Zn0PNTAEJKzKsBKDTxIOhzU7O6u1tTWTaqEk/xt/428YrFmt\nVrW3t/da60r6P1jMTAJ/WNeLFy/0i7/4i3rrrbfkcDj05ptv6p/+03+qfr+vr33ta/rGN75h0Jwk\nPXnyRF/72tf05MkTOZ1Off3rX//cMuP09NSe+FevXllqaCgUUqPRUCgUMh+NXC5nfQA0RPR9rVbL\ndmTiGVZXVw2bRfeGSz/DC5yDHA6HarWaotGo9vf3zWmTY31paUmVSsV4G2SmYPQNZEgiFKUJfGAc\njdgBaWbxzmu1WpbFN2kATi4K/nnkseBcmsvlDGeXdCsIHvYblrgsXlQzZ2dn5o3HToz2kewTTjaa\nZmpuTiCCN1/3+lxo7t/+23+rX//1X9e/+Bf/4i//pakp/cf/+B/v9MI/7Gtqakr/7J/9M4XDYVNb\n+Hw+tdttLS8vm/Qfkevc3JyCwaB2d3f14MEDu8HUtWToORwOqzepK7mRDDsmx8eUJuThxWIxvXz5\n0kg8pFN1u13zWYZ9RrPHcIOYhGg0ajU6cWw8uOFw2E4IZEidTkdLS0t69eqVAoGAsetCoZDxIaLR\nqPx+v0qlklKplDqdjpU6krS2tqYPP/zQSiSkVPQFjNmBAMvlsuUGolGMRqNWtrARUCPncjl1Oh1d\nXFwYH+Xq6kq/8zu/88P3mhsOh/rTP/1TvfHGG3ZkS/pLLLofpwv5vsPhUKFQ0MOHD2/5y0HywdgE\nF6NyuWwOmrgBud1uNZtNg45QUwBfdToddbtdxWIxo3dST2I+iLfE9PT0LQgNzgRqbE4G0A1JBqMx\n7j45ObFjHYybU+H09NSMFXFAYuLZaDRUrVb1/PlzO/pRcSOP+uSTTzQ3Nyev12vTO/Bi4DYaQ4S8\nYNTklEQiEWvqEagyOqf04/PDvwPpWbVaVT6f18cff3yn+/+50Fy329W/+lf/Sr/2a7+mb3zjG9rY\n2FAkEtFXv/rVH1uiEdAWmrbZ2VnjA0wqmoGsUEAT3D47O2vDBUbVwWBQOzs7ZhY+aReAMgQSDjXn\ngwcPzGqLmtfn8ykUChk01mw2bac6PT1VKpXS4uKi8R5YWJKMKMXigmDEg4LzfjQatfQAWHRQLOF+\n4CEtyTL4AoGA4e2MoT0ejxk/EsjD77+ysmIQ3eXlpdk6ZLNZq6W73a7m5uZu/TscDiscDisej9tJ\nNjMzYzv3j8xq4N/9u3+n73znO6pWq/qt3/otRaNRffOb39TTp09/qFDdD/PCG4OJG4JQaJHgq9ls\n1rSA6Pj4IDnWWagMimicIP2Dq8I2g7MM92E8Huvg4MBMAi8vL1UsFk0SBeuOQQlUSW5qIBAwpfdk\n1C+7OX/OyPz6+tokVLzfy8tLKyegqQK1gQWjcuH0kGQuQ2gEWaDIp87Pz40yIMnSCUiz5dRBcTJp\n/EhZBneGiDpU2ne5/tqhCZEHcIIzmYy+9KUv3elFf1RXu922HQ6R6MzMjGq1mqrVqjWHsP42NjbU\n6XSMHE+qElBWvV43Oypoj7lczlKZDg4O7CYiNAWhqNVqevjwoVnf4mkHdZOQHsLVm82mDg4OjGz0\n2WefmRqDMmNzc1Nut1uXl5fWJLLbTk1N6dNPPzWcfDIRqt1u6+HDh8Y54USCU4FXHiR/SdboUoaB\nbXu9Xu3u7mpjY0PBYNDKGhYoDykLF174aDSyiSMTV9ASHkiMa173+tya+Z/8k3+i9fV1+f1+vfPO\nO3r33Xf1K7/yK3d+en6UF7o7wnnotPGfazQa8vl8Nl6mhibC4PT01CZjWExNTU0Zmej09NRCc1iQ\nk/gvbL3hcGhO9clk0sbhMNvq9bqx5C4uLmx3LpVKcjgcCgaD5p3MiB4IEfEorDV2c9TVhEXOzc2Z\nHAoUh8+FBtjj8SgQCFhy1uTpQAkhyTyjA4GAjo6ODDsnzB0WXiAQsJIomUyaPx+WBDw4lD7j8Vgb\nGxtmOPkjs+dCU5ZKpTQ/P6/5+Xljsf24XpCMiAtDyArHOJ/Pm0WVy+VSNps19QQxCtTJkPrxco5E\nIkqn01pYWFAymVQ+n1c0GjW3Txa83+9XMBg00hI4bjKZtGxs1MnUpvA0wHInPebw2ohEIkomk0ok\nElZ781DCqiNIk56B+j0ejyuVShnGnc1mzXprPB5rfn5e09PTNqhaXl42Dg7yJuyAk8mkjfx9Pp89\n2IuLi4pEIlayQXkgk3s0GhmdIB6PW5Tc06dPLXtmdXX1Tvf/c3fm//7f/7uur6/16tUrvf/++/r3\n//7f6/vf/76i0ai+9KUv6V//6399pxf+UVwMS2CFuVw3Qew0KIPBQOFwWMVi0eo0PJOJJ3A4HDbF\nw49Zuim3+v2+jaBJeJpMlIJ5l0gkzC+CIQFKbeA7/i6LGO4zjkfVatUeJFAM6mAME1FJX1xcmFki\nNbp0w25DsDsajdTpdPTGG2/oj//4jy30E1w5mUyai2ir1bJgI4/HY8SmRCKhw8NDPXv2TK9evbIF\nCSx4eXmpx48fG08FSJBpJdHCnDQgM6PRyLIV73L9b2tm+MZf+cpX9JWvfEU/+7M/q52dHf3u7/7u\nnV70R3XRbIDnoowmiJPjs9/vKxKJGISF4z0DhWKxaCQZr9drxoQw8qQbU8RYLGYaQB4IpoosNo/H\nY+Yz7FaTdNPj42OzRICbQakxyUkGncFGq16vm+M8fGhJJndiAESdimqE0xZsNx6Pq9lsqt1umxsU\nrD92fMzVgQsZPHU6Hc3MzFjJxmd1cXGhYrFo01d+h6mpKfl8PssFJ5WWk/SujkafOzT53d/9XX3n\nO9/R+++/L6fTqXfffVc/+7M/q3fffVfPnj27s2X/D/uamprSb/7mb0qSGf4h4nz48KGq1aolJAEz\nscuiOibOoNFoWJkBhRJkBJUzyVGTMBg3IxKJ2FQN/BoaJUjAcDhUMpm0QQw0zWazaaw8mj98LZxO\npy0o+BPHx8fmHdftdrW2tqb9/X0lEgnVajUjwTcaDcOlr66ujFyFFItTQroR6qbTadVqNcViMYsV\nln4gD7u6urIxPmYyw+HQ2IrFYlHpdNryxxGyxmIxOw3a7bahM+FwWN1uV9/85jd/+Hzm/f19fe1r\nX9Of/MmfaG9vT7/3e7+nX/7lXzbH9h/HazLXmk6aC/gKw3FM/MBOaYYkGXYKsoAuTpKVAtA0KQMm\nudSTQZCTyhMIQyRUYVNFPggu+uPx2JJhJVm6KzsddTTEeuiyw+FQxWJR0WjUJnsE/9CErq2tmQ8f\n7kc8XGDNwHaYvzSbTW1vb2s0GtmUlM8bwWuxWLRTcZJKm0wmLc8QHjvcbl53MivlLtfn1sy/8zu/\nc6cf/P/i4uiMRqPa3d01ZUaj0bBIXmT+Xq9X77//vuG1IBdg1V6vV6VSSQsLCyoWi+ab1mg0FI/H\nValUVK/XbUdm4sXOe3x8rNXVVTNEXFhYUKvVMudR+BKURhCL8HZjioa/dCgU0sbGhjEGmVKCGEzC\nYzSMlUrF3lcgELCkp3Q6bU7/rVbLThv0iCcnJ2aWXqlUbEh0cnJiU8BWq6UXL14YFs0kE5IRJ95f\n1AIGg0E7qbBmgC77eaKL/9PrbuZeP2ZXvV5XrVZTv9+3GwbpHi80dsJ6va5sNmsTqlgsZhAXGYGh\nUOjWtGo4HGp5edmO/kwmYzcYc5Zut2vEfDw7aMzi8bj9N4gH+PX5+U32NBAePhvU/J1OxzyRGYag\nJL+6ujL4kfpTko3yqWWhgWKPgHkkNTJDJU4XdH+QmWq1mjWos7Oz9rBAJsJvDxI/pje8D9Kr2KGB\n6zCBvGtY6r1azDRgEI1YJNSDjJIbjYapS2Cr0YETIYZkqVwu68GDBxbnQGA5xoqT49t8Pq/l5WUT\nzdLcUFZQX87MzKjX6xlcR2P60z/902afhRJ7ZmZG8XjcFj8LdWZmRtVq1fDa4XBo0Bp1MhrFer1u\nOkNomXwfHhp4iRCRFgwGrSxjJ02n01ZiBoNBk03Nzc1pZmZGS0tL2t/fN6ekSbYeJRdlHWldIEaI\nGu5y3avFDHVxdnZWCwsLtnDYJdLptNloYZAYiUQUCASsk2dihd4Nu1jpBimYtH91u92mFZw0icFx\nCMEq5KFJuHCyrsQg0ePxWG3Je2dszs8hQgJNICNv3gsDGPoF0A9gNpQ4xJnxfoApmcxNT0+bvRb8\naNCISWteSjYMG3kgfD6fEfZ5APgzGlisEBD7TvY4r3Pdq8UM/OR2u7W5uWmsM/Khya6jJv3kk0/0\n8ccfq16vq1KpGPl+d3dX9XrdRs+ffPKJ8R3q9bpp7sZ/nrkNP3dvb0+FQkHHx8c6PDzUYDCw9CiI\n9DRu0B+x+IpEItrY2DCzFh6IWq2mcrmscrms8/NzbWxs2HvEbBFtJubm1WpVH330kXGKsRVgqMNn\nBa1zc3NToVBIhULBGjmmnXBb2KU5RchKxAEpGAxqc3PTFvnm5qYODw9vCVgZ8MCkwyyHvMX19fU7\n3f97tZhzuZyx4xYXF3VycqLV1VUtLCzo2bNnNp1bWFiQ2+3We++9p0AgYF1/JpPR3Nyc1tbW5PF4\nzHxxbW3N3I0wZGFs3u/3FYvFbOASiUR0dXWl1dVVq39phh49emRWB3gTI8DFeQlFDDsaOy3DnXQ6\nrbOzMz1+/NgmgtjNUgP7/X6zUwNWrNVqtqOiLEGkOj8/r+FwaAaJSMOWl5eVy+UsixvvaL/fr0Qi\nYeUY1l8Ib10ul1ZWViTJppuUGpQ+q6ur9jUYgT/1Uz91p/v/4+ka/poX5ixer1fFYtEmayATpK4C\nhX3yySfm0XZycmJDk/39fa2srKjRaCiTyeijjz7S6uqqeXUwuSITmtTVarWqs7MzPXv2zCikWHMB\no5GXB6car4rp6ZsMbUxsILaj7Li6ulImkzHZ0qeffqqnT5+acz3NGEc1nBPI9ERXkBQ1MzNjp8b+\n/r4ikYh5OsNmw4qs2+2aip0mt1Kp6Pj42OpkTjGfz2fi3nw+bza/nB6pVMr4zahTGGT9yLOz//90\nQWNE2Nrr9bS3t2eUTRqqq6src8WHEjmppmCXgbvMA0LMGI5IjKQxOKdmho1G5LDH4zH2GlAgGkhc\nis7Ozswgnd+DnZSjGSlVu91WLBbT1dWVNZogMjghDQYDtdttS4KiXp0MpKTmhaIK/5lQHofDoVKp\nZDwSkA58sEF4IBIBxWFKw0MOfRYYD6YgJ5QkM6u8y3WvFjMSp1AoZDo4+AfNZtMcK/v9vubm5kz1\nzVQNXHdSjYFBCePiWCwmr9drO6Ikc+wk+4T6lIQnUAjqW+RbHLP8N9TI0WhkDw0KGb/fb6UADviT\nUWosBCy7eBAqlYrBaWdnZwoGg6ZIgUTcr00AACAASURBVIDFTol0iWaTHHAeFsxq0CROxp0x0uZ7\nJmM3SKbCqw67Lx5k4D0gw9e97lWZQdQCMWnD4dCCdnw+n1ZXV2/xIxKJhLa3t+Xz+cw3g50KGI/J\n3sXFhZGUCMwh2JzF0el0zNYrHA6bUWMsFrOfT+YHyAsWr5QHLCr82Bh2XFxcmEyfEgaFOMaQQHQM\nJubn5zU1NWWu/+l02nZR8vkoX5B+MdGkF5BkuziLm8+HcHmfz6doNGo7MScafQQUWR40BkRI0kCL\nnjx5oq2trde+//dqZz47OzOao8PhMINr6rNqtWqlBUc/KomDgwNVq1XzF2bXIWEJGuNoNFI0GpXb\n7bbY4Umr2P39fRtTc5TSyeMJDecCvBcUhUHFJAw2GAzU7XbNzgxdIlAeZQu0z7m5OTs18OqA+YeI\nAHIVpcPJyYmZubTbbdNR0pxOT0+bMWK/3zcnfk4rJovU1MCOPGDdbtcGQnNzcxoMBpqbm7M0An7O\nXZN/79Vipl6VfoA5w0bz+/323/x7dXXVvNRANSCXX19fKx6Pm/caCw9MFWyY8gLMmFOAGhaVBe+H\nhZvJZOzhCIfDRtXk4eKhQ7mBwgQkBbwbDgi7Kjs/jqWkqIKNQ4clOAjKKMgKp8ekTxz1/cnJiWkU\nobTyGYAiEd6JgTkkIr7O58V7xAuEmvsu170qM0ajm4hfalnkUBh8n52dmbz/+vpaxWLRwm7cbrft\n3KSwHhwcaDweq9VqmaMR1Edq3263a2gAnGmOVKxwJZnuDn9lmlKaR/jRgUDAfJtRmhDHwa6Kvo7R\nOR7QuHlSW1P/Uzag22u320okEhoOh+aihHC13W7r9PTUrBZqtZqRgiaDeWgWl5eXLUAIZiDNJJ8P\nkjHs0yYdXGu1mrkg3dWQ9l4tZmpTQP1cLmfUyYWFBfNGhpo5OzureDyuWCxm5UO73dba2pok6enT\npzo5ObFBC7Kls7OzWyRzlBqT5t7wOSDpSDKWWjabtRB0FDGBQEDPnj2zSR16SwYTqLtZ3OzC1J2z\ns7N6+PChUTzJK0GPNznVg6HHn6P8oAanDCFCjd+L8KBcLnfLOB2HT+kHzp/U8qiTeED8fr+mp6dV\nKpUUjUZNBsZI/S7XvVrMPPGJRMI8GeAtI4HiaKVOGwwGSqVSt8a7jUZDy8vL5iYPdxfiDQ0mXTrW\nWQQ4SrIaevIoZucl7WowGOjRo0fa3t6+ZWvbaDTUarWsXkXNzc+jNo1Go8YzwaCQ9CgmfBB/8vm8\nRV3AR2ERUz+HQiEjK3Hs82A6nU4zQafJzmQy1mMkEgl7iNihsT7A+AWKgSQb+FBC+f3+O1va3qvF\nTNIUimpsrJigkevHUKBSqZi0CVUHi4rFzjCFXRDJEzwOSdaBezweNRoNI+SQ4QdCAOT32WefqV6v\n6+nTp5YkRflAbAV0zkmnz2KxaIT+8Xis/f19xeNxlUols8VlCALrDv828PPHjx+b+STmNpQgZLyA\nXsDBlm4cVl0ulylksCh7/vy57bToBimR2FyCwaCcTqe2trYUjUYN/eh2u/rwww8Vj8dvsf1e97pX\ni5kaDYGpJBOHFotFFQoFPXnyRNPT02Z/hTcGSAR5J5NBlIxcadJoDEEPsJlttVrm+QxxfXFx0Wx0\n2dkh/8PZwPMaUj6kdaRcRL0xlk4mk8bPZlrIKUT4EDU4bDjq52azqd3dXS0tLdnvhmVtJpMx8cFo\nNNLOzo7m5+ftIaGZJWWKaSIxxDxkTBIDgYCFd9IcwpVB3T1Zr98FlpPu2WKGUzypg3O73aaAAKdl\nEggZH1QBGRAoAkME2HNAXky9Jm8w/AxssxiInJ2dGRsNOIsmExQBthuaxMnsa/jQZFsjUep0OpZ7\n2Gq1bDg0PT1tpuuxWEw+n09TU1O2+xMif3p6asID4DzgQoYY8DEwbEmn08bNQFUDZZbPBk5yOp2W\n1+vV6uqqQX9EaFCuXF1dWVk2aW3wute9guaYvgELMfECYQByGo/HisfjtiOyo1HzAfp7PB7jdgDh\nYd0FhMauAiJC1h4UUWpIdi6Xy6VUKmUiVzBth8Nh8i0eQiCxSCRiAwpyQiTZe4UJB2f4/Pxc4/HY\npnmdTse4zozNQVKgm7LoecAkWVzFeDxWLpczGA81djabNbQIKi2LGnPF2dlZe3+cLhi0RyIRmwkE\ng0Gjyr7uda8WMzsV+CYmftgNQEzHtIQmBjNIuBWSjIuxsrKifr+vtbU1nZ+f2wLFWmtyx0NjR42O\nRIuYBHZrgiUnyxlJxpgD5+ZGOxwOy6JmsYNdk9IUCoWM75xMJi06jR2UkBwEvVzYFSBm5bUlmSm4\ny+Uywj+fVa/Xs9w+oEF2W/ybr6+vTcFN45lIJBSNRu0kA/XhfdzluldlBovt4uLCQiNJ/8QKi8GE\nx+PR4eGhYaf4q+EJB7kIbPTg4MCI/jgC4e/M1/CcA0EguGZ9fd1GypOedqAS1NQIZaenp41yCrR3\nfX2tQqGg+fl5mzCenp5qa2tLfr9fh4eHyuVyKpfLNirGER9k5fz8XNVq1VyLHA6HnE6nms2motGo\n8UAYoJAIxWcD8y+bzRqWPSkUvrq6MtNwsGzqfHBooE23222OoiQmMJh63ete7cw0bCcnJ5ZjMjMz\no0gkoqWlJa2trRl7DB4u6UuIVR0Oh548eWKox3g81tLSksFjk0JUdiG4Baurq8Ymm0yTImqNn0Fc\nwqQPxng8Vj6ft+ZNku2uIADhcNh295WVFQsHgveM0jmRSGh5edmOejgljPCbzaadFi6XSw8ePNDx\n8bE5/sN1ZkpH2RKJROzzYyLJe3S73Xry5Ik9OJQNk/mJnJrT09MaDAamFXz69KkpgO5y3avFTJhO\nIpEw9QfMMdhjk0R2dhPYbNlsVk6nUwcHB9aoxeNx26kh2XDzaMwgDpVKJaOPnp6eKpFIWIRbPB43\nVTYLAc4HAlqgNjR1DFvwiUNe5PP5LCwTCBEUgkazVCoZJHh5eanl5WUj7gPLTeYEThKaxn+enMpY\nmkaPwHdG6XwG7NLNZtP+HOQHnN/luknbwt4BmzDw9mAwaNPS173u1WKGIMOkbRL6oTm7uLiwBuT6\n+tpuGgYrlCFwGiRZ7Qs6AYoBM46SBoQEhGPSyQdrLW7kpLgTrgTNKfDWpAiVqSbvg8YLJTUPKgT9\n4XBo9T0IDlM/UAuGOZMcbiaAfJ7QU/k68B6/2+RnA9JDc8x7oLHmVGMgM2ltC5HrLte9WswEROJF\nDA0Utha6ucPDQ7OarVQq1jiWSiVbAHzQtVpNhULBwtvH47EajYY5v0ciEX366afGBtva2tLZ2Zlp\n6hjeYJM1Go300UcfqVAomAJmenraaknQEhz6T05ObCfloSEcnp2QieZoNDLeB5a5w+FQhUJBhULB\nyqiTkxNLkWUQgpoEjBh5FVEYh4eHcjgcOjg4sJobdTU+GjSE09PTNr3s9Xry+/3q9/t69eqVCV3B\n28vlsvr9vjn83+W6V4sZOCubzZp3MfDS+fm5ObsHAgG1Wi3lcjk79gaDgaU9sVNRT6fTaVNAo4Ym\ngLPRaOinf/qnDdUgNBN7XNQX19fX1tx98Ytf1OrqqorFopm4MBiRZJwNkBBol6AtkyIEShVYcDD2\npB+IBmKxmJ4+fWrfL92E7/DQEHoZCASMUchQCNrqz/zMz1jKFqR78lXgjvD7wWUG8280GnK5XHry\n5ImJWw8PD3V9fa21tTVLznrw4MGd7v+9WswMIE5OTrS0tGRke+LA4CiD847HY6VSKUlSOBxWr9cz\nIrnL5VI6nbahiiSr73D1oTSZZOex06CFg5MBy+zy8tIsrsgFofFqtVqq1+tmVijdqKiRVYHRTvrc\nMZDBmJHdcGbmJpmVEmZra8vwb1TSlDUsergbmNMQTD8ej7W9vW3NKf0GIllJOjo6snAfRvI+n0+9\nXk+Xl5fqdDr67LPPbg2Mjo6OVC6XrWfZ2dm50/2/V4uZJmkyGoGa8uzszOiSmPnhfEmAOzuKz+fT\n1dWVRYExnJBkeDR1IeQceM9wJyD+o4RGrYLS4vr6WplMxrjSOHNSRzPVA/NlqHF2dmZHOEoUVNn4\nf0w6dMKWQ3mOhS6ZgOSV8LszreMkYEHH43Fls1njfBARDEclGAwqm83eesiZhkIkYlBEjyLdTAp9\nPp8WFhaME/66173CmSVZfK4k21VpesB+gYDG47GKxaLm5+dtCtjtdm1AgSdGKBRSr9czIWq327Wh\nSKlUsoiD8/Nzq335XvyNI5GISqWS8TowJJRkujiv12tqEAg9TA7hRCQSCctKAfuliYQ4xJFfLpet\n6Wy32/ZzJZliutvt2s/Gv5pTBH7H+fn5rWmqdDNsYYgCMrKzs6P8n6ey8rCQSwjGDCzKdJBIO0n2\n3l73ulc7M0OIs7MzU/3Oz88rEAhYDgvcAKRT/D/wWDwe1/LyspLJpFKplDwej/lssOPgSYGbD3Ba\nIpHQwsKCOdVfXV1ZvTwej/VzP/dz5nM3OaIm+RS9IJazwHiJREKNRsPw2uvra0syCAaDNhZHb9du\nt40wz+ACq4B4PG7DiWw2q+vra4t1YBfHT+Phw4eKxWKGiU+GBuVyOdu9Cafndw6Hw5qfn7cTYjwe\n6+LiQvl83piABM7Pzs7qwYMHymQyevTo0Z3u/73amSfJNR999JHW1tbMqahQKMjj8ejly5dmRUUD\ntL29rVwup7OzMwuBR4FMUxgMBo1IDjOPnbfZbBozTZJF6VLysEu/fPnSiDlM29xut7a3t22Xm7TO\nRVgwMzNjHGjKEPRzGKsjUIU3gVkiLkqPHj26pWbp9/va2NiwXJFwOGyxFLVazYwnW62WyZ22t7c1\nPT1t1FBi3AaDgbH8cEMCoqRPoJzBaZ/anLwVkJ27XPdqZ3Y6neZ2ubCwYF5peLOdnp7qC1/4gvEC\nMpmMjZzhHqCaYNeiRAGq4nhFi4cLPKgAwwEMx+GDgGzQoLFwCFrHGAZYze12K51O38rII6ye2pMp\nG3hxs9m0zBTpJmU1kUjYYAP9HRZbTBShzC4sLJghO8MXCPYEEYFl81qUHsjEqMkx0Jms12kGIWYl\nk0nVajXjMt+1Zr5Xi7lWq9liJHeEEoKmjY4dF0+anHQ6baT0XC5ndR5EeRYYpPtYLGYOmy6XS4uL\ni1afd7tdm0DSmOGHzNfm5+dN8cx7kmQ7GH7L5XJZqVRKbrdbDx8+lMPhsObW6/Vahjc7N6oO6mka\nLRpUfgd2eKwGJBlvQ5KZjmPvlUwmzYx8enpaDx48sMjlq6srQ45gFqJej8VihpTw85CATY6wk8mk\nHj58eKf7f6/KDKfTqXK5bI1cPp/Xzs6OXC6XarWamfhhqYUW7urqStvb2+b+OVmiYNMF6sBot1Qq\naTgcKpPJ6OOPP7bm5fz8XEtLS2YVRjwDi+j8/FydTkd7e3t69uyZyuWy1caQ8XHsLxQK8vl8Wl9f\ntwUhyaJ94ZR0u13jQOMJzWlBOXJ9fa3NzU2lUikdHR3ZWHw4HGpvb08LCwtKpVLa3NzU4uKiotGo\nXr16pVwuZ4JaSWapRSRxMpnUeDzW5uamjf5nZ29C49966y1Vq1WzEMAHenNz02gH2JzVajV99NFH\nd7v/d/rbP2ZXOBw2vBfDE5oxwi3JOWEYcXp6qjfeeMNUE5DSfT6fFhcXLZOPXQ9bLqKEpZtGilIE\nclM0GjXuBGlU1KuBQECLi4vmhQzPFysAfhd8MxB7wguRpHw+b3wKrGr39/ftaE8mkxb8w4Jm8cN6\nQ6Hy4sULCyB68eKFNYOLi4smgD06OlI2m9Vnn32mTCajw8NDJRIJ+f1+NZtNLS0t2e7PA03tjlsT\nSAmQIJa3FxcXSiQSymazd7r/92oxc4Qj/3/rrbfsSD08PDTYDHeg3d1dO8rxeRuPxzY23tvbU6fT\nuSXOxM1oNBqpUCgonU7bgpV+YF3lcDi0u7urx48f6/Dw0AYeeMZBFWVYgOCUBV6pVGwUj2wJTgVH\ne7lcNqHp+vq6crmcBoOB9vf3bViDpe1bb71lUiYsCbBlePnypWKxmFKplA1plpeXzSuPOLrLy0s1\nGg3LH8GAUbrJwMFjg888EAioWCzaiUKD2Gw2lUqltLu7K0mGcHz44Yd3uv/3ajFDLF9YWLCFAO9i\nfn5ei4uLxn+4vLzU8+fPtb6+bgwwYC1c4c/Pz83OlqYRJ8vp6Wm9+eabVhZA+llcXDSbsOfPn6vZ\nbGptbc2MUo6Pj/XgwQN9+OGHxpE4PT1VJpNRtVo1GA5kxu/3my1BNps16X+n01E8HjctHoR8ms96\nvW72t4SvAwPyAJOU9fbbb6vb7VpeCim8jx49UjweV71eN7Qnn89LkokTpJu8QRhwUFybzab6/b6S\nyaRZPjCsoU5+6623TNAbCoX03nvvaW9v77Xv/71azN1u1zBb6JYE4HQ6HZPTE+NAuYG9K0QlUIdi\nsXjLcJBhwKTJ4OQgweVymUSJYcHc3JwNBsBboToSjh6LxYykc3h4aGXJ9PS0GdNQl8IPhlhECSTd\n4N4HBwcmNqjX63I6nRoOhya2JXZ50p+uWq1qfn5e1WrVeBToAvl9mfoRNnR4eGin1cHBgbLZrE0k\nGdAwTDo6OtLMzIw2Nzft5zocDnNkQgS8v79/p/t/r9AM6thAIGAqBrp2JEYw1+D6IiCVZDUwuyzE\n+Emn/MlygnIBKRZexIPBwJhjfzEOYlLrxgOFyz61PhRTyhoWLVwSUAG8QFCFoLXLZDJG1pdkrEHk\nSYlEQslk0t7/aDRSq9XS9PS0crmcNWX43MFNKZfL1pwiK3O73Rb3xsgf9APCFe+dfBeiLJBKIem6\nKzn/Xu3MOGpOOnfSwLED43gJ0adSqSgSiRjxndhfOLfoCOEXoCJB0IlBOTIoTAUZljBQYUESQind\nPDS5XE6np6daXFw0mVMkErG4NmRFZ2dnevr0qSTZ6JzgS4fDoVgspsFgoHw+bwaLlAbgy41GQ6lU\nSp1Ox3w2er2ehf8kk0mVSiVls1mNx2NFo1ElEolbJuDgwTgqTTbG7LggPHNzc/YgUxYxcAHfLxQK\n5grFRvG6171azJJsV2RaViwW5Xa7b3nAwRmAfO5yuQx6wkf49PTU8kvg+KZSKe3v79+KWoMZVqvV\n5Pf7jWjT6/V0cnIip9Op/f19zc/P289hR0Moil8GOzx+0UCJ7Lq7u7tmBUZUGWpsoECHw6Fer6do\nNKrNzU0bK+N9TGprv983rHs4HJqTJ1kjODtBowWFYAeFs4GFGJ87inbsxSBXwQHnRMQbG6szmtq7\nXPeqzHC5XDb+PT8/NzsBXDJxvj88PLRmETKRJGNz1Wo121kjkYglnDKomKzHOe4RaGIsQwOKagVJ\nP0dqtVq15pRJYzQaNVIR8Ws8XEiiIM4TgMkUcWpqSktLSzaxRC1NCH0wGDQ0RZItRrB2avpGo6HT\n01N5vd5bjvvn5+emloHvDJIBwZ/+Ap4I1gO8/0gkYgGioB7ch4uLizuT8+/Vzoz7ZLfbNRz3C1/4\ngmXiNZtNZbNZC9HBKBBOMw0eQT8ErjudTktHpdYjj4SFh7kKTVA8HjfJFkmxLNput2vYNNDbpAkL\n0ROTDL9ms2m2WN1uVysrK7dw5MePH6tarSqdTiufz8vj8ajT6Vj0BIlZmUxGtVpNKysrcrvdRk8F\nY0+lUkZ2yufz1qj1+30zUvT7/aZ29/l8qtVqBsdhdM5nDo+EJpbsRfK8y+WycrmcfD6fTSJf97pX\nO7MkO4Lb7bY6nY6azabVmDRxyKI6nY594GDMqVTK8FBuwpMnTwzwdzqdCofD5qw/aZMFbZKakNoU\n8o/f71cymTSR6XA4tNKEnBEiF/CeoAzBiOb8/NyaRWrexcVFk2dBMIKqCvd5fn5efr9fe3t7Np5H\niwiHudVqKZVKmbKcoMm5uTk9efLEOMmnp6cW91av122YMx6PrXwKBAKm76NmxyOaz8rj8ZhIAhTl\nLte9WswstslgRrgJkMK5gRz/mJpgYYUjPSUH3y/JHoTJ15FkZCbc5qn9kF653W57TSArSfYQTJL/\nMW2hLKFEkWQ5JSwKLkoE/PX4WQgL2O35Hdhtec/8vcmHktfn/0FY+Iffhdfn8+M1sVng504KhXld\nEBtJPxG0/sULdhzHP2gAciII+tVq1Rw92RFoTFBBezwem7a9evXK5EFzc3NqtVrqdDoG/5VKJTNZ\n4ShGmsUOOxwO1Ww2Layn1+tZRMPl5aUymYyJAi4uLlQsFjUajWwqyGSQvMJisWgNaqVSMRQGUj9H\nPc0XJKG1tTXDsGHb0fiB3GCyyN8dj8fa29uzQE4gxU6nY69FIwncRrpsKBQy3SClyeQpAt8FXs2d\n7v8PYxH9uFw0TTz1brfb5FBQMVutljU04KUsciAkxJ7Y0aIt5Puo+SSZxwXDGbfbrWAwaI5E3GCo\nln8R+8UxnmYRiReWWeFw2JCAXq9nhCZOH+l2M3d6eqpWq6Xz83Nr5sgGl2520aOjI1O08LnQ9NE4\nXl9f27gavjIjebJP6FGwQODzmZmZUalU0ng8tteH+83phU6QJhBs/y7XvWoAB4OBHV2Tpoj8Pw0V\nf8aAggne8fGxYdLo8kiTAj1oNBo2dTs5OTGnIHbuTqdjww7YcpKMtknJMunpdnJyYt4UnCyQ9/HJ\nw0QROwBkUqAVfr/fjn74JRDh4WOcnZ2p0WgYFIZ8SbopBQ4PDyXJ+ghcO6empizXG97JysqK2S2Q\nZwLxibCeXq8nSRY2JMkWMrbCxMxN8rBf97pXO3Or1TLiDTed5g8HoH6/bzuR2+3Wxx9/bKPhSf8H\niPW9Xs9U1+y8eGRMGpI3Gg1bMMPhUJVKRZlMxryhJ2mnCEZhs11cXJheD+hub2/P6KKkUzHuxeCG\nv8vJwG7JQ4RXHYp0Fhh1+fHxsRqNhiTZ12kMJ6HNWq1msCPlyieffKJwOGynARCedDMM2t7elvQD\npyVq7uPjY9XrdSM7YYoj3T1t6l7tzPPz84rFYqZ6SKfTcrlcCofDRgCCRL68vKxKpaJ8Pm8EG5Qc\np6enWllZMVuuVqtleSHxeNyGIU6nU+l0WoPBwLgVaArJ74C4PhkMBEbL8ZpOp01bd319rUqlorff\nftsWQigUUr/f13g8ViKRsCEPAZXtdlvHx8eWeYgKhbKGmjkYDGp+ft6C26emppTNZi32IpFIWN2P\nNdlwOFQ6nVa73TbVCNwNIEnKLkoZj8djekd6l1KpZFK1SUy9Xq+bmPeuaVP3amfGeqtarRpVkyNs\nElpDRgS/AjNB7LhQUFBXXl5eGndhamrKkp1QncD3gBbKNKvZbKpcLlt9zUPW7/dVrVaNV8FN3tvb\n02AwsHqYo5vd0uFwWOIVNXy/3zcIcTAYWBAOJ9JkSlSv17sVvE6dC/0VTjaWBJRPp6endmrgG4JG\nEU88Pj+IUEwoUYu7XC5VKhVrCvH+4ARisHOX614tZo66YDBozRE7DZ5plCGgDgsLC4YBM+ioVqu2\nw/IB4x0xHo9NjTw7O2skenDXhYUFI5/Ds4AdRt1IJiH/TRPk8XiMP8xrYgMwaWyO2SLCXMSovB/p\npibH79nn88nn81nIEDUrWSPIrjhJSAXgoUokEuZWxCbBogaRmHRS4sHDbJL4NJxAGdXPzc1pbW3N\nppjs7K973avFjOp3amrKHDkZ6YK34vwDzXLSDKXX6xkKQsAMNwAoanIQMImT4kEM14Odj7E2uxr0\nzUk/Z8a4sVjMBjatVst8JqgtKRfYtUFLIOFj0ELjBQejVqvZ10ejkR3p4M8gO5M52FiW4YTPz2T3\nZXBEypb0g2g4Fju/HxBgKBQyxAMGHTv6ZITE6173ajEjU4L0g/kg4+uNjQ3Lta7VagoGg7d82YCY\naFQA9x8/fmxoAdM1FlS1WtXx8bHxpK+vr+1IR8DKCBtOA6UOpwZMu8FgYAsb5ThK6MFgcIvjjLG3\nz+eT3++X3+//S9EK/X5fkrSysqJCoWDqlPPzG4NzoD7KoMPDQxs7Myzy+/02Xuf7wJDdbrdOT08t\nDLNWq5krEg821mJYEYCe0OR2Oh1jHfIwve51rxZzv983Hu/S0pIuLi4Uj8d1fX2tJ0+eKJfLKZPJ\nGH2T5FCCaDiSk8mkstmsMc64QdAiIcvg2xYMBo0ngQfy8vKyLi4u5Pf7rVbN5XJmeZBKpewhu76+\nNr3iF7/4RblcLtuZ4/G4Hj58aNNHNHN/MdsEcSp/Lt2kwiaTSXW7XSuHGDdjKJ5MJuX1eq3koka/\nvr6JHqYkYCJKLJzP5zPaLAaUy8vL5k2dz+dtMgi2joKc8fbjx4/15ptvGgvxrte9WsyA+mT8AUtd\nXFxofX391o2qVqvq9/va3t62CN7p6Wmr73q9nnZ3dzU7O2txZmDEkkwMG4lErGZmesYEj+MbHnSx\nWLQ01XK5fMuzGJ3i1taWkYwSiYRxRFKp1C1HTRq+er2ufr+vwWCgwWCgly9fGtckm80aa5DUqHQ6\nbRAY9S+NJo0ukzq89ig39vf3jR/SarVULpcVCAQs5BJ6bDgcNq8PegHITuz69Xrdsg07nY4ajcZP\nxtmTF1J+jqyZmRlz2BmPx2o2m2q1Wta8QAqamZm5ZQnb6XR0cXFhbvFQJzk2JdmE7uzs7NZUEePs\nySgEjl1qT34OMCANIKPinZ0dKz329vZUKpXMjWh6elqJREIffvihqtWq8SlQ1Ey+JoudxTU3N2cZ\n3BCrmFDu7u6aoGGS3YcesVgsyu/32yieoQfSKpyLut2uDaWw/YXDzWdwfn5uDlDYlyFTu8t1rxYz\neRpM0GCsMRHDLothB80VI+CzszMbZw+HQx0dHSkej5s0is7e7XbbsT43N2c0TLr68XhsgwtsrJig\nud1uLS0tyePxGD0TNTgCVnyPJ41YmNZNRp4hWJV+QGqiUQQeYwFPRvnCfGOHpjbGZT8ajZqhDRvE\nw4cPVSqVbPHW63VzgmKyOumP0IoICgAAIABJREFUBxUXPH16elrPnj2TJHuo4W2AQoH3v+51rxYz\nGCooACQcyEOFQkGlUsnigwmGnFRT4DQPFHZ8fGyZH4PBwPR6k77JvB7O9HTvNJ/VatWmkA6HQ4VC\nQWdnZ8pkMrdSn+CWsAhqtZpN/05OTrS7u2sxDKPRyAhT7K4shkKhoOPjY7XbbZXLZRWLRVNLIyuj\n3gdr73a7KhQKOj8/187Ojo23qc83Nzfl8/nUarWMAz2JpzPVg8DPydZsNnV6empiWYY4pFIxhez3\n+/rggw/udP/v1QSQwYgkS0Z6+PChyfODwaARerAfKJfLFi8GQfzy8lKpVMp2OpzsISVJskEMuyTT\nQWA+hLDj8Vjz8/MW10AzxkRvbm7OGHF09vhbBINB27UYPKBd5P06HDdBl5MEoS9+8YsajUZaWlqy\n3X1qasrITpxMOJ9eX19rbm5O8XhclUrFbHdx0ZdkQgZi4ZjWcVpJssnmaDTS/Pz8LR0leYdMDFHK\nM/CZm5v7Sajl5HV+fm6dd6VSMQMUSEDgseDMUBolmeC11+up0WjYpA21tdPptGMZmAzLAbfbfctE\nm6+hXDk5ObGaEV4HUBzvB287HiAw78vLy1uNmtfrNZU13GBwWvjPUCmxCSgWi4b/okbHmBxZFj8D\npiHvA0JSu91Wv99Xq9UyW10sDo6Pj436CfGfZnIyJ9Dv91tDSwQcMOLZ2dlPyPmTF/WpJOVyOTNv\nYfHg4r66umo2AKlUyv6c+o9wd0nGUcAd0+12m7so9FAWMDeZGxaJRCwCGUQBfggYcjweVzgctmzp\nybRXdmZMFGOxmC3qUCikcDhsTL90Om3H96RaZG5uTisrK+ZCBLzY7XbtwWdSSHzZysqKwXxwTzwe\nj42+gRKJb2ZX/YtWDTSjwJfkwvA7wamm/v9JzTxxjUYjHR8fa3d313gGjHUXFhZUr9fNfQjzQEbX\nIAyBQEDz8/OGw9IEDgYDg8Cur691cHCgRqOhQCBg3Xk2m7Uo41gsZhKlyViG4XBopimxWMykW0ir\nOp2O0VVRPUMGQoaFWxHj30ne9WAwMNstNHoMkmhYy+Wyrq+vlf/zEE2mjuz2PJjgwnjF5XI5g/qw\nwMWaNxQK3eJ0E49Bngl/BtsQqPHo6MgeIkqa173u1WKGexEIBHR0dKRAIKBPP/3URrk0T7u7u4pG\noybiRDFB3MPm5qYdrel0Wt/97ncVDoetXBgOh8rn80okEkbowaQ8EAgY046hAXwRjM+dTqe9L0bd\n7Lwul8umbiinwarx85ifn9f6+rqRimgW4R7Du5iZmVG73dbu7q7G47FarZZGo5GcTqfy+bx6vZ7B\nj8Bj9XpdBwcH1jQjpO31eqrX67eosTgUFYtFm1yi2MEo3eG4SZElmpkdGgIXQ6T19fWfxEBMXgwx\nJNnOhDSJ3QiIjfE1hKBms6lisWjjbHw0UInwoPAPuXncXAYBjUbjFizF2Bs8FX4Gbvc0ZRCAwMVh\nmf1FTw34FjST5LaAylCGoAYBLWFqCW+ZoRGc6cmgT2rfdrttRo+4j04SlfhsUWuDm3e7XeOxjEYj\nk4DReJbLZRuYSLJN5SdWAxMXR/Xs7KyWlpbkdDrNWjUUClktTCcOCgDGSoOECpn6Dkd+LFi5kVhV\nYb01Go3MCR6rA7gLOO8zRXz06JFxM+CUlEolM3kBsSDIZ2pqyqwOMBrEiJCT4Pz8XLFYTIuLi3I4\nbrICe72eVlZWjM9Mk0qdG41Gb9E9KamIV0smk2o2m4aFw8mgbKBOZpqJYjwejysWi9mAhVQAanQ8\n946PjxWNRu2Uuct1r3ZmjmmibuFpAJUxYKBpArKDEIR/MTsq3nE8BCACxJd5vV6TXuHS/+LFCxtY\n8LV0Om11KQw8rLKoc10ulwVLMo2ESJTL5STJjnfIRVdXVybpB1lhchiJRDQ7O6tkMmnuTCwcBKSw\n/hyOm4D7dDqt4+NjY8QxPEFRAlw5aXYIfxmJ2dTUlNbW1qxm9nq9xq+GCjs/P69MJmMxFOQTxuPx\nO93/e7UzT+b/QZxHFo90ia+Tq42MHnElkBjWXcisIMicnp4qHA6r0+mYCWG32zXzE24+mOvV1ZVa\nrZYtMv5h2ALMxkiaWpL6nK9jOebxeFQulzUYDIzfzFg6FAqZjwUSKwYb/AzKiYuLC2WzWRsA0ezS\nX6TTadXrdYMJoYViP4YpDCp1Jn+kS5XLZS0vL6vVahmOD0YP2hMKhXRwcKBwOKzl5eU7y6bu1c7M\nsU0kL/RJyg8mfKPRyJoN0kg5eqEown0+Pz83k0DCdNATUm4wcuYohRsBqYnmEw8KMN5JyBDMFkIT\ntS47IXwH3iv8YxrS8/NzU81UKhUT1YLnQvSfHHvzHiil8HLme9kIKJdwMUWhEgwGzUhy8nOHtQfP\nA0NI7GyxL0BUTL1PWtfrXvdqMePIid/D5KQLlXI4HLbs63g8bh5ppVJJR0dHmp+ftyZMko1tkQ5t\nb29bLghRYHzPeDxWoVBQtVrV9fW1Wq2WTQDn5uZuNWeTpQBUVUzKsbuSbhZ3vV6Xx+NRNBrV8fGx\npUHF43H5/X45nU7FYjGDuFBLT5q/gIQwmicLcXl52YY509PTKpfLxstGSUKiLAuRent2dtYeesb/\nksxbDkrtzMyMoTvJZNLoo/A+8PCDxPW6171azOFw2EwAUZKg4yO6i4673++rVCoZOT+fz8vv95um\nD0cfRsEsEixmXS6XRX+hpuj1esrlchbDS/3N7s9ABGYeaMLFxYVZy8I3xqz8+vraBhaE30D839/f\nt78PdZXfj0RYHmwaNUoqpGTshpPRatjkEmrEwwIOT7IsERmTpQ6vg4IGZAaZ2snJiUajker1urxe\nr7ELMbG5y3WvauaZmRnrtHO5nJxOpx4/fqyZmRmtrKyY1o0PkCOXoQDsrrffftsMWLjx8CimpqbM\nTwKWHqw6vJFHo5GhFdfX15YpyEDh+PhYfr/fungCcU5OThQOh20kHAqF5PF45HK5zHUonU6bwTeU\nT+RJkO/hpbBD+/1+m+Ktrq7emjJi7Njr9ZTNZi2qLRgM6smTJ4b8jMdjQ38ikcitUEuyCL1ery3Q\n1dVVq/PfeOMN7e3tWQoWEW7j8Vj7+/tqt9tKJBJ677337pRrcq8WM6UB5HHElIFAQHt7e3bsw0Po\n9/tqNBpWD8/MzBjfd2FhweyyqE+ZkIFWNBoNJZNJo35yCoRCIe3t7ZnKu1qtGswHSafdbpvROdgx\ndSqezpCJXC6XDg8Plc/ndXR0ZHEThMPTUFHXkr1CuYS9LMy13d1dJRIJMz4k4uHw8NCi1BAQgE3D\nE8ECjJOE34MHB31hpVKx9/L+++8rFAoZjxwWISjJcDjU2dmZPv300zvd/3u1mFOplCqVin1Q4Maz\ns7PmnIPDJt0/vAC4vzMzMzo+Ptby8rI5bALP0dzgU5dIJIwbfH5+bnj2aDQyX45gMGjhOXNzc2q3\n27Z7ovCmHkXjNzs7q+fPnxuq0ev1jI/MtPDJkycmafL7/bbDM1GjhmX6CBOvXq9rdXXVLLdQaPO9\nc3Nz5uhJuiuQGQ8wedqT0RRYBUwaIM7Pz6tQKCiXy5nHNexAUllpeBOJhF68eKHvfOc7r33/71XN\nXK/Xtba2Zjsig4fRaGSG4sPhUIeHh5qbm9PR0ZHxIXDawVPj8PBQjUZDlUpF+/v7VhcDncGmg3F2\ndXUT2cvkj7B3yoDhcGhmh1NTU8ZpgLSzurqq09NTU4jAQQYfppHz+/3K5XLa3t42GBCjRpo26KCg\nERcXF3aCRCKRW3mFPGC9Xk9er1fVatXQB0S9V1dXJj5gusnvjHIcmBN4j4nj6uqqIUjAoJR3k2pz\nt9v9k+zsyQsoDgIOPAQmcMBs1JMgEZJMBTE3N6elpSVdXl4a4w6hJwaEJycnkmRG2wwGUG2XSiXj\nfrBwkeRj7+r1ek1T1+/3tbe3Zx5vmL1gGrO4uKipqSnl83ldXl5qY2PDamZGwjwImMyg/nY6neYS\nCtejVqsZdxjVNJImGHTNZlPz8/NGsOp2uwa5odBxOBxWrmEHBj+a4Qz9BA8vr8MUs1AoWPP5kwng\nxEVaKEoSt9utmZkZy2rm/zEUJK4hnU6bOPPy8lKHh4emByQ7UJKVAEBW1KIENTIJhIAEpsyuOhwO\nLYW01+spn89rYWHB0lMTiYTxK1ZXVyVJS0tL5ge3vb0tj8ejfD5vDy019/HxsZaWlmxShyK60+nY\nrg1rTpKN30F+YA7ikxeJRFSpVOR0OuX3+y0nkWgISYab0y9g63V2dmYhl5RBkx7U+Evze6JjBGt/\n3eteLebz83OrjQ8ODjQajfTy5Ut1u11jfFUqFX3wwQdqtVra39+3/LnDw0NLVfr444/NOqrb7erj\njz+2I7VQKBgmWigUjMB+cnKi9fV1Ow0++OADg6eAsobDoTY3N9XpdLS/v28lxaRuDmI7fOP9/X29\nevVKW1tbcrvdqtVqmpub09bWlhF0IM3XajWTb9XrdSNAsVMjATs/P9f/+l//S5LMJ4+S6/vf/74t\nUlTXBPXs7+/bEKpSqaharWo4HBrpqlKpmEsq3h7D4VCdTsfwa062QqGgbrdrv3+329XOzs6d7v+9\nWswsAKZRJycnmp+fN19mQsszmYw8Ho+lLTmdTgPxi8WiHZH5fF6VSkWPHj3S5uamer2exRmPx+Nb\nYZjT09N66623JMkav2g0Kq/XaygI/IlwOKznz5/ba5+dnWl9fd387E5PT/Xq1SvT0i0tLZn54PT0\ntD777DNrcLe2toys1G63DUrMZDIGmU36TwMnQrpH/Ioz0pMnT8wHBL5Fu902cUIymTS3U4Y3cDDG\n47HxqnngeTBIzaXMwINDkpmPk2P4ute9WsyUGf1+36ZrUCxXVlZ0cXEhr9erlZUVK0N8Pp9hpdls\n1ojm4XDYRtMzMzNKpVLWRIFuoKRAX8ju/ejRI2skz89v4sVQtmBZgK8xXA/yAL1erx48eKCHDx/e\ncssMhUJaXl62Wv/Ro0f2nidNEekHYOIxqcNqADiNxbO6umqwIeiOJLPeAu8OhUI6OjqyMgaVC3Iz\nOMqw6ijnID+xe0syYcHp6ak5S3m93p8oTSYvxqXkTbvdbhWLRXOLb7VaKhaL+pM/+RNdXl5qc3NT\nDodD5XJZhUJB5XJZs7OzOjg4ULvd1s7Ojv0MQtuZeHW7XTWbTXk8HjM0mVSWgBZA3AEDl252IvKw\nEYBOT08rnU7b+BqCEmVAo9EwR3+v16uPPvrIyhI0h+zWlUpFu7u7GgwGqlQqZvaNmhzDGqLkwJoH\ng4H29vZULpdvGegg+Zqbm7OSBmkU00Ya0Wq1eivujVg1mkLsxUgmgKh1fHz8E3X25FUqlUwt4vP5\nLEea9KNGo2GMtrOzMzmdTn3/+9/X3/pbf0vBYNAWC3Umx3wymdTu7q4k2WAE7gT2AmgBUZD0+33t\n7OxY2ilTOhqxQCCg3d1d+f1+cwsCDSFagR3O7/cbG87lcpnTEP4eHOUbGxvGYcbQZXp6Wtvb2zYe\nn52dVa1Ws12Y9405C1NEGlGkTxsbG3rrrbfM8ouhjcPh0M7Ojg2EgsGgDaOg5LZaLRPuSjJzytFo\npMPDQy0sLJip412uqTGt9v/Pr6mpKf3mb/6mLZpSqaQXL16oUqkYtxa/CBQo0g02jRUADcvu7q4h\nBs+ePVOlUjFuMfAWPGnI9ygpuJm9Xs9i0qCWwuXI5/P69NNPLWa3UCiYJJ+J2KtXrwzyYyeMx+OK\nx+PmEJTL5VSv1y2BFsQA2VWpVDJU4s0337TyAptcn8+nRqOhSCSifr+vpaUlU047HA7V63V7OFCk\nMOGkH5F0S9zKP3/6p3+qXC6n0WhkabbIp2D/raysaG9vz9COwWCg//Sf/pNed0neqzIDHzNUIhhi\ngxHjVFkulw2aIt4XjBdTE6ILms2mmWpXq1Xr4q+vr80cHE+5er1u3hfAV4FAQNvb23I4HEb8YVSd\nTCatdEGajykKk0RKH5fLZegLvO3JkwjnU3ZiUIdisahGo6HDw0NVq1Wjl1KuYJCO716n09HGxoa8\nXq9qtZrVzUztcDXl70KaOjo6MnSCHZ5TB7NzPr9+vy+fz2faw3q9rmaz+ZMyY/JiQAG5BsM+SUao\nh4872UGzo7HbVKtVMy+k0ZmdnVUikVC1WlUymTQmWSaTUalUsp3J5/MpEolof3/f6sFcLmd2XIyJ\n8ZFgghaLxcxjg0hj9IRwGmhi2UlxForH4+bIhApdktbW1oy8hOHN9PS0IpGICoWCJCmRSKjRaBg3\nuVwua3FxUbOzs1pbWzMn/YWFBfMfYXLJlc1mrQHlVGg2m3r06JGdVGDyoVDIJohESSAmnpqa0ief\nfPLa9/9e7czwclEog68S3zAYDCyzGVchgH3Qj1qtZmUDu0Y0GjXLrlwup16vp7OzMx0fH2t9fd0c\njVwul9rttjY3N5XP5+2YxlhckqEZJKIyuq5UKiqVSjbsoBY/Pz/X/Py8LSCPx2NCV0n2d46PjzUz\nM2NBP4zUCZInoBIxLygIzkrUrMlk0mplSEyEHbVarVtQJE6kGK3DDByNRhaHTGYgE1hKvHQ6bdyT\nUqlk2PVdrnu1mBlbe71ebW9vGxJBhoh0UyNXq1U5HI5bbDA4E2dnZ9rZ2bFBiMPhMAbc+fm57ZYk\nVi0sLGgwGNjCCIfDVudGIhEbB5PbQXwwnGhJRohilAwiAh8YN6FCoWDcYEj+CwsLxolgvExtyyKn\nEaYuxcMZ0QF86+Pj41vRx4eHh9ZA4+bP+8XSl0moJJObTSpGeF0883gw8MtjJy+XyzbpfN3rXi1m\nj8dj/ATccsjzwPU9kUiYQiKZTJqwlLhev99vAlQEozC7UGbDgMOhKJ1OG493koaJAhmxJjwQfgY7\nJe75RCP0ej3FYjHjBzPwiEQiGg6Hcrvdmp2dNcEuo3eYdyi8cfdcWlqyUbXT6TSuBF55kJUmMWa4\nF4y5YRXC3wgEAsYjwWYLTBtDGKRkwIc4jIZCIXOEarfb8vv9t9h5r3vdq5oZVYPD4dCzZ8/MgAQO\nA048sLoItwGvJZQxHo9rYWHBGHC4zhMyw9Gbz+c1OztrO+bjx49NLMrP6vf75phPBBoLg0kbRH6g\nsmw2q9PTU4uCwDGTozoYDOrBgwcGbwE9TsZeTBoUbmxs2IAolUrp4OBAsVhMPp/PQoecTqfhxQw7\n3njjDeNvI+h1u90m/+Lzm2yieQCx9AX3Z+zP63IaoUvE8uzb3/72a9//e7WYz8/PzUKgVCqZ4jgY\nDJrRCbnXWFBRR2Nk6HK5VCwW7fgkmwOjRXSFoBcw66amplSpVGxEe3R0pEQiYd09I+3BYKDFxUVT\nWOCLkUwmzeQR5yPYd8B0mH1fX1/ru9/9rh4/fiyPx6PDw0PV63XF43E76mHJYacg3UimqtWqLi4u\n7L03Gg3D5eFhQBX98MMPzYOZxYgFA2774OHValXBYNBKtmq1alZol5eXVt6cnZ1Zzc+UlBJna2vr\nTvf/XpUZo9FIBwcHthglWW2I3F66CayZm5szORHHOBMuj8djPw96Y6fTsT+HzjgcDo2hRxmCpxs1\nNA8EDwuNI4Qijv6TkxNtb28bssECA9riH0hIqVRK09PTJv3HVxkj71gsZouPaSXvA99lzF9g0qGb\nhHRPCQHNE62h1+tVr9czX+qNjQ3TT3Li1Ov1W589U1Kmgb1ez6avPKR3zTW5V4s5FAppdXXVBKD4\nSnAcY8L96tUrzc7Oan193Try0Wiko6MjQzkkGWUSc8Fut6vhcGgWVZKUTCbNunU4HJp+Dv0eOzzS\nK1KryBeZmZmxY/7Ro0eGegBpwTdmJM9QolqtWrwyvIY333xTbrfbHhgWMjvt9fW1ZmdntbCwcKvR\nZOCCQgXPavjLcDFCoZDRNVOplKSb0g4HUyiil5eXyuVyNjDCUy6TyRi8F4lEdHx8bJBhv9/Xy5cv\n73T/712ZEYlEtLq6qu9///tyuVxG8olEIuZUxAePomR1dVXX19fG2vJ6vRY9LOlWJAJ47uzsrPF8\nV1ZWbKqFuoVUKukmH48oX3gU6XTadifkSizaw8NDsxgbDAaKxWI6Pj42UhOYttfrNdlWrVYzMUA8\nHrfGFwNIGjMeblARckeCwaCVFARzZrNZi7BgfA1HW7pZkGwG8XjcYFCSZ6nDKS/YZHBaQg8ITv7i\nxQttbGy89v2/VzuzdEN6pyab9H8AGoI0D447uWixKYCUw/eurKwYeQgYKxqN3gqQjEajNrZF7VGp\nVIwqeXV1ZXguI3EWNrwIEqooa05PT83rDVIOi5OdHgPERCKho6MjM1bp9/vGIZm02gULpp6WZOpz\nXJJAWxgeQXyamZnR6empms2miWr5jKGJwgRErMAUlVxwkKVut2v868mB012ue7WYoWfC52UHgU9A\nqYB8h6aNUTW6tsvLS+MAT01NaX9/31AQSPR7e3sG/1FL7uzs2C6O2crU1JSxxLD3YjACjIjagvd3\ndHRkY+JSqWQd/9HRkTqdjjl0SjLjQ76HHBGaLxTWMOMokyTZtJRaGp7JZCbMZJl0enpqlM9Wq2VT\nVKRRfHYkbzmdTiNDIb0CFuT94QFSr9e1ubl5p/v/I1vM/+gf/SMlk0k9f/7cvtZut/XlL39ZDx48\n0N/+23/7lh/vb/3Wb2ltbU2PHj3SH/3RH9nX/+zP/kzPnz/X2tqa/uW//Jf/29dMpVJyOp2GHEAi\nx0sD2A1sV5INRgiknMymJsOa8ElGz/V6XSsrK5JkCw+MmoYSSJCdHwsB/t/n89nCwMScMoiSKJPJ\nyOv1anl52cQDz549M74ydE+mdxCmUKdP8iOAIhEJYPsFz4LMbPK3gQEh74fDYZNoIaEKBoMGA4K2\n4PkBnJdMJpXJZAxHZ5MgE4aQUUwo73L9yBbzP/yH/1B/+Id/eOtrv/3bv60vf/nL2tra0t/8m39T\nv/3bvy1JWl9f1+///u9rfX1df/iHf6h//s//uY1rf/mXf1nf+MY3tL29re3t7b/0MycvGHOYXVOr\n0VEzet3b2zOXTiwBWq2WuezQeOHmw7SO5gd22cnJiZaWliy6t9PpKBAIyOfzaX9/X8Ph0MbhDF7I\n/atWq2YzcHFxoVgsZh5u+Lfhv/zhhx+q0WhoenpaH374oaEYLCZ0hmTxYY+L/YHL5VIqlbqVj727\nu2vDkHq9bmjC+vq6ms2mmZ1DEWWTmFRrw0lGtT4ajUwcwQnS7/d1dHSkSqVig5p+v6+trS1j/3Gi\n3NXR6Ee2mH/u535O4XD41tf+4A/+QL/0S78kSfqlX/ol/df/+l8lSf/tv/03/cIv/IJcLpfy+bxW\nV1f1wQcfmHL5nXfekST94i/+ov2dv+o6Pj42OT/RB+wEjKelGyuuSXyT7wmFQhZPhkwfh0xqUKIY\nWGgEy8zOziqTyZhCHLwYph5iTSiYk1L+k5MTFYtFU3IEg0HjiGDOiBUuIZqgDbVazeRiOzs7qtVq\nt4we4SV3Oh37GgE+kxYGnDrz8/PGAuT32Nra0mAwUKlUumV4XqvVDNPvdru3rALgZdOPzM7OqtVq\nmTkM01RI/cCdd7n+r6IZtVrNume4vdKNZu5LX/qSfR82US6Xy9TMkgzG+byL3QXFMYE8gUDAuLTB\nYFAnJyc2Xt7b2zM5lCQ79jCKYUTtcrnMrRN8VpKpNjjKOTIh4lBaQHRiFyL6eHp6WplMRpLsBHC5\nXHr48KGazabBXJgLUv+jRwwEAqaA5mdPljXZbNZSWoHRlpeXLXeFLEHkXYeHhxY0ubKyIpfLpeXl\nZTUaDZuK8lrRaNQmjaFQyMb8ICDEo83NzdkDibqG93J8fGyj8MFgcKf19f8MmiPv44d50Q3XajUd\nHh4qHA4bPZOIBtxBZ2dn9fHHH0u6aRAzmYxZBnz22We6vr7W8fGxLbRer2fYLX7K0B6ZDA4GAxPD\n0uEvLS1pfX3dDBgRAQBnNZtNHR4emofceDw2mA1VN3X2xsaGnj59akpnBipOp9Ocirrdrl6+fGmj\nddz8l5aWdHx8bMR7r9erTqdj6IzH49HW1pacTqe2t7eVyWRs5/X7/RoOhzbUYXGCpmB3xveBK8Pw\nc7vdajQaWllZsYzsaDSqZrOpQCCgQqGgi4sL/dmf/dmd7v//1cWcTCZVrVbNEpYwxPn5+Vu5yaVS\nSdlsVvPz87eOLmISPu/64z/+Yzta19bWdH5+ruXlZYPRqANZUCsrK4ZYpFIplctlOZ1Ovf3224an\nTk1NaXV11SC1QCCgYrFoIe2owaF2SjKfY2rCR48emWv+aDSS1+u18TULA49i/JZnZmb0zjvvaG9v\nz4YRb7/9tjwej6rVqnw+nwKBgFE4JRlZ/sWLF3I6nUqlUlb/gvJQtsDo8/v9CoVCKpfLZuD49OlT\nzc7O6itf+Yo+/fRTy0SJRqMmHOB3lmQSLGzHhsOhqtWqNdNzc3NKp9PyeDx68OCBzs7ObDPY3983\nwv5dN7f/q9DcV7/6VX3rW9+SJH3rW9/Sz//8z9vX/8t/+S8W9bu9va133nlHqVRKgUBAH3zwgcbj\nsf7zf/7P9nf+quvx48d68OCBcrmcqRu+973v2Qj65OTEAiipdeFHFAoFNZtNud3uW2lTEIkIZHz1\n6tUtKIxyhPE1SU3FYlHtdttqZkSpkI+IcQsEAspkMibLB9/2eDza2dnR9fW1qUXw0JBkLDTpB65E\nnBDUsN/73veMj0L/QQTxpD1AtVo1nkSr1dLLly/Vbrf17W9/23ZakCec8vl9JBnqAV+l1WqpUCgY\nskOtf3BwoKOjIxUKBR0cHJhU6+2339ZP/dRP3Zk19yNbzL/wC7+gd999V5ubm1pYWNA3v/lN/cZv\n/Ib+x//4H3rw4IH+5//8n/qN3/gNSdKTJ0/0ta99TU+ePNFXvvIVff3rX7en9Otf/7r+8T/+x1pb\nW9Pq6qr+zt/5O5/7mlhSBMdzAAAgAElEQVQJTHoIJ5NJa76oZUElnj9/rs8++8zUFvAVYI2hqr64\nuDCTbUm2qzGEoXwAy6YOZFrHnzNqRpHBA+Z0Oi3mmGZta2vLCPaSDJGIx+OGn/v9frMWQAkOWYqw\nHr/fr2QyaYlZkUjEjngQGnwzcGIi0oL6lunmZNgQk0Igy3g8brRZamhsvEByyAZHF+j3+3V4eGgW\nD3y+r3vdK0Hrr/7qrxobjt2GxmlSscxCY7LG8Qj8tL+/r4cPH6pcLuvBgweqVqvmiEmdzFgc7i8T\nRnw46vW6Hj9+rIODg1vmgI1GQ9ls1sqHeDxu7/Xo6EgrKys2yQOioxyBiIOLEYIBXIykG6ydXZRp\nIuJXhh7kIYbDYfv9h8OhCWShvRYKBeXzeR0eHtrEj/dwdnZm0CDm7DR/EIjy+bwRk/r9vmKxmG0y\n5+fnSiQSNvyJRCLa29vT7//+7/9E0CrJjmq84aAkwnuYRCfIyYvFYopEIibjwUKA2rrZbBqYXyqV\nzMXo/Pzcvo5am2O72WwqGo2qUqloYWHBvJ3xh8bnjokfJ8b19bW2trZMsuV0Oi39if/GYoCJIn+X\nyIVJg/TRaKROp6N2u21CWeIp4Jogo4I7QQ4i2r+TkxM73XD0ZDOAKnB5eWkJVXhteL1emwKC4iCL\nOjk5sQcRzzyUMXe57tVi5mYcHx8bwYWmDwtapk8nJyeKRqM2PWs2mwZrkQ1CfY2R4sLCgi3U6elp\n82I7Pz834kwoFDLjQrBYfC2A4iZl+8RNEGsGkZ0cEcJz0P/xvikRLi8vbYzMUAjuNeqXUChkbDwQ\nE04LrA1wLSWEEzdPjCFpPKempqz8Iv9akgqFgvb29sw8kWELsCZQnCQtLi5aycIs4vT01Caur3vd\nq8WMm2Yul1Oj0TBMmPqb3Ws0Glm2HjeQG8tujl4umUyalS0SKWJ0yTVhB8cCQJJ5YjDASCQSZp8F\naw1vZgxeEomEKbk5XVhQ0g0dgDKGBxWhK8pybMeoW5PJpKFAoBCJREKxWExXV1eSZBkqWGZRniUS\nCXX+P/beJDby9K7/f3upKperXPu+eHe3u6d7lqwTINzCcuHAASk5IIG4EAmQkMIZDigHjki5ESG4\nwC1IKERBECKSKMNkMj3d0z3d7b0W1+7ay1vZ/h3M6zNfB/E/tOH3y780X2lEmOnFdj3f5/k877Xd\ntlmaBNRkMqnxeKx8Pi/p+hRLJBLK5/M2g7MwKbnnsspOvbCwYDIBvlesba/6TJQElIuX1+vV3t6e\nLcRut2sqOUnGovV6PVWrVS0vLxvjRvzUwcGBsYgYXcniwHcHKgDOyq4+Go20v7+vzc1NeTwePX36\nVMvLyybquXfvnrVVcTog9A8Gg/bfsIAxBpBd0Wq1LF4Lq9TFxYV2dnbMbU73CnYvEBgEUvz5BKwz\nyzISRKNRbW9vKxwO69mzZ5qenjbWstFoWPYFzh1+JpTbP336VJubm9ZyhaiIy3i327WXke/lNjED\n0oTtzNPT07YoYLw45tA6cFEiMR7Vl9/vN80wOxDpO1yi0DHgaKZiwtlu9eGHH9pOSy9fIpEw2M2Z\ntE/INzd70IhYLGYF78ydXq/XGl1h09jlwY8ZGZwaDSxPlUrF3Og4U3DbECQ+Ho+tN1CSOclhO5mh\nU6mUcrmczc+MY2D8mF6Z63G+n52dGRKCnoPYM+rUbvX53+p3/5w94XDYCiOZm0kMcnb7IV0cDofy\n+/2W70Aod6VSMfNmIBDQYDCwX0dAOQsCNm1ubk6VSkWbm5tGB0Ojk1VM4DnHKhGzhULB7FL49Uaj\nkRXVS7qRP8dLQNXD1dWVvRToHXghuSNwhOP0mJ2dNVcIWhGkn2QsQ7BQrsMoBdvJ18/px92AIEhU\njBh3nU4VRplUKqVGo6Fer6fd3d1bff4TtZgR3ddqNaVSKSsmZwdydjkfHh7qtddek3TNQHJZmZmZ\nMXljKBS6URYJhJXJZKykhl2QmbPdbtuH3W63Lf0SwmJqasoMtdjts9msiZ6azaYFPAJ9segITfR4\nPLZLU1BJGGMqldLGxoa1skoy1/VwOLSKX2JsuSAiOeUyKN30BCKfBTbz+XyWrI/DhLsEXYX0n0Qi\nEcvqIz+Ek4nCImJ9b/NM1GLGtRAMBo2hww2BYAabD7sb2WuIxlkkHLvAZNDb7XbbEBBQEmSb5Nyh\nzwgGg9rf3zfojxYnukLIqsDDh92IrDpiYcmnSKfTCgaDppt2u922+zJ7w27yv7lk5XI5xeNxG3uQ\nbmL3B+25vLy0GRwyhgQkXihixbjYOf2BFHMSPcCLTEd3KpVSNBo1JGd+fl6pVMrqOm7zTNQFkHFi\namrK0vE3NzeNciagJZ1OWyZcIBAwIsHv95vugnEFiphsuHQ6bUcr9nzEO4uLizYXZrNZm2nZvX0+\nnxEiuVzOPnC0Iowi0jWEBgKC5JLOlcFgYFnOjA35fF6Hh4eamZnR5uamfD6fms2marWaEomEdnd3\nTUDfarXMpOD1epXL5XR6eqpMJqPBYGDdKLlczvKouSgi36zX63Z6cXnk5T4+PlYmk1EkEjFShJdz\nenraXgzabSGUnArJV3kmamcmnMXv91vE1pMnT8yKND8/b6wf9Qbf+c531Gq1LL4LQf1wONQ777xj\nlWggC+zieNu4LDWbTT158sQ0FAcHB6YM3N3dNf0Guzikzfn5uQ4ODqwibWpqSs1mUzs7O6rX63r5\n8qX29vY0Ho9N8cdpQq/fycmJ/vEf/1G9Xk97e3t6+fKl3n33XT179kzhcFhHR0d2MuDoJqMOPXS7\n3TaLE1Ff29vb6vV6KpVKVuNG3BhyTXKsJZkakCD3drtt3sJms6ler2fppe+8846NS0Q6fDIzO56L\niwuDruiZIz/D6/VaVhrM13g8tkoCdphQKGQZEPF43GbBk5MT9ft924mRRpKsybyKiJ6SeRAMHB7B\nYNAQBpAQZl6v12t0MjUOyWTSKiZ4qJdwCt/v37+vi4uLG0pEv9+vcrmsy8tLc9Mg5OdyxgJEiwIO\njwGAS2s6nbaXk4soEQtg5sCI1GbMzs6a+ZZTCmhzY2NDp6enev78+Y2Up9s8E7WYWTDoc8mkYNFw\nweNYxGlBJwdULjsvbU7EAXA0shP1+33r8hgMBjYOEBSIqIaoLsYVIDh2arDxVqtlOR00qmLhcib5\nM/pwkeLv5LTB7QJVXC6XzZPn9XqNGgfnhkGUZC8JoqBut2tmVNKbGBmIDSZ4BuxakuVhg17wtWP4\ndb4UQKdsNq/6TNTMDGKRy+W0t7enubk5y4JAvyzJdBggBSyKeDyuXq9n7Jkkw2+5qL3++utWP0am\nMpnP6JjJlAMew0MXi8VUq9XMCODxeBSNRnVwcGABhshBi8WiKfggJPL5vHWSPH/+3JwawWDQukum\np6ftYhgOhy1SN5/P28sbDAZVLpfl9Xo1GAzshUylUmYTQ8lH1pzP55PX69Xi4qL9O9SFqPGurq6s\nVxGjLAwlklciDjA+AGuSQfK9733vlT//iVrMZBDv7u5qcXHxxq5F5QHifhRla2trikQiJk5HYrm+\nvq5/+qd/MnwZ02iz2dSdO3cso21lZUWVSsWCC1G3nZ2daX9/X8vLy4ZN08BEgU0sFjMoC0Xb7u6u\n8vm87t+/r9PTU5XLZWu+QihErpzb7ba8CTyK6JLZNefm5hSNRm+UzIMdEwyDvoJTgpeCTvE7d+7Y\nLA+igVSUS+zx8bFdqMkSmZ6etpEPsyqLmRNiY2PDcPdPdmbHw255cXGh733ve/rlX/5lc/8WCgVL\nru92u3rw4IH+/d//Xaenp+bMIJf40aNHtvvu7e2ZE5kZ89GjRwoEAioUCpY3gU55d3dXKysrhhik\nUin98Ic/VCKRUK/XM1itWq0ql8tpMBioUChobW1NOzs7lpiPy9mplnO6PJyllicnJxb+Qk84liV+\nLqFQSIVCQUtLS+ZCR2hfKBTsa2WsiMVi+td//Ve99dZbKpfL2t7eVigU0uHhoXkSK5WKibPa7bYk\nWYxtMBi0bD7UjJxy9JpvbW0Zlb6xsaHvf//7t/r8Z/70T//0T2+7iH4enj/7sz/TL/3SL+no6Mjw\nXJRfjAGZTMbqc8/OznTv3j2dnJyYiN5pfeKoXFxctFQiMomZx9E7YOacnp62o7PdbisSidjFJhwO\nKxwO6+zsTMvLy/ZSZbNZjcdjg/RWV1dN24wwSrq+9C0tLVlnoc/n0/Lyss23aK3JdJNkuO/Z2Zk2\nNzclyUiQwWBgijgasZaWlowJBYOOx+MmWSV3mr8rkUjYqYDgKhAIKBwOW6c4o9LU1JRyuZwJv0aj\nkVZWViytn1i1H//4x3rVJTlRO7Pb7Tb3w8rKii2yQCBgNn7GDUTsTlJiYWHB5jpYLI5Pahsk2TFM\n+WWhUJAky6YIBoMWmsLDMcscPR6Pdf/+fYs4oHMFPciDBw/M8uRk/JgtSQIisuvs7MzymLk8wiyO\nRqMbJAhzMgudvA+itiAwcKBEo1HrUmFccr5oyWTSREjSNXKUTCZ1cXGhdDqt09NTnZ+fq1KpKBaL\nWR0cbhbQnf39/Vt9/hOFZozHY1WrVbXbbf3whz9Uu93Ws2fP7FJXrVZVKpVUKBTM73Z2dmb9e4iB\n8NkdHR1pdnZWz549M7Npo9EwRAKHNE1R5XL5BvKws7Ojw8ND1et1myupZqvX6zo6OrKAlGKxaAtp\nZmZG29vbKhaLtrOXy2XDop3d2ldXV2q321bAKUn7+/s6Pz9XsVhUoVDQhx9+qEqlYkEyNE3x/WIe\nuLy8VLvd1uPHj9Xv9y0fhNjaJ0+emDO82+1aKunBwYGFPZImCtV9cHCgi4sLa83i64e0KpVKGg6H\nt47mkiZsMVPPS3LOzMyMjRxHR0dWVENmBV3UXEDASSUZHcwuXq1W5Xa7bSzgIkaNGg4SwhTZ5e/e\nvaujoyPr0pOudbvYtCRZJECr1boRKgMEx+nRbDZ1enqqSqViORyVSsV0GIiOCDinGCgcDtsRzw7d\n6XQsAw/REmYDNBsoDDkRKNCUZCgGAv2Liwt70YHvGMNAllD1dToda8ClFCmVSmlra+tWn/9ELeZq\ntWrYJUHduIZ7vZ7tRpAIDx48sJ4OxPzMxxAjtJAiDWXm83q9RrwQBOh0qmCAff78uemoETPxdxLA\nwgyNVmN6elqVSsVcJ5eXl6pWq0YAEY/FSDQcDu2Ypp3q/Pxcy8vLFlXLqAH5c3R0ZNYm1HEEoFMA\nj4ipWCxaBwxB4ltbWxqPx1avwc+Kr79arZq/EGUhlqrj42Pt7++bZDccDluOyG2eiVrMJOzMzs6q\n0WgoGo0qHo+b8IUdr91ua3FxUR999JE1RgFRuVwuO/okmb2Jiw6ubGhuOuwQNRFJQNvq2tqazeBO\nbfDMzHVBerfb1d7enu2ILPxYLGZw2tnZmbLZrGHhYN4nJycGwW1tbWltbc3kmpwqZ2dn1iSA9pnS\nd0kGGw4GA1Pxoczr9/s2osEQcoe4f//+jbZbIs6oDF5cXLQ7CxkiuE+A5KTr0k8c3+g4XvWZqMWM\n1te5u0BOIEOkMLLf7+szn/mMES2dTketVkt37tyxm3koFLJqNCjdy8tL5fN5E7/jmeNih7h+f3/f\ndsD19XWLqcImdHp6auEoKysrSqVSxlSySBcWFkzhdnZ2pvX1dR0cHJjEk9wNn8+nt956y3JBMpmM\njQbhcFgbGxumwYalq1ar5tcjgdPv91sJJyRPKpUy7BxjLWMaUQJg3ljE5ubmTGeBUEm6RlLW1tYs\n1Mb5mUWjUX3qU5+61ec/UYtZklU2wEhhPsU5jY4CWxH0Ly1INLBS7siiY5dkrubvYeFxnIOW0DDF\ng5WJYhp2JHZE+vGIDhiPx6a2u3v3ru2OCwsLajQaSiaT5uQgaOXk5MR2NxYXZl0QA5/Pp16vp7W1\nNTvmqckAEUHQD3sHWkIkAlnPnFjNZtMy9zhZVldXTQNDahKXXjTlmUzGRP2E7tzmmajFTNjIxcWF\nisWipqendXh4aJcy/uHiNzU1ZRcV5KEk76B0gwTAKk9wCzMgeRlO5R05yFS0bW9vG2XOAiZWixJJ\nxPWSzI09HA7VbrdNiPPixQuj4kFiQCAajYaNHAcHB4bsoJkgDZRZuNfrmXuFr7/T6Wh6etqS8re3\nt83MiysH/JuRjcszhA4vK1oVEA++Pxzk6D74Puv1ugVpvuozcYsZHJWuaurEMH6enZ2ZAwP9b6PR\nUCQSMXklsx0xrLFYzGz0dJM4m5SAsfDAVSoVw10ZB9CHMJc7EzBZUMVi0Xx3hNUghAIHr9VqdkRL\nsmBIdBIk46MHAUOHOkeQRNUEO6mzDxsT7Orqqlmc8ERSEUG2Bo4SQmJ42SGVSFpFo4JnENobgVEi\nkXjl8BeeiVrMjAHsijMzM0YFUzDDjtpqtbS6umoNU3t7e0ZBI3iHDaxWq8rn8xqNRtrZ2bEIWEk3\n2DZOBahj8iuYp/H1oTJDV5HL5WyeZ5dkobjdboPiQE4wi8JQokSj+HJhYeFGSy3dfWdnZ9Zt0uv1\nlEgkDK5jrAAGJD3U5XKZBhypAG50xFRcUhEQ0ZctyYiaTqejbrdroxkWMRJYQT5u80zUYsZtzYfh\n7NhADsqHFgwGzVaEVQgXB0U9wFUsHOhcLjQU/zB3Hh4emh+QbAiIFbLWMITyMvB1IcE8Pz+374FU\nouPjYzuCj4+PDbEBRQD+cobKjMdjuziSYIock0q5ZrNp2g7peq4fDAa2ARwfH1u3NWwhXz8k0s/2\nZYMcEUYzGAxMf01gDbpm7GW8jCAhr/pMFJ2N341FMzMzYxc5jmA+EMIQMW8SaIKgH9G8dA0zEWlF\nbjGNSfPz85bwk8/nrYf7jTfesNCYXq93QzWXTCZN4zsYDLSzs2Ni+lAopEajYcq2SqWipaUlSddj\nUS6Xs7RSSTdKLdfW1kzUE4vFLPUoHA7baMVik3TDqsWIganV4/FodXXVbFftdttOMRZ2KBSyWC/G\nOYwGDx8+tJ8jSsHFxUVzjnNCxuNxM7WihX7VZ6J25vF4rNPTU8N9s9mswUe8/VQ6RCIRS3r3+Xx2\n0XF2ATLzkSvnxKulj10jQFi8SMyV0WhU7Xbbit4ptMQkyolAiQ1pncFg0P5evpfz83Otrq4qHo+b\n5oO+QihwionW19c1NTVlpw8+QnQg5NMRj0VDLT3iZFvw8rHr0tcCagFKROIRi5l4LunjUnkQIZAT\n8ul4aelsvM0zUYuZHZiESaK3oLVxbrPwcJ1gwiQK1ufzWV2EcyGgeuPXoVDDURGNRg2pYE4H3yVU\nEIkm2DfWJGSUOEn4PeFw2ALJZ2Zm1Gq1DM5zFt9j1yJHjrYsxO/YtEBgiLgFm15YWDB2k3GMv5OX\nnnsI8zwmW+4BTtKEiy1xYR6PR6VSyWojyNPgpGS0us0zUYuZ+bJarerFixe6uLiweC0Cv9Emd7td\nS9oh4RJL1KNHj7S3t2ch2ul0Wp1OR7u7u3Z5IbEIzQZGUf6edrut4+NjRaNRffTRR2o2mxYGg71L\nkn2N5+fnJiaCvSsWiyqXy2YS3dnZsaoKBE0wlMz0Xq9XP/rRjzQajbS1taXt7W2jucvlsqnoeHGI\nJdjf37cdGQbxvffeM+hte3vbugkR5p+enqpQKGg4HKrX66nb7Vq5JcjEzMyMSqWSut2ufD6farWa\nnj17ZiIkwiVnZ2d/fnsA/188brfb4lRzuZxF2cJawXQBJ62trZleoVgsKhQKKRqNam1tzYqELi6u\nyyShxcncWFhYUDQaNdE9u1c+nzcCgQiwXC5nzCNhLxTMT01NKZFIWG8fvwdSgV1tdnZWKysrZlpF\nTIWBlby6mZkZ3b9/30yuy8vLN0rh2Sk5fZBzEvY9GAyM9btz5465TySZKwbXzczMjJaXl61rkL4/\nLtVUOvN1oxshfAcvICE8/Mxf9ZmoCyBKOdqXpqamrHSRYvWrqysTyo/HY21ubtqCJ6k+EokomUyq\nUqlocXHRWlzJjHM6JiTZrMtOTHsSxzaQIGTLwsKCqtWqRcVSM5bL5WzBOON5nSWYHo/H4m5jsZgJ\n86GcQW4k2YsQj8eVSCSsoYrZGkEWGwDJQoircKdfXV1pY2PDSB9GNC54s7OzSqfTZnSA/EFLTofK\nwsKCRfeenZ1Zwbyz8PM2z0Qt5u3tbUv07HQ6yufz2traUjgc1u7urmZnZy3wu1QqWeUCOGcgEFAg\nENDLly81Pz+vTqejubk57e/vGyRHbtvBwYHBU91uV9VqVY1GQ4FAQPfv39fz588ViUQUDodVLBaN\nQu92u1pcXLRSSxLjuYgBkwHfIen0+XwajUaKRqMmkoJhROzz+PFjZTIZlUolxeNxy3AjrajT6ej5\n8+c6PDy0iyEJqVxKa7WaGQ/+4z/+Q2tra+p0Ojeko+DGq6urcrlcNmosLi6qUqmoUqno4OBADx8+\nVL/ft++F7pXHjx9bVQfifa/X+4k43/lwiSJ1/uTkxOSJVOWym8zNzWl1ddXs95FIxHaIjY0Nq0JA\nx0AD6u7urhk7ycPgeF9aWtL09LRKpZI8Ho9hzM4AGWd/CPUI4NcgAHwf7IL5fN4EPG63W4eHhxby\niHaZHR/SA502KUi1Ws3SR7kcUu82PT2tWq2mfD5vhNBoNFI2m7WZeTgc2pwMFAfJw8gFbsyOz0Lu\ndDry+/06PDy84fjx+Xx6/vy5NXc5mc1XeSZqZ97c3LSg8PF4bPJLICPw536/r2w2a2MEVnoo706n\no3Q6rVarpTfffFNPnjzRL/7iL6parer+/fumb2Y3X1hYkNvtVrfb1Wc+8xm71aOd4INn1CF0ELGR\nJMtvw18HSoLt/+rqSvfu3TMJ6ZMnT5TP5xWPxy0RKZvN2kK9urrSysqKLRBqle/evauXL19Kuh5D\nYrGYqQzpZAGBoccbDyDKwZmZGcvzwIDL7wuHw0qn07q4uLCo3VQqZZUYFGIi+PL5fOZHzGazt8po\nnqidmd3GGcqC4ZPZFXUcqff7+/uG/6LuIkQFgX80GjUm8ezszHQTzqZRUALcIYh/4vG49QGCW8Mw\nEsyCkfbk5ESVSsWEPnNzc5aMBGRXqVQs33g0GtmiQtNxfn5uKaOlUklHR0dGo19cXFgMAClEQHJO\nlAQ2Es0IgnxGqmg0ao4bIEsMAn6/X8Ph0KhqyoLA56HB5+fnrUoOsZEztelVnonamfGWQSM7TZmS\nrJhcknnVyLigeowoKcTxLOLZ2VlDI2DZ0FzwENjCrpPJZMxW76wBRtREwSUVbETYNptN2+mla6f4\ncDi0Ip75+XnLvUBEhNn29PRUS0tLNyhigsnZ4Rm9WEiMX4lEQoeHh3Yh5nKIBoQ4X/TYbAozMzN2\neWPsIHQRwwQsIDg0aUowq07jwqs+E7Uz06REbCvHmSQbJbh0oS9ANMNlanZ21uZPAhYhQCAkmF1p\nVmX34c+l7uH4+NjaSPl95EiQUD81NWUNUYj5oboXFhaMOEHvQaEkaAe7KtoQaHskmszPGFZxkMCG\nQrmzgHmxXC6XjVA4QSTZ/O/0E0KLg7+zCXB/YcflREEiK+lG2DkCr1d9JmpndjYtMQ+jOOMyRtIm\nKTsnJyfmxTs8PFSz2VSxWDSJoyTTcbBYJZliDBPmeDzW1taWRdHS7srOVa1WrXLC5/Ppgw8+UD6f\nt4wJBDzkWjx79szyi6vVqo0zGF0LhYLW19dthADx4KWYnp62+ZzmqV6vZ3/XRx99pAcPHqjRaJiO\n2e12G/5dq9V0cHCg9fV17e7uKhAIWGYd0Q3ZbNZq2RBG1et1hUIhvffeexaOCIqUzWaNIKlWq1YJ\nLV3P704y6VWeiVrMKNzYlbj8OeOnZmdnNT8/b0L8XC5nO8LMzIwikYiq1arVIMzMzJi1CB0w4hqe\n8/Nzud1uK+cBo6Vugq+NBYfjw1kC6fV6TaRDUTojD98DAim0HnQP0hAAuQHuTDKnkzYmugsDrTNc\nktMA5zUjSSQSuRHl5Qx7xN8IqoMGA/II9hKBPogROzgjIU6Y2zwTNWZwiUskEnaxglTAQkU4IrYk\nrEDEbw2HQzs6W62WxdsyrzKuAGFFo1GrEjs9PTVEodPpWNEj8B+jTzQavZGC6Sx/z2QydmFFD0Fa\naCAQUCwWM5c2KkG0HRTwMF8zN6NVgTxC3kn3HpYyYhn4eWF2hYRihmdxsiAZzSBLePEJWIddDIfD\nRuig38a8MD8/fyM051WeidqZWcA7OzuWU4FY//Dw0LDSXq+nVCpltiAkla1Wy+xR7GbPnj2TJCs+\nx8kSiUTM9oMmZGpqSi9evLDKtp2dHa2srBgSQDjiD3/4Q4u0qlar6vf7CgaDevbsmbWs9vt9K9pk\n9+10OrYbtlqtG9pkMuDAddEr416JRqPa39/X0tKSkTu8RE+ePFE6nTYsfmdnR5/97Gf16NEjPXz4\nUG632/LyXrx4oXv37unq6kqVSkWRSESdTke1Ws3ktI1GwwRa7XbbdM+tVst8lpFIRM+ePdPMzIwR\nXN/97ndv9flP1GJGlO/xeLS0tGQ/XI/Ho/v371tMbCQSUa/Xs5l6cXHRjrmjoyOtrq5qfX1dMzMz\nSqfTKhaL9mtmZ2etP4SYgVarpVAopFqtpng8rrW1NT1//lzZbNYiqObn520HRx56cnKiSCRiDux8\nPm8B4Zg+Md5SEoTOBKPr9PS0aU2A66hio9KXcQK9NRXGjFdra2umrAuFQnZy3bt3T9ls1jBx8uEQ\n+ePMbrfbVmrEC/Hy5Us7MXD08CIuLS3J6/Uqm81qa2tL6+vr8nq9JvZ65c//f2QV/Zw80Loul8si\nslCq0diUTqdttqNgBvknwSnRaFSdTsfmQqJZT05OrOaMDw1txXg8tjpgUo7IOnZ2bA+HQ6PKQQbK\n5bLBViAE0vVJw3E/Go1MCzwcDg0m7HQ6FoWA99FZs4Z4B+8fY0G73bYjniYoSSYPBbrj5YjH41pe\nXr5hs8L3h5GYvyhrDgEAACAASURBVIPLNUJ8ECNE+aAbR0dHRlTRNXObZ6IWcyqVMgyYiw0BgQsL\nCzo6OlKhULBLC5cuGkSRinJMg4CkUiklk0mjwbn9cyuXZDJQAs1BHpCCOjsDsSOxG3Lpikaj5hkk\ntJwFTcQB8yxZbel0WrFYzCosoMmTyaR5IGdmZvTy5UtdXl6q0+lY+tLFxYW2trZMewztjOsjGAxa\nBrPTphWJRBSPxw1yzGQy5lHEluZ2u+VyuW7YyhBjMWufnp7qww8/1Pn5uUql0q1Jk4lazPv7+5qd\nnbXZ+ejoyC5Pl5eXymQyRmQcHx9bFgbOaWa9eDxul5dyuSyXy6XDw0Nj20jfwRIEs5ZIJPTRRx9Z\nEDkuk8FgYJc5whPJnTg/P7dcC/7bxcWFnj59qq2tLWu5opSnXC7brL63t6dKpWIJTZTpOPXLFAHx\nYmBuBYMnL/ro6MgQEU418pv5fsbj8Q1DLHQ9eR0LCwuq1+s6PDy0OLNgMKh2u62pqSlVKhWrYqYs\n6d69e3YCokJ81WeiFjNiFwQxkUjEmDSqFI6OjuxYwyEC1JbP53V1daVyuWwogLOWOBaLmZYC5gzB\nOxBaLBZTPp9XOp029AM7knSdiQGyQOwAGC0WrIuLCy0vLxt7yX/PZDLmHueIBqft9Xo2U8diMZ2e\nntoMPjs7q1QqpUAgILfbbTplrGLT09fl7ijr+HcgD4FAwJANXiyfz2fZcMPhUHfu3NFoNLLMZtCZ\ncDisfD5vkWIul0vZbNaqKbBwkedxm2eiFjMXHahW3M3OMYIKX8p1iKsqFArqdDoW64VTGfYM6pXZ\nEDw5HA4bMUEwi3QdT8A8zthCAAzIiHRNyMCyOZVmuJm9Xq8KhYL6/b6FQFJ+yewO0sHOxt9FStDc\n3HUVMjQ04iOy4th5GRvAqBnXCJokiow4BOZyWgdGo5FBnv1+X9Fo1HZjiCeE/ORw0EY1Go1UKpVu\n9flP1GJGzTU7O2upRVQ0kCnM5YidmZIddkr8fpKsLQqyAkUaBAJsHlpd7EtOdhERjSTb8ciTm5+f\nN70ImgVKeySZi5o/m4eRA7oaCSn6B3Kc0TJjSIC2dzamYuwld46xbHZ21gROqOcgn5zMKDswPzd+\nxszrUOYE8tD5x+bijHy4bXXaREFzxEN1Oh2tr69rPB5raWnJcN+lpSW9fPnSdpfl5WVJH48M0WhU\nR0dH+tSnPiWPx2OXPxYoqZYct2dnZyYFXVtbs8vN1dWV7ty5Y3kVm5ublhXR7XbNicxYwe8BZmP3\nWl5etvYst9utUqlkCrtf/dVfNWMsId5EYOGWJgm/0+loaWlJc3NzSqfTeu+99xSPx5XJZFQul7Wy\nsmIVx1xAJenTn/70Dfb07OzMLra4yrkgUpeRz+eNSe31eoaTU645Pz9vdrXp6WklEgkVi0ULVfyk\nbeo/n8PDQ8M9i8WixQr0+30dHh7qo48+uqG7/dGPfmSpmalUyjQZT58+1ebmpvb3940R5CEiKxKJ\nqF6vG6S2u7uri4uLG6KZ4XCozc1NPXv2zNKCcEIT8F2tVvXy5Uutrq6apZ/IKuSeVFbw53a7Xf30\npz+1GC0ifHO5nIbDoR49eqRsNmtyTr5eIEOMA7VazS5kMJmEtsRiMT169EiLi4tWPBQOh3V4eKiN\njQ1zaE9NTalQKNhItLu7a86WTCajXq+nYrFoGSb4LZHGgnYkk0n94Ac/uNXnP1FjBjTq/Py83n77\nbaNp8aj5/X4lEglFo1FNT09rdXVV9Xpd4XBY0sdpQhTU+P1+PXjwQKFQyGhfAgTH47EV/kSjUcuA\nAKoiJAXdBRoHYgAYEdxut9544w2z/IPD0nMCTgyMh5INfBZ5KSE2wWDQLoqS7OthVwQ64/dNT0/r\n4cOHlr/B14g0gEIjzLuMCijr+LMw7KI5caoF0+m0/YyRBTCqRSIRpVIpDQYD+x5e9ZmoxSzJjrVe\nr2el8E4dMSk8QFDgpaenp8ZuUSwjXaMEBwcHNiun02n7sIH76P0A00awDs0MOREKhYyMIJsCuh14\nCikmCjp2NOl6nq7X62aYRUsBjo2AHwf14uKiZYSAguAC4cUhXoumqMFgYGMVmLkk0zZzuUT0TwMX\n7CvaGAT7uGmOj48tvoyTbn5+3uDK+fl5S2561WeiFjNetlarZW4QSXY0N5tNy74YDAYKBoNaWVmR\nJLukMNOCQPD7Sfuk25rujl6vZywYbhOE/pJuuEjQPRPLxdyIhpmLGPZ/nCs7Ozu2uGl+IoQQ0T3M\nIXrtZrNplzzGGVjMTqejvb0902s3Gg1zt5BPd35+XVAvSYVCwX6m7LiXl5ema+F7B7kZjUaGbfd6\nPTMDo9ZjjMJZ49SM3+rzv9Xv/jl76ORLJpPa2dmxGZZUfOl6dyNgsFQq6fHjx4a5Etjd6XSMGKBh\nFGIFeC8UCqndbhtlze5Mf0qlUjHXCyQGvSnUn2GKBVaDrDg6OtLW1pa63a4hL3SW8HIcHx+rXC6r\nWCwaS1iv19XpdPTkyROdnZ3ZQun3+zYScdkiEotFRb50MBiUJAu56XQ65ogBB97e3laj0TA3TqlU\nsq9BujbkNptNI1rI4oNsIrOZ7JK5uTk1Gg1jU1/1majFTDdHvV7X5z//eWPz5ubm9Pbbb5sElAjZ\nz372s6bdBdkIBoPK5/NaWlrSW2+9ZU5jZkh0CWiI6SHBpRGLxZRIJLS4uKh6va7Ly0stLS3J7XZr\ndXXV9NBer9dym5eXl60Mk/FkfX1dw+FQq6ur1qkHckBRJ6OEy+UyeeXy8rKy2azVH+dyOUWjUe3t\n7Zn4Ci3FaDTS8vKy/Z7V1VWD9y4vL3X//n3F43EbZ6anp+XxePT222/r05/+tNbX1yXJRFkIowaD\ngZaXl02DnUgkjCACgvN4PBb+grT1zp07t/r8J2oxY1jlwz4+PjZrE+EmKM5mZmbk8XisUgzJpzMQ\n5uLiwsopCQSEROACg4WJIz8YDFqDk1NIj8iH5ljqKbBSHR8fa3l52eZmrE80ojJ3SzI9B2OU3++/\noc6bnZ21wEUS+4PBoGHTKPAYxfhaoOolGW7NS0dmn9frtbQmMvGcLmzmeQJrCGgEysMRzuWTX+s0\n0r7qM1GLGeYsn8/r6dOnGgwGNjc+fvzYNLh4BWu1mprNptrttg4PD5VMJi0DrVarqVKpaG5uTs1m\n09AHsFZeCJARxOgQE0dHR7p7967N2OFw2OK4wuGwyuWyer2eNZQmEgk9f/7cILhWq6VwOKzRaKSf\n/vSnmpubs3EDc6nb7Tb2jgzpZrOp+fl50y7TvUJEAP8wJjDKQMqQODQcDq1CA92zc2w5OjqyYnou\n0oTLeDwe+2/OFCMuijhKqOtAQvpJ2LjjgZomaYgQbiICWq2W4c5cQvADouLiYog1iHmWzg9QkNFo\nZJem0Whkxz+euEAgYGU0Z2fXlb40vhI+iBIOzJZEocFgYAu53+9bnhyM3GAwUKPRMBaRqDASSEFv\nYOKq1aqxoJTQRyIRE82XSiWLV7i8vDSZKwVF9XrdZKGIoTAEX15eWmQA4Ti88OPx2C51Z2dn2tra\nMsKqVqtZFRsRZM1m81af/0QtZlgnoC52Li58sHCYTLmEsVNDNaNT6Pf7dulzipacKALHJU1KBBoW\ni0UTPKERnp2dNUMtRzTzK9QucBmXw6urK+3v75vhVZKFHzISgDAgDOLSiOqNuR5BTzAYtPRSekVo\nzcIEi/1qNBqZdhrEBIETBAzZcdwtzs7OVCgUTFwEZe+MPkCbwq/hgn6bZ6IWMz9MMtsgFtAaw5a1\nWi3rFAHdQLMQjUbN7YxwidkUtgqvGnQyjCIODV6ASqViRld2NJ/PZ2gJkBZxtMz46CEIUURnHQqF\nrLqBX+tyuW4kzgM7YkqQZKGI7NhQ0xAYYOiSbI5mlkWshc7j4ODgRpYyIYycYHw/OFOAIZ1BicPh\nUKlUSrVaTdls1u4On0hAHQ92IDqumRXn5uZMmwDRwaUNEU42m7VKh2QyqVwuZ04UxDcLCwuml5Bk\npT3RaNQIGWcyEmwaOxjhiaAsOJcJNgfaIvEHZAD2DaWf86RBtYbwaW5uTolEwr4fXmBmaaqO7969\naxdlXloaAlANogrk7xuNRspkMnaRxbHDi+H3+004hEOdmIOFhQWrnguFQkauOJVyn7RNOR6CV9BT\nQAAwO0NpwwyCcMzNzSmZTJrrGBYQDPjNN9805gvdB0n19OsdHx8rlUpJut6xmUkJcAGDlmTUOAIi\nyjUDgYDlcVQqFbvt53I5Q1WQrSLO554gyexPU1NTRrrgPCeQhlOIxdrpdJTJZMyPyC7OKNNutw1N\noZObiFrCG8GfM5mMzeXonqGuXS6XEomE5Xx4PB4r/kmlUjo9Pf2kBsL5xONxu9T0ej1TeNGl98EH\nH1jTFO5hWCnS3ufn5zU9PW1ZyrlcTjs7O5Y+JF33WmPepAhnMBhYLYPf7zdYTJJpGLDsszuCsUrX\nxgI0H/Pz81pcXLSXisKeTqdjiz0cDlvqKOPE0tKS4cGHh4cmKHK73YYVh0Ih+f1+o8p9Pp+azab1\n8sHgEUeA5sK5y3MhRb/sbHOF7SP6gF+DACscDlu2n3Q9AlUqFesxvM0zUao5LPv1el3FYlHRaFRb\nW1vWy0d6UafT0fLysra2tiyHjdkZ4TyzLVZ4EAcWW71eN2y31+vJ5/PZZevy8lKlUkmZTEatVkv1\net3GHf43/97ph7u4uLCdFIqZiyO0NPgwCrhkMmmJQjs7O5Ku1YPZbNaiEXCXEygpXY9kCwsL1vFH\nQCLoCslIROuigcYkfHp6arh2p9Ox3BGXy2UXXRL0QWgODw/tskcDAf0sR0dHevLkya0+/4lazHj6\nPB6PNjc35XK59ODBA01PT1vANxFbWO53dnb04MEDS7knpIW6MIyx5MNJMnMqUVylUskiAzjS19bW\nDL/d3Ny0ch7ICbQKx8fHZseq1WpGhpCixOWo0WjcSKJ3GnVZzOz29+7d02g0ssJ4IDROBVL36bVG\n00HmHcn9r732ml2kobSPj48VDAYtZouGgWg0qouLC7svUMg5Pz8vl8tlUBwtAzCC77//vp0Wi4uL\neu+9917585+oMQP6dGFhwcyk3NKBnobDoeLxuEFLThWYk3Hj9o2XENwa2SW0dLvd1urqqsWBSdeo\nSrfbVTAYNE8huRyI/AnyRtIJPouICKKGVH5gQ2fIOWwm7U2MU8fHxzeUbUtLSyZppUMFrx8hkNis\ngsGgRRo0m02DF71er05PT+2CHY1GFYvFzNHN7k78ACOWMzyS2jWwZkzAuNMRdr3qM3GLeWtrywiO\nra0ts09xrHEZ4yimODIcDhshQU4xGCraCy5UkC5+v9+qFpyzKywZFDZ/LwgE9WmML6QOsWhWV1fV\nbrdVLpeN6kVK6nSUjEYj7e3tqVarWRQXeXgELY7H4xtJTBcXF2arQvVGkigjDxkcvIAo7g4PDy2B\nCZKGCjXmak4dbGBAiMz4zNWwq6gLCSm/zTNRixl7ULvdViAQ0ObmphEGJA7h8MhkMsaWpdNpu7yF\nw2FzMpfL5RtKrsvLS7PNg9UyM0OdEy6ILw5REouw2+3q8PDQ9M9O1isSiejq6koffPCBvF6vSUmx\nQQHznZ6ean9/X/1+3ySsUMwwkoS6QFi8ePHCIg1Go5Hi8biJkWDznFQ1WXCdTsfy+cDXYQCpW4vF\nYnb6oHkGf768vDSzKvJbkB9+/oxjxWLxVp//RM3MCFiSyaRlnLGQy+WyLSCOX3BjLnXsSpLM/4cm\ngosRwhtwU6AnSbbIgdRgxWKxmLnCJVmot1NoxH8jUoBeEYpsfD6fIRCnp6daXFyU1+tVs9k0IVW9\nXlc0GpXb7VahULD4seXlZQUCAcuqI8uDOZ8AdgwCLNper6fXXntN1WrV5KyYeGkT4HJIsDsEEX8P\nqBG4OycEaaeE8Zyenn6imnM+4KdUJ4DHcpQmk0m7KJFfvL29LUmmIdjf3zeNb7lclt/vt99PB/XV\n1ZXVPZBrge4DxR0Xu6mpKVWrVWs6nZqaMudIq9UyZo7jGTSAWz7ogxPzJfkIep4gGaC8TqejWCym\n7e1t0xAzUjln7Ha7bcweqZ7Sx+WgHo/HNCOLi4va29uzy3C9Xr+RdpRMJm2hQqvzkuDM4SKLKyYW\ni1lcLtnZt3kmajE7K8LQTDit805KmQ/+7t27N+p9QSzY0ZklyXlwui340IDNQAMk2W7DbMkL4MS0\nCXzBN8fv+dk8aUQ+vGTHx8emc0BfQtQu5ZGMOiwiWldJIZJkuyjzNd8HJIczXgH3OhJSsGNwfMYo\ndCaSLGaBGZrRjF/vTNTHlHCbZ6IWMwEp1OnCArrdbouuKhQKpqkol8uqVCp2eWq32zo5ObEorl6v\np2azae2mSDsR4BQKBbsEYelHNcdlK5lM2q9vtVq2A/F3suDx8TF7M2c3Gg31+32dnp6q1+uZAGo0\nGtluzMkgXcsq9/b27O+r1Wra29szQgTbEgq/09NTHR4e2q85PT1VsVi0nxlBODjXuRzztRLXRaUw\nBgk2DYp/QGjAyBcXF1Uqlaw/8erqSs+fP7/V5z9Ri5kdlEuX07uHv49OaZiui4sLCzOB2oY0AAaD\nzePIl3SjhF26PlLL5fKNCC8CZ5xoCjro4+Nj0wSzq2EO4M9kZzs7O7Ovlb4QvhYQE+J6QRrIzWN8\nYYYn3JyZHBgS9V6/31elUjF9NAtNup6heTGRwtJZgoqPrw80g58hDVhUDNPdzc+bTvPbPBO1mEnx\nIdQbNAHlGnoMOvEymYxJQAk6BGaCksXdzQcPogA2itaBUBUiupiDj46ObDfkH4ykYLSRSMQ0z2iK\nOQnAtp0dIhTiMFLgfHn+/LllzEGwoHw7PDzUycl142uxWDTcnVQjZ8L/4uKi/VxisZj29/ft5ECZ\nCJZNQyyxDowmsK0YZok74KWp1Wryer06PDw0fJw4gld9JgrNmJubM48bP9SVlRX5fD4L7wMvvnPn\njg4PDyVd6yKA8DCdplIpU3uNx2ObwSUZebG4uHgjboC51WlbInCRHZLdV7qeWSlfx8pEyr/Twk9K\n0fn5uSUG4cJm4QwGA3OgEF/Azge8RlwtIeWoCSnVSSaTKpVKhosnEgnNzc0ZoyjJ9CUQUh6Px/yT\n6DMCgYDy+bwhREtLS2o2m+YsZ9Ein0Vxt76+rh//+Mev/PlP1M7MD7NWqxlJ0e125fF41Gw21Wg0\nTAfB2FGtVm2R8b8rlYq63a7Fa7FD4lYBsaCHm92a0cHtdqtcLtsuXyqVNBqNVK1WNRwOzTFCnBii\nedwo7HrYwOimxsHtrFmTZAuYr+Pw8NBidpnTcV8zonAJZJwiHgDmz+v16uXLl2bPqtfrdnpIstHp\n+PjYQh0ZlTAqzM/PW8g7+g0sYdxNJP2PBSdO1M5cr9dN3ELaJqXlhGwTAINtipT8WCxmyjZ2SLQb\nuVxOrVbLNM3YnnCCwMDFYjFVKhXt7OxYwSWXQ3IlyMaQdGNm58jFwcyiQczEpY2Ac7QSFGBif6J6\ngQUvyX4fLzeXUBYfxTvhcNhy4ghwwaJFVoYk06n8bCMVtrKTkxOFw2EdHx9bxlwoFLIsEsLWQTnA\ntz+ZmR1PIpEwiA261FmxgPmzWCwaLc0/nU7H0n3QCROTtbW1Za5sJI1TU1OmnKPj48MPP7SoAYgD\nTKaSLJNCki2Qbrerg4MDc7mgGT46OlK5XLYdrdls2slBCM1wOLTFUygUFIlE1Gq1tLe3Z3cHMuW4\nDKMhxhfJ/E7mXL/f187OjulcgO2Oj49VrVZt4XMx5KXf29sza5b0MVwJDLm/v6+5uTm7f7Ab8zkU\nCoVb52ZM1M7MXEbpzuzsrPXRMfOBaY7HY33hC1/Qu+++q0996lN2ZAcCAb399tuWN4cQnf/G7Isu\nF2w1kUgY7syRj4B/fX3dxP4sEHbKQCCgjY0N+f1+M3biXgHXZYR5/fXX1e/3TSnHTI8abX9/X+l0\nWl/84he1sLBgSkGv16tisWhqPJhL7gO5XE69Xs+kqclkUnNzc8pmsyaOgt3DoT0zM6Nms6nV1VUl\nEgnt7e1pampK8/Pzikaj1tIFe7q4uKjp6WnLyvB4PDo4OLAS+FwuJ+matHrVZ6J2Zt58JJxQpnjR\nENg0m01NTU1pf39fuVzOZkgyhaklq1armp+ft4gpXgJoXXBeci/Y/fmzQDq45PD1kJAEY0jpDnAi\nKMF4PFa1WjU/Ihc6tM+SbGRCgcZLVKvVVK/XDarjAus8kU5Orvu7nz9/bjt9r9dTvV43HyUXZrKX\nccug5UADTliis7KOnBHSWJmfh8Ohdnd3lUwmbdTCg3mbZ6J2ZmYxOjZwb4CjkqQD0xYMBnVwcGBB\ng51Oxy6Rg8FA6XTafHLc4p3tSuCvyCIx0Pb7fcN4waJpLuXP4wbvNK2iYmOxT09PG+JBNZokWyD0\nk4RCIa2srNjLgnSTwERJVv3W6XTMdYLhgOoMSkCd0WGRSESFQsFYyvF4bGZXtCKXl5fWA4MHkPpm\nt9ttATI0A0gfZ/DRZz4ejy1k/VWfiVrM/CBjsZhWV1fth0Wq0HA4VDQaVb1elyRLDwoGgwqFQkqn\n0yoUCpqenlYmk9H29rZdChkJJN0Ig2EWBW9lHqWZiWR9PmgW+HA4NJsTuze6D0gVNCR0iITDYSNf\nICCYn4HinE1TXKr4e8fjseLxuFqtloWu3L17115iuk0YZcj8QGyFcg4R/mAwMOiTwh38lph2nRfq\ndrtt/YOYG9LptP16lH6v+kzUYibB8/LyUgcHB4pGozd2SUkql8umHcAWT1gJFnnQCBg4dl3MmD9r\neAVWk2S6CbBe2DZ6P/iQEfo4Q8g5xn0+n+k5+H5OTk7UbreNWWs0GrZYnZdVgmvm5uasERVGlDnX\n5/Op0WgonU7fYN5Q04GI1Ot1LS0t2agFGUTQeiqVsghdJAIIpPg5k81BNh8iI/I54AOI/73NM1Ez\nM5lpBIYvLS2ZRhmvGogF8sV4PG7GTsTvc3NzisViknQj9XN2dtYWN/M5Rs9EImFNUwSPQ5NLsmOW\nzhHp46OWCyaBKKPRyC6YoBe4QRAX4X5OJBKKRCL2Z0EpS7JoMebmarVqLnT+POhosvM4FTwej4Uc\ngpggviKLhKAXdMwEvbhcLiNNONW4gGPPcmqppY/lAbd5Jmox49cjLQdqGCy3VqtZOjwVBhzjqNtw\nH6OMi0Qi2tjYUKPRMNknaAW+QbpDms2m9vb27DTodru2AIiyRYBP9oTTFY1rI5PJGIwHIkJwzXg8\n1sHBgSETuFVY9Lyc9JzMzs7aTjw9Pa1SqWSRA+R/MBoQrwVsB0kzPz9vSaWSTFmIbpyTBdZyOByq\nWCxaDANyVaA8xiI054w/oVDoVp//RC3mSqVi2RHQop1OR81mU5VKxebTra0t5fN5PX78WO+9955d\nGFGNPX782GxL+/v7evLkiekmvF6vCW6q1aoJ8sGAyZ+jJyQajWpnZ0ej0Ujb29uGlHAM9/t9lUql\nGzUJtVpNH3zwgWGv1AnDkPn9fm1tbVmoChfHmZkZ7e7uWh41oqCdnR0jNdhV+b+SzAVC7jOXNDpd\nWq2W3n//fTWbTW1tbandbpteHLMAOdGtVstQC2eiabPZtJ8xedW8TKjqvv/979/q85+oxYyWGVeE\nM11odnZWKysrmp6eNnH7m2++aVphdhsqCS4vL3X37t0bl0d+jcfjsSpeLj08ZLwRpHJ+fm7IBExd\nKBTSnTt37M/0eDxmWkUQ1e12zYBKfCz/PycGJgSw3Z2dHcPBA4GAmU5XV1ftAou+RJJZllKplP3M\ngsGgotGoMZrs8FNTU8rn8/azgK7m5768vKzLy0tFo1Hlcjkbd0hDQojPHWU8Huvu3bsWlzs9Pa37\n9+/f6vOfqMUcCAQMLspkMsZGEVICEoBjmHkWujcejysSiRgchiPb5XJpbW1NXq/XBPs4uQnSBg2I\nRCJa/s92VXoIwX8Jh/F6vbZDU+STzWbt0jgzM6M7d+4YisDtHxsS8Fm/31cqlTKXOEQP3j3aWelo\n8fl8RiC5XC4LapRk9im+N+K1QqGQQqGQ5ufn1Wq1FAwGLfiGWIdYLGYdKczBaFn4s1ALYiPjNMGA\nK+kTOtv5uFwu2wlLpZLhsMy05+fnKhQKZoPCP0eLKXNvqVSy4MOzszMrKccyhJiJcYP5kpAUYsHQ\nBCNaZ6FyrDLLcmzDCh4fH9spA0SHnR/89+joSP1+33bAcrmscDhsiaTUXLRaLRsN2u22xe86y3Eq\nlYrF2jIGSNdal2azaaTG06dP1Wg0LAEJynxubs5mZwIqnQpFkBWXy2WqRCxlxH0RinObZ6IWM9AU\nuyXIBjYgLn+SLPxbku0s4XDYbu9AXVxcksnkDcKA3Qp8GgYsHo8rlUrZ7iVdQ4BQ0+yEEBfE6yLw\nIaiF5HkwZkkmBWUWJQiGCykWp6urK4O78N+BfHCsg4VDU0syaadTL83uimF1bm7OEvIZwUAqaPqC\nUJFko54kI4tQIYLYMK6AIL3qM3V12+jFn5NnampKX/va1+wSd3V1ZSmUqVTKpJUQD6i1+v2+crmc\n2aswsCaTSX3wwQd6+PCh5dctLCyoUqkokUjYTg89DN3MuEHElrMMh1AXj8ejTqdj4we91oQixmIx\n+7vIlmMXZTdLpVJW4BkIBAzt4M9gvGDkIOyFhQQMCMJDahFjASWYIDFer/e/xC7QfzIejy0gnVGK\nkxHYr9FoWDeL0ziLAx0G9C//8i9fOQ10okiT2dlZSyH68MMPtbS0pFqtZh82GWjD4VD5fF4//vGP\n7b8h6Mc353Q612o1hcNhuVwuG036/b663a5isZiZRS8vL1WpVKyZ9P79++p0OiYjLRQKmpqaMj0I\ndRJ4F5kpGXWIxiKLotvtamVlRePxWPv7+3Z6NJtNIzUYRZLJpDmhz8/Ptbq6qlqtZhdM5lVC1nHj\n4Eekr4Wehmpn/QAAIABJREFUQYgmwslbrZY+85nPmKqQIBnp46IkQl3482q1mlHahEcydknS+++/\nf6vPf6LGjO3tbRPdgN/WajVJMs3CcDg0PDcajarValmlQ6vVMhwYoQ06CbS7HN9oMFCFIdtstVr2\ncnzwwQeGQhCHRVceCf9kN4OCNJtNuwgRh4D8Mx6Pm90IaI143tFopLOzM9NsFwoFYyk5JTDfwvqh\nvSBfBNMvXydUtsfjUa1Ws0oImmOxmeGZJCQHwojdnio53O8E1dCheH5+buWXt3kmajEjakfJxY7G\nIuQIpQeEGXZmZsYsQtDJfBiIdXB+II5nAQ4GAws5DIVCisfjppdwCs4RBhEaSITrxcWFzs/Ptbe3\np3Q6bQwfMBywG7O1z+ezyx6KOWcUL/+Xjj5UbGDivEzkUONOR8FHkr4ke2HI2oDxBF+GXgetIE+O\nUHPGD0m2AZCNAdvI6NLr9UwG+qrPxI0Z0M0sgHg8bgIar9drGgwYOILJCQCXZJ4/mEOsVhAPCGfS\n6bQZQ5nTobLj8bhdAGG9mFeBpoAMGTNwvkC3o1NG4UctWiAQMJJCkqEf0WjU4Efy8Ei+TyQSZiog\nIw+mEUYS/6Tf77fKZGBLgm0IVry4uFAqlbJLJ9/TzzrLFxYWlEwmdXx8bFS5dB3IzuL92Yvkqz4T\ntTNPT09rZWVFoVBIDx8+vCFLZAfg6CaSC0rbmUuByowqCQT6Tv8f2RxoJMBLW62WFhYWTBNMXjSn\nAJphtMCkeaJuY5ekuw/obzweq1KpWHgKUB+7IHFZjBnMprCBQHOSTPWG5gMtB7TzxcWFqeR4mbvd\nrtWvOeWvnCz7+/sWAEOiKUn5Tmrc6/Wq2+0qk8moWCwa4vQ/0TY1UTvz/Py8SqWSFhYW9OTJE8Vi\nMcXjcZ2dnZkIKRgMWmCKk0FDU0uNBJ2A7HK4WKTrI3FlZcUugUB0ODJ+Nj4rk8nYjuX1ehUIBOy4\nX1lZMZybnXh9fV2PHz+Wy+Uyxu34+Nj+frKTcZMHAoEbov3XX39d3W5XGxsbhomvrq6qWCwqHA6r\nVqvZeDIcDpXNZq3LD+RDuoYvGS9YmOiqnbkkONVJRmXXBsHhZ+3z+dTr9Swy4c6dO4b4ABv+y7/8\nyyt//hO1M/d6PSWTSaNV2fHG47HVqjHfxeNxlctluyCyW09NTWn5P6tyiYt1OkWYGencQ7OLM5sA\nFuZuUAco206no8PDQzUaDcXjcTOkYhbFAc5iWFhY0NramiKRiCEKZFggYkInwQtSq9XMjweRBBHC\nAiUSF1iMBcqfAQNIG4Cz8ySVSuni4kLtdlvJZFLZbFaVSsWiuKhM3tvbkyRls1mNRiM9evTIxo1Y\nLGZfF50ujCCv+kzUzoxGGMv+gwcPzI1dKBS0u7t7o4gRdu/hw4eWAYcVf3l52YiWmZkZLSwsWEax\n2+22aCtYvVarpaOjI6PC9/b2NDMzozfffNPir9xutx3j1ABjI6KiweVyWV40oh9wcShx9M6Iqvx+\nv168eKFsNmu7HwE0FGPyMn344YcqlUpKp9OW41wqlQxNIFpreXlZ7777ru7evavz83PrSEF153Sq\nFAoF01fv7e3J4/FoZ2dH6+vrOj4+1vb2tgnzsaVFo1EVCgWzazUaDT169Oh2n///0Dr6uXiAyUjG\nB04D/1xcXJR0vQsTJ7W6umrzq8vl0urqqt555x2DtHw+n82w8XhcjUbD5s1Go6FcLqdyuSyv12tI\nBo6UXC5nORrLy8tqNBq2K3FxxEMIvet2u5XL5SwABlREumYNqeaFAAIRyWaz8vv9ltnB1yNdC4HW\n19dVrVY1Nzen1dVVHR0d2SWVnxnCqGw2a4wh4Tj5fP6GgAmUAtYzEAj8l/EMixqFP1ROAMul02kt\nLS2ZPmVpaUk/+clPXvnzn6gxAx0BnSVc7AKBgHw+n3XdQQTE43ET2yQSCblcLtXrdc3Ozpqulw+X\n9E1JVuA4GAy0s7NjtDQpnKenp/Z7WFCNRsNo9W63q3A4bDjuycmJtra2TJh0cXGho6Mjq6bIZDIK\nhUK2u9E7giAedpBdzuv1KpPJaDAYWIbGRx99ZFjwaDSyGZ+vBYMrczPzLuGIJHiSQQICwhhzdnam\n1dVVu7A6tRkgHpQFOUVWoC+9Xu/WhtaJWsySzMFAPwhqrU6nYzOeJLPfVyoVNRoNS9tktwQGI1UT\n1wW+PubgXC5nowtF8NQ8LCwsqFarGQRHnVg8HjeL1+zsrKLRqFZXV22UkGRid0YiiCAWinR9ESQX\nD1gRLQkllsBm+XzelG1OssXtdtv3TXg5ixi4DtQHbYvb7dZwOJTf7zcsnbAXtB38HVygCeNBbooH\nEI35bXUZ0oQt5lAoZMorRESpVMpu2dz6E4mENUW99tprSiaT8vl8tqCQUj548MCc2EdHR6YtRqBD\nLADwHmTDz5IewE+8aGDMzMsI1YnsSqVS5sAOhUL2dzG7o4tAH82OCWaNiAe6GHiOgEUuh4Q7YvJl\nwUIySbqR5MSfgZGAymRgTVzwCJyg4iFeSDEF3aGFip/xz22nye/+7u8qmUzq4cOH9u++9rWv6d69\ne3rjjTf0m7/5m5aOI0lf//rXtbGxoc3NTX33u9+1f//ee+/p4cOH2tjY0B/90R/9f/6d/MDa7bb1\n/hUKBcOKyWujgBJGChE5DFexWFSv19P+/r7tItL1fFiv128IzyEunIuD4xKDJm4USIHxeGxySela\n7wAzhuQTPyL4LMwmqAGMHWgLR7p0jRdjFJVk9wGyQ5CoooYjpIURgRhfxFd0nUDoYDYIhUJGhVNI\nxB0F+SpGWu4IkiyQ5vj4WHNzc1a1zEj2qs//2mL+nd/5HX3nO9+58e9+5Vd+RU+fPtUHH3ygO3fu\n6Otf/7ok6dmzZ/r7v/97PXv2TN/5znf01a9+1UD53//939df/dVfaWtrS1tbW//lz3Q+dFhHo1HT\n3CKvZBfEvkN6JqUw7BoEsjCHsutxUUokEqbZZaxIp9MmNo/FYkYJx+NxE7VDM0Opo1E+PT1Vo9Gw\nUSMQCJhACGJGkqnfqPjF7eHUNCAHBRlBh+F0gYNz8zNBpLSzs2PqP4osgTFpiSUqjH4Vonfn5ubM\n3wipAurCBoOJls+DnRuZa7FY1LvvvnurNfe/tpi/+MUv/pe83S996Uu2y33+8583T9s//MM/6Mtf\n/rJcLpeWl5e1vr6ud955xySQn/vc5yRJv/3bv61vfetb/+3f6ewZuXPnjoWTSDIyIxQKWbxtIpHQ\n+vq6JRDBhqVSKSUSCZN1UsIDtUzdLk4Q0n24nLGrETbD1+DxeOxFAknw+Xx67bXXbJflzwmFQspk\nMgqHw5Z/HI/HrSmLeC0yo1GfYenCBhUOh027jV4b+hu9Cg4WcplzuZzZrMh3RhYQCATMTMBYwqxP\nzoezEg34kNRUskww+zICplIpra6u3mrN/T+D5r75zW/qy1/+sqTr4/jtt9+2/wbc5XK5bohPstms\nyuXyf/tn1ut12znL5bLtmIwbCG1wP7x48cLmRxqhOP6Bn2D1iMP1+/2q1+s2Q87Pz9tsKsmIEq/X\nq06no2AwaFAZlimOVZwrtVpNmUxG5XLZwl5Y2LCWlKfncjltb29bEQ4j1NTUlO22hUJBmUzGcjgu\nLi4Ms3Yq2vgzK5WKjVxoj6HhmaEpt8fJk0gkNBgMzNZFgRBjSTwetwt4sVg0tnA0GqlSqSiXy6lW\nq9lYKP3/VAL653/+53K73frKV77yP/rn8oNEXeYsNY/FYna7pis7n89b7C3mTmcA+NXVlRYXF01J\nxwLHyRIOh22HAqMFviPiABUcyjqQiNPTU5NhYicCccCZgpjp4uLCAgybzaYtIPLjYAY5FXCszM7O\n2oWRwEMWciKRMD+jx+NRKpWyjkMgTZAQ6tSoGCZbmZ0Wmef8/LwCgYBisZiNb8CLEEI4W1DiAVme\nnJxYaPmrPv/Xd+a//uu/1re//e0bHHw2m71RaFgqlZTL5ZTNZm8EUJdKJWWz2f/2z37//fdNcfbp\nT39awWDQfpg4TDjyWRQbGxsKhULq9/tKJBK6urpSPp/X/fv39YMf/EB+v1/RaNQSM6+urrS6umpM\n2L179/T8+XM7liFIer2enj9/rgcPHlgppSQ7WnF3E4BIxgcLEOlnuVy2Hm+iCVwulyV1DodDG2+c\nijjc1WTEIYgie4+LHM2ojAbUs3k8Hr3xxhtqtVpKp9N2NygUCiZqCoVChjUPBgP5fD5zj3NJBtYD\nnkTKysu5ubmpp0+fmrT2Ns//1cX8ne98R3/xF3+h73//+yZAkaTf+I3f0Fe+8hX98R//scrlsra2\ntvS5z31OU1NTCgQCeuedd/S5z31Of/u3f6s//MM//G///M9//vMmAyV+ChisWq3ahaperyubzZry\nrFQqGZzk9/stA4Pfh36CXaxYLKparVqQCuU93PKPj4+1v79v7BbjCgJ4XlJK6Xu9nmHKwFydTsdE\nOPv7+zbuIOs8ODiwE4Gkf4LTITFoBkCvjfC+0WgYFIaiLpVKKRwOq1gsKhAIKJVK6d1331U+n9fe\n3p69aGDqpBihTISMIjKYoErETxh90+m0bVxoQlZXV01XfpvGqf+1MePLX/6yfuEXfkEvXrxQPp/X\nN7/5Tf3BH/yBBoOBvvSlL+mtt97SV7/6VUnS/fv39Vu/9Vu6f/++fv3Xf13f+MY37Oj8xje+od/7\nvd/TxsaG1tfX9Wu/9mv/7d8J2wUezMgBVMWuzIXo8vJShULBJJAkgSJePz8/N9c2yjqiZlncsGwY\nUiVZnS8fJvQ5lC5IBlJLzJ84tpFEOpNDQQ6cGdBoK2DT+v2+pqenTVOdyWQUj8d1dXVlowLMHhpt\nxiK8hoxhV1dX5l1ElorDGoTHmeyJuo7WLqfXD8Wc2+02bQuWL+ZqklJv80yUofVP/uRP5HZfl4zn\n83l1u11j6ebn5/W9731P6+vrKhaL5qL+1re+pS984QumIeDWzXxLcxXHfb/ft6TMRqNh9n7mUUyt\nwG6kY8bjcS0sLJg+AxMpMBXpREBexOpiFADzTSaTlsaJa4WQFnbnZrOpUCikw8NDBYNBCxcnDIdR\njJBwlHfk0J2dnSmTyej4+FiRSETlctkue4eHh0qn0xoOh0aZ8/URcEMnIko82Ea+X8wMs7Oz2tnZ\nUTKZVCAQ0MHBgf7mb/7mlQ2tE8UActnLZDL653/+Z+Xzeb18+VKHh4f69re/bUbXp0+fampqSn/3\nd39nDm0IjL29Pf3bv/2bWq2W3n33XQ0GA1OkOY2nZEwsLCyYPrnZbGppacngK6SWlKRDeIRCIb18\n+dIE6R999JHcbrfFyIIeHB0d2UkBrQ3r+N5779mLcXJyop/85Cc2Qz969MgufLVazRzl0Mp4AzEb\nPH361KSpR0dHNo5sbW3ppz/9qSEhjButVsvw+vF4rFKpZGKsRqNh0J8ku0gyD1erVb18+dJ0GkTj\n7u/v6/Hjx7f6/CdqMc/OzppxE+E3mQwowBYWFrSxsaGTkxPdvXvXpIxOnJT0IjKM2aGRXoIIAJkd\nHBxoYWHBROeMA2dnZ1bEDsHC7g3FHQ6HtbS0ZI2opApBOoTDYXNwLC8v28U1lUpZx97FxYWWl5e1\ntbUlSXrzzTdtpPB6vXr99dfNhTIYDNRut8272Gw2lU6n5Xa7LTWVsWc8HmtjY8P6WdCtAEN2Oh3r\nMCQwEW3G5eWlpR65XC772fh8Pi0tLalard5AnMDBb/NM1GIej8d2y+d2DjIAmgABIsngK2bU0Whk\nOWkULcJSAbNJspRQjk3SK8/OzmxcAOfF9MrxikkW8T4yT36NM34LGlq6npGZ1akxJuibHZnwc5g6\n6foFR+VHRgaRuF6vV6+99pr1AXICEBtAvJfz5zUejxUKhSwPhCguCBTIFaohwL6ZwXO5nI6Pj5XJ\nZAwnJ/CdO8erPhOlZybrgVTP+/fvG5TE3Li7u6vT01Mlk0nTNCOywd3sDM7mAwwEAqrVapaVgWA9\nlUoZNMdNv9vt6sWLFwa9+f1+HR0dGWJSrVYtYIVLaywW0/b2tlWLQa3Tk4JoqVqtKp1O68WLF3rw\n4IExekB01WrVWlzPzs6MpEkmkwbH0QnI985pE41Gtbe3Z3rt8/NzVatVG4f8fr+Fu1AfzENXObUa\n8/PzajQa9sISXN5ut1WtVtXr9UybgW4F18+rPhO1M3NhAcFAjXV5eWkWfSd1u7W1ZS4PXMcIkNAV\n8N8R9sTjcdXrddMEd7tdk4pSHezxeJRMJo1K5pJEAWYikZDf7zdlGzsptWlgs4wEROMWCgWjz9k9\n0Y2Ew+EbowXRCeQjQzPPzMxoaWlJ+XzeLGUsZppagSwRN0FJg0BIsh0VPBlqH8EWli/kpHgcQTnQ\nOJP8TzH9bZ6JWsySLEormUza+MAMCCmCjmFzc9P6tMFsyX9wZlYAQXHcArGhcIOcAK6iDBKYEPEN\ncCFxCJAKhJ87iyc5lhH084LhQ+TPYkalSRW20Vn9gP4YRRxqRWBAElOdKaMEoTtT+vH2oYu+uLgw\nyBHyBBQIbH00GpkWBqcMeme+d0YqVHWv+kzUYq5WqwbroBZzOojb7baCwaBRsk4NLpFVJycnWlpa\nsrmZ3aterxuzRTtVIpGwWAJUY6PRyJLwcUZT6APCAMbNr2FRw9xxGXVWrVF9zAzM4iZbb3Z2Vi9e\nvLCXt1qtKhqNmuOG3R40hheNP8Pj8RjFzlyOFhvqmtPK+X0Nh0NrhiVhlBplGmeZmYk2m52dNSSE\nnkK0HLd5JmoxY1e/vLxUKpWyXQntAQozdqNQKGS3dFI1c7mcOZ/ZLWDHPB6P2ap4QVCBQUfTWIqP\njx3f7/crn8+bhgOUBAKFLA52YnQO/HrK7EFAsD3xPSCa9/l8ury8tPsDfSZECIAnMxNzEZRkDCdG\nAxYnvkbnyZPP563d6uHDh0byME6R5YEFDKjO5XIpFArZaSjJfn7r6+u3+vwnajFL0sHBgUkiA4HA\njSw1l8tlHzrxA+vr6+ZnQwnn9/tt7iUH2bmwnS1Uzpt/JBKR3+9XMpm0XY+jnKObI5YxgoWDOk66\n/nAZOcCeA4GA1Rc73RrMrNDB7L6JRMJqyVKplFHHzPmSbsTLjsdjra2tGXEDmjM9PW0uHX6mXOj4\nb+DfzMScZoihGJeAJJ2aDk4IRo7bPBO1mKF42VWgko+PjzU7O2sLG+H9wcGBDg4O7LLIcdztdjUa\njVQul+3IRQDPfEo6P4XpuI6hq8FZT06ui9yRX0of50Gj0kM/AlVNIypjhzPYkIXx7NmzG82utDoh\n4kFzcXV1ZRFZ5Igg3ySOF8MALzwLk/9N3giBN3gG+W9k7IGYMA7BTKIFB28nLDGRSFhID26VW33+\nt1s+P1+Ps0WU/DJ2N8ykhCNOTU1Z/BYXRAymXPxABMgn5thlpyoWi6b7RVFHLza2KqA251F+cHBg\nhlASRtFFMxOHw2EtLy+r3W5bCj+iJZR1V1dX1jsCZov/cHZ21i5eoB7Ozj7y8kB+aATA4ABBQrUb\nBZaYDngZuZMQPsOdgnkb8giYj+hcsPqzszNrxbqtam6iFnOn07FUnkKhoFarpUqlotFoZDGu9Dtf\nXl7q2bNnRuHW63VtbW3J7XarVCqp0WioUCjYQqLKwZlZjHmUwBUibYfDoQ4ODm7YnwhChEHjto8G\nGuQFYyoVDFj8ga2opID5I17h/PxcvV5PjUZDjx8/VrVaValUMugvFAoZ5U5YDhYoKHaidtkpq9Wq\nqeBI/aRyjpeKEQwUiRfj4ODAXsBOp6NSqWQOcr42TA68/Ld9JmoxO6tuU6mUJNncShom+mRCB7HJ\nY0Pq9/smHnd2cjBuENBN8g8ULcbXWCymfr+v1dXVGyGDqPagpglXRKDTbDZtV6cnD5aRGRZlG3oH\njnCyOrAi5XI5BYNBFYtF9ft91et1Iz9IUyLylpmVTr5EImGoC/cBsjOY852h4oxSuOK5H3AC4iYh\nqYmXBYy9UChI+ljEf5tnohhAmDp2lUgkYrYhEAw0ECzccDhsWot0Oq1yuaxAIGCp99DRkkzEzs4Z\nDoeVzWa1v7+vmZkZraysmBOFXY+vC3qdY5YPlAZYj8ejTCZjo9HMzIxd+Pr9vvL5vB3vzp4TqHoi\nvRiV3G63PvWpTxm8t7i4aNkbsHxAa5As4O9Y/hcXFy33ArUexTyI9bGFra+v2zjmcrmsphh6HMqd\nSyj4M+lPnBC3eSZqZz47O9Pjx4+1v79vSi8UXKenp2o2m2o2mxZKjiQS9ANAn0JLKh5AH7AL4ZFj\n/gWfde464NzsbM7kfvLjuIiCw2IgyGQyFi9QrVbV7XYtB4/xiTgt1G7EA3CRwqtITdqLFy8MX240\nGqrX68ZI0mpL0Q8v39bWllHq+AG73a45Z6rVqv2ccamQP4d5gO+5VCrZ+MLoU61W9ezZM4sYc/Yp\nvsozUYvZ7/fr4cOHSiQSlkhEJjBu4fPzc5vfmH/RR7CLQ+UeHh7aKCLJLmZc0ri5M2L0ej3L5OC2\nTs4xJATuGUJgJNklkV15d3fXlGfOv6PX6ymTydjlk/Bv/n7IGXKZERghwAJN2djYsFMCOxZzuzMg\nBiE+DVN8/WRisBmgZ0GnzOnB4iVR1FkLgSKRvI5er6cXL17c6vOfqMUMjnl6eqq9vT2bwVC/QXgM\nBgPTXHADh3oFx3UiA2CikmwkgIwheZ7ePuIALi8vtbW1pbm5OTsJgN+4WALn4bbAm8euCSJDXC4C\nKn49hAhifqScYL2BQEDn5+emowZPp7SIBXl0dGTRXN1u1/TMsKBzc3Mql8sWoOOMu8UQACNJZp0k\nS2pyuVwW1OhMgHJejvnnNs9ELWbwVj5s8F4ubhAGTgFQNpu1CwoKObBoXCuYQCFOwEORbkKbZzIZ\nNRoN9Xo9+Xw+pdNpU9EFg0ELLqRizOPxKBqNGlRIOmg2m9Xp6anlPgcCAUMjUOIxGgGvzc/P28x5\nenpqX6fL5TJGEfaTfOXxeKxWq2U7MZYp2FKSlWD6nJFknU7H8uFQwMEyYjODkUQ7TewD4wjzNEHq\nTl/oqzwTtZglmSaYCxK062AwsDTQi4uPCxsDgYCp5iSZq5nRhC5tsimwVRHlBbEAG+b3+y2G1pnk\ngwsbkRLqM74WrFn4DoHwwG1Z+LyQToOrM+FUkqUtBYNBw58RAF1eXtqcPh6PTasCxAjNThnR2dmZ\nzd2SrBQom81aYOPc3Jzu3r1roxPj0Pn5dV8h2hVYVsIWGUFgGj+ZmR0PRyARVMy+koxCRQV3fn6u\n5eVluzgRCgh8xg82m80aAwcctrS0ZBFcUM/0dozHYy0tLdkC6Ha7pgxjbiSABVQChV8ymbQFRxoR\nJBBOl2q1qsXFRVOuMe+S0Dk3N2fG3VAopEgkomw2ewMFYXZOpVIKBAIWN0A0GEq8SCRiLbCRSMQu\niLzQfK3k+BH4uLi4qHK5bNDf/2HvTX4jS9Oy78t22I7JdsyDI+wIT+msrCGzq7qbplDvXzaIFVJv\nkEBsYIPEsv8AkJDY9g6xYAHs6F1DSwxSS01RorrmSqencIRjngdHOByO8Lcwv7uOu18Qb1rwoVAf\nqUSTmbbD5zznee77uq+BwQz3gdoaLxE+N6qg173majETcAPVEVGoJEtSgvPAKJYhxc3NjQXibGxs\nmGMnw45ut2vQGKgADRcLi5203W4/sBHAaZ4XChJ6q9UymAulCUQdFCsLCwvmU0F9DZwGjj0YDIzQ\nQw3NjjscDtXpdNRoNKzW9Xq9NorH0ouhBfdA+noDWFpasvIN2wIkV/jM0UAz5na6hkKGApmh7Flc\nXNTl5aUFeD5//vxRz3+uFrNTewd5ZXNz0wYNkh6MtZHrMAbnaEYLCKnf7XZbiA2EHUn2M+r1uh3P\nwWDQ+MYMUSKRiHGKqc0ZxvBZI5HIA93c7u6u3G63OYiGQiElEgnt7+8rHA4bjBcMBq0eZ3Hz//v9\nfts9wXlBLWhGce6HJwIVFLyaXZyXkRet1WoZyYmyhxOPoRBqHpo+GIYoyxlU4ev3WEHrXA1NSqWS\nNVGkfzIiRqoj3U8Ki8Wi+RaHQiHzY4tGozo/P7e6rlarqVar2e6Yy+XMsYjRLZxgzE0ODw/tz4k8\nA9JrNBrW/PT7fSM2jcfjB1KqtbU188fDO4NGChQE7Lrb7ZrxTTabtYwSvPNms5lyuZyGw6FFGS8s\nLFidy4tQLpd1cXFhipt/+Zd/0XvvvWfTybu7O52dnWl/f9/uazgcNjHB1dWVksmkLi4udHJyomfP\nnpmP3XQ6tRenXC5bOBGm6MVi0Z7P615ztTMz/XMKPI+Pj83LYXFxUe12W9Vq9QFFE/gJEg6YNMc1\nVNLl5WUrH/BHcyo9JpOJMpmMvVCLi/dZIDRnkHaorcPhsC1W4ibYycB/JVlyFouTXRJvCun+xHnx\n4oXtuggAUKmsra1Zk0eZ5azz+d6UaihWRqOR4vG4QWhM9RC1EoHhcrmUTqdNTOD8vaLRqLmrtttt\nIzOBADHsKZfLj3r+c7WYIb9IsjEsiw/u8dbWlsLhsNlGMSVE9REKhRSJRKxrZwLIUer1em0Ysr6+\nrng8bgrlTCZjYlQeKNNCIhMkmYJ8Op1qfX3dlM6oTChTotGoAoGAIQ8cyzDmNjc3TZaVyWRMNMtw\nY3t7W3t7ewqHw0omk/byOknxuA1tb2/L6/VqPB5ra2vLmtCVlRUNBgOl02n7enZYNg/QEMQK/K63\nt7dKpVLm9zydTrWzs2OlHOgGRo47OzuPev5ztZh58Aw0UG0sLi4qm80aRspEi13M7/eb4xAqkHA4\nLI/HY5YAKysr5l5UKpV0c3NjWdc8RPDa4XBo7p1o9kA/8GHGFqvRaNhnlGR4MFKuUCik7e1tczri\nMwPRcTKgIUQVAiTWarVUrVZNwoSz0d7envl4ACeCXPB9UqmUVlZWrPRh/E3d32q1jJsNgYrQI/gl\n3AsJaP1RAAAgAElEQVSC7vH42NvbUzqd1pdffmnN48HBwaOe/1wtZhoNZ5LU9va2ZeNJMm+H29tb\n7e/v680337RdET4yC+fg4MDyn1Fn01gxwWJRO1GCZDJp9rmIQSUZasBOyCQR0g+7POJP4EAmg8id\nYOC5XC7jQjNUWV5efmBDGwgEdHh4aL8/8cFklQCPwVFx4tfQOp3TOZfLZYxEBipMFvGcpoYm3Ieo\njVAoZKFBhIL6fD4FAgGLa3vMNVeL2ev1KplMKhaLGY7bbrcNCpLuH2Y+n5fH49HFxYURfqCJoiEM\nBAI6Pz83JcTm5qYk2WCEI5kYBo77tbU184WTZCcFNSo+dM7FxA5LWA4qbMSnTjNzcGmaRfSBw+FQ\nsVjMJnvwndfX19VqtfTOO++YInpjY8OGF1hnUaujBOFnOaFMalt8RWhkNzY2zI2IoRP5JysrK4Yo\nEQkhyRCczc1NU7b/MjvbcTn5A5PJRIVCwVQWwEl4w4H/np+fG/yGfGkwGNg0rt1uG1WSJmlhYUHV\natUmbpVKxdKlLi4ulE6n7WeMx+MHo3B8KiSZAffd3Z3K5bLK5bLh4evr62o2m+Y2T+zvaDQyORgN\nGKjNF198oa2tLYuSIPfE4/Eon89rcXHRskuAI+GPgHPTS9zc3Fi9jwlNLBYzfkuz2VQ8Htft7a0K\nhYJRAvg37XZbkUhExWLROC7E08HjuLq6UqlUMo8RXqLXveZqMScSCVNC0NxAyWRRsfPiSo9wFaMU\nGpfJZKJ4PK5YLGakoLu7O4VCIfX7fe3t7RlZfjabqdfraWVlxYzJiR/D3xg+A3kh5+fnhq9CUAI/\nRmTAmLzX6ymbzRo3gzE1vGg8o99//31TnqCmwdCFUglSPuXP7u6uWQBggu5k+Hm9Xh0cHFhYPffZ\n4/EYfp9IJDQajbSzs2MvPC8w/QfRD9BlwcA5XcClH3PNVZlRKBTM6AUb1nq9ruFwqHq9Lkkmu2fR\n5vN59ft9y38GnltcXFS1WlWz2VStVjOucrVaVTweV7PZNKsqUAhKmOFwqFwuZzs8gxFJOjs7swbQ\nOVXDn+3u7s5cNieTidE5wbqr1ao8Ho9qtZpxUHhhj4+PjYyPaoVyByYdp1a1WjUOCMbkCwsLqtVq\npk0kkq3f79uQZjgcajQaqd1uW6g7ho/VatV6DnSPq6urFspJKBC8j+vra+M5T6dTffHFF496/nO1\nmJPJpD3Yq6srq+toUBhyMKV6+fKlKZS3tra0ubmpUqmkYrFogwTqWhq+QCCg4+NjgwCLxaLtoFdX\nVyaXCgQCltKEdGh9fd2srmisms2mcrmcLaJYLGZRxSx6opAvLi60ublpuze6Q3oDIowLhYK8Xq9F\nVRSLRa2trVm9isK70WiYTnE6ndoLQ9bhysqKyZlQ6NCQLi4uGibN/R0MBlpYWLByjmEQfBTclMDk\nb27uk1mJZUNY/LrXXC1mpkxIkPCAWFxcNMQBlMDtduvg4MCaG/DO1dVV7ezsaGdnx7wswKmBoUh4\n3drasjSs6XRqsiqgwdXVVfX7fW1ubppiGmI+R7bb7dbW1pbh1qi0/X6/tre3TdIEMw6oK51Oa3V1\nVZFIxH5vju10Oq14PK4nT54oEomYXweK89lsZnmF6Bex+uXvnDIzfKaxnWVIwvgf8lU8Hje7XULq\nITKBkCwtLRkS8/TpU7M+8Pl8j4bm5qpmBleuVCrW5VerVUWjUVNco9JIp9P66quvbPonyfi7R0dH\nJkwdDAaqVqsG06EtZCLGDocs68WLF0ZmTyQS5tYjyY5sMrX39vYM1eD4pWS5vr5+kGSKAWI4HFar\n1dJXX32l7e1ts/dqtVra2dlRu93W8fGxGdVIX1soDIdDE6ZeXl6aeqVSqdh4HWX57u6uPv74Y2Wz\nWbVaLavBnQoUrAaIRwuFQjYRJNEV3kq1WjUaqlOixf1ZXFzUj3/840c9/7namZEtraysqFqtmoCS\nkgBNH3IfcFEsrTA4cSYk9Xo9o3SS6wehHaMVoC6okdi/QuaBfUd5A5TFIm+328axgBgEXAisB62T\nMTrTOHjGGIgzuWP4AiMPDLrT6Zh9FsE63J/ZbKZwOGwNMzQArHFpNoHkaBrZEJyxxjSnkI84cRju\ncEKhyun1etre3n7U85+rxYyJCVxcKJnT6dSOx+3tbZMi4X7p9H1LpVIW27u+vq50Om2mK4lEQuPx\nWBsbG8pkMsbDGA6Hpvbg/7Lb1Ot1xWIxY8VhPUBqFEc/JQzmjZFIxMzDoY3u7u6aDhFeMHnZh4eH\nNs4Ph8Mmm4rFYnr69Kl9H3b6er2ueDyuZDKpQCCgcDhs9Ws2m31gKgPDLZVKmd8z5CfKG+zDGOAc\nHR3ZyQCbEYwcGRW9TSqV0ptvvvm/Nwj+/48LkN7pp8yCvr6+Ni4wu/FwODQ4iPEruzuLjli1Xq+n\nRqOhcDhstEcwbVKlIBSBwU4mEzMpx/XSmbUnyf5saWnJ9H24BSG2dbvdlvXHEARKJfxhSXbCgBoQ\nnUa0Gp8RaivTO0IvGd9jcMMuPhwOTZ19dXVldFQYeSAiYOKj0Uj7+/vyeDx2H2kgMWyPxWKG2OCc\n+p+l7/5XrrlazMBfzmgzmFi9Xs8WCzjoaDTS8fGxKpWKarWaZWCze11eXhq/F5iJ1CVCdzgieaiN\nRkOxWMzgPaiTOAMhvcrn83aEs/NSmuCihHg2l8vZ17OQncmyeOUh04LbAdnfGb+2uLho3ntOpbXH\n4zEYs9PpmAiBUToU1eFwqFKpZGE9oBIMU7i3UEkpL/iZkuwFQUvZ6/Xs5X3MNVcNIByHlZUVJZNJ\nLS0t6cmTJwoGg0qn0wahRaNRXV9fKx6Pm3kLSAFO9clkUoPBwDwwYJtBD4WoBAGJOh1yPuNt6kWO\nWr4HmSdra2va2toyOiYYNlAa5B1ODhh2iFUZE6NzBHsOBALm4cZwhs/hcrkMhYBnDXJD84YAOBQK\n2Us1Ho8ViUSsNGOqieM+/AuI+px0DKoYfaPD3N/f19HRkQ1hfgnNOa5Go2GNG/kgSIOcR9rFxYU1\nYDDGer2ecSsg7k+nUzPDxmMY2yzp3j6XnLtKpWKoAfkplBmUNv1+3+LLXC6XQWDlctmOcFQw8Bjw\nswPnXl5efjDm5niHi8yomeaSgRENLzngOHFC8Gck3m63bfHhwQGuTHY45CgoANBCsRa4urpSOp02\nh1T8SMCgianjNMKrpNPpPOr5z9ViXl1dtabj4ODAyOuQdLCXSqfTBquVy2VbhAD6JCZBOEIwysN1\nEuN7vZ6azaY2Nja0tLSkQqGgxcVFU6+gsUOuzw5O3e3kYXS7XUtDjUaj1oTNZrNfUI8gxEXHyBAI\n/wxQCQY3TsgP9bj0NQV1c3PTSg8YdvCVGe+zs2Okg/IFhySnMSTsQHoF7L0YsPCyNptNq7Ox6nrd\na67KDFhePp9Pn376qaLRqPlTYJjodruVy+VMPQJMBWQHhCXJhinUnclk0lhu+/v75ngUjUbtoRBF\nls/nH4TgYKTdbDb1rW99S5988okZqJRKJfl8PiPS12o109hJMm4xnAy/328KGnBrPq9TTUKCVKFQ\n0Pb2tuHs8ET8fr8ikYgtcmwZMLZBiEo6F+N7Sab65u+cvhxer1eFQsEabE6l58+fm1AXc3N+/tLS\nkg4ODvSP//iPr/3852oxX11dWRi6JCN9420GUE8UAggCFgW1Ws28hqlT2WmhVNZqNTOXoZzg69fW\n1h6EyGPGcnp6avxgtHbgz+jjnGoMuBChUMjsaGGYgXPjZYeVQbfbtR0VVh4jecol7Hb5/HBI/H6/\nlpaWVKlUzB5gY2PjQfnTbDatxsVPDr5yoVCwHoLcE7gnkgwxOTs7M/W6JKO4Mtb+pWzKcWFYgmHh\nbDYzvJeBAVq/brdrRy7WWtPp1MbTHLder1eDwcBscBkL4zi6u7trAxcnl4LasNvtKpVKGcbLLlgu\nl21HhVnm9XqtZNjc3LQyBzSBz0Ikw8bGhr28MPqYyDH0IEoNLgjc4V6vp0gkot3dXcPRb29v1el0\nDLJDYY4CnAQtxtW8FE64EPRmMBiYEQ9hRMipsBYul8tmcYBr1GOuuVrM0WhUvV7PygKGJYxYUZ1k\nMhlJsroSN6JwOKxms2mJSRsbG8YjWF5eVjQatUBL9IKnp6c29EilUgqFQvZ34K/swplMxgKBnLs4\nL9tgMFA0GjUzwdlsZhRU0Ae+P1ROUqCo8SXpjTfeMCoqsCHDDF6QYDBouycDm0QioUQiYU0ZtT8n\nT6VSeRBmv7i4aJ541OU+n88MbySZCJiUguvra4XDYblcLj158sTIW8vLy9rd3X3U85+rxbyxsWH1\n283NjbLZrKlMaPba7bbOz88VDAYfiFiRIPH1jUbDjkeaJ2y5QBg6nY5N0FqtlkqlkgX1JBIJhUIh\nJZNJMzTn+Ge0HY1GTWHh8Xj07NkzeTwelUolc16qVqv2+djRnJItTgzqcrfbrZOTE41GI2O/TadT\nM4tcX183eEySNbHdblelUkn9fl+FQkHr6+sGVyYSCbPymkwmOjg4sPgJ7psks7oleQqVPMJevJnB\n2iuVipnxkFr7mGuuFjM+xePxWOl02nI8cKa8u7uzXRCNnsfjsfgISg1wWAYr7FQco3TssOycrp71\net0IScRPsPBxAsWshcaUnbhcLptxOSE3QGqMrev1ug1IKAdgozHwYQrKgnIGciKpqtfrtgiHw6E1\nhtfX15bJR1mF0IB/0263bciEuyqZKFgsQOtcWFhQLpcztAjSFNRTIocjkYhKpdKjnv9cLWZuOho9\nLGGpLbEQcHqeUX7QvKBNg2eA/SoLAhJPp9Ox8TFUTbzq4GhAnWR8DoEfhTgNKS9PIBBQLBazBpZo\nXjSA0EhRepO1hwM/Oz90TaBKn8+ncrn8AB2BIosnh3RvOok9LW5KfG8ErcTOoamEwyLdxz1z74g/\nhuLKGN3tvs9b3NnZsaEPDSWuoq97zdViZmepVqs6OTmRJOvEy+Wyjo+PbWjidrv14Ycf6vz83DLt\ncJo/Pj629KR6va5Wq6VOp6Nut6vz83NLgMrlcppMJnr16pVp8k5OTqxEaTabFpsGNwSrAV6GVqtl\noTuUHG6325CFer1uZQ3uP/V6XY1GQ6VSyRQcNKkEqjO8GA6H+uqrr0yRAuRHlBu8i3w+b/7PpGqh\nncTV/+bmRsfHx2aLAGIzHA5NNdNoNJTP522ELkm5XM52ZUKCoNcOBgPLJTw+Pn7c83/UV/8vu3w+\nn0V+4cyeyWSUTCbV7/f1zW9+01QPd3d3evvtt03QubS0pGw2q8XFRT179kwbGxtKp9O2c6FC2d7e\nNqw1EokoGo1atMP29raCwaDi8bim06m2t7fNEUiSuV9SRzMFdLlcRruEIPT2229rOBzatC8Wi+nq\n6srCH8fjscUiowCn0cUDxOW6zz7M5/Pa3Nw0ZXalUrG6eTgcmhEM+kR2WoxhvF6v3n77bSWTSTup\nJBmGnE6nrdmV7kf9lBHc/3K5bEQpVCfAeTTqh4eHevXq1Ws//7lazE55PxCdk5XWbDZNj0fWCYOH\nvb09S2MFD3aWBa1WS9PpVPF4XOfn5woEAjo9PbWfId3vQG63W6VSyRZRNpu1HZLaluP47OxMq6ur\nevXqlSEsYM8QibCKZTro9Xp1cXFh8BsMvE6nY3AeI3ZCLVlkTkd9IuQgUVHaMHRxuVyKx+PWuHW7\nXbs/2X8Pp0TJDTGJEo9GORaL2XAHNyN6Dghay8vLdtI99pqrMsOJkdK8cKPYUXDf4YEWCgXzusCj\ngmYL/BbzRIYsEJqw0WLkDOIBew6pPccuZCDsDyAq7ezsWEiOE6orFAqaTCbWYAHXwcdG78hO+cUX\nX5jTpyR1u11TkGN+CGsQPBz+h/R14hML97PPPtPKyoo6nY6daNK9tcKrV6/MuleSlTbdbtdECiQC\njMdjLSwsWPlGcwyHPBQKaWFh4ZcJrc4rGo2q3+/r8vLSBgXAQC6XS/l83jRtZ2dnCgaD2tvbkyRT\ndmBUjn1rIBDQ7u6uEWuKxaId5/wbmheOS0nyeDw6OzvT3d2d9vb2LJqNHL3ZbGYNHA+VEiMWi5ka\nhQYMVAR1SzweN/d9OByZTEZ7e3tmlM6fQ36CWMQLjn+eJJsyAkNib3Z3d6dYLGa2Xrj+Oy23WJBL\nS0vy+XwmkIVgxZQzHo/L4/EYCZ9dnVzCx3rNzVWZ4cR5i8WiksmkkViazaYpRTi68/m8isWivvnN\nb1rHfnt7axyE2WxmwfKUCPB+Ofax3Lq8vLR8QHYd3Itg6X322WeKRCIWtTubzSzYEm7DeDxWq9Wy\nAQWxagx0nFYKYMjkt/R6PVtAzvRXKKdg01AtKVWAL7HdcgYAra+vW/OG0GBjY+MBJ4QXGsSFk5AB\nUbFYlN/vVz6fN1IVDqXS13DjV1999ajnP1c78+LiouXkEd2AKcrNzX1UsMfjkcvlMjn++vq6xuOx\nJZziNUG5ARqBGUuv11M6nbZp2s7OjsmcMJfx+/3mLooO8erqyiy6BoOBms2m7dCj0cjUFjSB/Dxo\nlpCEgOkodfCO43Pf3t6aFzVKcQQA+HIwoABmBBKUZFAepQf8ktFoZIHw5A+CiOAtB5q0sLBg5Q6N\nILwZvtfq6qq9dFgwpFKpRz3/udqZ3W63njx5ouXlZX3yySdmF4X1APhvvV7Xr/7qryqfz9sxjGtm\nrVazxKdutyu/36/z83Pt7++bdQGLZDqdamVlRfv7+8rn8/J6vdrZ2bG/d7lc8vv96na7Rlxyuh0B\nlzF82d7ets/Hy9HtdrW7u6tms2nNVyAQUC6X08rKilnZHh8f65133rHIimAwaNzo4+NjvXjxwth3\nIB6YuCDiJdG21+tpa2vLRAOcdgyGuK8kSq2urppWEgI+X8fPPD4+tkSsRCJhjDxKMRz9H3PN1c5M\nnYdIUvoaHcBQm2QpRtmlUsl4wq1WS/F4XNFoVLVazUjl2MZOp1NdXl6aYTkYMYoRsj8QpfLfxsaG\n5Zo0Gg2TRRHRhofFv/7rv6rT6ZgUn124WCxaXT4YDKwhZEfH9Pzi4sK4F/V6XaVSyXgVzsB76b5h\no9FEy8jXo9uD4N9oNFQuly3DBS44nwGeCX51OCOBW+NFd3Z2Zg3hcDjU6empzs/P7Xudn58/6vnP\n1WJmgdLkTCaTB87wqI/xr5hOp9ra2lKpVLKQGKJywZ4RdsLBzWazpuamuQRC4wWIxWJG4gfWAknh\nZUJWtb6+bkmqz58/N9+1yWSiRqPxoFSBuwBHG9SGHZYXDq9l8Ojnz59b88nIGUor8BjcZ+wCnIlY\n/X7fXJZQqTBRdNrQgtIgCA6FQkaqwl7YycWYTqd69uyZJpOJwuGwMRZf95qrMiOZTJpU6fb21pAF\nJO27u7s6OTkxz+Xl5WWLLZPu0ZByuaz333/fyEOBQMDYboysx+OxUqmUjo6OjNCfzWYlycwC9/f3\n1Ww2jaGWSqXU6XSMd8HwBTsBuCJQPSeTiZ4/f65Wq2WjY2A2SFTk521tbeni4sKaw0AgYGJS6d6D\nL5vNGmrw8ccf21Hf7XbNzpeFBjfj3Xff1cLCguLxuDV4lEgbGxuKxWJaWVkxHgwsO1AhDBKhk0Kp\n3d/ft1o/FAqZOOGxxolztZjRrzWbTeXzefl8PquLa7WaLi8vrQ599913VSgUjB5JyM7S0pKOjo50\ncHBgnGYUyww/JpOJkXU4dsnMJuGUZkmSuQTBb1heXtbl5aXeeOMNdTodvXz5Unt7e6rX6+ZSXyqV\nrCYHH2YSOJlMVCqVrA6nwaVpbLVahirc3NwYwZ/ReDQa1WAwsNE1X0NOeKFQ0PPnz/Xhhx8qm81q\naWnJSFiNRsP42aAw2C6wAfAfERwMRkKhkJlB0rxiG8zv/JhrrsoMvDJYUIyUnQ7zyWTShhSgHQTl\ncPTTfDFMYUoG18A5NGFwguMPrkLkbzOgwECQho+XbnFxUfv7++p2u+p0OsYDwbeOtNS7uzvT5NFg\ncbrAZS4UCtZ0QYInLJJBETg3ej+sZfGcoyllceLyBFriTI7lNIHABHebxY+o10mZxe4AU55isaiF\nhQUjXj3mmqvFTJnw6tUrU2kXCgXj7jJ+ns1mOj8/N9gJ825JNhFbXFxULpdTKpXS3t6e1YZOZhoQ\nHjUxC+fniUgQ1SXZUQ6fgmwQFBs3NzcPCPKSTLnR7/fVaDR0cXFhQwqsayXZosSchX4B4tJgMLBQ\n+MFgoF6vp1qtZkMWppOgPozCY7GYvbAMjGg6mRZigcvi7XQ6KhQKhnOjSpfuVTwY03Ba8TI95pqr\nxew86p34MosrmUwa5TIajZqgFfQB105sB8LhsHq9ns7OzmxAAjQHcYbdEX81SaZ08fv9ur6+VqVS\nMa4HY28nP5qHjCRpcXFR5+fnBtmxM5LhTfwao2k4JzACIcCTXz2dTrW5uWnJVlgesCOimiZUCJVO\no9GQy+XS0dGROp2Ojo6ODDOHzO9UVKM4x84LNffa2prt+Dc3N/aiUdODpbPDv+41V4vZqaSAnM5/\nSObhKQC7kUvCaJaMjdFoZIoJ+MzswFjOwjBz6t7gJnDMhkIhbWxsWM2LHEmSnR4YtBBmA07M4AbU\ngKmaz+ez6Rw2soycMfoGdqPswCCcMgypEyNoLho8mIe9Xs+CgZ48eWL3g+YZLjZjanz6sDOgYUb1\njZSLiavL5TICFYbwr3vNVQO4u7trSuy9vT2bxlGj0mVDlr+9vdVoNLKxN647eCUTzMiUEC5ELpfT\nixcvVK/Xlcvl9OTJkwdkpqWlJT179szck8rlsmn5nMR98q/x1aAmxisukUhoOBxqf39fjUZDs9lM\n+/v7qtVqSiaTDxomIhwIooxEIrZoeGEwiaHhpe7HqAYIDrHsaDRSIpEwZ1KnKDiRSMjtdpuggYVL\nP4HQN5VKyev1GguQzxYIBEySBYPxlxNAx1UsFu0hVCoVo3UmEgmTULGrIXlfWVkxjJndBr+4fD6v\ng4MDMztBzZ3JZPTy5Uvd3d1pe3tbuVzOyDXD4VCpVEq5XM5eHMoJ6b4Uisfjxh3h68LhsC4uLqzk\nQJJ0dXVlnAaXy6VPP/1UsVhM1WpVh4eHWltbMzEA43BQFjBlONSgOQsLCzo/P9dbb70l6d6ZiQX+\n6tUrIxGNRiPjYORyOStvPB6P2u32AzkWukbErpeXlzo8PNTV1ZWq1apqtZqdJDS7jOEZz5+dnT3q\n+c/VYkaPhs9ap9MxMozH47FdAM7uzs6O8THi8bhms5mx0yaTiba2tkx0yi5L+cH/rtfrZj2Af7H0\ntZwIJISYXuleeBsKhcychamfdE/49/v9+uijj+wEQVuYzWatKSRcqFKpyOv1mjrk9vZWu7u7FtIT\nCoUsZg3+NglUJFVh5gLzj0aMurfVapmlAVAgxHtyTvCs83q9lkWI0SSC3O3tbdVqNSv1wOJ9Pp9x\nQ37yk5+89vOfq8VMzehyubS/v28PwplRx7Rre3tbl5eXymazNpEju2R9fV2ZTEaff/65fd3a2prF\ne2GNNZlMlE6nVS6XTRy7vLys4XCoJ0+e6OrqyqZ1WHqRreIcEtA4Ut/2ej3t7e1ZowSdlVqbVCuf\nz2eLrNFomB/zcDi0ZpWmbDgcam1tzaLNiHeDk0L9zKja4/Ho8PBQ0+lUiUTCsk2wKtvY2FCv19Mb\nb7zxoD72+/0KBoNW69OMSvflGc8IkhONK1F2j7nmqgEE5oHTzMOHv0AaUq/Xs52w1WoZzZLsEiIR\nnFawOP60221jghFKg55tPB5bcyTJeAhAWOPxWJJMH4jbJibhmBWyyzJMoGSAi+1yuXR5eWmIBBAj\nTRXBk1xXV1eaTqc2wuaFdnKb+/2+UVcvLi4kSeVyWdPpVMViUeVy2Wp7GlNG6ZQjIEd4j6B9RE1O\n1gt2wxC78J57LDl/rnZm8kvgBOOTAQ1SkkUaYO7nVH9gGMMiRXBKp40JuNvtVrPZtIVD1Njl5aU1\nm3BDyA3B4BsnfWfi1Hg8VrVataEKL1+r1TJaJzIoRAGS7IUD+4aHXK1WrekivAeIjrDOb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+9rdtp/vggw90d3env/zLv9Rv/uZv/oc/A89ggmDAc0EHgOSoAdEJXl/fpzoBRWFFRVpU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\nM+YdVreO42h9fd0eDqix7mEPDTcbMp1Om78HHO6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4HA4kEgncv39fMGZN05DJZOD1euFyuUTUFAqF0G634ff7YTKZ4PF44PV65X0F\nAgFp7Ig1s8lkbZ9IJIRip1fQ7XYjkUgglUrBbDYjkUhgd3dXSCHGMPCz9Xg84lRxuVxwu91y6rzu\nda12ZtZoLpdLsNHZbCY6iKOjI/HXhUIh/PEf/zHW6zWq1So8Hg+Oj49FaE58l7tZpVKB1+uV0Bab\nzYZisYhWqyVlAMX7lEo2Gg3cu3cPz549g9PpvKTlpZ53tVrh+PgYiUQCxWIR4XBYMGHi41arFdPp\nVBRt/X4fs9kMiURC7GC1Wk0sXGwu9/f3BcqLRqMoFAoCI/I1dDodtFotKU2sVis6nQ5u376N4+Nj\nIW5arZb0EnyP/X5fmrxGowG73S4Sz3a7Lbgy3zOpbxJWjx49gqIo+OSTT7C9vX2lXRm4Zjtzu91G\nrVbDYrHAfD5Hv98XQqLT6WBra0vw0fl8ji9+8Yt49OgRIpEIXr58KU0cpZcX3dW0OdGhMRgMEA6H\nEQqF0Gq1xJ1BnJW7UavVgtPpFC3zfD4Xva/b7UY4HMbW1pZIJJllQQlpLBZDsVjEdDoVhIP2LcYK\nUHJZq9UwnU4RjUZFl00cnbEDg8EAo9EIjUZDJJ5kEllWud1uKQlcLhd6vR50XZfAmuVyiUKhIKcP\nyyOq69gf1Ot1iXS4GMjD+p4nTyQSkRPuKte1Wsy03qTTafkivF4vFosFgsEgAIhmVlVVPHv2DOl0\nGhaLBRsbG6KpCAQC8Pv92N/fRyKRgKIoyGazcpwTXaDTeWdnR7x6wWBQHM4+nw/L5RI3b94UBRoA\nWYilUklwXOpAdnd3EQqFLkUm0FOXzWbhdrslwosGXYryl8ulwI3L5VKc4IQSWTdHIhHBsi9mh2ia\nJhg4dd/UUbPuZbCL1+uVG8Ln88FqtYrLmyekz+cTcT4p+Gg0is3NTSmVWNZomobd3d0rff/XajGz\nZqVgxuFw4OTkRPBTpgQRnXA4HBIYU61WoSgKVquVeO6azSYGg4FkU1itVvGymUwmie1iidFsNtHr\n9dBut9HpdESo/9FHHwnNPBgMxPHN0BdCdoPBAJ1OB7quiyi/Wq2i1WqJvrjX6wnaQTgRAE5PT+V1\n7e/vYzabSRQWSwGSLLQvUWjEXZg0NJnQfD6PwWAgbGi9Xpe8DfoOWdpczOVot9soFApSVhACrVQq\nosY7OjrCfD6XaLLxeIxKpXKl7/9aJRp985vfFI1ur9cThosZEbTs12o1JJNJgag0TRNLFb9ILiiH\nwyGCI3rpgXcGAAAgAElEQVT8JpOJEBOkcG02m7BkrG8pKGK9DEB+3u12EQ6HRePAPAzu+Iz+oqKP\naAbdNIS8qMjTdV1uUGo82MgVCgVsbW1hPB4jGAxKk0yh0avPDy6XS3QZ4XAYw+FQtCcMhmk2m4jF\nYqLvZsadw+GQml5VVezv78Pv98Nms8FqtULXdXHAOJ1O1Ot1JJNJiQaj7ew3f/M33yYaAYCu69JY\nnZycSDLl3t4eLBYLjo+PEYlEUK/XxdVRqVRw7949SdqZzWY4PT3FvXv3cHh4iLt37+L58+e4ceOG\n7JT0FLLZ4S5DnDsUCmE0GgkzV6/X4ff7JamzWq1KXsfFpspms2E6nUpjyXqTATOVSgXRaBTBYBD1\nel1yKVhTU2HHBKaXL1/CbDbj6dOnl2IB6ERZLBaCP5P27nQ6gpfTCeP1evGjH/1IwhuZqKqqKj7z\nmc/g8PAQAETu6nK5hNUbDAaX3Oq8eb1eLwaDAcrlsqA3xL1f97pWO/PXvvY1JBIJ1Go1BAIBbG9v\n4+DgAPF4HNVqVaSh/JIePHiAP/qjP8KdO3dgsViQzWYl7GU2m+HevXv45JNPMBgMJLtiMBgIwwYA\niURCcjlqtZrseKlUCt1uV8oAitsXiwUCgYDoGEwmE6rVqsgj4/E4crmcMJfNZlN2cmZpsJkju0gC\n5f3338fDhw/RarWQTCYRDAbRarUEWQCAZDKJH/zgBwiFQlBVVVRrhmFI7U1vZDweR6fTQaFQgMlk\nElSFFrNYLCaUORlKn88HXdeF9ez1egLrsYcg6hMIBEQF6HA40Gg08Ad/8Advd2bgx9FcpFSdTieq\n1aqI0UulksBS6/UaP/rRj6DrunzZh4eHUlc7nU5873vfE7tQrVaTxxmPx7KAWq0WWq0Wtre3MRgM\nMJvNBB4DzmWpvV5PvsCL7ufBYCBIQr1eRyQSkXqXiaTdblfez0WTLBV79XodLpcLnU5HYDDWyuv1\nGt1uF+VyWUoS0vPtdhs2m008fvP5HF6vV3LvaMotl8tCqe/u7iKfz6NYLEoQjcPhwPPnz8UCRSnq\nixcv8MEHH6Db7Qpt3mg0xDNZLBZx69YtqdEZA3yV61ot5mg0KouDH2AmkxGigOk5JCBu376NFy9e\nIJvNSo1NyxDF4kyyVFVVogXS6TRqtRqsVqugDvxiWfuFQiHx11HySScKPXB0fvDE+MnaXFEU7O7u\n4uTkRBADZiiTfibyQnJntVoJ3svXzNDvi3Af47EODw8lDIYQnsfjgaZp6Ha7SCQSAktyx2bN/eUv\nf1nqcU3T5DGcTifef/99kafyJGK/QQOC3W6XBClmlFzlulZoRq/XEyuPx+MRmpbZxQxL6Xa7smPQ\nt+Z2u+Vo3tnZgd/vF3aOZYTD4YDVakWv1xP2i6whAxNXq5UIdoipmkwm1Ot1YQ3JVHIRs6EMBAK4\ndeuWiIbozmaJwKkA3W5XUBXutIZh4Ctf+YqUIQ6HQwwHjF7Y2NgQ9i4ajUozCkCaRWos+v0+QqEQ\nlsulnG6NRgOBQAC3b99GLBaTm5PiLjpZmF1y0aDK9CLqmQnZ8e8DkI3oda9rtTPv7u7KEdtsNiWN\nnYJ1TdPQbDYxHo+xtbUlwd1M62Rj+MMf/hDpdBqPHj3CrVu3UK/Xsbe3h1KphF6vh93dXXQ6HRmZ\ncHBwIOUD0ZOHDx+K1arX62E0GkniPj1z29vbIuJxOBwSaUvPIp0cz58/lxuLzN/BwQHu3LkDj8eD\nSqWC09NTiR2j8IdwHQmc09NTsXDVajXs7OwIDBkIBBAIBCQrOZ1O46OPPoLf74eiKOIkyeVyAjHy\nFKDemzkYmqah0WggEolITV+r1QR/7vV6YqAlW8v6+SrXtWoAf+3Xfg3pdBqNRgN+vx/b29t48eIF\nEomE0K0AUCgU4Ha78d577+EP//APcefOHQlOrNfrYiS9d+8eHj9+LLFTbrdbRErUKaTTaZGKNptN\n0T0kEgkRqNOISn0ETbLckS4iCqlUCqenpzJnhF495iYDEPVdMBiUHW+xWOBLX/oSvve976HVaiGV\nSiEQCIhiLpVKwWQyIRaL4fvf//6lHA2PxyOjMqjE83g8EhJO7TQRkQ8++ABPnjyRxKbZbCajIxhK\nQzy60+kIoUOokhJTWrPYADabTfz+7//+azeA12oxf/Ob34TP55NMDCbIUzfM2rdarYoplVRwqVSS\nfOblconJZILJZCLOFMMwEAwGJUaLBAsA2V2JqbIJ4mtheUEnB7v8YDAohA5F/rQoMeiFODMNscR2\nAUiyEL1/dDiPx2OEQqFLxAtLBqZ5djodSTBi2hFfC0uS4XAosCIXPKcEMH42GAyi0+lIWpSmaQiF\nQhKnQHktYx6o+S6Xy9jY2MDBwYE4TMxmM37rt37rLZoBQOhbpuik02l0Oh3s7Ozg2bNnElZCXLdY\nLOLo6Ai/9Eu/BJPJhNPTU0SjURwdHYm4BvixdWk0Gol/jSJ1NkpskE5OTiTettvtSuYF2T5FUUQZ\nRziu1+vB5XJhNBpJ6UNV2mAwEMlpq9WC1+uFruswDAM3btxAtVqV+n08HouGeDgcolKpIBgMolar\niQ6brKbT6bykIe71erh9+7bk2fV6PRweHkLTNJn9srW1hf39fWQyGdFhc7YLwxYnkwnK5bLk2zF6\nl2wlk5zS6TSOj49xdHSE4XCIeDyOR48eXen7v1YNIO3+Pp8PmUwGsVhMPkgygyQuWDoAuJSwmc/n\nZRKTxWLB/fv3MZlMZAZIPB6Hz+cT0oSLjbVrKBRCJpMR4TqllTS38r8pxp9Op+LrYwIQM+yY00aR\nEcuJcDgsoieiFmQwqXOgwo8sJwVUgUDg0pEfCASg67o0bWQwAUhjmk6nYbPZpA8hXV8ul6UGZonC\n7+FiBC/ZSiYlMfqB6AwjDiiXfd3rWu3M/X5f4lsDgQC8Xq+Ij0ajEe7du4f1eo1cLof1eo1f/MVf\nRKFQEBf3r/7qr6LdbmN3dxfr9Rq//Mu/jMPDQ/lS6FBhc7Zer7G7u4vZbAaXy4Xlcim75t27d8Vg\nu7m5iW63KwIeQncU+XQ6HVitVqRSKSQSCZnwxMgCegqZ2EnHNJPuuSi+8pWv4Lvf/a7Y/D//+c+j\n1Wqh0WiIN5DmWjpI+v0+7t69KygMhfmLxQJf+cpXxHsYi8Ukfvezn/0sjo+P5UZIp9NIJpOy+FVV\nFSc4R67REU9ZAbXcvLF4mj18+PC1v/9rVTP/xm/8hgSOx+NxoYopWD84OJAEIy58kgkM2bZYLJjP\n57hx4wa+//3v486dO4JyOBwO5PN5ZDIZ2VnD4TAODg7gdDrR6XSQSqVkxz47O5OJTGz2iAAUi0Xs\n7u6KCIgCKKrO6DKv1Wrw+XzybzKBdH9wgZVKJYxGI8nNY0QtpaFUEFosFmkgycjRysRSxDAMiQpg\n2hEx9NPTUwmX5MSAer0uDhQmI1HjQQyeJ+T29rYwgnSGc7zGw4cP8Sd/8idva2YAl/LTTk5OkMlk\nRKzPzDhivGSiSqWSgP5Ucem6jhcvXogQh40krVMU1U+nU9TrdSEp2L3Te0h7EbFqKueI5xKpuJia\nxEaMfyeRSIimghYqivy5ABlzexFRCAaDkqF8kWbmdKxgMChZdsxGJvLCcqPZbEpaKHCuU3a73RKT\nwPfD8qbdbiObzcLr9YqakDpoBk1e9ExSJ0Ip6duogQtXr9cTL1y9XhdXcaPRkGaOKjIOaCwWixgO\nh+j3+9B1HZ1OB6VSCcvlEq1WC5PJRMbzAucTSSuVClarlUR9DQYDFItFVKtVybB48eIF/H6/sGwU\nELXbbWENaZjN5XLyxQPn4xcePXqEZrOJ/f19iZZ9+fKlHNvdbhf5fB69Xk8gQebPMXCl2WzKv5ld\nx5mGpPHZHH700UeXXiNwLiut1WoYDodCz1erVZycnOCjjz6SiQP7+/uXUkEHgwH++q//Whg9Mn9U\nEzKXg+E0tJz98Ic/vNL3f612ZsZdUYTDwBWKYIh5sllSFEUEN5FIBBaLBdVqVVKMOBaiUqkglUpJ\nLAEhtXg8jlQqhfl8jtPTUxHqM2eNKT8Mn6FYh0gFa06Xy4VAICDHNIXzbKoikQh6vZ4YcGOxmEgn\niSUfHx/j/fffx2AwQK1WQyQSkcfj6eB0OhEKhVAuly9N3qL1iuaE0WgkzpfVaoVMJoNGoyGoTTab\nlXR+ZnhwABBw3gDev39f6PF4PI5arYZsNivZcqzh+dy6rl85Of9a7cyse7vdLjY2NjAajcSSw2gA\nfpBMHNrd3RWtcLFYhMPhwAcffCBw2XA4xP379y8lwnOK6Ww2k6ziVCoFTdOQz+dl+A4F8bFYTHZ6\n1qBUpLG+Zj7c2dkZlsulBJlfLDvu3buHVquFWq0mU6+Ojo4AnLtXLqYIcehQLBaTG5PBjjw9qF+h\n0L/f78Nmswkzubu7K3G+DGrkfEKOq+Bzc+oVoxaIXjAzj3U+iZ/pdIqzszO0Wi10Oh1EIhFx4rzu\nda0WM0MEedRdPMq501AcztqVc7OPj49FYD8cDkWrTHz34OBAJJccJkl9Ba1YFDKx1tY0TSBARVEk\nuJs5dcFgEFarFbu7u9JIUaNMPx5vSmK2xMoZysIoMTq3V6uVMIz0JRIn56nF/Ds2aD6fD9lsVnKs\nyUgSleCQn9VqJZ7CVqslOXIul0sE+2Q4OVtQVVWZ90fzLMu/3d1dsaIBEJ3I617XajFHo1HxyjFr\njSIb4LzepcTzopOYXT/F7aPRCMFg8JJL5aIwhynwwWBQ0ISLY8moT6AskjNHmLQJnBM8vNlo108k\nEpfm41HOyh2amDTjAbhDslyg04UiqYupRZx2xRuUN5TFYkG9XpeIrvV6LVOpSLCwDOBnVa/XJdeZ\naBDFWhy1xrFwlAZwhBwAubEp2KLV7arjhq/VYqbHjaMJSGK43W5EIhHJkgiHwzCZTPjGN74h9enF\n3ZM7zu3bt6GqKjY2NpDP50VzkEwmpYRgCihJCS4aTdOkkWJYDONgZ7MZ0um0mDkJZ5G9JEphMpmE\nOSNxQmPp/fv3xZ2xXq9x48YNRKNRCcC5mOi0ubkpRAwpd2Ljq9VK4nsJ9/GGDQaDcjLYbLZLUtn1\neo1sNivGBZZZvGlJhtAl73K5kEqlJCeaTCgD2JmncZXrWi1mBqMAkJ2XGC79f4xvtVgs+N3f/V2B\nkJj03m635QN++vQpPB4PCoWCDNCJx+Oi8wUg5ljuRjy2qVVg88UZH9xtdV1Ht9uV3Zy1K28KwzAQ\nCoXEo8jFdnh4iNlshkqlIrssiZdCoSCxsfQAMqSRr+0iEUNJZqPREP0IP5/1ei11fyQSwXq9Rj6f\nlxiGxWKBs7Mz0bgUCgUMh0OZgtvpdFCr1SRdaTAY4PDwUDLw1uu1jMAgucTv7nWva7WYCa15vV6Z\ntrpardDv99Hr9UThRYeyrut48uSJNCrValUWf7PZRLPZxGw2kwR6jnoYDAbo9XpYLpeo1+sAzskF\nSkSn06mI+pfLpUw25fxpjmTg8E2KcihCYrYFQ2lqtZrMHWQUVj6fF3qe8BthOr/fj1wuh7OzMwlS\nBCAWrVAohHw+j+l0KtoKDjSi2Ii1M8Nu2CMQmuTi43QpPn6tVsNyuZSkfAY8kjKnhIAacc7q5glz\nletaQXPdblfS4Cm8Zy1IZRe9emyCqEzjF7VcLsV8ydkhTNFknUn5JuEx4tLMkqOAnmqzP/uzP8PN\nmzelsWLcLlP4J5OJ1M2cbUIEoFQqSbJ8Pp/H7u6uRBWUy2WZoMoTwul04vj4WGxZxKQNw5BTh8J9\nyl1nsxnG4zEajQba7bYYU3O5HOLxuDB4RGAYS0AGks0x7VakwGnv4vAhOsGp075oAm40Gm8H9Fy8\n7Ha7hB/evHlTglISiYTMKSFExQ79wYMHmE6nuHPnDlRVRTwex+c//3nRC6uqir29Pezt7QGAfDHE\nbGlz4iTXyWSC0WiEW7duodVqST1rtVpl8DyDwWmMJYvGkoei/kAgAIvFIlG9X/jCF7BYLGCz2ZBM\nJhGNRuUGZRA5bf0mk0kEV3zf8Xhc3Dcsjbxer9TWZA8pZHrnnXdgGIY0nDS87u3t4b333pMmc2Nj\nA5qmSTOqqqrAmbxBORErlUqJu50eRTa2b0NgLlysvxKJBE5OTmTHYbCKy+WSBU+56Icffgiv1yu5\ncqenpzg7O4PX65XdsVwuo1arCRFis9nQaDQAQPDqSqUi6fbBYBCFQkG0FFy49NNxNPB8PkcoFBJI\najQaYWdnR2r/fr8vtDRwznD6/X5ks1lBFKivGI/H6Ha70HUd9+7dk3hceh8pSqJovt1ui/aZ5leq\nDBl8Q6wdOO8NOCelXC6jWCzKjcjPhG4ap9OJs7Mz0XirqiqnHxvG6XSKTCYjoiuGU17lulaLmePO\nDg4OsLe3JzYdMmfPnz+XcJf1ei3/jyHYtFwxmJBA/0UJI3FVAGK9Z/RUv9+XnYbjwjweDw4ODkRj\nbbVacXZ2Jk1krVZDPB4XAdDx8bFkTlD0RAd1rVZDrVYT10w+nxcUxul0yjFPkQ+H3qxWKxwdHaFc\nLsuUVe7qjNsi9HcxXIZzDKlb4XzCyWQiZYnFYpGsDeZSc1GrqoqXL1/KaVKtVqWUWa1WaDQaKBQK\n6Pf7Qt1f5bpWNTMpW5vNhrOzM3H9khb+hV/4BYlYJcZLS5PH40EqlQIAHB8fCxXLY5Y1bjQaFa0D\nm0IaTZkFx+aGOuV3331XBkl2u11sb2/j8PAQuVwOd+7cwcuXL5HNZmXeB61KzOoggcFRxXTChMNh\nafhY5wYCAaG7Cf01m03cunULjUYDDocDW1tbElAzmUxw8+ZNsWVRQ71YLJDJZGTMMo2zJFrcbjf8\nfr8I6ymBJc7MyNxMJiM3GrFlirRYc1NPAwB/9Vd/9drf/7VazPyCAAjiQG0EXdDUKjCSi116r9eD\nz+dDvV4XbJQNJZVkw+EQw+FQxjSwSSQZ0m63ZSYg0/UXiwUqlYpkdZByp0KPdXQ+n5dxCkRKKDxi\ngmir1RIyp9VqyQChVqslC4mjH6jKG41GKBaL2NzchMlkEhKl0+lcYuqoFmw0GjLtlWo2lhsUDjmd\nTgkPJyZP0wPnCc5mM9RqNcGsq9Wq3FgARB8D/Dg1dDweX+n7v1ZlhtfrhWEYQooAELaOXTU/dMJJ\njL/iImcsFus6Cvu5g9AadHHsA1N66ARhtgSz6OiqoG6CyAfrRJpSiX+T4CEdTe8c/YgApAShPprJ\nQbwBer2ePD4TUCnx5ONejOHt9/uXyin2Fsy5YF0MnDe5xNEpKeX4NFL7JF2YcsoShcmjfr9fSCc2\no2/p7AtXPp+HYRhyxIbDYclEowCJaAfhNuDH0tH5fI5kMimIw/b2tjRQ1WpVspNp8eGRzClPZPIo\npGfKZrVaRb1eFwz64OBA0BRS5P1+H3a7Haenp1Izq6oqxANd1s+fP8fBwYEsZir5eJOs12vs7+9L\nxCxxY07d0jQNtVpNfH4sWS6eIEQtyPA1m03k83kEg0H0+31sbGxIVDB9hKTMSbxwI6CICoCI+Dud\njkSSES8fDoc4Pj6+0vd/rcqMbDYr0FupVMJisZAoAArKWZtNp1Msl0upNznfw2KxoFwuY3t7G51O\nRwgPzgcMh8Oye3NqKScmud1u+X0KkkwmEx48eIBcLifYbzabxYsXLwBAdtubN28CgCzKg4MDaJqG\nO3fuoNlsSt4xg84phmKaEWFETdPwxS9+EScnJ3LDWiwWiT5YLpdIpVKXBksyTejGjRuo1WryfiqV\nCrLZLBqNhhA0LpdLCCHe9BsbG5LrQadMLpeTGz6RSFyaheLz+YTc4qajKApu376N73znO6/9/V+r\nxUzT6Gg0ws2bN2W4JDFYhh4Sp2Xjx1RNqti+/OUvXzK1MvqKRyqd2Ryyzi+R5lLGupJkGQwGUrcz\nRsDtdiOVSkmjBUAyLKxWK+7fvy8qQE5TpaieTpLVaoVUKiUjlEmH53I50SdPJhMZLq+qKmKxmCQW\nUU3H8oE0Nhc4zar0U7KeZ+NnGAa8Xu8llpGqxI2NDQlUZNnH98cJASR8SPaQCn/d61otZgBCXfPD\nyufzuHHjBp49ewafzyeeO2ZA5PN53L17FwBwdHQkKZxMQcpkMhiNRkilUmg2mzJplWMOMpmMDLBk\nTobX6xUfXjQaxenpqZAZlFFWq1WxQtXrdWEuB4OBjJKgXgM437HL5bLkJNfrdRH3cN4IAJlv4vF4\nRILZ7/elxqcvkjoNUtMUP7GM0jQNP/jBD3Dz5k2h6UOhEEqlkuiiOV6OJxFnZTscDpRKJdGxkLpn\nw8dm9y/+4i+QSqWENudp9brXtVrMgUBARPjEkblTkDHL5/PQNE0cIz6fTySWsVgMzWYTiURCnNjR\naFQICtLTi8UCkUhE/IYWiwVbW1vi8ubgRu7ioVBIbiSOLPP7/UKnMwaBijcOn+eQyovyUWK2Ozs7\nsiBjsRhevnwpJ8Lt27elAWM5Qe9iLBZDLpdDOp0WMoXZdgyUofz0/v37MJlMQjYtl0sJPGezerG8\nYcgM2VE2pZTjEl+m129zcxM2m02mxfKEet3rWjWAF8MJg8Gg1GSr1QrxeFx2QNbJDBnkbBIuPIac\n0BVBOxFNmOPxWBIvKbfkUKCLORdMMGKICwdk0p1MfBqANKZer1coeEJfxKuj0SgcDofMGnG73dLA\n+f1+gREZpEisG8ClkW7vvfeeCPndbre4wTVNExSDNwazLjRNw8bGBnw+H9555x3E43FprAlRMp/D\nbrcLle9yubC9vS2oEV//bDZDNpuVuYOBQEAkAa97XavFzN0DOC832u227GzEoLloGa/V6XRk5t1s\nNsNsNkMul5Nalzg05/Qx8YcKL5IEq9VKfk7pJ5NDya5R08Fxv/TDkV2j+P1iMpGiKKJEs9lsooku\nlUrC2AEQGahhGPL3uTPSB0mamTsrcI7NE1lgiUMTA3PnWPJwnBtDXwhB8vMDICZfMpcOh0PKHAbM\nDIdDzGYzOX14IrBUet3rWpUZDodDZjAXCgUEAgGpTUmdcmcFIEQE4SjivdVqFTdu3JB8t9FohFAo\nJNQrmbbj42PJVpvP53jx4oWMLeOIhWw2i48//hg3b97EaDSSDp8RBxxaTxMudRgWiwW5XE6cJnQ1\nEwmg/467f7ValezmRqMBn88nQ4esViseP34s4qOLcQesV5kNQudJOBzGixcvcOvWLamz1+s1KpUK\n7ty5AwAy4o2qOo4/I4O5s7ODDz/8UNAMEiiDwQA+nw8nJycolUrisKGc9nWva7UzD4dDxGIxgaIo\n0Kf+wul0iheQx6/H4xHBeSwWE3WXw+GQbAqyeszgIImQyWQkItbhcGBjYwN2ux2JREIym8fjMdLp\ntAyOZCNmt9vFrGqz2SRgnHnRrNdJPCiKIuTH9va2zMter9cSCAlAAiOpAGQCPnsIlgekqvk50Huo\nKMqlYHMK+4mY+Hw+wYqZthoIBLBYLC7NDySUmclkLvUlpOhJbxN14ed8letaLWZqhRl8clFj7HA4\nEA6HcffuXdnpvv71r+PFixfSXPEL59/d3t6WCazPnz+XLGLOiWbKPFVnHLfQ7/dlpAIDwxlGbjKZ\nEAgEkEgkhJpm+cPRYiyLdF0XmSjpcvrx3n33XcnQcLlciEQi2NnZkRqa7B6jCbi4xuMxVFUVCnw0\nGmFzc1OylcnIseS66CKnu4YL+otf/KIYIO7evQvDMGSHppyVtLzD4UAsFhMaezAYIJlMCmxK7+RV\nrmu3mDksnblubMbIVp2cnAiM9Du/8zvSsTscDmn0qG57+vSpTIn67Gc/i/V6Da/XK8J0UuZU1tG5\nzLwJDuBhPgZdK4qiIJ/PS6O2Wq1E6EQNM4MMiaIwXouumMePH0sJslgsUC6XxWHOHDdGDnBUMmN2\n6dEjRX94eCi0PN04F6WbDIqs1WoSx2u32/Hw4UOEw2EJnAQgWpXxeCz5cV6vF/P5HNVqVXZ9pqrS\ndMu5Kle5rtVipsKMNSE7eLJq/X5ffsaGZ7VaSQI+pZsAhOigOu3p06eCKFD1xWGYTqdThEntdhvD\n4RAAZNwC8V2GwLAhm81mWCwWwpaxGaVnzjAMcWdQ4wD8WFA1n89xcnIionqO/3358qUM4Wk0GjCb\nzQiHw7BardIQE5HhTTedTmVoD08aNop8LjZ1HBwfi8Uk3ovzCAHIeGbe5LRWsV6v1WoSKcxcDeqk\nr3Jdq8V8MZ3T6/XKFKaLugVCciaTSUYHZ7NZ6eAJ7VFZxgRQ1qs0krL+pD7jYs4Gm0WWOBe1yYw6\nYEnD2SuEyWaz2aVwFr/fL4IiLmafzyejzliLspxhOj5PJeBcGnt8fHxJa8zyhK+dsb4U2XMsBgNi\nFosFksmk4MK0hlE2qqqqSAYURREFIeE6mlXJ9nHYJwDs7OzITn6V61otZkJoJpMJ3/3ud8X/RmF7\nr9fD2dkZcrkcDMPAxx9/LJFcFOZTCMTjtlQqoVqtinlTURQR2p+enkodPRqN0Gw2pVkiccPxxWxu\nCHfVajVxKr98+VLeQygUEsr78PBQhj5SvEP9NTMwLBYLFosFjo6OJAjxyZMnUtPzPft8PokzsNvt\nYv7t9XrY399HvV5HLpeTkcI0InCQka7rODg4EPaQPj6iHMys43tyOp1y+i0WCxQKBei6jlarhXw+\nLyeRYRg4OjpCs9mUPLvXva5VpO03vvENAOeoxmKxQCqVwmg0QiAQkKHrmqbJh2i1WtHv9xGJROTI\nj8ViqFQqCAQCqNVqSCQSaLfbCIVCcLlcODk5kbhYHsG6rl9iz+je0HVdEoiAH4fQsAHlbl+r1aAo\nCrxeLwqFAnZ3d6EoClqtlkTvskzhjMLxeIzbt2+jUqkgmUyiWCxKvkWv1xOpJYfqkATh+7fZbPB6\nvYKIUIP86rNEIBDAeDyWnZgpTqxrecoBEKkrB3ayvGDqkdvtliE89EISdalWqxJa0+v1rjTT5Frt\nzMWIwIUAACAASURBVPzwmB3MLx+AfGDMauNR+vDhQ/T7fYTDYezs7GA6nUrGM0dGUNbInZb1KGWb\nTqcT0WhUnq9cLssOyFnWTBulmIfDNRmUyAZsY2MDg8EAlUoF3W5XalFOUiXURl1Fs9lEqVRCo9FA\nJpMR5u0nU/CZMLS1tSV2L6IZFBCREOFjhEIhSUelk5uqP5p5+dlQn00Eh8pCjm5jCcXanL0MCSwO\nPbrKda0WM+flLZdLsSExSYhlgt/vRzKZlKC+nZ0d+XtMsOe8kHg8LtJLkh1utxuZTEbqTSIjpVJJ\nglWYt8bSgNroiwlErG25Q1HYTgUelWqcnzKbzVCv1wWTdblcCIVCoi3e29sTXyEDYJgdnUwmJeiG\nC8nv9yMej0NRFESjUamhfT4fotGowHfUlXC8BCdUcb4KNSCksdmfMPOagemhUAiBQAAAZEIAMWcA\ngthc5bpWDCDLi9FohCdPniCVSiH3ag51pVKRBUlqmV8KVW8cTs6gFHb6xKnr9TpWqxVOT09hMplk\n5APrbe7ezNIg2cGGidOr6LrodDqIxWKCgrAWpdSUuzjw41xohh/+ZC5boVAQtIV5eazN2+02dnZ2\nUC6Xpf5l0OJyucSzZ89EZcfdlKpC4LwMInxIlKfT6eDo6Ag+nw+NRkPCKjkOIp/P4zOf+Yy42uv1\nupyGVPNRW16pVMTCdpXrWu3MTqcT8XgcgUBA7niyetytaGciAXDjxg2MRiPcv38fiqLA7XZjc3NT\nsGoGJJJ4YNYzmUZKNPln1tAckh4IBODz+SS3gySJw+GQNNJqtQqv1yu6aVqskskkFosFEokEotEo\n7t69KzQ2d0/CgNRHc+fmc1MvQgMqJ7/y7xK9GY/HsFgsYp5lWTKZTATF4HvkwCOyp7RjMcaLMxXp\nqzQMA4qiIJVK4ebNmwiHw6LRps0rFApdOdHoWjWAX//612V+HTHe4XAoi6zT6UiUQCQSkWiqvb09\n1Go1mXGiqqrY+y8SFVxsbOyIWTN+gKybqqoCyVFcREvRYDCQnTeTyUiSEgNROGbhohEAgGRyAJCd\n0zAMaQY5NJMQGLXDtExxRATHyZGJYyQYE/07nY5kWVA3wosZ1tvb26L7oD2MiUoM2Dk8PJSGk0QL\n4Tev14tWq4V4PI6PP/4Y8XhcXN/f+ta33s40Ac4bQCIFAKRBymQyUmb4/X7k83mZiVcqlcTQWiqV\nEA6H8cknn+DGjRsoFotSa2cyGSiKgv39faTTaQDnx+/t27dxenoq5AZhOWZZ+Hw+CQgkKbO1tYVW\nqyWlAC1QnU5HRDt0ceTzefj9fvR6PXGJ0LKfyWTk98vlMhwOhwiRaFz1+/1CdDBat1gsyk5JzyLt\nWERyVFXFyckJdnZ2pNZutVoCUzJsZz6fS7AkMX6WKCx5Op2OSGbZfHP8BU/K4XCIv/3bv73S93+t\nyox33nlHpJN0h4zHY/j9foGsCoUCer0ebDYb0uk0bt26JfFRzH4gW8WOu9Vqwel0Qtd1Cd02m83i\nFOEX7HA4sLe3h0wmI/44nnyRSETkqUyMZ0onSRiOEKMYirU6GTaeDBw5RnsTUzQzmYzQ0ER06Ptj\n1pvZbJbH4/Pruo7T01OEQiE0Gg0RVHHXZ7QBm1SKmkqlkliuqtUqhsOhmCMo2OJ74aINh8Oiw7bZ\nbIKQ8Ma+ynWtFvNoNILdbsft27eh67qwcMPhEOPxGNvb24hGo8hms/L7T58+leOWOmEmcZIgoAOE\nWDM1vIvFQgaqr1Yr9Ho9Yc04IZZ5yPP5HLFYDGazGVtbW0ilUqhWq3A4HJLyQ2EP6WxqJGazmexy\nzMpgXVutVkWmms/n5fgn0cL4r1AohHQ6DV3XZUfmPwyQ6XQ6yGazItzXdR3j8VgGVM7nc0SjUTQa\nDek7mPO8sbEhs17G47GMcrtYj5Oh5fPSNEGChWaI172uVZlBRVs+n0ckEkEwGJRwQIvFglarJeId\nBsL4/X4oiiINXzqdFpaNkBaF+0RKmBxKZzUdHABET3FRNRYIBGT4OaOumOcxm83EgkVVWzQalbgv\nwnM0oNIdTSE/cE5X7+3twePxiNE0Go3KDcJkUgASGH4xuszhcAhuTNNsPB7H9vY2FEUR7yFPvAcP\nHiCfzws+bLPZRDDFBZpOpyVEh2OMiV+rqioumKOjI5mJ8tZpcuHiLLrBYIBEIiGEAcNRqBpjorvd\nbsfe3p4sFsZ70d3N0b4cEQGcL9ZEIiGa4VQqJdpn6ik2Nzehqira7baEG3JSLMkFm80m7g9KUFVV\nlUDGdDqNjY0NjMdjaJom6UO1Wk2GaVJcRLZuPB5L4A2tVByswxKHo9CIaVOboaoqRqMRNE0T/Jnl\nxI0bNyTqS1VViXFwOBzi2GE+CWtzNqScB1Ov10WZyJtmPp8jm83C5XIhHA7LHO/Xva7Vzsxhj5qm\nSaPW6/XEDa2qKnK5nEBrxDt7vR7u3bsnCjRFUfDkyRMoioJisSiDdZj1fHp6eilln/kUvV5P/j99\ndX6/H/v7+/D5fMjlcgLV8Yter9c4ODiQm4KZ0C9fvpR6neo1UsqcPciprjabTY7y1Wol+dOcsnV2\ndoYHDx5IrV8qleByuUTUVCgUYDKZkE6nRS7rcrlQLpehqqroNfx+PwqFguiQL0Z8UYlHRSFRDWpX\nFEURBKZcLosYi/R4PB6/smruWkFz3/rWt8QFTbiL+RX0nGmahmaziXA4LEA9gX6OSqP4vN1uIxqN\notvtioictTHtV/T/EaMmKzgejy/Nhma9SHiK0BTDvinKobOExA71HhwqxKaJ8B0AoYh5IwMQRo+D\nKtPptLB1hmHI9FWOgCAuzc/m/2fvTWIjTdMzsScYjGDs+75zX5JVWdndVdXVDbUgoaUWIMD2QbB9\nMDAwfPPBgiEImjnqMjB8kQUddDIaAx0MDGBpZB1aLVVD6pbVKk11rZlkMrnFvm+MIIOMYGw+sJ6n\ngpZkA0nMtETUDzQ6i5kMBuP//u973+d9Fopt+/2+DBOtVqvkaGyKWdOz5LJYLHC5XHjx4gVWVlYU\nmtTtdtFsNnVCVatVxGIxHBwciLvt8/nw27/9219xMwAo8anb7SoMnuy3RTvWwWCg3D1292z25vM5\njEajUA2WKhSeLvoQkzBDtl2/30e32xW7jg9Et9tFvV4Xx5pQFxtDAKrHFw1VCGfRQ48sOJqJ03OZ\nOyJPB4bVk5dC+ii1jFSvUE3CYB3ymLvdrnBu/u6cYM7nc8TjcSlISB0tFAryZl5aWpJXHh1Tyf/m\n58TPjIkGvH8PuR5VmcH8PRqJs2FhhgdZYAyeoXjVYrFgPp+jUqnAbrfj+PhYjSJwN13joq/X6+L/\nslHkhOvq6koEnXK5rHQnBkESCru5uZHzktVqVWaJ2+1Gs9kUt5lWYTRx5OIhlEfiExvMRqOBJ0+e\n4OTkRLs/3z+xbFJj2fhS7sXFvyhwYIlGSI+BQp988sk9X7nFPER6WxPbbrVaeP/99+FyuTQQms/n\nsFqtqFQqMmpcWlr6ajEvXtT8+Xw+PH/+XJozjk37/b4ytdfW1mRgyDhhGhXm83nFJdAEnMd6KBRC\nuVyW2XYoFFLOx3Q6lT6QtNJUKiWnHrPZrOO01WrJ5Pvk5EQDCL/fL7NCngCMKO73+9jY2MDl5SXy\n+TxWV1e1E/d6PU3m6KBEeufFxQW++c1vqoyhRjKXyyko02AwCJEYDAYapWezWbkmVSoVGI1G+ekF\nAgGMRiMpdmiQw5OBGDRtvlKpFLLZrJxO+/0+vF6v3J9yudyD7v+jKjOq1ap25EQiIaql2WyW0plj\n3eFwiFgsJk0eFRtLS0vY3d2VDu/y8hLj8VhwV6FQ0FjZaDQqKm19fV0DjpubG43D6VZP7JY3mgwx\nxjqQgEPOBkWp1CNeX1/j3XfflQMpANXtVG3kcjk9SFdXV0ilUmK9MQAoEAig1Wqh1WpJSsWfSTst\np9OJTqej7JHz83OsrKyIXcif53A4pG+Mx+MajcdiMXQ6HfHAiYTQ0YibBi3PMpkMEokEvv71rz/o\n/j+qnZlukjQxJG7sdDrRbDYRCASwtLQkM+2vf/3r+L3f+z1873vfQ7/f140ol8uS0FNizykaSUP5\nfB7xeByJROIeMZ5DlWAwKDUGH6ZKpQIA4gHT0jYYDMpmgLU7c1BWV1fRbDZFZN/b25NaejabSVlN\n3jWZd6SbMkKZSQDME+dYvFqtIhKJSKS6uroqeiktgClrItxJJCIajaJSqWhgwkGIwWDAzs6OamHW\n/SzFrFarBjqJRAIGgwEul+vBCa2PajFzxEvcM5FI4ODgQAhHtVpFIpGQWvmv//qvcXFxoQV0e3ur\n3WM+n+OnP/0p1tfXtZsAdwuxUCiIRHR+fq5FzmleKpVS7shkMpFsi7vxaDQSsYcppcxFYYxEsVgU\nZmuxWPRQnZ2dod/va6o3GAyEP7Nc4I5Nf2oy05hJmM/nRS6irGtlZUU0VJKhWq0WvF6v8hHfeOMN\njEYjpNNpnJ6e4ujoCH6/X0Y6FxcX0lMeHx9rgbMBzGazWF1dRbVaVT/A/gbAgx2NHlWZ4Xa7EQgE\n7imfGQlGEj1r0EQigZubGzx79kxNT7fbRTweV5fPiRv9nUnhpIcdWWNckGTXkWhEwxia0jDPLxQK\nyRaBDkQczpAjQggNuPPQoxuR0+nE+vq6kBgS6jOZjDzq7HY77Ha7FjEpoNQ0kqq5vLyMZDKpBpY+\nHlarVTAdhaypVEqec8+fP4fdbleNHQ6HMRwOJd/qdDry0qPRIjNlaJDDptTj8Sj+Ymtr60H3/1Et\n5larJaiMHyitZ6m8praOuwCJ4TQ+zGaz99AQBlAyzIfjYZLyLy8vUSqVhBww+4OjZO5ANzc3aDQa\nSjbl2NhoNKq2pvi2XC7j/Pz8nkh2Mpng6OgIo9EI5XJZkn2qoOlHxwg1ohHX19figFBGRYiNjSQN\nZgaDgWLTFk84+tqRA12r1XQ63dzcKFGKjfJ0OkW321XaADeTfr8v1hwbZnqL3N7e4qc//emD7v+j\nKjOoPWMEwng8VvY0rVdpMUA+MY1ZKFUymUzI5/OiQTKhlAMCaveY/+f1etUI0nSQuyfVF5ubm+Jl\ncNTLJCzu2g6HQ1Fku7u7SKfTODs7Uz27tLSEJ0+eyDO63+8jGo1KEU03ImZ4MzWAUjCeFJRD8YRg\nzHEkEpG7Pl2NAoGAfEKAuxOCJwdfkyHwlHuxTn/y5Il6Ao76Kczl570IGXo8Huzv7+PHP/7xa9//\nR7Uzc9pGT2AAWFtbg8fjkT6PcBERD5YfVBVbLBY8ffpU3nHtdhvxeFxTMgo7eYTTrd7hcEhEy12d\n/A36qBEXpoKbRCP6UrCpy2azaDabKl+i0SgcDodISdPpFHt7e+Is076WCpJWqyW5FnV4tOWiiyjp\nq6PRSPKli4sLNceTyQTRaFTWvNPpFKurq8LAl5aWZDlG/2W/3w+XywWTySTlDADxOOh5R19nTkBD\noZAcjh5yParFTMcdyp3MZjNqtZoMSrrdriTxjUYDn3/+uaZ9i9a35D189NFHikVg3Tyfz/UAMAeQ\nWHa73ZYBOTkf3InYWLHM4f/oRkRVNHdS2oItLS3p4by4uJAq/Pj4WOWRy+XS1I6EKe5+zEAslUqa\nkLInWFSqMHyHsCLLBv6OzDShjIyjfzbOzWYTJycnmprm83n5i9Btlb8bdZS8Z/V6HdfX11+5gC5e\nHMlS2UAVBg2z6dc8n8+xvr6ORqOBjz/+WDesXq/fI/CQSUbrVyYnEbdttVqCxFiDz+dznJ2dKU11\neXlZN5mqEu5YbC65yMm9uL29lTEKPZhLpZJsb0ejEarVKgDcy22h1xxr0o8++gjZbFZlwnw+R61W\nk2av0+losPLixYt7UivgrqzodrsSLZDeenJycm+BM6+EZQNJ+q1WS/ZjRGpqtRqazabIXzS0oRPr\nQ65HtZiZn0FF9Xw+1wIlr4KKB8qQ1tbWMBgMxNug1eyibJ6dPxdRoVDQkUj4jP5qREuoEuGxCkD2\nWmSmkQtxe3t7L/GVpwPN0AEoQ7vb7crl/vLyEs1mU80nQ4goHiDxiTtot9vVJJFHPrPF6RNCxTkb\naUKXbK5Zo3Msv7KyIq4I7c4MBoM+Dy5QnobcTMhbuby8xNramsI4H3I9qgbQ7/dLjUweMXVxhI48\nHo9y8DweD3Z2dhRvRhGq1+tFMBhEOBzG6empMkuYyrq8vCz4y+12C9q7uLhANBrF5eUlMpmM7KYy\nX8QQEwlwOp3ySaZ6gw9SMpmU+/ze3h4ODw8Ri8XQarWwv7+vBNlAIACn04l6vS4FNAlKNpsNLpcL\nkUgEVqsVn332GWKx2L1m1eFwCFKjeJX1Kwc+a2tr4o6k02nZ/NJWN51Oi4uytraGXq+H1dVVQYGt\nVguxWAyJRAKtVgubm5sK2mw0GvjlX/5l/OAHP8B0OsXOzg6cTid+9rOfvfb9f1QU0N/8zd/UsXh8\nfCzSzfb2tthlhJii0aiErbSNslgs6Ha7ODk5wRtvvCH3916vB6fTKUUF4xs4GeQRzOOVmdCLlrcM\nvATumtLDw0OF5JDIw9oagOBE4tRkwIXDYRgMBlSrVQXIE4fmQ8UkVKYGXF1dYX19XTUwgzY3Nzfx\n6tUrAHcPAO26mHf42Wef4c033xTDr1AoyMe52WwikUjA4/Gg2+1qOEKrhOPjYyEs9KxmrszKyopE\nwjSlcTgcKBaL+NM//dOv1NnA3Q3xer2o1+vaBb7xjW8gFouJMONwOMT9/Y3f+A380R/9ETKZjPSD\ndAl1OBz4pV/6JTQaDaTTaVl/XV1dIZPJoFqtSnXSbrdhMBg0uiZhiOUKUQh6ylmtVuzv74tFR7UG\nc0TsdrvKH7fbjUqlIppmJBJBv9/Ht7/9bZVPe3t7aDQaKj04zGFgD2mYVHzf3NzIsZ7QGkk/HH0z\nu48PyHQ6xZMnT5DNZhEKhTAej7G/v696mFNPcrf39/cxmUzkgBoOh+9lKrrdbsnFVlZWpCd8yPWo\nFjO5zIwF3t3dxYsXLwDcBcdUq1WkUikFNf7kJz/BYDBAuVyWf1s8Hhd984MPPkA8Hsfx8TE2NzdF\nMG80GlJG0K6KTpfJZBLxeBxnZ2dihxGB4Eh7Mpng4OBARueVSgWxWEw1LP2Xy+WyXIdYw5+cnGB5\neRn5fF5EqVKphH6/j62tLdW1FxcXop7Sl5puRByGMJCSRzuFCLTK+uSTT7C+vq6fFw6H0Wq19Bof\nfPCB8kqIQbPefv78Ofb29hQiz82CfBDW+7RmuLq6wqeffvqg+/+oGkBygknYYe3IuAT6FlMpXC6X\nJRh9++234fP50O/3FZHAhi7zRQb1fD6X2xH1fLQIsNlseOedd7CysqLIXr/fj3Q6LX0crQSMRiMy\nmYxsb2liyAxv5gASv6bmkCN2NqSEE5lexXzCWq2GeDwuFUy/31cAJiVP8XhcwfTsNdLptAYpAFTy\n8DObTCYaxbNnYELVovnhZDLB22+/rTSAxRwZpnft7+/DZrPJWNLr9crf+XWvR7WYF6GinZ0dkdnJ\nXlsUktLIm01To9EQPTMYDMLlcmFjYwNerxflclnSIGZkL+r+GEhDp3qGXzLugTtWKBQCAJUBxF23\nt7dxc3Mjh3kA2NzchMlkkscdx9qMKeNr0PJg0Td6e3tbam9yJ7gzDgYDCW/JPQG+FAMzr5ClCNU6\nVKDQyZQIkN/vFxUAgEze2+22ZGNUyVCixaRX4C5dIBqNKhb5IdejWszU9Q2HQ3S7XQCQEw9H0Pl8\nHs1m8x5mTPiJ/hOExg4PD3UzFj3VOLXjpI96PUr4J5OJyoxFI0TyIsh+I4Zcr9dF9KGmrtFoiFvB\nQQ+taPv9vqBE4tSE9MijJixpNBoVIsQ0WYpKyQOhAIGnGndhui4tfpZseBkeNB6Ppf0zmUxy/a/V\nalrAnMz6/X4hOuRxUK1Dn5KHXI+qZp7NZojFYlhaWhIzK5lMIplMCsyPxWJSiAwGAxQKBZkaUmtH\nVyIqV2hGSF8KAHLuIRY9mUyws7Mjgj8HEIT5iFfzwfB6vYjFYsK+DQYDUqmUSEgXFxfY2NgAADV9\n5CEDwJtvvimVCL9G+RLH6E+fPhVRiKlR0WhUZQgDNjnVo3qbO/Y3vvENmEwmlUp+v19ezIt2B4QJ\nR6MRIpGIpF38nWw2GzqdjhTewWBQJRAN14PBIFZWVvCTn/zkte//o1rMHDRwFEuzlV6vJ+yWH2Sr\n1YLT6VSOc7ValekKhy6cBtIqi1atXPz0U6vVarJ2pUcG3Y2CwaBU3STgU19ITsbFxYVqzmKxiL29\nPUSjUTSbTXQ6HY2cS6WSsPCjoyMkEgkR9fk7U33On0FyUT6fh9vtRi6Xg9lsvmeHxYkhR/acmLKW\npyNppVJRnc/MbWoAyRFn6USzce7ALENoTMnXpUSLtIKHXI+qzOAO4fP5YLFYxFnmxUWczWYlvmSZ\nQMNvp9OJ4+NjSYAsFosolNw1CS/x6GZQZbvdlvFKuVxGJpPReBmA6k5OIGmE4nA4sLa2hkAggJ2d\nHblvcrLXbDblnETrWtapXDA2mw1ra2twu933vOn4bzY3N3F1dSVCUqVSuccZ4fCkWCzKe4/1Nf07\nGHVBshaNGInBEyun2ICDGrfbjdlsJhnXeDyWEIBTS+BfIDnfYDAkDQbDXxkMhgODwfDCYDD8T198\n3WcwGP7SYDAcGwyGvzAYDJ6F7/k3BoPhxGAwHBkMhl/9p16bdSMXCWMaOE6mm9HGxgbMZjPi8Tia\nzaZU1CsrKygUCjIJNxqN0sExooDUULLhms0mQqGQpnckLXE34nHKhFhi2H6/H4VCQcw1RjkQ8SiX\ny4qYoPKZBJ/JZCLiPyeH3A0HgwEymYyaPyqo6WfBppK+cBxDE6HIZDKSRxEPJkGJ7kqLKawcXZM1\nSPX7YlyF1WrV+yX2TIkaHzqLxSIPwNe9fh5lxhjA/zyfzz81GAwOAB8ZDIa/BPDfA/jL+Xz+vxoM\nht8B8K8B/GuDwbAH4L8BsAcgDuB9g8GwNZ/P/4Ez9e3tLYLBIFqtlhZbt9tFIpG4F25JP+VOpyNb\nK4L3Ho8Hp6enagRJdKcZIgBZurKB5Gv0+31cXV0hkUiorrTb7SgUCoLBhsOhFl0kEhEPmpO08/Nz\niU/J3+DAhikAsVhM7krMB6R1gslkQqlUkqSKooJSqYR2u60UAS484M6yy2w2o9FooFwuYzweCyMm\nGkS3fe7MfF+xWAzT6RS5XE7OT2woAQiqm06nsugisnJ6eipP6PF4rGDM173+s+/M8/m8Np/PP/3i\nz1cAXuJukf4XAP7dF//s3wH4r774838J4P+Yz+fj+XyeA3AK4J1/7LUX+QXlclnNVq/XU11IMSVN\nFXmM0juj2WwC+JIMdHNzI8YdvSqYFEXkhMoU7py3t7caFpDHSx+5+XwOp9Op98t/Wy6XNf2bTqf6\nb6YxcYGQj8EdkdRPADqBWNfyAer1enIkqtVqKqn4sNLei/YAJFXd3NyItGSxWGS2SBah2+0W25Bw\nIdESfjbAlwY67F/m87mwa46/6dH8kOvn2gAaDIYMgGcA/h5AeD6fk9BaBxD+4s8xAIsu1CXcLf5/\ncLGLn8/n2NzcxHA4RCqVklKZKVJer1e7IXcLhpmT9UaOMF8zEAjA4XDg7OxMcB71hhyEkKlntVrl\nHXd5eYlvfvObikQAoJExSe7hcFjDA7PZjNXVVSEHPp9PO1skEkEwGES329W4mp4diyVBMBgULk5H\nf1rzUoKVy+WQSCRUgvBUYh08m82wtbUlvjWpqUQdaEVLC2GauZB4tLGxAbvdLqNELnRuBGazGcFg\nEO12W7503/rWt/BXf/VXr72efm4N4Bclxv8J4Dfn8/nl4t/N70ZP/18MqH/079jscSchl8LhcMjQ\nhOR9h8MhMxhmBrbbbfj9foTDYXXwhJNYlgSDQeHJ5DuTgE+iPnP3GNLTbrflSkSXJZqfE9NmZBux\n6FqtphKCBB5yRAiPAVDOISeDoVAIhUJBJQRppDy1eMq4XC7t9AaDQYGgjICgfnE4HEoqRqEAHZWG\nwyFms5lI/Uzgury8VMDRzc0NKpWKsGbGdHD3rtVqmnbSiuF1r5/LYjYYDCbcLeQ/ms/n/+GLL9cN\nBkPki7+PAmh88fUygOTCtye++No/uD7++GP8+Mc/xt///d/j/PxcNSvrvFQqpVByGmm/fPlSdSZz\nTjhsKJfLkuRTjcGdvVarYT6f66FhA+V0OpHJZNDtdpFMJrVIybdgI0hZFXcvLm6n06lcwevra+22\n0+kU+Xwefr9frzebzTRtI/OOMq/FqGGiO1TceL1e6RrJX2b5wBRV2oqxZuaOTliNfBEOZejLwclh\ns9mUH14ymRRzLp/PC4NnQ3h4eIj3338fZ2dnD1pXPw80wwDgfwdwOJ/P/7eFv/q/APyrL/78rwD8\nh4Wv/7cGg8FsMBhWAWwC+I//2Gt/97vfxXe/+1289957WF9fh9ls1iJi88JO2mazyRibPAfu1vRi\n406zmHe9tbWlv6f1AO2uAGiqxbJhEYaLRqMAIJsANmyEqzj2Zh0diUQ0YGF+YK1WE8JC+wE6CwEQ\n8uL1esVUYxwFvfZ4wiya5ZAlxwUKQDAe/ZoXp30MBeLCpIKdOYRut1tYP/+O6AYHKhcXF0in03jj\njTfwne98B0+fPn3Q2vp51MzfBvDfAfjcYDB88sXX/g2A/wXAvzcYDP8DgByA/xoA5vP5ocFg+PcA\nDgFMAPyP83+ChE2CD8lCzL6LxWI4Pj7WzVp06WT07qIxCy24SL1kWVEqlaRqZijPs2fP5BTE8oaW\nVUajEcFgUDYATLciU44LL5vNIhaLoVgsyqX/9vYWR0dH2rEZJ7G0tAS73Y6joyNsbW3BaDSi3++j\n0WgIFiOB//LyUlyLdrstLnGv1xNJ6fb2Fp1OR/TXUqmkB+Gzzz7D6uoqyuUyms2mpojkbpBkJGAf\ntwAAIABJREFUXy6X0Wq1RA01Go3I5XLY2dnRiJoUVZ6CPp9PbMF+v4+VlRV89NFHD1pY/9kX83w+\n/7/xT58I3/0nvuffAvi3/3+vzc6aRnzdblflBN2GLBYL/H6/HOpZNnzxc9DpdODxeKTTMxgMSlIl\n2M9BwKKUyOFwCJpjfVqtVvHkyRMpM2j8wikep2wulwu5XE71Mz3xms2mLGd5qjCmjNNDmpFT3Gq3\n2xWISSOWZrMppTb9NJjbMh6P4XQ6sb29jfPzc/UX19fXSpCiqQ6RGVokMJNwOp2K9ERONi1sGTFM\nl9HFnZxCXkJ9RGNe93pUSpMvMuRgt9txcnIiHJdcB8JmDKW5uLhALpfDG2+8IViMOrV4PI6joyO8\n8cYb8mVjx06Xei5Gj8cjKI35KXTr4QImf6LVakkBs7y8jFQqhVKppHqUjLJ2u63kKdqF0dHIbDbj\n9PRU3iDX19f3FBvMRqGNVy6XwzvvvHNPZZ7NZrGzs4OjoyNJwTKZjFKnaMtFBIh+dhaLBePxGKPR\nCLFYTIgNfTQ4STw4OBAcaLPZcHZ2hlQqJToscxqbzabq7ouLC/z+7//+V2bjACT5pwMPdydip/1+\nX9Mr8mrJfONCDYVCwllJPL+9vdUOmM/ncXFxcU+Kz0aRr8shS7FYxM3NjRYrORg87imGpZXA5eUl\nTk9PFVlMKI/KZUZEMMlpNpvptQAIxisUCvKKBiAfOT4UXJw8SQjd1Wo1ABDfYjqdypKApCsGeZJF\nyH/Pet5qtUroenV1hYuLC+HHZN1xQ6G9AAn95G+87vWoFrPT6YTT6YTb7QYADS0WrQO4s3HETM4F\nORaTyQT9fl8LmiJQk8mE29tbPH36VFl2dDsKBALaOROJBCwWi2J6rVYrdnd3MZ1OJUsKhUK4ublB\nsViUupkB7NTVUXVNSyxi4LSoJRLAnZtj7mAwiF/7tV9Tbgo1f4twGxtg4O4B4AApnU4jEAiIuE/H\nVPKSKcliY8vmjs0o4xy4oMPhMJaWlhSLlk6n5S1HPz3SUxfzBV/3elSsOTLM+EFarVa8ePECb7/9\ntuq509NT3N7eIhQKCRajYplw1+3trVwtg8HgPTfNSqWi0oUBkJTtLy0toVQqCYWgK+bh4aF+PgDk\ncjkd6yyLzs7ONP3jjSUXmqPpVqslGI0m3zzaiV3P53P8+Z//uXR9jL148eLFvTSoRYst7qyLHnZv\nvvkmCoUCCoWCnPTtdjtyuZyQELIQ6SGXyWTkMc1BD6eshOqonVxeXsbBwYEyWtrt9r88aO4/5cVd\nmbvd5eWlrJ84cuYY1e12i1REz2LyoGnFCtx14fSlowplNptJ0c1deXl5GePxGLPZTCgIEQ42eTRs\nXPTR6Ha7oqNykdJmy2Kx6Huur6+VOEvaJ49yOp1SGNtsNjXm9ng8aLVaWF1dVRA8ADWCTKdiL8Df\nbdFnhEw4CgQikYiwZdolENlYWlrS5HUx74VOo6Tccgp4fX2NZrMpIfFDrke1mBknzKxqZpBw5yTX\nFgDy+TysViuq1apyOrgQer2ejm3CdoVCAcPhEMfHx3C5XFJqAFB9SmUIxavdbleK7H6/r52Xk0Bq\n5yKRiFQZtBXgzsdGtV6vo1AoKHCI3G1yT25ubnB+fi67A9bUDMM8PT2VmQtrfnIkWOIQ3+bwg2aI\nJFkxqKfX66kWprspkRUOkF69egWTyYRGo6EThaofg8GghpXcED5YD7ke1WJ2OBziBOfzeeGu1AXS\n9GQ0Ggle4k4VDofh8Xi0eMnl4BFLUSZdf0iSYRAjF+vy8jLS6TS8Xi/W1tbkl8yGyWKxiGnGq1qt\nqn5krC8XOuvpRCIBv9+PQCCgkEp6dxDm293dBQDEYjFRTVnDklttNps1+OFAhYobUmG50LhTj8dj\nxGIxTCaTe/0IbcrYQ7BkMxgMgjvj8biExDRD56kyHA7lnE/W4kOuR1UzM7z94uICm5ubiuH1+/2o\nVCqYTqdIpVJoNpuw2+1477338Ad/8Af42te+pskWiTlWqxXvvPMOXr58qeQmkmbi8TgajQYSiYQU\nGJwiUgnOCDHgDk1Ip9Pq3OfzOdLptP4tGyaqyvkQsflivQncuSP1+32Zs7hcLnFN2PRdX1+LTGUy\nmfQg0laWkWxUquzv7wv3ZV633++XPS/9OYLBIM7OzmSzsLu7i7OzM7mL0v3f5XJpgDIajeRnQoUL\nJ4hMAbPZbAiFQjg6OnrQ/X9Ui/no6Eg7R7lcxurqKlqtFoxGo3BncpgHgwFOTk6Qy+VQLpfV/Fmt\nVuTzeUQiERwdHaHZbKJer8s7jTed9TBrWDYy7Mi5OzmdTlSrVdWGtCe4vr6Wsz+TSVl78uexdiec\nyAUN3E07vV6vXqPT6eDs7ExpVUQx+v0+2u226m9yt/v9vtQkLKHoukQHVRKhaJyeTqfFua5UKjrp\nGo2GBL38jIvFItbX1++VVxzukILL5pnQX+6rtKkvL2aE0FOYQweaYpfLZfEFvF4v3n//fezv7+P2\n9lYeauTlWq1WHB4eaqoWiURgNBqRSqXQarU0FTMYDHC73VJTkHfALp/iTvJ22RBdXl7q/3u9ntyF\n5vM5otGodtbRaIRgMCgoiwoWckIoOuj3+0IZer2eFmG/39e42mKxqPSgb7PT6ZSZ+WAwuOdtPRwO\nBc8xQZZU03Q6rfE98CWSxJKMfBKbzSYHVd4jytGKxaI+AzIDH3I9qsVMLHaxM+aImLUa67PF3OdF\nX2fipotWUoxNYBLpdDqVdQEtBBZTT7lAyAF2u91yx2RmCJELvk9GCy+OsTn0WST+cFReq9VU1y7K\nmkjr5DSQfQObWQAKAaIFAut5ci46nY5EBPzdKO8iYYmRGre3tzAYDEry4i7carWUo0jvjX6/j2q1\nKq9sDrRcLpfEwA+5HtVi5k5jNBrlzENne+KdrHsZWcAPlz4Qi65DjEQYjUbadbl7D4dDRKNRrK6u\nisLJGjcYDAIAXC6X3PLZ0NEAkQ+IyWTC6uqqyoVFqigHDMRrWTIBEDne5/NpQZCFRyd9vj4d+Umm\nL5VKGqhwcETIEcC9FAEmEbDfMBgM8Pl8Ut6QGMXGjzs7SxZyqAkjUg5mNpsRCoXUN9BQ5yHXo6qZ\nuZi4IGgcSEvaXC4nWT4XSTKZRDgcFkIxn88VDE/bK07alpeXxfaiTwSRDx6vS0tLKg04vaMqhLnY\nXNDkKtCrgm5FVH4Ph0NhuhcXF+IQB4NB1ZnkYBPHpcqFtlxkxgF32dSJRAKZL+LNVlZW1Jh1u12k\n02nVwmx6J5OJmkFyukejkax70+m0jCdZKrHBY0m0u7srYQSnhBQMn56eIpFIwOVyYX9/H++///5r\n3/9HtZhJPTQajRpgEB1oNptCMcg5IPYM3CEhxKdbrZaQER6n3DkWw3rICiNaQNnT1dWVOvv19XVp\n9shj5uJvt9vaDdlw3d7eagfjrk5sl2YqwWBQNrvAndHMq1ev4PV6hZ3zQbq8vMTV1ZVQg3K5jHK5\njGQyqabs1atXKj2m0ykODg6wv7+Pg4MDRb7lcjmpz6mPJFpRqVRkcMOypdFo3HNIpQSt3+/rpGs2\nm2IQjkajB3kzA4+szCAHgEcaR7A0c6Fae7Hmo2cwFRgANDRgKI7b7dbomimwtJOiSpuDDE4OqQFc\nbABJdg+Hw/eO9dPTUxGU6H/MI5tQFk8IIghsasmRJueCVmMUCzCn8OnTp1KbJBIJDUfYfBkMBnQ6\nHR37w+EQ6+vrGu9HIhHRadnAcbHS29lgMIhzzQkpKZ/kX9DHg5g2zdWpmH/Q/X/Qd/8zu+r1um7S\neDxWZgenfixBaB3LnYSqCY6Db29v5dLT6XQ0bQMgGIvKDzLlaLXFUoKZ2awnCYsNBgMtbKIBDocD\nNzc393zyKIWiUeHNzQ0SiYRIRyylxuOxHtBOp6Nyh+Y2HHU3Go17sRDMxmYJwnCixei0brcreI/N\nLQDh6dQCknXIXXY+n6NSqYgeQASp0+nIuuHq6upeP8LS7CHXo1rMq6urMuErFAqIx+PodrsSnp6c\nnIjbTGJQuVxWXghDZkjcp2SoWq1KPcJdn6VFMpmEz+cTZk0G3vLyMtrtNpaXl5HL5RAIBDTZy+fz\n+PTTT5XNTZ3g9vY2AEj1QeyXRo/n5+caQ7Np5dSNPnuE5hi9TMrqzc0NyuUyrq6u0Ov1NKjhKdPt\ndoWjMw44l8uJy1KpVGA0GlGpVOByudDr9VAsFjV65/dQGADcOTidn58rANTj8SCZTOrnEvPm+2UC\n1etej2oxc7TKLp+7InkGZKPRZoAUS4582ZFPp1PdGIpJyW5j5C7DdBhFRhcfwmFUaJAHzCOYOYAu\nlwsABJ1R8LmIQrBJ5ciaDwqzwC8uLuSBQV4Fa1e+X5ZKixEThCT50LEJpfKaDkcUtVJDSaSHqhmW\nNnx9qkz4fii4pS/JYpQcSyzu8g6H46uAnsWLDDgGQVqtVni9XuVDUzbFiIXt7W1ZaNEgcTqdYnd3\nV8E+sVgMfr9fUz86hXLxcpchIkFaJRGBTqejE4N1Msn2HFkTuguFQuh0OkJh+CCwObTZbIjH42i3\n21hfX1dYJ5GJ2WymMX4qlZL9F1UxVKYTRuR4+dmzZ/eYbRzFv/nmm8Lb+VBsbGyI+01UwufzSYNI\njgZPJQYBjUYjJJNJXF9f69Qh1s3f8Tvf+c6DRtqPamfmolg0BqSQlFlzk8kE1WoVKysrEn6SqM/6\nstlsit98dXWFFy9eKEqBtSEneDabDYFAALPZTP4XhNeur6+RTCaRz+dVO3I8TfOWbreLUqkk/JhH\nOC14Wc+zVGJoJadqtVoNw+EQZ2dnsFgs8j4mw67Vasmckbss86zp8FSr1dBqtdRLcLyezWalsnn5\n8iUqlQqKxSLMZrNOJEKItFmgTpInEetwIjzz+VxlBZtdNqIP5TM/qp2ZsiiDwYBf/MVfxGg0QiKR\n0FHMJCUqKRgQ4/F4YDAYUKlUFJHgcrlUUqTTaXX8s9kMfr9fWjpOzajmsFqtOi5p5RWNRqXHI0uN\nzvoAsLGxoVEyd3oAGs1Tlu/3+3F5eSnTF7LOqFlcWlpSnDAALRxKtbjgWq0W4vG40BFCZ3t7e3j5\n8qUQG5PJhPF4rFhgmkuyWSVD8Pb2VsoVDof40DNjnBkqLGkcDge63S663S52d3c1KPrRj3702vf/\nUe3MrBcHgwFevnyJyWSCfD6P0WikepTYKLm+bI6o1DCZTKjVauLXEmpjmORgMEC1WhWiYDabtdNx\nvMtBhdPpFE+CyhMeszy2iRZwRM66nlg00RQGUXLH5+KgiTdtAhqNhnZrq9WKZDKpo547JbWMRFnI\nISE+zSB51sr8GSxLuBszEpkedQzbASBn/0gkcs+/j2Lfy8tLOBwORCIRTRNp4fu616NazDabTQuN\nOzQ7fTYtRqNRxoKRSAQABK+xCbJaraovibtypyYXgTwL7kR09+TpQJtaypMACDYbDAZot9sIh8Py\nsSDLjkcw1TKsX/l+2GQFg0HVrnQ74g7KB6RSqcjVkzAka1VafxHLrtVqaLfbSo4yGo3iT3BX5qCE\nggLCdW63W78r+RbkuBDy5AbCySVxbXKqCZE+5HpUi7larSLzRYrT4kKmTxp3NcafdbtdvHr1SkGY\n9E7j8GE0GiGXy2EwGKBer0sQC9xZBtBFs9frIR6PazzN5i4SiWgMzN2JCEgwGMT5+bkWJjFyRk1k\ns1mNmTkip/v+5eUlarUaKpWKVB5msxnFYhHNZlO7XjKZVK3Oh5hDDQAav3PHBCCjnMlkgu3tbcxm\nM2kBOdanxQKbw3q9Dq/XKzIVJ6GUgBEGZQjSbDZDLpeT+p2G7g+9HtViDgQC6qhp1kc4ik0Vechr\na2s4Pz/H1772tXvsr8lkgs8//1xYKamSvEksWRa5GYFAAPV6HblcDul0WlAf4S+6edJTw+FwoFKp\naAE8f/4cgUBAzD4OYDju5u54fX0tOihra5KHbm5uhDPTW/rs7Exyf4fDoV2f5CGy4BahOYpLDQYD\ncrmc/KYZFrpYlrEUInYPQPU0m2meCuTD8AFKp9PI5/OiDwD/Ap3z/1Ne9FJm3QxADphsqjqdDvL5\nvIy4Of6lbKfX62lnYQ1Zr9eVyX11dSXjQk7ygC9D3klIInuMN5blB2/YxcWF0I9FY3HW6eQqc2cn\nT5rSp1qtdi8QnsR2q9Uq6iaRgm63K5ISHy6WL6PRSGNq+twxQo2+ILlcThEWy8vLqNVqwuCJSjBo\nh9Af/7w4kaTqnbwWOjU5nU71Ig+5HtViJmC/qAJmDUlkgkaFBoMBmUxGpHpOo+jHTIk/m0Lu8GyK\n6LHhdDrVHFEzR0yaU0KbzaadnMMWppX6/X6EQiFxkFkaud1ukZJo+E1UgtnfixhyPB4XnXPRrXMx\nwJIPCG0S+HsyB8VqtQpa5GJfWVlBJBKBx+NR3AUxavIt+ACSB86SZXHQwywYNpGcUDIGmU6lD7ke\n1WKmspkCVlIeOW1j00YvYxoaTqdTPH36VDtHMpnUrpnP57GxsaE6k8aE5C9w0rWysoJ4PI5Op4O/\n/du/FTmJRB36UbB8oRSfEQtsCvP5vOREvLk+n08PBJOjiCgwB4WMuPF4LOSGxCXuzFyg9EOmap0O\nRDR6pH6PYoWLiwuN+DnoIJHeZDLds9alSxENxPl14uTkr3Dgwt7joQsZeGSLmbASox4YLk43eKo0\nqMW7vLzzOLfZbDg/P4fL5YLP5xNhfz6fIxgMyjKLRy89Meiwz5RShuPs7+9jNBopa4819NbWlhhm\n9OPg0IayKE4lSdjhkUyhbiQSQSaTEb+CqaYUsTJl1mQySenCXXE4HGpX93g8mnKSwcYdk4udu2oo\nFBJGzPqcDDtCkfTvo1BgUYzA7OzFv+dgyeVyweVyqcd5yPWoFjM9KiwWCzY2NhRxwBg1t9uNVCol\n9tnm5qZG4IlEAsAdPvorv/Ir8Pv92NnZQbfblQs8oTzuRExSpWNmIpEQwkHuBx8k4A6r5m5MI3KX\ny4VIJCIvC9bOJN7XajVEo1HM53O88847yOVyKJVKsv5iGP3a2ppKEWoBiWZQwUJLACI1LpdLll92\nu10cZ54w/Mw4rWSDabPZFNlM1Uk6ndbn4nK51PyxtHO5XMLLGbUcj8fRarUQDoeRyWTw1ltvPej+\nP6rFnE6nAUBUSx6pwN1Cr9fr0r2xI6cFVzabRbFYRCgUwocffohGo4FsNotEIiEuM0WiDI+nWTeP\n4mKxqB2zXC4r42QxCIgK5efPn2t3Go1GCIfDuLm5EbRGP7pgMIijoyO0222USqV7tgX8d9fX1ygU\nChIN0LTl5ORETk7T6RSlUkljeJZHNGphmXN5eYlisQiv14uPP/5YueOkbXLo0m63pWp3Op04OjpS\n6A/jhulD3ev1lAvOCaTH4xGpv9lsot1uI5vNPuj+PypL29/93d+V7dT19bWiFDKZDPL5PCaTCQKB\ngBxA7Xa7FjN1buQ3OJ1ODUwcDgeq1aqaMrphcuJF/jN5yYTlWLMuktSJUDC+12KxoNls6vuvrq7g\ncrng8Xhwfn6uOpdoCSeHALC7u6sRPYMvfT4fisWiIorZqLI8WCTIc3hEezK+7tXVFUKhkAzEq9Wq\n6ulF5IM7O8lG1WpVJjaMkgAg03OPx4NoNCrx6mQy0YLmfz/E0vZRcTMod7+9vdWxR8kSieLZbFZm\nKMCdlxwzoEm8r1arcLlcOD09RTQahcPhkGUtE1YdDgdarRYcDoekWOQEA5CZSiwWQzabFSRHXzoA\nGnszNoxDjX6/j1KpJDEo9YSLkWP9fh8vX76Ex+NRucOpIQW7FCYQ/uJAiIQs/v7Ly8vIZrMKJKLt\nFxtYckPoo8e+hIlbJCKtrq7KQ4PZJtPpVBpGk8mEo6MjTQHZILJpbbfbD7r/j2oxc7TK5CKHw4FU\nKiX/s5ubG0SjUUl/kskkPv30UxiNRglPWU7YbDZ84xvfQK/XkxyKI9xAIIBGo4FwOIxIJCJ8lLte\no9FAJpMRu45UTbpt0lOCdrQ+nw9Wq/Wec3w8HhfXl5Iqejqz/DAYDIIAidDw/dNf2mw2o9PpCKFg\no0dif6lU0o7LwREhtkwmoxqbdNVer4dUKoVsNov33nsP+XweJpMJOzs7Kh+oyqGo1Wg0IhqNij89\n/yLIMxgM4vj4WPfpK6+5hYv2ADTpo6UtfedOTk7QarVweHgIAPjhD3+IUqkkUhGbuXK5jMlkgr/7\nu7/DYDDAhx9+qAHIaDSSJRUX70cffYTb21s0m02VI8xVaTabcvHhBPLw8BA/+tGPZDrI3YxWs9Pp\nFCcnJ6jVauJg012oXq9jOp0qUctgMKBYLKJSqUiaxJ03l8uhVqvp3zIznM0gd8disags61KphEKh\ngMFggE6noxPi+fPnyGazyOVyqFaruL29xU9+8hMl4h4fH4tfMZvNcHp6KqaiyWTC+fk5Pv30U3FL\nSMI6PT1FuVzG8fHxg4lGj6pm/q3f+i34/X5Uq1UlONVqNaTTaU2zBoMBKpWKSOY8iuPxOA4PDxXs\nSAn/8vIyksmkGklK+GnISNir0+kgFouh2WxqBA7c2R8Ui0XBhoPBAPF4HIVCAdvb27i8vES5XIbJ\nZMLW1haazaZU4NPpFOHwXbbnYpYKoUVmllCAy1E5f+eXL1+K60FEAYC+32azyfOtVCphd3cX1WpV\naaxHR0dyZgKg9AE2tPxcV1ZW0Gg0sLm5iUKhoF6DJxjlWFar9Z4SZdHckZHNf/iHf/hVzQxAzDf6\nlq2trSnmloMJCjHPz88RCoXwZ3/2Z/jVX73LliedczQaaVGZTCZ17AT2uUszDy+fz8uHjUMW1pPE\nbkmyp46O2HYkElEA5tnZmWpdlkyFQkF1cbValYkiSyUiHL1eT6NkckKCwSAGgwGurq5E6uGEE4AG\nL9zhT09PtasPh0M10KSA7uzs6OuDwQD9fh8OhwMnJydwOp149eqVyplXr17pdTgm5/Tx5uYG/X4f\nT58+RS6X04T1q5p54VoMs+EukkqlFN3LuAOSyff29nB8fKwaFoD4DCTxl8tlDR/IxWWcmtlsVoNI\nhlgikcB4PL5ndsKMPnpnhEIh8Z/JQEskEiI00V6MMb0Oh0PvmYzAaDQqewNmsTBygegIcEe+mkwm\n8Hg8SnPl7ur1ehWvTHSHo3aiIA6HQykDrOlJed3b25PMazHLcDwe48033xS5iT+XsKjP58PGxoYG\nO8T4H1ozP6rFDEDTs9lsBofDgdwX0b80iInH4+j3+5jNZvjZz36GbDaLjY0NMdDy+bwWzuHhoeTv\n3F1MJpPCeqbTKc7Pz2E0GsW96PV6iEQimhAC0Gic3hKVSgWXl5dSf5MC6XQ61UCdnJwICiS+zZ/Z\n6XSkR2T4TTablVSJC3fR9bTRaAiiI5easioqSBjOyQXOJrHdbovlN5lMkEql0Gg0cHx8LN0im91n\nz55hPp/j5cuXiMViorVeXl6iWq0ilUqJOx0KhdDv9/Hxxx8jk8koIOh1r0e1mI1GI2KxmNhdixke\noVBIx7DX60U0GkWpVMLq6qqIRgys5Gu53W6B/JQj8Uin0npR7UFbLZKRKEeinIqOlxyVr66uiqcQ\niUQ0bjYYDNjd3RV5x2azKZ9veXkZq6urKicIjdFtiBNPwnckQFEpHo/Hpf+jqaPf70en00EkEkGt\nVpMsi7tzMpkUhOZwOMRNoR9dOp3W4qRCJRaL4fr6Gi6XC6urq8hmswiHw3A6nQiFQqLUXl5eSgWz\nvb2NH/zgB699/x8VmrG0tKQMkKOjI3Q6HRl8UzTKHYSRaAw05/96vR5evXqlWo+qZ5JyuIB4rNJU\nhhNFst4YbEPftcXwn3g8DqPRiGKxCI/Hg3w+L4I8hxilUgn9fh+5XA7n5+eo1+tSbVgsFiEq5JdQ\nrTEej9FoNDR1Ix86Go3CbDbj+PhYBCrgDs6s1+uaCpICS4HAbDYTmalQKOD29lbTRzLgms2mkgRI\nbT04OBDllcT+eDwuTPzk5ASz2Qyrq6sAvgysf9D9f9jy+ed1UYt3fX2N1dVV1WCcolGhTbmTx+NR\nPh6VETTwBqDdhznZvJE0bgFwT1VNpKPRaGhX5i5I6RbJ73a7HTabTYmnXEC8oYuOQaRu0g+j2Wwi\nEAiIUM9aepHuSrsrlky5XE7c4m63K4mVzWaTGyizXMj9ps0tveZWV1dRr9c13FmklfLzo0MqR/4c\ncdPgnPwT9gGVSgVms1lj9Ydcj6rMoGKZww1yeVkLkgvB0S5wl/8BQIOLarWKUCgkByPulG63G+Fw\nWIuFlgY8/umSD0BGMhx0UNHByRmhKA41aPfFGpsjdRLx2Rim02lJoviQ8Pgn14L6Pu6Uw+EQwWBQ\npQ2TV8nko0CBfGpaHTCHBAAGgwG2trZQKBSwuroqMevt7S1cLpdOFH6mJC3RNIecZWLwVMOT1ETF\nSjKZfND9f1Q7Mx2KqtWqMu2IGhiNRh17nELRfRK44w80Gg3tmJ1OR2UJd18ORrjD0MlnOp1qoEHV\nCh3xeTGfkKUKCfFUx3DHZpQaU2VZOnBnB+5OoFwuh/l8LnOY2WwmxGBRpe7xeNDr9bR4WeeSOVcq\nlSRoAKBShoJU5mMzP2Vxs/D5fIIvuVFwbtHtdlGpVKRdbLVaMkYcDu8yzk0mE8rlskbrvBevez2q\nndnn86mxuby8RCwWUxd+e3uL09NT0TgZ5P7ZZ58hlUrJ7op149ramkJziAxQ+Q18iVBQBV6r1dBs\nNuH3+xGPx1GtVmEwGLCxsYFyuYx6vQ6LxSICEtl1rFX584PBoFhr3OnoqcG8QJqb1+t1IQX5fB6p\nVOqeOWSz2USxWBTfArg7varVKsLhsCRdlUpF8WmMQ1tdXcXx8bHUJq9evUIymZSggQ9xLBbDYDBA\nLpdDJpNBsViEzWZDqVTSUIhCYpY4fB8vX76UqY3L5cKLFy8edP8f1c5M61faW5FTPJlx/k4sAAAg\nAElEQVRMZA1LRyNaxL777rvqwP1+P+x2O8rlsoSZRAVIKieLjgR07lwsH1ZXV7WjZTIZABCbjGUD\nv4fQG4/i9fV1OJ1ORCIRWCwW9Ho91bGkj8bjceG9NJ6hkU0ymZQZJG3CFqd8FMtmMhmZsNMujG6i\nbBgBaLdedCtiWP1in8FpH+0S3G43LBaLdmtO/KjmpnYxFAohlUqpnOEO/brXo9qZecROJhPs7++j\nXq9jf39fzkB2u10UxMlkgm9+85v44z/+Y2kBSREF7hZaMpnEaDTCW2+9JfdLq9WKUCgkMhLrXNa2\n5+fnSKVSCIfD6Pf7sFgsiEQi9wwVKRZg7ondbke321XtyiY1nU5LxUHlChEWwnPM7CbLzWazYX19\nHSsrK/D5fIq0oBiWvhf0ox6Px4ItOYnj0GZ3dxfxeBylUkmLlEMVDox6vR78fr8aYp6Oq6urGAwG\n8Hq9uLq6gt/v18Mci8XUuN7e3iIWi8FisWB3dxeffvrp69//hy+hfz4Xa1kAohrSI+Pi4gK1Wg3n\n5+ci2z9//lyj5m63i2KxiE6nI8X28fExjEYjyuWynHqcTqfIPBw9k9JIC9p6vS6XHgAapxM+A4Dz\n83PRK2u1mnZ82hcwJYr/hg8qFzZJ/IPBQO+fTqCkldI3r91uo9frodFooFKpSIFDbjUht4uLC0Ft\nV1dXOD8/v/d7sE4nS/CTTz7ReyP3hb1CPp8Xz3qxhON7orKGvPBarfaVCczixcbH5/NpFEu6ISdX\n4XD4Hjc4FotpzErVBhGBxeAZo9EoV1GLxaJjn/9N+iQlQMwCoSZvUTlNLzuiJGx8zGYz0uk03G63\nShjyiGns6PP5VNvSkZTHNodCHJ5wlG6327G1tYV0Oo1IJIJwOIxisSjeCrOyV1dXlfcHQNPPdrut\nE2tRyLu5uSmeRyAQuNeIcgEHAgH4fD54vV6dkORsUDLGrBj+jNe9HlWZQSVFpVJBtVrF+vq6uumr\nqyuVF8SiaQ5Oji5z6er1OhKJhAy6yc2gJIhqEDZdHF1zYEFYrVAoIBaLyfuNmC/pkZwu0ouOo2cS\nibi7UmnSarWwt7eHQqEg4hEZeqVSCclkEgaDQaw40kfL5TJOT08xHo+l2eMDSGcnn8+HbDaL8XiM\nk5MTvPPOOygUCkJV6MvB0HamyTJEk8iR2+3GxcWFNg265g8GAxwcHKgBTqfTODs7k41CMpnERx99\n9KD7/6gooL/zO78jWI27FfOoc7kcRqORSEI2mw3Pnj3D97//fXzrW9+ShJ4RBk6nE0+ePMHZ2Zls\nCxjKHovFZBBD6IuUTw5X2CCSief3+3W0cuelrIjTReBLc0EA8rCgkY3VasX6+rpIU2T4UY3O0oEB\nl5PJBEajEe12W+lQXq9XZUkymcTJyQmi0ahG53x4KJuixzSHJ8fHx1hfX0elUsHbb7+NXC6H6XQK\nv9+PVqslVh7jMGhKTlIRbQfo3lQqlRTddnh4iO9///uvTQF9VGUG67fr62usra0piqHX6wmQNxgM\nytOgyoFMLgpSDQYDQqGQambuqCwvSqWSeLjn5+cSyC7Gkd3c3AjDvbq6kvdwv98XdjwajcTjpWMm\nLWIZZUFGHSmnvV4PpVIJtVpNUivi2s1mE+FwWDERHF0ToqOdLrH2bDYra1367HG8TSHu/1vtQryc\nTMTl5WWsrKzg6OhIxCI2s4sj6qOjIxk38pTiQ7+ysoJisfhgq4FHVWbQPPD6+hoHBwdIJBKo1+vw\n+/04OjpS5hwAGRNSElWr1TS1YxNERh2nh/V6Xd07A3y4QzLyl75u5GrwlCC1lNavrVZLLvfkRRMT\nZtRDq9VCqVRCuVzWwuDrcpLGZpQiVT4szWbzni8zldVEM/gadNfncc/TYjQa4fz8XNwJn88nvz6n\n03nPo5kBPp9//jn8fr+UJpubmygWi4LnqJIhYsJm3OPxwO/3Cwp83etRLWbuwHSo5wh6PB6rWeFC\no7qCC5XCS/pjsEEZjUbK3vN6vUilUjg9PYXH40EgEJDmkHo3m80mToTRaEStVpP5CUe+5F2Qgced\nnObo5C/QJNHhcKDdbuuUCIfDmtrRO45+db1eT+PrdDoNs9mMbDYrbPjdd9/F+fk5HA6H4ERqABnn\nQBuFRVuv8Xh8b+TNMisej8sb2u126zNhMPzGxgZ2dnbw6tUrtFotRCIRWK1WRbiRsOTxeOB2ux90\n/x9VmUFZDj/ITqcjfPj8/FzwWLfbVWhPNBoVrNbv9xEOh/Hy5UtcXV3J8w2AiOs8TjlqJtmHO3O1\nWsVgMEC320WtVlOHTvhuPp8rAIg7IZ30XS6XIDc2gJ1OB5999tk9tTYXD4lALIGoyuZono0Xifjj\n8RjPnz8Xjxm4I0iVy2VFU9TrdTXJlUoF8/kcVqtVA6RmsyknJvpnkGgEQJ95uVzWg/7RRx+hUqkg\nFoshEAig3++rIY9Go/Lrq1QqD7r/j2oxkxzU7Xbl5BMMBmVjRa83RpZReUxPYpLtCbsR5spkMuIf\nmM1muQjR7IU8Az4cnI4xLoKdv9PphMPh0E1mBh5trOjvRi0fvYxZBxM9yOfzikKmYTlH6wzTTKfT\netjMZrPqd/qDcOqXzWbh8/mk2I7FYkKFEomETGUYJEQdod/v1+fBh4OQIA0TSZGNRqMiJDUaDSV7\nTadTYdc0jXzI9agWM+mcW1tb8hEmVDUYDPC1r31NZic8pjnOpa8zACEEnIrRe9hoNCIcDivAnJa2\nGxsbsqXiA0RxgMfjgcVigdfrFXuPMqR2uy0XT9oDMMC+UCjIPZONFydzW1tbqn25q7KMqdfr2Nra\nEh2TnIy9vT2N6ykc4GsCd9PTvb09YeWj0UjRwqPR6F5uCYdKfB0AWFtbk5DVZDLB7XbL5ZQnCqmq\nPJW2trawsbEBh8MBv98vL5PXvR5VzcysO0p+6G7EHZvydtZ/l5eXwnkJ29XrddWJ+Xz+3uia+rlF\nIjvDZ2gMfnt7K484LqpKpaKalROvm5sbUSTp2dFut+WuRJ8PMvI42OCkjSbiLKs++eQTpFIpxTgQ\nLaAZziKDcDabyUuDuz+V7FdXV1haWoLf70cul0MkEtHPqNVqwqfJByHMuJj9N51OUSgU9HC3Wi3M\n53MUCgVxRsgt53ubzWZf+TMvXhaLBbPZDC9fvtQRbLFYEI1GEQgE8Omnn8p8hRpBSpr4tUQiIUIS\nj+xIJKKkJ9bS3GGYh8LhBWmSNCSfzWZ6IADIgZ4DBuoHTSYTksmk9IV0z+eDR9NFeiuHw2G43W4k\nEgk4HA5sbGzA5/Nhe3tbEz/W60tLSzg5OUGz2dTQIhgMqo5ftNFiHW6325HJZNRE+3w+rK2tKd6C\nfA6ecicnJ4I8Wf/ToJyGi+RvMz/84uICn332mfzrHlpmPKqdOZ/Pa6RqtVpRr9cRjUa1Mzx58gRO\np1Mezkxj6vf78pXgjeURzV2YsboANP4m0jAcDhVGwx2LxJ9IJHLPPJz6QafTqe8ht4G7FIWtfr8f\n2WwWwWAQpVIJ4XBYyozr62u43W5RXEnuZwPKARDJ9sCXQgTW+AaDQWNoojwcV/Mzodqb9l4sERbj\n2qhhZIlBfz9OX3lSVKtVqcuXl5cRDocxm30Zk0w/vte9HtVi5oKhkTjN/FZXV1Eul2G1WtFsNgVH\nsd70er149uwZXr58KdB/fX0dzWYTRqMR6XRagwm73a7jsNVqySiQdTUJTWzaOF5frJFpEcZyod1u\nw+PxiPM7mUzw/PlzJVMRGiMyUyqV1GgBEDrD5pYihdlshuPjYxQKBbz77rvS8jEDfD6f4/r6Wr4X\n6+vr8t8gykGEqNVqiStit9v1WdIbJBqNiqhF83JuEIydAO4mnMViUQOW0WiEg4MDxGIxfPjhhw+6\n/49qMdPhx+l0wm63I5FIaCjx7NkzURlJByX0tba2huPjY3g8Hjntt1otyZJYG4fDYZmUM8ODvGnG\n5hLLpvVVuVxWfAJ3P+aGcNcmh2QxsjgUCskB32azodVqyUt6c3PzXrNF9IYqkrfeegtms1nwGVEM\ns9mMt956S3U+g4y+973voVAoKFObdE+a6EynUzWyFosFyWQS9XodwN0JRqdQlmMrKys4Pj7G1dUV\nrFYrvv71r8sqzOVyYX9/X1PWDz/8EJFIBPF4HPF4HB988MFr3/9HVTMTfyUWSrokc+mInxLdILON\ntrB09vz4449hsVhk+FKr1WRN2+l0lPZkNpvh8/nUVNESl7EL3KHJS+DxzEVLxQankGwgr66uhF0T\n/242m+h2u0JsWFZwh2UjS79nNoDM8qM0q1wu47PPPtPnsrS0pGhi8jg4dKJVASeG3W4Xw+EQL1++\nFCTJuGKPx4NmsylXqZWVFe3quVxO9rpEV0hA4glWr9e/ihtevKh0Hg6H+OlPfwq73Y4XL17INHE2\nm8lYkd5yFotF2YHM6/v8889V85I1RptZIiVWqxX5fB5PnjyRIzxjxoiKuN1u2O12fPzxx3LppEPR\n4eGhRKZnZ2f34D6OsTnYIbb7ySef4N1330Wv18Ph4SGSySQCgQCurq6Qz+fx3nvvod1u4/nz59jZ\n2VFqVDabxfe+9z3xRcLhMIbDIV69eoV2uy38+sMPP4TNZsPR0RG++c1v4vj4GH6/XxEQzWZTihvC\niJVKRQgPDWaYTejxeNDtdoX9h8Nh6TP5vSaTCe12W/yOh1yPjjXHXRiAMqvJXOv1egiHw6jX64jF\nYuh0Ouh2u1hbWxOmyrxpu90uk0LmcpCny1356uoKkUgE2WxWGPXl5SVSqRQqlYqGI5eXl+I0t9tt\nJJNJFAoFlRkAdFPD4bAyRRazr+kJTXU5BaeskYvFouRiXFjT6VSfx2w2g8vlgtvtltiAJZPNZkOl\nUsHm5iZyuZySpchXMZlMKoNIE+BnnEql0Ov1JNalZKzRaAjxIMGIlrxUYwNQ6ef1evH8+XP8yZ/8\nyVfGicDdzhyLxcQ79vv9KJfLamwKhYIyqJ1Op/6bEqfFnJKNjQ188MEH2N3dRa1WQzAYlFrCZrNh\nMpno39IJkzVuu91WihRxWmLEw+EQ2WxWnGhqDEn7vLq6QrVahdfrFbRFd/2trS3VuicnJ1hfXxcz\nsFqtYmVlRczBRVOaXC6HUCgky1zW2FycZ2dnsNlsImkZDAY5EhEeLBQKcDqdaDQaiMfjwoZZEpH+\nyfKl0WggEAig2+3qtCH8yUHV8vKykCZmiz/kelQ1c6/XQ7Va1Q5BthnwJTeY2jcS2yklMplMMnhh\nzEMqlRKnl69D9hm9l0mnpLg0n89rgEA4ixa19EB2uVxIp9Oy+6L0n+NfIiCMIiMSQOivVqthZ2dH\njZbRaMTOzg6azaZqdk74AIizQdMZNqF0dGJjNxgMBElyUMNIC/5+JCgBQDgchsPhwPn5ueA1jslN\nJhOKxaL43X6/X4YwLH/o70Fs/qHXo1rMmUwGy8vLYmkxLZXTr1AodG9gQjSA7kCM/2Vo+enpKdLp\ntEjubrcbsVhM3Amfz6chwsXFhVTYdNGkGeHa2hqi0ShSqZTYbMzL8/l8aDabwrj5b6ngpmiU6upg\nMIhnz56hWCyK1G4ymVAqleDxeOR8enFxgXq9rodlZ2dHrLetrS1ZH3CXvr29xdramoj0tNpiAmsw\nGJQYlzIvNrWpVEq8DA5crq6uEAwGtfiZyBUKhcT6m81mSKVScv7f2dl50P1/VIuZux5NANlNc3HR\ngZNQ02w2QzqdxtXVlRACwlNMaO31emLOEa7jzez1esrr4M2hFo/5gMFgUAw2vpfb21sAd2QdSpvI\ncWCX73A4NIl0OBz38qmLxaLyQnw+n6inwWBQ9XQymdQiouMRJ5a0CyiVSoL2KD4lt5nQ32Qy0YNJ\nvJhTPH5m1EmyRr+8vFQf0ul05MtMiJCvSz/mZrOplICHXI+qZqY6+vr6Wjo0OnQOh0OUy2UEAgFc\nXl7i4uICpVIJlUoFv/7rvy4nokgkgoODAzidTk2sSqUS9vf3NbYNh8NakIT4eIN41FNKxZqTxzj5\nu6VSCTs7O2om7XY7jo+PMZlM0O/3pZ/jcGU0GiEajYq7TFI9AHE06FrPcoW+cHQXomyKg5FIJCL6\na7lcRjAYVJbi5eUlstks1tbWMB6PUSqVEAgEUKlUtPPzQaOq2+VyadN4+fIlNjY2ZFDJxRsKhYSe\nUDUfj8cxGAxQLBYfdP8f1c5sMBjQ6/Vk4UrWGtXBpF8GAgEsLy/j6dOnwl8jkYgC1kOhEDwej4YA\nNBm0Wq1wu90yM7FarQqcpzzK4/HAbrdjNpuh1WohnU6ryaNXBCPOrFarGjY2THRIojGKy+WScTmd\n96mAJhLldDpVBrGUYQorGXtUwBAloV5wNpshHo/D6/UKxWAtzxLG6/UqLJOjfZYQ/FxYjtzc3Kih\nI3YfjUYVskl6KaPbqGC32+2aaL7u9agW83A4hM/nQyQSwYsXL+D3+8VQIxm90+kgm83C4XDgb//2\nb7G2tgYAcrt3OBw4PT2V3o6xwDy+3W63EA3eVMrlOX202WyIRCLY3t7GZDLB6ekpvF4vrFarFtpw\nOJTHB6VDlEt5vV5xQubzOUajkbJW6BzEUbDNZtPQhGjMwcGBrAgYO7G9vS3s1263w+PxCE2hbwZl\nVQyQZ+PKWIlcLidrXMq7iOrwwSRzMZlM3nNN4jCHLEH63pF6S/+Sh1yPqsxgrBibLh7hVBszg4Mw\n2d7eHprNpiZv4/EYq6urygnxeDxIp9M4PT2F2WxGOBwWLRSAwieLxSJWVlbk/O7xeJDL5eByuVCv\n17G3tyeqJKEvNj0sV4LBoBht8/lcLj9EFubzuRq3tbU1KbS5k29tbcnMkE0rw+MdDsc9ORcX5tbW\nltTrVqsVTqdTeYBGoxFra2uw2+1CQ4xGIxqNhhbfcDiE3W5XbDDlTwaDAQcHB0IwWFM7nU6p1KlE\nAe5ONdrfPsTR6FEtZhoLspli/cpxM2VJDNqhHRXLgclkgnK5DKPRiH6/j+vra5RKJZGEuCA5mr25\nucHh4aFI+4PBQDAVYTnCTvTooPIjGAzKsJD1eqfTEXGn1WqJdUbMl7tZsVgUgy2fz99z6KdNAvCl\neQvxcA5K6ApKI3FmrbDxJB7OHmQwGEhWxQaOCp75fC7dIHFs4K70YePX6/WwvLys+LhAIKByh54j\n5Eg/5HpUi5m8ZC4ujl45fWOsLlld7XZbsqjl5WVRJefzuWrHcDiM4+Nj2cVubGyoiaMglPgp60cu\nJvKQb25u4PV65dVMvJjcCI6XGUvMEEi6jvJ9LSIrfDASiYTw4WKxiHQ6LVJTvV5XaeFwONDtdvUe\nqL0jOYryLKZi0SmJPnKktjLKmWGda2trqFar6Ha7YtPd3t5FCweDQfE3yAbMZDLKBuT/aJfwlT3X\nwkWXeQDiVVCbRnegVqulepMfMgWgxKVZ21UqFTV4qVRK8BWtuMiqo7pjOp2q0eQpQViQkBwjkamA\nWVlZwcXFhZKq2Pg1Gg15JbPxY71psVgkeuXO2e12kUqltAgrlYqOd6ZLcZhjNpsVXNlqtVCpVMSr\nzmazauD40FGvR5SFjSeRE5qJm81mkZEajYagO74WuRu03QUgMtViJvjrXo9qZx4Oh/D7/crZm81m\nUpKQxEO5FLFTt9stmiUX3dOnT3F9fY1oNCqDGHoa83gkErBYCwJQxl0ikZAcPxKJKCaN/IlEIqFk\nrMWGj9wGu90uojwZdpyYcSJHFp/P5xNBnsHqHITQxoBBlZSOra+vw263IxAIYGVlRRFrpKsyJMhs\nNktk0Ol0YDAYkEgkcH5+Dp/PJ2ydD3AoFILNZlOqFDkt9AbhVNJkMklIyzjob3/72/iLv/iL177/\nj2pnph8w2WqsNwOBgLi+wWBQ07Tvfve7ir7l4IHZJx6PB++99x5cLpesrkajkfwsyBXm5I7uRExR\n4q5LbjPhNS5QNo/8Ghs0EvSn06lQEQphR6ORIMdf+IVf0EPK9z8ajWSHy1g2ogWsT7lzU/PI8oLU\nTSIqhMrMZjPcbjcGg4GaN6Is6XRaO6vdblcUBnO7w+GwxuGE/Vh+uN1uwXNseB8anfaoFjOtrgDc\niy6jNcBiYzMej/HDH/5Q6mPgjsHFGIbhcCgCztnZmRomhjbS2KVSqcBgMMhbglwJmnDTrpVxBxTP\nsrnkAqKjEZNKWdOXSiW0Wi2RhohsfPzxxxKQcldnZggx5GaziUKhINIRHZco3eIOztqaaEo2m8Vg\nMEC1Wr2XPcihDkuYv/mbv1GZdnh4eM+mlkaLtOfNZrOo1+uy7B0Oh6Kf0t+vUCg86P4/qjJjcaxa\nLBaxvr6Ofr+P+XyOy8tLWVldXFwgmUxKeEpjQTp5LhoQ2mw2IRIrKyu4vr5W112r1bQgiEtTyDqd\nTnFzc4NUKiXLrNFopM6d7LdqtSq3UO7a8/lccQuVSgWJREKEe46HOV2kCqbZbCIWi6FYLIrBBkB0\nS3qAjMdjjZxJuiedkyjQaDRSaZLP56UFtNvtqNVqyjQhOsRSA4CSa9lQs+5nUla73YbBYIDb7db4\nmp/zQ2vmR7Uz86ilRS1ruVQqpbqN7C/mhzCXj3gtJ15utxter1cwUiaTwXg8Fi7r8/kQCoWQyWTg\ncDhkVUXPDPKQOarmgMRoNCpSze/3w+FwiEfB5ClyJbxeL/b29oQ9c5Et5hDu7+/D5/OpPNjd3UUq\nlUIoFJInHEOLCL3Rf49oCq3HMpmMnOydTqcUJJFIREQhBgtx2sqp5fX1tSKPaRlMmRhjLRKJhMoX\nsvjY/DFD/CHXo1rM1Lsx05nOmeQXb29vi3Nrt9tVBozHY1EtabMFQMJVugCxWSL/mLsdp2m0q41E\nIvfypFmnk6O8tbUFp9OJ09NTTRQp0VpbW5NwYDweo9PpiMQUDAaRTqclElhdXUWtVpMxY71eR6PR\nEBON1gkmkwnb29siVW1vb8ujeTKZaBzNTBLqCVdXV2VDtr29LaiTA5N4PK54CbqXMn6COzXxe5ZE\nTqcTwWBQ7qbpdFpw5Pr6+oPu/6MqMxiW2Ol0kPsihPHk5ESB5PRxq9frkgWZTCZUq1V15oPBAKen\npxpeMB7C4/HI7XNpaUmmKH6/H7VaTcy55eVlHZmU3S8mmN7e3qLVauHk5ASbm5uo1+toNptKwWId\nyQDN5eVlUTfPzs7kz0y9nNfrVT37zjvvKGGW7LhyuYxcLodEIoFyuYxkMol8Pi8Vzc3NDQ4ODuBy\nufDq1SvV/W+//bZMXwjtcfhBfJ0Wv5xSUuFCo51WqyVuM51VKQUjgZ/ezwCUGvu616PamVdWVkQn\njEQionICdw0hlSg8TjOZDK6vrxUi2Ww2kUqlEAwGhQhQNkS1SK/XQ6vVUkdPcg1wdzO4k5LfzBKB\nmsLhcKhygSR3WnsBUOwDJ2x8IJi4ShkX0RI2VA6HQ7Upd06askSjURQKBT2Y0+lUJt8A1JiyPPD7\n/YL9mIO9+HmFQiHFWDAAiI2cyWTCzc0NisWipqQkIbEs4USUjS0X91cTwIWLUBgNRRhDRq0d61iS\nw5lRQl4AVddMJ7VYLLJs5XSLdTBVxZTwW61WjclZkrARSiQS4hgTVVlfX5f7D3HrdDqNYrEopCEU\nCmkX5MJhCi3hLZJ7CInRBiCRSKBYLMJisSgbm/ki5GQT8aH1LQBZc5lMJuzt7amW5yKMx+MaY/Nh\n4ENEPjM9rWlOEwgEUC6XxSkJBoMqlUg28nq9ePr06YOsBh7VYv5/2HuX2MbXNL3voSiRkijeREq8\n6a6Squqc03X6nD59mzFmpoE4i0Hs2SWzCbzILpuBYwdxso8RZJO17VUwNgLYm8DxwHA8iCcz4x50\nt/vUudRNUkmibhTFOylRlERJzKLO72mqPXaAUpzxCP0HDk5dVBTF//f/vvd93ufCbplMJvXy5Uub\ncePkzsCDsW+xWNTZ2Zn929rttq0I4GccHx+rXq9raWnJkB0ZJyhF2D0JvwQCxGLr5z//uZaWlmzO\nPTU1ZV4GJUE0GtX29vYdqwJ2O/gOuBmx60Ki73Q6zsPGJB2oD9ekxcVFM+Cogxk9l8tlN7DhcFhH\nR0c2A4cui/80pUa32/XDDb21UChY8IsYAYiQrJRAIOBhDqgMvG3I+u97PajFfHp6qtnZWTsSgRSM\njo5qYWFBh4eHd8IooV0Sh4ad69ramh3vGUqgxJ6amtL29rZlVpFIxLXy1NSUkQs69VqtpvX1dXtJ\ngBMHAgHbeDEe7nQ6ppEiAmVogRsR9EwGEghxiVA4Pz83P+Pp06dOV8Xi4NNPP9Xe3p6J9ciYIFJh\nkTA5OakPP/zQKhWULaOjo3r06JG2trbMwVhaWrKPHV597Xbb2TEff/yxXr9+rXK5bG7zxcWFlpaW\n9NOf/tR86sXFRf3BH/zBe9//B1Uzs6OBOLRaLZVKJfV6PW1vb5sMhPdZq9XS1taWpf27u7uSpD/+\n4z82ToxzJ6UIfsaMYEl1Qmp/cHCgbrerVqulYDDoxpK0VY5fkATYdbD9Tk5OtLu7q/39fVvbbm1t\nWQECzZP8FUkOh6QJhSvCgIRxfSAQULFY1M7OjimvIyMjevv2rSYmJmzHcH19rWazaZIQEQ4YhG9s\nbLie5j0DSZ6cnGhvb0/VatWl15dffmnJFYJgfDgoRSKRiH7605/e6/4/qJ0ZoguezKAHNBhjY2Mq\nFosmteDX1mw2XTfSgBHsiC0WTp9MGKGCNhoNhcNhLSwsaGNjww0lk0ZUJ2dnZ+780SDirUHzOdwg\nofeDyDMcbgl6kkqlnF3SaDQ8XKEePj4+to6R5Few8qOjI0WjUSMmmJEzOOLBweEIo3CIXKhQSMqC\nYDQ9PS1Jd/jW2C6QQ55MJu+ExmP7y7993+tBLWaEmnhNAOgDb7VaLePMlA4ctYyV8UCDAgp1kRKA\naDByOeBKo6/jRsEoQ8SKjReDG9w+oZlK8k4/MTFhKwQeCMhR0WjU4tTR0VFzrJfUCdUAACAASURB\nVDm+ISNBlOdBokyg/MGPj0ZYeicqODk5MWeEMT28auig8Cn4zHkNXP/hmvR6PWUyGX8eJApAxwXB\noQnkc3jf60GVGZlMxvG6sMAoO05PT7WxsWGft+3tbXvFQVHsdDreeTiuqYtxkz85OTHcxPes1Wo6\nPz/3wmFBM8plzM6EkXp8mFyExRXyLnZZID2C2ilX0OXBa2bHZ3cbjg+emJiwPxyhm2DqU1NTHhJB\nfaV86Xa7XpA42yMXgzZLM1mv1++M3FutlsbHx1UqlcxFQUYFPl0qlWwOAzx6n+tB7cz1et1sucPD\nQy0tLdkDbjiJ6vT0VPPz8yoWizo4OND3v/99L/zb21sVi0VTLTEyQdpDycGNh9vcbretwN7f31cw\nGFQkElE4HFaxWFQymdTe3p5mZmZsaQtBCHcjOvxer2fFCLvi5OSk7buoU4djzjCbwcARESuSsGEl\ndiKRMDzHsKPf73vsLP2C03Fzc3MncxAnz06no7W1NS9SsHAkVVBZgQbHxsZMCWXTGB0d9YInw/s+\n14NazNAlu92u1tfX1ev1tLq6qoWFBcViMcvfi8WiRkZG9Lu/+7v6+3//73uXAycF3ltZWVGj0VA8\nHtfOzo4//MePH6vVapm/S5NXq9U0NTWlmZkZL3BJHu9+8skn6nQ6ury8NOQlyRYC+H4woKA8gIif\nz+e9ED/++GMnQEFawpeCdFTQGlxM4U4z0WOw9PjxY/Oiz8/PfSI8ffrUwUYMU4AA4U8zoMJ2C1UL\nWDRG64FAwPkmTC3D4bDevn2rlZUVU2V//OMfv/f9f1BlBjtMJBLR9va2ZmZmtLm5qePjY21sbGhk\nZESbm5va3t7W9fW1/uiP/sh14v7+vvNMGOtubW1pYmJCf/Znf6bFxUUT5iENkWLKtI/FPD09rTdv\n3tjhBzYYabGBQEBv3771BLHT6SibzXohkDFCwhRDj3A4rJOTE83OzupP//RPzQhEywcXmjG8JPtu\nEEwEe29vb88ck6+++sp8aZyggsGgk2CxNsAjhIknuDH2vbjs93o9/Zt/82/8cxAkNDY2pkgkomKx\nqGq1qlKpZBLT+fm56/D3vR7UzsyMH+y4Vqvp6dOnPloZbYML09gtLS1Jkk0Ih9Oger2elpeXjTkj\n0BwfH1c2m3XE7/HxsSdxjUZDS0tLNjan3AGayuVyLn0WFhYsI6JBwsSFeAZ28Ovra62urno6yEgZ\niA2JF6YqWO9SbszOzhpJYac9OjryuDqdTjsQk1obGBBSFdNBBAYEbi4uLjoACKN3ml+abxIAEomE\nut2uCoWCSqWSJ7c0ou97Paid+fLyUrVaTb1ezxEPBwcHCgQC5lNQryG5J2CnVqvp7OzMIemYk2M6\nzo4WCAQ8vABL5e/QFmLq3el0dHp6qp2dHd3c3PgoZiFTEoCJYzvAQ4BRODl7wIiVSsViXB4Cfn5I\n7yzm8/NzJzsxqcPc/OjoyLnZ7Nq3t7ceTXe7XXOSydqmB6FnQGxQrVa9WDFlhOvBCcLDzESWr8P2\nlhi3970e1GLmOB0bG1OhUHDcWDgc9i7U7/cdYQABPhQKKZvNWjaFXIkunEBKbk6z2fRNn5iYUK1W\nUzqdNmwGJRJn+5GREbXbbd9UXDCR5+PEyeIHoyU7cHp62hYBiFuRd0E4wkgRUS8TymGBLAaN7XZb\njx49crJrtVqV9IuHYXt7W5FIRNFo1Lg0wyLp3aS12+2qVCo5fg0SlqQ7uYTwm8fGxhwNEY1GPW1F\nnVKr1e7Fy5AeWJnBTYYsgzUAi3RsbMx8AnDoSqVi5x+OyMXFRScxFQoFx6VhWwD3F0cgTGFSqZTx\nZSA3BAL4rREkiS6QhRAMBi0m5TRAtwgEB0e43+/ryZMnTkENBAIObOd98vNKsj0tZUAymfT4nWQo\nBkszMzOS5GaRsufjjz/W/v6+x+98lsOEouEyBN3ksL0XpdPExISazabm5uYcAB+NRrW+vq5/9a/+\n1Xvf/we1mEEXkPIwWfre976n3d1d1et1TU9Pe9pUqVTUbDYt/4fv+/nnn2txcdFEI3aQi4sL7e3t\nWdKPi2a9Xr9TMszMzJjsP1zSMG6/vr7W8fGxj140gzxYyPtp7Jg2AsuNjo5qf39fhULBOO/19bVq\ntZpj1hi0oBiJxWKOHx4m+PR6PUej8XMmEgm1221tb29bRf6zn/1MhUJBr1+/1vr6uiPdsDoACWq3\n2xoMBoYhyTSR5CaUevzNmzfG5QOBgF68eHGv+/+gygwWAGJQjm+GA1hHzc7O6vr6WpFIRAsLC3Zt\nLxQK7txnZ2eVzWaNXLBDIt1nPA2XGChqWIEMmsHolmlYOp02rjwxMaHz83ONjIxofn7e2DTMNKZp\npFCxg09NTfn4xok+n8/bkovmja9H1c176XQ6biCB7Six2FEvLi7cLDNc4nUgOoFQENLJ2D0ej7sE\ngjgFuQnoELMeyFmccO97PajFHIvFDMZzYzEVp6mizMBkEANAXIgYGaMmgXyPSSGdeCQSUTabVSQS\nsUQrkUj4e8RiMXOEUZ9QfpRKJU/POp2OxsfHbUYIY40RPJL9SCTicTwsNth/TPsoldAuwskm6/rm\n5sZ85EePHtnEhihkRAmUOysrKx7F83nwUKPC5qFiwBONRu1Vx4m1srJiO4PBYKC1tTXzzDc2NnR4\neOh7dp/rQS1mkAlk/DRdgUDAmR/hcNilCB8eDR3TuKOjIzdvyITQ4fX7fR/3dPG4EQ0GAw8ukNUz\nVcRSdmRkxOmr8CDg/WJ9xTHPkby9vW0VNiNjMg+RfGHCSOlCUgB6x0QiYV/ndrutvb09x7PRBGMs\ng1UWE7tisWgkZXgoA/w5HLUBMsTPO6xCWVtbM6OPIRQ7OIOm+1wPajEz8YrFYspkMlpYWLhjUIIO\n7c2bN7bVyufzdg1KpVL+Wiy0YJrhw4aBCsSdR48eqd/vO8R9YWHBLj7hcNjEGsLooZb2+33Nzc0Z\nG5+dnVU+n1cgEDC3hFOARQEDDQOVq6srra6uGiLj/QN7dbtdGxjyUGE1m8lkTCiCGjs5Oem4YiaE\nYOG4f+JeSvPJZwWKg4f1wsKCJicnFY/HXa9XKhUlk0kVCgU/aExVgQrvcz2oBpAOm9BKjj0IPNVq\nVWtra3acbDab+slPfqLf+Z3fMU9CeqfoKBQKNhGkMaLJYmrIrokBzBdffKGlpSUnn0ajUYsCUIfc\n3t56isaUjigGjFjIGWFHYzFeXl4qFos5aw9zSExeGo2GLQnIrIZkRVN8cHCgq6srVSoVvybE/M8/\n/1zRaFSdTkdXV1fa2NjQt771LWPGkJBoImHUYSIDsnJzc6ONjQ19/PHH2tvbs5cIzqWMwMHLCc7c\n2dm51/1/UDtzo9FQNBq9M7qV5GYvkUh4d6NUYGdm52NnhjQz7C8My02SNYB8DcoRGhv8ncFaY7GY\n+dNM2XCyB7KC0QfkBQUU939y/7CmhZ7KbinJQ5B+v+9/gy8Fp8DCwoK1i9Tr2NQOy6emp6fthB+P\nxzU7O2tYE0wb9TXO+5D5sXigtuczYyfu9/sKBoNulDOZzL35zH9hizkQCAQDgcDzQCDwf3zz++lA\nIPAvA4HAZiAQ+D8DgUBi6Gv/+0AgsBUIBN4EAoH/9N/zmncYbfF43PUdgxIUwagmIMeTeVcqlUz6\nQRERiURcdnCzmOpJ73BfdGwwykAFgsGgTwoeDEkWrZKvgjEMjDe0eRjbMGFE+c0pwvFOUzoYDFzz\nN5tNcx5ohuFugPgwyGF6R34J743mF4f9QCDgEwsCP+6e/X7fJ9jMzIw97piK8lkxNCIgk2EQm8/7\nXn+RZcbvSXolKfrN7/+OpH85GAz+50Ag8N998/u/EwgEPpD0X0j6QFJB0h8GAoH1wWBw+8svyNz/\n+vraw465uTmHwrBbocP7zne+o+PjY+8oIA7r6+taXFxUt9t1HY68H7n/4uKiDg8PjTywUIH2hkk/\nS0tLhukgxvOay8vLikQibigZ/ExMTOjJkyeuddvttubm5jyYoBeA5TczM+Pp4K//+q+r2Wzqhz/8\noUqlkl8fEtTLly8dAVev1xWNRr2DMkwhygFmnCStrKzo4OBAyWTSKVmkFcDNgMfS6XRMUWVAhDXY\n4uKiarWalSiJREKJREKFQuFeC+ovZDEHAoE5Sb8t6X+U9N9888d/XdJvfvPr/1XSH+ndgv4dSf/b\nYDDoSyoGAoG3kr4n6c+dfbLrFotFFQoFhyh2u12dnJwoGAzq5OREIyMj2tvbsz0U3IJ4PK7NzU1b\n37bbbTvaB4NBJ5XC38AbgjocUhApqwsLC3rx4sUd1yCQgnw+r2q1qsPDQz8c6XTaNer+/v4dU/Ji\nsXgnYSqVSrkhbTQabnh3dnY0Pj6uL774wgQkxu4w/TDJYcAxNTWl5eVlnZycqFwu69NPP7WY4ZfD\n3gnlhFGHri+fz2tra8sELgZT8Xhcx8fHxqNPTk4ccccJEwqF7h0E/xdVZvwvkv5bScO7a2YwGJx8\n8+sTSWho8pIOh77uUO926H/rormCIMQCQNqEjJ5mJJlMWscGPIcHBnUftgKMeamb4SWTz41ekPoY\nST6RC9TLwGCUB3hGh8Nhzc/PW6pF3jSsNIYb2OaiDcTPjQaNh4VaF4sAJoe4k3L0Q0/FVJFhysTE\nhPkVwHAY12CzMDxm5/OSZOydzPGbmxv3F3zmw+HxvD9KsPe9/n/fmQOBwH8mqTIYDJ4HAoHf+vO+\nZjAYDAKBwL+P3Prn/h3TulAo5FDFbDbr+DGoiIg78W1molar1dRut50dQjjNRx99pHa77boZwn4o\nFNLr1699XLKrXl1d6dGjRyarszAxER8ZGdGHH35oOKzZbDoEiIkbZcnY2Jg++eQTnZ2dqdVq2bgc\ntQrfA7YaDkZkpoAbgxqA+GCyPjIyopWVFQt/yefGM5nmGI7zN/fHglbKomQyaZsHuBnDAZl4ZpdK\nJQ9zMB4fHR01Ln6f6y+izPg1SX89EAj8tqRxSbFAIPD7kk4CgUB2MBiUA4FAThJxnUeS5of+/dw3\nf/ZvXV988YWB91gspu9973v2NJ6ennYgZavV8gcLPtxsNl2rknrKqHhjY0Orq6t3AiOldwoR6Z2v\nM9M0VNns6MiuSH7FQLBcLltPB6aLMyn+yZI8esepnxwQJoGzs7M6PDz0wARNIEproLP19XXjzMBl\nNKiSnA3Ybrc1MzNjRiAC2WFbA+r9i4sLraysmFZL5gpWva1Wy9NWgjNxPo1EIup2u/qDP/gDR3L8\npcsBHAwG/8NgMJgfDAbLkn5X0v81GAz+S0n/VNLf+ObL/oak//2bX/9TSb8bCARCgUBgWdKapD/X\nYOG3f/u39ezZM62trZn/S+cOBRJMFix32KkHqVG1WrXUX/oF6Z+Qeeo8vCzm5uYcPD/MVMOU5uTk\nxIMNJndAdvCEGSODIEjvTGGKxaIHGSyKer1uiwCUK6Ojo2bqVSoV+1ygpuZUOTo6Urvd1tHRkbF4\n+gqyTVDc7O7uuvkc9rkmLxHzRR5wBjAnJycW3TYaDdVqNVUqFY/JDw4O/P0eP36sH/zgB/r44481\nPz+v+1z/MQxNKBn+J0n/OBAI/FeSipL+c0kaDAavAoHAP9Y75ONa0n89+Hfoa6rVqlUW+BOvr69r\nZGREc3Nzkt5lBQ7nYuN8xL+5vr7WX/2rf9WOoWNjY/ZmhvM8jO3ifwz+G41GzZhDALq6umpeBOlT\n6ALHxsb06aef2pyGvG3qZWpqmlaiIPBQ5vtLMo0T9IQMFkbHy8vL+uijj3R8fKyTkxOl02lJ704x\n9IY0iRMTE/r000/NJ1lfX7dsbG5uzqUTPzfmktls1jwNKANra2va2dkx7rywsKDT01N9+OGH+vGP\nf2x+yO3trf7JP/kn772Q/kIX82Aw+L8l/d/f/Loh6T/5d3zd35X0d//fXo+SIpVKaWtry9RKIDLI\nRLgUgQEXCgVzjs/Oziyy5NhvNBpaXFyU9Av4j3oPDJejFr9jBiF4One73TvJVVjUfvDBB9ra2jLB\nKRaL6fj42Po8nP45NWCtYSADrRO/O7gn6A15n9lsVmdnZ9rY2PDUEAU2jeX8/LyazaaHJeDAQIZo\n9X72s595HI/iJpVK6fr6Fymr19fXymQyVspXq9U7tbn07sRbWVnx2P9XXnNDF5ZXjUZD9Xpd+Xze\nWDCSptHRUdVqNevwcOqBWIQh4dnZ2R19Xz6ft6kL/Irj42MtLS1pc3PTjvLUsQxOarWak6pAF2q1\nmtNih3dCxtE0i2C5mBFC3SQ0XpKnhLVaTdls1sMNdtRhr+RWq6V8Pq+XL19ay9hsNq173N3dNQNw\nfX3dD1Gj0TCKAc7OEIi+4ejoyIgN4e8IEnA9Oj4+dv0fCoW0ubnpxK2pqSltbGzc6/4/qHE2kBML\nSZLhKqxhWbT8Gd369fW1KpWKVlZWrELGlhbeLkEyTMskeVgCfsrNabVa3v1IfaJxBIeFpim9a6ym\np6ft5wxScHt7q16v598zBgbOg81HhsnU1JTDMMGgqcrgh9DwAqsx+kf6xGcjyc3p2NiY3aJIASAM\nE/UNjS9mjZKcnXJxceFGmM8LBToOUQxZ3vd6UIsZzBJVMHXwcKQXU8CJiQnl83kNBgP/HgJRNBq1\nkSHwHpgsujiCexqNhon25AF2u10tLCzo+vraTR6eyjh2krEC6kBziCxpamrKRCJI/MQwMJ3L5XL+\n3njcoSaHmE89/fbt2ztuTaurq8adZ2ZmjCfjTQ0vnOgKyoNQKKRkMqlMJmMrLiBMuBXBYNC+H/yM\neO71+33lcjkbmu/v7zvy+S8dzvwf8uLoe/TokXZ3dz2sgHr50UcfmVvMQqUehA/BDkJWB4lK6Ao5\nOiEFnZ+fu65Op9MuN4ikQEzL8Q36US6XVS6X9eTJEw9xoGaWSiV7JrNDYudF7iDuP2DCjLiDwaAN\nx1moR0dH+vVf/3Uf91NTUyqXy8pkMpqamrLi5PHjxyoWi16U6PPGxsYsdkBdTX4gD2omk/HPEIlE\n7jR1o6Oj2tnZUS6X84l2c3Oj9fV1P6jcp/tcD2oxswt2Oh1PskgXhUZJTZpKpXR6emqvDEz8aGJ6\nvZ4bP5ohdHVYFJAKdXZ2pkaj4bixQCCgcrmsZ8+e6eTkRLVaTZFIxFRPLK/y+byPeHKpr6+v7xzB\nktw4ogVkqHN+fu7d9urqyijKF198ofHxccdgEGjJRA5KKyaKfI9ms2mDxtvbW9XrdROOMK6B6Xd9\nfW0EhFMLnFuSU3Jvbm782SJawIcPshMptn9Zx9n/QS68JGCDochgVNrpdDw1g63GgKFcLlu9LMmE\nfbK3wWKr1ao9iMFx4WRcXl7q8PDQQ5nNzU03jJQRPBjSu4cPfw4eEtyLGJkz8pXkCWWv11Ov1zN+\njckLdgW4+Pf7fZXLZYfPl8tltdtt48g0j5CwSqWSm8VQKKS9vT0vUpAd2If0J9TmBHIyqaSGHx0d\ntWd1sVg0w25qakrFYtF9y8HBwb1Zcw9qMdOoIF1iAgY1sVqtqlarmUNRLBZtHJPNZh1Kw9exACET\nAV9h3s2xyGtK8s5FibG0tKRKpeKdb9jjmUZwf39fp6en1tSR7AT7jLoXQ0eEAZwO4XBYjUbDtS9E\nI+DCSqVi6y+4JSxMml0e8MFgYI/rdDpt9GR8fFyvX7/2yTHc6LKbk60tyZg0D4Yk5fN5G8+g4Lm+\nvtbW1pYGg8G9yfmB+/p7/cdyBQKBwd/6W3/LcBcmK1NTU8rn89re3nb6KhDT4uKi/sW/+Bf64IMP\nfETiLs+ABMEpMn4GAbgFpVIpCzUlefFls1mjEOyCPBzU54y4u92u6aGSjLB0u1078w8rsHu9nmZn\nZ+0YynhZeuctx/eA4IMOUJKj5c7Pz/X48WM9f/5cCwsLarVanohOTU0plUrZOw94LxwOa29vT/Pz\n86rVavrOd76jg4MDD094DUkO3+E9sMlQFvV6PaVSKQcSZbNZ7ezs6B/8g3+gwWDwXmLAB1UzU0dS\n25JGen197d0NJXM4HNZPfvIT+09QJ6Ktu7q60uvXr5XL5e4ouDmquTHDOxVGiAw1ZmZm7gTWUK+v\nrKzo1atXd6LLRkdHdXp6qkQiYUZeOBxWv993qYFdLH7LS0tL3pXJa0mn0y4ppHcTT5pTHOwh+PNz\nVCoVnZ2d2dsDkhWLmAVIniFmjGQs4oREmREKhRzYSVJXqVRylgvfhxMUaJTm8H2vB1VmQCXEIYc8\narwpKAtoxgqFgp4+feoINNzt8Y9IpVKmQ+bzeSuncR2CaA/PIh6P2+qViSHHLoR8giYhBA3j3DDq\nJicnbTMG8R/jRpAPfDTw1cDeAAuEfD5vAn4wGLSIlnE7Uz5IT7FYzLAfsij+L71DTWKxmOkAnFjk\ns7DwcW0aHx93VPJw5AbMQGi2qFw4Ue5zPajFDIzEWBpDPsB4dkDwT5ovFhq1JX83Pj5uGAt4D24x\nN4yRMfU2JQNNFjgsN4whAaXJYDCwSJTXxMWfMTwPGLsfv+50OpYcgZkzGCHm7fz83GUN5pAgMPF4\nXOl0WrOzs4YcIWdJMrxHrU0ZMuzzTOkEdIiA+OrqStvb2+Zew0ch/4+HiXr6+vr63r4ZD6rMqFar\nFna+ePFCq6urVkZUKhUNBgMzyrLZrBUgIBmJREJv3rzRl19+qd/8zd+0kpkdB/IP9loHBweu0VFS\nz8/P+wiHRvry5Uutrq7aVw6HfBYBHhNMLvGq6PV6zg8JBAKeUMJ6IxTz7OxML1++1NOnT1WtVrWx\nsaFcLqdGo+Fx9He/+12XLZRF5XJZlUrFKhC+dmpqynazKGSCwaDZe+zoqVTK6BCsw7GxMafFUtqh\ncUQ4gCHlMFJzeXmpP/3TP73X/X9QOzOE71qtpt/4jd9wk5HJZLS8vKz5+XktLCw4n2R9fd2LZ3V1\n1Q3Pr/3ar1kvFwgE9OzZMw8k4FeMjIyYGx2LxZRIJFyyUDJwcxcWFix2xfyQsS4awm63q5WVFe++\n+H+k02nbdi0sLLiBXV1dNRV1fHzcKbCRSERra2t69uyZDWeSyeQdESmZ36izP/vsM4VCIS0vL3t6\nGA6HVSgUbPV1c3NjSy6U2ZwSlEoEuSMmpsYfGxuzmePk5KS51PjTIXxdX1+/1/1/UIuZumtqakrP\nnz93t9/r9eygj4UAuSH7+/v2v7i+fpd/t7+/72O63+/rxYsXHrdCUuc4Zxfudrva3d3V9va2nYko\nGYrFot3lsYPllMDPrd1u6/PPPzc/gl3/8PDQ/s3lcvnOe5qamtLbt289TKlUKup0Oh748GAwkgbx\nAKEAa9/c3FQoFHJ8G+VSs9n0/8GxW62W6vW6tra2XM8jRfviiy9MGsIUcbicwqPj4ODAvBA41ldX\nV7bWfd/rQS1mTMAZSRP+gjKZm/fixQt33YxhqXXJMSGeASXH+vq6RkdHHXbDhA1HI0xMpqenzbTD\nTT+RSNg8EHSiWCx6XBwMBrW+vq5CoaDx8XH/HnbZy5cvzXir1WpaWVmR9G5w8eTJEx/ZcB6IsIBH\n0mq1lMvl7vQM+/v79rogSmJ8fNzxFcOefPQRpGphKonYAUwZvgkPOicM7qlTU1NaXFw0TwbWIpHN\n1Orvez0onPn3fu/3lM1m1Wg0bKGFDIhmj6kXyuGXL1/qu9/9rrnAML6y2ayCwaDi8bj29/eVSCTs\niTEzM+MdKxKJ2OGn0WioXC7rO9/5jqEtmkn8inu9niE7xursrMVi0bKpfD6vZrPpwcT4+LjlTJIs\nph02RJekR48eeQeHttlqtbS2tqZqterQTGBIbHNBLeCVrK2tObx9b2/PAl1Jd0QJ8Xj8TlOMAQ99\nCerwUqlkHSbKHjz2EB+fnJzoH/7Df/grnFl6B8Rj6DI6OmquMkOBi4sLVSoVXV1d2dwF8jkZJoyW\nk8mkyuWyHj16pFwu5zF0r9fzWJZUUtTfoVBIhULBhPTb21sT2OEqsDihmGLa0u12vdBisZgjgbHH\npRTK5XIaGRmxOaQkP0xjY2Pa2dlxTBzvczgDEZ4GFFJOj06n41qW6d75+bk2NjYMqd3e3nqgRK1M\ngxoMBm0GyWeJxxw6wvHxcSd9QZ2FAgvP+z7XgyozUDTTXAD9QBCnXkYrB6Go3++rUqlYaAm8l0gk\ndHZ2ZiiPY5obgHEMSg+GKOx81MKQlKiVaaAIk0SkCm8auBDkBEU4wxygr2g0ekeyhI+HJDsNhcNh\nN4gYGzKs4LMCB0Z9g/8GJQCnDiVHNps1FCfJ8B3qF7IRsRgGlSEujQcBExu41ffNNHlQOzPdM/oz\n4n+BwOD+QpznmIdUxDABKie+w+xawWDQDDgWifSOzsjCQ6UNtkr+nSR7QqPQJpJ3eCBycXHhiSRk\nf45m/i02XKFQSGdnZz6+Dw4OzGADRmSxMHnDu4PanNIJS1sIWaAWCAFo4ubm5kzQh4fNa/E9rq+v\n9ejRIx0eHnpkz3QTigAnZ7lctgUDzfX7Xg9qMRNZxqBi2ELgW9/6lssISXbvubi4cLYJcNWTJ0/s\n0wa0hPE41FH0hkzxMFak7MArAtMWDF8Izzk/P1cmk7FQFp41R7gkE+zBmUElMIohPIf0J4Y/cJQh\n1SM0CIfDWl5e9tCEU2Ztbc1iU+y6mEZSK/OQ7+/v69mzZ2bJkeN3dXWlVCrl4QruqIFAwA0zlNtM\nJqNGo6FkMmlZWzweVyaT0e///u+/9/1/UIt5fHxcf/RHf+SdgiYtl8upVCr561KplDY3N8304liE\n51sqlfThhx8at0W5EolEjAJcXFz4RqCZ29/f19XVlZ48eeJBCjU5Rzz1KiGQjLKpF0Elfvazn+n2\n9tbE/16v52ObcT2j6WGrWTLDYdxRokC7HB0d1fPnzzU6OqqFhQX1ej0dHh5qamrKTenBwYEk6U/+\n5E/0gx/8QMViUbFYTMVi0RRRSqm1tTVtbm6az8Ewh6DON2/e+H0Pm8GjEaOx9AAAIABJREFUECcl\nizyY+1wPajGHw2F9+umnkqT9/X3lcjlH+FIijI6+C1b/9NNPdXh4qF6v52EENTfqEbK4i8WiPvnk\nE11fXzuJ6vLy0vG/sNAI5gmFQjYCDAaDajQavmEMEEqlkkWylAT4shGTDHRFRjVlEWFCt7e3WlhY\nMI2T3BFG52NjY55IPn782Gbei4uLtpLF40LSHfQnnU7r6dOnVsYgJ2OqSMOXz+fVbrdNbY1Go8rl\ncsbBnzx5Ikku7dj15+bmLHIgGeu+QfAPqgEsl8sG4NvtttW/OOZj/8RiSqfTbrhIe4J0BCmJ8oDm\nBSQENQj/BpvXk5MTNRoNHR8fq9lsGhqTZG40rkl7e3u2O4DpRrjN0dGRzs7OdHx8rFKpZOwbo3Ho\np5VKxRERjKlRcnPasDPi7onNAZ8NMil8+n7Zfw81CGN/eBUoyodFBDjg02CD7fd6PeVyOUeo8VAg\nUSsUCsbP3/d6UDtzLBazgcrjx49teTU1NaVcLqfj42P/GVTJ2dlZzc/P6/j42LDXs2fPVK1WNTMz\no8nJSa2srDhhtVAoaHd31xmAwzawNzc3mp+f19XVlU1nzs7O9OGHH9ooER706Oio/fDm5+e9+MB7\nP/vsM52enro5ZKDA4lxcXDRaAc6MgSHSLcSxjN/Hx8f15MkTZ5VEo1ElEgmHvWNsODU1pWQyqU8/\n/VSnp6e2q/3ggw8s9wIbnpiY0OPHj90rMHqXZFbh48eP9ebNG4dwghrlcjk3iVdXV1pYWLjX/X9Q\nixlrq0QioVKp5KiFfD5vFly5XHbkGTsHtlpnZ2e6ubnRj3/8Y33rW99y140XBrs3MiiI5+zK4+Pj\ntrKlbgd7ZVedmJiwXS7/BqfQsbExk3tgvKFsBpID193Z2VE8HjeHGNNEyPws0FAopHQ67UnjxsaG\njcvJ1YarcXJyckf6BK4NQjJcKyOjokxidD5sc0sZ9ZOf/MQCBfLIm82mNxNgxb29vXvd/wdVZpD4\nSdmAATecY+KFi8Wij2QaL8a54MQ48EMNZSEP482tVsuvD46K5cDZ2Zl97pBnUZOXy2XX76VSyW7+\nTNRg6gFpoUZhAXGEg1+zOHEm2tvbs6PR8fGxnj9/rmaz6TobSwPwZkl3yqJarWYjckSzfGa8Bgy8\nwWBgmdmwRS/4+sHBgYW7ePbh88dDge8civP3vR7UYgZu6na7XqTIh4CvWHwTExN2pZTe5W1gVM6R\nSeeOjSzjbuRNNzc3hvWwo8IwkdEv+Sa8FgMG6lQaJ/Bd/p5pIwR2VM9o9fh9t9s1J4PviVsoPwdN\nMAMe6Z2YltMFQe74+Lj51ihv0OqBNTMlROaFVW4wGLQotVarmaeBcJWfgYeU04cTcnZ2VvelVjyo\nxcwVDAa1vLzsIxuMNBqNan5+XolEQhcXF/rBD35g0L5arToMZ3l5WZOTk1pdXbUyYnd31/q5+fl5\nm5vgEQdRplgsuhwAviPgkqHBsJ0AsQg3NzdWVMMVpoaFBD8sB4MkBD85Eono8PDQY/GrqytlMhml\n02nrBkdHRz00IZwoGAzahJEHj1JmeXnZpCDYfcMBltTt+DWTghuPx+12RLYgp2QikdDs7KzLQWIz\nxsfHf0XOH76AeJD/4OmAqzvZeq1WyzxjuvxGo+ExLYgGGR4498AMw1YA8hGCzX6/rw8++ECRSMSO\nP9PT08Z4G42GE5Y4MZB6Db5JumL8zi5IWUFyKxNN3gMcYXY3CEQ8PCxEBkgoxzudjndwYtqYgiIN\ny+VyTg0AMcHIhmEIERMTExOeeGK3xZifBAISAcbGxiz1QvnCoOde9///o3X0H8XFBwdJBnlRs9n0\nMckY++bmRm/evDFSQOglu1YikdBPf/pThUIhdbtdR5/RjK2srFgIcHNz45p4bm5OoVBIW1tbxnFR\nWDB5gwAFU4/3x8PCIAZ+Nb9Gzzc5OWmHfwSleOUhcZJkGwBI/MFg0DZbfC6UEWDYjMnJScHyi4XN\nw5tOp/Xll18afYFIBOF+b2/P3Be8q1HehEIhxx8TYTEyMqLl5eV73f8HtZhpKIC/kEQRkUsULoR8\nhghgwSw4GrJhuRA7LyNxFhnecoPBQMVi0c0Nejtiz1BR09TRLKLxY1QOoR2CDzZdkPrxjCuVSob4\n8NAALtzc3HR9jvMpNTLvqdFoSJIV7NI7E539/X1HOfT7fRspDlspDGsMKX8wlESHyImGPGs4l4XJ\nZKVS8ci+0+loa2vrXvf/QZUZ7DRM1MCG4/G4Op2OPzRQBTRs2AOcnZ157I1C5erqSktLS2o2m961\nEYOen59rd3fXgllkVLjgD0f1Yn5CREI8HjdPpNfr6e3bt0omk05xrdfrxmmZstVqNaXTadXrdYXD\nYXU6HfvrIdXCaCabzRodwN0JXLjT6ejRo0cKh8M2aeH0mJubU71e19LSkmOHpXcLHY8QCEtIyaDX\nYhozOTnpQKPhQVE4HNbBwYEfPKaUvBaU1ve9HhQ5/2//7b9tE0BgNpwrhxUoxA4fHBzo6OhIH3/8\nsQF91MZ4NeOPHA6HLTfiuIYlBs46Pz9vUxOk+pCKbm5u3CTmcjmVy2XzKOBo4JzPgwOpabjuTyQS\nHsljoojhOIT4arWqqakps/p2d3f1rW99y6cA/GfKGfyq4TpDA221WndqaWrtRqOhVCqlsbExG7lQ\nKpBwi9KdySBIzXATGolEVC6XXcIUi0X9o3/0j35Fzpd+kZQKCb9QKOj169f66KOPdHR05A+YkoIj\n+vDw0IaLpVLJE7z9/X1ze6EukmmXz+e1s7PjmjUQCOjVq1caHR3V/Py8tre3TX9kFI3ItVKpWKOH\nO1KlUjGnmZwTckRCoZDK5bINWCQZV6aUgNfADkzONrsmKpiVlRV9/fXXzvUmIqL4TcYgDSS2Yclk\nUq9fv9bl5aVPqNHRUX311VdaWlry5sH7hCW4u7vrhpoyazhVFjEDJdvs7KxLn/e9HtRipsumEWFh\n0T0DO0myjAlGHKPfhYUFvXnzxrBSOBx2OUANyKKEbgkMxTQPuf6wGgMOMv8el1Iu5PnDptx4SoC4\n5HI5fy+I/DwAkrzQh/8MNTe/ppfgfcPbgPuNcBc2HwaInEiSHF3MAmV83u12NTMzY9gvmUyagssO\nDe+70+l4uMQD/Jc2O/s/xMXkid2KxYeJCqaEQG+4V4bD4TvHH+76sVjMyIj0iywU0AteH8MW5Eaj\no6Mm+1BqYHGFcADlBgMTMFeIOPF43AMVoC3MbQiFpCSoVqu2t6L0IOUV4lK73VYkErEPxu3trfkg\ngUBAR0dHDrVkiMLPy4ibngFv5larZS43AxbEsbwOny0lCra7YMq9Xs96wPvuzA9qMe/t7dnkcHNz\nU5VKxSUFknZgqJGREdXrdauxSRUdGRlRIBDwv+t0OkqlUoaSJBlSqlar7uLh5lYqFW1tbdmNk1oX\nByJqaRCPq6sr7e7uqt1uW+18fX2tvb09HR4eOvEKXBgDGqBHPI1BZo6Pj/XixQtVKhVP5N6+favx\n8XEz3GC3EbFMs4k2sdfreZrKZwcfZXt725O7eDyuvb09NZtN1Wq1O2XeMCWAzwlC0eHhoX9ebL2C\nwaBr/Pe9HlSZMTwho7lCH5dKpRzzQPc+MTGhly9f6pNPPvGCxrQ7k8no6OjIaU/hcFiJREJbW1u+\nKdPT0x48gPXCJgPRYKiA38Xo6KinYcBSkjxgAPdNpVKq1+s2rMGAEKXK3t6ek6WwTYjH41a+MJYm\nLTUcDvsYj8fjOjk5sSkkk8TR0VEnP0nya8K4I59bkss50m4RCtDoSvLnv7y8rE6n44EKYuDZ2VkP\nfXiv97ke1M48Pj5un+VIJGLTQxx5jo6ObG5IVBqxwEj+b25uXFfncjlP0jhyce1BG4gtF8c2Rze1\n5vz8vKFCxtnUrTDjSqWSDcYh6qAdZPLG8CYYDNriQJK97vg54UxQ06LwiEQiOjs7UyQSUSAQ0PT0\ntJl2PCDSu9qdBxSXfQhJ8XjccCF1NbtwvV53WYVYFU8Oml1KsNvbWy0vL/uERIh73xiIB7WYLy4u\nLF0iVbRcLqvb7drWFVVErVZTLBYzk+7k5MTG3NVqVVdXV84ShCtMSCVCTsbYku6w6SYnJx1ttrW1\nZfhN0h2MGF0hN71eryuZTEqS3Y8Ir4fM1O12bWiDEBTK5eeff24PakmexjGoAUuvVCqmWxKPMezz\nDFR4enqqUCikZrMpSQ7WxAcDQhG5MMCQt7e32tracp2NrS5ErvPzc7169coPCyP7QqFwr/v/oHDm\nv/k3/6b9LSYnJ+11vLS0pGq1atEmVEQojESCkVaFofjr16/16aefegoGXgonAaMVjARZtMj2W62W\nZmdnFYvFvAuyWIgwi0QiHm0z6ctms1aiSPJkjaYVPkUmk/EQY39/326k4ONMMG9ubuw7jUcIA5xK\npeLSAtRmdHTUwZhg5kBoEJYuLi5MKOKziEajHgZtbm4qlUrZZqDdbmt2dtYNOCUIFINEIqFyuay/\n9/f+3nvjzA9qZ6ZZIw4XDsBwOurNzY3H04yHaYBAAMhEIX+DoHWon5hu85rDQZVIpBDSBoNB77KI\nXBlWkHjFAwVPuF6v69WrVz4xkGNhDTA7O6tKpWJZE5gzxPmzszPvqrw/rGYpFchqGRsb83u/vb3V\n4eGhkRGaulqtZvydMT4kfCIyIBIhNID/wqQUpyRQCx4APq+rq6tf8ZmHLxTZuOdDjEGRgcyJ3Qby\n0OjoqL3dIpGIXYkIkCfeiykXHNzhXZlG6fr62goN0ILhmpUSCBgREj2LnFOFDBXYf5Cc+v2+9vf3\nNTs761OEKSW2VzSsCFWLxaJOTk78M1C+9Pt98zCGLQYkGXmBT4JBzPX1tWKxmBYWFty0MjBhx2+1\nWnY0pQGm9JmYmLDpSzgc1tu3b9XpdMwVv8/1oNCMTCZjUj2NHBMxEkZRUXS7XaVSKYenN5tNLS8v\nq9Vqmby/t7enxcVFRydMTk56tA2vIZvN3jFVgRXGAgZDjsfjPimgg05MTNx5ULCNpYxgVxsZGXFp\n0+v1lEwmPXCZn5932QRj8MmTJ0Y0UJ/DEUkmk3r79q0fZBCUpaWlO7pBFtbs7KyazaYJVUCf7LoM\nXvCPnp2ddXgRmwrWuVAF1tfXfVp+9tlnOjo68s91n+tB7czDlq/Yxe7u7jqS6+XLl+YrDwYDY8Ow\nw6rVqiYnJ/X8+XPvcBzJkH8YVvT7fUNyHM+lUkntdtsunxMTE34vBwcH2t/fN+cXQtPV1ZVH2dgc\n4Ex0fHysRqOho6MjW9sy+Hj16pXZcjSvTD1PTk7sqk85sbu76+YWyRK8FeRNaCgrlYqCwaDevn2r\ns7MzZ6CAwQ+jQyhfGKcT6fb1118rEokYb2YMTloW+styuWxt4K9yAH/pIjt7GCojOJI/I4SSpgdI\nCIQBaQ8JSXTqGGzf3t7ao46bgyLl6OjIY2vk9LD0JicnPR0LBAKuw/H0GPa/oJwJBAKuMylVGGqA\nBtB8RiKROxM5xviSjF/jDEpwEfVxIpHw2Bk8m4cRghA2BmDplENMERnHU/dL8oAF/D8cDruZJYy+\n2+3ag+Q+14NazMSJQUHEBhZ39unpae9sKDaIGh4fH9fs7KxrQnBbeA0sOoz/CN6h+clms5qamtL8\n/LxisZjy+bxHyywscFmINpCUksmkuRt8fTQaVaFQsO0WC3xsbEzhcFjz8/Oul9kt8aKmHia+AsXJ\nzMyMm8KZmRkvUFANkgfAiYfH15QwsAQxv2FQg9Ib7jThQNFo1PcGrnQ2m7XmL5FIKBgMql6v6/Ly\n8l73/0Et5tPTU01OTt4xGMRwm6gCjmJUH+zA0i/Sqqh/WRRYyFILD4P9vB5BOZJct0tyXUrdiJE3\nO7kkZ1oz7GCSxi5HLcsDQSmBAyiNGpNHYEkmcwgU2OkZkPD+JHlHl+TPhZ9XkhOvKGcY4HDKwEXh\n52YXxraLyGIEwfwawS4ozX2uB9UA9no9k4kqlYpD0IkvYLGR2wwufHx8rEQi4SMPrLrZbLo0YKer\n1WqWWqGiGCYjATVdX197zAwP5Pr62to4BjeRSETVatXmiVggMBW8uLjQ0tKS5V63t7fmg/T7fW1v\nb3uHhf9MbY1O8PT0VIVCQRcX72IxGNUDPYJdk9gaCoX8ELNoQXIw0ME7D3gNTSO52PQEIDCcmBin\nQ7DCHAdnpvtcD2oxLy8vGzNGcZxMJq1CPjw81OPHj5VKpdTv9zU3N6dXr15pfn7ecBFyJYSfhULB\nZts8LGNjY44qGxsbs3QIgSrdPHUxnAjqRY7ieDzu6Ao0dHjWjY+P6/DwUOl02lTSwWCgQqFgk5dE\nIqGlpSXn+EnSwsKCNjc3lc/n7VIkyelXDMkogchDZKJJ/mA0GtXs7Kyte1lok5OTVphjMcBGAIQI\n1RZsGzuvTCbjUT5+GwiJf0U0+qXr4ODApUWxWLTioVKpaHt7W4uLi25MksmkSqWSTk5OdHx87NIh\nFAqpVCrd8U0jtw6SfCKR0Pb2tjqdjhKJhBs3Fj2aw2w2q2Qyqc8//1zZbFYHBwd30qVglDWbTU1P\nT1shgh4OTjUG6ATb4LbPrthoNDwlRHaFSoSQeDDg4bhfGkkGOIVC4Y5K5+XLl1ag9Pt9RSIR7ezs\naG5uTo1GQ6urq66nKUGgcw7bKVSrVYVCIW1sbCiRSHikT5gSGPe//tf/+l73/0Et5mg0asd7TALZ\nfZDhU16Ew2FFIhF7UCB5v7r6ReZ2qVTS3NycO/FoNKpHjx4Zw6aRHBsbU6PRMCowNTWlZrNpf2Ka\nQqZ0CFmHE5dwM6Khy+fzrpsZceP5wQ47MzPj0+Lt27cmWUny4uOBZleUZK+L4QwSGs12u22fan5m\n6V0/Uq1WvbiB59LptLrdrrLZrPsSppAkyaKuSaVSxt6Pjo4c0cY9+uSTT/TFF1+89/1/UA1gv9/X\nzMyMlcXkgQwntwJnsQgjkYgbJoSXLGgoiuwkHJGQe7gJ0WjUQw4W88rKitlmOAdJMkmIGpQpXyAQ\n0MzMjGKxmBlmDC5WVlaMqkBmIu0qlUopmUwqHo9rdnbWdl1YEpyfn5vrfHt7a3Ny6KcMlnhIoMwS\nJg/n4vb2Vul02uw5lC70EjyYpBA8fvzYmYeUQMOREisrK5qenvZgh//f53pQizkYDKpYLKpUKhnR\nOD4+NuQDrjucEzIctINjD5gsJBweBIB+OBksymKxeMfSCkrk4eGhPZ7xVy6Xy7a8BceGhbe3t6dS\nqeTG7fr6Wrlczuw/lDFo7oLBoBs8QnaGs/6gXvb7fR0cHBiRqVQqNrVhTI16He83vj/fo1aruYGr\n1+tqNBoO70FMgA0X0cvYDjBBxRZ4bm7O77vVajmo577Q3IMqM9LptJlaFxfvUptyuZzH18SmgTBk\nMhmVSqU71rTIq4aTVcfHx12KMHWjhOl2uyoUCo5JA6dOJpP2W0NIy0JbWFhwqYO5YiKRcCNZq9WU\nSCQsDqBUGQ7xkeSaE/gNm16a0lQqpYuLC83NzalQKNitlAdkuCxghx7edVFk39zcaGVlRZ1OR/Pz\n85JkhiENHgYygUDA42/qYh7m4XqdfgC52PDw6n2vB7UzQ9phuifJNShDABowPmiySKBBMsxgp8ag\npVQqmYIJXgu2C8wE8RybW2AvKJPgymQSgt1KMlEfNhlKFSaScDTAxYEGsYOdnJzU/v6+J5X8/DSM\ncJLB1xl0BINB02IhYPEz8JnwEEgyR5mwTH4GRueXl5f2qoNiixSMB3PYRBGN5rAS5n2vB7WYkd5L\nUqlUUr1eNxmcupfGplKp3HEMRV3MqJdjEItZoDMUI1dXV1pcXDQzj10WMjujbB6gYRUy+X/Fb7JC\nms2md2Wmfdvb22o0GpY04X9MzQuMeHx87IWJoSELCV7F6empVlZW/q0dm4eKRc1uC5TGAuckOjw8\nNP7c7/c1Ozvrmh9DRAZOu7u7pnpS+8/PzztCgxOC0yYQCCiXy93r/j+oxQwVkqgzpk2gAF988YVV\nJNTHWMjCRYabwDQMiI0F2Ww2Xe/y9fv7+3fGxzhlBoNBUzkvLy8t12fwMqzJA6GAqD83N+fdn4YT\n2T7Iy9jYmNbX1z1KluRAePLBcd7/+uuvbZEF/DY6Oqpqteoe4c2bN6a3UiqNjIwol8tpYmJCT58+\nteSMh5zT6M2bN45shlkIJRZBL1NIsPiXL19qa2tLvV7PlmX3uR5UzVwqlUzfpB5EECpJ3/72t5VM\nJp1wFI1GzX9eWVmx+SHcCthmHPk4XTLsuLi4UDabdX0IFfL8/FxLS0va39/3eykUCg5oJ0Ma/jFl\nDST9er2uo6MjPX36VF999ZUNBcHJQQzYAaemprS0tGTTcYhOWM7WajXNzc2Z34GKPZlMKpfL2csC\ngxxcP7HYYlIJ+sFGgLi13W7rt37rt1yCDAYDPX782J55w9mI2Dc0m039xm/8hv75P//nSqfTymQy\n2tnZudf9f1CLGQfNy8tL7e7uan193aNWVAy9Xs8y+WKxKElOlBoMBspkMnrz5o1dkRYWFiy5oo5k\nLHx5ealms+malPqXAQniWcoYasWxsTE9f/5cT58+VTAY1P7+vnOxEQUkk0ltb29renpaGxsbGhkZ\n8S6KgTcE/evra2vv4GdgUXBycmKIbGtrS4lEQuFw2AT+WCym3d3dO7yM29tbLS4u6mc/+5lWVlZM\nqJdk1IMyKZlMKhAI6MWLF4YtB4OBSqWSVlZWjOKAr8MrCYfD+slPfqKZmRlbKvAe3vd6UGVGo9Ew\nL4BdUJK9j+Ets4NVq1XX0wTq4IhJWurR0ZHd4rGgpU5sNpt3Xocmbmdnx4lWKDHgLWPyQh429SMl\ny97eno968kmgRkJ4Pz4+9micqIZyuWx6KqbjrVZL/X5fxW/yqiE6MZpGmAo0iRcdJjb4MdNUYzXG\nQwcVFHX1sEUtmwpTyuEkLHB0PhMU5m/evLnX/X9Qi3m4SwfQH5YtkXCEreqPfvQjHR0dKRKJ6O3b\nt5LeWVtls1lFo1Gtr6+bOwFycXR0ZC84OnCQA2AscGK8JiDdo+6QdGf4sry8rFQqZU820BEgLnas\n4Zhi8OlUKuWSgtiFRCLh4c34+LgFvcNMPPw7Tk9PlU6ndXNzo7W1NROFfnkEjv9GJBK54/rZ7/eV\nTqdVKBScIAs/Ay44TR42tpLMHuQU4IS7z/WgFjO0Q2pKfs0ABVgunU5rYWHBNrL8ORBRuVxWr9dT\np9PR9PS0R9O4u+fzeeXz+TuUS+pv6sTb21tTGkOhkGZnZ73j8YCgXAYxQIRKWGUkEjHjjxIoEonY\n4wNvEPgOTOuowcF1IS/hyREKhRSLxXR5eXnHN2Rvb88NLGSphYUFE51I5Uqn0woGgyqVSpqamlKv\n19Pe3p7a7bYDRCuVihc2ppPBYFDz8/Oan5/3OB8l+zAJ6n2vB7WYQSdubm5MbpFkiAmFdL1etzUU\nUb0wwJDic8RWq1Xb20LgOTo6Mk5aKpW82OLxuMWww87zt7e3llPhA4d8C686EABQEOpYBigc05eX\nlzo6OlKr1VKz2bScCuTg4ODAxCGwbyaGkvx/4o7fvn1rzJgUWUb62MyC/tDgMoBiweOfQfnBlJVJ\nI7xq+pCTkxMr3KmnR0ZG9OGHH97r/j+oxYw6hFKBiyYpHo87Xo1SAguqYWokBoUYfQ9LmbLZrCOH\n4/G4d2lQDMoWdmsGG+l02qgBJCdMt+GJ3N7eupRYWVnR6Oiocrmcxa9TU1OamJhQNptVLpdz+A7l\ny9TUlObm5pROpz3lxB9j2AIrFAo5LXV9fV3RaNS0WHR7DJbW1tb8QOLkORzaQ+kGR5zPPxKJ+GRC\nZhaNRpXNZrWwsKBcLmdRLCY4NOTvez0oNAMjwlarpePjY3344Yfa3t62pB0ojV3t8vJSGxsbZrHB\n9nr16pUSiYRevXqlxcVFHR4e6rPPPrNJYC6XszsPwtNIJKK9vT1Fo1Ftb2+b6TY+Pq6DgwNPxpia\nbW5u6oMPPlC1WlWxWLR6Gbejw8NDLx6mfNVqVYlEwkMVSoLz83Pt7+/b3LvZbCqZTOr4+NiZgtI7\n4QDE/1gspnQ6rd3dXbMCZ2dnLQpIp9N69eqVwuGwPTOmpqb085//XB999JEdkmjc+Gw7nY6mpqa0\nt7enTCajL7/80nU3zeBwhszOzo77mFevXt3r/j+oxQwPF4J5KBTS6uqq4vG4VlZWHGqDrzF1KHjr\n7u6uBoOBlpaWlEwm9fjxYy0sLHgYwq6CNVY6nVar1VIul3PjRkQE0cU3NzeOLSYkaHJyUh999JEG\ng4GHEpQwpFHlcjmXMzSGKysrFtKyMyPnxzqMmhUKLMY46AehrUJ/xUBcknd4pqOYiUvvUJ7b21s9\nefLEuj+GH4VCQZ1OR6FQyM3msCYS7nQoFFI0GvUDSv+xtrZmUevGxsZ73/8HVWZgmI0iA9wZZGJy\nctIZf7lcTs+ePdPp6an/TS6XcyYdiopOp6NcLmfhar1e947b6/X05MkT8z+y2azm5ua0sLDgehAD\nlGEjQ7gT/DqVSrk5oxbHIxp6JzxsBLnPnj1TMPguzphgnqWlJTeF7JpLS0tuvhKJhCYmJhzhQBmS\nzWYViUQ0PT2tQCDg7zkzM6NoNGojR0ozAjLhdo+Pj2t+ft4G7vQctVpNIyMj5jED5RUKBU1MTGhu\nbs5wYSqVcsDo+14PajHTZAUCARWLRVM9z87O9Pz5cxv6Yc79xRdfeHeDnthoNDQ2NqZyuazNzU1d\nXl7q5ORE09PTZr0dHx9bxEouyvARyv85HfCWgITPa1OfHx4emhkH6R/vOHZrYuGQXf385z83rl6t\nVvXy5Ut/b4KJcHhqNpuq1+uOOAZmBFEpFosql8u29cJa4OzsTLu7u44SrlarOj4+tpH45uamEaBS\nqaROp+NkXMx0sHmgyTw9PdXx8bFubm5Uq9XU6/X01VdfGWu+z/Vmi5xbAAAgAElEQVSgFjPuPufn\n5/qzP/szjY6O6uDgwDXbq1evbDyCbxyyIfDVRCKhr7/+Wqenp1ZWswhgjjWbTVMaOWa73a62tras\nzGDYIL0zAoetBkWTRXp5eelQH0QEqEe2trYcwdZqtcxXrlQqJgKdn5/r6upK6XTa9gmvX79WtVrV\n/v6+ud2zs7O6urqy1g8PO4ZI5+fnOjk5sWXY5eWltre3LQPb29sztMgIHNV1rVaz+oSHF2gQY5mj\noyMtLCwYbx7OP4lGo2q1Wvrqq6/udf8fVM2Mp0S329Vf+2t/TTc3N1pdXVUymbTwM51OK5vNuqwA\nm4Vi2e/39cknnyiXy5lY/1f+yl+R9M7+i/Ew/sZAcLFYTI8ePXLJwjDl7OxMn332mdrttknpg8FA\nT5488ZELNRQIL5FI3DHehnXGQGQwGCiVSpmFNkzFpJxKp9OeFHa7XZ2cnFjCRN4gCMfq6qrVHsvL\nyzau+e53v6vJyUlls1nnhwN3UpsD4/HgxmIxW56xIaTTaT+olHqS7A6VyWQUjUb1wx/+8FeyKS6i\nG2i0kCZVq1XVajWb/cEfGB0dVT6ft1VrLBZzHASKCZw48dGIx+M2Gm+32zZMCYVCHnsnEgl7uzF+\nHuYJgy9j78VuhhcGBi0saKyvksmkjo6OfByfnp7aVkt6xzvhCEcihqPTo0ePrG1sNBoeyRNkn06n\ntbi4aNYh6hTUO8COGNnABMT1iIcJByOMXqLRqEZGRlxyQGcl/HN5eVmRSMQeG/e5HtTOzA41MjKi\ng4MD+1kMa9ngAsTjcSeHRiIRzc3NmR4J3ZIanIFLIBAwDMdNGfaH6PV6HlpAA81kMqpWqx7jhkIh\n0zsxUcRNCLX08fGxJDk6DSiQIxlYEP8O6Z3NLjxl+A/QW4H2GGAkk0lVq1X1+33zVygjGLTADqxU\nKrYWuL29dckFFZQdGcadJH8O4XDYbkVwvYf1hpRK8GBAVd73elA7M4MPjPkwKSF+YGtry+6dmKnU\najW7WfKhV6tVx5uhhqBm5TUpBejGa7WaoT+kVTwMmLZIshigXC67QWNixn94KEMc6na7Oj091cbG\nhrHlVqulcrls1hqqDlhphPocHh5qb29Pt7e3Ojg4UKfT0dHRkUub6+tr1+ULCwuuo29vb23OyKST\nGh35GA/y7e2tzXBarZabOd47dAL43GDbp6enOjk58evcN6H1Qe3MkUjE5BYmaOl0WpFIxFkky8vL\nCoVCymQympmZ0T/7Z//MdrFAXN1uV/F4XPPz86ZPXl5e2hL38ePHDrZZWFhwlARY8/z8vKeINzc3\n9rgbHx+30yg70/T0tB49euTuH4LU4uKiY4lpULPZrNXTa2trNm6EaA9feGVlRaFQSIVCQWNjYy49\nstmswuGwCoXCHQiNYQdQH43tt7/9baXTaY+hCT2anp5WrVbT4uKid/JMJmMLsqurK+/8YMsgOXz/\ndrutxcVFvXz5Uo8fP5ake8dAPKidGf0eFyaFsNfW1tZULBZ1eXmper2ufr+vx48fO42KHWTY4xnC\nUCqV8g7/5ZdfWr1cLBatokAYgLcdWC0NHO8Ft3m8JqhhIfUMBgP7w11fX6tQKNh7mjp6a2tL5XLZ\nqAoPE0rnfD7vEwIbLHZ73O0xbxl+uCYnJ83rIBGA6R6CX8br8DcmJiZUr9edb0Itnclk/KDBz0D1\nDkc6l8vp+vra2dz3uR7UYoYQE41GTU4ni6Ner2tra8seD0jucTN6/fq11SdgsGSDfP3115bNM4lj\nocDvoFEkx3pkZMTaPpo+rArIy2PnpmY/Pj72Qs/n8/bG2N/fN5WUU6PT6SiTyVj9AhIivSsdSqWS\n2XTDVE6YhJRfk5OTOjo68klCs8tCRYMIFxufEMwVpV/0KsCgDITQVHY6HXU6HaMbpFIRiwG0R7nx\nvteDKjMgq+DbQJcNUiHJtSq7GRo9iEQ0gWj6qP/wRBv2bwP2YpLGwqRposnDt7jVat2hbCLkRCUO\nFRIvj3a7rWw2aztdGjHizIZ9pQn8wdeDxpDdEM4yu2a9Xrf9AaYvuC7xmWElgAfd0dGRxQXs5nwO\nqVTK2SUkVfF3nJYgIQyFyMvGz46I5Pe9HtTOjL8DKmwGHbgKUdtSa+I/zA4JjIYkfnT03bM+DPfF\nYjFDa8M7D7HAQE4ou6VfxDPE43Ef4SAVktwETk5OeudCns+uNzo6qkqlYuJQIBBwQxUOhy3jAn0B\nmqTxg8BEaQGycHFxYUISuylWDZw05+fnajabLr1wSqVR5AEafjCHjRVRyKPG4TNBcAukivPS+14P\najEPW8HSsLVaLY2NjdlDeHiHDoVCRjbYibChQqFC6lS1WvXRyhSPwJ2zszObvtze3nqU3O/3TSBi\nhI6ae3JyUqVSydxg5E/sbJubmybZB4NBR0O0Wi37Y8AVYRd+8+aNjo+Pjaogeo3FYo5sYFemUW40\nGuZkQHyiyUSXiHoaM/ZoNKpGo+ExOzg9kCeWZqAwlDfHx8dGT4YTsEBJMpnMve7/g1rMOBChZxtW\nOtze3lr9wI2U5OmWJDPeyPTAulZ6J+GnURkMBvYjnpqaMg68v7+vqakplxXVatU1eD6f9xEOJyOX\ny9kSgBAfJFM/+tGPvHujSwQdGB8fVzweN7EpHo8bffn2t79tHw+ywyORiHkbOBm1222dnp5accKw\nBGf8WCzmhjCXyymXy7khjUQiWlhY0OjoqG0dGDRR2tHwYRXM0ArHqPX1de3v79vPBAHCfa4HVTNf\nXFxoZmbGtS81LDZTn3zyic7PzzU2Nqa5uTmdnJwok8mYq0sjx/E/7J8xMjJiKRZH+KNHjzykgPnF\nTcRY8fb21mYneEpwOhCxEAwGlU6nTXIC2sKMkRAg2HZzc3OWKeFGNDIy4qRTalS8Nfr9vubn510m\nZDIZoweEaQJXnpyc2OIM32gQD+A58lpWV1c9jSR2jQYQZuHc3JwhS5z+8bRbXl5WPp/XxcWFy777\nXA9qZ+b4J5MOIWYsFjMzLh6P+6aOjIxYWZHJZFSr1TQ1NaXvf//7bhDZvSYnJ3VxceFSA8ssdnB4\nIZIc0l6r1WwEg6M9pcvs7KxqtZoajYZVypCGbm5unI+NF0ggELCDEIR2bHfhITMsgrjPxVSSBxw+\nBCcLDR9oDVNGeMoIejnhOJmIP+ZzB/HhgaLeZ3hECUbWYbfb1Zs3b4xr/yo6behClsRiBUKi7qS+\nQ7dHg3Vzc6ODgwMfc3/4h3/o6Vs6nb7jr4FWTpL1gpQUBwcHSiQSxrORUwEXwiG+vr5WuVxWLpez\nUeHc3JwpoMFgUAsLC3Ye2tzcVL1eN3S1urqqvb09n0SMg+Fw83PTA8zPzyuZTKrX63l48/r1ay88\nIosnJia0s7OjUqlk1Qk2teDltVrNBKphxAJbA1TjX3/9tYlc+PrBz8b+oNfraX5+3o1qtVq91/1/\nUIu53+8rk8nYlHA4nFGScU7MSIhE44hjEPLhhx+a9AJsB1yHmSHHMK6fMzMzSqVSOjw8dK2MTInd\niJIlEAhYAApKgkcH418WMuoQUBR2RkmGzXgAGBKxAPlMQAwoc66urjQ3N2cWW7/fNy8EVcpwqBEN\nGooWampMbVC2876ur6+VTCZtMHl+fm6yfr/f1+TkpObm5swN4QFH3vW+14OqmcfGxrS9va1ut6v1\n9XW1222LO/lw0+m0+RMTExPK5/NKp9M6OjrS6uqqaY1Pnz7VH/7hH7q+pWm8urrS0tKSDVlSqZQb\nRtJJ0+m0yuWy4yQWFxdNsfxlb4zBYGA0g51/cXFR8Xhc7XZbpVJJS0tLarVapo/u7++rUCi43oQA\nBewVCATuGH3jj4zdQSwWs3i11+vp29/+ti1siTzr9/taXV3VYDBwJgxGjuQR0hwTsoMpJWaR+EPj\nAYi6O5lMqt1ua35+3v7Ok5OTtvt63+tBLWacgjqdjv74j/9YH374oY1UmKKxQ62srOiLL76wb0M+\nn/ei3Nzc9OBhY2PDdTblwsbGhlKplN68eePdV5KxamRS1KTPnz/3AiADGyPFm5sbvXnzRoVC4U4Q\n/YsXL5xR0u/3nfsRi8XU6XRcH8M9xpUJke7c3JztDEqlkr7//e/r5OTEtbokl1kvXrywcQxj+Xg8\nrpcvX2p2dlbtdlt7e3v2jF5dXfVUMJFI3AkDBREJBAJKpVKqVqu2FeO0qlarmpmZ0cbGhnf9Z8+e\n6fnz5/e6/w9qMRcKBX9wy8vLVjUAh+FjAaUTdfT09LQHJ9Sr+XxeZ2dnWlxc9DGIrSuICYaJMOlQ\nZGOezQQQOf38/LxVzVAiB4OB1tbWPGRh6LC+vm63ULBnFCEYM/LAcOJQk1L6HB0dKRQKaW1tzQYu\nfAYMhvr9vpaWliTJiAjvIZVK2QckkUjo5OTEQlVKFghXPOwY1GD7tb+/r6WlJX8/kA5Mxo+Pjx1G\n+qMf/ehX5Hyuw8NDCzorlcodUxKgNaiRHIO/vMNJcnqT9E5XuLOz446dhcCxj29dv983T4JjFWND\neBPb29tqNpvmOMBt6HQ6LitCoZDq9br29/ctYRo2UgECxL8ZK9jz83NVq1W7McGVDgQCOjk5sWSJ\nKDbw55ubGyMdGExSu7bbbR0dHalYLOrg4MCIR6PRMC2AQM3h3MHb21v//4MPPrD0CtnY5eWl3r59\n69INMcHu7u697v+DWsyMR5m84QgEtMQOwoeeSCRUrVZtvI3hNzAeOCuqk1/2Nr65uTHri6HMsPsm\ni5YGk4UNG43mEzYauYHAhpisVKtV78qQ5nG5Z8qHhQJ1PQuEKGJOCppESaZk0pgyCkeMS0wyWS1o\n+ygrMDPHngBMGvRIksM8edgZxlByZDIZw5oHBwf3uv8PajGvrq6qXq+7XKDGhQ3X6XSMQ0vvatzF\nxUWl02nHeJHElMvlPEzI5/NOfo3H4zZNYYfLZDJ2KSoUCmo2mx6CAH0tLS356zBNhMMAzkt8wuzs\nrDKZjOMp4GywqHZ3d82RxkgRhyUsDdj5QXDgU1AOTExMWGXNZ4VHH+VONBpVIpFQLBa7Y8GwuLio\nTCZj9t34+LjLBsxlJNmHjocTQcDk5KS51LiXBgIBlzvvez2omnl3d9fH3fn5ufb29ky3pAbc39/3\nbtPtdo0NE2HQbret2aNmBXVgB4rFYrYy2NvbM/m/Vqvp9evXvpnpdNrQ14sXL2xSA77MtI2pZKvV\nUiqVsgni5eWlR+E0c1iQHR4euieg9gyHwx5Tw1tGvEpNzVTy+PhY6XTaGSmUQiAu4NK8FwI9JyYm\nrH2Eu4EFA+GeTFKhsqIXpDSbnJz0hJNBCZzw+1wPamcGhqNhWVpa8s41Njamer1ujgFH/szMjNLp\ntK25njx54ro7m81qeXnZdlWIUlm8iURC3/3ud40m3N7e6tGjR3ry5ImpoSwERr/sZrVaTY8ePdLk\n5KRSqZSWl5f15MkTTU5Oanp6Wqurq8rn81Z1EP1weXmpubk5188EZgIrzs3NuWxZXFzU5OSkgzuH\nU5442ldXV72rT09Pm3jEzwv3BKW79Isdt1qt2jASE5tkMmlbBfoLSplsNqu1tTVb7cIrhxY7PBN4\nn+tB7czNZtO7BbKeaDRqwxG692AwqEAgYG4E4D66t2HqJey6ra0t831xGu10Otrb25Mk7+KlUskN\nEWP1P/mTP7FUiRMhFoupWq2a2IRjJvzjvb09zczMqF6vK51OG47LZrPa2dnxoKVcLnsUD/H/5ubG\nSm44y5VKxZg0LkTdbte6O8j6MzMzprfigAoCwpj77du3Ojs707Nnz3R9fe3Phh0eI8jhGOZ4PG4y\nFqPysbExtdttnZ2dmXx1n+tBLWb4s+Pj43r27JnGx8ftS7G4uGhTP3Yl6suZmRl39clk0se99C48\n5/Xr15qdndX09LRGR0ddGnAShMNhZTIZ7e3t2RKLB6bdbnswgx6P4QEnxMLCgs1gcMn/4Q9/qO3t\nbT+c2GQxoSuVSkqn0/Z6ZtKILo8dk5+B8HXEslhvTU5OOrASg3ASAjKZjAqFgsOJarWaUqmUc1cu\nLi40Ozvr9yy98xbBfw+vEkk+qRYXF525CPID7fRXCa1DF2Pdbrerr7/+WsFg0C6UlUpFh4eHOjw8\n1Pb2tq6urlQul81VZiJ3cHBg0k+xWNTp6amurq684EBI8BVmZz05OTGBiCkd/GluEgw1/C2w/Nre\n3la/39fTp0+Nxe7s7NihHnJQsVhUvV5Xs9m0OppBTSAQ0OLiog4ODryA6vW6Tk9PtbW15UGGJDsN\nYbfVaDSc2wJXhZobH+dhx9F2u+3FSUIANNZGo2HUhO/faDTUbrfvOJyCclxdXRntuG/a1INazBi+\ndDodffbZZ7q6urLJCP/P5/MqFAoaHR3VysqKj2IooaFQSOvr60omk5qZmfHflctlp5hisBKLxVQu\nl410wMmlDDk+PtbU1JSNZiS5Ycpmsx7EzMzM3IkNptEjjAcxKcR3VN2SPCYeDAY6PT1VJpNx44m/\nRTabtf4RVQzSL3jR0WhU6XTaeeH8HdBkrVYzOR81tiSLGOCRsODBkDn52PUlGTFBG0lZ8ivW3NB1\ndnbmm4F3MtBQvV5XJpNRt9u1l1qn01GhUPAuglfa/8Pem8W4mqb3ff+vdq7FnawiWfvZ+3TPTPcs\nPZIVQbDsXDibA8QOksALYASyIyMXUSLpOjGsIIiQQFGgwIHgC8NyvMSJBRuxJDua0YxmJtPdc06f\npU7tVdyXIllkVbFIFvnlos7vaZY8stqn0NKoMB/QmDlbcfne732f5//8F4YfDA3gZ9Tr9Wv+Fycn\nJ3aDLi4ujCUG8uHz+XRycmJ/j+iDUChkWK7X6zWVSzKZVLfbNZ4EF2NxtHIsDLB03juEJthxDI3g\nI1OToh6BXcdnA3kBEep0OqbS5kFFZAveLslw9PHJJgOZ8SYYk5jRaKRGo2GRbGR0jyvr3+S6VYuZ\nZocvfLwOnJ6eVqFQUK/XUzKZtJ0DqIuufzgcqlwum98FmXh4x4HFMjjhxjO6ZljA+JZhBna2sNtg\nm0EjdRzHms+TkxOTYU1PTxtnpNvtmqMQMBZ4LU3VeA4KC4oHFJI+iAbMwG63q1gsZsoR2IAgEjAR\nWbRgz0xFWYy9Xk/BYNCQIWIiYAbyHYIwIezlYYco9abXrVrM3KS5uTnt7OxYfVYulw11IHdvbm5O\nh4eHevXqlVqtlo1Sy+WyKpWKOp2OXr58qVKppP39fSP8MMqWPnFOAtuFuTYYDNRsNs2lf2try3Zr\noK9ut2t1da1WM+U2D+Dh4aEajYbV0/ClIbWPuxSRa/Ls2TPt7OzYz2MgAQoiyVz6qXtxYqrX6yoU\nCte85qrVqgqFgl69eqXBYKCjoyOdn59re3tbH3zwgT1U5XLZhK0Q7fnewc9xNCoUCup2u2o2mybB\ngvOChdmbXrcKzYAVNjs7a74T4LH4zY0f7cQmjBu/rK2tWWNHLcmYHKUFU7l0Oq1oNKp6vW6umODQ\nTNimp6ctkmw0GlmtSmorUB96ORrRQCBgp8rc3Jza7bYeP34sScavjkQitosi58JR6PLyUvfv3zf+\nMPV3KBQyVfX8/LxWV1cNIvT5fKrX68pms5Kkx48fG1TH6FqSiRNwkEIQwNic/G3yFwlCmp2dVTab\nNR0iOze5KD+MGx674CVAcqFLhqhPpw3uymJhyIARIHnQMNTgYbCAqtWqDTDq9bp6vZ6psTHfxlIA\n+T9+ERDlu92uyuWyuc1jik7IPELRcd5xrVaT3+83425qbcxbwIFHo5F5YfAZxk3EsQgjnYqdejAY\nWN0K0X44HBr5CKuu+fl5223JIWRDQFVCI4wxD7Ck3++3iWy/3zfvulAodGNo7lYt5mg0qkgkokKh\nYOLSYDBoqAO79cXFhSWyOo5jdV6/31c6nbbMkng8bhgtJB5MEweDgebm5rS4uGgkdOpwGqZSqaR7\n9+7ZZAs9IdwI8vTG6aSSzLd5MBgY3XR9fd0kSeOpTjSO8D1odpmEjjv+czrxsyHEwxqkIUQIzPSO\nXPHZ2Vk7RR4/fmykpHg8rkqlorW1NaMPJBIJC4zHcyOTyVgjiusSiVSoT25y3aqaeTgcWpAMPhXj\nTu6O46hWqxkP4ejoSKenp2q323rw4IFc19Xu7q6VFb1ez3YTr9dreG6hULAaGuspWGWDwUB7e3u2\n29PQwf2l2UkkEub8Q/oVu9Q4rRQJPyHrkoxKiqAUfwpqZOwQ8K9AroT5DdNAtJDlctkoo3jZwbsm\nExxDGOwWwPMhNL399tv2OXH+pxGn9EKWdnR0ZL6AYPS8xk2uW7WYoXpClSS4Em9iVMnoz7LZrJUe\n3/72t826ikRWnDeRYlH/xeNx88AA2oJcdHZ2ZrIq13XNfAV30GAwqOFwqEKhYF7PxPQiIAU9YMJG\n3ggS/3A4bAgMTReav06no5WVFWPxjdfpeEoD68H8Y7oJlEZp1mg0bFQ+MzOjXC5nKMrx8bEkmSh2\nb2/P/iwYDNqmgvcefUa5XLYyJRaL6ezszPqDu3fv3uj+/5EsZsdxQo7j/APHcV46jvPCcZwvO44T\ncRznNxzH2XIc5587jhMa+/s/5zjOtuM4m47j/Knf7+dOT08rEolci0qAAE8dV6lULKidkJzz83Pz\nnjg7OzM+QrPZNOcecFdJ5itMJAR5KaSOttttOY6jeDxuDc/ExITtiDSmOPrAj8hkMnJd19yLqO0Z\nO1Mzw2mgnMFii52Qh+/Vq1fW2DJAcV1XqVRKk5OTNvk8OjpSu902FyIU1bgucbpxwd3gIfF6vTbh\nG41Garfb9v+Hw6Hy+byazaZarZbm5uZ0fn5uU85MJmMWDs+fP7/Ruvqj2pn/J0n/1HXdB5LelrQp\n6Wcl/Ybruncl/dbrX8txnIeS/pykh5L+bUm/7DjO933fiEYxGp+dnbXpGR04+DBTKhzoKTNojODc\nwqzDgwPeQyAQUCKRMGNE+MVgtfPz8xYdxrROktXmNEaSzK4AxQf4Nzki4zHANHyu6xqxn2YTDNjv\n9ysQCBjagUKk3+9b2tXc3JzV79Fo1LJO+FmUCvy54zhmAIN4F4QHtTbNsuM418LqU6nUNTNGPpPH\n4zEagN/vv7Gl7R96A+g4zrykP+G67l+QJNd1LyWdOI7z70r6t17/tb8t6f/V1YL+9yT9Xdd1B5IO\nHMfZkfQlSd/6vT8b1hYkcelKkoRe7uzsTKFQyGAhqJ+INuF2EAgJNZGpHLIpNIXo++AZcCLQFBKA\nOe4bDbTn8/mMAplMJm0Xm5iYsOEKY21qZfjQjH89Ho+Gw6H5QO/u7hqJfnZ21oYuLEpgPhpBHEZn\nZmasVobMBNKDD914rHAikdDR0ZGFEUlXo+9+v28qGNxYx08EPid8ZxpX8GZEE296/VGgGauSao7j\n/KqkdyR9IOm/lJR0XZcOoCIJF71FXV+4eUnf12IdIsu4USCwFDgpjRZ0THI6KDP44hkTh8NhVSoV\nvffee9rd3bVGyXVdO2oZomATAC7Lw0GpI8ketlwup4cPHxoSEg6HbXdlR8c6Fs+K09NTexBprFzX\nValUUq/X0+Liou3CTNbGbWcZ6AAjnp6e6vj42BTVGxsbOj4+VqPR0Be+8AWre0FoXr16ZQ0msROS\nrHbH5R/oMJPJWMPL0ARrAdydaFbpL25y/VGUGVOSviDpl13X/YKkM70uKbjcq2/J/df8jO/7Z1//\n+tf19OlT/c7v/I7K5bKx3eAFAC2NRiNFIhGr4Uajke7evWs1M+mq0lWA5dramh3zS0tLxkNgN0Wa\nv7S0ZE6kHPGUHkz/eGiWl5etdsb9k+YVhTOYLUJc6JfoB7GKzWQytthhrLE7k3GCpAo3VPjMTB3x\nuwiFQlZ2YCZD08hnmZ2dVSaTMd4HJ1W73TYc/f79+8aPdhzHyPgMdVKplPFfXrx4oWfPnv2xNBvP\nS8q7rvv/vf71P5D0c5LKjuOkXNctO46zIInZZkFSduzfZ17/3r9yvf/++woGg9rf39fjx4+Vz+dN\nlf1n/syf0Xe/+12l02kLbL97966Oj4+VSCSMHonuLR6Pa3Jy0tyPGE9DwifUZ3zI4vP5jFyPsoUA\nn36/ry996UsWSIn75vT0tBKJhIbDob785S9rNBppb2/POA/pdForKyv6xje+YUYrHo/HzBghwUME\ngqyUSCRUqVS0vr5u43z42DRulDmZTEa5XM4WNLXvvXv37Ncw/BAQSJ9MAgmUf/fdd61hHBcwzM/P\nq1Ao6K233lKhUDDbsoWFBSUSCX3hC1/QcDhUrVbTr/7qr77xwvpD35ld1y1LyjmOAw7zJyU9l/RP\nJP2F17/3FyT949f///+W9Ocdx5lxHGdV0h1J3/n9fr7f79edO3d0eHhoFrGdTkff+ta3bOdFqey6\nrnGDd3Z2jOlGbIQkiy7GT2J6elrJZNI87KhRUX7s7u4aW02SjbQDgYCOjo4sCapWq5kbfiAQUCqV\n0pMnT8xCYHFxUdFoVGdnZ/rggw/k8/ns10RGcMyDHCCQxbETE5ZkMmkuoOz40pWYYX5+XgcHBza2\nH1fk5PN5q9Nx65d0zXCcaWUwGDRUZnZ2Vs1m81oWOK5L0F0nJydVLpetzKKxvcn1R4Vm/LSkv+M4\nzhNdoRn/naS/KeknHcfZkvQTr38t13VfSPo/JL2Q9M8k/VWXu/h7LqRI2FrhDo+vMPBQo9FQo9HQ\n2dmZNYGUG6gqkFOBeLA4YYjRCKJ3k65KksXFRfl8Pu3s7NhkkYeAcoMhCOYxDEhw9gyHwxbtwMBn\n3INuvAzC9w6E5fT0VI1GwxYMI2YWMEkB29vb14ZLlBUw/4gD5iSC+TaeMYhwgLoetQt4P98ffQLf\nseu62traMniS/oWH5U2vP5Jxtuu6TyR98fv80Z/8ff7+35D0N/6gn7u/v28+EjRBzWZTmUzG/Ihp\ndlgkH374od59913DodvttprNpu1IhUJBHo9H+/v7isViRpf9F/kAACAASURBVNLhz4DUQAoobfCP\nwOYKwg4wHrgxLp6MoPGdK5fLikajZiWLOsXv95sGEDtckJpqtWqvg3Ib1hp4NGT6YDCok5MTVSoV\nTUxM2E6PpAs9Yb/fV6FQULvd1v379+0hazQaeuutt8zRkxocJAYNIUIG/pzPBhGJh9l13Ruz5pzf\nZ5P7Y3c5juP+1E/9lHEvBoOBZWQTlwDXgBqQnZyJGlwHkpok2S5KXvbOzo5FKwCdkaFHA4ZxeaFQ\nUCqVMgMURtIYhKPLI9RSkqEgpMBCHOp0Okb+l64SWe/fv69CoWA539FoVK1WyzIF3dchl61Wy0wd\nJyYmzKAGywRilsHoCdyp1+smppWuBk/j/Gswe4j9Z2dnZopDucL0r9lsWrTb5OSkTk5OlEgklMvl\nlEgkND09rU6no1/6pV+S67rOm6yBW0U0kqREImHxB6SNxmIxM/wG/mK3LhaLZpoCL5luv1QqGeGf\n3ZFFxoAD0j0LgeOUUTDaOSxxwcCfPXumjY0N40+ABkSjUZs8MoZnEQK10RxiNH58fKxKpWLlDGN3\n8vcKhYLV2T6fT5KszJmamtLBwYHFSBCjkUgklM/nDc0YLyvGVTLjkCH6SPyaeah5IDmZKLXw/CCk\nfnt7+0b3/lZxM46Pjw0tQLlALciuMC56JZnK4/GoWq1aKA1JpixGlBydTsdEpBCCQD2QNkHex64L\notH4EU+cAqNvuAnjJKBSqWTUU6it5XJZZ2dnajab8nq91zSJlBeSjOLJg5tMJnV0dGTfB4McShEM\naeLxuJ1OExMTisViZuCCUyrNM+XD5OSkTUr5njAWx2EVSsC4IABeBu6lYPM3uW7VzgxhptFomJAU\n90t2HaynIpGIyuWyQqGQ6edCoZA6nY6Wl5dVr9fN1w3CfDAY1Oc+9znt7++bnhAbXerTSCRi3T0L\nm3EwgxYsajn6x03ACe3JZrNqNpsKhULGkSAUiBEzyIvjOEbFJA9wfn7eCEi7u7s2+XzvvfdULBZV\nKBTshIrFYsaRwO2JYB7q52QyqcnJSS0vL2t5ednMJNPptObm5nRwcKCJiQkzsnnx4oUl0r7//vva\n3NzUxcWFotGoTQD5jJCn/vSf/tP6xje+8cb3/1YtZjr2QCCgjz/+WFNTU8rlcgoEAmbvSr7H0tKS\nGZY0m00jj09MTOg3f/M3zawchteDBw+uWbiSUbK0tKRqtWrSpuXlZUMF7t+/b5ZeOArhWt9sNk1j\nt7u7a4pxKKb9ft+wYaiseH34/X7t7+9rYWHB5P3tdtuGK1BJMX+UPrFheP78uS4uLoyuiaMnwtJO\np2N9xatXr8yGd3xHHw6H2t3dNbencUFAsViUx+NRr9fT0tKSarWavvGNb9g0E5YdBpPYdw2HQ/3O\n7/zOje7/rSozUP0eHx8btivJ1MKFQsEcjvCE2NzcNLEl4lAWO+JOasTxcJ5kMqmlpSVFIhHL4kin\n09dU1B9++KGRa9iRp6enTbNHPR4Ohw2VYPH1ej1ls1lNTEyY6z9OoShX8NigfAGOoy5nYpjL5Yy3\nzMO7vLyslZUVyy6kcWU3x8ARwQInHPxrn89nJ1ClUrFyCG4LiA+e0aBDEI8WFxeVSqUsExFx8E2u\nW7WYwSnZMRk+zMzMKJvNmko6FosZqwyXHjgFo9FIb731lo6Pj7W0tKRut6vHjx+bKhomWKlUUrlc\n1t7enrHCkNofHx9b8wX1E8dRFtv9+/dtTAwPQ5Jl6VFrM21stVrGqCuVSkYGgi8cDoctg5rm98mT\nJ+p2uzbw8fl85vMB6nF6emqEIyaIYMOgNYT9oErHYyObzaper2txcdE4HECGoBz5fN7gv5WVFTWb\nTRUKBWMKYugoychOb3z/b7Z8frCuWq1mjRhlxuHhoYbDoV69emXHOAOUXq9nNxKzbho3+L0XFxcW\n84tFFWUJNlOSTJpPAwdrDE+7mZkZPXv2zJAR1CqSzD8Ck/DLy6tQ+bOzM5uSgRNTElCPQ6Fk0AG2\nizkM6a0Er1MeNZtNy8YulUr2GXFwmpiYMDU2vwc6hCIbdAXsGUI/f7fX6ykSiZjC5OXLl+YuhdKF\nB50T9SbXraqZE4mETdXu3LmjYDCotbU1U4lwzDFChhoZCoVMgXJ+fq6lpSVrwkqlkpUE4/IpWG6x\nWEx7e3vmNgRvAlx1enraGqnFxUVLJKVkuLy8tAAcGsB8Pq8vf/nLJiWCggmCwQ7LLglBieEEdgU4\ngsIBSafTpslDezhOJ11cXLQsRDzwOIlIfcUzgzIDvvjS0pI1eHzXBHIS8wBCgjMSpye/f9Ohya3a\nmfGIqFar8vl8FlDOl39yciJJNr69vLzU9773PSPqP336VNVq1QYse3t7SiQSNnKVZAQaRrR7e3u2\nwPF1brfbKpfLyufzxvnlJJCu4hVAF6CtssjPzs5MQU2ADdxp/DxyuZypzDFZYZzMe52enlaxWDQM\nOp/P25gfqT/OozwsvF/G9+PKamwATk5OrOQBm8cnhNIBXd9wODSYFJErOkhOsl6vd213v8l1qyaA\nv/iLv2i1LTRDMN1SqXStpvN6vfJ4PKrVagqFQkZER1SZzWa1ublprvTUxXTePp9Pu7u7unfvng4O\nDuTz+YwzHIlETHAK3r24uGgZJSAUeEXTXPLzedAQxKL65oHE1406HU42JwElByLWer1u3nDpdNrk\nSclkUtVq1RYsxKTZ2Vlr+miiiUKGXI8Ui1IGtYjX67UcGU6bbDZrPGl4IgxbyB+cn5/X4eGhfu3X\nfu2HE0Dpyq2HQcLBwYE8Ho8KhYKZDm5vb5tNAFG+jK57vZ458DD5oubb3983b2acORmXE8eGVdW4\n9wMu9ASh49V2cnKiZrNpGDbZJRcXF0Zc4lhmbE5tSUmCkBXBK3g3fwavo9fraX9/3xrPaDRqC5fp\npCQzYwGaQ51OE8tirdfrCoVC5uCJvS9WBJwOPODwPhBFYCrOqPv58+dm27W8vHyj+3+rFnO/37dF\ngocx2je0euxseNJhtCLJaljsqbhJ8I1ZeDhuwl5DvoRChRvPlAwMFl4FuzxqcASio9FIhULBdIgs\nNuxomdjhSgQXA/MWIDZgOvJMHj58aJrBvb09SdLR0ZGSyaRhzYgVQGSIwcDsJhwOX3Mpvbi4sJwY\nEJmzszNr/i4vL80sHWV5LBazJvvly5cGb3LyYKH2ptetqpm73a6WlpasAfF4PEaux3gE21tEm0QZ\njJsFokaGCFQsFo2CCU4NnAYDT5Lxdlm4kNfhNkgyPwseFOkTWinowGg0Mv4FMN3l5VUcMlg0Pso8\nkFBbYaDVajXT1T179syGNkicCCOirsW9E485ooY7nY45GgEbjqu4R6ORNjc3jSJAGq4kO8WA3mq1\nmj34Ho/HOCzAksjP3vS6VYt5ZWXFDLPBMon8PTs7M34wg4FxC9bLy0tTi9DggOHiFgRrbNz9R5I1\nYJiwcJNoinK5nHw+nz0Q9XpdjUbDbizN38LCgtFHJyYm7H3AQIMjQWwCTDX4zAsLC5Jku3U+nzeI\nsFKp6OTkxEhU2CEA62G2yAKfnZ1VpVLR3NycEomEDg4O1Ov1VKvVtLa2ZiP6fr+vZDKpWq1mFrc0\ngZyE9Xpd9XrdNhYyB/EWwc/jpov5VpUZmLycn5/r/fffl8/n0/Lyssnoo9Go2WuRQ51MJs0wcGLi\nKmotHo9fE43SkYNaxGIxnZ6e2uKZmZkxmTwcDpJfB4OBRQmHw2FVq1WlUin7M4wVYZzNzMyoXq9b\niik163jshMfj0e7uriQpHo+bcWOpVDJbW04WdnvyrQnXYeKHfxyjcHjGo9FIqVTKyjCGJpRf1Pce\nj8caUSwdMJ5h9w2Hw4ZpI92KRCLK5XKmeD8/PzdF/Ztet2pnHo1GNgDAL5k8O6Z93Gik+Pl8Xr1e\nT48ePTILXOT5uLljGUBJAb0RrgZH7sTEhGWGIAtikXD802SxUCcnJ806llSsSCSii4sLe4jINcEq\ni9AeiE6YLbK4MAKv1+sW0QBNs9VqmZdevV7X9PS02QNgqcWonBRb2Hvsqixk6uSJiQmbrKLmgbFH\nrgpNKREax8fH8nq9kq4eSDw8bnLdqsWMDJ/AcrR4tVrNYgw4wmliEomEer2eqtWqstmsDTRQWjSb\nTUtiotyg5Bh3mQcSG+cUh8NhHRwcSJLtmI7jaGlpySZ9UC6DwaDtzKAm0WhUXq9Xx8fHdsowzEHF\njQwKrLpWq9lInVg213X16NEjs571+/2mIG+32+YoSo0MVElDSYZ4s9m0CGVG54FAwBiHPATs9ozf\nGdBQRiBNA6KjYU6nv6+DxKe+btViZlekloVvixQIEji46mAwsHoWbzl8MGZnZ21iiNIEPgYjaUmG\nOw+HQ8OWaf5OT0+NWklYDaw49IaYLuLKiTggk8nYGHlyclKNRsPMwJF1EYaJajoajZpqhh2RyIvd\n3V1VKhW5rqt+v29TOJyfRqOR1e64djabTRvagGM3m03LWSRuzuv1KpfLqdFomHhgcnLSUgcwJefk\nOTk5UTqdNtNHHJtQtLzpdasWM1a2pCnBSwBK63a7Ojg4MKioUCjozp07VpaMRyDQWGFmiFkJTDbS\nS/GhGPcvrtVq1hzB+2C6x8KDSYdqGRy42+3K4/HYuBoGIAMJkAw0jDSd4MU44NdqNTu2MW2k/h4X\nFzAyH4+owMiRUgqWHv+fEoLGGWIVzSuE//Pzc/n9fh0cHJgiB14Jqh5Jpg6v1+s3uv+3ajHjZClJ\nX/3qV008GggE9JWvfEXhcFgbGxsGHf3Ij/yIDRJc11UoFJLP59OdO3cUDof1+c9/XtFo1PKyA4GA\nTQzZ1djVfT6fKbDxgcbhh1Li4cOHdnpgyILPHQMK9HDoBBOJhEGNmBOS9uT3+5XJZDQxMaG1tTVz\nEM1kMlaCLCws2M4eCASMhzE/P28DlHv37ikSiVhGNtL/bDZrTL9kMqlQKKSFhQXzqctkMrq8vNT6\n+rpZLpBUgFCA4RJjc5K2JicntbS0JL/fbxvEvXv3bnT/b9Vi5smemprSkydP1O/39e1vf1u1Wk1f\n//rX1Wq1VCwWjZH25MkT20k3Nze1vb2tqakpvXjxQu12Wx988IEmJyf17Nkzo4OenZ3ZlJEF3mq1\n1G63lcvlVCgULHuPYxqFyc7OjkqlkrHO0MK1220Tqx4cHFjdC3b79OlTC6jf2dnRwsKCNjc3NRp9\nki4FJNloNFStVg0poDZmKMJg4/Dw0DSG+XzeTrMnT55Y2bO/v29KFrL8Njc3rcd4+fKlBRrhI42r\n0ZMnTyRdIT3U8jSEyL9evXplSM3x8bE2NzdvdP9v1WJm9Iv6gpwOEqAkGUke7wuv16u5uTlFIhEl\nk0mrCRlo0Cyii0ulUmo2m2Y9xYLAfmpjY8N2psXFRRugTE1NWXkBGZ9pYrlctp9DID01OM0V3GV2\nMYSlkPOpV/lcLN5+v28KFyy7ms2mTe0YM1cqFftMJycnchzHRAQ8SEwNIWwxhmfow9AIF1Lw9vn5\neftM+Nf1ej1Fo1GdnJxYHARkrje9btVipg6s1+vmfQxFEZ4AQxByAKkDHzx4YM0b6gjw54cPH5rR\nX6/Xs1gFjBlRHqPJOzo6MolWPB43SA8rWjgVmCpi/wWjjPo5lUqZsyaN0+TkpHZ2doybDeFHkpmw\ndLvdaza+eH10u11LmYWWOe5QCjKDTnJ9fd3iLsrlslKplIbDoSlIQDLG7RMQD4AzozpHpYJ/HfU4\naV2Li4tGanrT61YNTVgYUCsdx9H8/Lw1K3BtUTQsLi6aQThaQZosHOXn5uZMv5ZIJLS7u2s7PU0Z\nqmdqZmpRFiA7FaPuyclJ86qD7ww/hIEGqabjkB8BnKhKxnfLy8tL87igJ4AZyOtA5GHRj9vzIlJg\nkWIOiXHO8vKywXfU/XiLzM3NmXqFwQeuUdJVPuN4LiGYfyqVMgHxcDjU2traje7/rdqZganm5+dt\n6kT2M0GPHN00c/l83gYY9XpdpVLJ0I5ut2v13Dh0BfmdLp8d/fT0VOVy2WrdcDis8/Nz40wwZOl2\nuzbuHvdwAxtnOrm3t6dyuWziV5Qf8DtOT08Vi8WMQffy5Ut7T9LVWBu73mazqVwuZycJtlrS1QNU\nKBTsBKnVauYeSkNNn8FpVq/Xbdw9GAz08uVLM1qv1WrGQDw5OTGolO+gVCoZlo76u9Pp6MWLFze6\n/7dqZ85ms+ZLAfk8nU4rkUiYCDUYDNpU66/9tb+mX/iFXzAPOISo3MAvfvGL2tra0uPHj+3mjEYj\nZTIZFYtFOY5jtTCUURYE0BSu+Xja9ft9G6qM80NALiAynZ+fa3Fx0SZ3jKeJKtvY2DDVNAOOt99+\n20j6ExMTSiaT8nq9RtuEuca4GuNy3itjfxzx4WDgQx0Oh/Xq1Svz0FtZWTG+OEbiTAIzmcy17EBU\nOL1ez/4MLv14JMa//Jf/8o3v/63amTudjnkTY+iCGkOSTbTY/f7+3//7Njwpl8uSZBo7n8+n3/3d\n35XX67UdKBQKGdG/3+9fcxsCTsOHA981COnjOzjMNK/Xa7RR+A2UQ1BK5+fnNT8/bxBju922EwNO\nMUqZw8NDG/hQMvHaMzMz9vP4+5xkNIfspOSN06gNh0OFw2HlcjkbsmDZG4/HJclOn0AgYM75NJJw\npw8PD42aSpoV43mcp25y3arFPA7sAxXRyRN7Vq/XrYGjAWLc2u12jVPMTcaSgOOcmwtxqd/vq1ar\nmYkLAxMW5sLCggVMnpycaDgcql6vG4MPqAxuhPRJklO/37dFzWcgP3B/f/8a+y+fzxuSAfrR7/ft\n825ubqrdbluYJycDn7tUKhlVMxgM2tQOR3xckngQEfdyYVhTq9VssQI3sslg5+DxeMxU5uzszKao\nP6SAjl00Y5KME0w9eXFxYTAUhHFJhhOfnp6qUChoaWnJCDxwPbjJeKhxU1GzwAkuFovXvJipsyXZ\nRBBOsqRr1lbBYNDkU5Cazs7O1Gq19PHHH1vpVCwWzROZiLNxx/tEIqHDw0Pt7+8bguH3+3X//n1D\nVC4vL/XRRx+p3W6r1+vp6OjI0mnBnycnJ40mKl31I6FQyEoJTjnG9B6Pxx526eqEw8qr0+mY7ApD\nyd3dXeXzeWvKmbLe5LpVi5nYBUa+uFHCM6hWqwbY+3w+22GweI1EInr69Kny+bxlfbDjjsvnwV5b\nrZaOj4+vmZgztmbBhkIh5fN5i3qQPgms58httVoqlUomuSIsEzEqENjLly/l9/tNUABBH5717Oys\nCXK9Xq92dnZUrVb13e9+1xQhcI6Xl5dt5N/v9413Eo1GrwXCY7dQq9V0cHBg9E94JtgAg5lLnyTR\nAjcC5w0GA21ubqpWqymTyWg0GllOYy6X++HQZPyCF3BxcWG0SZ/PZ2lMsVjMjmF4FkBnCESTyaTV\nvjjpE3wDsR1FMbIgj8djP4Njc25uzvw1IpGIxZaBQnDMY6VLPQ2WjEYuHo/bBA/yEA5NlAjU3Ax3\nUqmUvSev16vV1VVDMkiUBesmBoLoMoZIlCx+v9+EqalUyqRc0WjUeCQ4IQWDwWtiWnD9ubk5eTwe\nG3kjgqXnIBXrj1102md5MV5mLAztEhgJVhaowscffyzpagyOLxrURXamg4MDQxMg9/AfWC8TMcj7\nR0dH8ng8xtRjV6TGjcViarVaJrlnJ4MoNQ4posZgMjk3N6disaiTkxOtrKxYElSr1bJhDdg5dXmh\nUND7779vOzluoUCVCAnGTxhKA04YnJl4LzTJUGl9Pp+2traUSqUM60bjiAL8448/Nm43jEHqZBTc\nN7lu1WLGzIXdmKw/dhGv12uKB6/Xq3fffVetVkupVEonJycKBAK6c+eOJFlIYywWU7FY1MrKis7O\nzozMRKkCRk19S22JEIAwdHLyGo2GWQZMTk4auYldeDgcKpfLaW1tTaVSyVCF+fl5ayYfPXqk7e1t\nLSws2EMDbkys2+TkpNLptLrdrpkyhkIhzc/Pa3Z21rw77t27Z4oQdsZQKGSKG1QyfHfsrNTMkJaY\n4qVSKfV6Pb148UIbGxsqlUomCGaIRdgRuzcKoLW1NT179uyN7/+tKjMcx9GLFy/UarX08uVL2xUx\nKkGDRuYGam74wXjIcQwnk0nDQMeDa3q9nvENKpWKKUCYLDJhG/euYDrJkUpJNDExYdyMUqmki4sL\n3b17V7lcznyXJVlmH9xov9+vi4sLvXz50thpqEowIOz1emYFlkgkJMlcQvHzODg4sHID1yKGLHjv\nlUoltVotbW5uWoMKDRU3JcbzEPTpHegPoAigAD84ODAfQPd1NPT6+vqN7v+t2pld19Xq6qqmpqZ0\nfHxsVlegBtgDXF5e6u7duzatw2qLUoCatFQqSZK58WAqjhPnaDRSOp22KR91KLo4ak90ewwriGAI\nBoM2WPD5fObNkcvlzCim2WxaMiociNnZWcPF79+/b6JXEBwI77xfGjDw93HUJxKJWD43vBHeDxku\nGNjgkMruih0Y5Qw7NjESjuMoHA4bp5nvdzgcKhaLWdlFljbf95tet2pnlmQDEnY+HInI3uPIh7zO\neJcRMpgvjQ6TL+RUDCN4ABgUINSEtwFKMhgMrHOnVod8VK1WrUypVqvmu4aamsULhMZrM1WDozEY\nDJTNZo07cffuXUMtpCsOSTAYvGYrO276uLS0dK1koJSiacOEkcaU75WHA/4HwlfkVzSmNMxwpply\nBgIBQ5GIobjJdasWM7TJV69emZ4MXu/k5KRxG2ZmZmwUzPgZNhwLDiYbqgoom0+fPrVhCqE4z58/\nV6VSUb1eV7FYNA7C8+fPDXaC/zw7O6tCoWDSKLzlKHm2t7c1Nzen9fV1I9VTo+/v7+vly5cql8ua\nmpoyeytJ9nPm5uaMu91sNs0v+uDgwIYcNKTn5+cWK0GzWqvVlMvlzIc6n8/bAsYWAfgTHBoFD8Mg\nfPZ6vZ65e3Y6He3v7xunut1ua3d312YAw+Hwh9Dc+NXtdrW4uKhEIqG9vT1jiyHTka4w3EKhYImm\n0BNpDEOhkCWxBgIBW4RwoHGgpw7ErsDr9SqZTJqk/+zszCISOPK73a6q1ap5w3HUExlMXdnpdFQo\nFIzrAYa8tLRksRCQ93E9hTxEHBsstXFjGxY+WDGG5PwaTH1+fv6aoxHoTTAYtAaTB7bdbst1XYPw\nCMoMhUJGzIItB0zJ+56bm7s2PcVM502vW7WYOdaxiR0MBkokEmaKCHAP9ZEjsdVq2YLD1gv3+NPT\nU6NEQtRBSMoIFlNuKKCtVkvxeNzIPSwGeMncYMoaBKODwUDdbteSnGgiPR6PsetYoJjQEEEsfeIg\nNBgMzHeO3EOSnhjeUBYh6yJmORKJ2HhZkrkiQaxnikmuN1Zo5JJzRSIRbWxs2MgaByWGK3BoGNn/\n0Dfj+1zj9E2spCCzMLSAOM6xSNMEdIZyYnyQwcJjLMuE8fLyUrlczky7yfTAplWS1Y2MiTGcwTJs\nNBqZ6mN8gcPHQKVN6TAcDpXP582yq9lsqlwu2zAG0hAXbMBxYny73bamEcSn0+moXC5f46x0Oh2z\nQHBd1/gu8EwY8ZNmBcEKXgg/C/iN/BWStXq9nsnOwL/f9LpVaAZfGPBbIpEwwlEwGLTdDb4uQlRJ\ntsij0ahyuZyxylKplNlhoaR2XVfhcNgmhjDfRqORiWA//PBDra6uWlOEhVWv11MymbT4CJh1i4uL\nisfjxogjAQBXJRYjNgHEp8F847RJJpP68MMPDb9m941EIjo9PTU7he3tbT169MgQD6BJDMj9fr/i\n8bipdIDpeNA4JYigazQaJmglb/wrX/mKMfYKhYJWVlbs5Op2u4pEIgqHw4buHB0d3ej+36qdGYyz\n2Wwqm81a182oFLfMaDRqZiePHj1SMBhUr9ezXSIcDisejxtRhiMfiT5cYEk2FQT2A79NJpPa2tqS\nJIMD4Q67rmtoADDf0dGR8vm8qajhSFMS8NonJyfmm0wphAMof8apcnx8bEc5C4UyLJVK2WdCKQ4N\ns9PpmIiBrGwml5IMkUEkwGmGb1yz2bSyDt+54XCoQqEg6eqkQB8Zj8fNBgFriDe9btVippZLpVLa\n39+Xx+Mxz7iDg4NrBoHsQjR4iUTCdl9UFoFAQPv7+1YPUlZAhzw8PNSLFy8sUD6dTl9TWaRSKSUS\nCXMSJccEUSsEJEoX13WVyWSUy+UsPJOpYjAY1OPHjxUKhUwlTi4IgxssdlnkeOKxq09PTxtezpSw\n0WiYqSQMQGwUUGnzOoVC4ZrrEpndKNa73a7ZGYCZz8/PW94h7vzjfiIHBwfa3t6+Zoj+ptetWsx0\n5gxKMMkOBoMWUwaJHg7E3t6ewuGwarWaDQSAoRzH0dramnmnnZ+f281qtVpaWlpSLBaT3+9XpVLR\n1taW6QWhW/IeQDgCgYCazaYqlYoWFhYUiURMI7i0tKTd3V1NT09bQtTS0pKazaYk6cmTJxoOh0ql\nUvL7/TYup5nNZrMKBALGGcZ9qdFo6PT0VLVaTUdHR9fQHFAOgjF5SKhpx8fcaAuhfhILhws+BCJC\njjgdYBvOzc3ZePz8/NweOgY5PzSBGbtQLAcCAe3s7Ei6UkDAZa7X6zo+PtarV690fn6u7e1ta0qo\n7RqNhmq1mhHZyScZt7yFoYbxIGNyCDcgAtS4eKnx0OAiBAGqWCxKkgVB4q5Uq9UstAaONcJRvCqm\np6cNBwbNgPMMjoxam9ra6/UaRMhkkQcYxbn0yQCqXq+r1Wppd3fXsHkebumKeovyBRwfZILyi3qd\nsoUafDzGeX9//2b3/0b/+gfsajQaVoNiLgjZxuPxWOhkNBo13wZwXJ/PZ0qOhw8fWpPjuq6Wl5eN\ndPT8+XMjzkB9pDZNp9OmQaR2xm6WJotdaH193bwr0um07d7Hx8c2Wt7Y2DDCEg0rECEq6Uqlovn5\neSWTyWvuSaRo8X1MTExoYWHBYDxJRrx3HEfpdNoek5NwsQAAIABJREFUjFgspm63a9/PaDSykM9U\nKmWm4eOMOtAXhjQzMzMW7zw1NWUiCMdxjB9CU57JZCRJ9+7d07e+9a03vv+3ameu1WoKh8PGe2Ch\nwpUAtoO8TiY2scKhUEihUMiUw9Ink7V0Om0WAOxO7CrjtSi7EKUGIe8Q4ceHL/CeaZxAKrrdrpUI\n+/v79jnW19fN9wJcGPNx3IT4M/IHiS/DCgEvjYmJCQtsh6sBnIlvB7g4nngYO4KcwPlGzQKvgwEJ\n9gcXFxcmskXjOD58wo0VUtWbXrdqMQcCAZ2cnFjeBrUtO0UymbT85/Pzc9OopdNpQwPK5bIODw/t\nJpJGWqvV7FiVZDcHTLper9sNgt4I9Adsh9l3p9O5Zh5OgwnmCy7caDR09+5d833+6KOPjMTjuq4O\nDg5s+NHr9Yzn4bqu4vG4/Tz4HdVq1ep/MgVZ4FBXCQjiIYBQNK6bPD8/V7fbVT6f12AwMHSILEDK\nBzLGyTCMRCJWUvR6PZ2dnVn9X6vVLAXrTa9btZhhniEZAj5jiEJKK/L9Uqmkzc1NG8uy63W7Xfu3\ns7OzJlfCHTQajdpQIZ1OW629u7trpwD0U3ZMpnsQ6eEu4PPGIsI6l9p2OLwKXS8UCgYBomaB7smk\nkkaNupnFmMvlrun2oMSCfBwcHKjdbqvRaFgdj1RKkjkaMUJnjM9IW/qE6M9ixuCcqWGr1TKy1LgL\nP/EUUGBvct2qmhnrWIB/eBPseqlUym4+Zt0/8RM/YclIuBnduXPHXCuLxaLVwpQO1WpV6+vryufz\nOjo6MkgtmUyq0WgYR2RcnoRKA5kS07JIJKLd3V27yfgrs7MfHBzo7t276nQ6+tKXvqQnT57YeDge\nj5siZXJy0nBcxs8LCwuGb8NUwzcDD+lWq2WOSvQJ4M7Ly8v2oFM/VyoVQ1sgaNVqNTv1kKKBtGCn\nAKTIWB+ivyTD3H/yJ39S3/ve9974/t+qnRluBrXqYDCwY5XjFII9HIdyuWwumhB/yA4cjUZaWFiw\nUetwOFQ2mzVN38LCglZXV826Fgx5dXXVcFMWNzo+UJHDw0O72djF0gAuLi7ae00mk6pUKqpUKtrc\n3LT6nbqdYzqRSJjWEV8L2ILUt4hyYdsx3ZucnNTi4qJBbKQJ8MCjm2RDmJ2d1UcffWTTwPn5eesn\n4LKAJTNyH41GCofDtrlAf2X0HgwGb4xm3KrFLMkaM75MmiPgJ0k2qm6320ZkR3EC/5lJFna0cHnR\n9zUaDdul4CJwk/L5vCYmJuyhQG2CL0e5XDbEgdIGsxZU3yAWNIZYEhAVPM65HjcER/FNSQJrDjUK\n3tBwLLBAoPw5Pz+3E47GTZINb8rlsvnFoVpnCgqRiMYaMQJqdt6767qmJOd7q9fr14hKb3LdqsWc\nTCbN7Jtuen193VhoyHSQLiWTSWN8ra6u2tBl3EQcw+7Z2VnNz8/bVC2dTmt1dfWaIWAoFDJuBibi\nuAWFw2H7/bt37xqCEAwGjTAfDAa1urqqxcVFmzbGYjFDKGZnZ7W+vm47siQbAnGSzM7OKplMWvg8\nzR3NJ+UMsOD5+blxQhYWFoxmymteXl7K6/Ua0pDJZDQcDg3dgQGYTqetROE1+PPV1VWl0+lrukoy\nWqSrzWVxcVFvvfXWje7/rVrMp6enOj8/1+HhoWKxmO2mSNyZMEFvhKnGDskOCqtubW3NFCk7OzvG\na56fn7eoBdhpYKdM2MLhsF68eHEt62Q8nw9iE+GTuVzO5E8c1Vjo4jcN8R8/DBJlkfOz88KFoFnk\n/Y67FGHZO86F5iRC5YLcrNvtqt1uGxWVhKxxORenCbj24uKiJiYmdHJyot3dXcOdoXvOzMwYZAgv\nJp/P3+j+36oG0HEc4+S+ePFCjx49MvdKQstZuDi4Z7NZGxZEIhH1ej2jjXY6HcViMVWrVS0vL5uS\nGB4xu56ka3wISWY8TlQvjRjhPpVKxUbflUpF8XjcGHwHr3OnKY8QnP5eNTS9AYstmUzKcRytrq5K\nkkmpdnZ29PnPf94miODBWGYRTs84OhAIWK3Nd0WTSZ0L24/3IslIVOgn5+bmNDU1pYWFBZVKJTuF\nMFHknuEIxbj8Ta9btTMTxLO3t2e1XrfbtfovEAhYbh1TwZOTE52dndn/ohVEnYKjPcJLjAX9fr8d\n17iKMtJGLgTnGRgNO1vG5RB7qLvhGAMfTkxMWJnBuB2/DeKK6Q0kqVgsGmLApA91SS6Xs0wUxtQs\naIYvkqy8oWxh9MzDhzKbeDaMKOGlsPvTn0DMAkFCoY6/HdAemYM3uW7VYuamozSBQH5yciKv16tS\nqaRkMmk5Ht1uV8lk0iAiBgNwcy8uLhSLxa6pK2DEnZ+fG29iMLgKU6c5wrzl4ODA0BVGv8T3gofD\ndDs+Pla5XLagenDtnZ0dI7SfnJwYOoK6BQsD6mKI+6VSSUdHRwoEAjbJm56eNvgOohCLj50WwhE2\nCIPBwCDEZDJ5TdU9HnlG6m2n01G1WlW1WrV/2263zYeOBhhBwsrKilZXV63Rvsl1q8oM/CSwWyUr\nr9vtan9/37znUD0fHh6qXq/rwYMH5ubD1BD3TCiWCwsLqtVqlnPHYpienjbnH8dx9J3vfEdvv/22\n5ufnTdlBQA1wITX04eGh+W+gTcRNHuU1DkKoVBiklMtli1K4vLy0Ic3du3fNeIb6l6gI0Amcn/r9\nvpGoUGGT15JKpdRoNEzSRU1NTV4qlcyoEScoyjigNkk2Rk8kEiaURRne7/f16tUrxeNxi0G+yXWr\ndma/36/19XUjsGSzWYOvYHqdnZ1ZAwYpRpJZ1RJVRtjMuK8bdaUka76wGUCgmc1m5fV6TUENkoJ2\nEM82PDfw0wCB4Ne8R+pmhjrg0tSjuGjG43G99dZb5kfHqTE7O6vJyUkj8M/MzBhfghoYWA1cGucj\n8r6hqSaTSc3OzioUCpkahc+zsLCgZDJpDw59CvYIxWLR4tbweJZkzXkikbBS502vW7WYJRleOt61\nx2Ixc72nSSSrJBqNanJyUuvr62ZMAv5KDU4q0nhCEtAYQevo4dDEUeb4/X7zhpZkYgHqdWpHhKe4\nEFFGoAph6IOnBlAZKVGUHjSwSMfwgwabHhee4v3M4GLcmwOCfqfTMTIWLkfY77IgUXdLsoaY7D8Q\nDaab+IcA3xHuCUR6k+tWLeb5+Xm9evXK5DgMHSDDhEIhJRIJU25/9atfleu6SqVSqlQqptAOhUKK\nx+NKJpNaW1vT5eWllQy7u7tGZVxaWjIEBGI7nfrFxYUNCwiDRzdHHjXTM+pjwoQkmT9cMpm0h4tJ\nJQ8jDvnU6ZjDrKysmKAWfBkBLIuJGp7QeIYv0idBR3A3Jicnlc1mFY/HTdGOMyjTRdhyDHfIaykW\ni1byABnCp5Zk6BNayZtct2oxS5906Oj9GCRAfEH9zM0olUp2nI/voHy57DQ0Rn6/35od/g3HJkQi\nVODIhjglxp3hKUvAiDFhxL+t3+9f88bgs7GYeGgoncgQpKHEHoydniBOdI4wCXnQ8PdAHQKRioFO\nsVg0PSETPH4+tTiuRxCfeEB5GMH+aaylTzD/cTeoN71uVQPY6/W0sbEhx3FsqMCxCiQ0GAyMnba3\nt2ewFLsgQ5JwOKy9vT2jPw4GA62urqpcLhuMxO/t7e0pFovZDWLRQXnEUouGNJlMmpMQixlS/8rK\niilY4GTDU8YSDLMXIo2npqb0/PlzjUYjO31w/8QCIR6Pa35+3miyDJRGo5ERgYAfiRxmkoqAdXd3\nV+l02pQ5RBsjICYyIhqN6t69e8Z79ng8Oj09ValUMo5Kr9fT4uKiqeeBTm9y3aqdeTxSjIhc3Nzx\nO/N4PAaN0X1T89ZqNeuq9/f37bhmR0KxzOiZ+hU9XbPZ1NOnT23Awa7FzinJjmheh4bKdV0dHR2p\n2Wzaboq6GQ72+IgYfzYml8vLy8ZJZqLGCYUXHBO3eDxurqVwqsGtcXYiQiMejxvllN+LxWLG2Ybg\n32g07LOdnZ2pUqnYqXJ6eqpms2mqd8oSeN3g/j/cmccudhlqwng8bg0M1gMYm/T7/WtSqk6nY9ZR\n77//vh31k5OT1vDQKFEzY0RI4+Tz+bS0tGSqZrBdJmqQ1TmWV1dXr5USGCrC9SVokqkj74dFzTSR\nqRrcEhhsDD7QGPr9flOQc2LAM0FZAurg8/m0sLCgSqVi8W8MbYApEUCQsDWOEIEtI9TlhKP5HI1G\nunPnjnl8cL9uct2qnRkmHGlMTK0uLi6u8X4hi2P4R8TX+fm5SqWScWoxDUQ1AT6KCyjNFDvk8fGx\n7VRg0HT046y2TCajqakpPXv2TB6PR8Vi0cSpsVhM9XpdW1tb5n3MMEWSmc+wmCDQn52dmc1tKpUy\nlfo4BjwajQzrpUHDdQiBqyQTClDuIHQlXPPVq1eGRzMVnJ2dNUgOD2dqfcS5SM56vZ6mp6dNZ0jT\n+8Ohydh1cnKiSCSifD5vhjDo/cZDKWlgtra2FI1G1Wq1TMSJETk7yatXr5RKpVQul82nji8dp6TD\nw0P1+/1r4TbRaFSlUkkLCwva29uz3R97g/PzcxOHEizEovX7/eZJgeJb+gQ7rtfrNvAAEmNUPRqN\n9I1vfEOJRMIguWq1aq+P4TcPRL/f19OnTxUMBlUul62kITGAU2QwGMjr9erk5MRq+UajYUKCcSMc\npqUkTUFz3d7etrqcDQfr29FopO9+97s3uv+3ajEDHVWrVX35y182emM0GtWDBw/0W7/1W9Z0ZDIZ\ny62DmkkE2dzcnJLJpClMwKTZpVzXtQkWJtxAXNlsVv1+X4uLizo4ODBJlMfj0aNHj1QsFo0Aj08y\ntex7772nQCCgFy9eGCpx//59C9jMZDIKBAJqNBp6+PCh4drVatVKkuXlZbXbbWUyGbVaLb311lt6\n+vSpWYoxoACtmJ6eVjqdNjcj1DR+v1/vvvuuITaIAegVJCmRSKhSqWhjY0Pb29taWVmxvzs5OWkZ\nJfPz86rX63rnnXes6fb7/YpEIubpEQqF9M477+i3f/u33/j+36oyg0WGYz4k9LOzM33ta1/T7Oys\n7UbtdtsciZrNpvL5/DUTxZOTEz158sSy8SgzSJzCSPvu3btGvBkMBjo/P1coFNLm5qYtjkAgYOhJ\nu9223ZqckKmpKW1sbKharerjjz+2B2xxcVGVSkXPnz/XysqKYcEbGxs6ODgwLJsd2ePxKJfLKRQK\nqd1ua2VlxUg+IC2gI5Is0erly5cm6K1WqxYKVCqVrKwaFwFg7HJ0dKTZ2VnlcjklEgnLHSfMB0SG\nYQ/oED4fOzs7FhDUaDR+KGgdv4bDoblwVqtVNZtNE7biGwcTrNVqmUyeAPOpqSmVy2Vtb28bK0yS\nkXywERhPFKWenpiY0NHRkRmOV6tV4+2yoOBFoL+jXi8Wi9rc3DSPDORLjUZDpVJJ5+fnJnWCNce0\njKy+jz76SJVKRfl83ohGW1tbNpIH48aajHE7JRYi13G1Ry6XU6VS0fb2tolqKdcgUVFKYAscDAZ1\ncXGhw8NDU8OQMUiPQbwzzqLhcFjT09NaWlq60f2/VWUGU6vt7W1z0KGjd11Xm5ubBkvxRcKYg7wP\nmoAxNgYnkHZYyBydExMTZtAC0f7i4sLc6i8vL80bjoHH2dmZGo2GHjx4IOmKP8FwolQqGYRYLBZt\nCIFZIpHJoVBIkmwYEg6HbTgCLDYYDLS3t6dAIGCMPmpcmH7wIWhYSZVlYY371DH8yWazymQyOj8/\nN2dTuNJwLVjoL168sNE28CVOqPl8XsVi0RxQfxidNna5rqvFxUWz0fJ6vVpaWjLTvsePHxslFE85\nbFrhaDQaDUUiETODAcqLRqPKZDLm1Xx+fq7l5WUL2ZGk1dVV4x1g0u3xeOw9YU3r8/ns9TBXgeRD\n4A11tSSrz+FjU/JQpzNqRwmDRzMSr3w+r0ePHqnRaCidTmtnZ8dc/c/Pz22kjx8eE9GNjQ2bGsL/\nCIVC5iOXTCYNhx43YZ+ZmdH9+/d1fn6uhw8fGlaPNx0CAcI0JV2btr7pdasWM4YjuPOcnp5qd3fX\nAhRxPCInBKgOWqLP5zNRp8/nM6wUFQlWWPV6XbFYTHt7e9f8JxhlZzIZbW1taXFxUbVa7Vr5gPcG\ndevCwoLRRCWZoSMuptLV7pjP522BEsa+vr5upH8GLLDsGKVTwyJghUiFema83GL8j9UWAyG4yel0\n2qBH1NW4H6G+wWw8l8tpcXHRdmJ25nFSEqcEYgBEA2963aqamYEBypLxXZLaDG4FRzIG5EiE4Cww\njUK2D6WRXYppI68xTrPkhrMLESYPnwP/NpTg0lXHDwRHUBC7NX4bYMzT09NmDcC4GBIRsiUWsNfr\nNVoowltG65KMoTfu98wuSTgoZRaZLQxqgC+xtB0MBlaiQayampqyMTx2YVgN+3w+k6zx729y3arF\nHI/HjZEG7bBardrCbjabOjg4MNJ5sVhUJpOxkTYO+Nvb26a6ZtfhhkLCIR4CV85er2dNFXKmdrtt\nXhLAeoxxJdnRDHoAAw4eBkHqPECVSsXgPKwOUJf4fD69/fbbSiaTyuVyCgaDyuVyqlarevr0qY23\nMVAkywUUA0cnRAOocUhT9Xq9JnCQZPxjsklwZmIYhVodOwLYfjTNDHZozpGk3eS6VWXGxMSEwU0r\nKyuKRqN6++23TZ6D5RXZfUtLSzo+PjZpVCKRsFIBvJkdlx0JP2HHccwbGUI9tTYNITwRZPZ4MwcC\nAStfJNlxzUPT6XTMGGY8FwWh6rjjJiPp6elpFYtFw2slaXl52XZ4HDk9Ho9KpZI8Ho9CoZAJDPr9\nvrLZrCEvvV5PqVTKyFIYvUhXPBdsAxYWFhSPx014gKIb43DeIwsd2RrUUUSx0WjUxttvfP/f+F/+\nAF4Q0sF5O52Ovve97xk/oVqtql6vW2xupVKxnDpJJj/66KOPbLQ9HA61s7Nj4e0sZBYinGLqWBQg\nuHsSwTBO6YSFxy6G0Tm4Mw7/lUrF4seoYfn3QG2gDHt7e1pYWNDZ2Zm+973vqd1uq1gsql6vG76L\nKxOG4L1eT6enpyoUCjo5OTECfrFY1OXlpQqFgvL5vH0OfOGgDcByY3JIuUWQD73BOCGrXq8b2gRe\nPzMzYxYNN7lu3WImYzqRSJiCGu0cR/3MzIy5djJpc11X5XLZ9Gho+uA1o9rArYd6OBqNand3V5J0\n584dDQaDa8LTi4sLpVIpMxAHzuLYnZqa0vz8vMLhsM7OziyBCYNC6n0QBwwXKXvOzs5sdx0OhwqH\nw8pms3ZyzM3NmZ3A8fGxDZZGo5EikYipPcbRDyKTSQUYjUba29szwQN8bVCcmZkZw6jZnXE9IrEA\nO9xUKqXV1VXNzs5aiTU5OaloNGrigje+//8mf9lxnElJPtd12zd61c/oopnr9Xp68OCBqtWq7t+/\nbzslLjqxWEyXl5eanZ21YMdyuaxwOKyZmRm9/fbbSqfThkhAQoejkclkVKvVzAfi3r17NiHDMJAY\nBb/fr9XVVSsLgK/wiFhYWDCr2bm5OZ2cnGh9fd3QFDjAPHShUEgnJyeKRqPKZrPq9Xr2v51Ox+p/\nRt3AdJREBGhyOgSDQSUSCUMTHjx4YIt9bW3NHv4/+2f/rIW4wyQEIqR8I/QSuzJQEHSQ8/Pz9jDg\nWgrRaG5uTnfu3NHXvva1N77/f+Cj4DjO33UcJ+g4jk/Sx5JeOo7zX7/xK36GFwgBEnh8g6n1RqOR\nqZilT+KJIeBQd1J6IKfCzZOdo1arGaTHRG58V4FiCl+a477dbtvuzBEPEsDi8/v9FolAljZTQ/Dk\nhYUFKxcYyrA7MrAA2SgWi/a61WpVPp/PLAWoc3O5nPkw8744vcDZ9/b2VKlUJMkYfvl8Xl6v1+wK\nmHoCc2IGSSMNH3p6etpw62KxqE6n86/4X7/J9Wn29Yevd+J/X9I/k7Qi6T+70at+RheKEfi01MJg\nn/jHccQWi0Xt7e1Jkg0PsBxgIUBrhAbJkARuNFwHOnsoleTqAcUx5Rt3l2cBIkbF7gt4DGol4gDY\nbozF0fV1Oh21220zLWQaiFSp1WpZA0qTjGP+eMoUAyV4JuygXOOGMyhuKEMYlvD5eMjYib1er0Uo\nYyDp9/vt9UBJbnJ9msU85TjOtK4W8z9xXXcgyb3xK38GF+UEEncYc6hLJF07chcWFvTOO++YoBTB\n6Y//+I9rNBpZ/QzKgS0rZCPootls1hY2+XhQUDkFwK2B1hh5s7jAXAOBgNbW1q69d6xqGV0Ph0Pd\nuXPH/D7AwiHL83qhUMgUNYhPMXxkUfr9fq2trRksCcrh9XqNc8J3kc1mFQqF9KUvfcnyUeB2SLKY\nNOwP4HlsbGxYc0gpRynDgg+FQjc2Tvw0NfOvSDqQ9FTS1xzHWZF0M0DwM7pwITo/P9fu7q6CwaC+\n9a1v6Ud/9Ee1s7NjyVGNRkNf/epXdXBwYP4XENJrtZp+/dd/XV/5yldscre9va0f/dEfVbvd1v7+\nvkFZhUJBmUzGDAr39/f13nvvaWZmRrlcTplMRtlsVt/85jd1//59mxYi6sTIGyQgHA7r4uLCnOsR\nB1BrDwYDPXjwQJeXl/rggw+sbsachViyr3/963r8+LFFyOHk1Ol0tLu7aycLusO9vT0jy4NaLCws\n6Gtf+5ru378vj8ej58+fG2nq8vLSaKfn5+fa29uzkwtvup2dHX3uc59TPp/Xhx9+aDK0arWqRqOh\n5eVllctlzc/Pm4XBd77znRvdfweN3Kf+B1cF6NTrHfoH5nIcx/2Zn/kZG3KwG5dKJWUyGZXLZdVq\nNcViMcukgxA/btDCIiMBCWdOJEfVatXQEca0kHQocwimxMibB2Z6elqVSsWQEgQBHPdg4EwLOQGk\nq2M9Go1a/Y9hYjKZVKlU0vT0tOr1ujKZjHq9niEY+N01Gg2FQiE9ePBAh4eH18oMJpypVEpHR0eG\nL+fzeVOQFAoFK6lWV1dt3P7WW2/ZRoHxOY6ed+7cMSLSYDCw8ofvrNVqqVQqaXFx0T7Hr/zKr8h1\nXedN1sAfuDM7jrMr6VuSvi7p667rPpf0A7WQucYd8V+9emV2rPV63XYvgs3ZUWGP+Xw+80A+ODiw\niSGEdLBe5PfYaY3Xw5QP7XbbRr0gHgTBY1VFmCZ0yng8rmKxaDUu0iZifsFjoazm83nFYjGLESaI\nh6FHvV43K4JWq6WNjY1rhurU4zShWAgMBgOVSiX5fD6DODGkGadvLiwsmAxL+iRQlFE3r4tTKqaU\nELtAiHBXgl56k+vT1MyPJP1vkqKS/gfHcXYdx/nHN3rVz+hijIq4k8YEISocAXYHJFWYgY9GI8vZ\n4z+OzfEAHbSAKysrymQyJqWiHq/X6wZZkfREcynJCPxwIaLR6LXBiHT1YFLnX1xcyOv1WkYgTRm2\nX9iDjUYjLS4umuZvPMxnOBxeS62lSWPnnZ2dVSKRsHQtvjeaVTwwELvyWYDnAoGATTah2PKQU6/j\nN02/QFnI93/T69Ms5ktd7cRDSSNJNUmVG7/yZ3BhLzAcDnV0dGRJpohJga2oXaPRqD766CODnsbh\nMgYY0WhUuVzOEIGzszNr8hCwglocHBxY/Ytx9vHxsdnXMinETRRjlXK5bLwLGr1ut2sWr6TGMv0D\nm6V8IOmp0+kYf6JWq5kRJA78LCgmjIy5MYDBEBI0A5Eru/yzZ890fHys4XCoFy9eWHxEv9+3GIfh\ncGjeHBD+KbNQgY9GI21vbxs2jdsopd2bXp+mAWzrCl/+HyX9Ldd1bxZw/BleGK/0ej198YtfVDqd\nNrPDRCKhcrmsVCplqMP5+bneeustxeNx4w0EAgEzCccHIhaLGQ6cyWRULBYVCATMwQdoKx6PG2cB\nHgX5eKAY1Lqzs7NaW1vTxcWF1tbWNBgMbHTNZBHXUZw4KWP6/b7eeecdY7/hjg93RJLtntTY4Mjv\nvPOOcrmcPVxM6nhwcB0KBoP64he/aMw7ThnU5YyyOVUQKqyvr5sPx9nZmXw+n+7evau9vT3t7e2Z\nsQxmOB9++KGSyaQZM97k+jSL+T+W9Cck/VVJf8VxnG9K+prrur95o1f+DC52AVhaHo9H29vbevjw\noQ4PD804EecfLGPxlaD5+fa3v6379++bEvni4sJYbNSweE8sLS3p8PDQUAqiziqVih2fqL7JBOSI\nHzdEJ9i9XC7b0AfYamtry6y0Tk9Plclk9OTJEz18+NBGy/Pz82YVViqVDFk4OTkxK7BaraYPP/zQ\n6npOiidPnpiqptVqKRqN6uLiQltbW8pkMrZj+3w+1Wo183JmkYPIzM3NWaJsvV7X+vq6Tk5O9J3v\nfMdOQp/PZ8kAT58+NTKV3+/XN7/5zRvd/z+wzHBd9/9yXfe/kvSfS/qnkv6ipF+/0at+hheiUtTH\n1MnjvGK4vpOTkzo6OtLKyorhqhiTo22DTI4ok4YxHo8rlUoZooEyHK5yIBCwwcm4uxBORpB3OKaZ\nusE9Hsdfl5aWzNwGhh0DDZyO9vf3bVERKeHxeBSNRlUsFrW1tWU7+Gg00t27dw01YfHCN4GHDKtu\ncnJSzWZT5XLZegmGIoyyQXJwB6WcgArK73U6HS0sLMjn85lglzKQz/6m16cZZ//D14jG/yzJq6vp\n382r9c/g4sgl9heerHQ1HSRllAUZDAaVSqVUrVaNcQeRCPYd0zJ4zBi8zMzM2ESLnGomXTDHfD6f\nTQ/hNAyHQ6sTEZdyJCMuwNqAEwYUArZaoVCwYUa9XjfOBGQfTF3QREYiES0uLlrje3Z2ZoFAYM7I\ntcbVNxi57Ozs6Pz83EoLOB7AcODYPJCSzLXUcRzjsbCYSQmo1Wq20OFS3+T6NA3g35R013XdP+W6\n7n/ruu5vu657M4vzz+jCOBvZOugExB8QhnF8GPyWKx6Pq1armamh1+u1rEBonFjLer1eSZ/kcdD0\nkd1H6hTlCfo6iOh4zGH7Ct+5Xq8rlUrZLogNeCw8AAAgAElEQVS5TaFQsIeLnzU1NWVc4ZWVFUMr\nBoOB9vf3Va/XDa5rNBqqVqu2YDm9xmN/m82mUVkrlYq63e61NNZ2u61wOGyeIixqWICIdxEUtFot\nRSIRHRwcWMwbRoyQp0Ch/jC4GU8k/Revd+h/6DjOT78eb//AXbj/xGIxrays2JGJ6iQYDJpDKI0S\nquDJyUlDHfr9vqmf2aV8Pp8SiYQNBxhPM+iQdC2x9OTkRPF43LDUcZsuJPlo/DY2NqwEikQi8vl8\nBl2xW+EC5LquEomE8aXBx1FTp9Npe9+xWEzxeFzLy8tmWEieYbFYNH4IavN0Om1xE6hdKJUQBnM6\n0AeMfw/EOMzMzFzLZuH98LNQ80hXQoNIJKJoNGo9xJten2Yx/6+SviDpf5H0y5Leff17P3AX7kPk\n0bXbbQP6ic+VrjpwZEhYZ1Fro4JGesWuAZkebjFMNgYFXq9X6XRax8fHlgHCkYq6mlNhdnZWzWbT\nrAsQw+7t7dnRz3iYnZNAGwYelC88hOScYJlFP3B6eqqdnR3LYGG3h7M8PoxBqAuNFvErpQTWuuPJ\nt/Qgkoznzfssl8vGL5+cnLR8FcdxdPfuXbNQQKp1Uw3gp0Ezvui67ttjv/4tx3Ge3uhVP6OrUqlo\naWnJGirAe3a6cDgsx3GUTqfl9XoNVqLOJKB9fHDgOI4x5SAiDYdDa5jgCxOnOx7T1uv1bHQNzg3f\neH193RAM1BycBrwnhKEQjIimIIAH5GZ6elp+v99chOr1ugKBgLLZrNndQpLHUZ/xOoMTSQb1jYt1\nedCAPXFA9Xg8RtACu6fkoQEOhUIKBAKWq0hdf3FxYRtENps12PMPIzrt0nGcDX7hOM66rgYpP3AX\nZQCIAmJTal+YYi9fvjQzFnbRO3fuWLAPI1oSq5rNpvkeM1XMZrOW/wFhKBAIKJlM2kMC/4HxNgrx\ndrutSqVi1gZEl0FgBzWZnZ01jw+EuugNB4OB4cHEU6TTadPvMQGdmZnRs2fPbKfl4VpZWZF09eCM\n/yyEuKhXsBNj55ZkJwzNLLwP6nl415RomUzGQj1pSAOBgNbX1+0hpQe4yfVpduafkfQvHMchcn5F\n0l+60at+Rheq54uLC3OPJ7Ac21oUI+wU7Lj4nVFzM3LN5/NaX1/X1taWwuGwxYvRCBKbAA/5448/\nNlck5PjIryDHY57SbDYNvcDUhZvKTk4TxaQRe15JFqg5MTFhYlRscSmTpqamtL6+btO8cVsu6RMn\nI4Sl+HtwCgQCAfOci8ViVgZRe/PvhsOhjfvhdvNaW1tblnTL7g0ve2dnRxsbG4rH4/awvOn1aXDm\n35J0V9Jfl/TTukI2/sWNXvUzuiYnJ6+hApj/4Q/XaDR0eXlpTQ7qj8vLSxu99vt9G4hgmHh4eGg8\nCZQS2OWizmaXx9YL8vp4/BmiAUSkXPh2jCtY4AqzKAnrxC631WrZwwvUxt+p1+v2HyNu+MNgv6Aa\n/X7fKAAEc5IQUKlUjIBUq9VUKBSuiXJLpZKdeM1m00oHIMVAIHAtPZamm/4EvB5K6f7+vm5y/b47\ns+M4/6GuSPiOrpPxN14/tf/oRq/8GVyMbi8vL/X2229rMBhoaWlJ6XTamg4sVfv9vvlM4I8GC45S\nAcL+j/3Yj1lgfDqdVr1eVzabtag1auHxvDvc5hOJhGHR8/PzCoVC8vl8SqVSFmsMlLW2tmYnCF55\naBaZHHLEJ5NJcwKC7ENdOzMzo0wmo+fPn19LsyI/vFqtanFx0Wr+xcVFtdttK03gayCMDQQCWllZ\nsbgLSfY6w+HQ6JvLy8tmfr6+vm7aRYYuWOAyGWX8jqrmvffe07Nnz974/v/rduZ/5/V/f1nS/y7p\nP3n93996/Xs/cBcWtgxP4Bng3bazs2OcW3ZNwPpWq2WTMxYNu+7e3p5BZVNTU3aT+/2+0TPZlWh6\ngsGgDWcQjmLYQtcOGoHhIMYzoVDIOMWXl5fK5/Mm8R9n5925c8e8PUAqGHS0220tLy/bEGM8FBPb\nLgzE9/b2bGGP8619Pp+9Byx8A4GAPB6PRTLDzgsEAsrlcpqenlYmkzGjSGifEPs9Ho9xQSYmJpTP\n5027+G/Krf+91++7M7uu+xclyXGc39CVDrD0+tcLkv72jV71M7oqlYqSyaSVECAHBM5IMoYZpoGN\nRkPJZFJTU1MWAww3gsECJQPBkAxUUGBLMltclB08WIhsqSNBRHAqwv4ABAYIDkhQuoISG42GxRrD\nUvP5fMbNQLoPOQrMnCnhxsaGBcjTsJHPAmIBUT+fz5utLn8nGo2arx0Kc3ysa7WaFhYWrLkDosNg\ncRzqk2RUgVQqZRAm9fdNrk/TPmYljbOmK5JuZqT7GV3pdNo4tHAvaM4wXEHrRo3YbDZtosZuDjYK\nLopLT7fb1dHRkR318HQhL6FgwQgR/gVKC8hAKEDOz8+ttse8cGJiwh5EYKyjoyPzNwZflmTlC2Y1\n+NAhTn358qU1i1A7cWaCv8L7Pzs7U6lUMtHtzMyMeULPz89re3vbhiRwj4EnM5mMhYLCN0H4wJS1\n3W7r7OzMMPbl5WUTQdB//GHEDf+mpP/HcZy/6DjOX9IV2eg3bvSqn9G1tbWlUqlkit+TkxO1Wi3b\nmVkMNHzJZNImhChRMPIGXmMkXSwWDYIjehi1Bg2jx+PR0dGR7V5wfyHTHBwcSJLBhghG2ZHZDaem\npsz1B1jP7/erUCiYRhAsmiYN3gcpAJJs4uf3+20xEXLfbDYlSYeHh2q329rb29PKyoo1qgx3Wq2W\njo+PTUlDY0mJAg+EOpgGkvg2fPFg1x0eHqrT6dgw5vj42B4O3tObXn+gBvC15u8/kPRjumoEv+a6\n7v95oxd1nJ+T9J/qiuz/sa6gPp+kvydpWVcC2v/Idd3W2N//y7oSCPx113X/+ff5me5P/dRPaXFx\nUdKVjGhpaUnFYtG8NAqFgk3QyDM5Pj7W5z73OYPNyOWDJUZHjmvl7OysDg8PLUN73HCRJoudmweF\ncTYTQyiV9+/fN3YctExSVsdRF4hHNEosHCaDKEn4XNT7eGdsb28bHPn5z3/eRKXhcNhKs1qtplQq\npVKpZMGY7My8TjAYVKVSUTabVbFYNEUP/iHkpXi9Xm1vb9s0dWVlRYeHh6pUKuaSBEvu29/+tjKZ\njJUnP//zP//ZaQDdq9X+j17/d+Prtbr7r0h64Lpuz3Gcvyfpz+tKnvUbruv+947j/DeSflbSzzqO\n81DSn5P0UFJa0m86jnPXdd3R7/3ZKC9Go5F1/+VyWaurq5Z5DUqB3xrEpMvLS+VyOUUiEf3u/8/e\nm8RGmqZ3fv+PjGBEkIx9j+C+JXOrrF4wXdOC1GrBGvukMWADvowxNnTTwePlYBuQL9bFbWAGXgAL\nsC0bmsPIkAFjoIPGGEEQ0FZ3Cb1VZlYWkzuDZARj3xgRDC5Bhg/M31PBbnWjkVRrWkR9QKOyM8lY\n3+99n+f//JePP9bS0pLVcqiwHccxmyp2H+rbwWCgZrOpRqOhubk5G5H7fD4dHBxYfYi5zNjYmB3N\nuF/m83kbN4fDYR0fH1vO9M3NjRYWFuwxX716pUePHlnjR24htSo3yPBd9C/ko52dHXPhZ1S+ublp\nZU2j0dDR0ZE++ugjvXz5Ul/+8pe1tbVlnyelA3rG6elp5XI5czwdHx9XqVTS4eGh0um0Go2GhRJh\nw9VqtXR6emqNbTgc1tnZmba3t++1tn4eCui/5zjOjuM4p47jdN797z72XKe6lWFNOo7j0i2t9ETS\nb+nzxvIPdevTIUn/UNIfDYfDq+FwmJO0K+nv/XUP7HK5jPZIbDDcDGLIOGqpH6mXwYcZYsBpLpfL\nSiQSVh9PTEwYMZ1mklDLVCplahV4FRMTE0aCZyxMnAM7MDVwKBSy0qNQKFhkGfRNOn7KF6xoQTiw\n2GKCyEjZ5XIZ6WpsbEyRSOTOsIgbiM8PEev5+blOTk4sBQB7LSRQpGxNTEwok8nccUh1uVxGBZie\nnjbSEoY1jOHpI3Bqvc/189TM/72k3xoOh4HhcOh/97/A+z7hcDhsSPqnko50u4hbw+HwzyQlh8Mh\n2sKyJDQ0GUn5kYfI63aH/olrNOcDDgURDNAWs9ms4akgGEBdmAjSFLlcLqVSKUkyGumTJ09MHvWl\nL31Jbrfb5D4TExNaXFw0LgW85S996Us2QUOuhA4wHA6b7xq+yVBDgel4rPPzc83NzRm5nXKC/Oxo\nNKqlpSX9+q//ur1u3lsymTS23agIIBQKaWFhQZFIRPPz88pms0qn05Zqtbi4qLGxMeMuM63Euovm\nlR6AhCmMeCBiBYNBra2tWVTx9PS0VlZWbCIai8XMVuF9r59nnF0aDodv7/UsI9c7bsd/qtuxeFvS\n/+04zj8a/ZnhcDh0HOdnFfN/7b/lcjlTSxQKBfN7WF9fty9iZ2fHdqdarabd3V3F43Gr/1qtlmGt\nb968MQ4HOxKPe3x8bMcukzTsvJDdY9n68ccfy+VyWWOaSqXMhRT8eGtryxTR7LY4ikYiEeNGcDoc\nHx/bgiyXyyb/L5fL+t73vqdkMqnNzU3Nzc1ZPTwYDAwVKRQKev78ufnModxmihmNRvXZZ5/ZuH58\nfNy0j3x209PT8vv9JqAFyoTfjRNSt9vVycmJ5ufnbWqaz+dt6MK4Hqu0971+nsX8g3d17b+UBOF0\neI8J4FclfXc4HNYlyXGc/0fS35dUchwnNRwOS++w7Mq7ny/oFh7kmnn3dz9xofxgQvb06VNjZlUq\nFW1sbGhubk67u7uamZmRx+OxwUM8Hre86lKppLW1NZuKERLZaDTk8Xg0HA5NfjQ7O6vvf//7Jvkn\nShgvuIWFBXMdlW5LoVwup7OzM62urhrSMjs7a+UDsv1RAWu/31cikbBdkpNkcXFR+/v7qlQq1mgW\ni0V5vV6trKyY9g8vurW1Nb169UorKytyu93mMkRcA7+bTCZNCbO0tKS3b99aRgkuS61WS9lsVvv7\n+xbtgHQsFApZNjgDmePjY62urlqkxfj4uF6/fm0sPKaH73v9PIs5KKkv6R/82N+/72LelPTfOI7j\nk3Qu6d+S9D1JPUn/WNK33v0Xb44/kfQvHMf5Z7otL1bf/fxPXN/85jdVqVRssibdTr4gvTNOfv78\nucbGxjQ3N6fx8XEtLS0ZZIdwtFgsanZ2VuFwWLVazSRBREokk0ldXFyo1+uZZEi6zc6bn5+3o31p\naUlbW1tG7mGxj3oSb25umrr7+PjYXgNum9fX10qn0+p0OjZZzGaz8vl8lgkeDoeVTqdVq9UUDAbt\n5kHVQZkC9ZObEY860rdG6304KOPj44pEIub8hA6RCR/E+kQiYQQsBLn9ft/MbpaWlgz5YKDzG7/x\nG4Z6FItF7e7uvuey+vnQjP/ovR/9r3+8V47j/HNJP9AtNPcj3ZrM+CX9seM4v6130Ny7n99wHOeP\nJW3olnr6O8OfgifCkzg/P9dnn32mpaUli0YoFApGsCkWi1pdXVWtVjPZDyoJLGWR+kMgf/TokarV\nqrHJ6vW6Go2GFhYWbHgBCgC2HAqFtLe3Z1wIkJFoNGrxZYRnJhIJ84vrdruqVCpG1ul0OkbKAZMG\nGpuamtLV1ZWZpXMiEOaDQIGPDGbg69evtbCwYDZZnU5Hy8vLpo2kOYQNSAkCBAjllWFNr9ez0mHU\nGHE4HFqjTAxFoVCwcpBGEGLTfa6fijM7jvNfDofDbzmO8z//Nf88HA6H/8m9nvlv+AJnBiP2er2W\nM724uGiTMHgYNFe7u7taXV01dtz4+LiZeUPHJGsalKHf71tWNf4a7HLdbtf4C+Pj40okEvrkk09M\nZzgYDBQIBNRqtcx48OTkxAY1IC9MJnl+wi7x+MCTAt0j+LXf71etVtPS0pLevHljzyXdNnvo8SKR\niN24PCZEIfwvXr58aQ79KFJIaJU+l2gRdcHOO+qPnc/n5ff7zY6XIdH8/Lyazab6/f6dsfy3vvWt\nXwjOPOE4zt/TrfvnqDjrx1l0vzTXqJXs7u6unjx5YpyHUQiKpNLPPvvMfM7wt7i8vFSpVDLvB4g0\nWFQh82+1Wmo0GsZ9htBEehOlB8c1Uy/Ce0YFnEBz4L+gKUi/kGmxWDFDhKtNc8hxTm1PY1YqlfT8\n+XNb9ChlENd++umnhgCBR/PYKEd4L5wWkPIjkYhlfR8cHFicBWJiXg8TRDgsjUbD6LDlclkLCwt6\n9erVvb7/nwXNhST9D7qF5n5b0rqkhqQ/GQ6Hv5REo0wmYx/awcGBJicnDXW4ublRsVg0eiVj3lar\npcnJSe3u7hrMVqlUDIdmQEHADf5wNGBgw71eT3t7e5qYmNDCwoLtlBzPfr/fund+FhSEkyCdTtuw\nBUoqo3HqWBYVvGS4DdTNOPOz642KTIlo43E4lSEpMarHibRSqdhkkzKt1+tpeXnZNo5R1iEsRZpO\nr9drPUw+n1coFDL/Ed4PAgBU6fe5fhZr7r+QJMdxPLpFIP6+bsfO/6vjOK3hcPj4Xs/8C7iYTEnS\n8+fPTaqDKSByonQ6LbfbrbW1Ndt9+QI9Ho+Wl5cVDofNRIVRMCJNiEeYwFCzosQIBAKanJw0S61k\nMqmbm9tMasJpGCLAFgMhyb0LWE8kEtbI4teBWhtFNqcJCbHhcNgcO5H8w1uGjI8OL5lMGj6MBRe5\nK4RkRiIRM3ZJJpNGKGLSyEmDGh2qK6mrCGD5/V6vp3A4rEgkYoQpPluv12ul0fteP8/QxCcpoFtU\nI6jbQcdfvfcz/gKvWq2meDyuubk5Cz5H4FkqlWyQcnR0pMFgoJ2dnTuY7tXVlXK5nOr1utrttkWt\nsdt7vV4tLCzYIj4+PjbqJCJO+M21Wk2rq6uWk3Jzc6NEImEyI8bUOCZVq1XDji8vL7WxsaFOp6NW\nq2U85e3tbeN91Go1GxPDad7Y2DAiEXYB3W5XzWZTa2trCgaDljEofX7zAzeCSlD2sPtyopXLZQUC\nAe3s7Ghzc9MQknK5bAxBmsV2u23BO5CgGOHzmmn8rq6urOm9z/VTF7PjOP+b4zjfkfR/6XZX/q6k\nf384HH5lOBz+UmoA2WVoKtxut03cqDX7/b55JqMSBttF8AlzjMYHeKzf7yuXy+nw8FC1Ws1G4YTG\nA0cR9jg6OIDX6/P5VC6XzSIWJTclAg0kMi2GJBMTE0qn08ZTZmDB1HM4HNp4fNSTg7H24eGhMfLY\nRUebZEnG/Gu320ZpdRzHxvzU1NlsVrOzs8YhwSxytE5OJBKKRCImmIWGC8JBw7e9vW0MPV7z+14/\na2eek+TRLZe58O5/rXs92y/4wuSFLBCQAZqw5eVlI9l7vV5lMhn7kihJ4GjgjE+WXigUUjqd1uzs\nrLLZrJaWlhSNRrWysmK7NlwEMNt4PG4LJpPJmEUV6auEBkm3w5R4PG64ONFk3CTRaNRG0tFo1B6X\nGxBVOgKC09NThUIhPXr0SIlEQrOzs5qfn5fL5dLc3Jwx/iRpbm5OLpdLyWRSw+FQS0tLCgaDhstP\nT0+bMfmoAQ7vB6V1PB43/JhyLxAI2Ag8lUopm80qmUyaEfvz58/tPT169Ohe3/9PXczD4fDf1i2h\n55/qFr34z3U7DfzXjuP8t/d61l/QRb1YLBZNY0bjBYeZY5whCObheBS7XC5ls1mbCgK9wa6jbqX5\nAT0hMpcsPhZ0rVYzr2VixChtwMXj8bg1Z+yYNFCRSOQOBHZ2dqZKpWJcYgYbyWTSbmZ8j+fn5++M\n4tvttlZWVrS3t6dYLGbeHmNjY0qlUmbs2Gw2dXZ2ZkptjBLx3lhcXDRCFwY3YOGxWEzJZFIfffSR\nNaDT09Om/mF0DbUWES285/tcP7NmHg6HN8Ph8FPdRqb9K0nfkbQi6Z/c61l/QRfmgzgYjTptgr/i\ngxwKhVSpVOTxeIxUhMHf0dGR7XZut9sk85i54HHBVIzamYw+mjUYbJDPCcUh4oEjHstcsGp2O+kW\nthsOh4Y7M1kEm2Z3Zrrn8/lMNABEdnx8bKbn2IXBCgRaq9VqdxIAaArZ4aGT3tzcKJfLqdvtqlar\n3Qm+9/l8qlQqFi40ekJKsiEP1rahUMgyGPHiu8/1s4Ym/0TS13VbLw90WzN/591/3wyHw+t7PfPf\n8OU4zvB3f/d3Jck6bgg6a2trKhaLajQaSiQShhsDSZEehUlLpVKxjh5KJvIpbABYlNiBwaOQZISl\n0dfAVA0tHPwR8kngXFSrVYXDYfOP5nkbjYbVmYhgM5mMOfoXCgW1Wi09evRI+/v7pphGroWiA4Ep\n3GPgNho1vEWy2axOTk6sJAD3pi8YDoeWYMXJNhgMzBQnn8+bwypZM+Pj44rH42a2zgQWJKjZbOoP\n/uAP3nto8rN25gVJfyzpo+FwuDQcDv/RcDj8/eFw+OqXbSFzEUbJUILdh66eQQVfHmppHI9goQE9\nTU9PGw+ahYpjEMoIVCxwFQjj8Xq9VkdTEuB3h4UXsiK0e2dnZ5bJR1Ks4zjmnwH1E1kTY2iIQpeX\nl8rn80omk8rn88pms3bz0oQ+evTIfC8g5yOoxZXT7/fbScWgZHd3VxcXF2anywib3bxYLFrDCNmI\nRhAJGjpHCP6SLIUqEAjcO9fkZ+HM/9m9HvnfwIV9QDAY1Pb2tpaXlw3yoXYEevL7/frOd76jQCBw\nR/rU6/XMOqtQKCibzaparRp6UK/XbUzLrsNjIkCdmppSu922mIfd3V1zHALFoKtn6oiAFJcl0AG4\nFX6/X2/evNHMzIzGxsaMbhqJRCxjm9ff6XQsiYrBDM1as9lUPB43Fh2vidH5qO7QcRydnJwYtk0Z\nQKj8ixcv7AaAmM+Usdlsmjzq5uZWFFQuly1KAl7G6Ibx8ccf3+v7v5+51y/ZValUzHkSGIvFygJi\n8oTJYqvVsgyRVqtlzu9+v98GLEiVLi8vNT8/b7s5gerj4+NGREdAyyABf7jz83OrVfv9voXY0PAR\nnM4OGYvF7OZgcSwuLtouGo/Hjdgvfe5NDQLCe4WHUq1WzdUU3BvzSEjxbrdbU1NT5pkMwZ7auV6v\nG087EAioWCwaJEgsHQ01Wkr8RdrttiFDICOodkCR4vH4vb7/B7WYGWrwBeKNAUoRDAZtiII0KhKJ\n2OiZ0gAiO7Xf6uqqHcmkOEky0jrWAktLS1pdXZXP51O327XYNaIggKvw0UilUjbKlaSPPvpI0q16\nu1qt2kJPJBIKh8P2vjAoxB2IiRuUUrgVwI08FnpFOBJAl1A7MU+PRqN287NrEiUMi47YtouLC+Mx\nYx+AYSRIkM/ns4g2DGUkqVqtmicgNNP7XD8Pn/nvzOVy3b4dMGTQiNEMQBQShNaQucHuGI1G7e+g\nQJIDPYowsIMD9ENOpxkCQ/Z6vXZEA235/X6Vy2VrwIgtY9diRI5gdHp62hpWxs7Y0DIahzQfCoXs\nJsBay+12G6cEngpeHzwHI3IQEPB27L+on8lKoS/gfbbbbaVSKRu4MKhCUIu2Ei1lMBg0qBI73l/k\n0OTv3NVqte7IkDAXqVQqyufzev36tQaDgWX9vXz5Un/1V39lmXws/N3dXZXLZaudX79+bRAY0iE4\nE9TR5+fn2tnZ0e7urprNpvb399XpdFStVnVzc2P4d7fbNfMZIK1YLKZwOKyNjQ1ThdBYVioVFQoF\n5fN59Xo9bW5uyu12a39/X2NjY6rX6+p0OiZurVQqOjk50Q9/+EPbpev1uk09p6amrPzAQvbt27cK\nBALK5XI6OjpSv9+3XgP0BK4F8GK73TbokRiLra0tG1BtbGwol8uZVAxCErs4sF6hULBk2vvwMqT3\nyM7+Zb0cxxn+3u/9nunbqJkxALy4uNDW1pbxIzAM/NM//VN97Wtf09jYmH3YLFhJtnNWKhXDRePx\nuHEWRt192E2heUI6qlQqluTabrdtWMJJ4nK5LMYNhAXkhb9DdQJ/YjRagjLl7OxMkUhEx8fHCoVC\nBschUCB+AkRh9GdHA3jOz8/tZMtkMuYw6vV6LeD+7OzMpGTU5PBPCOKhyQUxgYQEzRWjnEAgIL/f\nr2q1qt///d//xflm/F26cPicmpqyTD8avOPjY7OMAq/99NNPDVe9vLw0NcTBwYGWl5dVr9eVTqf1\n8uVLra6u2uLr9/t3jAxZxEQa4GcB3MZjs/BGx8+QdVwulw4ODowEFIlEdHJyYtq4wWBgiygUCunl\ny5d6/vy5TecKhYI5arKT4pMnyX4OxAOe8c3Njfb3902Eix9dMBi08Et8LqiDCXDHnBGuCqXKYDDQ\nycmJKX1Aas7OzpRIJCy5C2FCJpNRr9f7W8nO/jtz8aHlcjnzktvf37fdFd+K0S5fktrttkFz+NDB\nFQbG83g8llHSarWMgYaTPVM3BiPNZlO5XO4n/Juhh4IKjPows6DAxbGDRYlBWHq9XrcEKI5xXI4g\n5CNIiEQidgMx2CmXy9ZEMjUcdS6itLi8vNTx8bH5fmAkjsv+xMSECR7A5cHkGdlDfuK9jGL9pNNS\nhtEgv+/1oBaz4ziKxWIKhULmx0beCMaBlUrFuMv7+/t2BLKIgdP4kjD0ZjyO9wUjc9hl5+fn5i3B\nokokEgoEAkbqqdVq5l7E+FeSwWQoVjA8x/YA7JqAd8J4jo6OzLgF1Qp4LyXI4eGhmayw4xLJzO/i\n1olub2JiwmzM0Eey8CVZ2hQwIacbeDNeGpJsuMLv0l/wM+DoY2NjFr/xvteDKjMwDqTDR9WMixAJ\nrNRymUxGW1tbFgnGbkd3z+5IPU396Ti3gY1kdZDD3W63tbi4aKoLhgjxeNz8NKampoxJhqv+KCqC\nGpqGk8V7cXGhx48f224Ils5NR61+c3Njiauzs7MaHx83OzAMcaampqzOdpzbwCJUNKA8+HtA6A8E\nAsbTgJSF3wg8bkb9lHFYCMCak2RICfwYoobdbrc++OAD7e3tvff3/6B25m63q2w2a6QgiPEQhCDn\nYCkAdHdxcaHDw0OVSiV5PB7jXjDoIISKR+8AACAASURBVP4MTjM83WKxqHg8bgw4MlDOzs4MNaEe\nR+jZ6/VMj8jOzpGPgBZyETsXdq8MVsCD4WrTTMLfoBmVZFixJGvKGMOP2uzS3NbrdWPS8Z7ITpme\nnjb/PTBqSVbSMaaH4wFhqdVqmYsq5Z7P5zOZFOStL8qMkQtesqQ76gnqzmg0qomJCcsBXF1dNS+1\n6elpJRIJC8K5vLy04QnHsvS5TRfcA74gvjh83Xg8yheOXXbhmZkZW3A42UsyMg87NeR8Hj8QuHVG\nA5uG1zExMXGHtA8HWZJhu6N4s8/n09jYmDmcIh4AW8YmbGpqyvgko6QseoFRiy5eN+bnLGzouPBH\noOYyykZPyXt73+tBlRnsvnTy6OP6/b4qlYq5AlFXAkuRyoQqm7iw4+Njm/q9ePHCMF2CfcbGbvOv\nEWtisj18F8jT7/dtkII97tXVlUqlkgk54UozDmbYwOugKS2VSgaP4VYPN5vHHz1p4GzAtwYfxg+D\nG7dareri4sJKoWq1ar54GKbDMoSmCd/i6upKy8vLVtdTruEtIt3WyAgm8NVg5wa3Z7O5b0Lrg1rM\nREAMh0ODjZjizc/P3wl1hCcBofzi4kKJREKtVktPnjzRcDjU8+fPLf200+loZmbGcFrG4D6fT5lM\nxrzqUGfQSN3c3NjfoY1Doziac+LxePT8+XPb5bG+GhWN8hiEAXFK4Gz6+PFjIz2hOEEGxY4JxxiF\nus/nMwOaZDJpzR67p+M4lvVCZMTc3JyOjo4kyf4dLw2QDqaY4PBsJLiR4tfBOB2F+32uB7WYGdHi\nkwZyQB2J4oEsu/HxcbVaLQvcoaMvlUpaWFhQLpfT6uqqNZJwEuAeU1unUimzJpiZmdFwODToDW4G\namnCeMBrnzx5YqNoSpRKpaLT01MrCdjlILd7PB6zvUKLSKaI1+tVNpu18HlQkrm5OdtV8QJBTT4q\nG+t2u0ZWwo4WYpXH49HJyYn5P6P8pnTjJmKHHk3sOj091ezsrDXf8XhcyWTSft/v92t+fv5e3/+D\nWswsRkoKGkCO7r29PQWDQcvpYLxMXBkLhzHwcDg0SijNFpIi+BEYkDMqrlarymQydtOcnp5qYmLC\njv/BYKDPPvtMlUpFL168MCwarR/BPYPBQIPBQLFYTNItfZJBEGSfXC5n/nS9Xs/QmL29vTuBOvv7\n+3ajP3nyREdHRzaAAY6E3DQaJwwBSLplJIKJQwjK5XL68MMPJX1uYIOdLS5KoCsTExPa2tqydNZY\nLKZGo6Ef/ehHZjqJJvF9rwe1mMmbGyX7QGMsFArK5XJaW1uzWtfn8ykYDCoQCMjtduvw8NByTWKx\nmMFVHMfUjrFYzLjGsMEwPMGxBzk9rv3AVaMDE5qh09PTO0lVNzc3NohggY1mmHASQBoiVIgb8vLy\n0gKHer2evfbz83PVajUdHh5auYJHteM4ymQyhuJcXl5qf3/fBKiSzBgdewVJhrbwWJIsT4aYCXBw\npomoY3BspZHd2dm51/f/oBbz6ELhw4Mi6TiO1tfXNTExYTarkmzyhZ0tHhhMrEb/zAKg6WJqiIEL\n0iEMAqlnKW9gkIEQAI9NTk6aJhGnUeiczWbTuvxIJGI3KgMNYDEwW3IIkTWBwlAmcAOfn98G33c6\nHWMFYkHG0CUcDttJRjkGrXX0M85kMtYUYn2AS+nq6qrt5rVazWBNpqyYmTuOY+jO+14PCppjJAxm\nC20T/zSO+1ETbI5YSQbr8Wev12uKbBo2GhvHcayxAi1gB0LVwgBhcnLSOviJiQmLUJBkkBvDklFH\n/H6/L5/PZ+JPSUZlpaRiwEJwvKQ7aAPvgd0VTw/qfbfbbYoPsHnKLRJUr6+vze0UygCpBIPBQGdn\nZwqHwzalhGtCE4qtAhg/426c9omJ/oICOnLxoVEvI2eHW4FEiWOQJgb/NTjJTLX6/b5WV1fV7Xa1\ntrZmeCtDjmAwaJgqTdT6+rpCoZBRLuGF0LihsoCGylhcuvWSxu0IKiX/hQ8ChXJ098TrGZ9l6mq8\n3y4vL3V+fm7Oo5Qo4+Pj6nQ6hgFLMiiRhQ1ezedIT4G1wKjF7+j3wJCJngEP5ng8rlgsplgsJo/H\nY70HZdl9rgdVZhCySGoS7kIEk6OrQ2R6eHgot9ttYZI0RPgbT01NGTZ6eHgoj8cjt9utdrttnA+g\nMwwLRxXKjuMolUppY2PDBjBYX7Fj9/t97e/vm4s+ITg0sZJs4kYNS5lDg4krPZFmmIaXy2U7ibAE\nAEtvNBp2etTrdbMc4LGhBuCST+4hqATTQ6Z2GDFiGt5uty3YCPsBamZQF+ijpVLJLM/ucz2onZnj\nrdvtan193dha0WhUCwsLWltb09TUlHFsV1ZWbBeenJy0RmZ9fd3M/YbDoRYWFu5k/VE3D9+F6ExP\nT0uSVldXTcWCNo4Gje4eeA1oLxgMWuO1uLhoFrWQbyDuEy4Pg25packwXpyULi4uFAqFLKhnlFdB\nhDDsPU4hJqHkJuKkBI4NtDccDu35KRm4YTGZfPz4sZVnDH5GTyW86+hbeJxnz55ZuXSf60EtZmxb\nSQrt9XpGnSwWi1bLxmIxa85w1el0Ospms3K73crlckbpjEQiKhQKCgaDVs+Gw2EbWNBUeTwe7e3t\naTAYGNsO931JBj/RvSN+pY7HVjcSiZgWkVKlUCjo7OxMyWTSKJvdbtc8KFCMsJMz3ZyYmDAUZGFh\nwU4bHJTghuBexO8ydBpl5Pl8Phv983553Zwo1WrVZFr4hoCfS7JaHU75zMyMRWlwo9znelCLmZ14\nYmLCdlF4COwMqJYh01A/0jxJMvokO/2ojzG7lCQbFHQ6HcuF9ng8hlxAeJJkzDuaQOwNwLTJ2qOz\n73Q6xoHgtTLoobbE3oCamBD4ycnJn9AW0nhR89P8IXTldWBiKMmEstxUNHCjo3HKJur40cdFLCDJ\neM3Ak5FIxE440KXRBvx9rge3mOEpIObsdru2m7FzMkTY29tTsVg0g29yQlhQ4LpbW1uqVCpGm8TO\nCpkSzvMXFxfa2NhQr9dTMBjU/v6+8Q1ubm7MZ+OHP/yh9vb2TAFDHU6DiqqZBZhOp9Vut1Wr1ezG\n2d7eVqlUMjiS0oQ0V5z/qcn39vbM6pa6F7SlXq+r2Wyq2Wyq3W5bc8wJMPqZ5XI5M4bhxGq1Whbd\nTOkBR4OMlF6vp9evXxu9FkOYfD5vrLovlCYjF5AcujVGswwCiEvz+/2qVCqan5+3nZOj7uTkRJLs\nKAyFQpqfn7cR7+TkpMLhsMUHV6tVffWrXzVGWjabNRwXM29OA4hFX/7yl7WysqL9/X3FYjETozLo\noValmSQMh5RWslIg/jMGpx7nuKb+jsVievbsmYLBoGX+1Wo1K8PIOqHeBokAC7+8vDQbBIhavFew\ncXjao0gLCAbDpKdPnxpkSlj96uqqstmshVze53pQixk65vn5uVZWVkx2HwwGjcnGYmeKl81mjVoJ\nuM+XlclkLIaBoxlWHM0iI2/YeUdHRzaIoKzBeBvMNpfLSfrck25+fv5OFAL1tiSLbYPbQZ0LS216\netr8KDqdji1UGj+sAMi/pgQA4qNmvrq6MqlTrVbT5eWl5SBizI4hDQOTcrlsJU2xWLTyg/g0HEUv\nLi7UaDS0sbFhv+t2u1UsFnV8fGwG5PeJTZMe4GLGUw1d39nZmQ0ykD7h6IMIs91uG04KZkwuCpM/\n6mRI5/i1wfa6vr62yAhSpxg8RKNRgwAvLy8tTjiVShl1NJlMmkceYerssKAm0EUh42O7xXOAIODQ\niWzMcRzNzMyYATsEI2pavDKgeIKewDCEFMTrJU6DP8OhTqVStvPjZJRIJMy9FFxeko3iM5mMpqen\nTVRxn+tB4cyjnT87BJIiRtaYi4M3Hx4e6mtf+5qNpxuNhlnbTk9P2wgW8Wqv1zN8lEiJy8tLW9wM\nQ1BUwIIDFRl10a/VajZJ5FTh6G+1Wkb5pHnqdDpKpVLKvUt4RaolyULuYQeGw2EdHR0ZOoHODw9k\nEAfqXKIY0C/+uPAUSzE0kZRwbBx4bSwsLBgnBCf9er1uLk7oBkdZeCzw0cHL+1wPamdmgfX7fa2v\nr0uSZmdnFQqF9PWvf12SzM2eXTMej5vhSiAQUCaT0crKilKplO0aMzMzRirHg5laOxAIKJFIyOfz\nKZVKaeFdvPDMzIwRhqTbHf0b3/iG3G63KpWKotGoaejgE5N2Co1VktE7UXaDyszNzVmdy8IYHx9X\nOp22Zu7i4kLpdNokXLxfEAhMGJeWlgwSxC73+vpaa2trxmUmPQAkgtLI5/NZeHwqlTLR79zcnGWm\nQH4CR4/H41pYWLDT6NGjR0qn03rx4sW9vv8HtzPzgf/whz/U+vq6pYHu7+/L6/Xq5cuXdixSu21t\nbWl2dlb9fl/ValXlcllnZ2em2Mjn82YtQDbKzc2NmSJWq1Vr1CRZHUkuCZKjly9fyuu9zaWu1WoG\ng21vb1vpASJzfX1tte/ExITl8sEjoWaHHER9i3M/Xs+E+Kyvr9tujlh3a2tL19fX2tjYUDgctloa\nX2Zi1KTbEm5nZ8dKHaadJAFQw3NCADuO7sRMK+lDGNEfHh7aFPM+14PamScmJsxHYjRjA47B9fW1\nvvKVrxgpfH5+XuPj40YaJ8LA4/FodnbWUAKgrkAgYH501H/JZNJ4Bmjm0um0GZug/IhEIuYQj7AU\nzggZItSak5OTmpyc1MzMjNXk1NhYGJDWCl/j+vparVZLw+FQ6XRaLpdLjx8/ViqVstAhPECwJYhE\nIhYdgelko9FQJpMxPJpGGXN0dm9gykgkcsciAU/mdrutTCYjt/s2bB6uC/U/Kbf1el2xWMymhfe5\nHtRiLhaLCgaDlqgECsHuBgcZVhlN0iirzOPxaHFxUbVazRhi4KYME2B8TU1NmaHK4uKigf7NZtNy\nrAm9hCtC5EMmk1G9Xr9DPGKo0ul0LEf6+PjYQntWV1dN7Qxr7ujoyPBu9H48XqvVsgEOEqbR4RFD\nC4S3xWLRSjBQC/BuSiIa2+XlZUuhvbq6MqkYUN74+Liq1apisZg5QI0aW2JojiF8IpG4NzT3oMqM\nyclJlctl09qNjY0pl8tZKUBDM7pwILfs7u6aOvnVq1daWVlRsVjU8vKySqWS+SXD8iJUJhqN6tNP\nPzVRAHTJo6MjhUIhnZycGIaMyLbVamlvb08vXrywqAakRiTGkkft9/v19u1bw4xBTY6Pj0313Ol0\nTEWOuSGYdqVSMVrnzs6OksmkyuWy4dkMRObn55VKpbS1taWFhQXF43G9fftWCwsLZmOAZQPEoXq9\nrmQyKcdxtLOzo1gspkKhIJ/Pp/39fQWDQZXLZZ2enmowGBgFYGdnR91u12Ik4I3f1zjxQS1mZPAc\niRzvCF0rlYqpMoga7nQ6ev78uSRZvRkIBDQ1NaW5uTlLq6KRo95MJBK269FkMUJHps8NANYLujE9\nPW07OSoXsksoazCKATWAx4FAdHZ21soeMOP9/X1r6sj0Rp6EgGByctJeL2SqDz74wLjRH3zwgQKB\ngJVf4XBYY2NjKhaLmp2d1dbWllKplJ0YwIcLCwv2eV9cXGh2dtbsCKABBINBk4KlUikbArHzczq+\n7/WgFjN5eYeHh5adjWr4+PjYSgyGKtjCApnxb7VaTclkUvv7++amDw8Da67Ly0vt7e0pk8lYXcyk\nEQjw4OBA6+vryuVydpOhUQRCY1iwsLBgDWQoFFI+n7cBCc+NexHNV6FQsBJqY2NDMzMz6na7ZjuG\n9VWj0dCXv/xli46DjwLlc2dnR5FIxIwmgdgYXXs8HhUKBbNJCAaDJnqAT7K/v6/p6WlLEuB9ACPi\n3ES6bCaTsfzwbrerSCSiH/zgB/f6/h/UYgaam5mZsQUGFprJZEwtgWD0y1/+sl6/fm3kHVh3kUhE\n0WhU3W5Xjx8/VrFYNFEmtaTL5dKHH35oeDB16rNnz8yc8Pnz5xY7DN2x3W7r0aNH+uSTT9TpdLSw\nsGAu+vCfS6WSNZfEEgeDQc3NzUmSaRjj8bgNKdLptKTbcTN2tvCJA4GAnU7hcFiO46hQKBg/4qOP\nPlKtVtPc3Jz9zNjYmJ4/f65oNGpq9W63q+XlZY2NjVndi8F5Nps10tL09LRev36t09NTpVIpeTwe\nVatVzc7Omivr2NiYvvKVryiXyykajSoQCOib3/zmvey5HtRibjab5qQZiUQsEwQOA00QrpksTOxd\ncf7kS6Ih4oiE7I+F7fX1tcUEwwYjv9txHIubqFQqNg6nFpZkMFUikTAi/OHh4R1bLJThw+FQm5ub\nmpycNM0eNgYMRsbGxnR0dGT2AfV63f69VquZAAHjF4/Hc8fP+uTkxBz9WegMoPDV2N7eNhvbDz74\nwOimiAYCgYBqtZqJYsfGxlQqleR2u/XmzRvrB5gAsovj3nqf60GhGdSxYMHUc8ic4vG4TcKoMykb\nRvVvoA7EfoGQQLmUZIw6vrR2u23KDhzlp6amLC0Vq4JAIGCcCbBqrAJgyDHSptYm8AYzb+BGFg+w\nnN/v183NjdLptEKhkAkI0PiBzqRSKcXj8Tuvnx1+fn7elOJoJHFJLRaLZoKDtZbH49HCwoKhI2DL\nqVTK+hMoq7FYzAxtMJ+knoZMdZ/rQe3M7JjwehmiQHb3eDymJJZkaEM4HLa6lB2DsgMGHNkmEIjG\nxsYUj8fVbrdN88YR3e/3jf/baDQMBwYH5+fPz8/t+J6dnbXAHV4XvAuQk+fPn9tuBmaMoz6vZXFx\n0WRjlFuw30AfwOITiYQhOPV6XalUSicnJ5qbm7MbJxqNmvkknnlwPiDaEwY6Gl386tUrey8ej8eM\n2+lbSJo9PDzU5OSkqW3ucz2oxQw+CpcBjjK1LhjpqL0qUFu9XjdmG8cxLDZMs7PZrN6+fWt2ruz+\n7Xbb1NtQIplyoVzBdAVPDRQm3FjFYtEEoC6Xy9QoQG2BQEA7OzuamZkxQhC499nZmQ4PD03lgdpm\ne3vbFjRhlHCqeU9kWUO6Z7IYiUTMWQlZGEJWIMazszNls1kbxVNq4Y2RSqXM4RM2IAKGQqFgr33U\nOPI+14MqM9gtXC6XTZjAcKl9B4OBYcA0K6AMxBhQXwYCAYO2EomEBoOBKaChfqKkmJqaUq1WUygU\nUjAYvKO6wPNZkh2pOC1RCkxOTt6B+6ampgw1Ga3bObZRnZCt53K5tLa2Zp4ULEosFjh5qMUxAOfU\nQK3daDR0dXVlhHqGG0CHozZn3Eyw61DPwM4bHeTAq6YU4qTCSRR05T7Xg1rMLNZqtWq45+rqqi1m\n0I2FhQUzPwyFQobdMnnLZDJyuVxKJpMqFApyHEdbW1s2DaQp4kYAV56ZmbF6MxKJGG9ieXnZBgdM\nJzEq56aD64x5ICKCTCaj2dnZO/zjSqVihizwt5eWloyHMjMzo9XVVc3NzVmJ9PLlS6tLz87O7qAS\nhE9KMpuEy8tLZTIZSZ/7L1MXo58EN2cKyM2LhzPPTVwdpVcymdT6+rqazab5WYON3+d6UIsZKiFx\nD9A5KRvYEUAiWq3WHbI7bvrk3NXrdfn9fj179sx2NqAoToBRC1t2a45iYo45guFtwGMgealWq6lW\nqxlhiJuDCSVu9+Vy2ST/7HSdTkezs7M2SgdRYdLISHp2dtZI+nAo4F+w+KrVqilo0C/Cm3j27JmZ\nPmK2E4lELG6DlCmErgyJCEWikf1xLSRuTZCq7vX932/5/HJdo0EwUDZHrVZHhatEJow2HRB9sH9F\nncxNggIF05jRP/O7cHj5+R9/Dfw7/ybJNIc0dxB3EATwJUPzZHfkd0AQ4F2MEuAhyXMjjr5P/j9o\nA6+P5+S04L+S7PcoPUb/DjSDkoXXT8NKL4N7E802C/sLQeuPXbjHM75lUoW06PLyUqVSycg/+DdP\nTk7aQoeIDxKwsbFhjK7p6WnV63Wdnp4aMgGOSugOJQpHNuPwRqNh2C/TOXb2dDqtRqNhdSZ0yF6v\nZ65B4XBYiURC6XRa+XzepnEnJyemrAFrJ0dbkp1O5+fnWl1dtRE9C5b4BzynR+VZRDVsb2/r/Pzc\n8PBut2vPBTQJrk9zS47MqF4SBfhov0LP8oWgdeTCbV6S3e2VSsWGFUBWqCiwnAL5wHEHl3pyOvBx\no8Yd9S0eRSWQ8uMmxM0D0YiGjp0OY0een9EuC8JxHEMVUFCjFJE+j7pAsU3ji4YPXna/37+zi4NS\n8H7wrqCJBd2ABIQG8eLiwmxyyT7hhgQZAh0iwwSko9/v35GBAdHRSPI673M9KGgODoAks4nlKB2V\nzxOPBuEGqigWtfjGMdVCQT0+Pm41IjvM8fGx7erBYNC8niEyUZfy2vBiZiFKssEFEWlgwiAYQItM\nDCkbgP+kzw0V0fKR5oROkUFQtVq1gQe1MeUTXAyU6UwzJRnxiRtidXVVjUZD7XbbnJJARjCU5LWB\nunBBmmIkj1HjFw3gyIUvBHc6pBf4tCgxrq6u1Ol05PV69emnn1p6ab1ety+o2WzaLnR+fq5CoWBi\nUvwkzs/Prbms1WrWUPZ6PRWLRaXTaQUCAR0fH9ti4ngNhUKmAeTxkXydnp7q4ODAPPIajYZRNSXZ\nKYDdFQQefg6eNAMNIi64kSk5Wq2WjdpJnCVtC8bd6empqtWqms2mQYBnZ2d6/fq1wZtsAggALi4u\ntLu7awgH6I8kyzZsNBo2aqfZhPD/vteD2pmz2axp6Hq9npFrgOOgPQ6HQy0tLalUKimTyRg0Fg6H\nzWFneXlZR0dHlve8trZmvskMQ9ADspiJN/B4PIrFYkZ5pNYdtc+CHgnnwefzWY2bz+f11a9+1YZA\n8JQHg4GpPcrlsoLBoKLRqBqNhsmmWBiMsweDgRl++/1+zczMqFAoaGFhwT6zarWqxcVFYw5yw0Jh\npZ7HNRXuhtfrtXF9JBKxps/r9epXfuVX7kwq8bq7uLiwcTr8FWis902belA7M5l75XL5Tozazc2N\nIpGI5f9xHPZ6PduBOdZxkAdSk27H5FBBOX4JvIS4g3bv8PDQ3IWazaa5/tC5M4kkWF6SJS3lcjmb\n7HFME+COQz8Z1IFAwOrVZrOpyclJk2lhVA7agPVBu922Mgp0Ay4HTEJODj4nbjjIRtTRGKGDU1er\nVSNwBQIBe5/wR+hfpqenFYvFjDOyv79vU1DiJd73elCLeTSo0efz3TEFHFVzwC2QbhXKkowmeXl5\nqXw+b+aKkF9w+4FEE4/HjSzPFxQKhZTJZGxaNjZ2m+4EFAXKgisSEzj88fCGBsLCgKXb7Rp5CUiP\nunxyclLBYNAGKCxyn89n+SG4juJFTW4JJHxODzzlaABpbkmWBba8vLy0x2C3ZRI5GgcRCATk9/st\ngo3nkmSlycrKiiFA9BDvez2oxQykBJUSLwzcP9kBgMVohCTZTsNUjHEx42HUxKiPqfVAJyC6083D\nxKNZIyKYETZu/ldXVyoWi7bwCcZpNBrGJaFZBNbzer12CvHagCJZ/NLnXhrwMXBWmpqasgxB6m7+\nx46OExNj+W63a5pBSpDJyUl7TLBnRAj0CAxoUKHg6I8ImAHPaMza+14PajFzXJZKJYXDYeNFUGKg\nHIlGoyqXy+YVIckWG4gDkzhJevr0qU3k6vW6OfMTlomCgzJl1JCFGpQjHjQAhTK1InU+po/4bsCz\nAOkYHx83UhISLKwTut2uTezAgh3H0fLyst3cJycnVtKMYuXj4+MqFAoGnzF0gkgfDocttoFaGZSF\nkTVID5sKnynqG3gYIEcHBwfqdruG2LDZvO/1oBbz6empURHn5+ctK+/m5kbr6+tKp9MmwMReACk+\nwTYYpWQyGWUyGVu47ObsnuDCkP+xteIGWV1dtXqx2Wwa644FwFAF/sb5+W1g/IsXL+RyuVQuly31\nam1tzeito8R8himSrLbFFUmSqU0ajYa9N9AKRvI0n9LtZgBJCOEBC0+S4d6UF1gfzM3NmaodNIcR\nO1QBhLg0pZOTk1pfX9eXvvQly0tkcvm+14NazGDGoVDIKIbgt1tbWyboHAwGZj6+vb2ts7Mz1et1\njY+PWy3YarW0u7srj8djOyFNnCQbJMRiMQUCAQv8AZ7b39+3aRrO8fl83oxj8vm8Ybp42R0fH2t3\nd1exWMycji4vL1Wr1eymw1GT4HYGI8i2PvnkE2OpZbNZq7VDoZCR/5niBQIBOz1omIEC+/2+kayI\njDs4OFCpVNLFxYWazabFNRNyiRIem1vpdpBFWfPJJ59IklmTlUolHRwc2HPeN274QS1majqmbTDl\nMB/BoQc2FzXdaJIqi4QYMOpNJnWQhuDl0gxRC/b7fYOtIpGI5ZFQhxJfzCLimGYggmwfM+7Dw0NT\nhFxcXMjr9SqVSunly5fGnR7lkYTDYWPGIc7FK4/QTQhMqMnPz8+Vy+Ws1mewg6+e4zgmYgCrJtyH\nKeeoSypTv5mZGY2Pj5sXHacItgOY29AAYyf83t//38Qi+mW5IBphtI2BH7yAcDhs2C3NIYhHuVw2\nmKtararf7+v4+Nh4DtwAeNVNT09bCYIdLoqOwWCgWq1mkBicYgIy5+bm5PV67TXxWrEWw9sC7jKs\nPG4iyot0Om0GhfAbINkD7bndbsORWUzU16P84VEkBSswSFbn5+daXFy00E8kY7FYzD5L4iVojK+u\nrowJB8pBzgxcDuwYcD9NpVL3+v4f1GJGutRoNIxEnsvl5DiOer2ednd3tbe3Z8Yo5XJZ09PTRiyS\nZCVJt9s1N856vW5ec7u7uzo9PTWXecj2yPBh0JEXGAqFdHR0ZBitJO3v71uwPFNAdIo0ZS6XSycn\nJyY+bTab2trauiNIPTk5MaFtp9NRMpnU2NiYNjY21Gw2ValUlM/ntb+/bylUo3UplNNcLqdWq6Wd\nnR2dnZ1pc3NTLpdLe3t76na7CgQCVqbhkAqPhKQCHh+8GqPGUqmks7Mzy5U5Pz+3tIKTkxM1m02V\ny2W1Wi19/PHH9/v+7/Xbv2QXcF8LeQAAIABJREFUzR4exFNTU3ry5InBXuCoCFfn5+eVz+eNczA9\nPa10Oq0PP/zQwnpIWUKgOkqFRKDJhXG51+s1yb8k81pjV2OiJ8nQA4xeuGlIfRplAI6G+Yw68Uej\nUcNyb25u9NWvflXX19daXl42MStIChiy2+02kS22WslkUsViUclk0pxIUaYggZqfn7ebVJKJASBP\nob6em5uz7wEyFwgMwgXeM83pFxPAkQtegySzvYIDQFME3gpsBAsNxfMoF4HmCcwZCy60bAxHgJyA\n9dhlqR8pd3DKpKECwup0OuYkii8cXAlwXZzzwZmpvXlfqL3RPUoySOz4+PgOTEjkMX0A/BIWOT7J\n9AB8jrxOOBU0bM1m02i23Bh4QeMfTSMMG5GQIsbvo+Yx73s9uMXMhImAx2QyaWNeosPI6wsEAsat\noFFkx6KxQptXr9fNy5m6mYGKJKs3WYz8PKqM0Vo0Ho/blC+VSikYDCoUCplHxuTkpCTZ5A5eBLxg\n+ByRSMQYauSLkNDKa5qamtLy8rIZOJIuQI0/NjZm1gS4guKjHAwG7fPD0gCzGj4bzA8ZWUuyxhYU\nA/iSqerU1JQx5xDvZrNZM7l53+tBLWZUx/v7+6a88Hq9isVimp2dValU0sTEhDUwsNLAP6mdZ2Zm\nDC8ebXS63a4Rfvb3981BH2hrdnbWuL7pdNrsv/g7POVyuZzS6bRisZjq9bqZIlLvM7yhFs5kMpZw\n1ev1VC6XjfIJ2wxsmGaWSRzj50qloouLC5Nf3dzcaHZ2Vufn5za5I5OERnU08/rs7EwzMzNWzzPY\nwdsjGAyaQQ5jfiai1NJ8hmdnZ7YxoGqnhLrP9aAWM11zMBhUqVTS9PS0Xr16Zb5rHHG7u7sKhULG\ndwa+ikQiarVa5rrZaDQUj8f18ccf25cFiWh+fl6JRMI6d1AQiDflctnQA2C+fD4vt9utsbExffbZ\nZ1a/MlZm54L4z5QR0xXKgGw2q83NTQtgZwIXj8dtwog3R6PR0M7OjkVcwB1eXFw0828yXvDBy+Vy\nRpTis2m1WkZzRV+I7UIul7PSBlgSPw98peGXc0KA0OCctLGxce8YiAfVAKK4JqMPHLPdbuv4+NjU\nEORIj4ZEVqtV4zg7jmMSHoLSfT6fuYTe3Nyo1Wqp3W7bGNrtdiufz1tJ0Wq1LJEUyf5wODTDl4uL\nCxOcwm2AfHN2dqaJiQnlcjlNTU2pWCzaogc7x1IWOA6OtfS5nQGLw+VyGT7caDTk9XpVLBZNUtZu\nt80FCvEpGX4zMzM6OTkxuzM+E0n2mvELgQ2HvAqVN8aRYPDQQSXZTUb61H2uB7WY4UZMTU1pbW1N\n4+PjWltbk9vtNnYbx6fjOGbXBSoACQeHIzgPXq9X9XrdWGoQ/pmq4TyEIjsSiWhlZcWOek4MSoPh\ncKgnT57o8vJSU1NTSiaTxilJp9N3aJrgxG63W7Ozs5JkKAG/F4/HVa1W7QTAwDudTqvT6WhlZcVs\nsYAvYe7BJqRWT6VSJmAIhUJKJpOWkQK7DmEtpCq42dItooQFGB56P95XgIwQ2sP7wPzxfa8HVWbA\nxBpt+JjYkTON/gzvuFErW6ijfOEnJydGGR098uHtYisFod/tduvp06fm6sPQAv4DAxTq3kQiYTIj\nXIS4MUYVKTMzM/Z71Wr1Tvg8ODnKE0Si0WjUYDsITOQLor2TZIjI1dWVMpmMGo2GWZr5/X5dX18r\nEAhoOBwaZIcaHGN0Biaw7paWlswZNRAIWMQF8iwCffDgA68m+/B9rwe1M2NWwk7QbDbtAzw4ODC+\nAIudYxOaKEMSSUaIabfbNgABqgqHw6rX68ZSQ28HjEXnD3yH7g7ZPiNkmHTU4aOyo/Pzc2O9jToj\n+f1+M1ocjXSQZCPhyclJ7e3t3RHugpD0ej0b6XMjnZ6eWjAPtXI6nbYoCoZDg8HADBYh5jebTauj\nJyYmTBB7eHio5eVlG7nzGY5KwyYnJy2ldm5uzvg073s9qJ0ZlhpxAwg7O52ODTcQoiI8pdMGMoL6\neHPzeWA8jkgTExNaXFy0o5FmDpK79LmqBYdLSSZClW6nbhCe2JE47jFIgUAPaoC3HHYCOGfCgWCB\nwKkulUq2o5Limkql7nhiSDJzcAQCSLT4Wfge9BqBQMC8m8fHx41uOhwO5ff7zeuOTQJKZ71et5MM\n/BmO9MzMjPx+v66urkwA+77Xg1rMdN6jvGPqQ9h0kH9SqZQFzCCNL5VKmpmZsVICo5fR1KidnR3F\n43GLJKMOZeiAnwWG4Dc3N5aOyuID14aXcHl5aUrxZrNpX770eYD81NSUlQykTZHKivtoMBjU1NSU\n0UHxQeZxGJhEIhHDyFdWVgxVgNPMIsR/b1TOVKlU7Abxer3WUDOQYXA0iuuDtkiybEWwdkS32Dzc\n53pQizkYDBoODLeXZsbr9arVatk07vT0VEdHRwYfLS4u2gdaLBZNxsOiwpQc8/CJiQklEgmdnJxY\nXY7zJfUiuzn0RqZ3k5OTqlarlojV6/WswQQ7hhzFOPr6+lqFQsGwaHK6GVAwycRDA1X4xcWF0UbZ\nYVF048vM54UxJDg04UKSjBA1NTWlSqVi+DHOSqhLKIcuLi6MZsuGwusD36d5ZDD0haXtyMViubq6\n0vz8vCYmJrS+vi63263l5WXza/Z4PHd4GjDMUHJ8/etft90OhQS7h3R7AsA1JlDn/Pxcy8vLVluv\nra2ZOTmcDRY03m6JRMIifmlc4TKDV7OzgWOn02l7HXA2er2eQWpAdyAvqFlOT0/l8/m0urqqo6Mj\nE/USydZsNjU7O6ujoyNls1mFQiE9efJEwWDQnisSiaharRqKAxeDphnVi9vt1tLSkg1jPvjgAx0c\nHFgoKI06rvsul0vxeFzf+MY37pVr8qAWM7ZPNFfhcFjNZlPT09MmmaLBwguZqSDddrlc1tXVlebm\n5sy9EoIQnGS4uOVy2WAm3DCBr3K5nFKplLrdrkqlktFGLy8vzRosn8+bsJUdENplLpczce7Y2Jjy\n+bwWFhZUKpXMxDwQCOjk5MRMHrkhvV6vIRy4I0UiEZVKJVWrVR0dHZkDKgy8ZDKpXC6nwWCg3d1d\nPX36VMVi0XB6ScbjwBxyenra2Hng6dAEsOydmJjQ9773PaOdSrelCo002syzszN99tln9/r+H9Ri\nTiQSVvPBUkP1DOwD3IW0Ci8I4tRcLpdOT0+1uLiodruthYUFY6r1+31dXl7agCGZTCocDt+xlmI3\nX1hYULFYVDgcViqVshExvsl4sGGeAqEIBUg0GjUJ0unpqUUtXF7eBkY+ffrURuWkn9JYYpNAA4ry\ngxKBaOXRmwXl93A4NO8RanpcjdA8RqNRm/ThsM8p8eMsQozWm82mMQkZqLA7O45j9NW//Mu/fO/v\n/0HVzNVqVSsrK0omk7ag0dhBkez3+8rlcvJ6vTo+PtbExIQZIQJrYbtVLpdVKBR0cHBg0BOlAx7F\n1Isc2TDbIN5D4u/3+6Ya4XE46m9ubrSysmKex+Sc1Go1VatVQz3Ia5mZmdH29rY1WWNjt8lUNF/U\n2tgdQEzi1EBlg9kLcWlTU1MqlUoWR8GElIkn3nJAe3wOkuwzODk5MYSEBhMYFIQIrBoGHqqdL5Qm\nIxeulT9uV8vkCZiNThwHfEkWbEngDOyyTqej+fl5MzYhVAeH+Gq1alNFhhHHx8dmbTU2NmZQIIGX\nTP7Q1EH6R34EZMjNR1BlNpvVxcWF3r59a6GR+XzecHIiJigBYMUVCgXDh4HwwMIvLi5MZAvCglxs\nNAweigC8ZZAeDHLA1JlcEqrJaYZRZLfbNVVMKBTS/v6+Kbm/8JobuTDuZoExFmYKxSSPhYd9bDab\nVaFQMJbY0dGR7YLz8/N3DABxr6fpwUS72WzaUCWdThtNkhuGcTkj6VarpcXFRaM9RqNRJRIJq3XJ\nkV5eXjZYa39/X36/X0tLS2o2m6ZZBBFZWlqS2+1WJpOxGGU4IixcGkTG79Jtc8fQ5/LyUsViUbFY\nzFAdRvqpVEqNRsMmevw+pw8bxeXlpQ4ODsxQEfiQTQXPasoxMPovBK0jF2Z/LMirqyu9fv1a7XZb\ntVpN7XZbhUJBH3/8sRqNhg4ODmxadXx8bOSizz77zESep6enevXqlUUYYLd1dnZ2J2H07OxMOzs7\nkm5Pgu9///vGt2AAcnV1pe3tbZ2entoUjxICJQgeGtiG5XI5vX37Vjs7O5YNPj09re3tbVvQQHtI\nsKrVqnkhU/rQoELT/O53v2ufGdwLl8ulzz77zDSG/B2+HYeHhwYLUo5cXV2Z9VexWDTI7eTkxEqo\nVqtljSoN39HRkXk6X19fq9ls3ivQUnpgixk+hN/vt2OWXQq5/nA4VDablcfj0ezsrFkMQGg/OjpS\nOBzW+fm5jVifPHmivb09tVoto0MyhKhUKlbPfulLX5IkFQoF89CYnp42jSBc4UgkomfPnqnRaFhN\n++bNG8OKMTinMVxcXJTf7zfp/5s3b4xjsbOzI7/fr2g0qmazadzqdDpt4TrsypQC0EjhekxOTlpJ\n8PjxY8v6wxeDMiOVSlkkca/XUzwetyaU5pWdn/BQGry5ubk7TkfAcfQgTFLvcz2oxYyvBBHAPp/P\nlL9LS0sWKLO8vGyYssfjMayUSR2cW8xKIColEomfSKaanJw0mAuVy9OnTw3vPjs7UyqVskYHQ/BR\nBXU4HNbs7KyJQFdWVvTo0SMjH7lcLgUCAc3Pz5tuDwd8vDHwwmC4g8qFcojnCwaDZh8wNTWlpaUl\nS30ilJ6JIPyL6elpBYNB5fP5O8oUYtUo3UBkoBL4/X5TpCC7wqgRbL5UKsnv98vn833Bmhu9SDIa\nNVnJ5/OWLgo68e1vf9tU2C6XS9VqVQcHB8rn80Y2b7VaOjw8lM/n08HBgR3T1M2tVst2tVKppEql\noq2tLcvHps6mQYKeOsqjYCGAlGBsyASuXq/b85ycnBiWHAgE9IMf/MAQCfjKcI6Pjo60u7urfr9v\nHGHyChnMYBqDXzJKm/39fZVKJfN1JpKZ045dmoiM09NTxWIxHRwc2OdM8w1LEZ40wmBU2sB1NK7f\n//737/X9P6jFTI1brVYVjUaVz+eNrgmZBXUwC+Ht27d27HLkezwes2O9urrSzMyMDg4OVCwWTegq\nfW62CDw1NTWlTz/9VIeHh7q4uFAulzNXfrBoFCyBQMCmYjc3N8rlcsrlcjo6OlK1Wr0zaIHWyciZ\nm67T6diNJ0lv3761G6fT6ajT6eji4kKbm5uqVCqWaY21LBAheDEY+sTEhOkpOUVevXolSaZHhFDv\ncrm0u7trDa7P5zNVCaNykJnT01MrrSBnwZRjMHOf60ENTR4/fmxqjUKhYEc1/OXV1VV5vV4bCkxP\nT+vZs2fGGkun00bhjMViRuDnJqArJ1RmbW1NvV5PmUzGFCcffPCBTbqSyaQhDq1WS8vLyyoWi1pa\nWtLr16/NCJ2yaGFhwRbS5uammZYD1yWTSYsVxn6rUqlY6CVO9r/2a79mo2LMDj/88EML6gF9gP22\ntLSkTqej5eVlY9oNBgPNzMwYwvLixQsVCgXNzc2ZFArL3dXVVfOwo3Q7Pj42y6+nT5+qWq3acIpp\n57Nnz0w4IUnf/OY39ebNm/f+/h/Uznxzc2OLh8anWq3K7/eb1VS5XDZuAtpASUZu53fOz88Nt4bF\nViwWdXx8rEKhoJubG+3u7trud35+buQheBEoPzY3Nw1/vrm50f7+vi4vL5VMJtVqtcxhnuFNrVZT\nMBjUzc2Njo6ODFLM5XKGV5+enqpQKCgajRpa4Pf75TiONjc3jTVYKBQMuYHNB1LCZ7C3t6fT01O9\nfftWzWZTm5ubmpycVLFYNFuFUqlkCxsODBBfo9GwxCtOIHoPRuBMD4vFovFEjo6OJEmlUkm1Wk3f\n/va37/X9P6idGcVEv9+35gycl24fD2MmZGCmQFyzs7OanZ01kxPk/B6PR+l0WoVCwcbTsVhMMzMz\nZiSDOXksFlMul1MikVC1WtXS0pJ96YPBQOl02iaUcDFisZjpEqmlyfPGrZ5pWiwWM70jzSlmLnNz\ncwYxYhkQjUY1Oztr+dnRaFS7u7uam5tTMpk0u4Dp6Wnl83nNzs7K6/VqfX3dfEGWlpZsAVOWAOHN\nz8+r0WhYYzwcDlWpVLS+vm6mNo7jyO/3KxAIWI1Mg45d7tjYmJUz73M9qJ0ZWAquAWqGUaMRamIW\nKcB+OByW1+s1iy2mh5VKRfF4XIeHh+r1elpYWLhTl25ubt5p4mq1mnZ2dmzYkc1mLd0KtOHi4kKB\nQED1el29Xs92e2ptScbhuLi4UDabtckaihB4FESUnZ6eyu1227+53W6zLaBmZeSO4xAJqthpcVpw\nU9EE0izSkEoylALMmSkfhKy1tTV7L0RmAM3d3NxYwgDWXf1+/4tx9ujFlGlyclLb29vyeDzWFKK+\nANGglsbdvlKpmLKY8gG3+L29PSUSCZ2fn6tcLtuX1uv1TDSKIhrLglH3TTjVqKpvbm7ucKYxZYzF\nYlY/klhFedRut3VwcGCcajgcmJJzE5I6heMQpt69Xs8Yb8QpwwVhetnpdNRoNMyilgYaKic4OJKu\ns7MzS7Nlp+ZxuNlG3aBwCcW0Ef60JJ2cnKhSqdzr+39QixkpEl8cDpTQEofDoRKJhO2O8XjcvNU4\nPvFcwwJgenraQH1qV/jGICSw35Dej8qQwKkRmDIClmTavsnJSUszHR8ft+QoiEBYDIDCILLFX1n6\n3GwctTf5h5OTk5qfn7emdTTInlKKMT4nEnCfJEttZefv9XqmXIfYBE8D2RlCYqaeOB2NKmL4bmq1\nmo3L7xvQ86BqZmC4wWCgDz74QNJtAI/L5bJFzFCA4UM0Gr0TmgO1c25uzhoVFhYEJWRVdPHHx8dy\nu9168eKFscvYcbrdrubn541sj9KCBqnf72t9fd0W6XA41MzMjE5PTzU7O2uk95OTkztyJALlWSzo\n76Bk8viStLGxoYmJCQWDQWUyGR0eHiqRSMjv9xu9c3x8XIlEQgcHBwqHw3K5XHrx4oUkGSkJKwYY\nc8Cc6Ckpg9g8UOPwOpB8wWZE5Q2py+Px6C/+4i/e+/t/UIuZ6RwoAFyGUTX1zc2NHZcYrqCXa7fb\nRg2F54v+T5Idx9SSRDfA1Ds+Pr5z7I96O8O4g4W3v79vo/RqtWokHpz6GS6Ew+E7Tkkc5d/5znf0\n+PFjs+2tVqtKJBKSbssAEgAwICdFitIF05ZarWboiSTjdIRCIf3whz9UJpMxfJu+YNSknShliFpX\nV1cql8sqFos2FqfcoT/BWQlDSIxgNjc37/X9P6gy4/r6Wvl83tx6+OJZSOjrVlZWDO1gp8QEBTSB\nIQJaPlx/qAfBUKFTYrJCuCU3CZTPUddPThB2O3b7vb09s8qFXzwYDMxckOFMt9tVJpORx+Mx+uX5\n+bnZC1CmsNtDC8UOjIYNZiD5KLD8IPUjKGAhUw8Tq4zR+dbWlinSuemwHBhtaGu1mrlCUbbw+cHK\nu8/1oBZzMBjU0tKSfD6f6fskWWkBTvvmzRu5XC5tb2+bnOfq6kqVSsUceZD9Q9ZnTM3OTPOYyWSM\nD3J+fq5UKiW/32/oCPAbbDUGL1BFaQLdbreePHliE0NeFzug4zjK5/OGzDDRQyw6NzdnYTfcMBMT\nE/aaqYu9Xq9mZ2etlGi327bAaCoLhYJFGEtSKpVSOBw2mRNJsdy0wWBQ5XJZ8XjcbH8XFxeN7skN\nm8lk7DsJh8Nqt9vKZrNG6L8PLCc9sDLj7OxMsVhMS0tL2tjYkMfjUTabNSzz0aNH8ng8BrcNBgMd\nHByY3B7WltfrtUUtyerS8fFxzc3NGZ8Zgerq6qp5PlMPhsNha/4kmVbQ5XJpYmJC6XTaHDUJkCRy\n+PDwUF6v16RbDFdoEhG5UufjWMTrJYSSm7pWq8nr9RqRCMcmdkPHccy+tt/vWxIXTkqQkyBt4ZvH\ne2y32xbySXPIzs/PwPGAtcfzlkolyxR88eKFNjY23vv7f1A7s8vlMhUzihBJpm4eLTXAcVlk7Ewk\nn2IcMyppwgT8/PzcRrmEl8diMRvM4I1RKpUswxuCPibiPOfExIQNccCMYawB/TFZZNwO1ZPoCRor\npmuZTMbKIZfLpaWlJXMukmSliyRLXWUcj3H6+Pi49Rtkn1DOwKaDcgtXA1PGfr9vblIQpnD+9Pv9\nBllCaIJNSNP8vteDWsykgfp8PpviUTdDCSV1aVRS5PP5VC6XdXFxYWbhg8HARrHslOzQNzc3Ojg4\nMPd3bK5gjnG800TijUz9PnrzXFxcaHl52aZw9XpdpVLJeNMQcQaDgQqFwh0nJkSnHOUslmazKb/f\nr4uLCxUKBV1eXt5JkJVkcjE4x5CsEPQiFIAqe3Z2ZiUPXiBXV1dmAIOdGa8D7gfvncELjSjREHC9\ny+WyiRve9/qFLWbHcf4Px3HKjuN8OvJ3Ecdx/sxxnG3Hcf614zihkX/7rx3H2XEcZ9NxnH8w8vdf\ncRzn03f/9j/+rOdMJBIWcoMCotvt3unAsZylAWMHi0QiFuIIjssuTvzZqLn30tKSDT/g8DKqxaEI\nohKDA0nWmJGP53K57gxo4JZQj3u9XiP8uFwuPXv2zLDcUaOW6+trw8cZdYNf4+9BzAQ3KRNJSiaU\nKQTJw7CDsz2aQoXbfzQatbwXyjS425CVZmZmjKjFoudmSafTNuj6ZW4A/09J/86P/d1/JenPhsPh\nmqQ/f/f/5TjOE0n/gaQn737nf3FA/aXfl/Tbw+FwVdKq4zg//ph2wcvAV4JFjHIYSGpvb09XV1c6\nPz/Xo0ePVKlUrNtGuDoYDEzmM6oiJpEUtcX8/LyZMlYqFdvNGE1Xq1VTVJA/QqgjUqnz83OD1RzH\nsUVHatbr169tKvejH/3Idv7RsHeGF4PBQL1ez9yOGB7F43EbnDDVxHKX9K1oNKqtrS1DZEabUxQl\nfC04J0HColSChE9J02w2VSwWTUYViUTU7Xa1ubmpTqdjXnm4mt7n+oUt5uFw+P9Jav7YX/+WpD98\n9+c/lPTvvvvzP5T0R8Ph8Go4HOYk7Ur6muM4aUn+4XD4vXc/989HfucnLjR+kMHBM9l1KC2I7yVk\nkp0CtTBEelAIBgXYuNL1X11d2dEbCAQsdpcpIDcJRzzdvSSDzAjhOTo6Mg0ftFDCHqlhR4Mqg8Gg\nrq+vVavVDHo7OjpSvV63RpHnoLZH5FssFjU+Pm78C25U5E1YI9TrdU1OTmpnZ0fX19eWzgrGDk4O\ndk3utyQjHAFPIh8DimPIgxSMEuk+1982mpEcDofld38uS0q++3NG0l+N/FxeUlbS1bs/cxXe/f1P\nveD/MqHiiAOKgg6ayWQ0Njam/f19410gCWKMzaiY+nI07J2Fge/z2dmZmZr4fD4lEglDDtgNuQGw\nOQCmSiQSloLKLriwsKB6va6rqyuTGfF7oxxo9HvY2wKpUTKQ5YKu0ev1an5+3hyf4EP3+33L2c5m\nszYGHx8fN61kIpEw+RWnCRM/FienDTa2fO6Y5BB+hESMTEF6kftc/8agueFwOHQcZ/g3+ZhwgMvl\nsvL5vGVoY1RI/cdu+8knnxhJhklXpVLRzs6ONVkzMzPGuYD4D0qCOpoc7W63a6bbYNHLy8t6+/at\npqenrYSA1skE7ujoSKlUyqA0jF+wGKDO3tra0uPHjw2XxYeDgM61tTVThayvrxuZp1QqaXl52WRe\n1Kh4YuCev7u7K7fbrZ2dHaXTadVqNRukXF5eWoNGTMSoOSJMQEItGf/n83lNTk6q2WxqZWXFXlM0\nGlWj0bAJZiqVsmzt973+thdz2XGc1HA4LL0rIaBJFSTNjvzcjG535MK7P4/+/U91pP7zP/9zO47h\n/i4sLBhBB89hFtTi4qLliaRSKRUKBblcLlNZBwIBOY6jpaUla9Aw+2ZHZpoH3CbJoLtOp6OzszM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V1fX1cul9PNzY22trZ0dnZmhgGqG77++muzMaH7WFm5c5Dxc6RSKYMbh8OhhTs6c6pJ0/+t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\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zj9o3yJlgigksRhlDLSvJO+twOLRhIT8b2RcRx/A0JJmOiWK90+mYlonMC3Sm0+lYiAAa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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4d00a10>"
+        "<matplotlib.figure.Figure at 0x114254ad0>"
        ]
       }
      ],
@@ -586,16 +674,16 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "person          1.883164\n",
-        "bicycle         0.936994\n",
-        "unicycle        0.016907\n",
-        "banjo           0.013019\n",
-        "motorcycle     -0.024704\n",
-        "electric fan   -0.193420\n",
-        "turtle         -0.243857\n",
-        "cart           -0.289637\n",
-        "lizard         -0.307945\n",
-        "baby bed       -0.582180\n",
+        "person          1.835771\n",
+        "bicycle         0.866110\n",
+        "unicycle        0.057080\n",
+        "motorcycle     -0.006122\n",
+        "banjo          -0.028209\n",
+        "turtle         -0.189831\n",
+        "electric fan   -0.206788\n",
+        "cart           -0.214235\n",
+        "lizard         -0.393519\n",
+        "helmet         -0.477942\n",
         "dtype: float32\n"
        ]
       }
@@ -651,20 +739,20 @@
        "text": [
         "Top detection:\n",
         "name\n",
-        "person             1.883164\n",
-        "swimming trunks   -1.136701\n",
-        "rubber eraser     -1.251888\n",
-        "plastic bag       -1.286928\n",
-        "snowmobile        -1.304962\n",
+        "person             1.835771\n",
+        "swimming trunks   -1.150371\n",
+        "rubber eraser     -1.231106\n",
+        "turtle            -1.266037\n",
+        "plastic bag       -1.303265\n",
         "dtype: float32\n",
         "\n",
         "Second-best detection:\n",
         "name\n",
-        "bicycle     0.936994\n",
-        "unicycle   -0.372841\n",
-        "scorpion   -0.812350\n",
-        "lobster    -1.041506\n",
-        "lamp       -1.118889\n",
+        "bicycle     0.866110\n",
+        "unicycle   -0.359139\n",
+        "scorpion   -0.811621\n",
+        "lobster    -0.982891\n",
+        "lamp       -1.096808\n",
         "dtype: float32\n"
        ]
       },
@@ -673,15 +761,15 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "<matplotlib.patches.Rectangle at 0x4f59f10>"
+        "<matplotlib.patches.Rectangle at 0x118576a90>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+/ud/Dmsds/mMqqz5a1/4HPNyDgiUiuHtwzuPePz4MW+99RZt2/LBnTt84xt/\nwuc/c4t33nmXt958nRs3rlE1x8zmE+7e+4BelrO3t4cxnRcRmSnRkmZxoOiwz2VoIxPzJCTljRDo\n0OJT8gpiLq87JnrkdvHCKu9wc4dzkA9yZKZwwtFfH5IPe9y9+yFX8iuoQlO1NaPNdap5SQies9kY\nIxUqz9jb3QWt6a+PUEqxNlrj2dEho/V1ennBeDJma3eXtrV4a1F5xu6VSwSt+PDD99jY2uG9D96n\nMIaHDx/ggDc/93k213q8duszfOOb3yYz/ajgN9c52z/g5UuXMULjAow2NmmnNcoLBv0NxvWYZycH\n3LhxGbO2hbMBrw3T4LA2oHSORCC9Z3s0oj/I0SpHEPDO4mxNmzwUkRJkSkqk0QgReDo7IlctlYdx\n2MQMPL51kVI62CTb2XvhPCp6hlQNTaY1Ugh6eUGuNME6JtUk4o5aLRw36xrm1QwhFdYFhBbUrmJW\nnuF8RsgMMQYPqJ7idHzM5s6rHB2VFIMe07KklymkELjE99faoFWs9kXEPhoq8ddDwtGF1tH79J6m\nboDYm2Y6HYMQ+GCRQaCANlha5whCkBUGKUlKNypeJwR53uPDD+/xxms3CK5AKclsNiMzBds7O2xt\n7fHh3fsM+mu88fotfuqnfholNd/97ncZDg1SxgpK68DaZumNpzzRx4m3drF2lrAFtO3CPi0ghpgb\n6crUA0XH90886YguLPHw9MvxPOGj3mesll8qvuhJx4Rlt84777iDOEIXlQePcLGFQOiuQeR/CwJK\nisiOWTlHCDFJHxONUScptawYDcQEq/ceS5d7iQnT+CaX43GOUbOi+FeVuJB6obQ7qu0Pk0+PhaI1\n3jmGgwHOOfq9gjzPca2LnO+6RmlN6x1Xr15la2uL4XCID5Yie4vXX3+VjY1NPnP7Ns5WjM+O2Nvb\nY39/n/l8zrDXp6qqBHXEx5SJXihEV53YvRSVoJuOKw1K6iVJ3yWvJ2l6n5IdMSmauKzJy/eyK4mP\ni01KifURg7fWkuUxoemBzZ1txmdn2OGAd999lyLPaUPDpZcu4VrLrC7ZubTHo4ePKKuKV197HaXi\ndM97BQHY2t2laltO53Pe+/ADdvb26I1G7Fy+ihSKz9++xdnJCc+ODnj25AHHBwfsXblC2zS0TcCF\nwAdPHiLLOWtNiapmjLbXeHZ6zPRkyuXRJlpLvvC5L8I7hrVeIBcZbSMIRY+mLMm0wbhA4Ry+KkGB\nzgxB5TFTvBYYAAAgAElEQVSKsQKnIQRH09QEEUPQxltCa6OxzHtU3uEHa9x5OqWZH1J4S9E2DHsF\nwb0YC7Qqlj57AUiFl2B1IAiHx2GMWhRltG0bvSytMXkW+echLspMaG68cpPabvBwf0LwDlMYziZj\nNguYNi1vv3eHvPcyIi+SJxdi0VRqchIbhHVsCEfrGgIBJaM3a5SKRVEhtgJTSrO7u8PP/uzf4s6d\n92mammo8RspUi+BdrA5MHn50HPyCTdPMZgyGQ57uP4tJzEGP9bU1rl67xvHJKccnZ1y/9jI/uHOH\n27c/y6XLLzGdTNjY2EjOCgsqW9eASimF0pG9VVf1C8cc4RcUPFIic6HQkzJWMrFDnD+nuGZVGesM\ntI4sE+dAdD1/YtWm1Cqu048o8LDgmSNWWkOFjyb7BOC6uoEUeUeDI841Iuv+FiIm4Rc9vAIgovOh\npIxJd++SAhcxkd0paBl7x0ixwnDy/px33p1SJwMmvMdz3tsOKbJaJHjDeVjoRfKpKXCTZXGStw1C\nQJEbBLE3B0GiJQTfYvI8FpBohfOWTCmGwyGbm5vYpuXNN19nPp9jjAEzYmMj9mkgeStZltE0ybsI\nKyFWWIY3biVh08Et3i85prFZjVx4HEpLMiWxNqCNWGCVIQRswt6jCKx3GK8Y9fu0zrGxvo7SKnlt\nOnK2TcaN69fJ85zjoyPaJja8apuWpmmoq5osy7n52uvs7+/z/p33uHrtGlVVYXoFwmg2dza5fO0l\nLl2+Qq/fY3J8BCrjn/7BH/HTn/8Ma8M1KhcLMKbjCVubm0xnDUJJdvZ2+e7jb/PSzVfYevkK33v7\nB0zmNW/euk0dYP/ZYyZ2ytMnD8muDJm3giwf0TrPwdERl69eZ1AU9J0Do5BZDDtdEw2jVwbnmlim\njMRkBmSX8Y/0yjPrKfrrjF2Gc4I6CFzjcC0gBNWz6QvnUdlWNLZFZiYpcUntXaTFtRacS55WXKRK\nZ3GuxE9QUmDbNjVm2mQyc3jXIkSND47cZHg54PGzklkjcbrFGDg6mSGQaGPITIF1NbP5PLVuiIks\nnZg5wnu89QtsW6lIw7TeUbcNX/ziFymKgsePH+M3t/HeUXuHI7YYiPSN5OkFaL2ncY48y7l25Qqj\nYZ/t7S1wlvHZKd995x3Gkykb65s8Oz7ijTffIisK6trS6w+SYxG9SkTkTxeZwXbFKVIQcOjsxUaz\nw5YJIVULurQGll5wxNW7IhhSYleAkbTOxo6bSuL90hBEIwBCReWvknqK+Hb8l0oY9QLBTpc04jl+\neMKb41oPizyoECwqILt5Ef936TOR5krUF1rE/kUkZpIPKXLwXT/OpZ5QybCKxJ5Jg5SSqUteeAhR\nkXdR/5L3HmE+JZd01L9IPjUF7oNLnUWXdBmlBFoLmqZevKgmZaKVjoPWJqvW1g0Bx2w+jdza0OLa\nZVtImf7umCixoGRp2ZAvtmzncLE0uBYBruvURge7xZ4NYtlJLdZ7xWOllLFCUkf6mQ8BYxQhNZTy\nRPrWYHdvwakWQjDs9Rc4fV3VSCm5cvkyARgORwzXRmxsbTAajbj/4AFaa6z3bAnBeHKGygwQIY2T\n08eYrMeHdx+wtb3NK6++zu61G3znnfcgSH7qC58nMzmHx/vk/YKj2Zj7D+7TlxLnYf/RQyrfEmzD\ng/ffZrPI+c6TdxlkQ/7mz/zLfO/Oh7RCcPBsn0s33+Tpe+9x9PQhl65eYt7M2WCd7e0tirwg6w8o\nyxm9jS1GayOkjuX0J8enjKdTdp2mGAz5vT/7AfZMQK4wON689gpb23v84N6Dj74sIEsrM1hPSB6d\nlDlt7XCtQyHpD/psbGyk5klxXt2/f4/5bA4hUBQF45Mz9o9raqvxrsTkDbPZGTuXXseVgSf7FabY\niQlhOaPo9ZEythiYtzXBO4SCspnTNA0mM4sk+iDLCM5hsgwpNWVVUbcNznmKvBdpqHkeDbIyeEg0\nvdiPpisj9z5CKiiNBUbra0ymY8bTM/JeRq/IebT/JPa6kYLB+hr7Tw/4N/6tf5tZVaMmUzY31qKT\nQcSGJS41VnJIufRSwaaE5kfFtTUR4lSEhccYo5El/CgWSbsOb5dCILWIfX2URGFiR8iOHRLASwHB\ngQeXiHuRyhlZJL6tEwwa16IUAuc9WpgVTDnE4jEpkzFdMlhiZ8ZVOCOtZUGCZmCBw0OCwKKSF4kC\nK0JA6dRasrM6AYJzIGJ7gRU1AcSWXEJKtIrQWwOJl+6XXPk4qvH6Cabxf4ES/9QU+FIbpp+Cw/mu\nS98yYSBFZ9nP91yGEPtjLxlG50KP1efuoIwO3xJCLEpnu9/r/r3wLlgqcKXMuZdxTsmnCb8IkrwD\nn1qfrhyvUhMfIcSivDhi8hFnVzJSp7qozFrFcDCMdMTNzZjokZKd3R3e4A28d7z+xuuUZYVMkMCf\nf/8d8l6fpmp4VD/k9mc/w+z0JBYRa0XjA+ubG2xvbzEra8YnRxQmYysv8FXDMM/IQkBZx8bmBpUU\nNFrx6itv8vYfTZjuP8PmNbOjMfc/fB8jJcG2nB4+42Gmefu7f8rGoODtd454uP+Qv/3X/xWGvo3t\nR1WP2rb40jOrSobDIRvbW/RHa+SDAX014vD4lPe/8w7N+lXMZkYWWvYGQ15+6RW+/WfvvXAWfeH1\nN9k/eMa0aZjVNbroc3h0Sr8YoIXheDphuLHDzt41JpMJQghefuVldncvc3ZyxJ333mN8cowSgsu7\nO8xmLboYEPSYfu8l+mYX6bcpy1OUNGRmgFMt1TzOTaUlQQVaV2NyjVEK2UQ83KgMYyTSA6jY6yZX\n1G0svFI6QmGTyYTf+do/AWB6NkdlGc7HyDSTEtla1vMeTVmRDYfMRcCsjdjff8Jar08InrsffMDW\nzmZUcgJ6g36skgyWz37uszw7OKG2guPjY0KI7V4DltFohHOWum1RqaGZtdG58qF94ZiLlGAlJW1b\n18RwH5M6YupE3/V4B3rZQpCgJcJ13PtkqETXW6SrwI1FP9bHYpc8z7CNix50UqgdZONFhMojxLSk\nA3cKYBE5LxT1eT0i07qLHvCy93f3KwsnLemjRSS0wNVTS+BU57CINFbW/mqyUohYIKeERITY0ji2\nxY5ObG7Uwqn1K4nQj5NPTYEvlK0g9WBfNoRJWjt5tF0GmOjfrmSbY9lsp0z9IsxaNQ7d4IXQpSqi\nhOe+7yQ8p9jjZ35xXSlEDPFYWtjV46Vggd11iGC8o7A4VwcdumR5l2MSG/DI1Lfah9ibuGxriqJY\nhFghxK5ufd1jOBpS1RVBKG7deoODg0N2tzYptCRTmoMnRUzsDXr0N9YYjEb8zO4OIkSj0cwq3v/B\n+1xa3+CtV26QIdH9EVnW47Sco3oGd3ZCOz2jlyuGV65Qn5UcPH3C9kvX2RkOsULibc21q5cZ9AoO\nz4740ms/Q2s0h+2cXGgmZUPAU0hB2zRMZo4jW9E0LcoYtnqeOnisCMyqM9ZDTpEJ7rzzbY7PptQf\n0wdlqz9i79Yu87blaDrlez+4g2tA5BlFr09fKHTWZzKrqJqIZ47PJjx6cJc3X3uVyeYRbjalX2R4\nA6GsGfWH6GKEEIZyNuP4+Ii7d9/DWU0xGJH1WuomVtCNRgXrG31m81MIDZk2DAZDNta3GPQHnJ3V\nlPOafn8Qe013fV+EQGnNg0cP+Z2v/k6ERpqG4dqI1raoYMBZVNuwYXJuX3uZPMt478E9xtMxta8I\nXlAdn2AyzfRUcnTwFKUVg8EQ51ru37tHkfc4PjyKdQ8eyrqhsU1skdxaTk6fxWIak6O8QSpNr5ej\nM/WxykOnsnNIkKMwiwRrdFsimyvCB1172LTfh5ULTnVXLdvljyK2HT3PqBB1gjRTq6kQECEmCbvW\nscYYuj73MQpIbWQFC5ZNSP/F3JfAtZ1vD8LZzv1eruUUVUOkK3f4fFzmKXErRbp/GRt0+VhlutTd\nXQ+mwKpjGWGl+J3zsfOgNgaZvotoQSqo8izG6OPk08PAV9ghMXBIuE/6JKSEkEhtRjuIolOkfjEw\n3cvtWpGmqidWMOyUUOnOGy/7Yo5r562f8/Z9AFw0FiJO3g6/gjh1FlZckMJCcU6xq+escvd5WFkk\nPoRlQsx7HI6qjQyXejpFaRW9meCRTiy55qm3cZHnrI2GHB4esre3xeuvvcbx4TFN08bmVc4ijOL6\nlUsREnIOJTW9tTVGm0N+6rOfoWkb1jc3OTkbcxXB6dEhjx98SNbT/PQXfpqgFQ/v3KUpa65f3eO4\nrtm6fIkHd++RZXGDjI2dLXavvcRs2kTFG+B0PEMQyExG8J7cOtq02Uae54znJceHp7g8cPtzr2H6\nOUfvv81r125wevQMZ18cSj7e32dtcwPynD/78+/z4eNn9PrrzO2Y6XSfxwcPee21VyN1U8emZNV0\nwt7WBm1dcvPGKxhX8dnbb/E///Z/S09vsdPbQmSSe3efsLV5nVNbUc+PUXqd+ank4f2nOKnY29vE\nGAdnU87O9tHKU1cltvV88Qv/Ar/0C7+AUQX7T495dnBIVVVMy4rJbE5mMsq65vf/4P/ibDqFEAt1\nynoePXspEL5hbzTi89dfwY1nbA/7DN94HfngA+6eHjHsjXC1o9CaXr/AOsvO1jb37j9ga3uXpmrJ\npOH9937AjTc+g0NgMo33Lnax1GDQICAvCua1pa5LXHD4eXsuGj0nnUOUvOaOBrhaGRlCOMcF7/jx\nPkhkDJwTH19SVfWyaZNIvUMALQIST9u26G79J2jEGLlI6IYFLbGrCCU6hKn3yOruRFpqnG/pKms6\nHRGCT3z7pbceo291br12P8de+GqhyIUQCNe1RF42pGKFj766K1OEp1Ljr1TkJUJXHfpReuHHyafX\njXBlcgiRFKxPr0jIheXuwIuYJZYLxZseH4gZ4NXsrZQSufL8q2FTh3/Fc533tFePXfXclUi7ZXQw\nToJtQlgeg48RgBcyNc8J55Rz97ydIZHd1mJySWfERxw3RgoCB2RFvnJ/ISZTfCy9bRqLVir1ZVCA\nZ2trk+GgR24kR8cHaG0odIHqZTTexU5sKhCcRakAwrH7ylWuv3QJ0bQEDXMVWLu2i5tV7G2tcXl7\nxCuvXaGyNZc2dhG1Y3NtnfW9XfZyg8gydtbWkK1nXpUczyb0Nte4tJXHyUpMyHrbIkJndBTT6YSg\nDArNzLcM14d89rNv0WaSujrlpe0B2z3D+mCXCdUL59HjowOOm5JgMsSgD3nOSVMzm02YTecoBM8O\nDplNJ4yGQ+aTKbvrI/7ml77Ile3XOHjyiG9944852n/M7OwhP/Plz3N6WvP44SMe3n3Exuf3wM6R\nVPSyDdpaE2ogD/R6BXhLXVYMepqXXtpmd3uLo4MTgqs4fPqIQX+dra1t1tbW2btyhSf7z/jz73+f\n9z74kHfffY+nT5/GJLsXC29SK0EvU2zvrHN1uIZoG67vbLP/dJ/Na5fJhWeQK4RzFEqz1uuTFbFq\nM5OKzdGIyekJvd6Qcj7jH//2P+Lv/f03aJ2greYURUZbTanaGkLsHWRtC4k62zQV5xHc89LRZiNu\nK9B5nKPW2kVNRYxko9KyzmJM3OEJlwJsBME5nG9RUpDJPEalziYFp9BaxrYWQqRdrNJWZYuNFEgM\nI0vWW/aisdYS5FKRdjvsIGJRVNwCMPVTIZbRd5Cm6OAcHyN2n+CcBafb2eSNp60THQulrdIGJ89H\n9V0ODpbOW5cYjdHG0gmMx/glhLMCM79IPr1KTLviheIiwL9QcqB15wXn0Zte8YpXYY4lDWfpUUea\n1XmPdzmeUQF3Gwc8T9ORckVZpsRM3E8yTemkgF3o2k+e9+zl6gsUy89VakbfNeVxKxns7h1JocCn\nl9tBRe6j0EEHOWmdRUvuli1dvW0p8jyOodALb0ECuRAEG6gn5WKcpHSI9ox5x3tv4lj7po1N7pVk\nsLfH6PJlIKCVZvf6azR1vRgPBDBIGF4zYJOd2OgreT6dB7RKO+siqpBYANY7pNTcfHnGvK7xDsp5\nxXQ2padnvOHGL5xHp9Mpfe8RwFaWMby6y6ycs37zClJrRKUJUvDo8SO8FBwbyUtpk+157bn/+IiZ\nzXn//imN3cCpIa0Yc3TaYEPO02fPODmtgE2CzJnZCQ0BoUusL5FlgLphcjZht93h6MGE09M5LpSc\nXp/Tu7rNZBZ4/95jfnbjTbb2tuDuIWF4yJ39e7heoK1a+mqIagucEdhQMhCKlwc9LucKWbecTI/I\ndtdohznDvW3c+0cU/T7aGy5vbvDSxhqElqPxCZsvbfHdD+5yVo4JMqc5OOTb3/9Tfvqvf4GD/cfM\nZ4fkucZ6cDqjUVBNzhj1CjIDta1otYAXQ+CccEYmDaKNPb6diiyvum7o9wa0dYN3gaoqUUqR93qE\nEPeMdNojnUSjUVKljUUCXtSpTzgIJ8E6moTVa61j06jcIJXBhlil2DQWpRX90YCmGaNCdAYykcU+\nLMpR+TleggqauvJoMmxoIpYePAmnQMvU6AqBTAlYFQRGZyAVzsd9cxGpgti1CJ8i/2RYmp5Iyc64\nfZuzEVLxTfTkpZIxTRYChRVRgYuA9JLYvTHu2BNbBEQvccloe7F8agq8244Llgp5YTHFShlsp7hX\nwrTnk4+rv/f8Md1xq/KiCqjn5dzviKUdXC3PPZ8MOR9uvuiaL7p2hHvOH7usYhMrVvmjnNBlyHr+\nTzzH0kB2/V2e9wJWr/fciRe8WpcazK+K1nrRuKnbtaT7vLvfzBiclB+5Vnfe5w2w8T5us6YNGyH1\ns/CBumnJ8myxe8vzcvPmzUV/6g62ap2lqipm5Zx26rn28nU2t2/jQ+Dg+JCNzU2UVNy7/wGnZ8ds\nbI4Y9Ae4oHl6cEA1bzAmj0wDESuEjTFx30UR/bI1M0LWksGwx8lpCRju3n9EXc7o5316gx5HB4/Z\n3lnjB3fv81tf/V3M5jpvffYLPNs/5Zt/9GdMj2rmB1O2+iPacUmRLYs4hr0eW6M1ckAieXb4hPJ0\nzJXREG8t68MRs3lFv5/TyzVagRGG9X6fw9mUz92+xfuP9jk8m1HNT/nDf/p/cHT4iEs721Eh6gzb\nVEgR33G/l0EI1I2NFYvBL1hbz4sKkVVhnUPpDO/j/rBKCZq2IuCROvbxDyGOn0xFMs62sXDIKaRQ\nSAnCQUitDUTasEUiQMZdn4SMnrh1bexL71zc11LGPVvLqsXaikzEjUwiNBmwocVhoyMlBEWWk6kc\nZ9u0a0/CuhXobj662CIBYpRv/XLPXYh5uM45W01whhD3yfV+SUeM+7Dq2G7BxW6E3ZruNmSXIrak\n7RAEIbrNpiP88iObxOyKazo6T6cMup+7MGxBAVyBIJ5XZquVSy9ilKwqxO6756+/8JQ7ov1HPPyl\nPG8ofti9nGsrugqjiPNcz+cz16vHLeGcj95H99lqVny1kfzqOVdLe1cNyhKrPH/d1bF5/v5W+8W8\naCy6jo7Pj9nzxmZhcFJYvWj9RiyS6joYFvmQF8n2zlbci1MbmraltW2ke/noWck2shyqpo6dF3sw\nHk+Yty0Kwdp6j+Ar5rMzLl/dpm0dp+MzTs6OOTx8RlXNMJnGyIDCs7ezwfbWRlzkPtDOLNWsoZ7P\nGfRyrIf++oBiWLA/ecaTP37KBwdTJtUJ/WGfb37rT/m93/2/Maxz88bnCZVncvSMtWGPcnaGVobM\naDb7AwqTEeqatnUMRmsUJqOfF2wN1xmPp6ie4NL2iLVBgSKgBfSzjDVfcHJywutXr/CFz22xtrFD\nluesb23S6w2YTOd4HyuOtXPgW9rSYtHorA/Wo+WyAOl50c4jVCySsq5FCUFZzhFCYkzsCWJtu4jO\nYoXpssOhkOBdi3cWnaC/RV0GDic0PmiCa1OiL4pROU1TLXJFtqljj3mtMZkmWE/TVEihUUrQhhon\n476dwTVk0tH6BoRP3UVBp775PsQoDgFCpV4zKGoXveiuEtb5uEmMlDolO2MeLoSACQIhY0fC2Ioj\nIJ2LmycnnF7IuCuRMjp57p2uS7k32RmLZbuOHyafajOrhQe3sri77zp5sXfpFwrx474PISy2J1pV\nJJ2iWY0AnlfCH+f5Pq8UVw1DpwBfpKiev/bzyvL5c79I+a7ez+r5n++3sJqA/WeJMpYJmedpmufv\n5XzOQpwrtV41Iqv3tVodt3ovq4Zs8X23ozgSrSKX3WSKXBpsaz82lFxfX4+hZ+vQRpHlJiajSDRO\nZVE69jQRUnJ5sMelS7sopbBNSzUvqcuSqqxAB4zKGAxGTKdzdna2WN8YMpmMmc3n7Gxv8PrNN5hO\nS+49eYBtPPVkzq2bN9kYrfGtb36LtmnRxTo3P/dZ8rUeTw6ecvW1Ta6+epM/+MM/4MO7h8zmjsOj\nEzY2CtZ3rxOM5Nn+fUabBe5sxt5wm77JsE2LAiyBN966xcNnh5RlRTmZUQhF3i/YHOYYLMKBkTlG\n5zjjaV1Ae8fZ0yf83X/tK9z54IPIU5/NaRtAKQga4Vu0tRR5RuMMQQ1wbY1y7mN3g8mBqv5/mHvz\nINuO+77v0322u86+vXk78ABiIwASgLiIFCmRICVTosX8IZUsJ1JVLCvlpRynUlYqVbGqnNimSxWX\n7TiJU7JUoeQsFmXtkiVKpkhxByEQ+/Y2vHVm3qx3P2t3/uhz7vQ9c+48yIoLatTDnXvPOX16/fZv\n/yUoR+HX6ujkMPxyFJlgX3nOYRMYShiHNpWa2OEmWJYJL6Cy1ABcniokz82DIEWSxx7PLUBG4eDQ\n0UgbvZfrSFxHMRiOcKWHFIbijaIUJTK8wHACWZahs5gsjfFrAQpl/C+SFFIAjczFIUKIccpAJYyn\nrud5xoIlK9zsc/wYK9sUvnTzta/JpOEGyKl1E6Aq3+sopDDOfEkaj/UFtt7BEFbOdEVyXt5xCrws\nKoDJzW5TxGVqvbjXBrFynTZA2ddssLEPkeL3sv1mUZf93X6uaEsVJQ0QFDG0rX9vp4wtXSyQtPsx\n7XAp2lhVXxlwy1Y39sFQ1FNct8G+PD72IQLV3My0fiXG5dIYjmbZmFXV2WGC2erxyfLFLnFyp6ks\n3+QKnYeZVShh2HqVt09rDZ7D7Gwbb2GewWCADDySOGFhYZn7LtxHGI7wXGMiOQyHSOFQC5rsHXQ5\nc2GRbm/AsBPiizrX39rgoSe+i/4wpjMc8IWvvkDQqqEcwfLqPFtbV+l1Unx/ASFc5mbm6Hf3EAjq\nXpOzp89xZ/sWJxYXmW+0kBqG4QjhucjAY/uggwx8RnHMsD+i7gf4Acw2arhK4wkHqYzMdqHu02i0\nGKYJjz/6btJhj5majxs06G51uXpzmzhVnD+1wplza4SDPRzX58rNPXZHMTLLWG0JqNcrx9xRTh4L\nXjAcDJjJk1s4roPreSRZShwnJmm2pYuSovCGBhw5FtFkOjHGBYLxPyFMVFedKVKVmYNAgutI0iQl\nzaKccpaM4hC35qMzRaYiY68fOCRZNo4Q6eSUr+9J4+0NFLlcC52MMSDIuU5lAk8pKQy3ok3i8yzN\nP3OPayM6MlhmTA5l7p2au83nJpdCFvvFKCldz0U6Ild1HebWjNMEpQVK5bb6d4GJd9CRZ5LtLjKd\nFMBYdKgsAzoOuMogVvXdpiiPA5ZyO21gt3+zKWIb5KcB67SDqvy+qu82OBalKuB7AXhVIpfyeFVx\nCVU6hDIlXu6HfQCWOaRp7574u7iux9w0UkjiMDX2wFPsYY3XrovQEOeZawovW+k6qMyMme8H+IFP\nnCRjGWOz0SSJIhwhmZ2dRblGbBMPIxqNBvN6liQdMuwpavUZVAa+X6fZaBI0YrSQJHgkNLj3KcVe\nL+Hn//UvMRiE6EQTXz8ApXiNN3Fch3CYMtvq0T0Y8NQTT6GbLmkSIplhf/8O7oxm1hfUfJc0yxim\nMVnmGBGBDEmUYhgmLC0tEUZDHJmi4wSECb6llMZzXXzPY6buk/mCx971EL1kxOLcLFHmkmU9VtdO\nI72A0+tLLK/UiXoutza2uLOzz0C38YVk+fwpgvnZyjGv19ooP0UJk/jj3NIyvV6Pg26HJE3Q2oS9\ncFwvtxQxDjmO5+bx2CXaMVYpQmh84SByCjwTAi1ckJIsjcaZjjJVuLJrHM8Ze4kak0KXOPcD0UKR\nZhEqVSaAmANaOSbSYpbiublllOOgEGOlopHVp8ZTRB/GTUq1SUQ8NgjIHXYK+XucJQiVixGjdJzB\nHiBTcpzUW0h56KCTAULmoK5IktSIc1zXiGucw/g6Ysq6L8o7CuAFUINpfFnUUSV/LYsSit9sCtC+\nXgUa0+SwcEiVTiv2M+V7y8FrbM7Czmht11VFJU8TZVT9bh9wR5SjFaKYqvfcDejv1rYxRQsT82nP\nRXlejrRhHD7i0GQTbUK/cowxlR/kclqliwrGTlNKpYjMGTtMJMmIgqvVWpOEqXHTFrlLtjCUf71R\nBzRSKfygycJcO6eIVJ4tXEIWkWnQXo1LNzb5w69+m1cvvcUwTGnWmvT6ezipwhcObtjAdSRtoRjt\n7dJ2XZ79+h+yuLSMdB0WlhfpJ4qZpVMMtq7iqJhm4FL3PTJHMEzicYquVGfoVBHUAnzhIrUg8AM8\n7VJ36qAlizPztObmmD+5zMFuh8hRaMehn8Cbl6/QS32iNOP29QbtjzxB0/N49dU32dwZoeuCeDji\nUpAxs7ZWOeauX2dn4xavX7+OAm61WqysrLC8uoIWgp29fa5cvsTMzIw5CGfnSOKYOFE0aj5xkpu9\nSgcpFDJToDPCKEJ4PplICeoN0iwkiqOcElUEvgnXGgQBXuAaRWWa4HsuMk0RQpssU55nQlGkKcJ1\n0ZlmZWGB2UaL7t4e6XCA4wii3IrFxE3ReI5jXNvTw3hGQinjXSpMfJtROsLLfRlMWjeTyUdKiRN4\nY2s5g10ZSEgzRc2r5TGZjJw7jAb5etVkeTYeotxIS5iE4n+hlZhl4JpGjZblpTZI29fLisoqKt0G\nn9o5RAoAACAASURBVEI5YB8IhdK0qhwnH57WfptyH7vzWyKdIltQVZ+rKNcqwC23t+rgKve5fK2K\ny5k2H3Z7ymIm+55ppSpUARSAbf7SaIQlfz22PlFY2OTst9a5hYH5LLIp2e+0zTu11iblmxDEoYnx\nUchCpQCtUzrdEbVaDc8LQPomiH/7FCrT/NEX/ohvfes59vY7xPs9sihmc2RsrcM4ZPncWVYbZ7jx\n1jXCpIvfDuhFHeZOztAZdjh16h46o5DW4gqjUYjyPLSjidE4WhvFbJLhIDDpEPL4JdrkWxRaEsUJ\n9WYDMN+Xllaot5voDJJEMVAhiQC/PceTT7wXEcwyjGJmmuZwTJVmGMVGpKEV954/w4MP3cOLb7xZ\nPeiuy62bG/huDS00/d6I++9f5GC/x2tvvM7eQZcTJ05w8sQZ9nf3efWlNxgNR9QCn729O3i1Bh/6\n2Me4ePkKuztbtDyPc6fXWVtZYRCF3NzeZmZeMNeu0+l0aDSahMMRtTyVohCCvQMTtdFxHKTnGlk2\nJsbKYNgjTRVuUGM4GBGNElq1JmdOrHP21En+5JlvAZpazZgIIgSD/gjHddGOcSAsOD4nt/JK05RM\nZQSeR5bEuXmjf+gk5HmkwliwiJx6l46xynJ8I87TDmQqNUnJHdcS/x6KgV3fy6NAGo1AkvwFVWJO\nE2+UlXTl61ByipnGlnOoEqgCmGnxjqdRrdPaUgaHSWXf+MrE75PWGZPvL6jDKnAsf7ffVz60qgDY\n84rpPgqIZdn82xHvFO2pEsGAbVPPWJZnc0qHdWmUEuO/y8qc40qW5V5s5CZhgrG8UQhAVRzgRV/H\noyFAQGDSS4CWuaw2A+FQ95oI4TCKM4TI2D8Y8I1vX2Ljxm1e/tafku11cUcJT548TbvdINQRI5kS\nuhleO6Axf5L3Pno/X/nmH3Ft6zLNWZ+D/h1qTo2rV69w78l7qGlJO2jSC5ps3nmL9ZUFkA5pFON6\nHkprXFeaaIbSQzqaOI5NUg2jj0QI8B2PK9cusbZ+CtmoEyzO4SnPZBHyfJLOAZ3dPaSUrM6dZn9n\nh9Goz/LKMqnuolDsbd3gZXosLlbHYP/d3/89ao0m8wvLbGxssLK2wsrKCv/+93+fOElxpMPm7S1c\n4bO9eQdXSk6trKPSDM916A5HXLl6jWE4QmtzcF56/U0unD3Hd154kds7O5y9D1RcZ2FhgSiKqNXb\njKKMnZ0darU6vh8QpwkLc/OMwpCZoEm/f0CWJQS+B2RkmUYKj9u3r7PQnuUb3/gW66tLNJt1dnf3\nQEjCKMHza2RKoQWozIhylKNwhMSRGVGU4DpGNOJ6HiqXfxu+0M2zPQmkw2GyakeCUGgK88LDPe0H\nHmkUjx39TKgnY2aYpibKYWGC6ExJJViUPxeAnzt3jpmZmbEw/5lnnmFvb48f/dEf5dq1a5w7d45f\n+ZVfYW5u7sizVdSxzYpXsfF3A1dbsZn/cQTkis9Ca15lWleu/4hrPdODrU+rxxa5TCpEqwGqrOic\nRhVXiXDKvxcly6qBsepgPA7AjzvAbKXyZDsn21iu38GlCM8JMrcLtt47JXrkuM2asey7ZHmeHx56\nfDiORVelNshC9unkmcx1ztK6DnGSUWvN8Pf//j/izJl7GcoZXvnO8ywFDe5/7DzeMCQ66PDouXNk\nIuPS7evsjbqkachcS9O9s82HHn6EL+7dorvfYbY9g4oFMpPsbWzx1GPv4frly5w6uU63f4c00ya4\nlHBQUYr0PTwpkdrFc0x2IymNzFc4ApwM6XlkKubWjdvs9zroep3v/uQnWVhdJpMa4UhOrq+jlWI0\nGhFGIf5ck6Rdx3VdTp44RTSI0I6AQBrbzoqytDxPt9uns7XFqYVFWq0GGxu3EEKTZQkC48Qz6PUI\nh0OWF5aYbc7S6RwQjobs7e3jtJqkaBr1GjOBz5zvsb66Qu/ggIW5WW7fvIF78hSLCyucOb3OYBQy\nGA5ZWHS4ees26+snEXikyuHdjz3JPUuL7GxvsLl1gzs7dwgcF78xwyuvXWJ754DGEzOcP3OKvZ0t\nkIIkSajVGwiZEkURfi2gVqvT63VRaYZ0oFarmexEGDPCMIyIotwnIA8VHHgeWkMURQSeb9wJc+OG\nQrfjB7WxyW0URXksJY1w8ngwooibQm777pBlmiSNMNY208ufC8CFEHzpS19iYWFh/NtnP/tZnn76\naf7e3/t7/JN/8k/47Gc/y2c/+9mpz1eJN/5j2iGEOOIkUmzUqsNiYiNTLVuvorptADsO9Kv6Uz5g\n7AOrqk/2Z7neae+dJtawr5X7acvyy4fhcaXc/wLAyxY7Ve0ot8FkqRc5iBs5eKFs1MKOr1wutkhm\n8vDRGqQogpyNW52LsZKjY4rJZ6hUbudrjIIZDke0ZuZIleSv/1d/g69+7Rm+/m9/m1NrJzizMo9S\nQ1QNPvrpj5MNQgIc/tLHPsnO5ja3rt1geHOElgHDwZAHn/gEz1x8ieevXkY3WnTjCC8I+L1vfZEP\nfOC72N/dZP3ESUadPVzh4gZ1Yj3KwxQrmrUAk6pNmaQPaBxfMoyGxNGQulPj5OlVHnrkca7f2aPR\nboIjSVWKjmOkSgmEptnyyVo+odZIP2BtbQ1fOgRCsN/rcJAltLxG5YgHdY9TjVXmGrOEoxDtCPqD\nHufOn2Vnd49+f0i9VqNWC5hptU1Qta07dDoHKDdlcWkeITSNep10OCBo1phpt0jCkPNnTrHZ7TJf\nn6Pb7fPCCy9x6vQZwjhieXmV1bWT7B/0+c4LLxGGJhJntx/SeOzdNJs1lheXuXj5IlGSUW9rXn75\nVdCCq1ffou6eJ4pCHrj/YTKl6Q8G1GsNojjh4KBDr9en0ajTnmkbZ6EswdG5NYnWLC4u5kpPRb1W\nJ01TkjglThJqtRpxNMwToZuojqKgAKSJ2hjU6ibhtuOS+ApwUCod7z/XdUmyBCFNjl2lhOECjyl/\nbhFKeZP+1m/9Fl/+8pcB+Imf+Ak++tGPTgXwMohCBWWeE6ki/8+60bAuwvyqlR7v8eOUedOo+jLl\nPwkEk6BuU6xVlHJVP6v6WPW9yoLlOIq4XEdxb1nBepx8v+r3Ktm8/a7yOFS1uUoEZLdnQsmpDoHb\nCKkd8unFQOuUIkFrlcdWPoxnbeSQxvmCkreqiVhnt8FQQFHQQycObhrgZR4uPkooajWPMMl45Y3X\n+Bf/y79ie7fLDzzyYe69517q7QY3tjZYOrHGcGmJjtzj3ecu8PWXX8Tb79DKFPefbtCcm2F/MMDH\n4wG3iRqmfGWwQTbrwyhiXvk8/+KrSBKeevBB4sGIZr1BPNgnjAeIwCfRUHNcyIyVj1+v40kX8IhV\nxsr58+x1u3z4kz/A0toJTksXHBdkngUoD5qkVRHiwESHVEqjaooszZAI2s4cXhQdxkMulQ89+SSO\n41IPTFaoRKUMhyMylXH+1DL7e/vGS1M6JPEC4Sik2+0iA4dbmyGnVs+wuLLMxUuX8D2X/W4HL/B5\n7uKbqEad9dkZFhYX89gqCuG4rDSWSTPNxtYGw+HIRNPEgwiuvnaF2288y8LCIucv3EukXLb3OoQb\n+6AypNDsHdxhtzPD9WtXeeqhdzOjXXb3h+yPQi7duM0wSZhbXGammdKqZywvLrK2ehan1WF1ZZ2N\n65sMuxHJKGFuZpaHH3yIU6fWybTiT597nheefwGphybmv+saRScCrQSLiyeYnVshSyXbdzrMzy8h\ng4wwGuE4gu07t43OJOyjB12kznA9CR4k8ZR4Bnn5c1PgH//4x3Ech5/+6Z/mp37qp9ja2mJ1dRWA\n1dVVtra2Kp91LDaj2NhVFiBVVHoVYIAJSlNVqqhmG6CqZLnlusu/VVHzVdeK9x+XYWMa93EcNX1c\nKU7z4w6i8nvKfx/HAUwoH4UYBzea1v+i2GKnMtCb2GLTubBpbXdycQfOZNuK3JEFeNuesXZ95n7z\n6crABEFyJCJTZCRoZeI939rY5F/+8/+VcKh47IFHOXXmNJ1uh1EyIotj6rUat27d5vSp06yfO4cb\nhji7u9Drcae/SzYa4TcaLNUbPHji3Xy0qXnhC79JJ4zQqYvrBnhKoIRkFMW02m1jm56mtNpNtBSk\nQjAajXCEIHA9klQRNFyk4xFFEfe+6wE+8cCDDOKYQZoYD8g8NaAx5TskmEzCFKfaWzYnlaYd4gvz\n84eEgtYEjk+r2RiP8emT6yRJMjEXYRgSRTGjgbH39gOfZt1nMBxy0O3SajfJVMbW5iaDwYD5hXlW\nV5dZXV3LY2XHNIIGURhysLvNXKuBN+vjCJcwDHFkgzSTbG916XdTrl3bNiFpqTEa9Ni6tctce552\na4ntzS22Njd5+aUXGSqBqDdxpMuoP2B5cYmZ2VkeePBB5tttvMU+InO4kWW89sYbzDVm6ex3WFpc\nxPM9avUajUaLldUTjJJ9er0e9Xabudk5ksREE818j14Sc+vGFufO3cfVqze4sXGd9z7xXqLhiKUT\n5xBC0VIJnidAp2xt3Wam3aTbq44BVJQ/F4B/7Wtf48SJE2xvb/P000/zwAMPTFyvEjEU5XP/5vPj\na48/+jCPP/ZwpYeeHY+hDJK2ss9QXNXyojJYCCGOyss5Kt+1f7ffWf4sA1pV3wuzujKAVSkB/yxl\nGsVvW73c7RCYFm/F7kN5rG0ALIuCjvu7fACU+1A1fsetoyKIV7mdVeIpmzOp4qJUaFzSHaGRbgak\nKCGo11p89h//HE7m8fR3fwiUw2DUYzTsc+fKJsuLS1x58SVOnj3H1ltv8auvvMZ84HPv6jL33n+e\nkXeKg36XLM2Im3M0W3P86KOf4a3Nm/zRM99k6CgGMkYkDp5nvA5d10VkgiCooUmN40seE8RxXdNv\nBGGaUa9L/HqD2fl5hONQq9eMAjTVFHbETq7gPTzcVC5KyibGAg5toKeVNdu8UJi41kopE68kt3Qq\nTPmK9R4EgTG7mwGEIIpjZi7cg3BMCjKNseO/cP40aZKiBcYt3pHGOkdpRlFI0h9y5sQSruPS63ZZ\nmJ/l8Uc/SK0WENTqvPjSq2zd3iUcjBAIhqMh7WabYW/E1u1t7rv3HpZPrNKPI+4fDkkdj94oIkFw\nZ3uP7Y1b3Ll1najfYWdnm3MPzjI3s8DuVofAdUyYg9l5vv3tZ/ja177C6TNnqNUaXLlyhbkT88ws\nrdIdDLhz/RbD4YizZ88R6oxubw9dh9qCx7Kap68H+K06wnf49X//e2it+cj3fA8vPP8dhErZun2L\n2dlZRqPh1HmAPyeAnzhxAoDl5WU+85nP8Mwzz7C6usrm5iZra2tGQ71Srcn+L3/yx6ZSeUURwnhu\n3Y2yK8rd3E6rAGnyerWZW0HNTqvj7YCuvWkKECvs16vaOA0Eq/pU9VtZbHNcmTauZWAte2eWAbFq\n/or2H9eH8gFZ/q1437RS5SVbPFt4/MLReDHltgXpLELGaDlCiRGJTsi0RxrH/Ozf/5/4g9/8Q+qq\nTsML2Nx7i62NWzRcj/Bgh3Z7HicMEdLh4Ycf4PIbbzBwBM9du0zSkDz4wP28+6FHePX117i8ucn6\nQYOfeP9HePaP/4iwHpD4PouNNrdu32JjpsZjF87S2xqSJLGJX60dwjTF9QKE4xvRBZokjnB0k7/1\nd/9rRnFClCZI1wUtcBwBuXM6eZIQe2zLa8wei+wYjnEwGk4o5AvK2/d96vX6BIFkrxmlFPEwJFMZ\ncRQb1/skzhV8hmNo1zxqsy283IEnyVKiyNhraw1hGGLIOoFKtbFAygaoLGbj9i3euvISYdhlZbGR\nKxY9lBpyYm2WXmeDdutetvf3eP3iG4zCmLnFFYJ6jdOnz7F55w7n772Hfr+H40gOVueI1R10EjNT\nrzNkwLPPfJP3PvYedra3GQz7JGnCQw8/SrvdpuW2+MLv/CGf+sEfZHZ9lkajYTgmR7LR22Cm5vOn\n3/wSDz/8EOfOrnL79hUuvnmR97/vvdTrTaT0mJ1bwPcDzl94hGazhZCS3/31fzd17f9HA/hwOCTL\nMto5q/eFL3yBn/3Zn+XTn/40n/vc5/iZn/kZPve5z/HDP/zDlc9XnfxFsTd+kd26fK18fyH7rCpV\nyrsqsUr53mniA7vecj32tXIpu5bbFIpdt31gHOljRd+qSpWn6ds5aKY9Y4N02Wmq3L7jxrp8f/Gv\nYNfLzxSf09j5wkFqWh/SimiKdl/sdrl4CKFQjjSBsKSL0jU8Z443XnuVutsm3uvQ795mkO3QdGG2\n4ZMMQ+JBh41b15DNFi++8QrNVos7e3do1XzCfo/NV67w1gsX+ej3fy86HMGtDo1On3/4k3+bv/6L\n/5TYrTP0AuoNI4qZDxzarmK21WYU9nFqAdFgiHYkaZ7Sq96oUWvWac7Ps9/rETSaoDITkhTj/TjO\nYGWcxCfGwdaLlOenypO3KLVabSyfBiYIkyRJxtxfeb6llLieiZrYatVJEiPfLaJb2rFXTMx7U1/s\nxZj8oxnteo3RaITv+4RhSLPRpNfrE6f7zM15fOz7niLLNL1en/5gwO7uLuFoSBgO8fwF5mZdtAtn\n7z1HEqcMhxHb27t88Q9+l7WTJ9m4cYVUZ9x74R5qjTrnT15g2I84SPucP3OasydOkyQxg0GPXr/D\n6sqKSUKcZdx88xLnVtZ54/mXSeKUubl5PM9lb3+H+cVZZudaPHz+HKszTRzVYf3COo/df4Z+b8jM\n7DyZgosv95iba9Pr7nLrrSv/6Rx5tra2+MxnPgMYB5Ef//Ef5xOf+ARPPvkkP/IjP8Iv/MIvcO6c\nMSOsKrbFyDTRBUxSSDZAlMEQGAcxKpcqC5MqxV35XfZGP05+bV8rFm554G3gK1PIZeCv5ESmjM+0\nCb6bVU25/vL1qsOr6vkqh6zyfNn6jqpxLIBkUi799kRLNoAfx42U+1JF0TuORrsaLRSJFiTaQzot\nvvH1l9G6TuDW2d6/QtrbwQlSap6LTGMcMuJkyJkT9zMULptvXeED77qPmUaD02trfOP/+U0W77mX\nP/3tL/Dis9/ifR98kkfn13nu1Zd58v0f4C899UF++8rLjLKI1uwMmzeuMQoj1teXUdmQ//a/+xlW\n1k9yY2ODq29d5+rFS8a6JRri1Os89uSTzC0tc9DtIB3XxFnXhV4pMx6p0hkn97XH0ngHTreqqirF\nfJVjAVWt+bFddH5fmItZ6jVjWue6Lmkcj/eDK/N9LfWYQCyeD4Igl+dnDEd9pJwjThLqDZ9MNegc\n9BCOII4TmrUaZClJo8E9Z04TBD6u6+IHLol2ac/OgoZ1z+ddF+5Fqafo9ru4NZ8ojtjvdpht11ld\nWEQseHzt2je4dPEaS4sr3HPPPSDWqNVNiOFu54BWs0Gr5rK2eoLZ2Xl6/RGzcwvs7Oyy3FvG9eCF\nF57j4qXXidMIJ00YDkYEQUCt0aTTHdBuz+F4Pjf7B3R7A777uz9MEAT8n8fMhdB/VqHr/w9FCMF/\n+Pf/tnLBlL+70juyocsgOd6Q8iilXbB4xwFZFWjb91ZRmnadVaBVLlXXp1HuVWMy7RB5O+KVMgdR\nbm8VmNliifIBWxZX3O1wK3M1/7H9ePSpjx259vy3/vBtiWfKlH1ln1OHTCYEDZdRmuDV5vnKV1/i\nxlv7LLdXuP7qi+xffx2ZdpkJTBabwHfpRyNay8ssnD5DfWUNt9ZEaMH64jJXX3+DR9wZbrzxBo88\n8SgvXXuDG9s3ePjcBd6zfg9f/sIXyRbn+N3XX+TV3g7tmXl2bm1wZnWeR+49zQP3neMHf+hTxCiU\ndBDCwZcuvuMQpREJCikFaWbs3Z1cJ1BkiwIjcMhtbY6M9d3W63d9+FNHrn37q7931+fL7yF/f5Z7\nyaK1EYQIYUQ7Oje5s9aWIg8ljcids8iVo+owUl8u1/eEb0ImaJOEPE1T4ig2BzzaRAPExD5PXE2W\npMaFPslyW29jwjocDclQOJ7LYDQg8ASeEyBwSWMTQ3xmpsX5e8+DEOzuHfDyy6/RH4Sk7ojeYMjc\nzCKu66O1Q6PeojfoE0Uj2jNNmq06UTiiHhtHob2DffYPTAyZwSji2o0btGdn6fX7JvFDrcGvff7X\npq7xd9SV3mbFp57+enKR2KxZVWCnsrleQQUU38vsfxXreKQJUwDHpiyqnH3K90+jEO222u8rA7D9\nzNvZOOWDwXZemuaEVK6j6qCy33+32DHlequ+279VteG4/hZJJGy5drmU48UUgdPKJRUhruPQ6Y6Q\nssZv/cbv4Mg5XOXy1qVLpOGAxaVZ9rZ2GQ4ktZrHMIyot5osra1w+dY1Hj9zmkzA1sYWbgrbW9v8\n6cFlVpcWefnam6yfO8WZh+/lmeee5anv+wh/5bG/wz//uX/G6blFeoFkexBxYv0UKh2yvHqCT3z/\nD+D4HkJlaGGyl0dZaoJGCY2WGJGJKJTyApBoWxxRJAjIl9Hb0Y1MO2iLMSyLEsul0jQYgUmfmD8j\n8nSC+f8MfhuPSLRGuCZImTl8QCgT7U8IE3Nc58GtNJq0yEsmwFMaMEGuigBR5Gaj+D6zrZqJGKgU\nOlVkqQlYpbRiNmsTpzFpltJsmNSEaaJwnBpZavZNvV6j09lDAeEoZHFhjnY7I6bDqfU1pPRIU00U\nJbTbdYTOiBxBlihatRaNoElTBGxvbzO7uM7pex5kGI3o9vpEwtiQu802tUadg07n2Hl6R13py+BV\nxcrp7Hi59wTI6aOiCptSt2V1VZYXQoiJGCk2tV117zSX/re7oG1ALG+Iqg1U9Y7jREFVwFn0qxxC\n165fiKNmlvbYHScOqSrlcasqx8n63i44HwfyBXALYXJPlusH0I4mTTKW59d47tsv06RGPWjyypuv\n0z84oOlrgobHwvIKmzf3caRxcT977jx7/T4ukp2tO9z30GPc2bhDvdmgPjPD7u5thp2IlbU1hmnM\nyfYKf+1v/E02D/a5s32bT/3VH0PWa/z43/2b0J7Hr9fo9w544cWX+Vt/86fpDTrGu09KE2NDaaQC\npMil2kfBtOzV/HZA1xpIDGFcPZ7OWDGcc2jqaF32+w7nyAQOK9Lojf07pMiDdTHOcgOg83ag87Db\nUuZKaRMVUGHs/wEckpyQ12RZXDTPrAnHmK9q8wODbmL8a6Q01jmORLoCV4N06jSFiaDoOCYjk4nW\nYCJVFvb0cRKTqRSlUmZnGsRxCpnxrEySjHqryUiGOFoxv7RAp9vF9+uIFPr9EWquiV9rcdA5YL+3\ngRYQJiELSys4rkOmNZ7vc/LUGT4/fabeOQAvW2TAJPhWBXSyN7IdybB41nf9SnB0x2zl0QVcBsUq\nK5Qq0UH597sBlF1sICy7r1dRuXZ7y9/vBnx2mRZ6ttzmQgll96cssnq7fa269zgKvOq342zo7Yzj\nVXUUnIddZ/nwGbfRcWl5bb74B3/M6y9chMSh2dijlg1w65DEI7KkhhQt3EWHg9GQhy5coNsbkSUp\nbVlD9SPatTrLC4uEWUptdoYdEpLRkN7VHpvXb7B78w4rqye5/6GH+OLWV1lbnKPp1/nJv/Kf83/9\n3hcYDEcEtTpaw1e+9jUefuwhk78SjRaZyeiSKbQSFLlTTf8P489MjpcmjxZTKUIrF6X1ONFvVRlT\n9IWDVIXuqbyex/s2fy5HV9O6PLmw1noMyEAegtWkdhuvciXzw0Uj0YxjeavCPNgA/7h6IYwCNBcg\nCSFxkjyonDIBqgwVD1opk+cTSRanoEG5Cs8LyDKN63o4rgn1G3geUnoIDa5TI0kyCGM8zyOKQpOQ\nOTCha4XjUnNnyTJNOEpMbtq4y0xDUvPahFGE43lId4Fef0Cr3UK6Hr1ez6SmO6a8owAOkwBSlq3C\nIQU+dj6ooByLeqpibVfJt48Do+NEGcVvd6M+pwHJ2733OKucsvhomihkGvVe1Y5pfS63rUzVHdcH\nuxwHFkUpKzHtZ48ToZRDEJeLzU2UzQ3Lc6+F5K0rN/jal7/B2eUzzM/NcvnSRQbDLotL87iuYDSK\nqfkN/IUGS42ThGmMSFIabkCcKtL+iLg74OaNm8jAZxiFdDu7qMGAht8gDjWjm3tsvrXBX/sH/wPf\n/fGPc3NrkzdfeZ3HHnmUL3z9OfajASLMOHv+Xg46XTwvIExGJpE2hlqV0jggUazHPGQuAjKdWf00\nYhYpJI446rRTGc4YTFCvu8zV+B132QuyoJy1CRaVXzicN8eylLHrFaqIoG3MiQGUSQeuDyVCZEIj\nqJNkysQi0XqCd1AyD9OQdynIk30IKVHCHASpUiZ6IBJXCIQXIBGMVIQUzjiWSZalKJ2QpiYhsk4V\nUoSgJa7TIM0y8DwyBxw/INMmRHF7dpYshTnpkmYamfQOxy0PqjUKI5ZbCyYPbM1jzp8jreBu7PIO\nplQrFoIN3DnrJg6T8sp84lOtyJLDzBVjFi9fQI4UZCIYD4oQh/dopcdKEIkYv8fcVwYGWzRTbPCi\n2sLd265DMFmF7dWZUxoUz2vrHlNvlk2mUyruk/KwHWXAmaYHKAPfNKAat3QM6od9LNpi3mv+Np9m\nx1TFlCo8944UOenhetwhonUZpPV4DVBde2WfjhYX8uzjBeVm2GCjkDKgLkFIaqLN137/85yYWaVW\nb0BDolSXJgNkJJGNGQ7ChDl8zrzrXSzPNrn47DdpexIdDUmHIf3dHa48+yyNMCY+6LIQePQywVy9\nTcPxaK/OkvRD4s4u/8c//kf8F//9f0MzCBhubbAVDlk5sch8Os+tm5d45PFHeM8T7yZTGT6OiY2R\n98GMaz5fOeWqC+ASh2OsNehMo4UmE9WmmNM4vWljKzR5ECYzT1pUg0wVBT4xw3nbx8Qak2t9/Lf9\nbgqZeBFRUiPzT12IVHSuYxtvctCYUK9Ka+I8DZou1LrCJGrO1ZxkKn+PEHh5th3Hc8bvEyLI94sy\nCCoEAkmcJOPWxXEKuTxfShPyV2tzMAoh8DAhDZxcJOT6DrP1GbSGpoUfd9NXvKMAnmWHWW6K4IMm\nrgAAIABJREFUBWnK4YZPJ9KhmUtK5yeflIj8OaUlKrcrteXcQhgZG9oAQlakQ3JdhBRjlnOyHUVD\nCjGFDcimfQVHULxv3PIpYFkVhe/wnUfHJ7MyWJuDTlFkvC47R1QBs/3PftfkHBSHZiEumbx2eHBZ\nnBFHuSXG9g2TxYb142LBgElHVd6qxQF5XJl2gBVFjTHLpKkqMr84noOUDmhJmmZ4rsev/Jt/y/bG\nDqfXz9EfDrl09XXmAhcnE+hoiKjVjRt7q8m5lRNcfu0l9m7dQjkZvopNtEflmABNnk+apgSuj5IO\nvU6PRrtN4Ls89sRjiE7C9YM9vvSrv8EHPvZxagd9nEDQDjy6Wcj6iVV+6Zc/x1/+9P/Nzs4dfM9H\nawPgCI0y6GfmlUmK84iY6BgirsoRayxKnEZZlwgF+732vNj1VXFXBcUgintL3N/4GfvVh2/L/2/6\n7zrG2/SI6KjAAq3R0qRJG6/aon6lj65JYah0aYmdwHipGjm7rRMycb4Fh3H+fc8d96EQ89n7JVWY\niJN5QuQkSY4kQXk75R1VYtqnbVmmPS5j4GA8GFLKsSJFZRk6U5ApEwCGYmoNpSURSHFYt5b6MG60\nNWnl9Gi2mKbs7GCbMdoAWfxWtainTUgVNV2mpI0zylGTveNk7+XDaJoYYhoBO60tjjjal2l9s+WZ\nd7NWkZKcoqw60O9epopZNBg5scIEXNGgjOItiiJ8v04SZdy8fo3tWzssrp+kE43wFLiDhGHaZ36u\nTpokxAdd3v/4h2isnOLmyy+x+eYrOIO+SZzsOdT8AKWgF3ZZWT3H3p0BC4szzKwumrUbp+ze3uTS\nIOI9Zx7gZH2GVHhsv/oaC27Ar/+/v8rB/BzXewfcd98ZnnziPYTRiGazTprmYiDhoIWJ8yKFHLu8\nTwPJ8m/l+6aFQbjbfB2XBMQWW9l1ThNzFtfsz2mlSjRYvLuov8AJ+3pZJl+1Xqo4huL3ci7YKuyo\nEu3a7Z30AzGeq8V1P882VG7L3co7BuBlxdIhUIkJxw8p5Ri8i4Etci6OBzOXoUXjcI4eUkiT9kib\nwPeu547j+Jrs0kc9/4q22KBrvxMOvcayLKNWq01MOkyRKVqTXQWoxfNFUChb3n94XRUEy0SZBqZC\nHN24VcWM8dFNUyV+EUKAVkcW8dSFpifd1ct1lttxnKx7Wrkbi+m4EoPi+X3C/KkVzLTbdA56rK6c\n4Hd++/dpN+c4c9/9pErztd/8XZpRgu86HPS6NGt1/CQj7h4QLC7R690hSfok6ZBYK9q1JlmW4Dse\n8aCHpzVhr0c4GhHUayyfPkt4a4PN7Q10GPP12/v4rRlOrj7Jk5/8XmQ/4lc//3lIErI45PbN63zu\nl36e7Tu38XxjlWD4cZELqW2O8CjRUbXmqkrZU/XtUID2Xq0KB1EFsva18vVibxX1FM5Zx4U0vlv/\nCoefMlgXh0t5vR1yuvrIeh1zJKU+lA+tQzHoZP9sM9eir1l2eHjaxJ/drrcT0vkdFKEcletWZppX\neqz1nvgnZU5c5QtIg+scatqVSscg7Xk+kKdFyjNGa2XL06sdaop2FZ/lhVJ4sdmWM1WAOu20L05u\n27QPzIJOkmSql1x54RXFSBwsKWFps1QtdMO5Vsmkj1LwAJLDhXs3sJ226Kuey7I0v6d85c9GkR15\nWpixGDPf2sTRcF2X4SBitj3P88++wN6dfR57+H2EaPb291lcWkTu7KHUgCTKGGQjslHGxTffoD4a\nMLfUIozn2Mu6KJUxUimtepM01ogkI+oOqEmP4W6PWnOWSy+/xpNnzvPkB9+PQuOOMvx6k+35Gq/2\ndki6PT7ymU9zcWuTV//gd6jVFHE0wvMd4iRC5vFPtM45iEKUqI7Oa3nu7HE6Mp8V6/puIO55XqX1\n1NudlyqAm7b/yqXc1mlth+qIp8X7yhyJHauoqFdKOWHhVEWETK7rSee34t4ysSiEsLJjHbZ7WnrE\n48o7CuA2eBXFBkQAR7rGHVgXuSXzGA9SGiWEEGQYBtnVGYULsSMkvu/nIJ0rS0XOSAuRJ7o9fKe9\nsMugV7j82jksbZbHXgRlkK06zYu/C7GRfW3c75wamLaoy20sDrUyG3c3qlYIxmM2rVRxGHa/ppnw\npRalU66vuh1VIH/8Ir6bbL3IOViuJ4oSmvU2WaJ57tvPszS/zLXtLWZmZ7n51nVklrG4vEgWuTix\nw/buDkK61Fs+rWYNT0CrUWczShA6w1XgOCna8dnvdWknMbW5RWrtGebOruCmmu3tA8TWLvc/dD9z\nXpMoTNALbS686wFGB112vv4aO5ubJHHI3/nbP8Ogd4DjO0jHMS7nGfm6zYEgN6Yur5Oy+KusfzmO\nej2OSypKmqbj9ZkV6cVKADftYLDbaq/7Mni+3VJ+n71/j9t35UPHFosWbazCJnvdl+8vnKfKeDKO\n9ZJLDorfC3GwvY/uFvukXN5RGXjZBMweuOKeIpmo1gXFnOuKs8zYxUqQrkecpiAkSmX4vk8SJbi6\nsGbJlaU5gBfvFhxl78ptLP6VvfeKwbfbW1XKis4q4LVPf631OIJeFfVQ3ih2VDjbfttWBBf5+OBo\n6FjD+UwPB1uUww1W1F9stkJNUbXpTCadwprEWAYdPbAmWcjJw69s7lkuQhzPZoqShYTWGoSDIyFJ\nUp795nPs7uzyyIOPsh+49O/skXY6NDyXXhYyPzMLfcHqqQZDCc3lNXZ2d5n1JGtLS2zXZxkc7NOL\nYhyvgRt4LJ0+zamH3oVoz3H74AB3aRlncZfhbpftToeNr3+Tk3PLPPXwe6k7LeR+zHIwy0Mn7+Xf\n/fZv8ImnP8aTTzxJt78LGGJBI3CFRGgHpTMQ2liDUA3G5fUmxNGQEgXHU/xkni/AdTqQeFaiaFvx\nBkcP+KPzUS3uqHrmbmuyXMrUbxUAw2FkUPu5she31kZM5HneRB3FPrCBuzjAbOw9HH9DLBRj6+S5\nNYv22fVWjcXd+v2OAXgx8ceddsV9IhMonSFE7qggQCnT2TevXOXC/Q/g1RuEgwPqQQ3H9RA4ZHFM\no1YnjiJjfiUETsEOZZlRZsKE7L38/vLvdilkbMWnTUGUB75YCNMWsE0VlKnvYrJt2+9iAdnhUu2F\nVR7X4jAsAN+mIMpcQ9V8jDe/mqSiimtV/SpsicvjYo/P4cEwGdP77Ra77qo2OMLJg5wZsBLC2FJr\nIXGQfOfZ59CJorN3QPvCSbZ3t1lq1El1ykgpdnodAi1IXZd7H383J8+f48WvfpvR3jYH/SHN+SWi\nROOi8Jtt5hYXiFyHURjSnHcZxSmDMCZ1HYZa4WSaDMXNzi6LGzd54vRpFoIW/WjIzOoK3/f0x3n8\ne99HWqTpyhKyLCOoNQynpMkzpmfIPKxqFXCVqU8zNpMs/sRcTRALmuMIwTLHacty7Xm267b/Lq9T\nOzKpPY9VhNG0tWG/s0wplyn7Ko/sKmKpXJcdPbSqbZM6q+pD7W7F3kvTQl7Y5R135Jk20UVJUwPc\nRRJRKQXScUzOOgTf+va3+d/+9S/y4e/5CE9/30fQSUamJaSKml8jihMzKDJfaKKwNXfHIgwbPKsi\nqpUBzQZau91VwFRFedvfiwPCXmxVGu84NoF5HOlQWNAUoCxyBw4DTEeDNVVtNrsNZQ/N8mYYAy/g\n5naxZRCvWpzSmXQ2suuqal/VGE+ruyhVm3Hyem67O2ZMBGgT2e7NVy4y7A9YXz3Nwe4+lzevwMGA\nRb+O6zu4boOD6IA407QXlmkvrtEbJqyfOcfS+x5n4/ZtTjx4Py8/+x1knJIOe3iOi8gU4c4uyysn\nCcKUWqqJfJ+e5+JLgfI1sSt4c+8Ws9cuIa8ssvDAOcR9q5zpPsD8/CJSapIUavUGnu/RH4Sm+WZA\nAI2QhwkbyvNnz+MhZX1UzmyP9+E1s8+mjftxhJf9Xrst5XbZXGZ5rqv2iv3OqlIGX5tQKBM1tvXH\nUY5kEo/KxFnR3yqv5ipiyH7H21nP9mFWhUfl8o6KUMpyq/IAQKGIAIQxfNfabMhUKbx6Ay+o8ehj\nj+MGAb/4uX/DyZPrfP/HnmZ+pk2WJBYI5gOC8e5yOPT8cxxnTJ1WUeHFSWifpEqpcUyNqgkqn+42\n5VtmwYpP+31lADdONLntMhYYMrlRit8KkCzaUQSysttjL8qi3XfbnGWTqWmLGQoZ7aScsopTMd/F\nOK+pNg+BKCzJp8vxbQ6kqmiloHgPhoMQKFSW8cd//CUW5xfZ29llYW6RwaXr+BoOHJd6o452PZr1\nJgMlWDl/D1FoUl216nWaOiN1JWtnz3L50lvs37yJpxSDbocg8FGjAW3X4cT8DHXPR9RrdH0JwyH9\n0ZCOqxlmLt/6431wBPe3PeRsg8yFtRPr7He2yYQJtJSpBN8PjBJH6NyxXKKURumj4FW1j6rEV/ZB\nWp77Ki5yPK8Wl2avicrxr6D0CyKlTCGXjQru5q1drrf8OQ30px0E5XeUwdx+rixHt9tbftc0orCq\n2Jm7qix8jtx/7NX/hCXJwbUAl2kssJHdAaKgOiRRmjA3N0d3NKLVajNKDlhYWmZxaYmrV67wT//F\nv+AHP/FJ3v/Ek7jSRWcpaEOFVclEywBj3iuO/G5PhJRyHJDeXnhlNso+JKoA3r7P5giKUt4cxUFT\nbIB6vW6NVb54pcBx5MT9xYFTRRWUuYEClMuHlmnQ0Q03bVNplR3ZUPbhYffNdTyUIS/tGo4ATbmU\nD8ojbTA+eGMvPaU1rnB58aWXuH3zFveduw+n5dLvdFnMBCEpOILe9g4aCQuLeOsn8WZm0LEgHWYs\nnDvB3u2rvPSd55l16pxaPcFo6w6OSun1DmjNrOI1XCId0k9H9G73iKIBO9u3qe91UIEgrEObFq1h\nRvzSRdQ9Z5BnV/nAd72P559/nlOnT5hs5/WA3rCP5wW5LtZ4lUoykC5CHuUCq6henespqoCxDCiG\nQ60cbjNXOciUQdP+rcoO3AamIopkmdq0/9l9sa3Bpq2DKsq9eMa+bsu77cPuz3qAldd9Wd9lj3P5\nMJhW7sZplMs7BuCeV2hmjYLyaHAq8+kKRaI0WZYi0gzhClzfJ+708TyHhTOrXNvdYtnxiLXmvfc/\nyGP3vYuXX3mFi5cv8QOf/CTzczO4UiKyDM910VmGTBMTa1ypwzgITrEwc9vhEpjb8i9b9GKXIpph\n8VwVNVHOj1mesCrxUvG9eLdN/dv/bHZROg6Ok1PhQk6IZgpwLDZ10cdyNMYy0OtM556tjDPB573N\n+2e39yiVX6bsx4s9O6qsNNyEifMxbT2X21seNylMUoNiDlwnYDRSfPE/fIO1tbP0O33m6nX29jZo\n1uqEww5CJzhuSqoEne42p86fYtA9IOyOOHNqFRGP2H3zMsEw5Lkvf4lPfeoH2Z1vkw0EiQthmNIJ\nt9HBG3TjhKgbEngeTibRjk/d9UjiEVqHRAh2d7YY7uwj5xs0GnNs3bzN2vmTyFRCnOLVA1IBfgau\ncvLYHsayqvDYtQ++o+BazPfh+JZlv0fX2/GsexmUymv1boSMTYSUwaogJOw6beuNae2BoxyiXcr7\npPit6v12H6cBehnAq2TdNgFiH2BVVH5Ve/7CilCyLC0BxyQ1W/zL4oTYNex44LggzWZ3YxCOw8nz\nZ/jDP/kSQZRQb9Q56HbJHIfVtTXa83P8/C//Eh/8wAf4/qc/zrDTxUPiOg5SZ7gSUl3EaRN5PAnz\nLq0y4+VpnYhObs5VUA+FnTZUK0GqJrIAkgJMy5poW1ZdOPNIKckyRZqkE3WB4WSKsTqMjX24sW2O\nI80S0jTNQU/jOAW1fzjexbvL7LFZSGL8DltmPWlOaYGyPmpDO21Bpmk68X4hbJv+470CizZUiQ68\nwCEZxSwuLtDpDEjijK2tfbLMAxnQagQM9u4Q9fZItKReq4GKGKiYfhwzu3qCulbcunyRXn9I04ft\nzdskG3dYnZuhH4e88tJztFo1Nnd36B/0yZJ9XM+jFjSYW5hlMxzh+D5Bs0WaQnOuxawLaTxk2I9I\n65J6s0nm1/HdGm6ikNLBlx5kEdqDBBOF0FUOGkGWW2Q51hjYn+U1aMb/qNiuWhwBx4mtinIci3+U\nqj8UnUy7v8xtVnF3ZdPdKi7AJnSqSvkwqKKO7QPHNhqw31eu3+Zk7T5XpfUrE3F222xc/AsrQrEp\n7rKSAUxH0jRFZpIUcB0nl2UqMpWRKUGWSRYXFkijmFES0XbnWV2fJWg0WM8yBlHI937047z6ysv8\n4z/9Of6zH/oh7r9wgeGgT81xifJMHMKRJFliNPyuiUgmHAcsKsIG5zhPAWWLOgozw6Jf5Qn2fR84\nqiEvStVkTrCTiLH82y52bOvDcRzXOgbUYjHU63WLCirEJJNUkVImw3hRDkGVsbdoAZZVqe3KfSjX\nY/et+F4VVdA+PI6rv3yI2Idpt9un0ahx/fotGvU2Qa3Jc9/5Eo+8+zG2rt9CpDE3N27jIvAAFCRp\nRqM5i2woTp4+z15/yLAzYHVxkZ0bN7h++SLNLCVVCW6jxq2bN1mYn6fX64LKiEchUkr6nQOWVpdw\n2jXkTI1GOk8vjdECVleXGO7vIzKXpNfnha9/i/ec+EFUmrF94xbXn3uZC4/cz0gJvEQjPAflmsh6\njpbo3PRTlPprj18Z3KqVvEcPVDM31UBr13PcgVzmWo8TDRRtLwiDMgDah5C9fuxni1IcEsVn1fuq\nYo5UHYA20VGAflWy5moO5vCaLXKq8va021DloX5ceUeVmDAp4y3Lw7XWuHh4eZ+E0djgCg+hNY6U\n+EjmZ2a5vnmbh5bOsHcwQPZGjOKYRquJ5wQ89cT70VrxK7/263zgfe/jI9/zYeIkRgY+QmtAIaUJ\nRamUOSBAgDRKwyxLkeIwXksBmoVDQ6FIK07bApxsd+M4jicUNDbFe9z4FAtIZWoMzOVFVlZWFr/b\ni9v0K52ggqR0cJzJWOnFM7ZycEIUlE3K7+02loHEpl7sOa0Sq9jgPu7zMexwUfr9/ri9ZbESQFCv\n0+32WFhcIU00X/2Tb7CzvUt9fYZz58+xefUKrZkFwu4+WTQkUikKTa87YGZ5Bc9v4EYj5lqSQGk2\nbt3Aj2McKfCkpB4ExCozLvOBj8o0sl4jCHyiUWjEdUqRRiGuI4njyCQgFhqymNHeAXqUcStJ2fwP\nAR/8wIf4/o9/gn/4P/4Dfu6X/3cO7vRpCgcnhaEHsdD4mTI+EGjQRw/P8lxWXSuvs8lS7VVsP2NT\nzMdlsbLXPFTHpC/fb7erChzL7SqAv/jbFtOU94HdBntt2RyJ/c8myMrjWrVv7LVnr/syx2oTPmUO\n4G5iE7u84wBeli+VOyOlA05+4klMlnotEFITqwwnlXzofR/gyvOv0On3MHF5XWYaAY1ag1Qr9g/2\niNOID3/oIzz3/LPc2trg0z/0l6n5PkKleMI4Sag4xMuTPyBchOOCkHkQ+pIsWOuxUrCgvrMsG3ts\n2hSq67pjWa1NiUxj4WwN9KG3JibtkzrMLmSX4ntBIZfZVziM41LcU4C/zS4Wi6zgMsptlGKSUi4v\nevu9aTYpQimUvnYZi8pKXptFXXdjIQslbln+WGyuMAmpN5qMhhFxmPGtbz7LmTMXUJkiimM27tyh\n3moRuJJ6Umdvf59hrBC1Jq3FNbb2+vQGQ86sryPjEBGFBDpDKMGw36MfD5GBh1aaU6trDNQBKToP\ng5jRPzhAxhlpqo1ZZarQGWzevsPTn/wY1y5ehkwSNgKClUVudnY5e9+7uf+e+2jUGtQaDWSS4klJ\n4mhSYZTUXm466jAd4Mpc3t1EIpP3Tb+3WGu2B2a5TLVMOoYCL9pcBd7l9Vyux14nNsdRRVmX13QV\nN1z8s8Ufdl/L1jv2b2XlZdX42ARK1fW7Ud5FeUftwG0xSplVKj5TnZHlAXrtcJGO6yBR1JCcXT/F\nN770FZ5eO0Gn26XuB+b5JCEJQ5qeTyPwCVXI448/zubWJp/9n/8ZP/zpT/PUex9j2NnFUSmtekAS\nRwZ43SKTh9kwAnGkrWXgKmvWC9bLlrmVJ6aYSDjcEIWd6iQlSq5XnZzwshNEVRvBLK5iMRaedLYY\nw67TXoDlTaoyNUHBVHmNjvuvJzebzX6WWdDi0LOTL9gUy3Gsul3HEQrKd8gShe/UuPjWNeZmF1ha\nWERlcP3KFYbDAUIKZpot3BRmfRdGMfW5BZzmDLu3N5htt9E64+rli0iVEAQuUZyisoyD/S6J0Pi+\nz/L8Ao1mk16UEIURbj1g0O1Rm19mq9dlYX2NmbUTuP2QhuOwdvIcZ8/ciyt9Xrl5g3e95wlubNwm\n9iXvfuoJRNBgFEbU2m3SXh+hQPkQIqkpgdCQVeBsmYr9s4K4Gbu732uvk3KxvTWr9vbd3n+c+KCK\nW6uyIJvGfVQpG6tM/WxCa4ILzddplQjYHo9p41esbZuCtwkPuw93K+8gBe5g2mqLTyZlxwU4ucg8\nl17eUa2JtcYNfESqObG4jK65CB1DFpPGGgn4ns/S2iqdfg/pS7xggV44YGVpiQsPPs7v/s5vc7C/\ny8c+/EF8oRkN+8bLTZms1U6u2DTB3gUZagIcgiDIe2MBapqZwyanvG1q4CgoV2u1wzAcy9OBCfar\nTJW6FXLxgoLKsixnscG4XhvPvSI0iCNzbkFnlSaGxQE7uUAVjutNaNXLsTDGC1kddYcvgLYIiFTc\nPw4TbMXYKAcBqirFuGRZNjZNnWiL0uhU4Em4evEqJ1dOEPYHLCws0OvsIXXGaBjiC43nZERCUl9Y\n4MIjjzGIU+ZThacVne4BcTxCxzGe7+J5PqM0zg/GmJrnkagMMvCDANd16fZ77O0qnvqu99Pdus2J\nhx5E1xr0Lt9CRSkvvPgqZ0+eZGl2gYcvvIvl1iLLT57k6rWrvOuDT3Hl9TfwA4+N/T1mHReZgBYQ\nuxm11GTY0e5RRZrNYd0NTKrEHyaZyHRosL2oodpqyrbGsuepvMaqqNRizVS1/W4Ua/GOKiVhFeVt\n13kcF2PvubKosri3kL1Pa5tdEit3gf15N46zXN4xAIejsbDLi0wIkwTVQRhzMAFCCySCVBgvTV84\nxCrDazXYvrNJu9UmiSKCWoPhcECn0yGo12h5LToHHfrDPkhB4tT4vo98lDffeI1/+S//FX/1x36E\ntZUlHAGjQZ+gViOMQgM2rpfLoCdP2KqFK4QwZnZMUp9JkkxYmNjP2RSHLXqZpuW2FXdlBx075Vzx\nTFF/prIj9Womw1zaIFs1J/ZGLK4Vm7WKWrEB1W53MR5l0Lap9eLvaRsDmLAEKo+D67okKqZeryOV\ny5U3L3P+3H2MhkN6eztIHbO80EaHPqNen76OiJGcPn2exvwCvd097n/oAer/H3NvHmRZdtd3fs65\n+1vz5Z6VVdVVXVW9L1W9qUGrbQlkFoFgRrLkAEaYAYNNhGE8hhk7iBjP2BIzEx4HYTDMGIwAG5DZ\nJCMJLEC0JCSrpZbUa3V37Uvu29vvfu/8cfPkO+/my5Zw2NGciIzMvO++e8/yO7/z/X1/v/M7huDZ\nz3+WLE+xPYcwivFsSRbnmK6DEeXUphqEaUK/O6BmObiOQ9M0sBwHKSTHjh0nBFLboT0MyLKca7dX\n6HS7PPbweVxTEl+7xvEL95NYJmu9Pe47eSc7u5uEjlecypNkZHFCLCBNsuIosNLRZ6p/y3JZVnB6\n35aLEPspK44oZX/HpORQR1lMZeAyiRqZZDmU21EuR+UfLyPZ8vv0RU5Refp3y+2YtOBMUrpH1V1X\n1JOskqP+Pqq87gc6qAFVZsWhzjYlZIVA5YbAzAQyF/vn4AmcTJKaBrPHj7G+vs78Q/OEUcR2e48w\niKjWaqTkbO3sEYQh1VqNWs1jzx+yvdfh7rPnMOVd/Pwv/r/8vR/7UVzLZGlpnl57j2Zziijwiy3k\n2or+Wp2cZhl5engnpa5kJilqXVkqJFk24VT/lFG9eu5RpuOozmJiznFl7pbRRHm3XLk+qv0KTU9S\nDnq79bEuR52UKTXlBFbvPCofjU4X6E7TMAyLyWdkZGHO5z/9aVrNFi89+zwnTxzn1SsXsYwco+Iy\nXW1Qn25xq7/LwuwiZ++5j+3+kO7QZ35uhu7GbeIoYG5+lv5ehyBKSf0ADJNh6FOZmmJmYZH25jaW\n4+BV6kw1mliWxU57j/WLV5i75yzbez08w8JxbIwkJSFhSMSN7VWOeSeZn64yPT3F1KADYczWzRUq\n9Sq7/S5W1SY3JIaQOFIiTUiTw1n8ykpnkhI4KmZ+XGaOVh66LByFiHUZ1b83aR4dhX4nWQ1/GUtC\nPaustCf1j6qTDq6UT2tSvfSFQZf58r1HXZtkBXy9708qr6sCn0QJ6MIhpSTJQeSF1a9Oj87yDGGa\nWEIi/CIM7vidd/DCR/6Uk6dOEaUp1akpWo6HNEwMwyRNMlr7ccXSECzPVVmYbrG9vUuYJDzy6Bv4\nvY9+nEcvnKdar2M5LmEYEEcBruUVZ+9pnGxZoaliyCJni1Ik5fAjhSwnIeWy17w84GWkq/elHk6o\nKJ3RUWn7aF2aY3XPsqw4hJVxwVVKuTxW5fFTReVZL1/XJ4uqYxnpqzaWJ6CO7CbRTaqUI1mU3Bxw\n82aKiC3++I/+iEcfeILZqRaD9h5GGuMPupihzXB7i7pXJXU95heP4XpVdq7fplavEAU+N69dJYtD\nTMvGq9SIoozA75CbKcI0WDi2RJDEZIbEcR229vZI4oSlpSWyPKe3us7pc2cIHZNmZYpkfoZgZ4eM\nFD8ecvnGKzx/8WvctXqF75qbpW7YZAJWbl1lulKlOdMgyiQDIoQAK0wJFMWQfX30V1ZiOlqdrBCL\nmPGjlGXZGV2mR4QQB/6WcgjopLBSXXEeZR2UraxymcRrq++VQeGkCJtJkSZlZD3pHZPol3LRLZZy\nvcr1+3qLb7l8XQX+gz/4g3z84x9nfn6e559/HoDd3V3e+973cuPGDU6dOsVHPvIRpqadZLylAAAg\nAElEQVSmAPjgBz/Ir/zKr2AYBj/3cz/Ht3zLt0x8rsqnrZDWeMTFSIFLaWClOYnISNh3HmSQiKQ4\nIDQFYUruvOscH7/5q/R8H69SR9ouRqWCadqs3Fpj0OsjhaDmVZhqNGnWbIRlsHDvPex2h8wtLrNw\n7AR//tlP41Uczt5xHJIQ15bESUE+6lZDlmUHsd2gKxIQ+wpEHxz1uTqooewAKm88UMKvwhYn0Qnl\nvhsJSE7xZ4ae/yIMw7HJlqbFOaGuZx9C+XrmNX1i6u/R+cGjkE75PEA9ckdfrFSETBl96VbKpFLm\n3/WQSiEEUTJkdXWVeqWKZZiYnsflSzeQMsHIE2zTJgpDht2YCAspDVZWVvFcl7mZWbZXrtPd3YU4\nIswyWq0ZvEqTtmmw3W3TnJpCGEW+8EalRqXW4Oalawz6A6qNBgiJ40lSkWI4NjEJO51tBptrGDIn\ny3ysJMHI4MXPPMWVKyv8yI/9AxZmZ6ifvYtP/f7H+Pbv/k7aZsJeHlLJwQhi+jLFsEzMNJ+oWPQF\nsmyZfSNmuhBHf/ZaTu/yc8vjqfPz6jPHcSb6eCY996jQ26OiXtT39faX01ro95StxEkcuvpe2a+l\nj8NRfV1eiMr10EOSv5HydRX4Bz7wAX78x3+c7//+7z+49qEPfYh3vOMd/KN/9I/42Z/9WT70oQ/x\noQ99iJdeeonf/u3f5qWXXmJlZYW3v/3tvPrqqxMnn65Mys46vWRZSrYfAeIaJqaZQ0aBEIDMKg4U\nXXJqTN2xyF6vg2U7DPwBq2vrVKwKqTSYmp/DSFJcIWl39tjZ7SKkJE4zDMulUq+TZzlvfONb+MJ/\n/jJJknDfvefIDUE+DCHLSCXE+7sLDdMgz/JiJ5wouOTcEJBn6Id/6wOjlLRCt/ogT0K4SvnpDo+y\ncEwSCCnVJCg266h7bMfeP8AVhDQwLQF5XmQLKU0q3drQ0a2OvnQlKxi5csuUi46udCtDv6+MUiZx\nmpNKlucHp6SLfe5WGoI0jcmyHNuu8NKzLxEHCbVmg2cvPo2VRthGTq1RhyjCzGCYJtQdpzhVfnWN\n5dOniAc23d0tHCmxvToizfCHIdK0aB07hmzWaczN0g2GOFaFilNhY20dyzXJSdhtb3Ps+DJRHPHi\nyiWGWyYVwyPa2aOaSvI0QUoLBwNERi8N2fZ7fPjDv8o9J0/y1779W3no8QtYQULFsbFyi5SMTIJp\nMMrykqbkKRiGSZKDYZtkosjGbgDGftKrhHGL8SgUqETxqG4f7fIFEPv55Mt3KcWo5KGQTwV69MX7\nG40yKlvqepH7lu/+t7Q2TKZjJlm4xXPGd0br9+n36zI9escIxKEdBD5aMPKD30dZDPq1/yoI/M1v\nfjPXr18fu/axj32Mp556CoAf+IEf4G1vexsf+tCH+OhHP8r73vc+LMvi1KlTnD17lqeffponn3zy\n0HN1pKcPZtnUsO3CnCTLyZKEfL/hxv6A+maKQDDdjXnL97yDi3/+Je4+fYauHzJdqVGTHruhz8bu\nBk3TolFtsLg0jds4SRIVyq3f77OxuUW/3ycVOXNLx1nd7fHUr3+EH/nRH6Yet7EFhLLY8m+5DkYK\nRAkkRZKkmIxYgG0ZWKXIECnlwZZ3xTmrAdIdm0qZRVGEZVmFEy6OD1Z7mExnqAkwQjD6ST+jjRb6\nTtHie6NTQnTqQX9HGWXo7ywr+7KAT4ok0BcJ/beShfIEUp8fZc5neeETwZBF9klTEsUxSRrhujbk\nFV594Sp3n72fvU4Hv99j3hSwH+duppALE7NiM1urEvfamOmQuL/NxWdvcOvKNebqU1TcGkmaEMUx\naRAR1iwWzpxh+dhJXnzhRWqeQzQY0u90ybMYYRn4YZdO1yHuBfSzBHdmBrc5Q6tRox13SHKBmUAl\ntzCzlCTPGNoW59/wGK986Ut0M5/5B8+x9uoKRlalMdVgK9rDtC0qSU4WxwjHRiRFfkLbdvFsl0EU\nkJFBniLSFJmm5EIc7CyeZP0p+VD/jyvE8ZKmhy2zctEXa/X/JOriGyn6Qq/L4/i79GujxWP093gY\n4CS5VP2iRzapz3SLZhJFVTxLB5+HfXtFKgN1tq0YW/S+HjI/qvwXceAbGxssLCwAsLCwwMbGBgCr\nq6tjyvr48eOsrKxMfIa+cUM3y8t0gNrBqDpORReUhSFJEh5/+GG++PE/Yau9SRKDIyrk1QrzrTkW\nqi6eadBZ3yDo9Vjd3CoGPcuZmZnljhOnkIZEGJJ+MMCPAra3t/mNX/0N3vcd72Cq6iERWHkKQYIw\nJKZrQ150op0X+bKjNJk4CLp5pq7pik9XUio8UffuT1KAqr8UBVFWpDrvnuf5WGiXclIKIQ4OZ9Yd\nrOWNNXp7VJkk/KpORyGqcly5TgeVfQDluO7JpZiMSZJg2TZRFJAmCdVKlTTN+MOPfpLFO89ippJw\nt42R5wR5gityzCQlNwxwDE4sLTFz7DivXr1MqzUDWcbO2hqEIWHewa7VsKVFmKb0gpDclNSnW3SD\nIYbnMbcwx+XnnydJEsxcINKcZBiyt77NdGozb5q4aXGuZTZbZ6u/g5NJ3DRDmDa1xOJsbZYb3ZQX\nv/wlHnzTk9QaLbBd2ia4kU8QpzBTYbizw/HaFGHoE+c5oQGGaxHmKZnfw8rFwdmlqSEJjCIM1uQw\natSjoyZZRpOKrmzUnNSvq890pa0sSSkPW5JKdsvPKKNitchPkj0dcY+UMyjUq7dHD+/V31NW6opC\nKVMjZfn9ev00+Z7CZi3TjmVf1H8TBV6u5FHoSH3+WtfVoJQjENRnumLXV1ClYHTFY6YSt+YSi4S5\n6TlM4TDwI/prbQzLAgmNWpWKXWFx+QQbG1v0en06nS6b61vYrkOtXqVaq4Fh88gD59lt7/Hrv/c7\n/PAHfgAnBQuJa1oMo5CQnFwKDARmLrDSDMc0ye3x9LO6wE/Kg6zapwf4R1F0IGiKStFD+3SqQSk7\nwzAOdlqqZ6r36uhFoXpdSer1UEUX0skIJD9AEeU6jU+k0RgeFeKmK/ZJMnOUIFtq70DO/lFjAsv2\nGAYpt27eZnd7QGVmihPzx3jhyqc5uXSM3mAb/C55GNMVEVmlwjHTYc9PiIXB/MIimxsrDPf2qBoG\njsjobG1Sb7YwHQeHjNbyEnatwo0bt6k0qhi2heGYuJUKcbcD+wt6nMZsNUw8I6fl+8wzTavSoNmM\n8Ve3sKTD/B1LREnE3NJx8ueu8MCb34rzxoe4sr7DmeU600sLyJ02tm3wqc8/xVsfe5Ir19dYmJvB\nTwMSU5KLCFMaWAaYcYqRCxKRk4icQBZUn7nfhbrDWM+FX6a4jipj1FmJkpk0XkoxKYVaVlq6zKrv\n6nI2OrIs0541rhxN86j83Idluzz39O+UgY7ePv39Xy90UAjG5sP+HWP1OUrOv1HlDf+FCnxhYYH1\n9XUWFxdZW1tjfn4egOXlZW7dunVw3+3bt1leXp74jH/7a791UMnzD93P+YcfOBAcHYWXHQ66o0sp\nuANFNfQ5e+4sl69fxT3pQG7iTM9yYn4OmQhwTQahT7/bp9u/jR8E2LZDqzGF47hUq1U6nTbtdodu\nr4NpGcS+zyNveAO/8R8+wv/4vu8jGoSEWYxj20QyJxM5aZ5jZClZlhOFyRjXWF5VdWWrt0t3+Akh\nDlC4cpbqQqsLled5B1EgCsWq56vv6+/WkbGaODrSLYc16spbf79+feIGmgkL2GtZEjrS0ZFdmZop\nFyFyhFDflQjhEAwTms1Znv3Kpzi+fI6doMsXvvCfmdq3VKbnZ4n3DIJulyAJ8ZqzyEqTyxsbeE6F\n1bVNtm7domHZ1C2TNAgI/CFBFJI7DnMn7uDcXXfTHw4RUlJt1NheXSNOE2qNOsI0ycOI0A/JowQx\nZ2BZDnGWIpOcumWzePIUndzl8vVrCMvi/JOPYedQw+SLn3uKU02Xu+69AJnB9OlTXLz1pzR3Qh7q\nmXz69z7Jo9/2TlZFjiMsyBOMqMgT3ut1qXkeEYIEiDNJJgWmNMg4HM1j2/bYnBsh2q9/Ik/ZijrK\n7NfnrWGMsuzpllb52brslt85WfmOEliNZG3E55cXGV2mywuPDnjKdVL1mozAdbpmnLr5y5QvP/Ms\nX3rma9/Qvf9FCvxd73oXH/7wh/mpn/opPvzhD/Pd3/3dB9ff//7385M/+ZOsrKxw6dIlnnjiiYnP\n+KEP/O0xISlzW6rhk05uLq/ASgisNOPkiVM8/YWv8Ni5R/DDlK4/xO+HmEFON4tILEkjN3A9E9d1\nSJKUIAqI45C99g6ObTM302K61SBNYgbDPt0o4Nzd9/Erv/br/MB730/QH+AJQZ6m5Psn/OTkZIbA\nNm0cbSKotpU3nOim4KQMhoZhEMfxQfpafVKpZ+toSadLVCn3pe4sKi8Iql7qmeWddOq+SV5y/fvq\n2WUnjT7Ok5R4eUKU23pUyfIEFK+IhWl41GtVvvaVF6lWW4XPJIzpbe/iCoGZR4gsR3o10tym7rk0\nFo+zOozpBCGu57Hb3mbYG1AlgTzFyDOkY9OLA+I8oxEFtLe26Q2GnF4+DnnOWuCj9r5U6nVSJyKl\n2G08ZzcRScowDNjw2+xZKSCpH6vR79hs9jpEX3yGvDNkeXkWM8/YuniFWnUev9Nn4dgMtm0Rv3SV\nN3gtNsWAf/2bv8m9D97LX7twgTm3gdEfYCYxVqVKKjJikZFSnPvp5BJSiLP4kGKK4/iAltT7/rWU\njj6W+rjrY6isrcMoOjmInlKflfcRqL+Vw7wst2U5URTa4YUjQ8Wzlz/T31derBSYkFKOgSodFE1a\nRMaPoTscvaJbIEcdGi2E4InHL/DE4xcOrv3i//drE++Fb0CBv+997+Opp55ie3ubEydO8E//6T/l\np3/6p3nPe97DL//yL3PqVBFGCHDffffxnve8h/vuuw/TNPmFX/iFIwVBrch6GJyOwHUkYNv2gRJT\npRyLmuc5ru1y99m7WV3bZOgHeLVphOvCMCEM+mzsbGM0q2S5ybTl4LgO080WhmHS6/YY9HtsBwFp\nHFOpuszNznBsYYFZK+f2+ioPnH+Uf/Vvf4Uf/5EfJvIDXNNGZmkxKJYkTGOyJIJoZBoqpVc2AcsI\ntbztXs/zrSuyskJTE1D1qa5MdeeUCt/TI2D0sSnH3OrPUTnEC6pLHDxPxXWXQ/dGZuYod7h6vk5/\nqXFTyEwItQNwvF7qwI9JpYg3zkFKRJ4hhEHFqfGHH/sEj154AscxSfb2aFkm/rCHZwqiro+o1dk1\nXI6fuId77j/PF59/idOtBmG3zebaOjUErmlhmwLhWHT9gJScqekW1XqNl7/2HIZpcWx6jtW1Nfx2\nB1cIiGOwDIRt47YspOXhVlxWN9dwKh63tjd58uydzB1b4uKVy5iLLba32hgZSAlBEvCON76J33/u\nBU7Ygmi3w3Btm/OP3s3ndj5Hfc7loZNnGcwt8J++9DS3L1/jR9//PmwEluXgVTyCxCfLMiwhkalE\nRClxmpLKdKIcqjQESt4KZZu/hpLR07qO88c6ANAd9kqOFDLVKUNdtvVka0rGdH5cp3t0+UnT4v44\njsdCVYu0AOMnZcXxSCnryrvs0Pf9ENu2D2TXNMd5+1F/jOabAj4qHa+Sf/WZOikrz8dDX1XdyvP3\nqPDZg3fn3wjR8l+5CCH4sz/6nUOoTKdO9FW3HDtc5oLVIIRRSl6r8gcf/wTB7pB7H7pAKCy83KQm\nXKxGHW+mSdIZEEY7JGlMGBaHHBjSwHNdKq6LlIIkjgj8AWmSIk0L6ViEIiXKY/x+j7/xpjch+kNk\nHCFETiRTIpFhYWDkh8+51AVWtywm56KQB23V0UiWZWMOn0ncnY5e1T06fVJWtDo6L1tA+nip3wpB\nqWfrVoDevuLHOFRXvYyjnmSsv0bvGyXcuvf8Ww4944Wv/CnIYkJU3Qa9js/Tn3+WSy9fx5QO9Zk6\nvavXSXpdcIEgRmYmN30fFo9x7s77SWJBR2ScP1HnyksvsnXtCnlvl3zQpeaaZCIjyiEyTO686z5i\nBFubm9TqdUzDYmtzE9cycE1R5AOPQzJpEiRw90MPc2xpnhcvXSTOE7wk5/47zpLZkt0s5MUXX6KR\nmniZSZynGOmAx+56gFtrbYzpBe6+9z7Czi6Lx6c5eWKJ5//4cyw0l7hlGlyqJryydYvuzhbv/d7v\nZrrqYZNhZFkReZLkRW6fPEeYBmEWHwJUhZIelxeFFPM858KTh/dxfOlznzig6YpxH6H3o9SJem+a\njucAgcOZBHUrTZfLMngZr3Mxj/Ssn0UI7XhIbPHDWP3V/bplq4rjOERRdLAxScm6slh0+daVdXmh\nUqhbSgXsjDEFrc873fo1DIMLb3jHkf36uu3EHCWCOhzFoJtdMM65qgarFV6PRmnUmgzThG97+7fw\nr37+l7jHABfB5toGoVujc/smzdlZXNel3jQwTYN6cwbXcQnCkG63y9ZuGwFUKw6t1hzVaoXdrT1i\nitSkO70ec7PT/E8//dP84r/4F3Q3NsjiCGEbGKYxFgOu2qLqP4lb0zn9SU5HPT/6pET1k7IRqnth\n3ON+tPALTcgOh/aNFgEOJoR6no5s1Lio/w3DOmSelp2VIyH+y2VhOyiyOPTaMCz6vSFxmHHj6g3S\nKMayTfrbq2zduoqIQmqzDbI4g9ym1pqmcfIOhmHAzsYup88/yJWXv8rayk3qtonRaDBMYvw8IUwz\ngjRjfnmBxvQ0K6vrVCsVbCnZ3tygs7FJbJnU5qexTEm1VmP+jtMMMsk3vfWtLMxMc/9jj9Cam+bT\nH/2PrK+tc9f5B+j2drBqHmk3RBoSI4f1wOd3P/UJ/uaJR9j66tMkx+apP3SKThzQswyWT5+h88wV\nzj76MFEtZmlpgZW9Lf7JP/sgH/zg/07LqeDvtpmv1kizAD/wqbYaBFF46BDuQn4mxViPO//KxXEc\nkiQhiiLStKApdABmGAaO4xygbj2iSQc0qujzQ6dX1Nwugx+9jBRf4cBXiLl4poE6hUhZGYXesA7p\nnPKzy9SMbp2o58G4vOsWb56nY0AHimMkDUMeLCL66VYKmEIReed53ti1o8rreKRaOqZs8jwfM0uU\nIJS3aeuTXxX1t+8PsEyL2WYDu2ozDAYQ9JmbaVKrT9GYatDZ69DPIwbDbL/zdsmFQAhJtVLBq7ao\nei5C5AyDkG5/lzSISbIMwzE5c+wUcR7zHe96N//yl36Jv/W976biVsjiCJlJTCkxStygapcSUBV1\nM0lZKaSg7ld5ufXPdYU96RAD9ZnO3+n9XUa6uhNSr5OUcmwBKK5nY2Om7lPf052z+t/qczVxyvH/\nlnX4DMWj6De9SGkipInIBdPT03zqk39KHEUcW1wgjVJuXrmEYWY0PJeg1yFKMyLpUZ9fYL5WY2tn\njyeffIiBP+D66g1EEpIJgW1a2LUae702wzTH9CpYtQY3bq+SJynN6RrDXo9Br0PNtXHynO7GBouL\nC9xx/ARTC4sYUzPU6g2eu3aVE8tLtDd2sDFIwoi1azepTlV56+Nv4ItP/QVbu7t0dtvYlmCq2kRW\nTPaCLVZuXuL0nS1cu0Kn78NMjc2aIN9cRYYue1shtmfxdz/ww3ziE59ieXGRNz56ns2hj5NnOFWP\nOImI4hDbcg4hX30x1vv7tRZRIQSe52lyMR5RlmUZg8FgTDZGyu1wtInuOFfXy45snfKYVMqyWMif\nhb4QqXvSdARO9M/KCdbUe/XFRg8W0GV0ksyq7424dPWTkaaj9yv6SjENKnR60lFs5fK6H+gAk8+d\nUx2ittzrdIK+cuqDarkmmR/R39vl3ofu5fbaLS7ceR87ux38OESGKfeeu4eeTDBFlShKWF9fZ3t3\nF0MaDPrFBpqZVos0jQqe27EwEaRJQhbFhEOf2EhIgZnjy/yfv/jzfPBnfgaGQ/KBP5YbrswlK9ML\nRocr6CabGjD9JB+dJ1TP1JWczg+Wkbj6nuIA9WgTHVmXn6HqUEb3xViNx5mrZ8P4xCub1GUrAsqh\nk+mhtultOkqhRHGCACzTYX1tk7/47F9w7o672NpYZ3Z6lrzfIyXCj3MalokvJf0soVZxuP3yS2Su\nReBPEaxtksc+rmES+T6W4yIcF8+YIYl9qo0GcS7odzo0vArdQY8wHFJsosywpcTCREQxW7du4yc5\nT977ILZhgjQIewFf/YsvMNdq8E1veQt/8Ae/xzd90xsI99oIKQiigEpuYPQiao0qz29d4/H3vp3P\nff4L3HHuNFMnzrC32mbh/nNMf6tDqxvjRDntnW2kdOh1Qs7f9SDtcMAv/Ma/5/vf/x6kJTGSGCuO\nqToucTaeFlWn1vQ+L/5+DcexRmup8ddTL0gpcV33kGVXzOeRE1yX/fI4l61BJQvl6K3RwpCPpa8Y\nIexJETJy4rN1WkS3Csp1KwcAqDoEQaDRKCP+OwzD/ecWNIoQAtt2DuaDGgM9IiyKorFjDY8qr5sC\n100HhfR0haMaVY4pLq98egfHgU/VcoiyhDvvvIPr1z7D3Ow0ruuRCAMZprzyykVC18IfpFimjVep\n8OAD9yOkQZbl7O3uEIQ+3W4P0hQha6RSUql4mFKCyAnSAM+tsnTqOMKU/JsPf5j3fue7aNo2JClx\nlJBLQIAhJNn+IcIYBrkAspwkKlZdYYwjj/KipStzGD+FfVJO4rKFovdn4TBWiFcJ+2QHpp6hUL9e\nbFEfn3j6GOh1VY6ao3wb6v5iHCn66OAEdf0IuaNzXSRRAlISRCG/+5Hfh9Rie7dLr9/HQOL3fWqz\nTfKgR5pmRGlMvdXCtSXbazuYzSovfuVLhJubeI6JbQssz8XEJApCpOUwMz/L8TtOsXF7hdwYYrg1\n+sMttm+vMO141CseMkupSJcsTLByg87OHq+8fBGj0WRuYZHrL7xE06vw8qVXWL7vFN/zA+/jU5/4\nBDPT09iWQRaEtKSF7XnkSUqfmNXeDkG3xwuff5raO2dZPH4Hu9t7nL7wAGvPXqS/sUfVtugFPlXP\no7PbIxcpjz78KP/Pz/8Cf/u9/z1n5mZxnSrx0MdybNI8RxiiWDCzHLJsPx2EAVLsW6MC+ZpOTLXz\nMdsfq8n+HB0sjOb64fSrR/1fzseTZdnBHgb9HYVit5BSHNA5I+V7mKIp7h3NO112lVJV8tZoNMmy\nlCQuHPlJOpp/WZZBrhy5OdVqtZDJJCHLVOI5tau1sD5Urn/LisboUdU+ZXEra2A8Sd3h8ropcF0x\nqDKJo9X/102K8iBKKbFESiRjMCVztTpGknJr9TYyMTCFzczsAubxCsJxiJMBw8GAIPC5fvUFHMeh\nUZ+i6pq06g3mZup0Oz0GwwF+kuJnAY7tMFVr0KhUgRzRy3n03AWei77CMy+/zMOPPEwjSbFNi2Ee\nY7lF2lBbSKRhEEoYJjFSCFyK/BFlZagrYN2E1IWsHFZV5vNUn+iCUTyvCF8b70OBYZiH+luvjz5O\nI8R8OHPi4WRUI6RT5jD1cS8moIoyUly7PPS+ScXGwnBr/KennubKq9vMeE1qzSVWdttsX71KNhSE\nWwNyGbFnZAiRs1yvQejjmDlTlsn27i7dzg62MMhnWniNJgwynMxiEOcYlSbG1CxeL8aymgyJGa7d\npB4KqlGAWUuZPb1MHmZE611MXJZPnKLj94l6bYL2NrdXbhJEAVdvX+MDp3+IKA5ZPrXM7sYm040K\n7bpJP4xoRQZJkiIcm1cuXub07Al6V1dZ29nAeOwcwncIr+5gTi2SBhF22MepSFIX7CSjkZv4uyHv\nePBNvPTsZT7ff4b3vOvdTFcqJNEAYUuiLAKRYYoMgxwDCbkgxSDBKM7azGImiMT+mJhjIXWGMXnj\n2uh+pSzzAwpFt6bL1J8qkw4UKTvxR5FZCWk67icbAYJRHRQC1wGieq+u1A/ql6ZkeYY0JI7hYOf2\nwVmk6X6kSZ7lJKk6IHx8J7lhgJTpActgmvY+Hx+NUUdpmh4gbmXR6FbIUeV1PdChvOGkvBKWzb2y\n0tcRvJQSS7qESYpTdZlqOJw8foytrXUeP/8Y6ysbvHrpBSzPI8xTpptTOLbD3LElKpUKQ9/HMk06\nnS6rt2+Q5TnVSoWZVp1KY6pAeUOfKIjY3dqm1+/RmmniDh2++Q1v5F//m19gamqK+0/ege/71CoV\nhsMh0hDE0iAOQ4QU2Oybgfu7NSUcEsgy56+bhToVUnbC6H2j949StGry6M8VQpLtxwfr/Vvexq+E\nTTczRwvD4djh4ro4pOj1RUWnS8rcORzOST6p1FsNoljwF5/6U0ynip9HTE/VsaIYw7FJHRtp5oQp\nJFFMvTXFMEwZBG1OnzpFGAzZXr+NKzOSPGfY7xElCY1KC1yTqudw+tQdbG5uEgU+D91/H3Ea0ybB\nXBqQ+312u9vEvYDZ2hThtElSsRAzNTrrG0xh8eVnn+fk2dN85i8+wwd+6Afx/QBpwBvf9CZ+97c/\nglupcs9993HplUuExEhLEvgBSIFxbIGKaWILQQOLtGKzttnmznP34GYhfSG4vraKZQucqSaeVSEJ\nI1oyw+jucufpU/yv//NP8aN/9+9w59kTWFFETUqyMMaUOZiSRAhyBHmWFagcyMVrc+C67Ol0yohn\nPuy4hMKy030zOpVRnt/jERz5gRWp36crXJ1S0Te3lflpZV3qekQPiFBhs7ps6gyBDp4OInH2w6HV\n81QfGYZxcG5rlmWaczdGiMN8v6KBdFr5tcrrng9cb0DZlFKf6SE/ZeSpT2wpLCAkDmMSkbO0uMDz\nz75Au7PDzFyDuaVpTMclTGOGuz5RGLFy4zrVWg1DSizbwhSS6UYFz/MwTZNOp8P2uo9p20hhUHUr\nTB8/gWEI+sMBg6DHxuomP/j9P8hnP/85mpUK880GoR/RqtToBUP8PEGaEjsrDqNNcwhFjhRgZOPJ\nhcppaBWyVUXnpdX/5T496A9tYQAOztosj4NCymUaRv8ZhSAW3ysjpzJNUjhNRzK08GAAACAASURB\nVEhNH9MyLaPXUbW3/NlRCjzLMn77N36LY40mmVMlFYJrly8yZRgIr4ZZdRCmoOv3qbYa1BpTtNt9\nTCTd4ZDN29eoWhKSEGG6pHFIHCf4QYzdaHDqjjM4pgFRwMMXHsSVBns31vEsk8bsDE1vCeeWTZrE\ndPba5JbLPefv53pnB1sabLx0mTvuPM4rl17FrVR445veSJCEZBIMy+ad3/EdfPbP/pyYnLnlY9y4\ndg0jzXAdm0ajgWzWuPuhs1x75TLTd9xBMD3FMIfhc8/xxoce4IXbt5nCYm+vR8+S9K0hMi4SrDUr\nHv1Oj//tH/8MH/3jj7GTDnjs7N3YdoVhZ4Bd9YiyjMzISWURcijT/Zjoib1dFH1Phr4Yly023eIa\nKdHJud91C04pUxXzrcugLmO6HIVhePBOfVNb+UAG/UfNN/VsfVOTUtijqBGdulULSWExFtx2SpJk\nh94TRVEB5LQoFikljmOPLRSWZR1E5pXn4WuV102B64Oor2zliauERT/MAMbzEqh799ptvFq16BBD\ncs/dd3HxhefZ7WzTG1j0ej0s28XyXKbcWU6ePHnw/MGgR7fbpd3ZwzAMwiijWpvm3MJpotQgCCLa\nu212Njfw/WKjxMzsNNMzTYRlsLm2xZve8M185kuf5T3f9V3IYUR/MMSwLaI8xnYs7DjDyCDNM+I0\nw7EsLC3SRhcsXRHri1t5QMtn/+kKXTcJ9SiUo4r+XkVXlWNlDeMwBaLqrWdOFEIcTIgyMp+0QKgN\nXboCUJs4XguFbG9scv3Vyzx07kHs+XliU3Lj6a8x36iRWjlbO5skqcBttrjnwuN0BwHrncs0qzUw\nimiJeNBnqmKSOgaEOVkGm702liNZzAIq/pApz2V2pomHwdUvb7O5cRvf83Dn5gtrKwq5srLC4ukz\nfOWlF7E8h87tDe5bXODa3h5fefar/POf/SCpAMMySfIEy3VYWF7mybe8mS9/8Uv4YYzRqpH7ARXD\nxbZMNru7nK4Y5FHE4MY6XqtJVPEwfJ+Lzz5HOPCxgpgZt4JZtwgMQTaIWJyZpZ+nLFQdOjs7fM+7\n3s2v/t5vsvrqdb7r7W9ncX6RJByQJTFGTnHuq8jJjX35Ojq99hgPXQ6tK1Nkk5SmHoE1Dg5GuXrU\naTi6hV1G47pMua57MIeUwtXBng4W1WYa9Zle3zIHLuU4I6BHipTbW6vVx2hEHZiq36rtvj8Ys3T1\nsF+d7pm0I1ovr+up9GWErU/gEYqTYw3UBaNsolmWVcTTZhlJnGBKyezsLFEUs3zsBEtLJ5DSpO/7\npH7O7ZXV4h1C4FVcTNNiemZmX6BywjBidW2NOM6wLId6vUK95iHygnrwgyF7O7sIE0zLYGttnTtP\nneGD/9f/zT/80R9DZjlWEmOakjiIyJIMS0oyKTGkIEtSwiwZoyYmOfj0duqcsFKQupDo/VtGKhND\nxUY04UHRBW1c4ecFP6shg0mW1Giijh9+O4k7VxNIR0u6Ga5CHI8qX/j0Z8iDkG6vjcgTrIpLlviY\nrgFhRGpIogymWguEucNGt8vi6bsx85Qrz36ZgR/S9CoIYqQwkBSbLFzPwWnU2NnaZOf6Lc5fuICd\nprzwtWe4Y2aaZL5JuLdHv9fD8RwGSUDrnjM4rRk6tzchiKk7LlnV5plnvsqP/fjf58y5s0RxVOTP\nyTKwBFme0pieJkpzatMz3Ls0y8bKbUR7gEgFcRTy6af+nHc++ha8JKN7a5VLQZepwKRmSL75ice5\n8fRz+GFEO+oSuCZObrBy6zaxCbEpkRn0+32+7c3vYK/f5iOf/CTf++7vpOm6mKHAyjNknpHkGYlI\ngSIl8VELp45q1XiVwddR41z4YRijN9Q9Sub01BFhGB4oVLXIKz2hv08HD2UHpW7dFdQEY7KrZFOf\nX6MY7Ri5v9eg4KlHSH+0GaeIN1eOR/29CnyWQxaLnccjkKLTiDq78FqyD38FFLiqYDnaBIqGqtAc\nfVDUfWqVPTCdbIEUGaZj4wgBUvLYI0/wiU/+MaZdRQqHeqVOo9HEm/MwzcLDmyQJ/nBIGIWkcYLr\nOdRqNRzHodvtkvbadLu7bG+vY0qTRqNJo9Fkbm6apWMLDP0+27s77O3tkLsW3/d9/wO//8lP8u5v\n/zYs04YgQOayOIjZLt7pyCKDXi5G6BVGlokeqqX3i8pSqCPrSear6if1WYEcxnOojDg7Dr6nC56a\nVKNS7K4s7tXNZLW92kA/S7HMVyohnUSNSFnsxizaq8ZVYlkqRG2yKXn9pUvUbI9Ov4OXJaxe2cLv\n9ahMZdSFSW45NOpNWvPHuHp7g53OkPvvO0F7c41UGJiuRzgMcT2HPC14eyjQVHWqhZnB4sISlQyu\nv/ACHjnS9wmyCK/i0Rn02OsHxFWbex56gBvXbhKFEdHAx7QkH/mTp/kH/8tPcf/DDxc5uin6yrQM\n4iQhTFNsx2N6YZ7d7R1OLx/HtkxuXrxEsDfEM02cisXu3jZB2qXT3uL2zgZxbYZ0tsV2Z5fW/Bx5\nZ4+GCYZjINKcqueSWxa5bRD6ITWnwmDoszy3hFnx+D/+5c/xE3//R5mxHUDgGRbEPgYgpCDNjlYe\nZUtOD4VTP+W88mq8kyQ7cDYWERoKZIj9+Gy1hdzcV2gKcYNlHa7TSGGOOziVjOvZOsvRUeW5UqZn\nhRAkSUyeRwf3mqZ5oGiLthY7PgsFrW+vH7cQ9IR0hdynE9893iZxaLEsl9eVQlGdVBYIfWJP4oL1\n1deyrAMlk6RFCE6exoAgN0xs22boB7Sm54nDnNWVTdZu72JVrILzNgws28YwJKZp4LgeGCbt3gDR\nH5IkCbZjMT1zjIpXA6Df6+MPh3S7bVQGI9syOXHiJMMwZNDpkRsWL1+7zvkz56gIA0MKpCWIDcji\nBCvKChRmjnu9lQe6XPS+0vtnkpNQR7zjpuN4fLj+TDXxypEk+rP19+rXRiaxQD/NXD99RX1nUqRC\n8f7DedQhP1Dcutmrl2Z1CsersNbbJdnZZri2TmTA7TBkWtjsVTwu3H0/cZoT+RHTrRYrt26xev0S\nZhLRmGoRi5TuoE+aD3ENizCOWTp1Eq/WYPXadf7a2x4j6PW4ePFlmtUKjl2lNdWgn0ZsJj5+EnHX\n/N1ceeEVBsOIWr3OznDIqzcu8/d/6id44MLDxGlanJ9TrA/7DS/kotcf0Bv4LC6fYHNtm0qthtts\nkPQCqrZHagguXnqFNz/wGJ3tHdLb6wzmMuyKST/0OTHbwqy62MQ8d/MKqZQkMiQTkjQX5AKC/pBa\nrUrQ6RMNBvz4j/w9/uzPn+Ktb/4mWp5HLkHmgrrlEMVxEfJ6BP+q7z7U5WzSrl997BUFoctPWb71\n76gEcCNr8HDEVlkp6/OhHOmi5HsEQiYf0KDXzfOqY5+N6KAi787oXrkv/3IMhOl0jP6uOC449HJd\ny33yV9qJqYqqZHly64OqKyj9R/cO25aDyAANrTXrNaZnpnnx4kucWD7NiRPHmW7OMkh8Ot02Ozs7\npN0Ux3GoVqvkCBqOQxJGBMGQKIrJkgF77Q5SGlQqFRzHwa641KemsG0b3/cZDIYEfoRjOfQDn/MP\nn+e3fuvfMfPe93N6dg7bMojThOL8ILAExIIiLldrr+6hLzuBVD+o+3TqRb+njLB1tFAWkuLekflb\nzmpYpkDK13TroTxmulO6/Hx9vEembTbGY+ql7OBWpR8lTM01MXpt/E6XuVod38zohxGdXh93tsXe\n7i57bZ/5Yyep1etcv3YJKw/J04gUcGtNEsOl3+uwNRgyPT+P6VbY2dhmqtqkNdVidzDEzaC7vkFi\nWdjbHl0Hhi5Mzy9x6fmLWFj0woj7H3+Eqys3+Hv/8Cc4/8TDhHG6f9weRdbKrPgdxzHN5hSXXrnC\nwPdpphnb27vIPcFdd93D7SCjvbJOfziAHJ69+BxveeSb2N3e5pWrl3Fn6ly/eYMNY5WHzt3D6elF\nNjfX2QgGxBKa1TrBMCCRAstxCJIIMzOZsapsXrrBO9/2LfzG7/57vu3bvxVncREPGHSHOJaFMI/e\n9VjenVuOKFLjrY+d+r+s/HXao2xJjuRzfLONrpTLzxFCjNEtOlApo2JdpnSLd/z94w718gKh3l8s\nMiM+uzxvyn1jmgZZdnifRFnZH7WIHozFa37637BMqmBZYJSy0jkpdV0pA93czzNJnqaQZiCK4Hos\ni3vuO8sffvJPePzxR9m4ucHmxgq5adJoNjlz5jSVSoUgCPF9n16vz3A4ZDgc4DgujUYDx2wggCAK\nCcOM3Z1tfN8vFLltIw2J57pUqzVs26IiJL1uj5/56X/CH/3HjzH31rdQyQXCEMVOv8AnBQzTPEDw\nqk/Kf5cHsGyJlBPhqz7Uw6JGi+Bog8w4kj6sHMsTRF9Iy5znUfka9DEuL0R6fYt2jEfTlE9NOQqJ\nbA59ZrKc3dUtamlKSgRRjuPYhB4szkyTxwF+e4eBYbB++WUGgzY1T2J4BoE/JBE20q7h1iWiMUXi\nuXTDmDTOsasOt1ZX2L55AxHF2Aj6QZ80CMGu02y0WL98nVnh0W/3mT91HGGZZAY89sSjtP0O0nSL\n9grI0ow0V+FpJsOhz82bN2k0mqRJztKJk6zcuEHYG2JVK+yGA2wDRJqxvrXG1toq9504yUp3m+e+\n+hWmlhY58fBDZKZEtvvM5BY9w6BvGsRRhJHmCCGJk6igB2t1TGGSpykb12/zHX/9b/LsM1+j8oTF\nlGMxN1VnMPSRR2ycOko+ynOzHKWip8uYpJR0xTjJylM/esK2MpDTfSb6YSiqfuq6EiWdZinL2Ui2\nx+VQHRyht03RfeUAAr3+Zd9WmsYH4YwKpesWr2EY2Lb9VzcXSpkDzw+E2kB3oKlVsdwhk5R9lhem\njCEFUkBKRpqGnDl3iuQTQ4K4y+KxJjJr0RnEhHHM+toKlm3j2A627TA/N4NpmAyGQ4IgoNPeQwgL\ny7KpVipUqx6VWhMpJH7g02m3Gfb6+G5CmpoIy8c2JdJP+PyffIbW9DQvXL3E+QfvR4Yhwo9whIGw\nBHGeUWzZHCECnbdT18tHyOmDWkYjEx2VqIgUdRL9eDpNHTHrSEPVqdz3Uo7yJE9CLapMonv0MEnd\nwVpOiqUL/WtFz9z7xGNceuZZPGHi2QZkEXG3S7fXx1qYxSSj296hbhuY0ZBwZ5VgsIdZd2g2m7jV\nCkEEaWpi15osnj5OY2GO21eukvYCKpU6r1y5zN7NW8xaJv6wT+xKasfn2Rv06F6+gbk3JJUxb3/n\nt3LiiQtUji/wZ5/8OMQpmVn4OSTFuZVIiSmU3yfjheeex3E8bNPBdVz6wZDTp07z/Be+yNKpZezp\nJuHuLmaa0qzVuPjqizx874Mszc9w+vgJ3vGOd5BWbTZfuMzLX3ieEydP0qi4dFIfQ9pY0sTPc1zL\nBlMy8IeYpoUQkqgzIOn7vPXhN/DFz3+Rex6+B1F18eoO1fBoBX7UDuBJcqjAVxlslRF6WXbVc/Q8\nQuUcQroi1/OJqPt1PaEjW5VkTRU9gKCMsMvAYZI1qtdZDzJQi03ZOT9Jf6k+LCv8v7IUSnkH1FHp\nHcskvhDFLr2ikYqv3c8fYrhIITEMMMgRIiEVOVXX5fHHL/DMl7/IXafuJAliGs1jTNVrVOarGIbJ\nYDhgOAxY39nZV5oWrVaL5YVFbK/OYDCk1++zvb1Df9DHsW3qjTr33fcAju3QbrfZ29ul7/foBQE1\ny8KRJnfedRe/+bH/gFfzuHD6LLZIi8N3pSRLs+JQiBIqdRxnRKdo5iCM704rm5cqxElHKkWM6qjP\nFac8zrkddirqaEinOvSoIeM1eNJJk1XfYaZzhVJK4jgsUUZFbvDCstqP2phQvHqNIAxpmg4iC4kB\nhEG9VsOcmSPwfUgzluYWuHX1CrnfY77pkROztXGL6bllLLuKIxzmlo7RWF6gEw9ZPr7M3Jl7uHX5\nMiu3V2DQx7VMhsMhTm2KXp6DNLDjjEW3ztu++W1sJjk319e4s16n6TQY7PWJmymmsW9WqwVrv429\nXp/d3V163SHTUzOEYZ/6XIv2zds8+OB5Pvvlz3HXfWdZjUKSTp/OoEfi52zv7eBUKvz1t/8NZmZn\nubq7ztr6Gg1pIoch0zMtupkgHsY07Aa5zEnIMAwTPIMEgWd7OIaFZxisv3qN/+47vpuPfuaPiOo2\nx+fmqIisHJykTcJi+3ieZqDFdSuAoEBHIRsKPXNI7nSFVcgFB4ejQH4ojXSZX9ZBThm5j4CH2gGq\nO/tH80d3upqmeThiJBvpHA7yqkCWFnUsqlDMMb1uqq1qLhT14qAfdPSug6QDNqFkGRxVXtedmKqj\nddSnh/MUiigau1dlPtMHrOg4gRAJiCKENQXyTCCFRe7D2598G7/6ax9m+vFZkjSlu5Ex6A1omz6W\nU+SBMAyJW62QJCmmNAmGEb32Gqa9hmUVJs2pE7Pk+Qx5njPo99hZv3ZQB8/Kma63iOOYIE4wqXD9\n5hrf8tZv4/ataxyfW2amUUGaGVmWYMpiI7NqzwFa2M+NbewfhYXIyfKMPJ98pqQqo88y1FYM07T3\nOeoigsQwDtMhutJWP2X+Xb8+cgYdjnzR61ZW4Lp5qOgf/ZnFGJv7z1PHyVnEWYwpJ4tqeOk2d80s\nIERCe9Cm5xvsxILm9HFIGqwPNnAtj1ev38b0u8xXc7Kkx24QEWIQkGLEbaJ+j51+G778FP2tm2QV\nm4FhIfGYsVzuWJplL+jAVAOnMoU1bNGcqmO4KYuRxZUXX+ZaLrh99RIrv/47nHaPYRt1hN0miSMs\nLPL9HC9ZWijy9vYOt26uMDd/jDCRuF6Vtc0us0snSTtt7l48RbbW4fjJk1y9dZ3BrR0MPD5/6xbv\n/+f/GLm4yI3NdW5euUbSj2hNT2NFKc7OgCUzZTNL2DNCNoYx09NTEA2JsxjDgH40wDMM/CCn0fB4\n8dkXOHfiHp575grRmRRjeZqmMTkPR5jHiExg5hKZQ5QGY3N6FIY4UpoKcClAMYZahUQgECJHMFLy\nSTYCAcoK1X1EuuxJ6YxZdKNoFCXf6kzOcaCiA0Q9YuVAXuX+nEK9E0b5T9ifVwczUM2Asfmpz41R\nHcb3aejzQ9Vfp6KOKq9rFEqZT1UncOgJXKIoOhiYJBkPgRspdXVtPMe0YRjkQmDaFvEgpVKpcPXq\nVZpTTe6++xEqbpU4SWh327S7bZI0Jk0TarUqrUYLyzDpdnvstXfodApKxTAMarUalYpLa6pFtVIh\nz3N836ff77PXbhe7qlyPmakmURKz12lzfOk4n/3s5/hb3/NuhkmMQBQ5qy15wIGlaUKWZ/tJcQp0\ng5BIBJChYyLVxqS0hTfZT5xlmiOnSNm8LXOMZVSkVn/duVJGFer7oJL3HF58x/n3w7sudYSiEnip\n+9RGjrW1NZaXl9nY2JgoR51BQn+nzfx8E0tIbENw4aEHsLwpbtzcoHaiQrjaZrB1m7kZi+3hHvEg\nJ40sMlMShDnVSoNmbQHjXIObX9rBq8zSH+4ys+ix1d0mri1xtd2n1mrhOhkvv/xFbHMRryaxjSFD\nr0nUk2ynJms7Q56/+BXe+He+D7+yxzACNzdIswxDCgwtvM71XGq1Krt7eywt1ZFS0vIq1A2TKBdc\nfeVl5msVZmSLc/PLrMeSGxev857v/D7uqs/Qaff53X/3W5yZP8aZpRNE7Q4r3T3m/YwTC3Nsbawg\nKh533XUnW7dWmPOqxCRklsC0TCxTItIMx/Hwk5TEMjhz+hQvX3yBY1OPkOSTMXg48DGkAdLCEBJj\nX9EXiLGwXvVoptF4G6T7cfYGIyfiSA9Atj/PlY4o88eTZEnfpKZTjXrRLVWlP8bffRhs5Hl+cOqU\nTg0VtNDhowv1UNdyBNikUqZNlXWrip5q5Kjyuinw8pZxKDoiDEPCMDz4HziklPWiKyT1/5hjTwjy\nwMerVbn7rru5fvMG9957Lxur10jSlByJ63nUKi6WbZOmGcPhkHZ7B0FOHEfUahUWFuaRUtDr9QjD\ngCRJWFtdpUheb2M7NkE4xPUq5AjCKCDeTQmCAMu2sU0Tz6nw55/9DI+cfwjDtDCkCVmOYUqkIbAw\nyfOMKAoP6ANpGOSySHylo5lJPJkQ6jBkFeYkxnhDRT0djQ5Gwjsp+X/ZxCsvBur+o7hrHZnD+AQV\notgYoe5TCP3YsWOEYcj8/MJEOYqjPm6twk6vR25KnKkmp+45y8r6NuefeBA/2ePm2gtMTy3QC3fp\nRykN18WQDlZzitqZU/Q6PbxIsHnrFTr+HlXHI82aBDsxZ2pLzDeWSOtNbrZ7JD2DE/ZDDFhjb6tN\nY3qaTdsmchKGwYDexm3uvfMYj775UfKajRlKzMzAkPvLbz5qv23ZtNsdpqfn8VyHNI0xHJO9qEcv\nbHPm8Ye59rWv0r3Vw2nVYKHBuTPfzJ3f/BDdrQ0uv3yJ6QiqnYCk0mEgQvbyAe2r6yx1djl+fJHr\nsc+tm5dpWB5x4BOLnDjOyfaRZBgEeK6LNA2CLMGsuDz60MP8zu9+lG9/57dO7HMjzyCDMN2nAFBA\nYD+HPwlCgPn/M/feQZZld53n55xz/bPps7JcV3V1VZvqaquWYGQYWTSIlgRCjERgJEwsTDATK2Ij\ndmEhxE6sNBGYQWIXEKZnVsMs0sghCYQsciAJqZ26ukttypusykrz/Lv+nP3jvpt5MysbEcRGiPtP\nvnzvPnPP/Z3f+Z3v7/v7/pRdwC150RYPIbBUIeeQa02aFYJZSlY440JMeFrFYD0f3l7a4E6nXbXX\nnQnJKs5dJUaU/8P2uooS8tv53VV44x8LUnYGMdVj51wo50s1+Hk+eLJ6fM8ceNXJVjnQsLWqZlmG\n53nbVtedjqK65SifL1/bXDEtxdrKdU6cOMGHPvJhXvCCFzA9E1APGqSZptPpE41HKKmwLYu5mRk8\nzyHNYnq9HhsbfQaDAVoXUfz8/DytRpM0i4nCiGsr1xiOBoXTkxD4Hq7rY1sug36/iMwHA2anZxmM\ne1xeWWP/gb2gEyxZqgSWN8xg25OkhywMWm/CRtsTPtUbvAU5FeNqWbISkXODoVfHaieTpYyIq864\nNM7yHlUX32r0sttEKs+pfk55lP8nSbotWSulxHVdhsMhUiqydLyrHUkzxq3VQUuu99aYm5ljY7RB\nZka4XkJ36FP3DYuWxfLYw5o9hkrBz3KsqTZT+w9xKjqH7TrcsuATpDb5RoJbs3jbT76Fs08+hmVJ\nXvFjb+SZlXUunV3G6qRM+/Dhj3+KTmrAn+XSpWew8hF72zP85rvejW416Pc0ddcGtuAmuZlvEHiu\nR61WY2Z6CqMzXMdHC41rO4wtxdTSIisX2ojBiLAzoDU/jTvbYmHfHs586u/ZOH2Bq8+d4Z5XvgpM\nRpYnJDJjGG7QjBycVXAaHgePHKIzGpFbDrk2ICRCCzzHoeHW0FmG47v4OgVbMh4MeP0bf5Q//pP3\n8bpdxtwRCiw1EcEC22xRT8uFdxyOtmG7UkqEFBidIYXCkhJhKbQQEzx5Cx/ehF7MlvZJaVPV3WDV\nxnYreCnzRTuL42C72FXVr+xsvlKsKduDkKLW4UbxOCFulMLeeU55PB+Lpwxed17f8x3fcy2UqiM2\nZnt3DsuySJOtXnTVFa0c6CpVTqrtK6EUAiUEUZIwNTVFZgwvfMEDPPboY+xfnMGxPTy3juvWadRq\nuK7PaByRpWN6vS7apLiuzZ7FBVzXR+ucKIzobKyxev0ajuPg+x7z83P4nk+cRERpRqpz+qvXydMc\n3w3wHY96rUaURoRJzONPPsXUwjxOnpFmKUqCmrBEyqa+enNswJAjKbTFq9FBFUOrRhdlsqU6zsAN\nuHVpICWdqTx2TphqFFFyvqvnV+Gw6lGdHDtZJjujqDJhVUx2gOJ6XNfFsh2Gkw4vO490OIIwJpeG\nKdfhxNFjrHY3CgchBdeuXkHpIT0zRNXqOKaObWlM2MX3XTztsuC1OTLb4HT/Al7us2dqjpv3L/LN\nr3waYaf40zOcW13h3NoqJ158K92Lp1m40ua33/mf+NaF5zi1vsxGb5WWNc2//ZE3ENSmiYXPYt0l\n7F7DeGrzfpY7KSZywklUtPJrNNt4foBOUkaDIcO1LtMH9nDk9jv4wgc/StNxiMx5XnjTLXzi//kA\nL2ruR/VDfMdiebSBK2tcXVmm01slTHpEKyPun7mLKQzXL5zDXZxjddjBtX0Cu0ae5EgEyhR5pWwc\nYXuKLElQxtDtDHn5a/4NPPT/3jDmwiiSVJMogxGFJIQQArKUsuuMwRTa8WnRB9K2bSxhIbTBSINB\noo1AG02apBhRUPY2ITpzo1PeaWM7o92qbVd3p7s5/N0KbqpB4lZEvr15evUoA81y7lnWdjruzqBm\n52s7zxNCbDbCKH/Xd3Pi39NCniqeWsWMyghsi+kARYRavncHi2KzQKKCNeki228wBL5fVD5J+JE3\nvpE/et/7eOmL7i+YJf2QwWCNZnMWiUc9qE1IGZpMJ/QHXUKd4Loxjm1jOxazs0USs9fr0u12ELoQ\nc/c8D7/mI22bwPEY9IboLCXNU6LIoDyb6elZglaDD37ow/zcT7wFlcabRi9EoT8spUAg2YIgd+dD\nV7Gz8kZvacrcqBW+G5VrNwil+v8/hglWf0s1wi7PLYoVCly0hHOM2S7YX0Y4UloVrrAs1OVkju24\n5FrTak3takdWmBANV9FK0FzcQz42ZLpGMDNHX3v4V75CHGasiRq5VpD3MVbMOI/xmi0u9DbY6F7m\nwHQDfywJc81auMbqk5eYqrvcsv8gcQfaqs3Df/8p/vB3f5dX3H0XL1+8HYIUKxC86MhxPv/pD/O6\nH34zt957H6kLab6Onbp4UhFLhZiIIqGL6lKdF+L9YTjm8qVLzC+keK5LomA97BMqOHn5Mnka4x25\nibWzl4nOLRM8/AT333EXqWPRicYMyfj28jmEhPHqGnkeE4uIoe9ydbjG//OupwAAIABJREFU4enD\n5EnKE088wYYEx/bx/QaWcmk2p/DdAG1yWs06o0GHPM9IETSm54ifR1I21QYjBcq2QUqU2MpNFTkn\nB8tSJGm82V6wYK7kKFPMzSQpNE4QE5kMoVBCIycMF53npKaMkMtIurDroiCtnBdbjRu2onWDEFs7\n8cI2q7a8PZFf/VvljRffuVVNvBXIFInYKvRSPOaGeVH1Z1XopVqJXv3+6qLwLzoC337hW4+rzqF8\nrVpyWt2i7HRA5SHMVgwqKbg7eZbhBT6WY3PvPffwzUceYWnPfqbas0zPBGAUnU6HwWBATtH6qdmq\nYdk2tbo/gRSKSZdMtl/NZoO56WmCWoDWmm63y+rqKqMwRBpJq9FgZrpwPJbtMAhHjOKYIKjhOi6n\nnnmWY4cOooTCCzzC8RB7U0SoVM6QIEDsWODKMXIcp+IcS4fOpmOsGlB17HeOJ2ydX26Dy+eq7ymf\n2/ne3SMjvS3K2cL39LZJVPzurSgpTVMMRW4ACr60el5NiAynFtCPU/qJ5pGnnqWXaeb2Z3QHQ+bC\nFQ4ELVydk0jNVW1zPgyIbJel1gyB6OLeNE124C6Wn/wbao7g4KEl5hfn6K91OX/mCnOWw+q3vsy7\n3vajpD/5On73//wtVlvXOfPYM4j5+5g3FsoRvOqNr+bCWhfpg0lHpDokF9PkZqLzQuHAjQGBIPB8\nhsMBnY0e165d48mTJ3HmpxgOh0zXGsiaTyoMSyeOszSzl97FKwyWu6xPrfPRM4+w/+A+5mpzDMkQ\ntqB/fQUdhUg7Z5DkfPHkP5AJw9GFg9weNHmms0bdrePVfKjVuLB6lUvXV6nX66RRTM22Obx3H1K5\nnD1zAW3t7kD8xhSjOCLLcnKTF5Wm5NiWjbBscgkGSYoAaVGyMzKd4yGL3aRlFXz0SZd2o3OSKC4K\ncaQsckphMpFjKHJRWbaVjC853yXHvPAX+SRg2LLz0vkXjnOLRljaWtXZVm26nEt5nm6z8/IzS/ut\nQpPlubvtNnc65Oo83gkFPV/UvtvxL6KlWhU3rf5fvF5VuCv4mDudThlxlvdCsmOrJSW2ZSERxGHE\nsaNH+R8f/jb3veD7uXr5Kq6T0Ki1mZpqMr8wSxTGxGlMrnOGgxFROKZWC7AsiyiKSJKYLMsZDvqo\nCVbreR5B4LOvUccgSJOU4WDIcDREKglxVLBLpGAUhzxwz/387Rc+x9HDR8h0zjhKkNLCsi3yLMNo\nDaYQ2geBzjW6UvK+SbXapTS5iHJ3Rg03ShWU41+lT5WfvTOnUF0Ins9x77yPeb4Fje2Gk1cXY8ey\nSbJkonlStPZK0xTpWEglGQ53x8CfxWIQZfRHGUt1zVTaI+tfJstPUYs6nJVNTLhBs3ed9TTk0Xya\nk6ODNN2ARv4Yt0yvcGUt4elzLu1ayvXrXRprkvWV8wg5YnpxGhHM8+g6cCVBDJ/j3/38K/D3nGa1\nf4K//VqTxx5Z51f+93dzobtCbvvoXCII0EqQezW0GSGFhPJeTLZWzz33HMLAiRMnEFJRr9Xpx0Ma\nh25m3/weZvbswZ9qUXMDWjh85cOf4Asf/wRpd0xrZob7XvNKpvbvASmJTcJzzz3N3/yPv8DLU/zA\nYUzK3598mMGlaxy/+VbunpmjG8fEvQ6HbjnE3S+8h74wGOVgcoOVGhrKQwiLkYFUP4+UqV9Ucw56\nXVwvIJNpgb9HhSJfnIQANBo13CCYBBcaaVuYXKM373tOnuUFjVBKHM/C8bzCEecaz/M2W4xVNVSq\nuZtqxFy1s23QaiUxWKWsVm1wp81v4ffb7bv4/kKQa9O/bNr07kVt1aT+TumL6lyAG6mM3+34nrJQ\ngG3Y005IoIjGksp2aSuSq+K25fm2XQiiK7GdX5knKa7nok0Rms9Oz3Dinnv52te/wQte8ABZkhHG\nI1avnaNeb+J5Hu32NM321GSbOyBJE4bDAWmaEgQBrZZHs16fYMKa4XDImTNXSDKN67g0Wy3a7Tau\n6xYVnf0e3f4GYRiiLAfbc/mBH3g5v/rr7+Q/ves/4kgBOiWKQmxV8GKVkEWPQgRaaIzeak5cGkop\nZF+94YWBZdvwv2I7uL0CrRzjavedKu2wNO6qklsV165ihzudcnGIbfe3PL8KoZS/LY5icvLJRDUF\nnz/PSbK02FE8j6l+a2yTaQ/HDwj7Ibc7mmMzhnZ0Fses8IlnF3lazHHi2MuIZURvbZklSyLGPU6d\nPktwq+TonIMTfpOOn3E2GzByFtnbnGE6jjmwNM9fntJ8jX389gdWeZG6yu++2CFuXmHKP4KvJa61\nyBf/4Vl+4Zd+hKunn8XOcjJVo5dGWK7GNhoz4bIrpRCqaGH26GOPYlkWF86fZ3Z+jtXVFfbdvA+/\n4RCZELIME6d04x7rWc4LH3w1l9aW6Scp/+s7f53Es9nodlma20M/HbNw+ACWNHzuL/6CeDgmNim2\nsnhm+TxaZxw8fJBjR49xaW2N73z9q+zt3kbr0CHsVpso0yjpkaNAg6OszeYOO4+xFowjjee2EIDl\n+DhSTCSBi5Zstq3Y2FjjmWcv0u/3mZ+fZ2q6RWB5ZHmGFAYpLRzXQ8lC9S/JUozJsYSF5QiSJCOY\nLABlw99qkrFsiLCTwlp1xuV55f9F27Ibg8Cdzn8n660arZfP3whn7t6kuRrklL+5mvOpBmQ7v++7\n8cCF+afG6v8/HkII/v6Ln9xWMl91ANXsdZZFmwNW3dpUb9oWPusXANdmNrsYANu2idMEqRRSKYSU\n5J7DQ3/2EEePHGHPwgLuJMkSR0lRTYYgCOpEaUYQeDjOFtfVGIPJi752xhQwRokNC1TR0SUMidIE\n27GRSuF5Hq7rkk8i5tFozMpGl/bcPNevXuHlL/1XmCzGxCFKiAmNcLJdo3DKupKVL8drZ4eeYjy3\nCmG2sz92V2GrGmYZZeyUqd1p8DvhnPKztvPEzaY2RXWR2G1HII2FsMpFIUWLQoN9HMbUa00Go4T7\nH/hXN9jSkdf+B8ZhQjoaY8UDZqwhR6cNh6ZSpuyI9ZljnL+muTRs0EnAyTsccBJqSvH0ygbtmYAX\nz4x5gPNcwCZkipA2OsvY42pq2IxrRzgbHGIjDLkjX+H1fo/mfavU21OM87187kyDb11r8qK77+OF\nezLa1iphPeC68DC2j2USpDFIJMKISeGKZGOjw9PfeYbFxT1kWaFWqGVMKlLCUYyIwJM+lwddNvKE\nNE042J5h2gtQriLXFr5TIxdQb9fxAou6K/nchz7A+ulnaTkuvXDEWBqwDK3A5baDN3N0/2HCfkiY\ngTc7T/vAIepLBzBujf4oRloWjmORRyl3njh2w5h/+fFTqNxgZwYlFNpxkarkV5fOSyOVQEwS8wLo\n93vkWcR4HLK4MIfvOVy5dAHHtvA9ByVB5xl6YtO+tbXo76ZtUrX53eocqs6+9BUFrdbedu7OYKU8\nr/iu7VBt8TlFArrqrwq73t2VVufoziDm+SLw6ly7875//byQyvc0ifl80VsV7I/j8AYnUm5FYDvl\nME0Lx2spVeBjUoGyEFLQqNWJkrhw3jonSVNe/LKX8jd/9Ve86Q1vwJnAI1PTTeq1FlmmCccx692r\nXLp0HsdxCIKAWq1Gq9kkqPl43hRRFNHtdtnY2MCyLJqNFp7r0Ww0MKpo+rDR6XDx4kpRpKQsWo0G\n87NztGYWWB0MWNvosLx8lYWZNoEfkKcJUhQTXQMm12Qm34SToEpz2i2bXTj8KqZdGnl5lJ+jVNGz\nb2vHUwjje55XwQG3JDFhaxHbjZu/tc00KLWDRlaJSoqs/XYNimgYISTUagFxFmNZipmZGbJMs7S0\nZ1c7mtKCpuUQOhb9xKVnZjnZ1zzZGVOvKVoXP48FRJ2ckXWMvruXdJjx4hOH2OPu4eTTlxk+26N5\nS5sXvPYY68+u8Nxjp8lai4xuPszljTPMXPogb1iEaSfl0NE76Qws4o1Z5rwV7NpZjh17MeneV/LU\nN69y2+0xS0tXWBtrlu58KVc3BnhOQJZmmNwUMIq0sJTF3NwcC/OLhGGI47hobZB6hJQGZTnQz6jh\nkdYDVj1D5iisUYw9jhiOBkSjjHOXVzhz7TIf+8uPouMxrmO4/eZ9zGuo5Qosj76XcSXZoBYN6D49\nYHB9jUW7yXR9htnGHONLKxi3TW3/NHnNwQ0CLpw7zc17D+w65tJxUalGxzFhFGJcge04GMqmvhDU\nAoQQpGlEkuQ0Gg2aLYc0G+PXM5TrgqW4+djtBJ7H8pULXL54ASk0c3Nz1OsB2Wi4rbCvhAurtlT6\ngpJ+Vw0A0zTdZE1Vg8DRhNG0m18pnXiZW1Jqa6eZpukk4t9qbFJdSCxre+PuKl5fpceW0ftuu9Zy\nTvxTE5nfNQJ/+9vfzl//9V8zPz/PyZMnAXjnO9/Jn/7pnzI3NwfAu971Ll772tcC8O53v5uHHnoI\npRTvfe97efWrX33jlwrB1770ic3Jbtv2phhNdeWB3TVTysGpVgmWA1IVkt9KXtzIPw60w0ho/uTD\nf4HbqHPrgcM0tUvN83EaNQZJgqUsapaLciwsx97E3DqdzqbztCyLWq2GlJIkSciyZPM3VG+a7/tk\nWUaSJERRVPxuoxiMhview9/93Rd56799E41agCUMeZrhuh55WjS1cF0PCsLNVuQwcdKWXfw2JttY\nnWVINAUqZyYUNrCtIvKQgNBFclcbyHKJMRmFIkGOELrg7BpDloGQNko5mFwj0wFCOhjLIbdsUi3I\ndYalDMokOCqBPEahMdrBQFFBawzCdrAtl0xLQCGlgzFFAU/q5Hi2xfLly5w7fYZrK2v0BjFRLjl3\n8RKNRov3//F7b7ClEw/+B3qjYUFFywwi1Vha0Aqa9De6xH5IlmtQDrbtkmcJKh+z0JAsNQymd4mD\n0w5TNcVy0oFxwr0HjtDojdHXN9C+zXU3YNyYxq/VuGna5WAb9rY2aCwMqdsKa9hCeNMs9yUOszQ8\neOTyCqveHRy995Uoa4SXaJRKGQRjIuEREEDWQ8gMV1sYDV3LQWqbunQwWqPzHGtSkl7SQzVFEY5S\nClsE2FKCHnDm9LP8lz/5cy6cucbMdJs7Txyi11/GdlyeeuocifQQgcv+dpN528KOxrQCj+N33U19\nboknzlzBas2D22D/oZs4fvcxkjTllqO33jDmjz7+DGma4Ps+g+GIWOcEfh0hBEmcIIQ1KYlXZHlK\nvV4wtJIsRUdFtyvf90nSlEynuG7R6b3erGNMTqfTZW1tlWY9J44jtMmp132kFCgM6BQLEDoDrVFS\nohMbISVZluJ4DmEU4rp2UdlpJrtlCsqsJ2zykg0kZPHYFIur1jlKgJyomRrkNt9SJk1LLfDqUTZp\nqEIy1Qi//LtTOrb0aTsVCcvjjnte9s+PwN/2trfxy7/8y/zUT/3U5nNCCN7xjnfwjne8Y9u5p06d\n4oMf/CCnTp3iypUrvPKVr+TZZ5/dtkptXez2SHJn0qx8XJZYl99bXdGq0pKls65yysuVzLa3nG95\nruvaCAFvffOP89vv+T3uPXoHlpZISxWyssMBSZwWXbqVxPOKogvHKZrNuq5LGIb0ej1GoxGe59Fq\ntZibm0UINp8fjUabi02tVmNmZoZ6vT4pdkgIgnlWri9z//0P8IlPfooff/ObyNE0603yLEMqPWk0\n4aAxZHlGnptJJzSJpSwQoOytTLxUEiUURhSMHEWRN8uzfJJJkAglJ4q7GiEjhNYwMUopLNI0R+cG\nS1lYskisaq3Bb9Ko+XTWV/GVg8oSLMdBI0mlQ6Ia4LkE9RZxOOnSY1KSJCZOQrI8RUhNpnNGvVXW\n1tcZj0f0Bi7ReMTjDz/MeDwmSQ2OV8Pymgh3nuVetNOECqPPbITxi6o/mRPGPWxH0pgSHL3tGI7y\nWF3f4PTZSyQxSOFhUPTGOWmcINM2q/0Rc1MuK8ktDPs5K2GDVx9t0ZDX6HfXSZM97PMF0/Ish3VM\ns+Pix4sIL6fvjiE2eLWcqJYQiDXUeMDe2u0sX6tz+fQTHDl6O9rVDKM+DTxMEqGZxcqmsKzzhFZK\nnu+nFgFqRKaLJLbR+WYxi5STSW40mAxjchITMRhFBJ7k0JGj/Phb38Jvvet3WF27zsULNguLLdbX\n13Etm9jAMIxoHjrEYH2Flq1ILUliCWYPLPHG7/t+3v3eP+Lhk89w66238bVvzXL33fdyyy5jbkyK\nUoI4HtNs+KRas7RnDysr13GkWzRFMYay404YjhgPIzCglINtW6RZSK1WY3VjgNYJ2miyPEJIQa3m\nEQT7mGm7KCUZh2Oee+4ZkiRmfmYKS9nkSUwWZ7QaDXrdHs1GQBRGZDpBaoGQkE2qkC1hI5yJxo4U\nmLyMfivMks1rkxMHDqARcmuXmOc5YRgCAs/zN31U6bSTZAvureapqsFc6duqvquKvZe/55+KgX9X\nB/6Sl7yE8+fP73ITb1wRPv7xj/OWt7wF27a56aabOHLkCN/85jd50YtedMO55dao3LKXK1v186uZ\n4/K56mq40+lXBW/KRGc1UoZK5V+WkxuDQvPKl76UZ575Di950YtZu3adLErZt3cvlmNjpERrw3A4\nZDgcsra2im07OI4zcepFB/syoXjhwgWgjLoDZmeDzeROFEVcunSZPC+ia8t2cGyYn1tgbX2V6dkF\nnj17gaNHDtMPI3Sa4E06Dg3HQ4RdGqHEVlX6VIHzl9suJVWhFKeLZKDGYLTBsu1tOYIixtAImRXM\nAAxSWAgUgV8jTQqOuiDHsQUoi462EGmK41nYJsa3NaNxl5VOxFpk8cxyjzPLPXrjjDAcbuGLTAzV\naIQslOuULLaolrLAqhONx4jmYRpTNmGUkBlFbIr35m64q30q6RF4LlEckcRD6o0GWdJhdl+LTA+w\nohQdjSCPEMZCC5s4hRDJ2PFRwmZq6iDhTJujfp/VUc7pCxeYve7x4vm9HHA7LK+GrFzdoOe3CDsd\nDtnrZI1ppB3i16ERxaBj3JYGexk1NaQ/OkFj7xv45qmPsjBzCquxj6B2M2q4yrTK6IsxlpQEWQNP\nJ0Qmwc0zEpETq8l2XDigzSRYSWFS6afKvIjU1FpBATMiufX4cf7Vy76fL3z2c4zCCFggiSVJkhGm\nEXajSW8w4KY9S/RXLlN3bLx2k69/+2Ge+OhHePTp03TimKdOn+LqlTr79x/cdcwPHNyPUhJLSbIk\nQQjJaDhgaX6aNEk3Of2DQR9L2DR8G8/3iuYnabaZWFxbX2Zqaor+oEd7qsV4HOLYLkkSMjM7Q783\nKGiEwuLWoyfI8pSLF85DnmArhes0GYYavzbHOOngeDbK+EXQNoGkjCmYa3bpFDMNqpCb1qYgCiAm\nzCdjikhnEoljKHZsE4ds2/aETlx8ZnXnD9s7UFVhxwJKSjcddjlvyyi7bCBehVrgRrGr3Y5/Ngb+\n+7//+7z//e/n/vvv53d+53dot9ssLy9vc9b79u3jypUru76/xLPK6LTEscujuo2oJjVLJ14mx6o4\nbBnJVwdhexJva3tjOQ4y09SUwwN33c1DT7yfZ849y80HDiHilDwKERKubqwy1Z4mCHyazQZaF53M\nh8Mh4/GQ4bAQnwqCAAClrAlzJSSKks1rabfb+L7PwsLi5rWOhyOE5TAajZmdW8T2fD7z+c/TaLfZ\nMz9Pq1Fn3OviORY6U1DJUCdJgjYGOVnBs03qoSGXCj2h8KHKQokiyjCiKHSYjHIxTloiJrAGUqEN\nhGmIbUksS6BNirAM0rJxtMc4HDLXbvHUY48gpeDwkdtoug4f/dCnWQ8tIlOjOb2E5a8WCWFjIbEw\nWpIlGqO3AgCJQGuI4ghlT2G0ZhSnjBOB7XgoxykaYOTPQ2nLImzlEWY5lrSRaA4fvoXz5y8gZUbN\nBKxudBglI4xyQXnUWjVybbBsB2Ur1qKI9WvrSOsae/c0OXDrDMtnr3De1JmeMhxeinikF/K17n7i\nwTQvnOqydzXCiTIOLU5xVAWkaZ8kbePWbZy9NiMcvvzkNa7EFvXx4+TjDifXr3HPieNYmUUj0IxM\nh4gWymSg1hiYACEdhEjJsxw9kXTNdSFQZrQp6gEQaAxRPMJ2HSzLI45zfMfibb/wM0zNNPmbT36W\n7186wuragFozIer3iYdj0t4A0SxogAvzi2gN/f6Q7zzzNPVaGztokSWaW2+9jcU9S7sOuRCGK1cu\n0qgFuI7D1StXWF1d4/bb78APaniey2AwZGlpHp3ryQJjSOMQ23VwnYAoiZmbu5lOt8uBfUuMw5Bm\nPQAhCGyPNImp+XVGo5BavUaaJGQ57F06ROD75GmCQnBl+QqDcULQcMkoGmYgFZZlI3OzGVnLyZgh\nCudc0JGLOaBNUR4vpUQYhTH5JvsLnW466i34RGwKblUdbJalm76m+lq1pqKcuzsJGFXUYWdh3j92\n/LMc+C/+4i/yG7/xGwD8+q//Or/yK7/Cn/3Zn+167k6cqDwKbZGJqM9Ed6PqjKs4UZXutlMbAbbz\nmXeubmX0XZ4jRNFgdJDFOEiSUYRl2bzhDa/nve/7I370wTeQbgw4sLiEEi5Hjt5Mb6P4rZ1OZzOh\nMj09vUlx0rqgEXY6HRzHpVar0263qdfrRFHEeDwmjmPW19c3sf49e/bg2DZJHDE3O8fl5avUp6d5\n9Wtfxwc+9BF+/u1vIxwPqLkWSZoU3VwyjRQFO0UpUEZMJGkn0UApYmUEbilGVdDIAUM8Dgu4RcnJ\nFq+IQFRem1T+gFCSzOQgIJWaOAkRQuMomyyMQAuWL1/lO8+Mufve7+fRJ5/mP//HPyRM4NDNt5PE\nOVNtl7VLZ/CnG1jKRkkHJSxAgld8T5JERFmEZSksW6GHBW6qhcCShppnF+3njMFVEunuLm0ajq4x\nNbVI05fkWtHthjz6zcdZXJxlOBpi1xSZcHHbPkJK0ixHWaAQ5HlEEuYoZaG14eTwKPHFDV522Ke9\nN6eXrLGMZD6PWKr3uZpv8M1em49e3kM9iVHrHrecG/AjSxm37DEImshxje76k7SnDF/9xp9z5IFD\nuGmMpZ8kz4/wWx8c8pY3P8hs8izGTujYGqMMyljkSmFnBrvcJOUaZSmyNENZVoGxasOETITr2FhK\nEI0zWo0ZorBHblLe8GM/ynBk8BqzaKuG34Y7lvaQDSIWplrk/SH75hbJo4zxIOTC2Yu4wifXigP7\nD/PmH/8J7rv7Xi4vX911zM+cOcNUu4ARCyjScM89dxX3F0MYhdh2AT3EcThhXmlm5ufoDoasr19n\nfn6eJA6Znp4ijmLa7aJJSn8wwHNctNE4uHgzAVEUFQleYWHbNuNxoR2vHJdbjt2B1obHT/4tnluU\noVtSkmSmgAWNQWLAFBG1lAJpqUJIjqJ2RDGRndUaIQuKpzClL9LbdvtbWkPONqdbPLcd+tiphbIz\nmt7pp6rOe2cA+nzHP8uBz8/Pbz7+uZ/7OX74h38YgL1793Lp0qXN1y5fvszevXt3/YwPfOgTm87v\nnruPc/eJO7YxHqpZ2ZLMX17obtuNcsDK6qyy2XF1QMsbEUURwiuwMZUXTm5pYYHXv+H1PPPMM7zu\nZa8k7g9J04SN5ct4KsC1PYQxoA1ZlpKnGdE4xPd9XNeh1WjSajSI4pTxaMwgjguMVUosKWlNzzA/\nO0ev1yMMQ9I4IYtjsiyjs7FBq9WiO6kCvfW2O3j62Wc5ccetxFmM47voNMdVdpHc0sUM31y0pAQN\ntrJBTooc0qQoINo0AkMt8EmzBJ3n5HmhL46W2MYvGC5WjlAU5dRKIyyL9U7CcBgyNT1P4DVRSczS\nvlv49le+wV//wQdozu1lz/GXIJGkoyGKHjUxojFnsY7cFOYSUheVe2iUYyMdiUgF0lNI26ItfcAw\nHo4xeV4U9MitoodSF37nEY+u08lHCGnj2D51V9Dau4/XvOo1fPGLX2LdOEiZksQRyoAtQeagsxRp\nBLZyMSkYFBuNgDPJmMala7zoiIPpZTx5WXD7zB3sUxv8gLuMqQ/4un0PT6VLOPZe8t5ZVltD9g7G\nSNFhnObYDYtnn/kSrfo0er1FnDbZu3+dE23Nk8NjvP+rF3jb6xdpJmdwTYcsldQti1xfJkw8hDtL\nvdEijmOM0ZCDQRR5i5KyBlhSk8cpngxIwoQ8M2ghyIXkF3753/PFL3yDbpyQ6TFZOmLB9YnHfepu\noS0zs7hIpz9mY2PEK17+Wu5+4Yvx61MgJGfPXiKM4l3HvNlsMTczh+87nHrqKfYu7SOOM7wgINWa\nQufFMBj3cG0HlCIOU/IMXMdjasoiy4qmwOPhuEjwT6QuGrU6tmWTZhlkBlsJ3EYNU/MZR/EmRJpl\nGcPxmOF4DAIOHb4TKWA4HNDd2EDnCe1GgzyLQReyzVrnSGGIUo1lFWhinmmMLppdCFE0WdmCUTRC\nbZeELZyw2BYQlr6orMQsg82yVmUnm6RazLPTYSul+NbDj/OtRx7/J7FQ/lkO/OrVq+zZU9C6Pvax\nj3HnnXcC8OCDD/LWt76Vd7zjHVy5coXnnnuOBx54YNfP+Pm3/8QmCb8q67iTVlg672qyoIzM0zTF\ncRwsy2JtbY25uTlGoxElHW59fR0pJY7jkOf5Jm5tWRbnz5xhtjnFjN/ED3ziLOfO229j9eo1vvXo\nwxzcs5dmq8Xc9Ay9jRGFQkRxY+tBbbO58Wg0ot/rkyTJpDFyg+bCQsH5znNGoyFRFHPp4kUajTq2\n7TA/N1dE72nC1WtXybOUURyRa4PfqFELajx58kmWFubYt7TAOInQcULTs4ijeJLYLYwkTVOUo7CU\ntbniF7z0ApoajUabieBi4kscx8YISLMM13ERqcAoGMURylb04xFPnz6HFh659hmP4NyVDYaDyzTs\nkG6Ucun6kMUDx8EJIMlIRj0c10XFiuvXLjLTrNGaDtCkZJnBtgK0tElyQRQbjHKR1Oh1Y5Tr4sV9\njM4xwtBoNck1JHkBlSFtbBHsakc//G9ey0ZnlfWNdbq9AZ2NIeTzqSzxAAAgAElEQVQeT596AnSO\nbQRhlOFJizxLMTonzbNiV5YbstSgbBdhwMvWSOyEk+EQ+0LIPQvTBE2byyt9HBL2+X3uDzRp+zK3\nhxdozxwl78xwPlJcW3bR44R7j3jMDhpcPHuOWdZpDfej5BTWeI1jTsiDhxb46/UX8p6PnOaX3nQT\n8+EKc5aPNmNC2eQLX3uYL335G9TrNU6cuJPbb7+dAwcOonVOmmXbdk/xcMzc7Cwb6yGgsVyPeqvN\nV/7u7/nLj/8eP/RDbyKTEqM8Ll07R+q7TDdbjBKLWq3O1598ikO33sFP/9K/54GX/GvOXVwhSQED\n3W6HO08c33XMm/UWKyurrFxb5tixoyChPTNFtz/ADwK0LvTxHS9gOBzSajZpugGdTh+35hMEDU4/\nd5pbbjnM6soalrDwPK9ohBLGaDsnqNVI9BjPlcRxShRFWMLC9Rx8z2EURkVkzqQiM5VI22ZmqsHB\nvYe5dOk8Fy6cod0IqNUdtEmK4MGSWLibO2Hf90mTpIAcKRaVIrs/IUxMIMpqr00w2yoxS18lxHaU\noOq3dvLWS3pjiYmXi4DWmvvuPcE9dx/f9HV//Gd//ry++LvSCN/ylrfw5S9/mbW1NRYWFvjN3/xN\nvvSlL/H4448jhODQoUO8733vY2Gh0Gt+17vexUMPPYRlWbznPe/hNa+5UVNYCMFXv/CxbatX9QKr\nq9HO50tHVDr/8ihX5hKKKV+3bXsz6i5pQHmeE5ucpl9DTppEZBIyS7CyusZnPvVpXvbilzDVajEa\nj3HsOjpnG3xTRv/VxqRVkn7JT3VdFyHEZkVmmcwAsJRCG43tuijLZhxHxGlKb9BDSsGTT57kVa98\neUHDEgLiQhsiSYq/WZ5hWTZZniGk3Pyu8todp+jGY9s2STJJoii52RgiN5osyRgNQvyGT5QlpMJQ\na08jrIC//MvPMhhAPLZIYoXj+hi7T5zl7N1/iNX1HrVGkzhOECYjcEAnQ5JwQBwO0OmYOEqp15tF\nhOzWsJwGWD4pDlEiyYRCC0UqxmRJTBpFuLZNFCUoxyfThY5NlkR85g9/+QZbeuAHf4406xPHA1zX\nYeXadTy3jhAWlnJJElk0U5hsbwuoSICwyDQYoZC2S5pqAm0ROxrLCplK17ln3udgG6acMY3RNWT3\nGsZxGc8sckvSp88CF8U+Tg3qfG05Jawl3NwccWuWs9fpobOzTAWL/Mgr7sKfyRjVQ4RzlP/+9RN8\n8upRmOoyHX+bg6qLbyvaU7fTu36Gq5efQkjBzOxssdvTOXEc0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FhGAe9973uI4pI0bcji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pNGwkyzBgeP0INMnickpWVJiWRafb5ehgnyLLiOOYe/dOKWso6wbb8Rnt7tM0FWkaE4YnLGYL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/iex9mNTcIw5Pe/+Ht89KMf1fS7wRIHd/bwPJ84zvEDn/FojB8GONh0goAkiamq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\n3Yt9+/bh/vvvx759+/CJT3wCALB3717cc889+O3f/m3Mz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CKjRVWzPaXKeal4TgOZuNMVKh8oy93V3Qmv76CKUUa6M1nh8dMlpfp5cXjCdj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1y8chlzqcXWZgcjAykhSALe9oY38ofPPMdxWxDtdBmtbnH+sXv5zPZnqM+7nDtxD8P5Rf78S1/k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b9V2kEn/LVRlfCHm4HigqWxpN25ZTPfBB6aa2Shz9LGXRqAbLnFg+itBzKDyLY53z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fXVV/mVf/dLHB0d8a//1b9iZWWFH/6hH6Lb7dLr9Th//jz9bo9WENJtdxCVvm/nHnqExx57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\nYAMcO3qUM6dOc2xnh9lsJnn11Eoj1NYWR49sybXHk0vb1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9jY2KDIpBnD1Q6V97PxCF3m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\nwNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x3f5b2d0>"
+        "<matplotlib.figure.Figure at 0x116bcb210>"
        ]
       }
      ],
@@ -797,19 +885,19 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "scores: [ 0.93699419 -0.65612102 -1.32907355]\n"
+        "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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3uHXnNs/fvI1d1szOS8x0QuVbgm1YPHnMVpHTtseMsjF/+xf/Dn/53ofMhaCY\nbPCVl7/Io3ff5ejRPa7dusayWbLJBjs72xS5IcsUZblgMCiYTCdIHcvpT45POZ/PaZymGI35w++/\nzbtngiafMsDx2u19tnf2efvjuxS55Qu3pgzrQ85PT3j04CF3bt+icS1BSGzqxJjnQ2ztcK1FIRmO\nhmxubqbmSTFL4pNPPmZZLiEEiqJgdn7O4+Oa2moqV6Ly2Ibh+Wsv4coz9veGOKcYjDdxckEhhikf\nPTXI8qumYk3TYLKY4dG2LaMsIziHyTKk1LTWUrcNznmKfICUkvfee48333wzVRl7GhurN4WIfKq0\nUYEEIWiFoHSW7e1tZCo6evELzzMoct55+y3apsV7we3bz/H40QH/4X/06yAzppu7bG1O+fPv/ClK\nBbQE72ILVJl67oSuDFPEHHVCVD7Bx3Q9rWL9BanNgPOuL1CJLn/XUEyv5ZHHPiRSCKRetWhWyjyl\npEKIedddQaWMzRxSKmfMIvGpz0xXASuFwHmPFqZP0YQQi8dSR9GuL81/+g//wSXrfe27WDWq6nWH\nFH1rXZFSYEUIKJ1aS3bjFSA4ByK2F7iYpiGJed1aKaQQNJDy0v0qVz6Oarx+omn8j1Hin7sC7yQE\nh/Ndlz76nF7ZR3XFBeUaYn9ssf4SVgp6/bn7kuaumEOIvnS2+7vu/11O6uo8IU60p+51bdLFsDO9\nk+Qd+NT6dO14lZr4CCH68mIpZOo+1hV5SDqvzFrFeDSO6YhbW1gX24Hu7u3yCq/gveOlV16iLCuk\n1pg844dvvEU+GNJUDffre7z2+pdZnJ7EImKtaHxgY2uTnZ1tFmXN+ckRhcnYzgt81TDOM7IQUNax\nubVJJQWNVnzh+S/y5p/MmD9+gs1rFkfnfPLh+xgpCbbl9PAJ9zLNm3/xPTZHBW++dcS9x/f4pa/9\nMmPfxvajakBtW3zpWVSx78fmzjbDyZR8NGKoJhwen/L+D96i2biF2crIQsv+aMxzN5/nz7//LnNV\ns7U54KWtTdSi4vV/7V+nKHIeHzxh3jQs6hpdDDk8OmVYjNDCcDyfMd7cZXf/NrPZDCEEzz3/HHt7\n1zk7OeK9d9/l/OQYJQTX93ZZLFp0MSLoc4aDmwzNHtLvUJanKGnIzAinWqplnJtKS4IKtK7G5Bqj\nFLIxseBFZRgjkR5AxV43uaJuY+GV0pAPCmazGb/7rd8DYH62RGWxCZUQkEmJbC0b+YCmrMjGY5Yi\nYKYTHj9hQFvLAAAgAElEQVR+yHQwJATPRx98wPbuVlRyAgajYaySDJbXv/I6Tw5OqK3g+PiYEGK7\n14BlMpngnKVu26h0if3ohQQfWgSJ803z0juXuiXGua6loHVNdPcxqSOmTum7PnZ4XLUQJGiJcF3u\nfSrQEV1vka4CNxb9WB+LXfI8wzbJmCWFGtJa8jE1PDWkW8sW6XOwZV8Mdpn0raZhrWhGdjp5BdK6\nGgO/4rw7/eA8fYdMwtMgcNU7pQOTsUBOidS3J6W3+hDvJTeqL2bza/UCz5LPXYEHQerBvmoIk7R2\nQtypJ7AAgexTdELoyma7N+OTIk/mm5Vy7hL8Y6giXffC5/39XFDs8Xe+v64UApEa1/eJP2vHS0Hf\nMzqBhL47Q3eukHg2t56HRHy5AhkbyYtYVCCkpGxriqLoXawQYle3oR4wnoyp6oogFK+++goHB4fs\nbW9R6BgpP3hYIIVgMBow3Jwymkz4xb1dRIhGo1lUvP/2+1zb2ORLz79AhkQPJ2TZgNNyiRoY3NkJ\n7fyMQa4Y37hBfVZy8OghOzfvsDseY4XE25rbt64zGhQcnh3x9Rd/kdZoDtsludDMyoaAp5CCtmmY\nLRxHtqJpWpQxbA88dfBYEVhUZ2yEnCITvPfWn3N8No9dFgcZTxZz5GjARj5gcXTKzVdeZv/VPZZt\ny9F8zl++/R6uAZFnFIMhQ6HQ2ZDZoqJqIp95fjbj/t2P+OKLX2C2dYRbzBkWGd5AKGsmwzG6mCCE\noVwsOD4+4qOP3sVZTTGakA1a6iZ6fZNJwcbmkMXyFEJDpg2j0ZjNjW1GwxFnZzXlsmY4HMVe013f\nFyFQWnP3/j1+95u/iw/QNg3j6YTWtqhgwFlU27Bpcl67/Rx5lvHu3Y85n59T+4rgBdXxCSbTzE8l\nRwePUFoxGo1xruWTjz+myAccHx7FugcPZd3Q2Ca2SG4tJ6dPYjGNyVHeIJVmMMjRmerRs21aQKKE\noPWxHSxpjiulCMIklN3BloC1LZE+6Jpipf0+rOxzqmW3ptYLXxKPHBWiTkUxqdVUCIgQg4Rd61hj\nDF2fewh0m2XE63WwKvTr76I0bd3B79VaTl41gLWrBlpxmceEbSFFun8ZG3SlFgwr3d31YAqsA0vv\nXf9Z17JXG4NMn8W2xKmgytOP0bPkc1fgJjNEx6Frt7mquhNSIlKb0Y6i6BSp7weme7mrhkd9eW5H\nnUiV/n4NPYvLewx0aP0ptO9j8YIQq2qwjr+COHV6Ky6ILtcaqu+KGDpZp3bCmoX1IaReD6l1JY6q\njUGWej5HaRXRTPBIF0u+q7JMih+KPGc6GXN4eMj+/jYvvfgix4fHNE0bm1c5izCKOzeugXPgHEpq\nBtMpk60xf+v1L9O0DRtbW5ycnXMLwenRIQ/ufkg20PzsV3+WoBX33vuIpqy5c2uf47pm+/o17n70\nMVkWO/dt7m6zd/smi3lDbWNDq9PzBYJAZjKC9+TW0abNNvI853xZcnx4issDr33lRcww5+j9N3nx\n9gucHj3B2cDtO8+zvadpzh9wcHSEAEaPHzPd2oQ85/s/fIMPHzxhMNxgac+Zzx/z4OAeL774BYbD\nIbmOTcmq+Yz97U3auuTlF57HuIrXX/sS/9vv/GMGepvdwTYik3z80UO2t+5waivq5TFKb7A8ldz7\n5BFOKvb3tzDGwdmcs7PHaOWpqxLben7uqz/Pr/7dv4tRBY8fHfPk4JCqqpiXFbPFksxklHXNP/+j\nf8nZfA4hFuqU9TIieykQvmF/MuFn7jyPO1+wMx4yfuUl5N0P+Oj0iPFggqsdhdYMhgXWWXa3d/j4\nk7ts7+zRVC2ZNLz/7tu88MqXcQhMpvHexS6WGgwaBORFwbK21HWJCw6/bPsydNvE7pdFVnQTeA2I\nxCZeq3m9KpHXfSuCrqlX7Isto+McS9eFpKrqVdMmkXqHAFoEJJ62bdHd+k/UiDGpuRYidqNM1Env\nfadK0K5C9llItvPEQ/CphH6F1qP3rZ5ar93PsRe+6hW5EALhupbIq4ZUsLaLk1/tyqSU7O9XpCIv\nEbrq0KdB5Y+Sz12Bx/ScpGB9ekVC9pa7Iy9ilFj2irfrQQcxArweve048E7WKZKu+1g819NIe/3Y\ndeSuRGr639E4fRXV6hhSZzIvZGqeE55Szt1L7AyJ7LYWk6J3B/GekHYBCggckBX52v2FGEzxsfS2\naSxaqdSXQQGe7e0txqMBuZEcHR/EhacL1CCj8S52YlOB4CxKBRCOvedvcefmNUTTEjQsVWB6ew+3\nqNjfnnJ9Z8LzL96gsjXXNvcQtWNrusHG/h77uUFkGbvTKbL1LKuS48WMwdaUa9t5nKz4yMvaFhE6\no6Nir25lUGgWvmW8Meb1179Em0nq6pSbOyN2BoaN0R4zKo4ePiHPxzw+O+bx2TGTwZAHRwccNyXB\nZIjREPKck6ZmsZixmC9RCJ4cHLKYz5iMxyxnc/Y2Jvztr/8cN3Ze5ODhfb77p9/m6PEDFmf3+MVf\n+BlOT2se3LvPvY/us/kz+2CXSCoG2SZtrQk1kAcGgwK8pS4rRgPNzZs77O1sc3RwQnAVh4/uMxpu\nsL29w3S6wf6NGzx8/IQfvvEG737wIe+88y6PHj2KG0Z40aNJrQSDTLGzu8Gt8RTRNtzZ3eHxo8ds\n3b5OLjyjXCGco1Ca6WBIVsSqzUwqtiYTZqcnDAZjyuWCf/Y7/5R/8A9foXWCtlpSFBltNadqawix\nd5C1LaQeLk1T0WmxrmlT8HFbQpLT2JW3BwQ6j3PUWotPsYCuLNx7j3UWY+IOT7jkYCMIzsX2uFKQ\nyTx6pc4mBafQWsa2FkKkXazSVmX9Rgqktq+WbJD1NIe1liDX8qxlh54vERHL6DtKU3R0jo8eu090\nTmcAopKOTIB3Ftwqp1ulDU4uevXdZh+wAm9dYDR6GysQGI/xKwqHZ914lM9dgeNSMFLKnisTArTu\nUHAe0fQaKl6nOVZpOCtEHZsWPY14V+MZFXC3ccDFoKWUa8qSEHtrC9kTMyQF7ELXfvJpZC/XX6BY\n/V6lZvRdUx63FsHu3pEUCnx6uR1V5J7u+wEryknrLFpyt2rp6m1LkedxDIXu0YIEciHiRgWzsh8n\nKR2iPWPZNWBq4lj7po1N7pVktL/P5Pp1IKCVZu/OizR13Y9HhMKJw2tGbLEbG325Vc/rPqqeXKxV\n75H4O+sdUmpefm7Bsq7xDsplxXwxZ6AXvOLO2bm2i1IN7WDAxk5sNXA6nzP0Mcy8nWWMb+2xKJds\nvHwDqTWi0gQpuP/gPl4Kjo3kZtpke1l7PnlwxMLmvP/JKY3dxKkxrTjn6LTBhpxHT55wcloBWwSZ\ns7AzGgJCl1hfIssAdcPsbMZeu8vR3Rmnp0tcKDm9s2Rwa4fZIvD+xw/4Nza/yPb+Nnx0SBgf8t7j\nj3GDQFu1DNUY1RY4I7ChZCQUz40GXM8Vsm45mR+R7U1pxznj/R3c+0cUwyHaG65vbXJzcwqh5ej8\nhK2b2/zFBx9xVp4TZE5zcMifv/E9fvZrX+Xg8QOWi0PyXGM9OJ3RKKhmZ0wGBZmB2la0WkALA5+R\nmZxlU1K1M4rxgKZuyKRBtLHHt1OxhLyuG4aDEW3d4F2gqkqUUuSDASHEPSOd9kgn0WiUVGljkYAX\ndeoTDsJJsI4mcfVa69g0KjdIZbAhNq9rGovSiuFkRNOco0IEA5nIYh8W5aj8Ei9BhctVnbUNSIGW\nqdEVApl23VFBYHQGUuF83DcXkfYbdS3CJ88/GZZmIFKwM27f5mykVHyTkjJU7HfsQ6CwIipwEZBe\nErs3xh17YouAiBI7A/os+dwV+HqT9PXKpL51Zae419y0i8HH9b+7eEx33LpctJCXyVN/I1Z2sL+v\nC9dYD34+65qXXTvSPU8f22WkrI/DZTmh6yW/61/xHCu3r+vvchEFrF/vwon7JjouNZhfF611H/zp\ndi3pft/db2YMTspPXas770UDbLyP26xpw2ZI/Sx8oG5asjzrd29p24ZqXHDrxo2Itpo2ZYH4nrZq\nnaWqKhblknbuuf3cHbZ2XsOHwMHxIZtbWyip+PiTDzg9O2Zza8JoOMIFzaODA6plgzF5zDQQnqap\nY9BRRLde4JmaCbKWjMYDTk5LwPDRJ/epywXDfMhgNODo4AE7u1Pe/ugT/sk3/wCztcGXXv8qTx6f\n8p0/+T7zo5rlwZzt4YT2vKTIVkUc48GA7cmUHJBInhw+pDw958ZkjLeWjfGExbJiOMwZ5BqtwAjD\nxnDI4WLOV157lffvP+bwbEG1POWP/8X/ydHhfa7t7kSFqDNsUyFFfMfDQQYhUDcWoWOATRJ7oUih\n0EqxOS6o2goVYlaFdQ6lM7yP+8MqJWjaioBH6tjHP4Q4fjIVyTjbxsZqTqVt30A4CCJtrJI2bJEI\nkHHXJyEjEreujX3pnYv7Wsq4Z2tZtVhbkYm4kUmkJgM2tDhsBFLPgODGaFCgu/no4ibHEL1861d7\n7kKMw3XgbD3AGULcJ9f7VTpi3IdVx+ZaLnYj7NZ0tyG7FLElbccgCNFtNh3pl7/xQcyu2qhTBl21\nUqfYO/djnYK4qMzWK5cuyyhZV4jdZ7rbmDesIsqwchk/jfBXctFQ/Kh7WfWqdk/TKOLpXM+Lkev1\n41Z0zqfvY73kdr3a6zJl37uBF/h5vdY2df3z9bG5eH8dn9d1Brw4Fl1Hx4tjdtHY9AYnudV96zfi\nprxdOmmRj6mqiizTTMcxMGvbuCu6MYZMG5q2pbVtTPdK7UhlG7McqqaOnRcHcH4+Y9m2KATTjQHB\nVywXZ1y/tUPbOk7Pzzg5O+bw8AlVtcBkGiMDCs/+7iY725txkftAu7BUi4Z6uWQ0yLEehhsjinHB\n49kTHn77ER8czJlVJwzHQ77z3e/xh3/wrzBs8PILP0OoPLOjJ0zHA8rFGVrFMu6t4YjCZIS6pm0d\no8mUwmQM84Lt8Qbn53PUQHBtZ8J0VKAIaAHDLGPqC05OTnjp1g2++pVtppu7ZHnOxvYWg8GI2XyJ\n97HXvXYOfEtbWiwanQ3BerQMKGmwITajcs7RziqM1gjnEcphjMK6FiUEZblECIkxsZWwtW3vnflg\nkWHV4VCk9EXvLDpRf32PbhxOaHzQBNemQF8Uo3KapupjRbapY495rTGZJlhP01RIoVFK0IYaJ+O+\nncGtJySvxPnY8M6HlCwsQCiBlhKFonYRRUsZ+3Q7HzeJkVKnYGeMw4UQMEEgZOxI6F1MD5TOxc2T\nE08vZNyVSBmdkHun61LsTXbGIrIGFztvXpTPXYF36BCeVoydXI4ufa8Qn/V5CKHfnmhdkXSKptuQ\nd/0a69e8DPleVIrrhqFTgJcpqovXvqgsL577MuW7fj/r519Xyt1xz0LaF6X7G7fG3V12DDztYQgh\nyLLsqfu7OB7dO7p4zPq5nhqbbkdxZL/npckUuTRpMwWFGg3idVKxgxgUKYDlca1DG0WWG7odKmP2\nnkVpjcriXovXR/tcu7aHUgrbtFTLkrosqcoKdMCojNFowny+ZHd3m43NMbPZOYvlkt2dTV56+RXm\n85KPH97FNp56tuTVl19mczLlu9/5Lm3ToosNXv7K6+TTAQ8PHnHrxS1ufeFl/uiP/4gPPzpksXQc\nHp2wuVmwsXeHYCRPHn/CZKvAnS3YH+8wNBm2aVGAJfDKl17l3pNDyrKinC0ohCIfFmyNcwwW4cDI\nHKNznPG0LqC94+zRQ/7+v/MN3vvgA4SCcrGkbQClIGiEb9HWUuQZjTMENcK1Ncq5iG6bljYEcqXI\nlcSVcYu3qm7xypMVA0K7ar9c13H/19SuPG03J7De4W3sHQ4y9eeO71KEgE9bhaS9eRBYJCnNL2WA\nlNWi718eQox7aSXRyrNYlmhpkCIi3rq2eOEwefQEntWWtWkrsAABmegQIUS/ZaAXsXzeGINQAue6\nMvukP/pgmyeTOs39gEvbGpLQulKJFoln7dvztrbp4wWrvLYOWKlPefYX5XNX4BepAnh6sa8j4g75\ndWi9O3ZdiV0857qCWv9sXdmsBxnWt41aR7jdudZ/Xv+77l4uQ9IAeddDe+3rryJ9psuaklx/jmcZ\nl+4eLzvfRYV7Metm3TB05+k+X1f2F8dn3YjA5d7Ms56rdS2dAu92+A4hENzTG8x65wgavEvN8FPg\nSWuJ0rFnt0uL3BNSm1mPF9Gt9+n+QghgFBsbE8z2FovFApkb2qZle3uPV15+haoqMTqmSC6rJVIo\ninzE8ek5z728w/lswfKsIhMDPvnoIV/+2i8wXzacLRf87h99n3xc4JVg79oWjx9/yOzMkmXbCKHZ\nnG4yPz9GIBiYEc/feYEnB/e5sbPD1nCMDLCsSoTRyNxwcHqGzDPKpmE5LxlkOVkOG8MC7QNGKKSP\nnO32IGM4HLO0LT/71Z/BLmdMiwydDzl/fM6H9w5orOcLt/d57oXrVItjlM744N4xR2WDdI5rYwGD\nAWY4QWqNa1vcfM5AapRXqRe8YLlYMNVx8xOlFdoYWmdpmjZumr0Wi5JJMcaQ1WrvUBfamFwg6L+E\niF1dg/NY76IhkKCVxLYW6+qEnCVlU6GLjOA8ztcxXz9XtC5uvBB3FbqcQvHe9TGZmECQvE4ft1vz\nUkRvJcSNz51N311M+YvUUVSjMeVQpr0sU9l82ulHyG69xCClNhqpRAp1rfbWbGyLD3EjEGstP05N\nfO4KHOgR8Tqv2j3QRQ7oRymui0rssp/XEeWPUizrsk5LrCueixTIupJ/lmJ9lqG6eL3Lfl5Xjp1c\nhiw6hXcZ5XJxvC7zEi6LIVxE4hefY90AXvSQnnXtp/7ffR56bxopJE1lYz6wWO1O4n3a5ixEoKeU\nRgRobJtQUbpXrfAujlmW5WR5RtO2Pcc4Go5o6xolJBsbG3gdaZtmWTMcDtkKG7R2yXLmKQZTvIMs\nGzAajsiHDUFIWgwtQ176uud41vLf/w//mMWiIrSB5pNT8J43eQelFdXSsjGecX664Otf+zphpLFt\nhWTKyckT9DSwkQmKTGOdY2kbnFORIpAVrfcsq5bd3V2qeomSltC0IAwkSqTrpjcdZLhM8Lde/TKz\ntmRnc4PaaZybce36HaTJuXNzl739AfVMc//hY54cnrAIEzIh2fvCbfKtDe6dnHB0es6N7S22J9ss\nDp4wKIb4zOJF3Pjjhd09ZrMZp+dntLYlBBWpCW1SpkgsyFFGp37skqBiVooQgUwoRELgTgiC0HHj\nZVuTZTG7xPmulD2gjEppeKSUQk2T6kCC8FhX463HB+IerV7FjoCXiExV0pGrt7FSJNCDAxviRsR9\nQkAq2On498a1CJ9oxNr2O9gDOC/7Tb2FlKsCHQcImZS6T9vudWwESNVtHbdKm36WfO4KvENpWutP\nUR2X8a8XqYTud+sIcP3zy5TGs3hYWNue6xmy/jcXj73YvGbds1gP1q6f6zKU/Cwq47Lfrxu4TwVH\nL6FiLrvOj1P0P+7eekTL6v10vHp3/ovv5VP30LePWKVsEjxZqhOIrHg07l52W1axCv/60P/UFW14\nbxFO9QUTbVvSebUhBNrKxjJtkUqyRUT+g+EACEjvyfIR25uThIh82i1cgqtxAYIpeO/uI37vj/6M\nN977iGVlGRUjZvNjlPVkQqGrIVpJJsJTHh8x0Zrv/PHvsbO7h9SK7b0d5q1nunubxeMPUb5hlGsG\nmcEpwbJt+i26bHAE68mLnExoZBDkWY4JmoEaQJDsTLcYb26ydWuP06MzauUJSjFv4Z33P2BmM2rr\nePDJkMnf+RojY3jjjXd4dFgSBoJmWfJe7phev86ZbTifL3n1xVf4+lde58/+xb9AScnhw/u89ckn\neOD+eMz+/j571/YJQnB4fMIH77/HdDqNhnBjk7ZpaFrPsMho2pT2KhVSeKTzEBxVXSNMhhOWfDDE\nuoq6qRMS9eRZhveePM8xuY6BStuSGY20FiFC3GXKmNiKwlqE1gQX2E/73V4UpaNX4H3AKBVL29OW\nfwDC+1hdKuJGDaUtMamWIfYJX+0fqnLTZ8t1m0EjwTpPYYqYLioiz13VizRfAy411KJOSVoibnj9\nExHE7ORZaPQiX7qupNc/vxiovAylryufLjiwbhC6oOll8qP44Wfd/zpyX+1QvqJ0ut2CLnvmy5Dr\nZQr34v1eZrguPvPFzy7zcp71Ptbv5yLNtH7Ms+SyVgXQKez4v0BArO0F2J+v/1z2/S1kn4WT3O+w\n6jnjCf1uSuvXXE/vDCHELd+EoKlqSGlkEYFBCJaz85KiKDAmB5kRfIDJbbwLfOt3v8W3v/3nHJ+c\n0ZzMcHXDozLmWldNxd4Lz3Nt+Bx3P/qYqj0nm+TM6jM2b005W55x+/aLnJUV4519yrLCG0NQgYaA\nCiEGZluHQhBQyZA5Qoj7LYogqZuWwWgYRyNIdnf3GUxGBAdt61n4ilZANtnk57/2c4h8g2XdMB1F\n42h9YFk3kdIInpe+8ByvfflFfvD2O9w/OKBsa769qDh/+ICt0QiU4v69h2S6IIjAfFbyxS/ucHoy\n48233+L49JwbN25w68ZznByd8MZfvE25LCnyjOPjJ5hiyL/5y7/Mu+9/wNHhY8bG8MKdm1zf32dR\nV9w7OGC6JdicDDg7O2M4HFEtS4q0laIQguPTc6SMa0gajW8tEHusLJYzrPXovGC5KKnLlnExunQ+\nZlkGQrCYlyitCSoWEKqEfFWaX9ZanHfkxuDaJqU3Zn2KrDQGK2IGi0joXaqYlaWySOcFFYOmznpy\npdfo3xUNrDNDneZhBB1/w4OYnVzkVJ/l7sOFophnueWsQgKXKZj1INzFz9blWUj+WfTL08G+/pOn\nfv90dsbT1+/Q4WXK8eLP69e7aLQuU8DGdK/70wr2Ijf/V6F3uvu5jIKB9Zx6ei5v3VNanSvgvej/\nfzGY059DrMVLunz6RKfE3P9YHNXxjUIA/hID3j1rPxoCBORoBBKCTFytA6EYmBFCKMrGIYTj5HTB\nn/zZezy8+4C//PZ3ccfn6LLl52/dYTIZUoX/h7k3D7btuus7P2vt6Yz3njsPb7pPepplSbYkPGFs\nsCRDDAbnD1yENE7SAToQKklXF3R1VYdUuhNEUZ1KaNJNdxoKAz1gCGCDCRjwEOMBWciaZenpvac3\n3Ond8cxnT2v1H/usc9fZd5/7BKRbWaqrc94+e6+9ht/6rt/vt35DSF8mDNwUrx5QmTnFOx64ky99\n7U+4un2J6rTPYecmJafElSuXuf3UbZS0pB5UaQdVtm6+weriLEiHJIxwPQ+lNa4rSdMYR3pIRxNF\nUZaIOTuPRAjwHY/LV19nefU0slImmGvgKS9L8Ov5xM1Dmnv7SClZapzhYHeXfr/DwuICiW6hUOxv\nX+dF2szNLVKtlTk8aDLbmCLwPT77p3+MVopSpcrM7AKbm5ssLi+yuLjIf/jDPySKExzpsLWxjSt8\ndrZu4krJ6cVVVJLiuQ6tXp/LV67SG/TROts4X//ma1w4t8Y3nnuejd1dzt0BKiozOztLGIaUynX6\nYcru7i6lUhnfD4iSmNnGDP3BgKmgSqdzSJrGBL4HpKSpRgqPjY1rzNanC+l5f28fzy+RKoUWw1gv\ngHIUjpA4MiUMY1wnU424noca6r8zudAdMhMC6TACdOFIEAqNMS88WtN+4JGE0cjRLwv1lJkZJkkW\n5dCYIDpOMU6Z8tcC8LW1NaampkbK/Keeeor9/X0+9rGPcfXqVdbW1vjkJz9Jo9GYWIe9uGxRvEiM\nvxW42gebwy/HQM58mlPzItO6fP3HXOuZHGx9Uj22ymX8QLSYW80fdE7iiotUOPnrpqRpMTAWbYwn\nAfhJG5h9qDzezvE25ut3cDHhOUEO7YKt90oBOnOocuUwXoTWo2cy4wajQhnr9XDzMCmwjnwO8idE\n0ug+nWEmcz0UaV2HKE4p1ab4p//0X3L27O305BQvfeNZ5oMKdz54Hq83IDxs8sDaGqlIeX3jGvv9\nFkkyoFHTtG7u8K333c/n9tdpHTSZrk+hIoFMJfub2zz64Nu5dukSp0+t0urcJEmHEf2EgwoTpO/h\nSYnULp4jcUTmZKaFQjgCnBTpeaQqYv36BgftJrpc5r0f+hCzSwukUiMcyanVVbRS9Pt9BuEAv1El\nrpdxXZdTK6cJuyHaERBIUAJXCRZPrbB6epUwGVCueFTKFVqtDs3tbU7PzlGrVdjcXEcITZrGCDIn\nnm67zaDXY2F2nunqNM3mIYN+j/39A5xalQRNpVxiKvBp+B6rS4u0Dw+ZbUyzceM67qnTzM0ucvbM\nKt3+gG6vx+ycw431DVZXTyHwSJTD2x58hNvm59jd2WRr+zo3d28SOC5+ZYqXXnmdnd1DKg9PFdKz\nkIIwDPFLAaVSmXa7hUpSpAOlUokkTjKpR8JgEBKGQ5+AYRLvwPPQGsIwJPD8zJ1waNxgznb8oDQy\nuQ3DcBhLSSOcYTwYYeKmMLR9d0hTTZyEZNY2k8tfC8CFEHzhC19g1tIvPfnkkzz++OP85E/+JD/7\nsz/Lk08+yZNPPnliHfbnX7UdQhyZCuU3hSJVythCpli3XsR12wB2EugX9Se/wdgbVlGf7M98vZPe\nO0mtYf+W76ety89vhieVfP8NgOctdorakW9DlqVeDAE504Obw0YtjILcHHQKxmUsU+/45qM1SGGC\nnI1aPVRjxcfHFE061IdK6WbvEA69Xp/aVINESX7kv/ox/uzLT/GV3/g9Ti+vcHZxBqV6qBJ84COP\nkXYHBDj8jQ9+iN2tHdavXqd3o4+WAb1uj3sefoKnLr7As1cuoSs1WlGIFwT8wZ9/jne/+1s42Nti\ndeUU/eY+rnBxgzKR7mfqjVRRLQVk7ssKP/ABjeNLemGPKOxRdkqcOrPEvfc/xLWb+1TqVXAkiUrQ\nUYRUCYHQVGs+ac1noDXSD1heXsaXDoEQHLSbHKYxNa9Cv9XDFS79bpuD9gFT0zUcx+F0ZYlGZZpB\nf5RuJqQAACAASURBVIB2BJ1um7Xz59jd26fT6VEulSiVAqZq9Syo2vZNms1DlJswNz+DEJpKuUzS\n6xJUS0zVa8SDAefPnmar1WKm3KDV6vDccy9w+sxZBlHIwsISS8unODjs8I3nXmAwyCJxtjoDKg++\njWq1xMLcAhcvXSSMU8p1zYsvvgxacOXKG4W0WC5VCKOYw8Mm7XaHSqVMfaqeOQulMY4eWpNozdzc\nHJknpaJcKpMkCXGUEMUxpVKJKOwNE6FnUR2F4QBkFrUxKJWzhNuOS+wrwEGpZLT+XNclTmOEdLLD\nVSUyKfCE8tdWoeQX6ac//Wm++MUvAvDxj3+cD3zgAycCOBy3MR77HDKpYvifdWMmuojsqlZ6xMye\ndJg3iavPc/7jQDAO6jbHWsQp58tx1cpkyaLIguUkjjhfh7k3f8B6kn6/6Poku1nzjvw4FLW5SAVk\nt2fskFMdAXempHYYTi8ZtDKkA1A2UUvQWg1jK+sR15PpITPnCwP85i+LWGe3IeOAwqCNjh3cJMBL\nPVx8lFCUSh6DOOWlV1/h5//nX2Rnr8V33f8+br/tdsr1Cte3N5lfWaY3P09T7vO2tQt85cXn8Q6a\n1FLFnWcqVBtTHHS7+Hjc7VZRvYQvdTdJp33oh8won2effxlJzKP33EPU7VMtV4i6BwyiLiLwiTWU\nHBfSzMrHL5fxpAt4RCpl8fx59lst3veh72J+eYUz0gXHBZk5quhh0CStTIiDLDqkUhpVUqRJikRQ\ndxp4YQgKyrNTCAE1VWJhqcHZU0tDu3yXclBCA7FK6PX6pCrl/OkFDvYPMi9N6RBHswz6A1qtFjJw\nWN8acHrpLHOLC1x8/XV8z+Wg1cQLfJ65+BqqUmZ1eorZublhbBWFcFwWKwskqWZze5Ner59F08SD\nEK68cpmNV59mdnaO8xduJ1QuO/tNBpsHoFKk0Owf3iyk5e2NHV6/vkEvjmnMLTBVTaiVUxbm5lhe\nOodTa7K0uMrmtS16rZC4H9OYmua+e+7l9OlVUq34i2ee5blnn0PqHp7noF03O+hEoJVgbm6F6cYi\naSLZudlkZmYeGaQMwj6OI9i5uZGdmQw66G4LqVNcT4IHcRRPXIfwn4ADf+yxx3Achx/90R/lh3/4\nh9ne3mZpaQmApaUltre3T6yjyAY7/448OBQBBmRBaYpKEddsA1SRLjdfd/5aETdf9Jt5/0kZNiZJ\nHydx0ycVs5uftBHl35P/fpIEMHb4KMTQSqB447GLrXbKA30WW2yyFGb3wZXj1kpaC3DG25apx7Ld\n35bE8pJWdr+pN8iCIDkSkSpSYrTK4j2vb27xC//m3zLoKR68+wFOnz1Ds9WkH/dJo4hyqcT6+gZn\nTp9hdW0NdzDA2duDdpubnT3Sfh+/UmG+XOGelbfxgarmuc9+iuYgRCcurhvgKYESkn4YUavXM9v0\nJKFWr6KlIBGCfr+PIwSB6xEniqDiIh2PMAy5/a67eeLue+hGEd0kzjwgZXaQlpnyHUmdjuPgSKfY\nW3bIKplAUcaCKo6zODvGQU7rLOFA4PjUqpXRGJ85tUocx2NzMRgMCMOIfjez9/YDn2rZp9vrcdhq\nUatXSVXK9tYW3W6XmdkZlpYWWFpaHsbKjqgEFcLBgMO9HRq1Ct60jyNcBoMBjqyQpJKd7RadVsLV\nqztZSFpK9Lttttf3Cunq4msXEeUqjnTpd7oszM0zNT3N3ffcw0y9jjfXQaQO19OUV159lUZlmuZB\nk/m5OTzfo1QuUanUWFxaoR8f0G63KdfrNKYbxHEWTTT1PdpxxPr1bdbW7uDKletc37zGOx5+B2Gv\nz/zKGkIoairG8wTohO3tDabqVVrt1sQ1AX9NAP/yl7/MysoKOzs7PP7449x9991jvxepGI7KPwPg\nV37tJR564D4eevC+Qg89aXHdeZC0D/syjqtYX5QHCyHEcX05x/W79nX7nfnPPKAV9d2Y1eUBrOgQ\n8C9TJnH8ttXLrTaBSfFW7D7kx9oGwLwq6KTv+Q0g34ei8ZtER0Ubf16NUzQuk6QoNchc0h2hkW4K\nJCghKJdqPPkzP4eTejz+3m8F5dDtt+n3Oty8vMXC3DyXn3+BU+fW2H7jDX7rpVeYCXxuX1rg9jvP\n0/dOc9hpkSYpUbVBtdbgYw98lDe2bvAnT32NnqPoyggRO3he5nXoui4iFQRBCU2SOb4MY4I4rpsF\nL0MwSFLKZYlfrjA9M4NwHErlUnYAmmiMHbEzPOA92tzUUJWUjo0FHNlAmzE2ts3lcvk48YgsrrXJ\nRGQsnYwpn6H3IAgys7spQAjCKGLqwm0IJ0tBpsns+C+cP0MSJ2hB5hbvyMw6R2n64YC40+Psyjyu\n49JutZidmeahB7LEHkGpzPMvvMz2xh6Dbh+BoNfvUa/W6Q0DuOXL/ffdQ7sfEiO4ubPPzuY6N9ev\nEXaa7O7usHbPNI2pWfa2mwSuk4U5mJ7h619/ii9/+UucOXuWUqnC5cuXaazMMDW/RKvb5ea1dXq9\nPufOrTHQKa32ProMpVmPBTVDR3fxa2WE7/A7/+EP0Frz/m/7Np579hsIlbC9sc709DT9fq+w3ab8\ntQB8ZWUFgIWFBT760Y/y1FNPsbS0xNbWFsvLy9kJ9eLihKf/GQD/5d/57WNcnilCZDaat+LsTLmV\n22kRII3/Xmzmlnf3z9fxZkDXXjQGxIz9elEbJ4FgUZ+KruXVNieVSeOaB9a8d2YeEIvmz7T/pD7k\nN8j8NfM+ux2TQDh/r4l5A8fjxeTbFiTTCBmhZR8l+sQ6JtUeSRTx0//0f+SPPvXHlFWZihewtf8G\n25vrVFyPweEu9foMzmCAkA733Xc3l159la4jeObqJeKK5J677+Rt997Py998hUtbW6weVvj4u97P\n05//EwblgNj3mavUWd9YZ3OqxIMXztHe7hHHEamKQTsMkgTXCxCOn6ku0MRRiKOr/MN/8o/pRzFh\nEiNdF3SWopChczrDJCH22OZpzB6L1BorUwaDwegAzz6QN5y37/uUy+UxBsmmGaUUUW9AqlKiMMpc\n7+NoeMCXSQz1kkdpuoY3dOCJ04QwzKIOap21YWg8ikp0ZoGUdlFpxObGOm9cfoHBoMXiXGV4sOih\nVI+V5WIrlGopICiXOHNmja2bNzl/+210Om0cR3K41CBSN9FxxFS5TI8uTz/1Nd7x4NvZ3dmh2+sQ\nJzH33vcA9Xqdmlvjs7//x3z4u7+b6dVpKpVKJjE5ks32JlMln7/42he47757WTu3xMbGZS6+dpF3\nvfMdlMtVpPSYbszi+wHnL9xPtVpDSMlnfuffT1w7f2UA7/V6pGlKfSjqffazn+Wnf/qn+chHPsIn\nPvEJfuqnfopPfOITfN/3fd+J9dhBnkyxF74jnWNAki+jxXwLVYT5bl+z//L3TlIf2PXm67F/y5e8\na7nNodh12xvGsT4W9K2oFHmavpmNZtIzNkjnnaby7TtprPP3mz+j0so/Yz5Hqa90sVfspD4kBdEU\n7b7Y7XLxEEKhHJkFwpIuSpfwnAavvvIyZbdOtN+k09qgm+5SdWG64hP3BkTdJpvrV5HVGs+/+hLV\nWo2b+zeplXwGnTZbL13mjecu8oHv/Hb0oA/rTSrNDv/i7/wEP/LL/4rILdPzAsqVTBUzEzjUXcV0\nrU5/0MEpBYTdHtqRJMOUXuVKiVK1THVmhoN2m6BSBZVmIUnJvB9HGaw4stgxRWs9RpP2OOU9ebXW\n+L4/WqtGPw2MMSZxHI+kv/x8SylxvSxqYq1WJo4z/a6Jbqm0DfaZaWkcx0ReRJZ/NKVeLtHv9/F9\nn8FgQLVSpd3uECUHNBoeH/yOR0lTTbvdodPtsre3x6DfYzAo5mQrJY+dnT0+90efYfnUKTavXybR\nKbdfuI1Spcz5UxfodUIOkw7nz57h3MoZ4jii223T7jRZWlzMkhCnKTdee521xVVeffZF4iih0ZjB\n81z2D3aZmZtmulHjvvNrLE1VcVST1QurPHjnWTrtHlPTM6QKLr7YptGo027tsf7G5f/vHHm2t7f5\n6Ec/OprMH/zBH+SJJ57gkUce4fu///v5pV/6JdbWMjPCk4rtoTgJoIrUE3kwBEZBjPKlyMKk6OAu\n/y57oU8CvzyQGcLND7wNfHkOOQ/8hZLIhPGZNMG3sqrJ15//vWjzKno+v/kWzZdjmVUVjaMBknG9\ndHEERLtNk9RX+b4VbUZF0onjaLSr0UIRa0GsPaRT46tfeRGtywRumZ2DyyTtXZwgoeS5yCTCISWK\ne5xduZOecNl64zLvvusOpioVziwv89X/+1PM3XY7f/F7n+X5p/+cd77nER6YWeWZl1/kkXe9m7/x\n6Hv4vcsv0k9DatNTbF2/Sn8Qsrq6gEp7/Df/7U+xuHqK65ubXHnjGlcuvp5Zt4Q9nHKZBx95hMb8\nAoetJtJxERwFTsoyoSeZZYNzXNLNvAMnW1WZ8RIiO+swYS4ya4tx5qqI5kd20cP7BkM1S7mUmda5\nrksSRaP1YM43hNQjBtE8HwTBUJ+f0ut3kLJBFMeUKz6pqtA8bCMcQRTFVEslSBPiSoXbzp4hCHx+\n4RePdY0Lt69x14XbUepRWp0WbsknjEIOWk2m62WWZucQsx5fvvpVXr94lfm5RW677TYQy5TKWYjh\nVvOQWrVCreSyvLTC9PQM7U6f6cYsu7t7LLQXcD147rlnuPj6N4mSECeJ6XX7BEFAqVKl2epSrzdw\nPJ8bnUNa7S7vfe/7CIKAXzne7KO50X9Zpet/gpJNZvbaz//RbxX8dlRc6R1b0HmQHC1IeZzTNiLe\nSUBWBNr2vUWcpl3nrUAk/458XXmgKuIYJ20ib0a9kpcg8u0tAjNbLZHfYPPqilttbnmp5q/aD3Nf\nkYXNpJIHb9OWwj4nDqmMCSou/STGK83wpT97getvHLBQX+Tay89zcO2byKTFVJBlsQl8l07Yp7aw\nwOyZs5QXl3FLVYQWrM4tcOWbr3K/O8X1V1/l/ocf4IWrr3J95zr3rV3g7au38cXPfo50rsFnvvk8\nL7d3qU/NsLu+ydmlGe6//Qx337HGd3/Ph4lQKOkghIMvXXzHIUxCYhRSCpI0s3d3hok9TLYoyBQO\nQ1ubY+N1K3q178mrrk56vmheNJp06CWL1iMv2syMbGhyZ9GWYhhKGjF0zmJ4OKqOIvUN9fqe8EdO\nXY7jkSQJURhlDCIaxxG874nj2oA/+J1fH9p6ZyasvX6PFIXjuXT7XQJP4DkBApckyg7Mp6ZqnL/9\nPAjB3v4hL774Cp3ugMTt0+72aEzN4bo+WjtUyjXa3Q5h2Kc+VaVaKxMO+pSjzFFo//CAg8Mshky3\nH3L1+nXq09O0O50s8UOpwm//5m9PpPG33BPTcF6Tdn/0OJHYollRYKe8uZ7hAsy/8+J/keh4rAkT\nAMcGkiJnn/z9k7gbu632+/IAbD/zZhZOHuRs56VJTkj5Ooo2Kvv9t4odk6+36N/2taI22Ny7UgrP\n88aetVUrRSUfL8YETsuXRAxwHYdmq4+UJT79u7+PIxu4yuWN118nGXSZm59mf3uPXldSKnn0BiHl\nWpX55UUurV/lobNnSAVsb27jJrCzvcNfHF5iaX6OF6++xuraac7edztPPfM0j37H+/lbD/4j/s3P\n/WvONOZoB5KdbsjK6mlU0mNhaYUnvvO7cHwPoVK0yLKXh2mSBY0SGi3JVCbCHMoL4CjwF2SSqRDC\n8Exv6mykaKMt2sDtecrPSf45gSBLnzh8RgzTCQ7/l+F35hGJ1gg3C1KWbT4gVBbtT4gs5rgeBrfS\naBKTl0yApzSQBbkyAaKY0OX5+TnSJAtYpbRiOq0TJRFJmlCtZKkJk1jhOCXSJFs35XKJZnMfBQz6\nA+ZmG9TrKRFNTq8uI6VHkmjCMKZeLyN0SugI0lhRK9WoBFWqImBnZ4fpuVXO3HYPvbBPq90hFJkN\nuVutU6qUOWw2T5yntxzAYdy6I8+V6vRkvfcYyOnjqgqbU7d1dUWWF0ZMhPHkEJPuneTS/2YJ2gbE\n/II4aQHZ7zhJFVQEnKZfefNNu34hjptZ2mN3kjqkqOTHraicpOuzN8u8FPFmuEFjAWQsLvIbwGiD\ncjRJnLIws8wzX3+RKiXKQZWXXvsmncNDqr4mqHjMLiyydeMAR2Yu7ufWzrPf6eAi2d2+yR33PsjN\nzZuUqxXKU1Ps7W3Qa4YsLi/TSyJO1Rf5+z/242wdHnBzZ4MP/+0fQJZL/OA/+XGoz+CXS3Tahzz3\n/Iv8wx//UdrdZubdJ2UWY0NppAKkGGq1j4Np3qv5zYCuNZBkjHF2CGpdHj47qiWrtyDSn/2+o7nN\nAoeZNHoj/w4pjoKTyaM4N3rYDvQw7LaUw0PpLISCIrP/B3CIh4y8JjXJGwzz5xR7TcPQ9NiRSFfg\napBOmarIIig6TpaRKU1B6yxSpbGnj+KIVCUolTA9VSGKEkgzz8o4TinXqvTlAEcrZuZnabZa+H4Z\nkUCn00c1qvilGofNQw7am2gBg3jA7PwijuuQao3n+5w6fZbfnDxTbz2A2/bRNvgWBXSyuSyjizNF\na43v+oXg6I7EyuMEnAfFIiuUIs4jf/1WAGUXGwjz7utFXK7d3vy/bwV8dpkUejbfZnMIZffH3gj/\nMn0tuvckDrzoWn7jzVuTFB2amWIkj3x/i1QEwnGpeXU+90ef55vPXYTYoVrZp5R2ccsQR33SuIQU\nNdw5h8N+j3svXKDV7pPGCXVZQnVC6qUyC7NzDNKE0vQUu8TE/R7tK222rl1n78ZNFpdOcee99/K5\n7T9jea5B1S/zd/7Wf8H/+QefpdvrE5TKaA1f+vKXue/Be7P8lWi0SLOMLqlCK4HJnZr1/yj+zPjc\na4bRYgpVaPmitB4l+oUjWkxycfUxDlIFZ095eh6tW2E2BT3aEXSqhty3HgEyMAzBmqV2G1G5ksPN\nRSPRjGJ5K2MenAH/qHohsgPQCedjqTJcPGilsjyfSNIoAQ3KVXheQJpqXNfDcbNQv4HnIaWH0OA6\nJeI4hUGE53mE4SBLyBxkSb2F41Jyp0lTzaAfZ7lpoxZTFUnJqzMIQxzPQ7qztDtdavUa0vVot9tZ\naroTylsO4LYdal63CkcceNHBST72R1Gs7bwYnufK8wBzkirDXLsV9zkJSN7svSdZ5dgqGygG8FuJ\nvyfdP0m1ZN5ZNCa3AvKTwMKU/CGm/WxeZXPSQXFR38zGlTc3zM+9FpI3Ll/ny1/8KucWzjLTmObS\n6xfp9lrMzc/guoJ+P6LkV/BnK8xXTjFIIkScUHEDokSRdPpErS43rt9ABj69cECruYfqdqn4FaKB\npn9jn603Nvn7//y/572PPcaN7S1ee+mbPHj/A3z2K89wEHYRg5Rz52/nsNnC8wIGcT9LpE3GrUqZ\nOSBh6HEYMhcBqU6tfmZqFikkjjjutFMYzhiyoF4F9+bVaUafPamYdem6bgaIhomw15RjWcrY9Qpl\nImhn5sQAKksHro80QqRCIygTpyqLRaL1WIQhJbMwDUXFcb0sv6dSWfRAJK4QCC9AIuirECmcUSyT\nNE1QOiZJFAiFThRSDEBLXKdCkqbgeaQOOH5AqrMQxfXpadIEGtIlSTUybh+Nm5AMwpj+IGShNpvl\ngS15NPwGyYQ45qa85QAO44GdhMjm1iTllcOJT7QijY8yV4xEvCEBOVKQimA0KEIc3aOVHh2CyFFM\nDYb35SfWVs2YBW6qNe7edh2C8Spsr84hp4F5Xlv3ZPWm6Xg6JXOflEftyAPOpHOAPPBNAqpRS0eg\nftRH05bsvdn37DNbMUWJTYzn3rEixz1cT9pEtM5vGHpEA0difNaW/OZ7cnHJsn+rEeeWicHZgVQG\nShKEpCTqfPkPf5OVqSVK5QpUJEq1qNJFhhJZmeJwENPA5+xdd7EwXeXi01+j7kl02CPpDejs7XL5\n6aepDCKiwxazgUc7FTTKdSqOR31pmrgzIGru8b/9zL/kh/67/5pqENDb3mR70GNxZY6ZZIb1G69z\n/0P38/aH30aqUnycLDbGsA/ZuA7na8i5agNcwmZgyNLOCU0qisMjTJL0Rte1cY46Qs0sCNNwTkQx\nyBRx4GMzPGz7iFljnNZH3+22YnTiJpiZRg4/tVGp6KHxwmiRc8yEctSGNMvI42iGkkRKqobvEQJv\nmG3H8ZzR+4QIhutFZQgqBAJJFMej1kVRAkN9vpRZyF+ts41RCIFHFtLAGaqEXN9hujyF1lC18ONW\n5xVvOYCPqw3M1aMFPy62ZT8pPdz5pEQMn1NaooZ2pba4LUSmY0NngJCadEiui5BiJHLaIt8RQRs1\nhQ3IWfuMRGDeN2r5BLAsisJ39M7j45JaGawz7lRhMl7nnSOKgNn+s9+VH3ssW2G7GjMfRyA+3FA5\nLi0xsm8YLzasnxQLBrJ0VPmlajbI/DVxAmDkixphVpamymR+cTwHKR3QkiRJ8VyPT/76b7CzucuZ\n1TU6vR6vX/kmjcDFSQU67CFK5cyNvVZlbXGFS6+8wP76OspJ8VWURXtUThagyfNJkoTA9VHSod1s\nU6nXCXyXBx9+ENGMuXa4zxd+63d59wcfo3TYwQkE9cCjlQ5YXVniV3/tE3zvR/4vdndv4ns+Woth\nHC+N0kfqDZ3jL4+piU5g4oocsUaqxLG1MKxngirPvNeUSVZaY3M05BiEuTcn/Y2esd939Lbh/7P+\nu07mbXpMdWSwYJKUoK0YOnYRWQxvaamdIPNSzfTs9plQFudbcBTn3/fcUR+MKaW9XhJFFnFymBA5\njuNjSVDeTPnPAsBNpwt3mxFwMBoMKeVw58pEMp0qSFUWAAYztRmnJRFIcVS3lvoobrQ1afn0aLbI\nnnd2sEV4GyDNtSKinjQhRdx0npPOnFGOm+ydpHvPb0ZFKpHsemGzJrbFEcf7Mqlvtj7zVtYqUjIE\nZ3sjLb636HWT+pcdlkky6SkL3YnKFm0Yhvh+mThMuXHtKjvru8ytnqIZ9vEUuN2YXtJhplEmiWOi\nwxbveuhbqSye5saLL7D12ks43U6WONlzKPkBSkF70GJxaY39m11m56aYWprLaDdK2NvY4vVuyNvP\n3s2p8hSJ8Nh5+RVm3YDf+X9+i8OZBtfah9xxx1keefjtDMI+1WqZJBmqgYSDFlmcFynkyOV9Ekjm\nr+XvmxQGoWi+dG4+8+Nvr5+ihNaT1JzmN/tzUilSDZp3m/oNTti/T6rX9tQtkhjM9Xwu2CLsKFLt\n2u0d9wPJPFfN7/4w21C+LbcqbzmAG532EVCJMccPKeUIvM3AZpHUrAEd6tDCUThHDylklvZIZ4Hv\nXc8duQFn2aWPe/7BOBDk3wlHXmNpmlIqlcYmHSboFK3JLgJU87wJCmXr+49+V4ZhGSuTwNRwqbfa\n0bMxPk7cReoXIQRodYyIJxKaHndXz9eZb8dJCy1/75stjivJUHw4LyL7qhVM1es0D9ssLa7w+7/3\nh9SrDc7ecSeJ0nz5U5+hGsb4rsNhu0W1VMaPU6LWIcHcPO32TeK4Q5z0iLSiXqqSpjG+4xF123ha\nM2i3GfT7BOUSC2fOMVjfZGtnEz2I+MrGAX5tilNLj/DIh74d2Qn5rd/8TYhj0mjAxo1rfOJX/x07\nNzfw/MwqIZPHxVBJbUuEx5mOIporKnlP1Ty92Ooq+3DbPFcUDqIIZO3f8r+bd5h6jHPfSSGNb9U/\n4/BzxCUXj4G9bs05TJ5eRxJJrg/H1ZvFa802czV9TdOjzdNm/ux18GZCOr/lAG5KXiUxIhylR3as\nY39SDpmrIQFpcJ2jk3alkhFIe54PiKPIaoBWtj692KHGtMt85onAeLHZljNFgFpEQKaPhjjsCVNK\nZS7EE7zk7O9jqonsR0ZawtxiKSLiTHIt0kkf5+ABJEeEeysgnUT0Rc+laTK8J//L5M3lzZRsIzN/\ngM7iaLiuS68bMl2f4dmnn2P/5gEP3vdOBmj2Dw6Ym59D7u6jVJc4TOmmfdJ+ysXXXqXc79KYrzGI\nGuynLZRK6auEWrlKEmlEnBK2upSkR2+vTak6zesvvsIjZ8/zyHvehULj9lP8cpWdmRIvt3eJW23e\n/9GPcHF7i5f/6PcplRRR2MfzHaI4RA7jn2g9lCCMKlEdn9f83Nljdmw+C+i6aNMXQoyFeCiynnqz\nc1QEcJPWX77k23qSlGAzgpNo1bTfjlVkrhuLJ9t3JD+O43Q97vxm7s0zi0IIKzvWUbsnpUc8qbzl\nAJ4nNBsQARzpZu7A2uSWHMZ4kDI7uBCClExAdnWKcSF2hBzFbpAyC4SjxVCQFmKY6PbonTZh5wnX\nuPzaOSxtkcfm1PMgW7Sbm+9GbWT/Nuq35eBkj1XRd3tTy4txt+JqhWA0ZpNKkYRh92uSCV9iSUn5\n+orbUQTyRfeeHHXSLtk4WAA+LGEYUy3XSWPNM19/lvmZBa7ubDM1Pc2NN64h05S5hTnS0MWJHHb2\ndhHSpVzzqVVLeAJqlTJbYYzQKa4Cx0nQjs9Bu0U9jig15ijVp2icW8RNNDs7h4jtPe68904aXpVw\nEKNn61y46276hy12v/IKu1tbxNGAf/QTP0W3fYjjO0jHyVzOU4Z0OwSCoTF1nk7y6q/8+ctJ3Osk\nycvQss0dm3/bUrRNJ7faWGy6z4Pnmy3599nrN7/uikp+I8ubrObHyqb7/P3GeSqPJ6NYL0PNgblu\nx/cx6+hWsU/y5S0H8DwgmIGD4SHmMJmo1oZjHp4Vp2lmFytBuh5RkoCQKJXi+z5xGONqY80yDH40\nBHDD7QqOi3d2sYk3771nBt9ub1HJH3QWAa+9+2utR3q5Iu4hv1DsqHC2iGsfBJt8fPnxNs85zuRw\nsKYcLTBTv1lsRiddtOiyTDrGmiSzDDq+YY2LkOObX5HFSebN9+YcifIHnlprEA6OhDhOePprz7C3\nu8f99zzAQeDSublP0mxS8Vza6YCZqWnoCJZOV+hJqC4ss7u3x7QnWZ6fZ6c8TffwgHYY4XgVXSci\n0gAAIABJREFU3MBj/swZTt97F6LeYOPwEHd+AWduj95ei51mk82vfI1TjQUeve8dlJ0a8iBiIZjm\n3lO38+9/73d54vEP8sjDj9DqZDGsfT9LDuAKidBOltBC6MyckGIwztObEMdDShiJx1zKnjfgevzQ\n3aZNc90+eIPj6/n4fBSrO4qeuRVN5kue+72Vd7T9TN6LW+tMpWlC6Zpi1oEN3GYDs7H3aPwzZsGM\nrTPMrWnaZ9dbNBa36vdbDuBwnOs21+AoLrLSKUIMHRUEKJV19rXLV7hw59145QqD7iHloITjeggc\n0iiiUioThWFmfiUEjhGH0jQ7zIQx3Xv+/fnrdjHciPm0OYj8wBtCmETAtv4tz32bybZtvw0B5Q9h\n7I3AHlezGdqR/I5A8zi3lZ+P0eJX41yU+a2oX8aWOD8u9vgcbQzjMb2LysmgXjy2jjBOHBlYCZHZ\nUmshcZB84+ln0LGiuX9I/cIpdvZ2mK+USXRCXyl2200CLUhcl9sfehunzq/x/J99nf7+DoedHtWZ\necJY46Lwq3Uac7OErkN/MKA649KPErqDiMR16GmFk2pSFDeae8xt3uDhM2eYDWp0wh5TS4t8x+OP\n8dC3v5PEpOlKY9I0JShVMklJM8yYniKHYVWLgCvPfWZjMy7i58f2aAz1CIxsWsrPo8082DRRJMUW\nzaENnnkaNHSVL7eiDfv7mwHBItovqte0Mw+0dhk/syre1G5V7LU0KeSFXd5yAJ800aYkSQbcJomo\nlALpOFnOOgR//vWv87/8H7/M+77t/Tz+He9HxymplpAoSn6JMIqzQZFDQhPGFM0di6xmBr3IOSQP\naDbQ2u0uAqZJIpxNbPnDk6IT7yjKAvM40sFY0BhQFkMHjgyYjgdrKlpsdhvyHpr5xTACXsB1jjiw\nk6QXAOmMOxvZdRW1r2iM7boNx5i/Jy+52UWpoe3uSDARoLPIdq+9dJFep8vq0hkO9w64tHUZDrvM\n+WVc38F1KxyGh0Sppj67QH1umXYvZvXsGvPvfIjNjQ1W7rmTF5/+BjJKSHptPMdFpIrB7h4Li6cI\nBgmlRBP6Pm3PxZcC5WsiV/Da/jrTV19HXp5j9u41xB1LnG3dzczMHFJq4gRK5Qqe79HpDrLmZx0H\nNEIeJWzIz589j0ec9XE9sz2WR79l6yw/vvZzRevCfr9NP0XtsqXM/FwXrZWiduTfWdQ3e7MoKkXn\nNHadeebM9LfIq7mIGTL1Fn0Wlbxj43/2AG44Uxtk4GiyM/EEEJnhu9bZgkyUwitX8IISDzz4EG4Q\n8Muf+HVOnVrlOz/4ODNTddI4tkBwOCBk3l0OR55/juOMuNMiLtx214ajgTUxNYomyAYUm9O1Nwqb\nyOwJz59GC5FxXpk55LhJo2Z8oZhrBiRNO0wgK7s9NlGadt9qceZNpiYRMxgd7bieskhSyf4tRnlN\ndfYQCGNJPhwDYVQgt+ZkTNFKgXkPmQQhUKg05fOf/wJzM3Ps7+4x25ij+/o1fA2Hjku5Uka7HtVy\nla4SLJ6/jXCQpbqqlctUdUriSpbPnePS629wcOMGnlJ0W02CwEf1u9Rdh5WZKcqejyiXaPkSej06\n/R5NV9NLXf788wfgCO6se8jpCqkLyyurHDR3SIUiTRJSFeP7QXaII/TQsVyilEbp4+BVtI6K1Ff2\nRpqf+zyw2nUZkLElwkmgVMTpGyYlzyHnjQqKznEmcdTH+1vk13G8FG0wRZz7+GbIGNNlfi8C25OY\nwqJiZ+4qsvA5dv+Jv/7/UOI4HjstzpdMd8dw0WY23GES02g0aPX71Gp1+vEhs/MLzM3Pc+XyZf7V\nz/883/3Eh3jXw4/gShedJqAzLqxIJ5oHmOy94th1eyKklKOA9Dbh5cUoe5MoAnj7PlsiMCW/OMxG\nYxaAneZqRLxS4DjjgffNhlPEFeSlAQPK+U0ra9DxBTdpUWmVHltQ9uZh9811PFTGXto1jAHNqA5Z\nLGoX0Y8ms2IyXnpKa1zh8vwLL7BxY5071u7Aqbl0mi3mUsGABBxBe2cXjYTZObzVU3hTU+hIkPRS\nZtdW2N+4wgvfeJZpp8zppRX62zdxVEK7fUhtagmv4hLqAZ2kT3ujTRh22d3ZoLzfRAWCQRnq1Kj1\nUqIXLqJuO4s8t8S7v+WdPPvss5w+s5JlOy8HtHsdPC8YnsVmXqWSFKSLkMelwGJQArMRFklE489k\nf3bJ15/fxA2N2CA/mgOL1gxNua57TOIydGr+iuZ4EqDZ6zX/3pN04UWbg93Hk57J033+vMse56KN\nsKi8mU3HLm85gKvhAeXx4FTZpysUsdKkaYJIUoQrcH2fqNnB8xxmzy5xdW+bBccj0pp33HkPD95x\nFy++9BIXL73Od33oQ8w0pnClRKQpnuui0xSZxFmscaWO4iA47nDwhrbDOTC39V+26sUuJpqhea6I\nm8jnx8xPWJHXpvm3ebfN/dt/tupGOg6OM+TChRxTzRhwNIva9DEfjTEP9DrVQ89WRpngh70d9s9u\n73EuP8/Zj4g9LdZra7I4H2KoBdBKgR5vl93e/LhJkdGYmQPXCej3FZ/706+yvHyOTrNDo1xmf3+T\naqnMoNdE6BjHTUiUoNna4fT503Rbhwxafc6eXkJEffZeu0TQG/DMF7/Ahz/83ezN1Em7gtiFwSCh\nOdhBB6/SimLC1oDA83BSiXZ8yq5HHPXRekCIYG93m97uAXKmQqXSYPvGBsvnTyETCVGCVw5IBPgp\nuMoZxvbILKuMx6698R0HVzPfR+Nrz0fRuMHx+cjHHrLvz9PqrRgZmwnJg5VhJOw6beuNomJLr38Z\nALRVFvm68tJx0Xoco7UCXbfNgNgbWBGXb+6fNOZF5S0HcKNrS5Jxbtb8pVFM5GbieOC4ILPF7kYg\nHIdT58/yx//xCwRhTLlS5rDVInUclpaXqc80+He/9qu8593v5jsff4xes4WHxHUcpE5xJSTaxGkT\nw3gS2bu0SjMvT2tHdIbmXIZ7MHbaUHwIUjSRBkjyYigwxnmYDcJsbGmqSOJkrC7IJBgzVqZd9sK2\nJY4kjUmSZAjSGscx3P7ReJt358XjjJDE6B1FQaW0NlYNQ1DWx21oJxFkkiRj7xfCtuk/4saUVkg9\nLimZNhSpDrzAIe5HzM3N0mx2iaOU7e0D0tQDGVCrBHT3bxK294m1pFwqgQrpqohOFDG9tEJZK9Yv\nXaTd6VH1YWdrg3jzJkuNKTrRgJdeeIZarcTW3i6dww5pfIDreZSCCo3ZabYGfRzfJ6jWSBKoNmpM\nu5BEPXqdkKQsKVerpH4Z3y3hxgopHXzpQRqiPYjJohC6ykEjSIcWWUZWy/c7T4PZ+B9X2xWrIyCv\nLzf0bx+snSTiH+fqj1Qnk+7PS5tF0l3edLdICrAZnVuVfP35zczuc/59+fptmrT7XJTWL8/EmWIz\nWaYfJ5W3HMDtUK95ESxJEmQqSQDXcYa6TEWqUlIlSFPJ3OwsSRjRj0Pq7gxLq9MElQqraUo3HPDt\nH3iMl196kZ/5i5/jb37P93DnhQv0uh1Kjks4zMQhHEmcxtkJv5tFJBOOAxYXYYNzNEwBZas6jJmh\nAdz8BPu+D4zr5vIAn5/MMXESMdJ/28WObX00jqNaR4BqiKFcLltckFGTjHNFSmUZxk05AlVG3qIG\nLItS2+X7kK/H7pv5tz2Wtghqv8v+rQjAzXV7M221OlQqJa5dW6dSrhOUqjzzjS9w/9seZPvaOiKJ\nuLG5gYvAA1AQJymV6jSyojh15jz7nR69ZpeluTl2r1/n2qWLVNOERMW4lRLrN24wOzNDu90ClRL1\nB0gp6TQPmV+ax6mXkFMlKskM7SRCC1hamqd3cIBIXeJ2h+e+8ue8feW7UUnKzvV1rj3zIhfuv5O+\nEnixRngOys0i6zlaooemnyLX3/zY2J/Fh7zHN9Rsbia7oufPgopKXmo9STVg6jeMQR4A7Tm36cd+\n1qYd2zrsVpx4fozyddnrAo7oNE/X5v6i+g1Y25JF/s/cmz+DK6rTLm85gOcP0/L5E108vGGfRHZi\ngys8hNY4UuIjmZma5trWBvfOn2X/sIts9+lHEZVaFc8JePThd6G14pO//Tu8+53v5P3f9j6iOEIG\nPkJrQCFlFopSqWyDAAEyOzRM0wQpjuK1GNA0OQJt21hb52y7G0dRNHZAY3O8RcUmCqUUKlUjYM4T\nWf6w0ly3iTvrVzLGBUnp4DjjsdLNM7Z54pgqKB3X39ttzC92m3ux57RIrWKD+6jPOa7HXAvD8Bjh\nu657TK0EEJTLtFptZucWSWLNn/3Hr7K7s0d5dYq182tsXblMbWqWQeuANOwRqgSFpt3qMrWwiOdX\ncMM+jZokUJrN9ev4UYQjBZ6UlIOASKWZy3zgo1KNLJcIAp+wP8jUdUqRhANcRxJFYZaAWGhII/r7\nh+h+ynqcsPWnAe9597fynY89wb/4H/45P/dr/yuHNztUhYOTQM+DSGj8VGU+EGjQxzfP/FwW/Zan\nsxz1FVor2QdrNsd8UhYrm+ahOCZ9/n67XUXgmO+DAX7z3VbT5NfBsZ5a9eeZK9O3vDrRZhry12za\nG5MccxKrzfjkJYBbqU3s8pYDuN1x4FhnpHTAGe54kixLvRYIqYlUipNIvvWd7+bysy/R7LTJ4vK6\nTFUCKqUKiVYcHO4TJSHv+9b388yzT7O+vclHvud7Kfk+QiV4InOSUNEAbygRIFyE44KQwyD0OV2w\n1qNDQcN9p2k68ti0OVTXdUe6ZZsTKSJOe7INMRq1iFZHHE0Rtw5HHHJefIWjOC7mHgP+trhoiMxI\nGfk2SjHOKeeJ3n5vko6rUMyhr11GqrKc16apy+Ya7YPiPJeZ1z+axTWIB5QrVfq9kGiQ8udfe5qz\nZy+gUkUYRWzevEm5ViNwJeW4zP7BAb1IIUpVanPLbO93aHd7nF1dRUYDRDgg0ClCCXqdNp2ohww8\ntNKcXlqmqw5J0MMwiCmdw0NklJIkOjOrTBQ6ha2Nmzz+oQ9y9eIlSCWDSkCwOMeN5h7n7ngbd952\nB5VShVKlgowTPCmJHU0iskNqb2g66jAZ4CapB25VsvuOSzye541x1bYHZr5MtEw6gQM37ykC7zw9\n5+ux6cSmiSLO2i6mT/nxM3+2+sPua956x76WP7wsGh+bQSn6/VactylvOYAXGb/bn4lOSYcBeu1w\nkY7rIFGUkJxbPc1Xv/AlHl9eodlqUfaD7Pk4Jh4MqHo+lcBnoAY89NBDbG1v8eT/9K/5vo98hEff\n8SC95h6OSqiVA+IozEDCNZk8sgUjEMcmLQ9c+ZN1I3rZBzD5ibF1imZBmChl45wow3PV8QnPO0EU\ntREy4jLEaC9E+1DFbpOpL79IVXokTmpd7DU66r8eX2y2+JkXQc2mZydfsDkW+2ygaGFMEk8d3yGN\nFb5T4uIbV2lMzzI/O4dK4drly/R6XYQUTFVruAlM+y70I8qNWZzqFHsbm0zX62idcuXSRaSKCQKX\nMEpQacrhQYtYaHzfZ2Fmlkq1SjuMCQchbjmg22pTmllgu91idnWZqeUV3M6AiuOwfGqNc2dvx5U+\nL924zl1vf5jrmxtEvuRtjz6MCCr0ByGlep2k3UEoUD4MkJSUQGhIC3Apz8X+ZUE8G7vj9+ZB2aaT\nfDE0dlTfcQbtpPefpD4oktaKLMjeTF/zoGu3wWa0xqTQIZ3mz4nyTMWk9xvatjl4m/Gw+3Cr8pYD\neJra6pNx3bEBJxc5zKU37KjWRFrjBj4i0azMLaBLLkJHkEYkkUYCvuczv7xEs9NG+hIvmKU96LI4\nP8+Fex7iM7//exwe7PHB970HX2j6vU7m5aY0aZzgDA82s2DvghQ1Bg5BEAx7YQFqMgwQP+S8bW7g\nOCgXn2oPBoORPh3Gs9DkuVK3QC9uOKg0TYciNmSu15nnngkN4sihtKDTQhNDw5GPE6jKsphYYnQ+\nFsaIkNVxz0kDtJ7njY3JKEywFWNjUsTIIq48TVNiy+5/1Bal0YnAk3Dl4hVOLa4w6HSZnZ2l3dxH\n6pR+b4AvNJ6TEgpJeXaWC/c/SDdKmEkUnlY0W4dEUR8dRXi+i+f59JNouDFGlDyPWKWQgh8EuK5L\nq9Nmf0/x6Le8i9b2Biv33oMuVWhfWkeFCc89/zLnTp1ifnqW+y7cxUJtjoVHTnHl6hXues+jXP7m\nq/iBx+bBPtOOi4xBC4jclFKSZdjR7vGDNFvCuhWYFKk/smQi4y7z+ZJXFeTvta2x7HnK01jRZmxo\npqjtt+JYzTsmHRJO6kdRG+zf8zRXxHga3fukttkltnIX2J9v5uDVLm85gJuBKNIHC5ElQXUQmTmY\nAKEFEkEiMi9NXzhEKsWrVdi5uUW9VicOQ4JShV6vS7PZJCiXqHk1modNOr0OSEHslPiO93+A1159\nhV/4hV/kb//A97O8OI8joN/tEJRKDMJBBjauN9RBj++wRYQrhMjM7BjnPuM4HuMi7edsjsNWvUw6\n5Tb3GPAs4pjtZ0z9qUqP1asZD3Npg2zRnNgL0fxmFmsRt2IDqt1uMx550La5dfM97w1n/3uM48+N\ng+u6xCqiXC4jlcvl1y5xfu0O+r0e7f1dpI5YmK2jBz79doeODomQnDlznsrMLO29fe68927KjuC5\nr3wJpVP8ckAYxZR9iYo1binAiTS1xhRhmtBpdal5AaUgYNp18IIAKSSrq6cJgdQPOOwNUEpz5cY6\nzVaLRx58iJIria9c4fTb7yPxXDbbB9x79jb29m8SBuUsK0+iUHFCLCBNVJYKLJdu3Yxvni7zAGeP\nbb4IMQxZwXGAsUHVppU8AE4CzjzjUqQaKZIc8v3Il0nxx2/FydrvM6o8+3q+H0UbThHoTmq7DdRF\nUsmk75PKWw7gZjHahyOmCCHAlaCG5oaOwFUCqcUwD54gUJLUdZg/vcrW1haLDywSRhG7hweEg4hq\nrUaKZmfvgEEYUq3VqNXKHPR77B40uevCHbjyTv7tL/7v/PiP/QNKnsvKyiLtwwOmpxtEg37mQm7t\n6CcNcqoUOj3uSWmDTBFQ22BpOMm8CGfGJ8/Vm3oniY5HbRaFMcdtPaANknlvuXx7TP8NN10EDna/\nTTtsDsbUYTYjWyy19fn2OJmD4jyt2IemYRhmi89RqFDzlc9/npnpGV5+7gXOnjnNa5dewXM0TqXE\nbHWK+uwM1zv7LM0vc+Hue9nt9Gj1+iwuzNHavkEcDVhYnKdz0GQQpaT9ATguvbBPpdFgbmmZw5u7\neEFAuVKnMTWN53nsHR6w9colFu6+wO5Bm7LjEQQ+TpKSkNAj4uruBqvlsyzOVpmdbdDoNiGM2bm2\nTqVeZb/Twqv6aEfiCEkgJdKFNDkexS8POkUgYNPaJJA4Smd3VGz6sj+L6rBp1G5L0TqaxP0WSQ1/\nGUnC1HWSKsVug20VZRiKfKwh0wZ7Y7BpPn/vpGt58C6Sxt9MecsB3OiNTbGBQkpJokHoTOo32aOV\nVgjXxRMS0c/M4E7fdo4XP/mnnF1bI0pTqo0GM0EZ6bg4jkuaKGaGdsXSEZxaqLI0O8Pu7j5hkvCO\nh9/Jb3/qMzz89oeo1ut4QYkwHBBHA0peOcu9Z+lkizhAyA5ZsbjavPmR4SyLOOX8qXl+wvOcrilC\niDFzQqPSOUqVNuTW5bjJplIqS8LKOOEaULbrL5KOTDFx1vPXbTAxbcxz+qaP+QVoc3amzab/ZsMo\nAi9DNyPdvJsiYo8/+sM/5OH7v4X5xgzdwwOcNKbfbeGGPr3dHerlKmmpzOLyKqVylb03blCrV4gG\nfa5duYyKQ1zPp1ypEUWKQb+JdlOE67C0usIgiVGOJCgF7BwckMQJKysrKK1pb2xx/o7bCQOX6UqD\nZHGOwd4eipR+3OP1q6/ywivPcufGJb53YZ6646MErF+/zGylyvTcFJGSdIkQArwwZWBUDOrW3F8e\nxGxutRjcMpvxIuajqP68ekQIMTpvsVUhdh3mPtunwJ7HSe2cxNnnpVr7uVtx4HYfbHrKc9ZF7yhS\nv+SLbY2Sb1e+fbfafPPllgD+9/7e3+Mzn/kMi4uLvPDCCwDs7+/zsY99jKtXr7K2tsYnP/lJGo0G\nAD/zMz/DL//yL+M4Dj//8z/PE088cctGGCcOOz62GUApHbxUkwhFwvDwQEEikixBaArCldx25x18\n5tqv0O73KVfqSL+EU6nguj7r1zfptjtIIaiVKzSmppmu+QjPYemeu9lv9VhYPsXS6hm+8KXPU64E\nXDh3GpKQki+Jk0z5aKsClFIj226wD45ADAHEnhzzu0nUkD8AyjseGOI3Zot5dQIccam2+WVWNNlX\nhR3/IgzDscWWplme0FLZP8bl25HX7IVpv8fWD+ZBwrQ/nw/QttyxNytjIVMkqtvct+HAJ3GCtkml\nEIIo6bGxsUG9UsVzXNxymdcvXkXKBEcn+K5PFIb0WjERHlI6rK9vUC6VWJibZ3f9DVr7+xBHhEox\nMzNHuTLNoeuw2zpkutFAOFm88KlKjUptimsXr9DtdKlOTYGQBGVJKlKcwCcmYa+5S/fmJo7UKNXH\nSxIcBS/9xy9y6dI6P/pj/5il+TnqF+7kj3/n03z4+76HQzfhQIdUNDiDmI5McTwXN9WFwGJvkHnJ\n7M2I6UKMM1PjNF4MOnmGxP40v9n6efNbEASFZzxF9U4yvZ1k9VLUXrvkzfnyUmKRvbfpV/5cy56H\nSWM9aTM0n/lwvbcqtwTwv/t3/y4/8RM/wQ/90A+Nrj355JM8/vjj/ORP/iQ/+7M/y5NPPsmTTz7J\nyy+/zG/8xm/w8ssvs76+zmOPPcZrr712S8V8/rDOLkqlqKEFSMlxcV0NioxDAJSXJRRdCWo0zi1z\n0G7i+QHdfpeNzS0qXoVUOjQWF3CSlJKQHDYP2NtvIaQkThWOV6JSr6OV5r3v/Ta++rWnSZKEe++5\nA+0IdC8EpUglxEPvQsd10EpnnnAi0yVrR4BW2Mm/7YkxAGS4W3uSizhcA372gUeeOIoIQkqzCDJn\nHXOPH/jDBK4gpIPrCdA6ixaSW1S2tGFzYrYpoA2ygqOj3LzKxV74tpRh35fnUop0mqN3jfSzw2sa\npMgcjbJolYI0jVFK4/sVXn7uZeJBQm16iudeeQovjfAdTW2qDlGEq6CXJtSDIMsqv7HJqfNrxF2f\n1v4OgZT45ToiVfR7IdL1mFldRU7XmVqYpzXoEXgVKkGF7c0tvJKLJmH/cJfV06eI4oiX1i/S23Gp\nOGWivQOqqUSnCVJ6BDggFO00ZLff5hOf+BXuPnuWb//wh3jg0bfjDRIqgY+nPVIUSoLrcBTlJU3R\nKTiOS6LB8V2UyKKxO4AzDHqVMC4xTuICDSlml/RInaL10D/CzIPIiD+LJ0+u6FFd2Rxn9GmYHnvz\nzlsl2cVua15St4scSr7Dp6w+TFbHjLU2x2jBuFoy/86izWw8OfjRWjoye9Wjz0kSg33tPwkH/r73\nvY833nhj7NqnP/1pvvjFLwLw8Y9/nA984AM8+eSTfOpTn+IHfuAH8DyPtbU1Lly4wFNPPcW73vWu\nifXb3FReZINh5hsBKI1KEvSw485wQvtuikAw24r5tr/5OK984evcdf52Wv2Q2UqNmiyzH/bZ3t9m\n2vWYqk6xvDJLaeosSZSBW6fTYfvmDp1Oh1RoFlZOs7Hf5ou/9kl+9B/8CPX4EF9AKDOXf68U4KRA\nlECSBUmKUcQCfM/By1mGSClHLu9G52wmyD7YNGAWRRGe52WHcHE82u2hWJ1hm9llddmZfo4cLWxP\n0ey5oywhturBfkeey7DfmQf7PIEXWRLYm4T9abj+/AIyv9ttSlSKEJk/AGRnIjgyiz7pSqI4Jkkj\nSiUfdIXXXrzMXRfu46DZpN9ps+gKGNq5uylo4eJWfOZrVeL2IW7aI+7s8sr/S92bB1uWXeWdv73P\nfMd375sz82VlVlbWXDnUpEISEgYJCSGEBG0JySHTDI0NhoiGdje47SCwuxv14KAdHWZQ29AIy2aS\nADFUgYXQLFGSSlLNVco58+Wbhzuf+ez+47zz7r7nvSwRDncX3hE33rv3nnvOPmevvfa3vrX2Wk9f\n48alK8zWp6i4NZI0IYpj0iAirFnMnzrF0SPHef6556l5DtFwxKDbQ2UxwjLwwx7dnkPcDxhkCe70\nNG5zmlajRifukiiBmUBFWZhZSqIyRrbFudc8zMtf/jK9zGfugdOsfuMmRlalMdVgM9rFtC0qiSKL\nY4RjI5I8P6Ftu3i2yzAKyMhApYg0RaYpSoj9ncWHWX+FfBTvdYVYODSLFAyHWUnlpi/Wxfuyj+tv\n2vSFXpfHyWvpn40Xj/H/hytwXS6L56JHNhXf6RbNYRRVfi4dfI6R/LjPGePatmJi0ftmyPxW7T+J\nA19fX2d+fh6A+fl51tfXAVhZWZlQ1seOHePmzZuveK6yWV6mA4odjMWDK6ILysKQJAmPnD3Lk3/2\nl2x2NkhicEQFVa0w15plvurimQbdtXWCfp+Vjc180DPF9PQMty2dQBoSYUgGwRA/Ctja2uLDv/lh\n3vv2NzNV9ZAILJVCkCAMienaoPKHaKs8X3aUJocOgm6eFZ+VY5qLey7CE3Xv/mEKsHheBQVRVqQ6\n766UmgjtKpyUQoj94sy6g7W8sUa/H33sis915V4o78NaOa5cp4PKPoDD4rqzLAMpc3i4N0GFyDlX\ny7aJooA0SahWqqRpxp9+7AkWbr8DM5WEOx0MpQhUgisUZpKiDAMcg6XFRaaPHOMbly/Sak1DlrG9\nugphSKi62LUatrQI05R+EKJMSb3doheMMDyP2flZLj77LEmSYCqBSBXJKGR3bYt2ajNnmrhpXtcy\nm6mzOdjGySRumiFMm1picUdthmu9lOe/8mUeeP1j1BotsF06JriRTxCnMF1htL3NsdoUYegTK0Vo\ngOFahCol8/tYSuzXLk0NSWDkYbAmB1GjHh11mGWky9thwKGYk+Xvy8CksCTzoZuUad2ihIe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Qmmqk1kxWQ32OTm9QucvL2Fa1foDnyYrrFRE6iNFWTosrsZYnsW//CHfozHH/84RxcWeN1D59gY\n+Tgqw6l6xElEFIfYlnMA+eqLsf689YW8aIPBYF9Re56nycVYPgqZGw6HE7IxVm4Ho010x3nxedmR\nXfTvMJnQ+6krYsOw+GZRKPrGonKCteK6+mKjBwvoMnqYzBa/G3PpxSsjTceLQEFfFUxDETp9WCm2\ncvtbocD1JEQ6YlUqz7OsI1JdwcGkQrNck8yPGOzucM+Ze1hevcH52+9le6eLH4fIMOWe03fTlwmm\nqBJFCWtra2zt7GBIg+Eg30Az3WqRplHOczsWJoI0SciimHDkExsJKTB97Cj/+6/9Mh/4+Z+H0Qg1\n9Cdyw5W55ML0gnFxBd1kKwZMr+Sj84TFOXUlp/ODZSRe/K7gAPVoEx1Zl89R9KGM7vOxmowzL84N\nkxOvbFKXrQgoh06mB+5Nv6eCIoK9BV+AUhL2wgkt02FtdYPPf/bznL7tTjbX15hpz6AGfVIi/FjR\nsEx8KRlkCbWKw/JLL5C5FoE/RbC6gYp9XMMk8n0sx0U4Lp4xTRL7VBsNYiUYdLs0vAq9YZ8wHJFv\nosywpcTCREQxmzeW8RPFY/c8gG2YIA3CfsDXPv9FZlsNvuUNb+CP/ugP+JZveQ3hbgchBUEUUFEG\nRj+i1qjy7OYVHnnPm/jcF77IbadPMrV0it2VDvP3nab9FodWL8aJFJ3tLaR06HdDzt35AJ1wyK98\n+D/w99/3bqQlMZIYK46pOi5xNpkWVafW9Gee/z+JHsupDnRaqwhf1R2NrusesOzy+Tx2guuyPwkS\nJi0xXRbK0VvjhUFNpK8YW/Wv7MTUz63TIrpVUO5bOQCg6EPhz8ufzZj/LipI5U7Owq/n7M+HYgz0\niLAoiibKGt6qveoKvBAEfXNO8UDLSK04vvy++D8OfKqWQ5Ql3H77bVy98hlmZ9q4rkciDGSY8vLL\nLxK6Fv4wxTJtvEqFB+6/DyENskyxu7NNEPr0en1IU4SskUpJpeJhSglCEaQBnltl8cQxhCn5tx/6\nEO/5nnfQtG1IUuIoQUlAgCEk2d4ONgwDJYBMkUT5qiuMSeRRXrR0ZQ5jk03nz8uKsowMdKVcOHUs\nqxD2wx2Y5UolYwUcH5h4+hjofS0cNbfybRTH5+NI/oz2K6jrJeR05KNwDIuIDAWkUQJSEkQhH/29\nP4TUYmunR38wwEDiD3xqM01U0CdNM6I0pt5q4dqSrdVtzGaV57/6ZcKNDTzHxLYFludiYhIFIdJy\nmJ6b4dhtJ1hfvokyRhhujcFok63lm7Qdj3rFQ2YpFemShQmWMuhu7/LySy9iNJrMzi9w9bkXaHoV\nXrrwMkfvPcH3/eB7+fjjjzPdbmNbBlkQ0pIWtuehkpQBMSv9bYJen+e+8CVqb51h4dht7GztcvL8\n/aw+/SKD9V2qtkU/8Kl6Ht2dPkqkPHT2If7PX/4V/t57/i6nZmdwnSrxyMdybFKlEIbIF8xMQZbt\npYMwclpqb5zlnhNTT8YmRL7Q5oqtUPIKpQ735+hgYQzCDqZfvdX7cj6eLMsmeHhdvg3DQkqxT+eM\nle8r03J6niEdZBT33Wg0ybKUJM4d+Uk6nn9ZloEqHLmKarUKsOdoLRLPFbtac+ujyPVvWdEEPVrc\nX2FxF9bAZJK6g+1VV+C6iXAYR6u/102K8iBKKbFESiRjMCWztTpGknJjZRmZGJjCZnpmHvNYBeE4\nxMmQ0XBIEPhcvfwcjuPQqE9RdU1a9Qaz03V63T7D0RA/SfGzAMd2mKo1aFSqgEL0FQ+dPs8z0Vd5\n6qWXOPvgWRpJim1ajFSM5eZpQ20hkYZBKGGUxEghcMnzR5SVoa6AdRNSF7JyWNUYbUw+L10w8vPl\n4WuTz1BgGOahAl6+bnE9nbfT+3wwGdXYcihzmPq45xOwiDIquHZ54HoFWqkkNspMSVWGkUgMt8Z/\n/PSXuPSNLaa9JrXmIjd3Omxdvkw2EoSbQ5SM2DUyhFAcrdcg9HFMxZRlsrWzQ6+7jS0M1HQLr9GE\nYYaTWQxjhVFpYkzN4PVjLKvJiJjR6nXqoaAaBZi1lJmTR1FhRrTWw8Tl6NIJuv6AqN8h6GyxfPM6\nQRRwefkKP3TyR4nikKMnjrKzvkG7UaFTNxmEEa3IIElShGPz8osXOTmzRP/yCqvb6xgPn0b4DuHl\nbcypBdIgwg4HOBVJ6oKdZDSUib8T8uYHXs8LT1/kC4OnePc73kW7UiGJhghbEmURiAxTZBgoDCQo\nQYpBgpHX2sxilMq55VxW8kW9iALRQ+oM4/CNa0Ub0yNqn0LRreky9Ve0wwqKlJ3448ishDSd9JON\nAcGtCyXo9KxO4+z3L03JVIY0JI7hYCt7vxZpuhdpojJFkhYFwid3khsGSJnuswymae9F/UQT1FGa\npvuIu7BoXmnhKdqrrsD1MKPySlg29yaVw6SzT0qJJV3CJMWpukw1HI4fO8Lm5hqPnHuYtZvrfOPC\nc1ieR6hS2s0pHNth9sgilUqFke9jmSbdbo+V5WtkSlGtVJhu1ak0pnKUN/KJgoidzS36gz6t6Sbu\nyOG1r3kdv/pvf4WpqSnuO34bvu9Tq1QYjUZIQxBLgzgMEVJgs2cG7u3WlHBAIMucv24W6lSIrrR1\nGqRoZdO4mDz6eXNUFR94vuVt/IWw6WbmeGE4GDucfy4OKHp9UdHpkjJ3DgdzkhfjHKYpjjBIhcJu\nNYhiwec//glMp4qvItpTdawoxnBsUsdGmoowhSSKqbemGIUpw6DDyRMnCIMRW2vLuDIjUYrRoE+U\nJDQqLXBNqp7DyRO3sbGxQRT4nLnvXuI0pkOCuThE+QN2elvE/YCZ2hRh2ySpWIjpGt21daaw+MrT\nz3L8jpN85vOf4Yd+9Ifx/QBpwOte/3o++ru/h1upcve993Lh5QuExEhLEvgBSIFxZJ6KaWILQQOL\ntGKzutHh9tN342YhAyG4urqCZQucqSaeVSEJI1oyw+jtcPvJE/yP//3P8uP/8Ee4/Y4lrCiiJiVZ\nGGNKBaYkEQKFQGVZjsoBpW1E0ZVM8ZkuezqdUozhYY5LyBcB3Tej0yHl+T0ZwTFOmaAfpytcnVLR\nN7fp5z6sFQtScY4ibFaXTZ0h0MHTfiTOXlbQ4nzFMzIMA8/z9j8fO3djhDjI9xc0kE4rv1J71RW4\nfgPlVbL4Tg9ZKiNPfWCksICQOIxJhGJxYZ5nn36OTneb6dkGs4ttTMclTGNGOz5RGHHz2lWqtRqG\nlFi2hSkk7UYFz/MwTZNut8vWmo9p20hhUHUrtI8tYRiCwWjIMOizvrLBD//9H+azX/gczUqFuWaD\n0I9oVWr0gxG+SpCmxM7yYrSpglAopAAjm0wuVE5DWyDboum8dPG+aGUlWg5RKmpt6k1HymUaRn+N\nQxDz35WRU5kmyZ2m5i3N6nJ/y/db/k7/PrAyqkmGzDIyK+N3P/w7HGk0yZwqqRBcufgiU4aB8GqY\nVQdhCnr+gGqrQa0xRaczwETSG43YWL5C1ZKQhAjTJY1D4jjBD2LsRoMTt53CMQ2IAs6efwBXGuxe\nW8OzTBoz0zS9RZwbNmkS093toCyXu8/dx9XuNrY0WH/hIrfdfoyXL3wDt1Lhda9/HUESkkkwLJu3\nvv3tfPavPkWMYvboEa5duYKRZriOTaPRQDZr3HXmDq68fJH2bbcRtKcYKRg98wyvO3M/zy0vM4XF\n7m6fviUZWCNknCdYa1Y8Bt0+//yf/jwf+4s/Zjsd8vAdd2HbFUbdIXbVI8oyMkORyjzkUO6VOCyr\nDT1lQxltlxfp4nPd4hqDgcNzv+sWXKFM9cIdui4oI3chBGEY7l9T39SmB0Yc1vQCE/qmpkJhj6NG\ndOq2WEhyizHntlOSZOw3KF5RFOVATotikVLiOPbEQmFZ1v7u6/I8fMX+v+K3/z81fWUrT9xiA49e\nzAAm8xIUx+52Oni1av5ADMndd93Ji889y053i/7Qot/vY9kulucy5c5w/Pjx/fMPh316vR6d7i6G\nYRBGGdVam9PzJ4lSgyCI6Ox02N5Yx/fzjRLTM23a002EZbCxusnrX/NaPvPlz/Lu7/1e5ChiMBxh\n2BaRirEdCzvOMDJIVUacZjiWhaVF2uiIV1fE+uJWHtBy7T9doesmoR6FcqumX7egq8r5vw3jIAVS\n9FvPnCiE2J8QZWR+2AJR5DXXFYBeuEFvyhKQZARDn9FgyNVvXOTM6Qew5+aITcm1L32duUaN1FJs\nbm+QpAK32eLu84/QGwasdS/SrNbAyKMl4uGAqYpJ6hgQKrIMNvodLEeykAVU/BFTnsvMdBMPg8tf\n2WJjfRnf83Bn53JrKwq5dPMmCydP8dUXnsfyHLrL69y7MM+V3V2++vTX+MX/7QOkAgzLJFEJlusw\nf/Qoj73hW/nKk1/GD2OMVg3lB1QMF9sy2ejtcLJioKKI4bU1vFaTqOJh+D4vPv0M4dDHCmKm3Qpm\n3SIwBNkwYmF6hoFKma86dLe3+b53vIvf/IPfZuUbV/neN72JhbkFknBIlsQYirzuq1AoY0++0vF4\nFa2IltAX1HJoXZkiK4+zLlMHwcE4V09RDUe3vMpoXJcp13X351ChcHWwdyu5L1uuZQ5cyklGQI8U\nKd9vrVY/4Ogt+l/8Le7d94cTC6Ie9qsHIBy2I1pvr7oCLya5vjkH0FCcnLhBXTDKJpplWXk8bZaR\nxAmmlMzMzBBFMUePLLG4uISUJgPfJ/UVyzdX8msIgVdxMU2L9vT0nkApwjBiZXWVOM6wLId6vUK9\n5iFUTj34wYjd7R2ECaZlsLm6xu0nTvGB/+Nf8o9//CeQmcJKYkxTEgcRWZJhSUkmJYYUZElKmCUT\n1MRhDj79PnVOuHh2upAUTTfjdIEqK16NJtxvuqBNCr7K+VkNGegKuUzZFNy6PtnLaK2YQDpa0s3w\nIsRRfx6JkRKqjEajzif+6E9QQUiv30GoBKvikiU+pmtAGJEakiiDqdY8oXJY7/VYOHkXpkq59PRX\nGPohTa+CIEYKA0m+ycL1HJxGje3NDbav3uDc+fPYacpzX3+K26bbJHNNwt1dBv0+jucwTAJad5/C\naU3TXd6AIKbuuGRVm6ee+ho/8VM/yanTdxDFUZ4/J8vAEmQqpdFuE6WKWnuaexZnWL+5jOgMEakg\njkI++elP8daH3oCXZPRurHAh6DEVmNQMyWsffYRrX3oGP4zoRD0C18RRBjdvLBObEJsSmeUhgG/7\n1jezO+jwe088wfe/63toui5mKLBUhlQZicpIRJ7zW8rJYgYFMj4oE0wo4TK9WR7n3A/DBL1RHFOc\nX08dEYbhvkItFvlCT+jX08GDDly+GYrV6Rp9fo1jtGOkNBB7jvQoGiP98WacPN68cDzq1y3AZzm/\nfb7zeAxSdBpRXyRfCXDB3wIFXnSwHG0Ce+byXmiOPijFccUqu2862QIpMkzHxhECpOThBx/l8Sf+\nAtOuIoVDvVKn0WjizXqYZs6/J0mCPxoRRiFpnOB6DrVaDcdx6PV6pP0Ovd4OW1trmNKk0WjSaDSZ\nnW2zeGSekT9ga2eb3d1tlGvx/vf/1/zhE0/wru9+G5ZpQxAglcwLMdv5NR2ZZ9BTYoxeYbK4RcEt\n6s+lyFKoI+vDzNfiORXf5chhMofKmLMbj4UueMWk0kaLwqklhG4m5+hN7kXyFK3MV+pKQO8f5Egn\n21vM8hwWeTUhyypC1NTe5pEYsDA8j34n4OoLF6jZHt1BFy9LWLm0id/vU5nKqAsTZTk06k1ac0e4\nvLzOdnfEffcu0dlYJRUGpusRjkJcz0GlOW8POZqqTrUwM1iYX6SSwdXnnsNDIX2fIIvwKh7dYZ/d\nQUBctbn7zP1cu3KdKIyIhj6mJfm9v/wS/+0/+VnuO3s2z9FN/qxMyyBOEsI0xXY82vNz7Gxtc/Lo\nMWzL5PqLFwh2R3imiVOx2NndIkh7dDubLG+vE9emSWdabHV3aM3Norq7NEwwHAORKqqei7IslG0Q\n+iE1p8Jw5HN0dhGz4vE//6v/i5/+yR9n2nYAgWdYEPsYgJCCNJuci3qqi7IS10ysruEAACAASURB\nVEPhilc5r3wx3kmS7Tsb8wiNAmTkFaKUKraQm3vXLBA3WNZBhTZWmJMOzkLGdernsFbOzZ/L4njO\nJUmMUtG+DJumiU4jZVm+4zNX0Pr2+kkLQU9Il8v9ZDqJsk9Bv94rtVddgZeRNxzkPIu/hzn3LMva\nH5wkzUNwVBoDAmWY2LbNyA9oteeIQ8XKzQ1Wl3ewKlbOeRsGlm1jGBLTNHBcDwyTTn+IGIxIkgTb\nsWhPH6Hi1QAY9Af4oxG9Xocig5FtmSwtHWcUhgy7fZRh8dKVq5w7dZqKMDCkQFqC2IAsTrCiLEdh\n5qTXu/BAl1shUGUT9jAnoY54dYVsGJPx4fo5i4lXjiTRz30Yf60rZaUERfJ/mNwkUfzmsEiF/PoH\n86ijVYPJx8fJwxulwk9SBr0BzeoUjldhtb9Dsr3FaHWNyIDlMKQtbHYrHufvuo84VUR+RLvV4uaN\nG6xcvYCZRDSmWsQipTcckKoRrmERxjGLJ47j1RqsXLnK3/m2hwn6fV588SWa1QqOXaU11WCQRmwk\nPn4ScefcXVx67mWGo4havc72aMQ3rl3kJ3/2p7n//FniNM3r5+Trw96N53LRHwzpD30Wji6xsbpF\npVbDbTZI+gFV2yM1BC9eeJlvvf9hulvbpMtrDGcz7IrJIPRZmmlhVl1sYp65folUShIZkglJqgRK\nQDAYUatVCboDouGQn/oH/4i/+tSneeO3fgstz0NJkEpQtxyiOM5DXjWKQ0exuuLR5eywXb/62BcU\nhC4/ZfnWf1MkgBsj/4MRW+WQVX0+3CrSRW+Hfaf3zfOqE9+N6aA87874WLkn/3IChOl0jD5/4jjn\n0Mt9LT+Tv/VOTBgPdnly64OqKyj9pXuHbctBZICGApv1Gu3pNs+/+AJLR0+ytHSMdnOGYeLT7XXY\n3t4m7aU4jkO1WkUhaDgOSRgRBCOiKCZLhux2ukhpUKlUcBwHu+JSn5rCtm1832c4HBH4EY7lMAh8\nzp09x+/8zr9n+j3v4+TMLLZlEKcJef0gsATEgjwuV7tf3UNfdgIVz6E4Tqde9GPKCFtHC2UhyY8d\nm7/lrIZlCqT8mW49lMdMd0qXz6+Pd7EI6AtIWXB1WsdMBWatxie/9ikGUcLUbBOj38Hv9pit1fHN\njEEY0e0PcGda7O7ssNvxmTtynFq9ztUrF7BUiEojUsCtNUkMl0G/y+ZwRHtuDtOtsL2+xVS1SWuq\nxc5whJtBb22dxLKwtzx6DoxcaM8tcuHZF7Gw6IcR9z3yIJdvXuMf/eOf5tyjZwnjdK/cHnnWyiz/\nG8cxzeYUF16+xND3aaYZW1s7yF3BnXfezXKQ0bm5xmA0BAVPv/gMb3jwW9jZ2uLlyxdxp+tcvX6N\ndWOFM6fv5mR7gY2NNdaDIbGEZrVOMApIpMByHIIkwsxMpq0qGxeu8dZv+04+/NH/wNu++y04Cwt4\nwLA3wrEshDkJBooxKYeDlqkzXU71sdunv7St58VfHVQcLp+Tm210pVw+jxBigm75ZlSEjtj1aJrx\n9Scd6uUForh+LptjPrs8b8rPxjQNsuzgPomysn8l+gf+Fijwwzhf/X1xg+WqHYUy0M19lUlUmkKa\n54oGBZbF3ffewZ8+8Zc88shDrF9fZ2P9Jso0aTSbnDp1kkqlQhCE+L5Pvz9gNBoxGg1xHJdGo4Fj\nNhBAEIWEYcbO9ha+7+eK3LaRhsRzXarVGrZtURGSfq/Pz//cP+PP/+SPmX3jG6gogTBEvtMv8EkB\nwzT3ETwcjG0vf1a81y2RciL84hnqYVHjRXC8QWYSSR+MkS1PEH0hLXOetzJPdSEsL0R6f/P7mIym\n0dFc0Z+CS3SFjb874OtPPw8BTGeKnZVNamlKSgSRwnFsQg8WptuoOMDvbDM0DNYuvsRw2KHmSQzP\nIPBHJMJG2jXcukQ0pkg8l14Yk8YKu+pwY+UmW9evIaIYG8EgGJAGIdh1mo0WaxevMiM8Bp0BcyeO\nISyTzICHH32Ijt9Fmm5+vwKyNCNVRXiayWjkc/36dRqNJmmiWFw6zs1r1wj7I6xqhZ1wiG2ASDPW\nNlfZXF3h3qXj3Oxt8czXvsrU4gJLZ8+QmRLZGTCtLPqGwcA0iKMII1UIIYmTKKcHa3VMYaLSlPWr\ny7z927+Lp5/6OpVHLaYci9mpOsORjyz5nAqZKiuy8uJejGc5SkVPl3GYUtIV42FWXvG6VQnGYqdo\n4TPRi6Ho/Ss3HSEX/dCvbRiTclgUjtDvraD7ygEEev/Lei5N4/3NckUfdIvXMAxs2/4vIxcKjCe7\nHjJYPKCy4+KwFVUIQaZyU8aQAikgJSNNQ06dPkHy+Igg7rFwpInMWnSHMWEcs7Z6E8u2cWwH23aY\nm53GNEyGoxFBENDt7CKEhWXZVCsVqlWPSq2JFBI/8Ol2Ooz6A3w3IU1NhOVjmxLpJ3zhLz9Dq93m\nucsXOPfAfcgwRPgRjjAQliBWGfmWzbGJqfN2xeflEnL6oJbRyKGOSoqIlKISfTqhgHXErE/Qok/l\nZ1/EBN8KtRTtMLpHD5PUzddyUixd6HV6yTAMHOHy5Ke+QL3a5NiZ01x46mk8YeLZBmQRca9Hrz/A\nmp/BJKPX2aZuG5jRiHB7hWC4i1l3aDabuNUKQQRpamLXmiycPEZjfpblS5dJ+wGVSp2XL11k9/oN\nZiwTfzQgdiW1Y3PsDvv0Ll7D3B2Rypg3vfUtLD16nsqxef7qiT+DOCUzcz+HJK9biZSYovD7ZDz3\nzLM4jodtOriOyyAYcfLESZ794pMsnjiK3W4S7uxgpinNWo0Xv/E8Z+95gMW5aU4eW+LNb34zadVm\n47mLvPTFZ1k6fpxGxaWb+hjSxpImvlK4lg2mZOiPME0LISRRd0gy8Hnj2dfw5Bee5O6zdyOqLl7d\noRoenvxJl5fyAn+YHBbgqwy2ygi9LLvFefQ8QuUcQroi1/OJFMfreuJWyayKkMGxlTqJsA9EQB1i\njep91oMMisWm7Jy/lUVwmML/W0+hFApAV9xwcACLJkS+Sy+/yYKv3csfYrhIITEMMFAIkZAKRdV1\neeSR8zz1lSe588TtJEFMo3mEqXqNylwVwzAZjoaMRgFr29t7StOi1WpxdH4B26szHI7oDwZsbW0z\nGA5wbJt6o869996PYzt0Oh12d3cY+H36QUDNsnCkye133slv//Hv49U8zp+8A1ukefFdKcnSLC8K\nUUKljuOM6RTNHITJ3Wll87IIcdKRSh6jmp+7cAbCJEIoSNmySVoWpuL/Yqz0zTfldthk1XeY6Vyh\nlJI4DkumeZ4bPLes9qI29pLhB2HIjavXmGm18eo1gjCkaTqILCQGEAb1Wg1zepbA9yHNWJyd58bl\nSyi/z1zTQxGzuX6D9uxRLLuKIxxmF4/QODpPNx5x9NhRZk/dzY2LF7m5fBOGA1zLZDQa4dSm6CsF\n0sCOMxbcOt/22m9jI1FcX1vl9nqdptNguDsgbqaYxp5ZXSxYe/fY7w/Y2dmh3xvRnpomDAfUZ1t0\nri/zwAPn+OxXPsed997BShSSdAd0h30SX7G1u41TqfDtb/oOpmdmuLyzxuraKg1pIkch7ekWvUwQ\nj2IadgMlFQkZhmGCZ5Ag8GwPx7DwDIO1b1zhv3r7O/nYZ/6cqG5zbHaWisgLlYhxdxF7udrVnm9C\npRlocd0FQChARy4bBXrmgNzpCiuXC/aLo4A6kEa6zC/rIKeM3MfAo9gBergC19M0m6Z5MGIkG+sc\n9vOqQJYqil3DkM8xvW/FvRZzIe8X+89BR+86SNpnE9St+6y3Vz0f+Oc/+acTg1lGe0XYkf5d8b6M\n3ooIiaIppVAid+KEScxv/taH+PbvfDNJmtJbz50OhimxnDwPhGHIveD6FEuaWIZNHMWYNlhWbtJU\nq9V9RTcc9FF7g1UoM891ieOYIM5Lq/lxRL3ZYPnGFR558CzTjQoyiRBZAnsbmYv72UcLe7mxURkC\nBUKRKVDqICd+GG9YVL+WUmLbthaKdzDmtay0i1eZ59QRTxn96OfU+3ZYXH+BpAv6Z4zo473Fabxg\nZ1m233/J2GF1ebfLU5/7Mm4sCTYH+N0uQiR0hh16fsB2z6c5fQzHbTAaruNaLvFggDncpGVFZFnI\nThDRw6A1v4BBRjTok9qz0N1ksHmdrGIzNCwkHtOWy22LM+wGXbAtGpU2plqkMpVgRKsciSysUY0r\nSrDcMLm5usFJp8X/8E9+nN12BxUbWMrKFbi28C4vr/L5L3yJ2bkj2HYN16vSDUbMtKZQ3Q7bL79M\nFg4xGjaXb1zFv7FNK/OIqxXe94v/lOmFBcLugCuXrhBcXeOUcqlFGXLKY91M2chiArfKehjTbk9B\nNCLO4nx7d5biGQYyUVi2QyItRKPGM5cvceLUcU4fbdM0LCw/xEiTvPqRLUlMSSYMRCowM4kEIjVO\nuqTTanqEVAG4Cif3BGoVe4mDUAjG8zzJJuVbt/wOkz3dojvMX3PP2ddSbi98/fMH5Hby70GKV6nJ\nQhZj2Z/cyFP+nd6+mdrVFfyDj33nLY9/1RE4jCd6UYFDT+ASRdH+wCTJZAjcmHYoPpvMMW0YBkoI\nTNsiHqZUKhUuX75Mc6rJXXc9SMWtEicJnV6HTq9DksakaUKtVqXVaGEZJr1en93ONt1uTqkYhkGt\nVqNScWlNtahWKiil8H2fwWDAbqeT76pyPaanmkRJzG63w7HFY3z2s5/jB77vXYySGIHIc1Zbcp8D\nS9OETGV7SXFydIOQSASQ7RVvy1txjzo6L8w2yIu8FuilbN6WOcYyKipWf925UkYVxe+hSN6TlcZk\nkvsuK3L9XEqNE3gVxxUbOVZXVzl69Cjr6+u0221UmmE3j2PUbmD4Kd1hh8F2h7m5JpaQ2Ibg/Jn7\nsbwprl1fp7ZUIVzpMNxcZnbaYmu0SzxUpJFFZkqCUFGtNGjW5jFON7j+5W28ygyD0Q7TCx6bvS3i\n2iKXOwNqrRauk/HSS09imwt4NYltjBh5TaK+ZCs1Wd0e8eyLX+V1P/J+/MouowhcZZBmGYYUGFp4\nneu51GpVdnZ3WVysI6Wk5VWoGyaRElx++SXmahWmZYvTc0dZiyXXXrzKu7/n/dxZn6bbGfDRf/87\nnJo7wqnFJaJOl5u9Xeb8jKX5WTbXbyIqHnfeeTubN24y61WJScgsgWmZWKZEpBmO4+EnKYllcOrk\nCV568TmOTD1IogRTjo29V8UuDkOSUKCExJAGSAtDSPK0rQVNkVuvejTTeLwN0r04e0NbkMcKHrK9\neQ5M0C56K8uSvkmtvB+iaLdSgGX6pgxaiqpTOjWU00IHSxfqIbTlCLDDWpk2LazbohVW7iu1V12B\n6xSJUoowDAnDcP89cEAp66282hX0wf57IVCBj1erctedd3H1+jXuuece1leukKQpConredQqLpZt\nk6YZo9GITmcbgSKOI2q1CvPzc0gp6Pf7hGFAkiSsrqzknKxjYzs2QTjC9SooBGEUEO+kBEGAZdvY\nponnVPjUZz/Dg+fOYJgWhjQhUximRBoCCxOlMqIo3KcPpGGgZJ74qoyAD4veybfjFmFOYoI3LKin\nW6EDXXgPS/5fNvHKi0FxfJm71sdG7+8kLyoZJ04a5704cuQIYRgyNzePPxphmCbGYB3T38GTFeJo\ngFursN3vo0yJM9XkxN13cHNti3OPPoCf7HJ99TnaU/P0wx0GUUrDdTGkg9WconbqBP1uHy8SbNx4\nma6/S9XxSLMmwXbMqdoic41F0nqT650+Sd9gyT7DkFV2Nzs02m02bJvISRgFQ/rry9xz+xEe+taH\nUDUbM5SYmYEh95ZfNb5/27LpdLq023N4rpNbIY7JbtSnH3Y49chZrnz9a/Ru9HFaNZhvcPrUa7n9\ntWfoba5z8aULtCOodgOSSpehCNlVQzqX11js7nDs2AJXY58b1y/SsDziwCcWijhWZHtGcBgEeK6L\nNA2CLMGsuDx05iwf+ejH+O63vgXbtRnEERXLwDQtzD1USAZhukcBUACBvRz+JAgBpmGBgCzNy+Ih\nBKaRp3NIs4w4yRNmGVKLGRdiL04rf1i3yrhZyGBZaevyWnZIHtaK6KfD9lXoVoR+bZ3eeCWQUgYx\neivPhWK+6ODnb0KOvOoKXN9CrQ9WEQrkuu7E6lpWFMX7sslUrJpZliFNg631Dc6cOcPvf/QjPPLI\nI7SnK9QqdeIkY3e3RzAaYkgDyzSZnZ7GdW3iJKTb7bKz06Pf75NlOYqfm5ujWW8QJyGBH7C2vsZg\n2M+VnoSK5+I4Hpbp0O/1cmTe7zPTnqE/6rK8vsXS8aOQRZiyyBJYDJjCsvacHjIX6EwVAjKJGPQB\nLj4vFi/TlBoi54Cg68+qHMlSIGJdGeumcfH+MKrksIlUHKOfp2jF+yiKJ5y1Ukocx2EwGCClQRKP\n9tJw2lz5yqcwej3WdwOkGuFUa5BJNrpbzE7PsjPcIVFDHDeiM/CoeYoF02Rl5GLO3IURg5ekmK0p\nWksneSG4guXYnJ73qMQW6U6EUzX5ofe/l8vPfQ3TlHzH330XL69vc+PyCuZuTNuDj3zscXZjBd4M\nN268jJkOOTo1zT//xQ+QNev0uhk1xwLGoWhy398gcB2XarXKdLuFyhIc2yMTGY5lMzINWkcWWL82\nhegP8Xf7NOfaODNN5o8tcunxz7Nz8RqrFy5x/k1vBpWQpBGRTBj4OzQCG3sT7LrLbXecZHc4JDVt\n0kyBkIhM4No2dadKliTYnoOXxWBJRv0+3/uu7+f//jcf5Md/4seoVlyCJMHOMuxMYAoDTGMvCRZY\nahx6Wiy8I384we1KKRFSoLIEKQxMKRGmQSbEHp885ocL35ZQ49wnhUzp1qAuY4dteCn8Ra+EYssp\nHMrFV/I1ZRKE5DTQweRxQhxMhV0+Rp8PupIu9FcBXsv3d6v2qivwwiFXTH49xaJpmsTRuBadvqIV\nD1oPlZPG5EoohcAQgiCKaLVaJErxmkce5Wtf/RpLC9PYlovr1HCcGvVqFcfxGI4CknhEt9shUzGO\nY7G4MI/jeGRZSuAH7O5ssbmxhm3beJ7L3NwsnusRRgFBnBBnKb3NDdI4xXMqeLZLrVoliAP8KOTr\nzz1Pa34OO02IkxhDgrEXJVIU9c0oFilQpEjy3OI6OiieE4wXtCybLIelC+9hSaKA/XCmopUnjI4i\niphv/Xg9MkBvOq1SjjIpo6jCYZVPdoD8fhzHwbRsBsMhteYUf/3XT3L58jaOMLl88ToN1wM/JJWK\nlmNz5s672Ozs5ApCCtZWb2JkA7pqgFGtYasalpmh/A6e5+BmDvPuFHfM1LnYu4abeiy2Zjm1tMCX\nPvPnCCvGa09zZXOdK1ubnHn93XSuX2T+5hT/8hf+V7587QIvbK+w092kabb5ge97J5Vqm1B4LNQc\n/M4ayjX2x7OwpNhLJxwFeSm/emMK16uQRTHD/oDBVof28UXuuPc+PvG7f0DDtgnUVV5z4jR//KHf\n4bHGEkbPx7NNVoY7OLLK6voKu91N/KhLsD7k4emztFBsXLuCszDL5mAXx/KoWFXSKEUiMFTuC0pG\nAZZrkEQRhlJ0dgd8+1vexp9/8lN893e9hUbFQfohEoXIMqI4IzJyH1O2l16BJKaoOqNQee74OK8D\naVkWpjARmUJJhUKSKUGmMuIoRok8ZG+folMHlXJZxspoV5dt3Tp9pUiOYtu+7lwv/uZzJr7lbwug\nWcw905wMVSyDmvJ35eOEEPuFMIq+fzMl/qorcF0R64paj0zJ72HsyS6Q6P6A7W+Q0AYqy93nCkXF\n8/KdTxK+713v4tc++EHe8NjDeWRJz6ff36LRmEHiUqtU94IyMpIsotfv4GcRjhNiWxaWbTIzM41S\nim63Q6ezi8jyZO6u6+JVPaRlUbFd+t0BWRITpzFBoDBci3Z7hkqzzu/+/kf40b/3Xow43Bd6IfL8\nw1IKBBK1P8aT8dBF07mzYqDHO1sP5go/LJTrMApFf/9KnKDeFx1hF8fmmxVyXrSgc5SaTNhfIBwp\nxxVfTFPm2eVkimU7pFlGs9niC1/4ax7/syc4f+q1dDbWcDMb048IBptkhqCxsEg6UiRZlcr0LL3M\nxbv5GUI/YUtUSTMD0h7KDBmlIW6jybXuDjudZY6363gjiZ9mbPlbbD53g1bN4fTSbYS7MGVM8ZXP\nP86v/tIv8R3nzvLtC/dCJcasCB67437+8s8/wtu/593c/eBDxA7E6TZW7OBKg1AaiL2kSGR5BEeW\n5sn7fX/E8o0bzM3HuI5DZMC238M34NnlZdI4xL3jBFuXlwmurFD5yjM8fN9ZYttkNxgxIOHplSsI\nCaPNLdI0JBQBA89hdbDF7e3bSaOYZ555hh0JtuXheXVMw6HRaOE5FTKV0mzUGPZ3SdOEGEG9PUso\nFF5tiic+/pe85+3fQ5iGSJXfg5ICw7JASgwx9k3lPicb0zSI4nC/vCAiR9aGyudmFOU5TtircC+F\ngSEypGDv+aTEqsixXSDpXK7zDWnFvBgXbhijdYUQY0s8l83DKZQyStbjxvNrjncTj4FMHnWiUy/5\n/xyYF7o+06kXPTWBfn19UfgvAoGXH4KuHIrv9C2nuolyK25LqDEGleSxO2mS4FY8TNviwfPn+dJT\nT3FkcYnW1Azt6Qoog93dXfr9Pil56adGs4ppWVRr3h6lkE+6aC/UqNGoM9tuU6lWyLKMTqfD5uYm\nQ99HKknz/2XuzYMsO88yz9/3fWc/d8vMyqUyqyRVqVTaymUt3saWbGNkuT14azAwNtE0NjQRdEd3\nEBATMUMPhD0dY4gGmjZEdMOABUMDNpsXaIPxvoAXLZbskspSqapUa1bletezn/N988e5N/NmSoIO\nojvc5x+Vbp48N8/2fO/7vM/7vM0mc7MzAFi2wzCJiLKMIAhxHZfTz5zh1iM3ooTCCzySeIS9YyI0\ncc6QIEDsW+Am18hxnClwnAA6O8A4/QBNtv1db9MP0TT/PPls+ncmn+3/3ReOjPYa/+/ye3Uz0d6H\ndjctLooCQ10bgFovrSyLDz30u7RbHTquIBM5jipBVzhhwCArGOSax546Q7/UzB8u6Q1HzCdr3BC0\ncXVFLjXXtM2FJCC1XZbbcwSih3vTLOUNL2X1yb8mdAQ3HllmYWmewWaPC+euMm85bDzyJT7wnh+g\n+Gdv4T/8P7/MRnudc48/g1i4lwVjoRzBG//pg1zc7CF9MEVEoRMqMUtlxj4vjMHPgEAQeD6j0ZDu\ndp/r16/z5KlTOAszjEYjZsMmMvQphGH55AmW51boX7rKcLXH1swWHz33GIdvPMR8OM+IEmELButr\n6DRB2hXDvOILp75BKQzHF2/kjqDFM91NGm4DL/QhDLm4cY3L6xs0Gg2KNCO0bY6uHEIql/PnLqIt\ng0LR8Ns899wVDrbaNIIQR0miLKUsKypT1Z2mVNiWjbBsKgkGSYEAaTFRZ5S6wqNWrijLqvXo4ynt\nRlfkaVYrpqSsa0pJPrZjqGtRZblbjJ9ovica8xovqnHAsPucT8D/xSR5+xtspqPmWlhQ7HnOJ8ec\nPL/T1ORk3xfKNvcD8vR7vJ8KerGo/YW27zqAT5/A/hOqb9a0w12tx9wPOpOIc3IvJPtSLSmxLQuJ\nIEtSbj1+nD/5s29x78tfzbUr13CdnGbYYWamxcLiAdIkIysyKl0xGkakSUwYBliWRZqm5HlGWVaM\nhgPUmKv1PI8g8DnUbGAQFHnBaDhiFI2QSkKW1uoSKYiyhFfc/TI+/7nPcPzoMUpdEac5UlpYtkVV\nlrU80dRG+yDQlUZPtbzvSK1eoDW5jnL3Rw3PtyqYvAzThj+TY++vKUwvBC8G3PvvY1XtUmMvxJNP\nL8aOZZOXOUrVkjIjxs0ZjoVUktEo5vWvfwOnvn2KXtllNe3S92wGsWCYlgyikuWGZqboUw6uUFan\nCdMu52ULk2zT6q+zVSR8s5rlVHQjLTegWT3OLbNrXN3Mefo5l05YsL7eo7kp2Vq7gJARs0uziGCB\nb24BV3PE6Fn+1b/4XvyDZ9kYnOTzX23x+GNb/Oz/9Ytc7K1R2T66kggCtBJUXog2US2Vm9yLcWr1\n7LPPIgycPHkSIRWNsMEgG9E8cjOHFg4yd/Ag/kyb0A1o4/DlP/sLPveJv6DoxbTn5rj3TQ8wc/gg\nSElmcp599mn++k8+jFcV+IFDTMHfnXqU4eXrnLj5Nu6am6eXZWT9LkduOcJdr7ybgTAY5WAqg1UY\nmspDCIvIQKFLKlNPimkEHr600UUGloUlLIb9Hq4XUMqi5t/T2pEvyxMAms0QNwjGwYVG2ham0uid\n+15RlRVSyBq0PQvH82ogrjSe5+2MGJv2UJmu3UxHzNPP2R5q9UWUKTv48ALP/C5/v/f5rr+/NuSa\n/v36+C/c1DZd1N9vfTH9LsBe3/X/lu27DuCTCHK/nGZyckWRT6VLu5HcNG872d+2a0N0JeSeiLPK\nC1zPRZs6ND8wO8fJu+/hq1/7Oi9/+Sso85Iki9i4/hyNRgvP8+h0Zml1ZsZp7pC8yBmNhhRFQRAE\ntNserUZjzAlrRqMR585dJS81ruPSarfpdDq4rlt3dA769AbbJEmCshxsz+X1r38DP/fz7+OXPvDv\ncKQAXZCmCbaSCETNeYu6Kq+FxujdbsTJgzIxsp++4fUDVu7h/+p00N7zcE2u8fT0nWnZ4QRgJ983\nff2nCzUvBMr1titjnNQ6JpIv2JuuZmlGRTV+UQ2IsX1nWdQZBRavevWrOffcRR7rFTy9kbM5TCml\nT6k9HD8gGSTc4WhunTN00vM4Zo2/OLPE02Kek7e+jkym9DdXWbYkIu5z+ux5gtskx+cdnORhun7J\n+XJI5Cyx0ppjNsu4YXmBj5/WfJVD/MpHNniVusZ/uM8ha11lxj+GryWutcQXvnGGn/yX38+1s2ew\ny4pShfSLFMvV2EZjdIE1dtgTqh5h9s3Hv4llWVy8cIEDC/NsbKxx6OZDczLHDgAAIABJREFU+E2H\n1CRQlpisoJf12SorXvm2B7m8ucogL/g/3vfz5J7Ndq/H8vxBBkXM4tEbsKThMx/+MNkoJjMFtrJ4\nZvUCWpfcePRGbj1+K5c3N/nO177CSu922keOYLc7pKVGSY8KBRocZdXDHYzEEhrLWMRZjrIcSm2I\nU43nthGA5fg4UowtgeuRbLat2N7e5JkzlxgMBiwsLDAz2yawPMqqRAqDlBaO66Fk7fqXlwXGVFjC\nwnIEeV4SjBeAycDf6SLjZCDCfgnrNBhP9pvGg+lt4nu/H/z3q96mqZHJ58+nM194SPP04jH5m6dr\nPtMB2f7v+4c6Mb/rjTzf+MqnngcA09Xrskz36JGnQX4/b27bfl3K3qlm1xfAtm2yIkcqhVQKISWV\n5/DQhx7i+LFjHFxcxB0XWbI0r7vJEARBg7QoCQIPx9nVuhpjMFU9186YmsaYcMMCVU90SRLSIsd2\nbKRSeJ6H67pU44g5imLWtnt05hdYv3aVN7z2NZgyw2QJSoixjHCcrlGDsp6qyk+u1/4JPfUDwTgj\n2e/etldBMr0Awl5qZb9N7f6sZz+dMznWXp242Wkiml4kXigjkMZCWJNFoUCL2oM9TjIaYYthlBOG\nTT70oYf42Kmo5kkR2E5AnOQUUYyVDZmzRhyfNRyZKZixU7bmbuXCdc3lUZNuDk7V5QYnJ1SKp9e2\n6cwF3DcX8woucBGbhBkSOuiy5KCrCbGJw2OcD46wnSTcWa3xdr9P694NGp0Z4mqFz5xr8sj1Fq+6\n615eebCkY22QNALWhYexfSyTI41BIhFG1BEnku3tLk9/5xmWlg5SlrVboZYZhShIogyRgid9rgx7\nbFc5RZFzY2eOWS9AuYpKW/hOSCWg0WngBRYNV/KZP/0IW2fP0HZc+klELA1YhnbgcvuNN3P88FGS\nQUJSgndggc4NR2gs34BxQwZRhrQsHMeiSgukBttxyDBUtiLKshpMK4NdGpRQaMdFqonNwuT91kgl\nEOPCvAAGgz5VmRLHCUuL8/iew9XLF3FsC99zUBJ0VaLHz7Rv7S76f5+3ySSY2P8sToO9lJI7737t\n87Do1GNffl6wMvme+rv2UrU15tQF6Gm8qp/rF4bS6Xd0fxDzYhH49Lv2knu/50UziO96BL7fvnQa\npAGyLHkeiExSEdgrOSyKGngtpWp+TCpQFkIKmmGDNM9q8NYVeVFw3+tey1//1//KO9/xDpwxPTIz\n26IRtilLTRJnbPWucfnyBRzHIQgCwjCk3WoRhD6eN0OapvR6Pba3t7Esi1azjed6tJpNjKqHPmx3\nu1y6tFY3KSmLdrPJwoF52nOLbAyHbG53WV29xuJch8APqIocKeoXXQOm0pSm2qGTYFrm9ELV7Brw\npzntyUM+2aaLnr7vT2U8tTG+53lTPOBew5/p1ub9Mq3dNNOg1D4Z2VRUUlft93pQpKMUISEMA7Iy\nw7IUc3NzlKVmefkgcZxx9OhR7MfPgi5ohgFUFS3LIXEsBrlL3xzg1EDzZDemESralz6LBaTdisi6\nlYG7QjEque/kEQ66Bzn19BVGZ/q0bunw8jffytaZNZ59/Cxle4no5qNc2T7H3OU/5h1LMOsUHDn+\nErpDi2z7APPeGnZ4nltvvY9i5QGeevgat9+Rsbx8lc1Ys/yS13Jte4jnBJRFialMTaNIC0tZzM/P\ns7iwRJIkOI6L1gapI6Q0KMuBQUmIR9EI2PAMpaOwogw7ThlFQ9Ko5Lkra5y7foWPffyj6CzGdQx3\n3HyIBQ1hpcDyGHglV/NtwnRI7+khw/VNluwWs405DjTniS+vYdwO4eFZqtDBDQIuPneWm1duYNQd\n4FkuaZrQT0YI10E6LqrQ6CwjSROMK7AdB8PEFgOCMEAIQVGk5HlFs9mk1XYoyhi/UaJcFyzFzbfe\nQeB5rF69yJVLF5FCMz8/T6MRUEajPY19E7pw+lmaYMFEfjcdABZFsaOaerEIfGJZuz8KnjADSu1m\nmkVRjCP+3cEm08e1rL1OjdN8/bQ8dhK9v1DWOnkn/lsLmf9gBP7e976XT37ykywsLHDq1CkA3ve+\n9/E7v/M7zM/PA/CBD3yAN7/5zQD84i/+Ig899BBKKX7913+dBx988PlfOhWBf/WLf4lt2ztmNNMr\nDzx/avR0UWy6S3ByQaaN5KeLF3tTewi0QyQ0v/1nH8ZtNrjthqO0tEvo+TjNkGGeYymL0HJRjoXl\n2DucW7fb3QFPy7IIwxApJXmeU5b5zt8wfdN83x8PJMhJ07T+u41iGI3wPYe//dsv8O7/7Z00wwBL\nGKqixHU9qqIeauG6HtSCm50bO+HErfG0cMZprC5LJJqalTNjCRvYVk07SUDourirDZSVxJgSpQBR\nIYSuNbvGUJYgpI1SDqbSyGKIkA7Gcqgsm0ILKl1iKYMyOY7KocpQaIx2MFB30BqDsB1sy6XUElBI\n6VDbAygKp8KzLVavXOG5s+e4vrZJf5iRVpLnLl2m2WwjZN0NeOqqotQVtueAlPSjUS1FKw2i0Fha\n0A5aDLZ7ZH5CWWlQDrbtUpU5qopZbEqWmwbTv8yNsw4zoWI170Kcc88Nx2j2Y/T6Ntq3WXcD4uYs\nfhhy06zLjR1YaW/TXBzRsBXWqI3wZlkdSBwO0PTgsStrbHh3cvyeB1BWhJdrlCoYBjGp8AgIoOwj\nZImrLYyGnuUgtU1DOnXLfVVhScXEfAxMfT/HigtbBNhSgh5y7uwZfve3/4CL564zN9vhJSeP0B+s\nYjsuTz31HLn0EIHL4U6LBdvCTmPagceJl95FY36Zb5+7itVeALfJ4SM3ceKuW8mLgv4gYnZ+iX5v\nQFFVhEGDLM0oihzf9xmOIjJdEfgNhBDkWY4QFoK687KsChqNWqGVlwU6radd+b5PXhSUusB160nv\njVYDYyq63R6bmxu0GhVZlqJNRaPhI6VAYUAXWFDbUWiNkhKd2wgpKcsCx3NI0gTXtevOTlNnyy97\n5Zufh0XfevRLVNqM6dW63V8JkGM3U4Pcgy2TounEC3x6mwxpmKZkpqmZyX/3W8dOMG2/I+Fku/Pu\n170okP+DAP6Vr3yFRqPBj/7oj+4A+Pvf/36azSY/8zM/s2ff06dP8+53v5tHHnmEq1ev8sADD3Dm\nzJnnrX7TAP63n//E83TI0yT/pMV68nv7+fLpQkVZlnuquJN9bHsXfCf7tt0mmTCMhOZXPvgf+YG3\nvANPS3zLo8DQHQ3Js6Ke0q0knlc3XUzoEtd1SZKEfr9PURR4nke73abTaSME9Pt9oigiiqKdxSYM\nQ+bm5nYKMHGSo5TF2voqQhie/s6T/PAPvRNBRSto1MXMSpNnOUEYojGUVVlzbePrZymrBvZpjk7U\n+vex4qu+lgKqskIAinGkYKh1uCYDvWtQL4WgKOpCkqUsLKvuUK20Rro2zdCnu7WB7zlURY7lOGgk\nhVAY5YLtEjTapEk9pacyBXmekeUJZVUgpKbUFdGwz+bWFnEc0R+6pHHEE48+ShzH5IXB8UIsr4VQ\nFmmSMhr2GA174N5IqTWW62GEJEpTtC7BVCTDPoEjWZmf4+DSAo7y2Nja5uz5y+RaooUEU+CrikDl\nyKKHW0bMz4Ss5U1Gg4qj800ePK5pjh6n3xsylAdZPHSIWes6R1sZLeHSCpawb+hRuRlkDby5Jmt5\nzqzwacRDrkZ38Mj1O2nfHHDs+B0IpYnTDZotl1GRIjiEVbpY1gUyq6AqDuNUgIowgrFvSoW1L8Op\njKYyZc2RYZMlKYEncZTiqW99h1/+wK9SFSXHbl5hcanN1tYWmxt9BkYRYXjVHbdTbK3RtgRN3+XO\nl76U4y+9l/bKTfzir/8mj556httuu52Vgwe46657ePV995NkBY7jEcUJURTVKi9TZ3me51FozfLB\nFdbW1tGVqYeiGMNk4k6SRLUZmQGlHGy77jgOgpCN7U0sJdFG47i1KmWS/c11XJSSxEnMs88+Q55n\nLMzN1PvnGWWW0W6G9Ht9Ws0WaZJSVjmO5+7w3kopDILKGE7e/brn4du3Hv3SWCgwwR49BnAAjZC7\nWeIkCgeB5/k7GDXBpTzfpXun60aTIG46ot6PXfsz6enM9OTL3vCPp1Duv/9+Lly48LzPX+iAn/jE\nJ3jXu96FbdvcdNNNHDt2jIcffphXvepVL3r8ScffZGWbPv608mHy2fRquF8pMflswjHt8NJit/Fn\n52KXFZUxKDQPvPa1PPPMd7j/VfexeX2dMi04tLKC5dgYKdHaMBqNGI1GbG5uYNsOjuOMQb2eYD8p\nKF68eBGYRN0BBw4EO8WdNE25fPkKVVVH15bt4NiwML/I5tYGswcWOXP+IsePHWWQpOgixxtPHBrF\nI8S4Q1NIia2m5VM1zz95KJRUtVOcrouBGoPRBsu299QI6hhDI2RZKwMwSGEhUAR+SJHXGnVBhWML\nUBZdbSGKAsezsE2Gb2uiuMdaN2UztXhmtc+51T79uCRJRrv8IuMIxGiErJ3rlKxTVEtZYDVI4xjR\nOkpzxiZJc0qjyEz9u5Wb4IgAq6q7Mx3Xx7FdSq0IPJc0S8mzEY1mkzLvcuBQm1IPsdICnUZQpQhj\noYVNVkCCJHZ8lLCZmbmRZK7DcX/ARlRx9uJFDqx73Lewwg1ul9WNhLVr2/T9Nkm3yxF7i7I5i7QT\n/AY00wx0htvWYK+iZkYMopM0V97Bw6c/yuLcaazmIYLwZtRog1lVMhAxlpQEZRNP56Qmx61KclGR\nqXE6LhzQZhycFDDu9FOTuojUhO2gphmR3HbiBK953av53Kc/Q5SkwCJ5JsnzkqRIsZst+sMhNx1c\nZrB2hYZj43VafO1bj/Ltj/4533z6LN0s46mzp7l2tcHhwzcyGAxotDoIITh4cJGyGLe/K4mlJGWe\nI4QkGg1ZXpilyIsdTf9wOMASNk3fxvO9evhJUe4UFje3VpmZmWEw7NOZaRPHCY7tkucJcwfmGPSH\ntYxQWNx2/CRlVXDp4gWocmylcJ0Wo0Tjh/PEeRfHs1HGr4O2MSVlTK1cs1+EQlFKwthMyxhTRzrj\nSBxDnbGNAdm27TFe1ceczvwnWDbBo2nasaaSih3Anry3E9CeFFKnqRbYO5T8xbZ/NAf+G7/xG/z+\n7/8+L3vZy/jVX/1VOp0Oq6ure8D60KFDXL169e89ziQ6nfDYk206jZguak5AfFIcm+ZhJ8WK6Yuw\nP3qfHMNyHGSpCZXDK156Fw99+/d55rkz3HzDEURWUKUJQsK17Q1mOrMEgU+r1UTrepL5aDQijkeM\nRrX5VBAEAChljZUrCWma75xLp9PB930WF5d2zjUeRQjLIYpiDswvYXs+f/PZz9LsdDi4sEC72SDu\n9/AcC10qmKpQ53mONgY5Ts3KHemhoZIKPZbwoSaNErX+1kxlP0za97VEjGkNpEIbSIoE25JYlkCb\nAmEZpGXjaI84GTHfafPU448hpeDosdtpuQ4f/dNPsZVYpCakNbuM5W/UBWFjIbEwWlLmGqN3AwCJ\nQGtIsxRlz2C0JsoK4lxgOx7KceoBGFVJVSTYyiHwUwajTaoqx/VnsJVHUlZY0kaiOXr0Fi5cuIiU\nJaEJ2NjuEuVRnR0oj7AdUmmDZTsoW7GZpmxd30Ja11k52OKG2+ZYPX+VC6bB7Izh6HLKY/2Er/YO\nkw1neeVMj5WNFCctObI0w3EVUBQD8qKD27BxVmwiHL705HWuZhaN+AmquMuprevcffIEVmnRDDSR\n6ZLSRpkS1CZDEyCkgxAFVVmhx5aula4Nyow2dT8AAo0hzSJs18GyPLKswncs3vOTP8bMXIu//stP\n8+rlY2xsDglbOelgQDaKKfpDRKse6rC4sITWMBiM+M4zT9MIO9hBmzLX3Hbb7SwdXKbZaCGVYmtr\nm4uXLtHptFlcWODq1Us0wwDXcbh29SobG5vccced+EGI57kMhyOWlxfQlR4vMIYiS7BdB9cJSPOM\n+fmb6fZ63HBomThJaDUCEILA9ijyjNBvEEUJYSOkyHPKClaWjxD4PlWRoxBcXb3KMM4Jmi4l9cAM\nZO3bIivDBP4ke+mOySao6ZO6C1oijMKYakf9hS52gHqXPhE7hlvTAFuWxQ7WTP9suqdi8u7uF2BM\nsw77G/P+vu0fBeA/9VM/xS/8wi8A8PM///P87M/+LB/60Ide+AKJF75wk63b7e5E4dNgPM0TTcvd\n9nsjwF498/7VbRJ9T/YRoh4wOiwzHCR5lGJZNu94x9v59d/6TX7gbe+g2B5yw9IySrgcO34z/e0h\nWmu63e5OQWV2dnZH4qR1LSOsz8UlDBt0Oh0ajQZpmhLHMVmWsbW1tcP1Hzx4EMe2ybOU+QPzXFm9\nRmN2lgff/BY+8qd/zr9473tI4iGha5EXeU2HlBopanWKUqCMwJJqh6fTExMrI3AnZlS1jBwwZHFC\n7d8sxyleLVVUVTju/AGhJKWpQEAhNVmeIITGUTZlkoIWrF65xneeibnrnlfzzSef5tf+3X8myeHI\nzXeQZxUzHZfNy+fwZ5tYykZJByUsQIJXf0+ep6RlimUpLFuhRzVvqoXAkobQs+vxc8bgKol0bYRw\niYsI16kLtVk+JIkyZmaWaPmSSit6vYRvPvwES0sHGEUj7FBRChe34yOkpCgrlAUKQVWl5EmFUhZa\nG06NjpNd2uZ1R306KxX9fJNVJAtVynJjwLVqm4f7HT565SCNPENtedzy3JDvXy655aBB0ELGIb2t\nJ+nMGL7y9T/g2CuO4BYZln6SqjrGL//xiHf90Ns4kJ/B2DldW2OUQRmLSins0mBPkqRKoyxFWZQo\ny6o5Vm2YuCa7jo2lBGlc0m7OkSZ9KlPwjh/8AUaRwWseQFshfgfuXD5IOUxZnGlTDUYcml+iSkvi\nYcLF85dwhU+lFTccPsoP/fCPcO9d93Bl9Rqrq+s02x2EsFhYWARjOHfuHDOdFq7r4rr1sOy7735p\nfX8xJGkypkkMWZaMlVeauYV5esMRW1vrLCwskGcJs7MzZGlGp1MPSRkMh3iOW1MquHhzAWma1gVe\nYWHbNnFce8crx+WWW+9Ea8MTpz6P59Zt6JaU5GXd8o8xSAyYF27kUUqhqLs7jdYIWUs8hZlgkd6T\n7e96DTl7QLf+bFeLPqFcpjFwfzS9H6emwXt/APpi2z8KwBcWFnb+/RM/8RO89a1vBWBlZYXLly/v\n/OzKlSusrKy8yFHeB8AffuQUd991grtO3rlH8TBdlZ2I+Scn+kLpxuSCTbqzJsOOpy/o5EakaYrw\nFJawUVUNcsuLi7z9HW/nmWee4S2ve4BsMKIocrZXr+CpANf2EMaANpRlQVWUpHGC7/u4rkO72aLd\nbJJmBXEUM8wyiqxeaS0pac/OsXBgnn6/T5IkFFlOOZZkdbe3abfb9MZdoLfdfidPnznDyTtvIysz\nHN9FFxWussd+0vUbvrNoSQkabGWDHDc5FHndQMTkITCEgU9R5uiqoqo0xkjQEtv4tcLFqhCKup1a\naYRlsdXNGY0SZmYXCLwWKs9YPnQL3/ry1/nkf/oIrfkVDp64H4mkiEYo+oQiojlvsYXcMeYSUted\ne2iUYyMdiSgE0lNI26IjfcAQj2JMVdWprdxteijLHCVKGoHNKFf4tqLMEuKoR7eKENLGsX0arqC9\ncog3vfFNfOELX2TLOEhZkGcpyoAtQVagywJpBLZyMQUYFNvNgHN5TPPydV51zMH0S568Irhj7k4O\nqW1e765iGkO+Zt/NU8Uyjr1C1T/PRnvEyjBGii5xUWE3Lc4880XajVn0VpusaLFyeIuTHc2To1v5\n/a9c5D1vX6KVn8M1XcpC0rAsKn2FJPcQ7gEazTZZlmGMhoraKVvVDS/G1KytJTVVVuDJgDzJqUqD\nFoJKSH7yX/8bvvC5r9PLckodUxYRi65PFg9ouLW3zNzSEt1BzPZ2xPe+4c3c9cr78BszICTnz18m\nSTPCZoM0zTGAZUOWJLRabebn5vF9h9NPPcXK8iGyrMQLAgqtqX1eDMO4j2s7oBRZUlCV4DoeMzMW\nZVnXXOJRXBf4x1YXzbCBbdkUZQmlwVYCtxliQp84zXYo0rIsGcUxozgGAUeOvgQpYDQa0tveRlc5\nnWaTqsxA17bNL7RleY7R9bALIeohK7s0ikaovZawNQiLPQHhBIsmnZiTYHNS29uvJplu5tkP2Eop\nHnn0CR557In/PioUgAsXLvDWt751p4h57do1Dh48CMCv/dqv8cgjj/BHf/RHO0XMhx9+eKeIefbs\n2edF4dNFzC9++s93gHWyIk2D9QS89xP+E1B2HAfLstjc3GR+fp4oinai5CRJkLIealBV1Q5vbVkW\nZy6f50Brhjm/hR/4ZBJGlHz+c1/AN5IbD67Qardx2y362xG2susmHKVwXXdnuHEURQwGA/I8Hw9G\nbu5qvquKKBqRpnX03Ww26tFsY8qlKnKuXb+GEZK0qii1wW+GrK2vc/7sMzz4wPdwaHmRMk/RWU7L\nC0nTdKewq5SiKIo97fQw4eLqn0dRtLO/tNROlmIEFGWJa7mIwsaoiiiLUIGiF0c8ffY5tPCotE8c\ngdYuo2FM007opQWX14e0D94ITkCcl+RRH1XGVIMt8uEWc60QMXsQjaIsDbYToLVFXgnS0mCUTWEE\nUZqhXBcvG2DGLnS+61NpyKuaKkMXmCKBIkJUOaaEsOHTnmkhRMF2d4Ot7S16/SHd7RFKehw9cpxr\n1zYYmIAkjrEkVGWB0BW6KsczFGuwUbYLBnpOjqVy/OQy97QS7l6cpUxt0u0BR9nikD9gzWnyNecY\ncVLRmTtO1R1ysOrh2Sm6k3PPMZcDbPO1Z57j0fWQdvgGfuaHZrh55izCCXms9z18cuuVPLV1kX/5\nzptYSNaYkT7axCQm41OffZQvfunrNBohJ0++hDvuuIMbbrgRrSfNLLvZUx51mT8wx/ZWArgoFxpt\njy//7d/x8U98mu/7vnfyx3/yYYxIGFx/jiXfZbbVxlUWYdhgmBUcue1O7n7V/bzi/u/huUtr5EX9\navZ6XV5y8gRr61u0Wi3KKmNra5sbblhCV4bhoM/a9VVuvfU4QkIQtugNhvhBgNa1P75lWUSjEe1W\nC0tK0iTFDX2CIODss2e55ZajbKxtEgS1SqUoCvI8x7FtgjAkz2LC0CfLCtI0RQoL16/ni0ZJWkfm\n1IIASW2Y5ToOjcDj8uULXLx4jk4zIGw4YHJuP3H/87DtzFNfp8hzdDXJbsfpzXjKvbTZWTCma2nT\nnZgTrBJi1/52OqLexbzdfacHgcMuTTyhQ6e16Xe94oF/vArlXe96F1/60pfY3NxkcXGR97///Xzx\ni1/kiSeeQAjBkSNH+K3f+i0WFxeBWlL40EMPYVkWH/zgB3nTm970/C/dJyPcT9ZPr0b7P5+sWJOT\nnL4A0/rkyc9t296JuicyoKqqyExFyw+R4yERpYTSEqxtbPI3f/UpXnff/cy020RxjGM30NXeoa6T\n6H96MOm0SH+iT3VdFyHETkfmpJgBYCmFNhrbdVGWTZylZEVBf9hHSsGTT57ijQ+8oZZh1ZwBjuPU\nD7njUFYllmVTViVCyp3vmpz7ZJqNbdvk+biIouTOYIjKaMq8JBom+E2ftMwphCHszCKsgI9//NMM\nh5DFFnmmcFwfYw/IyoqVw0fY2OoTNltkWY4wJYEDOh+RJ0OyZIguYrK0oNFo1RGyG2I5TbB8ChzS\nXFIKhRaKQsSUeUaRpri2TZrmKMen1LWPTZmnVFlEVaZYqUucDKmqCK0jinJAlg1xXYe16+t4bgMh\nLCzlkueyHqYwTm9rqkiAsOqJL0IhbZei0ATaInM0lpUwU2xx94LPjR2YcWKa0XVk7zrGcYnnlrgl\nHzBgkUviEKeHDb66WpCEOTe3Im4rK1acPro8z0ywxPd/70vx50qiRoJwjvOHXzvJX147DjM9ZrNv\ncaPq4duKzswd9NfPce3KUwgpmDtwoM72dEWWZbXKqd0h8D0C36fTcGk3Ghw5cpxvPPIYl65e5tRT\n32ar12N9vcfJk3ezunoVbXIcmRI6mjRKmO3MUmlozhzgvT/1r/BbswTNOQwK3/PZ3ujS8ALKSuO4\nLlmWIS1Jp92g1+sR+jbXr6+xfHARpSSNZpPN7R6tdpvtXp9Gq12/B8KQRAme46DGQZczDnoC30fr\ngqKo3z/fc4mimKWledaub9LrbVGUMUEYMDc7X0t1hUApm6wokLIeLJEXBVIqsrQiTRKUlJRFQasd\noKuKfm+D/mCDPIt45ave+Dws+tajX0AKQTVWsMmdJp3JOzKlZtnJ5id2FXuDSaWeryjZT4VMcGqa\nBp4OWPdH9v9QI893vRPzK5/7OLB7MaYBcALG0xruyQlPwAkgDENOnTrFS1/6UtI03Wm71VrXAxXG\ndMr076dFjhSSqqjbjS3LwvI8hOew0d3mD//gD3jvj/woo+0eRVWD3uxMbVwF9Q25cuXKTlW5jr5D\n2u364V1fX6ff75PnOVmW0Ww2x5N8AixlUelq5+8vinqcmO3ZeJ4HUrK2vk5/NOLsuXP8wA/9IB4g\n0qRelCyLvKpwXAepJMMoot3pkI0pGct2yPOqjmryHNuqr5VlOUhLceXqKr1hXeHXBuaXDwOS61ub\nbPVGnP7OGba6I/q9hKXFw1QZBH4DjaSrh0ghaQZNQi8kHo3wHZc8z6lMgZaaLE9J0hi3e4VRNEQp\nTRyPkEqQlwWduQVm5pdwgjaFhiwv2WAB20AWDZlptzCVwfZCtHQox5F4nqcUeU7RK8EUCJMTx1tk\nSRdjEqKojzPu3ms2W4ziBDMaYITEsm2GcYyRivnFZSzXZ7s3IM0K0rSgMmBsXatW4gwlBbaqWPEL\n7pnT3DaT44g+cZzT75f07FnaXhvLbtFTh7msFzmfDDlz6Tu0UsWDN1rcwmOcWHFwD93DlYN3sjV3\nHK9QHD98F7/6p8+x2b6TNHsaa/1h5OoVbj/QYDS6CKqirCqa7RZ36Cv7AAAgAElEQVQYwzCO6ylR\n7TYAo8GQPMtwLYGgIisyikoThm02t7qkaYrnuliWoCwKlLAodYwbCKLhiDTJ+Lmf+7esrm/ymS99\nif/9//y3rK13OXb0OKbUONLgez7Xrm/QbDZZ39yg02nTCJokyYBzz55hZWWZQyvL+J5LWUGUZBgh\ncFyXotKMRlGdiVqKYb+P73qEgU9elvi+TxzHbG5ucPjQobFCqeKjH/tz/uZv/oZOu013uwuq4Pr1\n61RFydGjN/O2t72dN77xjYSNBlprtnt9Op0Z8rLElrXU1egaBNMsRUqDbUuErIebd1oHnodFl859\ni0G/h22NqdVxl+yOHlvt99m3kFIxAfidngxduxG+ENZNz92cKOP2W1JMAH66xX7yvf9TA/g3vvJX\ne05k0jY7iaKnee7JNi0bnBxvskJONNlQpz6e5+0A20TDnWUZlBqtJJXROJaDzoo6inYtIqn5zGc/\ng68lty3fiN+eRTm7g4YnXgau6+5kCJObUJYFruuhlBxTGfWszbrJpySKol0TeamwbLsebYWhKut9\nEIICGCU5l1ZX6Q1GPPC6+1mZ7WDZFkVR1i5uxlBRT7ORSqFFrb3t9QfkmabMC2ZmZ9lY28T3Q4SQ\nhM0mvcEIaVu4nk+claxu5ly8dJXt3oiyAqMFtqXI4gFJ1KUqRizMtUiyhCKcx5I2odvEVT5lZqhK\njbRshCXoRUNs366N/qMueTZiY+MqSuRAQVGktZJR2rRnF0lzzeyBRXRjBQtNOujT9FyKvCArDLmx\nKI0irwxFOZZmCU08GiGNQZmSKs8QpkBS4TqKdqdJmkZkeYqVlyRZRlYUOL5H0GhijCGKExASz3HQ\nZW3hu1X1aRmBjySuDJvDmBlp0Y57LAYVpRmCNAy2B3xp1ODEYsBd8w26vZRrmxFrvSEjx0eGNnc2\nDUetiLmOz2D+Rr65GrBR3o4WhnZjjdd/7/fx1SdyaPgstwuuP/opOtkTaBRathkMh7WxGXWwkqYJ\nllJIAUoq1LijeBT3idM+R4/ehO+2yRMo8wJTRYS+RKGoMonfDOklPT74wf/Ik9/6NnecOEGuK5zQ\n57cfeojbb72dE7ffydLcPP3hkGES4fsBaZrh+X7doKasevg0htlOCyHrnoKyEOPGKo/S1L78UVx7\n+pRZiaMUuqrfmUJrOp0W/X4f1/Uoi5zTp5/iqaeewrEtwiAADI5rk2QJaZywvr7O1UuX2d7eBiQz\nMzO85e1v48E3vYli/C7qqraMNVqgLEWS1FlonCa4noMRmtlm+DwseuqJv8OzLaJ4WLspirr9vzaS\nq2nGaXza7WpmT7Rcf1aD/HRjDrCDZdMDySdihv0t9dNMwmT//6kB/JG/+xTw/PZY2K3S7vf0mLaQ\nnIDhZEWbAPlkYC6wY0BVVXUqqpTCNopKCUoJnu2g8gqBIBGayIGtXpc/+93/wve//kFmlg7hhuHO\n4tLr9UjTlCzLsG17p8U+CAKM0aRpwtbW1g7lMunWnEwXqotTdYdkkmS4roVr1XaKVVWSlxUbvS7K\nbWCUzXOXrrJx7TLvfMs/YTAYMHtgbty6DNJWDKOINM+5eOki3V6XtbVNhoMMKSRvevCfMD+/SFUZ\nWs023f6AOMmwfZ/zFy5w/tJ1huUBqgpcp0Ge67qyrRM8u0AX6yTJKkL3SdOI7XKeA7OLCOOzOHuY\nPBM4bsgoTknLDK/hkeQx/cEAx3ZYmJ9ByYrAFeRJhNEVreYsVWVj8IkTTZZqtL1O03PIhn2KeDS2\nQfBIK0VSCZJck+YlZVWSqBHxKEVpCYVB5xWWgdBzOXRoieGoi+1I4mSIq336wyFFWVLpCtuzaTeb\nFEVGleWUWYIpC5SAljjKUK9TeAOwchZnW3Q8yWxnlsbMcYZFwNLSLI7cREYxT37tr+glPbrNA/jN\nNktlyIUnn2YUP0PbjOi0b2a7dYiCESvDWWaqNqvNnNVGE12EtK2ASLuEcy79ja8z76fISOJbIVGa\n4HreWFlkSJIY3/MwuvbhEUBhoB9vs7DUREmBKRxCZxad5nhOTuBWtP0m8UBzzyv+F+593cvxLZdf\n+fe/wk//7E9TCU3rwAynTz/Fb/3mf+KuO1/CO9/2dioBmVIkaYoQUFQlnuuxfHCZP/nTj/DGN7we\nozXtVpPedpfQa+EGIXGaomyHJKuLjRhwLQthIE8TPNfF2Barq1c5eHCJsqx44vHHOHv2WW666Uaa\njRAhBK1Wrd5Sto2pNIN+n+fOnefyxSu1MdxwwHMXLnDfa+/nR//5P2dhcQFh9LhL2aIqDVIpEFBW\nkBcaxwX/BQqZTz3+d6DLWi479qc39ShxtKkHqkw3B9bvs82kkLnXm2V3AtZ+GeC0RHpaJri/vmdZ\n1n9fDvx/xLafA9/PLU/++Am/PC21mfx7jxHSTvOOtedEp4854agn/+9hoYFKQikNpa4oigxH2VSV\nxg0D/vxjHwNpcd8rXoupNKNhD9dR2Ba0Ww2gNrZP0oI0K4jiFF1m+J6LZdnYjkuSpLQ7s6RZwWA4\nwrKcWrpk2eiqLqgZTB0tOjbF2OjeGENepASuj+f7XFvb4tNf+DL/7N3vZtDv87KX3YsuKvKipCgN\naVbwmc99kfbsAV72slfSaB3gP//2b+IGFvfcdYJXnjzB+oXLLB1YYpRLyrDDFx8/zVaUUJUZWpcI\nNK5jUeQ5lpBUhUFoSZ4ZLOkw6G1T5c9RVAVLy4fxgjZF5aBLn6qqGzuStItSGUUR8ZJjt1OUZe3z\nYTTd3gCpbEqtyfOSOM1otpo4jkM8jCh1SbMV4NjQ765hK02eJhSlRkuP7WEGlkeRSAx1kdp1bLzA\nJcszgsBlFEc4rk2cxBitqZIBvu/juw6mLOh1u3iuQ56luF5Q66rHLoFpUtYeNBhuOnyIdquJKfPx\n8wiDwRAh667cmw7dxOrqJdbXVlGqIM2GmKrAcV1WV9cJgg5S+XheSKIrHM+ri2QGTFUijamvs1Ro\nXVKktavk/IKHUpqtjU2qXOM4Pko6SOlRYZHkGtsNMdJG6gGBU2v9l5YP4fgBaZ4jAdeWGJ3zT9/2\nfQSujeMIBv0R291t/vIv/pIfe8+PEcUxCwsHcBwXx5H83u/9F3q9PneeOEF7dgbPc3BsC8sSJFHE\n5z/3We5/zWtYWFjiwOwcVVmSZTkaQaPVJE4y0jTHC0LyPK/nl6pdg7JmENLv9wj8ACkE16+t8pWv\nfJk7T9xRZ9GWVTs1IuthEXnNS0/qOL1ej/Pnz7O2tsZgMODxxx/nzW9+Mz/+4z+O47k7wd5u1l5r\naCcg+UJKlDNnnyWJh3WGIE1NO+U1hVZ7uO+ax8VpShCEVGO54Q74ylo+KY3eE1VP+xVNsvfdaHzX\nMGuCYQDGPJ8b//um0n/XAfzhv/1rYFcGuDuAV+zRcE8uxHRTz7Qcp74wezWTkxs5Ae49UsRcg5QI\nW6KlGFs0a6SpqQmUpDcc8ju/9//xw2/7QVzLQeuCJIkQaPr9HpZt4fo+jUYbjdiR95VlWRdrRhF+\n2KiLHgiKUjMcjkiz2mQnSwss6YCAIPRJ0gRt6nTeVorRaESaJHTabU7eczetuTk+/rGPksUx99xz\nN77rcfLkSQySjY0tuv0Rt9x6nI3NLo7f4f/90IeI84giizh++DCBkLz2Nfdj+W0ubo/49oWrRIUm\nzQrarSZZlpDnKaHvjwuTIIUiTyt0ZcjSBJFeA1kxiIbce+89RHFJmhqyWOP7AXme0Gp6tNshOs/H\nXXF191qvPyBsNOkPRyAk7c4MaZaiTa3dRUrW1lYJfAuhc8osotMM6Q2GtOeWWNscYKRNHmkqXTIz\nO8Pq6iqOV3fIZUVKs9lEKUW3t11bm5a1OlkgaDdDBr0uAhgOh/XzM+Y7W51ZyjKtrYRbLUaDHo6S\ntFtN0iQe9w/Uhbb63x6eayOpCAIbrTN6vS22t3ukaU4U57heg1Z7Fq08oijGthUzMzPEwwEznTa9\n7S2qqsRxLKg0YRigTYzjSIa9AYPegFajhWXZHJhfotsfkRUG5QUgJDobEjqSwSji2PHjdHt9bKdW\nYpRlxvGbb0JJiIY9mo0GrVaHZ589w7dPfYu3vvWtNBoNLMtiYWGOsqxwXZdut8fZc+d45uxZXNdh\nabE2lup3u/ze7z3Er/z7X8bzfBpBWLevZzlpXhtJxUlOpQ3zC/OkY/XYaDTEc10cx6YsC2xpYXQd\n6X74D/+QO+68A9etVWMTABdCoayxne14m1AKVVVx/fp1tra2OH36NGtra5w4cYJ/89P/mtq+eL8f\neD0rACaj+vZueZGTphHra2tEUR90ie+6RKMhtmPjqNooy/XceqrXuC5XjDN8baY6xavdua7THPf+\n4Sj1tteIbiJPzLJ8z2dCiL+3lf67DuBf//In95hS7Y7VsnZkctP0ygSQJ2APu5SL43g7+0yD9fRs\nvcnCUGW1csPIWo1Rt78LbGmT5BnKdVC+x//9S7/Ej/yv76jtZi0LlMR2HNywQZ4XdHt9Bv0BE4cy\ny/KptOHK1VVczwMEjlsb01uOi2XbSKGI0wTPaWK0qk3tLUk5fpmlECRxRDMM8VyHW48fY6O3ze0v\nuYNvfOPrXL54kUsXzjM3M4tSive858c4ffppGq0WjuPhuAFa+Tz2xOOcOv0kQsJoexMdxbz8rns5\nfPQYF9Y2udIb4TZnkCpka2sL17OZnZmj19saRywOVVUyGAwxprb6bCtDWUXE8SZ5NuDITUdoNTuY\nUiGFi+P4OwurpSqEFCRphmXZDKOIShuUcpCWRa/XR2PGvjIZZVURBH494NcCnSfkSYTve6xt9ahQ\nOF7IwQOLbG1ukRc5ldEcXDpIlqU4js1mt8v6+jo33HCYfn+AIMD3PDY31llZXuLc2bNgNEeOHGU4\nGNagIesidFJG5GnC8tISzWZI6Lm4tsPq1au0my1Wr11jZnaOYlwoHgz7JPEQx7HQ4yymzhpBo5DK\notnsUBESxxmWJXHsetSYqUqkqt02oUKXde3GcSykAduSBL7PsD+grOpeAWXZzC8toRHEeYYnBDON\nkOFoxMqhQ8RxhNYlWZYx02kRBj62XQM0SHq9IRcvnEdrzdr6Ovff9xqSJOHAgTkCz+XixYusrKyw\n3e0RhCHdbpdz588ShgEXzp9naWmBu+66i5XlQ7SaTZRlce3qdQCyoqDV7nBgfpbN7T6WbZOlCbat\ncF2HwPdIkoTeVo+ZdofvPP0kwtQ9FWYseZUTn/uxTYQyNfi12m2i0Wjn3XUch+FoxOrVqzz99NMM\nh0P6/z97bxZra3rW+f3ebx7XtPfa49nnnKpyja7ygLGhmwYMpoC2kqCk00FCNKGVNFcRl1aUvkly\nEYwUBYmopUgoIKUF3RDSrZZQRMd0JMBgsHHRHqpcruHMZ49r/ubxzcX7rXXWqXLZiaKkiFLv1dba\ne6+99re+9bzP+3/+Q7Tgc5/7HCcnJxRFgeN47yp6uv7uCp4VOYamIWn4xte/Ttsqmb5lKShD64zE\n2rbFtKwu8lDbhIa3TRc6s1VfHg01tccYcbqub/mPPz7rW9czXTcfq18AH/3kZ96zgL/vdrLbePW6\n2K6L+bp4b681TrT+3W0/gXcmsG9Pc7cLuqapvMlWPCLZ64DeKjms5zgkVYlpmIz394jiJUdPfIhF\nFCOlRlbAJJpTN5KibIkzoIWmgaRIFebmjzEtiyRNmUxidke7xEVBkxbdh1wnL0vaRlM+KUnCtWvX\nsG2TwHPxHAdNgGUY9Ec7lJrJvdMp82VOf3TAUatRlwVpGvMv/9Uf8LGPfBTfdbFsmzjJqGTDzZs3\nma1iLqdX9EcGsZzwV1//Bq3lMFmu2Bvvk5QFrdSxRIutG5RZTlmoUNqqKciyRA2BZAtSI0tqZKvj\neSGmXnB1fpts6eFaAc8/+1EaaVDmDZphKA5t2yhVZdviWjq26xHHCciWDz1xQpbnpFnKcBhS1Q2r\nZYzruHi2QSUkRbREly2HOwOmq4iqiJidxyRxzPXrN1guVzgiY7w/wHNdjvcGiOeeYrlc0Ld1skJF\nXV0/HlLmK/bHKsBaEwXHh0MWiwWmZWKa4Ic96tpFygramvkswtB1eqGHrgv298bUdUNdFlxNL9nf\n30PTWnzPoyxKykpx9NfD6zhJqeuCogBdGBhdvFev1yNJY0xTx3ItVB6qipCri4amlZR5ySqeES3m\npGlMrx9SNgV1W2BYJmUc4wUhSZ4CDacPb2ObBo5tMeq5+K7BcBiSpClv37rF7u4+t+/epShVsfCD\nkFe/9S1OHz7k+eeeUyc6z2MymQAa0+mCJEm5eeNJPM/h9u277B0cc3E1w7J9vvX6m1w7uY6Ukl7Y\nIzQtriZTJssVRVHgug5HR4ckSUQ8nZG6Dmmasr+jOnrTtFgt5uiG8gtfR6FJKTeJWrppgSaI4ugx\nP/CsyDFMg6Nrx8Rpwq1bt2iWNa/89Vc5PDzoPl/NVu14bzW4wrNbmkry0kc+QhStOD8/Zz6f4bgu\njgHUj3BwrYNLyq5TVvYVanNoBY/N3r6TivJR7eKxpnUbCv6/st73Ar4+bqy/hm256uOGTduqpu0i\nvR5ewrbyUK137mbr3VG3zM3XQoDWtohGYps2aZohDYPJfIbj+1xeXpJFCbYXotkBV4uYSuqkWYFm\nmugIbMtmuVhRVDp7+0fM5nN8oeP39ji5+Ryz5QK9bcmyjLjrJmxHxw1d+v0+mqapkOSoYuGYZFGM\naQhOjo64c/9t9g6O6O8esEpK4tWS0XDAJDpHYjKdLrl7/z7Xj49ZLuYcHJ0Q9gMu7tyjKCpkq5MW\nDUWr8dSzz3ExvWJ374D7D+/ieQENGePhkOUyxrYMAtukaiW2Y5NlCb0wZBktKIoSxwko05w4rbA1\nC13WRPMF0m3I4jlSmvjhECEMWiGhaTAtgyhOaaqSWmg4pkHdtpw+vIfrupi6zuTyFClgb3xMWzfM\nLi452d9ht+czGvRYrBb0eh5hf0Aez7HM6wih8bGXniUMe7z66mvUouHtN9/GsmyGwxHX9nbo7/Q3\nntLL1Yz5dM7Z2SkgCDwHzw2J44g8WwIh/V6PXhCQxBG2bZAnCQ/v3+Vg75CqqonjmCwtGB/vUBYJ\no+GAtmlwjIDR6DpFqfzhJ/MJ10+u8fD0AaauKctdAdEqxXHt7uiujL0cx1FGSbaFbQhWiyVnZ+cI\nWo7297hx4xrDYZ/JdELd1PTDkGi1JPA8HNuiH/j4jomQDXkW8/Zbb/PSR17izq23CPpDjq+fAAaa\nYWFIgYkkiiJmsyV/62//bW5ev8Ht27exuw5TCEFTg+cFSFnz5pu3+MIX/oinn3mGqqp5/oWXyIuG\nola2sGdnE5bRisPjI9Jlyt7+mFa2fPuNN6jriqos2RkO+NCHnmR6MeVb3/oWoe8T6Utg3a0qZods\nW3SVgExdqgbO932qSrHE1rbMpmkSRRF7e3ssFguqOuPLX/5LBoM+P/bpHyOKIwK/t/7Ud43euwu5\nrumADoYgileEvSFBOCDLcr797ddpdRXSbNs2eZqhGQZ1WXZui+o5hVBBFrphPgbrwtaJv8O711h4\n01QbVtwaGlLIw6PgGJVxUL3rNW+v9x1C+cs//V83R4s1bWb9z24PN9drezq77Xuihp6WwqF5hItv\nT49hy+VQU4Mw2bToQmJ0YatNLdEsi7St0UKP3/if/il7hk3oehh2QJQ3CMsnKVvKGgzTxrVddF10\n2J5LVSlq33wxJwiCzeu0bKszJgK74+kKQ2y467ZlE/gei/kM2daYGhwf7nNy7RjN9JivCt544w3C\nMMBxHBazK7I0RsiGnu+xvzvixRdfYBXH5I3gy1/9Oqu0VAMvoSFkzajv47oWZZHRSKnofqbGgwfn\nBMGIvf1r3Ll/RllLiqbFD33QUf7KjktVgaxzPF1iyZI6XaK3BVkS8eKHX8RxfWqpjKLWYocsyxFd\nWLNhWSRJRt3U9HoDVqsleV6gO5K6FeR5haGZDIMQz9QZBDaCmjSNsV2XtCzI0xWaUHjybDYnimL6\nvQF5XhKGqmDP5wscxyEt59i2RZYlDPp9PN+nqkukFOR5QRwleI5HmmVEuTIwUsd0A9MwuiOyJI0z\n0iRjOBphGTZCL0mSmLppMTQ1sM7zYgMXlHVOliXUdYUubJIkxwt8dNOkrBuk0Du6W4Fl2aqDLyvK\nIsF1bXaGQxzLpKnUc7br06UmiFYxhmUqIVZd0g9c8jTCFGDbOl7njqmZFvcenhMOhlxczMiKip3h\nCNdzCIMA27KoihzXtRkNho/iC6WgqsA0DeIk4l//6z+kkQ0///M/z737D/CDENOyiKKY4WCIJkUH\nKxkqQ7MqGQz6DAY9aCWChjzLmc1muJbL8889zauvfpM0WWGZaoi7LnxCe8Sz1g2dpml5/fVvkSQp\nTdPw5JNP8uSTT6gZTWejfHl5yVdf+TJXV1c888wz/NzP/Rzj3bG6/8R2cs+7IZSqrrshpJqTtOqX\naNtW+Rdd3GW1WuHaDlWVKw1GrmAXU9dUKlerBqRZ9Si6bZtksTbee1zJ+bjx1XqV5SPh0Lp2/d+y\nk/1/eq2xoXfKTreHAduWsGvZ+Pqxd9J21p36+rnWIptHfM1uuNl5XzdUW126wHJs0HQ806LSTc7O\nTjl86jk026WSAqEr8YuUMBwOkULZxBZVQ9s0mLqG5wUYuobvHRB0ooNVrHI1szSm1+sTrRYMR0OQ\n4DkeSZLSNpLz00ssy8A2bDQhGe/u09QQRXMup0uGgz79wZA0TdFNl6PjHaaTCy4uJiznC1768Evk\neUGU5egCfviHfoi798+YzGYEgU8SzTi/OmdnOKQqczTRglkTOpLx0KFMZ/gWUNf4vkeSpozGuziG\nxXQ6wRv06A13WU1mrFYNA2+H6cUDkAYIk6ptaNqalpqmMoiiCM/zSNKVUrMaulJG6gbxcollWeia\nTiNjTENDM5XMe393hzqNKfKc6eQUyzJpZYsX+lw7fJrpdEpZlt2HQ+P84hxDVyKJvb0Djo+P8D2f\nvAhV4o2mM53MOH14tgngCIM+N05usJgvcW2BZmjMZ+rovFrMcF0XOpGJZVnUlcbs8pzRaMTBwYD9\nnT5+EFLmNY2U3L17lzRZkqcryjzB8x1sXXBybY+ryYT5YkEcNUhNww9CRGvRD1zqvGaxXHF0eATC\nRdclVZkwj3Jsw4TGxjJUIpSum7iGj227CAvKJmU1n+O6LnkcEccrpnJCK2E6X3HzqQ/x6quvcnzt\nJpblUjcVVaVzdTXh8GAfz/UwDI1XX32Vmzdv0DQtSZwipYbnu5RlyTe+8XX+/s/+R5yenhKEIZqm\nE62UdW8cp5R5iR94XEwuKcqCk5NjDMPgwYMH7O7s8vDBAw7393nqqad5cO8Bt+/c58b1a9y9e4cs\njTAMBUtIqQyl6rrBNC0WyyVf/OIXmUwmCCGYzWa88tev8Nxzz/EzP/Mz1HWt7G7DkJ2dHYqi4C/+\n4i/44b/zwwRBgGN73XzrvU31dE1fZ0wDoKFSsDRNx3U9Do9OCMKIe/fuoQmJrqmmTaOlli0SgWUr\nHvq2WHCbiLFOvFp31EqZbW0eU4EdXf3pjP3eaVX7Xut978C/9Md/sOmU39lRr4vwNsSyzVLRNG0j\nTV8PPLcpOeu/tS0GEig/7aZuFGVJAEJgCl1ZsVYtju+TVBWlJvj8r/13/PSP/DiWYWJZLmleM5kv\nsZ2QWgr8oIdE4LoejZSIVhCvou6o1CkQNZXI47puJ+83yPIMTRisogTH9ojTDM/11JvWStq64Mb1\nE8Y7Q5bLGbt7e1RNQ1k1OJ7Hg7MLirIijiJEq6TmtqlT5SmjnQFPPvsUb7x5h15/n7KCJFdxZXVd\nMByETGdTdAS9wCWLz4njjOUyYb5MOb72BF44YrZYYdkuCINlFLOzO2JVzIiXCT17QGD5NEXO/m4f\ny2gxrZY0XeGFrjLdqg1FuRKC1WrF7u6Y1WqFpulKiNSqsAp1JC4xLAfHDdQGWTcEjklVZIwGAXXT\nUDaNCndIk647afH9gKpSlgICjbZpqKuGJEkoq4qe71M3NbZjbz7MSsVbk6Y5Ao00zTB0E80U1E3N\nZHrFeG/MvXt3GY/HzGcz9vb2sA2L0WiXIsuJV1OaVgVYR2mCYZgMh8MOf22xTIOyyJW50mJOXuSM\nDw4Y7OyS5xWabrFYrJANTK5mDPtDhr0BhtMgtBZD1zZJ9nXdUpet4lOXDUmckWUF/tDD69m0dY2h\nC5AtTVXR1BVlpcye2lYZNj04PeOHf+TTTGdTXNthb2+MrulkSYLj2Az6fVarZQdn6JimBUheeeUV\nXn31m3zm5c/Q66kNK8tyDFN14GEQ0Av7XE0muJ5LS8NqtcR11QBYCMHJ0TGz2Yy6qmkayeXlGTdO\njlgtp5gGtO0jQkLTSKQUCE3nL7/yZS4vLzedaBRFCCHIsoyXX36Z5557bgNJPHhwl1u33ubu3XvY\ntsOv/De/Qq+3hlA6OvF3KOLr6vdeRbDp6LWr1YrZdEJZ5MoITXSiPcuk7WxkFfLzKEbtnSyTdeyb\n+t7jTobrn62q5rH6pmkaL3zsR/7mduDvDHHY/sfXHNDtFHZ490XaFves1zZksp78gnqj2kbZcirm\njqIN1ip4DN1S6jcQfPWvvsrezh5CExR5jgBuHB9z7WCMEBqz+ZIki8mKkqRc0UqJazkMQxvbspRP\nSR0SRSuiOCaP480N6ToOfjDgxtEBhumwWiXkRUFZ1iAkWVXw7Ve/Tnx8iGObJLbO1XSCFDq9wS5V\nmRMnGZquWBS6lOiaIBiNuXXnTUqZ4ftDtLYiWyWUVY0X+tRIzi4uCPyApmo4Pb3CM8F1ezhun5ZL\n2rZkOj1jMFTFxnFtDCPE0FpCo+Lw5ADR2jiGiyF6lEVE1VZUWUVVlZS5gh7KKlcmX46NbmgsV/NO\n0m+gS6PzazGwTJ1h4KOZNqbtYrsOSRwxvTqn53s8OHuI716MtlUAACAASURBVAfolocQ4Hkhy8WS\nOI6QrerMPNdF03QC38exbXx3hKYJ0qSiLGJOTy8Z7QwJwlAlCMmCayf7lHlNHKfcvXMHz1Pileee\neYY333oT17HJ0ggpa/qhTxInXJw/JPR9jo/UALaoSmrZkhU5q3hJmiSYpsq83BmM8NyAVZQitYYH\nD89569Y95sslZVnheT47wx2G4YA6X1FZGqtVhGYIXM/Dse3NkGs0GpPEKbam4XkeWZZjuSbLeA4I\ncgFxlFCWBa7rcrA3xvFKNA1u377F8eEBdZESuDZCQFXkmJ5H2AuwLZskzdB1k9Goz3K5IE1j6rrk\njTe+Ra8X0AtDHNukyFMEAkvX2R0OSdOM+/fu4fgu8/mEMPQxDR0BFEXBznDIndt3lVeSJZjN54Rh\nj6JUgrq6LUE2aFIVb2Uba3J5cdHdpz55UZAlCUfHx9y7fx9N13n9jTd4+pln1Cm8aXAct0uxVzOr\nqq4pq3VCF5sT9nstAd+xSAp0mhYGg11s20G2NZeXFxR5hm0YavOxbcq8QBePGsZtZt2GKKHrW9bW\njxJ5Hs3ixAYteGfz+l7rfS/g2zDIuli/0/jlkV3jI+K74zibr9dRR2sXwvVFWa/vFHEkqgY0jUao\nbMkuIlKxWYoC1+vxpS/+GR//1A+wOxohaEmWK6p0Tuj5yKbhZNejalx0xwXNpGxqsrSgLArqOkE0\nJgYw6tk8cW0P3TAoy5ubwcx8OefyckIaKz8Ox/E5HI/UdRA9dnYHVEVGnqe01Qrf0jBdj0YqKCYI\netRVh/cLgWloLJYLXNdjNp8QLWN2h0eYmoFmG+hC0u+H9OixWCyJlimH+9exdakCE2TDtesBVV3z\n7LVj7ty5S1YkGJZOFKf0Ap/QALPJ6A8CHFtlTLa+kiAXect4tEealLiuzySZEPYU3OM4DpqukSQN\nUja0KC531XH3l1NJOBiQxAlpkRP2PA4O9kiTFTdu3CAvapZxTplVOKLF0HT6vT79sM/x4SGybSmL\nTIVpJCuSJEFKyc7OPuO9IePDEUmaIIyW84uHaLrG/Yf3qIqKumw4PDqi77sYlgrB/tQnP0GSxZRV\nxYN799jfHzM3dQZhn8lkwnyVMJ3NkLJluDvA9j0s22C0t6usG/Ka+6fntK2kFQZSuPhhn3Ck0x/t\nUdUFDx88QNNbej2bk8MnSOMY0xspbLuqqBoV5GDoJqtoTlnW1FUnWAPaouJw/4CH5xe4bsB8EfPp\nH/8pvvSlL4Fu43kqvuzZp57m9PyUNI6oa2X+JOtaDWodh8APKYqSMAxZRRF102CYGmXZYJg6H/no\niwihFMau4yGERppEaJqaExwc7JPlCbYVsoqWneJYFeS6rjENk3gZEycJUhOEYQ9koYqWJh6zRm5b\nSYPg4vyCqqpIO1dDy1YU3PVJ++HDh+hdEDlCeeT3ej1Wq5jZbMHXvvZ1Pv2jn0YgkFLZQ3zXJeU7\nyrtCCRoEmmZQljWuG9A0FQeHR9y9c4soyXEcBZ8Iw0A2jwaO7yy8a6j4UcP5KEbyUZF/FLX2zs78\nvdb7XsDX9L9t+uA2hmTb9qaz3pbUg3rTLcvaDEG3c/DWhX29m2134QCWVEPLVkjVcXdDat00CMKQ\n1994m8ViQej51HWBrGtMHaYXZ3iHB13oQI9SNGT5kkYzQTPwXR3f8dA758M4ilFqsIJ4tcRzXbIk\nQzQqAPjZp/eJkxSJKpJlscC1beJ4TlG06JpkOrvL/GqJZfUJh8opMPR9lmmKZXkkSYahC+Ikw3F9\ndLNhlcT0+gNkW5OlGa4fogklrJjMl9y8+RSB32BoBm2rkVeJol7pJk1Vcuv224z39/ACX3F8NR9d\nwH4/JIpytLogq3L8nkNRpNiOSVWqI2UWVeRpiWnq5EWK56s80bIscJwhyh/mUTK353m0iwjTsamF\nwIiXpGlCmizo90KiJKZpdYJen6KSkKwoq5K2rSiynDRWOOp4dwddqLwaIRoM3WC+OMfxXHTTRGiw\nipYEfYcoTnA8jZPr19HRKMuK1WqFbujkRY7lGHi+i2HonJwcc3r6gF4Ycnr6kBvXr5MVJrbjkRUp\nURYhZYNeCizToipr9sYHmIZLnhWcXsyYLSJs18bxHHRTMN4/5PDogJ1Bj2g65fLqIaamU7QNLRq6\nobx0pMYmdcmwG+WjLZVne5GX3L9/H9Nyee75F9Atl3sPz9k7OFY+102JSBOi+Rzbsri8OKPfHxAM\nAyzTwrYdZQZV1QwGA7Iso2layjLHNFVKku977O8r/38hBKalvHhGwwHRKka2LVdXF4zHY5bRAtMy\n0PQOy9Xazo3QQdd1ekFIlGecnZ3x3LNPUmRx91lXghpk5zHSKKHb2q55zdLK83wzUzo8PCTLsi0h\nn858vtzUkW984xvcuHGT3Z0xvu+9d7KNBIRkE/S5/ThKyNZKiWEa1HXT1ReTmzef5P6DO0ymV9i2\niS40jC3V5RohWM/31jVtncazTbZ4vNk0Hive76RRv3O97wV87de97pCrqnpsMLn2L1n/7DqwYX0k\nUewTZRmrqSx0VeilRDOszSBUdvjUeird6EJJdVtJo2nkhtqljaomSxP+6M/+nMObTyDziqR1iJMS\nU5dkUQlMuXa0z9VsRtgJJtA0srLsxBSC1XJFVdb0+yq5RwB7O7uUTYVuGkymE0QruTqbKUaKqbPT\n6+GOPUBgXjthMV8RRQm+vUfvxh6a3rJYRuz0PRarC0amhdAqNKOkbiSNBo5rMXT22B316ff7JEmC\nbmvMF5e4Ukmci8WMbBZg2S6Ty6ka+LguaVqyiDJcyyIMe7RxycC2QINayyiKgqT0OL75BEIzSNOM\nLK1oG4tlUjG9mmMdWDiOwHU1FqlLRYUmdZJVutmAm6YhzwvSNFPvRdNwfNCnzVqkhPF4l/3dHUzT\nVI6PJkyuzrEsVXSEreF6Nrbp0zYVRgtlkXJ6ltDIFoGO3+vheT4HN66T5wWNFFxNZlxezfA8QeD1\ncQKLaLEgz2L6QcDu4c7GAjhLU1arJfPpkrosefbZZ1nM5/iew9tvvwloOK7L3njMdWeEZVksFgss\n2+bt27eYX52xWC5ZLpeEYcCNo6A7CWrUZUVy8ZCmaTHrI/YPjhSrI1FeMVWpFL9Jooq1EIJ+L6TK\nS3Qh8D0fXdMojJy3z84YXbvG5P5bBHpN2xTItiQMbNpKUhQ5MoBGaLz0ie8DIXAcD920EegI3cD3\ndPIswzRtijzG0C2yrGAyneP7LpahYgR3R2PapmI1n5HFKxxX5U4OBwGz6QWGYeAYNmVeYjoGlayo\nqgLf98hLxbQyLY1nnvkQ09mUogEdHaHplGWFaenUdU7T1mhGxWinx3R2gZkpG9l4tSAMQ3zX5fq1\na8imoSwqFRTSNYHn51eYtsdw94C0aDifzPCzgjAIAftd9UeCiqrruvjNo2KtD1Ep9QBatwdohonU\nDW7eeAbfG6oQG9OkFQqGNXUNWddoUoWttHUDSOqyQqCp04B4RKFe12/FvGs2DSl87w78fR9ifuXP\n/vAx9eX65WzvWvBIFv9O74BtG9q2ZZOEIdaY+Racsu7CdV3Hblo0x2aeRPiuh4ZGVjdgW/zxn/wZ\nr/z5X/LyT7yM4TnYRkBT1ZRFSrKaUWQx++MRu+ORyu4LQ1opqdoG3w/VmyShKhuEVLhm3bYYpqFS\ncnRB0zbYpk1TNd3GVRLHauBZVzVuEDCbKse2vb0DqqpQBzpNp67VMLMoGxarCNvxSHNl2BUEIcvl\nAtc21U2ia0gkum5gWhbz+RLdtBDCxLZsmkaiGRpxkhAEPdIk6/zDCwLXQaPGtU2auqCuKgxXqSaF\n0HC9AE0op0XLMCjSmJ7vEsdL4nhJMDxSnRVq2q9pKs9Rwha1Sm3KQuY0TbsRQqhOBHxfHdnXfurL\n5RLDsZCywbFMaBr6fR/ZKNqXpusqAKCsKKsSTZM4nRMjmsm9+w+5du0EgcA0uy2/bZCypawfUTyF\nEMqvvakpsgzLstCE6N4jQVnVCKGRJMnm5x3H6bpUi8lsxnK5Yjgc4rrqccs0VdC0VM1EnuediyS4\nnt9BgFuWESgKmmkYlEWxsSWtq4oiL8iTBNdxeOqZp7m4vMK0bcWXRtJUJdDguw5Stkzmc/aOTrBt\nl7Kq0Q0LTVPOnIZuKF+TPFevDzVgPn14h9n8gh/8ge+jzAs8x0PJ3C3KsgDRdj+rb15znhcKNmqV\nH73rup2ls2qmGtmwnM85Pj6iqiuKNCYvMmUoZWpkSQLA/fv3yOuGxWLBbD6jzMtNgf3wh1/i+eef\n78zkLJCCi4szbt+5zSuv/FvG+wf8x7/4Dzk6OiaOY05OTijzgieuH7yrFlXVWvDzONV4zVj7TuPN\nd+pKkiTh9PSUZXSB77nK36iuaesKyzSQzRoq6mpYC0JTtvRr7Hvdqa8h4+3HP/zxH33PIeb7XsC/\n9Md/sBlCbr+U7VCEqqo2hPZ1p/7OJGdQzn7bw8xtKuG2vFUIga/pzKsMox/iYGA3ghzJn37tFf7J\nf/9P+OV/8I8IvQB6LtEyo0hSDg730WkJApery0vKPOH6jWtkWcb+/ljlX+a5wvpaZbV5fHyCaVro\nukFRVeRlwXy5IIpjLMPEtVwQEIQ+rut2gQ21ojF5Pq9/+w2KvFTChDDg9PSU0WiXnd09DMumKCpM\ny2axiAiCEM1QuKRp6FRVSZanREmiZMyLBZ4Xcnh0RJaVj1g7hobZCZh0zVD4cdMiREtTFRgaVGVG\nVRV85OMfx3Ec8rxiGaWq4NaNclEsMnaGPQxdJww98lonDEOm0ynT6XSzyeZ5rgQ8pko56vV6OLZi\ncaxdG9dujvfv3yfPShzHwXVdwjDEdm2yLGVydUGaxBi6xrWjIxaLhVL7HR4wGo26rg6mkxmzZURV\nNQyH441Hu+jgliDwCEKfIOyxWq2Ioog4jja4aC/wkW1LEPhMp4rStre3RxAEBEGgYvEWC779x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UUnmRd4ZNdVWoOL4sQzctLicTojjj2edVuHSel3iujaDF0ARNpWh7bSuVa6RuECcpjmXw+//8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\n2G8So0PJ84UBUFSEwrElrR+5PVkYbZFrDRhL/fjKwliDq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w7PUhxMaYuqqRUnLj+nUCMByOGK6N2NjaYDQa8elnn6G1xnrPlhCMJ2eoPPKE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W6Ho01Mx7k+XnVkV+t3SCYqsljKn0N1IirrMAUn5rlqgjX9XHPiMIMFTBk9Smb176Zcuv4ryPPp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wenNsjbnpcteZYHjsvsSnvQ2N63I73aBJkm6Ge5gS0tZwG2//+gtcCoL8R7bNYSxS5SGPqx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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x5a3a290>"
+        "<matplotlib.figure.Figure at 0x114f15d10>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -842,4 +930,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 8e068805d62..0bfdb5caf68 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Filter visualization",
   "include_in_docs": true,
   "priority": 2,
-  "signature": "sha256:526501b358e0f60c489eaf5799e8603a75019cc65401533baa307b5603fdefa1"
+  "signature": "sha256:44536e4f82eb5748b6a3bb6fcfca01bc6c5815dad2641c994dab031f452b7606"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -62,14 +62,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "caffe.set_phase_test()\n",
       "caffe.set_mode_cpu()\n",
       "net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
       "                       caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'))  # ImageNet mean\n",
-      "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+      "net.transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))  # ImageNet mean\n",
+      "net.transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+      "net.transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
      ],
      "language": "python",
      "metadata": {},
@@ -214,7 +213,7 @@
      "collapsed": false,
      "input": [
       "# index four is the center crop\n",
-      "plt.imshow(net.deprocess('data', net.blobs['data'].data[4]))"
+      "plt.imshow(net.transformer.deprocess('data', net.blobs['data'].data[4]))"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index a4fb4a66065..2932687da6a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Editing model parameters",
   "include_in_docs": true,
   "priority": 5,
-  "signature": "sha256:bf84bcbd78fe007310f86d71c5969ba4205b6e06f408029860eec94821844bee"
+  "signature": "sha256:811097f2151652d2b630c016a5f1de23bd824df3dfcfc72aa0aeb23b2d9686c0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -28,7 +28,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "!diff imagenet/imagenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+      "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
      ],
      "language": "python",
      "metadata": {},
@@ -146,7 +146,9 @@
       "import caffe\n",
       "\n",
       "# Load the original network and extract the fully-connected layers' parameters.\n",
-      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
+      "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
+      "                caffe.TEST)\n",
       "params = ['fc6', 'fc7', 'fc8']\n",
       "# fc_params = {name: (weights, biases)}\n",
       "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
@@ -181,7 +183,9 @@
      "collapsed": false,
      "input": [
       "# Load the fully-convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', \n",
+      "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+      "                          caffe.TEST)\n",
       "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
       "# conv_params = {name: (weights, biases)}\n",
       "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
@@ -279,19 +283,19 @@
       "import matplotlib.pyplot as plt\n",
       "%matplotlib inline\n",
       "\n",
-      "caffe.set_phase_test()\n",
-      "\n",
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
-      "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
-      "net_full_conv.set_channel_swap('data', (2,1,0))\n",
-      "net_full_conv.set_raw_scale('data', 255.0)\n",
+      "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
+      "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
+      "transformer.set_transpose('data', (2,0,1))\n",
+      "transformer.set_channel_swap('data', (2,1,0))\n",
+      "transformer.set_raw_scale('data', 255.0)\n",
       "# make classification map by forward and print prediction indices at each location\n",
-      "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
+      "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
       "print out['prob'][0].argmax(axis=0)\n",
       "# show net input and confidence map (probability of the top prediction at each location)\n",
       "plt.subplot(1, 2, 1)\n",
-      "plt.imshow(net_full_conv.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+      "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
       "plt.subplot(1, 2, 2)\n",
       "plt.imshow(out['prob'][0].max(axis=0))"
      ],
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index f9a13a39865..94dd063a2c7 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -14,33 +14,32 @@ class Classifier(caffe.Net):
     by scaling, center cropping, or oversampling.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
-                 gpu=False, mean=None, input_scale=None, raw_scale=None,
+                 mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
         """
         Take
         image_dims: dimensions to scale input for cropping/sampling.
             Default is to scale to net input size for whole-image crop.
-        gpu, mean, input_scale, raw_scale, channel_swap: params for
+            mean, input_scale, raw_scale, channel_swap: params for
             preprocessing options.
         """
-        caffe.Net.__init__(self, model_file, pretrained_file)
-        caffe.set_phase_test()
-
-        if gpu:
-            caffe.set_mode_gpu()
-        else:
-            caffe.set_mode_cpu()
+        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
+        # configure pre-processing
+        in_ = self.inputs[0]
+        self.transformer = caffe.io.Transformer(
+            {in_: self.blobs[in_].data.shape for in_ in self.inputs})
+        self.transformer.set_transpose(in_, (2,0,1))
         if mean is not None:
-            self.set_mean(self.inputs[0], mean)
+            self.transformer.set_mean(in_, mean)
         if input_scale is not None:
-            self.set_input_scale(self.inputs[0], input_scale)
+            self.transformer.set_input_scale(in_, input_scale)
         if raw_scale is not None:
-            self.set_raw_scale(self.inputs[0], raw_scale)
+            self.transformer.set_raw_scale(in_, raw_scale)
         if channel_swap is not None:
-            self.set_channel_swap(self.inputs[0], channel_swap)
+            self.transformer.set_channel_swap(in_, channel_swap)
 
-        self.crop_dims = np.array(self.blobs[self.inputs[0]].data.shape[2:])
+        self.crop_dims = np.array(self.blobs[in_].data.shape[2:])
         if not image_dims:
             image_dims = self.crop_dims
         self.image_dims = image_dims
@@ -82,7 +81,7 @@ def predict(self, inputs, oversample=True):
         caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
                             dtype=np.float32)
         for ix, in_ in enumerate(input_):
-            caffe_in[ix] = self.preprocess(self.inputs[0], in_)
+            caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index b78abd12a89..4ea07fb7b36 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -29,28 +29,27 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
                  context_pad=None):
         """
         Take
-        gpu, mean, input_scale, raw_scale, channel_swap: params for
+            mean, input_scale, raw_scale, channel_swap: params for
             preprocessing options.
         context_pad: amount of surrounding context to take s.t. a `context_pad`
             sized border of pixels in the network input image is context, as in
             R-CNN feature extraction.
         """
-        caffe.Net.__init__(self, model_file, pretrained_file)
-        caffe.set_phase_test()
-
-        if gpu:
-            caffe.set_mode_gpu()
-        else:
-            caffe.set_mode_cpu()
+        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
+        # configure pre-processing
+        in_ = self.inputs[0]
+        self.transformer = caffe.io.Transformer(
+            {in_: self.blobs[in_].data.shape for in_ in self.inputs})
+        self.transformer.set_transpose(in_, (2,0,1))
         if mean is not None:
-            self.set_mean(self.inputs[0], mean)
+            self.transformer.set_mean(in_, mean)
         if input_scale is not None:
-            self.set_input_scale(self.inputs[0], input_scale)
+            self.transformer.set_input_scale(in_, input_scale)
         if raw_scale is not None:
-            self.set_raw_scale(self.inputs[0], raw_scale)
+            self.transformer.set_raw_scale(in_, raw_scale)
         if channel_swap is not None:
-            self.set_channel_swap(self.inputs[0], channel_swap)
+            self.transformer.set_channel_swap(in_, channel_swap)
 
         self.configure_crop(context_pad)
 
@@ -76,12 +75,13 @@ def detect_windows(self, images_windows):
                 window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
+        in_ = self.inputs[0]
         caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
-                            + self.blobs[self.inputs[0]].data.shape[2:],
+                            + self.blobs[in_].data.shape[2:],
                             dtype=np.float32)
         for ix, window_in in enumerate(window_inputs):
-            caffe_in[ix] = self.preprocess(self.inputs[0], window_in)
-        out = self.forward_all(**{self.inputs[0]: caffe_in})
+            caffe_in[ix] = self.transformer.preprocess(in_, window_in)
+        out = self.forward_all(**{in_: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
         # Package predictions with images and windows.
@@ -170,7 +170,7 @@ def crop(self, im, window):
             # with mean padding
             context_crop = im[box[0]:box[2], box[1]:box[3]]
             context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
-            crop = self.crop_mean.copy()
+            crop = np.ones(self.crop_dims, dtype=np.float32) * self.crop_mean
             crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
 
         return crop
@@ -178,20 +178,30 @@ def crop(self, im, window):
 
     def configure_crop(self, context_pad):
         """
-        Configure amount of context for cropping.
+        Configure crop dimensions and amount of context for cropping.
         If context is included, make the special input mean for context padding.
 
         Take
         context_pad: amount of context for cropping.
         """
+        # crop dimensions
+        in_ = self.inputs[0]
+        tpose = self.transformer.transpose[in_]
+        inv_tpose = [tpose[t] for t in tpose]
+        self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
+        #.transpose(inv_tpose)
+        # context padding
         self.context_pad = context_pad
         if self.context_pad:
-            raw_scale = self.raw_scale.get(self.inputs[0])
-            channel_order = self.channel_swap.get(self.inputs[0])
+            in_ = self.inputs[0]
+            transpose = self.transformer.transpose.get(in_)
+            channel_order = self.transformer.channel_swap.get(in_)
+            raw_scale = self.transformer.raw_scale.get(in_)
             # Padding context crops needs the mean in unprocessed input space.
-            mean = self.mean.get(self.inputs[0])
+            mean = self.transformer.mean.get(in_)
             if mean is not None:
-                crop_mean = mean.copy().transpose((1,2,0))
+                inv_transpose = [transpose[t] for t in transpose]
+                crop_mean = mean.copy().transpose(inv_transpose)
                 if channel_order is not None:
                     channel_order_inverse = [channel_order.index(i)
                                             for i in range(crop_mean.shape[2])]
@@ -200,5 +210,4 @@ def configure_crop(self, context_pad):
                     crop_mean /= raw_scale
                 self.crop_mean = crop_mean
             else:
-                self.crop_mean = np.zeros(self.blobs[self.inputs[0]].data.shape,
-                                          dtype=np.float32)
+                self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)
diff --git a/python/detect.py b/python/detect.py
index b67b500aafd..cb0c2645761 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -102,6 +102,8 @@ def main(argv):
     mean, channel_swap = None, None
     if args.mean_file:
         mean = np.load(args.mean_file)
+        if mean.shape[1:] != (1, 1):
+            mean = mean.mean(1).mean(1)
     if args.channel_swap:
         channel_swap = [int(s) for s in args.channel_swap.split(',')]
 

From a1c160ba87f22c3aa7fd1997b7d6a08c3dc8ef34 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 19 Feb 2015 10:31:23 -0800
Subject: [PATCH 1168/2053] relax MemoryData transform check to warning

warning is still useful to keep from accidentally running data without
pre-processing.
---
 src/caffe/layers/memory_data_layer.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index c7d73f0925d..effdad90aff 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -81,9 +81,11 @@ void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
   CHECK(data);
   CHECK(labels);
   CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
-  // Refuse transformation parameters since a memory array is totally generic.
-  CHECK(!this->layer_param_.has_transform_param()) <<
-      this->type() << " does not transform data.";
+  // Warn with transformation parameters since a memory array is meant to
+  // be generic and no transformations are done with Reset().
+  if (this->layer_param_.has_transform_param()) {
+    LOG(WARNING) << this->type() << " does not transform array data on Reset()";
+  }
   data_ = data;
   labels_ = labels;
   n_ = n;

From 6f4bdd88dfc4b4f3f9b965f9bfb283c3dbafeefb Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 19 Feb 2015 11:03:23 -0800
Subject: [PATCH 1169/2053] Revert "Merge pull request #1878 from
 philkr/encoded"

This reverts the encoding cleanup since it breaks data processing for
existing inputs as discussed in #1901.
---
 include/caffe/util/io.hpp              | 36 ++++++++++------
 src/caffe/layers/data_layer.cpp        |  4 +-
 src/caffe/layers/window_data_layer.cpp |  2 +-
 src/caffe/test/test_io.cpp             | 31 ++++++++++---
 src/caffe/util/io.cpp                  | 60 +++++++++++++-------------
 tools/compute_image_mean.cpp           |  4 +-
 tools/convert_imageset.cpp             | 30 ++++++-------
 7 files changed, 95 insertions(+), 72 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 9d7540d62cc..a01f917abf5 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -92,14 +92,7 @@ inline bool ReadFileToDatum(const string& filename, Datum* datum) {
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color,
-    const std::string & encoding, Datum* datum);
-
-inline bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum) {
-  return ReadImageToDatum(filename, label, height, width, is_color,
-                          "", datum);
-}
+    const int height, const int width, const bool is_color, Datum* datum);
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, Datum* datum) {
@@ -116,12 +109,20 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, true, datum);
 }
 
-inline bool ReadImageToDatum(const string& filename, const int label,
-    const std::string & encoding, Datum* datum) {
-  return ReadImageToDatum(filename, label, 0, 0, true, encoding, datum);
+bool DecodeDatum(const int height, const int width, const bool is_color,
+  Datum* datum);
+
+inline bool DecodeDatum(const int height, const int width, Datum* datum) {
+  return DecodeDatum(height, width, true, datum);
+}
+
+inline bool DecodeDatum(const bool is_color, Datum* datum) {
+  return DecodeDatum(0, 0, is_color, datum);
 }
 
-bool DecodeDatumNative(Datum* datum);
+inline bool DecodeDatum(Datum* datum) {
+  return DecodeDatum(0, 0, true, datum);
+}
 
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
@@ -134,7 +135,16 @@ cv::Mat ReadImageToCVMat(const string& filename,
 
 cv::Mat ReadImageToCVMat(const string& filename);
 
-cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width, const bool is_color);
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width);
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const bool is_color);
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 9a2dfac9b1f..0f959a9d783 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -42,7 +42,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   Datum datum;
   datum.ParseFromString(cursor_->value());
 
-  if (DecodeDatumNative(&datum)) {
+  if (DecodeDatum(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
   // image
@@ -110,7 +110,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
     cv::Mat cv_img;
     if (datum.encoded()) {
-       cv_img = DecodeDatumToCVMatNative(datum);
+       cv_img = DecodeDatumToCVMat(datum);
     }
     read_time += timer.MicroSeconds();
     timer.Start();
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 1a6ffe2011d..3e9e9765ccf 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -278,7 +278,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       if (this->cache_images_) {
         pair<std::string, Datum> image_cached =
           image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
-        cv_img = DecodeDatumToCVMatNative(image_cached.second);
+        cv_img = DecodeDatumToCVMat(image_cached.second);
       } else {
         cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
         if (!cv_img.data) {
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 6b135efcf6e..4d941fa8683 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -289,8 +289,8 @@ TEST_F(IOTest, TestDecodeDatum) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  EXPECT_TRUE(DecodeDatumNative(&datum));
-  EXPECT_FALSE(DecodeDatumNative(&datum));
+  EXPECT_TRUE(DecodeDatum(&datum));
+  EXPECT_FALSE(DecodeDatum(&datum));
   Datum datum_ref;
   ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
   EXPECT_EQ(datum.channels(), datum_ref.channels());
@@ -309,17 +309,38 @@ TEST_F(IOTest, TestDecodeDatumToCVMat) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
+  cv::Mat cv_img = DecodeDatumToCVMat(datum);
   EXPECT_EQ(cv_img.channels(), 3);
   EXPECT_EQ(cv_img.rows, 360);
   EXPECT_EQ(cv_img.cols, 480);
 }
 
+TEST_F(IOTest, TestDecodeDatumToCVMatResized) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, 100, 200);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 100);
+  EXPECT_EQ(cv_img.cols, 200);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatResizedGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  const bool is_color = false;
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, 200, 100, is_color);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 200);
+  EXPECT_EQ(cv_img.cols, 100);
+}
+
 TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
-  EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
-  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum);
   cv::Mat cv_img_ref = ReadImageToCVMat(filename);
   EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
   EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 65531687b63..f738e053e86 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -98,36 +98,11 @@ cv::Mat ReadImageToCVMat(const string& filename,
 cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
-// Do the file extension and encoding match?
-static bool matchExt(const std::string & fn,
-                     std::string en) {
-  size_t p = fn.rfind('.');
-  std::string ext = p != fn.npos ? fn.substr(p) : fn;
-  std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
-  std::transform(en.begin(), en.end(), en.begin(), ::tolower);
-  if ( ext == en )
-    return true;
-  if ( en == "jpg" && ext == "jpeg" )
-    return true;
-  return false;
-}
+
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color,
-    const std::string & encoding, Datum* datum) {
+    const int height, const int width, const bool is_color, Datum* datum) {
   cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
   if (cv_img.data) {
-    if (encoding.size()) {
-      if ( (cv_img.channels() == 3) == is_color && !height && !width &&
-          matchExt(filename, encoding) )
-        return ReadFileToDatum(filename, label, datum);
-      std::vector<uchar> buf;
-      cv::imencode("."+encoding, cv_img, buf);
-      datum->set_data(std::string(reinterpret_cast<char*>(&buf[0]),
-                      buf.size()));
-      datum->set_label(label);
-      datum->set_encoded(true);
-      return true;
-    }
     CVMatToDatum(cv_img, datum);
     datum->set_label(label);
     return true;
@@ -156,24 +131,47 @@ bool ReadFileToDatum(const string& filename, const int label,
   }
 }
 
-cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width, const bool is_color) {
   cv::Mat cv_img;
   CHECK(datum.encoded()) << "Datum not encoded";
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
   const string& data = datum.data();
   std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
-  cv_img = cv::imdecode(vec_data, -1);
+  if (height > 0 && width > 0) {
+    cv::Mat cv_img_origin = cv::imdecode(cv::Mat(vec_data), cv_read_flag);
+    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
+  } else {
+    cv_img = cv::imdecode(vec_data, cv_read_flag);
+  }
   if (!cv_img.data) {
     LOG(ERROR) << "Could not decode datum ";
   }
   return cv_img;
 }
 
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const int height, const int width) {
+  return DecodeDatumToCVMat(datum, height, width, true);
+}
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum,
+    const bool is_color) {
+  return DecodeDatumToCVMat(datum, 0, 0, is_color);
+}
+
+cv::Mat DecodeDatumToCVMat(const Datum& datum) {
+  return DecodeDatumToCVMat(datum, 0, 0, true);
+}
+
 // If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
 // if height and width are set it will resize it
 // If Datum is not encoded will do nothing
-bool DecodeDatumNative(Datum* datum) {
+bool DecodeDatum(const int height, const int width, const bool is_color,
+                Datum* datum) {
   if (datum->encoded()) {
-    cv::Mat cv_img = DecodeDatumToCVMatNative((*datum));
+    cv::Mat cv_img = DecodeDatumToCVMat((*datum), height, width, is_color);
     CVMatToDatum(cv_img, datum);
     return true;
   } else {
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index b1fc7cae38f..dff63a09dca 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -50,7 +50,7 @@ int main(int argc, char** argv) {
   Datum datum;
   datum.ParseFromString(cursor->value());
 
-  if (DecodeDatumNative(&datum)) {
+  if (DecodeDatum(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
 
@@ -68,7 +68,7 @@ int main(int argc, char** argv) {
   while (cursor->valid()) {
     Datum datum;
     datum.ParseFromString(cursor->value());
-    DecodeDatumNative(&datum);
+    DecodeDatum(&datum);
 
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 816a91f971b..7fbf5b0514c 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -39,8 +39,6 @@ DEFINE_bool(check_size, false,
     "When this option is on, check that all the datum have the same size");
 DEFINE_bool(encoded, false,
     "When this option is on, the encoded image will be save in datum");
-DEFINE_string(encode_type, "",
-    "Optional: What type should we encode the image as ('png','jpg',...).");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -65,7 +63,6 @@ int main(int argc, char** argv) {
   const bool is_color = !FLAGS_gray;
   const bool check_size = FLAGS_check_size;
   const bool encoded = FLAGS_encoded;
-  const string encode_type = FLAGS_encode_type;
 
   std::ifstream infile(argv[2]);
   std::vector<std::pair<std::string, int> > lines;
@@ -81,8 +78,11 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  if (encode_type.size() && !encoded)
-    LOG(INFO) << "encode_type specified, assuming encoded=true.";
+  if (encoded) {
+    CHECK_EQ(FLAGS_resize_height, 0) << "With encoded don't resize images";
+    CHECK_EQ(FLAGS_resize_width, 0) << "With encoded don't resize images";
+    CHECK(!check_size) << "With encoded cannot check_size";
+  }
 
   int resize_height = std::max<int>(0, FLAGS_resize_height);
   int resize_width = std::max<int>(0, FLAGS_resize_width);
@@ -98,24 +98,18 @@ int main(int argc, char** argv) {
   int count = 0;
   const int kMaxKeyLength = 256;
   char key_cstr[kMaxKeyLength];
-  int data_size = 0;
+  int data_size;
   bool data_size_initialized = false;
 
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     bool status;
-    std::string enc = encode_type;
-    if (encoded && !enc.size()) {
-      // Guess the encoding type from the file name
-      string fn = lines[line_id].first;
-      size_t p = fn.rfind('.');
-      if ( p == fn.npos )
-        LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
-      enc = fn.substr(p);
-      std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
+    if (encoded) {
+      status = ReadFileToDatum(root_folder + lines[line_id].first,
+        lines[line_id].second, &datum);
+    } else {
+      status = ReadImageToDatum(root_folder + lines[line_id].first,
+          lines[line_id].second, resize_height, resize_width, is_color, &datum);
     }
-    status = ReadImageToDatum(root_folder + lines[line_id].first,
-        lines[line_id].second, resize_height, resize_width, is_color,
-        enc, &datum);
     if (status == false) continue;
     if (check_size) {
       if (!data_size_initialized) {

From a2f7f479a82290ca60126873f18ed54e37bb9e2c Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Thu, 19 Feb 2015 13:46:21 -0800
Subject: [PATCH 1170/2053] added a force_encoded_color flag to the data layer.
 Printing a warning if images of different channel dimensions are encoded
 together

---
 examples/images/cat_gray.jpg           | Bin 0 -> 92726 bytes
 include/caffe/util/io.hpp              |   2 +
 src/caffe/layers/data_layer.cpp        |  15 ++++-
 src/caffe/layers/window_data_layer.cpp |   2 +-
 src/caffe/proto/caffe.proto            |   2 +
 src/caffe/test/test_io.cpp             |  88 ++++++++++++++++++++++++-
 src/caffe/util/io.cpp                  |  23 ++++++-
 7 files changed, 126 insertions(+), 6 deletions(-)
 create mode 100644 examples/images/cat_gray.jpg

diff --git a/examples/images/cat_gray.jpg b/examples/images/cat_gray.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..43c5ce377167a49624c3e9d61e83a04becb8df9e
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literal 0
HcmV?d00001

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 9d7540d62cc..3a62c3c9fa9 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -122,6 +122,7 @@ inline bool ReadImageToDatum(const string& filename, const int label,
 }
 
 bool DecodeDatumNative(Datum* datum);
+bool DecodeDatum(Datum* datum, bool is_color);
 
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
@@ -135,6 +136,7 @@ cv::Mat ReadImageToCVMat(const string& filename,
 cv::Mat ReadImageToCVMat(const string& filename);
 
 cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
+cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 891d03911c2..7716406f672 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -42,7 +42,9 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   Datum datum;
   datum.ParseFromString(cursor_->value());
 
-  if (DecodeDatumNative(&datum)) {
+  bool force_color = this->layer_param_.data_param().force_encoded_color();
+  if ((force_color && DecodeDatum(&datum, true)) ||
+      DecodeDatumNative(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
   // image
@@ -90,6 +92,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
   const int batch_size = this->layer_param_.data_param().batch_size();
+  bool force_color = this->layer_param_.data_param().force_encoded_color();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
     // get a blob
@@ -98,7 +101,15 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
     cv::Mat cv_img;
     if (datum.encoded()) {
-       cv_img = DecodeDatumToCVMatNative(datum);
+      if (force_color)
+        cv_img = DecodeDatumToCVMat(datum, true);
+      else
+        cv_img = DecodeDatumToCVMatNative(datum);
+      if (cv_img.channels() != this->transformed_data_.channels())
+        LOG(WARNING) << "Your dataset contains encoded images with mixed "
+        << "channel sizes. Consider adding a 'force_color' flag to the "
+        << "model definition, or rebuild your dataset using "
+        << "convert_imageset.";
     }
     read_time += timer.MicroSeconds();
     timer.Start();
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index cceb4ffb1b5..73408c6e1f2 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -281,7 +281,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       if (this->cache_images_) {
         pair<std::string, Datum> image_cached =
           image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
-        cv_img = DecodeDatumToCVMatNative(image_cached.second);
+        cv_img = DecodeDatumToCVMat(image_cached.second, true);
       } else {
         cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
         if (!cv_img.data) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8d937420eba..8ba60753570 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -426,6 +426,8 @@ message DataParameter {
   // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
   // data.
   optional bool mirror = 6 [default = false];
+  // Force the encoded image to have 3 color channels
+  optional bool force_encoded_color = 9 [default = false];
 }
 
 // Message that stores parameters used by DropoutLayer
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 6b135efcf6e..4ab96311bbc 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -286,6 +286,60 @@ TEST_F(IOTest, TestReadFileToDatum) {
 }
 
 TEST_F(IOTest, TestDecodeDatum) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(DecodeDatum(&datum, true));
+  EXPECT_FALSE(DecodeDatum(&datum, true));
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMat) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, true);
+  EXPECT_EQ(cv_img.channels(), 3);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+  cv_img = DecodeDatumToCVMat(datum, false);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
+  cv::Mat cv_img = DecodeDatumToCVMat(datum, true);
+  cv::Mat cv_img_ref = ReadImageToCVMat(filename);
+  EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
+  EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
+  EXPECT_EQ(cv_img_ref.cols, cv_img.cols);
+
+  for (int c = 0; c < datum.channels(); ++c) {
+    for (int h = 0; h < datum.height(); ++h) {
+      for (int w = 0; w < datum.width(); ++w) {
+        EXPECT_TRUE(cv_img.at<cv::Vec3b>(h, w)[c]==
+          cv_img_ref.at<cv::Vec3b>(h, w)[c]);
+      }
+    }
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumNative) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
@@ -305,7 +359,7 @@ TEST_F(IOTest, TestDecodeDatum) {
   }
 }
 
-TEST_F(IOTest, TestDecodeDatumToCVMat) {
+TEST_F(IOTest, TestDecodeDatumToCVMatNative) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
@@ -315,7 +369,37 @@ TEST_F(IOTest, TestDecodeDatumToCVMat) {
   EXPECT_EQ(cv_img.cols, 480);
 }
 
-TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
+TEST_F(IOTest, TestDecodeDatumNativeGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat_gray.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  EXPECT_TRUE(DecodeDatumNative(&datum));
+  EXPECT_FALSE(DecodeDatumNative(&datum));
+  Datum datum_ref;
+  ReadImageToDatumReference(filename, 0, 0, 0, false, &datum_ref);
+  EXPECT_EQ(datum.channels(), datum_ref.channels());
+  EXPECT_EQ(datum.height(), datum_ref.height());
+  EXPECT_EQ(datum.width(), datum_ref.width());
+  EXPECT_EQ(datum.data().size(), datum_ref.data().size());
+
+  const string& data = datum.data();
+  const string& data_ref = datum_ref.data();
+  for (int i = 0; i < datum.data().size(); ++i) {
+    EXPECT_TRUE(data[i] == data_ref[i]);
+  }
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatNativeGray) {
+  string filename = EXAMPLES_SOURCE_DIR "images/cat_gray.jpg";
+  Datum datum;
+  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
+  EXPECT_EQ(cv_img.channels(), 1);
+  EXPECT_EQ(cv_img.rows, 360);
+  EXPECT_EQ(cv_img.cols, 480);
+}
+
+TEST_F(IOTest, TestDecodeDatumToCVMatContentNative) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 65531687b63..b243a9804ec 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -167,9 +167,21 @@ cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
   }
   return cv_img;
 }
+cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color) {
+  cv::Mat cv_img;
+  CHECK(datum.encoded()) << "Datum not encoded";
+  const string& data = datum.data();
+  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
+  cv_img = cv::imdecode(vec_data, cv_read_flag);
+  if (!cv_img.data) {
+    LOG(ERROR) << "Could not decode datum ";
+  }
+  return cv_img;
+}
 
 // If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
-// if height and width are set it will resize it
 // If Datum is not encoded will do nothing
 bool DecodeDatumNative(Datum* datum) {
   if (datum->encoded()) {
@@ -180,6 +192,15 @@ bool DecodeDatumNative(Datum* datum) {
     return false;
   }
 }
+bool DecodeDatum(Datum* datum, bool is_color) {
+  if (datum->encoded()) {
+    cv::Mat cv_img = DecodeDatumToCVMat((*datum), is_color);
+    CVMatToDatum(cv_img, datum);
+    return true;
+  } else {
+    return false;
+  }
+}
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";

From 06ceaf452f65535a42b4f8a07f9cd861878d03e8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 19 Feb 2015 17:49:12 -0800
Subject: [PATCH 1171/2053] [docs] add gitter chat badge

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0a60095bdf1..ebec286d550 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,9 @@ Check out the [project site](http://caffe.berkeleyvision.org) for all the detail
 
 and step-by-step examples.
 
-Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) to ask questions and talk about methods and models.
+[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
 Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
 
 Happy brewing!

From ccd837433df0bfadcd198419ee554e39f7f0a7f9 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 19 Feb 2015 17:54:49 -0800
Subject: [PATCH 1172/2053] Repeal revert of #1878

---
 include/caffe/util/io.hpp              | 36 ++++++----------
 src/caffe/layers/data_layer.cpp        |  4 +-
 src/caffe/layers/window_data_layer.cpp |  2 +-
 src/caffe/test/test_io.cpp             | 31 +++----------
 src/caffe/util/io.cpp                  | 60 +++++++++++++-------------
 tools/compute_image_mean.cpp           |  4 +-
 tools/convert_imageset.cpp             | 30 +++++++------
 7 files changed, 72 insertions(+), 95 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index a01f917abf5..9d7540d62cc 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -92,7 +92,14 @@ inline bool ReadFileToDatum(const string& filename, Datum* datum) {
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum);
+    const int height, const int width, const bool is_color,
+    const std::string & encoding, Datum* datum);
+
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const int height, const int width, const bool is_color, Datum* datum) {
+  return ReadImageToDatum(filename, label, height, width, is_color,
+                          "", datum);
+}
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, Datum* datum) {
@@ -109,20 +116,12 @@ inline bool ReadImageToDatum(const string& filename, const int label,
   return ReadImageToDatum(filename, label, 0, 0, true, datum);
 }
 
-bool DecodeDatum(const int height, const int width, const bool is_color,
-  Datum* datum);
-
-inline bool DecodeDatum(const int height, const int width, Datum* datum) {
-  return DecodeDatum(height, width, true, datum);
-}
-
-inline bool DecodeDatum(const bool is_color, Datum* datum) {
-  return DecodeDatum(0, 0, is_color, datum);
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const std::string & encoding, Datum* datum) {
+  return ReadImageToDatum(filename, label, 0, 0, true, encoding, datum);
 }
 
-inline bool DecodeDatum(Datum* datum) {
-  return DecodeDatum(0, 0, true, datum);
-}
+bool DecodeDatumNative(Datum* datum);
 
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
@@ -135,16 +134,7 @@ cv::Mat ReadImageToCVMat(const string& filename,
 
 cv::Mat ReadImageToCVMat(const string& filename);
 
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width, const bool is_color);
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width);
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const bool is_color);
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum);
+cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 0f959a9d783..9a2dfac9b1f 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -42,7 +42,7 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   Datum datum;
   datum.ParseFromString(cursor_->value());
 
-  if (DecodeDatum(&datum)) {
+  if (DecodeDatumNative(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
   // image
@@ -110,7 +110,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
 
     cv::Mat cv_img;
     if (datum.encoded()) {
-       cv_img = DecodeDatumToCVMat(datum);
+       cv_img = DecodeDatumToCVMatNative(datum);
     }
     read_time += timer.MicroSeconds();
     timer.Start();
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 3e9e9765ccf..1a6ffe2011d 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -278,7 +278,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
       if (this->cache_images_) {
         pair<std::string, Datum> image_cached =
           image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
-        cv_img = DecodeDatumToCVMat(image_cached.second);
+        cv_img = DecodeDatumToCVMatNative(image_cached.second);
       } else {
         cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
         if (!cv_img.data) {
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 4d941fa8683..6b135efcf6e 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -289,8 +289,8 @@ TEST_F(IOTest, TestDecodeDatum) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  EXPECT_TRUE(DecodeDatum(&datum));
-  EXPECT_FALSE(DecodeDatum(&datum));
+  EXPECT_TRUE(DecodeDatumNative(&datum));
+  EXPECT_FALSE(DecodeDatumNative(&datum));
   Datum datum_ref;
   ReadImageToDatumReference(filename, 0, 0, 0, true, &datum_ref);
   EXPECT_EQ(datum.channels(), datum_ref.channels());
@@ -309,38 +309,17 @@ TEST_F(IOTest, TestDecodeDatumToCVMat) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
   EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMat(datum);
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
   EXPECT_EQ(cv_img.channels(), 3);
   EXPECT_EQ(cv_img.rows, 360);
   EXPECT_EQ(cv_img.cols, 480);
 }
 
-TEST_F(IOTest, TestDecodeDatumToCVMatResized) {
-  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
-  Datum datum;
-  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMat(datum, 100, 200);
-  EXPECT_EQ(cv_img.channels(), 3);
-  EXPECT_EQ(cv_img.rows, 100);
-  EXPECT_EQ(cv_img.cols, 200);
-}
-
-TEST_F(IOTest, TestDecodeDatumToCVMatResizedGray) {
-  string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
-  Datum datum;
-  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  const bool is_color = false;
-  cv::Mat cv_img = DecodeDatumToCVMat(datum, 200, 100, is_color);
-  EXPECT_EQ(cv_img.channels(), 1);
-  EXPECT_EQ(cv_img.rows, 200);
-  EXPECT_EQ(cv_img.cols, 100);
-}
-
 TEST_F(IOTest, TestDecodeDatumToCVMatContent) {
   string filename = EXAMPLES_SOURCE_DIR "images/cat.jpg";
   Datum datum;
-  EXPECT_TRUE(ReadFileToDatum(filename, &datum));
-  cv::Mat cv_img = DecodeDatumToCVMat(datum);
+  EXPECT_TRUE(ReadImageToDatum(filename, 0, std::string("jpg"), &datum));
+  cv::Mat cv_img = DecodeDatumToCVMatNative(datum);
   cv::Mat cv_img_ref = ReadImageToCVMat(filename);
   EXPECT_EQ(cv_img_ref.channels(), cv_img.channels());
   EXPECT_EQ(cv_img_ref.rows, cv_img.rows);
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index f738e053e86..65531687b63 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -98,11 +98,36 @@ cv::Mat ReadImageToCVMat(const string& filename,
 cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
-
+// Do the file extension and encoding match?
+static bool matchExt(const std::string & fn,
+                     std::string en) {
+  size_t p = fn.rfind('.');
+  std::string ext = p != fn.npos ? fn.substr(p) : fn;
+  std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
+  std::transform(en.begin(), en.end(), en.begin(), ::tolower);
+  if ( ext == en )
+    return true;
+  if ( en == "jpg" && ext == "jpeg" )
+    return true;
+  return false;
+}
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, const bool is_color, Datum* datum) {
+    const int height, const int width, const bool is_color,
+    const std::string & encoding, Datum* datum) {
   cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);
   if (cv_img.data) {
+    if (encoding.size()) {
+      if ( (cv_img.channels() == 3) == is_color && !height && !width &&
+          matchExt(filename, encoding) )
+        return ReadFileToDatum(filename, label, datum);
+      std::vector<uchar> buf;
+      cv::imencode("."+encoding, cv_img, buf);
+      datum->set_data(std::string(reinterpret_cast<char*>(&buf[0]),
+                      buf.size()));
+      datum->set_label(label);
+      datum->set_encoded(true);
+      return true;
+    }
     CVMatToDatum(cv_img, datum);
     datum->set_label(label);
     return true;
@@ -131,47 +156,24 @@ bool ReadFileToDatum(const string& filename, const int label,
   }
 }
 
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width, const bool is_color) {
+cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
   cv::Mat cv_img;
   CHECK(datum.encoded()) << "Datum not encoded";
-  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
-    CV_LOAD_IMAGE_GRAYSCALE);
   const string& data = datum.data();
   std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());
-  if (height > 0 && width > 0) {
-    cv::Mat cv_img_origin = cv::imdecode(cv::Mat(vec_data), cv_read_flag);
-    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));
-  } else {
-    cv_img = cv::imdecode(vec_data, cv_read_flag);
-  }
+  cv_img = cv::imdecode(vec_data, -1);
   if (!cv_img.data) {
     LOG(ERROR) << "Could not decode datum ";
   }
   return cv_img;
 }
 
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const int height, const int width) {
-  return DecodeDatumToCVMat(datum, height, width, true);
-}
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum,
-    const bool is_color) {
-  return DecodeDatumToCVMat(datum, 0, 0, is_color);
-}
-
-cv::Mat DecodeDatumToCVMat(const Datum& datum) {
-  return DecodeDatumToCVMat(datum, 0, 0, true);
-}
-
 // If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum
 // if height and width are set it will resize it
 // If Datum is not encoded will do nothing
-bool DecodeDatum(const int height, const int width, const bool is_color,
-                Datum* datum) {
+bool DecodeDatumNative(Datum* datum) {
   if (datum->encoded()) {
-    cv::Mat cv_img = DecodeDatumToCVMat((*datum), height, width, is_color);
+    cv::Mat cv_img = DecodeDatumToCVMatNative((*datum));
     CVMatToDatum(cv_img, datum);
     return true;
   } else {
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index dff63a09dca..b1fc7cae38f 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -50,7 +50,7 @@ int main(int argc, char** argv) {
   Datum datum;
   datum.ParseFromString(cursor->value());
 
-  if (DecodeDatum(&datum)) {
+  if (DecodeDatumNative(&datum)) {
     LOG(INFO) << "Decoding Datum";
   }
 
@@ -68,7 +68,7 @@ int main(int argc, char** argv) {
   while (cursor->valid()) {
     Datum datum;
     datum.ParseFromString(cursor->value());
-    DecodeDatum(&datum);
+    DecodeDatumNative(&datum);
 
     const std::string& data = datum.data();
     size_in_datum = std::max<int>(datum.data().size(),
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 7fbf5b0514c..816a91f971b 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -39,6 +39,8 @@ DEFINE_bool(check_size, false,
     "When this option is on, check that all the datum have the same size");
 DEFINE_bool(encoded, false,
     "When this option is on, the encoded image will be save in datum");
+DEFINE_string(encode_type, "",
+    "Optional: What type should we encode the image as ('png','jpg',...).");
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
@@ -63,6 +65,7 @@ int main(int argc, char** argv) {
   const bool is_color = !FLAGS_gray;
   const bool check_size = FLAGS_check_size;
   const bool encoded = FLAGS_encoded;
+  const string encode_type = FLAGS_encode_type;
 
   std::ifstream infile(argv[2]);
   std::vector<std::pair<std::string, int> > lines;
@@ -78,11 +81,8 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  if (encoded) {
-    CHECK_EQ(FLAGS_resize_height, 0) << "With encoded don't resize images";
-    CHECK_EQ(FLAGS_resize_width, 0) << "With encoded don't resize images";
-    CHECK(!check_size) << "With encoded cannot check_size";
-  }
+  if (encode_type.size() && !encoded)
+    LOG(INFO) << "encode_type specified, assuming encoded=true.";
 
   int resize_height = std::max<int>(0, FLAGS_resize_height);
   int resize_width = std::max<int>(0, FLAGS_resize_width);
@@ -98,18 +98,24 @@ int main(int argc, char** argv) {
   int count = 0;
   const int kMaxKeyLength = 256;
   char key_cstr[kMaxKeyLength];
-  int data_size;
+  int data_size = 0;
   bool data_size_initialized = false;
 
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     bool status;
-    if (encoded) {
-      status = ReadFileToDatum(root_folder + lines[line_id].first,
-        lines[line_id].second, &datum);
-    } else {
-      status = ReadImageToDatum(root_folder + lines[line_id].first,
-          lines[line_id].second, resize_height, resize_width, is_color, &datum);
+    std::string enc = encode_type;
+    if (encoded && !enc.size()) {
+      // Guess the encoding type from the file name
+      string fn = lines[line_id].first;
+      size_t p = fn.rfind('.');
+      if ( p == fn.npos )
+        LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
+      enc = fn.substr(p);
+      std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
     }
+    status = ReadImageToDatum(root_folder + lines[line_id].first,
+        lines[line_id].second, resize_height, resize_width, is_color,
+        enc, &datum);
     if (status == false) continue;
     if (check_size) {
       if (!data_size_initialized) {

From 5c81243ad72234d8de6c347ec704653afc963bca Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 19 Feb 2015 18:14:57 -0800
Subject: [PATCH 1173/2053] [build] fix rpath for examples

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index b298d9d146e..29827270baf 100644
--- a/Makefile
+++ b/Makefile
@@ -540,7 +540,7 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 $(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
 	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
-		-Wl,-rpath,$(ORIGIN)/../lib
+		-Wl,-rpath,$(ORIGIN)/../../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 

From 2d155abc4bdc418304fd96eecf80c7ac083dd80a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 29 Jan 2015 09:26:12 -0800
Subject: [PATCH 1174/2053] [docs] send API link to class list

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index 7b87c24ce96..932b3b58d1d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -48,7 +48,7 @@ Tested on Ubuntu, Red Hat, OS X.
 BVLC suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
-* [API Documentation](/doxygen/)<br>
+* [API Documentation](/doxygen/annotated.html)<br>
 Developer documentation automagically generated from code comments.
 
 ### Examples

From 7867ead95af415320b50201bf0dd926528e8e0d5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 29 Jan 2015 10:53:44 -0800
Subject: [PATCH 1175/2053] [docs] add check mode hint to CPU-only mode error

---
 include/caffe/util/device_alternate.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 5a45691bb17..6ea595dba2d 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -7,7 +7,7 @@
 
 // Stub out GPU calls as unavailable.
 
-#define NO_GPU LOG(FATAL) << "CPU-only Mode: cannot make GPU call."
+#define NO_GPU LOG(FATAL) << "Cannot use GPU in CPU-only Caffe: check mode."
 
 #define STUB_GPU(classname) \
 template <typename Dtype> \

From 5d15ed7d302b7977c4df621c05013e2117409b3e Mon Sep 17 00:00:00 2001
From: Andre Ambrosio Boechat <boechat107@gmail.com>
Date: Fri, 30 Jan 2015 13:57:31 -0200
Subject: [PATCH 1176/2053] Brief explanation of SLICE layer's attributes

* A sample code was added.
* `slice_dim` and `slice_point` attributes were explained.
---
 docs/tutorial/layers.md | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 5f8f519cdc4..34bb48050e8 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -451,6 +451,26 @@ The `CONCAT` layer is a utility layer that concatenates its multiple input blobs
 
 The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
 
+* Sample
+
+     layers {
+       name: "slicer_label"
+       type: SLICE
+       bottom: "label"
+       ## Example of label with a shape N x 3 x 1 x 1
+       top: "label1"
+       top: "label2"
+       top: "label3"
+       slice_param {
+           slice_dim: 1
+           slice_point: 1
+           slice_point: 2
+       }
+     }
+
+`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). 
+
+
 #### Elementwise Operations
 
 `ELTWISE`

From 079e4168f55112f6cf62f1d9ee7f4927f15978c5 Mon Sep 17 00:00:00 2001
From: Brandon Amos <bdamos@vt.edu>
Date: Mon, 16 Feb 2015 15:09:24 -0500
Subject: [PATCH 1177/2053] Correct 'epochs' to 'iterations'

See https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/solver.prototxt
---
 examples/imagenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 41384f9475b..c2dd62ec963 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -67,7 +67,7 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * We will run in batches of 256, and run a total of 450,000 iterations (about 90 epochs).
 * For every 1,000 iterations, we test the learned net on the validation data.
 * We set the initial learning rate to 0.01, and decrease it every 100,000 iterations (about 20 epochs).
-* Information will be displayed every 20 epochs.
+* Information will be displayed every 20 iterations.
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 

From af01b9c7354b36620881b0d2e608df83ebcedfd5 Mon Sep 17 00:00:00 2001
From: e3 <erik@cpan.org>
Date: Thu, 19 Feb 2015 22:07:27 -0800
Subject: [PATCH 1178/2053] Updated the path for get_ilsvrc_aux.sh to match
 what is found in the current project

---
 examples/imagenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index c2dd62ec963..a6bdf49ca4d 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -26,7 +26,7 @@ We assume that you already have downloaded the ImageNet training data and valida
 
 You will first need to prepare some auxiliary data for training. This data can be downloaded by:
 
-    ./data/get_ilsvrc_aux.sh
+    ./data/ilsvrc12/get_ilsvrc_aux.sh
 
 The training and validation input are described in `train.txt` and `val.txt` as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See `synset_words.txt` for the synset/name mapping.
 

From eabbccd4d3f4ba5e1f7b37bf1e5ae3f7b67992cc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 20 Feb 2015 11:18:47 -0800
Subject: [PATCH 1179/2053] [build] fix dynamic linking of tools

set the right rpath for tools and examples respectively

thanks for the report @mees!
---
 Makefile | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 29827270baf..2a75d66e02a 100644
--- a/Makefile
+++ b/Makefile
@@ -537,7 +537,12 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
 	@ ln -s $(abspath $<) $@
 
-$(TOOL_BINS) $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
+$(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
+	@ echo CXX/LD -o $@
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+		-Wl,-rpath,$(ORIGIN)/../lib
+
+$(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
 	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../../lib

From dc12757074bf07f75594625a51327a2b5682f7bd Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@eecs.berkeley.edu>
Date: Fri, 20 Feb 2015 12:42:28 -0800
Subject: [PATCH 1180/2053] Add failing tests for LRNLayer due to large local
 region

---
 src/caffe/test/test_lrn_layer.cpp | 38 +++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 07425df9b3a..c4e2f8ea7f2 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -138,6 +138,22 @@ TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {
   }
 }
 
+TYPED_TEST(LRNLayerTest, TestForwardAcrossChannelsLargeRegion) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  LRNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<Dtype> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
 TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -159,6 +175,28 @@ TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(LRNLayerTest, TestGradientAcrossChannelsLargeRegion) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  LRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+  // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+  //   std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
+  //       << std::endl;
+  // }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From e27c369d3fc06e122a786230af4bb7859c990e86 Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@eecs.berkeley.edu>
Date: Fri, 20 Feb 2015 13:07:32 -0800
Subject: [PATCH 1181/2053] Bounds checks for cross-channel LRN.

---
 src/caffe/layers/lrn_layer.cu | 39 ++++++++++++++---------------------
 1 file changed, 16 insertions(+), 23 deletions(-)

diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 58c39926c72..24aa6a30130 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -26,26 +26,24 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
     Dtype accum_scale = 0;
     // fill the scale at [n, :, h, w]
     // accumulate values
-    while (head < post_pad) {
+    while (head < post_pad && head < channels) {
       accum_scale += in[head * step] * in[head * step];
       ++head;
     }
-    // until we reach size, nothing needs to be subtracted
-    while (head < size) {
-      accum_scale += in[head * step] * in[head * step];
-      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
-      ++head;
-    }
     // both add and subtract
     while (head < channels) {
       accum_scale += in[head * step] * in[head * step];
-      accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+      if (head - size >= 0) {
+        accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+      }
       scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
-      accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+      if (head - size >= 0) {
+        accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+      }
       scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
@@ -143,26 +141,19 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
     int post_pad = size - pre_pad - 1;
     Dtype accum_ratio = 0;
     // accumulate values
-    while (head < post_pad) {
+    while (head < post_pad && head < channels) {
       accum_ratio += top_diff[head * step] * top_data[head * step] /
           scale[head * step];
       ++head;
     }
-    // until we reach size, nothing needs to be subtracted
-    while (head < size) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
-      ++head;
-    }
     // both add and subtract
     while (head < channels) {
       accum_ratio += top_diff[head * step] * top_data[head * step] /
           scale[head * step];
-      accum_ratio -= top_diff[(head - size) * step] *
-          top_data[(head - size) * step] / scale[(head - size) * step];
+      if (head - size >= 0) {
+        accum_ratio -= top_diff[(head - size) * step] *
+            top_data[(head - size) * step] / scale[(head - size) * step];
+      }
       bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
           * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
           bottom_data[(head - post_pad) * step] * accum_ratio;
@@ -170,8 +161,10 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
     }
     // subtract only
     while (head < channels + post_pad) {
-      accum_ratio -= top_diff[(head - size) * step] *
-          top_data[(head - size) * step] / scale[(head - size) * step];
+      if (head - size >= 0) {
+        accum_ratio -= top_diff[(head - size) * step] *
+            top_data[(head - size) * step] / scale[(head - size) * step];
+      }
       bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
           * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
           bottom_data[(head - post_pad) * step] * accum_ratio;

From 5a2633370f460b6a4d57c7564a38b14311420ab3 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 20 Feb 2015 20:29:53 -0800
Subject: [PATCH 1182/2053] check caffe tool runs in runtest

---
 Makefile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile b/Makefile
index 2a75d66e02a..642bde3571d 100644
--- a/Makefile
+++ b/Makefile
@@ -450,6 +450,7 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
 
 runtest: $(TEST_ALL_BIN)
+	$(TOOL_BUILD_DIR)/caffe
 	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
 
 pytest: py

From a1e951dcf9f534796de9a0f73f4869b3df33ab58 Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 22 Feb 2015 18:58:12 +0300
Subject: [PATCH 1183/2053] ignore pycharm files

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 73bba6cb364..28f2aca854b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -44,6 +44,9 @@
 # QtCreator files
 *.user
 
+# PyCharm files
+.idea
+
 # OSX dir files
 .DS_Store
 

From fca05c34c67701368245410e6f7c5e118e84a09f Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 22 Feb 2015 19:03:47 +0300
Subject: [PATCH 1184/2053] set proper CMAKE_INSTALL_RPATH for _caffe.so and
 tools

---
 cmake/Misc.cmake | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 608a5f13a79..39569eaf996 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -32,6 +32,11 @@ endif()
 set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE CACHE BOOLEAN "Use link paths for shared library rpath")
 set(CMAKE_MACOSX_RPATH TRUE)
 
+list(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES ${CMAKE_INSTALL_PREFIX}/lib __is_systtem_dir)
+if(${__is_systtem_dir} STREQUAL -1)
+  set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_PREFIX}/lib)
+endif()
+
 # ---[ Funny target
 if(UNIX OR APPLE)
   add_custom_target(symlink_to_build COMMAND "ln" "-sf" "${PROJECT_BINARY_DIR}" "${PROJECT_SOURCE_DIR}/build"

From 645aa03207b24dc5f092686f399736c9334bc096 Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 22 Feb 2015 19:04:22 +0300
Subject: [PATCH 1185/2053] fixed bug in install-tree: _caffe.so installed by
 install(TARGET ...) was overwritten with symlink created at build time and
 installed with install(DIRECTORY ...)

---
 python/CMakeLists.txt | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 6afed4fa183..a2f82089cac 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -4,7 +4,7 @@ if(NOT HAVE_PYTHON)
 endif()
 
 include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
-file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
+file(GLOB_RECURSE python_srcs ${CMAKE_SOURCE_DIR}/python/*.cpp)
 
 add_library(pycaffe SHARED ${python_srcs})
 target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
@@ -22,9 +22,13 @@ if(UNIX OR APPLE)
 endif()
 
 # ---[ Install
-file(GLOB files *.py requirements.txt)
-install(FILES  ${files} DESTINATION python)
-install(DIRECTORY caffe DESTINATION python)
-install(TARGETS pycaffe DESTINATION python/caffe)
+file(GLOB files1 *.py requirements.txt)
+install(FILES ${files1} DESTINATION python)
+
+file(GLOB files2 caffe/*.py)
+install(FILES  ${files2} DESTINATION python/caffe)
+install(TARGETS pycaffe  DESTINATION python/caffe)
+install(DIRECTORY caffe/imagenet caffe/proto caffe/test DESTINATION python/caffe)
+
 
 

From 5e06d16d14d8a46d3c7a3f82497c6cb1401e160f Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Sun, 22 Feb 2015 20:14:40 +0300
Subject: [PATCH 1186/2053] minor cmake sumamry log fix

---
 cmake/Summary.cmake | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 3f7dff6b6e0..32931942846 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -107,8 +107,9 @@ function(caffe_print_configuration_summary)
   caffe_status("  C++ compiler      :   ${CMAKE_CXX_COMPILER}")
   caffe_status("  Release CXX flags :   ${__flags_rel}")
   caffe_status("  Debug CXX flags   :   ${__flags_deb}")
-  caffe_status("  BUILD_SHARED_LIBS :   ${BUILD_SHARED_LIBS}")
   caffe_status("  Build type        :   ${CMAKE_BUILD_TYPE}")
+  caffe_status("")
+  caffe_status("  BUILD_SHARED_LIBS :   ${BUILD_SHARED_LIBS}")
   caffe_status("  BUILD_python      :   ${BUILD_python}")
   caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
   caffe_status("  BUILD_docs        :   ${BUILD_docs}")
@@ -116,8 +117,9 @@ function(caffe_print_configuration_summary)
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
+  caffe_status("  Boost             :   Yes (ver. ${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION})")
   caffe_status("  glog              :   Yes")
-  caffe_status("  gflags            :   Yes")
+  caffe_status("  gflags            :   Yes")  
   caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
   caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
   caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )

From 569ae01cc309fc3e14352479735a137ae53cfb62 Mon Sep 17 00:00:00 2001
From: James Supancic III <arrummzen@gmail.com>
Date: Sun, 22 Feb 2015 10:16:45 -0800
Subject: [PATCH 1187/2053] cpp_lint.py fails silently with Python3 (which is
 the default on some systems). This commit specifies Python2 with which
 cpp_lint.py works :-)

---
 scripts/cpp_lint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 1b7c6c0536c..f750489f4f9 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python
+#!/usr/bin/python2
 #
 # Copyright (c) 2009 Google Inc. All rights reserved.
 #

From 845f9eac2b873e6d018ab83d5a32100ab443a35e Mon Sep 17 00:00:00 2001
From: spmallick <spmallick@gmail.com>
Date: Mon, 23 Feb 2015 16:13:20 -0800
Subject: [PATCH 1188/2053] APPLE was misspelled. in Line 27

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index adea37be565..2a48e062eca 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,7 +24,7 @@ caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
 include(cmake/Dependencies.cmake)
 
 # ---[ Flags
-if(UNIX OR APLE)
+if(UNIX OR APPLE)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 

From 54037d3942647ff3659028e1d7437f2e8f7cf911 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 17 Feb 2015 10:50:12 -0800
Subject: [PATCH 1189/2053] Making python3 work with cmake and the new python
 wrapper

---
 CMakeLists.txt           |  1 +
 cmake/Dependencies.cmake | 39 +++++++++++++++++++++++++++++++++------
 docs/installation.md     |  4 ++--
 python/caffe/_caffe.cpp  |  4 +++-
 python/caffe/io.py       |  9 ++++++++-
 python/caffe/pycaffe.py  |  5 ++++-
 python/classify.py       |  6 +++---
 python/detect.py         |  2 +-
 python/draw_net.py       |  2 +-
 9 files changed, 56 insertions(+), 16 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index adea37be565..626d5b44459 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -17,6 +17,7 @@ caffe_option(CPU_ONLY  "Build Caffe wihtout CUDA support" OFF) # TODO: rename to
 caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
+set(python_version "2" CACHE STRING "Specify which python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
 
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index aa2dcbe1d0d..b1ac96c6777 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -92,12 +92,39 @@ endif()
 
 # ---[ Python
 if(BUILD_python)
-  # disable Python 3 search
-  find_package(PythonInterp 2.7)
-  find_package(PythonLibs 2.7)
-  find_package(NumPy 1.7.1)
-  find_package(Boost 1.46 COMPONENTS python)
-
+  if(NOT "${python_version}" VERSION_LESS "3.0.0")
+    # use python3
+    find_package(PythonInterp 3.0)
+    find_package(PythonLibs 3.0)
+    find_package(NumPy 1.7.1)
+    # Find the matching boost python implementation
+    set(version ${PYTHONLIBS_VERSION_STRING})
+    
+    STRING( REPLACE "." "" boost_py_version ${version} )
+    find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
+    set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+    
+    while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
+      STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
+      STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
+      if("${has_more_version}" STREQUAL "")
+        break()
+      endif()
+      
+      STRING( REPLACE "." "" boost_py_version ${version} )
+      find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
+      set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+    endwhile()
+    if(NOT Boost_PYTHON_FOUND)
+      find_package(Boost 1.46 COMPONENTS python)
+    endif()
+  else()
+    # disable Python 3 search
+    find_package(PythonInterp 2.7)
+    find_package(PythonLibs 2.7)
+    find_package(NumPy 1.7.1)
+    find_package(Boost 1.46 COMPONENTS python)
+  endif()
   if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
     set(HAVE_PYTHON TRUE)
   endif()
diff --git a/docs/installation.md b/docs/installation.md
index 16575b54029..144e6a34f67 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -30,7 +30,7 @@ Caffe has several dependencies.
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
-* For Python Caffe:  `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
+* For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
 **cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
@@ -69,7 +69,7 @@ but we suggest first installing the [Anaconda](https://store.continuum.io/cshop/
 
 To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
 
-*Caffe's Python interface works with Python 2.7. Python 3 or earlier Pythons are your own adventure.*
+*Caffe's Python interface works with Python 2.7. Python 3.3+ should work out of the box without protobuf support. For protobuf support please install protobuf 3.0 alpha (https://developers.google.com/protocol-buffers/). Earlier Pythons are your own adventure.*
 
 #### MATLAB
 
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index a5d0e64605e..03967a21029 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -275,7 +275,9 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<vector<bool> >("BoolVec")
     .def(bp::vector_indexing_suite<vector<bool> >());
 
-  import_array();
+  // boost python expects a void (missing) return value, while import_array
+  // returns NULL for python3. import_array1() forces a void return value.
+  import_array1();
 }
 
 }  // namespace caffe
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 0ce9ecfeeed..f51e3a64d36 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -3,7 +3,14 @@
 from scipy.ndimage import zoom
 from skimage.transform import resize
 
-from caffe.proto import caffe_pb2
+try:
+    # Python3 will most likely not be able to load protobuf
+    from caffe.proto import caffe_pb2
+except:
+    if sys.version_info >= (3,0):
+        print("Failed to include caffe_pb2, things might go wrong!")
+    else:
+        raise
 
 ## proto / datum / ndarray conversion
 
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 31c145d77a5..d662d6cc282 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -4,7 +4,10 @@
 """
 
 from collections import OrderedDict
-from itertools import izip_longest
+try:
+	from itertools import izip_longest
+except:
+	from itertools import zip_longest as izip_longest
 import numpy as np
 
 from ._caffe import Net, SGDSolver
diff --git a/python/classify.py b/python/classify.py
index d435a572266..81d06369341 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -103,7 +103,7 @@ def main(argv):
             channel_swap=channel_swap)
 
     if args.gpu:
-        print 'GPU mode'
+        print('GPU mode')
 
     # Load numpy array (.npy), directory glob (*.jpg), or image file.
     args.input_file = os.path.expanduser(args.input_file)
@@ -115,12 +115,12 @@ def main(argv):
     else:
         inputs = [caffe.io.load_image(args.input_file)]
 
-    print "Classifying %d inputs." % len(inputs)
+    print("Classifying %d inputs." % len(inputs))
 
     # Classify.
     start = time.time()
     predictions = classifier.predict(inputs, not args.center_only)
-    print "Done in %.2f s." % (time.time() - start)
+    print("Done in %.2f s." % (time.time() - start))
 
     # Save
     np.save(args.output_file, predictions)
diff --git a/python/detect.py b/python/detect.py
index cb0c2645761..d395bd97abf 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -115,7 +115,7 @@ def main(argv):
             context_pad=args.context_pad)
 
     if args.gpu:
-        print 'GPU mode'
+        print('GPU mode')
 
     # Load input.
     t = time.time()
diff --git a/python/draw_net.py b/python/draw_net.py
index 4457b793e86..6320f775ef7 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -36,7 +36,7 @@ def main():
     args = parse_args()
     net = caffe_pb2.NetParameter()
     text_format.Merge(open(args.input_net_proto_file).read(), net)
-    print 'Drawing net to %s' % args.output_image_file
+    print('Drawing net to %s' % args.output_image_file)
     caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
 
 

From 2cf5089d273e0e46f51c1b4b7aa018cbf3b983fe Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 24 Feb 2015 16:02:06 -0800
Subject: [PATCH 1190/2053] Decoding the datum before feeding it into the
 reshaping data layer

---
 src/caffe/layers/data_layer.cpp | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 8877caf89c8..1861090f953 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -89,9 +89,17 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   // Reshape on single input batches for inputs of varying dimension.
   const int batch_size = this->layer_param_.data_param().batch_size();
   const int crop_size = this->layer_param_.transform_param().crop_size();
+  bool force_color = this->layer_param_.data_param().force_encoded_color();
   if (batch_size == 1 && crop_size == 0) {
     Datum datum;
     datum.ParseFromString(cursor_->value());
+    if (datum.encoded()) {
+      if (force_color) {
+        DecodeDatum(&datum, true);
+      } else {
+        DecodeDatumNative(&datum);
+      }
+    }
     this->prefetch_data_.Reshape(1, datum.channels(),
         datum.height(), datum.width());
     this->transformed_data_.Reshape(1, datum.channels(),
@@ -104,7 +112,6 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   if (this->output_labels_) {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
-  bool force_color = this->layer_param_.data_param().force_encoded_color();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
     // get a blob

From 4a3887ab1791f7a0ea6d17cafb861f53398a42d5 Mon Sep 17 00:00:00 2001
From: forresti <forresti@eecs.berkeley.edu>
Date: Tue, 24 Feb 2015 16:51:56 -0800
Subject: [PATCH 1191/2053] fixed matcaffe printout to specify num of args (now
 including train/test phase)

---
 matlab/caffe/matcaffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 996d3d2149c..da37d920b20 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -272,7 +272,7 @@ static void get_init_key(MEX_ARGS) {
 static void init(MEX_ARGS) {
   if (nrhs != 3) {
     ostringstream error_msg;
-    error_msg << "Expected 2 arguments, got " << nrhs;
+    error_msg << "Expected 3 arguments, got " << nrhs;
     mex_error(error_msg.str());
   }
 

From d2beb8ab20b55aebd25a2fb1f3eed3cf2923efaa Mon Sep 17 00:00:00 2001
From: Gustav Larsson <gustav.m.larsson@gmail.com>
Date: Tue, 24 Feb 2015 23:22:15 -0600
Subject: [PATCH 1192/2053] Replaced illegal tab in Makefile with spaces.

Commands, such as $(error ...), are not allowed to be indented with tabs
outside of targets, throwing an error instead of outputting the actual
error. The solution is to use innocuous spaces instead. Ideally, spaces
should be used everywhere outside targets, but since make does not mind
it if variable assignments are tab-indented outside targets, a complete
overhaul is not necessary. However, if more errors are added, it might
make more sense to be consistent.

Also, make will already add a period so I removed it.
---
 Makefile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 642bde3571d..033473ac45b 100644
--- a/Makefile
+++ b/Makefile
@@ -261,7 +261,8 @@ ifneq (,$(findstring clang++,$(CXX)))
 else ifneq (,$(findstring g++,$(CXX)))
 	STATIC_LINK_COMMAND := -Wl,--whole-archive $(STATIC_NAME) -Wl,--no-whole-archive
 else
-	$(error Cannot static link with the $(CXX) compiler.)
+  # The following line must not be indented with a tab, since we are not inside a target
+  $(error Cannot static link with the $(CXX) compiler)
 endif
 
 # Debugging

From 1377e1be4ee70e9f57f8cef53d767603124ee84f Mon Sep 17 00:00:00 2001
From: Sergey Karayev <sergeykarayev@gmail.com>
Date: Tue, 24 Feb 2015 21:52:32 -0800
Subject: [PATCH 1193/2053] Makefile fix for OS X 10.10

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 642bde3571d..933e9be93f3 100644
--- a/Makefile
+++ b/Makefile
@@ -319,7 +319,7 @@ else
 		# 10.10 has accelerate while 10.9 has veclib
 		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6')
 		ifneq (,$(findstring version: 6,$(XCODE_CLT_VER)))
-			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.10.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
 			LDFLAGS += -framework Accelerate
 		else
 			BLAS_INCLUDE ?= /System/Library/Frameworks/vecLib.framework/Versions/Current/Headers/

From 76519702a415467a61a3c1e9ce706f41647c7faf Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Wed, 25 Feb 2015 08:17:06 -0800
Subject: [PATCH 1194/2053] Fixing two bugs related to python3 and
 PROJECT_SOURCE_DIR

---
 python/CMakeLists.txt   | 2 +-
 python/caffe/pycaffe.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index a2f82089cac..df0401daa1c 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -4,7 +4,7 @@ if(NOT HAVE_PYTHON)
 endif()
 
 include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
-file(GLOB_RECURSE python_srcs ${CMAKE_SOURCE_DIR}/python/*.cpp)
+file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
 
 add_library(pycaffe SHARED ${python_srcs})
 target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index d662d6cc282..9cd894a576d 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -41,12 +41,12 @@ def _Net_params(self):
 
 @property
 def _Net_inputs(self):
-    return [self.blobs.keys()[i] for i in self._inputs]
+    return [list(self.blobs.keys())[i] for i in self._inputs]
 
 
 @property
 def _Net_outputs(self):
-    return [self.blobs.keys()[i] for i in self._outputs]
+    return [list(self.blobs.keys())[i] for i in self._outputs]
 
 
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):

From 25cdd35c9ab1447f06040720243526fc8d0343c5 Mon Sep 17 00:00:00 2001
From: Andre Ambrosio Boechat <boechat107@gmail.com>
Date: Wed, 25 Feb 2015 14:56:18 -0300
Subject: [PATCH 1195/2053] Small fix (visualization) on SLICE layer's
 documentation

The sample was missing some additional spaces to be correctly rendered on the HTML. The mistake was mine.
---
 docs/tutorial/layers.md | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 34bb48050e8..839939f5ad6 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -453,20 +453,20 @@ The `SLICE` layer is a utility layer that slices an input layer to multiple outp
 
 * Sample
 
-     layers {
-       name: "slicer_label"
-       type: SLICE
-       bottom: "label"
-       ## Example of label with a shape N x 3 x 1 x 1
-       top: "label1"
-       top: "label2"
-       top: "label3"
-       slice_param {
-           slice_dim: 1
-           slice_point: 1
-           slice_point: 2
-       }
-     }
+      layers {
+        name: "slicer_label"
+        type: SLICE
+        bottom: "label"
+        ## Example of label with a shape N x 3 x 1 x 1
+        top: "label1"
+        top: "label2"
+        top: "label3"
+        slice_param {
+            slice_dim: 1
+            slice_point: 1
+            slice_point: 2
+        }
+      }
 
 `slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). 
 

From 311abb7b377292bd94895a5d6088ac4f4b9e2981 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Wed, 25 Feb 2015 12:10:49 -0800
Subject: [PATCH 1196/2053] Fix incorrectly storing empty param_name

---
 src/caffe/net.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index c359be9b575..49b53956858 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -402,7 +402,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     // (i.e., not given a param_name) or explicitly given a name that we
     // haven't already seen.
     param_owners_.push_back(-1);
-    if (param_size) {
+    if (param_name.size()) {
       param_names_index_[param_name] = net_param_id;
     }
   } else {

From 101055c840ef3a713341735c97ca49b2a8de253e Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Thu, 26 Feb 2015 14:49:19 -0800
Subject: [PATCH 1197/2053] Fix siamese ipynb example

---
 examples/siamese/mnist_siamese.ipynb | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
index 5abd0469ba6..8e076663ca6 100644
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -3,7 +3,8 @@
   "description": "Extracting features and plotting the Siamese network embedding.",
   "example_name": "Siamese network embedding",
   "include_in_docs": true,
-  "priority": 6
+  "priority": 6,
+  "signature": "sha256:845bb18929f96543ba2611eb5eca744fd98939cbef876df6bc319c29f616fc64"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -55,10 +56,8 @@
       "MODEL_FILE = 'mnist_siamese.prototxt'\n",
       "# decrease if you want to preview during training\n",
       "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
-      "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE)\n",
-      "net.set_phase_test()\n",
-      "net.set_mode_cpu()\n",
-      "net.set_input_scale('data', 0.00390625)"
+      "caffe.set_mode_cpu()\n",
+      "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
      ],
      "language": "python",
      "metadata": {},
@@ -105,10 +104,7 @@
      "collapsed": false,
      "input": [
       "# reshape and preprocess\n",
-      "caffe_in = raw_data.reshape(n, 28, 28).transpose((1,2,0))\n",
-      "caffe_in = net.preprocess('data', caffe_in) \n",
-      "caffe_in = caffe_in.reshape((n,1,28,28))\n",
-      "# pass data through network\n",
+      "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
       "out = net.forward_all(data=caffe_in)"
      ],
      "language": "python",
@@ -143,9 +139,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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vvyz37G3axPI/+f2yMpZP6nCI5+9yMRH34YfMI+rxsP9NTSzf1OlkOZbReDG5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ipWuXwuRdj48c8KYHSM3FeaVGpvihiJtGPGGSeNCPWS5jMap9zmuiJU6VJAmirJo9byPotErYfM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\nfjV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AM0Y4baXXS9+3v83yHTsYuGwtqVDj93POILJLy8u5fOUK8StXkvJWARaXlXFhfJxL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V//ELwP+gpqYC2J4j95vbj2+//fZ54/E8evQob775JgMDamXzjz76iF/96ldJ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\nmQpmVsCdLcyKeM1mXGC6H+dTJdHH1XTny+FQP2S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DVVVKG7x6aYNEwD6xZw/6T5/Gjbt3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w6+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\nAABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f31d5144c50>"
+        "<matplotlib.figure.Figure at 0x7fadf4552a90>"
        ]
       }
      ],
@@ -155,4 +151,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file

From 1833566091481d31662f9d9acc60bb07c35c2951 Mon Sep 17 00:00:00 2001
From: Daniel Hamngren <daniel@narrativeteam.com>
Date: Sun, 1 Mar 2015 23:06:03 +0100
Subject: [PATCH 1198/2053] Added Pillow to requirements.txt

---
 python/requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/requirements.txt b/python/requirements.txt
index 908373bf452..6a90fd6f5b4 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -14,3 +14,4 @@ python-dateutil>=1.4,<2
 protobuf>=2.5.0
 python-gflags>=2.0
 pyyaml>=3.10
+Pillow>=2.7.0

From 1434e87a7410835500a8feb2e7b2f96431b0c00a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 25 Nov 2014 18:17:45 -0800
Subject: [PATCH 1199/2053] Blobs are ND arrays (for N not necessarily equals
 4).

vector<int> shape_ instead of (num, channels, height, width).
---
 include/caffe/blob.hpp       | 146 ++++++++++++++++++++++++++++++-----
 src/caffe/blob.cpp           |  92 +++++++++++++++++-----
 src/caffe/net.cpp            |  25 +-----
 src/caffe/proto/caffe.proto  |   7 +-
 src/caffe/solver.cpp         |  14 +---
 src/caffe/test/test_blob.cpp |   5 +-
 6 files changed, 213 insertions(+), 76 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 42e4420408c..2d4df34ec6b 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -1,11 +1,17 @@
 #ifndef CAFFE_BLOB_HPP_
 #define CAFFE_BLOB_HPP_
 
+#include <algorithm>
+#include <string>
+#include <vector>
+
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 
+const int kMaxBlobAxes = INT_MAX;
+
 namespace caffe {
 
 /**
@@ -19,10 +25,16 @@ template <typename Dtype>
 class Blob {
  public:
   Blob()
-       : data_(), diff_(), num_(0), channels_(0), height_(0), width_(0),
-       count_(0), capacity_(0) {}
+       : data_(), diff_(), count_(0), capacity_(0) {}
+
+  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
   explicit Blob(const int num, const int channels, const int height,
-    const int width);
+      const int width);
+  explicit Blob(const vector<int>& shape);
+
+  /// @brief Deprecated; use <code>Reshape(const vector<int>& shape)</code>.
+  void Reshape(const int num, const int channels, const int height,
+      const int width);
   /**
    * @brief Change the dimensions of the blob, allocating new memory if
    *        necessary.
@@ -37,25 +49,118 @@ class Blob {
    * an error; either Net::Forward or Net::Reshape need to be called to
    * propagate the new input shape to higher layers.
    */
-  void Reshape(const int num, const int channels, const int height,
-    const int width);
+  void Reshape(const vector<int>& shape);
   void ReshapeLike(const Blob& other);
-  inline int num() const { return num_; }
-  inline int channels() const { return channels_; }
-  inline int height() const { return height_; }
-  inline int width() const { return width_; }
+  inline string shape_string() const {
+    ostringstream stream;
+    for (int i = 0; i < shape_.size(); ++i) {
+      stream << shape_[i] << " ";
+    }
+    stream << "(" << count_ << ")";
+    return stream.str();
+  }
+  inline const vector<int>& shape() const { return shape_; }
+  /**
+   * @brief Returns the dimension of the index-th axis (or the negative index-th
+   *        axis from the end, if index is negative).
+   *
+   * @param index the axis index, which may be negative as it will be
+   *        "canonicalized" using CanonicalAxisIndex.
+   *        Dies on out of range index.
+   */
+  inline int shape(int index) const {
+    return shape_[CanonicalAxisIndex(index)];
+  }
+  inline int num_axes() const { return shape_.size(); }
   inline int count() const { return count_; }
+
+  /**
+   * @brief Compute the volume of a slice; i.e., the product of dimensions
+   *        among a range of axes.
+   *
+   * @param start_axis The first axis to include in the slice.
+   *
+   * @param end_axis The first axis to exclude from the slice.
+   */
+  inline int count(int start_axis, int end_axis) const {
+    CHECK_LE(start_axis, end_axis);
+    CHECK_GE(start_axis, 0);
+    CHECK_GE(end_axis, 0);
+    CHECK_LE(start_axis, num_axes());
+    CHECK_LE(end_axis, num_axes());
+    int count = 1;
+    for (int i = start_axis; i < end_axis; ++i) {
+      count *= shape(i);
+    }
+    return count;
+  }
+  /**
+   * @brief Compute the volume of a slice spanning from a particular first
+   *        axis to the final axis.
+   *
+   * @param start_axis The first axis to include in the slice.
+   */
+  inline int count(int start_axis) const {
+    return count(start_axis, num_axes());
+  }
+
+  /**
+   * @brief Returns the 'canonical' version of a (usually) user-specified axis,
+   *        allowing for negative indexing (e.g., -1 for the last axis).
+   *
+   * @param index the axis index.
+   *        If 0 <= index < num_axes(), return index.
+   *        If -num_axes <= index <= -1, return (num_axes() - (-index)),
+   *        e.g., the last axis index (num_axes() - 1) if index == -1,
+   *        the second to last if index == -2, etc.
+   *        Dies on out of range index.
+   */
+  inline int CanonicalAxisIndex(int axis_index) const {
+    CHECK_GE(axis_index, -num_axes())
+        << "axis " << axis_index << " out of range for " << num_axes()
+        << "-D Blob with shape " << shape_string();
+    CHECK_LT(axis_index, num_axes())
+        << "axis " << axis_index << " out of range for " << num_axes()
+        << "-D Blob with shape " << shape_string();
+    if (axis_index < 0) {
+      return axis_index + num_axes();
+    }
+    return axis_index;
+  }
+
+  /// @brief Deprecated legacy shape accessor num: use shape(0) instead.
+  inline int num() const { return LegacyShape(0); }
+  /// @brief Deprecated legacy shape accessor channels: use shape(1) instead.
+  inline int channels() const { return LegacyShape(1); }
+  /// @brief Deprecated legacy shape accessor height: use shape(2) instead.
+  inline int height() const { return LegacyShape(2); }
+  /// @brief Deprecated legacy shape accessor width: use shape(3) instead.
+  inline int width() const { return LegacyShape(3); }
+  inline int LegacyShape(int index) const {
+    CHECK_LE(num_axes(), 4)
+        << "Cannot use legacy accessors on Blobs with > 4 axes.";
+    CHECK_LT(index, 4);
+    CHECK_GE(index, -4);
+    if (index >= num_axes() || index < -num_axes()) {
+      // Axis is out of range, but still in [0, 3] (or [-4, -1] for reverse
+      // indexing) -- this special case simulates the one-padding used to fill
+      // extraneous axes of legacy blobs.
+      return 1;
+    }
+    return shape(index);
+  }
+
   inline int offset(const int n, const int c = 0, const int h = 0,
       const int w = 0) const {
     CHECK_GE(n, 0);
-    CHECK_LE(n, num_);
-    CHECK_GE(channels_, 0);
-    CHECK_LE(c, channels_);
-    CHECK_GE(height_, 0);
-    CHECK_LE(h, height_);
-    CHECK_GE(width_, 0);
-    CHECK_LE(w, width_);
-    return ((n * channels_ + c) * height_ + h) * width_ + w;
+    CHECK_LE(n, num());
+    CHECK_GE(channels(), 0);
+    CHECK_LE(c, channels());
+    CHECK_GE(height(), 0);
+    CHECK_LE(h, height());
+    CHECK_GE(width(), 0);
+    CHECK_LE(w, width());
+    return ((n * channels() + c) * height() + h) * width() + w;
   }
   /**
    * @brief Copy from a source Blob.
@@ -135,13 +240,12 @@ class Blob {
    */
   void ShareDiff(const Blob& other);
 
+  bool ShapeEquals(const BlobProto& other);
+
  protected:
   shared_ptr<SyncedMemory> data_;
   shared_ptr<SyncedMemory> diff_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
+  vector<int> shape_;
   int count_;
   int capacity_;
 
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index fbc1361a19d..4a6332f382d 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -1,3 +1,6 @@
+#include <climits>
+#include <vector>
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
@@ -8,15 +11,24 @@ namespace caffe {
 template <typename Dtype>
 void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
     const int width) {
-  CHECK_GE(num, 0);
-  CHECK_GE(channels, 0);
-  CHECK_GE(height, 0);
-  CHECK_GE(width, 0);
-  num_ = num;
-  channels_ = channels;
-  height_ = height;
-  width_ = width;
-  count_ = num_ * channels_ * height_ * width_;
+  vector<int> shape(4);
+  shape[0] = num;
+  shape[1] = channels;
+  shape[2] = height;
+  shape[3] = width;
+  Reshape(shape);
+}
+
+template <typename Dtype>
+void Blob<Dtype>::Reshape(const vector<int>& shape) {
+  CHECK_LE(shape.size(), kMaxBlobAxes);
+  count_ = 1;
+  shape_.resize(shape.size());
+  for (int i = 0; i < shape.size(); ++i) {
+    CHECK_GE(shape[i], 0);
+    count_ *= shape[i];
+    shape_[i] = shape[i];
+  }
   if (count_ > capacity_) {
     capacity_ = count_;
     data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
@@ -26,7 +38,7 @@ void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
 
 template <typename Dtype>
 void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
-  Reshape(other.num(), other.channels(), other.height(), other.width());
+  Reshape(other.shape());
 }
 
 template <typename Dtype>
@@ -37,6 +49,13 @@ Blob<Dtype>::Blob(const int num, const int channels, const int height,
   Reshape(num, channels, height, width);
 }
 
+template <typename Dtype>
+Blob<Dtype>::Blob(const vector<int>& shape)
+  // capacity_ must be initialized before calling Reshape
+  : capacity_(0) {
+  Reshape(shape);
+}
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::cpu_data() const {
   CHECK(data_);
@@ -345,12 +364,34 @@ void Blob<Dtype>::scale_diff(Dtype scale_factor) {
   }
 }
 
+template <typename Dtype>
+bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
+  if (other.has_num() || other.has_channels() ||
+      other.has_height() || other.has_width()) {
+    // Using deprecated 4D Blob dimensions --
+    // shape is (num, channels, height, width).
+    // Note: we do not use the normal Blob::num(), Blob::channels(), etc.
+    // methods as these index from the beginning of the blob shape, where legacy
+    // parameter blobs were indexed from the end of the blob shape (e.g., bias
+    // Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
+    return shape_.size() <= 4 &&
+           LegacyShape(-4) == other.num() &&
+           LegacyShape(-3) == other.channels() &&
+           LegacyShape(-2) == other.height() &&
+           LegacyShape(-1) == other.width();
+  }
+  vector<int> other_shape(other.dim_size());
+  for (int i = 0; i < other.dim_size(); ++i) {
+    other_shape[i] = other.dim(i);
+  }
+  return shape_ == other_shape;
+}
+
 template <typename Dtype>
 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
-  if (num_ != source.num() || channels_ != source.channels() ||
-      height_ != source.height() || width_ != source.width()) {
+  if (source.count() != count_ || source.shape() != shape_) {
     if (reshape) {
-      Reshape(source.num(), source.channels(), source.height(), source.width());
+      ReshapeLike(source);
     } else {
       LOG(FATAL) << "Trying to copy blobs of different sizes.";
     }
@@ -381,7 +422,23 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
 
 template <typename Dtype>
 void Blob<Dtype>::FromProto(const BlobProto& proto) {
-  Reshape(proto.num(), proto.channels(), proto.height(), proto.width());
+  vector<int> shape;
+  if (proto.has_num() || proto.has_channels() ||
+      proto.has_height() || proto.has_width()) {
+    // Using deprecated 4D Blob dimensions --
+    // shape is (num, channels, height, width).
+    shape.resize(4);
+    shape[0] = proto.num();
+    shape[1] = proto.channels();
+    shape[2] = proto.height();
+    shape[3] = proto.width();
+  } else {
+    shape.resize(proto.dim_size());
+    for (int i = 0; i < proto.dim_size(); ++i) {
+      shape[i] = proto.dim(i);
+    }
+  }
+  Reshape(shape);
   // copy data
   Dtype* data_vec = mutable_cpu_data();
   for (int i = 0; i < count_; ++i) {
@@ -397,10 +454,9 @@ void Blob<Dtype>::FromProto(const BlobProto& proto) {
 
 template <typename Dtype>
 void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
-  proto->set_num(num_);
-  proto->set_channels(channels_);
-  proto->set_height(height_);
-  proto->set_width(width_);
+  for (int i = 0; i < shape_.size(); ++i) {
+    proto->add_dim(shape_[i]);
+  }
   proto->clear_data();
   proto->clear_diff();
   const Dtype* data_vec = cpu_data();
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index c359be9b575..a6aa917b556 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -109,11 +109,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
       }
       blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
-      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
-          << top_vecs_[layer_id][top_id]->channels() << " "
-          << top_vecs_[layer_id][top_id]->height() << " "
-          << top_vecs_[layer_id][top_id]->width() << " ("
-          << top_vecs_[layer_id][top_id]->count() << ")";
+      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
       if (layer->loss(top_id)) {
         LOG(INFO) << "    with loss weight " << layer->loss(top_id);
       }
@@ -427,14 +423,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
           << "Shared parameter blobs must have the same count.";
     } else {
       // Strict dimension checking -- all dims must be the same.
-      CHECK_EQ(this_blob->num(), owner_blob->num())
-          << "Shared parameter blobs must have the same num.";
-      CHECK_EQ(this_blob->channels(), owner_blob->channels())
-          << "Shared parameter blobs must have the same channels.";
-      CHECK_EQ(this_blob->height(), owner_blob->height())
-          << "Shared parameter blobs must have the same height.";
-      CHECK_EQ(this_blob->width(), owner_blob->width())
-          << "Shared parameter blobs must have the same width.";
+      CHECK(this_blob->shape() == owner_blob->shape());
     }
     layers_[layer_id]->blobs()[param_id]->ShareData(
         *layers_[owner_layer_id]->blobs()[owner_param_id]);
@@ -640,10 +629,7 @@ void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
       Blob<Dtype>* source_blob = source_layer->blobs()[j].get();
-      CHECK_EQ(target_blobs[j]->num(), source_blob->num());
-      CHECK_EQ(target_blobs[j]->channels(), source_blob->channels());
-      CHECK_EQ(target_blobs[j]->height(), source_blob->height());
-      CHECK_EQ(target_blobs[j]->width(), source_blob->width());
+      CHECK(target_blobs[j]->shape() == source_blob->shape());
       target_blobs[j]->ShareData(*source_blob);
     }
   }
@@ -707,10 +693,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
     CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
-      CHECK_EQ(target_blobs[j]->num(), source_layer.blobs(j).num());
-      CHECK_EQ(target_blobs[j]->channels(), source_layer.blobs(j).channels());
-      CHECK_EQ(target_blobs[j]->height(), source_layer.blobs(j).height());
-      CHECK_EQ(target_blobs[j]->width(), source_layer.blobs(j).width());
+      CHECK(target_blobs[j]->ShapeEquals(source_layer.blobs(j)));
       target_blobs[j]->FromProto(source_layer.blobs(j));
     }
   }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 84b475ce3cd..e82b75d281b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -3,12 +3,15 @@ syntax = "proto2";
 package caffe;
 
 message BlobProto {
+  repeated int32 dim = 7 [packed = true];
+  repeated float data = 5 [packed = true];
+  repeated float diff = 6 [packed = true];
+
+  // 4D dimensions -- deprecated.  Use "dim" instead.
   optional int32 num = 1 [default = 0];
   optional int32 channels = 2 [default = 0];
   optional int32 height = 3 [default = 0];
   optional int32 width = 4 [default = 0];
-  repeated float data = 5 [packed = true];
-  repeated float diff = 6 [packed = true];
 }
 
 // The BlobProtoVector is simply a way to pass multiple blobproto instances
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 8ed8aec2fc8..034390e6824 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -420,16 +420,10 @@ void SGDSolver<Dtype>::PreSolve() {
   update_.clear();
   temp_.clear();
   for (int i = 0; i < net_params.size(); ++i) {
-    const Blob<Dtype>* net_param = net_params[i].get();
-    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
+    const vector<int>& shape = net_params[i]->shape();
+    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
   }
 }
 
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index e0678061173..a654896c654 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -31,10 +31,7 @@ TYPED_TEST(BlobSimpleTest, TestInitialization) {
   EXPECT_EQ(this->blob_preshaped_->height(), 4);
   EXPECT_EQ(this->blob_preshaped_->width(), 5);
   EXPECT_EQ(this->blob_preshaped_->count(), 120);
-  EXPECT_EQ(this->blob_->num(), 0);
-  EXPECT_EQ(this->blob_->channels(), 0);
-  EXPECT_EQ(this->blob_->height(), 0);
-  EXPECT_EQ(this->blob_->width(), 0);
+  EXPECT_EQ(this->blob_->num_axes(), 0);
   EXPECT_EQ(this->blob_->count(), 0);
 }
 

From 5407f82a184a0d35cc1a9265fc7cfa61dae22517 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 31 Dec 2014 16:05:52 -0800
Subject: [PATCH 1200/2053] Add BlobShape message; use for Net input shapes

---
 include/caffe/blob.hpp      |  1 +
 src/caffe/blob.cpp          | 25 ++++++++++++++++++-------
 src/caffe/net.cpp           | 24 ++++++++++++++++++------
 src/caffe/proto/caffe.proto | 16 +++++++++++++---
 4 files changed, 50 insertions(+), 16 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 2d4df34ec6b..07b996ee392 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -50,6 +50,7 @@ class Blob {
    * propagate the new input shape to higher layers.
    */
   void Reshape(const vector<int>& shape);
+  void Reshape(const BlobShape& shape);
   void ReshapeLike(const Blob& other);
   inline string shape_string() const {
     ostringstream stream;
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 4a6332f382d..c65dc2dd1ac 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -36,6 +36,16 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
   }
 }
 
+template <typename Dtype>
+void Blob<Dtype>::Reshape(const BlobShape& shape) {
+  CHECK_LE(shape.dim_size(), kMaxBlobAxes);
+  vector<int> shape_vec(shape.dim_size());
+  for (int i = 0; i < shape.dim_size(); ++i) {
+    shape_vec[i] = shape.dim(i);
+  }
+  Reshape(shape_vec);
+}
+
 template <typename Dtype>
 void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
   Reshape(other.shape());
@@ -380,9 +390,9 @@ bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
            LegacyShape(-2) == other.height() &&
            LegacyShape(-1) == other.width();
   }
-  vector<int> other_shape(other.dim_size());
-  for (int i = 0; i < other.dim_size(); ++i) {
-    other_shape[i] = other.dim(i);
+  vector<int> other_shape(other.shape().dim_size());
+  for (int i = 0; i < other.shape().dim_size(); ++i) {
+    other_shape[i] = other.shape().dim(i);
   }
   return shape_ == other_shape;
 }
@@ -433,9 +443,9 @@ void Blob<Dtype>::FromProto(const BlobProto& proto) {
     shape[2] = proto.height();
     shape[3] = proto.width();
   } else {
-    shape.resize(proto.dim_size());
-    for (int i = 0; i < proto.dim_size(); ++i) {
-      shape[i] = proto.dim(i);
+    shape.resize(proto.shape().dim_size());
+    for (int i = 0; i < proto.shape().dim_size(); ++i) {
+      shape[i] = proto.shape().dim(i);
     }
   }
   Reshape(shape);
@@ -454,8 +464,9 @@ void Blob<Dtype>::FromProto(const BlobProto& proto) {
 
 template <typename Dtype>
 void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
+  proto->clear_shape();
   for (int i = 0; i < shape_.size(); ++i) {
-    proto->add_dim(shape_[i]);
+    proto->mutable_shape()->add_dim(shape_[i]);
   }
   proto->clear_data();
   proto->clear_diff();
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a6aa917b556..60f387b92e9 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -48,8 +48,16 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-      << "Incorrect input blob dimension specifications.";
+  CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)
+      << "Must specify either input_shape OR deprecated input_dim, not both.";
+  if (param.input_dim_size() > 0) {
+    // Deprecated 4D dimensions.
+    CHECK_EQ(param.input_size() * 4, param.input_dim_size())
+        << "Incorrect input blob dimension specifications.";
+  } else {
+    CHECK_EQ(param.input_size(), param.input_shape_size())
+        << "Exactly one input_shape must be specified per input.";
+  }
   memory_used_ = 0;
   // set the input blobs
   for (int input_id = 0; input_id < param.input_size(); ++input_id) {
@@ -339,10 +347,14 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
     if (layer_id == -1) {
       // Set the (explicitly specified) dimensions of the input blob.
-      blob_pointer->Reshape(param.input_dim(top_id * 4),
-                            param.input_dim(top_id * 4 + 1),
-                            param.input_dim(top_id * 4 + 2),
-                            param.input_dim(top_id * 4 + 3));
+      if (param.input_dim_size() > 0) {
+        blob_pointer->Reshape(param.input_dim(top_id * 4),
+                              param.input_dim(top_id * 4 + 1),
+                              param.input_dim(top_id * 4 + 2),
+                              param.input_dim(top_id * 4 + 3));
+      } else {
+        blob_pointer->Reshape(param.input_shape(top_id));
+      }
       net_input_blob_indices_.push_back(blob_id);
       net_input_blobs_.push_back(blob_pointer.get());
     } else {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e82b75d281b..fdcb57366ad 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -2,12 +2,17 @@ syntax = "proto2";
 
 package caffe;
 
+// Specifies the shape (dimensions) of a Blob.
+message BlobShape {
+  repeated int64 dim = 1 [packed = true];
+}
+
 message BlobProto {
-  repeated int32 dim = 7 [packed = true];
+  optional BlobShape shape = 7;
   repeated float data = 5 [packed = true];
   repeated float diff = 6 [packed = true];
 
-  // 4D dimensions -- deprecated.  Use "dim" instead.
+  // 4D dimensions -- deprecated.  Use "shape" instead.
   optional int32 num = 1 [default = 0];
   optional int32 channels = 2 [default = 0];
   optional int32 height = 3 [default = 0];
@@ -50,10 +55,15 @@ message NetParameter {
   optional string name = 1; // consider giving the network a name
   // The input blobs to the network.
   repeated string input = 3;
-  // The dim of the input blobs. For each input blob there should be four
+  // The shape of the input blobs.
+  repeated BlobShape input_shape = 8;
+
+  // 4D input dimensions -- deprecated.  Use "shape" instead.
+  // If specified, for each input blob there should be four
   // values specifying the num, channels, height and width of the input blob.
   // Thus, there should be a total of (4 * #input) numbers.
   repeated int32 input_dim = 4;
+
   // Whether the network will force every layer to carry out backward operation.
   // If set False, then whether to carry out backward is determined
   // automatically according to the net structure and learning rates.

From 119a1c6699b7d97b39699a5764fa231f15bd6dfe Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 4 Feb 2015 15:00:03 -0800
Subject: [PATCH 1201/2053] add offset, {data,diff}_at nd blob accessors

---
 include/caffe/blob.hpp | 26 ++++++++++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 07b996ee392..82ca95a5f0f 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -163,6 +163,20 @@ class Blob {
     CHECK_LE(w, width());
     return ((n * channels() + c) * height() + h) * width() + w;
   }
+
+  inline int offset(const vector<int>& indices) const {
+    CHECK_LE(indices.size(), num_axes());
+    int offset = 0;
+    for (int i = 0; i < num_axes(); ++i) {
+      offset *= shape(i);
+      if (indices.size() > i) {
+        CHECK_GE(indices[i], 0);
+        CHECK_LT(indices[i], shape(i));
+        offset += indices[i];
+      }
+    }
+    return offset;
+  }
   /**
    * @brief Copy from a source Blob.
    *
@@ -177,12 +191,20 @@ class Blob {
 
   inline Dtype data_at(const int n, const int c, const int h,
       const int w) const {
-    return *(cpu_data() + offset(n, c, h, w));
+    return cpu_data()[offset(n, c, h, w)];
   }
 
   inline Dtype diff_at(const int n, const int c, const int h,
       const int w) const {
-    return *(cpu_diff() + offset(n, c, h, w));
+    return cpu_diff()[offset(n, c, h, w)];
+  }
+
+  inline Dtype data_at(const vector<int>& index) const {
+    return cpu_data()[offset(index)];
+  }
+
+  inline Dtype diff_at(const vector<int>& index) const {
+    return cpu_diff()[offset(index)];
   }
 
   inline const shared_ptr<SyncedMemory>& data() const {

From c4e9ec40427693f3a3848932519e175d11b1738b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 29 Nov 2014 16:11:07 -0800
Subject: [PATCH 1202/2053] TestBlob: test that legacy BlobProtos are correctly
 handled by ShapeEquals

---
 src/caffe/test/test_blob.cpp | 54 ++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)

diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index a654896c654..7da6423b67c 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -1,4 +1,5 @@
 #include <cstring>
+#include <vector>
 
 #include "gtest/gtest.h"
 
@@ -51,6 +52,59 @@ TYPED_TEST(BlobSimpleTest, TestReshape) {
   EXPECT_EQ(this->blob_->count(), 120);
 }
 
+TYPED_TEST(BlobSimpleTest, TestLegacyBlobProtoShapeEquals) {
+  BlobProto blob_proto;
+
+  // Reshape to (3 x 2).
+  vector<int> shape(2);
+  shape[0] = 3;
+  shape[1] = 2;
+  this->blob_->Reshape(shape);
+
+  // (3 x 2) blob == (1 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(1);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_TRUE(this->blob_->ShapeEquals(blob_proto));
+
+  // (3 x 2) blob != (0 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(0);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_FALSE(this->blob_->ShapeEquals(blob_proto));
+
+  // (3 x 2) blob != (3 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(3);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_FALSE(this->blob_->ShapeEquals(blob_proto));
+
+  // Reshape to (1 x 3 x 2).
+  shape.insert(shape.begin(), 1);
+  this->blob_->Reshape(shape);
+
+  // (1 x 3 x 2) blob == (1 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(1);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_TRUE(this->blob_->ShapeEquals(blob_proto));
+
+  // Reshape to (2 x 3 x 2).
+  shape[0] = 2;
+  this->blob_->Reshape(shape);
+
+  // (2 x 3 x 2) blob != (1 x 1 x 3 x 2) legacy blob
+  blob_proto.set_num(1);
+  blob_proto.set_channels(1);
+  blob_proto.set_height(3);
+  blob_proto.set_width(2);
+  EXPECT_FALSE(this->blob_->ShapeEquals(blob_proto));
+}
+
 template <typename TypeParam>
 class BlobMathTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;

From 559ff3ae05677801d4904f103de6dc4d88398e07 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 25 Nov 2014 22:48:03 -0800
Subject: [PATCH 1203/2053] InnerProductLayer weights are 2D; biases are 1D

---
 src/caffe/layers/inner_product_layer.cpp | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index b1ec6cb25c0..60a547c5b49 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -26,14 +26,18 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       this->blobs_.resize(1);
     }
     // Intialize the weight
-    this->blobs_[0].reset(new Blob<Dtype>(1, 1, N_, K_));
+    vector<int> weight_shape(2);
+    weight_shape[0] = N_;
+    weight_shape[1] = K_;
+    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.inner_product_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
     // If necessary, intiialize and fill the bias term
     if (bias_term_) {
-      this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, N_));
+      vector<int> bias_shape(1, N_);
+      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
           this->layer_param_.inner_product_param().bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
@@ -49,10 +53,14 @@ void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   M_ = bottom[0]->num();
   CHECK_EQ(bottom[0]->count() / bottom[0]->num(), K_) << "Input size "
     "incompatible with inner product parameters.";
-  top[0]->Reshape(bottom[0]->num(), N_, 1, 1);
+  vector<int> top_shape(2);
+  top_shape[0] = M_;
+  top_shape[1] = N_;
+  top[0]->Reshape(top_shape);
   // Set up the bias multiplier
   if (bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, M_);
+    vector<int> bias_shape(1, M_);
+    bias_multiplier_.Reshape(bias_shape);
     caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
 }

From cf9fdda4403996326b6a240aa94165df73135625 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 16 Feb 2015 01:29:17 -0800
Subject: [PATCH 1204/2053] Fix sparse GaussianFiller for new IPLayer weight
 axes

---
 include/caffe/filler.hpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index eebf565b1d5..bb18e8e1e28 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -79,9 +79,8 @@ class GaussianFiller : public Filler<Dtype> {
       // These have num == channels == 1; width is number of inputs; height is
       // number of outputs.  The 'sparse' variable specifies the mean number
       // of non-zero input weights for a given output.
-      CHECK_EQ(blob->num(), 1);
-      CHECK_EQ(blob->channels(), 1);
-      int num_outputs = blob->height();
+      CHECK_GE(blob->num_axes(), 1);
+      const int num_outputs = blob->shape(0);
       Dtype non_zero_probability = Dtype(sparse) / Dtype(num_outputs);
       rand_vec_.reset(new SyncedMemory(blob->count() * sizeof(int)));
       int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());

From 29581e6a4a2c9b88c2399a85628fcd0e9fa94383 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 29 Nov 2014 14:26:48 -0800
Subject: [PATCH 1205/2053] InnerProductLayer can multiply along any axis

---
 src/caffe/layers/inner_product_layer.cpp | 26 +++++++++++++++++-------
 src/caffe/proto/caffe.proto              |  5 +++++
 2 files changed, 24 insertions(+), 7 deletions(-)

diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 60a547c5b49..6b88724491b 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -15,7 +15,12 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
   N_ = num_output;
-  K_ = bottom[0]->count() / bottom[0]->num();
+  const int axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.inner_product_param().axis());
+  // Dimensions starting from "axis" are "flattened" into a single
+  // length K_ vector. For example, if bottom[0]'s shape is (N, C, H, W),
+  // N inner products with dimension CHW are performed.
+  K_ = bottom[0]->count(axis);
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
@@ -50,12 +55,19 @@ template <typename Dtype>
 void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   // Figure out the dimensions
-  M_ = bottom[0]->num();
-  CHECK_EQ(bottom[0]->count() / bottom[0]->num(), K_) << "Input size "
-    "incompatible with inner product parameters.";
-  vector<int> top_shape(2);
-  top_shape[0] = M_;
-  top_shape[1] = N_;
+  const int axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.inner_product_param().axis());
+  const int new_K = bottom[0]->count(axis);
+  CHECK_EQ(K_, new_K)
+      << "Input size incompatible with inner product parameters.";
+  // The first "axis" dimensions are independent inner products; the total
+  // number of these is M_, the product over these dimensions.
+  M_ = bottom[0]->count(0, axis);
+  // The top shape will be the bottom shape with the flattened axes dropped,
+  // and replaced by a single axis with dimension num_output (N_).
+  vector<int> top_shape = bottom[0]->shape();
+  top_shape.resize(axis + 1);
+  top_shape[axis] = N_;
   top[0]->Reshape(top_shape);
   // Set up the bias multiplier
   if (bias_term_) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index fdcb57366ad..e8a134865bb 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -561,6 +561,11 @@ message InnerProductParameter {
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
   optional FillerParameter weight_filler = 3; // The filler for the weight
   optional FillerParameter bias_filler = 4; // The filler for the bias
+
+  // The first axis to be lumped into a single inner product computation;
+  // all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 5 [default = 1];
 }
 
 // Message that stores parameters used by LRNLayer

From 94179cc242a02017538362842125f929e5acc2be Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 29 Nov 2014 17:02:18 -0800
Subject: [PATCH 1206/2053] ConvLayer biases are 1D

---
 src/caffe/layers/base_conv_layer.cpp | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index dccd5170c11..d4ab5e429b5 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -85,10 +85,10 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, initialize and fill the biases:
-    // 1 x 1 x 1 x output channels
+    // If necessary, initialize and fill the biases.
     if (bias_term_) {
-      this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
+      vector<int> bias_shape(1, num_output_);
+      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
           this->layer_param_.convolution_param().bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
@@ -144,7 +144,8 @@ void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   }
   // Set up the all ones "bias multiplier" for adding biases by BLAS
   if (bias_term_) {
-    bias_multiplier_.Reshape(1, 1, 1, height_out_ * width_out_);
+    vector<int> bias_multiplier_shape(1, height_out_ * width_out_);
+    bias_multiplier_.Reshape(bias_multiplier_shape);
     caffe_set(bias_multiplier_.count(), Dtype(1),
         bias_multiplier_.mutable_cpu_data());
   }

From a0fa2a9535bda318008c8a2a9edc9ca6218d5c41 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 25 Nov 2014 23:46:57 -0800
Subject: [PATCH 1207/2053] LossLayer output is 0D (scalar)

---
 src/caffe/layers/loss_layer.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index a5b6d11b065..3496a5c2a8a 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -24,7 +24,8 @@ void LossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
-  top[0]->Reshape(1, 1, 1, 1);
+  vector<int> loss_shape(0);  // Loss layers output a scalar; 0 axes.
+  top[0]->Reshape(loss_shape);
 }
 
 INSTANTIATE_CLASS(LossLayer);

From d8c6aeb3d213ead88328ec8e859ae91585c545f9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 29 Nov 2014 16:48:32 -0800
Subject: [PATCH 1208/2053] AccuracyLayer output is 0D (scalar)

---
 src/caffe/layers/accuracy_layer.cpp | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 3e8df34c0d6..7f28324541a 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -19,14 +19,15 @@ void AccuracyLayer<Dtype>::LayerSetUp(
 template <typename Dtype>
 void AccuracyLayer<Dtype>::Reshape(
   const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
+  CHECK_LE(top_k_, bottom[0]->count() / bottom[1]->count())
       << "top_k must be less than or equal to the number of classes.";
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  top[0]->Reshape(1, 1, 1, 1);
+  CHECK_GE(bottom[0]->num_axes(), bottom[1]->num_axes());
+  for (int i = 0; i < bottom[1]->num_axes(); ++i) {
+    CHECK_LE(bottom[0]->shape(i), bottom[1]->shape(i))
+        << "Dimension mismatch between predictions and label.";
+  }
+  vector<int> top_shape(0);  // Accuracy is a scalar; 0 axes.
+  top[0]->Reshape(top_shape);
 }
 
 template <typename Dtype>

From 6b8a765864e853397e15c8ef57c8110d6a1b4332 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 30 Jan 2015 23:23:27 -0800
Subject: [PATCH 1209/2053] AccuracyLayer generalized to N instance axes

---
 src/caffe/layers/accuracy_layer.cpp    |  4 ++--
 src/caffe/test/test_accuracy_layer.cpp | 10 ++++++++--
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 7f28324541a..186f9f8632c 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -36,8 +36,8 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype accuracy = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  int num = bottom[0]->count(0, bottom[1]->num_axes());
+  int dim = bottom[0]->count() / num;
   vector<Dtype> maxval(top_k_+1);
   vector<int> max_id(top_k_+1);
   for (int i = 0; i < num; ++i) {
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index fa59fab1e8a..1c58b767bfc 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -19,10 +19,16 @@ template <typename Dtype>
 class AccuracyLayerTest : public ::testing::Test {
  protected:
   AccuracyLayerTest()
-      : blob_bottom_data_(new Blob<Dtype>(100, 10, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(100, 1, 1, 1)),
+      : blob_bottom_data_(new Blob<Dtype>()),
+        blob_bottom_label_(new Blob<Dtype>()),
         blob_top_(new Blob<Dtype>()),
         top_k_(3) {
+    vector<int> shape(2);
+    shape[0] = 100;
+    shape[1] = 10;
+    blob_bottom_data_->Reshape(shape);
+    shape.resize(1);
+    blob_bottom_label_->Reshape(shape);
     // fill the probability values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);

From 8e96445ba57890a982f6e8f5570e3aac5eaf64e6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 13 Feb 2015 15:52:39 -0800
Subject: [PATCH 1210/2053] Test{Net,Solver} fixes for AccuracyLayer
 generalization

---
 src/caffe/test/test_net.cpp    | 17 +++++++++--------
 src/caffe/test/test_solver.cpp | 17 +++++++++--------
 2 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 1680a3f28d5..08106e79274 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -63,18 +63,19 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  name: 'data' "
         "  type: 'DummyData' "
         "  dummy_data_param { "
-        "    num: 5 "
-        "    channels: 2 "
-        "    height: 3 "
-        "    width: 4 "
-        "    num: 5 "
-        "    channels: 1 "
-        "    height: 1 "
-        "    width: 1 "
+        "    shape { "
+        "      dim: 5 "
+        "      dim: 2 "
+        "      dim: 3 "
+        "      dim: 4 "
+        "    } "
         "    data_filler { "
         "      type: 'gaussian' "
         "      std: 0.01 "
         "    } "
+        "    shape { "
+        "      dim: 5 "
+        "    } "
         "    data_filler { "
         "      type: 'constant' "
         "      value: 0 "
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index 1c2c9bbb740..ceabc9cdd2c 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -55,14 +55,15 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) {
      "    name: 'data' "
      "    type: 'DummyData' "
      "    dummy_data_param { "
-     "      num: 5 "
-     "      channels: 3 "
-     "      height: 10 "
-     "      width: 10 "
-     "      num: 5 "
-     "      channels: 1 "
-     "      height: 1 "
-     "      width: 1 "
+     "      shape { "
+     "        dim: 5 "
+     "        dim: 2 "
+     "        dim: 3 "
+     "        dim: 4 "
+     "      } "
+     "      shape { "
+     "        dim: 5 "
+     "      } "
      "    } "
      "    top: 'data' "
      "    top: 'label' "

From bf73cb18982712d03f096418e2aa33074457ae8c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 25 Nov 2014 23:47:10 -0800
Subject: [PATCH 1211/2053] EltwiseLayer need not assume old 4D dim names

---
 src/caffe/layers/eltwise_layer.cpp | 13 +++----------
 1 file changed, 3 insertions(+), 10 deletions(-)

diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index bbc34449588..a80700736bd 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -31,21 +31,14 @@ void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  const int num = bottom[0]->num();
-  const int channels = bottom[0]->channels();
-  const int height = bottom[0]->height();
-  const int width = bottom[0]->width();
   for (int i = 1; i < bottom.size(); ++i) {
-    CHECK_EQ(num, bottom[i]->num());
-    CHECK_EQ(channels, bottom[i]->channels());
-    CHECK_EQ(height, bottom[i]->height());
-    CHECK_EQ(width, bottom[i]->width());
+    CHECK(bottom[i]->shape() == bottom[0]->shape());
   }
-  top[0]->Reshape(num, channels, height, width);
+  top[0]->ReshapeLike(*bottom[0]);
   // If max operation, we will initialize the vector index part.
   if (this->layer_param_.eltwise_param().operation() ==
       EltwiseParameter_EltwiseOp_MAX && top.size() == 1) {
-    max_idx_.Reshape(bottom[0]->num(), channels, height, width);
+    max_idx_.Reshape(bottom[0]->shape());
   }
 }
 

From 1b97c06b16cae847578fb2d7130f347862b1a262 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 00:11:06 -0800
Subject: [PATCH 1212/2053] FlattenLayer: generalized Blob axes

---
 include/caffe/common_layers.hpp    |  6 ------
 src/caffe/layers/flatten_layer.cpp | 15 +++++----------
 src/caffe/layers/flatten_layer.cu  | 23 -----------------------
 3 files changed, 5 insertions(+), 39 deletions(-)
 delete mode 100644 src/caffe/layers/flatten_layer.cu

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index c67822c3738..a9c774f1485 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -216,8 +216,6 @@ class FlattenLayer : public Layer<Dtype> {
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the error gradient w.r.t. the concatenate inputs.
@@ -230,10 +228,6 @@ class FlattenLayer : public Layer<Dtype> {
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
 };
 
 /**
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index eb7b42bc10b..745f271ea45 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -9,12 +9,11 @@ namespace caffe {
 template <typename Dtype>
 void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  int channels_out = bottom[0]->channels() * bottom[0]->height()
-      * bottom[0]->width();
-  top[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
-  count_ = bottom[0]->num() * channels_out;
-  CHECK_EQ(count_, bottom[0]->count());
-  CHECK_EQ(count_, top[0]->count());
+  vector<int> top_shape(2);
+  top_shape[0] = bottom[0]->num();
+  top_shape[1] = bottom[0]->count() / bottom[0]->num();
+  top[0]->Reshape(top_shape);
+  CHECK_EQ(top[0]->count(), bottom[0]->count());
 }
 
 template <typename Dtype>
@@ -29,10 +28,6 @@ void FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   bottom[0]->ShareDiff(*top[0]);
 }
 
-#ifdef CPU_ONLY
-STUB_GPU(FlattenLayer);
-#endif
-
 INSTANTIATE_CLASS(FlattenLayer);
 REGISTER_LAYER_CLASS(Flatten);
 
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
deleted file mode 100644
index 42abdad4499..00000000000
--- a/src/caffe/layers/flatten_layer.cu
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  top[0]->ShareData(*bottom[0]);
-}
-
-template <typename Dtype>
-void FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  bottom[0]->ShareDiff(*top[0]);
-}
-
-INSTANTIATE_LAYER_GPU_FUNCS(FlattenLayer);
-
-}  // namespace caffe

From fb9caeef09e75dbb6196977f2cfa592eb80c37e1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 03:23:42 -0800
Subject: [PATCH 1213/2053] common_layers.hpp: remove unused "Blob col_bob_"

---
 include/caffe/common_layers.hpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index a9c774f1485..e9fe7cdeb9f 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -137,7 +137,6 @@ class ConcatLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  Blob<Dtype> col_bob_;
   int count_;
   int num_;
   int channels_;
@@ -452,7 +451,6 @@ class SliceLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  Blob<Dtype> col_bob_;
   int count_;
   int num_;
   int channels_;

From 704e524f6ec1e927db8cef57434002f6c34a30b7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 02:12:09 -0800
Subject: [PATCH 1214/2053] TestConcatLayer: fix style errors

---
 src/caffe/test/test_concat_layer.cpp | 68 ++++++++++++++--------------
 1 file changed, 34 insertions(+), 34 deletions(-)

diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index f14f1d2fa4f..3fc507ee24e 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -19,9 +19,9 @@ class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   ConcatLayerTest()
-      : blob_bottom_0(new Blob<Dtype>(2, 3, 6, 5)),
-        blob_bottom_1(new Blob<Dtype>(2, 5, 6, 5)),
-        blob_bottom_2(new Blob<Dtype>(5, 3, 6, 5)),
+      : blob_bottom_0_(new Blob<Dtype>(2, 3, 6, 5)),
+        blob_bottom_1_(new Blob<Dtype>(2, 5, 6, 5)),
+        blob_bottom_2_(new Blob<Dtype>(5, 3, 6, 5)),
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     // fill the values
@@ -29,30 +29,30 @@ class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
     FillerParameter filler_param;
     filler_param.set_value(1.);
     filler.reset(new ConstantFiller<Dtype>(filler_param));
-    filler->Fill(this->blob_bottom_0);
+    filler->Fill(this->blob_bottom_0_);
     filler_param.set_value(2.);
     filler.reset(new ConstantFiller<Dtype>(filler_param));
-    filler->Fill(this->blob_bottom_1);
+    filler->Fill(this->blob_bottom_1_);
     filler_param.set_value(3.);
     filler.reset(new ConstantFiller<Dtype>(filler_param));
-    filler->Fill(this->blob_bottom_2);
-    blob_bottom_vec_0.push_back(blob_bottom_0);
-    blob_bottom_vec_0.push_back(blob_bottom_1);
-    blob_bottom_vec_1.push_back(blob_bottom_0);
-    blob_bottom_vec_1.push_back(blob_bottom_2);
+    filler->Fill(this->blob_bottom_2_);
+    blob_bottom_vec_0_.push_back(blob_bottom_0_);
+    blob_bottom_vec_0_.push_back(blob_bottom_1_);
+    blob_bottom_vec_1_.push_back(blob_bottom_0_);
+    blob_bottom_vec_1_.push_back(blob_bottom_2_);
     blob_top_vec_.push_back(blob_top_);
   }
 
   virtual ~ConcatLayerTest() {
-    delete blob_bottom_0; delete blob_bottom_1;
-    delete blob_bottom_2; delete blob_top_;
+    delete blob_bottom_0_; delete blob_bottom_1_;
+    delete blob_bottom_2_; delete blob_top_;
   }
 
-  Blob<Dtype>* const blob_bottom_0;
-  Blob<Dtype>* const blob_bottom_1;
-  Blob<Dtype>* const blob_bottom_2;
+  Blob<Dtype>* const blob_bottom_0_;
+  Blob<Dtype>* const blob_bottom_1_;
+  Blob<Dtype>* const blob_bottom_2_;
   Blob<Dtype>* const blob_top_;
-  vector<Blob<Dtype>*> blob_bottom_vec_0, blob_bottom_vec_1;
+  vector<Blob<Dtype>*> blob_bottom_vec_0_, blob_bottom_vec_1_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
@@ -63,24 +63,24 @@ TYPED_TEST(ConcatLayerTest, TestSetupNum) {
   LayerParameter layer_param;
   layer_param.mutable_concat_param()->set_concat_dim(0);
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_1, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_1_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(),
-    this->blob_bottom_0->num() + this->blob_bottom_2->num());
-  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_0->channels());
-  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0->height());
-  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0->width());
+      this->blob_bottom_0_->num() + this->blob_bottom_2_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_0_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
 TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0, this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0->num());
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0_->num());
   EXPECT_EQ(this->blob_top_->channels(),
-    this->blob_bottom_0->channels()+this->blob_bottom_1->channels());
-  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0->height());
-  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0->width());
+      this->blob_bottom_0_->channels() + this->blob_bottom_1_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
 
@@ -88,22 +88,22 @@ TYPED_TEST(ConcatLayerTest, TestNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_0, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
   for (int n = 0; n < this->blob_top_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_0->channels(); ++c) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
           EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
-            this->blob_bottom_vec_0[0]->data_at(n, c, h, w));
+              this->blob_bottom_vec_0_[0]->data_at(n, c, h, w));
         }
       }
     }
-    for (int c = 0; c < this->blob_bottom_1->channels(); ++c) {
+    for (int c = 0; c < this->blob_bottom_1_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
-          EXPECT_EQ(this->blob_top_->data_at(n, c+3, h, w),
-            this->blob_bottom_vec_0[1]->data_at(n, c, h, w));
+          EXPECT_EQ(this->blob_top_->data_at(n, c + 3, h, w),
+              this->blob_bottom_vec_0_[1]->data_at(n, c, h, w));
         }
       }
     }
@@ -115,7 +115,7 @@ TYPED_TEST(ConcatLayerTest, TestGradient) {
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
-  checker.CheckGradient(&layer, this->blob_bottom_vec_0,
+  checker.CheckGradient(&layer, this->blob_bottom_vec_0_,
     this->blob_top_vec_);
 }
 

From d52e9a811bd0afa12602248cb377ed9234be1a33 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 02:24:41 -0800
Subject: [PATCH 1215/2053] TestConcatLayer: add forward/gradient tests for
 concatenation along num

---
 src/caffe/test/test_concat_layer.cpp | 42 ++++++++++++++++++++++++++--
 1 file changed, 40 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 3fc507ee24e..875468faa49 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -83,8 +83,36 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
   EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
+TYPED_TEST(ConcatLayerTest, TestForwardNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_concat_param()->set_concat_dim(0);
+  ConcatLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_1_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_1_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_vec_1_[0]->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+              this->blob_bottom_vec_1_[0]->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+  for (int n = 0; n < this->blob_bottom_vec_1_[1]->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          EXPECT_EQ(this->blob_top_->data_at(n + 2, c, h, w),
+              this->blob_bottom_vec_1_[1]->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+}
 
-TYPED_TEST(ConcatLayerTest, TestNum) {
+TYPED_TEST(ConcatLayerTest, TestForwardChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);
@@ -110,7 +138,17 @@ TYPED_TEST(ConcatLayerTest, TestNum) {
   }
 }
 
-TYPED_TEST(ConcatLayerTest, TestGradient) {
+TYPED_TEST(ConcatLayerTest, TestGradientNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_concat_param()->set_concat_dim(0);
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradient(&layer, this->blob_bottom_vec_1_,
+    this->blob_top_vec_);
+}
+
+TYPED_TEST(ConcatLayerTest, TestGradientChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConcatLayer<Dtype> layer(layer_param);

From 8afdcd02f06923a1ea61fff7bd35e72fb5c57ab4 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 00:03:36 -0800
Subject: [PATCH 1216/2053] ConcatLayer: generalized Blob axes

---
 include/caffe/common_layers.hpp      |  16 ++--
 src/caffe/layers/concat_layer.cpp    | 118 ++++++++++++---------------
 src/caffe/layers/concat_layer.cu     |  71 +++++-----------
 src/caffe/proto/caffe.proto          |  10 ++-
 src/caffe/test/test_concat_layer.cpp |  22 ++++-
 5 files changed, 108 insertions(+), 129 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index e9fe7cdeb9f..114f24a2b75 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -99,8 +99,8 @@ class ConcatLayer : public Layer<Dtype> {
    *   - K @f$ (N \times C \times H \times W) @f$
    *      the inputs @f$ x_K @f$
    * @param top output Blob vector (length 1)
-   *   -# @f$ (KN \times C \times H \times W) @f$ if concat_dim == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if concat_dim == 1:
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
    *      the concatenated output @f$
    *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
    *      @f$
@@ -115,8 +115,8 @@ class ConcatLayer : public Layer<Dtype> {
    *
    * @param top output Blob vector (length 1), providing the error gradient with
    *        respect to the outputs
-   *   -# @f$ (KN \times C \times H \times W) @f$ if concat_dim == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if concat_dim == 1:
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
    *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
    *      with respect to concatenated outputs @f$ y @f$
    * @param propagate_down see Layer::Backward.
@@ -138,11 +138,9 @@ class ConcatLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int count_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-  int concat_dim_;
+  int num_concats_;
+  int concat_input_size_;
+  int concat_axis_;
 };
 
 /**
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index fc88433c42b..1cac8fc3387 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -9,62 +9,63 @@ namespace caffe {
 template <typename Dtype>
 void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  concat_dim_ = this->layer_param_.concat_param().concat_dim();
-  CHECK_GE(concat_dim_, 0) <<
-    "concat_dim should be >= 0";
-  CHECK_LE(concat_dim_, 1) <<
-    "For now concat_dim <=1, it can only concat num and channels";
+  const ConcatParameter& concat_param = this->layer_param_.concat_param();
+  CHECK(!(concat_param.has_axis() && concat_param.has_concat_dim()))
+      << "Either axis or concat_dim should be specified; not both.";
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  const int num_axes = bottom[0]->num_axes();
+  const ConcatParameter& concat_param = this->layer_param_.concat_param();
+  if (concat_param.has_concat_dim()) {
+    concat_axis_ = static_cast<int>(concat_param.concat_dim());
+    // Don't allow negative indexing for concat_dim, a uint32 -- almost
+    // certainly unintended.
+    CHECK_GE(concat_axis_, 0) << "casting concat_dim from uint32 to int32 "
+        << "produced negative result; concat_dim must satisfy "
+        << "0 <= concat_dim < " << kMaxBlobAxes;
+    CHECK_LT(concat_axis_, num_axes) << "concat_dim out of range.";
+  } else {
+    concat_axis_ = bottom[0]->CanonicalAxisIndex(concat_param.axis());
+  }
   // Initialize with the first blob.
-  count_ = bottom[0]->count();
-  num_ = bottom[0]->num();
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
+  vector<int> top_shape = bottom[0]->shape();
+  num_concats_ = bottom[0]->count(0, concat_axis_);
+  concat_input_size_ = bottom[0]->count(concat_axis_ + 1);
+  int bottom_count_sum = bottom[0]->count();
   for (int i = 1; i < bottom.size(); ++i) {
-    count_ += bottom[i]->count();
-    if (concat_dim_== 0) {
-      num_ += bottom[i]->num();
-    } else if (concat_dim_ == 1) {
-      channels_ += bottom[i]->channels();
-    } else if (concat_dim_ == 2) {
-      height_ += bottom[i]->height();
-    } else if (concat_dim_ == 3) {
-      width_ += bottom[i]->width();
+    CHECK_EQ(num_axes, bottom[i]->num_axes())
+        << "All inputs must have the same #axes.";
+    for (int j = 0; j < num_axes; ++j) {
+      if (j == concat_axis_) { continue; }
+      CHECK_EQ(top_shape[j], bottom[i]->shape(j))
+          << "All inputs must have the same shape, except at concat_axis.";
     }
+    bottom_count_sum += bottom[i]->count();
+    top_shape[concat_axis_] += bottom[i]->shape(concat_axis_);
   }
-  top[0]->Reshape(num_, channels_, height_, width_);
-  CHECK_EQ(count_, top[0]->count());
+  top[0]->Reshape(top_shape);
+  CHECK_EQ(bottom_count_sum, top[0]->count());
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   Dtype* top_data = top[0]->mutable_cpu_data();
-  if (concat_dim_== 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->cpu_data();
-      int num_elem = bottom[i]->count();
-      caffe_copy(num_elem, bottom_data, top_data+top[0]->offset(offset_num));
-      offset_num += bottom[i]->num();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    for (int n = 0; n < num_concats_; ++n) {
+      caffe_copy(bottom_concat_axis * concat_input_size_,
+          bottom_data + n * bottom_concat_axis * concat_input_size_,
+          top_data + (n * top_concat_axis + offset_concat_axis)
+              * concat_input_size_);
     }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->cpu_data();
-      int num_elem =
-        bottom[i]->channels()*bottom[i]->height()*bottom[i]->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
-          top_data+top[0]->offset(n, offset_channel));
-      }
-      offset_channel += bottom[i]->channels();
-    }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
+    offset_concat_axis += bottom_concat_axis;
   }
 }
 
@@ -72,32 +73,19 @@ template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
-  if (concat_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      Blob<Dtype>* blob = bottom[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_cpu_diff();
-        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
-                   bottom_diff);
-      }
-      offset_num += blob->num();
-    }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      Blob<Dtype>* blob = bottom[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_cpu_diff();
-        int num_elem = blob->channels()*blob->height()*blob->width();
-        for (int n = 0; n < num_; ++n) {
-          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
-                     bottom_diff + blob->offset(n));
-        }
-      }
-      offset_channel += blob->channels();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  for (int i = 0; i < bottom.size(); ++i) {
+    if (!propagate_down[i]) { continue; }
+    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    for (int n = 0; n < num_concats_; ++n) {
+      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
+          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
+          bottom_diff + n * bottom_concat_axis * concat_input_size_);
     }
-  }  // concat_dim_ is guaranteed to be 0 or 1 by LayerSetUp.
+    offset_concat_axis += bottom_concat_axis;
+  }
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 88fc090025f..dbadb5aeb30 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -10,29 +10,18 @@ template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   Dtype* top_data = top[0]->mutable_gpu_data();
-  if (concat_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->gpu_data();
-      caffe_copy(bottom[i]->count(), bottom_data,
-        top_data + top[0]->offset(offset_num));
-      offset_num += bottom[i]->num();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    for (int n = 0; n < num_concats_; ++n) {
+      caffe_copy(bottom_concat_axis * concat_input_size_,
+          bottom_data + n * bottom_concat_axis * concat_input_size_,
+          top_data + (n * top_concat_axis + offset_concat_axis)
+              * concat_input_size_);
     }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      const Dtype* bottom_data = bottom[i]->gpu_data();
-      int num_elem =
-        bottom[i]->channels() * bottom[i]->height() * bottom[i]->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
-          top_data + top[0]->offset(n, offset_channel));
-      }
-      offset_channel += bottom[i]->channels();
-    }
-  } else {
-    LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
-      " not implemented yet";
+    offset_concat_axis += bottom_concat_axis;
   }
 }
 
@@ -40,34 +29,18 @@ template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
-  if (concat_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      Blob<Dtype>* blob = bottom[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_gpu_diff();
-        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
-                       bottom_diff);
-      }
-      offset_num += blob->num();
-    }
-  } else if (concat_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < bottom.size(); ++i) {
-      Blob<Dtype>* blob = bottom[i];
-      if (propagate_down[i]) {
-        Dtype* bottom_diff = blob->mutable_gpu_diff();
-        int num_elem = blob->channels()*blob->height()*blob->width();
-        for (int n = 0; n < num_; ++n) {
-          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
-                         bottom_diff + blob->offset(n));
-        }
-      }
-      offset_channel += blob->channels();
+  int offset_concat_axis = 0;
+  const int top_concat_axis = top[0]->shape(concat_axis_);
+  for (int i = 0; i < bottom.size(); ++i) {
+    if (!propagate_down[i]) { continue; }
+    Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
+    for (int n = 0; n < num_concats_; ++n) {
+      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
+          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
+          bottom_diff + n * bottom_concat_axis * concat_input_size_);
     }
-  } else {
-    LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
-      " not implemented yet";
+    offset_concat_axis += bottom_concat_axis;
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e8a134865bb..7a4ecf93c7a 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -378,9 +378,13 @@ message ArgMaxParameter {
 
 // Message that stores parameters used by ConcatLayer
 message ConcatParameter {
-  // Concat Layer needs to specify the dimension along the concat will happen,
-  // the other dimensions must be the same for all the bottom blobs
-  // By default it will concatenate blobs along channels dimension
+  // The axis along which to concatenate -- may be negative to index from the
+  // end (e.g., -1 for the last axis).  Other axes must have the
+  // same dimension for all the bottom blobs.
+  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 2 [default = 1];
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
   optional uint32 concat_dim = 1 [default = 1];
 }
 
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 875468faa49..662a50fa23b 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -61,7 +61,7 @@ TYPED_TEST_CASE(ConcatLayerTest, TestDtypesAndDevices);
 TYPED_TEST(ConcatLayerTest, TestSetupNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_concat_param()->set_concat_dim(0);
+  layer_param.mutable_concat_param()->set_axis(0);
   ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_1_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(),
@@ -83,10 +83,26 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
   EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
+TYPED_TEST(ConcatLayerTest, TestSetupChannelsNegativeIndexing) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  // "channels" index is the third one from the end -- test negative indexing
+  // by setting axis to -3 and checking that we get the same results as above in
+  // TestSetupChannels.
+  layer_param.mutable_concat_param()->set_axis(-3);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0_->num());
+  EXPECT_EQ(this->blob_top_->channels(),
+      this->blob_bottom_0_->channels() + this->blob_bottom_1_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_0_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
+}
+
 TYPED_TEST(ConcatLayerTest, TestForwardNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_concat_param()->set_concat_dim(0);
+  layer_param.mutable_concat_param()->set_axis(0);
   ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_1_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_1_, this->blob_top_vec_);
@@ -141,7 +157,7 @@ TYPED_TEST(ConcatLayerTest, TestForwardChannels) {
 TYPED_TEST(ConcatLayerTest, TestGradientNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_concat_param()->set_concat_dim(0);
+  layer_param.mutable_concat_param()->set_axis(0);
   ConcatLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradient(&layer, this->blob_bottom_vec_1_,

From b86891635dbb24f70d5634a679150070caf776e4 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 03:22:59 -0800
Subject: [PATCH 1217/2053] SliceLayer: generalized Blob axes

---
 include/caffe/common_layers.hpp     |   8 +-
 src/caffe/layers/slice_layer.cpp    | 142 ++++++++++++----------------
 src/caffe/layers/slice_layer.cu     |  70 ++++++--------
 src/caffe/proto/caffe.proto         |  12 ++-
 src/caffe/test/test_slice_layer.cpp |   6 +-
 5 files changed, 100 insertions(+), 138 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 114f24a2b75..4e47e55d5b6 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -450,11 +450,9 @@ class SliceLayer : public Layer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int count_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-  int slice_dim_;
+  int num_slices_;
+  int slice_size_;
+  int slice_axis_;
   vector<int> slice_point_;
 };
 
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 46c3acd6513..e4418c9cf9c 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -11,9 +11,8 @@ template <typename Dtype>
 void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const SliceParameter& slice_param = this->layer_param_.slice_param();
-  slice_dim_ = slice_param.slice_dim();
-  CHECK_GE(slice_dim_, 0);
-  CHECK_LE(slice_dim_, 1) << "Can only slice num and channels";
+  CHECK(!(slice_param.has_axis() && slice_param.has_slice_dim()))
+      << "Either axis or slice_dim should be specified; not both.";
   slice_point_.clear();
   std::copy(slice_param.slice_point().begin(),
       slice_param.slice_point().end(),
@@ -23,18 +22,27 @@ void SliceLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  count_ = 0;
-  num_ = bottom[0]->num();
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
+  const int num_axes = bottom[0]->num_axes();
+  const SliceParameter& slice_param = this->layer_param_.slice_param();
+  if (slice_param.has_slice_dim()) {
+    slice_axis_ = static_cast<int>(slice_param.slice_dim());
+    // Don't allow negative indexing for slice_dim, a uint32 -- almost
+    // certainly unintended.
+    CHECK_GE(slice_axis_, 0) << "casting slice_dim from uint32 to int32 "
+        << "produced negative result; slice_dim must satisfy "
+        << "0 <= slice_dim < " << kMaxBlobAxes;
+    CHECK_LT(slice_axis_, num_axes) << "slice_dim out of range.";
+  } else {
+    slice_axis_ = bottom[0]->CanonicalAxisIndex(slice_param.axis());
+  }
+  vector<int> top_shape = bottom[0]->shape();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  num_slices_ = bottom[0]->count(0, slice_axis_);
+  slice_size_ = bottom[0]->count(slice_axis_ + 1);
+  int count = 0;
   if (slice_point_.size() != 0) {
     CHECK_EQ(slice_point_.size(), top.size() - 1);
-    if (slice_dim_ == 0) {
-      CHECK_LE(top.size(), num_);
-    } else {
-      CHECK_LE(top.size(), channels_);
-    }
+    CHECK_LE(top.size(), bottom_slice_axis);
     int prev = 0;
     vector<int> slices;
     for (int i = 0; i < slice_point_.size(); ++i) {
@@ -42,94 +50,64 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       slices.push_back(slice_point_[i] - prev);
       prev = slice_point_[i];
     }
-    if (slice_dim_ == 0) {
-      slices.push_back(num_ - prev);
-      for (int i = 0; i < top.size(); ++i) {
-        top[i]->Reshape(slices[i], channels_, height_, width_);
-        count_ += top[i]->count();
-      }
-    } else {
-      slices.push_back(channels_ - prev);
-      for (int i = 0; i < top.size(); ++i) {
-        top[i]->Reshape(num_, slices[i], height_, width_);
-        count_ += top[i]->count();
-      }
+    slices.push_back(bottom_slice_axis - prev);
+    for (int i = 0; i < top.size(); ++i) {
+      top_shape[slice_axis_] = slices[i];
+      top[i]->Reshape(top_shape);
+      count += top[i]->count();
     }
   } else {
-    if (slice_dim_ == 0) {
-      CHECK_EQ(num_ % top.size(), 0)
-          << "Number of top blobs (" << top.size() << ") "
-          << "should evenly divide input num ( " << num_ << ")";
-      num_ = num_ / top.size();
-    } else {
-      CHECK_EQ(channels_ % top.size(), 0)
-          << "Number of top blobs (" << top.size() << ") "
-          << "should evenly divide input channels ( " << channels_ << ")";
-      channels_ = channels_ / top.size();
-    }
+    CHECK_EQ(bottom_slice_axis % top.size(), 0)
+        << "Number of top blobs (" << top.size() << ") should evenly "
+        << "divide input slice axis (" << bottom_slice_axis << ")";
+    top_shape[slice_axis_] = bottom_slice_axis / top.size();
     for (int i = 0; i < top.size(); ++i) {
-      top[i]->Reshape(num_, channels_, height_, width_);
-      count_ += top[i]->count();
+      top[i]->Reshape(top_shape);
+      count += top[i]->count();
     }
   }
-  CHECK_EQ(count_, bottom[0]->count());
+  CHECK_EQ(count, bottom[0]->count());
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  const Dtype* bottom_data = bottom[0]->mutable_cpu_data();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      Dtype* top_data = blob->mutable_cpu_data();
-      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
-                 top_data);
-      offset_num += blob->num();
+  int offset_slice_axis = 0;
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  for (int i = 0; i < top.size(); ++i) {
+    Dtype* top_data = top[i]->mutable_cpu_data();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    for (int n = 0; n < num_slices_; ++n) {
+      const int top_offset = n * top_slice_axis * slice_size_;
+      const int bottom_offset =
+          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
+      caffe_copy(top_slice_axis * slice_size_,
+          bottom_data + bottom_offset, top_data + top_offset);
     }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      Dtype* top_data = blob->mutable_cpu_data();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
-                   top_data + blob->offset(n));
-      }
-      offset_channel += blob->channels();
-    }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
+  int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->cpu_diff();
-      caffe_copy(blob->count(), top_diff,
-                 bottom_diff + bottom[0]->offset(offset_num));
-      offset_num += blob->num();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->cpu_diff();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    for (int n = 0; n < num_slices_; ++n) {
+      const int top_offset = n * top_slice_axis * slice_size_;
+      const int bottom_offset =
+          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
+      caffe_copy(top_slice_axis * slice_size_,
+          top_diff + top_offset, bottom_diff + bottom_offset);
     }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->cpu_diff();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, top_diff + blob->offset(n),
-                   bottom_diff + bottom[0]->offset(n, offset_channel));
-      }
-      offset_channel += blob->channels();
-    }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index b5c5e61533f..e6e65677bd8 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -9,58 +9,42 @@ namespace caffe {
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      Dtype* top_data = blob->mutable_gpu_data();
-      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
-                 top_data);
-      offset_num += blob->num();
+  int offset_slice_axis = 0;
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  for (int i = 0; i < top.size(); ++i) {
+    Dtype* top_data = top[i]->mutable_gpu_data();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    for (int n = 0; n < num_slices_; ++n) {
+      const int top_offset = n * top_slice_axis * slice_size_;
+      const int bottom_offset =
+          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
+      caffe_copy(top_slice_axis * slice_size_,
+          bottom_data + bottom_offset, top_data + top_offset);
     }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      Dtype* top_data = blob->mutable_gpu_data();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
-                   top_data + blob->offset(n));
-      }
-      offset_channel += blob->channels();
-    }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (!propagate_down[0]) { return; }
+  int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  if (slice_dim_ == 0) {
-    int offset_num = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->gpu_diff();
-      caffe_copy(blob->count(), top_diff,
-                 bottom_diff + bottom[0]->offset(offset_num));
-      offset_num += blob->num();
-    }
-  } else if (slice_dim_ == 1) {
-    int offset_channel = 0;
-    for (int i = 0; i < top.size(); ++i) {
-      Blob<Dtype>* blob = top[i];
-      const Dtype* top_diff = blob->gpu_diff();
-      const int num_elem = blob->channels() * blob->height() * blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, top_diff + blob->offset(n),
-                   bottom_diff +  bottom[0]->offset(n, offset_channel));
-      }
-      offset_channel += blob->channels();
+  const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    const int top_slice_axis = top[i]->shape(slice_axis_);
+    for (int n = 0; n < num_slices_; ++n) {
+      const int top_offset = n * top_slice_axis * slice_size_;
+      const int bottom_offset =
+          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
+      caffe_copy(top_slice_axis * slice_size_,
+          top_diff + top_offset, bottom_diff + bottom_offset);
     }
-  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+    offset_slice_axis += top_slice_axis;
+  }
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(SliceLayer);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 7a4ecf93c7a..7783a783dd7 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -674,12 +674,14 @@ message SigmoidParameter {
 
 // Message that stores parameters used by SliceLayer
 message SliceParameter {
-  // SliceLayer needs to know which dimension to slice across.
-  // Currently, SliceLayer only supports slicing across num (dim 0)
-  // and channels (dim 1).
-  // By default, SliceLayer slices across channels.
-  optional uint32 slice_dim = 1 [default = 1];
+  // The axis along which to slice -- may be negative to index from the end
+  // (e.g., -1 for the last axis).
+  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
+  optional int32 axis = 3 [default = 1];
   repeated uint32 slice_point = 2;
+
+  // DEPRECATED: alias for "axis" -- does not support negative indexing.
+  optional uint32 slice_dim = 1 [default = 1];
 }
 
 // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index 395be280089..ccd03646d19 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -62,7 +62,7 @@ TYPED_TEST_CASE(SliceLayerTest, TestDtypesAndDevices);
 TYPED_TEST(SliceLayerTest, TestSetupNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_slice_param()->set_slice_dim(0);
+  layer_param.mutable_slice_param()->set_axis(0);
   SliceLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_1_);
   EXPECT_EQ(this->blob_bottom_->num(), 3 * this->blob_top_0_->num());
@@ -91,7 +91,7 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) {
 TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.mutable_slice_param()->set_slice_dim(0);
+  layer_param.mutable_slice_param()->set_axis(0);
   SliceLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
   const int top_num = this->blob_bottom_->num() / 2;
@@ -166,7 +166,7 @@ TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
   // Gradient checks are slow; reduce blob size.
   this->ReduceBottomBlobSize();
   LayerParameter layer_param;
-  layer_param.mutable_slice_param()->set_slice_dim(0);
+  layer_param.mutable_slice_param()->set_axis(0);
   SliceLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

From abec30252ced89d9e2550ca47fca569f563479f6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 15 Feb 2015 13:26:36 -0800
Subject: [PATCH 1218/2053] SoftmaxLayer: generalized Blob axes

---
 include/caffe/common_layers.hpp    |  3 ++
 src/caffe/layers/softmax_layer.cpp | 62 +++++++++++++++---------------
 src/caffe/layers/softmax_layer.cu  | 35 ++++++++---------
 src/caffe/proto/caffe.proto        |  5 +++
 4 files changed, 54 insertions(+), 51 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 4e47e55d5b6..b1ac3a93eff 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -353,6 +353,9 @@ class SoftmaxLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  int outer_num_;
+  int inner_num_;
+  int softmax_axis_;
   /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
   /// scale is an intermediate Blob to hold temporary results.
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 25142fdec53..04712c9e653 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -10,14 +10,18 @@ namespace caffe {
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
-  sum_multiplier_.Reshape(1, bottom[0]->channels(), 1, 1);
+  softmax_axis_ =
+      bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
+  top[0]->ReshapeLike(*bottom[0]);
+  vector<int> mult_dims(1, bottom[0]->shape(softmax_axis_));
+  sum_multiplier_.Reshape(mult_dims);
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
-  for (int i = 0; i < sum_multiplier_.count(); ++i) {
-    multiplier_data[i] = 1.;
-  }
-  scale_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+  caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
+  outer_num_ = bottom[0]->count(0, softmax_axis_);
+  inner_num_ = bottom[0]->count(softmax_axis_ + 1);
+  vector<int> scale_dims = bottom[0]->shape();
+  scale_dims[softmax_axis_] = 1;
+  scale_.Reshape(scale_dims);
 }
 
 template <typename Dtype>
@@ -26,34 +30,32 @@ void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
   Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = bottom[0]->num();
-  int channels = bottom[0]->channels();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  int channels = bottom[0]->shape(softmax_axis_);
+  int dim = bottom[0]->count() / outer_num_;
   caffe_copy(bottom[0]->count(), bottom_data, top_data);
   // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
-  for (int i = 0; i < num; ++i) {
+  for (int i = 0; i < outer_num_; ++i) {
     // initialize scale_data to the first plane
-    caffe_copy(spatial_dim, bottom_data + i * dim, scale_data);
+    caffe_copy(inner_num_, bottom_data + i * dim, scale_data);
     for (int j = 0; j < channels; j++) {
-      for (int k = 0; k < spatial_dim; k++) {
+      for (int k = 0; k < inner_num_; k++) {
         scale_data[k] = std::max(scale_data[k],
-            bottom_data[i * dim + j * spatial_dim + k]);
+            bottom_data[i * dim + j * inner_num_ + k]);
       }
     }
     // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
-        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data + i * dim);
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_,
+        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data);
     // exponentiation
-    caffe_exp<Dtype>(dim, top_data + i * dim, top_data + i * dim);
+    caffe_exp<Dtype>(dim, top_data, top_data);
     // sum after exp
-    caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, 1.,
-        top_data + i * dim, sum_multiplier_.cpu_data(), 0., scale_data);
+    caffe_cpu_gemv<Dtype>(CblasTrans, channels, inner_num_, 1.,
+        top_data, sum_multiplier_.cpu_data(), 0., scale_data);
     // division
     for (int j = 0; j < channels; j++) {
-      caffe_div(spatial_dim, top_data + top[0]->offset(i, j), scale_data,
-          top_data + top[0]->offset(i, j));
+      caffe_div(inner_num_, top_data, scale_data, top_data);
+      top_data += inner_num_;
     }
   }
 }
@@ -66,20 +68,18 @@ void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_data = top[0]->cpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   Dtype* scale_data = scale_.mutable_cpu_data();
-  int num = top[0]->num();
-  int channels = top[0]->channels();
-  int dim = top[0]->count() / top[0]->num();
-  int spatial_dim = top[0]->height() * top[0]->width();
+  int channels = top[0]->shape(softmax_axis_);
+  int dim = top[0]->count() / outer_num_;
   caffe_copy(top[0]->count(), top_diff, bottom_diff);
-  for (int i = 0; i < num; ++i) {
+  for (int i = 0; i < outer_num_; ++i) {
     // compute dot(top_diff, top_data) and subtract them from the bottom diff
-    for (int k = 0; k < spatial_dim; ++k) {
+    for (int k = 0; k < inner_num_; ++k) {
       scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,
-          bottom_diff + i * dim + k, spatial_dim,
-          top_data + i * dim + k, spatial_dim);
+          bottom_diff + i * dim + k, inner_num_,
+          top_data + i * dim + k, inner_num_);
     }
     // subtraction
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim, 1,
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_, 1,
         -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);
   }
   // elementwise multiplication
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 6b8871a0b20..1f9c3a41203 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -90,36 +90,33 @@ void SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = top[0]->mutable_gpu_data();
   Dtype* scale_data = scale_.mutable_gpu_data();
   int count = bottom[0]->count();
-  int num = bottom[0]->num();
-  int channels = bottom[0]->channels();
-  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  int channels = top[0]->shape(softmax_axis_);
   caffe_copy(count, bottom_data, top_data);
   // We need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   // compute max
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_max<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+  kernel_channel_max<Dtype><<<CAFFE_GET_BLOCKS(outer_num_ * inner_num_),
+      CAFFE_CUDA_NUM_THREADS>>>(outer_num_, channels, inner_num_, top_data,
       scale_data);
   // subtract
   // NOLINT_NEXT_LINE(whitespace/operators)
   kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(count),
-      CAFFE_CUDA_NUM_THREADS>>>(count, num, channels, spatial_dim,
+      CAFFE_CUDA_NUM_THREADS>>>(count, outer_num_, channels, inner_num_,
       scale_data, top_data);
   // exponentiate
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(num * channels * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num * channels * spatial_dim, top_data,
-      top_data);
+  kernel_exp<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, top_data, top_data);
   // sum after exp
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_sum<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_data,
+  kernel_channel_sum<Dtype><<<CAFFE_GET_BLOCKS(outer_num_ * inner_num_),
+      CAFFE_CUDA_NUM_THREADS>>>(outer_num_, channels, inner_num_, top_data,
       scale_data);
   // divide
   // NOLINT_NEXT_LINE(whitespace/operators)
   kernel_channel_div<Dtype><<<CAFFE_GET_BLOCKS(count),
-      CAFFE_CUDA_NUM_THREADS>>>(count, num, channels, spatial_dim,
+      CAFFE_CUDA_NUM_THREADS>>>(count, outer_num_, channels, inner_num_,
       scale_data, top_data);
 }
 
@@ -131,18 +128,16 @@ void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   Dtype* scale_data = scale_.mutable_gpu_data();
   int count = top[0]->count();
-  int num = top[0]->num();
-  int channels = top[0]->channels();
-  int spatial_dim = top[0]->height() * top[0]->width();
-  caffe_copy(top[0]->count(), top_diff, bottom_diff);
+  int channels = top[0]->shape(softmax_axis_);
+  caffe_copy(count, top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff.
   // NOLINT_NEXT_LINE(whitespace/operators)
-  kernel_channel_dot<Dtype><<<CAFFE_GET_BLOCKS(num * spatial_dim),
-      CAFFE_CUDA_NUM_THREADS>>>(num, channels, spatial_dim, top_diff, top_data,
-      scale_data);
+  kernel_channel_dot<Dtype><<<CAFFE_GET_BLOCKS(outer_num_ * inner_num_),
+      CAFFE_CUDA_NUM_THREADS>>>(outer_num_, channels, inner_num_,
+      top_diff, top_data, scale_data);
   // NOLINT_NEXT_LINE(whitespace/operators)
   kernel_channel_subtract<Dtype><<<CAFFE_GET_BLOCKS(count),
-      CAFFE_CUDA_NUM_THREADS>>>(count, num, channels, spatial_dim,
+      CAFFE_CUDA_NUM_THREADS>>>(count, outer_num_, channels, inner_num_,
       scale_data, bottom_diff);
   // elementwise multiplication
   caffe_gpu_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 7783a783dd7..8fcb8def173 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -692,6 +692,11 @@ message SoftmaxParameter {
     CUDNN = 2;
   }
   optional Engine engine = 1 [default = DEFAULT];
+
+  // The axis along which to perform the softmax -- may be negative to index
+  // from the end (e.g., -1 for the last axis).
+  // Any other axes will be evaluated as independent softmaxes.
+  optional int32 axis = 2 [default = 1];
 }
 
 // Message that stores parameters used by TanHLayer

From 60c288b9de8ecd2b17ee2d2eff7a31fecfe3e98b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 9 Feb 2015 18:12:54 -0800
Subject: [PATCH 1219/2053] CuDNNSoftmaxLayer: generalized Blob axes

---
 src/caffe/layers/cudnn_softmax_layer.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 83a5b69a626..211701cad49 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -26,10 +26,10 @@ template <typename Dtype>
 void CuDNNSoftmaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   SoftmaxLayer<Dtype>::Reshape(bottom, top);
-  int N = bottom[0]->num();
-  int K = bottom[0]->channels();
-  int H = bottom[0]->height();
-  int W = bottom[0]->width();
+  int N = this->outer_num_;
+  int K = bottom[0]->shape(this->softmax_axis_);
+  int H = this->inner_num_;
+  int W = 1;
   cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, N, K, H, W);
   cudnn::setTensor4dDesc<Dtype>(&top_desc_, N, K, H, W);
 }

From 94d93da095486c3137aacd101f495acde673ba10 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 30 Jan 2015 23:22:26 -0800
Subject: [PATCH 1220/2053] SoftmaxLossLayer generalized like SoftmaxLayer

---
 include/caffe/loss_layers.hpp           |  2 ++
 src/caffe/layers/softmax_loss_layer.cpp | 42 ++++++++++++++-----------
 src/caffe/layers/softmax_loss_layer.cu  | 20 +++++-------
 3 files changed, 33 insertions(+), 31 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 36413ccd176..62d6df71a4a 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -754,6 +754,8 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   /// Whether to normalize the loss by the total number of values present
   /// (otherwise just by the batch size).
   bool normalize_;
+
+  int softmax_axis_, outer_num_, inner_num_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 0c9ba2c6626..132c30796a4 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -35,6 +35,14 @@ void SoftmaxWithLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
+  softmax_axis_ = this->layer_param_.softmax_param().axis();
+  outer_num_ = bottom[0]->count(0, softmax_axis_);
+  inner_num_ = bottom[0]->count(softmax_axis_ + 1);
+  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
+      << "Number of labels must match number of predictions; "
+      << "e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), "
+      << "label count (number of labels) must be N*H*W, "
+      << "with integer values in {0, 1, ..., C-1}.";
   if (top.size() >= 2) {
     // softmax output
     top[1]->ReshapeLike(*bottom[0]);
@@ -48,20 +56,18 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
   const Dtype* prob_data = prob_.cpu_data();
   const Dtype* label = bottom[1]->cpu_data();
-  int num = prob_.num();
-  int dim = prob_.count() / num;
-  int spatial_dim = prob_.height() * prob_.width();
+  int dim = prob_.count() / outer_num_;
   int count = 0;
   Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    for (int j = 0; j < spatial_dim; j++) {
-      const int label_value = static_cast<int>(label[i * spatial_dim + j]);
+  for (int i = 0; i < outer_num_; ++i) {
+    for (int j = 0; j < inner_num_; j++) {
+      const int label_value = static_cast<int>(label[i * inner_num_ + j]);
       if (has_ignore_label_ && label_value == ignore_label_) {
         continue;
       }
       DCHECK_GE(label_value, 0);
-      DCHECK_LT(label_value, prob_.channels());
-      loss -= log(std::max(prob_data[i * dim + label_value * spatial_dim + j],
+      DCHECK_LT(label_value, prob_.shape(softmax_axis_));
+      loss -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],
                            Dtype(FLT_MIN)));
       ++count;
     }
@@ -69,7 +75,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   if (normalize_) {
     top[0]->mutable_cpu_data()[0] = loss / count;
   } else {
-    top[0]->mutable_cpu_data()[0] = loss / num;
+    top[0]->mutable_cpu_data()[0] = loss / outer_num_;
   }
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
@@ -88,19 +94,17 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* prob_data = prob_.cpu_data();
     caffe_copy(prob_.count(), prob_data, bottom_diff);
     const Dtype* label = bottom[1]->cpu_data();
-    int num = prob_.num();
-    int dim = prob_.count() / num;
-    int spatial_dim = prob_.height() * prob_.width();
+    int dim = prob_.count() / outer_num_;
     int count = 0;
-    for (int i = 0; i < num; ++i) {
-      for (int j = 0; j < spatial_dim; ++j) {
-        const int label_value = static_cast<int>(label[i * spatial_dim + j]);
+    for (int i = 0; i < outer_num_; ++i) {
+      for (int j = 0; j < inner_num_; ++j) {
+        const int label_value = static_cast<int>(label[i * inner_num_ + j]);
         if (has_ignore_label_ && label_value == ignore_label_) {
-          for (int c = 0; c < bottom[0]->channels(); ++c) {
-            bottom_diff[i * dim + c * spatial_dim + j] = 0;
+          for (int c = 0; c < bottom[0]->shape(softmax_axis_); ++c) {
+            bottom_diff[i * dim + c * inner_num_ + j] = 0;
           }
         } else {
-          bottom_diff[i * dim + label_value * spatial_dim + j] -= 1;
+          bottom_diff[i * dim + label_value * inner_num_ + j] -= 1;
           ++count;
         }
       }
@@ -110,7 +114,7 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     if (normalize_) {
       caffe_scal(prob_.count(), loss_weight / count, bottom_diff);
     } else {
-      caffe_scal(prob_.count(), loss_weight / num, bottom_diff);
+      caffe_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
     }
   }
 }
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 215d589ffee..7e0f3da4552 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -35,10 +35,8 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
   softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
   const Dtype* prob_data = prob_.gpu_data();
   const Dtype* label = bottom[1]->gpu_data();
-  const int num = prob_.num();
-  const int dim = prob_.count() / num;
-  const int spatial_dim = prob_.height() * prob_.width();
-  const int nthreads = num * spatial_dim;
+  const int dim = prob_.count() / outer_num_;
+  const int nthreads = outer_num_ * inner_num_;
   // Since this memory is not used for anything until it is overwritten
   // on the backward pass, we use it here to avoid having to allocate new GPU
   // memory to accumulate intermediate results in the kernel.
@@ -49,7 +47,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
   // NOLINT_NEXT_LINE(whitespace/operators)
   SoftmaxLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
       CAFFE_CUDA_NUM_THREADS>>>(nthreads, prob_data, label, loss_data,
-      num, dim, spatial_dim, has_ignore_label_, ignore_label_, counts);
+      outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
   Dtype loss;
   caffe_gpu_asum(nthreads, loss_data, &loss);
   if (normalize_) {
@@ -57,7 +55,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
     caffe_gpu_asum(nthreads, counts, &count);
     loss /= count;
   } else {
-    loss /= num;
+    loss /= outer_num_;
   }
   top[0]->mutable_cpu_data()[0] = loss;
   if (top.size() == 2) {
@@ -102,24 +100,22 @@ void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_data = top[0]->gpu_data();
     caffe_gpu_memcpy(prob_.count() * sizeof(Dtype), prob_data, bottom_diff);
     const Dtype* label = bottom[1]->gpu_data();
-    const int num = prob_.num();
-    const int dim = prob_.count() / num;
-    const int spatial_dim = prob_.height() * prob_.width();
-    const int nthreads = num * spatial_dim;
+    const int dim = prob_.count() / outer_num_;
+    const int nthreads = outer_num_ * inner_num_;
     // Since this memory is never used for anything else,
     // we use to to avoid allocating new GPU memory.
     Dtype* counts = prob_.mutable_gpu_diff();
     // NOLINT_NEXT_LINE(whitespace/operators)
     SoftmaxLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
         CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, bottom_diff,
-        num, dim, spatial_dim, has_ignore_label_, ignore_label_, counts);
+        outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
     const Dtype loss_weight = top[0]->cpu_diff()[0];
     if (normalize_) {
       Dtype count;
       caffe_gpu_asum(nthreads, counts, &count);
       caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff);
     } else {
-      caffe_gpu_scal(prob_.count(), loss_weight / num, bottom_diff);
+      caffe_gpu_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
     }
   }
 }

From e2bc9f997d5ebfbefdc4a7e2fa0da9b029c1573a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 05:02:15 -0800
Subject: [PATCH 1221/2053] SplitLayer: change Reshape(n,h,c,w) to
 ReshapeLike(...)

---
 src/caffe/layers/split_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index d6929b99683..272cb59cd37 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -18,8 +18,7 @@ void SplitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     // some strange effects in practice...)
     CHECK_NE(top[i], bottom[0]) << this->type() << " Layer does not "
         "allow in-place computation.";
-    top[i]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-                       bottom[0]->height(), bottom[0]->width());
+    top[i]->ReshapeLike(*bottom[0]);
     CHECK_EQ(count_, top[i]->count());
   }
 }

From e6468e900f03b2c4f2d9ccf160b46bff0f52dfb9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 05:42:11 -0800
Subject: [PATCH 1222/2053] HDF5DataLayer shapes output according to HDF5 shape

---
 src/caffe/layers/hdf5_data_layer.cpp   | 11 ++++++++---
 src/caffe/test/test_hdf5data_layer.cpp | 16 +++++++---------
 src/caffe/util/io.cpp                  | 10 +++++-----
 3 files changed, 20 insertions(+), 17 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 3d856ec3001..1ceb6c24431 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -36,7 +36,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   hdf_blobs_.resize(top_size);
 
   const int MIN_DATA_DIM = 1;
-  const int MAX_DATA_DIM = 4;
+  const int MAX_DATA_DIM = INT_MAX;
 
   for (int i = 0; i < top_size; ++i) {
     hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
@@ -88,9 +88,14 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
   const int top_size = this->layer_param_.top_size();
+  vector<int> top_shape;
   for (int i = 0; i < top_size; ++i) {
-    top[i]->Reshape(batch_size, hdf_blobs_[i]->channels(),
-                    hdf_blobs_[i]->height(), hdf_blobs_[i]->width());
+    top_shape.resize(hdf_blobs_[i]->num_axes());
+    top_shape[0] = batch_size;
+    for (int j = 1; j < top_shape.size(); ++j) {
+      top_shape[j] = hdf_blobs_[i]->shape(j);
+    }
+    top[i]->Reshape(top_shape);
   }
 }
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 8d3b3d1e987..c9b027f88cf 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -77,15 +77,13 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_data_->height(), height);
   EXPECT_EQ(this->blob_top_data_->width(), width);
 
-  EXPECT_EQ(this->blob_top_label_->num(), batch_size);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  EXPECT_EQ(this->blob_top_label2_->num(), batch_size);
-  EXPECT_EQ(this->blob_top_label2_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label2_->height(), 1);
-  EXPECT_EQ(this->blob_top_label2_->width(), 1);
+  EXPECT_EQ(this->blob_top_label_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_label_->shape(0), batch_size);
+  EXPECT_EQ(this->blob_top_label_->shape(1), 1);
+
+  EXPECT_EQ(this->blob_top_label2_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_label2_->shape(0), batch_size);
+  EXPECT_EQ(this->blob_top_label2_->shape(1), 1);
 
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
 
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index b243a9804ec..77ef7f257f4 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -252,11 +252,11 @@ void hdf5_load_nd_dataset_helper(
   CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
   CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
 
-  blob->Reshape(
-    dims[0],
-    (dims.size() > 1) ? dims[1] : 1,
-    (dims.size() > 2) ? dims[2] : 1,
-    (dims.size() > 3) ? dims[3] : 1);
+  vector<int> blob_dims(dims.size());
+  for (int i = 0; i < dims.size(); ++i) {
+    blob_dims[i] = dims[i];
+  }
+  blob->Reshape(blob_dims);
 }
 
 template <>

From e56377d96e52fbfd2226bc35db6cd3e6e404afd0 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 05:34:47 -0800
Subject: [PATCH 1223/2053] DataLayer outputs 1D labels

---
 src/caffe/layers/data_layer.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 1861090f953..0f2d66776a9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -69,9 +69,9 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << top[0]->width();
   // label
   if (this->output_labels_) {
-    top[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
-    this->prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
-        1, 1, 1);
+    vector<int> label_shape(1, this->layer_param_.data_param().batch_size());
+    top[1]->Reshape(label_shape);
+    this->prefetch_label_.Reshape(label_shape);
   }
 }
 

From 7c8725bc6f2a017dc8c6979f75f9b3f0163c8a0a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 05:42:50 -0800
Subject: [PATCH 1224/2053] MemoryDataLayer outputs 1D labels

---
 src/caffe/layers/memory_data_layer.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index effdad90aff..42de4198bc4 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -19,10 +19,11 @@ void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(batch_size_ * size_, 0) <<
       "batch_size, channels, height, and width must be specified and"
       " positive in memory_data_param";
+  vector<int> label_shape(1, batch_size_);
   top[0]->Reshape(batch_size_, channels_, height_, width_);
-  top[1]->Reshape(batch_size_, 1, 1, 1);
+  top[1]->Reshape(label_shape);
   added_data_.Reshape(batch_size_, channels_, height_, width_);
-  added_label_.Reshape(batch_size_, 1, 1, 1);
+  added_label_.Reshape(label_shape);
   data_ = NULL;
   labels_ = NULL;
   added_data_.cpu_data();

From c87a136add3a43eb6ccf9f5b69a5a8a73ae4e753 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 12:56:14 -0800
Subject: [PATCH 1225/2053] ImageDataLayer outputs 1D labels

---
 src/caffe/layers/image_data_layer.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index f9046e1b3a1..38ebbd5ec14 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -81,8 +81,9 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
   // label
-  top[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+  vector<int> label_shape(1, batch_size);
+  top[1]->Reshape(label_shape);
+  this->prefetch_label_.Reshape(label_shape);
 }
 
 template <typename Dtype>

From 9505001d82698cb8028c479a238eb49ef9201068 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 26 Nov 2014 12:57:15 -0800
Subject: [PATCH 1226/2053] WindowDataLayer outputs 1D labels

---
 src/caffe/layers/window_data_layer.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 36e41560327..c127d56bc46 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -177,8 +177,9 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
   // label
-  top[1]->Reshape(batch_size, 1, 1, 1);
-  this->prefetch_label_.Reshape(batch_size, 1, 1, 1);
+  vector<int> label_shape(1, batch_size);
+  top[1]->Reshape(label_shape);
+  this->prefetch_label_.Reshape(label_shape);
 
   // data mean
   has_mean_file_ = this->transform_param_.has_mean_file();

From fcbb933ab5e31418fc4f8705d72d01f9c80363ce Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 29 Nov 2014 18:00:44 -0800
Subject: [PATCH 1227/2053] EuclideanLossLayer: generalized Blob axes

---
 src/caffe/layers/euclidean_loss_layer.cpp | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index b539d3487f5..80efa31b22c 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -11,11 +11,9 @@ template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Reshape(
   const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
-  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
-  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
-  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
-  diff_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
+  CHECK_EQ(bottom[0]->count(1), bottom[1]->count(1))
+      << "Inputs must have the same dimension.";
+  diff_.ReshapeLike(*bottom[0]);
 }
 
 template <typename Dtype>

From 7462c84ca0b44b0c1e270e56e381c618f6134857 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 31 Dec 2014 16:06:46 -0800
Subject: [PATCH 1228/2053] DummyDataLayer outputs blobs of arbitrary shape

---
 src/caffe/layers/dummy_data_layer.cpp | 57 +++++++++++++++++----------
 src/caffe/proto/caffe.proto           |  6 ++-
 2 files changed, 41 insertions(+), 22 deletions(-)

diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index d254eb1f961..6b0d617464c 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -16,18 +16,30 @@ void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
         num_data_filler == num_top)
       << "Number of data fillers must be 0, 1 or equal to the number of tops: "
       << num_top << "; you specified " << num_data_filler << " data fillers.";
-  CHECK(param.num_size() == 1 || param.num_size() == num_top)
-      << "Must specify either a single (1) 'num' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.num_size() << ".";
-  CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
-      << "Must specify either a single (1) 'channels' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.channels_size() << ".";
-  CHECK(param.height_size() == 1 || param.height_size() == num_top)
-      << "Must specify either a single (1) 'height' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.height_size() << ".";
-  CHECK(param.width_size() == 1 || param.width_size() == num_top)
-      << "Must specify either a single (1) 'width' or one for each top blob "
-      << "(" << num_top << "); you specified " << param.width_size() << ".";
+
+  const bool legacy_dims = param.num_size() || param.channels_size() ||
+                           param.height_size() || param.width_size();
+  if (legacy_dims) {
+    CHECK_EQ(0, param.shape_size())
+        << "Both shape and legacy fields were specified";
+    // Using deprecated 4D output dim specifiers.
+    CHECK(param.num_size() == 1 || param.num_size() == num_top)
+        << "Must specify 'num' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.num_size() << ".";
+    CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
+        << "Must specify 'channels' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.channels_size() << ".";
+    CHECK(param.height_size() == 1 || param.height_size() == num_top)
+        << "Must specify 'height' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.height_size() << ".";
+    CHECK(param.width_size() == 1 || param.width_size() == num_top)
+        << "Must specify 'width' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.width_size() << ".";
+  } else {
+    CHECK(param.shape_size() == 1 || param.shape_size() == num_top)
+        << "Must specify 'shape' once, or once per top blob "
+        << "(" << num_top << "); specified " << param.shape_size() << ".";
+  }
   // refill_[i] tells Forward i whether or not to actually refill top Blob i.
   // If refill_[i] is false, Forward does nothing for Blob i. We use this to
   // avoid wastefully refilling "constant" Blobs in every forward pass.
@@ -63,14 +75,19 @@ void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     }
   }
   for (int i = 0; i < num_top; ++i) {
-    const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
-    const int channels =
-        (param.channels_size() == 1) ? param.channels(0) : param.channels(i);
-    const int height =
-        (param.height_size() == 1) ? param.height(0) : param.height(i);
-    const int width =
-        (param.width_size() == 1) ? param.width(0) : param.width(i);
-    top[i]->Reshape(num, channels, height, width);
+    if (legacy_dims) {
+      const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
+      const int channels =
+          (param.channels_size() == 1) ? param.channels(0) : param.channels(i);
+      const int height =
+          (param.height_size() == 1) ? param.height(0) : param.height(i);
+      const int width =
+          (param.width_size() == 1) ? param.width(0) : param.width(i);
+      top[i]->Reshape(num, channels, height, width);
+    } else {
+      const int shape_index = (param.shape_size() == 1) ? 0 : i;
+      top[i]->Reshape(param.shape(shape_index));
+    }
   }
   // Run Forward once, with refill_ inverted, to fill the constant Blobs.
   this->Forward(bottom, top);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8fcb8def173..3b4794664b5 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -461,13 +461,15 @@ message DropoutParameter {
 // (or constant) data generated by "Fillers" (see "message FillerParameter").
 message DummyDataParameter {
   // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
-  // num, N channels, N height, and N width fields, and must specify 0, 1 or N
-  // data_fillers.
+  // shape fields, and 0, 1 or N data_fillers.
   //
   // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
   // If 1 data_filler is specified, it is applied to all top blobs.  If N are
   // specified, the ith is applied to the ith top blob.
   repeated FillerParameter data_filler = 1;
+  repeated BlobShape shape = 6;
+
+  // 4D dimensions -- deprecated.  Use "shape" instead.
   repeated uint32 num = 2;
   repeated uint32 channels = 3;
   repeated uint32 height = 4;

From 69fc1f642e8f87df44131aeaa0b55fb221897d40 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 15 Jan 2015 19:50:42 -0800
Subject: [PATCH 1229/2053] Add CHECK_EQ(4, ...)s to "vision layers" to enforce
 that the num/channnels/height/width indexing is valid.

---
 src/caffe/layers/base_conv_layer.cpp | 4 ++++
 src/caffe/layers/im2col_layer.cpp    | 2 ++
 src/caffe/layers/lrn_layer.cpp       | 2 ++
 src/caffe/layers/pooling_layer.cpp   | 2 ++
 4 files changed, 10 insertions(+)

diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index d4ab5e429b5..ccb3adc7e89 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -11,6 +11,8 @@ namespace caffe {
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   // Configure the kernel size, padding, stride, and inputs.
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
   CHECK(!conv_param.has_kernel_size() !=
@@ -101,6 +103,8 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   num_ = bottom[0]->num();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 112226116c8..1c802714e33 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -50,6 +50,8 @@ void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 5e3e7c429ef..36c1ace4c99 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -69,6 +69,8 @@ void LRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void LRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 6f4c69c861e..c8d41499455 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -81,6 +81,8 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();

From 269dafa37a96250012a10537ad773840b4621ff7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 1 Jan 2015 17:32:38 -0800
Subject: [PATCH 1230/2053] PyBlobs support generalized axes

---
 python/caffe/_caffe.cpp | 35 ++++++++++++++++++++++++++++++-----
 python/caffe/pycaffe.py |  2 --
 2 files changed, 30 insertions(+), 7 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 03967a21029..d4eda798192 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -163,9 +163,10 @@ struct NdarrayCallPolicies : public bp::default_call_policies {
     // the shape information from the blob.
     void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
     Py_DECREF(result);
-    npy_intp dims[] = {blob->num(), blob->channels(),
-                       blob->height(), blob->width()};
-    PyObject* arr_obj = PyArray_SimpleNewFromData(4, dims, NPY_FLOAT32, data);
+    const int num_axes = blob->num_axes();
+    vector<npy_intp> dims(blob->shape().begin(), blob->shape().end());
+    PyObject *arr_obj = PyArray_SimpleNewFromData(num_axes, dims.data(),
+                                                  NPY_FLOAT32, data);
     // SetBaseObject steals a ref, so we need to INCREF.
     Py_INCREF(pyblob.ptr());
     PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
@@ -174,6 +175,27 @@ struct NdarrayCallPolicies : public bp::default_call_policies {
   }
 };
 
+void Blob_Reshape(Blob<Dtype>* blob, bp::object shape_obj) {
+  PyArrayObject* shape_arr =
+      reinterpret_cast<PyArrayObject*>(shape_obj.ptr());
+  if (!(PyArray_FLAGS(shape_arr) & NPY_ARRAY_C_CONTIGUOUS)) {
+    throw std::runtime_error("new shape must be C contiguous");
+  }
+  if (PyArray_NDIM(shape_arr) != 1) {
+    throw std::runtime_error("new shape must be 1-d");
+  }
+  if (PyArray_TYPE(shape_arr) != NPY_INT32) {
+    throw std::runtime_error("new shape must be specified as int32 array");
+  }
+  npy_int32* shape_data = static_cast<npy_int32*>(PyArray_DATA(shape_arr));
+  const int num_axes = PyArray_SIZE(shape_arr);
+  vector<int> shape(num_axes);
+  for (int i = 0; i < num_axes; ++i) {
+    shape[i] = shape_data[i];
+  }
+  blob->Reshape(shape);
+}
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -218,8 +240,11 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("channels", &Blob<Dtype>::channels)
     .add_property("height",   &Blob<Dtype>::height)
     .add_property("width",    &Blob<Dtype>::width)
-    .add_property("count",    &Blob<Dtype>::count)
-    .def("reshape",           &Blob<Dtype>::Reshape)
+    .add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(
+        &Blob<Dtype>::count))
+    .def("reshape", static_cast<void (Blob<Dtype>::*)(int, int, int, int)>(
+        &Blob<Dtype>::Reshape))
+    .def("reshape", &Blob_Reshape)
     .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index d662d6cc282..ac387d51d96 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -85,8 +85,6 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.ndim != 4:
-                raise Exception('{} blob is not 4-d'.format(in_))
             if blob.shape[0] != self.blobs[in_].num:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob

From 71df6f90c8cc72c197db6bfe4b24480ab42b93bc Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 30 Jan 2015 23:16:44 -0800
Subject: [PATCH 1231/2053] Add option not to reshape to Blob::FromProto; use
 when loading Blobs from saved NetParameter

Want to keep the param Blob shape the layer has set, and not necessarily
adopt the one from the saved net (e.g. want to keep new 1D bias shape,
rather than take the (1 x 1 x 1 x D) shape from a legacy net).
---
 include/caffe/blob.hpp |  2 +-
 src/caffe/blob.cpp     | 36 ++++++++++++++++++++----------------
 src/caffe/net.cpp      |  4 ++--
 3 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 82ca95a5f0f..36579a5a545 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -227,7 +227,7 @@ class Blob {
   Dtype* mutable_cpu_diff();
   Dtype* mutable_gpu_diff();
   void Update();
-  void FromProto(const BlobProto& proto);
+  void FromProto(const BlobProto& proto, bool reshape = true);
   void ToProto(BlobProto* proto, bool write_diff = false) const;
 
   /// @brief Compute the sum of absolute values (L1 norm) of the data.
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index c65dc2dd1ac..6d2b3f502d9 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -431,24 +431,28 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
 }
 
 template <typename Dtype>
-void Blob<Dtype>::FromProto(const BlobProto& proto) {
-  vector<int> shape;
-  if (proto.has_num() || proto.has_channels() ||
-      proto.has_height() || proto.has_width()) {
-    // Using deprecated 4D Blob dimensions --
-    // shape is (num, channels, height, width).
-    shape.resize(4);
-    shape[0] = proto.num();
-    shape[1] = proto.channels();
-    shape[2] = proto.height();
-    shape[3] = proto.width();
-  } else {
-    shape.resize(proto.shape().dim_size());
-    for (int i = 0; i < proto.shape().dim_size(); ++i) {
-      shape[i] = proto.shape().dim(i);
+void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
+  if (reshape) {
+    vector<int> shape;
+    if (proto.has_num() || proto.has_channels() ||
+        proto.has_height() || proto.has_width()) {
+      // Using deprecated 4D Blob dimensions --
+      // shape is (num, channels, height, width).
+      shape.resize(4);
+      shape[0] = proto.num();
+      shape[1] = proto.channels();
+      shape[2] = proto.height();
+      shape[3] = proto.width();
+    } else {
+      shape.resize(proto.shape().dim_size());
+      for (int i = 0; i < proto.shape().dim_size(); ++i) {
+        shape[i] = proto.shape().dim(i);
+      }
     }
+    Reshape(shape);
+  } else {
+    CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
   }
-  Reshape(shape);
   // copy data
   Dtype* data_vec = mutable_cpu_data();
   for (int i = 0; i < count_; ++i) {
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 60f387b92e9..e8f7c05e09d 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -705,8 +705,8 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
     CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
-      CHECK(target_blobs[j]->ShapeEquals(source_layer.blobs(j)));
-      target_blobs[j]->FromProto(source_layer.blobs(j));
+      const bool kReshape = false;
+      target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
     }
   }
 }

From aa242aa5a121cf3afc0bacf4449da5a4ad76b236 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 2 Mar 2015 15:27:45 -0800
Subject: [PATCH 1232/2053] [pycaffe] expose Blob.reshape as *args function

---
 python/caffe/_caffe.cpp | 32 ++++++++++++--------------------
 1 file changed, 12 insertions(+), 20 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index d4eda798192..bfea0de661b 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -5,6 +5,7 @@
 
 #include <boost/make_shared.hpp>
 #include <boost/python.hpp>
+#include <boost/python/raw_function.hpp>
 #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
 #include <numpy/arrayobject.h>
 
@@ -175,25 +176,18 @@ struct NdarrayCallPolicies : public bp::default_call_policies {
   }
 };
 
-void Blob_Reshape(Blob<Dtype>* blob, bp::object shape_obj) {
-  PyArrayObject* shape_arr =
-      reinterpret_cast<PyArrayObject*>(shape_obj.ptr());
-  if (!(PyArray_FLAGS(shape_arr) & NPY_ARRAY_C_CONTIGUOUS)) {
-    throw std::runtime_error("new shape must be C contiguous");
+bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
+  if (bp::len(kwargs) > 0) {
+    throw std::runtime_error("Blob.reshape takes no kwargs");
   }
-  if (PyArray_NDIM(shape_arr) != 1) {
-    throw std::runtime_error("new shape must be 1-d");
+  Blob<Dtype>* self = bp::extract<Blob<Dtype>*>(args[0]);
+  vector<int> shape(bp::len(args) - 1);
+  for (int i = 1; i < bp::len(args); ++i) {
+    shape[i - 1] = bp::extract<int>(args[i]);
   }
-  if (PyArray_TYPE(shape_arr) != NPY_INT32) {
-    throw std::runtime_error("new shape must be specified as int32 array");
-  }
-  npy_int32* shape_data = static_cast<npy_int32*>(PyArray_DATA(shape_arr));
-  const int num_axes = PyArray_SIZE(shape_arr);
-  vector<int> shape(num_axes);
-  for (int i = 0; i < num_axes; ++i) {
-    shape[i] = shape_data[i];
-  }
-  blob->Reshape(shape);
+  self->Reshape(shape);
+  // We need to explicitly return None to use bp::raw_function.
+  return bp::object();
 }
 
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
@@ -242,9 +236,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("width",    &Blob<Dtype>::width)
     .add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(
         &Blob<Dtype>::count))
-    .def("reshape", static_cast<void (Blob<Dtype>::*)(int, int, int, int)>(
-        &Blob<Dtype>::Reshape))
-    .def("reshape", &Blob_Reshape)
+    .def("reshape",           bp::raw_function(&Blob_Reshape))
     .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,

From 8c79d65e333388e2bde9346899b7cdf812aab2c6 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 2 Mar 2015 15:54:11 -0800
Subject: [PATCH 1233/2053] [pytest] use non-4d blobs in test_python_layer

---
 python/caffe/test/test_python_layer.py | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index 383c283959d..dd99f6f15b9 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -11,8 +11,7 @@ def setup(self, bottom, top):
         pass
 
     def reshape(self, bottom, top):
-        top[0].reshape(bottom[0].num, bottom[0].channels, bottom[0].height,
-                bottom[0].width)
+        top[0].reshape(*bottom[0].data.shape)
 
     def forward(self, bottom, top):
         top[0].data[...] = 10 * bottom[0].data
@@ -21,17 +20,16 @@ def backward(self, top, propagate_down, bottom):
         bottom[0].diff[...] = 10 * top[0].diff
 
 def python_net_file():
-    f = tempfile.NamedTemporaryFile(delete=False)
-    f.write("""name: 'pythonnet' force_backward: true
-    input: 'data' input_dim: 10 input_dim: 9 input_dim: 8 input_dim: 7
-    layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
-      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
-    layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
-      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
-    layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
-      python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
-    f.close()
-    return f.name
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
+          python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+        layer { type: 'Python' name: 'two' bottom: 'one' top: 'two'
+          python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }
+        layer { type: 'Python' name: 'three' bottom: 'two' top: 'three'
+          python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
+        return f.name
 
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):

From 642619bcd45ab71be2483379ff4e7192628c11ab Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Mon, 23 Feb 2015 09:18:31 -0800
Subject: [PATCH 1234/2053] Add error checking for image mean

When setting the mean, assert that it is either one pixel or an array with
shape equal to the input data size.
---
 python/caffe/io.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index f51e3a64d36..f7f75b73cd8 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -238,11 +238,16 @@ def set_mean(self, in_, mean):
         """
         self.__check_input(in_)
         if mean.ndim == 1:
+            # broadcast pixel
             mean = mean[:, np.newaxis, np.newaxis]
-        mk, mh, mw = mean.shape
-        in_k, in_h, in_w = self.inputs[in_][1:]
-        #if mk != in_k or (mh, mw) != (in_h, in_w) and (mh, mw) != (1, 1):
-        #    raise Exception('Mean shape incompatible with input shape.')
+        else:
+            ms = mean.shape
+            if len(ms) == 2:
+                ms = (1,) + ms
+            if len(ms) != 3:
+                raise ValueError('Mean shape invalid')
+            if ms != self.inputs[in_][1:]:
+                raise ValueError('Mean shape incompatible with input shape.')
         self.mean[in_] = mean
 
 

From dec148e89d30020b0d81ea59c6d88539db83b019 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 4 Mar 2015 11:17:51 -0800
Subject: [PATCH 1235/2053] fix comment I forgot about from @shelhamer's review
 of #1970

---
 src/caffe/layers/inner_product_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 6b88724491b..89e0c8fbad7 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -19,7 +19,7 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       this->layer_param_.inner_product_param().axis());
   // Dimensions starting from "axis" are "flattened" into a single
   // length K_ vector. For example, if bottom[0]'s shape is (N, C, H, W),
-  // N inner products with dimension CHW are performed.
+  // and axis == 1, N inner products with dimension CHW are performed.
   K_ = bottom[0]->count(axis);
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {

From a3b0fbd9748114b981c56db5d2103843dbcb45b7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 4 Mar 2015 15:22:46 -0800
Subject: [PATCH 1236/2053] include/caffe/common.hpp: add <climits> for INT_MAX
 (now in blob.hpp)

---
 include/caffe/common.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 890673cd7e6..0e108f1fce8 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -5,6 +5,7 @@
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 
+#include <climits>
 #include <cmath>
 #include <fstream>  // NOLINT(readability/streams)
 #include <iostream>  // NOLINT(readability/streams)

From ccfa3dc10c4bfa3ff5740923a5c760e17316a514 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 4 Mar 2015 17:13:38 -0800
Subject: [PATCH 1237/2053] [pycaffe] check mean channels for transformation

follow-up to #2031: check that the input and mean channels are
compatible in the broadcast channels case.
---
 python/caffe/io.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index f7f75b73cd8..e362f3637c0 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -237,11 +237,14 @@ def set_mean(self, in_, mean):
         mean: mean ndarray (input dimensional or broadcastable)
         """
         self.__check_input(in_)
+        ms = mean.shape
         if mean.ndim == 1:
-            # broadcast pixel
+            # broadcast channels
+            if ms[0] != self.inputs[in_][1]:
+                raise ValueError('Mean channels incompatible with input.')
             mean = mean[:, np.newaxis, np.newaxis]
         else:
-            ms = mean.shape
+            # elementwise mean
             if len(ms) == 2:
                 ms = (1,) + ms
             if len(ms) != 3:

From c0219cc1916d60eb4b7d85d2058a92ecdd7dcd75 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 4 Mar 2015 17:18:26 -0800
Subject: [PATCH 1238/2053] expose Solver::Restore() as public and
 Solver.restore() in pycaffe

The solver can restore its state without entering the Solve() loop.
---
 include/caffe/solver.hpp | 8 ++++----
 python/caffe/_caffe.cpp  | 3 ++-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 2510de748de..4dcdc3dc20b 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -27,6 +27,10 @@ class Solver {
   virtual void Solve(const char* resume_file = NULL);
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
   void Step(int iters);
+  // The Restore function implements how one should restore the solver to a
+  // previously snapshotted state. You should implement the RestoreSolverState()
+  // function that restores the state from a SolverState protocol buffer.
+  void Restore(const char* resume_file);
   virtual ~Solver() {}
   inline shared_ptr<Net<Dtype> > net() { return net_; }
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
@@ -46,10 +50,6 @@ class Solver {
   void TestAll();
   void Test(const int test_net_id = 0);
   virtual void SnapshotSolverState(SolverState* state) = 0;
-  // The Restore function implements how one should restore the solver to a
-  // previously snapshotted state. You should implement the RestoreSolverState()
-  // function that restores the state from a SolverState protocol buffer.
-  void Restore(const char* resume_file);
   virtual void RestoreSolverState(const SolverState& state) = 0;
   void DisplayOutputBlobs(const int net_id);
 
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index bfea0de661b..dff7f627016 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -261,7 +261,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("iter", &Solver<Dtype>::iter)
     .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
           &Solver<Dtype>::Solve), SolveOverloads())
-    .def("step", &Solver<Dtype>::Step);
+    .def("step", &Solver<Dtype>::Step)
+    .def("restore", &Solver<Dtype>::Restore);
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(

From 596add755cdd1178ffd3ca5598a64909b361739d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 4 Mar 2015 23:00:45 -0800
Subject: [PATCH 1239/2053] [pycaffe] add missing import sys

---
 python/caffe/io.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index e362f3637c0..6ae2cf13cc0 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -7,6 +7,7 @@
     # Python3 will most likely not be able to load protobuf
     from caffe.proto import caffe_pb2
 except:
+    import sys
     if sys.version_info >= (3,0):
         print("Failed to include caffe_pb2, things might go wrong!")
     else:

From 65d84a595d187b39b7299f457967d7f91acf3cd9 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 5 Mar 2015 13:55:01 -0800
Subject: [PATCH 1240/2053] [docs] include boost-python in OSX pycaffe install

---
 docs/install_osx.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install_osx.md b/docs/install_osx.md
index 55b098731fc..0373a417847 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -32,7 +32,7 @@ If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
 
     # with Python pycaffe needs dependencies built from source
     brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost
+    brew install --build-from-source --fresh -vd boost boost-python
     # without Python the usual installation suffices
     brew install protobuf boost
 

From 87d3aa3d45cb11d8d05fdc6a0b301d0f85f00b33 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Thu, 5 Mar 2015 15:00:23 -0800
Subject: [PATCH 1241/2053] Adding correct hdf5 path

---
 cmake/Dependencies.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index b1ac96c6777..7a9b2af60b4 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -25,7 +25,7 @@ include(cmake/ProtoBuf.cmake)
 
 # ---[ HDF5
 find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(SYSTEM ${HDF5_INCLUDE_DIRS})
+include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
 
 # ---[ LMDB

From 5fec3f7c26225e295df8589be575c40564e4a3e2 Mon Sep 17 00:00:00 2001
From: tishibas67 <t_ishibas67@yahoo.co.jp>
Date: Fri, 6 Mar 2015 23:48:27 +0900
Subject: [PATCH 1242/2053] fix #1506

---
 examples/imagenet/make_imagenet_mean.sh | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/examples/imagenet/make_imagenet_mean.sh b/examples/imagenet/make_imagenet_mean.sh
index d3d0c9af5d2..57f43766c4b 100755
--- a/examples/imagenet/make_imagenet_mean.sh
+++ b/examples/imagenet/make_imagenet_mean.sh
@@ -1,8 +1,12 @@
 #!/usr/bin/env sh
-# Compute the mean image from the imagenet training leveldb
+# Compute the mean image from the imagenet training lmdb
 # N.B. this is available in data/ilsvrc12
 
-./build/tools/compute_image_mean examples/imagenet/ilsvrc12_train_leveldb \
-  data/ilsvrc12/imagenet_mean.binaryproto
+EXAMPLE=examples/imagenet
+DATA=data/ilsvrc12
+TOOLS=build/tools
+
+$TOOLS/compute_image_mean $EXAMPLE/ilsvrc12_train_lmdb \
+  $DATA/imagenet_mean.binaryproto
 
 echo "Done."

From 3d30510a50007e03067a028d4585bb1c358d49dc Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Fri, 6 Mar 2015 16:49:19 -0800
Subject: [PATCH 1243/2053] Making python layer work with cmake

---
 CMakeLists.txt           | 1 +
 cmake/Dependencies.cmake | 5 +++++
 2 files changed, 6 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 54b044d347b..74fa70c9d20 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -20,6 +20,7 @@ caffe_option(BUILD_python "Build Python wrapper" ON)
 set(python_version "2" CACHE STRING "Specify which python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
+caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index b1ac96c6777..8453b539186 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -127,6 +127,11 @@ if(BUILD_python)
   endif()
   if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
     set(HAVE_PYTHON TRUE)
+    if(BUILD_python_layer)
+      add_definitions(-DWITH_PYTHON_LAYER)
+      include_directories(SYSTEM ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
+      list(APPEND Caffe_LINKER_LIBS ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+    endif()
   endif()
 endif()
 

From 256366bf7a8e9b2fd3ae1390a3f08b9468a587e1 Mon Sep 17 00:00:00 2001
From: Christos Nikolaou <christnn@auth.gr>
Date: Wed, 5 Nov 2014 18:09:47 +0200
Subject: [PATCH 1244/2053] Fix references to plural names in API documentation

Changed each "&" character that conected the class name and the "s"
character to "%", so that it is properly displayed.
---
 include/caffe/blob.hpp  | 4 ++--
 include/caffe/layer.hpp | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 36579a5a545..472cc1841f7 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -246,7 +246,7 @@ class Blob {
 
   /**
    * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
-   *        data_ of Blob other -- useful in Layer&s which simply perform a copy
+   *        data_ of Blob other -- useful in Layer%s which simply perform a copy
    *        in their Forward pass.
    *
    * This deallocates the SyncedMemory holding this Blob's data_, as
@@ -255,7 +255,7 @@ class Blob {
   void ShareData(const Blob& other);
   /**
    * @brief Set the diff_ shared_ptr to point to the SyncedMemory holding the
-   *        diff_ of Blob other -- useful in Layer&s which simply perform a copy
+   *        diff_ of Blob other -- useful in Layer%s which simply perform a copy
    *        in their Forward pass.
    *
    * This deallocates the SyncedMemory holding this Blob's diff_, as
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 34e00d72c05..2d13ef97c05 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -17,11 +17,11 @@ namespace caffe {
  * @brief An interface for the units of computation which can be composed into a
  *        Net.
  *
- * Layer&s must implement a Forward function, in which they take their input
- * (bottom) Blob&s (if any) and compute their output Blob&s (if any).
+ * Layer%s must implement a Forward function, in which they take their input
+ * (bottom) Blob%s (if any) and compute their output Blob%s (if any).
  * They may also implement a Backward function, in which they compute the error
- * gradients with respect to their input Blob&s, given the error gradients with
- * their output Blob&s.
+ * gradients with respect to their input Blob%s, given the error gradients with
+ * their output Blob%s.
  */
 template <typename Dtype>
 class Layer {

From 724af109a84a7daa9c721e2091f0e8b68fb156fd Mon Sep 17 00:00:00 2001
From: Rohit Durvasula <durvasula.rohit@gmail.com>
Date: Sat, 7 Mar 2015 11:00:40 -0500
Subject: [PATCH 1245/2053] Update readme.md

In the resume training option.. the name of the solver state changed to 'caffenet_train_iter_10000.solverstate'
---
 examples/imagenet/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index a6bdf49ca4d..b1ebfafbf46 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -91,9 +91,9 @@ Resume Training?
 
 We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
 
-    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate
 
-where in the script `caffenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `caffenet_train_iter_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------

From d952ea35a46e1fc9fa8a850ee0c3df98f982f607 Mon Sep 17 00:00:00 2001
From: Charles Otto <ottochar@gmail.com>
Date: Sat, 7 Mar 2015 18:39:10 -0500
Subject: [PATCH 1246/2053] Update leveldb include variable name to match
 FindLevelDB.cmake

---
 cmake/Dependencies.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index c5c0fd26073..f328e8246ab 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -35,7 +35,7 @@ list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
 
 # ---[ LevelDB
 find_package(LevelDB REQUIRED)
-include_directories(SYSTEM ${LEVELDB_INCLUDE})
+include_directories(SYSTEM ${LevelDB_INCLUDE})
 list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
 
 # ---[ Snappy

From 35518c09c247148c81055a59c2b91de4699e1dd7 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 7 Mar 2015 16:54:30 -0800
Subject: [PATCH 1247/2053] comment grammar in net.cpp

---
 src/caffe/net.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index e8f7c05e09d..fd00b122630 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -44,7 +44,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
   InsertSplits(filtered_param, &param);
-  // Basically, build all the layers and set up its connections.
+  // Basically, build all the layers and set up their connections.
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
@@ -65,7 +65,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
   }
   DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-  // For each layer, set up their input and output
+  // For each layer, set up its input and output
   bottom_vecs_.resize(param.layer_size());
   top_vecs_.resize(param.layer_size());
   bottom_id_vecs_.resize(param.layer_size());

From c3aee354d8831e6fc7e7633a41c5e403ef0bb6b8 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 7 Mar 2015 18:16:18 -0800
Subject: [PATCH 1248/2053] whitespace in common.hpp

---
 include/caffe/common.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 0e108f1fce8..6cf80a37bc1 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -66,7 +66,7 @@ private:\
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
 
 // See PR #1236
-namespace cv {class Mat;}
+namespace cv { class Mat; }
 
 namespace caffe {
 

From a0087e49928005d041f1694460b4a43ff30722f8 Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Wed, 19 Nov 2014 17:30:46 -0600
Subject: [PATCH 1249/2053] Load weights from multiple caffemodels.

---
 tools/caffe.cpp | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index f04e28a3674..eb9e97f5e27 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -5,6 +5,7 @@
 #include <string>
 #include <vector>
 
+#include "boost/algorithm/string.hpp"
 #include "caffe/caffe.hpp"
 
 using caffe::Blob;
@@ -76,6 +77,19 @@ int device_query() {
 }
 RegisterBrewFunction(device_query);
 
+// Load the weights from the specified caffemodel(s) into the train and
+// test nets.
+void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
+  std::vector<std::string> model_names;
+  boost::split(model_names, model_list, boost::is_any_of(",") );
+  for (int i = 0; i < model_names.size(); ++i) {
+    LOG(INFO) << "Finetuning from " << model_names[i];
+    solver->net()->CopyTrainedLayersFrom(model_names[i]);
+    for (int j = 0; j < solver->test_nets().size(); ++j) {
+      solver->test_nets()[j]->CopyTrainedLayersFrom(model_names[i]);
+    }
+  }
+}
 
 // Train / Finetune a model.
 int train() {
@@ -112,8 +126,7 @@ int train() {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
     solver->Solve(FLAGS_snapshot);
   } else if (FLAGS_weights.size()) {
-    LOG(INFO) << "Finetuning from " << FLAGS_weights;
-    solver->net()->CopyTrainedLayersFrom(FLAGS_weights);
+    CopyLayers(&*solver, FLAGS_weights);
     solver->Solve();
   } else {
     solver->Solve();

From 1cd6fcb88a0fae9e6c54645843310ac8f7edf8cb Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Wed, 19 Nov 2014 17:39:30 -0600
Subject: [PATCH 1250/2053] extract_features preserves feature shape

---
 tools/extract_features.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index f86ff96ca82..364c436dfd8 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -147,9 +147,9 @@ int feature_extraction_pipeline(int argc, char** argv) {
       int dim_features = feature_blob->count() / batch_size;
       const Dtype* feature_blob_data;
       for (int n = 0; n < batch_size; ++n) {
-        datum.set_height(dim_features);
-        datum.set_width(1);
-        datum.set_channels(1);
+        datum.set_height(feature_blob->height());
+        datum.set_width(feature_blob->width());
+        datum.set_channels(feature_blob->channels());
         datum.clear_data();
         datum.clear_float_data();
         feature_blob_data = feature_blob->cpu_data() +

From fc35930e9dd366d87094213bf8a3c546edd2fefd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Mar 2015 16:23:43 -0800
Subject: [PATCH 1251/2053] [pycaffe] no need to squeeze output after #1970

fix #2041 reported by @dgmp88
---
 python/caffe/classifier.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 94dd063a2c7..3d433ec0dd4 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -83,7 +83,7 @@ def predict(self, inputs, oversample=True):
         for ix, in_ in enumerate(input_):
             caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2,3))
+        predictions = out[self.outputs[0]]
 
         # For oversampling, average predictions across crops.
         if oversample:

From 3e4d11b580a9e5c9267ef78664772ff07cbae924 Mon Sep 17 00:00:00 2001
From: ariandyy <ariandyy@gmail.com>
Date: Tue, 4 Nov 2014 16:48:02 +0100
Subject: [PATCH 1252/2053] [pycaffe] make classify.py print input + output
 file paths

...and fix up detector quote convention.
---
 python/classify.py | 8 +++++++-
 python/detect.py   | 2 +-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/python/classify.py b/python/classify.py
index 81d06369341..da383e6f532 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -103,16 +103,21 @@ def main(argv):
             channel_swap=channel_swap)
 
     if args.gpu:
-        print('GPU mode')
+        print("GPU mode")
+    else:
+        print("CPU mode")
 
     # Load numpy array (.npy), directory glob (*.jpg), or image file.
     args.input_file = os.path.expanduser(args.input_file)
     if args.input_file.endswith('npy'):
+        print("Loading file: %s" %s args.input_file
         inputs = np.load(args.input_file)
     elif os.path.isdir(args.input_file):
+        print("Loading folder: %s" % args.input_file)
         inputs =[caffe.io.load_image(im_f)
                  for im_f in glob.glob(args.input_file + '/*.' + args.ext)]
     else:
+        print("Loading file: %s" % args.input_file)
         inputs = [caffe.io.load_image(args.input_file)]
 
     print("Classifying %d inputs." % len(inputs))
@@ -123,6 +128,7 @@ def main(argv):
     print("Done in %.2f s." % (time.time() - start))
 
     # Save
+    print("Saving results into %s" % args.output_file)
     np.save(args.output_file, predictions)
 
 
diff --git a/python/detect.py b/python/detect.py
index d395bd97abf..d2c612b362c 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -119,7 +119,7 @@ def main(argv):
 
     # Load input.
     t = time.time()
-    print('Loading input...')
+    print("Loading input...")
     if args.input_file.lower().endswith('txt'):
         with open(args.input_file) as f:
             inputs = [_.strip() for _ in f.readlines()]

From 3f68c51753cd706412cc545b2c8cf20d9981e487 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Mar 2015 18:53:29 -0800
Subject: [PATCH 1253/2053] [pycaffe] fix CPU / GPU switch in example scripts

---
 python/caffe/detector.py |  2 +-
 python/classify.py       | 14 ++++++++------
 python/detect.py         | 13 ++++++++-----
 3 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 4ea07fb7b36..fec606fd974 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -24,7 +24,7 @@ class Detector(caffe.Net):
     Detector extends Net for windowed detection by a list of crops or
     selective search proposals.
     """
-    def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
+    def __init__(self, model_file, pretrained_file, mean=None,
                  input_scale=None, raw_scale=None, channel_swap=None,
                  context_pad=None):
         """
diff --git a/python/classify.py b/python/classify.py
index da383e6f532..1e26ff48a23 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -96,17 +96,19 @@ def main(argv):
     if args.channel_swap:
         channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
-    # Make classifier.
-    classifier = caffe.Classifier(args.model_def, args.pretrained_model,
-            image_dims=image_dims, gpu=args.gpu, mean=mean,
-            input_scale=args.input_scale, raw_scale=args.raw_scale,
-            channel_swap=channel_swap)
-
     if args.gpu:
+        caffe.set_mode_gpu()
         print("GPU mode")
     else:
+        caffe.set_mode_cpu()
         print("CPU mode")
 
+    # Make classifier.
+    classifier = caffe.Classifier(args.model_def, args.pretrained_model,
+            image_dims=image_dims, mean=mean,
+            input_scale=args.input_scale, raw_scale=args.raw_scale,
+            channel_swap=channel_swap)
+
     # Load numpy array (.npy), directory glob (*.jpg), or image file.
     args.input_file = os.path.expanduser(args.input_file)
     if args.input_file.endswith('npy'):
diff --git a/python/detect.py b/python/detect.py
index d2c612b362c..691098f5c53 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -107,16 +107,19 @@ def main(argv):
     if args.channel_swap:
         channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
+    if args.gpu:
+        caffe.set_mode_gpu()
+        print("GPU mode")
+    else:
+        caffe.set_mode_cpu()
+        print("CPU mode")
+
     # Make detector.
-    detector = caffe.Detector(args.model_def, args.pretrained_model,
-            gpu=args.gpu, mean=mean,
+    detector = caffe.Detector(args.model_def, args.pretrained_model, mean=mean,
             input_scale=args.input_scale, raw_scale=args.raw_scale,
             channel_swap=channel_swap,
             context_pad=args.context_pad)
 
-    if args.gpu:
-        print('GPU mode')
-
     # Load input.
     t = time.time()
     print("Loading input...")

From 0b398925d94df4445a6cf28c30cdf2802260903a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Mar 2015 19:34:32 -0800
Subject: [PATCH 1254/2053] [pycaffe] classifier + detector only have one input

---
 python/caffe/classifier.py | 2 +-
 python/caffe/detector.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 3d433ec0dd4..49f8003ce9d 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -28,7 +28,7 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
         # configure pre-processing
         in_ = self.inputs[0]
         self.transformer = caffe.io.Transformer(
-            {in_: self.blobs[in_].data.shape for in_ in self.inputs})
+            {in_: self.blobs[in_].data.shape})
         self.transformer.set_transpose(in_, (2,0,1))
         if mean is not None:
             self.transformer.set_mean(in_, mean)
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index fec606fd974..a67b818b93f 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -40,7 +40,7 @@ def __init__(self, model_file, pretrained_file, mean=None,
         # configure pre-processing
         in_ = self.inputs[0]
         self.transformer = caffe.io.Transformer(
-            {in_: self.blobs[in_].data.shape for in_ in self.inputs})
+            {in_: self.blobs[in_].data.shape})
         self.transformer.set_transpose(in_, (2,0,1))
         if mean is not None:
             self.transformer.set_mean(in_, mean)

From 5c84795572cf08f020dab31af43e7d8478628f44 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 6 Mar 2015 19:36:29 -0800
Subject: [PATCH 1255/2053] [pycaffe] align web demo with #1728 and #1902

---
 examples/web_demo/app.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index e456526fa55..208b1da0dc9 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -10,12 +10,13 @@
 import tornado.httpserver
 import numpy as np
 import pandas as pd
-from PIL import Image as PILImage
+import Image
 import cStringIO as StringIO
 import urllib
-import caffe
 import exifutil
 
+import caffe
+
 REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
 UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
 ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
@@ -80,7 +81,7 @@ def classify_upload():
 
 def embed_image_html(image):
     """Creates an image embedded in HTML base64 format."""
-    image_pil = PILImage.fromarray((255 * image).astype('uint8'))
+    image_pil = Image.fromarray((255 * image).astype('uint8'))
     image_pil = image_pil.resize((256, 256))
     string_buf = StringIO.StringIO()
     image_pil.save(string_buf, format='png')
@@ -114,15 +115,18 @@ class ImagenetClassifier(object):
                 "File for {} is missing. Should be at: {}".format(key, val))
     default_args['image_dim'] = 256
     default_args['raw_scale'] = 255.
-    default_args['gpu_mode'] = False
 
     def __init__(self, model_def_file, pretrained_model_file, mean_file,
                  raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
         logging.info('Loading net and associated files...')
+        if gpu_mode:
+            caffe.set_mode_gpu()
+        else:
+            caffe.set_mode_cpu()
         self.net = caffe.Classifier(
             model_def_file, pretrained_model_file,
             image_dims=(image_dim, image_dim), raw_scale=raw_scale,
-            mean=np.load(mean_file), channel_swap=(2, 1, 0), gpu=gpu_mode
+            mean=np.load(mean_file).mean(1).mean(1), channel_swap=(2, 1, 0)
         )
 
         with open(class_labels_file) as f:

From 08b595d539c0e624ceb73869ee7c95376d4972e5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 7 Mar 2015 00:34:24 -0800
Subject: [PATCH 1256/2053] [example] revise filter visualization

- download CaffeNet if it isn't there
- switch to caffe.Net
- reshape net for single input
- explain param, bias indexing
- update output for N-D blobs
---
 examples/filter_visualization.ipynb | 109 ++++++++++++++++------------
 1 file changed, 64 insertions(+), 45 deletions(-)

diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 0bfdb5caf68..7125907f35e 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Filter visualization",
   "include_in_docs": true,
   "priority": 2,
-  "signature": "sha256:44536e4f82eb5748b6a3bb6fcfca01bc6c5815dad2641c994dab031f452b7606"
+  "signature": "sha256:64c88129e2eeaa956e4c8a26467ff6119f24ea3d7ef15f8217326249973bea8f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -24,7 +24,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "First, import required modules and set plotting parameters"
+      "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
      ]
     },
     {
@@ -44,7 +44,12 @@
       "\n",
       "plt.rcParams['figure.figsize'] = (10, 10)\n",
       "plt.rcParams['image.interpolation'] = 'nearest'\n",
-      "plt.rcParams['image.cmap'] = 'gray'"
+      "plt.rcParams['image.cmap'] = 'gray'\n",
+      "\n",
+      "import os\n",
+      "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+      "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+      "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
      ],
      "language": "python",
      "metadata": {},
@@ -55,7 +60,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model, load the net, specify test phase and CPU mode, and configure input preprocessing."
+      "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
      ]
     },
     {
@@ -63,12 +68,16 @@
      "collapsed": false,
      "input": [
       "caffe.set_mode_cpu()\n",
-      "net = caffe.Classifier(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-      "                       caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+      "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
+      "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+      "                caffe.TEST)\n",
+      "\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "net.transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))  # ImageNet mean\n",
-      "net.transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-      "net.transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+      "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+      "transformer.set_transpose('data', (2,0,1))\n",
+      "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
+      "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+      "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
      ],
      "language": "python",
      "metadata": {},
@@ -79,25 +88,36 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Run a classification pass"
+      "Classify the image by reshaping the net for the single input then doing the forward pass."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "scores = net.predict([caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')])"
+      "net.blobs['data'].reshape(1,3,227,227)\n",
+      "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
+      "out = net.forward()\n",
+      "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Predicted class is #281.\n"
+       ]
+      }
+     ],
      "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The layer features and their shapes (10 is the batch size, corresponding to the the ten subcrops used by Krizhevsky et al.)"
+      "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
      ]
     },
     {
@@ -114,21 +134,21 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "[('data', (10, 3, 227, 227)),\n",
-        " ('conv1', (10, 96, 55, 55)),\n",
-        " ('pool1', (10, 96, 27, 27)),\n",
-        " ('norm1', (10, 96, 27, 27)),\n",
-        " ('conv2', (10, 256, 27, 27)),\n",
-        " ('pool2', (10, 256, 13, 13)),\n",
-        " ('norm2', (10, 256, 13, 13)),\n",
-        " ('conv3', (10, 384, 13, 13)),\n",
-        " ('conv4', (10, 384, 13, 13)),\n",
-        " ('conv5', (10, 256, 13, 13)),\n",
-        " ('pool5', (10, 256, 6, 6)),\n",
-        " ('fc6', (10, 4096, 1, 1)),\n",
-        " ('fc7', (10, 4096, 1, 1)),\n",
-        " ('fc8', (10, 1000, 1, 1)),\n",
-        " ('prob', (10, 1000, 1, 1))]"
+        "[('data', (1, 3, 227, 227)),\n",
+        " ('conv1', (1, 96, 55, 55)),\n",
+        " ('pool1', (1, 96, 27, 27)),\n",
+        " ('norm1', (1, 96, 27, 27)),\n",
+        " ('conv2', (1, 256, 27, 27)),\n",
+        " ('pool2', (1, 256, 13, 13)),\n",
+        " ('norm2', (1, 256, 13, 13)),\n",
+        " ('conv3', (1, 384, 13, 13)),\n",
+        " ('conv4', (1, 384, 13, 13)),\n",
+        " ('conv5', (1, 256, 13, 13)),\n",
+        " ('pool5', (1, 256, 6, 6)),\n",
+        " ('fc6', (1, 4096)),\n",
+        " ('fc7', (1, 4096)),\n",
+        " ('fc8', (1, 1000)),\n",
+        " ('prob', (1, 1000))]"
        ]
       }
      ],
@@ -138,7 +158,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The parameters and their shapes (each of these layers also has biases which are omitted here)"
+      "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
      ]
     },
     {
@@ -160,9 +180,9 @@
         " ('conv3', (384, 256, 3, 3)),\n",
         " ('conv4', (384, 192, 3, 3)),\n",
         " ('conv5', (256, 192, 3, 3)),\n",
-        " ('fc6', (1, 1, 4096, 9216)),\n",
-        " ('fc7', (1, 1, 4096, 4096)),\n",
-        " ('fc8', (1, 1, 1000, 4096))]"
+        " ('fc6', (4096, 9216)),\n",
+        " ('fc7', (4096, 4096)),\n",
+        " ('fc8', (1000, 4096))]"
        ]
       }
      ],
@@ -180,7 +200,7 @@
      "collapsed": false,
      "input": [
       "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-      "#  and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
+      "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
       "def vis_square(data, padsize=1, padval=0):\n",
       "    data -= data.min()\n",
       "    data /= data.max()\n",
@@ -212,8 +232,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# index four is the center crop\n",
-      "plt.imshow(net.transformer.deprocess('data', net.blobs['data'].data[4]))"
+      "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
      ],
      "language": "python",
      "metadata": {},
@@ -269,7 +288,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['conv1'].data[4, :36]\n",
+      "feat = net.blobs['conv1'].data[0, :36]\n",
       "vis_square(feat, padval=1)"
      ],
      "language": "python",
@@ -327,7 +346,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['conv2'].data[4, :36]\n",
+      "feat = net.blobs['conv2'].data[0, :36]\n",
       "vis_square(feat, padval=1)"
      ],
      "language": "python",
@@ -355,7 +374,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['conv3'].data[4]\n",
+      "feat = net.blobs['conv3'].data[0]\n",
       "vis_square(feat, padval=0.5)"
      ],
      "language": "python",
@@ -383,7 +402,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['conv4'].data[4]\n",
+      "feat = net.blobs['conv4'].data[0]\n",
       "vis_square(feat, padval=0.5)"
      ],
      "language": "python",
@@ -411,7 +430,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['conv5'].data[4]\n",
+      "feat = net.blobs['conv5'].data[0]\n",
       "vis_square(feat, padval=0.5)"
      ],
      "language": "python",
@@ -439,7 +458,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['pool5'].data[4]\n",
+      "feat = net.blobs['pool5'].data[0]\n",
       "vis_square(feat, padval=1)"
      ],
      "language": "python",
@@ -469,7 +488,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['fc6'].data[4]\n",
+      "feat = net.blobs['fc6'].data[0]\n",
       "plt.subplot(2, 1, 1)\n",
       "plt.plot(feat.flat)\n",
       "plt.subplot(2, 1, 2)\n",
@@ -500,7 +519,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['fc7'].data[4]\n",
+      "feat = net.blobs['fc7'].data[0]\n",
       "plt.subplot(2, 1, 1)\n",
       "plt.plot(feat.flat)\n",
       "plt.subplot(2, 1, 2)\n",
@@ -531,7 +550,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "feat = net.blobs['prob'].data[4]\n",
+      "feat = net.blobs['prob'].data[0]\n",
       "plt.plot(feat.flat)"
      ],
      "language": "python",
@@ -576,7 +595,7 @@
       "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
       "\n",
       "# sort top k predictions from softmax output\n",
-      "top_k = net.blobs['prob'].data[4].flatten().argsort()[-1:-6:-1]\n",
+      "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
       "print labels[top_k]"
      ],
      "language": "python",

From e2656e0e2f3e2434d3d16bf14d927a6be457d102 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 7 Mar 2015 00:46:05 -0800
Subject: [PATCH 1257/2053] [example] revise net surgery + add designer filters

- N-D blob parameter dimensions
- add filtering section to net surgery example
- make Gaussian blur and Sobel edge kernels
- alter biases
- do flat assignment instead of reshape and explain memory layout
- make dir for surgery files
---
 examples/net_surgery.ipynb                    | 386 +++++++++++-------
 .../bvlc_caffenet_full_conv.prototxt          |   2 +-
 examples/net_surgery/conv.prototxt            |  26 ++
 3 files changed, 272 insertions(+), 142 deletions(-)
 rename examples/{imagenet => net_surgery}/bvlc_caffenet_full_conv.prototxt (98%)
 create mode 100644 examples/net_surgery/conv.prototxt

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 2932687da6a..75c9889fb5a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Editing model parameters",
   "include_in_docs": true,
   "priority": 5,
-  "signature": "sha256:811097f2151652d2b630c016a5f1de23bd824df3dfcfc72aa0aeb23b2d9686c0"
+  "signature": "sha256:f21c804f76329e70847ccb87e28a91e5d8a375f5da0ba6dd85d3b87a05bebd72"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,15 +15,221 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Net Surgery for a Fully-Convolutional Model\n",
+      "# Net Surgery\n",
       "\n",
-      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully-convolutional model for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional neural network (CNN) structure by dynamic programming in the forward pass from shallow to deep layers.\n",
-      "\n",
-      "To do so we translate the inner product classifier layers of CaffeNet into convolutional layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding.\n",
+      "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
       "\n",
       "Roll up your sleeves for net surgery with pycaffe!"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
+      "import Image\n",
+      "\n",
+      "# Make sure that caffe is on the python path:\n",
+      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
+      "import caffe\n",
+      "\n",
+      "# configure plotting\n",
+      "plt.rcParams['figure.figsize'] = (10, 10)\n",
+      "plt.rcParams['image.interpolation'] = 'nearest'\n",
+      "plt.rcParams['image.cmap'] = 'gray'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Designer Filters\n",
+      "\n",
+      "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Load the net, list its data and params, and filter an example image.\n",
+      "caffe.set_mode_cpu()\n",
+      "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
+      "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
+      "\n",
+      "# load image and prepare as a single input batch for Caffe\n",
+      "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+      "plt.title(\"original image\")\n",
+      "plt.imshow(im)\n",
+      "plt.axis('off')\n",
+      "\n",
+      "im_input = im[np.newaxis, np.newaxis, :, :]\n",
+      "net.blobs['data'].reshape(*im_input.shape)\n",
+      "net.blobs['data'].data[...] = im_input"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "blobs ['data', 'conv']\n",
+        "params ['conv']\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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wOD+7rZYez/FQzmvBLddIBpl0ZLwxxp9nDRd1dKIXWRtjJ2NlZaU4fDbAGQCw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lUint2qvVaolWPu/c52sIw9DlCoYViATzjiKAfhyYLJ0bNr1eLxnQrVYr0ebu3buSzsOR8CRz\nwa5FQm0YI5JSeLDZbGp1dVWTySQZks+fP09ozdLSUmaXJP0ndFQsFtN7OKjcUVU3+KEbx+vQ+J6v\nF6eZhzucD0FffY1G5YJ8hZaO8tEYkxuSyDQUsstaDsYmf4n7yG8rFovpmCTeF3PzYjqAh4MceXDa\nRQfSDXJfi1xzneMKHsV8fHycZFBUyMhUDA6eQ4t6BpmLfIm5t64/HAX2e/29jirSD67H6I+HF/ke\n+ol3gy5JyuUlR/8JU8aUFEekGEdEEpHREcn1eYifMca8cGGv10sGvfclomx57YWF9rCSo+WJcnJm\n8Obb4Pnfn4FhIE0RF9plMVhX6DQEtYd9+NzDSW6ouSCKW1URbCT+ej4WC8WtaDfm/DgHXxTdbjfl\nungOkDRNrEeoRIPQtxnHxc3CdG/V0So8fZ4X0SpHnXyRuoHhuQaOvsXxQxc3WmKIyo0hD236nHiM\nO0LrvuBc8DD3LgAcvYoOgfOeP9/7g+Dz7zlqyHV/hzsVjspEDx0Uge85UgGfwDd4uvSZBGeUAAab\noxI4AKAqfg4dob6dnR3Nzc1pZWUlw3uHh4cJNvcz3ECpNjY2knDDURgOh6pWq8kYALGTsoVR3YBl\nXZGXw2eOFg+Hw7QOMZKgL+u+3++rUqlkio4SEqvVarp165YODw/TOm+1WlpeXk5lEU5OThJaRaHU\no6Mj7e7uZsofNBqNZEyAhMEbjka3Wq3kcNBfR04xJJkLHDme6xW64TNHQKQpkgGPuzHo/Op1mJzf\nxuNxJgEbRBMDx2tNxdxGShsUi9M6adFZcd7n/cgML+9BhfW8/CRPXHfnimc6XVxfFIvFdO4hSfDR\nGeczlxHoAhBCn1+/n7G6I45T4mvR54nP3fhgfqJMdFnvecbIDebMk61xRugrtPPQGPNKyRdfZ5Gu\nPjZpGn5nc4obTvydl5cEnTxE6A5VnFOXl5737IYiNkW/309OixvHEaSI7ar8wVW7alftql21q3bV\nrtr/sr0QRAqL0eGyGOby+Lt0bvV60bKIZrgF7l6WpnlYRwAAIABJREFUw5ERLnWPztExLGiSa90b\n43sxd4O+eJzcty7H+7zPHp6JSB1eF/TwEBxhnHq9fgEB8rHiDbg35JZ+tLTxKkhU9O/i7UAbpy/0\nxGuLCYlA9XhDsS/s7oIm0AjvM4ZuQSfdW/KxxDE73O5zH3O+3BuLKBPfiV6W52F5zpmP0Z8TE9VB\nQvCu47lwjMPzgCibgJcYn0li6Onp6YWigXjwEQEkNEWBUA8lg4IQUjs8PExzvLu7m1CuVquVQW7r\n9bru3buXSbZmXP1+X9vb27pz546Gw6FarVZmLZMDc/36dVWr1QthBzzshYUFDQaDlM/FPHh+hoci\n8DzhJxA5r/rN2YEehvMkX6/83Ww2U8ju7OwsHVgsKSFXoHeFwrS4nyMglCpxXiQVgP44gjYanVf7\ndj6E1+bm5lSv11WpVFJ4zJvzaVxrjm47euu8GkP30JhwuydcU6iU8caQN9/znarMEzssvSyDNK0I\nT+5YPG7MkXN4BNqAUHvaAeNnPkAgXA6Dxnq0ItLUEXDG5+vLS7j4PPO/o+0gc6Br/j4PsXp+sfON\nI+YxGsNYR6NRWjOe1H9ZXpCvJ66NRudlSCgc7HPqiBCywyNGbExgjI4Qud6JuXrQir8jwu95ZP7+\nKIdjdIGxlEqljAxmfV7WXlhoD4HkQhMmdKOD5nF+h0AhMELXBYpDiRFOdQZ3uFPKLghnFr+P5/J9\n7xv9Q5FL2WTz2D/uY3HH2DC5WrzLc5TYEeK7UqTp4sNgcLiT5wDh+tjjeKCrM7JvKfYF7PlMHhbk\nmv9G2NJXDARqYUV4mAUQIVYWALT1vvh8xnExZu5zvvAcCJ8jz3vK29gQDS7PJ0PxewjXhdtoNEo7\nvuI5dQhe4HMvY+BHo2BUeV8IYdF/KXuUj9cHkqb5WhhNvkaHw2HKgeF8PM6hW1lZ0f7+vsrlstbX\n1/XFF1+kkM6NGze0sLCgarWacSQY93A41IMHD5KgJSTEmX67u7va29tTo9HI5F0xTgwFD4EeHR1p\nPB6nhHXPyUMpQ08EpzQ9p63b7aat+t7Xs7Mz1ev1FKaCh4fDoRYXF1Wr1VIY/fPPP5ekZIhxkLcr\ndniNWk+EO+AZjAHyUJzffKOJ8ypOAJXgnQ+LxWIKGeXlfDj/ezmVmGODonGe93URc+tmZmbUbDZT\nyMplKPlHfIbRPxgMLhxjkrfGfG4ZPzIc2eKyIO6o9jQMHDNoFp1rfij1IU03PtAXf4enUuSFhtxR\njGkuHK+CU+Iyx8N4HlL0OWRMl+kcxsF9HuKLYTr4BjkWUxh6vV7iYz+Wx0uEcF+sHelz4Dt9XUe7\nUSspY+xFeRnH6LwL7aKcdJuCcCONOY285+2FIVIoWzdUnACxhpNvHwWJ4FqMgeYRJ8aRaUySE4oE\nPBZUzK+JBoJ7AihLR1G8nxhMbix4P5lkzyFA4dP36A1yOrqfxcXY+C4Lwxk3eofeQHmgqfeP/0GQ\n8oxU/z7XnDbRw6C/3OPCDhpwj+/U8DnyuXHPKjaMU4SbC2Ke44nJvpvJ+x8NYUd3oJ0vWBcwzjd4\n024Euwfr3hJ955ko99PT0xTbp98Yu9Hz9fGT5OuJ75zhJp2jCRQHxVA5OjpSs9nUnTt3UkFK6mCd\nnJxocXExIUnSuWIkgZvSEYyh0WhoOBxqZ2dH7XY7c5YdXjle/P7+vvb29iQp1bmamZlJRo/zEMaT\nF3d0/rp27VpS3l5jqt1uq9lsql6vJ/TBd2CBrrTbbTUajTR+0DAMv4ODgwwiA1oHmkd/oTXor+fm\nkP8yHo/VarUyCsNRY+QT95XL5VTmIDYcFhR3dLBYe6PR6ELNL3f0+D8mdjvq7oU+eaaXZYBuo9Eo\nc2ZgrC3F+nalTPK25zG6sQTtPe+Q53Edg9aNHnd8HBXBIHGHDkfBd/T5sUQ0N8xcJnk/oyPohm5E\n0FkPzEVE1uDXvIRq3umomOuEaHi5nnVD17/HmNgtGo3a+H7+j7miMUrgfB1lu+v1GOVhTIwz6ieu\nxYiJ63mQWX9HXo417YWVP2CSvXPA79HC9glD8XtyGYwNokFjEUVPTsomW8ekZfrg3pMjYlJWEcZJ\ndOQiImQIufg+/s9DSHzscSFijOL1OUyNgvV73HBFAMEkjtxgBDrCxjU+82J89NWFex6NXajSFzcM\nYpK+G5RR+DN2Ry4dUnceiIKNcUNDpzPj8ARgf7Y/x+nqhil9cx5xj9ZDdAhNVz4YLzMzM6lQY/TK\nxuOxBoOBzs7O6zB51ft+v5+ZTzfOR6ORWq2WisWims1mZkcbCgpjxsOCa2trWl9f1+LiopaXl3V2\ndpYqTVerVS0sLCRE5tq1a3rppZckTcN+MzMz2tvb0/HxsZaWltL4OCC53+/ryZMnevjwYaL322+/\nnalBFR2ier2e+LTb7aYQEn0hxOWhn1qtlgxEjF6eS82sYrGYUUiSdOvWrVSramVlRfV6PaNoQIY2\nNjYyPAdqzLMGg0EqjcC4QK1AIZxnkBuTySSF9nBwfF1ggGBEM1aMNmgaEWeXCePxOJVrcLq4IwTv\nerjJEVUKh8LLlG/o9/sJIfME/pOTE1WrVQ2Hw/R+nokxByLjicq0GNpiXB6Kjjt2oaevwxhNQK5y\nDQSOdcizjo6OkqEML+bJfcLv0bGJ6zo2n2saKRJuNESEjM/9+S6XcK5dvrkD6062n5JAnz0dh2cR\nMne56O/IQ4yYQ9cJjqhFMMTnijl0fenrB+NWypao4DseVYImDoDQN/9uXnth5Q+kiyEkfrvnz/dg\nzsiEKJr4PClb5ZrnRw+M+9w74F6ER4TGvS/+Tmd4R2VoPvlu4fv1+Ey8D645EuLGGl6XMx39xkCN\nwobvIDB9YTP+iBa5IuO6LwYY2+FhaOPeZRQehFtAbdyw8bF680UQjWEXir5wfOzej4hG8nekNzQG\nxXKUw9FN9+SlaQ5JNGjpC1vg5+bmVK1W03VQExSDw+2gO9xP8U3o7R4dh+UyDmgzGAwyVbfdY+eI\nEp+LmZkZ3bp1K5VpYO11Oh198cUXWl9fV7Va1bNnz1JtppWVlWRcg5RwX7FY1Guvvaaf/exnSQCT\nk/Xo0SNJ0le/+tWUS+F5IxzKu7e3l8lroq/Ly8t6/vy5ZmZmtLq6mikb4YUu3YuGP8mD8aNuWIvM\n77NnzzJClu+vrq6m3XnM4Xg8Tjk+boCheEHYyFOBZ4rFYqZ6uedtTCbnRy4RSozoZ61Wu6BIL3Me\nnYd5nh8P5OEOz1V0Be1Oj4eicEbcwfS++hz4WsWYg+cdOfZ1hMPosg9ax13XvBtaRjnkSLKjfMgm\n6Bl5kdwa38rP2PNQZPriO089SoGR6norprf4uJze/O8Ii/fHHXGXYYyPH78XOnNPNHqi3Isy03WQ\nP5PfLjv9Gs+KRpQ/33PnHL3nur/P6ev2RHy+j8H122XthSFS0chwQyYaGRGS88/cI3MDgGsolMss\nTUfG/D4IDTP6pPN5XiKyNI0x+4T6IkHQRBr4oo+QL/f5Ncab56VhkLl1DsLm1z1fI2+B8S6Hvwk3\nYThFww6kxJNcHbFBUXHNi/X5Nl+nFfPl0LAbQTHp0FG9aNj4QomwMYYgfYn0duPMBa17xXmIYl6h\nOeh9dnaWcnNAZ3guBgtz5/yNouj3+9rb28uc1VUul5NRsbi4qPX19YRm+JEwEdIej8fpSJlbt25l\nwsGdTkeTySTVpapUKplK23Nzczo4OEjHzGAIraysaDgcamlpSW+88Ybu3r2rnZ0dSefe/PLysh4+\nfKhKpaJarZb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OPk8YQq7YY0jd++IoDzLQHWE3XjC24BXQQ2rJeVjbK5s7qkdfHWFx9Gw2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\nJ30z36Xk8TmIYK3hpNqRMuWiTHmxFng2mzV4XrPZjHfv3iWV4uLiI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eYRUa6eoFaRyd8m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\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CizFwuhBPlujJi4SBtEIz5MjGQeAsrL1erxRH3CeEjNFRgo2yo7WIeg2WvLkRCMZ5f38f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\nl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j4jBfpSej2/+81P/g8v8pauFrLQIAAm7bnizYtvZ8ZRe8QuakOoysi7JJv6+Y7WbUB4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\no5hg26exMZZ+TpELSPFu2uPx8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+       "text": [
+        "<matplotlib.figure.Figure at 0x112797f50>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# helper show filter outputs\n",
+      "def show_filters(net):\n",
+      "    net.forward()\n",
+      "    plt.figure()\n",
+      "    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
+      "    for i in range(3):\n",
+      "        plt.subplot(1,4,i+2)\n",
+      "        plt.title(\"filter #{} output\".format(i))\n",
+      "        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
+      "        plt.tight_layout()\n",
+      "        plt.axis('off')\n",
+      "\n",
+      "# filter the image with initial \n",
+      "show_filters(net)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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4URgkf3g6BJoVFgCwUMY2oJwMJk7RC2ERFMyLr/xYR7A+Ed9++2392I/9WFJo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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x113e04090>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Raising the bias of a filter will correspondingly raise its output:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# pick first filter output\n",
+      "conv0 = net.blobs['conv'].data[0, 0]\n",
+      "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
+      "# set first filter bias to 10\n",
+      "net.params['conv'][1].data[0] = 1.\n",
+      "net.forward()\n",
+      "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "pre-surgery output mean -12.93\n",
+        "post-surgery output mean -11.93\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
+      "\n",
+      "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ksize = net.params['conv'][0].data.shape[2:]\n",
+      "# make Gaussian blur\n",
+      "sigma = 1.\n",
+      "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
+      "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
+      "gaussian = (g / g.sum()).astype(np.float32)\n",
+      "net.params['conv'][0].data[0] = gaussian\n",
+      "# make Sobel operator for edge detection\n",
+      "net.params['conv'][0].data[1:] = 0.\n",
+      "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
+      "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\n",
+      "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\n",
+      "show_filters(net)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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6rjiupf4Cj5aQOy8AIySOjNHfLJs/HKpUOi3GzfPTY+lRaO6ds8MGg0PE5N69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\nXi/AHOsJhfHw8BCsD5/n83m1Wi1Jq+A8gMTh4WFcy8fRgwJhcxgzZ46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lxGYlBsKBKwod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\nIiIi4pXfPRZNtgyLF3IAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x112bc2190>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Casting a Classifier into a Fully Convolutional Network\n",
+      "\n",
+      "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
+      "\n",
+      "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -37,101 +243,17 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1,2c1\r\n",
-        "< # This file is for the net_surgery.ipynb example notebook.\r\n",
-        "< name: \"CaffeNetConv\"\r\n",
-        "---\r\n",
-        "> name: \"CaffeNet\"\r\n",
-        "4c3\r\n",
-        "< input_dim: 1\r\n",
-        "---\r\n",
-        "> input_dim: 10\r\n",
-        "6,7c5,6\r\n",
-        "< input_dim: 451\r\n",
-        "< input_dim: 451\r\n",
-        "---\r\n",
-        "> input_dim: 227\r\n",
-        "> input_dim: 227\r\n",
-        "152,153c151,152\r\n",
-        "<   name: \"fc6-conv\"\r\n",
-        "<   type: CONVOLUTION\r\n",
-        "---\r\n",
-        ">   name: \"fc6\"\r\n",
-        ">   type: INNER_PRODUCT\r\n",
-        "155,156c154,155\r\n",
-        "<   top: \"fc6-conv\"\r\n",
-        "<   convolution_param {\r\n",
-        "---\r\n",
-        ">   top: \"fc6\"\r\n",
-        ">   inner_product_param {\r\n",
-        "158d156\r\n",
-        "<     kernel_size: 6\r\n",
-        "164,165c162,163\r\n",
-        "<   bottom: \"fc6-conv\"\r\n",
-        "<   top: \"fc6-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc6\"\r\n",
-        ">   top: \"fc6\"\r\n",
-        "170,171c168,169\r\n",
-        "<   bottom: \"fc6-conv\"\r\n",
-        "<   top: \"fc6-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc6\"\r\n",
-        ">   top: \"fc6\"\r\n",
-        "177,181c175,179\r\n",
-        "<   name: \"fc7-conv\"\r\n",
-        "<   type: CONVOLUTION\r\n",
-        "<   bottom: \"fc6-conv\"\r\n",
-        "<   top: \"fc7-conv\"\r\n",
-        "<   convolution_param {\r\n",
-        "---\r\n",
-        ">   name: \"fc7\"\r\n",
-        ">   type: INNER_PRODUCT\r\n",
-        ">   bottom: \"fc6\"\r\n",
-        ">   top: \"fc7\"\r\n",
-        ">   inner_product_param {\r\n",
-        "183d180\r\n",
-        "<     kernel_size: 1\r\n",
-        "189,190c186,187\r\n",
-        "<   bottom: \"fc7-conv\"\r\n",
-        "<   top: \"fc7-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc7\"\r\n",
-        ">   top: \"fc7\"\r\n",
-        "195,196c192,193\r\n",
-        "<   bottom: \"fc7-conv\"\r\n",
-        "<   top: \"fc7-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc7\"\r\n",
-        ">   top: \"fc7\"\r\n",
-        "202,206c199,203\r\n",
-        "<   name: \"fc8-conv\"\r\n",
-        "<   type: CONVOLUTION\r\n",
-        "<   bottom: \"fc7-conv\"\r\n",
-        "<   top: \"fc8-conv\"\r\n",
-        "<   convolution_param {\r\n",
-        "---\r\n",
-        ">   name: \"fc8\"\r\n",
-        ">   type: INNER_PRODUCT\r\n",
-        ">   bottom: \"fc7\"\r\n",
-        ">   top: \"fc8\"\r\n",
-        ">   inner_product_param {\r\n",
-        "208d204\r\n",
-        "<     kernel_size: 1\r\n",
-        "214c210\r\n",
-        "<   bottom: \"fc8-conv\"\r\n",
-        "---\r\n",
-        ">   bottom: \"fc8\"\r\n"
+        "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The only differences needed in the architecture are to change the fully-connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+      "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
      ]
     },
     {
@@ -145,7 +267,7 @@
       "\n",
       "import caffe\n",
       "\n",
-      "# Load the original network and extract the fully-connected layers' parameters.\n",
+      "# Load the original network and extract the fully connected layers' parameters.\n",
       "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
       "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
       "                caffe.TEST)\n",
@@ -163,27 +285,27 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "fc6 weights are (1, 1, 4096, 9216) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc7 weights are (1, 1, 4096, 4096) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc8 weights are (1, 1, 1000, 4096) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
+        "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
+        "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
+        "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Consider the shapes of the inner product parameters. For weights and biases the zeroth and first dimensions are both 1. The second and third weight dimensions are the output and input sizes while the last bias dimension is the output size."
+      "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Load the fully-convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', \n",
+      "# Load the fully convolutional network to transplant the parameters.\n",
+      "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
       "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
       "                          caffe.TEST)\n",
       "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
@@ -200,21 +322,23 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
-        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
+        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
+        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
+        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we need to roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices.\n",
+      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
+      "\n",
+      "The biases are identical to those of the inner product.\n",
       "\n",
-      "The biases are identical to those of the inner product -- let's transplant these first since no reshaping is needed."
+      "Let's transplant!"
      ]
     },
     {
@@ -222,33 +346,13 @@
      "collapsed": false,
      "input": [
       "for pr, pr_conv in zip(params, params_full_conv):\n",
+      "    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\n",
       "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The output channels have the leading dimension of both the inner product and convolution weights, so the parameters are translated by reshaping the flat input dimensional parameter vector from the inner product into the channel $\\times$ height $\\times$ width filter shape."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for pr, pr_conv in zip(params, params_full_conv):\n",
-      "    out, in_, h, w = conv_params[pr_conv][0].shape\n",
-      "    W = fc_params[pr][0].reshape((out, in_, h, w))\n",
-      "    conv_params[pr_conv][0][...] = W"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -261,18 +365,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "net_full_conv.save('imagenet/bvlc_caffenet_full_conv.caffemodel')"
+      "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "To conclude, let's make a classification map from the example cat image and visualize the confidence as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+      "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
      ]
     },
     {
@@ -297,7 +401,7 @@
       "plt.subplot(1, 2, 1)\n",
       "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
       "plt.subplot(1, 2, 2)\n",
-      "plt.imshow(out['prob'][0].max(axis=0))"
+      "plt.imshow(out['prob'][0,281])"
      ],
      "language": "python",
      "metadata": {},
@@ -307,33 +411,33 @@
        "stream": "stdout",
        "text": [
         "[[282 282 281 281 281 281 277 282]\n",
-        " [281 283 281 281 281 281 281 282]\n",
-        " [283 283 283 283 283 283 281 282]\n",
+        " [281 283 283 281 281 281 281 282]\n",
+        " [283 283 283 283 283 283 287 282]\n",
         " [283 283 283 281 283 283 283 259]\n",
         " [283 283 283 283 283 283 283 259]\n",
         " [283 283 283 283 283 283 259 259]\n",
         " [283 283 283 283 259 259 259 277]\n",
-        " [335 335 283 283 263 263 263 277]]\n"
+        " [335 335 283 259 263 263 263 277]]\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 11,
        "text": [
-        "<matplotlib.image.AxesImage at 0x7f283815ce50>"
+        "<matplotlib.image.AxesImage at 0x12379a690>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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P/fif5nv/qe/hg5//WQaLoKBJUC/M5oqGjBNHPybMpDJyq/KCK9VpXB1/gAkinmQjZXKY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0SuRppFZSBX+fZeq7gDcq1kdNEVCCp37RkBXEYwj0ZjdY7Gk475QiF5ewd/yux031FlX/md2FGy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\n6EbA8Fl/yEt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m4NvfEI5zItkeCZ5geiiRuN9wtdtQUsGXQtKCLRZNlegjxRowrkn05kqYWYy2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\nA43ph1xlbAoPAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7f285ddfca50>"
+        "<matplotlib.figure.Figure at 0x12666be50>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -341,7 +445,7 @@
      "source": [
       "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
       "\n",
-      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+      "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
       "\n",
       "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
      ]
diff --git a/examples/imagenet/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
similarity index 98%
rename from examples/imagenet/bvlc_caffenet_full_conv.prototxt
rename to examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 7b22bfa1404..3c951970fc1 100644
--- a/examples/imagenet/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,4 +1,4 @@
-# This file is for the net_surgery.ipynb example notebook.
+# Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
 input: "data"
 input_dim: 1
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
new file mode 100644
index 00000000000..9444c63ab74
--- /dev/null
+++ b/examples/net_surgery/conv.prototxt
@@ -0,0 +1,26 @@
+# Simple single-layer network to showcase editing model parameters.
+name: "convolution"
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 100
+input_dim: 100
+layer {
+  name: "conv"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv"
+  convolution_param {
+    num_output: 3
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}

From 124f6ef1a518ff8701302b368b827ca0912a29ca Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 7 Mar 2015 01:35:17 -0800
Subject: [PATCH 1258/2053] [example] revise hdf5 classification

- add a little explanation
- solve from Python and the command line
- time scikit-learn and caffe
- fix test iterations (number of instances / batch size)
---
 examples/hdf5_classification.ipynb            | 1258 +++++++++--------
 examples/hdf5_classification/solver.prototxt  |    6 +-
 examples/hdf5_classification/solver2.prototxt |    6 +-
 .../hdf5_classification/train_val.prototxt    |    4 +-
 .../hdf5_classification/train_val2.prototxt   |    4 +-
 5 files changed, 702 insertions(+), 576 deletions(-)

diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
index 51d854fa142..b90b79d962c 100644
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
@@ -4,7 +4,7 @@
   "example_name": "Off-the-shelf SGD for classification",
   "include_in_docs": true,
   "priority": 4,
-  "signature": "sha256:c3b84add3bb83e91137f396a48f46d46bf7921b242fc42c58390b30806e5a028"
+  "signature": "sha256:741422697d76b1667287180dc7c6360cf105ee774b1e2def800dc8fe80f78f67"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,32 +15,47 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "# Classification with HDF5 data\n",
+      "# Caffeinated Logistic Regression of HDF5 Data\n",
       "\n",
-      "In this example we'll use Caffe to do simple logistic regression on a simple binary dataset, showcasing HDF5DataLayer functionality."
+      "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "%matplotlib inline\n",
+      "\n",
+      "# Make sure that caffe is on the python path:\n",
+      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
+      "import caffe\n",
+      "\n",
       "import os\n",
       "import h5py\n",
       "import shutil\n",
-      "import sklearn\n",
       "import tempfile\n",
-      "import numpy as np\n",
-      "import pandas as pd\n",
+      "\n",
+      "import sklearn\n",
       "import sklearn.datasets\n",
-      "import sklearn.linear_model\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline"
+      "import sklearn.linear_model"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 1
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -51,17 +66,8 @@
       ")\n",
       "\n",
       "# Split into train and test\n",
-      "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
+      "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+      "\n",
       "# Visualize sample of the data\n",
       "ind = np.random.permutation(X.shape[0])[:1000]\n",
       "df = pd.DataFrame(X[ind])\n",
@@ -73,13 +79,20 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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sdhObNgUaQYyyAZsGC8ePo9br2INBVFluVOV1OlE7zKhqY2rN4TBht5v44Q+v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h3spVisI7TqfOfUBEqVAml9gTd/dBOdRqSSzbORL+B269H1jjG8I0Cj0SC0EiG8nqBeM6E0ZxH1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6hUakweD5Ik01Xr6RTyOA+Noq7lUKlUiE4n9XQa6Z6Gqtto3AuMfPDHy782b4gfpDSGw1Zy\nuQZWq55w2IrJ9MFnlYXCD0iGzNzcvvteKGRBEDR0uzKFQpPHHovx6qvrRCI2EokK1WqNzc0yrVaP\nU6fCvPjiGuVyi/FxD9NHPHz32xl8g0Mo9TITMQONpVvM/cPX6RSzGENhuu4Qid0GsckYrXqLRqNL\nqqegNtvItETIN3DKbQo18AzHyKeyVKptksvr9I1Ms7na4O7dLQ6OmKnevcpip4rHZwXRjOXACY5M\n9zHz9jyPX4zh8lq4+85dDg65WEgWqe3sIFhs+P1WOl2JeqmC0awjPZvm8mt32V7YwOq2E47FUNcL\nuPUN9J0S66/dIfz0cQ5MuunwKJWGwtCoDaW0hl/xMzzsplKIkUiUUAsdHGY1sZiDVHyPbElGtDdQ\nNpZodLMkk1XUKhWF7V2Gnnicel1Lvd6l0ej+xpKRRqPBK6+8wl//9V8/6FI+FH6gG/m4kxFFUQiF\nrCwu5n4sIM3lMv5czYii7B/fNptdwmErs7NpdncrWK16zpzpo1Jp74sxf0SQmcs1kCSZS5e2ePPN\nOBajivmbcfrCZoJuHeXNOAcP+dndrRMIWjGZ9KiR6QuZqNXadBstWqkdxmIeuooGldTFpG5SLGWg\nuEdyu4reE8ASiODoC5Jq59i8tIZgEDlyIkI1l2N1Totb7NEzWKimM7RaEn0DNlK5Nr7xEZKKlTNP\nHeH4pAX17gLWiXFWltJc+vPnOfsHz3LsyCCVQhmrTc/2bAqLw0xswIla6ZHYqbB14yrbd5eJ33Zz\n/HNPMDEV4PnvrXHiiAdtapHFlQyqXheXWseppx4h0zbj85lZWckjilpUqh5OX4CVO4s47Absjv11\n0JnN6G22D1yLRjZLPZP5sd+1SyWqe3sf6ntgMuk4dy5KIlHmzp00kYgNt9uEXi/c65TsG1dubpbI\nZBqIokC3KxGLOVhd7SDLAn/zNzcJhSy4XSJbaoX1jRKlYpNStkyu2GZi0s/5C1F6PYWdnSprawUW\nF3O4XEbUKNgtAs/+VoiI3CY5+zqlpoy9HCEyNsmRM0PUciWixyZ5/+1VHK0q/f4tVpZm8DoEOrkM\n3slJDEcPk711E3pd9AYBs8dFbS+Fye0hpJeYPn+AuZfeYua9BRRUaLo1crO3EUwmurUaxWNuaiuz\n9B15lJde3aRWbVFpyPhjYf7VvxiD9DZDQ34Sa2naXXAEfdj7+3GpbTjsRQxTUxSMxn1L+E4HjV5/\nv0v1YYhIZWeHwsYGUruNvb8fWzT6keuEHvQb4n8FjgG3gK/8vP+s0wkMDDgYGPjZGpFeT+bq1V0K\nhSZWq55g0MLlyzvo9QJ+vxlBUDMw4CCZrLG1VUatVuF2i6TTNer1DtlsA0HYTz1EUTgYbKLObdPa\nmKdSczB+ehJR3iOTyeKwaKjlChiGD9Gy9XN1tsypc0c44vawPbeO4pIQQ1HuLGSp1ns8Mm3B5LBw\n4/UN+gbD9I2YaauMrKyXSKV6qBQFQ2Wb2ZkF/H0elubTOJ0GhjGwV5ji6ENj+HwizUqN5PI6NuMA\nff0RRg5GSO2V0erA77dy8kSQRq1NudjYN8bxOskli5gsIv02HQaTSK9SopfdYf3tLpMXTzE9IaJ1\n+sjOzSPbnCyvFNDZ7Fx4fJyBqI1uvUHIL6KoBWqc5PZilaMmiepyioYgI0kK9VYXjUZNdTuO2T+J\nKAqYTL+5xzQvvfQS09PTuD8m+Q4/D+fPn+e5557jK1/5ubfbPzsURaEYj5NfXkZqt3GOjnJi2sfS\nSolms4vDIXL0qP+nbkB+8DdmZ9P3u6FvvBFHkpT7mgKXS8Rs1tHr/Xh3xeczkc83WFrKosgyNpOO\n4w+PkM3UGDgYorqxgkFdZOxsgPReif/hf7rInZsJkmu7nLg4hbrToCqXScyvU0wXMZhFVH80SSWd\nRW41QS0gtdvkl5YZ6TZp5POopA6Rfh+ZZBnUGuxOK35LHcvkJBNHByjH16llsoyGR6hr7XhUIjoh\nSCe/R9MUJdOEVreK2QCtnQ0C4TA1owOL8wi59U0sZgGXTSBXg36Hg603rtNDS7PVpVqXuXF1B0WW\nMbdTXHn+bSx2kQsXYiQ2UmRv38R79iKCoLqvz+l0JFo2N+HDB+i1c6BSobdY9p2qLZYPXA+51/uh\ndfmPQPqR8L2fB6tVz4ED+yO+tdoPPydJCkNDDur1LqVSG7NZh0aj4s6dNDqdwJNPDnH9+h4njgfJ\nbu7itxuYuRFHQYXHaSbg8WCziawtpjjzUJTvvLCOz2fmrbc2sZj16HVqOp0uY/168nN3ee1/+yqN\nQpnoWIiiRU0pX+HY1ClEa5AX/mkOfzTA9EE766++wPbtBU48eQKbTkN2aZHxz38BnUamsrmG1mim\nUSrTUwvUu2qKlQ6vvrXIiTOn0Bl0vPbyCp6pEOW7N6DVwep20ErtkN9MYIsUsdoMSIIR74AJm9dB\noa7CZzRibZYYnQqzvFFBbXVhNms5NT1M9vKbqDQazH4/vXabwLFj9JpNStvbaE0mDDYblmDwp17/\nciLB5ptv3hd+l7e28JXLhI4f/9Br+GHwIMnIUcAEnAP+d2Aa+NAxp5Ik02z2EEXhJxw+y+UWvZ5M\nudwmn28yOurCYBBYWysiivvufuvrBfR6gXK5TTJZpb/ffi/7BqamfDgcBiIRG0eGBFa/+Z+QO12G\n+2PcvTLHY48N0JhbxCyXEdVm3M9+kee/PU+9XcA+doD/8z9e47c/f5B6wEyxmue1F1Zp11v4Qk7y\nZRGdpMJsM1Ir12m1JR56dhrDyBjdy7s4TbB36xoen41Mukan00Nn0LKzvInNEmH2WoGRYSf+sAOd\nWuLlb17mk39gIxC2o1X1cLit2A0SB4eNXHn+XXRyC/9QGIPVjNntRtAohE4ewhoM8MZ/+BpWiwFR\nDKEStIRPn2Lv6lWaBhfvvBanVqmjSDJaucmFTx5h/ep7bC1p8Z17DHQi58+78Fo6rKdrdJotRkZc\npJLV/fXpdDGbdUxNeT/2zn//X/CP//iPPPvssw+6jA+Nc+fO8Sd/8icfm9bsj6K0ucnWpUvI3f0o\n9Ho2i+/gQX7rt47RavXuvWx+tptvNlvnzp00nY6ExaIjna7RaHSZnPQhigLttsT0tJt4vES9vu+i\n6nYbGR1102zuv9RMRg2zs0lWlvOgFshmGjx63EJETFO98yp62czomZNEAkN0FS2BiAu3x0Rh0Ekw\nusnsu3eZODqAIIpIZi/miJpSIoFSL+MeGKZbrXD0aIi1zSq7u1UK2RLTZ4dpN1os7+Yxzy5Ta8pU\n2hqsniADFjvajsTNd+6wfmcdbbOARpGwRGMEw05UBiNyt8f8O7ewhYKc/Mwhon1mNm/exREK4jV4\nefubbyHodfTaMirRgs5kYH0xia/fR2VrlXq5itLrIHU6RGNuenQIezRUZYFyuYXdvt8FSWU7hIYP\nMzm8r5vTW60YfkpXBEB0OtFZ9k0RfwCNXv+hNSM/ir4+G35/kVTqng26oGZkxM2tW8l71ucCjUYP\nnU5Do9aiW6lg76Xpi/Qo2A3UJAmjUYfFbkRQJPaSFWbeX2X1jgmt1ODIVJh6W2FpKc/6Wh6NGi6e\nD2NoZWhWkhjoIOlU9Nod9jbS1NopHjl2DK3JSa9SoJguYpzSU8/msDisVOsSys46nWqFwLETOCcO\nEzqZIDlzG/vQICaNEWtflJ1UB4vdzLeeu8PTnxpDQOL21XXcdi8Bu8zAoI3a0jt4Dx3l2nyZF747\nD4IeYzDC2UdM6IwiNleU7NVZxqI+Dj08wV5ZoFbr0FCZ6H/4YfLLy/TabSLnz9MulUjPzqIzmahs\nb9PM54k9/jjmn2Jol19evk9EYF8nVFhbwzUy8jPX/hfFg3xLnAReuffza8BpPiQZSSar3LmTplRq\nYbHomJryEYn88KKk03Vefz1OLtcA4O7dNE8+OcjTTw8zOupiZSVPtdqh15N5+OEI83MZ8oUmgiBy\n8mT43lFQk2DQgo0UUn4PZAWfVc+Jr1zEqm+wXckhdOt02gZuvzpHKV1AsDgxWc3spPJ8/7vzeLwW\nXv3ePLHxIH1TIkadjKQSsMcGkZdzuGP/D3nvGSTJed55/jKzvPeuu6raezs9Mz2uB2YwMDQ4iNJK\nS1EnyqxOp927jY3buI8XQd6ni71QaLUKKbQX0koh7ylBADUACDMw423PtPddXd57m1X3oYEBQQAk\nQPA4xN4/oiO6Kyu73+g3K/N5n/dvPIzP+jFoRVrhNaZ6TOypjbSqXZQSKro7UC03UGuU2AMe6g49\ni3eiGLQCOrOB88/NIX17kTsX73DimWMsPDaMXq/ALJXpVEpMnhyhsLOF2tuL5oSBQqFGtdLA4rfy\nrf/0x7TrVbRKgWalyvZ+mZQ+jbrQ4P79FMVMHrnRILO5SSWVwu4y4XVY2VveRbmxTmD8NOVyg1xD\nydB0L/GtPVqtNoNDDixWLb1np3AM+X9gS/3zjHq9zre//W1+8zd/82EP5ROjq6sLvV7PxsbGT5xT\nbHp9/UEhAkCn8+CmZ7Z+sBvaarXJZqsIgoDNpn2gqigU6tRqh54QkiTgcumpVlvodAoCATM6nQqX\nS0/QpyZ+kEGhFPH67TgcOpLJEkajClFQc/tWlGqhhMVuwKRps3n5FoFTbjS+IEurVYqLMTxeI2aH\nhTf/ZZ+SrCGVa6HqGPjl/+PnSe+FiWRFqj0naJXLeGY6mOQUFqeFZCJDb8DI1HwvS/eS6Ixa9Hol\nq7e32bp2j+OTRiqhbWZ+7qfZjtRRaDQIQoPl6xuYjWqUopZGLkNxdwvNyFnG53qwDA6xfnGHoRkt\n9b1V7v/5X6JzOJFsGg4OZArFGqJKh95ioFLr4LCqGB7zoDSZMbTsmBwWPG49SpWCfK5ES63m7v0M\nSqOMxaKl0ZCxWjW49Q0MzRhCyYLC2vWxD6NGqUQtl0NSq+manyd+5w6NUgmFVotrYgKD1/uprw+T\nSc0jjwQJh4uk0xU0GgWiKHD/fgKDQYVaLVGttqhWmmze2yN0a5HwTpwjxwMYKhE8E5M8+WQ/giix\nux4hH6tQLRTpOuZjZyWEvduNICh49tlB4FCNMupXcO0v3uDUqQBqrRKtTonDbSYPqNVKUqky9WoF\njcvHaMCFwetC4Q4gNBrozAYUDSMCHRrZJOtvJrEE+pgYHaVRrqI0WdB2Bbny7SsM2n3UutTUK03G\nutrMTU/SEpRMTPuQVy+xdjWN7qiddLGO0WpCVKmw+sxEIgVa5SI79+6wsZqieTeMWrdC75NPUkJB\nLFZm6Gw/tv5+Op0OpViMjcXFD3SymuUyhVDoY4uRRrn8odfazean6m59EjzMYsQCbL/7fR4Y/yQn\n5fM13nprj1zukDBVKNTJ5+s8+WQ/Dsdh9sPubu5BMBAcqmdu344xN+fDZtORTh+g0SjwGhpUN1YY\naOWZHvLRc2wQd8BNLFZibS3N9LQbW6XC8NwIjVIRk9OGUU6x953buEcGePPGKmML/TSLIkqFhH1o\nEFlSE4lWaFRqHJsPUi2VuXt5ja7npmnls0yMD5NuKrHNP0qXOc/6tWVsZonoXpLxLzxJvqRBaekh\nsxmnWq7hcuqotwRUvn5eeeuAaqHM+o0VjhclfuqcjflRJWqLE60Qxi4XUbT0XHtpkVJLhdOqZOmt\nW9h0bTpI9J85Rt+JY+y/8iJKmhjsOvR6BaVSHUmhZXnxgAGPmVRi55DYxGHWh0KnI7UfY3CwzXC/\nEZWihH/MztZWjldf3cFj6qahKKChSVevm/75aVxT4//dh+VdvHiRsbExPD+hwVMfh1OnTnH58uWf\nuGLko25unXabtix/4LVstsrVq2ESiTKCAF1dJo4f92EwqNFolCgUIq1WG0kSCQQshEJ5LBbtg0JE\nTO2y9cYrZEMxlEqR2mA/ncceQx/s5+TJbm7ejGKymtBrBLq7LTQKeYq5MoIzwOv/cBm9WYcqWyae\nUbBYN7J6ZwdHwIt7dJi7lw+4UMlz5pF+/vnFLfI7mxikGrTbjI27GVPksPd3c/fFe8SSDd58eQPJ\nYEYptrEaBCSjGWevj51wiPBOnMUtgZoYY+GEm56gmVgohcasQ6xUaDcbdHcZcHUPYhkeIjjRx8bl\nO9xZ3kPV1Ue9WqYld5hfGEAWJEJr+8iSmkC/B4ppJqa7uLeawzw4QHcoRLtWotZok8m3sU8GWNqq\nkkymOXs2wMKCH0UpQeLGLQSFTDQhkVo5XHWbv0dFltvbI3z1KvViEVGhwDYwQO8TTyA3Gii1WlR6\n/ae+NprVKum1NbI7OwgqLRq1i/ubh8ecTh0HB8UH5OZ0PMfIEQtLl3cpl+rsbyZ4+sl+PF4Fm4UO\nF1/fJLST5Oh8kL4RL2a9SKEo87d/cx+T3QSCgE6nJBi0kiq06RvykM63cQa8NJIRlM0i3h4/iaaF\nzYMm4fg6x04PsbOb48I7WbyuIGtvXKVRazAx7MfZK9KqVUldvY7t6WfYeulNJKuL5H6MTDzD6Bef\n5ubFyxh0Ovr9faxlOkQjWaLxGrFIFqdCYPLnvkZFYUbZ2UejkJFpoVGJ2MxKhHoZ2m2alSqNhozT\n70JXCJELpaFmoRBRYvL5EAQBudmk024jiCIqgwFBFGmUy8jfvQj4HliCQcrx+Ade01itaH7ELq4P\nU983BcjAMjALqIBr33X8G7lcjitXrvDGG28Qi8UwGAyUSgIrK2nq9RSyXEGh0NFoyLRaGQShjsFg\n4t69OJ1OFo2mRaulQq9X4vXK+P1q3G7HoVQ3s8P+9Uuk1kLkknkyuTgaqcTA1BhOtwm1uoQk58he\nucTm898inoiQCu0RmJ4hen+Fdv8Qks+PXG3gm5pCcFnQ+900GgoymSp9faCQcwyM9mJ1Wxka09E/\nYsKo05HIygT7RGLrK9TTRSRRRHCZuHLrgNhuAdHipmvciWRQY3H5OPqlBV65EUVrlHC6XQiSRFuq\n4u7W0drdQNkoUGhWSWeyCDoPb1/coGfCwebly1BvIxnMuMY9tKmjU6nJ10Qkh5aGJOHr9uI+coxL\nb98lvrfHwMwEej8/gZ4AACAASURBVIsRWVFDa1bSKLcRRInucQcaRRU9Mn1nTxKLx9m+fp1hj5K+\nPgvGgAXLYA+Dp04SmBxif3+fXC6H2WymnEyyvrRENpPB8W57dnd3l1wuh+XdC3p3d5ff/u3f5hvf\n+MaP9SL8LPjd3/1dpqamOHv27MMeyqdCJBLh1q1bfPnLX37YQwHgm9/8Jt/4xjdot1oUDg4+cMzg\n9eKamHiQN9PpdLh0KfTAy6bVOiSjS5JIV5cJrVZBsVgnmz28MQ8M2LDZtLjderxeI5N9SnZe+heu\nv3yL7bUIyWSZVr2G1WHC7PMgNCr4nRJOrwWDWU9bbmG165HqRQKDXl7+67c5fX4CZSGCza5jc7cE\ncot2rUyn3UGlViLRwmQzsbGRYvjIEEpJoFkuUq22mfvyWTqZCI6hQfJViY3tAnqLkXIqQy5dZPJY\nPz22JomDFN2jvaitNgRBIBXJMn+6n53NBGajGv9IgKFjw0xOdxO+cYuV1y/RNTbIznYGuVzk4O4S\n/rkZTH0DNOMHjE14OPLEcTw+E1aLgrWDNn3jQZKxIvuRKifPTzI21UW+rmDokVNs5PTsh0p0OpBM\nVjj3aID23j2EWolWpUyzXKZVq9Fut7H29T2Yn0a5zO7Fi9SyWeh06MgylVQKrcWCJRj8gDvze/P+\ng9DpdDi4u0x4L0NJVhMLpbn4929idtlYXC/jdOoxm9U4nXq0WiXDAxYi6ztsrkRoNmRMRgXjEx5S\n+3FC+zm0Rh0KtYZ8rsaxYz7SiRy3F1O0RA2NWgOzWcfOTo6BfhuSSkWhAmIhTlefh2q1ic5mpWd+\njr6FU6TyHVRKkdEJLwfhEqFwGW+/j5kTQ7TqdXrmjzD21OOklhYR1FrKsSiJpSVMdgupvQj5ZB6d\nUYc92I2iWaSr10VZYWNpMYZSqcBkNdLRGhH0FgJ+I2uLu5itBrx9fuw2NYOjHnpMFTTtMjq7Dc9g\nAE2rxMGNW+RyVczKOo1kDJ3TidpoRBBFqpkMkkpFMRymkkxi8Hhwjo19bJdLZTIh12o0K5XDQs3p\nPHQM/wFbNJ1Oh2omQ6NUQlKrEUWRb37zmwDf/Kj3P8yl62Xg14G/Bc4Bf/S9b/jP//k/f+ikzc00\nAGr1BwmDLlcXPT2HbaauLhPZrBuDAbq6DmXAh5yQPoDDZMsdkXCiiCgJSJJAj9OEuVGnnExi9vvp\n6elh7623WFtcxD0xjmp7m2o2SyEcpf/Rs1x6Z5daQ8Zk0tGvVzA2O83ORhJdp8rcpI2hQSvr60k0\nkszRcQOPPDJEtaXg26/FKBVl8vEMqy+sUy+XOT4fYHasl1u3t0kXUsgGB+ubQFPHyGyQIYOLteX7\nFJIZdOoozm4HLpcTZUXGNztGuwO3/+EFXD1+IskGVn8Xjf0oyWv3EVUqZK+b8UkPyys5HH6Z0H4e\njcbF5MI4nn4fr3zrJtHVKCPHR0mma0SKKnazbqxGBfM/P0Xs1k0mgxpC//gvmE+epByJULhzH593\nhOTdq4Rf3Mc7Okjb6acY9AM+enp6kJtNDq5cIbOxQatep6PXk5AkXGNjH4qy/zxG21+4cIE/+ZM/\nedjD+NQ4deoUf/AHf/Cwh/Eh2AYGaJRKZLe3abda6J1OfMePfyDYq1CoE49/uG28t5djZsaNSqXg\n5En/A9WdxaLhiSd6kSQRpULg4MZN7l3dJLJzKDUtFypUynWmnlWw+/rrlLIFYpE8ppqCYxOj3FYo\nMKrqDEzOYbXrmTh/itGZIK/9p+dp6Ww0RD/htT0cXXY8Jg2aepP+US86o4KRARPVyB6SUom1tw+1\nyUit1iS9uorhqB+9tsEzz81y916CTkmDVq3l5KkgmTtX2NjM0AlUeftWlPPn+xE1Mmazkq/92hnW\ntsrsb6fQNNXcee0WYipFs1wivbuP68gczVwvp37+WeL3Frn5B3+ExaZH++RZdO4e1mMCkTgcRKuI\nmjDh9T1MJiVvv11mZCZAXmkkExLYD73v9Gm1alBJbYrFIvm9PUqxGHKziaRSIUgSrUcfRaXTAVDP\n52kUvscltNOhGIng/AhH3E+CeCjNyy9vElqPkMtVkNQaBoYGKYV2UakGuHhxD7fbwLPPDiGKAi/8\n0zKpWBGVSsRoUNMWFKgNepqilnY+jyBJ2F1Gbr6zya2bEoNDDgwO6Op1k4nn2N3JsLySYmDARrsN\nN25k+Y2fHqGcj+B/9BzVRpuSyskr/+0tWnonqWQFl88KHRmDUGb9VoSoUYWn/xgZnYt4ooRnagrW\ntlh75SL5dAmfJGG2aFFq1WilJha/neuru3SUWpKRDOmlu5isRuJREcfQINuxBt2SmblpJ5vRDpVG\nC6mWZsDTjdtrpKb2kHn7HQSjlfDly1j8frxBKyazlla1SmZzE5PPh9poxBwIsPinf0ollTqcv0YD\nS0/Px7qxqnQ6gmfPUkmnabdaaG02FOrv7+/VqFSI3LhBYX+ftiyjczjonp//vuc8zGLkNlAD3nz3\n+0/EF3E69Vgs6gfbNHBo8+t2v+/yOTbmIJerEY+XaLc72GxaZmc9D8hvZrOWbp+ebNBCrS5jMqpw\nOHUPgrXkVotaLkcpHj+sJLNZzIEA1r4+qpU65uNTmLfKxC/dpGHQM7UwQY+1iFLaQTPQRbWtYnl5\nk+zV24cEuflBVv7uPl3Hj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s0mtVyOrhMnMHV1Eb5+nfT6OqVIBFGlwjhdRukcpmdugsTG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fIuVFOp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\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ofF0Zryw3GB2N4/FsnPDL5RqlkvyuOiOHDy+27MvlKhSLVR55pPstJdiOjSWQpBo6nYpM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\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8+Z//+YOexn3DR//u8R7wflv9ep1In0+mnkthsJrQyjKRy1Os/fRV/OMjJBaWsXidNCU9\nfV1BxHKK6twCapcNZ2cAz+4RNEY9DUHH3N0Q/k4f9UqdlduLdPs1vHQ2QqPRZPduD41Gi9u3o+Ry\nVUZGPMzPJ5mairGxkaOz08rCQhKdTs3FiyEmJ9tpNJo0GiaCQYFKRVnVTk/H6e21Mzzs5vXX1/mn\nf7rFH/3RfjY28gSDVvR6CVFUIYoC8XiJYNDGoUPt2Gw6ZmcT3L0bZc8eL4GAmbm5JMlkGVmWKZcb\nLC9DqRRmcjJAIlEin68Rj5fuSzHyfs7XO40RpF98STva/Vg+/Ulmby6h3VWlHN7gztkbqKp1ksUG\noalZeiYnOGDzcefVK4iSQDJdp6avsJUoc+XcEpY2P6WaivZ2C1/84hjlstJar1abbGzksFp93Lp1\ni1isyN69fpxOPfPzKQRRhcWio9Fo4HIauXxxlYlxL7/71TFcTj03r61z7/oKXUPt5LJlxYBvOMC1\nKyF++uN7WB1WLl/eJJlc5MjDnQwP2DFqYXdQpGPAz7WrWwjZCHM3l9l3cg9Gm4XL51Zxu7SUmhoS\n4TSVUz1shZNshssMDCgEyzt3Yuh0IpHlTerhFew+CznZzNiYl7Nn16nXmzgc+m05p5+pqSitlkyr\n5eb4cdNOMSKKKvr7HTuS2F8W3s/1o9NJb1LkpFJlEgktxWKdCxdC5PN1rFYtf/zH+7lyZZNkssxv\n/MYQdrtiNgYQ30xgMbVjMTTZva+bs68ssDm9js6ow+11MbrXQUevHZtQ4LVvfp9GsUSnf5JCPE3l\n6hWK2g7yhTov3w5z9IlxyjUdUzc2yJWamIxG2oMt9kz4WFtOUq3UGB52sRHK4PLbmZtN4HEb8DjU\nnP7uy2i0asZO7sXd3oWREnqDAUeHH51Ow+uvr6NWC3R22qjZ+hG0Hk4dOEKhoebmep2jR13oKXLp\n1QUsNi39/Q6MBpliVpH1jo56GR39r3lA1WqD8+fXWV39mRv22lqWU6e6sVp/Rmp90JyRZ599locf\nfhiPx/NAPv/DGn/48GFu3rz5jryRj+r83wm/UsXI+0UzvET62llCKwmaLdgz7kUtQrNaQWs2Yw74\nsfnc6K16jCao5gVqxTLTFy8T/PRvoxIEpq/MozFb0JjN7D3Ug92q5l9evcuBR/byhKGHe/NZPvnJ\nftLpMpcubeLzmTCbtbz88hI2mx6rVUtbm5lnn53B4dDx5JMDRCIFjh3rAmRUKvB6TdRqLX7wg2mC\nQSvLy2kaDZlduzz4/Was1tQ20a6Ax2PEbtfhcump15tEIgVu344Qi5VotWRu344yMuKlUKgBinfL\nG/v92Wx1R7LZ1WXbuXF/HNFotEilSsgyOJ16Mk0TuVKdaiyNs7cHmg3KqST1ao1caJ2RJz6F2h3g\n4iv3EAUVgiiiqueo12VklUA8XsTlMtJqyczMxHA49MTjRc6dC/H44/382Z8d4caNMMGghVyuSqFQ\n59q1Leanw3R2OTCatewZ91EpFJGoE9mq4nZokTQiZ15ZRKuVuHl2iVimiddrYfduD7LeRiJRZOrm\nJm6njjvn77Bnog2DxQRaDbN3N2nkSzi7OlCptYwd6OH8a4vUKjXKNZnhXW4W5+I4vFbcbsUvRpIE\nBAGi8yusXbzC1ItTGIwa7O1+Jh8+vi2XlTEaNRSLivS31VJIsCoV2O06Hn64Y6dbFwhYPhbKLJ/P\nRHu7hfV15QFaLjfw+82sr2coFhuEw3m2tnIUizVGRtzcuBHh5s0wPV0WohtJGvUmew90UG2AChlz\nbgqv18iWzUA8ViDY2cJvqqPTCtRbevo+9WlaxSypQpJcTUKfK6Dx1LF4zeiNSZZnQnT0tbFn0EAk\n3cLTZufUIz3cuxvlO9++hdmi4U//j2OMjbdRKzlo73TisanYuDuPUCtSrgqsXJliT4+IU07QrNto\ntdqYnVUsAs6dW+cHP7jH3r1+DAYNY2MejEaBzc08UqvJ8kISd4ePrUiJhVAeg1RHNlp3umZvh1is\nSCiUe9N7iUSJzc38m4qRB41vf/vb/OEf/uGDnsYHhtFoZGxsjEuXLn2sHGTfLX6lipH3o28up1Ik\npu8R8OrQCHZC61kKG2s4OtoRdXoko5F9v/MlkgtziIJIoVzEO9hLUaumXmtRuneF3/39TzK3uotM\nroGnzUqg3UY2U8ZgNnH9tbt0jA1iMGi4dy8OyMiyTK3W5MKFEHNzST7zmSHa2szbq04Zn8+kJMvG\ni5jNWh5/3Em1aiadrqDRiHzmM0NotRKxWBGzWUMuV+PZZ2cIBCzodBL9/Q70egmzWYNWqyaVKhON\nlggGLezZ42N1NUMkUiCfr9Debt32NJFxu/WMjWmo1SxoNAL79/vp7rbdtxvLh+Uz8l8hm1W8VjY3\ncySTZXQ6SeHXaHQsprQs3QthdKoYHezC2QFqowGdQYvXbyVgrtBqtrh9PUO1AY89tYeFtTKOUpPB\nQSdqtUCt1kCWVQSDViqVBjduTLNv3xBtbSa0WgmHQ+DVV1eQZZmZ6STZbIUv/s4E+54epFXKU62r\niCUrpNI1RElkZjaD3WVm36E+spkS8/NhBna1kSlCqSHj8DkQNGo8bXaMXh+jY36iKyEGd/t56ccZ\nFmeWmZtLMjbZz+99/SGS4RSyqIZ6hbVwmL5dbTSaRXK5Km63nrXlBDcXlnA5jBz+jSPMXp2Dcp7E\nzDQ6Yzf9/Q6sVh0//vHiNklai89nwmQqodVK9PS8sxvk/cT7uX50OomjRztYW8sQj5fo7bVz7twU\nL7+cQq8Xt38vG5ubOUwmLZlMhXK+RPTKXQqxBP0DLkzRMFpXL+dvFxmQyowEmuwdmyBbLSCVZSS7\nmavXY4RWEixPLdHZ4+KrXx1HK1eIrW3hnpjg2p0EvXsHGdvXSSlXor1HxWNtVlKpCMvrMj/5yRI9\n/W6CQTM3r2/QN+DmC58fYW5qnTM/nkKlsVNVm6mmkgzv8iCkN3nsySHKkpW51QqGQZF/fz6yIz+f\nn0/SbMocPNhGtdrE5TJw5fIGnV1WLl2NcfnCCmqNGr/fyOJcnFAoi8djfNtjXKs1d6IW/jNKpTen\nOT9In5GNjQ2uX7/Oc88990A+/8Me/wZv5O2KkY/y/N8Ov1LFyPtBrVSiUS6jVos4nQYS8RI1wQCC\nQPDIYeztATYvXaSaz6K12Og59BA1RKr1JpqeESIaP1d+cAHfUB/7joygE1W8/C+vozFZGT8yzL//\nv68xMKnB59NRq9VxOAykUlGSyTJ9fXampqLcuRPj8cd7mZjwMTMTR6tVVjThcAG1WqC3V8XVqyGe\nemqYwUEn58+v09lpxWzWcu7cGplMFVmGjo4CJ050cebMKvF4mcFBFxaLhm996xbJpOKqub6e48SJ\nLkBGrVbUNG/ICtPpCj09dsbGBlGp2Jadmmlre3cSvo8apqairK1lCYWyrK1labVkvF4TW1s5QrEm\npUIFWWwxfTtE78AE3SM9CKKIrDdj6B9FquYYsMncms7REHRMTioqDodDx8ZGHofDQKslY7fruHUr\nisVSYX4+zU9+ssDnPjdKuVwnlVIKSJfPis2moxhPISXL3L0d5sxP7yFp9fhGdpEqyPzayV5m5tNY\njCqMeiOpQp3BsQ4uXQ1TyZbweM2M723H5zGwvJLhhf+YQaLOE091MXagSiFfQRJVaDQC1WKJPQc6\nmbq5ycJKnuG9PsXWO5mjs93EKz9eoTNoIrq0xXrIyPC+HrofOkC9VKS/346uu5dGQ8Zm07J/v59M\npoIoCgiCCo2mhcv10eIFvBeYTBp271Za9plMiXv3Fra7jkqXpL/fSSJRRhRFzGYNHmON5QuzNOsN\njGKVQMCCqtxk9+4xyKvI3H6FxtoWJanJxORerkdUhKMlZElL20AnpWKRS5fDHNo7hrd/P6dfXkRr\nNOJuc/P9793FaNJiMWvxe3LotRnUkpfeHiUrR9VqEo8V0Rm0hCMFzG4HapePtYUwLreLY79+AJvL\nxFqhgK1QR9MMkb0xi28syK4+J9evh9FohO1IBwPlcoN4vMjwsIvZexEmhoxIagGHy0SrKeMN2Onq\ndbK6mmF83Pe2x9Fm02E0qikWf1Z8SJKAx/P20tNfJr7zne/w9NNPfywSet8Njh8/zl/8xV886Gnc\nF/xKFSPvpxpT6/VIOh31krLas1i1JOJNerv60feNkLz0Glu376B3OgnH68zPPMfxr3wKr9NDaayN\nK8/fRGe3c3cdXp+d4beeHmTkyBjx9TBOp44vfOMR4iUtn5j0oFIpe9VqtciVKxvodBJ//Mf7KRRq\nWK06trYKjI56uXMnCoBGI2y7qWoplbIUClWMRg2HDwdZWkpx6dIm0WgJm00RIt2+HcHnM3L4cDu5\nXI1du1x8//vTVKsNDAYNs7OKRbzZrMVi0XL0aJC1tSzDw26Gh900Gi28XiPj416KxTp6vfq+SjXv\nB2fkDZRKdba28tRqTaLR4s5WUzpdZmsrh87lYaTTSbOkSFhzggNbZycATqeRMgZyVZGOIQvnbt3j\nwktLeL0m3G4DTz01hM2mZ31dMd9Sq0UkSWBsbIjXX1/dUb2Mj3vR6yUkScBs1uNyG7HqW4RWEty6\nsko1lUJtUlNYEalZe9mMlHG5jBQW1xDlFhN7hiiVq8TDOSrVBsNj7VQqTV746Rp2q0RsLYIkyKhf\nXcHjt3Hy1ydw2STKNbh4JYLGbCZVVPHIr48RCJhZX4xw9CEv0UgBbS1NKpTB4rKxtFnn3/91iiee\n6OPepWlE68N85hMuDAbF2VSjkbh7N0Y+X8Nu1zE+PvCRMLp6N9dCo9EkHC6QyVQwmzX4/YovSbPZ\nIputoFYLTE7upl5fo9VSpL+zswkmJ9uJRoscezhIK3YPjQSiVk2zqXQ1NeU8Qr1CUe1APXgAIR3F\nJIJ5YITk/Do3bkQolRpUKzV8bj2jahOGtg5++vwSFZ2bPQfaef75eaZuhjBbtNgdBgRZ5vf+YAKP\ny4DdpmErlMVituO26TAY1KTTFV59dZVarcHhU7sZH3Fw+fQ016/MYBbKlNJpdh8Zw9XmJHp9kY6A\nirExL/PzKYJBK1arwgsZGnJhMmkIhwu01Do0QotgmwGVKOD3mzE7FC8RQVC97TF2Og3s29fGrVsR\nisUaOp3E0JCLtrY3E10fFGdElmW+/e1v8/d///cP5PPvx/jDhw9z48aNt+WNfJTn/3b4lSpG3g8M\nTifuXbuITk3RrNUItFtx93YhBIbIbmxRLDcQHV5yFZlcroRWI1DMFXAP7WXm/DQap5uKZGJ+IYvZ\naeHc+U3a201U6laODnYiqiVMkSLpdIW5uQSSJNLfb2dyMoAgqGg0WjQaTV56aZnZ2QSf/exuXC49\nbrdxR3o7NRXDZNLQ3W1ndjaBSgXj437C4aJCTqs1WV5Oo9dLJBIl4nFFeTM87CYaLWE0qkmnlc6I\nIKgolep0ddlYWEhup3uWUKlApVLRbLa2yZcG1OoHqfz+YNBoBLRaxcH2P3NelPAvFfl8ha4uL5Lk\nBsBo/9kNVGnlKwXf9etK0N2RI0G0WglZltnayuH3W3jqqSGq1SbJZInBQSeRSJFqtYXLZSQaLWA0\nBnnyyX7W17NYrVri8RKqfIKaxkSmJOPoCKDTCogaDZ1ddpqCCo9bR1ByQiFFd7DB9FKBow95KFRV\nJDMVpqai2BwGJEnA0e6nVmuQyrWwu2WuXY+xa5ebO3diaLUS1WqTYrHBxYshTp7sJrkRY/Vmivbh\nbuR6nXK1hdHZTja1giwr0vRAt49MRWJrq0BfnwOAzk7FkbVabaDXqz8W3BBQOntXrmwyO5uk0Wgh\nCCp6emzs2uVhakrhT4miivFxD6dO9TAzk0ClUvHkk/07RPF6vUUr2qJYqhOPl8hkqyQTJUYmBxga\nDTC3mCPbNBIpefD5TBTqaprNFqKoXH+iqKXSUGFxmCnVVaQzNbxeI+VynfnFFKWqjFiqE+xQ08xn\nqaZT2L1NnnqimxdeXsXtNZMttLDbdeTzNZLJErdvR3ZSh1fiAo5gB83oCpLFwfXLq3z604PEVjbR\nFhNM7h9jeNiNXq9cux0dVgYGHLRaCnm3Wm0ycrCfM68sImoknH4nWp2aoSH3uzrPQ0Mu/H4ThYJS\njLwhW/8o4PLly5TLZY4ePfqgp/KhwWQy/cryRn6lipH3u1fl27MHk89HOZVC0usx+f00VRJTxSJb\nghXR4oJYHIdNh683gMVhZfbKJdZvh1me3cLd2UawZ5C6oN7+g1dMrmLJKqOjNrw+My+8sIjFoqPZ\nlInHyyQSStjd88/PsbaW5ezZdXp7HXz3u3f40pdGGRvzcONGmHK5yeHDZjo7OymVaoTDhZ297t5e\nO7dvRzCZtAwMOInHS+zd69+5cYVCObxeI5VKnUJBMesymzVMTPjQ6STi8SJut1JwyDLbdtRpZmYS\nrK5mMBrV7Nrl3k6S/fBxPzkjkiQyPOwmlSpjteqoVApotSI+n5lisU4kUkSSRKrVBGaz9y38B4fD\ngNGobF+JokCrJXPu3DrpdJnRUS8dHSKrqxkOHWqnt9fGzZtR4vFN3G4DoqjC41F8Xo4cCTIx4ada\nreP3m1HHF0htxOgb9JJP5VDKJIFyscKRk1045DixuoqW1sK9OzNkFhO0XF3MLjbQu9x0eczkchUi\nkQJjYz4lZdioZnzcRy5XJZ9XHgqjo14WF9OsrKTJZqt43DVMOiuiVcDf4aSht2PQi7QECVu7n84O\nCzafG8nuRq3XUyzWfu54CkiS5n2ft/uBd5pHLFZkbi65E67XaslkMlXOnFkll/uZJ8qrr97i5MkJ\nPv/50TeN1+kkpqfjqId3MXVxHpNJgyxDtSVQtwfRmww89JCV3l47m5sFNjfXaW+3cPhwkOXlNKFQ\nDr/fjNttQKMRKBbrfOITfdy4sUW12kSnU1OpyeyZ8GAVC8zNzZOK6LhwaxOd3c5XvnScWKrJZrRC\noVBjcTFFV5eN1dUMkiQoickqATRqtAY9cqtJtVRjI1whUiwz0mVgfI+ftVCeQMBCIGBhdNS7Q04/\nerSDqakoDoeez31pL/l8DZfLQH+/g64u27s6xqDIiN+OV/agOCPf/OY3+frXv47wC9yZfxmff7/G\nnzx5kpdeeum/LEY+6vP/r/ArVYy8F8iyEipXqTQxmzVY29owt7W96Xs6hjvILg9gdluRywVS65u0\nTY6Ri8apZLIcPtZLOpFDqJXwGBsUtXoGBpyk01Xm5xPMzMS5cmWL8XEvJpOGixc3qNdb2Gw6AgEz\nGxtZjhzpwGpVwrI0GhGrVcff//01HA4Du3e7sdnA6VQC0qamYpw9u4bBoCGVKtPebiGfVyLtC4Ua\n+/cr7qr37sV2VBCHDweJxQrcvRtHkhTXTZVKxdxckocfDiKKIul0GVBuKoJQ3ZFwFos1cjklFdlk\n0uxsR3xc0N/vRKMR8ftNbG7mt91GZQ4daqdebxGJFKjXjUxOdhIMWt8yXqNRipdotMC///s8q6sZ\nmk2ZdLqCx6N0J15+eRmv14QgwK5dbtrb9VSrCtlzczNHpdLg+PEums0W1WoTi2cAfS2NztHJ6dPL\nrC3HcXa00Tfgp9On4af/ss71Cwuomg2sfi3t3g56ehzcSza4fTvCiRNd1GoN9HoNBoN62zBNh0oF\ne/e2odWKLC4mkWWZUqlOLlel0WhRq8vcXc1w9HCAbK7OwG4/S4tJ/C4b/UYLPp+Jy1NJnE4DPT3G\n+7bCbTWbCG8T3/5holis7chx34Asy6yuZt60/SjLsLSUZtcuD/V6k5WVDMvLaVQq6O21k0iI7Hn6\nU6TX19Fotei9PsI1Pel0Bb2+yaVLmyQSJSqVLJnMBlarjuPHu7DZdKyuZlhaSnPnToxEokx/v4PO\nThuCoOLAAR8ejwGfU830qzfo7vdiNUJN1SCzsYWYj3Hzag6Ty4XdrmzjtrWZOXIkSEeHFbtdz717\nMco1CYvZQStXwNNmwyDW8PqtWHr6uXorth182M7goOtNwYZer4lTp5TFilYrvcXI7uOMdDrND3/4\nQ/7qr/7qQU/lQ8cTTzzBN77xDf7yL//yQU/lQ8WD7Ld+Dfjd7f//X8B3f+7r8hsR1R826vUmN2+G\nmZtLUqs1MRjUTEz4GRpy7XxPo9Hk2qVVNLkNNq9eIxtP4xsdxd7dxcv/z79g11Sxex3ktD7WNwto\nXH5Mnb243QaeeeYOyWQZh0NHW5uZUqnBI490s7mZZ24uCSgy0y98YYTeXic3bmyxsZFnZiaG3a7n\n7/7uCgaDwtcoleq0t1s4dqwTtVrg2WdnkWWZ//bfDvOG9DKXq1Is1nf+L8syarXA8LCLer2JLKsQ\nBEV2d/NmmOXlNH19zp3gM0kSkGUZWYZXX1UyaUwmDeVyHYdDTyZTBhQjpt273fT02O9L2JRKpeJ+\nnfNWSyadLm/LUvWoVCoqlQa5nNKqttn0mM1vNQGORAp861u3uHEjzNZWDq1W2laZaBkZ8RCPFwkG\nbWQyFXQ6ke5uG0tLaTIZpQjo63MwMxPHYtEq2SQagVOTNshEiCSqNHQOii0tM/Npjj3k4Z//+kfE\no3mSiTJmiwaz1cSjnxohKXgol+t4vSYsFi2hUAZJUh4ssgyvvLLMww93cuJEF6+/vk44rHBhFhZS\nO1tvogCtRoMr55bYvduNRq1i974eOrvtPP/8AolECafTwGc+M8TRox0f6sMpv7VFfGaGSiaD2e/H\nNTyM3q50o+7XeQ+H87z44hK12s9kqm63gVAo95bY+44OK5/4RB9TU1GuXt3cUYlIkkAwaOb06VX8\nfjOxWJGFhRQul57/7eujzN8Jcf31adR6PTqXh9VIg2DQSrXaIJOpcObMKoGAGbNZS7PZYn4+yZ/9\n2RGsVi1arcStWxEykSSVcIi+bjMLF2/Q12nEYtbiGR6k2TGOWqvh9OkVnn12llZL5umnh+nqstLZ\naWNqKsbp0ytoJJjYZWa4W8/qnSUEq4tk00qgXXFp/vznRxkcO9yAoAAAIABJREFUdPFRwv38e//b\nv/1bLl++zHe+85378vMfJBqNBl6vl6mpKQKBwIOeznvC9nPjFz48HmRn5EXgf27P4RJvLUbuG7a2\n8ty5E9u54eTzSuKt223YWRGm0xUy8zNsXrvB0lIKQVBxb/EyJ37Hjc5sIp+rY2vUcBuz9BxuQxvo\n5vRUjVKpRi5TJh7N0Wo2GR52odEojqC7d7sRBNjaUnxAnE4D6XQZs1nLvXuL9PQ4sNl0PPKIkvSp\nUim+CNWqEqg2O5vYVtFotj0fBLRaifX1OMlkCbfbiMWiweczEQ7nuXUrilotMDLiYdcuFz/5yRJu\ntwm73UC5rPhfNBotHn+8D0kS2NhQvBd0OolXX13B4dCxuZknk6lw8GAAq1VHOl1Gq5Vob3+rG+NH\nGYKgetNqv1Cocv58iM3NPM1mC4tFy0MPte+Exb0Bl8tAR4fCl3jDGrxabVKtNpEkkdFRhewbDFrQ\n6UTm5lLMzyfp6LDi85lYX88yM5Ogo8NKKJSl2ZTR6SS6u4O8PLVErZYkm62iUqmwGCX0Rj25XBKV\noEKr12Jzmth1cJBUWeLs2bWdbTq7XY/FosbtNvIP/3CNhx5qR6+X+F//6yYTEz48HiPFYo1Pf7KH\noEeiki9g8vn5x3+aIldWkcjJ1BpN5n40xze+cZATJ7pptRTZeV+ffacQKcbjFGMxBEnC5PWis735\n+LwbFONxVs+coVZQeA7lZJJSMknPI4+gvo8qB4/HyNCQk5mZBPV6C1FU4febcTj0zM8nd7J11GqB\n3m4z8dUNbl1cplyU0ZgUk79Go0UolGd42MWlS5tcuxZGpYJdA1ZmXrvKvdk05WyeeqFIKpxAdHSw\nvNzk+PFuVlfTuN0GbDY9y8spxsa8TE4GABXXr21x6FCA3bs9VDt0rJxZZvq1K5gMIrNzKfbtb0Pn\nCxAttWjkimSzVex2xY9o1y4XN29GeOmlFQYHnXzhC6PbW3RVJLeTNbnG3XNxGo0cBw8GOHKk/W3/\nXt/guPyqQJZl/uEf/oFvfvObD3oq9wWSJPHYY4/xwgsv8Ad/8AcPejofGh5kMbK2/W8TaLzdN75b\nvNu9qmi0+BZ9fLGo5NK88cBqVcsk5heRWzLVapNKpY7QrDP78lkCo4Ms3bhKcm0Ta4dA94SVss2F\nKEQx61Xo1DJaSWZ02I7TrudWKMbCQpLduz0MDTkZGnLT3m7m3Ll1ZFl5+DcaMnNzCYaHXTgcOlIp\n5aFfKFTp6lK2cIJBC/39DkKhPFqtiNms3SZKqrl5M0ssVmL/fj9LS2nm55OMjnpoteDevTiyDLdu\nRVhf/5lJkU6nWJvXag0kSYPTacBkKjI1lSaVKvNGwqzVqhQlFotiIb6+nvlQipH77TPyBur1Jsmk\n0hVxOpXtlYWF1LZ7agKt1kU2W+X69TBer+lNq2Zla8tNqVTH5dITiRRJJkuMjXk4eNDPmTPrLC+n\nkSQBu72MSmVnaSlFpdLA5dITjRZotWQKhSrNpozJpMFu15NMlpiaiuHxGBFFAVH8/9t78+C4rvPA\n93e7b+/7ikZja2wEuIAbQBKURImiZFGylOh55MyMHHvieF5cfiNHznOSmky9vBfFcXnKlakoVtWU\npXmx68U1kR1viSKF1mKPJWsjJVAkKJIghZUgdnQ3et+X98cFmgQBLgAaG3l/VSqhL/uc8/X9zr33\nu+d8i0AyA5baWowDQeoaXVQ0WRgdV/HhmRBGs459+6qIRKSts/7+IDMzSTo7q6iuthCPZ/nZz3pK\nzsx791Zy/yEvudA0l35zipyQoG73QZTFHEqNBoVKjaKYx+GQqjjPGVlarVhayg/29zPy/vtkE1Km\nVa3Nhu+++5hOJJakg8joaMkQmSM+OUnC78dSU7MkXV7NzeaCUqmgo0M6P5FIGrNZjdstJayzWLQM\nDs6g0YgYdSGE4TOMRtJMX5wmHE5ia2jA4JKcmwUB6uutvPXWMF6vEZtNR0uNyEDXAOmCmanJGBqt\niN4O+VgEq7ea06fHAejrkxITTk3FS/48L/38LCfeG8JkuBtRUcTqMFC/q4nUTIBAPIbTYMa7YytZ\nnRMTSkDN4OAMarWSffsqOXlynAsXAgwOhhgcnOHDD8c4fNjHqVNjBIMpPB4TkcgkAwPSVpXTaWBi\nIkZjo33e+QkEEvT0+JmcjGG369i61bmgEm85/IPW2mfkpZdeQhAEDh06tKz2m8Hn4tFHH+XnP//5\nosbIZpB/MTaCz8hXgH9eywENBtWCY6I4F31RYGBghpg/RCKeIRZNS1k2p/KkwhGi034uTTbRfF8n\nxngMs9tO3eH7mPBnMaouo07H2d/h4SMhh6PCxM9+fg5RJeL3xxkfj3HkiA9RVDI6GiWbLVAsgtUq\nhRqKohKjUcP99zegUl1iZiZFU5MNrzfP6dN+2tu9DA2FueeeWjweAx98ME5PzxR79lTy6KNb+M1v\nhkincwwPh6mrs5QeKvl8kZ4eP42N9nnGSDqdw2zWSE55s5ESVVUWPv54Gq1WqjXicOhQq0WSySy5\nXAGVSskqrayuCsFggvffH5EiWQSorDTS2VnD6GiEQkEK0xRF6c0wEkkTDqfQauenvm9tdZJK5Rga\nCmGz6fB4jOzdW8nJk2NMTMSIxzOcOzeN2ZzkySfr8HpNBINJpqbiuFwG/H4pA+yWLQ5EUUFPjx+v\n18g999TO1oJJMTQUYutWBzWtdeSUGixmLR+duoiocdNzMUg0muHwYR81NWbsdi1Go5qPP55kbCxG\nR4eXt94aIh7PzupNycR4hPd/HWFXi4GGBjuf9EVJTk1Q5xaJZ/RXFWtUYTComJpKIIoKtmxx4HIZ\nyKXTTHZ3lwwRgNTMDFPnziHU1y9JB4XcwneNYrFIMX/zLJ8rRRQVVFebCYWS9PXNcPr0BA6HnuZm\nB7t2VSAIAh8eO0Z4ZASt1Yq31kqwK0zk8ghaiwWl+kqVYINBhVJpQBAEkokMA+eGOfDYIfyTYfxT\nUVRGNWa3mt3tlbz99iUGB0P8m3/TSiyWxes1olAIHDxYzbPf/jUCcGlohv0dHk6fvEwOJVs67sWl\nnKGpsZ5YQc/57mm0WhVbt7p48MEG3ntvBKtVx7lz01itWoxGFSAQCCSJxzM0NNgxmdTkckVyuQIe\nj5nGRhuTkzH6+oKllw+QjJS3377E1JSk30BAmq8PPdS44WrLLJWf/exnPPXUU6uylbxROHr0KF/9\n6ldJJpO3TQ6VtTBGKoAfXXNsHPgccAB4GPjfyjHQrVpj1dVmnE4ppHWO2loLLpee3t4g7747jMWi\nwdfWwJlfn8Tl0mOzqJgS49R37ERhMxPvv8TI2BR1LSn2qJU0NBjZu6eCrjc+YmdrLXfdd5j/9eYl\nvF6L5Pk+W5NmYiLKZz+7HaNRhcOhw+9PkkrluPvu2tlcDtIb3Je/3IHfL2XLDASS/N7vmXC7DRiN\nagwGFW+8McC5c1NMTcV59dU+Ojq8/Pt/v4N8Xtq+0enmG1yCUMTns9HfP8PISGS2yJ6ZqioT//qv\nvSgUsGWLk927W5iYELHZdLhcerZvd9PbG0SvVyGKClQqBbW1C509V1Nfy21TLBb56KNxRkejpWND\nQ2GMRg0mk4aengHi8Rxq9RRVVSYaGmwLfAkAdDoVBw/WsH27i3y+iNWqxe9PMD4ubbcJgsDYWBRB\nkHKPPPbYFrq7J0mnc3R21mCzafH740xNJejqGqOhwUYikSEYTNLR4eXkyXEqKw04HAYaGqw0N9t5\n9dV+NIZKFApIpfK43QZ6e4Ok0zk++SQ467BaSSiUJBZLUVtr4YEH6slmC2g0SuLhOIV8EZfbgDku\nYleryUxPcOSedrbnHRQK0u9oaXGi1YrMzKRwOnVUVpoQBIFMPD7PEJkjGQjQusQ3TlNlJdNqNfnM\nlQgdjdmM1m6/Qaubc6tzIZnM8s47w4yNSaszY2MxxsaiPPhgAxaLFmMqRQJIhcNsra8hm6lmbDSM\nWixS32Rn164Kstk8W7e6OHlyDKNRhT+mwF3lID99icP31zMdzKBSidz16H4CsSIOhx6TScO+fd7Z\nkgRJursnGR+LUuez43brmRn3k09ZOXy4jt+8O85EVEVr61ZefnOUs2cvUFNj4fBhH8Fgkk9/uhmL\nRYtarUSrVeH1mtBolPT2BgHJD0qrVaJUKujqGuPSJQGvV9pmPHNmiomJGCaThro6C9u3u5iais8r\ndAhSGYixseg8Y6QcUVNrmWekt7eXV199leeff37ZfWyGPB0ul4v29naOHTvGE088sebjr0b7tTBG\nJoH7FzleBfw34LeBRd+1/+iP/gjr7B51a2srnZ2dpR86V6Z4OZ8tFi3NzQqMRhBFOx6PAUEI03f2\nY6L+AjX2IuPhKQp6K9sO7SE5NYHRo6Hp7kaMxgrGB8ZIWXU4Kppo3OZDpdUyPHIZqy3DQw/UMJnQ\nMhMfxWxOotOLqFQiIyPDCEIGUTQxNhZhcnKEbdtcOBwWAoEkMMPevTo6O1uxWrVMT4/h9cLOnXWI\nooKJiREgQV2dm1OnxvH7xzAa40xNSQ6MFy/2YbensdkqaWiwlX6vRuNEqRSoqYHJyRH27fPS0VFJ\nPD6NWq0s+YSk034mJ0fR6drZu9dDIDCG3x9l926peqcghNDr4+zfv53qavOKzv+NPpeTaDS94IYL\nMDg4Q0ODbTaPS45UKkc+X+TQodobhihenejr6pcuvV7EbNaQyeRxOHQUCgW8XiP19TZ27/awc2cF\nAwMz/OQn52htdZLLFSgUYOtWF3a7js9/vo1oNItWqyQUklK1ezxGKXQTKdfM3MrNxESMVCqHzaZj\nYGCGw4frEAR4990RLl0KSW/IdRbMFg172l14LHmysQzbtrpIpXPUNBh4YF8bsVgWne5Kpd1rFzvU\nBgMqo3GBQaJ3ua5bjPB6mLxeqjs7mTx7llwyidZioXLvXrTmtfE7mpyMMTERn3csEEgyNibVUNE7\nnST8figWKUyPsLfBxq6dzVTs3IbbYynl2/h3/247jY02hodDDA2FufuJo4QudDP+cQ8V9R5aDu5h\nS1sN+XQSh1XDL4718vJLF0lnpYy1tbUWivk81RUigYkpFIKA06Gl2RKi7j/sYnomy8svf8I771xG\npVIyM5Pivfcu8+CDDSiVCj7zmVb8/iTJZI5EQvJTqqmxkM8X6OysJpvNkU7n8XiMHDxYg1arpK9P\n2rJVqRTk8wXOnJmkWCxit+sWXeGcC4PerHznO9/hy1/+MgbD2hZtXA+efPJJfvjDHy4wRjYr67lN\n838DbuDns58fAVJXf+Fv//Zvr9v4WuvL5/PNe6At9u9X09bWQttsWoFiscjUxxOMnvmY7vcHKAgi\nVbu2k3M3EVN7cPpa2dvuRVnIMvJRN8N6F0O9QygLeoomEdNoDJ/PR6G6mk+6LvDuS+9jdNqx2yqJ\nRfsJBKNcupTHbNZTW2vho48m0OtVmM1KisUolZVGMpkK2ttrqKoyz94sPCiVCgwG9YI9uFQqh0bj\nxOGwEgj4Z53X9FitHqqqTDQ22mczukYRRYGWFietrU7i8QzDw2Gi0QwWi5OhoRBjY9KqgUYjedp3\ndZ3nyScP8ZnPHGR6Oo5CIXD//T4EQUCjUaJWi7d0fm/l8430dT2Wsh+pUikXTdxWKBTx+xMcPuxj\nZGQYsOLxGJe0PG2366msNDEwMIPBMJfZM0o6neenP+1BqxUJBlNEoxnuvbcWr9dEY6OjFEZtMKhR\nKgXsdj1HjjRw7twk58756e6WnI6l6rJh4nEDyWQOUVRQUWFAqxXZscOA0aghEEgwPh5nYCAord4c\n8DI1EUZJgccea2ZbjUDk/CkoFplKpjAbHUSVDs6fn6a21loyRBZD1Gjw7NrFyPvvS/4egoDObse1\nbduS94QFQcDZ2oqlrk7KdGw2o1Qt3CpdKrcqRzZbWLTY41yUTcJsRme3kwwGKRYK5BNxqtp24PTO\nd9a12XQ8+GADg4MhzpyZYHgqTm+4DoXRxeCEkpHTk2hdYzBwEoVSxd6tbtTFFEm0NDRJK1Bjo2Gq\nKw04zQpaWpzUGqOoDSbqt1eSPD2JIIRKfkuiqCAUSpFIZNFolJjNWsxmKQX76dMTpfD+HTvc+HxW\nXnutj1gsQ39/kN7eQUIhLY2NNurqLKVt6HC4yOBgiNpayRE+Gr2yWqXVilRUzH+IbyafkWAwyD/8\nwz/wxhtvLLltOcZf6/ZPPPEEf/zHf0w4HMZiubJavVnkv5b1NEa+so5jzyM+Pc1EdzfFTAq7XcfI\n5QhDH5xm62MVDEW0VFTYsVZID+tiLRAcplqjQqtxkcgpOXFidNb5Uw2WCgy1jfQPhtjuUXLo3jq6\nuqT6EPv2VRGLpclmC9hsOtxuPZcuhWludlBTY8Fu1xEOpzh1aqIUmtnc7MBkmr+37vWa6Onxo9NJ\n6ZfD4RQmk4a2Nje1tVIOg3vvrSMWy6BSKUoPHatVh9UqPXADgQSnT09c95zY7bpVTQW/Fuh0Klpb\nnZw4cSVUUwp5dnH27CSZTAGVSoHBoGVmJjW7EnFrKBQC+/dXodOJjIxEZhPJGenuTuLzWbFatWg0\nIpcvhxkbi1Fba8bl0pPPX3nzFARpezCTyRMOZzh2TAqvdbsNHDlSjyAYiUalNi0tUgrvqak4RqOa\ndDqPw6HH7TYQCCSIhRN49ClqGpVQBFV8kvRInMrduykUigT7RomKVfSei5HNRujrm+HIEV9pPiyG\nrb4ejdlMwu9HUCoxVlSgMZmYjsev2+ZGqHS6VY2euR52u25BDRWNRonLJT14dVYrngcfJDYxQT6b\nxeByYfQsXpdFEATGx6MYjVLhyw9PTpLJ5Nm924NGI3LxYoAtdhfZ8UHUyRkaXXY0TgMZtRqdTuTg\nXXUUD1SjETLY9EX0Rg1GjwdBocBs1uB2GwiHi6Wkc4LAbPXtK5FgFRVGPvWpxpKRMucbVlVl5gc/\n6MbvT2C1qsnl1Jw/P80XvrALg0E1b1vabNZw1101nDo1TjSaQa9X0dbmXuDAupl44YUXePzxx3E6\nN1YI82phs9k4evQoP/jBD/jDP/zD9RZnxWwEB9aysVxrLptMUixIDwmv10Qumyc4kyIXCdHSsp2d\nOysA+OQTPy+90k9vbwCDQcXOnSI2m4ZIJE0olMJoVKPTq8iLWkIJgXfeG+XQoRq++MXd9PUFCAZT\nDA+HsNm0tLRITnEWi/SmY7frKBSKfPjhGAMDMyXZTp8e57775v+u6moze/Z4uHDBj1qtpKrKTHt7\n5YKwVKNRfd3fbLfrqKoylfacQUqV3t6+dVnncDmsts8ISFsher0UjaBUCtTX23A69Vy+HJ7dwrHN\n1uERF9TUuBlms4a7764lmcwiigoGB0OMjg7N+06xCJFIGrVaZP/+Kj74YJSZmSSiqKChQcqi29sb\n4OLFACqVsrSN8/HHk+zcWcuWLU5GRyMMDc3wzjvD2Gw6OjurgUypj7GxKIp0jIlzA6UNz7YdbhSK\nJLlUCk3DdqbOCcSDVx7Gfn+C4eHwDY0RkMol6B2OecfK4UdQDm5VDodDz4EDUoRLLJadzVDrprLS\nOK+fWw1bNhrVTEzEZrP7avD5rKhUCn79v4Kko8M8dLiSnQ1uJi+dZ3L4IhVNNajbDjE6GiUez/LI\nI02lHDFXU1FhoKGhAYUixPR0nEQiS0uLk/b2ytKK5BwKhbDg+jYa1bOZchVEo3qqqvT4fBZisTSF\nQoFstoAgQEODDb1eTV2dlAYgFpOMkWv9zK4+NythLXxGEokEzz33HK+99tqm9ZlYTvunn36aL33p\nSzz11FOlTLObSf6rua2MkaWSDAaZGRwkcvkySrUarcUC09M0NTtIJnP49tRStbMWhUIgGExy4v1L\npGMxtGqBSCTN8eOX+fSnm1Grr2wHeDxGqqstnDo1QbEIb711mUOHati1y1NK4+zzWTGbpVolcyG6\nIBVxm6s5AWA2q1EqFRw71svWrVI4cE2NGYNBw549km9IKiVFxCx2I7kRgiCwb58XrVbk8uUIKpUU\nSdHQsDKnwo2GUik9sK9N9z6XCtvvT5SquHo8xuv0cmPmzr3ZrEajUZJOX1lhkbZipAe+x2Pk4Ycb\nmZlJoSjmMWqLCMU8o6NRWpqt2FUxwsMjxBNZtJ5qCoUCUJwtyCcVPZucjHP+/BQPPdRIba0Vl0vP\nyEiEczNhFKKIgiK77msjEIWP++P4dgo02hZf9YlEMguO3a40Ndnxeo2lVYDFEtzdKrW10gO+rVmP\nmE/h8Jp46aWLKACTtkgwkOT4xBgtjR6Kl/yoDAZyOclKjMezpRw112IyabjvvjoqK43E41LxzMZG\n+7wVykKhyODgDL29AeKBEFUuJV6bgKXCgdEoFcELhVIUi6DXS3VyamosZDI5VColDQ02tm1zlfrT\naMQbbtdtFl544QUOHjzIzp0711uUNeXuu+/GbDbzT//0T5ved2Tzz8KrWMpeVTIUYvDXvyYZCFDI\n5wn29WHyerH6fCQDAdyNtVQ0+VAopCyBY30j9L93kmQsRSFRxFVjJRC14Pcn2LmzorSMKi3ZVpBK\nZZmaSuB26zGbtYyMRDh6tJHJyQSffOJnejqBViuyfburtFysUAglx0hRVKBQKHjjjX5aWkT6+5W8\n8UY/1dVmDh/2sXWrc7YmxPLPl9Go4eDBGnbvzqJUCqjV4prWHVmrPCOL4XJJWyEXL/bR1NRYljT3\niYSfHTvcnD07RTqdR6VS0NwsPQTnUKmUKGdGmDx7lvFUCn1FBTplFSffHqTnxEVsDj1eh5Kxd39D\n5+8eIRQyzTM0q6pMZDJSnSG9XsoJcvBgNZWVRvo8IjazSM+FAJfO9qPS6ckNp5hOjKDTxclkrjzU\nBIEF/gG3ymapTXMter26VIl4Rf0osziivUxP9+Ar5hBQU+PVkyeK3ewgn00TDcVRGbyYHFbMjVu4\nFJBq4czVqRkZiZDJ5DCZNDid+lIYajQ6hc/n4MyZSQYGZggEkrS1uUshuf39QalG0sg4wb5+uily\n4N4m6ky9uNvaaGiwcfFigHTaj0LhxG7Xs29fVSmdwVINj83gM5JMJvnrv/5rjh07tqz2Kx1/PdsL\ngsC3vvUtnnrqKX7rt34LtXqhj+Fqjl/O9reVMbIUIiMjJANSanaFUomtvp50LCalqm5txVJbW8rC\nGB0dJTU5CrkMolDAoMxSiExT6XbT0uJk927J2XR8PMo77wyTyxXQ6aSiebFYhmJRqnHhdBqoqDDi\n8RhIJLKYTJpSdV4Aq1VLba2Fnh4/ZrOGixf92O160ukEp04NA5SSd4mi5PtQDpa6qnK7IAgCWq2q\nbPV2FAqBPXsqZ2sGpdHrVbjdxnn9R8fHGTl+nHwmg6BQ4I8KvPHKm4STEA9HCU7OwLYqDnTUYhRi\nCBYtIGXGTSSy9PcHKRSK+HxWzpyZpLOzmvp6G1u3umhutNB7bpSZ9y5j9fnQ2myo9XqSyRwWi4hC\nIfnGqNVKGhqsZQvRvtOYOH2a+PAAJk2BtKFIRohTZc5StFgoFg04PTbyNi2+tka82xr55HIOyOJ2\n69m61ck771xmcDBELldAr1exe3cFO3ZIW8GxWIYPPrhEKCQZL+FwmpmZJA8/3ITFouXCBT/pVJbo\n+DjFfJ48cLFniuq7HAQuXqTjyEM4nXp6elLU1npoaLBtet+vm/HCCy+wf/9+du/evd6irAsPPfQQ\nLS0tfPOb3+Qb3/jGeouzbG4rY2Qp1lj2Gic8pVqN3m7H5PViqqllZCTCxPkRjEY1msAwmkyIxhYP\nF86MoNWqIA/bWqzs3u1Bp5OWQ0+dmmBmRgoIcjr17N1bicWioabGjNNpKKVcvp5vgiAI7N1biU6n\nIhRKlpJQdXdnEYQcer0KtVpJMJji448naW11lj2xz1q+8a6Fz8hy+8vnC4yORhgfj2EyqdFoRNRq\nJWaz5rrhv3N9VVQYqahYfMsnOjZWyrehMhjoG4mSDocwWZ2EkAzDdCyOu64ep1jE0mBjYGCGaDTD\n1FS8lJo/lysQjWY4dWoCj8eITqdCVKvR2aw0dmwnGs3Mq8lSV+ejrs5COJwuOW8uNwX4RlgVgfLJ\nsZR+0pEIkdFRQPL3ymYT5HR5rPoiGb0Dp0vShWerF6XFTjKYZMcODW63EaNRzfR0nIGBmZJDdSKR\npbt7Eq/XTDabJ502MjZ2ad4KTjSamc0ToiaTyVPI5ShkrySSy2fzFIoK8qk4KmWR7dvdbN/uXvNz\nU64+lvL9YDDIt771LX71q18te7yVjL9R2v/d3/0d7e3t7Nq1a8XbNbLPyBpjcLsRlMp5WSDVRiMa\ns5nu7klOn56YXeEQ8QpRxMnL7NrSgMfTysRkDJtNz5776rBapQdTPC5VzwXJEIlG05w+PYHBoOKx\nx7bgdN7akrjBoKajw0s2m8du1zE0JL0VG41qAoEkkUiKmZkUoigwOhqmunrptUJkbs65c9OcPDmG\nxaLh8uUIAwMz1NRIZdj37auiqWl5vjXX5ugoFqQwWp1RquQMoNVpUKqUmGsqcbkMPPBAPQMDIXK5\nPDt2uNHpxFL+lGg0XQrPvHgxwLlzUwwPh6mvt6HXqwiFUmg0Stxuw7xoKpnlISiVCLOOgoIgSFus\nyhh1v91KylJHOJLBbFYTDKZ4993LKJUKXC49Fy4EEASBeDyDw6EjHE6XfIt0OpHjxy8TDCYRRSVn\nz06XahvNUSyCKCqpq7MSCCTRmE3kklKYeGWNDXUhgb6iApV+daotb1SeeeYZPvvZz9I2l6fhDsXj\n8fDKK6/wyCOPcPHiRf70T/8UVRnC59eS26c6EktLnGWuqcHd1oZKr0dQKtGYzVR2dJBR6rl40V9K\n/pNM5sgbnYRTCrJTo7iKk+ypzWOzJamovvJA0mpF9HoRk0mN35/gV78aZGBghtHRKO+/PzIvauVW\nUKmUbN3qoq7OTGOjglyuQCCQwGzWolQK1NZa6eqaIBZsVu52AAATMUlEQVQrrxPiaiQfK+dY5ZZv\nsf6i0TTnz08DkE7n6eoaIxBIMjEh1Rfp6hojEkktaHcrspmrqkrbf9l4nNoaE+ZKN6bKSjRGI0ql\nkqqGCjxVdiKzhovbbaSzs5r9+6vIZPLzErlJ805Fd/cEH344SjyepVAo8u67wygU4HRKNW1SKf8y\nzs7irOUcuRHlkmMp/agNBuyNjfOy3gmAp8qGy53jwQcbyOfnnI6lsNwPPhjltdf6GR2VjNq5ek/A\n7BaewIULARKJHNlsAItFw/BwmERCuraNRnXJv2frVietrQ4qWxtw1HrYsrOGtlYLKoMBz65dpZXS\n9Tg35erjVr9/+vRpfvSjHy3YmlipzJu1/Z49ezhx4gTd3d34fD7+7M/+jLfeeotsNnvzxmUYf6Xt\n79iVEaUoUrVvH/aGBnKpFGqzGa3ZzMREbF40BMBkVKSl8x6M6XHC00HMHi8quxVRe2W5Xq0WaWur\n4Pz5abq6xigUimg0SrxeE4WCVASvudm+pKVxnU7F3r1eBCGC2ZzGbNaiUgmzKbyVBINJwuHUDUN4\nZZZOMpkjEkmjUAhcvhwuZapMJKSLOhbLEIlk5mVkvVUMLhd1993HdE8P6VAIT72dxxpb6B2M4fA6\nsJuV7N7tobLWxaXh4Xlta2ut9PfPlLZfVCqpiF+hUGBwMFSS0+UyYLFoyeeLfPrTzej1aoaGlpcb\nRGYh7rY2lBoNM/39CAoFji1bsDc1ER8ZASjl8xAEqS7UpUthlEqBbLaA3a5nejpBNJpGrVai04lk\ns3n0euktNpHIcvfdtfT0TKPRiNjtWrZtc2M2S9E/BoOaQ4fq2LHDTTazDb2QgnwOndOJqL5z7gOp\nVIovfOEL/M3f/M0dk1fkVqirq+Pb3/420WiUn/zkJ/zJn/wJAwMDPP7443z5y1+ms7NzvUW8LutZ\nSeg/AP8R0AD/A/j+Nf9eLK5DRbZEQko+FQxeefMtFouzxbKKJOJplKKSpiZHqYDW1QwNzfDTn/YQ\nDqewWLSlm4jTqeO3f7tl0ZC+W+H06QkGBoKAQDKZLeXGeOSRplveAtroCIIUubSeFItFenuD/PjH\nZwkEktjtOrq6xjGZ1NTWWvD5rGi1Ig8/3ITbvbLzXsjlSts2mUyOTKaAwaC6oR9QMJhkdDRCJpOn\nosKI12siHE5x7FjvvKReIEVuPP54a8lBeqOyEfS+HIqFAgjCAn199NE4XV1jKBQCFouGl166iNGo\nZscON2q1klQqx7ZtThwOPXa7jmAwyYcfjpXai6ICq1WqIzMxIfkJmc0a9u+vuq4v0mZkJXp/+umn\nGR0d5ac//eltXRCvHIyMjPDjH/+Y73znO+zcuZPvfve7VFdXr4sss7paVGHruU3zInAfcBfwn9ZR\njnno9Wra270lI0IUFTidepLJLH19M4yNJ7h8OcoHH4zi9y9826yrs9LR4aWmxlLqQxCgvt62bEME\npERnuVwBvz9BPJ5FoRBoarLjcNxZe8SrzcREjK6uMVpbXYiiAr1ehcWinl0qlyqvNjbacLlWft6v\n9h9Rq0WMRvVNb6x2u462tgra271UV5tRKASsVu2CHCmCAI2N9g1viGxmBIViUX01NdmprbUgCNJ2\na22tmbo6C2q1dP1brVp27HCzZ4+UqLC+3jovw2o+LyUnk5LoSYnSxsdjnDgxSiazsALyncYLL7zA\n66+/zve+9z3ZELkFqqur+frXv84nn3zCgQMH2Lt3Ly+//PJ6i7WA9TRG5q4qDVCWNeRy7ZPW19t4\n5JEmjh5t5OjRRrZtc5VquMwRDI7PWz2ZYy4iprnZjsGgwmSS3ohaW5e/lDg0NITTqeeBBxro6PCy\nbZuT++/30d5eWfaL8U73GZmaihOLZUgkstx1Vw1btjj4vd/bzb/9t9vZu9fD4cM+Ojq8i573tfBn\nWQxBEOjo8NLYaCvNuZ07K9iy5crKXTllu5N9Rm6lH7NZw5EjPh5+uIn29kq++MU9bNvmQq9X4XTq\n6eysnpd23WrVcf/9Pg4cqMLjyXL4sA+Xy7Dg/hIMJhe956zFb1rLPm70/RdffJFnnnmGV155pVRE\ndaXj3SntNRoNf/7nf87LL7/MV77yFZ577rk1Hf9mrLfPyP8D/AHw5+XuOJfLEwymEEUFNpt2yQ9t\nKaGY5BMQjfrnVbhUKgUUCq6bn8Js1nD4sI9wOI1CwbJ8CxbD4dDLKyFlIpcrMDMj+dwUi8XS/JhL\nCjVXzRekVYajR6WMpxsVi0XLkSP1hELSnF9JhlGZlaNWi6VEZQA+n4VQKEU+X0SnW3jbtdl02Gw6\nLJYUPp+D0dHIgu8olULZcuJsRp5//nn+6q/+il/+8pc0NTWttziblgMHDvDee+/x6KOP0tfXx7PP\nPotSufxV+3KxFmtcFcCPrjk2ATw5+7ca+BVS1d7YVd9Zts/I9HScEydGCASSKJUKfD4L7e3eZSf3\nikTSvPFGP+m05GiWSuWwWrXs3+/FbpeNg3KxVr4DwWCCEydGmZ5OoFAI1NSY6ejwYjCoCYdT/PKX\nAwQCydL3PR4jDzxQj8GwsR0EM5kcExNSTROrVcptsRm2aTarz8hSGB+XtnbnjMWmJjt79njm1ZzJ\n5wtMTsaJRNLk8wX6+2dK5SEEAVpbndxzT+1tszVxq3pPpVI8/fTTvPPOO7z00ks0NzevgXS3P6FQ\niCeeeAKj0ciLL76IwbD6voc38hlZz1mtBjKzMrwJPAZcvRdS/NrXvlZaimttbaWzs7OUUGVuKeja\nz9XVNbz2Wj8DA4MAaDTO2eJQAo2N9pu2v97n7u4L9PcH6epKUCwKOBxpvF4jjz66H4tFu+T+5M8L\nP9fX16/6Q6lQKPKrXw0wOBiad7y9vZL2di8gRUN88kkAvz+Bx2OkudmOzbax83Mkk1nee+8yQ0Mh\n8nkpkmvHDjd795Z/K6/c3O7GSDqd49ix3nkh2YIA995bR0uLtH2bzxfo6hrj/PlpstkCoqjA7dbj\ndErRNz6flaYm+22VLflW9H7ixAl+//d/nx07dvC9730Pk2nzVhXeiGQyGf7gD/6Anp4e/uVf/gXP\ndapVl4uNaoz8BXAYyWfkR8C1G1hLXhkZGhrCaHTxi1/0kUzOd/SqqDDw+OOtyxY2Hs/wyiufEA5L\naZrTaT8ajXPeQ2y1GNrg9WLKNdat3JxWKt/MTHJe5MmcHp1OHY8/3rrsrKTlkG0l/fX2BnjzzaF5\n24k6ncjRo1LUTzllK/fvXK4xUi45Vruf8fEor77aRzZbmHe8vt7Kpz7VCMDYWJTXXusjFptCo5EM\nFKVS4IEHGvD5lr49uJHOzfX6uJ7eh4aGUCgU/OVf/iXHjh3jueee47Of/ewtG9UrlflOa18sFvnG\nN77BCy+8wPPPP8/OnTtXbfyNGk3zl8D9SNE0i3vSLJHjx48jispFHygrfaOIRjPzDJze3m6AeVV2\nV4vjx4+v+hibZayVyqdSzZ8fc3rUaMQVb2mU+9wtpb9AIMm19/VkMkc8nllyX+WUazUplxyr3Y8o\nKhb19dBqr2zRRKNpstlCaT6ClKNkLqtzuWRZj35utY98Ps+bb77JU089xZ49e6isrKSnp4ff+Z3f\nWdLq3kplvtPaC4LAX/zFX/CP//iPfP3rX+dLX/oSx48fX/Zq5XLlv628oS5cuIDVqqWhwXp1gkQ0\nGiUtLQtzgiwFg0E17+YxONgHMC8kb7W4cOHCqo+xWcZaqXxGo5rm5ishr4ODfahUirLU+Sn3uVtK\nf3NlCa5Go1GWjPByyraWc+RGlEuO1e7H4dAvKEqo04k0NtpKnw0GNaKoKN1XQCq8OJceoFyyrEc/\nN+qjUCjw93//93zuc5+joqKCr33ta+TzeXp7e/nmN7953YiZ5Y4nt78+hw4d4ty5c+h0Oj7/+c+z\nZcsWvvrVr/L973+f119/nVOnTjEwMMDk5CTxeJxCobBoP8sdf72jaVaFPXsqMZs1DA6G0GrFUtz/\nSjCZNLS1uenqGitlaHU69TQ3L69Gicz60dbmxmBQMzAwg9Wq5fBh37KWwjcSNTVmamrMjIxEZuuY\nKGhpcaw4MZvMylEoBPbvr8Ju13HpUgiDQU1zs2NewUyPx0Bzs71kECsUAj6fFa/39vaRUCgUHD9+\nnCNHjvDtb3+bmpoannnmGex2+b66Hmg0Gvbt28crr7zCyZMnefvtt3nzzTcZHx9namqKSCRCPB4n\nFouRSqXQ6/UYDAaMRmPp/xMTE3R3d5c+Hzp0iN/93d+96di3lTESCklOiRqNyLZtbrZtK0/lyjm2\nbXNht+uYmorz/vsKHnywYdlvLkth7netBRt9rHLIp1aLtLY6aW118vrrCurrbTdvdAuU+9wtpT+D\nQc1999UxNhYjFstgt2uprDSVVoDKKdtazpEbUS451qIfnU5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ikQzBoItAYBQYobj4OhIRLz7VxtP7R7lm7Uz+n3vvPZ0qvJ2dO1vRNCeQRdNe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dJjiJgJ+tTFAgoOoKGjIvuDGOqs00PHJUVAGxWLFQ7SYAmbgJCYNjl0OY5wWzQHGcM+EygI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\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YTGe2SZKELx7HaDQyNjaGMhpFrVQiSVKi/ocoYk5JwWuxEAqFWLV4AT1HjjA82E+KR06f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\nIAIgiiLDqRQXTpx4ywUjvu9z7OmnOTg6elXKv6VpJGMxyqur6Lt2XdXKMG0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16fV9Hnvo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JUuMre6yvC11774GuX1dQpISFK8m4+EoNWp0OM5ZGWDjAiIgoj1loMLXDs8zOT4OIePHmXJ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xf38zLpcas7kCtVpDc3M7IyN7MBj0ZGYWnCKzTpdCT8/AhDECsGTJYtLSUvnyy4M4nTYW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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1155cf1d0>"
+        "<matplotlib.figure.Figure at 0x11dade4d0>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+     ]
     },
     {
      "cell_type": "code",
@@ -89,7 +102,7 @@
       "clf = sklearn.linear_model.SGDClassifier(\n",
       "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
       "\n",
-      "clf.fit(X, y)\n",
+      "%timeit clf.fit(X, y)\n",
       "yt_pred = clf.predict(Xt)\n",
       "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
      ],
@@ -100,11 +113,19 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Accuracy: 0.763\n"
+        "1 loops, best of 3: 499 ms per loop\n",
+        "Accuracy: 0.756\n"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Save the dataset to HDF5 for loading in Caffe."
+     ]
     },
     {
      "cell_type": "code",
@@ -139,15 +160,69 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Learn and evaluate logistic regression in Caffe."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def learn_and_test(solver_file):\n",
+      "    caffe.set_mode_cpu()\n",
+      "    solver = caffe.get_solver(solver_file)\n",
+      "    solver.solve()\n",
+      "\n",
+      "    accuracy = 0\n",
+      "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
+      "    for i in range(test_iters):\n",
+      "        solver.test_nets[0].forward()\n",
+      "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+      "    accuracy /= test_iters\n",
+      "    return accuracy\n",
+      "\n",
+      "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n",
+      "acc = learn_and_test('hdf5_classification/solver.prototxt')\n",
+      "print(\"Accuracy: {:.3f}\".format(acc))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 loops, best of 3: 240 ms per loop\n",
+        "Accuracy: 0.752"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
      "prompt_number": 5
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Do the same through the command line interface for detailed output on the model and solving."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Run caffe. Scroll down in the output to see the final\n",
-      "# test accuracy, which should be about the same as above.\n",
-      "!cd .. && ./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
+      "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt"
      ],
      "language": "python",
      "metadata": {},
@@ -156,9 +231,16 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.099238 2129298192 caffe.cpp:90] Starting Optimization\r\n",
-        "I0905 01:07:27.100469 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 1000\r\n",
+        "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\r\n",
+        "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
+        "test_iter: 250\r\n",
         "test_interval: 1000\r\n",
         "base_lr: 0.01\r\n",
         "display: 1000\r\n",
@@ -169,42 +251,43 @@
         "weight_decay: 0.0005\r\n",
         "stepsize: 5000\r\n",
         "snapshot: 10000\r\n",
-        "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
+        "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
         "solver_mode: CPU\r\n",
-        "net: \"examples/hdf5_classification/train_val.prototxt\"\r\n",
-        "I0905 01:07:27.100630 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val.prototxt\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0905 01:07:27.100988 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.101011 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0905 01:07:27.101022 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "net: \"hdf5_classification/train_val.prototxt\"\r\n",
+        "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\r\n",
+        "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
+        "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
+        "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
         "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
+        "state {\r\n",
+        "  phase: TRAIN\r\n",
+        "}\r\n",
+        "layer {\r\n",
+        "  name: \"data\"\r\n",
+        "  type: \"HDF5Data\"\r\n",
         "  top: \"data\"\r\n",
         "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
         "  include {\r\n",
         "    phase: TRAIN\r\n",
         "  }\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"hdf5_classification/data/train.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"data\"\r\n",
         "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
+        "  param {\r\n",
+        "    lr_mult: 1\r\n",
+        "    decay_mult: 1\r\n",
+        "  }\r\n",
+        "  param {\r\n",
+        "    lr_mult: 2\r\n",
+        "    decay_mult: 0\r\n",
+        "  }\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 2\r\n",
         "    weight_filler {\r\n",
@@ -217,72 +300,77 @@
         "    }\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: \"SoftmaxWithLoss\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  bottom: \"label\"\r\n",
         "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
         "}\r\n",
+        "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
+        "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\r\n",
+        "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\r\n",
+        "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\r\n",
+        "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\r\n",
+        "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
+        "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
+        "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
+        "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
+        "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\r\n",
+        "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\r\n",
+        "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\r\n",
+        "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\r\n",
+        "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\r\n",
+        "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\r\n",
+        "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\r\n",
+        "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\r\n",
+        "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\r\n",
+        "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\r\n",
+        "I0307 01:34:29.425789 2099749632 net.cpp:122]     with loss weight 1\r\n",
+        "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\r\n",
+        "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
+        "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\r\n",
+        "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\r\n",
+        "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\r\n",
+        "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\r\n",
+        "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\r\n",
+        "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
+        "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
+        "name: \"LogisticRegressionNet\"\r\n",
         "state {\r\n",
-        "  phase: TRAIN\r\n",
+        "  phase: TEST\r\n",
         "}\r\n",
-        "I0905 01:07:27.105614 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.105664 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.105698 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.105710 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.105717 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
-        "I0905 01:07:27.105813 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
-        "I0905 01:07:27.105828 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.109418 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.109501 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.109522 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.109531 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.109560 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.109570 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.109590 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.109618 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.115136 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.115190 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.115198 2129298192 net.cpp:394] loss <- fc1\r\n",
-        "I0905 01:07:27.115206 2129298192 net.cpp:394] loss <- label\r\n",
-        "I0905 01:07:27.115214 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.115224 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.115237 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.115244 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.115260 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.115267 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.115273 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.115278 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.115288 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.115295 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.115301 2129298192 net.cpp:220] Memory required for data: 284\r\n",
-        "I0905 01:07:27.115622 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val.prototxt\r\n",
-        "I0905 01:07:27.115644 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.115656 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"data\"\r\n",
+        "  type: \"HDF5Data\"\r\n",
         "  top: \"data\"\r\n",
         "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
         "  include {\r\n",
         "    phase: TEST\r\n",
         "  }\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"hdf5_classification/data/test.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"data\"\r\n",
         "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
+        "  param {\r\n",
+        "    lr_mult: 1\r\n",
+        "    decay_mult: 1\r\n",
+        "  }\r\n",
+        "  param {\r\n",
+        "    lr_mult: 2\r\n",
+        "    decay_mult: 0\r\n",
+        "  }\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 2\r\n",
         "    weight_filler {\r\n",
@@ -295,194 +383,176 @@
         "    }\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: \"SoftmaxWithLoss\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  bottom: \"label\"\r\n",
         "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"accuracy\"\r\n",
+        "  type: \"Accuracy\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  bottom: \"label\"\r\n",
         "  top: \"accuracy\"\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: ACCURACY\r\n",
         "  include {\r\n",
         "    phase: TEST\r\n",
         "  }\r\n",
         "}\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "I0905 01:07:27.115854 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.115864 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.116004 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.116024 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.116030 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
-        "I0905 01:07:27.116080 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n",
-        "I0905 01:07:27.116089 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
-        "I0905 01:07:27.117313 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
-        "I0905 01:07:27.117348 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.117357 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.117364 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.117377 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
-        "I0905 01:07:27.117384 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
-        "I0905 01:07:27.117393 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0905 01:07:27.117409 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0905 01:07:27.117419 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
-        "I0905 01:07:27.117427 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.117434 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.117444 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.117449 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.117470 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.117478 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.117506 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.117519 2129298192 net.cpp:67] Creating Layer fc1_fc1_0_split\r\n",
-        "I0905 01:07:27.117527 2129298192 net.cpp:394] fc1_fc1_0_split <- fc1\r\n",
-        "I0905 01:07:27.117534 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
-        "I0905 01:07:27.117543 2129298192 net.cpp:356] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
-        "I0905 01:07:27.117640 2129298192 net.cpp:96] Setting up fc1_fc1_0_split\r\n",
-        "I0905 01:07:27.117655 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.117662 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.117673 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.117679 2129298192 net.cpp:394] loss <- fc1_fc1_0_split_0\r\n",
-        "I0905 01:07:27.117687 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
-        "I0905 01:07:27.117696 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.117704 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.117717 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.117723 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.117743 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
-        "I0905 01:07:27.117749 2129298192 net.cpp:394] accuracy <- fc1_fc1_0_split_1\r\n",
-        "I0905 01:07:27.117756 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
-        "I0905 01:07:27.117764 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
-        "I0905 01:07:27.117774 2129298192 net.cpp:96] Setting up accuracy\r\n",
-        "I0905 01:07:27.117781 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.117789 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
-        "I0905 01:07:27.117794 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.117835 2129298192 net.cpp:170] fc1_fc1_0_split needs backward computation.\r\n",
-        "I0905 01:07:27.117842 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.117848 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
-        "I0905 01:07:27.117854 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.117861 2129298192 net.cpp:208] This network produces output accuracy\r\n",
-        "I0905 01:07:27.117866 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.117877 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.117926 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.117938 2129298192 net.cpp:220] Memory required for data: 528\r\n",
-        "I0905 01:07:27.117985 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
-        "I0905 01:07:27.117992 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
-        "I0905 01:07:27.118026 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
-        "I0905 01:07:27.123764 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.646801\r\n",
-        "I0905 01:07:27.123847 2129298192 solver.cpp:302]     Test net output #1: loss = 0.690777 (* 1 = 0.690777 loss)\r\n",
-        "I0905 01:07:27.123888 2129298192 solver.cpp:195] Iteration 0, loss = 0.689469\r\n",
-        "I0905 01:07:27.123898 2129298192 solver.cpp:210]     Train net output #0: loss = 0.689469 (* 1 = 0.689469 loss)\r\n",
-        "I0905 01:07:27.123915 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
-        "I0905 01:07:27.127096 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.128094 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.129258 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
-        "I0905 01:07:27.135226 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.745599\r\n",
-        "I0905 01:07:27.135296 2129298192 solver.cpp:302]     Test net output #1: loss = 0.573658 (* 1 = 0.573658 loss)\r\n",
-        "I0905 01:07:27.135315 2129298192 solver.cpp:195] Iteration 1000, loss = 0.49682\r\n",
-        "I0905 01:07:27.135325 2129298192 solver.cpp:210]     Train net output #0: loss = 0.49682 (* 1 = 0.49682 loss)\r\n",
-        "I0905 01:07:27.135334 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
-        "I0905 01:07:27.137315 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.137358 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.138335 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.140410 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
-        "I0905 01:07:27.147435 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.746399\r\n",
-        "I0905 01:07:27.147514 2129298192 solver.cpp:302]     Test net output #1: loss = 0.582127 (* 1 = 0.582127 loss)\r\n",
-        "I0905 01:07:27.147541 2129298192 solver.cpp:195] Iteration 2000, loss = 0.555272\r\n",
-        "I0905 01:07:27.147553 2129298192 solver.cpp:210]     Train net output #0: loss = 0.555272 (* 1 = 0.555272 loss)\r\n",
-        "I0905 01:07:27.147565 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
-        "I0905 01:07:27.148572 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.149441 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.152377 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n"
+        "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
+        "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\r\n",
+        "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\r\n",
+        "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\r\n",
+        "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\r\n",
+        "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
+        "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
+        "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
+        "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
+        "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
+        "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
+        "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
+        "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
+        "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
+        "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
+        "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\r\n",
+        "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\r\n",
+        "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\r\n",
+        "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\r\n",
+        "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\r\n",
+        "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\r\n",
+        "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\r\n",
+        "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
+        "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
+        "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\r\n",
+        "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\r\n",
+        "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\r\n",
+        "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
+        "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\r\n",
+        "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\r\n",
+        "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\r\n",
+        "I0307 01:34:29.428056 2099749632 net.cpp:122]     with loss weight 1\r\n",
+        "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
+        "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
+        "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\r\n",
+        "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
+        "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
+        "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\r\n",
+        "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\r\n",
+        "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
+        "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\r\n",
+        "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\r\n",
+        "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
+        "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
+        "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\r\n",
+        "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\r\n",
+        "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\r\n",
+        "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\r\n",
+        "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\r\n",
+        "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
+        "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
+        "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
+        "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.158655 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.696\r\n",
-        "I0905 01:07:27.158746 2129298192 solver.cpp:302]     Test net output #1: loss = 0.580239 (* 1 = 0.580239 loss)\r\n",
-        "I0905 01:07:27.158761 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.158768 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.159765 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.159843 2129298192 solver.cpp:195] Iteration 3000, loss = 0.476517\r\n",
-        "I0905 01:07:27.159873 2129298192 solver.cpp:210]     Train net output #0: loss = 0.476517 (* 1 = 0.476517 loss)\r\n",
-        "I0905 01:07:27.159983 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
-        "I0905 01:07:27.163079 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.163602 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.164567 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
-        "I0905 01:07:27.170277 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.745599\r\n",
-        "I0905 01:07:27.170344 2129298192 solver.cpp:302]     Test net output #1: loss = 0.573658 (* 1 = 0.573658 loss)\r\n",
-        "I0905 01:07:27.170364 2129298192 solver.cpp:195] Iteration 4000, loss = 0.49682\r\n",
-        "I0905 01:07:27.170375 2129298192 solver.cpp:210]     Train net output #0: loss = 0.49682 (* 1 = 0.49682 loss)\r\n",
-        "I0905 01:07:27.170385 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
-        "I0905 01:07:27.172350 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.172374 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.173084 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.175192 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
-        "I0905 01:07:27.181659 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.746399\r\n",
-        "I0905 01:07:27.181710 2129298192 solver.cpp:302]     Test net output #1: loss = 0.582127 (* 1 = 0.582127 loss)\r\n",
-        "I0905 01:07:27.181730 2129298192 solver.cpp:195] Iteration 5000, loss = 0.555272\r\n",
-        "I0905 01:07:27.181740 2129298192 solver.cpp:210]     Train net output #0: loss = 0.555272 (* 1 = 0.555272 loss)\r\n",
-        "I0905 01:07:27.181748 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
-        "I0905 01:07:27.182734 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.183248 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.186180 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n",
-        "I0905 01:07:27.192646 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7684\r\n",
-        "I0905 01:07:27.192751 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574538 (* 1 = 0.574538 loss)\r\n",
-        "I0905 01:07:27.192766 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.192773 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.193936 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.194007 2129298192 solver.cpp:195] Iteration 6000, loss = 0.464052\r\n",
-        "I0905 01:07:27.194036 2129298192 solver.cpp:210]     Train net output #0: loss = 0.464052 (* 1 = 0.464052 loss)\r\n",
-        "I0905 01:07:27.194051 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
-        "I0905 01:07:27.197053 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.198092 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.199162 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
-        "I0905 01:07:27.205195 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7684\r\n",
-        "I0905 01:07:27.205298 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574549 (* 1 = 0.574549 loss)\r\n",
-        "I0905 01:07:27.205327 2129298192 solver.cpp:195] Iteration 7000, loss = 0.495483\r\n",
-        "I0905 01:07:27.205338 2129298192 solver.cpp:210]     Train net output #0: loss = 0.495483 (* 1 = 0.495483 loss)\r\n",
-        "I0905 01:07:27.205353 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n"
+        "I0307 01:34:29.471674 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.4532\r\n",
+        "I0307 01:34:29.471724 2099749632 solver.cpp:315]     Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\r\n",
+        "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\r\n",
+        "I0307 01:34:29.471878 2099749632 solver.cpp:204]     Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\r\n",
+        "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
+        "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n",
+        "I0307 01:34:29.486868 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\r\n",
+        "I0307 01:34:29.486896 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
+        "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\r\n",
+        "I0307 01:34:29.486934 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
+        "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
+        "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
+        "I0307 01:34:29.501900 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\r\n",
+        "I0307 01:34:29.501941 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
+        "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\r\n",
+        "I0307 01:34:29.502003 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
+        "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
+        "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n",
+        "I0307 01:34:29.517227 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.6964\r\n",
+        "I0307 01:34:29.517300 2099749632 solver.cpp:315]     Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\r\n",
+        "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\r\n",
+        "I0307 01:34:29.518154 2099749632 solver.cpp:204]     Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\r\n",
+        "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.207471 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.207489 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.208534 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.210860 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
-        "I0905 01:07:27.216624 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.762\r\n",
-        "I0905 01:07:27.216704 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574515 (* 1 = 0.574515 loss)\r\n",
-        "I0905 01:07:27.216723 2129298192 solver.cpp:195] Iteration 8000, loss = 0.524565\r\n",
-        "I0905 01:07:27.216733 2129298192 solver.cpp:210]     Train net output #0: loss = 0.524565 (* 1 = 0.524565 loss)\r\n",
-        "I0905 01:07:27.216743 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
-        "I0905 01:07:27.217738 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.218291 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.221294 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
-        "I0905 01:07:27.227104 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7688\r\n",
-        "I0905 01:07:27.227171 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574278 (* 1 = 0.574278 loss)\r\n",
-        "I0905 01:07:27.227183 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.227190 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.228143 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.228210 2129298192 solver.cpp:195] Iteration 9000, loss = 0.461831\r\n",
-        "I0905 01:07:27.228240 2129298192 solver.cpp:210]     Train net output #0: loss = 0.461831 (* 1 = 0.461831 loss)\r\n",
-        "I0905 01:07:27.228252 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
-        "I0905 01:07:27.231314 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.232293 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.233417 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
-        "I0905 01:07:27.233680 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0905 01:07:27.233795 2129298192 solver.cpp:232] Iteration 10000, loss = 0.49554\r\n",
-        "I0905 01:07:27.233814 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
-        "I0905 01:07:27.240015 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.768\r\n",
-        "I0905 01:07:27.240099 2129298192 solver.cpp:302]     Test net output #1: loss = 0.574488 (* 1 = 0.574488 loss)\r\n",
-        "I0905 01:07:27.240110 2129298192 solver.cpp:237] Optimization Done.\r\n",
-        "I0905 01:07:27.240118 2129298192 caffe.cpp:114] Optimization Done.\r\n"
+        "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
+        "I0307 01:34:29.534873 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\r\n",
+        "I0307 01:34:29.534920 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
+        "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\r\n",
+        "I0307 01:34:29.534962 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
+        "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
+        "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n",
+        "I0307 01:34:29.549762 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\r\n",
+        "I0307 01:34:29.549789 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
+        "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\r\n",
+        "I0307 01:34:29.549828 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
+        "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
+        "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
+        "I0307 01:34:29.565335 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7476\r\n",
+        "I0307 01:34:29.565373 2099749632 solver.cpp:315]     Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\r\n",
+        "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\r\n",
+        "I0307 01:34:29.566086 2099749632 solver.cpp:204]     Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\r\n",
+        "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
+        "I0307 01:34:29.580993 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7524\r\n",
+        "I0307 01:34:29.581015 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\r\n",
+        "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\r\n",
+        "I0307 01:34:29.581050 2099749632 solver.cpp:204]     Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\r\n",
+        "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n",
+        "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
+        "I0307 01:34:29.595973 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7568\r\n",
+        "I0307 01:34:29.596002 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\r\n",
+        "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\r\n",
+        "I0307 01:34:29.596040 2099749632 solver.cpp:204]     Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\r\n",
+        "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
+        "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
+        "I0307 01:34:29.610930 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7432\r\n",
+        "I0307 01:34:29.610960 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\r\n",
+        "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\r\n",
+        "I0307 01:34:29.611583 2099749632 solver.cpp:204]     Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\r\n",
+        "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n",
+        "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
+        "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
+        "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\r\n",
+        "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0307 01:34:29.626454 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.752\r\n",
+        "I0307 01:34:29.626484 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\r\n",
+        "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\r\n",
+        "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\r\n"
        ]
       }
      ],
@@ -492,18 +562,33 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "If you look at the `train_val.prototxt`, you'll see that it's simple logistic regression.\n",
-      "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer neural network.\n",
+      "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n",
+      "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
       "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
       "\n",
-      "The final accuracy of the network we'll train below should be higher than for the network above!"
+      "The final accuracy of the new network be higher than logistic regression!"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "!cd .. && ./build/tools/caffe train -solver examples/hdf5_classification/solver2.prototxt"
+      "def learn_and_test(solver_file):\n",
+      "    caffe.set_mode_cpu()\n",
+      "    solver = caffe.get_solver(solver_file)\n",
+      "    solver.solve()\n",
+      "\n",
+      "    accuracy = 0\n",
+      "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
+      "    for i in range(test_iters):\n",
+      "        solver.test_nets[0].forward()\n",
+      "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+      "    accuracy /= test_iters\n",
+      "    return accuracy\n",
+      "\n",
+      "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n",
+      "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n",
+      "print(\"Accuracy: {:.3f}\".format(acc))"
      ],
      "language": "python",
      "metadata": {},
@@ -512,9 +597,50 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.466722 2129298192 caffe.cpp:90] Starting Optimization\r\n",
-        "I0905 01:07:27.468166 2129298192 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 1000\r\n",
+        "1 loops, best of 3: 333 ms per loop\n",
+        "Accuracy: 0.818"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Do the same through the command line interface for detailed output on the model and solving."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\r\n",
+        "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
+        "test_iter: 250\r\n",
         "test_interval: 1000\r\n",
         "base_lr: 0.01\r\n",
         "display: 1000\r\n",
@@ -525,36 +651,43 @@
         "weight_decay: 0.0005\r\n",
         "stepsize: 5000\r\n",
         "snapshot: 10000\r\n",
-        "snapshot_prefix: \"examples/hdf5_classification/data/train\"\r\n",
+        "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
         "solver_mode: CPU\r\n",
-        "net: \"examples/hdf5_classification/train_val2.prototxt\"\r\n",
-        "I0905 01:07:27.468351 2129298192 solver.cpp:72] Creating training net from net file: examples/hdf5_classification/train_val2.prototxt\r\n",
-        "I0905 01:07:27.469081 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.469100 2129298192 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0905 01:07:27.469110 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
+        "net: \"hdf5_classification/train_val2.prototxt\"\r\n",
+        "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\r\n",
+        "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
+        "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
+        "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
         "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
+        "state {\r\n",
+        "  phase: TRAIN\r\n",
+        "}\r\n",
+        "layer {\r\n",
+        "  name: \"data\"\r\n",
+        "  type: \"HDF5Data\"\r\n",
         "  top: \"data\"\r\n",
         "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
         "  include {\r\n",
         "    phase: TRAIN\r\n",
         "  }\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"hdf5_classification/data/train.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"data\"\r\n",
         "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
+        "  param {\r\n",
+        "    lr_mult: 1\r\n",
+        "    decay_mult: 1\r\n",
+        "  }\r\n",
+        "  param {\r\n",
+        "    lr_mult: 2\r\n",
+        "    decay_mult: 0\r\n",
+        "  }\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 40\r\n",
         "    weight_filler {\r\n",
@@ -567,21 +700,25 @@
         "    }\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  top: \"fc1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc2\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  top: \"fc2\"\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
+        "  param {\r\n",
+        "    lr_mult: 1\r\n",
+        "    decay_mult: 1\r\n",
+        "  }\r\n",
+        "  param {\r\n",
+        "    lr_mult: 2\r\n",
+        "    decay_mult: 0\r\n",
+        "  }\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 2\r\n",
         "    weight_filler {\r\n",
@@ -594,84 +731,91 @@
         "    }\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: \"SoftmaxWithLoss\"\r\n",
         "  bottom: \"fc2\"\r\n",
         "  bottom: \"label\"\r\n",
         "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
         "}\r\n",
+        "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
+        "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\r\n",
+        "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\r\n",
+        "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\r\n",
+        "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\r\n",
+        "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
+        "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
+        "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
+        "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
+        "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\r\n",
+        "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\r\n",
+        "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\r\n",
+        "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\r\n",
+        "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
+        "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
+        "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\r\n",
+        "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\r\n",
+        "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
+        "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\r\n",
+        "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
+        "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
+        "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\r\n",
+        "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\r\n",
+        "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\r\n",
+        "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\r\n",
+        "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\r\n",
+        "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\r\n",
+        "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\r\n",
+        "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\r\n",
+        "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\r\n",
+        "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\r\n",
+        "I0307 01:34:31.882844 2099749632 net.cpp:122]     with loss weight 1\r\n",
+        "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\r\n",
+        "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
+        "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
+        "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
+        "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\r\n",
+        "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\r\n",
+        "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\r\n",
+        "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\r\n",
+        "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\r\n",
+        "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
+        "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
+        "name: \"LogisticRegressionNet\"\r\n",
         "state {\r\n",
-        "  phase: TRAIN\r\n",
+        "  phase: TEST\r\n",
         "}\r\n",
-        "I0905 01:07:27.469447 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.469467 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.469493 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.469503 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.469511 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/train.txt\r\n",
-        "I0905 01:07:27.469558 2129298192 hdf5_data_layer.cpp:69] Number of files: 2\r\n",
-        "I0905 01:07:27.469569 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.471978 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.471997 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.472008 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.472015 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.472026 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.472033 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.472045 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.472060 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.476827 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.476857 2129298192 net.cpp:67] Creating Layer relu1\r\n",
-        "I0905 01:07:27.476865 2129298192 net.cpp:394] relu1 <- fc1\r\n",
-        "I0905 01:07:27.476872 2129298192 net.cpp:345] relu1 -> fc1 (in-place)\r\n",
-        "I0905 01:07:27.476881 2129298192 net.cpp:96] Setting up relu1\r\n",
-        "I0905 01:07:27.476888 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.476896 2129298192 net.cpp:67] Creating Layer fc2\r\n",
-        "I0905 01:07:27.476902 2129298192 net.cpp:394] fc2 <- fc1\r\n",
-        "I0905 01:07:27.476909 2129298192 net.cpp:356] fc2 -> fc2\r\n",
-        "I0905 01:07:27.476918 2129298192 net.cpp:96] Setting up fc2\r\n",
-        "I0905 01:07:27.476932 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.476955 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.476963 2129298192 net.cpp:394] loss <- fc2\r\n",
-        "I0905 01:07:27.476969 2129298192 net.cpp:394] loss <- label\r\n",
-        "I0905 01:07:27.476975 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.476984 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.477005 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.477040 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.477051 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.477058 2129298192 net.cpp:170] fc2 needs backward computation.\r\n",
-        "I0905 01:07:27.477063 2129298192 net.cpp:170] relu1 needs backward computation.\r\n",
-        "I0905 01:07:27.477069 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.477076 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.477080 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.477099 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.477105 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.477112 2129298192 net.cpp:220] Memory required for data: 3484\r\n",
-        "I0905 01:07:27.477455 2129298192 solver.cpp:156] Creating test net (#0) specified by net file: examples/hdf5_classification/train_val2.prototxt\r\n",
-        "I0905 01:07:27.477480 2129298192 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0905 01:07:27.477494 2129298192 net.cpp:39] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"data\"\r\n",
+        "  type: \"HDF5Data\"\r\n",
         "  top: \"data\"\r\n",
         "  top: \"label\"\r\n",
-        "  name: \"data\"\r\n",
-        "  type: HDF5_DATA\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"examples/hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
         "  include {\r\n",
         "    phase: TEST\r\n",
         "  }\r\n",
+        "  hdf5_data_param {\r\n",
+        "    source: \"hdf5_classification/data/test.txt\"\r\n",
+        "    batch_size: 10\r\n",
+        "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc1\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"data\"\r\n",
         "  top: \"fc1\"\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
+        "  param {\r\n",
+        "    lr_mult: 1\r\n",
+        "    decay_mult: 1\r\n",
+        "  }\r\n",
+        "  param {\r\n",
+        "    lr_mult: 2\r\n",
+        "    decay_mult: 0\r\n",
+        "  }\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 40\r\n",
         "    weight_filler {\r\n",
@@ -684,21 +828,25 @@
         "    }\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: \"ReLU\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  top: \"fc1\"\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: RELU\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"fc2\"\r\n",
+        "  type: \"InnerProduct\"\r\n",
         "  bottom: \"fc1\"\r\n",
         "  top: \"fc2\"\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: INNER_PRODUCT\r\n",
-        "  blobs_lr: 1\r\n",
-        "  blobs_lr: 2\r\n",
-        "  weight_decay: 1\r\n",
-        "  weight_decay: 0\r\n",
+        "  param {\r\n",
+        "    lr_mult: 1\r\n",
+        "    decay_mult: 1\r\n",
+        "  }\r\n",
+        "  param {\r\n",
+        "    lr_mult: 2\r\n",
+        "    decay_mult: 0\r\n",
+        "  }\r\n",
         "  inner_product_param {\r\n",
         "    num_output: 2\r\n",
         "    weight_filler {\r\n",
@@ -711,222 +859,200 @@
         "    }\r\n",
         "  }\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"loss\"\r\n",
+        "  type: \"SoftmaxWithLoss\"\r\n",
         "  bottom: \"fc2\"\r\n",
         "  bottom: \"label\"\r\n",
         "  top: \"loss\"\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: SOFTMAX_LOSS\r\n",
         "}\r\n",
-        "layers {\r\n",
+        "layer {\r\n",
+        "  name: \"accuracy\"\r\n",
+        "  type: \"Accuracy\"\r\n",
         "  bottom: \"fc2\"\r\n",
         "  bottom: \"label\"\r\n",
         "  top: \"accuracy\"\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: ACCURACY\r\n",
         "  include {\r\n",
         "    phase: TEST\r\n",
         "  }\r\n",
         "}\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "I0905 01:07:27.477839 2129298192 net.cpp:67] Creating Layer data\r\n",
-        "I0905 01:07:27.477850 2129298192 net.cpp:356] data -> data\r\n",
-        "I0905 01:07:27.477861 2129298192 net.cpp:356] data -> label\r\n",
-        "I0905 01:07:27.477870 2129298192 net.cpp:96] Setting up data\r\n",
-        "I0905 01:07:27.477876 2129298192 hdf5_data_layer.cpp:57] Loading filename from examples/hdf5_classification/data/test.txt\r\n",
-        "I0905 01:07:27.477902 2129298192 hdf5_data_layer.cpp:69] Number of files: 1\r\n"
+        "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
+        "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\r\n",
+        "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\r\n",
+        "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\r\n",
+        "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\r\n",
+        "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
+        "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
+        "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
+        "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
+        "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
+        "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
+        "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
+        "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
+        "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
+        "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
+        "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
+        "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\r\n",
+        "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\r\n",
+        "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\r\n",
+        "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\r\n",
+        "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
+        "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
+        "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\r\n",
+        "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\r\n",
+        "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
+        "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\r\n",
+        "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
+        "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
+        "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\r\n",
+        "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\r\n",
+        "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\r\n",
+        "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\r\n",
+        "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\r\n",
+        "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\r\n",
+        "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\r\n",
+        "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
+        "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
+        "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\r\n",
+        "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
+        "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\r\n",
+        "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\r\n",
+        "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
+        "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\r\n",
+        "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\r\n",
+        "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
+        "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\r\n",
+        "I0307 01:34:31.885337 2099749632 net.cpp:122]     with loss weight 1\r\n",
+        "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
+        "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
+        "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\r\n",
+        "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
+        "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
+        "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\r\n",
+        "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\r\n",
+        "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
+        "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\r\n",
+        "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\r\n",
+        "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
+        "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
+        "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
+        "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
+        "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\r\n",
+        "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\r\n",
+        "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\r\n",
+        "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\r\n",
+        "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\r\n",
+        "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
+        "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
+        "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
+        "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n",
+        "I0307 01:34:31.901005 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.5944\r\n",
+        "I0307 01:34:31.901049 2099749632 solver.cpp:315]     Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\r\n",
+        "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\r\n",
+        "I0307 01:34:31.901192 2099749632 solver.cpp:204]     Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\r\n",
+        "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
+        "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.477910 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/test.h5\r\n",
-        "I0905 01:07:27.478999 2129298192 hdf5_data_layer.cpp:49] Successully loaded 2500 rows\r\n",
-        "I0905 01:07:27.479014 2129298192 hdf5_data_layer.cpp:81] output data size: 10,4,1,1\r\n",
-        "I0905 01:07:27.479022 2129298192 net.cpp:103] Top shape: 10 4 1 1 (40)\r\n",
-        "I0905 01:07:27.479028 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.479038 2129298192 net.cpp:67] Creating Layer label_data_1_split\r\n",
-        "I0905 01:07:27.479044 2129298192 net.cpp:394] label_data_1_split <- label\r\n",
-        "I0905 01:07:27.479058 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0905 01:07:27.479069 2129298192 net.cpp:356] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0905 01:07:27.479079 2129298192 net.cpp:96] Setting up label_data_1_split\r\n",
-        "I0905 01:07:27.479086 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.479092 2129298192 net.cpp:103] Top shape: 10 1 1 1 (10)\r\n",
-        "I0905 01:07:27.479100 2129298192 net.cpp:67] Creating Layer fc1\r\n",
-        "I0905 01:07:27.480850 2129298192 net.cpp:394] fc1 <- data\r\n",
-        "I0905 01:07:27.480871 2129298192 net.cpp:356] fc1 -> fc1\r\n",
-        "I0905 01:07:27.480887 2129298192 net.cpp:96] Setting up fc1\r\n",
-        "I0905 01:07:27.480908 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.480978 2129298192 net.cpp:67] Creating Layer relu1\r\n",
-        "I0905 01:07:27.480986 2129298192 net.cpp:394] relu1 <- fc1\r\n",
-        "I0905 01:07:27.480994 2129298192 net.cpp:345] relu1 -> fc1 (in-place)\r\n",
-        "I0905 01:07:27.481003 2129298192 net.cpp:96] Setting up relu1\r\n",
-        "I0905 01:07:27.481009 2129298192 net.cpp:103] Top shape: 10 40 1 1 (400)\r\n",
-        "I0905 01:07:27.481017 2129298192 net.cpp:67] Creating Layer fc2\r\n",
-        "I0905 01:07:27.481024 2129298192 net.cpp:394] fc2 <- fc1\r\n",
-        "I0905 01:07:27.481031 2129298192 net.cpp:356] fc2 -> fc2\r\n",
-        "I0905 01:07:27.481041 2129298192 net.cpp:96] Setting up fc2\r\n",
-        "I0905 01:07:27.481055 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.481065 2129298192 net.cpp:67] Creating Layer fc2_fc2_0_split\r\n",
-        "I0905 01:07:27.481343 2129298192 net.cpp:394] fc2_fc2_0_split <- fc2\r\n",
-        "I0905 01:07:27.481360 2129298192 net.cpp:356] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
-        "I0905 01:07:27.481371 2129298192 net.cpp:356] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
-        "I0905 01:07:27.481379 2129298192 net.cpp:96] Setting up fc2_fc2_0_split\r\n",
-        "I0905 01:07:27.481387 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.481392 2129298192 net.cpp:103] Top shape: 10 2 1 1 (20)\r\n",
-        "I0905 01:07:27.481401 2129298192 net.cpp:67] Creating Layer loss\r\n",
-        "I0905 01:07:27.481407 2129298192 net.cpp:394] loss <- fc2_fc2_0_split_0\r\n",
-        "I0905 01:07:27.481413 2129298192 net.cpp:394] loss <- label_data_1_split_0\r\n",
-        "I0905 01:07:27.481421 2129298192 net.cpp:356] loss -> loss\r\n",
-        "I0905 01:07:27.481434 2129298192 net.cpp:96] Setting up loss\r\n",
-        "I0905 01:07:27.481446 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.481452 2129298192 net.cpp:109]     with loss weight 1\r\n",
-        "I0905 01:07:27.481466 2129298192 net.cpp:67] Creating Layer accuracy\r\n",
-        "I0905 01:07:27.481472 2129298192 net.cpp:394] accuracy <- fc2_fc2_0_split_1\r\n",
-        "I0905 01:07:27.481504 2129298192 net.cpp:394] accuracy <- label_data_1_split_1\r\n",
-        "I0905 01:07:27.481513 2129298192 net.cpp:356] accuracy -> accuracy\r\n",
-        "I0905 01:07:27.481521 2129298192 net.cpp:96] Setting up accuracy\r\n",
-        "I0905 01:07:27.481528 2129298192 net.cpp:103] Top shape: 1 1 1 1 (1)\r\n",
-        "I0905 01:07:27.481534 2129298192 net.cpp:172] accuracy does not need backward computation.\r\n",
-        "I0905 01:07:27.481540 2129298192 net.cpp:170] loss needs backward computation.\r\n",
-        "I0905 01:07:27.481545 2129298192 net.cpp:170] fc2_fc2_0_split needs backward computation.\r\n",
-        "I0905 01:07:27.481551 2129298192 net.cpp:170] fc2 needs backward computation.\r\n",
-        "I0905 01:07:27.481557 2129298192 net.cpp:170] relu1 needs backward computation.\r\n",
-        "I0905 01:07:27.481562 2129298192 net.cpp:170] fc1 needs backward computation.\r\n",
-        "I0905 01:07:27.481569 2129298192 net.cpp:172] label_data_1_split does not need backward computation.\r\n",
-        "I0905 01:07:27.481575 2129298192 net.cpp:172] data does not need backward computation.\r\n",
-        "I0905 01:07:27.481730 2129298192 net.cpp:208] This network produces output accuracy\r\n",
-        "I0905 01:07:27.481742 2129298192 net.cpp:208] This network produces output loss\r\n",
-        "I0905 01:07:27.481758 2129298192 net.cpp:467] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0905 01:07:27.481766 2129298192 net.cpp:219] Network initialization done.\r\n",
-        "I0905 01:07:27.481771 2129298192 net.cpp:220] Memory required for data: 3728\r\n",
-        "I0905 01:07:27.481814 2129298192 solver.cpp:46] Solver scaffolding done.\r\n",
-        "I0905 01:07:27.481822 2129298192 solver.cpp:165] Solving LogisticRegressionNet\r\n",
-        "I0905 01:07:27.481844 2129298192 solver.cpp:251] Iteration 0, Testing net (#0)\r\n",
-        "I0905 01:07:27.488900 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.4924\r\n",
-        "I0905 01:07:27.488932 2129298192 solver.cpp:302]     Test net output #1: loss = 0.693168 (* 1 = 0.693168 loss)\r\n",
-        "I0905 01:07:27.488962 2129298192 solver.cpp:195] Iteration 0, loss = 0.692972\r\n",
-        "I0905 01:07:27.488973 2129298192 solver.cpp:210]     Train net output #0: loss = 0.692972 (* 1 = 0.692972 loss)\r\n",
-        "I0905 01:07:27.488984 2129298192 solver.cpp:405] Iteration 0, lr = 0.01\r\n",
-        "I0905 01:07:27.495033 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.495604 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.497684 2129298192 solver.cpp:251] Iteration 1000, Testing net (#0)\r\n",
-        "I0905 01:07:27.504875 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.7744\r\n",
-        "I0905 01:07:27.504930 2129298192 solver.cpp:302]     Test net output #1: loss = 0.486552 (* 1 = 0.486552 loss)\r\n",
-        "I0905 01:07:27.504955 2129298192 solver.cpp:195] Iteration 1000, loss = 0.660151\r\n",
-        "I0905 01:07:27.504966 2129298192 solver.cpp:210]     Train net output #0: loss = 0.660151 (* 1 = 0.660151 loss)\r\n",
-        "I0905 01:07:27.504976 2129298192 solver.cpp:405] Iteration 1000, lr = 0.01\r\n",
-        "I0905 01:07:27.509419 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.509467 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.510288 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.514822 2129298192 solver.cpp:251] Iteration 2000, Testing net (#0)\r\n",
-        "I0905 01:07:27.522342 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8004\r\n",
-        "I0905 01:07:27.522444 2129298192 solver.cpp:302]     Test net output #1: loss = 0.447153 (* 1 = 0.447153 loss)\r\n",
-        "I0905 01:07:27.522483 2129298192 solver.cpp:195] Iteration 2000, loss = 0.505697\r\n",
-        "I0905 01:07:27.522495 2129298192 solver.cpp:210]     Train net output #0: loss = 0.505697 (* 1 = 0.505697 loss)\r\n",
-        "I0905 01:07:27.522507 2129298192 solver.cpp:405] Iteration 2000, lr = 0.01\r\n",
-        "I0905 01:07:27.524762 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.525921 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+        "I0307 01:34:31.924612 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7556\r\n",
+        "I0307 01:34:31.924646 2099749632 solver.cpp:315]     Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\r\n",
+        "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\r\n",
+        "I0307 01:34:31.924696 2099749632 solver.cpp:204]     Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\r\n",
+        "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
+        "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
+        "I0307 01:34:31.948729 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7824\r\n",
+        "I0307 01:34:31.948763 2099749632 solver.cpp:315]     Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\r\n",
+        "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\r\n",
+        "I0307 01:34:31.948812 2099749632 solver.cpp:204]     Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\r\n",
+        "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
+        "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.533335 2129298192 solver.cpp:251] Iteration 3000, Testing net (#0)\r\n",
-        "I0905 01:07:27.541055 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8144\r\n",
-        "I0905 01:07:27.541146 2129298192 solver.cpp:302]     Test net output #1: loss = 0.421441 (* 1 = 0.421441 loss)\r\n",
-        "I0905 01:07:27.541160 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.541167 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.542178 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.542261 2129298192 solver.cpp:195] Iteration 3000, loss = 0.242177\r\n",
-        "I0905 01:07:27.542284 2129298192 solver.cpp:210]     Train net output #0: loss = 0.242177 (* 1 = 0.242177 loss)\r\n",
-        "I0905 01:07:27.542310 2129298192 solver.cpp:405] Iteration 3000, lr = 0.01\r\n",
-        "I0905 01:07:27.549348 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.550144 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.552340 2129298192 solver.cpp:251] Iteration 4000, Testing net (#0)\r\n",
-        "I0905 01:07:27.560089 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.784001\r\n",
-        "I0905 01:07:27.560227 2129298192 solver.cpp:302]     Test net output #1: loss = 0.4395 (* 1 = 0.4395 loss)\r\n",
-        "I0905 01:07:27.560286 2129298192 solver.cpp:195] Iteration 4000, loss = 1.01631\r\n",
-        "I0905 01:07:27.560302 2129298192 solver.cpp:210]     Train net output #0: loss = 1.01631 (* 1 = 1.01631 loss)\r\n",
-        "I0905 01:07:27.560315 2129298192 solver.cpp:405] Iteration 4000, lr = 0.01\r\n",
-        "I0905 01:07:27.565016 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.565101 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.566145 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.570286 2129298192 solver.cpp:251] Iteration 5000, Testing net (#0)\r\n",
-        "I0905 01:07:27.577373 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.802\r\n",
-        "I0905 01:07:27.577426 2129298192 solver.cpp:302]     Test net output #1: loss = 0.463582 (* 1 = 0.463582 loss)\r\n",
-        "I0905 01:07:27.577452 2129298192 solver.cpp:195] Iteration 5000, loss = 0.632809\r\n",
-        "I0905 01:07:27.577463 2129298192 solver.cpp:210]     Train net output #0: loss = 0.632809 (* 1 = 0.632809 loss)\r\n",
-        "I0905 01:07:27.577564 2129298192 solver.cpp:405] Iteration 5000, lr = 0.001\r\n",
-        "I0905 01:07:27.579649 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.580368 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+        "I0307 01:34:31.972393 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7956\r\n",
+        "I0307 01:34:31.972411 2099749632 solver.cpp:315]     Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\r\n",
+        "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\r\n",
+        "I0307 01:34:31.973057 2099749632 solver.cpp:204]     Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\r\n",
+        "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n",
+        "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
+        "I0307 01:34:31.998638 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.798\r\n",
+        "I0307 01:34:31.998663 2099749632 solver.cpp:315]     Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\r\n",
+        "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\r\n",
+        "I0307 01:34:31.998718 2099749632 solver.cpp:204]     Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\r\n",
+        "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
+        "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.586956 2129298192 solver.cpp:251] Iteration 6000, Testing net (#0)\r\n",
-        "I0905 01:07:27.594288 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.822\r\n",
-        "I0905 01:07:27.594327 2129298192 solver.cpp:302]     Test net output #1: loss = 0.407026 (* 1 = 0.407026 loss)\r\n",
-        "I0905 01:07:27.594338 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.594344 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.594861 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.594897 2129298192 solver.cpp:195] Iteration 6000, loss = 0.214342\r\n",
-        "I0905 01:07:27.594910 2129298192 solver.cpp:210]     Train net output #0: loss = 0.214342 (* 1 = 0.214342 loss)\r\n",
-        "I0905 01:07:27.594919 2129298192 solver.cpp:405] Iteration 6000, lr = 0.001\r\n",
-        "I0905 01:07:27.601003 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.601380 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.603358 2129298192 solver.cpp:251] Iteration 7000, Testing net (#0)\r\n",
-        "I0905 01:07:27.610307 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8264\r\n",
-        "I0905 01:07:27.610323 2129298192 solver.cpp:302]     Test net output #1: loss = 0.403283 (* 1 = 0.403283 loss)\r\n",
-        "I0905 01:07:27.610342 2129298192 solver.cpp:195] Iteration 7000, loss = 0.894732\r\n",
-        "I0905 01:07:27.610352 2129298192 solver.cpp:210]     Train net output #0: loss = 0.894732 (* 1 = 0.894732 loss)\r\n",
-        "I0905 01:07:27.610359 2129298192 solver.cpp:405] Iteration 7000, lr = 0.001\r\n",
-        "I0905 01:07:27.614289 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.614297 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.614701 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.618602 2129298192 solver.cpp:251] Iteration 8000, Testing net (#0)\r\n",
-        "I0905 01:07:27.625637 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8216\r\n",
-        "I0905 01:07:27.625661 2129298192 solver.cpp:302]     Test net output #1: loss = 0.402446 (* 1 = 0.402446 loss)\r\n",
-        "I0905 01:07:27.625680 2129298192 solver.cpp:195] Iteration 8000, loss = 0.500503\r\n",
-        "I0905 01:07:27.625690 2129298192 solver.cpp:210]     Train net output #0: loss = 0.500503 (* 1 = 0.500503 loss)\r\n",
-        "I0905 01:07:27.625707 2129298192 solver.cpp:405] Iteration 8000, lr = 0.001\r\n",
-        "I0905 01:07:27.627665 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.628075 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n"
+        "I0307 01:34:32.024950 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.804\r\n",
+        "I0307 01:34:32.024981 2099749632 solver.cpp:315]     Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\r\n",
+        "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\r\n",
+        "I0307 01:34:32.025028 2099749632 solver.cpp:204]     Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\r\n",
+        "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
+        "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
+        "I0307 01:34:32.048216 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\r\n",
+        "I0307 01:34:32.048239 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\r\n",
+        "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\r\n",
+        "I0307 01:34:32.048809 2099749632 solver.cpp:204]     Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\r\n",
+        "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n",
+        "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
+        "I0307 01:34:32.071524 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8124\r\n",
+        "I0307 01:34:32.071542 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\r\n",
+        "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\r\n",
+        "I0307 01:34:32.071617 2099749632 solver.cpp:204]     Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\r\n",
+        "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0905 01:07:27.634202 2129298192 solver.cpp:251] Iteration 9000, Testing net (#0)\r\n",
-        "I0905 01:07:27.641368 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.8252\r\n",
-        "I0905 01:07:27.641412 2129298192 solver.cpp:302]     Test net output #1: loss = 0.404175 (* 1 = 0.404175 loss)\r\n",
-        "I0905 01:07:27.641422 2129298192 hdf5_data_layer.cpp:99] looping around to first file\r\n",
-        "I0905 01:07:27.641428 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.641960 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.642004 2129298192 solver.cpp:195] Iteration 9000, loss = 0.201587\r\n",
-        "I0905 01:07:27.642016 2129298192 solver.cpp:210]     Train net output #0: loss = 0.201587 (* 1 = 0.201587 loss)\r\n",
-        "I0905 01:07:27.642026 2129298192 solver.cpp:405] Iteration 9000, lr = 0.001\r\n",
-        "I0905 01:07:27.648680 2129298192 hdf5_data_layer.cpp:29] Loading HDF5 file/Users/sergeyk/work/caffe/examples/hdf5_classification/data/train.h5\r\n",
-        "I0905 01:07:27.649211 2129298192 hdf5_data_layer.cpp:49] Successully loaded 7500 rows\r\n",
-        "I0905 01:07:27.651327 2129298192 solver.cpp:319] Snapshotting to examples/hdf5_classification/data/train_iter_10000\r\n",
-        "I0905 01:07:27.651476 2129298192 solver.cpp:326] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0905 01:07:27.651564 2129298192 solver.cpp:232] Iteration 10000, loss = 0.935422\r\n",
-        "I0905 01:07:27.651582 2129298192 solver.cpp:251] Iteration 10000, Testing net (#0)\r\n",
-        "I0905 01:07:27.658738 2129298192 solver.cpp:302]     Test net output #0: accuracy = 0.826\r\n",
-        "I0905 01:07:27.658782 2129298192 solver.cpp:302]     Test net output #1: loss = 0.400826 (* 1 = 0.400826 loss)\r\n",
-        "I0905 01:07:27.658790 2129298192 solver.cpp:237] Optimization Done.\r\n",
-        "I0905 01:07:27.658797 2129298192 caffe.cpp:114] Optimization Done.\r\n"
+        "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
+        "I0307 01:34:32.095410 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.814\r\n",
+        "I0307 01:34:32.095432 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\r\n",
+        "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\r\n",
+        "I0307 01:34:32.095474 2099749632 solver.cpp:204]     Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\r\n",
+        "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
+        "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
+        "I0307 01:34:32.121587 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\r\n",
+        "I0307 01:34:32.121608 2099749632 solver.cpp:315]     Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\r\n",
+        "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\r\n",
+        "I0307 01:34:32.122181 2099749632 solver.cpp:204]     Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\r\n",
+        "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
+        "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
+        "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\r\n",
+        "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n",
+        "I0307 01:34:32.145747 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8148\r\n",
+        "I0307 01:34:32.145771 2099749632 solver.cpp:315]     Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\r\n",
+        "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\r\n",
+        "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\r\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -938,10 +1064,10 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 9
     }
    ],
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
index 040162076b8..65a6eb9e9fb 100644
--- a/examples/hdf5_classification/solver.prototxt
+++ b/examples/hdf5_classification/solver.prototxt
@@ -1,5 +1,5 @@
-net: "examples/hdf5_classification/train_val.prototxt"
-test_iter: 1000
+net: "hdf5_classification/train_val.prototxt"
+test_iter: 250
 test_interval: 1000
 base_lr: 0.01
 lr_policy: "step"
@@ -10,5 +10,5 @@ max_iter: 10000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
+snapshot_prefix: "hdf5_classification/data/train"
 solver_mode: CPU
diff --git a/examples/hdf5_classification/solver2.prototxt b/examples/hdf5_classification/solver2.prototxt
index 32a3693b4a1..32b9feba346 100644
--- a/examples/hdf5_classification/solver2.prototxt
+++ b/examples/hdf5_classification/solver2.prototxt
@@ -1,5 +1,5 @@
-net: "examples/hdf5_classification/train_val2.prototxt"
-test_iter: 1000
+net: "hdf5_classification/train_val2.prototxt"
+test_iter: 250
 test_interval: 1000
 base_lr: 0.01
 lr_policy: "step"
@@ -10,5 +10,5 @@ max_iter: 10000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
+snapshot_prefix: "hdf5_classification/data/train"
 solver_mode: CPU
diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
index b9ccc1a93ec..d5e8dbfa169 100644
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "examples/hdf5_classification/data/train.txt"
+    source: "hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "examples/hdf5_classification/data/test.txt"
+    source: "hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/train_val2.prototxt
index f9ef731fff9..8795e8facb6 100644
--- a/examples/hdf5_classification/train_val2.prototxt
+++ b/examples/hdf5_classification/train_val2.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "examples/hdf5_classification/data/train.txt"
+    source: "hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "examples/hdf5_classification/data/test.txt"
+    source: "hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }

From 11730440d048f5ae6e120926ddaf042c8ec4cc73 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 7 Mar 2015 12:53:34 -0800
Subject: [PATCH 1259/2053] [example] warm-start web demo

call forward to warm-start the demo so the first request isn't slow.
---
 examples/web_demo/app.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index 208b1da0dc9..bbeff5eb362 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -210,8 +210,9 @@ def start_from_terminal(app):
     opts, args = parser.parse_args()
     ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})
 
-    # Initialize classifier
+    # Initialize classifier + warm start by forward for allocation
     app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)
+    app.clf.net.forward()
 
     if opts.debug:
         app.run(debug=True, host='0.0.0.0', port=opts.port)

From c99b83cdd8ef5251dcfb572236a01225f1948b56 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Mar 2015 17:16:08 -0700
Subject: [PATCH 1260/2053] SoftmaxLossLayer fix: canonicalize input axis

---
 src/caffe/layers/softmax_loss_layer.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 132c30796a4..ba312f67fbc 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -35,7 +35,8 @@ void SoftmaxWithLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
   softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
-  softmax_axis_ = this->layer_param_.softmax_param().axis();
+  softmax_axis_ =
+      bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
   outer_num_ = bottom[0]->count(0, softmax_axis_);
   inner_num_ = bottom[0]->count(softmax_axis_ + 1);
   CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())

From 4a4118f2b15c40d071da5faca0c404bd7e887de9 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Mar 2015 17:47:20 -0700
Subject: [PATCH 1261/2053] Increment iter_ before snapshotting, remove +1
 logic -- fixes final snapshot being off by one

---
 src/caffe/solver.cpp | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 034390e6824..b3b77a002ec 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -167,7 +167,7 @@ void Solver<Dtype>::Step(int iters) {
   vector<Dtype> losses;
   Dtype smoothed_loss = 0;
 
-  for (; iter_ < stop_iter; ++iter_) {
+  while (iter_ < stop_iter) {
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())) {
       TestAll();
@@ -210,8 +210,12 @@ void Solver<Dtype>::Step(int iters) {
     ComputeUpdateValue();
     net_->Update();
 
+    // Increment the internal iter_ counter -- its value should always indicate
+    // the number of times the weights have been updated.
+    ++iter_;
+
     // Save a snapshot if needed.
-    if (param_.snapshot() && (iter_ + 1) % param_.snapshot() == 0) {
+    if (param_.snapshot() && iter_ % param_.snapshot() == 0) {
       Snapshot();
     }
   }
@@ -327,15 +331,14 @@ void Solver<Dtype>::Snapshot() {
   string model_filename, snapshot_filename;
   const int kBufferSize = 20;
   char iter_str_buffer[kBufferSize];
-  // Add one to iter_ to get the number of iterations that have completed.
-  snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_ + 1);
+  snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_);
   filename += iter_str_buffer;
   model_filename = filename + ".caffemodel";
   LOG(INFO) << "Snapshotting to " << model_filename;
   WriteProtoToBinaryFile(net_param, model_filename.c_str());
   SolverState state;
   SnapshotSolverState(&state);
-  state.set_iter(iter_ + 1);
+  state.set_iter(iter_);
   state.set_learned_net(model_filename);
   state.set_current_step(current_step_);
   snapshot_filename = filename + ".solverstate";

From 1535d5831ed5b18ef20132d5aa7ea945c14eecf6 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 8 Mar 2015 22:39:56 -0700
Subject: [PATCH 1262/2053] [example] pycaffe classification downloads the
 model automatically

---
 examples/classification.ipynb | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 6f8fa4252e6..0babf79f304 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -4,7 +4,7 @@
   "example_name": "ImageNet classification",
   "include_in_docs": true,
   "priority": 1,
-  "signature": "sha256:918b797b1b7d78125c8f1e3c84756b0679120cbe1071ce7fee7aeafef0fbae55"
+  "signature": "sha256:a2b12abaa1eb252f436d59833c08ab97948c8a7a0513197f31afad0a0690e318"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -18,9 +18,9 @@
       "Classifying ImageNet: the instant Caffe way\n",
       "===========================================\n",
       "\n",
-      "Caffe provides a general Python interface for models with `caffe.Net` in `python/caffe/pycaffe.py`, but to make off-the-shelf classification easy we provide a `caffe.Classifier` class and `classify.py` script. Both Python and MATLAB wrappers are provided. However, the Python wrapper has more features so we will describe it here. For MATLAB, refer to `matlab/caffe/matcaffe_demo.m`.\n",
+      "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n",
       "\n",
-      "Before we begin, you must compile Caffe and install the python wrapper by setting your `PYTHONPATH`. If you haven't yet done so, please refer to the [installation instructions](installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.\n",
+      "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n",
       "\n",
       "Ready? Let's start."
      ]
@@ -44,7 +44,12 @@
       "# and the image you would like to classify.\n",
       "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
       "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
-      "IMAGE_FILE = 'images/cat.jpg'"
+      "IMAGE_FILE = 'images/cat.jpg'\n",
+      "\n",
+      "import os\n",
+      "if not os.path.isfile(PRETRAINED):\n",
+      "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+      "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
      ],
      "language": "python",
      "metadata": {},

From 7a40f741ee8a6b33302406dbf225ad95ed5542ee Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 8 Mar 2015 23:00:10 -0700
Subject: [PATCH 1263/2053] Fixup AccuracyLayer like SoftmaxLossLayer in #1970
 -- fixes #2063

---
 include/caffe/loss_layers.hpp       |  1 +
 src/caffe/layers/accuracy_layer.cpp | 54 +++++++++++++++++------------
 src/caffe/proto/caffe.proto         |  7 ++++
 3 files changed, 39 insertions(+), 23 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 62d6df71a4a..da2c4be1e61 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -78,6 +78,7 @@ class AccuracyLayer : public Layer<Dtype> {
     }
   }
 
+  int label_axis_, outer_num_, inner_num_;
   int top_k_;
 };
 
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 186f9f8632c..6084ee7524b 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -21,11 +21,15 @@ void AccuracyLayer<Dtype>::Reshape(
   const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   CHECK_LE(top_k_, bottom[0]->count() / bottom[1]->count())
       << "top_k must be less than or equal to the number of classes.";
-  CHECK_GE(bottom[0]->num_axes(), bottom[1]->num_axes());
-  for (int i = 0; i < bottom[1]->num_axes(); ++i) {
-    CHECK_LE(bottom[0]->shape(i), bottom[1]->shape(i))
-        << "Dimension mismatch between predictions and label.";
-  }
+  label_axis_ =
+      bottom[0]->CanonicalAxisIndex(this->layer_param_.accuracy_param().axis());
+  outer_num_ = bottom[0]->count(0, label_axis_);
+  inner_num_ = bottom[0]->count(label_axis_ + 1);
+  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
+      << "Number of labels must match number of predictions; "
+      << "e.g., if label axis == 1 and prediction shape is (N, C, H, W), "
+      << "label count (number of labels) must be N*H*W, "
+      << "with integer values in {0, 1, ..., C-1}.";
   vector<int> top_shape(0);  // Accuracy is a scalar; 0 axes.
   top[0]->Reshape(top_shape);
 }
@@ -36,31 +40,35 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype accuracy = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->count(0, bottom[1]->num_axes());
-  int dim = bottom[0]->count() / num;
+  const int dim = bottom[0]->count() / outer_num_;
+  const int num_labels = bottom[0]->shape(label_axis_);
   vector<Dtype> maxval(top_k_+1);
   vector<int> max_id(top_k_+1);
-  for (int i = 0; i < num; ++i) {
-    // Top-k accuracy
-    std::vector<std::pair<Dtype, int> > bottom_data_vector;
-    for (int j = 0; j < dim; ++j) {
-      bottom_data_vector.push_back(
-          std::make_pair(bottom_data[i * dim + j], j));
-    }
-    std::partial_sort(
-        bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
-        bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
-    // check if true label is in top k predictions
-    for (int k = 0; k < top_k_; k++) {
-      if (bottom_data_vector[k].second == static_cast<int>(bottom_label[i])) {
-        ++accuracy;
-        break;
+  for (int i = 0; i < outer_num_; ++i) {
+    for (int j = 0; j < inner_num_; ++j) {
+      // Top-k accuracy
+      std::vector<std::pair<Dtype, int> > bottom_data_vector;
+      for (int k = 0; k < num_labels; ++k) {
+        bottom_data_vector.push_back(std::make_pair(
+            bottom_data[i * dim + k * inner_num_ + j], k));
+      }
+      std::partial_sort(
+          bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
+          bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
+      // check if true label is in top k predictions
+      const int label_value =
+          static_cast<int>(bottom_label[i * inner_num_ + j]);
+      for (int k = 0; k < top_k_; k++) {
+        if (bottom_data_vector[k].second == label_value) {
+          ++accuracy;
+          break;
+        }
       }
     }
   }
 
   // LOG(INFO) << "Accuracy: " << accuracy;
-  top[0]->mutable_cpu_data()[0] = accuracy / num;
+  top[0]->mutable_cpu_data()[0] = accuracy / outer_num_ / inner_num_;
   // Accuracy layer should not be used as a loss function.
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 3b4794664b5..7792695abfa 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -367,6 +367,13 @@ message AccuracyParameter {
   // the top k scoring classes.  By default, only compare to the top scoring
   // class (i.e. argmax).
   optional uint32 top_k = 1 [default = 1];
+
+  // The "label" axis of the prediction blob, whose argmax corresponds to the
+  // predicted label -- may be negative to index from the end (e.g., -1 for the
+  // last axis).  For example, if axis == 1 and the predictions are
+  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
+  // labels with integer values in {0, 1, ..., C-1}.
+  optional int32 axis = 2 [default = 1];
 }
 
 // Message that stores parameters used by ArgMaxLayer

From 6ea7a661379571a4e912f59a73c9c2037671eb21 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Sun, 22 Feb 2015 21:00:38 +0000
Subject: [PATCH 1264/2053] AccuracyLayer: add ignore_label param

---
 include/caffe/loss_layers.hpp       |  6 ++++++
 src/caffe/layers/accuracy_layer.cpp | 19 ++++++++++++++++---
 src/caffe/proto/caffe.proto         |  3 +++
 3 files changed, 25 insertions(+), 3 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index da2c4be1e61..d3eecd2e510 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -79,7 +79,13 @@ class AccuracyLayer : public Layer<Dtype> {
   }
 
   int label_axis_, outer_num_, inner_num_;
+
   int top_k_;
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
 };
 
 /**
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 6084ee7524b..90aad675ed3 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -14,6 +14,12 @@ template <typename Dtype>
 void AccuracyLayer<Dtype>::LayerSetUp(
   const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   top_k_ = this->layer_param_.accuracy_param().top_k();
+
+  has_ignore_label_ =
+    this->layer_param_.accuracy_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.accuracy_param().ignore_label();
+  }
 }
 
 template <typename Dtype>
@@ -44,8 +50,16 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int num_labels = bottom[0]->shape(label_axis_);
   vector<Dtype> maxval(top_k_+1);
   vector<int> max_id(top_k_+1);
+  int count = 0;
   for (int i = 0; i < outer_num_; ++i) {
     for (int j = 0; j < inner_num_; ++j) {
+      const int label_value =
+          static_cast<int>(bottom_label[i * inner_num_ + j]);
+      if (has_ignore_label_ && label_value == ignore_label_) {
+        continue;
+      }
+      DCHECK_GE(label_value, 0);
+      DCHECK_LT(label_value, num_labels);
       // Top-k accuracy
       std::vector<std::pair<Dtype, int> > bottom_data_vector;
       for (int k = 0; k < num_labels; ++k) {
@@ -56,19 +70,18 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
           bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
       // check if true label is in top k predictions
-      const int label_value =
-          static_cast<int>(bottom_label[i * inner_num_ + j]);
       for (int k = 0; k < top_k_; k++) {
         if (bottom_data_vector[k].second == label_value) {
           ++accuracy;
           break;
         }
       }
+      ++count;
     }
   }
 
   // LOG(INFO) << "Accuracy: " << accuracy;
-  top[0]->mutable_cpu_data()[0] = accuracy / outer_num_ / inner_num_;
+  top[0]->mutable_cpu_data()[0] = accuracy / count;
   // Accuracy layer should not be used as a loss function.
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 7792695abfa..e523efa50f1 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -374,6 +374,9 @@ message AccuracyParameter {
   // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
   // labels with integer values in {0, 1, ..., C-1}.
   optional int32 axis = 2 [default = 1];
+
+  // If specified, ignore instances with the given label.
+  optional int32 ignore_label = 3;
 }
 
 // Message that stores parameters used by ArgMaxLayer

From 2abbaca165e8fbebf35dad683d985e87b58af8ba Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 9 Mar 2015 12:16:30 -0700
Subject: [PATCH 1265/2053] AccuracyLayerTest: add tests for ignore_label and
 spatial axes

---
 src/caffe/test/test_accuracy_layer.cpp | 98 ++++++++++++++++++++++++--
 1 file changed, 93 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 1c58b767bfc..6cbf51df45e 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -29,6 +29,14 @@ class AccuracyLayerTest : public ::testing::Test {
     blob_bottom_data_->Reshape(shape);
     shape.resize(1);
     blob_bottom_label_->Reshape(shape);
+    FillBottoms();
+
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual void FillBottoms() {
     // fill the probability values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
@@ -39,14 +47,11 @@ class AccuracyLayerTest : public ::testing::Test {
     caffe::rng_t* prefetch_rng =
           static_cast<caffe::rng_t*>(rng->generator());
     Dtype* label_data = blob_bottom_label_->mutable_cpu_data();
-    for (int i = 0; i < 100; ++i) {
+    for (int i = 0; i < blob_bottom_label_->count(); ++i) {
       label_data[i] = (*prefetch_rng)() % 10;
     }
-
-    blob_bottom_vec_.push_back(blob_bottom_data_);
-    blob_bottom_vec_.push_back(blob_bottom_label_);
-    blob_top_vec_.push_back(blob_top_);
   }
+
   virtual ~AccuracyLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
@@ -112,6 +117,89 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
               num_correct_labels / 100.0, 1e-4);
 }
 
+TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
+  Caffe::set_mode(Caffe::CPU);
+  this->blob_bottom_data_->Reshape(2, 10, 4, 5);
+  vector<int> label_shape(3);
+  label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
+  this->blob_bottom_label_->Reshape(label_shape);
+  this->FillBottoms();
+  LayerParameter layer_param;
+  layer_param.mutable_accuracy_param()->set_axis(1);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  TypeParam max_value;
+  const int num_labels = this->blob_bottom_label_->count();
+  int max_id;
+  int num_correct_labels = 0;
+  vector<int> label_offset(3);
+  for (int n = 0; n < this->blob_bottom_data_->num(); ++n) {
+    for (int h = 0; h < this->blob_bottom_data_->height(); ++h) {
+      for (int w = 0; w < this->blob_bottom_data_->width(); ++w) {
+        max_value = -FLT_MAX;
+        max_id = 0;
+        for (int c = 0; c < this->blob_bottom_data_->channels(); ++c) {
+          const TypeParam pred_value =
+              this->blob_bottom_data_->data_at(n, c, h, w);
+          if (pred_value > max_value) {
+            max_value = pred_value;
+            max_id = c;
+          }
+        }
+        label_offset[0] = n; label_offset[1] = h; label_offset[2] = w;
+        const int correct_label =
+            static_cast<int>(this->blob_bottom_label_->data_at(label_offset));
+        if (max_id == correct_label) {
+          ++num_correct_labels;
+        }
+      }
+    }
+  }
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / TypeParam(num_labels), 1e-4);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter layer_param;
+  const TypeParam kIgnoreLabelValue = -1;
+  layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  // Manually set some labels to the ignore label value (-1).
+  this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  int count = 0;
+  for (int i = 0; i < 100; ++i) {
+    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      continue;
+    }
+    ++count;
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+    }
+  }
+  EXPECT_EQ(count, 97);  // We set 3 out of 100 labels to kIgnoreLabelValue.
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / TypeParam(count), 1e-4);
+}
+
 TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();

From 4cf2c5916c7331dc8b61bdb4922952ceb61e2651 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@gmail.com>
Date: Tue, 10 Mar 2015 13:55:32 -0700
Subject: [PATCH 1266/2053] Remove Gist from BVLC GoogleNet

---
 models/bvlc_googlenet/readme.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 2e22dfcf59a..061b6d74530 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -5,7 +5,6 @@ caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
 license: unrestricted
 sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
 caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
-gist_id: 866e2aa1fd707b89b913
 ---
 
 This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.

From 62b19125b91e071860968049c60bd6ae06e94926 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 11 Mar 2015 14:04:51 -0700
Subject: [PATCH 1267/2053] [docs] open release of BVLC models for unrestricted
 use

See BVLC model license details on the model zoo page.

[Re-commit of 6b84206 which somehow went missing.]
---
 docs/model_zoo.md | 43 +++++++++++++++++++++++++++----------------
 1 file changed, 27 insertions(+), 16 deletions(-)

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index ad30d0acd55..06dc0a49ec7 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -3,28 +3,30 @@ title: Model Zoo
 ---
 # Caffe Model Zoo
 
-Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications.
-To lower the friction of sharing these models, we introduce the model zoo framework:
+Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
+These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
+
+To help share these models, we introduce the model zoo framework:
 
 - A standard format for packaging Caffe model info.
-- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` parameters.
+- Tools to upload/download model info to/from Github Gists, and to download trained `.caffemodel` binaries.
 - A central wiki page for sharing model info Gists.
 
-## BVLC Reference Models
+## Where to get trained models
 
-First of all, we provide some trained models out of the box.
+First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
+<br>
+See the [BVLC model license](#bvlc-model-license) for details.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper. (Trained by Jeff Donahue @jeffdonahue)
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn). (Trained by Ross Girshick @rbgirshick)
-- **BVLC GoogleNet** in `models/bvlc_googlenet`: GoogleNet trained on ILSVRC 2012, almost exactly as described in [GoogleNet](http://arxiv.org/abs/1409.4842). (Trained by Sergio Guadarrama @sguada)
-
+- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
+- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
+- **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
-## Community Models
-
-The publicly-editable [Caffe Model Zoo wiki](https://github.com/BVLC/caffe/wiki/Model-Zoo) catalogues user-made models.
-Refer to the model details for authorship and conditions -- please respect licenses and citations.
+**Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+These models are subject to conditions of their respective authors such as citation and license.
+Thank you for sharing your models!
 
 ## Model info format
 
@@ -44,7 +46,7 @@ A caffe model is distributed as a directory containing:
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
 
-- `scripts/upload_model_to_gist.sh <dirname>`: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
+`scripts/upload_model_to_gist.sh <dirname>` uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If `gist_id` is already part of the `<dirname>/readme.md` frontmatter, then updates existing Gist.
 
 Try doing `scripts/upload_model_to_gist.sh models/bvlc_alexnet` to test the uploading (don't forget to delete the uploaded gist afterward).
 
@@ -56,4 +58,13 @@ It is up to the user where to host the `.caffemodel` file.
 We host our BVLC-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
-- `scripts/download_model_binary.py <dirname>`: downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
+`scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
+
+## BVLC model license
+
+The Caffe models bundled by the BVLC are released for unrestricted use.
+
+These models are trained on data from the [ImageNet project](http://www.image-net.org/) and training data includes internet photos that may be subject to copyright.
+
+Our present understanding as researchers is that there is no restriction placed on the open release of these learned model weights, since none of the original images are distributed in whole or in part.
+To the extent that the interpretation arises that weights are derivative works of the original copyright holder and they assert such a copyright, UC Berkeley makes no representations as to what use is allowed other than to consider our present release in the spirit of fair use in the academic mission of the university to disseminate knowledge and tools as broadly as possible without restriction.

From bb5bf436c10dee68a31825edf65f39149034c4e0 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Mon, 16 Feb 2015 09:52:47 -0800
Subject: [PATCH 1268/2053] PReLU Layer and its tests

described in Kaiming He et al, "Delving Deep into Rectifiers: Surpassing
Human-Level Performance on ImageNet Classification", arxiv 2015.

Belows are commit message histories that I had while developing.

PReLULayer takes FillerParameter for init

PReLU testing consistency with ReLU

Fix : PReLU test concistency check

PReLU tests in-place computation, and it failed in GPU

Fix: PReLU in-place backward in GPU

PReLULayer called an incorrect API for copying
data (caffe_gpu_memcpy). First argment of `caffe_gpu_memcpy` should be
size of memory region in byte. I modified to use `caffe_copy` function.

Fix: style errors

Fix: number of axes of input blob must be >= 2

Use 1D blob, zero-D blob.

Rename: hw -> dim
---
 include/caffe/neuron_layers.hpp      |  84 ++++++++++++
 src/caffe/layers/prelu_layer.cpp     | 140 +++++++++++++++++++
 src/caffe/layers/prelu_layer.cu      | 130 ++++++++++++++++++
 src/caffe/proto/caffe.proto          |  14 +-
 src/caffe/test/test_neuron_layer.cpp | 196 +++++++++++++++++++++++++++
 5 files changed, 563 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/prelu_layer.cpp
 create mode 100644 src/caffe/layers/prelu_layer.cu

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 0c306fb41bf..8669923d1fa 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -654,6 +654,90 @@ class ThresholdLayer : public NeuronLayer<Dtype> {
   Dtype threshold_;
 };
 
+/**
+ * @brief Parameterized Rectified Linear Unit non-linearity @f$
+ *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+ *        @f$. The differences from ReLULayer are 1) negative slopes are
+ *        learnable though backprop and 2) negative slopes can vary across
+ *        channels. The number of axes of input blob should be greater than or
+ *        equal to 2. The 1st axis (0-based) is seen as channels.
+ */
+template <typename Dtype>
+class PReLULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides PReLUParameter prelu_param,
+   *     with PReLULayer options:
+   *   - filler (\b optional, FillerParameter,
+   *     default {'type': constant 'value':0.25}).
+   *   - channel_shared (\b optional, default false).
+   *     negative slopes are shared across channels.
+   */
+  explicit PReLULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "PReLU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the computed outputs for each channel @f$i@f$ @f$
+   *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+   *      @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the PReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times ...) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
+   *      diff with gradients @f$
+   *        \frac{\partial E}{\partial x_i} = \left\{
+   *        \begin{array}{lr}
+   *            a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   *      If param_propagate_down_[0] is true, it fills the diff with gradients
+   *      @f$
+   *        \frac{\partial E}{\partial a_i} = \left\{
+   *        \begin{array}{lr}
+   *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            0 & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool channel_shared_;
+  Blob<Dtype> multiplier_;  // dot multipler for backward computation of params
+  Blob<Dtype> bottom_memory_;  // memory for in-place computation
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_NEURON_LAYERS_HPP_
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
new file mode 100644
index 00000000000..7119a274dd3
--- /dev/null
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -0,0 +1,140 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void PReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  PReLUParameter prelu_param = this->layer_param().prelu_param();
+  int channels = bottom[0]->channels();
+  channel_shared_ = prelu_param.channel_shared();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(1);
+    if (channel_shared_) {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(0)));
+    } else {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(1, channels)));
+    }
+    shared_ptr<Filler<Dtype> > filler;
+    if (prelu_param.has_filler()) {
+      filler.reset(GetFiller<Dtype>(prelu_param.filler()));
+    } else {
+      FillerParameter filler_param;
+      filler_param.set_type("constant");
+      filler_param.set_value(0.25);
+      filler.reset(GetFiller<Dtype>(filler_param));
+    }
+    filler->Fill(this->blobs_[0].get());
+  }
+  if (channel_shared_) {
+    CHECK_EQ(this->blobs_[0]->count(), 1)
+        << "Negative slope size is inconsistent with prototxt config";
+  } else {
+    CHECK_EQ(this->blobs_[0]->count(), channels)
+        << "Negative slope size is inconsistent with prototxt config";
+  }
+
+  // Propagate gradients to the parameters (as directed by backward pass).
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+  multiplier_.Reshape(vector<int>(1, bottom[0]->count() / bottom[0]->num()));
+  caffe_set(multiplier_.count(), Dtype(1), multiplier_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  top[0]->ReshapeLike(*bottom[0]);
+  if (bottom[0] == top[0]) {
+    // For in-place computation
+    bottom_memory_.ReshapeLike(*bottom[0]);
+  }
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+  const Dtype* slope_data = this->blobs_[0]->cpu_data();
+
+  // For in-place computation
+  if (bottom[0] == top[0]) {
+    caffe_copy(count, bottom_data, bottom_memory_.mutable_cpu_data());
+  }
+
+  // if channel_shared, channel index in the following computation becomes
+  // always zero.
+  const int div_factor = channel_shared_ ? channels : 1;
+  for (int i = 0; i < count; ++i) {
+    int c = (i / dim) % channels / div_factor;
+    top_data[i] = std::max(bottom_data[i], Dtype(0))
+        + slope_data[c] * std::min(bottom_data[i], Dtype(0));
+  }
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* slope_data = this->blobs_[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    bottom_data = bottom_memory_.cpu_data();
+  }
+
+  // if channel_shared, channel index in the following computation becomes
+  // always zero.
+  const int div_factor = channel_shared_ ? channels : 1;
+
+  // Propagte to param
+  // Since to write bottom diff will affect top diff if top and bottom blobs
+  // are identical (in-place computaion), we first compute param backward to
+  // keep top_diff unchanged.
+  if (this->param_propagate_down_[0]) {
+    Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
+    caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff);
+    for (int i = 0; i < count; ++i) {
+      int c = (i / dim) % channels / div_factor;
+      slope_diff[c] += top_diff[i] * bottom_data[i] * (bottom_data[i] <= 0);
+    }
+  }
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    for (int i = 0; i < count; ++i) {
+      int c = (i / dim) % channels / div_factor;
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+          + slope_data[c] * (bottom_data[i] <= 0));
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(PReLULayer);
+#endif
+
+INSTANTIATE_CLASS(PReLULayer);
+REGISTER_LAYER_CLASS(PReLU);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
new file mode 100644
index 00000000000..fd0eda5d191
--- /dev/null
+++ b/src/caffe/layers/prelu_layer.cu
@@ -0,0 +1,130 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+// CUDA kernele for forward
+template <typename Dtype>
+__global__ void PReLUForward(const int n, const int channels, const int dim,
+    const Dtype* in, Dtype* out, const Dtype* slope_data,
+    const int div_factor) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int c = (index / dim) % channels / div_factor;
+    out[index] = in[index] > 0 ? in[index] : in[index] * slope_data[c];
+  }
+}
+
+// CUDA kernel for bottom backward
+template <typename Dtype>
+__global__ void PReLUBackward(const int n, const int channels, const int dim,
+    const Dtype* in_diff, const Dtype* in_data, Dtype* out_diff,
+    const Dtype* slope_data, const int div_factor) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int c = (index / dim) % channels / div_factor;
+    out_diff[index] = in_diff[index] * ((in_data[index] > 0)
+        + (in_data[index] <= 0) * slope_data[c]);
+  }
+}
+
+// CUDA kernel for element-wise parameter backward
+template <typename Dtype>
+__global__ void PReLUParamBackward(const int n, const Dtype* in_diff,
+    const Dtype* in_data, Dtype* out_diff) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0);
+  }
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+  const Dtype* slope_data = this->blobs_[0]->gpu_data();
+  const int div_factor = channel_shared_ ? channels : 1;
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    caffe_copy(count, bottom_data, bottom_memory_.mutable_gpu_data());
+  }
+
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  PReLUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, channels, dim, bottom_data, top_data, slope_data, div_factor);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    bottom_data = bottom_memory_.gpu_data();
+  }
+
+  // Propagte to param
+  // Since to write bottom diff will affect top diff if top and bottom blobs
+  // are identical (in-place computaion), we first compute param backward to
+  // keep top_diff unchanged.
+  if (this->param_propagate_down_[0]) {
+    Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
+    // slope_diff is set as 0, then accumulated over batches
+    caffe_gpu_set<Dtype>(this->blobs_[0]->count(), Dtype(0), slope_diff);
+    int cdim = channels * dim;
+    Dtype dsum = 0.;
+    for (int n = 0; n < bottom[0]->num(); ++n) {
+      Dtype* temp_buff = multiplier_.mutable_gpu_diff();
+      // compute element-wise diff
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+          CAFFE_CUDA_NUM_THREADS>>>(
+          cdim, top_diff + top[0]->offset(n),
+          bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff());
+      CUDA_POST_KERNEL_CHECK;
+      if (channel_shared_) {
+        Dtype d;
+        caffe_gpu_dot<Dtype>(channels * dim, multiplier_.gpu_diff(),
+            multiplier_.gpu_data(), &d);
+        dsum += d;
+      } else {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
+            multiplier_.gpu_diff(), multiplier_.gpu_data(), 1.,
+            slope_diff);
+      }
+    }
+    if (channel_shared_) {
+      caffe_gpu_set(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+    }
+  }
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* slope_data = this->blobs_[0]->gpu_data();
+    int div_factor = channel_shared_ ? channels : 1;
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    PReLUBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(
+        count, channels, dim, top_diff, bottom_data, bottom_diff, slope_data,
+        div_factor);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(PReLULayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e523efa50f1..888371dc778 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -259,7 +259,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 131 (last added: python_param)
+// LayerParameter next available layer-specific ID: 132 (last added: prelu_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -323,6 +323,7 @@ message LayerParameter {
   optional MVNParameter mvn_param = 120;
   optional PoolingParameter pooling_param = 121;
   optional PowerParameter power_param = 122;
+  optional PReLUParameter prelu_param = 131;
   optional PythonParameter python_param = 130;
   optional ReLUParameter relu_param = 123;
   optional SigmoidParameter sigmoid_param = 124;
@@ -946,3 +947,14 @@ message V0LayerParameter {
 
   optional HDF5OutputParameter hdf5_output_param = 1001;
 }
+
+// Message that stores parameters used by PReLULayer
+message PReLUParameter {
+  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
+  // Surpassing Human-Level Performance on ImageNet Classification, 2015.
+
+  // Initial value of a_i. Default is a_i=0.25 for all i.
+  optional FillerParameter filler = 1;
+  // Whether or not slope paramters are shared across channels.
+  optional bool channel_shared = 2 [default = false];
+}
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index ad10720116d..c9d52f247a6 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <cstring>
 #include <vector>
 
@@ -99,6 +100,23 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
     GradientChecker<Dtype> checker(1e-2, 1e-3);
     checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
   }
+
+  void TestPReLU(PReLULayer<Dtype> *layer) {
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    const Dtype* slope_data = layer->blobs()[0]->cpu_data();
+    int hw = this->blob_bottom_->height() * this->blob_bottom_->width();
+    int channels = this->blob_bottom_->channels();
+    bool channel_shared = layer->layer_param().prelu_param().channel_shared();
+    for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+      int c = channel_shared ? 0 : (i / hw) % channels;
+      EXPECT_EQ(top_data[i],
+          std::max(bottom_data[i], (Dtype)(0))
+          + slope_data[c] * std::min(bottom_data[i], (Dtype)(0)));
+    }
+  }
 };
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
@@ -392,6 +410,184 @@ TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(NeuronLayerTest, TestPReLUParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* slopes = layer.blobs()[0]->cpu_data();
+  int count = layer.blobs()[0]->count();
+  for (int i = 0; i < count; ++i, ++slopes) {
+    EXPECT_EQ(*slopes, 0.25);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(layer.blobs()[0].get());
+  this->TestPReLU(&layer);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUForwardChannelShared) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_prelu_param()->set_channel_shared(true);
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  this->TestPReLU(&layer);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(layer.blobs()[0].get());
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUGradientChannelShared) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_prelu_param()->set_channel_shared(true);
+  PReLULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUConsistencyReLU) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter prelu_layer_param;
+  LayerParameter relu_layer_param;
+  relu_layer_param.mutable_relu_param()->set_negative_slope(0.25);
+  PReLULayer<Dtype> prelu(prelu_layer_param);
+  ReLULayer<Dtype> relu(relu_layer_param);
+  // Set up blobs
+  vector<Blob<Dtype>*> blob_bottom_vec_2;
+  vector<Blob<Dtype>*> blob_top_vec_2;
+  shared_ptr<Blob<Dtype> > blob_bottom_2(new Blob<Dtype>());
+  shared_ptr<Blob<Dtype> > blob_top_2(new Blob<Dtype>());
+  blob_bottom_vec_2.push_back(blob_bottom_2.get());
+  blob_top_vec_2.push_back(blob_top_2.get());
+  blob_bottom_2->CopyFrom(*this->blob_bottom_, false, true);
+  // SetUp layers
+  prelu.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  relu.SetUp(blob_bottom_vec_2, blob_top_vec_2);
+  // Check forward
+  prelu.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  relu.Forward(this->blob_bottom_vec_, blob_top_vec_2);
+  for (int s = 0; s < blob_top_2->count(); ++s) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[s], blob_top_2->cpu_data()[s]);
+  }
+  // Check backward
+  shared_ptr<Blob<Dtype> > tmp_blob(new Blob<Dtype>());
+  tmp_blob->ReshapeLike(*blob_top_2.get());
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(tmp_blob.get());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      this->blob_top_->mutable_cpu_diff());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      blob_top_2->mutable_cpu_diff());
+  vector<bool> propagate_down;
+  propagate_down.push_back(true);
+  prelu.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  relu.Backward(blob_top_vec_2, propagate_down, blob_bottom_vec_2);
+  for (int s = 0; s < blob_bottom_2->count(); ++s) {
+    EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Set layer parameters
+  LayerParameter ip_layer_param;
+  LayerParameter prelu_layer_param;
+  InnerProductParameter *ip_param =
+      ip_layer_param.mutable_inner_product_param();
+  ip_param->mutable_weight_filler()->set_type("gaussian");
+  ip_param->set_num_output(3);
+  InnerProductLayer<Dtype> ip(ip_layer_param);
+  PReLULayer<Dtype> prelu(prelu_layer_param);
+  InnerProductLayer<Dtype> ip2(ip_layer_param);
+  PReLULayer<Dtype> prelu2(prelu_layer_param);
+  // Set up blobs
+  vector<Blob<Dtype>*> blob_bottom_vec_2;
+  vector<Blob<Dtype>*> blob_middle_vec_2;
+  vector<Blob<Dtype>*> blob_top_vec_2;
+  shared_ptr<Blob<Dtype> > blob_bottom_2(new Blob<Dtype>());
+  shared_ptr<Blob<Dtype> > blob_middle_2(new Blob<Dtype>());
+  shared_ptr<Blob<Dtype> > blob_top_2(new Blob<Dtype>());
+  blob_bottom_vec_2.push_back(blob_bottom_2.get());
+  blob_middle_vec_2.push_back(blob_middle_2.get());
+  blob_top_vec_2.push_back(blob_top_2.get());
+  blob_bottom_2->CopyFrom(*this->blob_bottom_, false, true);
+  // SetUp layers
+  ip.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  prelu.SetUp(this->blob_top_vec_, this->blob_top_vec_);
+  ip2.SetUp(blob_bottom_vec_2, blob_middle_vec_2);
+  prelu2.SetUp(blob_middle_vec_2, blob_top_vec_2);
+  caffe_copy(ip2.blobs()[0]->count(), ip.blobs()[0]->cpu_data(),
+      ip2.blobs()[0]->mutable_cpu_data());
+  // Forward in-place
+  ip.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  ip.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  prelu.Reshape(this->blob_top_vec_, this->blob_top_vec_);
+  prelu.Forward(this->blob_top_vec_, this->blob_top_vec_);
+  // Forward non-in-place
+  ip2.Reshape(blob_bottom_vec_2, blob_middle_vec_2);
+  ip2.Forward(blob_bottom_vec_2, blob_middle_vec_2);
+  prelu2.Reshape(blob_middle_vec_2, blob_top_vec_2);
+  prelu2.Forward(blob_middle_vec_2, blob_top_vec_2);
+  // Check numbers
+  for (int s = 0; s < blob_top_2->count(); ++s) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[s], blob_top_2->cpu_data()[s]);
+  }
+  // Fill top diff with random numbers
+  shared_ptr<Blob<Dtype> > tmp_blob(new Blob<Dtype>());
+  tmp_blob->ReshapeLike(*blob_top_2.get());
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(tmp_blob.get());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      this->blob_top_->mutable_cpu_diff());
+  caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),
+      blob_top_2->mutable_cpu_diff());
+  // Backward in-place
+  vector<bool> propagate_down;
+  propagate_down.push_back(true);
+  prelu.Backward(this->blob_top_vec_, propagate_down, this->blob_top_vec_);
+  ip.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  // Backward non-in-place
+  prelu2.Backward(blob_top_vec_2, propagate_down, blob_middle_vec_2);
+  ip2.Backward(blob_middle_vec_2, propagate_down, blob_bottom_vec_2);
+  // Check numbers
+  for (int s = 0; s < blob_bottom_2->count(); ++s) {
+    EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+  }
+  for (int s = 0; s < ip.blobs()[0]->count(); ++s) {
+    EXPECT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);
+  }
+  for (int s = 0; s < ip.blobs()[1]->count(); ++s) {
+    EXPECT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);
+  }
+  for (int s = 0; s < prelu.blobs()[0]->count(); ++s) {
+    EXPECT_EQ(prelu.blobs()[0]->cpu_diff()[s],
+        prelu2.blobs()[0]->cpu_diff()[s]);
+  }
+}
+
 #ifdef USE_CUDNN
 template <typename Dtype>
 class CuDNNNeuronLayerTest : public ::testing::Test {

From ba933d3f9714d7aa6836842158829aba7c279c60 Mon Sep 17 00:00:00 2001
From: Zifei Tong <zifeitong@gmail.com>
Date: Thu, 12 Mar 2015 20:09:36 +0800
Subject: [PATCH 1269/2053] [docs] Add missing command in OS X installation
 guide

---
 docs/install_osx.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install_osx.md b/docs/install_osx.md
index 0373a417847..e1a1d569171 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -18,7 +18,7 @@ In other `ENV` settings, things may not work as expected.
     brew install --fresh -vd snappy leveldb gflags glog szip lmdb
     # need the homebrew science source for OpenCV and hdf5
     brew tap homebrew/science
-    hdf5 opencv
+    brew install hdf5 opencv
 
 If using Anaconda Python, a modification to the OpenCV formula might be needed
 Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.

From 58deb58084a61f70986e2ba6c2e98709febd6e35 Mon Sep 17 00:00:00 2001
From: Zifei Tong <zifeitong@gmail.com>
Date: Thu, 12 Mar 2015 20:11:52 +0800
Subject: [PATCH 1270/2053] [docs] Remove `--fresh` Homebrew option

Since it's removed from Homebrew [1].

[1]: Homebrew/homebrew@8cdf4d8ebf439eb9a9ffcaa0e455ced9459e1e41
---
 docs/install_osx.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/install_osx.md b/docs/install_osx.md
index e1a1d569171..fbfc71e8f39 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -15,7 +15,7 @@ In other `ENV` settings, things may not work as expected.
 
 **General dependencies**
 
-    brew install --fresh -vd snappy leveldb gflags glog szip lmdb
+    brew install -vd snappy leveldb gflags glog szip lmdb
     # need the homebrew science source for OpenCV and hdf5
     brew tap homebrew/science
     brew install hdf5 opencv
@@ -31,8 +31,8 @@ If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
 **Remaining dependencies, with / without Python**
 
     # with Python pycaffe needs dependencies built from source
-    brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
+    brew install --build-from-source --with-python -vd protobuf
+    brew install --build-from-source -vd boost boost-python
     # without Python the usual installation suffices
     brew install protobuf boost
 
@@ -78,9 +78,9 @@ To edit the formulae in turn, run
 
 After this, run
 
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
-    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python -vd protobuf
+    brew install --build-from-source -vd boost boost-python
 
 If this is not done exactly right then linking errors will trouble you.
 

From 249aba49ab3100d0409d93ef187ebcd5a53865b9 Mon Sep 17 00:00:00 2001
From: wieschol <patrick@wieschollek.info>
Date: Wed, 22 Oct 2014 15:22:36 +0200
Subject: [PATCH 1271/2053] shuffle data

---
 include/caffe/data_layers.hpp        |  2 ++
 src/caffe/layers/hdf5_data_layer.cpp | 43 ++++++++++++++++++++++++----
 src/caffe/layers/hdf5_data_layer.cu  | 12 ++++++--
 src/caffe/proto/caffe.proto          |  2 ++
 4 files changed, 51 insertions(+), 8 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 1f154408c27..2bb9d948169 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -171,6 +171,8 @@ class HDF5DataLayer : public Layer<Dtype> {
   unsigned int current_file_;
   hsize_t current_row_;
   std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
+  std::vector<unsigned int> data_permutation_;
+  std::vector<unsigned int> file_permutation_;
 };
 
 /**
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 1ceb6c24431..afa77e1e026 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -52,7 +52,20 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   for (int i = 1; i < top_size; ++i) {
     CHECK_EQ(hdf_blobs_[i]->num(), num);
   }
-  DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
+  //  permutation in file is identity by default
+  data_permutation_.clear();
+  data_permutation_.resize(hdf_blobs_[0]->num());
+  for (int i = 0; i < hdf_blobs_[0]->num(); i++)
+    data_permutation_[i] = i;
+
+  //  shuffle data when asked
+  if (this->layer_param_.hdf5_data_param().shuffle()) {
+    std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num()
+    << " rows (shuffled)";
+  } else {
+    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
+  }
 }
 
 template <typename Dtype>
@@ -81,9 +94,23 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GE(num_files_, 1) << "Must have at least 1 HDF5 filename listed in "
     << source;
 
+  file_permutation_.clear();
+  file_permutation_.resize(num_files_);
+  // default order-permutation is identity permutation
+  for (int i = 0; i < num_files_; i++)
+    file_permutation_[i] = i;
+
+  //  only change order, when asked
+  if (this->layer_param_.hdf5_data_param().shuffle()) {
+    std::random_shuffle(file_permutation_.begin(), file_permutation_.end());
+  }
+
   // Load the first HDF5 file and initialize the line counter.
-  LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
+  LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].c_str());
   current_row_ = 0;
+  if (this->layer_param_.hdf5_data_param().shuffle()) {
+    std::random_shuffle(file_permutation_.begin(), file_permutation_.end());
+  }
 
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
@@ -109,17 +136,23 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         ++current_file_;
         if (current_file_ == num_files_) {
           current_file_ = 0;
+          if (this->layer_param_.hdf5_data_param().shuffle())
+            std::random_shuffle(file_permutation_.begin(),
+              file_permutation_.end());
           DLOG(INFO) << "Looping around to first file.";
         }
-        LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
+        LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].
+          c_str());
       }
       current_row_ = 0;
+      if (this->layer_param_.hdf5_data_param().shuffle())
+        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
       int data_dim = top[j]->count() / top[j]->num();
       caffe_copy(data_dim,
-          &hdf_blobs_[j]->cpu_data()[current_row_ * data_dim],
-          &top[j]->mutable_cpu_data()[i * data_dim]);
+          &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
+            * data_dim], &top[j]->mutable_cpu_data()[i * data_dim]);
     }
   }
 }
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 02e3821d104..990cfa66920 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -26,17 +26,23 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         current_file_ += 1;
         if (current_file_ == num_files_) {
           current_file_ = 0;
+          if (this->layer_param_.hdf5_data_param().shuffle())
+            std::random_shuffle(file_permutation_.begin(),
+              file_permutation_.end());
           DLOG(INFO) << "Looping around to first file.";
         }
-        LoadHDF5FileData(hdf_filenames_[current_file_].c_str());
+        LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]]
+          .c_str());
       }
       current_row_ = 0;
+      if (this->layer_param_.hdf5_data_param().shuffle())
+        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
       int data_dim = top[j]->count() / top[j]->num();
       caffe_copy(data_dim,
-          &hdf_blobs_[j]->cpu_data()[current_row_ * data_dim],
-          &top[j]->mutable_gpu_data()[i * data_dim]);
+          &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
+            * data_dim], &top[j]->mutable_gpu_data()[i * data_dim]);
     }
   }
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 888371dc778..a7489c23088 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -518,6 +518,8 @@ message HDF5DataParameter {
   optional string source = 1;
   // Specify the batch size.
   optional uint32 batch_size = 2;
+  // Specify shuffling the order of training data
+  optional bool shuffle = 3 [default = false];
 }
 
 // Message that stores parameters used by HDF5OutputLayer

From 6fe2b04dcb2663543c8a5d0b3e139c00b5199d37 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 13 Mar 2015 01:27:59 -0700
Subject: [PATCH 1272/2053] HDF5DataLayer shuffle: minor cleanup; clarification
 in HDF5DataParameter

---
 src/caffe/layers/hdf5_data_layer.cpp | 41 +++++++++++++++-------------
 src/caffe/layers/hdf5_data_layer.cu  | 15 +++++-----
 src/caffe/proto/caffe.proto          |  7 ++++-
 3 files changed, 36 insertions(+), 27 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index afa77e1e026..1d3dc1e62ac 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -14,9 +14,9 @@
 #include "hdf5_hl.h"
 #include "stdint.h"
 
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -48,23 +48,24 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   CHECK_GE(status, 0) << "Failed to close HDF5 file: " << filename;
 
   // MinTopBlobs==1 guarantees at least one top blob
-  int num = hdf_blobs_[0]->num();
+  CHECK_GE(hdf_blobs_[0]->num_axes(), 1) << "Input must have at least 1 axis.";
+  const int num = hdf_blobs_[0]->shape(0);
   for (int i = 1; i < top_size; ++i) {
-    CHECK_EQ(hdf_blobs_[i]->num(), num);
+    CHECK_EQ(hdf_blobs_[i]->shape(0), num);
   }
-  //  permutation in file is identity by default
+  // Default to identity permutation.
   data_permutation_.clear();
-  data_permutation_.resize(hdf_blobs_[0]->num());
-  for (int i = 0; i < hdf_blobs_[0]->num(); i++)
+  data_permutation_.resize(hdf_blobs_[0]->shape(0));
+  for (int i = 0; i < hdf_blobs_[0]->shape(0); i++)
     data_permutation_[i] = i;
 
-  //  shuffle data when asked
+  // Shuffle if needed.
   if (this->layer_param_.hdf5_data_param().shuffle()) {
     std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
-    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num()
-    << " rows (shuffled)";
+    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0)
+               << " rows (shuffled)";
   } else {
-    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->num() << " rows";
+    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0) << " rows";
   }
 }
 
@@ -96,11 +97,12 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
   file_permutation_.clear();
   file_permutation_.resize(num_files_);
-  // default order-permutation is identity permutation
-  for (int i = 0; i < num_files_; i++)
+  // Default to identity permutation.
+  for (int i = 0; i < num_files_; i++) {
     file_permutation_[i] = i;
+  }
 
-  //  only change order, when asked
+  // Shuffle if needed.
   if (this->layer_param_.hdf5_data_param().shuffle()) {
     std::random_shuffle(file_permutation_.begin(), file_permutation_.end());
   }
@@ -131,25 +133,26 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == hdf_blobs_[0]->num()) {
+    if (current_row_ == hdf_blobs_[0]->shape(0)) {
       if (num_files_ > 1) {
         ++current_file_;
         if (current_file_ == num_files_) {
           current_file_ = 0;
-          if (this->layer_param_.hdf5_data_param().shuffle())
+          if (this->layer_param_.hdf5_data_param().shuffle()) {
             std::random_shuffle(file_permutation_.begin(),
-              file_permutation_.end());
+                                file_permutation_.end());
+          }
           DLOG(INFO) << "Looping around to first file.";
         }
-        LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].
-          c_str());
+        LoadHDF5FileData(
+            hdf_filenames_[file_permutation_[current_file_]].c_str());
       }
       current_row_ = 0;
       if (this->layer_param_.hdf5_data_param().shuffle())
         std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
-      int data_dim = top[j]->count() / top[j]->num();
+      int data_dim = top[j]->count() / top[j]->shape(0);
       caffe_copy(data_dim,
           &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
             * data_dim], &top[j]->mutable_cpu_data()[i * data_dim]);
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 990cfa66920..5e3e4ced141 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -10,9 +10,9 @@ TODO:
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -21,25 +21,26 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
   for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == hdf_blobs_[0]->num()) {
+    if (current_row_ == hdf_blobs_[0]->shape(0)) {
       if (num_files_ > 1) {
         current_file_ += 1;
         if (current_file_ == num_files_) {
           current_file_ = 0;
-          if (this->layer_param_.hdf5_data_param().shuffle())
+          if (this->layer_param_.hdf5_data_param().shuffle()) {
             std::random_shuffle(file_permutation_.begin(),
-              file_permutation_.end());
+                                file_permutation_.end());
+          }
           DLOG(INFO) << "Looping around to first file.";
         }
-        LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]]
-          .c_str());
+        LoadHDF5FileData(
+            hdf_filenames_[file_permutation_[current_file_]].c_str());
       }
       current_row_ = 0;
       if (this->layer_param_.hdf5_data_param().shuffle())
         std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
-      int data_dim = top[j]->count() / top[j]->num();
+      int data_dim = top[j]->count() / top[j]->shape(0);
       caffe_copy(data_dim,
           &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
             * data_dim], &top[j]->mutable_gpu_data()[i * data_dim]);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a7489c23088..5b21cf20028 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -518,7 +518,12 @@ message HDF5DataParameter {
   optional string source = 1;
   // Specify the batch size.
   optional uint32 batch_size = 2;
-  // Specify shuffling the order of training data
+
+  // Specify whether to shuffle the data.
+  // If shuffle == true, the ordering of the HDF5 files is shuffled,
+  // and the ordering of data within any given HDF5 file is shuffled,
+  // but data between different files are not interleaved; all of a file's
+  // data are output (in a random order) before moving onto another file.
   optional bool shuffle = 3 [default = false];
 }
 

From 4c090ae611c7cbed849b024e7228028a5d41050d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 13 Mar 2015 11:47:18 -0700
Subject: [PATCH 1273/2053] HDF5DataLayer: remove redundant shuffle

---
 src/caffe/layers/hdf5_data_layer.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 1d3dc1e62ac..8a782f7e524 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -110,9 +110,6 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Load the first HDF5 file and initialize the line counter.
   LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].c_str());
   current_row_ = 0;
-  if (this->layer_param_.hdf5_data_param().shuffle()) {
-    std::random_shuffle(file_permutation_.begin(), file_permutation_.end());
-  }
 
   // Reshape blobs.
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();

From e46cf85544ab8c4b40d529cd31e9b890a4fce19a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 13 Mar 2015 12:46:59 -0700
Subject: [PATCH 1274/2053] Fix for solver issue pointed out by @moskewcz in
 #1972

Use ReadNetParamsFromBinaryFileOrDie to read a net param when restoring
from a saved solverstate, which upgrades old nets, rather than
ReadProtoFromBinaryFile.
---
 src/caffe/solver.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 034390e6824..096980dd7af 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -349,7 +349,7 @@ void Solver<Dtype>::Restore(const char* state_file) {
   NetParameter net_param;
   ReadProtoFromBinaryFile(state_file, &state);
   if (state.has_learned_net()) {
-    ReadProtoFromBinaryFile(state.learned_net().c_str(), &net_param);
+    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
     net_->CopyTrainedLayersFrom(net_param);
   }
   iter_ = state.iter();

From 709e48438cfcb848d5fcd2c894f3c696563b8e46 Mon Sep 17 00:00:00 2001
From: Kenta Shoji <shoji@emosiv.com>
Date: Mon, 16 Mar 2015 08:26:31 +0900
Subject: [PATCH 1275/2053] Fix invalid syntax

---
 python/classify.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/classify.py b/python/classify.py
index 1e26ff48a23..4544c51b4c2 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -112,7 +112,7 @@ def main(argv):
     # Load numpy array (.npy), directory glob (*.jpg), or image file.
     args.input_file = os.path.expanduser(args.input_file)
     if args.input_file.endswith('npy'):
-        print("Loading file: %s" %s args.input_file
+        print("Loading file: %s" % args.input_file)
         inputs = np.load(args.input_file)
     elif os.path.isdir(args.input_file):
         print("Loading folder: %s" % args.input_file)

From 40ca4e68097589a6143a6754328a1f94786c118a Mon Sep 17 00:00:00 2001
From: tishibas67 <t_ishibas67@yahoo.co.jp>
Date: Wed, 18 Mar 2015 00:11:01 +0900
Subject: [PATCH 1276/2053] improved to load RGB image as grayscale image

---
 python/caffe/io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 6ae2cf13cc0..acd8a1427c8 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -285,7 +285,7 @@ def load_image(filename, color=True):
         of size (H x W x 3) in RGB or
         of size (H x W x 1) in grayscale.
     """
-    img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32)
+    img = skimage.img_as_float(skimage.io.imread(filename, as_grey=not color)).astype(np.float32)
     if img.ndim == 2:
         img = img[:, :, np.newaxis]
         if color:

From ccebe45b7a7b23813c9f508ad2d47aa34c72a512 Mon Sep 17 00:00:00 2001
From: TorosFanny <spiritofquantum@gmail.com>
Date: Thu, 19 Mar 2015 21:16:37 +0800
Subject: [PATCH 1277/2053] change resorce to resource

---
 include/caffe/syncedmem.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 2564e0716ef..1b726de9564 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -12,7 +12,7 @@ namespace caffe {
 // cudaMallocHost and cudaFree functions in order to create pinned memory.
 // However, those codes rely on the existence of a cuda GPU (I don't know
 // why that is a must since allocating memory should not be accessing the
-// GPU resorce, but it just creates an error as of Cuda 5.0) and will cause
+// GPU resource, but it just creates an error as of Cuda 5.0) and will cause
 // problem when running on a machine without GPU. Thus, we simply define
 // these two functions for safety and possible future change if the problem
 // of calling cuda functions disappears in a future version.

From dfdf2bbe296fa42361b3984c112d7fcffde6df78 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 19 Mar 2015 11:31:17 -0700
Subject: [PATCH 1278/2053] CUDA kernels for {Slice,Concat}Layer

---
 src/caffe/layers/concat_layer.cu | 44 ++++++++++++++++++++++--------
 src/caffe/layers/slice_layer.cu  | 47 ++++++++++++++++++++++----------
 2 files changed, 66 insertions(+), 25 deletions(-)

diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index dbadb5aeb30..8f2e85d8f52 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -6,21 +6,41 @@
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void Concat(const int nthreads, const Dtype* in_data,
+    const bool forward, const int num_concats, const int concat_size,
+    const int top_concat_axis, const int bottom_concat_axis,
+    const int offset_concat_axis, Dtype* out_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int total_concat_size = concat_size * bottom_concat_axis;
+    const int concat_num = index / total_concat_size;
+    const int concat_index = index % total_concat_size;
+    const int top_index = concat_index +
+        (concat_num * top_concat_axis + offset_concat_axis) * concat_size;
+    if (forward) {
+      out_data[top_index] = in_data[index];
+    } else {
+      out_data[index] = in_data[top_index];
+    }
+  }
+}
+
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   Dtype* top_data = top[0]->mutable_gpu_data();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
+  const bool kForward = true;
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    for (int n = 0; n < num_concats_; ++n) {
-      caffe_copy(bottom_concat_axis * concat_input_size_,
-          bottom_data + n * bottom_concat_axis * concat_input_size_,
-          top_data + (n * top_concat_axis + offset_concat_axis)
-              * concat_input_size_);
-    }
+    const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+    const int nthreads = bottom_concat_size * num_concats_;
+    Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, bottom_data, kForward, num_concats_, concat_input_size_,
+        top_concat_axis, bottom_concat_axis, offset_concat_axis, top_data);
     offset_concat_axis += bottom_concat_axis;
   }
 }
@@ -31,15 +51,17 @@ void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
+  const bool kForward = false;
   for (int i = 0; i < bottom.size(); ++i) {
     if (!propagate_down[i]) { continue; }
     Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    for (int n = 0; n < num_concats_; ++n) {
-      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
-          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
-          bottom_diff + n * bottom_concat_axis * concat_input_size_);
-    }
+    const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+    const int nthreads = bottom_concat_size * num_concats_;
+    Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, top_diff, kForward, num_concats_, concat_input_size_,
+        top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff);
     offset_concat_axis += bottom_concat_axis;
   }
 }
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index e6e65677bd8..796841d3f52 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -6,22 +6,41 @@
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void Slice(const int nthreads, const Dtype* in_data,
+    const bool forward, const int num_slices, const int slice_size,
+    const int bottom_slice_axis, const int top_slice_axis,
+    const int offset_slice_axis, Dtype* out_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int total_slice_size = slice_size * top_slice_axis;
+    const int slice_num = index / total_slice_size;
+    const int slice_index = index % total_slice_size;
+    const int bottom_index = slice_index +
+        (slice_num * bottom_slice_axis + offset_slice_axis) * slice_size;
+    if (forward) {
+      out_data[index] = in_data[bottom_index];
+    } else {
+      out_data[bottom_index] = in_data[index];
+    }
+  }
+}
+
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   int offset_slice_axis = 0;
   const Dtype* bottom_data = bottom[0]->gpu_data();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  const bool kForward = true;
   for (int i = 0; i < top.size(); ++i) {
     Dtype* top_data = top[i]->mutable_gpu_data();
     const int top_slice_axis = top[i]->shape(slice_axis_);
-    for (int n = 0; n < num_slices_; ++n) {
-      const int top_offset = n * top_slice_axis * slice_size_;
-      const int bottom_offset =
-          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
-      caffe_copy(top_slice_axis * slice_size_,
-          bottom_data + bottom_offset, top_data + top_offset);
-    }
+    const int top_slice_size = top_slice_axis * slice_size_;
+    const int nthreads = top_slice_size * num_slices_;
+    Slice<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, bottom_data, kForward, num_slices_, slice_size_,
+        bottom_slice_axis, top_slice_axis, offset_slice_axis, top_data);
     offset_slice_axis += top_slice_axis;
   }
 }
@@ -33,16 +52,16 @@ void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  const bool kForward = false;
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     const int top_slice_axis = top[i]->shape(slice_axis_);
-    for (int n = 0; n < num_slices_; ++n) {
-      const int top_offset = n * top_slice_axis * slice_size_;
-      const int bottom_offset =
-          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
-      caffe_copy(top_slice_axis * slice_size_,
-          top_diff + top_offset, bottom_diff + bottom_offset);
-    }
+    const int top_slice_size = top_slice_axis * slice_size_;
+    const int nthreads = top_slice_size * num_slices_;
+    Slice<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, top_diff, kForward, num_slices_, slice_size_,
+        bottom_slice_axis, top_slice_axis, offset_slice_axis, bottom_diff);
     offset_slice_axis += top_slice_axis;
   }
 }

From f61c374983f4d49f81e6dc2f7588ace5c00c7b36 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 19 Mar 2015 21:28:36 -0700
Subject: [PATCH 1279/2053] always call Layer::Reshape in Layer::Forward

There are no cases where Forward is called without Reshape, so we can
simplify the call structure.
---
 include/caffe/layer.hpp              | 1 +
 src/caffe/net.cpp                    | 1 -
 src/caffe/test/test_neuron_layer.cpp | 4 ----
 tools/caffe.cpp                      | 3 ---
 4 files changed, 1 insertion(+), 8 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 2d13ef97c05..8f924a75755 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -406,6 +406,7 @@ template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   Dtype loss = 0;
+  Reshape(bottom, top);
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Forward_cpu(bottom, top);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index fd00b122630..11503bce9c7 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -470,7 +470,6 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   }
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
-    layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index c9d52f247a6..030f4bbae7f 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -541,14 +541,10 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
   caffe_copy(ip2.blobs()[0]->count(), ip.blobs()[0]->cpu_data(),
       ip2.blobs()[0]->mutable_cpu_data());
   // Forward in-place
-  ip.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
   ip.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-  prelu.Reshape(this->blob_top_vec_, this->blob_top_vec_);
   prelu.Forward(this->blob_top_vec_, this->blob_top_vec_);
   // Forward non-in-place
-  ip2.Reshape(blob_bottom_vec_2, blob_middle_vec_2);
   ip2.Forward(blob_bottom_vec_2, blob_middle_vec_2);
-  prelu2.Reshape(blob_middle_vec_2, blob_top_vec_2);
   prelu2.Forward(blob_middle_vec_2, blob_top_vec_2);
   // Check numbers
   for (int s = 0; s < blob_top_2->count(); ++s) {
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index eb9e97f5e27..70b15f890f7 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -252,9 +252,6 @@ int time() {
     forward_timer.Start();
     for (int i = 0; i < layers.size(); ++i) {
       timer.Start();
-      // Although Reshape should be essentially free, we include it here
-      // so that we will notice Reshape performance bugs.
-      layers[i]->Reshape(bottom_vecs[i], top_vecs[i]);
       layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
       forward_time_per_layer[i] += timer.MicroSeconds();
     }

From 1a1ce5a4ef3897a0e3b40ebf243786b2f8a37667 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 4 Mar 2015 16:18:17 -0800
Subject: [PATCH 1280/2053] remove spurious net.hpp includes

---
 include/caffe/data_layers.hpp        | 1 -
 include/caffe/neuron_layers.hpp      | 1 -
 src/caffe/layers/base_data_layer.cpp | 1 -
 3 files changed, 3 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 2bb9d948169..24dfe723636 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -14,7 +14,6 @@
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
 
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 8669923d1fa..37553b9ee71 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -8,7 +8,6 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #define HDF5_DATA_DATASET_NAME "data"
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 352200915d7..931e4a9c0ab 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -2,7 +2,6 @@
 #include <vector>
 
 #include "caffe/data_layers.hpp"
-#include "caffe/net.hpp"
 #include "caffe/util/io.hpp"
 
 namespace caffe {

From 1e16c253a60918d7beee70ca163e6d7a973581dc Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Sun, 22 Mar 2015 18:53:02 +0900
Subject: [PATCH 1281/2053] Typos in documents

---
 docs/development.md | 2 +-
 docs/install_osx.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/development.md b/docs/development.md
index fe54864bd35..ccb6a29701d 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -30,7 +30,7 @@ Similarly for IPython notebooks: simply include `"include_in_docs": true` in the
 
 Other docs, such as installation guides, are written in the `docs` directory and manually linked to from the `index.md` page.
 
-We strive to provide provide lots of usage examples, and to document all code in docstrings.
+We strive to provide lots of usage examples, and to document all code in docstrings.
 We absolutely appreciate any contribution to this effort!
 
 ### Versioning
diff --git a/docs/install_osx.md b/docs/install_osx.md
index 0373a417847..c4ebd45f553 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -115,7 +115,7 @@ Then, whenever you want to update homebrew, switch back to the master branches,
     # Update homebrew; hopefully this works without errors!
     brew update
 
-    # Switch back to the caffe branches with the forumlae that you modified earlier
+    # Switch back to the caffe branches with the formulae that you modified earlier
     cd /usr/local
     git rebase master caffe
     # Fix any merge conflicts and commit to caffe branch

From 600c81d3da6dcc135734af789a9ffcad11515545 Mon Sep 17 00:00:00 2001
From: Simon Layton <slayton@nvidia.com>
Date: Mon, 9 Feb 2015 22:08:39 -0500
Subject: [PATCH 1282/2053] switch to cuDNN R2

---
 include/caffe/common_layers.hpp          |  4 +-
 include/caffe/neuron_layers.hpp          | 12 ++--
 include/caffe/util/cudnn.hpp             | 24 +++----
 include/caffe/vision_layers.hpp          |  8 ++-
 src/caffe/layers/cudnn_conv_layer.cpp    | 10 +--
 src/caffe/layers/cudnn_conv_layer.cu     | 89 ++++++++++++++++++------
 src/caffe/layers/cudnn_pooling_layer.cpp |  4 +-
 src/caffe/layers/cudnn_pooling_layer.cu  | 20 +++++-
 src/caffe/layers/cudnn_relu_layer.cpp    |  4 +-
 src/caffe/layers/cudnn_relu_layer.cu     | 24 +++++--
 src/caffe/layers/cudnn_sigmoid_layer.cpp |  4 +-
 src/caffe/layers/cudnn_sigmoid_layer.cu  | 24 +++++--
 src/caffe/layers/cudnn_softmax_layer.cpp |  4 +-
 src/caffe/layers/cudnn_softmax_layer.cu  | 22 ++++--
 src/caffe/layers/cudnn_tanh_layer.cpp    |  4 +-
 src/caffe/layers/cudnn_tanh_layer.cu     | 24 +++++--
 16 files changed, 199 insertions(+), 82 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index b1ac3a93eff..cae1c3e4ee6 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -386,8 +386,8 @@ class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
 
   bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 8669923d1fa..323215134c7 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -433,8 +433,8 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
 
   bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
@@ -516,8 +516,8 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
 
   bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
@@ -601,8 +601,8 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
 
   bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_;
-  cudnnTensor4dDescriptor_t top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
 };
 #endif
 
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index eaed7333df8..f4963623598 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -57,34 +57,34 @@ template<> class dataType<double> {
 };
 
 template <typename Dtype>
-inline void createTensor4dDesc(cudnnTensor4dDescriptor_t* desc) {
-  CUDNN_CHECK(cudnnCreateTensor4dDescriptor(desc));
+inline void createTensor4dDesc(cudnnTensorDescriptor_t* desc) {
+  CUDNN_CHECK(cudnnCreateTensorDescriptor(desc));
 }
 
 template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
     int n, int c, int h, int w,
     int stride_n, int stride_c, int stride_h, int stride_w) {
   CUDNN_CHECK(cudnnSetTensor4dDescriptorEx(*desc, dataType<Dtype>::type,
-      n, c, h, w, stride_n, stride_c, stride_h, stride_w));
+        n, c, h, w, stride_n, stride_c, stride_h, stride_w));
 }
 
 template <typename Dtype>
-inline void setTensor4dDesc(cudnnTensor4dDescriptor_t* desc,
+inline void setTensor4dDesc(cudnnTensorDescriptor_t* desc,
     int n, int c, int h, int w) {
   const int stride_w = 1;
   const int stride_h = w * stride_w;
   const int stride_c = h * stride_h;
   const int stride_n = c * stride_c;
   setTensor4dDesc<Dtype>(desc, n, c, h, w,
-      stride_n, stride_c, stride_h, stride_w);
+                         stride_n, stride_c, stride_h, stride_w);
 }
 
 template <typename Dtype>
 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
     int n, int c, int h, int w) {
   CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
-  CUDNN_CHECK(cudnnSetFilterDescriptor(*desc, dataType<Dtype>::type,
+  CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
       n, c, h, w));
 }
 
@@ -95,9 +95,9 @@ inline void createConvolutionDesc(cudnnConvolutionDescriptor_t* conv) {
 
 template <typename Dtype>
 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
-    cudnnTensor4dDescriptor_t bottom, cudnnFilterDescriptor_t filter,
+    cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
     int pad_h, int pad_w, int stride_h, int stride_w) {
-  CUDNN_CHECK(cudnnSetConvolutionDescriptor(*conv, bottom, filter,
+  CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
       pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
 }
 
@@ -110,14 +110,14 @@ inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
     *mode = CUDNN_POOLING_MAX;
     break;
   case PoolingParameter_PoolMethod_AVE:
-    *mode = CUDNN_POOLING_AVERAGE;
+    *mode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
     break;
   default:
     LOG(FATAL) << "Unknown pooling method.";
   }
   CUDNN_CHECK(cudnnCreatePoolingDescriptor(conv));
-  CUDNN_CHECK(cudnnSetPoolingDescriptor(*conv, *mode, h, w,
-        stride_h, stride_w));
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*conv, *mode, h, w,
+        0, 0, stride_h, stride_w));
 }
 
 }  // namespace cudnn
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 6cb507a5780..cd0ab8babb0 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -246,11 +246,13 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
   bool handles_setup_;
   cudnnHandle_t* handle_;
   cudaStream_t*  stream_;
-  vector<cudnnTensor4dDescriptor_t> bottom_descs_, top_descs_;
-  cudnnTensor4dDescriptor_t    bias_desc_;
+  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
+  cudnnTensorDescriptor_t    bias_desc_;
   cudnnFilterDescriptor_t      filter_desc_;
   vector<cudnnConvolutionDescriptor_t> conv_descs_;
   int bottom_offset_, top_offset_, weight_offset_, bias_offset_;
+  size_t workspaceSizeInBytes;
+  void *workspace;
 };
 #endif
 
@@ -445,7 +447,7 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
 
   bool handles_setup_;
   cudnnHandle_t             handle_;
-  cudnnTensor4dDescriptor_t bottom_desc_, top_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
   cudnnPoolingDescriptor_t  pooling_desc_;
   cudnnPoolingMode_t        mode_;
 };
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 4a69ca20d0a..524caf1320f 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -43,10 +43,10 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
 
   // Create tensor descriptor(s) for data and corresponding convolution(s).
   for (int i = 0; i < bottom.size(); i++) {
-    cudnnTensor4dDescriptor_t bottom_desc;
+    cudnnTensorDescriptor_t bottom_desc;
     cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
     bottom_descs_.push_back(bottom_desc);
-    cudnnTensor4dDescriptor_t top_desc;
+    cudnnTensorDescriptor_t top_desc;
     cudnn::createTensor4dDesc<Dtype>(&top_desc);
     top_descs_.push_back(top_desc);
     cudnnConvolutionDescriptor_t conv_desc;
@@ -104,12 +104,12 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
   if (!handles_setup_) { return; }
 
   for (int i = 0; i < bottom_descs_.size(); i++) {
-    cudnnDestroyTensor4dDescriptor(bottom_descs_[i]);
-    cudnnDestroyTensor4dDescriptor(top_descs_[i]);
+    cudnnDestroyTensorDescriptor(bottom_descs_[i]);
+    cudnnDestroyTensorDescriptor(top_descs_[i]);
     cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
   }
   if (this->bias_term_) {
-    cudnnDestroyTensor4dDescriptor(bias_desc_);
+    cudnnDestroyTensorDescriptor(bias_desc_);
   }
   cudnnDestroyFilterDescriptor(filter_desc_);
 
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 071014e1b48..4a70c69a34c 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -21,21 +21,59 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
+      Dtype alpha = 1.0;
+      Dtype beta = 0.0;
+
+      cudnnConvolutionFwdAlgo_t algo;
+
+      // get the desired convolution algorithm
+      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[g],
+        bottom_descs_[i],
+        filter_desc_,
+        conv_descs_[i],
+        top_descs_[i],
+        CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+        0,  // memoryLimitInBytes,
+        &algo));
+
+      // get minimum size of the workspace needed for the desired algorithm
+      size_t workspaceSizeInBytes_temp = 0;
+
+      CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[g],
+        bottom_descs_[i],
+        filter_desc_,
+        conv_descs_[i],
+        top_descs_[i],
+        algo,
+        &workspaceSizeInBytes));
+
+      if (workspaceSizeInBytes_temp > workspaceSizeInBytes) {
+        workspaceSizeInBytes = workspaceSizeInBytes_temp;
+        // free the existing workspace and allocate a new (larger) one
+        cudaFree(this->workspace);
+        cudaMalloc(&(this->workspace), workspaceSizeInBytes);
+      }
+
       // Filters.
       CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
-          bottom_descs_[i], bottom_data + bottom_offset_ * g,
-          filter_desc_, weight + weight_offset_ * g,
-          conv_descs_[i],
-          top_descs_[i], top_data + top_offset_ * g,
-          CUDNN_RESULT_NO_ACCUMULATE));
+            reinterpret_cast<void *>(&alpha),
+            bottom_descs_[i], bottom_data + bottom_offset_ * g,
+            filter_desc_, weight + weight_offset_ * g,
+            conv_descs_[i],
+            algo, workspace, workspaceSizeInBytes,
+            reinterpret_cast<void *>(&beta),
+            top_descs_[i], top_data + top_offset_ * g));
 
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
-        Dtype alpha = 1.;
-        CUDNN_CHECK(cudnnAddTensor4d(handle_[g], CUDNN_ADD_SAME_C, &alpha,
-            bias_desc_, bias_data + bias_offset_ * g,
-            top_descs_[i], top_data + top_offset_ * g));
+        Dtype alpha = 1.0;
+        Dtype beta = 1.0;
+        CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
+              reinterpret_cast<void *>(&alpha),
+              bias_desc_, bias_data + bias_offset_ * g,
+              reinterpret_cast<void *>(&beta),
+              top_descs_[i], top_data + top_offset_ * g));
       }
     }
 
@@ -67,21 +105,26 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     for (int g = 0; g < this->group_; g++) {
       // Gradient w.r.t. bias.
       if (this->bias_term_ && this->param_propagate_down_[1]) {
+        Dtype alpha = 1.0;
+        Dtype beta = 1.0;
         CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
-            top_descs_[i],  top_diff + top_offset_ * g,
-            bias_desc_, bias_diff + bias_offset_ * g,
-            CUDNN_RESULT_ACCUMULATE));
+              reinterpret_cast<void *>(&alpha),
+              top_descs_[i],  top_diff + top_offset_ * g,
+              reinterpret_cast<void *>(&beta),
+              bias_desc_, bias_diff + bias_offset_ * g));
       }
 
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = bottom[i]->gpu_data();
+        Dtype alpha = 1.0;
+        Dtype beta = 1.0;
         CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
-            bottom_descs_[i], bottom_data + bottom_offset_ * g,
-            top_descs_[i],    top_diff + top_offset_ * g,
-            conv_descs_[i],
-            filter_desc_, weight_diff + weight_offset_ * g,
-            CUDNN_RESULT_ACCUMULATE));
+              reinterpret_cast<void *>(&alpha),
+              bottom_descs_[i], bottom_data + bottom_offset_ * g,
+              top_descs_[i],    top_diff + top_offset_ * g,
+              conv_descs_[i], reinterpret_cast<void *>(&beta),
+              filter_desc_, weight_diff + weight_offset_ * g));
       }
 
       // Gradient w.r.t. bottom data.
@@ -90,12 +133,14 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           weight = this->blobs_[0]->gpu_data();
         }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+        Dtype alpha = 1.0;
+        Dtype beta = 0.0;
         CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
-            filter_desc_, weight + weight_offset_ * g,
-            top_descs_[i],    top_diff + top_offset_ * g,
-            conv_descs_[i],
-            bottom_descs_[i], bottom_diff + bottom_offset_ * g,
-            CUDNN_RESULT_NO_ACCUMULATE));
+              reinterpret_cast<void *>(&alpha),
+              filter_desc_, weight + weight_offset_ * g,
+              top_descs_[i],    top_diff + top_offset_ * g,
+              conv_descs_[i], reinterpret_cast<void *>(&beta),
+              bottom_descs_[i], bottom_diff + bottom_offset_ * g));
       }
     }
 
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index dd90195637b..b447f19b426 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -40,8 +40,8 @@ CuDNNPoolingLayer<Dtype>::~CuDNNPoolingLayer() {
   // Check that handles have been setup before destroying.
   if (!handles_setup_) { return; }
 
-  cudnnDestroyTensor4dDescriptor(bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(top_desc_);
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
   cudnnDestroyPoolingDescriptor(pooling_desc_);
   cudnnDestroy(handle_);
 }
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 1c113aad75f..be7c4a8edb8 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -14,8 +14,15 @@ void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
-      bottom_desc_, bottom_data, top_desc_, top_data));
+        reinterpret_cast<void *>(&alpha),
+        bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -28,9 +35,16 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
-      top_desc_, top_data, top_desc_, top_diff,
-      bottom_desc_, bottom_data, bottom_desc_, bottom_diff));
+        reinterpret_cast<void *>(&alpha),
+        top_desc_, top_data, top_desc_, top_diff,
+        bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNPoolingLayer);
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 0b8a6bc3248..759d83984ef 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -35,8 +35,8 @@ CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
   // Check that handles have been setup before destroying.
   if (!handles_setup_) { return; }
 
-  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  cudnnDestroyTensorDescriptor(this->bottom_desc_);
+  cudnnDestroyTensorDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 862508707a0..b9d0870a5a7 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -17,9 +17,16 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-      CUDNN_ACTIVATION_RELU,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+        CUDNN_ACTIVATION_RELU,
+        reinterpret_cast<void *>(&alpha),
+        this->bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        this->top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -39,10 +46,17 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-      CUDNN_ACTIVATION_RELU,
-      this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+        CUDNN_ACTIVATION_RELU,
+        reinterpret_cast<void *>(&alpha),
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        this->bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 67bd9c373b0..32637873d46 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -35,8 +35,8 @@ CuDNNSigmoidLayer<Dtype>::~CuDNNSigmoidLayer() {
   // Check that handles have been setup before destroying.
   if (!handles_setup_) { return; }
 
-  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  cudnnDestroyTensorDescriptor(this->bottom_desc_);
+  cudnnDestroyTensorDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 31b094e25d4..9bb915017b4 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -12,9 +12,16 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-      CUDNN_ACTIVATION_SIGMOID,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+        CUDNN_ACTIVATION_SIGMOID,
+        reinterpret_cast<void *>(&alpha),
+        this->bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        this->top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -29,10 +36,17 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-      CUDNN_ACTIVATION_SIGMOID,
-      this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+        CUDNN_ACTIVATION_SIGMOID,
+        reinterpret_cast<void *>(&alpha),
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        this->bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 211701cad49..77a3225adcd 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -39,8 +39,8 @@ CuDNNSoftmaxLayer<Dtype>::~CuDNNSoftmaxLayer() {
   // Check that handles have been setup before destroying.
   if (!handles_setup_) { return; }
 
-  cudnnDestroyTensor4dDescriptor(bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(top_desc_);
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
   cudnnDestroy(handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index f328afdd831..59c304f6338 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -16,9 +16,16 @@ void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
-      CUDNN_SOFTMAX_MODE_CHANNEL,
-      bottom_desc_, bottom_data, top_desc_, top_data));
+        CUDNN_SOFTMAX_MODE_CHANNEL,
+        reinterpret_cast<void *>(&alpha),
+        bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -29,9 +36,16 @@ void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = bottom[0]->gpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+    Dtype alpha = 1.0;
+    Dtype beta = 0.0;
+
     CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
-        CUDNN_SOFTMAX_MODE_CHANNEL,
-        top_desc_, top_data, top_desc_, top_diff, bottom_desc_, bottom_diff));
+          CUDNN_SOFTMAX_MODE_CHANNEL,
+          reinterpret_cast<void *>(&alpha),
+          top_desc_, top_data, top_desc_, top_diff,
+          reinterpret_cast<void *>(&beta),
+          bottom_desc_, bottom_diff));
   }
 }
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index b1d2b86384e..376faad324d 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -35,8 +35,8 @@ CuDNNTanHLayer<Dtype>::~CuDNNTanHLayer() {
   // Check that handles have been setup before destroying.
   if (!handles_setup_) { return; }
 
-  cudnnDestroyTensor4dDescriptor(this->bottom_desc_);
-  cudnnDestroyTensor4dDescriptor(this->top_desc_);
+  cudnnDestroyTensorDescriptor(this->bottom_desc_);
+  cudnnDestroyTensorDescriptor(this->top_desc_);
   cudnnDestroy(this->handle_);
 }
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index bf9ec7cfac4..e008b0dcde3 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -12,9 +12,16 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-      CUDNN_ACTIVATION_TANH,
-      this->bottom_desc_, bottom_data, this->top_desc_, top_data));
+        CUDNN_ACTIVATION_TANH,
+        reinterpret_cast<void *>(&alpha),
+        this->bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        this->top_desc_, top_data));
 }
 
 template <typename Dtype>
@@ -29,10 +36,17 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  Dtype alpha = 1.0;
+  Dtype beta = 0.0;
+
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-      CUDNN_ACTIVATION_TANH,
-      this->top_desc_, top_data, this->top_desc_, top_diff,
-      this->bottom_desc_, bottom_data, this->bottom_desc_, bottom_diff));
+        CUDNN_ACTIVATION_TANH,
+        reinterpret_cast<void *>(&alpha),
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        reinterpret_cast<void *>(&beta),
+        this->bottom_desc_, bottom_diff));
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);

From 17c3bb74ebf355b550d271ef1d2141213f770ec7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Feb 2015 16:01:18 -0800
Subject: [PATCH 1283/2053] replace cuDNN alphas and betas with coefficient
 values

Give cuDNN {0, 1} constants for controlling accumulation through the
alpha and beta coefficients.
---
 include/caffe/util/cudnn.hpp            | 14 ++++++----
 src/caffe/layers/cudnn_conv_layer.cu    | 35 +++++++++----------------
 src/caffe/layers/cudnn_pooling_layer.cu | 16 +++--------
 src/caffe/layers/cudnn_relu_layer.cu    | 16 +++--------
 src/caffe/layers/cudnn_sigmoid_layer.cu | 16 +++--------
 src/caffe/layers/cudnn_softmax_layer.cu | 15 +++--------
 src/caffe/layers/cudnn_tanh_layer.cu    | 15 +++--------
 src/caffe/util/cudnn.cpp                | 23 ++++++++++++++++
 8 files changed, 65 insertions(+), 85 deletions(-)
 create mode 100644 src/caffe/util/cudnn.cpp

diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index f4963623598..b531dd5fa7a 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -50,10 +50,14 @@ template <typename Dtype> class dataType;
 template<> class dataType<float>  {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+  static float oneval, zeroval;
+  static const void *one, *zero;
 };
 template<> class dataType<double> {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+  static double oneval, zeroval;
+  static const void *one, *zero;
 };
 
 template <typename Dtype>
@@ -102,9 +106,9 @@ inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
 }
 
 template <typename Dtype>
-inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
+inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
     PoolingParameter_PoolMethod poolmethod, cudnnPoolingMode_t* mode,
-    int h, int w, int stride_h, int stride_w) {
+    int h, int w, int pad_h, int pad_w, int stride_h, int stride_w) {
   switch (poolmethod) {
   case PoolingParameter_PoolMethod_MAX:
     *mode = CUDNN_POOLING_MAX;
@@ -115,9 +119,9 @@ inline void createPoolingDesc(cudnnPoolingDescriptor_t* conv,
   default:
     LOG(FATAL) << "Unknown pooling method.";
   }
-  CUDNN_CHECK(cudnnCreatePoolingDescriptor(conv));
-  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*conv, *mode, h, w,
-        0, 0, stride_h, stride_w));
+  CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode, h, w,
+        pad_h, pad_w, stride_h, stride_w));
 }
 
 }  // namespace cudnn
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 4a70c69a34c..ed0d91db9d7 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -21,9 +21,6 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
-      Dtype alpha = 1.0;
-      Dtype beta = 0.0;
-
       cudnnConvolutionFwdAlgo_t algo;
 
       // get the desired convolution algorithm
@@ -56,23 +53,21 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
       // Filters.
       CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
-            reinterpret_cast<void *>(&alpha),
+            cudnn::dataType<Dtype>::one,
             bottom_descs_[i], bottom_data + bottom_offset_ * g,
             filter_desc_, weight + weight_offset_ * g,
             conv_descs_[i],
             algo, workspace, workspaceSizeInBytes,
-            reinterpret_cast<void *>(&beta),
+            cudnn::dataType<Dtype>::zero,
             top_descs_[i], top_data + top_offset_ * g));
 
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
-        Dtype alpha = 1.0;
-        Dtype beta = 1.0;
         CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
-              reinterpret_cast<void *>(&alpha),
+              cudnn::dataType<Dtype>::one,
               bias_desc_, bias_data + bias_offset_ * g,
-              reinterpret_cast<void *>(&beta),
+              cudnn::dataType<Dtype>::one,
               top_descs_[i], top_data + top_offset_ * g));
       }
     }
@@ -105,25 +100,22 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     for (int g = 0; g < this->group_; g++) {
       // Gradient w.r.t. bias.
       if (this->bias_term_ && this->param_propagate_down_[1]) {
-        Dtype alpha = 1.0;
-        Dtype beta = 1.0;
         CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0*this->group_ + g],
-              reinterpret_cast<void *>(&alpha),
+              cudnn::dataType<Dtype>::one,
               top_descs_[i],  top_diff + top_offset_ * g,
-              reinterpret_cast<void *>(&beta),
+              cudnn::dataType<Dtype>::one,
               bias_desc_, bias_diff + bias_offset_ * g));
       }
 
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = bottom[i]->gpu_data();
-        Dtype alpha = 1.0;
-        Dtype beta = 1.0;
         CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
-              reinterpret_cast<void *>(&alpha),
+              cudnn::dataType<Dtype>::one,
               bottom_descs_[i], bottom_data + bottom_offset_ * g,
               top_descs_[i],    top_diff + top_offset_ * g,
-              conv_descs_[i], reinterpret_cast<void *>(&beta),
+              conv_descs_[i],
+              cudnn::dataType<Dtype>::one,
               filter_desc_, weight_diff + weight_offset_ * g));
       }
 
@@ -133,13 +125,12 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           weight = this->blobs_[0]->gpu_data();
         }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
-        Dtype alpha = 1.0;
-        Dtype beta = 0.0;
         CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
-              reinterpret_cast<void *>(&alpha),
+              cudnn::dataType<Dtype>::one,
               filter_desc_, weight + weight_offset_ * g,
-              top_descs_[i],    top_diff + top_offset_ * g,
-              conv_descs_[i], reinterpret_cast<void *>(&beta),
+              top_descs_[i], top_diff + top_offset_ * g,
+              conv_descs_[i],
+              cudnn::dataType<Dtype>::zero,
               bottom_descs_[i], bottom_diff + bottom_offset_ * g));
       }
     }
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index be7c4a8edb8..a952b855a48 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -14,14 +14,10 @@ void CuDNNPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnPoolingForward(handle_, pooling_desc_,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         top_desc_, top_data));
 }
 
@@ -35,15 +31,11 @@ void CuDNNPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_data = top[0]->gpu_data();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnPoolingBackward(handle_, pooling_desc_,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         top_desc_, top_data, top_desc_, top_diff,
         bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         bottom_desc_, bottom_diff));
 }
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index b9d0870a5a7..21d14857dd2 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -17,15 +17,11 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
         CUDNN_ACTIVATION_RELU,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
 }
 
@@ -46,16 +42,12 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
         CUDNN_ACTIVATION_RELU,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
 }
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 9bb915017b4..7a06cf721da 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -12,15 +12,11 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
         CUDNN_ACTIVATION_SIGMOID,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
 }
 
@@ -36,16 +32,12 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
         CUDNN_ACTIVATION_SIGMOID,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
 }
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index 59c304f6338..a9e2fcefaf7 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -16,15 +16,11 @@ void CuDNNSoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnSoftmaxForward(handle_, CUDNN_SOFTMAX_ACCURATE,
         CUDNN_SOFTMAX_MODE_CHANNEL,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         top_desc_, top_data));
 }
 
@@ -37,14 +33,11 @@ void CuDNNSoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* bottom_data = bottom[0]->gpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
 
-    Dtype alpha = 1.0;
-    Dtype beta = 0.0;
-
     CUDNN_CHECK(cudnnSoftmaxBackward(handle_, CUDNN_SOFTMAX_ACCURATE,
           CUDNN_SOFTMAX_MODE_CHANNEL,
-          reinterpret_cast<void *>(&alpha),
+          cudnn::dataType<Dtype>::one,
           top_desc_, top_data, top_desc_, top_diff,
-          reinterpret_cast<void *>(&beta),
+          cudnn::dataType<Dtype>::zero,
           bottom_desc_, bottom_diff));
   }
 }
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index e008b0dcde3..d287f6fee85 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -12,15 +12,11 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
         CUDNN_ACTIVATION_TANH,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
 }
 
@@ -37,15 +33,12 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
 
-  Dtype alpha = 1.0;
-  Dtype beta = 0.0;
-
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
         CUDNN_ACTIVATION_TANH,
-        reinterpret_cast<void *>(&alpha),
+        cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
-        reinterpret_cast<void *>(&beta),
+        cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
 }
 
diff --git a/src/caffe/util/cudnn.cpp b/src/caffe/util/cudnn.cpp
new file mode 100644
index 00000000000..1772f0099ce
--- /dev/null
+++ b/src/caffe/util/cudnn.cpp
@@ -0,0 +1,23 @@
+#ifdef USE_CUDNN
+#include "caffe/util/cudnn.hpp"
+
+namespace caffe {
+namespace cudnn {
+
+float dataType<float>::oneval = 1.0;
+float dataType<float>::zeroval = 0.0;
+const void* dataType<float>::one =
+    static_cast<void *>(&dataType<float>::oneval);
+const void* dataType<float>::zero =
+    static_cast<void *>(&dataType<float>::zeroval);
+
+double dataType<double>::oneval = 1.0;
+double dataType<double>::zeroval = 0.0;
+const void* dataType<double>::one =
+    static_cast<void *>(&dataType<double>::oneval);
+const void* dataType<double>::zero =
+    static_cast<void *>(&dataType<double>::zeroval);
+
+}  // namespace cudnn
+}  // namespace caffe
+#endif

From 6d1444fea9d701cbe3e1e9fa3216edc96979d41c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 16 Feb 2015 17:18:13 -0800
Subject: [PATCH 1284/2053] cuDNN pooling can pad now

---
 src/caffe/layers/cudnn_pooling_layer.cpp | 6 ++----
 src/caffe/test/test_pooling_layer.cpp    | 8 --------
 2 files changed, 2 insertions(+), 12 deletions(-)

diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index b447f19b426..c92c4e477b5 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -13,15 +13,13 @@ template <typename Dtype>
 void CuDNNPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   PoolingLayer<Dtype>::LayerSetUp(bottom, top);
-  // Sanity check: CUDNN currently only supports pad == 0.
-  CHECK_EQ(this->pad_h_, 0);
-  CHECK_EQ(this->pad_w_, 0);
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
   cudnn::createPoolingDesc<Dtype>(&pooling_desc_,
       this->layer_param_.pooling_param().pool(), &mode_,
-      this->kernel_h_, this->kernel_w_, this->stride_h_, this->stride_w_);
+      this->kernel_h_, this->kernel_w_, this->pad_h_, this->pad_w_,
+      this->stride_h_, this->stride_w_);
   handles_setup_ = true;
 }
 
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 435caa8381e..e9964e7f0b7 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -976,9 +976,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
-// This test and all following cuDNN pooling tests with padding are commented
-// for now, since cuDNN pooling does not currently support padding.
-/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
@@ -994,7 +991,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
   EXPECT_EQ(this->blob_top_->height(), 4);
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
-*/
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
@@ -1062,7 +1058,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
   }
 }
 
-/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
@@ -1107,7 +1102,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
   EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
 }
-*/
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
@@ -1175,7 +1169,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
   }
 }
 
-/*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
   Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
@@ -1194,7 +1187,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
     }
   }
 }
-*/
 
 #endif
 

From 3cc9b73a011b7ba8964d1bccc97b944814cbeb38 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 24 Mar 2015 14:17:57 -0700
Subject: [PATCH 1285/2053] note cuDNN v2 convolutional TODOs

---
 src/caffe/layers/cudnn_conv_layer.cu | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index ed0d91db9d7..08f5201bc22 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -23,7 +23,10 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
     for (int g = 0; g < this->group_; g++) {
       cudnnConvolutionFwdAlgo_t algo;
 
-      // get the desired convolution algorithm
+      // pick the convolution algorithm
+      // TODO(shelhamer) this should be done during reshape
+      // TODO(shelhamer) the choice of automatic or manual algorithm picking
+      // should be exposed in proto
       CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[g],
         bottom_descs_[i],
         filter_desc_,

From 655246dcd12b86fb804d53ed61236a6065a850e1 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 24 Mar 2015 17:42:56 -0700
Subject: [PATCH 1286/2053] Remove scikit-learn dependency

---
 python/requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/python/requirements.txt b/python/requirements.txt
index 6a90fd6f5b4..3a1d88099de 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -2,7 +2,6 @@ Cython>=0.19.2
 numpy>=1.7.1
 scipy>=0.13.2
 scikit-image>=0.9.3
-scikit-learn>=0.14.1
 matplotlib>=1.3.1
 ipython>=1.1.0
 h5py>=2.2.0

From 8be7842ae8809a2a8a663d7c10a31230f66e9a10 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 24 Mar 2015 18:15:08 -0700
Subject: [PATCH 1287/2053] Add note in example about installing scikit-learn

---
 examples/hdf5_classification.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
index b90b79d962c..19d27372754 100644
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
@@ -40,6 +40,7 @@
       "import shutil\n",
       "import tempfile\n",
       "\n",
+      "# You may need to 'pip install scikit-learn'\n",
       "import sklearn\n",
       "import sklearn.datasets\n",
       "import sklearn.linear_model"
@@ -1070,4 +1071,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}

From 3487d246fb3a665e3eee23d415a41ade1b7acf3f Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 25 Mar 2015 12:42:17 -0700
Subject: [PATCH 1288/2053] Downgrade Pillow pip requirement

2.7.0 isn't really necessary - 2.3.0 is sufficient. This is the version
available on Ubuntu 14.04 via apt-get, and seems to be a reasonable lowest
common denominator in general.

http://pillow.readthedocs.org/installation.html#old-versions
---
 python/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/requirements.txt b/python/requirements.txt
index 6a90fd6f5b4..28747f277fc 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -14,4 +14,4 @@ python-dateutil>=1.4,<2
 protobuf>=2.5.0
 python-gflags>=2.0
 pyyaml>=3.10
-Pillow>=2.7.0
+Pillow>=2.3.0

From add73fbea5767cc1f8a75d9c0715e334b9651335 Mon Sep 17 00:00:00 2001
From: Nuno Subtil <nsubtil@nvidia.com>
Date: Tue, 24 Mar 2015 15:48:16 -0700
Subject: [PATCH 1289/2053] Fallback to different cuDNN algorithm when under
 memory pressure

CUDNN_CONVOLUTION_FWD_PREFER_FASTEST requires a lot of GPU memory, which may
not always be available. Add a fallback path that uses
CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM when the allocation fails.
---
 src/caffe/layers/cudnn_conv_layer.cpp |  2 ++
 src/caffe/layers/cudnn_conv_layer.cu  | 19 +++++++++++++++----
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 524caf1320f..104d2b9d669 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -24,6 +24,8 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   // Initialize CUDA streams and cuDNN.
   stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
   handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+  workspaceSizeInBytes = 0;
+  workspace = NULL;
 
   for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
     CUDA_CHECK(cudaStreamCreate(&stream_[g]));
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 08f5201bc22..4a1a4c4f4f2 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -19,6 +19,11 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
     Dtype* top_data = top[i]->mutable_gpu_data();
     const Dtype* weight = this->blobs_[0]->gpu_data();
 
+    size_t workspace_limit_bytes = this->kernel_h_ *
+                                   this->kernel_w_ *
+                                   this->channels_ *
+                                   sizeof(int) + 1;
+
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
       cudnnConvolutionFwdAlgo_t algo;
@@ -32,8 +37,8 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
         filter_desc_,
         conv_descs_[i],
         top_descs_[i],
-        CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
-        0,  // memoryLimitInBytes,
+        CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+        workspace_limit_bytes,  // memoryLimitInBytes,
         &algo));
 
       // get minimum size of the workspace needed for the desired algorithm
@@ -45,13 +50,19 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
         conv_descs_[i],
         top_descs_[i],
         algo,
-        &workspaceSizeInBytes));
+        &workspaceSizeInBytes_temp));
 
       if (workspaceSizeInBytes_temp > workspaceSizeInBytes) {
         workspaceSizeInBytes = workspaceSizeInBytes_temp;
         // free the existing workspace and allocate a new (larger) one
         cudaFree(this->workspace);
-        cudaMalloc(&(this->workspace), workspaceSizeInBytes);
+        cudaError_t err = cudaMalloc(&(this->workspace), workspaceSizeInBytes);
+        if (err != cudaSuccess) {
+          // force zero memory path
+          algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
+          workspace = NULL;
+          workspaceSizeInBytes = 0;
+        }
       }
 
       // Filters.

From fe50c502db196a825bf1ebcb93044f5875c2b7df Mon Sep 17 00:00:00 2001
From: small-yellow-duck <kiri.nichol@gmail.com>
Date: Sun, 29 Mar 2015 14:30:21 -0700
Subject: [PATCH 1290/2053] Check if CPU_ONLY is set when determining CUDA
 version

Previously, CUDA_VERSION would appear to be < 7 if there was no CUDA
installed, and that would generate the wrong C++ flags for compiling on
recent OSX versions. Instead, skip the CUDA version check if CPU_ONLY is
set. This change only affects CPU_ONLY installations.
---
 Makefile | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/Makefile b/Makefile
index cb396794e37..706959ad0ca 100644
--- a/Makefile
+++ b/Makefile
@@ -232,13 +232,15 @@ endif
 # libstdc++ for NVCC compatibility on OS X >= 10.9 with CUDA < 7.0
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
-	CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
-	ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
-		CXXFLAGS += -stdlib=libstdc++
-		LINKFLAGS += -stdlib=libstdc++
+	ifneq ($(CPU_ONLY), 1)
+		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+		ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
+			CXXFLAGS += -stdlib=libstdc++
+			LINKFLAGS += -stdlib=libstdc++
+		endif
+		# clang throws this warning for cuda headers
+		WARNINGS += -Wno-unneeded-internal-declaration
 	endif
-	# clang throws this warning for cuda headers
-	WARNINGS += -Wno-unneeded-internal-declaration
 	# gtest needs to use its own tuple to not conflict with clang
 	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X

From 0035b2f01035c7a22891fb1ab29b18270f3539d9 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Mon, 30 Mar 2015 19:52:43 -0700
Subject: [PATCH 1291/2053] Fix: libm.* doesn not exist

---
 scripts/travis/travis_install.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 82f386cf029..0e8c37861b0 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -67,4 +67,3 @@ export PATH=/home/travis/miniconda/bin:$PATH
 conda update --yes conda
 conda install --yes numpy scipy matplotlib scikit-image pip
 pip install protobuf
-rm /home/travis/miniconda/lib/libm.*

From 10ddfd32cb5ca4132902e9453fb05242ef52ec07 Mon Sep 17 00:00:00 2001
From: Nuno Subtil <nsubtil@nvidia.com>
Date: Wed, 1 Apr 2015 19:10:55 -0700
Subject: [PATCH 1292/2053] Build gflags and glog through CMake if not found in
 the system

This adds functionality to fetch gflags and glog from GitHub and build
them during the Caffe build. This happens only if a system-wide
installed version is not found (i.e., when find_package() fails).

If built as part of the Caffe build, gflags and glog are compiled as
position-independent static libraries. This avoids doing a system-wide
install of these libraries during the Caffe install target.
---
 CMakeLists.txt              |  2 ++
 cmake/Dependencies.cmake    |  4 +--
 cmake/External/gflags.cmake | 56 +++++++++++++++++++++++++++++++++++++
 cmake/External/glog.cmake   | 56 +++++++++++++++++++++++++++++++++++++
 cmake/Targets.cmake         |  4 +++
 5 files changed, 120 insertions(+), 2 deletions(-)
 create mode 100644 cmake/External/gflags.cmake
 create mode 100644 cmake/External/glog.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 74fa70c9d20..f22aa5763a3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,6 +6,8 @@ project(Caffe C CXX)
 # ---[ Using cmake scripts and modules
 list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
+include(ExternalProject)
+
 include(cmake/Utils.cmake)
 include(cmake/Targets.cmake)
 include(cmake/Misc.cmake)
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index f328e8246ab..7cae5c9da25 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -11,12 +11,12 @@ find_package(Threads REQUIRED)
 list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
 
 # ---[ Google-glog
-find_package(Glog REQUIRED)
+include("cmake/External/glog.cmake")
 include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
 
 # ---[ Google-gflags
-find_package(GFlags REQUIRED)
+include("cmake/External/gflags.cmake")
 include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
 
diff --git a/cmake/External/gflags.cmake b/cmake/External/gflags.cmake
new file mode 100644
index 00000000000..a50a0925086
--- /dev/null
+++ b/cmake/External/gflags.cmake
@@ -0,0 +1,56 @@
+if (NOT __GFLAGS_INCLUDED) # guard against multiple includes
+  set(__GFLAGS_INCLUDED TRUE)
+
+  # use the system-wide gflags if present
+  find_package(GFlags)
+  if (GFLAGS_FOUND)
+    set(GFLAGS_EXTERNAL FALSE)
+  else()
+    # gflags will use pthreads if it's available in the system, so we must link with it
+    find_package(Threads)
+
+    # build directory
+    set(gflags_PREFIX ${CMAKE_BINARY_DIR}/external/gflags-prefix)
+    # install directory
+    set(gflags_INSTALL ${CMAKE_BINARY_DIR}/external/gflags-install)
+
+    # we build gflags statically, but want to link it into the caffe shared library
+    # this requires position-independent code
+    if (UNIX)
+        set(GFLAGS_EXTRA_COMPILER_FLAGS "-fPIC")
+    endif()
+
+    set(GFLAGS_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+    set(GFLAGS_C_FLAGS ${CMAKE_C_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+
+    ExternalProject_Add(gflags
+      PREFIX ${gflags_PREFIX}
+      GIT_REPOSITORY "https://github.com/gflags/gflags.git"
+      GIT_TAG "v2.1.2"
+      UPDATE_COMMAND ""
+      INSTALL_DIR ${gflags_INSTALL}
+      CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+                 -DCMAKE_INSTALL_PREFIX=${gflags_INSTALL}
+                 -DBUILD_SHARED_LIBS=OFF
+                 -DBUILD_STATIC_LIBS=ON
+                 -DBUILD_PACKAGING=OFF
+                 -DBUILD_TESTING=OFF
+                 -DBUILD_NC_TESTS=OFF
+                 -BUILD_CONFIG_TESTS=OFF
+                 -DINSTALL_HEADERS=ON
+                 -DCMAKE_C_FLAGS=${GFLAGS_C_FLAGS}
+                 -DCMAKE_CXX_FLAGS=${GFLAGS_CXX_FLAGS}
+      LOG_DOWNLOAD 1
+      LOG_INSTALL 1
+      )
+  
+    set(GFLAGS_FOUND TRUE)
+    set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include)
+    set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT})    
+    set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib)
+    set(GFLAGS_EXTERNAL TRUE)
+
+    list(APPEND external_project_dependencies gflags)
+  endif()
+
+endif()
diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake
new file mode 100644
index 00000000000..02b39dde676
--- /dev/null
+++ b/cmake/External/glog.cmake
@@ -0,0 +1,56 @@
+# glog depends on gflags
+include("cmake/External/gflags.cmake")
+
+if (NOT __GLOG_INCLUDED)
+  set(__GLOG_INCLUDED TRUE)
+
+  # try the system-wide glog first
+  find_package(Glog)
+  if (GLOG_FOUND)
+      set(GLOG_EXTERNAL FALSE)
+  else()
+    # fetch and build glog from github
+
+    # build directory
+    set(glog_PREFIX ${CMAKE_BINARY_DIR}/external/glog-prefix)
+    # install directory
+    set(glog_INSTALL ${CMAKE_BINARY_DIR}/external/glog-install)
+
+    # we build glog statically, but want to link it into the caffe shared library
+    # this requires position-independent code
+    if (UNIX)
+      set(GLOG_EXTRA_COMPILER_FLAGS "-fPIC")
+    endif()
+
+    set(GLOG_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+    set(GLOG_C_FLAGS ${CMAKE_C_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+
+    # depend on gflags if we're also building it
+    if (GFLAGS_EXTERNAL)
+      set(GLOG_DEPENDS gflags)
+    endif()
+    
+    ExternalProject_Add(glog
+      DEPENDS ${GLOG_DEPENDS}
+      PREFIX ${glog_PREFIX}
+      GIT_REPOSITORY "https://github.com/google/glog"
+      GIT_TAG "v0.3.4"
+      UPDATE_COMMAND ""
+      INSTALL_DIR ${gflags_INSTALL}
+      CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/..
+      LOG_DOWNLOAD 1
+      LOG_CONFIGURE 1
+      LOG_INSTALL 1
+      )
+
+    set(GLOG_FOUND TRUE)
+    set(GLOG_INCLUDE_DIRS ${glog_INSTALL}/include)
+    set(GLOG_LIBRARIES ${GFLAGS_LIBRARIES} ${glog_INSTALL}/lib/libglog.a)
+    set(GLOG_LIBRARY_DIRS ${glog_INSTALL}/lib)
+    set(GLOG_EXTERNAL TRUE)
+
+    list(APPEND external_project_dependencies glog)
+  endif()
+
+endif()
+
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index e3ad872313b..ed0ff9660fd 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -110,6 +110,10 @@ function(caffe_default_properties target)
     ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
+  # make sure we build all external depepdencies first
+  if (DEFINED external_project_dependencies)
+    add_dependencies(${target} ${external_project_dependencies})
+  endif()
 endfunction()
 
 ################################################################################################

From b45afa216deb99eecf838d1e927ee517a85b898f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Martin=20Ha=CC=88cker?= <spamfaenger@gmx.de>
Date: Thu, 2 Apr 2015 22:50:55 +0200
Subject: [PATCH 1293/2053] Add commented out helpers for homebrew users

---
 Makefile.config.example | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/Makefile.config.example b/Makefile.config.example
index 7a8aafd7c9f..a873502559f 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -37,6 +37,10 @@ BLAS := atlas
 # BLAS_INCLUDE := /path/to/your/blas
 # BLAS_LIB := /path/to/your/blas
 
+# Homebrew puts openblas in a directory that is not on the standard search path
+# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
+# BLAS_LIB := $(shell brew --prefix openblas)/lib
+
 # This is required only if you will compile the matlab interface.
 # MATLAB directory should contain the mex binary in /bin.
 # MATLAB_DIR := /usr/local
@@ -57,6 +61,10 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(ANACONDA_HOME)/lib
 
+# Homebrew installs numpy in a non standard path (keg only)
+# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
+# PYTHON_LIB += $(shell brew --prefix numpy)/lib
+
 # Uncomment to support layers written in Python (will link against Python libs)
 # WITH_PYTHON_LAYER := 1
 
@@ -64,6 +72,10 @@ PYTHON_LIB := /usr/lib
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 
+# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
+# INCLUDE_DIRS += $(shell brew --prefix)/include
+# LIBRARY_DIRS += $(shell brew --prefix)/lib
+
 # Uncomment to use `pkg-config` to specify OpenCV library paths.
 # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
 # USE_PKG_CONFIG := 1

From cbef35c76af15c8d133aae37e8029a0705f5aa69 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 5 Apr 2015 11:47:49 -0700
Subject: [PATCH 1294/2053] [docs] switch lmdb url for manual install, tweak
 formatting

---
 docs/install_apt.md | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index 89bc9a00aef..75f8bec0e95 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -8,12 +8,24 @@ title: Installation: Ubuntu
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
 
+**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
+Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+This can be skipped for CPU-only installation.
+
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+
+**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
+
 **Remaining dependencies, 14.04**
 
+Everything is packaged in 14.04.
+
     sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
 
 **Remaining dependencies, 12.04**
 
+These dependencies need manual installation in 12.04.
+
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
@@ -28,17 +40,10 @@ title: Installation: Ubuntu
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
     # lmdb
-    git clone git://gitorious.org/mdb/mdb.git
+    git clone https://gitorious.org/mdb/mdb.git
     cd mdb/libraries/liblmdb
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
 
-**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
-Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
-
-**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
-
-**Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
-
 Continue with [compilation](installation.html#compilation).

From cf5a306cf17f12f4dc1b9de6876182b320d75592 Mon Sep 17 00:00:00 2001
From: Ashwani001 <a.k.rai@live.com>
Date: Tue, 7 Apr 2015 17:44:01 +0800
Subject: [PATCH 1295/2053] Update generate_sample_data.py

Changed os.path.dirname(__file__) as it was returning empty...
---
 src/caffe/test/test_data/generate_sample_data.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index e5dbc3406d8..3b49773c3fe 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -27,12 +27,12 @@
 print data
 print label
 
-with h5py.File(os.path.dirname(__file__) + '/sample_data.h5', 'w') as f:
+with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5', 'w') as f:
     f['data'] = data
     f['label'] = label
     f['label2'] = label2
 
-with h5py.File(os.path.dirname(__file__) + '/sample_data_2_gzip.h5', 'w') as f:
+with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5', 'w') as f:
     f.create_dataset(
         'data', data=data + total_size,
         compression='gzip', compression_opts=1
@@ -46,6 +46,6 @@
         compression='gzip', compression_opts=1
     )
 
-with open(os.path.dirname(__file__) + '/sample_data_list.txt', 'w') as f:
-    f.write(os.path.dirname(__file__) + '/sample_data.h5\n')
-    f.write(os.path.dirname(__file__) + '/sample_data_2_gzip.h5\n')
+with open(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_list.txt', 'w') as f:
+    f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5\n')
+    f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5\n')

From c8323103758fc492771e07f143357fc403d9cc0e Mon Sep 17 00:00:00 2001
From: e3 <eerwitt@gmail.com>
Date: Wed, 8 Apr 2015 15:00:36 -0700
Subject: [PATCH 1296/2053] Changing Image import to be imported from PIL.

---
 examples/web_demo/app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index bbeff5eb362..c667ea94c11 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -10,7 +10,7 @@
 import tornado.httpserver
 import numpy as np
 import pandas as pd
-import Image
+from PIL import Image
 import cStringIO as StringIO
 import urllib
 import exifutil

From b963008a6591600e60ed6746d208e82e107f6a89 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Tue, 7 Apr 2015 17:51:22 -0700
Subject: [PATCH 1297/2053] Allow Transform of encoded datum. Allow initialize
 transformed_blob from datum or transform params. Allow force_color and
 force_gray as transform params.

---
 src/caffe/data_transformer.cpp | 59 +++++++++++++++++++++++++++++++---
 src/caffe/proto/caffe.proto    |  4 +++
 2 files changed, 59 insertions(+), 4 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index b0b98e478c1..454dabbebfd 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -125,10 +125,40 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
+  // If datum is encoded, decoded and transform the cv::image.
+  if (datum.encoded()) {
+    CHECK(!param_.force_color() && !param_.force_gray())
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // Transform the cv::image into blob.
+    return Transform(cv_img, transformed_blob);
+  } else {
+    if (param_.force_color() || param_.force_gray()) {
+      LOG(ERROR) << "force_color and force_gray only for encoded datum";
+    }
+  }
+
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
 
+  const int crop_size = param_.crop_size();
+
+  if (transformed_blob->count() == 0) {
+    // Initialize it.
+    if (crop_size) {
+      transformed_blob->Reshape(1, datum_channels, crop_size, crop_size);
+    } else {
+      transformed_blob->Reshape(1, datum_channels, datum_height, datum_width);
+    }
+  }
+  // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -139,8 +169,6 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   CHECK_LE(width, datum_width);
   CHECK_GE(num, 1);
 
-  const int crop_size = param_.crop_size();
-
   if (crop_size) {
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
@@ -200,6 +228,17 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
   const int img_height = cv_img.rows;
   const int img_width = cv_img.cols;
 
+  const int crop_size = param_.crop_size();
+
+  if (transformed_blob->count() == 0) {
+    // Initialize it.
+    if (crop_size) {
+      transformed_blob->Reshape(1, img_channels, crop_size, crop_size);
+    } else {
+      transformed_blob->Reshape(1, img_channels, img_height, img_width);
+    }
+  }
+
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -212,7 +251,6 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
 
   CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
 
-  const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
@@ -302,6 +340,19 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   const int input_height = input_blob->height();
   const int input_width = input_blob->width();
 
+  const int crop_size = param_.crop_size();
+
+  if (transformed_blob->count() == 0) {
+    // Initialize it.
+    if (crop_size) {
+      transformed_blob->Reshape(input_num, input_channels,
+                                crop_size, crop_size);
+    } else {
+      transformed_blob->Reshape(input_num, input_channels,
+                                input_height, input_width);
+    }
+  }
+
   const int num = transformed_blob->num();
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
@@ -313,7 +364,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   CHECK_GE(input_height, height);
   CHECK_GE(input_width, width);
 
-  const int crop_size = param_.crop_size();
+
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5b21cf20028..d66167eefc8 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -351,6 +351,10 @@ message TransformationParameter {
   // or can be repeated the same number of times as channels
   // (would subtract them from the corresponding channel)
   repeated float mean_value = 5;
+  // Force the decoded image to have 3 color channels.
+  optional bool force_color = 6 [default = false];
+  // Force the decoded image to have 1 color channels.
+  optional bool force_gray = 7 [default = false];
 }
 
 // Message that stores parameters shared by loss layers

From c3cd3c4d8c25c7b227e15aff0fe93e4151ffd8f6 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Wed, 8 Apr 2015 12:08:56 -0700
Subject: [PATCH 1298/2053] Added InferBlobShape to data_transformer.

---
 include/caffe/data_transformer.hpp |  36 ++++++++++
 src/caffe/data_transformer.cpp     | 105 ++++++++++++++++++++++-------
 2 files changed, 117 insertions(+), 24 deletions(-)

diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 880356601a4..0ad68c80216 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -62,6 +62,7 @@ class DataTransformer {
    */
   void Transform(const vector<cv::Mat> & mat_vector,
                 Blob<Dtype>* transformed_blob);
+
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a cv::Mat
@@ -87,6 +88,41 @@ class DataTransformer {
    */
   void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
 
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param datum
+   *    Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const Datum& datum);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<Datum> & datum_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param mat_vector
+   *    A vector of Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const cv::Mat& cv_img);
+
  protected:
    /**
    * @brief Generates a random integer from Uniform({0, 1, ..., n-1}).
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 454dabbebfd..6f75bdb3852 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -144,20 +144,11 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     }
   }
 
+  const int crop_size = param_.crop_size();
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
 
-  const int crop_size = param_.crop_size();
-
-  if (transformed_blob->count() == 0) {
-    // Initialize it.
-    if (crop_size) {
-      transformed_blob->Reshape(1, datum_channels, crop_size, crop_size);
-    } else {
-      transformed_blob->Reshape(1, datum_channels, datum_height, datum_width);
-    }
-  }
   // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
@@ -224,21 +215,12 @@ void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
   const int img_channels = cv_img.channels();
   const int img_height = cv_img.rows;
   const int img_width = cv_img.cols;
 
-  const int crop_size = param_.crop_size();
-
-  if (transformed_blob->count() == 0) {
-    // Initialize it.
-    if (crop_size) {
-      transformed_blob->Reshape(1, img_channels, crop_size, crop_size);
-    } else {
-      transformed_blob->Reshape(1, img_channels, img_height, img_width);
-    }
-  }
-
+  // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -335,15 +317,14 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
                                        Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
   const int input_num = input_blob->num();
   const int input_channels = input_blob->channels();
   const int input_height = input_blob->height();
   const int input_width = input_blob->width();
 
-  const int crop_size = param_.crop_size();
-
   if (transformed_blob->count() == 0) {
-    // Initialize it.
+    // Initialize transformed_blob with the right shape.
     if (crop_size) {
       transformed_blob->Reshape(input_num, input_channels,
                                 crop_size, crop_size);
@@ -446,6 +427,82 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   }
 }
 
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
+  if (datum.encoded()) {
+    CHECK(!param_.force_color() && !param_.force_gray())
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // InferBlobShape using the cv::image.
+    return InferBlobShape(cv_img);
+  }
+
+  const int crop_size = param_.crop_size();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
+  // Check dimensions.
+  CHECK_GT(datum_channels, 0);
+  CHECK_GE(datum_height, crop_size);
+  CHECK_GE(datum_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = datum_channels;
+  shape[2] = (crop_size)? crop_size: datum_height;
+  shape[3] = (crop_size)? crop_size: datum_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<Datum> & datum_vector) {
+  const int num = datum_vector.size();
+  CHECK_GT(num, 0) << "There is no datum to in the vector";
+  // Use first datum in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(datum_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {
+  const int crop_size = param_.crop_size();
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+  // Check dimensions.
+  CHECK_GT(img_channels, 0);
+  CHECK_GE(img_height, crop_size);
+  CHECK_GE(img_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = img_channels;
+  shape[2] = (crop_size)? crop_size: img_height;
+  shape[3] = (crop_size)? crop_size: img_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<cv::Mat> & mat_vector) {
+  const int num = mat_vector.size();
+  CHECK_GT(num, 0) << "There is no cv_img to in the vector";
+  // Use first cv_img in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(mat_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
   const bool needs_rand = param_.mirror() ||

From 3c46df772247e7bccfd23e340c75245f921ad0d1 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Wed, 8 Apr 2015 12:09:33 -0700
Subject: [PATCH 1299/2053] Simplify data_layer reshapes and encodings by
 letting data_transformer do the job.

---
 src/caffe/layers/base_data_layer.cpp | 10 ++--
 src/caffe/layers/base_data_layer.cu  |  6 +-
 src/caffe/layers/data_layer.cpp      | 90 ++++++++--------------------
 3 files changed, 35 insertions(+), 71 deletions(-)

diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 352200915d7..aa3f613d1a3 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -21,11 +21,11 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   } else {
     output_labels_ = true;
   }
-  // The subclasses should setup the size of bottom and top
-  DataLayerSetUp(bottom, top);
   data_transformer_.reset(
       new DataTransformer<Dtype>(transform_param_, this->phase_));
   data_transformer_->InitRand();
+  // The subclasses should setup the size of bottom and top
+  DataLayerSetUp(bottom, top);
 }
 
 template <typename Dtype>
@@ -63,13 +63,15 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
   JoinPrefetchThread();
   DLOG(INFO) << "Thread joined";
   // Reshape to loaded data.
-  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
-      this->prefetch_data_.height(), this->prefetch_data_.width());
+  top[0]->ReshapeLike(prefetch_data_);
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              top[0]->mutable_cpu_data());
   DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(prefetch_label_);
+    // Copy the labels.
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
                top[1]->mutable_cpu_data());
   }
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 775f6c47f7e..9335a5bc9a9 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -10,12 +10,14 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
   // First, join the thread
   JoinPrefetchThread();
   // Reshape to loaded data.
-  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
-      this->prefetch_data_.height(), this->prefetch_data_.width());
+  top[0]->ReshapeLike(this->prefetch_data_);
   // Copy the data
   caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
       top[0]->mutable_gpu_data());
   if (this->output_labels_) {
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(prefetch_label_);
+    // Copy the labels.
     caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
         top[1]->mutable_gpu_data());
   }
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 0f2d66776a9..161a75e0c8c 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -38,32 +38,17 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       cursor_->Next();
     }
   }
-  // Read a data point, and use it to initialize the top blob.
+  // Read a data point, to initialize the prefetch and top blobs.
   Datum datum;
   datum.ParseFromString(cursor_->value());
+  // Use data_transformer to infer the expected blob shape from datum.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape top[0] and prefetch_data according to the batch_size.
+  top_shape[0] = this->layer_param_.data_param().batch_size();
+  this->prefetch_data_.Reshape(top_shape);
+  top[0]->ReshapeLike(this->prefetch_data_);
 
-  bool force_color = this->layer_param_.data_param().force_encoded_color();
-  if ((force_color && DecodeDatum(&datum, true)) ||
-      DecodeDatumNative(&datum)) {
-    LOG(INFO) << "Decoding Datum";
-  }
-  // image
-  int crop_size = this->layer_param_.transform_param().crop_size();
-  if (crop_size > 0) {
-    top[0]->Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), crop_size, crop_size);
-    this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
-  } else {
-    top[0]->Reshape(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        datum.height(), datum.width());
-    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), datum.height(), datum.width());
-    this->transformed_data_.Reshape(1, datum.channels(),
-      datum.height(), datum.width());
-  }
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
@@ -86,25 +71,17 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   CHECK(this->prefetch_data_.count());
   CHECK(this->transformed_data_.count());
 
-  // Reshape on single input batches for inputs of varying dimension.
+  // Reshape according to the first datum of each batch
+  // on single input batches allows for inputs of varying dimension.
   const int batch_size = this->layer_param_.data_param().batch_size();
-  const int crop_size = this->layer_param_.transform_param().crop_size();
-  bool force_color = this->layer_param_.data_param().force_encoded_color();
-  if (batch_size == 1 && crop_size == 0) {
-    Datum datum;
-    datum.ParseFromString(cursor_->value());
-    if (datum.encoded()) {
-      if (force_color) {
-        DecodeDatum(&datum, true);
-      } else {
-        DecodeDatumNative(&datum);
-      }
-    }
-    this->prefetch_data_.Reshape(1, datum.channels(),
-        datum.height(), datum.width());
-    this->transformed_data_.Reshape(1, datum.channels(),
-        datum.height(), datum.width());
-  }
+  Datum datum;
+  datum.ParseFromString(cursor_->value());
+  // Use data_transformer to infer the expected blob shape from datum.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data according to the batch_size.
+  top_shape[0] = batch_size;
+  this->prefetch_data_.Reshape(top_shape);
 
   Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
@@ -112,48 +89,31 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   if (this->output_labels_) {
     top_label = this->prefetch_label_.mutable_cpu_data();
   }
+  timer.Start();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
-    timer.Start();
-    // get a blob
+    // get a datum
     Datum datum;
     datum.ParseFromString(cursor_->value());
-
-    cv::Mat cv_img;
-    if (datum.encoded()) {
-      if (force_color) {
-        cv_img = DecodeDatumToCVMat(datum, true);
-      } else {
-        cv_img = DecodeDatumToCVMatNative(datum);
-      }
-      if (cv_img.channels() != this->transformed_data_.channels()) {
-        LOG(WARNING) << "Your dataset contains encoded images with mixed "
-        << "channel sizes. Consider adding a 'force_color' flag to the "
-        << "model definition, or rebuild your dataset using "
-        << "convert_imageset.";
-      }
-    }
     read_time += timer.MicroSeconds();
     timer.Start();
-
     // Apply data transformations (mirror, scale, crop...)
     int offset = this->prefetch_data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
-    if (datum.encoded()) {
-      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
-    } else {
-      this->data_transformer_->Transform(datum, &(this->transformed_data_));
-    }
+    this->data_transformer_->Transform(datum, &(this->transformed_data_));
+    // Copy label.
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
     trans_time += timer.MicroSeconds();
-    // go to the next iter
+    timer.Start();
+    // go to the next item.
     cursor_->Next();
     if (!cursor_->valid()) {
       DLOG(INFO) << "Restarting data prefetching from start.";
       cursor_->SeekToFirst();
     }
   }
+  timer.Stop();
   batch_timer.Stop();
   DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
   DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";

From e048b1741e8a5dfafeecff96c5a69d3211224fd6 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Wed, 8 Apr 2015 12:44:48 -0700
Subject: [PATCH 1300/2053] Simplify image_data_layer reshapes by letting
 data_transformer do the job.

---
 src/caffe/layers/image_data_layer.cpp | 42 ++++++++++++---------------
 1 file changed, 18 insertions(+), 24 deletions(-)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 38ebbd5ec14..18c035cba9d 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -62,21 +62,15 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Read an image, and use it to initialize the top blob.
   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
-  const int channels = cv_img.channels();
-  const int height = cv_img.rows;
-  const int width = cv_img.cols;
-  // image
-  const int crop_size = this->layer_param_.transform_param().crop_size();
+  // Use data_transformer to infer the expected blob shape from a cv_image.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data and top[0] according to the batch_size.
   const int batch_size = this->layer_param_.image_data_param().batch_size();
-  if (crop_size > 0) {
-    top[0]->Reshape(batch_size, channels, crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
-    this->transformed_data_.Reshape(1, channels, crop_size, crop_size);
-  } else {
-    top[0]->Reshape(batch_size, channels, height, width);
-    this->prefetch_data_.Reshape(batch_size, channels, height, width);
-    this->transformed_data_.Reshape(1, channels, height, width);
-  }
+  top_shape[0] = batch_size;
+  this->prefetch_data_.Reshape(top_shape);
+  top[0]->ReshapeLike(this->prefetch_data_);
+
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
@@ -107,19 +101,19 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
-  const int crop_size = this->layer_param_.transform_param().crop_size();
   const bool is_color = image_data_param.is_color();
   string root_folder = image_data_param.root_folder();
 
-  // Reshape on single input batches for inputs of varying dimension.
-  if (batch_size == 1 && crop_size == 0 && new_height == 0 && new_width == 0) {
-    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
-        0, 0, is_color);
-    this->prefetch_data_.Reshape(1, cv_img.channels(),
-        cv_img.rows, cv_img.cols);
-    this->transformed_data_.Reshape(1, cv_img.channels(),
-        cv_img.rows, cv_img.cols);
-  }
+  // Reshape according to the first image of each batch
+  // on single input batches allows for inputs of varying dimension.
+  cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+      new_height, new_width, is_color);
+  // Use data_transformer to infer the expected blob shape from a cv_img.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data according to the batch_size.
+  top_shape[0] = batch_size;
+  this->prefetch_data_.Reshape(top_shape);
 
   Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
   Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();

From ef27496b2abbd7adf8369eeb0a85b57f99955c1a Mon Sep 17 00:00:00 2001
From: Akiomi KAMAKURA <akiomik@gmail.com>
Date: Fri, 10 Apr 2015 15:48:41 +0900
Subject: [PATCH 1301/2053] improved installation for osx

---
 docs/install_osx.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install_osx.md b/docs/install_osx.md
index c4ebd45f553..39cb02fe232 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -18,7 +18,7 @@ In other `ENV` settings, things may not work as expected.
     brew install --fresh -vd snappy leveldb gflags glog szip lmdb
     # need the homebrew science source for OpenCV and hdf5
     brew tap homebrew/science
-    hdf5 opencv
+    brew install hdf5 opencv
 
 If using Anaconda Python, a modification to the OpenCV formula might be needed
 Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.

From 7e2fceb1e91cfe48eddb3569e29aaef4b9ca1a2a Mon Sep 17 00:00:00 2001
From: Nick Carlevaris-Bianco <carlevar@umich.edu>
Date: Fri, 17 Apr 2015 14:16:26 +0930
Subject: [PATCH 1302/2053] Fixed contrastive loss layer to be the same as
 proposed in Hadsell et al 2006

---
 src/caffe/layers/contrastive_loss_layer.cpp    |  9 ++++++---
 src/caffe/layers/contrastive_loss_layer.cu     | 13 ++++++++-----
 src/caffe/test/test_contrastive_loss_layer.cpp |  3 ++-
 3 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 0692c11c257..d5e3c8a48bb 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -48,7 +48,8 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+      Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0);
+      loss += dist*dist;
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
@@ -76,10 +77,12 @@ void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
               Dtype(0.0),
               bout + (j*channels));
         } else {  // dissimilar pairs
-          if ((margin-dist_sq_.cpu_data()[j]) > Dtype(0.0)) {
+          Dtype dist = sqrt(dist_sq_.cpu_data()[j]);
+          Dtype mdist = (margin - dist);
+          if (mdist > Dtype(0.0)) {
             caffe_cpu_axpby(
                 channels,
-                -alpha,
+                -alpha * mdist / dist,
                 diff_.cpu_data() + (j*channels),
                 Dtype(0.0),
                 bout + (j*channels));
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 78a55995a0a..255480ced05 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -37,7 +37,8 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+      Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0));
+      loss += dist*dist;
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
@@ -45,7 +46,7 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
 }
 
 template <typename Dtype>
-__global__ void CLLForward(const int count, const int channels,
+__global__ void CLLBackward(const int count, const int channels,
     const Dtype margin, const Dtype alpha,
     const Dtype* y, const Dtype* diff, const Dtype* dist_sq,
     Dtype *bottom_diff) {
@@ -54,8 +55,10 @@ __global__ void CLLForward(const int count, const int channels,
     if (static_cast<int>(y[n])) {  // similar pairs
       bottom_diff[i] = alpha * diff[i];
     } else {  // dissimilar pairs
-      if ((margin-dist_sq[n]) > 0.0) {
-        bottom_diff[i] = -alpha * diff[i];
+      Dtype dist = sqrt(dist_sq[n]);
+      Dtype mdist = (margin - dist);
+      if (mdist > 0.0) {
+        bottom_diff[i] = -alpha * mdist / dist * diff[i];
       } else {
         bottom_diff[i] = 0;
       }
@@ -75,7 +78,7 @@ void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
           static_cast<Dtype>(bottom[0]->num());
       // NOLINT_NEXT_LINE(whitespace/operators)
-      CLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
           count, channels, margin, alpha,
           bottom[2]->gpu_data(),  // pair similarity 0 or 1
           diff_.gpu_data(),  // the cached eltwise difference between a and b
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index d269fbc26f2..5fab25f8832 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -79,7 +79,8 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
     if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
       loss += dist_sq;
     } else {
-      loss += std::max(margin-dist_sq, Dtype(0));
+      Dtype dist = std::max(margin - sqrt(dist_sq), 0.0);
+      loss += dist*dist;
     }
   }
   loss /= static_cast<Dtype>(num) * Dtype(2);

From c6414ea7cab5917c904729c87f786e0a5909475c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 17 Apr 2015 12:26:48 -0700
Subject: [PATCH 1303/2053] set default DISTRIBUTE_DIR -- fix #2328

---
 Makefile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile b/Makefile
index 706959ad0ca..db0f531eaa0 100644
--- a/Makefile
+++ b/Makefile
@@ -171,6 +171,7 @@ WARNINGS := -Wall -Wno-sign-compare
 # Set build directories
 ##############################
 
+DISTRIBUTE_DIR ?= distribute
 DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
 DIST_ALIASES := dist
 ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)

From 797587d73a8372db11b1f143a2868064f985fade Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Fri, 17 Apr 2015 15:53:12 -0700
Subject: [PATCH 1304/2053] Abort Makefile parsing if the configuration file
 cannot be found.

---
 Makefile | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/Makefile b/Makefile
index db0f531eaa0..55ca5f3734e 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,9 @@
 PROJECT := caffe
 
 CONFIG_FILE := Makefile.config
+ifeq ($(wildcard $(CONFIG_FILE)),)
+$(error $(CONFIG_FILE): file not found.)
+endif
 include $(CONFIG_FILE)
 
 BUILD_DIR_LINK := $(BUILD_DIR)

From d91c353577baf2b27d303962bb039ba39211871b Mon Sep 17 00:00:00 2001
From: Nick Carlevaris-Bianco <carlevar@umich.edu>
Date: Tue, 21 Apr 2015 17:14:47 +0930
Subject: [PATCH 1305/2053] added epsilon to prevent possible division by zero
 in gradient calculation

---
 src/caffe/layers/contrastive_loss_layer.cpp    |  2 +-
 src/caffe/layers/contrastive_loss_layer.cu     |  2 +-
 src/caffe/test/test_contrastive_loss_layer.cpp | 12 ++++++------
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index d5e3c8a48bb..4cf04d4dd15 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -82,7 +82,7 @@ void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           if (mdist > Dtype(0.0)) {
             caffe_cpu_axpby(
                 channels,
-                -alpha * mdist / dist,
+                -alpha * mdist / (dist + Dtype(1e-4)),
                 diff_.cpu_data() + (j*channels),
                 Dtype(0.0),
                 bout + (j*channels));
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 255480ced05..83ff3c90f0f 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -58,7 +58,7 @@ __global__ void CLLBackward(const int count, const int channels,
       Dtype dist = sqrt(dist_sq[n]);
       Dtype mdist = (margin - dist);
       if (mdist > 0.0) {
-        bottom_diff[i] = -alpha * mdist / dist * diff[i];
+        bottom_diff[i] = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i];
       } else {
         bottom_diff[i] = 0;
       }
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index 5fab25f8832..460fc8f32fc 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -22,15 +22,15 @@ class ContrastiveLossLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   ContrastiveLossLayerTest()
-      : blob_bottom_data_i_(new Blob<Dtype>(128, 10, 1, 1)),
-        blob_bottom_data_j_(new Blob<Dtype>(128, 10, 1, 1)),
-        blob_bottom_y_(new Blob<Dtype>(128, 1, 1, 1)),
+      : blob_bottom_data_i_(new Blob<Dtype>(512, 2, 1, 1)),
+        blob_bottom_data_j_(new Blob<Dtype>(512, 2, 1, 1)),
+        blob_bottom_y_(new Blob<Dtype>(512, 1, 1, 1)),
         blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
-    filler_param.set_mean(0.0);
-    filler_param.set_std(0.3);  // distances~=1.0 to test both sides of margin
-    GaussianFiller<Dtype> filler(filler_param);
+    filler_param.set_min(-1.0);
+    filler_param.set_max(1.0);  // distances~=1.0 to test both sides of margin
+    UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_data_i_);
     blob_bottom_vec_.push_back(blob_bottom_data_i_);
     filler.Fill(this->blob_bottom_data_j_);

From 23d28fdef8ba936b2a6a4a123de625084faec1dd Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Tue, 9 Dec 2014 09:20:38 -0800
Subject: [PATCH 1306/2053] Improvements to python log parser

Over version introduced in https://github.com/BVLC/caffe/pull/1384

Highlights:
* Interface change: column order is now determined by using a list of `OrderedDict` objects instead of `dict` objects, which obviates the need to pass around a tuple with the column orders.
* The outputs are now named according to their names in the network protobuffer; e.g., if your top is named `loss`, then the corresponding column header will also be `loss`; we no longer rename it to, e.g., `TrainingLoss` or `TestLoss`.
* Fixed the bug/feature of the first version where the initial learning rate was always NaN.
* Add optional parameter to specify output table delimiter. It's still a comma by default.

You can use Matlab code from [this gist](https://gist.github.com/drdan14/d8b45999c4a1cbf7ad85) to verify that your results are the same before and after the changes introduced in this pull request. That code assumes that your `top` names are `accuracy` and `loss`, but you can modify the code if that's not true.
---
 tools/extra/parse_log.py | 165 ++++++++++++++++++++++++---------------
 1 file changed, 102 insertions(+), 63 deletions(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 16ba077aee6..09ea216ced3 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -3,7 +3,7 @@
 """
 Parse training log
 
-Competitor to parse_log.sh
+Evolved from parse_log.sh
 """
 
 import os
@@ -11,18 +11,7 @@
 import extract_seconds
 import argparse
 import csv
-
-
-def get_line_type(line):
-    """Return either 'test' or 'train' depending on line type
-    """
-
-    line_type = None
-    if line.find('Train') != -1:
-        line_type = 'train'
-    elif line.find('Test') != -1:
-        line_type = 'test'
-    return line_type
+from collections import OrderedDict
 
 
 def parse_log(path_to_log):
@@ -36,70 +25,112 @@ def parse_log(path_to_log):
     for the two dict_lists
     """
 
-    re_iteration = re.compile('Iteration (\d+)')
-    re_accuracy = re.compile('output #\d+: accuracy = ([\.\d]+)')
-    re_train_loss = re.compile('Iteration \d+, loss = ([\.\d]+)')
-    re_output_loss = re.compile('output #\d+: loss = ([\.\d]+)')
-    re_lr = re.compile('lr = ([\.\d]+)')
+    regex_iteration = re.compile('Iteration (\d+)')
+    regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
+    regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
+    regex_learning_rate = re.compile('lr = ([\.\d]+)')
 
     # Pick out lines of interest
     iteration = -1
-    test_accuracy = -1
     learning_rate = float('NaN')
     train_dict_list = []
     test_dict_list = []
-    train_dict_names = ('NumIters', 'Seconds', 'TrainingLoss', 'LearningRate')
-    test_dict_names = ('NumIters', 'Seconds', 'TestAccuracy', 'TestLoss')
+    train_row = None
+    test_row = None
 
     logfile_year = extract_seconds.get_log_created_year(path_to_log)
     with open(path_to_log) as f:
         start_time = extract_seconds.get_start_time(f, logfile_year)
 
         for line in f:
-            iteration_match = re_iteration.search(line)
+            iteration_match = regex_iteration.search(line)
             if iteration_match:
                 iteration = float(iteration_match.group(1))
             if iteration == -1:
-                # Only look for other stuff if we've found the first iteration
+                # Only start parsing for other stuff if we've found the first
+                # iteration
                 continue
 
             time = extract_seconds.extract_datetime_from_line(line,
                                                               logfile_year)
             seconds = (time - start_time).total_seconds()
 
-            lr_match = re_lr.search(line)
-            if lr_match:
-                learning_rate = float(lr_match.group(1))
-
-            accuracy_match = re_accuracy.search(line)
-            if accuracy_match and get_line_type(line) == 'test':
-                test_accuracy = float(accuracy_match.group(1))
-
-            train_loss_match = re_train_loss.search(line)
-            if train_loss_match:
-                train_loss = float(train_loss_match.group(1))
-                train_dict_list.append({'NumIters': iteration,
-                                        'Seconds': seconds,
-                                        'TrainingLoss': train_loss,
-                                        'LearningRate': learning_rate})
-
-            output_loss_match = re_output_loss.search(line)
-            if output_loss_match and get_line_type(line) == 'test':
-                test_loss = float(output_loss_match.group(1))
-                # NOTE: we assume that (1) accuracy always comes right before
-                # loss for test data so the test_accuracy variable is already
-                # correctly populated and (2) there's one and only one output
-                # named "accuracy" for the test net
-                test_dict_list.append({'NumIters': iteration,
-                                       'Seconds': seconds,
-                                       'TestAccuracy': test_accuracy,
-                                       'TestLoss': test_loss})
-
-    return train_dict_list, train_dict_names, test_dict_list, test_dict_names
-
-
-def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names,
-                   test_dict_list, test_dict_names, verbose=False):
+            learning_rate_match = regex_learning_rate.search(line)
+            if learning_rate_match:
+                learning_rate = float(learning_rate_match.group(1))
+
+            train_dict_list, train_row = parse_line_for_net_output(
+                regex_train_output, train_row, train_dict_list,
+                line, iteration, seconds, learning_rate
+            )
+            test_dict_list, test_row = parse_line_for_net_output(
+                regex_test_output, test_row, test_dict_list,
+                line, iteration, seconds, learning_rate
+            )
+
+    fix_initial_nan_learning_rate(train_dict_list)
+    fix_initial_nan_learning_rate(test_dict_list)
+
+    return train_dict_list, test_dict_list
+
+
+def parse_line_for_net_output(regex_obj, row, row_dict_list,
+                              line, iteration, seconds, learning_rate):
+    """Parse a single line for training or test output
+
+    Returns a a tuple with (row_dict_list, row)
+    row: may be either a new row or an augmented version of the current row
+    row_dict_list: may be either the current row_dict_list or an augmented
+    version of the current row_dict_list
+    """
+
+    output_match = regex_obj.search(line)
+    if output_match:
+        if not row or row['NumIters'] != iteration:
+            # Push the last row and start a new one
+            if row:
+                # If we're on a new iteration, push the last row
+                # This will probably only happen for the first row; otherwise
+                # the full row checking logic below will push and clear full
+                # rows
+                row_dict_list.append(row)
+
+            row = OrderedDict([
+                ('NumIters', iteration),
+                ('Seconds', seconds),
+                ('LearningRate', learning_rate)
+            ])
+
+        # output_num is not used; may be used in the future
+        # output_num = output_match.group(1)
+        output_name = output_match.group(2)
+        output_val = output_match.group(3)
+        row[output_name] = float(output_val)
+
+    if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
+        # The row is full, based on the fact that it has the same number of
+        # columns as the first row; append it to the list
+        row_dict_list.append(row)
+        row = None
+
+    return row_dict_list, row
+
+
+def fix_initial_nan_learning_rate(dict_list):
+    """Correct initial value of learning rate
+
+    Learning rate is normally not printed until after the initial test and
+    training step, which means the initial testing and training rows have
+    LearningRate = NaN. Fix this by copying over the LearningRate from the
+    second row, if it exists.
+    """
+
+    if len(dict_list) > 1:
+        dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
+
+
+def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,
+                   delimiter=',', verbose=False):
     """Save CSV files to output_dir
 
     If the input log file is, e.g., caffe.INFO, the names will be
@@ -108,18 +139,22 @@ def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names,
 
     log_basename = os.path.basename(logfile_path)
     train_filename = os.path.join(output_dir, log_basename + '.train')
-    write_csv(train_filename, train_dict_list, train_dict_names, verbose)
+    write_csv(train_filename, train_dict_list, delimiter, verbose)
 
     test_filename = os.path.join(output_dir, log_basename + '.test')
-    write_csv(test_filename, test_dict_list, test_dict_names, verbose)
+    write_csv(test_filename, test_dict_list, delimiter, verbose)
 
 
-def write_csv(output_filename, dict_list, header_names, verbose=False):
+def write_csv(output_filename, dict_list, delimiter, verbose=False):
     """Write a CSV file
     """
 
+    dialect = csv.excel
+    dialect.delimiter = delimiter
+
     with open(output_filename, 'w') as f:
-        dict_writer = csv.DictWriter(f, header_names)
+        dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
+                                     dialect=dialect)
         dict_writer.writeheader()
         dict_writer.writerows(dict_list)
     if verbose:
@@ -141,16 +176,20 @@ def parse_args():
                         action='store_true',
                         help='Print some extra info (e.g., output filenames)')
 
+    parser.add_argument('--delimiter',
+                        default=',',
+                        help=('Column delimiter in output files '
+                              '(default: \'%(default)s\')'))
+
     args = parser.parse_args()
     return args
 
 
 def main():
     args = parse_args()
-    train_dict_list, train_dict_names, test_dict_list, test_dict_names = \
-        parse_log(args.logfile_path)
+    train_dict_list, test_dict_list = parse_log(args.logfile_path)
     save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
-                   train_dict_names, test_dict_list, test_dict_names)
+                   test_dict_list, delimiter=args.delimiter)
 
 
 if __name__ == '__main__':

From a57d19d9de33463f71ac66b3e7f6790d514deb40 Mon Sep 17 00:00:00 2001
From: PETER_GAO <pgao@berkeley.edu>
Date: Wed, 22 Apr 2015 11:33:41 -0700
Subject: [PATCH 1307/2053] Fix RCNN model fetching script

---
 examples/detection.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 2ccf21f09eb..e343feefd20 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -27,7 +27,7 @@
       "\n",
       "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
       "\n",
-      "-Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the Caffe R-CNN ImageNet model.\n",
+      "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n",
       "\n",
       "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
      ]

From caaf321a1dc6d51adb1ee0c80f76d4d172145884 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 24 Apr 2015 19:52:33 -0700
Subject: [PATCH 1308/2053] clarify Makefile.config check

---
 Makefile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 55ca5f3734e..15d30dfc0b7 100644
--- a/Makefile
+++ b/Makefile
@@ -1,8 +1,9 @@
 PROJECT := caffe
 
 CONFIG_FILE := Makefile.config
+# Explicitly check for the config file, otherwise make -k will proceed anyway.
 ifeq ($(wildcard $(CONFIG_FILE)),)
-$(error $(CONFIG_FILE): file not found.)
+$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
 endif
 include $(CONFIG_FILE)
 

From afa2d591b6e72a3c29c9efdff4c97d1141cd1ae0 Mon Sep 17 00:00:00 2001
From: Takuma Wakamori <wakamori.takuma@gmail.com>
Date: Sat, 25 Apr 2015 17:09:43 +0900
Subject: [PATCH 1309/2053] fix typo: swap the titles of xlabel and ylabel

---
 tools/extra/plot_log.gnuplot.example | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example
index 334ff1f2858..748b96e6925 100644
--- a/tools/extra/plot_log.gnuplot.example
+++ b/tools/extra/plot_log.gnuplot.example
@@ -39,8 +39,8 @@ set key right
 
 # Training loss vs. training iterations
 set title "Training loss vs. training iterations"
-set xlabel "Training loss"
-set ylabel "Training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
 plot "mnist.log.train" using 1:3 title "mnist"
 
 # Training loss vs. training time

From ccfd28f0ce802aa11b1e0eac0e3dab239a0c9028 Mon Sep 17 00:00:00 2001
From: gdh1995 <gdh1995@qq.com>
Date: Sun, 26 Apr 2015 22:51:21 +0800
Subject: [PATCH 1310/2053] fix a typo that GFLAGS_GFLAGS_H_ -> GFLAGS_GFAGS_H_

---
 include/caffe/common.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 6cf80a37bc1..5f86bc2625b 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -19,7 +19,7 @@
 #include "caffe/util/device_alternate.hpp"
 
 // gflags 2.1 issue: namespace google was changed to gflags without warning.
-// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
 // 2.1. If yes, we will add a temporary solution to redirect the namespace.
 // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
 // remove the following hack.

From 23685324b94ef1248ad5b377121f4e71cda24293 Mon Sep 17 00:00:00 2001
From: gdh1995 <gdh1995@qq.com>
Date: Sun, 26 Apr 2015 22:52:41 +0800
Subject: [PATCH 1311/2053] Net::Update: CPU_ONLY is in wrong place

---
 src/caffe/net.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index fd00b122630..888eec1d501 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -756,15 +756,15 @@ void Net<Dtype>::Update() {
       owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
       caffe_add(count, this_diff, owner_diff, owner_diff);
       break;
-#ifndef CPU_ONLY
     case Caffe::GPU:
+#ifndef CPU_ONLY
       this_diff = params_[i]->gpu_diff();
       owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
       caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
-      break;
 #else
       NO_GPU;
 #endif
+      break;
     default:
       LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
     }

From 4202c16d14ae445325803a1c44c4f123986df08f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sebasti=C3=A1n=20Ram=C3=ADrez?= <tiangolo@gmail.com>
Date: Thu, 2 Apr 2015 20:01:24 -0500
Subject: [PATCH 1312/2053] Import Pandas in HDF5 IPython notebook. Fix for
 issue BVLC/caffe#2247

---
 examples/hdf5_classification.ipynb | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
index 19d27372754..03c811b5120 100644
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
@@ -25,6 +25,7 @@
      "collapsed": false,
      "input": [
       "import numpy as np\n",
+      "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "%matplotlib inline\n",
       "\n",

From c1c493900271867da92cea8789a670b1d08c2f1d Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Wed, 29 Apr 2015 21:53:51 +0300
Subject: [PATCH 1313/2053] minor cmake fix - now Caffe complains when cmake is
 executed if glog/gflags are not found.

---
 cmake/Modules/FindGFlags.cmake | 2 +-
 cmake/Modules/FindGlog.cmake   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/Modules/FindGFlags.cmake b/cmake/Modules/FindGFlags.cmake
index 146e8455a50..29b60f05037 100644
--- a/cmake/Modules/FindGFlags.cmake
+++ b/cmake/Modules/FindGFlags.cmake
@@ -38,7 +38,7 @@ else()
     find_library(GFLAGS_LIBRARY gflags)
 endif()
 
-find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
 
 
 if(GFLAGS_FOUND)
diff --git a/cmake/Modules/FindGlog.cmake b/cmake/Modules/FindGlog.cmake
index 56c76434897..99abbe478a0 100644
--- a/cmake/Modules/FindGlog.cmake
+++ b/cmake/Modules/FindGlog.cmake
@@ -37,7 +37,7 @@ else()
         PATH_SUFFIXES lib lib64)
 endif()
 
-find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
 
 if(GLOG_FOUND)
   set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})

From 63703edb0a4debd92c2b4e843612502c6015d203 Mon Sep 17 00:00:00 2001
From: Delbert <code@delbert.me>
Date: Thu, 30 Apr 2015 10:15:58 +0800
Subject: [PATCH 1314/2053] Correct the REPO_DIRNAME

---
 examples/web_demo/app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index c667ea94c11..09411f33f10 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -17,7 +17,7 @@
 
 import caffe
 
-REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
+REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')
 UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
 ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
 

From e02e66ce0ed9381837f17065e3a70e53d673be22 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sat, 25 Apr 2015 20:57:34 -0700
Subject: [PATCH 1315/2053] Makefile bugfix: OTHER_BUILD_DIR name set
 incorrectly when empty due to lazy variable expansion when using the `?=`
 operator -- change them to explicit empty string checks with simple
 assignment operator `:=`.

---
 Makefile | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index db0f531eaa0..42df20d27ae 100644
--- a/Makefile
+++ b/Makefile
@@ -4,8 +4,12 @@ CONFIG_FILE := Makefile.config
 include $(CONFIG_FILE)
 
 BUILD_DIR_LINK := $(BUILD_DIR)
-RELEASE_BUILD_DIR ?= .$(BUILD_DIR)_release
-DEBUG_BUILD_DIR ?= .$(BUILD_DIR)_debug
+ifeq ($(RELEASE_BUILD_DIR),)
+	RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
+endif
+ifeq ($(DEBUG_BUILD_DIR),)
+	DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+endif
 
 DEBUG ?= 0
 ifeq ($(DEBUG), 1)

From ca673fdd05458980f62a909b64b51b22f0ddd21e Mon Sep 17 00:00:00 2001
From: Nick Carlevaris-Bianco <carlevar@umich.edu>
Date: Mon, 4 May 2015 11:41:44 +0930
Subject: [PATCH 1316/2053] Added support for original implementation, using
 (margin - d^2), through the legacy_version parameter.

---
 src/caffe/layers/contrastive_loss_layer.cpp   | 26 ++++++++---
 src/caffe/layers/contrastive_loss_layer.cu    | 31 ++++++++++---
 src/caffe/proto/caffe.proto                   |  9 +++-
 .../test/test_contrastive_loss_layer.cpp      | 43 +++++++++++++++++++
 4 files changed, 96 insertions(+), 13 deletions(-)

diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 4cf04d4dd15..25e167819d3 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -41,6 +41,8 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
       diff_.mutable_cpu_data());  // a_i-b_i
   const int channels = bottom[0]->channels();
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   Dtype loss(0.0);
   for (int i = 0; i < bottom[0]->num(); ++i) {
     dist_sq_.mutable_cpu_data()[i] = caffe_cpu_dot(channels,
@@ -48,8 +50,12 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0);
-      loss += dist*dist;
+      if (legacy_version) {
+        loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+      } else {
+        Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0);
+        loss += dist*dist;
+      }
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
@@ -60,6 +66,8 @@ template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
@@ -77,12 +85,20 @@ void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
               Dtype(0.0),
               bout + (j*channels));
         } else {  // dissimilar pairs
-          Dtype dist = sqrt(dist_sq_.cpu_data()[j]);
-          Dtype mdist = (margin - dist);
+          Dtype mdist(0.0);
+          Dtype beta(0.0);
+          if (legacy_version) {
+            mdist = margin - dist_sq_.cpu_data()[j];
+            beta = -alpha;
+          } else {
+            Dtype dist = sqrt(dist_sq_.cpu_data()[j]);
+            mdist = margin - dist;
+            beta = -alpha * mdist / (dist + Dtype(1e-4));
+          }
           if (mdist > Dtype(0.0)) {
             caffe_cpu_axpby(
                 channels,
-                -alpha * mdist / (dist + Dtype(1e-4)),
+                beta,
                 diff_.cpu_data() + (j*channels),
                 Dtype(0.0),
                 bout + (j*channels));
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 83ff3c90f0f..931239316ac 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -32,13 +32,20 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
       Dtype(0.0),
       dist_sq_.mutable_gpu_data());  // \Sum (a_i-b_i)^2
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   Dtype loss(0.0);
   for (int i = 0; i < bottom[0]->num(); ++i) {
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0));
-      loss += dist*dist;
+      if (legacy_version) {
+        loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+      } else {
+        Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]),
+                              Dtype(0.0));
+        loss += dist*dist;
+      }
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
@@ -47,7 +54,7 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 __global__ void CLLBackward(const int count, const int channels,
-    const Dtype margin, const Dtype alpha,
+    const Dtype margin, const bool legacy_version, const Dtype alpha,
     const Dtype* y, const Dtype* diff, const Dtype* dist_sq,
     Dtype *bottom_diff) {
   CUDA_KERNEL_LOOP(i, count) {
@@ -55,10 +62,18 @@ __global__ void CLLBackward(const int count, const int channels,
     if (static_cast<int>(y[n])) {  // similar pairs
       bottom_diff[i] = alpha * diff[i];
     } else {  // dissimilar pairs
-      Dtype dist = sqrt(dist_sq[n]);
-      Dtype mdist = (margin - dist);
+      Dtype mdist(0.0);
+      Dtype beta(0.0);
+      if (legacy_version) {
+        mdist = (margin - dist_sq[n]);
+        beta = -alpha;
+      } else {
+        Dtype dist = sqrt(dist_sq[n]);
+        mdist = (margin - dist);
+        beta = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i];
+      }
       if (mdist > 0.0) {
-        bottom_diff[i] = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i];
+        bottom_diff[i] = beta;
       } else {
         bottom_diff[i] = 0;
       }
@@ -74,12 +89,14 @@ void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const int count = bottom[0]->count();
       const int channels = bottom[0]->channels();
       Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+      const bool legacy_version =
+          this->layer_param_.contrastive_loss_param().legacy_version();
       const Dtype sign = (i == 0) ? 1 : -1;
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
           static_cast<Dtype>(bottom[0]->num());
       // NOLINT_NEXT_LINE(whitespace/operators)
       CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-          count, channels, margin, alpha,
+          count, channels, margin, legacy_version, alpha,
           bottom[2]->gpu_data(),  // pair similarity 0 or 1
           diff_.gpu_data(),  // the cached eltwise difference between a and b
           dist_sq_.gpu_data(),  // the cached square distance between a and b
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5b21cf20028..fe4ce366972 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -401,8 +401,15 @@ message ConcatParameter {
 
 // Message that stores parameters used by ContrastiveLossLayer
 message ContrastiveLossParameter {
-  //margin for dissimilar pair
+  // margin for dissimilar pair
   optional float margin = 1 [default = 1.0];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [default = false]; 
 }
 
 // Message that stores parameters used by ConvolutionLayer
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index 460fc8f32fc..1e9447cbc51 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -100,4 +100,47 @@ TYPED_TEST(ContrastiveLossLayerTest, TestGradient) {
       this->blob_top_vec_, 1);
 }
 
+TYPED_TEST(ContrastiveLossLayerTest, TestForwardLegacy) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // manually compute to compare
+  const Dtype margin = layer_param.contrastive_loss_param().margin();
+  const int num = this->blob_bottom_data_i_->num();
+  const int channels = this->blob_bottom_data_i_->channels();
+  Dtype loss(0);
+  for (int i = 0; i < num; ++i) {
+    Dtype dist_sq(0);
+    for (int j = 0; j < channels; ++j) {
+      Dtype diff = this->blob_bottom_data_i_->cpu_data()[i*channels+j] -
+          this->blob_bottom_data_j_->cpu_data()[i*channels+j];
+      dist_sq += diff*diff;
+    }
+    if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
+      loss += dist_sq;
+    } else {
+      loss += std::max(margin - dist_sq, Dtype(0.0));
+    }
+  }
+  loss /= static_cast<Dtype>(num) * Dtype(2);
+  EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6);
+}
+
+TYPED_TEST(ContrastiveLossLayerTest, TestGradientLegacy) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  // check the gradient for the first two bottom layers
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 1);
+}
+
 }  // namespace caffe

From 63ed23bd83d5c30e74a83a976524dadf22d6dd49 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Mon, 4 May 2015 11:44:44 -0700
Subject: [PATCH 1317/2053] Fix redundancy of parameter backward computation

---
 src/caffe/layers/prelu_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index fd0eda5d191..5fd69d6c4d4 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -89,7 +89,7 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       Dtype* temp_buff = multiplier_.mutable_gpu_diff();
       // compute element-wise diff
       // NOLINT_NEXT_LINE(whitespace/operators)
-      PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+      PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
           CAFFE_CUDA_NUM_THREADS>>>(
           cdim, top_diff + top[0]->offset(n),
           bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff());

From 4348c6f4c905e9eb2c4dee32614a1880e074b217 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Mon, 4 May 2015 11:45:33 -0700
Subject: [PATCH 1318/2053] Modify for better readability regarding temporary
 bufffer for backward computation

---
 include/caffe/neuron_layers.hpp  | 3 ++-
 src/caffe/layers/prelu_layer.cpp | 3 ++-
 src/caffe/layers/prelu_layer.cu  | 8 ++++----
 3 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 323215134c7..aff58233e5c 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -734,7 +734,8 @@ class PReLULayer : public NeuronLayer<Dtype> {
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   bool channel_shared_;
-  Blob<Dtype> multiplier_;  // dot multipler for backward computation of params
+  Blob<Dtype> multiplier_;  // dot multiplier for backward computation of params
+  Blob<Dtype> backward_buff_;  // temporary buffer for backward computation
   Blob<Dtype> bottom_memory_;  // memory for in-place computation
 };
 
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
index 7119a274dd3..7a38f9fac80 100644
--- a/src/caffe/layers/prelu_layer.cpp
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -45,7 +45,8 @@ void PReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
   // Propagate gradients to the parameters (as directed by backward pass).
   this->param_propagate_down_.resize(this->blobs_.size(), true);
-  multiplier_.Reshape(vector<int>(1, bottom[0]->count() / bottom[0]->num()));
+  multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  backward_buff_.Reshape(vector<int>(1, bottom[0]->count(1)));
   caffe_set(multiplier_.count(), Dtype(1), multiplier_.mutable_cpu_data());
 }
 
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index 5fd69d6c4d4..dfa238d85bd 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -86,22 +86,22 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     int cdim = channels * dim;
     Dtype dsum = 0.;
     for (int n = 0; n < bottom[0]->num(); ++n) {
-      Dtype* temp_buff = multiplier_.mutable_gpu_diff();
       // compute element-wise diff
       // NOLINT_NEXT_LINE(whitespace/operators)
       PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
           CAFFE_CUDA_NUM_THREADS>>>(
           cdim, top_diff + top[0]->offset(n),
-          bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff());
+          bottom_data + bottom[0]->offset(n),
+          backward_buff_.mutable_gpu_diff());
       CUDA_POST_KERNEL_CHECK;
       if (channel_shared_) {
         Dtype d;
-        caffe_gpu_dot<Dtype>(channels * dim, multiplier_.gpu_diff(),
+        caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(),
             multiplier_.gpu_data(), &d);
         dsum += d;
       } else {
         caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
-            multiplier_.gpu_diff(), multiplier_.gpu_data(), 1.,
+            backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1.,
             slope_diff);
       }
     }

From a046c571c5d66a09cd6d2dd51ea1ee0957c574fd Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 6 May 2015 17:40:12 -0700
Subject: [PATCH 1319/2053] check that count_ does not overflow in
 Blob::Reshape

---
 src/caffe/blob.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 6d2b3f502d9..94fdcc35fb6 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -26,6 +26,7 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
   shape_.resize(shape.size());
   for (int i = 0; i < shape.size(); ++i) {
     CHECK_GE(shape[i], 0);
+    CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
     count_ *= shape[i];
     shape_[i] = shape[i];
   }

From a0e9f95817ea593aeef6ad8b4bed0da417e5a66e Mon Sep 17 00:00:00 2001
From: Gustav Larsson <gustav.m.larsson@gmail.com>
Date: Mon, 11 May 2015 11:54:45 -0500
Subject: [PATCH 1320/2053] This imports the wrong io module in Python 3.

The Python standard lib has a module called io, so instead of Python 3
throwing an error, it imports the wrong module without complaining.
---
 python/caffe/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 37e8956da4f..fbe7112e868 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -3,4 +3,4 @@
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
-import io
+from . import io

From 7ed310f2835c6a7843fd3362f290dbfda1585936 Mon Sep 17 00:00:00 2001
From: Martin Thoma <info@martin-thoma.de>
Date: Mon, 11 May 2015 22:35:48 +0200
Subject: [PATCH 1321/2053] python: PEP8; changed docstring documentation style
 to NumPyDoc style

---
 python/caffe/classifier.py             |  5 +-
 python/caffe/detector.py               | 35 +++++++------
 python/caffe/draw.py                   | 45 ++++++++++-------
 python/caffe/io.py                     | 27 ++++------
 python/caffe/pycaffe.py                | 68 +++++++++++++++-----------
 python/caffe/test/test_net.py          |  2 +
 python/caffe/test/test_python_layer.py |  3 ++
 python/caffe/test/test_solver.py       |  1 +
 8 files changed, 103 insertions(+), 83 deletions(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 49f8003ce9d..7fb2ccc8ff3 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -29,7 +29,7 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
         in_ = self.inputs[0]
         self.transformer = caffe.io.Transformer(
             {in_: self.blobs[in_].data.shape})
-        self.transformer.set_transpose(in_, (2,0,1))
+        self.transformer.set_transpose(in_, (2, 0, 1))
         if mean is not None:
             self.transformer.set_mean(in_, mean)
         if input_scale is not None:
@@ -44,7 +44,6 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
             image_dims = self.crop_dims
         self.image_dims = image_dims
 
-
     def predict(self, inputs, oversample=True):
         """
         Predict classification probabilities of inputs.
@@ -78,7 +77,7 @@ def predict(self, inputs, oversample=True):
             input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
-        caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+        caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
                             dtype=np.float32)
         for ix, in_ in enumerate(input_):
             caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index a67b818b93f..f72b548ac9a 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -41,7 +41,7 @@ def __init__(self, model_file, pretrained_file, mean=None,
         in_ = self.inputs[0]
         self.transformer = caffe.io.Transformer(
             {in_: self.blobs[in_].data.shape})
-        self.transformer.set_transpose(in_, (2,0,1))
+        self.transformer.set_transpose(in_, (2, 0, 1))
         if mean is not None:
             self.transformer.set_mean(in_, mean)
         if input_scale is not None:
@@ -53,17 +53,18 @@ def __init__(self, model_file, pretrained_file, mean=None,
 
         self.configure_crop(context_pad)
 
-
     def detect_windows(self, images_windows):
         """
         Do windowed detection over given images and windows. Windows are
         extracted then warped to the input dimensions of the net.
 
-        Take
+        Parameters
+        ----------
         images_windows: (image filename, window list) iterable.
         context_crop: size of context border to crop in pixels.
 
-        Give
+        Returns
+        -------
         detections: list of {filename: image filename, window: crop coordinates,
             predictions: prediction vector} dicts.
         """
@@ -82,7 +83,7 @@ def detect_windows(self, images_windows):
         for ix, window_in in enumerate(window_inputs):
             caffe_in[ix] = self.transformer.preprocess(in_, window_in)
         out = self.forward_all(**{in_: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2,3))
+        predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
 
         # Package predictions with images and windows.
         detections = []
@@ -97,16 +98,17 @@ def detect_windows(self, images_windows):
                 ix += 1
         return detections
 
-
     def detect_selective_search(self, image_fnames):
         """
         Do windowed detection over Selective Search proposals by extracting
         the crop and warping to the input dimensions of the net.
 
-        Take
+        Parameters
+        ----------
         image_fnames: list
 
-        Give
+        Returns
+        -------
         detections: list of {filename: image filename, window: crop coordinates,
             predictions: prediction vector} dicts.
         """
@@ -120,17 +122,18 @@ def detect_selective_search(self, image_fnames):
         # Run windowed detection on the selective search list.
         return self.detect_windows(zip(image_fnames, windows_list))
 
-
     def crop(self, im, window):
         """
         Crop a window from the image for detection. Include surrounding context
         according to the `context_pad` configuration.
 
-        Take
+        Parameters
+        ----------
         im: H x W x K image ndarray to crop.
         window: bounding box coordinates as ymin, xmin, ymax, xmax.
 
-        Give
+        Returns
+        -------
         crop: cropped window.
         """
         # Crop window from the image.
@@ -175,14 +178,14 @@ def crop(self, im, window):
 
         return crop
 
-
     def configure_crop(self, context_pad):
         """
         Configure crop dimensions and amount of context for cropping.
         If context is included, make the special input mean for context padding.
 
-        Take
-        context_pad: amount of context for cropping.
+        Parameters
+        ----------
+        context_pad : amount of context for cropping.
         """
         # crop dimensions
         in_ = self.inputs[0]
@@ -204,8 +207,8 @@ def configure_crop(self, context_pad):
                 crop_mean = mean.copy().transpose(inv_transpose)
                 if channel_order is not None:
                     channel_order_inverse = [channel_order.index(i)
-                                            for i in range(crop_mean.shape[2])]
-                    crop_mean = crop_mean[:,:, channel_order_inverse]
+                                             for i in range(crop_mean.shape[2])]
+                    crop_mean = crop_mean[:, :, channel_order_inverse]
                 if raw_scale is not None:
                     crop_mean /= raw_scale
                 self.crop_mean = crop_mean
diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 6a4dbd47351..08b7c1de14b 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -11,19 +11,23 @@
 import pydot
 
 # Internal layer and blob styles.
-LAYER_STYLE_DEFAULT = {'shape': 'record', 'fillcolor': '#6495ED',
-         'style': 'filled'}
-NEURON_LAYER_STYLE = {'shape': 'record', 'fillcolor': '#90EE90',
-         'style': 'filled'}
-BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0',
-        'style': 'filled'}
+LAYER_STYLE_DEFAULT = {'shape': 'record',
+                       'fillcolor': '#6495ED',
+                       'style': 'filled'}
+NEURON_LAYER_STYLE = {'shape': 'record',
+                      'fillcolor': '#90EE90',
+                      'style': 'filled'}
+BLOB_STYLE = {'shape': 'octagon',
+              'fillcolor': '#E0E0E0',
+              'style': 'filled'}
+
 
 def get_pooling_types_dict():
     """Get dictionary mapping pooling type number to type name
     """
     desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
     d = {}
-    for k,v in desc.values_by_name.items():
+    for k, v in desc.values_by_name.items():
         d[v.number] = k
     return d
 
@@ -145,21 +149,24 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
                    label=edge['label']))
   return pydot_graph
 
+
 def draw_net(caffe_net, rankdir, ext='png'):
-  """Draws a caffe net and returns the image string encoded using the given
-  extension.
+    """Draws a caffe net and returns the image string encoded using the given
+    extension.
 
-  Input:
+    Parameters
+    ----------
     caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer.
     ext: the image extension. Default 'png'.
-  """
-  return get_pydot_graph(caffe_net, rankdir).create(format=ext)
+    """
+    return get_pydot_graph(caffe_net, rankdir).create(format=ext)
+
 
 def draw_net_to_file(caffe_net, filename, rankdir='LR'):
-  """Draws a caffe net, and saves it to file using the format given as the
-  file extension. Use '.raw' to output raw text that you can manually feed
-  to graphviz to draw graphs.
-  """
-  ext = filename[filename.rfind('.')+1:]
-  with open(filename, 'wb') as fid:
-    fid.write(draw_net(caffe_net, rankdir, ext))
+    """Draws a caffe net, and saves it to file using the format given as the
+    file extension. Use '.raw' to output raw text that you can manually feed
+    to graphviz to draw graphs.
+    """
+    ext = filename[filename.rfind('.')+1:]
+    with open(filename, 'wb') as fid:
+        fid.write(draw_net(caffe_net, rankdir, ext))
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 6ae2cf13cc0..e5feff38796 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -8,16 +8,16 @@
     from caffe.proto import caffe_pb2
 except:
     import sys
-    if sys.version_info >= (3,0):
+    if sys.version_info >= (3, 0):
         print("Failed to include caffe_pb2, things might go wrong!")
     else:
         raise
 
-## proto / datum / ndarray conversion
 
+## proto / datum / ndarray conversion
 def blobproto_to_array(blob, return_diff=False):
-    """Convert a blob proto to an array. In default, we will just return the data,
-    unless return_diff is True, in which case we will return the diff.
+    """Convert a blob proto to an array. In default, we will just return the
+    data, unless return_diff is True, in which case we will return the diff.
     """
     if return_diff:
         return np.array(blob.diff).reshape(
@@ -35,7 +35,7 @@ def array_to_blobproto(arr, diff=None):
     if arr.ndim != 4:
         raise ValueError('Incorrect array shape.')
     blob = caffe_pb2.BlobProto()
-    blob.num, blob.channels, blob.height, blob.width = arr.shape;
+    blob.num, blob.channels, blob.height, blob.width = arr.shape
     blob.data.extend(arr.astype(float).flat)
     if diff is not None:
         blob.diff.extend(diff.astype(float).flat)
@@ -81,7 +81,7 @@ def datum_to_array(datum):
     as one can easily get it by calling datum.label.
     """
     if len(datum.data):
-        return np.fromstring(datum.data, dtype = np.uint8).reshape(
+        return np.fromstring(datum.data, dtype=np.uint8).reshape(
             datum.channels, datum.height, datum.width)
     else:
         return np.array(datum.float_data).astype(float).reshape(
@@ -97,8 +97,9 @@ class Transformer:
     Note: this is mostly for illustrative purposes and it is likely better
     to define your own input preprocessing routine for your needs.
 
-    Take
-    net: a Net for which the input should be prepared
+    Parameters
+    ----------
+    net : a Net for which the input should be prepared
     """
     def __init__(self, inputs):
         self.inputs = inputs
@@ -108,13 +109,11 @@ def __init__(self, inputs):
         self.mean = {}
         self.input_scale = {}
 
-
     def __check_input(self, in_):
         if in_ not in self.inputs:
             raise Exception('{} is not one of the net inputs: {}'.format(
                 in_, self.inputs))
 
-
     def preprocess(self, in_, data):
         """
         Format input for Caffe:
@@ -155,7 +154,6 @@ def preprocess(self, in_, data):
             caffe_in *= input_scale
         return caffe_in
 
-
     def deprocess(self, in_, data):
         """
         Invert Caffe formatting; see preprocess().
@@ -179,7 +177,6 @@ def deprocess(self, in_, data):
             decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
         return decaf_in
 
-
     def set_transpose(self, in_, order):
         """
         Set the input channel order for e.g. RGB to BGR conversion
@@ -195,7 +192,6 @@ def set_transpose(self, in_, order):
                             'dimensions as the input.')
         self.transpose[in_] = order
 
-
     def set_channel_swap(self, in_, order):
         """
         Set the input channel order for e.g. RGB to BGR conversion
@@ -213,7 +209,6 @@ def set_channel_swap(self, in_, order):
                             'dimensions as the input channels.')
         self.channel_swap[in_] = order
 
-
     def set_raw_scale(self, in_, scale):
         """
         Set the scale of raw features s.t. the input blob = input * scale.
@@ -228,7 +223,6 @@ def set_raw_scale(self, in_, scale):
         self.__check_input(in_)
         self.raw_scale[in_] = scale
 
-
     def set_mean(self, in_, mean):
         """
         Set the mean to subtract for centering the data.
@@ -254,7 +248,6 @@ def set_mean(self, in_, mean):
                 raise ValueError('Mean shape incompatible with input shape.')
         self.mean[in_] = mean
 
-
     def set_input_scale(self, in_, scale):
         """
         Set the scale of preprocessed inputs s.t. the blob = blob * scale.
@@ -359,7 +352,7 @@ def oversample(images, crop_dims):
 
     # Extract crops
     crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
-                            im_shape[-1]), dtype=np.float32)
+                      im_shape[-1]), dtype=np.float32)
     ix = 0
     for im in images:
         for crop in crops_ix:
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 3c19261f690..e8a676a26d2 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -5,9 +5,9 @@
 
 from collections import OrderedDict
 try:
-	from itertools import izip_longest
+    from itertools import izip_longest
 except:
-	from itertools import zip_longest as izip_longest
+    from itertools import zip_longest as izip_longest
 import numpy as np
 
 from ._caffe import Net, SGDSolver
@@ -53,16 +53,19 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
 
-    Take
-    blobs: list of blobs to return in addition to output blobs.
-    kwargs: Keys are input blob names and values are blob ndarrays.
-            For formatting inputs for Caffe, see Net.preprocess().
-            If None, input is taken from data layers.
-    start: optional name of layer at which to begin the forward pass
-    end: optional name of layer at which to finish the forward pass (inclusive)
-
-    Give
-    outs: {blob name: blob ndarray} dict.
+    Parameters
+    ----------
+    blobs : list of blobs to return in addition to output blobs.
+    kwargs : Keys are input blob names and values are blob ndarrays.
+             For formatting inputs for Caffe, see Net.preprocess().
+             If None, input is taken from data layers.
+    start : optional name of layer at which to begin the forward pass
+    end : optional name of layer at which to finish the forward pass
+          (inclusive)
+
+    Returns
+    -------
+    outs : {blob name: blob ndarray} dict.
     """
     if blobs is None:
         blobs = []
@@ -99,14 +102,17 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
     """
     Backward pass: prepare diffs and run the net backward.
 
-    Take
-    diffs: list of diffs to return in addition to bottom diffs.
-    kwargs: Keys are output blob names and values are diff ndarrays.
+    Parameters
+    ----------
+    diffs : list of diffs to return in addition to bottom diffs.
+    kwargs : Keys are output blob names and values are diff ndarrays.
             If None, top diffs are taken from forward loss.
-    start: optional name of layer at which to begin the backward pass
-    end: optional name of layer at which to finish the backward pass (inclusive)
+    start : optional name of layer at which to begin the backward pass
+    end : optional name of layer at which to finish the backward pass
+        (inclusive)
 
-    Give
+    Returns
+    -------
     outs: {blob name: diff ndarray} dict.
     """
     if diffs is None:
@@ -146,13 +152,15 @@ def _Net_forward_all(self, blobs=None, **kwargs):
     """
     Run net forward in batches.
 
-    Take
-    blobs: list of blobs to extract as in forward()
-    kwargs: Keys are input blob names and values are blob ndarrays.
-            Refer to forward().
+    Parameters
+    ----------
+    blobs : list of blobs to extract as in forward()
+    kwargs : Keys are input blob names and values are blob ndarrays.
+             Refer to forward().
 
-    Give
-    all_outs: {blob name: list of blobs} dict.
+    Returns
+    -------
+    all_outs : {blob name: list of blobs} dict.
     """
     # Collect outputs from batches
     all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
@@ -175,14 +183,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     """
     Run net forward + backward in batches.
 
-    Take
+    Parameters
+    ----------
     blobs: list of blobs to extract as in forward()
     diffs: list of diffs to extract as in backward()
     kwargs: Keys are input (for forward) and output (for backward) blob names
             and values are ndarrays. Refer to forward() and backward().
             Prefilled variants are called for lack of input or output blobs.
 
-    Give
+    Returns
+    -------
     all_blobs: {blob name: blob ndarray} dict.
     all_diffs: {blob name: diff ndarray} dict.
     """
@@ -229,11 +239,13 @@ def _Net_batch(self, blobs):
     """
     Batch blob lists according to net's batch size.
 
-    Take
+    Parameters
+    ----------
     blobs: Keys blob names and values are lists of blobs (of any length).
            Naturally, all the lists should have the same length.
 
-    Give (yield)
+    Yields
+    ------
     batch: {blob name: list of blobs} dict for a single batch.
     """
     num = len(blobs.itervalues().next())
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 62b407da8aa..cc367477752 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -5,6 +5,7 @@
 
 import caffe
 
+
 def simple_net_file(num_output):
     """Make a simple net prototxt, based on test_net.cpp, returning the name
     of the (temporary) file."""
@@ -31,6 +32,7 @@ def simple_net_file(num_output):
     f.close()
     return f.name
 
+
 class TestNet(unittest.TestCase):
     def setUp(self):
         self.num_output = 13
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index dd99f6f15b9..6fba49143bb 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -4,6 +4,7 @@
 
 import caffe
 
+
 class SimpleLayer(caffe.Layer):
     """A layer that just multiplies by ten"""
 
@@ -19,6 +20,7 @@ def forward(self, bottom, top):
     def backward(self, top, propagate_down, bottom):
         bottom[0].diff[...] = 10 * top[0].diff
 
+
 def python_net_file():
     with tempfile.NamedTemporaryFile(delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
@@ -31,6 +33,7 @@ def python_net_file():
           python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
         return f.name
 
+
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):
         net_file = python_net_file()
diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index d59f23d973a..09b974dad66 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -6,6 +6,7 @@
 import caffe
 from test_net import simple_net_file
 
+
 class TestSolver(unittest.TestCase):
     def setUp(self):
         self.num_output = 13

From 7cf8b830cd24b73ebed89ef19e6bc8bd25d938cd Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Wed, 13 May 2015 21:16:28 -0700
Subject: [PATCH 1322/2053] [pycaffe] use bp::object instead of PyObject* for
 self in Python layer

This simply allows direct use of the nicer bp::object interface.
---
 include/caffe/python_layer.hpp | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
index 816ef453720..19cf18c9742 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/python_layer.hpp
@@ -14,12 +14,12 @@ template <typename Dtype>
 class PythonLayer : public Layer<Dtype> {
  public:
   PythonLayer(PyObject* self, const LayerParameter& param)
-      : Layer<Dtype>(param), self_(self) { }
+      : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
 
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
     try {
-      bp::call_method<bp::object>(self_, "setup", bottom, top);
+      self_.attr("setup")(bottom, top);
     } catch (bp::error_already_set) {
       PyErr_Print();
       throw;
@@ -29,7 +29,7 @@ class PythonLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
     try {
-      bp::call_method<bp::object>(self_, "reshape", bottom, top);
+      self_.attr("reshape")(bottom, top);
     } catch (bp::error_already_set) {
       PyErr_Print();
       throw;
@@ -42,7 +42,7 @@ class PythonLayer : public Layer<Dtype> {
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
     try {
-      bp::call_method<bp::object>(self_, "forward", bottom, top);
+      self_.attr("forward")(bottom, top);
     } catch (bp::error_already_set) {
       PyErr_Print();
       throw;
@@ -51,8 +51,7 @@ class PythonLayer : public Layer<Dtype> {
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
     try {
-      bp::call_method<bp::object>(self_, "backward", top, propagate_down,
-          bottom);
+      self_.attr("backward")(top, propagate_down, bottom);
     } catch (bp::error_already_set) {
       PyErr_Print();
       throw;
@@ -60,7 +59,7 @@ class PythonLayer : public Layer<Dtype> {
   }
 
  private:
-  PyObject* self_;
+  bp::object self_;
 };
 
 }  // namespace caffe

From e266a4d368b22a8940468d68f41fe4392f5200da Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 13 May 2015 22:08:57 -0700
Subject: [PATCH 1323/2053] remove superfluous empty destructors

The removed definitions do nothing; these classes already have virtual
destructors inherited from their respective base classes.
---
 include/caffe/data_layers.hpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 2bb9d948169..4dcf5501522 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -29,7 +29,6 @@ template <typename Dtype>
 class BaseDataLayer : public Layer<Dtype> {
  public:
   explicit BaseDataLayer(const LayerParameter& param);
-  virtual ~BaseDataLayer() {}
   // LayerSetUp: implements common data layer setup functionality, and calls
   // DataLayerSetUp to do special data layer setup for individual layer types.
   // This method may not be overridden except by the BasePrefetchingDataLayer.
@@ -58,7 +57,6 @@ class BasePrefetchingDataLayer :
  public:
   explicit BasePrefetchingDataLayer(const LayerParameter& param)
       : BaseDataLayer<Dtype>(param) {}
-  virtual ~BasePrefetchingDataLayer() {}
   // LayerSetUp: implements common data layer setup functionality, and calls
   // DataLayerSetUp to do special data layer setup for individual layer types.
   // This method may not be overridden.

From 6153231594b98c0933be21685708282bc6160b6c Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 12 Mar 2015 17:59:28 -0700
Subject: [PATCH 1324/2053] remove bogus implementation of
 SigmoidCrossEntropyLossLayer::Forward_gpu

It was a verbatim copy of Forward_cpu; there is no proper GPU
implementation.
---
 include/caffe/loss_layers.hpp                 |  2 --
 .../sigmoid_cross_entropy_loss_layer.cpp      |  2 +-
 .../sigmoid_cross_entropy_loss_layer.cu       | 22 +------------------
 3 files changed, 2 insertions(+), 24 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index d3eecd2e510..86c34241168 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -605,8 +605,6 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   /// @copydoc SigmoidCrossEntropyLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 077d949981c..cc236fe1e8e 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -71,7 +71,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(SigmoidCrossEntropyLossLayer);
+STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 08f7f492297..547fa80c72f 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -8,26 +8,6 @@
 
 namespace caffe {
 
-template <typename Dtype>
-void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  // The forward pass computes the sigmoid outputs.
-  sigmoid_bottom_vec_[0] = bottom[0];
-  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
-  // Compute the loss (negative log likelihood)
-  const int count = bottom[0]->count();
-  const int num = bottom[0]->num();
-  // Stable version of loss computation from input data
-  const Dtype* input_data = bottom[0]->cpu_data();
-  const Dtype* target = bottom[1]->cpu_data();
-  Dtype loss = 0;
-  for (int i = 0; i < count; ++i) {
-    loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
-        log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
-  }
-  top[0]->mutable_cpu_data()[0] = loss / num;
-}
-
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
@@ -51,7 +31,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   }
 }
 
-INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer);
+INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer);
 
 
 }  // namespace caffe

From 438cf0e9a660676b1526eb8178b020ad2d745f6f Mon Sep 17 00:00:00 2001
From: PETER_GAO <pgao@berkeley.edu>
Date: Sat, 21 Mar 2015 16:00:05 -0700
Subject: [PATCH 1325/2053] Spatial Pyramid Pooling Layer

---
 include/caffe/vision_layers.hpp   |  66 ++++++++++
 src/caffe/layers/spp_layer.cpp    | 193 ++++++++++++++++++++++++++++++
 src/caffe/proto/caffe.proto       |  20 +++-
 src/caffe/test/test_spp_layer.cpp | 131 ++++++++++++++++++++
 4 files changed, 409 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/spp_layer.cpp
 create mode 100644 src/caffe/test/test_spp_layer.cpp

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 6cb507a5780..e9023230439 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -451,6 +451,72 @@ class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
 };
 #endif
 
+/**
+ * @brief Does spatial pyramid pooling on the input image
+ *        by taking the max, average, etc. within regions
+ *        so that the result vector of different sized
+ *        images are of the same size.
+ */
+template <typename Dtype>
+class SPPLayer : public Layer<Dtype> {
+ public:
+  explicit SPPLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SPP"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  // MAX POOL layers can output an extra top blob for the mask;
+  // others can only output the pooled inputs.
+  virtual inline int MaxTopBlobs() const {
+    return (this->layer_param_.pooling_param().pool() ==
+            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  // calculates the kernel and stride dimensions for the pooling layer,
+  // returns a correctly configured LayerParameter for a PoolingLayer
+  virtual LayerParameter GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param);
+
+  int pyramid_height_;
+  int bottom_h_, bottom_w_;
+  int channels_;
+  int kernel_h_, kernel_w_;
+  int pad_h_, pad_w_;
+
+  /// the internal Split layer that feeds the pooling layers
+  shared_ptr<SplitLayer<Dtype> > split_layer_;
+  /// top vector holder used in call to the underlying SplitLayer::Forward
+  vector<Blob<Dtype>*> split_top_vec_;
+  /// bottom vector holder used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
+  /// the internal Pooling layers of different kernel sizes
+  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
+  /// top vector holders used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
+  /// pooling_outputs stores the outputs of the PoolingLayers
+  vector<Blob<Dtype>*> pooling_outputs_;
+  /// the internal Flatten layers that the Pooling layers feed into
+  vector<FlattenLayer<Dtype>*> flatten_layers_;
+  /// top vector holders used in call to the underlying FlattenLayer::Forward
+  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
+  /// flatten_outputs stores the outputs of the FlattenLayers
+  vector<Blob<Dtype>*> flatten_outputs_;
+  /// bottom vector holder used in call to the underlying ConcatLayer::Forward
+  vector<Blob<Dtype>*> concat_bottom_vec_;
+  /// the internal Concat layers that the Flatten layers feed into
+  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_VISION_LAYERS_HPP_
diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp
new file mode 100644
index 00000000000..795dd71693e
--- /dev/null
+++ b/src/caffe/layers/spp_layer.cpp
@@ -0,0 +1,193 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+using std::min;
+using std::max;
+
+template <typename Dtype>
+LayerParameter SPPLayer<Dtype>::GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param) {
+  LayerParameter pooling_param;
+  int num_bins = pow(2, pyramid_level);
+
+  // find padding and kernel size so that the pooling is
+  // performed across the entire image
+  int kernel_h = ceil(bottom_h / static_cast<double>(num_bins));
+  // remainder_h is the min number of pixels that need to be padded before
+  // entire image height is pooled over with the chosen kernel dimension
+  int remainder_h = kernel_h * num_bins - bottom_h;
+  // pooling layer pads (2 * pad_h) pixels on the top and bottom of the
+  // image.
+  int pad_h = (remainder_h + 1) / 2;
+
+  // similar logic for width
+  int kernel_w = ceil(bottom_w / static_cast<double>(num_bins));
+  int remainder_w = kernel_w * num_bins - bottom_w;
+  int pad_w = (remainder_w + 1) / 2;
+
+  pooling_param.mutable_pooling_param()->set_pad_h(pad_h);
+  pooling_param.mutable_pooling_param()->set_pad_w(pad_w);
+  pooling_param.mutable_pooling_param()->set_kernel_h(kernel_h);
+  pooling_param.mutable_pooling_param()->set_kernel_w(kernel_w);
+  pooling_param.mutable_pooling_param()->set_stride_h(kernel_h);
+  pooling_param.mutable_pooling_param()->set_stride_w(kernel_w);
+
+  switch (spp_param.pool()) {
+  case SPPParameter_PoolMethod_MAX:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_MAX);
+    break;
+  case SPPParameter_PoolMethod_AVE:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_AVE);
+    break;
+  case SPPParameter_PoolMethod_STOCHASTIC:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_STOCHASTIC);
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+
+  return pooling_param;
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  SPPParameter spp_param = this->layer_param_.spp_param();
+
+  bottom_h_ = bottom[0]->height();
+  bottom_w_ = bottom[0]->width();
+  CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero.";
+  CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero.";
+
+  pyramid_height_ = spp_param.pyramid_height();
+  split_top_vec_.clear();
+  pooling_bottom_vecs_.clear();
+  pooling_layers_.clear();
+  pooling_top_vecs_.clear();
+  pooling_outputs_.clear();
+  flatten_layers_.clear();
+  flatten_top_vecs_.clear();
+  flatten_outputs_.clear();
+  concat_bottom_vec_.clear();
+
+  // split layer output holders setup
+  for (int i = 0; i < pyramid_height_; i++) {
+    split_top_vec_.push_back(new Blob<Dtype>());
+  }
+
+  // split layer setup
+  LayerParameter split_param;
+  split_layer_.reset(new SplitLayer<Dtype>(split_param));
+  split_layer_->SetUp(bottom, split_top_vec_);
+
+  for (int i = 0; i < pyramid_height_; i++) {
+    // pooling layer input holders setup
+    pooling_bottom_vecs_.push_back(new vector<Blob<Dtype>*>);
+    pooling_bottom_vecs_[i]->push_back(split_top_vec_[i]);
+
+    // pooling layer output holders setup
+    pooling_outputs_.push_back(new Blob<Dtype>());
+    pooling_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+    pooling_top_vecs_[i]->push_back(pooling_outputs_[i]);
+
+    // pooling layer setup
+    LayerParameter pooling_param = GetPoolingParam(
+        i, bottom_h_, bottom_w_, spp_param);
+
+    pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+        new PoolingLayer<Dtype>(pooling_param)));
+    pooling_layers_[i]->SetUp(*pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+
+    // flatten layer output holders setup
+    flatten_outputs_.push_back(new Blob<Dtype>());
+    flatten_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+    flatten_top_vecs_[i]->push_back(flatten_outputs_[i]);
+
+    // flatten layer setup
+    LayerParameter flatten_param;
+    flatten_layers_.push_back(new FlattenLayer<Dtype>(flatten_param));
+    flatten_layers_[i]->SetUp(*pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+
+    // concat layer input holders setup
+    concat_bottom_vec_.push_back(flatten_outputs_[i]);
+  }
+
+  // concat layer setup
+  LayerParameter concat_param;
+  concat_layer_.reset(new ConcatLayer<Dtype>(concat_param));
+  concat_layer_->SetUp(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
+  channels_ = bottom[0]->channels();
+  bottom_h_ = bottom[0]->height();
+  bottom_w_ = bottom[0]->width();
+  SPPParameter spp_param = this->layer_param_.spp_param();
+  split_layer_->Reshape(bottom, split_top_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    LayerParameter pooling_param = GetPoolingParam(
+        i, bottom_h_, bottom_w_, spp_param);
+
+    pooling_layers_[i].reset(
+        new PoolingLayer<Dtype>(pooling_param));
+    pooling_layers_[i]->SetUp(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    pooling_layers_[i]->Reshape(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    flatten_layers_[i]->Reshape(
+        *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+  }
+  concat_layer_->Reshape(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  split_layer_->Forward(bottom, split_top_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    pooling_layers_[i]->Forward(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    flatten_layers_[i]->Forward(
+        *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+  }
+  concat_layer_->Forward(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  vector<bool> concat_propagate_down(pyramid_height_, true);
+  concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    flatten_layers_[i]->Backward(
+        *flatten_top_vecs_[i], propagate_down, *pooling_top_vecs_[i]);
+    pooling_layers_[i]->Backward(
+        *pooling_top_vecs_[i], propagate_down, *pooling_bottom_vecs_[i]);
+  }
+  split_layer_->Backward(split_top_vec_, propagate_down, bottom);
+}
+
+
+INSTANTIATE_CLASS(SPPLayer);
+REGISTER_LAYER_CLASS(SPP);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5b21cf20028..b11fdf62dec 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -259,7 +259,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 132 (last added: prelu_param)
+// LayerParameter next available layer-specific ID: 133 (last added: spp_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -328,6 +328,7 @@ message LayerParameter {
   optional ReLUParameter relu_param = 123;
   optional SigmoidParameter sigmoid_param = 124;
   optional SoftmaxParameter softmax_param = 125;
+  optional SPPParameter spp_param = 132;
   optional SliceParameter slice_param = 126;
   optional TanHParameter tanh_param = 127;
   optional ThresholdParameter threshold_param = 128;
@@ -768,6 +769,23 @@ message WindowDataParameter {
   optional string root_folder = 13 [default = ""];
 }
 
+// Message that stores parameters used by SPPLayer
+message SPPParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
+
 // DEPRECATED: use LayerParameter.
 message V1LayerParameter {
   repeated string bottom = 2;
diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp
new file mode 100644
index 00000000000..b2585f1a5fa
--- /dev/null
+++ b/src/caffe/test/test_spp_layer.cpp
@@ -0,0 +1,131 @@
+#include <algorithm>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SPPLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  SPPLayerTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_bottom_2_(new Blob<Dtype>()),
+        blob_bottom_3_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    blob_bottom_->Reshape(2, 3, 9, 8);
+    blob_bottom_2_->Reshape(4, 3, 1024, 765);
+    blob_bottom_3_->Reshape(10, 3, 7, 7);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_bottom_vec_2_.push_back(blob_bottom_2_);
+    blob_bottom_vec_3_.push_back(blob_bottom_3_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~SPPLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_bottom_3_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_bottom_vec_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_3_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(SPPLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(SPPLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 3)/(1 - 4) = 21
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 21 = 63)
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 63);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(5);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_2_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 5)/(1 - 4) = 341
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 341 = 1023)
+  EXPECT_EQ(this->blob_top_->num(), 4);
+  EXPECT_EQ(this->blob_top_->channels(), 1023);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_3_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 3)/(1 - 4) = 21
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 21 = 63)
+  EXPECT_EQ(this->blob_top_->num(), 10);
+  EXPECT_EQ(this->blob_top_->channels(), 63);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestForwardBackward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+}
+
+TYPED_TEST(SPPLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SPPParameter* spp_param = layer_param.mutable_spp_param();
+  spp_param->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+
+}  // namespace caffe

From 4fb3c9e6a5ac80804c910639d14651c2ecdcb5f3 Mon Sep 17 00:00:00 2001
From: Simon Safar <latanius@gmail.com>
Date: Wed, 15 Oct 2014 20:15:14 -0700
Subject: [PATCH 1326/2053] Added a Reshape layer for copying-free modification
 of blob dimensions.

---
 docs/tutorial/layers.md               |  42 +++++++++
 include/caffe/common_layers.hpp       |  35 ++++++++
 src/caffe/layers/reshape_layer.cpp    | 113 ++++++++++++++++++++++++
 src/caffe/layers/reshape_layer.cu     |  23 +++++
 src/caffe/proto/caffe.proto           |  16 +++-
 src/caffe/test/test_reshape_layer.cpp | 120 ++++++++++++++++++++++++++
 6 files changed, 348 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/reshape_layer.cpp
 create mode 100644 src/caffe/layers/reshape_layer.cu
 create mode 100644 src/caffe/test/test_reshape_layer.cpp

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 839939f5ad6..422ee01f201 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -419,6 +419,48 @@ The `SPLIT` layer is a utility layer that splits an input blob to multiple outpu
 
 The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`.
 
+#### Reshape
+
+* LayerType: `RESHAPE`
+* CPU implementation: `./src/caffe/layers/reshape_layer.cpp`
+* CUDA GPU implementation: `./src/caffe/layers/reshape_layer.cu`
+* Parameters (`ReshapeParameter reshape_param`)
+    - Optional: (also see detailed description below)
+        - `num` [default 0]
+        - `channels` [default 0]
+        - `width` [default 0]
+        - `height` [default 0]
+
+* Input
+    - a single blob with arbitrary dimensions
+* Output
+    - the same blob, with modified dimensions, as specified by `reshape_param`
+
+* Sample
+
+        layers {
+          name: "reshape"
+          type: RESHAPE
+          bottom: "input"
+          top: "output"
+
+          reshape_param {
+            num: 0  # copy the dimension from below
+            channels: 2
+            width: 3
+            height: -1 # infer it from the other dimensions
+          }
+        }
+
+The `RESHAPE` layer can be used to change the dimensions of its input, without changing its data. Just like the `FLATTEN` layer, only the dimensions are changed, no data is copied in the process.
+
+Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
+
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom layer has 2 channels, the top one will have 2 channels too, given `channels: 0` as target dimension. Since the default value of all the target dimensions is 0, omitting any of the target dimensions will also cause it to be copied.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. If this is not possible, an error is raised. Also, at most one -1 can be used in a reshape operation.
+
+As another example, giving `num: 0, channels: -1, height: 1, width: 1` as parameters makes the layer behave in exactly the same way as the `FLATTEN` layer.
+
 #### Concatenation
 
 * LayerType: `CONCAT`
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index cae1c3e4ee6..945c0cef1b6 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -297,6 +297,41 @@ class MVNLayer : public Layer<Dtype> {
   Blob<Dtype> sum_multiplier_;
 };
 
+/*
+ * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
+ *
+ * Note: similarly to FlattenLayer, this layer does not change the input values
+ * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
+ */
+template <typename Dtype>
+class ReshapeLayer : public Layer<Dtype> {
+ public:
+  explicit ReshapeLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<bool>& propagate_down,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  void FillInSingleUnspecifiedDimension(int bottom_count);
+
+  int num_out;
+  int channels_out;
+  int height_out;
+  int width_out;
+};
+
 /**
  * @brief Ignores bottom blobs while producing no top blobs. (This is useful
  *        to suppress outputs during testing.)
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
new file mode 100644
index 00000000000..7e8704e058a
--- /dev/null
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -0,0 +1,113 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(bottom.size(), 1) << "Reshape Layer takes a single blob as input.";
+  CHECK_EQ(top.size(), 1) << "Reshape Layer takes a single blob as output.";
+
+  num_out = this->layer_param_.reshape_param().num();
+  // Dimensions set to 0 (either by default or explicitly) will be copied from
+  // the bottom layer.
+  if (num_out == 0) {
+    num_out = bottom[0]->num();
+  }
+
+  channels_out = this->layer_param_.reshape_param().channels();
+  if (channels_out == 0) {
+    channels_out = bottom[0]->channels();
+  }
+
+  width_out = this->layer_param_.reshape_param().width();
+  if (width_out == 0) {
+    width_out = bottom[0]->width();
+  }
+
+  height_out = this->layer_param_.reshape_param().height();
+  if (height_out == 0) {
+    height_out = bottom[0]->height();
+  }
+
+  FillInSingleUnspecifiedDimension(bottom[0]->count());
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->Reshape(num_out, channels_out, height_out, width_out);
+
+  const size_t out_count = num_out * channels_out * height_out * width_out;
+  CHECK_EQ(out_count, bottom[0]->count()) <<
+      "Bottom layer count isn't equal to predicted; output layer size is " <<
+      num_out << "x" << channels_out << "x" << height_out << "x" << width_out;
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ShareData(*bottom[0]);
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  bottom[0]->ShareDiff(*top[0]);
+}
+
+/**
+ * @brief Fill in a single dimension left unspecified.
+ *
+ * If a dimension is set to -1, it will be filled in with a value inferred from
+ * the count of the bottom layer (if the product of the nonzero dimensions is a
+ * divisor of the count).
+ *
+ * @param bottom_count Count of the bottom layer.
+ */
+template <typename Dtype>
+void ReshapeLayer<Dtype>::FillInSingleUnspecifiedDimension(int bottom_count) {
+  int* const dimensions[] = {&num_out, &channels_out, &width_out, &height_out};
+  const size_t N_DIMENSIONS = 4;
+
+  // How many -1 dimensions do we have.
+  int n_unspecified = 0;
+  // Product of the remaining dimensions.
+  int product_without_unspecified_dim = 1;
+
+  for (size_t i = 0; i < N_DIMENSIONS; i++) {
+    if (*(dimensions[i]) == -1) {
+      n_unspecified++;
+    } else {
+      product_without_unspecified_dim *= *(dimensions[i]);
+    }
+  }
+
+  if (n_unspecified == 0) {
+    // Everything is filled out, nothing to do.
+    return;
+  }
+
+  CHECK_EQ(n_unspecified, 1) << "Only one dimension can be set -1.";
+  CHECK_EQ(bottom_count % product_without_unspecified_dim, 0) <<
+    "Bottom layer count " << bottom_count << " not divisible by product " <<
+    product_without_unspecified_dim;
+
+  // Fill up the one remaining dimension.
+  for (size_t i = 0; i < N_DIMENSIONS; i++) {
+    if (*(dimensions[i]) == -1) {
+      *(dimensions[i]) = bottom_count / product_without_unspecified_dim;
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ReshapeLayer);
+#endif
+
+INSTANTIATE_CLASS(ReshapeLayer);
+REGISTER_LAYER_CLASS(RESHAPE, ReshapeLayer);
+}  // namespace caffe
diff --git a/src/caffe/layers/reshape_layer.cu b/src/caffe/layers/reshape_layer.cu
new file mode 100644
index 00000000000..3023ce3ae88
--- /dev/null
+++ b/src/caffe/layers/reshape_layer.cu
@@ -0,0 +1,23 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  top[0]->ShareData(*bottom[0]);
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  bottom[0]->ShareDiff(*top[0]);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ReshapeLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 4516106428c..e8bf240c1b3 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -259,7 +259,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 133 (last added: spp_param)
+// LayerParameter next available layer-specific ID: 134 (last added: reshape_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -326,6 +326,7 @@ message LayerParameter {
   optional PReLUParameter prelu_param = 131;
   optional PythonParameter python_param = 130;
   optional ReLUParameter relu_param = 123;
+  optional ReshapeParameter reshape_param = 133;
   optional SigmoidParameter sigmoid_param = 124;
   optional SoftmaxParameter softmax_param = 125;
   optional SPPParameter spp_param = 132;
@@ -690,6 +691,19 @@ message ReLUParameter {
   optional Engine engine = 2 [default = DEFAULT];
 }
 
+// Message that stores parameters used by ReshapeLayer
+message ReshapeParameter {
+  // Specify the output dimensions. If some of the following parameters are
+  // omitted or set to 0 explicitly, the corresponding dimension from the bottom
+  // layer is used (unchanged). Also, if exactly one of them is set to -1, its
+  // value is calculated from the count of the bottom layer and the remaining
+  // dimensions, if possible.
+  optional int32 num = 1 [default = 0];
+  optional int32 channels = 2 [default = 0];
+  optional int32 height = 3 [default = 0];
+  optional int32 width = 4 [default = 0];
+}
+
 // Message that stores parameters used by SigmoidLayer
 message SigmoidParameter {
   enum Engine {
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
new file mode 100644
index 00000000000..878d40bb4d5
--- /dev/null
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -0,0 +1,120 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReshapeLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  ReshapeLayerTest()
+    : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+      blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~ReshapeLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ReshapeParameter* reshape_param =
+      layer_param.mutable_reshape_param();
+  reshape_param->set_channels(-1);
+  reshape_param->set_height(1);
+  reshape_param->set_width(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenValues) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ReshapeParameter* reshape_param =
+      layer_param.mutable_reshape_param();
+  reshape_param->set_channels(-1);
+  reshape_param->set_height(1);
+  reshape_param->set_width(1);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int c = 0; c < 3 * 6 * 5; ++c) {
+    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
+        this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
+    EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
+        this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
+  }
+}
+
+// Test whether setting output dimensions to 0 either explicitly or implicitly
+// copies the respective dimension of the input layer.
+TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ReshapeParameter* reshape_param =
+      layer_param.mutable_reshape_param();
+  // Omitting num to test implicit zeroes.
+  reshape_param->set_channels(0);
+  reshape_param->set_height(0);
+  reshape_param->set_width(0);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 6);
+  EXPECT_EQ(this->blob_top_->width(), 5);
+}
+
+// When a dimension is set to -1, we should infer its value from the other
+// dimensions (including those that get copied from below).
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ReshapeParameter* reshape_param =
+      layer_param.mutable_reshape_param();
+  // Since omitted, num is implicitly set to 0 (thus, copies 2).
+  reshape_param->set_channels(3);
+  reshape_param->set_height(10);
+  reshape_param->set_width(-1);
+
+  // Count is 180, thus height should be 180 / (2*3*10) = 3.
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 10);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+}  // namespace caffe

From fa6169ee799f97f80d33d6b4525c7fd4b891774a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 25 Mar 2015 17:44:37 -0700
Subject: [PATCH 1327/2053] ReshapeLayer fixups for ND blobs

---
 docs/tutorial/layers.md               |  33 +++----
 include/caffe/common_layers.hpp       |  31 +++---
 src/caffe/layers/reshape_layer.cpp    | 135 ++++++++------------------
 src/caffe/layers/reshape_layer.cu     |  23 -----
 src/caffe/proto/caffe.proto           |  14 +--
 src/caffe/test/test_reshape_layer.cpp |  42 ++++----
 6 files changed, 101 insertions(+), 177 deletions(-)
 delete mode 100644 src/caffe/layers/reshape_layer.cu

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 422ee01f201..c4529e6afc0 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -421,15 +421,11 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c *
 
 #### Reshape
 
-* LayerType: `RESHAPE`
-* CPU implementation: `./src/caffe/layers/reshape_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/reshape_layer.cu`
+* Layer type: `Reshape`
+* Implementation: `./src/caffe/layers/reshape_layer.cpp`
 * Parameters (`ReshapeParameter reshape_param`)
     - Optional: (also see detailed description below)
-        - `num` [default 0]
-        - `channels` [default 0]
-        - `width` [default 0]
-        - `height` [default 0]
+        - `shape`
 
 * Input
     - a single blob with arbitrary dimensions
@@ -438,28 +434,29 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c *
 
 * Sample
 
-        layers {
+        layer {
           name: "reshape"
-          type: RESHAPE
+          type: "Reshape"
           bottom: "input"
           top: "output"
-
           reshape_param {
-            num: 0  # copy the dimension from below
-            channels: 2
-            width: 3
-            height: -1 # infer it from the other dimensions
+            shape {
+              dim: 0  # copy the dimension from below
+              dim: 2
+              dim: 3
+              dim: -1 # infer it from the other dimensions
+            }
           }
         }
 
-The `RESHAPE` layer can be used to change the dimensions of its input, without changing its data. Just like the `FLATTEN` layer, only the dimensions are changed, no data is copied in the process.
+The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
 
 Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
 
-* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom layer has 2 channels, the top one will have 2 channels too, given `channels: 0` as target dimension. Since the default value of all the target dimensions is 0, omitting any of the target dimensions will also cause it to be copied.
-* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. If this is not possible, an error is raised. Also, at most one -1 can be used in a reshape operation.
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
 
-As another example, giving `num: 0, channels: -1, height: 1, width: 1` as parameters makes the layer behave in exactly the same way as the `FLATTEN` layer.
+As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
 
 #### Concatenation
 
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 945c0cef1b6..ccdfd62d5be 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -313,23 +313,28 @@ class ReshapeLayer : public Layer<Dtype> {
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
+  virtual inline const char* type() const { return "Reshape"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<bool>& propagate_down,
-      const vector<Blob<Dtype>*>& top);
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
+      const vector<Blob<Dtype>*>& top) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  void FillInSingleUnspecifiedDimension(int bottom_count);
-
-  int num_out;
-  int channels_out;
-  int height_out;
-  int width_out;
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+  /// @brief the current output shape
+  vector<int> top_shape_;
+  /// @brief vector of axes indices whose dimensions we'll copy from the bottom
+  vector<int> copy_axes_;
+  /// @brief the index of the axis whose dimension we infer, or -1 if none
+  int inferred_axis_;
+  /// @brief the product of the "constant" output dimensions
+  int constant_count_;
 };
 
 /**
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
index 7e8704e058a..618edf31824 100644
--- a/src/caffe/layers/reshape_layer.cpp
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -7,107 +7,58 @@ namespace caffe {
 
 template <typename Dtype>
 void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(bottom.size(), 1) << "Reshape Layer takes a single blob as input.";
-  CHECK_EQ(top.size(), 1) << "Reshape Layer takes a single blob as output.";
-
-  num_out = this->layer_param_.reshape_param().num();
-  // Dimensions set to 0 (either by default or explicitly) will be copied from
-  // the bottom layer.
-  if (num_out == 0) {
-    num_out = bottom[0]->num();
-  }
-
-  channels_out = this->layer_param_.reshape_param().channels();
-  if (channels_out == 0) {
-    channels_out = bottom[0]->channels();
-  }
-
-  width_out = this->layer_param_.reshape_param().width();
-  if (width_out == 0) {
-    width_out = bottom[0]->width();
-  }
-
-  height_out = this->layer_param_.reshape_param().height();
-  if (height_out == 0) {
-    height_out = bottom[0]->height();
+    const vector<Blob<Dtype>*>& top) {
+  inferred_axis_ = -1;
+  copy_axes_.clear();
+  const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+  const int top_num_axes = top_blob_shape.dim_size();
+  top_shape_.resize(top_num_axes);
+  constant_count_ = 1;
+  for (int i = 0; i < top_num_axes; ++i) {
+    top_shape_[i] = top_blob_shape.dim(i);
+    if (top_shape_[i] == 0) {
+      copy_axes_.push_back(i);
+    } else if (top_shape_[i] == -1) {
+      CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple "
+          << "-1 dims; at most a single (1) value of -1 may be specified";
+      inferred_axis_ = i;
+    } else {
+      constant_count_ *= top_shape_[i];
+    }
   }
-
-  FillInSingleUnspecifiedDimension(bottom[0]->count());
 }
 
 template <typename Dtype>
 void ReshapeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  top[0]->Reshape(num_out, channels_out, height_out, width_out);
-
-  const size_t out_count = num_out * channels_out * height_out * width_out;
-  CHECK_EQ(out_count, bottom[0]->count()) <<
-      "Bottom layer count isn't equal to predicted; output layer size is " <<
-      num_out << "x" << channels_out << "x" << height_out << "x" << width_out;
-}
-
-template <typename Dtype>
-void ReshapeLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  top[0]->ShareData(*bottom[0]);
-}
-
-template <typename Dtype>
-void ReshapeLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  bottom[0]->ShareDiff(*top[0]);
-}
-
-/**
- * @brief Fill in a single dimension left unspecified.
- *
- * If a dimension is set to -1, it will be filled in with a value inferred from
- * the count of the bottom layer (if the product of the nonzero dimensions is a
- * divisor of the count).
- *
- * @param bottom_count Count of the bottom layer.
- */
-template <typename Dtype>
-void ReshapeLayer<Dtype>::FillInSingleUnspecifiedDimension(int bottom_count) {
-  int* const dimensions[] = {&num_out, &channels_out, &width_out, &height_out};
-  const size_t N_DIMENSIONS = 4;
-
-  // How many -1 dimensions do we have.
-  int n_unspecified = 0;
-  // Product of the remaining dimensions.
-  int product_without_unspecified_dim = 1;
-
-  for (size_t i = 0; i < N_DIMENSIONS; i++) {
-    if (*(dimensions[i]) == -1) {
-      n_unspecified++;
-    } else {
-      product_without_unspecified_dim *= *(dimensions[i]);
-    }
-  }
-
-  if (n_unspecified == 0) {
-    // Everything is filled out, nothing to do.
-    return;
+    const vector<Blob<Dtype>*>& top) {
+  for (int i = 0; i < copy_axes_.size(); ++i) {
+    const int copy_axis_index = copy_axes_[i];
+    CHECK_GT(bottom[0]->num_axes(), copy_axis_index) << "new shape contains "
+        << "a 0, but there is no corresponding bottom axis to copy";
+    top_shape_[copy_axis_index] = bottom[0]->shape(copy_axis_index);
   }
-
-  CHECK_EQ(n_unspecified, 1) << "Only one dimension can be set -1.";
-  CHECK_EQ(bottom_count % product_without_unspecified_dim, 0) <<
-    "Bottom layer count " << bottom_count << " not divisible by product " <<
-    product_without_unspecified_dim;
-
-  // Fill up the one remaining dimension.
-  for (size_t i = 0; i < N_DIMENSIONS; i++) {
-    if (*(dimensions[i]) == -1) {
-      *(dimensions[i]) = bottom_count / product_without_unspecified_dim;
+  if (inferred_axis_ >= 0) {
+    // A -1 dim was specified; infer the correct dimension by computing the
+    // product of the other dimensions.
+    int explicit_count = constant_count_;
+    for (int i = 0; i < copy_axes_.size(); ++i) {
+      const int copy_axis_index = copy_axes_[i];
+      explicit_count *= top_shape_[copy_axis_index];
     }
+    CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count ("
+        << bottom[0]->count() << ") must be divisible by the product of "
+        << "the specified dimensions (" << explicit_count << ")";
+    const int inferred_dim = bottom[0]->count() / explicit_count;
+    top_shape_[inferred_axis_] = inferred_dim;
   }
+  top[0]->Reshape(top_shape_);
+  CHECK_EQ(top[0]->count(), bottom[0]->count())
+      << "output count must match input count";
+  top[0]->ShareData(*bottom[0]);
+  top[0]->ShareDiff(*bottom[0]);
 }
 
-#ifdef CPU_ONLY
-STUB_GPU(ReshapeLayer);
-#endif
-
 INSTANTIATE_CLASS(ReshapeLayer);
-REGISTER_LAYER_CLASS(RESHAPE, ReshapeLayer);
+REGISTER_LAYER_CLASS(Reshape);
+
 }  // namespace caffe
diff --git a/src/caffe/layers/reshape_layer.cu b/src/caffe/layers/reshape_layer.cu
deleted file mode 100644
index 3023ce3ae88..00000000000
--- a/src/caffe/layers/reshape_layer.cu
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <vector>
-
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void ReshapeLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-  top[0]->ShareData(*bottom[0]);
-}
-
-template <typename Dtype>
-void ReshapeLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  bottom[0]->ShareDiff(*top[0]);
-}
-
-INSTANTIATE_LAYER_GPU_FUNCS(ReshapeLayer);
-
-}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e8bf240c1b3..d36f1d511df 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -693,15 +693,11 @@ message ReLUParameter {
 
 // Message that stores parameters used by ReshapeLayer
 message ReshapeParameter {
-  // Specify the output dimensions. If some of the following parameters are
-  // omitted or set to 0 explicitly, the corresponding dimension from the bottom
-  // layer is used (unchanged). Also, if exactly one of them is set to -1, its
-  // value is calculated from the count of the bottom layer and the remaining
-  // dimensions, if possible.
-  optional int32 num = 1 [default = 0];
-  optional int32 channels = 2 [default = 0];
-  optional int32 height = 3 [default = 0];
-  optional int32 width = 4 [default = 0];
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom layer and the remaining dimensions.
+  optional BlobShape shape = 1;
 }
 
 // Message that stores parameters used by SigmoidLayer
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
index 878d40bb4d5..0c8e2427aa7 100644
--- a/src/caffe/test/test_reshape_layer.cpp
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -41,11 +41,11 @@ TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices);
 TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReshapeParameter* reshape_param =
-      layer_param.mutable_reshape_param();
-  reshape_param->set_channels(-1);
-  reshape_param->set_height(1);
-  reshape_param->set_width(1);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(-1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
 
   ReshapeLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -58,11 +58,11 @@ TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) {
 TYPED_TEST(ReshapeLayerTest, TestFlattenValues) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReshapeParameter* reshape_param =
-      layer_param.mutable_reshape_param();
-  reshape_param->set_channels(-1);
-  reshape_param->set_height(1);
-  reshape_param->set_width(1);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(-1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
   ReshapeLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -79,12 +79,11 @@ TYPED_TEST(ReshapeLayerTest, TestFlattenValues) {
 TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReshapeParameter* reshape_param =
-      layer_param.mutable_reshape_param();
-  // Omitting num to test implicit zeroes.
-  reshape_param->set_channels(0);
-  reshape_param->set_height(0);
-  reshape_param->set_width(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
   ReshapeLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
 
@@ -99,12 +98,11 @@ TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) {
 TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ReshapeParameter* reshape_param =
-      layer_param.mutable_reshape_param();
-  // Since omitted, num is implicitly set to 0 (thus, copies 2).
-  reshape_param->set_channels(3);
-  reshape_param->set_height(10);
-  reshape_param->set_width(-1);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(3);
+  blob_shape->add_dim(10);
+  blob_shape->add_dim(-1);
 
   // Count is 180, thus height should be 180 / (2*3*10) = 3.
 

From 6b64f121c272b3d1464004554b9e6a9c4033a8f5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 26 Mar 2015 02:25:48 -0700
Subject: [PATCH 1328/2053] basic tests (Forward, Gradient) for ReshapeLayer

---
 src/caffe/test/test_reshape_layer.cpp | 57 +++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
index 0c8e2427aa7..8635792a66e 100644
--- a/src/caffe/test/test_reshape_layer.cpp
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -115,4 +115,61 @@ TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
+TYPED_TEST(ReshapeLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[i],
+              this->blob_bottom_->cpu_data()[i]);
+  }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForwardAfterReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // We know the above produced the correct result from TestForward.
+  // Reshape the bottom and call layer.Reshape, then try again.
+  vector<int> new_bottom_shape(1, 2 * 3 * 6 * 5);
+  this->blob_bottom_->Reshape(new_bottom_shape);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[i],
+              this->blob_bottom_->cpu_data()[i]);
+  }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 }  // namespace caffe

From 21032b2b0911cd4d907df46c114b8e96e55c2313 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 26 Mar 2015 01:13:18 -0700
Subject: [PATCH 1329/2053] Add ReshapeParameter axis and num_axes to reshape
 only a particular span of the input shape

---
 include/caffe/common_layers.hpp       |   2 -
 src/caffe/layers/reshape_layer.cpp    |  53 ++++++++++---
 src/caffe/proto/caffe.proto           |  58 +++++++++++++-
 src/caffe/test/test_reshape_layer.cpp | 105 ++++++++++++++++++++++++++
 4 files changed, 204 insertions(+), 14 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index ccdfd62d5be..8da6d68096b 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -327,8 +327,6 @@ class ReshapeLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
 
-  /// @brief the current output shape
-  vector<int> top_shape_;
   /// @brief vector of axes indices whose dimensions we'll copy from the bottom
   vector<int> copy_axes_;
   /// @brief the index of the axis whose dimension we infer, or -1 if none
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
index 618edf31824..ffe970f2689 100644
--- a/src/caffe/layers/reshape_layer.cpp
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -12,18 +12,17 @@ void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   copy_axes_.clear();
   const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
   const int top_num_axes = top_blob_shape.dim_size();
-  top_shape_.resize(top_num_axes);
   constant_count_ = 1;
   for (int i = 0; i < top_num_axes; ++i) {
-    top_shape_[i] = top_blob_shape.dim(i);
-    if (top_shape_[i] == 0) {
+    const int top_dim = top_blob_shape.dim(i);
+    if (top_dim == 0) {
       copy_axes_.push_back(i);
-    } else if (top_shape_[i] == -1) {
+    } else if (top_dim == -1) {
       CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple "
           << "-1 dims; at most a single (1) value of -1 may be specified";
       inferred_axis_ = i;
     } else {
-      constant_count_ *= top_shape_[i];
+      constant_count_ *= top_dim;
     }
   }
 }
@@ -31,27 +30,59 @@ void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ReshapeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  const int input_start_axis = this->layer_param_.reshape_param().axis();
+  const int start_axis = (input_start_axis >= 0) ? input_start_axis :
+      bottom[0]->num_axes() + input_start_axis + 1;
+  CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range";
+  CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis
+      << " out of range for " << bottom[0]->num_axes() << "-D input blob";
+  const int num_axes = this->layer_param_.reshape_param().num_axes();
+  CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all";
+  const int end_axis =
+      (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes);
+  CHECK_LE(end_axis, bottom[0]->num_axes())
+      << "end_axis = axis + num_axes is out of range";
+  const int num_axes_replaced = end_axis - start_axis;
+  const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced;
+  const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+  const int num_new_axes = top_blob_shape.dim_size();
+  vector<int> top_shape(num_axes_retained + num_new_axes);
+  int top_shape_index = 0;
+  for (int i = 0; i < start_axis; ++i) {
+    top_shape[top_shape_index++] = bottom[0]->shape(i);
+  }
+  for (int i = 0; i < num_new_axes; ++i) {
+    top_shape[top_shape_index++] = top_blob_shape.dim(i);
+  }
+  for (int i = end_axis; i < bottom[0]->num_axes(); ++i) {
+    top_shape[top_shape_index++] = bottom[0]->shape(i);
+  }
+  CHECK_EQ(top_shape_index, top_shape.size());
   for (int i = 0; i < copy_axes_.size(); ++i) {
     const int copy_axis_index = copy_axes_[i];
-    CHECK_GT(bottom[0]->num_axes(), copy_axis_index) << "new shape contains "
-        << "a 0, but there is no corresponding bottom axis to copy";
-    top_shape_[copy_axis_index] = bottom[0]->shape(copy_axis_index);
+    CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index)
+        << "new shape contains a 0, but there was no corresponding bottom axis "
+        << "to copy";
+    top_shape[start_axis + copy_axis_index] =
+        bottom[0]->shape(start_axis + copy_axis_index);
   }
   if (inferred_axis_ >= 0) {
     // A -1 dim was specified; infer the correct dimension by computing the
     // product of the other dimensions.
     int explicit_count = constant_count_;
+    explicit_count *= bottom[0]->count(0, start_axis);
+    explicit_count *= bottom[0]->count(end_axis);
     for (int i = 0; i < copy_axes_.size(); ++i) {
       const int copy_axis_index = copy_axes_[i];
-      explicit_count *= top_shape_[copy_axis_index];
+      explicit_count *= top_shape[start_axis + copy_axis_index];
     }
     CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count ("
         << bottom[0]->count() << ") must be divisible by the product of "
         << "the specified dimensions (" << explicit_count << ")";
     const int inferred_dim = bottom[0]->count() / explicit_count;
-    top_shape_[inferred_axis_] = inferred_dim;
+    top_shape[start_axis + inferred_axis_] = inferred_dim;
   }
-  top[0]->Reshape(top_shape_);
+  top[0]->Reshape(top_shape);
   CHECK_EQ(top[0]->count(), bottom[0]->count())
       << "output count must match input count";
   top[0]->ShareData(*bottom[0]);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d36f1d511df..d43e560a1fa 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -696,8 +696,64 @@ message ReshapeParameter {
   // Specify the output dimensions. If some of the dimensions are set to 0,
   // the corresponding dimension from the bottom layer is used (unchanged).
   // Exactly one dimension may be set to -1, in which case its value is
-  // inferred from the count of the bottom layer and the remaining dimensions.
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
+  //
   optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [default = 0];
+  optional int32 num_axes = 3 [default = -1];
 }
 
 // Message that stores parameters used by SigmoidLayer
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
index 8635792a66e..9d08ec60d4e 100644
--- a/src/caffe/test/test_reshape_layer.cpp
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -115,6 +115,111 @@ TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecifiedWithStartAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(1);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(3);
+  blob_shape->add_dim(10);
+  blob_shape->add_dim(-1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 4);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 10);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesStart) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(0);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 1);
+  EXPECT_EQ(this->blob_top_->shape(1), 1);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 2);
+  EXPECT_EQ(this->blob_top_->shape(4), 3);
+  EXPECT_EQ(this->blob_top_->shape(5), 6);
+  EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(2);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 1);
+  EXPECT_EQ(this->blob_top_->shape(4), 1);
+  EXPECT_EQ(this->blob_top_->shape(5), 6);
+  EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(-1);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 6);
+  EXPECT_EQ(this->blob_top_->shape(3), 5);
+  EXPECT_EQ(this->blob_top_->shape(4), 1);
+  EXPECT_EQ(this->blob_top_->shape(5), 1);
+  EXPECT_EQ(this->blob_top_->shape(6), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(1);
+  layer_param.mutable_reshape_param()->set_num_axes(2);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(-1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
 TYPED_TEST(ReshapeLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From cf0e6b7215b8c571b02bae84d7674d867156660f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 25 Mar 2015 18:45:29 -0700
Subject: [PATCH 1330/2053] Update docs for ND blobs (#1970) and layer type is
 a string (#1694)

---
 docs/tutorial/data.md            |  12 +--
 docs/tutorial/layers.md          | 160 +++++++++++++++----------------
 docs/tutorial/loss.md            |  20 ++--
 docs/tutorial/net_layer_blob.md  |  30 +++---
 examples/mnist/readme.md         |  50 +++++-----
 examples/siamese/readme.md       |  66 +++++++------
 matlab/caffe/hdf5creation/demo.m |   4 +-
 7 files changed, 174 insertions(+), 168 deletions(-)

diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index 40605f7cd73..3bf7d932eda 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -10,15 +10,15 @@ New input types are supported by developing a new data layer -- the rest of the
 
 This data layer definition
 
-    layers {
+    layer {
       name: "mnist"
-      # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
-      type: DATA
+      # Data layer loads leveldb or lmdb storage DBs for high-throughput.
+      type: "Data"
       # the 1st top is the data itself: the name is only convention
       top: "data"
       # the 2nd top is the ground truth: the name is only convention
       top: "label"
-      # the DATA layer configuration
+      # the Data layer configuration
       data_param {
         # path to the DB
         source: "examples/mnist/mnist_train_lmdb"
@@ -46,9 +46,9 @@ The (data, label) pairing is a convenience for classification models.
 
 **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
 
-    layers {
+    layer {
       name: "data"
-      type: DATA
+      type: "Data"
       [...]
       transform_param {
         scale: 0.1
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 839939f5ad6..74d236c1194 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -23,7 +23,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 
 #### Convolution
 
-* LayerType: `CONVOLUTION`
+* Layer type: `Convolution`
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Parameters (`ConvolutionParameter convolution_param`)
@@ -43,15 +43,15 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
     - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-      layers {
+      layer {
         name: "conv1"
-        type: CONVOLUTION
+        type: "Convolution"
         bottom: "data"
         top: "conv1"
-        blobs_lr: 1          # learning rate multiplier for the filters
-        blobs_lr: 2          # learning rate multiplier for the biases
-        weight_decay: 1      # weight decay multiplier for the filters
-        weight_decay: 0      # weight decay multiplier for the biases
+        # learning rate and decay multipliers for the filters
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
         convolution_param {
           num_output: 96     # learn 96 filters
           kernel_size: 11    # each filter is 11x11
@@ -67,11 +67,11 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
         }
       }
 
-The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 
 #### Pooling
 
-* LayerType: `POOLING`
+* Layer type: `Pooling`
 * CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
 * Parameters (`PoolingParameter pooling_param`)
@@ -87,9 +87,9 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
     - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-      layers {
+      layer {
         name: "pool1"
-        type: POOLING
+        type: "Pooling"
         bottom: "conv1"
         top: "pool1"
         pooling_param {
@@ -101,7 +101,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 
 #### Local Response Normalization (LRN)
 
-* LayerType: `LRN`
+* Layer type: `LRN`
 * CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
 * CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
 * Parameters (`LRNParameter lrn_param`)
@@ -115,7 +115,7 @@ The local response normalization layer performs a kind of "lateral inhibition" b
 
 #### im2col
 
-`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
+`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
 
 ### Loss Layers
 
@@ -123,19 +123,19 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 #### Softmax
 
-* LayerType: `SOFTMAX_LOSS`
+* Layer type: `SoftmaxWithLoss`
 
 The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
 
 #### Sum-of-Squares / Euclidean
 
-* LayerType: `EUCLIDEAN_LOSS`
+* Layer type: `EuclideanLoss`
 
 The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
 
 #### Hinge / Margin
 
-* LayerType: `HINGE_LOSS`
+* Layer type: `HingeLoss`
 * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
 * CUDA GPU implementation: none yet
 * Parameters (`HingeLossParameter hinge_loss_param`)
@@ -149,17 +149,17 @@ The Euclidean loss layer computes the sum of squares of differences of its two i
 * Samples
 
       # L1 Norm
-      layers {
+      layer {
         name: "loss"
-        type: HINGE_LOSS
+        type: "HingeLoss"
         bottom: "pred"
         bottom: "label"
       }
 
       # L2 Norm
-      layers {
+      layer {
         name: "loss"
-        type: HINGE_LOSS
+        type: "HingeLoss"
         bottom: "pred"
         bottom: "label"
         top: "loss"
@@ -172,15 +172,15 @@ The hinge loss layer computes a one-vs-all hinge or squared hinge loss.
 
 #### Sigmoid Cross-Entropy
 
-`SIGMOID_CROSS_ENTROPY_LOSS`
+`SigmoidCrossEntropyLoss`
 
 #### Infogain
 
-`INFOGAIN_LOSS`
+`InfogainLoss`
 
 #### Accuracy and Top-k
 
-`ACCURACY` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
 
 ### Activation / Neuron Layers
 
@@ -193,7 +193,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 
 #### ReLU / Rectified-Linear and Leaky-ReLU
 
-* LayerType: `RELU`
+* Layer type: `ReLU`
 * CPU implementation: `./src/caffe/layers/relu_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
 * Parameters (`ReLUParameter relu_param`)
@@ -201,66 +201,66 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
         - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
 * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
 
-      layers {
+      layer {
         name: "relu1"
-        type: RELU
+        type: "ReLU"
         bottom: "conv1"
         top: "conv1"
       }
 
-Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
 
 #### Sigmoid
 
-* LayerType: `SIGMOID`
+* Layer type: `Sigmoid`
 * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
 * Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
 
-      layers {
+      layer {
         name: "encode1neuron"
         bottom: "encode1"
         top: "encode1neuron"
-        type: SIGMOID
+        type: "Sigmoid"
       }
 
-The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
+The `Sigmoid` layer computes the output as sigmoid(x) for each input element x.
 
 #### TanH / Hyperbolic Tangent
 
-* LayerType: `TANH`
+* Layer type: `TanH`
 * CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: TANH
+        type: "TanH"
       }
 
-The `TANH` layer computes the output as tanh(x) for each input element x.
+The `TanH` layer computes the output as tanh(x) for each input element x.
 
 #### Absolute Value
 
-* LayerType: `ABSVAL`
+* Layer type: `AbsVal`
 * CPU implementation: `./src/caffe/layers/absval_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: ABSVAL
+        type: "AbsVal"
       }
 
-The `ABSVAL` layer computes the output as abs(x) for each input element x.
+The `AbsVal` layer computes the output as abs(x) for each input element x.
 
 #### Power
 
-* LayerType: `POWER`
+* Layer type: `Power`
 * CPU implementation: `./src/caffe/layers/power_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
 * Parameters (`PowerParameter power_param`)
@@ -270,11 +270,11 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         - `shift` [default 0]
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: POWER
+        type: "Power"
         power_param {
           power: 1
           scale: 1
@@ -282,16 +282,16 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         }
       }
 
-The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x.
+The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
 
 #### BNLL
 
-* LayerType: `BNLL`
+* Layer type: `BNLL`
 * CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
@@ -309,7 +309,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr
 
 #### Database
 
-* LayerType: `DATA`
+* Layer type: `Data`
 * Parameters
     - Required
         - `source`: the name of the directory containing the database
@@ -322,7 +322,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr
 
 #### In-Memory
 
-* LayerType: `MEMORY_DATA`
+* Layer type: `MemoryData`
 * Parameters
     - Required
         - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
@@ -331,7 +331,7 @@ The memory data layer reads data directly from memory, without copying it. In or
 
 #### HDF5 Input
 
-* LayerType: `HDF5_DATA`
+* Layer type: `HDF5Data`
 * Parameters
     - Required
         - `source`: the name of the file to read from
@@ -339,7 +339,7 @@ The memory data layer reads data directly from memory, without copying it. In or
 
 #### HDF5 Output
 
-* LayerType: `HDF5_OUTPUT`
+* Layer type: `HDF5Output`
 * Parameters
     - Required
         - `file_name`: name of file to write to
@@ -348,7 +348,7 @@ The HDF5 output layer performs the opposite function of the other layers in this
 
 #### Images
 
-* LayerType: `IMAGE_DATA`
+* Layer type: `ImageData`
 * Parameters
     - Required
         - `source`: name of a text file, with each line giving an image filename and label
@@ -360,17 +360,17 @@ The HDF5 output layer performs the opposite function of the other layers in this
 
 #### Windows
 
-`WINDOW_DATA`
+`WindowData`
 
 #### Dummy
 
-`DUMMY_DATA` is for development and debugging. See `DummyDataParameter`.
+`DummyData` is for development and debugging. See `DummyDataParameter`.
 
 ### Common Layers
 
 #### Inner Product
 
-* LayerType: `INNER_PRODUCT`
+* Layer type: `InnerProduct`
 * CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
 * Parameters (`InnerProductParameter inner_product_param`)
@@ -387,13 +387,13 @@ The HDF5 output layer performs the opposite function of the other layers in this
     - `n * c_o * 1 * 1`
 * Sample
 
-      layers {
+      layer {
         name: "fc8"
-        type: INNER_PRODUCT
-        blobs_lr: 1          # learning rate multiplier for the filters
-        blobs_lr: 2          # learning rate multiplier for the biases
-        weight_decay: 1      # weight decay multiplier for the filters
-        weight_decay: 0      # weight decay multiplier for the biases
+        type: "InnerProduct"
+        # learning rate and decay multipliers for the weights
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
         inner_product_param {
           num_output: 1000
           weight_filler {
@@ -409,79 +409,79 @@ The HDF5 output layer performs the opposite function of the other layers in this
         top: "fc8"
       }
 
-The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
+The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
 
 #### Splitting
 
-The `SPLIT` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
+The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
 
 #### Flattening
 
-The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`.
+The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`
 
 #### Concatenation
 
-* LayerType: `CONCAT`
+* Layer type: `Concat`
 * CPU implementation: `./src/caffe/layers/concat_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
 * Parameters (`ConcatParameter concat_param`)
     - Optional
-        - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels.
+        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
 * Input
     - `n_i * c_i * h * w` for each input blob i from 1 to K.
 * Output
-    - if `concat_dim = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
-    - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
 * Sample
 
-      layers {
+      layer {
         name: "concat"
         bottom: "in1"
         bottom: "in2"
         top: "out"
-        type: CONCAT
+        type: "Concat"
         concat_param {
-          concat_dim: 1
+          axis: 1
         }
       }
 
-The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only.
+The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
 
 #### Slicing
 
-The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
 
 * Sample
 
-      layers {
+      layer {
         name: "slicer_label"
-        type: SLICE
+        type: "Slice"
         bottom: "label"
         ## Example of label with a shape N x 3 x 1 x 1
         top: "label1"
         top: "label2"
         top: "label3"
         slice_param {
-            slice_dim: 1
-            slice_point: 1
-            slice_point: 2
+          axis: 1
+          slice_point: 1
+          slice_point: 2
         }
       }
 
-`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). 
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one). 
 
 
 #### Elementwise Operations
 
-`ELTWISE`
+`Eltwise`
 
 #### Argmax
 
-`ARGMAX`
+`ArgMax`
 
 #### Softmax
 
-`SOFTMAX`
+`Softmax`
 
 #### Mean-Variance Normalization
 
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index aac561774bb..d2d0e77fbed 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -10,30 +10,30 @@ Hence, the goal of learning is to find a setting of the weights that *minimizes*
 The loss in Caffe is computed by the Forward pass of the network.
 Each layer takes a set of input (`bottom`) blobs and produces a set of output (`top`) blobs.
 Some of these layers' outputs may be used in the loss function.
-A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example:
+A typical choice of loss function for one-versus-all classification tasks is the `SoftmaxWithLoss` function, used in a network definition as follows, for example:
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "pred"
       bottom: "label"
       top: "loss"
     }
 
-In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $$1 \times 1 \times 1 \times 1$$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
+In a `SoftmaxWithLoss` function, the `top` blob is a scalar (empty shape) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
 
 ### Loss weights
 
-For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SOFTMAX_LOSS` layer and reconstructs it using a `EUCLIDEAN_LOSS` layer), *loss weights* can be used to specify their relative importance.
+For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SoftmaxWithLoss` layer and reconstructs it using a `EuclideanLoss` layer), *loss weights* can be used to specify their relative importance.
 
-By convention, Caffe layer types with the suffix `_LOSS` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
+By convention, Caffe layer types with the suffix `Loss` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
 However, any layer can be used as a loss by adding a field `loss_weight: <float>` to a layer definition for each `top` blob produced by the layer.
-Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
-So, the above `SOFTMAX_LOSS` layer could be equivalently written as:
+Layers with the suffix `Loss` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
+So, the above `SoftmaxWithLoss` layer could be equivalently written as:
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "pred"
       bottom: "label"
       top: "loss"
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 1f0966f88a4..e8b7bd316a9 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -11,22 +11,20 @@ We will go over the details of these components in more detail.
 
 ## Blob storage and communication
 
-A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is a 4-dimensional array that stores things in the order of (Num, Channels, Height and Width), from major to minor, and stored in a C-contiguous fashion.  The main reason for putting Num (the name is due to legacy reasons, and is equivalent to the notation of "batch" as in minibatch SGD).
+A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion.
 
-Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
+Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization.
 
 Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
 
-The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest. For example, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
+The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
 
 - Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
 - Channel / K is the feature dimension e.g. for RGB images K = 3.
 
-Note that although we have designed blobs with its dimensions corresponding to image applications, they are named purely for notational purpose and it is totally valid for you to do non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use blobs of dimensions (Num, Channels, 1, 1) and call the InnerProductLayer (which we will cover soon).
+Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon).
 
-Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
-
-Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 1024.
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1000 x 1024.
 
 For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
 
@@ -95,9 +93,9 @@ A simple logistic regression classifier
 is defined by
 
     name: "LogReg"
-    layers {
+    layer {
       name: "mnist"
-      type: DATA
+      type: "Data"
       top: "data"
       top: "label"
       data_param {
@@ -105,18 +103,18 @@ is defined by
         batch_size: 64
       }
     }
-    layers {
+    layer {
       name: "ip"
-      type: INNER_PRODUCT
+      type: "InnerProduct"
       bottom: "data"
       top: "ip"
       inner_product_param {
         num_output: 2
       }
     }
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "ip"
       bottom: "label"
       top: "loss"
@@ -135,19 +133,19 @@ Model initialization is handled by `Net::Init()`. The initialization mainly does
     I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb
     I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28
     I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176)
-    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 1 1 1 (64)
+    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 (64)
     I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip
     I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data
     I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip
     I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip
-    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 1 1 (128)
+    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 (128)
     I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss
     I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip
     I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label
     I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss
     # set up the loss and configure the backward pass
     I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss
-    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: 1 1 1 1 (1)
+    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: (1)
     I0902 22:52:17.941329 2079114000 net.cpp:109]     with loss weight 1
     I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation.
     I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation.
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index ef7f5da67d5..269e53ab9b9 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -38,9 +38,9 @@ Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.
 
 Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:
 
-    layers {
+    layer {
       name: "mnist"
-      type: DATA
+      type: "Data"
       data_param {
         source: "mnist_train_lmdb"
         backend: LMDB
@@ -57,14 +57,14 @@ Specifically, this layer has name `mnist`, type `data`, and it reads the data fr
 
 Let's define the first convolution layer:
 
-    layers {
+    layer {
       name: "conv1"
-      type: CONVOLUTION
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "Convolution"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       convolution_param {
         num_output: 20
-        kernelsize: 5
+        kernel_size: 5
         stride: 1
         weight_filler {
           type: "xavier"
@@ -81,15 +81,15 @@ This layer takes the `data` blob (it is provided by the data layer), and produce
 
 The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.
 
-`blobs_lr` are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
+`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
 
 ### Writing the Pooling Layer
 
 Phew. Pooling layers are actually much easier to define:
 
-    layers {
+    layer {
       name: "pool1"
-      type: POOLING
+      type: "Pooling"
       pooling_param {
         kernel_size: 2
         stride: 2
@@ -107,11 +107,11 @@ Similarly, you can write up the second convolution and pooling layers. Check `$C
 
 Writing a fully connected layer is also simple:
 
-    layers {
+    layer {
       name: "ip1"
-      type: INNER_PRODUCT
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "InnerProduct"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       inner_product_param {
         num_output: 500
         weight_filler {
@@ -125,15 +125,15 @@ Writing a fully connected layer is also simple:
       top: "ip1"
     }
 
-This defines a fully connected layer (for some legacy reason, Caffe calls it an `innerproduct` layer) with 500 outputs. All other lines look familiar, right?
+This defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right?
 
 ### Writing the ReLU Layer
 
 A ReLU Layer is also simple:
 
-    layers {
+    layer {
       name: "relu1"
-      type: RELU
+      type: "ReLU"
       bottom: "ip1"
       top: "ip1"
     }
@@ -142,11 +142,11 @@ Since ReLU is an element-wise operation, we can do *in-place* operations to save
 
 After the ReLU layer, we will write another innerproduct layer:
 
-    layers {
+    layer {
       name: "ip2"
-      type: INNER_PRODUCT
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "InnerProduct"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       inner_product_param {
         num_output: 10
         weight_filler {
@@ -164,9 +164,9 @@ After the ReLU layer, we will write another innerproduct layer:
 
 Finally, we will write the loss!
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "ip2"
       bottom: "label"
     }
@@ -178,7 +178,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 Layer definitions can include rules for whether and when they are included in the network definition, like the one below:
 
-    layers {
+    layer {
       // ...layer definition...
       include: { phase: TRAIN }
     }
@@ -190,7 +190,7 @@ In the above example, this layer will be included only in `TRAIN` phase.
 If we change `TRAIN` with `TEST`, then this layer will be used only in test phase.
 By default, that is without layer rules, a layer is always included in the network.
 Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.
-Also, there is an `ACCURACY` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
+Also, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
 
 ## Define the MNIST Solver
 
diff --git a/examples/siamese/readme.md b/examples/siamese/readme.md
index ce98ec10819..83db8c94395 100644
--- a/examples/siamese/readme.md
+++ b/examples/siamese/readme.md
@@ -39,13 +39,19 @@ exactly the same as the [LeNet model](mnist.html), the only difference is that
 we have replaced the top layers that produced probabilities over the 10 digit
 classes with a linear "feature" layer that produces a 2 dimensional vector.
 
-    layers {
+    layer {
       name: "feat"
-      type: INNER_PRODUCT
+      type: "InnerProduct"
       bottom: "ip2"
       top: "feat"
-      blobs_lr: 1
-      blobs_lr: 2
+      param {
+        name: "feat_w"
+        lr_mult: 1
+      }
+      param {
+        name: "feat_b"
+        lr_mult: 2
+      }
       inner_product_param {
         num_output: 2
       }
@@ -64,17 +70,19 @@ earlier. Each entry in this database contains the image data for a pair of
 images (`pair_data`) and a binary label saying if they belong to the same class
 or different classes (`sim`).
 
-    layers {
+    layer {
       name: "pair_data"
-      type: DATA
+      type: "Data"
       top: "pair_data"
       top: "sim"
-      data_param {
-        source: "examples/siamese/mnist-siamese-train-leveldb"
+      include { phase: TRAIN }
+      transform_param {
         scale: 0.00390625
+      }
+      data_param {
+        source: "examples/siamese/mnist_siamese_train_leveldb"
         batch_size: 64
       }
-      include: { phase: TRAIN }
     }
 
 In order to pack a pair of images into the same blob in the database we pack one
@@ -83,16 +91,16 @@ so we add a slice layer after the data layer. This takes the `pair_data` and
 slices it along the channel dimension so that we have a single image in `data`
 and its paired image in `data_p.`
 
-    layers {
-        name: "slice_pair"
-        type: SLICE
-        bottom: "pair_data"
-        top: "data"
-        top: "data_p"
-        slice_param {
-            slice_dim: 1
-            slice_point: 1
-        }
+    layer {
+      name: "slice_pair"
+      type: "Slice"
+      bottom: "pair_data"
+      top: "data"
+      top: "data_p"
+      slice_param {
+        slice_dim: 1
+        slice_point: 1
+      }
     }
 
 ### Building the First Side of the Siamese Net
@@ -105,17 +113,17 @@ parameters allows Caffe to share the parameters between layers on both sides of
 the siamese net. In the definition this looks like:
 
     ...
-    param: "conv1_w"
-    param: "conv1_b"
+    param { name: "conv1_w" ...  }
+    param { name: "conv1_b" ...  }
     ...
-    param: "conv2_w"
-    param: "conv2_b"
+    param { name: "conv2_w" ...  }
+    param { name: "conv2_b" ...  }
     ...
-    param: "ip1_w"
-    param: "ip1_b"
+    param { name: "ip1_w" ...  }
+    param { name: "ip1_b" ...  }
     ...
-    param: "ip2_w"
-    param: "ip2_b"
+    param { name: "ip2_w" ...  }
+    param { name: "ip2_b" ...  }
     ...
 
 ### Building the Second Side of the Siamese Net
@@ -133,9 +141,9 @@ an Invariant Mapping". This loss function encourages matching pairs to be close
 together in feature space while pushing non-matching pairs apart. This cost
 function is implemented with the `CONTRASTIVE_LOSS` layer:
 
-    layers {
+    layer {
         name: "loss"
-        type: CONTRASTIVE_LOSS
+        type: "ContrastiveLoss"
         contrastive_loss_param {
             margin: 1.0
         }
diff --git a/matlab/caffe/hdf5creation/demo.m b/matlab/caffe/hdf5creation/demo.m
index f554b87e5f6..4f9f7b5a454 100644
--- a/matlab/caffe/hdf5creation/demo.m
+++ b/matlab/caffe/hdf5creation/demo.m
@@ -52,9 +52,9 @@
 fprintf('HDF5 filename listed in %s \n', 'list.txt');
 
 % NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: 
-% layers {
+% layer {
 %   name: "data"
-%   type: HDF5_DATA
+%   type: "HDF5Data"
 %   top: "data"
 %   top: "labelvec"
 %   hdf5_data_param {

From adb955f8b38f1379d3213deba57e5ea129cb1289 Mon Sep 17 00:00:00 2001
From: Ashwani001 <a.k.rai@live.com>
Date: Fri, 15 May 2015 10:33:36 +0800
Subject: [PATCH 1331/2053] Update generate_sample_data.py

made code more clearer and more concise
---
 src/caffe/test/test_data/generate_sample_data.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 3b49773c3fe..ab5572685cb 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -5,6 +5,8 @@
 import numpy as np
 import h5py
 
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
 num_cols = 8
 num_rows = 10
 height = 6
@@ -27,12 +29,12 @@
 print data
 print label
 
-with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data.h5', 'w') as f:
     f['data'] = data
     f['label'] = label
     f['label2'] = label2
 
-with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
     f.create_dataset(
         'data', data=data + total_size,
         compression='gzip', compression_opts=1
@@ -46,6 +48,6 @@
         compression='gzip', compression_opts=1
     )
 
-with open(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_list.txt', 'w') as f:
-    f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5\n')
-    f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5\n')
+with open(script_dir + '/sample_data_list.txt', 'w') as f:
+    f.write(script_dir + '/sample_data.h5\n')
+    f.write(script_dir + '/sample_data_2_gzip.h5\n')

From 41d13c08e883588badf9d00b60c0dc24c7905fd1 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 14 May 2015 22:14:13 -0700
Subject: [PATCH 1332/2053] [pycaffe] correct exceptions from Python; remove
 PyErr_Print

Previously, PyErr_Print was used to print Python exceptions. This has
the side effect of clearing the exception, which results in (an
additional) SystemError in the Python interpreter. Exception printing
from the caffe binary tool is re-added in a future commit.
---
 include/caffe/python_layer.hpp | 29 ++++-------------------------
 1 file changed, 4 insertions(+), 25 deletions(-)

diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
index 19cf18c9742..9c30250c1b5 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/python_layer.hpp
@@ -18,22 +18,11 @@ class PythonLayer : public Layer<Dtype> {
 
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    try {
-      self_.attr("setup")(bottom, top);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("setup")(bottom, top);
   }
-
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    try {
-      self_.attr("reshape")(bottom, top);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("reshape")(bottom, top);
   }
 
   virtual inline const char* type() const { return "Python"; }
@@ -41,21 +30,11 @@ class PythonLayer : public Layer<Dtype> {
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    try {
-      self_.attr("forward")(bottom, top);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("forward")(bottom, top);
   }
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    try {
-      self_.attr("backward")(top, propagate_down, bottom);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("backward")(top, propagate_down, bottom);
   }
 
  private:

From cebce771309b1c07dd0fb2eb18b9b37667d955dc Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 14 May 2015 22:17:19 -0700
Subject: [PATCH 1333/2053] print Python exceptions when using Python layer
 with the caffe tool

---
 tools/caffe.cpp | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 70b15f890f7..6967a46f758 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -8,6 +8,11 @@
 #include "boost/algorithm/string.hpp"
 #include "caffe/caffe.hpp"
 
+#ifdef WITH_PYTHON_LAYER
+#include "boost/python.hpp"
+namespace bp = boost::python;
+#endif
+
 using caffe::Blob;
 using caffe::Caffe;
 using caffe::Net;
@@ -304,7 +309,16 @@ int main(int argc, char** argv) {
   // Run tool or show usage.
   caffe::GlobalInit(&argc, &argv);
   if (argc == 2) {
-    return GetBrewFunction(caffe::string(argv[1]))();
+#ifdef WITH_PYTHON_LAYER
+    try {
+#endif
+      return GetBrewFunction(caffe::string(argv[1]))();
+#ifdef WITH_PYTHON_LAYER
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      return 1;
+    }
+#endif
   } else {
     gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
   }

From 977023f171da5ff04253d413f1d2440f22dd510b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 14 May 2015 22:20:58 -0700
Subject: [PATCH 1334/2053] [pytest] check that Python receives (correct)
 exceptions from Python layers

---
 python/caffe/test/test_python_layer.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index 6fba49143bb..46c6e885d53 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -21,6 +21,13 @@ def backward(self, top, propagate_down, bottom):
         bottom[0].diff[...] = 10 * top[0].diff
 
 
+class ExceptionLayer(caffe.Layer):
+    """A layer for checking exceptions from Python"""
+
+    def setup(self, bottom, top):
+        raise RuntimeError
+
+
 def python_net_file():
     with tempfile.NamedTemporaryFile(delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
@@ -34,6 +41,16 @@ def python_net_file():
         return f.name
 
 
+def exception_net_file():
+    with tempfile.NamedTemporaryFile(delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
+          python_param { module: 'test_python_layer' layer: 'ExceptionLayer' } }
+          """)
+        return f.name
+
+
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):
         net_file = python_net_file()
@@ -61,3 +78,8 @@ def test_reshape(self):
         for blob in self.net.blobs.itervalues():
             for d in blob.data.shape:
                 self.assertEqual(s, d)
+
+    def test_exception(self):
+        net_file = exception_net_file()
+        self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST)
+        os.remove(net_file)

From c7c4c648c48177171c358f8c8c805ebe7e7cea9b Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Fri, 15 May 2015 11:17:00 +0200
Subject: [PATCH 1335/2053] Added "propagate_down" param to LayerParameter

---
 include/caffe/net.hpp       |   3 +
 src/caffe/net.cpp           |  43 ++++++++++-
 src/caffe/proto/caffe.proto |   4 +
 src/caffe/test/test_net.cpp | 145 ++++++++++++++++++++++++++++++++++++
 4 files changed, 191 insertions(+), 4 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 075afebc9b0..5665df1edf2 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -137,6 +137,9 @@ class Net {
   inline const vector<Dtype>& blob_loss_weights() const {
     return blob_loss_weights_;
   }
+  inline const vector<bool>& layer_need_backward() const {
+    return layer_need_backward_;
+  }
   /// @brief returns the parameters
   inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
     return params_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index fd00b122630..482b7c5ac32 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -79,10 +79,17 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
     // Setup layer.
     const LayerParameter& layer_param = param.layer(layer_id);
+    if (layer_param.propagate_down_size() > 0) {
+      CHECK_EQ(layer_param.propagate_down_size(),
+          layer_param.bottom_size())
+          << "propagate_down param must be specified "
+          << "either 0 or bottom_size times ";
+    }
     layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
     bool need_backward = false;
+
     // Figure out this layer's input and output
     for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
          ++bottom_id) {
@@ -151,15 +158,33 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // Go through the net backwards to determine which blobs contribute to the
   // loss.  We can skip backward computation for blobs that don't contribute
   // to the loss.
+  // Also checks if all bottom blobs don't need backward computation (possible
+  // because the skip_propagate_down param) and so we can skip bacward
+  // computation for the entire layer
   set<string> blobs_under_loss;
+  set<string> blobs_skip_backp;
   for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
     bool layer_contributes_loss = false;
+    bool layer_skip_propagate_down = true;
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
       if (layers_[layer_id]->loss(top_id) ||
           (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
         layer_contributes_loss = true;
+      }
+      if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {
+        layer_skip_propagate_down = false;
+      }
+      if (layer_contributes_loss && !layer_skip_propagate_down)
         break;
+    }
+    // If this layer can skip backward computation, also all his bottom blobs
+    // don't need backpropagation
+    if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {
+      layer_need_backward_[layer_id] = false;
+      for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
+               ++bottom_id) {
+        bottom_need_backward_[layer_id][bottom_id] = false;
       }
     }
     if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
@@ -178,6 +203,11 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       } else {
         bottom_need_backward_[layer_id][bottom_id] = false;
       }
+      if (!bottom_need_backward_[layer_id][bottom_id]) {
+        const string& blob_name =
+                   blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+        blobs_skip_backp.insert(blob_name);
+      }
     }
   }
   // Handle force_backward if needed.
@@ -367,9 +397,9 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
 
 // Helper for Net::Init: add a new bottom blob to the net.
 template <typename Dtype>
-int Net<Dtype>::AppendBottom(const NetParameter& param,
-    const int layer_id, const int bottom_id,
-    set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
+int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
+    const int bottom_id, set<string>* available_blobs,
+    map<string, int>* blob_name_to_idx) {
   const LayerParameter& layer_param = param.layer(layer_id);
   const string& blob_name = layer_param.bottom(bottom_id);
   if (available_blobs->find(blob_name) == available_blobs->end()) {
@@ -381,7 +411,12 @@ int Net<Dtype>::AppendBottom(const NetParameter& param,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  const bool need_backward = blob_need_backward_[blob_id];
+  bool propagate_down = true;
+  // Check if the backpropagation on bottom_id should be skipped
+  if (layer_param.propagate_down_size() > 0)
+    propagate_down = layer_param.propagate_down(bottom_id);
+  const bool need_backward = blob_need_backward_[blob_id] &&
+                          propagate_down;
   bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index e523efa50f1..bc175753803 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -280,6 +280,10 @@ message LayerParameter {
 
   // The blobs containing the numeric parameters of the layer.
   repeated BlobProto blobs = 7;
+  
+  // Specifies on which bottoms the backpropagation should be skipped.
+  // The size must be either 0 or equal to the number of bottoms.
+  repeated bool propagate_down = 11;
 
   // Rules controlling whether and when a layer is included in the network,
   // based on the current NetState.  You may specify a non-zero number of rules
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 08106e79274..782a96bc9b6 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -613,6 +613,103 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitSkipPropNet(bool test_skip_true) {
+    string proto =
+      "name: 'SkipPropTestNetwork' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'DummyData' "
+      "  dummy_data_param { "
+      "    shape { "
+      "      dim: 5 "
+      "      dim: 2 "
+      "      dim: 3 "
+      "      dim: 4 "
+      "    } "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    shape { "
+      "      dim: 5 "
+      "    } "
+      "    data_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'silence' "
+      "  bottom: 'label' "
+      "  type: 'Silence' "
+      "} "
+      "layer { "
+      "  name: 'innerproduct' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'innerproduct' "
+      "} "
+      "layer { "
+      "  name: 'ip_fake_labels' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'fake_labels' "
+      "} "
+      "layer { "
+      "  name: 'argmax' "
+      "  bottom: 'fake_labels' "
+      "  top: 'label_argmax' "
+      "  type: 'ArgMax' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  bottom: 'innerproduct' "
+      "  bottom: 'label_argmax' ";
+    if (test_skip_true)
+      proto += "  propagate_down: [true, false] ";
+    else
+      proto += "  propagate_down: [true, true] ";
+    proto +=
+      "  top: 'cross_entropy_loss' "
+      "  type: 'SigmoidCrossEntropyLoss' "
+      "  loss_weight: 0.1 "
+      "} ";
+    InitNetFromProtoString(proto);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -2224,4 +2321,52 @@ TYPED_TEST(NetTest, TestReshape) {
   }
 }
 
+TYPED_TEST(NetTest, TestSkipPropagateDown) {
+  // check bottom_need_backward if propagate_down is true
+  this->InitSkipPropNet(false);
+  vector<bool> vec_layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    string layer_name = this->net_->layer_names()[layer_id];
+    if (layer_name == "loss") {
+      // access to bottom_need_backward coresponding to label's blob
+      bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+      // if propagate_down is true, the loss layer will try to
+      // backpropagate on labels
+      EXPECT_TRUE(need_back) << "bottom_need_backward should be True";
+    }
+    // layer_need_backward should be True except for data and silence layers
+    if (layer_name.find("data") != std::string::npos ||
+          layer_name == "silence") {
+      EXPECT_FALSE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be False";
+    } else {
+      EXPECT_TRUE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be True";
+    }
+  }
+  // check bottom_need_backward if propagat_down is false
+  this->InitSkipPropNet(true);
+  vec_layer_need_backward.clear();
+  vec_layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    string layer_name = this->net_->layer_names()[layer_id];
+    if (layer_name == "loss") {
+      // access to bottom_need_backward coresponding to label's blob
+      bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+      // if propagate_down is false, the loss layer will not try to
+      // backpropagate on labels
+      EXPECT_FALSE(need_back) << "bottom_need_backward should be False";
+    }
+    // layer_need_backward should be False except for innerproduct and
+    // loss layers
+    if (layer_name == "innerproduct" || layer_name == "loss") {
+      EXPECT_TRUE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be True";
+    } else {
+      EXPECT_FALSE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be False";
+    }
+  }
+}
+
 }  // namespace caffe

From b866d14ae86df3bb1548117f22818c2fefb5778b Mon Sep 17 00:00:00 2001
From: Dmytro Mishkin <ducha.aiki@gmail.com>
Date: Fri, 15 May 2015 16:14:02 +0300
Subject: [PATCH 1336/2053] Remove unnecessary variance computation from
 backward in MVN layer

---
 include/caffe/common_layers.hpp |  1 +
 src/caffe/layers/mvn_layer.cpp  | 23 ++---------------------
 src/caffe/layers/mvn_layer.cu   | 23 +----------------------
 src/caffe/proto/caffe.proto     |  3 +++
 4 files changed, 7 insertions(+), 43 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 8da6d68096b..e6b42c14587 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -295,6 +295,7 @@ class MVNLayer : public Layer<Dtype> {
 
   /// sum_multiplier is used to carry out sum using BLAS
   Blob<Dtype> sum_multiplier_;
+  Dtype eps_;
 };
 
 /*
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index b74d7b4f300..3e79bddcdde 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -22,6 +22,7 @@ void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       bottom[0]->height(), bottom[0]->width());
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
   caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
+  eps_ = this->layer_param_.mvn_param().eps();
 }
 
 template <typename Dtype>
@@ -36,7 +37,6 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     num = bottom[0]->num() * bottom[0]->channels();
 
   int dim = bottom[0]->count() / num;
-  Dtype eps = 1e-10;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
     // put the squares of bottom into temp_
@@ -66,7 +66,7 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
           variance_.mutable_cpu_data());
 
-    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+    caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
 
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
@@ -102,7 +102,6 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     num = bottom[0]->num() * bottom[0]->channels();
 
   int dim = bottom[0]->count() / num;
-  Dtype eps = 1e-10;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
     caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
@@ -125,24 +124,6 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     // put the squares of bottom into temp_
     caffe_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_cpu_data());
-
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0.,
-        variance_.mutable_cpu_data());  // E(X^2)
-    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-        temp_.mutable_cpu_data());  // (EX)^2
-    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-        variance_.mutable_cpu_data());  // variance
-
-    // normalize variance
-    caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
-          variance_.mutable_cpu_data());
-
-    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
-
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
         temp_.mutable_cpu_data());
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 0667f50380f..3888a0c7106 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -36,8 +36,6 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
         variance_.mutable_gpu_data());  // variance
 
-    Dtype eps = 1e-10;
-
     // do mean and variance normalization
     // subtract mean
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
@@ -50,7 +48,7 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
 
-    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+    caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
 
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
@@ -87,8 +85,6 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   int dim = bottom[0]->count() / num;
 
-  Dtype eps = 1e-10;
-
   if (this->layer_param_.mvn_param().normalize_variance()) {
     caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
@@ -111,23 +107,6 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0.,
-        variance_.mutable_gpu_data());  // E(X^2)
-    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-        temp_.mutable_gpu_data());  // (EX)^2
-    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-        variance_.mutable_gpu_data());  // variance
-
-    // normalize variance
-    caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
-          variance_.mutable_gpu_data());
-
-    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
         temp_.mutable_gpu_data());
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index d43e560a1fa..1923627fd97 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -629,6 +629,9 @@ message MVNParameter {
 
   // This parameter can be set to true to perform DNN-like MVN
   optional bool across_channels = 2 [default = false];
+
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [default = 1e-9];
 }
 
 // Message that stores parameters used by PoolingLayer

From 04cb5401c96a5cd28748ab1b119f3bb648a83403 Mon Sep 17 00:00:00 2001
From: Martin Thoma <info@martin-thoma.de>
Date: Fri, 15 May 2015 16:06:49 +0200
Subject: [PATCH 1337/2053] Python: Formatted docstrings to numpydoc (Take,
 Give -> Parameters, Returns)

---
 python/caffe/classifier.py | 37 ++++++++-------
 python/caffe/detector.py   | 16 +++----
 python/caffe/io.py         | 97 ++++++++++++++++++++++----------------
 3 files changed, 86 insertions(+), 64 deletions(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 7fb2ccc8ff3..537193db8f8 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -12,17 +12,17 @@ class Classifier(caffe.Net):
     """
     Classifier extends Net for image class prediction
     by scaling, center cropping, or oversampling.
+
+    Parameters
+    ----------
+    image_dims : dimensions to scale input for cropping/sampling.
+        Default is to scale to net input size for whole-image crop.
+    mean, input_scale, raw_scale, channel_swap: params for
+        preprocessing options.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
                  mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
-        """
-        Take
-        image_dims: dimensions to scale input for cropping/sampling.
-            Default is to scale to net input size for whole-image crop.
-            mean, input_scale, raw_scale, channel_swap: params for
-            preprocessing options.
-        """
         caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
         # configure pre-processing
@@ -48,19 +48,24 @@ def predict(self, inputs, oversample=True):
         """
         Predict classification probabilities of inputs.
 
-        Take
-        inputs: iterable of (H x W x K) input ndarrays.
-        oversample: average predictions across center, corners, and mirrors
-                    when True (default). Center-only prediction when False.
+        Parameters
+        ----------
+        inputs : iterable of (H x W x K) input ndarrays.
+        oversample : boolean
+            average predictions across center, corners, and mirrors
+            when True (default). Center-only prediction when False.
 
-        Give
-        predictions: (N x C) ndarray of class probabilities
-                     for N images and C classes.
+        Returns
+        -------
+        predictions: (N x C) ndarray of class probabilities for N images and C
+            classes.
         """
         # Scale to standardize input dimensions.
         input_ = np.zeros((len(inputs),
-            self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
-            dtype=np.float32)
+                           self.image_dims[0],
+                           self.image_dims[1],
+                           inputs[0].shape[2]),
+                          dtype=np.float32)
         for ix, in_ in enumerate(inputs):
             input_[ix] = caffe.io.resize_image(in_, self.image_dims)
 
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index f72b548ac9a..75cd3b1202f 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -23,18 +23,18 @@ class Detector(caffe.Net):
     """
     Detector extends Net for windowed detection by a list of crops or
     selective search proposals.
+
+    Parameters
+    ----------
+    mean, input_scale, raw_scale, channel_swap : params for preprocessing
+        options.
+    context_pad : amount of surrounding context to take s.t. a `context_pad`
+        sized border of pixels in the network input image is context, as in
+        R-CNN feature extraction.
     """
     def __init__(self, model_file, pretrained_file, mean=None,
                  input_scale=None, raw_scale=None, channel_swap=None,
                  context_pad=None):
-        """
-        Take
-            mean, input_scale, raw_scale, channel_swap: params for
-            preprocessing options.
-        context_pad: amount of surrounding context to take s.t. a `context_pad`
-            sized border of pixels in the network input image is context, as in
-            R-CNN feature extraction.
-        """
         caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
         # configure pre-processing
diff --git a/python/caffe/io.py b/python/caffe/io.py
index e5feff38796..fc96266085f 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -16,8 +16,9 @@
 
 ## proto / datum / ndarray conversion
 def blobproto_to_array(blob, return_diff=False):
-    """Convert a blob proto to an array. In default, we will just return the
-    data, unless return_diff is True, in which case we will return the diff.
+    """
+    Convert a blob proto to an array. In default, we will just return the data,
+    unless return_diff is True, in which case we will return the diff.
     """
     if return_diff:
         return np.array(blob.diff).reshape(
@@ -125,12 +126,14 @@ def preprocess(self, in_, data):
         - subtract mean
         - scale feature
 
-        Take
-        in_: name of input blob to preprocess for
-        data: (H' x W' x K) ndarray
+        Parameters
+        ----------
+        in_ : name of input blob to preprocess for
+        data : (H' x W' x K) ndarray
 
-        Give
-        caffe_in: (K x H x W) ndarray for input to a Net
+        Returns
+        -------
+        caffe_in : (K x H x W) ndarray for input to a Net
         """
         self.__check_input(in_)
         caffe_in = data.astype(np.float32, copy=False)
@@ -182,9 +185,10 @@ def set_transpose(self, in_, order):
         Set the input channel order for e.g. RGB to BGR conversion
         as needed for the reference ImageNet model.
 
-        Take
-        in_: which input to assign this channel order
-        order: the order to transpose the dimensions
+        Parameters
+        ----------
+        in_ : which input to assign this channel order
+        order : the order to transpose the dimensions
         """
         self.__check_input(in_)
         if len(order) != len(self.inputs[in_]) - 1:
@@ -198,9 +202,10 @@ def set_channel_swap(self, in_, order):
         as needed for the reference ImageNet model.
         N.B. this assumes the channels are the first dimension AFTER transpose.
 
-        Take
-        in_: which input to assign this channel order
-        order: the order to take the channels.
+        Parameters
+        ----------
+        in_ : which input to assign this channel order
+        order : the order to take the channels.
             (2,1,0) maps RGB to BGR for example.
         """
         self.__check_input(in_)
@@ -216,9 +221,10 @@ def set_raw_scale(self, in_, scale):
         like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
         of these models must be 255.
 
-        Take
-        in_: which input to assign this scale factor
-        scale: scale coefficient
+        Parameters
+        ----------
+        in_ : which input to assign this scale factor
+        scale : scale coefficient
         """
         self.__check_input(in_)
         self.raw_scale[in_] = scale
@@ -227,9 +233,10 @@ def set_mean(self, in_, mean):
         """
         Set the mean to subtract for centering the data.
 
-        Take
-        in_: which input to assign this mean.
-        mean: mean ndarray (input dimensional or broadcastable)
+        Parameters
+        ----------
+        in_ : which input to assign this mean.
+        mean : mean ndarray (input dimensional or broadcastable)
         """
         self.__check_input(in_)
         ms = mean.shape
@@ -254,9 +261,10 @@ def set_input_scale(self, in_, scale):
         N.B. input_scale is done AFTER mean subtraction and other preprocessing
         while raw_scale is done BEFORE.
 
-        Take
-        in_: which input to assign this scale factor
-        scale: scale coefficient
+        Parameters
+        ----------
+        in_ : which input to assign this scale factor
+        scale : scale coefficient
         """
         self.__check_input(in_)
         self.input_scale[in_] = scale
@@ -268,13 +276,16 @@ def load_image(filename, color=True):
     """
     Load an image converting from grayscale or alpha as needed.
 
-    Take
-    filename: string
-    color: flag for color format. True (default) loads as RGB while False
+    Parameters
+    ----------
+    filename : string
+    color : boolean
+        flag for color format. True (default) loads as RGB while False
         loads as intensity (if image is already grayscale).
 
-    Give
-    image: an image with type np.float32 in range [0, 1]
+    Returns
+    -------
+    image : an image with type np.float32 in range [0, 1]
         of size (H x W x 3) in RGB or
         of size (H x W x 1) in grayscale.
     """
@@ -292,24 +303,28 @@ def resize_image(im, new_dims, interp_order=1):
     """
     Resize an image array with interpolation.
 
-    Take
-    im: (H x W x K) ndarray
-    new_dims: (height, width) tuple of new dimensions.
-    interp_order: interpolation order, default is linear.
+    Parameters
+    ----------
+    im : (H x W x K) ndarray
+    new_dims : (height, width) tuple of new dimensions.
+    interp_order : interpolation order, default is linear.
 
-    Give
-    im: resized ndarray with shape (new_dims[0], new_dims[1], K)
+    Returns
+    -------
+    im : resized ndarray with shape (new_dims[0], new_dims[1], K)
     """
     if im.shape[-1] == 1 or im.shape[-1] == 3:
         im_min, im_max = im.min(), im.max()
         if im_max > im_min:
-            # skimage is fast but only understands {1,3} channel images in [0, 1].
+            # skimage is fast but only understands {1,3} channel images
+            # in [0, 1].
             im_std = (im - im_min) / (im_max - im_min)
             resized_std = resize(im_std, new_dims, order=interp_order)
             resized_im = resized_std * (im_max - im_min) + im_min
         else:
             # the image is a constant -- avoid divide by 0
-            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]), dtype=np.float32)
+            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
+                           dtype=np.float32)
             ret.fill(im_min)
             return ret
     else:
@@ -323,12 +338,14 @@ def oversample(images, crop_dims):
     """
     Crop images into the four corners, center, and their mirrored versions.
 
-    Take
-    image: iterable of (H x W x K) ndarrays
-    crop_dims: (height, width) tuple for the crops.
+    Parameters
+    ----------
+    image : iterable of (H x W x K) ndarrays
+    crop_dims : (height, width) tuple for the crops.
 
-    Give
-    crops: (10*N x H x W x K) ndarray of crops for number of inputs N.
+    Returns
+    -------
+    crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
     """
     # Dimensions and center.
     im_shape = np.array(images[0].shape)

From 749306eb6438b4a09089f785471d1d9cb00b6666 Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Fri, 15 May 2015 17:39:11 +0300
Subject: [PATCH 1338/2053] minor fix in cmake.config generation - do not force
 client libs to include numpy include dirs

---
 cmake/ConfigGen.cmake | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index c82047dcc5f..a9101e34350 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -11,6 +11,17 @@ function(caffe_get_current_includes includes_variable)
   list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
   list(REMOVE_AT current_includes ${__index})
 
+  # removing numpy includes (since not required for client libs)
+  set(__toremove "")
+  foreach(__i ${current_includes})
+    if(${__i} MATCHES "python")
+      list(APPEND __toremove ${__i})
+    endif()
+  endforeach()
+  if(__toremove)
+    list(REMOVE_ITEM current_includes ${__toremove})
+  endif()
+
   caffe_list_unique(current_includes)
   set(${includes_variable} ${current_includes} PARENT_SCOPE)
 endfunction()

From dbd83195206f02bf3996cc5d97894571e7c26756 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 18 May 2015 00:45:26 -0700
Subject: [PATCH 1339/2053] clean up redundant message comments

---
 src/caffe/proto/caffe.proto | 33 +++------------------------------
 1 file changed, 3 insertions(+), 30 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 580e36ed130..307015f42c9 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -368,7 +368,9 @@ message LossParameter {
   optional bool normalize = 2 [default = true];
 }
 
-// Message that stores parameters used by AccuracyLayer
+// Messages that store parameters used by individual layer types follow, in
+// alphabetical order.
+
 message AccuracyParameter {
   // When computing accuracy, count as correct by comparing the true label to
   // the top k scoring classes.  By default, only compare to the top scoring
@@ -386,14 +388,12 @@ message AccuracyParameter {
   optional int32 ignore_label = 3;
 }
 
-// Message that stores parameters used by ArgMaxLayer
 message ArgMaxParameter {
   // If true produce pairs (argmax, maxval)
   optional bool out_max_val = 1 [default = false];
   optional uint32 top_k = 2 [default = 1];
 }
 
-// Message that stores parameters used by ConcatLayer
 message ConcatParameter {
   // The axis along which to concatenate -- may be negative to index from the
   // end (e.g., -1 for the last axis).  Other axes must have the
@@ -405,7 +405,6 @@ message ConcatParameter {
   optional uint32 concat_dim = 1 [default = 1];
 }
 
-// Message that stores parameters used by ContrastiveLossLayer
 message ContrastiveLossParameter {
   // margin for dissimilar pair
   optional float margin = 1 [default = 1.0];
@@ -418,7 +417,6 @@ message ContrastiveLossParameter {
   optional bool legacy_version = 2 [default = false]; 
 }
 
-// Message that stores parameters used by ConvolutionLayer
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
@@ -444,7 +442,6 @@ message ConvolutionParameter {
   optional Engine engine = 15 [default = DEFAULT];
 }
 
-// Message that stores parameters used by DataLayer
 message DataParameter {
   enum DB {
     LEVELDB = 0;
@@ -475,12 +472,10 @@ message DataParameter {
   optional bool force_encoded_color = 9 [default = false];
 }
 
-// Message that stores parameters used by DropoutLayer
 message DropoutParameter {
   optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
 }
 
-// Message that stores parameters used by DummyDataLayer.
 // DummyDataLayer fills any number of arbitrarily shaped blobs with random
 // (or constant) data generated by "Fillers" (see "message FillerParameter").
 message DummyDataParameter {
@@ -500,7 +495,6 @@ message DummyDataParameter {
   repeated uint32 width = 5;
 }
 
-// Message that stores parameters used by EltwiseLayer
 message EltwiseParameter {
   enum EltwiseOp {
     PROD = 0;
@@ -515,7 +509,6 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
-// Message that stores parameters used by ExpLayer
 message ExpParameter {
   // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
   // Or if base is set to the default (-1), base is set to e,
@@ -525,7 +518,6 @@ message ExpParameter {
   optional float shift = 3 [default = 0.0];
 }
 
-// Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
   optional string source = 1;
@@ -540,7 +532,6 @@ message HDF5DataParameter {
   optional bool shuffle = 3 [default = false];
 }
 
-// Message that stores parameters used by HDF5OutputLayer
 message HDF5OutputParameter {
   optional string file_name = 1;
 }
@@ -554,7 +545,6 @@ message HingeLossParameter {
   optional Norm norm = 1 [default = L1];
 }
 
-// Message that stores parameters used by ImageDataLayer
 message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
@@ -586,13 +576,11 @@ message ImageDataParameter {
   optional string root_folder = 12 [default = ""];
 }
 
-// Message that stores parameters InfogainLossLayer
 message InfogainLossParameter {
   // Specify the infogain matrix source.
   optional string source = 1;
 }
 
-// Message that stores parameters used by InnerProductLayer
 message InnerProductParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
@@ -605,7 +593,6 @@ message InnerProductParameter {
   optional int32 axis = 5 [default = 1];
 }
 
-// Message that stores parameters used by LRNLayer
 message LRNParameter {
   optional uint32 local_size = 1 [default = 5];
   optional float alpha = 2 [default = 1.];
@@ -618,7 +605,6 @@ message LRNParameter {
   optional float k = 5 [default = 1.];
 }
 
-// Message that stores parameters used by MemoryDataLayer
 message MemoryDataParameter {
   optional uint32 batch_size = 1;
   optional uint32 channels = 2;
@@ -626,7 +612,6 @@ message MemoryDataParameter {
   optional uint32 width = 4;
 }
 
-// Message that stores parameters used by MVNLayer
 message MVNParameter {
   // This parameter can be set to false to normalize mean only
   optional bool normalize_variance = 1 [default = true];
@@ -638,7 +623,6 @@ message MVNParameter {
   optional float eps = 3 [default = 1e-9];
 }
 
-// Message that stores parameters used by PoolingLayer
 message PoolingParameter {
   enum PoolMethod {
     MAX = 0;
@@ -668,7 +652,6 @@ message PoolingParameter {
   optional bool global_pooling = 12 [default = false];
 }
 
-// Message that stores parameters used by PowerLayer
 message PowerParameter {
   // PowerLayer computes outputs y = (shift + scale * x) ^ power.
   optional float power = 1 [default = 1.0];
@@ -676,13 +659,11 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
-// Message that stores parameters used by PythonLayer
 message PythonParameter {
   optional string module = 1;
   optional string layer = 2;
 }
 
-// Message that stores parameters used by ReLULayer
 message ReLUParameter {
   // Allow non-zero slope for negative inputs to speed up optimization
   // Described in:
@@ -698,7 +679,6 @@ message ReLUParameter {
   optional Engine engine = 2 [default = DEFAULT];
 }
 
-// Message that stores parameters used by ReshapeLayer
 message ReshapeParameter {
   // Specify the output dimensions. If some of the dimensions are set to 0,
   // the corresponding dimension from the bottom layer is used (unchanged).
@@ -763,7 +743,6 @@ message ReshapeParameter {
   optional int32 num_axes = 3 [default = -1];
 }
 
-// Message that stores parameters used by SigmoidLayer
 message SigmoidParameter {
   enum Engine {
     DEFAULT = 0;
@@ -773,7 +752,6 @@ message SigmoidParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
-// Message that stores parameters used by SliceLayer
 message SliceParameter {
   // The axis along which to slice -- may be negative to index from the end
   // (e.g., -1 for the last axis).
@@ -800,7 +778,6 @@ message SoftmaxParameter {
   optional int32 axis = 2 [default = 1];
 }
 
-// Message that stores parameters used by TanHLayer
 message TanHParameter {
   enum Engine {
     DEFAULT = 0;
@@ -810,12 +787,10 @@ message TanHParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
-// Message that stores parameters used by ThresholdLayer
 message ThresholdParameter {
   optional float threshold = 1 [default = 0]; // Strictly positive values
 }
 
-// Message that stores parameters used by WindowDataLayer
 message WindowDataParameter {
   // Specify the data source.
   optional string source = 1;
@@ -849,7 +824,6 @@ message WindowDataParameter {
   optional string root_folder = 13 [default = ""];
 }
 
-// Message that stores parameters used by SPPLayer
 message SPPParameter {
   enum PoolMethod {
     MAX = 0;
@@ -1053,7 +1027,6 @@ message V0LayerParameter {
   optional HDF5OutputParameter hdf5_output_param = 1001;
 }
 
-// Message that stores parameters used by PReLULayer
 message PReLUParameter {
   // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
   // Surpassing Human-Level Performance on ImageNet Classification, 2015.

From 4ceefaaf7159110b6c815b7d50f64465787abf32 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Mon, 18 May 2015 20:27:19 +0800
Subject: [PATCH 1340/2053] fix blob_loss_weights index in test() in caffe.cpp

Correct the index for blob_loss_weights during output. Previously it was set to test_score index by mistake.
---
 tools/caffe.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 70b15f890f7..0b7523fccf9 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -187,8 +187,8 @@ int test() {
   for (int i = 0; i < test_score.size(); ++i) {
     const std::string& output_name = caffe_net.blob_names()[
         caffe_net.output_blob_indices()[test_score_output_id[i]]];
-    const float loss_weight =
-        caffe_net.blob_loss_weights()[caffe_net.output_blob_indices()[i]];
+    const float loss_weight = caffe_net.blob_loss_weights()[
+        caffe_net.output_blob_indices()[test_score_output_id[i]]];
     std::ostringstream loss_msg_stream;
     const float mean_score = test_score[i] / FLAGS_iterations;
     if (loss_weight) {

From 2fed2a998ae8c8894866c9adb4afccc046620c4d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sebasti=C3=A1n=20Ram=C3=ADrez?= <tiangolo@gmail.com>
Date: Mon, 18 May 2015 10:21:44 -0500
Subject: [PATCH 1341/2053] Update IPython Notebooks to version 4

---
 examples/classification.ipynb        |  3667 ++++++-
 examples/detection.ipynb             |  9253 +++++++++++++++--
 examples/filter_visualization.ipynb  | 13754 +++++++++++++++++++++++--
 examples/hdf5_classification.ipynb   |  7329 +++++++++++--
 examples/net_surgery.ipynb           |  7231 ++++++++++++-
 examples/siamese/mnist_siamese.ipynb |  2049 +++-
 6 files changed, 39807 insertions(+), 3476 deletions(-)

diff --git a/examples/classification.ipynb b/examples/classification.ipynb
index 0babf79f304..a76cfb10773 100644
--- a/examples/classification.ipynb
+++ b/examples/classification.ipynb
@@ -1,397 +1,3342 @@
 {
- "metadata": {
-  "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "example_name": "ImageNet classification",
-  "include_in_docs": true,
-  "priority": 1,
-  "signature": "sha256:a2b12abaa1eb252f436d59833c08ab97948c8a7a0513197f31afad0a0690e318"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Classifying ImageNet: the instant Caffe way\n",
-      "===========================================\n",
-      "\n",
-      "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n",
-      "\n",
-      "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n",
-      "\n",
-      "Ready? Let's start."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# Set the right path to your model definition file, pretrained model weights,\n",
-      "# and the image you would like to classify.\n",
-      "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
-      "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
-      "IMAGE_FILE = 'images/cat.jpg'\n",
-      "\n",
-      "import os\n",
-      "if not os.path.isfile(PRETRAINED):\n",
-      "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-      "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
-      "\n",
-      "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
-      "                       channel_swap=(2,1,0),\n",
-      "                       raw_scale=255,\n",
-      "                       image_dims=(256, 256))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's take a look at our example image with Caffe's image loading helper."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "input_image = caffe.io.load_image(IMAGE_FILE)\n",
-      "plt.imshow(input_image)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda517da210>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])\n",
-      "print 'predicted class:', prediction[0].argmax()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n",
-        "predicted class: 281\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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j8dTduHFj/HlFRYUqKiom/LpIjxEBcHkKhUIKhUKTsu+0QeByuRSJROLLkUhEbrd7TDse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\nBABgOYIAACxHEACA5QgCALDc/wN1ylfVql6MNwAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda20473950>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Classifying ImageNet: the instant Caffe way\n",
+    "===========================================\n",
+    "\n",
+    "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n",
+    "\n",
+    "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n",
+    "\n",
+    "Ready? Let's start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# Set the right path to your model definition file, pretrained model weights,\n",
+    "# and the image you would like to classify.\n",
+    "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
+    "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "IMAGE_FILE = 'images/cat.jpg'\n",
+    "\n",
+    "import os\n",
+    "if not os.path.isfile(PRETRAINED):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
+    "\n",
+    "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
+    "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
+    "                       channel_swap=(2,1,0),\n",
+    "                       raw_scale=255,\n",
+    "                       image_dims=(256, 256))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at our example image with Caffe's image loading helper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "source": [
-      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
-      "\n",
-      "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
-      "\n",
-      "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image], oversample=False)\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])\n",
-      "print 'predicted class:', prediction[0].argmax()"
-     ],
-     "language": "python",
+     "data": {
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+       "7LS/GUURvX6f6NYtWq3WzBRV6q5Vokh6JjqONdNfT6WAU8PTKIro9/tEcYLj13C8OkUm6UOyd2MS\n",
+       "BBMWOoukWYmqSgMM2duRqh4U0KtpoaEbNJsdRCmwXRttouPoJq12E0UTREmC6XoyKCrPMQ1JjZr6\n",
+       "F05lgZqqM+gPsG0bRdGwLCkzS9OEZrMJSFMNx5FXyKIyofU8f0byL4qSZq1ZDXFyTN2i0aiTFxmK\n",
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+       "cR1pPlsZiliGPCHrukmSZKiqShyHM5ux8XjMYDTAr8sQrJ0jqeMPwpAiz9nc3JTvhYporWoa44nU\n",
+       "Hi8sLlKW0nW/5rvYy4vcOtzn5g+HaIpNmsbYjoPrtXANB9/3ObW+ShyGqBVHWNc0dENGnwghUBUV\n",
+       "BBwc3KIscwbDIZbn8lOra6imilAhCodYliLzrqwGTmpRqiqCosqNl3ScXAgs30WUgjAI0SyDmlan\n",
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+       "vV6Phx9+mHPnzvHOd76Twcs4YE888QRnz57lwoUL/PVf//Ub8cg/MXZ2dvi5n/s5Ll26xH333cen\n",
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+       "nridj/C6Y3t7+xVF8/z58+LWrVtCCFlgzp8/L4QQ4vHHHxcf//jHZ1/3yCOPiG984xu392FfB/zC\n",
+       "L/yC+MpXvvKmWncQBOKBBx4Qzz777Jti3Ts7O+Khhx4SX/3qV8W73vUuIcSb631+W6/ne3t7bGxs\n",
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+       "edaieDOs+9d//df5vd/7vVeYi78Z1j3FbS2aP06H/mbBy+OAf9zn/7ViMpnwnve8hz/4gz+gVqu9\n",
+       "4nN36rqntom7u7t8/etf52tf+9orPn8nrvsLX/gCS0tLXL169cfKDO/Edb8ct7Vo/lPruJ2dnVfs\n",
+       "QncilpeXuXXrFiANUKZqg9dio/f/O7Is4z3veQ+PPfYYv/iLvwi8OdY9xdQ28Zlnnrnj1/3000/z\n",
+       "+c9/nq2tLd773vfy1a9+lccee+yOX/fLcVuL5gMPPMDzzz8/E/d/7nOf49FHH72dj3Db8eijj/Lp\n",
+       "T38agE9/+tOzovLoo4/y2c9+ljSVphfPP/88b33rW9/IR/0XQQjBr/zKr3Dx4kV+7dd+bfbxO33d\n",
+       "JycnswlxFEV85Stf4erVq3f8uh9//HF2dnbY3t7ms5/9LO94xzv4sz/7szt+3a/A7W6ifvGLXxTn\n",
+       "zp0TZ86cEY8//vjtfvnXFb/8y78sTp06JQzDEOvr6+JP//RPRbfbFQ899JA4e/asePjhh0W/3599\n",
+       "/e/8zu+IM2fOiPPnz4svf/nLb+CT/8vx1FNPCUVRxOXLl8WVK1fElStXxJe+9KU7ft3f+973xNWr\n",
+       "V8Xly5fF/fffL373d39XCCHu+HW/HE8++eRsev5mWvdcRjnHHHPM8RowVwTNMcccc7wGzIvmHHPM\n",
+       "McdrwLxozjHHHHO8BsyL5hxzzDHHa8C8aM4xxxxzvAbMi+Ycc8wxx2vAvGjOMcccc7wGzIvmHHPM\n",
+       "McdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda517da210>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n",
-        "predicted class: 281\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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iLd0g8Pv9sFqtsFgsMBqNqK6uRmtra1yZrKwsOJ1OGI3GKdelmccgICIt3SAIhULIycmJ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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_image = caffe.io.load_image(IMAGE_FILE)\n",
+    "plt.imshow(input_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prediction shape: (1000,)\n",
+      "predicted class: 281\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit net.predict([input_image])"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w02WCx/FPnOaOG1xRKmJJulNoCgnQ1q4pDMsyU1dK\n",
+       "B06ytDo7XRn0dnvawUF393bXvX92FrxZseM4t2Jv5rqcv3VL//DGuh7magczQBVyCgyO9UfLtWcI\n",
+       "1mWBrvxQS+Nzf9TGJIX0BykBnvdrJtN8v9/n+ebJY/L95Hm+3y86jDFGAABrXZXtBgAAsosgAADL\n",
+       "EQQAYDmCAAAsRxAAgOUIAgCw3KhBEAwG5fV6VVRUpIaGhhHbW1tbVVpaqrKyMt18883asWNHfFtB\n",
+       "QYFKSkpUVlamRYsWZbblAICMcKS7jyAWi2nevHlqb2+Xy+VSeXm5mpub5fP54mVOnz6tqVOnSpLe\n",
+       "ffddVVdXq7u7W5I0e/ZsvfPOO5o+ffokvw0AwESlHRGEw2F5PB4VFBTI6XSqtrZWra2tSWWGQ0CS\n",
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+       "BABgOYIAACxHEACA5QgCALDc/wN1ylfVql6MNwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda20473950>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 355 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
+    "print 'prediction shape:', prediction[0].shape\n",
+    "plt.plot(prediction[0])\n",
+    "print 'predicted class:', prediction[0].argmax()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
+    "\n",
+    "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
+    "\n",
+    "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n",
-      "\n",
-      "Let's time classifying a single image with input preprocessed:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prediction shape: (1000,)\n",
+      "predicted class: 281\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
-      "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
-      "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
-      "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
-      "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
-      "%timeit net.forward(data=caffe_input)"
-     ],
-     "language": "python",
+     "data": {
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+       "EBFJjkFARCQ5BgERkeQYBEREkmMQEBFJ7v8B+P2XH1cBu2AAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 210 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prediction = net.predict([input_image], oversample=False)\n",
+    "print 'prediction shape:', prediction[0].shape\n",
+    "plt.plot(prediction[0])\n",
+    "print 'predicted class:', prediction[0].argmax()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "OK, so how about GPU? it is actually pretty easy:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 355 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "caffe.set_mode_gpu()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
+    }
+   ],
+   "source": [
+    "%timeit net.predict([input_image])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n",
+    "\n",
+    "Let's time classifying a single image with input preprocessed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Voila! Now we are in GPU mode. Let's see if the code gives the same result:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 210 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image])\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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wjsdTd+PGjfHnFRUVqqiomPDrIj1GBMDlKRQKKRQKTcq+0waBy+VSJBKJL0ciEbnd7jHt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\nIAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda1ab53650>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
+    }
+   ],
+   "source": [
+    "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
+    "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
+    "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
+    "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
+    "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
+    "%timeit net.forward(data=caffe_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OK, so how about GPU? it is actually pretty easy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_mode_gpu()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voila! Now we are in GPU mode. Let's see if the code gives the same result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prediction shape: (1000,)\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Full pipeline timing.\n",
-      "%timeit net.predict([input_image])"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
+      ]
+     },
+     "execution_count": 9,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 174 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Forward pass timing.\n",
-      "%timeit net.forward(data=caffe_input)"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w1OWBx/HPOtk7O1hRImLYTSeQDewCSUzdwFDKTFoJ\n",
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+       "+5246aabdO2112rv3r0yxui5556L10krEyc4Jmr79u1mzpw5prCw0Dz00EPZbMqk27Vrl3E4HKa0\n",
+       "tNTcfPPN5uabbzavvvqqOXbsmLn11ltNUVGRqaysNCdOnIjX+d3vfmcKCwvN3LlzTTAYzGLrJ08o\n",
+       "FIpfNWRrXxw4cMD4/X5TUlJiqqurTX9/v7V90dDQYObNm2cWLFhg7rzzTjMwMGBNX9TW1pq8vDzj\n",
+       "dDqN2+02Tz755ITe+1tvvWUWLFhgCgsLzX333Tem1+aGMgCwHP+rSgCwHEEAAJYjCADAcgQBAFiO\n",
+       "IAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fda1ab53650>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 34.2 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prediction = net.predict([input_image])\n",
+    "print 'prediction shape:', prediction[0].shape\n",
+    "plt.plot(prediction[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
-      "\n",
-      "To fully utilize the power of GPUs, you really want to:\n",
-      "\n",
-      "* Use larger batches, and minimize python call and data transfer overheads.\n",
-      "* Pipeline data load operations, like using a subprocess.\n",
-      "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 174 ms per loop\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# Full pipeline timing.\n",
+    "%timeit net.predict([input_image])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Parting Words\n",
-      "-------------\n",
-      "\n",
-      "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 34.2 ms per loop\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Forward pass timing.\n",
+    "%timeit net.forward(data=caffe_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
+    "\n",
+    "To fully utilize the power of GPUs, you really want to:\n",
+    "\n",
+    "* Use larger batches, and minimize python call and data transfer overheads.\n",
+    "* Pipeline data load operations, like using a subprocess.\n",
+    "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parting Words\n",
+    "-------------\n",
+    "\n",
+    "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
+  "example_name": "ImageNet classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 1
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index e343feefd20..e2a981a00d7 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -1,933 +1,8392 @@
 {
- "metadata": {
-  "description": "Run a pretrained model as a detector in Python.",
-  "example_name": "R-CNN detection",
-  "include_in_docs": true,
-  "priority": 3,
-  "signature": "sha256:5d53dc49c9b6b93c1a2714c99043a763029ec98aebfb44acfa8d9e61781c9499"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
-      "\n",
-      "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
-      "\n",
-      "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
-      "\n",
-      "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
-      "\n",
-      "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
-      "\n",
-      "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
-      "\n",
-      "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n",
-      "\n",
-      "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!mkdir -p _temp\n",
-      "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"R-CNN-ilsvrc13\"\r\n",
-        "input: \"data\"\r\n",
-        "input_dim: 10\r\n",
-        "input_dim: 3\r\n",
-        "input_dim: 227\r\n",
-        "input_dim: 227\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv1\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"conv1\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 96\r\n",
-        "    kernel_size: 11\r\n",
-        "    stride: 4\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv1\"\r\n",
-        "  top: \"conv1\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"pool1\"\r\n",
-        "  type: \"Pooling\"\r\n",
-        "  bottom: \"conv1\"\r\n",
-        "  top: \"pool1\"\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"norm1\"\r\n",
-        "  type: \"LRN\"\r\n",
-        "  bottom: \"pool1\"\r\n",
-        "  top: \"norm1\"\r\n",
-        "  lrn_param {\r\n",
-        "    local_size: 5\r\n",
-        "    alpha: 0.0001\r\n",
-        "    beta: 0.75\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv2\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"norm1\"\r\n",
-        "  top: \"conv2\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 256\r\n",
-        "    pad: 2\r\n",
-        "    kernel_size: 5\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r",
-        "\r\n",
-        "  name: \"relu2\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv2\"\r\n",
-        "  top: \"conv2\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"pool2\"\r\n",
-        "  type: \"Pooling\"\r\n",
-        "  bottom: \"conv2\"\r\n",
-        "  top: \"pool2\"\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"norm2\"\r\n",
-        "  type: \"LRN\"\r\n",
-        "  bottom: \"pool2\"\r\n",
-        "  top: \"norm2\"\r\n",
-        "  lrn_param {\r\n",
-        "    local_size: 5\r\n",
-        "    alpha: 0.0001\r\n",
-        "    beta: 0.75\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv3\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"norm2\"\r\n",
-        "  top: \"conv3\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 384\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu3\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv3\"\r\n",
-        "  top: \"conv3\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv4\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"conv3\"\r\n",
-        "  top: \"conv4\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 384\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu4\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv4\"\r\n",
-        "  top: \"conv4\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv5\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"conv4\"\r\n",
-        "  top: \"conv5\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 256\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu5\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv5\"\r\n",
-        "  top: \"conv5\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"pool5\"\r\n",
-        "  type: \"Pooling\"\r\n",
-        "  bottom: \"conv5\"\r\n",
-        "  top: \"pool5\"\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc6\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"pool5\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 4096\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu6\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"drop6\"\r\n",
-        "  type: \"Dropout\"\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  dropout_param {\r\n",
-        "    dropout_ratio: 0.5\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc7\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 4096\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu7\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"drop7\"\r\n",
-        "  type: \"Dropout\"\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  dropout_param {\r\n",
-        "    dropout_ratio: 0.5\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc-rcnn\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc-rcnn\"\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 200\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\r\n",
-        "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\r\n",
-        "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\r\n",
-        "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\r\n",
-        "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\r\n",
-        "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\r\n",
-        "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
-        "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\r\n",
-        "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\r\n",
-        "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\r\n",
-        "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\r\n",
-        "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\r\n",
-        "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\r\n",
-        "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\r\n",
-        "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\r\n",
-        "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\r\n",
-        "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\r\n",
-        "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\r\n",
-        "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\r\n",
-        "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\r\n",
-        "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\r\n",
-        "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\r\n",
-        "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\r\n",
-        "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\r\n",
-        "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\r\n",
-        "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\r\n",
-        "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\r\n",
-        "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\r\n",
-        "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\r\n",
-        "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\r\n",
-        "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\r\n",
-        "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\r\n",
-        "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\r\n",
-        "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\r\n",
-        "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\r\n",
-        "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\r\n",
-        "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\r\n",
-        "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\r\n",
-        "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\r\n",
-        "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\r\n",
-        "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\r\n",
-        "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\r\n",
-        "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\r\n",
-        "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\r\n",
-        "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\r\n",
-        "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\r\n",
-        "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\r\n",
-        "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\r\n",
-        "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\r\n",
-        "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\r\n",
-        "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\r\n",
-        "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\r\n",
-        "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\r\n",
-        "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\r\n",
-        "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\r\n",
-        "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\r\n",
-        "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\r\n",
-        "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\r\n",
-        "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\r\n",
-        "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\r\n",
-        "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\r\n",
-        "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\r\n",
-        "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\r\n",
-        "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\r\n",
-        "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\r\n",
-        "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\r\n",
-        "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\r\n",
-        "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\r\n",
-        "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\r\n",
-        "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\r\n",
-        "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\r\n",
-        "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\r\n",
-        "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\r\n",
-        "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\r\n",
-        "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\r\n",
-        "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\r\n",
-        "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\r\n",
-        "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\r\n",
-        "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\r\n",
-        "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\r\n",
-        "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\r\n",
-        "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\r\n",
-        "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\r\n",
-        "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\r\n",
-        "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\r\n",
-        "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\r\n",
-        "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\r\n",
-        "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\r\n",
-        "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\r\n",
-        "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\r\n",
-        "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\r\n",
-        "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\r\n",
-        "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\r\n",
-        "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\r\n",
-        "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\r\n",
-        "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\r\n",
-        "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\r\n",
-        "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\r\n",
-        "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\r\n",
-        "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\r\n",
-        "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\r\n",
-        "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\r\n",
-        "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\r\n",
-        "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\r\n",
-        "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\r\n",
-        "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\r\n",
-        "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\r\n",
-        "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\r\n",
-        "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\r\n",
-        "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\r\n",
-        "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\r\n",
-        "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\r\n",
-        "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\r\n",
-        "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\r\n",
-        "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\r\n",
-        "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\r\n",
-        "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\r\n",
-        "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\r\n",
-        "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\r\n",
-        "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\r\n",
-        "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\r\n",
-        "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\r\n",
-        "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\r\n",
-        "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\r\n",
-        "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\r\n",
-        "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\r\n",
-        "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\r\n",
-        "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\r\n",
-        "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\r\n",
-        "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "GPU mode\r\n",
-        "Loading input...\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Processed 1570 windows in 102.895 s.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \r\n",
-        "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
-        "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
-        "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Saved to _temp/det_output.h5 in 0.298 s.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
-      "\n",
-      "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
-      "(We only ran on one image, so the filenames will all be the same.)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import pandas as pd\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
-      "print(df.shape)\n",
-      "print(df.iloc[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1570, 5)\n",
-        "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
-        "ymin                                                     79.846\n",
-        "xmin                                                       9.62\n",
-        "ymax                                                     246.31\n",
-        "xmax                                                    339.624\n",
-        "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
+    "\n",
+    "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
+    "\n",
+    "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
+    "\n",
+    "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\n",
+    "\n",
+    "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
+    "\n",
+    "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
+    "\n",
+    "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n",
+    "\n",
+    "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
-      "\n",
-      "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
-      "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before InitGoogleLogging() is written to STDERR\n",
+      "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"R-CNN-ilsvrc13\"\n",
+      "input: \"data\"\n",
+      "input_dim: 10\n",
+      "input_dim: 3\n",
+      "input_dim: 227\n",
+      "input_dim: 227\n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv1\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"conv1\"\n",
+      "  convolution_param {\n",
+      "    num_output: 96\n",
+      "    kernel_size: 11\n",
+      "    stride: 4\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv1\"\n",
+      "  top: \"conv1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool1\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv1\"\n",
+      "  top: \"pool1\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"norm1\"\n",
+      "  type: \"LRN\"\n",
+      "  bottom: \"pool1\"\n",
+      "  top: \"norm1\"\n",
+      "  lrn_param {\n",
+      "    local_size: 5\n",
+      "    alpha: 0.0001\n",
+      "    beta: 0.75\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv2\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"norm1\"\n",
+      "  top: \"conv2\"\n",
+      "  convolution_param {\n",
+      "    num_output: 256\n",
+      "    pad: 2\n",
+      "    kernel_size: 5\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu2\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv2\"\n",
+      "  top: \"conv2\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool2\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv2\"\n",
+      "  top: \"pool2\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"norm2\"\n",
+      "  type: \"LRN\"\n",
+      "  bottom: \"pool2\"\n",
+      "  top: \"norm2\"\n",
+      "  lrn_param {\n",
+      "    local_size: 5\n",
+      "    alpha: 0.0001\n",
+      "    beta: 0.75\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv3\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"norm2\"\n",
+      "  top: \"conv3\"\n",
+      "  convolution_param {\n",
+      "    num_output: 384\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu3\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv3\"\n",
+      "  top: \"conv3\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv4\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"conv3\"\n",
+      "  top: \"conv4\"\n",
+      "  convolution_param {\n",
+      "    num_output: 384\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu4\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv4\"\n",
+      "  top: \"conv4\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv5\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"conv4\"\n",
+      "  top: \"conv5\"\n",
+      "  convolution_param {\n",
+      "    num_output: 256\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu5\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv5\"\n",
+      "  top: \"conv5\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool5\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv5\"\n",
+      "  top: \"pool5\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc6\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"pool5\"\n",
+      "  top: \"fc6\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 4096\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu6\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc6\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"drop6\"\n",
+      "  type: \"Dropout\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc6\"\n",
+      "  dropout_param {\n",
+      "    dropout_ratio: 0.5\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc7\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc7\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 4096\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu7\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc7\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"drop7\"\n",
+      "  type: \"Dropout\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc7\"\n",
+      "  dropout_param {\n",
+      "    dropout_ratio: 0.5\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc-rcnn\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc-rcnn\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 200\n",
+      "  }\n",
+      "}\n",
+      "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\n",
+      "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\n",
+      "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\n",
+      "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\n",
+      "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\n",
+      "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\n",
+      "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+      "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\n",
+      "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\n",
+      "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\n",
+      "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\n",
+      "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+      "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\n",
+      "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\n",
+      "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\n",
+      "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\n",
+      "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\n",
+      "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+      "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\n",
+      "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\n",
+      "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\n",
+      "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\n",
+      "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\n",
+      "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+      "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\n",
+      "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\n",
+      "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\n",
+      "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\n",
+      "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\n",
+      "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+      "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\n",
+      "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\n",
+      "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\n",
+      "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\n",
+      "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\n",
+      "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+      "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\n",
+      "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\n",
+      "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\n",
+      "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\n",
+      "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\n",
+      "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\n",
+      "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\n",
+      "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\n",
+      "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\n",
+      "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\n",
+      "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\n",
+      "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\n",
+      "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\n",
+      "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\n",
+      "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\n",
+      "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\n",
+      "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\n",
+      "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\n",
+      "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\n",
+      "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\n",
+      "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\n",
+      "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\n",
+      "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\n",
+      "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\n",
+      "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\n",
+      "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\n",
+      "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\n",
+      "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\n",
+      "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\n",
+      "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\n",
+      "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\n",
+      "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\n",
+      "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\n",
+      "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\n",
+      "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\n",
+      "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\n",
+      "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\n",
+      "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\n",
+      "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\n",
+      "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\n",
+      "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\n",
+      "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\n",
+      "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\n",
+      "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\n",
+      "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\n",
+      "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\n",
+      "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\n",
+      "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\n",
+      "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\n",
+      "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\n",
+      "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\n",
+      "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\n",
+      "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\n",
+      "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\n",
+      "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\n",
+      "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\n",
+      "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\n",
+      "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\n",
+      "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\n",
+      "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\n",
+      "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\n",
+      "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\n",
+      "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\n",
+      "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\n",
+      "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\n",
+      "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\n",
+      "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\n",
+      "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\n",
+      "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\n",
+      "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\n",
+      "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\n",
+      "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\n",
+      "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\n",
+      "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\n",
+      "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\n",
+      "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\n",
+      "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\n",
+      "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\n",
+      "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\n",
+      "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\n",
+      "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\n",
+      "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\n",
+      "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\n",
+      "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\n",
+      "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\n",
+      "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\n",
+      "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\n",
+      "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\n",
+      "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\n",
+      "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\n",
+      "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\n",
+      "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\n",
+      "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\n",
+      "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\n",
+      "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\n",
+      "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\n",
+      "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\n",
+      "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\n",
+      "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\n",
+      "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\n",
+      "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\n",
+      "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\n",
+      "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\n",
+      "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\n",
+      "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\n",
+      "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
+      "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\n",
+      "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\n",
+      "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\n",
+      "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\n",
+      "GPU mode\n",
+      "Loading input...\n",
+      "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\n",
+      "Processed 1570 windows in 102.895 s.\n",
+      "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \n",
+      "your performance may suffer as PyTables will pickle object types that it cannot\n",
+      "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\n",
       "\n",
-      "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
-      "\n",
-      "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+      "  warnings.warn(ws, PerformanceWarning)\n",
+      "Saved to _temp/det_output.h5 in 0.298 s.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
-      "    labels_df = pd.DataFrame([\n",
-      "        {\n",
-      "            'synset_id': l.strip().split(' ')[0],\n",
-      "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
-      "        }\n",
-      "        for l in f.readlines()\n",
-      "    ])\n",
-      "labels_df.sort('synset_id')\n",
-      "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
-      "print(predictions_df.iloc[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "name\n",
-        "accordion      -2.622471\n",
-        "airplane       -2.845788\n",
-        "ant            -2.851219\n",
-        "antelope       -3.208377\n",
-        "apple          -1.949950\n",
-        "armadillo      -2.472935\n",
-        "artichoke      -2.201684\n",
-        "axe            -2.327404\n",
-        "baby bed       -2.737925\n",
-        "backpack       -2.176763\n",
-        "bagel          -2.681061\n",
-        "balance beam   -2.722538\n",
-        "banana         -2.390628\n",
-        "band aid       -1.598909\n",
-        "banjo          -2.298197\n",
-        "...\n",
-        "trombone        -2.582361\n",
-        "trumpet         -2.352853\n",
-        "turtle          -2.360859\n",
-        "tv or monitor   -2.761043\n",
-        "unicycle        -2.218467\n",
-        "vacuum          -1.907717\n",
-        "violin          -2.757079\n",
-        "volleyball      -2.723689\n",
-        "waffle iron     -2.418540\n",
-        "washer          -2.408994\n",
-        "water bottle    -2.174899\n",
-        "watercraft      -2.837425\n",
-        "whale           -3.120338\n",
-        "wine bottle     -2.772960\n",
-        "zebra           -2.742913\n",
-        "Name: 0, Length: 200, dtype: float32\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+    }
+   ],
+   "source": [
+    "!mkdir -p _temp\n",
+    "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
+    "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
+    "\n",
+    "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
+    "(We only ran on one image, so the filenames will all be the same.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's look at the activations."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1570, 5)\n",
+      "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
+      "ymin                                                     79.846\n",
+      "xmin                                                       9.62\n",
+      "ymax                                                     246.31\n",
+      "xmax                                                    339.624\n",
+      "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.gray()\n",
-      "plt.matshow(predictions_df.values)\n",
-      "plt.xlabel('Classes')\n",
-      "plt.ylabel('Windows')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "<matplotlib.text.Text at 0x114f15f90>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<matplotlib.figure.Figure at 0x114254b50>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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4HA4kEgncv39fMGZN05DJZOD1euFyuUTUFAqF0G634ff7YTKZ4PF44PV65X0F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V1fX1cul9PNzY22trZ0dnZmhgGqG77++muzMaH7WFm5c5Dxc6RSKYMbh8OhhTs6c6pJ0/+t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\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zj9o3yJlgigksRhlDLSvJO+twOLRhIT8b2RcRx/A0JJmOiWK90+mYlonMC3Sm0+lYiAAa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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x114254ad0>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
+    "print(df.shape)\n",
+    "print(df.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
+    "\n",
+    "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
+    "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+    "\n",
+    "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
+    "\n",
+    "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's take max across all windows and plot the top classes."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "accordion      -2.622471\n",
+      "airplane       -2.845788\n",
+      "ant            -2.851219\n",
+      "antelope       -3.208377\n",
+      "apple          -1.949950\n",
+      "armadillo      -2.472935\n",
+      "artichoke      -2.201684\n",
+      "axe            -2.327404\n",
+      "baby bed       -2.737925\n",
+      "backpack       -2.176763\n",
+      "bagel          -2.681061\n",
+      "balance beam   -2.722538\n",
+      "banana         -2.390628\n",
+      "band aid       -1.598909\n",
+      "banjo          -2.298197\n",
+      "...\n",
+      "trombone        -2.582361\n",
+      "trumpet         -2.352853\n",
+      "turtle          -2.360859\n",
+      "tv or monitor   -2.761043\n",
+      "unicycle        -2.218467\n",
+      "vacuum          -1.907717\n",
+      "violin          -2.757079\n",
+      "volleyball      -2.723689\n",
+      "waffle iron     -2.418540\n",
+      "washer          -2.408994\n",
+      "water bottle    -2.174899\n",
+      "watercraft      -2.837425\n",
+      "whale           -3.120338\n",
+      "wine bottle     -2.772960\n",
+      "zebra           -2.742913\n",
+      "Name: 0, Length: 200, dtype: float32\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
+    "    labels_df = pd.DataFrame([\n",
+    "        {\n",
+    "            'synset_id': l.strip().split(' ')[0],\n",
+    "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
+    "        }\n",
+    "        for l in f.readlines()\n",
+    "    ])\n",
+    "labels_df.sort('synset_id')\n",
+    "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
+    "print(predictions_df.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the activations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "max_s = predictions_df.max(0)\n",
-      "max_s.sort(ascending=False)\n",
-      "print(max_s[:10])"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x114f15f90>"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "name\n",
-        "person          1.835771\n",
-        "bicycle         0.866110\n",
-        "unicycle        0.057080\n",
-        "motorcycle     -0.006122\n",
-        "banjo          -0.028209\n",
-        "turtle         -0.189831\n",
-        "electric fan   -0.206788\n",
-        "cart           -0.214235\n",
-        "lizard         -0.393519\n",
-        "helmet         -0.477942\n",
-        "dtype: float32\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114254b50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The top detections are in fact a person and bicycle.\n",
-      "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Find, print, and display the top detections: person and bicycle.\n",
-      "i = predictions_df['person'].argmax()\n",
-      "j = predictions_df['bicycle'].argmax()\n",
-      "\n",
-      "# Show top predictions for top detection.\n",
-      "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
-      "print('Top detection:')\n",
-      "print(f.order(ascending=False)[:5])\n",
-      "print('')\n",
-      "\n",
-      "# Show top predictions for second-best detection.\n",
-      "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
-      "print('Second-best detection:')\n",
-      "print(f.order(ascending=False)[:5])\n",
-      "\n",
-      "# Show top detection in red, second-best top detection in blue.\n",
-      "im = plt.imread('images/fish-bike.jpg')\n",
-      "plt.imshow(im)\n",
-      "currentAxis = plt.gca()\n",
-      "\n",
-      "det = df.iloc[i]\n",
-      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
-      "\n",
-      "det = df.iloc[j]\n",
-      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
-     ],
-     "language": "python",
+     "data": {
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+       "fUvSpCS5rvt/JH1PUkXSHziO8zdc1+1Iel/S/5L09yT9viRHUsd13W94/nly8zX+vqR/LGlW0seO\n",
+       "4yR+2T/gb8p62Mxf8XJd939ImnQc5+/wMcdxnul6c7Eikg5u/vtvShq7+bw5SQ3XdX9f15v2Q8dx\n",
+       "kpLGXNf9Q12XEN9wXbcnadNxnL988/ecm+8hx3Eeua77M9d1/6mkhqTiV/jj/lrXw2b+1ay/JOl3\n",
+       "bqC5LyT9M0lVfVmC/K6kv+U4znNJy5II9/htSc8dx/lE0l+V9C8lFST9T8dxPpX07yX9o5vP/YGk\n",
+       "v33zNb6Q9BdvPv4vbhrFVUk/cV3386/yB/11rgduxsMamfVwMj+skVkPm/lhjcx62MwPa2TWw2Z+\n",
+       "WCOzHjbzwxqZ9bCZH9bIrIfN/LBGZj1s5oc1Muv/AuHZAPr9VeA9AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114254ad0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Top detection:\n",
-        "name\n",
-        "person             1.835771\n",
-        "swimming trunks   -1.150371\n",
-        "rubber eraser     -1.231106\n",
-        "turtle            -1.266037\n",
-        "plastic bag       -1.303265\n",
-        "dtype: float32\n",
-        "\n",
-        "Second-best detection:\n",
-        "name\n",
-        "bicycle     0.866110\n",
-        "unicycle   -0.359139\n",
-        "scorpion   -0.811621\n",
-        "lobster    -0.982891\n",
-        "lamp       -1.096808\n",
-        "dtype: float32\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "<matplotlib.patches.Rectangle at 0x118576a90>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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CKjRVWzPaXKeal4TgOZuNMVKh8oy93V3Qmv76CKUUa6M1nh8dMlpfp5cXjCdj\ntnZ3aVuLtxaVZ+xeuUTQirt3P2Nja4fPvvicwhgePXqIA9752tfZXOvx5rvv84ff/DaZ6UcHv7nO\n2f4Br1+6jBEaF2C0sUk7rVFeMOhvMK7HPD854ObNy5i1LZwNeG2YBoe1AaVzJALpPdujEf1BjlY5\ngoB3Fmdr2oRQREqQKSmRRiNE4NnsiFy1VB7GYRMz8PjWRUnpYJNsZ++l86joGVI1NJnWSCHo5QW5\n0gTrmFSTyDtqtQBu1jXMqxlCKqwLCC2oXcWsPMP5jJAZYgweUD3F6fiYzZ03ODoqKQY9pmVJL1NI\nIXBJ76+1QatY7YuIfTRU0q+HxKMLrSP69J6mboDYm2Y6HYMQ+GCRQaCANlha5whCkBUGKUlONzpe\nJwR53uPu3fu8/eZNgitQSjKbzchMwfbODltbe9y994BBf42333qXn/iJn0RJzXe/+12GQ4OUsYLS\nOrC2WaLxlCf6KvPWLtbOkraAtl3sTwuKIeZGujL1QNHp/ZNOOrILSz48/XI8zkv6N8Vq+aXji0g6\nJiy7dd6h447iCF1UHjzCxRYCoTsHUf8tCCgpojpm5RghxCS97IqXVGrJEJb69uDj2rd0uZeYMI1P\ncjke5xQ1K45/1YkLqRdOu5Pa/jB7dSoUrfHOMRwMcM7R7xXkeY5rXdR81zVKa1rvuHr1KltbWwyH\nQ3ywFNl7vPXWG2xsbPL+7ds4WzE+O2Jvb4/9/X3m8znDXp+qqhLVEW9TJnmhEF11YvdQVKJuOq00\nKKmXIn2XUE/y9D4lO2JSNGlZE8r3siuJj4tNSon1kYO31pLlMaHpgc2dbcZnZ9jhgE8//ZQiz2lD\nw6XXLuFay6wu2bm0x+NHjymrijfefAul4nTPewUB2NrdpWpbTudzPrv7BTt7e/RGI3YuX0UKxddv\nv8vZyQnPjw54/vQhxwcH7F25Qts0tE3AhcAXTx8hyzlrTYmqZoy213h+esz0ZMrl0SZaSz782jfg\nY8NaL5CLjLYRhKJHU5Zk2mBcoHAOX5WgQGeGoPIYxViB0xCCo2lqgoghaOMtobVxs8x7VN7hB2vc\neTalmR9SeEvRNgx7BcG9nAu0KpY+ewFIhZdgdSAIh8dhjFoUZbRtG1GW1pg8i/rzEBdlJjQ3b9yi\nths82p8QvMMUhrPJmM0Cpk3LR5/dIe+9jsiLhORCLJpKTU5ig7BODeFoXUMgoGREs0apWBQVYisw\npTS7uzuyo8crAAAgAElEQVT87M/+De7c+ZymqanGY6RMtQjexerAhPAjcPALNU0zmzEYDnm2/zwm\nMQc91tfWuHrtGscnpxyfnHH92ut8cucOt29/wKXLrzGdTNjY2EhghYWUrWtApZRC6ajeqqv6pWOO\n8AsJHimRuXDoyRkrmdQhzp9zXLOqjHUGWkeViXMgup4/sWpTahXX6ZcceFjozBErraHCl5N9AnBd\nP5YUeccNR5xrRNb9HV8kFvMnofsdEcGHkjIm3b1LDlzERHbnoGXsHSPFisLJ+3PovDukThuY8B7P\nebQdUmS1SPCG87TQy+yVOXCTZXGStw1CQJEbBLE3B0GiJQTfYvI8FpBohfOWTCmGwyGbm5vYpuWd\nd95iPp9jjAEzYmNjI950QitZltE0CV2ElRArLMMbt5Kw6egW75ca09isRi4Qh9KSTEmsDWgjFlxl\nCAGbuPdoAusdxitG/T6tc2ysr6O0SqhNR822ybh5/Tp5nnN8dETbxIZXbdPSNA11VZNlObfefIv9\n/X0+v/MZV69do6oqTK9AGM3mziaXr73GpctX6PV7TI6PQGX8m9/9fX7y6++zNlyjcrEAYzqesLW5\nyXTWIJRkZ2+X7z75Nq/dusHW61f43kefMJnXvPPubeoA+8+fMLFTnj19RHZlyLwVZPmI1nkOjo64\nfPU6g6Kg7xwYhcxi2OmauDF6ZXCuiWXKSExmQHYZ/yivPLOeor/O2GU4J6iDwDUO1wJCUD2fvnQe\nlW1FY1tkZpITl9TeRVlca8G5hLTiIlU6i3MlfoKSAtu2qTHTJpOZw7sWIWp8cOQmw8sBT56XzBqJ\n0y3GwNHJDIFEG0NmCqyrmc3nqXVDTGTppMwR3uOtX3DbSkUZpvWOum34xje+QVEUPHnyBL+5jfeO\n2jscscVAlG8kpBeg9Z7GOfIs59qVK4yGfba3t8BZxmenfPfjjxlPpmysb/L8+Ii333mPrCioa0uv\nP0jAIqJKRNRPF5nBdsUpUhBw6Ozlm2bHLRNCqhZ0aQ0sUXDk1bsiGFJiV4CRtM7GjptK4v1yI4ib\nAAgVnb9K7iny2/G/VOKoFwx2OqURL+jDE98c13pY5EGFYFEB2c2L+H+XPhNprkR/oUXsX0RSJvmQ\nIgff9eNc+gmVNlaR1DNpkFIydakLDyE68i7qX+reI82n5FKO+qfZK3PgPrjUWXQpl1FKoLWgaerF\ng2pSJlrpOGht2tXauiHgmM2nUVsbWly7bAsp09+dEiUWlCx3Nr6isu8cL5YG1yLAdZ3a6Gi32LNB\nLDupxXqv+F0pZayQ1FF+5kPAGEVIDaU8Ub412N1baKqFEAx7/QVPX1c1UkquXL5MAIbDEcO1ERtb\nG4xGIx48fIjWGus9W0IwnpyhMgNESuPk9Akm63H33kO2tre58cZb7F67yXc+/gyC5Cc+/DqZyTk8\n3ifvFxzNxjx4+IC+lDgP+48fUfmWYBsefv4Rm0XOd55+yiAb8td/5t/le3fu0grBwfN9Lt16h2ef\nfcbRs0dcunqJeTNng3W2t7co8oKsP6AsZ/Q2thitjZA6ltOfHJ8ynk7ZdZpiMOS3/+QT7JmAXGFw\nvHPtBlvbe3xy/+GXHxaQpZUZrCckRCdlTls7XOtQSPqDPhsbG6l5UpxXDx7cZz6bQwgURcH45Iz9\n45raarwrMXnDbHbGzqW3cGXg6X6FKXZiQljOKHp9pIwtBuZtTfAOoaBs5jRNg8nMIok+yDKCc5gs\nQ0pNWVXUbYNzniLvRRlqnscNWRk8JJle7EfTlZF7HykVlMYCo/U1JtMx4+kZeS+jV+Q83n8ae91I\nwWB9jf1nB/zD/+g/ZlbVqMmUzY21CDKI3LDEpcZKDimXKBVsSmh+2VxbEylORVggxhiNLOlHsUja\ndXy7FAKpRezroyQKEztCduqQAF4KCA48uCTci1LOqCLxbZ1o0LgWpRA479HCrHDKIRaPSZk206WC\nJXZmXKUz0loWJGoGFjw8JAosOnmRJLAiBJROrSW7XSdAcA5EbC+w4iaA2JJLSIlWkXprIOnS/VIr\nH0c1nj/RNP5PceKvzIEvvWH6V3A433XpWyYMpOh29vM9lyHE/thLhdG50GP1vjsqo+O3hBCL0tnu\n97r/XqALlg5cKXPuYZxz8mnCL4Ik78Cn1qcr31epiY8QYlFeHDn5yLMrGaVTXVRmrWI4GEY54uZm\nTPRIyc7uDm/zNt473nr7LcqyQiZK4Ps/+Ji816epGh7Xj7j9wfvMTk9iEbFWND6wvrnB9vYWs7Jm\nfHJEYTK28gJfNQzzjCwElHVsbG5QSUGjFW/ceIePfn/CdP85Nq+ZHY15cPdzjJQE23J6+JxHmeaj\n7/4xG4OCjz4+4tH+I/7WX/33GPo2th9VPWrb4kvPrCoZDodsbG/RH62RDwb01YjD41M+/87HNOtX\nMZsZWWjZGwx5/bUbfPtPPnvpLPrwrXfYP3jOtGmY1TW66HN4dEq/GKCF4Xg6Ybixw87eNSaTCUII\nXr/xOru7lzk7OeLOZ58xPjlGCcHl3R1msxZdDAh6TL/3Gn2zi/TblOUpShoyM8Cplmoe56bSkqAC\nrasxucYohWwiH25UhjES6QFU7HWTK+o2Fl4pHamwyWTCr/3GrwMwPZujsgznY2SaSYlsLet5j6as\nyIZD5iJg1kbs7z9lrdcnBM+9L75ga2czOjkBvUE/VkkGywdf+4DnByfUVnB8fEwIsd1rwDIajXDO\nUrctKjU0szaCKx/al465SAlWUtK2dU0M9zGpI6ZO8l2Pd6CXLQQJWiJcp71PG5Xoeot0Fbix6Mf6\nWOyS5xm2cRFBJ4faUTZeRKo8UkxLOXDnABaR88JRn/cjMq27iICXvb+7X1mAtOSPFpHQgldPLYFT\nncMi0lhZ+6vJSiFigZwSEhFiS+PYFjuC2NyoBaj1K4nQr7JX5sAXzlaQerAvG8Ikr50QbZcBJuLb\nlWxzLJvtnKlfhFmrm0M3eCF0qYpo4YWfdxZecOzxM784rxQihngsd9jV70vBgrvrGMF4RWFxrI46\ndGnnXY5JbMAjU99qH2Jv4rKtKYpiEWKFELu69XWP4WhIVVcEoXj33bc5ODhkd2uTQksypTl4WsTE\n3qBHf2ONwWjEz+zuIELcNJpZxeeffM6l9Q3eu3GTDInuj8iyHqflHNUzuLMT2ukZvVwxvHKF+qzk\n4NlTtl+7zs5wiBUSb2uuXb3MoFdweHbET735M7RGc9jOyYVmUjYEPIUUtE3DZOY4shVN06KMYavn\nqYPHisCsOmM95BSZ4M7H3+b4bErd8Zkv2FZ/xN67u8zblqPplO99cgfXgMgzil6fvlDorM9kVlE1\nkc8cn014/PAe77z5BpPNI9xsSr/I8AZCWTPqD9HFCCEM5WzG8fER9+59hrOaYjAi67XUTaygG40K\n1jf6zOanEBoybRgMhmysbzHoDzg7qynnNf3+IPaa7vq+CIHSmoePH/Frv/prkRppGoZrI1rbooIB\nZ1Ftw4bJuX3tdfIs47OH9xlPx9S+InhBdXyCyTTTU8nRwTOUVgwGQ5xreXD/PkXe4/jwKNY9eCjr\nhsY2sUVyazk5fR6LaUyO8gapNL1ejs7UVzoPncrOIVGOwiwSrBG2RDVXpA+69rDpfR9WLjTVXbVs\nlz+K3HZEntEh6kRpplZTISBCTBJ2rWONMXR97mMUkNrIChYqm5D+F3NfAtd22B6Esx38Xq7lFFVD\nlCt3/Hxc5ilxK0W6fhkbdPlYZbr03V0PpsAqsIy0UvyZ87HzoDYGmX4W2YJUUOVZjNFX2avjwFfU\nITFwSLxP+iSkhJBIbUY7iqJzpH4xMN3D7RoepaonVjjslFDpjhtP+3KNa4fWz6F9HwAXNwsRJ2/H\nX0GcOotdXJDCQnHOsasXduXu87CySHwIy4SY9zgcVRsVLvV0itIqopngkU4steapt3GR56yNhhwe\nHrK3t8Vbb77J8eExTdPG5lXOIozi+pVLkRJyDiU1vbU1RptDfuKD92nahvXNTU7OxlxFcHp0yJOH\nd8l6mp/88CcJWvHozj2asub61T2O65qty5d4eO8+WRZfkLGxs8XutdeYTZvoeAOcjmcIApnJCN6T\nW0ebXraR5znjecnx4SkuD9z+2puYfs7R5x/x5rWbnB49x9mXh5JP9vdZ29yAPOdPvv8D7j55Tq+/\nztyOmU73eXLwiDfffCNKN3VsSlZNJ+xtbdDWJbdu3sC4ig9uv8f/+sv/Mz29xU5vC5FJ7t97ytbm\ndU5tRT0/Rul15qeSRw+e4aRib28TYxycTTk720crT12V2NbzjQ//LX7+7/wdjCrYf3bM84NDqqpi\nWlZMZnMyk1HWNb/zu/83Z9MphFioU9bziOylQPiGvdGIr1+/gRvP2B72Gb79FvLhF9w7PWLYG+Fq\nR6E1vX6BdZadrW3uP3jI1vYuTdWSScPnn33CzbffxyEwmcZ7F7tYajBoEJAXBfPaUtclLjj8vD0X\njZ6zDhAl1NzJAFcrI0MI57TgnT7eB4mMgXPS40uqql42bRKpdwigRUDiadsW3a3/RI0YIxcJ3bCQ\nJXYVoURAmHqPrL6dSEuN8y1dZU3nI0LwSW+/ROsx+lbn1mv379gLXy0cuRAC4bqWyMuGVKzo0Vff\nyhTpqdT4KxV5idBVh35ZXvhV9uq6Ea5MDiGSg/XpEQm52Lk78iJmieXC8abbB2IGeDV7K6VErtz/\natjU8V/xWOeR9up3V5G7EultGR2Nk2ibEJbfwccIwAuZmueEc865u99uI5Hdq8XkUs6IjzxujBQE\nDsiKfOX6Qkym+Fh62zQWrVTqy6AAz9bWJsNBj9xIjo4P0NpQ6ALVy2i8i53YVCA4i1IBhGP3xlWu\nv3YJ0bQEDXMVWLu2i5tV7G2tcXl7xI03r1DZmksbu4jasbm2zvreLnu5QWQZO2tryNYzr0qOZxN6\nm2tc2srjZCUmZL1tEaHbdBTT6YSgDArNzLcM14d88MF7tJmkrk55bXvAds+wPthlQvXSefTk6IDj\npiSYDDHoQ55z0tTMZhNm0zkKwfODQ2bTCaPhkPlkyu76iL/+U9/gyvabHDx9zLf+8A842n/C7OwR\nP/PTX+f0tObJo8c8uveYja/vgZ0jqehlG7S1JtRAHuj1CvCWuqwY9DSvvbbN7vYWRwcnBFdx+Owx\ng/46W1vbrK2ts3flCk/3n/P9H/yAz764y6effsazZ89ikt2LBZrUStDLFNs761wdriHahus72+w/\n22fz2mVy4RnkCuEchdKs9fpkRazazKRiczRicnpCrzeknM/417/8r/gnv/A2rRO01ZyiyGirKVVb\nQ4i9g6xtIUlnm6biPIN73jrZbORtBTqPc9Rau6ipiJFsdFrWWYyJb3jCpQAbQXAO51uUFGQyj1Gp\ns8nBKbSWsa2FEOktVulVZYsXKZAURpast+xFY60lyKUj7d6wg4hFUfEVgKmfCrGMvqM0RUfn+Bix\n+0TnLDTdziY0nl6d6Fg4bZVecPJiVN/l4GAJ3rrEaIw2liAwfscvKZwVmvll9uoqMe0KCsVFgn/h\n5EDrDgXnEU2voOJVmmMpw1ki6iizOo94l+MZHXDXnvRFmc6ybWkXkpHeJ5mmdHLALnTtJ88je7n6\nAMXyc5Wa0XdNedxKBrt7RlIo8OnhdlSR+zJ10FFOWmdxJ3fLlq7ethR5HsdQ6AVakEAuBMEG6km5\nGCcpHaI9Y97p3ps41r5pY5N7JRns7TG6fBkIaKXZvf4mTV0vxgMBDBKH1wzYZCc2+krIp0NAq7Kz\nLqIKSQVgvUNKza3XZ8zrGu+gnFdMZ1N6esbbbvzSeXQ6ndL3HgFsZRnDq7vMyjnrt64gtUZUmiAF\nj588xkvBsZG8du0aAPPa8+DJETOb8/mDUxq7gVNDWjHm6LTBhpxnz59zcloBmwSZM7MTGgJCl1hf\nIssAdcPkbMJuu8PRwwmnp3NcKDm9Pqd3dZvJLPD5/Sf87MY7bO1twb1DwvCQO/v3cb1AW7X01RDV\nFjgjsKFkIBSvD3pczhWybjmZHpHtrtEOc4Z727jPjyj6fbQ3XN7c4LWNNQgtR+MTNl/b4rtf3OOs\nHBNkTnNwyLd/8Mf85F/9kIP9J8xnh+S5xnpwOqNRUE3OGPUKMgO1rWi1gJdT4JxwRiYNoo09vp2K\nKq+6buj3BrR1g3eBqipRSpH3eoQQ3xnptEc6iUajpEovFgl4Uac+4SCcBOtoElevtY5No3KDVAYb\nYpVi01iUVvRHA5pmjAoRDGQii31YlKPyc7wEFTR15dFk2NBELj14Ek+BlqnRFQKZErAqCIzOQCqc\nj+/NRaQKYtcifIr808bS9ERKdsbXtzkbKRXfRCQvlYxpshAorIgOXASkl8TujfGNPbFFQESJS0Xb\ny+2VOfDudVywdMiLHVOslMF2jnslTHsx+bj6ey9+p/veqr2sAupFO/c7YrkPrpbnnk+GnA83X3bO\nl5070j3nv7usYhMru/KXNaHLkPX8n3iM5QbZ9Xd5EQWsnu+FAy90tS41mF81rfWicVP31pLu8+56\nM2NwUn7pXN1xX9yAjffxNWvasBFSPwsfqJuWLM8Wb2950W7durXoT93RVq2zVFXFrJzTTj3XXr/O\n5vZtfAgcHB+ysbmJkor7D77g9OyYjc0Rg/4AFzTPDg6o5g3G5FFpIGKFsDEmvndRRFy2ZkbIWjIY\n9jg5LQHDvQePqcsZ/bxPb9Dj6OAJ2ztrfHLvAb/0q7+F2VznvQ8+5Pn+Kd/8/T9helQzP5iy1R/R\njkuKbFnEMez12BqtkQMSyfPDp5SnY66MhnhrWR+OmM0r+v2cXq7RCowwrPf7HM6mfO32u3z+eJ/D\nsxnV/JTf+zf/J0eHj7m0sx0dos6wTYUU8Rn3exmEQN3YWLEY/EK19aKpEFUV1jmUzvA+vh9WKUHT\nVgQ8Usc+/iHE8ZOpSMbZNhYOOYUUCilBOAiptYFIL2yRCJDxrU9CRiRuXRv70jsX32sp4ztby6rF\n2opMxBeZRGoyYEOLw0YgJQRFlpOpHGfb9NaexHUr0N18dLFFAsQo3/rlO3ch5uE6cLaa4AwhvifX\n+6UcMb6HVcd2Cy52I+zWdPdCdiliS9qOQRCie9l0pF/+wiYxu+KaTs7TOYPu310YtpAArlAQLzqz\n1cqllylKVh1i97MXz79Ayp3Q/ksIf2kvbhQ/7FrOtRVdpVHEea3ni5nr1e8t6ZwvX0f32WpWfLWR\n/OoxV0t7VzeUJVd5/ryrY/Pi9a32i3nZWHQdHV8csxc3m8WGk8LqRes3YpFU18GwyIe8zLZ3tuK7\nOLWhaVta20a5l4/ISrZR5VA1dey82IPxeMK8bVEI1tZ7BF8xn51x+eo2bes4HZ9xcnbM4eFzqmqG\nyTRGBhSevZ0Ntrc24iL3gXZmqWYN9XzOoJdjPfTXBxTDgv3Jc57+wTO+OJgyqU7oD/t881t/zG//\n1v+DYZ1bN79OqDyTo+esDXuUszO0MmRGs9kfUJiMUNe0rWMwWqMwGf28YGu4zng8RfUEl7ZHrA0K\nFAEtoJ9lrPmCk5MT3rp6hQ+/tsXaxg5ZnrO+tUmvN2AyneN9rDjWzoFvaUuLRaOzPliPlssCpBdN\nO49QsUjKuhYlBGU5RwiJMbEniLXtIjqLFabLDodCgnct3ll0ov4WdRk4nND4oAmuTYm+aEblNE21\nyBXZpo495rXGZJpgPU1TIYVGKUEbapyM7+0MriGTjtY3IHzqLgo69c33IUZxCBAq9ZpBUbuIortK\nWOfjS2Kk1CnZGfNwIQRMEAgZOxLGVhwB6Vx8eXLi6YWMbyVSRifk3vm6lHuT3WaxbNfxw+yVNrNa\nILiVxd39rLOXo0u/cIhf9fMQwuL1RKuOpHM0qxHAi074q5Dvi05xdWPoHODLHNWL537RWb547Jc5\n39XrWT3+i/0WVhOwf5YoY5mQeVGmef5azucsxLlS69VNZPW6VqvjVq9ldSNb/Lx7ozgSraKW3WSK\nXBpsa78ylFxfX4+hZ+vQRpHlJiajSDJOZVE69jQRUnJ5sMelS7sopbBNSzUvqcuSqqxAB4zKGAxG\nTKdzdna2WN8YMpmMmc3n7Gxv8Natt5lOS+4/fYhtPPVkzru3brExWuNb3/wWbdOii3Vufe0D8rUe\nTw+ecfXNTa6+cYvf/b3f5e69Q2Zzx+HRCRsbBeu71wlG8nz/AaPNAnc2Y2+4Td9k2KZFAZbA2++9\ny6Pnh5RlRTmZUQhF3i/YHOYYLMKBkTlG5zjjaV1Ae8fZs6f8o7//97jzxRdRpz6b0zaAUhA0wrdo\naynyjMYZghrg2hrl3Fe+DSYHqrrFK09W9Ajtsv1yXcdmX+mdw7ExlIgFbd7G3uGxWVZsL+CdjQ4u\nvSokvZsHgUWSeo8nBUhZzZaFRiHmvbSSaOWZzUu0NEgREW9dW7xwmDxGAv8vc+8dY1uS3/d9qk66\nsXN4r1+eeTM7aWdmd2Y2cZdLchMpkivLf8iQLFsCJMiGAmQZBiQYsAzLVrAJC0q2JVAQTEqyTZES\nSTGI3KWWK23eHc5OTi/NvNTdr+PN94Sq8h91zu26p8/tNxQlDOuh3733hMr1rd/vV7+glMKoBJUl\nhLUIjbb2F2kGGYBB5uIQIcQkZKAW1lI3CAKrwaIKM/scPyaHbZpQ+vncNyhpuQFyat06qMrXOhop\nrDFfmiWT8wL33MESVt7sg+Q8ve8UeFlUANOL3aWIy9R68awLYuU8XYBy77lg424ixfWy/maRl/vb\nfa+oSxUlDRAVPrSdv/eSJpouDki67Zi1uRR1rMqvDLhlrRt3YyjyKe67YF/uH3cTgWpuZla7Umty\naRVHlZqwqkYdBZit7h+VT3aJlxtNqXyRa0zuZlajhWXrdV4/YwwEHvPzbYKlRQaDATIKSJOUpaVV\nHrr8EOPxiMC3KpLD8RApPGpRk/3DLucvL9PtDRh2xoSizs13NnnsmY/QHyZ0hgO+9PWXiFo1tCdY\nXV9ke/sGvU5GGC4hhM/C3AL97j4CQT1ocuHcRe7t3OH08jKLjRbSwHA8QgQ+MgrYOewgo5BRkjDs\nj6iHEWEE840avjYEwkNqK7Ndqoc0Gi2GWcrTT36QbNhjrhbiRw26211u3N4hyTSXzq5x/uIpxoN9\nPD/k+u199kYJUinWWwLq9co+97SX+4IXDAcD5vLgFp7v4QcBqcpIktQGzXbOoqQorKEBT05ENMqk\nVrlAMPkTwnp1NUqTaWU3Agm+J8nSjEzFOeUsGSVj/FqIURqlY6uvH3mkSk08RHo55RsG0lp7A0Us\n1+JMxioQ5Fynto6ntBSWWzE28LnK8s/c4tqKjiyWWZVDmVun5mbzucqlkMV6sYeUfuAjPZEfdR3F\n1kyyFG0EWue6+veBiffRkGea7S4inRTAWDSoLAM6CbjKIFb126UoTwKWcj1dYHevuRSxC/KzgHXW\nRlUur+q3C45FqnL4XgBelcil3F9VXELVGUKZEi+3w90AyxzSrLKnvhf3zYSbRgpJMs6sPvAMfVhr\ntesjDCR55JrCylb6HlrZPgvDiDAKSdJ0ImNsNpqkcYwnJPPz82jfim2SYUyj0WDRzJNmQ4Y9Ta0+\nh1YQhnWajSZRI8EISUpASoMHn9Ps91J++h/9LIPBGJMakpuHoDVv8Dae7zEeZsy3enQPBzz3zHOY\npk+WjpHMcXBwD3/OMB8KaqFPphTDLEEpz4oI5JhUa4bjlJWVFcbxEE9mmCQFYZ1vaW0IfJ8wCJir\nh6hQ8NQHHqOXjlhemCdWPkr1WD91DhlEnNtYYXWtTtzzubO5zb3dAwamTSgkq5fOEi3OV/Z5vdZG\nhxla2MAfF1dW6fV6HHY7pFmKMdbthecHuaaINcjxAj/3xy4xntVKEcIQCg+RU+BKCIzwQUpUFk8i\nHSldmLIbvMCbWIlalUKfJLcDMUKTqRidaetAzAOjPetpUWUEfq4Z5XloxORQ0crqM2spYo78JmXG\nBiKeKATkBjuF/D1RKULnYsQ4m0SwB1BaToJ6CymPDHQUIGQO6po0zaw4x/etuMY78q8jZsz7Ir2v\nAF4ANdjKl0UdVfLXsiihuOZSgO79KtCYJYeFI6p0VnLfKT9bdl7jchZuRGs3ryoqeZYoo+q6u8Ed\nOxytEMVUlXM/oL9f3SYULUyNpzsW5XE5VoeJ+4gjlU2Mdf3KCcpUYZTLabUpMpgYTWmdIZQ3MZhI\n0xEFV2uMIR1n1kxb5CbZwlL+9UYdMEitCaMmSwvtnCLSebRwCSpGGTBBjau3tvjy17/H61ffYTjO\naNaa9Pr7eJkmFB7+uIHvSdpCM9rfo+37PP/NL7O8sor0PZZWl+mnmrmVswy2b+DphGbkUw8DlCcY\npskkRFdmFCbTRLWIUPhII4jCiMD41L06GMny3CKthQUWz6xyuNch9jTG8+in8Pa16/SykDhT3L3Z\noP3pZ2gGAa+//jZbuyNMXZAMR1yNFHOnTlX2uR/W2d28w5s3b6KBO60Wa2trrK6vYYRgd/+A69eu\nMjc3ZzfC+QXSJCFJNY1aSJLmaq/SQwqNVBqMYhzHiCBEiYyo3iBTY+IkzilRTRRad61RFBFEvj2o\nzFLCwEdmGUIYG2UqCKwriixD+D5GGdaWlphvtOju75MNB3ieIM61WKzfFEPgeda0PTvyZyS0ttal\nwvq3GWUjgtyWwYZ1s5F8pJR4UTDRlrPYpUBCpjS1oJb7ZLJy7nE8yOerQeXReIhzJS1hA4r/vj7E\nLAPXLGq0LC91Qdq9Xz6orKLSXfApDgfcDaE4NK1KJ8mHZ9Xfpdwn5vyOSKeIFlTV5irKtQpwy/Wt\n2rjKbS7fq+JyZo2HW5+ymMl9ZlaqclUABWDbbwaDcOSvJ+YnCg2bnP02JtcwsJ9FNCW3TFe90xhj\nQ74JQTK2Pj4KWagUYExGpzuiVqsRBBHI0Drxb59FK8Nvfem3+M53XmD/oENy0EPFCVsjq2s9Tsas\nXt4QZZoAACAASURBVLzAeuM8t955l3HaJWxH9OIOC2fm6Aw7nD37AJ3RmNbyGqPRGB0EGM+QYPCM\nsQezqcJDYMMh5P5LjI23KIwkTlLqzQZgf6+srFFvNzEK0lQz0GNSAWF7gWef+TAimmcYJ8w17eaY\nacMwTqxIw2gevHSeRx97gJfferu6032fO7c3Cf0aRhj6vREPP7zM4UGPN956k/3DLqdPn+bM6fMc\n7B3w+itvMRqOqEUh+/v3CGoNPvmZz3Dl2nX2drdpBQEXz21wam2NQTzm9s4Oc4uChXadTqdDo9Fk\nPBxRy0MpCiHYP7ReGz3PQwa+lWVjfawMhj2yTONHNYaDEfEopVVrcv70BhfOnuHfffc7gKFWsyqC\nCMGgP8LzfYxnDQgLjs/LtbyyLENpRRQEqDTJ1RvDIyOhICATVoNF5NS79KxWlhdacZ7xQOnMBiX3\nfEf8eyQG9sMg9wJpTwTS9PfpIeYs8Ub5kK58H0pGMbPYco6OBKoAZpa/41lU66y6lMFh+rBvcmfq\n+rR2xnT5BXVYBY7l32555U2rCoCDoBju44BYls2/F/FOUZ8qEQy4OvVMZHkup3SUl0FrMflePsw5\nKSmVW7GRq4QJJvJGIQBdsYEXbZ30hgABkQ0vAUbmsloFwqMeNBHCY5QohFAcHA741veusnnrLq9+\n53dQ+138UcqzZ87RbjcYm5iRzBj7iqAd0Vg8w4effJivffu3eHf7Gs35kMP+PWpejRs3rvPgmQeo\nGUk7atKLmmzde4eNtSWQHlmc4AcB2hh8X1pvhjJAeoYkSWxQDXseiRAQegHX373KqY2zyEadaHmB\nQAc2ilAQknYO6eztI6VkfeEcB7u7jEZ9VtdWyUwXjWZ/+xav0mN5udoH+6/9xq9TazRZXFplc3OT\ntVNrrK2t8a9/4zdI0gxPemzd3cYXITtb9/Cl5OzaBjpTBL5Hdzji+o13GY5HGGM3zqtvvs3lCxf5\n/ksvc3d3lwsPgU7qLC0tEccxtXqbUazY3d2lVqsThhFJlrK0sMhoPGYuatLvH6JUShQGgEIpgxQB\nd+/eZKk9z7e+9R021ldoNuvs7e2DkIzjlCCsobTGCNDKinK0p/GExJOKOE7xPSsa8YMAncu/LV/o\n59GeBNLjKFi1J0FoDIV64dGaDqOALE4mhn7W1ZNVM8wy6+WwUEH0ZoQSLNLvCcCFEO8AXUABqTHm\nI0KIJeDngAvAO8AfNsYcVrx77NNlxavY+PuBq3uwmX85BnLFZ3FqXqVaV87/mGk9s52tz8rHFblM\nH4hWA1T5oHMWVVwlwilfL5JS1cBYtTGeBOAnbWDuofJ0PafrWM7fw6dwzwky1wt2yp3hPXJSZ8NE\n9l3SPM83DzPZHCeiq1IdZCH79PJI5iZnaX2PJFXUWnP8lb/y1zl//kGGco7Xvv8iK1GDh5+6RDAc\nEx92ePLiRZRQXL17k/1Rlywbs9AydO/t8MnHn+Ar+3foHnSYb8+hE4FUkv3NbZ576kPcvHaNs2c2\n6PbvkSljnUsJDx1nyDAgkBJpfALPRjeS0sp8hSfAU8ggQOmEO7fuctDrYOp1fuALX2BpfRUlDcKT\nnNnYwGjNaDRiHI8JF5qk7Tq+73Pm9FniQYzxBETS6nZWpJXVRbrdPp3tbc4uLdNqNdjcvIMQBqVS\nBNaIZ9DrMR4OWV1aYb45T6dzyHg0ZH//AK/VJMPQqNeYi0IWwoCN9TV6h4csLcxz9/Yt/DNnWV5a\n4/y5DQajMYPhkKVlj9t37rKxcQZBQKY9PvjUszywsszuziZb27e4t3uPyPMJG3O89sZVdnYPaTwz\nx6XzZ9nf3QYpSNOUWr2BkBlxHBPWImq1Or1eF50ppAe1Ws1GJ8KqEY7HMXGc2wTkroKjIMAYiOOY\nKAitOWGu3FCc7YRRbaJyG8dx7kvJILzcH4wo/KaQ6757KGVIsxirbTM7/V4pcAP8kDFm37n2l4Ev\nG2P+NyHEX8p//+Wql6vEA/8+qcinbCRSLNSqzWJqIVMtW6+iul0AOwn0q9pT3mDcDauqTe5nOd9Z\n5c4Sa7j3yu10ZfnlzfCkVG5/AeBljZ2qepTrYKPUixzErRy8OGw0wvWvXE6uSGZ68zEGpCicnE1q\nnYux0uN9io1nqHWu52uVghkOR7TmFsi05E//13+Gr3/ju3zz536Fs6dOc35tEa2H6Br80Bc/ixqM\nifD4A5/5ArtbO9x59xbD2yOMjBgOhjz6zOf57pVXePHGNUyjRTeJCaKIX//OV/j4xz/Cwd4WG6fP\nMOrs4wsfP6qTmFHupljTrEXYUG3aBn3A4IWSYTwkiYfUvRpnzq3z2BNPc/PePo12EzxJpjNMkiB1\nRiQMzVaIaoWMjUGGEadOnSKUHpEQHPQ6HKqUVtCo7PGoHnC2sc5CY57xaIzxBP1Bj4uXLrC7t0+/\nP6Req1GrRcy12tap2vY9Op1DtJ+xvLKIEIZGvU42HBA1a8y1W6TjMZfOn2Wr22WxvkC32+ell17h\n7LnzjJOY1dV11k+d4eCwz/dfeoXx2Hri7PbHNJ76IM1mjdXlVa5cu0KcKuptw6uvvg5GcOPGO9T9\nS8TxmEcefhylDf3BgHqtQZykHB526PX6NBp12nNtayykUjyTa5MYw/Lycn7oqanX6mRZRppkJGlK\nrVYjiYd5IHTr1VEUFIC0XhujWt0G3PZ80lADHlpnk/Xn+z6pShHSxtjVWlgu8IT0H0KEUl5ZXwQ+\nnX//GeCrzADwMohCBWWeE6ki/+c8aFkXYa8abSY1OekwbxZVX6b8p4FgGtRdirWKUq5qZ1Ubq35X\nabCcRBGX8yieLR+wniTfr7peJZt3yyr3Q1Wdq0RAbn2mDjn1EXBbIbVHPrxYaJ2RJBijc9/KR/6s\nrRzSGl9Qsla1HuvcOlgKKI56mNTDzyICFeATooWmVgsYp4rX3nqDv/v3/gE7e11+7IlP8eADD1Jv\nN7i1vcnK6VMMV1boyH0+ePEy33z1ZYKDDi2lefhcg+bCHAeDASEBj/hN9DDja4NN1HwIo5hFHfLi\ny68jSXnu0UdJBiOa9QbJ4IBxMkBEIamBmueDslo+Yb1OIH0gINGKtUuX2O92+dQXfoyVU6c5J33w\nfJB5FKDcaZLRhYsD6x1Sa4OuaVSmkAja3gJBHB/5Qy6lTz77LJ7nU49sVKhUZwyHI5RWXDq7ysH+\ngbXSlB5pssR4NKbb7SIjjztbY86un2d5bZUrV68SBj4H3Q5BFPLClbfRjTob83MsLS/nvlU0wvNZ\na6ySKcPm9ibD4ch60ySAGG68cZ27bz3P0tIyly4/SKx9dvY7jDcPQCukMOwf3mOvM8fNd2/w3GMf\nZM747B0MORiNuXrrLsM0ZWF5lblmRquuWF1e5tT6BbxWh/W1DTZvbjHsxqSjlIW5eR5/9DHOnt1A\nGc3vvPAiL734EtIMrc9/37cHnQiMFiwvn2Z+YQ2VSXbudVhcXEFGinE8wvMEO/fu2jOTcR8z6CKN\nwg8kBJAmM/wZ5Ok/BAX+W0IIBfxDY8xPA+vGmO38/jawXvWi57AZxcKu0gCpotKrAAOsU5rKSlZQ\nzS5AVclyy3mXr1VR81X3ivJPirAxi/s4iZo+KRW7+UkbUbmc8veTOICpw0chJs6NZrW/SK7YqQz0\n1rfYbC5sVt29XNyBN123InZkAd6uZaybn33efvoysk6QPIlQGkWK0dbf853NLf7+3/k/GA81Tz3y\nJGfPn6PT7TBKR6gkoV6rcefOXc6dPcfGxYv44zHe3h70etzr76FGI8JGg5V6g0dPf5Afahpe+tIv\n0xnHmMzH9yMCLdBCMooTWu221U3PMlrtJkYKMiEYjUZ4QhD5AWmmiRo+0guI45gHP/AIn3/kUQZJ\nwiBLrQVkHhrQqvIdEUw2YIpXbS2bk0qzNvGlxcUjQsEYIi+k1WxM+vjcmQ3SNJ0ai/F4TBwnjAZW\n3zuMQpr1kMFwyGG3S6vdRGnF9tYWg8GAxaVF1tdXWV8/lfvKTmhEDeLxmMO9HRZaDYL5EE/4jMdj\nPNkgU5Kd7S79bsa77+5Yl7TUGA16bN/ZY6G9SLu1ws7WNttbW7z6yssMtUDUm3jSZ9QfsLq8wtz8\nPI88+iiL7TbBch+hPG4pxRtvvcVCY57OQYeV5WWCMKBWr9FotFhbP80oPaDX61Fvt1mYXyBNrTdR\nFQb00oQ7t7a5ePEhbty4xa3Nm3z4mQ8TD0esnL6IEJqWTgkCASZje/suc+0m3V61D6Ai/V4B/AeM\nMZtCiFXgy0KIN92bxhgjjhx2T6Wf+ac/P5kwTz/5OE8/9XilhZ7rj6EMku5hn6W4quVFZbAQQhyX\nl3Ncvuted8ssf5YBrVxXOFKrKwNY1SHg7ybNovhdrZf7bQKz/K24bSj3tQuAZVHQSd/LG0C5DVX9\nVzUmRSqceJXrWSWecjmTKi5Kj61JuicM0ldAhhaCeq3F3/wbP4WnAj73A58E7TEY9RgN+9y7vsXq\n8grXX36FMxcusv3OO/zCa2+wGIU8uL7Kgw9fYhSc5bDfRWWKpLlAs7XAf/bkH+Kdrdv81ne/zdDT\nDGSCSD2CwFod+r6PUIIoqmHIrOFL7hPE833bbgTjTFGvS8J6g/nFRYTnUavX7AFoZij0iL38gPdo\nc9O5KElN9QUc6UDPSqdc9UJh/Vprra2/klzTqVDlK+Z7FEVW7W4OEII4SZi7/ADCsyHIDFaP//Kl\nc2RphhFYs3hPWu0cbRjFY9L+kPOnV/A9n163y9LiPE8/+QlqtYioVuflV15n++4e48EIgWA4GtJu\nthn2Rmzf3eGhBx9g9fQ6/STm4eGQzAvojWJSBPd29tnZvMO9OzeJ+x12d3e4+Og8C3NL7G13iHzP\nujmYX+R73/su3/jG1zh3/jy1WoPr16+zcHqRuZV1uoMB927eYTgcceHCRcZG0e3tY+pQWwpY1Yv0\nzYCwVUeEHr/4r38dYwyf/sEf5KUXv4/QGdt37zA/P89oNJw5DvB7BHBjzGb+uSOE+EXgI8C2EOKU\nMWZLCHEauFf17p/8E39kJpVXJCGs5db9KLsi3c/stAqQpu9Xq7kV1OysPN4L6LqLpgCxQn+9qo6z\nQLCqTVXXymKbk9Ksfi0Da9k6swyIVeNX1P+kNpQ3yPK1orxZqcpKtni3sPiF4/5iynWLsnmETDBy\nhBYjUpOiTECWJPyPf+V/4Td/+cvUdZ1GELG1/w7bm3do+AHjw13a7UW88RghPR5//BGuvfUWA0/w\nwrvXSBuSRx95mA8+9gSvv/kG17a22Dhs8Mc/9mme/+3fYlyPSMOQ5UabO3fvsDlX46nLF+htD0nT\nxPqvNh7jLMMPIoQXWtEFhjSJ8UyTP/cX/xtGSUqcpUjfByPwPAG5cTp5kBC3b8tzzO0LdQLHOBgN\npw7kC8o7DEPq9foUgeTOGa01yXCM0ookTqzpfZrkB3yWY2jXAmrzLYLcgCdVGXFs9bWNgfF4jCXr\nBDozVgNJDdAqYfPuHd65/grjcZe15UZ+sBig9ZDTp+bpdTZptx5k52CfN6+8xWicsLC8RlSvce7c\nRbbu3ePSgw/Q7/fwPMnh+gKJvodJE+bqdYYMeP673+bDT32I3Z0dBsM+aZby2ONP0m63afktvvSr\nX+bHf+InmN+Yp9FoWI7Jk2z2NpmrhfzOt7/K448/xsUL69y9e50rb1/hYx/9MPV6EykD5heWCMOI\nS5efoNlsIaTk137xX8yc+//eAC6EaACeMaYnhGgCnwf+J+BfAX8c+F/zz1+qer9q53fyBnK5rfSO\nAUlFXez1+4giiu/uNfev/Ows8YGbbzkf9145lU3LXQrFzdvdMI61saJtVanK0vS9bDSz3nFBumw0\nVa7fSX1dfr74K9j18jvF5yx2vjCQmtWGrMKbotsWt14+AUJotCetIyzpo02NwFvgrTdep+63SfY7\n9Lt3Gahdmj7MN0LS4Zhk0GHzzrvIZouX33qNZqvFvf17tGoh436Prdeu885LV/ihH/1hzHgEdzo0\nOn3+2p/48/zpf/y3SPw6wyCi3rCimMXIo+1r5lttRuM+Xi0iHgwxniTLQ3rVGzVqzTrNxUUOej2i\nRhO0si5JsdaPkwhW1kh8qh+MMVNz0u2nKkveItVqtYl8GpgiTNI0nXB/5fGWUuIH1mtiq1UnTa18\nt/Bu6fpesT7vbX5JkGDjjyra9Rqj0YgwDBmPxzQbTXq9Pkl2wMJCwGd+5DmUMvR6ffqDAXt7e4xH\nQ8bjIUG4xMK8j/HhwoMXSZOM4TBmZ2ePr/zmr3HqzBk2b10nM4oHLz9ArVHn0pnLDPsxh1mfS+fP\nceH0OdI0YTDo0et3WF9bs0GIleL221e5uLbBWy++SppkLCwsEgQ++we7LC7PM7/Q4vFLF1mfa+Lp\nDhuXN3jq4fP0e0Pm5hdRGq682mNhoU2vu8edd67/RzXkWQd+MR8kH/hnxpgvCSGeB/65EOJPkqsR\nVr3saozMEl3ANIXkAkQZDIGJE6NyqtIwqTq4K5flLvRZk7kMZMXELXe8C3xlCrkM/JWcyIz+mTXA\n99OqKedfvl+1eVW9X2WQVR4v97yjqh8LIHHB+72KllwAP4kbKbeliqL3PIPxDUZoUiNITYD0Wnzr\nm69iTJ3Ir7NzcJ2st4sXZdQCH5kleCiSdMj50w8zFD5b71zn4x94iLlGg3OnTvGt//eXWX7gQX7n\nV77Ey89/h49+4lmeXNzghddf5dmPfZw/8Nwn+JXrrzJSMa35ObZuvctoHLOxsYpWQ/67v/yXWNs4\nw63NTW68c5MbV65a7ZZ4iFev89Szz7Kwsspht4P0fOtn3RTnSspapEpvEtzX7UtrHThbq6oqFeNV\n9gVUNecnetH5c+NczFKvWdU63/fJkmSyHnyZr2tp322325P3oyjK5fmK4aiPlAskaUq9EaJ0g85h\nD+EJkiSlWauBykgbDR44f44oCvF9nzDySY1Pe34eDGwEIR+4/CBaP0e338WvhcRJzEG3w3y7zvrS\nMmIp4BvvfourV95lZXmNBx54AMQpanXrYrjbOaTVbNCq+ZxaP838/CK9/oj5hSV2d/dY7a3iB/DS\nSy9w5eqbJFmMl6UMByOiKKLWaNLpDmi3F/CCkNv9Q7q9AT/wA58iiiL+7xPGQvxuZa7/IZIQwvyb\nf/1zlROm/NuXwbEFXQbJyYKUxyntgsU7CciqQNt9torSdPOsAq1yqro/i3Kv6pNZm8h7Ea+UOYhy\nfavAzBVLlDfYsrjifptbmav5923Hk8995ti9F7/z5fcknilT9pVtzjyUTIkaPqMsJagt8rWvv8Kt\ndw5Yba9x8/WXObj5JjLrMhfZKDZR6NOPR7RWV1k6d5762in8WhNhBBvLq9x48y2e8Oe49dZbPPHM\nk7zy7lvc2rnF4xcv86GNB/i3X/oKanmBX3vzZV7v7dKeW2T3zibn1xd54sFzPPLQRX7iJ3+cBI2W\nHkJ4hNIn9DziLCZFI6UgU1bf3cvPBIpoUWAFDrmuzbG+vt98/cinfvzYve99/dfv+365HPLyVW4l\nizFWECKEFe0YY1UinLmlyV1JI3LjLPLDUX3kqS+X6wcitC4TjA1CnmUZSZzYDR5jvQFifZ+nvkGl\nmTWhT1Wu621VWIejIQqNF/gMRgOiQBB4EQKfLLE+xOfmWlx68BIIwd7+Ia+++gb9wZjMH9EbDFmY\nW8b3Q4zxaNRb9AZ94nhEe65Js1UnHo+oJ9ZQaP/wgIND60NmMIp599Yt2vPz9Pp9G/ih1uBf/vy/\nxBQ7cim9r6b0Lis+c/c305PEZc2qHDuV1fUKKqD4XWb/q1jHY1WYATguZVFl7FN+fhaF6NbVLa8M\nwO4772XhlDcG13hplhFSOY+qjcot/36+Y8r5Vv12r1XV4aT2FkEkXLl2OZX9xRSO08opE2N8z6PT\nHSFljX/1S7+KJxfwtc87V6+SjQcsr8yzv73HcCCp1QKG45h6q8nKqTWu3XmXp8+fQwnY3tzGz2Bn\ne4ffObzG+soyr777NhsXz3L+8Qf57gvP89yPfJo/+tRf4O/81N/m3MIyvUiyM4g5vXEWnQ1ZXT/N\n53/0x/DCAKEVRtjo5bHKrNMoYTASKzIRxaG8ACTGFUcUAQLyafRezkZmbbRFH5ZFieVUqRqMwIZP\nzN8ReTjB/D+L39YiEmMQvnVSZjcfENp6+xPC+hw3uXMrgyEr4pIJCLQBrJOrwkEUudooYch8q2Y9\nBmqNyTQqsw6rtNHMqzZJlpCpjGbDhibMUo3n1VCZXTf1eo1OZx8NjEdjlpcWaLcVCR3ObpxCyoAs\nM8RxSrtdRxhF7AlUqmnVWjSiJk0RsbOzw/zyBuceeJRhPKLb6xMLq0PuN9vUGnUOO50Tx+l9NaUv\ng1cVK2fUyXLvKZAzx0UVLqXuyuqqNC+EEFM+Ulxqu+rZWSb973VCu4BYXhBVC6iqjJNEQVXAWbSr\n7ELXzV+I42qWbt+dJA6pSuV+q0onyfreKzifBPIFcAthY0+W8wcwniFLFauLp3jhe6/SpEY9avLa\n22/SPzykGRqiRsDS6hpbtw/wpDVxv3DxEvv9Pj6S3e17PPTYU9zbvEe92aA+N8fe3l2GnZi1U6cY\nZgln2mv8qT/zZ9k6PODezl1+/I/9EWS9xn/+F/8stBcJ6zX6vUNeevlV/tyf/a/oDTrWuk9K62ND\nG6QGpMil2sfBtGzV/F5A1+lILGFc3Z/e5GA459D08bzc8o7GyDoOK8LoTew7pKDQVSui3ACYvB6Y\n3O22lPmhtPUKqLH6/wAeaU7IG5RKiurZOeFZ9VVjLzDopta+RkqrneNJpC/wDUivTlNYD4qeZyMy\nWW8N1lNloU+fpAlKZ2idMT/XIEkyUNayMk0V9VaTkRzjGc3iyhKdbpcwrCMy6PdH6IUmYa3FYeeQ\ng94mRsA4HbO0sobneyhjCMKQM2fP8/OzR+r9A/CyRgZMg2+VQyd3IbueDIt3Qz+sBEd/wlYen8Bl\nUKzSQqkSHZSv3w+g3OQCYdl8vYrKdetb/n0/4HPTLNez5ToXh1Bue8oiq/fa1qpnT6LAq66dpEPv\nRhyvyqPgPNw8y5vPpI6eTyto85Xf/G3efOkKpB7Nxj41NcCvQ5qMUGkNKVr4yx6HoyGPXb5MtzdC\npRltWUP3Y9q1OqtLy4xVRm1+jl1S0tGQ3o0eWzdvsXf7HmvrZ3j4scf4yvbXObW8QDOs8yf+6H/B\nP/v1LzEYjohqdYyBr33jGzz+1GM2fiUGI5SN6KI0RguK2Km2/Uf+Z6b7y5B7i6kUoZWTNmYS6Lcq\nTSj6wkCq4uypPJ8n6zZ/L0dXW7s8uLAxZgLIQO6C1YZ2m8xyLfPNxSAxTHx560I92AL/JHsh7AFo\nLkASQuKluVM5bR1UWSoejNY2zicSlWRgQPuaIIhQyuD7AZ5vXf1GQYCUAcKA79VIUwXjhCAIiOOx\nDcgcWde1wvOp+fMoZRiPUhubNuky15DUgjbjOMYLAqS/RK8/oNVuIf2AXq9nQ9OdkN5XAIdpACnL\nVuGIAp8YH1RQjkU+Vb62q+TbJ4HRSaKM4tr9qM9ZQPJenz1JK6csPpolCplFvVfVY1aby3UrU3Un\ntcFNJ4FFkcqHmO67J4lQyi6Iy8nlJsrqhuWxN0LyzvVbfOPffosLq+dZXJjn2tUrDIZdllcW8X3B\naJRQCxuESw1WGmcYZwkizWj4EUmmyfojku6A27duI6OQYTym29lDDwY0wgbJ2DC6vc/WO5v8qb/6\nP/ADn/0st7e3ePu1N3nqiSf50jdf4CAeIMaKC5ce5LDTJQgixunIBtLGUqtSWgMkivmYu8xFgDLK\naacVs0gh8cRxo51Kd8ZgnXrdZ6wmZdxnLciCcjbWWVR+42jcPEdTxs1X6MKDtlUnBtA2HLg5kgih\nhEFQJ1Xa+iIxZop30DJ305A3KcqDfQgp0cJuBJnW1nsgEl8IRBAhEYx0jBTexJeJUhnapGSZDYhs\nMo0UYzAS32uQKQVBgPLACyOUsS6K2/PzqAwWpE+mDDLtHfVb7lRrNI5ZbS3ZOLC1gIVwgayCu3HT\n+xhSrZgILnDnrJs4Csor84HPjEalR5ErJixePoE8KVAimnSKEEfPGG0mhyASMSnHPlcGBlc0Uyzw\nItvC3NvNQzCdhWvVmVMaFO8b5xmbr1LT4ZSK56Q8qkcZcGadA5SBbxZQTWo6AfWjNhZ1seXa7/bT\nrpgqn1KF5d6xJKctXE/aRIwpg7SZzAGqc69s0/HkQx59vKDcLBtsD6QsqEsQkppo843f+HlOz61T\nqzegIdG6S5MBMpbIxhyH45QFQs5/4AOszje58vy3aQcSEw/JhmP6e7tcf/55GuOE5LDLUhTQU4KF\nepuGF9Benyftj0k6e/zDv/HX+S//+/+WZhQx3N5kezxk7fQyi9kid25f5Ymnn+BDz3wQpRUhnvWN\nkbfB9ms+XjnlagrgEkd9bAwYZTDCoES1KuYsTm9W3wpD7oTJjpOpttWrpMCnRjiv+4RYY3quT767\nZVPIxAuPkgaZf5pCpGLyM7bJIgeDdfWqjSHJw6CZ4lhX2EDN+TEnSuflCEGQR9vxAm9SnhBRvl60\nRVAhEEiSNJ3ULkkyyOX5UlqXv8bYjVEIQYB1aeDlIiE/9Jivz2EMNB38uN95xfsK4EodRbkpJqRN\nRws+mwqHZm9pk+98UiLy97SR6Fyv1JVzC2FlbBgLCKoIh+T7CCkmLOd0PYqKFGIKF5Bt/QqOoChv\nUvMZYFnlhe+ozOP9o5wI1naj0xQRr8vGEVXA7P65ZU2PQbFpFuKS6XtHG5fDGXGcW2Ki3zCdXFg/\nyRcM2HBU5aVabJAnpVkbWJH0BLNsmKoi8osXeEjpgZFkmSLwA/75P/05djZ3Obdxkf5wyNUbb7IQ\n+XhKYOIhola3ZuytJhfXTnPtjVfYv3MH7SlCnVhvj9qzDpqCkCzLiPwQLT16nR6Ndpso9HnqfaY5\nhwAAIABJREFUmacQnZSbh/t89Rd+iY9/5rPUDvt4kaAdBXTVmI3T6/zsP/kZ/uAX/x92d+8RBiHG\nWABHGLRFPzuuTFOcx8REJxBxVYZYE1HiLMq6RCi45brj4uZXxV0VFIMoni1xf5N33KKPSsv/t+33\nPWttekx0VGCBMRhpw6RNZm2RvzbH56SwVLp0xE5grVStnN09E7J+vgVHfv7DwJ+0oRDzuesl01iP\nk3lA5DRNjwVBeS/pfT3EdHfbskx7kibAwaQzpJSTgxStFEZpUNo6gKEYWktpSQRSHOVtpDnyG+0M\nWjk8miumKRs7uGqMLkAW16om9awBqaKmy5S0NUY5rrJ3kuy9vBnNEkPMImBn1cUTx9syq22uPPN+\n2ipSklOUVRv6/dNMMYsBKyfWWIcrBrQ9eIvjmDCsk8aK2zffZefOLssbZ+jEIwIN/iBlmPVZXKiT\npSnJYZePPf1JGmtnuf3qK2y9/RreoG8DJwcetTBCa+iNu6ytX2T/3oCl5Tnm1pft3E0y9u5ucXUQ\n86Hzj3CmPkcmAnZef4MlP+IX/79f4HBxgZu9Qx566DzPPvMhxvGIZrNOluViIOFhhPXzIoWcmLzP\nAsnytfJzs9wg3G+8TgoC4oqt3DxniTmLe+7nrFQlGizKLvIvcMK9X5bJV82XKo6huF6OBVuFHVWi\nXbe+03Yg1nK1uB/m0YbKdblfet8AvHywdARUYsrwQ0o5Ae+iY4uYi5POzGVo8cSdY4AU0oY9Mtbx\nvR/4Ez++Nrr0ccu/oi4u6LplwpHVmFKKWq02NegwQ6boDHYVoBbvF06hXHn/0X1dECxTaRaYFi5o\n7rej2z4+vmiqxC9CCDD62CSeOdHMtLl6Oc9yPU6Sdc9K92MxPV9iUTx/TtivRsNcu03nsMf62ml+\n9Vd+g3ZzgfMPPUymDd/45V+jGaeEvsdhr0uzVidMFUn3kGh5hV7vHmnaJ82GJEbTrjVRKiX0ApJB\nj8AYxr0e49GIqF5j9dwFxnc22drZxIwTvnn3gLA1x5n1Z3n2Cz+M7Mf8ws//PKQpKhlz9/ZNfuZn\nf5qde3cJQquVYPlxkQupXY7wONFRNeeqUtlS9b1QgO5arXIHUQWy7r3y/WJtFfkUxlknuTS+X/sK\ng58yWBebS3m+HXG65th8nXAkpTaUN60jMeh0+1w116KtSh1tni7x59brvbh0fh9FKMflupWR5rWZ\nnHpP/UmZE1f5BDLge0cn7VpnE5AOghDIwyLlEaONduXp1QY1Rb2Kz/JEKazYXM2ZKkCdtdsXO7er\n2gd2QqdpOtNKrjzximQlDo6UsLRYqia65VyrZNLHKXgAydHEvR/Yzpr0Ve8pleXPlO/87iiyY28L\n2xcT5ttYPxq+7zMcxMy3F3nx+ZfYv3fAU49/lDGG/YMDlleWkbv7aD0gjRUDNUKNFFfefov6aMDC\nSotxssC+6qK1YqQzWvUmWWIQqSLuDqjJgOFej1pznquvvsGz5y/x7Cc+hsbgjxRhvcnOYo3Xe7uk\n3R6f/kNf5Mr2Fq//5q9Sq2mSeEQQeiRpjMz9nxiTcxCFKFEfH9fy2Ln9dGw8K+b1/UA8CIJK7an3\nOi5VADdr/ZVTua6z6g7VHk+L8socieurqMhXSjml4VRFhEzP62njt+LZMrEohHCiYx3Ve1Z4xJPS\n+wrgLngVyQVEAE/61hzYFLElcx8PUtpDCCFQWAbZN4rCYMkTkjAMc5DOD0tFzkgLkQe6PSrTndhl\n0CtMft0Yli7L406CMshW7ebF90Js5N6btDunBmZN6nIdi02tzMbdj6oVAmYYeU2VVd7Q3HbNUuHL\nHEqnnF91PapA/uRJfD/ZehFzsJxPHKc0621Uanjhey+ysrjKuzvbzM3Pc/udm0ilWF5dRsU+XuKx\ns7eLkD71VkirWSMQ0GrU2YpThFH4Gjwvw3ghB70u7TShtrBMrT3HwoU1/Myws3OI2N7j4cceZiFo\nEo9TzFKbyx94hNFhl91vvsHu1hZpMuYv/Pm/xKB3iBd6SM+zJueKfN7mQJArU5fnSVn8VT5/OYl6\nPYlLKlKWZZP5qYrwYiWAm7UxuHV1530ZPN9rKpfnrt+T1l1503HFokUdq7DJnffl5wvjqTKeTHy9\n5JKD4nohDnbX0f18n5TT+yoDL6uAuR1XPFMEEzWmoJjzs2KlrF6sBOkHJFkGQqK1IgxD0jjFN4U2\nS35YmgN4UbbgOHtXrmPxV7beKzrfrW9VKh90VgGvu/sbYyYe9Kqoh/JCcb3Cufrb7kFwEY8PjruO\ntZzPbHewRTpaYEX+xWIrjimqFp2NpFNok1jNoOMb1jQLOb35ldU9y0mIk9nMsjdjYwwID09CmmY8\n/+0X2Nvd44lHn+Qg8unf2yfrdGgEPj01ZnFuHvqC9bMNhhKaq6fY3dtjPpCcWllhpz7P4PCAXpzg\nBQ38KGDl3DnOPvYBRHuBu4eH+CureMt7DPe67HQ6bH7z25xZWOW5xz9M3WshDxJWo3keO/Mg/+JX\nfonPf+4zPPvMs3T7e4AlFgwCX0iE8dBGgTBWG4RqMC7PNyGOu5QoOJ7ikn2/ANfZQBI4gaLdgzc4\nvsEfH49qcUfVO/ebk+VUpn6rABiOPIO675WtuI2xYqIgCKbyKNaBC9zFBuZi71H/W2Kh6Fsvj61Z\n1M/Nt6ov7tfu9w3Ai4E/abcrnhNKoI1CiNxQQYDWtrFvX7/B5YcfIag3GA8OqUc1PD9A4KGShEat\nThLHVv1KCLyCHVLKHmbClOy9XH75upsKGVvx6VIQ5Y4vJsKsCexSBWXquxhsV/e7mECuu1R3YpX7\ntdgMC8B3KYgy11A1HpPFr6epqOJeVbsKXeJyv7j9c7QxTPv0fq/JzbuqDp7wcidnFqyEsLrURkg8\nJN9//gVMqunsH9K+fIadvR1WGnUykzHSmt1eh8gIMt/nwac/yJlLF3n5699jtL/DYX9Ic3GFODX4\naMJmm4XlJWLfYzQe01z0GSUZg3FC5nsMjcZTBoXmdmeP5c3bPHPuHEtRi348ZG59jR/53Gd5+oc/\nSlaE6VIpSimiWsNySoY8YrpC5m5Vq4CrTH3avplm8afGaopYMJxECJY5TleW646zm7f7vTxPXc+k\n7jhWEUaz5oZbZplSLlP2VRbZVcRSOS/Xe2hV3abPrKo3tfsldy3NcnnhpvfdkGfWQBcpyyxwF0FE\npRRIz7Mx6xB853vf4//8R/+YT/3gp/ncj3wakyqUkZBpamGNOEltp8h8oolC19yfiDBc8KzyqFYG\nNBdo3XpXAVMV5e3+LjYId7JVnXgniXXM40mPQoOmAGWRG3BYYDrurKlqsbl1KFtolhfDBHgBP9eL\nLYN41eSU3rSxkZtXVf2q+nhW3kWqWozT93Pd3QljIsBYz3Zvv3aFYX/Axvo5DvcOuLZ1HQ4HLId1\n/NDD9xscxockytBeWqW9fIreMGXj/EVWPvo0m3fvcvrRh3n1+e8jk4xs2CPwfITSjHf3WF07QzTO\nqGWGOAzpBT6hFOjQkPiCt/fvMP/uVeT1ZZYeuYh4aJ3z3UdYXFxGSkOaQa3eIAgD+oOxrb7tEMAg\n5FHAhvL4ueN4RFkflzO7/X10z66zWf1+EuHlluvWpVwvl8ssj3XVWnHLrEpl8HUJhTJR42p/HOdI\npvGoTJwV7a2yaq4ihtwy3st8djezKjwqp/dVhFKWW5U7AIqDCEBYxXdj7ILMtCaoNwiiGk8+9TR+\nFPGPf+afcubMBj/6mc+xONdGpakDgnmHYK27PI4s/zzPm1CnVVR4sRO6O6nWeuJTo2qAyru7S/mW\nWbDi0y2vDODWiCbXXcYBQ6YXSnGtAMmiHoUjK7c+7qQs6n2/xVlWmZo1maGQ0U7LKas4FftbTOKa\nGvsSiEKTfLYc3+VAqpLRGopysByEQKOV4rd/+6ssLy6zv7vH0sIyg6s3CQ0cej71Rh3jBzTrTQZa\nsHbpAeKxDXXVqtdpGkXmS05duMC1q+9wcPs2gdYMuh2iKESPBrR9j9OLc9SDEFGv0Q0lDIf0R0M6\nvmGofL7z2wfgCR5uB8j5BsqHU6c3OOjsoIR1tKR0ShhG9hBHmNywXKK1QZvj4FW1jqrEV+5GWh77\nKi5yMq4Ol+bOicr+r6D0CyKlTCGXlQruZ61dzrf8OQv0Z20E5TLKYO6+V5aju/UtlzWLKKxKbuSu\nKg2fY8+fePc/YkpzcC3AZRYLbGV3gCioDkmcpSwsLNAdjWi12ozSQ5ZWVlleWeHG9ev8rb/7d/mJ\nz3+Bjz3zLL70MSoDY6mwKploGWBsueLYdXcgpJQTh/TuxCuzUe4mUQXw7nMuR1Ck8uIoNppiAdTr\ndaev8skrBZ4np54vNpwqqqDMDRSgXN60bIWOL7hZi8podWxBuZuH2zbfC9CWvHRzOAY05VTeKI/V\nwdrgTaz0tDH4wuflV17h7u07PHTxIbyWT7/TZVkJxmTgCXo7uxgkLC0TbJwhmJvDJIJsqFi6eJr9\nuzd45fsvMu/VObt+mtH2PTyd0esd0ppbJ2j4xGZMPxvRu9sjjgfs7tylvt9BR4JxHdq0aA0VyStX\n0A+cR15Y5+Mf+SgvvvgiZ8+dttHO6xG9YZ8giPKzWGtVKlEgfYQ8zgVWUb0mP6eoAsYyoFgOtbK7\n7VjlIFMGTfdalR64C0yFF8kyten+uW1xtcFmzYMqyr14x73vyrvdze53u4GV5335vMvt5/JmMCvd\nj9Mop/cNwIOgOJm1B5THnVPZT19oUm1QKkNkCuEL/DAk6fQJAo+l8+u8u7fNqheQGMOHH36Upx76\nAK++9hpXrl3lx77wBRYX5vClRChF4PsYpZBZan2Na33kB8ErJmauO1wCc1f+5Ype3FR4Myzeq6Im\nyvExywNWJV4qfhdlu9S/++eyi9Lz8LycChdySjRTgGOxqIs2lr0xloHeKJNbtjKJBJ+3Nm+fW9/j\nVH6Zsp9MdnX8sNJyE9bPx6z5XK5vud+ksEENijHwvYjRSPOVf/MtTp26QL/TZ6FeZ39/k2atznjY\nQZgUz8/ItKDT3eHspbMMuoeMuyPOn11HJCP23r5GNBzzwr/9Kj/+4z/B3mIbNRCkPozHGZ3xDiZ6\ni26SEnfHREGApyTGC6n7AWkywpgxMYK93W2GuwfIxQaNxgLbt+9y6tIZZCYhyQjqEZmAUIGvvdy3\nh9WsKix23Y3vOLgW433Uv2XZ7/H5djLrXgal8ly9HyHjEiFlsCoICTdPV3tjVn3gOIfopvI6Ka5V\nle+2cRaglwG8StbtEiDuBlZF5VfV5/etCEWprAQc09Rs8aeSlMS37Hjk+SDtYvcTEJ7HmUvn+fK/\n+ypRnFJv1DnsdlGex/qpU7QXF/jpf/KzfOLjH+dHP/dZhp0uARLf85BG4UvITOGnTeT+JGxZRitr\n5ensiF6uzlVQD4WeNlQfglQNZAEkBZiWT6JdWXVhzCOlRClNlmZTeYHlZIq+OvKNfbSwXY4jUylZ\nluWgZ/C8gto/6u+i7DJ7bCeSmJThyqyn1SkdUDbHdWhnTcgsy6bKF8LV6T/ZKrCoQ5XoIIg80lHC\n8vISnc6ANFFsbx+gVAAyotWIGOzfI+7tkxpJvVYDHTPQCf0kYX79NHWjuXPtCr3+kGYIO1t3STfv\nsb4wRz8Z89orL9Bq1dja26V/2EelB/hBQC1qsLA0z9Z4hBeGRM0WWQbNhRbzPmTJkGE/JqtL6s0m\nKqwT+jX8VCOlRygDUDEmgBTrhdDXHgaByjWyPKcP3M/yHLT9f1xsVy2OgJPEVkU6icU/TtUfiU5m\nPV/mNqu4u7LqbhUX4BI6Vam8GVRRx+6G4yoNuOWV83c5WbfNVWH9ykScWzcXF3/filBcirt8yAC2\nIVmWIZUkA3zPy2WZGqUVSguUkiwvLZHFCaM0pu0vsr4xT9RosKEUg3jMD//QZ3n9tVf5G7/zU/yn\nP/mTPHz5MsNBn5rnE+eROIQnSVVqT/h965FMeB44VIQLzkkeAsoVdRRqhkW7ygMchiFw/IS8SFWD\nOcVOIibybze5vq2P+nGS6wRQi8lQr9cdKqgQk0xTRVrbCONFOgJVJtaiBVhWhbYrt6Gcj9u24neV\nV0F38zgp//Im4m6m3W6fRqPGzZt3aNTbRLUmL3z/qzzxwafYvnkHkSXc3ryLjyAA0JBmikZzHtnQ\nnDl3if3+kGFnwPryMru3bnHz2hWaKiPTKX6jxp3bt1laXKTX64JWJKMxUkr6nUNW1lfw2jXkXI1G\ntkgvSzAC1tdXGB4cIJRP2uvz0je/w4dO/wQ6U+zcusPNF17l8hMPM9KCIDWIwEP71rOeZyQmV/0U\npfa6/VcGt+pD3uMbqh2baqB18zlpQy5zrSeJBoq6F4RBGQDdTcidP+67RSo2ieKzqrwqnyNVG6BL\ndBSgXxWsuZqDObrnipyqrD3dOlRZqJ+U3tdDTJiW8Zbl4cYYfAKCvE3CntjgiwBhDJ6UhEgW5+a5\nuXWXx1bOs384QPZGjJKERqtJ4EU898zHMEbzz//lL/Lxj36UT//gp0jSBBmFCGMAjZTWFaXWdoMA\nAdIeGiqVIcWRv5YCNAuDhuIgrdhtC3ByzY2TJJk6oHEp3pP6p5hAWukJMJcnWfmwsrjuTm7brmyK\nCpLSw/OmfaUX77iHg1OiIDUtv3frWAYSl3pxx7RKrOKC+6TNJ7DDRer3+5P6lsVKAFG9TrfbY2l5\njSw1fP3ffYvdnT3qG3NcvHSRrRvXac0tMe4eoOIhsc7QGHrdAXOrawRhAz8esdCSRNqweecWYZLg\nSUEgJfUoItHKmsxHIVoZZL1GFIXEo7EV12lNFo/xPUmSxDYAsTCgEkb7h5iR4k6asfVvIj7x8U/y\no5/9PH/tf/6r/NQ/+b84vNenKTy8DIYBJMIQKm1tIDBgjm+e5bGsuleeZ9Op2qrYfcelmE+KYuXO\neaj2SV9+3q1XFTiW61UAf/HdFdOU14FbB3duuRyJ++cSZOV+rVo37txz532ZY3UJnzIHcD+xiZve\ndwAvy5fKjZHSAy/f8SQ2Sr0RCGlItMLLJJ/86Me5/uJrdPo9rF9en7lGRKPWIDOag8N9kizmU5/8\nNC+8+Dx3tjf54k/+QWphiNAZgbBGEjoZE+TBHxA+wvNByNwJfUkWbMzkULCgvpVSE4tNl0L1fX8i\nq3UpkVksnHsCfWStiQ37pI+iC7mp+F1QyGX2FY78uBTPFODvsovFJCu4jHIdpZimlMuT3i03U9Mi\nlOLQ100TUVnJarPI634sZHGIW5Y/FotrnI6pN5qMhjHJWPGdbz/P+fOX0UoTJwmb9+5Rb7WIfEk9\nrbN/cMAw0Yhak9byKbb3+/QGQ85vbCCTMSIeExmF0IJhv0c/GSKjAKMNZ9dPMdCHZJjcDaKif3iI\nTBRZZqxaZaYxCrbu3uNzX/gM7165BkoybkREa8vc7uxx4aEP8vADD9GoNag1Gsg0I5CS1DNkwh5S\nB7nqqMdsgCtzefcTiUw/N/vZYq65FpjlNFMz6QQKvKhzFXiX53M5H3eeuBxHFWVdntNV3HDx54o/\n3LaWtXfca+XDy6r+cQmUqvv3o7yL9L7qgbtilDKrVHxmRqFyB72uu0jP95BoakgubJzlW1/9Gp87\ndZpOt0s9jOz7aUo6HtMMQhpRyFiPefrpp9na3uJv/u9/m//ki1/kuQ8/xbCzh6czWvWINIkt8PpF\nJA+7YATiWF3LwFU+WS9YL1fmVh6YYiDhaEEUeqrTlCj5uer0gJeNIKrqCHZyFZOxsKRzxRhunu4E\nLC9SrfQUBVNlNTppv5lebC77WWZBi03PDb7gUiwnsepuHscoqNBDpZrQq3HlnXdZmF9iZWkZreDm\n9esMhwOEFMw1W/gZzIc+jBLqC0t4zTn27m4y325jjOLGtStInRJFPnGSoZXi8KBLKgxhGLK6uESj\n2aQXp8TjGL8eMej2qC2ust3rsrRxirlTp/H7Yxqex6kzF7lw/kF8GfLa7Vt84EPPcGvzLkko+eBz\nzyCiBqNxTK3dJuv1ERp0CGMkNS0QBlQFzpap2N8tiNu+u/+z7jwpJ9das2pt36/8k8QHVdxalQbZ\nLO6j6rCxStXPJbSmuNB8nlaJgN3+mNV/xdx2KXiX8HDbcL/0PlLgHraurvhkWnZcgJOPzGPp5Q01\nhsQY/ChEZIbTy6uYmo8wCaiELDFIIAxCVk6t0+n3kKEkiJbojQesraxw+dGn+bVf/RUOD/b4zKc+\nQSgMo2HfWrlpG7Xayw82rbN3gUJPgUMURXlrHEDNlN1scsrbpQaOg3L1qfZ4PJ7I04Ep9qtMlfoV\ncvGCglJK5Sw2WNNra7lXuAbxZM4tGFWpYlhssNMTVOP5wdSpetkXxmQi6+Pm8AXQFg6RiucnboId\nHxtlJ0BVqegXpdRENXWqLtpgMkEg4caVG5xZO824P2BpaYleZx9pFKPhmFAYAk8RC0l9aYnLTzzF\nIMlYzDSB0XS6hyTJCJMkBKFPEISMsiTfGBNqQUCqFSgIowjf9+n2e+zvaZ77yMfobt/l9GOPYmoN\netfuoOOMl15+nQtnzrAyv8Tjlz/AamuZ1WfPcOPdG3zgE89x/c23CKOAzYN95j0fmYIRkPiKWmYj\n7Bj/+EGay2HdD0yqxB82mMhsaHCtqKFaa8rVxnLHqTzHqqjUYs5U1f1+FGtRRtUhYRXl7eZ5Ehfj\nrrmyqLJ4tpC9z6qbm1IndoH7eT+Os5zeNwCH476wy5NMCBsE1UNYdTABwggkgkxYK81QeCRaEbQa\n7Nzbot1qk8YxUa3BcDig0+kQ1Wu0ghadww79YR+kIPVq/Minf4i333qDv//3/wF/7I/8YU6treAJ\nGA36RLUa43hswcYPchn09A5bNXGFEFbNjmnqM03TKQ0T9z2X4nBFL7NOud2Du7KBjhtyrninyF9p\ndSxfw7SbSxdkq8bEXYjFvWKxVlErLqC69S76owzaLrVefJ+1MIApTaByP/i+T6oT6vU6Uvtcf/sa\nly4+xGg4pLe/izQJq0ttzDhk1OvTNzEJknPnLtFYXKK3t8/Djz1C3RO89M2voY0irEfESUo9lOjU\n4NcivMTQWpgjVhn97oBWEFGLIuZ9jyCKkEKysXGWGFBhxOHw/2fuzYMsy+76zs85d39rvpd7bV3V\n1VW9V1f1pl0CWwIZiUUwgwwOzLAMWCwTJsYT4LGDiImZMbZngnHMGAwzDkAgIxAIkAwSIEC7hJaW\n1Gt1d+1b7svb737P/HHz5jvv5suWYDzRnIiMzLzvvnvP8ju/8/19z+/8fgFZprh2+w7dXo/HHzmP\na0ria9c4duFBEstktb/LAyfuZntng9Dx8qw8SUYWJ8QC0iTLU4GVUp8V/VuWy7KC0/u2XITYC1lx\nSCnvd0wLDnWYxVQGLtOokWmWQ7kd5XJY/PEyki2/T1/kCipP/265HdMWnGlK97C664p6mlVy2N+H\nlVc9oUMxoIVZcaCzTQlZLlDKEJiZQCqxlwdP4GSS1DSYO3aEtbU1Fs4tEEYRW51dwiCiWquRotjc\n3iUIQ6q1GrWax64/Ymu3y733nMGUZ/mlX/m/+cmfeA+uZbK8vEC/s0uzOUMU+PkRcm1Ff6VOTrMM\nlR48SakrmWmKWleWBZIsm3BF/5RRffHcw0zHcZ3F1JjjhblbRhPl03Ll+hTtL9D0NOWgt1sf67LX\nSZlSKzaBi3ceFo9Gpwv0TdMwDPPJZ2RkoeJzH/84rWaLF55+lhPHj/HylYtYhsKouLSrDertFrcG\nOyzOLXHPfQ+wNRjRG/kszM/SW79NHAXML8wx2O0SRCmpH4BhMgp9KjMzzC4u0dnYwnIcvEqdmUYT\ny7LY7uyydvEK8/fdw9ZuH8+wcBwbI0lJSBgRcWNrhSPeCRbaVdrtGWaGXQhjNm/eoVKvsjPoYVVt\nlCExhMSREmlCmhyM4ldWOtOUwGE+85Myc7jy0GXhMESsy6j+vWnz6DD0O81q+JtYEsWzykp7Wv8U\nddLBVbGnNa1e+sKgy3z53sOuTbMCvt73p5VXVYFPowR04ZBSkigQKrf6i+zRmcoQpoklJMLP3eCO\n3X0Xz33gLzlx8iRRmlKdmaHleEjDxDBM0iSjtedXLA3B0fkqi+0WW1s7hEnCo4+9hj/40J/w2IXz\nVOt1LMclDAPiKMC1vDz3nsbJlhVaUQyZx2wpFEnZ/ahAltOQcnnXvDzgZaSr96XuTlhQOuNUaXto\nXZoTdc+yLE/CyqTgFkq5PFbl8StKEWe9fF2fLEUdy0i/aGN5AurIbhrdVJSyJ0shN/vcvJkiYos/\n+9M/5bGHnmRupsWws4uRxvjDHmZoM9rapO5VSV2PhaUjuF6V7eu3qdUrRIHPzWtXyeIQ07LxKjWi\nKCPwuygzRZgGi0eWCZKYzJA4rsPm7i5JnLC8vEymFP2VNU6dOU3omDQrMyQLswTb22Sk+PGIyzde\n4tmLX+PsyhW+c36OumGTCbhz6yrtSpXmbIMokwyJEAKsMCUoKIbs66O/shLT0ep0hZj7jB+mLMub\n0WV6RAixv99SdgGd5laqK87DrIOylVUu03jt4ntlUDjNw2aap0kZWU97xzT6pVx0i6Vcr3L9vt7i\nWy5fV4ELIX4NeAewoZR6eO9aG/hd4C7gOvC9SqnO3mf/HPhhIAX+O6XUn097bhFPu0Bakx4XYwUu\npYGVKhKRkbC3eZBBIpI8QWgKwpTcffYMf3LzN+j7Pl6ljrRdjEoF07S5c2uVYX+AFIKaV2Gm0aRZ\nsxGWweL997HTGzG/dJTFI8f5xKc/jldxuOeuY5CEuLYkTnLyUbcasizb9+0GXZGA2FMg+uAUnxeJ\nGsobQOWDB4XwF26L0+iEct+NBUSR/5mhx78Iw3BisqVpnifU9ewDKF+PvKZPTP09Oj94GNIp5wPU\nPXf0xarwkCmjL91KmVbK/LvuUimEIEpGrKysUK9UsQwT0/O4fOkGUiYYKsE2baIwZNTHoIa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1y8chlzqcXWZgcjAykhSALe9oY38ofPPMdxWxDtdBmtbnH+sXv5zPZnqM+7nDtxD8P5Rf78S1/k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b9V2kEn/LVRlfCHm4HigqWxpN25ZTPfBB6aa2Shz9LGXRqAbLnFg+itBzKDyLY53z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fXVV/mVf/dLHB0d8a//1b9iZWWFH/6hH6Lb7dLr9Th//jz9bo9WENJtdxCVvm/nHnqExx57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\nwNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x116bcb210>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.gray()\n",
+    "plt.matshow(predictions_df.values)\n",
+    "plt.xlabel('Classes')\n",
+    "plt.ylabel('Windows')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's take max across all windows and plot the top classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "person          1.835771\n",
+      "bicycle         0.866110\n",
+      "unicycle        0.057080\n",
+      "motorcycle     -0.006122\n",
+      "banjo          -0.028209\n",
+      "turtle         -0.189831\n",
+      "electric fan   -0.206788\n",
+      "cart           -0.214235\n",
+      "lizard         -0.393519\n",
+      "helmet         -0.477942\n",
+      "dtype: float32\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "max_s = predictions_df.max(0)\n",
+    "max_s.sort(ascending=False)\n",
+    "print(max_s[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top detections are in fact a person and bicycle.\n",
+    "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def nms_detections(dets, overlap=0.3):\n",
-      "    \"\"\"\n",
-      "    Non-maximum suppression: Greedily select high-scoring detections and\n",
-      "    skip detections that are significantly covered by a previously\n",
-      "    selected detection.\n",
-      "\n",
-      "    This version is translated from Matlab code by Tomasz Malisiewicz,\n",
-      "    who sped up Pedro Felzenszwalb's code.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    dets: ndarray\n",
-      "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
-      "    overlap: float\n",
-      "        minimum overlap ratio (0.3 default)\n",
-      "\n",
-      "    Output\n",
-      "    ------\n",
-      "    dets: ndarray\n",
-      "        remaining after suppression.\n",
-      "    \"\"\"\n",
-      "    x1 = dets[:, 0]\n",
-      "    y1 = dets[:, 1]\n",
-      "    x2 = dets[:, 2]\n",
-      "    y2 = dets[:, 3]\n",
-      "    ind = np.argsort(dets[:, 4])\n",
-      "\n",
-      "    w = x2 - x1\n",
-      "    h = y2 - y1\n",
-      "    area = (w * h).astype(float)\n",
-      "\n",
-      "    pick = []\n",
-      "    while len(ind) > 0:\n",
-      "        i = ind[-1]\n",
-      "        pick.append(i)\n",
-      "        ind = ind[:-1]\n",
-      "\n",
-      "        xx1 = np.maximum(x1[i], x1[ind])\n",
-      "        yy1 = np.maximum(y1[i], y1[ind])\n",
-      "        xx2 = np.minimum(x2[i], x2[ind])\n",
-      "        yy2 = np.minimum(y2[i], y2[ind])\n",
-      "\n",
-      "        w = np.maximum(0., xx2 - xx1)\n",
-      "        h = np.maximum(0., yy2 - yy1)\n",
-      "\n",
-      "        wh = w * h\n",
-      "        o = wh / (area[i] + area[ind] - wh)\n",
-      "\n",
-      "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top detection:\n",
+      "name\n",
+      "person             1.835771\n",
+      "swimming trunks   -1.150371\n",
+      "rubber eraser     -1.231106\n",
+      "turtle            -1.266037\n",
+      "plastic bag       -1.303265\n",
+      "dtype: float32\n",
       "\n",
-      "    return dets[pick, :]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "scores = predictions_df['bicycle']\n",
-      "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
-      "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
-      "nms_dets = nms_detections(dets)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+      "Second-best detection:\n",
+      "name\n",
+      "bicycle     0.866110\n",
+      "unicycle   -0.359139\n",
+      "scorpion   -0.811621\n",
+      "lobster    -0.982891\n",
+      "lamp       -1.096808\n",
+      "dtype: float32\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.imshow(im)\n",
-      "currentAxis = plt.gca()\n",
-      "colors = ['r', 'b', 'y']\n",
-      "for c, det in zip(colors, nms_dets[:3]):\n",
-      "    currentAxis.add_patch(\n",
-      "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
-      "        fill=False, edgecolor=c, linewidth=5)\n",
-      "    )\n",
-      "print 'scores:', nms_dets[:3, 4]"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Rectangle at 0x118576a90>"
+      ]
+     },
+     "execution_count": 6,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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SWO+oFhVKacp+iXUpTAeE1IwnM2bVgsYYZrOKpmlojGPQLwnWdUpJCJU8FYlQGm89WkaY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W6Ho01Mx7k+XnVkV+t3SCYqsljKn0N1IirrMAUn5rlqgjX9XHPiMIMFTBk9Smb176Zcuv4ryPPp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wenNsjbnpcteZYHjsvsSnvQ2N63I73aBJkm6Ge5gS0tZwG2//+gtcCoL8R7bNYSxS5SGPqx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\nEqcl9b0u1vyhKNZYn87kIrmBtXVFXTbkaUqRO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-       "text": [
-        "<matplotlib.figure.Figure at 0x114f15d10>"
-       ]
-      }
-     ],
-     "prompt_number": 9
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
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+       "wNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116bcb210>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
-      "\n",
-      "You should try out detection on an image of your own next!"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Find, print, and display the top detections: person and bicycle.\n",
+    "i = predictions_df['person'].argmax()\n",
+    "j = predictions_df['bicycle'].argmax()\n",
+    "\n",
+    "# Show top predictions for top detection.\n",
+    "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
+    "print('Top detection:')\n",
+    "print(f.order(ascending=False)[:5])\n",
+    "print('')\n",
+    "\n",
+    "# Show top predictions for second-best detection.\n",
+    "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
+    "print('Second-best detection:')\n",
+    "print(f.order(ascending=False)[:5])\n",
+    "\n",
+    "# Show top detection in red, second-best top detection in blue.\n",
+    "im = plt.imread('images/fish-bike.jpg')\n",
+    "plt.imshow(im)\n",
+    "currentAxis = plt.gca()\n",
+    "\n",
+    "det = df.iloc[i]\n",
+    "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+    "\n",
+    "det = df.iloc[j]\n",
+    "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def nms_detections(dets, overlap=0.3):\n",
+    "    \"\"\"\n",
+    "    Non-maximum suppression: Greedily select high-scoring detections and\n",
+    "    skip detections that are significantly covered by a previously\n",
+    "    selected detection.\n",
+    "\n",
+    "    This version is translated from Matlab code by Tomasz Malisiewicz,\n",
+    "    who sped up Pedro Felzenszwalb's code.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dets: ndarray\n",
+    "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
+    "    overlap: float\n",
+    "        minimum overlap ratio (0.3 default)\n",
+    "\n",
+    "    Output\n",
+    "    ------\n",
+    "    dets: ndarray\n",
+    "        remaining after suppression.\n",
+    "    \"\"\"\n",
+    "    x1 = dets[:, 0]\n",
+    "    y1 = dets[:, 1]\n",
+    "    x2 = dets[:, 2]\n",
+    "    y2 = dets[:, 3]\n",
+    "    ind = np.argsort(dets[:, 4])\n",
+    "\n",
+    "    w = x2 - x1\n",
+    "    h = y2 - y1\n",
+    "    area = (w * h).astype(float)\n",
+    "\n",
+    "    pick = []\n",
+    "    while len(ind) > 0:\n",
+    "        i = ind[-1]\n",
+    "        pick.append(i)\n",
+    "        ind = ind[:-1]\n",
+    "\n",
+    "        xx1 = np.maximum(x1[i], x1[ind])\n",
+    "        yy1 = np.maximum(y1[i], y1[ind])\n",
+    "        xx2 = np.minimum(x2[i], x2[ind])\n",
+    "        yy2 = np.minimum(y2[i], y2[ind])\n",
+    "\n",
+    "        w = np.maximum(0., xx2 - xx1)\n",
+    "        h = np.maximum(0., yy2 - yy1)\n",
+    "\n",
+    "        wh = w * h\n",
+    "        o = wh / (area[i] + area[ind] - wh)\n",
+    "\n",
+    "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
+    "\n",
+    "    return dets[pick, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "scores = predictions_df['bicycle']\n",
+    "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
+    "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
+    "nms_dets = nms_detections(dets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(Remove the temp directory to clean up, and we're done.)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!rm -rf _temp"
-     ],
-     "language": "python",
+     "data": {
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+       "GuSEjnyS7YmSg/GILKPrgy4Jwirh3/cdnkAX+YhOnz4NDNUApWrQCF0DxnFVYIyC0WG/8RDZKwEa\n",
+       "9t2Br2xlK1vZH8aU6kAd7XgqU7NkpbIYo9f0b7UkqtF7OwJO1HW7h/sIJEtyDJO5ILDnLo7jj6ef\n",
+       "+8eui9rHZc+xw9bj1Y3llSbl93GUfmUrW9nKVvad2MuN0u+LA1/Zyla2spX94e3mc5orW9nKVray\n",
+       "V62tHPjKVrayld2mdssduDHmfcaYbxpjvmWM+flb/f77ZcaYXzXGXDLGPDq6b9MY82ljzO8ZY37T\n",
+       "GHN49NgvxDX6pjHmJ/fnqL+7Zoy5yxjzWWPM140xXzPG/PV4/4FdF2NMYYz5ojHmIWPMY8aYfxjv\n",
+       "P7BromaMccaYB40xvxF/P/BrsmcY5bv9H3DAE8BrgRR4CHjdrTyG/foPvBM4Dzw6uu8fA38r3v55\n",
+       "4Bfj7fvi2qRxrZ4A7H5/hu/CmpwCzsXbM+Bx4HWrdWEt/kyALwDvOOhrEj/r3wT+I/Dx+PuBX5Nb\n",
+       "HYG/DXgihHAhhNAA/xn46Vt8DPtiIYTfAa5ed/cHEck64s8/E2//NPDREEITQriAnIBvuxXHeSst\n",
+       "hHAxhPBQvL0LfAPRUj3o66IEGCIsKefNgV4TY8ydwPuBf4vSeR7wNYFbX0K5A/j90e/PxPsOqp0M\n",
+       "IVyKty8BJ+PtM8jaqH3Pr5Mx5rVIhvJFDvi6GGOsMeYh5LN/NoTwdQ74mgD/DPg5YDyOedDX5JY7\n",
+       "8BVm8WUsSO53s/X5nl07Y8wM+O+ICPbO+LGDuC4hBB9COAfcCfxJY8y7r3v8QK2JMeZPA5dDCA8y\n",
+       "RN977KCtidqtduDPAneNfr+LvTvlQbNLxphTAMaY08DleP/163RnvO97zowxKeK8PxJC+Fi8+8Cv\n",
+       "C0AIYQv438D9HOw1+WHgg8aYJ4GPAj9mjPkIB3tNgFvvwL8CnDXGvNYYkwF/Afj4LT6GV5N9HPhQ\n",
+       "vP0h4GOj+3/WGJMZY74fOAt8aR+O77tqRmaJ/x3wWAjhn48eOrDrYow5pmgKY8wE+AngQQ7wmoQQ\n",
+       "PhxCuCuE8P3AzwKfCSH8ZQ7wmvS2D53kn0LQBk8Av7DfXdxb+Lk/CjwH1Egf4K8Am8BvAb8H/CZw\n",
+       "ePT8D8c1+ibw3v0+/u/SmrwDqWk+hDipB4H3HeR1Ad4APBDX5BHg5+L9B3ZNrlufH2VAoRz4NVmN\n",
+       "0q9sZStb2W1qq0nMla1sZSu7TW3lwFe2spWt7Da1lQNf2cpWtrLb1FYOfGUrW9nKblNbOfCVrWxl\n",
+       "K7tNbeXAV7ayla3sNrWVA1/Zyla2stvUVg58ZStb2cpuU/t/6S2bnP6vZqYAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114f15d10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.imshow(im)\n",
+    "currentAxis = plt.gca()\n",
+    "colors = ['r', 'b', 'y']\n",
+    "for c, det in zip(colors, nms_dets[:3]):\n",
+    "    currentAxis.add_patch(\n",
+    "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+    "        fill=False, edgecolor=c, linewidth=5)\n",
+    "    )\n",
+    "print 'scores:', nms_dets[:3, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
+    "\n",
+    "You should try out detection on an image of your own next!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Remove the temp directory to clean up, and we're done.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!rm -rf _temp"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "Run a pretrained model as a detector in Python.",
+  "example_name": "R-CNN detection",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 7125907f35e..6d629c5b635 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,620 +1,13214 @@
 {
- "metadata": {
-  "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "example_name": "Filter visualization",
-  "include_in_docs": true,
-  "priority": 2,
-  "signature": "sha256:64c88129e2eeaa956e4c8a26467ff6119f24ea3d7ef15f8217326249973bea8f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n",
-      "\n",
-      "(This page follows DeCAF visualizations originally by Yangqing Jia.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "plt.rcParams['figure.figsize'] = (10, 10)\n",
-      "plt.rcParams['image.interpolation'] = 'nearest'\n",
-      "plt.rcParams['image.cmap'] = 'gray'\n",
-      "\n",
-      "import os\n",
-      "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-      "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-      "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-      "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-      "                caffe.TEST)\n",
-      "\n",
-      "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
-      "transformer.set_transpose('data', (2,0,1))\n",
-      "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
-      "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-      "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Classify the image by reshaping the net for the single input then doing the forward pass."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net.blobs['data'].reshape(1,3,227,227)\n",
-      "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
-      "out = net.forward()\n",
-      "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Predicted class is #281.\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(k, v.data.shape) for k, v in net.blobs.items()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "[('data', (1, 3, 227, 227)),\n",
-        " ('conv1', (1, 96, 55, 55)),\n",
-        " ('pool1', (1, 96, 27, 27)),\n",
-        " ('norm1', (1, 96, 27, 27)),\n",
-        " ('conv2', (1, 256, 27, 27)),\n",
-        " ('pool2', (1, 256, 13, 13)),\n",
-        " ('norm2', (1, 256, 13, 13)),\n",
-        " ('conv3', (1, 384, 13, 13)),\n",
-        " ('conv4', (1, 384, 13, 13)),\n",
-        " ('conv5', (1, 256, 13, 13)),\n",
-        " ('pool5', (1, 256, 6, 6)),\n",
-        " ('fc6', (1, 4096)),\n",
-        " ('fc7', (1, 4096)),\n",
-        " ('fc8', (1, 1000)),\n",
-        " ('prob', (1, 1000))]"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(k, v[0].data.shape) for k, v in net.params.items()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "[('conv1', (96, 3, 11, 11)),\n",
-        " ('conv2', (256, 48, 5, 5)),\n",
-        " ('conv3', (384, 256, 3, 3)),\n",
-        " ('conv4', (384, 192, 3, 3)),\n",
-        " ('conv5', (256, 192, 3, 3)),\n",
-        " ('fc6', (4096, 9216)),\n",
-        " ('fc7', (4096, 4096)),\n",
-        " ('fc8', (1000, 4096))]"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Helper functions for visualization"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-      "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
-      "def vis_square(data, padsize=1, padval=0):\n",
-      "    data -= data.min()\n",
-      "    data /= data.max()\n",
-      "    \n",
-      "    # force the number of filters to be square\n",
-      "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
-      "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
-      "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
-      "    \n",
-      "    # tile the filters into an image\n",
-      "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
-      "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
-      "    \n",
-      "    plt.imshow(data)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The input image"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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677sZausTsgLUkjKCiPQkQVnziTnp+Sw1UCw93swJ5bPOU/SdS6jIeRmAIGsn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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR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\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\nAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first layer filters, `conv1`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# the parameters are a list of [weights, biases]\n",
-      "filters = net.params['conv1'][0].data\n",
-      "vis_square(filters.transpose(0, 2, 3, 1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly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\nRK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv1'].data[0, :36]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+\n+uv2m7/5m/b666/bq6++aj/3cz93KhJORUVFRUVFRcUHEimNlMeP/diPTf/Lf/kv0xdffHH67rvv\nTqfT6fTWrVvTF198MWijPvvZz4b7/9bf+lvTz3/+81IjFZMw/EkV9y0uLs6cjjudTuMUq07LSgLi\nE7CSdiAtDofD6fLy8oxUwf/hxN9GWlL1g0aKf3/hhRemL7zwwsyJGnVtIxl46cX3V7/fn3Y6nVAO\na36uXbs2vXbtWrh/MBg0pByuN9oRk4a4PFzz46okUP8M6qLayxrB2D1cHv5jCQ1lKQkppjH07eA+\nn0cqPs3/eNxUXVK/xeqdktpPQ3Jt01+oS04L2Fb7lFvXqo9ykrXab8bj8YymWNVPadZK29FGI9VW\nw6i0I7E17OcEl9tWI5HqA+7LeeaYqkupVgzPpvaQ3FpBWdwOfg+Ab0+3201+p1jbqcpT3zF8A/v9\nfrAaseZKadTUXE/tBak9ejweJ60Qajzw/VlfX29cV+X0+3051uqscSKNFONb3/qWffGLX7RPfvKT\ndvv2bbt69aqZmV29ejXwkN555x179tlnwzPPPvusvf3226VFVFRUVFRUVFR8R6GIbP706VP7iZ/4\nCfuVX/mVQPYFmMSlELuGzNae9MZBJkHcXVpasnv37s3cNxUB4BQBlYmvitjM9Zz+fyIbPwNiHciI\n0+m0QSBMkeJz4LJAHNzY2LBvfOMbM/cNh8NQbirwGL9Hkb+Hw2Gj/fwOkBIvXbpk7777buN5Xx4T\nD1UIC+5z1JvDB4CUCbDDADCZTGYI+2ZxQjqTeGPwZfr70UaeT5iL9+7da4SF6HQ6kqTq34d7+d9Y\nXjB/v7oWcy/3z8T66iSBJ5VLfGodptqhrufK5+dU/VN55mKu656kW0pL4Lqqd6f6xcwazh/qPi4D\nf6f23dyzJfU3i2dCSCEWdBEAmRf9s7W11djL+dug1g9+U9+QnZ2dIueF2DVF6seegb5Q5eJ3s+O9\nIZf1QM1P7C87OzvJEDQc7gXOSQrb29uNkAO9Xq/xdbyGBgAAIABJREFUG/cHjzmCFjN8qBgGOzup\nTBipfQnv3d3dnZkLXE8ut9/vzzxjdtRXmGMoQ+X9m0wm4T2oc7/fT+7lMWQPUvv7+/YTP/ET9tM/\n/dP24z/+42Z2pIV699137dq1a3br1i27cuWKmZk988wz9uabb4Zn33rrLXvmmWfkeweDgZxYKysr\nwSMLHc6HqI2NDTMze/DgQfgN93W73XCIwLvZi40PED6cfafTkRsYns11ro92zhFt+ePpPxQcmRUT\n4datW6FcvENFSuaJj/ZsbW0lNz7uA35fylPu0qVLZmZ29+7dpOegqhf3Pf5Gny8sLNijR4/Ce8xm\nJzcQiwWmUPLx29raarRDRW3nAyG/12+WDCzqGPz455Lgpg5XsQMBf2RwzXsVKs8Wxjwx3Eo+WrHD\nf9tDZK5M1J8Pm+pe5elZ6hGYa5PyvPP9qTxvDw4OGv2vUqZMpzr5tkKqL+fhsfp5zvOYy0plR+D1\n5r3ccod/zGPez9ShOJciKFUG+mVxcTHsWWp9cxv9oYTbq1LJcF3wG3vgpfa9nFckyptOp41DU+kc\nHwwGoV4Yk/X19YZiA/fyfQqpa1yvDnmuKoGB40T55OvT6bQRKX00GjXm1nR6nEEAv3G52Eun06l9\n5jOfSdY7adqbTqf2sz/7s/bSSy/ZP/yH/zD8/qlPfcp+/dd/3czMfv3Xfz0csD71qU/Zb/zGb9je\n3p5985vftK9//ev2fd/3ffLdo9Eo+9GpqKioqKioqHi/0aHAnrmDVJJs/nu/93vTTqczffnll6ev\nvPLK9JVXXpn+x//4H6f37t2b/siP/IgMf/BP/+k/nX73d3/39MUXX5y++uqr8r0mSGNM8FLktStX\nrkyvXLkiyXeerOnfj79z5HCQ33CfIsktLi6G8pj8ze/G+1MERSbheUKeIsjH/kuRNNlVluuC8nIk\nU9WvnhjPBEdFsGQ3bz/mTIJMkWdV6IfYfyXurnxdkTTRP2ruqPFS5fl5Pu8YqnnMdTrt/it5n7qu\nSKfzEOzbluv73LvMx4iuar6ftgt2ai6q/lJOJ3zPSUjQbUMIpOYd14X3dO/Ak1uH2PO5TaoPuC6K\n3K/23tP8L0ea5/+w36UcGzyJPDbnuB94TgPLy8uN/sC+FesH3o/V/Lxw4cL0woUL2TmEfROEcDXW\nykEi9m2bd+3hnaPRKLtWUg5m3Bdqj8EcjyFp2vvBH/zBqNr3v/7X/yp///SnP22f/vSnU6+tqKio\nqKioqPhTgXPNtcccFGXrZ2I7eFO5qOfKZuzt0WwzBiltb28vlF1i62XEeC6eIB3jUShyo7o3xZFi\ngOC9v7/fIH3v7OxI/osiNXK0eQ/1Dh4bNQ5+jBWRldueGusY0TrlUKCeZf6aJwXz/GSeGPoF13Z3\ndxtzhudYiscQmzv+mdycyM13Bf9sjq/FmIdDhTJL24H5gn95bqq5MZ1OZ+Z5qrxUubn6c3kxxPqS\nia5mOsJ97N2+z3Nty6FkP+EyVF/xNbWHM1cN1zwvxUzv/34sudxcvkvflhiHy9dTOX8oHmgpb5P3\nmhyfTbVJrTM/HrH7GKm9HFBjrXi9OQcE/pbMu09gjpiluXRmx21nXjFzC08KkPpT6+3cDlKrq6v2\n9OnTBiHz8uXLdvfuXTObnejKU8qTbnlygwDI74599AE/6OpjzQtSpUJQmwSXmfPIiSE2iL5fbty4\nYe+8807j3XxwxCJQH2nuA38oKa2Dmdnq6qqZHXt8sNdhKn0LI+V5FeszVRf/DB+QAE6jgHKHw2Fw\nMuD3rq2tmZnZw4cPw/MXLlwws+MDP8+Jth9rBj9b8p7YARMCA4996sOX24BK7uP5ospS7VGJrxmp\n98yTKJo9RFPJedVHK3XojB1A/JxW4xXzCCs5XPP7+ECdc1AogSJL817IDii4LyUMpZKcs9MEtx97\nEtalam+bj6evn+pT3i9Sa5C/P6lDkxrzjY2NsFfG9kVFjE4J61BEsMc0Dhvdbre1N6bqX3iz7+zs\nyH5XTj2evK4wHA6lsAFwu9XcBkr3XjVP19fXzexoT0daudh7atLiioqKioqKioo5ca6mvdXV1Uao\nAwa0GuxWyupZ/8zS0lJ4N6sw/amUEx6revHJf15tAkuVpUk8Uwl5FVQySga77aLc9fX1mdARXF+z\n4xO5SnTLcUtS8U2URH3hwoUQ6oAlUa+14XAVKr4JoKRElna4XiVSEUuJ7M6MMcF86pAnh+orIGba\nS5nTUvfFNE2lSMUW47H38WGm02lRouXYb0BbbUEbsxVrR7xGKmeuTGlAfH081P056kFJ+JCTaKRU\nXbrdblK6LwWX77XK3D8ot9PpNLQObcY1pdVRe2Vq/ikNoVlTu6j2Pda28R7i1y3vjwCbwXhuQJOD\nslKWETzrk+2yOZW17bie0/hjT+D3KU09WyT4/hjUuKHtvV5P0nnmnZedTieYLdGXvMcpjbOax8CU\nQnYwPQR7ctVIVVRUVFRUVFScMs5NI+Wl+Hk0PyDnKS0UtBqdTmcm4raZts2WSvw5iSqlqTGbldbM\njk7leB8TlVOkP2UTZo0D/+2fee6552aCppodaYhUADMvSU2nzWjy0+lU9qvnVyleEpfLZEilQVJS\nkNeysLYoJTWtr68HaS2l/RuNRqFtmFus0WPJFpINB1ct4euoeXcSZ4MYudVLiTmybEqDFFsrag2X\ncFAYpdpbBdbkASyht43CrNDtdpOSeWr98xrIOWaUagFL0MaJYJ4+x3NewzGZTIo4izyf1P2soU7x\nYZQmpC0PsJRYHtMaAtA4KctHbP2Uam2ZBI+/FfcNdWCNj+ILA8vLy+FZjmaOvRn7rNIgzeP4wGtV\njSssF7jGjlI8njleZUn5ykLA45HTSJ2rac/suBNUJHJW0YHgCzx69KjRqOFw2DiUmB13iA8bz3+r\nhcGbZsprQplxTkLqjCHlbceHML7fH3xUaoAYlJnCq0JjGzSi3b/33nvJ+qe8SdRET21QMbNl6X3+\nYMbmSG6DH0f1gdzf30+mUWCUmMlyh6u2ZrCTmOliKImyHSNhnwbUQUqp9s2aQhU7Q+TI/N60a5b2\nMOS9waek8F6HgPLgKj1wlXxYTktwZJOS309yxG2upz9AxQ76fo2yMJYyX+eg9hpAHUQXFhakZzDa\nhL4dj8fhvpgDEu4v3Se8iWoe5MYfptO9vT3ZnyUepG0O8KUo2TOU8xQ/Mw9dopr2KioqKioqKirO\nCEVJi88CMMMoqcnnJhoMBjOu5mY6lgVLMNCI3L9/P7xPaTGUFJNzXfUnW5aKvLt37D2QXFhCU32R\nyo2WI5vH1N8pF12Y5A4ODqRE6FXJq6urQQ3MUrSqlyfx9Xq9oIliU2BK6lBaNJT74MGDhqSkJGCu\nWyrHn8Lh4WFD4t/f37fLly+bmdmdO3fCvSWaFyXlKGeI0vAHOVI0a8nU+/ycaEOGT8XDUnXJadty\nxO0UclpgH3YlRzbn93GMG8CXo2KVKVM8O7QwsEa8eaEEsbhrXIfcu9pqC5V2TplfeE/hZ/xew//P\n+wprosyO1qCyYKTMh6nYTApqf2RtFGvJ/R4eM58rrWbbfYKfUXGXeN75+3gNKy0qUx7g9MW/qfmk\ntFT+fr6eattwOJQ5cttadVIUBLUeJ5PJXFq0qpGqqKioqKioqJgT58aRQnDKEmkcz5hpIitwcHBg\nFy9eNLNj7RO/i6PsQtPD1z2nZTQaNSQKlYW9pL2+7ilwpmpVBiSg3d3dxunZc728rTim2Shxy/bB\nPv2zrKkr4SsoXgK79CrJmoNhqnHw5XI7XnjhBTM70hopkrmPwqzIsqyhYelUSWglHAYljXU6zWCz\npc8yV6Wt1qENfym1BpR78TwR0/2zJXwdL2Uroj1fZ9f1WCgPBoeISAUXzfEvUlw/BeWW38bZwCM3\nx9oSn/l9irjLdblx44aZHfMn2e2etZCeJ6YcVvwzvtxUm0oJ0jkHAwa0KHgvBy9V/c1OKqWEdhXG\ngYHfee8q5S/5PlpdXU1yvDBGOzs7kqTtMU/Ef/Usxnx9fT3s9bBuqP6NaXRzzmHAB5ZsjgWPiNCe\n1MsYjUaNOFKDwUCq0wEedFZn8jvMjlXE7InA11VHqw2eI8bi/lRUdPbe815FiiyXIspyO7xq2S+6\npaWl0BZ+nsProzw1NqlNnFPT+A1gZWWlcXhRZlKz9EdG1YnHPbXhfeITnzAzsy984QuNODR8AMl5\n/vkNXm14fIhIbdzqt1gKhpKPm+rT0yKqp57NEUtPy+EiZypUH5ecB6LZUZ978vBJ+jJGXvZ7R+xj\n6ddzKXE3Fg9N1fM0xkS110fb9vd78xgTt7nOqn6pOiuzW2qO59LGlEKZrdoKEGZlWRn478FgILNx\neKeJbrc7Q5Mx06l1GKkUPP1+P9QrFZ281+s1+uHg4ECOoeovNrf5aynEaDX+wL20tBTWVy7jQiWb\nV1RUVFRUVFScEc49/IFCadLglPkIp/vRaBROqCCsd7vdcOJWJh48y6bHtiYWszKybCyGSqmElNMM\nsPSCuqTyfSmpbmNjw8yOiPvcN/iXXXjxr9IqpRIK50yBkGJQPkdFZ9Mi6sB1Rl1QT46RArBmQM0/\nlbcKYLMLwjk8efIkOc9z41uamy6loeF3qcTY6pmSeaeIu2bNNVIaoTt3jccjlX+RSbXqnUDMJOa1\nT6wZxDtGo5F0CikNV+GxsbFh9+/fb/w+r0YK9zJiz51G+AluL68R1CNl2lehT1LhUJaXl8Pv2C8O\nDw/ld8JH/D5JPK7hcBieV/OZ12qJyZvDYAArKyuhbUqDydHVfUYHj1QdsC/2+/0wtzhvIe/rbbC8\nvBzGve2zpXMb+wCewb9ohyK+K4uH6h/1bWoTR6pqpCoqKioqKioq5sS5a6RSHCAm7AHQLuzv74eT\nOUIdMLGUA0EC165dMzOzd999t3EtxtdR9yluFksbqIsi7voI2Dmtm4Ii0LE2iwmynsQZsxmnAjWi\nf2/fvm2XLl0yM7O7d++G+1TeK3Wfh4qortp5cHCQJJEDrBliCeOv//W/bmZmv/Vbv2VmZlevXrXb\nt29Hn83VRV3z48l8nVzYgFKOyllFu55X+4n3mOUJzbzm8Xcuv1iJZpqlxFKCP4PL9e1TvD4VpJXb\nlBpXJdHG3lca3V+hVJup0HY+cXtLnuE1z2MFCwFrotAHHN7g5s2bZmb2rW99K1r34XAY9olU3rfS\nqPIxYv5JwnOU4EMf+pB9+9vfbvzO1odUcOCU40YbsHYfWF9fN7Nj7b7KRMB7NIdfyK1ntM3XNaZt\n9yE91LNtAtCqsf7Aks2VeQnXfCesrq6GhVEatZbf7zfkfr8frvOiThG8FZk7RdyMDaaPH8J9gfvZ\nW5AjZZcc9Lxniz/MKc8XRW7mwxgI3vfu3QsHGvTHo0eP5ELzfc79q9T36qOJxb+4uNj4oKlQ/v1+\nXx6GfByUWP+pjbHkYx6LGeTVy3y9dNmphMLzeMDh79JEoanDXxt40qrygFLkUDVPecx5PbKX52ke\npNS64LhpKVNrzGzpTe0cAT9FjPV1Ve+Olauu51KclCLlQcbR4nkfA1RUb2W+SQlbnAIotz+WzOOc\nGa+UoA9BcmdnJ7SZ6Q7KDK4O3qrtucNr6oCXMkMuLCw0PH5ZyIZA/fDhQ5lSDM+kMgRwWhvl1IUx\nUt/5nKdpKfiw5mNW8j7LwnE17VVUVFRUVFRUnBHO3bR31lhcXAwEMiUVtUXOBKSgJKEU8ZrBmjoV\nlTinKUtJjDkNjCeMcyR1nNpHo1G4rrQ3XBfl3ot+gAQSi5uFMjgxKurN7/XarrW1teBkkNLoKA2I\nUk1Pp8dJmlW4DCBmZio17aXGhjVxSur0Gk6uM9oYi8njTXGlcdNipsJU0uKURmcwGCRzreHadDqd\nIfirmD25uDZmR32VCruh8iYq6bhEa+SfLXWuOa0QEnhXqanDfyK8OdXsaFyUNsGPMc87brfPg1oa\neoTRVovH4EjzqXnC5HW1nz377LNmdrwf3717VzrKqLqrvYaBvRLzs9PpJPuN2465Dezu7oY2Yf2w\nRYEdeZSjCt6nQunMY0b01I3RaDTjvIR/1btZy2o2GxqJ90BvwWBLUiocCb69VSNVUVFRUVFRUXEG\nODeNVKfTsUuXLs3kJjOL50Ty0sb6+nrQNOA0a2YNImPO7fEk4JOtD/oZIzJ6KYbrznmk0M4Uj0Gd\nnmORzZV2hG3BzM8y09G6n3/+eXvjjTei9WF4jRvzJVLETeZLgNv0+PHjGe6E2awElIpOyxopRspd\nXWn5lLu10iTgPg7PoMB2eh/cTpHwmUunNBeqzopHxJJcShOiuBQpDkosjIcqw8+rHB+K36Gke1zf\n29uTvLQUZ4Trir8Vx0MRlFXg1hwPR9XFa7tOsiXHeFgn2ftSWtSS/cVMR5Wfh/isophjzbEmUQUY\nVih1aPBjpLQjinvLOeNYi4b9Ee1Q5XtNPMaQwxWoPeEk/VsKH/KGSeQ5zqAKIuo5pu/3sSTnOPCB\nJZsDHN/IbNazjVNx+CSe/PFXGxrewQTk3KHKx1/hd7Yd4FinK2I2P5MqI5Uygz8C/JuPg8MHi1Kv\nk9RBJXbw9W3p9XrhYPTgwYPwPLw/+DeA1b18qPLgQ5tSdasPFRPU0TYfNX04HDb6JmfG4UOi/+Dy\nfdynpYec1PxQZaRiLpXO41ITUIy87OeOiorMBG42r6m6qphlqs+BXq8nYwnxdSC11tXc5o9YKm5R\nKkUNv48TKadMYoA6cPM45BKBl3qBpg5h6iBVOheBnFmdvbj98wsLC2FuqQNtjpwcywih6sj1V/HE\nmHCN8i5evBj2E9zPsQ1VCjM8G0uXljq8cqytnPemf159e08CZcbn+iu03feYpM8ONf6b3+l0GueE\nNp6wlWxeUVFRUVFRUXFGOFfT3srKitQwKJS4dLIrLJPNVM6z0pxIXkpQpjiun3LtBJiozu+FtgVa\nNyV5sdReKkVxfZDMWWnCYiiNRA6tg3d/jYEJxSl3V2BlZSW8G9IWE99ZioGLLscR8wRVjg+kXGHV\nb0AsSrhfRjGyuUJp/jVFIo+9IwY2fXltJmscuU9LtFjz5ILLQfWLbzuXq/q8tDzWipSGMEhpzGKm\nfa6X2SwZnq+pMfaIRUX34xkzu6Y0ZTHSsgf3hQppwxpfs3hSbU9e5gwMrOXhcTc76u+SkDixNZVy\nMOD2pL4/THLGnswOKeoZH9NQhdpYWFgI72P6CM9T7G34TVkNcnP2tFBCAWAg/iCHiMgBfYBv4NOn\nT+X+he8T7t/c3JTaLqXhxvs4DEbVSFVUVFRUVFRUnBH6+VvOBtPptFgbpbQFfDLM5Qfyv7NdVQXB\nY5url8ZYwuC8czHbv5nZ5cuXzcxmomlDonrmmWfs7bffnimDeSRo5+PHj2eIzLjPBxRjqZzrpCSV\nnJaPc2ahnSqYZurdrCHwZMQbN24kIxSzZIPyVPksYfgo0YrnxME92TkAUg7mhAoBobggDE+KV+3h\nZ1nKBpQmTIW/YC0K6qzCcyiJNMbXAnIchZSWQkl+KQ1XTLsYCyvBdWIttEJMgvR1VBoY9d4YqRbg\nZzB3lKu+Gn8mc5doDNiFPdUHw+FwRrMBpDRMjBTXj6E0tX4uqnnV7/elRgjzEtcUn21paSmpkYJW\nfWFhIWjveI2mnuVvTkq7zI5BKT4pQzlXAIpPxN8ihppbav/07+v1eqHMXF39+2IhgEo0v6wd5X3Y\nz2O+D9+SpaWlsL+r7w/v1XgPZ8JAn/M3xJ8r+H2+/SmcO9kc4Eja7JFhFt8k/EdOqZIVyYyBRbWy\nstLK7OXhyY39fj+oLnFgzHnPKKItBp3Vn7yg8AxPLBVHCiYv7yWJ+/00UKRF3vRT6QdiJkxApY9B\nnRcXFxubw9LSUnieD3dqPF966SUzM3v99dfDb95EyRsjH5phYgUZP5bCJuUwAMRMe7GI/lwXM30Y\nScXc4jFX3melKVj8Zt3pdGZMa6i7Mrv5D5+KYm6WjgXEY+X7iuekMjvnzKlc53ljDqmPSCwdDA7m\nXEeUC6EoFg/JE/JjKI1BpaC8D0sPzX5P5TWV+qSwB5wS4NBn/A5O91JiVu/3+w2vPR4z0Bzu3bsX\nfmMPZvSlipXHccp8Wqj19XXpNOPLYHMk5kG3221QKLy5D+sLv62trYX1EEvRZBYfDx+7CX3Pv7EA\nVwqek17wVYfSXDw8vuZ/6/f7YS/AwWxhYSGMScqRiueOGn9GNe1VVFRUVFRUVJwRzjXXXqfTaRAP\n1akvFhtlXvBJP/c+zi9kNnt6ZkKoiricAqSLxcVFGedIoYTMx4Rcs1mCvVk8onVJriN2x2WzZswE\nYqYlfnbz9poNljqh0ev3+0HSS5FDzY4SEpsdm1FZg6BCLUA7sr293Vo1zeTvErK50lwokinqbTYr\nSSsNrdfM+PFHuUqrVBpbKJVHkqXa1FjyNa+RWlpaCvcxWdfvDdxXrBVUufYUqT7nDu4ldAVuU2qM\nmETOfQCpH9d8DkkgFe6D4bUOrEVt64CgwkfE9jFPfL548aLU0KZCj/C88/NYaZ9WVlbCdaZXeA3h\nxYsXZ7RNvi4qxxtbMNT3Z21tzcz03oF5rDTYPOasfeT9GOWjfqjTZDKZCRGCtnNdAdaUpcJ9cHvV\nfpwC2rG7u9voo1wsOIVUCCJ2Eos5bqHOmIv4XvA8ZOJ4SW7JhYWFMCa89qpGqqKioqKioqLijHCu\nHKnBYNA44bFUydoiLwGp0+54PG5omJg0zZocH0JgNBo1cqj1+/1iQvxJwBIIyvfEdyaMMo+FpQSz\nZrgHr5Eya0qJTG5WQfzYNq+CgirOg5fuWXJUQeHUNHzllVfMzOxLX/pS4xpLQKxl8ZI8E3dT4QI4\n6Cvz3JiMjt9UxnBF8PTSPYdd4P5RWoBUv7A2RgU+BVLSYEq6MpudB34slSs5a4uU1hDvG4/HQapX\nbVRaHn7fCy+8YGZm3/jGNxrXNzc3wztR3s7OTqOdsZAIpURhIKfN9tc5OrXiubGEnuLxAIrrleMn\nxojMuM8/E5snnkivuGmlITFiZfi2ra2thXWdqx+AtfLgwQOpRUG9S8c853gDpHiAvV6vwa8ymw02\nbXbURqy9yWTSyInX7XaTfLNYvVE/zEHPSeb7Ynyj0qwIvm1q3cUClGJs4Kz19OnT8A1sy9uKzZPS\ncBA5jdS5HqRisT1S4M2uRBVvpiPGerOBmtAcFTnllafiPq2vrwcPA/7I4W82jfj6YxGa6YWYApP5\n2HQK1fSjR49kv+U8rYBUTC6F1IedN31efCmzBk98f4hU0YZjdfJ1iSVTVeX6g4Iyp/DhqhTcB6mP\nei71i8fS0lKoK+YfH+qA4XAYPl4sfPj5wn2lxkqZPDn+Wsr8DfX8kydPGuOwsbER/kbdOVI/H0C5\n//xmWfrhVve1OYT5D63K0KAOL7H3YazZKzfVtpQZnO9TfQXEPg/IAoC+Z1OsQmqexszbvl94v+Cx\n4jmD96kPc0ldFhcXZ8xtZkdrAWOJvrp//76kL6BfONq6P9guLS2FuqqYgBi3w8PDGQ9t1AGCCB/I\nlPdZ6SHrtODX6/LyciOhPdcnd8j2czuXSqaUqoD5sr29nUyxxg5QT58+raa9ioqKioqKioqzwLlp\npM6h2IqKioqKioqK1qgaqYqKioqKioqKM8C5RTZvyx85a/xp1ZJ5QqnqdyaMl5IuU+CAeCpPX1tw\ncEMmh3r79s2bN8N9t27dMrMjjoQPfslctZTLL8J0mB3b+DlAJfpsOp2G93AQTM/X4fvaotPphDqA\n73b//v0i/kOv1wukWw7ICn4T+kJFGlaR1weDgb344otmZvbss8+amdmrr76arAPGZW9vL9QfoUX+\n7//9v/L+v/SX/pKZHUer/8pXvtK4bzgchnc/evQoSno2O+ZkMMcr54KtrpdyM08Tyr2c+RzKeYHH\nLZVnTHHCSvkmzLNKub8zXyuVlxTtWFpaCtwy7ElcBvhCT548CX+jHTs7O8HZAHPtzp07jbUSc1gB\nbty4Ecr1GRguXboUSN+4786dO2FOoP+uXLkS8ukBCwsLwZkA/Xv37t1GmBZ2lOp2u2F9Mh/OB4Jm\nxyH1Pcs5o6DfwCPinKXzoITMrbjSy8vLYY9BH3z9619P7p+lc5bz8KU4yFgzscj7M21IXv0OQVtv\nGwVOjBuL7XIaKI3tclbgDzwmXo70zwcC1JvvBzkeH+bDw0MZx6UtOM0MNij2ikNfYrO8d++eHDsf\nL4XniYqvxWX4RLYqvVDucMTphRRSHz7ExXrrrbdCvUE2HY1Gjai+Cs8++2w4bPBBitMxeKhsABxv\nBtf/+I//OFougz1SOYJyDAsLC+Hjm5pL+/v79vzzzyffxR6mqEPuAAWoMeOoz2ZxL0t8YN955x0z\nK0+grIQ6FfuK35WK/m0268WMuqTmbQkZv+R+1AVjzoINDkAXL160b3/72zPtuHDhwozAYKaT0vJB\nCs/u7OzYM888M1MGz3tey74P2CEA6wz14Gc5KwPatri4GOqAvVA5v3C8Lp+FwOz44DAej0P/Xb58\nWa5x763b6XRC2Zjjm5ubM+R3tJOv+7LRb88995y9+eabjXJLoeaFX3sqOvnTp0/DuoGn7vXr1+2N\nN96IloX3Mck9tX4XFxfD2CJNGyOVCqrRpuwdFRUVFRUVFRUVEn8qNFJ8sp3X1ZOl7NPQcMUAKfss\ntV45eA0J9xnarrQGnU4nSFjQQnW73SC55UI1QJ2NMm7fvj0jqeJ9XvLNaXLQl7HxwvPqOpsXcupn\nsyNppq02MaeRggQJCYjNH5Cst7a2gmTOZkT0KcNH2X/ppZfsT/7kTxr3Ybxg9mOoNmLMO51OkAw5\nKj9McZBg2SzA2k8gpS7v9Xoz0mkM4/E45G4sxcHBQSM0gArF0ev1GhqL2BxR5k9I1D/0Qz9kZma/\n8zu/U1Q/VYaaO7HI4NBm+phV/EwsunuqDhwziESDAAAgAElEQVRvyM8Pte9yuagfP4u+vXr1atBI\nAY8ePQrxgz70oQ+Zmc3MYX73tWvXzMzsy1/+cvgNmtKf/MmfNLMjjQO0Upzfztefo6fzv9izPvrR\nj5rZrJkZzz7zzDNhL0T9YCJjcDYI1iTiPdw2jAM0RAz+3qUS1D/33HNh30YfbG9v28c+9jEzO9J2\nm80mfMd9GxsbyZhY2LsuXbo0E9stBT+XOYwLz2OYREs1/8DTp09Dv6Mv1d7/4MGDMBd8CA1GyZmi\naqQqKioqKioqKubEnwqN1EkCjjFh2AeyOwuN1ElyBJ4GcvZetJnbDmmMg6oBKtt5v99vaJUmk4nk\nukAjwIRRz4dS4PxxqczhV65cCWXgPuZDQRqPaT04VxPfHwPuN2tybpi8zvOAo1KbHfUJ584zM/vI\nRz5i/+t//a9GeZ7wPB6PG5rVXq+X5OaoOQkJjaVU5pMxRwTl/sW/+BfNzCSPAe0YjUbhWcXNAra3\nt+2b3/ymmaU1UpcvX57Rcnkw34iJ0V4bxnXBePksAR4qIjvu4z4t1USp4IwMNZ9UwGDFh+IsBrg/\nFZ06lSszppFN8T8h6V++fDloGjB37927Zy+99JKZmb3++uvhmg8Oa3a8Tyi+lCoXWqqPfvSjQcsC\nrQeTjaF16fV6MpApygMBmjVSeMe1a9fCPECdmV8FTCaTmSCyqBP6CP28s7MT6qXmxGAwaATdVH3w\n5ptvBq0t+IQPHz4Me/f3f//3m5nZf/tv/62xl6r8iQwE4t3c3AyaQ69dLAHax5YQ9A002xsbG42c\njDGUWnx8JoR5LUXfMQep0lDubaGSjJYednJ18h80jlR7lkip6tn7iz9A3uuMoQ5XTLiFeQmLcGtr\na8aLxOyoD/A3+mAymTS85/hggA2SzS4qtQYDfc3pFHAvFstoNJLphVAHbF4LCwthM8XHPFYuDh7q\nsMkRjdEHbBJLmdhwkMEm5eEPGd1uN/QB+u/u3bshibNS0/PmgTkBkjsfpPiAizJgfnny5EmoCxN7\n/bM3btyQh29/QN3c3JSmBPQRri0vLze8ohhqbfI8xgeGD4acXsYT1flvPlh4oYvnLObE8vJyo+18\nGMK6UIl4VVt4fvq0Rfwb15k99PxHk6Pd8/rIJQoHMN98Si6z4w8uTEFmx1HAt7e3g6caAx9V9pjD\n+mPTjZpPADxCf/qnf9p+//d/38yO++j5558PcxXzc2lpaUagMTs6IGGOxcjjZkeHqx/4gR8wM7Pf\n+q3fMrMj72GsEV4Xvi+XlpbCPEG/3L17N4wDr0OAD8OYs7u7u/KQ4QUfbjMOrxsbG2H81TvQLxsb\nG2E/YWoE2pf6Lg6HwxlHAQDtxLxfXFxs9NH9+/cb5t6vfe1r4fpf+At/wczM/uAP/iD8prwZGWhH\nW3qARzXtVVRUVFRUVFTMie8YjZRXG5+WZorf47UNq6uryaTFKp4Hv8O/r9PpnCmR3dcrdk2ZxDiv\nkNlsbjRoGvb392dMV3gfJEKOQaPaB+lJxa2BdLS8vNwwu02n02TMHtR1bW0tSLGQdliCRJ2UdMfA\n9dh9KA9SzNLSUng3a5p8vKnBYNAIxXBwcBCkSWiLWJ2O+pfOl8PDwyD1w6xw9+7dMIaK/MpAHTAP\nGKjLzs5OIPh+/OMfNzOz3/7t37b//t//e/S96IvV1dWG2QjtN0ur7MfjcXCFhrvy06dPky7ROcDc\nzKE9UAfOAae0uyoHHMB58DCfY5oTH5dqb28v9D8nafXm5a2tLbt+/bqZHcdN4zWdMs91u92GhP70\n6VNpOikN2YB3oz1Ke8MaM+wHKysrUlvgf2OtMb/Hk5x7vV7YB6C5/JM/+ZNQns9ZyGXt7u6GdY91\n9PLLLweN1Oc///mZcsyOye6vvfZa0JgweH4Dnty8ublpN2/eNLPZdZta/zzHWOvpwwscHh7K/RO/\nYe50u90wdtD87u/vhzJQlydPnjS+MZ1OJ2iQVMJtYG9vL5gXWWMO8N7/4Q9/2MxmQ6xA64V/r127\nFrRKwM2bN4MWU5m+OQemd7745Cc/GUyTGHPOfRtD1UhVVFRUVFRUVMyJ7xiN1FllsGZpy2sLSqOv\nM1GdwWRfvO8kka098TVWbg6K9+HBrt+s8UHZqSCJ3KeQ6gaDQZAOU4HOdnd3G1GnmfcD7s7CwkKQ\n8CGdeMkEKOXXld4HSU4FcVNgDaAn7vf7/RC4MRWosrSs4XDYiE5869atBlE4FhwSv0GDdfHixfA3\nS7VYj9CcHBwcRPuf0e12Ax8G47a0tCTJ30qjC6kYayEXckOBtSc8NqxhMotHLvfzhLUFnEEA70Gd\nVXgW1rayBglrAHVi7TjPT8V98Xwofoa16ABL6F4juLS01Ojj2NxBnVM8QdYkod2TyaTRL6urqw3+\n3+HhYYO/dPny5QYnr9vtNur8uc99zj75yU+a2XF4jjfffLNB8FcEc9bOs7YYcwfr97XXXpNkZUUA\n5wjjHhgDDhURc3ZAHTGu/X6/oeEcDochi4DSXEIDtrOz0yiHg81CM7O0tBQ0VtDajMfjMF4pztpg\nMAh9CI0TRw7HHN/b2wuaqJRTxO3bt0PbwYcbj8eNbzi3Vzmv4Hu2t7cXHB8wN9jKEMN3zEHqrMAb\njN9s9vf3ZzaZNhiPxw1SKh+G5qmnJ3Cf9HCpzBWY0IpYOplMisnyXh0cM5F6UwIvFixqrgsWbq/X\na9TlwoULYSPjDUEdjNShiU1wZkfjpj7YODygzqXjsLe315gTvV4vbHzqcIpNKTf/8N7xeNw4APT7\n/bAp4N/9/X1pLkA7UZcrV66Efub3+ojAMYK0go/GzKpzlKE+Ptvb2yGWEOJrzbMG2Lyt5oGKfJ+K\nsRT74Pm1vrKyEuY3j6dfzyrq+OPHjxv0Br4vlR5DUQ8ODg4a5iXen1CXzc3Nxoc5ZupTcel8nfmw\njXLX1tYaB5DpdNowrR8cHIT5i/2EvfG4vcBzzz1nZkeHJqQzgil4Z2encZDa398PcxsfUOUty2C6\ng/rYY01xP+LdPL+wn+Cgcf369RDjKQeeV/hmoW3379+fyRIBsPMN6oA+54OZ32en06n9uT/350Jb\nzI7G0sev4yjxXE+0Dwel8XgcDi+o5//4H/+j0TYFXovoZyVc3bhxI8R1S+HJkydhvP0+n0I17VVU\nVFRUVFRUzIk/8xopSHJ8smVpFa6okKx2d3eTkbKhzeBccCwFzKuRMjs+mXvCdxtwXUrNgmhTm9AN\nyuzhESOlp6DCNHD9UuZKjhXkNWG9Xm8m+jLKgHTDIRR8NOF+vz8zP2Jg8xHquba2FuqlzDSlOQuZ\nnAxp/c//+T9vZkdmEk78apaPAo9/33nnnYaE94M/+IPhb5A6S6O9TyaTUDZL/pBmYbqNSY/etX7e\ncCIq8bDXnqgI3rE8eMr84PuYTR4cdsHnPGSND2tMvCYo5djCYG07a0D8uE6n0xlHEMAn4uVQMaoM\nbje0jfgtli/OjyPfh3V5cHDQ0A6ovTDWF//5P//nmf+/fv16cJ/n+QAHit/93d81szhlwNe10+lI\nzTG0T2jH9evXg4kNfTYYDELft8nx5sHfJ2gcp9Op3EfQl6jf9vZ2Q6uo5vve3p599atfDdd9uVgL\nV65cScaUYjMp1nsJsTsHpR0/PDwMWjSVJB3odrtz7S1VI1VRUVFRUVFRMSeqRur/S1Z7e3szOfvM\njiRSH/BuOByGZzjKrnLZB5gc7kmX83A8cNrm03MpmGsF9Hq9GekF9YKk3FbqjwUF9JwYzgQPrKys\nBII6RzbmvG0AXL9Rv0ePHklCvu9rHkPUZTQahbFml3Mf+XZ7e7vR591uN6mJAtiFHfXkHFqpSN8x\ngq/ndTGfDITQr3zlKw1SqpkFd2sOeAhAWmXJDlqvZ5991r7whS/MXC+dI6x9Qnvv3bsXfvuu7/ou\nMzviOajwE8zxwvtKwRonxbvw4So4snkq7IrKycfA2AwGgzA+0D4uLi6GflDcDvRrjHyvNE0eKro7\nR/dHG/v9fiPcA0v3OQ2YL4OBEAFqrm1ubiY19Zh3m5ubRfPswoULQQPDdYHmCFYGXm8Yo93d3WIn\nIwAaq+eff74xH9nBAGvqYx/7WNBIAd1utxGewbdVBez16HQ6je/Yyy+/HCKxK81xymEj9n3xhPKN\njY0wTmjbgwcPwringuaW1mU8HifHH98wDvqK9XZ4eBj2FkBppnLc2hj+zB6kFPnOdxybYrza0mw2\nsjHex2p6v7nxJD4NL8SS+C4KyjThyeC5tCJKzQ81O3sn4gM5Ho/DpMZi4f7A5sZxXNjUho8+PqSj\n0SgcrnhR+zovLCzMmOoAb2KIpQbAs2xS8oer0nHgZ9k7Dgs/lZpoNBpJ0iPex95niLWEcYGXktmx\nSYk3Gz4sYB6rOsDz53Of+1z4YGDMSzcdThTKfe49165fvy4PUrieiu8WA/pIpQgya65TZdpTODw8\nTApGylwGKJPd2tpag4zMf/PhKUV85/v9sxyDiFPiqJhWCr4tqi5mZUTdra2tRtwy9hbkvRcfUh+T\njvGRj3wkHKSwR/CBBu9l8zFMchcuXLA/+qM/mnkfR9lmUyL6BoeE9fX1xrxS9bt//35Yo4iBtbu7\n20gz5PtOHTbVnMJ4goR/48YN+57v+R4zO05QfP/+/eI1672J+/1+w4R5//79YKrH2Dx+/LiRmubR\no0fhujrsptDr9cJhUqWuYeEKfckx3HCYfOWVV8zsaB7w3sjlMEr2gGraq6ioqKioqKiYE39mNVIp\nNbRXeZvNuiYr6Y6TH/v3qoSoHwRwO7yJgLU37FoPaYk1UmxGM5tVSTMJ20vAo9EomNZ8dHQz7fLP\nrrqchBj1hJTIdVa5AkuRigGD982jXVTxuLgdbFrBNSXdc0RmsyMtGeLaqPaCzP3ee+8F8xLa2O/3\ng5YN489jifufPn06o0EEVB4/j93d3TB3lDkUkqaKrM7Rk1OJinPguc3zza9rdc3/jv/HOLBGR4Ur\n8Dg8PGwQ1dn8yaY2HzpDmSEODg4aDgVsFlTP8rgpYrmH0j4xbYGfQZugHWFtG7C3t9fItcfjj71h\nbW1txgxpprU0MPlzHzAwVkqrORgMghbLm6DRTrQL2hb02be+9S372Mc+NnOfwu3bt0P4DobPxsDx\nq8z0fsjZJGLXvva1r9lHPvIRMzt28OBwC7yv+P7lfZtpKdhHmIiPdY/I5ltbW6Evsa/s7e2FdkIr\nzqFseP75LBWbm5uhDn/tr/01MzvKq+ctHJwjk8cf2ieUsbq62nA66nQ6jX6v4Q8qKioqKioqKs4Q\nf2Y1Up5YGoNy8/USpiJ9MgGVr5Wcbt8vKK6FkqRY0zQPud3sSDJQbrQlJO3BYBD6XN3PweO89KSi\n7HY6nSDtKCItwBo4lmzm5bcpDcfu7m7gWEDLs7q6GjQvIKMPh8MkLwjvW15eDnVWfQXJ7+HDh0Ei\nhKSbC+OAPuj3+6EM1j6VkHS571hLgr+huVBu7aw5ZR7QPNqpkkCbqWuoN7fDbJbr5Z/Z2dlpSMCK\n9H1wcCCjnat8nkDKGUJx8xgYQ3ZeyYVTUGPtf+NxRRkbGxuSwO+1AHfu3Ananddee83MjrRATAo3\nO9awMNjhQoX78IFIGTyfVZ5F7pcf+IEfMDOzL3/5y+F9WF/QqG1tbTXqsLW11Qh10u12G3uVH4OU\nRp3Xkg+qurW1FbQxnGsT8xf5Ae/cuRPGBFrK3d1d+c0CZ5WBfkf53W63kRXh6tWrMzn7zGbDh7DG\nD3WAhv2dd94Jz4DHdv369RDYk4Mcp0jp0Iju7+83LA7T6VQGo87hz+xBCuCkj5h4PBHYRGR2NOg+\nUSR7uOGaUn8Ph8MP1EEKKD0ccWwsZfLynnBms2Rt/3EuLVd5RPHG6+OvxOrH5fpNklMroB2x6N8n\ngVLF+wjuy8vLoQ99ShkPzDfc1+/3wxxTh0iORI6DG6dHUMRob8pcX18PhzrewFVfeXLoZDJppGLi\n6MkqThgOk3xIYEFonoNUydzL3cMemH6+8YGRY0Gxuc3sqJ2KqK7Iyt48Z2bBJIYxZAGO36vMjN6D\nNLZm1KEzZqbMYXNzM8xtFgzUx8vPbfaiTAkLHA1cHaRia8nsaI75bBZ7e3vB+wxr6uDgIBzi2AyJ\nyPuI1P366683HDim02k4EDCR23/8d3d3Z8aw1PHB7zGHh4cNcjY7BLE3M+qIsel2uw0z6vb2dvib\nD6Uxhx2z4/V8+/btxnybTCYzacjMjvoZBx41XhC4ePzxN6cNwns5vRSX29ZDM4Zq2quoqKioqKio\nmBN/5jVSQKfTacTxODw8DKd1SA6K9Nnr9RqSvMrndZKI5B8U+DaxJgdSx/b2dpBivKbBTEvAkKz3\n9/dnYn/EUGISRD19Pi3WrKl3lpruWFuE9paGQlDSJRPGfXyomDTqzUss7XL8Ik8ev3HjRkMCZscB\nSNkc+whQGpirV6/KWF9Km+gJrTE1PJs18RwkZmjd+v1+Mv5WDKUJqn3mAxVKQIUN4Hqzy74i2vv5\nppIb43mz4z7tdrtBE8WOHEqb5dctz1M1t1KOKLFYVSlCO8Bu/tweFdUfmiVoTh89ehTejfmpknm/\n9dZbYT9hrSznnjM7Cueh5izezeRuhP7AuO3u7ja0NzxuKpYR0O/3wxh6U68H6ry5udnKcsD/MtD2\nCxcuhDnLoR8QcoQTUGO9og8mk0mgI+TWnp8z+/v7yXyEnCAZ9UrFaFM5KLe3txvhMZaXl8O70dcp\nDVpbVI1URUVFRUVFRcWc+FOlkSoNoMfAiZVP74rrwTnSIElzlGCACZT4Haf20wjCeVLEAucppCTL\nnITh2zoYDBpRqS9f/n/sfVmMZPdV/qm9uqr3np5uz4xn2p6xPR5P7PGS2CJWzBA7JgRCpCwiUQQP\nIQ+8RUECEQkwL8RIIERYJBQQL0H8o0hkIZKJIRhHTnCM7dhWbMbjbezMjKdn66V6qb3+D6Xv9HfP\n79xb1W3DxNH9Xnqmqu69v/3+znfO7zuzeoQYluPZs2dd5fDtio+i7OVyOZJPT6TfRyxnEVdHBqzo\nYrEY8bvjHhgTgyw0L2bDlqHVagXxK3FWmR3vGxsbGmeAv9VqVeMNEJ+ytramljQs8Hw+r58hJuS1\n114Lnlmv1wMrcHZ2NlBr5npyvcHQePOB289j5Wz7Mmu8HSTNAWaQkw43eMHhPCZs37RaLRUDfOaZ\nZ4LnWeZnUJm73W5ifNOggHo77nieceygt0ZaRpe/44BwzAv0/8bGhn4PRiCOGbIHLlj+AsHOcYci\n7JiYnp4OGIipqSn3uYAn58H1RHwQ5szY2JiuY97BEI41shIMY2NjOsc5Rgv/Xl9fDwLy3wpWVlaU\nNcNzPVaQ2xeq9Pv379dyoT9YtBTf3XzzzVrnp59+WuuUBHzPQfFe9gIW1LZr99raWsD0eeK+Hlhi\nA+8mLkscfqY2Uvl8ftsbKf59UkJXDiLHAoSB02q1Ii8jkWhqkmFOwvxfYVhXRiaTCdoyl8sF7gWv\nvUulUqBHk81mA6Xic+fODZ06AAsnFqClpSWdJNyudgIxrb7dF26xWNT6oi/X19cHLgZJ92M9FQAb\nTB5P9mSoVRcGsNijP9bX1+WGG24Qka024EUdixzrvyAwljdXSYrla2trwcvec1tnMpmIZgvqbdMu\niUQPeKC+aAPUrVarBXOI1djjkBQsnWQkdDqdIPCYjTX85SBdL4sB+iiTyegGioN07cas1WqpFhI2\np54rml22nmtxUH29JMi231utVuDeLBQK7hppx2qj0dBycVooO5aLxaLbR0jpgVNZlUolOM3oGS6T\nk5PabjAmrr/+enn88ccjv+M+uv7660WkHyyOzRX0kNbX1/UzfqmjrfDZwsKCbqT4JKzdiDYajUjC\nbpH+eMBGj+uEeXPu3LmhwxmGBZ4H1fE33nhjqOveeOMNLRfWRzZYUffXX39dXazWjcg4evSoqo7b\nssWB1w4+jAAkHeriTSDWY7yb6vV6oIA/TFqi1LWXIkWKFClSpEixQ/xMMVJvZcfOUgee9cYWOB9d\nts+2AesiW9bTICZjWDfT24Wk4FFO2AuUy+UgpyC7ujjHkg0o5aTQ2y3fzMyMWsCcH4utobgyiyQf\n745TbkaZtyt/EMcciURzcQFe4ulMJqNlwJgZHx8PpARKpZLeD+XM5XIRnRSRKNMAC4xx+PBhEelT\n99CU8SzCQ4cOiUifIQADizb1XCTsRka7rK6uBswgs5/M3mJOYXx5fTU2Nubqc3FQtXXBs+vUyxkJ\ntNttbX/Oq2mf1Ww2Azbby+02Ozur9/Pal+tnmVqv7q1WKzhI4bV5vV4P9Jds3QEeRyJRto0TvFtw\nMK8n48B9ZNnLS5cu6fcsGwDWjo+rA1aLiHHVVVcpIwUm23OLMVN79dVXi0jfpf29730vUs9cLhdh\ncLncjOnpaXV/4fmVSkWfwwH/ds7zuOJyoa12Et4wCKzxJyJy1113BaxdHFBGLxsD/8bLaQngXbJr\n1y65//77RUTk4YcfjpRtGGx3jeb3sH0nMPu0nfZOGakUKVKkSJEiRYod4meKkRoWcQHX2D3D2u71\nerrb5ViEpN0y+8FhEXKgMnbAcUraIn3LK2mXPezRbQYfB4W1yXEp+DfiVzwL0suh1mq1gmu57DZ4\nVWQrXoJzIjHwPFiRKysrbnsw44JneO1qn+H1fyaTCeKXvJgqjsOLC7CNw+joqBvQ6cV5wFoD05PL\n5ZQtAiOVy+WCo9XValW/Z2sdMTdsjWGc43ftdlvjQk6cOBGU5d3vfreI9BkpfIZ7eOKf3BcY2+Vy\nOYg54HZEn/OBgCTZkEqlEomHSArYZqFKOw/Z4se1HOvniW+y8KEdn71eL2CLLl26JAcOHBCRfvwI\nyoL7cH94Ae025rLX6+m9PQV0ZsltMDzLGniMLceB2bbkY/JApVLR4NwXXnhBLLyYFhZ4BDiw14pI\nNhoNjW/BdzyWMH95Trz66qsiInLttdcGcWTMoh48eFBEojFQ+D1ndMDf66+/XvsQ4PbDWNu7d28Q\neH7ttdeqcCcQt5azcOd2FbeHfU+A/Ww2m0EM7LDIZrMaZ3by5EkRiYp04vAKzx+MiVdffVXHAPpw\nenpa62vHwduJpPgnux4k4R2zkbJ6OrwQbBfeiRovzYN3CrDT6ehvETBYr9cjAZZcTjxPpL/Y2Xqw\nqjNTv0nuqDgdl6TfscsDA3jY4Gt+SXguO6u/lMvlguBhTn7Jgbl4cYNab7fbusjgfnGbSpsEeRDw\nshkZGQkU63u9nk5Y3G98fDxImeKl4CiVSkOXxasLXqDYqLZareAZXtoDb2HN5/O6WeLf48XH4wRl\n+e///m8R6S9e3gKKDRwDfbNv3z4R8U/3MTBn9uzZoy83DxgPi4uLOnb4pW1fDpcuXUpU2eZNM282\n+FAF/54/s8rSIr66NrtOeWyj/VGnS5cu6UbaUzPHJuKGG26QJ598MvIM3tRxOe192BVnQxX433wC\n0guaZ9hNBOs5AZcuXdIXKQPjCRpES0tL6p7HS3NtbU3d1jw2PDVuPNcLk0B9WVsKm5hnnnlG7rzz\nThER+eEPfxhciw0UzzuUr9vtBhvHZrMZGJYXL15UVyJcfJubm8GGet++fcFGiscB2ur8+fP6XFYT\nHxasuTWMZhK3N6u1J23gOBMB2vq2224Tka2TeiJb74vdu3frHMC1XpB7vV7X92vSITCRrX5nVXQP\n2OjDJbtdXa4kpK69FClSpEiRIkWKHeIdw0jZxJ5xgWDD0JmDWAN247ElivtyYmKRKKsEsPXAFp1V\nwM5ms0EQZy6X2zbb5lnWbDXybh6Uta2byBZDk8/n1SphNV/c03PzAZ1OR60IlIeD0lnDCdYBB25a\nVVqRLevw1ltvFZG+pYkjs0l9PjIyos9ltyXXCWW26s8bGxtaBquHIxI9cj6MG2pzc9PV18IRYVif\ncUHunlsQAJuRy+Xco7+wuMGO3HTTTWoJMkMQFzQuspWcVWRrjqDs1sIWibIoCNa99tprY+sgsmW1\n8/xhloyZUJF+vyXJlfR6vUDHLU5vzmOJkgLjWRbAMsLdblfLCkufj7hjPPP90Q8bGxtBWZiN4vXH\nlq/ZbAZH6/k3zELZ+eJJcjQajSB3WzabDZ7b6XRcKRMvPyDYJLjpRLZyMkLqgMuPecaJu71Aec4u\nsXfvXhGJslNgejz88z//s4iI3HffffoZAsaPHj0a0f0S6Y9ne7DkzJkz8tGPflREthippaWl4F11\n5syZSFJjkSgbxP2Audzr9QI9o0G6iey+ZlY0DiyngXKVy+WAEapWq0GC4unpaR0n+K5arep7AvW4\nfPmyylp4awar2GMtGBRMjufi2lqt5npbsH5ibFy6dGkoaYNhkDJSKVKkSJEiRYoUO8Q7hpECBrFJ\n22VyvOA6WBq5XE6tJlhHnU5HrQkOlrRWWy6XSzwybUU9GTtRavYYOu+zUqnkBoBbpsyLfRKRIFDd\ni1XjZ3jZuYetC98X/YNARmZn+Li3tTD4aDpYAGY72OLzWAXuT1s+DiK1TAmDpQxsDBJnJYdFhczl\njMuXLyfGxqF8XH+PqUMfjI2NKZsEluSVV15RyxVSB2fPnlWmhKUn7Hj3LDseN6j3oMBR9OvExEQg\nyJnJZIL29eIdGax8zHFsXgwiM1b2O/7Msr+5XC4SV4nf2bWlVqtpvBn3Mdoc/bC8vKxj1ZMcGKSA\njrWKWY9BCvl4FuqE/uS4KY4Js3IC+XzeVbYHOCcgys9xTla9mtkMDv5HG3lzHv1VrVbduYLyMTtj\n++iNN94YKhNFp9PRYHOWBbGsMSvvY419+eWX9fAHrz/4ntvCk93hMgyD9fV1ZbMQJ1Sr1YL1uNVq\nBYLG9XpdxxGvhVbqgplsTyoCv9+/f38sey0SfR/i30kxUocPH9aDMZxDEdd6quhgKcfGxlRYlPPv\noa1wj2Ha+R23kXq7kBSJz5osXsAmOrgay6EAACAASURBVBQDjE9U4LtSqeS68YBhE61yOXcaXM+I\n28x4wdIchCoSv0GywbyD6FK02/T0tG5a4dZaWloKFrDZ2Vn9nhcqq2gdd2Jv2PQAANqiUChoG3jt\nxpuYpBcUn+S0aDabugh5WjcYJ5ubm5EAUHtvvJQymYz2F2s3AVgkxsfHXVfMjTfeKCJbfeglha1U\nKkGKCA/eAuRtEj2srq5q/3qpNfACjJsTfCLNO2Vpy+alRxEJlfJZb45PtnmLPdqG3XToJ2h3nThx\nQjcZ6Jtms6nP4/lvN3g85tjVZU/R8jWcpikpkTHQbDaD5+bz+WAst9ttPY0LsGuX1b1tm05PT+u8\nZq00ezjl8uXLQdqWQqEQbKTiDk0ggP+zn/2siIh8+ctfDsZBuVzWzS42BJ67m09WY36Vy2V59NFH\ntVwi/bXTO9zDSbfxnTdf2IDbaUYFka2TeXv27BGR/lhDW7POFcrF4xnjiDcd3rzDmoB6Tk9Pq/GF\neR83XzFO5ubmRCTq2ktaW0+cOKGbQ4yhyclJXUs9dz3GFZ8qxNrKm3VPMT0OqWsvRYoUKVKkSJFi\nh7jijNRONJHeDnjHy+3Os9lsBlYbH4nGd7lczmWf8AwOruaAzbjnJpUzDt7Om8Fshy1Dt9vVcns6\nV7C8ms2mqxyNf7PlgM9gffZ6vSD/HVvMHHgKSwCurtXV1YDW9Y5qVyoVrb9nXSdJSoiE7TKsK7Ld\nbgcJqr378v3YsobllZQgVyRZzwv1ZlaA2w9gDR2PObQHArzxtLGxIdddd52IJAfADwKswGKxGFDw\nrAmGscQq0UChUHBdz8wW2Vxxnkub9Zfw3EKhEATu87jia71E257SN8oFd8T4+LjWifsVfcdjA8/m\n53pMmA2G5+Bw7nN7LJ/BLi97P5aP8K4BZmZmlJHgdkvKg4ZAeXZlsz6UF0Jhsbm5mXjog/vDrhOL\ni4v6DHbZAZ5rFowV54xj2QebpYCfy+8Nuy5Vq9VIWyUFXVuNMRFfSR39kcvldIzBxV+tVuXFF1+M\nPGtkZCTI2Tk6Oqp9zWs46gdJlHK5LLfffruIRMc7B4WL+G7BiYkJHZe8tg4KIRDpu2dRJ/zl/vLe\nqcwec6LoYZHISP3kJz+R48ePy0033SRHjx6VL33pSyIi8sADD8i+ffvk1ltvlVtvvVUeeughveaL\nX/yiXHfddXL48GGVe0+RIkWKFClSpPhZRCIjVSgU5M///M/l2LFjsra2Jrfffrvcd999kslk5POf\n/7x8/vOfj/z+hRdekK9+9avywgsvyJkzZ+Tee++VkydPJsYDYSe9XSXVtwuehelZsxynYWNzstms\nfobdcz6fjwRnikRjrlg6YdigwSQMClBntsNjz6xlFqf+7ln19nf5fN4NprdHlguFgraD5/9Piqfx\nrDO22j2wZW3rwWJ/+MsWNsdN4RruN3zG49iKkjJ7Z8cQ3yPuMxuXxHIUnP+RJSfwF+0Fi+7ChQtB\njFS1WpXTp0+LyJYS+vz8vPzkJz8JygXm4Nlnn9XPkhgOD3ys3YLXDC/mJ44BtHF9Xo5HHtteH3oM\nKzM0NhiZj40n5ctkBXQrCMqoVqsa14f4j0ajoe3Ac93ej5/Nf+1zisViIDXBB0e4PjYmLC6fJdhJ\nlIXlAWDdT09Pa1CwJ6XivSs4+NcGvk9OTgZs5rlz57TdPHzjG98QkT5bYcUbT58+rXE6NuZLJPlA\nyNraWhCv0263dd7yGsd5SUWiTAh+Nzc3pzIKHlhhHmOiUqmozAPmcrvdDuLSOp2O9gP+Tk1N6bxH\nYL4nKLq+vq5K5ZjD3I547uzsrLYT+vjVV1/VeiZ5WziulQ91eED/o0znzp3T8mCdmp6e1vJ76wjq\nsba2pkw5SzEMQuJGan5+Xgs3OjoqN954owafeo3wzW9+Uz75yU9KoVCQhYUFOXTokDzxxBNy1113\nxT7j7dhEvB3g+ngvPg5eRYeymwsdwpsmb3Nj0zJ4gZv/m2BXCCeIBfjFnBSQm4S4TZ2dEKz+zOWz\n7hl2sXA/gbbFwL9w4cJQ48mrb6VScXVLbFAzuxcGwepDvVV4CbF5UyoSfeGiLUqlUsQ9KxINIkf7\njY2NBfX1xubs7Ky7wOOFN+xGCuWr1WrBBqRSqegLhTWjAEv7A6g796F1e/MJTXYVWVeil+aFy+i5\n1ZKSYHP5UJeVlZUgGTVvLPjfKD+fOrPGnzc2vXnLmQaSTvHGrWF2MzU3N6duIwQ0eyemPAOMxxi7\n+AHrzmV4bsJut6vuIj65aNsZ89MCfcSGBq/XcVhaWtLy80bJu8Z+1m63Iy9zkX7f43ee4cgn6jhc\nApsgbCIuXrwYUcgX8ed1nCK4l9IJz8Bms1KpBAdPLl26pBs4BKqvrq5uO2ieQ2OSgD6fnJzUsrAB\nhLrzCUG0Aydzx33Y5TkIQwebnzp1Sn70ox/ppugv//Iv5ZZbbpHPfOYzOmHOnj2r/lGRvq/UO/WT\nIkWKFClSpEjxs4Chgs3X1tbkYx/7mPzFX/yFjI6Oym/91m/JH/zBH4iIyO///u/Lb//2b8vf//3f\nu9cOSvg3DMPxdsM76tztdoOAPU7mC3AyVXYFWLdAHGthg7WHSYj4diBJi4PL7+UcA1jrJCnAnF1n\nHGxuaXmPPWLXFKhVzlHI+jG4HtbRoLHEVqVNeBxnJSW5nFllP4ml4ntY9V92CwHsEmFr0DIz3rXF\nYlHbCrQ6SyJ4bQQrenR0VJ+NoE/P4m+1WkHCVpHQJTrIXY82t6ySSJR1g9XIgdksw8HWvQ2M9nR8\nODjcSwrMLIyXsQDP8OYSjwnPFWfZrHa7rQwJrx1emwDMHHiMkaeH5v3GO3BjD8h4v8vn88FzvWDy\n5eVl1y0EwA3GbjIe+5ZtGR0dDdp8cXExYFl4XnB7o8yeO4rhaSOBUUkKoajX6zru4F5bXFzUunM5\nPTeVlT9ZXl7W+7GuFtBoNPQ5zAbZrA38b87QYNvSc5MOAspUKpWCvu52u/o96jE7O6vf8yGQJNYR\n4NAYb71FXy8vL+v8YnV8lAUMU6lU0rnsBZbHjQ8PAxmpVqslH/3oR+XTn/60fOQjHxGRrZMImUxG\nfvM3f1OeeOIJEelLr3M8xenTp1WOPUWKFClSpEiR4p2GBx54IPH7REaq1+vJZz7zGTly5Ih87nOf\n08/ffPNNDUr7+te/Lu9617tEROTDH/6wfOpTn5LPf/7zcubMGXnppZfkPe95z1usws6QxMCwYrXH\nDHnCmTZIXCTKZllLmL/3dtHe/f434cX9cIAygDKWSqXAOmXrntsLlhTHjsHygaW0k8MESSrY3lFi\nhtfmfIjAg8ekeHIPsJS9uC1mR5KOz9rg5LiyMCOCfyMWqdfrqZgj2mBkZMRVjuZDECL9PrWKxa1W\nS5+Ba722jWPvuAxcxzgwK2xjvbz8j+Vy2bVcOa7GshPMnnhsAgdQW2Vz/h514v5g1ssbO7ZvWVDU\nW5d4vcB98JcZBCvnwP/2WErvcAVf68mCeEHznpwDwPFEYAG63a6WmdkK3AfMADNSzALYmJtdu3Zp\nIDMHZts1iduAWSqMHcS+xMWmep9j3iRJNzQaDc0jB0aXJSr4vjbnHYv/8v1w7eTkpLuegDXzAtoB\nL/5vYmIiCJhfWVkJBE9FtvoO606r1QqYq2azmcii4vmXLl3S+3DMLNhJfm/Yuc5eI29t4bLb+cWH\nxNBG5XJZ48gwB5aXl93+f+CBB+SP/uiPYuuXuJH6/ve/L1/5ylfk5ptv1mSxf/zHfyz/9E//JM88\n84xkMhm55ppr5G//9m9FROTIkSPyiU98Qo4cOSL5fF7+5m/+5v/MdWUxbJoV3ijZl2Y+nw9OXnmn\nWFiHib+zJ7Q4USjroNiFzKNd3yq4LMNsarzBVCqVAp0hdkNgkXmrJzCTEsoOq56+E10yz03mbb7s\nAp/L5XSx8V5GScjlcm4gMBYbaK2IbC1uBw8eFJFo0k+UvVAoBMrmm5ubwVgsFou6+OI7T2/KG4ec\nzNuWG98nwW46+BnY3PFCjs+4LQBOAcNl8F4snuvOM3y8wwj8ArUuJ64Lb0A4Cbl9rgdeB2z5vXHl\n3a/VagVuXO93/HLlQwlJCv48p7yXJn6Hcbe+vu6ms8HY9soFNz2f+GJY99za2lqwXnj9x2NskPFq\njederxdkrvCCq0X8uYn2wLV8chHYtWuXm2IH13gnW6+66iq9BuPE60NvM7y+vq5uQWzGer2ejjvO\nioC1Fn9x2IHBoRtJ6Ha7eviG1ySris5ufIADxj14G01+H2PcYS5vbm5qnVDf8fHxYNwhxCQJiRup\nu+++27VIP/jBD8Ze84UvfEG+8IUvDHxwihQpUqRIkSLFOx1XXNl8GHjBjYOwXSYsk8kEsgb5fD5g\nqfjob1LQNLsPAc+1x/pFnA/L0yV6KxiUtNQGCnuWaaPRcD/36G5rOfBxa3ZNWJdOLpdzNYLQ/4PU\nZllaQSQ+cTOXC9d5yvbe/e3vOp2Oa53aMcjX4rtisRj0CVt3Xi4u1InbnettZRI4XxratFQqBS7K\nTqejz01yycbNLfQNLL84QKcHbhqG51ICE9VqtQKmptfrRVgnT5fMMkLz8/PqiuLneO45227tdjvo\na09egPua5zUsYO7XJO0pgNucx0ZSsLmnc+XdD3VkXTLPbcgJy725YdlEri+PVbArniwA2paZfzwf\nAd8i0Xyoln32+s9LHM9gFsqTg7AJlHft2uXOebA1YKZOnDihEkJJ7nwvUTFL0Fy+fDmQhmAGCeN9\nkJQOu4fBRHEOPWanAOvuW19fj8gZ7BTo/4mJiUDnrl6vu9IjSfI2HgvN71GrX8bgDAJWC2wYOZc0\n116KFClSpEiRIsUO8Y5gpOJySiWBg7+TLD3eeVvBSI59SvKrs4XjSSLw/a21w0wYLJbV1VUNgoM1\nPqxwaVx92XK1wmRcBi/A2xMoZQvOE0xLCthkkTkbBO0JmQ5SLGfYmAyvHxqNhrIiaFcO/uSDALYs\ncUr0XkyWZc/m5+dVVw1tHxdHBSvIi0vBmGBGgi1vjnnBX8u2ViqVQBCx1Wqple0xf16sD89LlmXA\nX48JscrLDDAXLG/BAcNJx55Rfy5roVDQtkTA+Llz5wLrlWMHef7b/ucYlCSxTl4T+OCAjdPiIGOO\nn7TP9cZJHMNtGZdWqxUwCJ1OZyhBUY5VwfO8wOJMJqMHkJCbbt++ffpvjiuDpe+pjzPrZRkuZqTw\n2ejoaBBgzfMT5eQ6egwDr7d2fnOfow2q1aobw4XP7r33XhHpM1KWXeb+wb83NzdVgxHzgiU5NjY2\ngtikCxcuaO4/jO0LFy4oI4wyT09PB1kMeDwxq4RAfHx/6dKloD3q9XpwCKPb7SayYbwW4d5g4dbX\n13Xeo8/j3h+Yc15QPebMxz/+cfna174WW5YkCZi1tTUdn8PKuIi8QzZSmUxmx8HXcUq/9vRKp9MJ\nTgx5C3ev1wt0pOy9udwi0VQs1vUUdx8Mbj4tlLSZss+KA7srk2jZyclJLRf/zkutYelzbgNvovFL\nwgaPDwoSH5SCY9gNF07VMDCZWO8KtLbVfxLZepFms1mX5rcHC1g9l9vCujLZxeKlMEH5ZmdnNdgU\nn42NjWn/okzsrkDgZLPZdE/3JbnQ7QZNJGpg2ID2XC7nui2T1KExDnbt2hUkRPZemrYMtt06nY6W\nl/VrcB+8QNm1khR83Wg0IsriItHNhhfQjvvxxtc7Mctj22rtcKgAu8PtJoyzBfDmBeWDunen0wkU\n9/m0LbC6uuoefLDzbGRkJDA2eY55ekjssuMNHu5v14Jnn31WX/Q43eeNB0794p1C816MnE7HzgHP\nDRq3Tj3//PMiInLgwAH9zGYBmJiYkKuvvlpEJCIXhHKxawlrRrPZDMrFfYjTgoVCQdsE97t8+XKw\nKdjY2NC2RL+22229H8aJiLjjyep0VatVXT8xLzzNtVwup/OR72fXLA/tdluf6ym0o5w/+clP5J57\n7hERkUcffTS4j5d4Gm22e/dudW/iWcOECaWuvRQpUqRIkSJFih3iHcFIDdI8GfZagFWHmUVhut3+\nji0WTxHYUu8eFe9JLHhUPD8PO/RByY09BsNDu912WQfUGW6XpaUlN18RgHYZHR3Vz3HfYrGobgMv\naJn1XmxZPBcq18k7ypsUWD4IcBcwe8IMIihn/sxT8EW5vWPKuJZdCqylA5bojTfe0O/xXA7+Rflg\nQYJNYYyNjalFCKttc3NTLXM+ip2UT80DuyM9GQpm3FBmD3BdeEfFAa8vW62WO755/tjrPFap1+tp\nWZmJQhuh7ZkZ5PuifWGBx7E2tkysg8NzxZMpsM8dGRkJAp6ZpffcdJh7uVxOrXB28Vi2hscYM1O2\nTl6QO7MUfB3YFYQMcDu++OKLItI/xg9GCnVbWlpS5g/1qNVq6v4C4+SVZWJiQr/3xiCPWdQTbcCe\nCYDrhX6OY/PRbw899JCIiBw/flweeeSRyG8uXLgg1113nYhEGSkweOzyRNuPjIy47xSM1RdeeEFE\nonpT6POVlZUg/+Hy8rKr3I05jjY4ePCg5lBEP8zMzASs19ramr478LfT6ehcQdnr9XokmTruO6xc\nzTB5aR9//HH57Gc/KyJb7ff000/r9/jMS2R88eJF90DLIKSMVIoUKVKkSJEixQ7xjmCkGIPihIbd\n2dpj6L1eL1H9Ny6g3F7Lv7G/42BtvhZWAFs+NvZhEGswLENXKpXUEoDlwkHwnGSag4bxDFyLnbyN\nsxAZfCAAdfesi2Hr4TFXzBZ6IqgePMuSrXHLcGQyGdePj3InCTeyT57lD2y8FseRWDE/vh8zoSyJ\ngLKA6VpdXdU2QH9NTEwEMVxxCvE2Tozb1DsmbWO5LGDhIk7k6quvjljmIn1rEOMO7eiNNRGfyeEx\nZtnnQqEQSHawJAKL76JNWIjWSiyMjY2plcvsLVgvfpadx81mMzjAwfMR4P+Dqbl48aI7Dzx2zNYt\nl8vpGPNYfk8qgg8beOwI2oMD2zF+ef183/veJyIi3/ve90SkL4eBWD+sL61WSxkVPghi5/Lhw4fl\nxz/+ceQzZhkH5UtDW/E1llnjdsE48JhHka3YIjAgMzMzQRB5o9EIDjZ0Oh39N2IDefx1u91gzE5M\nTARzguvBYxd9iJRty8vLWlY+YIRnYOyurKzomMZ60m63lXXCvM3lctomPFeS8lcyi4+1IimXHjOF\ngwLAv/zlL4tIXwtTpN8PGEcssYDycVxaXOaGJLzjNlJJ2ImadZJLjN1unvYR/86e1uHfcICpl3LE\nlpv1Q3ZSN+uO5PKza8877cSwaSpY0ZoDs7d7EGBQYuQkeHpedlMskrzxzGazEfetSH9Bta4zkWhy\nWfzeJreO2/xZ9yZvHPmUJP7NitAAFjTv5KJV9eb68bWlUilIEDw5OakLtjdegLGxMb3G02dh4HnD\npj3CInzttdcG37G71HMLx40TWwdepPEi4JcPFlDvJJ9IcjA66lur1dzgV/tS4hehVw9eQ2wqDL4v\n+m1kZCSiAYbP7EveC1SPc4PzeEO78ObGlpWBIF0EMYtsvaSxyV5dXQ1cuWxIJB1EEAkV1b3fr6+v\n6wlCTy0cqFarrmGbFCIwSKXeGmYnT55UHSkGJ9MV6a8RdlzxeCmXy8GmaWVlJTDWeLzbwxoiIq++\n+qqIiOzZs0cNGi4fNlze6W2uG1yFfJjIriOcHJxhk9fzePf0vLwTycPiscceE5H+BhLzm0NWMM7x\nmbc5HQapay9FihQpUqRIkWKH+JlipHYC1p2xGjT8PWuAJCUjtteJRAOVAb6HtRK3s+v2jvd62il8\nbJe1UESiOl1gRRqNRpC4lq1ittqtBcX1ZPrWHnu2/8bvbf1Z5yqJBRqWtet2u0GeuV6v59L1Htvm\nWaNsmQGeZcPSALg/WA9PywptVigU9N4oE5cNv2fmhPN0oe/Qz6yRlaR6z+woJ4dN0mIBhu2P119/\nPfgsn88HVio/A/WwyV8tut2uWv3oD9aygdXuSSuMjY0FyVkzmUwkiB+w44QPUnjK/0lzwAsL8PTE\nvHHIgdSezhXDBqgXCoWALeBgeJSBE5ozrCL05OSktt+hQ4dEpM9qIHwArBG7vPm++B3Xw7rVz549\nGxyGGcRqAaVSKVjzOfzCq+MgPUOwrEePHhURkR//+MfuvEI9OBQB/cmJjzG+Z2Zm3PUE1zMDZ5nV\n+fl5Xduwnpw9e1brCRaqUCgEDCy/79grA3YKbtzFxUUdJzxvmXED7PuQxx3AEis87rCOJamNHzp0\nSJk3sKPnzp0LxjGvK0myC8MgZaRSpEiRIkWKFCl2iCvKSA0KDt+u1MGwiBNutP5vjpHi4G+bj4rj\ndfheSXFOnuSBjYsYBp4Ctc3nFXdPvhY7fY+VSVI273Q6anHjeeVyOWCz4tgOK4LKbelZ7W/XWBjE\nZAAoC+II2K+OupXLZb0fs3be2Ia1iO82Nze1zdnatQH5vV4vovosEm1TtrJwLazjq666KhCy46D0\nOHV1kSjr4al2M2CdbjdYs1wuB+OYcy6iXVZXVzXIFeWK60cOckW5vXgnVkq2zEatVguuYcFLgAUl\nOVbKrhO8nvD6Y9c5jnNBmUZGRvQZ3O9oG09CgfPR2d83m80gBoXvC0t+dXVVy4W2RyyUBVgnMFK8\nloBN4XUR8Uuzs7PKSnmyC8xOWObg/Pnz+lyoqMflvrTwFNq5DB7rBBY/DpivCJQXCZnDbDYbrLO3\n3367PPXUUyKyFQC/vr6u45vlShhYe5kNtvPv3LlzOo5Z0NSuT5lMJmBgS6VSwOjityJbTHw2mw3W\nGH6/e7n5mLmymRfW19f1M/wuLrOBRa1W09+h/by9Ri6Xk8OHD4tIX4Ee2L9/v4hE5WgG4YpupAY1\nytvx0oxL1eI9i0+04Hd28HKSYS+5JW+8LD3vqUD3ej2d9DvRQUoCT2Dv1CFrQXEaDpHoIsjtYTc0\nxWJRr8HLcG1tLQigbjQawYJSLpd1sUoKDu/1eon6QcO6kNCXvDDj+a1WK3Alzc/Pa7t4Qat8msye\nOhEJXR25XC7QKuIy4CXnuc44sJgXf9su7CLAPWZmZvSlit+vrq5u+2WTdDCDF9ztbqS4Puyq5g0U\ngPb1lOlFtl4UvOijHfCS4GS6rCCOdmNXEq7BC6NarQZBvLzB402GFyyLNuRn2PHLc8HLLgCwO5LT\nL7FGFeCNT4APa6CeuC+f1EXdSqWSu8nwTkAB2CjxCU30O58+RHtzPfhZmAOsTo3Tn9hIeSrgHtbW\n1iJK74BXN2vwxQEvYdRxeno66LtcLhfMb14P0I7s8j59+rQelgC4/9G+XtYOEXE3SPbUdLPZ1BOh\nWMvr9brOEdStVqvpaUisgRykjQ3V2NiYPsM7oe0dXkDd2Zjg0ALvIIs1gBYXF3UMcpkwjrHGLC0t\n6TjBhqper2tf3HzzzSIi8txzzwXPtEhdeylSpEiRIkWKFDvEOy7Y/O1QNo+DDfaOUye3LhZ2H7AG\njQ3c9HbTce7N7bItg8BMCKxhlIutd3Zb2ABvDtLlgGdr7eZyOb2GXTb2ucMqajNYtsCOgWKxqM/g\nHE+cv8uWiWH7h6ldT2uJlc0B7i+wFLDMWR+ImRAbRO65drit8JnHKDWbzWDcMqsAq41dO2izuHyB\nNjDWQ6VSSXQRemAXhlWY5n9zni5YwnHBvlY3iJkyMAMe8+v16+TkpFr8sFI3NzdVroEDWsHWeBIL\n3lrF84zXDJFoX+PfzEhinPI9+OAA8rwxo2EDkJmBYx07OwaYveNDJZ6bCW3PDCOSg4NB5HbGWOQg\nZzArrE4O1ogT6N56660iIvLwww+749b2sZdAWyScwzz3GCjfoLCLd7/73SIi8u1vf1tE+mMDUgLc\nB1aeAcrkqCfgjSdgfn5eWTiAc1nieUtLS4FMQqPR0HcC/p4/f16fDXmDsbExVaDHenjs2DFluFC3\nyclJZbMwrhqNhjJRXpuy3hzWAlYTxzhHG3hudZGtPuH8hZY1ZvcxK75jjmB95DAI1A1jOAkpI5Ui\nRYoUKVKkSLFDvOMYqWGZKC8/3LBgBsmzwj2L38YOcdAvx5hYSynOyn+7mCiA41Y4gFmkb3XCYuGg\nb8RJDauuPuzvOKv2sHFhNk6H+5XlHoYVB0XfwJfebreD/FGMQSyGB1sWPjYMcA41T7bCUwaGVclB\nmsy6wcLEZ5cvXw5YOY6HwXM9dtRrC89qr1QqQf97By64TmBYvNgHZkKATqejbcVB8xzzxuUR6beV\njf/jtvSENIG1tTX97fXXXy8ifYFFe7SaWTTOGWdjyrg9eH1KEsnEPTiGhqUzLDqdTkROgOsistUu\nXF+v7pwbzZaBpTMYp06dEpFozkXb/ysrK6quDXmDqampIPiay8TH5HntAMCYcHydlX7gXJrMLts2\n3LdvnyqQe/IxnpQFAyw0+pd/j7WGY7M49hLzAs8X2WKiKpVKUNZarRaJFRPptyXqjDYYHR3V9mIm\nDOMWfVmpVLSNINbJdQLr+swzz+i9AR7b3PbsGUgCGCEwf0tLS5H1C/Wwh3V4jQBLduDAgaAe3A9g\n3brdbpDl4/Tp09o3uBZsWRLecRspVKrZbA6VwHAnQCe1Wi03fYOnEmwl/wuFgpug2F7LJwy2izi6\n2gM/1y6CnU4nUZcDGB8fD6haTnHjuWfYbWCfwfQtn3DyTjtxWUX6E48Xe4C1sUSip6fYdYJ7ey+d\nYcEqvN6GISl9jp3AIlFNGCywvABgcWaXK/qDXyL2hXzx4kVdHDBeqtVqkL7D2/x7L45qterqJuHe\n/PLy2sWmP7L/FumPG29M2vqKRF/c1oXFGzKeKzY5a6PR0Pvg9+12W9ebkydP6nPRZ3ySz75sWEMJ\n4EBg7zSkF7bAIQM2PZOIr2tkle25D3kTbsMHuK3wXG+NiTNmbcqZSqUSuFg6nU5w8u38+fOBW2hj\nYyNwfx06dEhdWXzYgJPQohzoV84iiAAAIABJREFUN+5LgA/P2LVw9+7duiZwmw67zmIziXHKp+l4\ns+idNsV8RH9NT09Hkj3b+bC4uBhot9VqNR0TaLf5+Xnd4HuHZvg96iVd905p2hOhfLoTrjA+XMHZ\nPbx5bdNBZbNZnT+479raWvDOr1arej+0b61W01OCcAsykcDPt2XxDpgMo3SeuvZSpEiRIkWKFCl2\niHccIxWXLNKCLQjrihvWncQWPyucW8qeWRT+zqrmsg4GdvLbzVPH2I7bki1MTwsKFgPKNzMzo8F7\nwCuvvBJYBFwnrzysqI3nern+gHK5HLgemdlIchsyNc1WsxfEC8Dq6PV6QRsUCgW1aPg4cFL5OQjX\nO7LOR4P5viJbtDsncWXYZMTZbFatRU8TzKsvPmu1WloGPi4PizTJhc4BrfyXA7JF4sf2MO7yQqHg\nuudxLefL4vtZ6QW2OD03Hn5/yy23yLPPPisiW5ph586dC8a0F2Sdy+W0H5iZsgmUmeFOYoFwT5Go\ny9Zaz3yEnTMIePITSbn2UKZ6vR4cCGk2my5T5h0sADuCcb9nzx7tJ9YC8rSbcD/W0kNQP+YC9zMf\nBOG1DUBbeQmck9bNcrnsfo+6J117/PhxLRd+t7S0pOwIz0eoiXMwvm17ZhK9+ciadmj7RqMR5Ow8\nd+6cuqHf8573iEifOUMZmEHGs22iZQsv5ABlxHN37dqlcw6fLS0tRSRxUE8wbxgvY2NjWmbOWYpn\noD9brZaum2CSNjc3dQzC9Tk7O6vjB9fm83l3jUYZ8G6I081jpIxUihQpUqRIkSLFDvGOY6R2giRG\nwvudxzTgWhaKY2bCi4fiIF78tWzWWxUdtWWOU223x1/jAOvz0qVLierfrKjsHceGFYayxIk02nbw\nLL5hBTnj6uZZFF6OQq9sNhYgLp7NSmKI+OMIFlpSHi8va7tIOI6ZFeDyJTFR+Lu6uqptAOZobGxM\nrUUICnp15LKyhIIV1Yurg9cuVsVcZMsiTAr+Z1FX1IHr5AVVe7m9nn322SD3GN8HiFN3xtjjeBL0\nNc8L2+9xSvhWRZ4D6nE/vhbzY3Nz02Wc7f04YBzPrVQqAQNfqVSCeRUX+wag/zlgGc+qVqvu2LJs\nZrFYlJdeeinyG7AWItH1BGsbMzhgHcCqD+vJqNfrQbxrp9MJMg14uPPOO+VLX/qSiESZMBbaxX29\n9R/zHvNyY2ND477iBFnxOQsLY91mUUpISTz99NMisnVQQsQX2sX9OCaQwdk/8HubbaDVagWxSrw2\nYK1pt9tB/lCvvl68XiaTUcYS44BjyxDvVigUIrF7ItH284DxZAPrPbyjN1JJGx+G1UuJmww2maEX\nWM5S/Uy7cwCjiK83xfcedPpjWOB5LMufdNJLZGtg8IuKlYxxH5ygYO0RLw2IdTkUCgWtH5+y4cTP\neFbSBinp5CW7dNjtikUoKaAwn88nbqA8DKvCz/WJK7eIH8CIzxqNhrYVFlJ+QbPrE32JhdFzCYpI\n8HLd3NzUxRX3KJfLrioygAWIg/XhUuDAYu8UHcDZAnh8WlcRnxbjcWDno33J4//cD/ZAA/cLp4NA\nG/IL227C2ZDCS5r1mlD3bDYbJPHmwxUcFmDHOW82PSOF54fVcOMk0577zgt29xLL4sXHAcODkqpj\nTWB3lQ14LxaLOkbhktnY2NCxj3Qvr732mo55e7pUZKvtX3nllYhrUiS6+cNf3rwkGVEXL14Mxkkm\nkwlOuHlYWlrS58HFW6vV9GWNzRXPLcwZ1gnz3k/efFxdXQ1c8XxQBWVutVo6x1GWq6++Wt2QuPeh\nQ4fUlYe2X1hY0OfhfpwSiceTPSm5urqqv8NGdmZmJtj8lUqlYH0qFovBJifOyLYGpve7VqsVHIbJ\nZDJaPxwS8DDMQazUtZciRYoUKVKkSLFDZHpvt2DRMA8dInjr/6oMXvWZQbLWrGcZ8lFiDnxkyhy/\nt8F3mUwm0DfhgEwupw1U5wS/Xn2y2ay6KWAt8PHTJEX18fHxoY59MlD+arUaMG5e3j9+vsdSDAvP\nXcHfXYEh7iayFvGPpAOcIBdWM6z2l19+WT8DC7S4uKgWFQIt7ZF7ANYw+qhWq+lYxHetVkv7PMkt\nWSgU9BrUp1KpqBXLFqlFNpvVa5gRgGXNeRvxPAR/r62tKVvk5bdkFzueUSwWtU6YA6zqDuzevVvL\nj7E4Pj6uz+Mxa3WQbr/9dnn++ecjdWfwtTaoml2HSWsSB6V7uftQn7GxsSAJNjO/ljVg5HK5YB6O\njIxEpCRE+uMEaxr6eH5+XiUJvABvgNsZ+OAHPygPPfSQiIjcddddItJnpHA/jPGLFy8GrNf58+cj\nmRdE+qwHuxVForngOKgb7c/sPCeyRn2sq5jlLRCYXS6Xdb318qeCzd/Y2AjWqfn5+YhLGWCpExtE\nXi6Xta2ZDcb6wKEZ+IxZxRtvvFFE+rpQuJ93WAcsIcocx95YTSsP+/btU5Yd7Nfq6qqb5xLq6mDO\n3y4vzk6A9TzuXZIyUilSpEiRIkWKFDvEOzpGapiAYZHQ0uN4DFjAvV5Pd+t87N7G9bCvnQPncA1/\nBouQn2fz+LGgJft6YTHy870jzDamJS7fmed3hzWTy+UCP/MgNgp147gaWAyDAjs9NsHLb5ik9Ozl\nt2Mrz2O40M4TExNqoXGMkY1HaTQaem/Ez3Q6HbVEOV8erM2k+CCRLf89ypfL5bRvmE2C1cbB12AB\n+Bk2SNdeI9JnPdAnsOS9nIqe8F6pVNI24KPWrIYsEu3TOCZKpN+OlkG4fPlyEA+VzWYDUVDOD8b1\n5TlnVdM3NzcDFjCXy2kboR7nz58PYig5VornNyxuMBFPPfWU/g7PajQaroCmzavI8SY8ti37XK/X\nXfFNO0dqtZqymGApOp2OG2tl2alsNqt1R7+ura3p2Odcep5qP7OdIv1xYgUxvTHG6zfa9uqrr1ZG\nyjJEIluCnBwPhbJ7TDCPEZTpjjvukCeffFJEouPJBlKvra0FivBcFoyDp59+OvAQMEPIc9/KUGxs\nbARrlg3qt/Oa13SsP6z0Dybp7NmzWl5mxcBEcbsgTyPGfj6fD/L5xcFjomxM4OnTpyNi1FxflAEA\nq/jT4MEahHfcRsrbvGACc9AaD1T7GVN0XnA4axbZjRQHcwKc4oInEv7N98BkYnee3QzxwGENGvsC\n4sS9fCIpKXksB7IOe5LFgz1lYWFPbrESOX+HCc5t5FG4NkUMLyIc1I/y8GKEa/ESzmQySnvjPl4S\nWQYfJrCHDdjFMiixrw2O5sWRU5hgw8PJTPE9U/YYR9we2CR67egdHMBLwqvvgQMHVNXbaqWJbLVp\n3AlPq3q/uroaHMJgdzPGZrVa1fHJLy3WS0L9ue7WZZLL5YKgb3Z1cV1wH97QsDsQ94cBAtfE2NiY\n9kPcBs+CFaG9QzD2s9HRUffkq90MTU9PR05N4TcYOzzn0dZWDV5ka5xUKpXgUALr6wGXLl1SdXK8\nUBuNxsDQAxGRH//4x/oZ2uy1117Tzzx3tXWvMvjUq103GMViMUhAXSqVgtOMnMYJ4HGG5/FY84Dv\nRkZGgg1wu90O3iv5fN49ScfwMk1gA4I5sn//ft0MJZ1SE4kenHirKJVKwea/0Who+bDutFqt4MAF\nbyJ5zGD+e2mIvLbnvQHanMcuxiyf9rVr6jB6jalrL0WKFClSpEiRYof4qWOkmMlhi9Vq5zA9zwrC\nlqVi1W5mn6yFxDtWpnaTGIYkBVxWQOe62eSXHnOVzWYTpR2YwsZOHn/5956C8LCq7iJbFiiOQnN+\nK3xWLpcDpqLRaAT91Ww23bxXKGPSEdNqtartxhaVtWI6nU6Qj65erwduV9bVwe8mJia0H7hvbH8x\nC8gYRg/Mc69ymXGke2lpydXG8tTd0R6sFmzZkUwmo98zI5GUUNTLm4g+7/V6ykTBkltcXHSZA9QN\nDAu7rfFduVzWenIyYg92Plran1XJUUfLSDPw2dTUVOAi4gBl1rlCG/Ixedu3lUolVjvN1sPLH4Z2\n5/lj16xisRjonG1sbESOlaN8dn5xcLOnw4X25/FntcgYnU5HrwUj1Wq1dI1Gma+++mrVkULbg/ES\n8VlXjCt2g3KZwayiHbk94SL3NOYuXbqk1wL5fD5Y1ycnJ93xiGDoJKyvrwcuO9ZDYuacMyrwd0CS\nBqD3nkIbDeuaGwTWNMN4Qvt5TBe/e731EX3DCu18CMNT1LdM8vT0dESyR6TPrGLMsEo8WCw+1INx\nlqSZOAxSRipFihQpUqRIkWKH+KlhpFhQ0opz8b/tTh7XiESZBo5ZsSwVW7HMpthd/aD4JX7+oPgm\nkb4FiR0353izO2qWU+AdOCwMVlr1FIs94bykY/fValW/Rz9wQDGshWuvvTbi1xbpW1woFwebD8PQ\nsAgdwIKHDGvxeFIH1WpVrTlmRwapq8chn88Hlnwul4uwK7g/LG+OebLxAVNTUxrEySKDtl8LhYKK\n5XF7g6XgtvVYAstwlEoltf44pgRt5fUV7sF5ujB2e72extWApVpdXXWZLVs+jvuAxcmWtpUCsWBp\nEpF+G3P5wbKw1cmxLiL9cWqPaq+urkby34lExwsr71sG2ZNE8dhHjk9kxtmOE+9+zWYz+B23N1Ss\nmfGzMV8M7zDJ1NRUhB0SicZmoQ08hWkRP+YSc8XGLvJ3zFyg/zhGhmUfrPimyFY/gY1k6QOMU1sv\nkX7fQ5AV92UG1ovr4tgsPOcXf/EXRaTP/Fjph263m3hoBuB1kNfvJFFiVvAeFmjza665Rucd5y3E\nvED7cp+iHbjtk2KuvBhjryztdjsYq+VyWdsE68/o6KiymXguZ2jA79rtts511If3Aaj33NycvieS\nFOuHwU/NRooXVxu466UI4cBt/swGN3on0jhwG2g0GpG0EyLRRRoLgbcosQuI3XOWmqzX64FKb1yK\nCC9Q3abl8E648YkfnsA8URHIzAlCUUYsSisrK9veeHjAIBeJbpZFfDrV20R5pzK9l7+3CPLpL4Dr\nmwTvFCBvrrFoxpUbwAI/OTkZbLg7nY5ObA4sxwIA6txLoBsHu6GZmJhwNzkYT0m0di6XUxcGNmGj\no6O6UcFCe/nyZf0dv8jsRnRmZsYNfLa/Hx0dDU7ysZsW9WGXiEh0AyUS1V/CHBgdHdUNFPpwbW3N\nfVFgo+htPAYlHOcxIxIds2wA2bHI//fSxjDQNtgotNvtwN3iqclvbm7KsWPHRCSqI2TLzmuAVw+g\nWq26p7ZsH7PyPso+OTmpYQMcuG3B85vnAtqFP8O9Uf5msxkE5l++fFnd6Qg6f+WVV4L6ch8g6e/X\nv/51/YzTrWC8eGObtbc4WJrrYJ/H2l3eOmbH+yDgfXPixAk91QedrkuXLrkJtIfB+Pi4XsvzAp/h\nPcB9BD2qSqXiZsewrt+9e/fq+OD3GSeUF4mOT+/d4WUuwYY7m81qGRGIPsypwdS1lyJFihQpUqRI\nsUNccUbKHmf2FKGZBfDkBfhYvQ1u63Q6usO02h14nkh0F82qyNjB4zPPCuVjnrDkCoVCxPqzz0O9\ny+VyQCtyQDMrvlpGgo/2c7t4LkwGB42/nYBlVqlU1BKAJc+sohcEzzoowzIvANo8n89HAphF+tbM\nMLmSmClB2fkAAlv8wCClXfT1TTfdJCL9RMAYPyhznAQF2ARm8ZKU6JMORbTb7YB1Onz4sFp3SbT2\n7OysloHlEvA8SCOIbLFBVlWawZIASXo/rH3E7I09mm7ZEbRnUo46DgDm3+NeLAfgMVG2rflatIHn\nSmIcPnxYRPrMgA2g9YLIvQB0DtJlBgdjh12BNt/o2NiYMlFevjGPkUC7eG1y8OBBee655xLrDKD8\nYGI9lt9TbWdgjeAsCmDE2JXNDBjKz2MC6zr3l/Uk8JrPuecATq5sXVk8VsB+bmxsBEH9XEcOuPZk\nXoC3Il8jMnwQOh/wEumvAyw/ItJfP7H+Y1xxOyd5Fbx68DuA1wzoXJ04cSK4Bm09MTGhDBjnFsW4\nhe5XrVbT9kf5OGQE8ibDIGWkUqRIkSJFihQpdogrzkhZwTRmpDyrk4N5+Wgw/nrWixc3ZRkuZho4\nOJzzMvF3FlYVnZkhji3wBEOtwKMXZF8oFCLBmXiWZeA4SI+tIbAJ3lFSDgqE1eFZEMViUS09tMvG\nxobGIcCqi7PGYaF4FiH6kNuX46tgmaEeo6OjAWu3tLSk1k1SjEKr1QrkGbw4Ie8eIyMjQT+Njo4G\nSukiWz52T43bkzIAWP6CjxnDguc2SmKivFg0BMvefPPN8h//8R+x1wLz8/PKsvAYh0I1sxioU1LG\ngXq97s45q57d7Xa1r3l+W/FIFuHlMjCsACgzNAwbgM4CqhwEa4+hMyPFeek8JhTBzWxRW/HNer0e\nxCOtr68Ha0ej0QiCkVmyAe02NzenLLQnEcAClgD6kGPHkoLN40Rd7cGCTqcTUU0X6TNoSTkAPUV3\nvq9d8ycmJjQOlBkpjGPUt1gsKjP4+OOP6+/snOL1zKsn8iyKSMCYMlgqhMU5RfzMBDzetxtUvhPk\n8/ngYBa/Y9DO3lp58eJFLSvHSiblnsRadPbs2eDQFLcfxzajDaHez/2Le6ysrOgcwP3YQ+TlCuTx\nh3HiHVyKwxXfSFlZf95ssIvKLiJ8egbwdKR4knkn2zhwG/9G5/OmzXPTAVxmTjJpF0OmKHmz5KWX\nwb/55WUHtJf0l9uFn4FNwdTUVIQWF+n3gUfXY9DihdZqtXRRSXIHFYtFfUbSi35mZkbvg3Ytl8v6\nQuMNGuqEcvJnwwZG8gsw6TQMnxJBG+G5PHGBer3uvoywkcKYaLVa7ibXolqtRvRPRKJBkMPCewbK\ntLGxEQS8ejh06JC6itjN7J3WsSdS4zZSduyUy2UdYxhfnEEA/cYLvb2nBcpQr9cjbn6ReL05u2Dy\neLFrA8MzwjiND14wzWZTT0h5Rh3D6pzxIRIgm80Gp9JarVZEwV+k/+JGv+MlyCdmOaAcGwU2IryA\ne6va7r1s+IQmtz0nbBfpj1MvZMIeEmJg/nrK8PV6PWLQAPbwzPT0tAabM5LWE0/LCe1YKBQS3doM\njBMvzIHXbR5HduwdO3ZM64R1qdFoaB96p0CBfD6v7zfUqVqt6jX2JPkwQFngfltYWEh0waFurAXF\nxIBt6xdffHHosthnxR0MQRlYMxFGIt5DfJggDqlrL0WKFClSpEiRYoe44oyUpY2ZGWL3i0f5WY0n\n1hkB+Bil9zvW0rG78Fqt5gZCWrar2WxGcgnhM9ZkwvMtS+VJNuB6C8vAcPAh7uclMhbZYpWWl5cT\nc+3BSuF8UF4+qyQMOhYOMEXMrhM8jy1DtCH+VqvV4Ej3oOBJ7mvLHExNTem/0S7QLIm7D7uAPXcV\nmAFmP5NkEqx7izFMwDzgKQIDYES63a5arkkB681mU90KPBeSZCo8qx1lajQaQb+Vy+Xg6Hy73Q7G\nZ7FYDFguVm3nujCT56mhs+q3SH8ceElv8Tu0P+ddY80g637igHYOWrY6bB7LyIHlQK/X0zaEe/3C\nhQvBWOn1esG9Obdk0nF5j30oFosuW23736uHx1qPjIzo+ODn2TWt0+m4Ol0Az0HrPlxdXdW1gJk4\naFQxOM8fYBkufidxYLlFHGuM8Yl8flwnnqM24Tb/jssAPPPMM3L99deLiETkSNA3LJeDazmUBvOZ\nXfFJ69OwgKvznnvukYMHD4rIVu7E8+fPK3uPz44fPx6EqGxubuqY9Vxx20UulwsC/BuNRuDqLhQK\nOkfBTIGRTULKSKVIkSJFihQpUuwQmV5SsMb/1kPJEsGumVkZT/3XskDM5ABsMXsxCHytlSHg2CJm\neiwLxMdyPXBgK3bXsHBYXZUDxq0sQLfbda1Day00Gg29N8dmcfvZoDsGi35uN/4mCdlsNoihaDab\nWj/Er6ytrQ0V3xSnpJwEjkux/crB9YME6NDXYIuazeZQx457vZ4KHl5zzTUiIvLII49oHAGex+3E\nrGJSWfhaOyY4jshjpHAPWLAifiArrNS5ublAAkRkK2CXmQ0wJWBJvKWFmR/Ak3bgz7xgY6BYLEaO\nOHt5ytDvzBraIF8ODmcFd0/cFuDYIXs/DlQHdu3aFaiSM9sCSzhO3NKqZjOSpAJEtvoGfc1zCoHZ\nLIuCsnAdPMYU7TM6Opoofuix5ACXhZ/rlQHwxgQzzmiHpHvs379f64J+a7fbAePMYwMxVcxWe8/g\neySNtUGAYj3HYKINr7rqqogSPOpuxwLHXLLyvhdre6WBOTU6OqrtluRpKJVKbvYRb61AG6FvWEQ0\nSaEd907KvXvFNlI/TZ2XIkWKFClSpEgRh6R9S+raS5EiRYoUKVKk2CGuWLC5dTXxcf8kdw8o3ZmZ\nGfcoOgL7oA8ClVX7bNDZcE3U63WXtrVuIS/HH8PmrxLZyrV27ty5xEBgT38Hzy+XyxGXg8jgJLyT\nk5NKXXuuvSTXqXcN78a9PH+sxWGp90Eup0FIcl14x7OZTrdUPVsWNrCUn5XNZtVFtHv3bhHZCkAc\nBD5YwO3mJTf2+hGuSZQ5LnCXc8WJRFXxd+KuZRexSL8NOJkyvrPBytPT01oWjF8+2MD38/qf743/\no414rHmuWIwxdolzjjLcJ0mJPp/P64EMz9WJvGSbm5tDHb7Yv39/JBksAFcN6rG2thbM6263q+3B\nLopf//VfF5Gt9emRRx5xn21dGN1uV6677joR2eobL4D3pptukrvvvltERL7yla9Efs+Ym5vT8Qm3\niydN4R0W2dzcTNQJhAuSE3yzKxF9hN+dPn3aDZC+/fbbRUTkqaee0s8GuYhF+q49lJsD1dGW0AHj\nnHxYG1ZWVoL1nedtklaWyFa/YU0qlUr63M3NTXXpWg3EYTCMi5XvN6zHiPUH0a5ezti34oHisg9z\nHw4twe/faujKoOemjFSKFClSpEiRIsUOccXlDwAvONTL7TQolxV2xcwI2czTIyMjykjxkVjLcHFg\nLAeHA5wbC5YDP/fo0aMiIpHAZu/Ysa0vW4EcDA9LxTuOzLCCfIxBOdsYVrSQP0N92eLylIf5/2CO\nhs1UvhNrBkxUnPhp0v3ssXyPCeHP2eJOYhoZNth4ZGREg3jRLr1eT9kTqASvrq4G/c7WPf7ycXCM\ngxtuuEEDYVkgz2tfW/5utxswZp7Y4eXLl4dSX+50OtpH3L6W+Wi32wGDtJ3YSsz1TqcTMBC5XC4i\nIYDngW245ZZbRCTK+IB5ueGGGwJxS5RNZIvF+PCHPyx/9Vd/FZTLY2G8HKAYi5yf7d///d9FZCso\nfHJyUuvBR+utCGa5XNYxlrR2cFshgNpjpEZGRoIj4cxqc242MD0e62oD4Pnfs7OzgRDi0tKSPhes\nXNx8Qz/Ay3Dx4kUdT1hHmcH08pOifxcWFuS+++4TEZEvf/nLwbOwFs7MzOj7hO/DuUAB7+AIyoU2\nZ+X6YYPTWdaA36nDCLyKhGtjJpPRa/i+VqKIn8XB98NkXoiTwWBGVaTfRigL1jPODMKC1Z4gtM0Z\nyOBgfCuD5P3e4optpKwuBg8sTBbvpAVcK/l83n0RWNcPJ11FEsJutxsk7uU0Ct4mgelU+9xutxss\nOCMjI9qZWDB4A4FnVCoVHQBeMmE8Y3Z21j0NhZcl63Cg/Qap7HJbeakc7O9GR0cjKTBEfF0ge2+U\n2dus2bQcIv5GJelUF5+4sBsQ1vjx3GOeLhk/A+DNlU2mKyKu+2iYjeDm5qYukkh7kMvl1H2Ev1df\nfbVutDEHWq2WO0cAjMmnn35aP3vPe94jIiLPPfecPpfd1sPoyMQpJWOceydheHH1XGw2eXmn0wmU\n/D3XA6d+4Pt4qZD4/9g0YUysrq7q+vChD31IRPrtZjceJ0+eDF7wfOoML/B8Pi933HGHiIg8+eST\n+luUldvQc1ejXHAFZjKZYD4fPnxYjh8/LiIiX/ziF4N78EaFN1pxWF5eVvXopHEwPz8fjDse48O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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD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\n8lJsiPg+dMyicuh8ku+UxXQm+WFt1ieM1+LiopdYNGldEYnXhExKZ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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d91150>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second layer filters, `conv2`\n",
-      "\n",
-      "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "filters = net.params['conv2'][0].data\n",
-      "vis_square(filters[:48].reshape(48**2, 5, 5))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu\nXam1k3GyL7p2JGAuHJELbE2xNXr16tUDayBg3qHJ1hJoM5h/59mzFajlq/dD84Vx839Hn9maBo+R\nYecdNJ+WlpY6GWFN8tmWWkecmqemxTcePBvZbS2e9p/iWVhcvZ/DB2h17dpsX/Rcs0bbvc55ER2B\nn/mb8UzfBpgW+EG/8GHnzp0DOYeH8Jx5hz++DclQFqlCoVAoFAqFZaJq7RUKhUKhUChMQdXaKxQK\nhUKhUHiUsWI+Um9/+9u7e1nnIeL++TOf+UxE9LV5+PuqVasGEUKutcddbebLkNVacw01rGdkl6Zm\nUUR/1+toCurVUWvL/jqONqMGHe19P82dsGvWUd+q/Y5zq5AtmrpvjAe/LvjH/TQ1oqjNBp+hmTHQ\nz8UXXzyYH+AMs/T95je/OSJ6/tmXgvFS94lxcr/tWnU7d+4c1OWyj1NbpzEi4mMf+1hEDGtEAfhC\nP9RDo46T62E9/PDDnQ8E46TvzK/K46R9lm+Mz9DCWGdnZwd+Atzxsy7gITJknxGemdVmy3wFqJ/1\n1re+deBn4txt1PGjdpb9uDxO6ptR98850uyn9fGPf7yTrWmRP6xR5rOt2xjR+2nQD3uR68SxX7R7\n2YUXXjhBt31enE+M+T/ttNMioucXPLfvi2v5uV7amjVrur/RFtmyTyV0IwfsLeyj3tto5zpx8JHn\ntn5x/I22niP7vvFM5p9x2qfWexhzBN/bmw/7I0IL44THrp1ItKLruDnyDL64Zt1pp53W8TjLFs4a\npXZmmwer/WzZZU07Nxhj4Huf+MQn4m1ve9vE37xfsOaozQkPWQfeT5Ej3i/0Dy3MLX6w69ev7+im\nrfcW9w0ttPe73BGkruXqKh/bt28fvHNZz9DgiD/kmD09Q1mkCoVCoVAoFJaJFbNIzc/PDzKPOvqg\nbRvRaxW7du3qTrqZ5ukMx0SCUN0ZcBLn5EnmVzLaPuMZz5ho3+b0cHSAIxYcKeHxOtoki+JC83I+\nmb1793Z9wTNO3FlOK0C0Blqgo9k4ocMXa+xtZKWrmjvaxM+2BsW40eqyGmTOOo8MtNov8+kIQOTA\nVgznJrF11LTAb/pDc73++uu7mmHuG1qYG/p2FM4Yb1uaHEHWtqeNtVIAH5gbeEdOJmfN9zOQ7Szf\n2uzs7KCiPPPpCC9bNZk/W14BPOeZbm9LaPs/r8Es5wxwlu2ssoGtS0TUtXsANKAROwrXdbz4P3LB\nM8iz5/qLvAWnAAAgAElEQVR2jGWsPqj3IubGkaNZVDK0M3do/bTPau21+4Vr6mUg55WrJwBbZqGB\nCFXyKLn97OzswMrptWVrod9BXkeOMHbWekc/z83NDW4usjx7yLmj8UAWWWcgV61swwdHb7qKhmmB\nP75VyfIOQiN5+Ki32NaVZXzwhXnP9q4sOhOZdDS7ozvBzp0701sT4HmflhMRlEWqUCgUCoVCYZlY\nMYvUjh07Br4UrncHWktUxD5tmhO1swO7/haWGbRAn3ZdJZ66X1k9LGhbs2ZNd1J2zhXAZ5+C0WKt\neUED48XfCf7Yp2T16tUDrQUN0jXCrO3wLGcJNzIfqTYru/N8ORdTVt+Qv9uildFiC51pbGmwr4Zl\nCDj3kflkC5b9+MgUPjMzM7AYetzMN/S6b/gADdBkKwho+etxTouMxQcQuKag+ZjlPGvb20LkPEGA\n8Y3lHhrre+xZLcayD2d92grovGquveX2fMZShTWEz63V0H52rlDgtcvfae+cN7aeAfsabdy4cWBJ\ns4UOLZ615Mz19Dkt2zSwP+S6deu68fi7rtjw1Kc+dYIGMtYDzy/7om8dQGuN9q1Bti5M47R3kfnI\n3uw9emZmZmD1yuaxzQvX/vR7EjiLv63OLd88F86vldWta/2S236ydx00YdHle+0NidcFdNqfFfgd\nb0un36/eX9ubEvM+8+e0v+c0lEWqUCgUCoVCYZlYUR+pTDPzCdOnxY0bN3YatLVVfD/oE02av9tH\nypEi+DqgHdmy0568nf3Y/gbWGGgPbVndJ07afEbrtXb00EMPDZ4J76zNWhNBQ2Dc9tewP5dP961l\nx5qQv+s5so/UtCzbre9HxNBK1mpTtm7ZgmYNw5XS4bEzgANbftCWNm/enGbNBXzHVkFgHyloQha9\nTlrLTuZXATz/aM6sI2ukrBvPKZ9NeztWa+3mg+ua0SfznFWFd4b7sTHDQ/vE2NLo9pYl5tm0IBfI\n/09+8pOJ9q1FylY9fiKLmW+H/fuc8dq0w5c2gtAZvJ1t3lnhPf88m3GaD+7fY4joZch7FN+lT24B\n+K5rrzqKjfHaHwm0e5V9PDOLlHlufz+QVU5Afmzx3rlz5yA63Xuqx5lVhMgqPoAsmjVi6F9pq5At\nzoYzuWdWIJ7D3s7+0rbnWTyb+YYvvnHIrEK+0QDIlzOk79q1K+WZLW32lZ2GskgVCoVCoVAoLBMr\nZpFavXr1wNM/u4/3KfKAAw4YrXAdMTxpoyHwLPsO+O6Y+/tMm2qj13ynbU2Acfk073t74LtuNEzG\n5Er0c3Nzg7ptjq4DWeSIfRuAtX37b7Uaqq0WzmtjLTCz8vgZ/j/Yn3aJFuK6bVkEof0R7O+WWUf5\nibVocXFxYO0EjoCy3BjOk+XIGdOyd+/egUUtizbimVhaM3+czLpmayOYmZkZ8Mz17tyXc9q0dQvH\n2vNMxjhGCzzJ/EUsW9OsqV7/zl8HrbZUjNFi2HfMecZsqc18zUC7T9hi5KhFLKmOWgVZTcrMl8Y+\nZktLSx19ma+Xo3VteQSmzRZpz1Hbnj5d39F9eX/0T+A9zXvTmOy6j8zKwTpxxLSf7c+OJOb7LV8c\nETvN2uXbIsu9afGaxBI1tu48/7bUef69l9tqaP4io/Yxa88cwO9c8y5bu0ZZpAqFQqFQKBSWiaq1\nVygUCoVCoTAFVWuvUCgUCoVC4VHGivlInXvuuYPIGtfpyWqt7d27t4t4wbp1xRVXRERfU8p31Y6E\nct/cATuPDHe/F1xwQUT0Nch27do1yF/iWmjnnXfexHj4vyMkXDuJe1v7jHCHfvnll0dExDnnnJNG\nXbhG3DnnnDPKD/uxUFOKWkuu8+VIq61btw5q7fHT+aSghfpWzvjtaD7mCJ47ooLvzc3NdbWT3vKW\nt0zQ6Xt44Bpk9u+CBtdagy/IRRt5xThdI9K+cfCHyDnq1VEjyn479stirK2s27+M8Vx66aWjtBBd\ng9zg60V9K+qh2VeG7zHWlhbLkqNt4TlzZJ8q+0B5jrymPbeXXHLJoHaiNUjXQrPsmud8hhbWUZZ/\nZn5+flBrEWRRV9BCPUx4zP7iOaU2G3y0/9uqVas6eUAWWUMAueVZzD+1FqHF/jyObkJ2WdOtjDvS\n9xOf+MQEX1z30eu/reMYMfS1c644aGcfXVpaSqPvqPsIX+CdaWI88JG6b64Ll62jt7/97QOfIJ4B\nba5vx7xn/n7so14XrkXH3y+77LJub3HEsHPbXXbZZRHRy3nmz+t39Hve856Jv9N/G/3qNedISYCf\nErUWzzjjjIl2fM+54rzvjkVJZu8Wv7uy2owZyiJVKBQKhUKhsEysmEWqjeTgBOo8S8B5mLZv3z5a\nZTyiP61a4z7qqKMiYnjSRMuxtkCeFVud2ugeok2IgMkyLkODa2KNZcFtafD3bR1YXFzseEIeKD4f\nfvjhE23hEyfu1poz1rcz+zJfY9Estiw6z0+WmZr2zp/izObwCb4xN4zBfGyfzXdcGw04d5ejsLKI\nQWu6e/bsSbPgI2Nor6YBOLLKFhdrbm1Eoq1dzlTOHCCzRIDSzhFhXovQzmfnV5udnR3kmMnqVGVy\n4czfwHLiHGFjeXOsWWa+DciO884xZ1ndR+Sf/HRktR+TRVuLPb8epyOsXO8OQIMzPdtyEzFc9+Tq\nYe/yOG2J8po2xnLHZfl/rP3Dw8c//vETf3d774e2epjWxcXFQeSWLSvee/w5i15kLqiA4fxjoH0n\nOOLNvGQencMrW0/ZOmqj1EwHvIN+vus9ifEzHr8/sqhQkFmP2r9ZPpyjz+OxtZBzg5/N+wEa2nqy\nWUYA5J91kUVtZlixg1REzxA2dZeMADZD3n777R3TSVcAnNzrhS98YUT0G/+NN9440d5lGSi2iKC5\nICab9p49e7rDi1MQACaFjQL6t2zZEhHD1PcOZyWcnsV97LHHTrSfnZ3tDowcGCg+awHw5uVrRm9e\nNgE7iVw7Vpuqs3l0ezYKxsBLyN+jJAQ/+R78aF9eLvkCxl5wEUPzr6+u/JLm2YyRjXTTpk2Dw4vH\nx0/aeY5YDz4ouvgtaEP7GQey6IMRGx+yyMZJUkOnBaE9P+EDBwa/eOfm5gbJWrPSSYDxIbusUSsv\n3tRZd/vbpP1SYg05/J29h3lkzT7taU+LiD5BL2DOmCP4x7prXzB+iWfh2sDFmfketGWpO1hPd955\nZ/c/FxVm3vn5hCc8ISJ6mXKKAicqpKRQlkSZuWxLLSErTplghZESMczjzTffPNHeSgJzxMHLc8QY\nd+3a1Y0vK1rsslTs+y7r4nHynkE2mX+vu8XFxYEinaXisSuHFYbsEOsQfieZjejlnPHx3jzyyCNH\n+3SaB+Sa8WeHZF8rWgFv++BvGDngh0sEOX0M/ICfPrx6DnyIbOE9yy4vY0raGOpqr1AoFAqFQmGZ\nWDGL1MLCwqDcgstuALQ/tITHPe5xnQZgTYq/UwJl8+bNEdFrObfeeutEe7RbTrecSNF2bMmwFth+\n19cdtEVj4uTNd9E0TQvaH/xAexwruIqmgAbKKd9p9m3CNE99ineCSiwYYwV3rWFPM4taU29LW0QM\nNS9fz5qPHmtEP29Z6n/A57bYakTPa1te7BiPvB1yyCGDqxdrv8wRmpetI07yaOfrTLOfnZ3tZC+7\nZobnLuKdXRu5PBGWF9q5/dLS0uC6PUuC6msT1ge0WG6ysi60by1YLnzqouTum3GwT7zoRS+a+P8t\nt9wSY/CVxx133BERk2va18O2rNmCDT/YD60lZyVRsMAgL495zGMG+xb/wxqOHDiwBcBzl45yfwC+\nYunYuHHj4EobwBfGg6U5S2jr7z/72c+eGIv39NYq62TPpnva9aqtHdCCzPkGgLGAubm5wTqGpiwh\ns53us2tVJ1e2NbrtnzbeQ33VB+yGYvmw1dj8tWWnpZ29h/lHFnkn+drQ7wUnrPVtioNe+Dk3N5fu\nLfTBey4r+ZOhLFKFQqFQKBQKy8SKWaQWFxcH4fGccjMnVDT6TZs2dVpYpjHyf/s++FTPKRhtAUsU\np2ZbDVpfFDsB2gLh+2e0O07cPuVba4AGh9qCbdu2DdI0ZAUbXVgZTWzMwsT42v/bktMWwXRKBDv4\nZiVieAbjRpvxOHGcRw7gI9pf6w9lh21rROa5HRIzHyOApk97NJgHH3xwYBnz3b4dVV1I1MU8XcbD\nfl+tP5AdmD3/9GlLBTTbGoBlweVb7HQOFhcXu2dkzqQAGpFvO6mbL/b9wMcMy16rNbosiX00LItY\n3NwXhXRdQJf9AprxqbSfX9sXMmIa7NDNZ1sgkLVMO+aZbdkfz7+tvi6ZkpVZYZz25zGcguEXv/jF\nwHcS+JnwlGdYRu1rhWxSMNo+NR5j+7stUi29Ef1agwZbO7zuGRvraKyklItKZylrsoLJzHuWgoA9\nDv45WCGit/rQxmkbvFbNJ3iKjLk9/LN/FrLe7un2ceK79oEFLmvjkjiZNdXlz1atWjXom/95n+P8\nkBWBN8oiVSgUCoVCobBMVImYQqFQKBQKhSmoEjGFQqFQKBQKjzJWzEfqrW9966DMAverLoVBuYr2\nDt1lUz72sY91/UYMo3fsZ0H6eVLnOw+PkwCSfv7000+PiH33tvTJs1yqgHICPNP3y5xuP/jBD0ZE\nxFlnnRUR/d0vtHOXzHPakhLOj+LknZSfIRU+98v0DW38nVIYtOduGz4zB9CydevWQZp9l9fgHprS\nBsynfYY8ziuvvDIi+pT/8AXfIfzBHn744a7MAuUh6MvRWuahyxU4ioPvUzqB9oypbcfv8Pzss8+e\naOP7dpdZYf5p71IRwKVTtm3bNsjNwjxRIoaSH/hr2E+Lufrc5z4XEX3JD/xRiKzDPw2fkA9/+MMR\nsW/dwSvaOHEetCAvXv8AWXR750xzzqgrrriiK5vC/+yvx/qmjAuy6PVgvwvmiLJPwHKze/fuTraY\nf/sKuXwR42QvAl5HtKe8yZve9KaIGPp5zM7Odr+zz1HCAx8fxkl+IdZ3W9okYpj7iH2FOWWsp512\n2kS/bbQr4/De4sSU9o1lT6d9FmEHbdDerjv2XEeGU66GNWdaHfkGz5l/fKjYg/DvQXavuuqqiNi3\njuwDDBj3Bz7wgYjoZRE4Z1dbCimin6M2b1ZEz3vk4vLLL+94iN8uvCb3GL7En//85yNiuEYBvKZv\n+Mh+wbqBD/Bx3bp1XWkj3ov0kUXYsuZ453r9eM3CF7932/dSW9oson9f4KfldxDzXCViCoVCoVAo\nFH5FWNHM5o5SsgUD2GK1du3aQdZn4HxBttSMZWSOGGpFttQYS0tLU329fHrPopKAs8K63I1p2bt3\n78By5ig+4DIjtiwZjqhxfpA2ssbRFC6zYj7BD/rIMnaDLPs6mlj7d7Qy58kCLoVgyxM8d/4pt8ci\n00YFWracHR3ZyvgCnE8qi4KD5oceeqjjkTP1A8sJwEJhbd+fnfl9LB+XrT7QkMkiliv6Riaz6CRH\nDrbROMBryMiKmQNbSbI5ckZ8VwyI6OcHnnk/8N4Fn2wVtQULmF/wfffu3YOoPct5Zt0Dnks/2/vG\nWBUDl40BWaZuW5hMO/2wxl1Cx+0faXmPiCGPs2Ln8NxZtuEHe1I7JngMz7Os2VkhbMZtK6krJ5if\n7Zwwj9Cf5c8zLRmyOXJOq7F9lLasY1vezBfnQOQZ2V7kW6j2BsiyCE/56QhL710ZyiJVKBQKhUKh\nsEysaB4p5zIBWcZfTtPr169PtRffi9q/wH3bL8saqDWtNg+FrV/OUeFCmP5/5jNjTT6rWdfWN3Mf\nHodz/ICsIKZpdRbdlnZO+bb+2AIDsEA50282p6bBeZba9llGXhd2Ba7jyJyN+Xq0sBwdcMABA+3F\n4+Sn5R14LmyJsqbW+hRYm8syT/MMaLFvIMgsumDMyujcMZks0hf+JTzL1mBgzdvZp1u4uGrmh+Vx\nwDcsdFlh4Xa8LQ1jfnAurmtfF2eqt0XK7TNfsjGrvNeQ+WJLimWLv/PTuYmyAtptnc1sPoH912jn\nNer9gX69VkHLP1uzzUP70rpvryPndvMcen9ta7L6GVn9T9c5hEav0bEbivZnO1YXIbfPaFY1wcgq\nRPD9rDh0O6eZRd61Jk27ZdH1YoEtXD4LtHA1BlsaHynKIlUoFAqFQqGwTKyYRarVaGy5yOqhcdJ+\n+OGHB/V4gP1KQGZZsNZnLXl/GKv0PfbZfhuOIAGcyK3tZtFbCwsL3Umfvqz9mhZ47MzPWX4MaHGk\nRHtiN13QnWn11vb4vjM7A7dDA3EkYdvGGZlba+YYMt8IW5mg0b4xY9mkbe3Msm6bdmukmSy2z7Y/\nVka3LXVZXUR8zKyZZb5Hq1evHtQKyyyVYxXhW1heMiuCM6JH9DJhK06mKQNbAbNIMkeBZj50LX20\nda099215sF9bZsEGLc1ZjThXNLDvpPuy1SizvtsPdGFhYdAHsLXTPlOWF/vD2g/HaNs5W7hpsXUE\nWhx9BohyNO/3J+u2AgHTYh8gz1F2MzHN3zWif6/ZB9BRmcCWPFu092cFbGnO5rTty/tmtsf4vZm9\nuyxfra9e5q9nyyztMou0URapQqFQKBQKhWViRaP2rO36JA7s5d9GQlgjdGV5R5BlkSKcmNEKfKp1\n/63GNe2U7oierGq1T9quGWVa9u7dO9DWp0WqOKIh8wXIrErmr39vacj80lwpfJofiy10+7PUMD7a\nWFuzpu4oRtOcaeq2viwtLaWWF+h0lEnmx2Z/NFsFPNYNGzZM5bl9Wxytl/ml8T3XQxyruG6raBZt\nyWf7iGT5dmwddLReK/PZGsvWP33CD9plvmHeF/bnY8nvtpxkfknQ5tpqmUXKlor9Vao3X8C0KEfL\nTRbl51uF9v/eH7JI2cxH0pbabD8BLQ2ez8xqY3+bzPLmmxBbycyX2dnZQc1U5z8D9lvye8P7nX3L\nHCHX8gWrnn0ksxsMz9G0SGPftnj9tXzJouzG6I4Y+kRNq+Xq90krf5kVMMtpVrX2CoVCoVAoFH7F\nqFp7hUKhUCgUClNQtfYKhUKhUCgUHmWsmI/U29/+9kG+Gde5or4NdXy4t1xcXOzu6rO6fIA7UO6I\n2xpxEX1NIfKDOGcNn6mH1dZmsv+Qa0qdeeaZEdH73/AMaKFv1wiifXZvTy2/c845Z/A/TsyuhcU4\ns8zO8InaSdRmc1RgGyEXEfGhD32oq2+YRcbwGVrOP//8iOj9DRyVc+ihh0ZEX8fNfITv8G9hYaHj\nIfXn7BPhDM0XXnhhRPT1qhwR4igv+ocW+7ksLS11/kO0pY4T47TvG3451EOjb+ffcQ0q6mExRw8+\n+OAgrw2fP/KRj0REL1sgy9XjGoT2vULeXD/x/PPPH0S8OjcLNSUZZ+vj1baHNmqtUd8sq7HH9z70\noQ9169MZ3plf1hRrCNm1H4rraFJrkzpulqvW/435p29HBgHvc/RtnyI+05697owzzoiIYSTezp07\nu7+5Lf460ORM3PDFtTbhPf0yZ677Bj9Wr149kHu3hQbzkLXLHo0stvt/xNBHBj62+wVywPybh7xb\nGB/teBb8Yo2+733vm2hnHyLGQvt3vOMdHa+YH2e6p+0b3/jGib6cu4vvU/cTPpKnyjTTz8UXX9zV\nWgRZDT32f8uifYb4CR+pcWkfqdZ/i3qFzOdBBx000YaISOTF8+9oZ9fFo06o30etD6VrLTKfRCnb\nfxP5Ye/KUBapQqFQKBQKhWVixSxSu3btGmSsdR4lMKaJOhoPZLXzgKNwnC3XEUNZtu72u1mmYsZH\nn45OyOpb0Q+n4rvvvjsihhEjMzMzg1wajoBp20YMIwAzK5Jrcjmipq2Pl0WVAM8Bz8QShdZrPrl/\nzwVyMRa1ZQ3UdZ3c3tFrzogP6I//t1aGLP8V48y0V9PiWnW2poJ2TtHmoO/ggw+eaMs44IMtl1le\nnMyCab7s2rWrmw9HY2VZgl2/j+9nuasYG+2h/bDDDhv0nUW6OiLMEXLw3vuEv+9cRmN1vxzh6P3C\nPM9qhDl6E/D/ww8/PCL6/eK2224b1PN0HqAsAsq0MyfIoKOagSsI7C9Ky1HOtjCb987dxRrOIkjb\ntWueGq7j570oqwTx85//fOKzc4SBtg4mfWa1Vp3LyvXqsnx90I51kXbtunBeLFv1vLdMy3mW7dG8\no+jXlp2WFubVecSQNeB3ld9ZWaS6190BBxyQVvCwxfqR5o8CZZEqFAqFQqFQWCZWzCK1Zs2agUUK\nbcAayVg19KyOm/McobVaSwb446DV0R8na592scTMzs4OqthnmpGzpNqnAdgfadOmTRHRaxrmy5o1\nawZ3u1nWX1f9tqaW5cByJuwxzc5Znp0F2X17vtFAnIcIZPUR0fBaLcN+NvZHMP3wqfW3an86v5Yz\npCMPO3bsSHNO2ZKYZWR2n4wL/mQ5w/bu3duN0/4UwJq3/c1Mk/nAHDinF9izZ08nI86WbAsTz7ZM\n2f/K4+T/tvC12m5WK8vjd9+M3z6AXnP4n/DMQw45JCJ62W+tDZZ7+DKtpph9fzLN2xYd5GbNmjUD\nq4fz+eCfkuUR85x4jWYZwqGlzbbvtuxr8BrrqWUMuO4f37OfEuD78/PzA/+0LI9WlvuM+QW2ogMs\nL2M5jex3l+Xiym4q2vG0cMULaOd90a4LW4u9jr1fOGcf46Nvy5f911x1oh0TsgdvkQdo8q0Q+6Kt\nfdBi+cos4GvWrElvXuCPrcaV2bxQKBQKhULhV4wVs0jt2bNncB+ZVf+2n8OePXvSTKucLB2txwna\np1XuujkxZ1Ee7bNpby3PbX2azawi7vu+++6boAWY9l27dg0yLWcZ2bMMrVn7zHdqrLK4NWdbuTIf\nKfs8uNYUyOrBjY3NfaMRZb499gWyP5ZpsUbeZtvP/CmsrWV12iw/WBjgz/58R5yh12soy+ifzf9Y\nTcWWds/F3NxcmpHdbW15AfbbAM4+Dx8c1dSOw3sL6z/r21Zj2ltesD5nUYFtJmxbGOyPlNVazKoM\nZGsRqzRYWFhIZcsRgK5N6L6dwT+zMoE2mtpZ0QH02seJ9s4mbgukI0in7afteKbVcbTMei6gxevA\n6w8ceOCBg0hA0wRaC/PYOEwr/fJsfjJXLR+did9Z0L1G4S0/mbNsX7S8ZNU82r5s/WVt2UfKtVgz\nXzngW6l23bktsA9htuYylEWqUCgUCoVCYZlYMYvUWG2usUgo2kZMnnqzE6NPxI7Kye72fQLnBO/K\n1G3/1jizSIasvp3BXTHt7bfj/vfs2TPQYrJaUvYrwpKS1Yiincdgf4W2DzCtFhKwVsQ9vK0g1oZd\nNX6slhJ9O+Llf6oFemyuyN5aKK3VuUYYtNgHCri2IpobcpDxZcOGDQOfpsznwT4jWUSQ58Z+fdYm\nV61aNXgGMpTVmrPsZVYD+oNvjkxt+WjfBtNvWXS1d1u6s73IvpRjlejbHGMtndl6sCbtObHV0FGu\nWNG2b98+Vc6df86gT/xTePaYX2L7uY2Umrbus2jMzDqe1az0umgt47Z22SLh9QCfPM+Ado68RTbt\n37NmzZpB7byxeqXt5yyyLvPvxM8XOAfaGF18zuo++p1r62G253sPG4sKhXeOIMYSZct7FsXq9yPw\nfgLaGxxgq5YjjT3ODGWRKhQKhUKhUFgmqtZeoVAoFAqFwhRUrb1CoVAoFAqFRxkr5iN19tlnD3Jv\nONcFdXyon8W95SGHHNLdr9KW+nYXXHBBRPQ5Jnxny50otdbo2xEPZIom58XHP/7xiIg49dRTI2Lf\n3fLRRx898SzuX11TCDgikGdSD40aQa77xx0wn+HL+9///i7qELrx2XjMYx4TEX29MuoyMS54Tl4c\n7syh5fWvf31EDHN83HvvvRPP+fKXv9zRneWN4jP1jVz3zREe3NtTO4v2vodv+2c+mR9ki2e7rt8X\nv/jFiIh405veFBE9j+G9fSuuuOKKiOjn1NFdLY+QRdd9dEQZvEe2XIOMMdh/D76cc845EbHPlwpf\nFvs0uHYafbJ++InPE7L7ute9LiKGckL/8OXyyy+PiH11BfHpciQQ88n8QwvzCK3+PnJ++umnT/yd\nNQkNRLleddVVgzXnXFxZTUF4Dh9Z08wVdb/gORFG9r1cXFzs6nJS8w3esT5Ys3wX2aJv1qjrW0Kb\n6yfaf2337t0DupFF1jl+NbfccssELbSnvqHzkznb9kUXXTRBe/t/5oW+kXPopm9k0r4v1DdEXpwj\nj/5db5Ual7Ozsx3PPF/QTd1PqkcgY/AcWeO9wn7B32nHnLHGkYFTTz114FfG+Fh7X/jCFyIiBvXw\n7BPKOL1f0A95mXgO/N26dWu89rWvnaDbdV/ZH6+88sqI6GsQOooRPjIm3i+sI+9VvD/WrVvXyS10\nM67NmzdHRD8H7ps6fuafIydZ08w/e1WbS9B7NO9/5Bu+2AeW+cywYgeppaWl7qWMMLKg+AnshLpj\nx45u4u2AZydhmM7G6DIuwI6OLKgsjUDrXAvzXQCUyXDyQ57lpG5ODukFh1CO0eOkbA5/ZzwOlTav\nwZFHHjnxf/O1PSzZ+ddFN7Owbb8oMmd8hJ+Dtg+DLV+YXzvL8/csWRtzYmdTO1UDNhReinNzc93B\n2uO047ILgAIX/WXe/fICLY0Ow8+cQVlz/MycsI844oiJv7tUhGV9ZmZmEBSQpatwCLWThPqgzPqy\nQzB8siNtxDC5px2bgZU35IAXjHnuUGyH+Lfryeveh66sFIZLAmUJXLPkgatXrx7QbWdo5i8rP2QH\nZ77PeC27rIM2YMYHAfftpL/wy2vOCTitYFrOWJuto7cL3JqWLIQ+S0nCfuIEl95fDjrooIESwny1\nZbZaGl0qLHN4dsCD+23XNPu55YJnZHxxIFhWMsZ7VluWJWJyjbLG6IMDsdNiAAfSuByR5QuafRha\nvXp16mzu1Dt+h01DXe0VCoVCoVAoLBMrZpGKGF4BZEWLKSHQlnHIkr2hYTnle5ZyICs7kJVaedKT\nnvggY6MAACAASURBVNQ995hjjomI3iTp06utI2irY0nKIiJuvfXWCZpM+5gVAB66yGYWWk9fPt27\nyK1Lbbi4bctPh7jaMpUVlvZ1apaQkDl1SQBfN0T0V5pZiL3LtfgqB2sXc2Ur4D333BMRvdaLnK1b\nt25QNsFaHpopz/IcwTf+D63w3usCbWr16tUDS4Etr9CJ9gecsgBw9eNyJnympFILZCazQAJft3s8\nBrRYrlirbfi3r1HGUqe08DwyZ9m6YPwO2Wdu2/7hEfyANsbJNQuAVmQus7YDl+Bp5S9L2wAPuQay\n1Ry4LAf/tzUB2Pq8bt26NFQe2lpLQQvLC7T6FsLXMaBN2WLLWlbGK0s8aplkTqHZhYhtZdy4ceOg\nKDNw31j12j0lopctr1HkJLOyttZR9kWnO7H1BjgFgy1u2X5ha/NYOTRky1Y91oP3Lt+K+GbHc4qs\nOsXP/Pz8QBZdOs3W3SyRtVEWqUKhUCgUCoVlYsUsUuvWrRuUs+AkjZYI0NA5oS8sLKSFH/H94CTJ\nqb0tNtwis1ygHdj/Atp27drVfQcrhTUD/m7HZ8Zt7SUrBeISAOCBBx7o6EXbye6wOXkfddRRERHx\ns5/9LCJ6nttfi89oAbYatnPk0g4uFZNpr567rOSDk6W5IHWr2WFRYt75LvNonjupHePKaHHpIfo9\n5JBDBpqU6UWDgk8ev7V/P9taY5vQjmfwE/kF1qx4hhMvAvhgh3f4aetI65NnB1Vr4vZftDbo9rbg\nwSe04lZ2ocsWOng/LWGtEy16zVljtV9XO0f4fLj8BLQ42a8LZbfJLVvaAHNjf8g9e/YM/oa1Gzpt\nWbbvpANo4HGWqNJjfPjhhwcO+xkcCGTZuummmyZoHSuV1aJNOpoVTPfnzL/T7bHoYqmEP+wHpuWh\nhx4aFNfO1gWy5P2u9fkaGzd7EGvURb4j+v3edPMs7/9eP6bJVkAnsoY29pX2vYus2f+O79x1110T\nfXutuYC298Vs756bmxvw0H05ie402e1ofEStCoVCoVAoFAoDrJhFqj2hcqLmDnxa+YGZmZnudGoL\nE/fJ9p/J7nbRCq0d2KdgjH6+a98dj8vRSWBa+YlMKwZzc3ODIrp818+CVrQVLDbWONrxtTTx2Zav\ndhxoBvZ9s/Zqvwz6Yn5tNULzdjuXZ2jpQ5b4HxqX/ZjsMwJNaIHZ/Nsqcthhhw3k1qWBXF7H2o4j\naGxdyTTxtWvXDqyZmd+JrUUuuwIclWYt175mLW32j/A4bYFwGRfznHGytl3WqW1vixEy4igigO8I\n33OkVObfhT+H00i0/UOfaYGnlnNbMOE5fdtSDbAy8L1777134E8J4APW8rEo3PYztGYlZ4DThhx4\n4IGpDwtw+RmsyW5PP+ynLhRsawpreffu3YOi5Z5/xmU5yXzq7MfIszIfzB07dnR92PfVco5MmZax\nUkgtvEdj8WnfR07/Yf8k02JZdSklv+syy5VLEkUMi9q7FI7H6ULRft94Tx8rZk77zDru8l1Z0fIM\nZZEqFAqFQqFQWCaqREyhUCgUCoXCFFSJmEKhUCgUCoVHGSvmI3XeeecNfAjsS/LRj340IvoU8e0d\nqaP2KCdAKQTfv/ozKeJJhe87dPv7kK6eUgi7du3q7pndN6nwKW1gnyHnqrr44osjYlhShjFyPw1t\ntD/jjDMGPmGOhKRECH1n/jbQQhmPs88+OyKG/kj2Kbjooou6vp2Z3H5Vl1xyyQRf4AcRJdAELZR8\nYE6dTwV/hJmZmY4nLj/gPEIunWKeA0crZmV/2ggR5BZZpBSGM5zbjw1aKLVjvxXPLbTQfmlpaZDl\n2WUzaIvMEtUHbdAOLS6dRDt8Q/A9aefIvg72q6DMgksEeY4AsgjP6ceRVcjdpz/96W7+ga3fLstE\ne0eY2lcG2UUWs31i9erV3b6VldmwTwelLVgX9hFyhnCXThobK3Qhi6xn/KnYW/CnQS4s5/Y5gz8u\nh/W+971vgsbFxcWB3FJ+htI5ztlkHznaI4vA0X0uKQXfZ2ZmBpGgXhd+t/B/+8iZL/jSsAfhp4Uf\nF7S///3v73x8GK9zIPIugi/wy35njJe+od0Z050T65Of/GRXNsVRx/Clbdvy0L6zjt6GFpfxcf6u\n+fn5riwPbe0TjExadk877bSJZ9N3ttdRxscVJdo9Grpd2sY+U8wVtGT4pQ5Sxx57bGzatCnm5uZi\nfn4+rr766rjvvvvi937v9+Lmm2+OY489Nr70pS8NEs8VCoVCoVAo/P+AX+ogNTMzE1//+te7yIWI\nfRaKU045Jd71rnfFRz/60bjooou6k2iLnTt3DrIwZzmNHLV21113dadNW1b4jOZMToonPOEJEdFH\n6QBHp5G51gVR2zFH7LOOOOOqIzZcS8i5NZy7x1Eabcbmtj+wadOm7uR8++23T3wnq1eIxuRMxkTM\n+FmOlGCO2rFmmdqBI3wcXcJ4H/vYx0bEMNqCOcFy9W//9m8Rse8gHxHxlKc8pWtrq4VrrjmazVoc\nP/mec5QA/t/Ooa0fPKu1nEX0da8ynsNPIquYY8sutG3YsKFrQ7Sh54L5vu666yKi58fTn/70iBjm\nNIKGG2+8MSL67P0nnHBCRAxl84ADDujm1cXInaOorZkZ0c+r82gZjrxlPbVas2tlQUsWEeQsyOSh\no08rgdaCWdtjkbW2JJnerF4Zf6eYKxYs59dz9Gubp8prjnEgUzfffHNE9JUaeBaw5Zk9Psv1x9iw\ndCwsLHQ8dc4hrGL833mCvP6dCZ5nw3vvddB4++23d/sde0gWtYdMMU7453xs8IO8XNBAnU33v23b\ntkFhYOizbPHZUWd33nlnRAyrLHhPh8+st3ZN0/Z73/teRPTvx+c+97kRMVxztuZ4zXqOXJuQsYxF\nYvt9ccMNN0w801UTnNPpyU9+8gQtrFngqEdke8+ePYOzhSNqvY5tPc7wS/tIecP+m7/5m3jDG94Q\nERFveMMb4stf/vIv+4hCoVAoFAqF/yvxS1ukXvKSl8Tc3FycccYZcdppp8Xdd9/dnQA3b97cabJj\n4NRK5lo+28rEqZCfa9euHfgCAbR8TpannnpqRPSnVluNfC//3e9+NyKGGbEBn9esWdNpddBga4dz\nbjz1qU+d+Gxa+P5hhx0WEf0JHNrHagpdf/31E99Ba8mqXDsbsmkAzmXF6d/5qCKGh+lpNecYB5qT\nK8s7X873v//9iIi44447IiLiqquummj/93//911b50lBA4fntuowHvhGe9dHBM4X1PoB2arjrMEn\nnnhiRPQ8z/ICOb8WNLt9m4WXdWYNC7AunvjEJ0ZExAte8IKIiPjhD38YEcN1xLMe97jHRUTEe9/7\n3ojoLZ98r6UFrRN5tQ8hQItHRnkWtSuzHFjMxW233RYR/Zpt6+E5fxwWBfhjvvB/xvWMZzwjInqL\nm+cU2rCiQbPzbbWAbvY55N1t7TOE9dDWIUA/8BGa9u7dO6gR6IoPf/iHfxgREddee21ERPz4xz8e\nHSeyyj6T5Z2iHWt/1apVnezYz4Y2zvSNTNlSx37B/xkva9SWfb7/7Gc/u1s7yJytwDwb+pkjW6gB\n+wP9vfzlL4+Inve21O3YsWOQew++eN90hQxbRb0XMX72ftbFWLUC9lqs+L/zO78TERHf/OY3IyLi\nlltuGdAdMcyTZgsecM68H/zgBxHRz1W7pvkba5I1lOXsYxzcWPz3f/93RPRr1xZPaIM/P/nJTyJi\n3xq1VY+2rp0JvVn9T+OXOkj9+7//exx55JFxzz33xCmnnNKZiUFbVLdQKBQKhULh/zf8UgcpfD0e\n85jHxCtf+cq4+uqrY/PmzXHXXXfFEUccEXfeeedolfiIiH/913/tToMbN26MI444ojt0WZty9exD\nDz00vbs84ogjIqI/jaI5cPp1HR9HL/n+2vSjdT/44INdW9fbAmgK9EGf3/72tyNiaAWwtuv6SNa8\n7r///k5L4SdaTZbBG80E7YVnEhEHfACG92O0oIU4woe+rb1y+meu4A9akS1vz3zmMyMi4pRTTomI\nXlO/7LLLImJSO6Jv/2T8+BsAxsHcMWdYJGzZg8+2VOzevXugecPDk08+OSIifvrTn0ZEL4uMH6D9\nwGv4hp+SLVJt3Ts0LfvwAMaHNvwf//EfEdFrjvg+AWoywg+sr1j/nDl7165dndaaZWYGXg/0ldWO\ngx+uocXctbLLOsZ6yTqAJlu72CdOOumkifGyb1xzzTUT7W15YmyMfSyLN+ODfp5hvxTWJnPnDPDW\nvNmL2MNYByeccEK3DwDohedYd77xjW9ERK/tAyw30M6zuW3ILHWM7YEHHkhvIxgn/LCvlK3GjJO5\nfc5znhMR/frC4gDaCFPkwJFzANlybVHamRb+jm8R/PnOd74TEb2vFNi8eXOamdz+V8gDvGyrJkQM\nbw+QbfZN+wG2ezhyzvz93d/9XUREfOtb34qIfl8wkBu+z/i8v8AH1gf/x+fM7/SIoVWf79qPCSsf\n703+730EYH21z+lhhx2W1isci6i+9dZbO/mZhmX7SG3fvr2b2G3btsU//MM/xNOf/vR4xSte0V29\nXHXVVfHbv/3bo98/6aST4vnPf348//nPH7xQCoVCoVAoFFYKxxxzTLzgBS/o3CD2h2VbpO6+++54\n5StfGRH7tLLXvOY18dKXvjROOumkeNWrXhWf//znu/QHY9i4cWN3UudkiUZqbRctgf8/9NBD3SkV\nzRlwwuQnWpGjCVo6IvpT8fHHHz/xzKym2MzMTHc657Tu6CSsPo504eDoyCeAFoDFzzWawMLCQtcG\ncKq3ZoT2Ah/gG33bguWcV4zRUZER/SkeDSOrCeW+GT/jggbzBa3gRz/6UUT0mhxa1OGHHx5f/epX\nI6KXETQiW0ds7UCLs8ZKVI7henltvS9ru7ZeMT60f9dx4v/Ov+NcXqCdI9o4F4vpRntlXUCLacci\nA++JlGRu8WsAe/fu7eQaCxEy4vpWrvcG77EW2/KK7DFG15ZrI4IcycTewmdH+LieHfyhnS3Stgqy\n5nluq3lDF3sJcg3d9qeBRkc/MnfeX/i7rfB33HHHYP7Z55gLxvdrv/ZrE32ZFvZLfjoqFjB+rOir\nVq0aRG65b2hiz2W8tmRhbcUyw1xhibI1Df620Yvw3lZj2tI30d383e8iaGF9MN7jjjtuYmyglTfm\nj3m3lcZR4IB9weuCNcuax3o2VsvREXKM62lPe1pEDPcW9nJHsyE/jmbn/4wfPkJDa8G077DfsTas\nwHP4g59Xex5o4XyDbX5C7/8gy2X3SAu/LPsgddxxxw3M3hH7THlf+9rXltttoVAoFAqFwv8zqFp7\nhUKhUCgUClNQtfYKhUKhUCgUHmWsWK29M888c3D3a98a6ltRJ4i75XXr1g1qYlEL6bzzzouIYWZy\n2nMvT00h6hsRMeEs69BIHZ8zzjgjIiaziuM3wX0wbak/RR/cw+K/4HpV1JSiP2cV5/754x//eETs\nq7WV1SnyOOkb3yj4RnQO/KE9NcWg2Xzhvv+KK67o6jJxWqdv10SjHh5zZB8SaKYfxsn8u7ZWm03X\n43SEF/4arlf3ute9LiL6+3bmlHauzUidKNdP27lzZ/c7dFPHyXXwAH1k9Q3t58W44SO11g466KBB\nzSzmlTX07ne/OyJ63wjn4vH8U1PSeYPa7NkRfc2qt73tbQOfL77jepWnn376xPgdvWaeUycQuBoB\nMnnJJZd0dLu+JbKDbMFz2jtSCP8VnvXhD384Inr5cmRUq6nCc/YW+/5ldfyQLfvhuQIAdb/gI2ij\nvVyvlDWHz47l1/XqqEEIj+1jyN9ZR9R9Y48+4IADuj7hFfNJW1enMC+ZI/ho/zfLz4c+9KGI2Fff\nLmKf7wzfoW1WgxA/HGfRZu5cm9N1UL224eN5553X/a/13WrBuuDd4lxwznhP3/DR+aVoh+/Q1q1b\nu/mkL/ZzR6szR+wtXve0Z79xLU/2cEegR/Q85N3iOrheq6wL3i/wg//7PeN91+/RhYWFjjf0DQ+9\n5mjHvoCcZyiLVKFQKBQKhcIysWIWqZmZme5Uy+mPU7KzifJ/Tos7d+4cWC8AGgLRRpyIaZ9FlPF3\n8l4QteVoJk65hx9++CDiz3B9Ir7rSDLA+KEZzSuLlJmZmenoRtNEO7OWAi2OrONnZk1wezSTlhbG\n4T7QODxHfjZWQzR2WyjaqIuIXkvIarJF9DyD58iO+cI4+Tt9thmaWzAnjJn+d+zYMZgf2hKFZBlz\nBAnPchZ5Z5kHWCqOPPLIjm5HpQEiWeAdMkaf5mUWgcnfHYk1OzvbWbv4HxFv5ovnne+R48a0wKds\nDbdzlFUZyKJ1GA+8JnKQ+bW8IIvIOpYMvtfOEePG8sqzsHaZJmeB5v+u/2hA05YtWyJi39xmPHed\nS0fjGa6V1mbTb8HYsII89NBD3Xp2tBk08JMoLaI4s8g6RwE6/xCAjw8++GC3jpFFy45rcjr3mfc0\nv4uQG6L+xmpzet7oO+MhYM06uhPAJ57tCiAt7dDLd2yRzqKZbaHLMpvzLPqBBvjVzmmW4y6rb0pf\nrp9JNQLv0XyGpvb94/m3lZCf7NHTItBBWaQKhUKhUCgUlokVs0jNzs52J1HnkfIdsvNO7Ny5sztl\nOtsvJ2esXeQc4rTrkzQnTmta5OTIatHNz893+UvQpHwydm4ftEA0NWsgjNOnenJCjVmwnDerzeI6\nBvpwNXhrR/ALrchZlltasszm1mJAZonh79ZgAfzB2oi8OHdLRD+fyAd0Z3W50HbgNdpOlmWb7+GL\ntLCwkGppzhuEbGVWE/uCTKtA/uCDD8bjH//4iOh5Y9mib3iINQhazBdbHm3pGbMyMk6sM7RFuwPM\nN/wi35pz0QBogPfkHWKsrQXLewXyi7w6I7cz4GM9pB+vafteYamBhlZebO10RnPz0NYRaGecbu8a\nbjxnfn5+YJnkuzwDKyB8Gss8HdHzgX3UViS3a2tPZlYdeMT4+D8WNedwQ64YL/UiWU9ed9AyNzfX\nrU/+Zlm0nyt5xJBdaAL2uXK+JWNpaanjAzzns+XcFknnE/Ne5GzsrGXWUyu79rOypcl7kX3K2ozf\nbT/A8k8FibHKGTwz46H9FenTcsJazqzw9Nfun37POY8ga41cVZaXDGWRKhQKhUKhUFgmVswitWbN\nmsFp2NYlwKmxrReFpcinV98bu+aUT6SOznAkgO/r0Q7uvffeQaZi981pnkg5PnP6tabGM33itr9S\nO1b6zLKEA07naJZt1tuIoeWNsXDadzX4Vgt0hBvw+ADzyXiYK/ox7a49h9UIbbP1QUCGMq3HVj1X\nf0fLxWJnbccWz9bq6PEzTlcc51meT+bINbP4u/mJFemee+4Z+CLYr4L5pi9ngzbPWVe2njqCDMzO\nzg78sDwHwLXjyM7PuLMs+/CNeUcG27HyTGTCdeiyHHZYxV07b8wvMWJo+bLGH9HzGo0arR1LhLNm\n0xeyhWUmy1bPXuR9Z/369QMe8izoY7zek4AtDvZ5yeYIq0hLk60dzD/PdE3OLIqT9lhcWFeWRdBW\nvchuFhy1iAy2vl4tsiztyNuYlZF5ok1mQWGcrd9lRG71gk9YUaFt7B1gi6ErWkzzkbJvoHnu9jzP\nctP2ZWs3vMxuDVgXWLwzf+DMH3JxcXGwF8FTZAlLGueGzP/ZKItUoVAoFAqFwjKxYhapubm57jTI\nCZ3TrE+YY7XGOCk6IsL5dPAdGbOkRAzzI7V5gSKGWmCbV+XHP/5xRPQag61XjoygPpn9E4AjQujX\neUXasTqSYSxKAnrbvtEUfG8PbGWyljRWx8l5YMaqkEdM5vWI6PnjvCmAz/Ce6C4sGq1VEllx9B3j\nzKL2oPnmm2+OiN4S4zmyj1RrbXJNOebAljfGk0Un8X9bdGzZauUCy0vmCwRtzL9rLbo9fIF/niNb\ngmdnZzseYu2w7xxwfiysf/Y/AV7/aODQZFraZzDurG+DNQo/bZmhH+eCA62s8zvP9By5b/sUolkz\nBluNbNHHgrVhw4bUX5O/s4agzRZMWw2wYLKOTIsjDOfm5gaWZ+BoXvwRve+ZFs8pe5L3l3aO8XXh\nHZNF+PEewKKG5dXrwnudo1vH3kfQy97jiF9gywu0OgoNsMfBH/wevWe33/XtCWvKljZHFmaR1+04\nI3p+sKbhY2vZ854LL1nHfkczHmhiLln/WX4+Rz3Pzs4OeA4NWNM5L0B3FilrlEWqUCgUCoVCYZmo\nWnuFQqFQKBQKU5Adl1bsao9yCBG9SRJgFiQVPunqCdG98847O5MkPymFQMkHl+VwuPrll18eEX2K\neDvJuWwNZTkoPxDRm6T9ncsuuywi+jILTkDItQpmQ0ohUArFjsyAMZGW/8wzz+zMxb7+4lmf+9zn\nJnhIH9CKCZPP0EJaftqTwBHzOyHo733ve7u+XfLBVxSUHyCFP8B5kJBql7eg/ABywRVJW+aA8gAu\nm3HttddGRMQTnvCEiIh49rOfHRH93NA3ssVVFvyDFnhOqZWxKyOXNqEsB/MMr7mSdLkK+OLrNjuK\n0j9jWLt2bWfu5ooGE/VFF100QTfw9RJOln/6p38aERGnnnrqBK1ceT7rWc+KiP6agvX2zne+s5NX\nX6NjNocWSn4wTtYk7bgCveCCCyKiXxfwGkdQnoMsXnTRRd18uowE40TG6BueI0vMiXlPeQtKitg5\nme/fd999naxQTgY+wEPWPz/PP//8jocRw3WP3FgWWaP8nbnftm1b90z2OdZom0Imol9T8JLSGbS3\nCwXtCAtnj4aP7Me33XbbIFDB5WrgNWuO77rUFnOaJcNkjmgPLW36A67FkdsLL7wwInqe+yrLASHs\n6W7P3nXiiSdGiw9+8IMREfGa17ym4zF7CvJN0Axr0yVfeHe1KVYiIi699NIJPsI39mj2R65jP/ax\nj3XzCXDYf97znhcR/bXaH/3RH03wELg0DPJw5ZVXRkRfDs2pCriG37BhQydb9A1vuS62mwp7ut/p\ndqGAT95HXZpt7dq1HV3IInsofcM79j2ueikRlaGu9gqFQqFQKBSWiRVNyMlJGyfJ//W//ldEDE+c\nnHLREq699tp46UtfGhG9Y6vRnkIj+pNm5phoRz5OpHYIRlvauXNnpxE985nPjIih0yufOb2jMTz3\nuc+NiKEjo5/NsxweCmZmZlLNm5M0cEhsFmoKmBu0YWj5rd/6rYjonVUjhkUn0V4Yvx08XVAare7k\nk0+OiD4cHjgp4E9+8pOIiDjllFMiYnLu/Gy+87KXvSwiessUcCg27bGOWF7sCM73165dO0i1AQ/5\nO3Py5Cc/eYIPwKHJBDM87nGPi4ihozzrZHFxMW644YaIiDjppJMiYqghM/8u/Mz4LAdOH8BcHXfc\ncRExdPB8+OGHBwWwsV5lyWHRVtGOX/WqV0XEcC0is9DOWJ/znOdERM/PiGFIPDzCYueUAy4zcv31\n10dE76RsB3+nVzA/2zWKrGDtg5anPe1pE88ETjCJDGLZGSvLE9HPIfvLpk2bBnSz13gP4bNTFJh/\n/MQS1a7/Fszlbbfd1o3TDvl25IeGsRQSEf2aRv55D3gtgjbJ6k033RQR0b0vslB5O+63yU1bsE8g\n63wf64+d8BcWFrp5ue666yKil/Nf//Vfn2iL5cqlosYcttv/83f4ioW2DSCCLvqyddzy0hbAjujn\nhHXkOaU/9l36w9Lfyp2TgSL3rDmnv0DO2aOgCVp83ebgLMa4a9euQVAV+0FbbDuiP4tk70ejLFKF\nQqFQKBQKy8SKpj/gZInGhRZgK5DLt+zdu3dQlBbYOtJq7RFDjYzTLKdXtDsX1jX27NnTnbo5taKN\nmW6f3jm1Z2U8+J4T8ZmW1atXd+OxpmQrgMNcXTg5S4JJv4wVC+BYUjmXhvHfgRNNMl4sk+aLS8jA\nP6wkY1ZJtHksM/SJ9Qs4RJ1noWFi/QIugwOfxpICum/7m3mcTqhnPz1ru22hUOQgSyRqCxQ8hH6n\n1nBxZj5TQmGs7IvTV/CMrJg1mqf9VzJZNC1Yx9rQfSdvZdxZKg6Hg0Mbsma+0G+WqLDdu/gbGjHz\nCU1Y5KbR4tQlHqtLCo2Ve2EcLvyNJcEWCad38Jr2HLmcycaNGwcFjwHj8Hy6YDCw/6r3Rc9Ra5Hw\nPufbDuTBqVuAaUFmee/ARyyZbSkU+nXqjawcD/D7wUV9TTs0IV98hqb22W36nojeb883GLbQsK/Q\nt+XBKRrgM3LVts/KtNhnzuO0xR4ap5Vaavv3GkKmvJ6hIUueapRFqlAoFAqFQmGZWDGL1OLiYnfS\nJnoFC0OWBAvN7oQTTujuhTPtFfj0m6XC52Tqop7WSForGtochS7tI8Up3do70X7WpFzkk9M8J29b\n6jZu3JiWNsg0RifDhOe21DlyhJ/f+973ImLcv8uJ9uxHA3iWLWzQjpZk2tH28HNC0219k8w7aMES\nZT8Da2poi5kswjcnS33wwQcH1g5ocKmQrLCwIy/REq3lAeZ+9erVnVXOET6G7/yRE1tN6RvLJjIM\nX7yOZmZmujYucZIlEoXXzOtPf/rT/dLiyDrmtG2fae3mNfD+gH9eph17PbjA+Bjd/KTPG2+8MSL6\nuQKsiyxqLytvxb7Q+oeZh60vX0tTts+x/p3cMEtUyPPYo7ds2dJZXrPSUcwn+1tWCsmJKl0yy3xp\no7/wL2TPtfy7QLAttpn/DbyGJsZq2jds2DCwLLH+s6S5LlNmi4xpgR/sAWPJZJl370VZomrT4gLc\nnlPauczbWPJpW6/Y55ADW43Y580X+Or3DLT5Vmp2dnYwTm7DoIH55j33SLNDlUWqUCgUCoVCYZlY\nMYtUqwGh3WRFTu0PtWHDhtR3iT78/6y9T7M8yxYa/z9iWEzTp3GXCLE2ZK2eZ/I9F4I02igEvgMN\nPtU7OgVe2ocE2KcCjJV9oM+seLH7QBNzKRlHa7TjbJ+D5QMaWj5Ct6OpeEZW8sUWvczfhHZjaIOU\nCwAAIABJREFUvhPZPFnG7AuX9e1SMuZjq6E5ysYWGPsruUCsZTfzP6FfWyRmZ2e7+c98VkBWAiIr\n+WHNHNjXrn2Wy0/YEgt4piN9nFfItI75KxrOe4Q1h/m0vGdlbLACeL+wrDOWsb2ONcd47H+XjdNW\ncs8tGIuGhq5M/nm2fWQyK5D/Dv+yMi6zs7MDK6Z5m/nYIucelyORmVusqmNFbu2f6shiYAuj5zEr\n5uw8Y/xs17SLlmNpZpy2dtPOc+d3NcjKgI0VloZ38MH7Xlbkmv97bWfvmzH/1+xGKlsHjxRlkSoU\nCoVCoVBYJqpETKFQKBQKhcIUZMelskgVCoVCoVAoLBMr5iP1tre9bZCThc9YrD72sY9FRMR73vOe\niJi8x/R9OnV5qIXFfbKfQRQBNahOO+20ib59N849NjXr2vpZ9r/gM3X5qBHlO3LuaV0jCtodAeQ7\n3yuuuCIi9tUU8v0w4FnUN3JNQUdAQOOnPvWpifaOsPAYPvvZz3ZtDfs2UCOOcTprLrS4Nh98NNp8\nNNBNnSXnbrKfDTXC6NtRO4brPtqHZnZ2tqOb+XGttSyvlGtE+d7eUTsf+chHJmhvabaf4datW0f5\nYj8VaEFeqCnG/x0xar68+c1v7v7m3Er4VVCvDlpcx81RWNR9dN0vgOwz/q1bt3Y1Ag37YfzZn/1Z\nRPQ8tB+GfYqy/YL2rW8M65kaYQA67euB7FpenMuOOWBOvS7a6CTWBm2pV5jxAx8axknfll3n2WJO\nWRdj/mrML7Lypje9KSKG/jf264P2ti5rxHAPYgzUw4Pvs7Ozg7VmHiK3ziPleqHeXzxGZ+v2umvH\nSxvmizUHLfaVc9RiVoMwy9f2qU99KuWh4fXsvIp+70K768qC9h3Ae5G6fPj+2b+TSgDe5ywfrkpC\ne+rhOndeRC/n1P1kzdkvy3nhGGeGskgVCoVCoVAoLBMrZpGK6E97aBacHH06dEX21vPeJ2BO2s6X\n4iguMC2iKjvl7927d6ClZXmBiKpAo3DuEuDoGyxT0OSov5Z2R9NlFbJ5hvMnOdok4yvfG4tSAtN8\n4PxdR1ra+mENztpCy0drKeaxkUVzZlna4SNzBE1zc3MD66BlaBpfrNUi7850bhoXFxcH0XhZLh7X\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg\nAElEQVTZwkJDH2j1zPMwrRHtlr/tI9PXR9aWeUbz/vGPfxwRrUZtKzNgruxDVK5RrEDIBRYoeJv5\nDtIHfLRVHbDGsSqwz+y0004dSwrjgA9XXHFFRLQWWO9z/M2aY276LNIR7b7ImO+7775mj7HPG33y\nuaNU7fPiuqlYMqHBfOR34u67725kjfVgvyR4yv7GnsTatC8Ya5pnsN/Qvy2Bk5OTzV7Bs5gLr/+s\nHqBpNZAXfieRTazxEd3s6ETb8htjC7YztTOHyLLniPH7ZqfPH5D59xry76fhaFbmwr+j9pNkXSxa\ntKhjHfe5gHXDOOwLmqFapCoqKioqKioq5oh5s0hFtJYnayyc4AGnXSIlRkdHO/WZvva1r0VEexpF\ne0FT4kRsaxfaHidVNFZHwwFOvyMjI53K0W7LSdjRA0RKGPbXYgzQ2FcnzbXCHHUGrO1yv9xnYYro\n1hp07qM+i5f7sGUKOGIQTT3L+cSY8FdBAyWSrLR4ua6hIwitSaOJ8Op21rx439Gem266aWf8fBeZ\ns+xZXhwpBl+wBmRzVFqw0OpsvWJ8WOhcB81WMOYZvy/4SHtrx08++WTzDLR1tF1r9dBGX2jxfeMp\naYdmZNjRYBFdXyhH4dg66hp0rufocTLv0MAr+0wp664H6txdli1HMdE3/LNPpX1noHlqaiq1pOCn\nw7ORLctmNgdY6ky797qlS5d2rBygrAkY0c5BtrfwLNYe68bWJFCOHZ5jrfH8A/fJrYH3Lr7P747r\nI5r20jLMbww1ZjMrEPNvC4yjOi0froNZzqHXOd/FQmernuubMgfsL5YP2sNv5Il2JR/oI/OR8s2L\nnwH82wT8G8j3lixZ0lnP8BT59e+jfeoyVItURUVFRUVFRcUcUWvtVVRUVFRUVFQMQa21V1FRUVFR\nUVHxO8a8+Ui9613v6txt24ufOnGu4zQ5Odm5/6TODjWCuNt0fS76pi6T61txf+u7bvLnUINqcnKy\nuTd2Vmz6Pumkkwbed0QDljnGSd0f4IzpjJ/aTCeddFJzzwzdjkKgrWtnufYUr7Q/7rjjBtrbb4PX\nj33sY01b31U7eoIaYdRa8ufAdcJcU4y5Ke/MoZu6XKYB2vib9tSIYo7o274Ubg8tpW8B9HziE58Y\naEuf+GEg99BC7Szam+fmK+0Z6/T0dIfucn76aMnyLMHzk08+eYAW+5ZBE3N63HHHdeq3OToz43nm\n08C6oL6h/d08t+edd16njpvllnF7XTjbPnz0GjUfXV9ybGysmR/XTnPuKddxZP3bV8j+eK7l6P7L\nvrNaePaVcR1H+nY9M+8vrm9W7pseJ3XZaMsezXccrQbtniP7s0Fbn3xltx+0hS/0aV8p70Uep/nI\nKzULy3qLtmZ4L0JevB765DyiXf/2B/Q6+fjHP96pb9kntxHtfLpOJO28TqC9rPsZ0f4eIQMLFy5s\n2rK30Iezpvv3Itu77FNL/6wj+i9rF/IM5tO19hx96300Q7VIVVRUVFRUVFTMEfNmkZqenu5oFun9\nozTy0grgaANr885tZA3FGVldqds0lf1bS7NlxXWKsvp32VgcpdCXR8aZlkGm/dg6Noz35ldfHSza\nZNqf33c9xKx+EzD/0KqyqMAStnqYFsuJtaOsb1s8yrpehvseBs+Vo1v62jlaNeO5I4GycZrnw2S3\npHOYPGQ0+nPD/OibG2vOWV3LjGbXi/S6svZvq3TJl8zimtHv2qOe0yyPmOVqbGys8x7Pcn1LRyGW\nfZTPthXY/Tv79NTUVFo7Fdiq68oIpt17crZHl9bHbB903xnc3pF1w9Z2yYfMYlS27XtW9rnf9z5R\n0u42XoOmxdawYX7NthayRzsbefl/r6XZ/v67H9OW5duanp5OeZo9Y5h8NO1m1aqioqKioqKioqKD\nebNIlT4ozllhjdSaV5nDxH5WzuvCyZjnuT15mOiT3DTklchomZiY6Giz1hhstTCyzMaZZtGneTE+\nXhmfaXHdpsx6Zjg7eZ8Fy3TZQjdMYxjW3loxY7VVpfy/5y3LTcK4kJdsDgG5YMzHjRs3dnKrDNOG\nM7lw31l+rZJvXkOZtmuNui8jdx+t1tz6LB5uY4tsBvvpZdaULDda+b41aFsvsr4z66jliDE65xs5\ngcp9zbJjn43MOmKfKF4tT94vyhptzlHmcfEd2mVr1HwwnwC57ko/xmwt8jd77bD8Qa57Cu+hPbNI\nlc/KrN5+f7bWdfsKZZap8nm2dpoW5zIE9DlsjjKfqj76hq1J+wpmOZsMW6a8tiO6+bPYe/lOtk/y\nPnxyPjLTQE4r73l9fWfIrONGtUhVVFRUVFRUVMwR82aRKjX/zBIBbPEpo/Z8eucE6TvsPutFRFcL\nQvufjY+E/UiyzNa2GmX+OvyNRmrN1JltSyvAsHFa05rt3be1pj7areUafp/x2D8ju6/2PbstNH13\n4h5f5mdkC1um/QOezVyWGqnnx1F3w3zeMl8K+854jNPT053otGxeod+WqEyrs/+KfchKWjJLi2He\nwjfXwXL7zGpQ0mJZ9Hczi4TXKLBGmq3dvooCmc9LJueOcoPnrmVp2u3Xaat7+R7WHFv5Mv/MzEpk\nWlwJYP369b10lN/NLO5ZBn9bLjzHoLRgZxZ4w7KUWUHdzvvLMIt22WaYT6Wzx7uvzBLXZzVyFCKv\n2bqwJTK7mfGz+R5z7+jX8v+2TPJdrJvAEcmONM6sxraiTUxMdHiaRdL6N2kY5u0gVTLW5uXMfNhn\nlswOLxYqGOWwdooS2vRJ+5kYSVubQU0Lm3MWzg58YMgcRMvnZ2HJ2SYGX+wcm20Y2ULrw7AfPOCF\nYPNvdlWYlWvoo9vfyX4QfHj1uH048uY9mwPpbK8ZfY0626vABQsWpH0CeG6nYW8gGU1ZAeqSNrfN\n5NyHvWE/EFkKi74DvJ89THGgD4diu7i52yMvXrMlLZ4T7yVZiRi/P4x2H3qffvrpNAgHeL/InMef\nbb7mklYfSt2Gqxn/WFtueJ/DGmty2H5RwkqK37eMzeSwXPaT7R9gcnIyVYAM3s9+e2br+Nx3IM0U\no2EH6eyK2+19uPU6LMfCPDoFz7CAIP+++HfVtPSlPsmUGx+gnu1Bql7tVVRUVFRUVFTMEbVETEVF\nRUVFRUXFENQSMRUVFRUVFRUVv2PMm4/UCSeckDrocer75Cc/GRER73jHOwbaTUxMdO6mKZtQls0o\nX7mrxaH1nHPOiYi2XAn3qSTotM8UqfMpV7BkyZLOvbtLPhx55JER0Tp48jkOv/gIfPrTn254EtF1\nMuZO2Wn8TzzxxI4/he+2XU4gKw3icgWk/Df/oAWfkgsvvLBTTgD49E5a/ve///0DfZgW7r4pKXH0\n0UcPvG952WSTTRoeepz20+BZtKdEAM82zxk3pTOQl5n8npBbl+Ww3x7jcIkg2jlJLLRTgoYSRGNj\nYw39TuNxwQUXRERb8sc+QP4b2Tr22GMH+mGuoI11Ah/f8573pL4gyD+0vP3tbx9oZ78K1gftKW9h\nR3fKUMCniy66qDM/Gc+ZI2TLtEA7IdQu++S1XwZQMJ/HHHPMQF9Zws2sFAbPdhoJ1gXt7f/2+OOP\nN+P1vkgb9kHWB3sRfbPPuT17EDyn/RFHHBERg3489k9EVujbco6MMW72LuY/K5nD+/D9rW99a0QM\n7j/2CSzltqTVDvzAZXyQPQdIwB9oP+aYYzrJW/1bhJy7FJbLeTEe5pRyaF4/8JHv/cM//EOznp22\nA7485znPGaCF9vYVtJ+Wy/LY964MnDn77LMjouW5y9TY74x9jr0LGfW+Alyai/6hpaQduin540Sz\nLkdG3xmqRaqioqKioqKiYo6YN4tUCVsmhoXcTk9Pd6w2wNYhlx2whcIe/2gaWURRqfkPK4mRJRLN\nQosdteAyDW4/Pj7e0bhdAgSgxfG+rVxZlNJsE5L1tc34khVMhib34xQFTrJaahpO0pdZgwB9WIvr\nK/lRwknixsfHOzy0BYLP/SzTniVgnSmalXlnPE4R4XXA31nYP/3x6jnrQxbhmSV/taUWjTOTRctJ\nn7+CLQqep2HpD8yfYWs6+37ZBzz0d7yevRc99thjEdFNLAhsZSstelmkrK1EWYJGv0/EMHLVl4ql\nbDc1NRXLly/vHSfILBJe//DRVjQwUyQmvGH/s2zZosjnWQJX02RroWV9/fr1HQtTn4Wk7JN9INtP\n/Tffw3o8U9RiFhGa7S3eD0GWwgQ+OEFr2d6Rg9nvm2ln3SMHWXRjFoG6cOHCNJ2J12+WHDRDtUhV\nVFRUVFRUVMwR82qRsh8Sp0DnqnFStcnJyeY71rxt1eHuNysnQIkYl0zghJpp9uPj4813spwTzpdh\n7SfTAuyfM1NJCU7nvLp8AkBDsEUhK52DNgktvPp7M9E57DQP36Dd99LAc2Jtoa9EDPC9u2lC20e7\neeihhwY+9xzBx76cYZn10qU/stwkjM/avpPnAeZoeno6zY8DbO2xb0dWWDezWMyUt4rPLIMgK76b\nWY2h3SVH6Kcci9erLZLD4Nw0w/Kxsb/0WUdtebZ2bnmBdicazNrzTFtyR0dHO3xg3s3LzIqYJcnN\naPHf4+PjjXXK1gwnhbSFwbRAM3PB2OwjVz474hl+e6/1emYN0ZfHk82RrW22roINGzY08+M15TXo\nOSPHYWaxcdkv7xMl3+2X5d/a2ZbtgkbvNzyLOff6KfluH2FoYO/NinlntHhvypJrjo6OdubHlnpk\nqS//1UwYapF629veFitXroz999+/ee/hhx+O1772tbHXXnvF6173uqYuXUTEWWedFXvuuWesXr06\nvv3tb8+KiIqKioqKioqKP0QMVdOOOOKIeNe73hVvectbmvfOPvvseO1rXxvvfe9745xzzomzzz47\nzj777Lj++uvjS1/6Ulx//fVx1113xZ/+6Z/GzTff3Gt5mZiY6PhGgEzz6ivDYMuAyw+gnaAV2CLh\nYrU+iXLvDMpMr77T9zh8j+6ovSwtf1auxJr66OhoMy5HMvmUvm7dugFas9IfwM+yT81MpTAynwXg\nLNLAhaaBNS9rV6W82IcD7STzebK26AKpHgvyYItln1XIFoWyfEb5OeBZpj3zeyoz3TsC1tqrLbJ8\n174NAD7YHw0LjK0M5fctv+7bvh22lrhvF/udyXdsmLU385GAt7MtoO21aT/Psi/6dqSk5wirhvci\na83ZWErLnn1h3Mb7o+fIVuCM94DnMdaFCxemWcKzig+sRfPFvmK2eHq/6LshgC7/XtiCb7/GzFKX\nZdM2Fi1a1LG0s29lFihHGALTwp5u9GUIt2UoiyAF0GYLU5at3nscv0N9Prjw3OWqsqhsQDvkJPOl\ntJ9w6feU/c7ZKp7Nf4ahFqmDDz44VqxYMfDeV7/61Tj88MMjIuLwww+Pr3zlKxERcdlll8Vhhx0W\nY2Njscsuu8Qee+wRV1111awIqaioqKioqKj4Q8OcfKTuu+++xrdo5cqVcd9990VExN133x0vfelL\nm3arVq2Ku+66q7eP6enp5vSX+TkBF+9ctGhRGk3mSABO1C4yCdBEHNWR1c8ra225rbUS50ey1pvV\nt/N4s8iJkgeZNgt8EueO30U3s/aO+is/z+6ws9N8ZjXxnTbw/Tt/l9ovsLXPOXysHcM33t9ss80G\n3s/40lestC+qMqJbUyzzS3EOlmHWkVLe0HKzyCdoySLF3J75pl9owSrgNbt+/frOWsnq8zkPEJ9n\nkZVeV5n1qHzP/nqMf1j9Que/yaKcbF3qs764jqWtGX0+j+V47GNlebGfS2nRzLR6Wzuy6KQ+6/9M\ntPh7pf+PeWw5H1ZrzXu5965sjFNTU50C8JZF+8TQLotStXwM8wddtmxZ57eE/cBrCCsO4/M6MWyh\nx3+zz1LnHEzD8kPZmoqc2HIHXDDb+26fhRj6Mj9Ew+shs6baWlxaU/t8+frofbb4X0ftjYyMzOhU\nXMvBVFRUVFRUVPz/FtOzwNq1a6f322+/5u+99957+p577pmenp6evvvuu6f33nvv6enp6emzzjpr\n+qyzzmravf71r5++8sorO/1FRP1X/9V/9V/9V//Vf/XfH8y/DHOySB1yyCHx+c9/PiIiPv/5z8eb\n3vSm5v0vfvGLMT4+HmvXro01a9bEi1/84rk8oqKioqKioqLi/zyGXggedthh8YMf/CAefPDB2HHH\nHeP000+Pk08+OQ499NC4+OKLY5dddol//dd/jYiIfffdNw499NDYd999Y+HChfGpT30qvdr7u7/7\nuyZtAveSW2+9dUS0d5zUw6G+Fd76k5OTHf8TaqFR84m+8Om47bbbIqK9E/2Xf/mXho6Ibk4ifGWI\n0oIWai1FtNEk+ANAC3WZqPvF+Pic6AxoueSSSyKirSnoPDKOCqRO1Dvf+c6GVzvvvHNEtL49PIPa\nSdCy/fbbR0TEDjvsEBERN9xwQ0S0fhmu44VfAnfjvr8/77zzmr4dXem8P9QUYz7djnt2XmkPz+07\nxBgnJiYaHtKW8TD/zkVGLSzmn2g05vLuu++OiNZHgPmnZpn93rbbbrt48MEHIyLizDPPHOibunTI\nwf333z8wDtfDQ654hqNBmf93v/vdTT/2M2C8lkV8G5lXeMjcUYOOWlv2MSFSCD5eeOGFEfFMzSpH\nwGW10KDbkT2Mk36ob8ecOi8VNCE/5513XsNzxsf80zeyRd/UN3MNsi233HLgffYX1gXPxi+F/WXp\n0qVNW9fxs38Zz2KO3DdjYC9jrlzfzH4eGzdu7NSrYz55Nq/ILH9TUww5tx8rvGZfRBbpv5QBr2v4\nwvxvs802A30h915z1OaDVsbJHoYcsV9Q4/Dxxx9v5oe9g9+Qiy66aIDuLbbYIiLaNYmc8yzkhdps\n9rWxbEL7scce2/hhOds3r5/73Ocioq0RyF7kqFdeoZ39gn74zbK/47nnntvw0PmxysjfiJaHzD98\nYB2wP/Is17fj98ER2MuXL4+zzjorItpae661aL8tfosYJ3KBvDBO+IWsu35qOZfQx3yalswPlTWa\nYehB6tJLL+19/7vf/W7v+6eeemqceuqpw7qtqKioqKioqPiDx7xmNt91110jotVibrzxxojoes5z\nokQj22effRqtzZmo0W4feOCBiIi44447Bt7nRAo4BTuLbJYJnRPq7rvv3lgW1qxZExGt5gico2dY\nRla0IUeSYW1y9t3R0dFm/NCJ9SOzBMIXNAa0vCwjOM9+5JFHIqK/Hp61nGxcwDlH0G7QLKARoIEw\nV8gN/L7zzjs742S+4Qd9OvdKWZ08orXY8D5yYTC3e+65Z/OeI6UYd5mBPKLVfk0LfW677bYD32fe\n3b7MfbXddttFRDufzvYMsAJvtdVWERGx4447RkRrUQHOxs8cIbumZXp6ulmTw3Lx8Pcuu+wSEe0c\nsY4yvtga6hxvEd0IT9YHPM9y80Dz7rvvHhHPWNcjugojsu39oS93keWecdgKBKxhM1dYR007+wT8\nwNr82GOPddYEdN97770Dz0bWDOc0g59ZnVBHpJb5kzxPjirDEoWcO1KSdvTN9xm/97oy6pHUPVgk\nHUXOPgENtqpncwStrAvky/vF1NRU0wdWQnjq/dxWP1v/LE+Mc9WqVRHRyg17dWltYk0hKzvttFNE\ntFYyIu8B7dhjneMpy7LunHDO01cC+mhTRuWX8PwzFtcmBI7+LKPhPZ/wlD0IHsKPLGLSqLX2Kioq\nKioqKirmiHmzSK1bt66xLHCaR/OyBstpF2vBqlWr4p577mn66WvL+7TDcsDJE/iu2O3te0K7O+64\no9Hq8aewLwunXzQnNNEspwl8OOCAAwa+jyZ+8803D7Sfnp5uTszQjWblcXIqR+t3rUJb6tACbB1z\nVtqyb8NZlA2sBWiJmdXAGhq0QmNplUTbQ0vBaoi2g8YB0EjWrl0bEa025zpYgLEiB8jZwoULO5o0\nNOCHBj9cY8rjXL16dUS08s7cXn/99b3tH3jggUZrxYqRZf296aabIqJdazzDVgNoZL7pd6Ys27SF\nh9DH+gBooNCArKKxm488k7mlX9fHimg1ZfuLZBY61iIaKOuHV1tHoRH/Hiwel19+eUR0LRgReRbs\nvrZle6xHjBOfIOD8ZPB5+fLlnflBnpFXLBJYBW+55ZaB9vCY7zGnyInnFL5gZXn88ccbnmf1SukT\n2GIPkOUXvvCFEdGuK68H0zIxMdGMF8tLdiPB/MNL+2MCfptYD8wRMpzlTItoedYntyVtzD+0s9cg\n0wBZ59YFvtiKXI6DvZJn24cUIFvcSOy9994D32d9AFtbWReuMxnR8tB7tWkF8MX5uOjbPHe1D+Z+\ncnKy8yza8rto65d5nqFapCoqKioqKioq5oh5s0gtW7as0dSBI2wAGgqn4LIYMlYMt/VJOotS4CS+\n2267DXzPmVoBmscjjzzSWArwM7C1wzXSnIHYlhw0evjiukbW1JYuXdr4RVj7M92c3g888MCIaDUw\n+OI7bzRRNAz45vv+iMFaRn3jy7KD04d9q6yRoFFAw5VXXjnwPD6HJxGtFuMMxOaL/W7QMNEGbdlD\nU+WZ1157bUQ8o8FYS2e+rSFlNeUY9ze/+c2mz4h+nke0fN1mm20a7R66MisAVmDWUp+FsRy3x+Bo\nyLJ/5gNZyqx69O31XEZAlnA2bawBzGlJi3nMZ173Br4gaNysQWukrEmsw/CF55XWV7Ry+xlhBTTP\n+S6yh7VoWJZy1s0VV1wREc+MObNIY0lDzvHp8V5kiwz9OVu727MGRkdHG+uV93PPJ3ICXzILxa23\n3hoR7Rwg6xktm2yySWMhY5263Bnj5naBvpHlrMIDfknMP+vE1pFNNtmkY/Xok9vyWTzb2cUzvyT2\nOPvMlXxh3Ox3rkdneYEG5Nw1WW3Bguc8Gx891k9pCWT8zB/PhhbfSCFz8Iu+oNHtAWMs60VaFqHb\n8+79YxiqRaqioqKioqKiYo6Y16g9TpacnF3PJ2u/bNmyNHrA1hHnqPDp1bW4fO+anXZXrFjR+U5W\nGRuNy3WarGm6/hXgRN5X94/v2NplWlwxG03BWhBw/SbaZ3XfoKfsE2S5OeALp37nzwLmM9/rs6ZB\nn6Op6NuyZflA03StNmANFE12amqq4wtmHyFr4uY5tGNldY1F919GRTFPWeSk14u1QPPcNSiB5ayE\n86n1+UdEdP0zsAbb9wFAG++jXfdF1DAetHNHzpqHWE08n7TP9hfze6YadPDFNTS9/p3LznnYMgs2\n7/OcxYsXd/r23oGVw7w1LbZMZXucrZHj4+Md+t3WdTGxHmWRUpklxygt2+xbWTSzfWJsWcraOx/X\nTL54tihl0ayO7qa9o2EB/PLe5bxTJejDkePIEvC6cW67YXs6/dOupAVeeY1llld4zpy4dmVWJ5R9\nqHy2ee7obvZe0zgM1SJVUVFRUVFRUTFHjExnJpff50NrIeOKioqKioqKPyBkx6VqkaqoqKioqKio\nmCPmzUfquOOOa+7Qs2yj1CyjNlt5f+2og7PPPjsi2po/gHbOnkqtPer4cD/LnbozO7vW3sKFCzt3\ntvRBTaGTTz45Irp5b7h3hXZqkNEe4AvCXThjoO7P8ccf3/TljM6uhUb9KVsDfWcOLfCczxkjvjO8\nnn/++U1beEVbR2FQC4k58l03NLsenvunPTRMTU11aqHRN6/OYA4PPf/2kYCvZ5xxRkREnHLKKb20\nPPnkk804Xa/MecNcQxHZok4ctDjqjfdpT52o0dHRjp8hfzOf1JTjc9dBA9Taom/XtATIEevuuOOO\na8aJLCGvrEHq+L3vfe8beLb97nil1pbrmzkaFD6ed955TX27zPeLOfjsZz87ME5rmo7yo6Yc8uV8\nQ4xx4cKFnZqSHle2/l3fjr7tf1Wu/7I9+8VTTz3V4eEHPvCBgT6ggXFCP+Nkv3BknH0qGSv188r6\nkI6E8hp1xCNgvPCF+Ue+TINrs7KmFyxY0FkX8Mo1JR0h5/XNXsT6B4768u9R+TsH7EcFz4866qje\nvgH9wEdk12seIOsf+chHmvWf9c28ffjDH46IZ2rsluNxtCbtv/CFL0RE97fLud82btw4UH+wbOOc\ndbxSPxXZBfYVY/4/+MEPRkQri2WdP8bCvFJrD1n0GcRzBO0ZqkWqoqKioqKiomKOmDeL1MjISHOS\ntDXAsFa5cOHCjoXBbTmlO/+NT+2OkOAUm0WngMnJyU5kgr/jCLFh0QmuNQQ/stwt5bPNI7d1zhme\n4ShH4MhC194qx2ZtzJFzRmYNyebI/VjT7cvibb5YcwSWQbTeLEeJI2vK5/k9W8NcA8pwtM0wPpbP\ntnUmowXAhyzrvK2JzoXVRzvPpu8sLxifI0NZLjPTYhnvi361xmyeZ5GPbm/raEZL1i6iKyu2pDki\nKIv69LhNe19UWJZlP4ugy3hP+2F5hEBpLcoigh0hx9rMaPQcOqLKvC/3NEenmeeOzvbcZFnpHXmb\nZfyfnp7uyBjIqg9klv2sHZ/7ZqTc68xDr+dMFrN59liyfcc0lnTxW0IOvGx/RDazyhZ9NWgjulF+\no6OjaV1G0z8sR5VRLVIVFRUVFRUVFXPEvFqkfHLONFhOrpwwly1b1qkEDnznzymW/BDWGFzNnKzJ\n2f19+Ty0HZ7lvunDGpg1B0B+FF7xT8hqM0Xk2l52qrcWl2mi9hHxHXLZf6ZBZvPJnNhamFmqrBVk\nNEd0eWStznyx3w3wXAHnsCppy7QX+2fYZw5k/LLGCUrN3HLcVwuv7CPTxE2z12aW02zJkiWppcV8\nseUq8wUBmeWBsZTt7QPmHDSmzTnebEXK+OhalVgyy/b4etjS6nxCwHm0rBV7/tmbGAOa+lNPPdWh\nm3xZmc9blqnaczWML6WlLsvdZSs3dNuvBtjKyDPYP8zH0rKVyQ5g3hifb0eyNeqxZTcZ4+PjnbXn\ncQFkCQsLfMjySDGHWe68sr1/JzMrL7CV2HOY3ez4b8ZU8gXeMU5eybeXVXCgL0CBubIAACAASURB\nVCqIUHXDvxfAOcSefPLJzu9a5kOb/UZnmNeEnN4wfPgxYMLExES6oTNwmE0JFR+w3Cdg84OxWWHR\n9evXN4euviu3iG4SSG+o3sy82dG/k6OBRYsWNX17czYPnWDOJlcvUpuuae+ki+V3s2uULKmd/3bi\nQuDNOzuYlf/3BgqfssAGX/05UScgMR39Y5bebLPNUqdZH7SzqywnJuUgjVxkB7Xp6emGDvr0ZuSN\nE7lnk7a8WE7849dHi8eZHUbdlw+a2UHSV0UOlCi/67IS7guw3imV4mCDviS4Ea2c0H/fdYrHO5Pc\nRnQPafCzdOAu4cAPSstMT0+nV7tek5lMWUl08sRs3y33DQd8mO7MJSBL9ujDTnYt1SfT2T5nGp1w\nN3Nspp1dB7xflA7OjCtbFw7WsKxlV9tOBkr/ZTJdX8n64GBZtIIBmLvMFYRDEX+zPkpa6JNybMi1\nA8IAxc3ZsyzDppF+eC2v/rIk2F572fkiQ73aq6ioqKioqKiYI/5POJtbK/JJ3e02btyYmtycun9Y\nqQTg0g++IjDGxsY6ZVfct7U5axrWpCiYigUCYPJ0/wsWLGjMlra8ZOPLeJtZgXw1lmmZ5Xey1P3A\nzrEO3x2mkfoKrGxvGoYlf3X5FZvuLZv87SLRo6Oj6fVIVrQ6uzawdpRpXmVJIWvQmandGpdDyoGd\n0X2V2XdFbitGFiSRWUsyc7odQo2yfXb9ZxM+sPyjibvgKbC8OFijtBplVy2ZNcjWDxdBz0on9TlQ\n2/JqixHavcv5mHYXge67Ti1pKANkHFQAvP7t0G459xUnV4G29JuW8sobOc9KhPlqN7PUQWNZCLcc\nS5811YE9ICttYovusCAVMNP1mwMeTG9mqfFeNSxgyjT20Y5ce76zK2wsSsgsn2c3GL6Opt9Fixal\nbgbmS/Z7maFapCoqKioqKioq5ohaIqaioqKioqKiYghqiZiKioqKioqKit8x5s1HivIJEe29NHfe\n3EtSfoDSCbyPX1BExP333x8RbZp90sNzn8p9LHeh3PWTwj8rncIz8JUgRTztJyYmmvtUl/KgremG\nFu78eaX8gMssGNwFUzrhPe95T3OvzniJ9MMPAb5QfsS+PY6AoXQCZRaYE/hGO+61zzrrrIZu4Pv4\nslRBRFsihM/vuuuugTHQ/p//+Z8H+ALNzA3fW7BgQSMrLhHEHMF76Eb+KClCqCxwJOlnPvOZiIg4\n7bTTIqIb/bZx48amb+afchL4vBFVZX8T5ujtb3/7AM32qaM95Q3gy5NPPtnwzD6CWbkiotWYX+SI\nvinjsHLlyoiI2HbbbSMi4r777ouINvSYcjinnHJK0yeRjdAEb5FzxrnddtsNjNcRtS77BP+QWVKa\nEBl0+umnxxFHHDEwTkeb4V/DHFF+xMld2VfwmSzHWdJq36Knn366WUNHHnlkRLRy7jB2ZPL0008f\nGCc0Oz2E55/+7Xu1ZMmSpi08L8umRLTRzIyT8TBOaEH22FecFJF1x75YRlTZb499C7lF9rxPuNTS\n3//930cJ5Al5cAki9rqFCxd2EqYyn8gWexHj45W58b7IOmIPgs+sC6I/KW9y4okndsp3sTbpm/JT\nrDlgnynauxSOfzfpn+eec845zXyy5nbccceIiLj22msjIuK2226LiIhLL700ItryM/SF/65/P9i7\nXDrngQceiIhBP0HKlUGL5RtfKPpm/pGt0tep5I9LBMFH5oi9esOGDR3ZYv2zRzu6G5mkLE+GapGq\nqKioqKioqJgj5s0itW7duuYUiIZOdFoWzcBpeptttok1a9b09uucRFlBVOCcPXyPk6nvRNEKHnvs\nseYz6M+iTbbaaquIaE+3aOymhffpD82Lk7qjX9avX9/0OSz/iaOvnPvJgG/33nvvQDssE8xFRDcn\nif82X7KU/mjJ/hw+OKKiL/oJLRUNCp7R9y677NLbt/MAZdFJ5GuiXzTziG6OKmhxKZSsXA3v8z0+\nR34MZPbhhx+OW265JSIidthhh4hoNU6ABQnZcsK8LGJs1113jYhn1lxEV/MG09PTzXq2NdiWJnh9\nxx13RESrUUKz5YW5wOIFDfCnnFNH8mSRfqDUVstX9pwy/01Eu86Yd2hzpG1JryPksPJllmcnqmRu\n2B8A7zuScHp6Os2XxLqlb3L09CX7Ld93BOqwfWNsbKyTiw6YH8yZE22aBvjGvMPzvmhmxsz8Ma9Z\nKRR/nq1/sNNOO0VEO8fsC+7/iSee6JSRgYe+yfC+UEblRnRlGUss68jroZSBrbfeOiIiDjrooIE+\ns1xcnjPnp7PsMqdOQu0yahEtz1y+x3nlAHODhYnP6SfL9YZ1sYwk9XzSFgsatKxatap3nBmqRaqi\noqKioqKiYo6YN4tUmRmcUyCnXWfw5US5YsWKiHjmZJnleXA+GPq29Qug1VvzBD6RoomvX7++sRRk\nJT9cANe5fKw1Qgsnb5dAsAaz+eabN+OxZcraS2aByIrXctr3qd95U8r3XCIkO80zDqxEAM3z1ltv\nHXjfuZ1crqK0BNm3ie+gMTEeo8wKHdHNWA3s54JcLV++PM0mj/aPVQfrEFYzwHpwpm/GaxnFQvHY\nY4+l1gmApSrLk2TrGGuNdjfffHNEtJqbLRj33Xdf8x6WZeTYFkZkE2sIMgzPSx/I8llo/ba+lhYs\n+8Ix31n5HWv7nl+vUVuJXAGhXHfInC2M0Oi9yFaQrJQScK43MD093ZlPLChY/e68886IaPlUWlYj\nutnj2ZucdR24nMno6GjHugcYP33ax8cWSdYN7W+//faIyLNvsyZHR0ebcfUVz41o93PGhdwzN7bU\n7L///hHR8hFavP5KWjILm2kp/S3L8bkMjceJfDHWvuoWe+yxx8B70M13LUPe983rrJA8a9g55Up5\ncX456EUu+ip4RLQ+kYzbmdwBc9dXLNptGT9t2bvMy2GoFqmKioqKioqKijli3ixSm2++eXOqtQ+M\nT6ScXO+5556IeOb0y4nZp3ROqS4YygnZ969Zvacsc2tZUNg1nbJTemb18WmXk7a13Cyz85NPPtmp\nsZYVI0abc/2mbLy0QwvglM9rSYstUs4Sm/lTMFeOgPP8ozXAPxfCLLUpPsOfwlEm5iHjwRrkiDrz\n0bX2wMTERMfy4uzBaL/24wO2xKCh8X0/k7GsXLmyE21ii4ELhKLloS27Jh3rBKsRcwCf+jKI4z/H\nuKDbfUMjssU6cr0uj5N+Xe+xHGtWzLxPViK6ma1Ni+HIQct6KS+ev6z4LmActuRl1QccWQTt9qWK\naP3KHKWX7XPsn2jm9nfymsZqVEbiZfsioK1rlnqfxAqKFQX+IVee09Iq4kz+XkPIPzz3nmXaoRU+\n8sre7d+XpUuXduYdntMX6MuOX9Ls3wv7Flp2S0sYvCM6j9+NzP+Kv22ZyQpFuyh4Vuw8ol0XtHFl\nC8+nrb+2uGVzCkoaskzl3I7gS5ZVH8lQLVIVFRUVFRUVFXPEvFmkFi5c2Jz+0ExcSRtgqShfs9pZ\n1s6yGkvAGizt0chMC/1PTU11NKjMquPq1lmtNecA8im/D9ak4ZH79p23eW1NCiuhtWqsAaXm7Qra\nrq+U8d615exnAXhmWWux7L+UAUfOoVExB+4bTRJ/N/iQ+bE5n1bpF2YriC2W2bwD+1nYQpv58S1Z\nsqSTO8ZabZ8lcSaasEQBLDH2JQKbb755JwISfpiHtMs0T1uwGBNaIzRgRSjH6lxk9o0yLdDqWmuZ\n7LoGXaaZR3S1eVvSMp86+69lkWPQauvi2NhYar10tGkWnQyttPP+MMy6/sQTTzQyYkua8+jZmm5r\nqi30nktbILC+r1+/vmPt8LrwPEOD5Qg4Gti3CObjihUrmra+/TAtyLNpyfz7HBnniOxyTrBI2hKZ\n0W3fW9f/NC2Zb2FfhLp5zt+Z1djzbB9C72n4uTl6/Omnn+6sC37fsI6bFu8XGapFqqKioqKioqJi\njqi19ioqKioqKioqhqDW2quoqKioqKio+B1j3nykqBMU0b0Ldt0vavOUfgnOtUMtHOo4+T6au17u\nRM8888yIaOs40d7+OrR3LbfyXtY5rajjwxiz3DVY5qjjRG0+A9q4t4Yvp5xySkOnfXug+5xzzhmg\n2+0A9/PUZjv++OMHaHQuE94///zzm7pMwBlq+ZtaSPDcNaR8p02dMNrbkln6P9C3a+3Zl45xw3P6\ntl+Gc/QgX65BVkZBmofvfe97B+i07wO+QLSHFmi0Txh8gRbqfm3cuLFztw+QW/PctOBnQT1E5tQ+\nAvapQRZPPPHEjq+Cc8vQN7IF7OvmWpvmuf3TeD3//PPjne9850AfribAd6n7ZVoAcwBfqBMHH00r\nNCxevLgZJzXf7E/kyFp4yH7hNca48Vc699xzB2i3f1tZB5S9iPpjjqRjnOwtrkFonxrXO/3whz88\nQHsp6/Y/y/Y5y5jrRNLe+wp8ct2/vpqlzJd5zjiz6G36pjYfPLfvlGsvwsfjjz++E9npvYj1zJpz\nDjzAOmL+qRdqH7E++YInjozz3mR5Ye7wmcPXttz/S76wP9g3bcWKFc1vEbQwPkcO8sysjh+fe8+m\nf+onOi/Zhg0bmveob1nuoSUfHCnL+s9QLVIVFRUVFRUVFXPEvFmkyrvGYW5afbmPskg2W5acRdXW\noez9LLKqjH5x/b7MYmJa+3JrlH1nmcJN0/j4eJolN8uXkfVlWIuaCTzLURgZLbaKmCY/01qTNdNs\nrH3I5MZZ511pHNgSxZxNTEx05sCalvni9o5i9DOyqNDy/1nOGfeRVQYw3K/5U/afjdPIMnTbGmBk\nkXIlHNnFd5yZH6Bhm+dZHiFbwvl8WG22vr6ycWTjzyLxzPeZ1vZs54h2ttRk0dLOGzQ9Pd3Zg903\nsPXQ69/r3bJolBFplvc+uTXdZftszfZlze7D5ORkh2fD1rOt4VkEsb8/LMqvD8OqT/BMbok8zwBL\nlH+P+vie/T5kayiLYsz2uqwW6+TkZGodzn6jZ4tqkaqoqKioqKiomCPmzSI1OjraOaFnlhpXlC5P\n4lmOEvfVl1ujfN+5Puyn4P4nJiY6Wonz32TZgxmHNQxnenWuD2fCnpycTKt0G9lJPMts7my8mYZe\nwppUVgvMp31r0ln1b1tR7P9WjtNWrswS54rrINPc3V9Wq67sI7OOZtpuaeUqn+ncTaVm72cZ1iQz\nyyzIMh5nlqypqamORTWzXtknKtMKM9pmso45B5vnN5tP84/veX/xvLPenMun/P9s8wJ5/LYGef5p\nbx+Zvr5dg5PPnf/Hz56NNbTve+Pj46mFOpMtr2vg9U+OJ1c6AH259DKeZxapbL9zriOvZdMyMTGR\nWof9/jBrybBciDPlesosbn17aDkO5IPfNlvNTUN2C1PSzjidPzKzjtkSPeyWyb/55R6Q3XZkmd1r\nHqmKioqKioqKit8z5s0iNTY21rFEZNq0rSYjIyOdDOaAkzQnYrLcOhuq4dMu/WSZi9evX9/RRoZp\nGPbXsPbiLLIek8c6MjLSeeawU701j8wXIPOl6dOKMhqyO3prRbZguD9rgb63LzME+9m2NJj+rDZd\nJouZH8/SpUs7/lTW+tyn22djyPzVyjka5vuW1UPMLJLmubOtW45GRkbSepWZtdNRTJlmav810GfB\nsrbqTMue/7JSQdkODZzoV4CsOarHfpwlLZlvT1+0Xd+4svWE7OLnVdLUNz/lK+NgXoetf8M00l9p\nyXFGd7ellqD3f69Frx9oZo7cf7nXW8695jxH9gnMLBX0C5+zuoILFizoyFZmqbMlhf0tqxDA31mG\n71JeTDey4z5AFlGXWXCyfbSvogDzxVzQhnFadgF89M2N+ZLRWvo1u89hvnTDUC1SFRUVFRUVFRVz\nxLxZpJYsWdI5UWZe+5x2S98in6iB/ZT8eXaf6oiRrL5ZWU/MGqa1HWihb0cODau153w55suiRYua\n0/2wunb2HXEuliw6DQyrtTQTMh+ZzJLnvx1B4vp/5RybR3yW1XECzCtaUebHZAtHKZuWlcwq4L4A\n1lP7hMw0/7S3xTWzXjgCiL+t1QP6Y2yZH9PY2FhnPdtqAzJfr2H17cBMUWnWsD1f2fqn1hZzkM2/\nZdG+NOVYrXHzWVZDj/0Crd71/1ybjRxHtopNT0+ntfPsb8errePec0FmwaA9/JuamurUFjXdmYXG\n8mGZppZaZk1jLkva7Rtmui1TWaRcdsuQ3XQsWrSo06bPn7KEaxRm+yTjd3411mFJk9cBz8huMPx7\naBrMR+bUlrw+PzZkxH3h2zYsOhFkVkDvnyVNmW9otp6rj1RFRUVFRUVFxe8ZtdZeRUVFRUVFRcUQ\n1Fp7FRUVFRUVFRW/Y8ybj9Sxxx7b1Gt65JFHImLwfj2irbXm+llLly7tRJFRC+n9739/RLSRLNzd\nPvjggxER8eijj0ZExKWXXhoREcccc0xEtHe43K/yN/fN1Ik66qijmn7xAYBefHeohUTtpCw3lWsK\nUoPIfj1Et0ATNYhOOOGETvQi333ggQcG6Kam1Lp16yKi67dV1iuj74iIbbfdduBzapbhr/GBD3wg\nreNmHwZqIcFz+3wwR1tuueUAX5h/R2sgPxFtbavTTjttgA9r1qwZ4B1+KIzzLW95S0S0PlKOUuOu\nn/49p/izbLPNNg1djBO+QAsyyRysXLkyIto6TkcfffQAjfATPuFDQG0u6pstWrSoWUPQiyxSO8tz\nxPjuv//+AV4yzg984AO9NMMf1iq0n3DCCc179Mla4n3opgYhNDDvzpdEe+p+QSM8hzZ8Zs4555x4\nxzveERGtzLF2dtxxxwH6qbVJ3/Cc9vYhcd1P+IjvEf0+/PDDzfqkFhrjevzxxwfGzfseJ7LK/mJf\nqQsuuCAi2lp+0I78LVmypOER88kaYr1vv/32A7x86KGHBsaJbDFOeM0zGPdnP/vZiGhll3U0MjLS\nieSiBiH7nGUV4PtyxhlnRES7d1k+4At8Zd+F9rGxsWa/QkbgqWttMi5klvW0zTbbDLR3TTn/hjFm\n+Hj66ac3MnTXXXcNPIvv8jvnOqHwbeutt46Idv0zziOOOGLgmXffffcAzfz2fexjH2vGiXw7cpL5\nRBapV8f8u/4fc2T5Yq9jTuDT4sWL4+KLL46Ids/1vPs3lz2a3wvnn2IMyBHrDnlhbGWtWtdaZI2y\nfpnHbI4yVItURUVFRUVFRcUcMW8WqUWLFsUdd9wREe1J/eCDD46IbpQPmkiZfXz16tUREXH11VcP\ntOUkjCWFU+xvf/vbiOhGp6DVEOmBxsLpn9M94JS/ZMmS5sTLez5hOz/G3nvvHRHPaK0lTR4nWh20\n3nzzzRHRH1mFtsOpe+3atRHRWgE8TjQzW7kcGQHfbrvttoiI+M53vhMREfvvv39EROy7776dtvRF\n35zu4Q9w/hv6vP322yOitVAAvg+/9ttvv4ho+fHDH/4wDDTsK664IiJa7RaLJGDO0FSuv/76iIh4\nwxveEBGtZQ8gm/fdd19ERBxwwAER8YzcIHuAv7EYMCfIrCNIaMdcYgV64QtfGBHtXIAy4++VV14Z\nERHPf/7zIyJihx12GGjr7Pi77757RLTa/b333jvQHlqZk5e+9KUDtH31q1/t0OIcMsxvaTmMaDVJ\nxsMz/uqv/ioi2jkAjjj88pe/HBHR7AF77rln09bRha7ThrWzpDuilZeDDjooItq5MC3A0T533nln\nRAyuO56N3LIPvOhFL4qIrmwxN5ZJ9iTz0VZ05mbHHXfsyDn00je0XXXVVRHRXaOu1oAsY+FZtWrV\nQHv4yHOXL1/e7J3sA4C9ZquttoqIlvfORG3anZ3/lltuiYh2vwTI17p16zpRauxNpgWrz29+85uB\nz9kvAXzi92G33XaLiIivf/3rA++DxYsXN3RD76te9aqIiM5+4ezjrB/G4HFCC/sEv6PIcCkvtrA/\n73nPi4iI6667LiKi2T8A42DdMEesceYOwHNe4SdWsr48UuylzEkWte/cV9C01157DTwDMEb6h29P\nPvlkw1PA/LPGbrrppoho5d7zn2HeDlKLFy9uFtib3/zmiGg3lhtvvHGgLYxlsx8bG+uE0AKEioPR\nj370o+Z5ERH77LPPQHsY61BThJKNAyBIm222WdMXE+lFZDMx48uKrzrkFtoQNG+kk5OTzbjuueee\niGgnnh8ZQB8sKK6V2DjcN8+G53/2Z38WEe0BtTwE2tzLouM1+1H3hvEnf/InEdEK9Te+8Y2IaBcO\nGw/8fOUrXxkRg/ICz9nQX/ziF0dE+wP37W9/e4AWFj5zwwER+YKvgIXnA+lDDz3UKW3j6x/mJCtL\ng2yVP0YRLc+5MnP/Tz31VDNOXn0YhdfMJz9y0OZrVuYIXl9zzTUR0fKnr/gvmw/rwpsWYH65duUw\nutNOO0VExA9+8IPe73HgesUrXhEREYcccsjA+32AZyhtXv++or3hhhsior0KRJ4AfGSOkBtflUe0\nssGh+41vfGNEtHLswyv7hw8vzKV/YJxEkv4eeeSRhpfAyQzZ35DjTHnlR8ipW/yjTv8oicuWLYtf\n//rXEdEtYWNakDG7VQArfezJjNeHQNrffffdzRUm+2RWbJm+OSDC86y8DUAZQBlk/N/61rci4hm5\nQs6RU9YzP9qA+YfWP/qjP4qIiG9+85sDNAH/9v3t3/5tRLT7cblHs79DLwrmS17ykoho5fjHP/5x\nRHT3IOSBdp4jlxJCYaF9aWRwyo1sPwR2FcFYwPhs7HDhZA51Dz/8cGc+WWPQxxUlMsa+MQz1aq+i\noqKioqKiYo6YN4vU1NRUc2pFu7n22msjomvyRBPj5HnLLbc02i6mVcApHc2A72INyEqEcKLmtMwp\nNksONjEx0WjzaEY+7dokjeWE6xdbgaCB8XNdgLXAWuPY2FhDD9+hT2uBLseBZoU2aA0TPuyyyy4R\nEXHggQdGRMT3vve9zljtZO5EpE6SCt+wEqFxYQXYY489emnne7/61a8iomvhiWi1MTRtLG9oYFgs\nTTvjRbOkH1tH4R9jQnMbHx/vmIHpE00S8z/jzJL4YU2FNqwkto6WFqnnPve5EdFeRXGNAJgLxvWT\nn/wkIloN3NfS8HzXXXeNiNYitfPOO0dEyyc07+np6WZNwjM7ywJkB2sBMsYVtmXXcoUsMtaf//zn\nTVsnb3Tyv4zn8AerAFcXvn6zwzM0cVVaXmPxTKxDtP3lL3858Ezg4Az4g1XA+wv7gp2vH3744c41\nqx314TH7Z5Y0l33FlhzPEWPhOVtttVVDly2pvmZiX8ey730RWtijoAnromWX/pctW9aMlz3aLgz8\n7Xlmn3ByUP5Grr773e9GRMSrX/3qiOheBY2NjTXvYc3NChzbeZr2WGBswSxvRyLaPZ09ukyey3pm\nLWJ5wjrGngMcCMCc0I9dR2jPLQR7ARaw0hLs0mDw1IlFgRN3QgvWIluksgSoExMTnd+5bF/Emsq6\nHoZqkaqoqKioqKiomCPmzSI1MTHRaGhoz3ayBNx58/luu+3W+K5klhdOrzikAd/Dor34Dt2Ov+5/\n48aNjYXAFgTACXu77bYbGAfWsqxcCVqxQ/btQDo1NdUJBXUYPHApHDQHTvf2kXHRSiw6vI/D7CWX\nXNIpv4DlCJ5mafnRarAw0De0mR9Yl+ysXPLdtNi/Akf1yy67bGB80Ir1B63XzsnwD7njddmyZc28\nAuhEo2KukJssuRsy6SLW1o4Z45IlSxrrJXRjMcLPzP5a/O0AAUA/aLBY//BL8LpYsWJFszaQQeTW\nZWXs4IsPCTLKegFo3vARixf94lz7rW99q1PgFvnG8mI/RpfT4BXriB2lXTrC6UbK/plHtGB8pbB2\nZeWrTKOdkIHLO8HvBQsWdHx5GIcderOyJcwRew408X1bcFwGZ926dQ1dthiYfuQaa2dWQJd1wziR\nXe8X8AlrS0Qr9+YLz0beWc8uc2SaeTY+Rsibf4/Gx8eb+aYNltTMLxFrzhe/+MWIaHlvvjj1za23\n3hoR7boqbwLgHesB/1Lf/gD45f2T3zpbPF1Kxj675U0A47ScI6Pec225Q06Qq8yaDr8yv67yPXiI\nlYtzg/17M1SLVEVFRUVFRUXFHFFLxFRUVFRUVFRUDEEtEVNRUVFRUVFR8TvGvPlIvfvd7+5Eb3Av\nz9+UQiC3A1iyZEknOuess86KiDblO/ev9q/ge6STJ80+7bif5W6cO1Snwl+8eHEnGSb0U06AUgX4\nxjgXi9PsUzrD9/TOF1OWiPDdNH1CP6nwKW1gvtn/hlI7lAigX57DXTv8vfDCC5uyKdBnXzHGTYkI\nSmHgO8Kznf+D9vCxLIHh51BOgPk3r/ku/geUQqDMBu38yrOQL8Zq+ShzvFx00UUDdAPGaV8HeE57\neM74aA9fXMYnohsJxXgZJ23pA7qdeI91QRkP88G+NJROOPbYYzvRY/Yvoe273vWugT4tizyD8jaU\nfAD4lkAz6+XjH/94I+eOrnOkELJF315j8IVnwUdo99yUfzM/Lm3Sl1g4oi2zwvp3KRn6Zg2yXyC7\n9rUq+c5eBN2sMfcNmP9Mzvk+46ZcCfJVykfWN+OED54bl+WhvAm04LeDvDD+z33ucxHR7tEbN27s\nyArf+cxnPjNAi8dJe2jzOL2/eM8uy5vBK8sivEK2oBtaXL7L+//JJ58cfWCOGOsnP/nJZj6hxfsF\nYJ9j/TuiDt7zN3yEFvgGH6B9amqq4QmJd112irb4dpF495RTThmg3T6DPIukyy7NVc4R32VvKX/P\nI1rfMOeXY01nqBapioqKioqKioo5Yt4sUmNjY82J0jlc7EPFqbk8kdpyADgx+1TuMhOAUyunYJ4N\nbY5OoJ8FCxY0p3o0K9Pi0znfZbyOIKEfZ/625gUWLVrUjAdafLov25bjp0+PD1jTtoZWjtUagu+R\ns9wtHhd/u0SErQU8r48W/u8SISDLl2ONyyVV/H0Xc92wYUNvCZ+SFr5rEskLHQAAIABJREFUixNA\nC8p477xDtNu4cWPTFhmynAOXRKBv89xz6bly/6Ojo01fjI/veA5smbOGbjm3VcFz3Ac+s6x4/om0\nZO9xln6vUfPDEbblHNm64XnP1omjsXjf46Vfr82NGzd2eIhsOidPxkN/7vItnlNbqqanp5vxmYem\noSxsW/ZheB2ZXzONw30A+vItAfPbJ+fl93yb0reOvI4z3iP//u1xOSLgOfCeVLb37Yn5Yh66tJLh\ncZaWp/L7tqaXNPDaR28ffF4wP00br+U+k2VP9zzSzha7DNUiVVFRUVFRUVExR8yrRQpYG7Cm7pP3\n2NhY2jbTJMgOnBXnJacRp2IsE87dU2ZKdtFQ1+VijLTDJ8inXpDlrPKdOVi0aFHHf4I2fdarsi8X\ndpxJq4voamIlX3x3PQzQ5jvu7N7eVgWPqczHwjicNwvtLrN22LfIzwLWuPje0qVLOxncnasHHtqS\naThvTMbXcqy2dmU5p2zVQzadY8UaZZbzqBxrlrHY82n/EvM0G6+1YNqX7zOf9GEabO1ALmjvcWUR\nxvaLpN/SKgldpRV7JtgfxRYpWweclbnUojNrheeTds5UbZrI1eP8acAWuZGRkdTC6Hm2Zcl7ka0F\n8Jj1Y9qZw6VLl3Z8dbIbCX/XvoFu77xZyJ1pWbBgQSefXmYtz6yg2Y0EyCz8JR9tMfPeYp4zHvPP\n1h4An2w1ol1Zd9N+yyCzvJnmYfunKwEgL3351bz/2ye4WqQqKioqKioqKn7PmNfM5o5KyzQYZ5+e\nmppK6/Jk9Xs4lVqTsoXGvjJG6VMEXbyX3Sf7WZkvkceQWQPAhg0bUr8LW1QYN+PkNE/f9pXKsuny\nPGelLZFleAfWMKHFUVjAlh1bsErrC3T5Hp3xeZymxfJjzQu5sEV0yZIlaaZ6Rw7alwHwLFsL+J4z\nIcOnkZGRob4gmdbqWpOAtWj5ymRx/fr1DU9ma1EzsvaeU/jkKLiIlleZb1RW385rDl7bImE/Fvdb\nWpWhi2c4ciyzSHuN8jrM97CUG4/TfoY8w5Z40wLtyD2RVl5H8K30OcysfMi9rcTZ3mK/nsz3DpQW\nT8utLbWZz5AtlW5vq5grRoCJiYnOvmU/POBbA/svmnZHOQNb48u+MkuU59OyaHk374nmtnW5b0+3\nVY9nETHvPRd5gVZH1nqO8Hv0Dcdjjz3W2efsSw1t/r0chmqRqqioqKioqKiYI+bVImWrgbVm4Hwa\n4+PjqSbtHEOc4q31AEdtuE5PlqNlamqq449lzSCLTnJfAB8bxovWgxbY58fi3CuOzgC2uFgbympz\nQROn+z7LjnloDPMzsW+Aafe47XNSWiScg8cahu/VnR/Hls7M18S+AhMTEx06bQ3xszKfL/MROTIt\n1obL72bRKba4IIPmi6PenPsoi34q6XFUGTBfbEVye6wh0GxLZp+VGGRRrAAt2NZx+zcCywUWHc9t\nRKtJMy5k0JZIwOf2FXKOIsCzzefJyclOBKl9IlnXjpxze487W+v4pZQRZ/DIllRgP6TMUgdPXYs1\ns0y5xmXZp606WdQq8J7OXNi/M/MdW7x4cad2pmkCtrTaN8hz6hsc+/eU/fs31jUGTUtm0c2i+TKr\nc59lz/U+7UtoZFbwzKfSfClvHbKbGmTRv2+ZH5ZRLVIVFRUVFRUVFXNErbVXUVFRUVFRUTEEtdZe\nRUVFRUVFRcXvGPPmI3XcccelGX4BNYWoQdYXMcDdJnWZyvpjEd38FtyXUjvJ9aoyPx/qW7l+Wvkd\n6HJb0+3Mvq6dZb44CqOsQWXfJ0epfPSjH42I6NQgy/Jp0Tf18DLQ/pOf/GRT28j8ADwTnlPHyZEg\n9j+BL64T15eNmL6pnWSe+29qkGV88TNcD8v8Hh0dbeaHmlKe/yxPCu1Ni79nvlA/rQ98hxpRzFHm\n8wBcU3JY//Dl+OOP79BtnwfXTszWP3JA331rrvw+7c8///yOLLotdLvu30yyFdHdXxytV66nsv7g\nTCjXUES7LuzrAlwn0LXZSvBdaoohW1kknfcixmn/G79SJ7JvrJZbxknfzvXlXE/wnH3R0W6mhbGW\n8pLtvawLatBl0amu5WrZBc67Bu3vfOc7h97AZHOUyaTHmUWBl+si2xf9W9RXx7OE5QFZpB5etq+M\njo7GmWeeGRHd+cz449qMwFVKmAvm6LTTTkvb850PfehDEZH/Rnv9s6YzVItURUVFRUVFRcUcMW8W\nqRLWFjOrUHnitqUh68vRR7PNr+PnzASfYvvoLZ+V9Z09y7k5yveznE2Ousmsflnfw6Kd+vpw9uxs\nPMPqwA3jubNz9yHjbRaFl33PfLPlqtQeHQk57LvDaM60JLefnp7uzFcme36dzVrzs2bqv/zMuWuG\n9Z0hq02Y9Ve2ne2eMmx9ZM/K1ttcMNsM72CmNevPhtUcHOYqm1mD5oKsjmNWwcAyC4ZF1pafZXtS\nNv/QNGxP99990azZb5WR7UUZjZnVfTYY1jbrO9s3snqifVGeHuewvevZ0p5Fx5f5J0HGu2F7tVEt\nUhUVFRUVFRUVc8S8WaRGRkaGatp935np84hurg5rLdaOsxpBWSbkMp/KTBWuZ6LTvgPA2YftA9KX\nG2hYNu2MFvMl85nIxlBa7rLs8hmG1VQcpgXOpu9hPlJ+Zvb3/wYezzAriTVPW94yH4hSFjP6s77B\ns53/meZ8rnnFMpiPtlD19TeM/gyZtpwh860rv+t5z3ygyKfjdZ/Jy0zr59ladbL5HEbDbJBVR7Dv\nZ7bOh9WYy24Ryrkfts/NxpJS/m3LU/b7Un5/2Brt87cr/872dD+7b11ke03GF2OYVTijqe/5rn/p\nuoX+LjnPnJcsyzvHbzr9krdq48aNQ9d3xtNhqBapioqKioqKioo54v+Ej9T/xkow7K52tsh8SGa6\nU5+ttmsrQKZhWIMd5s/Td/+e3e1m/Jgtn2aak2G+YMP6nK2/xrC56etzmL/WbP263H8fbZlfmunO\nfEFM02yfPZs2mZUr84UbZi17Ns8YZmEexvthc9f33rO1SGYybI3UsjeTb9T/1tqZ8dP99VnfZuv7\nNsznza+ZpebZ1FX0Hm05ebZ7d8aXku7MUpLN3zDLU4Zn4/+XWRhnu58Os/7M9OzZ7ncZstsUWzD9\nvPL/9lvOqol4DoftRbZ0lc+ZrWzNxg+3RLVIVVRUVFRUVFTMEfNmkRodHX3W1oPy8+xk6RPksBOl\nNRKf/v15nyWGZ2R5X4bl7vGz3T4bQ8kDf2dYNOOw9s/GUjNbCxTIIopmaw2aKVrFWvqzjRBz37ZI\nZHl4pqenU+12thFhw3zHMixYsKAT6ZVphNbWhkVtzlaD64vay6rce5yznaOZ/C4yzFamhu05wPwy\nn0qaMt+gZ2sdyfYX0zTTe9kzh0Xx2YrKq31HZ7IOD3tmFvHlfob5iIG+dZTRMkz2spqSvwsZnas/\nY9aPZbB89lx9gYZFO4LZ+k6V/7dPVMYr17uzH6znyPXzSsvUbG8/QLbmjGqRqqioqKioqKiYI2qt\nvYqKioqKioqKIciOS9UiVVFRUVFRUVExR8ybj1RZmwtwF8r7F154YUS0dX+4U52cnGyiBFzfjJo/\nTzzxxEBfK1asiIiIp556aqC9a4o9/vjjEdHmnthmm20ioq3NAy0bN27s1GOCfmpKUSPIeS64T4Z2\n6sSVfZdgDDyPOk7HH398Y92D7s033zwi2lwbH/7whyMi4qSTThrgy/r16yMiYrvttht4v6zjV9LK\ns53L6vzzz29qhIHMX4c6TtTlIt8Hd9rkF8nqftkvo+SPawpCLzSQowfaqBF24oknRkQ7R74T531o\nZ6y+lx8bG+vUN3QdJ3j5nOc8JyJanrvWlv2Z7DvndVF+h/Ex3nPOOSciWjmHRl6Rc+YA2k8++eSB\nfpl3RwSVNQ5pyzrYbLPNIqJdc9TOor7dML8Vau3Bc8YEDc6I/fGPf7xTCw+eIe/IGDxnXTjCB5pZ\nR/CFdcGzH3744Yho19HExERHbr3e7ZeCLLJf8Gx4Th4dxuQ1alnfsGFD857rfkKDecczkBdoR/6Z\n0+XLlw/wi32UdVTuF3wHWpAV6GacjGvlypUREfHoo49GRLvmkF3Tzuumm24aEW3dt3Jd8B3mnz3H\nbemLdYDsMgfIC7KLXNg3iOdR9+3YY49teOX8R4A5Ys15P2QOeOX3Bdk1P/ge/D///PObvpE9+MHc\nWBZd99VrjrUIX+AjtPPsbbfdtnme1wXjca1F84X20Ayt/I38XHzxxRERcfTRR0dE17fwiSeeaOi+\n5JJLIqK7RzM+5IRnsHdlmLeD1NTUVMcpDIbyCrwBTU5ONgLuH1cmhx8pO0fyTMCzeX/dunUR0Qq9\nD3vlwoQefhh9YPKzhpVt4XMn5mSsbBglLbSlTeY0aMc7BJ325rk3Z/5GEMvNwAenYWHMXrxOzJc5\n1zNW+No3VqcWoC0HBujP+oSPbOqWLx9Yyh9o8xD6fCBi/JnjI32CzOGxLGvEOPxD5z5ox8EbWHbt\n4Alf7CgKFi9e3DnwsRllTvXwnM9RdrKSMj7sMJdl+yw4wH0BaCgPIeU4zXvGxCHAiSv7EnJ6nNCd\nHdrdV5ZaABr8AzM2NtbIO/B8WSHy3zybA8VDDz008AzLF/sm/SxatKjpg73UtJjuYY7/8AvZzfY6\nDmKTk5NDHZM9B7RHIWXcwAmcn3zyyYH33V/p4DwsNQ+yBy/ZTzjkZnsR8GGx3OuyfZE23ruQH9qz\nr0Cbec/apb9HHnkkIlo+MicRrYzw+uCDDw6Mj2cBJ6r1b5j5AL8AY1u4cGFn/v17xp7qPoahXu1V\nVFRUVFRUVMwR82aRWrRoUXOyfOyxxyKiPSVbI+EUzIm7tAZl6Qm23nrriOiaAbMSMZxMOeVCC7QB\nTsWPP/545xrFloSs8CWap0/HnNCxpqF5ZabPUov0NZotDDZFcyXBODNrGv2ieVlz6YND6z1OF4hm\nHFmiUvhhDX8m6xjaDW2gH1kCWPmYu3vuuSciWi0JTQtAK3KCprV48eKOtYtxYjngb56VaUeMwaUU\nLIvldSR0Qc+wEkHwNLMwwSdossXTFoyNGzd2QuSh3+vCtPD57bff3vu5rxVpbz6Vz0ajNg9NN+0Z\nL/zzNSxgLml/7733RkS7j/Aa0fIU7Zb1DO+9lngmNEIzPEfLB3z/gQceiIjWerL55pt39gqeyZpj\nPQDLi2V3WMJfrAj0+8gjjzS8tCXdVlHkOtvnaMf4bXmxfG211VYNrfDuvvvui4iupdWgL8bvdQEf\n6df86bO++ro4o4Hv+mbGe5bbM3fQZotf+X++Aw2+NgTe/y2b/h2lPXOOTGLZL8G8ITOsMebIv+ms\nZWix244tWIzV+8bSpUs7libvWXwnm88M1SJVUVFRUVFRUTFHzGuJGLTdnXfeOSLa0581b+7p0Ww2\nbtzYccAGWF449doiZU2MfmjPidVWE8Dpef369R3LkU/p22+/fUR0tT1O7/ah4mTNCRzNCliTmZqa\n6viPZHTbJ4ZxZAn5XEjSTrtl/3aetX/JsFO9/Q5skdh1110HaLEVrdRgoM+OjDzDGiYyuO+++0ZE\nxHOf+9yIaLVHLFQACxX8KeXMPLSFypY3ywvtrbFl/ZVWkMxyBLbYYouI6FouWS/WSPFtsD8C8N/T\n09MdqwVrzfIP3WiryDt9YuUp+47oOtMy1nJN0+b+++8feBZr1Rrp7rvvPjBe+IP1w2sUPrN+PIaS\n78gQ72Elt4wC5oIxeNy2jiEf+++//0C/69at66w5gmZ4pq2kWYkgt2dNex2xV7GXr1ixolk7tqTx\nLPhBXzzDoB38sH9aaQWMaPm4cOHC5v9Zgln76/AK7V5H8DwrlGwsX768oaH8/YrIrePMs53TSz+j\niHZuaAefWB/lWC07XnNeo7Zq0Re/zV5HLhDMOuHmowT0uhixLbjAt0nwCVpswWJubJnasGFDp+9h\nhcNnW6S7WqQqKioqKioqKuaIebNIrV+/vuNL41Mt8Ml9yZIlnagygHbKSZjTaqZ5Z2VdHDEDOD1v\nueWWjaXM980ArTiL2vCzHc1lHyD7VCxfvrw5xdt3xad0RwSBLPIhi9abTTHH2Rb69Pih2e1vu+22\niOhax0D5t/2MrN17/MwR/iVobrbYAPho37rp6emOlSYrvpmNE5nle2hcjv4DZeSl5yfzQ/K8ZhqX\nLRa2LvbNv7U5R8IALE7wnPVu64H7Rf6RA4eHR7QWg0yztJxff/31A7RCSxa1B9BsHXna56/nSGL6\ntGwxHr4H7dnaZV+09X3BggXpvgVfsFBlFmxr7rY62yKFfxsWvCVLlnSs/QCLAvun11wWtWpLhq3s\n4K677mr+bznwuoDntqRk68IRx/6tMt/vueeezv4N77KoO15Zg5lPLd+nX55N+9IKZWsf33VEPGCO\neHVEnWmBX8gVYK5KqzHjYz0gH8yn+7Y82P/VtPNM37o88cQTnXl19HrmYzwMQy1Sb3vb22LlypWN\n+Tgi4oMf/GCsWrUqDjrooDjooIPiG9/4RvPZWWedFXvuuWesXr06vv3tbz8rYioqKioqKioq/qAw\nPQQ//OEPp3/xi19M77fffs17H/zgB6fPO++8Ttvrrrtu+oADDpgeHx+fXrt27fTuu+8+PTk52WkX\nEfVf/Vf/1X/1X/1X/9V/fzD/Mgy1SB188MGdEPB4psfOe5dddlkcdthhMTY2FrvsskvssccecdVV\nVw17REVFRUVFRUXFHyTm7CP1iU98Ir7whS/EC1/4wjjvvPNi8803j7vvvjte+tKXNm1WrVo1cF9d\n4h3veEfzfw5q5JEggoYU8cccc0xEtD4je+yxR3Pn6/IT73nPeyKi62dgn4+LLrpooG/u60lpzx0p\n+TBc9mPp0qWxww47RETEb37zm4ho74/POOOMiGhLoRAxRXt8Q8hVRCkEUuHb/wK/J+6QKRFw3HHH\nNeM64IADIqKNkMHfpiwnU46T19/+9rcR0d5L0ze0O2LyzjvvjIj2frosEePMxPYXoEQEPOTzl73s\nZRERceONN0ZEKwcXXHDBQHvmZO+99x6g/amnnmrKplDagLt6t8WXg75drmLVqlUDfOR+Hr689a1v\njYhWRonauvnmm5txUn7g7W9/e0S0crHHHntERMRNN90UEa0yAi3wnLlAbhzFiqzTfuHChU20IeNk\n/uELbZF/ohRvueWWiGh5+9nPfjYiWj4SCUQ0Fu3xzylLZyC3u+22W0S06wK/GeTc88+6YK+ARvqG\ndtA3/4wVWcT3AZpc8oM1etRRRw3QsuOOO0ZEG7UFLZ/5zGcG2tMvc8QY+0phOMcU6x/fFvYWaMcP\nZ8stt4yIdg9yCaIjjjhigBZkccOGDY3vH/NJ+RlkC57fcccdEdEtKcR+wVqmBA60w3PmiDmFlu22\n266RQXxhWP+U8EC27CPDHLF3uaQI43QpHcrVUMZlfHy8iZx2niPm0+WqaH/33XcPPJP1D+3I0377\n7RcREf/zP/8zQEu5p+PTg2zBQ/hDKSTodhkWV4zwusBPa5dddomIdi+in09/+tMN3cjUnnvuGRGt\njyB7DL+L/D6z77uSCL5EyCK/o3xOFCdyODIy0vDQey57CzS4dJbLG9nvkfcpb0OZOGf+X7hwYTP+\n008/PSLa8kPIFHN09dVXD/TNms4wp6i9Y489NtauXRvXXHNNbLfddg1j+pA5Gl999dXNPzbEioqK\nioqKior5xp133hk//elP46c//enQtnOySJWe+UceeWS88Y1vjIhntBw0HAhB8zFe9rKXNZo6ViBO\npPbSBzvttFNEPHOCvfXWWyMirxHmIr5onI74QoNCI7EWlWVC33zzzZvvcmo3LZyY0Syck8WHTNo7\no3lWV3BqaqrR6miT5fngWfAH/mWRkmjanOCxGjo3R0Rev8oRcwAewje0f+f+KccZ0S3I3BfN6Hn8\n2te+FhGtVYg+gKNNiPTg+x4bfHB+oiVLlqQ85DvwwZl6AZ8zPtqhwTordymLjuxxDirGyfvIgXNX\nAfiA/GM9dRQsWLx4ccey4AhHgGzCF2TYVgOA3DN3yI35HdFdK1g32ZecT4dxODKSv007NJe5ikra\nyzUNfY6QzObfkXVZ1mngjOFYCZ944onOZ+w5rsWYVWVAFpkr52zL6gTy+vDDD6dRdVhzWGNEdMEX\n0w4tfI/1zrrI+Ljppps2bV0IGDhS2nuvgQwiF+xd9O91t27dunRvyepZOlKO9o44g1/e8+inlHX2\ncb6DVZzfC3gLeDbv833mCoud2zN+/u6rQ5tFPrPPe10zN8w3z/atADAfyog87y3+LaHv5cuXx+rV\nq2OfffaJiIgrr7wyZsKcLFJlksL/+I//aCL6DjnkkPjiF78Y4+PjsXbt2lizZk28+MUvnssjKioq\nKioqKir+z2OoReqwww6LH/zgB/Hggw/GjjvuGB/60Ifi+9//flxzzTUxMjISu+66a+OHse+++8ah\nhx4a++67byxcuDA+9alPpVd7m266aeNv8+///u8REfH6178+IrrarqvCn3vuuc0p9DWvec1AW57n\nPrJcTGgk+MSsWbMmItrTLtoy4HR87bXXxg033BAREa961atm7Jt7aLSAAw88MCK6OTc4maMVYQXg\n5G6n/8WLFzfPJAWF78ABGgJ9wnvaWevlmaSwuOaaayIi4i//8i8jorW2RHRzy7juVmapwwLF3far\nX/3qiIiBVBsR7X07B/hPfepTA+3Lw7q1tp///OcR0d59lz58Ea28MP9f+tKXIiLioIMOiojW7wBY\nEy35mGXZ//GPfzxAy1ve8pZeWhknfLviiisiovXbIMM7KPMM4duCj9cf//EfD7RFM4RuLHVvetOb\nIqJrecVCgd/a9773vYiIeMMb3hARbUZwsGDBgsYn6ic/+UlEPBOoEtFdQ8gHfiX4K0ELvmQAPmJV\nOe200yKizUL/kpe8ZICOiJZnzDs0ubIBcoy2+/3vfz8iIvbaa6+I6Grq9lshmIYx4UsU0c4va2ft\n2rUR8YzCWdIKkB/ybPGKxZ61DZxDDqvbkiVLOpZXtHzW/3e+852IiHjBC14QEV0LNuNEg4d/0MIr\nwIrCGNesWdOsIdPiWwOsXOzp/s3gb9ber371q4ho5cHywj551113xY9+9KOBNr4hcf4v1urq1asj\novs7YovOpZdeGhGD/oolttxyy2bNsbew5+LXCry3uMKF87E5+zi+k+yLJd/pi9+i//zP/4yI1s/O\n43ROL/rKsonzO8Sefvnll0dE+1tXypdvh9hbeAZrFvAsZA6ZZb+xNRXa7YO1YMGCTqUC6P7Zz34W\nERE//OEPI6L9neurFdiHoQcpBKXE2972trT9qaeeGqeeeuqsHl5RUVFRUVFR8YeMec1sjkXmL/7i\nLyKi1ZrR8gEnS07Nr3rVq5q2+KgAtB1O4Jxmaec7bEdEoNG7/hso7+uxblh7c1s0Ck63nMh9OrZf\nlrM027KzcePG5jtEMmVZf/kumuMrXvGKgfGjkQM0d7QneM+r+Q495bjsbwJ4Jjz+m7/5m4ho+eQs\nvGhBaJpEzjHHaEHlM/ns8MMPH3jfWfKhFRpf+9rXRkSrHZkW+Gh/nU022STNPI6cH3bYYQPjdg0y\n5IfPiQjib8tXWf8QDaqvzlY5TtPCPFtemF8sO0TxQIsjCaemphrZIwoTDdI8ZNxYWKDF0buAOYPX\nf/7nfz7weelrgvXLtSGZL1teGDdrjTWNPHiO4Bd8hmYsW+Ua5dlYQ7ByDaudxjgZC+Oz5m2/pbJG\nm3lOH9DN+odP2bpAu///2HvXWE3L6v5/7T17z5lxhrMwnAURlEJt0Dba/nqg6ZvaNiamtlpLFLCV\nKRCUg6iAQqCIQaytoBKkaZPWV9U0adqYpjZVYxPPCsr5zACio8xxH2b/X5DP/VzP577XPPz2n2b/\n+8/6vtkzz3M/172udR3ua617rfXFA5nxZ3I/PHLHHXdc188s5tVce/THc5cxoh3zHXofRa8bN27s\nvDO0nemQ8cbL6SrhgHnPOrrqqqvG+uB9dM+ePd08/7Vf+7WI6GcpAjN9mFvQnjpnpv75n//5WDvt\nmKITPOxktjF3HPNEv7/xjW9ExMgjyfPRnhrGmucDe4C5btu26R+xz+jQzxZX+Hc1csPMIMRKTU1N\n9fZodMpeiyz0z+siQ3HtFQqFQqFQKCwTK+aRWlpa6k6emfXfXhsxOokecsgh3anbljEnYn7DdVgF\nPsU62wbLNOOUa2tSYI1yavX7cU7ClsmM2gBZaQfrESvY7S8tLXXyYcU6Ow9wnT0wyGDLi+uQ/Vd+\n5VciYuT9aa1p2sBy4Lf839mG5pDD64EebakhO3+ZL8yfIYsX+bgGWTI+PGe6ZJxyXMf3zKu5ubme\nHIwbcTZYOcS+eC6iR/TnjKmML+u5557rdEib3AM4q4/rGAtnp3ltYslx/ZA3lf5jtXIvW3Wea3iu\nGBvr3HxdeAMYi5ZhHh15rdE/e6SALdXWim3BuPPXlnk7v9ARsrC3ZB4as9abH9DrHz3ZqzI1NdWb\n58629G+9RkHLLRox0oszSL0u2rbtvXJsoD0U1rljLp156H205Xh0PI7lNmcr/WQMrEf2JuYTMlDr\ny7LPzc1193CNQnuvaIs4Lu6RcbkiMx5cvCnI3urdvH3OqPb6Zz44K5y+ZPOFz1ln7COtXuin+S2B\n93/z5Lo2pWOTkYGxZE/ftWtXb67gcaYN9i72FHuNM5RHqlAoFAqFQmGZmFoa4nr5n75pkslXKBQK\nhUKh8P9FZMel8kgVCoVCoVAoLBMrFiN18cUXd54p3k86NgIeH/jzwMzMTC+25aabboqIEb+ZYxn8\nTvezn/1sRIy4k5wph2y8v73mmmsiYsRZtWbNmu49sLMKrr766oiIeN/73teTO2L0jpj31NQAgmuL\n7505yH3o6yWXXNKLN7C3D34r+KqA66eYa8t1Udwuv7/lllt64wMXvJyeAAAgAElEQVRoG/nhzoJS\niDbdPwC/kXm/HEs2NzfXXQtHHG05y4i/1Fwx75tjH+gDta58fVvFnTnG+HAtbWWeWGSHa8sVrgHz\nCx6vdr5wb8cGZtxptO1YQPjKzG+XAT2269lcWB5/xojvnWnI7z2mrlcG+Pymm27qOL8A4+m9xTxe\nbgvZ0Dl8aKwj1q5jq1atWtVxijGejnm0njxGXMdf4lvoQ7Yu2Kvm5+d743nFFVeM9Q+ZvA9ce+21\nY/20PvjL59dff/3Y9W1fHX/JumDeAu+H/M7cbK6b5Zgh2m/1MhRXGpHzfvpZ5HXE/u915ow7rn/X\nu96V7qFcSx1Gz13vd6xZxh89opcsbumGG27o+sk923i6iJEu4f28/PLLx9pw9XHisJhfHn+ub+cL\nz1Cyrs30AJDNvK+Wxc9Tnxc816emprpr4drznuu4Va6nnxnKI1UoFAqFQqGwTKyYR2pqamosu6L9\na3Bde7LkM3sx7Knyqd6WKb8355Z/18od8cJpOqvACzJPhOuEgOz//D5rf6hf2bXcm+/5nbNZgK0+\nW7AR/XHL+ufv/X/X9MquB8jsvh/ot5M8eIA+WS9ZrZuFhYU0q8r9yjxwHhNbxe5n+/9sPRjc21lW\n2bqwB2Lo3vzfnpZMFus8u/ek/w+tC1vck+aivWa2vD1fuI75YdnavmQeaI+r2/bek83RbB6tXr26\nN2+drekstmzv8dzNxhS9tZZ9Jp8/dz+z5wCfu67egfZhj6/bHlrHEX1vOrBeJ8X7Tk9P93TojGng\nuWv+R+/FfhPi9dDOxRe7DkBWbymDvY+eP+3+4X0QuL+W3c+wTHbLlK3l9hrPvSxrP0N5pAqFQqFQ\nKBSWiRXzSEX0379n73h9XURec4RaI7b6bGkArEbHMWT1MrCGVq9enVqUGXyizrxIfE79EMeztLAV\nA9y2PW2OHcpO9ZmXoZXFp/gX65HKrFtbJENWTdv+gSwvx0pltbuMzCNpPYI2bs9tOJ4is3LsRfXY\nZLFBQ97R7B5uM7O8+b//Zp6JpaWliR7V7HPr2P+3l+hAntzMEp6kD2BvoH9nffh3Q/3gHo55MxwD\nxF+85ZnX2Jb81NRUGuthbx7IPM8g8yIA7u01cCB5s7XneW6vqOP8rPvMUxWRezOymlSZ18i1vQ7k\nLWnHZahN3yvz0E7a0w/0DPCay9a1f2tPTfZ2xOvsQF5C17RzDatJXvUDvZkZ+jz7PiL3SGbfZyiP\nVKFQKBQKhcIysaKVzTNr8MW8r590urcV66ws35MKrFh9ZBJk745b2bN4Gv6fWSuGrUX3e8jycgZL\n5jFxDM2kasJYhR6ToT7Y2zOpnyCLFcg8FJlHopXd4z50zYFksOWVxXe44vH09HQ6bzN+MluQeEN9\nXeZNbWW0NyuzpP3/bPxdLRhkMVPt/z1nJnmcsv76ess8FGvkcbPuJnlMvC58vauve921smRzx5lS\nwPGKfJ95ojyvWm5L39t7UcZTBrJYwEnzCxn27t2b7nv/t3Eorsbtiukeo3YsJsWZOrbHbzAMz6NJ\nbyVmZ2d7HiNnhFpue+yztw4Z995QvOQkT9Ikz8yB4hKH7v1i1l1WyT+rVG6YSQNksbNDnvtJfH0v\ntuZleaQKhUKhUCgUlokVjZGy9ZhlHvlU22YITXpvbGQnb59MOdU6rsEei1beF5ttkHlu/E7XdWcO\nlM2WZYb5e+t4kmcvywRpZZmUvWj4/bxlymKhMmty6N6ZdTcpBmKStWy9tfWbbDlNyogz7GHI4nFA\ny4I+KdPNFvekLCyQeY+GLPHMU5Rl59hjZc+DZfc8OZCn1/2ctB9ksTLZmgbemw60Jr0Gs1hA928S\nH55rQrVtAXvOQBavlM3dzKM1NC8yj1oWU5mNlWXPvEGWcWlpqbcPTNK5ufcMtzPJ8zszM/OidBWR\nxwwC38trOot/a6/JYocPlH3a3jt7vtrjiZdpKBMvq4+V7bmT4pS8Lg6UFZ09F7OM6kmeW1AeqUKh\nUCgUCoVlorj2CoVCoVAoFCYgOy6t2Ku9yy67rHOfUbLAJQhcxr+lTuC1Bm1Qwh2KGNyGBI3jasTF\neMcdd0REn8Zhx44dY/fic8rVI8u+fft6BeIIZDflh18zIEvWttN76SuD2NJbOFUaN+fzzz8/1jYl\n/Ll3lnIK/Qi0DLRLQJ9fO950001d29krKX5D2/QTih1k8usEy44s/A538p49ezr6EVMV2HXNfLjh\nhhvGZAHozzQ9H/nIRyIi4sILLxyTYagoJuMPVYFf5QzpMGJEhcK4+9Uu/WWuI8vatWt7/URu2oZm\ngbbRA/MEoHNoNkz1YJc9spj2o+031zL+pmXgFQX0TNzT9CNOJPArr49+9KNdP9EHpQMOPvjgiBgl\nlbC3QMthOiYHwEKFQ/vck/bBwsJCpxMoopAXqhfuxRy67rrrIqKvQ79uok+mt/G6mZmZ6XRlyh90\njNyM/6T1jz78Gsbj376+pJ+mn2Lecu2mTZvG7kF/aZv5wpz9+c9/PvY72snosCJGOkeH0Il4X/Rr\nN9Yg+ws693OFZxj6bZ8BfhWJTlnPtI1eaPPlL3/5mP7ot2UHDkehzzfccEM3Fx3gzb7+spe9LCL6\nFGEu3Mv/mf+f+9znxq6nj8wrrlu7dm1HP+PnHNe4MLf3IuDkLvppGifLsri42I0ba8jXWgbT8mSo\nV3uFQqFQKBQKy8SKeaT27dvXneKxErEwbOXZK3L//fd3J+QsVZITNpbo9u3bI6JvgTtIlHtxEnWw\nGe3v3bs3fvKTn0TE6PR6+OGHj13rV5g/+9nPut+2bQHTd/B95j1Ys2ZNd2/0wOkczxpwUOyWLVvG\n+v3UU0+NXU//bbnQzjHHHBOGU1yzgD1ktbWbpTPbi2Yv3BAlBL/BisuocBygSb+ZP8xNwHXMXebX\nIYcc0s0H99NBxsi0efPmGAKy43Fxv0E7f5hbfIa3A9AGFjP94178dT+ZR5NkaVPN8byga+aa2+Z7\nPK7cI1v/6O+5554b+74lPWX8WBe0zVgwXoDP6T+yIbNT1NEfYAy5D/OiBd95TVoWF8tlnvD5oYce\nOnY9c5c5yths2rSpt1d4P2OsPL6AttDLs88+OyYb8we4HEQb6O1SIXx+0EEHRUTEscceGxERP/7x\njwevZz789Kc/jYiIp59+euz3hxxyyKAse/bs6fpHf70uLBPwugf83qUYMnqT1atXd58hC/J5/2ec\nDzvssIiIOO2008Y+p9/uJ/OAPeCII47oycI16JD93EHkwEHXfnvkNcp8Yc7yvEVfrd6Rm8/4Lf3L\nShTZW4wsft3Gmvaa3L9/fy95zJRy7C2ZxzlDeaQKhUKhUCgUlokV80ht3LixZ91zYveJlNMyVsLe\nvXs7a87eC06SnEo5lWNJZpYXJ3Xa47SceQEOP/zw7h6PPPLIWD8A/eNkzD043ds68jt0rOLsdDw9\nPd31h9N86zFrgazoEAvTXjCD32Etcj8sj1bellQ6oh8TBBgLPBcA2TMqFOYH3jPuh2Ua0Y8PQAYs\nDesFMCb2KhmMJfrC+tu8efOYTlr56CdegkkFFvGwYPUju3/X6p1+Y5VmKdTokPE/8sgjI2LkDQbI\nwJxGb8hgb9qmTZs6XSPDJILboeKFEX2PhFOT7ckaSlF2XBFjk9GPZEVwM6+B95chfTPPHdPlODPQ\nltJo2+R6e0nwKrAvnHDCCRHxwtz32kIGPAWOBWUeAHteHOdny5750q5R5oG9V45fnVQM0mn+zD17\nESz74uJiz+PiZ4tjoRwz5uvtdWV9eA8Ea9eu7fTAtfTH+z9t0PYTTzwREaP9zWOKbN7zua7dF13W\ngblE/y0L/WdenHjiiRER8eijj4793rLQN55drIG2fZNO02/mg98y0bbjlnkm+bnI/2m33atanbRt\ne43awzoJ5ZEqFAqFQqFQWCZWzCO1a9eu7mR5yimnRMTIkvUpkJMop9yZmZk0zsjeHxdk8+nVcRku\nN2/rqLVckQuLyxYmp3H+2lNjWbBY7E1BRlsBCwsLPSs3i0tx/BG/s7UH/H8sClMBtHJmhdQcK9Zm\nUbT38rtz4Fgx/g7Fsdm6xXPpuCqAbLTJHMQazLyGyMT1jz/+eC8GxkX77HGydcTcG7Jq23u7/dnZ\n2S5+xvFGvtbfO1PK/ST+xNmMnrtzc3Pdd7SVxRk6Hs1xCl7TyI7eHFPTtu+CrPYgZJY0wCOJnux5\nY4zwcKEH+t5607g3bdg7Zsvb3i1k45721Dm+E9kXFxfTfczZvawP69wFFpmrWaFiZGjndFZI1J4Z\n9gPPSWBPDN5TPs/2rnXr1nXxRvZmAPrhmKGMasqeKmemDpHcszbtObIOHaf3ox/9aExmzxdkRibG\nEg9dGyfHPEUW77leB5aJ5ymyeX7Zg4ssQwVcuRaZHOuVrX/u7b74TZCvbz13WRFs9nnmVuZ5zVAe\nqUKhUCgUCoVlYsU8UlNTU91JkpM2J02fAk2guXnz5rREP9e6DP2kDBKfQJ31AtqTOPfA42RZnOGA\nZZq9fzXxrD0cQ5lVtO134LZe/LljQjKdc5rHwzVEkTNEKt3+zWJlbA1k1AmA60zf0XrCHDdDf7Fe\nMjJSW9qmLwC0x/X83b59ezqerqeTUZtgiWFpE8eUZT/S15mZmU4uLPCM2oR7OGMyyyAFmVcV7Nmz\np2vD9X3skbTnra01E5HHJbQeuIjRemplNYk33+GJsteUMcq8xJ6LJsx1val2jNrxiRitIbw22Vzk\nd65t5Rg5e9/suWlhj7y9hda5ZcfDwOcZaW27pjNaJntDkDvTh2s2Od7JY8q+OzU11fNEZXMrq1E2\niSKJuW4PHti7d2/vjUqWQey4O5DJAlhPrAt7eiPy8bSnGjhrm7hUP18zGR3v146Rn7nOnB5689K2\n4We791H64tjilloO0D90jwfe63oSyiNVKBQKhUKhsEwURUyhUCgUCoXCBGTHpfJIFQqFQqFQKCwT\nKxYjdemll/ZqkzhrB26et7/97RExese8tLTUvbvkN3BhXXHFFRHRr9XBddwTvqLLLrts7HpnYfB+\nHt4veH8WFxd7bfIuFh6f888/PyJG71+JM3Elb3OQAVfuNtfWe9/73p4M5vH68Ic/HBERV199dUT0\n4xEcMwMH3XnnnTcmC+/MkYX7fepTn4rLL7987DPAGPH3xhtvjIiIP/7jP46IftaR64r8/d//fUSM\n+BPRr2MjFhYWOp3AV+WYFcffwMvFuPJeHf04pgzZ4axCf20mHte+//3vH9Ohq88bt912W0SMeJ+c\ntWm+K/pK+6tWrerFyNF/1tA111wTEf2MKVfEv/baayNitC6c7enxh8vvbW97WzevXUWfeX/nnXeO\n9RM4awfZ4c664IILxj73XOS+t912Wzc+9MuxX9yDfjK3mCdZjBRcWx/84Acjoh/HQV9XrVrVzRXz\nPnJvZ+fR9lVXXRURo+w799dzMePmbLN54SvzvHUsCzLC+wfvo+PzHAvDvssYMY+mpqZ6WWWf+cxn\nIiLiQx/6UEREr+q4ZWKNXnnllRHRjxF13SHahfdt7dq1nTye98jNfuEYL4As6BH+TPTo+CTGouVy\nc1yq+8H48+xy3KLjr7gefkPHf3E//v/xj3+8e7Zk2ZaWheecYyu9RuFDRC9tFfGI8RpQ5v107S3H\nPbN3sS6y2m78n/GHF5W9vG2fezLPf/d3f3dMZ44d5nOeRRnKI1UoFAqFQqGwTKyYR2p2drZX4ZrT\nn0/JrmC9fv367pTuatLORnE9GdcRwfqjJo153cxv1dZfcp0U19ZAXtdqyWraUI/Knp2sHsvi4mLP\nQmx5plqgLz7n3kOVZyP6DOvOamnbt0fJNWtco4TMMtcooiaTq4vjibQFyn3bDEv6SX/MvO74PMYd\nS4p+83tzq9E3y7h+/fretXAvWnfci/4Cc0rZ0rTsLccj93YmDDAvG3/tsTX4nMr5mVdt3bp1nQx4\ndeinrXw+tyfKnheArMwXZ6q213sO2tLMuLZYq6xBPBT2HjA/0AdeFX531FFHdddyL+Q1x6Dr33gO\nDtVmamE90Pe5ublehqdrNZkBImMfYO2ae89ZXmRQttla5kYD1Dl64IEHImK0l7BeYAsArDV705m7\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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\ni8T/A2aklexSCttuAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd630658d90>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n",
+    "\n",
+    "(This page follows DeCAF visualizations originally by Yangqing Jia.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'\n",
+    "plt.rcParams['image.cmap'] = 'gray'\n",
+    "\n",
+    "import os\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
+    "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+    "                caffe.TEST)\n",
+    "\n",
+    "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
+    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+    "transformer.set_transpose('data', (2,0,1))\n",
+    "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
+    "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+    "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Classify the image by reshaping the net for the single input then doing the forward pass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is #281.\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "net.blobs['data'].reshape(1,3,227,227)\n",
+    "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
+    "out = net.forward()\n",
+    "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv2'].data[0, :36]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[('data', (1, 3, 227, 227)),\n",
+       " ('conv1', (1, 96, 55, 55)),\n",
+       " ('pool1', (1, 96, 27, 27)),\n",
+       " ('norm1', (1, 96, 27, 27)),\n",
+       " ('conv2', (1, 256, 27, 27)),\n",
+       " ('pool2', (1, 256, 13, 13)),\n",
+       " ('norm2', (1, 256, 13, 13)),\n",
+       " ('conv3', (1, 384, 13, 13)),\n",
+       " ('conv4', (1, 384, 13, 13)),\n",
+       " ('conv5', (1, 256, 13, 13)),\n",
+       " ('pool5', (1, 256, 6, 6)),\n",
+       " ('fc6', (1, 4096)),\n",
+       " ('fc7', (1, 4096)),\n",
+       " ('fc8', (1, 1000)),\n",
+       " ('prob', (1, 1000))]"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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44sWLqd6jk/OahTXn6zYWLlwYeU2rRmu6rL29PbLeq6++6uJDhw4Nux//99rc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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d73990>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(k, v.data.shape) for k, v in net.blobs.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "[('conv1', (96, 3, 11, 11)),\n",
+       " ('conv2', (256, 48, 5, 5)),\n",
+       " ('conv3', (384, 256, 3, 3)),\n",
+       " ('conv4', (384, 192, 3, 3)),\n",
+       " ('conv5', (256, 192, 3, 3)),\n",
+       " ('fc6', (4096, 9216)),\n",
+       " ('fc7', (4096, 4096)),\n",
+       " ('fc8', (1000, 4096))]"
+      ]
+     },
+     "execution_count": 5,
      "metadata": {},
-     "source": [
-      "The third layer output, `conv3` (rectified, all 384 channels)"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(k, v[0].data.shape) for k, v in net.params.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper functions for visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
+    "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
+    "def vis_square(data, padsize=1, padval=0):\n",
+    "    data -= data.min()\n",
+    "    data /= data.max()\n",
+    "    \n",
+    "    # force the number of filters to be square\n",
+    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+    "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
+    "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
+    "    \n",
+    "    # tile the filters into an image\n",
+    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+    "    \n",
+    "    plt.imshow(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv3'].data[0]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
+     "data": {
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+       "j9hIEREREXnERoqIiIjIIzZSRERERB6xkSIiIiLyiI0UERERkUdspIiIiIg8egFnCUKNTa56BwAA\n",
+       "AABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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B7uNGubfNcM590u9p113ONSw8C9FuvmcaZUhbcj9G9/fiOB8Dgb6M4kNd8Xvfo819SO6+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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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C/MCPAMUmVb+sXzmRIFU/rJTol8H1sc5YD/w/vo4ptQh6HecjifX9cv6R+FZQLQDlAn83LFp8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\nVOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer filters, `conv1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJNCAYAAAARaCA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmQZVd95/m77737ttyXysyqytpUpdIuVkFjNrEIjKch\n",
+       "mMF2e6Lb45ihZ9x7G7dBEkJGgJBEY+PGbUfHTAf2KOwxmIhxNNhWu23RIDAGyVjCgCRUWmpfsir3\n",
+       "fPu79775Q27s8/t+K/L1RXbi8fcTURF1fnHufeee7Z1893u+JxoMBgMTQgghhBD/XRR2ugBCCCGE\n",
+       "EH8b0SJKCCGEECIHWkQJIYQQQuRAiyghhBBCiBxoESWEEEIIkQMtooQQQgghcvCCL6L+4A/+wK6+\n",
+       "+mq78sor7WMf+9gLfXshhBBCiB8IohfSJypNU7vqqqvswQcftL1799pNN91kn/70p+2aa655oT5C\n",
+       "CCGEEOIHghf0l6hHHnnEjhw5YgcPHrQ4ju0nfuIn7HOf+9wL+RFCCCGEED8QlF7Im509e9b27dv3\n",
+       "vfTi4qI9/PDDQZ59+/fbmdOnX8iPFUIIIYT4a+NyL+1e0EVUFEXb5jlz+rS997b3m5nZV7/yZXv1\n",
+       "a19n1XJ4XVzE+xQL+ADFYhjr9vuQp9tNINZph7FuykqKP9JVS2F1/eInfhHy3HbnB/FWruitZgey\n",
+       "NBqbEEuzsJylEtZLpVwOy1itQJ5i4S+f5at//FV79WtebaU4vC4yvPc9d98NsX/+z342SPeTHuRp\n",
+       "tdoQ830jLmH9Fkj/qZbjIF2pYJeN4xhiBX8r0jXv+bcfh9gHP3JXkM4yMnBIbJCGsbT/l53qK3/8\n",
+       "ZXvta15naYp9MUnCWNbHe/f7eF1UCOuPjb1//x/+HcRuv/X2IF2KyRQAlWdWqYT9ZXJqBPJMTI5D\n",
+       "rNvvBumz55Yhz4kT5yG2tdkI0rHr52Zmn77/U0H6Qx/8wPf+/8UvPWRvuPn1lpK28lVVrmD/qVQx\n",
+       "Vq2EY6tYwjxpgp/n27jb7UKeThtjH7zrw0H6bqIxLUZFiJXcmCmR+TSOsT59vUQRmXPJu4te+/k5\n",
+       "4IEH/rP9yI+8zXpdnBMGGfbhn3tf2Bc/Rsbj1uYWxJaXVoJ0o9GEPFOzUxibCvtnrUbmyjLWZ9IL\n",
+       "n6fdxPktSzOIffTe+4L0e9/7XshTrtYw5tqmVMQyFf6iTR/8oz+yN99yixVIW7UaLYg1m2FsQMpd\n",
+       "LJA+5TrHPZ/Atnr/7bdDrBi5DlPEcja28PuwPjoapOcWJyHPdx59Gj8vDevuptddB3n+5OsPQ2zv\n",
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+       "mZkpiqLI7OzsHzv+8OGDlJaWitFoFJfLlcTk6YW3fSEiIiKKA69YTkRERBQHDlFEREREceAQRURE\n",
+       "RBQHDlFEREREceAQRURERBQHDlFEREREceAQRURERBQHDlFEREREcfgLd2vbS3y+X88AAAAASUVO\n",
+       "RK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The fourth layer output, `conv4` (rectified, all 384 channels)"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the parameters are a list of [weights, biases]\n",
+    "filters = net.params['conv1'][0].data\n",
+    "vis_square(filters.transpose(0, 2, 3, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv4'].data[0]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
+     "data": {
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+       "iRPRsWPHoiNHjkR/+Zd/GUVR8vvm/6osQ4mYgICAgICAgIAtImQ2DwgICAgICAjYIsJGKiAgICAg\n",
+       "ICBgiwgbqYCAgICAgICALSJspAICAgICAgICtoiwkQoICAgICAgI2CLCRiogICAgICAgYIsIG6mA\n",
+       "gICAgICAgC0ibKQCAgICAgICAraI/wdvk9M+hXkCqAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd621d91150>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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SrrrqKknNc3HPjAL+z/Nyrpv/E5N16KGHSmriG975zndKStvK8uXLR66LatLge/ylapQQ\nB/HmN79ZknTeeeeNnC/P52uz5Hjuz3UTT/Ga17xGknT++edLarLxUnW6vN4T50MsEbFqqUwf+vGs\ns86SVF5LpRbae//73y+psQvGifgdvDzPliV+getOZfMS+/b6179+bEo37bz3ve+V1GSJYUucq8da\nEaPEHN64cePI+4whtkfcIe3lrq/trhAOMSjvfve7JUkf/OAHJc2PH8XmmFNuQ7k97DgO8Zannnqq\npKY/ybLi+okL9HawIWzF96LD5nyPxLe85S2SpE996lOSpG3bti14nn3BuF188cWS5q/9zAniQFkz\niMFjrjNHWKO5bvqZcfmjP/ojSc1axhxiHLFL4hcZD7dH1mRinOgnxp2Yv5e97GUj11kK8wV7L11j\nS+dDX2Tn3VjOIgiCIAiCYBdkcaQrdaTvfZBy4DV51V28Re7Ceb3lllskNd4EXkbq7ty9XLxPvFw8\ndP7vNVVQnr785S8veHy8HTJ/POvwmmuuGTmuV5KvrcN16623tvo8/ZCqkI4CmVNcPFOp7/2eXAnz\nrFKv54VC4xlb/N/tAu/W63vxeR9/z+IcGsbJ7TanEJdWMk9lxY4DH0v63K8VyM5LVSzH88cm21Zz\nZw1IrR05xcoVJD9O6fnkagGmVGSvZs/nUraasyEUGBQyj3HxDN6h4SkFayxrpZ8Ha8ZFF1204HGw\nD1R51grmNopUbi3zDHZANX/yk58sqclK9Yzhrr+tbetkTSuhSAVBEARBEFTyiFCkSiul94XXBAG8\nQO7CUXz8e159GC+T5+YpD9zr6uCN4F2UxogRx0EslVdXxnvBO0V588rTbSFeonTvuJx3XKq4+Ofo\nZ+wGr9a9+No94Dxeg/Y9fqEUzguvFoULL7x2T8Oh9hED4oGwszvvvLPqOJOsA+Z9U9pXrvQwxxk7\nVMTcbgxOau5wfI9tmhRcn6ulzCXWEuZYaveEUlJV/GtjyWpB0eG6vBZh6VrCbwVPNfhe2zhgzgeF\nlNg06mGxhrhyCEPHHS8WQpEKgiAIgiCo5BGhSOUg8+OZz3ympEYRwBu89tprd/p94hog5+25p49i\nhCJDzBSkYkyc22+/feR88N7wGvCCUFxSSgXP7fFCPKPFFRWuB+8yFZuVw3fozsF1dPWuXTnkelCI\nvKI+XtqSJUskNeoC9uLXzfFTFd1LqzCXgtfdNdYLxYhXYuH68uKJr3DFte141uyrRkYqWUmorrXV\n8yGnmKCs0IeontgU6lrt2KXU4FKVs23l6KFgbWLu1cbz8T36uVYtZ03lNwLIwiuFNZU1uTZzGzvy\nNdrXshz0L/3DvECh2rJli6T0nBzXnobTTihSQRAEQRAElSxKRQoPua9sPI7H/lnsw8Vd/hvf+Mad\nfr+tF4DXh7fE3f5PfvKTVsdJQb/48XiOnou74LpRCPz63ItCQfJ4BsDbyXnZpeOJgoGCR8xZbZ0n\n98JTNYCA66N/UDTJliTDJRVb1TVbjoyf2j33SmsNYSd48SinbeN2cqTqrQ0Jnjxj3FemZq5GHLaL\n6okywVz1bLUUKCTMvb5qglGLbtLQT6nYHNRdYspyKmbXDTzoV17ZH7ZtTUBUbJTH2vpVzEVeUVRZ\nkyC3r6nHgfr+lqjl/N/V+b7ouqaVQjwm/cS86/rbG4pUEARBEARBJVOtSOE5c7eK99h3pgWxRV//\n+tclNTFCpbE6bTNJ+roLdvDiyLjg/F3pyXlPZAVy905mCPh5czyel9Muf9cqRSnwrlCSuN7adlxB\ny/UP3r9nWHm9sBSemdM2O66tAuq0nT9UL6Zf+H7f9jsJ+q4ZhlKEcuG2lMtyYq6iNKTOD5tBcWCN\nROnwOV+qnHgGZNdK6bV4bTeHNWDc2YdXXnnlyGtbUPxqsw8B5QaFi7XaySlxqMv8trIWEiPFmsRa\nh133vUXv0EoUeOZ8X0+1QpEKgiAIgiCoZKoVKe4e8c6IHxiqkjH7DPG8mZgX7sb79l5TUK22bV0d\nvGGe/+L14HW0fb5N/3sMkXsj7u3STluvyyvBp8BL9gryDrFvxDChBrgX4gpPrpYL3gyvnC9Kpvez\ne/XubaMklipS46oG/IxnPENSo0h9+9vfljScEsV4eZxQ34rmkDAHarOxmDMphQGwYWwL20nN8dLz\ncRssVaLYb/NZz3qWpCa26fLLLy/6voPStquB8pObwyhCzAXswnc9YM1hDfTYutLYJtZS1nLWQP7m\nONhbrX1PmqHWrlCkgiAIgiAIKplqRQq4G09lcPQFygLenseEpMg9z29L7V0z54/3wfmjKLVVpPBC\ncgqNeym1z/9La5IwHrSTihnieT+KD/3gipR74V6pPXWerj649wbYB+16TFZb+/F2UhleXatBE7dw\n1VVXSZLuueeequOUQr9zfXjlXF+fNWs8lgkbIt6uthp8110UUvsjOswBXrmOtlX2ndrzx8bIQuvq\n+e+qihT95Cor18vTBHaXoB+J4/U9GJmjvjsClK792L/HIXtFfI7XNcZrVyMUqSAIgiAIgkoWhSI1\ntBIFrmygIOS8w76rAddmonj2F15I7V5peCG566fdofc05Hzw2vGeqCflCgxeGupCKmPKvTWvPk2c\nAl4k//c4Ao5DfxHz44pU6rpKKc3A6joeVNjvW3FNgQLF9WG3jEefGVooUihQZD/RZq0i1TWGBFWu\nbVaUq7O1WVW1dYKYi9iM2x5jWxrv1lVZc4beN7IUj+8Exos1gvhY/xzZmfSP7yGIggSldsh5+VrP\nXEQxCyVqYUKRCoIgCIIgqGRRKFJtq8fWwt0/WV4oBbnn/V3r+nTFFQO8FLyI0lgnJ5cJBF5HqpZc\nzRr+z/kQH0CVYX9+X6pgpCqbc13uTeM9ej0r+tmzD90LTsVHQK4fuP6c6tDXfMHL5bpK921rC8dF\nJaJdXtvuxbgz8OCZO4xJ2zgw3x+QDF+upe2cQxkjUzlXd8rpWt+nVn3EJlFMOA/PKizF4whr4Tgr\nVqyQNHycXw7U7RTMWTLT161bJ6mxL34LUVQZL+wQ+4NS+0ut3fx/XHWeFiuhSAVBEARBEFSyKBSp\ncdWs4O4ejxhFgLpSpeT2N3IOOuggSc2+Rm2VHbxBvFcUGa8w3pbrr7++6HPuPdbu94UXnqoLheKD\nPTBejFOtnXiMEl4cx03F6NEeMVruTaa8QVf4XHlC4UopP66C9F1l2BmXN0ptnKVLl0pqlOG2Ow2U\nwJxgbJkzbSsdY7OMBa+1WWesAaksrBy+j2dbPMamLaxBnuVV2q/UozrwwAMlNePCGnPbbbdJKrd5\nvo8S1XUvwq6Uxi2iXDEHWBOJQeO6fG6mdp0A1g4UWfqR8aKfOU/+TtW9QiF7pBOKVBAEQRAEQSWL\nQpHqCl5Rzhugojif526eu/KUZ+4Vz9sqBG3jIBxvb6gYFvBK7/zNc3v6OXddXnUXhcmvx+tB8Xni\nL+644w5J82OiSvcJc6+/VNnie3jbtZXvXfHMjd+uHq9w9913S2q83bYV/muo3XMLBQm67knHNbdV\nkbH1rvWXiAfsCiot6uK1114rKR+zw1xgDUDdrc1mhKHmTGnlcChd67lelDTsIZc1l+sfztd3D+B7\n9BP2hD2gCjtkJvdNKi4aRW3aCEUqCIIgCIKgkqlWpFKKglclzpHzFrirxptDWfAMiZRX4+fnmS8c\nFy8A78Jjsfbaa6+R9tj7z70MdhBPQXyB1xjJKXJ4H3iRKHObN28e+Zx7lXiNqfHAu0JR8popfhzH\nY5bwUvg/7dLPbWv5eFwB+5xhF/QH4+bZerTH+OH1ET/D5zlfH0/PCuQ4KFNch2fP8X/+5nzwHrEv\nz1Tab7/9JDX9hl1yHcwX91ZdKeP4ZGpxvK57AfJ92p3m2jWM1QMPPCCp2Z8T2+HceT+lNK1Zs0aS\ndMQRR0hqbJmK1sCaxBqADbgyksoYdTgOx+0rW47zYs1sm73oa0GfVe37pO2uEcy13G8XazbHJVaK\nrM5SFXzZsmWSmrXT1X3sDLvluLRLzJqvGYznoYceOtIe8wG7Y03z2nyp6+e81q5dK2m+EslvYwq+\nT39hf7kMbn4z+e1GaX7qU5+60+9BKFJBEARBEASVzDw8dMrPQo3OzGh2dnbczQZBEARBELRmdnY2\nGYMWilQQBEEQBEElE4uRmp2dnXue2jWmwiuLE7Px5je/WZL0iU98YuR9nodSt2br1q0j71NNlrtP\njxHieTKZKTxXPvXUUyVJ//RP/ySpyTYixobz5P88Xz/66KMlNc+VL7300gWvk+e3PP997WtfK0n6\n53/+Z0nzY2noV7++E044YeT97373uyPfI7aH+AliY0455RRJ0nnnnScp/3ye75fGSRA/QL+ffvrp\nkqQvfelLkprn6zzXJ66DOBKOf9hhh0lq+pf+5vNcJ9fFeb7whS+UJJ155pkj7eXweJVUhouDKptT\nZ7E34hb8+LyfijHz9rAXjuPxNcRGrVq1SlITr8B82bZt28jf1GIie5N4lre//e2SpI997GOS5o8/\n45iLX+Bz2D92wt/Y4emnnz53bcRW8FnawOaJ2fD9KMkK4pUYD8aWOXzyySdLkj760Y9Kmj8mnBu2\nQR+zhx+xHsTjka3mc4oYl1e/+tWS8rbifVW7Zx3tcH2+fyW7CTCHeD322GMlNf1JLTquy2NjnvOc\n50iSjjvuOEnSWWedJSkd37hy5cqR6/KsydS4pq7v7LPPljR/t4K2NemIiWOcaZfjnHbaaSPtDg3t\ntF3LHN+lIdfeuK+vtD3mMxXuWbuIj83tAZlrJxSpIAiCIAiCSiaatddViQK/2/YsH7wXvBTuPlN7\n6G3atGmn7eF90Y63h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2\nX8prIVMpBe0xDoyT1yZhvFCWyCRBAXFvorQiewqum5ovqAqoCF7XCMUt1d9Qu2ckakqq4nvb6tdc\nD+OFXaJcMr7YGwoe71PnCeXSs2hddaA/UW49y5D2UuqJqwWoLSjCO36Psa8N/cypeg6qOh6uZxcx\n5tgItkv2FWObqh3Wtr4VY/nc5z5XkvTNb35z5P22NphSV6kw7nzrW9+S1IyNr1XOJZdcIqlRpHLn\nRX9wHdgCWWXY5K233rrT40DbuZM7r2kjp0Rhp/R7n7sHjIPSOl6uerMGMl9zilSOUKSCIAiCIAgq\nmeo6UrW4Z4u3wN02XqffxebubvF+iElBAcrdzXbdK9A9eG/PY4RQGlL7Zt18880L/h/vGqWH/nKv\n1L2WtjVe6GePP0kdj+fb9AN1l/AqUteTgu9hF75juntxeC2p66TfcwpcrR3kKp2XxmQB3pnvyehK\nI/1z8cUXL3gcPo/deByNfw7lkfHnlf7zemt+PpwnSirf31ENwMMure6fmvNcE33l+zsCY+4KGNeQ\nqhFGuyhVXmMOUipkCmyXWCyUIdYCYohyqnstXGeth496icKXep/+IlaL8bvrrrskldcYRBXn8/50\ngfhW3vfzQglDvR7XvrB9wT6v9ANPU7zCvj+VmBba9jcV9sHjq2sJRSoIgiAIgqCSXVKRclxBScVm\n5Z6z4kW69+Leb99eicc+OZ5t5d5uKSheeF201/au3fdp8kyk3I7w7pUTe4QSiHJRu0chXmYqzsMV\nTVdSFjvErXgV7LZ4plLKTnyHAuzKd45HfeG4qUr8HGeh+Yp6yZzns6lYkdScxwZ8Lrt6mVJeaA8l\niJgorjGVeekZtrUe83XXXTfyN8pJTjWdNF652pU9+hWFhLUYRZD+SsVt+l6CfN6VRuyC/Vf5HO36\nHnipNd9tfNpwdd/n6JYtWyZzYoV03dvSM8FrYytDkQqCIAiCIKjkEaFItQXvBOUERQUFCq+J98ly\ny9E2Y8b3bAPPNsRL5nxQatruUYYXjzeeymrMwV1+KgMkl+HiKgFZhnjTpftVpSDuJeWFuMrA+dRm\n+JTWYml7nNRelDl8n65aL4z4G84jVVcMVcaVJO9P/i5VGhcaD86F19o+Z+7gsfv+isDcdIWJ76P+\nEYviMTXMEVdE+oZaX33Tdt/THB6DxFpEbBe2m4qhovaZ1wkCn9v87ftgYqvYEWs8Sg1xqbn4xGmP\nmWJ+YNfkKbm1AAAgAElEQVTMVTJzp3mfyz5JxWWWEopUEARBEARBJY8IRQrlAe8J74+7bTxbj4FJ\nedh4LcTauMJCBgTeCHEbZOzgHVLNOAXnxfc5rsc5cN5cZ20tEM7Ha9zUKjG1d/cOsV5cF16i1+Uq\nhRg5V3aGgnHHm63NaHJ1pTY+AAUzV5k+B9dBbaSUqpJTKGtZKGaQsexrC1GPGfE+T2XVodCgSGFz\nrgxxXNYSr1LfNs6RtQD12+cgWW6p+MC29KVE5Y6H7aAc5L5fOqdz2Z0bN26U1FSYHxeMH/ZVW6E+\nB2uKZ+4yf0rrNC12uv5WhSIVBEEQBEFQyUQVqb5iR3J4HSDusvk/Sg+veJ2p6rg8fyejw+9mPRbK\nMwPw3Hke7ftiuVeG4oW36pW/eR8vzL029yZ8rzkgboPrIj6krVfcN64U8jf9xnWXem1eu2fo+BTi\nD8g+LK26PBTYJfZfW3GdCu/Mq5SddI0TYXxc0dqZl9yXB+1zxOdmqarH94itcVCdmdsoUm2VCK47\n1efUYUJtbluDbFKUKk2shb62ptTQnO2ztqPgjSvmifMmBo/z4LW0TloOsg9ZC333AX4TsZNdXZmq\nJRSpIAiCIAiCSiaqSKWq+XYlVdHbn7OjEHGXz9146fkQ/+DeAX/7XmZ4U3juXk8nFW+B15val4v4\nC9rJxeCkYou8fZQUr+0yNF7rhfHkuvDa8Y5Q/Eq9dz6Pl+uKYteaIg5KH2rApPF+6uplY38pb5Xx\nbJtlSCwXGVOoOb6f3Y4qkZ9D32Pp9KUMUFG6bSXzFLlYrlys0bTB+OXi7HwNz30+Z/veT+NSpLBp\nzn+odvktIj4WtR97Yc4y10KRWphQpIIgCIIgCCqZqCI1VI2KlJfoVWbxVrnL5q681HtFyfA4CdrH\ni6Adj80Cfy7t4F3yOd/JmvMv9WZTmTHUBaI9YnrGjfeP7/Tu2Ze8X5oNRz/xPVcwUzVFUrFlOTyT\natKUZqiUqjnEcdCvqCtAf7oilYtPQZHyGj+AUsX+atL8udFViUJFTGX29oXvTtAVHzsyR4l1QU1f\nbLgt+VzkevtSCofKlitlXAoY/UrGNvaDCtw1w3dXJxSpIAiCIAiCSh4RdaSoistzczxiVwpKvQ8U\nERQbj0dAKfHn3K5MOanYLNrj1etWuULj7XmcADFGeDt4w16BOrfn2VC4N4n36c/nUUAY31LIkGLc\n/fsoLGROEStGLRnG1ZWXFE960pMkNf2Zqso8LrAXV4SxZ+wTO0idL59HGSIWytUOjuP27RXqXT3K\n1d0a0lunj6iM7Zm3Q7Xn11oby+R9+eCDD0pavDEu9H9tDbZg5zB3eSpBP3sl/2BhQpEKgiAIgiCo\n5BGhSOHV4YGjPNXebeNB+w7ggKfO5/CcUUDaxm2QFZWqicLO3XgRrrg5nB/ngbeK8kD1ZfZbGrrO\nl+PjguJBDBtxMFxnW0XKFRau//jjj5ckrV+/XtL8/sZb4+9SRYr4FFf+JgVVtF2RxC68vhvni0KH\n3Xj9Lezd7SVlP57VynGZr3jJ2L+zUJxPW/UUJYi2+X4qjnCo7D/mqleSfvKTn9xrO/Rp7T6afXP0\n0Ue3+nypComNsybSn7U14zgeleG3bNkiaf6cWKywOwFrHL+VPA14pFQ4Z354TckcoUgFQRAEQRBU\nMlFFiliUvr0j7q4h5dF2jf1JedqueOCtdM2USe3gjjLmMS8p743MHa/X5FWOPQOqFGKQar/v2YJk\nZXl1X0iNby0oca44uaJZyr//+79Lmp74jlQmTtvzQ8njNUWqenaqxg+KGPW32tBWGei6V1zb2lgp\nvP4RqlxbtdXPCwUGJSHVP+6JA0oMqiFzAzjP3JwgA9M/7zFgK1eulNTMPXZZoN4R/ZSyHZQTjoua\nyv99jeQpAe8Tx4jtoY4fddRRkprxYC3n++NW7YG1lvZR71Pvc/6MJ39z/fwWs+bS39gH1+sq9q4C\na2DbtTAUqSAIgiAIgkompkitXLlyzvsh9gWvDi+Avz3GCK+Cu2qvfE1MSt9wXqm99WDt2rWSpE2b\nNklq7t6pSYPHjTfs3pBnzXksi++1l6vHhVdKf3u2GnEieJtds6G88rSfd06BTGUcjYvUXni1tVSG\nVqLa7pU3bu+Z+Yi9e6V8+sdVDbdr5g3zheP0oUiWKivO0572NEmNgkLfMha8skbxvseHoc77Guhr\ni481fcHnUWqYQ16Dy+eeK18eQ+S7BzipTFrGnMzL1D6JxBodc8wxkhpb4Dz4G1vIVSrnuOyN53jc\nqI+TXw/nsXnzZkmNDfN0YNxzyW3fayF6LUGeCrjyh3LF9aHAsX8mT1U4rh8fpWrcGd3TSihSQRAE\nQRAElcw8PIF0g5mZGc3Ozo672SAIgiAIgtbMzs4mYwxDkQqCIAiCIKhkYjFSX/ziF+eeS5NhwvNe\nnoeT6UFsEc/HeU7Nc3ue2/JKvMCxxx4rSVn1y6sV14p0tJNrj+fVnGdpdhvnSX/99V//tSTpk5/8\npKTmebXHY6SyBT0egufvxEswPsQ2vfKVr5SUv76+oJ2zzjpr5HxSYCe1lcNLxy8FNViIpcrFD3Rt\nry3T3h72TR0v4mO2bt068jnPCsVu3/Oe9+gTn/iEpGYMvDYVx2QtYS4ddthhI+97xiZtkkV20kkn\nSZI++tGPjrRHOx7LxJrmsUasAR67w99kIL/oRS+SNLmxo5/oz9o1kn6kH+iXv/mbvxlpb2ho57LL\nLpPUZJaSHbhq1SpJjR0QW3bIIYdIauY6GdPYIn/zW0SNv1NPPXWk3aGhnb/927+V1GRfspce53vt\ntddKatYqxnm//faT1MQv8/9DDz1UUnP92PtznvMcSc1vkWd3em06WLNmjaQm5oz+d/g+53H66aeP\nXOfQ5NoJRSoIgiAIgqCSiSlS999//1y2DXeZKcWBarIoJXfccYekJiMEL4dMBpSJHKkaMHgjtEeG\nAnfNvhccd+/gtU/wVmkHxQhvBe8AhWjFihWSGi/J9/vy2jJktdF/eNW5fcE8A4a/PVtq0jt/l2aj\n9VV1l/FDLcDLxAv3/kE5ZdxQxB544AFJzXhRc6W2vlaKVMX7WjhfMr7GlZlE/+Ilp6pQM29RSnfc\nexIP2klltjKnbr75ZklpW2PMqMHl/8f2fAx8n0zmPmsUawTHoX2uiWy+trC2YINd6VonC7raPnOI\nNa6rbXrNOGyeNdfXvlK1m+O4vYwb7Au7or/4v6vmjLPPI58n2HnpLg2pceIeIJctO6k6XaWEIhUE\nQRAEQVDJxBSpPffcs7gqLDVBUvv9eK2SlBeHV4jSwN00d+V4gQceeODI+7RPdVeUHs7Hq7tSkyPl\nBePteE0X7vo5j5T3416SKxL0a1/1izweoq9qzn2TUs4WUi52hscF5GKdaPe73/3ugu/TLq9dq2lj\nb27vXSvng++z1RX6n+smToPzTc3/lLpD3BDn2aY/U7Eapaqnfy6l+jInWWu8kjR1lnz/TZ/zbWu6\nsUZRJ8gVKdR2jztbLDB+Rx55pCTpqquu6nQ81mBXRGrXTuYi9lW65gwFMUw+l7yuVCn85qHCu2rc\ndi/DtjUCfTeOUlgzUzFYXQlFKgiCIAiCoJKJKVJ77733nLeFF1ZatRbw9rjbxKtIPbfFc+U43F2j\nOPD/733ve5Kau3Y8X68mDK5YcF5tY0z4XCrOI4Wff+p5M9lQq1evltR4pd///veLzgvGrUThzddm\nCrX1CvuKNXJyFehz0A+uwHrMXl/4jgG1uP14de9SmFeoOsSulRyHvkspUqV4X6P8ED+JjfqedX7N\nrHlcE2qbq4BtbZ6xuvHGGxd8f7EqUUDcKGsYWWTEIrUd19R+jrVrXF/7xvouF/vvv7+kJqaotJo/\ndshvn2e4twXFDWXHlS2vxN8XnG9tHCzzbChCkQqCIAiCIKhkYorUQw89NOd9+T5PpR6w12rhbjUV\n24GH7TuD+/5YHIfzSe1/xff8bpcsQr6/ffv2kfe5u2ZHcf4mFsu9277wWJ2hlIzFRu1z93GDvXZV\ntkrB7rvGxHkMU+35M984H1ScHRXHVAYjcymXyZrC9zgDr2nl+4LSZ3jyKFisGfQN32ct5PyH3qdx\nseKK3rp16yRJ11133cTOqU9cRUdxazsHsVfsF7usnQccz/e9BK8flQN7z60JtSo2EMdKtiy/fV3j\nVSEUqSAIgiAIgkompkj99Kc/nXvOy10yd7ulXphn7aSq7vpzYc/e8895BXX+5hWvl3bIYABillIZ\nCdxV8zmq5XJeeB2lNWBKnxvj1aTqYU0rXWOkUHJS17tYvP6U19Y17ifFUPbhXmwpeOnEoTAPd4xH\nysW31caG8D2PAfG1wbPvvCIzyhQ2SU08vu+KQ20NNypPs7Zs2bJFUvssqWmDNZv+ZC2j33jakKtL\ntNioVYO9kj9zxn8LS5WZu+66S1KTeesZ620VnnHF23LdKGCsGcxTYu9qCUUqCIIgCIKgkokpUlLj\nveE91N6d8j28Rc+i4y4U5QiPmLtUPO9Sb81rwvhdOUpbrgbMTTfdJKl5rszdse9513dMzGJRoqCr\n10Icy2K77lI8Vi9X92rcUDOJ+edxRW2zJFFgvZ5WCfRNqiZdCjxtjy3xa/A1iM+ThcXnyRYjQzcV\ns1Fb74e1jDW2r2yyFCgbjC39WrvvZQr6g7Wb66K9obLGxo3PkVpQpLAvflMYL5RL1F6UUn7DfM3E\nnjhu1zje2littnhcMLXoWEO6KlKdbqSWL1+uxz/+8dptt920xx57aOPGjfrxj3+sl7zkJdq2bZuW\nL1+uL3zhC/NuNIIgCIIgCHYFOt1IzczM6Nvf/vacxylJGzZs0PHHH693vetdOvfcc7VhwwZt2LBh\nwe96hk3Xu9tUrIhn93EXzF112+fpfD51N13qIXtVY+A6hs7Sosox7d15552Spk/R6Mpijwsppa9K\n5H2DGkNcAn/X7hGIfWK3bRTLrvsx+tzwvz1rD+WLVxQaPsdawue9ErXHX7YlVSepb1DSiAFjdwgU\nlb7i9zx7y20eVXZcma1D0bZCeAp/2uP273HFvJ+LJeS3s6ti1tf+qDn4rcZuUgpzLZ1XXr8J+OpX\nv6pXvOIVkqRXvOIV+vKXv9y1iSAIgiAIgqmksyL1B3/wB9ptt930+te/Xq997Wv1wAMPzO08vu++\n+yYzzx5++OG5u0GPA6it7cDdt3/fvRi8Pz7Pc+NU3ESqjg7eqN+Vl9bd4XktXqfXqRrKm8RbpDow\nz4eHqug9adruV7ZYcKW170ylrtmSwHliX54x1DZr0uM0xuXVSo2aBqkMROY+fchcZq1Axef7rE0+\nhpPeq60tvtsEa1tpJe4cKE30F7FEfe8POWn6iudkPHyOMBdZO7BLlLBcrFnKXtuCffAbnMt4r8UV\nUeZVX09fOt1IXX755dp///31gx/8QMcff7zWrFkz8v7MzExSOvvpT386Nwi77757dVpyEARBEATB\nUFx88cU7fb/T3Qv7/+yzzz466aSTtHHjRu277766//77td9++2n79u1zWQHOnnvuOXLXvWOl866V\nlN0r8YwOPz7VhlHGSj1cvNOuz1mpFYOnPfRNJedLxsu9994rafx76AXdwIvDXr1CP9R66X1V1kd1\ncUUKNaGt8krGDSoEtZjafLd0P8v99ttv5DW1b6BD37k6zrXzPn3j9ZBgsSlSnC/XPVSNNlfz2Z2g\na8xO39TOPa7PVWFXoXOk9oX1pzPYHYpU6W9aX2ow7Y2r/ldbxe+4447TJZdckny/Wgf9+c9/Pjdp\nfvazn+kb3/iGjjjiCJ144om64IILJEkXXHCBXvjCF9Y2EQRBEARBMNVUSx8PPPCATjrpJEm/ubt9\n+ctfrhe84AU66qijdPLJJ+uTn/zkXPmDhXjUox6VzHDhrrjUuyC7je+xnw54HSlUMn+eT9aa414i\n32fn8eXLly94PqVVibl+zofvs8N236CkoRCUVlAPhsErr7tqAe6F8j5eJhlT/M3xiImbFB6HUVP/\naUdQHzjujsc5/PDDJTVzmc8wZ1euXCmpWSv4nGcQok4zN1FWXC1uW3mcsfF9L1Nq8NC71vcNigLX\nNVTcJWokNs84DV0vqy21Kn9KDW4b7+mKae43JWfP/Eb7Prm1sEZh5+OqK9U31TdSK1as0LXXXjvv\n/3vttZcuuuiiTicVBEEQBEGwGJhYhDdFPKVGIeEu1+s95WI18HDxKvFYAS+UmAzufjmuZyw4nlHA\nXf62bdskNd4qdZnaPn91L+Oee+5p9f1S8NpcuRhn1lMNKH6oB4yHZ+4sXbpUUuMF3nDDDZIaLwp7\nIn6D/nB7GTfE7WzdulVSY4eMC6+cP/bu3ncqM6qvjKlaiP9hnuAV19rdpk2bJDXXv+P6gGLksTnM\nVe8zbMnnLMoK8YOlsIYxVtgex/N9Nvk8qiS2zPk/6UlPatU+eDbU9u3bR9rrOx6S62UODq0seOwP\nytdiVTRSoKRi12SzldblQo2mfhlKK8oTa6HbI5/jt4k1ilhB/zywluZi41h7OS5/Dx3j5nGl2A1r\nKufRtiL/rpErGgRBEARBMAEmpkj95Cc/mbsbxEPl7rbtc/XUvj/HHHPMyPt45nhj3I1TR8k9ZJQB\nr5KLN8BdvXsHpd4e1+v7HQ0Fd914E4slNgo1AXw88NJQFRhHvKlUXAH/97gAvC7ssu9K76gO4PaC\n14QXipfE91AUb731VknN9VOXCXWD4+6488CO4L1j56gmuR3hsSO8SdpnHqFKgPdf12xAxn+hcU3F\nOQJj7Xvg0ac59c5jpOgLr7LONeLZ8zfn7J93W2VNueuuuxY8Dzx/xgybZ03hvOgr2mOtKVU2OB42\n5Gst/XbwwQdLauJT6UdsgX6nv7GJXEyOP62gv1A5eXVKlZFpx+tm+XjldgfAPug/j8ddKM5Qaua+\n2yn/57w8yy63VrI2cR6enVg7Xswz30/XY/ZSFe+XLVs28n3WstKYu1CkgiAIgiAIKpl5uK9iMW0a\nnZnR7OzsuJsNgiAIgiBozezsbFJND0UqCIIgCIKgkonFSJ199tlzz/F5/k6kPM8nSyFWw+vxvO51\nr5OksalftPPpT39aUhPf0HdWHM+TzzjjjJF2gf4kTsDjLF7wghdIauITiLXheTjH98wO2jn//PMl\nNTFEPF8n/oJxIF6C59I8P+eunnHyveI4j7e97W0LXt9Q0M6//Mu/SJpfO4g4AyppE79BZgyfp1I3\ncQBcP8/f6a83vvGNI+0ODe184AMfGDkv4HzZsYD4i9tvv33kc2SSET9w9913j7yfs8+hmJ2dndcW\n5+IVnH3XeyeX3UY755xzjqTyisycB31buq8o7fn1sSsDtkgMDTbI2kPcHXF2niFNDAn99Za3vGXB\n9oaCdt73vvdJKo+TXbJkycjnS6vk097nPve5Bd8nU5gK+Lyypq1YsUJSM37MEeYCawZr8aTmeq49\nfiOwc9ZkrpMYODLJPQYKu3vHO94hSdqwYcPIcfl811g1Yrqw3z//8z+XVN6fuViyHLl2QpEKgiAI\ngiCoZGKK1C9/+cs578Gr/bbFvZBaBaivTA8yVoaqz5QLa6PdVC2Mb3/72yPH8fPEG0l5zVyfZ5D4\njt38jULFK//vOxuuL7huzs+VNK9K7fuL0S8oUHh71Ejpa2f3WlKZK6gTuTpm119//U7fn0DYZRLO\npbTuDpRm3rbdG4w+bqtIpWDtyykx2N607UXntFUM2tb5cuh/sr5QYHi9+uqrJTVPSXi95ZZbFjwe\n2WKo8yiQ0wq/dVwvaxNrnteZcvw329fMvuB4tZX+h6qwD6FIBUEQBEEQVDIxRWpHUIK422y7Gzzf\nb7vvlUPNk5tvvnnk/8QhlJ7XuHds5/pRktauXTtyHt/5zndGPp+6O0cxOuKIIyQ13ojXcaI/qJbs\n4G37zuzEEtEOx+3qlffN5s2bJZXvdZgab6+IP237gE0bbXe2X8z0rQwR88T+n8TwoP763oN9Kwep\nGBR2lfA4u2nB1zbWUPqp7W8Ra9tQFeT7Zt9995UkPe95z5PUzEFiwm677bZWx+sai5Sj9jeecaVG\nIEocMYG5+nM5QpEKgiAIgiCoZCoUqZSyUUqugnUpHhvCXSzPz0tpG4/RFWJeOE/ab6v08Jycytb0\nR+m+Q57151WH2UtuaOiHtvErUPs9oB9Q7rZs2dLpeNNK3wqS74k47nm0mHHlA8/78MMPl9SorFdc\ncYWk/pQoniKQnYZNoD6n4vGmFda6WiUJ20UJbPvbMW44v6OPPlqS9PSnP12StHHjRknSWWed1ep4\nQ6vJteOCUsZuJ4wTMXahSAVBEARBEEyIqVCkahUEPFjPtqu9a/VsPerpQC6baVxwdw30H/3gO3eX\ngiKFd8nzfhQqSO3Rl9oPKsVQz9OpbULGVtvjo0TiVbbNtOL6+Tz9yfFqsznx8om9In6h7fF8v7W2\nYGfEwBFP0RXmc9vxYrwfyeBhU8fI91kktiSnRBHbUwoxWdgm0P60ZubmqI3bZBxQpYfOFusKNQa/\n8Y1vSGoUxNrM4mmNbyTelXhW9tbrS/UORSoIgiAIgqCSiSlSMzMzc8oJygcefGnWG554ql5PLXjG\nPP9HAegbPHvu4ku9IL/r5/zwzDn/tufNXTvHoWqwK3VdM1E4r6HqbLWtjO9QPReFlP7EuycDKee1\n8T5eO96+VwIvBftYtWqVpMabaht7llIdUOIYb66fecC442VjL8y/rlmzrAPYRamaMe21eoYEW0V9\nxUaovo9tMOa5vmob00N8K2sFsVm0h01dc801I+fZlaGz4ujXtooac55+XCyKHLs58DouUO6Gjodk\nXpDBjoLKb11XewpFKgiCIAiCoJKJKVKPfexj55QJ7kp5bVuHiee6PJ9GQehyblITE4Vy1Dd4+ryW\nxmC5IoUH79l6bb0h2keRQGlAKegLYs+mJebMoT9RXhgf+gOvm9igVD/z/kEHHSSpu7Lpe9uhOtQe\nx2HeuXeIiuHX2Xf8B0qixwDmKK33NQ30nenox0upvMQ/sr9pqj5Q2zGlztLll18uSVqzZo2k+QoV\nCoDv+9kWfiOYS0NVakehoJ3S7ENU3K6Zv4uVUmWHNZV4TX5z2DVjKKgbxuvBBx8sqbFPrytWSihS\nQRAEQRAElUxMkXrc4x43p/TwXJnn53gd3LXiDZA9xv+5e+3rLtbvpr0OUt/gge+9996SGkWtbRwB\n3ijnzSvHa4vvX9V170GHfp3W+AG8SfqVv4kBwuvPqQrYLbVpul4v7XWtx5XyllNqxLhqAfm8n7aK\n910YKhYERSZniyhXObW+VilDzWQtR/Wk3b7Gkt+A2rg4xiFH7EJQR+kuI6yFKJpDxSHnYC33zPS2\nhCIVBEEQBEFQycQUqcc85jFzmQ14MSgo3B2izBBZTyaFZ0vh5eFR12aGoOR0zT5qi8fitPXevG4U\n508/tH3O795z31lRfStcfYO3RP/xN/2KnZbW3EGRikrdO6dU6VuMDDX2pX3F52ozRnOwxhBHCChm\nXWOZWIP4bShdo/ntQDErjfdkTW4bO0M/EIu2WG2ZOEXstjSbre1vBf087n5ye2q7p6ITilQQBEEQ\nBEElE1Okdt999znlxWN8UERQArjLR4nyvd8We4ZEVwWMzBi8CO6yydRBQbnqqqt2ehxitdavXy9p\n/rg8UuB6PQOK/kA5zClSeOeMB9mK017tGLtJxYlgT3yOGEXib2rrgy32atjTTOl+mbV4prDv+1lq\n8zx14DfA42ZRlEprxbnS4Wstc5nzxrapGUfGLbs55Gxz+fLlkholbLHaMrFObZXEthn3vmvEuOCe\ngjWra5x1KFJBEARBEASVTEyReuihh+ayw1CU8GK4S8TLmdRz1No9AMcNigf9Q3+hmJTG8qAIcBy+\nN1S/44WghFF3Ca9uUqxcuVJSU6cJO6A2Ds/X8YpTMW2+5x/9ecABBwxx2sXgbbsXxvWhnJEd6HWa\nGC+nr0r1067YBfNh7SAOExtru2cbihNziuPxf16Zk7l40lz7rDUoVa7A+L6ZKYWJ80K5YveDxQKK\nn9eMa7v218YCjjtDl99MlLeu+76GIhUEQRAEQVDJxBSphe7YU1VhJ5X5MK1KlO92j2LA832y4lBC\nSis/o1BcccUVI8fzCuReqXvz5s07PU+8DfdW8GI93qGv/bhqITbowQcfHPk/14E3k4sf4PvuFXet\nWdIWt5dUPAAKsdcRcxifrnsa9kXflfcXgrlANtauimfdlYKKy76SxM+1rRTN3n1OX9l/zq233ipp\nfhwoShhzJRcnyhxvq8D1Ta52YKpWIWvzPvvsM/J3raIIrD0oXqjWtJ9SwJYtWzbyfdZSlM5aUKDW\nrl0rqZnPHLe2dmQoUkEQBEEQBJVMTJHaZ5995rwWanVwd4qiwt0hni93rewovnr1aknNfj0pTxov\nCWWGGA8yDPByeD7qz0+JHeEuGi9r6dKlI+cJZJ649+SVm1P1lDz7js9xt+7eRGrfrFq+//3vS0rH\nvFBzI+dt5jI4Us/Ta/c76otU+20VmJQX6/EA++67r6Qm7gNFzLNWsVvsg5gr/x79TqYW8wzcK83t\nj4VXiLfq2aClNVjwAplPnC/zlvaZh1wn85B2ieFK7Q04BPSVZzKytnCuxMiwBjA2d955p6R0LTff\nQ444UVfAvJYefcFawRqXq12GzaEud83MZc5gE1x37X6Q4yJ33UNlLJNpTX/x24cdsEZgT8D7jBvj\nzxxmXIE5yxzFTlJKk+/bmoPfMmCusmZwfZw3axh2zm8bv5XYN/ONtaF2H06HfuUpCr+pXdX1UKSC\nIAiCIAgqmXl4AgEpMzMzmp2dHXezQRAEQRAErZmdnU3G74YiFQRBEARBUMnEYqTGoUjRxmc+8xlJ\nzXPQVBYbsVc8h+V5Mc+veV7O9z2GiPbOO+88ScPv2Ud741L3Flt7xAcQ18Jz/1TmyqSuD3sh1o/M\nJTMqscQAACAASURBVOIIeI7PK58jlojPE5dAfAr2ynW+4Q1vGGl3aGhnw4YNkpr4CDJwOH/OFzxb\nlrgJj+Ui3oFMobe+9a3Ja0vVzoLSmnHEmJx22mmSpHPOOUdS0/cew0EcF6+8T7wX7REjwlqD7RJP\n9id/8ieSxj925557rqTh6vwwF8844wxJ0vvf/35Jw2e/cX1nnnmmpOF3b6C9888/X9L8jGAHO6F/\niJnjNbXrAHPlbW97myTprLPOktSsHbl9V4mxSsUO0T72y+f+8i//cuQ6+8Yr0NPO1772NUnNdXH+\n2OuNN94oqVk7nv3sZ0tqYrPI2uR9YrmI26a6wItf/OKdnl8oUkEQBEEQBJVMTJGSmgj/VPZaKdy1\n8+reBXexuXpKvh+Ve8DcreYyGvCQXUnwukq11815BDsnldU16TpVDpXO8aZcFbn77rtH/l6zZo2k\nxg54RVXxWjLjyGrbGXixzC+vHkxmGtePl8j6wP/JPHJvfsfj0RZjzNx11csprRnnNblQqGgHm2Pt\nILuKtYdzR/1GeeF9xgqFatK2OrRSk6pnNC7GvY8oNo0doDzxm+G7VHB+2AtPQVKKlEN2IPaNPfI3\ncwp7Q/lhDfFdLlCi+Ny4MqxTFeVR55/ylKdIkg4++GBJ8zPdeTpE/2NnrC2sqaxFXGepEhuKVBAE\nQRAEQSUTlTa6KlGAV5Py3lLPg3O4t8JdbG6Xe+6CUQrwOnjeXOtlpvYDwpuZdFXdSYMX5TFE7s3k\n6iaNG8YVu8CuUlW0r7zyygX/v2rVKkmN+uJxN23By6MfsS9Ul9L6Ubl6Ylw314vXiNfse0jujNSc\nTFXMzkHMSYqcyo1n67XqUrsB4OnT16X7ZDrPeMYzJDW2j9LB+Vx44YWS8msRtjmuXR5QXmrHq2+Y\nO6jGKBvYdNs1xNdo7IF2iOXzeEnWtpwS5XOdmEDGkVgir5XIeWzatEnS/H1WuX7mKL9ltb+tfeFr\nOfOMewv2b73lllskSTfffLOkpl+8lh6KlsdkZc+jwzUEQRAEQRA8onlEBNv0tSs9d/G5u9RST70t\nKY/8ka5EAcrc+vXrJTXe4zXXXCOp8ao9C3PSEDuEF4rXx2up4ujZbLzm9t9KwffWrVsnqbH/m266\nSVJ/dk68CNfp8Rgou7U7s3cBDxcVmmw6wMPn1eO38Hxzqhxj3VeMEH3JHnh42jfccIOkclU8d94O\ne/Uxx0pjeaBUifKK7LTT11MOwAZpDxu86qqrqo6X2jeWuYodsBawhtFurj99rnNcfgP57eK42Ddz\nO6U8plTtofcNzT39Yfy5LtZIlDL6m/dTv92sZSh3tFtqT6FIBUEQBEEQVDIxRepxj3vcXEbB1q1b\nOx2Lu/dURkNfNVCGqqUS9APxFex/xj5V7PfF374T+aTxmCPiD1BmsG8yZFJqAt4W9o+91mZ5opTh\n1RFPMVSmDsdFBeD6iUOapPKKp+9xa2T9cK47yyjcEZQbamtxjbSDwlWrUF199dWSmrEn9oaYkaHA\nkye7sa0iVQq/HbRDvaC+FSnmIrFDXa8nZ8PsLcdcxp5cpUZx8TXMFRfsDPvEnnhljfA6Sm53vs8l\ndjsUKF0oq8QsuULK9aP8eqzUPffcU9Qe/Uy71G8r/Y0IRSoIgiAIgqCSiSlSj3rUo5LP33ke6zUs\nvDIy4DmnntemnksHuxZe6RvvzjNLPKuybVYbChHZcV7nqS14f8wHvGCvrI/37c//ge/jxeGdts38\nQg055JBDJDXz6v777x9pp29Q2lAVUlXIxwkKANl79C0wBql4MRQm1i6ORywGY8m18vlczEoOYmA4\nHmOGDQ2l7rEWD632oi577M9Q9KWs5WLTuC5eATUYO+K6vZ/9qQlzmfHHLphjXm8JRZE1x9thHrB2\nkgUHKGC0g73nslsdj1Xiun3N43z4HPbua2oO1jbOFwWs9LchFKkgCIIgCIJKJqZI7czb5K7dM2Tw\nprjr5K6du9S+n48Hiwu8issvv1xS4827IuVxK23jULAzV3pyFfZT+B6AgHdZmq2G97V69WpJjVfX\nVn3A+6R9FDf3PodiWrIppcaTRvFwD5WYo5QC44oUni7fc3Wdta8vxYjjsUaSxefn0RXfC47+Kt0N\noi0ogJz/JDI6ayh9OuJ7P2InxCql7MPnDn/zyprF2lCq3FCHiZhAlCD/zfU6aChUbRUpr+ye2h+V\n9rzuVerzObgvYZ5jzzlCkQqCIAiCIKhkKutIcReKksDdLV4I2VlBsBDuveFFoVh1BXtEISWeBQWn\nrRfE54lnALzStt42x/OaPqXQHgqU7wDPdXO+ffUruHrhascksvdQWNgNHnKxQF4NnmsrjX2qrUrv\nkG1F9Xuupy9FirhBj3Mb6inBpCtq11I67kuXLpXUxBqhIPHbWLrGsIZgp6ndMXJgL65MeWwaf7eN\nUXJ8VwNi78jGg1TtPP7P2lxqL8w3lNRQpIIgCIIgCAZmKhUpSHmeQ2doBNOFZ3Hm8OfqZGK4cuLx\nAm3B2+H8auuMcZ5e2bt2T0a8ObzR2srmrkShQOGNjiurzqsXTxOlMUCopKh5qbHFBrCtrrE/jP2B\nBx44cr59K0VkyFKviv6Ypni3LjDHeR06JsvridXWvsPeWDtZ61gb2q6tfM/3kQVX3EoVKV+rUeKw\nH+4FfA3w7ELaZx7x9KpUkSIum++RLZkjFKkgCIIgCIJKplqRgrVr10pqdkxfrM/HgzpSikpODfB4\nDcCLaVtfyeG5fVfvlPPwPQDxsjyDJwdeFd5UV+WI9qkmjRfaNhOnlmmqA8eea+A25LvRA2OJh56y\nPfqWsetas8uVL2JM7rrrrk7HTcF1l8aWLBZYa1B4XK0tpbSuEp8jzpPfPMYPpcpjhLzOGXbEWgVc\nB58vtbMnPelJkhrl0Wvo+RpVmq2JnXpsFOdHP/h1sGZ6lihr4IoVKySV2zvtMQ9LfyNCkQqCIAiC\nIKhkUShSKFG1cPfel5KVUwiIIeHu2e/Kucvl7jfqX+2clPKEF5N6H2/MvUe8HV5rqzD3VeH7yCOP\nlCStW7dOUuN1up1t2bJl5G+HfqDGC8/5a/faA28PL5jj5hS52pou04hXuHa1LDWniXmiz1IqG0pF\nX2ofnjWeO+c1VOzSrhYbBYwbqiwKUNvfFN9b0eG3g5g22sPuLrvssp2263OV47HfKL89KEqsEaVr\nGe0yl/t+OuRrMe2wlpOJDF4/irpSbRUlYFyI+Yu99oIgCIIgCAZmUShSgIcOnp2UunvES+zr7jkX\nq+L7JDl4a6FElZHK1srFzqDsDBVTl4qHcfCiDj30UEnNTvWAF3TnnXdKarxdV5Jy+6+hBnzrW9+S\n1KgQxFscffTRI8ehX1NKEbVs8GqpRUTMle8FyPkTfwHEN6BckYEzhGpROia1kE0ErD2prCJAJeT8\nUnu3EVvU1151eObbtm2TVL93Xym76prGGkL2ZW2Gbi5Wh9+O733ve5Kk5cuXS2rsCqUkha+JW7du\nldScN2sNv4ltlc9/+7d/2+n7XSvZu91zPBQzf/+mm24a+T+KFGsUtSdL40ypncd8Zr4/73nP2+n3\nQpEKgiAIgiCoZGKKFBV2pcZTxVvj7pu7TO5uuYtOVWnFs3YPO7L8HlnglfCKkoJygneCt+EVxQ8/\n/HBJ0pIlSyQ1cQp4Obxu375dUuMtoYAtW7ZMknTYYYdJaqo+e/VnwAtK7WVHjBHxDBwHVSOlXnhV\nYEAhI04CxcqVKm8fxYlMHb6HcpWqf+XVjr1KM2oNShbH8axIr13DuO1Yb44+YiwYc49NYcy8Oj2e\nO8oQahy4rXAuXCPH4ZwYg1z19wMOOGDkeJxH2yrujBV9iHKSUor4PP3EnAH6h37wvc0YG88qoz2v\nh4TNEfPidYiYOynlxmu3gcfKoDz4PpZ8vxbiLWtrs2HrKCXgSuQ999wz8lqLt8Oca6tEcb2cZyou\nknnG+PM3a5VXJgfmttfU4/uctytdKHcet8nftRm/bbNaQ5EKgiAIgiCoZObhCaTSzMzMaHZ2dtzN\nBkEQBEEQtGZ2djYZTxqKVBAEQRAEQSUTi5EahyJFG7Vttd2HiHbOPPNMSU0VWM+U4Hkxz3HJaiJe\ngOfaxOL4fj/EpJx00kmSpE9/+tOSmswc4hqIFyEWiOvh/zzHJh7D4wuIJyDOguv77Gc/K6mJPaJ6\nLMe96KKLJDX1v17+8pdLkp785CdLkr7xjW9IauINiL+gf4g3Wb9+vSTpM5/5jKQmXiCXGZV6Du/Q\nTzxXf8tb3jJynUNDOxs2bJCU30uOWK1cvE2uPb8+4ksYB2K/vF3iFahJQ1wMdkt8x+rVqyVJp5xy\nyoLtDcXs7Kw+9rGPSWpsm3PzKvdts/qI7eH7p59++lyb44B2PvShD0lq5tp9990nqYlZ8dphnkFM\nDBJzPmVzXdfOFF5TjDn7tre9TZJ09tlnS2oqU7MGcR0e68W4EGPGWostegYpa8Ob3vQmSc3cI26S\nWC76pzb7zLPEhurPFG4vqTpRnCd2Qz+X/uYxnmeccYYk6YMf/KCkxh6H2pNwUv2ZIhSpIAiCIAiC\nSqa6jhTZRV67ZVx03eMLpQYPHy+Lu34UJM9AQanBq0IJIGPCvSyUIbwtFCtePfODCtp4KWQ+oDDg\nZVD3iIra4BkwZPJwfe6NkH3Fca688kpJjQKFl0wWmO8Nh/eaU2xKlSjg+j1zaNzkrgtqlagcqDOu\nRHm7qfbd60xlH44D1DXPzmIuM3dStpKq48T3ahWKvsBmU9lTnqEJKC2eGZ2DuZ36PBW4WdM4HxQi\nXrEdX4u8VhoKCWsDa2NK2UDd990KXGHCHnzcuS5e264hKYau11VKrmI554my2RaPGeJ4u1pl+xyh\nSAVBEARBEFQy1YpUX0pU21inHMSS4A25t8Td+A033CCpUVjwylB+iIXKVULPVbOl5gVKFq+33Xab\npPleCbFLOe+UWBivZcPx+D+Kjtf5AWKmUBg5P5Qo/7y3h9LF+FHPCLru5bbYvaddZS+75z73uZKk\nSy+9dOT/K1eulCTdfvvt2WMwt3hFicDWc5W3UzW5mOOTVqTagirt1fNT+O4RHrfoUNOM76HooC7T\nbqoekitoHIe1gvZZM4jXJG4UxYzPsaay5qPWszbVqrqpit2027be165KXxX5FxuhSAVBEARBEFQy\n1YpUX/SlRAFeUS4jAe+Kz6N8tFVA3BvyuAK8Iif1fLw0kwJv3pVBvA68fvqX/+NNepaZe214k/QP\n7bhqwPP7lOJCf9R6Q213CJ82ul5/33jMXynYB0qlx9eUgM0yx8hG4pyYK2RlleLf74vavck8jtBh\nbtEPpbs7eGxPak85r3Ttu0qgRLXtZxQq5iSxaa5cpdYSlDPfo42/a1VbzsOzAYeK32XnD+zulltu\nGaSdoB8W9y9IEARBEATBBHlEKFJ9U+qF4B3h1fHaVgFxb9XjFXIxVrWk9mPCu8M7x4sljsH3SkyB\nkoV3Tf+44sb7qfiWUoWPDB73zoeqdTIupkWJAnasbwtqAXE9Hh9TAnWFmDMoVBwLlRTVdOvWrUXH\n9f1A+2KomCtsonSPtBQpRYqxIn6T/Re7Qjyk19bzfVUdXxNRclibfC+3thAP6+0NNX6ldtkXZGgz\nX4iPHUpxY3zIIOe3YNOmTYO0NzShSAVBEARBEFQSitSApGKz8I5zGUQp3Atyb6kvUrVQ8MZQgmgf\nBQsFKVXTBsjkwVsmE8e9/r4UI+IoSuNFphX6Czviemq9Y69sTvxJ25o6eP3UFmoLGWCHHHKIpEZV\nQZG69tprs8fA5rBJroUK2tRkQ+Hw6v0piPXBhrqCIkYcWN+1t1h7chm/XcEWDz/8cEmNzfCKbaXU\nbcfHifEhTq5UnWRtYvxRKj0rMQd2gpLJcVBUrr766lbHm1boL+bc0LUbsU+UR16JBVts2bGhSAVB\nEARBEFQy1YrUtNfHyZ2fe694VygvKD5tY1xqs6L6wiuXo6zRD3gTuRorXD/xFSgRHiOVi48ozcpM\n7SG42EDFQEGijlhpdhvjQj/gpfN3rhpyCo/HaQs1gDx2rk3leY+VYczxsD3TtLTCd+r4paBooNzw\nN32G4pXLxps2sKUlS5ZIauYU/d42Y9orjWNLuTpNrInYCudB/Ch1rdqeD8dDZSVbD1v1ell9k6q0\n3zfjjsnyjHaukzjWvirMj4tQpIIgCIIgCCqZakVqWpUoyJ0fNWyA59B4n7UVtV2xGTfEPfhzdbw9\nvP/Sar9eh8q9fhS8VExZaS0evl+rmJSSU9C6wnWyd2HbGDm8PuJRiNdhHOintjF8jF+tonXTTTdJ\nml8Lij0rS/CxRanARlCkateW2qwvbAIFgzHARvqKvWqLz+W2sD8j30dRo7/b7jnH2sH3ec2dH+Ps\nSofXqGurQjMujDvKDbFaKHK021cGNfaBvdDetOzh1xWvUchv4lBrJkpi7dqUIxSpIAiCIAiCSqZa\nkZo0XZ9Pu/eJ99fVqyhVVLruMYiX5efrsTW8z3mRgVEa70G8CAqePx/PKVtt1YWhMkLGpRR2zcTy\nPRF9PzXsdqj6ZClQi4j9QilrM19cMcL2OUZbW2HOEiNTm/FJHBu2zvmg+g0VY5Oj6z6TZEkyd3mt\n3dPOx4v+R5VOxagx7j63UV1Zg9v2M0oQ14OCwnkwnthVX3OGcSF7kbU1t1fiYoFx8Bg4/mYc+4oN\nG0qJglCkgiAIgiAIKglFagHwqnheW3tXjCLkO9Bzt932eX3bfblQHPBu2iphKSWL6/G6THh9VKbG\neyPDJcVhhx0mSVq5cqWkxnv39ugvHw/6FWUl5X3gtbZVzEoZd+0T3z+M15zKgB24V+jZe7V0rf7N\nOOONt/HyuQbfA84zCkvnNH1JX3e9NhQc5hZ/l8YTTivEoGA7uRpyKRgn+oc1DFvwOlJ8ns+5Yuj2\nUAuxUdSRwi5QpvpWipgDXE9ttmhbavd+rIVxwW5qM9knTShSQRAEQRAElYQitQDcFddmEKDMoJDg\nveBl1Xq1eAlkyuToWicp5ZUQF0DcAgqQZ8jklChA2eJ4xMYAMTNe5dj39Mt5MXitQ1WCHxfU7KHf\n8CJvv/12SXlFirgcFDkyZlAVumbOUO35BS94QdX3sR/mS5uaMlw7toQigWdfaisOtc5qs/YA5YLz\n61ovp+86Q6jJbWGtaGs7XmmctZe1hMzUlMpMPF9KjcbWWTO67mrgKnbpWlxLbWxfLeNW1ffff39J\nzbjdeuutY22/L0KRCoIgCIIgqGRRKFJ4XXibvA51l167Bx7g9RDrs9iqFecghgzv1RWk++67T1Lj\nLTJevn8TXjkKF+Ps8QB4m+7tovyVKk27Sg0Wz9pbvny5pHJVwvsRL7Qvpa5rP6NGYEdt5jlqL7aG\nbXk1d6Av/HspW227V5uDysY11da3Ya7k4hjpS+Yo7aUyP/3/XhNtn332GTl/1FHiDlGhU3GMZNFh\na8Q+AdeD8sfnUjFCvrefwxpE+22q5JcwtIKDErqrwvgutkrmTihSQRAEQRAElUylIkXsB94f3o3H\nHKH84J3hdeB1TQrOk/NBwfG6S7nMFr5PZWe8NDJHJsW1114rqYkPwCtz5Yn/p7x4Pk/cCN4sfx9x\nxBGSmuwyXjkuXmrXuAcH++G4xCC1jf9AFeirJopnKKECYF+oCd5OVxUlB+eBd4lSmMIrl7uCxf/J\n1kNN8axCxn9HxcpjVugT5hIKFXWh6CtUY9rwavrYXteMRjJ5saVUTbYcOVtizSAGBVhzUoqUq39P\nf/rTJTXKlis8jBWKAtfBnoJcH2s0Kj0KnKugKFQch/7yNYC5kIoHRC0nvpK57HOYfSuf+MQnSmps\nGdvzbEpirbiugw46SNL8yvXY7ObNmxc8P0DhIxuxVH3lvBkH7L62ZqAzVPae1xrEbvg/CmfXWnk5\nuD7mR1flLxSpIAiCIAiCSmYensCGdjMzM5qdnR13s0EQBEEQBK2ZnZ1NKoahSAVBEARBEFSSjZF6\n9atfrX/913/VE5/4RN1www2SfhNP8JKXvETbtm3T8uXL9YUvfGHu2fA555yjf/zHf9Ruu+2mj3zk\nI8laMueff/7cc1Kej5LRQQwOz7UPPfRQSU1MBM/ZeS5NRWziGnje/MpXvlKS9KUvfWmkbZ7Dc7w7\n7rjjN53x/5+bUmmbWJ/bbrtNUvNcnmsllovzOPHEEyWpWG1rGxfh0A6vxMoQd8D5ES9AXALP0+l3\n4kZ4n7tuxoPjvOxlL5Mk/d3f/d3I/z2GjRgjsq7oH86DfqdaMNfP94gbOPXUUyVJZ599tqSm/+k3\nrzzP+PG5tv1KP2Iv2BPjTY0T6jURq/anf/qnkpr4je985zuSmrgS+hc7pV9e9KIXSZLe+973SpLW\nrVu34HUSR0DcBv+n/z0ugvfpd+I4nvGMZ0iSLrvsMknz+5/r5PqI81izZo2kJl6CWDbOhzgixpe/\n3/rWt0qSPv7xj0tq7NKrhfv8Zzy9DhnXlaqPNjs7Ozal2+fertreP/zDP0iaH7NCjND69eslNbbB\nWortEePE91lbmDvE073mNa8ZaRd4vzYOkjmMjTO3Tj/99AXbK8WzIh3WYmLSaOeLX/yipGbusuYu\nW7ZMUjMniROlHc6bNY+5wJrrMVovfvGLi66Pdvne9ddfv9PPO6zZ73jHO3baHufvig5rPefPnPds\nWtYMrvuv/uqvdtpe3+TaySpSr3rVq3ThhReO/G/Dhg06/vjjdeutt+r5z3++NmzYIOk3gXWf//zn\ntXnzZl144YV64xvf2HlTzCAIgiAIgmklq0gdc8wx8/YR+upXv6pLLrlEkvSKV7xCxx57rDZs2KCv\nfOUreulLX6o99thDy5cv16pVq7Rx40Y985nPnHfcBx98cK6iMni2FKSqnXLXyl0sGRW+wzd3+aX7\nFeUyBny/J+7m29J3XSPPAiTjJNV/eP6pjIVUbY9cvaFUxkduzzT6A8UG8FZQLryGjiswPj4pUFy8\nZhD95sqlZ4SR6fWpT31qp+2QYePHB/rpuuuuKzrvUlAD8OpQpBh35hn96NWp+RyZLd///vclNQot\nXjcKFPbnXjr9m6pfRf/maimVVOpPeb5Oau+2YBTWVIexQMHwtRV1caHMSqmZO7kae10zcnM1AVmr\nXF3Prc05WyRb0Ndkzgdbp3/5LfTfrlQ7zB3vH1/LcjAuT3nKUyTNV6SoUZfaS7C0/llqPrIWorz5\nGuFCzLTWAqyKkXrggQfmDGXfffed+4G87777RrYYWLp06eBpjEEQBEEQBJOicx2pmZmZne4/1WZv\nqrbVfTl2rh4Tz5Hda2pbKwPlwyusd90RvvZ8ppXa80/Vacp5pV4jKAdKTQpihFAruJ7avQvxMvG+\n77nnnqrjlIJih9179ehLL710we+594ciSVyH4+Oc6h/Op+vej15leyFQQtyzd1CReUW17htUT1RJ\n1iBUwGmvXI1SkFJ2UHK4LmyIv1NPAbBN4mT7hrWU+lCucoOrothqV1L1nFBUsE9smf7oSttadYwr\nayxrI+PK69BMq9JUStUdwL777jsnSW7fvn3OWJcsWTKyMNxzzz1zBbaCIAiCIAgWGxdffPFO369S\npE488URdcMEFOu2003TBBRfohS984dz/X/ayl+ntb3+77r33Xm3ZsmWuMm4JJR7njqS8T1eIuNtH\nESCTw2NDcnDXzF16rrpuW2qVHM6H76eOUxo/4ky6UnwO+j/ldTqMey6eoG28gUN/exxECtQLlBu3\nKxRRMl3wHokzoB3PWvRK46gwOdUmBfbA4/3cPlmlsYk5StaF0msi3s33iewbFA5UQdasm2++edB2\ngUxQQizajrlXeAdsirUVW0PZyKmPKCddq/2nYI0rVTpYM3NqdSk5dZY5xG8Vyl/tnKyF6/7Wt74l\naf5vYi6GEOWvFP+Nx1763p3CYc2j/7kXKK0Ef9xxx83FhS9Ethde+tKX6pJLLtEPf/hDHXjggTrz\nzDP17ne/WyeffLI++clPzpU/kKS1a9fq5JNP1tq1a7X77rvr/PPPb/VoLwiCIAiCYDGRvZH67Gc/\nu+D/L7roogX/f/rpp8/V6MjhNULa7mmWunt3BQXvhLt/vCDujtl3qzReAQ+b5/tdY6Tw7HkMirfq\n2V0puF5uWlPKR20R+677i00rqTpeZKn5nm9tob/x2lEjUl4yj8hRwmgfr4n5gb0yfzg/FCmvNeNe\nNrFkPn9Kd2Dn+DmFDfra/6tPxuUJE795yy23SGo8/XHtdo/NlMaOOanP02/YWNt+ZA3OKR4oCNh6\nKvbKlQ5srm3sTV/xqalYK86T8eA3py9ljuMCcz81B1lj/DeXfvPjOW2FEs6n9ulIV7w2XV9EZfMg\nCIIgCIJKOmftdYG7f+7S8TpKlRgndfftFa9p17PvvApuykvwjJPa8wUUhVrlY6i7bOgaKzSt+HiD\nKzlkk9aOM95eqj0/H+yRecH58D5eIvWcqPSfws+7a8wS51/q7TNf2mbl7kqgvLgH7llvfUNdoNrY\nn9x55epA5fA4QNR9bB+1njXcbYjYF6/5RmYsc4ZXr4vllbP7Uk85L8/GYy7SDsqkV/nnfIjhS9Xu\nc2XHf7Ny10M/8zSEtZ7flNya11ZJ65q5W0vpU65aQpEKgiAIgiCoZKKKVF8ZEp4J4XfhxPigeNEu\nd/mpoqHc7fPqx8HbaJu5sNjo6i3nntN3pfZ5e67qMeeL11arSNEOSlIqDoP/0w79TvucD8ejYn0q\na5R+8fPuq+5ZKW3HnXlV099da7G5QtEXKdtkzRpKkaLdaa1N5/GXvosB44n6iS1h295vKEEeZ4iy\n4xmsjHdun862axj97nWYmLt+fB8fvpdb0zgvvt/26QHf47eQuMehsiknRdenRjlCkQqCIAiCIKhk\nolIKXgAZH3gjbSHLClxp4O7evZFU1hbgDfDqe7v5XmbBKNQ7YnyH8grwatseP+Vd+vP0rt4ZO26j\npQAAIABJREFUShRecaoSP/EDxIG4902dLM4n562m6rL1VUV5KLqoM22VF5QPMiHp46EUImArLcYC\nJWGxV3hui9tiqp6T74mH7aM4eSa2j59niwHjn1Oc2qqqtJ+KCUpdn1daz9kh55+rIZiCtYbv7WpK\n1LiIO4AgCIIgCIJKJqpIec2P2rgEFCHu8lGMwLMC+TztcleeyyjwGCm8iHHVhJkUtXWkhs4mhFql\ny9UAwC7wzrrWGqL/nvCEJ0iar5jiVXrtG7zE2rgdj7GCvhRUjrPXXntJauaBe7Vt22Me+/UOEWtH\njBLnPi4l6uCDD5bUxGcuViWqa0yZ11vieB675Hv+kQVJv6FQkUnNODK+/Na4YuO1BbuOA+ebitdl\nLvqcqI1h6ysLbhprvS0mQpEKgiAIgiCoZKKKFF4G3gN36bVZWHgD/tyddjg+XhCfy93V++d9r71J\n1cYYF7UKRpfsqx0hxqhv5S9VlZf2fEf72n2w6D+8Ts/kwRv2eAVXWFP9mJovrvhCbn+5XOwgHH74\n4SPHQ9mjdhHQn7ksSWC+ubI1xHZTXesgtYUxJEuqdH/IaYX6TKy9bfvTbRbb4//8zVqCTbst8H/W\nYmyIuYbtuXLmGbEcl++3rT/E04qDDjpIUpNZC8ztobZOq3164FmSQ2e57WqEIhUEQRAEQVDJRBUp\nVzrwgL02Rg5qX3gFaUDJYF8jj4vIed54xtyl40Xi5fR9946XlKppMm5qY4S8SnFtddnabM4cKYWL\n8URhwR5rq1BTZZk9FFPX49l8eMk5xTM3X2j3hBNOkCStW7dOUlMZHfCmiXnauHHjgscjG3PFihUj\nx09V5m8b/5GaTyXHaTtGVM5mDRl67y/fdX6xe/7YHvtEtlWkfEy9cjnj4qoma6M/hfBxzK1d/nQB\nPG4xtVuGnz/tsXb4+DL3U2tA15iz2linVCZw33XVap82oUx6hv60EIpUEARBEARBJRNVpNwDr401\n8rt+j2VBieL43GW70sPdMoqWe0d4L3greD8e89IW7rZRBKjOy3XkFCm+l/K+8H5WrVolqfEaUWSG\nivEqjYlx/Dn/uLL/AAWpL7wfUsqce2set+DjhB0To+QxUby6Qrtp0yZJ0vbt2xc8j1xMHOdxySWX\nSKqPHSslFTO1EFxrTpFizpBJ6Woac5o+JBsstedZDhQVroXzdLV72sipydjesmXLRv6P2pkbs1RN\nNWBvPN8jr+1xUqC0sHay1vp+lyhP7OnHOPo+l3yOOEFfuz3jO3U+taQUKZRD3/eS81m/fr2kpp9Z\nG44++mhJzVMY1o4U2HVKEWyrRHE81sb99tuv1ffHRShSQRAEQRAElezam8T9f9xb8cwPr2Ce2qU+\n5cGnvN+2z4O9rlVpvEEuDoDrpR/wEmuVKLx1vFG8KF7xrvmb9vGKiEvBO8KLQ+FzBcX33/KsNtrx\nrE/P+HHlkP5wb3vovQFTuJ1wHh6vwfly3SiLqfN1Re+KK67Y6XnkvOK2SiMxiag5qXgMV4Sxa7z3\nEkXKz53YHWwDm0eJAt83E9vxvdq87lEpHo9HHaRpJxfXuG3btpHXvqD/qTjPXGBtREHk/yiGvg+q\nr31+PakahqwVxANim/yfNcgzijnv1G8ICpbbH3A8r0PFXGEOpZ5S5Cqz+3kxx773ve9Jmj/HiH8s\ntdfly5dLavqJWDHGh+P4bwLjhR3x/SVLlix4HG+PNY74TuYpaxW/pak4S8aD62e+l+6jG4pUEARB\nEARBJTMPD52mslCjMzOanZ0dd7NBEARBEAStmZ2dTT5dCkUqCIIgCIKgkonFSA2pSB1wwAGSpNe9\n7nWDt7UjtEPWF8/ViRki1ufLX/6ypOZ591FHHSWpeR6/ZcsWSU2GyOrVqyU1GQvEaq1cuVKS9LWv\nfU1S85yX9nhuzH5ePC/m+Tv95PtVkTXnlbVPPPHEkescGto555xzJM2PT6nNCuS6yI6kv0477bSR\ndqlZQj8SR8H3UzE7XhncaxsRx/HOd75TkvThD3945PuMD+NP3ARxDsQRcDyqKHuFdOJCuM5XvepV\nkqTzzjtP0vxqz9gncSnY1+233y6psWv2iaPfsEeP26Afx2kvH/rQhySlY1ToC9/lwCtp830yEt32\n3vOe98y1OQ5o5+///u8lNdlVZMsRA8KYEivisTVArAmxIH/8x38sSfr4xz8+0t5nPvMZSU1sCzZG\nrImPuccz0n5qrzufC+Puz0984hOS8hXmWRM985s13rM7mRvM5b/4i7+QJH3gAx+Q1PQT/c+awlzj\n+KzRzFHWIsaBtQW75vWUU06RJL3vfe+T1PQ/awlxql55/aUvfenIdX7nO9+R1MSAcZ6cPzFEb37z\nmyUNP3709xlnnCFJOv/88yU18bWeyexxum1rAHp7KUKRCoIgCIIgqGSXzNrL1Rzpi1QV5a9//euS\n5tcowVtw7/Cqq65a8PhkeODF3XLLLZKkO++8U5L0hje8QVKjUHDdtIdX4V4j3jbHg1QVW8+iGzec\nf18V3vEec7VnyPjy5+J4PynoP7xRFB7GBy8T8HqwD5QoMllonwwWvErsADvES0UN4NUzXVAByIQB\nvLY77rhDUqPOPPWpT5XUKHSoNPRfblxQXG+44QZJjT15/3P9a9asGTk/1BDfG5P+3bp169wxch6n\n17wqrbpfkjE4Dnxt8zpGkMv4ZQ3yV4d6UKWhtPRj6S4Gtbsm9EXpXoee4ZzKbPV+TNWRYs6ztru6\nzvFzNe0YFxQwbw8FCXxvQYfzYVw8W8+VxnHPC7dDP7+u+6Lm2ksRilQQBEEQBEElu6QiVVsdlue9\neAu5u+2U14XX6N9vW5cI74G7brwNjzPwCth49sRJoFzk+oUYLc6Tdmpr54wbYnqI2elasZ2K+Kgc\nXi8rBeOe2k/NK4cTf4BSSBwEn8M7TLWb21/Oxw9FjHY5P9/PjNg6+hV78urIHo/k14dimlMWiUNB\nQfNK7SiBnO9Ce/u19ZBr93+sZenSpZIatc1VwUmR2neS8/WYsV2FvvYBLf2+11JLPQUoraTPWs9a\n5+PjayBrS2q3CBTOlFo/bRX4+c0b9zx2QpEKgiAIgiCoZJdUpPy5cClkveEB56q5khngXgCxNXjM\nqQyXHMSSeFaUV8XlfF2x4rVUoeP7tIsyUfscvO+dw3PQT56xURtbRb/i1bUtuUb7qA54l5wfoPSg\nKNHfeLu5/svFBaWun+PjZRITRfuoFN/85jdHvvesZz1L0vw9EcHPt9SL9dguYsWI9/G4H7zRVLXi\nHanddb5vuu7j2FVByR3XoW8n7fEPBWpu2+vL7UeZwtfS0lirFAupsjXwm4l6XDKnpoFpsctQpIIg\nCIIgCCrZJRUpv8sv9eKIwSj1WlN37Xj2eMGQa5+sLN9TzRUFPy6KBB468QwpJcn3bvNMB88W4++2\noMwddthhkhrFiP2b+gbliP4gq6utIkV8AvW7UBE8likHMWdku5GxQ1wDMA6MC+df6hX6Dul+fm53\nV1999YLHSWUj0n/Yl9fTylHqveOFX3fddZLm762Iksc4MK9L+mnSSlRfYDuMFWpm6b6cKVizHNTE\noWJjapUd8JptbandT9PrE00Lvp8oaw/KFXOGV9Ry+iGV8c7cZ076fp+PdEKRCoIgCIIgqGSXVKRq\nvc+230tV1ub/OW8Hb4rK1HjcfD+l3LjShIJR6qHjtaBgeOYQXgbvp2JhcuDd0K+pzKC+ob3ajCi8\nW6/e29Y+UAlQDVJxOnh5XjGddnMxUChv4IpUX1mX2BWZPaX1xbxaeIpUFiBQjRlvGKV02jKJhsRj\nmdpWak6Rqufk6nffpGKzSulaW27cdaxYe5k7PAXoGpOE+u1rAePHmo69lK5lHI/fMs6fDN9c/zG+\n/Ia0/Q1g7aB/ahXEoQlFKgiCIAiCoJJdUpFyxl19FQ8ZLwBFgGw7FCc8bJ5jEy+QU1Lcm+AuvdTb\nyCkD9BfeXtf+6yuzZFwwfniLjFdbRYpxwQvDe3MvHCWK42MHeK05L47vpeIVusahOCht7POWy87s\nKz6JGCraq83OnSTE3/GKbeWq5aeoVeN8zFI2Vqpmog4yJqVxhKiuk2JS2Zz0e5vM051BfKjHuqUy\nz3NK3vLly0fOj90DsDf2fc0pRF4pvS2MS2o3hmkhFKkgCIIgCIJKFoUihZdT+nzUPfBxexupOkx4\nAXgPKDXc5XPevE+GDteN9+RenO/b1BXOm/MtrWni8Dyd4yzWzCn6FyWprXeF/aa8e9732DTsAW8M\nxdHnQW2dslq84niuunBf4+7HT2WaTSOod3j6jDF1e0rp65pdPUypzqXxkZ5pXEoqznRcoKSNK1aK\nucCc57eidI1Mqb+pfmwb44Z9HnnkkZKaCug77mcpNap0brxLd4NIwfGnZa/LFKFIBUEQBEEQVLIo\nFKm2kfrTWtuCmBuP8cBr4H3uvlEivBL20NeHwuD1g9rSV0bRuGF8eEVJqs1g4jioCV6Xi/FEWfK9\nHl2Z8jgBvLZUvEztOKS8X+yW+IucV+oKcVuFOcWk1Yw2oDLTV/RB25iPobLoUnO87a4Iiw3m1LgU\nKeY+7aJA+W4CgCLI+DCX/HP8RriCSMYwNfzA5yTfX7FihSTpwAMPlNT8JtG+K6LjqqM17U8zQpEK\ngiAIgiCoZFEoUpOiNqMj5d1wHM+yI2uP/xM7xd2+xyi5V5qLjSq9Dj7Hc3u8ELILHymgFngdqVol\nEG+d8XRvkAwnFBbGwWOl8Ab5nMdUpdSKWm8bbxbvF68Zr7pUHXG1o69aMNOeybMjxJpgW4xl24rk\nQ+2B5vWHACWtlJzC47W/sLFJ0bWOVQ7PLGX3CuYEc9mr9gNrMWtESl0+4IADJM23D3aXcEWKNR0F\ni34gG8/jMlHlUao4/xtvvHHB83mkEYpUEARBEARBJaFILQDZclRvbZtZUxovwOeoFF2KK0vUpMFL\n8FgrrgevJuXVeqwW5zeuiuTTAv2G14xX7nWeShUqrxHk3qF7mR43QfYj3vzee+89cn54l6gbqdox\n4DF6qYwYrg/1hM9RD412safbbrtt5P/A+fo+Xrl+zO2jNu1xEwuR2suslL4UKRQGxjSnzLC2oCBh\ns55xzFrEnPGxdTVy0pmX2DL7afaNXy82e++990pq1NTUuHr8ZIrULhip3QcYP5QwFCjsgt+Au+++\nW1Iznthv7W4JbdfOxUIoUkEQBEEQBJWEIrUA3G27cuDwvBvvAtp6yl33CsO7SMUzlNaBwktYrBk4\npeB9ofSkauUM9fzfK72nvE3iTHj90Y9+tODnbr311lbtk5njXqZ7iagU9JPvmUh8h3vzrrqkvP2c\nV5qzw9Lq2YsJbBE13Ofu4YcfLqnpU2KbGCPfHYHjMVZLly6V1KjPqIjYhEP8JsoVCofvC4qCwVrm\nMTaeecz1HXzwwQu2Oy7aPm1oi8dIbdq0qdX3U3O+lNT1YVduXyhozE3/bWOca2mrRGGnqPGx114Q\nBEEQBMEuxsQUqf/H3rkGaVZV5/+ZRCpJaSr5q0GUAWYYGIbhjggogxQBvEQhRFMUpCRGJV4RBRTl\norSAMCiIBoXSaOGtolKViLdQISBgQLkqIAx3BpCLVvxoVapMqvh/sH5zpp+e1Xuffc7bb4+s35eu\n7n7fc/bZe+19znrOWmu/8IUvnKP88DtP8WQ58RTLU6nHrHgsBe+94YADDpDUve/FK8Nrw4vC88eL\n4vx4V3vttdes85IRce+9984630JXYe2bWTMpfM849z6Is2Ac8ZYZd8ahpNC1ZlPiFdNOlKDWGDDs\ngp3XUQOeeuopSXGGzaQzhUqgQpSI4nn6ZgGilgB2QOwev2PHntHkyhQqDLFYQxXdIeywww6S5l4L\nbUctjOrtYDOsJStXrpTU2YireV5hGlWOMaFPOB5zhb5FgXAV9Jprrtlk+1A0aKdX+Ueh8jHmej2+\nju+zNkxaESrh/cA4sjbRfmystCciawz3FsZhsRFlV95xxx3zfo9+YXxrawxiP3zed+OIlCa/l9RW\n2l9oUpFKkiRJkiRpZMkzU0h9WbJkiWZmZhb6tEmSJEmSJL2ZmZkJ34KkIpUkSZIkSdLI1GKkFkKR\n4hylcxFXsGrVKknS9ddfP+h8F198saQu9sbfw1M9NopBIe7A9xLj/TJxGUcfffSs806a2v4c+3zn\nn3++pP6xYMRteKxd6Xxr166d9XmPbyHjiDiIvqIucRennnqqJOnCCy+U1MVuUXeJuAIyZYhncPug\nX4jPoNaPV6v+m7/5m1nXORbEuBHnQLvPOOOMiZwvYmZmZsFt8yc/+Ykk6a677pLUzVFiiMjQZM4z\ndxm7devWSepszXcTICbk9a9//azzLlu2TFJXg25oXR4ykIlHO/744yVJX/3qVyXNrXBNrFWpqrxX\nxCZO1eNIo7XFY2ta93YjJoc14UMf+pAk6eyzz5YUx+gQb8t5PVOUOccc9Oti/DjfBRdcIGludiPH\nZe4zDowr9sR1kE2JfXF++uvkk0+WVJ57HrfaukvAWPeGUvwr8ajvec97JHX3BvqN7FPmldfUY+1m\nXB955BFJ3ZrMcZin9C/32ohUpJIkSZIkSRrJOlLqnjqjWip4f48++mjV8Uq1P/CKosyJqK4Qf/e6\nQaWneLwUvCevBYJysVjr8uy8886SuqrJKH1kx/meeGQ20V9423hdeB983pXBKNsOJYj20I9UFcbL\nwQuPFEevqeP97uNDO7meKGuO7/n3GX8UqbFxL3yaVYsjNXdSXHfddZLKNa+wSc+6i3BPGkUKGGNs\nuTVzF1ulv3zt8b3xUFhKNfYA24iUqKg9fA5b6ls/yNvN2uj9xFoczanSWl5ScHxNRnXmunzuc53+\nd28f18Haz3XW7n8JjPe2224raW7tPLIO2TVhUns9Qknd9/72vQLp36gfuHd4pjbzl7pZZPbXzqtU\npJIkSZIkSRpZFIoUT5HTrlqKgoBSRE0Rr07bCt4j1Xx5X4u3wfvaSBEB3uP6cf14eJn+nt0p1Ttq\njUsYC7wh2sN14bXxu+//hPeEN0z/oMDx/9r9z7z/8OIYD4572223zXuc2torgKKG94WdMK4oTtE4\nuUK00KpNLYwf44RagJdYE4u20DW6iP2pVZrGYmiFaSCejrnkNbk4Dx68VzYv7euIily7u4IrVigL\npXsEc4Q6XLxlIFYGIvtgDpeupy+ujHB90T6SteMaKSWtSfjMtV122UVSp/zQX6X6WUAsIPbkdcyG\n3uOxJ6A/Wfuw36F7OLJG19aETEUqSZIkSZKkkUWhSI1dyqqvxw8eA9W3gnMJvA+8Jbwfnt5rY0v8\nKRlFqaRkRQoE3qXHA0xbiQKqO//3f/+3pM5L8jgCz/DBrvg73hDH4e+1oIwRE4UXh5KCd8/xI/ra\nlVfyRknj+n3vvhLEPSw2RQq7du+1z/rAGJWI4hP7Ql/6XB5LXe9ro/Ca17xGUpdNSDyh47bj6jvf\nY23i99q1oTULDFCKojm10047SZIOP/xwSdINN9wgaa4SBW5Lvp/l2LhyxHhi66ivKH60D/W5NkbH\nM4n7sn79eknd+HMv6Xs8xoufzEfufWO/dfJ7AO13ZZV7BrFzJaWNeVG7a0IqUkmSJEmSJI0sCkUK\nz9730Ov7fZ6ivX7OYoOYD7yQvllOrXvD+fvjHXfccdbfh+40PinwHkre43bbbSep80rIbsT7G2sP\nxCiOgVisEh7LFeF7S6J6UDuoVTFE0fKaR9Pcs25jJq0SSN010xelrLsIxmKsmCUnWhuiGBv4u7/7\nO0ldTbuDDjpIUqxMoYB4RqmfH0WhNnurVYFgDqMcRooUsVf8v7SP5ELvg+q4Chyt5X2zMFvvCa5A\nsaa0ZuexRhFLx1rn2ZitRG9dsFPWDFemua5ahdf3Ay2RilSSJEmSJEkjU1OklixZMifbjKdYnsZ5\niix5i3gtPPUu1h2igaddno77KlJD6/TQn0O9j4UCr6CkmLCTPJkjk7ouquuihNIuVA6PCwD+j72W\n4LjEKbjX2IrXDFrs409GFkruGNRmkZUgfm+hKa2Jl112maSu3lMpdgylwJU1r1FX8uhZy1njsF1X\nIlBbI4UCJatUu48Yl3/7t3+TNN0aZpsj9PNY6q/HCKKUjRVvG8U/16rYtfHErIm1GfupSCVJkiRJ\nkjQyNUXqj//4j+dUbMY7ccWkBE+/KFx9q7suNFzftJQAvEGUu9b36wtF7XjiZdRWoG8FJQplCjvl\n/L5PFl4NilTfWkd4WVHcRClexlkssVC1oEixPtTWtHk2c+211876WSJSJ4mZYo3AdlH/PauLz/nb\nBlRQ5ggK11hrNUoIc61v1lsyDszRsdRzp5SRP1btQ2oLesxgRCpSSZIkSZIkjUxNkdrYU8Djbn2/\n7e/18YIWK0Nrq9RmEkTgrdHvfesQ9YX6R2QF9lXi8DJbY8ocvBrspG+2Ip/Hm8b74ScKqWfERFml\nJS/K9wZk7vB3KqxT38rx9/yl+IDFAnbjtV/GgDg6xnLa2VyLFcbAY11QZT1jGlBHGTsHG26tkxXB\n3FssShS15Z4tYB+TilWr3atxrDjS2goCqUglSZIkSZI0sijqSI399LrYY36GMjQrEa8NJQ+lg2q0\ntXvP1TLUO/AaIEPthdgmrztVCzFGZH6RbYr3QvuimCWviUMWX9RPeFnsSI5KgBrA99mDz48z7T0s\n+4J9s6+W76c1BoxRVNX/2QZ97n3saw22iM2VlB/fDzL6/+8rY2WHbi5wD+Gegj3V7jhQomRvYynL\nXsesRCpSSZIkSZIkjSwKRQr6Zh89WxlaRdkVjRe84AWSupifsRWpF7/4xZLm7itV663RTrwd2oky\n1FeB5Lxcd6tigwKF18Lv2G/0ft3jRkqVzmkv/YgC5vvFLZa9EYdCvA3jy+9jZusxh1AnFyrLC093\noVXC0t6C2GRJzewb37jQbweYG/Tz2Ofve48aex/ZhQbFtjauF+WRNQ17IVasrzLl/e32R7wpawX9\nzdreqjSz60dtXGYqUkmSJEmSJI1MTZF6znOes+Hpte8+OI4/tU6qjhTH5f0v72NLMUtje6FDvWZi\nT3iKp9/GrBy9MShK9Jvv2F2Cz/uO6a3jjMJx9913N30fL4X4joceeqjqe3j9xGaB77/lcN2cZ8WK\nFZI6u+vbn16/bSh4rdjR0Ewlz7yZRN0ojk1fsIb0nVuuspYorQHYFmvGWPuGlio+R567q8asGdgk\n/Uc7+9ri2DC37rnnnlGPi3KJms8uCozn2LXZUBBdadlpp50kSffff/+o58P+uR7sevny5ZKk2267\nreo4ZJR7JjNrRF9FypU/V0w9vpR7G2ttqyJFBjT3mDe84Q3zfj4VqSRJkiRJkkampkj9yZ/8yZyd\nxFvrBW255ZaSuve4vB8F6uywLxZPzSgTeFeliH+edvFa+TznB/f4F1vWFDuk423S75OqpYPSxbjg\n/dTiXsXQOlwlUHpQ0vBKXBHDW3JvFPsiXoPjcN1eBwzvCXv0jBdYtmyZJGmXXXaZdVxXB0pK0557\n7imp86q9/bX7bmE3BxxwwKy/j5WlOXas3qbAllrjDrHpsWJxvO9aVUPi6VDzWtegSC2tXTP7MrQy\ntWfEOn0zdIF7Ee1iLpeUqNbaZ9jVvvvuK2ny1fxf85rXSJJWrlwpSbrxxhubzkv/u5I5qTWb87AW\n164ZL3nJSyR14/nrX/96k5/LOlJJkiRJkiQTZmqK1J/+6Z/OqViMh413SF2ciG222UaStPvuu0vq\nlANXPPDq8Co8G6gEXkz0fhfPHrweEwoEShbtob3uddJ+YmF+8YtfSOriD1ort/OenxgW+p/23Xnn\nnU3HLUHldPpxofd6K2WeuL3ssccekuYqPNgL4xh5v147h9/9+8B56B/3gnxvRNqDPfB5vCzaFcWk\nEc+Cwor3y/zj+6gRnJd56fuloTj6vDrwwANnXRfVxImBQrFjXOgXjzvxebrDDjvM6oeNFT7P1vH4\nSa+ZxneZi9Ec5zj0EUwqrhBc8aGdJU8ZRYrPP/XUU03nn1TWHf3pe5mVlCiuy/dnxTZLCopnzNb2\nJ2BHtD9SMqC0N1zE448/LqmbC8yBSdU7u+qqqyR1byvWr18vqV6dBvoFZYvvM04+t1kbPN6VWDDW\nBj7vcdT0L+PH8TxukzWY8fdnD5Qt2se6UJ2tWPWpJEmSJEmSZA5LnplCoYslS5ZoZmZmoU+bJEmS\nJEnSm5mZmVC5TEUqSZIkSZKkkanFSNUoUvvss4+kLkaIWJu+5yida9WqVZK62hF94X3sSSedVHU+\n3r8OrVDu10fsE8ctZT0SJ1J6zx+db2y8ujTnOffccyV1NUM8I4SYGa/n5O/diW/xGCVidT7wgQ/M\nOq/HSHm8De/ryawhVu7pp5+W1L1/x4shRoiYpfe///2SpHPOOWfWeWhPqb4UNW2iecH/aS/n+8Qn\nPiGpiwegn7Afj6ch9or4lNtvv32T52P8iOF75zvfKUn69Kc/PetzjKNXdH/00Uc3eVyH7EXfx+vD\nH/7whmsjHpCx4LO0jbkR1T2K6iLRV2eeeaakzla4djJ4OT5rlx+XNYCxIRYJW+H/9P2RRx4pSfr4\nxz8+6zzYTN+1ETw+lZgQruuzn/2spPo1IqJUEdzXligDlbi+oXvY9V3Ltt56a0ld+/v2R+l8pcr6\nzJUoS5L/s6a/4x3vkCRdfPHFkuLYKtYuxj26BxKzROa7x05xXZdeeqmkbr75OHlMk2eTsjYwf6L+\n4Hyf+tSnJHVrZW2mf9+dDEp2kopUkiRJkiRJI1Pda4/IeTxwz7hAWWn1tmrZf//9JXVP0Z4tSN2e\nqFpu3wrbeA0HH3ywJOnb3/72rP+TjUh/uBfndbIAhaS2Dlerl1nyKrw9pXagdHCd7iXghUVeKIoE\nXrVnlQGKi2daufIT7Uvm3iDXddNNN22yXZ6BhDqBWgJeobykREGpZk40b3xPwJJ3hkLB0qclAAAg\nAElEQVRV2l+M73sWpCuv9CP9UKtEAZ/n+xsrW2T30Dc+BvRxySYjpSqKkeA8nsEbHZc5xBzxvqXP\nvC89S612d/oIr0DtWVFj7d9YW2+K87sSxRijoPzwhz/sddxWfPcNMlGvueYaSePVRyopI6XrxC7d\nPkvtQ2EqZXWSxVeyB9oZZcSX6qKx5kb1t3wXEeYHn4/uaa7ojb2n5lQfpFj0onT40tYbdKIPbt/4\n+bvuuktS/Epj++23lxQ/SNWWUQCu67jjjpMkLV26VJL01a9+VdLc1wFOZGRD5fdaeD1RktdrZVY+\nFx2vNJ612w7Uvg7gwd4fpEitrb2uSI7347ZO6tp2OP7gz/n977w+4jpq7avkWJRSwjnvy1/+cknS\nrbfeKmnu/PRNozcGJ41r81dmfbeqKNG34KVvz+RwA49sAxvydH4gLIJXm7VlGtym+q5tEVGR2dL5\ngTnEjT96sPBXpox763XQHh7Mv/vd70pqL7Q5KXhA8YKUtD96VV1bFqP2nsqD29Dtp3iFyzxlvH1t\nYZ6UnEq+z+d5duDBbOh6kK/2kiRJkiRJGpmaIrXllltu8PwJhOtb/M29OV7tcNxaKH7mXgseeEke\nLb3yiPj6178uqXs6rr3+sWXJvoxdMcODsh28EBShyNtBQYy8rNpCoJF3gpeEXZQUIdQCxhVvyBWU\nsTYPrqV0Pl6BosQyPrQ/UkxRHQhyj3Av39lxxx0lSa961askdeN/xRVXzPoc9rCp+RepZyhHYwUt\nt1JaM2oVFO9DxuCwww6T1BVG/PKXvyxJuv766/s0c/CrPX/dPhTuFdHraG/vWOcF7Geh1mDeVqDE\n9WWs7clq1/yx1jJeXbP2oCh5O7D/vm8/GL+xxjEVqSRJkiRJkkampkj93//935yg5aHgffT1Qkht\n5WkVRQLv5r/+67/m/b6/t8Wj53hRmQPeZ/d9Pxs9fdcGd6OA8ZTfGmvjZRw4rm/yG3lFpKJTPoL3\n+B7/gH14IKyDEoWSsttuu0mSvvOd78z7PY/ZieIvuC6C46OAZnCF0Tc+7UsU5xCBYuObJEfzDfWA\nmCgPjC4penijpesjmN7LHwAbpv785z+ft73e7hpYG1A0KEmBbbVupTIp+q5lXBdjtXz5ckndRtWR\nIjW2cgRjH492YmOuKHDdHqRfW1Kklb7p9A5zges79NBDJUlnnXWWJOmnP/2pJOmiiy6SJN1xxx2b\nPA5Kq1O7ZkSMrZqzprz2ta+V1CXssKai2Lpy6/3beu8am1SkkiRJkiRJGpmaIvU///M/G1KFfSPI\nhd7cFuWAGBveS5PNV3rqdY85ep/rEMdRKlbnRFlPtU/nrTFd4BkPZBCRbYUXTGyZe098jzIPHtvm\n3iTUKjmUryhteg213hbjSXtdIcJu8Zp4v+/t7rsRKPT1KvvGc6CMEttFu2lvbbxFqdAsSiSxYrSP\neCXsozY2kTigGvgOcVwUGiTua7EpUn3BRn7wgx9I6sbQs7kc1NaS6jtpSsoRikU0hzwdHpsdK1Yo\ngnb7Zsq18Hl+UhKH60BpisaHucQaBH03ZY6Oix2NVW6CfqKswuZOKlJJkiRJkiSNTFWRAjx8vAkU\nl2233VZS99Q6VEmJKBUEJOYGb7X03r+vctD3uiZdhK72/CgyHqOCF4PCB17Gn/EeWutloSBjCG+q\nNrbNFceFztJzZbdU54lxaFWEa71/Pw9xIvQXf48KpKJ8tagojB0Zu5u7EuVQq47ri+pNAUrGtOsj\nlVRn5lBUo414QK6HtXjScw6lbOz++9a3vlX1OfrB5wjjXlLB+Rz3XIpBsx0T9jT2vefuu+8e9XjT\nIhWpJEmSJEmSRqZa2dzxLCee7smqw7taaI+ep3BiO6atnEw7jsGz/fD+iPki1gXvhiy3e++9V1I3\nfl4xm+/13XJnoegbc8R1LpbMEih5lUPbW4ptQnGif5hP2JHXdmIrmMir9q2l5oNYFuIgUSyGZjUt\nNLUZuig8ZMaSUexqKp+b1Nwrbbrr7YhABafW2AMPPCCpu3cwvpxvqNLYN1N2rC11+sK90mvUecxU\nhNfo4y0BqvC07jm1djNtUpFKkiRJkiRpZFEoUpHCg7dVm301Np49FNW9WWiirLaFAu8MLwEvjPEi\nFopNc/EKydLE20HZ8RixsSunj01przhYLEqUe6WluAk+77FO2D/XHylzpfHDfomtw+v1Pf/IAi3t\nVFA7Hhufm2NOq7J5LShnjmfMliB2KKqQPWklZSxFgfFiTWYcsSFsdixlzeP2JhWnOxTWXL831PYD\n18XaTv+i9E1rLWON8LcYi42iIvXWt75VL3rRizYUN5SkmZkZLV26VHvttZf22msvXXnllRv+d955\n52nHHXfUqlWrdNVVV02m1UmSJEmSJIuAoiv3lre8Re9973v193//9xv+tmTJEp100kk66aSTZn12\n3bp1+ta3vqV169bpySef1KGHHqoHHnig+f0qisa0IK6Ap+Fo/65SRszYLNQ+T8SERe/ZIyWRmjyM\nu+/EDmTBTZu+41cbdwAoJtgR/bpQvPCFL5z1e0mRirLuvNZNK08//bSkTiVxu+B37KaU2dNnZwA8\nd8YQZWexQWxOtHb2zagk62paEOcW7a/Yl/vuu2+Tf0dRGUu5wAZRR6N9JmsZWgG9L7X7t6JITvue\n67AWsSZMKwatRPEJ58ADD9xk2flNyfff+c53dMwxx2iLLbbQsmXLtMMOO+iWW24Zp6VJkiRJkiSL\njOYYqYsvvlhf/epXtc8+++jCCy/Un//5n+upp57S/vvvv+EzS5cu1ZNPPjlKQ2ug3lNrTJXHhuBF\noSRECkrJQx97n6dSBslY7/NrvZnoe+yV5nu9DYUszrFsy717FJxIgaxVWD0+AWXKFbCh1YdLeBwQ\nSitKzthVn1FTIpgvJXWiVCEdonHaFMwJFAG3pVa1bey96pjjUTzkYo8jBJQcbGIsRSqCtWeszG7m\nbN+4VCrnu8Iz9lxD3ea6h8bPcr1kAQ7dXSTa3aGWhdrdJKIUnwlN79ze9a53af369brjjjv04he/\nWCeffHL42cWayp4kSZIkSTKUJkUKL0OSjjvuOB1++OGSfufdbfwO+Yknntjg8c3HH/zBH+gP/uAP\nNngRPMXyNIgyRCYBMRbOUIVixYoVkroMBbzT0sNgKWZmaA2Ovt7uYskswWsuZUahWEUKhMcVTNqr\nxWuMau5EMTlRHIh7xz6Ori7U1giqxb1Bfh/LO3Yl9wUveMEox50k7JZAjTNUyGhtiSAWh5+1c7R2\nTk+7vhXqZak2mMPazff7qIZDGLvGIGtp33pU3Duive/Gwt9y9B0nB4WrVUHyXQhajxOtvUNh3jEu\npaxX1rRrr7123s813eE3Xmy+/e1vb8joO+KII/TNb35Tv/3tb7V+/Xo9+OCD2nfffYvHm3aBySRJ\nkiRJko157nOfq+c+97k6+OCD5/1cUZE65phjdP311+vXv/61ttlmG33sYx/TddddpzvuuENLlizR\n8uXL9fnPf16StHr1ah111FFavXq1nvOc5+iSSy6pegJ3L4I92njfy1N3yVssKTEoXbwH5v0rtVp4\n6OMp9eGHH571uQj3KvHIUeNad7jGm6Mf8HJalQR/n75YIH6CfnSb8QyXSWdulBQ07AUVg3HBG0Nh\nirxwP74rbiR3YM/E/mG//MROOS+KLPW6PFsQsKfttttOUufFYq94bb/85S83+T2Uw6222mrW+bne\njRXr+eBzeJ8PPvigpHr7jvZ6nA+PT6MydiuMfdQGxoC24gnzs6RITbtmHGNbq3Rgy14rLBpTr8Q9\nFiWVG1hrmFNRfGmtys/3IyWF4zD+rBWt6vPYMVfEAaOw9n0bUtseYsi4ft+doFaJiuKPuXci0tB+\nzlcbY1irpBYfpL7xjW/M+dtb3/rW8POnnXaaTjvttKqTJ0mSJEmSbM4seWYK6R9LlizRzMzMQp82\nSZIkSZKkNzMzM6GSlcFJSZIkSZIkjUxtr70aRSqK8YBSRgnnmLT6xfvk008/fdb5SvWIxj5f32yv\n2vpWfr5zzz1XUherxDgR20PcSG0dIOIKiGvgffaJJ54oSfrCF74w6zyPP/74rO/zvp3+vueee2b9\nn2w6Yo1uvvnmTbaDfiTmj/f9vgfcNttss8nzEO9BjBzfJy4CO91hhx0kSUcddZQk6bLLLpPUZfsR\nrxH1H+NGzJtnqxJ7RH+SSfuhD31o1nVyHq6HmKja/dsA++B8XPcJJ5ww63wloj3+apmZmdHHP/5x\nSfV76dGHeJq+lnAcbI9rfc973iNJ+tSnPiVpbowNx/GsO47DNXLNjBnt4HPErJxyyimStOH6onpX\nURxoXxizT37yk5Jim8B2oorfHjNFf3D9vrZ85jOfkRSP29577y2p2zPQY2u8Xdg0/UX/EH7yiU98\nYtb/x9oT0KE/yf569NFHJc3dO5C1gTntGbasdcuXL5fUXR/1qogBfO973zvrvBH0J+fxXQRWr14t\nqYu7jOyA83z5y1+edTzufYwzayH3dOyUtYPvcS/Bfv2e5tfHPCKmq+8aBtgHcE95+9vfPu/3UpFK\nkiRJkiRpZGqKlNQ9paKk4A3w9FmqCTJWpfChRF7MtttuK6lTKO6//35JXXbUAQccIEm6/PLL5z1+\nSZkDnsbdC911110lzfU2avvPFQK8TLwH2uXKSy18D3vwasDYR7QnHt4ZWWiuFL3iFa+Q1HnFt912\nm6RY+SBjJMrUWbdu3Sb/jhdU8ob8+/Qf41YqB8K4ReNH/5f29WKeoYJ4NmAttJfj9N1HDKUQFeH6\n66/v9f2NoS9d0fD6NlDKYEUdpa+8ajtzjvP5/pIR9BXtpU4RY4BS5h4ya0G05qxcuVJSNzc9K7Fv\nfZ6SLZT2nsMWIpvw6/DrdVAIo9p91AWj/7x9nvHLOExKiXJQGKO1kjkd9ReqdbQnXvS9KBuRTHhK\nGHH8NWvWSOrWpGjN83FAKfR7lfe7v03ytzYol9gD7Y7sg7W8VYmK2llrF6lIJUmSJEmSNDJVRcoV\ngb61NMaq/DwU32mcp+af/vSnm/z8kUceKUn67Gc/K6mLj+B3h6d7Yn2ip273DlBw8MYjr7yE93Pk\nxUeKmStq9Jc/7ePNOLQ3Umrw5qP+Jt4Abw+7i/a4K3khePXEM6A01saE+Tj5PBi6v1Sp5gvn5zqw\nPz9vbcyS1+Lx/ivtJcjemH2VrE0RVYkn1qHW9qmRhefPcakhBihS/L+kRNHXeP7eV3jEzF33kEt9\nhNJELT6nVCmduQqluMtSvSaUB9YerwWIigwlNZv4xqifUV5qK9TX2hyxRNEaUwv9H9UdQ3GL4ldb\n73lR7T36iTnKGo0qXIrv9fZgP9yriOHyecnaE423K7al64Do3lJag6J6WbU1F1ORSpIkSZIkaWSq\nihRPs5NWlqKn1LFwj52n2+jpmQrOxEahaJQo7THnigJP8zxVt74/dsUtglgwbwdewPve9z5Jnbd8\nySWXSOoyQiLwJqLr9yw+h5goJ/JOuI7ofHj9eLOHHHKIJOlf//Vf520HuL14O0rzoaQsomYQIxWd\nn/nAePF34oDIEMJLw64feuihWcfj/5EiWVuqru8+d/PhMUb0Bb+XYmNQNrB9FC2yiYCx8jHlPMuW\nLZPUxSqV9mxDiYjiHUtw/Og8pTXQx7A0dsR7lhSp6P/+95LiUFL8yIYbG8Z9//33l9SN749+9KNe\nx2HtiMahFP/YSmkc++4lGB2XtYC57HGa9FuUbekwr/rGCXv/0g6UMu4prKXRPbi0jy6kIpUkSZIk\nSdLIVBWphWLSGRn+VF7KPLnqqqskST/84Q8ljbdjOUoE7+Hx7vrGnnAc30OuBN6i7+mHV3HNNddI\nkv76r/961vEd32sPVaCkqLXuVO+UYoIY77vuumvWz1ZQgNw7j7xv+jfqD+JWiMfxvR7pT+zU42Dw\n3vg+3mDktaLekD1ZGys2STy2wT1iFAZsxT9PvJ7Hqtx3332SpEMPPVRSHOvCHOo791C+gCyvaVFS\nMkpzEluM4h+937G9SJFhLWIcmTv0c0m1r1XXHTKBV6xYISnOYnM805jzR7FqHqPWF9ZAKMUGTRpf\nw4iNYl65uh3BPCuNX9Sv9APzCTsrKZiZtZckSZIkSTJhnhWK1KTxp31XVGq/1wrvfT22xr1v3s+X\nMnd46kfRqn0q53x4D5GXT+ZL9P7Z/06cSCk+Ai+D62vt36G1SCKiDChUCPqL//v1Ylclr5V+oKaO\nqyYch89xPH7Sb3j7qApA/5LRRjv5nqsqiwEUC9TGUgyKxz5Fn/daXIAN0feoe6XYENRpr18FUT2g\nWrCJseLRovhElBiyHL2GXUQpJoy1ibpdpcr1Tqv6T/YZ2ZylbDbmmCtSKEZRHS/W0NoMa19T3F6m\npUSxlmD/zB/6EUWKeNSS8sr3o7jPEt6PrTUUI1KRSpIkSZIkaWSzUKRa6x9Ni9J7esD7QIHhKZ2n\n9lrwSlAQeLp3b6j2/TteW9+qv6V4ELL1UNCIN/Dqw97ukoIGxOZ4Ndy+1H6P8cLLLGW+RNl4jD/j\nGNXLYjxKWYr0J+qDe1+cj1grFCdX8hgHPof9EFfk1ZVRW1BfpkGk+pU8S5Qe+saz8yIPNlJQmDM+\nl1FUyHbzStBetyo6biu18Y61eD8TA/O6171OUqfMlOpNRcdzSjXSSni/lqr/A/1W2/+cx9vr6q6D\nPdXe67y/at+GTBrsOcrCbM1OHKPWXB/IXC6RilSSJEmSJEkji1qRYudpvBzqDeE9ogAMrQQ9LfDw\nUQJaa4fgxbJnGV4W3jneFIpQKa4gqlZbAm+T8XHI0GDvO/ZWc9yLqVUkWxWoVujPWsUswvfdKsVx\nlOqi0X9k67kag9qCgonihP15tifXxzigKDq0v7b2yiRACeibrcQYsNYQ51Wak309ZMaMOetjzf+j\nPhxac2/MWl0bwxqx3377SeraT7/WKlIlhipSjtf8i5RL1p6+bwvcPkoZ3ajbfK9UWZvj0f6hWX99\nKSlgrfbKGkcMVd9+7wvKk6vstQpuKlJJkiRJkiSNLGpFCqWJp27iCviJN1GbEbLY4Gm7pGhE+y45\nXnEbLwVvivOVMn/ci6r1KnwPO4f2uALiuNe5WN77O8TCRQpcLSiHXGdJcUJJQlmMVBfa5f1H7BNq\nCPMMb5bfqbHCdVKlOwI7cfuZRoyjx8nVVkbm86iNjAFquH+/NqvHKSlZKGNj15Eaq2adg43xlgA1\nFNWZPfUmVXm8FeYO9sFcjOZe36w6t49S5XbmdO2a58db6DjikuLbam/e/668RXi/RRn1/J01EpUd\nxZR1IOtIJUmSJEmSTJhFrUjxNI8HTdwCtUn8febmRm1sV23slHsj9B9P2XhdfWOJ+n6+5IVQg6U2\n3qE1dgzvEq9j7IwPvJqhcSv0A/ZQUriiGjROVHUbr47sPvoFJYx2EE+Dt4ZKEmWl0g8eg0fM3qTr\nc0ldLA6xJihJrCGlqvcoKMT63HvvvZLmKhEwqTo9JXW3Fdo/djwhnjtV/hmTVatWSZJ22GEHSdL1\n118/6nkjahVIYrlof2kusxYxR/vaNHOH87pihX1Sr6pvTFmtYjOt+lJ9ITYKFT4aH9aYaO8/YH7T\nr9tuu62k+N6YdaSSJEmSJEkmzKJWpIjp2WuvvSR1T6M777yzpPrYqKHVgEu0ZimNnW0Yvc/lqbxU\njTeir/dS2iMOvH5ULcTIPfnkk5v8P3aD98F5x/aG8R6HKlJeN2osO0W58vHDDrA/6j8Ri+eKEl49\nas+DDz64yfNFak9fr53sU9pP5hLXMV81Z1Q1V45qx+jlL3+5pE5JufPOOyXFHmttDEXffSCjelhD\nqVUzW2GMUC+JjVrobLLa2Bw+V9vPY2XoRvuRonxSA46acbVrcFSDjrnNvZDYO+yS629V/6E2lqkv\nHC+61/rbBt+Tj3Zhl/xkvrPbhlP71iQVqSRJkiRJkkYWtSLF0yDeJb8Tt3DNNddUHQeviHgJjocX\nUKrV4fA+lve2pf1/ovfhC1X3CC94aFXkvtA/K1eulCTttttukrrK13j7fYmUKIf367Wf78tYakGr\nMleitho3dhHVF8NO6ce+ClNtbBpKI/FJjB+xkOvWrZM0f7/7Hm++tx1tdw//gAMOkNTZKLEsZJ/5\nHniA0sLc5icKALaPSoqKzudoH/+n/SgLk1aQJgW2hAr8yCOPVH1vUopGRN+1f6zzRWsxihcV78eK\nZYoqidcqpLWMPW7cW5l/tfcw/xzzFEWO+QnR261ahS4VqSRJkiRJkkYWtSJ13333zfrZCpkKZP0R\n6+ExGLXgZUaKlu/fFEX+89RMfR48cJ6aeRpurVUDXo9rKKUdu1EA8d5RXFADaIerBVTc5r19K0Nr\n76CIUPOGmKLacUBVoBYRCgrjHdU24brpB8YfbyyyU4+rAOIEUFWGUtpDErtvtTPm47XXXiupLa6G\nvr3lllskzc168rWAPqfN9BXqF1lokQrGWPN9zsdYczxsiTWBSsqsRYwV30eZ8L4cq0L4pMFmmfuR\n8uFjXFI0+saatYKd8LNVffaYnigO0eEewFrrGdjEf7JGcDzfp3ShGTsrkOuOlKgoltCzXbFH5iMx\nUvRnpLLT/yVSkUqSJEmSJGlkyTNTKCixZMkSzczMLPRpkyRJkiRJejMzMxMqbalIJUmSJEmSNDK1\nGKmPf/zjE88iQ/Xqq361Zo5wns9//vOSpN13311SFw9xzz33SJJ+/vOfS+re6+69996zficmjPfr\nZBIRe3T//fdLkl7/+tfPOu+k4TznnXeepK5ydimWq7Y/iX8gjuDtb3/7rPNOGrcX4laIO/AYpZ12\n2kmS9NBDD0nqrp8YJ7wXst74yThznosvvnjW8YmD4Xdin4ibieIrqE1D3AkxS8QLvO1tb5t13knj\n/UlcArFjVE5nHcDeuQ7iF7zGDvOJ8aFfjz76aF100UWSujGgL8j8ow85V1SdntgIzkHMCm0+7bTT\nZl0btss1MnbRWGHjxC9yfjKKOS/tP/744yVJn/vc52b1AbZCVhx73QHHIZbEM4WpOE57iGs8+uij\nZ12fQ+atxzkydtT64/9R1h5z65RTTpEkffGLX5TUxY3S7h/96Eeb/D7jQj945XBqkrFW8X/Wlksv\nvVTS3LnHeGBHDz/8sKQu1oY5RfsYN85DPzN36c+1a9dKimN/1qxZI6mzU89spj2MU5QRy7idc845\ns9rtRBnlEXye/sG+fD5MGs5zySWXSOrmmfcn9k173Q6JNeN7xClH54tIRSpJkiRJkqSRqSlS8+0h\nteeee0qS7rjjjk3+/9BDD5XUeYcoPTA0Ow0vyRUUvCQycHga96dgvNk99thDUudFkBngNT3IMnMv\nBe+WekyrV6+WNNfrXGjw4vFi8coi77tW2RurUvhYMA5RthzKoBP1Q2TzeLHYAf2FV//Sl75UknTj\njTdu8vivfe1rJXVKJzVoUKTYK68vkX33BfugH5mfnl2J4kpGHZ+nP/DS+R7H3XhPQZQBV5o8Oyyq\nVM5c9UxXbD2yTdqCohHtIsDxSvVpUHw8C8vr6eBBR550aexQU10BKuFKFLALBf140003zXsc3w+S\nvc8OPPBASd043nrrrZLmKjCMn++7yjhh+zvuuKOkrgYh+Pe8H6PrpF2s2axdnk3oa0epUjZzN9pH\nlrmPnZVqtJUqvPfNCPfPj70XZES0dyKZvtF1Mk6RIsq9upSRXCIVqSRJkiRJkkampkjNp1JEShTg\nzUVP97X7LEUQm8H7dzxkrwkSeXt4CexJhneJJ+0xNRF4H3gneEeTqtRdC2PH9fi+ZniZtfsUOUP3\ne1qsRBkfPp54tSifeEv8nf49+OCDJXX7wl155ZWzjoMd9q2OjSqAIkrMXut40l7sPfKi2XkAL9f3\nZnT1CNVh4+PRx7SVz7iK58dirnNujslPYqCiMURtZk64DaMmEzNDzA3KFZ/nfFEldRQtFDPWoNa9\n+VBsmMNDK31fd911vT7vtkn76U9UyNaK2cwdFLJS/SYHVZfxd6UIJQ/FhLUd+tbEY85HMNexi2nX\nE1uo3Tmie3rpHso8i2A+D31mSEUqSZIkSZKkkUVd2TyC9+7R0/5YMTZ4j7yPrvXIecr1GBDPQqo9\nDvD0P7TS+VjQH94v//iP/yhJ2mqrrSRJX/rSlyTV77f1bMMr4bsqElVxJkZw6I70DjF5ZIahfLFD\nOvun1YLqgTLFddFun6945fQLqgvxQr6v3cbKlceZ1eKZlU5pP0TaGH0OpYI+JZsITxhFir4gVgal\nzM+DkoVC1epR8z2UmoVSGCJoD0oSNoLi1hrTg831VXBcmXTIQOU8jD/9OHZmOnNo2krUYoG1JYpl\nc8jiRP1mDY2+X9pHF1KRSpIkSZIkaWSzVKQgetrHox6Kv4fGKyrVRcKrJMYEr9JjrErgpXI+FDjf\nU60WPHcyWOgn9jarhXgA+oN+4PpQVF73utdJklasWCGpq6USsfXWW0tq967JwCBz6Oqrr246zkJD\nDB5eEeNM1lyUpVmKJaQ/iA+phSxYvHBipogPYa/JBx54oOp4nB9l17NVo32uXKHzelNk0m18ffwP\n2x5brWuFOcJeX4xxFL/G5z0rCqWDNcFVvr748cZaO1thbUGNxzZas8PoR2yPzNCxIAuQGDNX9Fr2\ni5S69lKr7vHHH5dUnvMOazT3oFY76YtnmxLTB2Qik6HP24t//ud/ljRXIYoyiGuVZxTgt771rZK6\nNY74z0iR4rwlUpFKkiRJkiRpZGqK1BZbbBEqSlR3/cu//EtJXVzBZZddJkm67bbbJHVP1zxt4w2M\n9dRN+/AOePpFQYiUArxivEWeamvftzoeW9XqNZKRg+LT6q27YoS3SPv+/d//XVIX20N/8X46ipVC\nAURp6AvKxEEHHSSp6//vf//7TcdbKFAw3ZuttePIW2NcWhU+xgn7Y3zIQKuF757PlOwAACAASURB\nVBE/QjyOe6mAWoPdEA+EPXGdXN/GSq/XOGPODo39Ke1qX1shmjnINUV9AF7XyePFUJJ8DayF8zN3\nPFNyoaEfWSu5flekandLwNZQZ8eue0T/1SoXtTDnbr755kHH2VRm60KAEhfFd3IPO/zwwyV1andU\nAzJ6VqiNFePtEHGeKHul+Re130lFKkmSJEmSpJGpKVLzZTP4flh4qGRI+Pto98KG1oQAvFrOT72f\nyCsFvCm+z3W0ZnCQWUA8xdCsvb7v2Ut4f5ChxM/aGi7EzLR6jR67RUYHdjPtivCRquHKIF5Wrb34\nPmOAt1VbrToCu2Pe9d05gJgo4l1QETy+Z99995XUxfBRAwZFC++QfkSF2Xg+8FnmzFgwpyMPtnZO\n9q1j5EoCihFzirFFEYkqbJfwfRmnTRQHR+Xx2tgYr0PVN5szqqgNzAn2bItie55tlOwPtZsMb1cO\nx4ZnBOI6vd7XUFKRSpIkSZIkaWRRZu2xh9mPf/xjSV2EPe+L8dJaMyJKcFze4+JtepZaBJ62KwF4\ne143qARxFSgOrRWmFwqPF+nrhZcUvwjeg6MgkqmBPU1bkUJBcS/VVQ7iGkpxORDt68ZxvRZRLXiH\nKIWoA56RUwIVoeTVoyBiN6gJrjjTH2PHRM4H11yKqZjUecFVQfqCMWbN4v+om/Q96qVnIrfGJY4N\n14cay1zAFqM9BUtwHLfB0n6SrNWoqV7zjP6P1vbFkjW62CBe0tc2xr815i8CJQr7H5tUpJIkSZIk\nSRpZlIoUist//Md/SJrrmaMY4aWMHQ/h1YZRoPAOS4oQT9N4eyg0PA23xqxsLu/b8YbxcvvurN0a\nA4ZagLfNeJH9NW24Lvf+3Uvue/0lxaq1P+k3FFayZ/sqUqWdBvD2iUFcunSppLLixN8XQqHtq6qO\nhauJrCFeZ4k+QJ2MatahmDAm/F7aa2+hFDkUNcaedrYqCbSbOEl/m1CKCSspSvyfe5Lb4tiVzX9f\nKO1ZOXaF/VKF+qGkIpUkSZIkSdLIolSkHH96dcVoKNHO6ez5hVezfPlySZ2HHsHTNEoMCoTXlaqF\n73Ec9glarPDUj4LRN9uw1etFiULZQLEcquQRc0X7o0rcJfBOI69/UrTuPck4ohL0zXjqe37UFyrc\nM7+pMROxEHtPTmt/S1dEfLd6bIo5U/LkXb1323aPPVLhh8awMCe9Jh5rHYoaqqjHqdbaFJ+LsvZa\nbRr8OlrjO5PfUZrrY7HllltKqt+jr0QqUkmSJEmSJI1sFoqUw/v8sTIiSu/J8fbwimr3zCNegads\n4iz67tyNN0jW2bp16yRJRxxxRK/jAE/jXDc1Wth3aCx42u/rzbd6idQuQUmk36P9zPoed6wYtb5x\nE0P3jWtVbrF7VAsU0do99qC26jfqBnE77PHHPmPR9W9OmVHE+tSq6h4/56omfdY6xk888cSsn762\noUjxd2yC/T7pezI7a0FZ8nhRrsPjS2lfX3WVtfOKK66Q1PXnbrvt1us4EcwJ9lBM+rFs2TJJ3bi2\njnMt3Ov63oNLpCKVJEmSJEnSyGapSLXGqETg/ZQ8ZryZ0tMstU54qsarG/qUTcwPilwrKFJ33323\npPHeE0fnIWZp0hksKE+33HKLpM4bHVrZG6aVgYMS1jc+BGoVVId4Feye+I++yiXtd0XX5xsV9zkv\ndaVKcTjTil9qgba2xhYxlyYVZ+d9ydix5vJ/VN9WuH6vBcheaFwfc3hoLBNV8seGNWHsuNVaFXdz\nZ6gd9WVS2bepSCVJkiRJkjSyKBSpVk8b8GB539rXW6tVGmr358HbIsaDGBe8O+os8V498k6p1P3K\nV75SkrTHHntIiuMRSlV6yaIjvgEvjf5vVfp8/BgPMntcmZo0Q2OiFhqvIA4oMuyvRv8Ss1WaL/T/\nzjvv3NQu4hX8eH3xzLDSfOO8jONC2c1CMDSeixgPFAt+sqbQZ7X1clijItV87Bp9DvFwgGKArQ1V\ngbE9GDv2hrcTffefLEHsFeM4VJGrhf7qu1fjtCjthbhQpCKVJEmSJEnSyJJnplD4YsmSJZqZmVno\n0yZJkiRJkvRmZmYmrBOWilSSJEmSJEkjU4uROvvss8OMBN4PU9U2iuwvvR9F9frkJz8pqYshirLu\nDjroIEnS6tWrJUk333yzJOmuu+6SJO2yyy6SulgPYlqoF3X66afPOu+k4Tx9z0eMFO0vvX8nDuMj\nH/mIJGnt2rWzvkcWIZ+L+pf9rqgVw/t/vs94M/5HHnmkpN/ZilTOYNlqq60kzd0hnhgu4kD4nfMT\np3HSSSdJ6vqTdvl1ErfhMWXEOhHn4fEYxI5xvve9732SpLPOOktSFxdSim/hONEec/QDsYJc7wc/\n+MFZ1zcpmJdnnHGGpC7Lj6w8vDquY/fdd5fU1acic4uaS8QYEoeCfTGfjznmGEm/G1+/tu22205S\nt0Zg8953xB7xd7cNxoZ4tbe85S2S5vZlKdvq4IMPltTVY2KNcZgD2Nqpp54qSfriF78oqYsjo130\nEWuRH4f4SPoBG6UPaTf9QJ9Oey1jTcCWPWaK8WWOekwXNsbcJYbszDPPlNTdG5jL9Ce2xpoRVdzm\nuL4WEN9IzNGxxx67yevDnjhf7b6kvv+s4/0ZxSHvuuuukro12e+1tN/jaH0covFjLaCdPi+wN+6t\nN9xwwybP7xm8xx13nKRu7eS6OF5rdp6PP+095ZRT5v1eKlJJkiRJkiSNTE2Ret7znhdm1/F0jDcY\nPWXWhnfV1hG6/vrrJXXKCU+leHF4M7SHv+PtbS5EikmEV/R2b55+KCkp1KtyBRGFAQXCx6u2loor\nUcDx8OJoP144mUuOe5ml+mGl6s6c1/cXYxxqM60iJQq8H0rZdn1r1pQ+7+NLvbKo/tSdd965yb/j\nnZe89EsvvVSSdPLJJ8/5Hxl/payeyINF6WBsUHiANYLPRX2ydOlSSdLRRx8tSbr88svnbQ/n8+NF\nmbWuRAG29Ytf/GJWOyM1v2RbkwYFwtcmV0AYBxSVH/zgB5s8HmsXCqDvk+r9ieLBvYWMY1ekOK7X\nwQJstlTDjXZFx3FoP0oW7eIeFGXbRWs9ClS09vD24v77769qH6DUcV3Yldsz8y6qzI8iGO33SX/Q\nj6x12Effiv/c26G2AnoqUkmSJEmSJI1MTZGqqfvAUz31k3hP6jFKtU+NpT314Bvf+IYkac2aNbP+\nzlPv8uXLJXV1oGqPu1jo63X65/39PN5MaS+60pjjVY9VMwW1gHZGNXxKdadcdaiFfsI7Qhkba8++\nWsauDN63Fs+kqgnDfF7n0Poy3jeuOnocXQRq7JVXXilJ+uEPf9jrvICHTp9Sa452EaeHYoGSQ804\nvu/txUbdIx8KigTXU3qLQLuYO9Gc5bgPPvjgvMdjbeprB7TX1WPwPQ8jSmsLx+m7V6GvIVEMV4mS\nYtNXiaJd22yzjaROIXrkkUckxWsR8ZMO6jpvifxe62sR9tM67/ke41K75qcilSRJkiRJ0sjUFKk+\nVbSJoSCGhqykWiUKRaGvZ0wGAd4aXgpeBt5Sa7VispXuueeeWcdf7OBluFI1dE87vKPW6sN423hD\nKFy00+MsiF8oeWVcF95pabzxlmkPqsW0qu/2VUyJU/nc5z4nSbrxxhsldd7/NddcM+/3fS/ISe3k\nDltvvfVEj78xHktSe214tldcccW8n4uywMAVEtY0jo9tRhm5tN+VIeZAbUxJ7W4U2HxtHJ6vKfSH\nKxms/aV7QGuZRGJvuOeUsuQivHJ7dJ7atwR8blK7N7Sq74A9YEf019B7A7Ffvi+s2xP90roLg8+7\n2vmdilSSJEmSJEkji2KvvVrwPnjKrX16xptp9Yx5uqa+FApVlElQC/WpyAzxp+3Fimei0A+uuJDx\n4bV5St6XZ0bVwp5yb3jDGyR1tXZuv/12SdItt9wy6/N4M1GmC94w7S21G6+LWD7UAffeFzozqq8X\nTX8Qt7By5UpJXW2hm266SVKsmlDbB0pe+VCGbM6wYsWKWT+JCXnssceqvl+r4DBHiGny46N8uG34\nXCDWBPzzpfaMtZEFx4n2iXSYA31V99b9P6FV5UeBYm1nfPq+1fBsPF8LSvekkmLVN+O2BG97Hn/8\n8abve/wy9zT60dvrylEUQ8Va6v3AvEKJ4vio6rV1uYDxYt65uh6RilSSJEmSJEkjm5UiBcS67LXX\nXpLmVgd2L2CsLDBqbuy9996Sxsu+2lyUKPCnfrwLjx8hdgWlCEXxe9/73rzHr62p4qxbt05Sp6Bw\nHGLQ+kIGE5k5XCfZm4D3QnYp3jr1k2gHCuqkY4bG4sILL5z1+6pVqyTNjTVzGAfw2j1j4+PRB5So\nV7/61ZI6G61VpLwyeLTWlOIpa+M9o+O75zxUySmBYkP9okiR2nPPPSV19bz6KgRObWzWUFD2uK7W\nzFMfB7IpPbYtYqeddpr1+7333itpmM1vCtbMl73sZZI6+6edfddQ1kC/PtRpKtbzuaeeemre46E8\n+XWj/jMPsYvWGCnWapTWUh0wSEUqSZIkSZKkkc1akYqykTxWx5WjVoWKp2EUJI5b8tB/36AfSrVh\n8Grw7mpjg1q9LRQx6oANBa8GRSWyG1QNqizTft7bc93YS5TBMjRjZtJElclLRFW3x6KljhvV7PHE\niQl56KGHeh0HD7hkI7VKimeG1sZgYXN4+rQjWpuiDFQ8/BJcB2sAe6WhPBELhqrLeYYqUtFaM6m1\nuFYpjPDx7ru2kYFM7BKK1Ngwh7zSOfbUF5QnX/N5i0Glf+Joqcl42223bfJ4rI0oRcC8o3+492Nv\npRgzYt+IC0WB4zz8v0QqUkmSJEmSJI1MTZH6oz/6o2LF5Qi8jlrvhqdZGOrxkw3Gcf34v+/U1kOq\n3SvNGfv9f1+8ZkypFgreDu/X8baoJk39pShTBfvB+yvFC/RlaA2XoUx6PFviV1BeUFDw9PvW52Et\nKa0pjDmxTJFK17fWmGdB4YmXlJlI6eob94ltEyeIZ4+Sg7LRGrPiRIpUlLXWt06Tw3iU6ntF1Gal\nRbBH4tVXXy1prp211rcC1iyUQ+YDcaE77rhj03G5TlfZqcCOYkXFcuZjpJT6HpEQjTtrHtfl84Pj\nkWGMHbNWcc+iFmCJVKSSJEmSJEkamZoiNYaXXPv+Gq8AL80j8fvW4KDt0Q7tyTCmHSOEF+XeT5TB\nQe0hMmzIVsQrw14ibxT7nJQdTTtLsFV5rqVlLSEbC8WB2JBJgWdL5iO7Jjie+epgK8wRYk5QEGqV\nF2wUBQ5PvK+6h21Ftdjw7MlWmxSRjQ+1fY8H7RuL1Xp+1h7icX1cUb1Rr1Gk+tZsw/6ffvppSZ0C\nNrTfUNe5DuwKRc/3dsSut99+e0ndbibA/PG1hDWZ47r6jWLHPKF/UKb4vn8Pxbb2bUoqUkmSJEmS\nJI1MTZEqeV411O6YjcLAU6tX5cUrq33PTCVynqpra85EDH3P/fsGmRxjw/twvK9I+cKLwvshnoVM\npAiqTuM94f0sW7ZM0txqvzB0z8bFzqSzWvE2+4ACFXm6Ea1V2hl7drNvhZgf1gpXSkrXwedY+4j3\nhLH3g6R/ae9YlbhRMErXy1xuVblRgojdQamptenWel6MA0qjgyLl49da29DvYdyT+o4TyiPZbvQ7\ndsDxuHeiJqNgUZfMY9u4R/tbAvqJ9jMu3PP5P+OG3bPWEsfq0E7uFSVSkUqSJEmSJGlkqjFSpfew\nY3kvPF17zQieVvFuauMLUNN4+h36PjmVqNm0xlN43Se8uTVr1kiqVxDxIj0bs+RdEiuFt8j7dewY\ne6vNBEnqqInr8awrxhabqVXI/XOMJWtHaS2I6lSxNqF4RZ4wa47vXYZnT90hPG6PI8Wjj5SOvuph\nKWaIv6PosBZHbxNqY5BqFUTWgtbMUdZmzldb6boWYuaiGm3ebsaPWCYnilWD0j2VtwEcp6+ixnFR\n8b2WHlApHXsgvjTajxTlzfeexI7cXiIFks9z3rHeAqQilSRJkiRJ0sjUFKktttii6FXgxeA91So3\n7jVECkTklZWofW/6+wpeQW3V5b60KjbYE14osWy082c/+1nVcbA7aqjgPeGt4WV5zaH99ttPUlfl\nmfageqAOTLquknudXg14odlnn30kxVWLS6BQRvO1JkbKPWs8WBSQVlWYtrWOKbbGHmeomhHU3eF8\neNS0H9UzymhGUYuyFPvWxCvFVHE+fpYUjtYK5Sg1vttC37pgEZPKPHUFE0WS7DXqm/G5M844Q5L0\nn//5n5K6uk+1UAE8+h7nZxx8D8cSrLWlewPj45XDS/bkx22NOfQagUNJRSpJkiRJkqSRqSlSNR4H\nniZPjbUK0qTrOpH9xfvW1vfm1NDwGA8UhNqsxFaIq8ALwRsoeV9kr7k3hbfD9fjxfT8yvCIyNfAq\n3Svuu+M7ihleO3Epte/78fLJwsObRomMjkNcwK233ippbq0ir/kyKciA4TpqM3lcyaLa8ND90Xbb\nbTdJ0s9//nNJc+0LleXVr361pM6+GDf2xVu3bp0k6fbbb5fUKX0bV1/2a/XsHVctUT+xPWzVs5b4\nv3vMqJSMdRSbwXG9QjNrHBWfS568x/8xxqwlzJG+yg7t4Di11Noy7S2tzbXKHvtaomgQQ9O3HhgZ\ntcQEsdbQz9TbYk1h7rPWsUcjdY/8+kpz74EHHpj1O3OQe8Ahhxwiqetn7K01G7CkYHFcriOKxRoK\n/ed1oLgXbG772KYilSRJkiRJ0siSZ6aQMrZkyRLNzMws9GmTJEmSJEl6MzMzEyqwqUglSZIkSZI0\nMrUYqXPOOWdDvEHfGBgnqnaK6vWNb3xD0twIf+ILyJwhQ4FYlug8fB7IkDnzzDNnnRd8B2yyybzC\nteMVz71mzOmnny5JOuussyR1sSK8dyfegc8Tw7PDDjtI6uIDiOmh36hFw3trMl8++MEPSpIuvPBC\nSV2Miu9kv+eee0qS7rrrLklzx7V2vOlHfo5VV6x0vi984QuSulgj+sV3RscOiKPAvrAT4jeI+SH+\ngziYf/iHf5DU9Sf9TTxNFAfB+aJ4B85HfAHjdPzxx8+6zknDeS699FJJXW2YKNaROBRipsi2Ja6J\n+UnsFv9nfp166qm64IILZv2NvvbYJfrE48BoAz+Zo8TJMSeZ65/4xCckdX2NzXhm77777iupi8Uh\nloc+wWaIgSFminbTl//yL/8iqRt7j/0itobrYu76WkOtM9rDT7IQTzzxREnS+eefP+v6+8J1MecZ\nQ/qL8eH61q5dK6lbu1ij+RxrE2sB101/+Zrie7kxfu94xztmnXfS+Fq22M9H7GApZo1Ysg9/+MOS\npEsuuURSN15RHSxi8vpmVRI75fc+7Ar7pV1R3TbmN/aAPdIu5g8Z26V+TEUqSZIkSZKkkakpUhtn\nv+BdtCpSHAulxb0vfscT5qmW8/H3UlYg5+m7Q7pnq5Wy/Ki7Q9YYT+0oMZ7Vhgf/ile8QlLnpXm9\nJxQOvGCUNK6b6rIHHHCApE5puvvuu2edr7Q33B133DHv9UXjTHYWmTDOpLMxgf7CS2G83D64fq9K\njNfFuKFUYXe+XxvePv1SysgpZdG5l9eyF53UKZZ9M6Ec1IIS9LtnMgH94vu3bZwFSF/iCUd1kfgO\nSgy/R6osx/UYCfe4oxpzHIe+8MrNnLeUJcXciHZhIHuwlPHre7SB2/jQPVE9y7G05x3XFfUjay+Z\n07SPccbWXWlbqLVjsdGa/VabPelri8+jCOpk+VrF2hjZnV8HayZrMWtvZN8QZcdif6W3RU4qUkmS\nJEmSJI1MTZHamLFqRbiXCSgzvjeen5caFrUedC3uxeJt4fUS07Jy5cpZ/4/eH/vTvtcp4mmcuABX\nOPi/P7Vz3fQfMSh9n86d2piovlV6JwVxFXg3KFL0S8lb8/HxWDf3yvEaS14c9N3JnvO3svfee0vq\nlCDOf8sttww6bl+89gwQcyjNVR6i2KhoreD7qLXY7lCItRhKaQ5F9a5aQSGgXzj/WMdHra8FJcH3\nagN+r903tRZicOgPjwudNJyf65vUrhJ98XsDylBkp9iNv+UgJmnnnXeWJP3oRz9qak/tWkf8arQD\nQF/mXSV+8Ytf6OCDD9Yuu+yiXXfdVf/0T/8k6Xc3+MMOO0wrV67Uq171qlmNOe+887Tjjjtq1apV\nuuqqq0ZpZJIkSZIkyWJkXkVqiy220EUXXaQ999xTv/nNb/TSl75Uhx12mC677DIddthhOuWUU3T+\n+edr7dq1Wrt2rdatW6dvfetbWrdunZ588kkdeuiheuCBBwZ7dXz/r/7qryRJ3//+9zf5uSi+wP/u\nlbnxXsZWoiCKJaJdHqcR7QQOrqTxlI+SwtM9cQMoKKWSYRyH46Pk9a127NTGwOH1Ev8wLeg/f+/u\newCiJJVi6zgOXpCPw9A4lBKtlfeJjeInewhGMUwRKEmt+2KRCUc1ayqcw8ZxUK5+YrvEDDGWrAEo\nVB4HhuKATQ7NLB4L+oK1yhU4FBPGHJUbBaDvnoD0F/3E8aMYJldfI7CJoWqpn2dSa/hRRx0lSTr2\n2GMlSV/72tckSZdddtlEzue0VjJfrJXBXTFkvt18881Nx2Mtju61zAfuaSUlivWhdjeVeZ9wttpq\nqw2p7M973vO0884768knn9R3v/tdvfnNb5YkvfnNb9YVV1whSfrOd76jY445RltssYWWLVumHXbY\nYcHl/yRJkiRJkoWiOkbq0Ucf1c9+9jPtt99++tWvfrUhnuhFL3rRBi/gqaee0v7777/hO0uXLh0l\n7uXd7363JOkNb3iDJOnqq6+W1P4enKfRkjfkNVA83sLrQ0WUvNnarEHwdvM0/+CDD0rqvDK88dri\n9Xgvnt3o8RBcNwpLKYaqr1cUKRdjx39EYFcoRYwf5+3r1QNxDrV7343FWOcrxfl4rSDoGwfjoF5Q\nE8bHfz6ly+cmnj01wFC5UINRWqIYlCgLcKHg/FEWmnvkQx1Z5gJKlCte2JbXh/LYM4c1JnpbwXk4\n/1gbcFBnqC+sdcQLPvzww5I6ZWrSa9KkYT5gV61rHPbZ997M+LLGl/Z7dbA7xpe1m/nPddXeY/vW\nt6p65/ab3/xGb3zjG/WZz3xmTjHKJUuWzPtAMlS6TZIkSZIkmRbXXnvtvP8vKlL/+7//qze+8Y06\n9thjdeSRR0r6nQr1y1/+UltttZWefvrpDVkzW2+99ax4pCeeeGJDFeb5IPYEL8e9Kqr1Uj+HGJYo\nJsornbtyVBvngJdBfADVgIm34H1rqWbL2PEV0cMp/cN5Su1yPJMJJcEVJfqTduCN+nt8Hrqj99YR\nkcJX6/XVVkBH6XDvA6/Fr7uvl+TgvUexVlzf2NtftsZIlfCaL7Tb7XyoIkVMFMf1/tvYLvzcke2h\nYt12222zfi/RN2NybFiDhtb2qoW1j7nEHOd3fjIGtXOUtSOyTWxm0vGDtbA7Brs1/OQnP5E0nhJV\n+3bDawOOxZo1ayRJK1askCRdeeWVkjrlrdQewF5a3xbttttukrpYqb5rIf1X6kdiDVn7S/fmgw8+\nWNdff334/3kVqWeeeUZve9vbtHr1ar3//e/f8PcjjjhCX/nKVyRJX/nKVzY8YB1xxBH65je/qd/+\n9rdav369HnzwwQ1bIyRJkiRJkvy+Ma8ideONN+rrX/+6dt99d+21116Sflfe4MMf/rCOOuoofelL\nX9KyZct0+eWXS5JWr16to446SqtXr9ZznvMcXXLJJVWv9ngf6xWf4Zvf/KakLp4BhcoVKb7vXkJt\nLE8EygReg+95x1N4RF+vBUWNp2R/Wo6enlGAhipgVA+maq17jXhDJa+I71F5PdrD0GlVZNinDG+o\nVIHeK4oD7Wa8vbp1hNcocrguj7PBPlCmxlY9avu9FuzM+43rc2VyaE0fP09r/MamKClRk97fsS+u\nAEwabJqx9fg36LvGMUci2+i7e0Tf8/YFO2nNPC1RmvPcR1FSxlKk/O3N+vXrJcVZmcC9we2hNgYp\ngrWq9R5Qmqdk46Fqj1UPbN4HqTVr1oQ3YwK+ndNOO02nnXba8JYlSZIkSZIschZFZXOIPP77779f\nUld5PMqcib7vMRV94WESbwSPGG9q7IwNjlcb24LXiFLH763xBZ5J05owQHYkP1v3faoFbwRlirpH\nxI5hRxB5T16dF/sp1cgpebuMh3txUcXzsSgpOLXxGYA3WpuR26oulPbd2hS111JSDwE12yueL3bw\nvPtmHznMBWyf/TCHMi2Fb9oxbq2wRpSUolawExSa0j2NONrWfTwjon1Wa/HxRXEj5o7fx1S1pdxr\nL0mSJEmSpJlFpUiV4KmyVGnbFZWS91PrnfpT7KRrh9R6bfQHXihKFjU1+r63JguTDKGhe+1BSYnC\n60V5dKLYL64XBYN2o0gRS+eKVIRXLKcf6V9Uib5eNd7SpOI/IjwOwsHbrVUM+9aG85jC2ti9SIki\naxelcWNqd1GoVUXpm0llPvalNsaHGBpXUbFhbLAUi4LNEpuF2osi8cgjj1S3fWOwtcUSe7a50Brj\nFWVWcw9jTeNz22+/vaROoYpiiYbGRE0azybF7oYqtU4qUkmSJEmSJI1sVooUT8WlzBX3eEvvxfHY\n8VKnvZ9WX/CWUezIqsIb5Wm8dr8mvM5ly5ZJkm644YbR2jofeMdR/SuvUcL1esVwxpGYtqjeWATH\nw2vxDCPOgx3WemW+g/tC4UV0HfprUqoL/Td0XmGXL33pSyV1Vbs3Vkw9CyxSwfg7cz9aI7CBxVJY\nuLY2HHOJ+D7G2Gt+1YLCRYzOUDWe+oJ9a8wNZWhNs80N1PRIkQKUxZ122mnW3yO7L709WChqYyKZ\n/9jx2PXJUpFKkiRJkiRpZLNSpKhwXKrb5JSUmNadtWvpmxXVFxQSYqU4D3EMfbPkUCZqa4qMTdRe\nshJRIGgn7/dRLNmTrRWOS80WMrdQA7A/lB76ueTl4w0tX758UPv6Uqs05L+doAAAIABJREFUTSpe\nxeM6Wuuc7bPPPpI6FalGnaGvvUIzx6hVZmo/h+IxtHZWRGm/Q28HtsrvfXc7oAYcqisKEmPY93pR\nSJhbC60QDd2dYHODe4JnIHvNOhSau+++W1IXb1q6124qTnEhqb2n1lY8d2rXzlSkkiRJkiRJGtms\nFCm8nr7e3tj7EvWFp3+8WjIiarPIHI95wUukKmxfr9NhHy8UHvfG8UZ5WncFyWvOoGh5bBGZQ3y+\n5C1ynlbvwqEGiu9UjzJD9iJeHN4d3hrXQX+UaqDg1S+0F16rpqA+ML+ID0K9ieJi+B4qg/cD/UM2\n5RNPPFHddqnrZ5RdVJkaBS3ak442Da0n41lxk1KioFY1RGFgzrWC4sC+lGRMogL3vV4+T+YnCtVC\nsVizBFtVWvqPtcl3MSCG6b777pNUtnfWdNbG6F6Cvbdm7a1atUpSOSuwL6zNrNlR+0tvifg+b0FK\npCKVJEmSJEnSyGalSDmeUUAsS986PUP3pitBDBZKEj/xFn71q1/1Op57cTx1R8fhKZ2ncLxVvA9+\np12PPfaYpM5L8EryeCM8rePl8TvfQ5nwyt0oT65w0B9RjBTjjTfD+3u825JXg5eFUoZ3TVwAEBsW\n2QNKFUoNx0VxYRyi63DFhkwZvkd/o6RyHN9LEbUBxQ+vlB3US+0AjhN5aaUMLcYTb9gV05UrV0rq\nskA5fqk+GeNM7SOyL+erY+XqWaRAcEzUNI7ZN8OQbKiS6s35YOw6Ng7XH52ndv9Lz/aDFStWSOpi\n0H76059K6sY0UkFZuzyb7NmC17xjzUPNZc5GSiqw1vga5HOd8WUeeMyPzw/WAuyZ/3N85jKKpCtS\nzP1SNiYK2VBYs1jTsSuUVFekPL6VtQg7ZN6zlta+zUpFKkmSJEmSpJElz7RuszzkpEuWaGZmZqFP\nmyRJkiRJ0puZmZlQaU1FKkmSJEmSpJGpxUhdcMEF4ftH3t/zHtlrwNSC6sVPsn94D0oGA+/7ySaK\nsseITeE9LO9neR/70Y9+dNb5Jo1fXwT9OLSaa+35xoLznHPOOZK6ceI9PrFNjANxIXvvvbekblzI\nyuM9OPEIjDdxBSeeeOKs8+F9eBVmj10iZgo89od4BmL4yFx605veJEm66KKLJEnbbLONpC5OgvZ7\nZlgEcThcL9mBxOideeaZkqTPfOYzkubGElI5nPlBfI3HXZDRwvwh7oJxYZ6ccsopkqRLLrlE0tx+\n4bqIVaM9feuecd7TTz99w7UxRvRZKc6LayhldRErctJJJ0maOxeGzjX6jtgZruPkk0+edT7iEek7\nYkGiuj5R7IrbJrsBHH/88bPOB8SYEFtS+0KDfsH2PVbnjDPOkCRdfPHFkrrr53vMVY8DJTYG27/j\njjtmHf+Vr3ylpM5GiBn627/9201eH/cI5jyxc0MzvznPueeeO+u6/F7EGsDcJS4Qu+D/XC/xrNg3\nMUs+92g//Y/d0J9cL+PpsXPYB/bC2ks7TjjhhFnX2ZdS5XWgH7jXnnXWWZK6+cBxaL/He65evVpS\nF0/rNQk5DvcI4k/f9773zd+uef+bJEmSJEmShExNkZqv/ghP55F3iBdSqtvj8HTqWWg8vZbqGOE9\n0C68vFL20bRx75gK0Xhbk6pc7nvftYK3hYLhey0yrngrVDYnswRQPFBasDMyX4DxxTvjc1E2aGn8\n8abxglwdwYvES3VVAQWo1ltDNcBeXTXw60BhpR9KexPSH4wH/cVPz8SK+od2tVbgh42/h+LSdy+4\n2vpCpTo8Q22dsWDsouwnbKGUqcz/fS4AtukZvRGtlcH9uNH4cN3cHzif13oD7gF+L2A8mUvYtvcT\nawmKDf2AIsb5x6pFyJzh+nwOlbL12N2DrLJShno091zZ414QVfLGfrwumdtDrbLk1H7es2lRiN2+\novpQrG0+r+h/+ovfa+vMpSKVJEmSJEnSyNQUqfk8Rp5+oxojxHK0KlL+tNo3ngHviKfV1sTH3Xff\nXZJ07733ShruzdbCeWqVKK8LVAvj595wqVK2g9KCchPV+MG7K1XbxevCjtw78fEca38uqkNHtXuI\nT3G4fpRErypNP1Mp3/dBczzGiXGIVIsI7yfUFJS16HzO0GrgG9eT66tE9cXVS2fo+Wtr4LGG0Xd+\nXjz1/fbbT1Kn0kbxpqxpkTKAUsOYD12roj36/O/MPVTWvms1n0dh8jmBYofixNrBXOxbhb8E10M7\nmDP8vfZeUmsnzD1imVjzWSN8HCNlFiWK44H3J/38yCOPVLVvKKzd2Adre2RfpfpW4GtjiVSkkiRJ\nkiRJGlnUlc15f+07mT/wwAPzfq92x+ZayMry981E+PetTA7Eeu2xxx6SpK997WutTezFnXfe2evz\nrft1ucJx0EEHSerGr1YRYzyJd4i80pe97GWSOtXghhtu2OTn3Isvebm1XkwEihFeYOTllLwo5sOe\ne+4pqeu/H/zgB7M+75lArhBxHP7fN54hgvO6ssZ5mL8lhQ8vvXYfLzKQWui7qwEKRl/w1BnL2tiL\nSFWkvVEMDOpnpDw42GS0dqKA+W4SQKY1NllSViJbJ34xshG+RwYvCkpUKRsFzpUL4DxcF3OV3znf\nWDFS7GLguwlwHsazr/JGxXG/N3J9HvtTUlYhirsE75colg08G3IoHss0dO9MxqFv/6cilSRJkiRJ\n0siiVKTwoNnHiadh3leX9odqjemJiLy+obEdKDY77rjjoONMmrH2BeM9fd84EuI98DaiWJ7ttttO\nkrTLLrtI6t7Tu73gneOV+Xt/BwUnirErgZ2UYrxK/XLddddJkm6++WZJZa+J6/MsVbdblNVop/Ra\nSsod/VhSpPoqZEOyZvvur9mqdpf2oIsotS9Smh588EFJXRZYyWaxCd8T0Inaj23XXl80h5mLvueb\nt/Oee+6pOg/H4a2CZ/x6hixqKMojCkurIuXKD7bNcVFUSjbPGhgpPlF8JfdOn/MoTaUsO+wvyhz2\nuRzZGd8/5JBDJHX1vu66665Nfj7C1fWx8X6KFFgnFakkSZIkSZJGpqZI/dmf/VkYA8FTMB463sD6\n9esllbOA3NuJdrUfSm0MRwQxPGNnhiw28Iqo4dHXq2CcSwrM97//fUmdd1ZSLqPjeVYhKkStd+Lg\ndeIFRtQqdbXv7zmeK0XuVdbGgHnNnb7UqhWTzrwbQm29Kac1s7fvmuUxX7XfxxNvjQEbK86O9qKU\nDI1NQrGjX1yddfg/cx6Vu7XWnitSrWt9KfYo+j9zyZVNX0NoJ3Pc76EoqiX7iN7SoAxSmZ17eF9F\nqnUN7ovHsJVIRSpJkiRJkqSRRRkjxXtXnm49vgBFozZCf6iH2zezpy+larYlJt2+VvAqPWOJOAy8\nz+j9PtQqMHjFP/7xj/s3diPwRrAbVIjaGJ8I7NczhyaN24XvK1erWgxVB4YquIuBVmVpoRi6BrRm\n6I4Fc5i55rsjRLFCpdp0rLHLli2b9/zMbXYhoNZcqxrbGkfLmkm8L+2Pri9SUGrvfbW15FCS+JzH\nDEZrCXOf2L3WNXRoVl4tUQ3LiFSkkiRJkiRJGlmUe+0BMS48zeKN9FWYeEpv9Q54Oo1iSSadSVBi\n0kpUKbYngvfZtI/9ofDy+lbSrmVo1WW/XuwGr88VnVpQoqZtL5OuoB/ZS9/4hqExWZOgti1RnaRJ\ngzKD6ts3o3Gh1dIIlAfmGjYbrXUlRQoFy7P0HDKUWeupID6WDdbu6sAaSUwR1x1VDHfFDsa+N3jN\nRM+CLEGdK+4Fk6bvLhqAslabpZuKVJIkSZIkSSNTU6Rq3pF6/SKefnm69P2YInivW/IOo4rKeGmu\nSJXqD9XSt5LzQsHTfKuXihe3evVqSdIOO+wgqXvaH1q3aFKgGBGbRVyM/94XFK3WzK++4J36/Jh0\nnE+kSPX1CheTEgW16uxQJYrz9I1Zoo+ZY2RZ1aq/Y1WcHoqv8RDFyNTG3JTUWFRT5s7QXQ0cj7+M\nYPyYq0uXLp319yj7L1pTPdasFV+7+lYA57pRbFHcyOgem9b4aOysNlYqFakkSZIkSZJGFmXWnsPT\nq/+s9bJqK3NHihDeAJkcgBc0VGEgM2OxKVI8zQ/NeiQTh37Cyxvb22vFY3f4HSUOLw5vsG/cAfEA\nKKqRWkEsH95jVJunNkaL/nUFyuuqoawOVdyAjKPfRyJVeuxsImwsUoNLmbrYGGp3LX0Vhlaiitoe\nh8jvXKfbfF+lpWTbKFG0j/4fK56zbwYyc5SYN/qlth4V6jo/W3epYE1kT0WUy751zlCZGc+x6kK5\ngtuaFQi19aM2nH/Q2ZIkSZIkSZ7FbBaKFN4VGRTE7gx96nRWrVolqVMOUL72228/Sd3+QDBWrAve\nFPtBoQDVeg881eNtoYDQX63wlB/tQF8LNVBoZ2sW4KRwLxXvzWuzkAFVqz6whx1eLdftmS9Qq3jV\n2l3kffvxuR6ul3GKvE2PvfJ5OGlllfHwHRCmwaTr2kQVq0s28sIXvlCStNVWW0nqFJWhaya27Mfx\nNaIUIxYpSCh82DhjG9ly35gfjyHienxPPpSTsStpu5KI0uRrPVl7xJfS36wpN910k6SywsW9gH7k\nenkLgsIa2QXXT4V31GYUKc8SLIGShuL3+OOP9/q+w7MA9oIC69mqzAdfO6Iahn33VV1cd7QkSZIk\nSZLNiM1CkQK8qpI39hd/8RdNx1+xYoWkzishNqr1eLWgULTWJ+J7tdVpa6Gfh3rdvO+/++67B7dp\nIUCRouYM3gxKS9QfeG/RvlV4vaXYsEkrLNHxaXfk5eLF4oVGcRqebYd3TYwhKgLerSt0keoBfI//\n962V1AIe9NZbb930fWJLqI2HZ7z99ttLku67776hTZwFfUpfYcNDs/KitwFk5HJdKD+PPfbYJo9T\nqoC9UDAXuJ4777xz1v/HyswGr2kYvXWgAjj3JOYU/Vkba+VvK1xtRrHi/9gNyhl2z5yP5motfd+2\nOF5/yteyaC1A2WU+jLU3JKQilSRJkiRJ0sjUFKn/9//+3wYPd9ttt5U0t04UT5G8x+Qp2d/z8jTN\n03NtNVKHGChqr9x7772SFq4SdWvMFe+vUTrw/KO4AvqN6yKWyuMNSsqAHw/lDiWnpLzgndEOvu+K\nj9fZ8qyzWvCmURzxStwLxmvz+AjiQIhT4Lo9m5T20W+egVOL17PqC+1zokyn0nlK8RTgWXtPP/30\nJj/nWbDePvrf42361JxhzWCN4ZprlRnWEtamVnWa8zMmKAx9Y0xqbR81mb4fS+V0T5727LLLLpI6\nG1q5cqWkrv+iitwRrD2ePdU305e1kTXE+w3FiXZia/QfSiL3lvvvv3/W5/pS+z0UvRtvvFFSN+f6\nxr1yHM8Y9nbw9oXrZBxZG7Efv7e6+kxMHmuhZ+lh77WZz9gBn++7Bx4w7qW1w3dTqI2RS0UqSZIk\nSZKkkSXPTGE78yVLlmhmZmahT5skSZIkSdKbmZmZ8C1PKlJJkiRJkiSNTC1GapKKFO9VTz31VEnS\n+eefL6l7H966xxvvZ3m/TCwHv3NN/OR9Me/1/T18Kc7Bd5Dn+8Rwvfe97511PvAMoVbIxCGu5C1v\neYsk6bzzzpvVfuIYoh3aPXPE/06/8l6a99Qf+MAHJPW3FcbZs9A8Jojx4+cpp5wy63yl6tFQu58Z\nGSd87swzz5QkrV27VtLcuAXsmLgCYsfoR/qf+AXa65lB/P2jH/2oJOmCCy6Q1NkXsUq+D1hUEZ2/\nl2r40I+f/vSnJXWZY4wz/RDFjUQ7t9Nuxpn6b6961av08Y9/XFKX7cQcvPXWWyXNjfEh3s2r+Hs2\nEn3INZ944omSpLPPPnvW5zzOsVT5vDZT19eWPffcc1Y7yTbzviRejTlFVhPnoy+xMWzq7W9/uyTp\n8ssvl9TFqTKXauP2aB/9QNwr52d83vnOd0qSLr30Uklzs8PYk43x8pp+HJ//kxVGbJHHop1wwgmS\n4rWldu6X4kiZM6eddpqkzl44bumFUBQnyVz1NY74z3e/+92SpHPPPVdSt/bQXsadmCviWoHjRFlw\n9DPfZ2357Gc/K6nrf+Y814t90t7I7n1e0A/cM1irP/axj81qb1Sjz9vd9xmgdA9KRSpJkiRJkqSR\nzaqOVC3uHfAU2/cpdO+995bUZb7wlM1TMseNsgTxaqKaFSg+Dz300Cb/jxfh9Xe8BgdP616/aCh4\n557V5v1byuKKvC7+7t6WZ4L0Be+/5FXSn66seIZL9H284Nr2RspcpMjQr3iLeF0onK7UuNcH7o3T\nXv9cSWHqm3Xo5+tb3yzKNPPMo40zwrxuEpm3PgeXLVsmqZu7eNa+xxu2Sb2nqC4SfexZVRwv8vD7\nZuqifu6+++6SujHzGm2cN6og7bspLF++fJPtI7uxtb5TVP0efG5FNki7ovpDzPlI+etbt6ikRKF4\nMjej66N/AUUEpaq0Z16k/EVrl48fChT3CPoHu4vuXaXsQL7HPcf/Tv9hN30r6vu8iPqBtbQ2m7Pv\nM0BtHbFUpJIkSZIkSRqZmiL1h3/4h811k6KKyJHXF3kppT3p7rrrLkmxZ1yqzRLVz4FIiQK8KPc6\nXNHgaZ+n9rESMfFGPV4EL5fzlvqhtEM7la8Zz7Fq3tTWKnFqFaa+Fd8jr7j03p44kl133VVSp2yx\n31bJG0NNcZh/tfEgtfheinjDpXbyPa6TPRqj/eZgY7tCefrJT34iKVZ8iB/Eg3YbxxapP0RMiStb\nzAVs3GGtYgzp46iGVgna6/tW+nXye6l+E33stfugb92mvnitMxQA5grKDf3L57n+vuq1Kyh9wQ5o\nx8MPPzzv531c6OdSO2rnpMfROnzf32bQf9GaXFKnWftdpcbOgXvS2PviOpPa87L2uKlIJUmSJEmS\nNLJZKlKRgtR3z63SU/JQZWSoMlT7FE98Bt5qayyLg1eCVw5476Wndb6H9xPFyOCd8dMVjVqIWyh5\niU5rJXyH+BX6pXYciOeJFCkypqKdygGFFS+VcYoUHR8fn1etldW9GjBeccnL5u+MI+oEylvExvtv\neUxIlB3kY8PvXrkc20DB8u/Rt1EfM6YoK0Pj/4C1BWVrqJoYqdn049h7k4ErKR6LRX/zk9i2PfbY\nQ1IXG1aKNcImPeYFpQv78H50m6W9njXosJZF94BSRnXteJbuEVyXr7GtldkBFd3Hi3swawnxpqXs\n1UmDUoYiR3vG2pc2FakkSZIkSZJGpqZI9VFN8Brw+HmqLsUglZjW03HEYYcdJqnzdry2RwTeLv2D\nlzA0viHau5Cx46ker83PV5vpg9eEl9S6nxL2gBdEu0vtcLUCRQe7i/qxVnFzPB6Hnd5bYdxRDTg+\nakikrEbtpQ7ZmjVrJEm33367pHqlzxVFxhOFszT3v/3tb1edBzaO50Dde/7zny+pU2xKajU24Kof\nSpPXHAOuLVJs8MxRJoYqAZ7JSRzjUBU6ah9q96QUKd/zsLRmsVcatl2rJnN9bpusndH1uTLEXGOO\nrFu3bt7zeSyc92dr7BBKodeDcjzzuBT7VEt0Po+d4nrpL+KCh86DvnD9ZOJz709FKkmSJEmSZMps\nFnWkeIrGC2uNoamFp3281LFijkpwHpSOSJFyLwyvmTiHsZQ2vAuPefH+L2VVlfCaJq3tH1thLHlv\nKF1eK6aEe6Gt8S2oLmS5oWy5HWDPteBN4631VQi932or+beycdwRfYk6WaqHA1GFcdQ8+tKvbeut\nt5bUKSWlejcRKAteYd2hfV7lvpQZWwKbdLW0No61VOF7LJhzrXWtXFHsa5PMBdZA1G+PfaPf/Hz0\nk6+pfcHOqP/F75HSg5147cPWOOWSQsl18vYIu/TdQPrSmnWJIt03jrqWVKSSJEmSJEka2SwUKWes\nejcRfbOUxgKvtlRN1Wuv4B3xtB/VvOkLx+V4Tqs34+Cd4C1NenxL9PWWhnqXHmdTqy74/mh4gR47\n2Neeiet44IEHJA2PI5j0eG6s9pSUKGJiyDryvcfuu+++TX4v6kPqNJUyfFFqokxIz66KjoeSQHYh\n32NN6FvBG7A9rwNUm2U4aSVqKPRbaU4xl7EHHydiumqVTj8f9ob9eYxYXxgfr+3nsFZ4RXSP4XO4\nF/n3WHN87eP/tAN77LtHY8TQtXZSpCKVJEmSJEnSyGapSI1F9H57WpAxxB58Ee6tuhc7llLEcT0m\ni/4aS2nAi1ms3kYEMUpLly6VVM6Si4gUxpL3jL3g5UWxan3tgfNS9TqilLEGk87Q2ThepqQU4Enz\nHcYQm45ifZgDPra1Y13KAMXzp8p/pIwRP0m7XUlqVaRon8/BxZbZDNG+lRHMAY+t8jWM43FvwA6i\n+lLRvQPlMaqTNdbuDeyhyBqycU01qVOisC/sn+thDlOHy/uT+RCtRf55lDzsnP4ZKz5yrF07xiYV\nqSRJkiRJkkaelYoUcRJDFSm8w9YMEoenfrwK2ulP895eapuwP9hYtUK4Ps/koV1jKQ14w3h5C5Ul\nORTPmKrdU85B+eP7qB+Mc+SF0f+lcWC8xgZvvRTXM2mlsaUyPUqLxy7huY+VWQme+RjVXCt53KwN\nzEnmDO1uzcYiZmdSttJKpDxxnfQHv6PIRWuIH4e1M9pVIBr30j3D7zHAuI0dhxutOVwv58POUKpY\nu0p2V3tP8aw6xmFolmB0/MVCKlJJkiRJkiSNLM7HuwnDU/LQ2Cie7sfaq42nfo5b6wXglaII4P3y\ne6v3Q2aQe3ce0zT0vXVrXMe0IR4HdaPVDvi+7wM1VjxAlHU5lNo4j0ln7W28P16r5+t1dqL/t1Ib\nV1iqyeYxKFwnf0dZ67u21dpuSX3si5/Xx4/YH+8X1rhtttlGUjduKEHEDjk+vtHbhNpdJSJc+QGu\np7TX3lgKDnZCPCXKG2sMbx2ifTD7xshxPM8cRpnjXtJ6TxqrhiTHwR6G9nMqUkmSJEmSJI08KxUp\n3lPzlLxy5UpJXf2cyJuJYH+vsSjtLO5Q5wdvgmrLeFWtT/9UgXVvEK9vWvW2Fht4562qhfcvXmAU\nI1ci8uodVIxJZ9WV4lCGsrHK4J4l1x7FkPB5st8ipWWoAjNWZjBzDjWU47J2tZ6Hekae9eWMpUTB\nihUrZv1OrFa09qAkUNEbhYz2lzJIfY6OfT2O1wSkfaU5jV1GdcegtqK8xxYxH1gDUM68Zly0prGb\nwt133z3r75GyyXmG3jta1W2PP6W/uO6ha1MqUkmSJEmSJI08qxQpvFO8Njx/fm+N1RkrS64Vnu69\neu9QxYj36s6LXvQiSZ1y5zFaffHsx9b34NF+ZdTm8ff2Q0FpcXuirhS1WUpEdcGw11pvac2aNZI6\nL5Wd1qP9pfDG+ipSJZXHFbAo0yaKtynFh6Akc57ddtttw/+op4Tn2vfamDujx1AEMSh9P3fPPfdI\n6vrssccekzSeqtj3OltVU1i/fv2s30trFv3CvpLY0FiZ02Pjdc1od20/l/qjtOaytrImuWLJ2hKd\nx8eVLD/ewniWp18v40MsGwpjawzatttu2/Q9FCnWRpTBsVTyVKSSJEmSJEkaWdSKFE/T2223nSTp\n3nvvldQ9laKI1CpC7kHzdMx5ogyFksJQu++S05qZ4e+h/f04+3/5+22u02Nj+npznJ+fPO1HsVSc\n16sL0w73klrfgxOPgGJB3ETf6yvFJfB/FCn2SOT6+u4wvmrVKkmdQkS/YdfYJe3hOvEO99prL0nS\nfvvtJ6mbJ8S7REpr1E4UR9QYvNLaGLBIqUIZxB74nfZRzbsU7+F7DG6sYDJX8dRd8YhA3SIbjLnB\n74ytq7R45owFnrYrBbV9x5yIqv1zHjIVaSfHJ1aK31EESrXNiFXydh9yyCGSpIcfflhStwbyuUiJ\nYg4yl/mcH9/ra7HWoxTwf2w+2v/T6yTx/2XLlknqbMrbiy15pXjGkfMRR0v7qdlHv7IWeIyR2zKK\nCNfD2teqKDLHGXeH6+Enaxd2Rb/V7pXotfL8XkS/AP2LPaBMYResQYxL1A9Ds0Vpb+mtU61yPOd7\nTa1KkiRJkiRJtOSZKWxes2TJEs3MzCz0aZMkSZIkSXozMzMTKsupSCVJkiRJkjQytRip+RQp3m/z\nHpn3psQpbLnllpK6miNUifX3n2eccYYk6VOf+pSkOJvNK3hHMTV8Lso44Jr4yftwMomIr/D30Vwn\nsV/Rzu/EDxCTddJJJ0mSzj33XEnd++OhtTY87gK4rrVr1876nEPMT3QdtXh/Rhx00EGz2nPzzTfP\n+j/xL6WqwqXzEUcRvWfnPT79H40D/fyhD31o3vOV7K0WYp7e9a53zTpfabwd4nNqYxJrx8/pGw9B\nu04//fQNc93nHDESYwnwXNN5550nqRxjgu1tv/32krqMSq97RKwNawFZeh/5yEckSWeffbYk6SUv\necms45EN5rZNDBdrEf3BWsr5DjzwQEnd2nLooYdKki688EJJ3VgQK0Rc3jXXXCOpixPcaaedJHWZ\nlI8++qgk6bbbbpPUxccR28NYv+lNb5IU2woxXMQC+RpNXSNidHzOcD5iro477jhJ0qWXXiqpi1Xi\n3uNxiYcffrgk6YYbbpj1eWBtJkaKmCDiRk844YR5r28ozAE4/fTTJ3o+h/Mw/0oV+oF7XxRDB9g7\nnzv++OMlSV/60pckzbVr74/aGDDg+8zDY489dt7PpyKVJEmSJEnSyKLM2ivtao/3gzLgdYMioqfS\nPfbYQ5J09dVXz/t9vJmSQuBVXPk9Oj/eT6TgcJyoFgj/x7vjJ14BXiftKNWHImMmyvQpZZgMVaL6\n7vCNFxqdl+su7W9VopTxUaug1NbcqfXqSkTj2LfO2Fj10krZqqV+xNtHzdlY+aPPmCsoU2P1pVPr\n6TIGd95557yfQ9FAOfM+8sxQVEXPFgNsluO5Ike/oER59pfbDkoQawRrMTAXUcJQovz7tKNUSR24\n7mhtIBsMpc9hzvned6xlnt2GcsYc+d73vjdv+/y89JvXWYogi45cf65JAAAgAElEQVS1Kpqb2DX9\nQW085qb3J+dnjngdJUBJxB64RzDXuOe4/WB/gIITVTyn3by14HyltZnz+9rJ9UdrRl8lCuiv6v1u\nm86SJEmSJEmSTE+Rev7zn7/hKZWn9761NEr7KkHJG8X7QHGqPW6EK2p9FRbHY7e8GiuKFu+b/Xx4\nq1FVWLwcjoPXxlN53/4gtoe4EI7L032pmmzfyub0c1QDZGiM0djU1g0reVNc74477ihJuv/++zf5\nub47uLcSzR9XaIeqQ15/bOP+ZE1Zt27dJr9L9XdigBZb9jDKBPGfHgvE9aEs4MlHY1yKl+T/KEv/\nv71zjbGqOt/4c2JJajK1WFsGZGwGuch9oBBKPxhrBJMmDdpAGm2wNGJMjEljNbb9YjO9qLVJS5G0\nsRdNSBqt8UNLmwoxTfBSEjpYII1OU6QMBhCxgk2gjcE2+/+B/+8cZs2sWXvvc9kzw/P7MnDOPvuy\nbnu9z3rfd6XU0lChCKEv/vWvfx32eZj3Cd+uvKT2Ety7d++w64fQJsNyChUHyjflS5d6V9B38yoi\nXC+mROFj9ZnPfEaS9OCDD456HO0ivE8Ux7A9cDx7F4Z9KlQQAeUrVNxifZvrUP4cl3csiB3H9WO7\nQ6TKP9aeef68Y7UVKWOMMcaYklSmSF155ZV1z3giHlhXxapYvXq1pMascd++fZLiUXVYc0Ujc/Cj\nIBKFyIyQouutKDL4C/BcRRUClIeYNYaVkdr5HSUo3DGc2TzKFdZjrBxT1hgKUFklqOi+XUQGlYX2\nVZZ7771XUiOzOBElKeusWW655RZJjWjCmCLVKWLtAT8KFNNmI+ioL6zsi63G1O7y+IKsX79eUmNM\n+cMf/lDqXsruThADS5ooJXYpAK4TqvnNQt2FvlZhpCZjSEw1jvm38v8wo3fe/S9TYxttIZZhHMI2\nFyoZlG+s7zL2kTGdqMqQsu+iGChA+LGGGcQh9H8Nn493RajY7Nq1a8zrU67UO+UQlhO7jYSE91HW\nXzVcbYmt9uR9h6RWwVCGU1iRMsYYY4wpSWWK1D//+c/6LJVZbjg7HBgYkNTIE4Ri9OKLL456zrKz\nf2bx5GKJUXSnaKwILOiyvlLh/kyhgpKyflCYYopBaMWkZvNld3ofr6Ss3RRYsatWrZKUXvcvG0kS\nQr6svJFPVUH5oBKh1Jb1lWLcoF8UiSakzFCgmlXxQpWtWcII3BixKKqyhPtmAmMLz4k/aZinJy/U\nGWN92fOEoBxw/pgiFUI5M6alcpjNnDlTUtpvNNzrsFnI3ffyyy9LKr+/LIR7OMagvilfokXDaM0U\n4f6sKQU3tuoRqvnh2E15U/6MNbFySEUIxyLcQ6xIGWOMMcaUpDJFKo81ynru7t27xzwu5bOTmpWy\no3kqwgWrJe+sGh8hFIiyFji/5/5CK471a74PfchS68BE6eG7xSw9pnSlZvGp+iA78qFDh8Y8z3gH\n6weF9Iknnujo9fEzGO8KIe2Hdtjs/YZ+NRdbqai1sWzt+GE+/PDDktJKEgpN+AzQ6rKnD6b8C1Hx\nUz5BeWHMwJ8TeL7QJyZvzrSQ0D+O85aFul+xYoWkhoKQ1wcnbB/smkF5hGNYUb/PMG9VWXjn7Nmz\np9DvWI2g3TKm51W0wnKIRcdBbDeGlO9iCOUWln+opIXvbO6HfsHfsrkN8/ZvK1LGGGOMMSUZl5nN\ni5Jar07NKvl93vXsvBE6zL7zrkfHYNaNNReeD6s5jOTJu+ceVhhWHFbHjBkzJI30DUv5ZYTZc7nf\ntWvXSmr4GWAlxSI9xjuUd7OZ3Ft1HzGabX/NgnrCfcSiVmmHWNGxdhH2v4tVItp8qo3m9WlKWdCt\n8ncD7psotNR1UdubhTKMKVuholFWkeJ31GGr7h8fnli9x1YRQmWGsS5Wr6l6CWlVNCcKDe+oEydO\nSEq38/CdVtanLm9904dRpIgoxscuFnEfgt8n5+H6qfunnXKdMFN/UfLmNLQiZYwxxhhTksoUqQ9/\n+MOFM5nnpejOz1gheXOa5PWR4rhWWa34J4QWfahYxZQo9jcibxb5l7jP2HlTikcIVi3KFJm3yawe\nZiKfqLRajShLSjVpNnIIaxLrEp/CvIyWgXw0sLq5HhFCqXHi4vYettVm8/ngw8M9tEphSNGsJV2W\nWAQrYyp/i+7TGBJGV5WFPkgmdRQu8nChstO2Ur5kYRRfs/fXqqg9FBnGbvJIpXydwvbabN4zovhi\nfTK2NyTlQD2k/IUpf5RGlKm8exc22z4hb87Hif0mM8YYY4ypkEp9pMKIglZRdJf6opnGUxEIWLE8\nX9H8UxD6tnC+0L8DayXl94EvD0pQat+sspnJUQiwXrBG3nzzTUmN8s67Xm7GJmXdNWsVY8WW7aeo\nBCk14B//+Iekxr5fqB+p6168j1uoEnJtxoSiliq7LtBnwl3t20XeaKFmo/XyghKCwsB1UQ/LjqGt\ngrZDxvHY+cMcfGG+pzCPV7NjFFFjzYIyw33hF5t614XtnectqtRSLjwPvmKxDPZh+XE99nVlNSTW\nbg4fPjzq50VXR/KCTxbPxXWc2dwYY4wxps1Upki9//779dkts9dQGWEWy2yR4/CdaBXMmvHhSUWR\npaxarLRUNGEKrGksbqLoml3/xZrB4o8pWaG/QFE4PxEmKFyxfb1Me2i2vdA+yraDMBtxzBqO5WpK\ncbFyG94jClVZf7bBwcFSv2sW+nrKb7Ns9FyMUEFhLAvVQcaksooUbYrnbBbqHTU8VJ5QGlBEIOYr\nhALXrCLVKl+3MDI7b0b4sK/ljeQOoXwZy2l3odpNuwjLlevzLi+bQ7BdftXhGMX95/WbtiJljDHG\nGFOSMRWpY8eO6ctf/rLeeecd1Wo13X333frqV7+q/v5+/fKXv6xnMX3kkUf0uc99TpL06KOP6qmn\nntJll12mxx9/XDfffPOo5+7q6hqxdxyzXRQLsuwyy2VWHMvmi09OWSsCK6TZvEZYAc0qAfi+MDvm\nuULlLu8sPVxfx58A6wIfJmg2Ko1ZPvVq2kOz7T4vZdvDmTNnhv0f6xS/o9BKRt3I238utn7D34T3\nTFnRt1q1V12rwCeL+8xrEbeKcExF+cAXB18Zxuyy0V/U8dDQUKnfh9CGwgjmkFQbjvn+lKVVfZL7\nIU9TXh8z3mm0ozBqL6zfFOE7pLu7e9j3vKvDfsX58X0qq6Q2uy9qjNg4kFfBG3MiNWXKFG3ZskXL\nli3TuXPntGLFCq1du1a1Wk3333+/7r///mHHDw4O6tlnn9Xg4KBOnDihNWvW6NChQxM+zN0YY4wx\nZjTGnEhNnz69PqPt6urSggULxsyoumPHDt1+++2aMmWKent7NWfOHA0MDGj16tUjjr3iiiuS+yEx\nGyR/DbP72Hp82dk/s2sUthixfYRCsBZSlnfR+0OJC/enyjtLxwpg3T6mRKXg+VI+M62yNltF3vor\nCn0k707hrQa/j6qiILFSY1ZmzNqN9YeivnN5fLfog6jc/L9opuqQOXPmSGo8e+j/l7L0Ua+XLl0q\nqaH0kG+H/EhVESpjPF/ZsSykWdWe8kMZoS/Eyi1v329VHqJWQcQ19583Epx3Bf62+KShHIXRqChV\n4fsdpZTf8w5hVQliCmreXT64X1YzQjU7dnyz/six6/DuTZFbKjp69KgOHDhQnxRt27ZNfX192rx5\nc33QeOutt4Y58/X09HhZxxhjjDGTllxRe+fOndOGDRu0detWdXV16Z577tG3vvUtSdJDDz2kBx54\nQE8++eSov43NQPPszs06d2gdxCzfMBdEXrAa//KXvwz7nNk6s+O8/hRYyFgBYebwotFPYYQGCkhZ\nmt0bjujGWK4PqNr/BPUB66nVShQ5RrgO7a7TGc9Tyl+zuWyuvfZaSdKRI0ckNaxA2nFKdQnzlWHF\nMjY0a01eHKFFXwkjZxkzyDfUKkK/Rcokr+8QfSTsSyhSeeG5GbPCNphSxVFrw9+F0Vn8nv+HCgZq\nNWNfTDnh9/j8lIW2GNuHtCiM1bE9BVEoaHP4LjG2Ux6M0WHfK5pZnNUYFJOiqjP3F0YxMhbSzhBA\n6DeUJ/VJOeNbyO9jeaRCwqhBxk6ei/bJ81LOKUWK9srxofJFu2Zsjo1Vsc9RZFMkFakPPvhA69ev\n18aNG3XrrbdKutD4a7WaarWa7rrrLg0MDEi6sBntsWPH6r89fvx4fYNaY4wxxpiJwtmzZ3X27Fnt\n3r17zONq2RiyQZZl2rRpk6666ipt2bKl/vnJkyfra6VbtmzRvn379PTTT2twcFBf+tKXNDAwUHc2\nP3z48AgroVarqb+/v4nHM8YYY4zpDP39/dFVljGX9vbs2aNf/epXWrp0qZYvXy7pQqqDZ555RgcP\nHlStVtOsWbP0s5/9TJK0cOFCffGLX9TChQv1oQ99SD/96U+bllqNMcYYY8YrYypSbbtoraapU6fW\nndTxbWJ9k3VW1lFZZ2ddHX+AMPKAdWqWEzdu3ChJdfUr5icQrkOH4COV8n3hOj/4wQ+G3U9IGOWU\nun4Iz/3Nb35z2HXbDdfhb97oPSi6t2J4vRR56ynv9a655hpJjQgs/AbCzPCxve7wS6Ad0O7wk3jo\noYeGXa/dFC3PVl1v27Ztkhr9GDU7lrMH/xr8Gyg/yp9yp9/w+b333qvvfOc7kloXVRajbFnybKl9\nMWPX+/a3vy2p4bNCGRAxmuqLlB3DfszflOt973vfG/O4VsH1fvzjH0saGbkZGzt4HvokfZ+xie/x\nScKwv++++4ZdN9WnP//5z0tq+Pbu379/2Pf4QoX7SlLPsefjXcDz8Q7kfnk38i4Moyd5Hv7S7rne\nE088IanhC8WuILQT+hb3vWjRIkmNcj548KCk+NiKT9XXv/71Ydct+m4oms8qb/+j/Civsrsz9Pf3\nj6lIOcGTMcYYY0xJKttr7+KZHREv4WwUq2D+/PmSGhYsyk2oYEEsmixmpaaUIHKUkPGc2Tb3G85S\nU/sghfeRV4mCmKKD0kEkTGhFtjpqrejsvl37JEGro+UuDpy4mNBqjBHL71U2G/REJVzeT2WPxopH\nQY5FKhEZhTUvtV+JapaiSlQIYw1tq2imZxSQvHmS2q1EhcTaROxzxuDw3VF0LOD8sXLhnRKLeEZh\nSuVAC88f1mcIfSUWic7vY0oJfSeV4452uXfv3lG/j5VnbNWFd1HeHIp5lajYdWJjat4I/jDqryhW\npIwxxhhjSlKZInWxhRHORvFNYV33pptuktRQVPbt2ycpnhOGWWqrCPfeS1kBYS6V0Aptl9XMrBwl\nj3Xh6667TpLq+yH+6U9/ktTwVeH5YgrMpRowgF8A7TOst5gi12wm+5BO7aXXLshRg/VK/4ipDJR7\n6nlp32NtQRXz7VixYoWkkbnjJjtXX321pPz5tFJ+h4xx+AhRF/SN1F6B4Z5x7VatQ1Ai6OMxBS6V\new9fIdo45RauTuA3SXmG+ZnKkvIh63Sm9k5dr1l1n7kC70h8wopiRcoYY4wxpiSVKVLTpk2LKiDM\nElk3JtoHC3Tu3LmS4r5QRWep+FpgTaWyrqfWc7Fq2pXhOuWDFWaKxkoiM/X1118vqWEF7dmzZ8zz\nVZ2hvFnK7sdEO8Jqzfv7mBIV25MuZfWj0LZqf7hOg+pABnj+H/MnydtvKMexlL/YuVauXCmpoRC8\n+uqrktJ1XFadDVWzVqmVsbYTRnOFY2qrIaqMXQ/wyaGOYv6ZVfsLhsoQSkrKNyyMKA99hTgvUWNA\nfUFKiULx42+eHUEuBqWKd1ZZpYg+S31VVW/h3n4pUmo+UYphPRXFipQxxhhjTEkqU6TG8mtgXZ1Z\n/ZtvvimpMbv+1Kc+1dJ7YX0fK6PorD+E2W+7fKGKRjgQFYgC+Prrr0uaeMpGWbDKi+YqwSrNuwN4\nWVIKTCpyZ7xD+8N6p58VrY8Qyq1MbpiXXnpJ0sg901IUVWex5L/2ta9JauQjIh/UH//4x0Lnw8eJ\nthBTGPB/RIFC+Th16lSh66XaJv6f+JaEPiYXR1SORqhslM2zVZYw/xLKXUqRiu0hGBK2F/b9TIFC\ngoJFvrDYuynWfskb1Sxl8y+1mlDRS5GqR9pbsz5dVqSMMcYYY0pSmSI11hprzApi1p3a7b4oqdwf\nRRlvuWxCv49Q2SAT/IkTJzp2T52krF8ImbWxBv/+9783dR9YR0X9bDqdy6fVhMoRz4PPWFlFinaM\nolyEVBRWq+CZd+zYIamhhMXy9aTA9yg1xjSrqldFp5QooC8W9Z/MS9i28yo74RidygN1qVA0cjml\nNKHQkiGgLFakjDHGGGNKMi4ymxeFCJBW5ethvbtdES3jnaVLl0pqWEGxbLUTnbztJYzSI/KjVf4b\nRdv+rFmzJElz5syRlI6yHG9glaNAhVmk8Uvhb1F/jKKRPFXQKgUs71g3XnKP4dPS6fxQeaHtNJvH\nKUarcxpOFJrd9zRGq1d7UG6J2iyLFSljjDHGmJJUpkg14/eBf1XMsi/qg0L0UFElhj3tWhUZ0WmI\nAMJaHCuScjJABFGqnomYIdoM/wQUlbKwDl/U+uW65JOaaIpUqj+WzU1DfV6qmffHolUKEGWbV0Xl\neBQJlDHU/rCOUznx2k27x7xOR9qOl77QrhyKRUmN+XzO6kNZJW1yvzmNMcYYY9pIZYrU1KlTkzlN\nsMRZF+UvuV9ivipFfVDK+gThMzPRFCny+PDcg4ODkia/j1jRjNn46rTKui+bYR1FjP2gyE00XnK7\npOA+6ZcouZRr0f5HOaImTHYltQytUgQo25RaSOQkdUMfSv2esXyy0umI23DvwkudvCo3ylVZnzaP\nQMYYY4wxJalMkcqzNs56bxht1a6dpZnN59nDS8q/gzoU9TdoF3mz8k40UkoNvk+p7MJEOtHuqK9m\n6436L+oLRH4vrKbFixdLkvbv39/U/XQK1JFQWS7rHxMqeqns2aY8qO7k2kNBYgwOM4KHe/qlxuqq\nfXrGu6pb1GdnvPgmjRcoDzL9s3rEGEx7Zh/Q1157rdR1rEgZY4wxxpSkMkUqj18R+WfCPD4oRkRE\n5J2Fx/IIsa5PJmv29stLXquqrKKBr9hEz3Ddbsj/RH3QLsi/tGDBAknS73//+zHP0y6rrqwvHtYT\nGdbxlZooihT9lj002fOR50LFKKrU0Z8nUq6eqvM7Fc2FRp0wBhHpS1sOVVbGOMaqVJtPqf60eSJo\nmyX0IWLM6FTuvJ6eHknS8ePHcx2PPyvvS78DyhHLNUe7btY/2IqUMcYYY0xJKlOkilj9oQ9E2X2R\nQusHBYooIjKmF1WOsNLaRausEKyvdvmYVU1MaaT9/PnPf+74PUkN5aRsRA3WFOv5fX19kqS//e1v\nkqSDBw82e4tthczsoWpB1F5KHaEe+R3td9WqVZI6n6unDLTBZiMMUSjK+jkWzcofHn/o0KFh/8f3\nJFS6WE1IjaXhPqf4zeX9fVFCn6h27bEXo2hm7om6Z2K7KNt/Yv7MtL9m1X0rUsYYY4wxJalMkZo+\nfXrSTwDrBGsHSxSrDIu27M7YWHX8LbuPD1FVUNbno9VgBYd7m0008vp1oNzRPmhfhw8fllQ8HxRR\nftRnKtovBu2qbHtg/f65556T1PCzafW+U+0CJY7M7ihslAeRSXxOPZIBnt9TDnyPf8PF7QLVjmO7\nu7uH/Qalg+OoW/y2UnCvecGCnj59uqSG6l0Wxqowc3i7ctnFosYoe3yLwszljN2UO+VM+fP/3t7e\nYeflewjVRlYPOD8qO32b+zp69Giu50uNCYwBPH9RX6pQhc77+zBH2kTxjaIPoyTSHsKI6aJjIe2c\nMaTVNBs9akXKGGOMMaYktayCpEa1Wk39/f2dvqwxxhhjTGH6+/ujPntWpIwxxhhjSlKZj9Rzzz1X\n92FhHR7/AXwjrr32WkkNX5DQjyH0ucAfgnXYO++8U5L03e9+V1Ijeyl/WRcNs5mSd4h18fC67CvF\n+jX38ZWvfEWS9POf/1xSPOLiRz/6kSRp9+7dkkbmNVqzZo2kht/B3r17h31PlODdd98tSdqyZYuk\nhk8QvlGszxNZw/OEfhr4ILFuzfesS7O+zfMZY4wx5gJWpIwxxhhjSlKZIvXuu+/q1KlTYx5z5MiR\nMb8PIzrIFhtGA6K0cD3WOWP7LBHlFSPMfYKCBqncH08//bQk6dVXXx31+xUrVkiSnnnmmVG/D3Of\nhM9BJAp/iRSJRX6QPTiMfqQcO5X11xhjjJloWJEyxhhjjClJZYrU+++/X/cxIidI2T3OUpmN8aHi\n/Py/VTuPh7llUjt2x5QofK3wjYrlhgn3BQpzr4SkAjPxQcOnKlS4Uuc3xhhjLlWsSBljjDHGlKQy\nRercuXN1JaSsEpWX0Men1b4/KGpQNoM1vlbbt2+XFM+6G+52n8p6m8q0zucch1JXQYoxY4wxZkJh\nRcoYY4wxpiSVKVKjwS7vze7IHe5vBKFyxHEoMEX3YuP3+FxBs/si4QMVU5Ji0YYhPG9ehYzyn6h7\n8hljjDGdxoqUMcYYY0xJKlOkrrjiihHRdq2KDgsVG5SZMEqPKDl8pYoqUmQEZ8fxGEQn4hOWgkzi\nKENlfa74Xeq65I+66qqrSl3HGGOMuVSxImWMMcYYU5LKFKmPfexjIxSpUEnCR+gTn/iEpJGZt6Gr\nq2vY/8MoQJQuFCn2okNJCq+DQoUixPnDDOb4MoU+UiFcJ69vE+dtVqFDicqriNk3yhhjjCmGFSlj\njDHGmJJUpkhdfvnldWUo5puEgpJSSj7+8Y9LavgEnTlzZtTjyIt0+eWXD/uLIoUShe9Ub2/vsPOG\nihTKVyofVV4lClJKVJhJPUVMUQvxnnrGGGNMMaxIGWOMMcaUpDJF6tSpU/VoupgihYJEJvIYZ8+e\nldSIokspQCgzXB8fInyZiF5DsXr33XfHPB+/7xRhPqwU3B95oigf76FnjDHGNIcVKWOMMcaYklSm\nSL333nsjMoCn9oSLcfr0aUkNX6qU0oIvEL5D+Grhk8X1jx49Ouz8MVL3i88VSlBMgZs6deqw72PH\nEdWXFzKw40t2+PDhUY/zHnvGGGNMMaxIGWOMMcaUpDJFarT96Mpm8Ia8UWcch+8TihF5rdjrL+Wb\nBbH8TB/96EclNRQkng8fJ/7iu4Ry9K9//SvXdfPyzjvvSEr7jlmJMsYYY4phRcoYY4wxpiSVKVLN\ngC8VPkUoKShDeRUdlKJwT7uiGb5RngCFC2WL85IBfdq0aZIaiheKVCrPU1m4H6IQYz5YlGerFTFj\njDFmsmJFyhhjjDGmJONKkULZQUHBtyfcOw+Fh73xyJNEdF2oEMXOT74o/LVee+21XPd59dVXS2oo\nTDNnzhz2fUxZQjkjGhDCPQdjcN+xaL4Yn/zkJyWN3GMQTp06pcsuu6yujFmRqpahoSHNmjWr6tsw\n/4/rY/zguhhfuD4uYEXKJDczNp0lnGibanF9jB9cF+ML18cFKlOkbrjhBt14440duVZ/f3+u4zZs\n2NDR67WKZq+3e/fujtWFMcYYM5mwImWMMcYYU5JaVkHyoM9+9rN66aWXOn1ZY4wxxpjC3HDDDXrx\nxRdH/a6SiZQxxhhjzGTAS3vGGGOMMSXxRMoYY4wxpiQdn0jt2rVL8+fP19y5c/XYY491+vJGUm9v\nr5YuXarly5dr1apVkqQzZ85o7dq1mjdvnm6++WbnkmoTd955p7q7u7VkyZL6Z2OV/aOPPqq5c+dq\n/vz5euGFF6q45UnNaPXR39+vnp4eLV++XMuXL9fOnTvr37k+2suxY8d04403atGiRVq8eLEef/xx\nSe4jVRCrC/ePUcg6yH//+99s9uzZ2dDQUHb+/Pmsr68vGxwc7OQtmCzLent7s9OnTw/77MEHH8we\ne+yxLMuy7Pvf/372jW98o4pbm/S8/PLL2f79+7PFixfXP4uV/euvv5719fVl58+fz4aGhrLZs2dn\n//vf/yq578nKaPXR39+f/fCHPxxxrOuj/Zw8eTI7cOBAlmVZdvbs2WzevHnZ4OCg+0gFxOrC/WMk\nHVWkBgYGNGfOHPX29mrKlCm67bbbtGPHjk7egvl/siDG4He/+502bdokSdq0aZN++9vfVnFbk57r\nr79eV1555bDPYmW/Y8cO3X777ZoyZYp6e3s1Z84cDQwMdPyeJzOj1Yc0sn9Iro9OMH36dC1btkyS\n1NXVpQULFujEiRPuIxUQqwvJ/SOkoxOpEydO6Jprrqn/v6enp14xpnPUajWtWbNGK1eu1C9+8QtJ\nF7aJ6e7uliR1d3fr1KlTVd7iJUWs7N966y319PTUj3N/6Rzbtm1TX1+fNm/eXF9Gcn10lqNHj+rA\ngQP69Kc/7T5SMdTF6tWrJbl/hHR0IlWr1Tp5ORNhz549OnDggHbu3Kmf/OQneuWVV4Z9X6vVXFcV\nkSp710v7ueeeezQ0NKSDBw9qxowZeuCBB6LHuj7aw7lz57R+/Xpt3bpVH/nIR4Z95z7SWc6dO6cN\nGzZo69at6urqcv8YhY5OpGbOnKljx47V/3/s2LFhM1jTGWbMmCHpwqbPX/jCFzQwMKDu7m69/fbb\nkqSTJ09q2rRpVd7iJUWs7MP+cvz48REbZJvWM23atPrL+q677qovT7g+OsMHH3yg9evX64477tCt\nt94qyX2kKqiLjRs31uvC/WMkHZ1IrVy5Um+88YaOHj2q8+fP69lnn9W6des6eQuXPP/5z3909uxZ\nSdK///1vvfDCC1qyZInWrVun7du3S5K2b99e7zSm/cTKft26dfr1r3+t8+fPa2hoSG+88UY9ytK0\nj5MnT9b//Zvf/KYe0ef6aD9Zlmnz5s1auHCh7rvvvvrn7iOdJ1YX7h+j0Gnv9ueffz6bN29eNnv2\n7OyRRx7p9OUveY4cOZL19fVlfX192aJFi+p1cPr06eymm27K5s6dm61duzZ77733Kr7Tycltt92W\nzZgxI5syZUrW09OTPfXUU2OW/cMPP5zNnj07u+6667Jdu+tes7EAAACgSURBVHZVeOeTk7A+nnzy\nyeyOO+7IlixZki1dujS75ZZbsrfffrt+vOujvbzyyitZrVbL+vr6smXLlmXLli3Ldu7c6T5SAaPV\nxfPPP+/+MQreIsYYY4wxpiTObG6MMcYYUxJPpIwxxhhjSuKJlDHGGGNMSTyRMsYYY4wpiSdSxhhj\njDEl8UTKGGOMMaYknkgZY4wxxpTEEyljjDHGmJL8HyFOTlbukMv2AAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d25190>"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv1'].data[0, :36]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer filters, `conv2`\n",
+    "\n",
+    "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXuMr1V199fMnDmcO1c5wAEFFfBuVaiXat7GiMb+YWrS\n",
+       "WhtvpcoteI2gomKoaETF6FFMuWgoaY2paVrbNKmttrWttRFbQ1VEBZH7RZCLcO4zc94/Tj7Ps3+f\n",
+       "51nnR0d8533frO8/M7+Z/dvP2muvvZ+91l6Xmb179+6NQqFQKBQKhcL/GLMrTUChUCgUCoXC/6uo\n",
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd630658d90>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The fifth layer output, `conv5` (rectified, all 256 channels)"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filters = net.params['conv2'][0].data\n",
+    "vis_square(filters[:48].reshape(48**2, 5, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv5'].data[0]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
+     "data": {
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+       "z+XW1hYURUG5XIYgCBgYGMDR0REA6+aS384jIiIi0oFvLCciIiLSgSWKiIiISAeWKCIiIiIdWKKI\n",
+       "iIiIdGCJIiIiItKBJYqIiIhIB5YoIiIiIh1+Ad4CxRoGhD90AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd621d73990>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nxEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv2'].data[0, :36]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The third layer output, `conv3` (rectified, all 384 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmwZVV1/78t4IBoolFRmbqlabqBBoFuhqAFBBpsgxTG\n",
+       "ImpiRDEOIWUoLcuRxEfwJ21MaZwSE4eExCqc4gAkEhAbFLBpgZ4YuwUkCEqMZnCIcQi/P6jPO+9+\n",
+       "31u99zn33Htfw/r8c/vdPvcMezp7ffdaay944IEHHlCSJEmSJEnSmkdM+gaSJEmSJEl2VHIilSRJ\n",
+       "kiRJ0pGcSCVJkiRJknQkJ1JJkiRJkiQdyYlUkiRJkiRJR3IilSRJkiRJ0pGRTKQuvfRSLV26VPvt\n",
+       "t5/e9a53jeISSZIkSZIkE2dB33mkfvnLX2r//ffXl7/8Ze2xxx5auXKlLrzwQi1btqzPyyRJkiRJ\n",
+       "kkyc3hWp9evXa/HixVq4cKF22WUXvehFL9IXv/jFvi+TJEmSJEkycXqfSN17773aa6+9pv/ec889\n",
+       "de+99/Z9mSRJkiRJkomzc98nXLBgQfGYRz7ykfr5z3/e96WTJEmSJEl6Z5999tG3vvWtOf+v94nU\n",
+       "HnvsoXvuuWf673vuuUd77rnnwDE///nP9djHPla//OUvJUmPecxj9KhHPUqLFy+WJP3oRz+SJD32\n",
+       "sY+VJJ1xxhmSpEc/+tGSpOuuu06SdN999w18/sqv/IokaaeddpIkHXnkkZKkqamp7d7zr/7qrw78\n",
+       "/u6775Yk7bzzg8Xz9Kc/XZL0r//6r3P+nuPOOeec7V6P5/nxj3888P0ee+whSfqv//ovSc3zR3Cf\n",
+       "r3vd6yRJ73//+yVJxxxzjCTpe9/7nqQHJ6yS9LOf/UySdOedd0qS/u3f/k3Sg+XO/++888763//9\n",
+       "X0nSM57xjIHn2nXXXSVJp512miTp3HPPlSS5e51fr8STnvQkSdK///u/z/n/lCOfxx9/vCRNK5zc\n",
+       "3yMe8aCwetttt0lqJvOPetSjJM0uzyOOOEKS9IQnPEFS87y77777wPUiqC/u//vf/74kabfddpPU\n",
+       "tNP/+Z//kSTdfvvtVc8HO++8sx75yEfqJz/5yXbvI+Lggw+W1JQL9/eKV7xizuv1Dc//5je/ec7r\n",
+       "Ue5+f8B48d///d8DnxG77LKLJOltb3ubzjvvPEmaHlvawj393//938D3tLVf/OIXkppnWrt2rSTp\n",
+       "qquuktSMPcuXLx/4/caNGzvdD0RtZVTM1+vttttuevzjHz895reFvvvKV75SkvQP//APkqT7779f\n",
+       "UnnsjfD2AYxBb3nLWyRJH/rQhyQ1Y33tWNmWSdXfZz7zGUnNGHTIIYdIkk4++WRJ0qc+9SlJ0gc/\n",
+       "+EFJ0lOf+lRJTbkxUXnKU54iSXrBC14g6cFJjCQ9+clPlqTpOcY73vGOgd8DY8i3v/3tTs/zpCc9\n",
+       "ST/72c+mx55jjz12uo/PRe9LeytWrNC2bdv0rW99Sz/72c/0qU99Sqeeeuqs43bbbTftuuuu2nXX\n",
+       "XacbW5IkSZIkyaRBGJAaIz6id0Vq55131gc/+EGdfPLJ+uUvf6lXvOIVc0bs3X///dPKAQoL3HHH\n",
+       "HZKkH/7wh9PHStK+++4rqVGMmM1ikaIYuSJV4nGPe5wk6YADDpAkLVmyRFKjcERKlP++xBOf+ERJ\n",
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+       "N785+XmPx2DZtm1bZ2xsrDM+Pt5Zu3ZtZ+3atZ177rnHa2QETDcWd999t9fHNLhEjDHGGGNMJc5s\n",
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+       "VOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The fifth layer after pooling, `pool5`"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv3'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fourth layer output, `conv4` (rectified, all 384 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['pool5'].data[0]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
+     "data": {
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+       "7OyRRx7p9OUveY4cOZL19fVlfX192aJFi+p1cPr06eymm27K5s6dm61duzZ77733Kr7Tycltt92W\n",
+       "zZgxI5syZUrW09OTPfXUU2OW/cMPP5zNnj07u+6667Jdu+tes7EAAACgSURBVHZVeOeTk7A+nnzy\n",
+       "yeyOO+7IlixZki1dujS75ZZbsrfffrt+vOujvbzyyitZrVbL+vr6smXLlmXLli3Ldu7c6T5SAaPV\n",
+       "xfPPP+/+MQreIsYYY4wxpiTObG6MMcYYUxJPpIwxxhhjSuKJlDHGGGNMSTyRMsYYY4wpiSdSxhhj\n",
+       "jDEl8UTKGGOMMaYknkgZY4wxxpTEEyljjDHGmJL8HyFOTlbukMv2AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd621d25190>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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GTMu3v7/fiGn7PS8vz2q72udN3M9bTUFBgRGzPW9tc9Gm9Gu0c1nT3t5uxOrq\n6ozYa6+9ZsTS8Rj5FiSXhoYGI6Z99mmfLdrU+7jvl7lwJQoAAMABRRQAAIADiigAAAAHFFEAAAAO\nImksd5WVlWXEtMZU38rLy62W0xpWkdmGhoa8rk9rzNRoDb+zs7NGTGvg1Bo9NdpNG0GsXr3aiGnN\n5h0dHUasu7v7A9d/8uRJIzY8PGyVm7afRkdHrV4bd1qTtva7BWkit6UdjzNnzhgx289S7fyx/bwG\nRPSbdOaDK1EAAAAOKKIAAAAcUEQBAAA4oIgCAABwENvGctuGXa2Z1rfq6mqr5RZaY/mPf/xjI1ZR\nURFBJguPNlF3fHzceX3aDRq+pwX/9re/NWLalOyF9j5a6LTzTIvNZ4p0plq8eLHVcp2dnSnORL8Z\nQ6M1/9u+VnuSgPaZ4fsGn/ngShQAAIADiigAAAAHFFEAAAAOKKIAAAAcxLaxXJueqzW/zszMpDwX\n2wnPvtlOUh0ZGUlxJva06ciavr4+r9utra21Wk6bjpyOtIZL7b1g24w7NTUVOKcPok3J9jkV3LZZ\nNQjb89tWXl6eESsrKzNi2vR428++OE1et50MrzUjT0xMGDHtM1J7bwRhe4PFQmt8154gEgbf7/Og\n359ciQIAAHBAEQUAAOCAIgoAAMABRRQAAICD2DaWa81jWmN5GKJqLNeaKzVxaizv6OiIOoUFobKy\n0ojZNsB2d3dbLed7Ynmq1dfXGzHb94bWuK01ePtuwC8vLzdi2kRmLb905LsxPwzaeaAZHBw0Yr6f\nqBHGJHJb2k0RmiA5a/tvenraeX2pwJUoAAAABxRRAAAADiiiAAAAHFBEAQAAOIiksfzixkmtUUyb\nhqo1m+fn5xux6urqANmZSktLrZYbGhoyYkGm2Pqe6h2E1uyqTZW31dDQECQdg7bvF5rx8XGv6wty\n7mqN7xqtGbeurs6I2byntWn02j5ZtMju344DAwNWywWhbUObzB3kyQzaDQK+p2vb3vSj3aSj/b62\nDffaZ+Qbb7xhlYst2/NF+87y3VgehpKSEiOmffdqDeO+p4kvWbLEiGnf+dp5oN2MsW7dOiO2ePFi\nx+z+F1eiAAAAHFBEAQAAOKCIAgAAcEARBQAA4CCR9N1h+EEbTCS8NzUCAACkwqXqFq5EAQAAOKCI\nAgAAcEARBQAA4IAiCgAAwEEkE8u1CbqppjWFRZGHSLBctAmuZ8+etXrt1NSUcy7aFGmN7XIHDx50\nzkXT1NTL6yctAAAgAElEQVRkxDo6OoxYcXGxEdMm72bK+eJbkFyWLVtmtVx7e3tK8/DNNpeysjKr\n9QWZlG6bS15entX6tGni2mRubaK6bS62sSDTv21zsT1Hbc+1kydPOufim+0+1Sa0h3EzmO1+0SaM\na9872tMKtCcknD592iqXuXAlCgAAwAFFFAAAgAOKKAAAAAcUUQAAAA4iaSy3UV1dbbXcuXPnvG63\nsbHRiL344otWr3300UeN2A9/+MOgKV1Aa4LTGj2DNGFq+vv7rbZr21jum21z/fDwcIozgYhIQUGB\nEfvoRz9q9drvf//7vtO5wNVXX2213K9//Wuv29UaxsvLy61i2vsvCK1h3JbvJmNtfVE91cLmpgYR\nkeXLl6c4E72pOsh+sX1t3J8okpuba7Vcfn6+EdMay4PiShQAAIADiigAAAAHFFEAAAAOYtsTpQ1F\n1GgDtTK570UbtqnReqeCsO2h0IZohqGnpyeS7SL9NDQ0WC3nuycqU/jut9Ro/SyTk5OR5KLRhmj6\npvX+BOlly2TT09NWy9n2U80HV6IAAAAcUEQBAAA4oIgCAABwQBEFAADgIJEMebKW7dOqS0pKjJg2\n3FFrIteazNLxqe8a343lmbJffCMXXVxyCZJHRUWF1XJ9fX0pz8W3TMlFawDWGsvDyMU321yCDNvU\nvis12ndl3PeL7e9m20Q+Njb2gbkkEok59z1XogAAABxQRAEAADigiAIAAHDgXET19/fL3XffLVdc\ncYU0NzfLa6+9Jr29vdLS0iKrV6+WrVu3en9oJgAAQFw4N5Zv375dbrzxRrnvvvtkenpaRkZG5Ctf\n+YpUV1fLww8/LE888YT09fXJzp07L9xgzJvWwhAkl6KiIiNWVVVl9Vptym6m7BffyEUXJJfGxkar\nmKa1tdVbHr6Riy5ILuXl5VbLDQ0NGbGZmRmvudTV1Rkx7XNY+3wNcpOTbX7a+mxvnujt7XXerm++\nz92cnBwjNjU15ZSL98bygYEB+fnPfy733XefiIhkZ2dLWVmZ7N69W7Zv3y4i/1tkvfDCCy6rBwAA\niD2nIurEiRNSU1Mj9957r2zcuFG++MUvysjIiHR3d5+v2uvq6qS7u9trsgAAAHHh9ADi6elpefPN\nN+Xb3/62XHvttfLggw+qf7aL6rIgAACAi9bWVqOFYC5ORVRDQ4M0NDTItddeKyIid999t+zYsUPq\n6+ulq6tL6uvrpbOzU2pra11WDwAAEInNmzfL5s2bz//8+OOPz7msUxFVX18vy5Ytk6NHj8rq1atl\n7969snbtWlm7dq3s2rVLHnnkEdm1a5fceeedLqufU2VlpdVyWrNcptCmsBYWFkaQSbxoVz3z8/ON\nWHV1tRFrb29PSU640HXXXWfEtAn84+PjRsz2X4XIDLY3HBw7dsyIjYyMeM1F+8zwTZvCbXvDUJBJ\n7ulI+76rqamxem1HR4cR05r/58OpiBIR+da3viWf+9znZHJyUlasWCFPP/20zMzMyD333CNPPfWU\nNDY2yvPPPx8oOQAAgLhyLqKuvvpq+eUvf2nE9+7dGyghAACAdMDEcgAAAAcUUQAAAA6cJ5Y7b/AS\nkz8BAADixPvEcgAAgIWOIgoAAMABRRQAAIADiigAAAAHznOigrh4uvSf/umfGstcf/31RuzgwYNW\n6/+Hf/gHI6Y1hWlTrv/sz/7MiG3cuNGIaVOu//Vf/9WItbW1OeeiKSoqslrOdmpvkFx8IxddOuai\nTdbXaK/VJjWfPn3aKY8wZEouTU1NVssdOXIk5bn4FiSXnJwcI7ZokXn9YWJiwogVFxcbsaGhIedc\nfIv7MSooKDBif/EXf2HEPvrRj1qtb//+/UbsW9/6lhEbHh6eM8+LcSUKAADAAUUUAACAA4ooAAAA\nBxRRAAAADiJpLL/YT3/6UyOmNS9qDd5R0Zpfs7NTvzttG8Z9W7p0qdVyHR0dKc4Ec1m8eLER+9jH\nPmb12qefftprLlrjrYanF0Rj2bJlRuzw4cNWr/3CF75gxL773e8655KVlWUVm5ycdN5GEIWFhVbL\naY3l82lQXkhsm9fHx8dTnEnw71SuRAEAADigiAIAAHBAEQUAAOCAIgoAAMBBIhlyZ2ecpqFquQSZ\nCK5Ntp2amnLOJQy2uZSUlFitT5vG6zuXMKRjLmE0ltvmojUGa2ZmZoyYdq5dfF6l4/GxlZ+fb8Rs\nG2xtc9Eay0+ePGm1DdvG8kw+RkGQi76N2dlZq+Vsv7e1z6DBwUEjpu2Di2OJRGLOm2C4EgUAAOCA\nIgoAAMABRRQAAIADiigAAAAHNJZfpLi42Gp92iRabX02TWtzvTYMtrloTfMarZHedy5hIBddGLl8\n6EMfMmIXv9+6u7tTnoetdGws19h+ftlaaOetLXLRxTkXGssBAAA8o4gCAABwQBEFAADggCIKAADA\nQXbUCcSN1jBuK+QefSDtVVVVGbHa2lojpk02z1S2TeS+aZ9fS5YssXrt6dOnveaSnW1+NWnnika7\n6QBIFa5EAQAAOKCIAgAAcEARBQAA4IAiCgAAwEFsG8uXLVtmtdzo6KgR6+npsXptXl6eEZuYmLB6\nraawsNCIafmlo6KiIqvl+vv7U5xJdLKysqxi69ats1rf7OysETt48OD8E0sTWrOwNpFY2y9nz55N\nSU7pzPc05zVr1hixhoYGq9f6bixfv369EdMmuWtsG8tLSkqMmNZcX1FRYbW+wcFBI2b7pIc40b57\ntRuu+vr6jJj2PaGt79ixY47ZxQ9XogAAABxQRAEAADigiAIAAHBAEQUAAOAgkQx5zHYikWCyNwAA\nSAuXqlu4EgUAAOCAIgoAAMABRRQAAIADiigAAAAHkUwst5m0azshe9Eisw4cGhoyYlpTmJZHQUGB\n1XbHxsasltPY5qJNw9amPmtT1ktLS42YNlHXNpcwkIvONpfc3Fyr9U1OTqY8l1SLSx4i9rls2bLF\nan3Hjx+3Wu799993ziUMQXKxnRKuTc0OksvnPvc5q/X96le/slru0KFDzrloamtrrZY7c+aM1XLp\neL5UV1cbsY9//ONGTJuK/rOf/cw5l7lwJQoAAMABRRQAAIADiigAAAAHFFEAAAAOImksv9iaNWuM\n2BVXXGHEtGavV155xWsus7OzXtcXxMzMjFVMozWRZ4rVq1cbsUceecTqtdpy586dC5zT71u+fLnV\ncidPnvS6Xa1hfP369Vavfeutt7zmAtPBgwetlhsdHU1xJtEpKyszYgMDA0bMtmHct+9///uRbFez\nYsUKI7Zp0yYjpt3ktHv37pTkFAfa+dLW1hZ+Iv+HK1EAAAAOKKIAAAAcUEQBAAA4oIgCAABwEIvG\n8iB6enq8rk+b/h0VbWp7fn6+EdMaC8NoTt22bZvVcj/+8Y+N2MjIiO90IqFNldemxceddl6Nj49H\nkEnm6u/vt1rOdvI8otPc3Gy1nDax3Jb2/tNuGMqUz1JbU1NTRkz7jgkLV6IAAAAcUEQBAAA4oIgC\nAABwQBEFAADgIJHUxoCncoOJRJibO0/7NeOei9ZgWlJSYsS0hlXbyeZB9ovvxvJ0PEYa7Rhp6xse\nHk55LgUFBUZMu3nCdlJ/XI5RXPIQIZe5ZEou2ntoy5YtVq996aWXvObyqU99yuq1x48fN2IHDhzw\nmotvcc4lkUio+YlwJQoAAMAJRRQAAIADiigAAAAHFFEAAAAO0mpiuTZVWWuInZycDCOdlNN+D98T\n2oPYt2+fEauurjZi5eXlRiyTp+wODQ1FncJ52jR7xIf2mZadbX4sa02t6fgeqqioMGJ9fX1et+G7\nGVnb93H6jtHOIYSHK1EAAAAOKKIAAAAcUEQBAAA4oIgCAABwENvG8ubmZiOWk5NjxLTG2aNHj1pt\no6qqyogNDAxYvXZ6etpquUzW1NRkxG6//Xar1/7N3/yN73RiQ2uk16bKLzSVlZVWy/X29qY4k2ho\n58WGDRusXtvR0WHEbD/n4k5rBNc+6zVag3deXl7gnH7f+Pi4EXv33Xe9bsOWdh5o++rgwYNet6tt\nY2pqynl9UU0iTwWuRAEAADigiAIAAHBAEQUAAOCAIgoAAMBBIqmNY03lBhMJdQIsAABA3FyqbuFK\nFAAAgAOKKAAAAAcUUQAAAA4oogAAABxEMrHcZlppdraZmu3Eco3WFGY7NXXlypVGTJs+fOLECSPW\n09PjNZcgcnNzjdjExEQkuWii2i+aTMll/fr1VstpE5i191Zc9ktc8hAJlkt+fr4R0yZBz8zMeM2l\noKDAan1ZWVlWyw0PDzvnom3jox/9qNV2z5w5Y8Teeecdr7kUFRUZMe27SHvahfZki3Q8dxsbG63W\n19DQYLXcK6+8YpWLdjy02kCbXF9WVmbEbJ9IMp+b37gSBQAA4IAiCgAAwAFFFAAAgAOKKAAAAAeR\nNJb7pDVLa01mQTQ3NxuxZcuWGbHx8XEjpjWWR8X3fgFgT/usWrFihdVrDx06ZMSCPPnBtuFZu7nA\ndxN0TU2NEWtqarJ6bV9fn9dctEZ/ral/cHDQ63ZtlZSUWC03NDTkdbttbW1Wy2lN30HMzs4asbq6\nOqvXFhYWGjGt2fzkyZPzT+z3cCUKAADAAUUUAACAA4ooAAAABxRRAAAADmLbWK41PmqxONGa4OJE\na2xdaLTGTN9NmHHy1ltvGTGtmXnJkiVG7L333ktJTguVdmNHb2+v1WuDNJFrFi0y//0cZCq6b8eP\nHzdixcXFKd8udIsXLzZiWpO29h2tHcsgtO8x7WYH7QkimqDN8FyJAgAAcEARBQAA4IAiCgAAwIFz\nEbVjxw5Zu3atXHnllfLZz35WJiYmpLe3V1paWmT16tWydetW6e/v95krAABAbCSSDl2CbW1tcvPN\nN8vhw4clLy9PPvWpT8ltt90m77zzjlRXV8vDDz8sTzzxhPT19cnOnTsv3KDnabe2tF8zTrlojZ6+\nGzi1hryJiQkjFqf9Qi7+c1m3bp3Vcm+//XbKc3EVlzxEyGUunLe6TMmlqKjIiI2MjKQ8F227Gp+5\nJBKJOb+Pna5ElZaWSk5OjoyOjsr09LSMjo7KkiVLZPfu3bJ9+3YREdm+fbu88MILLqsHAACIPaci\nqrKyUr785S/L8uXLZcmSJVJeXi4tLS3S3d19/rk2dXV10t3d7TVZAACAuHAakPDee+/JN77xDWlr\na5OysjL54z/+Y/ne9753wTKJRCKyS5QAAAAuWltbpbW11WpZpyLqwIEDcsMNN0hVVZWIiHzyk5+U\nX/ziF1JfXy9dXV1SX18vnZ2dUltb67J6AACASGzevFk2b958/ufHH398zmWdiqimpib5u7/7Oxkb\nG5P8/HzZu3evXHfddVJUVCS7du2SRx55RHbt2iV33nmny+rTQkFBgdVyY2NjVstpV+18N5ZrE5N9\na2xstFqura3N63YX2iTyIE6fPh11CsAlrVy50oiVlpZGkAnmEqRxO5M4FVFXX321fOELX5BNmzbJ\nokWLZOPGjfInf/InMjQ0JPfcc4889dRT0tjYKM8//7zvfAEAAGLBacRBoA2m4a2cmiBXorRcsrKy\njFgYz+LzvV+CXIkKkovvK1GZchuyprKy0mo57blucdkvcclDhFzmEiQX7UqUbXvI/v37vebiG7no\nFtSIAwAAgIWOIgoAAMCBU08U/AvjT3eZYsmSJUZMazo9evSoEcuU/aztg4qKCiOmXdLW/pSq/RlW\n20YQ2sR8jesNEL4nKAdRVlZmxPLz842Ydsy0Pxv09PRYxWxpT0jQjs/4+LjzNoJYsWKFEdNyHhgY\nCCMdxIjvP+cF/fMlV6IAAAAcUEQBAAA4oIgCAABwQBEFAADgIJI5USFvEgAAwAlzogAAADyjiAIA\nAHBAEQUAAOCAIgoAAMBBJBPLXSeElpeXGzFtou7U1JQRm56e9pZHUOn40Mcw2OaiTS6+6qqrjNjw\n8LARO378uHMuvh8SrU2I1qZ12+6X4uJiq+1qv4dmcHDQiGm/r+35cuedd1ot97Of/cyIXfww5HQ8\nb7Oz7T5utc+qILlo0/xtPzc12nmmncvaRPW4H6MwBMklyAPefefim20uy5YtM2JVVVVG7Ny5c0ZM\ne6j66OioVS5z4UoUAACAA4ooAAAABxRRAAAADiiiAAAAHETSWH4xrVFY09/fn+JMRPLy8pxfOzEx\n4TGTeNEa/LSGVc3AwIDXXHJycozY8uXLrV5r21iuCdJErtGayIOoqKgwYl/60pesXvuXf/mXXnMJ\norCw0IhpDaGppn0uBTkHtIbxpqYmq9ceOXLEebtDQ0POr9VoN2xkspUrV1otF+SzxdaSJUuslrNt\nLE9HXV1dRizKp6BwJQoAAMABRRQAAIADiigAAAAHFFEAAAAOYtFY7rthNwhtmrMW890UHHda4542\n0ToMWgP/2NhYBJnES1lZmRG75pprIshE98ILLxix/Px8I6ZN045ClM2qiI8wGsZt7d+/P+oUIqdN\n1j99+rTVa1PxnuZKFAAAgAOKKAAAAAcUUQAAAA4oogAAABwkkiF3T2qTr8Og/ZrkQi5zyZRcPvzh\nDxuxmZkZI3bgwIGU5+JTXPIQyZxcfDf5Z8p+8Y1cdLa5ZGfb3Q+n3bCmbcMmlkgk5mxK50oUAACA\nA4ooAAAABxRRAAAADiiiAAAAHKR9Y3leXp4R0yZap2MDXRgyJZfi4mIjpjUWjo6OpjwX38glvnmI\nZE4u2k0IJSUlRkybDn3o0CGvudDkHo50zKW8vNxqff39/d5yobEcAADAM4ooAAAABxRRAAAADiii\nAAAAHNiN/vTs4mYxrWFr06ZNVusaHh42YkeOHHFLDGlLa0TVaM2pWgN6psjJyTFiU1NTEWSSerY3\nmdiqrKwMkk5s2J4D9fX1RmzJkiVGTJt4rzWWBzE9Pe11femotLTUarmhoSEjpp27Id9DljJBGsZT\ngStRAAAADiiiAAAAHFBEAQAAOKCIAgAAcBDJxPJMaXADAACZjYnlAAAAnlFEAQAAOKCIAgAAcEAR\nBQAA4CAWE8ubmpqsXtfb22u1nDaNV3vtxXmERWtQs81l69atVsu9/PLLKc/FtyC5ZGVlWS2nTVv2\nnYtvmZzLQw89ZLXc17/+dac8cnNzrdZfUVFhxLRz6vTp00YsyD65/vrrrZZ79dVXrZazzaWkpMRq\nfdo0bFtB9stHPvIRq+WOHTtmxM6cOeM1F9+C5KJNkM/ONr/GBwYGrGJB3kcbN240Yl1dXVYx7ckR\nvo9RcXGxVUzLbz43v3ElCgAAwAFFFAAAgAOKKAAAAAcUUQAAAA4iaSy/WF9fnxG77bbbrF77n//5\nn0ZMa6DLFAcOHIg6hfM+8YlPWC33X//1XynOZOHRGi5tmyGXLl1qtVxHR8e8coqjyclJq+VuuOEG\nq+U4l4MpLS01YoODgxFkkp60GxvKyspSvt2WlhYj9pWvfMXqtX/1V39lxH70ox8553LVVVcZsbq6\nOiM2OztrxH75y186b3cuXIkCAABwQBEFAADggCIKAADAAUUUAACAg1g0lnd3dzu/NpObyDW2U9vD\noE3KjYrtJHLEy8WTyH2znXisTVAOw2uvvRbJduczkTkK+/fvt1oujN9j3bp1Vsu9/fbbKc5EF/fv\nwIKCAq/rKywsNGK2x+iNN97wmosIV6IAAACcUEQBAAA4oIgCAABwQBEFAADgIJEMucPQttHTN+3X\nJBdymQu56OKSi20etrkF+RgMsk+ysrKslrO9ccI2F9tm37GxMavlguSiKS8vN2Ja87/tDQFBcvHd\nWB6X95BIsPPlgQceMGLazUY//elPjdjrr7/unIs2nfzyyy83Ytq58dZbbxkxzcW5JBKJOT8juBIF\nAADggCIKAADAAUUUAACAA4ooAAAABzSWR4BcdOSiI5do8qioqDBifX19keRiK4xc6uvrjVhXV5fX\nXDZu3Gi13OHDh42Y1gy/0I6RrSC55ObmGjGtwVvz7rvves3FNxrLAQAAUowiCgAAwAFFFAAAgAOK\nKAAAAAfmeNEQ5OTkXPCz1qCmGRkZSUU6saBNLradUoxwaOfp5ORkBJmILFpk/vtndnbW6rV5eXlG\nLD8/34gNDAzMP7H/43sits8GU+131aYbFxUVGTGtsTyTaU3ksFdZWel1fdr7IOR7wy5J+44eHBx0\nXl+Q3zfIZ+R8cCUKAADAAUUUAACAA4ooAAAABxRRAAAADiKZWB6nRjgAAIC5MLEcAADAM4ooAAAA\nBxRRAAAADiiiAAAAHEQysbywsPCCn22nFgehNYX5nII8H+Sis81l48aNVus7duyY1XJDQ0POudgq\nKSmJTS5BpuPb5qJNC9amN587d85qu655aLRJ5BrbJyQEyeX++++3Wu6pp55KeS7aJHvNxMREynPx\nLUgupaWlRkybfD08POw1F+09tHjxYqttdHR0eM0lO9ssFRoaGoxYV1eXEdOeBhAklzDM5+Y3rkQB\nAAA4oIgCAABwQBEFAADggCIKAADAQSSN5RfLycmxWm5qairFmei0Bj+N1mwI/44fP261nNboGdW0\nfK1hPCq2TeRB+H4v2H5GpJuCgoKoU0BMae8h24Zx32pqaiLZbjrgShQAAIADiigAAAAHlyyi7rvv\nPqmrq5Mrr7zyfKy3t1daWlpk9erVsnXrVunv7z//33bs2CGrVq2SpqYmefnll1OXNQAAQMQuWUTd\ne++9smfPngtiO3fulJaWFjl69Khs2bJFdu7cKSIihw4dkueee04OHToke/bskQceeIAeIQAAkLEu\n2Vj+R3/0R9LW1nZBbPfu3bJv3z4REdm+fbts3rxZdu7cKS+++KJ85jOfkZycHGlsbJSVK1fK66+/\nLtdff72x3osnlMe9aZRi0H5Srjax1ncz9+DgoNVy2mRuTZBGa+3cveuuu6xe++///u9ec4k71+nk\nIn7fg7aTyMNge5OEdp75vtHGdhL5QmP7eRMn2vT5IMe3r6/Pajnb6eSZZN49Ud3d3VJXVyciInV1\nddLd3S0iIqdPn75gDHxDQ0NkdxIAAACkWqDG8kQiccln20T13BsAAIBUm/ecqLq6Ounq6pL6+nrp\n7OyU2tpaERFZunSptLe3n1/u1KlTsnTpUn+ZAgAApFhra6u0trZaLTvvK1Hbtm2TXbt2iYjIrl27\n5M477zwff/bZZ2VyclJOnDghx44dk+uuu26+qwcAAIjM5s2b5bHHHjv/v0u55JWoz3zmM7Jv3z45\nd+6cLFu2TP72b/9WHn30UbnnnnvkqaeeksbGRnn++edFRKS5uVnuueceaW5uluzsbPnOd75j/ee8\nIA2SUTWlFxUVGTGt+fXiJvp01dvba7VcVBPBNWHcEBDk/Lv88suN2HvvvRcknYyVqQ33F9/9LEIb\nBILzfZOA9tSO5uZmI6Z95mqfaQMDA34Si4FEMuRvPd8fELZfYpOTk15zCVJEabs8qg9O21y0uz00\nQd68vveL7Wu17drmUlhYaMS2bdtmtd0DBw4YMe0DRzuv4n6+LJQ8RNLzvA0DuejSMRftc853ERXn\n/ZJIJOa8QMDEcgAAAAcUUQAAAA4oogAAABzMe8TBQrRy5UojVlVVZcS0qa5Hjx71msv69euNWE1N\njRHTesB+N2neRTpOM46qyb28vNyILV++3Ihp59B3vvOdlOSE9BGnmzOAuZSUlBix3NxcI6Z9d2RS\nYzlXogAAABxQRAEAADigiAIAAHBAEQUAAOAgksby4uLiC37WBlLaTigOMu3c1sX5zmVkZCTFmYhU\nV1cbsbKyMiN29uxZ521oA86CNLv6Xl+cjI6Oel1fpuyXKGjvU+0GCy0GfXCxFhsfHzditk8H0D4L\nbAcX277XbD+vYU9rIl+7dq0Rq6ystHrtO++84yexGOBKFAAAgAOKKAAAAAcUUQAAAA4oogAAABwk\nkiF3sl7qacgAAABxcqm6hStRAAAADiiiAAAAHFBEAQAAOKCIAgAAcBDJxHJtam2qaU1hWh7l5eVW\n6+vv7095LraWLl1qtVxHR0fKcwlCy2XRIrPOz8rKct6Gtj5tgnXc90vcc9EmF2t6e3tTmsfGjRuN\nmDbhf2BgwGq7b775pnMuGt/7SctFe7/YThi3lZeXZ8S0yebafqmvr7faxoc+9CGr5fbv32/E0vE9\nVFBQYLU+7YkfQXLRtqs9GUR7eoaWy+DgoHMucTpGc+FKFAAAgAOKKAAAAAcUUQAAAA4oogAAABxE\n0lgeZ1qTMUSuuuoqI/alL33JiJ07d86IPfLII87b1Rr8pqenrV6bnR3N6V1RUWHE+vr6Ur7dyy67\nzGo520bPIDnffPPNVst1dXVZLffKK6845aE1gmvWrFnjtP65aDcw+G7mzsnJsVquoaHBiJ08eTKS\nXDS2Tf3pKMhNMEEaxoPQbgiw/T2CnAdB3HjjjVbL7du3z/u2uRIFAADggCIKAADAAUUUAACAA4oo\nAAAABzSWX2R0dDTqFOZNm0QelaiauVesWGHEtCbHI0eOpDyXMJrI425mZibqFObl3XffjWS7rhPb\nReY3VTnOioqKjFhVVZURO3XqlBHz3SDvW7q9D+ai/R7d3d0RZBI/XIkCAABwQBEFAADggCIKAADA\nAUUUAACAg0Qy5O7ERCIR5ubO035NcgmWS15enhHTGhBtJ4wHyaWmpsaI5ebmGjHbJvx0PEbV1dVW\n69OmyvvOpbGx0Wp9/f39RkybsP3+++9f8LM2dT3uxycMWi5ak3aQhvYguWj7JT8/34iVl5cbMW2C\nt+20c9/HyPYGGu2zL+7nC7mYuSQSiTlv5OBKFAAAgAOKKAAAAAcUUQAAAA4oogAAABwwsRzOJiYm\nok7hvLNnz0adAn5PW1ubEdOahZcsWWLEtEbozs5OL3ktRGE0kQcxPj5uxLq6uiLIxJ72NASN7U01\nSF9ciQIAAHBAEQUAAOCAIgoAAMABRRQAAICDWDSWL1pk1nJZWVlGbGpqKox0gLQUZBJ5ENr0Zq2h\ndqLs8QcAAAYuSURBVHJy0mp92k0CPT09H/g6rdnX9jMjyGs19fX1Rmx2dtaInTlzxmp92jR67ekA\nmoKCAiOmTf8OQrtpIAjtaQO254+t2tpaI2Z7PGz3X2Vl5bxy+iBFRUVGTNsvfFeGhytRAAAADiii\nAAAAHFBEAQAAOKCIAgAAcJBIJpPJUDeYSEjImwQAAHByqbol0itRra2tUW4eF+F4xAfHIl44HvHC\n8YiPhX4sKKJwHscjPjgW8cLxiBeOR3ws9GNBTxQAAIADiigAAAAHoTeWb968Wfbt2xfmJgEAAJzc\neOONc/7ZMvQiCgAAIBPw5zwAAAAHFFEAAAAOKKIAAAAcRFJE7dmzR5qammTVqlXyxBNPRJHCgtbe\n3i433XSTrF27VtatWyff/OY3RUSkt7dXWlpaZPXq1bJ161bp7++PONOFY2ZmRjZs2CB33HGHiHAs\notTf3y933323XHHFFdLc3CyvvfYaxyNCO3bskLVr18qVV14pn/3sZ2ViYoLjEaL77rtP6urq5Mor\nrzwfu9T+37Fjh6xatUqamprk5ZdfjiLlUIVeRM3MzMif//mfy549e+TQoUPyzDPPyOHDh8NOY0HL\nycmRr3/96/LOO+/Iq6++Kv/0T/8khw8flp07d0pLS4scPXpUtmzZIjt37ow61QXjySeflObmZkkk\nEiIiHIsIfelLX5LbbrtNDh8+LAcPHpSmpiaOR0Ta2trkn//5n+XNN9+U3/zmNzIzMyPPPvssxyNE\n9957r+zZs+eC2Fz7/9ChQ/Lcc8/JoUOHZM+ePfLAAw/I7OxsFGmHJxmy/fv3J2+55ZbzP+/YsSO5\nY8eOsNPA7/n4xz+e/NGPfpRcs2ZNsqurK5lMJpOdnZ3JNWvWRJzZwtDe3p7csmVL8ic/+Uny9ttv\nTyaTSY5FRPr7+5OXX365Eed4RKOnpye5evXqZG9vb3Jqaip5++23J19++WWOR8hOnDiRXLdu3fmf\n59r/X/3qV5M7d+48v9wtt9yS/MUvfhFusiEL/UpUR0eHLFu27PzPDQ0N0tHREXYa+D9tbW3yq1/9\nSj784Q9Ld3e31NXViYhIXV2ddHd3R5zdwvDQQw/J1772NVm06P+/HTkW0Thx4oTU1NTIvffeKxs3\nbpQvfvGLMjIywvGISGVlpXz5y1+W5cuXy5IlS6S8vFxaWlo4HhGba/+fPn1aGhoazi+3EL7fQy+i\nfvfnCkRveHhY7rrrLnnyySelpKTkgv+WSCQ4ViF46aWXpLa2VjZs2DDnU8I5FuGZnp6WN998Ux54\n4AF58803paioyPhTEccjPO+995584xvfkLa2Njl9+rQMDw/L9773vQuW4XhE64P2f6Yfm9CLqKVL\nl0p7e/v5n9vb2y+oXBGOqakpueuuu+Tzn/+83HnnnSLyv/+i6OrqEhGRzs5Oqa2tjTLFBWH//v2y\ne/duufzyy+Uzn/mM/OQnP5HPf/7zHIuINDQ0SENDg1x77bUiInL33XfLm2++KfX19RyPCBw4cEBu\nuOEGqaqqkuzsbPnkJz8pv/jFLzgeEZvr8+ni7/dTp07J0qVLI8kxLKEXUZs2bZJjx45JW1ubTE5O\nynPPPSfbtm0LO40FLZlMyv333y/Nzc3y4IMPno9v27ZNdu3aJSIiu3btOl9cIXW++tWvSnt7u5w4\ncUKeffZZufnmm+W73/0uxyIi9fX1smzZMjl69KiIiOzdu1fWrl0rd9xxB8cjAk1NTfLqq6/K2NiY\nJJNJ2bt3rzQ3N3M8IjbX59O2bdvk2WeflcnJSTlx4oQcO3ZMrrvuuihTTb0oGrF+8IMfJFevXp1c\nsWJF8qtf/WoUKSxoP//5z5OJRCJ59dVXJ9evX59cv3598oc//GGyp6cnuWXLluSqVauSLS0tyb6+\nvqhTXVBaW1uTd9xxRzKZTHIsIvTWW28lN23alLzqqquSn/jEJ5L9/f0cjwg98cQTyebm5uS6deuS\nX/jCF5KTk5McjxB9+tOfTi5evDiZk5OTbGhoSP7Lv/zLJff/V77yleSKFSuSa9asSe7ZsyfCzMPB\ns/MAAAAcMLEcAADAAUUUAACAA4ooAAAABxRRAAAADiiiAAAAHFBEAQAAOKCIAgAAcPD/ADWWZox7\nIqDoAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv4'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fifth layer output, `conv5` (rectified, all 256 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XuM3Gd1//HPEN/vXtu7vqzjTeI4xiShoSFQCUpTCBVS\n",
+       "GwUh0oYKIqBV1UoBmnJJ3RYGihRTFaFfC0H8gUQqJEjUKoR/QCFVSJXQYG5pEjuO7ZD1Zb273ptv\n",
+       "sZM4iX9/ROeZM/HXOzPPfG8z835JKF+eXe88Ozvzne/3nPOcp3Lu3LlzAgAAQMveUPQEAAAAOhUX\n",
+       "UgAAAJG4kAIAAIjEhRQAAEAkLqQAAAAicSEFAAAQKZMLqR//+MfaunWrLr/8cn3lK1/J4iEAAAAK\n",
+       "V0m7j9Qrr7yiK664Qg8++KA2bNigt771rfre976nN77xjWk+DAAAQOFSj0jt3LlTmzdv1tDQkObO\n",
+       "nas/+7M/0/3335/2wwAAABQu9QupkZERbdy4Mfz/wcFBjYyMpP0wAAAAhZuT9g+sVCqpfA8AAEAZ\n",
+       "bNq0ScPDw4lfS/1CasOGDTp06FD4/4cOHdLg4GDT/37hwoXh+MyZM6nOzVSr1cTjIjCXZMwlGXNJ\n",
+       "Zo9f1Dzmzp0bjv/hH/4h1bnYjWdMOWvRz4vHXJK1OpeLLrooHL/66quSpEWLFoWxBQsWSJKmp6fD\n",
+       "2GyvHft+Sbrjjjtamssb3vCGMJ+zZ8829W+aVaa/0bve9S49/PDDF/x66qm9a6+9Vvv27dPw8LBe\n",
+       "eukl3XPPPbrxxhub/vdnzpwJ/wOATnD27Nnwv7SdO3cu6iIK3emVV14J/7PXxgsvvBD+Z+xrjV47\n",
+       "L7/8cvhfq1599dXMXvdlcv3118/69dQjUnPmzNHXv/51/dEf/ZFeeeUVffzjH2fFHgAA6EqpX0hJ\n",
+       "0vve9z69733vy+JHw/FpUDt+5ZVXwphF9V566aV8J4bgd3/3d8OxhcCffPLJMEbkFZ3kDW94LYlh\n",
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+       "r66uqv/oY96FT0QwOjqKxsZGfPjwQd3OMa7BjF+4b25uisfjEZfLJdPT02aUQHl0enoqiqKIoijS\n",
+       "1NSkZnx9fS3d3d3idrult7dXfvz4YXKl9ByDg4Nit9vFarWKw+GQxcXFv2Y8NTUlLpdL6urqZGtr\n",
+       "y8TKSY+neS8sLMjw8LB4vV5paWmRN2/eyOXlpXo88y5sBwcHYrFYRFEU8fl84vP5JBwOc4xr4CVi\n",
+       "iIiIiHTiyuZEREREOrGRIiIiItKJjRQRERGRTmykiIiIiHRiI0VERESkExspIiIiIp3YSBERERHp\n",
+       "xEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The first fully connected layer, `fc6` (rectified)\n",
-      "\n",
-      "We show the output values and the histogram of the positive values"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv5'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fifth layer after pooling, `pool5`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['fc6'].data[0]\n",
-      "plt.subplot(2, 1, 1)\n",
-      "plt.plot(feat.flat)\n",
-      "plt.subplot(2, 1, 2)\n",
-      "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-     ],
-     "language": "python",
+     "data": {
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+       "IqDoAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\ni4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064b890>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['pool5'].data[0]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first fully connected layer, `fc6` (rectified)\n",
+    "\n",
+    "We show the output values and the histogram of the positive values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "g4svozE4j5dsHvPkoibe4maDuegYV101vjbGNT6sPjFhn2yQZBUjwfaCAYxCYryEKKCAMxN0RG4i\n",
+       "MFPPH2MPPT3V3XU5p+pU9ef9evFipqfqnFNVp6p+fc6pUwXP8zwBAADAmP3SLgAAAEDeEGABAAAY\n",
+       "RoAFAABgGAEWAACAYQRYAAAAhhFgAQAAGBYowNqyZYtOPfVUHXHEETryyCO1YsUKdXd3q7W1VRMn\n",
+       "TtSsWbO0ZcsW22UFAADIhEAB1le/+lV94hOf0LPPPqs//OEPmjRpktrb29Xa2qo1a9Zo5syZam9v\n",
+       "t11WAACATCjUm2j09ddfV3Nzs9auXTvg80mTJumhhx5SU1OTOjs71dLSoueee85qYQEAALKgbgvW\n",
+       "unXrdPDBB+vss8/We9/7Xn3+85/Xjh071NXVpaamJklSU1OTurq6rBcWAAAgC+oGWHv37tWTTz6p\n",
+       "L37xi3ryySf1N3/zN4O6AwuFggqFgrVCAgAAZMmQeguMHTtWY8eO1fve9z5J0qmnnqqrr75ao0eP\n",
+       "Vmdnp0aPHq2Ojg6NGjVq0LqHH364XnjhBfOlBgAAMGz8+PF6/vnnjaRVtwVr9OjROvTQQ7VmzRpJ\n",
+       "0pIlSzR58mSddNJJmj9/viRp/vz5mj179qB1X3jhBXme13D/vvWtb6VeBrab7Wa72W62m+1mu8P9\n",
+       "M9koVLcFS5JuvvlmnXnmmdq9e7fGjx+vO+64Qz09PTrttNN0++23a9y4cVqwYIGxQgEAAGRZoABr\n",
+       "2rRpevzxxwd9vmTJEuMFAgAAyDpmcregpaUl7SKkgu1uLGx3Y2G7G0ujbrdJdefBipV4oSCLyQMA\n",
+       "ABhjMm6hBQsAAMAwAiwAAADDCLAAAAAMI8ACAAAwjAALAADAMAIsAAAAwwiwAAAADCPAAgAAMIwA\n",
+       "CwAAwDACLABoAB0d0iGHpF0KoHEQYAFAA1izRnr55bRLATQOAiwAAADDCLAAAAAMI8ACgAa3cyfj\n",
+       "swDTCLAAoMG9+irjswDTCLAAAAAMI8ACAAAwjAALAADAMAIsAAAAwwiwAAAADCPAAgAAMIwACwAA\n",
+       "wDACLAAAAMMIsAAAAAwjwAIAADCMAAsAAMAwAiwAAADDCLAAAAAMI8AC0LCWLJH+9V/TLgWAPCLA\n",
+       "AtCwLr9cuvDCtEsBII8IsAAAAAwbEmShcePGafjw4dp///01dOhQrVy5Ut3d3frMZz6jF198UePG\n",
+       "jdOCBQv0jne8w3Z5AcCYQiHtEgDIq0AtWIVCQcViUatWrdLKlSslSe3t7WptbdWaNWs0c+ZMtbe3\n",
+       "Wy0oAABAVgTuIvQ8b8DvixYtUltbmySpra1NCxcuNFsyALCMFiwAtgRuwTrxxBN1zDHH6LbbbpMk\n",
+       "dXV1qampSZLU1NSkrq4ue6UEAAsIsADYEmgM1rJlyzRmzBht3rxZra2tmjRp0oC/FwoFFbhSAUAm\n",
+       "cfkGzAsUYI0ZM0aSdPDBB+uUU07RypUr1dTUpM7OTo0ePVodHR0aNWqU77rz5s3r/7mlpUUtLS2x\n",
+       "Cw0AMKdiBAjQMIrFoorFopW0C17l4KoKO3fuVE9Pj4YNG6YdO3Zo1qxZ+ta3vqUlS5booIMO0qWX\n",
+       "Xqr29nZt2bJl0ED3QqEwaOwWAHM8T9pvP26QUX3gA9KKFQP336239s2NtXNneuWy4aGHpJYW/7qy\n",
+       "YYP0rndRjwCTcUvdFqyuri6dcsopkqS9e/fqzDPP1KxZs3TMMcfotNNO0+23394/TQMAZIlf19jy\n",
+       "5dIbbyRfFgD5UjfAOuyww7R69epBn48cOVJLliyxUigAAIAsYyZ3AA2Lwd3Z8dJLaZcACIcAC0DD\n",
+       "IsDKBs+T/u7v0i4FEA4BFgAAgGEEWECG8dQXALiJACtnNm5MuwRIA4EWALiFACtnDj1UevzxtEsB\n",
+       "ZANjsADYQoCVQ9u3p10CIBsIsADYQoAFAHBaqQucrnBkCQEWgIaV1RasrVvTLoF5y5YRQCFfCLAA\n",
+       "IEOefFI68MC0S2Heccf1vRMRyAsCLAc98kjfP6Aeuk7Mc31fdnebT9OVljzX9z0QRt13ESJ5J5zQ\n",
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+       "y8ulqKhIrFarPHr0SDY3N8Xn88n58+fF7/fL1tZWvpd5rExOTorFYhG32y0ej0c8Ho+Mjo6yu0aJ\n",
+       "REJM0xS32y0ul0vu3LkjIsLmOTIxMSGBQEBE2Fynjx8/itvtFrfbLU6nM/u6yeZ6xeNx8Xq9UldX\n",
+       "J8FgUNLptNLm3GiUiIiISDEtG40SERERnWQcsIiIiIgU44BFREREpBgHLCIiIiLFOGARERERKcYB\n",
+       "i4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd63064b890>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The second fully connected layer, `fc7` (rectified)"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc6'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second fully connected layer, `fc7` (rectified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['fc7'].data[0]\n",
-      "plt.subplot(2, 1, 1)\n",
-      "plt.plot(feat.flat)\n",
-      "plt.subplot(2, 1, 2)\n",
-      "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-     ],
-     "language": "python",
+     "data": {
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+       "ItmGDx8uSTriiCM0Z84cbdq0KeaI4EVNTY1aW1slSS0tLRo6dGjMEcGLoUOH9lyUL774Yo6/BDtw\n",
+       "4IDmzp2r+fPn69xzz5XE8Zcm+fq78MILe+rP1PFnPLk68cQT9eqrr2rr1q3av3+/7r77bp199tmm\n",
+       "F4OQ7N27Vx0dHZKkPXv26IEHHuj1JBOS7+yzz9aqVaskSatWreo5aSAdWlpaen5et24dx19CWZal\n",
+       "hQsX6vjjj9dll13W8znHXzo41Z+p4y+Uca42btyoyy67rGeA0Wuuucb0IhCSLVu2aM6cOZJyI/Ff\n",
+       "cMEF1F+CzZs3T48++qh27NihmpoaXXfddTrnnHN0/vnn680339SoUaO0du1affazn407VNgorr/F\n",
+       "ixcrm81q8+bNymQyGj16tFasWNHThwfJ8cQTT+jUU0/VpEmTem793XjjjZoyZQrHXwrY1d8NN9yg\n",
+       "NWvWGDn+GEQUAADAoFAGEQUAAKhWJFcAAAAGkVwBAAAYRHIFAABgEMkVAACAQSRXAAAABpFcAQAA\n",
+       "GERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd6306fb450>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\nAACAQSRXAAAABpFcAQAAGERyBQAAYBDJFQAAgEE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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd6306fb450>"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc7'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final probability output, `prob`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
-     "source": [
-      "The final probability output, `prob`"
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['prob'].data[0]\n",
-      "plt.plot(feat.flat)"
-     ],
-     "language": "python",
+     "data": {
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+       "oIqkHN544434wAc+ENdff31cc801EVH7rWD37t0REfHUU0/FO3+/BPbwc7pr165oamo68QdN/OIX\n",
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+       "+njXu94VV199tfM3AbS3t8eGDRvi1VdfjaIoYv369XHhhRc6fxPI0f5fee6558Zpp50WGzdujKIo\n",
+       "4nvf+97QPnWN4RMMjtq6deuKCy64oJg5c2bx1a9+NeWhMIIHH3ywqFQqxZw5c4pLLrmkuOSSS4of\n",
+       "//jHxXPPPVf8yZ/8SdHW1lYsXLiweOGFF4b2+cpXvlLMnDmzmDVrVtHd3Z3w6BnU09Mz9OxI527i\n",
+       "+PWvf110dnYWs2fPLpYsWVLs2bPH+ZtAVqxYUVx44YXFRRddVNxwww3F/v37nb+SWrp0aTFt2rSi\n",
+       "sbGxaG5uLu66665jOlcPP/xwcdFFFxUzZ84sPvWpT43qY1usFQAggaSLtQIA5EqEAQAkIMIAABIQ\n",
+       "YQAACYgwAIAERBgAQAIiDAAgAREGAJDA/wckYxa5Es1/mgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd630627290>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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+acS6dRH/+q8RU6akPhoAJpqx7Ja6I2ENDQ2xcuXKWLRoUVSr1Vi2bFl0dHTEqlWrIiJi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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd630627290>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['prob'].data[0]\n",
+    "plt.plot(feat.flat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see the top 5 predicted labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's see the top 5 predicted labels."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+      " 'n02127052 lynx, catamount']\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# load labels\n",
-      "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-      "try:\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-      "except:\n",
-      "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-      "\n",
-      "# sort top k predictions from softmax output\n",
-      "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-      "print labels[top_k]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-        " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-        " 'n02127052 lynx, catamount']\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
     }
    ],
-   "metadata": {}
+   "source": [
+    "# load labels\n",
+    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "try:\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "except:\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "\n",
+    "# sort top k predictions from softmax output\n",
+    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+    "print labels[top_k]"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
+  "example_name": "Filter visualization",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
index 03c811b5120..e98d13dd501 100644
--- a/examples/hdf5_classification.ipynb
+++ b/examples/hdf5_classification.ipynb
@@ -1,1075 +1,6290 @@
 {
- "metadata": {
-  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Off-the-shelf SGD for classification",
-  "include_in_docs": true,
-  "priority": 4,
-  "signature": "sha256:741422697d76b1667287180dc7c6360cf105ee774b1e2def800dc8fe80f78f67"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Caffeinated Logistic Regression of HDF5 Data\n",
-      "\n",
-      "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import pandas as pd\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "import os\n",
-      "import h5py\n",
-      "import shutil\n",
-      "import tempfile\n",
-      "\n",
-      "# You may need to 'pip install scikit-learn'\n",
-      "import sklearn\n",
-      "import sklearn.datasets\n",
-      "import sklearn.linear_model"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "X, y = sklearn.datasets.make_classification(\n",
-      "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
-      "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
-      ")\n",
-      "\n",
-      "# Split into train and test\n",
-      "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
-      "\n",
-      "# Visualize sample of the data\n",
-      "ind = np.random.permutation(X.shape[0])[:1000]\n",
-      "df = pd.DataFrame(X[ind])\n",
-      "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ikQzBoItAYBQYobj4OhIRLz7VxtP7R7lm7Uz+n3vvPZ0qvJ2dO1vRNCeQRdNe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dJjiJgJ+tTFAgoOoKGjIvuDGOqs00PHJUVAGxWLFQ7SYAmbgJCYNjl0OY5wWzQHGcM+EygI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\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YTGe2SZKELx7HaDQyNjaGMhpFrVQiSVKi/ocoYk5JwWuxEAqFWLV4AT1HjjA82E+KR06f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\nIAIgiiLDqRQXTpx4ywUjvu9z7OmnOTg6elXKv6VpJGMxyqur6Lt2XdXKMG0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16fV9Hnvo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JUuMre6yvC11774GuX1dQpISFK8m4+EoNWp0OM5ZGWDjAiIgoj1loMLXDs8zOT4OIePHmXJ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xf38zLpcas7kCtVpDc3M7IyN7MBj0ZGYWnCKzTpdCT8/AhDECsGTJYtLSUvnyy4M4nTYW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\nb765DbXaTXf3XtLSLCxdOge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-       "text": [
-        "<matplotlib.figure.Figure at 0x11dade4d0>"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Caffeinated Logistic Regression of HDF5 Data\n",
+    "\n",
+    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "import os\n",
+    "import h5py\n",
+    "import shutil\n",
+    "import tempfile\n",
+    "\n",
+    "# You may need to 'pip install scikit-learn'\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import sklearn.linear_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Train and test the scikit-learn SGD logistic regression.\n",
-      "clf = sklearn.linear_model.SGDClassifier(\n",
-      "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
-      "\n",
-      "%timeit clf.fit(X, y)\n",
-      "yt_pred = clf.predict(Xt)\n",
-      "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
-     ],
-     "language": "python",
+     "data": {
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+       "sjEiIyMjIyMjE1dkY0RGRkZGRkYmrsjGiIyMjIyMjExc+f8Bo2OouhAS2pgAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11dade4d0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 499 ms per loop\n",
-        "Accuracy: 0.756\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(\n",
+    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+    ")\n",
+    "\n",
+    "# Split into train and test\n",
+    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "\n",
+    "# Visualize sample of the data\n",
+    "ind = np.random.permutation(X.shape[0])[:1000]\n",
+    "df = pd.DataFrame(X[ind])\n",
+    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Save the dataset to HDF5 for loading in Caffe."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 499 ms per loop\n",
+      "Accuracy: 0.756\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Write out the data to HDF5 files in a temp directory.\n",
-      "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
-      "dirname = os.path.abspath('./hdf5_classification/data')\n",
-      "if not os.path.exists(dirname):\n",
-      "    os.makedirs(dirname)\n",
-      "\n",
-      "train_filename = os.path.join(dirname, 'train.h5')\n",
-      "test_filename = os.path.join(dirname, 'test.h5')\n",
-      "\n",
-      "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
-      "# To show this off, we'll list the same data file twice.\n",
-      "with h5py.File(train_filename, 'w') as f:\n",
-      "    f['data'] = X\n",
-      "    f['label'] = y.astype(np.float32)\n",
-      "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
-      "    f.write(train_filename + '\\n')\n",
-      "    f.write(train_filename + '\\n')\n",
-      "    \n",
-      "# HDF5 is pretty efficient, but can be further compressed.\n",
-      "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
-      "with h5py.File(test_filename, 'w') as f:\n",
-      "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
-      "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
-      "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
-      "    f.write(test_filename + '\\n')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "# Train and test the scikit-learn SGD logistic regression.\n",
+    "clf = sklearn.linear_model.SGDClassifier(\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
+    "\n",
+    "%timeit clf.fit(X, y)\n",
+    "yt_pred = clf.predict(Xt)\n",
+    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the dataset to HDF5 for loading in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Write out the data to HDF5 files in a temp directory.\n",
+    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+    "dirname = os.path.abspath('./hdf5_classification/data')\n",
+    "if not os.path.exists(dirname):\n",
+    "    os.makedirs(dirname)\n",
+    "\n",
+    "train_filename = os.path.join(dirname, 'train.h5')\n",
+    "test_filename = os.path.join(dirname, 'test.h5')\n",
+    "\n",
+    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+    "# To show this off, we'll list the same data file twice.\n",
+    "with h5py.File(train_filename, 'w') as f:\n",
+    "    f['data'] = X\n",
+    "    f['label'] = y.astype(np.float32)\n",
+    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    \n",
+    "# HDF5 is pretty efficient, but can be further compressed.\n",
+    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+    "with h5py.File(test_filename, 'w') as f:\n",
+    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+    "    f.write(test_filename + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate logistic regression in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Learn and evaluate logistic regression in Caffe."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 240 ms per loop\n",
+      "Accuracy: 0.752\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def learn_and_test(solver_file):\n",
-      "    caffe.set_mode_cpu()\n",
-      "    solver = caffe.get_solver(solver_file)\n",
-      "    solver.solve()\n",
-      "\n",
-      "    accuracy = 0\n",
-      "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
-      "    for i in range(test_iters):\n",
-      "        solver.test_nets[0].forward()\n",
-      "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-      "    accuracy /= test_iters\n",
-      "    return accuracy\n",
-      "\n",
-      "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n",
-      "acc = learn_and_test('hdf5_classification/solver.prototxt')\n",
-      "print(\"Accuracy: {:.3f}\".format(acc))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 240 ms per loop\n",
-        "Accuracy: 0.752"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+    }
+   ],
+   "source": [
+    "def learn_and_test(solver_file):\n",
+    "    caffe.set_mode_cpu()\n",
+    "    solver = caffe.get_solver(solver_file)\n",
+    "    solver.solve()\n",
+    "\n",
+    "    accuracy = 0\n",
+    "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
+    "    for i in range(test_iters):\n",
+    "        solver.test_nets[0].forward()\n",
+    "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    accuracy /= test_iters\n",
+    "    return accuracy\n",
+    "\n",
+    "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n",
+    "acc = learn_and_test('hdf5_classification/solver.prototxt')\n",
+    "print(\"Accuracy: {:.3f}\".format(acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Do the same through the command line interface for detailed output on the model and solving."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\n",
+      "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\n",
+      "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "net: \"hdf5_classification/train_val.prototxt\"\n",
+      "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\n",
+      "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n",
+      "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n",
+      "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"LogisticRegressionNet\"\n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  include {\n",
+      "    phase: TRAIN\n",
+      "  }\n",
+      "  hdf5_data_param {\n",
+      "    source: \"hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"fc1\"\n",
+      "  param {\n",
+      "    lr_mult: 1\n",
+      "    decay_mult: 1\n",
+      "  }\n",
+      "  param {\n",
+      "    lr_mult: 2\n",
+      "    decay_mult: 0\n",
+      "  }\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"gaussian\"\n",
+      "      std: 0.01\n",
+      "    }\n",
+      "    bias_filler {\n",
+      "      type: \"constant\"\n",
+      "      value: 0\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"fc1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\n",
+      "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\n",
+      "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\n",
+      "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\n",
+      "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\n",
+      "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n",
+      "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+      "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
+      "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\n",
+      "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\n",
+      "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\n",
+      "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\n",
+      "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\n",
+      "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\n",
+      "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\n",
+      "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\n",
+      "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\n",
+      "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\n",
+      "I0307 01:34:29.425789 2099749632 net.cpp:122]     with loss weight 1\n",
+      "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\n",
+      "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\n",
+      "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\n",
+      "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\n",
+      "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\n",
+      "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\n",
+      "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\n",
+      "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n",
+      "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"LogisticRegressionNet\"\n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  include {\n",
+      "    phase: TEST\n",
+      "  }\n",
+      "  hdf5_data_param {\n",
+      "    source: \"hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"fc1\"\n",
+      "  param {\n",
+      "    lr_mult: 1\n",
+      "    decay_mult: 1\n",
+      "  }\n",
+      "  param {\n",
+      "    lr_mult: 2\n",
+      "    decay_mult: 0\n",
+      "  }\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"gaussian\"\n",
+      "      std: 0.01\n",
+      "    }\n",
+      "    bias_filler {\n",
+      "      type: \"constant\"\n",
+      "      value: 0\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"fc1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"fc1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "  include {\n",
+      "    phase: TEST\n",
+      "  }\n",
+      "}\n",
+      "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\n",
+      "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\n",
+      "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\n",
+      "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\n",
+      "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\n",
+      "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n",
+      "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+      "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\n",
+      "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\n",
+      "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
+      "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\n",
+      "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\n",
+      "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\n",
+      "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\n",
+      "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\n",
+      "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\n",
+      "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\n",
+      "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\n",
+      "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\n",
+      "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\n",
+      "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\n",
+      "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\n",
+      "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\n",
+      "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\n",
+      "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\n",
+      "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\n",
+      "I0307 01:34:29.428056 2099749632 net.cpp:122]     with loss weight 1\n",
+      "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\n",
+      "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\n",
+      "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n",
+      "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\n",
+      "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\n",
+      "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\n",
+      "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\n",
+      "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\n",
+      "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\n",
+      "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\n",
+      "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\n",
+      "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\n",
+      "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\n",
+      "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\n",
+      "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\n",
+      "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n",
+      "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\n",
+      "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+      "I0307 01:34:29.471674 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.4532\n",
+      "I0307 01:34:29.471724 2099749632 solver.cpp:315]     Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\n",
+      "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\n",
+      "I0307 01:34:29.471878 2099749632 solver.cpp:204]     Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\n",
+      "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n",
+      "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+      "I0307 01:34:29.486868 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\n",
+      "I0307 01:34:29.486896 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n",
+      "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\n",
+      "I0307 01:34:29.486934 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n",
+      "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+      "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+      "I0307 01:34:29.501900 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\n",
+      "I0307 01:34:29.501941 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n",
+      "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\n",
+      "I0307 01:34:29.502003 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n",
+      "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+      "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+      "I0307 01:34:29.517227 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.6964\n",
+      "I0307 01:34:29.517300 2099749632 solver.cpp:315]     Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\n",
+      "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\n",
+      "I0307 01:34:29.518154 2099749632 solver.cpp:204]     Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\n",
+      "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+      "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+      "I0307 01:34:29.534873 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\n",
+      "I0307 01:34:29.534920 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n",
+      "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\n",
+      "I0307 01:34:29.534962 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n",
+      "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+      "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+      "I0307 01:34:29.549762 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\n",
+      "I0307 01:34:29.549789 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n",
+      "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\n",
+      "I0307 01:34:29.549828 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n",
+      "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+      "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+      "I0307 01:34:29.565335 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7476\n",
+      "I0307 01:34:29.565373 2099749632 solver.cpp:315]     Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\n",
+      "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\n",
+      "I0307 01:34:29.566086 2099749632 solver.cpp:204]     Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\n",
+      "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+      "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+      "I0307 01:34:29.580993 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7524\n",
+      "I0307 01:34:29.581015 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\n",
+      "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\n",
+      "I0307 01:34:29.581050 2099749632 solver.cpp:204]     Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\n",
+      "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+      "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+      "I0307 01:34:29.595973 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7568\n",
+      "I0307 01:34:29.596002 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\n",
+      "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\n",
+      "I0307 01:34:29.596040 2099749632 solver.cpp:204]     Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\n",
+      "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+      "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+      "I0307 01:34:29.610930 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7432\n",
+      "I0307 01:34:29.610960 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\n",
+      "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\n",
+      "I0307 01:34:29.611583 2099749632 solver.cpp:204]     Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\n",
+      "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+      "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\n",
+      "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+      "I0307 01:34:29.626454 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.752\n",
+      "I0307 01:34:29.626484 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\n",
+      "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\n",
+      "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\r\n",
-        "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 250\r\n",
-        "test_interval: 1000\r\n",
-        "base_lr: 0.01\r\n",
-        "display: 1000\r\n",
-        "max_iter: 10000\r\n",
-        "lr_policy: \"step\"\r\n",
-        "gamma: 0.1\r\n",
-        "momentum: 0.9\r\n",
-        "weight_decay: 0.0005\r\n",
-        "stepsize: 5000\r\n",
-        "snapshot: 10000\r\n",
-        "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
-        "solver_mode: CPU\r\n",
-        "net: \"hdf5_classification/train_val.prototxt\"\r\n",
-        "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\r\n",
-        "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TRAIN\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TRAIN\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
-        "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
-        "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\r\n",
-        "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\r\n",
-        "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:29.425789 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\r\n",
-        "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\r\n",
-        "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: \"Accuracy\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"accuracy\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
-        "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
-        "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
-        "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
-        "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
-        "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
-        "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\r\n",
-        "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\r\n",
-        "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\r\n",
-        "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
-        "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
-        "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\r\n",
-        "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\r\n",
-        "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
-        "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:29.428056 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
-        "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
-        "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\r\n",
-        "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
-        "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
-        "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\r\n",
-        "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
-        "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\r\n",
-        "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
-        "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\r\n",
-        "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\r\n",
-        "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
-        "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
-        "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
-        "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.471674 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.4532\r\n",
-        "I0307 01:34:29.471724 2099749632 solver.cpp:315]     Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\r\n",
-        "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\r\n",
-        "I0307 01:34:29.471878 2099749632 solver.cpp:204]     Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\r\n",
-        "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
-        "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n",
-        "I0307 01:34:29.486868 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\r\n",
-        "I0307 01:34:29.486896 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
-        "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\r\n",
-        "I0307 01:34:29.486934 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
-        "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
-        "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
-        "I0307 01:34:29.501900 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\r\n",
-        "I0307 01:34:29.501941 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
-        "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\r\n",
-        "I0307 01:34:29.502003 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
-        "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
-        "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n",
-        "I0307 01:34:29.517227 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.6964\r\n",
-        "I0307 01:34:29.517300 2099749632 solver.cpp:315]     Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\r\n",
-        "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\r\n",
-        "I0307 01:34:29.518154 2099749632 solver.cpp:204]     Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\r\n",
-        "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
-        "I0307 01:34:29.534873 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\r\n",
-        "I0307 01:34:29.534920 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
-        "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\r\n",
-        "I0307 01:34:29.534962 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
-        "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
-        "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n",
-        "I0307 01:34:29.549762 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\r\n",
-        "I0307 01:34:29.549789 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
-        "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\r\n",
-        "I0307 01:34:29.549828 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
-        "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
-        "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
-        "I0307 01:34:29.565335 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7476\r\n",
-        "I0307 01:34:29.565373 2099749632 solver.cpp:315]     Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\r\n",
-        "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\r\n",
-        "I0307 01:34:29.566086 2099749632 solver.cpp:204]     Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\r\n",
-        "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
-        "I0307 01:34:29.580993 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7524\r\n",
-        "I0307 01:34:29.581015 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\r\n",
-        "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\r\n",
-        "I0307 01:34:29.581050 2099749632 solver.cpp:204]     Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\r\n",
-        "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n",
-        "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
-        "I0307 01:34:29.595973 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7568\r\n",
-        "I0307 01:34:29.596002 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\r\n",
-        "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\r\n",
-        "I0307 01:34:29.596040 2099749632 solver.cpp:204]     Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\r\n",
-        "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
-        "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
-        "I0307 01:34:29.610930 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7432\r\n",
-        "I0307 01:34:29.610960 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\r\n",
-        "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\r\n",
-        "I0307 01:34:29.611583 2099749632 solver.cpp:204]     Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\r\n",
-        "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n",
-        "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
-        "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\r\n",
-        "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.626454 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.752\r\n",
-        "I0307 01:34:29.626484 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\r\n",
-        "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\r\n",
-        "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+    }
+   ],
+   "source": [
+    "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n",
+    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+    "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
+    "\n",
+    "The final accuracy of the new network be higher than logistic regression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n",
-      "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
-      "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
-      "\n",
-      "The final accuracy of the new network be higher than logistic regression!"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 333 ms per loop\n",
+      "Accuracy: 0.818\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def learn_and_test(solver_file):\n",
-      "    caffe.set_mode_cpu()\n",
-      "    solver = caffe.get_solver(solver_file)\n",
-      "    solver.solve()\n",
-      "\n",
-      "    accuracy = 0\n",
-      "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
-      "    for i in range(test_iters):\n",
-      "        solver.test_nets[0].forward()\n",
-      "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-      "    accuracy /= test_iters\n",
-      "    return accuracy\n",
-      "\n",
-      "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n",
-      "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n",
-      "print(\"Accuracy: {:.3f}\".format(acc))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 333 ms per loop\n",
-        "Accuracy: 0.818"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+    }
+   ],
+   "source": [
+    "def learn_and_test(solver_file):\n",
+    "    caffe.set_mode_cpu()\n",
+    "    solver = caffe.get_solver(solver_file)\n",
+    "    solver.solve()\n",
+    "\n",
+    "    accuracy = 0\n",
+    "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
+    "    for i in range(test_iters):\n",
+    "        solver.test_nets[0].forward()\n",
+    "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    accuracy /= test_iters\n",
+    "    return accuracy\n",
+    "\n",
+    "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n",
+    "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n",
+    "print(\"Accuracy: {:.3f}\".format(acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Do the same through the command line interface for detailed output on the model and solving."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\n",
+      "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\n",
+      "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "net: \"hdf5_classification/train_val2.prototxt\"\n",
+      "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\n",
+      "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n",
+      "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n",
+      "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"LogisticRegressionNet\"\n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  include {\n",
+      "    phase: TRAIN\n",
+      "  }\n",
+      "  hdf5_data_param {\n",
+      "    source: \"hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"fc1\"\n",
+      "  param {\n",
+      "    lr_mult: 1\n",
+      "    decay_mult: 1\n",
+      "  }\n",
+      "  param {\n",
+      "    lr_mult: 2\n",
+      "    decay_mult: 0\n",
+      "  }\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"gaussian\"\n",
+      "      std: 0.01\n",
+      "    }\n",
+      "    bias_filler {\n",
+      "      type: \"constant\"\n",
+      "      value: 0\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc1\"\n",
+      "  top: \"fc1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc1\"\n",
+      "  top: \"fc2\"\n",
+      "  param {\n",
+      "    lr_mult: 1\n",
+      "    decay_mult: 1\n",
+      "  }\n",
+      "  param {\n",
+      "    lr_mult: 2\n",
+      "    decay_mult: 0\n",
+      "  }\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"gaussian\"\n",
+      "      std: 0.01\n",
+      "    }\n",
+      "    bias_filler {\n",
+      "      type: \"constant\"\n",
+      "      value: 0\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"fc2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\n",
+      "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\n",
+      "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\n",
+      "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\n",
+      "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\n",
+      "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n",
+      "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+      "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
+      "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\n",
+      "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\n",
+      "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\n",
+      "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\n",
+      "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\n",
+      "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\n",
+      "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n",
+      "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\n",
+      "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\n",
+      "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\n",
+      "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\n",
+      "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\n",
+      "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\n",
+      "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\n",
+      "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\n",
+      "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\n",
+      "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\n",
+      "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\n",
+      "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\n",
+      "I0307 01:34:31.882844 2099749632 net.cpp:122]     with loss weight 1\n",
+      "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\n",
+      "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\n",
+      "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\n",
+      "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\n",
+      "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\n",
+      "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\n",
+      "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\n",
+      "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\n",
+      "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\n",
+      "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n",
+      "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"LogisticRegressionNet\"\n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  include {\n",
+      "    phase: TEST\n",
+      "  }\n",
+      "  hdf5_data_param {\n",
+      "    source: \"hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"fc1\"\n",
+      "  param {\n",
+      "    lr_mult: 1\n",
+      "    decay_mult: 1\n",
+      "  }\n",
+      "  param {\n",
+      "    lr_mult: 2\n",
+      "    decay_mult: 0\n",
+      "  }\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"gaussian\"\n",
+      "      std: 0.01\n",
+      "    }\n",
+      "    bias_filler {\n",
+      "      type: \"constant\"\n",
+      "      value: 0\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc1\"\n",
+      "  top: \"fc1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc1\"\n",
+      "  top: \"fc2\"\n",
+      "  param {\n",
+      "    lr_mult: 1\n",
+      "    decay_mult: 1\n",
+      "  }\n",
+      "  param {\n",
+      "    lr_mult: 2\n",
+      "    decay_mult: 0\n",
+      "  }\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"gaussian\"\n",
+      "      std: 0.01\n",
+      "    }\n",
+      "    bias_filler {\n",
+      "      type: \"constant\"\n",
+      "      value: 0\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"fc2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"fc2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "  include {\n",
+      "    phase: TEST\n",
+      "  }\n",
+      "}\n",
+      "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\n",
+      "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\n",
+      "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\n",
+      "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\n",
+      "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\n",
+      "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n",
+      "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+      "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\n",
+      "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\n",
+      "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\n",
+      "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
+      "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\n",
+      "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\n",
+      "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\n",
+      "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\n",
+      "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\n",
+      "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\n",
+      "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n",
+      "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\n",
+      "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\n",
+      "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\n",
+      "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\n",
+      "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\n",
+      "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\n",
+      "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\n",
+      "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\n",
+      "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\n",
+      "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\n",
+      "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\n",
+      "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\n",
+      "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\n",
+      "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\n",
+      "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\n",
+      "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\n",
+      "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\n",
+      "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\n",
+      "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\n",
+      "I0307 01:34:31.885337 2099749632 net.cpp:122]     with loss weight 1\n",
+      "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\n",
+      "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\n",
+      "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n",
+      "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\n",
+      "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\n",
+      "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\n",
+      "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\n",
+      "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\n",
+      "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\n",
+      "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\n",
+      "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\n",
+      "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\n",
+      "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\n",
+      "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\n",
+      "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\n",
+      "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\n",
+      "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\n",
+      "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n",
+      "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\n",
+      "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+      "I0307 01:34:31.901005 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.5944\n",
+      "I0307 01:34:31.901049 2099749632 solver.cpp:315]     Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\n",
+      "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\n",
+      "I0307 01:34:31.901192 2099749632 solver.cpp:204]     Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\n",
+      "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n",
+      "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+      "I0307 01:34:31.924612 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7556\n",
+      "I0307 01:34:31.924646 2099749632 solver.cpp:315]     Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\n",
+      "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\n",
+      "I0307 01:34:31.924696 2099749632 solver.cpp:204]     Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\n",
+      "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+      "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+      "I0307 01:34:31.948729 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7824\n",
+      "I0307 01:34:31.948763 2099749632 solver.cpp:315]     Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\n",
+      "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\n",
+      "I0307 01:34:31.948812 2099749632 solver.cpp:204]     Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\n",
+      "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+      "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+      "I0307 01:34:31.972393 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7956\n",
+      "I0307 01:34:31.972411 2099749632 solver.cpp:315]     Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\n",
+      "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\n",
+      "I0307 01:34:31.973057 2099749632 solver.cpp:204]     Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\n",
+      "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+      "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+      "I0307 01:34:31.998638 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.798\n",
+      "I0307 01:34:31.998663 2099749632 solver.cpp:315]     Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\n",
+      "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\n",
+      "I0307 01:34:31.998718 2099749632 solver.cpp:204]     Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\n",
+      "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+      "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+      "I0307 01:34:32.024950 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.804\n",
+      "I0307 01:34:32.024981 2099749632 solver.cpp:315]     Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\n",
+      "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\n",
+      "I0307 01:34:32.025028 2099749632 solver.cpp:204]     Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\n",
+      "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+      "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+      "I0307 01:34:32.048216 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\n",
+      "I0307 01:34:32.048239 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\n",
+      "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\n",
+      "I0307 01:34:32.048809 2099749632 solver.cpp:204]     Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\n",
+      "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+      "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+      "I0307 01:34:32.071524 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8124\n",
+      "I0307 01:34:32.071542 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\n",
+      "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\n",
+      "I0307 01:34:32.071617 2099749632 solver.cpp:204]     Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\n",
+      "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+      "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+      "I0307 01:34:32.095410 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.814\n",
+      "I0307 01:34:32.095432 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\n",
+      "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\n",
+      "I0307 01:34:32.095474 2099749632 solver.cpp:204]     Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\n",
+      "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+      "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+      "I0307 01:34:32.121587 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\n",
+      "I0307 01:34:32.121608 2099749632 solver.cpp:315]     Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\n",
+      "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\n",
+      "I0307 01:34:32.122181 2099749632 solver.cpp:204]     Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\n",
+      "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+      "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\n",
+      "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+      "I0307 01:34:32.145747 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8148\n",
+      "I0307 01:34:32.145771 2099749632 solver.cpp:315]     Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\n",
+      "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\n",
+      "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\r\n",
-        "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 250\r\n",
-        "test_interval: 1000\r\n",
-        "base_lr: 0.01\r\n",
-        "display: 1000\r\n",
-        "max_iter: 10000\r\n",
-        "lr_policy: \"step\"\r\n",
-        "gamma: 0.1\r\n",
-        "momentum: 0.9\r\n",
-        "weight_decay: 0.0005\r\n",
-        "stepsize: 5000\r\n",
-        "snapshot: 10000\r\n",
-        "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
-        "solver_mode: CPU\r\n",
-        "net: \"hdf5_classification/train_val2.prototxt\"\r\n",
-        "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\r\n",
-        "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TRAIN\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TRAIN\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 40\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc2\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
-        "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
-        "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
-        "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\r\n",
-        "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\r\n",
-        "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
-        "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\r\n",
-        "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
-        "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\r\n",
-        "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\r\n",
-        "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\r\n",
-        "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\r\n",
-        "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\r\n",
-        "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\r\n",
-        "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:31.882844 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
-        "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
-        "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\r\n",
-        "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\r\n",
-        "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 40\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc2\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: \"Accuracy\"\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"accuracy\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
-        "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
-        "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
-        "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
-        "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
-        "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
-        "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
-        "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\r\n",
-        "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\r\n",
-        "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
-        "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\r\n",
-        "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
-        "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\r\n",
-        "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\r\n",
-        "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\r\n",
-        "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\r\n",
-        "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\r\n",
-        "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\r\n",
-        "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\r\n",
-        "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
-        "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
-        "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\r\n",
-        "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\r\n",
-        "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
-        "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:31.885337 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
-        "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
-        "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\r\n",
-        "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
-        "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
-        "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\r\n",
-        "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
-        "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\r\n",
-        "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
-        "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
-        "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
-        "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\r\n",
-        "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\r\n",
-        "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
-        "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
-        "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
-        "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n",
-        "I0307 01:34:31.901005 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.5944\r\n",
-        "I0307 01:34:31.901049 2099749632 solver.cpp:315]     Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\r\n",
-        "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\r\n",
-        "I0307 01:34:31.901192 2099749632 solver.cpp:204]     Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\r\n",
-        "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
-        "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.924612 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7556\r\n",
-        "I0307 01:34:31.924646 2099749632 solver.cpp:315]     Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\r\n",
-        "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\r\n",
-        "I0307 01:34:31.924696 2099749632 solver.cpp:204]     Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\r\n",
-        "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
-        "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
-        "I0307 01:34:31.948729 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7824\r\n",
-        "I0307 01:34:31.948763 2099749632 solver.cpp:315]     Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\r\n",
-        "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\r\n",
-        "I0307 01:34:31.948812 2099749632 solver.cpp:204]     Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\r\n",
-        "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
-        "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.972393 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7956\r\n",
-        "I0307 01:34:31.972411 2099749632 solver.cpp:315]     Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\r\n",
-        "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\r\n",
-        "I0307 01:34:31.973057 2099749632 solver.cpp:204]     Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\r\n",
-        "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n",
-        "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
-        "I0307 01:34:31.998638 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.798\r\n",
-        "I0307 01:34:31.998663 2099749632 solver.cpp:315]     Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\r\n",
-        "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\r\n",
-        "I0307 01:34:31.998718 2099749632 solver.cpp:204]     Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\r\n",
-        "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
-        "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:32.024950 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.804\r\n",
-        "I0307 01:34:32.024981 2099749632 solver.cpp:315]     Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\r\n",
-        "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\r\n",
-        "I0307 01:34:32.025028 2099749632 solver.cpp:204]     Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\r\n",
-        "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
-        "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
-        "I0307 01:34:32.048216 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\r\n",
-        "I0307 01:34:32.048239 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\r\n",
-        "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\r\n",
-        "I0307 01:34:32.048809 2099749632 solver.cpp:204]     Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\r\n",
-        "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n",
-        "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
-        "I0307 01:34:32.071524 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8124\r\n",
-        "I0307 01:34:32.071542 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\r\n",
-        "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\r\n",
-        "I0307 01:34:32.071617 2099749632 solver.cpp:204]     Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\r\n",
-        "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
-        "I0307 01:34:32.095410 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.814\r\n",
-        "I0307 01:34:32.095432 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\r\n",
-        "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\r\n",
-        "I0307 01:34:32.095474 2099749632 solver.cpp:204]     Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\r\n",
-        "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
-        "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
-        "I0307 01:34:32.121587 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\r\n",
-        "I0307 01:34:32.121608 2099749632 solver.cpp:315]     Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\r\n",
-        "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\r\n",
-        "I0307 01:34:32.122181 2099749632 solver.cpp:204]     Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\r\n",
-        "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
-        "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\r\n",
-        "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n",
-        "I0307 01:34:32.145747 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8148\r\n",
-        "I0307 01:34:32.145771 2099749632 solver.cpp:315]     Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\r\n",
-        "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\r\n",
-        "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
-      "shutil.rmtree(dirname)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
     }
    ],
-   "metadata": {}
+   "source": [
+    "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+    "shutil.rmtree(dirname)"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "example_name": "Off-the-shelf SGD for classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 75c9889fb5a..1fb99bd3726 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -1,464 +1,6827 @@
 {
- "metadata": {
-  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
-  "example_name": "Editing model parameters",
-  "include_in_docs": true,
-  "priority": 5,
-  "signature": "sha256:f21c804f76329e70847ccb87e28a91e5d8a375f5da0ba6dd85d3b87a05bebd72"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Net Surgery\n",
-      "\n",
-      "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
-      "\n",
-      "Roll up your sleeves for net surgery with pycaffe!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "import Image\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# configure plotting\n",
-      "plt.rcParams['figure.figsize'] = (10, 10)\n",
-      "plt.rcParams['image.interpolation'] = 'nearest'\n",
-      "plt.rcParams['image.cmap'] = 'gray'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Designer Filters\n",
-      "\n",
-      "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Load the net, list its data and params, and filter an example image.\n",
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
-      "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
-      "\n",
-      "# load image and prepare as a single input batch for Caffe\n",
-      "im = np.array(Image.open('images/cat_gray.jpg'))\n",
-      "plt.title(\"original image\")\n",
-      "plt.imshow(im)\n",
-      "plt.axis('off')\n",
-      "\n",
-      "im_input = im[np.newaxis, np.newaxis, :, :]\n",
-      "net.blobs['data'].reshape(*im_input.shape)\n",
-      "net.blobs['data'].data[...] = im_input"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "blobs ['data', 'conv']\n",
-        "params ['conv']\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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wOD+7rZYez/FQzmvBLddIBpl0ZLwxxp9nDRd1dKIXWRtjJ2NlZaU4fDbAGQCw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lUint2qvVaolWPu/c52sIw9DlCoYViATzjiKAfhyYLJ0bNr1eLxnQrVYr0ebu3buSzsOR8CRz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\nfKhKpaJarZb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OPk8YQq7YY0jd++IoDzLQHWE3XjC24BXQQ2rJeVjbK5s7qkdfHWFx9Gw2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\nJ30z36Xk8TmIYK3hpNqRMuWiTHmxFng2mzV4XrPZjHfv3iWV4uLiI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eYRUa6eoFaRyd8m\nv2dnEUcTwTI3w/NgxcRJOG6iz+Rnk6OtNMwLy84skS3v4Th7RIVs8jveTMzN09NT3N7e1hzefr9f\n5tf9YAyvX7+O9XpdSKusPQ6ueRM+DYPsse7mBzCHh4e7m9p9TYg5UHa23SdkgEtA6bsdYDuuPA/5\nwAgig1YioGX0BfmHAP7dd99FRMT79+9LuQF4OD4J980338Rf//rXgtjRF+48Qz6/fPlSHIl2e3e/\nHg5Uq1XVQluv14UvB5cCzuHj42MMBoNy3Q7Iqfc7z8Dw55NX/r85REavfTzeAaBRK+YebmFGE82x\nYl1w8EGocJ7t9KC8XZ6AqDiTmH2whbnHcTFnpdGoymUg03bUMSDZGcr8VAy8kTzrYOtJ81TR4+ZY\n8V3WwEiWUSC3VqsV3333XaxWq/jhhx9iPB6XS6LzNVWQ0plvo2lPT1UNIjss7DnztbLOsk7EmWMv\nZSfSyBTOPmvBWB0g571sp3W7rV9jw3hMhqeved54nrlcRt2Yf3SZZdkHqH7rpDey5+CBAHKzqd+P\nyfdyPSrPKTIMQsozsZW2t3zGOuZx2dml8UzLApmynC3LqJ7bszlSEVGL9vedmjNZk0ldLpe10xTA\njUYJeA5CbOWQoUR77vaWgQRttCPqpHicDBNHSRHiRLBJ8YJZlExGRZE6VcczgcbZqBYEPiOKR/Fg\nHEgvTiaTWoFMKzungZg3lB19zg6h4W0TGTebTXz8+DHOz8/LJsj9iqg7ITyPZxpiR1njGLkKNesA\napCVDf0jIjVakZ1jK7eIKCkEn1BxypENZ5QDBwj0xOli+kZUjQLlHTmNQN+QWaJ9EAEa91PxnuzU\ns69AN1gDR7NOeyLDkMK/++67eP/+fUTs7tP7l3/5lzg4OIj/+q//ipOTk3j16lVE7NCqu7u7uLm5\niYeHh9oJuuPj40KMHo1G0Ww2y95nP5+cnBQHjMb36QupNtYCB2swGNT2IWNcrVYFBXXFcxwrZNx7\nitTPyclJQW0dYPkAxsPDQ1nH5XJZCosif3a6+Bmpa/pDtG5Umd93LaN+v18zrugDn5BiDCaS814b\nK4KPiCoFyr9B63LAlJ0a9BljRN68j63fIio9472f98i+tHYOkCKq0hDdbjfOzs5iOBx+ha47lWZn\nkb2KfkT27QTiODozwHx7bvns6ekp5vN52btZlxIgWyc6Bcf/mWMHxYzB9AocGZAp20v+DeqaM0Am\n1FuHIYPoQztLnGjDEbXcoa+dQrNjY/TecoGso989r6wZyBF6ivlA9jJKZPuBw8n7MiptB4y95YDP\nyPD/5kRFPCNHCpjQJ6EYCM6DlRtGgdM4hjkRSBbMgunN3GhUx/+d42YBUTa8z6mwfWMAIfCx44OD\ng1KBGmXDMxk3C2OjDG+C/rmuDUUa7TFH1IuWNZu7o9PwUlAk/M7Z2VlMp9PixFq48e4zPIpQMUf8\nnO9ZcTKnmbPGWuAIYJgcuUdUjit9M/TfbDbj4uIiNptNjMfjEl2ykdhUTiuguKxQmdvPnz/H09NT\nXFxclOs3aCim9XpdjtxbBtjw7i/f8+ZEdoz0oLhcUZtnGv1zetvy4XpbXot9R7lx9H1qC3kHwcTR\n6PV6ZS045XJ4eBhXV1dxfX0dP/30U0RE/PM//3NcXV3Fv//7v8dwOIx//dd/LcqGAp6r1SouLi6i\n1+uVyJNIdbVaxd3dXa3QIykh+m+nEM4NhsnGAM4R8+UghHeCxOB822CyD1GuDgY4IccfdJSjV97j\n9R2NRnF0dBSnp6dxe3tb5AM0A+fK6SRzwAiK+B5GBHQEh5Q+MH6CwGzIrL/sKA+Hwzg5OYlut1sL\nPpAT9o0DW+TNDpMdBpBYI6R2bBzY2CkAMaSBavF962bXykIv3t3dFZ7jt99+W+YUBDYHZk45s/7Z\nkWLNfNqXZ7AmDnZZH3QvThXy7XSaUR4KCmP7DAJQOgbd5SDCts+OhlEwvo/TlB0pI0UeI2vCz+0Q\ngsqwJ5yWRdcgG8wxKWaQZdKCyBvlQkDXLcNGvtx/mp0yo77oWcAMAiyP9elpd20aY/B7mTNnHv63\ntF7EMxbk3Gx2VYfhprD5DTd6EUlPAeHnaIXFiqgEwkRRCwKfkU4Cyt/Hs0FBZIcK4adYZESUu8eI\nsM0Hyn3K0WVERazjGfQhC7ohRzsr3CPFfDin3+l0ym33PCvPsfPFkEeNGvFOoiiUt4XThGZvRMPb\nfg7vxtBlB6vZbJZrBUBmMN6Qwp0eyAKPkXGqCbI0Ssx5dKJRNo8RoMFgUIPqGTPNKBKlJ0xUJ9WW\no33PrbkRNL6DwvY7I+r3eTk4oC84YSgU114CjSEtAupGam80GpU73H7/+9/Hf/zHf8Tnz5/j3/7t\n3+Lg4KDcwweacHFxEZ1Op2bYIf2Px+NinO7u7iIiyvwSqXvclhUUW1ZuzClOodcR9IG0t6NKp7iz\nXsDQMEdGfChrAkqUkczr6+v4/e9/H4PBoMwp84qSdnXn9XpH+saozufz8j6QDiJ2owB8F+fEe8by\nwVF/p3GPjo5iOp2WquYucJs5S5YnHCfQIKdoPLfoAOstZIo96vQ0Tk+O/NELjH84HNaoIOjRX3/9\nNT58+FCrMYb+y6lG7ADy6WDI+jjrXPpNf7wORu3zdWNZ39uOIO/+mR1MO6I4h7yPdK5tofeGUT07\nxZZr5NzvJDDNDhGyYOfZhynM87Q8gIYSkFoWjV4yT0admHNssPc26+xglPHRRwcrXif+jUzzfZxB\nyke4EZT9Vnspf/DSXtpLe2kv7aW9tJf2d7ZnQaSIgrfbbYki8AINM2evkOOOERUxjWj31atXBc3I\nRHWnBTKHxnCiv+Ncqb+XoU4QE8ZALhkYmsjT5F9QFEcYQMwHBwdxd3dXoGFQJ8ZgLxoEj0ji6emp\nnE5xocGcQuS7GbWyx79arQqXxH01zwfSrlMRoIdOtzF+5of3Eb3QR9bcXBcXkiSV6iPw4/G4Fn3k\ndAScj30RBkRpl3BABpHRRqNREMfBYFDSMhzH5b2WXZCu2WxW3uvrjUA1kFOjCVl+Sd1RcDFD8Ya8\nc/rLc+so2b97eHgYHz58qJ1aQ/4hf799+zYidoVjf/nll/inf/qnaDab8cMPP5Tn9Xq9glQis6RS\nKbrJGt/d3RUOWrfbraWSHHmDUBKpWp4gLVMtfR/Pj1QDe8R8Ra+XOR2gFIzB8kSVc1CJdrtd9BdF\nNofDYSlOCtrOaT5OnZpG0O12y54h1eQUItwxUFCnzeDjUWrFyAJyStrESHG73S5pPd9DR5kMo0PM\np9FQo+smamfU38greiNzQPm+9YVRDfpwfHwc5+fncXNzExFRkI3VahWz2Syur69L1XPeY6TE6TD3\nLaNBIDXsT/oHdwiujrlNRpXg74L8I3eupo4Mm6vldDJzy+9EVFeMMXbSUqZYmH5BSswcNMZhBDvr\nHsaVv8ucMJ+et+l0WuNist8sF5R9MVrm8bP3nYJ1RsPvM9/Up2/ppw+BOP0H2gaH1adSmXuKg4Ke\nMWdkxH6rPetdezYKuYZFRAWr+pjzdDqtOVksFEezqSHi75uv44Xi+0wQv08/yAXnnK8JqoaiSYdx\nrBxnhGe3Wq2iGP0+w8ZsZHhAzAuGwmOgTz4tyPdGo1EMBoMiuIzfuWAElZQcY0FpzGazr4y335lh\nXOaAvjpd6E1hUj7PzFwEGu8xNI8jxf175tPxPvM1SCXQX5cnmEwm5ToXZM0pouPj41K9G9gew0Xl\n3IhqA6NsmQPWmtN/+5SbTzJlhwBlBx/CcsO7rODNyzGJ3ScoI6rLiVG4jJFgBeVsnsiPP/4Yg8Eg\nDg4O4qeffqrtQ4wavBSud4mo7iEkvdftdsta4Fizp+BM8b31el3qkrmEw2KxKDwQFKpTNexNHGTL\nDYbCqXGXDeH3ebYPKTAnOOLmlcDjIWXGs0hduoQHqU0CHhP/aRj236I74HjhCPG+w8PDmM/n5YJp\nOwStVium02ms1+u4uLionYayc4BMe1+iJ3BqkVn6gx7ACWNPkpY2l4fP0G3sU1Me0AV8p9/vl0Dx\n06dPZf/haHGAgT3tZv1ASQS/x+PHaYUSEVHtNVNJkEWCcfZvpoGQpuMAQ05DZZ1Hc3radtHpKObX\nup13klKz/s7pzkwo93wZZDAvzCnXiCicYM8ffeP/7HM79TzTZHPr74io6T2aaSmtVr1avKkX1PZz\nag/7jVwxBkAG9IjlEAcs98PtWRwpn8JyLhfuBpvfXIHtdhuz2eyr0194wiyE66kQBZjPY+8UD5p3\no8Q48mzF4U1DTp8J9zMbjUatcNc+j98kWhqb1E4FY2DD2TGIqAieRol4H0iNDbEVscm1/pu+mIRn\nATePANQhH1HO/Cs3b+j8MzshfE4EnSPkiChEcdZvPB4XYXd0heLwprGMmFt2dHQUl5eXpf8YGxoG\nFufNXCnehTNhJGy9Xsd4PC6cFK8xRscbnHHC/WMNPW+sqb+Xlfs+hMAcFu5sMz+u1WqV8gzv3r0r\nBQ3n83m8efMmRqPRVzwYnHWcJDun0+m0zMnp6Wmcnp6W+W6323F2dlYrAGlnIvNYTLiF94Wz4b1j\n3eGCuTyDtTPi4sYzzTujzArzZMU6mUyKEV0ulzEYDGpGAQXOOqMjZrNZnJ+f1/a/+2KF7n2BU847\n4Z9FVLWpbm9va/IQsXPczs7OYrlcxmg0in6/X9bJKHuzWV0v47ljbtCN/NyBlZEd3m3UOQd0OIWZ\nf5iDDTiRrOtkMinXax0cVJfvbre7y6HNu2PeTHTGCTMyjqHH4SIQ9ljQHy78zNhAbjwGozmscZY1\nnr1PZxJoZztjMr6dU+wrJ44dmKEPQEF9yIh5wgm5v78vzim614RyNx+gMDqGXKPjzKNFJoyk0bDr\nBKoEhW6np6eFe+q5Qvf7PsKISkcTKOeA3air14gx/P8OkUIAXQzOpxqIThnMdDotkbEJ4jzDxjIj\nKxCNjUDxN9+xoaTRH28svscC5VNUPoaaIU76aCE0ugN0aCFmfETBRi1o9/f3JXLkhEREdRs8pyLy\n0VtHXRZ6GhsOpZg3yXq9rtWgYb7tvPAczztzYEK9YXcrWMZxdHRUijpmcreh2KOjo/jy5UttLdjg\nRIQRUUMvQF981x7RyT7EjY2P7Jocyd+OTP1ODDOy7RQeP8uHG9brdYn4qOPC9wxn4/zbKWUfGRX0\n3KHYSCdH7Gps3d/fx2QyiTdv3hRSckSUC4SROc8p+wQD7wurqUr/9PRULikFOb26uqqltI1yeX5B\nMbMz6HnPSIdTe/tSnqzJPuMVUZH37bw6qs4pMwzcarUqRTE9RqJyggOex7w1Go1YLBa1u8Gc3vIa\nYhScTkOGDw8PYzAYxOXlZQyHw1gsFjVndDAY1Iq+IudGVdin1jXMox1E61MjLXZemENSJH4na4HD\n4aDNcz6fz0s1/YgoqZn1elfaJdM5kIl8QtZrBvpLAy3ySTHS/ybH08+ctnfQk4MX1shzwNo76LRO\nzMG/aRL8n/2Sswa8D0fKFBXmw7YpoiKxozOQS2QKCgpyYEQMPZsDOmRjX4rut5xG5gFHDzn0HiBz\n4EwNDRTKJ8g9pzTsgufWiFruS5ZLt2dxpHL0SGMRnCKIqC6hHAwGZUCuRE3OE0OEMFJp2sgAk+MU\nGY6ZNyDRsbkdfGYUKC8GjhyolRUfAsz3Mupjz9kKmg1jQ0lf/LehdrhGhljzOJhzDHwWfiMlOdok\nYvA62Vm0gucz5p+xu6w/x40zUkZ0gJPVarVqhoZNxdzQz+FwWFIscAqIvPv9fkH5Wq1W9Pv9MhtC\n9csAACAASURBVPfj8ThGo1G5VgauUET9VCVpHRfUw3lpNBo1tIjvUtOIOXdqNxsiO6AogG63WzPQ\nnst9yCLFP70eNIIOInMbttvb2+j3+3F4eBjX19e1S025WcDHxiMqnpkRGXPn6Av9N7IAsgkny4YG\npbxvfHZA8l4gzb/dbr+6mJi153lGSLzHnPrmd/luhvqREwzqarWqnYRkDY6Pj2vHrk9PT2sGLKJK\nsdh4ZFTCOoM+Iac3NzexWCzi7du3cXJyEp8+fapF9uv1rrQHCIn1IIEhhtfp5Jx6+a3+sSbeNy4C\n6jnNgSF7ww2DuFwuy5w6C5FT3/BgnPFwIJ2RL2SGOUUGMxJPYIIsZS4jc2QZYl54b9bfvpjaOtiB\nKE6PP0OOWDOPlcY6ZIQsol5RPqfh0PsEBP6eHUP/DNTNzhXzho1AXrz2/l3LIv1gDbzvSWmDGNpR\nZt32lU1ANul7Djz5PnNtR9N7b197tjpSTIaPUOI547wYNvYGv7u7i8+fP0fEblK5EoAjxI5aEGJD\nuHzPnrkFdTKZlPdtNjtODEaYRUBI891ILJ55DxFVJMMCYWwjohwl5nlGjpgbNkM2mnZ2ECDmjD4w\nVnN2UKKsg9NGKDMbNKNWQNsWdsZv/lHuq//vjcia0R8r18ViUYo4np6elhIEEVWFcqITOGoRu036\n6dOnaDabJXWVSbwoNztnIE1E/Mvlspb2g+fCMXWnNa2QQRDpa6vVKmkv0kom2rKGrLOLweFInp6e\nlut3WAvkFjn0++gXCtxpXRAp1pO1ABWK2Bnkk5OTgjrhBJ2entaQWBpV9I+OjqLX6xWEhLFThXy5\nXJaUAc/AMXdai3Ejj4bcQWDNZ8u8O5AxG3vmBLQHJyobLtI11guOaJEPv49CpHCUkJvJZFLumWQ8\nfMY8YhS979ATBCx2Fk5OTspeIxBhDDhxh4eHcXl5Ga9evarxXxyYGDkkELWsECjY6PAuIw04EZSV\nsaNtZBuHynOKfDr1x2c8p9frxWg0KgjRaDQqe4mrgByIoivZG8gV/cQhcvCFEXbAuY+a4SrvEdVd\nbOxd6wKPFZ1pZ2a9rooME2Qx3xnFsSFHDvdlU0yQx07ZnmRagtfQNZc2m00tLc5tFYzP+x9b43Qz\njWdZfnivnalMk4moHHkDHwYWWG90opHinPFgv/B31hf+/Qw6+B372kv5g5f20l7aS3tpL+2lvbS/\nsz0LIhVRoTf2ToGAiUrwJDudTrmvCxgUb3E4HBZ4m9QADSg9R7K8L0cPJtxyegjvnb4AJeLtG+Ll\nma5ebk8ZZMnpvYiKqGfYme+Z3EckmcfoNF2upA4MTVTrKsJwvZjT3IjIHZU5ktvHsbA372ie+SL9\n5TQkaVqQSJPB4XJMp9Ov0qytVqugUPATQJaIrrgvzpEu/fGamQtA/0CKHBXBZYO/gzwxB/TfRFDG\n0el0ylp6/pxeZT0cQdNAF00AhfuWo0sTYA1le+zA1ZkAyrvOz8+j1WoV9ATCLKmr4+PjghAQTT8+\nPsbl5WVst9vCu3r9+nXc3NzUIkhOCSLrIDzmV4BiGC0yx8Gpa+bM4wB5Y0w+qs+cIY+ZX4L+MeeS\nSDYjiMhup9MpJ7PgNvLZ2dlZ7bQSn0G+59Jic9pIXbLfTD/g+piDg4OyPtxfyOXny+WyoIBG+tB7\nyJPTmv7/er2uFevkfXALM5ePueP53hMglcy/v+f0EjqQNeVz0Fini9FfyKOv5yHVSRrazXxF9ilj\ndDYgp5G9R9nnNL7jK7QYX5brfSn9XC4G+TLdxPNi7hConhGyiPq9kaZ8gEbxPc+p18LjQdfSV1A9\nt30onNN4UEJymtH20FQQ1n+z2dRoHx4zupj3QeNYLBbl1B7rT6ke9DNzQV/Qp/TJ47Fc7mvP4kgh\nXDnHTPO1KxERl5eXNeVmSI4FYELyUUg3Q3MIl6Fl+tBq7a5dIH3jflp49zlg/DyTVHkuStHQ6sPD\nQ0lTkN7zpZQIN6kvK33Dj84V+7QfQmHnBW4RZDwLqlOShtw9DpzgbrdbOy3z+PhYq3HiOcjOgSsj\nt9vtUmHc78KxWa93FapJu7LWlCdwOoy1oPYKc+sN5ZOjHp8rImNg2VxWMN1uNzabTVknDDlyZEct\nojpJZQVkp4d1xRC7BhPrinzSH3hT6/W6EGX5HStpxumUAo6lT7DRjo+Po9/vx3q9jru7u9p6NRqN\nmM/npW6bHeXhcBjdbjdOT0/j48ePNY7BbDaLt2/fxmKxiKurqzJ2k6uRGada2PPZcDHHrBHGhblB\n6ZNmyukkjD77g8b6oRc875Q8wfnabreldhE/e3x8jMlkUkuLcO8ka7BcLmsne5GFfLyalA8Oix2p\nx8fHwp/EqSAgefPmTdzd3ZV9mnki6DAcGr/35OSkpged4uPAj51zp5StQ3LQgs7L3CrG7r9zehdZ\n9R5HL8FF9clqKrfbuTHXyzowE5U9H6wp823ZcH9tQzglZtvAPOMY2DlCdnmO+5Fl1in9ZrNZLt7m\n3XZQPE95/dk7dnwjqjJD6E1/bz6fF71p3ULLoERucETN5aPlAzbMW54P5BK9g33x7y6Xy7i7u6vt\ntxyI4RSi55kXvsPBFq+hSf/72rM4UkRk7pw3dzaKLDboko8i+tilnxNRGSgEMp/ow/HhcwutT1b5\ntJCjyexgRFROIv3KUYSRMCsQrjIBgfE77DQ6V8x3UbDmSHmzmkeSCbdcG2PUjZNsFjK+B4mTwpjm\nQjAfGGcLI3NHP3yShJ/zvHzM36dIjORERHE6KHZpx6Xf78f5+Xnc3t4WdJKx0wecRvME2LTInfP2\n/IHvxPhwXDE2djD8TkfBzK8NFHwOHDQ7EE9PT4V7FVFFyVzr4popILy824VTGZORMPp3enoajUYj\nJpNJQTUonkl/cN6enp4KOsbJvIuLixiNRjGZTMr7XQ6g0WhEt9utnThkDTNX0QGSOS+WH9bIDq9l\nln5STJN5pKFzMv/EPCbPv3lOllN0GSccvYcPDw9LTSe4Scz34+NjDSX2HvHJPMsf/cZpIyBiXBym\nQP8YxfOpMiPVPJ/Cif1+v7Z3vT9wUO2E2gEBrTLXCZm37PE8DCm6LuvX7XZbC/iYbxwk86/y2kdU\nqJf7b32Y+XGWB+tL/nZGgrVgvDhc5pfaeWTd/Cz+YLDdT5rl00gd+tL2xDXzmPMcvNEXo5XsRes8\nvjeZTL7imjmgx1HKBU+t67D56IzM1fL3CP58ctCymJFsnLP7+/tasWTuDaXPHq/BG+ya0S0aeigj\ncG7P4khl5nxEFe0b2kYxLJfLWhTrNM1sNqvBjo44HH2tVquSBomoCi9CfrUj4X6iII0eRFTk2Kz4\nnTbxGOzx0kwIRpggnXoj5pMtdgaBN7OhMRLTau1OplF9mv6hwKjwahQK+B4HkOPqs9msEDyppmxj\nYkXgzYZhMYnfhsYb3uNHQVjJG+2h72wOKz5QKCI3YOqzs7PyTKBek/Adydh44eTiwKA8IuqVh7vd\nbjnubhnmO6B5bMzBYFAzJC7ah2FmnC5Y6Sj17OysKB/mwcqVOeFv5N3KkHGgoNg7duSRN2q4/O1v\nfyvvf/XqVQyHwwL98z3WdDab1W4BoC/ed74zE0eGk4cumZERaeSK+Vqv18V5NCLLd3wowg44cmxn\nne+BAmVEgf4Q3FHj6Pz8vMjpbDar1eui4QDjJHst0FutVis6nU7NWWIMpPEGg0FBxwj+fKDAhzB8\n0tSNgHGz2VXuN/kZmSf9yLr5WP1isahlDLLcgP7YUcnZAxfKZO2Y97u7u9rhJHQ6joWJ8RRB3Ww2\npfAsDUeSPZ77YDI573NAbuSZz2zUnU3hO+i+HODYKfL+xTm0Pst2kfmzvmbeeKbfwXPQi0aimBcj\nkHYyQao43OOTvkaUmWcHqw5OszPCvBFkGHiwDUCGvE7YOFfnZ3+ia72+/r/T1/zNHzt2/1/bszlS\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CizFwuhBPlujJi4SBtEIz5MjGQeAsrL1erxRH3CeEjNFRgo2yo7WIeg2WvLkRCMZ5f38f\nP/30U+lLTgu9evWqjJd0l9NYNCJ0ogzDyk7d0V8LGvO8b4z8nZUshgXFYRTEBGFD0vTl6ekplstl\nOca7z7BRbweZcS2nzWZTyMD8vg1QNsI5HWBo3NwQK0Q+I4XH/NPs/Dv9yzgyguE0If1DBjL/wpwq\nr2FO69B8LYn7xJgwRBi2nMZCpiynnz59Kr93fX1djFzEbq/ZIeFqk4hdhW76MZ/Pa1XWIf7jZJPi\n5Tkc03bh3Jz2xOBmNI/5zqjqer07Mk4aLyOOrBXf974g9eGAjM+8DkazQdvRlQ48mX/SKqTzaK1W\nqzhuRrLMi+MdONSgqBhhGz2cWWTOSD7vc9rLzYEl85gDFjv1ds5ms1mRj9lsVvYwP+OP9yEBxOvX\nr6PX69U4kBh97EROw7OP8rpwbN5IjsdHP7J9MocUXZXBA6NA1sME3kavWSeXSOFZdnT9HT/T6Sr0\ngwvfdjqdstaAHTxzMpnUHB4jZNbpdl7QTfxxOhLZ9p+cyeFZDj6wldb1zCllfdB/OKm8L4MmzqZg\nf0A5vU5G7fe1l/IHL+2lvbSX9tJe2kt7aX9nexZECh4PKauI+nUAREQmFhJRkgrAO+Q0gSMiPHVz\nHfZFguZROJIy6gEilGFtPNSMVsBN4DnO+RKRZoSAZs5P9vrzBaaMgWf7RE1ERX5l/PAXvvvuu9JX\nOGOgM/AP8Pg5CeIoGUTCpEXe6agSNIfv5cq7hsmZe9ICvJv3Ad+SAjL5m2gb2aEvpJJAKUDgeCbz\nvF6vy/U3EbuojHcSkTtqcYqk3W7HYDAofWm32yVyBWkx3wUOivk3ERWXjz+MhzV2FOl5A0HwPDvN\nCroFquj8v7lnTjnkfZQROU6DzWaz6Pf7Ze6Wy2V88803ZQ06nU45vLBareLk5CSGw2FBDowac8ko\nqS1QUw6lgEY4KmUeOEGH7JgjR9TtPW6Z4gLsfegwiMt8Pv8KjSZq9VrQWEuXMXAKgetjzC/h2fCS\nHM1nsiyNiu6gwp1Op/AYjWRAZjafJyIKSo/cek2MOBuRMPJJoV5H7ehN5HFf+s78JMZtVHSxWJSD\nLT/++GPc3NyU+SRrQV9ZcxAIp8EuLy/j1atXhVuVdQbr+FsUkswZBJ0BlfIBBqOdNMbkTAPvxj6h\nz9AlOWUGwpznk3XEZoKe5DQga+uMilFs0Ni8xhEV9QN96u9jO7K+dzqT+QC55XtGeninm+0e46a/\nRobJotAPI1KgrKSC6QtpevaH9Rr6Mp8gZ+ym4uxrz+JIuQ5Q6YhOugGl0XELXq/Xi06nU5TUwcHu\n1nmcLhsMjGur1YrJZFKu2fD7fA0Dgsh34AH4NJRz0iZsMx6qOJMW8+kNp2S8kCbPmSzHZwhuhhaZ\nKwQtp30Wi0XtuorxeFyc2IuLizLfOBZWUu43HBTGb8VmZzFzg5zeYf2c1vX7nBO3s8hzXQfFBFcT\nIDl9yLtcSTqiqnzd7XbLFRhOtUbUT6Iwl3bqbFRNqiUVyrgg1zudhlzCAcMJ4fd8EsUwOfNhBUEf\nzPewMnNqIh9ggPjtk4xuOCSso8fQaDSKg+6ThxcXF7Fe747rX15elis8InYpuvV6d/VHfmbEzglD\n8Tvd//j4WNtjNsBHR0cxHA7LaVyUK86ygxKUJ59tt9uSJsIZcaAFiZnx2rGBU2jj6DWMqBxu3ucT\nUyhxE4XZKwR8JrV6b5h7Yl7hPgcIvg7cOT5rt9vleXyGw+u6e8ynU3AYLIy/Tx9SJds63XNjMnUO\nSPiMNNft7W1ERAyHw/jy5Uvc3NyUfjCn7Pmjo6Na6i5it7+4VDyiCjhYi/l8Xpxrp0tJW7EGDlCc\nXkIPZHqCA+4cQNPsrFh3M347vDzP9AzWnL3CuzL/Kn83Uywi6ulk+or8kDbn9+ERIgfIkcdIn1ar\nVbEzzLlTxt4n9I858DOzXaEhR6QJj46Oio63Q+p38n8feMjyjZwyd56ffUGT27MV5KRjTKr5P0ag\nInYL2+l0Sr7bXKfLy8tyuoPfN2vfJ198XJJNDwKQvdNc98mKD6HNRhjF741KM/pGiQN7cuaZAgAA\nIABJREFU+0Zw9kVJzjPn5/K3NzInVY6Pj2MymZRo1YRrol0QJ96FAOLxm9th7sS+/ngtnU9H4aMQ\nLJR2HHL0YWQwol4HhfcTkRodQ2HacLCGg8GgnEZEDs0twunx/XcR1Y3z3mjmchHxgK40m80y38yd\nx+IoHfn0VSl8xkbGONuRYo585RGySO01F+20XIFYeZ38b6M/rCHPxvFzNHt9fR3v3r2Lx8fHuL29\nLcVgW61WQUDY10bH7u7uiqNkZ4kSHEblGN9oNIqnp921RhDfOegREQVtshHhu1wwjYEFYaGvrAEH\nIzK6AKfOhHcrYpBzrzt7DfK8uUrIE9w7O0kYKYI9notT52icv3GGMhpPX+gPjm5GG/hjRMrIBnw/\nrslBVlqtVgkiHXCiC3imZcpZAAj8vvOz0WjEdDqNu7u7mkOCTNsYw7vjeivGsVqtiv4bjUbFwfYf\n+uRg0HPD5+bLZITCqG7mq+F4O1DgWXZK7Chnuc3vyjwkyw0OBXvA/DT+oL/4HgVzWU/khDmNqLiJ\n+4qJmvzOGLmmbLFY1AKiiCoww75YLzCH7Ll9iHJExYXMTibv8il7I1roGfsf9IF3+5nes/vas57a\nc2TiiMUQfkRFGPalnBZwECMMGMIAYuLFyKkFnuf3sThGmvievXwrWP5er9dF0WZ0jN/B83ZxTIwN\nEauFhu9ltAIHaF/EgYM0GAyKU+XSEHd3d+VyYMPWfldEfKUAbSTw8C2Mnh+iTfrKJsMpcIrT76Zk\nA435oB/8LlGjjYn7iUywvswbztfp6Wl5Hv10VVzkyugQlznTB9aQlKChfJNPeY+jX69tji5pJmIy\nD5yuQdmRxvUYGQsKgNRvfq5TmJZvO+VGDj0OR3+j0SgGg0EcHx/Hzc1NzUA9PDzU0micqEGGbm9v\no91ux9XVVQ3lIaXHOhwdHdUKnOIocmLNaTCQYaOG/i6OIM4xDSSTvkXU7wPNhOF82ADlbQXOAQoc\nYgJC5pTvshd5dqfTKbKBYbChcRBH2pi1NaqELuAzBx9eZ+ZovV6X6tbIBQEuCADpdx9rd0rMe4Q1\nxcBaRjFcm80mPn78GB8/fqyhIOx75sDGjGet1+vodrvFPmADWK/5fF4cKcopoDN8uTT6hSAb+WDt\ns/PiMaAnmYeMzGU7wJxiA52moy/ekwYd6BcoEXonIz127GxPCKLYB3bY2C/0w4R21w9DtzLfRhit\nh5BBkN+cRuf9GXVjL6NLeRbvcDbFjib7hXkxoMHPQdWto+zcGeVHzvZlhNyetSBnxG974M1ms3bM\nnQ2XYT7gaRaVCDyiqprcbDYLKmWDzXuJtuwQdDqdGl/Eih+h9QZ0P1Fe3hg+RbRYLGppODsVhhv5\nLKLaDPboOZHE9xCSiKpOBv188+ZNOaXEGszn84K6tNvV1QwHBwelrhSCbmTICs0Kk4gUAbXwZcVj\nZYPQ2oB6LegDa4/xyjLhuVsul19d6WIl2G63yyWwpM0Yw2ZTpVet+PicyMUKkrlmI8/n8xgOhzVj\nDLJgY4hMoTBAshg/6Rbk2gqaueNZv2XIcKic3jGaiNPPOOgnishjZD+gcBhfp9MptY2QVd53c3NT\nngNSxjPH43GsVqt49epVbY6RUQwgR9iNHOH8b7fbePXqVRwcHJR0Kf1yqQmvP0EVDpX5JaxFs9ks\n6HLELq0E5+L09LRWK4o5Zy/ZOQaFAgFEL7EW/swpG+YQx4a54Hs4GMiU0xvZWPoUlNfRkT7yh6xY\nnzCudrtdUnsOWtl77B+nshkLv2ddT2s0GjEej+N//ud/alyv4XBYgg4bffYf6TmuguJd6Ajq5+Ec\n45h5r7oiPDo2IxDoc+su17Nj7tA1diKRUebF68ReyWkjO5vZgDvt7sDSmRh/17bKTibyz/epEcj+\n9HNwgAAYXE/KJ40dWNAX7MA+gMIOIHuDZrvgvhCws17WQ+wVZ7XyiXyekZE1z31GInOQmtuzpfZA\nV+x1I8Sk2kyMJL0HidQGGqV/dHQUJycnNSKnc8URX9dsMgRKY6FwpvZVGiY9kBf58fGxcDYspDzT\nKZYcmTkKsKKxIsybG+VFxIZgoExxJNjs5onglCBUvB+j9eXLl6JsvGmazWZBFWz0czRmw29HcTab\nFePCmHxlT85He/1cBRujZ2XC3/BxVquqSByfHRwcFFTn5OSk5iw4PWPlx/f53AY3okpDrdc7ntB4\nPK5ddWP0Edkz0Zh1hLPB99jscNI838wVCF+73S6KjHQfht/H3B00MAZHiY707IBlHgHoC880tG/+\nFc+hRIERzul0Gufn59FqtWI+n9f4eNQwOzzc3QmWUWoU5uXlZUEyaexpAq3tdlvmO9d/MlLdaDQK\n0mKibESUK0dwQFwviL3Ivttut7XinZ1OJ2azWTG2fMbeZOzHx8fl0Md8Pi+6pN1ux8XFRY0OYAR4\nX4rKQYYdKe99O7zdbrdwi9CjdrIODg7KtSy80/okI2o0B0n79jd9PT8/j81mEz/88ENERFkDHA1n\nDjy/V1dXtVQqKPVsNovJZFI7MICxNippZ5B5zKi5ETfmz0FMDqzcsjNqZ5AACf2HnsXBdSCZOTv0\nOZdMQQcb9bYNw+7hMBnpQS/jlDrAZD/wXdpsNis8O+xNTouZU+h9jKPDM22nT09PS61Hk8at90hj\n0+xcOpNB35AlAg/bWfY032cMDhB+q72UP3hpL+2lvbSX9tJe2kv7O9uzIFJ4fYY5ncckteDoa7lc\nxsePH+N3v/td4SFF1AskEj3zTLxhIganAEnvEB35Ql/4FkRX9Ie+m+Nl5MynWohmTIzm/4bhI+op\nwdVqVSumlnPgPqFANAliMZ/Py7xA+uQZRH1454Y0STkalQAuJ32R89P8ntN+pD2JZI0G4dEzp0Sw\nEfW0QYb+Gbu5YE5tIkv39/c1/oWfDZJjFILn7LsihnUAgTKXy2k4TnDxPdaXIndOERoFog/MI/07\nODgo62ekw1GnUz+kYUCDvL6UKCAd5aPjcAUcsTqSZm9mNJF3wDHw+Ej5cBKOasZ8HxknCvbVMp1O\nJ25ubgqnyVd9kCbNp+Q8P5BZ1+t1QUjMU2TPMR7k5uzsrJYG8vg5AZq5TkTDeZ1Mokb2+d5gMCjr\na54X3wPJIso2Wsh63d/fR7/fL3uf/XpyclLG57W0jBuRcoqMvWuS+uHhYSkbYmTJZT32Veo+ODgo\nl0475U4DzUF+8v1/8/k8Wq1WvHv3Lv76179GRMRf/vKXMldGQJBFOLFOqyOLyE3mwvD7ZD7W66r8\nCYdzzImkWX9xwnkftYODIs5EGJHKtAh+x2lFv9Of79PZzLXRHNaI5zklStkM0LDMn2JtSFVmCgxp\naCO8EVFOiIO6+io20HdTdLyGTm2a9oCu4BAOaX2nZVkrj93FYp2iZbzOhNh2weFifLaHeW1ye7bU\nHik54Nl9REQb7O12d+/br7/++pXy8xUPNg4IHgbAKSIUD5PLwkVUqSSMFD/j7+zE0Xy6ACeC76EM\nUZqZm4DSwdmzEnLe3kqRNBdCDs8gYkcmRzHgVGUuFpsJ5Y+goqAMWTOOXCU455+Bh7mbyRuD57Va\nrej1erWNQVqS8bBOTi/kzeO0RualoEibzV1tL6fSSGUwt05HopQx3CaqIh/MiU+Bkj5CcXv96Ctz\nh7IyRw5lgpNi54W5gOjs+SYNmZ1znL7pdFqD11lDxm5Z5x18hlNppx4Zx4jb2WIdUNQmd/NeHCx+\n9ubNm0KWJ8XnvQ38ztqavI9D6Jo53kco88yjIDXL/uWEntcWJ86pD9/7x/zntUDmHx4eSvqYtI2v\nZWJNcXKdmnFwieyuVtX1OxFRHCjz++zwO3hysMNn5qv4vayRT3wxZ5YB7xnkm1Srj+7z3cxz9BH1\nL1++xPX1dTm9eX5+HhG7E1+sz2QyKfIYEV+l9eywsP7I2eHhYSFR41ijD1wtnc+tA/I1Sk7xmU5g\n59QBNLLDuNnfrCFEbZP2mWfsGjrOOop3npycFF1rh81XOzHmiPpVPXkd0dHor/v7+6/2EH30wYej\no6M4Ozsr9eDMceX3Scs6aMXxJI0KVYZ+5sDJV9hkPeDmOUMnWQ5x6H0LiteH30Un4hhbTnJ7FkeK\n/LvvEuLOK5P17IRERHGmvKFQ6iwWiE7E/gt/zYWBmGYCZkR1CtAnmuyouViZERqe6xolfObTNe6D\nx0Cfs3PG99hAvvA1olp0bxgULwgJCBNzavRsNBoVZU8fbDC8Kc2DwmgYQeDdOGlshG63W5xnjKIj\nXjZqPo7P/DpHzffsYBgl4Xsce2Z9fS0GRGUXFuVZ8OPYvBQInE6nxXnAYNj5NCKFY44iOjk5KQqa\nKMyGLyKKExJRlalA1n1C08TKrHhsFJkDHKqM5vD7R0dHNZlDEeVIjDXDaTNP5OnpqTZG9hbvYY1w\ncJkX+sZccRiDhpJlfRj7fD6Ps7OzYtTyiS7ewZ7YbKoSJr6yw46X55H18T133PdoZI/G+JCzw8PD\nWgFYz5NPipn/0m63Y7lc1vQD1zUR0TM3Rt7Mn2PO7FQ4Kke3Mp8Y84j6lVOg6uarwfkDseZd/M1Y\nWHcHbehR8wojojg50+k0/vznPxf0IWIXuPT7/RgOh2VvWxbfvHkT/X6/hi6zlvP5vNiTfGKVPqGn\nzE3FHvCujNagoxzQ8Szz0RxgmnNjXqFrwjkI9Lp5Tfy9x8fHghzybJ+cs23KNsrBF9kefk5RWOvi\niKqgMDaIE7MRVSYCGwu3EZnieaBk5pbRjJ4xBmqcAUBkJMu8RjugRvzQD36HMw40Aj+cqX08vyxD\ntbX6zU/+D9tgMIinp6cC2UdUG9E1nfZ5/Cb70fgMJZWNMIuybzHwkBGuiChRPBOfC74RJWcSGu/y\nouSIzgrUhhSY1saKMZjg7MjAkRb9otK2U4pcagp5LyLKEeyHh4eYTqc1UjFwP0egQZkidoam0+nE\n4eFhSVOwaVzPC8PoU4Kkl3AADDc7mjXUi3PCGtqpIxXBnBjJsuIl8rFjiDE7OjqKbrdbDBOGm8j8\n9PQ03r59GxG7i6A/fvwY0+m0GFynEp26cLoyokKzDLX71I/RKUefRNXIi+UwHyW2I2Vj5tQn7wCZ\nQMaNMtIn0q92sDEoHLawEeZ7rFFWRkSBEZUjAEG52+1+lfLFqOFEWg5pmVhqMjLvY208V5BUiWjt\nUPF/jGx2vCy7GR0FcTWSgfE4PT0t83Z5eVm+z95zmg9ZZJ2NqCIXXHjcau1qX/n+RAwvz3IaGUOf\n5cnpG1LD3k927iOqUiGsO303OuPPLCfMN87J0dFRPDw8xIcPHwoCTDDm0i085+rqKi4uLgrq4EAY\nfcfzbStYx31IBuvtAxg52GF+fa+na6Txe9bftO22XqqFuTZy58AZvQBC5LX03X9ZNph/9lpGB0Eo\n6Rvrip05ODgodRstH3bkcrYFHbJYLOLk5KScAMdeYEc8DkAH9oRJ9F5/DgyAQiKLOH527AFP0AXY\neM8N+9GOm9OOzhCwFpaFfe3ZECk8SqfJUCZGnPjMnAh+FlEvkmZBjqgfjweWd6TAd4wiRVTHOfnD\naSO/1wowIwTeHHzm3Kx5LhH1iMX5/Ih6Ne2np6dSE4pmRbPZbAr0z+Lf3d0VxTidTmunQhyxcxop\nIkpF8PPz8+h0OtHtdsupjHZ7VyZhMBjEwcFB3N7elv644vV8Po9Op1PWdz6fx/n5+VenLGi8H4Qo\np1RRfrkuiIv/2ZFgXTKfLSIKqoDSd8oXtKrb7ZYNykW5r1+/jm+//TZ++OGH+Omnn2qOMgoB5QhU\nbcfEacucFvIm9rhZWzhARnEjvr740w4hcgDfh+85TZxlzkoj86BwdLiWxcqbz1DKRrlIkbCP+/1+\nkZmnp6fodrsxGAyKMbXDg6PbbrdraZiLi4vYbDYlGON5KFu4Vqy1nR8cVT7DeWbe7HSDgrNOpB55\nhmUZeTDyGxEF1To+Po7RaBTdbrcYmi9fvhTejiuFe16pbeV1Ql8S9IBO0A9+x+P1M5E50yIcyeNw\n8SxQepDNRqNRDCTzZLTBXBh0jNEr1mk8Hsd0Oi2lQqbTaaEnYNhJi67X66Lfrq6uyryxt5GN0WhU\n6uRhMJ3aJHXO3rdtITjBbiDDGFZslDlLPN+oDA0nHtk3hSSvVXbQWbd8GtIBFPbPzqplmsLLDqyw\nP9hEZzEYG+vFvFGyCCcJPcH6bja7WwXOzs6KXeEz9Ln3GfOGDMKvIigHEGk2m4Ub6MCCtSM16ufy\nbJ5vm8+c2yeIqCqiQ4Oxw8sa5L3p9iyOlBWz0zM4Ihg+p/Qi6l5+9rAj6gXaIqLGVzEUyLPYOEDW\n5oegLDFMjjxBxPyHts+o80xvTBtvzwnHPf08+spYzWchEjUxMGJnSF6/fl1+h42BoC6Xy1J1FofD\nqS+M/Wq1uyft+++/L+/E0YjYOcWOhDEkpAIMm/NdhNbKHUXovDzzwsZHRowCOS3CumUZIRLMUDyf\n+2gt88RVRL1erxi9VqsVg8Egvvvuu/jTn/4U//mf/xnD4bCsL0bW8DTrStSKI2VEEtTJt5bbgLHu\nKAejjTiEzIH5T0boLBvmXvBzFBGRPc+m0Cj9BOEBceT5RpNBHu3wub6YDUGn0ynOOd/xFVI4y6vV\nKhaLRRkf/BajypPJpOwd7u1j/3ivYWAc/duxg6SMM4GBtrJGvrLj4CryyBRR+XQ6jc1mE+fn5zU0\ng8Kbs9msoL3MKVE58m/n1U4VBomfo18w7Nad6CbW2PrV6Y8czDp1OZvN4vT0tFbAMafrbaQyOokj\n9de//jVubm7i5uYmvnz5EqPRqHbHqp2gi4uL4khh6OCKUeogoqp677XyGK1PPI/MicuheI6dqiZg\njqicTPa7qRrIBuiJUTxSdqyJMy12kIx2Mh70DX/b3uRg3ugRQQLvpaRBRNTmbD6fF3pKRJRUHraN\nqvo8v9lsxmAwKOUznCpnXo0wM6fISa/Xi6enpxKwj8fjGI/H5Wo3gxlXV1clqwV5nn6iW0Cb7ACZ\ne+xMB82p1Jze+9+cqIiX8gcv7aW9tJf20l7aS3tpf3d7FkSK9J0JmeTA8c5NArTnmKNLokJH8TRg\nPaNbjsyI7vHQDXHzBxItzeRW0kw5dwp8nI8QO5WYYU6e7Zw4P8PzNnxLP5fLZZycnESv16shK0RA\ng8GgHAflZFnELjIZj8clmjHScX9/X+654udEwhwZBynhpA9z6xTtdDqtpW45jt/r9Uqaj3nh+Zy0\nc7TCWuS1N3cORCYjYEbzvE5E0ETRPBeiJLwdrpKJiFIcsd/vl2j8v//7vyNix58immV9OZEVESXd\nQ9RDusMyBVpocmxOJxithCdAhEqqw+MzGZ1m+crROXsPJMipa6LXfr9fSyVFVAVJfQqJMZCaY67N\n2QBhcArHiCtzBtrE73uujci47IW5aKR5eC4IIRGs0UHGn1PJlq+cnvfedVorouJUNhqNODs7K6kM\n5DQiSuTtS7aREVLGRqMiKg6JTy7yPpel8PicyrJei6hO6zIvvjnBd+ixVk6BHh0dlfQWaAoNdGBf\nqnG5XMYvv/wSNzc3MR6PayejWAf2InLE+9DPy+UyptNp3NzcRESUA0kej/Ui68ffToM7HeR+ooc8\nNsuCT95ZT8GnMvLrkiiZo+jsijl/tm3m8NAnZ2accqMotVE+xpdTwNjZ+/v7ImvoPqPi2CKfggUh\nuru7q6FORgKZX5p1HPab752fn8doNIrb29u4vb2tpei2223JhDAe0wGcsTCdJcuBqRnOLpGVYH5A\n0X0gJ7dncaQMX9q4sehZUIEUUXImgtnostA53cBGtlGwYPq294jKkQKKNMTpMaBkrbzNE7Bid5/J\nPTtdyN84BZ4Xb0znyk9PT2O9XpfqzxjciArGxKHKRFE7hAiQlcuXL1/i1atXtes8IqLwWRBscwVI\ntZ2cnBRY1afafK0A9YNYdxOUzZNptapq3/w7H6unL05t9Xq9kkqh9pYNtMefa9Q8Pj7Ghw8fipJi\ncwMh4/Sfn5/HH//4xzJnP//8czHA9M1ke1KakJF9Qo/vLBaLWK1WNeeN9edIvlMSXjPzXZgXnIFM\nnrSxxfGPqO6OI3VLfRi+d3V1VQ5nWGbg6TC/BwcHZQw+4YWh9TVOELwhKzsdyd43p493MC/T6TS6\n3W7hbiFfOFOcfDMH0EECZG3Gj9NOanofcZh58/gxNjg4yDckXMZ/c3PzVdrMxHGnIni2dQfvpuU0\nhJ0Dp4b4zKevTLrm/zjlJlR7jUipuT/oGTsFTjUhY+bLsP7w37jSibWg7+fn57XDCBFVSQl4NfP5\nvAQivnEiOxnI0j5iOGuIo5ADV5PQ85wyfqetmQOCOQIJ82ztrDm4MoeL5+f+8gxsm/mVPjDhsbLe\nXnf+9tyzf7PdIUC0TUQv4dS7Vhpj9z5yStDcJI9ru91Gr9crJzdxrCKinMhsNpvl5C6O4mKxKAEs\nNoUDWOgA9JffBzjAHwfX2JBcb87t2RApIi07NgxwH3HRvBOUcUTl9CCEv3UaA+OVo90cqfAZKA7H\n5BGMXq9Xi/JyVIp3jBfrz/OxUnvYmd+VlTdzY0/ZxMDJZFK7RgOHk/eiyFyqwIiaBX29Xsd4PI5P\nnz6VInteJyKd4XBYnFA+I6ImmspFGVFuNiSNxq4WDgqMUxyM24RFbzhQCis8RxEoAtbdPAHeAVJH\nY21Ho1F5rj/v9/tlXMhQxA6pe3h4iOFwWE4uOorCECHXJp0iWyjxZrNZfoZzimPsvWBFzJz6KDN/\n81k+vWIjQWOfwIcyIjsYDGqkbDtnzCVzYkeRnyP3j4+PBVmyobGjxztwrEHIMm+Sz7iXLyt371Hm\nlNIIrIk5HeYNsXZGJRy5Zh4MsoS88TmBgU/yOlCYTqe1k8Pwh3CiGWNGpPJdmzwTh5r5Mr+H3+Xd\nx8fHNZTT+xK0h59D0sYJ84lhAhk7GEY52eM4P+adcfqY+TWKPRgMCprrZ7NX0NVwelgL+owM2JEw\nNw7Zo4GoIgdGI1lHZDyjSOwH633PtYM5non+Bi3JzyIzs4+jg244OzurodXsNetgB9jMPX8bPYXD\nyqEmH5Biv+D8eE5Bxwjmrb9AW3HQ2fsg271erwYmMG/sFUrRoC/JIFm3ucYfVwKhvwjwWN+M+vFe\nuF6MzXNuXb6vPYsjhaA5ZcciUYsmoo44+Hi5UShHQHzPzsvBwa7iro9UR1RwIv82pOyjtrlmEwoa\nRYkDwHvZtPzbDtH9/X0h7x4dHRWFmcmduQaJj8lamZg0mNEhb2afhLNTsNlsahdX+oRZRMTPP/8c\nh4eH8bvf/a4QOc/OzmKxWJRNul6va0eNqevCOjraYa0cDUdE2Ujr9boUdWPecCqog+Xostvtxng8\nLmlG+mC5oFKzDRupM5OYMVKGrUejUfk8YpcyePfuXTl16jlrt9txfn5eNud4PK45B5vNphRbpZ9O\nb1HzKh8tRqFzIMCKwAaCuWK+UfoYbTvpGE8Uh1E3DAunffr9fq0vjup86pZnsDfsuCJ7jMPGiwAK\n4w9hH7ll7/Ne3ud0wOHhYQ3Cj9idAmWd7UBH7BBAB17IAb/D2tLPjEowDqcw0GcuIEozCsEYjGI3\nGo0SgNhxPzk5KU4suggdh7OGgjdiAZkeo8d88Rnr2263a0VVI6qsAOiokWwcL6ejnBnImQajpfTj\n4eEhrq+v48OHDxGxu9B6u92WuwhJ4zFG3/lnfYIzZtQw2wEMO3/TN+8fp7iQJ88Hn7laNpkNIxY8\nL1M97HDxu5nEDJJnpNbUEz+LuXbakv3mQxrsa+qEeR1Jw6JnrBetz/LJNf4mEEQ2jfjzPAchLhzt\nvUFAy7Ndm8oEe4IyZw6MdkbUbT52EefeqXJsBX2xrCAbIJ9e1wya5PYsjhTXN1i5w6lwusLKDUcL\nA+S6ETgsmYXvWin27mn8m1M7OdpFkFHk7hMbzt68I4yIqI2PvrLRDg4OSkRHCmq73Zajzl5Ep1Ai\n6mgDEfBms4nxeFw7Een0m3PqfM4fjAf95mTJ09NTfPr0qbbByIHj4JjvwfyyoTi6zjiMlqCw3U5O\nTuLi4qLmnD09VSf92ARG8nAOObWFXBi+Pjg4iMFgUFN8zM8+B9Och+FwWIz3x48f429/+1tcXV2V\nvlhm2u12dLvdchLUChwFtNlsas53RJRSCzhChtSdTsBo2CjyO0a8aHbOLft2uLMRQsmcnJwUR8bO\nMO+9v78vR8xZu/F4XL7PdS80X9KbT3AZ3bLRZb5Q0vP5vMgQSDFjmM/ntdQEjj9Ij1M/8PDy3EVU\naVY7oMw3eySnaDwOR7s23kTQcBORfRxQAh87505pwm9kj+IAohvNAWPeQJBwcJFD5pqUGuPgmDnO\nXaPRqBkvn9g0AoBsZP2ZeTKbzSYmk0nc3NzE+/fvI2LHLZxOp+WkpGvj5bSQUQF+H7qAg1bQGfYf\ne5z5zs5OPkXOnDnNCvqDg2FEBj4pfbUNYxzIVj45bgfI82l9QeDptCZOFH0hOOdzbA160KnrvE6W\nWWeEvFcZE/bNgRJOFb/vsfMs+mL7i5whk0axj4+PaydCTZPhO7ZtjAmbTf/tyLKmzFd2vplj5sB2\nNtuq3J7trr2IekSNgC8Wi5qXzO8xmdmjt4OTc8mZN+L3odgwxuYD8LtEnxGVgIPgIDSOYJw7t7Ph\nvlowvbldNfjk5KQYHsO9/G52qoBNMeARUbvviOjJhFRHMhjr/BlHi3/55ZfyPjYKzzMSgBA7heTr\nLugj68mmGo/HpTZJp9MpRUJZJ37O77iiLZEsRofvMcfMj4nf1DhiDb3ZqLdENLfdbosBvrm5ievr\n6zg6Oiq1tLzx2ZxET8iy5xTFYmeKqJT0IGhZRBUJU+IiEx7t9Du9Y8csNyJxp1wcpbMXMWyuobZe\nr0tpCx80GI1G8fj4WI7vcww6Iso1O8fHx3F5eVnQtX3zZmdhu62KWObrGUBnSCuY94PJAAAgAElE\nQVSwnuZ44GA1m82vCvrRb2TJTqmd8OyYImc4Wk5fgSqxd3jm4eFh3N3dlSr7nU6nli5cr9c1nei+\nGBGwzuC59NXBHo3negyZk+Pv8Bz012AwKEGESfggFXYm0InIh9EMdMt8Po/RaBTX19dFp/z8889l\nXeCn7HNCqHhuJwD9wdwYeWCeKbZrpGNfHaKISlc5Tcn6Ukep3+/XSr4gO1nvOzuC3aLmGbIPqtnp\ndIoDa12a9y79w+mgYDDvdWrc+hzbyb+dDcIp4TOoECA5Rn6c9nbQwPtAY/cFagZO6FdOXZLGQxYY\nHylz0zdAnAiGnSqnL6BgyEmmBjgrgL50cGZd6nna117KH7y0l/bSXtpLe2kv7aX9ne1ZECkiOnt9\nIA1E5M5dR1Qn8CIqz5xngdo4qqZlbxwPNJO6IyqIk1QZUYUJzuSusyfL951+dPRHdEmaarvd1nLM\nnKwDIs6pBiJhv9upJZNJI6I2f0QB5k0ZkQIVMCJnyPXx8TE+fvxYPnN/XOiUuTafIeffibR8pQVo\nA+lVUkMRUS5/7ff7cXZ2Fp1Op5zeoHgn7z08PCzRLRyC4+PjGrE4IgqR0vA5c5qRyXa7XQpykl4a\nDoclzcRnRqcajV1BTxNAiYDgpZkL6OrHHDGnEaGDHJGqolGdF/k3QmDo3PuA3yUyN/JKOmO73ZaU\nI+tLZM/cRkRBAJfLZUENT09P4/GxupDcldDhZ3iOQZ2I7B01kk5gHzEGkGHkb1/qYD6fl+dxMCKi\nOilIP4xuEO16n2d9wt/ef4PBoBCjiXzNzeI+y8vLy3KCL6KOGu1LheX9nnmgPt5vHovTki6M+/T0\nFN9++21Zn1evXtWQGF8XQnqQ7202mxqnynJozo0RTr57f38f4/E4bm5uauly+mi03il4dBYIqfUX\nqInTmMgwXKter1e7TxCOGDqKdHxElbr23COnoNfw1rwPzVXi3x4DXC4u5zYRnfkCrTYCxhyB8PgC\nYb6HHjP3yOhK5hIh8yBjtl+M0ffqGZXhO5nWwrtZR8+Nsz408wOZB2TYfDX/rnmc6C37AT4FbOI4\nv88YeB9/3E/rJGea3M/fas/iSJFnt5JCmbFghvJQdk5BeXAokXwKyXlOfteTYSfKBF/exxHqLDQ0\nE8D9PvrkmiEoChwKw+pWCjyLaxLgFiFkwM55TOv1unYCh7niGRkmxmjjKLnuj51baifRbm9vi1IF\n5vcpGxSH6y/Rz4gojs9qtSopM04GQgLmvRE7p2cwGES/34/Ly8saN8ZH8/meDS4OAQqSucGJg2Rs\nXgpkZxxCG0QcC5z66+vrr4jIPKfT6USr1SrOhDkO2XCSQrChpK9Uusc4+AAEyqXf79fSB6w5f5t/\nSF8YXyZdcu2HFbTLP0TsHBR+jqGlZlm32y1zk3lXcEmQG75HatPGjX7ijMORywEGBs9lTlgL0rNw\naVgr+G/MleXTwYeNE59l5ZvvroTgbsVMgESq3Pucgw7oGe9tZA0Z9jrZ6Nop83dx2nEc+Z3RaFT6\n6TQnv8dcIXeWUeQok6gz/cBtu92VU7m+vi4pMo6kOz2ZHUf6i/PB6S3LFO8y+ZuggxS703AEyDgL\nrorN/HvvmANmI22Hn5+ht+ycIMP39/elrh7OKsE0e82cpOVyWTtV6nRZPiCQObQ4LgRMtl84Uug4\n02hciiBzGVn/fbYWGUWGLafIinl6lmGn/MxJcyqRMZijad6hHTUfdMmfmSKUU/c5Jcm8IoNOA+5r\nz4ZIkce0cmdBF4tFiWojqlN0VhgmAdrDjKgjUlmg7LVHVJGtT/9F7ISVSMWePXwM+uuoFGGirxZg\nnCeIeZC8eT4C4FNZjBXBN5LAZwgu9TMcsToK9ZHoiMoJzBFJRB2ZazabBWXguXDZIPtlwvV6vS53\np3kc/L3dbmvcGxRoRBQukB2pXq9XDJUdglzozsfBKRgJyulrOYhy920OSLy0HIW02+1ymedisSik\nZiNvdrB5FkYeZWO+mhUR/ATL9z4eg+fUXCWOFi+XyxiNRjVukeeb92I8/H1OEbo//D7HzHu9XtnD\nrBNF+a6vr2O5XNaOHXOlDPObo2ATRB0A4DxhYLn3sN1uF86REVLGa4SPUg6eL1AyuFRGDmmZC2Id\nwbyBSBIg4UDBzaMvzNf9/X25sDiiQs326Sbkx84s/UNGMBbZiex2u3F1dRXD4bAWUCK37D/zchz4\n8ZmdM0jdyI33Po19tc+AsQ7Hx8fxzTffFDlFpphro0cELaAojJH+cTDH/TBaYx4o/YPUTLCH7Ltk\nQHZcCbaM+toxwJYhwz7NaWTDwQABBCimneNer1fj6nkf0qzHMn/H8uFj/qBQ7p9lw2vm/zM2n6i2\n3s+Ode5P5kcxfvalHXRkzXwzj8GIK33LfC36lfuAc4bM8Dsmydt583j+N47UszhSEdXmslHMpQZQ\nLK5ia2URUZ3QcLqQzzxwNoGFJqJeA8QeL+kfo1/8PpMOouZNyu/Td6d7GDffNbLCO4l0MELr9bqc\nUGETW2g8T2y8ffPM73lunN7LiAVz2e124927dyWCPDg4iMViER8+fIjr6+ty1NRziIL1vXMRO+eG\nQmlee6pio0x4BvPGpsGhBEngokscUSt+iKTeAD7pSZ/pK30BqWKuXHsLInqr1SplLPxZLvppdAXD\nCjKBEaAPGWmFdOm0TE7rMF9GRmgcqaYir42knUWfaOIz1/ixQzCfz2O73Ua/3y9oCXJBSYjb29uY\nTqdf3cGIXK3X69qJHyJ51jDXSkKZ8z2fArUDleePVC/pZxO3nY7OQZRT2tmJzk5UruoPQooDZ4cV\nJ3KxWJQLvZlj5iaiIuCyzqvVqoYYOdVkXZNTEaQuz8/PizPFOlHM0vsmoqpLxR2DnK7mmegtB2me\nN36Wg1bQee5TYywRlW4nNejTWjhCOGEmm/Oex8fH2gER5tQHJFhz+hJR7U8/E6ceGbTu9NxmKgl/\ngxQ5jQ5SttlsirzwTJe5yXaDvWKk3X3nOw7a6SN72nKc0SDmyPbE5RDsDPKunG6zk4XTaxuS598Z\nn4ioIetGqegfwRzP9cELbI33gdfHQYazV54rk9SxMaxXdqL2Oatuz4ZIWVAiqguHSY84usTb57SP\nuT4oUzaVI3anL1CWjiZc3dn/NqQZUVUi59/2xP1/n7TIkZojZBSdDaRz196k8FIYZ04z4gAxpz5e\nmzemlRvPw9jbeUF5PT09xdXVVbx7965E0XDZUEKu0sxcoJydhuSKBxTV01N1QSXKzDwaR0bb7bZE\nrg8PD4UjxWWnpImbzfqlxYeHh8WZWiwWRUHb8BrVi9gpb4w1StqpS1If/K6LanJaCYXK/PJdw82O\nkkk1ohidFj0+Po7JZFIiea8pCginpd1u12qTRVTH/532pNbXPmeXsXAdB+OK2O3Rs7Ozks61zGw2\nmxiNRjGZTMoaY5C63e5XqKidN/YFStFyaDlHtiIqFA45Y12sNFk/5pp3mk/D33xmNMjPiqiuVLHO\nsiPE6cJmsxm9Xq98n9Sd69tkPgaGwwbHCLfTJpZv+uZaWOiPyWQSl5eXcXV1VagCj4+PBRHPJSqc\nZmGvWSfakBA0OkBEpnKgiB7nJgbv06enXcFXir0eHx/H+fl5RFQXWm82m5hOp185vBSX9PUnrA/7\njHUxv4Y+YGP4HmMB/bZ843wakbPMYD9arVZJyzFPToWC6tAX9KxRPq8HqLBr0DF//Az9nU/XGm2y\nk8n/87iNKoFkGbhg3rBHNH7PKBeN72E3vE68y7yyjN4x3znNzvwbyOB7ZEroi/WJ05m23TxzHxrF\nc3Jg5fZsjlTOiRKZkitHOCPqV8Uw4c4zO41mD5QUAgLqlAK/7yObNnoYdoSZ75G3diTo5hQBSpyf\nO9LJUR3OgmHKiIpfAYKR04UR9UiIfqIgMpRKy9F8RNTG32zujqOfnZ3Ft99+W5QbimA+n8fFxUWN\nxEsDnneEQiSGMnRfSYeYJ5TnYLFYxHa7LXVoInaOFFXE90UNfAcj5Tz6ZDKppVFdxgDndTab1coN\nEOkBz9tw46zbwWg0GjXEwiiA1xGD73U0UntwcBDj8bimNFlPIjWiQqclURLMC/LnO9bgLTn6zKgi\nxnYwGBQ5BVXzPpzNZsUxtzPIAQH2qvcTc0Pw5OBqX7rL+gIUDB6ZycGuep1T5uZeuA/MtxEeI0Q2\nFDhyTnMYMTZnx4VzncbhmSDSEXWUwgRukEnWkGCF9+V0NJXCHx4e4uLiogQH5iV2u92vUpcusmrS\nsB2pnO5FxtiH7EXvfdJ6yKJ1NH93u92SAmZuCDJ5nknFjUajxo3LurjVapX37iu4Sr9zyQGccHQB\nv2eZPTio6j/1+/1arb19NQkt40YVPV/W18wRAQjrijyxV4w+edz8HvrP8sZaOrhzX/m3gQEH5Pvs\nBfNjqk1E5YA43UtfkWHsutFRUoiM0/1jDf1er72DVQcfyA2ygpPKs60XbIP+39CoiJfyBy/tpb20\nl/bSXtpLe2l/d3sWRMq8JLxNkB48TUjZERUE6sg5F+jjuY7YHanjpRLR+Fl4tDyTyACY0MRgIEaQ\nhcxJ4r0RUXumI2O8crxhCOxEWY7YSE9Cms2et087MRaeCfzriMARBlE373AqA+Tl/Pw8zs/PC6mW\nMRGR93q9EsHyXubTcCwRkBE9omM4DnBAKAQZUXHnptNpjEajGsEb0uxmszutlhFHIF4QAtIboGPc\nydRut786WkyF5cViUb7nQoMgIiawwzEhskGWmDfzuEA8WWOOR19eXpZIO6Lil9CXiApRAqbmea6I\nTz/4Tq4kDzzPeJk3n2KKiHj9+nV5H3eaudI4awgyBEJGWobPQBtJhznVQLROFLnvdAzrnPkO7G+e\nYZ1BQw7yQQz+D+GXfyMHGeUwVwVUxgi00wTsd55Jior0tm9IAMEgtZWJ7+xVEGv6ws/pp6NrUr4g\nGhcXFxER5aAEXKmMyvl+P+s9kEanzbLOsO7KqASIFQiYUQnmB1n25cOkG41gR1TpXtBSI4DIGIgK\nKcCIqJ1KhGJhPhPIdZZTz5M5Qcw340ZOzQfy+M3PY//RF8s988d8k6nhfTldaKQLnh7fz5kJ7xmn\n+Iw25TUEoeJ5mROHns0ps2xT/DOjwvYJLN/IisfnPmcCPuNzCnYfny8j404L0y+vef5/bs921x4E\nWCuqTIpjkOZBIUSZxxQRX8GcTushjBYU/m8YMCJqBu7u7q4cteYdbHz3KaJevRvIOJ8iQsE1Go3a\nVQi87/HxMbrdbg1KBdL3JoyonBYfo+dZKCHmgLGa6Ggh8nzCZeEklisDNxqNcoy30djVSxoOh6Wv\nvIP19aYhXYTRd6VpGv2lkfZgQ1CLhTGgFEgb2hCycXHcmDvGjoK1U9vtdktKkFQRY3eqmMa84Xii\nnDMXxOuEcvW1DfP5PMbjcTSbza+u+0DhGvpmvlkrO6i8o9lslgMMVN2OiCJ/PjThqu849Dj9cNIe\nHh7i9PQ0Op1OjMfjuLu7K+M/Pz8vzh77zWlmO/sm9LJuKDCcFH7PTpiblSAOgOXIqSV+3/w5GyP3\nh7QkgQtX/dAfHNRWqzotyDNxnigZwglKZI7ncGkycoODzjvMWbEzQwqYMftwgxU9PCzW8ODgoHzv\n4uKiHJRwatDzjZPvNCvzgMNgg8P7bVhJcyM32VmhOe0KfcE6ykaPtCjr6YMS3pc4xvQNKgJzwx/2\niw9TOJhGrhiTU+WZ5wa30Zcns2bMGfrdTkY+KJXT8ayRHRE7eKxBPjDiFJWDk+wg2kFxmo059QEV\n/rZzw/f4m+fZJtqJRp95juHcGgTJB0gcqJgaYeDAfzslSrPNxkG1DfS8ICu0HCjl9mx1pEA1zDGI\nqOpV2NP0orMomQti3pI3d/Y+feO1c8QINf1brVYFjbAy9ak3E9loOC9EdNlRBAnx9xCiw8PDUuoh\nEwfZ1CipiHokZOGIiK+EYh+HiD7tO1p8eHgYvV4v+v1+DV1Amd/c3BTlgcGAr2RkwdwWNia/Y+cM\nRUg/mTeUD3eCUXbBMmPFzmeQna3cnX+3HDIO5AJl02w24/9h7816G9uO8/0iRWrgrKlbPZwcx7Gd\nxBe5yvf/CrmKgQSGY5+xWwPFUdTA4X9BPMVnL6nzAwwE+l9oAwc6LYp777VWrRreeqvWbDbL59B+\ngWdaHhgr9wIhKBFA3tuKkUObiZTX63UFIeNdQSu4Z6vVSie72dydTu/1dlEFMudjOHgnV81w1Mtm\ns0mSr2VqNBrluY6sPf2jQHqMuiyXy9xDyBLybeJteSQNCtPor5W2ESj4LDinRoNZWxsDxsO6lRG7\n+T3WP8yrCcIRkc7m2dlZrhXjwDGv1XbnJfLeGBbzjqwbjKCbs+LWDQ4m+Fuc/hLlQ74YC+NizlwN\nbX1C0QGOD/e23ubffM8VrUaBeD+ew/2QbRBn7yHW3xWl3gu12o6PCPLptXPTUeaM90QGkR/0YRn8\nWFZKPYQzjB4uOWsEEzg3XAR1jJP3dXEEsm6bUNo47oXM2sFwUMk7IgPmFRoFM3rDZ3bYjA67kIR3\neMnBfykY4vcEhdYZ7K9y3tAJyJsRN9aX35nj6MCgfE8j3gRLdpTtcL50vRrZHI/ekSnRHMrbhzAC\nyTJICziTRqqjREFsVDwZbHaTObnKqjYuOx4lyc2wtpUe74KhtyKO2LUUKEt2+QyFz0+MBU6NEQVH\nl5Ab/Xt+lv2NTH5/fHyMXq8XJycncXx8nEhSxK4/0WQyqVRxROwqYlqtViyXy0rkidLiXUtvn7ll\nbR2VktLg/nxmJMrjZlxl+sDpFH9vs9nEzc1Nju/4+LjSfqJMm7pSy6kkV1ExZiOLjppL2Biir5Gw\niEgnCceOXj68mxEltw6o1WpJDjfszjjYLxg4R/MoRObIChO0gmfyPcr6QQ0cwd/d3cV4PI7T09Nn\nqbvlcpkHPS+XyxgMBhUSvpFjOzxOwfJOzWYzU3Q44KS2bXSNABg95WKeQTztZCF/q9Wq4rhtNpv4\n9OlTrvXBwUGmqJbLZeqhsqAEpIX96ojdxODlclmpsOO+rLXXd7OpUhgODg6yTQUo27eaDWMwmW/u\n6SaQdsDsSFnO+X/eEyNV6tOyn5VRBJ7PWNw0GFTbwZQd1/v7+wxQPFcgXiCP3ofYH+TOqLEDPAoZ\nQDgh0e/t7eWJBg6wnJosg10yDUZb+Gl94u/ZObLT46q+Ui4sG8ivG1OWf29nhe+VqCdjtHOP01fu\nU/7fziK/s71zGg5ZYC1eyrzw3g6S7LCXqT+nij0e5tdj9/h4l48fP8ZL16s4UpTfGhpm8vf29ipQ\nb8R28CiAiGojRnvKbGxvbgQ/Ip4ZBd7B1QMRu8V1btbGG+XKhHtRcRJ5vo2eo3/SVDwvIjItYo5K\nt9tNw4TBszBTOo9Rs5E3j+Ilp8/polqtVulrhFFiM/PZcDjMXkERUUE6SDExjs1mUzl+wfCyv0cv\nKBwsp6gwJmx4O1lErHt7ewmps/bL5TIbiZaOratznNLleWx2KtOYb9YF2fHam7tiKNpGx6kWR/PT\n6TQajW2PJH5vp8cw9tHRUUWB8zdE5DyPPWY0xfPmVC97gHsxjyAlvCfOFYgw88CF01dykFgbd/3m\nYrwYE7fMwLjyPjZ8/lnyRSIinfnxeJz713uYOeEzR+VOx5RUAWQVx4TvnZycZAqJZsI2bGVazIEJ\nvCnm1I5brbbtEUagVzopDsqc9np4eEjnez6f5/vSBd9pYsZAdSzrZ0TnJU4NY+OZGCKOTyK15/1m\nQ8k4jOx4vzm7wJ5yqtyVya6EZK5IGRnhRodaT1uWMJqk+fgeDp9TWuV7rtfbNg1lypX0LO1YjDQZ\nTbRuL/meXien60BquFdEpIPpLIT3vmXEOpN7gBg6gC51q4NXZ16MSPOuZAf8rp4bAsgSEeX9S54X\nqBm/cxrPDlxpn0E9zVFzup99yrt5f9h2vnS9iiOFQrUBIx1mpe6XJ8IrkSwGD2xHuWvETuAwevy9\nL4x06Z3ymSN137NUaFx2zCKi4tXyfRSK/+alKI73tYMU8fwIF/+/HSkUgcnbfk+E3pAtYwWtG4/H\nFY98Pp/H1dVVxZHiXTG6jO3+/j6jcvrEcE5V6dgYKnfKxJuSsTvt4jmP2PHbvC4o6JKHQsRiWWy1\nWhkplqkWNqI3ael82+CU5FEcSIyf4WUceZQysmFiaEQ8i/aQXZxfv7Ojfz+vbNtgQ+PfEy2bM4jM\nYKRKbgKO0NHRUfYJe3x8TJJz+RwQAIz7YrGopFlRkvTS4XKKiN9b8ZNKIQVUIqA2gmVEzBqXaXue\nQed9Chz43nA4jHa7nXMKgkTK9eDgINNWfk/4PG53YHl7qfyfMdspKNtbYCjr9XpcXV1FRMT5+Xki\njpZP/t4yab1UBmJ2cjyvj4+PMRqNYjKZ5Fhd8GJDGVFFwTDWDjjtPDkwZQ9j/FmXiK2s0werdP5s\nL0rUxYgTMsI6cS+coYidQ848O2BjfUgxko2wrsFAOwiwA1JmU+ys2EkHWfJakQYt18jvWepKO3Ps\nuZe+R2BnFMiBg+WUlDV70ek/9j3tKdwAln1Arz4HhbbT5gIjgyXowUUgQ4sPZITLDqqfZ+f7W9db\n+4O36+16u96ut+vterverr/zehVEyhBmvog8QBAbIy9EbF+/fs0IIGLXFRVv8yWEiIjOeVe8fxCi\nx8fHZxwSOBg+M8xpP3OlPA7gW+fYSRcQfZSQNvd2RBQRCY9TyfWSBw2079TH/v5+JRLyO0fsCLUg\nCEYlWJ/JZBKXl5cxn89zjIvFIptDgiAYfjdMXKvVErm6vLzMKJ1xOlrwmUu8f0Q1lVrmykEk+N3T\n01MlLdpqtXJOLHNGuZADIihIwuYGOPIyP8ERG6kpGlh6nZFToiy+zztAFPe6OBI2OuV0Ycl/sBzy\nPubRlGhQKUc8w+lAw9pEa6TpXF3Knux2u9Fut2M2myXvDITRcu/IezweJ6/IqMVLKTEjGozRqT1z\n/0C5PL989lLpP3JH9Oln80z0DWgL8jaZTHJsnPPo1NbBwUEi5bVaLT+zzuMZrD0I5WKxyK7gXE6h\ngriA/q7X62y4WSJL7PdyP0Rs036k540UMc4yg+DP2WOkS+7v77NtyGQyybWwfuQ+INRO7XAxp6Cu\nRl1AlCk0MOqE3ivXl3EYqXKatF6vV1LQyBP/b46uW7QwbtA3I9Xs9TIlxmeWeSO7RnzNmyRV6H1b\nVtvxk33guWFe0R2MH/2FTfQ+BRUrW4lE7NKE2ALrT/QF/GYj4/P5PCaTSUyn0yxmYN7oLO+xlf6B\nEUX+1ilmrxHjY6z4AeZou9mt9aALN751vYojZTj8pTQVEJ8NBsb04eEhxuNxLobPGGPA3B8yrY1m\naRRQjK4g5FnNZjPLxl1NYAHld9wfISuNt/+O3zFeFIgFmqtMQbD5eE/4KqVSsGLCGBviRticRuFA\n2Ha7HZ1OJ2F6Q9Wj0SghYypcmF9KxuEucGZXROT5a8y1nQW4TvAySGexvqQ+MG4mx+JAeQ24p1Nd\n3hg2zNwPmeEdgY1xnlgXjDYKGUfZaQH+zr2bHCggW4aODeWbS4Iswk2yDJMKsUL0+pPSBFb3OMwj\nfKnC06kzKykcdkr8kYu9vb04OzuLXq8XNzc38csvv1ScHj/74eEhq/0oJmCt9/b20uFgLcwh8bxQ\n5epAxHID38dGnr/jd8w53yv7PJXpKK9xs9ms9IlbrVYxGAxis9nEly9fct6Yq4eHhwpHLmJXFeie\nYE6R0LUd54Z34Z4ljwvZf3x8zM7vrgi0nvPfc7HvTZC2nPjvS74Uzs5LKWHSKdAPvBcptPF+9HPY\nR66EhFPI+Gy80ROuYmUt0D1OCzp4Q+59aD3ri8OHTjDdgj1tPpTniJS8gxsCATvETtkh4yV/iL93\nxZ/twsHBQQY5nAHq9J15U6QA+Qx7gcNnR4SghL1kpwQnsky/uYCAcbh9Tdn7rpQl3tNBtp2aUn8R\noJdcLJ7Pfbrd7jMfw4Gm0/+M+3/jSb2KI1VykiJ2+WYLpD8jv8rkwL+4v7/PzYliLKMaR2V2qngX\n8rB2spi4b+WfEWRvGjYlQgCfJGKHKJngZm4RwoKwefwRO6cBEjHf5z3xzM1xGAwG8d1338X5+Xm0\nWq2KMWEz2alAcdATqNvtJoeFPiyz2SyVXuloMI8oG5en7+3tZRNAjlOwMR8MBtFoNNJw4yBjeMoq\nTeQCRcUmsSNlZ4J59k9vLDsLlh9zKDC4oH2utkEWarVaOpLmGeAIehy8K+Pgd+UzI3ZOnJ0g/g5l\nUvYcIhJFIdsB9Rwhr6xFKYMlz8pyDA/o+Pg46vV6/Pzzz3FzcxPr9boS5IDSULXGe5qXQ38lG2zP\nQ8lnMukXQ2J+I8a0dArMH3PJOmOFH4czYGVrJQ7iwjg4hxDd5BYH3Ofo6KjS84rAAq4HbWGYb8sJ\n+43vbTabLEbBGeWyLEVEBallv+Bkm8SLE4XzZSPEVRotPmce2ScOFAkkcJTKwg10ETLIOqHfCEyM\nUtiZcGBWOkasKfOG0cTJ9vid4SgzDZ5H26/FYlEJMGxn7LTd398/O07LSJP1jmU1YleswnsSfPKe\ndpY5K3C93h0VZD6Ugy7QPp6Pw4+Dilx5/AcHB5XeinzP728bhc1A1sp+buj9EiXF5iNfjKF0suxH\nMNcGElzNjGw6mOL3PAO9aZl3kPrS9SqOlHtqMBCiDgwKJMyI54eE2mGgDB/hsSHkJHEqWGww7JE7\nRcTzUKQ2WhE7Q4tBsYIGZrYidiRgheJIgPfFsBvCRkD5XlkeDBR9dHRUUVBEVN9//32OqUTIeA+n\nSSN2yABGzaRFHK7RaBSz2awStfD3rsC008K8OvXFGLk/CpjPkAEcUxSgv1c6H8wpBEi+42jWG8My\n46qxUjEzJ6yDDRsG2+OyYfeckzryOkK2LNEFO78gQE6fGrEp03yOqrzOL0r9hz4AACAASURBVP3O\nc4dcl+/C/sFB8f3puXZ7e5tIru9Zr9dzHx4eHqbs39zcpEOGs4Eclmiyf1oOHBRYEZNeQzbKlAqH\nPbfb7WeNF7mXnSyu1Wp7qG8ZYBHlPjw8ZHEF42AvcLahURcQEAyyAzN0kBHCiKhE8uzX0qkhSCwJ\nuI7mPS92bko5tRyUTr5lir/f399PhLvT6cR8Pk+ytaN/5Iy9Y5kxisw82CjyDuwRrxMpOJBpp5Ih\n8GMsjSxxHwwyzye1aj1jNJYABwfArXt4P9a9dLDZ34vFohJkg046dc09jWyWjp9/8re8K/Ns3cgY\nsVugwk4XMk4cP9Au1ubu7i5Tey+thQn8pk3YGbIDip4BZHjJibGe5p6MCafY54qyruwr20KvRakT\n3bvsW9erOFI+zJcJAolAkRkSPDs7q6AB6/U6ERJY/44AHLWsVqvk5XhjIJj8zoYNQXGlnb1vIEnn\nZvk7M/+tMCJ2R2WU71mmpdiQEZHpMZyp5XLXrNNdq2mc6YNinZZxGsP/5t44OqwFCIbTSoxhsVhk\nry8rcO6JoLIGjJdndLvdilNrhIn343coIs+nq/0ceXgNidAeHh7SkNkIs4m5p40skTmGtIwOee58\nPq8cj4OSQMm9xGGAK+IxOnePgfQzUQx3d3cVRxrkzsiUEYQyhWUFgbPQbDYrvA0+93whn8w9zoK5\nR3S7xzlZLnedrUnBHBwcRLvdjvV6nRwjl+m7WSDPc/TJe/szO9F2GIjYQczKI6VIa4OQ2rlE6ePY\n2TmDy0FQxNyw35gDZIh79vv9NHy0u4jYdZnnvc3/xAFgjNYl/J53LXtMkfqLiIrzgWzZmHndmW9+\nX6Z1mTsjwdyX4LAMQJhDH95cogteY6eHQEeQ17LlB/sJ2eK5HLtT6nf0kINgyxTpV+7tzwjcTDPx\nvLFOBAsR1SOHarVapmr5HqgQ+ob5xlF3IO35BG2jythOqNsfcFnv22EwWIAdMOfMz8Qh4p2NcEN1\nKOeUABZns3TIrbOcvgP4YL6tj1g/ZN+ABWvG+hlZ5/vYb1/MM/cymlvazpeuV3GkHL1zTSaTNAoM\nngm4ubmJ9+/fV9JbFxcXEbHdGNfX1xGxg7Md0TldZ8PGxOBNm+vTaDQqTdmswIigMEDm4fhyCsrP\nJ+r0uXkIE0JuRypi1y6CCJNIr9frpQOFE+UNbJjdabOInWFHQbzETSBn73QSUCkG0Uq5zEVHRCUy\nY1M4zcTziMR5lhW0N1KJALo8vHx35p1/28ii/MrvReyOsWDuzCNDHsr1ZQ4pjy55RyhzI6Ceb2Qe\nJWjH1albyzBjQg55Dz8Tg2+H11E66+/0ldEIo4VE1lZcRlXZC/QS42JPksIiamUNO51OOjYR1dJ+\nFDsOjtFbK+ASOWYezYHyOBhfrbZNxRrNQS/wOXvK84ETyTv3+/0cM58xDvf0Yp6McuIk8zfmZ3lf\n2pg4pYN8uImsZc6y5rUmALWuKVMlvr71e9Z/OBxm/yXLAAEXAbIROQw0PEgjsDgfNpZ2CsfjcUwm\nkwzOGBd0BBtExohcYMTt1BHcWhaQbzt0XiP+Dr0MbcGcLL5vhCxi197h6enpWcoXuX3pNIX9/f1n\nfFOey7NIETMHvofTe6ZN2KlhzxlM4KfllTESPGM3S7TSOpw5YT/xX1kcAIpdIuMeh5FF1rdE6nGw\nmW/aLRjYsD5hH3rflaBIeb21P3i73q636+16u96ut+vt+juvV2t/QITq/HBE1fN1ftxw+MHBQZyf\nn+ff1ev1jEyMDhF5ANGbC8G9iFzwZHk/R4dl8zpXiZRRGpGL78E9I3ZwvSvaHL3CsXqJbN7v9+P4\n+DgRqZOTkzg5OYlOp5P3dTlnibgYdSs5Fa5KAP0ADSovUEFzzJhv7odHz2eOyl9Ci0Cx+K75S8wf\n//kzZAli90uctBJBIqp0tUz5Gd9zJOQIjfUllUqqGeTDuX/e1ZwA39scn9VqFbPZLNtGkKJiv7hR\nKBG001tOi/C5Uyfl88rCDCNQ/J3HwJhANElvgP447cVe6/V6icKCGrvM2UhfCfXT0gKUmKtEHplf\noytGJRyVU/RQq9WyZYD5J/V6PQ/HdkrU6XhQLMYxnU7zeBAqW09PT/OePAtOFu9vvpWRGd4flA/k\nkDEQXYNu8V3uCZ/I5f6MgfE6nek58Bw7feN5L/92tVrl8VF0f/dFA0SoCS5CgbdkYjlrityiE01V\nME/V5fLORLD3ywtd5H3Id5jTkmwPr2ixWFT0M4U8JSUiYptpYZ+5KzvPs36zDsZeGTn3+Fh75MDo\nGegtYzDvzkiUES4+Q5eY5sGFHeVz3sfyVtpFo+geA+PAxrJXGT8Vty5MK3WG19JrZ/3mtK5pCeY5\nM3ZzqKwv+L3f/Zk8ffOT/8MLgaP6KaLK27BBjIiKgel0OhUYM2I3CfB2bKCB050LjqgeFAycW1Yd\n+Pe8D++LMDrdgECYTOhKMUOGFgR4PKR29vb2ctPxHoeHh9HtdmMwGORxD/1+P51DK1su4Gt3fmbe\nECIg8zJlxGfmCHEPBA3n16kENpBTcVwWTDs2QNBs/NJxZSy+v58LBF5+BoT7ktInpYtyNMRrgw4X\nis9QeDhSXE6fMu67u7sKb8NKsuQKoERQGK5429vbVizBh3AVHf9h9O1ksZeczvX6mmjrNCX/NmTu\n9eX7ZcGE95KfV3K0GEtEVNIlOAplhSbpIKeRS4fWijtiu9/m83mlw7TfCw4dh1bbyYzYVqdiBHG2\nTk5OknfJmiAbe3t70ev10jiWfXAwpCWBN6KaHi2JunzHqbmIqOgm84UidmkZ5NH7wuvpg3x5vg1Q\neTllw37kfjiIw+Ew01gm+k6n0xgOh0lPYF+wn9BVnh/0noMeOx4UGC2XyyTxezzWXXaGMZLs+9KR\nIlhxyhzj/fT0FMfHx2noeQ56mKIpdON8Pq84dawJ33N6nnEx9pIK4fQVOsp8L2wGwQx2xHw587NK\nHhx6sV6vP9OdOEqr1Sp7RlmfWM+UdADLnNtU4CTh1DlFT4BBmpgUdcS2KpE1MoeW5+E/MH92epGh\nMv3Id5HB0jcpeaTl9SqOFIrB3q4/Y+HttHwrJ3p4eBjHx8c5kdPpNAfMwqNkOJcuokrYI+r3RJUI\nFJNO9YYjayt3Frd0FGkOyec8N6J6GCz/76gMQWi32zEYDJILQW4aB8oePQLKBkUZWWkbkXCE8RKf\nw0aR79vg8T2+WxJOXQ1j5c9nRBUoVBs2bxLPm9eL55pbZQNbVuYYBZvP54kAMa9uGuiqOeaUcbjK\nhujqJQVeGusSBSNyQ24wNETMNipGM3DAN5tNxXChDOBSlO9kx82KDkeWy9ElCt+RnflMrBtryBio\nKmO+zdlxBa8rJrmnnT4jWciyo0iPg+/aebVj4/5xfO4xwpskUkYuCOJoOsr6ttvt/B5z73Mmedf5\nfB7Hx8fPKuTm83kiM8gbetDcTz4DAWMP22Cw9+v1eiLHNvrw1Ox08xlrbqJxKQf+W/+u1WrF2dlZ\npRcV74osLhaLRPsYP/qXefMasiceHx8rhHKjjA7qeFf2DQ4t62tSN/NipGhvby8mk0k6zC60oAlt\nxNapNtmc90YueR/zLZEh5NC/512M6ltfWrej890nq9SLRq/s2LAHy3YZETuHnwxNeSHDAB6WU/it\nOB3sReYR3e49bsTcup95BGBwdTRzaGfHAaR1RhlcMW/fQikJgLH3Rj/xJb51vYojRQrK8CLOAwrD\njhQKpOwSHrEbJH2Ibm9vnx0GzN/b8FHeiUPHYZ4RUTH0LBZC6vQUxslwu2HIshEeqIU3dkQVkSgh\n8dlsFrPZLKNcHDKeV8LEVnCMA+E3cR4hMSQKSsBGxGgYSgXdw6GwsrXX7k0fUT3YMqLaKblU9GXK\nibFx/5J8yf+bSItjiuIsz5djDkBH+Iy/x6Ep78n7oCCtvEBPTKwso31ko3x3fl8qAae5/OyISIXN\nnNgQsZ5EipYtPxuDY6cWRYpi9Pjr9XqldLpMFaH8+/1+9Hq9fJ7nn4g5IrLAxIqb5xHcuJKG98Sp\nBFFiznEmn56e8vDcx8fH6HQ6FZQPJInneYwgdXQrt+O6Wq2y0SXGnfk2quk0Ow0nI7YEaVAmPnPk\n3Ov1KujB4eFh3N3dZbDIe4PSm7hsnYIBoyu6969TSHaEcKxLJNnz8i3ECv30D//wDxlIcb4fa06F\n3Xw+z15bOKyQtUHleCYBEfLswBCd8NJei9hWi56cnDxrqExAVDqLrBE6zvoN42rkhcvBvasumRfL\nrh3ziCqR2VmTcs96rdAfBG2mKHgty6pPfg8aD6rmTIEdJWcA2u12xV6bUM+cmIjv5/k+1us8z/Lo\nLIWzG8iDn1eiTRG7fVGigJYZO9LlM2xj7GOUgUR5vXpDTsOLrug4PDysNJFjkah6s7I1d4NGiRGR\nB+E6D2r4l/41RKgoWibN0W5p2Bzle8IjdgJi5WblWkZfhndLCBZFCZRa5v9LbgljtdOAk1GiYDy3\nFHKcKMZQjt9jdprSOe0SIWBMzEPpPLF5S5jdskIEZ0eyREts9EAh3ZgyYufU1mq15Ok5VeQmjd7k\nvA+yw5xylQqjTIUgVzb8yDAb10hZxK6CzakUfoeMgP6AmEREVnAig1burJODFz4Htkepl/LhVILR\nI5BkIjnzH+2Mkjbgu/v7+zEajWKz2SRKZLlAVh4fHytNc5EjHPqyxBkdQosQzw0VZYzT69TpdOLo\n6Cj6/X7SCcyLQk/ByXPTSebIFYqMA90C4meHAL31+PgY0+k0uVWknknPG80wVQC5cEAHgvISr4O/\ntZFnznAGSK97r5WpXu7FvFv+G41GnJ2dRcSWJ9RutyvHQKH7Hh8fU9czJuQb59oojeWYvktO1fl7\n6/U6kQR+h+OGDjbKGbFLNZPa4jOcZ3qb2YCjB9AXTsWi7325pxPPZJ+xXk5/od9LVA1g4FsIo9e6\nRFwJFo1WoZP4jN6EEbsejugEUn0ROyeTVCJ/53fgOy/xmZC1MlAyqmZ0GVnBttmu4TSWVZsRO32C\n3vcaYHvsK/gq5b68XsWRIlLyyyKEeLZGQVgEoHZ7vD4+BO/TpEujMm7Qxf1BXSzs3NvRhx2oiCr8\nXUblLIpz+ihroit3cDbkSMTA8/b39zOdiDKz0ua+Nj78tIPGRi3hSZQFyjpilxJljCgej5t3i6j2\nmmEM/J0Rx8VikeMoOVne/IbUuS//BoFgDdm0PmIj4nmDTMPDKHLWyLwFKx7Wq0yPls5TxC5d7b8x\nsub0FH9jTgFyhBLge6yd0wEocZQgcmFIHQOBsrSTaeTBqdqISKcTFPju7q6ShmHdUESMH+4i7/ZS\n6gCn3Kli1on5K+fX6S4jLqAbDiJKpMTGxdwyDOJoNMpxU6rf7Xbj48ePue9ms1l+RmoKBMVR+d7e\n7mgbHB4rd4ISCMTlWXtHR0fR7XbzKBXemaNJ3KIhYpfa6/V6z6Jq5JuAwagDsvgS+kTaDcfXaV4b\n1ZcCU/7tgIG5+fjxYzw8PMRkMsk14Ygg0pO8q/d6q9Wq7HenZF7ah9bhZDB8Tqgv97FCZ9B4mD3v\nZxOUkWYk4OIeDhSs25FJt5owOsYzvDciqvu8RAedtsaZdMBjNAfdZkqLnSPf3+vKu5Ryw/1s2/xO\n6M5SPzDPJurzLp4/yyL7y8Vb/I2pIyWAYPv7UnoaPet7lrL0/0Kgyuut/cHb9Xa9XW/X2/V2vV1v\n1995vQoiRdSNhxux8+SJSs2DMgrTbDZjNptV+Ex8D6jWnuRqta1EaLVa0W63s+IN6JBqC6eJeEci\nb0c0RsmMcPCTvy3z4URQjMvvSTQBauBUA+9qr53vAXUbPSrz3eTS7amX3zW8yZwSBdj791hJGTnq\n9/1KzpbhWSMSjMeImc+jMp9psVhUogh+wqugeZvvCdTsgoG9vd0p5D6Lje85Bed0AvczIuo14Tus\no+ebOWc+WRfuQXQGImkkK6J6dh7vY9JnCet7L1jOvPaOQP08p2Udwfoz/h5koV6vx3w+r9zT1atG\nijkuhHE5Ted7m0uETnCE67QUqcQypQ3fqWwCyzqR5kCfXF9fZ1UQ62V0DR6To2DGAZJepsBcNk+z\nSGScDtLr9TrTkOahWMaMTIAar1arRAiM4h4fH2c0D8rPvdiDPId5ubu7i59//jkRI9MInOIvES7W\nGFmzfkJGTk5OYjQaPePDwS1DVnkOa4TuYs5MqEfG0N2me3A00MPDQx58zjhcqWlOFrrFdBDrIjeT\nNW8WJA1kyLYEdNkpTC70DjzUzWZX7UeazEiTdSlotCv4LG/oEfad541CGqNIETuqAP9vHeGWISWH\njndFr1pn8BxQvpJzauTfdt52zVXnrIPPRLScIofoe2d3/G9/h8/M5yrRqP8XOvUqjlREtS9HxA6C\ndG7YJ6tjmBEuSpIhYnJEgxcRRwjFcH5+nlUflNzDJbETVCozbwzSaCxI6SzYcBreR/DNbbCBdkrI\nm6fdbsdqtaocemwYsyQplkosYnc0jeF2ExlRHM4bG6b1JkWZojycu3Zqz+vM85yDt/HGCOFEbTab\nSqFBrVarVNaU/CwUpufe6VycPjs9GGZIw76nFaONIunoEj5mDOU8lXPn9zcvC4XKOvozy5G5BIzD\nKXGKESKe9+3y9xgbStXOcrmWJYfECg7Dz9w4RWlCPX3OaO9BKoN5I03HcSnu7owjxHqRTvERIHDO\nms1mpUs1c8062MnC0eK7yD69kNbrdeWEBMbulLDnhXdjbY+OjlJHRew6apdOtOeKquKSb4U+KI0q\nfCIoC3YIeNfSCPN+dL72PmSfQQR3+op9XXJZuOysvxTwnZ6eZvXedDpNeePZ1ouWNfau0/YRUdnX\nflZE5JmVDib9XVMwXI3FeNFRbm/B2HAYLIvI6XQ6rZTsI084NAcHB9HpdCq8NRwUeLp8bzgcZqq1\nXt+dp8p32NcOKk0xsb50/zVS9qUsW26cAuNijzL2krbAWpqu4HVFfsqiCJ7LfuR7OP+WQV+m7fgd\nDFJgG2yD+Ftk34GfbZd5bsyRdUJ5vZojhQIzh6YctDcbCpfIi0E+PDzEzc1NLJfLODk5qZDUjIBw\nnAqIlPuE+D24rIh9WZnxd3ZsbGQtiCi1knPF91CypXPGpnHU4Dm6v7+vKGkrTiIEHAcLDpwNR3tu\nroizxN+VeXVHJnYWI3aKqlRe3rSr1Sq5IDRDvbu7i4eHh2cl0DgRcHe8WT1PZWTC5xg9rxPjs/PO\nOqGIcGxMDvW8oHg8H1yes/JznAOeSwsA5vIlpW+Oj50eGzhImH4+37eSctNMoxWWS+btWygPChFn\nASfOCBCK/+DgICaTSdzd3cVms6kYE/rsOOhwBErFFc8teRwlWdZVbEZBPQ4bCxSqHQH4NSBwPiII\n2ej3+xVC/d7etiEmfXsspwQyPiLHFWKME91hvcc4eV8739wDbs/x8XGukx1P6yz2EOthOeTdMZKO\n5nE6/T6WF+aOvy2NFAgC+9vBVjleF1Ogf8rDlz2H7AEjciZS39/fVxwL/h4HxeiJuZvmMXpvlkES\npOnj4+OKrPOerDF7mHvSRoN38BjgJ47H41ittsTuk5OTiKg6X+xDEGjemywGQSpzaoQeveJed0bN\nvYYgd0aMvd5Gs+xk2WHj37ZB7Fk7+tyTIK9Ef9n36CGADcue5cpZCtYfXWJdShaGgIL7uGnw/68c\nKSJMR7sIJ8JhyNWKgAm08DuCt9PRarWS+NfpdKLT6VTKw70IpbfJhsHQlsiKoycvsqFKDE/EzpHi\ndx5TGcWVqJqf53dhYRE4k5S5DFeWa0BF2mq1rVKhlxKC/1KqwlEFSoJ3L9MGRqv4LkalTOE5rUn0\nw+UKmnq9ngfeopzYjE5TPj4+xnw+TyKz54D1JrVXEtuJ4hyd+DPkr0zbMX7ey0bR8+X3QE55P9Ai\ny3OZ9nIbENYXcrRJno68mWPWAdlGzsoKUu5txcQYWd/5fJ7Po+qsXq8n8ZzP6GiNzJrETKoMOSNg\n4p57e3sVON5pNubZCKoVMpVWbhkQEYk0gvZ6bozg8b6lYu52u9mfiXsPBoN8frfbzdYLPA/nkf1G\nawhkEATRUT1/64CGq0SgV6tdbyDObavVahXjwBoyBigS3Pf+/j6urq7S0D49PVUQfN+n1CeeO+sL\nf9ZsNmM6nVbWCeeQUnwbVQJg9ken03nWBw8kj88jIh1FZzDsDDNe94XiM9ae/9wShv1QOv7IVqvV\nyoDdhHIj7g4wWCOj1A4MaZtB3y1kjc7x/X4/74cNQKaMRtqRBIHHvrbb7bxvq9VKXWPknbkh4C2z\nDuhzvlfqRuaPueC92HNUNpZZg4idzsGx9zpxeV9gX+wQ2uHl3yU6xj7BL0HfRuyyDS/JfL7DNz/5\nP7zgVdiZIOpwtFimvnBq7GFH7LqN4wjwbxaIA33tGXtTgIaVufLSEYjYRWZMtHO+CKEXiO87j196\n2SgEQ5yGbMt0lh0polkcQvfJcjoNBM7RkJ3RWq0Wo9Eo70seuoyuKRu2YHkcZQqJ8TO/ID12bOwQ\nAZ8breLvqWIqBbzT6VSUKrJgpWfHl02JQ1XKoQ2BS3lttMu14KfHUkatdkbLYMDzao4U70/AQJQe\nsUOW2u12lu3zWYkUGpFB2dEry9GXUT07/YyRMVOF5L28Wq2Sm1NGqSBLyCNzCoLMvnLHaI+XPWpU\njd42/K3XDbSZ5pAOtsyNQnka6cABMeKH7DPPOP7oGoKyXq+XaJajZNZ8uVzGbDZLY9pqtRLB8Poh\nM94vOFx85pSRKyZBWjD2Jd+UdAmHnXstZrNZjMfj+PTpU7x//z4/43slks5lXYBuY97QFRcXF/HL\nL7/EZDKpIGt3d3eJbMJbjYhKaxrGUvIxSePhhPNZxI7n46o1Ak90vA9BtjPDOOxIGsVw8OVgCZ1s\ndJW/Jw2LE03F4ksUg81mE4PBIPb39+P29jYzDxERV1dXcXBwEIvFIo6PjysBpNcKYIKAmPHbDs3n\n85Q5pyvLVjvWOWUGh7kkOAeB5Hu2oQ7M+DcZJv/eGSrskd8FfYk+9t72XHh9uJfpEB57xG7/Gzlz\nsGLH29erOFKUAFv4mVAm2oYPRWIHxz1hmBQWEWEk3WCinC9D/mXulsm0YeInC8IY/FlEtZeNvWWn\nRkoHzZFQqbDKCMAOJhsGo8jfuVGjkb7SeLvkl4teKygaFFrEThGhbOy5R+xIzn4/xr5eryvlryaA\nGmms1+vpSBGlYkBwxnhPyKG8P99DiTKPjoQctSBvzAv/9tzZWbLxtRwyXygAjLHRLOYLhekNz/qx\nyZk3lAVOhlO7oD5HR0e5RjZodl4x/FwQX9l3VjZeh729vZxT5BQ0wKmter0eZ2dnKRtloDMej+P+\n/j76/X4lPWD5Ho1GGVCx9hg2ggK/hwOSRqNR4SSZY+GjLSwjpLRLw2jH0c5bt9uNfr+fjliv16ug\nfZB6F4tFdDqdvA9pmIeHh0QHMIroJkrr2ZO8B3KCIbFu855oNBqp5O1A7O3tPTsiBONeBkSDwSDO\nzs7i6uoqhsNhdLvddCTYY+zjUkdZNlgb65TNZtt5//T0tOJIU3hA/zJ3y0cn4IC4iS/B3cHBQTZh\nZt6sYwm6S/SDe5VG3UHAS4G+G8va0BIA8rfm8rF3Sb+zTpzNGLHricV7gkahb6fTacot5xYOh8O4\nvr6OXq+XGRfGCMG+2+1Gq9XK3mQcm2QbwkVmAtsHCZ6/I5jFoXXwVdI/bDdJ6aOvcLJw8tEXTgki\nI+wVOH3smdJhdxNuZK8MvngmOtGpuvV6XeFm2jllj/xviNRb+4O36+16u96ut+vterverr/zerUj\nYpyrjagiPnzuPHqZV/e9QGWI5k24dXrFED4eJ6iT03gmpJE+chRC7t0oUsTz9ge+uIc9bv8/0VHJ\nxwD98vw4mmg0GpUT440clZV6TicZDiU95tQTYyO68PeIIohATUB39Zyf91LDUxAER+AgPY5mI6pH\nrHCRrpxOp8/I3bwzESbrybu4fNZRGd8zF6DkpvB+Xm8iT1IA7gzu9ycK87uSkqRCx6kII6Dm80VU\nKxOdgoyI5Nu4oWYZXdfr9SxUMFfA6JWP1/A5ZUZBI3aVeYyRueB7e3t7cXx8nKgNa8EcLhaLPOTZ\n8srflpwNokZS/uaXMDfMf5lqdFdzIlOj0SBUrD+f0VkdfpXPY9zb21YzzWazePfuXRZORET0er1M\n7Ww2m0zpMEZQzvl8XiH/Ov2IfjKJud/vZyr4/v6+kjJiHGUqharAyWQS19fXFVnb29tLQvPNzU0c\nHx8ngR0OCUhQicow58yPOTqmFFxcXMT9/X1WBpJROD09TT4biBRpXloHkHVgjKQw4ROZqE3aeTab\nVc7RRKch204HIwesu2XKyCNyZ3tkGgGHi/P3Tota7/vs14gqknN0dJScsPv7+zg9Pc15ub29zcOh\nn562x72QrWGMNHAFmTLCPZvNUtfw91ymOpTZFNJpjNk2x2uPjkM+jAwig9yj0WgkXWOz2VTmDR8B\nmoptt9+7TNuX2RxTQbBZ6Eae5+akoI6uAPbavHS9Wh8pJhdIDkUBdGiYz04GisWKDw4M3YZduYNB\nYUIt/NzX/CU+8zvxDvwkzQDUWRLmMXwWRnLFVtrepOZ6OD/rXiLcwwLFu2AoyiNFSOuRZrLgYODg\njvEOjIs18HyzSXhHuGdcTqnZiXW6hE1Vcmj4W6d1MeQYPVI1EZFcFfd2spK0jJgL4TRR2d6CDe3/\nXiJAlukLp/DI+Tt9h+FHFuxoNBqNyuHDJrlioG1UbYT5m5IXwLOXy2VWBdqpZR2RN74PP83Kn5QB\nBgpi9P7+fhrtw8PDJMTCLbFjfnFxkfLhvd1oNGI8HqdD7nQK6QOfVeegCGiesZE25bt7e3upFHFk\nLANWsrxP6XSTGkPe6DtX6gVSU5yJN5lMKlxNUu3wR5zWZp65h51zZty5IgAAIABJREFU5gPnCqcY\n5wkeE2ensb6sn3khXKSkN5tNFmREbB2+wWAQ5+fn8ec//zlub2/j06dP+TzmjHcxRcF6kfn1vmE+\ne71ezk9E5AHBR0dH6dyXQSMBBE5DxNYoci/SUcw3jhzjs55Bbzt9Z2I478tewz459YMDx1jZa3Z4\nuRfvjd7a29sVTEwmk9QTpOjNLdrf34/BYJD3gxR+enoa8/k8ZrNZchUPDw/TkTZxm0CDi/Sl9b+D\nAeaeQNH/RhZxRlyggp1lvbx/0K/MMwGGKQwR1cKOl7horD29wSKi4oiyTgSVJYVms9mk00YKnjEw\nV4+PjxUqA/OJ7fzuu+/ipetVHCmM4mQyqZC8yH1inGyEMDDk/b2BUaRE515ghMWC5av8bkS1HQGX\nnSs7e5AhuZdJch6D+Tg4Jy+dJehoKGIX0bzkDaO4XqqE87g3m03lfLmI6tlJKBQT3BFilw0zfnPE\nyKszBngnlN8aBWE88EScY4fcXvYCQ9HxO6MgzH2z2axwrzzv5irYOcVxRGF4fm3kjYQaGXELAeYH\nzhBrwv0Zo422KxaJeOzYgNa57Ydl1GtiJM/IktElc8Qwshhjj3k+n+d5YrwbChoEEKVP9VrE7lgK\n95vBYGDovbfZv6PRKLrdbnS73exFxcW6gpx5r3G5cMGE65ITVxp9B0LlfrPjYUIyjToxmsgtzzg+\nPo5arRZfv35NI8hYceTQEcy7jRxOPe95d3eX+wgOVnle4Hg8jnfv3kW3283v3d/fV/af0WfmhKo8\nOwv1+rby7J//+Z/znZFDxm7DbV1aoumuBqTQgmDQxSvIHA64nUzrByOEyM1kMsl97AADOUGnQKqP\niCw8cjDvve9WJF4bileoiOv3+xlEWCaMoETsAivkxXLIGm42m0pbHr6HE8R+LZ1veHfwj3gf9Lz1\nTYnK8H3WhGc6qPa7urChtG0OwAmSbEtKuSt/Vxbi8DwCJYIM6yjujd7z941io7N4Ns9C97EPWTf2\nPwic19BOXXm9miMFzM3EEXljoL3R2EQIvZUNG6hETfie008mlDu6x5Ba+B2VOJ3GpmZRTCzEMcEg\nsJBc3MPoD89zJFymCN0N3R42UYBhdkdXjIE5sxDzXgioFSH3NNnXwmgFbeIiY0dJ2Qnh/VlL3olx\nOO3puXEaDfKjHeWIXeWXWxawVm4LgNFjzKQFXlonZMLpOUPzoHyeb77HvFkmUVCO1IwuUGGGo2mE\nxA6BI33GjOK2Axqxq3h8enrKrtIREefn5xkhllE5zhxOoJE92hogo14nnK+Tk5Not9uVoGU2m8WX\nL1/SKTTZejAYxGAwiNlslvc1wRljWDoeyIxlPiLSsVutVs+iahupTqeTMmw95GianzhSOJRHR0cx\nGAwqyh0jNp/Pk1TLvqDP3Xq9TjSrdPoJdpx6IoKG6mA0qNPp5P1ns1mcnZ1V0sFG4O3Ez+fz7Pfl\nc/UitqkmUmKnp6cxHo8rRh9HxwbFASYXDhjvY0OJw+t7kO6lp5gdAnQ21YRGemi/MRgMKtQE9k+t\nVsuSft7PvbXKjtkQ2tF/TpfjALNn5vN5fs9NJ/lbX/69U6F21Ph/3un+/j7u7u4qjnCJ4PO+Tqnx\nXfQBY/VnzE35GYE8Py0bUGYIwCzDpsmURS04gbxPidJbzxmpZkz1ej11tG2wD1dH5hiDnSD/P/vA\ntBzLLTad5t4GAco1La9XcaTgZZToEIuL0WdybKwxWChMO0OlJ44gvAQxR0Tl78o0nqMrvhuxi7wR\nrDJ3iuEHtvQCIIQ82/dGAEGK7JlzLxtyX077OOK384PRsfFjs7AeRiF4FgqKuUIpMV+eT1BFxmfn\nzP8PemOHyBvBUSLRFuMwQsAY4SUYdcKI4kjRw4bne60pr+Yq00dWNI7mQJ4itkbIfDrQIKInlBCy\nY2eCjU0bh9IZZDwoPe8bFOxqtapEX+T4QUJc0eaO7ay/kRXkqByj0yk4OXyPeV8sFomQsE4Yebo+\nTyaTHCM9fzBw+/v7le7OtC9g7v085hKn1Nwr5IP9Z5mi6SSIolOjGFEiUNYlYme84DzZWQDZcSNE\nIwI2EsvlMt69e5f3vL29TfTKe5T3Qj/ZwSb9y5Ez4/G4QjFg/Pxng4i8vXv3LmazWQyHw3w2nEO6\nj/O84XAY+/v78f79+0rQYVn0c1xJy/xw+agUOoX3+/18H+sCG/DpdJopQRA1HAjLxnw+j06nk+vl\nlO90Oq0EgZZd0ousgYNkDCuUBHMVqR72XHu+cXrpeF6mz0Cbms1m6oTpdBrj8bgSYHDPdrsd3W43\nBoNBIvLsrYhIDtR6vc4UZGn37NzyE3QXygR6jjUD7GDflylDrwVygY1wWs+UBv+t6R4EFryT9V63\n262ker234fxhw6wT5vN5OuPouPIwZ9r9lClP3udb16s4UnjmHiQoTwm/R8SzyMAIkR2aMjoyIlMi\nOl4YOx08A6NneJl7WhE42vE72bGL2B3L4fE7SjM6g+MTUeUm+Ds8z4RSc6CAmf18O4i8z3Q6zZO2\njQ76dPsS1sRDd08jfm8nAqfDa+LowBvRzlnJaWA85cZg/HZczTGwk+s0Fs6YYWzek7/HCbFzimGB\n01Gr1dJRQpEBR7vFQsQ2FYGicT4+IioGAHTNRtfBQnkRWSPbzB0ybc6aI3YMD6iKo0s7Guv1rplh\nvV7tTO20gJEfDLs5Qhj+VquVTkNEJCkbuXl4eIivX7+mTADfk9Y0pxIn0YGGHSLGZaQ3YpeidBrR\nQZtl0k42aSee69QexsmXCdaz2Syenp6y8zWoy97eloR/dnYW8/k8z7iL2BrEwWCQZxiSUo3YcdJs\nOLz2ZdqjROWm02keWcKFjJJaxnBGRHz9+jU6nU6cnJxUUh5lMMyYXiKk8/ePj49JYqeRK4Yd3g/j\niIhMk3748CGfMZ/PKy1fHHARkDqjYCSe37t4gPfmHe/v7/NsRNYSR8sE74hdGgo7g/5kfDj6tHYw\nR4iiBdBH1tD6g2DRuhOOjx1NIzbsXTew5ZmkzJkXy7cdIqNgOJfQR0hxsj7YIWy1u9MvFosciwPh\n6XSa6DV/Z8eG/e9CAr8L7+jAkzYSs9ksgwZ09Hg8TpQPvqMDVZxd0ro8k33vwKC83tofvF1v19v1\ndr1db9fb9Xb9nderIFI0net2u5WUAHAw5FQfd+F8v73okuhWpvJMGHYEDWzp/K0vf6+Exg37ObI1\n98fRCZ+5SsH3NApH+uqltJzJ5L5HmT/mJ6kBR6VuWkeUDKfDc1Ov1xOVKvPz5OXLc8qYj4hdCtQo\nChGLIz+/s+/h6Jq5dKqC9eVepLbKiM5oHnNkNJGUi9HB9XpXAgvMy2dElLwPqQY4DSVqSGS2WCwy\n2gbp4AK+BmEzodMIYhl5mi/C35lfYySiXq9XiJWr1SpLzS3Pnmfvs4hdStAkfaJSGoKC2O3t7cXl\n5WVEbGW41+vlGji6hne1WCxiPB7HZDLJyhgaDzKuUgYh7ZeFIVzNZjPu7u5ynzBfpFhANEq0FfSK\ndyyPweF+Lpgg9cPBvCAu3PPdu3dRq21PDzg8PMzP4YQxR+12O6uGTC7u9/uZxouIlBPWx80qQbxB\neYzG8f/NZjOGw2EeJ8M9QQ+QPeR7NBrFYDDIPVA2OGZOeHf4ZP4MXQE/kfUH4aCKyqg632u32/Hx\n48eck19//TVub2/j8fExptNpHB0dJWGbPWsCOJf/TSf5Ml2KbrLe8xmJzWYzWq1WpZDIfCzzcthn\nk8kkDzW2HLN2EfHsMxAwPydiu99/+umnaLVa8fnz52i1WpUO9XDdSH+RrWAtQKE3m20bD+tT0CGI\n8MzNdDqt8EKhUzCnoMW8g9OMpPJB6vis2WwmcmS9zthNGuf9IiLT5mUxmmXYSBM2k7Y3/DS9hLnh\nvpZR9pFtb3m9iiPV7/efEaVZeKcIUKJAm6WR9uW0idsfRFTLLF9yiMoc8ktkcH9mx8RpL94ThWGO\ngR09vyvfsxPonK/5QEDI3li8e5m69P2YV48DJ2N/fz8VuVMzODMoD0iu5qGhaEziJu8MsdYVOHYG\nXjJezKVz6lwoRhO8cXR5l9LJgqjLmpSlrqS3mNeI3WZ7fHxMhcQ68V4mW9o5wXFeLneHantD2zjb\n8FnR8878HcrkpTlBLsw3tJwzJ/x0t/iDg4NMq6GIucy34DwvrxOOIDwUPiPlzVph2AgAlstdXyvP\nAyX3h4eHcX5+nob98vIyvn79mkqYd2KdPEfmhjHvpSNpIm8p915j9h96xEax5P255Hy1WmUBw4cP\nH/LZm80muYb1ej1OT08zvQkvg7PSHHyw3pzjt1qtMiVIwDkajZ5xNR2gMXe8M2kpAoYff/wxn3Nx\ncRF7e3vJ+cEoRUSutx35cn5Jozw9PcXp6WklBch8Ybw9hxhSjJu5o+w/HF86dLOncdapqkOmcGbK\ntBjrhMNYr+8O3iZdt7+/n0UB5f5GT9mQ8z4Onpm3xWIR0+k0gyh/pySMs2a8C845vEG/J7YR58z0\nFOYNh8N6n98zH6bJIHukrk3mHo/HmX6MiMq5tegTU0NstyJ2nf/dl86yiW607ceeAU7wPeSB/VLa\nNQ6C5vsELcynA3cHnoAG3NfFBHAgv3W9iiMFv8QbnFLE2WyWSrdsjbBcLp8hHSh3lByGkM8c1Vg5\nIvA2qiYBskAIlhcfgw6HyGgQThjfc57fqJcFGI4P72CUy/wunmXBZxw2xtwfJwpCp4UfIiLfh2vD\nWhjpqdfraRS9EZbLZSoK3gfjSgk2Df6IDNgwRlFwEsh7LxaLigNmlMToiT9DNphvVw/S98ib2orL\nhwTjzOF4MBc8zwrGjhuInpVNyZGDn4ACRR7gTiBn7iVEWwSTwK0I7EDaocI4sf7srYht/5p2u51j\n8D7EUUJe4YNFRFZM4jAZPSjXZr1ep5Nxf3+fDQRZXwyBW4CUvb84+mIymeSxK1w446ztZrOpkFVd\nQUQ06R5TyD5G0bJBNGpdwT0ho242mzg+Ps53wvnCKWy1WhX+FEYCUrEDSNaQ9i8YxHK9MfCeAxqY\nGsXEEJgTyQU/ptlsxvn5eczn8/j5558jIrInHLLkgojT09PKWYA2XlzD4TCdhaenpzg/P8/7Gm3o\n9/s5pwQrIKB2KkAwyh5cyAqGcTweV46IabVa0el0cj+iX5lvI9F2bB4fH2MymWThio2py+XLAiRQ\nVs5/5F6MD7lkndFtIGd2Rrkn2Rre1foLvtx6vY4vX77E1dVVdDqddDJBKuEq2Qnhwqk3Lwv5ALFG\njvx71n+1WiVaOR6PMyDAiWK+W61WxbHCnkRE6mz+xs4g+9D6xM43z8D58fE8OFLYcfsK2AuDD7xX\n6QCaU8n+/Nb1Ko4UG8PnDi2Xy1yYiK2nXSo3lGaZSkLh4927Uy+OgjdQxC464vtl2i9ih2SV8CJR\ny0spKf/OhpaF4/dWmCWa4FRdSTT2GHCqcBBLlAujDOpgpI1IkPG5d1Cn00nFUaYMy54pEPj4Hqka\n0CiUBukUjJbTRhhunBejNVyeozKdC9HRf/f09BSz2SydH883/2bdDdOXFWwPDw+pFIhkUTTInNea\nEn/elfsapmbjGz0ySdNKA0Iz8LmhaN6JMVlOnMqlWaMPgnZUaqSHMfV6vTg+Pq6k39mDdqK81/jJ\n+to5pfM5KW5XUD49PeV+9/52ZGiSqsfeaDSyms3IImO0EbOSZq5x7P09HEIqPm34WDMcKt4VJ+T4\n+DgPkWbv48RiOIjgGb8rOtFHjO/x8TH3khFnZMmHRrsTc4mMcpHq5Do7O4tff/01IiKur6+j2WzG\naDTKTvNl2icisk+c5Y13gjjO3zE31qOcWcczifR5X1fzGiVx81/k+x/+4R/iy5cvMZ1Oc/yz2Szl\niOIP3p1AG7mxjttsNnkfnDHvfewQOtepYhwk0CH2DM6ou4+zZgSi6FqjK65QLnWxEfzLy8u4vb2N\nTqcT//iP/xgRW6f37u4uKy+dwbGzQoViGXwR0BN8+8Jhsm6bTqeZ9nexBPLd7XYzVYjjHLE7g9J0\nGKfvmBPmyfckwMMOs/bOWrGmzkyVtAWvPTbQBQl8xnO/db2aI+XNErHjCtgLR4iZ1Iid124DTzXL\n0dFRVjFE7BQf9/NCEa2+hEhF7Jyoer3aOJNFJ3KCE8JlR8e5WyNc/Nscl4hI6NwX0eFLjpc/Y+N5\nDAgNyFmZ52V8KG82OPNDVdfj42Pc3Nzks1Fu9PIwT8ZIhisqUJ7T6TQjfkeXKGbQKTcJRHGVKQxH\nk0YXGDu9glBELuPHEep0Ool2shb+Wz+PeSZlYNTCDpbljMtjMjISEdkHx+lkvk/FFvLksmvfG2eC\n9QX1JQ3A+0VsjSkGmujOfAAcJDeAjNhVQlr5sYZHR0f5rowFBMyBCdWc5nvgyIzH46zi4TOca4xs\nabjLd/Kc4FywtsgL1Wmnp6eZUuTIEpwdI7KmGDC20omwjKxW26o3o54475PJpIIcgsItl9u2A+X+\nZ1zoqjIY4//tfDutz2XklPeBk+Z+Zg8PD4lQ2ZiQlqWS8PHxscLfwSEwWoMscuAs8miHEAe/5LlE\n7OQdvXVwcFBJF8PZIhC7urrKzwgU+dzOGvdG97uFCVVim8321AY3ypzP5xUeJPdEN79UBet0J++P\nPD09PcX19XU2Ae12u4niYuOGw2HyK603bONwtli3m5ub5Dk5A8G7EpxYr0ZEBpS1Wi3Tr7bTzWYz\nUWKvE20aCAqcdvceKoNPbKhtiCsTcfSwX85QsW/YHw5oWXPGYn0CKu6/s1yw1wxKOEgmqCqvV3Gk\n2IQWRoQDEmmpGMo2ByguTih3p2WXXfO3GDffs3RMXOrJ37CgRkEQRj7333sBrMxwEF4qZydyXi6X\nKYR8RoqKVCeRdsSuf5QNq6Nu7gP6YljVJHLIo2yQ0og4ZcIYfLSE58bIAj2AIrZw/sePH19M3WL0\ncEyIbCOqKSPmzQ4U788clGRc1qtsqVCm9vw9b1Dn7RuNRoWYW6ZoWJunp6d06rkvcDNRIo5KRCTS\nan4RRoj3wTA5LYlcUTTg93FLhdlsFrPZLBEp0CBImXAXmJtWqxW9Xi96vV4FAUMuFotFpX8N74G8\nMUfwgHDkut1uclYs+/v7+3k0zXg8TjmEQGtyr1NK7GnLCM4pv38J5YXgihEy34V7gGSyPhGRARtr\nRCqbiz0FksTcgCQul8sYDAa5fyKqPZVAyMq+aaXTFvG8hYoRXkfZyL+dqna7nWmsxWKRzsL9/X1c\nX19X+EBGCGazWfK5cEDQ36PR6FmvMuTs8vIyzs7OMt1p7hUBD7zEktOzXq8zZbi/v5/zxhwMBoNn\naMZoNEo6iA2p18IIr8ePM4hMIP+DwSB6vV7ynXD+uCfPQAcwPsYLFxBHLWKbCr29vY1arRYnJyfR\n6/XyXY6OjnJPuK0Ka02gzx7FyY2IdMj5e/c0sywQiFgPky6mNUAJWIAgO7CHqmCagBFXUp7ePzwP\nO+LsAt/D7uLEsLcJ4nge9obvkUWgeawDKZxI9jfvUrb7wcn0nL2Uzk5Z/OYnb9fb9Xa9XW/X2/V2\nvV1v1/96vQoiFbFDb1zmTxoOUjQRBtE1HiIRc8Suyyu/A+aPqDZfK/Oe5hQAIePV4qkTdZdduM0d\ncCrP0KMrGiKqh6Jyf5+CTarPUTZjBloEPnbFmzkEnk/fn6hof38/yd9EMUDOpF24iHhOT08rCOD9\n/X3c3t7msRdOYQFBN5vNuLi4yFw68wB35P7+Pm5ubrLihkoi1sh8FhAJ5KNM1RC1MWaTjb22/D+y\nx5zzfe65Xq8T+SSSK/lZJkA6SgE52N/fz7QiqBPRD5ENURzf46wx5tpcHJNejbKAtPG5D1IFxr67\nu4vb29uYTqdxe3ub8rDZbOLu7i5arVYlgl6v1/Hhw4fkh7iBIBGlU8tccEQYt2WRKrFut5vRIvdg\nfagUBJmM2O7tyWSSyJHTRUSJruRxeq8krnodifo3m012OvbeBSWjczhrSLoDBNQoJ6gDJH7uxXwj\nw6ytkTMIwRzfwkW1WSlnXOZt+iqRca8VSDx7ZjweJ3K0Xq+zkzbNfE0S5mw7UNyIXWX1ZDLJcdJC\nwBwxUqkUGjgbYESURqDMN6gnc2YE1BwZ5I55I4UDr4r5RieBZJti8O7du9xTf/vb3+Ly8jLRSLiC\nZA8Wi0UlXcj68LlTnEZWPHdwhU0hccYApPrg4KCS4kdekUPub2oAe4GWBdbtvJtToMwbmQlQS5/d\niv1hXaGC9Pv9Co2A9DjyBk8MvWAkz4R4tyKhjQiFCS4icxEUGR5XjjOf6GrTWdBp2HCnGXlfUCn2\nCLLnOSyvV3GkDNFZ8WP8MTpOvwC5NpvNhFkjtpArsCGGpCSN45zYyUKYTGg1L8ZKyqkmFh5j7Od4\nAUkZlL0nSKeZX2MHjs3B2MsqpJIcyLsDR5ojw7sgJFQMcT8UP0oPnsh8Pk/eDrC7HUNg6OFwWHFA\nIXhzoOfp6WnOM1D/4eFhwtAUFxjqhwhqZwkeh40Pn8HD4vsoPhtzeGKG39nQ/I1TQFQXomic+qU9\ngavbWEOgYZTYarU7nNaON8qV78Kp4H7O+TMe5MJwe8TufKgylcU7oPycMiH9sFwuK5VOEbuzJCm/\nNu+F1B6p2Ol0+ozX1Wq10jHFQMORcTDBuLjXbDaL//mf/4lPnz5VeB3mL5YOiA2LeYDMCzIC6Z65\n6Xa7eU9aQJiTR0Uba2UHHNIzhtXrw96m+7kvnGd+z/dIAeOAOD1JdaCDv5JD5fSd+Zhlaq50fn/8\n8cf45Zdf4pdffokffvghIiLXDb3h3kSs93g8juFwmPJrWkOZIqWCDQ7i9fV1DIfDuLq6yvf5/Plz\nrFarXIf7+/uURThDh4eHcXl5WTla6OnpKf/daDRiMBhU9v5oNIp6fXtOG5w1xsH7LJfL6Ha7cXZ2\nl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j4jBfpSej2/+81P/g8v8pauFrLQIAAm7bnizYtvZ8ZRe8QuakOoysi7JJv6+Y7WbUB4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\no5hg26exMZZ+TpELSPFu2uPx8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-       "text": [
-        "<matplotlib.figure.Figure at 0x112797f50>"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# helper show filter outputs\n",
-      "def show_filters(net):\n",
-      "    net.forward()\n",
-      "    plt.figure()\n",
-      "    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
-      "    for i in range(3):\n",
-      "        plt.subplot(1,4,i+2)\n",
-      "        plt.title(\"filter #{} output\".format(i))\n",
-      "        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
-      "        plt.tight_layout()\n",
-      "        plt.axis('off')\n",
-      "\n",
-      "# filter the image with initial \n",
-      "show_filters(net)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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4URgkf3g6BJoVFgCwUMY2oJwMJk7RC2ERFMyLr/xYR7A+Ed9++2392I/9WFJo\nABGTxCu1O52OKpVK2s6Z1UZ8P5lMVK/XNR6P1ev1CuCD9h4cHKRnRrj3/v6+nn766TQGTq3TNz6x\n3QBHQwwy5jsKcaFyffURE86NuqN1trEm1+lRASm2uNvvumQVY/Jxo98IMhyo+GcOImKUGKN6b5tT\nu9L5FwF6VMw9ABOuV5JSpIkOHh8f69atW+eMbhlw8g3c+O3Ax9sICPD2NxqNwl4U7nRI/zEfvG/L\nAIAHKPRFBEURKHoBL4yi93e0d6tW4bi9uqoSmWGeDefjDGZkFaRioSo/Dgax7+48qesjHYy4j6Bt\n/oONQDexydPpNNXSRUDlLI37FnZFvn37tu7cuVNgDZwF85qmyWSSbC2+hPNgfTqdTgqsmSv1el3d\nble3b9/Ww4cP0xJpQMRisUjpbhdf0eR9DuD2eeNz3/2cAyfPKMzn81SM66wR/ppnp46x3W4XFq64\nT8SGx/a7XLm0DsIDOzXkfzttRCdRiOObvDAIsTgny7ICzcY93Vj5oPG3G2RP/9Tr9UJxKYjfn4ff\nLL2sVqv69re/rdu3b+vll19ONTAMphsBT62gVBhjVu7AZjBZAQNHR0caj8e6d+9eyr060oZmRbiH\n52IdEKKYPE80PB7d+KQfj8eF/Uz8xVqknqD+nA2CeiUv7crtIPMqgJMyWQXGL5Pe4XykzJl6hBnv\nW+YAy66HlLUHZ4tRpWao2+1qPB4n44ue0vann35a+/v72traKgCmMom0fAw+vO/cAWCsvQg49h9t\nj8yaj4kDLP6OgRESGQ9vqwcnDq4cTHsA5e2MIOiqAZQYbTswkc4v+faatchaOePktRNeJ+Q6je45\nW4E4YPe+5b5e4O/to/6j0WikGinpzC7B9jrIwOa88MILun//vg4ODs6x/FzfAwVYBHyXB7L4ljxf\n7inVbDa1u7ur559/XoeHh2l5sLOZ9F2z2Ux+h89cnOHwANzrSdwX4GM4lwDR0zP0ny9p9g00fczo\nex8b/MOVBCceOV3EnjiSc5Tn1J2kROujBCA+SYU8FwrCeTi+sloFH7iYX3ZE7jQ6VB/sgkdSRAcY\nyXa7ndIZ3/72t/VDP/RDkooRWKQ8QbTOklDVTVThWzqPRiPN53MdHR3p9ddfT0oDWHNWhWv6+MS/\nHZzEfCbfuUGu1+upDZ5j9T5lzGjbYDAoGHtH+uRcPcp3pukqpHXKpAxQSE92Pm6I4rER2Di1G89d\n1aYyBiMpQdUAAAAgAElEQVRKHAvGuFKpaDQapT6fz+cJqKCzTmdftE21t8FX7EjLZdHoBzaA4IDi\n9gh+ot5GEH1Rf3hQ4Ncs+wyJKdnoqNwplPUvziPeOz7LuqQsRcX/rt8OJImSfb5zDvOYfndWzuc8\nOuWpg9gfDkZivwFwPGrnOrPZLNUlwmjgNwicSMXQvsFgoCzL9OKLL6b3taGzrj/cx5lt6kHm87n2\n9/fV7XZVrVbT3iWz2Uy9Xk97e3t68cUXdffuXXW73QKTT78yt2GkAQ8O+qL/igDK/Zr7VUBPzExE\nNorrkIp33XYw6j7c5wHB9CpZ+4v/yiI7JKZAYuEZD45BJO/u1LMXQ8WqbCJul4gIIyXujhgDjBFm\nmS+5716vp8FgUIg8fQK1221Np1O98847+vDDD7W7u5vaTsWztHQKTFhf3VKtni1T29nZSVvzHx8f\nK89zPXr0SKenp/rGN76RlNHBCG1xqg0nAOJ1YIiB8NwvCsh5fAfz1W63NRgMCikemB6vIXHnw1h5\nvtdRdqTLYc2uCnNykdO/bHrHDXd0WDGSLxO/x0XO5KJniMbeAwKPMom2+v1+Ai/V6tk+P+TLy+4f\nGSUciUdfnqpxB0kRNE4h2hFsA33HRl2rntX7w+/v93XmA/12QB7lMixIZFw8TepA6KqwKBH8+f+u\nn3yHfYm1fW4nqtVqAgkONKXivksXAcxoE/gd9Y3jvJ5jOBwWbJ37iUqlkuqouOZ8Ptf169eTrUfX\nCL4Wi0XaJsHv529Pr1arqT5jOp2mgPrll19Ws9nU3bt3Cyt1XAf5jX8jpRkDFPwshcduR5xJIUMB\n++gMPudFEBHBhjMiMDDebr837QJQrZK1bsLmNGyM5CJ1KC2LVBeLRVpKykP7Rl7QdSBSp8F8NYsb\nD1dkAIpUpOgioHJkyvlON2NonXImpcNkbLVaGgwGeuONN/TZz362sKKHIk/qSxaLRYpUccSOoKG4\nK5WK7t+/r4cPH+rtt99OBhXlYZK6YsGcOE3vRcAcByUZx8z3KaB+gD5sNpsaDAYFI+4Rtiu2KzXg\nj/YRdaAL/HaEfhXE9Tu26bLARFrSomUOq0w8aoq6LRXBwJPa5OeQOiMf7oC+VqsVGJTZbKatrS0d\nHx+r0+no9PRUH374oW7cuJEcE2CWZ+FllDyDt6fMwWdZpt3dXR0cHBScSmy/Py+6VdZ/Hr0zTyI4\ncFra+xsQ7f3vQUCZeFtjPU0ZEI39sA6JtvgiHYr97qwvgQnBCk5SWq6SJEjyKD8yBj4WHvn7/z5u\nnOPpNlZl+UoZT7VUKmepaGwiQPzk5ESj0Uif//zn9eabb6bnunfvXmJ5YPPzfPmqFBy8B7oA7Ha7\nrS9+8YvK81zD4TAxJqukVqulnWdJvWAn6QNP53hKB7DowTttJlB0hhq/6+Prfe/pe0/NegDt/hHm\nijm58hlXfvMJSYzkfEBQSJ8QGKL5fJ5e0+479oEevXN9MxxJBQcdGRE+j4Y8IvM4MSJFyXf8Pjk5\nUafTObcklxc4feMb30jfZ1mmnZ0d9fv9VGtB5XSv11O32y0t4kUxxuOxPvzwQ925c6ewj4qkQkTo\ntB3KNBgMEmJ3JgLwEAvSJKV0kjuvGDWBzmezWWFVldO49JWPlwMo/5GWS5yvyjJil4sAyqrPOM/1\nCIYrpg1W3bNM3LhfxOqUtQ+DHR2In9/tdhMobbfbaVdh6PTnnnsuFW878OZ6MCtPYiCoc9nd3dWD\nBw9KgUlZH1FM6cxLdKb8dqAgFanwstSaf+cAIwIWF783ztCvWcYCXDXxKNiBoAc9/p33PfOdBQpc\nz5lSaWmnfTxiH7s9iAFmBCnxtzPnvm0C1yWAOjk5KWzQSZp+b28v2avT09NUMEvw6YCYcwFlgB3p\njEF/6aWXCqlRB3VefO7MPas+T09PE+jzVZA8j+vofD5Xt9st2EzGIII0fGmWZYXNHdFZZ7kARowL\n13f77cdh3y7S77W/ldjpprJjyj5zcACLQirFQQcTBcV3JOiV1g4wUEhvG2jPAYBPNraAZw8Vvuc8\ntnBn4rVaLY1Go5RzbLVaevDggb7+9a+nDdB2d3fV6/XUbreTIsxmM127dk1bW1tqt9vq9XqSzsDI\n1tZWymV+85vf1P3799Vut5OyQI26ktN/GODBYJB2t6W9zkr4JkYwI0xGJhrHepU2+xSwp8l4PE5M\nCjseSsV9ZRws+q690pItisDEt4e+KrJKj1exKjGl4MeUGWHpyamiaLQv2+YyQx/vhaFZLBapQHZv\nby/VGvV6veSEfI4DumDDKGr1wm8MPgWK3W5XzWZTH374YWqT0+QxWsaI+1b+0Un587pNkc7vRRJp\naY4p6zsvGr9IcHS0fVVNyrqZE6l8pViZDvtxCEtZ3f4QWbte+1g4MPE+8L7y+7ueRlDiwAldhZ3w\nqJ40PIw2tvH09DTt8orNabVa6UWB7EUlKYEF7CXpTna13t7eTun8mzdv6vOf/7yOjo4KBcHurxys\nxr+r1WoqpPUVb85wRh9G4O7jQ0Eyxx4fH6vRaGh7e7uQPpKWQI7+xwZ4MXwZ0wJj5u27qE5wrW8l\nXmUsLzK2lUol0cV0pufb3LBG+imiT0eUfh6f8T8d65/TFpiAR48epTf7gmop2MMAAVLq9br6/X5a\nCsY1j46OUq7ywYMHevDgQWIQyNEeHBykXRO3t7fT81+/fl2SdOfOHd2/f1/dbje10YEJEz72P0rG\nToP0qae3EGeh/D0PvnqJCnOcTa/X08HBgR49epQUlHvQTq7N2Hhxb3TMXggLEIzvi1i3lIGHyGJE\nI48j9vyxg2K/9mVYkHj8ZQEN4gXftMVXnAAQK5WKHj16lPZtIFgYDoeFVCX39iDCgTBAHIDrugtw\nR38jHc08jlE8+ua1BJ4CKBsjaclq+BzlnAhCaKuzQ2UMTexzZ6ViWkhaOqCLVjV8EhIZ7chWxb5z\nACcp7c+E7cLBe996MMkYx927nen2NFxkzOhDHyv0jF3EnSUhTeLMPLUg6OpoNNLOzo46nU56gSw6\nOZvNUl0TOsdqUFI8BwcHGo/Hab+Rev3sbchHR0eF9jq45n/3QV6r4/0FUPEl0PSXrw7ylBrP6nOT\n3ycnJxoMBtrZ2UnAif5kPBwwck3aHcGVA8wry5wgbpjjJEbhPXJgoOr1unZ3d1Wr1dIyWe9wp/Nw\nbjhfN3w4uGh8+Jx2uLLQPo/W7969q8FgkJwpqSYcNxR2o9FIK3qgC3n3Qa/XS89AAZazP7Az77//\nfkL2/X4/PQ/LbA8ODgrpK/rOWROcuhtxd4ZQmBRsuRIxoWNE7wW0gJc4eVDymHsFxERlZgLE8XGB\nsmd8r7qsmowOWkiHRAcaj7/oeh+1HWWO06Mfj+59LwV0r1ar6fr166kYG0cN5Y3RIjXDZmdOfTOv\n/EVwXgzutDPHe+2VR8HofZkRjMEIDqqsb5yBuagfcTa+Gs1ZFq93KTPaZZ/Hvl+nRHbJ+5r/Oc7t\nt5/PuOLcPOXs58OqYBcYW16p4e2QVKh3iixJDFCr1eUW8JVKJTlfzqemDmACi02qGyDSbDZ1fHys\nnZ2dpMsAFrfZbGM/n8+1s7OTNjID9LRarXQe/YD+kGr3vsc2AERgaTxzEAGHVEwvwmDAeESw7+eR\njoUB9Wu5T3Cd8Pussk+XWVl5JWpOLkrpeNrB0THROIM1Ho8L18IwudP1LXMXi0Vh+2R+s7wJRfbv\nYtQonRklgAHtYldA0jYUUR0eHqpWq6U3THJtEDaK4G/JjHUfTPLBYJCQO9ElEUTsz7KoGwNAH52e\nnqrX66VJwcukKpVKWjkU2Rb6wHO3cZyYaDBGe3t7CaBgtGNeP0YKMfqJUZIX+l5FuYgddFDi4kWZ\nZZFz2TVXOTc/J0bA3hb+xlnHdnlRrBt+xvDw8DBFkNKy4FBSMsLs3oxjYjwx1G64HRDx0kFJhcLa\nOObxfwcyZRE+fRuZJSQypav6lmNX1YysoubLzr+qgm0pAx9Ska3wc/gO0Opg1HXJbbczJw5Q2fnb\nARu2OjpX7ulMMedi+6j/o8aCN8pLyxqn8Xic2Hr8xmg0Urvd1nA4TLuosq/U8fFx0mX07+HDh9rb\n20uF4tPpVDdu3NBkMkkpTKkYEABMYD1gL7AJ7veYi7G2kj7gu7Ixc9uLuC6T3nLgF1nQMvvlvjKC\n1jgPy+RK7hDrx0jFqI3vpOUab5TT9/Hw853aoqCTDkP5nSHxIs1IrcXrMYCgUBSY+1AIyq6trDBC\nydnvBDDjRaJOczabzYTqfe07hpsUEbvIes6btnt9DUoMWzOfz/XUU0+lGhcYJU/vuDEo22/AgUlZ\nZC6dpYF2dnbSpHIWqwykeiQR9aLsvldBLtJr5CL9jxP4spT+RY7T7xsdSAQpzlxR5MffjMd8Pk81\nBLCDRJvHx8fJUPMZG0Wx/NLrBciBE7F2u920AohrA0x4TvonRuw8B6ADXY9sifetBygXjd2T+tf3\nybjseCDObn6ce3+vJQJS16OyzxH/HsaEwMf3hHL76nrI+fRrDMY8gCxzkFyHe5AC5Lvt7W09evSo\nsI0COt7tdpPtARhMJpP0Zm4CLJ4DRoLXamAzAeXxeHSaa8d5Tjt4dtrNrq2NRkOHh4eFNCV9hs57\nutT3j/Hr099ufwEWznhGm+TnRabQWZkyPWLMrhw4cbS2yhCUfe6Dx0N3Op0ETBx4+HVitL+KGfBc\nXYww4yT0CB/jCZvB24aZeKenp5pMJglJ93q9lN6BOoQBAnF7kSnUHceDZllr74WEgCFHql5j48vE\nPNed57meeeaZ1EcODKiDAD37mn3GwiPUaLwAN0TDtJH2eZ5eOq/ADtQ8Gohg9aoAlCcBE5eLnOFH\nqTPw9GdkkWI/rWJMytpBv3e73VQsyPGMKztsbm1taTqdam9vTw8ePNDW1lZyJjgj0oaMv+95w28v\nbGZextVKOKYItjjGI7dovMtSxfTLRWP3JNCJrn8UiYCwTJ7E2HwS4vMxMiPuhJ4UcErLvUwcmOBM\no47CcuCkfZmx1+hEhob7wS47WJCUmGI+w45mWZbS8u6U8zxP9YOj0Uj9fj8Fb7B9HvTyDNSxtNvt\nxLAA9LHx+C/u7WlGZ9NY9gzYIPWOfXYg7rZdUtoXCB11QOW/y/rUt7BnjGKhuAf3PLsDTtcjZ7Iu\nmk9r3SGWvy+KHpFIr/rDoTSkTzyq92ugiO6kWbZFDpJzy6Io/xsUy3UlJeWrVCoFxQe1eoFpp9PR\nwcFBUhKuQ3SaZZlarVZSWgAF92S7eYw3IIt2xpwubfZUjkcs9XpdN2/eVKWyLKD1zY+IgJlI9B+5\nT+7rrJKn43hOJrAj6viaAq7hNKRTvlCUPoGvGnsiXc75X/azy4g7Ywd68bplc6Psnm602JHY91Hw\nczudjsbjcVrl8NRTT2kymaTjOA+WLIIHxg8g6rl2dNyXRbrBdMfouhVZIOYiALmsRiGCPBfXxY8C\nFvy6Zd+tEmc2r4I4o7cqOFh1jLS0Sxzrf2MvYhDoq0wcYPi13A54m3CcXvsmLW13v99P774hsHTW\njWCSth4fH2t3dzcxKg5GHJg6U0ib40v08DOkOZ3BJ/h1W0cKp9vtajKZ6ObNm2lxgfs7MgnOtrhP\ni0xiBJ2eiikLCH1MnIkpAyc+Dv75YrHcr+vKMScurryeM3dlipM6Oi83RnyO4vi5rthScdc+BnQV\nq4OCMtherYxzxjgTITIQAAVSM7Sh3++nzdV4LpSRiNKZCNDybDZLG8yRs5RUoBQpFAW00S9Ohzpw\n6Pf7aWmyrzyAxYGy3traKuSMQfoRPfOdgzNH2/QvYxiBifc/Ch4NkhuIyBZcBYlRedn3Uvlqh3jM\nk8Rzyu4sypzEk67rjpSxAhz4d6RtML5ZliWdGwwGBRaPa0GrMzdOT0/TCgNnyDD06KB/54aUom2u\nT/uYG9S/wDgins7x/lgFTGJfP6nffNwvut5F3zmFv27mRDrPUMYoOR4XdTo6ObcV2DBnyNARt+Ow\nLewT4sWZzoRxnAc12FRJqViVZb3UMXkAiW/wPakcsLhjH41GyQ9EIE/bKY7lM3SQ15BQaOsbEqLz\n6DI+ZzKZ6M6dO2m7CPoWf+DbLPCiWOaAsx0+l7zvHdTgT+IGb5H5cAadcY/gB9DHWFw55kQqN45P\nmoD+MJFG9EIg74gIMDiOgZ/NZim/Tb2FVFQMJhMrZvJ8ufeGdAZ62u12KrCFVfD8M9flPQjSkmnx\niQWLsb29nZbfwWYAAlA8FJxzcBL0I5PXKVQMHudyzvb2dgIqcXI7kyIt30nkII22OzCK6R4mBYDJ\no5I4/vSxT4BoAHnusvqXqyBPmnzIk0BKvM5F142OzKPZeL/LCPrebrdTnRDX9b1tpGVQ4Pn3WKcC\nYOUaRFC+2stX8qCnOHEAPufgvAHcvuSRa/imWFFgAr2fvEgWiYAt9vEqoyxdbNcu+i469XXKKtC8\nirGMnzlQZpylYv0QOuLP6oGkM8ySUv0eQvDlRaWIA2VvB2kZbCvMntsdD5YJPvkcxh37SOEsjp5V\nPezc7bpCyh8gDQDHXvL3cDhMby5mKXS1WtV7772Xns/7jDlLX+ELR6NR2qoCJt99Zhwjntn3BJN0\njvWgn3xbB+ZkZGdYVu3nrZK1bsJWZpQR7+zIoETk5wgwrhpxY4rik3/DuVLkhLFzh+vsBfcmP8kx\nAJ5ut5v+n06nqeDJ834MNNvv53me3lBcqVTS2zEpbEVhQdQs6WLlhLTcCt7TIEwWFNEn3WJxtmaf\n5W/VajWxOBH88Vyj0Sg9U6fTSREAtTNuSJl4DkgiW+KTxkEef3uBWpZlhaVnPrb04WWBwDrko4KU\ni/6O/0dAs4p5ig7uojZFJo/8uBcxSstISSpuWEa0yVyLbAS/iRTRHWfYMHCkDQGiq5yPAxOeHdbu\n4OAgLRctEw9mmN+eNvQ2x7+9n1dJBDNl//t13J7F/ZvWLatYkzKWMDo7ByU+Z3H+XuDKddArB46S\nErssFdM53ja+A4QAJObzs51YDw4OUt+SivegFXDBWJAmRyepccQmHx0daXt7O92j0+no3r17un37\ntqTlTso+T/EFgHgCxHgcz+8rO9l+wrePR2ByAFNem8jzMJ+kJfjzNqzyzz5OPqbMN77jGq7vHvyX\nzWOXK8GcXIZFKUPTHq04nYdT90nhk4Gljy5eKOoTMHa8t9UroXd3d7W/v5/W4g+HQw2HwwQMKpVK\nYkJms1kqlIKNYRBxuIAJIrvF4uz11ZPJpJAf5b0KPmkdXBEx+DMTvbIMjt1m2TAr9g1g7uTkJD2T\nMyBMJp4hKqOnG8qYgXg8EzNuj8x5XCfWr1wF6tslPjPycYFU2XkOJi9ygKuu59fwaAn9yrIs0d4U\nCqKPUjHF6uABnfS0ZbPZTICaSNJrqTqdzjnGArDCNXzpOfl2GEvmK20mMqVoHr33fsRIeqrzo4xN\n1OmyICuOC32MREaG/igbq3VJBCDIRcA5SgwkY82Gz3G3Gc4Ee32a109wnrQEK86IwQB4vQV2BhBN\n6poiWAJGQDOpCIJB7xsYPIpUt7e3dXR0pGvXrhXS+ZJS0Ij+NhqNtGzZ00gEtwSasB71el1f+cpX\nCr7OXwXgfVCtVtOKUPqY4NHbDyBxkOMAxD9jTDwQ8DQSx8Sx8jovr31cJWuvObms+ASWzqNjqfge\nAf7ne1dEvvPqZ2m5ix4GxH/8etXq8i2a7hwZTF8JxL29AHc0GiUFJU/INsgc78W6FGxVq1U9fPgw\nnVOr1Qq7DfqKCPoKpZ9MJundPc7gnJyc6KWXXtKNGze0WCxXUrix98nMe3RwLkx2B4kOXGKajGM8\nokIwJvQV+9JgaLyv+Zxr+9heFYnMHvLnASurQMeqlIOfdxG48b99nKTiC+58fjgFj0Nx1gFwzByD\nIcSQslvmfD5PdSc+rr5TbtRFj6K5J22nLVDisKGRoUA8beTR/GWkrP+ik/Bcvh8bGa0I+K4a6C4D\nSB4suK2M33GuB3vSsnCeMfaaDw8U6UPArL9Kg3v5+EXWfDqdpqCPdna73WSjfFsIGAq/frPZTMzJ\n0dGRut1ugUXMskxbW1uJbcnzs4JRtq5Hr7HnnItuVqvVVEDO/XkdCiuBTk9PC6uE6J+oY8wNB1je\n3+6vmMdS0UZ7n8eUEcEqNoF7SCqAHnQ7gkjaGEFSlLWmdZ4k0ZnxmS9L5TOUyhEgiNdzaxxPkarn\np924cLwbLYwyaJQIbTabpSVdZR3uu6JS40I6qVarpRQOk4Tc4mQySe9zkM62t+cNlr46idoWVzzS\nQJXK2S6I/X4/0aCAB87p9/uFCNwnNhMLNobPcABc0wuLnamJURH3ROgHJhy/OY7zPFqJxtrH/ipI\nZCSepO8XgZVVTvMiQOMGK9K1q9icVeLREzVKDgYd9PiSxvl8nhg+38tHUgF0Y8SZp1mWFXLavtTU\nGQV36g6YYEwAUkTDHkFf5vk/Lrvl4NCDk7LUtB/jjCO/rwooQdxRrZprZdF2DBhdB9FTZ8M4x1N4\neZ6nFAVsHPrAvTy9g+33PVHYdZv3NO3t7Wk8Hms2myXWDp1DCLZYgXNycpKCRWqqnMVGz2u1WtrX\nylND/loHBxBcA+Z6OBymYI1AFtDCPiv4ntgXzvhwXR877ycPNPhfUiEdy+cOQtyX0E8OLl2HncXx\nY580v64cc+LG2HONvrTQaSgGzjvfd9tDKdyQSUpvNN7a2kosCPd3IEM7UEoUj/zlaDTSZDLRw4cP\nU1vYbEdabjmM4Cyazaa2t7dTzcbOzk6qQwGhc42jo6N0HRArUaFTZIAVBx9Q8bQ7bnglLTezI/2E\n0A++PDv++CSICk/7vT+YbL6XBeMYqWyPhnmmVVHRVTPkZRNv1YS8iFlZlapxwy0VV6gxHpHGXgWW\nIgDieg5AB4NBum+lsqzLiECM/+fzedoskPomSYWiPwclTnsTSeJUInihnZ7W4xlhYnguj8ovAxbj\n80Qm6knfl6Vo4jh6P3LN2P84m48DkL5X4kyGVEyroytebO/Pgi4CXklre2TPGNI3/uwU4XPt8Xhc\nGNfI3viSWuwX86VWq6UFBzAdXA/QzDVhPL773e/qww8/TGDjpZdeSky7gwdPYfhnMB29Xk+DwSC9\nc8fZM3SAII2XosKMw6awGg4d8XmIX4S98RoQZ+s8PeO2nLZEW+DpVOw540nw7eO4WCw0Ho+TL2Y8\n8SOM50VyJcCJd4Qr+3g8LtQsRNoe6l9aTgBf5hqjO67Npmg46Nu3b6fB9UIg2sObd8fjsdrtdmFn\nTElpTTsG1NMZPpnzPFe/30+vrH7mmWeS4X/77bfPpVycBfJ184AIPtva2kpLKh2hSkuAxhIz6gAA\nB61WS71eL1GcPgYxveVOg+MYu0jX8n1kNHgmLzhD6HtnsRjDVqulra2tc3Qw4814XgVZ5VAu62i8\nT70g8iLWJEayGC4MEQ47nkv/lZ3rKbSYI+Y6vuwR4+VV/b4HD0YaHZpMJmq324Xoy1NH/rlfP+qc\nz3mYRNrBSjwPUlYBvstI7PdV59Ie/i7r5+iEow0k8Lgqeh2BWaTpoftd7zgPR+3Le+N4eGTO9+5g\n2SyzVqvpvffeS9fzY/mbtDN2L8/zlKpZLJZbsnc6neQHsEfs6IrOHB8f6+7du2m7hfv37+uP//iP\n9dprr6ler6etF2ifB7j1ej0x5dvb26mehQ0LY+EpOsGqzqOjI7Xb7UI9yuHhYSpQBwhhDwGFnoql\nb5k3/rcHmnGOeHG6v7xzFUiNjDhz0Oc03w2HwwQIV8nawcmqaJJNa3BMdAAdSt7R99mIOXBfT01n\nYhhx0MPhUL1eLympOwOuNR6Pk5ITAbLja7VaTecDlDzSY2ICiNrttp555pn0Tp7r16/r2rVrmkwm\n+s53vpNQLytnWKNOesXz5+QiDw8PUx945MizMglQVq4Jc0TKJ8/zlPd0gOMvIkRZMUDObnku08Gg\nVCzQ4v84/u6AmQyMG5PQoxPuzTLuq2TEPyoQcXEn7RGNf+8GpUwwNN4nGPoYqbkzdEeBzsQVI+ga\nbCJtxOiwm+y7776rF198UdevX9f+/n7STSJBzvWi2Pl8rna7XajLQhecKfE5DdChpspTIjgqvwfP\n5IbYDbvLZYCLsyM+Vt6fMU3jUWaZHjgI59UA6xR3XogDAkCD70Lq5zmblWVZYkH4H/DiG08ylgBc\nD1xY+cJxpLG9MJ9xrVQqaZ8plu26ffO+JcVMbcWNGzf01ltv6fbt23rjjTf03HPP6eHDh+r3+3r4\n8KF6vV4BRJ2cnCQQ5Kzlzs6OHj16lEAMthpdRtc5j+fZ3d1N/cfxb775pnZ3dzUYDNTtdlMQQNvp\nS57d7UkEc4xfzD4wPtw7BkfYBNh8AmBshjOoi8UilSLgQ1khe5GdvBLgRDpPZUMVoZDecZ5KIOXi\nUT7OHHCQZVlSQOiuo6Oj9NnBwUFqA/uXgFIlJVTtkSPpHKebmSgOFKTlAKE8eZ7r1q1bunv3rp5+\n+mm9++67eu655/T2228n2hBFopaFNtBPtVpNn/nMZ3R6eqqDg4OU+sG4O4iAYaIynPbwHhNyo3me\nazAYaD4/2z3R33SJcXVQUubcpCUDwuf0mdfxOCDxFRmkthh3vvNxB7iyz8H+/r76/X4hPbdOicVf\nUXj+mMZySjTWPMVjPOLw6BqDzrWjM3FnKZ1n9kif8T15dahmZ1PcKANWtra2dHx8rKefflrb29sa\nDodpGSdOixefsVkVhpPrEyT0er1CatB1340gTs2fqdFopP0hKGCMYxMNLp99FHAZAUYZ4PAl2D5n\nvJ8d4DN+6MBwOLwwwvwkxB0tvyOY4ofniCCS60hnzs8jZ9e76Fh5fk/N+EaRnEtRtYMeBzr4Ed+b\nBL0m6CMAbLVa2tvbS6vLHjx4oPv376clvF/60pf0W7/1W3rxxRcLgB8g7Xat2Wzq61//evIXtVpN\nO7tsnBgAACAASURBVDs7unbtWkGXnfEg6OY53WYSrPtKTEmp/ZGlcxbSg3fsjAeWUnFDRw9IPbD0\nNB1tYMsBSeeuSWDMpnHb29sp6F0lawEnh4eH6Y29TmlJZw9OjhADy+C4USbXhXKhfOTDYDF8tUee\n50nx/B6wGjgG1q+DtGkbqN5Xk3hBZ7PZTO1i0pAWIl+5u7uryWSip556Srdu3VKe5/rGN76hV199\nNTEhGEicsG9RvL29reeee05PPfVU2iL84cOHOjg40DvvvJNYGi9clJQAGn06nU7V6XS0s7OT6l7o\nP69TcSqdAkP6pSzKZMJzz0qlkgxGs9lMfQQAZCKgtFyHQjUHOCcnJ2l1Eud6cTFb+q9bYgSN4Mil\n8++J8sjaDQmfx4nu7BjAZLFYLjnEgMT8sUf50SHyN4YLA+ibSmFAY5tY4k4kR8rxxRdf1B//8R/r\n+eefT5sOOqMYgRjOodfr6eTkRKPRSLdu3Upvo+W5HESg4x7FM0/j/j7+3FzPo2g+9/SE9xvnlElZ\nH0tKqV7GyW2e972L2x2PRNclOzs7CWhGUIJN9b6LKSv6nufyMWDuR+BAWkY606+jo6MEWEl90AYi\nfVgWZ3F9/B1E4VjRl8VikcCyAxnY2Z2dnWSb3n777bQNA/bLyxB4Zkn65je/qdPTU/3ET/yE/t7f\n+3u6detW6qNnn322MPeY217/B0hpNBr60z/907QCk5dy0q+k1bgOwV2/30/z2Df29BU39LkXtLo+\ne/qH7/GdtN0XQdAmlj870Gb7C16HskrWAk4Gg4FOTk4K0bm0ZExgC6SlU3KEiZMEnKCY5Jw5l85j\nkOmkavVsKTDvVvCqbpBirPp3qtKdPk7CC0zpcAykU8kffvihXnjhBd27d09PPfWUsizTF7/4xULa\nhAnJczJxms2mrl27phdffFH37t3TM888oxs3bqT2fPDBBwkY0RaPEpm49OnNmzd148aNtINhr9cr\nMBNeGAZoIZ3FVsxx1ZKnxqjNcYbLAYhTmp6qAhy6k6xWq0kvYIFY6QRrRL+tUzzyLUvXSOdXmrhE\nx8k1/douXksiKekyRgrn65FPZAn8mk6FM6e4j7fHDSnXbrfbevrpp3Xnzh3t7e2pWj1b+s7qCF+m\n7sCHe3o+/s0331Sj0UhzBeDuTtpXAEUn79vnOxPhgM/ZO4yug3f6M/a5j0MZAI2sGHOOKNiP9fnp\nTjWCkcsyOd8rabVaqtfrOj4+PrdZI4GMs9e+ygqnzzi7HvEdNsOvQf/AbDiwdSAdGXW+YwxwwlKx\n9kJa6rUDAvTy6OhI/X5fr7zyij744AM999xzOjk50Y0bN3Tnzh299tprGo1GhTe5+7Pmea7Dw0O9\n++67unnzpn7zN39TjUZDOzs7SQ+w8bQNn0NJAv6KawLO8Ce+pBow4i+dBbQA5lh15N87MHFGPLKo\n+AY+A0iyRQD3935mXBgTFkPwve+iHmUt4GQ4HCZFu379emHpFvtoOI3ra8Sl5YubcIiSEhrDsXGc\nKz3Rvnc+CuBK71G9O1sMPwrBdWLxEeyJ5y4p5vzggw/U7/d1eHiowWCQHOvt27fTLrXxWaWzAe71\nenr55Zc1nU71kz/5k6pUKnrnnXdS5TdKAENBPzJhvU30zXA41OnpaaK9URb2MolAkLTLeDxOb+ck\n5+n7VDDJPDVE7Q59DPihXUQibDaH4/IIObIJTAqe6SpJWR2B051OkyORSXSJDiqCFa4VDUMZMFnV\nVj/ewQ3O2hkEDBXOd3t7W2+88Yb29vaSsWcDtP39/TRHfG5xLwBvv9/XvXv31Gg09Nxzz+n+/ftp\njwneiuz9ik4Q1TGXmQ8+F2OqzOt6Yt+UgZgI5Lw/0EdPffnqOMCX0/dRygCPB2XrFBwPAaTXMuFU\nHRB4wEefABKwCW5b6ZcsyxKIc9tdqVTSHh9E5d6HjD22wPXfN5ZE//J8udmls/bOumVZpsPDQ3U6\nHe3u7qZnPzk50ec+97kUMHmNIcEVYOrevXtpB1l05Pnnn9e3v/1tzefztPs2/s7BBH3sATzXp68J\nADkX2x/HzmsVve98paf7Bc6hPV73A6gA/JOF4NrOurhvl4rbgpQFWy5rASdEuFmWqdfrJVQuLfNm\nvkTYKWyUeDKZJOcLymTSewEP/7M9MMp9eHhYeHcLRs4jQo9ypOWyYlJADDaoVVoCKUfznh7Jskxv\nvPGG+v2+jo+PU3+88847kpTe7grFTd1JvX72cqi9vb20E+3W1pb+6I/+SC+++GIaZIpcSZ94eqTZ\nbOro6KhQIU50QF9KS+p8PB4XwAypJgwudTA4Mq8bQNygwWp5HRB7A/AzHo/V6/VS9EKbvAiLZ/XU\n0kepE/ikJLbJUxBScQlvrFPx1BDXKQM6fh83Ur40153mqjQGOhvb68CbucP/njqp1c5eJb+zs5Mi\nOgD/cDgs1ELhgHz+MEcBQq1WS7u7u3r48GEynnEHWq878P7yfo4MqEfmHj171B6Bg+t0HBPEU20I\nKVDf6BHH6mwWoCfeAwdVdu1PWtxGEpljRxz0uZ65TSA1ApChaDSu2PCUEA6+Xq/r2rVr6V4OSLEt\n3D/+po85F7uCA+Y47DTPStuuX7+u8Xic3meDHt69ezc9J2CEZ6fG6tGjR9rd3dU777yja9eupdTP\n7//+7+uFF17QYnFW18hqS1+4gH7GeU4hOS+ZrVQqhb+jfkYGhGt5RoD54+DOQYikQhDCcxIQYMPd\nfpWlr2P9EZ+tkrWAE2coACpQtDj9GEWQi3bDyOez2UyPHj1KqM/BCdfFiI1Go7SMyZXcgQmgyGlt\nlBCA4imjWENB2gRFc6qc9elHR0c6ODhIwGNnZyetNvDVOkQagK8HDx6o3W7r/fff1+3bt/XjP/7j\nev/991NOdjAYKMuyVOXtyyo9lZJlZ8W/g8EgLYEjvQMAo/LcJwoK6n0DuHQAwSTFMPlvV243OIwr\n+wAgtN0NeJk+rTsvLxXTaDEi98/8WSJ7EqlnruHGir50sOZO39sirY5SympfPLWAcfJnwYFznLMD\nfo0sy9JSSNKCFHsDdKncr1arOjo60tNPP63xeKxGo6G7d++eCy78uZi3MYVGP3hhtqcSor5EnXIw\nEMeJzznfr+MADhDiu9cSnfLcTplHBsb70VMX6xJAgDtx1ynG3qNi9NidEscDcHwlJud7bU6tVtPe\n3l4BTDrA9nbxP/3lNgv7AsPR6/VSG7HTjI8X2mIfSdfgY2Dy+v1+quPwFDXB3s7Ojj772c9qMBjo\n+eefV7VaTanoz3/+8ymI4EdSAvn0G5/DzMCOHx8fazabpTovfAdsPMDcwTf+JAbf2GGvtyGAdH/o\nLAzj5sCFQILxYX6UpSk9iC+TtYATBh7QgEL6qg06F4X0CIKHAmGChH0vD2db6CAKX0kr8T3O1pc6\n+YRxx8jkigW7pKN8MnB9KN1a7WzXQFYKsawMNAy74bsK8pykTv7gD/5AL7/8csrns4x4f38/7emw\nWJwVbXU6nbSMMoItwANMyt7eXlIi9nSRzvZQ2dnZKeRDmeBEyETFHhECcHxnV4CcGxlfukzbvDYF\nJ8bfkd5m/KKDX5d4e5yVkFYXyl4kXkgZNz6L1/I0hTs39Bew4QyUzxXO8+8jEHHn4REqlfcYSKem\nqYHxqJe0j9PxN2/e1Lvvvps2BORzVv0Afn0Vnb9i3qM5jwi9H2BmGCdSiHGJpfdtGauBrAI6ztT4\nMQ5guJ+nbiNIiX+vSzwFE/eNwcZKSzbTwYY7X5/XOEoK5r0ImFUcDnqd8fDI3NORzjL6XPE5OJ/P\nUyCGnjrDg83BrhFwYl99bjx48EC7u7vpGjwLYz+fz/WZz3xG+/v7mk6XL4P9whe+oDzPE8AAiNE/\nvAzQnw2bzbt4YJmcjeRZASj+PePEPRxs4acoMuY85pzbfknn5rKPNWPp9i8GWj4/VslawInvnueG\nxYFJGXXqA056wEEOSND3+kCR3aFyHQAAhXh+X2lJZflqmZjPzLIsGVNpSdsSFUhKSNcpN/ZlQMko\nZKI/eDZPZQEa/uRP/kSdTke3bt1KTMP9+/dTbQsRG+DAl4L69UHGrIrgGTDcgDloWJxFrMZnHKEp\nuf7JyUkyAgAsj064B32M+OR3xF3GZhHZew58neJRfFma6UnAxIEt/eJOCpr6IpqfsfHUgVO1fkx0\nxDggDwrQZ8CGM13OdFYqZzsmV6vVtJLAa46cSYyMB/rNNuLsA1SpVBIAB+TTfoCGR2fohzNN6BI1\nAqy6q1arhSJqgIKPW4zI0X2eqQxExH6WlrUOOFokAs1o5CMYX5f4/MLmOiNEu2PahWfF/knLmkEc\nPfVxbu+d0ZBUGNcY+EnLlEEMSr0WBpBB+3G0jCO+AYaClYGVytmGlzdv3iwEWKPRSN1uN6UrmXOw\n4YzjdDrV7u6uJKXNMg8PD5MP8PS2z1X6LabK8jxPzA/PDcPTaDS0vb2d7uX1MzwrYzifzwt9IC33\nBcLfuT1wEoH/GUfGlWs7uPG6k2q1mlhUGJpVsva3ErtSoFxMBI8u3EjCVETamS3pSWc4clxVVBYN\ns1NdKLFHrtwrFvr48lnAENFfrKEhHzkYDJTnZxXdvV5P0nLrfSYTO2iiYL4Py3vvvZeQ9M2bN5Mj\nkpZFuR6NlTl3BxEYhF6vlybmaDTS/fv3tb29XQAobiAc2HlkSyrMDZFTrCg3zoPr+P4X3tdetEVf\nY9x4tqsi0WHFtNSTGJRI9/s5MZ0jFd9p4oYsRiZlxsANl+sKgrF0YOtACbbOHQIggLdYR8AFa+JR\nlxt01wFfAk/huzMS3NeBHM8VozYMa+w/N7re/3EMo5P2Yz1S538PqHxO+nX529Mg9DMOZN3g22l6\nxt37SyruCM24eMqEMaNPsHXoj5/vTpPxhpGDVYhMatRtricVC5xdfxCCOWfZ8SXT6VR7e3uJvWZM\nFotFes8PO2wDZggQfT76LtaAVH+1gz87eoHNi6k9mBJqFKln4d6wW9hfn9c8H3PV3xbOPKxUKikQ\n8HStM1OrBEDjY4feeLqU41bJWsAJnc2EhGrG6WI8fJJiDCWd25QMFO+Rm6cPnC3BAHA/irToLCYV\n9wdBOoKXllXgEZHSdugx2tjr9dK1BoOBjo+PE8KG/bh586Y6nY4ePnxYOBZAEukzwA8Tl36JhhUA\ngALGaJBCVO/b7e1t7e3t6eDgQPP5PBXHQoU6TY0TAdy5UXDAhxFytgxWx8GJR0keaXp7eX6nES9C\n4Z+UuBFELprIUSJbJJ1/s+0qwOKfScs3iK5ybPRbDAa4Btf2JbBu5AAtrEIBqABG/T1P0PEYdliQ\nXq9XqE3ytIu03IAMBsaZEGeDPKqkfRzvDp95QHvd4HqUGMfRDaz3v38fI0dnr6Rlca6PR9QNT/M4\nUFo3g+JpLwcW0vll3P4bJ+6BCc/o6S4Hv4wNfcr40gb00VkWqViXJRXfVs33UnG3agIb9JR24qBP\nT0+1t7eXtlngOo1GQ7dv39brr7+u/f39VPB6dHSkGzduqFo9qy1xANrpdDQYDBJL7bUd9BUCs+CB\nM0wxesk2DrVaLe1Bc3BwUNjw0H0b+o3v9D5xPeWa3u/O6tCvzD/muzM+HhhH9tv16coxJzjLSL/i\nnGIEIhVrD1xhMWieciCSosNw7K6UODnqPpgYbiS5L2jWQZTTrjAbOHDOZ4DzPE9AZDAY6IMPPijs\nepjnuR49eqR79+7ps5/9rCQlULK9va379++nTa24P2iZ/SN8Z77FYlmvg1NwFoq+caBANIRytVqt\ntEmb061MEKdtuT6GiB8fR0CTT1ZJ54wubfA+lnTu7zIHcVVkVdtWRQn+bBGARKcbrxmdm/c5feuG\nJ7anjKGJ90GnAKZHR0ep4I8NnlgOyXujYCJ4Jwg7DwOo0V/moVR8W3GsL0BnKVb06BtjylxyGpr/\neX6Oc3DtDnMVEPC8vDNXXM/ZFe8/Z8oceDqA9PtFVsyPW6fEqJ05T6rWAa7bRbcdnBtBh7O5UjHy\nduZsNjtb/dRqtXR0dFQAks6GeK1RTEtij2EPSN/Qxvl8+cJKnmkwGBRqMQiMRqORXnvtNb3xxhtJ\n927cuKFWq5U2t/SxJRUEsOd+tVotMYX0dSyYxm85AHAbycaLgBQfg8iWOwHA3OZ/dNnvKS2DEfoQ\n8OH2nftFW+L1pD7O3HuVrAWc+EYukRKlvsNzmihZZFK8M1FAf5WzU74cB9BAIdjVj9xejJZQIpCu\nTzofePLsvkS51Wqp1WppNBoltOr7L6BsTJZ3331X9Xo9FVgdHBwoz8+KpmiT/87zs132vEAQEMVq\nIZ4dytInoeeH3TjPZrNEx+OQMA5EgyihOwJfigzoi5PAxwaaDxTN94A6Z4uYuF6z4LR9BD1XRdAh\nf2Y+p71OPUcmQDq/gsMlRj5+fcakbHUF142fex9iqGOhYLVaTcvP2cyvWq3q3r176vf7Ojo6SjrJ\nPjq9Xi+9UG2xWKQ3rjLGtNXtwNbWVnp7baz5oo+IfD2v7oAOI+0sCc/moA1b43Pe+8aPpY9wys6y\nuPGNQMZtEp9H4FlWa8GcW6fQZrd/6IKDQ0kFQMnYOVsCoAF0RLbUI3ie28fJAUbZnOA8Uv9ScYVX\ns9ksFEKz1B0fwsaBW1tbGg6HevbZZzWdTgsgxdP0165d0/7+frKXnU4nvRIFPwbIcp8Vi2djKpvg\n2hkKntltB8ujfUECfYY98eDE2RrYfx8vjvc+4/oRfNBeBzi0i/s7K+ZgO86XKGtjTtyxz+fzQtW2\nVESKjpy9aphruJOD6pJUMEpc2wfG84uei445tYg2uS9g4OHDh2kSOfU4mUzU7XZ169YtvfHGG2mv\nEkn6zGc+k9iRbrebjPC9e/fS1sS1Wi1tXuXbM/tkd+fjL5Ki3UwI0jYYB/o3rohiZcVsNkvUom+l\n7JMD5XblZFkdq3hwUp4C86JZ71s3Sp739PF0AyktAVkcs3VJdPKuz25M/bhoQCNo4btV0bl0nh5l\nbDnGjRbihsIpeNJ3vlcJ+XY3thRwP3z4UJK0t7en09NTdTqdlDqpVCppTpIfJ5XnYBrwyfPgPHyT\nLEmJCseAx1oOB9/SMmdf1keMh3/nDKC3hz6NEagzI+hxGfsbGZCY3vEINdbo+NxYl7hDon/c4fDs\n/I/d9a0MfBx4Lq8n4hxsvbPP9CkLCLyuzZ2wdH7HX28XAIAl7thdtm9gkUKj0dD+/r4k6dGjR6rX\n6wmQE6xRb+JgzVffUKOHTtRqNe3u7iaGnT4YjUYFfwg75G33ue86689JcI5fYe54Gh4g7zrqdjwC\nbrdBTg5gbxkzacnqe8rG/XScKzzXKlkLOHGnifHDwdCB0hJ9oYBS8eVkXiCJkSK941E4g+ab9XgE\nJamwBS/3ccVgUBlY8ovNZjNVXAOQ+Pv09FT7+/vq9Xp6/vnndXh4qHq9rldffVX3798vOKlr166d\nc2i8kA/Q4I45y7JElbfb7aSATFwHH4eHhwVFZsLwjBgC/uYdNlSTO4JfLBZpySh9xfcYLU/heH7S\niyDdALtyM76wWTESiCk9ZxWugpSxHU5rXnRsBB9lkWFkVFaJ1zfECB7x6IbrAVIYY/TGgTuAFkYE\nVnBrayuttpGW0VG1Wk10tjMisB6ka6Ix9JUKDl4crMb+jfrggYszbG4cI5CINTrOfnkEHEEmx0WQ\nHO/BuT4HYnqHc5wRWqe4vZCWO8A6ve/94cDP0+jOlKJTETjH1IuDE2dfsCWeUpCKK85YUFCr1dLL\nWre3twsBEuc1Gg0dHx+rXq8X3gR9+/Ztvf/++3r06FEBCACw0V1W4jA/RqNRWvlCehMg5PtgbW1t\nJRYSVsmDLfeX0tJ/YbO5Hz6xWq0WXtsCWxkzD9gH37CR8cOHon8UIMcgC10ASPKeOXwRUsYyogOr\nZG37nPiD4vCgkF25XBkciNAhoFhp2bFujJwa9E3c3MgNh8M0CHQqiukGg1VC0lmn7uzsFKqc+Z3n\nedrpFGaFHQ7v3r2rra0tLRaLtMxXUlJaUkHsporx95c8eRTi+wGs6ueYSqDGhueNNCAgBYDlG+/4\ne3C8z1E6JqOn7ubzeWJAIgXuQJRaBCYxoIu2eWqOcUFHrgpzQt+5I+SZ6cMYNayKJDwalIqbfEXx\nz9xISyqMb2QSYrqC6JT5546BOUg+3PPTpBiHw6GGw2GqLYFBAWiiP6Q7+S0pGVfvv/F4rH6/n95h\ngpPziG1VSoZ2OTChH/jfdTCCOAdlZaxIHDdnbvyzOM5x/CJb48/gAGyd4u8gw+ETwDAenl5jLB18\n8JuUr1TcFM/HCGE+sacN4JAxdb30FJCDKXSM4+PeSp1OJ62aIbj88MMPtbu7q62tLX3ta1/TrVu3\n0rmw4pJSMMg29Owe3u1203j7bsjUB2IXqtWqhsNh6jMWaNB+T/14qs9tOyDX57mkBOzcVjtjg067\njvoSXx8Tgvsy4M1LCambgc1nVV7Ue59zF8lawInvM+Bpm0ajUaie9sLLsvfZkKP2yCvStC44CC+U\nhZr2NeG+PTMKxKQAuaPcPMvJyUnaVZUUFe8LoYBrPB7rxo0b6e2tktJeEN1uN+Xnh8NhMug7Ozs6\nPj5OeVFpiWpB+L7k0tNhtElaGnAmOIjb2QsUDwTvyl6v19XpdAoOwDcwos/5ATgywbiH9yPiY0VE\nTr97FMwKEHcEPsGugrgRZkLzf5zYZcyJS3SAq6Jov04EM4ALqbgMNBoymDf0JdKwgHbmXHzeVqul\nDz74IL1SHjDqG3b5VvPT6TSlf3ye43iYn/V6PRXPkmZicyrmIYYvpgf4G/EAIj6jU/8OTOh3ZwEc\nlHlfenTv4xL7mZo0HIuDHwf+Mehap9C/9A1stKf+vL0cTx9iE7Ar9JWkBGCRCMqjA/UFAdjCyWRy\nLk1HX1IDRSBHaob9R3hJJMCh1WolJvvOnTt69tlnNZ/PdXh4mBgVxo3/YZjRDYI66Txopm0wLfg8\nNs2MfYg9Rkcig0UfMReYK/Szv0gWYOh22hkVabnSxus8AdBxPJ3d8nHDtwBY6Qc+KwPpUdYCTqDZ\nJKVcHyCFKB2D6KkWAIhU3OwKB+YdhtGMBt1pas7tdrvKsixVOns1P9dhMvlyL2n5ojxPGfkSNopT\nJWl7ezvlOgEH0OC+pT4reaSzHVo93eXGAWPPM/veJExgULojXGdImDhcH6ViddBkMkm5WIq04hj6\nWABkHGUTRftEig6AZ4IxidG8O3oU2yNZANdVkRgtSuep+yhlKZwYua86DvE+8VQEuhxpWfTMUznR\n2fr3jJFveLhYLNKS806nk/LdUnEHVc5lHCeTSdqBk/s5a0nE65/hAHwjQHQJ48lz+1x3wwlL4cDN\nwa6ncbxvPUjiHHeiDubKxgZg7sytAypnspxxuwriAQK1dLQTe1nGbkvFeitsss8NZ6h8IUQMeqRi\nioi0s9sQrjObzdLeH6xo9IAzyzJ1u109ePBAe3t7qlQq6R06bg9feeWVtIs2tXgAHXS92WwmsEKK\npl6vF5hfdM7rIt1e8a4hxNlhDyI9+HQd53t8J+3xNwAzv7wfuY6ngRijWMTq7XW2DIbTx9uZc/df\nzNPoh8tkbe/WibQpoABE7lQW7ABRB+I5LxQZWhjUCPJkwJzKRcG9AIlIgJUmGHTPlznNCqhibxBW\n6zDwi8WiMNC0FWfNJGYwKVyl2Orhw4fpGWBjuHae5+lzvx4/kbFwSjwiWJTIlYX7zefz1E84E4/W\nPYqEzkVR6TeiXQdGPqG4BnsDuPPwqAFGiWdkEse6gXUJho+2EaWUAQOpCABo/yomxK/v/3Ms14hR\nKDpW5ugwth7N+zWYCzgg3/PEi/Yo4KZNkWL3dgJoJKW5yT1dt5zFZM44W+JGjzw+OuL9A1PjdW2S\nClF8ZEK8HoR2O5DzovR4jKfDfIz82Jju4V5loOgqiI8Pv9FlZ00iw+ERt4N0T62hz85QY68BzFyH\nH+wS4IAxZgxarZYGg0EK7tBdauV4v40Hj9h1d8osB+Y3vsB1GBsPoOR1Hw64uJ7Pf+pQXNccIPtq\nJvc9jIEHKwjLo6lpwUc4+0eb+d83yvQNNj3odFvk4A0ggg13sOl2niADPbiMfq91h1iPpjwv5XlE\nR7t0nDtTouroYOkofz03n3M9nL60fK00gMVTFgy4p3VgGRaLRTKwvmLA8+DVajVRbTgBR6m+Ex/f\nocikijAAfn8iOc7BaDD4kcZG+RAHiLSZ+9Bfk8kk9QfGn70saI9PHNrRbDYLbxyFvvT35nik6Xl8\n3/DLz3fnTQTtOdSrIPSDRxXO8pQdy3HovQMN71eu5UbGgbK0HGscLN+5YXO2yYtmnVlwY0X0NZ/P\nNRgMUlQGeJWUisK97RhmB2c+T7y93g/+bMxXH3//m2LY0WhUqC1xI++6Tduk4kZRzsgSLETG1QOT\nuKLHGVVPzfhY8T8GOqaEfJykYsooXueTllj7h02i/7yo3leHEPU7uyGp8L4lAlNsrgNP9MVX7bju\nuG/w8SY9E+0S7aDGER3FefIc6CO6AJvr15OKWxugq7GepowlcJCBfnuQQiDpab+YHo6AnTQ/fQNo\ngBViVZBvN+/Pw/3cV8KEO1sjLQNKxsrnegw0fK56DSHtXKlzH19dP754wV2WZYnmipMRxZSWyL0s\nQqZjSItADeK8iJDcOLkT8S2Hm81mSrG4gfFIwRkRZx74n8EkDeWokgGMlcuR+WEQXRH9f8Rpc56R\n6Nap1LhduAM5Z1K4FudT4EU/kB/mfUKeanOnkGXFF1kxfl5T5BEWz04/k7LCAMUlpYwN11238Ubc\nkESn5SmasvZG50xfer846EXnoqDzzB1pmTLhezc+buydHWCZZdQbfmq1mvr9fgGUuPPl/1V95IDa\nv3M99M/cGdIGdBDw7xGbp3hoB/dCP7FF3g5/YWUE3/7b03T8HR1QZFcisKKdPiel4ovVvN3raEGS\npgAAIABJREFUEuYfOoWjY+7hFLFxMaXIc7vN8fPdPsLUuf1H1+NCBwIplu3CqMR30jhw4l7MHfaK\n4jgKZrvdbqon9OJenyN5nqe0oxfs4icc8MegDNvmPs1ZE0mpXoQ5SDtInTqL5Wwec6GMnfZaEvqF\na/OsnONMErruQNPtugP7arV6Ljhl7Dz4vojtXtsmbBhQImqMDBMZA+FpgIi+fVAADCBmIj6AB6DF\nIylyhp4CYrAoZGXwcdbSkipHwZm4IFSWXcEuOP3pyuD35TiUAQVjsnqE6cyPVKTOPZJ2cFWpVAoT\nFmTNMzu4AszQZnKqCCkj+seNsOeJiaAcvHj6KU5OnsmL2zyCANjE/iPHexUAikfgGBFfpRP7wh2c\ngxY3ZO6YHKwg8RjG1j/HaDImGChPM3r6h4jJ2+eGEPBf1k7GmPb6/PXjI0j36MxXDXCver2eVo24\nrrdarcTuuX57ZB3bGZkVdC8CyAgm/VoYbx8bd8gRUHgwJC1XEjEHGLtVY79OcWYPXUJfcHxeS8PY\n8DzuyLxQlN9uw6NDo3+4L6llUjXMCZhexJkIB03YUk89S8tdrLkvb8L2FSfoFHtKAQDKarK8VlIq\nroZ0fVsF5F2/ou9zJsn/pw95fxvPCHhykIjQj/ge/57r8lzYA/wm53JdHyufzx50xA1IV8naCmLd\nueR5nhz6YrFIqNEpTwwSkmVZWmeP0vmD+rIwruP0oOfGJKVrcV+Mna8SKGMayiIbb6+DBaedI2vi\nLIykhEZjJbz3gVNmOATahIPneXgmz5W6o/eoBkq7Uqmk1RL9fr/w9s08z1OKhvMYPxQYw4XS4yic\nuYpLAgeDQRpfn/yc646ZtkoqGPh1igM1npHCT09XuQNy9sJZCeYGn3sfl9GhTqdKq2sjPJqi3zyF\nKp1/10aZvsc2u+PiHjgPJBpH12HqB4h8eU5nZnxHWHSGueoAnWvHokwXn2+AaS+gj88YgYrn8f0Z\nGSPvQ9oQmRhnsSLLEHVlnRJTVWyoh91y5xMdkqTCSkJ0xAE0feHpIbf5zt5Jy8JQCmIJUNyeuZ9B\nfBWig3P+Z+yxvXxPigib5n4gjqvrBXMqgk/XG19aTTvoczZDlIqr75xB4rquv6RiB4PBOcaDWjHv\na+y2tFzN5Asg0EkCU56XdlDX4sGur4p1RpB+ATCukrUtJfbohY4hx+1gwzsFhfV8l0d50nJDGAwc\ngpIy4DG/V6vVNBgMCq95j4Yalod2OdvAPaRiLUe8B4ZVWg44QMKRsCNyd9DOyHBPJgvHMbH8PlzX\nt12ODs8/Y4L5tsVUmvNMTh/6TpCMQzQuDv5wJEQlzgShA05h8uxMTO9n2uxGaF0SGQSPNBy4SOVv\nUeUa8Vox4nc63A2Ui4N/9IiIDgDh0Zm329uBOEvAvQFA7jSkJSiBAfHAwJ895viZNwASoiyKILk3\nx0Clk250IOXsm7fN54Pbm1g0uEqnIliIgQnX8HP9uh69u464M4y6sm7g7eOEbQag+J5Q7vwdHAOc\neU7pvL3kPogHMP45fzsTjaDPnlrxrd1h5Pk/6hU/vgmhrxQ8OTnR9vZ2wT5LZ2kuavF8rLmH2zi3\nafV6Xd1uV4PBIPlFr3dy4O9MC3rt98MudDqdtGqIOj8YqzhPOY/+5B7uv7DVkgrZDQelPA/j6syS\nB7xcz9N1q2Qt4ITB9j0TcD7tdjtF6OwqGY2Eb5kund811iMPp3opPHUWxB0dvwEwoMR6vZ4oMe5N\nu9iYDeOGOHCJkZgrlztekCfRo2+6xoTjuVFEEDxGnEI1p1g5hr5zytqNdyw8zPOzzeQePnyoTqeT\nHAHH0g6MPffxqJjP3KA46IvgSzqLWlBu0mZs1OW0phv46EjXJZHWjnSrR/YxdSAVNyh0NjBOYhya\nj188zh1bo9FI+yq4XnhaLTpWxo/reg0Q18Wh8wzM6bgB0//X3rsst5UkWbsLAEmRBAHwJqWUWdWV\n1tWTfv8n6KfoQVn34M+bUuIFd1IkAfwDnC/2t0PMLLNj5xQ0QJjJKInA3nHxcF++3MPDFSedP2SA\nZS/aawrz1O/3SxzbwGiz2bRqqyRt4MdaeK5gpkxPO/ZfP8PsksHFH7ErfrcdjtqLtJPh9ah13q6B\nNwYeNhp9C8NVMyteU+bfOjtpwLMBODqM37umTdIYa2TIR2UpZlY7XDyXd3gPAhD4LH848eIj8bDZ\nDlkQ0mB+fHmlHVL67nckKaXsOfbc7XZbzqVrkliOXhunGWhOoM5ms9Lvbre5kbjeJ3X4iDVljMwX\nJIDzfBySS1JspfNvWCPbSDM/r7WdaHQvMoM3q1CfisEzB3ERTsAAsxHIjUAQ2TAUtzGFZMPJBJ2d\nnZX8l36/Xyoh2vMyg2H2wErKeSwgYRKr8PD4ztPTU6mySAljPkfNEo4L2xOmP4yXOeBGTeaUeaDP\nDw8PBRB5bGYzrGxfXl5KP3w6iWqIZk34bg06WHMSz9wvmBfmAaXHJgNUoqyRAZR4zYLtutVMRu35\nWhnz7yRfzXud52HPmTnCGwGovDZ+5Nx1CFCeNCs++vAa4PNaJm2AlDT33hj0o+A8J8iKn2slxak2\nPn9yclL2AUmAlrsk5Qh+rXx5fu1ho18Mcl+rgHl8fNyioL0W9kLt3Lj58wbodlK8tn6WDem3AL7R\nWYBCQjM4R97vSZPDAYiBMcDJS/KV0TXQdTi4Ds8kzUlHDOlisSg6CSDFu10MkD2BXMDq8Zk6x8S2\nijASjjQ2BEMNMOD72AuD76enpzw8PBRZTtp6AXC22Wwyn89LH2zMnVeIDPH7N2/elL1hvXx0dJTR\naFTGAJAycDR7yPqxzn/E0hpw+N9mtW0b2aP/TK53VueEY08gK9e3cF0SAAAG6+npKdPpNOv1ulQs\nZaPUNSUwZISLzs7OSpYzjAqTzh+U1dHRUTmJwIQiyPZoLXQII5vYpdwpve1iQNQyeXl5KQAFRoLv\nwcrwHkJOvIt5dFIXmw+BhP1J0nqH45ZG1QgUz5xOpzk/P28J2ePjY2azWZ6enjIcDnN+fp71et1K\nnvXcYGS73W7rWB7CbwOCEUC54MXYCyGc5Qx5NvUuG2trg1MbLCsuFL2ZBeTc+TU1yIGVIV7Nv2tF\nmDSxdeYfZez+ITNWenVYhL/zDgMiG56kfW8PFZOdkGgDTR/t/dkTwxDQDLapMkuf2Nf2DP13jwnZ\nr6lz5srsJ+NzSJVx+99mof6o1QCIPtWsgvfOrpmTJEVP2dAAdCkmSbVVnEFARb/fLwwo+x02D6BS\ns77r9bqUnTfbAEuctMOIhDCS9rUFNfBFv6NLkS2D6i9fvpRrRpbLZa6uropM8hnkhPpbNfOHTXO9\nKOTW4aSk7dSwp7vdbgHHrgdk1sT7BjmCnTw5Ocl8Pi+OLxXHT05OMplMcnNz05qzpH3akJAnDBU6\nhvA+TiLfc80YrrBIGt3FvDCPh4eHLSepbjsDJ574ZCsYGNnxeJzNZpPLy8si3NQxAG1x3Hc0GhXg\nYqDB0baHh4c8Pj6Wd/GThQbM2Iv58uVLKXnsDYbi4nsg7ZeXpvy9lQiZ4wcHB+XoHUqOI7oGFe6P\n6WcMCieGzs7OWhRqTf8yBhQlmdgYDJ6JoNTMkNfEXiVzBNC7u7vLr7/+mul0mm53W3wIBcM4UCIG\nWoAK1gsFhDJxnxxSY3xsRjaRQeyu2x9R+vzbhgZAggwhn8yhPXkrINbQ+Sz1SYQ6xGAlyl4x2OTz\ngBTLup9Jf/27mtIF9PtZHrdpaOaFOXD+FM9Ejvm+HRFocVdqNjjmfYy1VubIE+927ke32xydd3jC\nxpN1dI6D2SSzZx6rw2ZmpAA2NnQY7F02+mWnxmDu5OSk6EHmnRwHHLGknXsDSLCjuNlsL5CELbPx\ns2fPHwz+yclJzs/P8+nTp9Yx26Rx2JIU55c5RzfiGCFbHCN+fn4upzp7vV4rfGjGwCyecy+QRd/F\ngzx6DyDvPAP9bfmAYa4TcutIgUMosNMAlZOTk1xdXSVJZrNZqVtkB9IOfpLC0iKP3l/IA7YQW8X8\nAF49X0RLPFevtZ1whWx+aHl7cSic8XhcBskC4SENh8MMh8PWEd7VqrlLBmXJxXs8D8UEkkya0uos\nMKeGxuNxJpNJoQWTtJAifcWzB+myeCjPpIkr1hvcFFfSgAH6b5qTRGDTlXjK9rIAPwgtxeWMuu2h\nooTdN97NZ0H7SQozcnx8nLdv36bb7eb29jY3Nzet9XXIis2MgDKPfKauycL6cImc69DYUHhc9jx2\n2ezt160GkihaU7t16IJmNgQ5drGnOixDcwgCI85nbPBQTO67jSR9wuO0MeY7PplVZ+GbvXN+AuNE\nOdrD5DPIH33BmD08PJS9S9+d7O3QsD3vmvVM2iEpzykOk/NsalYEQGSWqw4duBmk1YCtDg/VRmtX\nDX2RNKG6pM2wOVmefY18T6fT3N3dFbYFAzUYDFqsno3ja/KNgfZckWuC00Qfa5as291eVUKpCYdf\ner1eOQxh9psTm85XxBjzbOtwO5zot6QNPM0O8sz6WgSveT0OO5BmL+28UwWXiwTZU58/f87t7W26\n3W2pfYAZc227UTvE2Ft0sKMF7NE3b95kMBgUAGXAn3wdOv0zhnFngcw6KZJNDyqHQkxSyqZbiK+v\nr3N+ft6iwyeTSSsfBG+Iz2AUvdEx+izq8fFxCTsQwwQUWPGv19v7BObzeau0PIvFcSwotjphlf/D\nIDmkwR/6zhxxN06tBJlPPsdcoezt7dGshO1N0hBmfgIUHh4eSp8uLi7y17/+Nf1+P9PpNLPZLN1u\nt1C3vAcgAohbLBaZz+ctRsreODHT1WpVnlsjeoOUbwWYJG1vvQYMhAbxLpxMZm8cpW5g6rwaDJYr\nnFo50uqN79CQmZmaQUNZ0+ekDRAwBniT3W63dd2D9xNj94kL9oUNM0oXAFMfc6bPprMJlRq48HuP\ngfnzvvfnaGYjn56ecnp6WpISTbVbZ/Fun2pjvgzI6+e7ua9189h22XxhqHNBki04oKYUexjG2vkF\nDpWdnp7m6uqqsBKsPaULADfowqRhnWqQjy6mUQeFPplhYAzIO8zYbDYrdznhdD48POTi4qKAT/ar\nQxHdbrfkvbhMPEwQDig2xHuXk0113pMdSjMr7MWkCakh8wYz1jvuN3N/f3+fp6enDAaDXF9fp9PZ\n5nWxh60XsFcHBwctsJM0ABBnEr3f6XQK6ERecLitr+wcvNZ2FtbxZgd0MLmj0agMCEOOUJHwZPT4\n/Pyc33//PUnyl7/8pQgjSt7xTzMHbAh7qfwffSIhyRUQnaTFIq7X67LR7DX5hMFkMslqtc1AZzNZ\nuSKwPMuCwTw4PJOkZexq0IJyt7CZ+UFIeL49EtOJvOf5+bnUSkHw/vrXv+bx8TH39/f56aefihJa\nLBaFnnW+CRubSwl5tt+TbC88PDo6yu3tbbkIsd/vFwWDwtq1R1m31zxrxs66oBBZC5SUq7rWBjZp\n19XguXg4VtoYdCt1+gZFmzQlyfnsa7kkSTs0MZ/Pi3d5cHDQOiHDM90Ps4T0l/+jnw41GiyjHLlo\njZMh9M2gFgVt1oI5qvd8zSYxDu+Hbreb2WyWwWBQQryLxSL9fr8YIxwI1sWAlHf/Eagw2GI9DF7M\nJDhMtMtWM3cGl7PZrMiFQ8BJw1Cx9uSokMxeh/3MRtghxQ54jxAaShoGi/fBwjHXAN+kAcuup4Ms\nwcqNRqOiE2FPfEKF77BnDIScFIr8O1xq+4EsJU3Yh/6anWD+HRZhHSzzjCFpcnHMIn769CmHh4cZ\njUa5vLwsfSbBl0rrOBrL5bLMk2WA9R0MBuWCWIAn+8x2nLlCJjzu19pOwAkCtlqtChVYK1KEyqEJ\nFIizt1erVUHonHCBirWyQchNpTlWzqQjYBYSUDXvBlUT22SSqVdC4hdCMhwOC1PAM8zusMFchZZn\ngtRr78tGi88yHtOKjq0naWWkW9hqmpaGEAKA5vN568TN8fFxfvzxxyTbGOZvv/1WhPPp6SlXV1dl\n7lgLCgsx90bi3FA9GAwKQMMLYS7qPiavF9naRbNhdm6Bc4HwxGmAUtbfe8EsB+vld7mQFYaMdXdy\nLH0hodEx8qShq53Ma08naY5v813H6tmL7FPT2O43cg9I4nkoXCrAoszYh0lal8CZ2fFeZfxmEpN2\nPlXNsKArzMowXupXAJABSxzzr/UHCYZepzpU5PwiJxE7pOPvfwtyzdpyoZ4Ba7fbLbkmBlroHdab\nBEiMf5KiQ2t2CF1qjxsmPGlYSEINOIVJU5CMPpo5MHimn91uN6PRqOQNPjw8lMv7aNPptLAB6Fdk\nDPkzqERODLjq/WkHgn4jxzWg9sknjDzA2HmQvMtrQRgLFufNmzf59ddf8/LykvPz8/T7/cJU4SCZ\nmWL8rjDr8Azrt1gs8ubNm8I+Yb9xYLBjzJEdhdfaTuA4tzGCtGoKuNfbZjo7KxvlhXCs1+tyKR1J\nPu/fv0+SknvCZUcusgMrYsYBAwLNt9lsyoLybgAQzInZGIQNYZzNZkX4QJyM8+joqJzdrz1eK05A\nEoKIYDAn3vw1GjVt6dAAdKLDIg7dAFIQchtH/r1arXJ/f19O6qzX29ye//iP/8j19XW+fPlSlBdM\nF54mG2g0GuXs7CzHx8cl1MNczmazkjeEkYNFcSjQuSrIyLcQ2sHjAVBZGRlwJmltUN/fYUVDjk7y\ntdFiTu1d83dTxQ578T4rfZqVrZWjARaghH2C0UE2AVo29PboanbPgAJgYuVlT5HfMZeAAffdgAej\nRJ/dH36SH2adYG8Vuv7s7KzsJxLsWSMrWIdoaWZJvL/QSzUL6BDft8CYJE3VUMoBMJ66fAMyzFyy\n/y2PvV4v9/f3+eWXX3J7e5vk6zouHP81m5akZS+QQ/QXIWxkG5aGfpMAagCF/uUE6GazKZdYbjbb\n2inoc4fhsQ3ezzgd3tPoXN5lwG9GPPn6vieHzniX9zfyyNzwfOsX7AN2z3P422+/5fPnz8WJAUSc\nnp629kXNShoE+bb5WrckjY5CHmx32PN/1HZ2K3Gvty2ty7XWGHbH1RgwE85mcAiDCbq+vs7T01MW\ni0VeXl5ydnZWkPZ6vS7I3hQpiogFgNZKUsI4fM4erD176i8YEYI+UWCgd55PbNO0lgEAfQDcoJyd\nL2Mq0nkbPMv0m2lNCzBKmz+vGXcAnecBpeDEsOFwmL///e/5+eefi5fJBqX5JAZCbqrdiL1mHmoP\nOGkbU/q362aAmTS5B2xanyZB0bB2zrFgLK5uiaeE3LmxT6wETT/72TXbQvPvDRhNufNsji9D+fp7\nzIPZSwClT9kAYpAlxgFwZ+/WHiJePPLCPrOSRkH6Ejnvj6R9/xF99r6xgl0ulzk7OyuyDQh7zcu0\no4EMADj4WXuRXgMzJX8UFtpFA5TZaQOU1Dk26BvkdrPZFF3A3HAP2nA4LMma7O/Dw8PixLK2lll0\nGBVfsQvIDwYedp71n06nJXcCGYLNRM8AqqbTabEjgAYzNYzV7DaOJb/DtrA3vObsK8t2p7OtVYVM\nERKCkQNsmY1L2owke5F9jk0F1HBSdL1e5+PHj6VvsOlmZer5d4jGa4F981w6fIxMOILxz8D3zi7+\n464RkBMUEAYbwQHJUonVnv/5+Xnm83kR5sPDw3z8+LHcJ3BxcVEW/e7uLsvlssU6bDabkuhFVna/\n3y9on4kjsxtFCJq+vr7O5eVlxuNxQcmENJ6fn7NYLAoLZAqPvBkLq8M7pp0RRv6OssJztZCYQTGI\nMVXufBo2kGPHfJ9/0z/Ago3F4+NjBoNBQc8kLt/d3eX29rZlBJ0M6vABf1B01L6ZzWYFwXvu2Mhc\n9mZU/i0ocp9KcTjNRr6m8W2QDAIMUpJm7NPptMwH62pDyk+vVR3br8MRKG+MDUqfz/sIZNKEWBzf\nBiQgVwaoVm44DYAnn/7BwCFrHovfjw6pCxvCaKJw/R2eZcXOvJOAiJya9qcfPJM1BmjWeVwAsz8K\nbWH88JgNUh2O8nN33WysYH/pL/k4nHY5ONheBYL8JU1CJ04b84gskHBpRpTvO9Thk1rIFM+wswYL\n3uv1slgscn5+3rIjyC3POzw8zN3dXQaDQZKUPCeO3yaNkTUjUQNZO1wG96xvzQwyN94fSQPuGRtH\nfgmHmblzw04AqqbTaZJtjh85exTQJCxGBOPi4uKrkPNr4X7GzN4ghaHuk3WAdRnj/LN8k2RH4MQo\nCiOXNPUCfNwLAANASJp4I4ILc0DZc46hgrwBLlZ4AAiy8k29omAQWhQ2i4nC5cw5qBMljEANBoNy\nXw+KHNSKQPM+/i9p0LJzD2xoWPQ6b8ZGxkLCGHq95g4SmCGAg5u/v9lsslwuy7Hqi4uLcpppPp+X\nSrpJE/46OTkpNCog0JuW9TJK513O3jc74FCDWR7mraZAd9WIN9f5BEnjsZs9cX4Km5c1wUtjnQeD\nQSaTSYslqVkyyzh5LA6JAUCsaGgYGrNryAo5FuRz4SFyooU9wx5gXIAOG3z+Tt9gUSyzjMWKnn4x\nt4Q+GQP9JAm7VviM3z+9r/z/zCmspT1IanA4NEvzfPq9rC3zbwDH/zssVyfJ7rqx/0gypa6JQ1Do\nQyeNouvt3OCAouc+f/5c1skhQcLGzA3OG4ADPeJwh+cRY0y4x3uE3BJYlcVikYuLiwJeuJuGd7KH\nALGMNWnCLWb/AJ8+Zo48GvAawPLv15ws7jECTDEOmllB9g1sBn3APmIDYKeYm8ViUcZEfx2OYy7Y\nuwbiDpPa+Wftea5DYHX4s247ASdQegio2RAmhUVYLBa5u7trHY+lmbbiRMxf/vKXzGazIrg25mdn\nZ0XhsCBfvnzJ/f19rq+vW/kB/LG3RCG1Xq+X4XDYyv6GmnQoaT6f5+LiIvf39y3602yACwQxNwYY\nzIU3OayOF9YCwb95n71e2BqEq45b0oekEXjmC+YKAXcs+fDwMOPxuLzb9QscxkGh1YwPc9/tNsdS\nHS7DiHiDwxpgqGp6fReNsJ6TW+u59Xw79GJwXDfWHY/VANeeNw1gnzRg3sCFviBTDkEkaa3dw8ND\nYbRMzQJGkpTwDvvWILMGy8gVwAS2EoDCs63QfZzTuSkYBZwNF8xij9XhHBpj8RFYx9XZqzWb4vmo\nPWav8WvsVG2IWHuPhfd5Lr6FxtyzF50/Y10MC83eRQ5xPliTfr+fN2/e5O7uLt3u9jRep9Mpzo/3\nNMm0yOBgMMh4PC65SN4/nrNer1ee63oonU6nJPc7ZI9jQCI0uo5+Pz4+luex3sxF0oTlLeNmdy0n\nyBQ63uCJ7zgM62P27HlaDebpB7reoK/f7xcbhO5lvs2UJ+2aNowvafJjzFbxfdbEibrYUsbBuF9j\nf2g7AydsyvPz88xms21n/h+FQx4DKOzp6amEakCA/D+5JLPZLF++fMnFxUW+++67LJfLkg9CApNp\n1IeHh5yenibJV7FzI0F7M0lKYixH4VxA7OzsrNRaIQnr8fExFxcXmc1mRcFyAyWVVZP2aSEzIRjp\n+hSAE14t1LVSR+klTbKmvdTX8lVq5gR0nKQ1h0nKunCjJgq20+m0CuwBEO1tIKSMkXeDtE3D4o0A\nDm1YvBm/hcbcODZPA1glbQBgBozPGbihpNgbSVNDxrQp84BnV+cTmZ3hGZYbhyCs4E1jm85mz6Cw\nGRN0OM4A64aCQjly4Rmy4P7ZmAP4kLV63JYHvFyHaWg1UDGDC/sKMDF97wYoYxyeMzsXnlOPC8Nj\nx4N9w7o4n8PrtatG6BYHwd55khJi8/wyPwBr1pFTWRwl3mw2JTRuBhsmPWkcWnQn76yNtMOi6Abe\nYzYNRw0QAjB9enoqpyt5hpOuT05OCpCuWbhku6dns1mRZxwtwpc03pc0Cf7IIQ4I3wM4sb8sP+6D\ndTfAjn6iP1gDPzdp8gkBZ3Z+vC/tEPlyVpxXO0/YGu8Jvksf/6ztBJxwkgaQYu9ovW5yJJxklbRR\nHII5m80KNTeZTPL4+JgffvihKE0mwFQYhjPZnhzivaakvdjEsvv9flFYRvYoQICTz9vD/mC8fUTO\ntUxQ+owZxYZx5x3O43CyZdIkRdlj9700SQMueJa98JomtJJgY+E1Esqxt2RmA6FHCeC5G/igMLy+\n9NEljzmeSpjEdCNzhwx8C835A/aW+Il8edz2zB3qcZVNA1XnkNgw2itPGian9ubdHLrAwzF4xcOv\nk7JZF8CuT5+5qqaPA9dKCnYiaZJtzRog34At64zXGBEDhKQNAGn8nvmmD+wDlD/j90++X/+s38v/\nA0CSpqAdY6XvHu9rOUe1Ad5FY10BZi8vL2XfY/yRac+Hx4ze22w2Bdz1er1SqZQwO7rRjpbnxbf3\nmvVjLkmQpaJ4XZsHPYXugxkBMKHjFotF3r17V5zXJCVUUoe3GDtOnNkQ+s0e8lzUDsrBwUHJVcTA\nW4/w3VqneA+gg3kG6QWLxaIFprFdyKLnBl0LQeC+Mv8AE+wnzigy0O/3c3p6mtlsVsZe5yH+Geje\nCThxLJ4wAZckkafAImP0+ZwZFehD0DnCfXd3l+FwmG63W042+MiwjQKby4wFz+d5bCaOvjoHBZqT\nCT87OysABAFAufN8WBAnlNWgiGfWeRQWJH7PRnVRMyvLfr+fpJ1RXytihNsCbzBB8hoesalBknYB\nFPQRlgkQgkA6fOexf/nypYCMugLily9f8unTp3KHBgarXstdN9butTAGgAJjY48EEMZPK/I6ORLW\nIGnnRtQGuDZqBgj8G8XjMtt1SMI5JrWxZB1IhERpYzgMovCskZvValVAO8rZ/UShO/GWd3N6ok64\ndKsBcf2TseHIeJ55Xx0qqMGHGaV6TnkO/XutiBjfOz09LTF/+vCacd5VW61WxUAfHm4Lc3HJH+tL\nf2E4yUfiz3q9LqcpMWrIAU6ic/KSRh8RjkAmYLpYD55F+fokBTh0u93y3KSdRA7YIW9fRaVLAAAg\nAElEQVSQ/LzlcpnRaJTxeFyqqCIjTvB2CI69ioPq0EfNQvAcGiGjGihTm8ThSebF85O0i2qyD52Y\nzZxjTxeLRetZ7DM32wLARLfbzbt370rkgfIPdtyxT+S8jMfjVt4g4O3P5HpnR4nPzs5aniBC5QJH\nSVpn1KG8EKLXBsfmnk6nJanWCgVjTJ4KaDlpZ03jSSJkhBUQZJ9QsbFIGkWTpLVQ/JuNxLMcrzV4\nwsCwqGxIxgFAQ/AQQgTE5/WTtMIsDl/VSNyNTUU2NmACkIbBOjs7a3kKrxno2gMCzMzn89zc3GQy\nmWQwGOTt27ctg87zHh4eSoEk5+2wKf8ZTfivaA63Je1L7vhjEMXcWnGYQUIRO+RphgSP1qybjWzN\nxKFAauCAkfV+sjGn8WzABuDUIIKGl83zUVTsQUAoa8j7DX4I57AHTPX7OC4nBRwOShovk797XIzH\nx5qTJhfEAMXryZ6rGZmkCeUBfNgjNEJTPKvX65UrHTBQvpeL/u66kU9wcHBQQALXSiyXy+JUsF6M\ngzXnfjGAHLoCxvrk5KTlqNTsFH9H58HYmWE22EM3oBORdwMgwCb6czgctsrmv7y8lCTZzWZTLv3j\nvRhskkSZH4fqXGTOsuhkaN7P9+i7c7aQFduHmi1BHs24UweGvz89PWU0GrWAjAEeesOOMk65w0AA\nabNXziVinpOUFAjGTB5M8ucJ3zsBJ46bY8Sh1pgE8kkQCo6o4i0xSCdFMll4ecQ5kzbNjQK3EFkx\nW0GS/5A09xmQ6c1zCDEx4ShS2BZT9PaMTHMlTUVKKqryPhuTpO0Vm9qn7yhwV7O0ENoztsKm1cqQ\nTbxerwtaPjw8zHA4zP39fUuRAIxQIqwV/w8bRe4AoKfe+KwL8dvhcJizs7NMp9NMp9PWJWM2LN9C\nAwywLovFohwnN/NQU6pWXjXbhtz5CK5ZIzMSKGP/vVb2KD/+rzaYKGj3mXchmz6NQHM9B4wV/49M\n1Bf2MR7mjv1cy6wT562MGYt/x1j+SJnbg7ODwd6xPDl2bwXtUBzjqZU788T3PM/M22QyycXFRTl+\naxaH8e9avjudTs7Ozop+4WfNusIckDD/5s2bjEajVj0SA0N0tMEE82ZGPEmxB+iNuoYNewT5BDgg\nY4PBoAAn9hZsFiALMMAxdIe9AQ7IHseikWf0nJ0JH2YwE8begnVk7pK07APzw/o7NFSz3F4HLkxl\nTxwfH+fTp09J2kd50VNm4n1K6e3btxkMBlkulxmPxyWPM2lCV+PxuLV29PXu7q7koBjcM78121u3\nnYETJv3+/r5sRAwRwrbZbMrFb6enp3nz5k0mk0nu7u5yfn5ekB4CzHc5qcAkAkT4N6EgNk2v1yuX\nVxEeccEhTiEYyd7c3JQNdH5+XsJI1DCBWeHZ9ma5KM//h2KDxcE7Sxoa38JnWh2aESBkqtGVZtlU\n9kBgqSzkyddeJnkgLlPN/5kNYnPSJ451Pj4+5vb2tgjy27dvy2VTbM6Tk5OyzlwkhZLp9Xr529/+\nVvqLEdxsNuWc/beQc+LcD+hTQhdJ+yivE1aTJg+oHoeVSb1OBqcoUmQV8OB+8dMXU9ahHFPWNuh4\nW8gOBgejAIPhEJEZNN6LwjJrw15ARjE8VuTuZw2meQZgAaXLe0191+Fdym47ZICD4bmjOaeI9aQP\nPqX12ndZAzNQh4dNHQvH4+vQ0i6bnbj379+Xu7Mw2knDGpKXARtoet9z4bW1E2l2CueP8hDsjeVy\nWULJrl6L48g6I5voeeoy3d3dZbFY5Pn5OcPhsPTJIcPz8/PC/CWN00HIBFk/OTkptsvsGzowaVeD\nTtosEM+mhgngjHlHLvj8a7JQs4PsMdh5bNvj42PG43GRW54L2HQOEbZrsVhkOp2WPKHRaFTqoWDv\nCPlTvoO8xF9//bXcVJyk5F9anv6o7QScIKyr1So3Nzfpdrs5Pz8vCh1kbsqbDcDv7L3Y4wE5M+F1\nvJaS9tfX160kURSnwyYk26LEQbnHx8e5uLgoAIWaKp50noERJ/sbpQ5qB4w4Ucgeq4+lMi7mg7Gh\naAFfvMcGkX8zn9CX9ljd/G824cHBQekPioey3gChmnrlrpz1el1qopydnRUwN5/PS80IQCDgjrAA\nd16QYEWpacZX5wLssmHYam/BFK7zY+rEb8BeHa7EINZ5Nc5dQalDqWOUUU42AE5oNFOALFnRE66p\n84ZgBBwqAlgk7evRWdc6Dp40OWhmkQyqHeJ1bROvOc830LYhpKEQ6Xev1yvhT37PT8uX2Zo6P8X7\n0IUSAZP+nJ/N3GO07HHXa/zaWP6VDSPPvjQD4BMayEeSwhQDHgBvBpgwF/P5PMnXYUpygi4vL1ul\nzm3MeRdyi6dPTuL9/X1eXl6KUeb76H/LIbrLOSDr9Trn5+eZTCY5OzvLbDbLcDgsa8iFgDC/fr7X\nzkCf+at1ugEL+525Muv/mmFnHybJp0+fMp/PMx6Pc3p62tpbdujdJ+wbtogaXbbLyCpy7VweGBvW\nF/v7/PycyWRSdAPOLev1R21ntxI7CzhpCrBxXw4NdIey5OdyucxgMCiIFKV+c3OT6+vrEsNk07AR\nENLJZJKrq6skKcKVpCjjTmdbVpubhLldeLPZJmadn58naWJvCDZ9ZtOS2Y7QJimnf0DfPDdplABC\nTaKRKV7ewf85Ju4cEQtIHf5h0zmmbg8zaViTk5OT/Pjjj3n79m0BWgA2U4q8s/YSki0ABMD5lBDv\nPzzcFgTC4LEZh8NhS6H0er2iGJKUo4UHBwdFwe2ysVY0jyVpjKOTHvl9/T0bYf6Nwsdw8516zS0v\ni8Wi1B4w6wTIR+E5QY93+hZeg8A63o2TgOJhPC5G533quXGYludbQfNdxkr/6VM9B47R1wygmUEc\nEHujzhlJ2vfiuLFXTNvzf95nNRPpUBZ70kDRoSj6+Boo/Vc3OyWcisTweK87/OCj/4wZ1gigcnh4\nWMAEpwrRATwf58S5HehNGMKkSYq9uLhohd4xxBwdRkbQRewj9iFsg/MLyauhpD17AVlz3aikufaj\nBhgwOOjL2sHk/8zA8lzn17mZNUEf9/v9UqV7Op2W+Tw6OipgBZl1/hhrRk0r+oCsYutgdxi3c34I\nY9MX5qcuKgkI/KO2E3CCB9Xr9TIajcqxMdcvcUweBWZPI2kqbcJCHB0d5fPnzzk6Osrl5WURIMeq\nnc3NJK9WqyJ8gJ+Dg6ac8nK5bCVrsRimwDabTQEiSXN8kskHiXNMmveafkSgjYpZTN5pDwpa0zQ6\nY+Vn7SlaaQIMkvYJiZrKx+gz1qurq5Zw1rFDMt95LkJNOI21A4SRaMuJD9aYNfDmY62TFIbgNW9z\nl405NPCiOXSDgVqtVq0Mf5RyHYNOXk/w9O+Yz5omJ7wENetEPj4LeHec36EgK0uHUuhTt9s+HQeA\n4CfA254zMlon5nrMZiMwILA3KEf2t/vn/tKs7DebTTlCyvxYlmsQVQMUgIXBC+/lHQ7LmOVinvmO\nAY0NkB2PXTYcxm63W8LUm82m0Po+8p40Ce+Mg3HDMFhnDofD9Pv9Uu8KUJA0BySo5sq6dzqdXF5e\nZj6fF1Z3vW5KqZOT4svreC4sM/NqJoI9RO4fn0U+ABDURzGw4BkOTfkdzAtHm9nvZoutk1erVetE\nGvPL+F4D26wFNsBgnbHDgNA3l9BAXwEeASs+pZo0trMGFzj3SftaBuaYxGjWxvu8bjsBJyjPzWZb\n6Y/YFpQhVI9DMi58wySRF4KBBzCAtEHhpnvX63VGo9FXSUqcxBkMBsUzXa1Wuby8LKd6iGWywCBv\nb0gEpKZq6R/lglerpgAVVBuK0YKO0sZgma5mk+IFmxUxI4JCJ9kURYPRchjBSpgxUAbazUesMS5O\nDgRU8Lmjo6OSfwKFyXhZb7wXQJ3Xj2fbwJFF7hyXXTcbWPeZubQXjGJjHn0S5bWNS3gGZez3Je3q\nrrXn1ev1Sv0Gx7MpxW6PJmnAtRkTP5P3YGQZ7+3tbZG15OuifnXo0r+v6WszL1bGBtR4wyhe7xe/\nL0nrPcx7XajL4MTPq2n4PwI9zImpfINVAzOU92vNc1yzartozq+pveCkAcGeb7Nm1imsF59D/gwa\n0c8OH74G2M7OzsrhidVqVUo5rNfrcvO5nT3XvUoaxp7cEQNlmEROpKDLeQb949kGA8iwwcp6vS5F\nQQHFZop5LzqZOUiaECHvrfdCDY7m83nRyci8WXk+x5idgM34GBd2ot4z2FucSdgXOxyev8fHxwJs\n0S91+NptZzknLPZwOCxIdL1ujgQ6wZFFgFFhoyTNwBH04XDYin+aXvMCARaMcslwJmzDZ8mkH4/H\nX93vw79Z1E6nk9FoVBK4eL69McANFF/SZj2S5iQP40aY7KXCSDB/VhA29DBTSb4CBt48tXGnL8Ph\nsMSGARJQtKPRqCStcmytNjQ+6cDY2AAcC2eu+Q5KCc+FsZMke3R0VPJWxuNxWc9dt3qz4TGY+uYz\nKHKzG/y/4+32tpmbOgznsJH7gPzzbIP81WpVvLg696WmlpN2PgBrTEPuXA7czgXf5Xm1MXKioOfH\n/ajZBhRiv98vSa3Mr2lzGu/h/YASktNfAzTMtefQHmG9Dih6xoF3CWBxWOw1JsbhTjzvb4EVZN2f\nn5/LHrSjyO9ms1krL4X9yzOQc7632WyK08c8OefOyf0YWwzlYrHIcDjM1dVVFotFbm5u0ul0cnp6\nmru7u8KIo9ORJQNjwCZhIPaGk7DtdL3mCBtoGczbOUTu2MPj8bgAK/aiTx9Np9NSS4p38Szm0vuV\n/sG2UHUZ1sesFnaHP8ik5ZXPG+h77wKccLQs28yRc2g4PYrtYy7+zKHc2cV/LN6bN29Khm/STkQj\nDOCbi5O0QizE0lEc0IYog1pYHGbxM8ni/vnnn8sdMTAjxEY5XeLnI9CEpFg8qsk6y7nf75fjgsRv\nTW/buJpm5N9JO3YNdQk9DeJO0jJ0zBkAEBDhvBPmx4aITTUajVoxcjaTGQ4E7zU6ns8CtuxtORRB\ngSYAqr01GxVAIYDQRnHXDRlzSMBMSR0CsPKjsT/MvvB55sThEcB3kq++hwdIkUPytQCZBjVJE9ox\n64NiJAaNguH5Tua2gYUxg2qu995rXh+KzeOm3DlAjX4hi1D+ptiZR4Mt5sXgy9dWeD7quamBxJ+F\nW9A7jMf5RV5L//Tf2We851sAJ4Qe0L3j8bgwqhg79rqPoaOn7dXjYPIdEklxVJLmxIoveSSPjzWm\n+FeSXFxclBwTnNwkBZiQm2ZjDONqJ9WeP418F4d8krRyMpJ2CfgkX4EfO9s4dIASf+f5+bnYRDOQ\nSVqVs5Ft5BkZ73a7xS5RHZb+sS5mwWwja/adubEdOTra3kLtCwRZH+bODjDpDKwF9rpOB6jbTsAJ\nAmyqExCC4TFlhgCAIFFuNYXr0zYsPr/j0j8W3HQehpOjw9PpNOPxuBxXNuJz38mbAM1zmgTlykZM\nGi/w5OSk1HbBsENDsmgIitkNhM+gxSEmFCDjZ1PDLPm6ANN1r8XYaZwmQkDn83lBwvTFng1eS705\nraSZK5QSf7+8vCxIH7kAePBObqd2PhBskzf4LlsdzjGFn6QFXJB9mj0Pb1pkgVBikuKN4inVe8Js\nQ6ezzaKnUJY/WxtYclFsUJFnswcvL+1TSabtMSpWUJ4fsyrev8yTQYGZFL5XG2z64T7YSEC3I6M1\nyHsN/DI/Bkx/tNZ2evh/K3uvR31U3PNs5oY59X7dZXP/0XVmm+inwz0YUoeO0ck2dj/++GMxlA41\n/PTTTyXxlpLxOC/dbnNBKO8HxJJcb0YLucGIUr12PB6X/CiHaJIGePtE283NTS4uLpK0QauZgtp2\nAdjrOZhMJjk/Py/MkcEFwJ659zvMRNfRhfF4nN9//711whPHF0fJDiI6wpEK6w3mmjk8PT0tegTm\ng/3oongAReacsJmZliStfVO3nYETBmGEy6QATpwYSWIqyZZ1YhAK5B//+MdXl/o9Pj7mw4cPef/+\nfasPGEMUoMEMGyVJOZ3iWD7Ik8v9MNy+J8JGGg80aW7KTFIQLuOndbvdcsmUq+kh+PSFXA6O1bHJ\nHPtkU5iWs6JOvmZqkq3S//vf/15OPzFvGBCE0LVZGCPALWkE0HONR09hH7MhSTuUR1iKTeZ7imC1\nmOdvqaGYrCBrqtifsTJN2qEQvE3WGS/NNDdejJkp3luDHcCwlaa9Hj7HOry8vLSO8dJ3/0Qhwzii\nCD02xod8Grj57/5JBdEaCKAjrDw99zyz9kx5BrUf2FMwsZbvOizk/eJ1qoEx64W8m9mxh8q7DHTM\nTmIYd80KAkrJA3OIgpoX9JMxHhxsa5RQRgH9hRO5Xm9DzhRgZE8TipnNZi3g6DvXHMKGOTs7Oyt6\nGO8eveokcbOHw+GwMCE0h1iQF3IDGevBwUE5lcK+TlJkyswZdgU54d0AMj6TNBcs1o5kt9tthcuS\ntPQenzs5OSmXKGKD+v1+kSPWxc4ln7P+sGNbyzeRAW55RmZtp6w77GTyrDoZ/7W2E3BitAQaxrA/\nPDxkPp+X41oGDPP5PI+Pj3n79m0ZGEg62RrmH374IYvFooCBwWCQTqfJA0EBEA7qdrvluPB6vU2W\nXa/Xubu7y9nZWU5PT3N7e9tCkWwO+nV6eloWfzabtSrgEgayUibL2wwLWel4rTYgKEkWlfnjWFjS\nJDBipMjLQDBcPhhWwsJUhwoMvi4vL4uXwp0aCJtPQpGgyhzB1lCQablctvJKSF4jNmwDCUKvlbir\n59JflIiPa++qOW6Ol+PTVIAJnzCycTQgMUPAnJi9SBrji+K3geP3BkJJkx+FrDhJLkmLGbM3Zwqb\nZ7vZi/b72ItmA01z41wYxHk+/e71et1yHPwMK/LaY3YYhd/55EfSJFfWSe4GOjQDTFP3ngvCReQP\nvRYGslfMO9z/Ogl+V409TCVvDh2sVquyJ22k0S8YW4f6ABaU7f+v//qv9Hq9vH//Pr1erzDLPv4O\nUEu28sVRVY7K9nrbAmvD4fArJgJ2mTmFSZ5Op0madWXdyXHju4AR1pe9c3Z2Vu5xe3x8LGwNehEA\nbCeAED9A2KEfxsb+pK/0n9AaMu1wC2P7+eef0+l0ynsHg0GxQWZbfDQc4GcdZGBkub+/v8/Z2Vlx\nsK2DDfJ8mitJOZHJ8znl+WdH5Hd2KzEThGGjABf0fdJ4fklasUcm2YmuKIC//e1vpZLs77//XhgP\n19ZImrAP7wdVQglOJpOStFQfhURw8BDxEBA6jFKS4lkAUAA1pjVrYIDQ2ICgtGAPMEpJY4AYj4ur\n2Ztljgxc2CQYPDb0mzdvcnV1lfPz8yJUT09PmU6nBX2jbOy9stEYvw0fY+X/Dw621XWn02nr4igQ\nPIALQ8lmfHp6+up69Tr5cVeNeSfsQWlrU6woBDMmNdPlcB7KfLFYZLPZFG8PltGJmskf5y7QJydo\nWmFgWFy7woCmNsgoYmSBMZldYE/5HT6uD9BwX2t2ws/lXfz0HjYLQeO7BoJJWvkJyCw6Ablz43Nu\nrwG2+v/sELwmy2ZpkubUi9fyNVCzi4ZM+oQJMu4+moWlKBe/96kPchoosOacBDN2yJBDEg4bdLvd\nwmKQW9fpdArbi/OHc4t8oEMoJIksole95hhS5Isxc5CCPlAtFtZ7s9km8dvZJMfG+XXMpfWfnRUa\njik2yGtze3ubxWJRWJ7T09McHBwU/cqdSMy/dRIy6WRkQCE2zPkqjN25OmaMWGv2LPuL39e5LK+1\nnR1vYNCc8mAxT09PW7dZMmlJWrfr0jBKeIDL5bIYBMI2KD8WwwlWKF0r8Tdv3pRLoJjg1WpVEL2Z\nBaNYgBV1UQ4PDwvFRr//iMLFM4a6dOU9NrfpUnsFVmyAILM7q9WqVXYeJQmar9E3LMh3332X0WhU\ngM90Oi2nZJKtgrcX7zFCx9M/0/HUlXDVUmKksAC+0RiF7qRO5pHnM5+7bsiaDSfr7Lo9fK6m+R32\nqY0860fhMIy7jWzNNBkQ06c6edugo+4nzYra/alDSIyJfwPeUbaUG2eN6xAX7+D7Bi6uaUJugR0U\nU8n2+gz2aDaA6A47DXWrGRqzWu67f+K9Mp9ea/4YjLJe3s8e2y6b8yVms1lWq+3Fosxdt9ttXfDG\n/sUDR48DHLx/X15eyr09m832BBVJnITdAAacgHT46Pl5W6jt5uamgI2kCaPaLsDkuAQFoIFnJe3a\nM96PrI3B8dNTc5ke80G4BFnBJgHOYJWYW8JOyL0rPHe73VKDq9vttu6Xo+G83N/fJ9kCr6urq6Kj\n+/1+2TOuP4N8Ol0A+waAcp6cw2r0FZsDeATIuTI1wM9lOlxA77W2E3AC+Dg8PCxHiaGW8NrJ41iv\nt/FCMsMRLhaNRcSTpJAN5YsREt+DwOZJmnPuKABO3rx7964V/+T39MPC+fLyUrLBWVhOSJCTwsaz\np5Y08c2aumRzgzCNNvmML9FC2aGA7R2b7cFDmE6nRch8OiPZbszT09NcX18XtI1SIDGN00sodYMQ\n+uNcCCdS4snwXl8Uxtx6w9BH1itpjlrbc67DDLtoVmTOF3C+hQ2ywZyNEXNqehlGDuAOa3VyctIC\nCkk7J8JABGNtTxYlPBqNMpvNinzacLsvGCpCfVaivLs23Ky7nQGeC3BlXKbg8a6TNqBAwdpwm4F0\niInwCuMh7Ov+MBeMz3H518Ju/DTA8HgNQgwybYj8O3/PToWdn102mIJut1sMrQtKIluu8oqeRW9Z\nPpOmiCT3p71586YAHwzxwcFBbm5uiuPK2tr7h/0jN5DwMmEHvk+1V9YZHYKuAiyQs8j77ehhqAEz\nvjMIefa6scbOQ0Q3ADRcfI1cF/IpkW3kkXoutVF/enrK3d1dut3tCbR3797l+fk58/m8HB5g39dO\nMe92agDrSmidEgSU9OckEPaSvjNfgEGckJOTk6KvsJlOaH6t7azOCZML9cZEoLgQMiaVBfHGTtpX\nT+NZDYfDVmweqmu5XObs7Czv3r3LfD4vTAiKh4mlL1CEm80mt7e3mc/n+f7770tsdDweF8ADs0Ll\nu9VqVSoeAlBYYECH47NsEDYB3wEEobQstD51YaXMhqUZRAFOjG4BQqzD8fFx3r9/3zp5tF6vCzCz\nd4wn7FAW68Ia1YXaSGp23PXu7i7L5TL39/eF8YI94r4MU7vr9bqEAgE/NdW5iwZbBMuRpKXMknYd\nDRtSGziavWg2N4ZqvV4XeXI5d472JQ1ITZq7adg/yBZrOh6Pyxoa7AAWHfo0M9Dtdlsgy8weDS+K\nz6IY+cm7MHQ0lKi9M8+NqWaHKGFaANBeH3vdGFEfdTaAM71tI2yGw142fbYz4jli3uh/7bHyOdqf\nnWb4Vzb0CuOHZWBvr9fbPD30X9IGacwfYQzGvtlsT4CgS5Ptnri4uChl7ZOtDl8ul+XaEDPi1kNJ\nAzL7/X7+7d/+LUdHR/nll19aFaXn83lxdjH42CLnObKm6BkYAJhxHGISSPmugb3HnzRhS+fQWK7Z\nE3VtE8CRGRl+9/PPP5fcSWp0Mac1W8uJPOfFuW/oGv+byIZPqCKzXNaKHDA3R0dHmUwmhVAgvAR4\nNYPzWtv53TpJAzAwvMk2hENGcJ3tmzRHHK3goP2Ojo4yGo1asa9ff/01z8/POT8/z/Hxcd69e5f7\n+/vWZU6wE/Ymid2t19sblP/7v/876/U6//7v/96qLeIy2NyiybhMVTMWUD4hGJA1G6Tb7ZakYFfk\nIyZqpgCjD6gDgNCsZBEue8EWzMPDw1xeXubDhw+ty99IjCUkxvwAMhiDDTNKiA3OXDFOxgEljPH1\n0eqnp6eMx+Ny/xDvOzk5yWg0SpIWw7XrVlPwKBEbHMBp0lDPNuT+vsMyAAZ74tx9Azvn8AnPNaC0\nMUcOVqvt3VHj8bjE7k1Rw2aaAncYyt4+wMTG3UDHgCppco/s1RkcOB5vb9RHVpFF5M5MlNk8vs/p\nDowLfXBoir7WjIXBsdcIT5zfYVgMZvAw+T3yzv5xmNlMlIHPrprXHXBiIJikhFfq8RPCQH48fozn\n6elp5vN5AffT6bSE1mFAyO04OjrK7e1trq6uCoNjxmS5XObx8TE//fRTye/gRBA5RT4skDS62Sww\nHr9P6cCe83eSVNGTPAfnEjtl5xE7Q+4chp1TUMg/z/HJNqqi26j71KLBl0O1Blnsm263W5z92rFA\n/ngm/aI/dSLrarUq4PDk5CQnJyd5+/ZtyXt5eXnJu3fvis53GsUftZ2AE5BajebcMFj21j15TDYL\nmjR3ukAnUhr/+vo6s9ks0+m0CD6K03TzbDbLaDQqz4AJOTo6yvX1dY6PjzMej/Pysr38yuf4bZh5\nJgbItPrLy0vrfpMkJXHK4SEWmL7gpaKoURCcQGKxXQ+AMYL0if3xXgML+nl0dJSLi4t0Op3iUbA5\n8TApwYzw41WTQ0DfADHMyWq1at1jwsZwAi+XeZkd6vV6mUwmJeRgj4E+7Vp50wwA7H3UoAWAYhYF\nIFJ/1h55kpZ8PTw8FOXtuUZWFotFicED3pAfU72Pj4+lEjIhSYAFMW76YWXm/rOPzNr4plbWDuNk\nJsXxfa8lex85RGc41EQ/7N3jjda5Oy8vLxkMBgVA0U/mhWd5/g1yDJbYdw77OBxjZqXum/OmzKok\nbQaNPrs/u2ibTVOdmfkw8EvSMjoAZYwuupKQu4GBQ3sYwfl8nl9++aXldBGe4B1c8GdWGtaZI/c/\n//xzer1ezs/PW7VPyLuyDYJZRk+ZuQC4cFIJmYYp+vLlS7nFl+fU689ewy6ROEufHBZN2se3yWc0\ns8F8/vbbb5nNZkUPECoCCJCrSWQC547nEqojnMX+5fsOowH+WU/6ip62PcFusK99ei35up5T3XYC\nTqDqoeXxlOv4K4thpYbnDrVFAi3AgEUDZYJQR6NRzs/Pi0HnOCyeJyEFjudi1EiSfa8AACAASURB\nVDHCnKO/vr4ux2lZNIBM0tBgIE5QMoiesSUpRoES2syDj6oRWnmtzgMLPZ/Py2YhMzxJy0ggHIvF\n4quYpY3BcDjMxcVFnp+fc3d3Vyg6AACK5fn5uVScRUEAMnq9Xuv+IjxUxue4a5ISx3S4wmCU9bTB\nR1mgEM3Q7LLVRq/Oi0iaUAVjxMB7szuUSaufiwH48uVLOZaNPNb5ESgW1hKamoZB4H0oe6+J85gY\nh40Q8sizeYZzXKzgWNt6Hth7AHhCePTb4NR5AM4jqBvvYe5qw8gYUZzOQ2OsdijsMAGWeK4VukNA\nrLG9U5prsfjk2bdSw4f5Zw/CZDIvjJN9jyyQP8J8wUJzZUCSMnfkhKD7zs7O8ssvv5TwCyG6+Xye\n4XCYXq9XShCQIE4jwRZWdTab5ezsrADDpMlhYg0JL9nBShqn4fDwMPP5vMWmsD/QQThLXn+zpDBI\nZsmT9u3kyJOPGicNs22W5enpKWdnZ+Xfw+GwsHE4tOxZ1sqX35IzQ8KtIwWbzaY4F94HXnMDMOtg\ngKYLxmGP0GN/BkySHYETFs+5A0mjuFhkKyGEg88lW6GeTqflCBkxZqO82WyWT58+5f/8n/+T//zP\n/8xgMEiSkg9CbQMWltMp0It4p5PJpCw8TASTjWBMp9NCuVGrxUfKfLKChSPEg2GHhWGRYTEQ6IeH\nh9bpn6enp5JgnLTvbUnaxdAAATaEzHfSCNSvv/5avO46zwBFwXq4P7PZrBgLvGLWimdYaQFubOz4\nPJsAT9OeAs9DhpyAtutm78fy62ampO6z859gLZApswBWhm/evMnnz58zHA5byXvkohggeV4NCGBH\nYK1sfGuPB8DPPgB80D9T9zBEZh2QsxrkGKQl7Vwd53y4/6aok6bsNrLGu0xV86euX0FfcDDMCnmO\n6IvL3vPd5Ouj0PZ2a2+a+bIxNMjmd/9Mkf//3egreRW14U22sn93d9eqb9LpdHJ3d9cKpbhx9BZn\nEq/83/7t39Lr9fLbb7+1gB0s7ZcvX/L7778XJxOGhdMp19fXSVJYE8CzD19QhI2TjMlWtn17uoEF\nzAL5LbAesNtcxcI1HEkK++95JFyEPq1BOo4tgIk9hq5w+MdMUK/Xy3Q6LeUwcNwdmfARbPQLDbBj\n9tDF61wegs8zJphMA6mkyQO1LcDu2TF4re0EnKAg8Q4ZGIaTDiM8PpWB8OC9Y3CdK8LGMJvAGfea\nOgedkqOC1+eMafcNj8FKmLL3bA4MAhSfE7WShskgPou3S3P45/HxsSBdh1VYWAwA36efbBrQLz/N\nrPAulDaeMmvCpgKkEM5hrDBfjIHicgYkfgdUqzeikycxnklz4gPa0bkWPNPPr3+/q4airL0uy7Vz\nJJg/fm9Dz3zAeNAMLJyMZ2aBuXXuBobVzbWGkGmHjQA5vIe+Wrm436zVH8mYDTTNjAb/9nN4lgEp\nSX027H424+ffda6OmTr2OR4nc+4QE2EAJ63TT+bN4JG+oEu87vzdfTYw5xlej12HdZhj5/kxX2Y4\nWS/rNeaNeTg7O8tgMGhd4wHgob158ya///57qXhKCIIkWfY8a4jsM5fUHDFgZ47RmQ6zoO8BMk9P\nTyWPizUYDAYF2Jgt9EEKZIm9gt5lDgEe/BtnDp3qvetogNlAy5vvzQH4mZlyWQCHgp1AzN6DyQE8\nmJUkB8YOCn1l7XBasZNms2z7cGjZnwDJuu0sIZYBseBJWh4iAm4lzWKzkCS1LZfLVu0EX5+OYA2H\nwxJrRtFgqK1kOYLMYtugcLSZvtXJdCyUT98gvFRyRUAAGNDb9rq8oGZnmDMfPXbSI2NwXgm/h5JN\n2hUtrdwpuFavFaj86OioVcb+5aW558LrlzT3HBl8mvaHSQHIOUGLGCjKgSQvh3KsAJnbb4E5Sdpl\n59ncZgf8b7NC/p4NMvuD79vTwhvlmB8nmnyCyh4QILQGzEkb8PlEGSEay2gNqF8LW7E3bHQNGmx0\nzaoYrKL42fc8g1AhPz2HNkTur3Ma6j+vGTAD/DqB0CDIFD5j593+Wcvua8+0DJutqEHlv7rBYKFv\n2cMOH6LbACPPz8+tmlXodp9qZF0Ya6+3rVPy6dOnTCaTUjMK7x+QPhqNyj5I2ndKwerCohHyZD19\nGMOna2CLkQHrddbg4GBb1Ozq6qrFHJjdQkbRs4CRJCVVgfnjj8NLDqHQN4dJkLenp6dysMJsnp1s\n9q33Ms7u8fFxsZu9Xq8w+wAa9i9JrXwfe4uuwY4C6gGPdXjSziPMkZm3r2Tu/xPJ/X/RmIDlcpl+\nv9/K+kaIEWRiYb5nxiyKQxCcrWazQ2Nx8Z+LABG/5D2155I0RpiFMRUHyHJcznF8H6mzx4miWa1W\nGQwGhdZ04R17TbyTvmBQDKLot5WhPTOe5TCJQwUYeU4d4fGhaBxjxih1Op1y/wXvNYB0QhS/e3h4\nyGKxKJsHBgYjxHFxnkdCMnRtnW+Dcf4WqO+kWQN757UhB2i+BkoMRGqalFNgBhmwgev1ulV+HaoZ\nGa1Bda0AUUT8zrKbtK9op5ltqWUNA2C2o2YV+J3XzvPEv82esHf4npNpeY/77eewj+p18hg9PkA9\n78MI1ODRTKFPfBhcJE11VN7Dd2vWlHcz1po520VDhg4PD3N+fl6OndvIek7JY0AOanZzvd4eQSWU\nzj4xE8GJD+Ydufzw4UOSdrI04IV5Y3+5sjiMOv3w7cIYSzONDvWgY8bjcVnPxWJR2GKMO/3CFsDu\neU+ORqNi7wyazUIbUPNMg2vmk8hC0gAAHMNOp1McN++bJAUwnpyclDGgS11LBTCHvUUHOyGWvUBY\nzqFkAx3WnLnyd19rOwEnRmyfP38uA2JSXJqcxUgaJoEkK9PAj4+Pub+/z93dXf7nf/6nRUGb6mOR\n6gRNK1Umn36xMIQsMC6msqG0UH7kU5jmRPgxyPaI+DtGwIiSRXYSMMJudsc0HQjdIMfeFwrRyJY8\nAvrg3x0fHxcAmKS1Pg7T+eg34JECPKyvjbWBEkKMR0z/GDfJZKwHsuC8hG+hISvINKcEnMdRb0qU\nkQ21P2MK2Zudz5lC5/c0e/O1ATbI43nIDN6P2UHWnLyOmhkwnc+znNthJsgGwMAaAIz3y3fZW2Zb\nmFOHavkdzyX3hjE5Zm6QTD/tHGAU6JNZQAMgGxjez3gd1/f46U8N8vic5aAGW//qhuwlKXl7GPfD\nw8Ny2zqshfPLjo+Pi3OIbjg6OipHgdEBTjDFsfEJM9bGsgTbwncAHcvlsoB5O4027qwh+nS9XhdH\nmM+ancap4uQiSbe1zLHPqCHV7XZLDiIGnn4Q5vD9WDBUdnRfc5xJGyCEUx+N5hQR8mXG1s49dhEw\nSZkH9CkX/LH/AVnYtOFw2JoH5hUiAAcTmeCzTrh/re3sVuIkBY3++uuvefv2bQuRJs1RVz6XbA3w\nzc1NASgIyu3tbWazWWsToyAw7jAcfMY1P8wu1Emjjlvyf0m7ZPd6vS65MCwg5+2TJiHURsJ9eO2E\nQdJmXQ4ODnJ2dlYUIEdrUcAYBdPTgBL+JM0dGY6R867BYFAMPXPBM3zMzMaI76L0B4NBAXLMOxsD\noOON5DDeYrEo68/vicnCFJCBT3P9lF03xkk7ODgo7B8G3UesARL2tg2yzD7Q6vCBQTC/N4PgdTZd\n+xowtvJOvr7ML2m8OP+ecdMf11PAcJthcz/piz1MKztk38/i72aXaAZtBu7sP7xkgyje4X1P/7g1\nm/U0kKidKOdg4EB5jeo9mDSshMMNfv8/8zD/FQ3n4ODgoLDdm82mhA87nU7Oz89bYAFHp9PpFA98\ntVqVat528Lx/qTyaNGuIHfCRdkLjABzXg8LrX62aqztsGHu9XmHsefZgMCjHcXGIcIAIqWBQzXDX\njoENOwmqgAcDm6R9bJxnWNaYe/YGMtLpdPLhw4dSHHS5XJY8HmSFAwqj0aicXEUXw5ZQ9DJJiSIc\nHR2V5zhXZLPZtHSXw3JmSLDjHOZwHpz3q+3ga21nOScvLy8l+zlJxuNxRqNRUSaLxSLj8fgr42eg\nkWwVKwlNFlyEm9LIbCAEwMmuCJ6peNA/DAt9cJKPY3mbzaZFa1HREM8hSXkmmwZDW7MVTpxz3slw\nOEy32y2Cg8K2N+6Yueun1BsTQ290TF+vrq6SpChYNsXh4WEmk0lZQ/6fRKk3b96U43qOIaN4AGje\nyDSU3GuGOUlr7VH4GNM/ovJ31Uzzkk/g/AvnDzFe+o7HxJpi1A2Ka+NrA8nvMbQGDcylEweRXX/W\n1HLSPk3jMJQNrcfN352QSv/5jD04Pt/pdEqBK37PPgbM0l8Uqve9wbnZOPa5w3/Ik0Mu1McwhY7c\nQ8PX4TbWyawp4Z26VoYdBIdCoMENRgziasC6i8baPT09Fb1G4cSLi4ui28ziWU7MxvlOGXSbHVDq\ncfhwgkEPup938Xz0KBffUXTNBtDOnNkv1o/3WVeaLYZlTJpwEP2z04DMOPdmtVoVloE5RcbsEDAu\nyxiOqI8Bv7y85Pz8vNQyQhe4+dAFz16tVplOp8WZvru7K6zRcDgsZS04rm1WxkeDCTeSc4IcW18B\nYJO0dJLX44/aTsAJx9Gge05OTjKdTjOZTPL8vL1g7suXLyU0Y3TsBpK7vb1tAQrAg9kJKDDTw8lW\nUbtGRK34fILFl0Xx+6RRzgizvUBaTdXzjF6v16Ie6SPAw/+G6kT5+bkWCAw9v6cvbMyTk5NcXFzk\n06dPrRozX758yf39fYvGxkCgRKEh/S5/niNsoPlut5u3b98W+pF5hklwkiuGhDUDxDh8ZvapNqbf\nAjih/ygsFBj0qgFH0sgD6+gxYGQBZK7IiTGzwfNzUHR8x4rB1LhDQABHg2OaZRuQxRq4DLWBDcCY\n33s/0GeHUBhPDdDpk0MLgC2zIwYOyI4ZIBtU1goFy1iQZYM2DI+TC5FLwgKcrMMwAZjsecIgsNeQ\nAYdQzXDV87nL5vwO5KTT6ZTilDhRPkHFOlpeAHusB/Wf0C+Pj4+ZTqetMvgOsxB68y3v1puACRtj\n5r4Ob6Dn0WOwCbWcueFMU6jQjBwAC9lF9jlhyf/zf87hsDwDWJgzfkfVXAMZ5ufx8bFUPsZZ9x1I\nw+GwOMXUBasdysFg0Dr5R2gMJxWwZ/sKI2OWxfoY0I8tYS+9xhLXbSfgBOFhstjk0+m0RemjQJMG\nZTIB9eDW66bcsCvQvnnzJu/fvy+VR/0s3k8SJrRh3RAE0CNelz2zOnwzn89bXpE9fzYZYSjeDXXK\ngidNTgCl7Dudbc7J4eFhPn78WASQd5v65vuuoGoQMRgMSr9RfvP5vFRThN2B1rZiYZ34DgLJuFer\nVdm4HIeG+bm9vW3FmJkPBBow4pwDxk1irpXPrhW3G/Rm0jYwyJqBCc0eZm2M+Ls3P7LvBDzmnO/A\nKFhRYxzNeNhDA5g7pOKcDjyg2nDTLwCMmRrHvW2MDbT4ye+RMd7B/nKOCyymvXIciLu7u3z48KEV\n+ttsNqXgFHJGHspyuSwKmdMlljH0jC+NYz9tNptMp9MkTeEvvEU8f8snesO5KayF6XEML47IrhNi\nmX/6iaOSpDhXLnOetAvLGRADRizrOKo1s9HrbU9Jmv0yo4KMozu73W4xvkla8unQGWwuurHf75dS\nB9gRnLzBYFBSCBgHxhog471pBytpnIx+v9/af3WBM5pzdmCCkGvfrMweuby8zP/+7/8WRodIQa/X\nKzklPAdQslqtWiUqut1uxuNxYU8cwq+jDOxz9AlADOYsSZlfwm1mUrHVq9WqdZKpbju7lZhTB6Ax\nBuBEqjqkwyQmTc4DwubENT5vhsG0k424gcN6vS5389CHpNl8LAxCYDaHeCSbBQYHhZWkJEg+PDxk\nNpu1jnuS68HnWMzaG6QPVEa0wgclJ41woFjtQZIHwVFssxd4JDxzvV7nu+++K7cGI6BO2GVzLpfL\nAmBQYNRGQeE7nGBPHmPrNTVdyvtIXrNnzTiceLirxlrZ+0GR8DszBQYezqEw+HXIBkXHXjFl6zwW\n/pC8lrSP8CdNMSfTywZIhDRN69a1UljjpM0KIpckSdI/My98lvlgTgxOeJaPHpKXgqzwDE51AWLN\nuPEOAAh7HObUoB4ZRTegdA3eAEckzDNX7pdlnXGwLg4TeU39e/d/12Ed+oUcms3wsWLGyHolaSVQ\n+u8OY/Ms7pqB4WIu2Qsu2AlgA6zA6mK8zVpRD2S5XBYHkXcQgmXNALXIkp0BqodTMXw6nRYH0bko\n6/W6nCRlXQ8PD0v+Gf1NUhxPnEY7D4TB6PPnz5/z448/lvfQ5/Pz81Y4HZYFUI0NY2/BXPd6vbJf\nkNebm5skTZ0xWG+zYOgRKtQmyWQyKfk+7K1Op1NO0OKwAozqpN+67Yw5scLivhU2qBcYL4QFc0JO\nTcXWIRdimy6PjsGz0fYEdTqdElNN0gIlzjWp7yZA8fF+cg36/X5B8aB01zhhfNBp3LvgvhntJk21\nQhrje3x8LDcwY/AYL2MzQOAkDV5esjUWVKxlUyDgMCnMNYARJYA3vF5vj7US+3XxovF4nOvr6zL/\n9TFoh5KGw2G53t5AldM/yRaYUGDvWwAnSdtIO7Rg4GHvGAWfpDXW2kM3I8H3AA4opZp1oD+mjU2D\nW76ReYNuvCYzmA6/8NN0Nv2Greh2uwWgAjwZj49U+h29Xq8Y+k6nU+6ccnVk56Qw32dnZ7m7u0uS\nVn0Le7x2KDgOyvcZP8bIFVEd6nJdGeaU7xh08EzXhEhSEiMdRuLd9op5758p8X9FGw6Hmc1mJUy5\n2WxKkqvZMhto1gfd69Cs5dyfJYeCtaQmCrqcvVXX5EC/wDyjV/l/dDcgBEYXgE4fkNXlcllkpd/v\nl/L5sDfkkgyHw1aelKteU0UWUNXr9UrBzrdv35YwIDbC+sEMFHqXMAxgZzqdlitZzs7OSv0Z9Kjl\nE3uArvHlqpxuury8LDbr4OAg9/f3JVTnveB57/W2dwS9efOmBUjJZ+HvvHcwGLwKcl9rOwEnnz9/\nTr/fL2iPP6BaAwyDlKShrI12ASgsJhsCOtDPYlFhSEzvooiTtJQSRWhcCdbxThe/SppLr1DO5+fn\npdwyZ+5ns1kBEaenp2Ujfvz4sVCmLCZAwgmuxLjn83kmk0mZIwStjgEiEMfHxxmNRuW4GPPF3T3c\nO3R2dlYE8Keffiq3FFMcDU8Vj9pJbvydPB+El9COAUntTQN+mCeML5s3acJIq9WqJHMxzl032Cd7\nGVbYSfs4oA1RDVpQlvbqa1Dz9PRUkvKShir2RY3OR0raV7S79oYZMbMF9ioBI6w962+Wx8wO4MPH\n3h2GYXzsQQx90lT9pLAiYT7mzewac8++ZmwYiX6/X/aPy3ATggWgmBkaDodFhwBuksZhcSVNAwh+\n8hzXj2Bf2UGzoq91nR2UXTbWh3Lx3PtVh9/xnM0+ITsvLy9FVuv8FQ4KPD1t70FjvgAcAG+cV3IW\n0Q/sDYq2wWZ0Op3i/QP2AA3kglCUkLmfzWaFJfnhhx+KnGIrGM98Ps90Ok2/3y/63g7acrlslX1w\nQUTfccZ+MTto4Pb4+Jibm5tiG3HYnp6eyjFiElw7nW0+0+XlZcnhZG7Ze+hnbBB26NOnTzk8PMz3\n33+fu7u7HB8fF0cVVopnUUmX577G0HNaDeaSz7FnHeJ6re2MORmPx0lSQMR4PC5F1rxRQXiOjZvG\nht5DWSXNLb945kwu8WQMt5Pb8ITYXH6uQydsQsCL2RnXRGHTQf8dHx/n48ePxXNC0blEMpsd4UZA\nEQjmzoyFQRKgzciYhtBQcwCUfH19XWKNeCv2EjFyHz9+zPn5eZImj4XNxm3Px8fHuby8LEqm3+/n\nl19+KYoVNgvwiFKj0qGPBvJ+EDveDO9PmvhunQS3y4YiQ/EmTQjSYQoa4AEZ9+ZmQ/Nv55JguAlz\nEB6ELQAI1AnEVob2ZpN2QTbei8dXn1hg/9gr9NUIeMfsE+hf9iYyYYbH7JCBFP0E0Fg+aFDNPl2W\nNLebG5TwDkAJ7Bs5KXyGXJSaZsewEqZFfxjAoFdQ6hh0M0WMDwfLYUz6jmLfNSuI8cTYj0ajYswZ\nJ/92bRIcDfQD7LLHjXMEeFssFnn//n055cKNu93uti4WenY+n7cYKBwjJ2EDihxKplihw5jonV6v\nl6urq6KDAVKcvgLIEBq8ubnJxcVFK+zOn9PT0wIeAPuAIhwIZMrH570/Op1OPn/+nPF4XJ738PCQ\n4XCY4XCYzWaTjx8/5t27d+U4NKCF/YYz++HDh9zf37dOz7BPqeHS7/cLwHt5eSml5X1IBObMR7sn\nk0k6nW2CdK/Xy6dPn1q5nIzdJ19J6fijthNwMhgMSt7F09NTbm5uCvrlj+lr0DHAAAEETePZIHAk\ncHKSxLUxKCDExPEs2BEjffrB7/HwQLPkzjjc4mS/zWaT+/v7Et4ALSYp5+kxNMvlMm/fvs3nz5/L\nAkKJs4kAHpQPpnQx2esYqTpxkr4lWyF7//59AWPX19c5PT0tawCFCWLebDZ5+/ZtZrNZJpNJTk9P\nyy3K1DsYDocFyXNjKP9OmsQwMvHxWBw2QkE5qZlj4N1ut8RPURjOjUEJ7pr6TtoFwjCSDnuhMPlc\nHXKjAaIdiknSAiYAcudEGZSaIsZYOmQEPVwrGjMUzDnvR4adz4VhN8vJZ5zUx0kznu2wBrLGH/pn\n8PRa+Mvz1u1uk60vLi6KHDnEQj4CMkSuCUaNPlIKoAa9zAWGxSd96I9ZKEIAyRbkkZPlOeCEBXOF\nIXe4Gcp8l+3Tp0+FPUYfAdDQw4BadJ3HgV4y0HOeHpemrtfrsn7owX6/n/l8Xhi4pDkpCWvtyuDo\nAoA/evng4KB1uzuAK0lhO9mz2AUDVBg/O8Xv378v8orMJimMG+9dLpelRIMdu6TJ2XM+DXOUJD/+\n+GMrD/Ho6KgcTe52u7m+vi6y9+nTp7JGT09PJRyXbCMWRAfMmFIlFnZytVqVUNN0Om2dwOL3OBn3\n9/fFeXz//n0JeY1Go9ze3rZyFZ0bhP3+M4dyJ+Dku+++K7TYr7/+WjqL0kgaj9LCTAO4cConScnb\nICSBkpvP50UR2asajUZJ2ln/UL/2+Jy7AbpF+AEdJH+SBwL9hbInDJI09CheII2wj+OMbHKus2aT\nOyuevroUM58zAqdNJpP84x//KHkfk8mkABs2J8d9uYob2pnEKI4Ls2G92Z6envLx48eSCIuRhgHB\n0Jo+RxmQCAf4OTg4KN5Bp9Mp8UpAJCeG2Ji7ThpMGjk1aE6aM/7IJYbNORxJmxVE9lA0DpMgQ47z\nOhHbMV3nhdigO//EBQSde1IzBKbwUdJJu0gbYyC5EfBppwOljVK2PHNywgAC+XPisOccY0FxMIcc\niZGzHvZQ+Sz5AA6vAeBRog6T4iwBTviMw2f8ZDzkaBBenUwmLZ1E+BZmgXlCXnbdmAtqIpHUiPw6\nFMnet4PnUzaEN9brdcbjcZHjs7OzfP78uYR/Tk9PMx6PWyA1SWGH6UfSsKxm28m9MPuHLHW73cIS\nmLGm/0lzMs2hO4AHSfiLxSKnp6dFpwNIkVvrcoy6k2/Pz89LMbTXjPUvv/xS9udqtSoOdpISImQP\nMKfsOeqHPT8/l9OhLy8vubi4KGF8n8Kibz6B9do9XHzeckG43Swm15sYXGNz7QS91nZWhA1BPDo6\nysXFRW5vb1sdRQmy4fGYLKQYxaSpEooX4qIxxMdM3dm7TBqBtQDxfxyrYvFJuqqpexI/AS0IOooL\ndsBsAcoZANTtdkvlQk4S1OEBjJSFg8UG6fomTNPmg8Ego9GoeIar1aqAKTw1gEeSFoBIthvu8+fP\nubq6KgqVvBLi9ggx3hWKHqEEpGCcaYvFIhcXF0lSlHTSnNYidstJJYcn/hlF+K9qyAwN42dPHw/P\nXrkTWVkXvGg2sY2ewyWsncEPtCxzxHdsKMy0AIJhEgz26pwXJ2w77OmTdvwfYzXLBYhHNvjpEAaG\nhr4sl8uMRqNScdPzTZtMJrm4uCh7ybk4zJvDSfSJtcDR2Gw2OT09zWQyKdU1ofQZD/uZvzNG1pk5\nA4DhicPOcPoBz3U2m5VcBOfcOMS1yzYYDAowpl/MDSCXELqdsSQlodWMUtLkFMGUJMnNzU0Bw0dH\nR5nP562SDxjT2WxWTjlyMov5dWiEhHz2EuNItvoGubYjYUehrqjqPDj2HQw6DDi3KBP2ZI/A5qH/\n2XOE0y2bBilHR0e5u7vLd99918rXSVLsEUCH7wFmcGpJnrXNcZ4abDe6wuweugDGmj7DRB4dHRXd\nP5/PS07jeDzOu3fvWhV6Aeg885tLiP348WNRMHjNzupmEEwkE5Y0J17wPhDMJC0PxJOQpEXRouz4\nO54TeSMu9wuNdnCwPY5GXJTEJP7/+Pi4MAoOQVlJIYwO19ze3rb62utt70R49+5dRqNRPnz4UKh8\nDIKVHQ3l7rABPxk7Xs+HDx9KaAUBwRsEYZOEljTH9mArhsNhbm5uWsi609leioXxQTGAmA3WACn0\nlY2aJPf39y1QyO9B/z7yZgDrmgq7bDXrUBvnpDmNkjTGFeBlWh+ZQZm8dk8GzwNY21t1sjYso3Mq\n6BO5Tj5tZrmh1TkUdUiKPpghq8t3A9wdtqjngmdgAJLm8jV7sRg6gPxgMMh4PM7FxUUxaABjmFiO\nc8IoEXIyo4G+waj1+/3MZrMis/SFNbChMmAz2+Kj2OghJ3QCqiwDzLe91102xojedM4HoMsAbrPZ\nFIBJGBwASSjNdZTI5eBZSaM/ky34xOg6rw7bwXoljZPAmlBuALnjQAAev/P1zDwjH+PxOC8vL6Ua\nK+NBFjnhOR6PW3dpJSmOw3Q6bbFi7DUcXeRms9km5cIU+7Z4HBWiA9ieTqdT7u6pWeRud5t35Rvl\nYYmQL4clfaACZ55QO/ucd+Dk+D48F+D01SWbzSa3t7e5urpq1V75/vvv1OWGhQAAB0pJREFUX5W3\nnYATx43tUUEvYby5cMjUdz35hDTY2CBFBDFJC2UmKQuOADrhFDorSVFahIpQEiBkP5cKfYeHhyVx\n9MuXL61kOLyAwWCQ09PTfPjwoSR64Tk4J+Xy8rIIvFmE1WrVqvg3GAxyc3NTKGIy3AFyKF7m8/ff\nfy+KmYRUo1o2MJRcst1A5+fneX7eVvDF2wQ9A/LwDii0hDJzKGKz2ZT5s5Ew0+R7G0DpTjhcr9et\nBOdut1tYl103gwN7ZfbW+R39xyuELqY6Ixsb4A34cTIfQAjDh4G18nC4wkYWeWLdYFoM+vCE8fr6\n/X7ranlACUod40GoDoWKh2baHcCdtG8ato7g+Un75l+AweXlZYmrQ7UnDciBYTKNj9FjfIC3yWRS\nZI81AHQD2jkh5fViHv1+jDN7xeARGWAPcdoCZy1pEvAnk0nryPQu2nw+L3OO7rSjQWgFXYisEVbB\naTk/Py96EvYoad9Sj1zBUqCTLy4uCvhwAiynYtBhsM/cNO8QJmEQ2DuSe7nUFF1yenqaT58+5d27\ndyU/0CEtFymDaR8MBmWPAAR4H2kMLy/bAyA4pTA/7FHk6/T0tFWXi6qvJAkjQ5wGAnDAFpL3AThg\nj7E35/N5FotFAfTMD3PEQQ7W4e7uriR0w5ACKNnH2DnmfDKZZLPZlr0YjUY5Pz//in0i4fa1thNw\ncnV1VSaM4jhJQ7ViqJ2sljQhHbwLNq+pLZgTFtWeFsbw4OCgCG7SgBc2DcYaQ4+yxgvgvfQJQHJ3\nd1c2MXFBAACb9OHhIYvFosTjfWw4Sen/8/NzPn782Ep2xQNFgfk+BeoDQCPzPd7NnxoYYFxgZCz4\nrrY7n88zn89zcXFRjt8ZvEDrnZ+fp9fbFvIhhHN0dJTb29uyxtfX1616AEmK0XTYjjoUvV6v3POA\nYb25uSnH3fg89/7sutUhPQy+ATbKjo3uvAVySepYuZtzTvA0ydZH2fEee58wDSgj5ARP1EYXdi1J\nAb5JinfI3vLeSFJqLgCOXTDw/v6+gC4YIcsgbGmv1yvAnT3W6WwLOhmooexrFpS97QrK7I3n5+ec\nnZ2VfIHRaFQ8Z/bAly9fSrgBDxanpPbGyX0ajUa5v79vlTj/8uVL2af0t9vtFiaQ5FrCITBT5N5g\n/P+M/v5XNHTjwcH21CNrw3ohT9TaQP5IyiQnDpkGPAPcmDP2RX0a7fT0tIBIdD/rBWipLyZl3gk9\nOS/GuYKwIVS3dn4VsgPwHI1GrRQAZIk54EQN4MRHj7FB3333Xfk8cmpW/fDwsFzpgj7GmYHlZP84\nXw02nWdiW9EvHz9+TK/Xy+XlZbrdbs7Pz8spOoOZ4+Pj/PLLL7m8vCxsliuXA3jQbTAqnz9/bpXb\n4HQXoR1OZDqfaDKZfFvMCYlnKLD7+/s8Pz+3PB6ED48JQUSg7UmxaLVnjreBR8+kOp/CoRDeSzIX\niwyz4eqDgA6EebPZFBTpOKVDTHiBJBk6Ix0vL9l6f6enp1kul63jYHim9JvNnzQ5ISh2F2QzM5E0\nFLNDKhgqnuXwEMfS+Dx06A8//FCOgHNyiPlmDh4fH/Pdd98VT4BTRskWyPB3DC1z6hg13/Emd0Ip\nNQr+rBTyv7Ixz05GTRoQ7JguSof8KR/DZHwOESUN64IHBFMC24hS9f6pQ172dmAnqQ/B+mPweZeP\nhLKPzF45yZCGbAHCkHufdKvDr8zXcrksoA2vEqAH43lwcFBAlStR8j3+wN5gbCaTSdlnzLlP+5HM\nBwgnTOky5svlMrPZrCTX393dZTAYZDqdlpu5fS0GLOJ6vS61fyaTSVkrG3fndlgudtW8fpeXl2X9\nfcqF36PHAQUYNWQQZhemD6AL6+cCk+RNwMSyXrwHMIcXTz/ZYzCEDnOik5ElwvROCH16esrV1VXZ\nG5YT7wdYOGSfvtEIj3D6hfkARMPS+fQN7DQ6HrtBCM3MInIOuwxYBGytVqtSa4s9CghbLpf5/vvv\nW3k6zMn19XU6nU7r9ul+v5+PHz+W00Aw5AbzgCaYxufn59ze3ubgYHsIBSLBl9f+Ues4Fr5v+7Zv\n+7Zv+7Zv+7brtvuqVfu2b/u2b/u2b/u2b2p7cLJv+7Zv+7Zv+7Zv31Tbg5N927d927d927d9+6ba\nHpzs277t277t277t2zfV9uBk3/Zt3/Zt3/Zt376ptgcn+7Zv+7Zv+7Zv+/ZNtT042bd927d927d9\n27dvqu3Byb7t277t277t2759U20PTvZt3/Zt3/Zt3/btm2p7cLJv+7Zv+7Zv+7Zv31Tbg5N927d9\n27d927d9+6baHpzs277t277t277t2zfV9uBk3/Zt3/Zt3/Zt376ptgcn+7Zv+7Zv+7Zv+/ZNtT04\n2bd927d927d927dvqu3Byb7t277t277t2759U20PTvZt3/Zt3/Zt3/btm2p7cLJv+7Zv+7Zv+7Zv\n31Tbg5N927d927d927d9+6baHpzs277t277t277t2zfV/i+IAQDEy/wsagAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x113e04090>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Raising the bias of a filter will correspondingly raise its output:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# pick first filter output\n",
-      "conv0 = net.blobs['conv'].data[0, 0]\n",
-      "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
-      "# set first filter bias to 10\n",
-      "net.params['conv'][1].data[0] = 1.\n",
-      "net.forward()\n",
-      "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "pre-surgery output mean -12.93\n",
-        "post-surgery output mean -11.93\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
-      "\n",
-      "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ksize = net.params['conv'][0].data.shape[2:]\n",
-      "# make Gaussian blur\n",
-      "sigma = 1.\n",
-      "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
-      "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
-      "gaussian = (g / g.sum()).astype(np.float32)\n",
-      "net.params['conv'][0].data[0] = gaussian\n",
-      "# make Sobel operator for edge detection\n",
-      "net.params['conv'][0].data[1:] = 0.\n",
-      "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
-      "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\n",
-      "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\n",
-      "show_filters(net)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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6rjiupf4Cj5aQOy8AIySOjNHfLJs/HKpUOi3GzfPTY+lRaO6ds8MGg0PE5N69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\nXi/AHOsJhfHw8BCsD5/n83m1Wi1Jq+A8gMTh4WFcy8fRgwJhcxgzZ46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lxGYlBsKBKwod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\neJp22rYCTlKa3xVsVpDoJqXjI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-       "text": [
-        "<matplotlib.figure.Figure at 0x112bc2190>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Net Surgery\n",
+    "\n",
+    "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
+    "\n",
+    "Roll up your sleeves for net surgery with pycaffe!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import Image\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# configure plotting\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'\n",
+    "plt.rcParams['image.cmap'] = 'gray'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Designer Filters\n",
+    "\n",
+    "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "blobs ['data', 'conv']\n",
+      "params ['conv']\n"
      ]
     },
     {
-     "cell_type": "markdown",
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112797f50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "## Casting a Classifier into a Fully Convolutional Network\n",
-      "\n",
-      "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
-      "\n",
-      "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the net, list its data and params, and filter an example image.\n",
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
+    "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
+    "\n",
+    "# load image and prepare as a single input batch for Caffe\n",
+    "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+    "plt.title(\"original image\")\n",
+    "plt.imshow(im)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "im_input = im[np.newaxis, np.newaxis, :, :]\n",
+    "net.blobs['data'].reshape(*im_input.shape)\n",
+    "net.blobs['data'].data[...] = im_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
-     ],
-     "language": "python",
+     "data": {
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+       "31Tbg5N927d927d927d9+6baHpzs277t277t277t2zfV/i+IAQDEy/wsagAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113e04090>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# helper show filter outputs\n",
+    "def show_filters(net):\n",
+    "    net.forward()\n",
+    "    plt.figure()\n",
+    "    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
+    "    for i in range(3):\n",
+    "        plt.subplot(1,4,i+2)\n",
+    "        plt.title(\"filter #{} output\".format(i))\n",
+    "        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis('off')\n",
+    "\n",
+    "# filter the image with initial \n",
+    "show_filters(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raising the bias of a filter will correspondingly raise its output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pre-surgery output mean -12.93\n",
+      "post-surgery output mean -11.93\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# pick first filter output\n",
+    "conv0 = net.blobs['conv'].data[0, 0]\n",
+    "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
+    "# set first filter bias to 10\n",
+    "net.params['conv'][1].data[0] = 1.\n",
+    "net.forward()\n",
+    "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
+    "\n",
+    "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# Load the original network and extract the fully connected layers' parameters.\n",
-      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
-      "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
-      "                caffe.TEST)\n",
-      "params = ['fc6', 'fc7', 'fc8']\n",
-      "# fc_params = {name: (weights, biases)}\n",
-      "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
-      "\n",
-      "for fc in params:\n",
-      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "JyIiIuIVBSciIiLiFQUnIiIi4hUFJyIiIuIVBSciIiLiFQUnIiIi4hUFJyIiIuIVBSciIiLiFQUn\n",
+       "IiIi4pXfPRZNtgyLF3IAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112bc2190>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
-        "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
-        "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ksize = net.params['conv'][0].data.shape[2:]\n",
+    "# make Gaussian blur\n",
+    "sigma = 1.\n",
+    "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
+    "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
+    "gaussian = (g / g.sum()).astype(np.float32)\n",
+    "net.params['conv'][0].data[0] = gaussian\n",
+    "# make Sobel operator for edge detection\n",
+    "net.params['conv'][0].data[1:] = 0.\n",
+    "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
+    "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\n",
+    "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\n",
+    "show_filters(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Casting a Classifier into a Fully Convolutional Network\n",
+    "\n",
+    "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
+    "\n",
+    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Load the fully convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
-      "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-      "                          caffe.TEST)\n",
-      "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
-      "# conv_params = {name: (weights, biases)}\n",
-      "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
-      "\n",
-      "for conv in params_full_conv:\n",
-      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
-        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
-        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+    }
+   ],
+   "source": [
+    "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
-      "\n",
-      "The biases are identical to those of the inner product.\n",
-      "\n",
-      "Let's transplant!"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
+      "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
+      "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for pr, pr_conv in zip(params, params_full_conv):\n",
-      "    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\n",
-      "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
+    }
+   ],
+   "source": [
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# Load the original network and extract the fully connected layers' parameters.\n",
+    "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
+    "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
+    "                caffe.TEST)\n",
+    "params = ['fc6', 'fc7', 'fc8']\n",
+    "# fc_params = {name: (weights, biases)}\n",
+    "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
+    "\n",
+    "for fc in params:\n",
+    "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, save the new model weights."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
+      "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
+      "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
+    }
+   ],
+   "source": [
+    "# Load the fully convolutional network to transplant the parameters.\n",
+    "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
+    "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+    "                          caffe.TEST)\n",
+    "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
+    "# conv_params = {name: (weights, biases)}\n",
+    "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
+    "\n",
+    "for conv in params_full_conv:\n",
+    "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
+    "\n",
+    "The biases are identical to those of the inner product.\n",
+    "\n",
+    "Let's transplant!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for pr, pr_conv in zip(params, params_full_conv):\n",
+    "    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\n",
+    "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, save the new model weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[282 282 281 281 281 281 277 282]\n",
+      " [281 283 283 281 281 281 281 282]\n",
+      " [283 283 283 283 283 283 287 282]\n",
+      " [283 283 283 281 283 283 283 259]\n",
+      " [283 283 283 283 283 283 283 259]\n",
+      " [283 283 283 283 283 283 259 259]\n",
+      " [283 283 283 283 259 259 259 277]\n",
+      " [335 335 283 259 263 263 263 277]]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# load input and configure preprocessing\n",
-      "im = caffe.io.load_image('images/cat.jpg')\n",
-      "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
-      "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
-      "transformer.set_transpose('data', (2,0,1))\n",
-      "transformer.set_channel_swap('data', (2,1,0))\n",
-      "transformer.set_raw_scale('data', 255.0)\n",
-      "# make classification map by forward and print prediction indices at each location\n",
-      "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
-      "print out['prob'][0].argmax(axis=0)\n",
-      "# show net input and confidence map (probability of the top prediction at each location)\n",
-      "plt.subplot(1, 2, 1)\n",
-      "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
-      "plt.subplot(1, 2, 2)\n",
-      "plt.imshow(out['prob'][0,281])"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x12379a690>"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[282 282 281 281 281 281 277 282]\n",
-        " [281 283 283 281 281 281 281 282]\n",
-        " [283 283 283 283 283 283 287 282]\n",
-        " [283 283 283 281 283 283 283 259]\n",
-        " [283 283 283 283 283 283 283 259]\n",
-        " [283 283 283 283 283 283 259 259]\n",
-        " [283 283 283 283 259 259 259 277]\n",
-        " [335 335 283 259 263 263 263 277]]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x12379a690>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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m4NvfEI5zItkeCZ5geiiRuN9wtdtQUsGXQtKCLRZNlegjxRowrkn05kqYWYy2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\nA43ph1xlbAoPAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x12666be50>"
-       ]
-      }
-     ],
-     "prompt_number": 11
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "9LP99Jddvv7oRNMF4OW9xh7gYeB7wAvAv7GH1SxACdgGKnvRlG/nz8nJybkPyHeA5uTk5NwH5ME8\n",
+       "Jycn5z4gD+Y5OTk59wF5MM/Jycm5D8iDeU5OTs59QB7Mc3Jycu4D8mCek5OTcx+QB/OcnJyc+4D/\n",
+       "A43ph1xlbAoPAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12666be50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
-      "\n",
-      "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
-      "\n",
-      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "*A thank you to Rowland Depp for first suggesting this trick.*"
-     ]
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# load input and configure preprocessing\n",
+    "im = caffe.io.load_image('images/cat.jpg')\n",
+    "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
+    "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
+    "transformer.set_transpose('data', (2,0,1))\n",
+    "transformer.set_channel_swap('data', (2,1,0))\n",
+    "transformer.set_raw_scale('data', 255.0)\n",
+    "# make classification map by forward and print prediction indices at each location\n",
+    "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
+    "print out['prob'][0].argmax(axis=0)\n",
+    "# show net input and confidence map (probability of the top prediction at each location)\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(out['prob'][0,281])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
+    "\n",
+    "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+    "\n",
+    "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*A thank you to Rowland Depp for first suggesting this trick.*"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
+  "example_name": "Editing model parameters",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
index 8e076663ca6..11cae120db2 100644
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -1,154 +1,1909 @@
 {
- "metadata": {
-  "description": "Extracting features and plotting the Siamese network embedding.",
-  "example_name": "Siamese network embedding",
-  "include_in_docs": true,
-  "priority": 6,
-  "signature": "sha256:845bb18929f96543ba2611eb5eca744fd98939cbef876df6bc319c29f616fc64"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Setup\n",
-      "\n",
-      "Import Caffe and the usual modules."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Load the trained net\n",
-      "\n",
-      "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "MODEL_FILE = 'mnist_siamese.prototxt'\n",
-      "# decrease if you want to preview during training\n",
-      "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Load some MNIST test data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
-      "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
-      "n = 10000\n",
-      "\n",
-      "with open(TEST_DATA_FILE, 'rb') as f:\n",
-      "    f.read(16) # skip the header\n",
-      "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
-      "\n",
-      "with open(TEST_LABEL_FILE, 'rb') as f:\n",
-      "    f.read(8) # skip the header\n",
-      "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Generate the Siamese features"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# reshape and preprocess\n",
-      "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
-      "out = net.forward_all(data=caffe_in)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Visualize the learned Siamese embedding"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Import Caffe and the usual modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the trained net\n",
+    "\n",
+    "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_FILE = 'mnist_siamese.prototxt'\n",
+    "# decrease if you want to preview during training\n",
+    "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load some MNIST test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
+    "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
+    "n = 10000\n",
+    "\n",
+    "with open(TEST_DATA_FILE, 'rb') as f:\n",
+    "    f.read(16) # skip the header\n",
+    "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
+    "\n",
+    "with open(TEST_LABEL_FILE, 'rb') as f:\n",
+    "    f.read(8) # skip the header\n",
+    "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the Siamese features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# reshape and preprocess\n",
+    "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
+    "out = net.forward_all(data=caffe_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the learned Siamese embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = out['feat']\n",
-      "f = plt.figure(figsize=(16,9))\n",
-      "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
-      "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
-      "for i in range(10):\n",
-      "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
-      "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
+     "data": {
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+       "AABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fadf4552a90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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V//ELwP+gpqYC2J4j95vbj2+//fZ54/E8evQob775JgMDamXzjz76iF/96ldJ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\nmQpmVsCdLcyKeM1mXGC6H+dTJdHH1XTny+FQP2S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DVVVKG7x6aYNEwD6xZw/6T5/Gjbt3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w6+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\nAABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fadf4552a90>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "feat = out['feat']\n",
+    "f = plt.figure(figsize=(16,9))\n",
+    "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
+    "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
+    "for i in range(10):\n",
+    "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
+    "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "Extracting features and plotting the Siamese network embedding.",
+  "example_name": "Siamese network embedding",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 6
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 83b5dcd40bd61fe61b88fb90c3615e2096316724 Mon Sep 17 00:00:00 2001
From: Kibum Bae <kibum85.bae@samsung.com>
Date: Tue, 19 May 2015 22:43:49 +0900
Subject: [PATCH 1342/2053] fix typos in docs

fix typos in install_osx.md and performance_hardware.md
---
 docs/install_osx.md          | 2 +-
 docs/performance_hardware.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/install_osx.md b/docs/install_osx.md
index 39cb02fe232..ad98a85d47f 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -10,7 +10,7 @@ In the following, we assume that you're using Anaconda Python and Homebrew.
 
 **CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
 
-**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
 In other `ENV` settings, things may not work as expected.
 
 **General dependencies**
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
index b35246feabd..cdd4b361dea 100644
--- a/docs/performance_hardware.md
+++ b/docs/performance_hardware.md
@@ -48,7 +48,7 @@ and then set the clock speed with
 
     sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
 
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to intialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
+but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
 
 ## NVIDIA Titan
 

From 7a8fcc763dc89e717a58319a77da9d9813a829c0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 19 May 2015 22:41:50 -0700
Subject: [PATCH 1343/2053] avoid dangerous state in pooling layer CUDA kernels

Previously, pointers were modified with the assumption that they would
only be modified once. While this is true so far in practice, the
introduction of CUDA_KERNEL_LOOP makes this a dangerous assumption.
---
 src/caffe/layers/pooling_layer.cu | 57 +++++++++++++++++--------------
 1 file changed, 32 insertions(+), 25 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index d1d48501af3..a1080ebf33a 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -28,12 +28,13 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
     int maxidx = -1;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        if (bottom_data[h * width + w] > maxval) {
+        if (bottom_slice[h * width + w] > maxval) {
           maxidx = h * width + w;
-          maxval = bottom_data[maxidx];
+          maxval = bottom_slice[maxidx];
         }
       }
     }
@@ -67,10 +68,11 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     hend = min(hend, height);
     wend = min(wend, width);
     Dtype aveval = 0;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        aveval += bottom_data[h * width + w];
+        aveval += bottom_slice[h * width + w];
       }
     }
     top_data[index] = aveval / pool_size;
@@ -94,11 +96,12 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     int wstart = pw * stride_w;
     int wend = min(wstart + kernel_w, width);
     Dtype cumsum = 0.;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     // First pass: get sum
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
       }
     }
     float thres = rand_idx[index] * cumsum;
@@ -106,10 +109,10 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     cumsum = 0;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
         if (cumsum >= thres) {
           rand_idx[index] = ((n * channels + c) * height + h) * width + w;
-          top_data[index] = bottom_data[h * width + w];
+          top_data[index] = bottom_slice[h * width + w];
           return;
         }
       }
@@ -137,12 +140,13 @@ __global__ void StoPoolForwardTest(const int nthreads,
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     // First pass: get sum
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
-        cumvalues += bottom_data[h * width + w] * bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
+        cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];
       }
     }
     top_data[index] = cumvalues / cumsum;
@@ -231,22 +235,22 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     int offset = (n * channels + c) * pooled_height * pooled_width;
-    top_diff += offset;
+    const Dtype* const top_diff_slice = top_diff + offset;
     if (mask) {
-      mask += offset;
+      const int* const mask_slice = mask + offset;
       for (int ph = phstart; ph < phend; ++ph) {
         for (int pw = pwstart; pw < pwend; ++pw) {
-          if (mask[ph * pooled_width + pw] == h * width + w) {
-            gradient += top_diff[ph * pooled_width + pw];
+          if (mask_slice[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff_slice[ph * pooled_width + pw];
           }
         }
       }
     } else {
-      top_mask += offset;
+      const Dtype* const top_mask_slice = top_mask + offset;
       for (int ph = phstart; ph < phend; ++ph) {
         for (int pw = pwstart; pw < pwend; ++pw) {
-          if (top_mask[ph * pooled_width + pw] == h * width + w) {
-            gradient += top_diff[ph * pooled_width + pw];
+          if (top_mask_slice[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff_slice[ph * pooled_width + pw];
           }
         }
       }
@@ -274,7 +278,8 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
     int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice =
+        top_diff + (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         // figure out the pooling size
@@ -283,7 +288,7 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
         int hend = min(hstart + kernel_h, height + pad_h);
         int wend = min(wstart + kernel_w, width + pad_w);
         int pool_size = (hend - hstart) * (wend - wstart);
-        gradient += top_diff[ph * pooled_width + pw] / pool_size;
+        gradient += top_diff_slice[ph * pooled_width + pw] / pool_size;
       }
     }
     bottom_diff[index] = gradient;
@@ -310,12 +315,14 @@ __global__ void StoPoolBackward(const int nthreads,
     int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
     int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    rand_idx += (n * channels + c) * pooled_height * pooled_width;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const rand_idx_slice =
+        rand_idx + (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice =
+        top_diff + (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
-        gradient += top_diff[ph * pooled_width + pw] *
-            (index == static_cast<int>(rand_idx[ph * pooled_width + pw]));
+        gradient += top_diff_slice[ph * pooled_width + pw] *
+            (index == static_cast<int>(rand_idx_slice[ph * pooled_width + pw]));
       }
     }
     bottom_diff[index] = gradient;

From 44d35bd071517a6bab8f2d2d7fc3ddf2d5df9762 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 19 May 2015 22:52:12 -0700
Subject: [PATCH 1344/2053] avoid dangerous state in LRN layer CUDA kernels

---
 src/caffe/layers/lrn_layer.cu | 58 +++++++++++++++++++----------------
 1 file changed, 31 insertions(+), 27 deletions(-)

diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 24aa6a30130..e50ae8df83b 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -18,8 +18,8 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
     int n = index / width / height;
     int offset = (n * channels * height + h) * width + w;
     int step = height * width;
-    in += offset;
-    scale += offset;
+    const Dtype* const in_off = in + offset;
+    Dtype* const scale_off = scale + offset;
     int head = 0;
     int pre_pad = (size - 1) / 2;
     int post_pad = size - pre_pad - 1;
@@ -27,24 +27,26 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in,
     // fill the scale at [n, :, h, w]
     // accumulate values
     while (head < post_pad && head < channels) {
-      accum_scale += in[head * step] * in[head * step];
+      accum_scale += in_off[head * step] * in_off[head * step];
       ++head;
     }
     // both add and subtract
     while (head < channels) {
-      accum_scale += in[head * step] * in[head * step];
+      accum_scale += in_off[head * step] * in_off[head * step];
       if (head - size >= 0) {
-        accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+        accum_scale -= in_off[(head - size) * step]
+                       * in_off[(head - size) * step];
       }
-      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+      scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
       if (head - size >= 0) {
-        accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+        accum_scale -= in_off[(head - size) * step]
+                       * in_off[(head - size) * step];
       }
-      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+      scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
   }
@@ -131,43 +133,45 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
     int n = index / width / height;
     int offset = (n * channels * height + h) * width + w;
     int step = height * width;
-    bottom_data += offset;
-    top_data += offset;
-    scale += offset;
-    top_diff += offset;
-    bottom_diff += offset;
+    const Dtype* const bottom_off = bottom_data + offset;
+    const Dtype* const top_off = top_data + offset;
+    const Dtype* const scale_off = scale + offset;
+    const Dtype* const top_diff_off = top_diff + offset;
+    Dtype* const bottom_diff_off = bottom_diff + offset;
     int head = 0;
     int pre_pad = size - (size + 1) / 2;
     int post_pad = size - pre_pad - 1;
     Dtype accum_ratio = 0;
     // accumulate values
     while (head < post_pad && head < channels) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
+      accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+          scale_off[head * step];
       ++head;
     }
     // both add and subtract
     while (head < channels) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
+      accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+          scale_off[head * step];
       if (head - size >= 0) {
-        accum_ratio -= top_diff[(head - size) * step] *
-            top_data[(head - size) * step] / scale[(head - size) * step];
+        accum_ratio -= top_diff_off[(head - size) * step] *
+            top_off[(head - size) * step] / scale_off[(head - size) * step];
       }
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+      bottom_diff_off[(head - post_pad) * step] =
+          top_diff_off[(head - post_pad) * step]
+            * pow(scale_off[(head - post_pad) * step], negative_beta)
+          - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
       if (head - size >= 0) {
-        accum_ratio -= top_diff[(head - size) * step] *
-            top_data[(head - size) * step] / scale[(head - size) * step];
+        accum_ratio -= top_diff_off[(head - size) * step] *
+            top_off[(head - size) * step] / scale_off[(head - size) * step];
       }
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+      bottom_diff_off[(head - post_pad) * step] =
+          top_diff_off[(head - post_pad) * step]
+            * pow(scale_off[(head - post_pad) * step], negative_beta)
+          - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
       ++head;
     }
   }

From d73f02b5aa81ce6808cb204f2540f2f585450890 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 19 May 2015 22:59:23 -0700
Subject: [PATCH 1345/2053] more const in pooling layer CUDA kernels

This treats pointer arguments in the same way as non-pointer arguments,
and should help to avoid issues like the previous dangerous state issue.
---
 src/caffe/layers/pooling_layer.cu | 161 +++++++++++++++---------------
 1 file changed, 81 insertions(+), 80 deletions(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index a1080ebf33a..ca4b13f7c41 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -9,21 +9,21 @@
 namespace caffe {
 
 template <typename Dtype>
-__global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
-    int* mask, Dtype* top_mask) {
+__global__ void MaxPoolForward(const int nthreads,
+    const Dtype* const bottom_data, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* const top_data, int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
-    int hend = min(hstart + kernel_h, height);
-    int wend = min(wstart + kernel_w, width);
+    const int hend = min(hstart + kernel_h, height);
+    const int wend = min(wstart + kernel_w, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
@@ -48,21 +48,22 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
 }
 
 template <typename Dtype>
-__global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
+__global__ void AvePoolForward(const int nthreads,
+    const Dtype* const bottom_data, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
     int hend = min(hstart + kernel_h, height + pad_h);
     int wend = min(wstart + kernel_w, width + pad_w);
-    int pool_size = (hend - hstart) * (wend - wstart);
+    const int pool_size = (hend - hstart) * (wend - wstart);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     hend = min(hend, height);
@@ -81,20 +82,20 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
 
 template <typename Dtype>
 __global__ void StoPoolForwardTrain(const int nthreads,
-    const Dtype* bottom_data,
+    const Dtype* const bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* rand_idx, Dtype* top_data) {
+    const int stride_w, Dtype* const rand_idx, Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_h, height);
-    int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_w, width);
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
+    const int hstart = ph * stride_h;
+    const int hend = min(hstart + kernel_h, height);
+    const int wstart = pw * stride_w;
+    const int wend = min(wstart + kernel_w, width);
     Dtype cumsum = 0.;
     const Dtype* const bottom_slice =
         bottom_data + (n * channels + c) * height * width;
@@ -104,7 +105,7 @@ __global__ void StoPoolForwardTrain(const int nthreads,
         cumsum += bottom_slice[h * width + w];
       }
     }
-    float thres = rand_idx[index] * cumsum;
+    const float thres = rand_idx[index] * cumsum;
     // Second pass: get value, and set index.
     cumsum = 0;
     for (int h = hstart; h < hend; ++h) {
@@ -123,20 +124,20 @@ __global__ void StoPoolForwardTrain(const int nthreads,
 
 template <typename Dtype>
 __global__ void StoPoolForwardTest(const int nthreads,
-    const Dtype* bottom_data,
+    const Dtype* const bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* top_data) {
+    const int stride_w, Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_h, height);
-    int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_w, width);
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
+    const int hstart = ph * stride_h;
+    const int hend = min(hstart + kernel_h, height);
+    const int wstart = pw * stride_w;
+    const int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
@@ -214,27 +215,27 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 
 template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-    const int* mask, const Dtype* top_mask, const int num, const int channels,
-    const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_h, const int kernel_w,
-    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
-    Dtype* bottom_diff) {
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* const top_diff,
+    const int* const mask, const Dtype* const top_mask, const int num,
+    const int channels, const int height, const int width,
+    const int pooled_height, const int pooled_width, const int kernel_h,
+    const int kernel_w, const int stride_h, const int stride_w, const int pad_h,
+    const int pad_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart =
-        (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
-    int phend = min((h + pad_h) / stride_h + 1, pooled_height);
-    int pwstart =
-        (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
-    int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart =
+         (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
+    const int phend = min((h + pad_h) / stride_h + 1, pooled_height);
+    const int pwstart =
+         (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
+    const int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    int offset = (n * channels + c) * pooled_height * pooled_width;
+    const int offset = (n * channels + c) * pooled_height * pooled_width;
     const Dtype* const top_diff_slice = top_diff + offset;
     if (mask) {
       const int* const mask_slice = mask + offset;
@@ -260,23 +261,23 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
 }
 
 template <typename Dtype>
-__global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
+__global__ void AvePoolBackward(const int nthreads, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w,
-    Dtype* bottom_diff) {
+    Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width + pad_w;
-    int h = (index / width) % height + pad_h;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width + pad_w;
+    const int h = (index / width) % height + pad_h;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     const Dtype* const top_diff_slice =
         top_diff + (n * channels + c) * pooled_height * pooled_width;
@@ -298,22 +299,22 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
 
 template <typename Dtype>
 __global__ void StoPoolBackward(const int nthreads,
-    const Dtype* rand_idx, const Dtype* top_diff,
+    const Dtype* const rand_idx, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* bottom_diff) {
+    const int stride_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     const Dtype* const rand_idx_slice =
         rand_idx + (n * channels + c) * pooled_height * pooled_width;

From 5a6b0d6883df97a8f329f5ba7615b0a74b6ea651 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 19 May 2015 22:59:27 -0700
Subject: [PATCH 1346/2053] more const in LRN layer CUDA kernels

---
 src/caffe/layers/lrn_layer.cu | 44 +++++++++++++++++------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index e50ae8df83b..001b3c34ac1 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -7,22 +7,22 @@
 namespace caffe {
 
 template <typename Dtype>
-__global__ void LRNFillScale(const int nthreads, const Dtype* in,
+__global__ void LRNFillScale(const int nthreads, const Dtype* const in,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype alpha_over_size,
-    const Dtype k, Dtype* scale) {
+    const Dtype k, Dtype* const scale) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int n = index / width / height;
-    int offset = (n * channels * height + h) * width + w;
-    int step = height * width;
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int n = index / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    const int step = height * width;
     const Dtype* const in_off = in + offset;
     Dtype* const scale_off = scale + offset;
     int head = 0;
-    int pre_pad = (size - 1) / 2;
-    int post_pad = size - pre_pad - 1;
+    const int pre_pad = (size - 1) / 2;
+    const int post_pad = size - pre_pad - 1;
     Dtype accum_scale = 0;
     // fill the scale at [n, :, h, w]
     // accumulate values
@@ -70,8 +70,8 @@ void LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 // TODO: check if it would be faster to just put it into the previous kernel.
 template <typename Dtype>
-__global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
-    const Dtype* scale, const Dtype negative_beta, Dtype* out) {
+__global__ void LRNComputeOutput(const int nthreads, const Dtype* const in,
+    const Dtype* const scale, const Dtype negative_beta, Dtype* const out) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     out[index] = in[index] * pow(scale[index], negative_beta);
   }
@@ -120,27 +120,27 @@ void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 template <typename Dtype>
-__global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
-    const Dtype* top_data, const Dtype* scale, const Dtype* top_diff,
+__global__ void LRNComputeDiff(const int nthreads,
+    const Dtype* const bottom_data, const Dtype* const top_data,
+    const Dtype* const scale, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype negative_beta,
-    const Dtype cache_ratio,
-    Dtype* bottom_diff) {
+    const Dtype cache_ratio, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int n = index / width / height;
-    int offset = (n * channels * height + h) * width + w;
-    int step = height * width;
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int n = index / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    const int step = height * width;
     const Dtype* const bottom_off = bottom_data + offset;
     const Dtype* const top_off = top_data + offset;
     const Dtype* const scale_off = scale + offset;
     const Dtype* const top_diff_off = top_diff + offset;
     Dtype* const bottom_diff_off = bottom_diff + offset;
     int head = 0;
-    int pre_pad = size - (size + 1) / 2;
-    int post_pad = size - pre_pad - 1;
+    const int pre_pad = size - (size + 1) / 2;
+    const int post_pad = size - pre_pad - 1;
     Dtype accum_ratio = 0;
     // accumulate values
     while (head < post_pad && head < channels) {

From 74a8aefaf7082601ca611f12380386633b5ba643 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sebasti=C3=A1n=20Ram=C3=ADrez?= <tiangolo@gmail.com>
Date: Thu, 21 May 2015 20:19:59 -0500
Subject: [PATCH 1347/2053] Update python/requirements.txt to have
 ipython>=3.0.0

---
 python/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/requirements.txt b/python/requirements.txt
index 7bc164a42b5..e0c86c7e51e 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -3,7 +3,7 @@ numpy>=1.7.1
 scipy>=0.13.2
 scikit-image>=0.9.3
 matplotlib>=1.3.1
-ipython>=1.1.0
+ipython>=3.0.0
 h5py>=2.2.0
 leveldb>=0.191
 networkx>=1.8.1

From 25538ce07d0e71e2ed1a523f50b8f9a40daeb424 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:15 -0700
Subject: [PATCH 1348/2053] Refactor types FloatCPU and DoubleCPU into a new
 type CPUDevice<T>

Similarly, FloatGPU and DoubleGPU are replaced by a new type GPUDevice<T>.
---
 include/caffe/test/test_caffe_main.hpp | 28 ++++++++++----------------
 1 file changed, 11 insertions(+), 17 deletions(-)

diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index bd5f31e063f..89b44052c3c 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -40,34 +40,28 @@ class MultiDeviceTest : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestDtypes;
 
-struct FloatCPU {
-  typedef float Dtype;
-  static const Caffe::Brew device = Caffe::CPU;
-};
-
-struct DoubleCPU {
-  typedef double Dtype;
+template <typename TypeParam>
+struct CPUDevice {
+  typedef TypeParam Dtype;
   static const Caffe::Brew device = Caffe::CPU;
 };
 
 #ifdef CPU_ONLY
 
-typedef ::testing::Types<FloatCPU, DoubleCPU> TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>,
+                         CPUDevice<double> > TestDtypesAndDevices;
 
 #else
 
-struct FloatGPU {
-  typedef float Dtype;
-  static const Caffe::Brew device = Caffe::GPU;
-};
-
-struct DoubleGPU {
-  typedef double Dtype;
+template <typename TypeParam>
+struct GPUDevice {
+  typedef TypeParam Dtype;
   static const Caffe::Brew device = Caffe::GPU;
 };
 
-typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
-    TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>, CPUDevice<double>,
+                         GPUDevice<float>, GPUDevice<double> >
+                         TestDtypesAndDevices;
 
 #endif
 

From 2cd27fd1d53c0dfded35ea34baed59f1898a038d Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:28 -0700
Subject: [PATCH 1349/2053] Split class MathFunctionsTest into
 CPUMathFunctionsTest and GPUMathFunctionsTest

---
 src/caffe/test/test_math_functions.cpp | 51 ++++++++++++++++----------
 1 file changed, 31 insertions(+), 20 deletions(-)

diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index 667f744bdd7..a095b544e17 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -15,8 +15,10 @@
 
 namespace caffe {
 
-template<typename Dtype>
-class MathFunctionsTest : public ::testing::Test {
+template <typename TypeParam>
+class MathFunctionsTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   MathFunctionsTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -64,14 +66,19 @@ class MathFunctionsTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_;
 };
 
-TYPED_TEST_CASE(MathFunctionsTest, TestDtypes);
+template <typename Dtype>
+class CPUMathFunctionsTest
+  : public MathFunctionsTest<CPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(CPUMathFunctionsTest, TestDtypes);
 
-TYPED_TEST(MathFunctionsTest, TestNothing) {
+TYPED_TEST(CPUMathFunctionsTest, TestNothing) {
   // The first test case of a test suite takes the longest time
   //   due to the set up overhead.
 }
 
-TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestHammingDistance) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   const TypeParam* y = this->blob_top_->cpu_data();
@@ -79,7 +86,7 @@ TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
             caffe_cpu_hamming_distance<TypeParam>(n, x, y));
 }
 
-TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   TypeParam std_asum = 0;
@@ -90,7 +97,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSign) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -100,7 +107,7 @@ TYPED_TEST(MathFunctionsTest, TestSignCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSgnbit) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -110,7 +117,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestFabs) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_abs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -120,7 +127,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestScale) {
   int n = this->blob_bottom_->count();
   TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
                                                    this->blob_bottom_->count()];
@@ -133,11 +140,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestCopy) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   TypeParam* top_data = this->blob_top_->mutable_cpu_data();
-  Caffe::set_mode(Caffe::CPU);
   caffe_copy(n, bottom_data, top_data);
   for (int i = 0; i < n; ++i) {
     EXPECT_EQ(bottom_data[i], top_data[i]);
@@ -146,8 +152,14 @@ TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
 
 #ifndef CPU_ONLY
 
+template <typename Dtype>
+class GPUMathFunctionsTest : public MathFunctionsTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes);
+
 // TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
+TYPED_TEST(GPUMathFunctionsTest, DISABLED_TestHammingDistance) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   const TypeParam* y = this->blob_top_->cpu_data();
@@ -158,7 +170,7 @@ TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
   EXPECT_EQ(reference_distance, computed_distance);
 }
 
-TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   TypeParam std_asum = 0;
@@ -170,7 +182,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
   EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSign) {
   int n = this->blob_bottom_->count();
   caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -181,7 +193,7 @@ TYPED_TEST(MathFunctionsTest, TestSignGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSgnbit) {
   int n = this->blob_bottom_->count();
   caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -192,7 +204,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestFabs) {
   int n = this->blob_bottom_->count();
   caffe_gpu_abs<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -203,7 +215,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestScale) {
   int n = this->blob_bottom_->count();
   TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
                                                    this->blob_bottom_->count()];
@@ -216,11 +228,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestCopy) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
   TypeParam* top_data = this->blob_top_->mutable_gpu_data();
-  Caffe::set_mode(Caffe::GPU);
   caffe_copy(n, bottom_data, top_data);
   bottom_data = this->blob_bottom_->cpu_data();
   top_data = this->blob_top_->mutable_cpu_data();

From 65af68d8914b6f509f776a9ffae785127c477fa7 Mon Sep 17 00:00:00 2001
From: Nick Carlevaris-Bianco <carlevar@umich.edu>
Date: Mon, 16 Feb 2015 15:49:43 +1030
Subject: [PATCH 1350/2053] Added MSRAFiller, an Xavier-like filler designed
 for use with ReLUs

...instead of tanh. Based on paper: He et al, "Delving Deep into
Rectifiers: Surpassing Human-Level Performance on ImageNet
Classification," 2015.

- add VarianceNorm option to FillerParameters which allows one to
  normalize by fan_in, fan_out or their average.
- update XavierFiller to use the VarianceNorm option (default behavior
  unchanged).
- add tests for MSRAFiller and XavierFiller.
---
 include/caffe/filler.hpp       | 68 +++++++++++++++++++----
 src/caffe/proto/caffe.proto    |  8 +++
 src/caffe/test/test_filler.cpp | 98 ++++++++++++++++++++++++++++++++++
 3 files changed, 165 insertions(+), 9 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index eebf565b1d5..0125b3093f2 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -127,17 +127,18 @@ class PositiveUnitballFiller : public Filler<Dtype> {
 };
 
 /**
- * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$
- *        is set inversely proportional to the number of incoming nodes.
+ * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$ is
+ *        set inversely proportional to number of incoming nodes, outgoing
+ *        nodes, or their average.
  *
  * A Filler based on the paper [Bengio and Glorot 2010]: Understanding
- * the difficulty of training deep feedforward neuralnetworks, but does not
- * use the fan_out value.
+ * the difficulty of training deep feedforward neuralnetworks.
  *
- * It fills the incoming matrix by randomly sampling uniform data from
- * [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
- * of input nodes. You should make sure the input blob has shape (num, a, b, c)
- * where a * b * c = fan_in.
+ * It fills the incoming matrix by randomly sampling uniform data from [-scale,
+ * scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their
+ * average, depending on the variance_norm option. You should make sure the
+ * input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c
+ * = fan_out. Note that this is currently not the case for inner product layers.
  *
  * TODO(dox): make notation in above comment consistent with rest & use LaTeX.
  */
@@ -149,7 +150,16 @@ class XavierFiller : public Filler<Dtype> {
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
     int fan_in = blob->count() / blob->num();
-    Dtype scale = sqrt(Dtype(3) / fan_in);
+    int fan_out = blob->count() / blob->channels();
+    Dtype n = fan_in;  // default to fan_in
+    if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_AVERAGE) {
+      n = (fan_in + fan_out) / Dtype(2);
+    } else if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_FAN_OUT) {
+      n = fan_out;
+    }
+    Dtype scale = sqrt(Dtype(3) / n);
     caffe_rng_uniform<Dtype>(blob->count(), -scale, scale,
         blob->mutable_cpu_data());
     CHECK_EQ(this->filler_param_.sparse(), -1)
@@ -157,6 +167,44 @@ class XavierFiller : public Filler<Dtype> {
   }
 };
 
+/**
+ * @brief Fills a Blob with values @f$ x \sim N(0, \sigma^2) @f$ where
+ *        @f$ \sigma^2 @f$ is set inversely proportional to number of incoming
+ *        nodes, outgoing nodes, or their average.
+ *
+ * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically
+ * accounts for ReLU nonlinearities.
+ *
+ * It fills the incoming matrix by randomly sampling Gaussian data with std =
+ * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on
+ * the variance_norm option. You should make sure the input blob has shape (num,
+ * a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this
+ * is currently not the case for inner product layers.
+ */
+template <typename Dtype>
+class MSRAFiller : public Filler<Dtype> {
+ public:
+  explicit MSRAFiller(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK(blob->count());
+    int fan_in = blob->count() / blob->num();
+    int fan_out = blob->count() / blob->channels();
+    Dtype n = fan_in;  // default to fan_in
+    if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_AVERAGE) {
+      n = (fan_in + fan_out) / Dtype(2);
+    } else if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_FAN_OUT) {
+      n = fan_out;
+    }
+    Dtype std = sqrt(Dtype(2) / n);
+    caffe_rng_gaussian<Dtype>(blob->count(), Dtype(0), std,
+        blob->mutable_cpu_data());
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
 
 /**
  * @brief Get a specific filler from the specification given in FillerParameter.
@@ -177,6 +225,8 @@ Filler<Dtype>* GetFiller(const FillerParameter& param) {
     return new UniformFiller<Dtype>(param);
   } else if (type == "xavier") {
     return new XavierFiller<Dtype>(param);
+  } else if (type == "msra") {
+    return new MSRAFiller<Dtype>(param);
   } else {
     CHECK(false) << "Unknown filler name: " << param.type();
   }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 84b475ce3cd..f9d4d614d10 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -41,6 +41,14 @@ message FillerParameter {
   // The expected number of non-zero output weights for a given input in
   // Gaussian filler -- the default -1 means don't perform sparsification.
   optional int32 sparse = 7 [default = -1];
+  // Normalize the filler variance by fan_in, fan_out, or their average.
+  // Applies to 'xavier' and 'msra' fillers.
+  enum VarianceNorm {
+    FAN_IN = 0;
+    FAN_OUT = 1;
+    AVERAGE = 2;
+  }
+  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
 }
 
 message NetParameter {
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index e04b0fd22af..728b8dc5f0d 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -142,4 +142,102 @@ TYPED_TEST(GaussianFillerTest, TestFill) {
   EXPECT_LE(var, target_var * 5.);
 }
 
+template <typename Dtype>
+class XavierFillerTest : public ::testing::Test {
+ protected:
+  XavierFillerTest()
+      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+        filler_param_() {
+  }
+  virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n) {
+    this->filler_param_.set_variance_norm(variance_norm);
+    this->filler_.reset(new XavierFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int count = this->blob_->count();
+    const Dtype* data = this->blob_->cpu_data();
+    Dtype mean = 0.;
+    Dtype ex2 = 0.;
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      ex2 += data[i] * data[i];
+    }
+    mean /= count;
+    ex2 /= count;
+    Dtype std = sqrt(ex2 - mean*mean);
+    Dtype target_std = sqrt(2.0 / n);
+    EXPECT_NEAR(mean, 0.0, 0.1);
+    EXPECT_NEAR(std, target_std, 0.1);
+  }
+  virtual ~XavierFillerTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  FillerParameter filler_param_;
+  shared_ptr<XavierFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(XavierFillerTest, TestDtypes);
+
+TYPED_TEST(XavierFillerTest, TestFillFanIn) {
+  TypeParam n = 2*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillFanOut) {
+  TypeParam n = 1000*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillAverage) {
+  TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
+template <typename Dtype>
+class MSRAFillerTest : public ::testing::Test {
+ protected:
+  MSRAFillerTest()
+      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+        filler_param_() {
+  }
+  virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n) {
+    this->filler_param_.set_variance_norm(variance_norm);
+    this->filler_.reset(new MSRAFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int count = this->blob_->count();
+    const Dtype* data = this->blob_->cpu_data();
+    Dtype mean = 0.;
+    Dtype ex2 = 0.;
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      ex2 += data[i] * data[i];
+    }
+    mean /= count;
+    ex2 /= count;
+    Dtype std = sqrt(ex2 - mean*mean);
+    Dtype target_std = sqrt(2.0 / n);
+    EXPECT_NEAR(mean, 0.0, 0.1);
+    EXPECT_NEAR(std, target_std, 0.1);
+  }
+  virtual ~MSRAFillerTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  FillerParameter filler_param_;
+  shared_ptr<MSRAFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(MSRAFillerTest, TestDtypes);
+
+TYPED_TEST(MSRAFillerTest, TestFillFanIn) {
+  TypeParam n = 2*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillFanOut) {
+  TypeParam n = 1000*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillAverage) {
+  TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
 }  // namespace caffe

From 59de6c7190eb57bfa7511ede30c965c1d5963a06 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 26 May 2015 12:39:14 -0700
Subject: [PATCH 1351/2053] include comment on Saxe and sqrt(2) scaling factor

although different and independent, the derivation of Saxe et
al. with regards to the scaling factor might be of interest.
---
 include/caffe/filler.hpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index 0125b3093f2..446f5b5c4eb 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -175,6 +175,9 @@ class XavierFiller : public Filler<Dtype> {
  * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically
  * accounts for ReLU nonlinearities.
  *
+ * Aside: for another perspective on the scaling factor, see the derivation of
+ * [Saxe, McClelland, and Ganguli 2013 (v3)].
+ *
  * It fills the incoming matrix by randomly sampling Gaussian data with std =
  * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on
  * the variance_norm option. You should make sure the input blob has shape (num,

From 43d538f0969601a6cb96b51d64741482b45f9e0b Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:32 -0700
Subject: [PATCH 1352/2053] Split class StochasticPoolingLayerTest into
 CPUStochasticPoolingLayerTest and GPUStochasticPoolingLayerTest

---
 src/caffe/test/test_stochastic_pooling.cpp | 35 +++++++++++++++-------
 1 file changed, 24 insertions(+), 11 deletions(-)

diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 12962c65d85..f84464c322c 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -16,8 +16,10 @@ using std::min;
 
 namespace caffe {
 
-template <typename Dtype>
-class StochasticPoolingLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class StochasticPoolingLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   StochasticPoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -45,9 +47,14 @@ class StochasticPoolingLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes);
+template <typename Dtype>
+class CPUStochasticPoolingLayerTest
+  : public StochasticPoolingLayerTest<CPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(CPUStochasticPoolingLayerTest, TestDtypes);
 
-TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
+TYPED_TEST(CPUStochasticPoolingLayerTest, TestSetup) {
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -60,8 +67,16 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
-  Caffe::set_mode(Caffe::GPU);
+#ifndef CPU_ONLY
+
+template <typename Dtype>
+class GPUStochasticPoolingLayerTest
+  : public StochasticPoolingLayerTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUStochasticPoolingLayerTest, TestDtypes);
+
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochastic) {
   LayerParameter layer_param;
   layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -104,8 +119,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   EXPECT_GE(total / this->blob_top_->count(), 0.55);
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochasticTestPhase) {
   LayerParameter layer_param;
   layer_param.set_phase(TEST);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -142,8 +156,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   }
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestGradient) {
   LayerParameter layer_param;
   layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -158,6 +171,6 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
       this->blob_top_vec_);
 }
 
-
+#endif
 
 }  // namespace caffe

From 8d2010489a2e291b4747d3b21b5cadc97d84cb00 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:36 -0700
Subject: [PATCH 1353/2053] Add classes GPUDeviceTest and CPUDeviceTest.

These new classes can be used to implement test cases that are only
running on the GPU or the CPU. The goal is to move all calls to
Caffe::set_mode() inside the test framework, to discourage any test to
change the mode halfway through the execution, which is documented to
be illegal.
---
 include/caffe/test/test_caffe_main.hpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index 89b44052c3c..fc156091476 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -46,6 +46,10 @@ struct CPUDevice {
   static const Caffe::Brew device = Caffe::CPU;
 };
 
+template <typename Dtype>
+class CPUDeviceTest : public MultiDeviceTest<CPUDevice<Dtype> > {
+};
+
 #ifdef CPU_ONLY
 
 typedef ::testing::Types<CPUDevice<float>,
@@ -59,6 +63,10 @@ struct GPUDevice {
   static const Caffe::Brew device = Caffe::GPU;
 };
 
+template <typename Dtype>
+class GPUDeviceTest : public MultiDeviceTest<GPUDevice<Dtype> > {
+};
+
 typedef ::testing::Types<CPUDevice<float>, CPUDevice<double>,
                          GPUDevice<float>, GPUDevice<double> >
                          TestDtypesAndDevices;

From 8a5abbfcd4a51e92c3731ad03ce95786a7e395b9 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:39 -0700
Subject: [PATCH 1354/2053] Make class Im2colKernelTest derive from
 GPUDeviceTest

---
 src/caffe/test/test_im2col_kernel.cu | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index ee684c00255..0017ac23e69 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -25,7 +25,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
 template <typename Dtype>
-class Im2colKernelTest : public ::testing::Test {
+class Im2colKernelTest : public GPUDeviceTest<Dtype> {
  protected:
   Im2colKernelTest()
         // big so launches > 1024 threads
@@ -68,8 +68,6 @@ class Im2colKernelTest : public ::testing::Test {
 TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
 
 TYPED_TEST(Im2colKernelTest, TestGPU) {
-  Caffe::set_mode(Caffe::GPU);
-
   // Reshape the blobs to correct size for im2col output
   this->blob_top_->Reshape(this->blob_bottom_->num(),
           this->channels_ * this->kernel_size_ * this->kernel_size_,

From 8437c649a2701e34321dfa54860ecdefe3ea8815 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:41 -0700
Subject: [PATCH 1355/2053] Make class AccuracyLayerTest derive from
 CPUDeviceTest

---
 src/caffe/test/test_accuracy_layer.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 6cbf51df45e..c14b67cc0e9 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -16,7 +16,7 @@
 namespace caffe {
 
 template <typename Dtype>
-class AccuracyLayerTest : public ::testing::Test {
+class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   AccuracyLayerTest()
       : blob_bottom_data_(new Blob<Dtype>()),
@@ -92,7 +92,6 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
 
 TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   AccuracyLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -118,7 +117,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
-  Caffe::set_mode(Caffe::CPU);
   this->blob_bottom_data_->Reshape(2, 10, 4, 5);
   vector<int> label_shape(3);
   label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
@@ -162,7 +160,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
   const TypeParam kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);

From f48ceadc1f7e4ff60f7fdc0ada1d7298870e7084 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:44 -0700
Subject: [PATCH 1356/2053] Make class CuDNNNeuronLayerTest derive from
 GPUDeviceTest

---
 src/caffe/test/test_neuron_layer.cpp | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 030f4bbae7f..37b54713b46 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -586,7 +586,7 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNNeuronLayerTest : public ::testing::Test {
+class CuDNNNeuronLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNNeuronLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -609,7 +609,6 @@ class CuDNNNeuronLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -624,7 +623,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
@@ -633,7 +631,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CHECK(google::protobuf::TextFormat::ParseFromString(
       "relu_param { negative_slope: 0.01 }", &layer_param));
@@ -653,7 +650,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CHECK(google::protobuf::TextFormat::ParseFromString(
       "relu_param { negative_slope: 0.01 }", &layer_param));
@@ -664,7 +660,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -681,7 +676,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
@@ -690,7 +684,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -713,7 +706,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);

From 5ca280a3df52fd8693dce00711b39b1cd9391ae3 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:46 -0700
Subject: [PATCH 1357/2053] Make class ArgMaxLayerTest derive from
 CPUDeviceTest

---
 src/caffe/test/test_argmax_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 3487d42f21e..895c3d372ff 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -13,13 +13,12 @@
 namespace caffe {
 
 template <typename Dtype>
-class ArgMaxLayerTest : public ::testing::Test {
+class ArgMaxLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   ArgMaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
         blob_top_(new Blob<Dtype>()),
         top_k_(5) {
-    Caffe::set_mode(Caffe::CPU);
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;

From 4feaa0e2ecae38f7a68540507d9fef53c36a2dee Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:48 -0700
Subject: [PATCH 1358/2053] Make class CuDNNConvolutionLayerTest derive from
 GPUDeviceTest

---
 src/caffe/test/test_convolution_layer.cpp | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index c1fe3b58c58..67d41fff844 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -424,7 +424,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
 #ifdef USE_CUDNN
 
 template <typename Dtype>
-class CuDNNConvolutionLayerTest : public ::testing::Test {
+class CuDNNConvolutionLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNConvolutionLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
@@ -467,7 +467,6 @@ class CuDNNConvolutionLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
@@ -505,7 +504,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
@@ -541,7 +539,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -572,7 +569,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   // Test separable convolution by computing the Sobel operator
   // as a single filter then comparing the result
   // as the convolution of two rectangular filters.
-  Caffe::set_mode(Caffe::GPU);
+
   // Fill bottoms with identical Gaussian noise.
   shared_ptr<GaussianFiller<TypeParam> > filler;
   FillerParameter filler_param;
@@ -665,7 +662,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -683,7 +679,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();

From 307c4b63a172f1c7445f486b2443ff7f83ead224 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:50 -0700
Subject: [PATCH 1359/2053] Make class CuDNNPoolingLayerTest derive from
 GPUDeviceTest

---
 src/caffe/test/test_pooling_layer.cpp | 13 +------------
 1 file changed, 1 insertion(+), 12 deletions(-)

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index e9964e7f0b7..69f2d5c1135 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -608,7 +608,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNPoolingLayerTest : public ::testing::Test {
+class CuDNNPoolingLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNPoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -963,7 +963,6 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -977,7 +976,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -994,7 +992,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   layer_param.set_kernelsize(3);
   layer_param.set_stride(2);
@@ -1020,7 +1017,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
@@ -1030,7 +1026,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
 // the corresponding backward test.
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_top_vec_.push_back(this->blob_top_mask_);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
@@ -1039,7 +1034,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1059,7 +1053,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -1105,7 +1098,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1126,7 +1118,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -1152,7 +1143,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1170,7 +1160,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;

From 58f9ea37a9c5c6a0253be5235504b8399aae54ec Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:53 -0700
Subject: [PATCH 1360/2053] Make class DummyDataLayerTest derive from
 CPUDeviceTest

---
 src/caffe/test/test_dummy_data_layer.cpp | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 99548352746..c9ed38db3a5 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -13,7 +13,7 @@
 namespace caffe {
 
 template <typename Dtype>
-class DummyDataLayerTest : public ::testing::Test {
+class DummyDataLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   DummyDataLayerTest()
       : blob_top_a_(new Blob<Dtype>()),
@@ -44,7 +44,6 @@ class DummyDataLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes);
 
 TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -74,7 +73,6 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
 }
 
 TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -113,7 +111,6 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
 }
 
 TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);

From 89bf3c3d99aee902c49de56c25a32d5837f8962f Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:56 -0700
Subject: [PATCH 1361/2053] Make class CuDNNSoftmaxLayerTest derive from
 GPUDeviceTest

---
 src/caffe/test/test_softmax_layer.cpp | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index f6674422e56..996da4b8f7c 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -82,7 +82,7 @@ TYPED_TEST(SoftmaxLayerTest, TestGradient) {
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNSoftmaxLayerTest : public ::testing::Test {
+class CuDNNSoftmaxLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNSoftmaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 2, 3)),
@@ -104,7 +104,6 @@ class CuDNNSoftmaxLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNSoftmaxLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -138,7 +137,6 @@ TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
 }
 
 TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);

From 68133e7848517c0bf4b25ae60e11985600942130 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Tue, 26 May 2015 11:21:58 -0700
Subject: [PATCH 1362/2053] Make class MultinomialLogisticLossLayerTest derive
 from CPUDeviceTest

---
 src/caffe/test/test_multinomial_logistic_loss_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 9038017e3e2..b2db984feb1 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -16,7 +16,7 @@
 namespace caffe {
 
 template <typename Dtype>
-class MultinomialLogisticLossLayerTest : public ::testing::Test {
+class MultinomialLogisticLossLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   MultinomialLogisticLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -51,7 +51,6 @@ TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes);
 
 TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   MultinomialLogisticLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<TypeParam> checker(1e-2, 2*1e-2, 1701, 0, 0.05);

From 9ea3da42fec3d1ec54de7ad064ae5070cdede524 Mon Sep 17 00:00:00 2001
From: Mohammad Norouzi <mohammad.n@gmail.com>
Date: Tue, 26 May 2015 17:25:56 -0400
Subject: [PATCH 1363/2053] add leading zeros to keys in feature DB files

---
 tools/extract_features.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 364c436dfd8..1ffbfbed08d 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -157,7 +157,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        int length = snprintf(key_str, kMaxKeyStrLength, "%d",
+        int length = snprintf(key_str, kMaxKeyStrLength, "%010d",
             image_indices[i]);
         string out;
         CHECK(datum.SerializeToString(&out));

From ca816676ac9caf28c8627980549a2e12aa529294 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 18 May 2015 18:30:00 -0700
Subject: [PATCH 1364/2053] Refactor solvers regularization and logging code

---
 include/caffe/solver.hpp |  12 +-
 src/caffe/solver.cpp     | 500 ++++++++++++++++-----------------------
 2 files changed, 214 insertions(+), 298 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 4dcdc3dc20b..c92067917c8 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -39,8 +39,8 @@ class Solver {
   int iter() { return iter_; }
 
  protected:
-  // Get the update value for the current iteration.
-  virtual void ComputeUpdateValue() = 0;
+  // Get and apply the update value for the current iteration.
+  virtual void MakeUpdate() = 0;
   // The Solver::Snapshot function implements the basic snapshotting utility
   // that stores the learned net. You should implement the SnapshotSolverState()
   // function that produces a SolverState protocol buffer that needs to be
@@ -80,7 +80,9 @@ class SGDSolver : public Solver<Dtype> {
  protected:
   void PreSolve();
   Dtype GetLearningRate();
-  virtual void ComputeUpdateValue();
+  virtual void MakeUpdate();
+  virtual void Regularize(int param_id);
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
   virtual void ClipGradients();
   virtual void SnapshotSolverState(SolverState * state);
   virtual void RestoreSolverState(const SolverState& state);
@@ -102,7 +104,7 @@ class NesterovSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param_file) {}
 
  protected:
-  virtual void ComputeUpdateValue();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
 
   DISABLE_COPY_AND_ASSIGN(NesterovSolver);
 };
@@ -116,7 +118,7 @@ class AdaGradSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
 
  protected:
-  virtual void ComputeUpdateValue();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
   void constructor_sanity_check() {
     CHECK_EQ(0, this->param_.momentum())
         << "Momentum cannot be used with AdaGrad.";
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 877b19b86f8..88f6d314fc7 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -207,8 +207,7 @@ void Solver<Dtype>::Step(int iters) {
         }
       }
     }
-    ComputeUpdateValue();
-    net_->Update();
+    MakeUpdate();
 
     // Increment the internal iter_ counter -- its value should always indicate
     // the number of times the weights have been updated.
@@ -456,95 +455,118 @@ void SGDSolver<Dtype>::ClipGradients() {
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::ComputeUpdateValue() {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  // get the learning rate
+void SGDSolver<Dtype>::MakeUpdate() {
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
   ClipGradients();
-  Dtype momentum = this->param_.momentum();
+  for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) {
+    Regularize(param_id);
+    ComputeUpdateValue(param_id, rate);
+  }
+  this->net_->Update();
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Regularize(int param_id) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // Compute the value to history, and then copy them to the blob's diff.
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
+  case Caffe::CPU: {
+    Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_cpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->cpu_data(),
+            temp_[param_id]->mutable_cpu_data());
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
       }
-
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->cpu_diff(), momentum,
-                history_[param_id]->mutable_cpu_data());
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          history_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
     }
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // Compute the value to history, and then copy them to the blob's diff.
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
+    Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_gpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->gpu_data(),
+            temp_[param_id]->mutable_gpu_data());
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
       }
-
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->gpu_diff(), momentum,
-                history_[param_id]->mutable_gpu_data());
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          history_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
     }
 #else
     NO_GPU;
 #endif
     break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype momentum = this->param_.momentum();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // Compute the value to history, and then copy them to the blob's diff.
+    Dtype local_rate = rate * net_params_lr[param_id];
+
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              history_[param_id]->mutable_cpu_data());
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    // Compute the value to history, and then copy them to the blob's diff.
+    Dtype local_rate = rate * net_params_lr[param_id];
+
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->gpu_diff(), momentum,
+              history_[param_id]->mutable_gpu_data());
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
@@ -571,252 +593,144 @@ void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
 }
 
 template <typename Dtype>
-void NesterovSolver<Dtype>::ComputeUpdateValue() {
+void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   const vector<float>& net_params_lr = this->net_->params_lr();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = this->GetLearningRate();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  SGDSolver<Dtype>::ClipGradients();
   Dtype momentum = this->param_.momentum();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // save history momentum for stepping back
-      caffe_copy(net_params[param_id]->count(),
-          this->history_[param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // update history
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->cpu_diff(), momentum,
-                this->history_[param_id]->mutable_cpu_data());
-
-      // compute udpate: step back then over step
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-          this->history_[param_id]->cpu_data(), -momentum,
-          this->update_[param_id]->mutable_cpu_data());
-
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
-    }
+  case Caffe::CPU: {
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    Dtype local_rate = rate * net_params_lr[param_id];
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              this->history_[param_id]->mutable_cpu_data());
+
+    // compute update: step back then over step
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->cpu_data(), -momentum,
+        this->update_[param_id]->mutable_cpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // save history momentum for stepping back
-      caffe_copy(net_params[param_id]->count(),
-          this->history_[param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // update history
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->gpu_diff(), momentum,
-                this->history_[param_id]->mutable_gpu_data());
-
-      // compute udpate: step back then over step
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-          this->history_[param_id]->gpu_data(), -momentum,
-          this->update_[param_id]->mutable_gpu_data());
-
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
-    }
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    Dtype local_rate = rate * net_params_lr[param_id];
+
+    // update history
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->gpu_diff(), momentum,
+              this->history_[param_id]->mutable_gpu_data());
+
+    // compute update: step back then over step
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->gpu_data(), -momentum,
+        this->update_[param_id]->mutable_gpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
 #else
     NO_GPU;
 #endif
     break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
 }
 
 template <typename Dtype>
-void AdaGradSolver<Dtype>::ComputeUpdateValue() {
+void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   const vector<float>& net_params_lr = this->net_->params_lr();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = this->GetLearningRate();
   Dtype delta = this->param_.delta();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  SGDSolver<Dtype>::ClipGradients();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // compute square of gradient in update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history
-      caffe_add(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          this->history_[param_id]->cpu_data(),
-          this->history_[param_id]->mutable_cpu_data());
-
-      // prepare update
-      caffe_powx(net_params[param_id]->count(),
-                this->history_[param_id]->cpu_data(), Dtype(0.5),
-                this->update_[param_id]->mutable_cpu_data());
-
-      caffe_add_scalar(net_params[param_id]->count(),
-                delta, this->update_[param_id]->mutable_cpu_data());
-
-      caffe_div(net_params[param_id]->count(),
-                net_params[param_id]->cpu_diff(),
-                this->update_[param_id]->cpu_data(),
-                this->update_[param_id]->mutable_cpu_data());
-
-      // scale and copy
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-          this->update_[param_id]->cpu_data(), Dtype(0),
-          net_params[param_id]->mutable_cpu_diff());
-    }
+  case Caffe::CPU: {
+    Dtype local_rate = rate * net_params_lr[param_id];
+
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_add(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->history_[param_id]->mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+              this->history_[param_id]->cpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+              net_params[param_id]->cpu_diff(),
+              this->update_[param_id]->cpu_data(),
+              this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // compute square of gradient in update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          this->history_[param_id]->gpu_data(),
-          this->history_[param_id]->mutable_gpu_data());
-
-      // prepare update
-      caffe_gpu_powx(net_params[param_id]->count(),
-                this->history_[param_id]->gpu_data(), Dtype(0.5),
-                this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add_scalar(net_params[param_id]->count(),
-                delta, this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_div(net_params[param_id]->count(),
-                net_params[param_id]->gpu_diff(),
-                this->update_[param_id]->gpu_data(),
-                this->update_[param_id]->mutable_gpu_data());
-
-      // scale and copy
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-          this->update_[param_id]->gpu_data(), Dtype(0),
-          net_params[param_id]->mutable_gpu_diff());
-    }
+    Dtype local_rate = rate * net_params_lr[param_id];
+
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        this->history_[param_id]->gpu_data(),
+        this->history_[param_id]->mutable_gpu_data());
+
+    // prepare update
+    caffe_gpu_powx(net_params[param_id]->count(),
+              this->history_[param_id]->gpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_div(net_params[param_id]->count(),
+              net_params[param_id]->gpu_diff(),
+              this->update_[param_id]->gpu_data(),
+              this->update_[param_id]->mutable_gpu_data());
+
+    // scale and copy
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->gpu_data(), Dtype(0),
+        net_params[param_id]->mutable_gpu_diff());
 #else
     NO_GPU;
 #endif
     break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }

From a85f7f1955c434e46a39cbbc91df82601d5e9646 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 May 2015 16:34:43 -0700
Subject: [PATCH 1365/2053] deduplicate decay and local rate in solver updates

---
 src/caffe/solver.cpp | 23 +++++------------------
 1 file changed, 5 insertions(+), 18 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 88f6d314fc7..6a0151837bb 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -475,9 +475,9 @@ void SGDSolver<Dtype>::Regularize(int param_id) {
       this->net_->params_weight_decay();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
+  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
   switch (Caffe::mode()) {
   case Caffe::CPU: {
-    Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
     if (local_decay) {
       if (regularization_type == "L2") {
         // add weight decay
@@ -501,7 +501,6 @@ void SGDSolver<Dtype>::Regularize(int param_id) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
     if (local_decay) {
       if (regularization_type == "L2") {
         // add weight decay
@@ -536,15 +535,13 @@ void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  // Compute the update to history, then copy it to the parameter diff.
   switch (Caffe::mode()) {
   case Caffe::CPU: {
-    // Compute the value to history, and then copy them to the blob's diff.
-    Dtype local_rate = rate * net_params_lr[param_id];
-
     caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
               net_params[param_id]->cpu_diff(), momentum,
               history_[param_id]->mutable_cpu_data());
-    // copy
     caffe_copy(net_params[param_id]->count(),
         history_[param_id]->cpu_data(),
         net_params[param_id]->mutable_cpu_diff());
@@ -552,13 +549,9 @@ void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    // Compute the value to history, and then copy them to the blob's diff.
-    Dtype local_rate = rate * net_params_lr[param_id];
-
     caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
               net_params[param_id]->gpu_diff(), momentum,
               history_[param_id]->mutable_gpu_data());
-    // copy
     caffe_copy(net_params[param_id]->count(),
         history_[param_id]->gpu_data(),
         net_params[param_id]->mutable_gpu_diff());
@@ -597,6 +590,7 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
   switch (Caffe::mode()) {
   case Caffe::CPU: {
     // save history momentum for stepping back
@@ -604,8 +598,6 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
         this->history_[param_id]->cpu_data(),
         this->update_[param_id]->mutable_cpu_data());
 
-    Dtype local_rate = rate * net_params_lr[param_id];
-
     // update history
     caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
               net_params[param_id]->cpu_diff(), momentum,
@@ -629,8 +621,6 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
         this->history_[param_id]->gpu_data(),
         this->update_[param_id]->mutable_gpu_data());
 
-    Dtype local_rate = rate * net_params_lr[param_id];
-
     // update history
     caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
               net_params[param_id]->gpu_diff(), momentum,
@@ -660,10 +650,9 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
+  Dtype local_rate = rate * net_params_lr[param_id];
   switch (Caffe::mode()) {
   case Caffe::CPU: {
-    Dtype local_rate = rate * net_params_lr[param_id];
-
     // compute square of gradient in update
     caffe_powx(net_params[param_id]->count(),
         net_params[param_id]->cpu_diff(), Dtype(2),
@@ -696,8 +685,6 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    Dtype local_rate = rate * net_params_lr[param_id];
-
     // compute square of gradient in update
     caffe_gpu_powx(net_params[param_id]->count(),
         net_params[param_id]->gpu_diff(), Dtype(2),

From e1cc9d3c78fb3a7cf779abf36c15dd0d38496d46 Mon Sep 17 00:00:00 2001
From: Mohammad Norouzi <mohammad.n@gmail.com>
Date: Wed, 27 May 2015 10:33:19 -0400
Subject: [PATCH 1366/2053] fix the bug with db_type when the number of
 features to be extracted is larger than 1

---
 tools/extract_features.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 1ffbfbed08d..365dd495bbf 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -122,9 +122,10 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   std::vector<shared_ptr<db::DB> > feature_dbs;
   std::vector<shared_ptr<db::Transaction> > txns;
+  const char* db_type = argv[++arg_pos];
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening dataset " << dataset_names[i];
-    shared_ptr<db::DB> db(db::GetDB(argv[++arg_pos]));
+    shared_ptr<db::DB> db(db::GetDB(db_type));
     db->Open(dataset_names.at(i), db::NEW);
     feature_dbs.push_back(db);
     shared_ptr<db::Transaction> txn(db->NewTransaction());

From 76db47e1de6159daa8e38b33d3930308e4078f66 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 27 May 2015 12:24:06 -0700
Subject: [PATCH 1367/2053] Solver::MakeUpdate() -> Solver::ApplyUpdate

Designate `Solver::ApplyUpdate()` as the core method to compute
and apply parameter updates given the current state of the Net.

Make `Solver::ComputeUpdateValue()` a subordinate call overloaded by the
`SGDSolver`s to take care of optimization algorithm details.
---
 include/caffe/solver.hpp | 8 ++++----
 src/caffe/solver.cpp     | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index c92067917c8..da1bab13663 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -11,7 +11,7 @@ namespace caffe {
 /**
  * @brief An interface for classes that perform optimization on Net%s.
  *
- * Requires implementation of ComputeUpdateValue to compute a parameter update
+ * Requires implementation of ApplyUpdate to compute a parameter update
  * given the current state of the Net parameters.
  */
 template <typename Dtype>
@@ -39,8 +39,8 @@ class Solver {
   int iter() { return iter_; }
 
  protected:
-  // Get and apply the update value for the current iteration.
-  virtual void MakeUpdate() = 0;
+  // Make and apply the update value for the current iteration.
+  virtual void ApplyUpdate() = 0;
   // The Solver::Snapshot function implements the basic snapshotting utility
   // that stores the learned net. You should implement the SnapshotSolverState()
   // function that produces a SolverState protocol buffer that needs to be
@@ -80,7 +80,7 @@ class SGDSolver : public Solver<Dtype> {
  protected:
   void PreSolve();
   Dtype GetLearningRate();
-  virtual void MakeUpdate();
+  virtual void ApplyUpdate();
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
   virtual void ClipGradients();
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 6a0151837bb..fa334edaa60 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -207,7 +207,7 @@ void Solver<Dtype>::Step(int iters) {
         }
       }
     }
-    MakeUpdate();
+    ApplyUpdate();
 
     // Increment the internal iter_ counter -- its value should always indicate
     // the number of times the weights have been updated.
@@ -455,7 +455,7 @@ void SGDSolver<Dtype>::ClipGradients() {
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::MakeUpdate() {
+void SGDSolver<Dtype>::ApplyUpdate() {
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;

From 41cf06cc6e40e1b41d04b5b26e19395611bdcf5d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 11 Aug 2014 21:38:59 -0700
Subject: [PATCH 1368/2053] zero-init param diffs and accumulate gradients

(With layers whose backward accumulates gradients), this effectively
decouples the computational batch from the SGD minibatch. Each
iteration accumulates gradients over iter_size batches, then parameters
are updated.
---
 src/caffe/proto/caffe.proto |  4 +++-
 src/caffe/solver.cpp        | 27 ++++++++++++++++++++++++++-
 2 files changed, 29 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index c471fa0a93e..94836421a42 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -96,7 +96,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 36 (last added: clip_gradients)
+// SolverParameter next available ID: 37 (last added: iter_size)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -149,6 +149,8 @@ message SolverParameter {
   // Display the loss averaged over the last average_loss iterations
   optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
+  // accumulate gradients over `iter_size` x `batch_size` instances
+  optional int32 iter_size = 36 [default = 1];
   optional string lr_policy = 8; // The learning rate decay policy.
   optional float gamma = 9; // The parameter to compute the learning rate.
   optional float power = 10; // The parameter to compute the learning rate.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index fa334edaa60..ad041b8f268 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -168,6 +168,25 @@ void Solver<Dtype>::Step(int iters) {
   Dtype smoothed_loss = 0;
 
   while (iter_ < stop_iter) {
+    // zero-init the params
+    for (int i = 0; i < net_->params().size(); ++i) {
+      shared_ptr<Blob<Dtype> > blob = net_->params()[i];
+      switch(Caffe::mode()) {
+      case Caffe::CPU:
+        caffe_set(blob->count(), static_cast<Dtype>(0),
+            blob->mutable_cpu_diff());
+        break;
+      case Caffe::GPU:
+#ifndef CPU_ONLY
+        caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
+            blob->mutable_gpu_diff());
+#else
+        NO_GPU;
+#endif
+        break;
+      }
+    }
+
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())) {
       TestAll();
@@ -175,7 +194,13 @@ void Solver<Dtype>::Step(int iters) {
 
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
-    Dtype loss = net_->ForwardBackward(bottom_vec);
+    // accumulate the loss and gradient
+    Dtype loss = 0;
+    for (int i = 0; i < param_.iter_size(); ++i) {
+      loss += net_->ForwardBackward(bottom_vec);
+    }
+    loss /= param_.iter_size();
+    // average the loss across iterations for smoothed reporting
     if (losses.size() < average_loss) {
       losses.push_back(loss);
       int size = losses.size();

From 539f8798233e25ac8110f545995f5b8f7340718f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Dec 2014 22:52:07 -0800
Subject: [PATCH 1369/2053] zero-init param diffs in gradient checker

---
 include/caffe/test/test_gradient_check_util.hpp | 7 +++++--
 src/caffe/solver.cpp                            | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 22937711b58..cc5dcbad0ee 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -80,11 +80,14 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
       CHECK_EQ(top_count, bottom[blob_id]->count());
     }
   }
-  // First, figure out what blobs we need to check against.
+  // First, figure out what blobs we need to check against, and zero init
+  // parameter blobs.
   vector<Blob<Dtype>*> blobs_to_check;
   vector<bool> propagate_down(bottom.size(), check_bottom < 0);
   for (int i = 0; i < layer->blobs().size(); ++i) {
-    blobs_to_check.push_back(layer->blobs()[i].get());
+    Blob<Dtype>* blob = layer->blobs()[i].get();
+    caffe_set(blob->count(), static_cast<Dtype>(0), blob->mutable_cpu_diff());
+    blobs_to_check.push_back(blob);
   }
   if (check_bottom < 0) {
     for (int i = 0; i < bottom.size(); ++i) {
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ad041b8f268..d104522002b 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -171,7 +171,7 @@ void Solver<Dtype>::Step(int iters) {
     // zero-init the params
     for (int i = 0; i < net_->params().size(); ++i) {
       shared_ptr<Blob<Dtype> > blob = net_->params()[i];
-      switch(Caffe::mode()) {
+      switch (Caffe::mode()) {
       case Caffe::CPU:
         caffe_set(blob->count(), static_cast<Dtype>(0),
             blob->mutable_cpu_diff());

From 3262e464b06f1ecd8a04db1487cc5878c0cfd852 Mon Sep 17 00:00:00 2001
From: Sergio <sguada@gmail.com>
Date: Fri, 26 Sep 2014 23:03:26 -0700
Subject: [PATCH 1370/2053] accumulate gradients in inner product layer

---
 src/caffe/layers/inner_product_layer.cpp | 4 ++--
 src/caffe/layers/inner_product_layer.cu  | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 89e0c8fbad7..83c3235eb71 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -101,13 +101,13 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
     caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.cpu_data(), (Dtype)0.,
+        bias_multiplier_.cpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index a9e1784a205..dd90cac12a8 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -33,13 +33,13 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
     caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.gpu_data(), (Dtype)0.,
+        bias_multiplier_.gpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {

From 8cc9af01941cd8c7c32c664672e5f31106d2cc40 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 30 Dec 2014 22:29:35 -0800
Subject: [PATCH 1371/2053] accumulate gradients in (de)conv layers

---
 src/caffe/layers/conv_layer.cpp   | 7 -------
 src/caffe/layers/conv_layer.cu    | 7 -------
 src/caffe/layers/deconv_layer.cpp | 7 -------
 src/caffe/layers/deconv_layer.cu  | 7 -------
 4 files changed, 28 deletions(-)

diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index c0c9f6f3371..928ef5ee468 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -39,13 +39,6 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_cpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
     const Dtype* bottom_data = bottom[i]->cpu_data();
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 3902fdf3930..b8a98ff7cc9 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -31,13 +31,6 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     // Bias gradient, if necessary.
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index e6d65ab526b..a4612963b6b 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -39,13 +39,6 @@ void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_cpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
     const Dtype* bottom_data = bottom[i]->cpu_data();
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index 9198dd64c72..39bc4de8c66 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -31,13 +31,6 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     const Dtype* bottom_data = bottom[i]->gpu_data();

From 67b1ff3114320188b5046ff899d4fb0f87fd7b63 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 13 Sep 2014 17:41:59 -0700
Subject: [PATCH 1372/2053] accumulate gradients in cudnn conv layer

---
 src/caffe/layers/cudnn_conv_layer.cu | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 4a1a4c4f4f2..b4e802e13d1 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -101,12 +101,10 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (this->param_propagate_down_[0]) {
     weight = this->blobs_[0]->gpu_data();
     weight_diff = this->blobs_[0]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
   if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();

From 55585f5bfab61328a61125b3d49627a69022d817 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 May 2015 17:06:42 -0700
Subject: [PATCH 1373/2053] adjust local learning rate and decay according to
 gradient accumulation

Divide local rate by `iter_size` to normalize the gradient according to
the full minibatch size and not only the computational batch size.

Multiply the local decay by `iter_size` to counter the division of the
local learning rate since the decay is multiplied by the rate in the
update equation.
---
 src/caffe/solver.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index d104522002b..4c8fa25c955 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -488,7 +488,7 @@ void SGDSolver<Dtype>::ApplyUpdate() {
   ClipGradients();
   for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) {
     Regularize(param_id);
-    ComputeUpdateValue(param_id, rate);
+    ComputeUpdateValue(param_id, rate / this->param_.iter_size());
   }
   this->net_->Update();
 }
@@ -500,7 +500,8 @@ void SGDSolver<Dtype>::Regularize(int param_id) {
       this->net_->params_weight_decay();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
-  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+  Dtype local_decay = weight_decay * net_params_weight_decay[param_id]
+                                   * this->param_.iter_size();
   switch (Caffe::mode()) {
   case Caffe::CPU: {
     if (local_decay) {

From adf31899fde826b98d64e4c344be1924bfa52b7e Mon Sep 17 00:00:00 2001
From: Martin Thoma <info@martin-thoma.de>
Date: Wed, 27 May 2015 23:20:16 +0200
Subject: [PATCH 1374/2053] python/draw_net.py and python/caffe/draw.py:
 Simplified code; added more docstrings; adjusted code according to PEP8

---
 python/caffe/draw.py | 163 +++++++++++++++++++++++++++----------------
 python/draw_net.py   |  13 ++--
 2 files changed, 109 insertions(+), 67 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 08b7c1de14b..834ea15ac9a 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -1,13 +1,15 @@
 """
 Caffe network visualization: draw the NetParameter protobuffer.
 
-NOTE: this requires pydot>=1.0.2, which is not included in requirements.txt
-since it requires graphviz and other prerequisites outside the scope of the
-Caffe.
+
+.. note::
+
+    This requires pydot>=1.0.2, which is not included in requirements.txt since
+    it requires graphviz and other prerequisites outside the scope of the
+    Caffe.
 """
 
 from caffe.proto import caffe_pb2
-from google.protobuf import text_format
 import pydot
 
 # Internal layer and blob styles.
@@ -32,15 +34,15 @@ def get_pooling_types_dict():
     return d
 
 
-def determine_edge_label_by_layertype(layer, layertype):
-    """Define edge label based on layer type
+def get_edge_label(layer):
+    """Define edge label based on layer type.
     """
 
-    if layertype == 'Data':
+    if layer.type == 'Data':
         edge_label = 'Batch ' + str(layer.data_param.batch_size)
-    elif layertype == 'Convolution':
+    elif layer.type == 'Convolution':
         edge_label = str(layer.convolution_param.num_output)
-    elif layertype == 'InnerProduct':
+    elif layer.type == 'InnerProduct':
         edge_label = str(layer.inner_product_param.num_output)
     else:
         edge_label = '""'
@@ -48,8 +50,19 @@ def determine_edge_label_by_layertype(layer, layertype):
     return edge_label
 
 
-def determine_node_label_by_layertype(layer, layertype, rankdir):
-    """Define node label based on layer type
+def get_layer_label(layer, rankdir):
+    """Define node label based on layer type.
+
+    Parameters
+    ----------
+    layer : ?
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+
+    Returns
+    -------
+    string :
+        A label for the current layer
     """
 
     if rankdir in ('TB', 'BT'):
@@ -61,26 +74,26 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
         # horizontal space is not; separate words with newlines
         separator = '\n'
 
-    if layertype == 'Convolution':
+    if layer.type == 'Convolution':
         # Outer double quotes needed or else colon characters don't parse
         # properly
         node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
                      (layer.name,
                       separator,
-                      layertype,
+                      layer.type,
                       separator,
                       layer.convolution_param.kernel_size,
                       separator,
                       layer.convolution_param.stride,
                       separator,
                       layer.convolution_param.pad)
-    elif layertype == 'Pooling':
+    elif layer.type == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
                      (layer.name,
                       separator,
                       pooling_types_dict[layer.pooling_param.pool],
-                      layertype,
+                      layer.type,
                       separator,
                       layer.pooling_param.kernel_size,
                       separator,
@@ -88,12 +101,12 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
                       separator,
                       layer.pooling_param.pad)
     else:
-        node_label = '"%s%s(%s)"' % (layer.name, separator, layertype)
+        node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
     return node_label
 
 
 def choose_color_by_layertype(layertype):
-    """Define colors for nodes based on the layer type
+    """Define colors for nodes based on the layer type.
     """
     color = '#6495ED'  # Default
     if layertype == 'Convolution':
@@ -106,48 +119,62 @@ def choose_color_by_layertype(layertype):
 
 
 def get_pydot_graph(caffe_net, rankdir, label_edges=True):
-  pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir)
-  pydot_nodes = {}
-  pydot_edges = []
-  for layer in caffe_net.layer:
-    name = layer.name
-    layertype = layer.type
-    node_label = determine_node_label_by_layertype(layer, layertype, rankdir)
-    if (len(layer.bottom) == 1 and len(layer.top) == 1 and
-        layer.bottom[0] == layer.top[0]):
-      # We have an in-place neuron layer.
-      pydot_nodes[name + '_' + layertype] = pydot.Node(
-          node_label, **NEURON_LAYER_STYLE)
-    else:
-      layer_style = LAYER_STYLE_DEFAULT
-      layer_style['fillcolor'] = choose_color_by_layertype(layertype)
-      pydot_nodes[name + '_' + layertype] = pydot.Node(
-          node_label, **layer_style)
-    for bottom_blob in layer.bottom:
-      pydot_nodes[bottom_blob + '_blob'] = pydot.Node(
-        '%s' % (bottom_blob), **BLOB_STYLE)
-      edge_label = '""'
-      pydot_edges.append({'src': bottom_blob + '_blob',
-                          'dst': name + '_' + layertype,
-                          'label': edge_label})
-    for top_blob in layer.top:
-      pydot_nodes[top_blob + '_blob'] = pydot.Node(
-        '%s' % (top_blob))
-      if label_edges:
-        edge_label = determine_edge_label_by_layertype(layer, layertype)
-      else:
-        edge_label = '""'
-      pydot_edges.append({'src': name + '_' + layertype,
-                          'dst': top_blob + '_blob',
-                          'label': edge_label})
-  # Now, add the nodes and edges to the graph.
-  for node in pydot_nodes.values():
-    pydot_graph.add_node(node)
-  for edge in pydot_edges:
-    pydot_graph.add_edge(
-        pydot.Edge(pydot_nodes[edge['src']], pydot_nodes[edge['dst']],
-                   label=edge['label']))
-  return pydot_graph
+    """Create a data structure which represents the `caffe_net`.
+
+    Parameters
+    ----------
+    caffe_net : object
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+    label_edges : boolean, optional
+        Label the edges (default is True).
+
+    Returns
+    -------
+    pydot graph object
+    """
+    pydot_graph = pydot.Dot(caffe_net.name,
+                            graph_type='digraph',
+                            rankdir=rankdir)
+    pydot_nodes = {}
+    pydot_edges = []
+    for layer in caffe_net.layer:
+        node_label = get_layer_label(layer, rankdir)
+        node_name = "%s_%s" % (layer.name, layer.type)
+        if (len(layer.bottom) == 1 and len(layer.top) == 1 and
+           layer.bottom[0] == layer.top[0]):
+            # We have an in-place neuron layer.
+            pydot_nodes[node_name] = pydot.Node(node_label,
+                                                **NEURON_LAYER_STYLE)
+        else:
+            layer_style = LAYER_STYLE_DEFAULT
+            layer_style['fillcolor'] = choose_color_by_layertype(layer.type)
+            pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)
+        for bottom_blob in layer.bottom:
+            pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,
+                                                            **BLOB_STYLE)
+            edge_label = '""'
+            pydot_edges.append({'src': bottom_blob + '_blob',
+                                'dst': node_name,
+                                'label': edge_label})
+        for top_blob in layer.top:
+            pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))
+            if label_edges:
+                edge_label = get_edge_label(layer)
+            else:
+                edge_label = '""'
+            pydot_edges.append({'src': node_name,
+                                'dst': top_blob + '_blob',
+                                'label': edge_label})
+    # Now, add the nodes and edges to the graph.
+    for node in pydot_nodes.values():
+        pydot_graph.add_node(node)
+    for edge in pydot_edges:
+        pydot_graph.add_edge(
+            pydot.Edge(pydot_nodes[edge['src']],
+                       pydot_nodes[edge['dst']],
+                       label=edge['label']))
+    return pydot_graph
 
 
 def draw_net(caffe_net, rankdir, ext='png'):
@@ -156,8 +183,14 @@ def draw_net(caffe_net, rankdir, ext='png'):
 
     Parameters
     ----------
-    caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer.
-    ext: the image extension. Default 'png'.
+    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+    ext : string, optional
+        The image extension (the default is 'png').
+
+    Returns
+    -------
+    string :
+        Postscript representation of the graph.
     """
     return get_pydot_graph(caffe_net, rankdir).create(format=ext)
 
@@ -166,6 +199,14 @@ def draw_net_to_file(caffe_net, filename, rankdir='LR'):
     """Draws a caffe net, and saves it to file using the format given as the
     file extension. Use '.raw' to output raw text that you can manually feed
     to graphviz to draw graphs.
+
+    Parameters
+    ----------
+    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+    filename : string
+        The path to a file where the networks visualization will be stored.
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
     """
     ext = filename[filename.rfind('.')+1:]
     with open(filename, 'wb') as fid:
diff --git a/python/draw_net.py b/python/draw_net.py
index 6320f775ef7..ec76a744da3 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -2,7 +2,7 @@
 """
 Draw a graph of the net architecture.
 """
-import argparse
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
 from google.protobuf import text_format
 
 import caffe
@@ -14,7 +14,8 @@ def parse_args():
     """Parse input arguments
     """
 
-    parser = argparse.ArgumentParser(description='Draw a network graph')
+    parser = ArgumentParser(description=__doc__,
+                            formatter_class=ArgumentDefaultsHelpFormatter)
 
     parser.add_argument('input_net_proto_file',
                         help='Input network prototxt file')
@@ -22,10 +23,10 @@ def parse_args():
                         help='Output image file')
     parser.add_argument('--rankdir',
                         help=('One of TB (top-bottom, i.e., vertical), '
-                              'RL (right-left, i.e., horizontal), or another'
-                              'valid dot option; see'
-                              'http://www.graphviz.org/doc/info/attrs.html#k:rankdir'
-                              '(default: LR)'),
+                              'RL (right-left, i.e., horizontal), or another '
+                              'valid dot option; see '
+                              'http://www.graphviz.org/doc/info/'
+                              'attrs.html#k:rankdir'),
                         default='LR')
 
     args = parser.parse_args()

From 92ab737adad6d686ac75cdf934472f6a97b52fe7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 21 May 2015 18:14:16 -0700
Subject: [PATCH 1375/2053] test equivalence of solving with accumulating
 gradients

Compare the parameters after solving with a given batch size and the
halved batch size + two iter accumulation of gradients equivalent.

Note: the test net dummy data layer now makes constant data and random
gaussian targets. This assures the standard and gradient accumulation
cases check the same data. Otherwise the difference in batch sizes
causes different orders of random number draws.
---
 src/caffe/test/test_gradient_based_solver.cpp | 82 ++++++++++++++++++-
 1 file changed, 79 insertions(+), 3 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index eb2569c04f2..c9135d64e70 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -23,7 +23,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   GradientBasedSolverTest() :
-      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+      seed_(1701), num_(4), channels_(3), height_(10), width_(10) {}
 
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
@@ -56,19 +56,21 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   }
 
   void RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
+      const int iter_size = 1) {
     ostringstream proto;
     proto <<
        "max_iter: " << num_iters << " "
        "base_lr: " << learning_rate << " "
        "lr_policy: 'fixed' "
+       "iter_size: " << iter_size << " "
        "net_param { "
        "  name: 'TestNetwork' "
        "  layer { "
        "    name: 'data' "
        "    type: 'DummyData' "
        "    dummy_data_param { "
-       "      num: " << num_ << " "
+       "      num: " << num_ / iter_size << " "
        "      channels: " << channels_ << " "
        "      height: " << height_ << " "
        "      width: " << width_ << " "
@@ -76,6 +78,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "      height: 1 "
        "      width: 1 "
        "      data_filler { "
+       "        type: 'constant' "
+       "        value: 1.0 "
+       "      } "
+       "      data_filler { "
        "        type: 'gaussian' "
        "        std: 1.0 "
        "      } "
@@ -270,6 +276,45 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
+      const Dtype kMomentum, const int kNumIters, const int kIterSize) {
+    const double kPrecision = 1e-2;
+    const double kMinPrecision = 1e-7;
+    // Solve without accumulation and save parameters.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters);
+    // Save parameters for comparison.
+    Net<Dtype>& net = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    vector<shared_ptr<Blob<Dtype> > > noaccum_params(param_blobs.size());
+    for (int i = 0; i < param_blobs.size(); ++i) {
+      noaccum_params[i].reset(new Blob<Dtype>());
+      noaccum_params[i]->CopyFrom(*param_blobs[i], false, true);
+    }
+    // Solve by equivalent accumulation of gradients over divided batches.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters, kIterSize);
+    Net<Dtype>& net_accum = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& accum_params =
+        net_accum.layer_by_name("innerprod")->blobs();
+    // Compare accumulated parameters against no accumulation standard.
+    const int D = this->channels_ * this->height_ * this->width_;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_param = noaccum_params[0]->cpu_data()[i];
+      const Dtype accum_param = accum_params[0]->cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_param), fabs(accum_param)));
+      EXPECT_NEAR(expected_param, accum_param, error_margin);
+    }
+    ASSERT_EQ(1, accum_params[1]->count());
+    const Dtype expected_bias = noaccum_params[1]->cpu_data()[0];
+    const Dtype accum_bias = accum_params[1]->cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+        std::min(fabs(expected_bias), fabs(accum_bias)));
+    EXPECT_NEAR(expected_bias, accum_bias, error_margin);
+  }
+
   // Test that the correct update is computed for a regularized least squares
   // problem:
   //
@@ -372,6 +417,16 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -416,6 +471,16 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
 
 template <typename TypeParam>
 class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -482,4 +547,15 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
 }  // namespace caffe

From 0e7a0785db224aa7cf2bd925d8b7910bdc3f7a98 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 28 May 2015 12:43:29 -0700
Subject: [PATCH 1376/2053] directly normalize accumulated gradients

`SGDSolver::Normalize()` normalizes accumulated gradients by scaling
inversely to the accumulation as `1 / iter_size`.

This fixes accumulation for AdaGrad and is more obvious than fooling
with rates and decays in 55585f5.
---
 include/caffe/solver.hpp |  1 +
 src/caffe/solver.cpp     | 32 +++++++++++++++++++++++++++++---
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index da1bab13663..c2ced487d6f 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -81,6 +81,7 @@ class SGDSolver : public Solver<Dtype> {
   void PreSolve();
   Dtype GetLearningRate();
   virtual void ApplyUpdate();
+  virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
   virtual void ClipGradients();
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 4c8fa25c955..aabe0edec80 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -487,12 +487,39 @@ void SGDSolver<Dtype>::ApplyUpdate() {
   }
   ClipGradients();
   for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) {
+    Normalize(param_id);
     Regularize(param_id);
-    ComputeUpdateValue(param_id, rate / this->param_.iter_size());
+    ComputeUpdateValue(param_id, rate);
   }
   this->net_->Update();
 }
 
+template <typename Dtype>
+void SGDSolver<Dtype>::Normalize(int param_id) {
+  if (this->param_.iter_size() == 1) { return; }
+  // Scale gradient to counterbalance accumulation.
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 template <typename Dtype>
 void SGDSolver<Dtype>::Regularize(int param_id) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
@@ -500,8 +527,7 @@ void SGDSolver<Dtype>::Regularize(int param_id) {
       this->net_->params_weight_decay();
   Dtype weight_decay = this->param_.weight_decay();
   string regularization_type = this->param_.regularization_type();
-  Dtype local_decay = weight_decay * net_params_weight_decay[param_id]
-                                   * this->param_.iter_size();
+  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
   switch (Caffe::mode()) {
   case Caffe::CPU: {
     if (local_decay) {

From ee0c93137472981554ad30a766a1542cf155bf7b Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 22 May 2015 01:45:14 +0800
Subject: [PATCH 1377/2053] MatCaffe3 : a powerful matlab interface for caffe

Added matcaffe3, a powerful matlab interface. To test it, run 'make mattest'
---
 Makefile                                 |  22 +-
 matlab/+caffe/+test/test_net.m           |  74 +++
 matlab/+caffe/+test/test_solver.m        |  43 ++
 matlab/+caffe/Blob.m                     |  71 +++
 matlab/+caffe/Layer.m                    |  32 ++
 matlab/+caffe/Net.m                      | 133 ++++++
 matlab/+caffe/Solver.m                   |  56 +++
 matlab/+caffe/get_net.m                  |  37 ++
 matlab/+caffe/get_solver.m               |  11 +
 matlab/+caffe/io.m                       |  22 +
 matlab/+caffe/private/CHECK.m            |   7 +
 matlab/+caffe/private/CHECK_FILE_EXIST.m |   7 +
 matlab/+caffe/private/caffe_.cpp         | 546 +++++++++++++++++++++++
 matlab/+caffe/private/is_valid_handle.m  |  28 ++
 matlab/+caffe/reset.m                    |   8 +
 matlab/+caffe/run_tests.m                |  12 +
 matlab/+caffe/set_device.m               |  11 +
 matlab/+caffe/set_mode_cpu.m             |   7 +
 matlab/+caffe/set_mode_gpu.m             |   7 +
 matlab/CMakeLists.txt                    |   8 +
 20 files changed, 1141 insertions(+), 1 deletion(-)
 create mode 100644 matlab/+caffe/+test/test_net.m
 create mode 100644 matlab/+caffe/+test/test_solver.m
 create mode 100644 matlab/+caffe/Blob.m
 create mode 100644 matlab/+caffe/Layer.m
 create mode 100644 matlab/+caffe/Net.m
 create mode 100644 matlab/+caffe/Solver.m
 create mode 100644 matlab/+caffe/get_net.m
 create mode 100644 matlab/+caffe/get_solver.m
 create mode 100644 matlab/+caffe/io.m
 create mode 100644 matlab/+caffe/private/CHECK.m
 create mode 100644 matlab/+caffe/private/CHECK_FILE_EXIST.m
 create mode 100644 matlab/+caffe/private/caffe_.cpp
 create mode 100644 matlab/+caffe/private/is_valid_handle.m
 create mode 100644 matlab/+caffe/reset.m
 create mode 100644 matlab/+caffe/run_tests.m
 create mode 100644 matlab/+caffe/set_device.m
 create mode 100644 matlab/+caffe/set_mode_cpu.m
 create mode 100644 matlab/+caffe/set_mode_gpu.m

diff --git a/Makefile b/Makefile
index d2e5e5720ed..74b167763c3 100644
--- a/Makefile
+++ b/Makefile
@@ -66,6 +66,7 @@ NONGEN_CXX_SRCS := $(shell find \
 	include/$(PROJECT) \
 	python/$(PROJECT) \
 	matlab/$(PROJECT) \
+	matlab/+$(PROJECT)/private \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
@@ -81,10 +82,13 @@ PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
 PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
 # MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
 MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
+# MAT$(PROJECT)_PKG_SRC is the mex entrance point of matlab package for $(PROJECT)
+MAT$(PROJECT)_PKG_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
 ifneq ($(MATLAB_DIR),)
 	MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
 endif
 MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT)
+MAT$(PROJECT)_PKG_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
 
 ##############################
 # Derive generated files
@@ -447,7 +451,7 @@ $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
 
 mat$(PROJECT): mat
 
-mat: $(MAT$(PROJECT)_SO)
+mat: $(MAT$(PROJECT)_SO) $(MAT$(PROJECT)_PKG_SO)
 
 $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 	@ if [ -z "$(MATLAB_DIR)" ]; then \
@@ -460,6 +464,18 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
+			
+$(MAT$(PROJECT)_PKG_SO): $(MAT$(PROJECT)_PKG_SRC) $(STATIC_NAME)
+	@ if [ -z "$(MATLAB_DIR)" ]; then \
+		echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \
+			"to build mat$(PROJECT)."; \
+		exit 1; \
+	fi
+	@ echo MEX $<
+	$(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_PKG_SRC) \
+			CXX="$(CXX)" \
+			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
+			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
 
 runtest: $(TEST_ALL_BIN)
 	$(TOOL_BUILD_DIR)/caffe
@@ -467,6 +483,9 @@ runtest: $(TEST_ALL_BIN)
 
 pytest: py
 	cd python; python -m unittest discover -s caffe/test
+	
+mattest: mat
+	cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'
 
 warn: $(EMPTY_WARN_REPORT)
 
@@ -582,6 +601,7 @@ clean:
 	@- $(RM) -rf $(DISTRIBUTE_DIR)
 	@- $(RM) $(PY$(PROJECT)_SO)
 	@- $(RM) $(MAT$(PROJECT)_SO)
+	@- $(RM) $(MAT$(PROJECT)_PKG_SO)
 
 supercleanfiles:
 	$(eval SUPERCLEAN_FILES := $(strip \
diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m
new file mode 100644
index 00000000000..4958d50f9e3
--- /dev/null
+++ b/matlab/+caffe/+test/test_net.m
@@ -0,0 +1,74 @@
+classdef test_net < matlab.unittest.TestCase
+  
+  properties
+    num_output
+    model_file
+    net
+  end
+  
+  methods (Static)
+    function model_file = simple_net_file(num_output)
+      model_file = tempname();
+      fid = fopen(model_file, 'w');
+      fprintf(fid, [ ...
+        'name: "testnet" force_backward: true\n' ...
+        'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ...
+        'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ...
+        '    num: 5 channels: 1 height: 1 width: 1\n' ...
+        '    data_filler { type: "gaussian" std: 1 }\n' ...
+        '    data_filler { type: "constant" } } }\n' ...
+        'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ...
+        '  convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ...
+        '    weight_filler { type: "gaussian" std: 1 }\n' ...
+        '    bias_filler { type: "constant" value: 2 } }\n' ...
+        '    param { decay_mult: 1 } param { decay_mult: 0 }\n' ...
+        '    }\n' ...
+        'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ...
+        '  inner_product_param { num_output: ' num2str(num_output) ...
+        '    weight_filler { type: "gaussian" std: 2.5 }\n' ...
+        '    bias_filler { type: "constant" value: -3 } } }\n' ...
+        'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ...
+        '  top: "loss" }' ]);
+      fclose(fid);
+    end
+  end
+  methods
+    function self = test_net()
+      self.num_output = 13;
+      self.model_file = caffe.test.test_net.simple_net_file(self.num_output);
+      self.net = caffe.Net(self.model_file, 'train');
+      % also make sure get_solver runs
+      caffe.get_net(self.model_file, 'train');
+      
+      % fill in valid labels
+      self.net.blobs('label').set_data(randi( ...
+        self.num_output - 1, self.net.blobs('label').shape));
+      
+      delete(self.model_file);
+    end
+  end
+  methods (Test)
+    function test_forward_backward(self)
+      self.net.forward_prefilled();
+      self.net.backward_prefilled();
+    end
+    function test_inputs_outputs(self)
+      self.verifyEqual(self.net.inputs, cell(0, 1))
+      self.verifyEqual(self.net.outputs, {'loss'});
+    end
+    function test_save_and_read(self)
+      weights_file = tempname();
+      self.net.save(weights_file);
+      model_file2 = caffe.test.test_net.simple_net_file(self.num_output);
+      net2 = caffe.Net(model_file2, weights_file, 'train');
+      delete(model_file2);
+      delete(weights_file);
+      for l = 1:length(self.net.layer_vec)
+        for i = 1:length(self.net.layer_vec(l).params)
+          self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+            net2.layer_vec(l).params(i).get_data());
+        end
+      end
+    end
+  end
+end
\ No newline at end of file
diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m
new file mode 100644
index 00000000000..9774ec1d2fd
--- /dev/null
+++ b/matlab/+caffe/+test/test_solver.m
@@ -0,0 +1,43 @@
+classdef test_solver < matlab.unittest.TestCase
+  
+  properties
+    num_output
+    solver
+  end
+  
+  methods
+    function self = test_solver()
+      self.num_output = 13;
+      model_file = caffe.test.test_net.simple_net_file(self.num_output);
+      solver_file = tempname();
+      
+      fid = fopen(solver_file, 'w');
+      fprintf(fid, [ ...
+        'net: "'  model_file  '"\n' ...
+        'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ...
+        'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ...
+        'display: 100 max_iter: 100 snapshot_after_train: false\n' ]);
+      fclose(fid);
+      
+      self.solver = caffe.Solver(solver_file);
+      % also make sure get_solver runs
+      caffe.get_solver(solver_file);
+      caffe.set_mode_cpu();
+      % fill in valid labels
+      self.solver.net.blobs('label').set_data(randi( ...
+        self.num_output - 1, self.solver.net.blobs('label').shape));
+      self.solver.test_nets(1).blobs('label').set_data(randi( ...
+        self.num_output - 1, self.solver.test_nets(1).blobs('label').shape));
+      
+      delete(solver_file);
+      delete(model_file);
+    end
+  end
+  methods (Test)
+    function test_solve(self)
+      self.verifyEqual(self.solver.iter(), 0)
+      self.solver.solve()
+      self.verifyEqual(self.solver.iter(), 100)
+    end
+  end
+end
\ No newline at end of file
diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m
new file mode 100644
index 00000000000..a6326d1a36c
--- /dev/null
+++ b/matlab/+caffe/Blob.m
@@ -0,0 +1,71 @@
+classdef Blob < handle
+  % Wrapper class of caffe::Blob in matlab
+  
+  properties (Access = private)
+    hBlob_self
+  end
+  
+  methods
+    function self = Blob(hBlob_blob)
+      CHECK(is_valid_handle(hBlob_blob), 'invalid input handle');
+      
+      % setup self handle and attributes
+      self.hBlob_self = hBlob_blob;
+    end
+    function shape = shape(self)
+      shape = caffe_('blob_get_shape', self.hBlob_self);
+    end
+    function reshape(self, shape)
+      shape = self.check_and_preprocess_shape(shape);
+      caffe_('blob_reshape', self.hBlob_self, shape);
+    end
+    function data = get_data(self)
+      data = caffe_('blob_get_data', self.hBlob_self);
+    end
+    function set_data(self, data)
+      data = self.check_and_preprocess_data(data);
+      caffe_('blob_set_data', self.hBlob_self, data);
+    end
+    function diff = get_diff(self)
+      diff = caffe_('blob_get_diff', self.hBlob_self);
+    end
+    function set_diff(self, diff)
+      diff = self.check_and_preprocess_data(diff);
+      caffe_('blob_set_diff', self.hBlob_self, diff);
+    end
+  end
+  
+  methods (Access = private)
+    function shape = check_and_preprocess_shape(~, shape)
+      CHECK(isempty(shape) || isnumeric(shape) && isrow(shape), ...
+        'shape must be a integer row vector');
+      shape = double(shape);
+    end
+    function data = check_and_preprocess_data(self, data)
+      CHECK(isnumeric(data), 'data or diff must be numeric types');
+      self.check_data_size_matches(data)
+      data = single(data);
+    end
+    function check_data_size_matches(self, data)
+      % check whether size of data matches shape of this blob
+      % note: matlab arrays always have at least 2 dimensions. To compare
+      % shape between size of data and shape of this blob, extend shape of
+      % this blob to have at least 2 dimensions
+      data_size = size(data);
+      self_shape_extended = self.shape;
+      if isempty(self_shape_extended)
+        % target blob is a scalar (0 dim)
+        self_shape_extended = [1, 1];
+      elseif isscalar(self_shape_extended)
+        % target blob is a vector (1 dim)
+        self_shape_extended = [self_shape_extended, 1];
+      end
+      is_matched = (length(self_shape_extended) == length(data_size)) ...
+        && all(self_shape_extended == data_size);
+      CHECK(is_matched, ...
+        sprintf('%s, data size: [ %s], blob shape: [ %s]', ...
+        'data size does not match blob shape', ...
+        sprintf('%d ', data_size), sprintf('%d ', self_shape_extended)));
+    end
+  end
+end
\ No newline at end of file
diff --git a/matlab/+caffe/Layer.m b/matlab/+caffe/Layer.m
new file mode 100644
index 00000000000..7587ed7ddf7
--- /dev/null
+++ b/matlab/+caffe/Layer.m
@@ -0,0 +1,32 @@
+classdef Layer < handle
+  % Wrapper class of caffe::Layer in matlab
+  
+  properties (Access = private)
+    hLayer_self
+    attributes
+    % attributes fields:
+    %     hBlob_blobs
+  end
+  properties (SetAccess = private)
+    params
+  end
+  
+  methods
+    function self = Layer(hLayer_layer)
+      CHECK(is_valid_handle(hLayer_layer), 'invalid input handle');
+      
+      % setup self handle and attributes
+      self.hLayer_self = hLayer_layer;
+      self.attributes = caffe_('layer_get_attr', self.hLayer_self);
+      
+      % setup weights
+      self.params = caffe.Blob.empty();
+      for n = 1:length(self.attributes.hBlob_blobs)
+        self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+      end
+    end
+    function layer_type = type(self)
+      layer_type = caffe_('layer_get_type', self.hLayer_self);
+    end
+  end
+end
diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
new file mode 100644
index 00000000000..5319634dc8b
--- /dev/null
+++ b/matlab/+caffe/Net.m
@@ -0,0 +1,133 @@
+classdef Net < handle
+  % Wrapper class of caffe::Net in matlab
+  
+  properties (Access = private)
+    hNet_self
+    attributes
+    % attribute fields
+    %     hLayer_layers
+    %     hBlob_blobs
+    %     input_blob_indices
+    %     output_blob_indices
+    %     layer_names
+    %     blob_names
+  end
+  properties (SetAccess = private)
+    layer_vec
+    blob_vec
+    inputs
+    outputs
+    name2layer_index
+    name2blob_index
+    layer_names
+    blob_names
+  end
+  
+  methods
+    function self = Net(varargin)
+      % decide whether to construct a net from model_file or handle
+      if ~(nargin == 1 && isstruct(varargin{1}))
+        % construct a net from model_file
+        self = caffe.get_net(varargin{:});
+        return
+      end
+      % construct a net from handle
+      hNet_net = varargin{1};
+      CHECK(is_valid_handle(hNet_net), 'invalid input handle');
+      
+      % setup self handle and attributes
+      self.hNet_self = hNet_net;
+      self.attributes = caffe_('net_get_attr', self.hNet_self);
+      
+      % setup layer_vec
+      self.layer_vec = caffe.Layer.empty();
+      for n = 1:length(self.attributes.hLayer_layers)
+        self.layer_vec(n) = caffe.Layer(self.attributes.hLayer_layers(n));
+      end
+      
+      % setup blob_vec
+      self.blob_vec = caffe.Blob.empty();
+      for n = 1:length(self.attributes.hBlob_blobs);
+        self.blob_vec(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+      end
+      
+      % setup input and output blob and their names
+      % note: add 1 to indices as matlab is 1-indexed while C++ is 0-indexed
+      self.inputs = ...
+        self.attributes.blob_names(self.attributes.input_blob_indices + 1);
+      self.outputs = ...
+        self.attributes.blob_names(self.attributes.output_blob_indices + 1);
+      
+      % create map objects to map from name to layers and blobs
+      self.name2layer_index = containers.Map(self.attributes.layer_names, ...
+        1:length(self.attributes.layer_names));
+      self.name2blob_index = containers.Map(self.attributes.blob_names, ...
+        1:length(self.attributes.blob_names));
+      
+      % expose layer_names and blob_names for public access
+      self.layer_names = self.attributes.layer_names;
+      self.blob_names = self.attributes.blob_names;
+    end
+    function layer = layers(self, layer_name)
+      CHECK(ischar(layer_name), 'layer_name must be a string');
+      layer = self.layer_vec(self.name2layer_index(layer_name));
+    end
+    function blob = blobs(self, blob_name)
+      CHECK(ischar(blob_name), 'blob_name must be a string');
+      blob = self.blob_vec(self.name2blob_index(blob_name));
+    end
+    function blob = params(self, layer_name, blob_index)
+      CHECK(ischar(layer_name), 'layer_name must be a string');
+      CHECK(isscalar(blob_index), 'blob_index must be a scalar');
+      blob = self.layer_vec(self.name2layer_index(layer_name)).params(blob_index);
+    end
+    function forward_prefilled(self)
+      caffe_('net_forward', self.hNet_self);
+    end
+    function backward_prefilled(self)
+      caffe_('net_backward', self.hNet_self);
+    end
+    function res = forward(self, input_data)
+      CHECK(iscell(input_data), 'input_data must be a cell array');
+      CHECK(length(input_data) == length(self.inputs), ...
+        'input data cell length must match input blob number');
+      % copy data to input_blobs
+      for n = 1:length(self.inputs)
+        self.blobs(self.inputs{n}).set_data(input_data{n});
+      end
+      self.forward_prefilled();
+      % retrieve data from output_blobs
+      res = cell(length(self.outputs), 1);
+      for n = 1:length(self.outputs)
+        res{n} = self.blobs(self.outputs{n}).get_data();
+      end
+    end
+    function res = backward(self, output_diff)
+      CHECK(iscell(output_diff), 'output_diff must be a cell array');
+      CHECK(length(output_diff) == length(self.outputs), ...
+        'output diff cell length must match output blob number');
+      % copy diff to output_blobs
+      for n = 1:length(self.outputs)
+        self.blobs(self.outputs{n}).set_diff(output_diff{n});
+      end
+      self.backward_prefilled();
+      % retrieve diff from input_blobs
+      res = cell(length(self.inputs), 1);
+      for n = 1:length(self.inputs)
+        res{n} = self.blobs(self.inputs{n}).get_diff();
+      end
+    end
+    function copy_from(self, weights_file)
+      CHECK(ischar(weights_file), 'weights_file must be a string');
+      CHECK_FILE_EXIST(weights_file);
+      caffe_('net_copy_from', self.hNet_self, weights_file);
+    end
+    function reshape(self)
+      caffe_('net_reshape', self.hNet_self);
+    end
+    function save(self, weights_file)
+      CHECK(ischar(weights_file), 'weights_file must be a string');
+      caffe_('net_save', self.hNet_self, weights_file);
+    end
+  end
+end
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
new file mode 100644
index 00000000000..80fa5394739
--- /dev/null
+++ b/matlab/+caffe/Solver.m
@@ -0,0 +1,56 @@
+classdef Solver < handle
+  % Wrapper class of caffe::SGDSolver in matlab
+  
+  properties (Access = private)
+    hSolver_self
+    attributes
+    % attribute fields
+    %     hNet_net
+    %     hNet_test_nets
+  end
+  properties (SetAccess = private)
+    net
+    test_nets
+  end
+  
+  methods
+    function self = Solver(varargin)
+      % decide whether to construct a solver from solver_file or handle
+      if ~(nargin == 1 && isstruct(varargin{1}))
+        % construct a solver from solver_file
+        self = caffe.get_solver(varargin{:});
+        return
+      end
+      % construct a solver from handle
+      hSolver_solver = varargin{1};
+      CHECK(is_valid_handle(hSolver_solver), 'invalid input handle');
+      
+      % setup self handle and attributes
+      self.hSolver_self = hSolver_solver;
+      self.attributes = caffe_('solver_get_attr', self.hSolver_self);
+      
+      % setup net and test_nets
+      self.net = caffe.Net(self.attributes.hNet_net);
+      self.test_nets = caffe.Net.empty();
+      for n = 1:length(self.attributes.hNet_test_nets)
+        self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n));
+      end
+    end
+    function iter = iter(self)
+      iter = caffe_('solver_get_iter', self.hSolver_self);
+    end
+    function restore(self, snapshot_filename)
+      CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string');
+      CHECK_FILE_EXIST(snapshot_filename)
+      caffe_('solver_restore', self.hSolver_self, snapshot_filename);
+    end
+    function solve(self)
+      caffe_('solver_solve', self.hSolver_self);
+    end
+    function step(self, iters)
+      CHECK(isscalar(iters) && iters > 0, 'iters must be positive integer');
+      iters = double(iters);
+      caffe_('solver_step', self.hSolver_self, iters);
+    end
+  end
+end
diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m
new file mode 100644
index 00000000000..04872b263c8
--- /dev/null
+++ b/matlab/+caffe/get_net.m
@@ -0,0 +1,37 @@
+function net = get_net(varargin)
+% net = get_net(model_file, phase_name) or
+% net = get_net(model_file, weights_file, phase_name)
+%   Construct a net from model_file, and load weights from weights_file
+%   phase_name can only be 'train' or 'test'
+
+CHECK(nargin == 2 || nargin == 3, ['usage: ' ...
+  'net = get_net(model_file, phase_name) or ', ...
+  'net = get_net(model_file, weights_file, phase_name)']);
+if nargin == 3
+  model_file = varargin{1};
+  weights_file = varargin{2};
+  phase_name = varargin{3};
+elseif nargin == 2
+  model_file = varargin{1};
+  phase_name = varargin{2};
+end
+
+CHECK(ischar(model_file), 'model_file must be a string');
+CHECK(ischar(phase_name), 'phase_name must be a string');
+CHECK_FILE_EXIST(model_file);
+CHECK(strcmp(phase_name, 'train') || strcmp(phase_name, 'test'), ...
+  sprintf('phase_name can only be %strain%s or %stest%s', ...
+  char(39), char(39), char(39), char(39)));
+
+hNet = caffe_('get_net', model_file, phase_name);
+net = caffe.Net(hNet);
+
+% load weights from weights_file
+if nargin == 3
+  CHECK(ischar(weights_file), 'weights_file must be a string');
+  CHECK_FILE_EXIST(weights_file);
+  net.copy_from(weights_file);
+end
+
+end
+
diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m
new file mode 100644
index 00000000000..855709c725e
--- /dev/null
+++ b/matlab/+caffe/get_solver.m
@@ -0,0 +1,11 @@
+function solver = get_solver(solver_file)
+% solver = get_solver(solver_file)
+%   Construct a Solver object from solver_file
+
+CHECK(ischar(solver_file), 'solver_file must be a string');
+CHECK_FILE_EXIST(solver_file);
+
+pSolver = caffe_('get_solver', solver_file);
+solver = caffe.Solver(pSolver);
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
new file mode 100644
index 00000000000..95d58909abd
--- /dev/null
+++ b/matlab/+caffe/io.m
@@ -0,0 +1,22 @@
+classdef io
+  % a class for input and output functions
+  
+  methods (Static)
+    function im_data = load_image(im_file)
+      CHECK(ischar(im_file), 'im_file must be a string');
+      CHECK_FILE_EXIST(im_file);
+      %   load an image from disk into Caffe-supported data format
+      %   switch channels from RGB to BGR, make width the fastest dimension, and
+      %   convert to single
+      im = imread(im_file);
+      im_data = im(:, :, [3, 2, 1]);
+      im_data = permute(im_data, [2 1 3]);
+      im_data = single(im_data);
+    end
+    function mean_data = read_mean(mean_proto_file)
+      CHECK(ischar(mean_proto_file), 'im_file must be a string');
+      CHECK_FILE_EXIST(mean_proto_file);
+      mean_data = caffe_('read_mean', mean_proto_file);
+    end
+  end
+end
\ No newline at end of file
diff --git a/matlab/+caffe/private/CHECK.m b/matlab/+caffe/private/CHECK.m
new file mode 100644
index 00000000000..1cf21032b36
--- /dev/null
+++ b/matlab/+caffe/private/CHECK.m
@@ -0,0 +1,7 @@
+function CHECK(expr, error_msg)
+
+if ~expr
+  error(error_msg);
+end
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/private/CHECK_FILE_EXIST.m b/matlab/+caffe/private/CHECK_FILE_EXIST.m
new file mode 100644
index 00000000000..1db464c8258
--- /dev/null
+++ b/matlab/+caffe/private/CHECK_FILE_EXIST.m
@@ -0,0 +1,7 @@
+function CHECK_FILE_EXIST(filename)
+
+if exist(filename, 'file') == 0
+  error('%s does not exist', filename);
+end
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
new file mode 100644
index 00000000000..96a1920ac39
--- /dev/null
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -0,0 +1,546 @@
+//
+// caffe_.cpp provides wrappers of the caffe::Solver class, caffe::Net class,
+// caffe::Layer class and caffe::Blob class and some caffe::Caffe functions,
+// so that one could easily use Caffe from matlab.
+// Note that for matlab, we will simply use float as the data type.
+
+// Internally, data is stored with dimensions reversed from Caffe's:
+// e.g., if the Caffe blob axes are (num, channels, height, width),
+// the matcaffe data is stored as (width, height, channels, num)
+// where width is the fastest dimension.
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "mex.h"
+
+#include "caffe/caffe.hpp"
+
+#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
+
+using namespace caffe;  // NOLINT(build/namespaces)
+
+// Do CHECK and throw a Mex error if check failsf
+inline void mxCHECK(bool expr, const std::string &msg) {
+  if (!expr) {
+    LOG(ERROR) << msg;
+    mexErrMsgTxt(msg.c_str());
+  }
+}
+inline void mxERROR(const std::string &msg) { mxCHECK(false, msg); }
+
+// Check if a file exists and can be opened
+void mxCHECK_FILE_EXIST(const char* file) {
+  std::ifstream f(file);
+  if (!f.good()) {
+    f.close();
+    mxERROR("Could not open file " + string(file));
+  }
+  f.close();
+}
+
+// The pointers to caffe::Solver and caffe::Net instances
+static vector<shared_ptr<Solver<float> > > solvers_;
+static vector<shared_ptr<Net<float> > > nets_;
+static double init_key = static_cast<double>(caffe_rng_rand());
+
+/** -----------------------------------------------------------------
+ ** data conversion functions
+ **/
+// Enum indicates which blob memory to use
+enum WhichMemory { DATA, DIFF };
+
+// Copy matlab array to Blob data or diff
+static void mx_mat_to_blob(const mxArray* mx_mat, Blob<float>* blob,
+    WhichMemory data_or_diff) {
+  mxCHECK(blob->count() == mxGetNumberOfElements(mx_mat),
+      "number of elements in target blob doesn't match that in input mxArray");
+  const float* mat_mem_ptr = reinterpret_cast<const float*>(mxGetData(mx_mat));
+  float* blob_mem_ptr = NULL;
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    blob_mem_ptr = (data_or_diff == DATA ?
+        blob->mutable_cpu_data() : blob->mutable_cpu_diff());
+    break;
+  case Caffe::GPU:
+    blob_mem_ptr = (data_or_diff == DATA ?
+        blob->mutable_gpu_data() : blob->mutable_gpu_diff());
+    break;
+  default:
+    mxERROR("Unknown Caffe mode");
+  }
+  caffe_copy(blob->count(), mat_mem_ptr, blob_mem_ptr);
+}
+
+// Copy Blob data or diff to matlab array
+static mxArray* blob_to_mx_mat(const Blob<float>* blob,
+    WhichMemory data_or_diff) {
+  const int num_axes = blob->num_axes();
+  vector<mwSize> dims(num_axes);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    dims[mat_axis] = static_cast<mwSize>(blob->shape(blob_axis));
+  }
+  // matlab array needs to have at least one dimension, convert scalar to 1-dim
+  if (num_axes == 0) {
+    dims.push_back(1);
+  }
+  mxArray* mx_mat =
+      mxCreateNumericArray(dims.size(), dims.data(), mxSINGLE_CLASS, mxREAL);
+  float* mat_mem_ptr = reinterpret_cast<float*>(mxGetData(mx_mat));
+  const float* blob_mem_ptr = NULL;
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    blob_mem_ptr = (data_or_diff == DATA ? blob->cpu_data() : blob->cpu_diff());
+    break;
+  case Caffe::GPU:
+    blob_mem_ptr = (data_or_diff == DATA ? blob->gpu_data() : blob->gpu_diff());
+    break;
+  default:
+    mxERROR("Unknown Caffe mode");
+  }
+  caffe_copy(blob->count(), blob_mem_ptr, mat_mem_ptr);
+  return mx_mat;
+}
+
+// convert vector<int> to matlab vector
+static mxArray* int_vec_to_mx_vec(const vector<int>& int_vec) {
+  mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL);
+  double* vec_mem_ptr = mxGetPr(mx_vec);
+  for (int i = 0; i < int_vec.size(); i++) {
+    vec_mem_ptr[i] = int_vec[i];
+  }
+  return mx_vec;
+}
+
+// convert vector<string> to matlab string cell vector
+static mxArray* str_vec_to_mx_strcell(const vector<std::string>& str_vec) {
+  mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1);
+  for (int i = 0; i < str_vec.size(); i++) {
+    mxSetCell(mx_strcell, i, mxCreateString(str_vec[i].c_str()));
+  }
+  return mx_strcell;
+}
+
+/** -----------------------------------------------------------------
+ ** handle and pointer conversion functions
+ ** a handle is a struct array with the following fields
+ **   (uint64) ptr      : the pointer to the C++ object
+ **   (double) init_key : caffe initialization key
+ **/
+// Convert a handle in matlab to a pointer in C++. Check if init_key matches
+template <typename T>
+static T* handle_to_ptr(const mxArray* mx_handle) {
+  mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr");
+  mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key");
+  mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64");
+  mxCHECK(mxGetScalar(mx_init_key) == init_key, "incorrect handle init_key");
+  return reinterpret_cast<T*>(*reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)));
+}
+
+// Create an empty handle struct array
+template <typename T>
+static mxArray* create_handles(int ptr_num) {
+  const int handle_field_num = 2;
+  const char* handle_fields[handle_field_num] = { "ptr", "init_key" };
+  return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields);
+}
+
+// Set up each handle in a handle struct array
+template <typename T>
+static void setup_handle(const T* ptr, int index, mxArray* mx_handles) {
+  mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL);
+  *reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)) =
+      reinterpret_cast<uint64_t>(ptr);
+  mxSetField(mx_handles, index, "ptr", mx_ptr);
+  mxSetField(mx_handles, index, "init_key", mxCreateDoubleScalar(init_key));
+}
+
+// Convert a pointer in C++ to a handle in matlab
+template <typename T>
+static mxArray* ptr_to_handle(const T* ptr) {
+  mxArray* mx_handle = create_handles<T>(1);
+  setup_handle(ptr, 0, mx_handle);
+  return mx_handle;
+}
+
+// Convert a vector of shared_ptr in C++ to handle struct array in matlab
+template <typename T>
+static mxArray* ptr_vec_to_handles(const vector<shared_ptr<T> >& ptr_vec) {
+  mxArray* mx_handle = create_handles<T>(ptr_vec.size());
+  for (int i = 0; i < ptr_vec.size(); i++) {
+    setup_handle(ptr_vec[i].get(), i, mx_handle);
+  }
+  return mx_handle;
+}
+
+/** -----------------------------------------------------------------
+ ** matlab command functions: caffe_(api_command, arg1, arg2, ...)
+ **/
+// Usage: caffe_('get_solver', solver_file);
+static void get_solver(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+      "Usage: caffe_('get_solver', solver_file)");
+  const char* solver_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(solver_file);
+  shared_ptr<Solver<float> > solver;
+  solver.reset(new caffe::SGDSolver<float>(solver_file));
+  solvers_.push_back(solver);
+  plhs[0] = ptr_to_handle<Solver<float> >(solver.get());
+}
+
+// Usage: caffe_('solver_get_attr', hSolver)
+static void solver_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_get_attr', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  const int solver_attr_num = 2;
+  const char* solver_attrs[solver_attr_num] = { "hNet_net", "hNet_test_nets" };
+  mxArray* mx_solver_attr = mxCreateStructMatrix(1, 1, solver_attr_num,
+      solver_attrs);
+  mxSetField(mx_solver_attr, 0, "hNet_net",
+      ptr_to_handle<Net<float> >(solver->net().get()));
+  mxSetField(mx_solver_attr, 0, "hNet_test_nets",
+      ptr_vec_to_handles<Net<float> >(solver->test_nets()));
+  plhs[0] = mx_solver_attr;
+}
+
+// Usage: caffe_('solver_get_iter', hSolver)
+static void solver_get_iter(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_get_iter', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  plhs[0] = mxCreateDoubleScalar(solver->iter());
+}
+
+// Usage: caffe_('solver_restore', hSolver, snapshot_file)
+static void solver_restore(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('solver_restore', hSolver, snapshot_file)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  const char* snapshot_file = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(snapshot_file);
+  solver->Restore(snapshot_file);
+}
+
+// Usage: caffe_('solver_solve', hSolver)
+static void solver_solve(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_solve', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  solver->Solve();
+}
+
+// Usage: caffe_('solver_solve', hSolver, iters)
+static void solver_step(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+      "Usage: caffe_('solver_solve', hSolver, iters)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  int iters = mxGetScalar(prhs[1]);
+  solver->Step(iters);
+}
+
+// Usage: caffe_('get_net', model_file, phase_name)
+static void get_net(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('get_net', model_file, phase_name)");
+  const char* model_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(model_file);
+  const char* phase_name = mxArrayToString(prhs[1]);
+  Phase phase;
+  if (strcmp(phase_name, "train") == 0) {
+      phase = TRAIN;
+  } else if (strcmp(phase_name, "test") == 0) {
+      phase = TEST;
+  } else {
+    mxERROR("Unknown phase.");
+  }
+  shared_ptr<Net<float> > net;
+  net.reset(new caffe::Net<float>(model_file, phase));
+  nets_.push_back(net);
+  plhs[0] = ptr_to_handle<Net<float> >(net.get());
+}
+
+// Usage: caffe_('net_get_attr', hNet)
+static void net_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_get_attr', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  const int net_attr_num = 6;
+  const char* net_attrs[net_attr_num] = { "hLayer_layers", "hBlob_blobs",
+      "input_blob_indices", "output_blob_indices", "layer_names", "blob_names"};
+  mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num,
+      net_attrs);
+  mxSetField(mx_net_attr, 0, "hLayer_layers",
+      ptr_vec_to_handles<Layer<float> >(net->layers()));
+  mxSetField(mx_net_attr, 0, "hBlob_blobs",
+      ptr_vec_to_handles<Blob<float> >(net->blobs()));
+  mxSetField(mx_net_attr, 0, "input_blob_indices",
+      int_vec_to_mx_vec(net->input_blob_indices()));
+  mxSetField(mx_net_attr, 0, "output_blob_indices",
+      int_vec_to_mx_vec(net->output_blob_indices()));
+  mxSetField(mx_net_attr, 0, "layer_names",
+    str_vec_to_mx_strcell(net->layer_names()));
+  mxSetField(mx_net_attr, 0, "blob_names",
+      str_vec_to_mx_strcell(net->blob_names()));
+  plhs[0] = mx_net_attr;
+}
+
+// Usage: caffe_('net_forward', hNet)
+static void net_forward(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_forward', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->ForwardPrefilled();
+}
+
+// Usage: caffe_('net_backward', hNet)
+static void net_backward(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_backward', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->Backward();
+}
+
+// Usage: caffe_('net_copy_from', hNet, weights_file)
+static void net_copy_from(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('net_copy_from', hNet, weights_file)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  const char* weights_file = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(weights_file);
+  net->CopyTrainedLayersFrom(weights_file);
+}
+
+// Usage: caffe_('net_reshape', hNet)
+static void net_reshape(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_reshape', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->Reshape();
+}
+
+// Usage: caffe_('net_save', hNet, save_file)
+static void net_save(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('net_save', hNet, save_file)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  const char* weights_file = mxArrayToString(prhs[1]);
+  NetParameter net_param;
+  net->ToProto(&net_param, false);
+  WriteProtoToBinaryFile(net_param, weights_file);
+}
+
+// Usage: caffe_('layer_get_attr', hLayer)
+static void layer_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('layer_get_attr', hLayer)");
+  Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+  const int layer_attr_num = 1;
+  const char* layer_attrs[layer_attr_num] = { "hBlob_blobs" };
+  mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num,
+      layer_attrs);
+  mxSetField(mx_layer_attr, 0, "hBlob_blobs",
+      ptr_vec_to_handles<Blob<float> >(layer->blobs()));
+  plhs[0] = mx_layer_attr;
+}
+
+// Usage: caffe_('layer_get_attr', hLayer)
+static void layer_get_type(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('layer_get_attr', hLayer)");
+  Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+  plhs[0] = mxCreateString(layer->type());
+}
+
+// Usage: caffe_('blob_get_shape', hBlob)
+static void blob_get_shape(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_shape', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  const int num_axes = blob->num_axes();
+  mxArray* mx_shape = mxCreateDoubleMatrix(1, num_axes, mxREAL);
+  double* shape_mem_mtr = mxGetPr(mx_shape);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    shape_mem_mtr[mat_axis] = static_cast<double>(blob->shape(blob_axis));
+  }
+  plhs[0] = mx_shape;
+}
+
+// Usage: caffe_('blob_reshape', hBlob, new_shape)
+static void blob_reshape(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+      "Usage: caffe_('blob_reshape', hBlob, new_shape)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  const mxArray* mx_shape = prhs[1];
+  double* shape_mem_mtr = mxGetPr(mx_shape);
+  const int num_axes = mxGetNumberOfElements(mx_shape);
+  vector<int> blob_shape(num_axes);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    blob_shape[blob_axis] = static_cast<int>(shape_mem_mtr[mat_axis]);
+  }
+  blob->Reshape(blob_shape);
+}
+
+// Usage: caffe_('blob_get_data', hBlob)
+static void blob_get_data(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_data', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  plhs[0] = blob_to_mx_mat(blob, DATA);
+}
+
+// Usage: caffe_('blob_set_data', hBlob, new_data)
+static void blob_set_data(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+      "Usage: caffe_('blob_set_data', hBlob, new_data)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  mx_mat_to_blob(prhs[1], blob, DATA);
+}
+
+// Usage: caffe_('blob_get_diff', hBlob)
+static void blob_get_diff(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_diff', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  plhs[0] = blob_to_mx_mat(blob, DIFF);
+}
+
+// Usage: caffe_('blob_set_diff', hBlob, new_diff)
+static void blob_set_diff(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+      "Usage: caffe_('blob_set_diff', hBlob, new_diff)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  mx_mat_to_blob(prhs[1], blob, DIFF);
+}
+
+// Usage: caffe_('set_mode_cpu')
+static void set_mode_cpu(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_cpu')");
+  Caffe::set_mode(Caffe::CPU);
+}
+
+// Usage: caffe_('set_mode_gpu')
+static void set_mode_gpu(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_gpu')");
+  Caffe::set_mode(Caffe::GPU);
+}
+
+// Usage: caffe_('set_device', device_id)
+static void set_device(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsDouble(prhs[0]),
+      "Usage: caffe_('set_device', device_id)");
+  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
+  Caffe::SetDevice(device_id);
+}
+
+// Usage: caffe_('get_init_key')
+static void get_init_key(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('get_init_key')");
+  plhs[0] = mxCreateDoubleScalar(init_key);
+}
+
+// Usage: caffe_('reset')
+static void reset(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('reset')");
+  mexPrintf("cleared %d solvers and %d stand-alone nets\n", solvers_.size(),
+      nets_.size());
+  solvers_.clear();
+  nets_.clear();
+  init_key = static_cast<double>(caffe_rng_rand());
+}
+
+// Usage: caffe_('read_mean', mean_proto_file)
+static void read_mean(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+      "Usage: caffe_('read_mean', mean_proto_file)");
+  const char* mean_proto_file = mxArrayToString(prhs[0]);
+  Blob<float> data_mean;
+  mexPrintf("Loading mean file from: %s\n", mean_proto_file);
+  BlobProto blob_proto;
+  bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto);
+  mxCHECK(result, "Couldn't read the file");
+  data_mean.FromProto(blob_proto);
+  mwSize dims[4] = {data_mean.width(), data_mean.height(),
+                    data_mean.channels(), data_mean.num() };
+  mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
+  float* data_ptr = reinterpret_cast<float*>(mxGetData(mx_blob));
+  caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
+  mexPrintf("Remember that Caffe saves in [width, height, channels]"
+                " format and channels are also BGR!\n");
+  plhs[0] = mx_blob;
+}
+
+/** -----------------------------------------------------------------
+ ** Available commands.
+ **/
+struct handler_registry {
+  string cmd;
+  void (*func)(MEX_ARGS);
+};
+
+static handler_registry handlers[] = {
+  // Public API functions
+  { "get_solver",         get_solver      },
+  { "solver_get_attr",    solver_get_attr },
+  { "solver_get_iter",    solver_get_iter },
+  { "solver_restore",     solver_restore  },
+  { "solver_solve",       solver_solve    },
+  { "solver_step",        solver_step     },
+  { "get_net",            get_net         },
+  { "net_get_attr",       net_get_attr    },
+  { "net_forward",        net_forward     },
+  { "net_backward",       net_backward    },
+  { "net_copy_from",      net_copy_from   },
+  { "net_reshape",        net_reshape     },
+  { "net_save",           net_save        },
+  { "layer_get_attr",     layer_get_attr  },
+  { "layer_get_type",     layer_get_type  },
+  { "blob_get_shape",     blob_get_shape  },
+  { "blob_reshape",       blob_reshape    },
+  { "blob_get_data",      blob_get_data   },
+  { "blob_set_data",      blob_set_data   },
+  { "blob_get_diff",      blob_get_diff   },
+  { "blob_set_diff",      blob_set_diff   },
+  { "set_mode_cpu",       set_mode_cpu    },
+  { "set_mode_gpu",       set_mode_gpu    },
+  { "set_device",         set_device      },
+  { "get_init_key",       get_init_key    },
+  { "reset",              reset           },
+  { "read_mean",          read_mean       },
+  // The end.
+  { "END",                NULL            },
+};
+
+/** -----------------------------------------------------------------
+ ** matlab entry point: caffe_(api_command, arg1, arg2, ...)
+ **/
+void mexFunction(MEX_ARGS) {
+  mexLock();  // Avoid clearing the mex file.
+  if (nrhs == 0) {
+    mxERROR("No API command given");
+    return;
+  }
+
+  { // Handle input command
+    char* cmd = mxArrayToString(prhs[0]);
+    bool dispatched = false;
+    // Dispatch to cmd handler
+    for (int i = 0; handlers[i].func != NULL; i++) {
+      if (handlers[i].cmd.compare(cmd) == 0) {
+        handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
+        dispatched = true;
+        break;
+      }
+    }
+    if (!dispatched) {
+      ostringstream error_msg;
+      error_msg << "Unknown command '" << cmd << "'";
+      mxERROR(error_msg.str().c_str());
+    }
+    mxFree(cmd);
+  }
+}
diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m
new file mode 100644
index 00000000000..f03469c4f86
--- /dev/null
+++ b/matlab/+caffe/private/is_valid_handle.m
@@ -0,0 +1,28 @@
+function valid = is_valid_handle(hObj)
+% valid = is_valid_handle(hObj) or is_valid_handle('get_new_init_key')
+%   Check if a handle is valid (has the right data type and init_key matches)
+%   Use is_valid_handle('get_new_init_key') to get new init_key from C++;
+
+% a handle is a struct array with the following fields
+%   (uint64) ptr      : the pointer to the C++ object
+%   (double) init_key : caffe initialization key
+
+persistent init_key;
+if isempty(init_key)
+  init_key = caffe_('get_init_key');
+end
+
+% is_valid_handle('get_new_init_key') to get new init_key from C++;
+if ischar(hObj) && strcmp(hObj, 'get_new_init_key')
+  init_key = caffe_('get_init_key');
+  valid = true;
+  return
+else
+  % check whether data types are correct and init_key matches
+  valid = isstruct(hObj) ...
+    && isscalar(hObj.ptr) && isa(hObj.ptr, 'uint64') ...
+    && isscalar(hObj.init_key) && isa(hObj.init_key, 'double') ...
+    && hObj.init_key == init_key;
+end
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/reset.m b/matlab/+caffe/reset.m
new file mode 100644
index 00000000000..2df812b3f1d
--- /dev/null
+++ b/matlab/+caffe/reset.m
@@ -0,0 +1,8 @@
+function reset()
+% reset()
+%   reset Caffe to initial status
+
+caffe_('reset');
+is_valid_handle('get_new_init_key');
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
new file mode 100644
index 00000000000..20834d497c2
--- /dev/null
+++ b/matlab/+caffe/run_tests.m
@@ -0,0 +1,12 @@
+function results = run_tests()
+% results = run_tests()
+%   run all tests in this caffe matlab wrapper package
+
+caffe.reset();
+results = [...
+  run(caffe.test.test_net) ...
+  run(caffe.test.test_solver)
+  ];
+caffe.reset();
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/set_device.m b/matlab/+caffe/set_device.m
new file mode 100644
index 00000000000..43f00bddee6
--- /dev/null
+++ b/matlab/+caffe/set_device.m
@@ -0,0 +1,11 @@
+function set_device(device_id)
+% set_device(device_id)
+%   set Caffe's GPU device ID
+
+CHECK(isscalar(device_id) && device_id >= 0, ...
+  'device_id must be non-negative integer');
+device_id = double(device_id);
+
+caffe_('set_device', device_id);
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/set_mode_cpu.m b/matlab/+caffe/set_mode_cpu.m
new file mode 100644
index 00000000000..8c0576ccbe4
--- /dev/null
+++ b/matlab/+caffe/set_mode_cpu.m
@@ -0,0 +1,7 @@
+function set_mode_cpu()
+% set_mode_cpu()
+%   set Caffe to CPU mode
+
+caffe_('set_mode_cpu');
+
+end
\ No newline at end of file
diff --git a/matlab/+caffe/set_mode_gpu.m b/matlab/+caffe/set_mode_gpu.m
new file mode 100644
index 00000000000..ecd13b59bba
--- /dev/null
+++ b/matlab/+caffe/set_mode_gpu.m
@@ -0,0 +1,7 @@
+function set_mode_gpu()
+% set_mode_gpu()
+%   set Caffe to GPU mode
+
+caffe_('set_mode_gpu');
+
+end
\ No newline at end of file
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index 791a4e70f43..63182abcc44 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -32,7 +32,9 @@ endfunction()
 
 # global settings
 file(GLOB Matlab_srcs caffe/matcaffe.cpp)
+file(GLOB Matlab_pkg_srcs +caffe/private/caffe_.cpp)
 set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex)
+set(Matlab_caffe_pkg_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex)
 
 caffe_get_current_cflags(cflags)
 caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
@@ -50,9 +52,15 @@ if(build_using MATCHES "Matlab")
       ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags}
       DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM)
   add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
+  caffe_fetch_and_set_proper_mexext(Matlab_caffe_pkg_mex)
+  add_custom_command(OUTPUT ${Matlab_caffe_pkg_mex} COMMAND ${Matlab_mex}
+      ARGS -output ${Matlab_caffe_pkg_mex} ${Matlab_pkg_srcs} ${cflags} ${link_folders} ${libflags}
+      DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_pkg_mex}" VERBATIM)
+  add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_pkg_mex} SOURCES ${Matlab_pkg_srcs})
 
 elseif(build_using MATCHES "Octave")
 
+  # Note: Matlab Caffe package cannot be used in Octave, so we don't build it
   if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
     set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
   elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")

From a53df9892c49758bfee1de3455054dec9fc6dbdb Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Wed, 27 May 2015 12:37:20 +0800
Subject: [PATCH 1378/2053] Fix matlab tailing dimension 1 issue for shape
 match

Matlab cannot have tailing dimension 1 for ndim > 2, so you
cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10.
Extend matlab arrays to have tailing dimension 1 during shape match.
---
 matlab/+caffe/+test/test_net.m           |  2 +-
 matlab/+caffe/+test/test_solver.m        |  2 +-
 matlab/+caffe/Blob.m                     | 15 ++++++++++-----
 matlab/+caffe/get_net.m                  |  1 -
 matlab/+caffe/get_solver.m               |  2 +-
 matlab/+caffe/io.m                       |  2 +-
 matlab/+caffe/private/CHECK.m            |  2 +-
 matlab/+caffe/private/CHECK_FILE_EXIST.m |  2 +-
 matlab/+caffe/private/is_valid_handle.m  |  2 +-
 matlab/+caffe/reset.m                    |  2 +-
 matlab/+caffe/run_tests.m                |  2 +-
 matlab/+caffe/set_device.m               |  2 +-
 matlab/+caffe/set_mode_cpu.m             |  2 +-
 matlab/+caffe/set_mode_gpu.m             |  2 +-
 14 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m
index 4958d50f9e3..5d9ba000209 100644
--- a/matlab/+caffe/+test/test_net.m
+++ b/matlab/+caffe/+test/test_net.m
@@ -71,4 +71,4 @@ function test_save_and_read(self)
       end
     end
   end
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m
index 9774ec1d2fd..682dad48a3b 100644
--- a/matlab/+caffe/+test/test_solver.m
+++ b/matlab/+caffe/+test/test_solver.m
@@ -40,4 +40,4 @@ function test_solve(self)
       self.verifyEqual(self.solver.iter(), 100)
     end
   end
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m
index a6326d1a36c..f9b64096adc 100644
--- a/matlab/+caffe/Blob.m
+++ b/matlab/+caffe/Blob.m
@@ -51,7 +51,6 @@ function check_data_size_matches(self, data)
       % note: matlab arrays always have at least 2 dimensions. To compare
       % shape between size of data and shape of this blob, extend shape of
       % this blob to have at least 2 dimensions
-      data_size = size(data);
       self_shape_extended = self.shape;
       if isempty(self_shape_extended)
         % target blob is a scalar (0 dim)
@@ -60,12 +59,18 @@ function check_data_size_matches(self, data)
         % target blob is a vector (1 dim)
         self_shape_extended = [self_shape_extended, 1];
       end
-      is_matched = (length(self_shape_extended) == length(data_size)) ...
-        && all(self_shape_extended == data_size);
+      % also, matlab cannot have tailing dimension 1 for ndim > 2, so you
+      % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10
+      % extend matlab arrays to have tailing dimension 1 during shape match
+      data_size_extended = ...
+        [size(data), ones(1, length(self_shape_extended) - ndims(data))];
+      is_matched = ...
+      (length(self_shape_extended) == length(data_size_extended)) ...
+        && all(self_shape_extended == data_size_extended);
       CHECK(is_matched, ...
         sprintf('%s, data size: [ %s], blob shape: [ %s]', ...
         'data size does not match blob shape', ...
-        sprintf('%d ', data_size), sprintf('%d ', self_shape_extended)));
+        sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended)));
     end
   end
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m
index 04872b263c8..d60979d04b5 100644
--- a/matlab/+caffe/get_net.m
+++ b/matlab/+caffe/get_net.m
@@ -34,4 +34,3 @@
 end
 
 end
-
diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m
index 855709c725e..30366d851e4 100644
--- a/matlab/+caffe/get_solver.m
+++ b/matlab/+caffe/get_solver.m
@@ -8,4 +8,4 @@
 pSolver = caffe_('get_solver', solver_file);
 solver = caffe.Solver(pSolver);
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
index 95d58909abd..7fad9686ac1 100644
--- a/matlab/+caffe/io.m
+++ b/matlab/+caffe/io.m
@@ -19,4 +19,4 @@
       mean_data = caffe_('read_mean', mean_proto_file);
     end
   end
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/private/CHECK.m b/matlab/+caffe/private/CHECK.m
index 1cf21032b36..21706549cfa 100644
--- a/matlab/+caffe/private/CHECK.m
+++ b/matlab/+caffe/private/CHECK.m
@@ -4,4 +4,4 @@ function CHECK(expr, error_msg)
   error(error_msg);
 end
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/private/CHECK_FILE_EXIST.m b/matlab/+caffe/private/CHECK_FILE_EXIST.m
index 1db464c8258..8c80fb8094f 100644
--- a/matlab/+caffe/private/CHECK_FILE_EXIST.m
+++ b/matlab/+caffe/private/CHECK_FILE_EXIST.m
@@ -4,4 +4,4 @@ function CHECK_FILE_EXIST(filename)
   error('%s does not exist', filename);
 end
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m
index f03469c4f86..77abf21dd11 100644
--- a/matlab/+caffe/private/is_valid_handle.m
+++ b/matlab/+caffe/private/is_valid_handle.m
@@ -25,4 +25,4 @@
     && hObj.init_key == init_key;
 end
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/reset.m b/matlab/+caffe/reset.m
index 2df812b3f1d..c1cfea41a76 100644
--- a/matlab/+caffe/reset.m
+++ b/matlab/+caffe/reset.m
@@ -5,4 +5,4 @@ function reset()
 caffe_('reset');
 is_valid_handle('get_new_init_key');
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
index 20834d497c2..fb1089c2d96 100644
--- a/matlab/+caffe/run_tests.m
+++ b/matlab/+caffe/run_tests.m
@@ -9,4 +9,4 @@
   ];
 caffe.reset();
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/set_device.m b/matlab/+caffe/set_device.m
index 43f00bddee6..f94068cbe98 100644
--- a/matlab/+caffe/set_device.m
+++ b/matlab/+caffe/set_device.m
@@ -8,4 +8,4 @@ function set_device(device_id)
 
 caffe_('set_device', device_id);
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/set_mode_cpu.m b/matlab/+caffe/set_mode_cpu.m
index 8c0576ccbe4..a87e0e2852b 100644
--- a/matlab/+caffe/set_mode_cpu.m
+++ b/matlab/+caffe/set_mode_cpu.m
@@ -4,4 +4,4 @@ function set_mode_cpu()
 
 caffe_('set_mode_cpu');
 
-end
\ No newline at end of file
+end
diff --git a/matlab/+caffe/set_mode_gpu.m b/matlab/+caffe/set_mode_gpu.m
index ecd13b59bba..78e5f6773a1 100644
--- a/matlab/+caffe/set_mode_gpu.m
+++ b/matlab/+caffe/set_mode_gpu.m
@@ -4,4 +4,4 @@ function set_mode_gpu()
 
 caffe_('set_mode_gpu');
 
-end
\ No newline at end of file
+end

From 9735f4b3b257379ac4e6c9d310aab32bfb198661 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 28 May 2015 13:40:26 +0800
Subject: [PATCH 1379/2053] Aesthetic changes on code style and some minor fix

---
 matlab/+caffe/Blob.m                    |  22 ++--
 matlab/+caffe/Layer.m                   |   2 +-
 matlab/+caffe/Net.m                     |  10 +-
 matlab/+caffe/Solver.m                  |   2 +-
 matlab/+caffe/get_net.m                 |   3 +-
 matlab/+caffe/get_solver.m              |   1 -
 matlab/+caffe/io.m                      |  15 ++-
 matlab/+caffe/private/caffe_.cpp        | 154 ++++++++++++------------
 matlab/+caffe/private/is_valid_handle.m |   1 -
 matlab/+caffe/run_tests.m               |   8 +-
 10 files changed, 113 insertions(+), 105 deletions(-)

diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m
index f9b64096adc..e39f7ee3f20 100644
--- a/matlab/+caffe/Blob.m
+++ b/matlab/+caffe/Blob.m
@@ -7,9 +7,9 @@
   
   methods
     function self = Blob(hBlob_blob)
-      CHECK(is_valid_handle(hBlob_blob), 'invalid input handle');
+      CHECK(is_valid_handle(hBlob_blob), 'invalid Blob handle');
       
-      % setup self handle and attributes
+      % setup self handle
       self.hBlob_self = hBlob_blob;
     end
     function shape = shape(self)
@@ -37,14 +37,16 @@ function set_diff(self, diff)
   
   methods (Access = private)
     function shape = check_and_preprocess_shape(~, shape)
-      CHECK(isempty(shape) || isnumeric(shape) && isrow(shape), ...
+      CHECK(isempty(shape) || (isnumeric(shape) && isrow(shape)), ...
         'shape must be a integer row vector');
       shape = double(shape);
     end
     function data = check_and_preprocess_data(self, data)
       CHECK(isnumeric(data), 'data or diff must be numeric types');
-      self.check_data_size_matches(data)
-      data = single(data);
+      self.check_data_size_matches(data);
+      if ~isa(data, 'single')
+        data = single(data);
+      end
     end
     function check_data_size_matches(self, data)
       % check whether size of data matches shape of this blob
@@ -59,17 +61,17 @@ function check_data_size_matches(self, data)
         % target blob is a vector (1 dim)
         self_shape_extended = [self_shape_extended, 1];
       end
-      % also, matlab cannot have tailing dimension 1 for ndim > 2, so you
+      % Also, matlab cannot have tailing dimension 1 for ndim > 2, so you
       % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10
-      % extend matlab arrays to have tailing dimension 1 during shape match
+      % Extend matlab arrays to have tailing dimension 1 during shape match
       data_size_extended = ...
         [size(data), ones(1, length(self_shape_extended) - ndims(data))];
       is_matched = ...
-      (length(self_shape_extended) == length(data_size_extended)) ...
+        (length(self_shape_extended) == length(data_size_extended)) ...
         && all(self_shape_extended == data_size_extended);
       CHECK(is_matched, ...
-        sprintf('%s, data size: [ %s], blob shape: [ %s]', ...
-        'data size does not match blob shape', ...
+        sprintf('%s, input data/diff size: [ %s] vs target blob shape: [ %s]', ...
+        'input data/diff size does not match target blob shape', ...
         sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended)));
     end
   end
diff --git a/matlab/+caffe/Layer.m b/matlab/+caffe/Layer.m
index 7587ed7ddf7..4c2023101a5 100644
--- a/matlab/+caffe/Layer.m
+++ b/matlab/+caffe/Layer.m
@@ -13,7 +13,7 @@
   
   methods
     function self = Layer(hLayer_layer)
-      CHECK(is_valid_handle(hLayer_layer), 'invalid input handle');
+      CHECK(is_valid_handle(hLayer_layer), 'invalid Layer handle');
       
       % setup self handle and attributes
       self.hLayer_self = hLayer_layer;
diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
index 5319634dc8b..a6761060dfb 100644
--- a/matlab/+caffe/Net.m
+++ b/matlab/+caffe/Net.m
@@ -33,7 +33,7 @@
       end
       % construct a net from handle
       hNet_net = varargin{1};
-      CHECK(is_valid_handle(hNet_net), 'invalid input handle');
+      CHECK(is_valid_handle(hNet_net), 'invalid Net handle');
       
       % setup self handle and attributes
       self.hNet_self = hNet_net;
@@ -64,7 +64,7 @@
       self.name2blob_index = containers.Map(self.attributes.blob_names, ...
         1:length(self.attributes.blob_names));
       
-      % expose layer_names and blob_names for public access
+      % expose layer_names and blob_names for public read access
       self.layer_names = self.attributes.layer_names;
       self.blob_names = self.attributes.blob_names;
     end
@@ -91,12 +91,12 @@ function backward_prefilled(self)
       CHECK(iscell(input_data), 'input_data must be a cell array');
       CHECK(length(input_data) == length(self.inputs), ...
         'input data cell length must match input blob number');
-      % copy data to input_blobs
+      % copy data to input blobs
       for n = 1:length(self.inputs)
         self.blobs(self.inputs{n}).set_data(input_data{n});
       end
       self.forward_prefilled();
-      % retrieve data from output_blobs
+      % retrieve data from output blobs
       res = cell(length(self.outputs), 1);
       for n = 1:length(self.outputs)
         res{n} = self.blobs(self.outputs{n}).get_data();
@@ -106,7 +106,7 @@ function backward_prefilled(self)
       CHECK(iscell(output_diff), 'output_diff must be a cell array');
       CHECK(length(output_diff) == length(self.outputs), ...
         'output diff cell length must match output blob number');
-      % copy diff to output_blobs
+      % copy diff to output blobs
       for n = 1:length(self.outputs)
         self.blobs(self.outputs{n}).set_diff(output_diff{n});
       end
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
index 80fa5394739..daaa8022b91 100644
--- a/matlab/+caffe/Solver.m
+++ b/matlab/+caffe/Solver.m
@@ -23,7 +23,7 @@
       end
       % construct a solver from handle
       hSolver_solver = varargin{1};
-      CHECK(is_valid_handle(hSolver_solver), 'invalid input handle');
+      CHECK(is_valid_handle(hSolver_solver), 'invalid Solver handle');
       
       % setup self handle and attributes
       self.hSolver_self = hSolver_solver;
diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m
index d60979d04b5..4b5683eb82e 100644
--- a/matlab/+caffe/get_net.m
+++ b/matlab/+caffe/get_net.m
@@ -5,7 +5,7 @@
 %   phase_name can only be 'train' or 'test'
 
 CHECK(nargin == 2 || nargin == 3, ['usage: ' ...
-  'net = get_net(model_file, phase_name) or ', ...
+  'net = get_net(model_file, phase_name) or ' ...
   'net = get_net(model_file, weights_file, phase_name)']);
 if nargin == 3
   model_file = varargin{1};
@@ -23,6 +23,7 @@
   sprintf('phase_name can only be %strain%s or %stest%s', ...
   char(39), char(39), char(39), char(39)));
 
+% construct caffe net from model_file
 hNet = caffe_('get_net', model_file, phase_name);
 net = caffe.Net(hNet);
 
diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m
index 30366d851e4..74d576eb31b 100644
--- a/matlab/+caffe/get_solver.m
+++ b/matlab/+caffe/get_solver.m
@@ -4,7 +4,6 @@
 
 CHECK(ischar(solver_file), 'solver_file must be a string');
 CHECK_FILE_EXIST(solver_file);
-
 pSolver = caffe_('get_solver', solver_file);
 solver = caffe.Solver(pSolver);
 
diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
index 7fad9686ac1..7a30bfb5772 100644
--- a/matlab/+caffe/io.m
+++ b/matlab/+caffe/io.m
@@ -3,17 +3,20 @@
   
   methods (Static)
     function im_data = load_image(im_file)
+      % im_data = load_image(im_file)
+      %   load an image from disk into Caffe-supported data format
+      %   switch channels from RGB to BGR, make width the fastest dimension
+      %   and convert to single
       CHECK(ischar(im_file), 'im_file must be a string');
       CHECK_FILE_EXIST(im_file);
-      %   load an image from disk into Caffe-supported data format
-      %   switch channels from RGB to BGR, make width the fastest dimension, and
-      %   convert to single
-      im = imread(im_file);
-      im_data = im(:, :, [3, 2, 1]);
-      im_data = permute(im_data, [2 1 3]);
+      im_data = imread(im_file);
+      im_data = im_data(:, :, [3, 2, 1]);
+      im_data = permute(im_data, [2, 1, 3]);
       im_data = single(im_data);
     end
     function mean_data = read_mean(mean_proto_file)
+      % mean_data = read_mean(mean_proto_file)
+      %   read image mean data from binaryproto file
       CHECK(ischar(mean_proto_file), 'im_file must be a string');
       CHECK_FILE_EXIST(mean_proto_file);
       mean_data = caffe_('read_mean', mean_proto_file);
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 96a1920ac39..4e0ebc1c00a 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -21,21 +21,22 @@
 
 using namespace caffe;  // NOLINT(build/namespaces)
 
-// Do CHECK and throw a Mex error if check failsf
-inline void mxCHECK(bool expr, const std::string &msg) {
+// Do CHECK and throw a Mex error if check fails
+inline void mxCHECK(bool expr, const char* msg) {
   if (!expr) {
-    LOG(ERROR) << msg;
-    mexErrMsgTxt(msg.c_str());
+    mexErrMsgTxt(msg);
   }
 }
-inline void mxERROR(const std::string &msg) { mxCHECK(false, msg); }
+inline void mxERROR(const char* msg) { mexErrMsgTxt(msg); }
 
 // Check if a file exists and can be opened
 void mxCHECK_FILE_EXIST(const char* file) {
   std::ifstream f(file);
   if (!f.good()) {
     f.close();
-    mxERROR("Could not open file " + string(file));
+    std::string msg("Could not open file ");
+    msg += file;
+    mxERROR(msg.c_str());
   }
   f.close();
 }
@@ -43,6 +44,7 @@ void mxCHECK_FILE_EXIST(const char* file) {
 // The pointers to caffe::Solver and caffe::Net instances
 static vector<shared_ptr<Solver<float> > > solvers_;
 static vector<shared_ptr<Net<float> > > nets_;
+// init_key is generated at the beginning and everytime you call reset
 static double init_key = static_cast<double>(caffe_rng_rand());
 
 /** -----------------------------------------------------------------
@@ -104,17 +106,17 @@ static mxArray* blob_to_mx_mat(const Blob<float>* blob,
   return mx_mat;
 }
 
-// convert vector<int> to matlab vector
+// Convert vector<int> to matlab row vector
 static mxArray* int_vec_to_mx_vec(const vector<int>& int_vec) {
   mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL);
   double* vec_mem_ptr = mxGetPr(mx_vec);
   for (int i = 0; i < int_vec.size(); i++) {
-    vec_mem_ptr[i] = int_vec[i];
+    vec_mem_ptr[i] = static_cast<double>(int_vec[i]);
   }
   return mx_vec;
 }
 
-// convert vector<string> to matlab string cell vector
+// Convert vector<string> to matlab cell vector of strings
 static mxArray* str_vec_to_mx_strcell(const vector<std::string>& str_vec) {
   mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1);
   for (int i = 0; i < str_vec.size(); i++) {
@@ -135,44 +137,46 @@ static T* handle_to_ptr(const mxArray* mx_handle) {
   mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr");
   mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key");
   mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64");
-  mxCHECK(mxGetScalar(mx_init_key) == init_key, "incorrect handle init_key");
+  mxCHECK(mxGetScalar(mx_init_key) == init_key,
+      "Could not convert handle to pointer due to invalid init_key. "
+      "The object might have been cleared.");
   return reinterpret_cast<T*>(*reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)));
 }
 
-// Create an empty handle struct array
+// Create a handle struct vector, without setting up each handle in it
 template <typename T>
-static mxArray* create_handles(int ptr_num) {
+static mxArray* create_handle_vec(int ptr_num) {
   const int handle_field_num = 2;
   const char* handle_fields[handle_field_num] = { "ptr", "init_key" };
   return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields);
 }
 
-// Set up each handle in a handle struct array
+// Set up a handle in a handle struct vector by its index
 template <typename T>
-static void setup_handle(const T* ptr, int index, mxArray* mx_handles) {
+static void setup_handle(const T* ptr, int index, mxArray* mx_handle_vec) {
   mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL);
   *reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)) =
       reinterpret_cast<uint64_t>(ptr);
-  mxSetField(mx_handles, index, "ptr", mx_ptr);
-  mxSetField(mx_handles, index, "init_key", mxCreateDoubleScalar(init_key));
+  mxSetField(mx_handle_vec, index, "ptr", mx_ptr);
+  mxSetField(mx_handle_vec, index, "init_key", mxCreateDoubleScalar(init_key));
 }
 
 // Convert a pointer in C++ to a handle in matlab
 template <typename T>
 static mxArray* ptr_to_handle(const T* ptr) {
-  mxArray* mx_handle = create_handles<T>(1);
+  mxArray* mx_handle = create_handle_vec<T>(1);
   setup_handle(ptr, 0, mx_handle);
   return mx_handle;
 }
 
-// Convert a vector of shared_ptr in C++ to handle struct array in matlab
+// Convert a vector of shared_ptr in C++ to handle struct vector
 template <typename T>
-static mxArray* ptr_vec_to_handles(const vector<shared_ptr<T> >& ptr_vec) {
-  mxArray* mx_handle = create_handles<T>(ptr_vec.size());
+static mxArray* ptr_vec_to_handle_vec(const vector<shared_ptr<T> >& ptr_vec) {
+  mxArray* mx_handle_vec = create_handle_vec<T>(ptr_vec.size());
   for (int i = 0; i < ptr_vec.size(); i++) {
-    setup_handle(ptr_vec[i].get(), i, mx_handle);
+    setup_handle(ptr_vec[i].get(), i, mx_handle_vec);
   }
-  return mx_handle;
+  return mx_handle_vec;
 }
 
 /** -----------------------------------------------------------------
@@ -182,12 +186,12 @@ static mxArray* ptr_vec_to_handles(const vector<shared_ptr<T> >& ptr_vec) {
 static void get_solver(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
       "Usage: caffe_('get_solver', solver_file)");
-  const char* solver_file = mxArrayToString(prhs[0]);
+  char* solver_file = mxArrayToString(prhs[0]);
   mxCHECK_FILE_EXIST(solver_file);
-  shared_ptr<Solver<float> > solver;
-  solver.reset(new caffe::SGDSolver<float>(solver_file));
+  shared_ptr<Solver<float> > solver(new caffe::SGDSolver<float>(solver_file));
   solvers_.push_back(solver);
   plhs[0] = ptr_to_handle<Solver<float> >(solver.get());
+  mxFree(solver_file);
 }
 
 // Usage: caffe_('solver_get_attr', hSolver)
@@ -202,7 +206,7 @@ static void solver_get_attr(MEX_ARGS) {
   mxSetField(mx_solver_attr, 0, "hNet_net",
       ptr_to_handle<Net<float> >(solver->net().get()));
   mxSetField(mx_solver_attr, 0, "hNet_test_nets",
-      ptr_vec_to_handles<Net<float> >(solver->test_nets()));
+      ptr_vec_to_handle_vec<Net<float> >(solver->test_nets()));
   plhs[0] = mx_solver_attr;
 }
 
@@ -219,9 +223,10 @@ static void solver_restore(MEX_ARGS) {
   mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
       "Usage: caffe_('solver_restore', hSolver, snapshot_file)");
   Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
-  const char* snapshot_file = mxArrayToString(prhs[1]);
+  char* snapshot_file = mxArrayToString(prhs[1]);
   mxCHECK_FILE_EXIST(snapshot_file);
   solver->Restore(snapshot_file);
+  mxFree(snapshot_file);
 }
 
 // Usage: caffe_('solver_solve', hSolver)
@@ -232,10 +237,10 @@ static void solver_solve(MEX_ARGS) {
   solver->Solve();
 }
 
-// Usage: caffe_('solver_solve', hSolver, iters)
+// Usage: caffe_('solver_step', hSolver, iters)
 static void solver_step(MEX_ARGS) {
   mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
-      "Usage: caffe_('solver_solve', hSolver, iters)");
+      "Usage: caffe_('solver_step', hSolver, iters)");
   Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
   int iters = mxGetScalar(prhs[1]);
   solver->Step(iters);
@@ -245,21 +250,22 @@ static void solver_step(MEX_ARGS) {
 static void get_net(MEX_ARGS) {
   mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]),
       "Usage: caffe_('get_net', model_file, phase_name)");
-  const char* model_file = mxArrayToString(prhs[0]);
+  char* model_file = mxArrayToString(prhs[0]);
+  char* phase_name = mxArrayToString(prhs[1]);
   mxCHECK_FILE_EXIST(model_file);
-  const char* phase_name = mxArrayToString(prhs[1]);
   Phase phase;
   if (strcmp(phase_name, "train") == 0) {
       phase = TRAIN;
   } else if (strcmp(phase_name, "test") == 0) {
       phase = TEST;
   } else {
-    mxERROR("Unknown phase.");
+    mxERROR("Unknown phase");
   }
-  shared_ptr<Net<float> > net;
-  net.reset(new caffe::Net<float>(model_file, phase));
+  shared_ptr<Net<float> > net(new caffe::Net<float>(model_file, phase));
   nets_.push_back(net);
   plhs[0] = ptr_to_handle<Net<float> >(net.get());
+  mxFree(model_file);
+  mxFree(phase_name);
 }
 
 // Usage: caffe_('net_get_attr', hNet)
@@ -273,15 +279,15 @@ static void net_get_attr(MEX_ARGS) {
   mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num,
       net_attrs);
   mxSetField(mx_net_attr, 0, "hLayer_layers",
-      ptr_vec_to_handles<Layer<float> >(net->layers()));
+      ptr_vec_to_handle_vec<Layer<float> >(net->layers()));
   mxSetField(mx_net_attr, 0, "hBlob_blobs",
-      ptr_vec_to_handles<Blob<float> >(net->blobs()));
+      ptr_vec_to_handle_vec<Blob<float> >(net->blobs()));
   mxSetField(mx_net_attr, 0, "input_blob_indices",
       int_vec_to_mx_vec(net->input_blob_indices()));
   mxSetField(mx_net_attr, 0, "output_blob_indices",
       int_vec_to_mx_vec(net->output_blob_indices()));
   mxSetField(mx_net_attr, 0, "layer_names",
-    str_vec_to_mx_strcell(net->layer_names()));
+      str_vec_to_mx_strcell(net->layer_names()));
   mxSetField(mx_net_attr, 0, "blob_names",
       str_vec_to_mx_strcell(net->blob_names()));
   plhs[0] = mx_net_attr;
@@ -308,9 +314,10 @@ static void net_copy_from(MEX_ARGS) {
   mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
       "Usage: caffe_('net_copy_from', hNet, weights_file)");
   Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
-  const char* weights_file = mxArrayToString(prhs[1]);
+  char* weights_file = mxArrayToString(prhs[1]);
   mxCHECK_FILE_EXIST(weights_file);
   net->CopyTrainedLayersFrom(weights_file);
+  mxFree(weights_file);
 }
 
 // Usage: caffe_('net_reshape', hNet)
@@ -326,10 +333,11 @@ static void net_save(MEX_ARGS) {
   mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
       "Usage: caffe_('net_save', hNet, save_file)");
   Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
-  const char* weights_file = mxArrayToString(prhs[1]);
+  char* weights_file = mxArrayToString(prhs[1]);
   NetParameter net_param;
   net->ToProto(&net_param, false);
   WriteProtoToBinaryFile(net_param, weights_file);
+  mxFree(weights_file);
 }
 
 // Usage: caffe_('layer_get_attr', hLayer)
@@ -342,14 +350,14 @@ static void layer_get_attr(MEX_ARGS) {
   mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num,
       layer_attrs);
   mxSetField(mx_layer_attr, 0, "hBlob_blobs",
-      ptr_vec_to_handles<Blob<float> >(layer->blobs()));
+      ptr_vec_to_handle_vec<Blob<float> >(layer->blobs()));
   plhs[0] = mx_layer_attr;
 }
 
-// Usage: caffe_('layer_get_attr', hLayer)
+// Usage: caffe_('layer_get_type', hLayer)
 static void layer_get_type(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
-      "Usage: caffe_('layer_get_attr', hLayer)");
+      "Usage: caffe_('layer_get_type', hLayer)");
   Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
   plhs[0] = mxCreateString(layer->type());
 }
@@ -446,10 +454,12 @@ static void get_init_key(MEX_ARGS) {
 // Usage: caffe_('reset')
 static void reset(MEX_ARGS) {
   mxCHECK(nrhs == 0, "Usage: caffe_('reset')");
-  mexPrintf("cleared %d solvers and %d stand-alone nets\n", solvers_.size(),
-      nets_.size());
+  // Clear solvers and stand-alone nets
+  mexPrintf("Cleared %d solvers and %d stand-alone nets\n",
+      solvers_.size(), nets_.size());
   solvers_.clear();
   nets_.clear();
+  // Generate new init_key, so that handles created before becomes invalid
   init_key = static_cast<double>(caffe_rng_rand());
 }
 
@@ -457,21 +467,15 @@ static void reset(MEX_ARGS) {
 static void read_mean(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
       "Usage: caffe_('read_mean', mean_proto_file)");
-  const char* mean_proto_file = mxArrayToString(prhs[0]);
+  char* mean_proto_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(mean_proto_file);
   Blob<float> data_mean;
-  mexPrintf("Loading mean file from: %s\n", mean_proto_file);
   BlobProto blob_proto;
   bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto);
-  mxCHECK(result, "Couldn't read the file");
+  mxCHECK(result, "Could not read your mean file");
   data_mean.FromProto(blob_proto);
-  mwSize dims[4] = {data_mean.width(), data_mean.height(),
-                    data_mean.channels(), data_mean.num() };
-  mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-  float* data_ptr = reinterpret_cast<float*>(mxGetData(mx_blob));
-  caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
-  mexPrintf("Remember that Caffe saves in [width, height, channels]"
-                " format and channels are also BGR!\n");
-  plhs[0] = mx_blob;
+  plhs[0] = blob_to_mx_mat(&data_mean, DATA);
+  mxFree(mean_proto_file);
 }
 
 /** -----------------------------------------------------------------
@@ -516,31 +520,27 @@ static handler_registry handlers[] = {
 };
 
 /** -----------------------------------------------------------------
- ** matlab entry point: caffe_(api_command, arg1, arg2, ...)
+ ** matlab entry point.
  **/
+// Usage: caffe_(api_command, arg1, arg2, ...)
 void mexFunction(MEX_ARGS) {
   mexLock();  // Avoid clearing the mex file.
-  if (nrhs == 0) {
-    mxERROR("No API command given");
-    return;
-  }
-
-  { // Handle input command
-    char* cmd = mxArrayToString(prhs[0]);
-    bool dispatched = false;
-    // Dispatch to cmd handler
-    for (int i = 0; handlers[i].func != NULL; i++) {
-      if (handlers[i].cmd.compare(cmd) == 0) {
-        handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
-        dispatched = true;
-        break;
-      }
-    }
-    if (!dispatched) {
-      ostringstream error_msg;
-      error_msg << "Unknown command '" << cmd << "'";
-      mxERROR(error_msg.str().c_str());
+  mxCHECK(nrhs > 0, "Usage: caffe_(api_command, arg1, arg2, ...)");
+  // Handle input command
+  char* cmd = mxArrayToString(prhs[0]);
+  bool dispatched = false;
+  // Dispatch to cmd handler
+  for (int i = 0; handlers[i].func != NULL; i++) {
+    if (handlers[i].cmd.compare(cmd) == 0) {
+      handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
+      dispatched = true;
+      break;
     }
-    mxFree(cmd);
   }
+  if (!dispatched) {
+    ostringstream error_msg;
+    error_msg << "Unknown command '" << cmd << "'";
+    mxERROR(error_msg.str().c_str());
+  }
+  mxFree(cmd);
 }
diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m
index 77abf21dd11..a0648ecdf61 100644
--- a/matlab/+caffe/private/is_valid_handle.m
+++ b/matlab/+caffe/private/is_valid_handle.m
@@ -15,7 +15,6 @@
 % is_valid_handle('get_new_init_key') to get new init_key from C++;
 if ischar(hObj) && strcmp(hObj, 'get_new_init_key')
   init_key = caffe_('get_init_key');
-  valid = true;
   return
 else
   % check whether data types are correct and init_key matches
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
index fb1089c2d96..afdd8f3309d 100644
--- a/matlab/+caffe/run_tests.m
+++ b/matlab/+caffe/run_tests.m
@@ -2,11 +2,15 @@
 % results = run_tests()
 %   run all tests in this caffe matlab wrapper package
 
+% reset caffe before testing
 caffe.reset();
+
+% put all test cases here
 results = [...
   run(caffe.test.test_net) ...
-  run(caffe.test.test_solver)
-  ];
+  run(caffe.test.test_solver) ];
+
+% reset caffe after testing
 caffe.reset();
 
 end

From 6af8efc0bf51d475077be146e5ab7b7899ad705d Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 28 May 2015 14:52:47 +0800
Subject: [PATCH 1380/2053] Add MatCaffe docs to docs/tutorial/interfaces.md

---
 docs/tutorial/interfaces.md | 209 +++++++++++++++++++++++++++++++++++-
 1 file changed, 205 insertions(+), 4 deletions(-)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 17430b35c57..f824c7cc54b 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -67,10 +67,211 @@ Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be i
 
 ## MATLAB
 
-The MATLAB interface -- matcaffe -- is the `caffe` mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix.
+The MATLAB interface -- matcaffe -- is a `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.
 
-A MATLAB demo is in caffe/matlab/caffe/matcaffe_demo.m
+In MatCaffe, you can
 
-Note that MATLAB matrices and memory are in column-major layout counter to Caffe's row-major layout! Double-check your work accordingly.
+* Creating multiple Nets in Matlab
+* Do forward and backward computation
+* Access any layer within a network, and any parameter blob in a layer
+* Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs
+* Save a network's parameters to file, and load parameters from file
+* Reshape a blob and reshape a network
+* Edit network parameter and do network surgery
+* Create multiple Solvers in Matlab for training
+* Resume training from solver snapshots
+* Access train net and test nets in a solver
+* Run for a certain number of iterations and give back control to Matlab
+* Intermingle arbitrary Matlab code to with gradient steps
 
-Compile matcaffe by `make matcaffe`.
+A MATLAB demo is in caffe/matlab/matcaffe_demo.m
+
+### Build MatCaffe
+
+Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`.
+
+Common issue: if you run into error messages `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it means that your Matlab's runtime libraries does not match your compile-time libraries. You may need to do the following before you start matlab:
+
+    export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+    export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+
+Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.
+
+After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window.
+
+    addpath ./matlab
+
+You can save your Matlab search PATH by running `savepath` so that you don't have to run the command above again every time you use MatCaffe.
+
+### Use MatCaffe
+
+MatCaffe is very similar to PyCaffe in usage.
+
+Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
+
+    model = './models/bvlc_reference_caffenet/deploy.prototxt';
+    weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
+
+#### Set mode and device
+
+**Mode and device should always be set BEFORE you create a net or a solver.**
+
+Use CPU:
+
+    caffe.set_mode_cpu();
+
+Use GPU and specify its gpu_id:
+
+    caffe.set_mode_gpu();
+    caffe.set_device(gpu_id);
+
+#### Create a network and access its layers and blobs
+
+Create a network:
+
+    net = caffe.Net(model, weights, 'test'); % create net and load weights
+
+Or
+
+    net = caffe.Net(model, 'test'); % create net but not load weights
+    net.copy_from(weights); % load weights
+
+which creates `net` object as
+
+      Net with properties:
+
+               layer_vec: [1x23 caffe.Layer]
+                blob_vec: [1x15 caffe.Blob]
+                  inputs: {'data'}
+                 outputs: {'prob'}
+        name2layer_index: [23x1 containers.Map]
+         name2blob_index: [15x1 containers.Map]
+             layer_names: {23x1 cell}
+              blob_names: {15x1 cell}
+
+The two `containers.Map` objects are useful to find the index of a layer or a blob by its name.
+
+You have access to every blob in this network. To fill blob 'data' with all ones:
+
+    net.blobs('data').set_data(ones(net.blobs('data').shape));
+
+To multiply all values in blob 'data' by 10:
+
+    net.blobs('data').set_data(net.blobs('data').get_data() * 10);
+
+**Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are `[width, height, channels, num]`, and `width` is the fastest dimension. Also be aware that images are in BGR channels.** Also, Caffe uses single-precision float data. If your data is not single, `set_data` will automatically convert it to single.
+
+You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10:
+
+    net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights
+    net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias
+
+Alternatively, you can use
+
+    net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10);
+    net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10);
+
+To save the network you just modified:
+
+    net.save('my_net.caffemodel');
+
+To get a layer's type (string):
+
+    layer_type = net.layers('conv1').type;
+
+#### Forward and backward
+
+Forward pass can be done using `net.forward` or `net.forward_prefilled`. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
+    
+    res = net.forward({data});
+    prob = res{1};
+
+Or
+
+    net.blobs('data').set_data(data);
+    net.forward_prefilled();
+    prob = net.blobs('prob').get_data();
+
+Backward is similar using `net.backward` or `net.backward_prefilled` and replacing `get_data` and `set_data` with `get_diff` and `set_diff`. After creating some gradients for output blobs like `prob_diff = rand(net.blobs('prob').shape);` you can run
+
+    res = net.backward({prob_diff});
+    data_diff = res{1};
+
+Or
+
+    net.blobs('prob').set_diff(prob_diff);
+    net.backward_prefilled();
+    data_diff = net.blobs('data').get_diff();
+    
+**However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.**
+
+After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:
+
+    pool5_feat = net.blobs('pool5').get_data();
+
+#### Reshape
+
+Assume you want to run 1 image at a time instead of 10:
+
+    net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data'
+    net.reshape();
+
+Then the whole network is reshaped, and now `net.blobs('prob').shape` should be `[1000 1]`;
+
+#### Training
+
+Assume you have created training and validation lmdbs following our [ImageNET Tutorial](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html), to create a solver and train on ILSVRC 2012 classification dataset:
+
+    solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt');
+
+which creates `solver` object as
+
+      Solver with properties:
+
+              net: [1x1 caffe.Net]
+        test_nets: [1x1 caffe.Net]
+
+To train:
+
+    solver.solve();
+
+Or train for only 1000 iterations (so that you can do something to its net before training more iterations)
+
+    solver.step(1000);
+
+To get iteration number:
+
+    iter = solver.iter();
+
+To get its network:
+
+    train_net = solver.net;
+    test_net = solver.test_nets(1);
+
+To resume from a snapshot "your_snapshot.solverstate":
+
+    solver.restore('your_snapshot.solverstate');
+
+#### Input and output
+
+`caffe.io` class provides basic input functions `load_image` and `read_mean`. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running `./data/ilsvrc12/get_ilsvrc_aux.sh`):
+
+    mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto');
+
+To read Caffe's example image and resize to `[width, height]` and suppose we want `width = 256; height = 256;`
+
+    im_data = caffe.io.load_image('./examples/images/cat.jpg');
+    im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
+
+**Keep in mind that `width` is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image.** If you don't want to use `caffe.io.load_image` and prefer to load an image by yourself, you can do
+
+    im_data = imread('./examples/images/cat.jpg'); % read image
+    im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR
+    im_data = permute(im_data, [2, 1, 3]); % permute width and height
+    im_data = single(im_data); % convert to single precision
+
+We do not provide extra functions for data output as Matlab itself is already quite powerful in output.
+
+#### Clear nets and solvers
+
+Call `caffe.reset()` to clear all solvers and stand-alone nets you have created.

From d3d7d07be981ab046da99e1ad5d84217bf0ec4c3 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 28 May 2015 16:24:30 +0800
Subject: [PATCH 1381/2053] Clean up old matcaffe wrapper and rename
 caffe.reset to caffe.reset_all

Remove old matlab wrapper but keep the classification demo and hdf5 demo

Change 'caffe.reset()' to 'caffe.reset_all()' to avoid potential name conflict.
Otherwise, Matlab R2015a complains:
Warning: Function reset has the same name as a MATLAB builtin. We suggest you rename the
function to avoid a potential name conflict.
---
 Makefile                                      |  25 +-
 docs/tutorial/interfaces.md                   |   8 +-
 .../imagenet}/ilsvrc_2012_mean.mat            | Bin
 matlab/+caffe/reset.m                         |   8 -
 matlab/+caffe/reset_all.m                     |   8 +
 matlab/+caffe/run_tests.m                     |   4 +-
 matlab/CMakeLists.txt                         |  12 +-
 matlab/caffe/matcaffe.cpp                     | 421 ------------------
 matlab/caffe/matcaffe_batch.m                 |  75 ----
 matlab/caffe/matcaffe_demo_vgg.m              |  96 ----
 matlab/caffe/matcaffe_demo_vgg_mean_pix.m     | 102 -----
 matlab/caffe/matcaffe_init.m                  |  41 --
 matlab/caffe/prepare_batch.m                  |  41 --
 matlab/caffe/print_cell.m                     |  42 --
 matlab/caffe/read_cell.m                      |  21 -
 .../matcaffe_demo.m => classification_demo.m} |  64 ++-
 matlab/{caffe => }/hdf5creation/.gitignore    |   0
 matlab/{caffe => }/hdf5creation/demo.m        |   0
 matlab/{caffe => }/hdf5creation/store2hdf5.m  |   0
 19 files changed, 63 insertions(+), 905 deletions(-)
 rename matlab/{caffe => +caffe/imagenet}/ilsvrc_2012_mean.mat (100%)
 delete mode 100644 matlab/+caffe/reset.m
 create mode 100644 matlab/+caffe/reset_all.m
 delete mode 100644 matlab/caffe/matcaffe.cpp
 delete mode 100644 matlab/caffe/matcaffe_batch.m
 delete mode 100644 matlab/caffe/matcaffe_demo_vgg.m
 delete mode 100644 matlab/caffe/matcaffe_demo_vgg_mean_pix.m
 delete mode 100644 matlab/caffe/matcaffe_init.m
 delete mode 100644 matlab/caffe/prepare_batch.m
 delete mode 100644 matlab/caffe/print_cell.m
 delete mode 100644 matlab/caffe/read_cell.m
 rename matlab/{caffe/matcaffe_demo.m => classification_demo.m} (56%)
 rename matlab/{caffe => }/hdf5creation/.gitignore (100%)
 rename matlab/{caffe => }/hdf5creation/demo.m (100%)
 rename matlab/{caffe => }/hdf5creation/store2hdf5.m (100%)

diff --git a/Makefile b/Makefile
index 74b167763c3..3748b52ca47 100644
--- a/Makefile
+++ b/Makefile
@@ -65,7 +65,6 @@ NONGEN_CXX_SRCS := $(shell find \
 	src/$(PROJECT) \
 	include/$(PROJECT) \
 	python/$(PROJECT) \
-	matlab/$(PROJECT) \
 	matlab/+$(PROJECT)/private \
 	examples \
 	tools \
@@ -80,15 +79,12 @@ NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
 PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
-# MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
-MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
-# MAT$(PROJECT)_PKG_SRC is the mex entrance point of matlab package for $(PROJECT)
-MAT$(PROJECT)_PKG_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
+# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
+MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
 ifneq ($(MATLAB_DIR),)
 	MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
 endif
-MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT)
-MAT$(PROJECT)_PKG_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
+MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
 
 ##############################
 # Derive generated files
@@ -451,7 +447,7 @@ $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
 
 mat$(PROJECT): mat
 
-mat: $(MAT$(PROJECT)_SO) $(MAT$(PROJECT)_PKG_SO)
+mat: $(MAT$(PROJECT)_SO)
 
 $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 	@ if [ -z "$(MATLAB_DIR)" ]; then \
@@ -464,18 +460,6 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
-			
-$(MAT$(PROJECT)_PKG_SO): $(MAT$(PROJECT)_PKG_SRC) $(STATIC_NAME)
-	@ if [ -z "$(MATLAB_DIR)" ]; then \
-		echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \
-			"to build mat$(PROJECT)."; \
-		exit 1; \
-	fi
-	@ echo MEX $<
-	$(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_PKG_SRC) \
-			CXX="$(CXX)" \
-			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
-			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
 
 runtest: $(TEST_ALL_BIN)
 	$(TOOL_BUILD_DIR)/caffe
@@ -601,7 +585,6 @@ clean:
 	@- $(RM) -rf $(DISTRIBUTE_DIR)
 	@- $(RM) $(PY$(PROJECT)_SO)
 	@- $(RM) $(MAT$(PROJECT)_SO)
-	@- $(RM) $(MAT$(PROJECT)_PKG_SO)
 
 supercleanfiles:
 	$(eval SUPERCLEAN_FILES := $(strip \
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index f824c7cc54b..a57e17339b0 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -67,7 +67,7 @@ Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be i
 
 ## MATLAB
 
-The MATLAB interface -- matcaffe -- is a `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.
+The MATLAB interface -- matcaffe -- is the `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.
 
 In MatCaffe, you can
 
@@ -84,7 +84,7 @@ In MatCaffe, you can
 * Run for a certain number of iterations and give back control to Matlab
 * Intermingle arbitrary Matlab code to with gradient steps
 
-A MATLAB demo is in caffe/matlab/matcaffe_demo.m
+An ILSVRC image classification demo is in caffe/matlab/classification_demo.m
 
 ### Build MatCaffe
 
@@ -181,7 +181,7 @@ To get a layer's type (string):
 
 #### Forward and backward
 
-Forward pass can be done using `net.forward` or `net.forward_prefilled`. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
+Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
     
     res = net.forward({data});
     prob = res{1};
@@ -274,4 +274,4 @@ We do not provide extra functions for data output as Matlab itself is already qu
 
 #### Clear nets and solvers
 
-Call `caffe.reset()` to clear all solvers and stand-alone nets you have created.
+Call `caffe.reset_all()` to clear all solvers and stand-alone nets you have created.
diff --git a/matlab/caffe/ilsvrc_2012_mean.mat b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat
similarity index 100%
rename from matlab/caffe/ilsvrc_2012_mean.mat
rename to matlab/+caffe/imagenet/ilsvrc_2012_mean.mat
diff --git a/matlab/+caffe/reset.m b/matlab/+caffe/reset.m
deleted file mode 100644
index c1cfea41a76..00000000000
--- a/matlab/+caffe/reset.m
+++ /dev/null
@@ -1,8 +0,0 @@
-function reset()
-% reset()
-%   reset Caffe to initial status
-
-caffe_('reset');
-is_valid_handle('get_new_init_key');
-
-end
diff --git a/matlab/+caffe/reset_all.m b/matlab/+caffe/reset_all.m
new file mode 100644
index 00000000000..a8b33dee8d5
--- /dev/null
+++ b/matlab/+caffe/reset_all.m
@@ -0,0 +1,8 @@
+function reset_all()
+% reset_all()
+%   clear all solvers and stand-alone nets and reset Caffe to initial status
+
+caffe_('reset');
+is_valid_handle('get_new_init_key');
+
+end
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
index afdd8f3309d..8773c9f638b 100644
--- a/matlab/+caffe/run_tests.m
+++ b/matlab/+caffe/run_tests.m
@@ -3,7 +3,7 @@
 %   run all tests in this caffe matlab wrapper package
 
 % reset caffe before testing
-caffe.reset();
+caffe.reset_all();
 
 % put all test cases here
 results = [...
@@ -11,6 +11,6 @@
   run(caffe.test.test_solver) ];
 
 % reset caffe after testing
-caffe.reset();
+caffe.reset_all();
 
 end
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index 63182abcc44..4b0d549f07f 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -31,10 +31,8 @@ function(caffe_fetch_and_set_proper_mexext mexfile_variable)
 endfunction()
 
 # global settings
-file(GLOB Matlab_srcs caffe/matcaffe.cpp)
-file(GLOB Matlab_pkg_srcs +caffe/private/caffe_.cpp)
-set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex)
-set(Matlab_caffe_pkg_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex)
+file(GLOB Matlab_srcs +caffe/private/caffe_.cpp)
+set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex)
 
 caffe_get_current_cflags(cflags)
 caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
@@ -52,15 +50,9 @@ if(build_using MATCHES "Matlab")
       ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags}
       DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM)
   add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs})
-  caffe_fetch_and_set_proper_mexext(Matlab_caffe_pkg_mex)
-  add_custom_command(OUTPUT ${Matlab_caffe_pkg_mex} COMMAND ${Matlab_mex}
-      ARGS -output ${Matlab_caffe_pkg_mex} ${Matlab_pkg_srcs} ${cflags} ${link_folders} ${libflags}
-      DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_pkg_mex}" VERBATIM)
-  add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_pkg_mex} SOURCES ${Matlab_pkg_srcs})
 
 elseif(build_using MATCHES "Octave")
 
-  # Note: Matlab Caffe package cannot be used in Octave, so we don't build it
   if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
     set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
   elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
deleted file mode 100644
index da37d920b20..00000000000
--- a/matlab/caffe/matcaffe.cpp
+++ /dev/null
@@ -1,421 +0,0 @@
-//
-// matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from matlab.
-// Note that for matlab, we will simply use float as the data type.
-
-#include <sstream>
-#include <string>
-#include <vector>
-
-#include "mex.h"
-
-#include "caffe/caffe.hpp"
-
-#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
-
-// Log and throw a Mex error
-inline void mex_error(const std::string &msg) {
-  LOG(ERROR) << msg;
-  mexErrMsgTxt(msg.c_str());
-}
-
-using namespace caffe;  // NOLINT(build/namespaces)
-
-// The pointer to the internal caffe::Net instance
-static shared_ptr<Net<float> > net_;
-static int init_key = -2;
-
-// Five things to be aware of:
-//   caffe uses row-major order
-//   matlab uses column-major order
-//   caffe uses BGR color channel order
-//   matlab uses RGB color channel order
-//   images need to have the data mean subtracted
-//
-// Data coming in from matlab needs to be in the order
-//   [width, height, channels, images]
-// where width is the fastest dimension.
-// Here is the rough matlab for putting image data into the correct
-// format:
-//   % convert from uint8 to single
-//   im = single(im);
-//   % reshape to a fixed size (e.g., 227x227)
-//   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-//   % permute from RGB to BGR and subtract the data mean (already in BGR)
-//   im = im(:,:,[3 2 1]) - data_mean;
-//   % flip width and height to make width the fastest dimension
-//   im = permute(im, [2 1 3]);
-//
-// If you have multiple images, cat them with cat(4, ...)
-//
-// The actual forward function. It takes in a cell array of 4-D arrays as
-// input and outputs a cell array.
-
-static mxArray* do_forward(const mxArray* const bottom) {
-  const vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  if (static_cast<unsigned int>(mxGetDimensions(bottom)[0]) !=
-      input_blobs.size()) {
-    mex_error("Invalid input size");
-  }
-  for (unsigned int i = 0; i < input_blobs.size(); ++i) {
-    const mxArray* const elem = mxGetCell(bottom, i);
-    if (!mxIsSingle(elem)) {
-      mex_error("MatCaffe require single-precision float point data");
-    }
-    if (mxGetNumberOfElements(elem) != input_blobs[i]->count()) {
-      std::string error_msg;
-      error_msg += "MatCaffe input size does not match the input size ";
-      error_msg += "of the network";
-      mex_error(error_msg);
-    }
-
-    const float* const data_ptr =
-        reinterpret_cast<const float* const>(mxGetPr(elem));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(input_blobs[i]->count(), data_ptr,
-          input_blobs[i]->mutable_cpu_data());
-      break;
-    case Caffe::GPU:
-      caffe_copy(input_blobs[i]->count(), data_ptr,
-          input_blobs[i]->mutable_gpu_data());
-      break;
-    default:
-      mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-  const vector<Blob<float>*>& output_blobs = net_->ForwardPrefilled();
-  mxArray* mx_out = mxCreateCellMatrix(output_blobs.size(), 1);
-  for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    // internally data is stored as (width, height, channels, num)
-    // where width is the fastest dimension
-    mwSize dims[4] = {output_blobs[i]->width(), output_blobs[i]->height(),
-      output_blobs[i]->channels(), output_blobs[i]->num()};
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    mxSetCell(mx_out, i, mx_blob);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(),
-          data_ptr);
-      break;
-    case Caffe::GPU:
-      caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(),
-          data_ptr);
-      break;
-    default:
-      mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-
-  return mx_out;
-}
-
-static mxArray* do_backward(const mxArray* const top_diff) {
-  const vector<Blob<float>*>& output_blobs = net_->output_blobs();
-  const vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  if (static_cast<unsigned int>(mxGetDimensions(top_diff)[0]) !=
-      output_blobs.size()) {
-    mex_error("Invalid input size");
-  }
-  // First, copy the output diff
-  for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    const mxArray* const elem = mxGetCell(top_diff, i);
-    const float* const data_ptr =
-        reinterpret_cast<const float* const>(mxGetPr(elem));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(output_blobs[i]->count(), data_ptr,
-          output_blobs[i]->mutable_cpu_diff());
-      break;
-    case Caffe::GPU:
-      caffe_copy(output_blobs[i]->count(), data_ptr,
-          output_blobs[i]->mutable_gpu_diff());
-      break;
-    default:
-        mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-  // LOG(INFO) << "Start";
-  net_->Backward();
-  // LOG(INFO) << "End";
-  mxArray* mx_out = mxCreateCellMatrix(input_blobs.size(), 1);
-  for (unsigned int i = 0; i < input_blobs.size(); ++i) {
-    // internally data is stored as (width, height, channels, num)
-    // where width is the fastest dimension
-    mwSize dims[4] = {input_blobs[i]->width(), input_blobs[i]->height(),
-      input_blobs[i]->channels(), input_blobs[i]->num()};
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    mxSetCell(mx_out, i, mx_blob);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr);
-      break;
-    case Caffe::GPU:
-      caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
-      break;
-    default:
-        mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-
-  return mx_out;
-}
-
-static mxArray* do_get_weights() {
-  const vector<shared_ptr<Layer<float> > >& layers = net_->layers();
-  const vector<string>& layer_names = net_->layer_names();
-
-  // Step 1: count the number of layers with weights
-  int num_layers = 0;
-  {
-    string prev_layer_name = "";
-    for (unsigned int i = 0; i < layers.size(); ++i) {
-      vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
-      if (layer_blobs.size() == 0) {
-        continue;
-      }
-      if (layer_names[i] != prev_layer_name) {
-        prev_layer_name = layer_names[i];
-        num_layers++;
-      }
-    }
-  }
-
-  // Step 2: prepare output array of structures
-  mxArray* mx_layers;
-  {
-    const mwSize dims[2] = {num_layers, 1};
-    const char* fnames[2] = {"weights", "layer_names"};
-    mx_layers = mxCreateStructArray(2, dims, 2, fnames);
-  }
-
-  // Step 3: copy weights into output
-  {
-    string prev_layer_name = "";
-    int mx_layer_index = 0;
-    for (unsigned int i = 0; i < layers.size(); ++i) {
-      vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
-      if (layer_blobs.size() == 0) {
-        continue;
-      }
-
-      mxArray* mx_layer_cells = NULL;
-      if (layer_names[i] != prev_layer_name) {
-        prev_layer_name = layer_names[i];
-        const mwSize dims[2] = {static_cast<mwSize>(layer_blobs.size()), 1};
-        mx_layer_cells = mxCreateCellArray(2, dims);
-        mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
-        mxSetField(mx_layers, mx_layer_index, "layer_names",
-            mxCreateString(layer_names[i].c_str()));
-        mx_layer_index++;
-      }
-
-      for (unsigned int j = 0; j < layer_blobs.size(); ++j) {
-        // internally data is stored as (width, height, channels, num)
-        // where width is the fastest dimension
-        mwSize dims[4] = {layer_blobs[j]->width(), layer_blobs[j]->height(),
-            layer_blobs[j]->channels(), layer_blobs[j]->num()};
-
-        mxArray* mx_weights =
-          mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-        mxSetCell(mx_layer_cells, j, mx_weights);
-        float* weights_ptr = reinterpret_cast<float*>(mxGetPr(mx_weights));
-
-        switch (Caffe::mode()) {
-        case Caffe::CPU:
-          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(),
-              weights_ptr);
-          break;
-        case Caffe::GPU:
-          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(),
-              weights_ptr);
-          break;
-        default:
-          mex_error("Unknown Caffe mode");
-        }
-      }
-    }
-  }
-
-  return mx_layers;
-}
-
-static void get_weights(MEX_ARGS) {
-  plhs[0] = do_get_weights();
-}
-
-static void set_mode_cpu(MEX_ARGS) {
-  Caffe::set_mode(Caffe::CPU);
-}
-
-static void set_mode_gpu(MEX_ARGS) {
-  Caffe::set_mode(Caffe::GPU);
-}
-
-static void set_device(MEX_ARGS) {
-  if (nrhs != 1) {
-    ostringstream error_msg;
-    error_msg << "Expected 1 argument, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
-  Caffe::SetDevice(device_id);
-}
-
-static void get_init_key(MEX_ARGS) {
-  plhs[0] = mxCreateDoubleScalar(init_key);
-}
-
-static void init(MEX_ARGS) {
-  if (nrhs != 3) {
-    ostringstream error_msg;
-    error_msg << "Expected 3 arguments, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  char* param_file = mxArrayToString(prhs[0]);
-  char* model_file = mxArrayToString(prhs[1]);
-  char* phase_name = mxArrayToString(prhs[2]);
-
-  Phase phase;
-  if (strcmp(phase_name, "train") == 0) {
-      phase = TRAIN;
-  } else if (strcmp(phase_name, "test") == 0) {
-      phase = TEST;
-  } else {
-    mex_error("Unknown phase.");
-  }
-
-  net_.reset(new Net<float>(string(param_file), phase));
-  net_->CopyTrainedLayersFrom(string(model_file));
-
-  mxFree(param_file);
-  mxFree(model_file);
-  mxFree(phase_name);
-
-  init_key = random();  // NOLINT(caffe/random_fn)
-
-  if (nlhs == 1) {
-    plhs[0] = mxCreateDoubleScalar(init_key);
-  }
-}
-
-static void reset(MEX_ARGS) {
-  if (net_) {
-    net_.reset();
-    init_key = -2;
-    LOG(INFO) << "Network reset, call init before use it again";
-  }
-}
-
-static void forward(MEX_ARGS) {
-  if (nrhs != 1) {
-    ostringstream error_msg;
-    error_msg << "Expected 1 argument, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  plhs[0] = do_forward(prhs[0]);
-}
-
-static void backward(MEX_ARGS) {
-  if (nrhs != 1) {
-    ostringstream error_msg;
-    error_msg << "Expected 1 argument, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  plhs[0] = do_backward(prhs[0]);
-}
-
-static void is_initialized(MEX_ARGS) {
-  if (!net_) {
-    plhs[0] = mxCreateDoubleScalar(0);
-  } else {
-    plhs[0] = mxCreateDoubleScalar(1);
-  }
-}
-
-static void read_mean(MEX_ARGS) {
-    if (nrhs != 1) {
-        mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'");
-        return;
-    }
-    const string& mean_file = mxArrayToString(prhs[0]);
-    Blob<float> data_mean;
-    LOG(INFO) << "Loading mean file from: " << mean_file;
-    BlobProto blob_proto;
-    bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
-    if (!result) {
-        mexErrMsgTxt("Couldn't read the file");
-        return;
-    }
-    data_mean.FromProto(blob_proto);
-    mwSize dims[4] = {data_mean.width(), data_mean.height(),
-                      data_mean.channels(), data_mean.num() };
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
-    mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]"
-                  " format and channels are also BGR!");
-    plhs[0] = mx_blob;
-}
-
-/** -----------------------------------------------------------------
- ** Available commands.
- **/
-struct handler_registry {
-  string cmd;
-  void (*func)(MEX_ARGS);
-};
-
-static handler_registry handlers[] = {
-  // Public API functions
-  { "forward",            forward         },
-  { "backward",           backward        },
-  { "init",               init            },
-  { "is_initialized",     is_initialized  },
-  { "set_mode_cpu",       set_mode_cpu    },
-  { "set_mode_gpu",       set_mode_gpu    },
-  { "set_device",         set_device      },
-  { "get_weights",        get_weights     },
-  { "get_init_key",       get_init_key    },
-  { "reset",              reset           },
-  { "read_mean",          read_mean       },
-  // The end.
-  { "END",                NULL            },
-};
-
-
-/** -----------------------------------------------------------------
- ** matlab entry point: caffe(api_command, arg1, arg2, ...)
- **/
-void mexFunction(MEX_ARGS) {
-  mexLock();  // Avoid clearing the mex file.
-  if (nrhs == 0) {
-    mex_error("No API command given");
-    return;
-  }
-
-  { // Handle input command
-    char *cmd = mxArrayToString(prhs[0]);
-    bool dispatched = false;
-    // Dispatch to cmd handler
-    for (int i = 0; handlers[i].func != NULL; i++) {
-      if (handlers[i].cmd.compare(cmd) == 0) {
-        handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
-        dispatched = true;
-        break;
-      }
-    }
-    if (!dispatched) {
-      ostringstream error_msg;
-      error_msg << "Unknown command '" << cmd << "'";
-      mex_error(error_msg.str());
-    }
-    mxFree(cmd);
-  }
-}
diff --git a/matlab/caffe/matcaffe_batch.m b/matlab/caffe/matcaffe_batch.m
deleted file mode 100644
index f6d1aa83b84..00000000000
--- a/matlab/caffe/matcaffe_batch.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function [scores,list_im] = matcaffe_batch(list_im, use_gpu)
-% scores = matcaffe_batch(list_im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-%   list_im  list of images files
-%   use_gpu  1 to use the GPU, 0 to use the CPU
-%
-% output
-%   scores   1000 x num_images ILSVRC output vector
-%
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-%  scores = matcaffe_batch({'peppers.png','onion.png'});
-%  scores = matcaffe_batch('list_images.txt', 1);
-if nargin < 1
-  % For test purposes
-  list_im = {'peppers.png','onions.png'};
-end
-if ischar(list_im)
-    %Assume it is a file contaning the list of images
-    filename = list_im;
-    list_im = read_cell(filename);
-end
-% Adjust the batch size and dim to match with models/bvlc_reference_caffenet/deploy.prototxt
-batch_size = 10;
-dim = 1000;
-disp(list_im)
-if mod(length(list_im),batch_size)
-    warning(['Assuming batches of ' num2str(batch_size) ' images rest will be filled with zeros'])
-end
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
-  matcaffe_init(use_gpu);
-else
-  matcaffe_init();
-end
-
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-
-% prepare input
-
-num_images = length(list_im);
-scores = zeros(dim,num_images,'single');
-num_batches = ceil(length(list_im)/batch_size)
-initic=tic;
-for bb = 1 : num_batches
-    batchtic = tic;
-    range = 1+batch_size*(bb-1):min(num_images,batch_size * bb);
-    tic
-    input_data = prepare_batch(list_im(range),IMAGE_MEAN,batch_size);
-    toc, tic
-    fprintf('Batch %d out of %d %.2f%% Complete ETA %.2f seconds\n',...
-        bb,num_batches,bb/num_batches*100,toc(initic)/bb*(num_batches-bb));
-    output_data = caffe('forward', {input_data});
-    toc
-    output_data = squeeze(output_data{1});
-    scores(:,range) = output_data(:,mod(range-1,batch_size)+1);
-    toc(batchtic)
-end
-toc(initic);
-
-if exist('filename', 'var')
-    save([filename '.probs.mat'],'list_im','scores','-v7.3');
-end
-
-
-
diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m
deleted file mode 100644
index 4e5a98eb5f4..00000000000
--- a/matlab/caffe/matcaffe_demo_vgg.m
+++ /dev/null
@@ -1,96 +0,0 @@
-function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-%
-% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
-%
-% INPUT
-%   im - color image as uint8 HxWx3
-%   use_gpu - 1 to use the GPU, 0 to use the CPU
-%   model_def_file - network configuration (.prototxt file)
-%   model_file - network weights (.caffemodel file)
-%   mean_file - mean BGR image as uint8 HxWx3 (.mat file)
-%
-% OUTPUT
-%   scores   1000-dimensional ILSVRC score vector
-%
-% EXAMPLE USAGE
-%  model_def_file = 'zoo/VGG_CNN_F_deploy.prototxt';
-%  model_file = 'zoo/VGG_CNN_F.caffemodel';
-%  mean_file = 'zoo/VGG_mean.mat';
-%  use_gpu = true;
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file);
-% 
-% NOTES
-%  the image crops are prepared as described in the paper (the aspect ratio is preserved)
-%
-% PREREQUISITES
-%  You may need to do the following before you start matlab:
-%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%   $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-%  Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-matcaffe_init(use_gpu, model_def_file, model_file);
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im, mean_file)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-% size(scores)
-scores = squeeze(scores);
-% scores = mean(scores,2);
-
-% [~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im, mean_file)
-% ------------------------------------------------------------------------
-IMAGE_DIM = 256;
-CROPPED_DIM = 224;
-
-d = load(mean_file);
-IMAGE_MEAN = d.image_mean;
-
-% resize to fixed input size
-im = single(im);
-
-if size(im, 1) < size(im, 2)
-    im = imresize(im, [IMAGE_DIM NaN]);
-else
-    im = imresize(im, [NaN IMAGE_DIM]);
-end
-
-% RGB -> BGR
-im = im(:, :, [3 2 1]);
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-
-indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
-indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
-center_y = floor(indices_y(2) / 2)+1;
-center_x = floor(indices_x(2) / 2)+1;
-
-curr = 1;
-for i = indices_y
-  for j = indices_x
-    images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :)-IMAGE_MEAN, [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
-  end
-end
-images(:,:,:,5) = ...
-    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:)-IMAGE_MEAN, ...
-        [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
diff --git a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m b/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
deleted file mode 100644
index 5f7898a7029..00000000000
--- a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
+++ /dev/null
@@ -1,102 +0,0 @@
-function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
-%
-% Demo of the matlab wrapper based on the networks used for the "VGG" entry
-% in the ILSVRC-2014 competition and described in the tech. report 
-% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
-% http://arxiv.org/abs/1409.1556/
-%
-% INPUT
-%   im - color image as uint8 HxWx3
-%   use_gpu - 1 to use the GPU, 0 to use the CPU
-%   model_def_file - network configuration (.prototxt file)
-%   model_file - network weights (.caffemodel file)
-%
-% OUTPUT
-%   scores   1000-dimensional ILSVRC score vector
-%
-% EXAMPLE USAGE
-%  model_def_file = 'zoo/deploy.prototxt';
-%  model_file = 'zoo/model.caffemodel';
-%  use_gpu = true;
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
-% 
-% NOTES
-%  mean pixel subtraction is used instead of the mean image subtraction
-%
-% PREREQUISITES
-%  You may need to do the following before you start matlab:
-%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%   $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-%  Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-matcaffe_init(use_gpu, model_def_file, model_file);
-
-% mean BGR pixel
-mean_pix = [103.939, 116.779, 123.68];
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im, mean_pix)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-% size(scores)
-scores = squeeze(scores);
-% scores = mean(scores,2);
-
-% [~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im, mean_pix)
-% ------------------------------------------------------------------------
-IMAGE_DIM = 256;
-CROPPED_DIM = 224;
-
-% resize to fixed input size
-im = single(im);
-
-if size(im, 1) < size(im, 2)
-    im = imresize(im, [IMAGE_DIM NaN]);
-else
-    im = imresize(im, [NaN IMAGE_DIM]);
-end
-
-% RGB -> BGR
-im = im(:, :, [3 2 1]);
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-
-indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
-indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
-center_y = floor(indices_y(2) / 2)+1;
-center_x = floor(indices_x(2) / 2)+1;
-
-curr = 1;
-for i = indices_y
-  for j = indices_x
-    images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
-  end
-end
-images(:,:,:,5) = ...
-    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
-        [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
-
-% mean BGR pixel subtraction
-for c = 1:3
-    images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
-end
diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m
deleted file mode 100644
index 5d0a0a70bde..00000000000
--- a/matlab/caffe/matcaffe_init.m
+++ /dev/null
@@ -1,41 +0,0 @@
-function  matcaffe_init(use_gpu, model_def_file, model_file)
-% matcaffe_init(model_def_file, model_file, use_gpu)
-% Initilize matcaffe wrapper
-
-if nargin < 1
-  % By default use CPU
-  use_gpu = 0;
-end
-if nargin < 2 || isempty(model_def_file)
-  % By default use imagenet_deploy
-  model_def_file = '../../models/bvlc_reference_caffenet/deploy.prototxt';
-end
-if nargin < 3 || isempty(model_file)
-  % By default use caffe reference model
-  model_file = '../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
-end
-
-
-if caffe('is_initialized') == 0
-  if exist(model_file, 'file') == 0
-    % NOTE: you'll have to get the pre-trained ILSVRC network
-    error('You need a network model file');
-  end
-  if ~exist(model_def_file,'file')
-    % NOTE: you'll have to get network definition
-    error('You need the network prototxt definition');
-  end
-  % load network in TEST phase
-  caffe('init', model_def_file, model_file, 'test')
-end
-fprintf('Done with init\n');
-
-% set to use GPU or CPU
-if use_gpu
-  fprintf('Using GPU Mode\n');
-  caffe('set_mode_gpu');
-else
-  fprintf('Using CPU Mode\n');
-  caffe('set_mode_cpu');
-end
-fprintf('Done with set_mode\n');
diff --git a/matlab/caffe/prepare_batch.m b/matlab/caffe/prepare_batch.m
deleted file mode 100644
index 345c8eb5f0b..00000000000
--- a/matlab/caffe/prepare_batch.m
+++ /dev/null
@@ -1,41 +0,0 @@
-% ------------------------------------------------------------------------
-function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
-% ------------------------------------------------------------------------
-if nargin < 2
-    d = load('ilsvrc_2012_mean');
-    IMAGE_MEAN = d.image_mean; 
-end
-num_images = length(image_files);
-if nargin < 3
-    batch_size = num_images;
-end
-
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-center = floor(indices(2) / 2)+1;
-
-num_images = length(image_files);
-images = zeros(CROPPED_DIM,CROPPED_DIM,3,batch_size,'single');
-
-parfor i=1:num_images
-    % read file
-    fprintf('%c Preparing %s\n',13,image_files{i});
-    try
-        im = imread(image_files{i});
-        % resize to fixed input size
-        im = single(im);
-        im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-        % Transform GRAY to RGB
-        if size(im,3) == 1
-            im = cat(3,im,im,im);
-        end
-        % permute from RGB to BGR (IMAGE_MEAN is already BGR)
-        im = im(:,:,[3 2 1]) - IMAGE_MEAN;
-        % Crop the center of the image
-        images(:,:,:,i) = permute(im(center:center+CROPPED_DIM-1,...
-            center:center+CROPPED_DIM-1,:),[2 1 3]);
-    catch
-        warning('Problems with file',image_files{i});
-    end
-end
\ No newline at end of file
diff --git a/matlab/caffe/print_cell.m b/matlab/caffe/print_cell.m
deleted file mode 100644
index 864340d4be9..00000000000
--- a/matlab/caffe/print_cell.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function res=print_cell(input,file,linesep,cellsep)
-assert(iscell(input),'The input should be a cell')
-if nargin < 4
-    cellsep = '\t';
-end
-if nargin < 3
-    linesep = '\n';
-end
-if exist('file','var') && ~isempty(file)
-    %%
-    fid = fopen(file,'w');
-    for l=1:length(input)
-        if iscell(input{l})
-            for i=1:length(input{l})
-                fprintf(fid,['%s' cellsep],input{l}{i});
-            end
-            fprintf(fid,linesep);
-        else
-            if size(input,2) > 1
-                for i=1:size(input,2)
-                    fprintf(fid,'%s ',input{l,i});
-                end
-                fprintf(fid,linesep);
-            else
-                fprintf(fid,['%s' linesep],input{l});
-            end
-        end
-    end
-    fclose(fid);
-else
-    res = '';
-    for l=1:length(input)
-        if iscell(input{l})
-            for i=1:length(input{l})
-                res = [res sprintf([cellsep{1} '%s' cellsep{2}],input{l}{i})];
-            end
-            res = [res sprintf(linesep)];
-        else
-            res = [res sprintf(['%s' linesep],input{l}(:))];
-        end
-    end
-end
\ No newline at end of file
diff --git a/matlab/caffe/read_cell.m b/matlab/caffe/read_cell.m
deleted file mode 100644
index 19831167106..00000000000
--- a/matlab/caffe/read_cell.m
+++ /dev/null
@@ -1,21 +0,0 @@
-function res=read_cell(filename,linesep,cellsep)
-if nargin < 2, linesep='\n'; end
-if nargin < 3, cellsep = '\t'; end
-if exist(filename,'file')
-    fid = fopen(filename);
-else
-    % Assume that filename is either a file ide or a string
-    fid = filename;
-end
-
-fileLines = textscan(fid,'%s','delimiter',linesep,'BufSize',100000);
-
-fileLines = fileLines{1};
-
-if regexp(fileLines{1},cellsep,'once')
-    fileLines = regexprep(fileLines,['^' cellsep '|' cellsep '$'],'');
-    res = regexp(fileLines,cellsep,'split');
-    res = cell2matcell(res);
-else
-    res = fileLines;
-end
diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/classification_demo.m
similarity index 56%
rename from matlab/caffe/matcaffe_demo.m
rename to matlab/classification_demo.m
index a931f910cbf..43a7bf62fdf 100644
--- a/matlab/caffe/matcaffe_demo.m
+++ b/matlab/classification_demo.m
@@ -1,7 +1,16 @@
-function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
-% scores = matcaffe_demo(im, use_gpu)
+function [scores, maxlabel] = classification_demo(im, use_gpu)
+% scores = classification_demo(im, use_gpu)
 %
-% Demo of the matlab wrapper using the ILSVRC network.
+% Image classification demo using BVLC CaffeNet.
+%
+% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
+% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
+%
+% ****************************************************************************
+% For detailed documentation and usage on Caffe's Matlab interface, please
+% refer to Caffe Interface Tutorial at
+% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
+% ****************************************************************************
 %
 % input
 %   im       color image as uint8 HxWx3
@@ -16,8 +25,8 @@
 % Or the equivalent based on where things are installed on your system
 %
 % Usage:
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo(im, 1);
+%  im = imread('../examples/images/cat.jpg');
+%  scores = classification_demo(im, 1);
 %  [score, class] = max(scores);
 % Five things to be aware of:
 %   caffe uses row-major order
@@ -26,7 +35,7 @@
 %   matlab uses RGB color channel order
 %   images need to have the data mean subtracted
 
-% Data coming in from matlab needs to be in the order 
+% Data coming in from matlab needs to be in the order
 %   [width, height, channels, images]
 % where width is the fastest dimension.
 % Here is the rough matlab for putting image data into the correct
@@ -42,20 +51,27 @@
 
 % If you have multiple images, cat them with cat(4, ...)
 
-% The actual forward function. It takes in a cell array of 4-D arrays as
-% input and outputs a cell array. 
-
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
-  matcaffe_init(use_gpu);
+% Set caffe mode
+if exist('use_gpu', 'var') && use_gpu
+  caffe.set_mode_gpu();
+  gpu_id = 0;  % we will use the first gpu in this demo
+  caffe.set_device(gpu_id);
 else
-  matcaffe_init();
+  caffe.set_mode_cpu();
 end
 
+% Initialize the network using BVLC CaffeNet for image classification
+% Weights (parameter) file needs to be downloaded from Model Zoo.
+model_dir = '../models/bvlc_reference_caffenet/';
+net_model = [model_dir 'deploy.prototxt'];
+net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
+phase = 'test';
+net = caffe.Net(net_model, net_weights, phase);
+
 if nargin < 1
-  % For demo purposes we will use the peppers image
-  im = imread('peppers.png');
+  % For demo purposes we will use the cat image
+  fprintf('using ../examples/images/cat.jpg as input image\n');
+  im = imread('../examples/images/cat.jpg');
 end
 
 % prepare oversampled input
@@ -67,7 +83,10 @@
 % do forward pass to get scores
 % scores are now Width x Height x Channels x Num
 tic;
-scores = caffe('forward', input_data);
+% The net forward function. It takes in a cell array of N-D arrays
+% (where N == 4 here) containing data of input blob(s) and outputs a cell
+% array containing data from output blob(s)
+scores = net.forward(input_data);
 toc;
 
 scores = scores{1};
@@ -77,10 +96,13 @@
 
 [~,maxlabel] = max(scores);
 
+% call caffe.reset_all() to reset caffe
+caffe.reset_all();
+
 % ------------------------------------------------------------------------
 function images = prepare_image(im)
 % ------------------------------------------------------------------------
-d = load('ilsvrc_2012_mean');
+d = load('+caffe/imagenet/ilsvrc_2012_mean.mat');
 IMAGE_MEAN = d.image_mean;
 IMAGE_DIM = 256;
 CROPPED_DIM = 227;
@@ -98,13 +120,13 @@
 for i = indices
   for j = indices
     images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
+      permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
     images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
     curr = curr + 1;
   end
 end
 center = floor(indices(2) / 2)+1;
 images(:,:,:,5) = ...
-    permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
-        [2 1 3]);
+  permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
+  [2 1 3]);
 images(:,:,:,10) = images(end:-1:1, :, :, curr);
diff --git a/matlab/caffe/hdf5creation/.gitignore b/matlab/hdf5creation/.gitignore
similarity index 100%
rename from matlab/caffe/hdf5creation/.gitignore
rename to matlab/hdf5creation/.gitignore
diff --git a/matlab/caffe/hdf5creation/demo.m b/matlab/hdf5creation/demo.m
similarity index 100%
rename from matlab/caffe/hdf5creation/demo.m
rename to matlab/hdf5creation/demo.m
diff --git a/matlab/caffe/hdf5creation/store2hdf5.m b/matlab/hdf5creation/store2hdf5.m
similarity index 100%
rename from matlab/caffe/hdf5creation/store2hdf5.m
rename to matlab/hdf5creation/store2hdf5.m

From 0f13feef342fc59aa113bafb45041b35ff97b649 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 28 May 2015 18:33:54 +0800
Subject: [PATCH 1382/2053] Move demo to demo/ and check weights file existence

Move all Matlab demo to caffe/matlab/demo. Since we want the user to add
caffe/matlab to Matlab search PATH, we don't want to mess it up with too
many files

Check if CaffeNet is already downloaded in classification demo.
---
 docs/tutorial/interfaces.md             | 10 +++++----
 matlab/{ => demo}/classification_demo.m | 27 ++++++++++++++++++-------
 2 files changed, 26 insertions(+), 11 deletions(-)
 rename matlab/{ => demo}/classification_demo.m (84%)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index a57e17339b0..a59a410d964 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -84,18 +84,18 @@ In MatCaffe, you can
 * Run for a certain number of iterations and give back control to Matlab
 * Intermingle arbitrary Matlab code to with gradient steps
 
-An ILSVRC image classification demo is in caffe/matlab/classification_demo.m
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m
 
 ### Build MatCaffe
 
 Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`.
 
-Common issue: if you run into error messages `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it means that your Matlab's runtime libraries does not match your compile-time libraries. You may need to do the following before you start matlab:
+Common issue: if you run into error messages like `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it usually means that your Matlab's runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab:
 
     export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
     export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
 
-Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.
+Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.
 
 After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window.
 
@@ -270,7 +270,9 @@ To read Caffe's example image and resize to `[width, height]` and suppose we wan
     im_data = permute(im_data, [2, 1, 3]); % permute width and height
     im_data = single(im_data); % convert to single precision
 
-We do not provide extra functions for data output as Matlab itself is already quite powerful in output.
+Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image.
+
+We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output.
 
 #### Clear nets and solvers
 
diff --git a/matlab/classification_demo.m b/matlab/demo/classification_demo.m
similarity index 84%
rename from matlab/classification_demo.m
rename to matlab/demo/classification_demo.m
index 43a7bf62fdf..453582476be 100644
--- a/matlab/classification_demo.m
+++ b/matlab/demo/classification_demo.m
@@ -1,5 +1,5 @@
 function [scores, maxlabel] = classification_demo(im, use_gpu)
-% scores = classification_demo(im, use_gpu)
+% [scores, maxlabel] = classification_demo(im, use_gpu)
 %
 % Image classification demo using BVLC CaffeNet.
 %
@@ -18,6 +18,7 @@
 %
 % output
 %   scores   1000-dimensional ILSVRC score vector
+%   maxlabel the label of the highest score
 %
 % You may need to do the following before you start matlab:
 %  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
@@ -25,7 +26,7 @@
 % Or the equivalent based on where things are installed on your system
 %
 % Usage:
-%  im = imread('../examples/images/cat.jpg');
+%  im = imread('../../examples/images/cat.jpg');
 %  scores = classification_demo(im, 1);
 %  [score, class] = max(scores);
 % Five things to be aware of:
@@ -51,6 +52,13 @@
 
 % If you have multiple images, cat them with cat(4, ...)
 
+% Add caffe/matlab to you Matlab search PATH to use matcaffe
+if exist('../+caffe', 'dir')
+  addpath('..');
+else
+  error('Please run this demo from caffe/matlab/demo');
+end
+
 % Set caffe mode
 if exist('use_gpu', 'var') && use_gpu
   caffe.set_mode_gpu();
@@ -62,16 +70,21 @@
 
 % Initialize the network using BVLC CaffeNet for image classification
 % Weights (parameter) file needs to be downloaded from Model Zoo.
-model_dir = '../models/bvlc_reference_caffenet/';
+model_dir = '../../models/bvlc_reference_caffenet/';
 net_model = [model_dir 'deploy.prototxt'];
 net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
-phase = 'test';
+phase = 'test'; % run with phase test (so that dropout isn't applied)
+if ~exist(net_weights, 'file')
+  error('Please download CaffeNet from Model Zoo before you run this demo');
+end
+
+% Initialize a network
 net = caffe.Net(net_model, net_weights, phase);
 
 if nargin < 1
   % For demo purposes we will use the cat image
-  fprintf('using ../examples/images/cat.jpg as input image\n');
-  im = imread('../examples/images/cat.jpg');
+  fprintf('using caffe/examples/images/cat.jpg as input image\n');
+  im = imread('../../examples/images/cat.jpg');
 end
 
 % prepare oversampled input
@@ -102,7 +115,7 @@
 % ------------------------------------------------------------------------
 function images = prepare_image(im)
 % ------------------------------------------------------------------------
-d = load('+caffe/imagenet/ilsvrc_2012_mean.mat');
+d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
 IMAGE_MEAN = d.image_mean;
 IMAGE_DIM = 256;
 CROPPED_DIM = 227;

From 18adbb8d1a1be91598aa23bad6550eed954e32a9 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 29 May 2015 00:23:06 +0800
Subject: [PATCH 1383/2053] Fix automatic header file dependency for MatCaffe

Automatic header file dependency was introduced in #1472, but
not correctly applied to matcaffe. Fix it by moving ./caffe_.d
to build/matlab/+caffe/private/caffe_.d and add it to DEPS
---
 Makefile | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 3748b52ca47..e4e66dfd138 100644
--- a/Makefile
+++ b/Makefile
@@ -118,7 +118,7 @@ GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
 # Output files for automatic dependency generation
 DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
-	${TEST_CU_OBJS:.o=.d}
+	${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
@@ -460,6 +460,9 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
+	@ if [ -f "$(PROJECT)_.d" ]; then \
+		mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \
+	fi
 
 runtest: $(TEST_ALL_BIN)
 	$(TOOL_BUILD_DIR)/caffe

From d07e5f796907a2bc048bdab3cdb4ace05fa60d7a Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 29 May 2015 07:50:23 +0800
Subject: [PATCH 1384/2053] More tests for Blob, Layer, copy_from and step, fix
 some typos

More testes are added into test_net.m and test_solver.m
---
 docs/tutorial/interfaces.md       |  4 ++--
 matlab/+caffe/+test/test_net.m    | 24 +++++++++++++++++++++++-
 matlab/+caffe/+test/test_solver.m |  2 ++
 matlab/+caffe/Net.m               |  2 +-
 matlab/+caffe/Solver.m            |  2 +-
 matlab/+caffe/io.m                |  2 +-
 matlab/+caffe/run_tests.m         |  3 +++
 7 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index a59a410d964..12963318485 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -82,9 +82,9 @@ In MatCaffe, you can
 * Resume training from solver snapshots
 * Access train net and test nets in a solver
 * Run for a certain number of iterations and give back control to Matlab
-* Intermingle arbitrary Matlab code to with gradient steps
+* Intermingle arbitrary Matlab code with gradient steps
 
-An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
 
 ### Build MatCaffe
 
diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m
index 5d9ba000209..3dabe84d111 100644
--- a/matlab/+caffe/+test/test_net.m
+++ b/matlab/+caffe/+test/test_net.m
@@ -48,6 +48,24 @@
     end
   end
   methods (Test)
+    function self = test_blob(self)
+      self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape));
+      self.verifyEqual(self.net.blobs('data').get_data(), ...
+        10 * ones(self.net.blobs('data').shape, 'single'));
+      self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape));
+      self.verifyEqual(self.net.blobs('data').get_diff(), ...
+        -2 * ones(self.net.blobs('data').shape, 'single'));
+      original_shape = self.net.blobs('data').shape;
+      self.net.blobs('data').reshape([6 5 4 3 2 1]);
+      self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]);
+      self.net.blobs('data').reshape(original_shape);
+      self.net.reshape();
+    end
+    function self = test_layer(self)
+      self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]);
+      self.verifyEqual(self.net.layers('conv').params(2).shape, 11);
+      self.verifyEqual(self.net.layers('conv').type(), 'Convolution');
+    end
     function test_forward_backward(self)
       self.net.forward_prefilled();
       self.net.backward_prefilled();
@@ -60,13 +78,17 @@ function test_save_and_read(self)
       weights_file = tempname();
       self.net.save(weights_file);
       model_file2 = caffe.test.test_net.simple_net_file(self.num_output);
-      net2 = caffe.Net(model_file2, weights_file, 'train');
+      net2 = caffe.Net(model_file2, 'train');
+      net2.copy_from(weights_file);
+      net3 = caffe.Net(model_file2, weights_file, 'train');
       delete(model_file2);
       delete(weights_file);
       for l = 1:length(self.net.layer_vec)
         for i = 1:length(self.net.layer_vec(l).params)
           self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
             net2.layer_vec(l).params(i).get_data());
+          self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+            net3.layer_vec(l).params(i).get_data());
         end
       end
     end
diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m
index 682dad48a3b..739258b0e85 100644
--- a/matlab/+caffe/+test/test_solver.m
+++ b/matlab/+caffe/+test/test_solver.m
@@ -36,6 +36,8 @@
   methods (Test)
     function test_solve(self)
       self.verifyEqual(self.solver.iter(), 0)
+      self.solver.step(30);
+      self.verifyEqual(self.solver.iter(), 30)
       self.solver.solve()
       self.verifyEqual(self.solver.iter(), 100)
     end
diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
index a6761060dfb..e6295bba1a4 100644
--- a/matlab/+caffe/Net.m
+++ b/matlab/+caffe/Net.m
@@ -111,7 +111,7 @@ function backward_prefilled(self)
         self.blobs(self.outputs{n}).set_diff(output_diff{n});
       end
       self.backward_prefilled();
-      % retrieve diff from input_blobs
+      % retrieve diff from input blobs
       res = cell(length(self.inputs), 1);
       for n = 1:length(self.inputs)
         res{n} = self.blobs(self.inputs{n}).get_diff();
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
index daaa8022b91..f8bdc4e22b2 100644
--- a/matlab/+caffe/Solver.m
+++ b/matlab/+caffe/Solver.m
@@ -41,7 +41,7 @@
     end
     function restore(self, snapshot_filename)
       CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string');
-      CHECK_FILE_EXIST(snapshot_filename)
+      CHECK_FILE_EXIST(snapshot_filename);
       caffe_('solver_restore', self.hSolver_self, snapshot_filename);
     end
     function solve(self)
diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
index 7a30bfb5772..c9e07aee9e8 100644
--- a/matlab/+caffe/io.m
+++ b/matlab/+caffe/io.m
@@ -17,7 +17,7 @@
     function mean_data = read_mean(mean_proto_file)
       % mean_data = read_mean(mean_proto_file)
       %   read image mean data from binaryproto file
-      CHECK(ischar(mean_proto_file), 'im_file must be a string');
+      CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
       CHECK_FILE_EXIST(mean_proto_file);
       mean_data = caffe_('read_mean', mean_proto_file);
     end
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
index 8773c9f638b..93896855ac2 100644
--- a/matlab/+caffe/run_tests.m
+++ b/matlab/+caffe/run_tests.m
@@ -2,6 +2,9 @@
 % results = run_tests()
 %   run all tests in this caffe matlab wrapper package
 
+% use CPU for testing
+caffe.set_mode_cpu();
+
 % reset caffe before testing
 caffe.reset_all();
 

From 2e03d89fd4578849d7793e166a1993ae521b6561 Mon Sep 17 00:00:00 2001
From: TorosFanny <spiritofquantum@gmail.com>
Date: Fri, 29 May 2015 21:19:56 -0700
Subject: [PATCH 1385/2053] [example] fix path for diff in net surgery

---
 examples/net_surgery.ipynb | 88 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 86 insertions(+), 2 deletions(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 1fb99bd3726..303c22ba3fd 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -5494,12 +5494,96 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n"
+      "1,2c1\r\n",
+      "< # Fully convolutional network version of CaffeNet.\r\n",
+      "< name: \"CaffeNetConv\"\r\n",
+      "---\r\n",
+      "> name: \"CaffeNet\"\r\n",
+      "4c3\r\n",
+      "< input_dim: 1\r\n",
+      "---\r\n",
+      "> input_dim: 10\r\n",
+      "6,7c5,6\r\n",
+      "< input_dim: 451\r\n",
+      "< input_dim: 451\r\n",
+      "---\r\n",
+      "> input_dim: 227\r\n",
+      "> input_dim: 227\r\n",
+      "152,153c151,152\r\n",
+      "<   name: \"fc6-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "---\r\n",
+      ">   name: \"fc6\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      "155,156c154,155\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   top: \"fc6\"\r\n",
+      ">   inner_product_param {\r\n",
+      "158d156\r\n",
+      "<     kernel_size: 6\r\n",
+      "164,165c162,163\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc6\"\r\n",
+      "170,171c168,169\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc6\"\r\n",
+      "177,181c175,179\r\n",
+      "<   name: \"fc7-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   name: \"fc7\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      ">   inner_product_param {\r\n",
+      "183d180\r\n",
+      "<     kernel_size: 1\r\n",
+      "189,190c186,187\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      "195,196c192,193\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      "202,206c199,203\r\n",
+      "<   name: \"fc8-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc8-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   name: \"fc8\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc8\"\r\n",
+      ">   inner_product_param {\r\n",
+      "208d204\r\n",
+      "<     kernel_size: 1\r\n",
+      "214c210\r\n",
+      "<   bottom: \"fc8-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8\"\r\n"
      ]
     }
    ],
    "source": [
-    "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+    "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
    ]
   },
   {

From 4874c01487f552dd4b16313d0a6723634f1912e5 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Fri, 29 May 2015 22:32:25 -0700
Subject: [PATCH 1386/2053] Add a simple C++ classification example

Closes #2487

Example usage:
./build/examples/cpp_classification/classification.bin \
  models/bvlc_reference_caffenet/deploy.prototxt \
  models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
  data/ilsvrc12/imagenet_mean.binaryproto \
  data/ilsvrc12/synset_words.txt \
  examples/images/cat.jpg
---
 .../cpp_classification/classification.cpp     | 255 ++++++++++++++++++
 examples/cpp_classification/readme.md         |  77 ++++++
 2 files changed, 332 insertions(+)
 create mode 100644 examples/cpp_classification/classification.cpp
 create mode 100644 examples/cpp_classification/readme.md

diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
new file mode 100644
index 00000000000..1c6371e382b
--- /dev/null
+++ b/examples/cpp_classification/classification.cpp
@@ -0,0 +1,255 @@
+#include <caffe/caffe.hpp>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <iosfwd>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+using namespace caffe;  // NOLINT(build/namespaces)
+using std::string;
+
+/* Pair (label, confidence) representing a prediction. */
+typedef std::pair<string, float> Prediction;
+
+class Classifier {
+ public:
+  Classifier(const string& model_file,
+             const string& trained_file,
+             const string& mean_file,
+             const string& label_file);
+
+  std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
+
+ private:
+  void SetMean(const string& mean_file);
+
+  std::vector<float> Predict(const cv::Mat& img);
+
+  void WrapInputLayer(std::vector<cv::Mat>* input_channels);
+
+  void Preprocess(const cv::Mat& img,
+                  std::vector<cv::Mat>* input_channels);
+
+ private:
+  shared_ptr<Net<float> > net_;
+  cv::Size input_geometry_;
+  int num_channels_;
+  cv::Mat mean_;
+  std::vector<string> labels_;
+};
+
+Classifier::Classifier(const string& model_file,
+                       const string& trained_file,
+                       const string& mean_file,
+                       const string& label_file) {
+#ifdef CPU_ONLY
+  Caffe::set_mode(Caffe::CPU);
+#else
+  Caffe::set_mode(Caffe::GPU);
+#endif
+
+  /* Load the network. */
+  net_.reset(new Net<float>(model_file, TEST));
+  net_->CopyTrainedLayersFrom(trained_file);
+
+  CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
+  CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
+
+  Blob<float>* input_layer = net_->input_blobs()[0];
+  num_channels_ = input_layer->channels();
+  CHECK(num_channels_ == 3 || num_channels_ == 1)
+    << "Input layer should have 1 or 3 channels.";
+  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());
+
+  /* Load the binaryproto mean file. */
+  SetMean(mean_file);
+
+  /* Load labels. */
+  std::ifstream labels(label_file.c_str());
+  CHECK(labels) << "Unable to open labels file " << label_file;
+  string line;
+  while (std::getline(labels, line))
+    labels_.push_back(string(line));
+
+  Blob<float>* output_layer = net_->output_blobs()[0];
+  CHECK_EQ(labels_.size(), output_layer->channels())
+    << "Number of labels is different from the output layer dimension.";
+}
+
+static bool PairCompare(const std::pair<float, int>& lhs,
+                        const std::pair<float, int>& rhs) {
+  return lhs.first > rhs.first;
+}
+
+/* Return the indices of the top N values of vector v. */
+static std::vector<int> Argmax(const std::vector<float>& v, int N) {
+  std::vector<std::pair<float, int> > pairs;
+  for (size_t i = 0; i < v.size(); ++i)
+    pairs.push_back(std::make_pair(v[i], i));
+  std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);
+
+  std::vector<int> result;
+  for (int i = 0; i < N; ++i)
+    result.push_back(pairs[i].second);
+  return result;
+}
+
+/* Return the top N predictions. */
+std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
+  std::vector<float> output = Predict(img);
+
+  std::vector<int> maxN = Argmax(output, N);
+  std::vector<Prediction> predictions;
+  for (int i = 0; i < N; ++i) {
+    int idx = maxN[i];
+    predictions.push_back(std::make_pair(labels_[idx], output[idx]));
+  }
+
+  return predictions;
+}
+
+/* Load the mean file in binaryproto format. */
+void Classifier::SetMean(const string& mean_file) {
+  BlobProto blob_proto;
+  ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+
+  /* Convert from BlobProto to Blob<float> */
+  Blob<float> mean_blob;
+  mean_blob.FromProto(blob_proto);
+  CHECK_EQ(mean_blob.channels(), num_channels_)
+    << "Number of channels of mean file doesn't match input layer.";
+
+  /* The format of the mean file is planar 32-bit float BGR or grayscale. */
+  std::vector<cv::Mat> channels;
+  float* data = mean_blob.mutable_cpu_data();
+  for (int i = 0; i < num_channels_; ++i) {
+    /* Extract an individual channel. */
+    cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
+    channels.push_back(channel);
+    data += mean_blob.height() * mean_blob.width();
+  }
+
+  /* Merge the separate channels into a single image. */
+  cv::Mat mean;
+  cv::merge(channels, mean);
+
+  /* Compute the global mean pixel value and create a mean image
+   * filled with this value. */
+  cv::Scalar channel_mean = cv::mean(mean);
+  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
+}
+
+std::vector<float> Classifier::Predict(const cv::Mat& img) {
+  Blob<float>* input_layer = net_->input_blobs()[0];
+  input_layer->Reshape(1, num_channels_,
+                       input_geometry_.height, input_geometry_.width);
+  /* Forward dimension change to all layers. */
+  net_->Reshape();
+
+  std::vector<cv::Mat> input_channels;
+  WrapInputLayer(&input_channels);
+
+  Preprocess(img, &input_channels);
+
+  net_->ForwardPrefilled();
+
+  /* Copy the output layer to a std::vector */
+  Blob<float>* output_layer = net_->output_blobs()[0];
+  const float* begin = output_layer->cpu_data();
+  const float* end = begin + output_layer->channels();
+  return std::vector<float>(begin, end);
+}
+
+/* Wrap the input layer of the network in separate cv::Mat objects
+ * (one per channel). This way we save one memcpy operation and we
+ * don't need to rely on cudaMemcpy2D. The last preprocessing
+ * operation will write the separate channels directly to the input
+ * layer. */
+void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
+  Blob<float>* input_layer = net_->input_blobs()[0];
+
+  int width = input_layer->width();
+  int height = input_layer->height();
+  float* input_data = input_layer->mutable_cpu_data();
+  for (int i = 0; i < input_layer->channels(); ++i) {
+    cv::Mat channel(height, width, CV_32FC1, input_data);
+    input_channels->push_back(channel);
+    input_data += width * height;
+  }
+}
+
+void Classifier::Preprocess(const cv::Mat& img,
+                            std::vector<cv::Mat>* input_channels) {
+  /* Convert the input image to the input image format of the network. */
+  cv::Mat sample;
+  if (img.channels() == 3 && num_channels_ == 1)
+    cv::cvtColor(img, sample, CV_BGR2GRAY);
+  else if (img.channels() == 4 && num_channels_ == 1)
+    cv::cvtColor(img, sample, CV_BGRA2GRAY);
+  else if (img.channels() == 4 && num_channels_ == 3)
+    cv::cvtColor(img, sample, CV_BGRA2BGR);
+  else if (img.channels() == 1 && num_channels_ == 3)
+    cv::cvtColor(img, sample, CV_GRAY2BGR);
+  else
+    sample = img;
+
+  cv::Mat sample_resized;
+  if (sample.size() != input_geometry_)
+    cv::resize(sample, sample_resized, input_geometry_);
+  else
+    sample_resized = sample;
+
+  cv::Mat sample_float;
+  if (num_channels_ == 3)
+    sample_resized.convertTo(sample_float, CV_32FC3);
+  else
+    sample_resized.convertTo(sample_float, CV_32FC1);
+
+  cv::Mat sample_normalized;
+  cv::subtract(sample_float, mean_, sample_normalized);
+
+  /* This operation will write the separate BGR planes directly to the
+   * input layer of the network because it is wrapped by the cv::Mat
+   * objects in input_channels. */
+  cv::split(sample_normalized, *input_channels);
+
+  CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
+        == net_->input_blobs()[0]->cpu_data())
+    << "Input channels are not wrapping the input layer of the network.";
+}
+
+int main(int argc, char** argv) {
+  if (argc != 6) {
+    std::cerr << "Usage: " << argv[0]
+              << " deploy.prototxt network.caffemodel"
+              << " mean.binaryproto labels.txt img.jpg" << std::endl;
+    return 1;
+  }
+
+  ::google::InitGoogleLogging(argv[0]);
+
+  string model_file   = argv[1];
+  string trained_file = argv[2];
+  string mean_file    = argv[3];
+  string label_file   = argv[4];
+  Classifier classifier(model_file, trained_file, mean_file, label_file);
+
+  string file = argv[5];
+
+  std::cout << "---------- Prediction for "
+            << file << " ----------" << std::endl;
+
+  cv::Mat img = cv::imread(file, -1);
+  CHECK(!img.empty()) << "Unable to decode image " << file;
+  std::vector<Prediction> predictions = classifier.Classify(img);
+
+  /* Print the top N predictions. */
+  for (size_t i = 0; i < predictions.size(); ++i) {
+    Prediction p = predictions[i];
+    std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
+              << p.first << "\"" << std::endl;
+  }
+}
diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
new file mode 100644
index 00000000000..a086db1a035
--- /dev/null
+++ b/examples/cpp_classification/readme.md
@@ -0,0 +1,77 @@
+---
+title: CaffeNet C++ Classification example
+description: A simple example performing image classification using the low-level C++ API.
+category: example
+include_in_docs: true
+priority: 10
+---
+
+# Classifying ImageNet: using the C++ API
+
+Caffe, at its core, is written in C++. It is possible to use the C++
+API of Caffe to implement an image classification application similar
+to the Python code presented in one of the Notebook example. To look
+at a more general-purpose example of the Caffe C++ API, you should
+study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
+
+## Presentation
+
+A simple C++ code is proposed in
+`examples/cpp_classification/classification.cpp`. For the sake of
+simplicity, this example does not support oversampling of a single
+sample nor batching of multiple independant samples. This example is
+not trying to reach the maximum possible classification throughput on
+a system, but special care was given to avoid unnecessary
+pessimization while keeping the code readable.
+
+## Compiling
+
+The C++ example is built automatically when compiling Caffe. To
+compile Caffe you should follow the documented instructions. The
+classification example will be built as `examples/classification.bin`
+in your build directory.
+
+## Usage
+
+To use the pre-trained CaffeNet model with the classification example,
+you need to download it from the "Model Zoo" using the following
+script:
+```
+./scripts/download_model_binary.py models/bvlc_reference_caffenet
+```
+The ImageNet labels file (also called the *synset file*) is also
+required in order to map a prediction to the name of the class:
+```
+./data/ilsvrc12/get_ilsvrc_aux.sh.
+```
+Using the files that were downloaded, we can classify the provided cat
+image (`examples/images/cat.jpg`) using this command:
+```
+./build/examples/cpp_classification/classification.bin \
+  models/bvlc_reference_caffenet/deploy.prototxt \
+  models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
+  data/ilsvrc12/imagenet_mean.binaryproto \
+  data/ilsvrc12/synset_words.txt \
+  examples/images/cat.jpg
+```
+The output should look like this:
+```
+---------- Prediction for examples/images/cat.jpg ----------
+0.3134 - "n02123045 tabby, tabby cat"
+0.2380 - "n02123159 tiger cat"
+0.1235 - "n02124075 Egyptian cat"
+0.1003 - "n02119022 red fox, Vulpes vulpes"
+0.0715 - "n02127052 lynx, catamount"
+```
+
+## Improving Performance
+
+To further improve performance, you will need to leverage the GPU
+more, here are some guidelines:
+
+* Move the data on the GPU early and perform all preprocessing
+operations there.
+* If you have many images to classify simultaneously, you should use
+batching (independent images are classified in a single forward pass).
+* Use multiple classification threads to ensure the GPU is always fully
+utilized and not waiting for an I/O blocked CPU thread.

From 97b4c1453377e6257f6b5efc32e2d5bc472e9323 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 30 May 2015 09:37:32 +0800
Subject: [PATCH 1387/2053] Update ilsvrc_2012_mean.mat to W x H x C, update
 demo and add comments

Update previously ilsvrc_2012_mean.mat stores 'image_mean' variable in
H x W x C with BGR channels, which is inconsistent with Caffe's data format
and inconsistent with caffe.io.read_mean(..). Replace 'image_mean' with
'mean_data' variable in W x H x C and update classification_demo.m. Also add
some comments.
---
 matlab/+caffe/imagenet/ilsvrc_2012_mean.mat | Bin 786640 -> 606799 bytes
 matlab/+caffe/io.m                          |  10 ++-
 matlab/demo/classification_demo.m           |  64 ++++++++++----------
 3 files changed, 42 insertions(+), 32 deletions(-)

diff --git a/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat
index f1da25c84a1a4bdd6d32aee386bcdbed48bba99c..21df3d39aaa3c43b9c2b41f88bd2e6f48585b8c5 100644
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diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
index c9e07aee9e8..af8369ddfab 100644
--- a/matlab/+caffe/io.m
+++ b/matlab/+caffe/io.m
@@ -7,16 +7,24 @@
       %   load an image from disk into Caffe-supported data format
       %   switch channels from RGB to BGR, make width the fastest dimension
       %   and convert to single
+      %   returns im_data in W x H x C. For colored images, C = 3 in BGR
+      %   channels, and for grayscale images, C = 1
       CHECK(ischar(im_file), 'im_file must be a string');
       CHECK_FILE_EXIST(im_file);
       im_data = imread(im_file);
-      im_data = im_data(:, :, [3, 2, 1]);
+      % permute channels from RGB to BGR for colored images
+      if size(im_data, 3) == 3
+        im_data = im_data(:, :, [3, 2, 1]);
+      end
+      % flip width and height to make width the fastest dimension
       im_data = permute(im_data, [2, 1, 3]);
+      % convert from uint8 to single
       im_data = single(im_data);
     end
     function mean_data = read_mean(mean_proto_file)
       % mean_data = read_mean(mean_proto_file)
       %   read image mean data from binaryproto file
+      %   returns mean_data in W x H x C with BGR channels
       CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
       CHECK_FILE_EXIST(mean_proto_file);
       mean_data = caffe_('read_mean', mean_proto_file);
diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m
index 453582476be..2b60332970b 100644
--- a/matlab/demo/classification_demo.m
+++ b/matlab/demo/classification_demo.m
@@ -40,15 +40,17 @@
 %   [width, height, channels, images]
 % where width is the fastest dimension.
 % Here is the rough matlab for putting image data into the correct
-% format:
-%   % convert from uint8 to single
-%   im = single(im);
-%   % reshape to a fixed size (e.g., 227x227)
-%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-%   % permute from RGB to BGR and subtract the data mean (already in BGR)
-%   im = im(:,:,[3 2 1]) - data_mean;
+% format in W x H x C with BGR channels:
+%   % permute channels from RGB to BGR
+%   im_data = im(:, :, [3, 2, 1]);
 %   % flip width and height to make width the fastest dimension
-%   im = permute(im, [2 1 3]);
+%   im_data = permute(im_data, [2, 1, 3]);
+%   % convert from uint8 to single
+%   im_data = single(im_data);
+%   % reshape to a fixed size (e.g., 227x227).
+%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+%   % subtract mean_data (already in W x H x C with BGR channels)
+%   im_data = im_data - mean_data;
 
 % If you have multiple images, cat them with cat(4, ...)
 
@@ -94,7 +96,7 @@
 toc;
 
 % do forward pass to get scores
-% scores are now Width x Height x Channels x Num
+% scores are now Channels x Num, where Channels == 1000
 tic;
 % The net forward function. It takes in a cell array of N-D arrays
 % (where N == 4 here) containing data of input blob(s) and outputs a cell
@@ -103,43 +105,43 @@
 toc;
 
 scores = scores{1};
-size(scores)
-scores = squeeze(scores);
-scores = mean(scores,2);
+scores = mean(scores, 2);  % take average scores over 10 crops
 
-[~,maxlabel] = max(scores);
+[~, maxlabel] = max(scores);
 
 % call caffe.reset_all() to reset caffe
 caffe.reset_all();
 
 % ------------------------------------------------------------------------
-function images = prepare_image(im)
+function crops_data = prepare_image(im)
 % ------------------------------------------------------------------------
+% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
+% is already in W x H x C with BGR channels
 d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
-IMAGE_MEAN = d.image_mean;
+mean_data = d.mean_data;
 IMAGE_DIM = 256;
 CROPPED_DIM = 227;
 
-% resize to fixed input size
-im = single(im);
-im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-% permute from RGB to BGR (IMAGE_MEAN is already BGR)
-im = im(:,:,[3 2 1]) - IMAGE_MEAN;
+% Convert an image returned by Matlab's imread to im_data in caffe's data
+% format: W x H x C with BGR channels
+im_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR
+im_data = permute(im_data, [2, 1, 3]);  % flip width and height
+im_data = single(im_data);  % convert from uint8 to single
+im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data
+im_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)
 
 % oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
 indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-curr = 1;
+n = 1;
 for i = indices
   for j = indices
-    images(:, :, :, curr) = ...
-      permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
+    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
+    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
+    n = n + 1;
   end
 end
-center = floor(indices(2) / 2)+1;
-images(:,:,:,5) = ...
-  permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
-  [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
+center = floor(indices(2) / 2) + 1;
+crops_data(:,:,:,5) = ...
+  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
+crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);

From 66583ec8a2c613fccd5037d0fd873ddd74c44864 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 29 May 2015 23:33:26 -0700
Subject: [PATCH 1388/2053] [travis] install lmdb through git mirror

switch to github mirror of latest tag as tarball to fix stale url.
---
 scripts/travis/travis_install.sh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 0e8c37861b0..b6e6f6ce821 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -47,12 +47,12 @@ if $WITH_CUDA; then
 fi
 
 # Install LMDB
-LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz
-LMDB_FILE=/tmp/openldap.tgz
+LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
+LMDB_FILE=/tmp/lmdb.tar.gz
 pushd .
-curl $LMDB_URL -o $LMDB_FILE
+wget $LMDB_URL -O $LMDB_FILE
 tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/openldap*/libraries/liblmdb/
+cd /tmp/lmdb*/libraries/liblmdb/
 $MAKE
 $MAKE install
 popd

From a02a8ba0c252dfa41e80f8dcab071c3166ced7e3 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Thu, 26 Feb 2015 18:59:27 -0800
Subject: [PATCH 1389/2053] PReLU accumulates grad

---
 src/caffe/layers/prelu_layer.cpp | 1 -
 src/caffe/layers/prelu_layer.cu  | 6 ++----
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
index 7a38f9fac80..81831755512 100644
--- a/src/caffe/layers/prelu_layer.cpp
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -113,7 +113,6 @@ void PReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   // keep top_diff unchanged.
   if (this->param_propagate_down_[0]) {
     Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
-    caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff);
     for (int i = 0; i < count; ++i) {
       int c = (i / dim) % channels / div_factor;
       slope_diff[c] += top_diff[i] * bottom_data[i] * (bottom_data[i] <= 0);
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index dfa238d85bd..e1f20048f60 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -75,14 +75,12 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     bottom_data = bottom_memory_.gpu_data();
   }
 
-  // Propagte to param
+  // Propagate to param
   // Since to write bottom diff will affect top diff if top and bottom blobs
   // are identical (in-place computaion), we first compute param backward to
   // keep top_diff unchanged.
   if (this->param_propagate_down_[0]) {
     Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
-    // slope_diff is set as 0, then accumulated over batches
-    caffe_gpu_set<Dtype>(this->blobs_[0]->count(), Dtype(0), slope_diff);
     int cdim = channels * dim;
     Dtype dsum = 0.;
     for (int n = 0; n < bottom[0]->num(); ++n) {
@@ -106,7 +104,7 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       }
     }
     if (channel_shared_) {
-      caffe_gpu_set(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+      caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
     }
   }
   // Propagate to bottom

From 86758be9323a2bbefa751132dd58376e7667294d Mon Sep 17 00:00:00 2001
From: Kibum Bae <kibum85.bae@samsung.com>
Date: Tue, 2 Jun 2015 22:20:27 +0900
Subject: [PATCH 1390/2053] Remove unnecessary filler parameter in the sample
 model

In bvlc_googlenet model, unnecessary filler parameters, 'std', were used
for Xavier weight fillers.
---
 models/bvlc_googlenet/train_val.prototxt | 63 ------------------------
 1 file changed, 63 deletions(-)

diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
index 79ede2b9d9c..5dee3abe28f 100644
--- a/models/bvlc_googlenet/train_val.prototxt
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -61,7 +61,6 @@ layer {
     stride: 2
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -115,7 +114,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -148,7 +146,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -202,7 +199,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -234,7 +230,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -267,7 +262,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -299,7 +293,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -332,7 +325,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -376,7 +368,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -417,7 +408,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -449,7 +439,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -482,7 +471,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -514,7 +502,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -547,7 +534,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -591,7 +577,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -643,7 +628,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -675,7 +659,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -708,7 +691,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -740,7 +722,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -773,7 +754,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -817,7 +797,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -869,7 +848,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.08
     }
     bias_filler {
       type: "constant"
@@ -900,7 +878,6 @@ layer {
     num_output: 1024
     weight_filler {
       type: "xavier"
-      std: 0.02
     }
     bias_filler {
       type: "constant"
@@ -940,7 +917,6 @@ layer {
     num_output: 1000
     weight_filler {
       type: "xavier"
-      std: 0.0009765625
     }
     bias_filler {
       type: "constant"
@@ -997,7 +973,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1029,7 +1004,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1062,7 +1036,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1094,7 +1067,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1127,7 +1099,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1171,7 +1142,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -1212,7 +1182,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1244,7 +1213,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1277,7 +1245,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1309,7 +1276,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1342,7 +1308,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1386,7 +1351,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -1427,7 +1391,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1459,7 +1422,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1492,7 +1454,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1524,7 +1485,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1557,7 +1517,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1601,7 +1560,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -1653,7 +1611,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.08
     }
     bias_filler {
       type: "constant"
@@ -1684,7 +1641,6 @@ layer {
     num_output: 1024
     weight_filler {
       type: "xavier"
-      std: 0.02
     }
     bias_filler {
       type: "constant"
@@ -1724,7 +1680,6 @@ layer {
     num_output: 1000
     weight_filler {
       type: "xavier"
-      std: 0.0009765625
     }
     bias_filler {
       type: "constant"
@@ -1781,7 +1736,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1813,7 +1767,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1846,7 +1799,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1878,7 +1830,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1911,7 +1862,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1955,7 +1905,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -2007,7 +1956,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2039,7 +1987,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -2072,7 +2019,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2104,7 +2050,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -2137,7 +2082,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2181,7 +2125,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -2222,7 +2165,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2254,7 +2196,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -2287,7 +2228,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2319,7 +2259,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -2352,7 +2291,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2396,7 +2334,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"

From 5bde20b1e9274b11b71e1c0970d4a22a5256b205 Mon Sep 17 00:00:00 2001
From: manuele <manuele.tamburrano@gmail.com>
Date: Fri, 29 May 2015 15:54:38 +0200
Subject: [PATCH 1391/2053] Filter Layer implemented

---
 include/caffe/common_layers.hpp      |  63 +++++++++++++
 src/caffe/layers/filter_layer.cpp    | 128 +++++++++++++++++++++++++++
 src/caffe/layers/filter_layer.cu     |  70 +++++++++++++++
 src/caffe/test/test_filter_layer.cpp | 128 +++++++++++++++++++++++++++
 4 files changed, 389 insertions(+)
 create mode 100644 src/caffe/layers/filter_layer.cpp
 create mode 100644 src/caffe/layers/filter_layer.cu
 create mode 100644 src/caffe/test/test_filter_layer.cpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index e6b42c14587..5d018e7389d 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -180,6 +180,69 @@ class EltwiseLayer : public Layer<Dtype> {
   bool stable_prod_grad_;
 };
 
+/**
+ * @brief Takes two+ Blobs, interprets last Blob as a selector and
+ *  filter remaining Blobs accordingly with selector data (0 means that
+ * the corresponding item has to be filtered, non-zero means that corresponding
+ * item needs to stay).
+ */
+template <typename Dtype>
+class FilterLayer : public Layer<Dtype> {
+ public:
+  explicit FilterLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Filter"; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_1 @f$
+   *   -# ...
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_K @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the selector blob
+   * @param top output Blob vector (length 1+)
+   *   -# @f$ (S \times C \times H \times W) @f$ () 
+   *        the filtered output @f$ x_1 @f$ 
+   *        where S is the number of items
+   *        that haven't been filtered
+   *      @f$ (S \times C \times H \times W) @f$
+   *        the filtered output @f$ x_K @f$ 
+   *        where S is the number of items
+   *        that haven't been filtered
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the forwarded inputs.
+   *
+   * @param top output Blob vector (length 1+), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2+), into which the top error
+   *        gradient is copied
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool first_reshape_;
+  vector<int> indices_to_forward_;
+};
+
 /**
  * @brief Reshapes the input Blob into flat vectors.
  *
diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp
new file mode 100644
index 00000000000..d7fc59e0ebe
--- /dev/null
+++ b/src/caffe/layers/filter_layer.cpp
@@ -0,0 +1,128 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(top.size(), bottom.size()-1) <<
+      "Top.size() should be equal to bottom.size() - 1";
+  first_reshape_ = true;
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // bottom[0...k-1] are the blobs to filter
+  // bottom[last] is the "selector_blob"
+  int selector_index = bottom.size() - 1;
+  for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) {
+    CHECK_EQ(bottom[selector_index]->shape(i), 1)
+        << "Selector blob must have all shapes == 1 (except the first one)";
+  }
+  for (int i = 0; i < bottom.size()-1; i++) {
+    CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) <<
+        "Each bottom should have the same dimension as the selector blob";
+  }
+
+  const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data();
+  indices_to_forward_.clear();
+
+  // look for non-zero elements in bottom[0]. Items of each bottom that
+  // have the same index as the items in bottom[0] with value == non-zero
+  // will be forwarded
+  for (int item_id = 0; item_id < bottom[selector_index]->shape(0); ++item_id) {
+    // we don't need an offset because item size == 1
+    const Dtype* tmp_data_selector = bottom_data_selector + item_id;
+    if (*tmp_data_selector) {
+      indices_to_forward_.push_back(item_id);
+    }
+  }
+  // only filtered items will be forwarded
+  int new_tops_num = indices_to_forward_.size();
+  // init
+  if (first_reshape_) {
+    new_tops_num = bottom[0]->shape(0);
+    first_reshape_ = false;
+  }
+  for (int t = 0; t < top.size(); t++) {
+    int num_axes = bottom[t]->num_axes();
+    vector<int> shape_top(num_axes);
+    shape_top[0] = new_tops_num;
+    for (int ts = 1; ts < num_axes; ts++)
+      shape_top[ts] = bottom[t]->shape(ts);
+    top[t]->Reshape(shape_top);
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int new_tops_num = indices_to_forward_.size();
+  // forward all filtered items for all bottoms but the Selector (bottom[last])
+  for (int t = 0; t < top.size(); t++) {
+    const Dtype* bottom_data = bottom[t]->cpu_data();
+    Dtype* top_data = top[t]->mutable_cpu_data();
+    int dim = bottom[t]->count() / bottom[t]->shape(0);
+    for (int n = 0; n < new_tops_num; n++) {
+      int data_offset_top = top[t]->offset(n);
+      int data_offset_bottom =  bottom[t]->offset(indices_to_forward_[n]);
+      caffe_copy(dim, bottom_data + data_offset_bottom,
+          top_data + data_offset_top);
+    }
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[bottom.size() - 1]) {
+    LOG(FATAL) << this->type()
+               << "Layer cannot backpropagate to filter index inputs";
+  }
+  for (int i = 0; i < top.size(); i++) {
+    // bottom[last] is the selector and never needs backpropagation
+    // so we can iterate over top vector because top.size() == bottom.size() -1
+    if (propagate_down[i]) {
+      const int dim = top[i]->count() / top[i]->shape(0);
+      int next_to_backward_offset = 0;
+      int batch_offset = 0;
+      int data_offset_bottom = 0;
+      int data_offset_top = 0;
+      for (int n = 0; n < bottom[i]->shape(0); n++) {
+        data_offset_bottom = bottom[i]->offset(n);
+        if (next_to_backward_offset >= indices_to_forward_.size()) {
+          // we already visited all items that were been forwarded, so
+          // just set to zero remaining ones
+          caffe_set(dim, Dtype(0),
+              bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+        } else {
+          batch_offset = indices_to_forward_[next_to_backward_offset];
+          if (n != batch_offset) {  // this data was not been forwarded
+            caffe_set(dim, Dtype(0),
+                bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+          } else {  // this data was been forwarded
+            data_offset_top = top[i]->offset(next_to_backward_offset);
+            next_to_backward_offset++;  // point to next forwarded item index
+            caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top,
+                bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+          }
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(FilterLayer);
+#endif
+
+INSTANTIATE_CLASS(FilterLayer);
+REGISTER_LAYER_CLASS(Filter);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu
new file mode 100644
index 00000000000..4a9e674de1a
--- /dev/null
+++ b/src/caffe/layers/filter_layer.cu
@@ -0,0 +1,70 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int new_tops_num = indices_to_forward_.size();
+  // forward all filtered items for all bottoms but the Selector (bottom[last])
+  for (int t = 0; t < top.size(); t++) {
+    const Dtype* bottom_data = bottom[t]->gpu_data();
+    Dtype* top_data = top[t]->mutable_gpu_data();
+    int dim = bottom[t]->count() / bottom[t]->shape(0);
+    for (int n = 0; n < new_tops_num; n++) {
+      int data_offset_top = top[t]->offset(n);
+      int data_offset_bottom =  bottom[t]->offset(indices_to_forward_[n]);
+      caffe_copy(dim, bottom_data + data_offset_bottom,
+          top_data + data_offset_top);
+    }
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[bottom.size() - 1]) {
+    LOG(FATAL) << this->type()
+                << "Layer cannot backpropagate to filter index inputs";
+  }
+  for (int i = 0; i < top.size(); i++) {
+    // bottom[last] is the selector and never needs backpropagation
+    // so we can iterate over top vector because top.size() == bottom.size() -1
+    if (propagate_down[i]) {
+      const int dim = top[i]->count() / top[i]->shape(0);
+      int next_to_backward_offset = 0;
+      int batch_offset = 0;
+      int data_offset_bottom = 0;
+      int data_offset_top = 0;
+      for (int n = 0; n < bottom[i]->shape(0); n++) {
+        if (next_to_backward_offset >= indices_to_forward_.size()) {
+          // we already visited all items that were been forwarded, so
+          // just set to zero remaining ones
+          data_offset_bottom = top[i]->offset(n);
+          caffe_set(dim, Dtype(0),
+              bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+        } else {
+          batch_offset = indices_to_forward_[next_to_backward_offset];
+          data_offset_bottom = top[i]->offset(n);
+          if (n != batch_offset) {  // this data was not been forwarded
+            caffe_set(dim, Dtype(0),
+                bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+          } else {  // this data was been forwarded
+            data_offset_top = top[i]->offset(next_to_backward_offset);
+            next_to_backward_offset++;  // point to next forwarded item index
+            caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top,
+                bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+          }
+        }
+      }
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(FilterLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp
new file mode 100644
index 00000000000..c641b6ef6e8
--- /dev/null
+++ b/src/caffe/test/test_filter_layer.cpp
@@ -0,0 +1,128 @@
+#include <cstring>
+#include <limits>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class FilterLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  FilterLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(4, 3, 6, 4)),
+        blob_bottom_labels_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_bottom_selector_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_labels_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    Caffe::set_random_seed(1890);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    // fill the selector blob
+    Dtype* bottom_data_selector_ = blob_bottom_selector_->mutable_cpu_data();
+    bottom_data_selector_[0] = 0;
+    bottom_data_selector_[1] = 1;
+    bottom_data_selector_[2] = 1;
+    bottom_data_selector_[3] = 0;
+    // fill the other bottom blobs
+    filler.Fill(blob_bottom_data_);
+    for (int i = 0; i < blob_bottom_labels_->count(); ++i) {
+      blob_bottom_labels_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+    }
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_labels_);
+    blob_bottom_vec_.push_back(blob_bottom_selector_);
+    blob_top_vec_.push_back(blob_top_data_);
+    blob_top_vec_.push_back(blob_top_labels_);
+  }
+  virtual ~FilterLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_labels_;
+    delete blob_bottom_selector_;
+    delete blob_top_data_;
+    delete blob_top_labels_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_labels_;
+  Blob<Dtype>* const blob_bottom_selector_;
+  // blobs for the top of FilterLayer
+  Blob<Dtype>* const blob_top_data_;
+  Blob<Dtype>* const blob_top_labels_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(FilterLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(FilterLayerTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  // In the test first and last items should have been filtered
+  // so we just expect 2 remaining items
+  EXPECT_EQ(this->blob_top_data_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_labels_->shape(0), 2);
+  EXPECT_GT(this->blob_bottom_data_->shape(0),
+      this->blob_top_data_->shape(0));
+  EXPECT_GT(this->blob_bottom_labels_->shape(0),
+      this->blob_top_labels_->shape(0));
+  for (int i = 1; i < this->blob_bottom_labels_->num_axes(); i++) {
+    EXPECT_EQ(this->blob_bottom_labels_->shape(i),
+        this->blob_top_labels_->shape(i));
+  }
+}
+
+TYPED_TEST(FilterLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_labels_->data_at(0, 0, 0, 0),
+      this->blob_bottom_labels_->data_at(1, 0, 0, 0));
+  EXPECT_EQ(this->blob_top_labels_->data_at(1, 0, 0, 0),
+      this->blob_bottom_labels_->data_at(2, 0, 0, 0));
+
+  int dim = this->blob_top_data_->count() /
+      this->blob_top_data_->shape(0);
+  const Dtype* top_data = this->blob_top_data_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_data_->cpu_data();
+  // selector is 0 1 1 0, so we need to compare bottom(1,c,h,w)
+  // with top(0,c,h,w) and bottom(2,c,h,w) with top(1,c,h,w)
+  bottom_data += dim;  // bottom(1,c,h,w)
+  for (size_t n = 0; n < dim; n++)
+    EXPECT_EQ(top_data[n], bottom_data[n]);
+
+  bottom_data += dim;  // bottom(2,c,h,w)
+  top_data += dim;  // top(1,c,h,w)
+  for (size_t n = 0; n < dim; n++)
+    EXPECT_EQ(top_data[n], bottom_data[n]);
+}
+
+TYPED_TEST(FilterLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  // check only input 0 (data) because labels and selector
+  // don't need backpropagation
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+}  // namespace caffe

From 0ccf336bfb19a82144d63399034cd886f02d8745 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 2 Jun 2015 18:21:15 -0700
Subject: [PATCH 1392/2053] FilterLayer cleanup and bugfix for GPU backward

-caffe_set -> caffe_gpu_set (backward was segfaulting before)
-remove uses of 'offset' (to support >4D blobs)
-change var++ -> ++var (per Google style guide)
-cleanup comments/whitespace
---
 include/caffe/common_layers.hpp   |  6 +++---
 src/caffe/layers/filter_layer.cpp | 25 ++++++++++++-------------
 src/caffe/layers/filter_layer.cu  | 26 +++++++++++++-------------
 3 files changed, 28 insertions(+), 29 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 5d018e7389d..3155b45bbbc 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -211,12 +211,12 @@ class FilterLayer : public Layer<Dtype> {
    *   -# @f$ (N \times 1 \times 1 \times 1) @f$
    *      the selector blob
    * @param top output Blob vector (length 1+)
-   *   -# @f$ (S \times C \times H \times W) @f$ () 
-   *        the filtered output @f$ x_1 @f$ 
+   *   -# @f$ (S \times C \times H \times W) @f$ ()
+   *        the filtered output @f$ x_1 @f$
    *        where S is the number of items
    *        that haven't been filtered
    *      @f$ (S \times C \times H \times W) @f$
-   *        the filtered output @f$ x_K @f$ 
+   *        the filtered output @f$ x_K @f$
    *        where S is the number of items
    *        that haven't been filtered
    */
diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp
index d7fc59e0ebe..be1db32dbaa 100644
--- a/src/caffe/layers/filter_layer.cpp
+++ b/src/caffe/layers/filter_layer.cpp
@@ -10,8 +10,7 @@ namespace caffe {
 template <typename Dtype>
 void FilterLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(top.size(), bottom.size()-1) <<
-      "Top.size() should be equal to bottom.size() - 1";
+  CHECK_EQ(top.size(), bottom.size() - 1);
   first_reshape_ = true;
 }
 
@@ -23,11 +22,11 @@ void FilterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   int selector_index = bottom.size() - 1;
   for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) {
     CHECK_EQ(bottom[selector_index]->shape(i), 1)
-        << "Selector blob must have all shapes == 1 (except the first one)";
+        << "Selector blob dimensions must be singletons (1), except the first";
   }
-  for (int i = 0; i < bottom.size()-1; i++) {
+  for (int i = 0; i < bottom.size() - 1; ++i) {
     CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) <<
-        "Each bottom should have the same dimension as the selector blob";
+        "Each bottom should have the same 0th dimension as the selector blob";
   }
 
   const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data();
@@ -50,11 +49,11 @@ void FilterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     new_tops_num = bottom[0]->shape(0);
     first_reshape_ = false;
   }
-  for (int t = 0; t < top.size(); t++) {
+  for (int t = 0; t < top.size(); ++t) {
     int num_axes = bottom[t]->num_axes();
     vector<int> shape_top(num_axes);
     shape_top[0] = new_tops_num;
-    for (int ts = 1; ts < num_axes; ts++)
+    for (int ts = 1; ts < num_axes; ++ts)
       shape_top[ts] = bottom[t]->shape(ts);
     top[t]->Reshape(shape_top);
   }
@@ -65,13 +64,13 @@ void FilterLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   int new_tops_num = indices_to_forward_.size();
   // forward all filtered items for all bottoms but the Selector (bottom[last])
-  for (int t = 0; t < top.size(); t++) {
+  for (int t = 0; t < top.size(); ++t) {
     const Dtype* bottom_data = bottom[t]->cpu_data();
     Dtype* top_data = top[t]->mutable_cpu_data();
     int dim = bottom[t]->count() / bottom[t]->shape(0);
-    for (int n = 0; n < new_tops_num; n++) {
-      int data_offset_top = top[t]->offset(n);
-      int data_offset_bottom =  bottom[t]->offset(indices_to_forward_[n]);
+    for (int n = 0; n < new_tops_num; ++n) {
+      int data_offset_top = n * dim;
+      int data_offset_bottom = indices_to_forward_[n] * bottom[t]->count(1);
       caffe_copy(dim, bottom_data + data_offset_bottom,
           top_data + data_offset_top);
     }
@@ -95,7 +94,7 @@ void FilterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       int data_offset_bottom = 0;
       int data_offset_top = 0;
       for (int n = 0; n < bottom[i]->shape(0); n++) {
-        data_offset_bottom = bottom[i]->offset(n);
+        data_offset_bottom = n * dim;
         if (next_to_backward_offset >= indices_to_forward_.size()) {
           // we already visited all items that were been forwarded, so
           // just set to zero remaining ones
@@ -107,7 +106,7 @@ void FilterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
             caffe_set(dim, Dtype(0),
                 bottom[i]->mutable_cpu_diff() + data_offset_bottom);
           } else {  // this data was been forwarded
-            data_offset_top = top[i]->offset(next_to_backward_offset);
+            data_offset_top = next_to_backward_offset * dim;
             next_to_backward_offset++;  // point to next forwarded item index
             caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top,
                 bottom[i]->mutable_cpu_diff() + data_offset_bottom);
diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu
index 4a9e674de1a..cf929eeeadf 100644
--- a/src/caffe/layers/filter_layer.cu
+++ b/src/caffe/layers/filter_layer.cu
@@ -11,13 +11,13 @@ void FilterLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   int new_tops_num = indices_to_forward_.size();
   // forward all filtered items for all bottoms but the Selector (bottom[last])
-  for (int t = 0; t < top.size(); t++) {
+  for (int t = 0; t < top.size(); ++t) {
     const Dtype* bottom_data = bottom[t]->gpu_data();
     Dtype* top_data = top[t]->mutable_gpu_data();
     int dim = bottom[t]->count() / bottom[t]->shape(0);
-    for (int n = 0; n < new_tops_num; n++) {
-      int data_offset_top = top[t]->offset(n);
-      int data_offset_bottom =  bottom[t]->offset(indices_to_forward_[n]);
+    for (int n = 0; n < new_tops_num; ++n) {
+      int data_offset_top = n * dim;
+      int data_offset_bottom = indices_to_forward_[n] * dim;
       caffe_copy(dim, bottom_data + data_offset_bottom,
           top_data + data_offset_top);
     }
@@ -29,9 +29,9 @@ void FilterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   if (propagate_down[bottom.size() - 1]) {
     LOG(FATAL) << this->type()
-                << "Layer cannot backpropagate to filter index inputs";
+               << "Layer cannot backpropagate to filter index inputs";
   }
-  for (int i = 0; i < top.size(); i++) {
+  for (int i = 0; i < top.size(); ++i) {
     // bottom[last] is the selector and never needs backpropagation
     // so we can iterate over top vector because top.size() == bottom.size() -1
     if (propagate_down[i]) {
@@ -40,22 +40,22 @@ void FilterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       int batch_offset = 0;
       int data_offset_bottom = 0;
       int data_offset_top = 0;
-      for (int n = 0; n < bottom[i]->shape(0); n++) {
+      for (int n = 0; n < bottom[i]->shape(0); ++n) {
         if (next_to_backward_offset >= indices_to_forward_.size()) {
           // we already visited all items that were been forwarded, so
           // just set to zero remaining ones
-          data_offset_bottom = top[i]->offset(n);
-          caffe_set(dim, Dtype(0),
+          data_offset_bottom = n * dim;
+          caffe_gpu_set(dim, Dtype(0),
               bottom[i]->mutable_gpu_diff() + data_offset_bottom);
         } else {
           batch_offset = indices_to_forward_[next_to_backward_offset];
-          data_offset_bottom = top[i]->offset(n);
+          data_offset_bottom = n * dim;
           if (n != batch_offset) {  // this data was not been forwarded
-            caffe_set(dim, Dtype(0),
+            caffe_gpu_set(dim, Dtype(0),
                 bottom[i]->mutable_gpu_diff() + data_offset_bottom);
           } else {  // this data was been forwarded
-            data_offset_top = top[i]->offset(next_to_backward_offset);
-            next_to_backward_offset++;  // point to next forwarded item index
+            data_offset_top = next_to_backward_offset * dim;
+            ++next_to_backward_offset;  // point to next forwarded item index
             caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top,
                 bottom[i]->mutable_gpu_diff() + data_offset_bottom);
           }

From 8c72fe310977b217c47d8a505aafc5d57666a087 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 1 Jan 2015 23:07:44 -0800
Subject: [PATCH 1393/2053] Add LogLayer

---
 include/caffe/neuron_layers.hpp       |  66 +++++++++++++
 include/caffe/util/math_functions.hpp |   6 ++
 include/caffe/util/mkl_alternate.hpp  |   1 +
 src/caffe/layers/log_layer.cpp        | 136 ++++++++++++++++++++++++++
 src/caffe/proto/caffe.proto           |  14 ++-
 src/caffe/test/test_neuron_layer.cpp  | 125 +++++++++++++++++++++++
 src/caffe/util/math_functions.cpp     |  10 ++
 src/caffe/util/math_functions.cu      |  21 ++++
 8 files changed, 378 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/log_layer.cpp

diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index 9cf233f0eb3..c2e0774aaa2 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -267,6 +267,72 @@ class ExpLayer : public NeuronLayer<Dtype> {
   Dtype inner_scale_, outer_scale_;
 };
 
+/**
+ * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class LogLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides LogParameter log_param,
+   *     with LogLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit LogLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Log"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = log_{\gamma}(\alpha x + \beta)
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype base_scale_;
+  Dtype input_scale_, input_shift_;
+  Dtype backward_num_scale_;
+};
+
 /**
  * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
  *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index f43036fcebc..2cacd8e72cd 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -88,6 +88,9 @@ void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
 template <typename Dtype>
 void caffe_exp(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_log(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_abs(const int n, const Dtype* a, Dtype* y);
 
@@ -203,6 +206,9 @@ void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 32fdbf79932..3355b6658a3 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -33,6 +33,7 @@ extern "C" {
 
 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
+DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
 
 // A simple way to define the vsl unary functions with singular parameter b.
diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
new file mode 100644
index 00000000000..45f73950d7e
--- /dev/null
+++ b/src/caffe/layers/log_layer.cpp
@@ -0,0 +1,136 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  const Dtype base = this->layer_param_.log_param().base();
+  if (base != Dtype(-1)) {
+    CHECK_GT(base, 0) << "base must be strictly positive.";
+  }
+  // If base == -1, interpret the base as e and set log_base = 1 exactly.
+  // Otherwise, calculate its log explicitly.
+  const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
+  CHECK(!isnan(log_base))
+      << "NaN result: log(base) = log(" << base << ") = " << log_base;
+  CHECK(!isinf(log_base))
+      << "Inf result: log(base) = log(" << base << ") = " << log_base;
+  base_scale_ = Dtype(1) / log_base;
+  CHECK(!isnan(base_scale_))
+      << "NaN result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+  CHECK(!isinf(base_scale_))
+      << "Inf result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+  input_scale_ = this->layer_param_.log_param().scale();
+  input_shift_ = this->layer_param_.log_param().shift();
+  backward_num_scale_ = input_scale_ / log_base;
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  caffe_copy(count, bottom_data, bottom_diff);
+  if (input_scale_ != Dtype(1)) {
+    caffe_scal(count, input_scale_, bottom_diff);
+  }
+  if (input_shift_ != Dtype(0)) {
+    caffe_add_scalar(count, input_shift_, bottom_diff);
+  }
+  caffe_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+  if (backward_num_scale_ != Dtype(1)) {
+    caffe_scal(count, backward_num_scale_, bottom_diff);
+  }
+  caffe_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LogLayer);
+#else
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_gpu_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_gpu_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  caffe_copy(count, bottom_data, bottom_diff);
+  if (input_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, input_scale_, bottom_diff);
+  }
+  if (input_shift_ != Dtype(0)) {
+    caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
+  }
+  caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+  if (backward_num_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
+  }
+  caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LogLayer);
+
+#endif
+
+INSTANTIATE_CLASS(LogLayer);
+REGISTER_LAYER_CLASS(Log);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 94f713e3cb7..619642f2d32 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -269,7 +269,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 134 (last added: reshape_param)
+// LayerParameter next available layer-specific ID: 135 (last added: log_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -332,6 +332,7 @@ message LayerParameter {
   optional ImageDataParameter image_data_param = 115;
   optional InfogainLossParameter infogain_loss_param = 116;
   optional InnerProductParameter inner_product_param = 117;
+  optional LogParameter log_param = 134;
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
   optional MVNParameter mvn_param = 120;
@@ -607,6 +608,17 @@ message InnerProductParameter {
   optional int32 axis = 5 [default = 1];
 }
 
+// Message that stores parameters used by LogLayer
+message LogParameter {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
+}
+
+// Message that stores parameters used by LRNLayer
 message LRNParameter {
   optional uint32 local_size = 1 [default = 5];
   optional float alpha = 2 [default = 1.];
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 37b54713b46..c6e4d27b903 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -117,6 +117,49 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
           + slope_data[c] * std::min(bottom_data[i], (Dtype)(0)));
     }
   }
+
+  void LogBottomInit() {
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
+    caffe_exp(this->blob_bottom_->count(), bottom_data, bottom_data);
+  }
+
+  void TestLogForward(const float base, const float scale, const float shift) {
+    LogBottomInit();
+    LayerParameter layer_param;
+    layer_param.mutable_log_param()->set_base(base);
+    layer_param.mutable_log_param()->set_scale(scale);
+    layer_param.mutable_log_param()->set_shift(shift);
+    LogLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    const Dtype kDelta = 2e-4;
+    const Dtype* bottom_data = blob_bottom_->cpu_data();
+    const Dtype* top_data = blob_top_->cpu_data();
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      const Dtype bottom_val = bottom_data[i];
+      const Dtype top_val = top_data[i];
+      if (base == -1) {
+        EXPECT_NEAR(top_val, log(shift + scale * bottom_val), kDelta);
+      } else {
+        EXPECT_NEAR(top_val, log(shift + scale * bottom_val) / log(base),
+                    kDelta);
+      }
+    }
+  }
+
+  void TestLogGradient(const float base, const float scale, const float shift) {
+    LogBottomInit();
+    LayerParameter layer_param;
+    layer_param.mutable_log_param()->set_base(base);
+    layer_param.mutable_log_param()->set_scale(scale);
+    layer_param.mutable_log_param()->set_shift(shift);
+    LogLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-2);
+    checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
+  }
 };
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
@@ -339,6 +382,88 @@ TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1Scale3) {
   this->TestExpGradient(kBase, kScale, kShift);
 }
 
+TYPED_TEST(NeuronLayerTest, TestLogLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
 TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
   const float kDropoutRatio = 0.5;
   this->TestDropoutForward(kDropoutRatio);
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 13e17be582b..0aab6b17b85 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -206,6 +206,16 @@ void caffe_exp<double>(const int n, const double* a, double* y) {
   vdExp(n, a, y);
 }
 
+template <>
+void caffe_log<float>(const int n, const float* a, float* y) {
+  vsLn(n, a, y);
+}
+
+template <>
+void caffe_log<double>(const int n, const double* a, double* y) {
+  vdLn(n, a, y);
+}
+
 template <>
 void caffe_abs<float>(const int n, const float* a, float* y) {
     vsAbs(n, a, y);
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 43e65eb9a69..2631a0740d6 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -324,6 +324,27 @@ void caffe_gpu_exp<double>(const int N, const double* a, double* y) {
       N, a, y);
 }
 
+template <typename Dtype>
+__global__ void log_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = log(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_log<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  log_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_log<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  log_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
 template <typename Dtype>
 __global__ void powx_kernel(const int n, const Dtype* a,
     const Dtype alpha, Dtype* y) {

From eb442b9bc9ca206bd0606f259115f01b53144e7a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 31 Dec 2014 18:02:12 -0800
Subject: [PATCH 1394/2053] FlattenLayer gets a FlattenParameter with an axis,
 end_axis

---
 src/caffe/layers/flatten_layer.cpp    | 16 ++++++++--
 src/caffe/proto/caffe.proto           | 16 +++++++++-
 src/caffe/test/test_flatten_layer.cpp | 46 +++++++++++++++++++++++----
 3 files changed, 68 insertions(+), 10 deletions(-)

diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 745f271ea45..f7e5c9c2172 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -9,9 +9,19 @@ namespace caffe {
 template <typename Dtype>
 void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  vector<int> top_shape(2);
-  top_shape[0] = bottom[0]->num();
-  top_shape[1] = bottom[0]->count() / bottom[0]->num();
+  const int start_axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.flatten_param().axis());
+  const int end_axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.flatten_param().end_axis());
+  vector<int> top_shape;
+  for (int i = 0; i < start_axis; ++i) {
+    top_shape.push_back(bottom[0]->shape(i));
+  }
+  const int flattened_dim = bottom[0]->count(start_axis, end_axis + 1);
+  top_shape.push_back(flattened_dim);
+  for (int i = end_axis + 1; i < bottom[0]->num_axes(); ++i) {
+    top_shape.push_back(bottom[0]->shape(i));
+  }
   top[0]->Reshape(top_shape);
   CHECK_EQ(top[0]->count(), bottom[0]->count());
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 619642f2d32..f79cf80cc05 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -269,7 +269,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 135 (last added: log_param)
+// LayerParameter next available layer-specific ID: 136 (last added: flatten_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -326,6 +326,7 @@ message LayerParameter {
   optional DummyDataParameter dummy_data_param = 109;
   optional EltwiseParameter eltwise_param = 110;
   optional ExpParameter exp_param = 111;
+  optional FlattenParameter flatten_param = 135;
   optional HDF5DataParameter hdf5_data_param = 112;
   optional HDF5OutputParameter hdf5_output_param = 113;
   optional HingeLossParameter hinge_loss_param = 114;
@@ -533,6 +534,19 @@ message ExpParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+/// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [default = 1];
+
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [default = -1];
+}
+
+// Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
   optional string source = 1;
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 3042d293cf7..7b6757cba32 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -42,13 +42,48 @@ TYPED_TEST(FlattenLayerTest, TestSetup) {
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_->num(), 2);
-  EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
-  EXPECT_EQ(this->blob_top_->height(), 1);
-  EXPECT_EQ(this->blob_top_->width(), 1);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6 * 5);
 }
 
-TYPED_TEST(FlattenLayerTest, Test) {
+TYPED_TEST(FlattenLayerTest, TestSetupWithAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_axis(2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 6 * 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithEndAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_end_axis(-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithStartAndEndAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_axis(0);
+  layer_param.mutable_flatten_param()->set_end_axis(-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2 * 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(1), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
@@ -71,5 +106,4 @@ TYPED_TEST(FlattenLayerTest, TestGradient) {
       this->blob_top_vec_);
 }
 
-
 }  // namespace caffe

From 823d055bc927912528b0f53aa15fab6d988d4a4c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 2 Nov 2014 17:21:37 -0800
Subject: [PATCH 1395/2053] Add ReductionLayer to reduce any number of "tail"
 axes to a scalar value

Currently implements operations SUM, MEAN, ASUM (sum of absolute
values), and SUMSQ (sum of squares)
---
 include/caffe/common_layers.hpp         |  45 ++++
 src/caffe/layers/reduction_layer.cpp    | 132 +++++++++++
 src/caffe/layers/reduction_layer.cu     |  93 ++++++++
 src/caffe/proto/caffe.proto             |  33 ++-
 src/caffe/test/test_reduction_layer.cpp | 297 ++++++++++++++++++++++++
 5 files changed, 599 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/reduction_layer.cpp
 create mode 100644 src/caffe/layers/reduction_layer.cu
 create mode 100644 src/caffe/test/test_reduction_layer.cpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 3155b45bbbc..d2c0ce6d0c6 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -399,6 +399,51 @@ class ReshapeLayer : public Layer<Dtype> {
   int constant_count_;
 };
 
+/**
+ * @brief Compute "reductions" -- operations that return a scalar output Blob
+ *        for an input Blob of arbitrary size, such as the sum, absolute sum,
+ *        and sum of squares.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class ReductionLayer : public Layer<Dtype> {
+ public:
+  explicit ReductionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reduction"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief the reduction operation performed by the layer
+  ReductionParameter_ReductionOp op_;
+  /// @brief a scalar coefficient applied to all outputs
+  Dtype coeff_;
+  /// @brief the index of the first input axis to reduce
+  int axis_;
+  /// @brief the number of reductions performed
+  int num_;
+  /// @brief the input size of each reduction
+  int dim_;
+  /// @brief a helper Blob used for summation (op_ == SUM)
+  Blob<Dtype> sum_multiplier_;
+};
+
 /**
  * @brief Ignores bottom blobs while producing no top blobs. (This is useful
  *        to suppress outputs during testing.)
diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp
new file mode 100644
index 00000000000..8ae6329ebe4
--- /dev/null
+++ b/src/caffe/layers/reduction_layer.cpp
@@ -0,0 +1,132 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  op_ = this->layer_param_.reduction_param().operation();
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  axis_ = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.reduction_param().axis());
+  // In the output, we'll keep all axes up to the reduction axis, but
+  // throw away any after that.
+  // Note: currently reducing along non-tail axes is not supported; otherwise,
+  // we'd need to also copy any axes following an "end_axis".
+  vector<int> top_shape(bottom[0]->shape().begin(),
+                        bottom[0]->shape().begin() + axis_);
+  top[0]->Reshape(top_shape);
+  num_ = bottom[0]->count(0, axis_);
+  dim_ = bottom[0]->count(axis_);
+  CHECK_EQ(num_, top[0]->count());
+  if (op_ == ReductionParameter_ReductionOp_SUM ||
+      op_ == ReductionParameter_ReductionOp_MEAN) {
+    vector<int> sum_mult_shape(1, dim_);
+    sum_multiplier_.Reshape(sum_mult_shape);
+    caffe_set(dim_, Dtype(1), sum_multiplier_.mutable_cpu_data());
+  }
+  coeff_ = this->layer_param().reduction_param().coeff();
+  if (op_ == ReductionParameter_ReductionOp_MEAN) {
+    coeff_ /= dim_;
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* mult_data = NULL;
+  if (sum_multiplier_.count() > 0) {
+    mult_data = sum_multiplier_.cpu_data();
+  }
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < num_; ++i) {
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      *top_data = caffe_cpu_dot(dim_, mult_data, bottom_data);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      *top_data = caffe_cpu_asum(dim_, bottom_data);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      *top_data = caffe_cpu_dot(dim_, bottom_data, bottom_data);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    ++top_data;
+  }
+  if (coeff_ != Dtype(1)) {
+    // Reset the top_data pointer.
+    top_data = top[0]->mutable_cpu_data();
+    caffe_scal(num_, coeff_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* bottom_data = NULL;
+  switch (op_) {
+  // Operations that don't need bottom_data
+  case ReductionParameter_ReductionOp_SUM:
+  case ReductionParameter_ReductionOp_MEAN:
+    break;
+  // Operations that need bottom_data
+  case ReductionParameter_ReductionOp_ASUM:
+  case ReductionParameter_ReductionOp_SUMSQ:
+    bottom_data = bottom[0]->cpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown reduction op: "
+        << ReductionParameter_ReductionOp_Name(op_);
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  for (int i = 0; i < num_; ++i) {
+    const Dtype bottom_coeff = (*top_diff) * coeff_;
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_set(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_cpu_sign(dim_, bottom_data, bottom_diff);
+      caffe_scal(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_cpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    bottom_diff += dim_;
+    ++top_diff;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ReductionLayer);
+#endif
+
+INSTANTIATE_CLASS(ReductionLayer);
+REGISTER_LAYER_CLASS(Reduction);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu
new file mode 100644
index 00000000000..2dbd3bc9f94
--- /dev/null
+++ b/src/caffe/layers/reduction_layer.cu
@@ -0,0 +1,93 @@
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* mult_data = NULL;
+  if (sum_multiplier_.count() > 0) {
+    mult_data = sum_multiplier_.gpu_data();
+  }
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < num_; ++i) {
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_gpu_dot(dim_, mult_data, bottom_data, top_data);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_gpu_asum(dim_, bottom_data, top_data);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_gpu_dot(dim_, bottom_data, bottom_data, top_data);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    ++top_data;
+  }
+  if (coeff_ != Dtype(1)) {
+    // Reset the top_data pointer.
+    top_data = top[0]->mutable_gpu_data();
+    caffe_gpu_scal(num_, coeff_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* bottom_data = NULL;
+  switch (op_) {
+  // Operations that don't need bottom_data
+  case ReductionParameter_ReductionOp_SUM:
+  case ReductionParameter_ReductionOp_MEAN:
+    break;
+  // Operations that need bottom_data
+  case ReductionParameter_ReductionOp_ASUM:
+  case ReductionParameter_ReductionOp_SUMSQ:
+    bottom_data = bottom[0]->gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown reduction op: "
+        << ReductionParameter_ReductionOp_Name(op_);
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  for (int i = 0; i < num_; ++i) {
+    const Dtype bottom_coeff = (*top_diff) * coeff_;
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_gpu_set(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_gpu_sign(dim_, bottom_data, bottom_diff);
+      caffe_gpu_scal(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_gpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    bottom_diff += dim_;
+    ++top_diff;
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ReductionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f79cf80cc05..81a8c69d88e 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -269,7 +269,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 136 (last added: flatten_param)
+// LayerParameter next available layer-specific ID: 137 (last added: reduction_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -341,6 +341,7 @@ message LayerParameter {
   optional PowerParameter power_param = 122;
   optional PReLUParameter prelu_param = 131;
   optional PythonParameter python_param = 130;
+  optional ReductionParameter reduction_param = 136;
   optional ReLUParameter relu_param = 123;
   optional ReshapeParameter reshape_param = 133;
   optional SigmoidParameter sigmoid_param = 124;
@@ -704,6 +705,36 @@ message PythonParameter {
   optional string layer = 2;
 }
 
+// Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+  }
+
+  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [default = 0];
+
+  optional float coeff = 3 [default = 1.0]; // coefficient for output
+}
+
+// Message that stores parameters used by ReLULayer
 message ReLUParameter {
   // Allow non-zero slope for negative inputs to speed up optimization
   // Described in:
diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp
new file mode 100644
index 00000000000..f568a18089a
--- /dev/null
+++ b/src/caffe/test/test_reduction_layer.cpp
@@ -0,0 +1,297 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReductionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ReductionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ReductionLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+
+  void TestForward(ReductionParameter_ReductionOp op,
+                   float coeff = 1, int axis = 0) {
+    LayerParameter layer_param;
+    ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+    reduction_param->set_operation(op);
+    if (coeff != 1.0) { reduction_param->set_coeff(coeff); }
+    if (axis != 0) { reduction_param->set_axis(axis); }
+    shared_ptr<ReductionLayer<Dtype> > layer(
+        new ReductionLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* in_data = this->blob_bottom_->cpu_data();
+    const int num = this->blob_bottom_->count(0, axis);
+    const int dim = this->blob_bottom_->count(axis);
+    for (int n = 0; n < num; ++n) {
+      Dtype expected_result = 0;
+      for (int d = 0; d < dim; ++d) {
+        switch (op) {
+          case ReductionParameter_ReductionOp_SUM:
+            expected_result += *in_data;
+            break;
+          case ReductionParameter_ReductionOp_MEAN:
+            expected_result += *in_data / dim;
+            break;
+          case ReductionParameter_ReductionOp_ASUM:
+            expected_result += fabs(*in_data);
+            break;
+          case ReductionParameter_ReductionOp_SUMSQ:
+            expected_result += (*in_data) * (*in_data);
+            break;
+          default:
+            LOG(FATAL) << "Unknown reduction op: "
+                << ReductionParameter_ReductionOp_Name(op);
+        }
+        ++in_data;
+      }
+      expected_result *= coeff;
+      const Dtype computed_result = this->blob_top_->cpu_data()[n];
+      EXPECT_FLOAT_EQ(expected_result, computed_result)
+          << "Incorrect result computed with op "
+          << ReductionParameter_ReductionOp_Name(op) << ", coeff " << coeff;
+    }
+  }
+
+  void TestGradient(ReductionParameter_ReductionOp op,
+                    float coeff = 1, int axis = 0) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+    reduction_param->set_operation(op);
+    reduction_param->set_coeff(coeff);
+    reduction_param->set_axis(axis);
+    ReductionLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 2e-3);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReductionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReductionLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 0);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis1) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reduction_param()->set_axis(1);
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 1);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reduction_param()->set_axis(2);
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSum) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeff) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumGradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMean) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradientAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSum) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquares) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+}  // namespace caffe

From 72d70892ad489815589b8e680813c350610b3f2a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 3 Jun 2015 10:46:53 -0700
Subject: [PATCH 1396/2053] [bug] fix double instantiation of GPU methods in
 LogLayer

---
 src/caffe/layers/log_layer.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
index 45f73950d7e..1e86a751e9e 100644
--- a/src/caffe/layers/log_layer.cpp
+++ b/src/caffe/layers/log_layer.cpp
@@ -126,8 +126,6 @@ void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
 }
 
-INSTANTIATE_LAYER_GPU_FUNCS(LogLayer);
-
 #endif
 
 INSTANTIATE_CLASS(LogLayer);

From f8efc628dfb6cd979f3aeb798099dcbb6f119694 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20St=C3=A6r=20Nissen?= <nissen@di.ku.dk>
Date: Thu, 4 Jun 2015 11:13:45 +0200
Subject: [PATCH 1397/2053] LogLayer gpu functionality moved to .cu file

---
 src/caffe/layers/log_layer.cpp | 47 ----------------------------
 src/caffe/layers/log_layer.cu  | 57 ++++++++++++++++++++++++++++++++++
 2 files changed, 57 insertions(+), 47 deletions(-)
 create mode 100644 src/caffe/layers/log_layer.cu

diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
index 1e86a751e9e..55a227f6226 100644
--- a/src/caffe/layers/log_layer.cpp
+++ b/src/caffe/layers/log_layer.cpp
@@ -79,53 +79,6 @@ void LogLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 
 #ifdef CPU_ONLY
 STUB_GPU(LogLayer);
-#else
-
-template <typename Dtype>
-void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    const vector<Blob<Dtype>*>& top) {
-  const int count = bottom[0]->count();
-  const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = top[0]->mutable_gpu_data();
-  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
-    caffe_gpu_log(count, bottom_data, top_data);
-  } else {
-    caffe_copy(count, bottom_data, top_data);
-    if (input_scale_ != Dtype(1)) {
-      caffe_gpu_scal(count, input_scale_, top_data);
-    }
-    if (input_shift_ != Dtype(0)) {
-      caffe_gpu_add_scalar(count, input_shift_, top_data);
-    }
-    caffe_gpu_log(count, top_data, top_data);
-  }
-  if (base_scale_ != Dtype(1)) {
-    caffe_gpu_scal(count, base_scale_, top_data);
-  }
-}
-
-template <typename Dtype>
-void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  if (!propagate_down[0]) { return; }
-  const int count = bottom[0]->count();
-  const Dtype* bottom_data = bottom[0]->gpu_data();
-  const Dtype* top_diff = top[0]->gpu_diff();
-  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  caffe_copy(count, bottom_data, bottom_diff);
-  if (input_scale_ != Dtype(1)) {
-    caffe_gpu_scal(count, input_scale_, bottom_diff);
-  }
-  if (input_shift_ != Dtype(0)) {
-    caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
-  }
-  caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
-  if (backward_num_scale_ != Dtype(1)) {
-    caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
-  }
-  caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
-}
-
 #endif
 
 INSTANTIATE_CLASS(LogLayer);
diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu
new file mode 100644
index 00000000000..847c86cd10c
--- /dev/null
+++ b/src/caffe/layers/log_layer.cu
@@ -0,0 +1,57 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_gpu_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_gpu_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+    const int count = bottom[0]->count();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_copy(count, bottom_data, bottom_diff);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, bottom_diff);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
+    }
+    caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+    if (backward_num_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
+    }
+    caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LogLayer);
+
+}  // namespace caffe

From eeb428e38029003572ca52182e11e62ab33e4e50 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Mon, 30 Mar 2015 17:29:50 -0700
Subject: [PATCH 1398/2053] Split db.hpp into leveldb_db.hpp and lmdb_db.hpp

---
 include/caffe/util/db.hpp         | 136 ------------------------------
 include/caffe/util/db_leveldb.hpp |  73 ++++++++++++++++
 include/caffe/util/db_lmdb.hpp    |  91 ++++++++++++++++++++
 src/caffe/util/db.cpp             |  57 +------------
 src/caffe/util/db_leveldb.cpp     |  21 +++++
 src/caffe/util/db_lmdb.cpp        |  51 +++++++++++
 6 files changed, 238 insertions(+), 191 deletions(-)
 create mode 100644 include/caffe/util/db_leveldb.hpp
 create mode 100644 include/caffe/util/db_lmdb.hpp
 create mode 100644 src/caffe/util/db_leveldb.cpp
 create mode 100644 src/caffe/util/db_lmdb.cpp

diff --git a/include/caffe/util/db.hpp b/include/caffe/util/db.hpp
index afdb8d2c4f8..59ec3d390ba 100644
--- a/include/caffe/util/db.hpp
+++ b/include/caffe/util/db.hpp
@@ -3,10 +3,6 @@
 
 #include <string>
 
-#include "leveldb/db.h"
-#include "leveldb/write_batch.h"
-#include "lmdb.h"
-
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
@@ -49,138 +45,6 @@ class DB {
   DISABLE_COPY_AND_ASSIGN(DB);
 };
 
-class LevelDBCursor : public Cursor {
- public:
-  explicit LevelDBCursor(leveldb::Iterator* iter)
-    : iter_(iter) { SeekToFirst(); }
-  ~LevelDBCursor() { delete iter_; }
-  virtual void SeekToFirst() { iter_->SeekToFirst(); }
-  virtual void Next() { iter_->Next(); }
-  virtual string key() { return iter_->key().ToString(); }
-  virtual string value() { return iter_->value().ToString(); }
-  virtual bool valid() { return iter_->Valid(); }
-
- private:
-  leveldb::Iterator* iter_;
-};
-
-class LevelDBTransaction : public Transaction {
- public:
-  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
-  virtual void Put(const string& key, const string& value) {
-    batch_.Put(key, value);
-  }
-  virtual void Commit() {
-    leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
-    CHECK(status.ok()) << "Failed to write batch to leveldb "
-                       << std::endl << status.ToString();
-  }
-
- private:
-  leveldb::DB* db_;
-  leveldb::WriteBatch batch_;
-
-  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
-};
-
-class LevelDB : public DB {
- public:
-  LevelDB() : db_(NULL) { }
-  virtual ~LevelDB() { Close(); }
-  virtual void Open(const string& source, Mode mode);
-  virtual void Close() {
-    if (db_ != NULL) {
-      delete db_;
-      db_ = NULL;
-    }
-  }
-  virtual LevelDBCursor* NewCursor() {
-    return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
-  }
-  virtual LevelDBTransaction* NewTransaction() {
-    return new LevelDBTransaction(db_);
-  }
-
- private:
-  leveldb::DB* db_;
-};
-
-inline void MDB_CHECK(int mdb_status) {
-  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
-}
-
-class LMDBCursor : public Cursor {
- public:
-  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
-    : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
-    SeekToFirst();
-  }
-  virtual ~LMDBCursor() {
-    mdb_cursor_close(mdb_cursor_);
-    mdb_txn_abort(mdb_txn_);
-  }
-  virtual void SeekToFirst() { Seek(MDB_FIRST); }
-  virtual void Next() { Seek(MDB_NEXT); }
-  virtual string key() {
-    return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
-  }
-  virtual string value() {
-    return string(static_cast<const char*>(mdb_value_.mv_data),
-        mdb_value_.mv_size);
-  }
-  virtual bool valid() { return valid_; }
-
- private:
-  void Seek(MDB_cursor_op op) {
-    int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
-    if (mdb_status == MDB_NOTFOUND) {
-      valid_ = false;
-    } else {
-      MDB_CHECK(mdb_status);
-      valid_ = true;
-    }
-  }
-
-  MDB_txn* mdb_txn_;
-  MDB_cursor* mdb_cursor_;
-  MDB_val mdb_key_, mdb_value_;
-  bool valid_;
-};
-
-class LMDBTransaction : public Transaction {
- public:
-  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
-    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
-  virtual void Put(const string& key, const string& value);
-  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
-
- private:
-  MDB_dbi* mdb_dbi_;
-  MDB_txn* mdb_txn_;
-
-  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
-};
-
-class LMDB : public DB {
- public:
-  LMDB() : mdb_env_(NULL) { }
-  virtual ~LMDB() { Close(); }
-  virtual void Open(const string& source, Mode mode);
-  virtual void Close() {
-    if (mdb_env_ != NULL) {
-      mdb_dbi_close(mdb_env_, mdb_dbi_);
-      mdb_env_close(mdb_env_);
-      mdb_env_ = NULL;
-    }
-  }
-  virtual LMDBCursor* NewCursor();
-  virtual LMDBTransaction* NewTransaction();
-
- private:
-  MDB_env* mdb_env_;
-  MDB_dbi mdb_dbi_;
-};
-
 DB* GetDB(DataParameter::DB backend);
 DB* GetDB(const string& backend);
 
diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp
new file mode 100644
index 00000000000..10623554b67
--- /dev/null
+++ b/include/caffe/util/db_leveldb.hpp
@@ -0,0 +1,73 @@
+#ifndef CAFFE_UTIL_DB_LEVELDB_HPP
+#define CAFFE_UTIL_DB_LEVELDB_HPP
+
+#include <string>
+
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+class LevelDBCursor : public Cursor {
+ public:
+  explicit LevelDBCursor(leveldb::Iterator* iter)
+    : iter_(iter) { SeekToFirst(); }
+  ~LevelDBCursor() { delete iter_; }
+  virtual void SeekToFirst() { iter_->SeekToFirst(); }
+  virtual void Next() { iter_->Next(); }
+  virtual string key() { return iter_->key().ToString(); }
+  virtual string value() { return iter_->value().ToString(); }
+  virtual bool valid() { return iter_->Valid(); }
+
+ private:
+  leveldb::Iterator* iter_;
+};
+
+class LevelDBTransaction : public Transaction {
+ public:
+  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
+  virtual void Put(const string& key, const string& value) {
+    batch_.Put(key, value);
+  }
+  virtual void Commit() {
+    leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
+    CHECK(status.ok()) << "Failed to write batch to leveldb "
+                       << std::endl << status.ToString();
+  }
+
+ private:
+  leveldb::DB* db_;
+  leveldb::WriteBatch batch_;
+
+  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
+};
+
+class LevelDB : public DB {
+ public:
+  LevelDB() : db_(NULL) { }
+  virtual ~LevelDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (db_ != NULL) {
+      delete db_;
+      db_ = NULL;
+    }
+  }
+  virtual LevelDBCursor* NewCursor() {
+    return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
+  }
+  virtual LevelDBTransaction* NewTransaction() {
+    return new LevelDBTransaction(db_);
+  }
+
+ private:
+  leveldb::DB* db_;
+};
+
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_LEVELDB_HPP
diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
new file mode 100644
index 00000000000..cc7c90afc4c
--- /dev/null
+++ b/include/caffe/util/db_lmdb.hpp
@@ -0,0 +1,91 @@
+#ifndef CAFFE_UTIL_DB_LMDB_HPP
+#define CAFFE_UTIL_DB_LMDB_HPP
+
+#include <string>
+
+#include "lmdb.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+inline void MDB_CHECK(int mdb_status) {
+  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
+}
+
+class LMDBCursor : public Cursor {
+ public:
+  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
+    : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
+    SeekToFirst();
+  }
+  virtual ~LMDBCursor() {
+    mdb_cursor_close(mdb_cursor_);
+    mdb_txn_abort(mdb_txn_);
+  }
+  virtual void SeekToFirst() { Seek(MDB_FIRST); }
+  virtual void Next() { Seek(MDB_NEXT); }
+  virtual string key() {
+    return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
+  }
+  virtual string value() {
+    return string(static_cast<const char*>(mdb_value_.mv_data),
+        mdb_value_.mv_size);
+  }
+  virtual bool valid() { return valid_; }
+
+ private:
+  void Seek(MDB_cursor_op op) {
+    int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
+    if (mdb_status == MDB_NOTFOUND) {
+      valid_ = false;
+    } else {
+      MDB_CHECK(mdb_status);
+      valid_ = true;
+    }
+  }
+
+  MDB_txn* mdb_txn_;
+  MDB_cursor* mdb_cursor_;
+  MDB_val mdb_key_, mdb_value_;
+  bool valid_;
+};
+
+class LMDBTransaction : public Transaction {
+ public:
+  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
+    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+  virtual void Put(const string& key, const string& value);
+  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+
+ private:
+  MDB_dbi* mdb_dbi_;
+  MDB_txn* mdb_txn_;
+
+  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
+};
+
+class LMDB : public DB {
+ public:
+  LMDB() : mdb_env_(NULL) { }
+  virtual ~LMDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (mdb_env_ != NULL) {
+      mdb_dbi_close(mdb_env_, mdb_dbi_);
+      mdb_env_close(mdb_env_);
+      mdb_env_ = NULL;
+    }
+  }
+  virtual LMDBCursor* NewCursor();
+  virtual LMDBTransaction* NewTransaction();
+
+ private:
+  MDB_env* mdb_env_;
+  MDB_dbi mdb_dbi_;
+};
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_LMDB_HPP
diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
index 7f7018107ec..f55420e9840 100644
--- a/src/caffe/util/db.cpp
+++ b/src/caffe/util/db.cpp
@@ -1,64 +1,11 @@
 #include "caffe/util/db.hpp"
+#include "caffe/util/db_leveldb.hpp"
+#include "caffe/util/db_lmdb.hpp"
 
-#include <sys/stat.h>
 #include <string>
 
 namespace caffe { namespace db {
 
-const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
-
-void LevelDB::Open(const string& source, Mode mode) {
-  leveldb::Options options;
-  options.block_size = 65536;
-  options.write_buffer_size = 268435456;
-  options.max_open_files = 100;
-  options.error_if_exists = mode == NEW;
-  options.create_if_missing = mode != READ;
-  leveldb::Status status = leveldb::DB::Open(options, source, &db_);
-  CHECK(status.ok()) << "Failed to open leveldb " << source
-                     << std::endl << status.ToString();
-  LOG(INFO) << "Opened leveldb " << source;
-}
-
-void LMDB::Open(const string& source, Mode mode) {
-  MDB_CHECK(mdb_env_create(&mdb_env_));
-  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
-  if (mode == NEW) {
-    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
-  }
-  int flags = 0;
-  if (mode == READ) {
-    flags = MDB_RDONLY | MDB_NOTLS;
-  }
-  MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
-  LOG(INFO) << "Opened lmdb " << source;
-}
-
-LMDBCursor* LMDB::NewCursor() {
-  MDB_txn* mdb_txn;
-  MDB_cursor* mdb_cursor;
-  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
-  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
-  MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
-  return new LMDBCursor(mdb_txn, mdb_cursor);
-}
-
-LMDBTransaction* LMDB::NewTransaction() {
-  MDB_txn* mdb_txn;
-  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
-  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
-  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
-}
-
-void LMDBTransaction::Put(const string& key, const string& value) {
-  MDB_val mdb_key, mdb_value;
-  mdb_key.mv_data = const_cast<char*>(key.data());
-  mdb_key.mv_size = key.size();
-  mdb_value.mv_data = const_cast<char*>(value.data());
-  mdb_value.mv_size = value.size();
-  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
-}
-
 DB* GetDB(DataParameter::DB backend) {
   switch (backend) {
   case DataParameter_DB_LEVELDB:
diff --git a/src/caffe/util/db_leveldb.cpp b/src/caffe/util/db_leveldb.cpp
new file mode 100644
index 00000000000..06c46627d31
--- /dev/null
+++ b/src/caffe/util/db_leveldb.cpp
@@ -0,0 +1,21 @@
+#include "caffe/util/db_leveldb.hpp"
+
+#include <string>
+
+namespace caffe { namespace db {
+
+void LevelDB::Open(const string& source, Mode mode) {
+  leveldb::Options options;
+  options.block_size = 65536;
+  options.write_buffer_size = 268435456;
+  options.max_open_files = 100;
+  options.error_if_exists = mode == NEW;
+  options.create_if_missing = mode != READ;
+  leveldb::Status status = leveldb::DB::Open(options, source, &db_);
+  CHECK(status.ok()) << "Failed to open leveldb " << source
+                     << std::endl << status.ToString();
+  LOG(INFO) << "Opened leveldb " << source;
+}
+
+}  // namespace db
+}  // namespace caffe
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
new file mode 100644
index 00000000000..a054b796806
--- /dev/null
+++ b/src/caffe/util/db_lmdb.cpp
@@ -0,0 +1,51 @@
+#include "caffe/util/db_lmdb.hpp"
+
+#include <sys/stat.h>
+
+#include <string>
+
+namespace caffe { namespace db {
+
+const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
+
+void LMDB::Open(const string& source, Mode mode) {
+  MDB_CHECK(mdb_env_create(&mdb_env_));
+  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
+  if (mode == NEW) {
+    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+  }
+  int flags = 0;
+  if (mode == READ) {
+    flags = MDB_RDONLY | MDB_NOTLS;
+  }
+  MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+  LOG(INFO) << "Opened lmdb " << source;
+}
+
+LMDBCursor* LMDB::NewCursor() {
+  MDB_txn* mdb_txn;
+  MDB_cursor* mdb_cursor;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
+  return new LMDBCursor(mdb_txn, mdb_cursor);
+}
+
+LMDBTransaction* LMDB::NewTransaction() {
+  MDB_txn* mdb_txn;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+}
+
+void LMDBTransaction::Put(const string& key, const string& value) {
+  MDB_val mdb_key, mdb_value;
+  mdb_key.mv_data = const_cast<char*>(key.data());
+  mdb_key.mv_size = key.size();
+  mdb_value.mv_data = const_cast<char*>(value.data());
+  mdb_value.mv_size = value.size();
+  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+}
+
+}  // namespace db
+}  // namespace caffe

From cdab89a2109956c3514d881c5456522e4181066f Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 9 Jun 2015 10:45:16 -0700
Subject: [PATCH 1399/2053] Small platform-specific bugfix for draw.py

Close #2376

On Gentoo and CentOS (and others?), you get this error:
    Warning: /tmp/tmpjqPQBC:6: string ran past end of line
---
 python/caffe/draw.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 834ea15ac9a..324929deca4 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -72,7 +72,7 @@ def get_layer_label(layer, rankdir):
     else:
         # If graph orientation is horizontal, vertical space is free and
         # horizontal space is not; separate words with newlines
-        separator = '\n'
+        separator = '\\n'
 
     if layer.type == 'Convolution':
         # Outer double quotes needed or else colon characters don't parse

From 543afd36b769675b6de113b3903aaa9866d517d0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 10 Jun 2015 17:43:30 -0400
Subject: [PATCH 1400/2053] [docs] drop out-of-date reference to dev branch

---
 docs/tutorial/layers.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index ff2ee491244..806374e3f93 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -5,9 +5,7 @@ title: Layer Catalogue
 
 To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
 
-Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The latest definitions are in the [dev caffe.proto](https://github.com/BVLC/caffe/blob/dev/src/caffe/proto/caffe.proto).
-
-TODO complete list of layers linking to headings
+Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
 
 ### Vision Layers
 
@@ -507,7 +505,7 @@ The `Slice` layer is a utility layer that slices an input layer to multiple outp
         }
       }
 
-`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one). 
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
 
 
 #### Elementwise Operations

From d512d0c5c5efc276db7c864b6b5b6da41824cd88 Mon Sep 17 00:00:00 2001
From: Aaron Schumacher <ajschumacher@gmail.com>
Date: Mon, 15 Jun 2015 19:53:05 -0400
Subject: [PATCH 1401/2053] typo: "a fixed steps" to "at fixed steps"

fixing in the correct place as per @shelhamer's advice from #2602
---
 examples/mnist/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 269e53ab9b9..413d4a1f40b 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -283,5 +283,5 @@ and you will be using CPU for training. Isn't that easy?
 
 MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.
 
-### How to reduce the learning rate a fixed steps?
+### How to reduce the learning rate at fixed steps?
 Look at lenet_multistep_solver.prototxt

From 94a5be14cb7775ba0567348631baa6d595ddf568 Mon Sep 17 00:00:00 2001
From: Luke Yeager <lukeyeager@users.noreply.github.com>
Date: Tue, 16 Jun 2015 09:12:32 -0700
Subject: [PATCH 1402/2053] Fix HTML display for docs

Doesn't change display in Markdown
---
 docs/development.md | 33 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 18 deletions(-)

diff --git a/docs/development.md b/docs/development.md
index ccb6a29701d..107c2c3b281 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -62,28 +62,25 @@ The following is a poetic presentation of the protocol in code form.
 #### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”
 
 Make the `feature` branch off of the latest `bvlc/master`
-```
-git checkout master
-git pull upstream master
-git checkout -b feature
-# do your work, make commits
-```
+
+    git checkout master
+    git pull upstream master
+    git checkout -b feature
+    # do your work, make commits
 
 Prepare to merge by rebasing your branch on the latest `bvlc/master`
-```
-# make sure master is fresh
-git checkout master
-git pull upstream master
-# rebase your branch on the tip of master
-git checkout feature
-git rebase master
-```
+
+    # make sure master is fresh
+    git checkout master
+    git pull upstream master
+    # rebase your branch on the tip of master
+    git checkout feature
+    git rebase master
 
 Push your branch to pull request it into `BVLC/caffe:master`
-```
-git push origin feature
-# ...make pull request to master...
-```
+
+    git push origin feature
+    # ...make pull request to master...
 
 Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses.
 

From 98fb438c0d14f279d434719abd3e79b7f9d231ae Mon Sep 17 00:00:00 2001
From: Dylan Paiton <dpaiton@yahoo-inc.com>
Date: Tue, 16 Jun 2015 17:57:07 -0700
Subject: [PATCH 1403/2053] fixed two bugs with prototext format

The first bug was in InitUnsharedWeightsNet. Proto var was of type
ostringstream, which converted the bool bias_term into an int. I
wrote an inline conditional to convert the term into a string.
This allows backwards compatibility with earlier prototext
versions (e.g. version 2.3.0 on Redhat was failing without this).

The second bug was in the syntax for repeated bool parameters,
assigned to the propagate_down parameter. The style used for e.g.
propagate_down: [true,true] does not work with earlier prototext
versions (failed with version 2.3.0 on Redhat). New syntax works
for all versions.
---
 src/caffe/test/test_net.cpp | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 782a96bc9b6..56959f4793b 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -288,6 +288,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
       const bool force_backward = false, const bool bias_term = false,
       const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
       const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
+    string bias_str = bias_term ? "true ":"false ";
     ostringstream proto;
     proto << "name: 'UnsharedWeightsNetwork' ";
     if (force_backward) {
@@ -314,7 +315,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: " << bias_term <<
+        "    bias_term: " << bias_str <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
@@ -340,7 +341,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: " << bias_term <<
+        "    bias_term: " << bias_str <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
@@ -699,9 +700,11 @@ class NetTest : public MultiDeviceTest<TypeParam> {
       "  bottom: 'innerproduct' "
       "  bottom: 'label_argmax' ";
     if (test_skip_true)
-      proto += "  propagate_down: [true, false] ";
+      proto += "  propagate_down: true "
+               "  propagate_down: false ";
     else
-      proto += "  propagate_down: [true, true] ";
+      proto += "  propagate_down: true "
+               "  propagate_down: true ";
     proto +=
       "  top: 'cross_entropy_loss' "
       "  type: 'SigmoidCrossEntropyLoss' "

From e7f543bb264ad8597a3edaf3b938e9c3cc57bf33 Mon Sep 17 00:00:00 2001
From: ih4cku <ih4cku@gmail.com>
Date: Wed, 17 Jun 2015 12:15:28 +0800
Subject: [PATCH 1404/2053] register a dummy reducer to prevent mincepie
 runtime error

---
 tools/extra/resize_and_crop_images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extra/resize_and_crop_images.py b/tools/extra/resize_and_crop_images.py
index c844f590c06..fd2c3134edb 100755
--- a/tools/extra/resize_and_crop_images.py
+++ b/tools/extra/resize_and_crop_images.py
@@ -101,7 +101,7 @@ def map(self, key, value):
         yield value, FLAGS.output_folder
 
 mapreducer.REGISTER_DEFAULT_MAPPER(ResizeCropImagesMapper)
-
+mapreducer.REGISTER_DEFAULT_REDUCER(mapreducer.NoPassReducer)
 mapreducer.REGISTER_DEFAULT_READER(mapreducer.FileReader)
 mapreducer.REGISTER_DEFAULT_WRITER(mapreducer.FileWriter)
  

From e342e155c41887379f3088e3e5b42b2a776d0b87 Mon Sep 17 00:00:00 2001
From: Manuel <mlopezantequera@users.noreply.github.com>
Date: Mon, 22 Jun 2015 11:49:45 +0200
Subject: [PATCH 1405/2053] Update parse_log.py

Correct parsing (exponential notation learning rates were not being interpreted correctly)
---
 tools/extra/parse_log.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 09ea216ced3..48f9bee0b49 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -28,7 +28,7 @@ def parse_log(path_to_log):
     regex_iteration = re.compile('Iteration (\d+)')
     regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
     regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
-    regex_learning_rate = re.compile('lr = ([\.\d]+)')
+    regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
 
     # Pick out lines of interest
     iteration = -1

From d2acfedb9256bb5b12614348e932ec448cdf60c9 Mon Sep 17 00:00:00 2001
From: berleon <github@leon-sixt.de>
Date: Mon, 22 Jun 2015 14:41:59 +0200
Subject: [PATCH 1406/2053] fixed _force_color check, fixes #2635

---
 src/caffe/data_transformer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 6f75bdb3852..22633922a01 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -127,7 +127,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
   // If datum is encoded, decoded and transform the cv::image.
   if (datum.encoded()) {
-    CHECK(!param_.force_color() && !param_.force_gray())
+    CHECK(!(param_.force_color() && param_.force_gray()))
         << "cannot set both force_color and force_gray";
     cv::Mat cv_img;
     if (param_.force_color() || param_.force_gray()) {
@@ -430,7 +430,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
 template<typename Dtype>
 vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
   if (datum.encoded()) {
-    CHECK(!param_.force_color() && !param_.force_gray())
+    CHECK(!(param_.force_color() && param_.force_gray()))
         << "cannot set both force_color and force_gray";
     cv::Mat cv_img;
     if (param_.force_color() || param_.force_gray()) {

From ba8899301ee5033a30eecd7726fb898279db3032 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 23 Jun 2015 09:59:22 -0700
Subject: [PATCH 1407/2053] copyright 2015

---
 LICENSE | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/LICENSE b/LICENSE
index efcc5c5b6b0..d69d16f5bc7 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,11 +1,11 @@
 COPYRIGHT
 
 All contributions by the University of California:
-Copyright (c) 2014, The Regents of the University of California (Regents)
+Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
 All rights reserved.
 
 All other contributions:
-Copyright (c) 2014, the respective contributors
+Copyright (c) 2014, 2015, the respective contributors
 All rights reserved.
 
 Caffe uses a shared copyright model: each contributor holds copyright over

From c23722302e1306ace7bce9e9469304ce040aabf2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 15 Jan 2015 01:27:42 -0800
Subject: [PATCH 1408/2053] [pycaffe] basic net specification

---
 python/caffe/__init__.py |   1 +
 python/caffe/net_spec.py | 203 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 204 insertions(+)
 create mode 100644 python/caffe/net_spec.py

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index fbe7112e868..1b2da510a90 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -4,3 +4,4 @@
 from .classifier import Classifier
 from .detector import Detector
 from . import io
+from .net_spec import layers, params, NetSpec, to_proto
diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
new file mode 100644
index 00000000000..f54328d56f1
--- /dev/null
+++ b/python/caffe/net_spec.py
@@ -0,0 +1,203 @@
+"""Python net specification.
+
+This module provides a way to write nets directly in Python, using a natural,
+functional style. See examples/python_nets/caffenet.py for an example.
+
+Currently this works as a thin wrapper around the Python protobuf interface,
+with layers and parameters automatically generated for the "layers" and
+"params" pseudo-modules, which are actually objects using __getattr__ magic
+to generate protobuf messages.
+
+Note that when using to_proto or Top.to_proto, names of intermediate blobs will
+be automatically generated. To explicitly specify blob names, use the NetSpec
+class -- assign to its attributes directly to name layers, and call
+NetSpec.to_proto to serialize all assigned layers.
+
+This interface is expected to continue to evolve as Caffe gains new capabilities
+for specifying nets. In particular, the automatically generated layer names
+are not guaranteed to be forward-compatible.
+"""
+
+from collections import OrderedDict
+
+from .proto import caffe_pb2
+from google import protobuf
+
+
+def param_name_dict():
+    """Find out the correspondence between layer names and parameter names."""
+
+    layer = caffe_pb2.LayerParameter()
+    # get all parameter names (typically underscore case) and corresponding
+    # type names (typically camel case), which contain the layer names
+    # (note that not all parameters correspond to layers, but we'll ignore that)
+    param_names = [s for s in dir(layer) if s.endswith('_param')]
+    param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
+    # strip the final '_param' or 'Parameter'
+    param_names = [s[:-len('_param')] for s in param_names]
+    param_type_names = [s[:-len('Parameter')] for s in param_type_names]
+    return dict(zip(param_type_names, param_names))
+
+
+def to_proto(*tops):
+    """Generate a NetParameter that contains all layers needed to compute
+    all arguments."""
+
+    if not isinstance(tops, tuple):
+        tops = (tops,)
+    layers = OrderedDict()
+    autonames = {}
+    for top in tops:
+        top.fn._to_proto(layers, {}, autonames)
+    net = caffe_pb2.NetParameter()
+    net.layer.extend(layers.values())
+    return net
+
+
+def assign_proto(proto, name, val):
+    """Assign a Python object to a protobuf message, based on the Python
+    type (in recursive fashion). Lists become repeated fields/messages, dicts
+    become messages, and other types are assigned directly."""
+
+    if isinstance(val, list):
+        if isinstance(val[0], dict):
+            for item in val:
+                proto_item = getattr(proto, name).add()
+                for k, v in item.iteritems():
+                    assign_proto(proto_item, k, v)
+        else:
+            getattr(proto, name).extend(val)
+    elif isinstance(val, dict):
+        for k, v in val.iteritems():
+            assign_proto(getattr(proto, name), k, v)
+    else:
+        setattr(proto, name, val)
+
+
+class Top(object):
+    """A Top specifies a single output blob (which could be one of several
+    produced by a layer.)"""
+
+    def __init__(self, fn, n):
+        self.fn = fn
+        self.n = n
+
+    def to_proto(self):
+        """Generate a NetParameter that contains all layers needed to compute
+        this top."""
+
+        return to_proto(self)
+
+
+class Function(object):
+    """A Function specifies a layer, its parameters, and its inputs (which
+    are Tops from other layers)."""
+
+    def __init__(self, type_name, inputs, params):
+        self.type_name = type_name
+        self.inputs = inputs
+        self.params = params
+        self.ntop = self.params.get('ntop', 1)
+        # use del to make sure kwargs are not double-processed as layer params
+        if 'ntop' in self.params:
+            del self.params['ntop']
+        self.in_place = self.params.get('in_place', False)
+        if 'in_place' in self.params:
+            del self.params['in_place']
+        self.tops = tuple(Top(self, n) for n in range(self.ntop))
+
+    def _get_name(self, top, names, autonames):
+        if top not in names:
+            n = autonames.setdefault(top.fn.type_name, 1)
+            autonames[top.fn.type_name] += 1
+            names[top] = top.fn.type_name + str(n)
+        return names[top]
+
+    def _to_proto(self, layers, names, autonames):
+        if self in layers:
+            return
+        bottom_names = []
+        for inp in self.inputs:
+            inp.fn._to_proto(layers, names, autonames)
+            bottom_names.append(layers[inp.fn].top[inp.n])
+        layer = caffe_pb2.LayerParameter()
+        layer.type = self.type_name
+        layer.bottom.extend(bottom_names)
+
+        if self.in_place:
+            layer.top.extend(layer.bottom)
+        else:
+            for top in self.tops:
+                layer.top.append(self._get_name(top, names, autonames))
+        layer.name = self._get_name(self.tops[0], names, autonames)
+
+        for k, v in self.params.iteritems():
+            # special case to handle generic *params
+            if k.endswith('param'):
+                assign_proto(layer, k, v)
+            else:
+                try:
+                    assign_proto(getattr(layer,
+                        _param_names[self.type_name] + '_param'), k, v)
+                except (AttributeError, KeyError):
+                    assign_proto(layer, k, v)
+
+        layers[self] = layer
+
+
+class NetSpec(object):
+    """A NetSpec contains a set of Tops (assigned directly as attributes).
+    Calling NetSpec.to_proto generates a NetParameter containing all of the
+    layers needed to produce all of the assigned Tops, using the assigned
+    names."""
+
+    def __init__(self):
+        super(NetSpec, self).__setattr__('tops', OrderedDict())
+
+    def __setattr__(self, name, value):
+        self.tops[name] = value
+
+    def __getattr__(self, name):
+        return self.tops[name]
+
+    def to_proto(self):
+        names = {v: k for k, v in self.tops.iteritems()}
+        autonames = {}
+        layers = OrderedDict()
+        for name, top in self.tops.iteritems():
+            top.fn._to_proto(layers, names, autonames)
+        net = caffe_pb2.NetParameter()
+        net.layer.extend(layers.values())
+        return net
+
+
+class Layers(object):
+    """A Layers object is a pseudo-module which generates functions that specify
+    layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
+    specifying a 3x3 convolution applied to bottom."""
+
+    def __getattr__(self, name):
+        def layer_fn(*args, **kwargs):
+            fn = Function(name, args, kwargs)
+            if fn.ntop == 1:
+                return fn.tops[0]
+            else:
+                return fn.tops
+        return layer_fn
+
+
+class Parameters(object):
+    """A Parameters object is a pseudo-module which generates constants used
+    in layer parameters; e.g., Parameters().Pooling.MAX is the value used
+    to specify max pooling."""
+
+    def __getattr__(self, name):
+       class Param:
+            def __getattr__(self, param_name):
+                return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
+       return Param()
+
+
+_param_names = param_name_dict()
+layers = Layers()
+params = Parameters()

From ad2e7f467d610d21c22d40f37a5a137ef9939a2b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Mon, 9 Mar 2015 17:43:53 -0700
Subject: [PATCH 1409/2053] [examples] caffenet python spec

---
 examples/pycaffe/caffenet.py | 54 ++++++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 examples/pycaffe/caffenet.py

diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py
new file mode 100644
index 00000000000..f9801d7cbb8
--- /dev/null
+++ b/examples/pycaffe/caffenet.py
@@ -0,0 +1,54 @@
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+
+# helper function for common structures
+
+def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+                                num_output=nout, pad=pad, group=group)
+    return conv, L.ReLU(conv, in_place=True)
+
+def fc_relu(bottom, nout):
+    fc = L.InnerProduct(bottom, num_output=nout)
+    return fc, L.ReLU(fc, in_place=True)
+
+def max_pool(bottom, ks, stride=1):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def caffenet(lmdb, batch_size=256, include_acc=False):
+    data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
+        transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
+
+    # the net itself
+    conv1, relu1 = conv_relu(data, 11, 96, stride=4)
+    pool1 = max_pool(relu1, 3, stride=2)
+    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
+    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
+    pool2 = max_pool(relu2, 3, stride=2)
+    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
+    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
+    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
+    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
+    pool5 = max_pool(relu5, 3, stride=2)
+    fc6, relu6 = fc_relu(pool5, 4096)
+    drop6 = L.Dropout(relu6, in_place=True)
+    fc7, relu7 = fc_relu(drop6, 4096)
+    drop7 = L.Dropout(relu7, in_place=True)
+    fc8 = L.InnerProduct(drop7, num_output=1000)
+    loss = L.SoftmaxWithLoss(fc8, label)
+
+    if include_acc:
+        acc = L.Accuracy(fc8, label)
+        return to_proto(loss, acc)
+    else:
+        return to_proto(loss)
+
+def make_net():
+    with open('train.prototxt', 'w') as f:
+        print >>f, caffenet('/path/to/caffe-train-lmdb')
+
+    with open('test.prototxt', 'w') as f:
+        print >>f, caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True)
+
+if __name__ == '__main__':
+    make_net()

From 1cdad89d8c0e1b31b5509f91b932ee3a2d376fd3 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 18 Jun 2015 13:25:08 -0700
Subject: [PATCH 1410/2053] [pytest] minimal testing of net specification

---
 python/caffe/test/test_net_spec.py | 67 ++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 python/caffe/test/test_net_spec.py

diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
new file mode 100644
index 00000000000..65b73b96f73
--- /dev/null
+++ b/python/caffe/test/test_net_spec.py
@@ -0,0 +1,67 @@
+import unittest
+import tempfile
+import caffe
+from caffe import layers as L
+from caffe import params as P
+
+def lenet(batch_size):
+    n = caffe.NetSpec()
+    n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                         dict(dim=[batch_size, 1, 1, 1])],
+                                  transform_param=dict(scale=1./255), ntop=2)
+    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.ip1 = L.InnerProduct(n.pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    n.relu1 = L.ReLU(n.ip1, in_place=True)
+    n.ip2 = L.InnerProduct(n.relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
+    return n.to_proto()
+
+def anon_lenet(batch_size):
+    data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                     dict(dim=[batch_size, 1, 1, 1])],
+                              transform_param=dict(scale=1./255), ntop=2)
+    conv1 = L.Convolution(data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    ip1 = L.InnerProduct(pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    relu1 = L.ReLU(ip1, in_place=True)
+    ip2 = L.InnerProduct(relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    loss = L.SoftmaxWithLoss(ip2, label)
+    return loss.to_proto()
+
+class TestNetSpec(unittest.TestCase):
+    def load_net(self, net_proto):
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.write(str(net_proto))
+        f.close()
+        return caffe.Net(f.name, caffe.TEST)
+
+    def test_lenet(self):
+        """Construct and build the Caffe version of LeNet."""
+
+        net_proto = lenet(50)
+        # check that relu is in-place
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        # check that all layers are present
+        self.assertEqual(len(net.layers), 9)
+
+        # now the check the version with automatically-generated layer names
+        net_proto = anon_lenet(50)
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.layers), 9)

From ccda10250d8adeee82cdc577204069b79a9d7d6f Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 12 Mar 2015 01:03:51 -0700
Subject: [PATCH 1411/2053] [examples] draft Python solving example

---
 examples/python_solving.ipynb | 5148 +++++++++++++++++++++++++++++++++
 1 file changed, 5148 insertions(+)
 create mode 100644 examples/python_solving.ipynb

diff --git a/examples/python_solving.ipynb b/examples/python_solving.ipynb
new file mode 100644
index 00000000000..de6c40196ad
--- /dev/null
+++ b/examples/python_solving.ipynb
@@ -0,0 +1,5148 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python solving with LeNet\n",
+    "\n",
+    "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('..')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2015-03-12 11:54:02--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 9912422 (9.5M) [application/x-gzip]\n",
+      "Saving to: 'train-images-idx3-ubyte.gz'\n",
+      "\n",
+      "train-images-idx3-u 100%[=====================>]   9.45M   194KB/s   in 51s    \n",
+      "\n",
+      "2015-03-12 11:54:54 (188 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
+      "\n",
+      "--2015-03-12 11:54:54--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 28881 (28K) [application/x-gzip]\n",
+      "Saving to: 'train-labels-idx1-ubyte.gz'\n",
+      "\n",
+      "train-labels-idx1-u 100%[=====================>]  28.20K   104KB/s   in 0.3s   \n",
+      "\n",
+      "2015-03-12 11:54:55 (104 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
+      "\n",
+      "--2015-03-12 11:54:55--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1648877 (1.6M) [application/x-gzip]\n",
+      "Saving to: 't10k-images-idx3-ubyte.gz'\n",
+      "\n",
+      "t10k-images-idx3-ub 100%[=====================>]   1.57M   224KB/s   in 9.2s   \n",
+      "\n",
+      "2015-03-12 11:55:04 (176 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
+      "\n",
+      "--2015-03-12 11:55:04--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Resolving yann.lecun.com... 128.122.47.89\n",
+      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 4542 (4.4K) [application/x-gzip]\n",
+      "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
+      "\n",
+      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  --.-KB/s   in 0.09s  \n",
+      "\n",
+      "2015-03-12 11:55:04 (50.0 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
+      "\n",
+      "Unzipping...\n",
+      "train-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? ^Ctrain-labels-idx1-ubyte already exists -- do you wish to overwrite (y or n)? \n",
+      "\tnot overwriting\n",
+      "t10k-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? Creating lmdb...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!data/mnist/get_mnist.sh\n",
+    "!examples/mnist/create_mnist.sh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need two external files to help out:\n",
+    "* the solver prototxt, defining the learning parameters\n",
+    "* the net prototxt, defining the architecture and train/test data\n",
+    "\n",
+    "Here are the learning parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# The train/test net protocol buffer definition\r\n",
+      "net: \"examples/mnist/lenet_train_test.prototxt\"\r\n",
+      "# test_iter specifies how many forward passes the test should carry out.\r\n",
+      "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
+      "# covering the full 10,000 testing images.\r\n",
+      "test_iter: 100\r\n",
+      "# Carry out testing every 500 training iterations.\r\n",
+      "test_interval: 500\r\n",
+      "# The base learning rate, momentum and the weight decay of the network.\r\n",
+      "base_lr: 0.01\r\n",
+      "momentum: 0.9\r\n",
+      "weight_decay: 0.0005\r\n",
+      "# The learning rate policy\r\n",
+      "lr_policy: \"inv\"\r\n",
+      "gamma: 0.0001\r\n",
+      "power: 0.75\r\n",
+      "# Display every 100 iterations\r\n",
+      "display: 100\r\n",
+      "# The maximum number of iterations\r\n",
+      "max_iter: 10000\r\n",
+      "# snapshot intermediate results\r\n",
+      "snapshot: 5000\r\n",
+      "snapshot_prefix: \"examples/mnist/lenet\"\r\n",
+      "# solver mode: CPU or GPU\r\n",
+      "solver_mode: GPU\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples/mnist/lenet_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here is the version of LeNet we'll be using. In future version of Caffe, networks can be defined directly in Python (see [PR #2086](https://github.com/BVLC/caffe/pull/2086)).\n",
+    "\n",
+    "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name: \"LeNet\"\r\n",
+      "layer {\r\n",
+      "  name: \"mnist\"\r\n",
+      "  type: \"Data\"\r\n",
+      "  top: \"data\"\r\n",
+      "  top: \"label\"\r\n",
+      "  include {\r\n",
+      "    phase: TRAIN\r\n",
+      "  }\r\n",
+      "  transform_param {\r\n",
+      "    scale: 0.00390625\r\n",
+      "  }\r\n",
+      "  data_param {\r\n",
+      "    source: \"examples/mnist/mnist_train_lmdb\"\r\n",
+      "    batch_size: 64\r\n",
+      "    backend: LMDB\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"mnist\"\r\n",
+      "  type: \"Data\"\r\n",
+      "  top: \"data\"\r\n",
+      "  top: \"label\"\r\n",
+      "  include {\r\n",
+      "    phase: TEST\r\n",
+      "  }\r\n",
+      "  transform_param {\r\n",
+      "    scale: 0.00390625\r\n",
+      "  }\r\n",
+      "  data_param {\r\n",
+      "    source: \"examples/mnist/mnist_test_lmdb\"\r\n",
+      "    batch_size: 100\r\n",
+      "    backend: LMDB\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"conv1\"\r\n",
+      "  type: \"Convolution\"\r\n",
+      "  bottom: \"data\"\r\n",
+      "  top: \"conv1\"\r\n",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 20\r\n",
+      "    kernel_size: 5\r\n",
+      "    stride: 1\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"pool1\"\r\n",
+      "  type: \"Pooling\"\r\n",
+      "  bottom: \"conv1\"\r\n",
+      "  top: \"pool1\"\r\n",
+      "  pooling_param {\r\n",
+      "    pool: MAX\r\n",
+      "    kernel_size: 2\r\n",
+      "    stride: 2\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"conv2\"\r\n",
+      "  type: \"Convolution\"\r\n",
+      "  bottom: \"pool1\"\r\n",
+      "  top: \"conv2\"\r\n",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 50\r\n",
+      "    kernel_size: 5\r\n",
+      "    stride: 1\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"pool2\"\r\n",
+      "  type: \"Pooling\"\r\n",
+      "  bottom: \"conv2\"\r\n",
+      "  top: \"pool2\"\r\n",
+      "  pooling_param {\r\n",
+      "    pool: MAX\r\n",
+      "    kernel_size: 2\r\n",
+      "    stride: 2\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"ip1\"\r\n",
+      "  type: \"InnerProduct\"\r\n",
+      "  bottom: \"pool2\"\r\n",
+      "  top: \"ip1\"\r\n",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 500\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"relu1\"\r\n",
+      "  type: \"ReLU\"\r\n",
+      "  bottom: \"ip1\"\r\n",
+      "  top: \"ip1\"\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"ip2\"\r\n",
+      "  type: \"InnerProduct\"\r\n",
+      "  bottom: \"ip1\"\r\n",
+      "  top: \"ip2\"\r\n",
+      "  param {\r\n",
+      "    lr_mult: 1\r\n",
+      "  }\r\n",
+      "  param {\r\n",
+      "    lr_mult: 2\r\n",
+      "  }\r",
+      "\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 10\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "    bias_filler {\r\n",
+      "      type: \"constant\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"accuracy\"\r\n",
+      "  type: \"Accuracy\"\r\n",
+      "  bottom: \"ip2\"\r\n",
+      "  bottom: \"label\"\r\n",
+      "  top: \"accuracy\"\r\n",
+      "  include {\r\n",
+      "    phase: TEST\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"loss\"\r\n",
+      "  type: \"SoftmaxWithLoss\"\r\n",
+      "  bottom: \"ip2\"\r\n",
+      "  bottom: \"label\"\r\n",
+      "  top: \"loss\"\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples/mnist/lenet_train_test.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "solver = caffe.SGDSolver('examples/mnist/lenet_solver.prototxt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('data', (64, 1, 28, 28)),\n",
+       " ('label', (64,)),\n",
+       " ('conv1', (64, 20, 24, 24)),\n",
+       " ('pool1', (64, 20, 12, 12)),\n",
+       " ('conv2', (64, 50, 8, 8)),\n",
+       " ('pool2', (64, 50, 4, 4)),\n",
+       " ('ip1', (64, 500)),\n",
+       " ('ip2', (64, 10)),\n",
+       " ('loss', ())]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# each output is (batch size, feature dim, spatial dim)\n",
+    "[(k, v.data.shape) for k, v in solver.net.blobs.items()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('conv1', (20, 1, 5, 5)),\n",
+       " ('conv2', (50, 20, 5, 5)),\n",
+       " ('ip1', (500, 800)),\n",
+       " ('ip2', (10, 500))]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# just print the weight sizes (not biases)\n",
+    "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accuracy': array(0.07000000029802322, dtype=float32),\n",
+       " 'loss': array(2.375746726989746, dtype=float32)}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solver.net.forward()  # train net\n",
+    "solver.test_nets[0].forward()  # test net (there can be more than one)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
+     ]
+    },
+    {
+     "data": {
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+       "hBCW1cdm4CgruVjkOpkQZZ3M32Me1DcfAj8QQuiFELlAIXB9C9r3zLBqeGTW5vn51vbVY9Smhedk\n",
+       "XD12PuwnQZKkRSHEPwLOsKIY+WtJku7E4lzPIWnAqdWyWHHA30qS9JkQ4iZPWSfz9wEhxN8BB4AU\n",
+       "IcQQ8G95QA1RSZLahRC/AtqBReAfrs4svxXcp6/+HfCSEGJdnh/41vfVE9WmfZb7Sg1NV1FRUXlO\n",
+       "UCMdVVRUVJ4TVIOtoqKi8pygGmwVFRWV5wTVYKuoqKg8J6gGW0VFReU5QTXYKioqKs8JqsFWUVFR\n",
+       "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109ed4650>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we use a little trick to tile the first eight images\n",
+    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
+    "print solver.net.blobs['label'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
+     ]
+    },
+    {
+     "data": {
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+       "MU8am3eBbwghDEKIXKAAuBkB+3YMq45H5hwr5xZ8gcdqHb1pYZecV+uuh70RJEkKCiH+GDjPimLk\n",
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+       "f3Vm+YXgMWP110CdEOKBOj/whR+rDfWm3cljpaamq6ioqOwS1ExHFRUVlV2C6rBVVFRUdgmqw1ZR\n",
+       "UVHZJagOW0VFRWWXoDpsFRUVlV2C6rBVVFRUdgmqw1ZRUVHZJagOW0VFRWWX8P8BCxPUWfGXxrcA\n",
+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11d221bd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
+    "print solver.test_nets[0].blobs['label'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both train and test nets seem to be loading data, and to have correct labels.\n",
+    "\n",
+    "Let's take one step of (minibatch) SGD and see what happens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x123c20e90>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x122785190>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
+    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
+    "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
+    "* logging will continue to happen as normal\n",
+    "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n",
+    "* testing will happen at the interval specified (here, every 500 iterations)\n",
+    "\n",
+    "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n",
+    "* write a custom stopping criterion\n",
+    "* change the solving process by updating the net in the loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 13 s, sys: 134 ms, total: 13.1 s\n",
+      "Wall time: 13 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\n",
+    "output = zeros((niter, 8, 10))\n",
+    "\n",
+    "# the main solver loop\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    \n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    \n",
+    "    # store the output on the first test batch\n",
+    "    # (start at conv1 to avoid loading new data)\n",
+    "    solver.test_nets[0].forward(start='conv1')\n",
+    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the train loss. (Normally we should plot the test loss as well, but we're only testing every 500 iterations, and loss drops very quickly for this example.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x123c2ebd0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "6zSKUeZV/Cb32lPonhR9gjwHMaw8AuSTxz8E/szMdgIws0PNbErJ56YDDyZheBqxdm/G9uzzBa4A\n",
+       "Tkt5jdnA84mpksuWxxSiL1AvRQwbWY99BfBUCg+dA3yWCOlcl+bJ30CsL1BcSesHwNvN7GbgViK0\n",
+       "lPFFYvrka939ddnn3P1CM3t2OqYDf+XuG9Ji8MVyQZUPir5ApaxCCCEaUFhJCCFEAxIHIYQQDUgc\n",
+       "hBBCNCBxEEII0YDEQQghRAMSByGEEA1IHIQQQjQgcRBCCNHA/webgPPorxOoYAAAAABJRU5ErkJg\n",
+       "gg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12276b250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(arange(niter), train_loss)\n",
+    "xlabel('iteration')\n",
+    "ylabel('train loss')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems to have dropped quickly and coverged (except for stochasticity); hooray!\n",
+    "\n",
+    "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x123da73d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1241bfa10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124252150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1236dcf10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "YII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1234d7e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1243fea50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1246ea5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1247e3910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "u8/2HbbX2F5n+2Pt5xm7HmJ7iu3Vtr/VXmb8elhXFVS2p0j6tKQLJZ0h6WLbp3c2KxzGZ9Uaq7Eu\n",
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+       "Xw/rqoJK0iskPRARD0fEqKSvSHpTh3PCBCLiVklbD3r6Ikmfbz/+vKQ3T2pSKBYRGyNiTfvxdknr\n",
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+       "SdLJnUwGZWwPSTpb0h1iDHuC7RNsr1FrjG6OiPvE2PWSf5D0YUn7xjzH+PWwbiuomMPhOBKtOTkY\n",
+       "0y5ne7akr0m6JCJGxr7GGHaviNjXvuR3iqTX2L7goNcZuy5l+w2SNkfEav3y7NSzMH69p9sKqp9L\n",
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+       "AAA0REEFAADQEAUVAABAQxRUADrO9sr2/y+yffExXvfl420LAI4l5qEC0DVsn6/WJIdvTLxnakTs\n",
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+       "d9pea/s97ec+Lmlme33Xjt2WWz5p+17b99h+65h1L7f977bX2/7i5H4aAHrR1E4nAAD6Zeubj0j6\n",
+       "0P4zVO0CaltEvML2DEnft/29duzZks6MiP9pL787Ira2e9utsv3ViLjM9vsi4uxxtvV7kn5D0osl\n",
+       "DUr6oe1b2q+9RNIZkv5X0krbSyKCS4UAJsQZKgDdxAct/7akd9peLekHkk6U9Kvt11aNKaYk6RLb\n",
+       "ayTdLmmhpBccYVuvkvTlaNksaYWkl6tVcK2KiF9E656INZKGGvybAPw/wBkqAN3u/RFx49gn2vda\n",
+       "7Tho+bWSzomIXbZvltR3hPWGDi3g9p+9embMc3vFsRLAEXCGCkA3GZE09gby70p6r+2pkmT7hbb7\n",
+       "x3nfXElb28XUr0s6Z8xro/vff5BbJb2tfZ/WoKTXSFqlQ4ssADgi/qsLQDfYf2bobkl725fuPivp\n",
+       "U2pdbvuRbUvaLOl32/Fjf6L8HUl/YXudpJ+oddlvv6sl3WP7roh4x/73RcR/2D63vc2Q9OGI2Gz7\n",
+       "9IPWrXGWAeBZmDYBAACgIS75AQAANERBBQAA0BAFFQAAQEMUVAAAAA1RUAEAADREQQUAANAQBRUA\n",
+       "AEBDFFQAAAAN/R9mVy2QoO8DbAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124972f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(8):\n",
+    "    figure(figsize=(2, 2))\n",
+    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+    "    figure(figsize=(10, 2))\n",
+    "    imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n",
+    "    xlabel('iteration')\n",
+    "    ylabel('label')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
+    "\n",
+    "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x125e0d510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124940ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1259dc350>"
+      ]
+     },
+     "metadata": {},
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+    },
+    {
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+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12592e410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1242c3b90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x124a05cd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(8):\n",
+    "    figure(figsize=(2, 2))\n",
+    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+    "    figure(figsize=(10, 2))\n",
+    "    imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n",
+    "    xlabel('iteration')\n",
+    "    ylabel('label')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From b8cc2974fc557fa163187e9ce0a879d0e13833b4 Mon Sep 17 00:00:00 2001
From: Yangqing Jia <jiayq84@gmail.com>
Date: Tue, 17 Mar 2015 10:50:42 -0700
Subject: [PATCH 1412/2053] [examples] flickr fine-tuning notebook

---
 .../Finetune with Flickr Style Data.ipynb     | 951 ++++++++++++++++++
 .../finetune_flickr_style/assemble_data.py    |   8 +
 .../finetune_flickr_style/train_val.prototxt  |   1 +
 3 files changed, 960 insertions(+)
 create mode 100644 examples/Finetune with Flickr Style Data.ipynb

diff --git a/examples/Finetune with Flickr Style Data.ipynb b/examples/Finetune with Flickr Style Data.ipynb
new file mode 100644
index 00000000000..d8cfc7a3925
--- /dev/null
+++ b/examples/Finetune with Flickr Style Data.ipynb	
@@ -0,0 +1,951 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finetune a Pretrained Network with Flickr Style Data\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network, and finetune the last few layers using your custom data.\n",
+    "\n",
+    "The upside of such approach is that, since pre-trained networks are trained on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/jiayq/Research/caffe\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "print os.getcwd()\n",
+    "\n",
+    "import caffe\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2015-03-17 10:51:07--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
+      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
+      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 17858008 (17M) [application/octet-stream]\n",
+      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
+      "\n",
+      "100%[======================================>] 17,858,008   287KB/s   in 55s    \n",
+      "\n",
+      "2015-03-17 10:52:02 (318 KB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
+      "\n",
+      "Unzipping...\n",
+      "Done.\n",
+      "Model already exists.\n",
+      "Downloading 2000 images with 3 workers...\n",
+      "Writing train/val for 1903 successfully downloaded images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "\n",
+    "# You won't need to run this - we should have already created it for you.\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1 --images=2000 --seed=1701 --label=5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's show what is the difference between the finetune network and the original caffe model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1c1\r\n",
+      "< name: \"CaffeNet\"\r\n",
+      "---\r\n",
+      "> name: \"FlickrStyleCaffeNet\"\r\n",
+      "4c4\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "15,26c15,19\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
+      "<     batch_size: 256\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/train.txt\"\r\n",
+      ">     batch_size: 50\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "31c24\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "42,51c35,36\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/test.txt\"\r\n",
+      "53c38,39\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "323a310\r\n",
+      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
+      "360c347\r\n",
+      "<   name: \"fc8\"\r\n",
+      "---\r\n",
+      ">   name: \"fc8_flickr\"\r\n",
+      "363c350,351\r\n",
+      "<   top: \"fc8\"\r\n",
+      "---\r\n",
+      ">   top: \"fc8_flickr\"\r\n",
+      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
+      "365c353\r\n",
+      "<     lr_mult: 1\r\n",
+      "---\r\n",
+      ">     lr_mult: 10\r\n",
+      "369c357\r\n",
+      "<     lr_mult: 2\r\n",
+      "---\r\n",
+      ">     lr_mult: 20\r\n",
+      "373c361\r\n",
+      "<     num_output: 1000\r\n",
+      "---\r\n",
+      ">     num_output: 20\r\n",
+      "384a373,379\r\n",
+      ">   name: \"loss\"\r\n",
+      ">   type: \"SoftmaxWithLoss\"\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      ">   bottom: \"label\"\r\n",
+      ">   top: \"loss\"\r\n",
+      "> }\r\n",
+      "> layer {\r\n",
+      "387c382\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      "393,399d387\r\n",
+      "< }\r\n",
+      "< layer {\r\n",
+      "<   name: \"loss\"\r\n",
+      "<   type: \"SoftmaxWithLoss\"\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "<   bottom: \"label\"\r\n",
+      "<   top: \"loss\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0, loss=3.786610, scratch_loss=3.163587\n",
+      "iter 10, loss=2.556661, scratch_loss=8.774073\n",
+      "iter 20, loss=2.035326, scratch_loss=2.266603\n",
+      "iter 30, loss=1.943101, scratch_loss=1.703273\n",
+      "iter 40, loss=1.982698, scratch_loss=1.831079\n",
+      "iter 50, loss=1.559268, scratch_loss=2.041238\n",
+      "iter 60, loss=1.464433, scratch_loss=1.836157\n",
+      "iter 70, loss=1.481868, scratch_loss=1.705826\n",
+      "iter 80, loss=1.394870, scratch_loss=1.695532\n",
+      "iter 90, loss=1.055422, scratch_loss=1.867379\n",
+      "iter 100, loss=1.407976, scratch_loss=1.881758\n",
+      "iter 110, loss=1.569579, scratch_loss=1.701803\n",
+      "iter 120, loss=0.951682, scratch_loss=1.764299\n",
+      "iter 130, loss=0.905122, scratch_loss=1.879305\n",
+      "iter 140, loss=1.020678, scratch_loss=1.746009\n",
+      "iter 150, loss=0.784985, scratch_loss=1.739624\n",
+      "iter 160, loss=0.911735, scratch_loss=1.673230\n",
+      "iter 170, loss=0.965255, scratch_loss=1.725484\n",
+      "iter 180, loss=1.028102, scratch_loss=1.676103\n",
+      "iter 190, loss=0.905020, scratch_loss=1.885763\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = np.zeros(niter)\n",
+    "scratch_train_loss = np.zeros(niter)\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "# We create a solver that finetunes from a previously trained network.\n",
+    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "# For reference, we also create a solver that does no finetuning.\n",
+    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "\n",
+    "# We run the solver for niter times, and record the training loss.\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    scratch_solver.step(1)\n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
+    "    if it % 10 == 0:\n",
+    "        print 'iter %d, loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
+    "print 'done'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss produced by the two training procedures respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f39dad72390>,\n",
+       " <matplotlib.lines.Line2D at 0x7f39dad72610>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "ra0pAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3a2803d590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f39d50acc90>,\n",
+       " <matplotlib.lines.Line2D at 0x7f39d50acf10>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "EijayVSswJOSYLXY5ephz0BYjpgh8N594MXHfrg+fDyCn0BE4IPByEmk6Eh+K/A1sWdAI3hFaTK8\n",
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+       "zc+8gxCsQxKx9Ry41RHuAz4B3B17/EXg2ZPTZuS9VGsBUcvGfhrBK4rSWT4K/MW5/0+k93la9G5Z\n",
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+       "BCxLmaSkYajAK4pSMl6AzFD1x5wFm0Tgvd1IP6CsAWab6ZA9U1vUg1cUpQZ4Hyuw0EqkfryrWzQU\n",
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+       "W2Yu0lMhi1o39H++MbfRO9CNmNvoHehmzC2+aPBxCM6o2Z50DzqsnUUi+DmIwK9AptX6KXBJwnJe\n",
+       "R3dC6RBzG70D3Yi5jd6Bbsbc4ot6/wPeP2q2J89zigj8eMJm+SBTX42PLRMgzYUeB24Hjipl7xRF\n",
+       "UZQOU6QOvsjlwSPARMRLuwC4BemNrCiKojSIIrbK6Ug7y/PN/Q8hE+x+NuM1y5H+yVudx5bQPlej\n",
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+       "RVEURVGalbyBUko2K4AnkEFkD5jHhgF3Ac8AdyLzRirJXAdsIDotWtbx+xDyXV0IvLRO+9gsJB1L\n",
+       "H6muswMdL3Ce02OZzURkspSnEBfkavN403w/iwyUUrJZjnzgLp8DPmBu/xfwf3Xdo+bihcj8oK4o\n",
+       "pR2/o5DvaG/kO7sEbenhknQsPwb8e8KyeizzGQOcYG63IFb4kTTR9/MM4A7n/gfNn1Kc5cDw2GML\n",
+       "keQ2yJdkYV33qPmYTFSU0o7fh4heZd6B5pTiTKZS4P8jYTk9ltVzC/AvlPT9rIfyFxkopWQTAH9C\n",
+       "pkCzEw+MRi6VMf9HJ7xOSSft+I1DvqMW/b4W49+QgY4/ILQT9FhWx2Tk6uh+Svp+1kPgtQdN53kB\n",
+       "8sFfgFQsvTD2fIAe586Qd/z02GbzbWAKYjWsA76Ysawey2RagF8B1wDPxZ7r8PezHgK/FkkkWCYS\n",
+       "PQMp+awz/zcBv0HGGmxALt0AxgIbG7BfzUza8Yt/XyeQPdmyIsfOitD3ke8n6LEsSm9E3G9ELBoo\n",
+       "6ftZD4F/CJhBOFDqteR3nlRCBgCt5vZAJGs+HzmGbzKPv4nwi6EUI+343QpcjnxXpyDf3QcqXq24\n",
+       "jHVuv4LQn9djmY+H2FoLgK84jzfV9zNpoJRSjClI1vwxpIzKHr9hiC+vZZL53Aw8i0yKvBp4C9nH\n",
+       "77+R7+pC4Ly67mnXJ34s3wrcgJTxPo4IkZsP0mOZzVlI65fHCMtMz0e/n4qiKIqiKIqiKIqiKIqi\n",
+       "KIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqidBf+P41gkbjYnj6JAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f39dad22150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for fine-tuning: 0.570000001788\n",
+      "Accuracy for training from scratch: 0.224000000954\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_iters = 10\n",
+    "accuracy = 0\n",
+    "scratch_accuracy = 0\n",
+    "for it in arange(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    scratch_solver.test_nets[0].forward()\n",
+    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "scratch_accuracy /= test_iters\n",
+    "print 'Accuracy for fine-tuning:', accuracy\n",
+    "print 'Accuracy for training from scratch:', scratch_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/finetune_flickr_style/assemble_data.py b/examples/finetune_flickr_style/assemble_data.py
index b4c995e8eae..09bfa2618a4 100755
--- a/examples/finetune_flickr_style/assemble_data.py
+++ b/examples/finetune_flickr_style/assemble_data.py
@@ -9,6 +9,7 @@
 import argparse
 import numpy as np
 import pandas as pd
+from skimage import io
 import multiprocessing
 
 # Flickr returns a special image if the request is unavailable.
@@ -27,6 +28,7 @@ def download_image(args_tuple):
             urllib.urlretrieve(url, filename)
         with open(filename) as f:
             assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+        test_read_image = io.imread(filename)
         return True
     except KeyboardInterrupt:
         raise Exception()  # multiprocessing doesn't catch keyboard exceptions
@@ -48,6 +50,10 @@ def download_image(args_tuple):
         '-w', '--workers', type=int, default=-1,
         help="num workers used to download images. -x uses (all - x) cores [-1 default]."
     )
+    parser.add_argument(
+        '-l', '--labels', type=int, default=0,
+        help="if set to a positive value, only sample images from the first number of labels."
+    )
 
     args = parser.parse_args()
     np.random.seed(args.seed)
@@ -56,6 +62,8 @@ def download_image(args_tuple):
     csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
     df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
     df = df.iloc[np.random.permutation(df.shape[0])]
+    if args.labels > 0:
+        df = df.loc[df['label'] < args.labels]
     if args.images > 0 and args.images < df.shape[0]:
         df = df.iloc[:args.images]
 
diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index aa9c73e17ce..848a426c914 100644
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -374,6 +374,7 @@ layer {
   type: "SoftmaxWithLoss"
   bottom: "fc8_flickr"
   bottom: "label"
+  top: "loss"
 }
 layer {
   name: "accuracy"

From a6cb8ec62cb6f92f48e8a19da3c71902d3282bfc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 17 Mar 2015 19:01:02 -0700
Subject: [PATCH 1413/2053] [examples] sequence and revise notebooks

- combine classification + filter visualization
- order by classification, learning LeNet, brewing logreg, and
  fine-tuning to flickr style
- improve flow of content in classification + filter visualization
- include solver needed for learning LeNet
- edit notebook descriptions for site catalogue
---
 examples/00-classification.ipynb              | 13187 +++++++++++++++
 ..._solving.ipynb => 01-learning-lenet.ipynb} |  4414 +++---
 examples/02-brewing-logreg.ipynb              |  5771 +++++++
 examples/03-fine-tuning.ipynb                 |   947 ++
 .../Finetune with Flickr Style Data.ipynb     |   951 --
 examples/classification.ipynb                 |  3342 ----
 examples/detection.ipynb                      |     2 +-
 examples/filter_visualization.ipynb           | 13214 ----------------
 examples/hdf5_classification.ipynb            |  6290 --------
 .../nonlinear_auto_test.prototxt              |    54 +
 .../nonlinear_auto_train.prototxt             |    54 +
 .../nonlinear_solver.prototxt                 |    15 +
 ....prototxt => nonlinear_train_val.prototxt} |    10 +-
 examples/hdf5_classification/solver.prototxt  |     5 +-
 examples/hdf5_classification/solver2.prototxt |    14 -
 .../hdf5_classification/train_val.prototxt    |     7 +-
 examples/mnist/lenet_auto_solver.prototxt     |    24 +
 examples/net_surgery.ipynb                    |     2 +-
 examples/siamese/mnist_siamese.ipynb          |     2 +-
 19 files changed, 22296 insertions(+), 26009 deletions(-)
 create mode 100644 examples/00-classification.ipynb
 rename examples/{python_solving.ipynb => 01-learning-lenet.ipynb} (53%)
 create mode 100644 examples/02-brewing-logreg.ipynb
 create mode 100644 examples/03-fine-tuning.ipynb
 delete mode 100644 examples/Finetune with Flickr Style Data.ipynb
 delete mode 100644 examples/classification.ipynb
 delete mode 100644 examples/filter_visualization.ipynb
 delete mode 100644 examples/hdf5_classification.ipynb
 create mode 100644 examples/hdf5_classification/nonlinear_auto_test.prototxt
 create mode 100644 examples/hdf5_classification/nonlinear_auto_train.prototxt
 create mode 100644 examples/hdf5_classification/nonlinear_solver.prototxt
 rename examples/hdf5_classification/{train_val2.prototxt => nonlinear_train_val.prototxt} (87%)
 delete mode 100644 examples/hdf5_classification/solver2.prototxt
 create mode 100644 examples/mnist/lenet_auto_solver.prototxt

diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
new file mode 100644
index 00000000000..46bbb193fe7
--- /dev/null
+++ b/examples/00-classification.ipynb
@@ -0,0 +1,13187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Instant Recognition with Caffe\n",
+    "\n",
+    "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n",
+    "\n",
+    "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'\n",
+    "plt.rcParams['image.cmap'] = 'gray'\n",
+    "\n",
+    "import os\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
+    "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+    "                caffe.TEST)\n",
+    "\n",
+    "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
+    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+    "transformer.set_transpose('data', (2,0,1))\n",
+    "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
+    "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+    "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set net to batch size of 50\n",
+    "net.blobs['data'].reshape(50,3,227,227)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feed in the image (with some preprocessing) and classify with a forward pass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is #281.\n"
+     ]
+    }
+   ],
+   "source": [
+    "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
+    "out = net.forward()\n",
+    "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What did the input look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f665b02ae90>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "+ObuVwgcQuw1tt8C8Asi0i4iowDGAVxqQv0CHxD0YWr4IvLaBmKsDzUkJ+R8BcDVlNKf0Vexth8T\n",
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+       "fBLALwF4U0Qua9lXEGv7sREpYgKBQCAQCAT2iR9r7YdAIBAIBAKB/w/iIBUIBAKBQCCwT8RBKhAI\n",
+       "BAKBQGCfiINUIBAIBAKBwD4RB6lAIBAIBAKBfSIOUoFAIBAIBAL7RBykAoFAIBAIBPaJOEgFAoFA\n",
+       "IBAI7BP/B0EEnTIvM42+AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f66a46f8a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adorable, but was our classification correct?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+      " 'n02127052 lynx, catamount']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load labels\n",
+    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "try:\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "except:\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "\n",
+    "# sort top k predictions from softmax output\n",
+    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+    "print labels[top_k]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indeed! But how long did it take?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 3: 7.14 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CPU mode\n",
+    "net.forward()  # call once for allocation\n",
+    "%timeit net.forward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's a while, even for a batch size of 50 images. Let's switch to GPU mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 90.9 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "# GPU mode\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "net.forward()  # call once for allocation\n",
+    "%timeit net.forward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Much better. Now let's look at the net in more detail.\n",
+    "\n",
+    "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('data', (50, 3, 227, 227)),\n",
+       " ('conv1', (50, 96, 55, 55)),\n",
+       " ('pool1', (50, 96, 27, 27)),\n",
+       " ('norm1', (50, 96, 27, 27)),\n",
+       " ('conv2', (50, 256, 27, 27)),\n",
+       " ('pool2', (50, 256, 13, 13)),\n",
+       " ('norm2', (50, 256, 13, 13)),\n",
+       " ('conv3', (50, 384, 13, 13)),\n",
+       " ('conv4', (50, 384, 13, 13)),\n",
+       " ('conv5', (50, 256, 13, 13)),\n",
+       " ('pool5', (50, 256, 6, 6)),\n",
+       " ('fc6', (50, 4096)),\n",
+       " ('fc7', (50, 4096)),\n",
+       " ('fc8', (50, 1000)),\n",
+       " ('prob', (50, 1000))]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(k, v.data.shape) for k, v in net.blobs.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('conv1', (96, 3, 11, 11)),\n",
+       " ('conv2', (256, 48, 5, 5)),\n",
+       " ('conv3', (384, 256, 3, 3)),\n",
+       " ('conv4', (384, 192, 3, 3)),\n",
+       " ('conv5', (256, 192, 3, 3)),\n",
+       " ('fc6', (4096, 9216)),\n",
+       " ('fc7', (4096, 4096)),\n",
+       " ('fc8', (1000, 4096))]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(k, v[0].data.shape) for k, v in net.params.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Helper functions for visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
+    "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
+    "def vis_square(data, padsize=1, padval=0):\n",
+    "    data -= data.min()\n",
+    "    data /= data.max()\n",
+    "    \n",
+    "    # force the number of filters to be square\n",
+    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+    "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
+    "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
+    "    \n",
+    "    # tile the filters into an image\n",
+    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+    "    \n",
+    "    plt.imshow(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The input image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer filters, `conv1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "EXCWLhlLmgDeAhYpyzc+b6Hs+05u+xIRERFRQ64jEREREVFDmqiIiIiIGtJERURERNSQJioiIiKi\n",
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffaf3877710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the parameters are a list of [weights, biases]\n",
+    "filters = net.params['conv1'][0].data\n",
+    "vis_square(filters.transpose(0, 2, 3, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "IiIiIvaI/w/CAMCOMj3yxQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01f952d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv1'].data[0, :36]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer filters, `conv2`\n",
+    "\n",
+    "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "KBQKhUJhg6jK5oVCoVAoFAobRB2kCoVCoVAoFDaIOkgVCoVCoVAobBB1kCoUCoVCoVDYIOogVSgU\n",
+       "CoVCobBB1EGqUCgUCoVCYYOog1ShUCgUCoXCBlEHqUKhUCgUCoUN4v8BaJ+sNGqpC6EAAAAASUVO\n",
+       "RK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01f42290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filters = net.params['conv2'][0].data\n",
+    "vis_square(filters[:48].reshape(48**2, 5, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "pAAAAGlJREFUngTuS3INeBt4j21yWFWXk5wGLgO3gVfK/+/SAmyTy3eAw0kOMlyq+wV4GaabS/87\n",
+       "T5IkaYQdfw8HSZKkRXCIkiRJGsEhSpIkaQSHKEmSpBEcoiRJkkZwiJIkSRrBIUqSJGmEPwDOrQm6\n",
+       "MQ8HvQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01f42310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv2'].data[0, :36]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The third layer output, `conv3` (rectified, all 384 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "nbLv+/7hruv+WtLDkv5F0m/03opgpqxTH1dJelXXdafrwLLEfkm/Lrk+FsR2Se+QtLfruvtX37tS\n",
+       "7iPLYL26+KCkS9w/JvEWMcYYY4wxjTizuTHGGGNMI55IGWOMMcY04omUMcYYY0wjnkgZY4wxxjTi\n",
+       "iZQxxhhjTCOeSBljjDHGNOKJlDHGGGNMI55IGWOMMcY08v8BGKNoaFbqcjsAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01dfb3d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv3'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fourth layer output, `conv4` (rectified, all 384 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "ZZ9z3pNS+o6kPZL+JOkj2VsRjJVl6uM6SRemlN6s+WWJfZI+LLk+psTbJV0uaXdK6ZHBe5+S+8gs\n",
+       "WK4uPi3p/e4fi/EWMcYYY4wxjTizuTHGGGNMI55IGWOMMcY04omUMcYYY0wjnkgZY4wxxjTiiZQx\n",
+       "xhhjTCOeSBljjDHGNOKJlDHGGGNMI55IGWOMMcY08v87vVmfz9SwBgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01cf9110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv4'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fifth layer output, `conv5` (rectified, all 256 channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "O4CDwEWSOB9JP3YEON36CFZj7WJ8BnhG0nZJu4DdwPkK+mcdlF5IM0+S5Dk43rUnScAMsBAR7+d2\n",
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+       "ugq8BbxDixhHxIKkk8AC0ABeCi+rUCst4v02cEDSXpJbOL8BL4Lj3SP2A88BFyTNp23HcI5v4iVi\n",
+       "zMzMzArqizkezMzMzLrBhZSZmZlZQS6kzMzMzApyIWVmZmZWkAspMzMzs4JcSJmZmZkV5ELKzMzM\n",
+       "rCAXUmZmZmYF/Q0kXnLFpKb3UQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01c2f0d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv5'].data[0]\n",
+    "vis_square(feat, padval=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fifth layer after pooling, `pool5`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "14rIL0Xk9+TyexXnIweq+rBc/qBeEZHXRORfi0iLcD4yoapPiMh9IrJFLvc//QcR+YGsc/xV9U9E\n",
+       "5F+JyJJcbhX5cQ5pZ4bvzgMAAIhQ0fMbAAAASoUiCgAAIAJFFAAAQASKKAAAgAgUUQAAABEoogAA\n",
+       "ACJQRAEAAET4/wEtHY5P7rJ1tgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01bbe590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['pool5'].data[0]\n",
+    "vis_square(feat, padval=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first fully connected layer, `fc6` (rectified)\n",
+    "\n",
+    "We show the output values and the histogram of the positive values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "Hqwkt4BNjicg3eh+kVMuyTawDMzT3hM/Al4Cz4ALwDfgdlX9+Ff3+L+ZRq+9BT5y/Nj4Ae1fDMx9\n",
+       "gCRLtA9Nz0zLVlU9TnIeMx8uyTJwv6pWzXycJIu0p1bQXl09raoNMx8ryRXaQI4Z4Atwj9ZbumTu\n",
+       "RKOSJEmdDZloVJIk6TSzYEmSJHVmwZIkSerMgiVJktSZBUuSJKkzC5YkSVJnFixJkqTOLFiSJEmd\n",
+       "/QRSxC44KICduwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01ae5a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc6'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second fully connected layer, `fc7` (rectified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "TIsdV9wgl54nri+QZNuSLpX08ZTSW0sfhVxfBhnnyvYzJb1Vdw8w+tPhC8FWsH2eFrVV0mJQ23dz\n",
+       "vGDJ9nskPUnS6Vr0f/hRSf+vpF+V9BWSbpH0gpTS30wVI/JRcbxcImm/Fk2CSdLNkl5R6lODHWX7\n",
+       "CZL+UNINurvp7w1aPHGm9/WFQUQBAAACDTKIKAAAwK4iuQIAAAhEcgUAABCI5AoAACAQyRUAAEAg\n",
+       "kisAAIBAJFcAAACBSK4AAAAC/f9A40wIgLpJlQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb01953f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc7'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final probability output, `prob`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ffb01f12a50>]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "kNVSOJsuZF99e1U9K8mTu3tnGY1/sPGYfY2MsMMsBMtCrH+y+5YkH0zy1d396fVNn86Xfj3P12S1\n",
+       "H3fYp2P9tST/bR69ZpF9dzp8XZLPVNXPVNWvVtVPV9WVsf9Ohe7+XJL/Ncn/l1V8/XZ3vz/232ly\n",
+       "oftq9/UP5hD7cGSEeUfAKVFVT0ryfyR5U3c/ahHBXr2z47AL7nFCqup7k/y77v5I9llg0L5btMuz\n",
+       "WqDyb3f3S7J65/lf3ryD/bdcVfXcJP9VkudkdXB+UlX94OZ97L/T4xD76qKNjLAHk1yzcfmaPLoi\n",
+       "WYCq+rKsAuxnu/vd66s/vf79oFkPwe6sHr57nz47X/qVPZysP5Hk+6rqN5L8XJLvrqqfjX13WjyQ\n",
+       "5IHu/vD68j/JKsp+0/47Fb4tyb/s7s9298NJ3pnkP4n9d5pcyPfKB9bXP3vX9Vv34cgI++JCsFX1\n",
+       "hKxOdLt94Pawy/pkw1uTfLy7//rGTbcn+XPrz/9ckndvXP/aqnpCVX1dVr/u5VJ+zQQXqbv/Sndf\n",
+       "091fl9UJwf+iu38o9t2p0N2/meT+qtr5/YevSPLrSX4+9t9pcHeSl1bVV6y/j74iqzfI2H+nxwV9\n",
+       "r1y/Zn93/S7mSvJDG4/Z17BfDGsx11PhZUl+MMnHquoj6+vekuR/TPKPqur1Se5L8v1J0t0fr6p/\n",
+       "lNU3m4eT/MW2EN1S7OwH++70+NEk71j/kPpvs1oI+0zsv8Xr7o9W1T/IarDhkSS/muSnkjw59t/i\n",
+       "VNXPZfXrFr+qqu5P8uO5uO+VfzHJ38vqd6S+p7v/2davbT8DAJw865AAAAwgwgAABhBhAAADiDAA\n",
+       "gAFEGADAACIMAGAAEQYAMIAIAwAY4P8Hf+iH2xY5ngUAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ffb019b9dd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['prob'].data[0]\n",
+    "plt.plot(feat.flat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see the top 5 predicted labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+      " 'n02127052 lynx, catamount']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load labels\n",
+    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "try:\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "except:\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "\n",
+    "# sort top k predictions from softmax output\n",
+    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
+    "print labels[top_k]"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.",
+  "example_name": "Image Classification and Filter Visualization",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 1
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/python_solving.ipynb b/examples/01-learning-lenet.ipynb
similarity index 53%
rename from examples/python_solving.ipynb
rename to examples/01-learning-lenet.ipynb
index de6c40196ad..3562c7adaf2 100644
--- a/examples/python_solving.ipynb
+++ b/examples/01-learning-lenet.ipynb
@@ -56,59 +56,59 @@
      "output_type": "stream",
      "text": [
       "Downloading...\n",
-      "--2015-03-12 11:54:02--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "--2015-06-30 14:41:56--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
       "Resolving yann.lecun.com... 128.122.47.89\n",
       "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 9912422 (9.5M) [application/x-gzip]\n",
       "Saving to: 'train-images-idx3-ubyte.gz'\n",
       "\n",
-      "train-images-idx3-u 100%[=====================>]   9.45M   194KB/s   in 51s    \n",
+      "train-images-idx3-u 100%[=====================>]   9.45M   146KB/s   in 57s    \n",
       "\n",
-      "2015-03-12 11:54:54 (188 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
+      "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
       "\n",
-      "--2015-03-12 11:54:54--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
       "Resolving yann.lecun.com... 128.122.47.89\n",
       "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 28881 (28K) [application/x-gzip]\n",
       "Saving to: 'train-labels-idx1-ubyte.gz'\n",
       "\n",
-      "train-labels-idx1-u 100%[=====================>]  28.20K   104KB/s   in 0.3s   \n",
+      "train-labels-idx1-u 100%[=====================>]  28.20K   107KB/s   in 0.3s   \n",
       "\n",
-      "2015-03-12 11:54:55 (104 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
+      "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
       "\n",
-      "--2015-03-12 11:54:55--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
       "Resolving yann.lecun.com... 128.122.47.89\n",
       "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 1648877 (1.6M) [application/x-gzip]\n",
       "Saving to: 't10k-images-idx3-ubyte.gz'\n",
       "\n",
-      "t10k-images-idx3-ub 100%[=====================>]   1.57M   224KB/s   in 9.2s   \n",
+      "t10k-images-idx3-ub 100%[=====================>]   1.57M   205KB/s   in 8.2s   \n",
       "\n",
-      "2015-03-12 11:55:04 (176 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
+      "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
       "\n",
-      "--2015-03-12 11:55:04--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "--2015-06-30 14:43:02--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
       "Resolving yann.lecun.com... 128.122.47.89\n",
       "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 4542 (4.4K) [application/x-gzip]\n",
       "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
       "\n",
-      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  --.-KB/s   in 0.09s  \n",
+      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  26.9KB/s   in 0.2s   \n",
       "\n",
-      "2015-03-12 11:55:04 (50.0 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
+      "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
       "\n",
       "Unzipping...\n",
-      "train-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? ^Ctrain-labels-idx1-ubyte already exists -- do you wish to overwrite (y or n)? \n",
-      "\tnot overwriting\n",
-      "t10k-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? Creating lmdb...\n",
+      "Done.\n",
+      "Creating lmdb...\n",
       "Done.\n"
      ]
     }
    ],
    "source": [
+    "# Download and prepare data\n",
     "!data/mnist/get_mnist.sh\n",
     "!examples/mnist/create_mnist.sh"
    ]
@@ -118,67 +118,57 @@
    "metadata": {},
    "source": [
     "We need two external files to help out:\n",
+    "* the net prototxt, defining the architecture and pointing to the train/test data\n",
     "* the solver prototxt, defining the learning parameters\n",
-    "* the net prototxt, defining the architecture and train/test data\n",
     "\n",
-    "Here are the learning parameters."
+    "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+    "\n",
+    "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# The train/test net protocol buffer definition\r\n",
-      "net: \"examples/mnist/lenet_train_test.prototxt\"\r\n",
-      "# test_iter specifies how many forward passes the test should carry out.\r\n",
-      "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
-      "# covering the full 10,000 testing images.\r\n",
-      "test_iter: 100\r\n",
-      "# Carry out testing every 500 training iterations.\r\n",
-      "test_interval: 500\r\n",
-      "# The base learning rate, momentum and the weight decay of the network.\r\n",
-      "base_lr: 0.01\r\n",
-      "momentum: 0.9\r\n",
-      "weight_decay: 0.0005\r\n",
-      "# The learning rate policy\r\n",
-      "lr_policy: \"inv\"\r\n",
-      "gamma: 0.0001\r\n",
-      "power: 0.75\r\n",
-      "# Display every 100 iterations\r\n",
-      "display: 100\r\n",
-      "# The maximum number of iterations\r\n",
-      "max_iter: 10000\r\n",
-      "# snapshot intermediate results\r\n",
-      "snapshot: 5000\r\n",
-      "snapshot_prefix: \"examples/mnist/lenet\"\r\n",
-      "# solver mode: CPU or GPU\r\n",
-      "solver_mode: GPU\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!cat examples/mnist/lenet_solver.prototxt"
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def lenet(lmdb, batch_size):\n",
+    "    # our version of LeNet: a series of linear and simple nonlinear transformations\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+    "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+    "    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+    "    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
+    "    \n",
+    "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And here is the version of LeNet we'll be using. In future version of Caffe, networks can be defined directly in Python (see [PR #2086](https://github.com/BVLC/caffe/pull/2086)).\n",
-    "\n",
-    "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
+    "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -187,17 +177,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "name: \"LeNet\"\r\n",
       "layer {\r\n",
-      "  name: \"mnist\"\r\n",
+      "  name: \"data\"\r\n",
       "  type: \"Data\"\r\n",
       "  top: \"data\"\r\n",
       "  top: \"label\"\r\n",
-      "  include {\r\n",
-      "    phase: TRAIN\r\n",
-      "  }\r\n",
       "  transform_param {\r\n",
-      "    scale: 0.00390625\r\n",
+      "    scale: 0.00392156862745\r\n",
       "  }\r\n",
       "  data_param {\r\n",
       "    source: \"examples/mnist/mnist_train_lmdb\"\r\n",
@@ -206,43 +192,16 @@
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
-      "  name: \"mnist\"\r\n",
-      "  type: \"Data\"\r\n",
-      "  top: \"data\"\r\n",
-      "  top: \"label\"\r\n",
-      "  include {\r\n",
-      "    phase: TEST\r\n",
-      "  }\r\n",
-      "  transform_param {\r\n",
-      "    scale: 0.00390625\r\n",
-      "  }\r\n",
-      "  data_param {\r\n",
-      "    source: \"examples/mnist/mnist_test_lmdb\"\r\n",
-      "    batch_size: 100\r\n",
-      "    backend: LMDB\r\n",
-      "  }\r\n",
-      "}\r\n",
-      "layer {\r\n",
       "  name: \"conv1\"\r\n",
       "  type: \"Convolution\"\r\n",
       "  bottom: \"data\"\r\n",
       "  top: \"conv1\"\r\n",
-      "  param {\r\n",
-      "    lr_mult: 1\r\n",
-      "  }\r\n",
-      "  param {\r\n",
-      "    lr_mult: 2\r\n",
-      "  }\r\n",
       "  convolution_param {\r\n",
       "    num_output: 20\r\n",
       "    kernel_size: 5\r\n",
-      "    stride: 1\r\n",
       "    weight_filler {\r\n",
       "      type: \"xavier\"\r\n",
       "    }\r\n",
-      "    bias_filler {\r\n",
-      "      type: \"constant\"\r\n",
-      "    }\r\n",
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
@@ -261,22 +220,12 @@
       "  type: \"Convolution\"\r\n",
       "  bottom: \"pool1\"\r\n",
       "  top: \"conv2\"\r\n",
-      "  param {\r\n",
-      "    lr_mult: 1\r\n",
-      "  }\r\n",
-      "  param {\r\n",
-      "    lr_mult: 2\r\n",
-      "  }\r\n",
       "  convolution_param {\r\n",
       "    num_output: 50\r\n",
       "    kernel_size: 5\r\n",
-      "    stride: 1\r\n",
       "    weight_filler {\r\n",
       "      type: \"xavier\"\r\n",
       "    }\r\n",
-      "    bias_filler {\r\n",
-      "      type: \"constant\"\r\n",
-      "    }\r\n",
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
@@ -295,20 +244,11 @@
       "  type: \"InnerProduct\"\r\n",
       "  bottom: \"pool2\"\r\n",
       "  top: \"ip1\"\r\n",
-      "  param {\r\n",
-      "    lr_mult: 1\r\n",
-      "  }\r\n",
-      "  param {\r\n",
-      "    lr_mult: 2\r\n",
-      "  }\r\n",
       "  inner_product_param {\r\n",
       "    num_output: 500\r\n",
       "    weight_filler {\r\n",
       "      type: \"xavier\"\r\n",
       "    }\r\n",
-      "    bias_filler {\r\n",
-      "      type: \"constant\"\r\n",
-      "    }\r\n",
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
@@ -322,31 +262,11 @@
       "  type: \"InnerProduct\"\r\n",
       "  bottom: \"ip1\"\r\n",
       "  top: \"ip2\"\r\n",
-      "  param {\r\n",
-      "    lr_mult: 1\r\n",
-      "  }\r\n",
-      "  param {\r\n",
-      "    lr_mult: 2\r\n",
-      "  }\r",
-      "\r\n",
       "  inner_product_param {\r\n",
       "    num_output: 10\r\n",
       "    weight_filler {\r\n",
       "      type: \"xavier\"\r\n",
       "    }\r\n",
-      "    bias_filler {\r\n",
-      "      type: \"constant\"\r\n",
-      "    }\r\n",
-      "  }\r\n",
-      "}\r\n",
-      "layer {\r\n",
-      "  name: \"accuracy\"\r\n",
-      "  type: \"Accuracy\"\r\n",
-      "  bottom: \"ip2\"\r\n",
-      "  bottom: \"label\"\r\n",
-      "  top: \"accuracy\"\r\n",
-      "  include {\r\n",
-      "    phase: TEST\r\n",
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
@@ -360,7 +280,56 @@
     }
    ],
    "source": [
-    "!cat examples/mnist/lenet_train_test.prototxt"
+    "!cat examples/mnist/lenet_auto_train.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# The train/test net protocol buffer definition\r\n",
+      "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
+      "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
+      "# test_iter specifies how many forward passes the test should carry out.\r\n",
+      "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
+      "# covering the full 10,000 testing images.\r\n",
+      "test_iter: 100\r\n",
+      "# Carry out testing every 500 training iterations.\r\n",
+      "test_interval: 500\r\n",
+      "# The base learning rate, momentum and the weight decay of the network.\r\n",
+      "base_lr: 0.01\r\n",
+      "momentum: 0.9\r\n",
+      "weight_decay: 0.0005\r\n",
+      "# The learning rate policy\r\n",
+      "lr_policy: \"inv\"\r\n",
+      "gamma: 0.0001\r\n",
+      "power: 0.75\r\n",
+      "# Display every 100 iterations\r\n",
+      "display: 100\r\n",
+      "# The maximum number of iterations\r\n",
+      "max_iter: 10000\r\n",
+      "# snapshot intermediate results\r\n",
+      "snapshot: 5000\r\n",
+      "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat examples/mnist/lenet_auto_solver.prototxt"
    ]
   },
   {
@@ -372,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -380,7 +349,7 @@
    "source": [
     "caffe.set_device(0)\n",
     "caffe.set_mode_gpu()\n",
-    "solver = caffe.SGDSolver('examples/mnist/lenet_solver.prototxt')"
+    "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
    ]
   },
   {
@@ -392,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -412,7 +381,7 @@
        " ('loss', ())]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -424,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -438,7 +407,7 @@
        " ('ip2', (10, 500))]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -457,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -465,11 +434,10 @@
     {
      "data": {
       "text/plain": [
-       "{'accuracy': array(0.07000000029802322, dtype=float32),\n",
-       " 'loss': array(2.375746726989746, dtype=float32)}"
+       "{'loss': array(2.301163673400879, dtype=float32)}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -481,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -699,7 +667,7 @@
        "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109ed4650>"
+       "<matplotlib.figure.Figure at 0x7f7939901710>"
       ]
      },
      "metadata": {},
@@ -714,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -920,7 +888,7 @@
        "AAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d221bd0>"
+       "<matplotlib.figure.Figure at 0x7f7939901490>"
       ]
      },
      "metadata": {},
@@ -943,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -961,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -969,10 +937,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x123c20e90>"
+       "<matplotlib.image.AxesImage at 0x7f79383819d0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -980,470 +948,437 @@
      "data": {
       "image/png": [
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+       "WflXIuKfxEsyfk7OP306y1nO8tLI+ZcCZznLWV4aOQPtLGc5y0sjZ6Cd5SxneWnkDLSznOUsL42c\n",
+       "gXaWs5zlpZEz0M5ylrO8NHIG2lnOcpaXRs5AO8tZzvLSyP8P5bdSohzrzUEAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x122785190>"
+       "<matplotlib.figure.Figure at 0x7f7939901850>"
       ]
      },
      "metadata": {},
@@ -1472,7 +1407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -1481,16 +1416,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 13 s, sys: 134 ms, total: 13.1 s\n",
-      "Wall time: 13 s\n"
+      "Iteration 0 testing...\n",
+      "Iteration 25 testing...\n",
+      "Iteration 50 testing...\n",
+      "Iteration 75 testing...\n",
+      "Iteration 100 testing...\n",
+      "Iteration 125 testing...\n",
+      "Iteration 150 testing...\n",
+      "Iteration 175 testing...\n",
+      "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
+      "Wall time: 15.7 s\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
     "niter = 200\n",
+    "test_interval = 25\n",
     "# losses will also be stored in the log\n",
     "train_loss = zeros(niter)\n",
+    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
     "output = zeros((niter, 8, 10))\n",
     "\n",
     "# the main solver loop\n",
@@ -1501,21 +1446,33 @@
     "    train_loss[it] = solver.net.blobs['loss'].data\n",
     "    \n",
     "    # store the output on the first test batch\n",
-    "    # (start at conv1 to avoid loading new data)\n",
+    "    # (start the forward pass at conv1 to avoid loading new data)\n",
     "    solver.test_nets[0].forward(start='conv1')\n",
-    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]"
+    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
+    "    \n",
+    "    # run a full test every so often\n",
+    "    # (Caffe can also do this for us and write to a log, but we show here\n",
+    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
+    "    if it % test_interval == 0:\n",
+    "        print 'Iteration', it, 'testing...'\n",
+    "        correct = 0\n",
+    "        for test_it in range(100):\n",
+    "            solver.test_nets[0].forward()\n",
+    "            correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
+    "                           == solver.test_nets[0].blobs['label'].data)\n",
+    "        test_acc[it // test_interval] = correct / 1e4"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot the train loss. (Normally we should plot the test loss as well, but we're only testing every 500 iterations, and loss drops very quickly for this example.)"
+    "Let's plot the train loss and test accuracy."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1523,258 +1480,340 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x123c2ebd0>"
+       "<matplotlib.text.Text at 0x7f793878f490>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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+       "LuN/8dT415rZP2SfM7PfSnphOqcB/2ZmD0jancEVKSMsNRgzRBh2EARB0BHCBRcEQRB0hBCgIAiC\n",
+       "oCOEAAVBEAQdIQQoCIIg6AghQEEQBEFHCAEKgiAIOkIIUBAEQdARQoCCIAiCjvD/AXFRJnS871y9\n",
+       "AAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12276b250>"
+       "<matplotlib.figure.Figure at 0x7f79387025d0>"
       ]
      },
      "metadata": {},
@@ -1782,23 +1821,27 @@
     }
    ],
    "source": [
-    "plot(arange(niter), train_loss)\n",
-    "xlabel('iteration')\n",
-    "ylabel('train loss')"
+    "_, ax1 = subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(arange(niter), train_loss)\n",
+    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+    "ax1.set_xlabel('iteration')\n",
+    "ax1.set_ylabel('train loss')\n",
+    "ax2.set_ylabel('test accuracy')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It seems to have dropped quickly and coverged (except for stochasticity); hooray!\n",
+    "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
     "\n",
     "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -1908,7 +1951,7 @@
        "WjSakdGi0YyMFo1mZLRoNCPz/yU19i71FpCwAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x123da73d0>"
+       "<matplotlib.figure.Figure at 0x7f7938797710>"
       ]
      },
      "metadata": {},
@@ -1918,92 +1961,92 @@
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1241bfa10>"
+       "<matplotlib.figure.Figure at 0x7f7938323190>"
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      },
      "metadata": {},
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1242c3350>"
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      "metadata": {},
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1236dcf10>"
+       "<matplotlib.figure.Figure at 0x7f793811c290>"
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      },
      "metadata": {},
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        "YII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1234d7e10>"
+       "<matplotlib.figure.Figure at 0x7f79380a2550>"
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      },
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x123187410>"
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1243d6c90>"
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       "text/plain": [
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       "text/plain": [
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      "metadata": {},
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       ],
       "text/plain": [
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12477ad10>"
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      "metadata": {},
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       "text/plain": [
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x124972f50>"
+       "<matplotlib.figure.Figure at 0x7d792b91c3d0>"
       ]
      },
      "metadata": {},
@@ -3504,7 +3549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -3614,7 +3659,7 @@
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      },
      "metadata": {},
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x125d7d610>"
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       "text/plain": [
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x124940ad0>"
+       "<matplotlib.figure.Figure at 0x7d792b2b79d0>"
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      },
      "metadata": {},
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        "YII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1259c29d0>"
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       "text/plain": [
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12592e410>"
+       "<matplotlib.figure.Figure at 0x7d792b127d90>"
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      "metadata": {},
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1249e5110>"
+       "<matplotlib.figure.Figure at 0x7d792b06c950>"
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      },
      "metadata": {},
@@ -4796,7 +4843,7 @@
        "SUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1242c3b90>"
+       "<matplotlib.figure.Figure at 0x7d792af4a550>"
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      },
      "metadata": {},
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       ],
       "text/plain": [
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      "metadata": {},
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       "text/plain": [
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+       "FZFQAQAAVERCBQAAUBEJFQAAQEUkVAAAABWRUAEAAFREQgUAAFARCRUAAEBF/w/CsMbhRL/ldgAA\n",
+       "AABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x124a05cd0>"
+       "<matplotlib.figure.Figure at 0x7d792baffb50>"
       ]
      },
      "metadata": {},
@@ -5125,6 +5169,9 @@
   }
  ],
  "metadata": {
+  "description": "Define, train, and test the classic LeNet with the Python interface.",
+  "example_name": "Learning LeNet",
+  "include_in_docs": true,
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",
@@ -5141,7 +5188,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
    "version": "2.7.9"
-  }
+  },
+  "priority": 2
  },
  "nbformat": 4,
  "nbformat_minor": 0
diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb
new file mode 100644
index 00000000000..d36871fcdfd
--- /dev/null
+++ b/examples/02-brewing-logreg.ipynb
@@ -0,0 +1,5771 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brewing Logistic Regression then Going Deeper\n",
+    "\n",
+    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "import h5py\n",
+    "import shutil\n",
+    "import tempfile\n",
+    "\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import sklearn.linear_model\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "GLGwsLCwsLDIKZYxYmFhYWFhYZFTLGPEwsLCwsLCIqdYxoiFhYWFhYVFTrGMEQsLCwsLC4ucYhkj\n",
+       "FhYWFhYWFjnl/wPBByFp6Gp27QAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11dbd0090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(\n",
+    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+    ")\n",
+    "\n",
+    "# Split into train and test\n",
+    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "\n",
+    "# Visualize sample of the data\n",
+    "ind = np.random.permutation(X.shape[0])[:1000]\n",
+    "df = pd.DataFrame(X[ind])\n",
+    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.783\n",
+      "Accuracy: 0.783\n",
+      "Accuracy: 0.783\n",
+      "Accuracy: 0.783\n",
+      "1 loops, best of 3: 508 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# Train and test the scikit-learn SGD logistic regression.\n",
+    "clf = sklearn.linear_model.SGDClassifier(\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
+    "\n",
+    "clf.fit(X, y)\n",
+    "yt_pred = clf.predict(Xt)\n",
+    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the dataset to HDF5 for loading in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Write out the data to HDF5 files in a temp directory.\n",
+    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+    "if not os.path.exists(dirname):\n",
+    "    os.makedirs(dirname)\n",
+    "\n",
+    "train_filename = os.path.join(dirname, 'train.h5')\n",
+    "test_filename = os.path.join(dirname, 'test.h5')\n",
+    "\n",
+    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+    "# To show this off, we'll list the same data file twice.\n",
+    "with h5py.File(train_filename, 'w') as f:\n",
+    "    f['data'] = X\n",
+    "    f['label'] = y.astype(np.float32)\n",
+    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    \n",
+    "# HDF5 is pretty efficient, but can be further compressed.\n",
+    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+    "with h5py.File(test_filename, 'w') as f:\n",
+    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+    "    f.write(test_filename + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def logreg(hdf5, batch_size):\n",
+    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "    \n",
+    "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to learn and evaluate our Caffeinated logistic regression in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.782\n",
+      "Accuracy: 0.782\n",
+      "Accuracy: 0.782\n",
+      "Accuracy: 0.782\n",
+      "1 loops, best of 3: 287 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n",
+      "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n",
+      "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+      "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+      "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
+      "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
+      "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.652724 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
+      "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n",
+      "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+      "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+      "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
+      "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
+      "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
+      "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
+      "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
+      "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:32.655422 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
+      "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n",
+      "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n",
+      "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n",
+      "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
+      "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+      "I0318 00:58:32.683643 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.3736\n",
+      "I0318 00:58:32.683686 2013098752 solver.cpp:315]     Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n",
+      "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n",
+      "I0318 00:58:32.683861 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.3\n",
+      "I0318 00:58:32.683871 2013098752 solver.cpp:204]     Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n",
+      "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
+      "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+      "I0318 00:58:32.701917 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
+      "I0318 00:58:32.701961 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
+      "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n",
+      "I0318 00:58:32.702029 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:32.702041 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
+      "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+      "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+      "I0318 00:58:32.721529 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
+      "I0318 00:58:32.721562 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
+      "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n",
+      "I0318 00:58:32.721603 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:32.721613 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
+      "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+      "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+      "I0318 00:58:32.743494 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.77\n",
+      "I0318 00:58:32.743544 2013098752 solver.cpp:315]     Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n",
+      "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n",
+      "I0318 00:58:32.744231 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
+      "I0318 00:58:32.744249 2013098752 solver.cpp:204]     Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n",
+      "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+      "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+      "I0318 00:58:32.769110 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
+      "I0318 00:58:32.769170 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
+      "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n",
+      "I0318 00:58:32.769242 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:32.769255 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
+      "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+      "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+      "I0318 00:58:32.788722 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
+      "I0318 00:58:32.788751 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
+      "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n",
+      "I0318 00:58:32.788833 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:32.788846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
+      "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+      "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+      "I0318 00:58:32.808061 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7856\n",
+      "I0318 00:58:32.808112 2013098752 solver.cpp:315]     Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n",
+      "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n",
+      "I0318 00:58:32.808753 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:32.808773 2013098752 solver.cpp:204]     Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n",
+      "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+      "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+      "I0318 00:58:32.831614 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
+      "I0318 00:58:32.831657 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n",
+      "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n",
+      "I0318 00:58:32.831728 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
+      "I0318 00:58:32.831745 2013098752 solver.cpp:204]     Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n",
+      "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+      "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+      "I0318 00:58:32.852712 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7796\n",
+      "I0318 00:58:32.852748 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n",
+      "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n",
+      "I0318 00:58:32.852840 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:32.852852 2013098752 solver.cpp:204]     Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n",
+      "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+      "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+      "I0318 00:58:32.871438 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7816\n",
+      "I0318 00:58:32.871461 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n",
+      "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n",
+      "I0318 00:58:32.872131 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:32.872143 2013098752 solver.cpp:204]     Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n",
+      "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+      "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n",
+      "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+      "I0318 00:58:32.893728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.782\n",
+      "I0318 00:58:32.893757 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n",
+      "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n",
+      "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n",
+    "\n",
+    "The final accuracy of the new network should be higher than logistic regression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def nonlinear_net(hdf5, batch_size):\n",
+    "    # one small nonlinearity, one leap for model kind\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    # define a hidden layer of dimension 40\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    # score the (now non-linear) features\n",
+    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    # same accuracy and loss as before\n",
+    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "    \n",
+    "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.832\n",
+      "Accuracy: 0.832\n",
+      "Accuracy: 0.832\n",
+      "Accuracy: 0.831\n",
+      "1 loops, best of 3: 386 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n",
+      "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n",
+      "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+      "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
+      "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n",
+      "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n",
+      "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
+      "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n",
+      "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
+      "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n",
+      "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n",
+      "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n",
+      "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n",
+      "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
+      "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
+      "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.662909 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
+      "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n",
+      "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n",
+      "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n",
+      "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+      "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n",
+      "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n",
+      "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n",
+      "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n",
+      "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n",
+      "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
+      "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
+      "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
+      "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
+      "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n",
+      "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
+      "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
+      "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n",
+      "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n",
+      "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
+      "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n",
+      "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n",
+      "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n",
+      "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n",
+      "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n",
+      "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n",
+      "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
+      "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n",
+      "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
+      "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
+      "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n",
+      "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n",
+      "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n",
+      "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n",
+      "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
+      "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
+      "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
+      "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
+      "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
+      "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n",
+      "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
+      "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
+      "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n",
+      "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n",
+      "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n",
+      "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
+      "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
+      "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n",
+      "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n",
+      "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n",
+      "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n",
+      "I0318 00:58:43.665282 2013098752 net.cpp:122]     with loss weight 1\n",
+      "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n",
+      "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
+      "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
+      "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n",
+      "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n",
+      "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n",
+      "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
+      "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n",
+      "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n",
+      "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n",
+      "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
+      "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n",
+      "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n",
+      "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n",
+      "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n",
+      "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
+      "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
+      "I0318 00:58:43.683938 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.5184\n",
+      "I0318 00:58:43.683981 2013098752 solver.cpp:315]     Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n",
+      "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n",
+      "I0318 00:58:43.684267 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
+      "I0318 00:58:43.684285 2013098752 solver.cpp:204]     Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n",
+      "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
+      "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
+      "I0318 00:58:43.721762 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8168\n",
+      "I0318 00:58:43.721818 2013098752 solver.cpp:315]     Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n",
+      "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n",
+      "I0318 00:58:43.721917 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.721932 2013098752 solver.cpp:204]     Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n",
+      "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
+      "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
+      "I0318 00:58:43.754590 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8224\n",
+      "I0318 00:58:43.754621 2013098752 solver.cpp:315]     Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n",
+      "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n",
+      "I0318 00:58:43.754672 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.754683 2013098752 solver.cpp:204]     Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n",
+      "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
+      "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
+      "I0318 00:58:43.789728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8176\n",
+      "I0318 00:58:43.789777 2013098752 solver.cpp:315]     Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n",
+      "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n",
+      "I0318 00:58:43.790510 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.790530 2013098752 solver.cpp:204]     Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n",
+      "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
+      "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
+      "I0318 00:58:43.821740 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8252\n",
+      "I0318 00:58:43.821770 2013098752 solver.cpp:315]     Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n",
+      "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n",
+      "I0318 00:58:43.821835 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.821846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n",
+      "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
+      "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
+      "I0318 00:58:43.852102 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8256\n",
+      "I0318 00:58:43.852145 2013098752 solver.cpp:315]     Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n",
+      "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n",
+      "I0318 00:58:43.852200 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.852210 2013098752 solver.cpp:204]     Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n",
+      "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
+      "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
+      "I0318 00:58:43.880080 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8328\n",
+      "I0318 00:58:43.880105 2013098752 solver.cpp:315]     Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n",
+      "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n",
+      "I0318 00:58:43.880718 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.880729 2013098752 solver.cpp:204]     Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n",
+      "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
+      "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
+      "I0318 00:58:43.917851 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8316\n",
+      "I0318 00:58:43.917876 2013098752 solver.cpp:315]     Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n",
+      "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n",
+      "I0318 00:58:43.917971 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.917989 2013098752 solver.cpp:204]     Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n",
+      "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
+      "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
+      "I0318 00:58:43.947589 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8312\n",
+      "I0318 00:58:43.947615 2013098752 solver.cpp:315]     Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n",
+      "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n",
+      "I0318 00:58:43.947664 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
+      "I0318 00:58:43.947674 2013098752 solver.cpp:204]     Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n",
+      "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
+      "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
+      "I0318 00:58:43.977033 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.834\n",
+      "I0318 00:58:43.977056 2013098752 solver.cpp:315]     Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n",
+      "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n",
+      "I0318 00:58:43.977735 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
+      "I0318 00:58:43.977746 2013098752 solver.cpp:204]     Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n",
+      "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
+      "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n",
+      "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
+      "I0318 00:58:44.008216 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8308\n",
+      "I0318 00:58:44.008252 2013098752 solver.cpp:315]     Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n",
+      "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n",
+      "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+    "shutil.rmtree(dirname)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "example_name": "Off-the-shelf SGD for classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb
new file mode 100644
index 00000000000..cc90b16bbfa
--- /dev/null
+++ b/examples/03-fine-tuning.ipynb
@@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-tuning a Pretrained Network for Style Recognition\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+    "\n",
+    "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('..')\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "\n",
+    "import caffe\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1 --images=2000 --seed=1701 --label=5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's show what is the difference between the fine-tuning network and the original caffe model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1c1\r\n",
+      "< name: \"CaffeNet\"\r\n",
+      "---\r\n",
+      "> name: \"FlickrStyleCaffeNet\"\r\n",
+      "4c4\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "15,26c15,19\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
+      "<     batch_size: 256\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/train.txt\"\r\n",
+      ">     batch_size: 50\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "31c24\r\n",
+      "<   type: \"Data\"\r\n",
+      "---\r\n",
+      ">   type: \"ImageData\"\r\n",
+      "42,51c35,36\r\n",
+      "< # mean pixel / channel-wise mean instead of mean image\r\n",
+      "< #  transform_param {\r\n",
+      "< #    crop_size: 227\r\n",
+      "< #    mean_value: 104\r\n",
+      "< #    mean_value: 117\r\n",
+      "< #    mean_value: 123\r\n",
+      "< #    mirror: true\r\n",
+      "< #  }\r\n",
+      "<   data_param {\r\n",
+      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
+      "---\r\n",
+      ">   image_data_param {\r\n",
+      ">     source: \"data/flickr_style/test.txt\"\r\n",
+      "53c38,39\r\n",
+      "<     backend: LMDB\r\n",
+      "---\r\n",
+      ">     new_height: 256\r\n",
+      ">     new_width: 256\r\n",
+      "323a310\r\n",
+      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
+      "360c347\r\n",
+      "<   name: \"fc8\"\r\n",
+      "---\r\n",
+      ">   name: \"fc8_flickr\"\r\n",
+      "363c350,351\r\n",
+      "<   top: \"fc8\"\r\n",
+      "---\r\n",
+      ">   top: \"fc8_flickr\"\r\n",
+      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
+      "365c353\r\n",
+      "<     lr_mult: 1\r\n",
+      "---\r\n",
+      ">     lr_mult: 10\r\n",
+      "369c357\r\n",
+      "<     lr_mult: 2\r\n",
+      "---\r\n",
+      ">     lr_mult: 20\r\n",
+      "373c361\r\n",
+      "<     num_output: 1000\r\n",
+      "---\r\n",
+      ">     num_output: 20\r\n",
+      "384a373,379\r\n",
+      ">   name: \"loss\"\r\n",
+      ">   type: \"SoftmaxWithLoss\"\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      ">   bottom: \"label\"\r\n",
+      ">   top: \"loss\"\r\n",
+      "> }\r\n",
+      "> layer {\r\n",
+      "387c382\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8_flickr\"\r\n",
+      "393,399d387\r\n",
+      "< }\r\n",
+      "< layer {\r\n",
+      "<   name: \"loss\"\r\n",
+      "<   type: \"SoftmaxWithLoss\"\r\n",
+      "<   bottom: \"fc8\"\r\n",
+      "<   bottom: \"label\"\r\n",
+      "<   top: \"loss\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
+      "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
+      "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
+      "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
+      "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
+      "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
+      "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
+      "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
+      "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
+      "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
+      "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
+      "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
+      "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
+      "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
+      "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
+      "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
+      "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
+      "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
+      "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
+      "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = np.zeros(niter)\n",
+    "scratch_train_loss = np.zeros(niter)\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "# We create a solver that fine-tunes from a previously trained network.\n",
+    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "# For reference, we also create a solver that does no finetuning.\n",
+    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
+    "\n",
+    "# We run the solver for niter times, and record the training loss.\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    scratch_solver.step(1)\n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
+    "    if it % 10 == 0:\n",
+    "        print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
+    "print 'done'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss produced by the two training procedures respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "Vvn5SNJ9pNt+5Lrt5kUnAdd2PAfrRGfb7zrgOElDJO1E+r3Oe/qhfFuMHDptjiHtn+Bt2S1JIt1y\n",
+       "eEFEnF/yVk32z57cJqBD4YuSamEC8Mv02TMIuDIibpZ0H3CVpJNJP3j7l/1XxIFN0s9J99zeRtIz\n",
+       "pB8EPpsOtl9ELJB0FenHhzcCf5troEaH27IANEmaRmoGWAzMBG/LMu0PnAg8JKntzo7/QI32T198\n",
+       "ZGZWR3rl4iMzM+sfDnUzszriUDczqyMOdTOzOuJQNzOrIw51M7M64lA3M6sjDnUzszry/wFBsEB8\n",
+       "UlvRigAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbb37f20990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "fElLgWXAvgraN2fkBNTwEKl/gmPZkyQB24GjEfFG06lS+mffl+yLDoVS/f7cGhkCPk39gJuADyJi\n",
+       "r6T9wC5JTwATwKPVNXF2k7QTuA+4TdIp4GVgG23iFxFHJe0CjgKXgafznanRNpZbgHFJK0iPCk4C\n",
+       "jSJIx7K3lcDjwGFJB/KxTZTUP13oZGZWU16T1cysppzgzcxqygnezKymnODNzGrKCd7MrKac4M3M\n",
+       "asoJ3sysppzgzcxq6j+vUsbacqJa4gAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for fine-tuning: 0.570000001788\n",
+      "Accuracy for training from scratch: 0.224000000954\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_iters = 10\n",
+    "accuracy = 0\n",
+    "scratch_accuracy = 0\n",
+    "for it in arange(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "    scratch_solver.test_nets[0].forward()\n",
+    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "scratch_accuracy /= test_iters\n",
+    "print 'Accuracy for fine-tuning:', accuracy\n",
+    "print 'Accuracy for training from scratch:', scratch_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+  "example_name": "Fine-tuning for Style Recognition",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/Finetune with Flickr Style Data.ipynb b/examples/Finetune with Flickr Style Data.ipynb
deleted file mode 100644
index d8cfc7a3925..00000000000
--- a/examples/Finetune with Flickr Style Data.ipynb	
+++ /dev/null
@@ -1,951 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Finetune a Pretrained Network with Flickr Style Data\n",
-    "\n",
-    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network, and finetune the last few layers using your custom data.\n",
-    "\n",
-    "The upside of such approach is that, since pre-trained networks are trained on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we will need to prepare the data. This involves the following parts:\n",
-    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
-    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
-    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/jiayq/Research/caffe\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "os.chdir('..')\n",
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "print os.getcwd()\n",
-    "\n",
-    "import caffe\n",
-    "import numpy as np\n",
-    "from pylab import *\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading...\n",
-      "--2015-03-17 10:51:07--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
-      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
-      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 17858008 (17M) [application/octet-stream]\n",
-      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
-      "\n",
-      "100%[======================================>] 17,858,008   287KB/s   in 55s    \n",
-      "\n",
-      "2015-03-17 10:52:02 (318 KB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
-      "\n",
-      "Unzipping...\n",
-      "Done.\n",
-      "Model already exists.\n",
-      "Downloading 2000 images with 3 workers...\n",
-      "Writing train/val for 1903 successfully downloaded images.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
-    "# and a subset of 2000 images for the style recognition task.\n",
-    "\n",
-    "# You won't need to run this - we should have already created it for you.\n",
-    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
-    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
-    "    --workers=-1 --images=2000 --seed=1701 --label=5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's show what is the difference between the finetune network and the original caffe model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1c1\r\n",
-      "< name: \"CaffeNet\"\r\n",
-      "---\r\n",
-      "> name: \"FlickrStyleCaffeNet\"\r\n",
-      "4c4\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "15,26c15,19\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
-      "<     batch_size: 256\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/train.txt\"\r\n",
-      ">     batch_size: 50\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "31c24\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "42,51c35,36\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/test.txt\"\r\n",
-      "53c38,39\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "323a310\r\n",
-      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
-      "360c347\r\n",
-      "<   name: \"fc8\"\r\n",
-      "---\r\n",
-      ">   name: \"fc8_flickr\"\r\n",
-      "363c350,351\r\n",
-      "<   top: \"fc8\"\r\n",
-      "---\r\n",
-      ">   top: \"fc8_flickr\"\r\n",
-      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
-      "365c353\r\n",
-      "<     lr_mult: 1\r\n",
-      "---\r\n",
-      ">     lr_mult: 10\r\n",
-      "369c357\r\n",
-      "<     lr_mult: 2\r\n",
-      "---\r\n",
-      ">     lr_mult: 20\r\n",
-      "373c361\r\n",
-      "<     num_output: 1000\r\n",
-      "---\r\n",
-      ">     num_output: 20\r\n",
-      "384a373,379\r\n",
-      ">   name: \"loss\"\r\n",
-      ">   type: \"SoftmaxWithLoss\"\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      ">   bottom: \"label\"\r\n",
-      ">   top: \"loss\"\r\n",
-      "> }\r\n",
-      "> layer {\r\n",
-      "387c382\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "---\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      "393,399d387\r\n",
-      "< }\r\n",
-      "< layer {\r\n",
-      "<   name: \"loss\"\r\n",
-      "<   type: \"SoftmaxWithLoss\"\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "<   bottom: \"label\"\r\n",
-      "<   top: \"loss\"\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
-    "\n",
-    "<code>\n",
-    "build/tools/caffe train \\\n",
-    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
-    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
-    "    -gpu 0\n",
-    "</code>\n",
-    "\n",
-    "However, we will train using Python in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter 0, loss=3.786610, scratch_loss=3.163587\n",
-      "iter 10, loss=2.556661, scratch_loss=8.774073\n",
-      "iter 20, loss=2.035326, scratch_loss=2.266603\n",
-      "iter 30, loss=1.943101, scratch_loss=1.703273\n",
-      "iter 40, loss=1.982698, scratch_loss=1.831079\n",
-      "iter 50, loss=1.559268, scratch_loss=2.041238\n",
-      "iter 60, loss=1.464433, scratch_loss=1.836157\n",
-      "iter 70, loss=1.481868, scratch_loss=1.705826\n",
-      "iter 80, loss=1.394870, scratch_loss=1.695532\n",
-      "iter 90, loss=1.055422, scratch_loss=1.867379\n",
-      "iter 100, loss=1.407976, scratch_loss=1.881758\n",
-      "iter 110, loss=1.569579, scratch_loss=1.701803\n",
-      "iter 120, loss=0.951682, scratch_loss=1.764299\n",
-      "iter 130, loss=0.905122, scratch_loss=1.879305\n",
-      "iter 140, loss=1.020678, scratch_loss=1.746009\n",
-      "iter 150, loss=0.784985, scratch_loss=1.739624\n",
-      "iter 160, loss=0.911735, scratch_loss=1.673230\n",
-      "iter 170, loss=0.965255, scratch_loss=1.725484\n",
-      "iter 180, loss=1.028102, scratch_loss=1.676103\n",
-      "iter 190, loss=0.905020, scratch_loss=1.885763\n",
-      "done\n"
-     ]
-    }
-   ],
-   "source": [
-    "niter = 200\n",
-    "# losses will also be stored in the log\n",
-    "train_loss = np.zeros(niter)\n",
-    "scratch_train_loss = np.zeros(niter)\n",
-    "\n",
-    "caffe.set_device(0)\n",
-    "caffe.set_mode_gpu()\n",
-    "# We create a solver that finetunes from a previously trained network.\n",
-    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-    "# For reference, we also create a solver that does no finetuning.\n",
-    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "\n",
-    "# We run the solver for niter times, and record the training loss.\n",
-    "for it in range(niter):\n",
-    "    solver.step(1)  # SGD by Caffe\n",
-    "    scratch_solver.step(1)\n",
-    "    # store the train loss\n",
-    "    train_loss[it] = solver.net.blobs['loss'].data\n",
-    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
-    "    if it % 10 == 0:\n",
-    "        print 'iter %d, loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
-    "print 'done'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the training loss produced by the two training procedures respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f39dad72390>,\n",
-       " <matplotlib.lines.Line2D at 0x7f39dad72610>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "WE19PLWP72hd1u9wrI/3GdScEjoYbZ8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIikk//DzX8Jz0M\n",
-       "ra0pAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3a2803d590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f39d50acc90>,\n",
-       " <matplotlib.lines.Line2D at 0x7f39d50acf10>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f39dad22150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy for fine-tuning: 0.570000001788\n",
-      "Accuracy for training from scratch: 0.224000000954\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_iters = 10\n",
-    "accuracy = 0\n",
-    "scratch_accuracy = 0\n",
-    "for it in arange(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "    scratch_solver.test_nets[0].forward()\n",
-    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "scratch_accuracy /= test_iters\n",
-    "print 'Accuracy for fine-tuning:', accuracy\n",
-    "print 'Accuracy for training from scratch:', scratch_accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
-    "\n",
-    "http://demo.vislab.berkeleyvision.org/"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
deleted file mode 100644
index a76cfb10773..00000000000
--- a/examples/classification.ipynb
+++ /dev/null
@@ -1,3342 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classifying ImageNet: the instant Caffe way\n",
-    "===========================================\n",
-    "\n",
-    "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n",
-    "\n",
-    "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n",
-    "\n",
-    "Ready? Let's start."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-    "import sys\n",
-    "sys.path.insert(0, caffe_root + 'python')\n",
-    "\n",
-    "import caffe\n",
-    "\n",
-    "# Set the right path to your model definition file, pretrained model weights,\n",
-    "# and the image you would like to classify.\n",
-    "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
-    "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
-    "IMAGE_FILE = 'images/cat.jpg'\n",
-    "\n",
-    "import os\n",
-    "if not os.path.isfile(PRETRAINED):\n",
-    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
-    "\n",
-    "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "caffe.set_mode_cpu()\n",
-    "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-    "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
-    "                       channel_swap=(2,1,0),\n",
-    "                       raw_scale=255,\n",
-    "                       image_dims=(256, 256))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at our example image with Caffe's image loading helper."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "/e/8zu+IM2fOiPPnz4svf/nLb+CT/8vx1FNPCUVRxOXLl8WVK1fElStXxJe+9KU7ft3f+973xNWr\n",
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-       "McdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fda517da210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "input_image = caffe.io.load_image(IMAGE_FILE)\n",
-    "plt.imshow(input_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "prediction shape: (1000,)\n",
-      "predicted class: 281\n"
-     ]
-    },
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fda20473950>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
-    "print 'prediction shape:', prediction[0].shape\n",
-    "plt.plot(prediction[0])\n",
-    "print 'predicted class:', prediction[0].argmax()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
-    "\n",
-    "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
-    "\n",
-    "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "prediction shape: (1000,)\n",
-      "predicted class: 281\n"
-     ]
-    },
-    {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "prediction = net.predict([input_image], oversample=False)\n",
-    "print 'prediction shape:', prediction[0].shape\n",
-    "plt.plot(prediction[0])\n",
-    "print 'predicted class:', prediction[0].argmax()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 355 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit net.predict([input_image])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n",
-    "\n",
-    "Let's time classifying a single image with input preprocessed:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 210 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
-    "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
-    "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
-    "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
-    "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
-    "%timeit net.forward(data=caffe_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OK, so how about GPU? it is actually pretty easy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "caffe.set_mode_gpu()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Voila! Now we are in GPU mode. Let's see if the code gives the same result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "prediction shape: (1000,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fda1ab53650>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "prediction = net.predict([input_image])\n",
-    "print 'prediction shape:', prediction[0].shape\n",
-    "plt.plot(prediction[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 loops, best of 3: 174 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Full pipeline timing.\n",
-    "%timeit net.predict([input_image])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 loops, best of 3: 34.2 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Forward pass timing.\n",
-    "%timeit net.forward(data=caffe_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
-    "\n",
-    "To fully utilize the power of GPUs, you really want to:\n",
-    "\n",
-    "* Use larger batches, and minimize python call and data transfer overheads.\n",
-    "* Pipeline data load operations, like using a subprocess.\n",
-    "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Parting Words\n",
-    "-------------\n",
-    "\n",
-    "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "example_name": "ImageNet classification",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 1
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index e2a981a00d7..6a03c996245 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -8385,7 +8385,7 @@
    "pygments_lexer": "ipython2",
    "version": "2.7.9"
   },
-  "priority": 3
+  "priority": 6
  },
  "nbformat": 4,
  "nbformat_minor": 0
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
deleted file mode 100644
index 6d629c5b635..00000000000
--- a/examples/filter_visualization.ipynb
+++ /dev/null
@@ -1,13214 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n",
-    "\n",
-    "(This page follows DeCAF visualizations originally by Yangqing Jia.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-    "import sys\n",
-    "sys.path.insert(0, caffe_root + 'python')\n",
-    "\n",
-    "import caffe\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = (10, 10)\n",
-    "plt.rcParams['image.interpolation'] = 'nearest'\n",
-    "plt.rcParams['image.cmap'] = 'gray'\n",
-    "\n",
-    "import os\n",
-    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "caffe.set_mode_cpu()\n",
-    "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-    "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-    "                caffe.TEST)\n",
-    "\n",
-    "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
-    "transformer.set_transpose('data', (2,0,1))\n",
-    "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
-    "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-    "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classify the image by reshaping the net for the single input then doing the forward pass."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted class is #281.\n"
-     ]
-    }
-   ],
-   "source": [
-    "net.blobs['data'].reshape(1,3,227,227)\n",
-    "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
-    "out = net.forward()\n",
-    "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('data', (1, 3, 227, 227)),\n",
-       " ('conv1', (1, 96, 55, 55)),\n",
-       " ('pool1', (1, 96, 27, 27)),\n",
-       " ('norm1', (1, 96, 27, 27)),\n",
-       " ('conv2', (1, 256, 27, 27)),\n",
-       " ('pool2', (1, 256, 13, 13)),\n",
-       " ('norm2', (1, 256, 13, 13)),\n",
-       " ('conv3', (1, 384, 13, 13)),\n",
-       " ('conv4', (1, 384, 13, 13)),\n",
-       " ('conv5', (1, 256, 13, 13)),\n",
-       " ('pool5', (1, 256, 6, 6)),\n",
-       " ('fc6', (1, 4096)),\n",
-       " ('fc7', (1, 4096)),\n",
-       " ('fc8', (1, 1000)),\n",
-       " ('prob', (1, 1000))]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(k, v.data.shape) for k, v in net.blobs.items()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('conv1', (96, 3, 11, 11)),\n",
-       " ('conv2', (256, 48, 5, 5)),\n",
-       " ('conv3', (384, 256, 3, 3)),\n",
-       " ('conv4', (384, 192, 3, 3)),\n",
-       " ('conv5', (256, 192, 3, 3)),\n",
-       " ('fc6', (4096, 9216)),\n",
-       " ('fc7', (4096, 4096)),\n",
-       " ('fc8', (1000, 4096))]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(k, v[0].data.shape) for k, v in net.params.items()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Helper functions for visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-    "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
-    "def vis_square(data, padsize=1, padval=0):\n",
-    "    data -= data.min()\n",
-    "    data /= data.max()\n",
-    "    \n",
-    "    # force the number of filters to be square\n",
-    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
-    "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
-    "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
-    "    \n",
-    "    # tile the filters into an image\n",
-    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
-    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
-    "    \n",
-    "    plt.imshow(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The input image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
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-       "AABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first layer filters, `conv1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "RK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# the parameters are a list of [weights, biases]\n",
-    "filters = net.params['conv1'][0].data\n",
-    "vis_square(filters.transpose(0, 2, 3, 1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "gICAgICAgC0ibKQCAgICAgICAraI/wdvk9M+hXkCqAAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd621d91150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['conv1'].data[0, :36]\n",
-    "vis_square(feat, padval=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second layer filters, `conv2`\n",
-    "\n",
-    "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "KfGTn/xkcNpppw1OO+20wUknndTp/Zlnnhm8/vWvHxx77LGDs88+e7Bp06YllvTFhbe85S2Dww47\n",
-       "bDA9PT1YvXr14K//+q93q/Mrr7xysHbt2sGrXvWqwde+9rUllPzFA4/BbbfdNnjb2942OOWUUwan\n",
-       "nnrq4Hd/93cHTz75ZHd9jcGexTe+8Y3BxMTE4LTTThucfvrpg9NPP31w55131jrYixg3Bl/96ldr\n",
-       "HexBFEVMoVAoFAqFwiJ6dsjvAAAAYElEQVRRlc0LhUKhUCgUFok6SBUKhUKhUCgsEnWQKhQKhUKh\n",
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-       "i8T/A2aklexSCttuAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd630658d90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "filters = net.params['conv2'][0].data\n",
-    "vis_square(filters[:48].reshape(48**2, 5, 5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd621d73990>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['conv2'].data[0, :36]\n",
-    "vis_square(feat, padval=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The third layer output, `conv3` (rectified, all 384 channels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "N785+XmPx2DZtm1bZ2xsrDM+Pt5Zu3ZtZ+3atZ177rnHa2QETDcWd999t9fHNLhEjDHGGGNMJc5s\n",
-       "bowxxhhTiR+kjDHGGGMq8YOUMcYYY0wlfpAyxhhjjKnED1LGGGOMMZX4QcoYY4wxphI/SBljjDHG\n",
-       "VOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['conv3'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The fourth layer output, `conv4` (rectified, all 384 channels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "zZgxI5syZUrW09OTPfXUU2OW/cMPP5zNnj07u+6667Jdu+tes7EAAACgSURBVHZVeOeTk7A+nnzy\n",
-       "yeyOO+7IlixZki1dujS75ZZbsrfffrt+vOujvbzyyitZrVbL+vr6smXLlmXLli3Ldu7c6T5SAaPV\n",
-       "xfPPP+/+MQreIsYYY4wxpiTObG6MMcYYUxJPpIwxxhhjSuKJlDHGGGNMSTyRMsYYY4wpiSdSxhhj\n",
-       "jDEl8UTKGGOMMaYknkgZY4wxxpTEEyljjDHGmJL8HyFOTlbukMv2AAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd621d25190>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['conv4'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The fifth layer output, `conv5` (rectified, all 256 channels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "y8TKSY+neS8sLMjw8LB4vV5paWmRN2/eyOXlpXo88y5sBwcHYrFYRFEU8fl84vP5JBwOc4xr4CVi\n",
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-       "xEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['conv5'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The fifth layer after pooling, `pool5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "IqDoAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['pool5'].data[0]\n",
-    "vis_square(feat, padval=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first fully connected layer, `fc6` (rectified)\n",
-    "\n",
-    "We show the output values and the histogram of the positive values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "y8ulqKhIrFarPHr0SDY3N8Xn88n58+fF7/fL1tZWvpd5rExOTorFYhG32y0ej0c8Ho+Mjo6yu0aJ\n",
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-       "J8FgUNLptNLm3GiUiIiISDEtG40SERERnWQcsIiIiIgU44BFREREpBgHLCIiIiLFOGARERERKcYB\n",
-       "i4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd63064b890>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['fc6'].data[0]\n",
-    "plt.subplot(2, 1, 1)\n",
-    "plt.plot(feat.flat)\n",
-    "plt.subplot(2, 1, 2)\n",
-    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second fully connected layer, `fc7` (rectified)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "hQsX6vjjj9dll13W8znHXzo41Z+p4y+Uca42btyoyy67rGeA0Wuuucb0IhCSLVu2aM6cOZJyI/Ff\n",
-       "cMEF1F+CzZs3T48++qh27NihmpoaXXfddTrnnHN0/vnn680339SoUaO0du1affazn407VNgorr/F\n",
-       "ixcrm81q8+bNymQyGj16tFasWNHThwfJ8cQTT+jUU0/VpEmTem793XjjjZoyZQrHXwrY1d8NN9yg\n",
-       "NWvWGDn+GEQUAADAoFAGEQUAAKhWJFcAAAAGkVwBAAAYRHIFAABgEMkVAACAQSRXAAAABpFcAQAA\n",
-       "GERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd6306fb450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['fc7'].data[0]\n",
-    "plt.subplot(2, 1, 1)\n",
-    "plt.plot(feat.flat)\n",
-    "plt.subplot(2, 1, 2)\n",
-    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final probability output, `prob`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "+njXu94VV199tfM3AbS3t8eGDRvi1VdfjaIoYv369XHhhRc6fxPI0f5fee6558Zpp50WGzdujKIo\n",
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-       "+PWvf110dnYWs2fPLpYsWVLs2bPH+ZtAVqxYUVx44YXFRRddVNxwww3F/v37nb+SWrp0aTFt2rSi\n",
-       "sbGxaG5uLu66665jOlcPP/xwcdFFFxUzZ84sPvWpT43qY1usFQAggaSLtQIA5EqEAQAkIMIAABIQ\n",
-       "YQAACYgwAIAERBgAQAIiDAAgAREGAJDA/wckYxa5Es1/mgAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd630627290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "feat = net.blobs['prob'].data[0]\n",
-    "plt.plot(feat.flat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see the top 5 predicted labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-      " 'n02127052 lynx, catamount']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load labels\n",
-    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-    "try:\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-    "except:\n",
-    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-    "\n",
-    "# sort top k predictions from softmax output\n",
-    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-    "print labels[top_k]"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "example_name": "Filter visualization",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 2
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
deleted file mode 100644
index e98d13dd501..00000000000
--- a/examples/hdf5_classification.ipynb
+++ /dev/null
@@ -1,6290 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Caffeinated Logistic Regression of HDF5 Data\n",
-    "\n",
-    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-    "import sys\n",
-    "sys.path.insert(0, caffe_root + 'python')\n",
-    "\n",
-    "import caffe\n",
-    "\n",
-    "import os\n",
-    "import h5py\n",
-    "import shutil\n",
-    "import tempfile\n",
-    "\n",
-    "# You may need to 'pip install scikit-learn'\n",
-    "import sklearn\n",
-    "import sklearn.datasets\n",
-    "import sklearn.linear_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dade4d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X, y = sklearn.datasets.make_classification(\n",
-    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
-    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
-    ")\n",
-    "\n",
-    "# Split into train and test\n",
-    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
-    "\n",
-    "# Visualize sample of the data\n",
-    "ind = np.random.permutation(X.shape[0])[:1000]\n",
-    "df = pd.DataFrame(X[ind])\n",
-    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 499 ms per loop\n",
-      "Accuracy: 0.756\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train and test the scikit-learn SGD logistic regression.\n",
-    "clf = sklearn.linear_model.SGDClassifier(\n",
-    "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
-    "\n",
-    "%timeit clf.fit(X, y)\n",
-    "yt_pred = clf.predict(Xt)\n",
-    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Save the dataset to HDF5 for loading in Caffe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Write out the data to HDF5 files in a temp directory.\n",
-    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
-    "dirname = os.path.abspath('./hdf5_classification/data')\n",
-    "if not os.path.exists(dirname):\n",
-    "    os.makedirs(dirname)\n",
-    "\n",
-    "train_filename = os.path.join(dirname, 'train.h5')\n",
-    "test_filename = os.path.join(dirname, 'test.h5')\n",
-    "\n",
-    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
-    "# To show this off, we'll list the same data file twice.\n",
-    "with h5py.File(train_filename, 'w') as f:\n",
-    "    f['data'] = X\n",
-    "    f['label'] = y.astype(np.float32)\n",
-    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
-    "    f.write(train_filename + '\\n')\n",
-    "    f.write(train_filename + '\\n')\n",
-    "    \n",
-    "# HDF5 is pretty efficient, but can be further compressed.\n",
-    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
-    "with h5py.File(test_filename, 'w') as f:\n",
-    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
-    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
-    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
-    "    f.write(test_filename + '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Learn and evaluate logistic regression in Caffe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 240 ms per loop\n",
-      "Accuracy: 0.752\n"
-     ]
-    }
-   ],
-   "source": [
-    "def learn_and_test(solver_file):\n",
-    "    caffe.set_mode_cpu()\n",
-    "    solver = caffe.get_solver(solver_file)\n",
-    "    solver.solve()\n",
-    "\n",
-    "    accuracy = 0\n",
-    "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
-    "    for i in range(test_iters):\n",
-    "        solver.test_nets[0].forward()\n",
-    "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "    accuracy /= test_iters\n",
-    "    return accuracy\n",
-    "\n",
-    "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n",
-    "acc = learn_and_test('hdf5_classification/solver.prototxt')\n",
-    "print(\"Accuracy: {:.3f}\".format(acc))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same through the command line interface for detailed output on the model and solving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\n",
-      "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\n",
-      "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \n",
-      "test_iter: 250\n",
-      "test_interval: 1000\n",
-      "base_lr: 0.01\n",
-      "display: 1000\n",
-      "max_iter: 10000\n",
-      "lr_policy: \"step\"\n",
-      "gamma: 0.1\n",
-      "momentum: 0.9\n",
-      "weight_decay: 0.0005\n",
-      "stepsize: 5000\n",
-      "snapshot: 10000\n",
-      "snapshot_prefix: \"hdf5_classification/data/train\"\n",
-      "solver_mode: CPU\n",
-      "net: \"hdf5_classification/train_val.prototxt\"\n",
-      "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\n",
-      "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n",
-      "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n",
-      "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \n",
-      "name: \"LogisticRegressionNet\"\n",
-      "state {\n",
-      "  phase: TRAIN\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  include {\n",
-      "    phase: TRAIN\n",
-      "  }\n",
-      "  hdf5_data_param {\n",
-      "    source: \"hdf5_classification/data/train.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"fc1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"fc1\"\n",
-      "  param {\n",
-      "    lr_mult: 1\n",
-      "    decay_mult: 1\n",
-      "  }\n",
-      "  param {\n",
-      "    lr_mult: 2\n",
-      "    decay_mult: 0\n",
-      "  }\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"gaussian\"\n",
-      "      std: 0.01\n",
-      "    }\n",
-      "    bias_filler {\n",
-      "      type: \"constant\"\n",
-      "      value: 0\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"fc1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\n",
-      "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\n",
-      "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\n",
-      "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\n",
-      "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\n",
-      "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n",
-      "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
-      "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
-      "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\n",
-      "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\n",
-      "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\n",
-      "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\n",
-      "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\n",
-      "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\n",
-      "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\n",
-      "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\n",
-      "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\n",
-      "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\n",
-      "I0307 01:34:29.425789 2099749632 net.cpp:122]     with loss weight 1\n",
-      "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\n",
-      "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\n",
-      "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\n",
-      "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\n",
-      "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\n",
-      "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\n",
-      "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\n",
-      "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n",
-      "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \n",
-      "name: \"LogisticRegressionNet\"\n",
-      "state {\n",
-      "  phase: TEST\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  include {\n",
-      "    phase: TEST\n",
-      "  }\n",
-      "  hdf5_data_param {\n",
-      "    source: \"hdf5_classification/data/test.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"fc1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"fc1\"\n",
-      "  param {\n",
-      "    lr_mult: 1\n",
-      "    decay_mult: 1\n",
-      "  }\n",
-      "  param {\n",
-      "    lr_mult: 2\n",
-      "    decay_mult: 0\n",
-      "  }\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"gaussian\"\n",
-      "      std: 0.01\n",
-      "    }\n",
-      "    bias_filler {\n",
-      "      type: \"constant\"\n",
-      "      value: 0\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"fc1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"fc1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "  include {\n",
-      "    phase: TEST\n",
-      "  }\n",
-      "}\n",
-      "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\n",
-      "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\n",
-      "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\n",
-      "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\n",
-      "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\n",
-      "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n",
-      "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
-      "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\n",
-      "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\n",
-      "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
-      "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\n",
-      "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\n",
-      "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\n",
-      "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\n",
-      "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\n",
-      "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\n",
-      "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\n",
-      "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\n",
-      "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\n",
-      "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\n",
-      "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\n",
-      "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\n",
-      "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\n",
-      "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\n",
-      "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\n",
-      "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\n",
-      "I0307 01:34:29.428056 2099749632 net.cpp:122]     with loss weight 1\n",
-      "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\n",
-      "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\n",
-      "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n",
-      "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\n",
-      "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\n",
-      "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\n",
-      "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\n",
-      "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\n",
-      "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\n",
-      "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\n",
-      "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\n",
-      "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\n",
-      "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\n",
-      "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\n",
-      "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\n",
-      "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n",
-      "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\n",
-      "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n",
-      "I0307 01:34:29.471674 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.4532\n",
-      "I0307 01:34:29.471724 2099749632 solver.cpp:315]     Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\n",
-      "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\n",
-      "I0307 01:34:29.471878 2099749632 solver.cpp:204]     Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\n",
-      "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n",
-      "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
-      "I0307 01:34:29.486868 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\n",
-      "I0307 01:34:29.486896 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n",
-      "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\n",
-      "I0307 01:34:29.486934 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n",
-      "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n",
-      "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
-      "I0307 01:34:29.501900 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\n",
-      "I0307 01:34:29.501941 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n",
-      "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\n",
-      "I0307 01:34:29.502003 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n",
-      "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n",
-      "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
-      "I0307 01:34:29.517227 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.6964\n",
-      "I0307 01:34:29.517300 2099749632 solver.cpp:315]     Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\n",
-      "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\n",
-      "I0307 01:34:29.518154 2099749632 solver.cpp:204]     Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\n",
-      "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n",
-      "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
-      "I0307 01:34:29.534873 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\n",
-      "I0307 01:34:29.534920 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n",
-      "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\n",
-      "I0307 01:34:29.534962 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n",
-      "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n",
-      "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
-      "I0307 01:34:29.549762 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\n",
-      "I0307 01:34:29.549789 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n",
-      "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\n",
-      "I0307 01:34:29.549828 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n",
-      "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n",
-      "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
-      "I0307 01:34:29.565335 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7476\n",
-      "I0307 01:34:29.565373 2099749632 solver.cpp:315]     Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\n",
-      "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\n",
-      "I0307 01:34:29.566086 2099749632 solver.cpp:204]     Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\n",
-      "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n",
-      "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
-      "I0307 01:34:29.580993 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7524\n",
-      "I0307 01:34:29.581015 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\n",
-      "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\n",
-      "I0307 01:34:29.581050 2099749632 solver.cpp:204]     Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\n",
-      "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n",
-      "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
-      "I0307 01:34:29.595973 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7568\n",
-      "I0307 01:34:29.596002 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\n",
-      "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\n",
-      "I0307 01:34:29.596040 2099749632 solver.cpp:204]     Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\n",
-      "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n",
-      "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
-      "I0307 01:34:29.610930 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7432\n",
-      "I0307 01:34:29.610960 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\n",
-      "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\n",
-      "I0307 01:34:29.611583 2099749632 solver.cpp:204]     Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\n",
-      "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n",
-      "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n",
-      "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n",
-      "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\n",
-      "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
-      "I0307 01:34:29.626454 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.752\n",
-      "I0307 01:34:29.626484 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\n",
-      "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\n",
-      "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n",
-    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
-    "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
-    "\n",
-    "The final accuracy of the new network be higher than logistic regression!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 loops, best of 3: 333 ms per loop\n",
-      "Accuracy: 0.818\n"
-     ]
-    }
-   ],
-   "source": [
-    "def learn_and_test(solver_file):\n",
-    "    caffe.set_mode_cpu()\n",
-    "    solver = caffe.get_solver(solver_file)\n",
-    "    solver.solve()\n",
-    "\n",
-    "    accuracy = 0\n",
-    "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
-    "    for i in range(test_iters):\n",
-    "        solver.test_nets[0].forward()\n",
-    "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "    accuracy /= test_iters\n",
-    "    return accuracy\n",
-    "\n",
-    "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n",
-    "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n",
-    "print(\"Accuracy: {:.3f}\".format(acc))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same through the command line interface for detailed output on the model and solving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\n",
-      "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\n",
-      "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \n",
-      "test_iter: 250\n",
-      "test_interval: 1000\n",
-      "base_lr: 0.01\n",
-      "display: 1000\n",
-      "max_iter: 10000\n",
-      "lr_policy: \"step\"\n",
-      "gamma: 0.1\n",
-      "momentum: 0.9\n",
-      "weight_decay: 0.0005\n",
-      "stepsize: 5000\n",
-      "snapshot: 10000\n",
-      "snapshot_prefix: \"hdf5_classification/data/train\"\n",
-      "solver_mode: CPU\n",
-      "net: \"hdf5_classification/train_val2.prototxt\"\n",
-      "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\n",
-      "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n",
-      "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n",
-      "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \n",
-      "name: \"LogisticRegressionNet\"\n",
-      "state {\n",
-      "  phase: TRAIN\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  include {\n",
-      "    phase: TRAIN\n",
-      "  }\n",
-      "  hdf5_data_param {\n",
-      "    source: \"hdf5_classification/data/train.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"fc1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"fc1\"\n",
-      "  param {\n",
-      "    lr_mult: 1\n",
-      "    decay_mult: 1\n",
-      "  }\n",
-      "  param {\n",
-      "    lr_mult: 2\n",
-      "    decay_mult: 0\n",
-      "  }\n",
-      "  inner_product_param {\n",
-      "    num_output: 40\n",
-      "    weight_filler {\n",
-      "      type: \"gaussian\"\n",
-      "      std: 0.01\n",
-      "    }\n",
-      "    bias_filler {\n",
-      "      type: \"constant\"\n",
-      "      value: 0\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"relu1\"\n",
-      "  type: \"ReLU\"\n",
-      "  bottom: \"fc1\"\n",
-      "  top: \"fc1\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"fc2\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"fc1\"\n",
-      "  top: \"fc2\"\n",
-      "  param {\n",
-      "    lr_mult: 1\n",
-      "    decay_mult: 1\n",
-      "  }\n",
-      "  param {\n",
-      "    lr_mult: 2\n",
-      "    decay_mult: 0\n",
-      "  }\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"gaussian\"\n",
-      "      std: 0.01\n",
-      "    }\n",
-      "    bias_filler {\n",
-      "      type: \"constant\"\n",
-      "      value: 0\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"fc2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\n",
-      "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\n",
-      "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\n",
-      "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\n",
-      "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\n",
-      "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n",
-      "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
-      "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
-      "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\n",
-      "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\n",
-      "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\n",
-      "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\n",
-      "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
-      "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\n",
-      "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\n",
-      "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n",
-      "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\n",
-      "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\n",
-      "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\n",
-      "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\n",
-      "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\n",
-      "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\n",
-      "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\n",
-      "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\n",
-      "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\n",
-      "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\n",
-      "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\n",
-      "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\n",
-      "I0307 01:34:31.882844 2099749632 net.cpp:122]     with loss weight 1\n",
-      "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\n",
-      "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\n",
-      "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\n",
-      "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\n",
-      "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\n",
-      "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\n",
-      "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\n",
-      "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\n",
-      "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\n",
-      "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n",
-      "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \n",
-      "name: \"LogisticRegressionNet\"\n",
-      "state {\n",
-      "  phase: TEST\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  include {\n",
-      "    phase: TEST\n",
-      "  }\n",
-      "  hdf5_data_param {\n",
-      "    source: \"hdf5_classification/data/test.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"fc1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"fc1\"\n",
-      "  param {\n",
-      "    lr_mult: 1\n",
-      "    decay_mult: 1\n",
-      "  }\n",
-      "  param {\n",
-      "    lr_mult: 2\n",
-      "    decay_mult: 0\n",
-      "  }\n",
-      "  inner_product_param {\n",
-      "    num_output: 40\n",
-      "    weight_filler {\n",
-      "      type: \"gaussian\"\n",
-      "      std: 0.01\n",
-      "    }\n",
-      "    bias_filler {\n",
-      "      type: \"constant\"\n",
-      "      value: 0\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"relu1\"\n",
-      "  type: \"ReLU\"\n",
-      "  bottom: \"fc1\"\n",
-      "  top: \"fc1\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"fc2\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"fc1\"\n",
-      "  top: \"fc2\"\n",
-      "  param {\n",
-      "    lr_mult: 1\n",
-      "    decay_mult: 1\n",
-      "  }\n",
-      "  param {\n",
-      "    lr_mult: 2\n",
-      "    decay_mult: 0\n",
-      "  }\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"gaussian\"\n",
-      "      std: 0.01\n",
-      "    }\n",
-      "    bias_filler {\n",
-      "      type: \"constant\"\n",
-      "      value: 0\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"fc2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"fc2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "  include {\n",
-      "    phase: TEST\n",
-      "  }\n",
-      "}\n",
-      "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\n",
-      "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\n",
-      "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\n",
-      "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\n",
-      "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\n",
-      "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n",
-      "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
-      "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\n",
-      "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\n",
-      "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\n",
-      "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\n",
-      "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\n",
-      "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\n",
-      "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\n",
-      "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\n",
-      "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
-      "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\n",
-      "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\n",
-      "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n",
-      "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\n",
-      "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\n",
-      "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\n",
-      "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\n",
-      "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\n",
-      "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\n",
-      "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\n",
-      "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\n",
-      "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\n",
-      "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\n",
-      "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\n",
-      "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\n",
-      "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\n",
-      "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\n",
-      "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\n",
-      "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\n",
-      "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\n",
-      "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\n",
-      "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\n",
-      "I0307 01:34:31.885337 2099749632 net.cpp:122]     with loss weight 1\n",
-      "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\n",
-      "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\n",
-      "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n",
-      "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\n",
-      "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\n",
-      "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\n",
-      "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\n",
-      "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\n",
-      "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\n",
-      "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\n",
-      "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\n",
-      "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\n",
-      "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\n",
-      "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\n",
-      "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\n",
-      "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\n",
-      "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\n",
-      "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n",
-      "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\n",
-      "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n",
-      "I0307 01:34:31.901005 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.5944\n",
-      "I0307 01:34:31.901049 2099749632 solver.cpp:315]     Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\n",
-      "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\n",
-      "I0307 01:34:31.901192 2099749632 solver.cpp:204]     Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\n",
-      "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n",
-      "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
-      "I0307 01:34:31.924612 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7556\n",
-      "I0307 01:34:31.924646 2099749632 solver.cpp:315]     Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\n",
-      "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\n",
-      "I0307 01:34:31.924696 2099749632 solver.cpp:204]     Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\n",
-      "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n",
-      "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
-      "I0307 01:34:31.948729 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7824\n",
-      "I0307 01:34:31.948763 2099749632 solver.cpp:315]     Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\n",
-      "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\n",
-      "I0307 01:34:31.948812 2099749632 solver.cpp:204]     Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\n",
-      "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n",
-      "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
-      "I0307 01:34:31.972393 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7956\n",
-      "I0307 01:34:31.972411 2099749632 solver.cpp:315]     Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\n",
-      "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\n",
-      "I0307 01:34:31.973057 2099749632 solver.cpp:204]     Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\n",
-      "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n",
-      "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
-      "I0307 01:34:31.998638 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.798\n",
-      "I0307 01:34:31.998663 2099749632 solver.cpp:315]     Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\n",
-      "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\n",
-      "I0307 01:34:31.998718 2099749632 solver.cpp:204]     Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\n",
-      "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n",
-      "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
-      "I0307 01:34:32.024950 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.804\n",
-      "I0307 01:34:32.024981 2099749632 solver.cpp:315]     Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\n",
-      "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\n",
-      "I0307 01:34:32.025028 2099749632 solver.cpp:204]     Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\n",
-      "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n",
-      "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
-      "I0307 01:34:32.048216 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\n",
-      "I0307 01:34:32.048239 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\n",
-      "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\n",
-      "I0307 01:34:32.048809 2099749632 solver.cpp:204]     Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\n",
-      "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n",
-      "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
-      "I0307 01:34:32.071524 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8124\n",
-      "I0307 01:34:32.071542 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\n",
-      "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\n",
-      "I0307 01:34:32.071617 2099749632 solver.cpp:204]     Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\n",
-      "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n",
-      "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
-      "I0307 01:34:32.095410 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.814\n",
-      "I0307 01:34:32.095432 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\n",
-      "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\n",
-      "I0307 01:34:32.095474 2099749632 solver.cpp:204]     Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\n",
-      "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n",
-      "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
-      "I0307 01:34:32.121587 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\n",
-      "I0307 01:34:32.121608 2099749632 solver.cpp:315]     Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\n",
-      "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\n",
-      "I0307 01:34:32.122181 2099749632 solver.cpp:204]     Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\n",
-      "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n",
-      "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n",
-      "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n",
-      "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\n",
-      "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
-      "I0307 01:34:32.145747 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8148\n",
-      "I0307 01:34:32.145771 2099749632 solver.cpp:315]     Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\n",
-      "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\n",
-      "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
-    "shutil.rmtree(dirname)"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Off-the-shelf SGD for classification",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/hdf5_classification/nonlinear_auto_test.prototxt b/examples/hdf5_classification/nonlinear_auto_test.prototxt
new file mode 100644
index 00000000000..53eda6ee8a0
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_auto_test.prototxt
@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/hdf5_classification/nonlinear_auto_train.prototxt b/examples/hdf5_classification/nonlinear_auto_train.prototxt
new file mode 100644
index 00000000000..fc0688fa652
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_auto_train.prototxt
@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/hdf5_classification/nonlinear_solver.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt
new file mode 100644
index 00000000000..b4aacf6e423
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_solver.prototxt
@@ -0,0 +1,15 @@
+train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
+test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
+test_iter: 250
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 5000
+display: 1000
+max_iter: 10000
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/hdf5_classification/data/train"
+solver_mode: CPU
diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/nonlinear_train_val.prototxt
similarity index 87%
rename from examples/hdf5_classification/train_val2.prototxt
rename to examples/hdf5_classification/nonlinear_train_val.prototxt
index 8795e8facb6..8f7ef04f58a 100644
--- a/examples/hdf5_classification/train_val2.prototxt
+++ b/examples/hdf5_classification/nonlinear_train_val.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
@@ -41,8 +41,7 @@ layer {
   inner_product_param {
     num_output: 40
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
@@ -72,8 +71,7 @@ layer {
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
index 65a6eb9e9fb..8587b5a1e5a 100644
--- a/examples/hdf5_classification/solver.prototxt
+++ b/examples/hdf5_classification/solver.prototxt
@@ -1,4 +1,5 @@
-net: "hdf5_classification/train_val.prototxt"
+train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
+test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
 test_iter: 250
 test_interval: 1000
 base_lr: 0.01
@@ -10,5 +11,5 @@ max_iter: 10000
 momentum: 0.9
 weight_decay: 0.0005
 snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
+snapshot_prefix: "examples/hdf5_classification/data/train"
 solver_mode: CPU
diff --git a/examples/hdf5_classification/solver2.prototxt b/examples/hdf5_classification/solver2.prototxt
deleted file mode 100644
index 32b9feba346..00000000000
--- a/examples/hdf5_classification/solver2.prototxt
+++ /dev/null
@@ -1,14 +0,0 @@
-net: "hdf5_classification/train_val2.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
index d5e8dbfa169..13ddf47524a 100644
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
@@ -41,8 +41,7 @@ layer {
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt
new file mode 100644
index 00000000000..fa4bbf02710
--- /dev/null
+++ b/examples/mnist/lenet_auto_solver.prototxt
@@ -0,0 +1,24 @@
+# The train/test net protocol buffer definition
+train_net: "examples/mnist/lenet_auto_train.prototxt"
+test_net: "examples/mnist/lenet_auto_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 303c22ba3fd..ff780fbb9f7 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -6884,7 +6884,7 @@
   }
  ],
  "metadata": {
-  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
+  "description": "How to do net surgery and manually change model parameters for custom use.",
   "example_name": "Editing model parameters",
   "include_in_docs": true,
   "kernelspec": {
diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
index 11cae120db2..1a4e30eda43 100644
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -1902,7 +1902,7 @@
    "pygments_lexer": "ipython2",
    "version": "2.7.9"
   },
-  "priority": 6
+  "priority": 7
  },
  "nbformat": 4,
  "nbformat_minor": 0

From 7003d1b8e24416cb5bdb5537a7805cb5a9de2ca1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 30 Jun 2015 15:35:21 -0700
Subject: [PATCH 1414/2053] [examples] add Euclidean loss PythonLayer

---
 examples/pycaffe/layers/pyloss.py | 37 +++++++++++++++++++
 examples/pycaffe/linreg.prototxt  | 60 +++++++++++++++++++++++++++++++
 2 files changed, 97 insertions(+)
 create mode 100644 examples/pycaffe/layers/pyloss.py
 create mode 100644 examples/pycaffe/linreg.prototxt

diff --git a/examples/pycaffe/layers/pyloss.py b/examples/pycaffe/layers/pyloss.py
new file mode 100644
index 00000000000..6200e6bbc55
--- /dev/null
+++ b/examples/pycaffe/layers/pyloss.py
@@ -0,0 +1,37 @@
+import caffe
+import numpy as np
+
+
+class EuclideanLossLayer(caffe.Layer):
+    """
+    Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
+    to demonstrate the class interface for developing layers in Python.
+    """
+
+    def setup(self, bottom, top):
+        # check input pair
+        if len(bottom) != 2:
+            raise Exception("Need two inputs to compute distance.")
+
+    def reshape(self, bottom, top):
+        # check input dimensions match
+        if bottom[0].count != bottom[1].count:
+            raise Exception("Inputs must have the same dimension.")
+        # difference is shape of inputs
+        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
+        # loss output is scalar
+        top[0].reshape(1)
+
+    def forward(self, bottom, top):
+        self.diff[...] = bottom[0].data - bottom[1].data
+        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
+
+    def backward(self, top, propagate_down, bottom):
+        for i in range(2):
+            if not propagate_down[i]:
+                continue
+            if i == 0:
+                sign = 1
+            else:
+                sign = -1
+            bottom[i].diff[...] = sign * self.diff / bottom[i].num
diff --git a/examples/pycaffe/linreg.prototxt b/examples/pycaffe/linreg.prototxt
new file mode 100644
index 00000000000..c0fb0776d0a
--- /dev/null
+++ b/examples/pycaffe/linreg.prototxt
@@ -0,0 +1,60 @@
+name: 'LinearRegressionExample'
+# define a simple network for linear regression on dummy data
+# that computes the loss by a PythonLayer.
+layer {
+  type: 'DummyData'
+  name: 'x'
+  top: 'x'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+layer {
+  type: 'DummyData'
+  name: 'y'
+  top: 'y'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+# include InnerProduct layers for parameters
+# so the net will need backward
+layer {
+  type: 'InnerProduct'
+  name: 'ipx'
+  top: 'ipx'
+  bottom: 'x'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'InnerProduct'
+  name: 'ipy'
+  top: 'ipy'
+  bottom: 'y'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'Python'
+  name: 'loss'
+  top: 'loss'
+  bottom: 'ipx'
+  bottom: 'ipy'
+  python_param {
+    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
+    module: 'pyloss'
+    # the layer name -- the class name in the module
+    layer: 'EuclideanLossLayer'
+  }
+  # set loss weight so Caffe knows this is a loss layer.
+  # since PythonLayer inherits directly from Layer, this isn't automatically
+  # known to Caffe
+  loss_weight: 1
+}

From 41df4cdce942c22f0d89ad8b0990d3ee7bd9cf31 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Thu, 26 Mar 2015 19:04:15 -0700
Subject: [PATCH 1415/2053] bilinear filler -- useful for interpolation with
 DeconvolutionLayer

This filler is a convenience for interpolating with DeconvolutionLayer
or smoothing + downsampling with ConvolutionLayer for stride > 1.
---
 include/caffe/filler.hpp | 56 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index ff3542e1f99..888f4a4ba3b 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -208,6 +208,60 @@ class MSRAFiller : public Filler<Dtype> {
   }
 };
 
+/*!
+@brief Fills a Blob with coefficients for bilinear interpolation.
+
+A common use case is with the DeconvolutionLayer acting as upsampling.
+You can upsample a feature map with shape of (B, C, H, W) by any integer factor
+using the following proto.
+\code
+layer {
+  name: "upsample", type: "Deconvolution"
+  bottom: "{{bottom_name}}" top: "{{top_name}}"
+  convolution_param {
+    kernel_size: {{2 * factor - factor % 2}} stride: {{factor}}
+    num_output: {{C}} group: {{C}}
+    pad: {{ceil((factor - 1) / 2.)}}
+    weight_filler: { type: "bilinear" } bias_term: false
+  }
+  param { lr_mult: 0 decay_mult: 0 }
+}
+\endcode
+Please use this by replacing `{{}}` with your values. By specifying
+`num_output: {{C}} group: {{C}}`, it behaves as
+channel-wise convolution. The filter shape of this deconvolution layer will be
+(C, 1, K, K) where K is `kernel_size`, and this filler will set a (K, K)
+interpolation kernel for every channel of the filter identically. The resulting
+shape of the top feature map will be (B, C, factor * H, factor * W).
+Note that the learning rate and the
+weight decay are set to 0 in order to keep coefficient values of bilinear
+interpolation unchanged during training. If you apply this to an image, this
+operation is equivalent to the following call in Python with Scikit.Image.
+\code{.py}
+out = skimage.transform.rescale(img, factor, mode='constant', cval=0)
+\endcode
+ */
+template <typename Dtype>
+class BilinearFiller : public Filler<Dtype> {
+ public:
+  explicit BilinearFiller(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK_EQ(blob->num_axes(), 4) << "Blob must be 4 dim.";
+    CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
+    Dtype* data = blob->mutable_cpu_data();
+    int f = ceil(blob->width() / 2.);
+    float c = (2 * f - 1 - f % 2) / (2. * f);
+    for (int i = 0; i < blob->count(); ++i) {
+      float x = i % blob->width();
+      float y = (i / blob->width()) % blob->height();
+      data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
+    }
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
+
 /**
  * @brief Get a specific filler from the specification given in FillerParameter.
  *
@@ -229,6 +283,8 @@ Filler<Dtype>* GetFiller(const FillerParameter& param) {
     return new XavierFiller<Dtype>(param);
   } else if (type == "msra") {
     return new MSRAFiller<Dtype>(param);
+  } else if (type == "bilinear") {
+    return new BilinearFiller<Dtype>(param);
   } else {
     CHECK(false) << "Unknown filler name: " << param.type();
   }

From 7d3a8e9f34bb8ac44ec9ab91b7229f6c57fd7ee1 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 1 Jul 2015 16:44:05 -0700
Subject: [PATCH 1416/2053] Update installation docs for boost - close #2454

---
 docs/install_apt.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index 75f8bec0e95..0fa205ab2cf 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -6,7 +6,8 @@ title: Installation: Ubuntu
 
 **General dependencies**
 
-    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
+    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev
+    sudo apt-get install --no-install-recommends libboost-all-dev
 
 **CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
 Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.

From 90750550d5f6ee7dd1937777b522189b9266634f Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Thu, 2 Jul 2015 11:42:37 -0700
Subject: [PATCH 1417/2053] Optimize inner product layer for special case M ==
 1

Using gemm with one of the operand being a vector or a scalar is
suboptimal. Functions gemv and axpy should be prefered in these cases.
---
 src/caffe/layers/inner_product_layer.cu     | 19 ++++++---
 src/caffe/test/test_inner_product_layer.cpp | 43 ++++++++++++++++++++-
 2 files changed, 54 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index dd90cac12a8..c0ebd2c47da 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -15,12 +15,19 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   const Dtype* weight = this->blobs_[0]->gpu_data();
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
-      bottom_data, weight, (Dtype)0., top_data);
-  if (bias_term_) {
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        bias_multiplier_.gpu_data(),
-        this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
+  if (M_ == 1) {
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_, K_, (Dtype)1.,
+                         weight, bottom_data, (Dtype)0., top_data);
+    if (bias_term_)
+      caffe_gpu_axpy<Dtype>(N_, bias_multiplier_.cpu_data()[0],
+                            this->blobs_[1]->gpu_data(), top_data);
+  } else {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+                          bottom_data, weight, (Dtype)0., top_data);
+    if (bias_term_)
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
+                            bias_multiplier_.gpu_data(),
+                            this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
   }
 }
 
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index c03df17383a..fbf0c851220 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -23,16 +23,21 @@ class InnerProductLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   InnerProductLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_nobatch_(new Blob<Dtype>(1, 2, 3, 4)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
     UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
-    blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
   }
-  virtual ~InnerProductLayerTest() { delete blob_bottom_; delete blob_top_; }
+  virtual ~InnerProductLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_nobatch_;
+    delete blob_top_;
+  }
   Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_nobatch_;
   Blob<Dtype>* const blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
@@ -42,6 +47,7 @@ TYPED_TEST_CASE(InnerProductLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
   LayerParameter layer_param;
   InnerProductParameter* inner_product_param =
       layer_param.mutable_inner_product_param();
@@ -57,6 +63,38 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
 
 TYPED_TEST(InnerProductLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* data = this->blob_top_->cpu_data();
+    const int count = this->blob_top_->count();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_GE(data[i], 1.);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);
   bool IS_VALID_CUDA = false;
 #ifndef CPU_ONLY
   IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
@@ -87,6 +125,7 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
 
 TYPED_TEST(InnerProductLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
   bool IS_VALID_CUDA = false;
 #ifndef CPU_ONLY
   IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;

From 3fa0de93b059753ed378b474e1568980ed131a10 Mon Sep 17 00:00:00 2001
From: AdamStelmaszczyk <stelmaszczykadam@gmail.com>
Date: Sat, 4 Jul 2015 20:57:17 +0100
Subject: [PATCH 1418/2053] Deprecated OpenCV consts

---
 examples/cpp_classification/classification.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
index 1c6371e382b..0683a4975ac 100644
--- a/examples/cpp_classification/classification.cpp
+++ b/examples/cpp_classification/classification.cpp
@@ -186,13 +186,13 @@ void Classifier::Preprocess(const cv::Mat& img,
   /* Convert the input image to the input image format of the network. */
   cv::Mat sample;
   if (img.channels() == 3 && num_channels_ == 1)
-    cv::cvtColor(img, sample, CV_BGR2GRAY);
+    cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
   else if (img.channels() == 4 && num_channels_ == 1)
-    cv::cvtColor(img, sample, CV_BGRA2GRAY);
+    cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
   else if (img.channels() == 4 && num_channels_ == 3)
-    cv::cvtColor(img, sample, CV_BGRA2BGR);
+    cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
   else if (img.channels() == 1 && num_channels_ == 3)
-    cv::cvtColor(img, sample, CV_GRAY2BGR);
+    cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
   else
     sample = img;
 

From 58b114ff68416c24fe7b7a344c51e686edc0c2fb Mon Sep 17 00:00:00 2001
From: semitrivial <alexander@math.ohio-state.edu>
Date: Tue, 7 Jul 2015 08:29:16 +0100
Subject: [PATCH 1419/2053] List protobuf-compiler dependency in the correct
 place (it is in the package managers for both 14.04 and 12.04)

---
 docs/install_apt.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index 0fa205ab2cf..f588b74dcdb 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -6,7 +6,7 @@ title: Installation: Ubuntu
 
 **General dependencies**
 
-    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev
+    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
     sudo apt-get install --no-install-recommends libboost-all-dev
 
 **CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
@@ -21,7 +21,7 @@ This can be skipped for CPU-only installation.
 
 Everything is packaged in 14.04.
 
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
 
 **Remaining dependencies, 12.04**
 

From a08bfb5c9200533c845ac05ae2a6d419fcae69f2 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 7 Jul 2015 21:30:33 -0700
Subject: [PATCH 1420/2053] Fix CMake typos

No functional changes, just fixing whitespace errors and typos in
comments
---
 CMakeLists.txt                              |  4 ++--
 cmake/ConfigGen.cmake                       |  2 +-
 cmake/External/gflags.cmake                 |  4 ++--
 cmake/External/glog.cmake                   |  2 +-
 cmake/Misc.cmake                            |  2 +-
 cmake/ProtoBuf.cmake                        |  4 ++--
 cmake/Summary.cmake                         |  4 ++--
 cmake/Targets.cmake                         |  6 +++---
 cmake/Templates/CaffeConfig.cmake.in        |  6 +++---
 cmake/Templates/CaffeConfigVersion.cmake.in |  2 +-
 cmake/Utils.cmake                           | 10 +++++-----
 cmake/lint.cmake                            |  4 ++--
 12 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index f22aa5763a3..e202350224f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -15,14 +15,14 @@ include(cmake/Summary.cmake)
 include(cmake/ConfigGen.cmake)
 
 # ---[ Options
-caffe_option(CPU_ONLY  "Build Caffe wihtout CUDA support" OFF) # TODO: rename to USE_CUDA
+caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
 caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
 set(python_version "2" CACHE STRING "Specify which python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
-caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
+caffe_option(BUILD_python_layer "Build the Caffe python layer" ON)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index a9101e34350..566d6ca0aa7 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -88,7 +88,7 @@ function(caffe_generate_export_configs)
 
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
 
-  # Install the CaffeConfig.cmake and export set to use wuth install-tree
+  # Install the CaffeConfig.cmake and export set to use with install-tree
   install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
   install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
 
diff --git a/cmake/External/gflags.cmake b/cmake/External/gflags.cmake
index a50a0925086..e3dba04f33f 100644
--- a/cmake/External/gflags.cmake
+++ b/cmake/External/gflags.cmake
@@ -43,10 +43,10 @@ if (NOT __GFLAGS_INCLUDED) # guard against multiple includes
       LOG_DOWNLOAD 1
       LOG_INSTALL 1
       )
-  
+
     set(GFLAGS_FOUND TRUE)
     set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include)
-    set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT})    
+    set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT})
     set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib)
     set(GFLAGS_EXTERNAL TRUE)
 
diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake
index 02b39dde676..a44672f2753 100644
--- a/cmake/External/glog.cmake
+++ b/cmake/External/glog.cmake
@@ -29,7 +29,7 @@ if (NOT __GLOG_INCLUDED)
     if (GFLAGS_EXTERNAL)
       set(GLOG_DEPENDS gflags)
     endif()
-    
+
     ExternalProject_Add(glog
       DEPENDS ${GLOG_DEPENDS}
       PREFIX ${glog_PREFIX}
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 39569eaf996..7676754fe04 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -1,4 +1,4 @@
-# ---[ Configurations types
+# ---[ Configuration types
 set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE)
 mark_as_advanced(CMAKE_CONFIGURATION_TYPES)
 
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index 8946d66c57b..fc799bd3906 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -1,12 +1,12 @@
 # Finds Google Protocol Buffers library and compilers and extends
-# the standart cmake script with version and python generation support
+# the standard cmake script with version and python generation support
 
 find_package( Protobuf REQUIRED )
 include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
 
 # As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
-# and should be installed  separately as in: sudo apt-get install protobuf-compiler
+# and should be installed separately as in: sudo apt-get install protobuf-compiler
 if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE})
   message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
 else()
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 32931942846..e094ac0040e 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -87,7 +87,7 @@ endfunction()
 
 
 ################################################################################################
-# Prints accumulatd caffe configuration summary
+# Prints accumulated caffe configuration summary
 # Usage:
 #   caffe_print_configuration_summary()
 
@@ -119,7 +119,7 @@ function(caffe_print_configuration_summary)
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
   caffe_status("  Boost             :   Yes (ver. ${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION})")
   caffe_status("  glog              :   Yes")
-  caffe_status("  gflags            :   Yes")  
+  caffe_status("  gflags            :   Yes")
   caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
   caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
   caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index ed0ff9660fd..2401f252e93 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -31,7 +31,7 @@ endfunction()
 ################################################################################################
 # Collecting sources from globbing and appending to output list variable
 # Usage:
-#   caffe_source_group(<output_variable> GLOB[_RECURSE] <globbing_expression>)
+#   caffe_collect_sources(<output_variable> GLOB[_RECURSE] <globbing_expression>)
 function(caffe_collect_sources variable)
   cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN})
   if(CAFFE_COLLECT_SOURCES_GLOB)
@@ -144,12 +144,12 @@ function(caffe_configure_testdatafile file)
   set(result "")
   foreach(line ${__lines})
     set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n")
-  endforeach()  
+  endforeach()
   file(WRITE ${file}.gen.cmake ${result})
 endfunction()
 
 ################################################################################################
-# Filter outs all files that are not inlcuded in selected list
+# Filter out all files that are not included in selected list
 # Usage:
 #   caffe_leave_only_selected_tests(<filelist_variable> <selected_list>)
 function(caffe_leave_only_selected_tests file_list)
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
index a4b03d961e0..8f23742e52e 100644
--- a/cmake/Templates/CaffeConfig.cmake.in
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -4,7 +4,7 @@
 #   Caffe and this config file depends on opencv,
 #   so put `find_package(OpenCV)` before searching Caffe
 #   via `find_package(Caffe)`. All other lib/includes
-#   dependencies are hard coded int the file
+#   dependencies are hard coded in the file
 #
 # After successful configuration the following variables
 # will be defined:
@@ -15,7 +15,7 @@
 #
 #   Caffe_HAVE_CUDA    - signals about CUDA support
 #   Caffe_HAVE_CUDNN   - signals about cuDNN support
- 
+
 
 # OpenCV dependency
 
@@ -40,7 +40,7 @@ get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
 set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
 
 @Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
- 
+
 # Our library dependencies
 if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
   include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
diff --git a/cmake/Templates/CaffeConfigVersion.cmake.in b/cmake/Templates/CaffeConfigVersion.cmake.in
index cbfa514f1a6..19f85309a5f 100644
--- a/cmake/Templates/CaffeConfigVersion.cmake.in
+++ b/cmake/Templates/CaffeConfigVersion.cmake.in
@@ -1,5 +1,5 @@
 set(PACKAGE_VERSION "@Caffe_VERSION@")
- 
+
 # Check whether the requested PACKAGE_FIND_VERSION is compatible
 if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
   set(PACKAGE_VERSION_COMPATIBLE FALSE)
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index a56c7c300c0..a1bde1ae95b 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -1,7 +1,7 @@
 ################################################################################################
 # Command alias for debugging messages
 # Usage:
-#   dmgs(<message>)
+#   dmsg(<message>)
 function(dmsg)
   message(STATUS ${ARGN})
 endfunction()
@@ -19,9 +19,9 @@ macro(caffe_list_unique)
 endmacro()
 
 ################################################################################################
-# Clears variables from lsit
+# Clears variables from list
 # Usage:
-#   caffe_list_unique(<variables_list>)
+#   caffe_clear_vars(<variables_list>)
 macro(caffe_clear_vars)
   foreach(_var ${ARGN})
     unset(${_var})
@@ -118,7 +118,7 @@ macro(caffe_parse_header FILENAME FILE_VAR)
         if(__add_cache)
           set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
         elseif(__parnet_scope)
-          set(${name} "${${name}}" PARENT_SCOPE)          
+          set(${name} "${${name}}" PARENT_SCOPE)
         endif()
       else()
         unset(${name} CACHE)
@@ -303,7 +303,7 @@ function(caffe_get_current_cflags cflags_var)
 endfunction()
 
 ################################################################################################
-# Helper function to parse current linker libs into link directoris, libflags and osx frameworks
+# Helper function to parse current linker libs into link directories, libflags and osx frameworks
 # Usage:
 #   caffe_parse_linker_libs(<Caffe_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
 function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var)
diff --git a/cmake/lint.cmake b/cmake/lint.cmake
index 585babb3587..70a006572bb 100644
--- a/cmake/lint.cmake
+++ b/cmake/lint.cmake
@@ -5,7 +5,7 @@ set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc)
 set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
 set(LINT_DIRS include src/caffe examples tools python matlab)
 
-cmake_policy(SET CMP0009 NEW)  # supress cmake warning
+cmake_policy(SET CMP0009 NEW)  # suppress cmake warning
 
 # find all files of interest
 foreach(ext ${SRC_FILE_EXTENSIONS})
@@ -26,7 +26,7 @@ list(REMOVE_ITEM LINT_SOURCES ${EXCLUDED_FILES})
 
 execute_process(
     COMMAND ${LINT_COMMAND} ${LINT_SOURCES}
-    ERROR_VARIABLE LINT_OUTPUT 
+    ERROR_VARIABLE LINT_OUTPUT
     ERROR_STRIP_TRAILING_WHITESPACE
 )
 

From 7093b0b6c9216e116046ab1d4d2b9bf2e487a641 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 7 Jul 2015 16:32:46 -0700
Subject: [PATCH 1421/2053] Making the net_spec python3 compatible

---
 examples/pycaffe/caffenet.py |  5 +++--
 python/caffe/net_spec.py     | 11 ++++++-----
 python/requirements.txt      |  1 +
 3 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py
index f9801d7cbb8..06c5a02d4ee 100644
--- a/examples/pycaffe/caffenet.py
+++ b/examples/pycaffe/caffenet.py
@@ -1,5 +1,6 @@
 from caffe import layers as L, params as P, to_proto
 from caffe.proto import caffe_pb2
+from __future__ import print_function
 
 # helper function for common structures
 
@@ -45,10 +46,10 @@ def caffenet(lmdb, batch_size=256, include_acc=False):
 
 def make_net():
     with open('train.prototxt', 'w') as f:
-        print >>f, caffenet('/path/to/caffe-train-lmdb')
+        print(caffenet('/path/to/caffe-train-lmdb'), file=f)
 
     with open('test.prototxt', 'w') as f:
-        print >>f, caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True)
+        print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
 
 if __name__ == '__main__':
     make_net()
diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index f54328d56f1..1b4814a45c6 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -22,6 +22,7 @@ class -- assign to its attributes directly to name layers, and call
 
 from .proto import caffe_pb2
 from google import protobuf
+import six
 
 
 def param_name_dict():
@@ -63,12 +64,12 @@ def assign_proto(proto, name, val):
         if isinstance(val[0], dict):
             for item in val:
                 proto_item = getattr(proto, name).add()
-                for k, v in item.iteritems():
+                for k, v in six.iteritems(item):
                     assign_proto(proto_item, k, v)
         else:
             getattr(proto, name).extend(val)
     elif isinstance(val, dict):
-        for k, v in val.iteritems():
+        for k, v in six.iteritems(val):
             assign_proto(getattr(proto, name), k, v)
     else:
         setattr(proto, name, val)
@@ -131,7 +132,7 @@ def _to_proto(self, layers, names, autonames):
                 layer.top.append(self._get_name(top, names, autonames))
         layer.name = self._get_name(self.tops[0], names, autonames)
 
-        for k, v in self.params.iteritems():
+        for k, v in six.iteritems(self.params):
             # special case to handle generic *params
             if k.endswith('param'):
                 assign_proto(layer, k, v)
@@ -161,10 +162,10 @@ def __getattr__(self, name):
         return self.tops[name]
 
     def to_proto(self):
-        names = {v: k for k, v in self.tops.iteritems()}
+        names = {v: k for k, v in six.iteritems(self.tops)}
         autonames = {}
         layers = OrderedDict()
-        for name, top in self.tops.iteritems():
+        for name, top in six.iteritems(self.tops):
             top.fn._to_proto(layers, names, autonames)
         net = caffe_pb2.NetParameter()
         net.layer.extend(layers.values())
diff --git a/python/requirements.txt b/python/requirements.txt
index e0c86c7e51e..e7d89e67f48 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -14,3 +14,4 @@ protobuf>=2.5.0
 python-gflags>=2.0
 pyyaml>=3.10
 Pillow>=2.3.0
+six>=1.1.0
\ No newline at end of file

From c51e8d623260bb921daf799ba846e13d8b47e843 Mon Sep 17 00:00:00 2001
From: AdamStelmaszczyk <stelmaszczykadam@gmail.com>
Date: Fri, 10 Jul 2015 21:41:46 +0100
Subject: [PATCH 1422/2053] One command less

Also more readable and compatible with format of instructions for MNIST https://github.com/BVLC/caffe/tree/master/examples/mnist
---
 examples/cifar10/readme.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 4a95cee9e8f..5d8d81e3efb 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -22,9 +22,8 @@ Prepare the Dataset
 
 You will first need to download and convert the data format from the [CIFAR-10 website](http://www.cs.toronto.edu/~kriz/cifar.html). To do this, simply run the following commands:
 
-    cd $CAFFE_ROOT/data/cifar10
-    ./get_cifar10.sh
     cd $CAFFE_ROOT
+    ./data/cifar10/get_cifar10.sh
     ./examples/cifar10/create_cifar10.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.

From d0423a8682b92a101f26b3093c88f08c235fb3d1 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Fri, 10 Jul 2015 17:03:03 -0700
Subject: [PATCH 1423/2053] Removes a unused variable warning

---
 src/caffe/layers/absval_layer.cu | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index 91f3c77fe9a..bb310e1afbb 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -18,7 +18,6 @@ template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
-  const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
   if (propagate_down[0]) {
     const Dtype* bottom_data = bottom[0]->gpu_data();

From 4c8515a05ae63400166e9311feac8037df3d59c6 Mon Sep 17 00:00:00 2001
From: Gleb Mazovetskiy <glex.spb@gmail.com>
Date: Sun, 12 Jul 2015 04:16:14 +0100
Subject: [PATCH 1424/2053] examples/imagenet: fix broken link

---
 examples/imagenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index b1ebfafbf46..65174d601f2 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -102,4 +102,4 @@ Hope you liked this recipe!
 Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks.
 **Caffe lets you explore different network choices more easily by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb).
+And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).

From 4ccc052e6e2bd4293dfb530febc9bf441786363d Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Sun, 12 Jul 2015 20:06:31 +0900
Subject: [PATCH 1425/2053] Update net_layer_blob.md

---
 docs/tutorial/net_layer_blob.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index e8b7bd316a9..d6df737439a 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -19,7 +19,7 @@ Blobs conceal the computational and mental overhead of mixed CPU/GPU operation b
 
 The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
 
-- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
+- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images N = 256.
 - Channel / K is the feature dimension e.g. for RGB images K = 3.
 
 Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon).

From 4702f85a1f31d5541cba0bf5500b2daea222da91 Mon Sep 17 00:00:00 2001
From: Youssef Kashef <youssef.kashef@gmail.com>
Date: Tue, 14 Jul 2015 11:26:03 +0200
Subject: [PATCH 1426/2053] tiny fix in Layer::Backward documentation

---
 include/caffe/layer.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 8f924a75755..e2eba196346 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -139,7 +139,7 @@ class Layer {
    * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
    * top blob diffs.
    *
-   * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
+   * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
    */
   inline void Backward(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,

From 6d92d8fcfe0eea9495ffbc326256ec5b70c3eed1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 16 Jul 2015 21:30:37 -0700
Subject: [PATCH 1427/2053] [examples] fix link to point to new tutorial
 notebook

---
 examples/feature_extraction/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index 6c8917e27e1..a980b8b3203 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -10,7 +10,7 @@ Extracting Features
 ===================
 
 In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
-Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
 
 Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.
 If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.

From 4d3b1844fc70ab52fe9e22343f9a1154617be6a4 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Wed, 8 Jul 2015 14:30:29 -0700
Subject: [PATCH 1428/2053] Travis scripts for python3 and pytest for cmake.
 Also fixes CUDA CMake build issue #2722.

---
 .travis.yml                                   | 13 +++--
 CMakeLists.txt                                |  4 ++
 Makefile                                      |  4 +-
 cmake/Dependencies.cmake                      |  9 ++--
 python/caffe/test/test_net.py                 |  7 +--
 python/caffe/test/test_net_spec.py            |  2 +-
 python/caffe/test/test_python_layer.py        |  5 +-
 python/caffe/test/test_solver.py              |  7 +--
 scripts/travis/travis_build_and_test.sh       | 11 +++-
 scripts/travis/travis_install.sh              | 52 ++++++++++++++-----
 .../travis/travis_setup_makefile_config.sh    |  4 +-
 src/caffe/layer_factory.cpp                   |  5 ++
 12 files changed, 90 insertions(+), 33 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 955aa8c3ba2..b920a935d0d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,24 +6,31 @@ env:
     - WITH_CUDA=false WITH_CMAKE=true
     - WITH_CUDA=true WITH_CMAKE=false
     - WITH_CUDA=true WITH_CMAKE=true
+    - WITH_CUDA=false WITH_CMAKE=true PYTHON_VERSION=3
 
 language: cpp
 
 # Cache Ubuntu apt packages.
-cache: apt
+cache:
+  apt: true
+  directories:
+  - /home/travis/miniconda
+  - /home/travis/miniconda2
+  - /home/travis/miniconda3
 
 compiler: gcc
 
 before_install:
   - export NUM_THREADS=4
   - export SCRIPTS=./scripts/travis
+  - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION"
 
 install:
   - sudo -E $SCRIPTS/travis_install.sh
 
 before_script:
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64
-  - export PATH=/home/travis/miniconda/bin:$PATH
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib
+  - export PATH=$CONDA_DIR/bin:$PATH
   - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
 
 script: $SCRIPTS/travis_build_and_test.sh
diff --git a/CMakeLists.txt b/CMakeLists.txt
index e202350224f..ef599b68922 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -62,6 +62,10 @@ add_subdirectory(docs)
 # ---[ Linter target
 add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
 
+# ---[ pytest target
+add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python )
+add_dependencies(pytest pycaffe)
+
 # ---[ Configuration summary
 caffe_print_configuration_summary()
 
diff --git a/Makefile b/Makefile
index e4e66dfd138..05b783af35c 100644
--- a/Makefile
+++ b/Makefile
@@ -228,7 +228,7 @@ ifeq ($(LINUX), 1)
 	CXX ?= /usr/bin/g++
 	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
 	# older versions of gcc are too dumb to build boost with -Wuninitalized
-	ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
+	ifeq ($(shell echo | awk '{exit $(GCCVERSION) < 4.6;}'), 1)
 		WARNINGS += -Wno-uninitialized
 	endif
 	# boost::thread is reasonably called boost_thread (compare OS X)
@@ -243,7 +243,7 @@ ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
 	ifneq ($(CPU_ONLY), 1)
 		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
-		ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
+		ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
 			CXXFLAGS += -stdlib=libstdc++
 			LINKFLAGS += -stdlib=libstdc++
 		endif
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 7cae5c9da25..7c86dd55a30 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -106,14 +106,15 @@ if(BUILD_python)
     
     while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
       STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
-      STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
-      if("${has_more_version}" STREQUAL "")
-        break()
-      endif()
       
       STRING( REPLACE "." "" boost_py_version ${version} )
       find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
       set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+      
+      STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
+      if("${has_more_version}" STREQUAL "")
+        break()
+      endif()
     endwhile()
     if(NOT Boost_PYTHON_FOUND)
       find_package(Boost 1.46 COMPONENTS python)
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index cc367477752..aad828aa8aa 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -2,6 +2,7 @@
 import tempfile
 import os
 import numpy as np
+import six
 
 import caffe
 
@@ -10,7 +11,7 @@ def simple_net_file(num_output):
     """Make a simple net prototxt, based on test_net.cpp, returning the name
     of the (temporary) file."""
 
-    f = tempfile.NamedTemporaryFile(delete=False)
+    f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
     f.write("""name: 'testnet' force_backward: true
     layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
       dummy_data_param { num: 5 channels: 2 height: 3 width: 4
@@ -47,7 +48,7 @@ def setUp(self):
     def test_memory(self):
         """Check that holding onto blob data beyond the life of a Net is OK"""
 
-        params = sum(map(list, self.net.params.itervalues()), [])
+        params = sum(map(list, six.itervalues(self.net.params)), [])
         blobs = self.net.blobs.values()
         del self.net
 
@@ -67,7 +68,7 @@ def test_inputs_outputs(self):
         self.assertEqual(self.net.outputs, ['loss'])
 
     def test_save_and_read(self):
-        f = tempfile.NamedTemporaryFile(delete=False)
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.close()
         self.net.save(f.name)
         net_file = simple_net_file(self.num_output)
diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
index 65b73b96f73..909a101bbfb 100644
--- a/python/caffe/test/test_net_spec.py
+++ b/python/caffe/test/test_net_spec.py
@@ -43,7 +43,7 @@ def anon_lenet(batch_size):
 
 class TestNetSpec(unittest.TestCase):
     def load_net(self, net_proto):
-        f = tempfile.NamedTemporaryFile(delete=False)
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.write(str(net_proto))
         f.close()
         return caffe.Net(f.name, caffe.TEST)
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index 6fba49143bb..f41e283fa4d 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -1,6 +1,7 @@
 import unittest
 import tempfile
 import os
+import six
 
 import caffe
 
@@ -22,7 +23,7 @@ def backward(self, top, propagate_down, bottom):
 
 
 def python_net_file():
-    with tempfile.NamedTemporaryFile(delete=False) as f:
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
         input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
         layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
@@ -58,6 +59,6 @@ def test_reshape(self):
         s = 4
         self.net.blobs['data'].reshape(s, s, s, s)
         self.net.forward()
-        for blob in self.net.blobs.itervalues():
+        for blob in six.itervalues(self.net.blobs):
             for d in blob.data.shape:
                 self.assertEqual(s, d)
diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index 09b974dad66..9cfc10d29a9 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -2,6 +2,7 @@
 import tempfile
 import os
 import numpy as np
+import six
 
 import caffe
 from test_net import simple_net_file
@@ -11,7 +12,7 @@ class TestSolver(unittest.TestCase):
     def setUp(self):
         self.num_output = 13
         net_f = simple_net_file(self.num_output)
-        f = tempfile.NamedTemporaryFile(delete=False)
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.write("""net: '""" + net_f + """'
         test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
         weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
@@ -45,8 +46,8 @@ def test_net_memory(self):
 
         total = 0
         for net in nets:
-            for ps in net.params.itervalues():
+            for ps in six.itervalues(net.params):
                 for p in ps:
                     total += p.data.sum() + p.diff.sum()
-            for bl in net.blobs.itervalues():
+            for bl in six.itervalues(net.blobs):
                 total += bl.data.sum() + bl.diff.sum()
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index 8ff63f31fdd..9ba737e28a9 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -7,8 +7,17 @@ MAKE="make --jobs=$NUM_THREADS --keep-going"
 if $WITH_CMAKE; then
   mkdir build
   cd build
-  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+  CPU_ONLY=" -DCPU_ONLY=ON"
+  if ! $WITH_CUDA; then
+    CPU_ONLY=" -DCPU_ONLY=OFF"
+  fi
+  PYTHON_ARGS=""
+  if [ "$PYTHON_VERSION" = "3" ]; then
+    PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
+  fi
+  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" ..
   $MAKE
+  $MAKE pytest
   if ! $WITH_CUDA; then
     $MAKE runtest
     $MAKE lint
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index b6e6f6ce821..d6c6e228b58 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -4,7 +4,6 @@
 set -e
 
 MAKE="make --jobs=$NUM_THREADS"
-
 # Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
 
 # This ppa is for gflags and glog
@@ -12,9 +11,8 @@ add-apt-repository -y ppa:tuleu/precise-backports
 apt-get -y update
 apt-get install \
     wget git curl \
-    python-dev python-numpy \
+    python-dev python-numpy python3-dev\
     libleveldb-dev libsnappy-dev libopencv-dev \
-    libboost-dev libboost-system-dev libboost-python-dev libboost-thread-dev \
     libprotobuf-dev protobuf-compiler \
     libatlas-dev libatlas-base-dev \
     libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
@@ -24,9 +22,10 @@ apt-get install \
 # if needed.  By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
 # Caffe requires a minimum CMake version of 2.8.8.
 if $WITH_CMAKE; then
-  add-apt-repository -y ppa:ubuntu-sdk-team/ppa
-  apt-get -y update
-  apt-get -y install cmake
+  # cmake 3 will make sure that the python interpreter and libraries match
+  wget http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
+  chmod +x cmake3.sh
+  ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
 fi
 
 # Install CUDA, if needed
@@ -60,10 +59,37 @@ rm -f $LMDB_FILE
 
 # Install the Python runtime dependencies via miniconda (this is much faster
 # than using pip for everything).
-wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-chmod +x miniconda.sh
-./miniconda.sh -b
-export PATH=/home/travis/miniconda/bin:$PATH
-conda update --yes conda
-conda install --yes numpy scipy matplotlib scikit-image pip
-pip install protobuf
+export PATH=$CONDA_DIR/bin:$PATH
+if [ ! -d $CONDA_DIR ]; then
+	if [ "$PYTHON_VERSION" -eq "3" ]; then
+		wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+	else
+		wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+	fi
+	chmod +x miniconda.sh
+	./miniconda.sh -b -p $CONDA_DIR
+	
+	conda update --yes conda
+	conda install --yes numpy scipy matplotlib scikit-image pip
+	# Let conda install boost (so that boost_python matches)
+	conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
+fi
+
+# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
+if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
+	pushd .
+	wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
+	tar -C /tmp -xzvf protobuf-3.tar.gz
+	cd /tmp/protobuf-3*/
+	./autogen.sh
+	./configure --prefix=$CONDA_DIR
+	$MAKE
+	$MAKE install
+	popd
+fi
+
+if [ "$PYTHON_VERSION" -eq "3" ]; then
+	pip install --pre protobuf
+else
+	pip install protobuf
+fi
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index ba326262bf8..1440be2af8b 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -12,7 +12,9 @@ if $WITH_CUDA; then
 fi
 
 cat << 'EOF' >> Makefile.config
-ANACONDA_HOME := $(HOME)/miniconda
+# Travis' nvcc doesn't like newer boost versions
+NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
+ANACONDA_HOME := $(CONDA_DIR)
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 		$(ANACONDA_HOME)/include/python2.7 \
 		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index d6a1cac5090..926c7d8ff78 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,3 +1,8 @@
+// Make sure we include Python.h before any system header
+// to avoid _POSIX_C_SOURCE redefinition
+#ifdef WITH_PYTHON_LAYER
+#include <boost/python.hpp>
+#endif
 #include <string>
 
 #include "caffe/layer.hpp"

From b2d7b9a31613d69dc4ea1fe6f3d0fe10ac7ee95d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 21 Jul 2015 10:33:31 -0700
Subject: [PATCH 1429/2053] [docs] matlab 2015a compatible

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 144e6a34f67..d535c6d093d 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -75,7 +75,7 @@ To import the `caffe` Python module after completing the installation, add the m
 
 Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
-*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.*
+*Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
 
 #### Windows
 

From 2d328815afa90c2b9bf10f1e5795bbcc2bc877ed Mon Sep 17 00:00:00 2001
From: Eric Zeiberg <ejzeiberg@gmail.com>
Date: Tue, 21 Jul 2015 22:12:48 -0700
Subject: [PATCH 1430/2053] [docs] set lmdb url to github mirror

---
 docs/install_apt.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index f588b74dcdb..d5deee1c093 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -41,7 +41,7 @@ These dependencies need manual installation in 12.04.
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
     # lmdb
-    git clone https://gitorious.org/mdb/mdb.git
+    git clone https://github.com/LMDB/lmdb
     cd mdb/libraries/liblmdb
     make && make install
 

From 075321931671330a8591554182dfa7e9f94697f4 Mon Sep 17 00:00:00 2001
From: Franck Dernoncourt <franck.dernoncourt@gmail.com>
Date: Wed, 22 Jul 2015 17:32:12 -0700
Subject: [PATCH 1431/2053] Fix path to mnist_autoencoder.prototxt

---
 docs/tutorial/layers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 806374e3f93..eabc792b704 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -213,7 +213,7 @@ Given an input value x, The `ReLU` layer computes the output as x if x > 0 and n
 * Layer type: `Sigmoid`
 * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
-* Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
+* Sample (as seen in `./examples/mnist/mnist_autoencoder.prototxt`)
 
       layer {
         name: "encode1neuron"

From 129d222fbd885c1a9e727e6134d2387676fc3dac Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 23 Jul 2015 20:23:42 -0700
Subject: [PATCH 1432/2053] [pycaffe] remove dead code

---
 python/caffe/net_spec.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 1b4814a45c6..95c03bcb9c5 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -44,8 +44,6 @@ def to_proto(*tops):
     """Generate a NetParameter that contains all layers needed to compute
     all arguments."""
 
-    if not isinstance(tops, tuple):
-        tops = (tops,)
     layers = OrderedDict()
     autonames = {}
     for top in tops:

From 7488163bf4b3d44be5fbc931ba58ccde07098818 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 23 Jul 2015 20:26:46 -0700
Subject: [PATCH 1433/2053] [pycaffe] use a Counter instead of a dict for
 counting net spec names

---
 python/caffe/net_spec.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 95c03bcb9c5..37e333d7cc5 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -18,7 +18,7 @@ class -- assign to its attributes directly to name layers, and call
 are not guaranteed to be forward-compatible.
 """
 
-from collections import OrderedDict
+from collections import OrderedDict, Counter
 
 from .proto import caffe_pb2
 from google import protobuf
@@ -45,7 +45,7 @@ def to_proto(*tops):
     all arguments."""
 
     layers = OrderedDict()
-    autonames = {}
+    autonames = Counter()
     for top in tops:
         top.fn._to_proto(layers, {}, autonames)
     net = caffe_pb2.NetParameter()
@@ -107,9 +107,8 @@ def __init__(self, type_name, inputs, params):
 
     def _get_name(self, top, names, autonames):
         if top not in names:
-            n = autonames.setdefault(top.fn.type_name, 1)
             autonames[top.fn.type_name] += 1
-            names[top] = top.fn.type_name + str(n)
+            names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
         return names[top]
 
     def _to_proto(self, layers, names, autonames):
@@ -161,7 +160,7 @@ def __getattr__(self, name):
 
     def to_proto(self):
         names = {v: k for k, v in six.iteritems(self.tops)}
-        autonames = {}
+        autonames = Counter()
         layers = OrderedDict()
         for name, top in six.iteritems(self.tops):
             top.fn._to_proto(layers, names, autonames)

From f16195aa8b1569e0260c48d4159b7d2ce0ea2fab Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 23 Jul 2015 20:32:04 -0700
Subject: [PATCH 1434/2053] [pycaffe] add Top._to_proto convenience function

This makes it possible to serialize Functions or Tops with a uniform
interface.
---
 python/caffe/net_spec.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 37e333d7cc5..5fb26ed45ba 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -87,6 +87,9 @@ def to_proto(self):
 
         return to_proto(self)
 
+    def _to_proto(self, layers, names, autonames):
+        return self.fn._to_proto(layers, names, autonames)
+
 
 class Function(object):
     """A Function specifies a layer, its parameters, and its inputs (which
@@ -116,7 +119,7 @@ def _to_proto(self, layers, names, autonames):
             return
         bottom_names = []
         for inp in self.inputs:
-            inp.fn._to_proto(layers, names, autonames)
+            inp._to_proto(layers, names, autonames)
             bottom_names.append(layers[inp.fn].top[inp.n])
         layer = caffe_pb2.LayerParameter()
         layer.type = self.type_name
@@ -163,7 +166,7 @@ def to_proto(self):
         autonames = Counter()
         layers = OrderedDict()
         for name, top in six.iteritems(self.tops):
-            top.fn._to_proto(layers, names, autonames)
+            top._to_proto(layers, names, autonames)
         net = caffe_pb2.NetParameter()
         net.layer.extend(layers.values())
         return net

From 96c2fe1de80c9752b992c4578a3ce46028d21fc5 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 23 Jul 2015 20:35:42 -0700
Subject: [PATCH 1435/2053] [pycaffe] allow layers to have names different from
 their first tops

Previously, net spec only allowed names to be assigned to Tops, giving
layers the names of their first tops. Now, names can be assigned to
Functions, which become layer names in serialization. Unnamed Functions
still get named after their first top, if present, or autogenerated, if
not. (This will allow top-less layers in a natural way.)
---
 python/caffe/net_spec.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 5fb26ed45ba..16f30008579 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -108,7 +108,15 @@ def __init__(self, type_name, inputs, params):
             del self.params['in_place']
         self.tops = tuple(Top(self, n) for n in range(self.ntop))
 
-    def _get_name(self, top, names, autonames):
+    def _get_name(self, names, autonames):
+        if self not in names and self.ntop > 0:
+            names[self] = self._get_top_name(self.tops[0], names, autonames)
+        elif self not in names:
+            autonames[self.type_name] += 1
+            names[self] = self.type_name + str(autonames[self.type_name])
+        return names[self]
+
+    def _get_top_name(self, top, names, autonames):
         if top not in names:
             autonames[top.fn.type_name] += 1
             names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
@@ -129,8 +137,8 @@ def _to_proto(self, layers, names, autonames):
             layer.top.extend(layer.bottom)
         else:
             for top in self.tops:
-                layer.top.append(self._get_name(top, names, autonames))
-        layer.name = self._get_name(self.tops[0], names, autonames)
+                layer.top.append(self._get_top_name(top, names, autonames))
+        layer.name = self._get_name(names, autonames)
 
         for k, v in six.iteritems(self.params):
             # special case to handle generic *params

From b464a6e5d2e381185bd8aa0b9cbcba53c9819585 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 23 Jul 2015 20:40:02 -0700
Subject: [PATCH 1436/2053] [pycaffe] net spec layers can have ntop=0

In this case, the Function is returned instead of a Top, which can be
assigned a name if desired.

Thanks @philkr for an earlier implementation of this.
---
 python/caffe/net_spec.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 16f30008579..31cde7ad946 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -188,7 +188,9 @@ class Layers(object):
     def __getattr__(self, name):
         def layer_fn(*args, **kwargs):
             fn = Function(name, args, kwargs)
-            if fn.ntop == 1:
+            if fn.ntop == 0:
+                return fn
+            elif fn.ntop == 1:
                 return fn.tops[0]
             else:
                 return fn.tops

From c80f6f585549334022996395015cd490cc2644c8 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 23 Jul 2015 20:41:40 -0700
Subject: [PATCH 1437/2053] [pytest] simple test of top-less layers

---
 python/caffe/test/test_net_spec.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
index 65b73b96f73..b344946932a 100644
--- a/python/caffe/test/test_net_spec.py
+++ b/python/caffe/test/test_net_spec.py
@@ -41,6 +41,14 @@ def anon_lenet(batch_size):
     loss = L.SoftmaxWithLoss(ip2, label)
     return loss.to_proto()
 
+def silent_net():
+    n = caffe.NetSpec()
+    n.data, n.data2 = L.DummyData(shape=[dict(dim=[3]), dict(dim=[4, 2])],
+                                  ntop=2)
+    n.silence_data = L.Silence(n.data, ntop=0)
+    n.silence_data2 = L.Silence(n.data2, ntop=0)
+    return n.to_proto()
+
 class TestNetSpec(unittest.TestCase):
     def load_net(self, net_proto):
         f = tempfile.NamedTemporaryFile(delete=False)
@@ -65,3 +73,10 @@ def test_lenet(self):
                 net_proto.layer[6].top)
         net = self.load_net(net_proto)
         self.assertEqual(len(net.layers), 9)
+
+    def test_zero_tops(self):
+        """Test net construction for top-less layers."""
+
+        net_proto = silent_net()
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.forward()), 0)

From de6d444445261fc9859143bfd969d538ae7a2108 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 27 Jul 2015 10:58:11 -0700
Subject: [PATCH 1438/2053] [docs] clear up PYTHONPATH confusion

Use the same language as the installation page to explain the Python
module path.

reported by @sdemyanov
---
 docs/tutorial/interfaces.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 12963318485..40602948cc3 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -11,8 +11,8 @@ The command line interface -- cmdcaffe -- is the `caffe` tool for model training
 
 **Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:
 
-* All training requires a solver configuration through the `-solver solver.prototxt` argument. 
-* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. 
+* All training requires a solver configuration through the `-solver solver.prototxt` argument.
+* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot.
 * Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
 
 For example, you can run:
@@ -31,8 +31,7 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
 
 **Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
 
-    #
-    # score the learned LeNet model on the validation set as defined in the 
+    # score the learned LeNet model on the validation set as defined in the
     # model architeture lenet_train_test.prototxt
     caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
 
@@ -63,7 +62,8 @@ The Python interface -- pycaffe -- is the `caffe` module and its scripts in caff
 
 Tutorial IPython notebooks are found in caffe/examples: do `ipython notebook caffe/examples` to try them. For developer reference docstrings can be found throughout the code.
 
-Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be installed in your PYTHONPATH for `import caffe`.
+Compile pycaffe by `make pycaffe`.
+Add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like for `import caffe`.
 
 ## MATLAB
 
@@ -182,7 +182,7 @@ To get a layer's type (string):
 #### Forward and backward
 
 Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
-    
+
     res = net.forward({data});
     prob = res{1};
 
@@ -202,7 +202,7 @@ Or
     net.blobs('prob').set_diff(prob_diff);
     net.backward_prefilled();
     data_diff = net.blobs('data').get_diff();
-    
+
 **However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.**
 
 After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:

From e4aed047a2203bbc1f5bd36ed90bb7d9882b2e11 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 28 Jul 2015 10:27:39 -0700
Subject: [PATCH 1439/2053] [docs] fix lmdb fetch url and path

---
 docs/install_apt.md | 2 +-
 docs/install_yum.md | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index d5deee1c093..2976e3cd07c 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -42,7 +42,7 @@ These dependencies need manual installation in 12.04.
     make && make install
     # lmdb
     git clone https://github.com/LMDB/lmdb
-    cd mdb/libraries/liblmdb
+    cd lmdb/libraries/liblmdb
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
diff --git a/docs/install_yum.md b/docs/install_yum.md
index 478e7d952cc..2104912e482 100644
--- a/docs/install_yum.md
+++ b/docs/install_yum.md
@@ -28,8 +28,8 @@ title: Installation: RHEL / Fedora / CentOS
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
     # lmdb
-    git clone git://gitorious.org/mdb/mdb.git
-    cd mdb/libraries/liblmdb
+    git clone https://github.com/LMDB/lmdb
+    cd lmdb/libraries/liblmdb
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.

From 7f7085439cbe4eb9d5fff95b41d6345168398142 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 29 Jul 2015 17:20:31 -0700
Subject: [PATCH 1440/2053] [docs] fix contrastive loss eq

make documented equation match the correct implementation of the
`max(margin - d, 0)^2` term in the loss. see #2321
---
 include/caffe/loss_layers.hpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 86c34241168..528266394f1 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -128,9 +128,9 @@ class LossLayer : public Layer<Dtype> {
 /**
  * @brief Computes the contrastive loss @f$
  *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
- *              \left(1-y\right) \max \left(margin-d, 0\right)
+ *              \left(1-y\right) \max \left(margin-d, 0\right)^2
  *          @f$ where @f$
- *          d = \left| \left| a_n - b_n \right| \right|_2^2 @f$. This can be
+ *          d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
  *          used to train siamese networks.
  *
  * @param bottom input Blob vector (length 3)
@@ -144,9 +144,9 @@ class LossLayer : public Layer<Dtype> {
  *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
  *      the computed contrastive loss: @f$ E =
  *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
- *          \left(1-y\right) \max \left(margin-d, 0\right)
+ *          \left(1-y\right) \max \left(margin-d, 0\right)^2
  *          @f$ where @f$
- *          d = \left| \left| a_n - b_n \right| \right|_2^2 @f$.
+ *          d = \left| \left| a_n - b_n \right| \right|_2 @f$.
  * This can be used to train siamese networks.
  */
 template <typename Dtype>

From ccc35d361d6c90b7691b3ed8de7ea0678b488dff Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 29 Jul 2015 20:16:31 -0700
Subject: [PATCH 1441/2053] [docs] add CONTRIBUTING.md which will appear on
 GitHub new Issue/PR pages

---
 CONTRIBUTING.md | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 CONTRIBUTING.md

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000000..8cd5e56ca49
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,30 @@
+# Contributing
+
+## Issues
+
+Specific Caffe design and development issues, bugs, and feature requests are maintained by GitHub Issues.
+
+_Please do not post usage, installation, or modeling questions, or other requests for help to Issues._
+Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead. This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
+
+When reporting a bug, it's most helpful to provide the following information, where applicable:
+
+* What steps reproduce the bug?
+* Can you reproduce the bug using the latest [master](https://github.com/BVLC/caffe/tree/master), compiled with the `DEBUG` make option?
+* What hardware and operating system/distribution are you running?
+* If the bug is a crash, provide the backtrace (usually printed by Caffe; always obtainable with `gdb`).
+
+Try to give your issue a title that is succinct and specific. The devs will rename issues as needed to keep track of them.
+
+## Pull Requests
+
+Caffe welcomes all contributions.
+
+See the [contributing guide](http://caffe.berkeleyvision.org/development.html) for details.
+
+Briefly: read commit by commit, a PR should tell a clean, compelling story of _one_ improvement to Caffe. In particular:
+
+* A PR should do one clear thing that obviously improves Caffe, and nothing more. Making many smaller PRs is better than making one large PR; review effort is superlinear in the amount of code involved.
+* Similarly, each commit should be a small, atomic change representing one step in development. PRs should be made of many commits where appropriate.
+* Please do rewrite PR history to be clean rather than chronological. Within-PR bugfixes, style cleanups, reversions, etc. should be squashed and should not appear in merged PR history.
+* Anything nonobvious from the code should be explained in comments, commit messages, or the PR description, as appropriate.

From 63e45b79932f3f990db32d75f157693809679230 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 9 Jun 2015 17:50:40 -0700
Subject: [PATCH 1442/2053] Change log levels in upgrade_proto

It's not an error if the upgrade succeeds
---
 src/caffe/util/upgrade_proto.cpp | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 38a06026adf..92e5cf55fa9 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -588,8 +588,8 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
   if (NetNeedsV0ToV1Upgrade(*param)) {
     // NetParameter was specified using the old style (V0LayerParameter); try to
     // upgrade it.
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "V0LayerParameter: " << param_file;
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "V0LayerParameter: " << param_file;
     NetParameter original_param(*param);
     if (!UpgradeV0Net(original_param, param)) {
       success = false;
@@ -599,29 +599,29 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
       LOG(INFO) << "Successfully upgraded file specified using deprecated "
                 << "V0LayerParameter";
     }
-    LOG(ERROR) << "Note that future Caffe releases will not support "
+    LOG(WARNING) << "Note that future Caffe releases will not support "
         << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
         << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
         << "weights upgrade this and any other net protos to the new format.";
   }
   // NetParameter uses old style data transformation fields; try to upgrade it.
   if (NetNeedsDataUpgrade(*param)) {
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "transformation parameters: " << param_file;
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "transformation parameters: " << param_file;
     UpgradeNetDataTransformation(param);
     LOG(INFO) << "Successfully upgraded file specified using deprecated "
               << "data transformation parameters.";
-    LOG(ERROR) << "Note that future Caffe releases will only support "
-               << "transform_param messages for transformation fields.";
+    LOG(WARNING) << "Note that future Caffe releases will only support "
+                 << "transform_param messages for transformation fields.";
   }
   if (NetNeedsV1ToV2Upgrade(*param)) {
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "V1LayerParameter: " << param_file;
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "V1LayerParameter: " << param_file;
     NetParameter original_param(*param);
     if (!UpgradeV1Net(original_param, param)) {
       success = false;
       LOG(ERROR) << "Warning: had one or more problems upgrading "
-          << "V1LayerParameter (see above); continuing anyway.";
+                 << "V1LayerParameter (see above); continuing anyway.";
     } else {
       LOG(INFO) << "Successfully upgraded file specified using deprecated "
                 << "V1LayerParameter";

From e7b2b4eab67390885b8cb360750cfc1553fbb70e Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 4 Aug 2015 10:27:34 -0700
Subject: [PATCH 1443/2053] ImageData layer default batch size of 1, and check
 for zero batch size

---
 src/caffe/layers/image_data_layer.cpp | 3 +++
 src/caffe/proto/caffe.proto           | 6 +++---
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 18c035cba9d..dcc53348304 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -62,11 +62,13 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Read an image, and use it to initialize the top blob.
   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
+  CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
   // Use data_transformer to infer the expected blob shape from a cv_image.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
   this->transformed_data_.Reshape(top_shape);
   // Reshape prefetch_data and top[0] according to the batch_size.
   const int batch_size = this->layer_param_.image_data_param().batch_size();
+  CHECK_GT(batch_size, 0) << "Positive batch size required";
   top_shape[0] = batch_size;
   this->prefetch_data_.Reshape(top_shape);
   top[0]->ReshapeLike(this->prefetch_data_);
@@ -108,6 +110,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   // on single input batches allows for inputs of varying dimension.
   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
       new_height, new_width, is_color);
+  CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
   // Use data_transformer to infer the expected blob shape from a cv_img.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
   this->transformed_data_.Reshape(top_shape);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 81a8c69d88e..8c3f0723600 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -290,7 +290,7 @@ message LayerParameter {
 
   // The blobs containing the numeric parameters of the layer.
   repeated BlobProto blobs = 7;
-  
+
   // Specifies on which bottoms the backpropagation should be skipped.
   // The size must be either 0 or equal to the number of bottoms.
   repeated bool propagate_down = 11;
@@ -431,7 +431,7 @@ message ContrastiveLossParameter {
   // Hadsell paper. New models should probably use this version.
   // legacy_version = true uses (margin - d^2). This is kept to support /
   // reproduce existing models and results
-  optional bool legacy_version = 2 [default = false]; 
+  optional bool legacy_version = 2 [default = false];
 }
 
 message ConvolutionParameter {
@@ -579,7 +579,7 @@ message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
   // Specify the batch size.
-  optional uint32 batch_size = 4;
+  optional uint32 batch_size = 4 [default = 1];
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not

From 4c23e93a95db0c6c6c5d4239070966e3bdc24fdc Mon Sep 17 00:00:00 2001
From: Gustavo Serra Scalet <gsscalet@gmail.com>
Date: Thu, 6 Aug 2015 14:20:55 -0300
Subject: [PATCH 1444/2053] Fix download model script to use zip archive

Currently GitHub is not using tarballs as archive for downloading gists
therefore the script was broken as actually a zip archive was being downloaded.
---
 scripts/download_model_from_gist.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/download_model_from_gist.sh b/scripts/download_model_from_gist.sh
index a1dccf78b5b..89527b7516f 100755
--- a/scripts/download_model_from_gist.sh
+++ b/scripts/download_model_from_gist.sh
@@ -18,7 +18,7 @@ fi
 
 echo "Downloading Caffe model info to $MODEL_DIR ..."
 mkdir -p $MODEL_DIR
-wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.tar.gz
-tar xzf $MODEL_DIR/gist.tar.gz --directory=$MODEL_DIR --strip-components=1
-rm $MODEL_DIR/gist.tar.gz
+wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.zip
+unzip -j $MODEL_DIR/gist.zip -d $MODEL_DIR
+rm $MODEL_DIR/gist.zip
 echo "Done"

From fda9229a426d9c0e4496c700099a7126da72ba64 Mon Sep 17 00:00:00 2001
From: Gustavo Serra Scalet <gsscalet@gmail.com>
Date: Thu, 6 Aug 2015 14:37:20 -0300
Subject: [PATCH 1445/2053] Fix download model binary script to get correct
 lines on parsing table

The base reference of "bottom" variable was relative to the "top+1" and
not to the whole readlines output. It ended up without all the lines.

That could work for some gists however for the model I was looking for (see
below) the sha1 key was not being parsed, as it was missing the last line.

tested with the following gist:
longjon/1bf3aa1e0b8e788d7e1d
---
 scripts/download_model_binary.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index 48e9015fd26..03a50f6776a 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -32,7 +32,7 @@ def parse_readme_frontmatter(dirname):
     with open(readme_filename) as f:
         lines = [line.strip() for line in f.readlines()]
     top = lines.index('---')
-    bottom = lines[top + 1:].index('---')
+    bottom = lines.index('---', top + 1)
     frontmatter = yaml.load('\n'.join(lines[top + 1:bottom]))
     assert all(key in frontmatter for key in required_keys)
     return dirname, frontmatter

From d958b5a45c95db4f4e90abda29b69d5bfed2ef07 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 6 Aug 2015 13:03:50 -0700
Subject: [PATCH 1446/2053] [pycaffe,build] include Python first in caffe tool

---
 tools/caffe.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 9de3abdc722..46f99594800 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -1,3 +1,8 @@
+#ifdef WITH_PYTHON_LAYER
+#include "boost/python.hpp"
+namespace bp = boost::python;
+#endif
+
 #include <glog/logging.h>
 
 #include <cstring>
@@ -8,11 +13,6 @@
 #include "boost/algorithm/string.hpp"
 #include "caffe/caffe.hpp"
 
-#ifdef WITH_PYTHON_LAYER
-#include "boost/python.hpp"
-namespace bp = boost::python;
-#endif
-
 using caffe::Blob;
 using caffe::Caffe;
 using caffe::Net;

From 1934feec285c901066ef28567ccc6a7cb5b4ce92 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 6 Aug 2015 13:04:15 -0700
Subject: [PATCH 1447/2053] [pytest] open exception file with mode for python3

---
 python/caffe/test/test_python_layer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index ff070a35aee..a1e11bc248d 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -43,7 +43,7 @@ def python_net_file():
 
 
 def exception_net_file():
-    with tempfile.NamedTemporaryFile(delete=False) as f:
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
         input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
         layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'

From a756cfec0056fc59bf8719997df7f3b233babf38 Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Fri, 27 Feb 2015 19:34:51 -0800
Subject: [PATCH 1448/2053] PythonLayer takes parameters by string

---
 include/caffe/python_layer.hpp                |  2 +
 .../test/test_python_layer_with_param_str.py  | 59 +++++++++++++++++++
 src/caffe/proto/caffe.proto                   |  5 ++
 3 files changed, 66 insertions(+)
 create mode 100644 python/caffe/test/test_python_layer_with_param_str.py

diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
index 9c30250c1b5..2957e7426be 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/python_layer.hpp
@@ -18,6 +18,8 @@ class PythonLayer : public Layer<Dtype> {
 
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+    self_.attr("param_str") = bp::str(
+        this->layer_param_.python_param().param_str());
     self_.attr("setup")(bottom, top);
   }
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
diff --git a/python/caffe/test/test_python_layer_with_param_str.py b/python/caffe/test/test_python_layer_with_param_str.py
new file mode 100644
index 00000000000..3d0f107b3bb
--- /dev/null
+++ b/python/caffe/test/test_python_layer_with_param_str.py
@@ -0,0 +1,59 @@
+import unittest
+import tempfile
+import os
+import six
+
+import caffe
+
+
+class SimpleParamLayer(caffe.Layer):
+    """A layer that just multiplies by the numeric value of its param string"""
+
+    def setup(self, bottom, top):
+        try:
+            self.value = float(self.param_str)
+        except ValueError:
+            raise ValueError("Parameter string must be a legible float")
+
+    def reshape(self, bottom, top):
+        top[0].reshape(*bottom[0].data.shape)
+
+    def forward(self, bottom, top):
+        top[0].data[...] = self.value * bottom[0].data
+
+    def backward(self, top, propagate_down, bottom):
+        bottom[0].diff[...] = self.value * top[0].diff
+
+
+def python_param_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10'
+          python_param { module: 'test_python_layer_with_param_str'
+                layer: 'SimpleParamLayer' param_str: '10' } }
+        layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2'
+          python_param { module: 'test_python_layer_with_param_str'
+                layer: 'SimpleParamLayer' param_str: '2' } }""")
+        return f.name
+
+
+class TestLayerWithParam(unittest.TestCase):
+    def setUp(self):
+        net_file = python_param_net_file()
+        self.net = caffe.Net(net_file, caffe.TRAIN)
+        os.remove(net_file)
+
+    def test_forward(self):
+        x = 8
+        self.net.blobs['data'].data[...] = x
+        self.net.forward()
+        for y in self.net.blobs['mul2'].data.flat:
+            self.assertEqual(y, 2 * 10 * x)
+
+    def test_backward(self):
+        x = 7
+        self.net.blobs['mul2'].diff[...] = x
+        self.net.backward()
+        for y in self.net.blobs['data'].diff.flat:
+            self.assertEqual(y, 2 * 10 * x)
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 8c3f0723600..adcf4e2fd5a 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -703,6 +703,11 @@ message PowerParameter {
 message PythonParameter {
   optional string module = 1;
   optional string layer = 2;
+  // This value is set to the attribute `param_str` of the `PythonLayer` object
+  // in Python before calling the `setup()` method. This could be a number,
+  // string, dictionary in Python dict format, JSON, etc. You may parse this
+  // string in `setup` method and use it in `forward` and `backward`.
+  optional string param_str = 3 [default = ''];
 }
 
 // Message that stores parameters used by ReductionLayer

From f5116a1d2827a5e1490ce047df7835b66e92f99f Mon Sep 17 00:00:00 2001
From: "koki1.saitoh" <koki0702@gmail.com>
Date: Fri, 7 Aug 2015 18:22:28 +0900
Subject: [PATCH 1449/2053] Fix typo

---
 include/caffe/layer.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index e2eba196346..0771b6a8fb4 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -86,7 +86,7 @@ class Layer {
       const vector<Blob<Dtype>*>& top) {}
 
   /**
-   * @brief Adjust the shapes of top blobs and internal buffers to accomodate
+   * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
    *
    * @param bottom the input blobs, with the requested input shapes
@@ -95,7 +95,7 @@ class Layer {
    * This method should reshape top blobs as needed according to the shapes
    * of the bottom (input) blobs, as well as reshaping any internal buffers
    * and making any other necessary adjustments so that the layer can
-   * accomodate the bottom blobs.
+   * accommodate the bottom blobs.
    */
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) = 0;

From a3c7425b80fd83ef550323836304a7255d35e033 Mon Sep 17 00:00:00 2001
From: Tian Zhi <tianzhi0549@163.com>
Date: Sat, 8 Aug 2015 02:31:19 +0800
Subject: [PATCH 1450/2053] add [] to "delete pixels".

see https://isocpp.org/wiki/faq/freestore-mgmt#delete-array-built-ins.
---
 examples/mnist/convert_mnist_data.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 2749e4521b6..54443f11dd3 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -166,7 +166,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     }
     LOG(ERROR) << "Processed " << count << " files.";
   }
-  delete pixels;
+  delete[] pixels;
 }
 
 int main(int argc, char** argv) {

From f973819240768df207ed8e4d307564b105950333 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 29 Jul 2015 17:27:04 -0700
Subject: [PATCH 1451/2053] add double_data, double_diff to BlobProto for
 weights/snapshots saved when using Dtype == double

---
 src/caffe/blob.cpp          | 49 +++++++++++++++++++++++++++++++------
 src/caffe/proto/caffe.proto |  2 ++
 2 files changed, 44 insertions(+), 7 deletions(-)

diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 94fdcc35fb6..8450aa140be 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -456,10 +456,25 @@ void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
   }
   // copy data
   Dtype* data_vec = mutable_cpu_data();
-  for (int i = 0; i < count_; ++i) {
-    data_vec[i] = proto.data(i);
+  if (proto.double_data_size() > 0) {
+    CHECK_EQ(count_, proto.double_data_size());
+    for (int i = 0; i < count_; ++i) {
+      data_vec[i] = proto.double_data(i);
+    }
+  } else {
+    CHECK_EQ(count_, proto.data_size());
+    for (int i = 0; i < count_; ++i) {
+      data_vec[i] = proto.data(i);
+    }
   }
-  if (proto.diff_size() > 0) {
+  if (proto.double_diff_size() > 0) {
+    CHECK_EQ(count_, proto.double_diff_size());
+    Dtype* diff_vec = mutable_cpu_diff();
+    for (int i = 0; i < count_; ++i) {
+      diff_vec[i] = proto.double_diff(i);
+    }
+  } else if (proto.diff_size() > 0) {
+    CHECK_EQ(count_, proto.diff_size());
     Dtype* diff_vec = mutable_cpu_diff();
     for (int i = 0; i < count_; ++i) {
       diff_vec[i] = proto.diff(i);
@@ -467,20 +482,40 @@ void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
   }
 }
 
-template <typename Dtype>
-void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
+template <>
+void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
+  proto->clear_shape();
+  for (int i = 0; i < shape_.size(); ++i) {
+    proto->mutable_shape()->add_dim(shape_[i]);
+  }
+  proto->clear_double_data();
+  proto->clear_double_diff();
+  const double* data_vec = cpu_data();
+  for (int i = 0; i < count_; ++i) {
+    proto->add_double_data(data_vec[i]);
+  }
+  if (write_diff) {
+    const double* diff_vec = cpu_diff();
+    for (int i = 0; i < count_; ++i) {
+      proto->add_double_diff(diff_vec[i]);
+    }
+  }
+}
+
+template <>
+void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
   proto->clear_shape();
   for (int i = 0; i < shape_.size(); ++i) {
     proto->mutable_shape()->add_dim(shape_[i]);
   }
   proto->clear_data();
   proto->clear_diff();
-  const Dtype* data_vec = cpu_data();
+  const float* data_vec = cpu_data();
   for (int i = 0; i < count_; ++i) {
     proto->add_data(data_vec[i]);
   }
   if (write_diff) {
-    const Dtype* diff_vec = cpu_diff();
+    const float* diff_vec = cpu_diff();
     for (int i = 0; i < count_; ++i) {
       proto->add_diff(diff_vec[i]);
     }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index adcf4e2fd5a..03daa808bf8 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -11,6 +11,8 @@ message BlobProto {
   optional BlobShape shape = 7;
   repeated float data = 5 [packed = true];
   repeated float diff = 6 [packed = true];
+  repeated double double_data = 8 [packed = true];
+  repeated double double_diff = 9 [packed = true];
 
   // 4D dimensions -- deprecated.  Use "shape" instead.
   optional int32 num = 1 [default = 0];

From 1e740e1e8568063ffe69162a30a97961c5c62af0 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 29 Jul 2015 17:27:58 -0700
Subject: [PATCH 1452/2053] TestGradientBasedSolver: add TestSnapshot to verify
 behavior when restoring net/solver from snapshot

---
 src/caffe/test/test_gradient_based_solver.cpp | 112 +++++++++++++++++-
 1 file changed, 107 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index c9135d64e70..94b500124aa 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -10,6 +10,7 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/util/io.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -25,6 +26,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   GradientBasedSolverTest() :
       seed_(1701), num_(4), channels_(3), height_(10), width_(10) {}
 
+  string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
   int num_, channels_, height_, width_;
@@ -36,9 +38,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   virtual void InitSolverFromProtoString(const string& proto) {
     SolverParameter param;
     CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
-    // Disable saving a final snapshot so the tests don't pollute the user's
-    // working directory with useless snapshots.
-    param.set_snapshot_after_train(false);
     // Set the solver_mode according to current Caffe::mode.
     switch (Caffe::mode()) {
       case Caffe::CPU:
@@ -55,11 +54,13 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
          param.delta() : 0;
   }
 
-  void RunLeastSquaresSolver(const Dtype learning_rate,
+  string RunLeastSquaresSolver(const Dtype learning_rate,
       const Dtype weight_decay, const Dtype momentum, const int num_iters,
-      const int iter_size = 1) {
+      const int iter_size = 1, const bool snapshot = false,
+      const char* from_snapshot = NULL) {
     ostringstream proto;
     proto <<
+       "snapshot_after_train: " << snapshot << " "
        "max_iter: " << num_iters << " "
        "base_lr: " << learning_rate << " "
        "lr_policy: 'fixed' "
@@ -119,9 +120,30 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     if (momentum != 0) {
       proto << "momentum: " << momentum << " ";
     }
+    MakeTempDir(&snapshot_prefix_);
+    proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' ";
+    if (snapshot) {
+      proto << "snapshot: " << num_iters << " ";
+    }
     Caffe::set_random_seed(this->seed_);
     this->InitSolverFromProtoString(proto.str());
+    Caffe::set_random_seed(this->seed_);
+    if (from_snapshot != NULL) {
+      this->solver_->Restore(from_snapshot);
+      vector<Blob<Dtype>*> empty_bottom_vec;
+      for (int i = 0; i < this->solver_->iter(); ++i) {
+        this->solver_->net()->Forward(empty_bottom_vec);
+      }
+    }
     this->solver_->Solve();
+    if (snapshot) {
+      ostringstream resume_file;
+      resume_file << snapshot_prefix_ << "/_iter_" << num_iters
+                  << ".solverstate";
+      string resume_filename = resume_file.str();
+      return resume_filename;
+    }
+    return string();
   }
 
   // Compute an update value given the current state of the train net,
@@ -348,6 +370,51 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Check that the solver's solution matches ours.
     CheckLeastSquaresUpdate(updated_params);
   }
+
+  void TestSnapshot(const Dtype learning_rate = 1.0,
+      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+      const int num_iters = 1) {
+    // Run the solver for num_iters * 2 iterations.
+    const int total_num_iters = num_iters * 2;
+    bool snapshot = false;
+    const int kIterSize = 1;
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                          total_num_iters, kIterSize, snapshot);
+
+    // Save the resulting param values.
+    vector<shared_ptr<Blob<Dtype> > > param_copies;
+    const vector<shared_ptr<Blob<Dtype> > >& orig_params =
+        solver_->net()->params();
+    param_copies.resize(orig_params.size());
+    for (int i = 0; i < orig_params.size(); ++i) {
+      param_copies[i].reset(new Blob<Dtype>());
+      const bool kReshape = true;
+      for (int copy_diff = false; copy_diff <= true; ++copy_diff) {
+        param_copies[i]->CopyFrom(*orig_params[i], copy_diff, kReshape);
+      }
+    }
+
+    // Run the solver for num_iters iterations and snapshot.
+    snapshot = true;
+    string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
+        momentum, num_iters, kIterSize, snapshot);
+
+    // Reinitialize the solver and run for num_iters more iterations.
+    snapshot = false;
+    RunLeastSquaresSolver(learning_rate, weight_decay,
+        momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str());
+
+    // Check that params now match.
+    const vector<shared_ptr<Blob<Dtype> > >& params = solver_->net()->params();
+    for (int i = 0; i < params.size(); ++i) {
+      for (int j = 0; j < params[i]->count(); ++j) {
+        EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
+            << "param " << i << " data differed at dim " << j;
+        EXPECT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
+            << "param " << i << " diff differed at dim " << j;
+      }
+    }
+  }
 };
 
 
@@ -428,6 +495,18 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
       kIterSize);
 }
 
+TYPED_TEST(SGDSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -482,6 +561,18 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
       kIterSize);
 }
 
+TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+
 template <typename TypeParam>
 class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -558,4 +649,15 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
       kIterSize);
 }
 
+TYPED_TEST(NesterovSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 }  // namespace caffe

From 50195e353293a7f41e7c95e158844eef53a70a2b Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 6 Aug 2015 13:46:11 -0700
Subject: [PATCH 1453/2053] pycaffe: add shape accessor

---
 python/caffe/_caffe.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index dff7f627016..bb5130fddd0 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -230,6 +230,11 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
     "Blob", bp::no_init)
+    .add_property("shape",
+        bp::make_function(
+            static_cast<const vector<int>& (Blob<Dtype>::*)() const>(
+                &Blob<Dtype>::shape),
+            bp::return_value_policy<bp::copy_const_reference>()))
     .add_property("num",      &Blob<Dtype>::num)
     .add_property("channels", &Blob<Dtype>::channels)
     .add_property("height",   &Blob<Dtype>::height)

From ada055bbf155882534907a7fb98a35e4f7bba392 Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@eecs.berkeley.edu>
Date: Wed, 22 Jul 2015 16:17:01 -0700
Subject: [PATCH 1454/2053] Snapshot model weights/solver state to HDF5 files.

Summary of changes:
- HDF5 helper functions were moved into a separate file util/hdf5.cpp
- hdf5_save_nd_dataset now saves n-d blobs, can save diffs instead of
  data
- Minor fix for memory leak in HDF5 functions (delete instead of
  delete[])
- Extra methods have been added to both Net/Solver enabling
  snapshotting and restoring from HDF5 files
- snapshot_format was added to SolverParameters, with possible values
  HDF5 or BINARYPROTO (default HDF5)
- kMaxBlobAxes was reduced to 32 to match the limitations of HDF5
---
 include/caffe/blob.hpp                    |   2 +-
 include/caffe/net.hpp                     |   4 +
 include/caffe/solver.hpp                  |  21 ++-
 include/caffe/util/hdf5.hpp               |  39 +++++
 include/caffe/util/io.hpp                 |  18 ---
 src/caffe/layers/hdf5_data_layer.cpp      |   2 +-
 src/caffe/layers/hdf5_output_layer.cpp    |   2 +-
 src/caffe/layers/hdf5_output_layer.cu     |   1 -
 src/caffe/net.cpp                         | 122 +++++++++++++++-
 src/caffe/proto/caffe.proto               |   7 +-
 src/caffe/solver.cpp                      | 164 ++++++++++++++++++----
 src/caffe/test/test_hdf5_output_layer.cpp |   1 +
 src/caffe/util/hdf5.cpp                   | 160 +++++++++++++++++++++
 src/caffe/util/io.cpp                     |  74 ----------
 14 files changed, 482 insertions(+), 135 deletions(-)
 create mode 100644 include/caffe/util/hdf5.hpp
 create mode 100644 src/caffe/util/hdf5.cpp

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 472cc1841f7..9b813e739e9 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -10,7 +10,7 @@
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 
-const int kMaxBlobAxes = INT_MAX;
+const int kMaxBlobAxes = 32;
 
 namespace caffe {
 
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 5665df1edf2..dfd2e556a2b 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -98,8 +98,12 @@ class Net {
    */
   void CopyTrainedLayersFrom(const NetParameter& param);
   void CopyTrainedLayersFrom(const string trained_filename);
+  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
+  void CopyTrainedLayersFromHDF5(const string trained_filename);
   /// @brief Writes the net to a proto.
   void ToProto(NetParameter* param, bool write_diff = false) const;
+  /// @brief Writes the net to an HDF5 file.
+  void ToHDF5(const string& filename, bool write_diff = false) const;
 
   /// @brief returns the network name.
   inline const string& name() const { return name_; }
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index c2ced487d6f..703434b5fcf 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -27,9 +27,9 @@ class Solver {
   virtual void Solve(const char* resume_file = NULL);
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
   void Step(int iters);
-  // The Restore function implements how one should restore the solver to a
-  // previously snapshotted state. You should implement the RestoreSolverState()
-  // function that restores the state from a SolverState protocol buffer.
+  // The Restore method simply dispatches to one of the
+  // RestoreSolverStateFrom___ protected methods. You should implement these
+  // methods to restore the state from the appropriate snapshot type.
   void Restore(const char* resume_file);
   virtual ~Solver() {}
   inline shared_ptr<Net<Dtype> > net() { return net_; }
@@ -46,11 +46,15 @@ class Solver {
   // function that produces a SolverState protocol buffer that needs to be
   // written to disk together with the learned net.
   void Snapshot();
+  string SnapshotFilename(const string extension);
+  string SnapshotToBinaryProto();
+  string SnapshotToHDF5();
   // The test routine
   void TestAll();
   void Test(const int test_net_id = 0);
-  virtual void SnapshotSolverState(SolverState* state) = 0;
-  virtual void RestoreSolverState(const SolverState& state) = 0;
+  virtual void SnapshotSolverState(const string& model_filename) = 0;
+  virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;
+  virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
   void DisplayOutputBlobs(const int net_id);
 
   SolverParameter param_;
@@ -85,8 +89,11 @@ class SGDSolver : public Solver<Dtype> {
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
   virtual void ClipGradients();
-  virtual void SnapshotSolverState(SolverState * state);
-  virtual void RestoreSolverState(const SolverState& state);
+  virtual void SnapshotSolverState(const string& model_filename);
+  virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
+  virtual void SnapshotSolverStateToHDF5(const string& model_filename);
+  virtual void RestoreSolverStateFromHDF5(const string& state_file);
+  virtual void RestoreSolverStateFromBinaryProto(const string& state_file);
   // history maintains the historical momentum data.
   // update maintains update related data and is not needed in snapshots.
   // temp maintains other information that might be needed in computation
diff --git a/include/caffe/util/hdf5.hpp b/include/caffe/util/hdf5.hpp
new file mode 100644
index 00000000000..ce568c5eb0d
--- /dev/null
+++ b/include/caffe/util/hdf5.hpp
@@ -0,0 +1,39 @@
+#ifndef CAFFE_UTIL_HDF5_H_
+#define CAFFE_UTIL_HDF5_H_
+
+#include <string>
+
+#include "hdf5.h"
+#include "hdf5_hl.h"
+
+#include "caffe/blob.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void hdf5_load_nd_dataset_helper(
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
+
+template <typename Dtype>
+void hdf5_load_nd_dataset(
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
+
+template <typename Dtype>
+void hdf5_save_nd_dataset(
+    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
+    bool write_diff = false);
+
+int hdf5_load_int(hid_t loc_id, const string& dataset_name);
+void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
+string hdf5_load_string(hid_t loc_id, const string& dataset_name);
+void hdf5_save_string(hid_t loc_id, const string& dataset_name,
+                      const string& s);
+
+int hdf5_get_num_links(hid_t loc_id);
+string hdf5_get_name_by_idx(hid_t loc_id, int idx);
+
+}  // namespace caffe
+
+#endif   // CAFFE_UTIL_HDF5_H_
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 3a62c3c9fa9..c0938ad0625 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -5,15 +5,11 @@
 #include <string>
 
 #include "google/protobuf/message.h"
-#include "hdf5.h"
-#include "hdf5_hl.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-#define HDF5_NUM_DIMS 4
-
 namespace caffe {
 
 using ::google::protobuf::Message;
@@ -140,20 +136,6 @@ cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
-template <typename Dtype>
-void hdf5_load_nd_dataset_helper(
-    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
-
-template <typename Dtype>
-void hdf5_load_nd_dataset(
-    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
-
-template <typename Dtype>
-void hdf5_save_nd_dataset(
-    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob);
-
 }  // namespace caffe
 
 #endif   // CAFFE_UTIL_IO_H_
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 8a782f7e524..8ced51039cf 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -16,7 +16,7 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/util/hdf5.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index f63375c3dc6..56788c21e5e 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index ae497c34fc2..eb6d0e470b0 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -6,7 +6,6 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a18ee63818e..0812b367ac3 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -5,12 +5,14 @@
 #include <utility>
 #include <vector>
 
+#include "hdf5.h"
+
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/util/insert_splits.hpp"
-#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
@@ -747,11 +749,72 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
+  if (trained_filename.size() >= 3 &&
+      trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
+    CopyTrainedLayersFromHDF5(trained_filename);
+  } else {
+    CopyTrainedLayersFromBinaryProto(trained_filename);
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
+    const string trained_filename) {
   NetParameter param;
   ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
   CopyTrainedLayersFrom(param);
 }
 
+template <typename Dtype>
+void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
+  hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
+                           H5P_DEFAULT);
+  CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;
+  hid_t data_hid = H5Gopen2(file_hid, "data", H5P_DEFAULT);
+  CHECK_GE(data_hid, 0) << "Error reading weights from " << trained_filename;
+  int num_layers = hdf5_get_num_links(data_hid);
+  for (int i = 0; i < num_layers; ++i) {
+    string source_layer_name = hdf5_get_name_by_idx(data_hid, i);
+    if (!layer_names_index_.count(source_layer_name)) {
+      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+      continue;
+    }
+    int target_layer_id = layer_names_index_[source_layer_name];
+    DLOG(INFO) << "Copying source layer " << source_layer_name;
+    vector<shared_ptr<Blob<Dtype> > >& target_blobs =
+        layers_[target_layer_id]->blobs();
+    hid_t layer_hid = H5Gopen2(data_hid, source_layer_name.c_str(),
+        H5P_DEFAULT);
+    CHECK_GE(layer_hid, 0)
+        << "Error reading weights from " << trained_filename;
+    // Check that source layer doesn't have more params than target layer
+    int num_source_params = hdf5_get_num_links(layer_hid);
+    CHECK_LE(num_source_params, target_blobs.size())
+        << "Incompatible number of blobs for layer " << source_layer_name;
+    for (int j = 0; j < target_blobs.size(); ++j) {
+      ostringstream oss;
+      oss << j;
+      string dataset_name = oss.str();
+      int target_net_param_id = param_id_vecs_[target_layer_id][j];
+      if (!H5Lexists(layer_hid, dataset_name.c_str(), H5P_DEFAULT)) {
+        // Target param doesn't exist in source weights...
+        if (param_owners_[target_net_param_id] != -1) {
+          // ...but it's weight-shared in target, so that's fine.
+          continue;
+        } else {
+          LOG(FATAL) << "Incompatible number of blobs for layer "
+              << source_layer_name;
+        }
+      }
+      hdf5_load_nd_dataset(layer_hid, dataset_name.c_str(), 0, kMaxBlobAxes,
+          target_blobs[j].get());
+    }
+    H5Gclose(layer_hid);
+  }
+  H5Gclose(data_hid);
+  H5Fclose(file_hid);
+}
+
 template <typename Dtype>
 void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   param->Clear();
@@ -773,6 +836,63 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
+  hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(file_hid, 0)
+      << "Couldn't open " << filename << " to save weights.";
+  hid_t data_hid = H5Gcreate2(file_hid, "data", H5P_DEFAULT, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(data_hid, 0) << "Error saving weights to " << filename << ".";
+  hid_t diff_hid = -1;
+  if (write_diff) {
+    diff_hid = H5Gcreate2(file_hid, "diff", H5P_DEFAULT, H5P_DEFAULT,
+        H5P_DEFAULT);
+    CHECK_GE(diff_hid, 0) << "Error saving weights to " << filename << ".";
+  }
+  for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
+    const LayerParameter& layer_param = layers_[layer_id]->layer_param();
+    string layer_name = layer_param.name();
+    hid_t layer_data_hid = H5Gcreate2(data_hid, layer_name.c_str(),
+        H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+    CHECK_GE(layer_data_hid, 0)
+        << "Error saving weights to " << filename << ".";
+    hid_t layer_diff_hid = -1;
+    if (write_diff) {
+      layer_diff_hid = H5Gcreate2(diff_hid, layer_name.c_str(),
+          H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+      CHECK_GE(layer_diff_hid, 0)
+          << "Error saving weights to " << filename << ".";
+    }
+    int num_params = layers_[layer_id]->blobs().size();
+    for (int param_id = 0; param_id < num_params; ++param_id) {
+      ostringstream dataset_name;
+      dataset_name << param_id;
+      const int net_param_id = param_id_vecs_[layer_id][param_id];
+      if (param_owners_[net_param_id] == -1) {
+        // Only save params that own themselves
+        hdf5_save_nd_dataset<Dtype>(layer_data_hid, dataset_name.str(),
+            *params_[net_param_id]);
+      }
+      if (write_diff) {
+        // Write diffs regardless of weight-sharing
+        hdf5_save_nd_dataset<Dtype>(layer_diff_hid, dataset_name.str(),
+            *params_[net_param_id], true);
+      }
+    }
+    H5Gclose(layer_data_hid);
+    if (write_diff) {
+      H5Gclose(layer_diff_hid);
+    }
+  }
+  H5Gclose(data_hid);
+  if (write_diff) {
+    H5Gclose(diff_hid);
+  }
+  H5Fclose(file_hid);
+}
+
 template <typename Dtype>
 void Net<Dtype>::Update() {
   // First, accumulate the diffs of any shared parameters into their owner's
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 03daa808bf8..96e975be566 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 37 (last added: iter_size)
+// SolverParameter next available ID: 38 (last added: snapshot_format)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -175,6 +175,11 @@ message SolverParameter {
   // whether to snapshot diff in the results or not. Snapshotting diff will help
   // debugging but the final protocol buffer size will be much larger.
   optional bool snapshot_diff = 16 [default = false];
+  enum SnapshotFormat {
+    HDF5 = 0;
+    BINARYPROTO = 1;
+  }
+  optional SnapshotFormat snapshot_format = 37 [default = HDF5];
   // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
   enum SolverMode {
     CPU = 0;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index aabe0edec80..75271138bdd 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -4,9 +4,13 @@
 #include <string>
 #include <vector>
 
+#include "hdf5.h"
+#include "hdf5_hl.h"
+
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
@@ -348,42 +352,58 @@ void Solver<Dtype>::Test(const int test_net_id) {
 
 template <typename Dtype>
 void Solver<Dtype>::Snapshot() {
-  NetParameter net_param;
-  // For intermediate results, we will also dump the gradient values.
-  net_->ToProto(&net_param, param_.snapshot_diff());
+  string model_filename;
+  switch (param_.snapshot_format()) {
+    case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+      model_filename = SnapshotToBinaryProto();
+      break;
+    case caffe::SolverParameter_SnapshotFormat_HDF5:
+      model_filename = SnapshotToHDF5();
+      break;
+    default:
+      LOG(FATAL) << "Unsupported snapshot format.";
+  }
+
+  SnapshotSolverState(model_filename);
+}
+
+template <typename Dtype>
+string Solver<Dtype>::SnapshotFilename(const string extension) {
   string filename(param_.snapshot_prefix());
-  string model_filename, snapshot_filename;
   const int kBufferSize = 20;
   char iter_str_buffer[kBufferSize];
   snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_);
-  filename += iter_str_buffer;
-  model_filename = filename + ".caffemodel";
-  LOG(INFO) << "Snapshotting to " << model_filename;
-  WriteProtoToBinaryFile(net_param, model_filename.c_str());
-  SolverState state;
-  SnapshotSolverState(&state);
-  state.set_iter(iter_);
-  state.set_learned_net(model_filename);
-  state.set_current_step(current_step_);
-  snapshot_filename = filename + ".solverstate";
-  LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
-  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
+  return filename + iter_str_buffer + extension;
 }
 
 template <typename Dtype>
-void Solver<Dtype>::Restore(const char* state_file) {
-  SolverState state;
+string Solver<Dtype>::SnapshotToBinaryProto() {
+  string model_filename = SnapshotFilename(".caffemodel");
+  LOG(INFO) << "Snapshotting to binary proto file " << model_filename;
   NetParameter net_param;
-  ReadProtoFromBinaryFile(state_file, &state);
-  if (state.has_learned_net()) {
-    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
-    net_->CopyTrainedLayersFrom(net_param);
-  }
-  iter_ = state.iter();
-  current_step_ = state.current_step();
-  RestoreSolverState(state);
+  net_->ToProto(&net_param, param_.snapshot_diff());
+  WriteProtoToBinaryFile(net_param, model_filename);
+  return model_filename;
+}
+
+template <typename Dtype>
+string Solver<Dtype>::SnapshotToHDF5() {
+  string model_filename = SnapshotFilename(".caffemodel.h5");
+  LOG(INFO) << "Snapshotting to HDF5 file " << model_filename;
+  net_->ToHDF5(model_filename, param_.snapshot_diff());
+  return model_filename;
 }
 
+template <typename Dtype>
+void Solver<Dtype>::Restore(const char* state_file) {
+  string state_filename(state_file);
+  if (state_filename.size() >= 3 &&
+      state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {
+    RestoreSolverStateFromHDF5(state_filename);
+  } else {
+    RestoreSolverStateFromBinaryProto(state_filename);
+  }
+}
 
 // Return the current learning rate. The currently implemented learning rate
 // policies are as follows:
@@ -618,17 +638,76 @@ void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::SnapshotSolverState(SolverState* state) {
-  state->clear_history();
+void SGDSolver<Dtype>::SnapshotSolverState(const string& model_filename) {
+  switch (this->param_.snapshot_format()) {
+    case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+      SnapshotSolverStateToBinaryProto(model_filename);
+      break;
+    case caffe::SolverParameter_SnapshotFormat_HDF5:
+      SnapshotSolverStateToHDF5(model_filename);
+      break;
+    default:
+      LOG(FATAL) << "Unsupported snapshot format.";
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
+    const string& model_filename) {
+  SolverState state;
+  state.set_iter(this->iter_);
+  state.set_learned_net(model_filename);
+  state.set_current_step(this->current_step_);
+  state.clear_history();
   for (int i = 0; i < history_.size(); ++i) {
     // Add history
-    BlobProto* history_blob = state->add_history();
+    BlobProto* history_blob = state.add_history();
     history_[i]->ToProto(history_blob);
   }
+  string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
+  LOG(INFO)
+    << "Snapshotting solver state to binary proto file" << snapshot_filename;
+  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
+    const string& model_filename) {
+  string snapshot_filename =
+      Solver<Dtype>::SnapshotFilename(".solverstate.h5");
+  LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename;
+  hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC,
+      H5P_DEFAULT, H5P_DEFAULT);
+  CHECK_GE(file_hid, 0)
+      << "Couldn't open " << snapshot_filename << " to save solver state.";
+  hdf5_save_int(file_hid, "iter", this->iter_);
+  hdf5_save_string(file_hid, "learned_net", model_filename);
+  hdf5_save_int(file_hid, "current_step", this->current_step_);
+  hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(history_hid, 0)
+      << "Error saving solver state to " << snapshot_filename << ".";
+  for (int i = 0; i < history_.size(); ++i) {
+    ostringstream oss;
+    oss << i;
+    hdf5_save_nd_dataset<Dtype>(history_hid, oss.str(), *history_[i]);
+  }
+  H5Gclose(history_hid);
+  H5Fclose(file_hid);
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
+void SGDSolver<Dtype>::RestoreSolverStateFromBinaryProto(
+    const string& state_file) {
+  SolverState state;
+  ReadProtoFromBinaryFile(state_file, &state);
+  this->iter_ = state.iter();
+  if (state.has_learned_net()) {
+    NetParameter net_param;
+    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
+    this->net_->CopyTrainedLayersFrom(net_param);
+  }
+  this->current_step_ = state.current_step();
   CHECK_EQ(state.history_size(), history_.size())
       << "Incorrect length of history blobs.";
   LOG(INFO) << "SGDSolver: restoring history";
@@ -637,6 +716,31 @@ void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
   }
 }
 
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
+  hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT);
+  CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file;
+  this->iter_ = hdf5_load_int(file_hid, "iter");
+  if (H5LTfind_dataset(file_hid, "learned_net")) {
+    string learned_net = hdf5_load_string(file_hid, "learned_net");
+    this->net_->CopyTrainedLayersFrom(learned_net);
+  }
+  this->current_step_ = hdf5_load_int(file_hid, "current_step");
+  hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT);
+  CHECK_GE(history_hid, 0) << "Error reading history from " << state_file;
+  int state_history_size = hdf5_get_num_links(history_hid);
+  CHECK_EQ(state_history_size, history_.size())
+      << "Incorrect length of history blobs.";
+  for (int i = 0; i < history_.size(); ++i) {
+    ostringstream oss;
+    oss << i;
+    hdf5_load_nd_dataset<Dtype>(history_hid, oss.str().c_str(), 0,
+                                kMaxBlobAxes, history_[i].get());
+  }
+  H5Gclose(history_hid);
+  H5Fclose(file_hid);
+}
+
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index a23034f284a..b56277b53ae 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -6,6 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
new file mode 100644
index 00000000000..d0d05f70f8f
--- /dev/null
+++ b/src/caffe/util/hdf5.cpp
@@ -0,0 +1,160 @@
+#include "caffe/util/hdf5.hpp"
+
+#include <string>
+#include <vector>
+
+namespace caffe {
+
+// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
+template <typename Dtype>
+void hdf5_load_nd_dataset_helper(
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob) {
+  // Verify that the dataset exists.
+  CHECK(H5LTfind_dataset(file_id, dataset_name_))
+      << "Failed to find HDF5 dataset " << dataset_name_;
+  // Verify that the number of dimensions is in the accepted range.
+  herr_t status;
+  int ndims;
+  status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
+  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
+  CHECK_GE(ndims, min_dim);
+  CHECK_LE(ndims, max_dim);
+
+  // Verify that the data format is what we expect: float or double.
+  std::vector<hsize_t> dims(ndims);
+  H5T_class_t class_;
+  status = H5LTget_dataset_info(
+      file_id, dataset_name_, dims.data(), &class_, NULL);
+  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
+  CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
+
+  vector<int> blob_dims(dims.size());
+  for (int i = 0; i < dims.size(); ++i) {
+    blob_dims[i] = dims[i];
+  }
+  blob->Reshape(blob_dims);
+}
+
+template <>
+void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
+        int min_dim, int max_dim, Blob<float>* blob) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+  herr_t status = H5LTread_dataset_float(
+    file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
+}
+
+template <>
+void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
+        int min_dim, int max_dim, Blob<double>* blob) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+  herr_t status = H5LTread_dataset_double(
+    file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
+}
+
+template <>
+void hdf5_save_nd_dataset<float>(
+    const hid_t file_id, const string& dataset_name, const Blob<float>& blob,
+    bool write_diff) {
+  int num_axes = blob.num_axes();
+  hsize_t *dims = new hsize_t[num_axes];
+  for (int i = 0; i < num_axes; ++i) {
+    dims[i] = blob.shape(i);
+  }
+  const float* data;
+  if (write_diff) {
+    data = blob.cpu_diff();
+  } else {
+    data = blob.cpu_data();
+  }
+  herr_t status = H5LTmake_dataset_float(
+      file_id, dataset_name.c_str(), num_axes, dims, data);
+  CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
+  delete[] dims;
+}
+
+template <>
+void hdf5_save_nd_dataset<double>(
+    hid_t file_id, const string& dataset_name, const Blob<double>& blob,
+    bool write_diff) {
+  int num_axes = blob.num_axes();
+  hsize_t *dims = new hsize_t[num_axes];
+  for (int i = 0; i < num_axes; ++i) {
+    dims[i] = blob.shape(i);
+  }
+  const double* data;
+  if (write_diff) {
+    data = blob.cpu_diff();
+  } else {
+    data = blob.cpu_data();
+  }
+  herr_t status = H5LTmake_dataset_double(
+      file_id, dataset_name.c_str(), num_axes, dims, data);
+  CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
+  delete[] dims;
+}
+
+string hdf5_load_string(hid_t loc_id, const string& dataset_name) {
+  // Get size of dataset
+  size_t size;
+  H5T_class_t class_;
+  herr_t status = \
+    H5LTget_dataset_info(loc_id, dataset_name.c_str(), NULL, &class_, &size);
+  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name;
+  char *buf = new char[size];
+  status = H5LTread_dataset_string(loc_id, dataset_name.c_str(), buf);
+  CHECK_GE(status, 0)
+    << "Failed to load int dataset with name " << dataset_name;
+  string val(buf);
+  delete[] buf;
+  return val;
+}
+
+void hdf5_save_string(hid_t loc_id, const string& dataset_name,
+                      const string& s) {
+  herr_t status = \
+    H5LTmake_dataset_string(loc_id, dataset_name.c_str(), s.c_str());
+  CHECK_GE(status, 0)
+    << "Failed to save string dataset with name " << dataset_name;
+}
+
+int hdf5_load_int(hid_t loc_id, const string& dataset_name) {
+  int val;
+  herr_t status = H5LTread_dataset_int(loc_id, dataset_name.c_str(), &val);
+  CHECK_GE(status, 0)
+    << "Failed to load int dataset with name " << dataset_name;
+  return val;
+}
+
+void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i) {
+  hsize_t one = 1;
+  herr_t status = \
+    H5LTmake_dataset_int(loc_id, dataset_name.c_str(), 1, &one, &i);
+  CHECK_GE(status, 0)
+    << "Failed to save int dataset with name " << dataset_name;
+}
+
+int hdf5_get_num_links(hid_t loc_id) {
+  H5G_info_t info;
+  herr_t status = H5Gget_info(loc_id, &info);
+  CHECK_GE(status, 0) << "Error while counting HDF5 links.";
+  return info.nlinks;
+}
+
+string hdf5_get_name_by_idx(hid_t loc_id, int idx) {
+  ssize_t str_size = H5Lget_name_by_idx(
+      loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, NULL, 0, H5P_DEFAULT);
+  CHECK_GE(str_size, 0) << "Error retrieving HDF5 dataset at index " << idx;
+  char *c_str = new char[str_size+1];
+  ssize_t status = H5Lget_name_by_idx(
+      loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, c_str, str_size+1,
+      H5P_DEFAULT);
+  CHECK_GE(status, 0) << "Error retrieving HDF5 dataset at index " << idx;
+  string result(c_str);
+  delete[] c_str;
+  return result;
+}
+
+}  // namespace caffe
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 77ef7f257f4..6f03314202c 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -228,79 +228,5 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   datum->set_data(buffer);
 }
 
-// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
-template <typename Dtype>
-void hdf5_load_nd_dataset_helper(
-    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob) {
-  // Verify that the dataset exists.
-  CHECK(H5LTfind_dataset(file_id, dataset_name_))
-      << "Failed to find HDF5 dataset " << dataset_name_;
-  // Verify that the number of dimensions is in the accepted range.
-  herr_t status;
-  int ndims;
-  status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
-  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
-  CHECK_GE(ndims, min_dim);
-  CHECK_LE(ndims, max_dim);
-
-  // Verify that the data format is what we expect: float or double.
-  std::vector<hsize_t> dims(ndims);
-  H5T_class_t class_;
-  status = H5LTget_dataset_info(
-      file_id, dataset_name_, dims.data(), &class_, NULL);
-  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
-  CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
-
-  vector<int> blob_dims(dims.size());
-  for (int i = 0; i < dims.size(); ++i) {
-    blob_dims[i] = dims[i];
-  }
-  blob->Reshape(blob_dims);
-}
-
-template <>
-void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<float>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
-  herr_t status = H5LTread_dataset_float(
-    file_id, dataset_name_, blob->mutable_cpu_data());
-  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
-}
-
-template <>
-void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<double>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
-  herr_t status = H5LTread_dataset_double(
-    file_id, dataset_name_, blob->mutable_cpu_data());
-  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
-}
-
-template <>
-void hdf5_save_nd_dataset<float>(
-    const hid_t file_id, const string& dataset_name, const Blob<float>& blob) {
-  hsize_t dims[HDF5_NUM_DIMS];
-  dims[0] = blob.num();
-  dims[1] = blob.channels();
-  dims[2] = blob.height();
-  dims[3] = blob.width();
-  herr_t status = H5LTmake_dataset_float(
-      file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data());
-  CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
-}
-
-template <>
-void hdf5_save_nd_dataset<double>(
-    const hid_t file_id, const string& dataset_name, const Blob<double>& blob) {
-  hsize_t dims[HDF5_NUM_DIMS];
-  dims[0] = blob.num();
-  dims[1] = blob.channels();
-  dims[2] = blob.height();
-  dims[3] = blob.width();
-  herr_t status = H5LTmake_dataset_double(
-      file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data());
-  CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
-}
 
 }  // namespace caffe

From 5c89c64ff1cf97a3578b11803f83d73e3e933592 Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@eecs.berkeley.edu>
Date: Wed, 29 Jul 2015 18:40:38 -0700
Subject: [PATCH 1455/2053] TestSnapshot expects .h5 snapshots, explicitly
 checks history.

---
 src/caffe/test/test_gradient_based_solver.cpp | 25 ++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 94b500124aa..78bf4b3121e 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -139,7 +139,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     if (snapshot) {
       ostringstream resume_file;
       resume_file << snapshot_prefix_ << "/_iter_" << num_iters
-                  << ".solverstate";
+                  << ".solverstate.h5";
       string resume_filename = resume_file.str();
       return resume_filename;
     }
@@ -394,6 +394,18 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       }
     }
 
+    // Save the solver history
+    vector<shared_ptr<Blob<Dtype> > > history_copies;
+    const vector<shared_ptr<Blob<Dtype> > >& orig_history = solver_->history();
+    history_copies.resize(orig_history.size());
+    for (int i = 0; i < orig_history.size(); ++i) {
+      history_copies[i].reset(new Blob<Dtype>());
+      const bool kReshape = true;
+      for (int copy_diff = false; copy_diff <= true; ++copy_diff) {
+        history_copies[i]->CopyFrom(*orig_history[i], copy_diff, kReshape);
+      }
+    }
+
     // Run the solver for num_iters iterations and snapshot.
     snapshot = true;
     string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
@@ -414,6 +426,17 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
             << "param " << i << " diff differed at dim " << j;
       }
     }
+
+    // Check that history now matches.
+    const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+    for (int i = 0; i < history.size(); ++i) {
+      for (int j = 0; j < history[i]->count(); ++j) {
+        EXPECT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
+            << "history blob " << i << " data differed at dim " << j;
+        EXPECT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
+            << "history blob " << i << " diff differed at dim " << j;
+      }
+    }
   }
 };
 

From c9b333e06212365e3ea166bacbd4b560158316f0 Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@eecs.berkeley.edu>
Date: Fri, 7 Aug 2015 13:17:26 -0700
Subject: [PATCH 1456/2053] Update example bash scripts to expect .h5, new
 extensions in .gitignore

---
 .gitignore                           | 2 ++
 examples/cifar10/train_full.sh       | 4 ++--
 examples/cifar10/train_quick.sh      | 2 +-
 examples/imagenet/resume_training.sh | 2 +-
 4 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/.gitignore b/.gitignore
index 28f2aca854b..53c1fb056bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,7 +61,9 @@ Makefile.config
 data/*
 models/*
 *.caffemodel
+*.caffemodel.h5
 *.solverstate
+*.solverstate.h5
 *.binaryproto
 *leveldb
 *lmdb
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 4285a5d6468..ef112e1f6db 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -8,9 +8,9 @@ $TOOLS/caffe train \
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 2830c40945c..6b7d228879b 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -8,4 +8,4 @@ $TOOLS/caffe train \
 # reduce learning rate by factor of 10 after 8 epochs
 $TOOLS/caffe train \
   --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
-  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index d1febff8d39..bf7945c0fd0 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -2,4 +2,4 @@
 
 ./build/tools/caffe train \
     --solver=models/bvlc_reference_caffenet/solver.prototxt \
-    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5

From f81ed077cbfd96b3a9ec98898347eb5d2966ccea Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 7 Aug 2015 15:27:02 -0700
Subject: [PATCH 1457/2053] TestGradientBasedSolver: restore Gaussian filler to
 all tests except accumulation one

---
 src/caffe/test/test_gradient_based_solver.cpp | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 78bf4b3121e..235a7d85ad0 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -24,12 +24,14 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   GradientBasedSolverTest() :
-      seed_(1701), num_(4), channels_(3), height_(10), width_(10) {}
+      seed_(1701), num_(4), channels_(3), height_(10), width_(10),
+      constant_data_(false) {}
 
   string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
   int num_, channels_, height_, width_;
+  bool constant_data_;
   Dtype delta_;  // Stability constant for AdaGrad.
 
   virtual SolverParameter_SolverType solver_type() = 0;
@@ -79,7 +81,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "      height: 1 "
        "      width: 1 "
        "      data_filler { "
-       "        type: 'constant' "
+       "        type: '" << string(constant_data_ ? "constant" : "gaussian")
+                         << "' "
+       "        std: 1.0 "
        "        value: 1.0 "
        "      } "
        "      data_filler { "
@@ -302,6 +306,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       const Dtype kMomentum, const int kNumIters, const int kIterSize) {
     const double kPrecision = 1e-2;
     const double kMinPrecision = 1e-7;
+    constant_data_ = true;
     // Solve without accumulation and save parameters.
     this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
         kNumIters);

From c251b2581b02904ea6b5501786d4514734aef3d6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 7 Aug 2015 15:34:00 -0700
Subject: [PATCH 1458/2053] TestGradientBasedSolver: make tests across solver
 types more consistent

---
 src/caffe/test/test_gradient_based_solver.cpp | 91 ++++++++++++-------
 1 file changed, 59 insertions(+), 32 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 235a7d85ad0..54606dbd7ed 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -466,23 +466,38 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
   this->TestLeastSquaresUpdate();
 }
 
-TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneHundredth) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
+  const Dtype kLearningRate = 0.01;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
-  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+  const Dtype kMomentum = 0;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecayMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
 }
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -492,8 +507,8 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -504,8 +519,8 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.9;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.5;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
@@ -515,7 +530,7 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
+  const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
   const int kNumIters = 4;
   const int kIterSize = 2;
@@ -526,7 +541,7 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
 TYPED_TEST(SGDSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
+  const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
@@ -554,15 +569,15 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
   this->TestLeastSquaresUpdate();
 }
 
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneHundredth) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
+  const Dtype kLearningRate = 0.01;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
 }
@@ -570,8 +585,8 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
 TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
@@ -581,8 +596,8 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
 TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
@@ -592,8 +607,8 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
 TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
     this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
@@ -620,23 +635,35 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
   this->TestLeastSquaresUpdate();
 }
 
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneHundredth) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
+  const Dtype kLearningRate = 0.01;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
 }
 
+TYPED_TEST(NesterovSolverTest,
+           TestNesterovLeastSquaresUpdateWithWeightDecayMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -646,8 +673,8 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
 
 TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -658,7 +685,7 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
+  const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -669,7 +696,7 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
 TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
+  const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
   const int kNumIters = 4;
   const int kIterSize = 2;
@@ -680,8 +707,8 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
 TYPED_TEST(NesterovSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
     this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);

From d5b42bf7d6fa0ed85069745c56104503b7888b63 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 20 Jul 2015 20:50:50 -0700
Subject: [PATCH 1459/2053] Net: add learnable_params_ used by solvers to
 correctly handle shared params

-Params now share diffs as well as data (works due to layers
accumulating gradients into param diffs, rather than overwriting)
-It's now required that any shared params with specified lr_mult's,
decay_mult's match
-TestGradientBasedSolver checks that behavior remains correct with
shared weights
---
 include/caffe/net.hpp                         |  41 +++-
 src/caffe/net.cpp                             |  89 +++++----
 src/caffe/solver.cpp                          |  44 ++---
 src/caffe/test/test_gradient_based_solver.cpp | 177 +++++++++++++++++-
 src/caffe/test/test_net.cpp                   |  29 ++-
 5 files changed, 276 insertions(+), 104 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index dfd2e556a2b..bf997553ee2 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -57,6 +57,12 @@ class Net {
    */
   string Forward(const string& input_blob_protos, Dtype* loss = NULL);
 
+  /**
+   * @brief Zeroes out the diffs of all net parameters.
+   *        Should be run before Backward.
+   */
+  void ClearParamDiffs();
+
   /**
    * The network backward should take no input and output, since it solely
    * computes the gradient w.r.t the parameters, and the data has already been
@@ -84,6 +90,13 @@ class Net {
 
   /// @brief Updates the network weights based on the diff values computed.
   void Update();
+  /**
+   * @brief Shares weight data of owner blobs with shared blobs.
+   *
+   * Note: this is called by Net::Init, and thus should normally not be
+   * called manually.
+   */
+  void ShareWeights();
 
   /**
    * @brief For an already initialized net, implicitly copies (i.e., using no
@@ -148,11 +161,19 @@ class Net {
   inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
     return params_;
   }
-  /// @brief returns the parameter learning rate multipliers
+  inline const vector<Blob<Dtype>*>& learnable_params() const {
+    return learnable_params_;
+  }
+  /// @brief returns the learnable parameter learning rate multipliers
   inline const vector<float>& params_lr() const { return params_lr_; }
+  inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
+  /// @brief returns the learnable parameter decay multipliers
   inline const vector<float>& params_weight_decay() const {
     return params_weight_decay_;
   }
+  inline const vector<bool>& has_params_decay() const {
+    return has_params_decay_;
+  }
   const map<string, int>& param_names_index() const {
     return param_names_index_;
   }
@@ -213,9 +234,6 @@ class Net {
   /// @brief Helper for displaying debug info in Update.
   void UpdateDebugInfo(const int param_id);
 
-  /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
-  void GetLearningRateAndWeightDecay();
-
   /// @brief The network name
   string name_;
   /// @brief The phase: TRAIN or TEST
@@ -254,10 +272,21 @@ class Net {
   vector<Blob<Dtype>*> net_output_blobs_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
-  /// the learning rate multipliers
+  vector<Blob<Dtype>*> learnable_params_;
+  /**
+   * The mapping from params_ -> learnable_params_: we have
+   * learnable_param_ids_.size() == params_.size(),
+   * and learnable_params_[learnable_param_ids_[i]] == params_[i].get()
+   * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer
+   * and learnable_params_[learnable_param_ids_[i]] gives its owner.
+   */
+  vector<int> learnable_param_ids_;
+  /// the learning rate multipliers for learnable_params_
   vector<float> params_lr_;
-  /// the weight decay multipliers
+  vector<bool> has_params_lr_;
+  /// the weight decay multipliers for learnable_params_
   vector<float> params_weight_decay_;
+  vector<bool> has_params_decay_;
   /// The bytes of memory used by this net
   size_t memory_used_;
   /// Whether to compute and display debug info for the net.
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 0812b367ac3..0e5ed804b73 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -244,7 +244,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
     layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
-  GetLearningRateAndWeightDecay();
+  ShareWeights();
   debug_info_ = param.debug_info();
   LOG(INFO) << "Network initialization done.";
   LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
@@ -441,6 +441,9 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
   param_id_vecs_[layer_id].push_back(net_param_id);
   param_layer_indices_.push_back(make_pair(layer_id, param_id));
+  ParamSpec default_param_spec;
+  const ParamSpec* param_spec = (layer_param.param_size() > param_id) ?
+      &layer_param.param(param_id) : &default_param_spec;
   if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
     // This layer "owns" this parameter blob -- it is either anonymous
@@ -450,6 +453,13 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
     if (param_name.size()) {
       param_names_index_[param_name] = net_param_id;
     }
+    const int learnable_param_id = learnable_params_.size();
+    learnable_params_.push_back(params_[net_param_id].get());
+    learnable_param_ids_.push_back(learnable_param_id);
+    has_params_lr_.push_back(param_spec->has_lr_mult());
+    has_params_decay_.push_back(param_spec->has_decay_mult());
+    params_lr_.push_back(param_spec->lr_mult());
+    params_weight_decay_.push_back(param_spec->decay_mult());
   } else {
     // Named param blob with name we've seen before: share params
     const int owner_net_param_id = param_names_index_[param_name];
@@ -474,23 +484,25 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
       // Strict dimension checking -- all dims must be the same.
       CHECK(this_blob->shape() == owner_blob->shape());
     }
-    layers_[layer_id]->blobs()[param_id]->ShareData(
-        *layers_[owner_layer_id]->blobs()[owner_param_id]);
-  }
-}
-
-template <typename Dtype>
-void Net<Dtype>::GetLearningRateAndWeightDecay() {
-  LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
-  ParamSpec default_param_spec;
-  for (int i = 0; i < layers_.size(); ++i) {
-    vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
-    for (int j = 0; j < layer_blobs.size(); ++j) {
-      const ParamSpec* param_spec =
-          (layers_[i]->layer_param().param_size() > j) ?
-          &layers_[i]->layer_param().param(j) : &default_param_spec;
-      params_lr_.push_back(param_spec->lr_mult());
-      params_weight_decay_.push_back(param_spec->decay_mult());
+    const int learnable_param_id = learnable_param_ids_[owner_net_param_id];
+    if (param_spec->has_lr_mult()) {
+      if (has_params_lr_[learnable_param_id]) {
+        CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])
+            << "Shared param '" << param_name << "' has mismatched lr_mult.";
+      } else {
+        has_params_lr_[learnable_param_id] = true;
+        params_lr_[learnable_param_id] = param_spec->lr_mult();
+      }
+    }
+    if (param_spec->has_decay_mult()) {
+      if (has_params_decay_[learnable_param_id]) {
+        CHECK_EQ(param_spec->decay_mult(),
+                 params_weight_decay_[learnable_param_id])
+            << "Shared param '" << param_name << "' has mismatched decay_mult.";
+      } else {
+        has_params_decay_[learnable_param_id] = true;
+        params_weight_decay_[learnable_param_id] = param_spec->decay_mult();
+      }
     }
   }
 }
@@ -895,39 +907,38 @@ void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
 
 template <typename Dtype>
 void Net<Dtype>::Update() {
-  // First, accumulate the diffs of any shared parameters into their owner's
-  // diff. (Assumes that the learning rate, weight decay, etc. have already been
-  // accounted for in the current diff.)
-  for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] < 0) { continue; }
-    if (debug_info_) { UpdateDebugInfo(i); }
-    const int count = params_[i]->count();
-    const Dtype* this_diff;
-    Dtype* owner_diff;
+  for (int i = 0; i < learnable_params_.size(); ++i) {
+    learnable_params_[i]->Update();
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::ClearParamDiffs() {
+  for (int i = 0; i < learnable_params_.size(); ++i) {
+    Blob<Dtype>* blob = learnable_params_[i];
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      this_diff = params_[i]->cpu_diff();
-      owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
-      caffe_add(count, this_diff, owner_diff, owner_diff);
+      caffe_set(blob->count(), static_cast<Dtype>(0),
+                blob->mutable_cpu_diff());
       break;
     case Caffe::GPU:
 #ifndef CPU_ONLY
-      this_diff = params_[i]->gpu_diff();
-      owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
-      caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
+      caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
+                    blob->mutable_gpu_diff());
 #else
       NO_GPU;
 #endif
       break;
-    default:
-      LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
     }
   }
-  // Now, update the owned parameters.
+}
+
+template <typename Dtype>
+void Net<Dtype>::ShareWeights() {
   for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] >= 0) { continue; }
-    if (debug_info_) { UpdateDebugInfo(i); }
-    params_[i]->Update();
+    if (param_owners_[i] < 0) { continue; }
+    params_[i]->ShareData(*params_[param_owners_[i]]);
+    params_[i]->ShareDiff(*params_[param_owners_[i]]);
   }
 }
 
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 75271138bdd..32276ac148a 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -173,24 +173,7 @@ void Solver<Dtype>::Step(int iters) {
 
   while (iter_ < stop_iter) {
     // zero-init the params
-    for (int i = 0; i < net_->params().size(); ++i) {
-      shared_ptr<Blob<Dtype> > blob = net_->params()[i];
-      switch (Caffe::mode()) {
-      case Caffe::CPU:
-        caffe_set(blob->count(), static_cast<Dtype>(0),
-            blob->mutable_cpu_diff());
-        break;
-      case Caffe::GPU:
-#ifndef CPU_ONLY
-        caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
-            blob->mutable_gpu_diff());
-#else
-        NO_GPU;
-#endif
-        break;
-      }
-    }
-
+    net_->ClearParamDiffs();
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())) {
       TestAll();
@@ -462,7 +445,7 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
 template <typename Dtype>
 void SGDSolver<Dtype>::PreSolve() {
   // Initialize the history
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   history_.clear();
   update_.clear();
   temp_.clear();
@@ -478,12 +461,10 @@ template <typename Dtype>
 void SGDSolver<Dtype>::ClipGradients() {
   const Dtype clip_gradients = this->param_.clip_gradients();
   if (clip_gradients < 0) { return; }
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   Dtype sumsq_diff = 0;
   for (int i = 0; i < net_params.size(); ++i) {
-    if (this->net_->param_owners()[i] < 0) {
-      sumsq_diff += net_params[i]->sumsq_diff();
-    }
+    sumsq_diff += net_params[i]->sumsq_diff();
   }
   const Dtype l2norm_diff = std::sqrt(sumsq_diff);
   if (l2norm_diff > clip_gradients) {
@@ -492,9 +473,7 @@ void SGDSolver<Dtype>::ClipGradients() {
         << l2norm_diff << " > " << clip_gradients << ") "
         << "by scale factor " << scale_factor;
     for (int i = 0; i < net_params.size(); ++i) {
-      if (this->net_->param_owners()[i] < 0) {
-        net_params[i]->scale_diff(scale_factor);
-      }
+      net_params[i]->scale_diff(scale_factor);
     }
   }
 }
@@ -506,7 +485,8 @@ void SGDSolver<Dtype>::ApplyUpdate() {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
   }
   ClipGradients();
-  for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) {
+  for (int param_id = 0; param_id < this->net_->learnable_params().size();
+       ++param_id) {
     Normalize(param_id);
     Regularize(param_id);
     ComputeUpdateValue(param_id, rate);
@@ -518,7 +498,7 @@ template <typename Dtype>
 void SGDSolver<Dtype>::Normalize(int param_id) {
   if (this->param_.iter_size() == 1) { return; }
   // Scale gradient to counterbalance accumulation.
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
   switch (Caffe::mode()) {
   case Caffe::CPU: {
@@ -542,7 +522,7 @@ void SGDSolver<Dtype>::Normalize(int param_id) {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::Regularize(int param_id) {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_weight_decay =
       this->net_->params_weight_decay();
   Dtype weight_decay = this->param_.weight_decay();
@@ -604,7 +584,7 @@ void SGDSolver<Dtype>::Regularize(int param_id) {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
   Dtype local_rate = rate * net_params_lr[param_id];
@@ -743,7 +723,7 @@ void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
 
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
   Dtype local_rate = rate * net_params_lr[param_id];
@@ -803,7 +783,7 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 
 template <typename Dtype>
 void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
   Dtype local_rate = rate * net_params_lr[param_id];
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 54606dbd7ed..e1bbbf17726 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -25,13 +25,13 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
  protected:
   GradientBasedSolverTest() :
       seed_(1701), num_(4), channels_(3), height_(10), width_(10),
-      constant_data_(false) {}
+      constant_data_(false), share_(false) {}
 
   string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
   int num_, channels_, height_, width_;
-  bool constant_data_;
+  bool constant_data_, share_;
   Dtype delta_;  // Stability constant for AdaGrad.
 
   virtual SolverParameter_SolverType solver_type() = 0;
@@ -93,10 +93,26 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    } "
        "    top: 'data' "
        "    top: 'targets' "
-       "  } "
+       "  } ";
+    if (share_) {
+      proto <<
+         "  layer { "
+         "    name: 'slice' "
+         "    type: 'Slice' "
+         "    bottom: 'data' "
+         "    top: 'data1' "
+         "    top: 'data2' "
+         "    slice_param { "
+         "      axis: 0 "
+         "    } "
+         "  } ";
+    }
+    proto <<
        "  layer { "
        "    name: 'innerprod' "
        "    type: 'InnerProduct' "
+       "    param { name: 'weights' } "
+       "    param { name: 'bias' } "
        "    inner_product_param { "
        "      num_output: 1 "
        "      weight_filler { "
@@ -108,9 +124,42 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "        std: 1.0 "
        "      } "
        "    } "
-       "    bottom: 'data' "
-       "    top: 'innerprod' "
-       "  } "
+       "    bottom: '" << string(share_ ? "data1": "data") << "' "
+       "    top: '" << string(share_ ? "innerprod1": "innerprod") << "' "
+       "  } ";
+    if (share_) {
+      proto <<
+         "  layer { "
+         "    name: 'innerprod2' "
+         "    type: 'InnerProduct' "
+         "    param { name: 'weights' } "
+         "    param { name: 'bias' } "
+         "    inner_product_param { "
+         "      num_output: 1 "
+         "      weight_filler { "
+         "        type: 'gaussian' "
+         "        std: 1.0 "
+         "      } "
+         "      bias_filler { "
+         "        type: 'gaussian' "
+         "        std: 1.0 "
+         "      } "
+         "    } "
+         "    bottom: 'data2' "
+         "    top: 'innerprod2' "
+         "  } "
+         "  layer { "
+         "    name: 'concat' "
+         "    type: 'Concat' "
+         "    bottom: 'innerprod1' "
+         "    bottom: 'innerprod2' "
+         "    top: 'innerprod' "
+         "    concat_param { "
+         "      axis: 0 "
+         "    } "
+         "  } ";
+    }
+    proto <<
        "  layer { "
        "    name: 'loss' "
        "    type: 'EuclideanLoss' "
@@ -388,8 +437,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
     // Save the resulting param values.
     vector<shared_ptr<Blob<Dtype> > > param_copies;
-    const vector<shared_ptr<Blob<Dtype> > >& orig_params =
-        solver_->net()->params();
+    const vector<Blob<Dtype>*>& orig_params =
+        solver_->net()->learnable_params();
     param_copies.resize(orig_params.size());
     for (int i = 0; i < orig_params.size(); ++i) {
       param_copies[i].reset(new Blob<Dtype>());
@@ -422,7 +471,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
         momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str());
 
     // Check that params now match.
-    const vector<shared_ptr<Blob<Dtype> > >& params = solver_->net()->params();
+    const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
     for (int i = 0; i < params.size(); ++i) {
       for (int j = 0; j < params[i]->count(); ++j) {
         EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
@@ -527,6 +576,18 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
@@ -538,6 +599,18 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
       kIterSize);
 }
 
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
 TYPED_TEST(SGDSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
@@ -549,6 +622,18 @@ TYPED_TEST(SGDSolverTest, TestSnapshot) {
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -593,6 +678,19 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(AdaGradSolverTest,
+           TestAdaGradLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
@@ -604,6 +702,18 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
       kIterSize);
 }
 
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
 TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
@@ -615,6 +725,18 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
   }
 }
 
+TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 
 template <typename TypeParam>
 class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -693,6 +815,19 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(NesterovSolverTest,
+           TestNesterovLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
@@ -704,6 +839,18 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
       kIterSize);
 }
 
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
 TYPED_TEST(NesterovSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
@@ -715,4 +862,16 @@ TYPED_TEST(NesterovSolverTest, TestSnapshot) {
   }
 }
 
+TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 56959f4793b..12998d8912f 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1107,11 +1107,10 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
   Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
-  // Check that data blobs of shared weights share the same location in memory.
+  // Check that data and diff blobs of shared weights share the same memory
+  // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-  // Check that diff blobs of shared weights are at different locations in
-  // memory.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
   this->net_->Forward(bottom);
   this->net_->Backward();
   // Compute the expected update as the data minus the two diffs.
@@ -1124,11 +1123,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // Make sure the diffs are non-trivial.
   for (int i = 0; i < count; ++i) {
     EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
-    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
-    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
   }
-  caffe_axpy(count, Dtype(1), ip2_weights->cpu_diff(),
-             shared_params.mutable_cpu_diff());
   caffe_axpy(count, Dtype(-1), shared_params.cpu_diff(),
              shared_params.mutable_cpu_data());
   const Dtype* expected_updated_params = shared_params.cpu_data();
@@ -1165,8 +1160,8 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
     EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
     EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
     EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
-    EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
-              shared_params.cpu_diff()[i]);
+    EXPECT_FLOAT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
+                    shared_params.cpu_diff()[i]);
   }
   caffe_axpy(count, Dtype(-1), ip1_weights->cpu_diff(),
              unshared_params1.mutable_cpu_data());
@@ -1196,11 +1191,10 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
   Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
-  // Check that data blobs of shared weights share the same location in memory.
+  // Check that data and diff blobs of shared weights share the same memory
+  // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-  // Check that diff blobs of shared weights are at different locations in
-  // memory.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
   this->net_->ForwardBackward(bottom);
   this->net_->Update();
   Blob<Dtype> shared_params;
@@ -1223,14 +1217,13 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   ASSERT_FALSE(NULL == ip1_weights);
   ASSERT_FALSE(NULL == ip2_weights);
   EXPECT_NE(ip1_weights, ip2_weights);
-  // Check that data blobs of shared weights share the same location in memory.
+  // Check that data and diff blobs of shared weights share the same memory
+  // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
   for (int i = 0; i < count; ++i) {
     EXPECT_FLOAT_EQ(shared_params.cpu_data()[i], ip1_weights->cpu_data()[i]);
   }
-  // Check that diff blobs of shared weights are at different locations in
-  // memory.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
 }
 
 TYPED_TEST(NetTest, TestParamPropagateDown) {

From 4227828ad43b78ad0c2ab4ab276dd0991ecb2ccb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 7 Aug 2015 20:03:04 -0700
Subject: [PATCH 1460/2053] temporarily switch the snapshot_format default back
 to BINARYPROTO

out of anticipation for user issues due to issue #2885, which causes
Caffe to crash when it attempts to snapshot nets with duplicate layer
names
---
 src/caffe/proto/caffe.proto                   | 2 +-
 src/caffe/test/test_gradient_based_solver.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 96e975be566..a13c0e79d80 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -179,7 +179,7 @@ message SolverParameter {
     HDF5 = 0;
     BINARYPROTO = 1;
   }
-  optional SnapshotFormat snapshot_format = 37 [default = HDF5];
+  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
   // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
   enum SolverMode {
     CPU = 0;
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index e1bbbf17726..7bb0ec18a09 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -192,7 +192,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     if (snapshot) {
       ostringstream resume_file;
       resume_file << snapshot_prefix_ << "/_iter_" << num_iters
-                  << ".solverstate.h5";
+                  << ".solverstate";
       string resume_filename = resume_file.str();
       return resume_filename;
     }

From 443b16f84fb8948dbddecaf775c7da44dac6c6b1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 21 Jan 2015 22:21:13 -0800
Subject: [PATCH 1461/2053] Add gpu_util.cuh, with caffe_gpu_atomic_add

(double impl from NVIDIA dev docs; float impl included in CUDA as
"atomicAdd")
---
 include/caffe/util/gpu_util.cuh | 35 +++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 include/caffe/util/gpu_util.cuh

diff --git a/include/caffe/util/gpu_util.cuh b/include/caffe/util/gpu_util.cuh
new file mode 100644
index 00000000000..994202f2a1a
--- /dev/null
+++ b/include/caffe/util/gpu_util.cuh
@@ -0,0 +1,35 @@
+#ifndef CAFFE_UTIL_GPU_UTIL_H_
+#define CAFFE_UTIL_GPU_UTIL_H_
+
+namespace caffe {
+
+template <typename Dtype>
+inline __device__ Dtype caffe_gpu_atomic_add(const Dtype val, Dtype* address);
+
+template <>
+inline __device__
+float caffe_gpu_atomic_add(const float val, float* address) {
+  return atomicAdd(address, val);
+}
+
+// double atomicAdd implementation taken from:
+// http://docs.nvidia.com/cuda/cuda-c-programming-guide/#axzz3PVCpVsEG
+template <>
+inline __device__
+double caffe_gpu_atomic_add(const double val, double* address) {
+  unsigned long long int* address_as_ull =  // NOLINT(runtime/int)
+      // NOLINT_NEXT_LINE(runtime/int)
+      reinterpret_cast<unsigned long long int*>(address);
+  unsigned long long int old = *address_as_ull;  // NOLINT(runtime/int)
+  unsigned long long int assumed;  // NOLINT(runtime/int)
+  do {
+    assumed = old;
+    old = atomicCAS(address_as_ull, assumed,
+        __double_as_longlong(val + __longlong_as_double(assumed)));
+  } while (assumed != old);
+  return __longlong_as_double(old);
+}
+
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_GPU_UTIL_H_

From 6067869f8339344ecd68c486a6e47d07e8997b6f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 21 Jan 2015 14:23:34 -0800
Subject: [PATCH 1462/2053] test_gradient_check_util: check_bottom < -1 only
 checks params

---
 include/caffe/test/test_gradient_check_util.hpp | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index cc5dcbad0ee..25f35d1589e 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -45,6 +45,10 @@ class GradientChecker {
   void CheckGradientEltwise(Layer<Dtype>* layer,
       const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
 
+  // Checks the gradient of a single output with respect to particular input
+  // blob(s).  If check_bottom = i >= 0, check only the ith bottom Blob.
+  // If check_bottom == -1, check everything -- all bottom Blobs and all
+  // param Blobs.  Otherwise (if check_bottom < -1), check only param Blobs.
   void CheckGradientSingle(Layer<Dtype>* layer,
       const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
       int check_bottom, int top_id, int top_data_id, bool element_wise = false);
@@ -83,21 +87,22 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
   // First, figure out what blobs we need to check against, and zero init
   // parameter blobs.
   vector<Blob<Dtype>*> blobs_to_check;
-  vector<bool> propagate_down(bottom.size(), check_bottom < 0);
+  vector<bool> propagate_down(bottom.size(), check_bottom == -1);
   for (int i = 0; i < layer->blobs().size(); ++i) {
     Blob<Dtype>* blob = layer->blobs()[i].get();
     caffe_set(blob->count(), static_cast<Dtype>(0), blob->mutable_cpu_diff());
     blobs_to_check.push_back(blob);
   }
-  if (check_bottom < 0) {
+  if (check_bottom == -1) {
     for (int i = 0; i < bottom.size(); ++i) {
       blobs_to_check.push_back(bottom[i]);
     }
-  } else {
+  } else if (check_bottom >= 0) {
     CHECK_LT(check_bottom, bottom.size());
     blobs_to_check.push_back(bottom[check_bottom]);
     propagate_down[check_bottom] = true;
   }
+  CHECK_GT(blobs_to_check.size(), 0) << "No blobs to check.";
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
   // Ignore the loss from the layer (it's just the weighted sum of the losses

From 4d299c3071039e7c49c01b2435e11549f764df88 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 15 Feb 2015 16:00:04 -0800
Subject: [PATCH 1463/2053] Add EmbedLayer for inner products with sparse input
 (one-hot vectors), with unit tests

---
 include/caffe/common_layers.hpp     |  38 ++++++
 src/caffe/layers/embed_layer.cpp    | 122 +++++++++++++++++++
 src/caffe/layers/embed_layer.cu     |  80 ++++++++++++
 src/caffe/proto/caffe.proto         |  18 ++-
 src/caffe/test/test_embed_layer.cpp | 183 ++++++++++++++++++++++++++++
 5 files changed, 440 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/embed_layer.cpp
 create mode 100644 src/caffe/layers/embed_layer.cu
 create mode 100644 src/caffe/test/test_embed_layer.cpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index d2c0ce6d0c6..691e755f88f 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -180,6 +180,44 @@ class EltwiseLayer : public Layer<Dtype> {
   bool stable_prod_grad_;
 };
 
+/**
+ * @brief A layer for learning "embeddings" of one-hot vector input.
+ *        Equivalent to an InnerProductLayer with one-hot vectors as input, but
+ *        for efficiency the input is the "hot" index of each column itself.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EmbedLayer : public Layer<Dtype> {
+ public:
+  explicit EmbedLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Embed"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  Blob<Dtype> bias_multiplier_;
+};
+
 /**
  * @brief Takes two+ Blobs, interprets last Blob as a selector and
  *  filter remaining Blobs accordingly with selector data (0 means that
diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp
new file mode 100644
index 00000000000..be6b2cd2727
--- /dev/null
+++ b/src/caffe/layers/embed_layer.cpp
@@ -0,0 +1,122 @@
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  N_ = this->layer_param_.embed_param().num_output();
+  CHECK_GT(N_, 0) << "EmbedLayer num_output must be positive.";
+  K_ = this->layer_param_.embed_param().input_dim();
+  CHECK_GT(K_, 0) << "EmbedLayer input_dim must be positive.";
+  bias_term_ = this->layer_param_.embed_param().bias_term();
+  // Check if we need to set up the weights
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    if (bias_term_) {
+      this->blobs_.resize(2);
+    } else {
+      this->blobs_.resize(1);
+    }
+    // Initialize the weights --
+    // transposed from InnerProductLayer for spatial locality.
+    vector<int> weight_shape(2);
+    weight_shape[0] = K_;
+    weight_shape[1] = N_;
+    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
+    // fill the weights
+    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
+        this->layer_param_.embed_param().weight_filler()));
+    weight_filler->Fill(this->blobs_[0].get());
+    // If necessary, initialize and fill the bias term
+    if (bias_term_) {
+      vector<int> bias_shape(1, N_);
+      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
+      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
+          this->layer_param_.embed_param().bias_filler()));
+      bias_filler->Fill(this->blobs_[1].get());
+    }
+  }  // parameter initialization
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // Figure out the dimensions
+  M_ = bottom[0]->count();
+  vector<int> top_shape = bottom[0]->shape();
+  top_shape.push_back(N_);
+  top[0]->Reshape(top_shape);
+  // Set up the bias multiplier
+  if (bias_term_) {
+    vector<int> bias_shape(1, M_);
+    bias_multiplier_.Reshape(bias_shape);
+    caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  int index;
+  for (int n = 0; n < M_; ++n) {
+    index = static_cast<int>(bottom_data[n]);
+    DCHECK_GE(index, 0);
+    DCHECK_LT(index, K_);
+    DCHECK_EQ(static_cast<Dtype>(index), bottom_data[n]) << "non-integer input";
+    caffe_copy(N_, weight + index * N_, top_data + n * N_);
+  }
+  if (bias_term_) {
+    const Dtype* bias = this->blobs_[1]->cpu_data();
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
+        bias_multiplier_.cpu_data(), bias, Dtype(1), top_data);
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
+  if (this->param_propagate_down_[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    // Gradient with respect to weight
+    Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
+    int index;
+    for (int n = 0; n < M_; ++n) {
+      index = static_cast<int>(bottom_data[n]);
+      DCHECK_GE(index, 0);
+      DCHECK_LT(index, K_);
+      DCHECK_EQ(static_cast<Dtype>(index), bottom_data[n])
+          << "non-integer input";
+      caffe_axpy(N_, Dtype(1), top_diff + n * N_, weight_diff + index * N_);
+    }
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+    caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
+        bias_multiplier_.cpu_data(), Dtype(1), bias_diff);
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(EmbedLayer);
+#endif
+
+INSTANTIATE_CLASS(EmbedLayer);
+REGISTER_LAYER_CLASS(Embed);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
new file mode 100644
index 00000000000..37a4f7e35cd
--- /dev/null
+++ b/src/caffe/layers/embed_layer.cu
@@ -0,0 +1,80 @@
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void EmbedForward(const int nthreads, const Dtype* bottom_data,
+    const Dtype* weight, const int M, const int N, const int K,
+    Dtype* top_data) {
+  CUDA_KERNEL_LOOP(top_index, nthreads) {
+    const int n = top_index / N;
+    const int d = top_index % N;
+    const int index = static_cast<int>(bottom_data[n]);
+    const int weight_index = index * N + d;
+    top_data[top_index] = weight[weight_index];
+  }
+}
+
+template <typename Dtype>
+__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
+    const Dtype* top_diff, const int M, const int N, const int K,
+    Dtype* weight_diff) {
+  CUDA_KERNEL_LOOP(weight_index, nthreads) {
+    const int index = weight_index / N;
+    const int output_index = weight_index % N;
+    for (int n = 0; n < M; ++n) {
+      if (static_cast<int>(bottom_data[n]) == index) {
+        weight_diff[weight_index] += top_diff[n * N + output_index];
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  const int count = top[0]->count();
+  EmbedForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, weight, M_, N_, K_, top_data);
+  if (bias_term_) {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
+        bias_multiplier_.gpu_data(),
+        this->blobs_[1]->gpu_data(), Dtype(1), top_data);
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
+  if (this->param_propagate_down_[0]) {
+    const int count = this->blobs_[0]->count();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
+    EmbedBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, top_diff, M_, N_, K_, weight_diff);
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+    caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
+        bias_multiplier_.gpu_data(), Dtype(1), bias_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(EmbedLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a13c0e79d80..705cceb098b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -276,7 +276,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 137 (last added: reduction_param)
+// LayerParameter next available layer-specific ID: 138 (last added: embed_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -332,6 +332,7 @@ message LayerParameter {
   optional DropoutParameter dropout_param = 108;
   optional DummyDataParameter dummy_data_param = 109;
   optional EltwiseParameter eltwise_param = 110;
+  optional EmbedParameter embed_param = 137;
   optional ExpParameter exp_param = 111;
   optional FlattenParameter flatten_param = 135;
   optional HDF5DataParameter hdf5_data_param = 112;
@@ -533,6 +534,21 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
+// Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  // The input is given as integers to be interpreted as one-hot
+  // vector indices with dimension num_input.  Hence num_input should be
+  // 1 greater than the maximum possible input value.
+  optional uint32 input_dim = 2;
+
+  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+  optional FillerParameter weight_filler = 4; // The filler for the weight
+  optional FillerParameter bias_filler = 5; // The filler for the bias
+
+}
+
+// Message that stores parameters used by ExpLayer
 message ExpParameter {
   // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
   // Or if base is set to the default (-1), base is set to e,
diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
new file mode 100644
index 00000000000..7a4fb9800f2
--- /dev/null
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -0,0 +1,183 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+#endif
+
+template <typename TypeParam>
+class EmbedLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  EmbedLayerTest()
+      : blob_bottom_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~EmbedLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(EmbedLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(EmbedLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  embed_param->set_num_output(10);
+  embed_param->set_input_dim(5);
+  shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 5);
+  EXPECT_EQ(this->blob_top_->shape(0), 4);
+  EXPECT_EQ(this->blob_top_->shape(1), 1);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 1);
+  EXPECT_EQ(this->blob_top_->shape(4), 10);
+}
+
+TYPED_TEST(EmbedLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  const int kNumOutput = 10;
+  const int kInputDim = 5;
+  embed_param->set_num_output(kNumOutput);
+  embed_param->set_input_dim(kInputDim);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  embed_param->set_bias_term(false);
+  shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(1, layer->blobs().size());
+  vector<int> weight_shape(2);
+  weight_shape[0] = kInputDim;
+  weight_shape[1] = kNumOutput;
+  ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape());
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim;
+  }
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> weight_offset(2, 0);
+  vector<int> top_offset(5, 0);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    weight_offset[0] = static_cast<int>(this->blob_bottom_->cpu_data()[i]);
+    weight_offset[1] = 0;
+    top_offset[0] = i;
+    top_offset[4] = 0;
+    for (int j = 0; j < kNumOutput; ++j) {
+      EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset),
+                this->blob_top_->data_at(top_offset));
+      ++top_offset[4];
+      ++weight_offset[1];
+    }
+  }
+}
+
+TYPED_TEST(EmbedLayerTest, TestForwardWithBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  const int kNumOutput = 10;
+  const int kInputDim = 5;
+  embed_param->set_num_output(kNumOutput);
+  embed_param->set_input_dim(kInputDim);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler());
+  embed_param->set_bias_term(true);
+  shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(2, layer->blobs().size());
+  vector<int> weight_shape(2);
+  weight_shape[0] = kInputDim;
+  weight_shape[1] = kNumOutput;
+  ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape());
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim;
+  }
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> bias_offset(1, 0);
+  vector<int> weight_offset(2, 0);
+  vector<int> top_offset(5, 0);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    weight_offset[0] = static_cast<int>(this->blob_bottom_->cpu_data()[i]);
+    weight_offset[1] = 0;
+    top_offset[0] = i;
+    top_offset[4] = 0;
+    bias_offset[0] = 0;
+    for (int j = 0; j < kNumOutput; ++j) {
+      EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) +
+                layer->blobs()[1]->data_at(bias_offset),
+                this->blob_top_->data_at(top_offset));
+      ++top_offset[4];
+      ++weight_offset[1];
+      ++bias_offset[0];
+    }
+  }
+}
+
+TYPED_TEST(EmbedLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  embed_param->set_num_output(10);
+  embed_param->set_input_dim(5);
+  embed_param->set_bias_term(false);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  EmbedLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  this->blob_bottom_->mutable_cpu_data()[0] = 4;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 2;
+  this->blob_bottom_->mutable_cpu_data()[3] = 3;
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, -2);
+}
+
+TYPED_TEST(EmbedLayerTest, TestGradientWithBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  embed_param->set_num_output(10);
+  embed_param->set_input_dim(5);
+  embed_param->set_bias_term(true);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler());
+  EmbedLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  this->blob_bottom_->mutable_cpu_data()[0] = 4;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 2;
+  this->blob_bottom_->mutable_cpu_data()[3] = 3;
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, -2);
+}
+
+}  // namespace caffe

From ac9e29fd7b90a665a956f460715669bf05445a13 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 21 Jan 2015 16:12:12 -0800
Subject: [PATCH 1464/2053] EmbedBackward with no loops -- use
 caffe_gpu_atomic_add instead

---
 src/caffe/layers/embed_layer.cu | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
index 37a4f7e35cd..672fb9c608c 100644
--- a/src/caffe/layers/embed_layer.cu
+++ b/src/caffe/layers/embed_layer.cu
@@ -5,6 +5,7 @@
 #include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/gpu_util.cuh"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -22,18 +23,21 @@ __global__ void EmbedForward(const int nthreads, const Dtype* bottom_data,
   }
 }
 
+template <typename Dtype>
+__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
+    const Dtype* top_diff, const int M, const int N, const int K,
+    Dtype* weight_diff);
+
 template <typename Dtype>
 __global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
     const Dtype* top_diff, const int M, const int N, const int K,
     Dtype* weight_diff) {
-  CUDA_KERNEL_LOOP(weight_index, nthreads) {
-    const int index = weight_index / N;
-    const int output_index = weight_index % N;
-    for (int n = 0; n < M; ++n) {
-      if (static_cast<int>(bottom_data[n]) == index) {
-        weight_diff[weight_index] += top_diff[n * N + output_index];
-      }
-    }
+  CUDA_KERNEL_LOOP(top_index, nthreads) {
+    const int n = top_index / N;
+    const int d = top_index % N;
+    const int index = static_cast<int>(bottom_data[n]);
+    const int weight_index = index * N + d;
+    caffe_gpu_atomic_add(top_diff[top_index], weight_diff + weight_index);
   }
 }
 
@@ -59,13 +63,14 @@ void EmbedLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
   if (this->param_propagate_down_[0]) {
+    const int top_count = top[0]->count();
     const int count = this->blobs_[0]->count();
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = bottom[0]->gpu_data();
     Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
     EmbedBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
-        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, bottom_data, top_diff, M_, N_, K_, weight_diff);
+        <<<CAFFE_GET_BLOCKS(top_count), CAFFE_CUDA_NUM_THREADS>>>(
+        top_count, bottom_data, top_diff, M_, N_, K_, weight_diff);
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();

From 2c356a4ed4c33f662288166dfa74dd5d71d6c194 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 8 Aug 2015 12:57:45 -0700
Subject: [PATCH 1465/2053] TestGradientBasedSolver: drop doubled seed
 inititialization

---
 src/caffe/test/test_gradient_based_solver.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 7bb0ec18a09..30b041fa6b5 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -180,7 +180,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
     Caffe::set_random_seed(this->seed_);
     this->InitSolverFromProtoString(proto.str());
-    Caffe::set_random_seed(this->seed_);
     if (from_snapshot != NULL) {
       this->solver_->Restore(from_snapshot);
       vector<Blob<Dtype>*> empty_bottom_vec;

From 6019246930f54b108decc100f9cb801e4aea81a4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 8 Aug 2015 13:07:13 -0700
Subject: [PATCH 1466/2053] TestGradientBasedSolver: replace dummy data with
 hdf5

Rely on fixed hdf5 data for determinism of solver tests.

- draw random Gaussian data and targets for test and save to hdf5
- use the same data for all tests without constant / gaussian condition
  previously needed for accumulation
- avoid test artifacts due to order of random draws in dummy data
---
 .../test/test_data/generate_sample_data.py    |  28 ++++++++++++-
 src/caffe/test/test_data/solver_data.h5       | Bin 0 -> 11776 bytes
 src/caffe/test/test_data/solver_data_list.txt |   1 +
 src/caffe/test/test_gradient_based_solver.cpp |  39 ++++++++----------
 4 files changed, 45 insertions(+), 23 deletions(-)
 create mode 100644 src/caffe/test/test_data/solver_data.h5
 create mode 100644 src/caffe/test/test_data/solver_data_list.txt

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index ab5572685cb..3703b41823b 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -1,5 +1,5 @@
 """
-Generate data used in the HDF5DataLayer test.
+Generate data used in the HDF5DataLayer and GradientBasedSolver tests.
 """
 import os
 import numpy as np
@@ -7,6 +7,8 @@
 
 script_dir = os.path.dirname(os.path.abspath(__file__))
 
+# Generate HDF5DataLayer sample_data.h5
+
 num_cols = 8
 num_rows = 10
 height = 6
@@ -51,3 +53,27 @@
 with open(script_dir + '/sample_data_list.txt', 'w') as f:
     f.write(script_dir + '/sample_data.h5\n')
     f.write(script_dir + '/sample_data_2_gzip.h5\n')
+
+# Generate GradientBasedSolver solver_data.h5
+
+num_cols = 3
+num_rows = 8
+height = 10
+width = 10
+
+data = np.random.randn(num_rows, num_cols, height, width)
+data = data.reshape(num_rows, num_cols, height, width)
+data = data.astype('float32')
+
+targets = np.random.randn(num_rows, 1)
+targets = targets.astype('float32')
+
+print data
+print targets
+
+with h5py.File(script_dir + '/solver_data.h5', 'w') as f:
+    f['data'] = data
+    f['targets'] = targets
+
+with open(script_dir + '/solver_data_list.txt', 'w') as f:
+    f.write(script_dir + '/solver_data.h5\n')
diff --git a/src/caffe/test/test_data/solver_data.h5 b/src/caffe/test/test_data/solver_data.h5
new file mode 100644
index 0000000000000000000000000000000000000000..7ee05ea7aac988bee3e769434dbb66eb668d65bc
GIT binary patch
literal 11776
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HcmV?d00001

diff --git a/src/caffe/test/test_data/solver_data_list.txt b/src/caffe/test/test_data/solver_data_list.txt
new file mode 100644
index 00000000000..a6552f50073
--- /dev/null
+++ b/src/caffe/test/test_data/solver_data_list.txt
@@ -0,0 +1 @@
+src/caffe/test/test_data/solver_data.h5
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 30b041fa6b5..a8b211b7d43 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -25,15 +25,26 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
  protected:
   GradientBasedSolverTest() :
       seed_(1701), num_(4), channels_(3), height_(10), width_(10),
-      constant_data_(false), share_(false) {}
+      share_(false) {
+        input_file_ = new string(
+        CMAKE_SOURCE_DIR "caffe/test/test_data/solver_data_list.txt" CMAKE_EXT);
+      }
+  ~GradientBasedSolverTest() {
+    delete input_file_;
+  }
 
   string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
+  // Dimensions are determined by generate_sample_data.py
+  // TODO this is brittle and the hdf5 file should be checked instead.
   int num_, channels_, height_, width_;
-  bool constant_data_, share_;
+  bool share_;
   Dtype delta_;  // Stability constant for AdaGrad.
 
+  // Test data: check out generate_sample_data.py in the same directory.
+  string* input_file_;
+
   virtual SolverParameter_SolverType solver_type() = 0;
   virtual void InitSolver(const SolverParameter& param) = 0;
 
@@ -71,25 +82,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "  name: 'TestNetwork' "
        "  layer { "
        "    name: 'data' "
-       "    type: 'DummyData' "
-       "    dummy_data_param { "
-       "      num: " << num_ / iter_size << " "
-       "      channels: " << channels_ << " "
-       "      height: " << height_ << " "
-       "      width: " << width_ << " "
-       "      channels: 1 "
-       "      height: 1 "
-       "      width: 1 "
-       "      data_filler { "
-       "        type: '" << string(constant_data_ ? "constant" : "gaussian")
-                         << "' "
-       "        std: 1.0 "
-       "        value: 1.0 "
-       "      } "
-       "      data_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
+       "    type: 'HDF5Data' "
+       "    hdf5_data_param { "
+       "      source: '" << *(this->input_file_) << "' "
+       "      batch_size: " << num_ / iter_size << " "
        "    } "
        "    top: 'data' "
        "    top: 'targets' "
@@ -354,7 +350,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       const Dtype kMomentum, const int kNumIters, const int kIterSize) {
     const double kPrecision = 1e-2;
     const double kMinPrecision = 1e-7;
-    constant_data_ = true;
     // Solve without accumulation and save parameters.
     this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
         kNumIters);

From abe99e8748ad7f583c87d1a6132ff2d79e70dd9c Mon Sep 17 00:00:00 2001
From: Eren Golge <erogol@hotmail.com>
Date: Sat, 8 Aug 2015 23:45:08 -0700
Subject: [PATCH 1467/2053] Implement RMSProp Solver

Implement RMSProp solver and cleaned up to adjust to new solver interface that uses
accumulated gradients and refactored regularization.
---
 examples/mnist/lenet_solver_rmsprop.prototxt  |  27 ++
 examples/mnist/train_lenet_rmsprop.sh         |   3 +
 include/caffe/solver.hpp                      |  25 ++
 src/caffe/proto/caffe.proto                   |  25 +-
 src/caffe/solver.cpp                          |  76 ++++++
 src/caffe/test/test_gradient_based_solver.cpp | 245 ++++++++++++++----
 6 files changed, 353 insertions(+), 48 deletions(-)
 create mode 100644 examples/mnist/lenet_solver_rmsprop.prototxt
 create mode 100755 examples/mnist/train_lenet_rmsprop.sh

diff --git a/examples/mnist/lenet_solver_rmsprop.prototxt b/examples/mnist/lenet_solver_rmsprop.prototxt
new file mode 100644
index 00000000000..74dadc51069
--- /dev/null
+++ b/examples/mnist/lenet_solver_rmsprop.prototxt
@@ -0,0 +1,27 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.0
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_rmsprop"
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: RMSPROP
+rms_decay: 0.98
diff --git a/examples/mnist/train_lenet_rmsprop.sh b/examples/mnist/train_lenet_rmsprop.sh
new file mode 100755
index 00000000000..621cab238bf
--- /dev/null
+++ b/examples/mnist/train_lenet_rmsprop.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 703434b5fcf..fbade9389ff 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -135,6 +135,29 @@ class AdaGradSolver : public SGDSolver<Dtype> {
   DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
 };
 
+
+template <typename Dtype>
+class RMSPropSolver : public SGDSolver<Dtype> {
+ public:
+  explicit RMSPropSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit RMSPropSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.momentum())
+        << "Momentum cannot be used with RMSProp.";
+    CHECK_GE(this->param_.rms_decay(), 0)
+        << "rms_decay should lie between 0 and 1.";
+    CHECK_LT(this->param_.rms_decay(), 1)
+        << "rms_decay should lie between 0 and 1.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
+};
+
 template <typename Dtype>
 Solver<Dtype>* GetSolver(const SolverParameter& param) {
   SolverParameter_SolverType type = param.solver_type();
@@ -146,6 +169,8 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
       return new NesterovSolver<Dtype>(param);
   case SolverParameter_SolverType_ADAGRAD:
       return new AdaGradSolver<Dtype>(param);
+  case SolverParameter_SolverType_RMSPROP:
+      return new RMSPropSolver<Dtype>(param);
   default:
       LOG(FATAL) << "Unknown SolverType: " << type;
   }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a13c0e79d80..89f14595ba6 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 38 (last added: snapshot_format)
+// SolverParameter next available ID: 39 (last added: rms_decay)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -153,7 +153,23 @@ message SolverParameter {
   optional int32 max_iter = 7; // the maximum number of iterations
   // accumulate gradients over `iter_size` x `batch_size` instances
   optional int32 iter_size = 36 [default = 1];
-  optional string lr_policy = 8; // The learning rate decay policy.
+
+  // The learning rate decay policy. The currently implemented learning rate
+  // policies are as follows:
+  //    - fixed: always return base_lr.
+  //    - step: return base_lr * gamma ^ (floor(iter / step))
+  //    - exp: return base_lr * gamma ^ iter
+  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+  //    - multistep: similar to step but it allows non uniform steps defined by
+  //      stepvalue
+  //    - poly: the effective learning rate follows a polynomial decay, to be
+  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+  //    - sigmoid: the effective learning rate follows a sigmod decay
+  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+  //
+  // where base_lr, max_iter, gamma, step, stepvalue and power are defined
+  // in the solver parameter protocol buffer, and iter is the current iteration.
+  optional string lr_policy = 8;
   optional float gamma = 9; // The parameter to compute the learning rate.
   optional float power = 10; // The parameter to compute the learning rate.
   optional float momentum = 11; // The momentum value.
@@ -198,11 +214,16 @@ message SolverParameter {
     SGD = 0;
     NESTEROV = 1;
     ADAGRAD = 2;
+    RMSPROP = 3;
   }
   optional SolverType solver_type = 30 [default = SGD];
   // numerical stability for AdaGrad
   optional float delta = 31 [default = 1e-8];
 
+  // RMSProp decay value
+  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
+  optional float rms_decay = 38;
+
   // If true, print information about the state of the net that may help with
   // debugging learning problems.
   optional bool debug_info = 23 [default = false];
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 32276ac148a..43834c0c569 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -859,9 +859,85 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
 }
 
+template <typename Dtype>
+void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+
+  // get the learning rate
+  Dtype delta = this->param_.delta();
+  Dtype rms_decay = this->param_.rms_decay();
+  Dtype local_rate = rate * net_params_lr[param_id];
+
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id] -> count(),
+        Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
+        rms_decay, this->history_[param_id]-> mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+        delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  case Caffe::GPU:
+#ifndef CPU_ONLY
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_axpby(net_params[param_id] -> count(),
+        Dtype(1-rms_decay), this->update_[param_id]->gpu_data(),
+        rms_decay, this->history_[param_id]-> mutable_gpu_data());
+
+    // prepare update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        this->history_[param_id]->gpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add_scalar(net_params[param_id]->count(),
+        delta, this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_div(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->gpu_data(), Dtype(0),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 INSTANTIATE_CLASS(Solver);
 INSTANTIATE_CLASS(SGDSolver);
 INSTANTIATE_CLASS(NesterovSolver);
 INSTANTIATE_CLASS(AdaGradSolver);
+INSTANTIATE_CLASS(RMSPropSolver);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 7bb0ec18a09..b09189228ba 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -52,13 +52,14 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
         LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
     }
     InitSolver(param);
-    delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD) ?
-         param.delta() : 0;
+    delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD ||
+        solver_type() == SolverParameter_SolverType_RMSPROP) ?
+        param.delta() : 0;
   }
 
   string RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters,
-      const int iter_size = 1, const bool snapshot = false,
+      const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay,
+      const int num_iters, const int iter_size = 1, const bool snapshot = false,
       const char* from_snapshot = NULL) {
     ostringstream proto;
     proto <<
@@ -173,6 +174,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     if (momentum != 0) {
       proto << "momentum: " << momentum << " ";
     }
+    if (rms_decay != 0) {
+      proto << "rms_decay: " << rms_decay << " ";
+    }
     MakeTempDir(&snapshot_prefix_);
     proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' ";
     if (snapshot) {
@@ -204,7 +208,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // updated_params will store the updated weight and bias results,
   // using the blobs' diffs to hold the update values themselves.
   void ComputeLeastSquaresUpdate(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum,
+      const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay,
       vector<shared_ptr<Blob<Dtype> > >* updated_params) {
     const int N = num_;
     const int D = channels_ * height_ * width_;
@@ -287,6 +291,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       case SolverParameter_SolverType_ADAGRAD:
         update_value /= std::sqrt(history_value + grad * grad) + delta_;
         break;
+      case SolverParameter_SolverType_RMSPROP:
+        update_value /= std::sqrt(rms_decay*history_value
+            + grad * grad * (1 - rms_decay)) + delta_;
+        break;
       default:
         LOG(FATAL) << "Unknown solver type: " << solver_type();
       }
@@ -352,13 +360,14 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   }
 
   void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
-      const Dtype kMomentum, const int kNumIters, const int kIterSize) {
+      const Dtype kMomentum, const Dtype rms_decay, const int kNumIters,
+      const int kIterSize) {
     const double kPrecision = 1e-2;
     const double kMinPrecision = 1e-7;
     constant_data_ = true;
     // Solve without accumulation and save parameters.
     this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
-        kNumIters);
+        rms_decay, kNumIters);
     // Save parameters for comparison.
     Net<Dtype>& net = *this->solver_->net();
     const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
@@ -370,7 +379,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
     // Solve by equivalent accumulation of gradients over divided batches.
     this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
-        kNumIters, kIterSize);
+        rms_decay, kNumIters, kIterSize);
     Net<Dtype>& net_accum = *this->solver_->net();
     const vector<shared_ptr<Blob<Dtype> > >& accum_params =
         net_accum.layer_by_name("innerprod")->blobs();
@@ -408,18 +417,19 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // matches the solver's (K+1)th update.
   void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
       const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
-      const int iter_to_check = 0) {
+      const Dtype rms_decay = 0.0, const int iter_to_check = 0) {
     // Initialize the solver and run K (= iter_to_check) solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
+        iter_to_check);
 
     // Compute the (K+1)th update using the analytic least squares gradient.
     vector<shared_ptr<Blob<Dtype> > > updated_params;
     ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-                              &updated_params);
+        rms_decay, &updated_params);
 
     // Reinitialize the solver and run K+1 solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-                          iter_to_check + 1);
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
+        iter_to_check + 1);
 
     // Check that the solver's solution matches ours.
     CheckLeastSquaresUpdate(updated_params);
@@ -427,12 +437,12 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
   void TestSnapshot(const Dtype learning_rate = 1.0,
       const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
-      const int num_iters = 1) {
+      const Dtype rms_decay = 0.0, const int num_iters = 1) {
     // Run the solver for num_iters * 2 iterations.
     const int total_num_iters = num_iters * 2;
     bool snapshot = false;
     const int kIterSize = 1;
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
                           total_num_iters, kIterSize, snapshot);
 
     // Save the resulting param values.
@@ -463,12 +473,12 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Run the solver for num_iters iterations and snapshot.
     snapshot = true;
     string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
-        momentum, num_iters, kIterSize, snapshot);
+        momentum, rms_decay, num_iters, kIterSize, snapshot);
 
     // Reinitialize the solver and run for num_iters more iterations.
     snapshot = false;
-    RunLeastSquaresSolver(learning_rate, weight_decay,
-        momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str());
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
+        total_num_iters, kIterSize, snapshot, snapshot_name.c_str());
 
     // Check that params now match.
     const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
@@ -548,9 +558,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -559,9 +571,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -570,9 +584,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.5;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -581,10 +597,12 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.5;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -593,10 +611,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
-      kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
 }
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -604,11 +623,12 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
-      kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
 }
 
 TYPED_TEST(SGDSolverTest, TestSnapshot) {
@@ -616,9 +636,10 @@ TYPED_TEST(SGDSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
   }
 }
 
@@ -627,10 +648,11 @@ TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
   }
 }
 
@@ -672,22 +694,26 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
 TYPED_TEST(AdaGradSolverTest,
-           TestAdaGradLeastSquaresUpdateWithEverythingShare) {
+      TestAdaGradLeastSquaresUpdateWithEverythingShare) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -696,10 +722,11 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
-      kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
 }
 
 TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -707,11 +734,12 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
-      kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
 }
 
 TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
@@ -719,9 +747,10 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
   }
 }
 
@@ -730,10 +759,11 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
   }
 }
 
@@ -787,9 +817,11 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -798,9 +830,11 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -821,10 +855,12 @@ TYPED_TEST(NesterovSolverTest,
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
   }
 }
 
@@ -833,10 +869,11 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
-      kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
 }
 
 TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -844,11 +881,12 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
-      kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
 }
 
 TYPED_TEST(NesterovSolverTest, TestSnapshot) {
@@ -856,9 +894,10 @@ TYPED_TEST(NesterovSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
   }
 }
 
@@ -867,10 +906,124 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
+  const Dtype kRMSDecay = 0;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+  }
+}
+
+template <typename TypeParam>
+class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new RMSPropSolver<Dtype>(param));
+  }
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_RMSPROP;
+  }
+};
+
+TYPED_TEST_CASE(RMSPropSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithRmsDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.0;
+  const Dtype kRMSDecay = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const Dtype kRMSDecay = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest,
+      TestRMSPropLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const Dtype kRMSDecay = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
+        kRMSDecay, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const Dtype kRMSDecay = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const Dtype kRMSDecay = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
+      kNumIters, kIterSize);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0.95;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
   }
 }
 

From 4dcbe9287d334a06049bc95af37143fc73f15536 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sun, 9 Aug 2015 01:32:25 -0700
Subject: [PATCH 1468/2053] Encapsulate kRMSDecay in solver tests

Instead of introducing another argument kRMSDecay and setting it for every test, this param could be set by the RMSProp test class for encapsulation.
---
 src/caffe/test/test_gradient_based_solver.cpp | 156 +++++++-----------
 1 file changed, 58 insertions(+), 98 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index b09189228ba..e3c6b6d7964 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -58,8 +58,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   }
 
   string RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay,
-      const int num_iters, const int iter_size = 1, const bool snapshot = false,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
+      const int iter_size = 1, const bool snapshot = false,
       const char* from_snapshot = NULL) {
     ostringstream proto;
     proto <<
@@ -174,9 +174,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     if (momentum != 0) {
       proto << "momentum: " << momentum << " ";
     }
-    if (rms_decay != 0) {
-      proto << "rms_decay: " << rms_decay << " ";
-    }
     MakeTempDir(&snapshot_prefix_);
     proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' ";
     if (snapshot) {
@@ -208,7 +205,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // updated_params will store the updated weight and bias results,
   // using the blobs' diffs to hold the update values themselves.
   void ComputeLeastSquaresUpdate(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay,
+      const Dtype weight_decay, const Dtype momentum,
       vector<shared_ptr<Blob<Dtype> > >* updated_params) {
     const int N = num_;
     const int D = channels_ * height_ * width_;
@@ -291,9 +288,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       case SolverParameter_SolverType_ADAGRAD:
         update_value /= std::sqrt(history_value + grad * grad) + delta_;
         break;
-      case SolverParameter_SolverType_RMSPROP:
+      case SolverParameter_SolverType_RMSPROP: {
+        const Dtype rms_decay = 0.95;
         update_value /= std::sqrt(rms_decay*history_value
             + grad * grad * (1 - rms_decay)) + delta_;
+        }
         break;
       default:
         LOG(FATAL) << "Unknown solver type: " << solver_type();
@@ -360,14 +359,13 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   }
 
   void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
-      const Dtype kMomentum, const Dtype rms_decay, const int kNumIters,
-      const int kIterSize) {
+      const Dtype kMomentum, const int kNumIters, const int kIterSize) {
     const double kPrecision = 1e-2;
     const double kMinPrecision = 1e-7;
     constant_data_ = true;
     // Solve without accumulation and save parameters.
     this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
-        rms_decay, kNumIters);
+        kNumIters);
     // Save parameters for comparison.
     Net<Dtype>& net = *this->solver_->net();
     const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
@@ -379,7 +377,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
     // Solve by equivalent accumulation of gradients over divided batches.
     this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
-        rms_decay, kNumIters, kIterSize);
+        kNumIters, kIterSize);
     Net<Dtype>& net_accum = *this->solver_->net();
     const vector<shared_ptr<Blob<Dtype> > >& accum_params =
         net_accum.layer_by_name("innerprod")->blobs();
@@ -417,18 +415,17 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // matches the solver's (K+1)th update.
   void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
       const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
-      const Dtype rms_decay = 0.0, const int iter_to_check = 0) {
+      const int iter_to_check = 0) {
     // Initialize the solver and run K (= iter_to_check) solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
-        iter_to_check);
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
 
     // Compute the (K+1)th update using the analytic least squares gradient.
     vector<shared_ptr<Blob<Dtype> > > updated_params;
     ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-        rms_decay, &updated_params);
+        &updated_params);
 
     // Reinitialize the solver and run K+1 solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
         iter_to_check + 1);
 
     // Check that the solver's solution matches ours.
@@ -437,13 +434,13 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
   void TestSnapshot(const Dtype learning_rate = 1.0,
       const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
-      const Dtype rms_decay = 0.0, const int num_iters = 1) {
+      const int num_iters = 1) {
     // Run the solver for num_iters * 2 iterations.
     const int total_num_iters = num_iters * 2;
     bool snapshot = false;
     const int kIterSize = 1;
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
-                          total_num_iters, kIterSize, snapshot);
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+        total_num_iters, kIterSize, snapshot);
 
     // Save the resulting param values.
     vector<shared_ptr<Blob<Dtype> > > param_copies;
@@ -473,11 +470,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Run the solver for num_iters iterations and snapshot.
     snapshot = true;
     string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
-        momentum, rms_decay, num_iters, kIterSize, snapshot);
+        momentum, num_iters, kIterSize, snapshot);
 
     // Reinitialize the solver and run for num_iters more iterations.
     snapshot = false;
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay,
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
         total_num_iters, kIterSize, snapshot, snapshot_name.c_str());
 
     // Check that params now match.
@@ -558,11 +555,9 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -571,11 +566,9 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -584,11 +577,9 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.5;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -597,12 +588,10 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.5;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -611,11 +600,10 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -623,12 +611,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(SGDSolverTest, TestSnapshot) {
@@ -636,10 +623,9 @@ TYPED_TEST(SGDSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -648,11 +634,10 @@ TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -694,11 +679,9 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -708,12 +691,10 @@ TYPED_TEST(AdaGradSolverTest,
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -722,11 +703,10 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -734,12 +714,11 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
@@ -747,10 +726,9 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -759,11 +737,10 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -817,11 +794,9 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -830,11 +805,9 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -855,12 +828,10 @@ TYPED_TEST(NesterovSolverTest,
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -869,11 +840,10 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -881,12 +851,11 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(NesterovSolverTest, TestSnapshot) {
@@ -894,10 +863,9 @@ TYPED_TEST(NesterovSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -906,11 +874,10 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
-  const Dtype kRMSDecay = 0;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -920,7 +887,10 @@ class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
 
  protected:
   virtual void InitSolver(const SolverParameter& param) {
-    this->solver_.reset(new RMSPropSolver<Dtype>(param));
+    const Dtype rms_decay = 0.95;
+    SolverParameter new_param = param;
+    new_param.set_rms_decay(rms_decay);
+    this->solver_.reset(new RMSPropSolver<Dtype>(new_param));
   }
   virtual SolverParameter_SolverType solver_type() {
     return SolverParameter_SolverType_RMSPROP;
@@ -941,11 +911,9 @@ TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithRmsDecay) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -954,11 +922,9 @@ TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -968,12 +934,10 @@ TYPED_TEST(RMSPropSolverTest,
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum,
-        kRMSDecay, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -982,11 +946,10 @@ TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   const int kIterSize = 2;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
@@ -994,12 +957,11 @@ TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   const int kIterSize = 2;
   this->share_ = true;
-  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay,
-      kNumIters, kIterSize);
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
 }
 
 TYPED_TEST(RMSPropSolverTest, TestSnapshot) {
@@ -1007,10 +969,9 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshot) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
@@ -1019,11 +980,10 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
   const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0;
-  const Dtype kRMSDecay = 0.95;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 1; i <= kNumIters; ++i) {
-    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i);
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 

From b97ca6d4d17b4a661d553c233c2dfe03b88033c5 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sun, 9 Aug 2015 11:03:23 -0700
Subject: [PATCH 1469/2053] Use net_->learnable_params() instead of
 net_->params() in RMSprop

In RMSProp solver, use const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params(); instead of const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
---
 src/caffe/solver.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 43834c0c569..54e085a63e5 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -861,7 +861,7 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 
 template <typename Dtype>
 void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
 
   // get the learning rate

From 9b71fd038018d7c910ed0b75003b76edce1c5af9 Mon Sep 17 00:00:00 2001
From: Fisher Yu <fy@cs.princeton.edu>
Date: Sun, 9 Aug 2015 14:22:04 -0700
Subject: [PATCH 1470/2053] from __future__ imports must occur at the beginning
 of the file

---
 examples/pycaffe/caffenet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py
index 06c5a02d4ee..82af2294435 100644
--- a/examples/pycaffe/caffenet.py
+++ b/examples/pycaffe/caffenet.py
@@ -1,6 +1,6 @@
+from __future__ import print_function
 from caffe import layers as L, params as P, to_proto
 from caffe.proto import caffe_pb2
-from __future__ import print_function
 
 # helper function for common structures
 

From d94ca3f4b15d1e02bdf520733b04999f1134d078 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 28 Apr 2015 14:28:04 -0700
Subject: [PATCH 1471/2053] Add BlockingQueue for inter-thread communication

---
 include/caffe/util/blocking_queue.hpp | 47 +++++++++++++++
 src/caffe/util/blocking_queue.cpp     | 86 +++++++++++++++++++++++++++
 2 files changed, 133 insertions(+)
 create mode 100644 include/caffe/util/blocking_queue.hpp
 create mode 100644 src/caffe/util/blocking_queue.cpp

diff --git a/include/caffe/util/blocking_queue.hpp b/include/caffe/util/blocking_queue.hpp
new file mode 100644
index 00000000000..955e12cc567
--- /dev/null
+++ b/include/caffe/util/blocking_queue.hpp
@@ -0,0 +1,47 @@
+#ifndef CAFFE_UTIL_BLOCKING_QUEUE_HPP_
+#define CAFFE_UTIL_BLOCKING_QUEUE_HPP_
+
+#include <queue>
+#include <string>
+
+#include "caffe/common.hpp"
+
+namespace caffe {
+
+template<typename T>
+class BlockingQueue {
+ public:
+  explicit BlockingQueue();
+
+  void push(const T& t);
+
+  bool try_pop(T* t);
+
+  // This logs a message if the threads needs to be blocked
+  // useful for detecting e.g. when data feeding is too slow
+  T pop(const string& log_on_wait = "");
+
+  bool try_peek(T* t);
+
+  // Return element without removing it
+  T peek();
+
+  size_t size() const;
+
+ protected:
+  /**
+   Move synchronization fields out instead of including boost/thread.hpp
+   to avoid a boost/NVCC issues (#1009, #1010) on OSX. Also fails on
+   Linux CUDA 7.0.18.
+   */
+  class sync;
+
+  std::queue<T> queue_;
+  shared_ptr<sync> sync_;
+
+DISABLE_COPY_AND_ASSIGN(BlockingQueue);
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
new file mode 100644
index 00000000000..73c9564c0dd
--- /dev/null
+++ b/src/caffe/util/blocking_queue.cpp
@@ -0,0 +1,86 @@
+#include <boost/thread.hpp>
+#include <string>
+
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+template<typename T>
+class BlockingQueue<T>::sync {
+ public:
+  mutable boost::mutex mutex_;
+  boost::condition_variable condition_;
+};
+
+template<typename T>
+BlockingQueue<T>::BlockingQueue()
+    : sync_(new sync()) {
+}
+
+template<typename T>
+void BlockingQueue<T>::push(const T& t) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+  queue_.push(t);
+  lock.unlock();
+  sync_->condition_.notify_one();
+}
+
+template<typename T>
+bool BlockingQueue<T>::try_pop(T* t) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  if (queue_.empty()) {
+    return false;
+  }
+
+  *t = queue_.front();
+  queue_.pop();
+  return true;
+}
+
+template<typename T>
+T BlockingQueue<T>::pop(const string& log_on_wait) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  while (queue_.empty()) {
+    if (!log_on_wait.empty()) {
+      LOG_EVERY_N(INFO, 1000)<< log_on_wait;
+    }
+    sync_->condition_.wait(lock);
+  }
+
+  T t = queue_.front();
+  queue_.pop();
+  return t;
+}
+
+template<typename T>
+bool BlockingQueue<T>::try_peek(T* t) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  if (queue_.empty()) {
+    return false;
+  }
+
+  *t = queue_.front();
+  return true;
+}
+
+template<typename T>
+T BlockingQueue<T>::peek() {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  while (queue_.empty()) {
+    sync_->condition_.wait(lock);
+  }
+
+  return queue_.front();
+}
+
+template<typename T>
+size_t BlockingQueue<T>::size() const {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+  return queue_.size();
+}
+
+}  // namespace caffe

From 45d792e8b1e44acb467ab9be3debdd9e819c11d1 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 27 Apr 2015 19:48:10 -0700
Subject: [PATCH 1472/2053] Thread-local Caffe

---
 include/caffe/common.hpp                | 13 +++++------
 include/caffe/internal_thread.hpp       | 12 ++++++++--
 src/caffe/common.cpp                    | 11 ++++++++-
 src/caffe/internal_thread.cpp           | 29 +++++++++++++++++++++---
 src/caffe/test/test_internal_thread.cpp | 30 +++++++++++++++++++++++++
 5 files changed, 82 insertions(+), 13 deletions(-)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 5f86bc2625b..3fa81431314 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -98,12 +98,12 @@ void GlobalInit(int* pargc, char*** pargv);
 class Caffe {
  public:
   ~Caffe();
-  inline static Caffe& Get() {
-    if (!singleton_.get()) {
-      singleton_.reset(new Caffe());
-    }
-    return *singleton_;
-  }
+
+  // Thread local context for Caffe. Moved to common.cpp instead of
+  // including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010)
+  // on OSX. Also fails on Linux with CUDA 7.0.18.
+  static Caffe& Get();
+
   enum Brew { CPU, GPU };
 
   // This random number generator facade hides boost and CUDA rng
@@ -158,7 +158,6 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
-  static shared_ptr<Caffe> singleton_;
 
  private:
   // The private constructor to avoid duplicate instantiation.
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 815ca54605e..bcff318e866 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -14,14 +14,19 @@ namespace caffe {
 /**
  * Virtual class encapsulate boost::thread for use in base class
  * The child class will acquire the ability to run a single thread,
- * by reimplementing the virutal function InternalThreadEntry.
+ * by reimplementing the virtual function InternalThreadEntry.
  */
 class InternalThread {
  public:
   InternalThread() : thread_() {}
   virtual ~InternalThread();
 
-  /** Returns true if the thread was successfully started. **/
+  /**
+   * Caffe's thread local state will be initialized using the current
+   * thread values, e.g. device id, solver index etc. The random seed
+   * is initialized using caffe_rng_rand.
+   * Will not return until the internal thread has exited.
+   */
   bool StartInternalThread();
 
   /** Will not return until the internal thread has exited. */
@@ -34,6 +39,9 @@ class InternalThread {
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
+ private:
+  void entry(int device, Caffe::Brew mode, int rand_seed);
+
   shared_ptr<boost::thread> thread_;
 };
 
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index af96cac40aa..0215c76ef76 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,3 +1,4 @@
+#include <boost/thread.hpp>
 #include <glog/logging.h>
 #include <cstdio>
 #include <ctime>
@@ -7,7 +8,15 @@
 
 namespace caffe {
 
-shared_ptr<Caffe> Caffe::singleton_;
+// Make sure each thread can have different values.
+static boost::thread_specific_ptr<Caffe> thread_instance_;
+
+Caffe& Caffe::Get() {
+  if (!thread_instance_.get()) {
+    thread_instance_.reset(new Caffe());
+  }
+  return *(thread_instance_.get());
+}
 
 // random seeding
 int64_t cluster_seedgen(void) {
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index c2d19d433b4..2be88b31fa0 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,8 +1,14 @@
 #include <boost/thread.hpp>
+
 #include "caffe/internal_thread.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
+InternalThread::~InternalThread() {
+  StopInternalThread();
+}
+
 InternalThread::~InternalThread() {
   WaitForInternalThreadToExit();
 }
@@ -11,20 +17,37 @@ bool InternalThread::is_started() const {
   return thread_.get() != NULL && thread_->joinable();
 }
 
-
 bool InternalThread::StartInternalThread() {
   if (!WaitForInternalThreadToExit()) {
     return false;
   }
+
+  int device = 0;
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaGetDevice(&device));
+#endif
+  Caffe::Brew mode = Caffe::mode();
+  int rand_seed = caffe_rng_rand();
+
   try {
-    thread_.reset(
-        new boost::thread(&InternalThread::InternalThreadEntry, this));
+    thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
+          rand_seed));
   } catch (...) {
     return false;
   }
   return true;
 }
 
+void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) {
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaSetDevice(device));
+#endif
+  Caffe::set_mode(mode);
+  Caffe::set_random_seed(rand_seed);
+
+  InternalThreadEntry();
+}
+
 /** Will not return until the internal thread has exited. */
 bool InternalThread::WaitForInternalThreadToExit() {
   if (is_started()) {
diff --git a/src/caffe/test/test_internal_thread.cpp b/src/caffe/test/test_internal_thread.cpp
index 31882b6db1d..390c8eda19b 100644
--- a/src/caffe/test/test_internal_thread.cpp
+++ b/src/caffe/test/test_internal_thread.cpp
@@ -2,6 +2,7 @@
 #include "gtest/gtest.h"
 
 #include "caffe/internal_thread.hpp"
+#include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -19,5 +20,34 @@ TEST_F(InternalThreadTest, TestStartAndExit) {
   EXPECT_FALSE(thread.is_started());
 }
 
+class TestThreadA : public InternalThread {
+  void InternalThreadEntry() {
+    EXPECT_EQ(4244559767, caffe_rng_rand());
+  }
+};
+
+class TestThreadB : public InternalThread {
+  void InternalThreadEntry() {
+    EXPECT_EQ(1726478280, caffe_rng_rand());
+  }
+};
+
+TEST_F(InternalThreadTest, TestRandomSeed) {
+  TestThreadA t1;
+  Caffe::set_random_seed(9658361);
+  EXPECT_TRUE(t1.StartInternalThread());
+  EXPECT_TRUE(t1.WaitForInternalThreadToExit());
+
+  TestThreadA t2;
+  Caffe::set_random_seed(9658361);
+  EXPECT_TRUE(t2.StartInternalThread());
+  EXPECT_TRUE(t2.WaitForInternalThreadToExit());
+
+  TestThreadB t3;
+  Caffe::set_random_seed(3435563);
+  EXPECT_TRUE(t3.StartInternalThread());
+  EXPECT_TRUE(t3.WaitForInternalThreadToExit());
+}
+
 }  // namespace caffe
 

From 73b3d13b68bedad9d19f70755b0ee4ef376e2a30 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 28 Apr 2015 14:46:20 -0700
Subject: [PATCH 1473/2053] Change the way threads are started and stopped

- Interrupt the thread before waiting on join
- Provide a method for looping threads to exit on demand
- CHECK if start and stop succeed instead of returning an error
---
 include/caffe/internal_thread.hpp       |  8 +++---
 src/caffe/internal_thread.cpp           | 33 +++++++++++++------------
 src/caffe/layers/base_data_layer.cpp    |  4 +--
 src/caffe/test/test_internal_thread.cpp | 16 ++++++------
 4 files changed, 32 insertions(+), 29 deletions(-)

diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index bcff318e866..be6ff7fbac7 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -25,12 +25,11 @@ class InternalThread {
    * Caffe's thread local state will be initialized using the current
    * thread values, e.g. device id, solver index etc. The random seed
    * is initialized using caffe_rng_rand.
-   * Will not return until the internal thread has exited.
    */
-  bool StartInternalThread();
+  void StartInternalThread();
 
   /** Will not return until the internal thread has exited. */
-  bool WaitForInternalThreadToExit();
+  void StopInternalThread();
 
   bool is_started() const;
 
@@ -39,6 +38,9 @@ class InternalThread {
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
+  /* Should be tested when running loops to exit when requested. */
+  bool must_stop();
+
  private:
   void entry(int device, Caffe::Brew mode, int rand_seed);
 
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 2be88b31fa0..d6c26559925 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,4 +1,5 @@
 #include <boost/thread.hpp>
+#include <exception>
 
 #include "caffe/internal_thread.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -9,18 +10,19 @@ InternalThread::~InternalThread() {
   StopInternalThread();
 }
 
-InternalThread::~InternalThread() {
-  WaitForInternalThreadToExit();
+bool InternalThread::is_started() const {
+  return thread_ && thread_->joinable();
 }
 
-bool InternalThread::is_started() const {
-  return thread_.get() != NULL && thread_->joinable();
+bool InternalThread::must_stop() {
+  return thread_ && thread_->interruption_requested();
 }
 
-bool InternalThread::StartInternalThread() {
-  if (!WaitForInternalThreadToExit()) {
-    return false;
-  }
+void InternalThread::StartInternalThread() {
+  // TODO switch to failing once Caffe prefetch thread is persistent.
+  // Threads should not be started and stopped repeatedly.
+  // CHECK(!is_started());
+  StopInternalThread();
 
   int device = 0;
 #ifndef CPU_ONLY
@@ -32,10 +34,9 @@ bool InternalThread::StartInternalThread() {
   try {
     thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
           rand_seed));
-  } catch (...) {
-    return false;
+  } catch (std::exception& e) {
+    LOG(FATAL) << "Thread exception: " << e.what();
   }
-  return true;
 }
 
 void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) {
@@ -48,16 +49,16 @@ void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) {
   InternalThreadEntry();
 }
 
-/** Will not return until the internal thread has exited. */
-bool InternalThread::WaitForInternalThreadToExit() {
+void InternalThread::StopInternalThread() {
   if (is_started()) {
+    thread_->interrupt();
     try {
       thread_->join();
-    } catch (...) {
-      return false;
+    } catch (boost::thread_interrupted&) {
+    } catch (std::exception& e) {
+      LOG(FATAL) << "Thread exception: " << e.what();
     }
   }
-  return true;
 }
 
 }  // namespace caffe
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 26a1118282f..facaed7f11c 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -47,12 +47,12 @@ void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
   this->data_transformer_->InitRand();
-  CHECK(StartInternalThread()) << "Thread execution failed";
+  StartInternalThread();
 }
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
+  StopInternalThread();
 }
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_internal_thread.cpp b/src/caffe/test/test_internal_thread.cpp
index 390c8eda19b..93f1cc541cd 100644
--- a/src/caffe/test/test_internal_thread.cpp
+++ b/src/caffe/test/test_internal_thread.cpp
@@ -14,9 +14,9 @@ class InternalThreadTest : public ::testing::Test {};
 TEST_F(InternalThreadTest, TestStartAndExit) {
   InternalThread thread;
   EXPECT_FALSE(thread.is_started());
-  EXPECT_TRUE(thread.StartInternalThread());
+  thread.StartInternalThread();
   EXPECT_TRUE(thread.is_started());
-  EXPECT_TRUE(thread.WaitForInternalThreadToExit());
+  thread.StopInternalThread();
   EXPECT_FALSE(thread.is_started());
 }
 
@@ -35,18 +35,18 @@ class TestThreadB : public InternalThread {
 TEST_F(InternalThreadTest, TestRandomSeed) {
   TestThreadA t1;
   Caffe::set_random_seed(9658361);
-  EXPECT_TRUE(t1.StartInternalThread());
-  EXPECT_TRUE(t1.WaitForInternalThreadToExit());
+  t1.StartInternalThread();
+  t1.StopInternalThread();
 
   TestThreadA t2;
   Caffe::set_random_seed(9658361);
-  EXPECT_TRUE(t2.StartInternalThread());
-  EXPECT_TRUE(t2.WaitForInternalThreadToExit());
+  t2.StartInternalThread();
+  t2.StopInternalThread();
 
   TestThreadB t3;
   Caffe::set_random_seed(3435563);
-  EXPECT_TRUE(t3.StartInternalThread());
-  EXPECT_TRUE(t3.WaitForInternalThreadToExit());
+  t3.StartInternalThread();
+  t3.StopInternalThread();
 }
 
 }  // namespace caffe

From ddcdc9d711e81312caf127e8aa512c3298101297 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 18 May 2015 17:45:20 -0700
Subject: [PATCH 1474/2053] Persistent prefetch thread

---
 include/caffe/data_layers.hpp          | 31 +++++----
 include/caffe/syncedmem.hpp            |  4 ++
 src/caffe/internal_thread.cpp          |  5 +-
 src/caffe/layers/base_data_layer.cpp   | 88 ++++++++++++++++++--------
 src/caffe/layers/base_data_layer.cu    | 15 ++---
 src/caffe/layers/data_layer.cpp        | 26 ++++----
 src/caffe/layers/image_data_layer.cpp  | 28 ++++----
 src/caffe/layers/window_data_layer.cpp | 20 +++---
 src/caffe/syncedmem.cpp                | 12 ++++
 src/caffe/util/blocking_queue.cpp      |  4 ++
 10 files changed, 153 insertions(+), 80 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 3958cb7ecb0..f57ab6b0dba 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -15,6 +15,7 @@
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
 #include "caffe/util/db.hpp"
 
 namespace caffe {
@@ -50,12 +51,17 @@ class BaseDataLayer : public Layer<Dtype> {
   bool output_labels_;
 };
 
+template <typename Dtype>
+class Batch {
+ public:
+  Blob<Dtype> data_, label_;
+};
+
 template <typename Dtype>
 class BasePrefetchingDataLayer :
     public BaseDataLayer<Dtype>, public InternalThread {
  public:
-  explicit BasePrefetchingDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param) {}
+  explicit BasePrefetchingDataLayer(const LayerParameter& param);
   // LayerSetUp: implements common data layer setup functionality, and calls
   // DataLayerSetUp to do special data layer setup for individual layer types.
   // This method may not be overridden.
@@ -67,14 +73,17 @@ class BasePrefetchingDataLayer :
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
-  // The thread's function
-  virtual void InternalThreadEntry() {}
+  // Prefetches batches (asynchronously if to GPU memory)
+  static const int PREFETCH_COUNT = 3;
 
  protected:
-  Blob<Dtype> prefetch_data_;
-  Blob<Dtype> prefetch_label_;
+  virtual void InternalThreadEntry();
+  virtual void load_batch(Batch<Dtype>* batch) = 0;
+
+  Batch<Dtype> prefetch_[PREFETCH_COUNT];
+  BlockingQueue<Batch<Dtype>*> prefetch_free_;
+  BlockingQueue<Batch<Dtype>*> prefetch_full_;
+
   Blob<Dtype> transformed_data_;
 };
 
@@ -93,7 +102,7 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
-  virtual void InternalThreadEntry();
+  virtual void load_batch(Batch<Dtype>* batch);
 
   shared_ptr<db::DB> db_;
   shared_ptr<db::Cursor> cursor_;
@@ -235,7 +244,7 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
  protected:
   shared_ptr<Caffe::RNG> prefetch_rng_;
   virtual void ShuffleImages();
-  virtual void InternalThreadEntry();
+  virtual void load_batch(Batch<Dtype>* batch);
 
   vector<std::pair<std::string, int> > lines_;
   int lines_id_;
@@ -307,7 +316,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
 
  protected:
   virtual unsigned int PrefetchRand();
-  virtual void InternalThreadEntry();
+  virtual void load_batch(Batch<Dtype>* batch);
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
   vector<std::pair<std::string, vector<int> > > image_database_;
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 1b726de9564..4d339bf4e57 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -56,6 +56,10 @@ class SyncedMemory {
   SyncedHead head() { return head_; }
   size_t size() { return size_; }
 
+#ifndef CPU_ONLY
+  void async_gpu_push(const cudaStream_t& stream);
+#endif
+
  private:
   void to_cpu();
   void to_gpu();
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index d6c26559925..b193826c1a6 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -19,10 +19,7 @@ bool InternalThread::must_stop() {
 }
 
 void InternalThread::StartInternalThread() {
-  // TODO switch to failing once Caffe prefetch thread is persistent.
-  // Threads should not be started and stopped repeatedly.
-  // CHECK(!is_started());
-  StopInternalThread();
+  CHECK(!is_started()) << "Threads should persist and not be restarted.";
 
   int device = 0;
 #ifndef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index facaed7f11c..9288d91339a 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -1,7 +1,9 @@
+#include <boost/thread.hpp>
 #include <string>
 #include <vector>
 
 #include "caffe/data_layers.hpp"
+#include "caffe/net.hpp"
 #include "caffe/util/io.hpp"
 
 namespace caffe {
@@ -27,56 +29,92 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   DataLayerSetUp(bottom, top);
 }
 
+template <typename Dtype>
+BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
+    const LayerParameter& param)
+    : BaseDataLayer<Dtype>(param),
+      prefetch_free_(), prefetch_full_() {
+  for (int i = 0; i < PREFETCH_COUNT; ++i) {
+    prefetch_free_.push(&prefetch_[i]);
+  }
+}
+
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
-  // Now, start the prefetch thread. Before calling prefetch, we make two
-  // cpu_data calls so that the prefetch thread does not accidentally make
-  // simultaneous cudaMalloc calls when the main thread is running. In some
-  // GPUs this seems to cause failures if we do not so.
-  this->prefetch_data_.mutable_cpu_data();
-  if (this->output_labels_) {
-    this->prefetch_label_.mutable_cpu_data();
+  // Before starting the prefetch thread, we make cpu_data and gpu_data
+  // calls so that the prefetch thread does not accidentally make simultaneous
+  // cudaMalloc calls when the main thread is running. In some GPUs this
+  // seems to cause failures if we do not so.
+  for (int i = 0; i < PREFETCH_COUNT; ++i) {
+    prefetch_[i].data_.mutable_cpu_data();
+    if (this->output_labels_) {
+      prefetch_[i].label_.mutable_cpu_data();
+    }
   }
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    for (int i = 0; i < PREFETCH_COUNT; ++i) {
+      prefetch_[i].data_.mutable_gpu_data();
+      if (this->output_labels_) {
+        prefetch_[i].label_.mutable_gpu_data();
+      }
+    }
+  }
+#endif
   DLOG(INFO) << "Initializing prefetch";
-  this->CreatePrefetchThread();
-  DLOG(INFO) << "Prefetch initialized.";
-}
-
-template <typename Dtype>
-void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
   this->data_transformer_->InitRand();
   StartInternalThread();
+  DLOG(INFO) << "Prefetch initialized.";
 }
 
 template <typename Dtype>
-void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
-  StopInternalThread();
+void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
+#ifndef CPU_ONLY
+  cudaStream_t stream;
+  if (Caffe::mode() == Caffe::GPU) {
+    cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking);
+  }
+#endif
+
+  try {
+    while (!must_stop()) {
+      Batch<Dtype>* batch = prefetch_free_.pop();
+      load_batch(batch);
+#ifndef CPU_ONLY
+      if (Caffe::mode() == Caffe::GPU) {
+        batch->data_.data().get()->async_gpu_push(stream);
+        cudaStreamSynchronize(stream);
+      }
+#endif
+      prefetch_full_.push(batch);
+    }
+  } catch (boost::thread_interrupted&) {
+    // Interrupted exception is expected on shutdown
+  }
 }
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  DLOG(INFO) << "Thread joined";
+  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
   // Reshape to loaded data.
-  top[0]->ReshapeLike(prefetch_data_);
+  top[0]->Reshape(batch->data_.num(), batch->data_.channels(),
+      batch->data_.height(), batch->data_.width());
   // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+  caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
              top[0]->mutable_cpu_data());
   DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
     // Reshape to loaded labels.
     top[1]->ReshapeLike(prefetch_label_);
     // Copy the labels.
-    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-               top[1]->mutable_cpu_data());
+    caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
+        top[1]->mutable_cpu_data());
   }
-  // Start a new prefetch thread
-  DLOG(INFO) << "CreatePrefetchThread";
-  CreatePrefetchThread();
+
+  prefetch_free_.push(batch);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 9335a5bc9a9..56439bc506a 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -7,22 +7,21 @@ namespace caffe {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  // First, join the thread
-  JoinPrefetchThread();
+  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
   // Reshape to loaded data.
-  top[0]->ReshapeLike(this->prefetch_data_);
+  top[0]->ReshapeLike(batch->data_);
   // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+  caffe_copy(batch->data_.count(), batch->data_.gpu_data(),
       top[0]->mutable_gpu_data());
   if (this->output_labels_) {
     // Reshape to loaded labels.
-    top[1]->ReshapeLike(prefetch_label_);
+    top[1]->ReshapeLike(batch->label_);
     // Copy the labels.
-    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+    caffe_copy(batch->label_.count(), batch->label_.gpu_data(),
         top[1]->mutable_gpu_data());
   }
-  // Start a new prefetch thread
-  CreatePrefetchThread();
+
+  prefetch_free_.push(batch);
 }
 
 INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 161a75e0c8c..22d9f436185 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -17,8 +17,8 @@
 namespace caffe {
 
 template <typename Dtype>
-DataLayer<Dtype>::~DataLayer<Dtype>() {
-  this->JoinPrefetchThread();
+DataLayer<Dtype>::~DataLayer() {
+  this->StopInternalThread();
 }
 
 template <typename Dtype>
@@ -54,21 +54,23 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       << top[0]->width();
   // label
   if (this->output_labels_) {
-    vector<int> label_shape(1, this->layer_param_.data_param().batch_size());
+    vector<int> label_shape(1, batch_size);
     top[1]->Reshape(label_shape);
-    this->prefetch_label_.Reshape(label_shape);
+    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+      this->prefetch_[i].label_.Reshape(label_shape);
+    }
   }
 }
 
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void DataLayer<Dtype>::InternalThreadEntry() {
+// This function is called on prefetch thread
+template<typename Dtype>
+void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   CPUTimer batch_timer;
   batch_timer.Start();
   double read_time = 0;
   double trans_time = 0;
   CPUTimer timer;
-  CHECK(this->prefetch_data_.count());
+  CHECK(batch->data_.count());
   CHECK(this->transformed_data_.count());
 
   // Reshape according to the first datum of each batch
@@ -81,13 +83,13 @@ void DataLayer<Dtype>::InternalThreadEntry() {
   this->transformed_data_.Reshape(top_shape);
   // Reshape prefetch_data according to the batch_size.
   top_shape[0] = batch_size;
-  this->prefetch_data_.Reshape(top_shape);
+  batch->data_.Reshape(top_shape);
 
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Dtype* top_data = batch->data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
 
   if (this->output_labels_) {
-    top_label = this->prefetch_label_.mutable_cpu_data();
+    top_label = batch->label_.mutable_cpu_data();
   }
   timer.Start();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
@@ -97,7 +99,7 @@ void DataLayer<Dtype>::InternalThreadEntry() {
     read_time += timer.MicroSeconds();
     timer.Start();
     // Apply data transformations (mirror, scale, crop...)
-    int offset = this->prefetch_data_.offset(item_id);
+    int offset = batch->data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_->Transform(datum, &(this->transformed_data_));
     // Copy label.
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index dcc53348304..223ba3a75ca 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -17,7 +17,7 @@ namespace caffe {
 
 template <typename Dtype>
 ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
-  this->JoinPrefetchThread();
+  this->StopInternalThread();
 }
 
 template <typename Dtype>
@@ -70,8 +70,10 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int batch_size = this->layer_param_.image_data_param().batch_size();
   CHECK_GT(batch_size, 0) << "Positive batch size required";
   top_shape[0] = batch_size;
-  this->prefetch_data_.Reshape(top_shape);
-  top[0]->ReshapeLike(this->prefetch_data_);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].data_.Reshape(top_shape);
+  }
+  top[0]->Reshape(top_shape);
 
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
@@ -79,7 +81,9 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  this->prefetch_label_.Reshape(label_shape);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].label_.Reshape(label_shape);
+  }
 }
 
 template <typename Dtype>
@@ -89,15 +93,15 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
   shuffle(lines_.begin(), lines_.end(), prefetch_rng);
 }
 
-// This function is used to create a thread that prefetches the data.
+// This function is called on prefetch thread
 template <typename Dtype>
-void ImageDataLayer<Dtype>::InternalThreadEntry() {
+void ImageDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   CPUTimer batch_timer;
   batch_timer.Start();
   double read_time = 0;
   double trans_time = 0;
   CPUTimer timer;
-  CHECK(this->prefetch_data_.count());
+  CHECK(batch->data_.count());
   CHECK(this->transformed_data_.count());
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
@@ -114,12 +118,12 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   // Use data_transformer to infer the expected blob shape from a cv_img.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
   this->transformed_data_.Reshape(top_shape);
-  // Reshape prefetch_data according to the batch_size.
+  // Reshape batch according to the batch_size.
   top_shape[0] = batch_size;
-  this->prefetch_data_.Reshape(top_shape);
+  batch->data_.Reshape(top_shape);
 
-  Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();
+  Dtype* prefetch_data = batch->data_.mutable_cpu_data();
+  Dtype* prefetch_label = batch->label_.mutable_cpu_data();
 
   // datum scales
   const int lines_size = lines_.size();
@@ -133,7 +137,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     read_time += timer.MicroSeconds();
     timer.Start();
     // Apply transformations (mirror, crop...) to the image
-    int offset = this->prefetch_data_.offset(item_id);
+    int offset = batch->data_.offset(item_id);
     this->transformed_data_.set_cpu_data(prefetch_data + offset);
     this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
     trans_time += timer.MicroSeconds();
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index c127d56bc46..f637f2ec6d4 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -27,7 +27,7 @@ namespace caffe {
 
 template <typename Dtype>
 WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
-  this->JoinPrefetchThread();
+  this->StopInternalThread();
 }
 
 template <typename Dtype>
@@ -171,7 +171,9 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   top[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i)
+    this->prefetch_[i].data_.Reshape(
+        batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
@@ -179,7 +181,9 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  this->prefetch_label_.Reshape(label_shape);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].label_.Reshape(label_shape);
+  }
 
   // data mean
   has_mean_file_ = this->transform_param_.has_mean_file();
@@ -217,9 +221,9 @@ unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
   return (*prefetch_rng)();
 }
 
-// Thread fetching the data
+// This function is called on prefetch thread
 template <typename Dtype>
-void WindowDataLayer<Dtype>::InternalThreadEntry() {
+void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
   CPUTimer batch_timer;
@@ -227,8 +231,8 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   double read_time = 0;
   double trans_time = 0;
   CPUTimer timer;
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+  Dtype* top_data = batch->data_.mutable_cpu_data();
+  Dtype* top_label = batch->label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   const int context_pad = this->layer_param_.window_data_param().context_pad();
@@ -252,7 +256,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  caffe_set(this->prefetch_data_.count(), Dtype(0), top_data);
+  caffe_set(batch->data_.count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 7617ccfb27f..0da7a3bac79 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -108,6 +108,18 @@ void* SyncedMemory::mutable_gpu_data() {
 #endif
 }
 
+#ifndef CPU_ONLY
+void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
+  CHECK(head_ == HEAD_AT_CPU);
+  if (gpu_ptr_ == NULL) {
+    CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+  }
+  const cudaMemcpyKind put = cudaMemcpyHostToDevice;
+  CUDA_CHECK(cudaMemcpyAsync(gpu_ptr_, cpu_ptr_, size_, put, stream));
+  // Assume caller will synchronize on the stream before use
+  head_ = SYNCED;
+}
+#endif
 
 }  // namespace caffe
 
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
index 73c9564c0dd..6ab6ba06780 100644
--- a/src/caffe/util/blocking_queue.cpp
+++ b/src/caffe/util/blocking_queue.cpp
@@ -1,6 +1,7 @@
 #include <boost/thread.hpp>
 #include <string>
 
+#include "caffe/data_layers.hpp"
 #include "caffe/util/blocking_queue.hpp"
 
 namespace caffe {
@@ -83,4 +84,7 @@ size_t BlockingQueue<T>::size() const {
   return queue_.size();
 }
 
+template class BlockingQueue<Batch<float>*>;
+template class BlockingQueue<Batch<double>*>;
+
 }  // namespace caffe

From bcc8f50a95ecad954d1887f3fb273eaa298e2274 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 18 May 2015 18:06:09 -0700
Subject: [PATCH 1475/2053] Add DataReader for parallel training with one DB
 session

- Make sure each solver accesses a different subset of the data
- Sequential reading of DB for performance
- Prefetch a configurable amount of data to host memory
- Distribute data to solvers in round-robin way for determinism
---
 include/caffe/data_layers.hpp         |   8 +-
 include/caffe/data_reader.hpp         |  82 +++++++++++++++++
 src/caffe/data_reader.cpp             | 121 ++++++++++++++++++++++++++
 src/caffe/layers/base_data_layer.cpp  |   2 +-
 src/caffe/layers/data_layer.cpp       |  55 +++++-------
 src/caffe/proto/caffe.proto           |   4 +
 src/caffe/test/test_layer_factory.cpp |  14 ++-
 src/caffe/test/test_upgrade_proto.cpp |  12 +++
 src/caffe/util/blocking_queue.cpp     |   3 +
 9 files changed, 259 insertions(+), 42 deletions(-)
 create mode 100644 include/caffe/data_reader.hpp
 create mode 100644 src/caffe/data_reader.cpp

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index f57ab6b0dba..12e6c366620 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -5,11 +5,11 @@
 #include <utility>
 #include <vector>
 
-#include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/data_reader.hpp"
 #include "caffe/data_transformer.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/internal_thread.hpp"
@@ -90,8 +90,7 @@ class BasePrefetchingDataLayer :
 template <typename Dtype>
 class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  public:
-  explicit DataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
+  explicit DataLayer(const LayerParameter& param);
   virtual ~DataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
@@ -104,8 +103,7 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
  protected:
   virtual void load_batch(Batch<Dtype>* batch);
 
-  shared_ptr<db::DB> db_;
-  shared_ptr<db::Cursor> cursor_;
+  DataReader reader_;
 };
 
 /**
diff --git a/include/caffe/data_reader.hpp b/include/caffe/data_reader.hpp
new file mode 100644
index 00000000000..8ed5542cb8d
--- /dev/null
+++ b/include/caffe/data_reader.hpp
@@ -0,0 +1,82 @@
+#ifndef CAFFE_DATA_READER_HPP_
+#define CAFFE_DATA_READER_HPP_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/util/blocking_queue.hpp"
+#include "caffe/util/db.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Reads data from a source to queues available to data layers.
+ * A single reading thread is created per source, even if multiple solvers
+ * are running in parallel, e.g. for multi-GPU training. This makes sure
+ * databases are read sequentially, and that each solver accesses a different
+ * subset of the database. Data is distributed to solvers in a round-robin
+ * way to keep parallel training deterministic.
+ */
+class DataReader {
+ public:
+  explicit DataReader(const LayerParameter& param);
+  ~DataReader();
+
+  inline BlockingQueue<Datum*>& free() const {
+    return queue_pair_->free_;
+  }
+  inline BlockingQueue<Datum*>& full() const {
+    return queue_pair_->full_;
+  }
+
+ protected:
+  // Queue pairs are shared between a body and its readers
+  class QueuePair {
+   public:
+    explicit QueuePair(int size);
+    ~QueuePair();
+
+    BlockingQueue<Datum*> free_;
+    BlockingQueue<Datum*> full_;
+
+  DISABLE_COPY_AND_ASSIGN(QueuePair);
+  };
+
+  // A single body is created per source
+  class Body : public InternalThread {
+   public:
+    explicit Body(const LayerParameter& param);
+    virtual ~Body();
+
+   protected:
+    void InternalThreadEntry();
+    void read_one(db::Cursor* cursor, QueuePair* qp);
+
+    const LayerParameter param_;
+    BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
+
+    friend class DataReader;
+
+  DISABLE_COPY_AND_ASSIGN(Body);
+  };
+
+  // A source is uniquely identified by its layer name + path, in case
+  // the same database is read from two different locations in the net.
+  static inline string source_key(const LayerParameter& param) {
+    return param.name() + ":" + param.data_param().source();
+  }
+
+  const shared_ptr<QueuePair> queue_pair_;
+  shared_ptr<Body> body_;
+
+  static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
+
+DISABLE_COPY_AND_ASSIGN(DataReader);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_READER_HPP_
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
new file mode 100644
index 00000000000..60606f0d6c8
--- /dev/null
+++ b/src/caffe/data_reader.cpp
@@ -0,0 +1,121 @@
+#include <boost/thread.hpp>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
+#include "caffe/data_reader.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+using boost::weak_ptr;
+
+map<const string, weak_ptr<DataReader::Body> > DataReader::bodies_;
+static boost::mutex bodies_mutex_;
+
+DataReader::DataReader(const LayerParameter& param)
+    : queue_pair_(new QueuePair(  //
+        param.data_param().prefetch() * param.data_param().batch_size())) {
+  // Get or create a body
+  boost::mutex::scoped_lock lock(bodies_mutex_);
+  string key = source_key(param);
+  weak_ptr<Body>& weak = bodies_[key];
+  body_ = weak.lock();
+  if (!body_) {
+    body_.reset(new Body(param));
+    bodies_[key] = weak_ptr<Body>(body_);
+  }
+  body_->new_queue_pairs_.push(queue_pair_);
+}
+
+DataReader::~DataReader() {
+  string key = source_key(body_->param_);
+  body_.reset();
+  boost::mutex::scoped_lock lock(bodies_mutex_);
+  if (bodies_[key].expired()) {
+    bodies_.erase(key);
+  }
+}
+
+//
+
+DataReader::QueuePair::QueuePair(int size) {
+  // Initialize the free queue with requested number of datums
+  for (int i = 0; i < size; ++i) {
+    free_.push(new Datum());
+  }
+}
+
+DataReader::QueuePair::~QueuePair() {
+  Datum* datum;
+  while (free_.try_pop(&datum)) {
+    delete datum;
+  }
+  while (full_.try_pop(&datum)) {
+    delete datum;
+  }
+}
+
+//
+
+DataReader::Body::Body(const LayerParameter& param)
+    : param_(param),
+      new_queue_pairs_() {
+  StartInternalThread();
+}
+
+DataReader::Body::~Body() {
+  StopInternalThread();
+}
+
+void DataReader::Body::InternalThreadEntry() {
+  shared_ptr<db::DB> db(db::GetDB(param_.data_param().backend()));
+  db->Open(param_.data_param().source(), db::READ);
+  shared_ptr<db::Cursor> cursor(db->NewCursor());
+  vector<shared_ptr<QueuePair> > qps;
+  try {
+    // int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
+    // TODO single solver until multi-gpu merge
+    int solver_count = 1;
+
+    // To ensure deterministic runs, only start running once all solvers
+    // are ready. But solvers need to peek on one item during initialization,
+    // so read one item, then wait for the next solver.
+    for (int i = 0; i < solver_count; ++i) {
+      shared_ptr<QueuePair> qp(new_queue_pairs_.pop());
+      read_one(cursor.get(), qp.get());
+      qps.push_back(qp);
+    }
+    // Main loop
+    while (!must_stop()) {
+      for (int i = 0; i < solver_count; ++i) {
+        read_one(cursor.get(), qps[i].get());
+      }
+      // Check no additional readers have been created. This can happen if
+      // more than one net is trained at a time per process, whether single
+      // or multi solver. It might also happen if two data layers have same
+      // name and same source.
+      CHECK_EQ(new_queue_pairs_.size(), 0);
+    }
+  } catch (boost::thread_interrupted&) {
+    // Interrupted exception is expected on shutdown
+  }
+}
+
+void DataReader::Body::read_one(db::Cursor* cursor, QueuePair* qp) {
+  Datum* datum = qp->free_.pop();
+  // TODO deserialize in-place instead of copy?
+  datum->ParseFromString(cursor->value());
+  qp->full_.push(datum);
+
+  // go to the next iter
+  cursor->Next();
+  if (!cursor->valid()) {
+    DLOG(INFO) << "Restarting data prefetching from start.";
+    cursor->SeekToFirst();
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 9288d91339a..20f76f62994 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -108,7 +108,7 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
   DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
     // Reshape to loaded labels.
-    top[1]->ReshapeLike(prefetch_label_);
+    top[1]->ReshapeLike(batch->label_);
     // Copy the labels.
     caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
         top[1]->mutable_cpu_data());
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 22d9f436185..0932d9feff3 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -11,11 +11,15 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/util/rng.hpp"
 
 namespace caffe {
 
+template <typename Dtype>
+DataLayer<Dtype>::DataLayer(const LayerParameter& param)
+  : BasePrefetchingDataLayer<Dtype>(param),
+    reader_(param) {
+}
+
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer() {
   this->StopInternalThread();
@@ -24,31 +28,19 @@ DataLayer<Dtype>::~DataLayer() {
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  // Initialize DB
-  db_.reset(db::GetDB(this->layer_param_.data_param().backend()));
-  db_->Open(this->layer_param_.data_param().source(), db::READ);
-  cursor_.reset(db_->NewCursor());
+  const int batch_size = this->layer_param_.data_param().batch_size();
+  // Read a data point, and use it to initialize the top blob.
+  Datum& datum = *(reader_.full().peek());
 
-  // Check if we should randomly skip a few data points
-  if (this->layer_param_.data_param().rand_skip()) {
-    unsigned int skip = caffe_rng_rand() %
-                        this->layer_param_.data_param().rand_skip();
-    LOG(INFO) << "Skipping first " << skip << " data points.";
-    while (skip-- > 0) {
-      cursor_->Next();
-    }
-  }
-  // Read a data point, to initialize the prefetch and top blobs.
-  Datum datum;
-  datum.ParseFromString(cursor_->value());
   // Use data_transformer to infer the expected blob shape from datum.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
   this->transformed_data_.Reshape(top_shape);
   // Reshape top[0] and prefetch_data according to the batch_size.
-  top_shape[0] = this->layer_param_.data_param().batch_size();
-  this->prefetch_data_.Reshape(top_shape);
-  top[0]->ReshapeLike(this->prefetch_data_);
-
+  top_shape[0] = batch_size;
+  top[0]->Reshape(top_shape);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].data_.Reshape(top_shape);
+  }
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
@@ -76,12 +68,11 @@ void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   // Reshape according to the first datum of each batch
   // on single input batches allows for inputs of varying dimension.
   const int batch_size = this->layer_param_.data_param().batch_size();
-  Datum datum;
-  datum.ParseFromString(cursor_->value());
+  Datum& datum = *(reader_.full().peek());
   // Use data_transformer to infer the expected blob shape from datum.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
   this->transformed_data_.Reshape(top_shape);
-  // Reshape prefetch_data according to the batch_size.
+  // Reshape batch according to the batch_size.
   top_shape[0] = batch_size;
   batch->data_.Reshape(top_shape);
 
@@ -91,11 +82,10 @@ void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   if (this->output_labels_) {
     top_label = batch->label_.mutable_cpu_data();
   }
-  timer.Start();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
+    timer.Start();
     // get a datum
-    Datum datum;
-    datum.ParseFromString(cursor_->value());
+    Datum& datum = *(reader_.full().pop("Waiting for data"));
     read_time += timer.MicroSeconds();
     timer.Start();
     // Apply data transformations (mirror, scale, crop...)
@@ -107,13 +97,8 @@ void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
       top_label[item_id] = datum.label();
     }
     trans_time += timer.MicroSeconds();
-    timer.Start();
-    // go to the next item.
-    cursor_->Next();
-    if (!cursor_->valid()) {
-      DLOG(INFO) << "Restarting data prefetching from start.";
-      cursor_->SeekToFirst();
-    }
+
+    reader_.free().push(const_cast<Datum*>(&datum));
   }
   timer.Stop();
   batch_timer.Stop();
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 89f14595ba6..41165410f33 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -500,6 +500,7 @@ message DataParameter {
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the database.
+  // DEPRECATED. Each solver accesses a different subset of the database.
   optional uint32 rand_skip = 7 [default = 0];
   optional DB backend = 8 [default = LEVELDB];
   // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
@@ -515,6 +516,9 @@ message DataParameter {
   optional bool mirror = 6 [default = false];
   // Force the encoded image to have 3 color channels
   optional bool force_encoded_color = 9 [default = false];
+  // Prefetch queue (Number of batches to prefetch to host memory, increase if
+  // data access bandwidth varies).
+  optional uint32 prefetch = 10 [default = 4];
 }
 
 message DropoutParameter {
diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
index efb1b37ac42..c86fafd000c 100644
--- a/src/caffe/test/test_layer_factory.cpp
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -1,11 +1,14 @@
 #include <map>
 #include <string>
 
+#include "boost/scoped_ptr.hpp"
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -21,11 +24,20 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
   typename LayerRegistry<Dtype>::CreatorRegistry& registry =
       LayerRegistry<Dtype>::Registry();
   shared_ptr<Layer<Dtype> > layer;
-  LayerParameter layer_param;
   for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
        registry.begin(); iter != registry.end(); ++iter) {
     // Special case: PythonLayer is checked by pytest
     if (iter->first == "Python") { continue; }
+    LayerParameter layer_param;
+    // Data layers expect a DB
+    if (iter->first == "Data") {
+      string tmp;
+      MakeTempDir(&tmp);
+      boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
+      db->Open(tmp, db::NEW);
+      db->Close();
+      layer_param.mutable_data_param()->set_source(tmp);
+    }
     layer_param.set_type(iter->first);
     layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
     EXPECT_EQ(iter->first, layer->type());
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index eec627656ef..006720231a5 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2,12 +2,15 @@
 #include <string>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -2901,6 +2904,15 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
       continue;  // Empty string isn't actually a valid layer type.
     }
     layer_param.set_type(v2_layer_type);
+    // Data layers expect a DB
+    if (v2_layer_type == "Data") {
+      string tmp;
+      MakeTempDir(&tmp);
+      boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
+      db->Open(tmp, db::NEW);
+      db->Close();
+      layer_param.mutable_data_param()->set_source(tmp);
+    }
     layer = LayerRegistry<float>::CreateLayer(layer_param);
     EXPECT_EQ(v2_layer_type, layer->type());
   }
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
index 6ab6ba06780..f7c53f22225 100644
--- a/src/caffe/util/blocking_queue.cpp
+++ b/src/caffe/util/blocking_queue.cpp
@@ -2,6 +2,7 @@
 #include <string>
 
 #include "caffe/data_layers.hpp"
+#include "caffe/data_reader.hpp"
 #include "caffe/util/blocking_queue.hpp"
 
 namespace caffe {
@@ -86,5 +87,7 @@ size_t BlockingQueue<T>::size() const {
 
 template class BlockingQueue<Batch<float>*>;
 template class BlockingQueue<Batch<double>*>;
+template class BlockingQueue<Datum*>;
+template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;
 
 }  // namespace caffe

From d2f045768cba7d494abb4d168fc366d6fce80b85 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 18 May 2015 20:07:36 -0700
Subject: [PATCH 1476/2053] Allocate host memory through cudaMallocHost

thanks to discussion by @thatguymike and @flx42
---
 include/caffe/syncedmem.hpp | 31 +++++++++++++++++--------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 4d339bf4e57..4a1a2f3fcc0 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -8,26 +8,29 @@
 
 namespace caffe {
 
-// Theoretically, CaffeMallocHost and CaffeFreeHost should simply call the
-// cudaMallocHost and cudaFree functions in order to create pinned memory.
-// However, those codes rely on the existence of a cuda GPU (I don't know
-// why that is a must since allocating memory should not be accessing the
-// GPU resource, but it just creates an error as of Cuda 5.0) and will cause
-// problem when running on a machine without GPU. Thus, we simply define
-// these two functions for safety and possible future change if the problem
-// of calling cuda functions disappears in a future version.
-//
-// In practice, although we are creating unpinned memory here, as long as we
-// are constantly accessing them the memory pages almost always stays in
-// the physical memory (assuming we have large enough memory installed), and
-// does not seem to create a memory bottleneck here.
-
+// If CUDA is available and in GPU mode, host memory will be allocated pinned,
+// using cudaMallocHost. It avoids dynamic pinning for transfers (DMA).
+// The improvement in performance seems negligible in the single GPU case,
+// but might be more significant for parallel training. Most importantly,
+// it improved stability for large models on many GPUs.
 inline void CaffeMallocHost(void** ptr, size_t size) {
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    CUDA_CHECK(cudaMallocHost(ptr, size));
+    return;
+  }
+#endif
   *ptr = malloc(size);
   CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
 
 inline void CaffeFreeHost(void* ptr) {
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    CUDA_CHECK(cudaFreeHost(ptr));
+    return;
+  }
+#endif
   free(ptr);
 }
 

From e5575cf17a43a56e4ba9bc5465548ac0512197d8 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 19 May 2015 11:11:05 -0700
Subject: [PATCH 1477/2053] Multi-GPU

- Parallelize batches among GPUs and tree-reduce the gradients
- The effective batch size scales with the number of devices
- Batch size is multiplied by the number of devices
- Split batches between GPUs, and tree-reduce the gradients
- Detect machine topology (twin-GPU boards, P2P connectivity)
- Track device in syncedmem (thanks @thatguymike)
- Insert a callback in the solver for minimal code change
- Accept list for gpu flag of caffe tool, e.g. '-gpu 0,1' or '-gpu all'.
  Run on default GPU if no ID given.
- Add multi-GPU solver test
- Deterministic architecture for reproducible runs
---
 include/caffe/caffe.hpp                       |   1 +
 include/caffe/common.hpp                      |   7 +
 include/caffe/internal_thread.hpp             |   3 +-
 include/caffe/layer_factory.hpp               |   4 +-
 include/caffe/parallel.hpp                    | 118 +++++
 include/caffe/solver.hpp                      |  38 ++
 include/caffe/syncedmem.hpp                   |   7 +-
 src/caffe/common.cpp                          |   5 +-
 src/caffe/data_reader.cpp                     |   4 +-
 src/caffe/data_transformer.cpp                |   4 +-
 src/caffe/internal_thread.cpp                 |   9 +-
 src/caffe/net.cpp                             | 180 +++++---
 src/caffe/parallel.cpp                        | 430 ++++++++++++++++++
 src/caffe/solver.cpp                          |  57 ++-
 src/caffe/syncedmem.cpp                       |  34 +-
 src/caffe/test/test_gradient_based_solver.cpp |  75 ++-
 src/caffe/util/blocking_queue.cpp             |   3 +
 tools/caffe.cpp                               | 111 +++--
 18 files changed, 949 insertions(+), 141 deletions(-)
 create mode 100644 include/caffe/parallel.hpp
 create mode 100644 src/caffe/parallel.cpp

diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index 3c829f2f9b0..68a5e1d1d1a 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -10,6 +10,7 @@
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
 #include "caffe/net.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
 #include "caffe/util/benchmark.hpp"
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 3fa81431314..1df6b9a14fb 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -149,6 +149,11 @@ class Caffe {
   static void SetDevice(const int device_id);
   // Prints the current GPU status.
   static void DeviceQuery();
+  // Parallel training info
+  inline static int solver_count() { return Get().solver_count_; }
+  inline static void set_solver_count(int val) { Get().solver_count_ = val; }
+  inline static bool root_solver() { return Get().root_solver_; }
+  inline static void set_root_solver(bool val) { Get().root_solver_ = val; }
 
  protected:
 #ifndef CPU_ONLY
@@ -158,6 +163,8 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
+  int solver_count_;
+  bool root_solver_;
 
  private:
   // The private constructor to avoid duplicate instantiation.
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index be6ff7fbac7..6a8c5a02892 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -42,7 +42,8 @@ class InternalThread {
   bool must_stop();
 
  private:
-  void entry(int device, Caffe::Brew mode, int rand_seed);
+  void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count,
+      bool root_solver);
 
   shared_ptr<boost::thread> thread_;
 };
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 2fcd93869a0..32e849de0d2 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -71,7 +71,9 @@ class LayerRegistry {
 
   // Get a layer using a LayerParameter.
   static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
-    LOG(INFO) << "Creating layer " << param.name();
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "Creating layer " << param.name();
+    }
     const string& type = param.type();
     CreatorRegistry& registry = Registry();
     CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp
new file mode 100644
index 00000000000..85fc2b55984
--- /dev/null
+++ b/include/caffe/parallel.hpp
@@ -0,0 +1,118 @@
+#ifndef CAFFE_PARALLEL_HPP_
+#define CAFFE_PARALLEL_HPP_
+
+#include <boost/date_time/posix_time/posix_time.hpp>
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+// Represents a net parameters. Once a net is created, its parameter buffers can
+// be replaced by ones from Params, to allow parallelization. Params ensures
+// parameters are allocated in one consecutive array.
+template<typename Dtype>
+class Params {
+ public:
+  explicit Params(shared_ptr<Solver<Dtype> > root_solver);
+  virtual ~Params() {
+  }
+
+  inline size_t size() const {
+    return size_;
+  }
+  inline Dtype* data() const {
+    return data_;
+  }
+  inline Dtype* diff() const {
+    return diff_;
+  }
+
+ protected:
+  const size_t size_;           // Size of buffers
+  Dtype* data_;                 // Network parameters
+  Dtype* diff_;                 // Gradient
+
+DISABLE_COPY_AND_ASSIGN(Params);
+};
+
+// Params stored in GPU memory.
+template<typename Dtype>
+class GPUParams : public Params<Dtype> {
+ public:
+  GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device);
+  virtual ~GPUParams();
+
+  void configure(Solver<Dtype>* solver) const;
+
+ protected:
+  using Params<Dtype>::size_;
+  using Params<Dtype>::data_;
+  using Params<Dtype>::diff_;
+};
+
+class DevicePair {
+ public:
+  DevicePair(int parent, int device)
+      : parent_(parent),
+        device_(device) {
+  }
+  inline int parent() {
+    return parent_;
+  }
+  inline int device() {
+    return device_;
+  }
+
+  // Group GPUs in pairs, by proximity depending on machine's topology
+  static void compute(const vector<int> devices, vector<DevicePair>* pairs);
+
+ protected:
+  int parent_;
+  int device_;
+};
+
+// Synchronous data parallelism using map-reduce between local GPUs.
+template<typename Dtype>
+class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
+    public InternalThread {
+ public:
+  explicit P2PSync(shared_ptr<Solver<Dtype> > root_solver,
+                   P2PSync<Dtype>* parent, const SolverParameter& param);
+  virtual ~P2PSync();
+
+  inline const shared_ptr<Solver<Dtype> >& solver() const {
+    return solver_;
+  }
+
+  void run(const vector<int>& gpus);
+
+ protected:
+  void on_start();
+  void on_gradients_ready();
+
+  void InternalThreadEntry();
+
+  P2PSync<Dtype>* parent_;
+  vector<P2PSync<Dtype>*> children_;
+  BlockingQueue<P2PSync<Dtype>*> queue_;
+  const int initial_iter_;
+  Dtype* parent_grads_;
+  shared_ptr<Solver<Dtype> > solver_;
+
+  using Params<Dtype>::size_;
+  using Params<Dtype>::data_;
+  using Params<Dtype>::diff_;
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index fbade9389ff..89a6c76d5f7 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -32,12 +32,27 @@ class Solver {
   // methods to restore the state from the appropriate snapshot type.
   void Restore(const char* resume_file);
   virtual ~Solver() {}
+  inline const SolverParameter& param() const { return param_; }
   inline shared_ptr<Net<Dtype> > net() { return net_; }
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
     return test_nets_;
   }
   int iter() { return iter_; }
 
+  // Invoked at specific points during an iteration
+  class Callback {
+   protected:
+    virtual void on_start() = 0;
+    virtual void on_gradients_ready() = 0;
+
+    template <typename T>
+    friend class Solver;
+  };
+  const vector<Callback*>& callbacks() const { return callbacks_; }
+  void add_callback(Callback* value) {
+    callbacks_.push_back(value);
+  }
+
  protected:
   // Make and apply the update value for the current iteration.
   virtual void ApplyUpdate() = 0;
@@ -62,10 +77,33 @@ class Solver {
   int current_step_;
   shared_ptr<Net<Dtype> > net_;
   vector<shared_ptr<Net<Dtype> > > test_nets_;
+  vector<Callback*> callbacks_;
 
   DISABLE_COPY_AND_ASSIGN(Solver);
 };
 
+/**
+ * @brief Solver that only computes gradients, used as worker
+ *        for multi-GPU training.
+ */
+template <typename Dtype>
+class WorkerSolver : public Solver<Dtype> {
+ public:
+  explicit WorkerSolver(const SolverParameter& param)
+      : Solver<Dtype>(param) {}
+
+ protected:
+  void ApplyUpdate() {}
+  void SnapshotSolverState(const string& model_filename) {
+    LOG(FATAL) << "Should not be called on worker solver.";
+  }
+  void RestoreSolverStateFromBinaryProto(const string& state_file) {
+    LOG(FATAL) << "Should not be called on worker solver.";
+  }
+  void RestoreSolverStateFromHDF5(const string& state_file) {
+    LOG(FATAL) << "Should not be called on worker solver.";
+  }
+};
 
 /**
  * @brief Optimizes the parameters of a Net using
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 4a1a2f3fcc0..62aadef498d 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -45,14 +45,15 @@ class SyncedMemory {
  public:
   SyncedMemory()
       : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
-        own_cpu_data_(false) {}
+        own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {}
   explicit SyncedMemory(size_t size)
       : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
-        own_cpu_data_(false) {}
+        own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {}
   ~SyncedMemory();
   const void* cpu_data();
   void set_cpu_data(void* data);
   const void* gpu_data();
+  void set_gpu_data(void* data);
   void* mutable_cpu_data();
   void* mutable_gpu_data();
   enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };
@@ -71,6 +72,8 @@ class SyncedMemory {
   size_t size_;
   SyncedHead head_;
   bool own_cpu_data_;
+  bool own_gpu_data_;
+  int gpu_device_;
 
   DISABLE_COPY_AND_ASSIGN(SyncedMemory);
 };  // class SyncedMemory
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 0215c76ef76..7077f3789d7 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -51,7 +51,8 @@ void GlobalInit(int* pargc, char*** pargv) {
 #ifdef CPU_ONLY  // CPU-only Caffe.
 
 Caffe::Caffe()
-    : random_generator_(), mode_(Caffe::CPU) { }
+    : random_generator_(), mode_(Caffe::CPU),
+      solver_count_(1), root_solver_(true) { }
 
 Caffe::~Caffe() { }
 
@@ -95,7 +96,7 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU) {
+    mode_(Caffe::CPU), solver_count_(1), root_solver_(true) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
index 60606f0d6c8..16378203a88 100644
--- a/src/caffe/data_reader.cpp
+++ b/src/caffe/data_reader.cpp
@@ -76,9 +76,7 @@ void DataReader::Body::InternalThreadEntry() {
   shared_ptr<db::Cursor> cursor(db->NewCursor());
   vector<shared_ptr<QueuePair> > qps;
   try {
-    // int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
-    // TODO single solver until multi-gpu merge
-    int solver_count = 1;
+    int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
 
     // To ensure deterministic runs, only start running once all solvers
     // are ready. But solvers need to peek on one item during initialization,
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 22633922a01..4666d9bd881 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -19,7 +19,9 @@ DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
     CHECK_EQ(param_.mean_value_size(), 0) <<
       "Cannot specify mean_file and mean_value at the same time";
     const string& mean_file = param.mean_file();
-    LOG(INFO) << "Loading mean file from: " << mean_file;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "Loading mean file from: " << mean_file;
+    }
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index b193826c1a6..104884e0295 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -27,21 +27,26 @@ void InternalThread::StartInternalThread() {
 #endif
   Caffe::Brew mode = Caffe::mode();
   int rand_seed = caffe_rng_rand();
+  int solver_count = Caffe::solver_count();
+  bool root_solver = Caffe::root_solver();
 
   try {
     thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
-          rand_seed));
+          rand_seed, solver_count, root_solver));
   } catch (std::exception& e) {
     LOG(FATAL) << "Thread exception: " << e.what();
   }
 }
 
-void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) {
+void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed,
+    int solver_count, bool root_solver) {
 #ifndef CPU_ONLY
   CUDA_CHECK(cudaSetDevice(device));
 #endif
   Caffe::set_mode(mode);
   Caffe::set_random_seed(rand_seed);
+  Caffe::set_solver_count(solver_count);
+  Caffe::set_root_solver(root_solver);
 
   InternalThreadEntry();
 }
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 0e5ed804b73..5d0f4322d19 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -10,6 +10,7 @@
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/hdf5.hpp"
 #include "caffe/util/insert_splits.hpp"
@@ -41,8 +42,10 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // the current NetState.
   NetParameter filtered_param;
   FilterNet(in_param, &filtered_param);
-  LOG(INFO) << "Initializing net from parameters: " << std::endl
-            << filtered_param.DebugString();
+  if (Caffe::root_solver()) {
+    LOG(INFO) << "Initializing net from parameters: " << std::endl
+              << filtered_param.DebugString();
+  }
   // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
   InsertSplits(filtered_param, &param);
@@ -66,7 +69,8 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     const int layer_id = -1;  // inputs have fake layer ID -1
     AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
   }
-  DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+  DLOG_IF(INFO, Caffe::root_solver())
+      << "Memory required for data: " << memory_used_ * sizeof(Dtype);
   // For each layer, set up its input and output
   bottom_vecs_.resize(param.layer_size());
   top_vecs_.resize(param.layer_size());
@@ -89,7 +93,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
     layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
-    LOG(INFO) << "Creating Layer " << layer_param.name();
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "Creating Layer " << layer_param.name();
+    }
     bool need_backward = false;
 
     // Figure out this layer's input and output
@@ -119,20 +125,30 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     // After this layer is connected, set it up.
-    LOG(INFO) << "Setting up " << layer_names_[layer_id];
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "Setting up " << layer_names_[layer_id];
+    }
     layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
       }
       blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
-      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
+      if (Caffe::root_solver()) {
+        LOG(INFO) << "Top shape: "
+                  << top_vecs_[layer_id][top_id]->shape_string();
+      }
       if (layer->loss(top_id)) {
-        LOG(INFO) << "    with loss weight " << layer->loss(top_id);
+        if (Caffe::root_solver()) {
+          LOG(INFO) << "    with loss weight " << layer->loss(top_id);
+        }
       }
       memory_used_ += top_vecs_[layer_id][top_id]->count();
     }
-    DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+    if (Caffe::root_solver()) {
+      DLOG(INFO) << "Memory required for data: "
+                 << memory_used_ * sizeof(Dtype);
+    }
     const int param_size = layer_param.param_size();
     const int num_param_blobs = layers_[layer_id]->blobs().size();
     CHECK_LE(param_size, num_param_blobs)
@@ -191,10 +207,14 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     }
     if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
     if (layer_need_backward_[layer_id]) {
-      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
+      if (Caffe::root_solver()) {
+        LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
+      }
     } else {
-      LOG(INFO) << layer_names_[layer_id]
-                << " does not need backward computation.";
+      if (Caffe::root_solver()) {
+        LOG(INFO) << layer_names_[layer_id]
+                  << " does not need backward computation.";
+      }
     }
     for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
          ++bottom_id) {
@@ -234,7 +254,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // In the end, all remaining blobs are considered output blobs.
   for (set<string>::iterator it = available_blobs.begin();
       it != available_blobs.end(); ++it) {
-    LOG(INFO) << "This network produces output " << *it;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "This network produces output " << *it;
+    }
     net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
     net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
   }
@@ -246,8 +268,10 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   }
   ShareWeights();
   debug_info_ = param.debug_info();
-  LOG(INFO) << "Network initialization done.";
-  LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+  if (Caffe::root_solver()) {
+    LOG(INFO) << "Network initialization done.";
+    LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+  }
 }
 
 template <typename Dtype>
@@ -286,27 +310,33 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
   // Check whether the rule is broken due to phase.
   if (rule.has_phase()) {
       if (rule.phase() != state.phase()) {
-        LOG(INFO) << "The NetState phase (" << state.phase()
-          << ") differed from the phase (" << rule.phase()
-          << ") specified by a rule in layer " << layer_name;
+        if (Caffe::root_solver()) {
+          LOG(INFO) << "The NetState phase (" << state.phase()
+                    << ") differed from the phase (" << rule.phase()
+                    << ") specified by a rule in layer " << layer_name;
+        }
         return false;
       }
   }
   // Check whether the rule is broken due to min level.
   if (rule.has_min_level()) {
     if (state.level() < rule.min_level()) {
-      LOG(INFO) << "The NetState level (" << state.level()
-          << ") is above the min_level (" << rule.min_level()
-          << ") specified by a rule in layer " << layer_name;
+      if (Caffe::root_solver()) {
+        LOG(INFO) << "The NetState level (" << state.level()
+                  << ") is above the min_level (" << rule.min_level()
+                  << ") specified by a rule in layer " << layer_name;
+      }
       return false;
     }
   }
   // Check whether the rule is broken due to max level.
   if (rule.has_max_level()) {
     if (state.level() > rule.max_level()) {
-      LOG(INFO) << "The NetState level (" << state.level()
-          << ") is above the max_level (" << rule.max_level()
-          << ") specified by a rule in layer " << layer_name;
+      if (Caffe::root_solver()) {
+        LOG(INFO) << "The NetState level (" << state.level()
+                  << ") is above the max_level (" << rule.max_level()
+                  << ") specified by a rule in layer " << layer_name;
+      }
       return false;
     }
   }
@@ -319,8 +349,10 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
       if (rule.stage(i) == state.stage(j)) { has_stage = true; }
     }
     if (!has_stage) {
-      LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
-                << "' specified by a rule in layer " << layer_name;
+      if (Caffe::root_solver()) {
+        LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
+                  << "' specified by a rule in layer " << layer_name;
+      }
       return false;
     }
   }
@@ -333,8 +365,10 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
       if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }
     }
     if (has_stage) {
-      LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
-                << "' specified by a rule in layer " << layer_name;
+      if (Caffe::root_solver()) {
+        LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
+                  << "' specified by a rule in layer " << layer_name;
+      }
       return false;
     }
   }
@@ -356,7 +390,9 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
   if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
       blob_name == layer_param->bottom(top_id)) {
     // In-place computation
-    LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
+    if (Caffe::root_solver()) {
+      LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
+    }
     top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
     top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
   } else if (blob_name_to_idx &&
@@ -366,10 +402,12 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
   } else {
     // Normal output.
-    if (layer_param) {
-      LOG(INFO) << layer_param->name() << " -> " << blob_name;
-    } else {
-      LOG(INFO) << "Input " << top_id << " -> " << blob_name;
+    if (Caffe::root_solver()) {
+      if (layer_param) {
+        LOG(INFO) << layer_param->name() << " -> " << blob_name;
+      } else {
+        LOG(INFO) << "Input " << top_id << " -> " << blob_name;
+      }
     }
     shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
     const int blob_id = blobs_.size();
@@ -409,7 +447,9 @@ int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
                << " (at index " << bottom_id << ") to layer " << layer_id;
   }
   const int blob_id = (*blob_name_to_idx)[blob_name];
-  LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
+  if (Caffe::root_solver()) {
+    LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
+  }
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
@@ -468,9 +508,10 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
         param_layer_indices_[owner_net_param_id];
     const int owner_layer_id = owner_index.first;
     const int owner_param_id = owner_index.second;
-    LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
-              << "layer '" << layer_names_[owner_layer_id] << "', param "
-              << "index " << owner_param_id;
+    LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name
+        << "' owned by "
+        << "layer '" << layer_names_[owner_layer_id] << "', param "
+        << "index " << owner_param_id;
     Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
     Blob<Dtype>* owner_blob =
         layers_[owner_layer_id]->blobs()[owner_param_id].get();
@@ -595,8 +636,10 @@ void Net<Dtype>::InputDebugInfo(const int input_id) {
   const Blob<Dtype>& blob = *net_input_blobs_[input_id];
   const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
   const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-  LOG(INFO) << "    [Forward] "
-     << "Input " << blob_name << " data: " << data_abs_val_mean;
+  if (Caffe::root_solver()) {
+    LOG(INFO) << "    [Forward] "
+              << "Input " << blob_name << " data: " << data_abs_val_mean;
+  }
 }
 
 template <typename Dtype>
@@ -605,9 +648,12 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
     const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
     const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    LOG(INFO) << "    [Forward] "
-       << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
-       << " data: " << data_abs_val_mean;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "    [Forward] "
+                << "Layer " << layer_names_[layer_id]
+                << ", top blob " << blob_name
+                << " data: " << data_abs_val_mean;
+    }
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
@@ -615,9 +661,12 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
     const int net_param_id = param_id_vecs_[layer_id][param_id];
     const string& blob_name = param_display_names_[net_param_id];
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    LOG(INFO) << "    [Forward] "
-       << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name
-       << " data: " << data_abs_val_mean;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "    [Forward] "
+                << "Layer " << layer_names_[layer_id]
+                << ", param blob " << blob_name
+                << " data: " << data_abs_val_mean;
+    }
   }
 }
 
@@ -629,18 +678,24 @@ void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
     const Blob<Dtype>& blob = *bottom_vec[bottom_id];
     const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
     const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
-    LOG(INFO) << "    [Backward] "
-        << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
-        << " diff: " << diff_abs_val_mean;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "    [Backward] "
+                << "Layer " << layer_names_[layer_id]
+                << ", bottom blob " << blob_name
+                << " diff: " << diff_abs_val_mean;
+    }
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
     if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
     const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
     const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
-    LOG(INFO) << "    [Backward] "
-        << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
-        << " diff: " << diff_abs_val_mean;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "    [Backward] "
+                << "Layer " << layer_names_[layer_id]
+                << ", param blob " << param_id
+                << " diff: " << diff_abs_val_mean;
+    }
   }
 }
 
@@ -653,17 +708,22 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
   const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
   if (param_owner < 0) {
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    LOG(INFO) << "    [Update] Layer " << layer_name
-        << ", param " << param_display_name
-        << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "    [Update] Layer " << layer_name
+                << ", param " << param_display_name
+                << " data: " << data_abs_val_mean
+                << "; diff: " << diff_abs_val_mean;
+    }
   } else {
     const string& owner_layer_name =
         layer_names_[param_layer_indices_[param_owner].first];
-    LOG(INFO) << "    [Update] Layer " << layer_name
-        << ", param blob " << param_display_name
-        << " (owned by layer " << owner_layer_name << ", "
-        << "param " << param_display_names_[param_owners_[param_id]] << ")"
-        << " diff: " << diff_abs_val_mean;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "    [Update] Layer " << layer_name
+                << ", param blob " << param_display_name
+                << " (owned by layer " << owner_layer_name << ", " << "param "
+                << param_display_names_[param_owners_[param_id]] << ")"
+                << " diff: " << diff_abs_val_mean;
+    }
   }
 }
 
@@ -720,8 +780,8 @@ void Net<Dtype>::Backward() {
     const Dtype l2norm_data = std::sqrt(sumsq_data);
     const Dtype l2norm_diff = std::sqrt(sumsq_diff);
     LOG(ERROR) << "    [Backward] All net params (data, diff): "
-        << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
-        << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
+               << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
+               << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
   }
 }
 
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
new file mode 100644
index 00000000000..3fef8cfdb33
--- /dev/null
+++ b/src/caffe/parallel.cpp
@@ -0,0 +1,430 @@
+#ifndef CPU_ONLY
+#include <cuda_runtime.h>
+#endif
+#include <glog/logging.h>
+#include <stdio.h>
+#include <sys/ioctl.h>
+#include <sys/mman.h>
+#include <sys/stat.h>
+
+#include <cstdlib>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "boost/thread.hpp"
+#include "caffe/caffe.hpp"
+#include "caffe/parallel.hpp"
+
+namespace caffe {
+
+enum Op {
+  copy,
+  replace_cpu,
+  replace_gpu,
+  replace_cpu_diff,
+  replace_gpu_diff
+};
+
+template<typename Dtype>
+static void apply_buffers(const vector<Blob<Dtype>*>& blobs,
+                          Dtype* buffer, size_t total_size, Op op) {
+  Dtype* ptr = buffer;
+  for (int i = 0; i < blobs.size(); ++i) {
+    int size = blobs[i]->count();
+    switch (op) {
+      case copy: {
+        // Init buffer to current values of blobs
+        caffe_copy(size,
+                   reinterpret_cast<const Dtype*>(blobs[i]->data()->cpu_data()),
+                   ptr);
+        break;
+      }
+      case replace_cpu:
+        blobs[i]->data()->set_cpu_data(ptr);
+        break;
+      case replace_gpu:
+        blobs[i]->data()->set_gpu_data(ptr);
+        break;
+      case replace_cpu_diff:
+        blobs[i]->diff()->set_cpu_data(ptr);
+        break;
+      case replace_gpu_diff:
+        blobs[i]->diff()->set_gpu_data(ptr);
+        break;
+    }
+    ptr += size;
+  }
+  CHECK_EQ(total_size, ptr - buffer);
+}
+
+// Buffer size necessary to store given blobs
+template<typename Dtype>
+static size_t total_size(const vector<Blob<Dtype>*>& params) {
+  size_t size = 0;
+  for (int i = 0; i < params.size(); ++i)
+    size += params[i]->count();
+  return size;
+}
+
+template<typename Dtype>
+Params<Dtype>::Params(shared_ptr<Solver<Dtype> > root_solver)
+    : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
+      data_(),
+      diff_() {
+}
+
+template<typename Dtype>
+GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
+    : Params<Dtype>(root_solver) {
+#ifndef CPU_ONLY
+  int initial_device;
+  CUDA_CHECK(cudaGetDevice(&initial_device));
+
+  // Allocate device buffers
+  CUDA_CHECK(cudaSetDevice(device));
+  CUDA_CHECK(cudaMalloc(&data_, size_ * sizeof(Dtype)));
+
+  // Copy blob values
+  const vector<Blob<Dtype>*>& net =
+      root_solver->net()->learnable_params();
+  apply_buffers(net, data_, size_, copy);
+
+  CUDA_CHECK(cudaMalloc(&diff_, size_ * sizeof(Dtype)));
+  caffe_gpu_set(size_, Dtype(0), diff_);
+
+  CUDA_CHECK(cudaSetDevice(initial_device));
+#else
+  NO_GPU;
+#endif
+}
+
+template<typename Dtype>
+GPUParams<Dtype>::~GPUParams() {
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaFree(data_));
+  CUDA_CHECK(cudaFree(diff_));
+#endif
+}
+
+template<typename Dtype>
+void GPUParams<Dtype>::configure(Solver<Dtype>* solver) const {
+  const vector<Blob<Dtype>*>& net =
+      solver->net()->learnable_params();
+  apply_buffers(net, data_, size_, replace_gpu);
+  apply_buffers(net, diff_, size_, replace_gpu_diff);
+}
+
+void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
+#ifndef CPU_ONLY
+  vector<int> remaining(devices);
+
+  // Group GPUs by board
+  for (int i = 0; i < remaining.size(); ++i) {
+    for (int j = i + 1; j < remaining.size(); ++j) {
+      cudaDeviceProp a, b;
+      CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
+      CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
+      if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
+        if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
+          pairs->push_back(DevicePair(remaining[i], remaining[j]));
+          DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
+          remaining.erase(remaining.begin() + j);
+          break;
+        }
+      }
+    }
+  }
+  ostringstream s;
+  for (int i = 0; i < remaining.size(); ++i) {
+    s << (i ? ", " : "") << remaining[i];
+  }
+  DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str();
+
+  // Group by P2P accessibility
+  for (int i = 0; i < remaining.size(); ++i) {
+    for (int j = i + 1; j < remaining.size(); ++j) {
+      int access;
+      CUDA_CHECK(cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
+      if (access) {
+        pairs->push_back(DevicePair(remaining[i], remaining[j]));
+        DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
+        remaining.erase(remaining.begin() + j);
+        break;
+      }
+    }
+  }
+  s.str("");
+  for (int i = 0; i < remaining.size(); ++i) {
+    s << (i ? ", " : "") << remaining[i];
+  }
+  DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str();
+
+  // Group remaining
+  for (int i = 0; i < remaining.size(); ++i) {
+    for (int j = i + 1; j < remaining.size(); ++j) {
+      pairs->push_back(DevicePair(remaining[i], remaining[j]));
+      DLOG(INFO) << "Remaining pair: " << remaining[i] << ":" << remaining[j];
+      remaining.erase(remaining.begin() + j);
+      break;
+    }
+  }
+  CHECK_EQ(remaining.size(), 1);
+  pairs->insert(pairs->begin(), DevicePair(-1, remaining[0]));
+
+  CHECK(pairs->size() == devices.size());
+  for (int i = 0; i < pairs->size(); ++i) {
+    CHECK((*pairs)[i].parent() != (*pairs)[i].device());
+    for (int j = i + 1; j < pairs->size(); ++j) {
+      CHECK((*pairs)[i].device() != (*pairs)[j].device());
+    }
+  }
+#else
+  NO_GPU;
+#endif
+}
+
+//
+
+template<typename Dtype>
+P2PSync<Dtype>::P2PSync(shared_ptr<Solver<Dtype> > root_solver,
+                        P2PSync<Dtype>* parent, const SolverParameter& param)
+    : GPUParams<Dtype>(root_solver, param.device_id()),
+      parent_(parent),
+      children_(),
+      queue_(),
+      initial_iter_(root_solver->iter()),
+      solver_() {
+#ifndef CPU_ONLY
+  int initial_device;
+  CUDA_CHECK(cudaGetDevice(&initial_device));
+  const int self = param.device_id();
+  CUDA_CHECK(cudaSetDevice(self));
+
+  if (parent == NULL) {
+    solver_ = root_solver;
+  } else {
+    Caffe::set_root_solver(false);
+    solver_.reset(new WorkerSolver<Dtype>(param));
+    Caffe::set_root_solver(true);
+  }
+  this->configure(solver_.get());
+  solver_->add_callback(this);
+
+  if (parent) {
+    // Enable p2p access between devices
+    const int peer = parent->solver_->param().device_id();
+    int access;
+    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
+    if (access) {
+      CUDA_CHECK(cudaDeviceEnablePeerAccess(peer, 0));
+    } else {
+      LOG(INFO)<< "GPU " << self << " does not have p2p access to GPU " << peer;
+    }
+    // Allocate receiving buffer on parent
+    CUDA_CHECK(cudaSetDevice(peer));
+    CUDA_CHECK(cudaMalloc(&parent_grads_, size_ * sizeof(Dtype)));
+    CUDA_CHECK(cudaSetDevice(self));
+  }
+
+  CUDA_CHECK(cudaSetDevice(initial_device));
+#else
+  NO_GPU;
+#endif
+}
+
+template<typename Dtype>
+P2PSync<Dtype>::~P2PSync() {
+#ifndef CPU_ONLY
+  int initial_device;
+  CUDA_CHECK(cudaGetDevice(&initial_device));
+  const int self = solver_->param().device_id();
+  CUDA_CHECK(cudaSetDevice(self));
+
+  if (parent_) {
+    CUDA_CHECK(cudaFree(parent_grads_));
+    const int peer = parent_->solver_->param().device_id();
+    int access;
+    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
+    if (access) {
+      CUDA_CHECK(cudaDeviceDisablePeerAccess(peer));
+    }
+  }
+
+  CUDA_CHECK(cudaSetDevice(initial_device));
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::InternalThreadEntry() {
+  Caffe::SetDevice(solver_->param().device_id());
+  CHECK(Caffe::root_solver());
+  Caffe::set_root_solver(false);
+  // See if there is a defined seed and reset random state if so
+  if (solver_->param().random_seed() >= 0) {
+    // Fetch random seed and modulate by device ID to make sure
+    // everyone doesn't have the same seed.  We seem to have some
+    // solver instability if we have everyone with the same seed
+    Caffe::set_random_seed(
+        solver_->param().random_seed() + solver_->param().device_id());
+  }
+  solver_->Step(solver_->param().max_iter() - initial_iter_);
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::on_start() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  int device;
+  CUDA_CHECK(cudaGetDevice(&device));
+  CHECK(device == solver_->param().device_id());
+#else
+//  CHECK(false);
+#endif
+
+  // Wait for update from parent
+  if (parent_) {
+    P2PSync<Dtype> *parent = queue_.pop();
+    CHECK(parent == parent_);
+  }
+
+  // Update children
+  for (int i = 0; i < children_.size(); ++i) {
+    Dtype* src = data_;
+    Dtype* dst = children_[i]->data_;
+
+#ifdef DEBUG
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+    CHECK(attributes.device == device);
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+    CHECK(attributes.device == children_[i]->solver_->param().device_id());
+#endif
+
+    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),  //
+        cudaMemcpyDeviceToDevice, cudaStreamDefault));
+  }
+  if (children_.size()) {
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+  }
+  for (int i = 0; i < children_.size(); ++i) {
+    children_[i]->queue_.push(this);
+  }
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::on_gradients_ready() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  int device;
+  CUDA_CHECK(cudaGetDevice(&device));
+  CHECK(device == solver_->param().device_id());
+#endif
+
+  // Sum children gradients as they appear in the queue
+  for (int i = 0; i < children_.size(); ++i) {
+    P2PSync<Dtype> *child = queue_.pop();
+    Dtype* src = child->parent_grads_;
+    Dtype* dst = diff_;
+
+#ifdef DEBUG
+    bool ok = false;
+    for (int j = 0; j < children_.size(); ++j) {
+      if (child == children_[j]) {
+        ok = true;
+      }
+    }
+    CHECK(ok);
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+    CHECK(attributes.device == device);
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+    CHECK(attributes.device == device);
+#endif
+
+    caffe_gpu_add(size_, src, dst, dst);
+  }
+
+  // Send gradients to parent
+  if (parent_) {
+    Dtype* src = diff_;
+    Dtype* dst = parent_grads_;
+
+#ifdef DEBUG
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+    CHECK(attributes.device == device);
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+    CHECK(attributes.device == parent_->solver_->param().device_id());
+#endif
+
+    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),  //
+        cudaMemcpyDeviceToDevice, cudaStreamDefault));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+    parent_->queue_.push(this);
+  } else {
+    // Loss functions divide gradients by the batch size, so to compensate
+    // for split batch, the root solver divides by number of solvers.
+    caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_);
+  }
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::run(const vector<int>& gpus) {
+  // Pair devices for map-reduce synchronization
+  vector<DevicePair> pairs;
+  DevicePair::compute(gpus, &pairs);
+  ostringstream s;
+  for (int i = 1; i < pairs.size(); ++i) {
+    s << (i == 1 ? "" : ", ") << pairs[i].parent() << ":" << pairs[i].device();
+  }
+  LOG(INFO)<< "GPUs pairs " << s.str();
+
+  SolverParameter param(solver_->param());
+  vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
+
+  // Build the GPU tree by finding the parent for each solver
+  for (int attempts = 0; attempts < pairs.size(); ++attempts) {
+    for (int i = 1; i < pairs.size(); ++i) {
+      if (!syncs[i].get()) {
+        P2PSync<Dtype>* parent = NULL;
+        for (int j = 0; j < syncs.size(); ++j) {
+          P2PSync<Dtype>* sync = j == 0 ? this : syncs[j].get();
+          if (sync) {
+            const SolverParameter& p = sync->solver()->param();
+            if (p.device_id() == pairs[i].parent()) {
+              parent = sync;
+            }
+          }
+        }
+        if (parent) {
+          param.set_device_id(pairs[i].device());
+          syncs[i].reset(new P2PSync<Dtype>(solver_, parent, param));
+          parent->children_.push_back((P2PSync<Dtype>*) syncs[i].get());
+        }
+      }
+    }
+  }
+
+  LOG(INFO)<< "Starting Optimization";
+
+  for (int i = 1; i < syncs.size(); ++i) {
+    syncs[i]->StartInternalThread();
+  }
+
+  // Run root solver on current thread
+  solver_->Solve();
+
+  for (int i = 1; i < syncs.size(); ++i) {
+    syncs[i]->StopInternalThread();
+  }
+}
+
+INSTANTIATE_CLASS(Params);
+INSTANTIATE_CLASS(GPUParams);
+INSTANTIATE_CLASS(P2PSync);
+
+}  // namespace caffe
+
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 54e085a63e5..b6fd6b642f1 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -19,13 +19,13 @@ namespace caffe {
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const SolverParameter& param)
-    : net_() {
+    : net_(), callbacks_() {
   Init(param);
 }
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const string& param_file)
-    : net_() {
+    : net_(), callbacks_() {
   SolverParameter param;
   ReadProtoFromTextFileOrDie(param_file, &param);
   Init(param);
@@ -33,17 +33,19 @@ Solver<Dtype>::Solver(const string& param_file)
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
-  LOG(INFO) << "Initializing solver from parameters: " << std::endl
-            << param.DebugString();
+  LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
+    << std::endl << param.DebugString();
   param_ = param;
   CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
-  if (param_.random_seed() >= 0) {
+  if (Caffe::root_solver() && param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
   // Scaffolding code
   InitTrainNet();
-  InitTestNets();
-  LOG(INFO) << "Solver scaffolding done.";
+  if (Caffe::root_solver()) {
+    InitTestNets();
+    LOG(INFO) << "Solver scaffolding done.";
+  }
   iter_ = 0;
   current_step_ = 0;
 }
@@ -59,19 +61,22 @@ void Solver<Dtype>::InitTrainNet() {
       << "one of these fields specifying a train_net: " << field_names;
   NetParameter net_param;
   if (param_.has_train_net_param()) {
-    LOG(INFO) << "Creating training net specified in train_net_param.";
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net specified in train_net_param.";
     net_param.CopyFrom(param_.train_net_param());
   } else if (param_.has_train_net()) {
-    LOG(INFO) << "Creating training net from train_net file: "
-              << param_.train_net();
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net from train_net file: " << param_.train_net();
     ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);
   }
   if (param_.has_net_param()) {
-    LOG(INFO) << "Creating training net specified in net_param.";
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net specified in net_param.";
     net_param.CopyFrom(param_.net_param());
   }
   if (param_.has_net()) {
-    LOG(INFO) << "Creating training net from net file: " << param_.net();
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net from net file: " << param_.net();
     ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);
   }
   // Set the correct NetState.  We start with the solver defaults (lowest
@@ -88,6 +93,7 @@ void Solver<Dtype>::InitTrainNet() {
 
 template <typename Dtype>
 void Solver<Dtype>::InitTestNets() {
+  CHECK(Caffe::root_solver());
   const bool has_net_param = param_.has_net_param();
   const bool has_net_file = param_.has_net();
   const int num_generic_nets = has_net_param + has_net_file;
@@ -175,10 +181,14 @@ void Solver<Dtype>::Step(int iters) {
     // zero-init the params
     net_->ClearParamDiffs();
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
-        && (iter_ > 0 || param_.test_initialization())) {
+        && (iter_ > 0 || param_.test_initialization())
+        && Caffe::root_solver()) {
       TestAll();
     }
 
+    for (int i = 0; i < callbacks_.size(); ++i) {
+      callbacks_[i]->on_start();
+    }
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
     // accumulate the loss and gradient
@@ -198,7 +208,8 @@ void Solver<Dtype>::Step(int iters) {
       losses[idx] = loss;
     }
     if (display) {
-      LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss;
+      LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
+          << ", loss = " << smoothed_loss;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
@@ -213,12 +224,15 @@ void Solver<Dtype>::Step(int iters) {
             loss_msg_stream << " (* " << loss_weight
                             << " = " << loss_weight * result_vec[k] << " loss)";
           }
-          LOG(INFO) << "    Train net output #"
+          LOG_IF(INFO, Caffe::root_solver()) << "    Train net output #"
               << score_index++ << ": " << output_name << " = "
               << result_vec[k] << loss_msg_stream.str();
         }
       }
     }
+    for (int i = 0; i < callbacks_.size(); ++i) {
+      callbacks_[i]->on_gradients_ready();
+    }
     ApplyUpdate();
 
     // Increment the internal iter_ counter -- its value should always indicate
@@ -226,7 +240,9 @@ void Solver<Dtype>::Step(int iters) {
     ++iter_;
 
     // Save a snapshot if needed.
-    if (param_.snapshot() && iter_ % param_.snapshot() == 0) {
+    if (param_.snapshot()
+        && iter_ % param_.snapshot() == 0
+        && Caffe::root_solver()) {
       Snapshot();
     }
   }
@@ -234,6 +250,7 @@ void Solver<Dtype>::Step(int iters) {
 
 template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
+  CHECK(Caffe::root_solver());
   LOG(INFO) << "Solving " << net_->name();
   LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
 
@@ -278,6 +295,7 @@ void Solver<Dtype>::TestAll() {
 
 template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
+  CHECK(Caffe::root_solver());
   LOG(INFO) << "Iteration " << iter_
             << ", Testing net (#" << test_net_id << ")";
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
@@ -328,13 +346,14 @@ void Solver<Dtype>::Test(const int test_net_id) {
                       << " = " << loss_weight * mean_score << " loss)";
     }
     LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
-        << mean_score << loss_msg_stream.str();
+              << mean_score << loss_msg_stream.str();
   }
 }
 
 
 template <typename Dtype>
 void Solver<Dtype>::Snapshot() {
+  CHECK(Caffe::root_solver());
   string model_filename;
   switch (param_.snapshot_format()) {
     case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
@@ -379,6 +398,7 @@ string Solver<Dtype>::SnapshotToHDF5() {
 
 template <typename Dtype>
 void Solver<Dtype>::Restore(const char* state_file) {
+  CHECK(Caffe::root_solver());
   string state_filename(state_file);
   if (state_filename.size() >= 3 &&
       state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {
@@ -480,6 +500,7 @@ void SGDSolver<Dtype>::ClipGradients() {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::ApplyUpdate() {
+  CHECK(Caffe::root_solver());
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
     LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
@@ -723,6 +744,7 @@ void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
 
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  CHECK(Caffe::root_solver());
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
@@ -783,6 +805,7 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 
 template <typename Dtype>
 void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  CHECK(Caffe::root_solver());
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 0da7a3bac79..a667a867af0 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -12,8 +12,14 @@ SyncedMemory::~SyncedMemory() {
   }
 
 #ifndef CPU_ONLY
-  if (gpu_ptr_) {
+  if (gpu_ptr_ && own_gpu_data_) {
+    int initial_device;
+    cudaGetDevice(&initial_device);
+    if (gpu_device_ != -1) {
+      CUDA_CHECK(cudaSetDevice(gpu_device_));
+    }
     CUDA_CHECK(cudaFree(gpu_ptr_));
+    cudaSetDevice(initial_device);
   }
 #endif  // CPU_ONLY
 }
@@ -48,13 +54,17 @@ inline void SyncedMemory::to_gpu() {
 #ifndef CPU_ONLY
   switch (head_) {
   case UNINITIALIZED:
+    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     caffe_gpu_memset(size_, 0, gpu_ptr_);
     head_ = HEAD_AT_GPU;
+    own_gpu_data_ = true;
     break;
   case HEAD_AT_CPU:
     if (gpu_ptr_ == NULL) {
+      CUDA_CHECK(cudaGetDevice(&gpu_device_));
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+      own_gpu_data_ = true;
     }
     caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
@@ -92,6 +102,26 @@ const void* SyncedMemory::gpu_data() {
 #endif
 }
 
+void SyncedMemory::set_gpu_data(void* data) {
+#ifndef CPU_ONLY
+  CHECK(data);
+  if (own_gpu_data_) {
+    int initial_device;
+    cudaGetDevice(&initial_device);
+    if (gpu_device_ != -1) {
+      CUDA_CHECK(cudaSetDevice(gpu_device_));
+    }
+    CUDA_CHECK(cudaFree(gpu_ptr_));
+    cudaSetDevice(initial_device);
+  }
+  gpu_ptr_ = data;
+  head_ = HEAD_AT_GPU;
+  own_gpu_data_ = false;
+#else
+  NO_GPU;
+#endif
+}
+
 void* SyncedMemory::mutable_cpu_data() {
   to_cpu();
   head_ = HEAD_AT_CPU;
@@ -112,7 +142,9 @@ void* SyncedMemory::mutable_gpu_data() {
 void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
   CHECK(head_ == HEAD_AT_CPU);
   if (gpu_ptr_ == NULL) {
+    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+    own_gpu_data_ = true;
   }
   const cudaMemcpyKind put = cudaMemcpyHostToDevice;
   CUDA_CHECK(cudaMemcpyAsync(gpu_ptr_, cpu_ptr_, size_, put, stream));
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index eaa7a759b9b..1cede07f774 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -8,6 +8,7 @@
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
 #include "caffe/util/io.hpp"
@@ -35,6 +36,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
   string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
+  shared_ptr<P2PSync<Dtype> > sync_;
   int seed_;
   // Dimensions are determined by generate_sample_data.py
   // TODO this is brittle and the hdf5 file should be checked instead.
@@ -70,8 +72,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
   string RunLeastSquaresSolver(const Dtype learning_rate,
       const Dtype weight_decay, const Dtype momentum, const int num_iters,
-      const int iter_size = 1, const bool snapshot = false,
-      const char* from_snapshot = NULL) {
+      const int iter_size = 1, const int devices = 1,
+      const bool snapshot = false, const char* from_snapshot = NULL) {
     ostringstream proto;
     proto <<
        "snapshot_after_train: " << snapshot << " "
@@ -184,7 +186,20 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
         this->solver_->net()->Forward(empty_bottom_vec);
       }
     }
-    this->solver_->Solve();
+    if (devices == 1) {
+      this->solver_->Solve();
+    } else {
+      LOG(INFO) << "Multi-GPU test on " << devices << " devices";
+      vector<int> gpus;
+      for (int i = 0; i < devices; ++i) {
+        gpus.push_back(i);
+      }
+      Caffe::set_solver_count(gpus.size());
+      this->sync_.reset(new P2PSync<Dtype>(
+          this->solver_, NULL, this->solver_->param()));
+      this->sync_->run(gpus);
+      Caffe::set_solver_count(1);
+    }
     if (snapshot) {
       ostringstream resume_file;
       resume_file << snapshot_prefix_ << "/_iter_" << num_iters
@@ -410,20 +425,38 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
       const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
       const int iter_to_check = 0) {
-    // Initialize the solver and run K (= iter_to_check) solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
-
-    // Compute the (K+1)th update using the analytic least squares gradient.
-    vector<shared_ptr<Blob<Dtype> > > updated_params;
-    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-        &updated_params);
-
-    // Reinitialize the solver and run K+1 solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-        iter_to_check + 1);
-
-    // Check that the solver's solution matches ours.
-    CheckLeastSquaresUpdate(updated_params);
+    const int kNum = num_;
+    const int kIterSize = 1;
+    // Test over all numbers of devices.
+    int available_devices = 1;
+#ifndef CPU_ONLY
+    if (Caffe::mode() == Caffe::GPU) {
+      CUDA_CHECK(cudaGetDeviceCount(&available_devices));
+    }
+#endif
+    for (int devices = 1; devices <= available_devices; ++devices) {
+      // Configure batch size for single / multi device equivalence.
+      // Constant data is needed for multi device as for accumulation.
+      num_ = kNum * devices;
+
+      // Initialize the solver and run K (= iter_to_check) solver iterations
+      // (on single device).
+      RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                            iter_to_check, kIterSize, 1);
+
+      // Compute the (K+1)th update using the analytic least squares gradient.
+      vector<shared_ptr<Blob<Dtype> > > updated_params;
+      ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+          &updated_params);
+
+      // Reinitialize the solver and run K+1 solver iterations.
+      num_ = kNum;
+      RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+          iter_to_check + 1, kIterSize, devices);
+
+      // Check that the solver's solution matches ours.
+      CheckLeastSquaresUpdate(updated_params);
+    }
   }
 
   void TestSnapshot(const Dtype learning_rate = 1.0,
@@ -433,8 +466,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const int total_num_iters = num_iters * 2;
     bool snapshot = false;
     const int kIterSize = 1;
+    const int kDevices = 1;
     RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-        total_num_iters, kIterSize, snapshot);
+        total_num_iters, kIterSize, kDevices, snapshot);
 
     // Save the resulting param values.
     vector<shared_ptr<Blob<Dtype> > > param_copies;
@@ -464,12 +498,13 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Run the solver for num_iters iterations and snapshot.
     snapshot = true;
     string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
-        momentum, num_iters, kIterSize, snapshot);
+        momentum, num_iters, kIterSize, kDevices, snapshot);
 
     // Reinitialize the solver and run for num_iters more iterations.
     snapshot = false;
     RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-        total_num_iters, kIterSize, snapshot, snapshot_name.c_str());
+        total_num_iters, kIterSize, kDevices,
+        snapshot, snapshot_name.c_str());
 
     // Check that params now match.
     const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
index f7c53f22225..d1d1fa864c3 100644
--- a/src/caffe/util/blocking_queue.cpp
+++ b/src/caffe/util/blocking_queue.cpp
@@ -3,6 +3,7 @@
 
 #include "caffe/data_layers.hpp"
 #include "caffe/data_reader.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/util/blocking_queue.hpp"
 
 namespace caffe {
@@ -89,5 +90,7 @@ template class BlockingQueue<Batch<float>*>;
 template class BlockingQueue<Batch<double>*>;
 template class BlockingQueue<Datum*>;
 template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;
+template class BlockingQueue<P2PSync<float>*>;
+template class BlockingQueue<P2PSync<double>*>;
 
 }  // namespace caffe
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 46f99594800..9f31b37ac2b 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -17,13 +17,17 @@ using caffe::Blob;
 using caffe::Caffe;
 using caffe::Net;
 using caffe::Layer;
+using caffe::Solver;
 using caffe::shared_ptr;
+using caffe::string;
 using caffe::Timer;
 using caffe::vector;
+using std::ostringstream;
 
-
-DEFINE_int32(gpu, -1,
-    "Run in GPU mode on given device ID.");
+DEFINE_string(gpu, "",
+    "Optional; run in GPU mode on given device IDs separated by ','."
+    "Use '-gpu all' to run on all available GPUs. The effective training "
+    "batch size is multiplied by the number of devices.");
 DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
@@ -31,8 +35,8 @@ DEFINE_string(model, "",
 DEFINE_string(snapshot, "",
     "Optional; the snapshot solver state to resume training.");
 DEFINE_string(weights, "",
-    "Optional; the pretrained weights to initialize finetuning. "
-    "Cannot be set simultaneously with snapshot.");
+    "Optional; the pretrained weights to initialize finetuning, "
+    "separated by ','. Cannot be set simultaneously with snapshot.");
 DEFINE_int32(iterations, 50,
     "The number of iterations to run.");
 
@@ -66,6 +70,29 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
   }
 }
 
+// Parse GPU ids or use all available devices
+static void get_gpus(vector<int>* gpus) {
+  if (FLAGS_gpu == "all") {
+    int count = 0;
+#ifndef CPU_ONLY
+    CUDA_CHECK(cudaGetDeviceCount(&count));
+#else
+    NO_GPU;
+#endif
+    for (int i = 0; i < count; ++i) {
+      gpus->push_back(i);
+    }
+  } else if (FLAGS_gpu.size()) {
+    vector<string> strings;
+    boost::split(strings, FLAGS_gpu, boost::is_any_of(","));
+    for (int i = 0; i < strings.size(); ++i) {
+      gpus->push_back(boost::lexical_cast<int>(strings[i]));
+    }
+  } else {
+    CHECK_EQ(gpus->size(), 0);
+  }
+}
+
 // caffe commands to call by
 //     caffe <command> <args>
 //
@@ -74,10 +101,13 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
 
 // Device Query: show diagnostic information for a GPU device.
 int device_query() {
-  CHECK_GT(FLAGS_gpu, -1) << "Need a device ID to query.";
-  LOG(INFO) << "Querying device ID = " << FLAGS_gpu;
-  caffe::Caffe::SetDevice(FLAGS_gpu);
-  caffe::Caffe::DeviceQuery();
+  LOG(INFO) << "Querying GPUs " << FLAGS_gpu;
+  vector<int> gpus;
+  get_gpus(&gpus);
+  for (int i = 0; i < gpus.size(); ++i) {
+    caffe::Caffe::SetDevice(gpus[i]);
+    caffe::Caffe::DeviceQuery();
+  }
   return 0;
 }
 RegisterBrewFunction(device_query);
@@ -106,34 +136,49 @@ int train() {
   caffe::SolverParameter solver_param;
   caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
 
-  // If the gpu flag is not provided, allow the mode and device to be set
+  // If the gpus flag is not provided, allow the mode and device to be set
   // in the solver prototxt.
-  if (FLAGS_gpu < 0
+  if (FLAGS_gpu.size() == 0
       && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
-    FLAGS_gpu = solver_param.device_id();
+      if (solver_param.has_device_id()) {
+          FLAGS_gpu = ""  +
+              boost::lexical_cast<string>(solver_param.device_id());
+      } else {  // Set default GPU if unspecified
+          FLAGS_gpu = "" + boost::lexical_cast<string>(0);
+      }
   }
 
-  // Set device id and mode
-  if (FLAGS_gpu >= 0) {
-    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
-    Caffe::SetDevice(FLAGS_gpu);
-    Caffe::set_mode(Caffe::GPU);
-  } else {
-    LOG(INFO) << "Use CPU.";
+  vector<int> gpus;
+  get_gpus(&gpus);
+  if (gpus.size() == 0) {
     Caffe::set_mode(Caffe::CPU);
+  } else {
+    ostringstream s;
+    for (int i = 0; i < gpus.size(); ++i) {
+      s << (i ? ", " : "") << gpus[i];
+    }
+    LOG(INFO) << "Using GPUs " << s.str();
+
+    solver_param.set_device_id(gpus[0]);
+    Caffe::SetDevice(gpus[0]);
+    Caffe::set_mode(Caffe::GPU);
+    Caffe::set_solver_count(gpus.size());
   }
 
-  LOG(INFO) << "Starting Optimization";
-  shared_ptr<caffe::Solver<float> >
-    solver(caffe::GetSolver<float>(solver_param));
+  shared_ptr<Solver<float> > solver(caffe::GetSolver<float>(solver_param));
 
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
-    solver->Solve(FLAGS_snapshot);
+    solver->Restore(FLAGS_snapshot.c_str());
   } else if (FLAGS_weights.size()) {
-    CopyLayers(&*solver, FLAGS_weights);
-    solver->Solve();
+    CopyLayers(solver.get(), FLAGS_weights);
+  }
+
+  if (gpus.size() > 1) {
+    caffe::P2PSync<float> sync(solver, NULL, solver->param());
+    sync.run(gpus);
   } else {
+    LOG(INFO) << "Starting Optimization";
     solver->Solve();
   }
   LOG(INFO) << "Optimization Done.";
@@ -148,9 +193,11 @@ int test() {
   CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
 
   // Set device id and mode
-  if (FLAGS_gpu >= 0) {
-    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
-    Caffe::SetDevice(FLAGS_gpu);
+  vector<int> gpus;
+  get_gpus(&gpus);
+  if (gpus.size() != 0) {
+    LOG(INFO) << "Use GPU with device ID " << gpus[0];
+    Caffe::SetDevice(gpus[0]);
     Caffe::set_mode(Caffe::GPU);
   } else {
     LOG(INFO) << "Use CPU.";
@@ -213,9 +260,11 @@ int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
 
   // Set device id and mode
-  if (FLAGS_gpu >= 0) {
-    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
-    Caffe::SetDevice(FLAGS_gpu);
+  vector<int> gpus;
+  get_gpus(&gpus);
+  if (gpus.size() != 0) {
+    LOG(INFO) << "Use GPU with device ID " << gpus[0];
+    Caffe::SetDevice(gpus[0]);
     Caffe::set_mode(Caffe::GPU);
   } else {
     LOG(INFO) << "Use CPU.";

From 335bee737cb2e715abe685e6029afc83ccd8f404 Mon Sep 17 00:00:00 2001
From: mhouston <mhouston@nvidia.com>
Date: Fri, 10 Jul 2015 16:05:48 -0700
Subject: [PATCH 1478/2053] Detect topology corner cases and improve broadcast
 order

- Start with distant nodes in broadcast
- Fix outside loop to loop for full tree depth
---
 src/caffe/parallel.cpp | 73 ++++++++++++++++++++++++------------------
 1 file changed, 41 insertions(+), 32 deletions(-)

diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 3fef8cfdb33..5a08df6c1c8 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -119,18 +119,23 @@ void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
 #ifndef CPU_ONLY
   vector<int> remaining(devices);
 
+  // Depth for reduction tree
+  int remaining_depth = static_cast<int>(ceil(log2(remaining.size())));
+
   // Group GPUs by board
-  for (int i = 0; i < remaining.size(); ++i) {
-    for (int j = i + 1; j < remaining.size(); ++j) {
-      cudaDeviceProp a, b;
-      CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
-      CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
-      if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
-        if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
-          pairs->push_back(DevicePair(remaining[i], remaining[j]));
-          DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
-          remaining.erase(remaining.begin() + j);
-          break;
+  for (int d = 0; d < remaining_depth; ++d) {
+    for (int i = 0; i < remaining.size(); ++i) {
+      for (int j = i + 1; j < remaining.size(); ++j) {
+        cudaDeviceProp a, b;
+        CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
+        CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
+        if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
+          if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
+            pairs->push_back(DevicePair(remaining[i], remaining[j]));
+            DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
+            remaining.erase(remaining.begin() + j);
+            break;
+          }
         }
       }
     }
@@ -142,15 +147,19 @@ void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
   DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str();
 
   // Group by P2P accessibility
-  for (int i = 0; i < remaining.size(); ++i) {
-    for (int j = i + 1; j < remaining.size(); ++j) {
-      int access;
-      CUDA_CHECK(cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
-      if (access) {
-        pairs->push_back(DevicePair(remaining[i], remaining[j]));
-        DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
-        remaining.erase(remaining.begin() + j);
-        break;
+  remaining_depth = ceil(log2(remaining.size()));
+  for (int d = 0; d < remaining_depth; ++d) {
+    for (int i = 0; i < remaining.size(); ++i) {
+      for (int j = i + 1; j < remaining.size(); ++j) {
+        int access;
+        CUDA_CHECK(
+            cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
+        if (access) {
+          pairs->push_back(DevicePair(remaining[i], remaining[j]));
+          DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
+          remaining.erase(remaining.begin() + j);
+          break;
+        }
       }
     }
   }
@@ -161,15 +170,19 @@ void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
   DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str();
 
   // Group remaining
-  for (int i = 0; i < remaining.size(); ++i) {
-    for (int j = i + 1; j < remaining.size(); ++j) {
-      pairs->push_back(DevicePair(remaining[i], remaining[j]));
-      DLOG(INFO) << "Remaining pair: " << remaining[i] << ":" << remaining[j];
-      remaining.erase(remaining.begin() + j);
-      break;
+  remaining_depth = ceil(log2(remaining.size()));
+  for (int d = 0; d < remaining_depth; ++d) {
+    for (int i = 0; i < remaining.size(); ++i) {
+      pairs->push_back(DevicePair(remaining[i], remaining[i + 1]));
+      DLOG(INFO) << "Remaining pair: " << remaining[i] << ":"
+                 << remaining[i + 1];
+      remaining.erase(remaining.begin() + i + 1);
     }
   }
+
+  // Should only be the parent node remaining
   CHECK_EQ(remaining.size(), 1);
+
   pairs->insert(pairs->begin(), DevicePair(-1, remaining[0]));
 
   CHECK(pairs->size() == devices.size());
@@ -289,7 +302,7 @@ void P2PSync<Dtype>::on_start() {
   }
 
   // Update children
-  for (int i = 0; i < children_.size(); ++i) {
+  for (int i = children_.size() - 1; i >= 0; i--) {
     Dtype* src = data_;
     Dtype* dst = children_[i]->data_;
 
@@ -301,13 +314,9 @@ void P2PSync<Dtype>::on_start() {
     CHECK(attributes.device == children_[i]->solver_->param().device_id());
 #endif
 
-    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),  //
+    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),
         cudaMemcpyDeviceToDevice, cudaStreamDefault));
-  }
-  if (children_.size()) {
     CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-  }
-  for (int i = 0; i < children_.size(); ++i) {
     children_[i]->queue_.push(this);
   }
 #endif

From 8771d0f4317fc0081d86b7637f5f5ceef5b92dfb Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 7 Aug 2015 13:56:49 -0700
Subject: [PATCH 1479/2053] [docs] add multi-gpu usage note to interfaces

---
 docs/tutorial/interfaces.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 40602948cc3..9006179d0f1 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -50,6 +50,13 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
     # query the first device
     caffe device_query -gpu 0
 
+**Parallelism**: the `-gpu` flag to the `caffe` tool can take a comma separated list of IDs to run on multiple GPUs. A solver and net will be instantiated for each GPU so the batch size is effectively multiplied by the number of GPUs. To reproduce single GPU training, reduce the batch size in the network definition accordingly.
+
+    # train on GPUs 0 & 1 (doubling the batch size)
+    caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1
+    # train on all GPUs (multiplying batch size by number of devices)
+    caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all
+
 ## Python
 
 The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.

From 1ce3380f172336cadaa649a6e077a42a246a534d Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Sat, 20 Sep 2014 19:01:28 +0200
Subject: [PATCH 1480/2053] Implement AdaDelta; add test cases; add mnist
 examples

---
 examples/mnist/lenet_adadelta_solver.prototxt |  22 ++
 ...mnist_autoencoder_solver_adadelta.prototxt |  17 ++
 .../mnist/train_mnist_autoencoder_adadelta.sh |   4 +
 include/caffe/solver.hpp                      |  23 ++
 src/caffe/proto/caffe.proto                   |   1 +
 src/caffe/solver.cpp                          | 199 ++++++++++++++++++
 src/caffe/test/test_gradient_based_solver.cpp | 100 ++++++++-
 7 files changed, 364 insertions(+), 2 deletions(-)
 create mode 100644 examples/mnist/lenet_adadelta_solver.prototxt
 create mode 100644 examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
 create mode 100755 examples/mnist/train_mnist_autoencoder_adadelta.sh

diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt
new file mode 100644
index 00000000000..b77b451d56a
--- /dev/null
+++ b/examples/mnist/lenet_adadelta_solver.prototxt
@@ -0,0 +1,22 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+momentum: 0.95
+weight_decay: 0.0005
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_adadelta"
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: ADADELTA
+delta: 1e-6
diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
new file mode 100644
index 00000000000..cc4f0bbb4a7
--- /dev/null
+++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
@@ -0,0 +1,17 @@
+net: "examples/mnist/mnist_autoencoder.prototxt"
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
+test_compute_loss: true
+momentum: 0.95
+display: 100
+max_iter: 65000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train"
+# solver mode: CPU or GPU
+solver_mode: GPU
+solver_type: ADADELTA
+delta: 1e-8
diff --git a/examples/mnist/train_mnist_autoencoder_adadelta.sh b/examples/mnist/train_mnist_autoencoder_adadelta.sh
new file mode 100755
index 00000000000..4be0ebddedc
--- /dev/null
+++ b/examples/mnist/train_mnist_autoencoder_adadelta.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+./build/tools/caffe train \
+  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index fbade9389ff..4b408380119 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -158,6 +158,27 @@ class RMSPropSolver : public SGDSolver<Dtype> {
   DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
 };
 
+template <typename Dtype>
+class AdaDeltaSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaDeltaSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit AdaDeltaSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+
+ protected:
+  virtual void PreSolve();
+  virtual void ComputeUpdateValue();
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.base_lr())
+        << "Learning rate cannot be used with AdaDelta.";
+    CHECK_EQ("", this->param_.lr_policy())
+        << "Learning rate policy cannot be applied to AdaDelta.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
+};
+
 template <typename Dtype>
 Solver<Dtype>* GetSolver(const SolverParameter& param) {
   SolverParameter_SolverType type = param.solver_type();
@@ -171,6 +192,8 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
       return new AdaGradSolver<Dtype>(param);
   case SolverParameter_SolverType_RMSPROP:
       return new RMSPropSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADADELTA:
+      return new AdaDeltaSolver<Dtype>(param);
   default:
       LOG(FATAL) << "Unknown SolverType: " << type;
   }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 89f14595ba6..7cfcaa8bac7 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -215,6 +215,7 @@ message SolverParameter {
     NESTEROV = 1;
     ADAGRAD = 2;
     RMSPROP = 3;
+    ADADELTA = 4;
   }
   optional SolverType solver_type = 30 [default = SGD];
   // numerical stability for AdaGrad
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 54e085a63e5..d8749a1b939 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -934,10 +934,209 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
 }
 
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::PreSolve() {
+  // Initialize the history
+  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  this->history_.clear();
+  this->update_.clear();
+  this->temp_.clear();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const Blob<Dtype>* net_param = net_params[i].get();
+    this->history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+    this->update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+    this->temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+  }
+  for (int i = 0; i < net_params.size(); ++i) {
+    const Blob<Dtype>* net_param = net_params[i].get();
+    this->history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
+        net_param->num(), net_param->channels(), net_param->height(),
+        net_param->width())));
+  }
+}
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue() {
+  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  Dtype delta = this->param_.delta();
+  Dtype momentum = this->param_.momentum();
+  Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
+  size_t update_history_offset = net_params.size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+
+      if (local_decay) {
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay,
+              net_params[param_id]->cpu_data(),
+              net_params[param_id]->mutable_cpu_diff());
+        } else if (regularization_type == "L1") {
+          caffe_cpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->cpu_data(),
+              this->temp_[param_id]->mutable_cpu_data());
+          caffe_axpy(net_params[param_id]->count(),
+              local_decay,
+              this->temp_[param_id]->cpu_data(),
+              net_params[param_id]->mutable_cpu_diff());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
+      }
+
+      // compute square of gradient in update
+      caffe_powx(net_params[param_id]->count(),
+          net_params[param_id]->cpu_diff(), Dtype(2),
+          this->update_[param_id]->mutable_cpu_data());
+
+      // update history of gradients
+      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+          this->update_[param_id]->cpu_data(), momentum,
+          this->history_[param_id]->mutable_cpu_data());
+
+      // add delta to history to guard against dividing by zero later
+      caffe_set(net_params[param_id]->count(), delta,
+          this->temp_[param_id]->mutable_cpu_data());
+
+      caffe_add(net_params[param_id]->count(),
+          this->temp_[param_id]->cpu_data(),
+          this->history_[update_history_offset + param_id]->cpu_data(),
+          this->update_[param_id]->mutable_cpu_data());
+
+      caffe_add(net_params[param_id]->count(),
+          this->temp_[param_id]->cpu_data(),
+          this->history_[param_id]->cpu_data(),
+          this->temp_[param_id]->mutable_cpu_data());
+
+      // divide history of updates by history of gradients
+      caffe_div(net_params[param_id]->count(),
+          this->update_[param_id]->cpu_data(),
+          this->temp_[param_id]->cpu_data(),
+          this->update_[param_id]->mutable_cpu_data());
+
+      // jointly compute the RMS of both for update and gradient history
+      caffe_powx(net_params[param_id]->count(),
+          this->update_[param_id]->cpu_data(), Dtype(0.5),
+          this->update_[param_id]->mutable_cpu_data());
+
+      // compute the update
+      caffe_mul(net_params[param_id]->count(),
+          net_params[param_id]->cpu_diff(),
+          this->update_[param_id]->cpu_data(),
+          net_params[param_id]->mutable_cpu_diff());
+
+      // compute square of update
+      caffe_powx(net_params[param_id]->count(),
+          net_params[param_id]->cpu_diff(), Dtype(2),
+          this->update_[param_id]->mutable_cpu_data());
+
+      // update history of updates
+      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+          this->update_[param_id]->cpu_data(), momentum,
+          this->history_[update_history_offset + param_id]->mutable_cpu_data());
+    }
+    break;
+  case Caffe::GPU:
+#ifndef CPU_ONLY
+    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
+      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+
+      if (local_decay) {
+        if (regularization_type == "L2") {
+          // add weight decay
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay,
+              net_params[param_id]->gpu_data(),
+              net_params[param_id]->mutable_gpu_diff());
+        } else if (regularization_type == "L1") {
+          caffe_gpu_sign(net_params[param_id]->count(),
+              net_params[param_id]->gpu_data(),
+              this->temp_[param_id]->mutable_gpu_data());
+          caffe_gpu_axpy(net_params[param_id]->count(),
+              local_decay,
+              this->temp_[param_id]->gpu_data(),
+              net_params[param_id]->mutable_gpu_diff());
+        } else {
+          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+        }
+      }
+
+      // compute square of gradient in update
+      caffe_gpu_powx(net_params[param_id]->count(),
+          net_params[param_id]->gpu_diff(), Dtype(2),
+          this->update_[param_id]->mutable_gpu_data());
+
+      // update history of gradients
+      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+          this->update_[param_id]->gpu_data(), momentum,
+          this->history_[param_id]->mutable_gpu_data());
+
+      // add delta to history to guard against dividing by zero later
+      caffe_gpu_set(net_params[param_id]->count(), delta,
+          this->temp_[param_id]->mutable_gpu_data());
+
+      caffe_gpu_add(net_params[param_id]->count(),
+          this->temp_[param_id]->gpu_data(),
+          this->history_[update_history_offset + param_id]->gpu_data(),
+          this->update_[param_id]->mutable_gpu_data());
+
+      caffe_gpu_add(net_params[param_id]->count(),
+          this->temp_[param_id]->gpu_data(),
+          this->history_[param_id]->gpu_data(),
+          this->temp_[param_id]->mutable_gpu_data());
+
+      // divide history of updates by history of gradients
+      caffe_gpu_div(net_params[param_id]->count(),
+          this->update_[param_id]->gpu_data(),
+          this->temp_[param_id]->gpu_data(),
+          this->update_[param_id]->mutable_gpu_data());
+
+      // jointly compute the RMS of both for update and gradient history
+      caffe_gpu_powx(net_params[param_id]->count(),
+          this->update_[param_id]->gpu_data(), Dtype(0.5),
+          this->update_[param_id]->mutable_gpu_data());
+
+      // compute the update and copy to net_diff
+      caffe_gpu_mul(net_params[param_id]->count(),
+          net_params[param_id]->gpu_diff(),
+          this->update_[param_id]->gpu_data(),
+          net_params[param_id]->mutable_gpu_diff());
+
+      // compute square of update
+      caffe_gpu_powx(net_params[param_id]->count(),
+          net_params[param_id]->gpu_diff(), Dtype(2),
+          this->update_[param_id]->mutable_gpu_data());
+
+      // update history of updates
+      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+          this->update_[param_id]->gpu_data(), momentum,
+          this->history_[update_history_offset + param_id]->mutable_gpu_data());
+    }
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 INSTANTIATE_CLASS(Solver);
 INSTANTIATE_CLASS(SGDSolver);
 INSTANTIATE_CLASS(NesterovSolver);
 INSTANTIATE_CLASS(AdaGradSolver);
 INSTANTIATE_CLASS(RMSPropSolver);
+INSTANTIATE_CLASS(AdaDeltaSolver);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index eaa7a759b9b..db89e285a9f 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -64,7 +64,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
     InitSolver(param);
     delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD ||
-        solver_type() == SolverParameter_SolverType_RMSPROP) ?
+        solver_type() == SolverParameter_SolverType_RMSPROP ||
+        solver_type() == SolverParameter_SolverType_ADADELTA) ?
         param.delta() : 0;
   }
 
@@ -164,6 +165,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    bottom: 'targets' "
        "  } "
        "} ";
+    if (learning_rate != 0) {
+      proto << "base_lr: " << learning_rate << " ";
+      proto << "lr_policy: 'fixed' ";
+    }
     if (weight_decay != 0) {
       proto << "weight_decay: " << weight_decay << " ";
     }
@@ -266,7 +271,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
           ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
       // Finally, compute update.
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
-      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      if (solver_type() != SolverParameter_SolverType_ADADELTA) {
+        ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      } else {
+        ASSERT_EQ(4, history.size());  // additional blobs for update history
+      }
       Dtype update_value = learning_rate * grad;
       const Dtype history_value = (i == D) ?
             history[1]->cpu_data()[0] : history[0]->cpu_data()[i];
@@ -289,6 +298,19 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
             + grad * grad * (1 - rms_decay)) + delta_;
         }
         break;
+      case SolverParameter_SolverType_ADADELTA:
+      {
+        const Dtype update_history_value = (i == D) ?
+            history[3]->cpu_data()[0] : history[2]->cpu_data()[i];
+        const Dtype weighted_gradient_average =
+            momentum * history_value + (1 - momentum) * (grad * grad);
+        update_value = grad * std::sqrt((update_history_value + delta_) /
+            (weighted_gradient_average + delta_));
+        // not actually needed, just here for illustrative purposes
+        // const Dtype weighted_update_average =
+        //   momentum * update_history_value + (1 - momentum) * (update_value);
+        break;
+      }
       default:
         LOG(FATAL) << "Unknown solver type: " << solver_type();
       }
@@ -981,4 +1003,78 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
   }
 }
 
+template <typename TypeParam>
+class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
+  }
+
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_ADADELTA;
+  }
+};
+
+TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.0;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.95;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 500;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 500;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 }  // namespace caffe

From 4c58741ce2e031b61aef53914128801e6edd673d Mon Sep 17 00:00:00 2001
From: Kevin Bache <kevin.bache@gmail.com>
Date: Thu, 19 Mar 2015 15:56:51 -0700
Subject: [PATCH 1481/2053] Updated AdaDelta for modern Caffe; reduced
 iterations on multi-iter tests

---
 ...mnist_autoencoder_solver_adadelta.prototxt |  2 +-
 include/caffe/solver.hpp                      |  6 ++--
 src/caffe/solver.cpp                          | 32 ++++++-------------
 src/caffe/test/test_gradient_based_solver.cpp |  4 +--
 4 files changed, 15 insertions(+), 29 deletions(-)

diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
index cc4f0bbb4a7..4e43468a71f 100644
--- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
@@ -6,6 +6,7 @@ test_iter: 100
 test_interval: 500
 test_compute_loss: true
 momentum: 0.95
+delta: 1e-8
 display: 100
 max_iter: 65000
 weight_decay: 0.0005
@@ -14,4 +15,3 @@ snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train"
 # solver mode: CPU or GPU
 solver_mode: GPU
 solver_type: ADADELTA
-delta: 1e-8
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 4b408380119..495cd4f159e 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -82,12 +82,12 @@ class SGDSolver : public Solver<Dtype> {
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
  protected:
-  void PreSolve();
   Dtype GetLearningRate();
   virtual void ApplyUpdate();
   virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  virtual void PreSolve();
   virtual void ClipGradients();
   virtual void SnapshotSolverState(const string& model_filename);
   virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
@@ -162,9 +162,9 @@ template <typename Dtype>
 class AdaDeltaSolver : public SGDSolver<Dtype> {
  public:
   explicit AdaDeltaSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+      : SGDSolver<Dtype>(param) { PreSolve(); constructor_sanity_check(); }
   explicit AdaDeltaSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+      : SGDSolver<Dtype>(param_file) { PreSolve(); constructor_sanity_check(); }
 
  protected:
   virtual void PreSolve();
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index d8749a1b939..34a290ffe3d 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -936,35 +936,21 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 
 template <typename Dtype>
 void AdaDeltaSolver<Dtype>::PreSolve() {
-  // Initialize the history
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  this->history_.clear();
-  this->update_.clear();
-  this->temp_.clear();
-  for (int i = 0; i < net_params.size(); ++i) {
-    const Blob<Dtype>* net_param = net_params[i].get();
-    this->history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-    this->update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-    this->temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
-  }
+  // Add the extra history entries for AdaDelta after those from
+  // SGDSolver::PreSolve
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
   for (int i = 0; i < net_params.size(); ++i) {
-    const Blob<Dtype>* net_param = net_params[i].get();
-    this->history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(
-        net_param->num(), net_param->channels(), net_param->height(),
-        net_param->width())));
+        const vector<int>& shape = net_params[i]->shape();
+        this->history_.push_back(
+                shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
   }
 }
 
 template <typename Dtype>
 void AdaDeltaSolver<Dtype>::ComputeUpdateValue() {
-  vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  vector<float>& net_params_weight_decay = this->net_->params_weight_decay();
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<float>& net_params_weight_decay =
+          this->net_->params_weight_decay();
   Dtype delta = this->param_.delta();
   Dtype momentum = this->param_.momentum();
   Dtype weight_decay = this->param_.weight_decay();
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index db89e285a9f..277aa3a5c8e 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -1060,7 +1060,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   const Dtype kLearningRate = 0.0;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.95;
-  const int kNumIters = 500;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
       this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
@@ -1071,7 +1071,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
   const Dtype kLearningRate = 0.0;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
-  const int kNumIters = 500;
+  const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
       this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }

From f2e523e479b89902b644f3a8bb2ac51a6dc28eee Mon Sep 17 00:00:00 2001
From: Matthias Plappert <matthiasplappert@me.com>
Date: Sat, 18 Jul 2015 18:46:51 +0200
Subject: [PATCH 1482/2053] Clean up and modernize AdaDelta code; add learning
 rate support; add additional test cases

---
 examples/mnist/lenet_adadelta_solver.prototxt |   2 +
 ...mnist_autoencoder_solver_adadelta.prototxt |   2 +
 include/caffe/solver.hpp                      |  16 +-
 src/caffe/solver.cpp                          | 274 ++++++++----------
 src/caffe/test/test_gradient_based_solver.cpp | 211 +++++++++-----
 5 files changed, 260 insertions(+), 245 deletions(-)

diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt
index b77b451d56a..776d1e06139 100644
--- a/examples/mnist/lenet_adadelta_solver.prototxt
+++ b/examples/mnist/lenet_adadelta_solver.prototxt
@@ -7,6 +7,8 @@ test_iter: 100
 # Carry out testing every 500 training iterations.
 test_interval: 500
 # The base learning rate, momentum and the weight decay of the network.
+base_lr: 1.0
+lr_policy: "fixed"
 momentum: 0.95
 weight_decay: 0.0005
 # Display every 100 iterations
diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
index 4e43468a71f..065647df31b 100644
--- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
@@ -5,6 +5,8 @@ test_state: { stage: 'test-on-test' }
 test_iter: 100
 test_interval: 500
 test_compute_loss: true
+base_lr: 1.0
+lr_policy: "fixed"
 momentum: 0.95
 delta: 1e-8
 display: 100
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 495cd4f159e..5fefd01e549 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -82,12 +82,12 @@ class SGDSolver : public Solver<Dtype> {
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
  protected:
+  void PreSolve();
   Dtype GetLearningRate();
   virtual void ApplyUpdate();
   virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
-  virtual void PreSolve();
   virtual void ClipGradients();
   virtual void SnapshotSolverState(const string& model_filename);
   virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
@@ -162,19 +162,13 @@ template <typename Dtype>
 class AdaDeltaSolver : public SGDSolver<Dtype> {
  public:
   explicit AdaDeltaSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { PreSolve(); constructor_sanity_check(); }
+      : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
   explicit AdaDeltaSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { PreSolve(); constructor_sanity_check(); }
+      : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
 
  protected:
-  virtual void PreSolve();
-  virtual void ComputeUpdateValue();
-  void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.base_lr())
-        << "Learning rate cannot be used with AdaDelta.";
-    CHECK_EQ("", this->param_.lr_policy())
-        << "Learning rate policy cannot be applied to AdaDelta.";
-  }
+  void AdaDeltaPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
 
   DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
 };
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 34a290ffe3d..78902ca0ebc 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -935,10 +935,10 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 template <typename Dtype>
-void AdaDeltaSolver<Dtype>::PreSolve() {
+void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
   // Add the extra history entries for AdaDelta after those from
   // SGDSolver::PreSolve
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   for (int i = 0; i < net_params.size(); ++i) {
         const vector<int>& shape = net_params[i]->shape();
         this->history_.push_back(
@@ -947,172 +947,134 @@ void AdaDeltaSolver<Dtype>::PreSolve() {
 }
 
 template <typename Dtype>
-void AdaDeltaSolver<Dtype>::ComputeUpdateValue() {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  const vector<float>& net_params_weight_decay =
-          this->net_->params_weight_decay();
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
   Dtype momentum = this->param_.momentum();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
+  Dtype local_rate = rate * net_params_lr[param_id];
   size_t update_history_offset = net_params.size();
   switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
 
-      // compute square of gradient in update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history of gradients
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->cpu_data(), momentum,
-          this->history_[param_id]->mutable_cpu_data());
-
-      // add delta to history to guard against dividing by zero later
-      caffe_set(net_params[param_id]->count(), delta,
-          this->temp_[param_id]->mutable_cpu_data());
-
-      caffe_add(net_params[param_id]->count(),
-          this->temp_[param_id]->cpu_data(),
-          this->history_[update_history_offset + param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      caffe_add(net_params[param_id]->count(),
-          this->temp_[param_id]->cpu_data(),
-          this->history_[param_id]->cpu_data(),
-          this->temp_[param_id]->mutable_cpu_data());
-
-      // divide history of updates by history of gradients
-      caffe_div(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          this->temp_[param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // jointly compute the RMS of both for update and gradient history
-      caffe_powx(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(), Dtype(0.5),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // compute the update
-      caffe_mul(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(),
-          this->update_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
-
-      // compute square of update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history of updates
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->cpu_data(), momentum,
-          this->history_[update_history_offset + param_id]->mutable_cpu_data());
-    }
+    // update history of gradients
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[param_id]->mutable_cpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[update_history_offset + param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->temp_[param_id]->mutable_cpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_div(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->temp_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_powx(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // compute the update
+    caffe_mul(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+
+    // compute square of update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history of updates
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_cpu_data());
+
+    // apply learning rate
+    caffe_cpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->mutable_cpu_diff());
     break;
-  case Caffe::GPU:
+  }
+  case Caffe::GPU: {
 #ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
 
-      // compute square of gradient in update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history of gradients
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->gpu_data(), momentum,
-          this->history_[param_id]->mutable_gpu_data());
-
-      // add delta to history to guard against dividing by zero later
-      caffe_gpu_set(net_params[param_id]->count(), delta,
-          this->temp_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->temp_[param_id]->gpu_data(),
-          this->history_[update_history_offset + param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->temp_[param_id]->gpu_data(),
-          this->history_[param_id]->gpu_data(),
-          this->temp_[param_id]->mutable_gpu_data());
-
-      // divide history of updates by history of gradients
-      caffe_gpu_div(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          this->temp_[param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // jointly compute the RMS of both for update and gradient history
-      caffe_gpu_powx(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(), Dtype(0.5),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // compute the update and copy to net_diff
-      caffe_gpu_mul(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(),
-          this->update_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
-
-      // compute square of update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history of updates
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-          this->update_[param_id]->gpu_data(), momentum,
-          this->history_[update_history_offset + param_id]->mutable_gpu_data());
-    }
+    // update history of gradients
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->gpu_data(), momentum,
+        this->history_[param_id]->mutable_gpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_gpu_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->temp_[param_id]->gpu_data(),
+        this->history_[update_history_offset + param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->temp_[param_id]->gpu_data(),
+        this->history_[param_id]->gpu_data(),
+        this->temp_[param_id]->mutable_gpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_gpu_div(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        this->temp_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_gpu_powx(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // compute the update and copy to net_diff
+    caffe_gpu_mul(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(),
+        this->update_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+
+    // compute square of update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history of updates
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->gpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_gpu_data());
+
+    // apply learning rate
+    caffe_gpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->gpu_diff(),
+        net_params[param_id]->mutable_gpu_diff());
 #else
     NO_GPU;
 #endif
     break;
+  }
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
   }
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 277aa3a5c8e..c97d4ede3b3 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -165,10 +165,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "    bottom: 'targets' "
        "  } "
        "} ";
-    if (learning_rate != 0) {
-      proto << "base_lr: " << learning_rate << " ";
-      proto << "lr_policy: 'fixed' ";
-    }
     if (weight_decay != 0) {
       proto << "weight_decay: " << weight_decay << " ";
     }
@@ -897,6 +893,139 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
   }
 }
 
+template <typename TypeParam>
+class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
+  }
+
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_ADADELTA;
+  }
+};
+
+TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.95;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest,
+           TestAdaDeltaLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 template <typename TypeParam>
 class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
@@ -1003,78 +1132,4 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
   }
 }
 
-template <typename TypeParam>
-class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  virtual void InitSolver(const SolverParameter& param) {
-    this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
-  }
-
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_ADADELTA;
-  }
-};
-
-TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  this->TestLeastSquaresUpdate(kLearningRate);
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.5;
-  const Dtype kMomentum = 0.95;
-  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.5;
-  const int kNumIters = 1;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
-  }
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.95;
-  const int kNumIters = 1;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
-  }
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.0;
-  const Dtype kMomentum = 0.95;
-  const int kNumIters = 4;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
-TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
-  typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.0;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.95;
-  const int kNumIters = 4;
-  for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
-  }
-}
-
 }  // namespace caffe

From 918c2aca42a0e9f87dab6f7b40d648f2c41321cb Mon Sep 17 00:00:00 2001
From: mfigurnov <michael@figurnov.ru>
Date: Mon, 10 Aug 2015 13:59:35 +0300
Subject: [PATCH 1483/2053] Fix truncation of value warning

---
 src/caffe/common.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index af96cac40aa..b40760efe16 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,4 +1,5 @@
 #include <glog/logging.h>
+#include <cmath>
 #include <cstdio>
 #include <ctime>
 
@@ -25,7 +26,7 @@ int64_t cluster_seedgen(void) {
 
   pid = getpid();
   s = time(NULL);
-  seed = abs(((s * 181) * ((pid - 83) * 359)) % 104729);
+  seed = std::abs(((s * 181) * ((pid - 83) * 359)) % 104729);
   return seed;
 }
 

From dde32b4ee631a647776e33ea92096558a2b37cbd Mon Sep 17 00:00:00 2001
From: Russell Stewart <Russell.S.Stewart@gmail.com>
Date: Mon, 10 Aug 2015 13:50:01 -0700
Subject: [PATCH 1484/2053] Update net_spec.py

---
 python/caffe/net_spec.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 31cde7ad946..77a0e0070ae 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -1,7 +1,7 @@
 """Python net specification.
 
 This module provides a way to write nets directly in Python, using a natural,
-functional style. See examples/python_nets/caffenet.py for an example.
+functional style. See examples/pycaffe/caffenet.py for an example.
 
 Currently this works as a thin wrapper around the Python protobuf interface,
 with layers and parameters automatically generated for the "layers" and

From bd2591e31e53e159c84874d26dcfe4a45d3c904e Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 11 Aug 2015 13:18:41 -0700
Subject: [PATCH 1485/2053] fix for learnable_param_ids_

---
 src/caffe/net.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 0e5ed804b73..91883a10a0a 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -485,6 +485,7 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
       CHECK(this_blob->shape() == owner_blob->shape());
     }
     const int learnable_param_id = learnable_param_ids_[owner_net_param_id];
+    learnable_param_ids_.push_back(learnable_param_id);
     if (param_spec->has_lr_mult()) {
       if (has_params_lr_[learnable_param_id]) {
         CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])

From 0d34d5ba0fbdc09ac8f372cb581ccaec599f10bc Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Tue, 11 Aug 2015 21:38:06 -0700
Subject: [PATCH 1486/2053] Data Layers Parallel for Multi-GPU

Allow data layers (and also PythonLayer when used as data layer) to be shared
among worker solver's training net, and also test net for future-proof if one
wants to do Multi-GPU testing. Data layers are locked during forward to ensure
sequential forward.
---
 include/caffe/data_layers.hpp  | 11 ++++++++++-
 include/caffe/layer.hpp        | 15 +++++++++++++++
 include/caffe/net.hpp          |  8 +++++---
 include/caffe/python_layer.hpp |  4 ++++
 include/caffe/solver.hpp       | 14 ++++++++++----
 src/caffe/net.cpp              | 32 ++++++++++++++++++++++++++++----
 src/caffe/parallel.cpp         |  3 +--
 src/caffe/proto/caffe.proto    |  4 ++++
 src/caffe/solver.cpp           | 23 +++++++++++++++++------
 9 files changed, 94 insertions(+), 20 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 12e6c366620..552d814131e 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -34,6 +34,8 @@ class BaseDataLayer : public Layer<Dtype> {
   // This method may not be overridden except by the BasePrefetchingDataLayer.
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
   // Data layers have no bottoms, so reshaping is trivial.
@@ -94,7 +96,8 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual ~DataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-
+  // DataLayer uses DataReader instead for sharing for parallelism
+  virtual inline bool ShareInParallel() const { return false; }
   virtual inline const char* type() const { return "Data"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int MinTopBlobs() const { return 1; }
@@ -118,6 +121,8 @@ class DummyDataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
@@ -151,6 +156,8 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual ~HDF5DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
@@ -192,6 +199,8 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual ~HDF5OutputLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 0771b6a8fb4..d82197a9c29 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -1,6 +1,7 @@
 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_
 
+#include <boost/thread.hpp>
 #include <algorithm>
 #include <string>
 #include <vector>
@@ -85,6 +86,14 @@ class Layer {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
+  /**
+   * @brief Whether a layer should be shared by multiple nets during data
+   *        parallelism. By default, all layers except for data layers should
+   *        not be shared. data layers should be shared to ensure each worker
+   *        solver access data sequentially during data parallelism.
+   */
+  virtual inline bool ShareInParallel() const { return false; }
+
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
@@ -396,6 +405,10 @@ class Layer {
     }
   }
 
+ private:
+  // mutex to lock layer to ensure sequential forward
+  boost::mutex forward_mutex_;
+
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 
@@ -405,6 +418,8 @@ class Layer {
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  // Lock during forward to ensure sequential forward
+  boost::mutex::scoped_lock lock(forward_mutex_);
   Dtype loss = 0;
   Reshape(bottom, top);
   switch (Caffe::mode()) {
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index bf997553ee2..1bf07d28d13 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -23,8 +23,9 @@ namespace caffe {
 template <typename Dtype>
 class Net {
  public:
-  explicit Net(const NetParameter& param);
-  explicit Net(const string& param_file, Phase phase);
+  explicit Net(const NetParameter& param, const Net* root_net = NULL);
+  explicit Net(const string& param_file, Phase phase,
+      const Net* root_net = NULL);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
@@ -291,7 +292,8 @@ class Net {
   size_t memory_used_;
   /// Whether to compute and display debug info for the net.
   bool debug_info_;
-
+  /// The root net that actually holds the shared layers in data parallelism
+  const Net* const root_net_;
   DISABLE_COPY_AND_ASSIGN(Net);
 };
 
diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
index 2957e7426be..c43c1e8a91b 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/python_layer.hpp
@@ -27,6 +27,10 @@ class PythonLayer : public Layer<Dtype> {
     self_.attr("reshape")(bottom, top);
   }
 
+  virtual inline bool ShareInParallel() const {
+    return this->layer_param_.python_param().share_in_parallel();
+  }
+
   virtual inline const char* type() const { return "Python"; }
 
  protected:
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 89a6c76d5f7..f583324acef 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -17,8 +17,9 @@ namespace caffe {
 template <typename Dtype>
 class Solver {
  public:
-  explicit Solver(const SolverParameter& param);
-  explicit Solver(const string& param_file);
+  explicit Solver(const SolverParameter& param,
+      const Solver* root_solver = NULL);
+  explicit Solver(const string& param_file, const Solver* root_solver = NULL);
   void Init(const SolverParameter& param);
   void InitTrainNet();
   void InitTestNets();
@@ -79,6 +80,10 @@ class Solver {
   vector<shared_ptr<Net<Dtype> > > test_nets_;
   vector<Callback*> callbacks_;
 
+  // The root solver that holds root nets (actually containing shared layers)
+  // in data parallelism
+  const Solver* const root_solver_;
+
   DISABLE_COPY_AND_ASSIGN(Solver);
 };
 
@@ -89,8 +94,9 @@ class Solver {
 template <typename Dtype>
 class WorkerSolver : public Solver<Dtype> {
  public:
-  explicit WorkerSolver(const SolverParameter& param)
-      : Solver<Dtype>(param) {}
+  explicit WorkerSolver(const SolverParameter& param,
+      const Solver<Dtype>* root_solver = NULL)
+      : Solver<Dtype>(param, root_solver) {}
 
  protected:
   void ApplyUpdate() {}
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 5d0f4322d19..14f8385c6ee 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -22,12 +22,14 @@
 namespace caffe {
 
 template <typename Dtype>
-Net<Dtype>::Net(const NetParameter& param) {
+Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
+    : root_net_(root_net) {
   Init(param);
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file, Phase phase) {
+Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)
+    : root_net_(root_net) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
   param.mutable_state()->set_phase(phase);
@@ -36,6 +38,8 @@ Net<Dtype>::Net(const string& param_file, Phase phase) {
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  CHECK(Caffe::root_solver() || root_net_)
+      << "root_net_ needs to be set for all non-root solvers";
   // Set phase from the state.
   phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
@@ -79,6 +83,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_id_vecs_.resize(param.layer_size());
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+    // For non-root solvers, whether this layer is shared from root_net_.
+    bool is_shared_layer = !Caffe::root_solver()
+        && root_net_->layers_[layer_id]->ShareInParallel();
     // Inherit phase from net if unset.
     if (!param.layer(layer_id).has_phase()) {
       param.mutable_layer(layer_id)->set_phase(phase_);
@@ -91,7 +98,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
           << "propagate_down param must be specified "
           << "either 0 or bottom_size times ";
     }
-    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    if (is_shared_layer) {
+      LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
+      layers_.push_back(root_net_->layers_[layer_id]);
+    } else {
+      layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    }
     layer_names_.push_back(layer_param.name());
     if (Caffe::root_solver()) {
       LOG(INFO) << "Creating Layer " << layer_param.name();
@@ -125,10 +137,22 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     // After this layer is connected, set it up.
+    if (is_shared_layer) {
+      // Set up size of top blobs using root_net_
+      const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
+      const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];
+      for (int top_id = 0; top_id < base_top.size(); ++top_id) {
+        this_top[top_id]->ReshapeLike(*base_top[top_id]);
+        LOG(INFO) << "Created top blob " << top_id << " (shape: "
+            << this_top[top_id]->shape_string() <<  ") for shared layer "
+            << layer_param.name();
+      }
+    } else {
+      layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
+    }
     if (Caffe::root_solver()) {
       LOG(INFO) << "Setting up " << layer_names_[layer_id];
     }
-    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 5a08df6c1c8..6e7d802bb99 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -218,7 +218,7 @@ P2PSync<Dtype>::P2PSync(shared_ptr<Solver<Dtype> > root_solver,
     solver_ = root_solver;
   } else {
     Caffe::set_root_solver(false);
-    solver_.reset(new WorkerSolver<Dtype>(param));
+    solver_.reset(new WorkerSolver<Dtype>(param, root_solver.get()));
     Caffe::set_root_solver(true);
   }
   this->configure(solver_.get());
@@ -436,4 +436,3 @@ INSTANTIATE_CLASS(GPUParams);
 INSTANTIATE_CLASS(P2PSync);
 
 }  // namespace caffe
-
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 41165410f33..e78c6686049 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -740,6 +740,10 @@ message PythonParameter {
   // string, dictionary in Python dict format, JSON, etc. You may parse this
   // string in `setup` method and use it in `forward` and `backward`.
   optional string param_str = 3 [default = ''];
+  // Whether this PythonLayer is shared among worker solvers during data parallelism.
+  // If true, each worker solver sequentially run forward from this layer.
+  // This value should be set true if you are using it as a data layer.
+  optional bool share_in_parallel = 4 [default = false];
 }
 
 // Message that stores parameters used by ReductionLayer
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index b6fd6b642f1..a44ff88dfd6 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -18,14 +18,14 @@
 namespace caffe {
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const SolverParameter& param)
-    : net_(), callbacks_() {
+Solver<Dtype>::Solver(const SolverParameter& param, const Solver* root_solver)
+    : net_(), callbacks_(), root_solver_(root_solver) {
   Init(param);
 }
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const string& param_file)
-    : net_(), callbacks_() {
+Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
+    : net_(), callbacks_(), root_solver_(root_solver) {
   SolverParameter param;
   ReadProtoFromTextFileOrDie(param_file, &param);
   Init(param);
@@ -33,6 +33,8 @@ Solver<Dtype>::Solver(const string& param_file)
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
+  CHECK(Caffe::root_solver() || root_solver_)
+      << "root_solver_ needs to be set for all non-root solvers";
   LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
     << std::endl << param.DebugString();
   param_ = param;
@@ -88,7 +90,11 @@ void Solver<Dtype>::InitTrainNet() {
   net_state.MergeFrom(net_param.state());
   net_state.MergeFrom(param_.train_state());
   net_param.mutable_state()->CopyFrom(net_state);
-  net_.reset(new Net<Dtype>(net_param));
+  if (Caffe::root_solver()) {
+    net_.reset(new Net<Dtype>(net_param));
+  } else {
+    net_.reset(new Net<Dtype>(net_param, root_solver_->net_.get()));
+  }
 }
 
 template <typename Dtype>
@@ -163,7 +169,12 @@ void Solver<Dtype>::InitTestNets() {
     net_params[i].mutable_state()->CopyFrom(net_state);
     LOG(INFO)
         << "Creating test net (#" << i << ") specified by " << sources[i];
-    test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+    if (Caffe::root_solver()) {
+      test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+    } else {
+      test_nets_[i].reset(new Net<Dtype>(net_params[i],
+          root_solver_->test_nets_[i].get()));
+    }
     test_nets_[i]->set_debug_info(param_.debug_info());
   }
 }

From 6b50ed6fc1897ce1ccd673cf0287788b38b58a6d Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Wed, 12 Aug 2015 12:05:56 -0700
Subject: [PATCH 1487/2053] Apply mutex only to shared layers and fix NVCC
 warning

---
 include/caffe/layer.hpp | 43 ++++++++++++++++++++++++++++++++++++-----
 src/caffe/layer.cpp     | 27 ++++++++++++++++++++++++++
 src/caffe/net.cpp       |  7 ++++---
 3 files changed, 69 insertions(+), 8 deletions(-)
 create mode 100644 src/caffe/layer.cpp

diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index d82197a9c29..a0d1d4ecc94 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -1,7 +1,6 @@
 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_
 
-#include <boost/thread.hpp>
 #include <algorithm>
 #include <string>
 #include <vector>
@@ -12,6 +11,12 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/device_alternate.hpp"
 
+/**
+ Forward declare boost::thread instead of including boost/thread.hpp
+ to avoid a boost/NVCC issues (#1009, #1010) on OSX.
+ */
+namespace boost { class mutex; }
+
 namespace caffe {
 
 /**
@@ -33,7 +38,7 @@ class Layer {
    * layer.
    */
   explicit Layer(const LayerParameter& param)
-    : layer_param_(param) {
+    : layer_param_(param), is_shared_(false) {
       // Set phase and copy blobs (if there are any).
       phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
@@ -61,6 +66,7 @@ class Layer {
    */
   void SetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+    InitMutex();
     CheckBlobCounts(bottom, top);
     LayerSetUp(bottom, top);
     Reshape(bottom, top);
@@ -94,6 +100,22 @@ class Layer {
    */
   virtual inline bool ShareInParallel() const { return false; }
 
+  /** @brief Return whether this layer is actually shared by other nets.
+   *         If ShareInParallel() is true and using more than one GPU and the
+   *         net has TRAIN phase, then this function is expected return true.
+   */
+  inline bool IsShared() const { return is_shared_; }
+
+  /** @brief Set whether this layer is actually shared by other nets
+   *         If ShareInParallel() is true and using more than one GPU and the
+   *         net has TRAIN phase, then is_shared should be set true.
+   */
+  inline void SetShared(bool is_shared) {
+    CHECK(ShareInParallel() || !is_shared)
+        << type() << "Layer does not support sharing.";
+    is_shared_ = is_shared;
+  }
+
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
@@ -406,8 +428,18 @@ class Layer {
   }
 
  private:
-  // mutex to lock layer to ensure sequential forward
-  boost::mutex forward_mutex_;
+  /** Whether this layer is actually shared by other nets*/
+  bool is_shared_;
+
+  /** The mutex for sequential forward if this layer is shared */
+  shared_ptr<boost::mutex> forward_mutex_;
+
+  /** Initialize forward_mutex_ */
+  void InitMutex();
+  /** Lock forward_mutex_ if this layer is shared */
+  void Lock();
+  /** Unlock forward_mutex_ if this layer is shared */
+  void Unlock();
 
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
@@ -419,7 +451,7 @@ template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   // Lock during forward to ensure sequential forward
-  boost::mutex::scoped_lock lock(forward_mutex_);
+  Lock();
   Dtype loss = 0;
   Reshape(bottom, top);
   switch (Caffe::mode()) {
@@ -450,6 +482,7 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
+  Unlock();
   return loss;
 }
 
diff --git a/src/caffe/layer.cpp b/src/caffe/layer.cpp
new file mode 100644
index 00000000000..3b9128986ae
--- /dev/null
+++ b/src/caffe/layer.cpp
@@ -0,0 +1,27 @@
+#include <boost/thread.hpp>
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void Layer<Dtype>::InitMutex() {
+  forward_mutex_.reset(new boost::mutex());
+}
+
+template <typename Dtype>
+void Layer<Dtype>::Lock() {
+  if (IsShared()) {
+    forward_mutex_->lock();
+  }
+}
+
+template <typename Dtype>
+void Layer<Dtype>::Unlock() {
+  if (IsShared()) {
+    forward_mutex_->unlock();
+  }
+}
+
+INSTANTIATE_CLASS(Layer);
+
+}  // namespace caffe
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 14f8385c6ee..7f5bdf7e2ba 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -84,7 +84,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
     // For non-root solvers, whether this layer is shared from root_net_.
-    bool is_shared_layer = !Caffe::root_solver()
+    bool share_from_root = !Caffe::root_solver()
         && root_net_->layers_[layer_id]->ShareInParallel();
     // Inherit phase from net if unset.
     if (!param.layer(layer_id).has_phase()) {
@@ -98,9 +98,10 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
           << "propagate_down param must be specified "
           << "either 0 or bottom_size times ";
     }
-    if (is_shared_layer) {
+    if (share_from_root) {
       LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
       layers_.push_back(root_net_->layers_[layer_id]);
+      layers_[layer_id]->SetShared(true);
     } else {
       layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     }
@@ -137,7 +138,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     // After this layer is connected, set it up.
-    if (is_shared_layer) {
+    if (share_from_root) {
       // Set up size of top blobs using root_net_
       const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
       const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];

From 3c6485a95e2c5653c601f07fd7f5875cf956f3e6 Mon Sep 17 00:00:00 2001
From: Ajinkya Kale <kaleajinkya@gmail.com>
Date: Thu, 13 Aug 2015 17:10:46 -0700
Subject: [PATCH 1488/2053] fixing the database param

The example talks about LevelDB as the db backend but has lmdb as the param in the execution.
---
 examples/feature_extraction/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index a980b8b3203..f3ec3609859 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -51,7 +51,7 @@ Extract Features
 
 Now everything necessary is in place.
 
-    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb
+    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb
 
 The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
 We can use any other layer, as well, such as `conv5` or `pool3`.

From 4c7e58e201ab2a06bb3b08d7c148e3b67988f853 Mon Sep 17 00:00:00 2001
From: PatWie <patrick@wieschollek.info>
Date: Fri, 14 Aug 2015 13:06:13 +0200
Subject: [PATCH 1489/2053] information about new implemented solvers

---
 docs/tutorial/solver.md | 79 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 78 insertions(+), 1 deletion(-)

diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index 17f793ef778..b150f6487bc 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -6,7 +6,14 @@ title: Solver / Model Optimization
 The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
 The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.
 
-The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NESTEROV).
+The Caffe solvers are:
+
+- Stochastic Gradient Descent (`SGD`), 
+- AdaDelta (`ADADELTA`),
+- Adaptive Gradient (`ADAGRAD`),
+- Adam (`ADAM`),
+- Nesterov's Accelerated Gradient (`NESTEROV`) and
+- RMSprop (`RMSPROP`)
 
 The solver
 
@@ -104,6 +111,32 @@ If learning diverges (e.g., you start to see very large or `NaN` or `inf` loss v
     [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
     *Advances in Neural Information Processing Systems*, 2012.
 
+### AdaDelta
+
+The **AdaDelta** (`solver_type: ADADELTA`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are
+
+$$
+\begin{align}
+(v_t)_i &= \frac{\operatorname{RMS}((v_{t-1})_i)}{\operatorname{RMS}\left( \nabla L(W_t) \right)_{i}} \left( \nabla L(W_{t'}) \right)_i
+\\
+\operatorname{RMS}\left( \nabla L(W_t) \right)_{i} &= \sqrt{E[g^2] + \varepsilon}
+\\
+E[g^2]_t &= \delta{E[g^2]_{t-1} } + (1-\delta)g_{t}^2
+\end{align}
+$$
+
+and 
+
+$$
+(W_{t+1})_i =
+(W_t)_i - \alpha
+(v_t)_i.
+$$
+
+[1] M. Zeiler
+    [ADADELTA: AN ADAPTIVE LEARNING RATE METHOD](http://arxiv.org/pdf/1212.5701.pdf).
+    *arXiv preprint*, 2012.
+
 ### AdaGrad
 
 The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
@@ -124,6 +157,28 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa
     [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf).
     *The Journal of Machine Learning Research*, 2011.
 
+### Adam
+
+The **Adam** (`solver_type: ADAM`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are
+
+$$
+(m_t)_i = \beta_1 (m_{t-1})_i + (1-\beta_1)(\nabla L(W_t))_i,\\
+(v_t)_i = \beta_2 (v_{t-1})_i + (1-\beta_2)(\nabla L(W_t))_i^2
+$$
+
+and
+
+$$
+(W_{t+1})_i =
+(W_t)_i - \alpha \frac{\sqrt{1-(\beta_2)_i^t}}{1-(\beta_1)_i^t}\frac{(m_t)_i}{\sqrt{(v_t)_i}+\varepsilon}.
+$$
+
+Kingma et al. [1] proposed to use $$\beta_1 = 0.9, \beta_2 = 0.999, \varepsilon = 10^{-8}$$ as default values. Caffe uses the values of `momemtum, momentum2, delta` for $$\beta_1, \beta_2, \varepsilon$$, respectively.
+
+[1] D. Kingma, J. Ba.
+    [Adam: A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980).
+    *International Conference for Learning Representations*, 2015.
+
 ### NAG
 
 **Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
@@ -149,6 +204,28 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we
     [On the Importance of Initialization and Momentum in Deep Learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf).
     *Proceedings of the 30th International Conference on Machine Learning*, 2013.
 
+### RMSprop
+
+The **RMSprop** (`solver_type: RMSPROP`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
+
+$$
+(v_t)_i = 
+\begin{cases}
+(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
+(v_{t-1})_i \cdot (1-\delta), & \text{else}
+\end{cases}
+$$
+
+$$
+(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i,
+$$
+
+If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$.
+
+[1] T. Tieleman, and G. Hinton.
+    [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
+    *COURSERA: Neural Networks for Machine Learning.Technical report*, 2012.
+
 ## Scaffolding
 
 The solver scaffolding prepares the optimization method and initializes the model to be learned in `Solver::Presolve()`.

From e696f85abe2c50107fbb67b211bf7dad0f87ade0 Mon Sep 17 00:00:00 2001
From: LI Yi <sincereli@tencent.com>
Date: Fri, 14 Aug 2015 20:51:45 +0800
Subject: [PATCH 1490/2053] Destroy CUDA stream when finished

---
 src/caffe/layers/base_data_layer.cpp | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 20f76f62994..5303fe9c5d3 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -74,7 +74,7 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
 #ifndef CPU_ONLY
   cudaStream_t stream;
   if (Caffe::mode() == Caffe::GPU) {
-    cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking);
+    CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
   }
 #endif
 
@@ -85,7 +85,7 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
 #ifndef CPU_ONLY
       if (Caffe::mode() == Caffe::GPU) {
         batch->data_.data().get()->async_gpu_push(stream);
-        cudaStreamSynchronize(stream);
+        CUDA_CHECK(cudaStreamSynchronize(stream));
       }
 #endif
       prefetch_full_.push(batch);
@@ -93,6 +93,11 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
   } catch (boost::thread_interrupted&) {
     // Interrupted exception is expected on shutdown
   }
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    CUDA_CHECK(cudaStreamDestroy(stream));
+  }
+#endif
 }
 
 template <typename Dtype>

From 4e4c89b026cb0b7f296aae4dfbb45e2eb1654f43 Mon Sep 17 00:00:00 2001
From: PatWie <patrick@wieschollek.info>
Date: Mon, 3 Aug 2015 17:31:14 +0200
Subject: [PATCH 1491/2053] Adam solver

This commit implements the Adam solver by Kingma et. al for CPU and
GPU. All solver parameters are defined in the caffe.proto. This also
adds an example for the MNIST dataset.
---
 examples/mnist/lenet_solver_adam.prototxt     |  26 +++
 examples/mnist/train_lenet_adam.sh            |   3 +
 include/caffe/solver.hpp                      |  17 ++
 src/caffe/proto/caffe.proto                   |   7 +-
 src/caffe/solver.cpp                          | 104 +++++++++++
 src/caffe/test/test_gradient_based_solver.cpp | 170 +++++++++++++++---
 6 files changed, 299 insertions(+), 28 deletions(-)
 create mode 100644 examples/mnist/lenet_solver_adam.prototxt
 create mode 100755 examples/mnist/train_lenet_adam.sh

diff --git a/examples/mnist/lenet_solver_adam.prototxt b/examples/mnist/lenet_solver_adam.prototxt
new file mode 100644
index 00000000000..d22c5718f3f
--- /dev/null
+++ b/examples/mnist/lenet_solver_adam.prototxt
@@ -0,0 +1,26 @@
+# The train/test net protocol buffer definition
+# this follows "ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION"
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# All parameters are from the cited paper above
+base_lr: 0.001
+momentum: 0.9
+momentum2: 0.999
+# since Adam dynamically changes the learning rate, we set the base learning
+# rate to a fixed value
+lr_policy: "fixed"
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+solver_type: ADAM
+solver_mode: GPU
diff --git a/examples/mnist/train_lenet_adam.sh b/examples/mnist/train_lenet_adam.sh
new file mode 100755
index 00000000000..a32ecf2d9c2
--- /dev/null
+++ b/examples/mnist/train_lenet_adam.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index d2b99923f23..582aa1427d3 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -217,6 +217,21 @@ class AdaDeltaSolver : public SGDSolver<Dtype> {
   DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
 };
 
+template <typename Dtype>
+class AdamSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdamSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { AdamPreSolve();}
+  explicit AdamSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
+
+ protected:
+  void AdamPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(AdamSolver);
+};
+
 template <typename Dtype>
 Solver<Dtype>* GetSolver(const SolverParameter& param) {
   SolverParameter_SolverType type = param.solver_type();
@@ -232,6 +247,8 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
       return new RMSPropSolver<Dtype>(param);
   case SolverParameter_SolverType_ADADELTA:
       return new AdaDeltaSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADAM:
+      return new AdamSolver<Dtype>(param);
   default:
       LOG(FATAL) << "Unknown SolverType: " << type;
   }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index fc0d961abda..d4c97d2bd06 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 39 (last added: rms_decay)
+// SolverParameter next available ID: 40 (last added: momentum2)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -216,10 +216,13 @@ message SolverParameter {
     ADAGRAD = 2;
     RMSPROP = 3;
     ADADELTA = 4;
+    ADAM = 5;
   }
   optional SolverType solver_type = 30 [default = SGD];
-  // numerical stability for AdaGrad
+  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
   optional float delta = 31 [default = 1e-8];
+  // parameters for the Adam solver
+  optional float momentum2 = 39 [default = 0.999];
 
   // RMSProp decay value
   // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 248f238eb76..9348e11c249 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -1114,11 +1114,115 @@ void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
 }
 
+template <typename Dtype>
+void AdamSolver<Dtype>::AdamPreSolve() {
+  // Add the extra history entries for Adam after those from
+  // SGDSolver::PreSolve
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const vector<int>& shape = net_params[i]->shape();
+    this->history_.push_back(
+            shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+template <typename Dtype>
+void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  const Dtype beta1 = this->param_.momentum();
+  const Dtype beta2 = this->param_.momentum2();
+
+  // we create aliases for convenience
+  size_t update_history_offset = net_params.size();
+  Blob<Dtype>* val_m = this->history_[param_id].get();
+  Blob<Dtype>* val_v = this->history_[param_id + update_history_offset].get();
+  Blob<Dtype>* val_t = this->temp_[param_id].get();
+
+  const int t = this->iter_  + 1;
+  const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) /
+      (Dtype(1.) - pow(beta1, t));
+  const int N = net_params[param_id]->count();
+  const Dtype eps_hat = this->param_.delta();
+
+  switch (Caffe::mode()) {
+    case Caffe::CPU: {
+    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
+    caffe_cpu_axpby(N, Dtype(1)-beta1,
+        net_params[param_id]->cpu_diff(), beta1,
+        val_m->mutable_cpu_data());
+
+    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
+    caffe_mul(N,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->cpu_diff(),
+    val_t->mutable_cpu_data());
+    caffe_cpu_axpby(N, Dtype(1)-beta2,
+        val_t->cpu_data(), beta2,
+        val_v->mutable_cpu_data());
+
+    // set update
+    caffe_powx(N,
+        val_v->cpu_data(), Dtype(0.5),
+        val_t->mutable_cpu_data());
+    caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data());
+    caffe_div(N,
+        val_m->cpu_data(),
+        val_t->cpu_data(),
+        val_t->mutable_cpu_data());
+
+    caffe_cpu_scale(N, local_rate*correction,
+        val_t->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
+    caffe_gpu_axpby(N, Dtype(1)-beta1,
+        net_params[param_id]->gpu_diff(), beta1,
+        val_m->mutable_gpu_data());
+
+    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
+    caffe_gpu_mul(N,
+        net_params[param_id]->gpu_diff(),
+        net_params[param_id]->gpu_diff(),
+        val_t->mutable_gpu_data());
+    caffe_gpu_axpby(N, Dtype(1)-beta2,
+        val_t->gpu_data(), beta2,
+        val_v->mutable_gpu_data());
+
+    // set update
+    caffe_gpu_powx(N,
+        val_v->gpu_data(), Dtype(0.5),
+        val_t->mutable_gpu_data());
+    caffe_gpu_add_scalar(N, eps_hat,
+        val_t->mutable_gpu_data());
+    caffe_gpu_div(N,
+        val_m->gpu_data(),
+        val_t->gpu_data(),
+        val_t->mutable_gpu_data());
+
+    caffe_gpu_scale(N, local_rate*correction,
+        val_t->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 INSTANTIATE_CLASS(Solver);
 INSTANTIATE_CLASS(SGDSolver);
 INSTANTIATE_CLASS(NesterovSolver);
 INSTANTIATE_CLASS(AdaGradSolver);
 INSTANTIATE_CLASS(RMSPropSolver);
 INSTANTIATE_CLASS(AdaDeltaSolver);
+INSTANTIATE_CLASS(AdamSolver);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 1d255a86621..dcbfff1cad2 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -42,7 +42,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // TODO this is brittle and the hdf5 file should be checked instead.
   int num_, channels_, height_, width_;
   bool share_;
-  Dtype delta_;  // Stability constant for AdaGrad.
+  Dtype delta_;  // Stability constant for RMSProp, AdaGrad, AdaDelta and Adam
 
   // Test data: check out generate_sample_data.py in the same directory.
   string* input_file_;
@@ -65,10 +65,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
         LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
     }
     InitSolver(param);
-    delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD ||
-        solver_type() == SolverParameter_SolverType_RMSPROP ||
-        solver_type() == SolverParameter_SolverType_ADADELTA) ?
-        param.delta() : 0;
+    delta_ = param.delta();
   }
 
   string RunLeastSquaresSolver(const Dtype learning_rate,
@@ -216,7 +213,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // updated_params will store the updated weight and bias results,
   // using the blobs' diffs to hold the update values themselves.
   void ComputeLeastSquaresUpdate(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
       vector<shared_ptr<Blob<Dtype> > >* updated_params) {
     const int N = num_;
     const int D = channels_ * height_ * width_;
@@ -282,7 +279,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
           ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
       // Finally, compute update.
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
-      if (solver_type() != SolverParameter_SolverType_ADADELTA) {
+      if (solver_type() != SolverParameter_SolverType_ADADELTA
+          && solver_type() != SolverParameter_SolverType_ADAM) {
         ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
       } else {
         ASSERT_EQ(4, history.size());  // additional blobs for update history
@@ -312,16 +310,31 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       case SolverParameter_SolverType_ADADELTA:
       {
         const Dtype update_history_value = (i == D) ?
-            history[3]->cpu_data()[0] : history[2]->cpu_data()[i];
+            history[1 + num_param_blobs]->cpu_data()[0] :
+            history[0 + num_param_blobs]->cpu_data()[i];
         const Dtype weighted_gradient_average =
             momentum * history_value + (1 - momentum) * (grad * grad);
         update_value = grad * std::sqrt((update_history_value + delta_) /
-            (weighted_gradient_average + delta_));
+            (weighted_gradient_average + delta_)) * learning_rate;
         // not actually needed, just here for illustrative purposes
         // const Dtype weighted_update_average =
         //   momentum * update_history_value + (1 - momentum) * (update_value);
         break;
       }
+      case SolverParameter_SolverType_ADAM: {
+        const Dtype momentum2 = 0.999;
+        const Dtype m = history_value;
+        const Dtype v = (i == D) ?
+            history[1 + num_param_blobs]->cpu_data()[0] :
+            history[0 + num_param_blobs]->cpu_data()[i];
+        const Dtype val_m = (1 - momentum) * grad + momentum * m;
+        const Dtype val_v = (1 - momentum2) * grad * grad + momentum2 * v;
+        Dtype alpha_t = learning_rate *
+            std::sqrt(Dtype(1) - pow(momentum2, num_iters)) /
+            (Dtype(1.) - pow(momentum, num_iters));
+        update_value = alpha_t * val_m / (std::sqrt(val_v) + delta_);
+        break;
+      }
       default:
         LOG(FATAL) << "Unknown solver type: " << solver_type();
       }
@@ -465,7 +478,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       // Compute the (K+1)th update using the analytic least squares gradient.
       vector<shared_ptr<Blob<Dtype> > > updated_params;
       ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-          &updated_params);
+          iter_to_check + 1, &updated_params);
 
       // Reinitialize the solver and run K+1 solver iterations.
       num_ = kNum;
@@ -946,13 +959,13 @@ TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
 
 TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.95;
   this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
@@ -960,64 +973,64 @@ TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
 
 TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
   }
 }
 
 TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
   }
 }
 
 TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.0;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
 TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
 TYPED_TEST(AdaDeltaSolverTest,
            TestAdaDeltaLeastSquaresUpdateWithEverythingShare) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
   this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
-      this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
 TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
@@ -1028,7 +1041,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
 
 TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
@@ -1040,7 +1053,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
 
 TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
@@ -1051,7 +1064,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) {
 
 TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
   const Dtype kMomentum = 0.95;
   const int kNumIters = 4;
@@ -1061,6 +1074,111 @@ TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) {
   }
 }
 
+template <typename TypeParam>
+class AdamSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    SolverParameter new_param = param;
+    const Dtype momentum = 0.9;
+    new_param.set_momentum(momentum);
+    const Dtype momentum2 = 0.999;
+    new_param.set_momentum2(momentum2);
+    this->solver_.reset(new AdamSolver<Dtype>(new_param));
+  }
+  virtual SolverParameter_SolverType solver_type() {
+    return SolverParameter_SolverType_ADAM;
+  }
+};
+
+TYPED_TEST_CASE(AdamSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
+  const Dtype kMomentum = 0.9;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdamSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdamSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 template <typename TypeParam>
 class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;

From bf42e6ebf7c56ff2f0d13bdcc7294d357d7592c6 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 13 Aug 2015 22:41:21 -0700
Subject: [PATCH 1492/2053] Cite Adam paper in solver.hpp

---
 include/caffe/solver.hpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 582aa1427d3..ab12ef1b1bd 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -217,6 +217,14 @@ class AdaDeltaSolver : public SGDSolver<Dtype> {
   DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
 };
 
+/**
+ * @brief AdamSolver, an algorithm for first-order gradient-based optimization
+ *        of stochastic objective functions, based on adaptive estimates of
+ *        lower-order moments. Described in [1].
+ *
+ * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization."
+ *     arXiv preprint arXiv:1412.6980v8 (2014).
+ */
 template <typename Dtype>
 class AdamSolver : public SGDSolver<Dtype> {
  public:

From 1d820f7fa81e6b02a64140a93edbfd30dc529e8b Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 14 Aug 2015 09:19:48 -0700
Subject: [PATCH 1493/2053] Malloc at least one byte in Parallel

---
 src/caffe/parallel.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 6e7d802bb99..d48136c56b8 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -64,7 +64,9 @@ static size_t total_size(const vector<Blob<Dtype>*>& params) {
   size_t size = 0;
   for (int i = 0; i < params.size(); ++i)
     size += params[i]->count();
-  return size;
+  // Size have at least one byte, otherwise cudaMalloc fails if net has no
+  // learnable parameters.
+  return (size > 0) ? size : 1;
 }
 
 template<typename Dtype>

From 98593e3aa64d9a8f42723fb03fa46a1343e12c33 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Fri, 14 Aug 2015 11:15:56 -0700
Subject: [PATCH 1494/2053] Make classification.bin support models with less
 than 5 classes

The example program would crash if the number of classes was less than
5, since it was still attempting to get the top 5 predictions.

Close #2585
---
 examples/cpp_classification/classification.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
index 1c6371e382b..dc8b863f53f 100644
--- a/examples/cpp_classification/classification.cpp
+++ b/examples/cpp_classification/classification.cpp
@@ -2,6 +2,7 @@
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
+#include <algorithm>
 #include <iosfwd>
 #include <memory>
 #include <string>
@@ -101,6 +102,7 @@ static std::vector<int> Argmax(const std::vector<float>& v, int N) {
 std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
   std::vector<float> output = Predict(img);
 
+  N = std::min<int>(labels_.size(), N);
   std::vector<int> maxN = Argmax(output, N);
   std::vector<Prediction> predictions;
   for (int i = 0; i < N; ++i) {

From e94be07e2d7e186821698df053ef77a35fe71c26 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Thu, 23 Jul 2015 08:33:58 -0700
Subject: [PATCH 1495/2053] Exposing blob loss weight to python

---
 python/caffe/_caffe.cpp |  4 ++++
 python/caffe/pycaffe.py | 10 ++++++++++
 2 files changed, 14 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index bb5130fddd0..e1ae3ec73c7 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -211,6 +211,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
         &Net<Dtype>::CopyTrainedLayersFrom))
     .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
+    .add_property("_blob_loss_weights", bp::make_function(
+        &Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
     .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
         bp::return_internal_reference<>()))
     .add_property("layers", bp::make_function(&Net<Dtype>::layers,
@@ -293,6 +295,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def(bp::vector_indexing_suite<vector<string> >());
   bp::class_<vector<int> >("IntVec")
     .def(bp::vector_indexing_suite<vector<int> >());
+  bp::class_<vector<Dtype> >("DtypeVec")
+    .def(bp::vector_indexing_suite<vector<Dtype> >());
   bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
   bp::class_<vector<bool> >("BoolVec")
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index e8a676a26d2..4f980a92c38 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -27,6 +27,15 @@ def _Net_blobs(self):
     return OrderedDict(zip(self._blob_names, self._blobs))
 
 
+@property
+def _Net_blob_loss_weights(self):
+    """
+    An OrderedDict (bottom to top, i.e., input to output) of network
+    blob loss weights indexed by name
+    """
+    return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
+
+
 @property
 def _Net_params(self):
     """
@@ -270,6 +279,7 @@ def _Net_batch(self, blobs):
 
 # Attach methods to Net.
 Net.blobs = _Net_blobs
+Net.blob_loss_weights = _Net_blob_loss_weights
 Net.params = _Net_params
 Net.forward = _Net_forward
 Net.backward = _Net_backward

From 7f4ffcd7c4d3896fd2a40cc4dd153ba04b1ba968 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 14 Aug 2015 12:52:39 -0700
Subject: [PATCH 1496/2053] [net] improve net config and shape mismatch error
 messages

---
 src/caffe/net.cpp                | 37 ++++++++++++++++++++++++++------
 src/caffe/util/insert_splits.cpp |  3 ++-
 2 files changed, 33 insertions(+), 7 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 7875285fea1..31644422e59 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -424,7 +424,8 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
              blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
     // If we are not doing in-place computation but have duplicated blobs,
     // raise an error.
-    LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
+    LOG(FATAL) << "Top blob '" << blob_name
+               << "' produced by multiple sources.";
   } else {
     // Normal output.
     if (Caffe::root_solver()) {
@@ -468,8 +469,8 @@ int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
   const LayerParameter& layer_param = param.layer(layer_id);
   const string& blob_name = layer_param.bottom(bottom_id);
   if (available_blobs->find(blob_name) == available_blobs->end()) {
-    LOG(FATAL) << "Unknown blob input " << blob_name
-               << " (at index " << bottom_id << ") to layer " << layer_id;
+    LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
+               << layer_param.name() << "', bottom index " << bottom_id << ")";
   }
   const int blob_id = (*blob_name_to_idx)[blob_name];
   if (Caffe::root_solver()) {
@@ -545,10 +546,19 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                                   ParamSpec_DimCheckMode_PERMISSIVE)) {
       // Permissive dimension checking -- only check counts are the same.
       CHECK_EQ(this_blob->count(), owner_blob->count())
-          << "Shared parameter blobs must have the same count.";
+          << "Cannot share param '" << param_name << "' owned by layer '"
+          << layer_names_[owner_layer_id] << "' with layer '"
+          << layer_names_[layer_id] << "'; count mismatch.  Owner layer param "
+          << "shape is " << owner_blob->shape_string() << "; sharing layer "
+          << "shape is " << this_blob->shape_string();
     } else {
       // Strict dimension checking -- all dims must be the same.
-      CHECK(this_blob->shape() == owner_blob->shape());
+      CHECK(this_blob->shape() == owner_blob->shape())
+          << "Cannot share param '" << param_name << "' owned by layer '"
+          << layer_names_[owner_layer_id] << "' with layer '"
+          << layer_names_[layer_id] << "'; shape mismatch.  Owner layer param "
+          << "shape is " << owner_blob->shape_string() << "; sharing layer "
+          << "expects shape " << this_blob->shape_string();
     }
     const int learnable_param_id = learnable_param_ids_[owner_net_param_id];
     learnable_param_ids_.push_back(learnable_param_id);
@@ -775,7 +785,11 @@ void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
       Blob<Dtype>* source_blob = source_layer->blobs()[j].get();
-      CHECK(target_blobs[j]->shape() == source_blob->shape());
+      CHECK(target_blobs[j]->shape() == source_blob->shape())
+          << "Cannot share param " << j << " weights from layer '"
+          << source_layer_name << "'; shape mismatch.  Source param shape is "
+          << source_blob->shape_string() << "; target param shape is "
+          << target_blobs[j]->shape_string();
       target_blobs[j]->ShareData(*source_blob);
     }
   }
@@ -839,6 +853,17 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
     CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
+      if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {
+        Blob<Dtype> source_blob;
+        const bool kReshape = true;
+        source_blob.FromProto(source_layer.blobs(j), kReshape);
+        LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"
+            << source_layer_name << "'; shape mismatch.  Source param shape is "
+            << source_blob.shape_string() << "; target param shape is "
+            << target_blobs[j]->shape_string() << ". "
+            << "To learn this layer's parameters from scratch rather than "
+            << "copying from a saved net, rename the layer.";
+      }
       const bool kReshape = false;
       target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
     }
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 416f80ab3c2..475a2a9f618 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -32,7 +32,8 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       const string& blob_name = layer_param.bottom(j);
       if (blob_name_to_last_top_idx.find(blob_name) ==
           blob_name_to_last_top_idx.end()) {
-        LOG(FATAL) << "Unknown blob input " << blob_name << " to layer " << j;
+        LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
+                   << layer_param.name() << "', bottom index " << j << ")";
       }
       const pair<int, int>& bottom_idx = make_pair(i, j);
       const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];

From 660cd1239038bac54942c1c4fefe694413e633a0 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 14 Aug 2015 16:22:34 -0700
Subject: [PATCH 1497/2053] New make target to only build the library.

---
 Makefile | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 05b783af35c..80bc37375be 100644
--- a/Makefile
+++ b/Makefile
@@ -386,11 +386,13 @@ endif
 ##############################
 # Define build targets
 ##############################
-.PHONY: all test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
+.PHONY: all lib test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples
+all: lib tools examples
+
+lib: $(STATIC_NAME) $(DYNAMIC_NAME)
 
 everything: $(EVERYTHING_TARGETS)
 

From 3778a2a6ca1c5d909f12add066478d04fe44b8cd Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 14 Aug 2015 13:52:01 -0700
Subject: [PATCH 1498/2053] Fix MultiGPU solver test with TEST_GPUID != 1

This is a patch for multi-gpu testing issue (#2926). The problem fixed in this
commit is that when calling make runtest with TEST_GPUID != 0 on a MultiGPU
machine, solver tests will crash because gpu ids in multi-gpu tests doesn't
match that of single GPU test.
---
 src/caffe/test/test_gradient_based_solver.cpp | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index dcbfff1cad2..7ad7467f86f 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -73,12 +73,19 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       const int iter_size = 1, const int devices = 1,
       const bool snapshot = false, const char* from_snapshot = NULL) {
     ostringstream proto;
+    int device_id = 0;
+#ifndef CPU_ONLY
+    if (Caffe::mode() == Caffe::GPU) {
+      CUDA_CHECK(cudaGetDevice(&device_id));
+    }
+#endif
     proto <<
        "snapshot_after_train: " << snapshot << " "
        "max_iter: " << num_iters << " "
        "base_lr: " << learning_rate << " "
        "lr_policy: 'fixed' "
        "iter_size: " << iter_size << " "
+       "device_id: " << device_id << " "
        "net_param { "
        "  name: 'TestNetwork' "
        "  layer { "
@@ -189,8 +196,12 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     } else {
       LOG(INFO) << "Multi-GPU test on " << devices << " devices";
       vector<int> gpus;
-      for (int i = 0; i < devices; ++i) {
-        gpus.push_back(i);
+      // put current device at the beginning
+      int device_id = solver_->param().device_id();
+      gpus.push_back(device_id);
+      for (int i = 0; gpus.size() < devices; ++i) {
+        if (i != device_id)
+          gpus.push_back(i);
       }
       Caffe::set_solver_count(gpus.size());
       this->sync_.reset(new P2PSync<Dtype>(

From 09868aced72a20bc30198b84a225e58fdd6f13a6 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 15 Aug 2015 19:02:57 -0700
Subject: [PATCH 1499/2053] Malloc at least 1 byte for MultiGPU P2PSync buffers

---
 src/caffe/parallel.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index d48136c56b8..a6d154e168e 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -55,7 +55,8 @@ static void apply_buffers(const vector<Blob<Dtype>*>& blobs,
     }
     ptr += size;
   }
-  CHECK_EQ(total_size, ptr - buffer);
+  // total_size is at least one byte
+  CHECK_EQ(total_size, (ptr == buffer ? 1 : ptr - buffer));
 }
 
 // Buffer size necessary to store given blobs

From 7453bbf6ea1aeb03330b5892a06276b69434f699 Mon Sep 17 00:00:00 2001
From: mhouston <mhouston@nvidia.com>
Date: Tue, 18 Aug 2015 13:40:00 -0700
Subject: [PATCH 1500/2053] Add some documentation on Multi-GPU support

---
 docs/multigpu.md | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)
 create mode 100644 docs/multigpu.md

diff --git a/docs/multigpu.md b/docs/multigpu.md
new file mode 100644
index 00000000000..4b202347417
--- /dev/null
+++ b/docs/multigpu.md
@@ -0,0 +1,24 @@
+---
+title: Multi-GPU Usage, Hardware Configuration Assumptions, and Performance
+---
+
+# Multi-GPU Usage
+
+Currently Multi-GPU is only supported via the C/C++ paths and only for training.
+
+The GPUs to be used for training can be set with the "-gpu" flag on the command line to the 'caffe' tool.  e.g. "build/tools/caffe train --solver=models/bvlc_alexnet/solver.prototxt --gpu=0,1" will train on GPUs 0 and 1.
+
+**NOTE**: each GPU runs the batchsize specified in your train_val.prototxt.  So if you go from 1 GPU to 2 GPU, your effective batchsize will double.  e.g. if your train_val.prototxt specified a batchsize of 256, if you run 2 GPUs your effective batch size is now 512.  So you need to adjust the batchsize when running multiple GPUs and/or adjust your solver params, specifically learning rate.
+
+# Hardware Configuration Assumptions
+
+The current implementation uses a tree reduction strategy.  e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate
+updated model, 0\-\>2, and then 0\-\>1, 2\-\>3. 
+
+For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
+
+Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
+
+# Scaling Performance
+
+Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
\ No newline at end of file

From 26a9880d72e81d415d1dc3bf449586ce54185ea4 Mon Sep 17 00:00:00 2001
From: mhouston <mhouston@nvidia.com>
Date: Tue, 18 Aug 2015 15:29:26 -0700
Subject: [PATCH 1501/2053] Add information about how to get GPU topology from
 nvidia-smi

---
 docs/multigpu.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/multigpu.md b/docs/multigpu.md
index 4b202347417..01cfb8938b5 100644
--- a/docs/multigpu.md
+++ b/docs/multigpu.md
@@ -19,6 +19,8 @@ For best performance, P2P DMA access between devices is needed. Without P2P acce
 
 Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
 
+"nvidia-smi topo -m" will show you the connectivity matrix.  You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.
+
 # Scaling Performance
 
 Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
\ No newline at end of file

From 3b00ca6d32647e683d9808d13243bd0240550901 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Thu, 20 Aug 2015 09:01:58 +0000
Subject: [PATCH 1502/2053] In BasePrefetchingDataLayer::Forward_cpu hanged
 top[0]->Reshape to top[0]->ReshapeLike, in line with other calls.

---
 src/caffe/layers/base_data_layer.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 5303fe9c5d3..b90bd4e0caf 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -105,8 +105,7 @@ void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
   // Reshape to loaded data.
-  top[0]->Reshape(batch->data_.num(), batch->data_.channels(),
-      batch->data_.height(), batch->data_.width());
+  top[0]->ReshapeLike(batch->data_);
   // Copy the data
   caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
              top[0]->mutable_cpu_data());

From 51b172ce2fcd7f63aa7830389af54d353f53a3bc Mon Sep 17 00:00:00 2001
From: Luke Yeager <lyeager@nvidia.com>
Date: Fri, 14 Aug 2015 16:53:39 -0700
Subject: [PATCH 1503/2053] Expose LayerFactory::LayerTypeList in pycaffe

Useful for validating NetParameters without crashing on SIGABRT
---
 include/caffe/layer_factory.hpp           | 31 +++++++++++++++--------
 python/caffe/__init__.py                  |  2 +-
 python/caffe/_caffe.cpp                   |  2 ++
 python/caffe/test/test_layer_type_list.py | 10 ++++++++
 4 files changed, 34 insertions(+), 11 deletions(-)
 create mode 100644 python/caffe/test/test_layer_type_list.py

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 32e849de0d2..2c2fde4d979 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -41,6 +41,7 @@
 
 #include <map>
 #include <string>
+#include <vector>
 
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
@@ -77,26 +78,36 @@ class LayerRegistry {
     const string& type = param.type();
     CreatorRegistry& registry = Registry();
     CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
-        << " (known types: " << LayerTypeList() << ")";
+        << " (known types: " << LayerTypeListString() << ")";
     return registry[type](param);
   }
 
+  static vector<string> LayerTypeList() {
+    CreatorRegistry& registry = Registry();
+    vector<string> layer_types;
+    for (typename CreatorRegistry::iterator iter = registry.begin();
+         iter != registry.end(); ++iter) {
+      layer_types.push_back(iter->first);
+    }
+    return layer_types;
+  }
+
  private:
   // Layer registry should never be instantiated - everything is done with its
   // static variables.
   LayerRegistry() {}
 
-  static string LayerTypeList() {
-    CreatorRegistry& registry = Registry();
-    string layer_types;
-    for (typename CreatorRegistry::iterator iter = registry.begin();
-         iter != registry.end(); ++iter) {
-      if (iter != registry.begin()) {
-        layer_types += ", ";
+  static string LayerTypeListString() {
+    vector<string> layer_types = LayerTypeList();
+    string layer_types_str;
+    for (vector<string>::iterator iter = layer_types.begin();
+         iter != layer_types.end(); ++iter) {
+      if (iter != layer_types.begin()) {
+        layer_types_str += ", ";
       }
-      layer_types += iter->first;
+      layer_types_str += *iter;
     }
-    return layer_types;
+    return layer_types_str;
   }
 };
 
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 1b2da510a90..6cc44e729f4 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
 from .pycaffe import Net, SGDSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index bb5130fddd0..f9b2dba1d64 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -200,6 +200,8 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::def("set_mode_gpu", &set_mode_gpu);
   bp::def("set_device", &Caffe::SetDevice);
 
+  bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
+
   bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
     bp::no_init)
     .def("__init__", bp::make_constructor(&Net_Init))
diff --git a/python/caffe/test/test_layer_type_list.py b/python/caffe/test/test_layer_type_list.py
new file mode 100644
index 00000000000..7edc80df069
--- /dev/null
+++ b/python/caffe/test/test_layer_type_list.py
@@ -0,0 +1,10 @@
+import unittest
+
+import caffe
+
+class TestLayerTypeList(unittest.TestCase):
+
+    def test_standard_types(self):
+        for type_name in ['Data', 'Convolution', 'InnerProduct']:
+            self.assertIn(type_name, caffe.layer_type_list(),
+                    '%s not in layer_type_list()' % type_name)

From 35657e31ad82f73a0682deb52cb1606b33a202be Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 20 Aug 2015 11:54:08 -0700
Subject: [PATCH 1504/2053] DeconvolutionLayer Backward_gpu fix: don't redo
 im2col

---
 src/caffe/layers/deconv_layer.cu | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index 39bc4de8c66..8a1eed8aa16 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -52,7 +52,8 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
           this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n));
+              bottom_diff + bottom[i]->offset(n),
+              this->param_propagate_down_[0]);
         }
       }
     }

From 2f5889cb84b8c7f8f64a6b7eb48e1dc624b18162 Mon Sep 17 00:00:00 2001
From: Luke Yeager <lyeager@nvidia.com>
Date: Thu, 20 Aug 2015 14:29:02 -0700
Subject: [PATCH 1505/2053] Use input_shape instead of input_dim in examples

---
 examples/cifar10/cifar10_full.prototxt                | 10 ++++++----
 examples/cifar10/cifar10_quick.prototxt               | 10 ++++++----
 examples/mnist/lenet.prototxt                         | 10 ++++++----
 examples/net_surgery/bvlc_caffenet_full_conv.prototxt | 10 ++++++----
 examples/net_surgery/conv.prototxt                    | 10 ++++++----
 examples/siamese/mnist_siamese.prototxt               | 10 ++++++----
 models/bvlc_alexnet/deploy.prototxt                   | 10 ++++++----
 models/bvlc_googlenet/deploy.prototxt                 | 10 ++++++----
 models/bvlc_reference_caffenet/deploy.prototxt        | 10 ++++++----
 models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt   | 10 ++++++----
 models/finetune_flickr_style/deploy.prototxt          | 10 ++++++----
 11 files changed, 66 insertions(+), 44 deletions(-)

diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index c16f7dca49f..446479da961 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -2,10 +2,12 @@ name: "CIFAR10_full_deploy"
 # N.B. input image must be in CIFAR-10 format
 # as described at http://www.cs.toronto.edu/~kriz/cifar.html
 input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 32
-input_dim: 32
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 32
+  dim: 32
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 1ad190e185f..9352fbf65df 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -1,9 +1,11 @@
 name: "CIFAR10_quick_test"
 input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 32
-input_dim: 32
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 32
+  dim: 32
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index cb42610fe1e..dff7123bf73 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -1,9 +1,11 @@
 name: "LeNet"
 input: "data"
-input_dim: 64
-input_dim: 1
-input_dim: 28
-input_dim: 28
+input_shape {
+  dim: 64
+  dim: 1
+  dim: 28
+  dim: 28
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 3c951970fc1..0cadde9b58b 100644
--- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,10 +1,12 @@
 # Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
 input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 451
-input_dim: 451
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 451
+  dim: 451
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
index 9444c63ab74..6b3e5c768d5 100644
--- a/examples/net_surgery/conv.prototxt
+++ b/examples/net_surgery/conv.prototxt
@@ -1,10 +1,12 @@
 # Simple single-layer network to showcase editing model parameters.
 name: "convolution"
 input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 100
-input_dim: 100
+input_shape {
+  dim: 1
+  dim: 1
+  dim: 100
+  dim: 100
+}
 layer {
   name: "conv"
   type: "Convolution"
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 0e903f85909..332731bd75f 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -1,9 +1,11 @@
 name: "mnist_siamese"
 input: "data"
-input_dim: 10000
-input_dim: 1
-input_dim: 28
-input_dim: 28
+input_shape {
+  dim: 10000
+  dim: 1
+  dim: 28
+  dim: 28
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index ced055b85d0..ff10daa9399 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -1,9 +1,11 @@
 name: "AlexNet"
 input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+  dim: 10
+  dim: 3
+  dim: 227
+  dim: 227
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index 4648bf26efc..1f90ee21630 100644
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -1,9 +1,11 @@
 name: "GoogleNet"
 input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 224
-input_dim: 224
+input_shape {
+  dim: 10
+  dim: 3
+  dim: 224
+  dim: 224
+}
 layer {
   name: "conv1/7x7_s2"
   type: "Convolution"
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 29ccf1469f7..127f1e265fd 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -1,9 +1,11 @@
 name: "CaffeNet"
 input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+  dim: 10
+  dim: 3
+  dim: 227
+  dim: 227
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ea9cf98a926..ae1df967742 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -1,9 +1,11 @@
 name: "R-CNN-ilsvrc13"
 input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+  dim: 10
+  dim: 3
+  dim: 227
+  dim: 227
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index 4a924f74927..0f07e47acab 100644
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -1,9 +1,11 @@
 name: "FlickrStyleCaffeNet"
 input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+input_shape {
+  dim: 10
+  dim: 3
+  dim: 227
+  dim: 227
+}
 layer {
   name: "conv1"
   type: "Convolution"

From 7146e596347db81869b5bfa9b4cb014e80be9732 Mon Sep 17 00:00:00 2001
From: David Larson <dplarson@ucsd.edu>
Date: Thu, 20 Aug 2015 15:18:03 -0700
Subject: [PATCH 1506/2053] [examples] fix link to feature visualization
 notebook

---
 examples/feature_extraction/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index a980b8b3203..2bc3dacbb69 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -64,7 +64,7 @@ If you meet with the error "Check failed: status.ok() Failed to open leveldb exa
 
     rm -rf examples/_temp/features/
 
-If you'd like to use the Python wrapper for extracting features, check out the [layer visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+If you'd like to use the Python wrapper for extracting features, check out the [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
 
 Clean Up
 --------

From 08086c7580ddfd0d2b996157b3f3b4c1a52fd2b5 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 21 Aug 2015 21:23:22 -0700
Subject: [PATCH 1507/2053] remove superfluous code in Net::ToProto

---
 src/caffe/net.cpp | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index a18ee63818e..554343f9767 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -763,12 +763,6 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   DLOG(INFO) << "Serializing " << layers_.size() << " layers";
   for (int i = 0; i < layers_.size(); ++i) {
     LayerParameter* layer_param = param->add_layer();
-    for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
-      layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);
-    }
-    for (int j = 0; j < top_id_vecs_[i].size(); ++j) {
-      layer_param->add_top(blob_names_[top_id_vecs_[i][j]]);
-    }
     layers_[i]->ToProto(layer_param, write_diff);
   }
 }

From ff19d5f5c010dd8d6bfcf768b4fe27d0458f17df Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Fri, 3 Apr 2015 16:11:23 -0500
Subject: [PATCH 1508/2053] Add signal handler and early exit/snapshot to
 Solver.

Add signal handler and early exit/snapshot to Solver.

Add signal handler and early exit/snapshot to Solver.

Also check for exit and snapshot when testing.

Skip running test after early exit.

Fix more lint.

Rebase on master.

Finish rebase on master.

Fixups per review comments.

Redress review comments.

Lint.

Correct error message wording.
---
 include/caffe/solver.hpp            |  37 ++++++++-
 include/caffe/util/signal_handler.h |  24 ++++++
 src/caffe/solver.cpp                |  70 +++++++++++++++--
 src/caffe/util/signal_handler.cpp   | 115 ++++++++++++++++++++++++++++
 tools/caffe.cpp                     |  32 +++++++-
 5 files changed, 268 insertions(+), 10 deletions(-)
 create mode 100644 include/caffe/util/signal_handler.h
 create mode 100644 src/caffe/util/signal_handler.cpp

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index ab12ef1b1bd..aba3e036004 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -1,6 +1,6 @@
 #ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
 #define CAFFE_OPTIMIZATION_SOLVER_HPP_
-
+#include <boost/function.hpp>
 #include <string>
 #include <vector>
 
@@ -8,6 +8,28 @@
 
 namespace caffe {
 
+/**
+  * @brief Enumeration of actions that a client of the Solver may request by
+  * implementing the Solver's action request function, which a
+  * a client may optionally provide in order to request early termination
+  * or saving a snapshot without exiting. In the executable caffe, this
+  * mechanism is used to allow the snapshot to be saved when stopping
+  * execution with a SIGINT (Ctrl-C).
+  */
+  namespace SolverAction {
+    enum Enum {
+      NONE = 0,  // Take no special action.
+      STOP = 1,  // Stop training. snapshot_after_train controls whether a
+                 // snapshot is created.
+      SNAPSHOT = 2  // Take a snapshot, and keep training.
+    };
+  }
+
+/**
+ * @brief Type of a function that returns a Solver Action enumeration.
+ */
+typedef boost::function<SolverAction::Enum()> ActionCallback;
+
 /**
  * @brief An interface for classes that perform optimization on Net%s.
  *
@@ -23,6 +45,12 @@ class Solver {
   void Init(const SolverParameter& param);
   void InitTrainNet();
   void InitTestNets();
+
+  // Client of the Solver optionally may call this in order to set the function
+  // that the solver uses to see what action it should take (e.g. snapshot or
+  // exit training early).
+  void SetActionFunction(ActionCallback func);
+  SolverAction::Enum GetRequestedAction();
   // The main entry of the solver function. In default, iter will be zero. Pass
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
@@ -84,6 +112,13 @@ class Solver {
   // in data parallelism
   const Solver* const root_solver_;
 
+  // A function that can be set by a client of the Solver to provide indication
+  // that it wants a snapshot saved and/or to exit early.
+  ActionCallback action_request_function_;
+
+  // True iff a request to stop early was received.
+  bool requested_early_exit_;
+
   DISABLE_COPY_AND_ASSIGN(Solver);
 };
 
diff --git a/include/caffe/util/signal_handler.h b/include/caffe/util/signal_handler.h
new file mode 100644
index 00000000000..fb84c65bd2e
--- /dev/null
+++ b/include/caffe/util/signal_handler.h
@@ -0,0 +1,24 @@
+#ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
+#define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
+
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+namespace caffe {
+
+class SignalHandler {
+ public:
+  // Contructor. Specify what action to take when a signal is received.
+  SignalHandler(SolverAction::Enum SIGINT_action,
+                SolverAction::Enum SIGHUP_action);
+  ~SignalHandler();
+  ActionCallback GetActionFunction();
+ private:
+  SolverAction::Enum CheckForSignals() const;
+  SolverAction::Enum SIGINT_action_;
+  SolverAction::Enum SIGHUP_action_;
+};
+
+}  // namespace caffe
+
+#endif  // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 9348e11c249..394ec3b3ad7 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -17,15 +17,31 @@
 
 namespace caffe {
 
+template<typename Dtype>
+void Solver<Dtype>::SetActionFunction(ActionCallback func) {
+  action_request_function_ = func;
+}
+
+template<typename Dtype>
+SolverAction::Enum Solver<Dtype>::GetRequestedAction() {
+  if (action_request_function_) {
+    // If the external request function has been set, call it.
+    return action_request_function_();
+  }
+  return SolverAction::NONE;
+}
+
 template <typename Dtype>
 Solver<Dtype>::Solver(const SolverParameter& param, const Solver* root_solver)
-    : net_(), callbacks_(), root_solver_(root_solver) {
+    : net_(), callbacks_(), root_solver_(root_solver),
+      requested_early_exit_(false) {
   Init(param);
 }
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
-    : net_(), callbacks_(), root_solver_(root_solver) {
+    : net_(), callbacks_(), root_solver_(root_solver),
+      requested_early_exit_(false) {
   SolverParameter param;
   ReadProtoFromTextFileOrDie(param_file, &param);
   Init(param);
@@ -195,6 +211,10 @@ void Solver<Dtype>::Step(int iters) {
         && (iter_ > 0 || param_.test_initialization())
         && Caffe::root_solver()) {
       TestAll();
+      if (requested_early_exit_) {
+        // Break out of the while loop because stop was requested while testing.
+        break;
+      }
     }
 
     for (int i = 0; i < callbacks_.size(); ++i) {
@@ -250,12 +270,20 @@ void Solver<Dtype>::Step(int iters) {
     // the number of times the weights have been updated.
     ++iter_;
 
+    SolverAction::Enum request = GetRequestedAction();
+
     // Save a snapshot if needed.
-    if (param_.snapshot()
-        && iter_ % param_.snapshot() == 0
-        && Caffe::root_solver()) {
+    if ((param_.snapshot()
+         && iter_ % param_.snapshot() == 0
+         && Caffe::root_solver()) ||
+         (request == SolverAction::SNAPSHOT)) {
       Snapshot();
     }
+    if (SolverAction::STOP == request) {
+      requested_early_exit_ = true;
+      // Break out of training loop.
+      break;
+    }
   }
 }
 
@@ -265,6 +293,9 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   LOG(INFO) << "Solving " << net_->name();
   LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
 
+  // Initialize to false every time we start solving.
+  requested_early_exit_ = false;
+
   if (resume_file) {
     LOG(INFO) << "Restoring previous solver status from " << resume_file;
     Restore(resume_file);
@@ -279,6 +310,10 @@ void Solver<Dtype>::Solve(const char* resume_file) {
       && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
     Snapshot();
   }
+  if (requested_early_exit_) {
+    LOG(INFO) << "Optimization stopped early.";
+    return;
+  }
   // After the optimization is done, run an additional train and test pass to
   // display the train and test loss/outputs if appropriate (based on the
   // display and test_interval settings, respectively).  Unlike in the rest of
@@ -296,10 +331,11 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   LOG(INFO) << "Optimization Done.";
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::TestAll() {
-  for (int test_net_id = 0; test_net_id < test_nets_.size(); ++test_net_id) {
+  for (int test_net_id = 0;
+       test_net_id < test_nets_.size() && !requested_early_exit_;
+       ++test_net_id) {
     Test(test_net_id);
   }
 }
@@ -317,6 +353,21 @@ void Solver<Dtype>::Test(const int test_net_id) {
   const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
   Dtype loss = 0;
   for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
+    SolverAction::Enum request = GetRequestedAction();
+    // Check to see if stoppage of testing/training has been requested.
+    while (request != SolverAction::NONE) {
+        if (SolverAction::SNAPSHOT == request) {
+          Snapshot();
+        } else if (SolverAction::STOP == request) {
+          requested_early_exit_ = true;
+        }
+        request = GetRequestedAction();
+    }
+    if (requested_early_exit_) {
+      // break out of test loop.
+      break;
+    }
+
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
         test_net->Forward(bottom_vec, &iter_loss);
@@ -341,6 +392,10 @@ void Solver<Dtype>::Test(const int test_net_id) {
       }
     }
   }
+  if (requested_early_exit_) {
+    LOG(INFO)     << "Test interrupted.";
+    return;
+  }
   if (param_.test_compute_loss()) {
     loss /= param_.test_iter(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
@@ -361,7 +416,6 @@ void Solver<Dtype>::Test(const int test_net_id) {
   }
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::Snapshot() {
   CHECK(Caffe::root_solver());
diff --git a/src/caffe/util/signal_handler.cpp b/src/caffe/util/signal_handler.cpp
new file mode 100644
index 00000000000..5d764ec524f
--- /dev/null
+++ b/src/caffe/util/signal_handler.cpp
@@ -0,0 +1,115 @@
+#include <boost/bind.hpp>
+#include <glog/logging.h>
+
+#include <signal.h>
+#include <csignal>
+
+#include "caffe/util/signal_handler.h"
+
+namespace {
+  static volatile sig_atomic_t got_sigint = false;
+  static volatile sig_atomic_t got_sighup = false;
+  static bool already_hooked_up = false;
+
+  void handle_signal(int signal) {
+    switch (signal) {
+    case SIGHUP:
+      got_sighup = true;
+      break;
+    case SIGINT:
+      got_sigint = true;
+      break;
+    }
+  }
+
+  void HookupHandler() {
+    if (already_hooked_up) {
+      LOG(FATAL) << "Tried to hookup signal handlers more than once.";
+    }
+    already_hooked_up = true;
+
+    struct sigaction sa;
+    // Setup the handler
+    sa.sa_handler = &handle_signal;
+    // Restart the system call, if at all possible
+    sa.sa_flags = SA_RESTART;
+    // Block every signal during the handler
+    sigfillset(&sa.sa_mask);
+    // Intercept SIGHUP and SIGINT
+    if (sigaction(SIGHUP, &sa, NULL) == -1) {
+      LOG(FATAL) << "Cannot install SIGHUP handler.";
+    }
+    if (sigaction(SIGINT, &sa, NULL) == -1) {
+      LOG(FATAL) << "Cannot install SIGINT handler.";
+    }
+  }
+
+  // Set the signal handlers to the default.
+  void UnhookHandler() {
+    if (already_hooked_up) {
+      struct sigaction sa;
+      // Setup the sighub handler
+      sa.sa_handler = SIG_DFL;
+      // Restart the system call, if at all possible
+      sa.sa_flags = SA_RESTART;
+      // Block every signal during the handler
+      sigfillset(&sa.sa_mask);
+      // Intercept SIGHUP and SIGINT
+      if (sigaction(SIGHUP, &sa, NULL) == -1) {
+        LOG(FATAL) << "Cannot uninstall SIGHUP handler.";
+      }
+      if (sigaction(SIGINT, &sa, NULL) == -1) {
+        LOG(FATAL) << "Cannot uninstall SIGINT handler.";
+      }
+
+      already_hooked_up = false;
+    }
+  }
+
+  // Return true iff a SIGINT has been received since the last time this
+  // function was called.
+  bool GotSIGINT() {
+    bool result = got_sigint;
+    got_sigint = false;
+    return result;
+  }
+
+  // Return true iff a SIGHUP has been received since the last time this
+  // function was called.
+  bool GotSIGHUP() {
+    bool result = got_sighup;
+    got_sighup = false;
+    return result;
+  }
+}  // namespace
+
+namespace caffe {
+
+SignalHandler::SignalHandler(SolverAction::Enum SIGINT_action,
+                             SolverAction::Enum SIGHUP_action):
+  SIGINT_action_(SIGINT_action),
+  SIGHUP_action_(SIGHUP_action) {
+  HookupHandler();
+}
+
+SignalHandler::~SignalHandler() {
+  UnhookHandler();
+}
+
+SolverAction::Enum SignalHandler::CheckForSignals() const {
+  if (GotSIGHUP()) {
+    return SIGHUP_action_;
+  }
+  if (GotSIGINT()) {
+    return SIGINT_action_;
+  }
+  return SolverAction::NONE;
+}
+
+// Return the function that the solver can use to find out if a snapshot or
+// early exit is being requested.
+ActionCallback SignalHandler::GetActionFunction() {
+  return boost::bind(&SignalHandler::CheckForSignals, this);
+}
+
+}  // namespace caffe
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 9f31b37ac2b..ff63860a3c1 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -12,6 +12,7 @@ namespace bp = boost::python;
 
 #include "boost/algorithm/string.hpp"
 #include "caffe/caffe.hpp"
+#include "caffe/util/signal_handler.h"
 
 using caffe::Blob;
 using caffe::Caffe;
@@ -39,6 +40,12 @@ DEFINE_string(weights, "",
     "separated by ','. Cannot be set simultaneously with snapshot.");
 DEFINE_int32(iterations, 50,
     "The number of iterations to run.");
+DEFINE_string(sigint_effect, "stop",
+             "Optional; action to take when a SIGINT signal is received: "
+              "snapshot, stop or none.");
+DEFINE_string(sighup_effect, "snapshot",
+             "Optional; action to take when a SIGHUP signal is received: "
+             "snapshot, stop or none.");
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
@@ -126,6 +133,22 @@ void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
   }
 }
 
+// Translate the signal effect the user specified on the command-line to the
+// corresponding enumeration.
+caffe::SolverAction::Enum GetRequestedAction(
+    const std::string& flag_value) {
+  if (flag_value == "stop") {
+    return caffe::SolverAction::STOP;
+  }
+  if (flag_value == "snapshot") {
+    return caffe::SolverAction::SNAPSHOT;
+  }
+  if (flag_value == "none") {
+    return caffe::SolverAction::NONE;
+  }
+  LOG(FATAL) << "Invalid signal effect \""<< flag_value << "\" was specified";
+}
+
 // Train / Finetune a model.
 int train() {
   CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
@@ -165,7 +188,14 @@ int train() {
     Caffe::set_solver_count(gpus.size());
   }
 
-  shared_ptr<Solver<float> > solver(caffe::GetSolver<float>(solver_param));
+  caffe::SignalHandler signal_handler(
+        GetRequestedAction(FLAGS_sigint_effect),
+        GetRequestedAction(FLAGS_sighup_effect));
+
+  shared_ptr<caffe::Solver<float> >
+    solver(caffe::GetSolver<float>(solver_param));
+
+  solver->SetActionFunction(signal_handler.GetActionFunction());
 
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;

From 374fb8c79c3f23ee36c46d0bcaeb2176037aa4b8 Mon Sep 17 00:00:00 2001
From: Ran <ran.manor@gmail.com>
Date: Sat, 15 Aug 2015 20:09:43 +0300
Subject: [PATCH 1509/2053] Output accuracies per class.

Fixed case where number of samples in class can be zero.

- Fixed ignore_label case, also added a test.
- Two other fixes.

Fixed lint errors.

Small fix.
---
 include/caffe/loss_layers.hpp          |   8 +-
 src/caffe/layers/accuracy_layer.cpp    |  20 +++++
 src/caffe/test/test_accuracy_layer.cpp | 107 +++++++++++++++++++++++++
 3 files changed, 134 insertions(+), 1 deletion(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 528266394f1..02687a944fb 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -39,7 +39,11 @@ class AccuracyLayer : public Layer<Dtype> {
 
   virtual inline const char* type() const { return "Accuracy"; }
   virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+  // If there are two top blobs, then the second blob will contain
+  // accuracies per class.
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlos() const { return 2; }
 
  protected:
   /**
@@ -86,6 +90,8 @@ class AccuracyLayer : public Layer<Dtype> {
   bool has_ignore_label_;
   /// The label indicating that an instance should be ignored.
   int ignore_label_;
+  /// Keeps counts of the number of samples per class.
+  Blob<Dtype> nums_buffer_;
 };
 
 /**
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 90aad675ed3..e2d8d9f8a24 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -38,6 +38,13 @@ void AccuracyLayer<Dtype>::Reshape(
       << "with integer values in {0, 1, ..., C-1}.";
   vector<int> top_shape(0);  // Accuracy is a scalar; 0 axes.
   top[0]->Reshape(top_shape);
+  if (top.size() > 1) {
+    // Per-class accuracy is a vector; 1 axes.
+    vector<int> top_shape_per_class(1);
+    top_shape_per_class[0] = bottom[0]->shape(label_axis_);
+    top[1]->Reshape(top_shape_per_class);
+    nums_buffer_.Reshape(top_shape_per_class);
+  }
 }
 
 template <typename Dtype>
@@ -50,6 +57,10 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int num_labels = bottom[0]->shape(label_axis_);
   vector<Dtype> maxval(top_k_+1);
   vector<int> max_id(top_k_+1);
+  if (top.size() > 1) {
+    caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());
+    caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());
+  }
   int count = 0;
   for (int i = 0; i < outer_num_; ++i) {
     for (int j = 0; j < inner_num_; ++j) {
@@ -58,6 +69,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (has_ignore_label_ && label_value == ignore_label_) {
         continue;
       }
+      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
       DCHECK_GE(label_value, 0);
       DCHECK_LT(label_value, num_labels);
       // Top-k accuracy
@@ -73,6 +85,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int k = 0; k < top_k_; k++) {
         if (bottom_data_vector[k].second == label_value) {
           ++accuracy;
+          if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
           break;
         }
       }
@@ -82,6 +95,13 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   // LOG(INFO) << "Accuracy: " << accuracy;
   top[0]->mutable_cpu_data()[0] = accuracy / count;
+  if (top.size() > 1) {
+    for (int i = 0; i < top[1]->count(); ++i) {
+      top[1]->mutable_cpu_data()[i] =
+          nums_buffer_.cpu_data()[i] == 0 ? 0
+          : top[1]->cpu_data()[i] / nums_buffer_.cpu_data()[i];
+    }
+  }
   // Accuracy layer should not be used as a loss function.
 }
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index c14b67cc0e9..94e529b5eee 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -22,6 +22,7 @@ class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
       : blob_bottom_data_(new Blob<Dtype>()),
         blob_bottom_label_(new Blob<Dtype>()),
         blob_top_(new Blob<Dtype>()),
+        blob_top_per_class_(new Blob<Dtype>()),
         top_k_(3) {
     vector<int> shape(2);
     shape[0] = 100;
@@ -34,6 +35,8 @@ class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
     blob_bottom_vec_.push_back(blob_bottom_data_);
     blob_bottom_vec_.push_back(blob_bottom_label_);
     blob_top_vec_.push_back(blob_top_);
+    blob_top_per_class_vec_.push_back(blob_top_);
+    blob_top_per_class_vec_.push_back(blob_top_per_class_);
   }
 
   virtual void FillBottoms() {
@@ -56,12 +59,15 @@ class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
     delete blob_top_;
+    delete blob_top_per_class_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
   Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_per_class_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  vector<Blob<Dtype>*> blob_top_per_class_vec_;
   int top_k_;
 };
 
@@ -90,6 +96,20 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
   EXPECT_EQ(this->blob_top_->width(), 1);
 }
 
+TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
+  LayerParameter layer_param;
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_per_class_->num(), 10);
+  EXPECT_EQ(this->blob_top_per_class_->channels(), 1);
+  EXPECT_EQ(this->blob_top_per_class_->height(), 1);
+  EXPECT_EQ(this->blob_top_per_class_->width(), 1);
+}
+
 TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   LayerParameter layer_param;
   AccuracyLayer<TypeParam> layer(layer_param);
@@ -228,4 +248,91 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
               num_correct_labels / 100.0, 1e-4);
 }
 
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  const int num_class = this->blob_top_per_class_->num();
+  vector<int> correct_per_class(num_class, 0);
+  vector<int> num_per_class(num_class, 0);
+  for (int i = 0; i < 100; ++i) {
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+      ++correct_per_class[max_id];
+    }
+  }
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / 100.0, 1e-4);
+  for (int i = 0; i < num_class; ++i) {
+    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                static_cast<float>(correct_per_class[i]) / num_per_class[i],
+                1e-4);
+  }
+}
+
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  const TypeParam kIgnoreLabelValue = -1;
+  layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  // Manually set some labels to the ignore label value (-1).
+  this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  const int num_class = this->blob_top_per_class_->num();
+  vector<int> correct_per_class(num_class, 0);
+  vector<int> num_per_class(num_class, 0);
+  int count = 0;
+  for (int i = 0; i < 100; ++i) {
+    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      continue;
+    }
+    ++count;
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+      ++correct_per_class[max_id];
+    }
+  }
+  EXPECT_EQ(count, 97);
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / TypeParam(count), 1e-4);
+  for (int i = 0; i < 10; ++i) {
+    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                TypeParam(correct_per_class[i]) / num_per_class[i],
+                1e-4);
+  }
+}
+
 }  // namespace caffe

From 4bed0ac9bab45246183184d84ff1b742e60574c7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 24 Aug 2015 19:44:18 -0700
Subject: [PATCH 1510/2053] TestConcatLayer: add gradient check for bottom[1]
 only (to verify propagate_down[0] == false correctness)

---
 src/caffe/test/test_concat_layer.cpp | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 662a50fa23b..088e0a41685 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -173,4 +173,13 @@ TYPED_TEST(ConcatLayerTest, TestGradientChannels) {
     this->blob_top_vec_);
 }
 
+TYPED_TEST(ConcatLayerTest, TestGradientChannelsBottomOneOnly) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradient(&layer, this->blob_bottom_vec_0_,
+    this->blob_top_vec_, 1);
+}
+
 }  // namespace caffe

From 6a7d4d6652018245f7bde1499d5208996912f3fb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 24 Aug 2015 19:22:54 -0700
Subject: [PATCH 1511/2053] bugfix for ConcatLayer with propagate_down set

if propagate_down[i] was set, offset_concat_axis was not correctly
updated for subsequent bottoms i+1, i+2, ...
---
 src/caffe/layers/concat_layer.cpp | 13 +++++++------
 src/caffe/layers/concat_layer.cu  | 17 +++++++++--------
 2 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 1cac8fc3387..95fba105b9a 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -76,13 +76,14 @@ void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
   for (int i = 0; i < bottom.size(); ++i) {
-    if (!propagate_down[i]) { continue; }
-    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    for (int n = 0; n < num_concats_; ++n) {
-      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
-          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
-          bottom_diff + n * bottom_concat_axis * concat_input_size_);
+    if (propagate_down[i]) {
+      Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+      for (int n = 0; n < num_concats_; ++n) {
+        caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
+            (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
+            bottom_diff + n * bottom_concat_axis * concat_input_size_);
+      }
     }
     offset_concat_axis += bottom_concat_axis;
   }
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 8f2e85d8f52..3c64c7ef224 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -53,15 +53,16 @@ void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const int top_concat_axis = top[0]->shape(concat_axis_);
   const bool kForward = false;
   for (int i = 0; i < bottom.size(); ++i) {
-    if (!propagate_down[i]) { continue; }
-    Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
-    const int nthreads = bottom_concat_size * num_concats_;
-    Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
-        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
-        nthreads, top_diff, kForward, num_concats_, concat_input_size_,
-        top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff);
+    if (propagate_down[i]) {
+      Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+      const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+      const int nthreads = bottom_concat_size * num_concats_;
+      Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+          nthreads, top_diff, kForward, num_concats_, concat_input_size_,
+          top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff);
+    }
     offset_concat_axis += bottom_concat_axis;
   }
 }

From 6aecb431319df1e6c97a6d15cda77ed62cb04148 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Wed, 19 Aug 2015 15:11:30 -0700
Subject: [PATCH 1512/2053] Allow the python layer have weight/parameter blobs.

---
 python/caffe/_caffe.cpp | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 020a5bee16b..1638e9d7cd2 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -189,7 +189,18 @@ bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
   // We need to explicitly return None to use bp::raw_function.
   return bp::object();
 }
-
+bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
+  if (bp::len(kwargs) > 0)
+    throw std::runtime_error("BlobVec.add_blob takes no kwargs");
+  typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
+  BlobVec* self = bp::extract<BlobVec*>(args[0]);
+  vector<int> shape(bp::len(args) - 1);
+  for (int i = 1; i < bp::len(args); ++i)
+    shape[i - 1] = bp::extract<int>(args[i]);
+  self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  // We need to explicitly return None to use bp::raw_function.
+  return bp::object();
+}
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -288,7 +299,8 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   // vector wrappers for all the vector types we use
   bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
-    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>())
+    .def("add_blob", bp::raw_function(&BlobVec_add_blob));
   bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
     .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
   bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")

From 60c0d58baab7be6c770d81f4c5a7cc1fce0ef7af Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 25 Aug 2015 10:20:53 -0700
Subject: [PATCH 1513/2053] Python parameter test added

---
 python/caffe/_caffe.cpp                |  8 +++-
 python/caffe/test/test_python_layer.py | 54 ++++++++++++++++++++++++++
 2 files changed, 60 insertions(+), 2 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 1638e9d7cd2..cc49f60ab13 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -189,18 +189,22 @@ bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
   // We need to explicitly return None to use bp::raw_function.
   return bp::object();
 }
+
 bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
-  if (bp::len(kwargs) > 0)
+  if (bp::len(kwargs) > 0) {
     throw std::runtime_error("BlobVec.add_blob takes no kwargs");
+  }
   typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
   BlobVec* self = bp::extract<BlobVec*>(args[0]);
   vector<int> shape(bp::len(args) - 1);
-  for (int i = 1; i < bp::len(args); ++i)
+  for (int i = 1; i < bp::len(args); ++i) {
     shape[i - 1] = bp::extract<int>(args[i]);
+  }
   self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
   // We need to explicitly return None to use bp::raw_function.
   return bp::object();
 }
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index a1e11bc248d..8ed86655ec3 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -28,6 +28,21 @@ class ExceptionLayer(caffe.Layer):
     def setup(self, bottom, top):
         raise RuntimeError
 
+class ParameterLayer(caffe.Layer):
+    """A layer that just multiplies by ten"""
+
+    def setup(self, bottom, top):
+        self.blobs.add_blob(1)
+        self.blobs[0].data[0] = 0
+
+    def reshape(self, bottom, top):
+        top[0].reshape(*bottom[0].data.shape)
+
+    def forward(self, bottom, top):
+        pass
+
+    def backward(self, top, propagate_down, bottom):
+        self.blobs[0].diff[0] = 1
 
 def python_net_file():
     with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
@@ -52,6 +67,16 @@ def exception_net_file():
         return f.name
 
 
+def parameter_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
+          python_param { module: 'test_python_layer' layer: 'ParameterLayer' } }
+          """)
+        return f.name
+
+
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):
         net_file = python_net_file()
@@ -84,3 +109,32 @@ def test_exception(self):
         net_file = exception_net_file()
         self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST)
         os.remove(net_file)
+
+    def test_parameter(self):
+        net_file = parameter_net_file()
+        net = caffe.Net(net_file, caffe.TRAIN)
+        # Test forward and backward
+        net.forward()
+        net.backward()
+        layer = net.layers[list(net._layer_names).index('layer')]
+        self.assertEqual(layer.blobs[0].data[0], 0)
+        self.assertEqual(layer.blobs[0].diff[0], 1)
+        layer.blobs[0].data[0] += layer.blobs[0].diff[0]
+        self.assertEqual(layer.blobs[0].data[0], 1)
+
+        # Test saving and loading
+        h, caffemodel_file = tempfile.mkstemp()
+        net.save(caffemodel_file)
+        layer.blobs[0].data[0] = -1
+        self.assertEqual(layer.blobs[0].data[0], -1)
+        net.copy_from(caffemodel_file)
+        self.assertEqual(layer.blobs[0].data[0], 1)
+        os.remove(caffemodel_file)
+        
+        # Test weight sharing
+        net2 = caffe.Net(net_file, caffe.TRAIN)
+        net2.share_with(net)
+        layer = net.layers[list(net2._layer_names).index('layer')]
+        self.assertEqual(layer.blobs[0].data[0], 1)
+
+        os.remove(net_file)

From 251e67ab3141bc8ac2adf97ea4e961e5664ae008 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 31 Dec 2014 14:07:00 -0800
Subject: [PATCH 1514/2053] Add TileLayer

---
 include/caffe/common_layers.hpp    |  29 ++++++
 src/caffe/layers/tile_layer.cpp    |  62 +++++++++++
 src/caffe/layers/tile_layer.cu     |  42 ++++++++
 src/caffe/proto/caffe.proto        |  13 ++-
 src/caffe/test/test_tile_layer.cpp | 162 +++++++++++++++++++++++++++++
 5 files changed, 307 insertions(+), 1 deletion(-)
 create mode 100644 src/caffe/layers/tile_layer.cpp
 create mode 100644 src/caffe/layers/tile_layer.cu
 create mode 100644 src/caffe/test/test_tile_layer.cpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 691e755f88f..8e64b3e5dc5 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -644,6 +644,35 @@ class SliceLayer : public Layer<Dtype> {
   vector<int> slice_point_;
 };
 
+/**
+ * @brief Copy a Blob along specified dimensions.
+ */
+template <typename Dtype>
+class TileLayer : public Layer<Dtype> {
+ public:
+  explicit TileLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Tile"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
+};
+
 }  // namespace caffe
 
 #endif  // CAFFE_COMMON_LAYERS_HPP_
diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp
new file mode 100644
index 00000000000..f55008cc53a
--- /dev/null
+++ b/src/caffe/layers/tile_layer.cpp
@@ -0,0 +1,62 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void TileLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const TileParameter& tile_param = this->layer_param_.tile_param();
+  axis_ = bottom[0]->CanonicalAxisIndex(tile_param.axis());
+  CHECK(tile_param.has_tiles()) << "Number of tiles must be specified";
+  tiles_ = tile_param.tiles();
+  CHECK_GT(tiles_, 0) << "Number of tiles must be positive.";
+  vector<int> top_shape = bottom[0]->shape();
+  top_shape[axis_] = bottom[0]->shape(axis_) * tiles_;
+  top[0]->Reshape(top_shape);
+  outer_dim_ = bottom[0]->count(0, axis_);
+  inner_dim_ = bottom[0]->count(axis_);
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < outer_dim_; ++i) {
+    for (int t = 0; t < tiles_; ++t) {
+      caffe_copy(inner_dim_, bottom_data, top_data);
+      top_data += inner_dim_;
+    }
+    bottom_data += inner_dim_;
+  }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  for (int i = 0; i < outer_dim_; ++i) {
+    caffe_copy(inner_dim_, top_diff, bottom_diff);
+    top_diff += inner_dim_;
+    for (int t = 1; t < tiles_; ++t) {
+      caffe_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff);
+      top_diff += inner_dim_;
+    }
+    bottom_diff += inner_dim_;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(TileLayer);
+#endif
+
+INSTANTIATE_CLASS(TileLayer);
+REGISTER_LAYER_CLASS(Tile);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu
new file mode 100644
index 00000000000..3af8e2eb72f
--- /dev/null
+++ b/src/caffe/layers/tile_layer.cu
@@ -0,0 +1,42 @@
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void TileLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  for (int i = 0; i < outer_dim_; ++i) {
+    for (int t = 0; t < tiles_; ++t) {
+      caffe_copy(inner_dim_, bottom_data, top_data);
+      top_data += inner_dim_;
+    }
+    bottom_data += inner_dim_;
+  }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  for (int i = 0; i < outer_dim_; ++i) {
+    caffe_copy(inner_dim_, top_diff, bottom_diff);
+    top_diff += inner_dim_;
+    for (int t = 1; t < tiles_; ++t) {
+      caffe_gpu_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff);
+      top_diff += inner_dim_;
+    }
+    bottom_diff += inner_dim_;
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(TileLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 35264610bdd..aa299f8660b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -301,7 +301,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 138 (last added: embed_param)
+// LayerParameter next available layer-specific ID: 139 (last added: tile_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -383,6 +383,7 @@ message LayerParameter {
   optional SliceParameter slice_param = 126;
   optional TanHParameter tanh_param = 127;
   optional ThresholdParameter threshold_param = 128;
+  optional TileParameter tile_param = 138;
   optional WindowDataParameter window_data_param = 129;
 }
 
@@ -919,6 +920,16 @@ message TanHParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
+// Message that stores parameters used by TileLayer
+message TileParameter {
+  // The index of the axis to tile.
+  optional int32 axis = 1 [default = 1];
+
+  // The number of copies (tiles) of the blob to output.
+  optional int32 tiles = 2;
+}
+
+// Message that stores parameters used by ThresholdLayer
 message ThresholdParameter {
   optional float threshold = 1 [default = 0]; // Strictly positive values
 }
diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp
new file mode 100644
index 00000000000..540aac3c2d3
--- /dev/null
+++ b/src/caffe/test/test_tile_layer.cpp
@@ -0,0 +1,162 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class TileLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  TileLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+    FillerParameter filler_param;
+    filler_param.set_mean(0.0);
+    filler_param.set_std(1.0);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(blob_bottom_);
+  }
+
+  virtual ~TileLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(TileLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(TileLayerTest, TestTrivialSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 1;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) {
+    layer_param.mutable_tile_param()->set_axis(i);
+    TileLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes());
+    for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) {
+      EXPECT_EQ(this->blob_top_->shape(j), this->blob_bottom_->shape(j));
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) {
+    layer_param.mutable_tile_param()->set_axis(i);
+    TileLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes());
+    for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) {
+      const int top_dim =
+          ((i == j) ? kNumTiles : 1) * this->blob_bottom_->shape(j);
+      EXPECT_EQ(top_dim, this->blob_top_->shape(j));
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestForwardNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kTileAxis = 0;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_axis(kTileAxis);
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+       for (int h = 0; h < this->blob_top_->height(); ++h) {
+         for (int w = 0; w < this->blob_top_->width(); ++w) {
+           const int bottom_n = n % this->blob_bottom_->num();
+           EXPECT_EQ(this->blob_bottom_->data_at(bottom_n, c, h, w),
+                     this->blob_top_->data_at(n, c, h, w));
+         }
+       }
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestForwardChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+       for (int h = 0; h < this->blob_top_->height(); ++h) {
+         for (int w = 0; w < this->blob_top_->width(); ++w) {
+           const int bottom_c = c % this->blob_bottom_->channels();
+           EXPECT_EQ(this->blob_bottom_->data_at(n, bottom_c, h, w),
+                     this->blob_top_->data_at(n, c, h, w));
+         }
+       }
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestTrivialGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 1;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(TileLayerTest, TestGradientNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kTileAxis = 0;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_axis(kTileAxis);
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(TileLayerTest, TestGradientChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kTileAxis = 1;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_axis(kTileAxis);
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe

From cbff2255bc8470299e15cc155ae7957a3acdd688 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 18 Aug 2015 18:15:20 -0700
Subject: [PATCH 1515/2053] TileLayer: add CUDA kernels

---
 src/caffe/layers/tile_layer.cu | 53 +++++++++++++++++++++++++---------
 1 file changed, 39 insertions(+), 14 deletions(-)

diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu
index 3af8e2eb72f..7fd3bc47d0f 100644
--- a/src/caffe/layers/tile_layer.cu
+++ b/src/caffe/layers/tile_layer.cu
@@ -6,17 +6,45 @@
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void Tile(const int nthreads, const Dtype* bottom_data,
+    const int tile_size, const int num_tiles, const int bottom_tile_axis,
+    Dtype* top_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int d = index % tile_size;
+    const int b = (index / tile_size / num_tiles) % bottom_tile_axis;
+    const int n = index / tile_size / num_tiles / bottom_tile_axis;
+    const int bottom_index = (n * bottom_tile_axis + b) * tile_size + d;
+    top_data[index] = bottom_data[bottom_index];
+  }
+}
+
 template <typename Dtype>
 void TileLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  for (int i = 0; i < outer_dim_; ++i) {
-    for (int t = 0; t < tiles_; ++t) {
-      caffe_copy(inner_dim_, bottom_data, top_data);
-      top_data += inner_dim_;
+  const int bottom_tile_axis = bottom[0]->shape(axis_);
+  const int nthreads = top[0]->count();
+  Tile<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+      nthreads, bottom_data, inner_dim_, tiles_, bottom_tile_axis, top_data);
+}
+
+template <typename Dtype>
+__global__ void TileBackward(const int nthreads, const Dtype* top_diff,
+    const int tile_size, const int num_tiles, const int bottom_tile_axis,
+    Dtype* bottom_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int d = index % tile_size;
+    const int b = (index / tile_size) % bottom_tile_axis;
+    const int n = index / tile_size / bottom_tile_axis;
+    bottom_diff[index] = 0;
+    int top_index = (n * num_tiles * bottom_tile_axis + b) * tile_size + d;
+    for (int t = 0; t < num_tiles; ++t) {
+      bottom_diff[index] += top_diff[top_index];
+      top_index += bottom_tile_axis * tile_size;
     }
-    bottom_data += inner_dim_;
   }
 }
 
@@ -26,15 +54,12 @@ void TileLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (!propagate_down[0]) { return; }
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  for (int i = 0; i < outer_dim_; ++i) {
-    caffe_copy(inner_dim_, top_diff, bottom_diff);
-    top_diff += inner_dim_;
-    for (int t = 1; t < tiles_; ++t) {
-      caffe_gpu_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff);
-      top_diff += inner_dim_;
-    }
-    bottom_diff += inner_dim_;
-  }
+  const int bottom_tile_axis = bottom[0]->shape(axis_);
+  const int tile_size = inner_dim_ / bottom_tile_axis;
+  const int nthreads = bottom[0]->count();
+  TileBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+      nthreads, top_diff, tile_size, tiles_, bottom_tile_axis, bottom_diff);
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(TileLayer);

From 1f3f9529df6285a5be5f8e72bd1922a6a0cec4d8 Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Sun, 23 Aug 2015 18:57:16 -0500
Subject: [PATCH 1516/2053] MVNLayer fixes.

Fix the MVNLayer tests so they actually test what they claim.

MVNLayer fixes: sum_multiplier_ sized correctly; backward gradient calculation.

Gradient calculation per analysis of seanbell, found here:
https://github.com/BVLC/caffe/issues/1938

Fixes according to review comments.
---
 src/caffe/layers/mvn_layer.cpp    | 15 ++++++++++++---
 src/caffe/layers/mvn_layer.cu     |  7 ++++++-
 src/caffe/test/test_mvn_layer.cpp | 13 +++++++++----
 3 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 3e79bddcdde..325691b1875 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -18,8 +18,12 @@ void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       1, 1);
   temp_.Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
-  sum_multiplier_.Reshape(1, 1,
-      bottom[0]->height(), bottom[0]->width());
+  if ( this->layer_param_.mvn_param().across_channels() ) {
+    sum_multiplier_.Reshape(1, bottom[0]->channels(), bottom[0]->height(),
+                            bottom[0]->width());
+  } else {
+    sum_multiplier_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+  }
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
   caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
   eps_ = this->layer_param_.mvn_param().eps();
@@ -130,7 +134,12 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 
     caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
   } else {
-    caffe_copy(temp_.count(), top_diff, bottom_diff);
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, top_diff,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+      mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+      temp_.mutable_cpu_data());
+    caffe_add(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 3888a0c7106..d86a2e73fc2 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -113,7 +113,12 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
     caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
   } else {
-    caffe_copy(temp_.count(), top_diff, bottom_diff);
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, top_diff,
+            sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+            temp_.mutable_gpu_data());
+    caffe_gpu_add(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff);
   }
 }
 
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index 933b4326417..be23d86e9c3 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -6,6 +6,7 @@
 #include "caffe/common.hpp"
 #include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -73,7 +74,8 @@ TYPED_TEST(MVNLayerTest, TestForward) {
 TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{normalize_variance: false}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -105,7 +107,8 @@ TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
 TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{across_channels: true}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{across_channels: true}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -149,7 +152,8 @@ TYPED_TEST(MVNLayerTest, TestGradient) {
 TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{normalize_variance: false}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
@@ -159,7 +163,8 @@ TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
 TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{across_channels: true}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{across_channels: true}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

From c548a7972b554b3ababaa0dc52db15a5f5a28be8 Mon Sep 17 00:00:00 2001
From: Jonas Maaskola <jonas.maaskola@scilifelab.se>
Date: Sun, 2 Aug 2015 23:47:12 +0200
Subject: [PATCH 1517/2053] Draw Deconvolution layers like Convolution layers

---
 python/caffe/draw.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 324929deca4..a002b60b59a 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -40,7 +40,7 @@ def get_edge_label(layer):
 
     if layer.type == 'Data':
         edge_label = 'Batch ' + str(layer.data_param.batch_size)
-    elif layer.type == 'Convolution':
+    elif layer.type == 'Convolution' or layer.type == 'Deconvolution':
         edge_label = str(layer.convolution_param.num_output)
     elif layer.type == 'InnerProduct':
         edge_label = str(layer.inner_product_param.num_output)
@@ -74,7 +74,7 @@ def get_layer_label(layer, rankdir):
         # horizontal space is not; separate words with newlines
         separator = '\\n'
 
-    if layer.type == 'Convolution':
+    if layer.type == 'Convolution' or layer.type == 'Deconvolution':
         # Outer double quotes needed or else colon characters don't parse
         # properly
         node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
@@ -109,7 +109,7 @@ def choose_color_by_layertype(layertype):
     """Define colors for nodes based on the layer type.
     """
     color = '#6495ED'  # Default
-    if layertype == 'Convolution':
+    if layertype == 'Convolution' or layertype == 'Deconvolution':
         color = '#FF5050'
     elif layertype == 'Pooling':
         color = '#FF9900'

From 292dbc5866c0b0b2ad56278591dce8b519166b20 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Mon, 24 Aug 2015 14:46:23 -0700
Subject: [PATCH 1518/2053] Fix previous mistake on unimplemented top and
 address pyramid_height_==1 in SPPLayer

also, do nothing in SPPLayer Reshape if already reshaped once and bottom size unchanged
---
 include/caffe/vision_layers.hpp | 10 +++------
 src/caffe/layers/spp_layer.cpp  | 36 +++++++++++++++++++++++++++++++++
 2 files changed, 39 insertions(+), 7 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index a6bd86a93f5..211e3d9042d 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -471,13 +471,7 @@ class SPPLayer : public Layer<Dtype> {
 
   virtual inline const char* type() const { return "SPP"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  // MAX POOL layers can output an extra top blob for the mask;
-  // others can only output the pooled inputs.
-  virtual inline int MaxTopBlobs() const {
-    return (this->layer_param_.pooling_param().pool() ==
-            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
-  }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -491,9 +485,11 @@ class SPPLayer : public Layer<Dtype> {
 
   int pyramid_height_;
   int bottom_h_, bottom_w_;
+  int num_;
   int channels_;
   int kernel_h_, kernel_w_;
   int pad_h_, pad_w_;
+  bool reshaped_first_time_;
 
   /// the internal Split layer that feeds the pooling layers
   shared_ptr<SplitLayer<Dtype> > split_layer_;
diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp
index 795dd71693e..d7622910495 100644
--- a/src/caffe/layers/spp_layer.cpp
+++ b/src/caffe/layers/spp_layer.cpp
@@ -66,8 +66,11 @@ void SPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   SPPParameter spp_param = this->layer_param_.spp_param();
 
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
   bottom_h_ = bottom[0]->height();
   bottom_w_ = bottom[0]->width();
+  reshaped_first_time_ = false;
   CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero.";
   CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero.";
 
@@ -82,6 +85,15 @@ void SPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   flatten_outputs_.clear();
   concat_bottom_vec_.clear();
 
+  if (pyramid_height_ == 1) {
+    // pooling layer setup
+    LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_,
+        spp_param);
+    pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+        new PoolingLayer<Dtype>(pooling_param)));
+    pooling_layers_[0]->SetUp(bottom, top);
+    return;
+  }
   // split layer output holders setup
   for (int i = 0; i < pyramid_height_; i++) {
     split_top_vec_.push_back(new Blob<Dtype>());
@@ -135,10 +147,26 @@ void SPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
       << "corresponding to (num, channels, height, width)";
+  // Do nothing if bottom shape is unchanged since last Reshape
+  if (num_ == bottom[0]->num() && channels_ == bottom[0]->channels() &&
+      bottom_h_ == bottom[0]->height() && bottom_w_ == bottom[0]->width() &&
+      reshaped_first_time_) {
+    return;
+  }
+  num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   bottom_h_ = bottom[0]->height();
   bottom_w_ = bottom[0]->width();
+  reshaped_first_time_ = true;
   SPPParameter spp_param = this->layer_param_.spp_param();
+  if (pyramid_height_ == 1) {
+    LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_,
+        spp_param);
+    pooling_layers_[0].reset(new PoolingLayer<Dtype>(pooling_param));
+    pooling_layers_[0]->SetUp(bottom, top);
+    pooling_layers_[0]->Reshape(bottom, top);
+    return;
+  }
   split_layer_->Reshape(bottom, split_top_vec_);
   for (int i = 0; i < pyramid_height_; i++) {
     LayerParameter pooling_param = GetPoolingParam(
@@ -159,6 +187,10 @@ void SPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SPPLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (pyramid_height_ == 1) {
+    pooling_layers_[0]->Forward(bottom, top);
+    return;
+  }
   split_layer_->Forward(bottom, split_top_vec_);
   for (int i = 0; i < pyramid_height_; i++) {
     pooling_layers_[i]->Forward(
@@ -175,6 +207,10 @@ void SPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (!propagate_down[0]) {
     return;
   }
+  if (pyramid_height_ == 1) {
+    pooling_layers_[0]->Backward(top, propagate_down, bottom);
+    return;
+  }
   vector<bool> concat_propagate_down(pyramid_height_, true);
   concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_);
   for (int i = 0; i < pyramid_height_; i++) {

From ac87850887f064752c2ad815367484c07eaf5449 Mon Sep 17 00:00:00 2001
From: Marco Castelluccio <mcastelluccio@mozilla.com>
Date: Wed, 26 Aug 2015 19:03:59 -0700
Subject: [PATCH 1519/2053] No need to squeeze the output of the network

---
 python/caffe/detector.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 75cd3b1202f..ef1f91730bf 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -83,7 +83,7 @@ def detect_windows(self, images_windows):
         for ix, window_in in enumerate(window_inputs):
             caffe_in[ix] = self.transformer.preprocess(in_, window_in)
         out = self.forward_all(**{in_: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
+        predictions = out[self.outputs[0]]
 
         # Package predictions with images and windows.
         detections = []

From 4f64b9ee3ed6c1267c4252cf79b2ccf0d042f0b2 Mon Sep 17 00:00:00 2001
From: Matt Dawkins <matt.dawkins@kitware.com>
Date: Thu, 27 Aug 2015 10:51:36 -0400
Subject: [PATCH 1520/2053] Add extra openblas search path

---
 cmake/Modules/FindOpenBLAS.cmake | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/cmake/Modules/FindOpenBLAS.cmake b/cmake/Modules/FindOpenBLAS.cmake
index b8434927a4d..a6512ae7e4e 100644
--- a/cmake/Modules/FindOpenBLAS.cmake
+++ b/cmake/Modules/FindOpenBLAS.cmake
@@ -2,8 +2,10 @@
 
 SET(Open_BLAS_INCLUDE_SEARCH_PATHS
   /usr/include
+  /usr/include/openblas
   /usr/include/openblas-base
   /usr/local/include
+  /usr/local/include/openblas
   /usr/local/include/openblas-base
   /opt/OpenBLAS/include
   $ENV{OpenBLAS_HOME}

From 4d7fe4de7e7bddfe107f4a37a7ec85c6f6178469 Mon Sep 17 00:00:00 2001
From: J Yegerlehner <jyegerlehner@yahoo.com>
Date: Thu, 27 Aug 2015 10:47:14 -0500
Subject: [PATCH 1521/2053] Fix EmbedLayer compiler warning for unused
 variable.

---
 src/caffe/layers/embed_layer.cu | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
index 672fb9c608c..62a4db81336 100644
--- a/src/caffe/layers/embed_layer.cu
+++ b/src/caffe/layers/embed_layer.cu
@@ -64,7 +64,6 @@ void EmbedLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
   if (this->param_propagate_down_[0]) {
     const int top_count = top[0]->count();
-    const int count = this->blobs_[0]->count();
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = bottom[0]->gpu_data();
     Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();

From 846f2c3cce8b937637e0b46a7f62be068b835ade Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 28 Aug 2015 21:27:11 -0700
Subject: [PATCH 1522/2053] fix GPU data race

Previously, the prefetch GPU -> top GPU and prefetch CPU -> prefetch GPU
copies were launched concurrently in separate streams, allowing the next
batch to be copied in before the current one is read.

This patch explicitly synchronizes the prefetch -> top copy wrt the
host, preventing the CPU -> GPU from being launched until its
completion.
---
 src/caffe/layers/base_data_layer.cu | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 56439bc506a..ff6e412aba6 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -20,7 +20,9 @@ void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     caffe_copy(batch->label_.count(), batch->label_.gpu_data(),
         top[1]->mutable_gpu_data());
   }
-
+  // Ensure the copy is synchronous wrt the host, so that the next batch isn't
+  // copied in meanwhile.
+  CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
   prefetch_free_.push(batch);
 }
 

From a6751723234926bdd03b6167ea6414da109854a3 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 1 Sep 2015 13:11:26 -0700
Subject: [PATCH 1523/2053] Compute backward for negative lr_mult

---
 src/caffe/net.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index f1fc63aba95..89d14013dc9 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -182,7 +182,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
       const ParamSpec* param_spec = (param_id < param_size) ?
           &layer_param.param(param_id) : &default_param_spec;
-      const bool param_need_backward = param_spec->lr_mult() > 0;
+      const bool param_need_backward = param_spec->lr_mult() != 0;
       need_backward |= param_need_backward;
       layers_[layer_id]->set_param_propagate_down(param_id,
                                                   param_need_backward);

From b04000e4204753803f4cdead66af8a830bc7c4ca Mon Sep 17 00:00:00 2001
From: Darren Garvey <darren.garvey@gmail.com>
Date: Tue, 1 Sep 2015 15:42:26 +0100
Subject: [PATCH 1524/2053] Cleanup: Fixup capitalisation of Caffe_POSTFIX.

Replaces CAffe_POSTFIX -> Caffe_POSTFIX.
---
 cmake/Misc.cmake        | 4 ++--
 examples/CMakeLists.txt | 2 +-
 matlab/CMakeLists.txt   | 4 ++--
 python/CMakeLists.txt   | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 7676754fe04..9dd2609b36a 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -46,7 +46,7 @@ endif()
 # ---[ Set debug postfix
 set(Caffe_DEBUG_POSTFIX "-d")
 
-set(CAffe_POSTFIX "")
+set(Caffe_POSTFIX "")
 if(CMAKE_BUILD_TYPE MATCHES "Debug")
-  set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
+  set(Caffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
 endif()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index f29fc7e5522..663d7360b7d 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -24,7 +24,7 @@ foreach(source_file ${examples_srcs})
   if(UNIX OR APPLE)
     # Funny command to make tutorials work
     # TODO: remove in future as soon as naming is standartaized everywhere
-    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX})
     add_custom_command(TARGET ${name} POST_BUILD
                        COMMAND ln -sf "${__outname}" "${__outname}.bin")
   endif()
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index 4b0d549f07f..f420df8d412 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -43,7 +43,7 @@ string(REPLACE ";" ";-L" link_folders "-L${folders}")
 string(REPLACE ";" ":"  rpath_folders   "${folders}")
 
 if(build_using MATCHES "Matlab")
-  set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
+  set(libflags -lcaffe${Caffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
 
   caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex)
   add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex}
@@ -56,7 +56,7 @@ elseif(build_using MATCHES "Octave")
   if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
     set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
   elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
+    set(libflags -Wl,--whole-archive -lcaffe${Caffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
   endif()
 
   add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler}
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index df0401daa1c..0e2bc7e66a8 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -18,7 +18,7 @@ if(UNIX OR APPLE)
                        COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto
                        COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py
                        COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/
-                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${Caffe_POSTFIX}.so")
 endif()
 
 # ---[ Install

From e8f96f58aa6b64726f62f7304964d1c0a82b5c38 Mon Sep 17 00:00:00 2001
From: Darren Garvey <darren.garvey@gmail.com>
Date: Mon, 10 Aug 2015 02:16:20 +0100
Subject: [PATCH 1525/2053] Fix memory leak in convert_mnist_siamese_data.

This fixes a memory leak by using delete[] rather than plain delete.
---
 examples/siamese/convert_mnist_siamese_data.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
index 71c56a0ae61..8008b4439c5 100644
--- a/examples/siamese/convert_mnist_siamese_data.cpp
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -102,7 +102,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   }
 
   delete db;
-  delete pixels;
+  delete [] pixels;
 }
 
 int main(int argc, char** argv) {

From 583194a00811a312e0bdfa151a8ad6a222c2dd4d Mon Sep 17 00:00:00 2001
From: Darren Garvey <darren.garvey@gmail.com>
Date: Tue, 1 Sep 2015 20:09:25 +0100
Subject: [PATCH 1526/2053] cifar10: Fix examples by setting snapshot_format.

Commit 4227828a set the default binary format from HDF5 to BINARYPROTO to
fix #2885. This broke the cifar10 examples which relied on this default.

This commit specifies the snapshot_format explicitly since the rest of the
example relies on this being HDF5.
---
 examples/cifar10/cifar10_full_solver.prototxt      | 1 +
 examples/cifar10/cifar10_full_solver_lr1.prototxt  | 1 +
 examples/cifar10/cifar10_full_solver_lr2.prototxt  | 1 +
 examples/cifar10/cifar10_quick_solver.prototxt     | 1 +
 examples/cifar10/cifar10_quick_solver_lr1.prototxt | 1 +
 5 files changed, 5 insertions(+)

diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt
index f30b3986142..882daa2d2b5 100644
--- a/examples/cifar10/cifar10_full_solver.prototxt
+++ b/examples/cifar10/cifar10_full_solver.prototxt
@@ -21,6 +21,7 @@ display: 200
 max_iter: 60000
 # snapshot intermediate results
 snapshot: 10000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_solver_lr1.prototxt b/examples/cifar10/cifar10_full_solver_lr1.prototxt
index 59bc5721f4c..55f4be44053 100644
--- a/examples/cifar10/cifar10_full_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr1.prototxt
@@ -21,6 +21,7 @@ display: 200
 max_iter: 65000
 # snapshot intermediate results
 snapshot: 5000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_solver_lr2.prototxt b/examples/cifar10/cifar10_full_solver_lr2.prototxt
index d4ed5d8e041..7c3d2da31de 100644
--- a/examples/cifar10/cifar10_full_solver_lr2.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr2.prototxt
@@ -21,6 +21,7 @@ display: 200
 max_iter: 70000
 # snapshot intermediate results
 snapshot: 5000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index 14b4401ba16..5de276f722f 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -20,6 +20,7 @@ display: 100
 max_iter: 4000
 # snapshot intermediate results
 snapshot: 4000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_quick_solver_lr1.prototxt b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
index d3af70c05e7..f8f1efd54af 100644
--- a/examples/cifar10/cifar10_quick_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
@@ -20,6 +20,7 @@ display: 100
 max_iter: 5000
 # snapshot intermediate results
 snapshot: 5000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU

From 6f5812c4547dd912dd0569330ebdd44a5afd278e Mon Sep 17 00:00:00 2001
From: Darren Garvey <darren.garvey@gmail.com>
Date: Wed, 2 Sep 2015 00:54:06 +0100
Subject: [PATCH 1527/2053] Fix up documentation errors.

Fix some doxygen warnings about an undocumented argument in Blob and
incorrect documentation for SoftmaxWithLossLayer::Forward_cpu().
---
 include/caffe/blob.hpp        | 2 +-
 include/caffe/loss_layers.hpp | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 9b813e739e9..dda7b1f8372 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -109,7 +109,7 @@ class Blob {
    * @brief Returns the 'canonical' version of a (usually) user-specified axis,
    *        allowing for negative indexing (e.g., -1 for the last axis).
    *
-   * @param index the axis index.
+   * @param axis_index the axis index.
    *        If 0 <= index < num_axes(), return index.
    *        If -num_axes <= index <= -1, return (num_axes() - (-index)),
    *        e.g., the last axis index (num_axes() - 1) if index == -1,
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 02687a944fb..8d41af34e88 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -712,7 +712,6 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
-  /// @copydoc SoftmaxWithLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,

From 6ca0ab66077c578dff14aa775858035b2a69fed6 Mon Sep 17 00:00:00 2001
From: Luke Yeager <lyeager@nvidia.com>
Date: Tue, 1 Sep 2015 17:20:37 -0700
Subject: [PATCH 1528/2053] Show output from convert_imageset tool

---
 tools/convert_imageset.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 816a91f971b..aad1f1fe216 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -44,6 +44,8 @@ DEFINE_string(encode_type, "",
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
+  // Print output to stderr (while still logging)
+  FLAGS_alsologtostderr = 1;
 
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
@@ -140,13 +142,13 @@ int main(int argc, char** argv) {
       // Commit db
       txn->Commit();
       txn.reset(db->NewTransaction());
-      LOG(ERROR) << "Processed " << count << " files.";
+      LOG(INFO) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
     txn->Commit();
-    LOG(ERROR) << "Processed " << count << " files.";
+    LOG(INFO) << "Processed " << count << " files.";
   }
   return 0;
 }

From d1a0345eaaff501d11a1705986cac8f124dd2545 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 7 Oct 2014 11:55:54 -0700
Subject: [PATCH 1529/2053] SliceLayer: allow trivial operation with single top
 Blob

---
 include/caffe/common_layers.hpp     |  2 +-
 src/caffe/layers/slice_layer.cpp    |  7 ++++++-
 src/caffe/layers/slice_layer.cu     |  3 ++-
 src/caffe/test/test_slice_layer.cpp | 27 +++++++++++++++++++++++++++
 4 files changed, 36 insertions(+), 3 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 8e64b3e5dc5..6d4a9e3cb81 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -625,7 +625,7 @@ class SliceLayer : public Layer<Dtype> {
 
   virtual inline const char* type() const { return "Slice"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 2; }
+  virtual inline int MinTopBlobs() const { return 1; }
 
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index e4418c9cf9c..0a059ae88fe 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -67,11 +67,16 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     }
   }
   CHECK_EQ(count, bottom[0]->count());
+  if (top.size() == 1) {
+    top[0]->ShareData(*bottom[0]);
+    top[0]->ShareDiff(*bottom[0]);
+  }
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (top.size() == 1) { return; }
   int offset_slice_axis = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
@@ -92,7 +97,7 @@ void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  if (!propagate_down[0]) { return; }
+  if (!propagate_down[0] || top.size() == 1) { return; }
   int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index 796841d3f52..e8dc6cd98fc 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -28,6 +28,7 @@ __global__ void Slice(const int nthreads, const Dtype* in_data,
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (top.size() == 1) { return; }
   int offset_slice_axis = 0;
   const Dtype* bottom_data = bottom[0]->gpu_data();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
@@ -48,7 +49,7 @@ void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  if (!propagate_down[0]) { return; }
+  if (!propagate_down[0] || top.size() == 1) { return; }
   int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index ccd03646d19..2d2d0fdc005 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -88,6 +88,21 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) {
   EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
 }
 
+TYPED_TEST(SliceLayerTest, TestTrivialSlice) {
+  // Test the trivial (single output) "slice" operation --
+  // should be the identity.
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SliceLayer<Dtype> layer(layer_param);
+  this->blob_top_vec_0_.resize(1);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_0_->shape());
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_bottom_->cpu_data()[i],
+              this->blob_top_0_->cpu_data()[i]);
+  }
+}
+
 TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -161,6 +176,18 @@ TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) {
   }
 }
 
+TYPED_TEST(SliceLayerTest, TestGradientTrivial) {
+  // Test the trivial (single output) "slice" operation --
+  // should be the identity.
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SliceLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  this->blob_top_vec_0_.resize(1);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_0_);
+}
+
 TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
   typedef typename TypeParam::Dtype Dtype;
   // Gradient checks are slow; reduce blob size.

From 784dfddd42cc787fd9661a954f2a360990867df2 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 23 Jan 2015 12:52:44 -0800
Subject: [PATCH 1530/2053] ConcatLayer: allow trivial operation with single
 bottom Blob

---
 include/caffe/common_layers.hpp      |  2 +-
 src/caffe/layers/concat_layer.cpp    |  6 ++++++
 src/caffe/layers/concat_layer.cu     |  2 ++
 src/caffe/test/test_concat_layer.cpp | 23 +++++++++++++++++++++++
 4 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 6d4a9e3cb81..89bab8d6f3a 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -85,7 +85,7 @@ class ConcatLayer : public Layer<Dtype> {
       const vector<Blob<Dtype>*>& top);
 
   virtual inline const char* type() const { return "Concat"; }
-  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MinBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 95fba105b9a..86b500de859 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -48,11 +48,16 @@ void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   }
   top[0]->Reshape(top_shape);
   CHECK_EQ(bottom_count_sum, top[0]->count());
+  if (bottom.size() == 1) {
+    top[0]->ShareData(*bottom[0]);
+    top[0]->ShareDiff(*bottom[0]);
+  }
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1) { return; }
   Dtype* top_data = top[0]->mutable_cpu_data();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
@@ -72,6 +77,7 @@ void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bottom.size() == 1) { return; }
   const Dtype* top_diff = top[0]->cpu_diff();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 3c64c7ef224..617701e2621 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -28,6 +28,7 @@ __global__ void Concat(const int nthreads, const Dtype* in_data,
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1) { return; }
   Dtype* top_data = top[0]->mutable_gpu_data();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
@@ -48,6 +49,7 @@ void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bottom.size() == 1) { return; }
   const Dtype* top_diff = top[0]->gpu_diff();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 088e0a41685..ccd97eb1d66 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -99,6 +99,19 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannelsNegativeIndexing) {
   EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
+TYPED_TEST(ConcatLayerTest, TestForwardTrivial) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  this->blob_bottom_vec_0_.resize(1);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_0_->count(); ++i) {
+    EXPECT_EQ(this->blob_bottom_0_->cpu_data()[i],
+              this->blob_top_->cpu_data()[i]);
+  }
+}
+
 TYPED_TEST(ConcatLayerTest, TestForwardNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -154,6 +167,16 @@ TYPED_TEST(ConcatLayerTest, TestForwardChannels) {
   }
 }
 
+TYPED_TEST(ConcatLayerTest, TestGradientTrivial) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  this->blob_bottom_vec_0_.resize(1);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_0_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(ConcatLayerTest, TestGradientNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From cf1516634d677cb8d2b2068e2b795c9b58a7c098 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 15 Feb 2015 15:18:56 -0800
Subject: [PATCH 1531/2053] Net: expose param_display_names_

---
 include/caffe/net.hpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 1bf07d28d13..bed241d2a6c 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -179,6 +179,9 @@ class Net {
     return param_names_index_;
   }
   inline const vector<int>& param_owners() const { return param_owners_; }
+  inline const vector<string>& param_display_names() const {
+    return param_display_names_;
+  }
   /// @brief Input and output blob numbers
   inline int num_inputs() const { return net_input_blobs_.size(); }
   inline int num_outputs() const { return net_output_blobs_.size(); }

From 46c3f00bc91819668fb384b7982706d19b2a9fd6 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 3 Sep 2015 14:57:19 -0700
Subject: [PATCH 1532/2053] net.cpp fix debug_info params -> learnable_params

---
 src/caffe/net.cpp | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 89d14013dc9..ebb8b5d28c2 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -810,12 +810,11 @@ void Net<Dtype>::Backward() {
   BackwardFromTo(layers_.size() - 1, 0);
   if (debug_info_) {
     Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;
-    for (int i = 0; i < params_.size(); ++i) {
-      if (param_owners_[i] >= 0) { continue; }
-      asum_data += params_[i]->asum_data();
-      asum_diff += params_[i]->asum_diff();
-      sumsq_data += params_[i]->sumsq_data();
-      sumsq_diff += params_[i]->sumsq_diff();
+    for (int i = 0; i < learnable_params_.size(); ++i) {
+      asum_data += learnable_params_[i]->asum_data();
+      asum_diff += learnable_params_[i]->asum_diff();
+      sumsq_data += learnable_params_[i]->sumsq_data();
+      sumsq_diff += learnable_params_[i]->sumsq_diff();
     }
     const Dtype l2norm_data = std::sqrt(sumsq_data);
     const Dtype l2norm_diff = std::sqrt(sumsq_diff);

From c2484747d813d616bcb504d97f93071b26bb372d Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 21 Aug 2015 17:29:06 -0700
Subject: [PATCH 1533/2053] NetSpec: don't require lists to specify
 single-element repeated fields

---
 python/caffe/net_spec.py           | 10 ++++++++--
 python/caffe/test/test_net_spec.py |  3 +--
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 77a0e0070ae..93fc01927db 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -56,8 +56,14 @@ def to_proto(*tops):
 def assign_proto(proto, name, val):
     """Assign a Python object to a protobuf message, based on the Python
     type (in recursive fashion). Lists become repeated fields/messages, dicts
-    become messages, and other types are assigned directly."""
-
+    become messages, and other types are assigned directly. For convenience,
+    repeated fields whose values are not lists are converted to single-element
+    lists; e.g., `my_repeated_int_field=3` is converted to
+    `my_repeated_int_field=[3]`."""
+
+    is_repeated_field = hasattr(getattr(proto, name), 'extend')
+    if is_repeated_field and not isinstance(val, list):
+        val = [val]
     if isinstance(val, list):
         if isinstance(val[0], dict):
             for item in val:
diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
index b4595e6531a..fee3c0aaebe 100644
--- a/python/caffe/test/test_net_spec.py
+++ b/python/caffe/test/test_net_spec.py
@@ -43,8 +43,7 @@ def anon_lenet(batch_size):
 
 def silent_net():
     n = caffe.NetSpec()
-    n.data, n.data2 = L.DummyData(shape=[dict(dim=[3]), dict(dim=[4, 2])],
-                                  ntop=2)
+    n.data, n.data2 = L.DummyData(shape=dict(dim=3), ntop=2)
     n.silence_data = L.Silence(n.data, ntop=0)
     n.silence_data2 = L.Silence(n.data2, ntop=0)
     return n.to_proto()

From 1bdc18c5beb4c6e679ed359eb707be8822306ea5 Mon Sep 17 00:00:00 2001
From: Lumin Zhou <CDLuminate@users.noreply.github.com>
Date: Fri, 4 Sep 2015 04:38:43 +0000
Subject: [PATCH 1534/2053] Update extract_features.cpp

---
 tools/extract_features.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 365dd495bbf..084c9bf88df 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -42,7 +42,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
     "  save_feature_dataset_name1[,name2,...]  num_mini_batches  db_type"
     "  [CPU/GPU] [DEVICE_ID=0]\n"
     "Note: you can extract multiple features in one pass by specifying"
-    " multiple feature blob names and dataset names seperated by ','."
+    " multiple feature blob names and dataset names separated by ','."
     " The names cannot contain white space characters and the number of blobs"
     " and datasets must be equal.";
     return 1;

From aa40ab98717507a60c23fb9cd4bf61c8b0bbb38d Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 3 Sep 2015 21:44:45 -0700
Subject: [PATCH 1535/2053] Fix AccuracyLayerTest for per-class accuracy.

Fix AccuracyLayerTest for per-class accuracy. Previously in #2935, it crashes since the test accuracy is nan (0/0) when a class never appear.
---
 src/caffe/test/test_accuracy_layer.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 94e529b5eee..ef0e57a37a1 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -250,7 +250,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
 
 TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   AccuracyLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
@@ -279,16 +278,16 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
   EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
               num_correct_labels / 100.0, 1e-4);
   for (int i = 0; i < num_class; ++i) {
+    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
+       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
     EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
-                static_cast<float>(correct_per_class[i]) / num_per_class[i],
-                1e-4);
+                accuracy_per_class, 1e-4);
   }
 }
 
 
 TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   const TypeParam kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
   AccuracyLayer<TypeParam> layer(layer_param);
@@ -329,9 +328,10 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
   EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
               num_correct_labels / TypeParam(count), 1e-4);
   for (int i = 0; i < 10; ++i) {
+    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
+       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
     EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
-                TypeParam(correct_per_class[i]) / num_per_class[i],
-                1e-4);
+                accuracy_per_class, 1e-4);
   }
 }
 

From 5cc76ad2e38f19a140497ff09c475500da9d76cf Mon Sep 17 00:00:00 2001
From: Daniel Gordon <xkcd@cs.washington.edu>
Date: Fri, 4 Sep 2015 10:12:00 -0700
Subject: [PATCH 1536/2053] enabling the alternate solvers to be accessed by
 the python interface

---
 python/caffe/__init__.py | 2 +-
 python/caffe/_caffe.cpp  | 9 +++++++++
 python/caffe/pycaffe.py  | 3 ++-
 3 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 6cc44e729f4..ccda1bcae4f 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,4 +1,4 @@
-from .pycaffe import Net, SGDSolver
+from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
 from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index cc49f60ab13..ccd5776ac40 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -297,6 +297,15 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
         "AdaGradSolver", bp::init<string>());
+  bp::class_<RMSPropSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
+        "RMSPropSolver", bp::init<string>());
+  bp::class_<AdaDeltaSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
+        "AdaDeltaSolver", bp::init<string>());
+  bp::class_<AdamSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>(
+        "AdamSolver", bp::init<string>());
 
   bp::def("get_solver", &GetSolverFromFile,
       bp::return_value_policy<bp::manage_new_object>());
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 4f980a92c38..8ea24da4fdd 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -10,7 +10,8 @@
     from itertools import zip_longest as izip_longest
 import numpy as np
 
-from ._caffe import Net, SGDSolver
+from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
+        RMSPropSolver, AdaDeltaSolver, AdamSolver
 import caffe.io
 
 # We directly update methods from Net here (rather than using composition or

From 1394cdc383e2f41d7435862442b15151e8ac1237 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 4 Sep 2015 14:16:31 -0700
Subject: [PATCH 1537/2053] disallow PythonLayer in Multi-GPU training

---
 include/caffe/python_layer.hpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp
index c43c1e8a91b..b839d52684e 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/python_layer.hpp
@@ -18,6 +18,12 @@ class PythonLayer : public Layer<Dtype> {
 
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+    // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
+    // Details: https://github.com/BVLC/caffe/issues/2936
+    if (this->phase_ == TRAIN && Caffe::solver_count() > 1
+        && !ShareInParallel()) {
+      LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training";
+    }
     self_.attr("param_str") = bp::str(
         this->layer_param_.python_param().param_str());
     self_.attr("setup")(bottom, top);

From 8fbac04ac32672ae8a97f8a1171f1d39456b97ca Mon Sep 17 00:00:00 2001
From: Sean Bell <sbell@cs.cornell.edu>
Date: Wed, 9 Sep 2015 12:52:52 -0400
Subject: [PATCH 1538/2053] Minor: missing space in string formatting

---
 src/caffe/solver.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 394ec3b3ad7..2e59a881d82 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -732,7 +732,7 @@ void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
   }
   string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
   LOG(INFO)
-    << "Snapshotting solver state to binary proto file" << snapshot_filename;
+    << "Snapshotting solver state to binary proto file " << snapshot_filename;
   WriteProtoToBinaryFile(state, snapshot_filename.c_str());
 }
 

From 3456259d400f7eef27e07c15c34f22b8d5e13bdd Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sun, 13 Sep 2015 20:46:24 -0700
Subject: [PATCH 1539/2053] Use EXPECT_NEAR in EltwiseLayer test

Otherwise there seem to be some numerical issues causing BLAS results not exactly same as evaluated results in test code.
---
 src/caffe/test/test_eltwise_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index be0c1347709..8031f6e9022 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -80,7 +80,7 @@ TYPED_TEST(EltwiseLayerTest, TestProd) {
   const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
   const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i], 1e-4);
   }
 }
 
@@ -99,7 +99,7 @@ TYPED_TEST(EltwiseLayerTest, TestSum) {
   const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
   const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i], 1e-4);
   }
 }
 

From ab554cb4918cf7bccfada00339b4d1d5ccf3b4af Mon Sep 17 00:00:00 2001
From: Sean Bell <sbell@cs.cornell.edu>
Date: Wed, 9 Sep 2015 12:49:27 -0400
Subject: [PATCH 1540/2053] Check that the snapshot directory is writeable
 before starting training

---
 include/caffe/solver.hpp |  2 ++
 src/caffe/solver.cpp     | 19 +++++++++++++++++++
 2 files changed, 21 insertions(+)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index aba3e036004..8d52785ac6e 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -82,6 +82,8 @@ class Solver {
     callbacks_.push_back(value);
   }
 
+  void CheckSnapshotWritePermissions();
+
  protected:
   // Make and apply the update value for the current iteration.
   virtual void ApplyUpdate() = 0;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 394ec3b3ad7..47493174f46 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -55,6 +55,7 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
     << std::endl << param.DebugString();
   param_ = param;
   CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
+  CheckSnapshotWritePermissions();
   if (Caffe::root_solver() && param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
@@ -434,6 +435,24 @@ void Solver<Dtype>::Snapshot() {
   SnapshotSolverState(model_filename);
 }
 
+template <typename Dtype>
+void Solver<Dtype>::CheckSnapshotWritePermissions() {
+  if (Caffe::root_solver() && param_.snapshot()) {
+    CHECK(param_.has_snapshot_prefix())
+        << "In solver params, snapshot is specified but snapshot_prefix is not";
+    string probe_filename = SnapshotFilename(".tempfile");
+    std::ofstream probe_ofs(probe_filename.c_str());
+    if (probe_ofs.good()) {
+      probe_ofs.close();
+      std::remove(probe_filename.c_str());
+    } else {
+      LOG(FATAL) << "Cannot write to snapshot prefix '"
+          << param_.snapshot_prefix() << "'.  Make sure "
+          << "that the directory exists and is writeable.";
+    }
+  }
+}
+
 template <typename Dtype>
 string Solver<Dtype>::SnapshotFilename(const string extension) {
   string filename(param_.snapshot_prefix());

From b7f9cba875c6db5c4ae33446dc80cd010c1c392c Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Tue, 15 Sep 2015 17:18:32 +0200
Subject: [PATCH 1541/2053] removed bug in caffe.io.resize_image when applied
 to Nd images

---
 python/caffe/io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index fc96266085f..0cad7211291 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -329,7 +329,7 @@ def resize_image(im, new_dims, interp_order=1):
             return ret
     else:
         # ndimage interpolates anything but more slowly.
-        scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
+        scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))
         resized_im = zoom(im, scale + (1,), order=interp_order)
     return resized_im.astype(np.float32)
 

From 3d3a8b2ca09b64d94836652d0c9b5ffbb31551f6 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Wed, 16 Sep 2015 12:06:16 -0700
Subject: [PATCH 1542/2053] Get back 'USE CPU' print for caffe train

---
 tools/caffe.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ff63860a3c1..e3f684b5ab3 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -174,6 +174,7 @@ int train() {
   vector<int> gpus;
   get_gpus(&gpus);
   if (gpus.size() == 0) {
+    LOG(INFO) << "Use CPU.";
     Caffe::set_mode(Caffe::CPU);
   } else {
     ostringstream s;

From f3a933a620b8b089a7fe29ba76ec27f5408ff68d Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Sat, 27 Jun 2015 11:44:56 +0800
Subject: [PATCH 1543/2053] Separate IO dependencies

OpenCV, LMDB, LevelDB and Snappy are made optional via switches
(USE_OPENCV, USE_LMDB, USE_LEVELDB) available for Make and CMake
builds. Since Snappy is a LevelDB dependency, its use is determined by
USE_LEVELDB. HDF5 is left bundled because it is used for serializing
weights and solverstates.
---
 .travis.yml                                   | 11 ++---
 CMakeLists.txt                                |  7 +++-
 Makefile                                      | 28 +++++++++++--
 Makefile.config.example                       |  5 +++
 cmake/ConfigGen.cmake                         | 12 ++++++
 cmake/Dependencies.cmake                      | 41 ++++++++++++-------
 cmake/Summary.cmake                           | 18 +++++---
 cmake/Templates/CaffeConfig.cmake.in          | 26 ++++++------
 cmake/Templates/caffe_config.h.in             |  5 +++
 docs/installation.md                          |  9 ++--
 .../cpp_classification/classification.cpp     |  8 ++++
 examples/mnist/convert_mnist_data.cpp         | 12 ++++++
 .../siamese/convert_mnist_siamese_data.cpp    |  9 +++-
 include/caffe/data_layers.hpp                 |  3 +-
 include/caffe/data_transformer.hpp            |  5 ++-
 include/caffe/util/db_leveldb.hpp             |  2 +
 include/caffe/util/db_lmdb.hpp                |  2 +
 include/caffe/util/io.hpp                     |  2 +
 python/caffe/test/test_layer_type_list.py     |  1 +
 scripts/travis/travis_build_and_test.sh       | 14 ++++++-
 .../travis/travis_setup_makefile_config.sh    |  6 +++
 src/caffe/data_transformer.cpp                | 16 +++++++-
 src/caffe/layers/data_layer.cpp               |  3 +-
 src/caffe/layers/image_data_layer.cpp         |  2 +
 src/caffe/layers/memory_data_layer.cpp        |  4 ++
 src/caffe/layers/window_data_layer.cpp        |  2 +
 src/caffe/test/test_data_layer.cpp            |  6 +++
 src/caffe/test/test_data_transformer.cpp      |  2 +
 src/caffe/test/test_db.cpp                    |  2 +
 src/caffe/test/test_image_data_layer.cpp      |  2 +
 src/caffe/test/test_io.cpp                    |  2 +
 src/caffe/test/test_layer_factory.cpp         |  4 ++
 src/caffe/test/test_memory_data_layer.cpp     |  5 ++-
 src/caffe/test/test_upgrade_proto.cpp         | 12 +++++-
 src/caffe/util/db.cpp                         | 14 +++++--
 src/caffe/util/db_leveldb.cpp                 |  2 +
 src/caffe/util/db_lmdb.cpp                    |  2 +
 src/caffe/util/io.cpp                         | 10 ++++-
 tools/compute_image_mean.cpp                  |  4 ++
 tools/convert_imageset.cpp                    |  4 ++
 40 files changed, 264 insertions(+), 60 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index b920a935d0d..4dc7ed72d6c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,11 +2,12 @@
 # one using CMake, and one using make.
 env:
   matrix:
-    - WITH_CUDA=false WITH_CMAKE=false
-    - WITH_CUDA=false WITH_CMAKE=true
-    - WITH_CUDA=true WITH_CMAKE=false
-    - WITH_CUDA=true WITH_CMAKE=true
-    - WITH_CUDA=false WITH_CMAKE=true PYTHON_VERSION=3
+    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true
+    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3
+    - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true
+    - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true
+    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false
+    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3
 
 language: cpp
 
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ef599b68922..838723beca2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -16,13 +16,16 @@ include(cmake/ConfigGen.cmake)
 
 # ---[ Options
 caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
-caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
+caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
 set(python_version "2" CACHE STRING "Specify which python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
-caffe_option(BUILD_python_layer "Build the Caffe python layer" ON)
+caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
+caffe_option(USE_LMDB "Build with lmdb" ON)
+caffe_option(USE_LEVELDB "Build with levelDB" ON)
+caffe_option(USE_OPENCV "Build with OpenCV support" ON)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
diff --git a/Makefile b/Makefile
index 80bc37375be..ddaed59b0a9 100644
--- a/Makefile
+++ b/Makefile
@@ -169,9 +169,18 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARY_DIRS += $(CUDA_LIB_DIR)
 	LIBRARIES := cudart cublas curand
 endif
-LIBRARIES += glog gflags protobuf leveldb snappy \
-	lmdb boost_system hdf5_hl hdf5 m \
-	opencv_core opencv_highgui opencv_imgproc
+
+LIBRARIES += glog gflags protobuf boost_system m hdf5_hl hdf5
+
+ifeq ($(USE_LEVELDB), 1)
+	LIBRARIES += leveldb snappy
+endif
+ifeq ($(USE_LMDB), 1)
+	LIBRARIES += lmdb
+endif
+ifeq ($(USE_OPENCV), 1)
+	LIBRARIES += opencv_core opencv_highgui opencv_imgproc
+endif
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
@@ -290,6 +299,17 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
+# i/o libraries configuration
+ifeq ($(USE_OPENCV), 1)
+	COMMON_FLAGS += -DUSE_OPENCV
+endif
+ifeq ($(USE_LEVELDB), 1)
+	COMMON_FLAGS += -DUSE_LEVELDB
+endif
+ifeq ($(USE_LMDB), 1)
+	COMMON_FLAGS += -DUSE_LMDB
+endif
+
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
@@ -472,7 +492,7 @@ runtest: $(TEST_ALL_BIN)
 
 pytest: py
 	cd python; python -m unittest discover -s caffe/test
-	
+
 mattest: mat
 	cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'
 
diff --git a/Makefile.config.example b/Makefile.config.example
index a873502559f..32e67ee493e 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -7,6 +7,11 @@
 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 1
 
+# comment out to disable IO dependencies
+USE_LEVELDB := 1
+USE_LMDB := 1
+USE_OPENCV := 1
+
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
 # CUSTOM_CXX := g++
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 566d6ca0aa7..8b259965359 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -56,6 +56,18 @@ function(caffe_generate_export_configs)
     list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
   endif()
 
+  if(USE_OPENCV)
+    list(APPEND Caffe_DEFINITIONS -DUSE_OPENCV)
+  endif()
+
+  if(USE_LMDB)
+    list(APPEND Caffe_DEFINITIONS -DUSE_LMDB)
+  endif()
+
+  if(USE_LEVELDB)
+    list(APPEND Caffe_DEFINITIONS -DUSE_LEVELDB)
+  endif()
+
   if(NOT HAVE_CUDNN)
     set(HAVE_CUDNN FALSE)
   else()
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 7c86dd55a30..d68d7bfba66 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -29,19 +29,27 @@ include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
 
 # ---[ LMDB
-find_package(LMDB REQUIRED)
-include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+if(USE_LMDB)
+  find_package(LMDB REQUIRED)
+  include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+  add_definitions(-DUSE_LMDB)
+endif()
 
 # ---[ LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(SYSTEM ${LevelDB_INCLUDE})
-list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+if(USE_LEVELDB)
+  find_package(LevelDB REQUIRED)
+  include_directories(SYSTEM ${LevelDB_INCLUDE})
+  list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+  add_definitions(-DUSE_LEVELDB)
+endif()
 
 # ---[ Snappy
-find_package(Snappy REQUIRED)
-include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+if(USE_LEVELDB)
+  find_package(Snappy REQUIRED)
+  include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+endif()
 
 # ---[ CUDA
 include(cmake/Cuda.cmake)
@@ -57,13 +65,16 @@ if(NOT HAVE_CUDA)
 endif()
 
 # ---[ OpenCV
-find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
-if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
-  find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+if(USE_OPENCV)
+  find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
+  if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
+    find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+  endif()
+  include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
+  list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+  message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
+  add_definitions(-DUSE_OPENCV)
 endif()
-include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
-message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
 
 # ---[ BLAS
 if(NOT APPLE)
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index e094ac0040e..3d12e81a130 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -114,6 +114,9 @@ function(caffe_print_configuration_summary)
   caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
   caffe_status("  BUILD_docs        :   ${BUILD_docs}")
   caffe_status("  CPU_ONLY          :   ${CPU_ONLY}")
+  caffe_status("  USE_LMDB          :   ${USE_LMDB}")
+  caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
+  caffe_status("  USE_OPENCV        :   ${USE_OPENCV}")
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
@@ -121,10 +124,16 @@ function(caffe_print_configuration_summary)
   caffe_status("  glog              :   Yes")
   caffe_status("  gflags            :   Yes")
   caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
-  caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
-  caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
-  caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
-  caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  if(USE_LMDB)
+    caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
+  endif()
+  if(USE_LEVELDB)
+    caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
+    caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
+  endif()
+  if(USE_OPENCV)
+    caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  endif()
   caffe_status("  CUDA              : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" )
   caffe_status("")
   if(HAVE_CUDA)
@@ -165,4 +174,3 @@ function(caffe_print_configuration_summary)
   caffe_status("  Install path      :   ${CMAKE_INSTALL_PREFIX}")
   caffe_status("")
 endfunction()
-
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
index 8f23742e52e..73f57ac2d74 100644
--- a/cmake/Templates/CaffeConfig.cmake.in
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -17,22 +17,24 @@
 #   Caffe_HAVE_CUDNN   - signals about cuDNN support
 
 
-# OpenCV dependency
+# OpenCV dependency (optional)
 
-if(NOT OpenCV_FOUND)
-  set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
-  if(Caffe_OpenCV_CONFIG_PATH)
-    get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+if(@USE_OPENCV@)
+  if(NOT OpenCV_FOUND)
+    set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
+    if(Caffe_OpenCV_CONFIG_PATH)
+      get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
 
-    if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
-      message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
-      include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
-    endif()
+      if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
+        message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
+        include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+      endif()
 
-  else()
-    find_package(OpenCV REQUIRED)
+    else()
+      find_package(OpenCV REQUIRED)
+    endif()
+    unset(Caffe_OpenCV_CONFIG_PATH)
   endif()
-  unset(Caffe_OpenCV_CONFIG_PATH)
 endif()
 
 # Compute paths
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 6039e8f6b21..9302022d7da 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -30,3 +30,8 @@
 
 /* Matlab */
 #cmakedefine HAVE_MATLAB
+
+/* IO libraries */
+#cmakedefine USE_OPENCV
+#cmakedefine USE_LMDB
+#cmakedefine USE_LEVELDB
diff --git a/docs/installation.md b/docs/installation.md
index d535c6d093d..89a8c71c71a 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -17,16 +17,19 @@ When updating Caffe, it's best to `make clean` before re-compiling.
 
 ## Prerequisites
 
-Caffe has several dependencies.
+Caffe has several dependencies:
 
 * [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
     * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
     * 5.5, and 5.0 are compatible but considered legacy
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
 * [Boost](http://www.boost.org/) >= 1.55
+* `protobuf`, `glog`, `gflags`, `hdf5`
+
+Optional dependencies:
+
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
-* `protobuf`, `glog`, `gflags`
-* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb`
+* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
index dc8b863f53f..de48fb692c8 100644
--- a/examples/cpp_classification/classification.cpp
+++ b/examples/cpp_classification/classification.cpp
@@ -1,7 +1,9 @@
 #include <caffe/caffe.hpp>
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
+#endif  // USE_OPENCV
 #include <algorithm>
 #include <iosfwd>
 #include <memory>
@@ -9,6 +11,7 @@
 #include <utility>
 #include <vector>
 
+#ifdef USE_OPENCV
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::string;
 
@@ -255,3 +258,8 @@ int main(int argc, char** argv) {
               << p.first << "\"" << std::endl;
   }
 }
+#else
+int main(int argc, char** argv) {
+  LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
+}
+#endif  // USE_OPENCV
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 54443f11dd3..8f29bafde85 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -9,9 +9,13 @@
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <google/protobuf/text_format.h>
+
+#if defined(USE_LEVELDB) && defined(USE_LMDB)
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
 #include <lmdb.h>
+#endif
+
 #include <stdint.h>
 #include <sys/stat.h>
 
@@ -20,6 +24,8 @@
 
 #include "caffe/proto/caffe.pb.h"
 
+#if defined(USE_LEVELDB) && defined(USE_LMDB)
+
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::string;
 
@@ -196,3 +202,9 @@ int main(int argc, char** argv) {
   }
   return 0;
 }
+#else
+int main(int argc, char** argv) {
+  LOG(FATAL) << "This example requires LevelDB and LMDB; " <<
+  "compile with USE_LEVELDB and USE_LMDB.";
+}
+#endif  // USE_LEVELDB and USE_LMDB
diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
index 8008b4439c5..ad08036fb08 100644
--- a/examples/siamese/convert_mnist_siamese_data.cpp
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -10,12 +10,14 @@
 
 #include "glog/logging.h"
 #include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
 #include "stdint.h"
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/math_functions.hpp"
 
+#ifdef USE_LEVELDB
+#include "leveldb/db.h"
+
 uint32_t swap_endian(uint32_t val) {
     val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
     return (val << 16) | (val >> 16);
@@ -121,3 +123,8 @@ int main(int argc, char** argv) {
   }
   return 0;
 }
+#else
+int main(int argc, char** argv) {
+  LOG(FATAL) << "This example requires LevelDB; compile with USE_LEVELDB.";
+}
+#endif  // USE_LEVELDB
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 552d814131e..90fd0d19917 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -4,7 +4,6 @@
 #include <string>
 #include <utility>
 #include <vector>
-
 #include "hdf5.h"
 
 #include "caffe/blob.hpp"
@@ -275,8 +274,10 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> {
   virtual inline int ExactNumTopBlobs() const { return 2; }
 
   virtual void AddDatumVector(const vector<Datum>& datum_vector);
+#ifdef USE_OPENCV
   virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
       const vector<int>& labels);
+#endif  // USE_OPENCV
 
   // Reset should accept const pointers, but can't, because the memory
   //  will be given to Blob, which is mutable
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 0ad68c80216..97b4ee6a8c4 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -50,6 +50,7 @@ class DataTransformer {
   void Transform(const vector<Datum> & datum_vector,
                 Blob<Dtype>* transformed_blob);
 
+#ifdef USE_OPENCV
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a vector of Mat.
@@ -74,6 +75,7 @@ class DataTransformer {
    *    set_cpu_data() is used. See image_data_layer.cpp for an example.
    */
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
+#endif  // USE_OPENCV
 
   /**
    * @brief Applies the same transformation defined in the data layer's
@@ -113,6 +115,7 @@ class DataTransformer {
    * @param mat_vector
    *    A vector of Mat containing the data to be transformed.
    */
+#ifdef USE_OPENCV
   vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
   /**
    * @brief Infers the shape of transformed_blob will have when
@@ -122,6 +125,7 @@ class DataTransformer {
    *    cv::Mat containing the data to be transformed.
    */
   vector<int> InferBlobShape(const cv::Mat& cv_img);
+#endif  // USE_OPENCV
 
  protected:
    /**
@@ -148,4 +152,3 @@ class DataTransformer {
 }  // namespace caffe
 
 #endif  // CAFFE_DATA_TRANSFORMER_HPP_
-
diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp
index 10623554b67..e9fa0d32b66 100644
--- a/include/caffe/util/db_leveldb.hpp
+++ b/include/caffe/util/db_leveldb.hpp
@@ -1,3 +1,4 @@
+#ifdef USE_LEVELDB
 #ifndef CAFFE_UTIL_DB_LEVELDB_HPP
 #define CAFFE_UTIL_DB_LEVELDB_HPP
 
@@ -71,3 +72,4 @@ class LevelDB : public DB {
 }  // namespace caffe
 
 #endif  // CAFFE_UTIL_DB_LEVELDB_HPP
+#endif  // USE_LEVELDB
diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
index cc7c90afc4c..4e1568ace50 100644
--- a/include/caffe/util/db_lmdb.hpp
+++ b/include/caffe/util/db_lmdb.hpp
@@ -1,3 +1,4 @@
+#ifdef USE_LMDB
 #ifndef CAFFE_UTIL_DB_LMDB_HPP
 #define CAFFE_UTIL_DB_LMDB_HPP
 
@@ -89,3 +90,4 @@ class LMDB : public DB {
 }  // namespace caffe
 
 #endif  // CAFFE_UTIL_DB_LMDB_HPP
+#endif  // USE_LMDB
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index c0938ad0625..6070b4c7f3a 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -120,6 +120,7 @@ inline bool ReadImageToDatum(const string& filename, const int label,
 bool DecodeDatumNative(Datum* datum);
 bool DecodeDatum(Datum* datum, bool is_color);
 
+#ifdef USE_OPENCV
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
 
@@ -135,6 +136,7 @@ cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
 cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
+#endif  // USE_OPENCV
 
 }  // namespace caffe
 
diff --git a/python/caffe/test/test_layer_type_list.py b/python/caffe/test/test_layer_type_list.py
index 7edc80df069..47f4cf6d008 100644
--- a/python/caffe/test/test_layer_type_list.py
+++ b/python/caffe/test/test_layer_type_list.py
@@ -5,6 +5,7 @@
 class TestLayerTypeList(unittest.TestCase):
 
     def test_standard_types(self):
+        #removing 'Data' from list 
         for type_name in ['Data', 'Convolution', 'InnerProduct']:
             self.assertIn(type_name, caffe.layer_type_list(),
                     '%s not in layer_type_list()' % type_name)
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index 9ba737e28a9..bbc8213347b 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Script called by Travis to do a CPU-only build of and test Caffe.
+# Script called by Travis to build and test Caffe.
 
 set -e
 MAKE="make --jobs=$NUM_THREADS --keep-going"
@@ -15,7 +15,12 @@ if $WITH_CMAKE; then
   if [ "$PYTHON_VERSION" = "3" ]; then
     PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
   fi
-  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" ..
+  if $WITH_IO; then
+    IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON"
+  else
+    IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF"
+  fi
+  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS ..
   $MAKE
   $MAKE pytest
   if ! $WITH_CUDA; then
@@ -28,6 +33,11 @@ else
   if ! $WITH_CUDA; then
     export CPU_ONLY=1
   fi
+  if $WITH_IO; then
+    export USE_LMDB=1
+    export USE_LEVELDB=1
+    export USE_OPENCV=1
+  fi
   $MAKE all test pycaffe warn lint || true
   if ! $WITH_CUDA; then
     $MAKE runtest
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index 1440be2af8b..83aacf11fb0 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -11,6 +11,12 @@ if $WITH_CUDA; then
   echo "CUDA_ARCH := $GENCODE" >> Makefile.config
 fi
 
+# Remove IO library settings from Makefile.config
+# to avoid conflicts with CI configuration
+sed -i -e '/USE_LMDB/d' Makefile.config
+sed -i -e '/USE_LEVELDB/d' Makefile.config
+sed -i -e '/USE_OPENCV/d' Makefile.config
+
 cat << 'EOF' >> Makefile.config
 # Travis' nvcc doesn't like newer boost versions
 NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 4666d9bd881..7189d67e289 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,4 +1,6 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
+#endif  // USE_OPENCV
 
 #include <string>
 #include <vector>
@@ -124,11 +126,13 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   }
 }
 
+
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
   // If datum is encoded, decoded and transform the cv::image.
   if (datum.encoded()) {
+#ifdef USE_OPENCV
     CHECK(!(param_.force_color() && param_.force_gray()))
         << "cannot set both force_color and force_gray";
     cv::Mat cv_img;
@@ -140,6 +144,9 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
     }
     // Transform the cv::image into blob.
     return Transform(cv_img, transformed_blob);
+#else
+    LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
   } else {
     if (param_.force_color() || param_.force_gray()) {
       LOG(ERROR) << "force_color and force_gray only for encoded datum";
@@ -194,6 +201,7 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
+#ifdef USE_OPENCV
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
                                        Blob<Dtype>* transformed_blob) {
@@ -315,6 +323,7 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
     }
   }
 }
+#endif  // USE_OPENCV
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
@@ -432,6 +441,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
 template<typename Dtype>
 vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
   if (datum.encoded()) {
+#ifdef USE_OPENCV
     CHECK(!(param_.force_color() && param_.force_gray()))
         << "cannot set both force_color and force_gray";
     cv::Mat cv_img;
@@ -443,8 +453,10 @@ vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
     }
     // InferBlobShape using the cv::image.
     return InferBlobShape(cv_img);
+#else
+    LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
   }
-
   const int crop_size = param_.crop_size();
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
@@ -474,6 +486,7 @@ vector<int> DataTransformer<Dtype>::InferBlobShape(
   return shape;
 }
 
+#ifdef USE_OPENCV
 template<typename Dtype>
 vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {
   const int crop_size = param_.crop_size();
@@ -504,6 +517,7 @@ vector<int> DataTransformer<Dtype>::InferBlobShape(
   shape[0] = num;
   return shape;
 }
+#endif  // USE_OPENCV
 
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 0932d9feff3..71f8cb099e8 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,5 +1,6 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
-
+#endif  // USE_OPENCV
 #include <stdint.h>
 
 #include <string>
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 223ba3a75ca..3d2190f8bbb 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
 
 #include <fstream>  // NOLINT(readability/streams)
@@ -164,3 +165,4 @@ INSTANTIATE_CLASS(ImageDataLayer);
 REGISTER_LAYER_CLASS(ImageData);
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 42de4198bc4..2370aa04d3b 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,4 +1,6 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
+#endif  // USE_OPENCV
 
 #include <vector>
 
@@ -53,6 +55,7 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   has_new_data_ = true;
 }
 
+#ifdef USE_OPENCV
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
     const vector<int>& labels) {
@@ -76,6 +79,7 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
   Reset(top_data, top_label, num);
   has_new_data_ = true;
 }
+#endif  // USE_OPENCV
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index f637f2ec6d4..f8db61c9258 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <opencv2/highgui/highgui_c.h>
 #include <stdint.h>
 
@@ -468,3 +469,4 @@ INSTANTIATE_CLASS(WindowDataLayer);
 REGISTER_LAYER_CLASS(WindowData);
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index afe2a40d227..9e03954a543 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <string>
 #include <vector>
 
@@ -348,6 +349,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
 TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
 
+#ifdef USE_LEVELDB
 TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
@@ -385,7 +387,9 @@ TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCrop(TEST);
 }
+#endif  // USE_LEVELDB
 
+#ifdef USE_LMDB
 TYPED_TEST(DataLayerTest, TestReadLMDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
@@ -424,4 +428,6 @@ TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
   this->TestReadCrop(TEST);
 }
 
+#endif  // USE_LMDB
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 16570e20356..8a1013744e8 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <string>
 #include <vector>
 
@@ -353,3 +354,4 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
 }
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_db.cpp b/src/caffe/test/test_db.cpp
index 5b2ac230a0b..1b487b14c58 100644
--- a/src/caffe/test/test_db.cpp
+++ b/src/caffe/test/test_db.cpp
@@ -1,3 +1,4 @@
+#if defined(USE_LEVELDB) && defined(USE_LMDB) && defined(USE_OPENCV)
 #include <string>
 
 #include "boost/scoped_ptr.hpp"
@@ -132,3 +133,4 @@ TYPED_TEST(DBTest, TestWrite) {
 }
 
 }  // namespace caffe
+#endif  // USE_LEVELDB, USE_LMDB and USE_OPENCV
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 931a5ebf137..481fcef7b27 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <map>
 #include <string>
 #include <vector>
@@ -177,3 +178,4 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
 }
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 4ab96311bbc..c2c919e90dc 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
@@ -420,3 +421,4 @@ TEST_F(IOTest, TestDecodeDatumToCVMatContentNative) {
 }
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
index c86fafd000c..7d5d39d8b91 100644
--- a/src/caffe/test/test_layer_factory.cpp
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -31,12 +31,16 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
     LayerParameter layer_param;
     // Data layers expect a DB
     if (iter->first == "Data") {
+#ifdef USE_LEVELDB
       string tmp;
       MakeTempDir(&tmp);
       boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
       db->Open(tmp, db::NEW);
       db->Close();
       layer_param.mutable_data_param()->set_source(tmp);
+#else
+      continue;
+#endif  // USE_LEVELDB
     }
     layer_param.set_type(iter->first);
     layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index a79033f59f1..7269a4d441b 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,4 +1,6 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
+#endif  // USE_OPENCV
 
 #include <string>
 #include <vector>
@@ -113,6 +115,7 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   }
 }
 
+#ifdef USE_OPENCV
 TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   typedef typename TypeParam::Dtype Dtype;
 
@@ -292,5 +295,5 @@ TYPED_TEST(MemoryDataLayerTest, TestSetBatchSize) {
     }
   }
 }
-
+#endif  // USE_OPENCV
 }  // namespace caffe
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 006720231a5..ee05b151e72 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2892,6 +2892,7 @@ TEST_F(NetUpgradeTest, TestImageNet) {
   this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
 }  // NOLINT(readability/fn_size)
 
+#ifdef USE_OPENCV
 TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
   LayerParameter layer_param;
   shared_ptr<Layer<float> > layer;
@@ -2906,16 +2907,25 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
     layer_param.set_type(v2_layer_type);
     // Data layers expect a DB
     if (v2_layer_type == "Data") {
+      #ifdef USE_LEVELDB
       string tmp;
       MakeTempDir(&tmp);
       boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
       db->Open(tmp, db::NEW);
       db->Close();
       layer_param.mutable_data_param()->set_source(tmp);
+      #else
+      continue;
+      #endif  // USE_LEVELDB
     }
+    #ifndef USE_OPENCV
+    if (v2_layer_type == "ImageData" || v2_layer_type == "WindowData") {
+     continue;
+    }
+    #endif  // !USE_OPENCV
     layer = LayerRegistry<float>::CreateLayer(layer_param);
     EXPECT_EQ(v2_layer_type, layer->type());
   }
 }
-
+#endif  // USE_OPENCV
 }  // NOLINT(readability/fn_size)  // namespace caffe
diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
index f55420e9840..ccda054d881 100644
--- a/src/caffe/util/db.cpp
+++ b/src/caffe/util/db.cpp
@@ -8,23 +8,31 @@ namespace caffe { namespace db {
 
 DB* GetDB(DataParameter::DB backend) {
   switch (backend) {
+#ifdef USE_LEVELDB
   case DataParameter_DB_LEVELDB:
     return new LevelDB();
+#endif  // USE_LEVELDB
+#ifdef USE_LMDB
   case DataParameter_DB_LMDB:
     return new LMDB();
+#endif  // USE_LMDB
   default:
     LOG(FATAL) << "Unknown database backend";
   }
 }
 
 DB* GetDB(const string& backend) {
+#ifdef USE_LEVELDB
   if (backend == "leveldb") {
     return new LevelDB();
-  } else if (backend == "lmdb") {
+  }
+#endif  // USE_LEVELDB
+#ifdef USE_LMDB
+  if (backend == "lmdb") {
     return new LMDB();
-  } else {
-    LOG(FATAL) << "Unknown database backend";
   }
+#endif  // USE_LMDB
+  LOG(FATAL) << "Unknown database backend";
 }
 
 }  // namespace db
diff --git a/src/caffe/util/db_leveldb.cpp b/src/caffe/util/db_leveldb.cpp
index 06c46627d31..f5c4d8a660d 100644
--- a/src/caffe/util/db_leveldb.cpp
+++ b/src/caffe/util/db_leveldb.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_LEVELDB
 #include "caffe/util/db_leveldb.hpp"
 
 #include <string>
@@ -19,3 +20,4 @@ void LevelDB::Open(const string& source, Mode mode) {
 
 }  // namespace db
 }  // namespace caffe
+#endif  // USE_LEVELDB
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index a054b796806..78dd880ac41 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_LMDB
 #include "caffe/util/db_lmdb.hpp"
 
 #include <sys/stat.h>
@@ -49,3 +50,4 @@ void LMDBTransaction::Put(const string& key, const string& value) {
 
 }  // namespace db
 }  // namespace caffe
+#endif  // USE_LMDB
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 6f03314202c..f2b1dd98423 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -3,9 +3,11 @@
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
 #include <opencv2/core/core.hpp>
+#ifdef USE_OPENCV
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
 #include <opencv2/imgproc/imgproc.hpp>
+#endif  // USE_OPENCV
 #include <stdint.h>
 
 #include <algorithm>
@@ -67,6 +69,7 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
   CHECK(proto.SerializeToOstream(&output));
 }
 
+#ifdef USE_OPENCV
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color) {
   cv::Mat cv_img;
@@ -98,6 +101,7 @@ cv::Mat ReadImageToCVMat(const string& filename,
 cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
+
 // Do the file extension and encoding match?
 static bool matchExt(const std::string & fn,
                      std::string en) {
@@ -111,6 +115,7 @@ static bool matchExt(const std::string & fn,
     return true;
   return false;
 }
+
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color,
     const std::string & encoding, Datum* datum) {
@@ -135,6 +140,7 @@ bool ReadImageToDatum(const string& filename, const int label,
     return false;
   }
 }
+#endif  // USE_OPENCV
 
 bool ReadFileToDatum(const string& filename, const int label,
     Datum* datum) {
@@ -156,6 +162,7 @@ bool ReadFileToDatum(const string& filename, const int label,
   }
 }
 
+#ifdef USE_OPENCV
 cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
   cv::Mat cv_img;
   CHECK(datum.encoded()) << "Datum not encoded";
@@ -227,6 +234,5 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   }
   datum->set_data(buffer);
 }
-
-
+#endif  // USE_OPENCV
 }  // namespace caffe
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index b1fc7cae38f..2035d515195 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -24,6 +24,7 @@ DEFINE_string(backend, "lmdb",
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
 
+#ifdef USE_OPENCV
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
 #endif
@@ -115,5 +116,8 @@ int main(int argc, char** argv) {
     }
     LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
   }
+#else
+  LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index aad1f1fe216..e51a2631077 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -43,6 +43,7 @@ DEFINE_string(encode_type, "",
     "Optional: What type should we encode the image as ('png','jpg',...).");
 
 int main(int argc, char** argv) {
+#ifdef USE_OPENCV
   ::google::InitGoogleLogging(argv[0]);
   // Print output to stderr (while still logging)
   FLAGS_alsologtostderr = 1;
@@ -150,5 +151,8 @@ int main(int argc, char** argv) {
     txn->Commit();
     LOG(INFO) << "Processed " << count << " files.";
   }
+#else
+  LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
   return 0;
 }

From 2349c6de69bf5043508cde41bb1d337fdb78e188 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Thu, 17 Sep 2015 15:02:45 +0800
Subject: [PATCH 1544/2053] Fix case in CMake notices

---
 CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 838723beca2..37f937fe489 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -19,10 +19,10 @@ caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to
 caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
-set(python_version "2" CACHE STRING "Specify which python version to use")
+set(python_version "2" CACHE STRING "Specify which Python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
-caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
+caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON)
 caffe_option(USE_LMDB "Build with lmdb" ON)
 caffe_option(USE_LEVELDB "Build with levelDB" ON)
 caffe_option(USE_OPENCV "Build with OpenCV support" ON)

From 68c9e2b4703ce18fd9a7ab541addf701129a8080 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Tue, 8 Sep 2015 12:20:40 +0800
Subject: [PATCH 1545/2053] Add a comment indicating that Travis CI tests are
 CPU only

---
 scripts/travis/travis_build_and_test.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index bbc8213347b..174f1ee5a0a 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -1,5 +1,6 @@
 #!/bin/bash
 # Script called by Travis to build and test Caffe.
+# Travis CI tests are CPU-only for lack of compatible hardware.
 
 set -e
 MAKE="make --jobs=$NUM_THREADS --keep-going"

From e75ae965519444fb64d67c0aa6323bc2ef4049ef Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 17 Sep 2015 15:05:12 -0700
Subject: [PATCH 1546/2053] [build] include IO dependencies by default

keep old behavior by including leveldb, lmdb, and opencv by default
---
 Makefile                | 7 ++++++-
 Makefile.config.example | 8 ++++----
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/Makefile b/Makefile
index ddaed59b0a9..a911133661f 100644
--- a/Makefile
+++ b/Makefile
@@ -172,6 +172,11 @@ endif
 
 LIBRARIES += glog gflags protobuf boost_system m hdf5_hl hdf5
 
+# handle IO dependencies
+USE_LEVELDB ?= 1
+USE_LMDB ?= 1
+USE_OPENCV ?= 1
+
 ifeq ($(USE_LEVELDB), 1)
 	LIBRARIES += leveldb snappy
 endif
@@ -299,7 +304,7 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
-# i/o libraries configuration
+# configure IO libraries
 ifeq ($(USE_OPENCV), 1)
 	COMMON_FLAGS += -DUSE_OPENCV
 endif
diff --git a/Makefile.config.example b/Makefile.config.example
index 32e67ee493e..a20bad2f5ce 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -7,10 +7,10 @@
 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 1
 
-# comment out to disable IO dependencies
-USE_LEVELDB := 1
-USE_LMDB := 1
-USE_OPENCV := 1
+# uncomment to disable IO dependencies and corresponding data layers
+# USE_LEVELDB := 0
+# USE_LMDB := 0
+# USE_OPENCV := 0
 
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++

From b4f9add57fa468ab43aa40f0a95badf3e9ace243 Mon Sep 17 00:00:00 2001
From: Gustav Larsson <gustav.m.larsson@gmail.com>
Date: Thu, 17 Sep 2015 20:32:33 -0500
Subject: [PATCH 1547/2053] Expose `Snapshot` to pycaffe

- Solver::Snapshot is made public
- It is also added as `snapshot` to pycaffe

Addressing #3077
---
 include/caffe/solver.hpp | 10 +++++-----
 python/caffe/_caffe.cpp  |  3 ++-
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 8d52785ac6e..51f8d495c37 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -60,6 +60,11 @@ class Solver {
   // RestoreSolverStateFrom___ protected methods. You should implement these
   // methods to restore the state from the appropriate snapshot type.
   void Restore(const char* resume_file);
+  // The Solver::Snapshot function implements the basic snapshotting utility
+  // that stores the learned net. You should implement the SnapshotSolverState()
+  // function that produces a SolverState protocol buffer that needs to be
+  // written to disk together with the learned net.
+  void Snapshot();
   virtual ~Solver() {}
   inline const SolverParameter& param() const { return param_; }
   inline shared_ptr<Net<Dtype> > net() { return net_; }
@@ -87,11 +92,6 @@ class Solver {
  protected:
   // Make and apply the update value for the current iteration.
   virtual void ApplyUpdate() = 0;
-  // The Solver::Snapshot function implements the basic snapshotting utility
-  // that stores the learned net. You should implement the SnapshotSolverState()
-  // function that produces a SolverState protocol buffer that needs to be
-  // written to disk together with the learned net.
-  void Snapshot();
   string SnapshotFilename(const string extension);
   string SnapshotToBinaryProto();
   string SnapshotToHDF5();
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index ccd5776ac40..6c2ccaa5794 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -286,7 +286,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
           &Solver<Dtype>::Solve), SolveOverloads())
     .def("step", &Solver<Dtype>::Step)
-    .def("restore", &Solver<Dtype>::Restore);
+    .def("restore", &Solver<Dtype>::Restore)
+    .def("snapshot", &Solver<Dtype>::Snapshot);
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(

From f75d594bbec1efab69cdc09c04bed1762aebd0e1 Mon Sep 17 00:00:00 2001
From: Yan Chen <yanchen036@gmail.com>
Date: Fri, 18 Sep 2015 17:02:16 +0800
Subject: [PATCH 1548/2053] refine format of switch case in solver

---
 include/caffe/solver.hpp | 14 +++++++-------
 src/caffe/solver.cpp     | 16 ++++++++--------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 8d52785ac6e..2ecf539baef 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -283,19 +283,19 @@ Solver<Dtype>* GetSolver(const SolverParameter& param) {
 
   switch (type) {
   case SolverParameter_SolverType_SGD:
-      return new SGDSolver<Dtype>(param);
+    return new SGDSolver<Dtype>(param);
   case SolverParameter_SolverType_NESTEROV:
-      return new NesterovSolver<Dtype>(param);
+    return new NesterovSolver<Dtype>(param);
   case SolverParameter_SolverType_ADAGRAD:
-      return new AdaGradSolver<Dtype>(param);
+    return new AdaGradSolver<Dtype>(param);
   case SolverParameter_SolverType_RMSPROP:
-      return new RMSPropSolver<Dtype>(param);
+    return new RMSPropSolver<Dtype>(param);
   case SolverParameter_SolverType_ADADELTA:
-      return new AdaDeltaSolver<Dtype>(param);
+    return new AdaDeltaSolver<Dtype>(param);
   case SolverParameter_SolverType_ADAM:
-      return new AdamSolver<Dtype>(param);
+    return new AdamSolver<Dtype>(param);
   default:
-      LOG(FATAL) << "Unknown SolverType: " << type;
+    LOG(FATAL) << "Unknown SolverType: " << type;
   }
   return (Solver<Dtype>*) NULL;
 }
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 3574ce75046..12c13dd8385 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -422,14 +422,14 @@ void Solver<Dtype>::Snapshot() {
   CHECK(Caffe::root_solver());
   string model_filename;
   switch (param_.snapshot_format()) {
-    case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
-      model_filename = SnapshotToBinaryProto();
-      break;
-    case caffe::SolverParameter_SnapshotFormat_HDF5:
-      model_filename = SnapshotToHDF5();
-      break;
-    default:
-      LOG(FATAL) << "Unsupported snapshot format.";
+  case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+    model_filename = SnapshotToBinaryProto();
+    break;
+  case caffe::SolverParameter_SnapshotFormat_HDF5:
+    model_filename = SnapshotToHDF5();
+    break;
+  default:
+    LOG(FATAL) << "Unsupported snapshot format.";
   }
 
   SnapshotSolverState(model_filename);

From 4c2ff1693ea509dc4758e73b913f4cbec6c1ac3a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 4 Mar 2015 19:27:56 -0800
Subject: [PATCH 1549/2053] caffe.proto: generalize ConvolutionParameter to N
 spatial axes

---
 src/caffe/proto/caffe.proto | 37 +++++++++++++++++++++++++++----------
 1 file changed, 27 insertions(+), 10 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index aa299f8660b..86683eb45da 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -471,18 +471,24 @@ message ContrastiveLossParameter {
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
   // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in height and width or as Y, X pairs.
-  optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X)
-  optional uint32 pad_h = 9 [default = 0]; // The padding height
-  optional uint32 pad_w = 10 [default = 0]; // The padding width
-  optional uint32 kernel_size = 4; // The kernel size (square)
-  optional uint32 kernel_h = 11; // The kernel height
-  optional uint32 kernel_w = 12; // The kernel width
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
   optional uint32 group = 5 [default = 1]; // The group size for group conv
-  optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X)
-  optional uint32 stride_h = 13; // The stride height
-  optional uint32 stride_w = 14; // The stride width
+
   optional FillerParameter weight_filler = 7; // The filler for the weight
   optional FillerParameter bias_filler = 8; // The filler for the bias
   enum Engine {
@@ -491,6 +497,17 @@ message ConvolutionParameter {
     CUDNN = 2;
   }
   optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
 }
 
 message DataParameter {

From 0813f32038bf7477d343ae369981166cfed783b5 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 4 Mar 2015 21:31:34 -0800
Subject: [PATCH 1550/2053] Blob: add SyncedMemory shape accessor for GPU shape
 access

---
 include/caffe/blob.hpp |  2 ++
 src/caffe/blob.cpp     | 11 +++++++++++
 2 files changed, 13 insertions(+)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index dda7b1f8372..fea5117ef10 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -219,6 +219,7 @@ class Blob {
 
   const Dtype* cpu_data() const;
   void set_cpu_data(Dtype* data);
+  const int* gpu_shape() const;
   const Dtype* gpu_data() const;
   const Dtype* cpu_diff() const;
   const Dtype* gpu_diff() const;
@@ -268,6 +269,7 @@ class Blob {
  protected:
   shared_ptr<SyncedMemory> data_;
   shared_ptr<SyncedMemory> diff_;
+  shared_ptr<SyncedMemory> shape_data_;
   vector<int> shape_;
   int count_;
   int capacity_;
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 8450aa140be..c86fd5d1d94 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -24,11 +24,16 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
   CHECK_LE(shape.size(), kMaxBlobAxes);
   count_ = 1;
   shape_.resize(shape.size());
+  if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
+    shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
+  }
+  int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
   for (int i = 0; i < shape.size(); ++i) {
     CHECK_GE(shape[i], 0);
     CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
     count_ *= shape[i];
     shape_[i] = shape[i];
+    shape_data[i] = shape[i];
   }
   if (count_ > capacity_) {
     capacity_ = count_;
@@ -67,6 +72,12 @@ Blob<Dtype>::Blob(const vector<int>& shape)
   Reshape(shape);
 }
 
+template <typename Dtype>
+const int* Blob<Dtype>::gpu_shape() const {
+  CHECK(shape_data_);
+  return (const int*)shape_data_->gpu_data();
+}
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::cpu_data() const {
   CHECK(data_);

From 9d8206e0f906069e7c04f08dfddefa1357f3915c Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 4 Mar 2015 19:30:17 -0800
Subject: [PATCH 1551/2053] Im2col and Convolution layers support N spatial
 axes

---
 include/caffe/util/im2col.hpp               |  24 ++
 include/caffe/vision_layers.hpp             | 108 +++++-
 src/caffe/layers/base_conv_layer.cpp        | 241 ++++++++----
 src/caffe/layers/conv_layer.cpp             |  32 +-
 src/caffe/layers/conv_layer.cu              |  16 +-
 src/caffe/layers/cudnn_conv_layer.cpp       |  46 ++-
 src/caffe/layers/cudnn_conv_layer.cu        |  18 +-
 src/caffe/layers/deconv_layer.cpp           |  32 +-
 src/caffe/layers/deconv_layer.cu            |  16 +-
 src/caffe/layers/im2col_layer.cpp           | 171 +++++---
 src/caffe/layers/im2col_layer.cu            |  41 +-
 src/caffe/proto/caffe.proto                 |   7 +
 src/caffe/test/test_convolution_layer.cpp   | 409 ++++++++++++++++----
 src/caffe/test/test_deconvolution_layer.cpp | 159 +++++++-
 src/caffe/test/test_im2col_kernel.cu        |  87 ++++-
 src/caffe/test/test_im2col_layer.cpp        |  30 +-
 src/caffe/util/im2col.cpp                   | 116 ++++++
 src/caffe/util/im2col.cu                    | 306 ++++++++++++++-
 src/caffe/util/upgrade_proto.cpp            |   6 +-
 19 files changed, 1554 insertions(+), 311 deletions(-)

diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index 0051e2fa067..531fd29c57a 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -3,24 +3,48 @@
 
 namespace caffe {
 
+template <typename Dtype>
+void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_col);
+
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, Dtype* data_col);
 
+template <typename Dtype>
+void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_im);
+
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int patch_h, const int patch_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, Dtype* data_im);
 
+template <typename Dtype>
+void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
+    const int col_size, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_col);
+
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, Dtype* data_col);
 
+template <typename Dtype>
+void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
+    const int im_size, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_im);
+
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int patch_h, const int patch_w,
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 211e3d9042d..eae65820c40 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -64,46 +64,101 @@ class BaseConvolutionLayer : public Layer<Dtype> {
   // Compute height_out_ and width_out_ from other parameters.
   virtual void compute_output_shape() = 0;
 
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
+  /// @brief The spatial dimensions of a filter kernel.
+  Blob<int> kernel_shape_;
+  /// @brief The spatial dimensions of the stride.
+  Blob<int> stride_;
+  /// @brief The spatial dimensions of the padding.
+  Blob<int> pad_;
+  /// @brief The spatial dimensions of the convolution input.
+  Blob<int> conv_input_shape_;
+  /// @brief The spatial dimensions of the input.
+  Blob<int> input_shape_;
+  /// @brief The spatial dimensions of the col_buffer.
+  vector<int> col_buffer_shape_;
+  /// @brief The spatial dimensions of the output.
+  vector<int> output_shape_;
+
+  int num_spatial_axes_;
+  int bottom_dim_;
+  int top_dim_;
+
+  int channel_axis_;
   int num_;
   int channels_;
-  int pad_h_, pad_w_;
-  int height_, width_;
   int group_;
+  int out_spatial_dim_;
+  int weight_offset_;
   int num_output_;
-  int height_out_, width_out_;
   bool bias_term_;
   bool is_1x1_;
+  bool force_nd_im2col_;
 
  private:
   // wrap im2col/col2im so we don't have to remember the (long) argument lists
   inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
-    im2col_cpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_cpu(data, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
+    } else {
+      im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
+          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+          pad_.cpu_data(), stride_.cpu_data(), col_buff);
+    }
   }
   inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
-    col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_cpu(col_buff, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
+    } else {
+      col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
+          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+          pad_.cpu_data(), stride_.cpu_data(), data);
+    }
   }
 #ifndef CPU_ONLY
   inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
-    im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_gpu(data, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
+    } else {
+      im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
+          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+          kernel_shape_.gpu_data(), pad_.gpu_data(),
+          stride_.gpu_data(), col_buff);
+    }
   }
   inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
-    col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_gpu(col_buff, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
+    } else {
+      col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
+          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          data);
+    }
   }
 #endif
 
+  int num_kernels_im2col_;
+  int num_kernels_col2im_;
   int conv_out_channels_;
   int conv_in_channels_;
   int conv_out_spatial_dim_;
-  int conv_in_height_;
-  int conv_in_width_;
   int kernel_dim_;
-  int weight_offset_;
   int col_offset_;
   int output_offset_;
 
@@ -250,7 +305,7 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
   cudnnTensorDescriptor_t    bias_desc_;
   cudnnFilterDescriptor_t      filter_desc_;
   vector<cudnnConvolutionDescriptor_t> conv_descs_;
-  int bottom_offset_, top_offset_, weight_offset_, bias_offset_;
+  int bottom_offset_, top_offset_, bias_offset_;
   size_t workspaceSizeInBytes;
   void *workspace;
 };
@@ -287,11 +342,22 @@ class Im2colLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
+  /// @brief The spatial dimensions of a filter kernel.
+  Blob<int> kernel_shape_;
+  /// @brief The spatial dimensions of the stride.
+  Blob<int> stride_;
+  /// @brief The spatial dimensions of the padding.
+  Blob<int> pad_;
+
+  int num_spatial_axes_;
+  int bottom_dim_;
+  int top_dim_;
+
+  int channel_axis_;
+  int num_;
   int channels_;
-  int height_, width_;
-  int pad_h_, pad_w_;
+
+  bool force_nd_im2col_;
 };
 
 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index ccb3adc7e89..a5b90a549bb 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <vector>
 
 #include "caffe/filler.hpp"
@@ -11,50 +12,103 @@ namespace caffe {
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
-      << "corresponding to (num, channels, height, width)";
   // Configure the kernel size, padding, stride, and inputs.
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  CHECK(!conv_param.has_kernel_size() !=
-      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(conv_param.has_kernel_size() ||
-      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
-      && conv_param.has_pad_w())
-      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
-      << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
-      && conv_param.has_stride_w())
-      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
-      << "Stride is stride OR stride_h and stride_w are required.";
-  if (conv_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  force_nd_im2col_ = conv_param.force_nd_im2col();
+  channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
+  const int first_spatial_axis = channel_axis_ + 1;
+  const int num_axes = bottom[0]->num_axes();
+  num_spatial_axes_ = num_axes - first_spatial_axis;
+  CHECK_GE(num_spatial_axes_, 0);
+  // Setup input dimensions (input_shape_).
+  vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
+  input_shape_.Reshape(bottom_dim_blob_shape);
+  int* input_shape_data = input_shape_.mutable_cpu_data();
+  for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
+    input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
+  }
+  vector<int> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1));
+  // Setup filter kernel dimensions (kernel_shape_).
+  kernel_shape_.Reshape(spatial_dim_blob_shape);
+  int* kernel_shape_data = kernel_shape_.mutable_cpu_data();
+  if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "kernel_h & kernel_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.kernel_size_size())
+        << "Either kernel_size or kernel_h/w should be specified; not both.";
+    kernel_shape_data[0] = conv_param.kernel_h();
+    kernel_shape_data[1] = conv_param.kernel_w();
   } else {
-    kernel_h_ = conv_param.kernel_h();
-    kernel_w_ = conv_param.kernel_w();
+    const int num_kernel_dims = conv_param.kernel_size_size();
+    CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
+        << "kernel_size must be specified once, or once per spatial dimension "
+        << "(kernel_size specified " << num_kernel_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+      for (int i = 0; i < num_spatial_axes_; ++i) {
+        kernel_shape_data[i] =
+            conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
+      }
+  }
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";
   }
-  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
-  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (!conv_param.has_pad_h()) {
-    pad_h_ = pad_w_ = conv_param.pad();
+  // Setup stride dimensions (stride_).
+  stride_.Reshape(spatial_dim_blob_shape);
+  int* stride_data = stride_.mutable_cpu_data();
+  if (conv_param.has_stride_h() || conv_param.has_stride_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "stride_h & stride_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.stride_size())
+        << "Either stride or stride_h/w should be specified; not both.";
+    stride_data[0] = conv_param.stride_h();
+    stride_data[1] = conv_param.stride_w();
   } else {
-    pad_h_ = conv_param.pad_h();
-    pad_w_ = conv_param.pad_w();
+    const int num_stride_dims = conv_param.stride_size();
+    CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||
+          num_stride_dims == num_spatial_axes_)
+        << "stride must be specified once, or once per spatial dimension "
+        << "(stride specified " << num_stride_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+    const int kDefaultStride = 1;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
+          conv_param.stride((num_stride_dims == 1) ? 0 : i);
+      CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";
+    }
   }
-  if (!conv_param.has_stride_h()) {
-    stride_h_ = stride_w_ = conv_param.stride();
+  // Setup pad dimensions (pad_).
+  pad_.Reshape(spatial_dim_blob_shape);
+  int* pad_data = pad_.mutable_cpu_data();
+  if (conv_param.has_pad_h() || conv_param.has_pad_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "pad_h & pad_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.pad_size())
+        << "Either pad or pad_h/w should be specified; not both.";
+    pad_data[0] = conv_param.pad_h();
+    pad_data[1] = conv_param.pad_w();
   } else {
-    stride_h_ = conv_param.stride_h();
-    stride_w_ = conv_param.stride_w();
+    const int num_pad_dims = conv_param.pad_size();
+    CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||
+          num_pad_dims == num_spatial_axes_)
+        << "pad must be specified once, or once per spatial dimension "
+        << "(pad specified " << num_pad_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+    const int kDefaultPad = 0;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
+          conv_param.pad((num_pad_dims == 1) ? 0 : i);
+    }
   }
   // Special case: im2col is the identity for 1x1 convolution with stride 1
   // and no padding, so flag for skipping the buffer and transformation.
-  is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
-      && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
+  is_1x1_ = true;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    is_1x1_ &=
+        kernel_shape_data[i] == 1 && stride_data[i] == 1 && pad_data[i] == 0;
+    if (!is_1x1_) { break; }
+  }
   // Configure output channels and groups.
-  channels_ = bottom[0]->channels();
+  channels_ = bottom[0]->shape(channel_axis_);
   num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
   group_ = this->layer_param_.convolution_param().group();
@@ -71,8 +125,29 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Handle the parameters: weights and biases.
   // - blobs_[0] holds the filter weights
   // - blobs_[1] holds the biases (optional)
+  vector<int> weight_shape(2);
+  weight_shape[0] = conv_out_channels_;
+  weight_shape[1] = conv_in_channels_ / group_;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    weight_shape.push_back(kernel_shape_data[i]);
+  }
   bias_term_ = this->layer_param_.convolution_param().bias_term();
+  vector<int> bias_shape(bias_term_, num_output_);
   if (this->blobs_.size() > 0) {
+    CHECK_EQ(1 + bias_term_, this->blobs_.size())
+        << "Incorrect number of weight blobs.";
+    if (weight_shape != this->blobs_[0]->shape()) {
+      Blob<Dtype> weight_shaped_blob(weight_shape);
+      LOG(FATAL) << "Incorrect weight shape: expected shape "
+          << weight_shaped_blob.shape_string() << "; instead, shape was "
+          << this->blobs_[0]->shape_string();
+    }
+    if (bias_term_ && bias_shape != this->blobs_[1]->shape()) {
+      Blob<Dtype> bias_shaped_blob(bias_shape);
+      LOG(FATAL) << "Incorrect bias shape: expected shape "
+          << bias_shaped_blob.shape_string() << "; instead, shape was "
+          << this->blobs_[1]->shape_string();
+    }
     LOG(INFO) << "Skipping parameter initialization";
   } else {
     if (bias_term_) {
@@ -82,20 +157,20 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     }
     // Initialize and fill the weights:
     // output channels x input channels per-group x kernel height x kernel width
-    this->blobs_[0].reset(new Blob<Dtype>(
-        conv_out_channels_, conv_in_channels_ / group_, kernel_h_, kernel_w_));
+    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
     // If necessary, initialize and fill the biases.
     if (bias_term_) {
-      vector<int> bias_shape(1, num_output_);
       this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
           this->layer_param_.convolution_param().bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
+  kernel_dim_ = this->blobs_[0]->count(1);
+  weight_offset_ = conv_out_channels_ * kernel_dim_ / group_;
   // Propagate gradients to the parameters (as directed by backward pass).
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
@@ -103,52 +178,68 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
-      << "corresponding to (num, channels, height, width)";
-  num_ = bottom[0]->num();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
-    " convolution kernel.";
+  const int first_spatial_axis = channel_axis_ + 1;
+  CHECK_EQ(bottom[0]->num_axes(), first_spatial_axis + num_spatial_axes_)
+      << "bottom num_axes may not change.";
+  num_ = bottom[0]->count(0, channel_axis_);
+  CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
+      << "Input size incompatible with convolution kernel.";
   // TODO: generalize to handle inputs of different shapes.
   for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
-    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
-    CHECK_EQ(channels_, bottom[bottom_id]->channels())
-        << "Inputs must have same channels.";
-    CHECK_EQ(height_, bottom[bottom_id]->height())
-        << "Inputs must have same height.";
-    CHECK_EQ(width_, bottom[bottom_id]->width())
-        << "Inputs must have same width.";
+    CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
+        << "All inputs must have the same shape.";
   }
   // Shape the tops.
   compute_output_shape();
+  vector<int> top_shape(bottom[0]->shape().begin(),
+      bottom[0]->shape().begin() + channel_axis_);
+  top_shape.push_back(num_output_);
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    top_shape.push_back(output_shape_[i]);
+  }
   for (int top_id = 0; top_id < top.size(); ++top_id) {
-    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+    top[top_id]->Reshape(top_shape);
   }
   if (reverse_dimensions()) {
-    conv_in_height_ = height_out_;
-    conv_in_width_ = width_out_;
-    conv_out_spatial_dim_ = height_ * width_;
+    conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
   } else {
-    conv_in_height_ = height_;
-    conv_in_width_ = width_;
-    conv_out_spatial_dim_ = height_out_ * width_out_;
+    conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
   }
-  kernel_dim_ = conv_in_channels_ * kernel_h_ * kernel_w_;
-  weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_;
-  col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_;
+  col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
   output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
+  // Setup input dimensions (conv_input_shape_).
+  vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
+  conv_input_shape_.Reshape(bottom_dim_blob_shape);
+  int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
+  for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
+    if (reverse_dimensions()) {
+      conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i);
+    } else {
+      conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
+    }
+  }
   // The im2col result buffer will only hold one image at a time to avoid
   // overly large memory usage. In the special case of 1x1 convolution
   // it goes lazily unused to save memory.
-  if (reverse_dimensions()) {
-    col_buffer_.Reshape(1, kernel_dim_, height_, width_);
-  } else {
-    col_buffer_.Reshape(1, kernel_dim_, height_out_, width_out_);
+  col_buffer_shape_.clear();
+  col_buffer_shape_.push_back(kernel_dim_ * group_);
+  const int* input_shape_data = input_shape_.cpu_data() + 1;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    if (reverse_dimensions()) {
+      col_buffer_shape_.push_back(input_shape_data[i]);
+    } else {
+      col_buffer_shape_.push_back(output_shape_[i]);
+    }
   }
+  col_buffer_.Reshape(col_buffer_shape_);
+  bottom_dim_ = bottom[0]->count(channel_axis_);
+  top_dim_ = top[0]->count(channel_axis_);
+  num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
+  num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
   // Set up the all ones "bias multiplier" for adding biases by BLAS
+  out_spatial_dim_ = top[0]->count(first_spatial_axis);
   if (bias_term_) {
-    vector<int> bias_multiplier_shape(1, height_out_ * width_out_);
+    vector<int> bias_multiplier_shape(1, out_spatial_dim_);
     bias_multiplier_.Reshape(bias_multiplier_shape);
     caffe_set(bias_multiplier_.count(), Dtype(1),
         bias_multiplier_.mutable_cpu_data());
@@ -167,7 +258,7 @@ void BaseConvolutionLayer<Dtype>::forward_cpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
-        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        group_, conv_out_spatial_dim_, kernel_dim_,
         (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)0., output + output_offset_ * g);
   }
@@ -177,7 +268,7 @@ template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::forward_cpu_bias(Dtype* output,
     const Dtype* bias) {
   caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
+      out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
       (Dtype)1., output);
 }
 
@@ -189,7 +280,7 @@ void BaseConvolutionLayer<Dtype>::backward_cpu_gemm(const Dtype* output,
     col_buff = input;
   }
   for (int g = 0; g < group_; ++g) {
-    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_,
         conv_out_spatial_dim_, conv_out_channels_ / group_,
         (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
         (Dtype)0., col_buff + col_offset_ * g);
@@ -209,7 +300,7 @@ void BaseConvolutionLayer<Dtype>::weight_cpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
-        kernel_dim_ / group_, conv_out_spatial_dim_,
+        kernel_dim_, conv_out_spatial_dim_,
         (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)1., weights + weight_offset_ * g);
   }
@@ -218,7 +309,7 @@ void BaseConvolutionLayer<Dtype>::weight_cpu_gemm(const Dtype* input,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::backward_cpu_bias(Dtype* bias,
     const Dtype* input) {
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, out_spatial_dim_, 1.,
       input, bias_multiplier_.cpu_data(), 1., bias);
 }
 
@@ -236,7 +327,7 @@ void BaseConvolutionLayer<Dtype>::forward_gpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
-        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        group_, conv_out_spatial_dim_, kernel_dim_,
         (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)0., output + output_offset_ * g);
   }
@@ -246,7 +337,7 @@ template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::forward_gpu_bias(Dtype* output,
     const Dtype* bias) {
   caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
+      out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
       (Dtype)1., output);
 }
 
@@ -258,7 +349,7 @@ void BaseConvolutionLayer<Dtype>::backward_gpu_gemm(const Dtype* output,
     col_buff = input;
   }
   for (int g = 0; g < group_; ++g) {
-    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_,
         conv_out_spatial_dim_, conv_out_channels_ / group_,
         (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
         (Dtype)0., col_buff + col_offset_ * g);
@@ -278,7 +369,7 @@ void BaseConvolutionLayer<Dtype>::weight_gpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
-        kernel_dim_ / group_, conv_out_spatial_dim_,
+        kernel_dim_, conv_out_spatial_dim_,
         (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)1., weights + weight_offset_ * g);
   }
@@ -287,7 +378,7 @@ void BaseConvolutionLayer<Dtype>::weight_gpu_gemm(const Dtype* input,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::backward_gpu_bias(Dtype* bias,
     const Dtype* input) {
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, out_spatial_dim_, 1.,
       input, bias_multiplier_.gpu_data(), 1., bias);
 }
 
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 928ef5ee468..5cf26970a0b 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -10,10 +10,18 @@ namespace caffe {
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::compute_output_shape() {
-  this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
-      / this->stride_h_ + 1;
-  this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
-      / this->stride_w_ + 1;
+  // input_shape_ + 1 to skip channel axis
+  const int* input_shape_data = this->input_shape_.cpu_data() + 1;
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int* stride_data = this->stride_.cpu_data();
+  const int* pad_data = this->pad_.cpu_data();
+  this->output_shape_.clear();
+  for (int i = 0; i < this->num_spatial_axes_; ++i) {
+    const int input_dim = input_shape_data[i];
+    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i])
+        / stride_data[i] + 1;
+    this->output_shape_.push_back(output_dim);
+  }
 }
 
 template <typename Dtype>
@@ -24,11 +32,11 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->forward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->cpu_data();
-        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -47,20 +55,20 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
       for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),
-              top_diff + top[i]->offset(n), weight_diff);
+          this->weight_cpu_gemm(bottom_data + n * this->bottom_dim_,
+              top_diff + n * this->top_dim_, weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n));
+          this->backward_cpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_);
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index b8a98ff7cc9..b429d2b47d0 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -16,11 +16,11 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->forward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->forward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->gpu_data();
-        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_gpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -37,7 +37,7 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
@@ -46,13 +46,13 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_gpu_gemm(bottom_data + bottom[i]->offset(n),
-              top_diff + top[i]->offset(n), weight_diff);
+          this->weight_gpu_gemm(bottom_data + n * this->bottom_dim_,
+              top_diff + n * this->top_dim_, weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->backward_gpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n));
+          this->backward_gpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_);
         }
       }
     }
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 104d2b9d669..3514fe2aba5 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -34,14 +34,15 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   }
 
   // Set the indexing parameters.
-  weight_offset_ = (this->num_output_ / this->group_)
-      * (this->channels_ / this->group_) * this->kernel_h_ * this->kernel_w_;
   bias_offset_ = (this->num_output_ / this->group_);
 
   // Create filter descriptor.
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int kernel_h = kernel_shape_data[0];
+  const int kernel_w = kernel_shape_data[1];
   cudnn::createFilterDesc<Dtype>(&filter_desc_,
       this->num_output_ / this->group_, this->channels_ / this->group_,
-      this->kernel_h_, this->kernel_w_);
+      kernel_h, kernel_w);
 
   // Create tensor descriptor(s) for data and corresponding convolution(s).
   for (int i = 0; i < bottom.size(); i++) {
@@ -68,29 +69,36 @@ template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   ConvolutionLayer<Dtype>::Reshape(bottom, top);
-  bottom_offset_ = (this->channels_ / this->group_)
-      * this->height_ * this->width_;
-  top_offset_ = (this->num_output_ / this->group_)
-      * this->height_out_ * this->width_out_;
+  CHECK_EQ(2, this->num_spatial_axes_)
+      << "CuDNNConvolution input must have 2 spatial axes "
+      << "(e.g., height and width). "
+      << "Use 'engine: CAFFE' for general ND convolution.";
+  bottom_offset_ = this->bottom_dim_ / this->group_;
+  top_offset_ = this->top_dim_ / this->group_;
+  const int height = bottom[0]->shape(this->channel_axis_ + 1);
+  const int width = bottom[0]->shape(this->channel_axis_ + 2);
+  const int height_out = top[0]->shape(this->channel_axis_ + 1);
+  const int width_out = top[0]->shape(this->channel_axis_ + 2);
+  const int* pad_data = this->pad_.cpu_data();
+  const int pad_h = pad_data[0];
+  const int pad_w = pad_data[1];
+  const int* stride_data = this->stride_.cpu_data();
+  const int stride_h = stride_data[0];
+  const int stride_w = stride_data[1];
 
   for (int i = 0; i < bottom.size(); i++) {
     cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
         this->num_,
-        this->channels_ / this->group_,
-        this->height_, this->width_,
-        this->channels_ * this->height_ * this->width_,
-        this->height_ * this->width_,
-        this->width_, 1);
+        this->channels_ / this->group_, height, width,
+        this->channels_ * height * width,
+        height * width, width, 1);
     cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
         this->num_,
-        this->num_output_ / this->group_,
-        this->height_out_, this->width_out_,
-        this->num_output_ * this->height_out_ * this->width_out_,
-        this->height_out_ * this->width_out_,
-        this->width_out_, 1);
+        this->num_output_ / this->group_, height_out, width_out,
+        this->num_output_ * this->out_spatial_dim_,
+        this->out_spatial_dim_, width_out, 1);
     cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
-        filter_desc_, this->pad_h_, this->pad_w_,
-        this->stride_h_, this->stride_w_);
+        filter_desc_, pad_h, pad_w, stride_h, stride_w);
   }
 
   // Tensor descriptor for bias.
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index b4e802e13d1..691152021a3 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -14,15 +14,15 @@ __global__ void sync_conv_groups() { }
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int kernel_h = kernel_shape_data[0];
+  const int kernel_w = kernel_shape_data[1];
+  const size_t workspace_limit_bytes =
+      kernel_h * kernel_w * this->channels_ * sizeof(int) + 1;
+  const Dtype* weight = this->blobs_[0]->gpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
-    const Dtype* weight = this->blobs_[0]->gpu_data();
-
-    size_t workspace_limit_bytes = this->kernel_h_ *
-                                   this->kernel_w_ *
-                                   this->channels_ *
-                                   sizeof(int) + 1;
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
@@ -69,7 +69,7 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
       CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
             cudnn::dataType<Dtype>::one,
             bottom_descs_[i], bottom_data + bottom_offset_ * g,
-            filter_desc_, weight + weight_offset_ * g,
+            filter_desc_, weight + this->weight_offset_ * g,
             conv_descs_[i],
             algo, workspace, workspaceSizeInBytes,
             cudnn::dataType<Dtype>::zero,
@@ -128,7 +128,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
               top_descs_[i],    top_diff + top_offset_ * g,
               conv_descs_[i],
               cudnn::dataType<Dtype>::one,
-              filter_desc_, weight_diff + weight_offset_ * g));
+              filter_desc_, weight_diff + this->weight_offset_ * g));
       }
 
       // Gradient w.r.t. bottom data.
@@ -139,7 +139,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
         CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
               cudnn::dataType<Dtype>::one,
-              filter_desc_, weight + weight_offset_ * g,
+              filter_desc_, weight + this->weight_offset_ * g,
               top_descs_[i], top_diff + top_offset_ * g,
               conv_descs_[i],
               cudnn::dataType<Dtype>::zero,
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index a4612963b6b..f1d1abf288a 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -10,10 +10,18 @@ namespace caffe {
 
 template <typename Dtype>
 void DeconvolutionLayer<Dtype>::compute_output_shape() {
-  this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_
-      - 2 * this->pad_h_;
-  this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_
-      - 2 * this->pad_w_;
+  // input_shape_ + 1 to skip channel axis
+  const int* input_shape_data = this->input_shape_.cpu_data() + 1;
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int* stride_data = this->stride_.cpu_data();
+  const int* pad_data = this->pad_.cpu_data();
+  this->output_shape_.clear();
+  for (int i = 0; i < this->num_spatial_axes_; ++i) {
+    const int input_dim = input_shape_data[i];
+    const int output_dim = stride_data[i] * (input_dim - 1)
+        + kernel_shape_data[i] - 2 * pad_data[i];
+    this->output_shape_.push_back(output_dim);
+  }
 }
 
 template <typename Dtype>
@@ -24,11 +32,11 @@ void DeconvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->backward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->cpu_data();
-        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -47,21 +55,21 @@ void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
       for (int n = 0; n < this->num_; ++n) {
         // Gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_cpu_gemm(top_diff + top[i]->offset(n),
-              bottom_data + bottom[i]->offset(n), weight_diff);
+          this->weight_cpu_gemm(top_diff + n * this->top_dim_,
+              bottom_data + n * this->bottom_dim_, weight_diff);
         }
         // Gradient w.r.t. bottom data, if necessary, reusing the column buffer
         // we might have just computed above.
         if (propagate_down[i]) {
-          this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n),
+          this->forward_cpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_,
               this->param_propagate_down_[0]);
         }
       }
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index 8a1eed8aa16..ea83f56f1b2 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -16,11 +16,11 @@ void DeconvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->backward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->gpu_data();
-        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_gpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -39,20 +39,20 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
       for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_gpu_gemm(top_diff + top[i]->offset(n),
-              bottom_data + bottom[i]->offset(n), weight_diff);
+          this->weight_gpu_gemm(top_diff + n * this->top_dim_,
+              bottom_data + n * this->bottom_dim_, weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n),
+          this->forward_gpu_gemm(top_diff + this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_,
               this->param_propagate_down_[0]);
         }
       }
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 1c802714e33..595c9dbbe5e 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -11,54 +11,106 @@ template <typename Dtype>
 void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  CHECK(!conv_param.has_kernel_size() !=
-      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(conv_param.has_kernel_size() ||
-      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
-      && conv_param.has_pad_w())
-      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
-      << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
-      && conv_param.has_stride_w())
-      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
-      << "Stride is stride OR stride_h and stride_w are required.";
-  if (conv_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  force_nd_im2col_ = conv_param.force_nd_im2col();
+  const int input_num_dims = bottom[0]->shape().size();
+  channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
+  const int first_spatial_dim = channel_axis_ + 1;
+  num_spatial_axes_ = input_num_dims - first_spatial_dim;
+  CHECK_GE(num_spatial_axes_, 1);
+  vector<int> dim_blob_shape(1, num_spatial_axes_);
+  // Setup filter kernel dimensions (kernel_shape_).
+  kernel_shape_.Reshape(dim_blob_shape);
+  int* kernel_shape_data = kernel_shape_.mutable_cpu_data();
+  if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "kernel_h & kernel_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.kernel_size_size())
+        << "Either kernel_size or kernel_h/w should be specified; not both.";
+    kernel_shape_data[0] = conv_param.kernel_h();
+    kernel_shape_data[1] = conv_param.kernel_w();
   } else {
-    kernel_h_ = conv_param.kernel_h();
-    kernel_w_ = conv_param.kernel_w();
+    const int num_kernel_dims = conv_param.kernel_size_size();
+    CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
+        << "kernel_size must be specified once, or once per spatial dimension "
+        << "(kernel_size specified " << num_kernel_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+      for (int i = 0; i < num_spatial_axes_; ++i) {
+        kernel_shape_data[i] =
+            conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
+      }
   }
-  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
-  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (!conv_param.has_pad_h()) {
-    pad_h_ = pad_w_ = conv_param.pad();
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";
+  }
+  // Setup stride dimensions (stride_).
+  stride_.Reshape(dim_blob_shape);
+  int* stride_data = stride_.mutable_cpu_data();
+  if (conv_param.has_stride_h() || conv_param.has_stride_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "stride_h & stride_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.stride_size())
+        << "Either stride or stride_h/w should be specified; not both.";
+    stride_data[0] = conv_param.stride_h();
+    stride_data[1] = conv_param.stride_w();
   } else {
-    pad_h_ = conv_param.pad_h();
-    pad_w_ = conv_param.pad_w();
+    const int num_stride_dims = conv_param.stride_size();
+    CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||
+          num_stride_dims == num_spatial_axes_)
+        << "stride must be specified once, or once per spatial dimension "
+        << "(stride specified " << num_stride_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+    const int kDefaultStride = 1;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
+          conv_param.stride((num_stride_dims == 1) ? 0 : i);
+      CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";
+    }
   }
-  if (!conv_param.has_stride_h()) {
-    stride_h_ = stride_w_ = conv_param.stride();
+  // Setup pad dimensions (pad_).
+  pad_.Reshape(dim_blob_shape);
+  int* pad_data = pad_.mutable_cpu_data();
+  if (conv_param.has_pad_h() || conv_param.has_pad_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "pad_h & pad_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.pad_size())
+        << "Either pad or pad_h/w should be specified; not both.";
+    pad_data[0] = conv_param.pad_h();
+    pad_data[1] = conv_param.pad_w();
   } else {
-    stride_h_ = conv_param.stride_h();
-    stride_w_ = conv_param.stride_w();
+    const int num_pad_dims = conv_param.pad_size();
+    CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||
+          num_pad_dims == num_spatial_axes_)
+        << "pad must be specified once, or once per spatial dimension "
+        << "(pad specified " << num_pad_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+    const int kDefaultPad = 0;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
+          conv_param.pad((num_pad_dims == 1) ? 0 : i);
+    }
   }
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
-      << "corresponding to (num, channels, height, width)";
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  top[0]->Reshape(
-      bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
-      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
-      (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
+  vector<int> top_shape = bottom[0]->shape();
+  const int* kernel_shape_data = kernel_shape_.cpu_data();
+  const int* stride_data = stride_.cpu_data();
+  const int* pad_data = pad_.cpu_data();
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    top_shape[channel_axis_] *= kernel_shape_data[i];
+    const int input_dim = bottom[0]->shape(channel_axis_ + i + 1);
+    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i])
+        / stride_data[i] + 1;
+    top_shape[channel_axis_ + i + 1] = output_dim;
+  }
+  top[0]->Reshape(top_shape);
+  num_ = bottom[0]->count(0, channel_axis_);
+  bottom_dim_ = bottom[0]->count(channel_axis_);
+  top_dim_ = top[0]->count(channel_axis_);
+
+  channels_ = bottom[0]->shape(channel_axis_);
 }
 
 template <typename Dtype>
@@ -66,10 +118,27 @@ void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  for (int n = 0; n < bottom[0]->num(); ++n) {
-    im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + top[0]->offset(n));
+  for (int n = 0; n < num_; ++n) {
+    DCHECK_EQ(bottom[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);
+    DCHECK_EQ(top[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);
+    DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_);
+    DCHECK_EQ(pad_.count(), num_spatial_axes_);
+    DCHECK_EQ(stride_.count(), num_spatial_axes_);
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_cpu(bottom_data + n * bottom_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          top_data + n * top_dim_);
+    } else {
+      im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
+          bottom[0]->shape().data() + channel_axis_,
+          top[0]->shape().data() + channel_axis_,
+          kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
+          top_data + n * top_dim_);
+    }
   }
 }
 
@@ -78,10 +147,22 @@ void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
+  for (int n = 0; n < num_; ++n) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_cpu(top_diff + n * top_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          bottom_diff + n * bottom_dim_);
+    } else {
+      col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_,
+          bottom[0]->shape().data() + channel_axis_,
+          top[0]->shape().data() + channel_axis_,
+          kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
+          bottom_diff + n * bottom_dim_);
+    }
   }
 }
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 9c338b14cb7..cd507623c78 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -12,10 +12,23 @@ void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  for (int n = 0; n < bottom[0]->num(); ++n) {
-    im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + top[0]->offset(n));
+  const int num_kernels = channels_ * top[0]->count(channel_axis_ + 1);
+  for (int n = 0; n < num_; ++n) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_gpu(bottom_data + n * bottom_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          top_data + n * top_dim_);
+    } else {
+      im2col_nd_gpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
+          num_kernels, bottom[0]->gpu_shape() + channel_axis_,
+          top[0]->gpu_shape() + channel_axis_,
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          top_data + n * top_dim_);
+    }
   }
 }
 
@@ -24,10 +37,22 @@ void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
+  for (int n = 0; n < num_; ++n) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_gpu(top_diff + n * top_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          bottom_diff + n * bottom_dim_);
+    } else {
+      col2im_nd_gpu(top_diff + n * top_dim_, num_spatial_axes_, bottom_dim_,
+          bottom[0]->gpu_shape() + channel_axis_,
+          top[0]->gpu_shape() + channel_axis_,
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          bottom_diff + n * bottom_dim_);
+    }
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 86683eb45da..f52c941b05e 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -508,6 +508,13 @@ message ConvolutionParameter {
   // N independent 3D convolutions, sliding (C/g)-channels
   // filters across the spatial axes (D, H, W) of the input.
   optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
 }
 
 message DataParameter {
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 67d41fff844..9df979a2d27 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -19,54 +19,87 @@ template <typename Dtype>
 void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
     const vector<shared_ptr<Blob<Dtype> > >& weights,
     Blob<Dtype>* out) {
+  const bool has_depth = (out->num_axes() == 5);
+  if (!has_depth) { CHECK_EQ(4, out->num_axes()); }
   // Kernel size, stride, and pad
   int kernel_h, kernel_w;
-  if (conv_param->has_kernel_size()) {
-    kernel_h = kernel_w = conv_param->kernel_size();
-  } else {
+  if (conv_param->has_kernel_h() || conv_param->has_kernel_w()) {
     kernel_h = conv_param->kernel_h();
     kernel_w = conv_param->kernel_w();
+  } else {
+    kernel_h = kernel_w = conv_param->kernel_size(0);
   }
   int pad_h, pad_w;
-  if (!conv_param->has_pad_h()) {
-    pad_h = pad_w = conv_param->pad();
-  } else {
+  if (conv_param->has_pad_h() || conv_param->has_pad_w()) {
     pad_h = conv_param->pad_h();
     pad_w = conv_param->pad_w();
+  } else {
+    pad_h = pad_w = conv_param->pad_size() ? conv_param->pad(0) : 0;
   }
   int stride_h, stride_w;
-  if (!conv_param->has_stride_h()) {
-    stride_h = stride_w = conv_param->stride();
-  } else {
+  if (conv_param->has_stride_h() || conv_param->has_stride_w()) {
     stride_h = conv_param->stride_h();
     stride_w = conv_param->stride_w();
+  } else {
+    stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1;
+  }
+  int kernel_d, pad_d, stride_d;
+  if (has_depth) {
+    kernel_d = kernel_h;
+    stride_d = stride_h;
+    pad_d = pad_h;
+  } else {
+    kernel_d = stride_d = 1;
+    pad_d = 0;
   }
   // Groups
   int groups = conv_param->group();
-  int o_g = out->channels() / groups;
-  int k_g = in->channels() / groups;
+  int o_g = out->shape(1) / groups;
+  int k_g = in->shape(1) / groups;
   int o_head, k_head;
   // Convolution
-  const Dtype* in_data = in->cpu_data();
-  const Dtype* weight_data = weights[0]->cpu_data();
+  vector<int> weight_offset(4 + has_depth);
+  vector<int> in_offset(4 + has_depth);
+  vector<int> out_offset(4 + has_depth);
   Dtype* out_data = out->mutable_cpu_data();
-  for (int n = 0; n < out->num(); n++) {
+  for (int n = 0; n < out->shape(0); n++) {
     for (int g = 0; g < groups; g++) {
       o_head = o_g * g;
       k_head = k_g * g;
       for (int o = 0; o < o_g; o++) {
         for (int k = 0; k < k_g; k++) {
-          for (int y = 0; y < out->height(); y++) {
-            for (int x = 0; x < out->width(); x++) {
-              for (int p = 0; p < kernel_h; p++) {
-                for (int q = 0; q < kernel_w; q++) {
-                  int in_y = y * stride_h - pad_h + p;
-                  int in_x = x * stride_w - pad_w + q;
-                  if (in_y >= 0 && in_y < in->height()
-                    && in_x >= 0 && in_x < in->width()) {
-                    out_data[out->offset(n, o + o_head, y, x)] +=
-                        in_data[in->offset(n, k + k_head, in_y, in_x)]
-                        * weight_data[weights[0]->offset(o + o_head, k, p, q)];
+          for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
+            for (int y = 0; y < out->shape(2 + has_depth); y++) {
+              for (int x = 0; x < out->shape(3 + has_depth); x++) {
+                for (int r = 0; r < kernel_d; r++) {
+                  for (int p = 0; p < kernel_h; p++) {
+                    for (int q = 0; q < kernel_w; q++) {
+                      int in_z = z * stride_d - pad_d + r;
+                      int in_y = y * stride_h - pad_h + p;
+                      int in_x = x * stride_w - pad_w + q;
+                      if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1)
+                          && in_y >= 0 && in_y < in->shape(2 + has_depth)
+                          && in_x >= 0 && in_x < in->shape(3 + has_depth)) {
+                        weight_offset[0] = o + o_head;
+                        weight_offset[1] = k;
+                        if (has_depth) { weight_offset[2] = r; }
+                        weight_offset[2 + has_depth] = p;
+                        weight_offset[3 + has_depth] = q;
+                        in_offset[0] = n;
+                        in_offset[1] = k + k_head;
+                        if (has_depth) { in_offset[2] = in_z; }
+                        in_offset[2 + has_depth] = in_y;
+                        in_offset[3 + has_depth] = in_x;
+                        out_offset[0] = n;
+                        out_offset[1] = o + o_head;
+                        if (has_depth) { out_offset[2] = z; }
+                        out_offset[2 + has_depth] = y;
+                        out_offset[3 + has_depth] = x;
+                        out_data[out->offset(out_offset)] +=
+                            in->data_at(in_offset)
+                            * weights[0]->data_at(weight_offset);
+                      }
+                    }
                   }
                 }
               }
@@ -79,11 +112,18 @@ void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
   // Bias
   if (conv_param->bias_term()) {
     const Dtype* bias_data = weights[1]->cpu_data();
-    for (int n = 0; n < out->num(); n++) {
-      for (int o = 0; o < out->channels(); o++) {
-        for (int y = 0; y < out->height(); y++) {
-          for (int x = 0; x < out->width(); x++) {
-            out_data[out->offset(n, o, y, x)] += bias_data[o];
+    for (int n = 0; n < out->shape(0); n++) {
+      for (int o = 0; o < out->shape(1); o++) {
+        for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
+          for (int y = 0; y < out->shape(2 + has_depth); y++) {
+            for (int x = 0; x < out->shape(3 + has_depth); x++) {
+              out_offset[0] = n;
+              out_offset[1] = o;
+              if (has_depth) { out_offset[2] = z; }
+              out_offset[2 + has_depth] = y;
+              out_offset[3 + has_depth] = x;
+              out_data[out->offset(out_offset)] += bias_data[o];
+            }
           }
         }
       }
@@ -150,8 +190,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -188,8 +228,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
@@ -217,13 +257,98 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  const int kNumOutput = 3;
+  convolution_param->set_num_output(kNumOutput);
+  convolution_param->set_axis(3);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  vector<int> top_shape = this->blob_bottom_->shape();
+  top_shape[3] = kNumOutput;
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(top_shape, this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  vector<int> weight_offset(2);
+  const Blob<Dtype>* weight = layer->blobs()[0].get();
+  const Blob<Dtype>* bias = layer->blobs()[1].get();
+  const int num = this->blob_top_->count(3);
+  const int dim = this->blob_top_->shape(3);
+  const int bottom_dim = this->blob_bottom_->shape(3);
+  for (int n = 0; n < num; ++n) {
+    for (int d = 0; d < dim; ++d) {
+      weight_offset[0] = d;
+      Dtype value = bias->cpu_data()[d];
+      for (int bottom_d = 0; bottom_d < bottom_dim; ++bottom_d) {
+        weight_offset[1] = bottom_d;
+        value += weight->data_at(weight_offset) *
+                 this->blob_bottom_->cpu_data()[n * bottom_dim + bottom_d];
+      }
+      EXPECT_NEAR(value, this->blob_top_->cpu_data()[n * dim + d], 1e-4);
+    }
+  }
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 5;
+  bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
+  bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_2_));
+  top_data = this->blob_top_2_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(1);
-  convolution_param->set_stride(1);
+  convolution_param->add_kernel_size(1);
+  convolution_param->add_stride(1);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
@@ -249,8 +374,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
@@ -288,8 +413,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
   shared_ptr<Layer<Dtype> > layer(
@@ -350,14 +475,11 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_bias_term(false);
   layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
-  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 1, 1, 3));
   Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
-  for (int c = 0; c < 3; ++c) {
-    int i = c * 3;  // 1 x 3 filter
-    weights_2[i +  0] = -1;
-    weights_2[i +  1] =  0;
-    weights_2[i +  2] =  1;
-  }
+  weights_2[0] = -1;
+  weights_2[1] =  0;
+  weights_2[2] =  1;
   layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
   layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
@@ -368,6 +490,124 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kernel_h = 11;
+  const int kernel_w = 13;
+  vector<int> bottom_shape(4);
+  bottom_shape[0] = 15;
+  bottom_shape[1] = 18;
+  bottom_shape[2] = kernel_h * 2;
+  bottom_shape[3] = kernel_w * 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_num_output(12);
+  convolution_param->set_bias_term(false);
+  convolution_param->set_group(6);
+  convolution_param->set_kernel_h(kernel_h);
+  convolution_param->set_kernel_w(kernel_w);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  Blob<Dtype> weights;
+  Blob<Dtype> top_diff;
+  // Shape and fill weights and top_diff.
+  bool copy_diff;
+  bool reshape;
+  {
+    ConvolutionLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    top_diff.ReshapeLike(*this->blob_top_);
+    filler.Fill(&top_diff);
+    ASSERT_EQ(1, layer.blobs().size());
+    copy_diff = false; reshape = true;
+    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+  }
+  vector<bool> propagate_down(1, true);
+  Blob<Dtype> result_2d;
+  Blob<Dtype> backward_result_2d;
+  Blob<Dtype> backward_weight_result_2d;
+  // Test with 2D im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_2d.
+    convolution_param->set_force_nd_im2col(false);
+    ConvolutionLayer<Dtype> layer_2d(layer_param);
+    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_2d.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_2d.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_2d.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+  }
+  Blob<Dtype> result_nd;
+  Blob<Dtype> backward_result_nd;
+  Blob<Dtype> backward_weight_result_nd;
+  // Test with ND im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_nd.
+    convolution_param->set_force_nd_im2col(true);
+    ConvolutionLayer<Dtype> layer_nd(layer_param);
+    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_nd.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_nd.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_nd.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+  }
+  ASSERT_EQ(result_nd.count(), result_2d.count());
+  for (int i = 0; i < result_2d.count(); ++i)  {
+    EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
+  }
+  ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+  for (int i = 0; i < backward_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+              backward_result_nd.cpu_diff()[i]);
+  }
+  ASSERT_EQ(backward_weight_result_nd.count(),
+            backward_weight_result_2d.count());
+  for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+              backward_weight_result_nd.cpu_diff()[i]);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -375,8 +615,36 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestGradient3D) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 5;
+  bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
+  bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -393,8 +661,8 @@ TYPED_TEST(ConvolutionLayerTest, Test1x1Gradient) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(1);
-  convolution_param->set_stride(1);
+  convolution_param->add_kernel_size(1);
+  convolution_param->add_stride(1);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -409,8 +677,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
@@ -472,8 +740,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -509,8 +777,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
@@ -542,8 +810,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
@@ -581,8 +849,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
   shared_ptr<Layer<TypeParam> > layer(
@@ -643,14 +911,11 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   convolution_param->set_bias_term(false);
   layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
   layer->blobs().resize(1);
-  layer->blobs()[0].reset(new Blob<TypeParam>(1, 3, 1, 3));
+  layer->blobs()[0].reset(new Blob<TypeParam>(1, 1, 1, 3));
   TypeParam* weights_2 = layer->blobs()[0]->mutable_cpu_data();
-  for (int c = 0; c < 3; ++c) {
-    int i = c * 3;  // 1 x 3 filter
-    weights_2[i +  0] = -1;
-    weights_2[i +  1] =  0;
-    weights_2[i +  2] =  1;
-  }
+  weights_2[0] = -1;
+  weights_2[1] =  0;
+  weights_2[2] =  1;
   layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
   layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
@@ -667,8 +932,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -682,8 +947,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
index fc63d5efbe3..770e7b277ee 100644
--- a/src/caffe/test/test_deconvolution_layer.cpp
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -58,8 +58,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -96,8 +96,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestSimpleDeconvolution) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("constant");
   convolution_param->mutable_weight_filler()->set_value(1);
@@ -144,8 +144,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(2);
-  convolution_param->set_stride(1);
+  convolution_param->add_kernel_size(2);
+  convolution_param->add_stride(1);
   convolution_param->set_num_output(1);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -155,4 +155,151 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(DeconvolutionLayerTest, TestNDAgainst2D) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kernel_h = 11;
+  const int kernel_w = 13;
+  vector<int> bottom_shape(4);
+  bottom_shape[0] = 15;
+  bottom_shape[1] = 12;
+  bottom_shape[2] = kernel_h * 2;
+  bottom_shape[3] = kernel_w * 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_num_output(18);
+  convolution_param->set_bias_term(false);
+  convolution_param->set_group(6);
+  convolution_param->set_kernel_h(kernel_h);
+  convolution_param->set_kernel_w(kernel_w);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  Blob<Dtype> weights;
+  Blob<Dtype> top_diff;
+  // Shape and fill weights and top_diff.
+  bool copy_diff;
+  bool reshape;
+  {
+    DeconvolutionLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    top_diff.ReshapeLike(*this->blob_top_);
+    filler.Fill(&top_diff);
+    ASSERT_EQ(1, layer.blobs().size());
+    copy_diff = false; reshape = true;
+    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+  }
+  vector<bool> propagate_down(1, true);
+  Blob<Dtype> result_2d;
+  Blob<Dtype> backward_result_2d;
+  Blob<Dtype> backward_weight_result_2d;
+  // Test with 2D im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_2d.
+    convolution_param->set_force_nd_im2col(false);
+    DeconvolutionLayer<Dtype> layer_2d(layer_param);
+    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_2d.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_2d.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_2d.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+  }
+  Blob<Dtype> result_nd;
+  Blob<Dtype> backward_result_nd;
+  Blob<Dtype> backward_weight_result_nd;
+  // Test with ND im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_nd.
+    convolution_param->set_force_nd_im2col(true);
+    DeconvolutionLayer<Dtype> layer_nd(layer_param);
+    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_nd.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_nd.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_nd.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+  }
+  ASSERT_EQ(result_nd.count(), result_2d.count());
+  for (int i = 0; i < result_2d.count(); ++i)  {
+    EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
+  }
+  ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+  for (int i = 0; i < backward_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+              backward_result_nd.cpu_diff()[i]);
+  }
+  ASSERT_EQ(backward_weight_result_nd.count(),
+            backward_weight_result_2d.count());
+  for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+              backward_weight_result_nd.cpu_diff()[i]);
+  }
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestGradient3D) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 2;
+  bottom_shape[3] = 3;
+  bottom_shape[4] = 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(2);
+  convolution_param->add_stride(2);
+  convolution_param->add_pad(1);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  DeconvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 0017ac23e69..f0b75fcc68d 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -22,6 +22,12 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height_col, const int width_col,
     Dtype* data_col);
 
+template <typename Dtype, int num_axes>
+__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_col);
+
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
 template <typename Dtype>
@@ -30,11 +36,18 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
   Im2colKernelTest()
         // big so launches > 1024 threads
       : blob_bottom_(new Blob<Dtype>(5, 500, 10, 10)),
+        blob_kernel_shape_(new Blob<int>()),
+        blob_stride_(new Blob<int>()),
+        blob_pad_(new Blob<int>()),
         blob_top_(new Blob<Dtype>()),
         blob_top_cpu_(new Blob<Dtype>()) {
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
+    vector<int> dim_blob_shape(1, 2);
+    blob_kernel_shape_->Reshape(dim_blob_shape);
+    blob_stride_->Reshape(dim_blob_shape);
+    blob_pad_->Reshape(dim_blob_shape);
 
     height_ = blob_bottom_->height();
     width_ = blob_bottom_->width();
@@ -44,14 +57,26 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
     kernel_size_ = 3;
     height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
     width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+
+    for (int i = 0; i < 2; ++i) {
+      blob_kernel_shape_->mutable_cpu_data()[i] = kernel_size_;
+      blob_stride_->mutable_cpu_data()[i] = stride_;
+      blob_pad_->mutable_cpu_data()[i] = pad_;
+    }
   }
 
   virtual ~Im2colKernelTest() {
-      delete blob_bottom_;
-      delete blob_top_;
-      delete blob_top_cpu_;
+    delete blob_bottom_;
+    delete blob_top_;
+    delete blob_top_cpu_;
+    delete blob_kernel_shape_;
+    delete blob_stride_;
+    delete blob_pad_;
   }
 
+  Blob<int>* const blob_kernel_shape_;
+  Blob<int>* const blob_stride_;
+  Blob<int>* const blob_pad_;
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
   Blob<Dtype>* const blob_top_cpu_;
@@ -67,7 +92,7 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
 
 TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
 
-TYPED_TEST(Im2colKernelTest, TestGPU) {
+TYPED_TEST(Im2colKernelTest, Test2D) {
   // Reshape the blobs to correct size for im2col output
   this->blob_top_->Reshape(this->blob_bottom_->num(),
           this->channels_ * this->kernel_size_ * this->kernel_size_,
@@ -122,4 +147,58 @@ TYPED_TEST(Im2colKernelTest, TestGPU) {
   }
 }
 
+TYPED_TEST(Im2colKernelTest, TestND) {
+  // Reshape the blobs to correct size for im2col output
+  this->blob_top_->Reshape(this->blob_bottom_->num(),
+      this->channels_ * this->kernel_size_ * this->kernel_size_,
+      this->height_col_,
+      this->width_col_);
+
+  this->blob_top_cpu_->ReshapeLike(*this->blob_top_);
+
+  const TypeParam* bottom_data_cpu = this->blob_bottom_->cpu_data();
+  TypeParam* top_data_cpu = this->blob_top_cpu_->mutable_cpu_data();
+
+  // CPU Version
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    im2col_nd_cpu(bottom_data_cpu + this->blob_bottom_->offset(n), 2,
+        this->blob_bottom_->shape().data() + 1,
+        this->blob_top_cpu_->shape().data() + 1,
+        this->blob_kernel_shape_->cpu_data(),
+        this->blob_pad_->cpu_data(), this->blob_stride_->cpu_data(),
+        top_data_cpu + this->blob_top_cpu_->offset(n));
+  }
+
+  // GPU version
+  int num_kernels = this->channels_ * this->height_col_ * this->width_col_;
+  int default_grid_dim = CAFFE_GET_BLOCKS(num_kernels);
+  const TypeParam* bottom_data_gpu = this->blob_bottom_->gpu_data();
+
+  // Launch with different grid sizes
+  for (int grid_div = 2; grid_div <= 8; grid_div++) {
+    for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+      const int grid_dim = default_grid_dim / grid_div;
+      TypeParam* top_data_gpu = this->blob_top_->mutable_gpu_data();
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      im2col_nd_gpu_kernel<TypeParam, 2><<<grid_dim, CAFFE_CUDA_NUM_THREADS>>>(
+          num_kernels, bottom_data_gpu + this->blob_bottom_->offset(n),
+          this->blob_bottom_->gpu_shape() + 1, this->blob_top_->gpu_shape() + 1,
+          this->blob_kernel_shape_->gpu_data(), this->blob_pad_->gpu_data(),
+          this->blob_stride_->gpu_data(),
+          top_data_gpu + this->blob_top_->offset(n));
+      CUDA_POST_KERNEL_CHECK;
+    }
+
+    // Compare results against CPU version
+    for (int i = 0; i < this->blob_top_->count(); ++i) {
+      TypeParam cpuval = top_data_cpu[i];
+      TypeParam gpuval = this->blob_top_->cpu_data()[i];
+      EXPECT_EQ(cpuval, gpuval);
+      if (cpuval != gpuval) {
+        break;
+      }
+    }
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index f50abe103f8..293aa262059 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -21,6 +21,7 @@ class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
+    Caffe::set_random_seed(1701);
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
@@ -41,8 +42,8 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
@@ -56,8 +57,8 @@ TYPED_TEST(Im2colLayerTest, TestForward) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -73,14 +74,27 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
       this->blob_top_vec_);
 }
 
+TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_force_nd_im2col(true);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
 
 TYPED_TEST(Im2colLayerTest, TestRect) {
   typedef typename TypeParam::Dtype Dtype;
@@ -89,7 +103,7 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_h(5);
   convolution_param->set_kernel_w(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -108,7 +122,7 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) {
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_h(5);
   convolution_param->set_kernel_w(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index c48f31f35d4..b0a7be50e5c 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -1,6 +1,7 @@
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
+#include <vector>
 
 #include "caffe/util/im2col.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -44,6 +45,98 @@ template void im2col_cpu<double>(const double* data_im, const int channels,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, double* data_col);
 
+template <typename Dtype>
+inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
+    const int num_spatial_axes, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_output) {
+  if (!im2col) {
+    int im_size = im_shape[0];
+    for (int i = 0; i < num_spatial_axes; ++i) {
+      im_size *= im_shape[1 + i];
+    }
+    caffe_set(im_size, Dtype(0), data_output);
+  }
+  int kernel_size = 1;
+  for (int i = 0; i < num_spatial_axes; ++i) {
+    kernel_size *= kernel_shape[i];
+  }
+  const int channels_col = col_shape[0];
+  vector<int> d_offset(num_spatial_axes, 0);
+  vector<int> d_iter(num_spatial_axes, 0);
+  for (int c = 0; c < channels_col; ++c) {
+    // Loop over spatial axes in reverse order to compute a per-axis offset.
+    int offset = c;
+    for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
+      if (d_i < num_spatial_axes - 1) {
+        offset /= kernel_shape[d_i + 1];
+      }
+      d_offset[d_i] = offset % kernel_shape[d_i];
+    }
+    for (bool incremented = true; incremented; ) {
+      // Loop over spatial axes in forward order to compute the indices in the
+      // image and column, and whether the index lies in the padding.
+      int index_col = c;
+      int index_im = c / kernel_size;
+      bool is_padding = false;
+      for (int d_i = 0; d_i < num_spatial_axes; ++d_i) {
+        const int d = d_iter[d_i];
+        const int d_pad = d * stride[d_i] - pad[d_i] + d_offset[d_i];
+        is_padding |= d_pad < 0 || d_pad >= im_shape[d_i + 1];
+        index_col *= col_shape[d_i + 1];
+        index_col += d;
+        index_im *= im_shape[d_i + 1];
+        index_im += d_pad;
+      }
+      if (im2col) {
+        if (is_padding) {
+          data_output[index_col] = 0;
+        } else {
+          data_output[index_col] = data_input[index_im];
+        }
+      } else if (!is_padding) {  // col2im
+        data_output[index_im] += data_input[index_col];
+      }
+      // Loop over spatial axes in reverse order to choose an index,
+      // like counting.
+      incremented = false;
+      for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
+        const int d_max = col_shape[d_i + 1];
+        DCHECK_LT(d_iter[d_i], d_max);
+        if (d_iter[d_i] == d_max - 1) {
+          d_iter[d_i] = 0;
+        } else {  // d_iter[d_i] < d_max - 1
+          ++d_iter[d_i];
+          incremented = true;
+          break;
+        }
+      }
+    }  // while(incremented) {
+  }  // for (int c = 0; c < channels_col; ++c) {
+}
+
+template <typename Dtype>
+void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_col) {
+  const bool kIm2Col = true;
+  im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape,
+                  kernel_shape, pad, stride, data_col);
+}
+
+// Explicit instantiation
+template void im2col_nd_cpu<float>(const float* data_im,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    float* data_col);
+template void im2col_nd_cpu<double>(const double* data_im,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    double* data_col);
+
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int patch_h, const int patch_w,
@@ -80,4 +173,27 @@ template void col2im_cpu<double>(const double* data_col, const int channels,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, double* data_im);
 
+template <typename Dtype>
+void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_im) {
+  const bool kIm2Col = false;
+  im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape,
+                     kernel_shape, pad, stride, data_im);
+}
+
+// Explicit instantiation
+template void col2im_nd_cpu<float>(const float* data_col,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    float* data_im);
+template void col2im_nd_cpu<double>(const double* data_col,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    double* data_im);
+
+
 }  // namespace caffe
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index c90f93eb67b..5a478ba62d2 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -59,7 +59,6 @@ void im2col_gpu(const Dtype* data_im, const int channels,
   CUDA_POST_KERNEL_CHECK;
 }
 
-
 // Explicit instantiation
 template void im2col_gpu<float>(const float* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
@@ -70,6 +69,156 @@ template void im2col_gpu<double>(const double* data_im, const int channels,
     const int pad_h, const int pad_w, const int stride_h, const int stride_w,
     double* data_col);
 
+template <typename Dtype, int num_axes>
+__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_col) {
+  int d_temp[num_axes];  // NOLINT(runtime/arrays)
+  int d_iter[num_axes];  // NOLINT(runtime/arrays)
+  int i;
+  CUDA_KERNEL_LOOP(index, n) {
+    // Initialize channel_in, computed in the loop below, with intermediate
+    // computations used to compute the spatial indices.
+    int channel_in = index;
+    int channel_out = 1;
+    for (i = num_axes - 1; i >= 0; --i) {
+      d_temp[i] = channel_in % col_shape[i + 1];
+      channel_in /= col_shape[i + 1];
+      channel_out *= kernel_shape[i];
+    }
+    channel_out *= channel_in;
+    int data_col_inc = 1;
+    for (i = 0; i < num_axes; ++i) {
+      channel_out *= col_shape[i + 1];
+      channel_out += d_temp[i];
+      d_temp[i] = d_temp[i] * stride[i] - pad[i];
+      channel_in *= im_shape[i + 1];
+      channel_in += d_temp[i];
+      data_col_inc *= col_shape[i + 1];
+      d_iter[i] = 0;
+    }
+    Dtype* data_col_ptr = data_col + channel_out;
+    const Dtype* data_im_ptr = data_im + channel_in;
+    bool incremented;
+    do {
+      bool in_range = true;
+      for (i = 0; i < num_axes; ++i) {
+        const int d_iter_im = d_iter[i] + d_temp[i];
+        in_range &= d_iter_im >= 0 && d_iter_im < im_shape[i + 1];
+        if (!in_range) { break; }
+      }
+      if (in_range) {
+        int data_im_offset = d_iter[0];
+        for (i = 1; i < num_axes; ++i) {
+          data_im_offset *= im_shape[i + 1];
+          data_im_offset += d_iter[i];
+        }
+        *data_col_ptr = data_im_ptr[data_im_offset];
+      } else {
+        *data_col_ptr = 0;
+      }
+      data_col_ptr += data_col_inc;
+      incremented = false;
+      for (i = num_axes - 1; i >= 0; --i) {
+        const int d_max = kernel_shape[i];
+        if (d_iter[i] == d_max - 1) {
+          d_iter[i] = 0;
+        } else {  // d_iter[i] < d_max - 1
+          ++d_iter[i];
+          incremented = true;
+          break;
+        }
+      }  // for (int i = num_axes - 1; i >= 0; --i)
+    } while (incremented);  // do
+  }  // CUDA_KERNEL_LOOP(index, n)
+}
+
+template <typename Dtype>
+void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
+    const int num_kernels, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_col) {
+  switch (num_spatial_axes) {
+  case 1:
+    im2col_nd_gpu_kernel<Dtype, 1>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 2:
+    im2col_nd_gpu_kernel<Dtype, 2>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 3:
+    im2col_nd_gpu_kernel<Dtype, 3>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 4:
+    im2col_nd_gpu_kernel<Dtype, 4>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 5:
+    im2col_nd_gpu_kernel<Dtype, 5>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 6:
+    im2col_nd_gpu_kernel<Dtype, 6>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 7:
+    im2col_nd_gpu_kernel<Dtype, 7>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 8:
+    im2col_nd_gpu_kernel<Dtype, 8>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 9:
+    im2col_nd_gpu_kernel<Dtype, 9>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  case 10:
+    im2col_nd_gpu_kernel<Dtype, 10>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, data_col);
+    break;
+  default:
+    LOG(FATAL) << "im2col_nd_gpu does not support computation with "
+               << num_spatial_axes << " spatial axes";
+  }
+  CUDA_POST_KERNEL_CHECK;
+}
+
+// Explicit instantiation
+template void im2col_nd_gpu<float>(const float* data_im,
+    const int num_spatial_axes, const int col_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    float* data_col);
+template void im2col_nd_gpu<double>(const double* data_im,
+    const int num_spatial_axes, const int col_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    double* data_col);
+
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     const int height, const int width, const int channels,
@@ -141,4 +290,159 @@ template void col2im_gpu<double>(const double* data_col, const int channels,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, double* data_im);
 
+template <typename Dtype, int num_axes>
+__global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_im) {
+  int d_im[num_axes];  // NOLINT(runtime/arrays)
+  int d_col_iter[num_axes];  // NOLINT(runtime/arrays)
+  int d_col_start[num_axes];  // NOLINT(runtime/arrays)
+  int d_col_end[num_axes];  // NOLINT(runtime/arrays)
+  CUDA_KERNEL_LOOP(index, n) {
+    // Initialize channel_in, computed in the loop below, with intermediate
+    // computations used to compute the spatial indices.
+    int channel_im = index;
+    // Calculate d_im (image dimensions).
+    for (int i = num_axes - 1; i >= 0; --i) {
+      d_im[i] = channel_im % im_shape[i + 1] + pad[i];
+      channel_im /= im_shape[i + 1];
+    }
+    // Calculate col start/end indices.
+    bool done = false;
+    for (int i = 0; i < num_axes; ++i) {
+      d_col_start[i] = d_col_iter[i] =
+          (d_im[i] < kernel_shape[i]) ?
+          0 : (d_im[i] - kernel_shape[i]) / stride[i] + 1;
+      d_col_end[i] = min(d_im[i] / stride[i] + 1, col_shape[i + 1]);
+      if (d_col_start[i] >= d_col_end[i]) {
+        // Skip computation if the dimension is 0 at any spatial axis --
+        // final val will be 0.
+        data_im[index] = 0;
+        done = true;
+        break;  // for (int i = 0; i < num_axes; ++i)
+      }
+    }
+    if (done) {
+      continue;  // CUDA_KERNEL_LOOP(index, n)
+    }
+    // Loop over the col to compute the output val.
+    Dtype val = 0;
+    bool incremented = true;
+    do {
+      // Compute the final offset.
+      int final_offset = 0;
+      int kernel_shape_prod = 1;
+      for (int i = num_axes - 1; i >= 0; --i) {
+        final_offset +=
+            (d_im[i] - d_col_iter[i] * stride[i]) * kernel_shape_prod;
+        kernel_shape_prod *= kernel_shape[i];
+      }
+      final_offset += kernel_shape_prod * channel_im;
+      for (int i = 0; i < num_axes; ++i) {
+        final_offset *= col_shape[i + 1];
+        final_offset += d_col_iter[i];
+      }
+      val += data_col[final_offset];
+      incremented = false;
+      for (int i = num_axes - 1; i >= 0; --i) {
+        const int d_max = d_col_end[i];
+        if (d_col_iter[i] == d_max - 1) {
+          d_col_iter[i] = d_col_start[i];
+        } else {  // d_col_iter[i] < d_max - 1
+          ++d_col_iter[i];
+          incremented = true;
+          break;  // for (int i = num_axes - 1; i >= 0; --i)
+        }
+      }  // for (int i = num_axes - 1; i >= 0; --i)
+    }  while (incremented);
+    data_im[index] = val;
+  }  // CUDA_KERNEL_LOOP(index, n)
+}
+
+template <typename Dtype>
+void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
+    const int im_size, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    Dtype* data_im) {
+  switch (num_spatial_axes) {
+  case 1:
+    col2im_nd_gpu_kernel<Dtype, 1>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 2:
+    col2im_nd_gpu_kernel<Dtype, 2>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 3:
+    col2im_nd_gpu_kernel<Dtype, 3>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 4:
+    col2im_nd_gpu_kernel<Dtype, 4>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 5:
+    col2im_nd_gpu_kernel<Dtype, 5>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 6:
+    col2im_nd_gpu_kernel<Dtype, 6>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 7:
+    col2im_nd_gpu_kernel<Dtype, 7>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 8:
+    col2im_nd_gpu_kernel<Dtype, 8>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 9:
+    col2im_nd_gpu_kernel<Dtype, 9>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  case 10:
+    col2im_nd_gpu_kernel<Dtype, 10>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, data_im);
+    break;
+  default:
+    LOG(FATAL) << "col2im_nd_gpu does not support computation with "
+               << num_spatial_axes << " spatial axes";
+  }
+  CUDA_POST_KERNEL_CHECK;
+}
+
+// Explicit instantiation
+template void col2im_nd_gpu<float>(const float* data_col,
+    const int num_spatial_axes, const int im_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    float* data_im);
+template void col2im_nd_gpu<double>(const double* data_col,
+    const int num_spatial_axes, const int im_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    double* data_im);
+
 }  // namespace caffe
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 92e5cf55fa9..ac379e50f4f 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -193,7 +193,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_pad()) {
       if (type == "conv") {
-        layer_param->mutable_convolution_param()->set_pad(v0_layer_param.pad());
+        layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad());
       } else if (type == "pool") {
         layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
       } else {
@@ -203,7 +203,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_kernelsize()) {
       if (type == "conv") {
-        layer_param->mutable_convolution_param()->set_kernel_size(
+        layer_param->mutable_convolution_param()->add_kernel_size(
             v0_layer_param.kernelsize());
       } else if (type == "pool") {
         layer_param->mutable_pooling_param()->set_kernel_size(
@@ -224,7 +224,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_stride()) {
       if (type == "conv") {
-        layer_param->mutable_convolution_param()->set_stride(
+        layer_param->mutable_convolution_param()->add_stride(
             v0_layer_param.stride());
       } else if (type == "pool") {
         layer_param->mutable_pooling_param()->set_stride(

From 328df2450c534119f239ce1d606f8502922c6825 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 19 Sep 2015 13:50:57 -0700
Subject: [PATCH 1552/2053] clarify im2col + col2im var names

- clarify indices by naming *_im for indices in image
  and *_col for indices in column
- mark corresonding im2col + col2im quantities  by renaming
  patch_* -> kernel_*
- fix out-of-date names in equivalent col2im loop
---
 include/caffe/util/im2col.hpp |  4 +-
 src/caffe/util/im2col.cpp     | 72 +++++++++++++++++------------------
 src/caffe/util/im2col.cu      | 69 ++++++++++++++++-----------------
 3 files changed, 73 insertions(+), 72 deletions(-)

diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index 531fd29c57a..d3eb6ccd6fc 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -23,7 +23,7 @@ void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, Dtype* data_im);
 
@@ -47,7 +47,7 @@ void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, Dtype* data_im);
 
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index b0a7be50e5c..afeb5e5d9b3 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -17,19 +17,19 @@ void im2col_cpu(const Dtype* data_im, const int channels,
   int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
   int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
   int channels_col = channels * kernel_h * kernel_w;
-  for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % kernel_w;
-    int h_offset = (c / kernel_w) % kernel_h;
-    int c_im = c / kernel_h / kernel_w;
-    for (int h = 0; h < height_col; ++h) {
-      for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride_h - pad_h + h_offset;
-        int w_pad = w * stride_w - pad_w + w_offset;
-        if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
-          data_col[(c * height_col + h) * width_col + w] =
-            data_im[(c_im * height + h_pad) * width + w_pad];
+  for (int c_col = 0; c_col < channels_col; ++c_col) {
+    int w_offset = c_col % kernel_w;
+    int h_offset = (c_col / kernel_w) % kernel_h;
+    int c_im = c_col / kernel_h / kernel_w;
+    for (int h_col = 0; h_col < height_col; ++h_col) {
+      for (int w_col = 0; w_col < width_col; ++w_col) {
+        int h_im = h_col * stride_h - pad_h + h_offset;
+        int w_im = w_col * stride_w - pad_w + w_offset;
+        if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width)
+          data_col[(c_col * height_col + h_col) * width_col + w_col] =
+            data_im[(c_im * height + h_im) * width + w_im];
         else
-          data_col[(c * height_col + h) * width_col + w] = 0;
+          data_col[(c_col * height_col + h_im) * width_col + w_im] = 0;
       }
     }
   }
@@ -64,9 +64,9 @@ inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
   const int channels_col = col_shape[0];
   vector<int> d_offset(num_spatial_axes, 0);
   vector<int> d_iter(num_spatial_axes, 0);
-  for (int c = 0; c < channels_col; ++c) {
+  for (int c_col = 0; c_col < channels_col; ++c_col) {
     // Loop over spatial axes in reverse order to compute a per-axis offset.
-    int offset = c;
+    int offset = c_col;
     for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
       if (d_i < num_spatial_axes - 1) {
         offset /= kernel_shape[d_i + 1];
@@ -76,17 +76,17 @@ inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
     for (bool incremented = true; incremented; ) {
       // Loop over spatial axes in forward order to compute the indices in the
       // image and column, and whether the index lies in the padding.
-      int index_col = c;
-      int index_im = c / kernel_size;
+      int index_col = c_col;
+      int index_im = c_col / kernel_size;
       bool is_padding = false;
       for (int d_i = 0; d_i < num_spatial_axes; ++d_i) {
         const int d = d_iter[d_i];
-        const int d_pad = d * stride[d_i] - pad[d_i] + d_offset[d_i];
-        is_padding |= d_pad < 0 || d_pad >= im_shape[d_i + 1];
+        const int d_im = d * stride[d_i] - pad[d_i] + d_offset[d_i];
+        is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1];
         index_col *= col_shape[d_i + 1];
         index_col += d;
         index_im *= im_shape[d_i + 1];
-        index_im += d_pad;
+        index_im += d_im;
       }
       if (im2col) {
         if (is_padding) {
@@ -139,25 +139,25 @@ template void im2col_nd_cpu<double>(const double* data_im,
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
     Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
-  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
-  int channels_col = channels * patch_h * patch_w;
-  for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % patch_w;
-    int h_offset = (c / patch_w) % patch_h;
-    int c_im = c / patch_h / patch_w;
-    for (int h = 0; h < height_col; ++h) {
-      for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride_h - pad_h + h_offset;
-        int w_pad = w * stride_w - pad_w + w_offset;
-        if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
-          data_im[(c_im * height + h_pad) * width + w_pad] +=
-              data_col[(c * height_col + h) * width_col + w];
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  int channels_col = channels * kernel_h * kernel_w;
+  for (int c_col = 0; c_col < channels_col; ++c_col) {
+    int w_offset = c_col % kernel_w;
+    int h_offset = (c_col / kernel_w) % kernel_h;
+    int c_im = c_col / kernel_h / kernel_w;
+    for (int h_col = 0; h_col < height_col; ++h_col) {
+      for (int w_col = 0; w_col < width_col; ++w_col) {
+        int h_im = h_col * stride_h - pad_h + h_offset;
+        int w_im = w_col * stride_w - pad_w + w_offset;
+        if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width)
+          data_im[(c_im * height + h_im) * width + w_im] +=
+              data_col[(c_col * height_col + h_col) * width_col + w_col];
       }
     }
   }
@@ -165,11 +165,11 @@ void col2im_cpu(const Dtype* data_col, const int channels,
 
 // Explicit instantiation
 template void col2im_cpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, float* data_im);
 template void col2im_cpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, double* data_im);
 
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 5a478ba62d2..897e3c92c1f 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -16,22 +16,23 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
-    int w_out = index % width_col;
     int h_index = index / width_col;
-    int h_out = h_index % height_col;
-    int channel_in = h_index / height_col;
-    int channel_out = channel_in * kernel_h * kernel_w;
-    int h_in = h_out * stride_h - pad_h;
-    int w_in = w_out * stride_w - pad_w;
+    int h_col = h_index % height_col;
+    int w_col = index % width_col;
+    int c_im = h_index / height_col;
+    int c_col = c_im * kernel_h * kernel_w;
+    int h_offset = h_col * stride_h - pad_h;
+    int w_offset = w_col * stride_w - pad_w;
     Dtype* data_col_ptr = data_col;
-    data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+    data_col_ptr += (c_col * height_col + h_col) * width_col + w_col;
     const Dtype* data_im_ptr = data_im;
-    data_im_ptr += (channel_in * height + h_in) * width + w_in;
+    data_im_ptr += (c_im * height + h_offset) * width + w_offset;
     for (int i = 0; i < kernel_h; ++i) {
       for (int j = 0; j < kernel_w; ++j) {
-        int h = h_in + i;
-        int w = w_in + j;
-        *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
+        int h_im = h_offset + i;
+        int w_im = w_offset + j;
+        *data_col_ptr =
+            (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
             data_im_ptr[i * width + j] : 0;
         data_col_ptr += height_col * width_col;
       }
@@ -222,35 +223,35 @@ template void im2col_nd_gpu<double>(const double* data_im,
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     const int height, const int width, const int channels,
-    const int patch_h, const int patch_w,
+    const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
     const int height_col, const int width_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
     Dtype val = 0;
-    int w = index % width + pad_w;
-    int h = (index / width) % height + pad_h;
-    int c = index / (width * height);
+    int w_im = index % width + pad_w;
+    int h_im = (index / width) % height + pad_h;
+    int c_im = index / (width * height);
     // compute the start and end of the output
-    int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1;
-    int w_col_end = min(w / stride_w + 1, width_col);
-    int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1;
-    int h_col_end = min(h / stride_h + 1, height_col);
+    int w_col_start = (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1;
+    int w_col_end = min(w_im / stride_w + 1, width_col);
+    int h_col_start = (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1;
+    int h_col_end = min(h_im / stride_h + 1, height_col);
     /*
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         // the col location: [c * width * height + h_out, w_out]
-        int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize
-            + (w - w_col * stride_w);
+        int c_col = c_im * kernel_h * kernel_w
+            + (h_im - h_col * stride_h) * kernel_w + (w_im - w_col * stride_w);
         val += data_col[(c_col * height_col + h_col) * width_col + w_col];
       }
     }
     */
     // equivalent implementation
-    int offset =
-        (c * patch_h * patch_w + h * patch_w + w) * height_col * width_col;
-    int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
+    int offset = (c_im * kernel_h * kernel_w + h_im * kernel_w + w_im)
+        * height_col * width_col;
+    int coeff_h_col = (1 - stride_h * kernel_w * height_col) * width_col;
     int coeff_w_col = (1 - stride_w * height_col * width_col);
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
@@ -263,18 +264,18 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, Dtype* data_im) {
-  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
   int num_kernels = channels * height * width;
   // To avoid involving atomic operations, we will launch one kernel per
   // bottom dimension, and then in the kernel add up the top dimensions.
   // NOLINT_NEXT_LINE(whitespace/operators)
   col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_col, height, width, channels, patch_h, patch_w,
+      num_kernels, data_col, height, width, channels, kernel_h, kernel_w,
       pad_h, pad_w, stride_h, stride_w,
       height_col, width_col, data_im);
   CUDA_POST_KERNEL_CHECK;
@@ -282,11 +283,11 @@ void col2im_gpu(const Dtype* data_col, const int channels,
 
 // Explicit instantiation
 template void col2im_gpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, float* data_im);
 template void col2im_gpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
     const int stride_w, double* data_im);
 
@@ -302,11 +303,11 @@ __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
   CUDA_KERNEL_LOOP(index, n) {
     // Initialize channel_in, computed in the loop below, with intermediate
     // computations used to compute the spatial indices.
-    int channel_im = index;
+    int c_im = index;
     // Calculate d_im (image dimensions).
     for (int i = num_axes - 1; i >= 0; --i) {
-      d_im[i] = channel_im % im_shape[i + 1] + pad[i];
-      channel_im /= im_shape[i + 1];
+      d_im[i] = c_im % im_shape[i + 1] + pad[i];
+      c_im /= im_shape[i + 1];
     }
     // Calculate col start/end indices.
     bool done = false;
@@ -338,7 +339,7 @@ __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
             (d_im[i] - d_col_iter[i] * stride[i]) * kernel_shape_prod;
         kernel_shape_prod *= kernel_shape[i];
       }
-      final_offset += kernel_shape_prod * channel_im;
+      final_offset += kernel_shape_prod * c_im;
       for (int i = 0; i < num_axes; ++i) {
         final_offset *= col_shape[i + 1];
         final_offset += d_col_iter[i];

From ec77358c2d2e05b3aa39221bd3ec093789bd40f6 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 19 Sep 2015 14:00:14 -0700
Subject: [PATCH 1553/2053] harmonize the im2col_{cpu,gpu} assignment

---
 src/caffe/util/im2col.cpp | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index afeb5e5d9b3..018ff0cdc8c 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -25,11 +25,9 @@ void im2col_cpu(const Dtype* data_im, const int channels,
       for (int w_col = 0; w_col < width_col; ++w_col) {
         int h_im = h_col * stride_h - pad_h + h_offset;
         int w_im = w_col * stride_w - pad_w + w_offset;
-        if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width)
-          data_col[(c_col * height_col + h_col) * width_col + w_col] =
-            data_im[(c_im * height + h_im) * width + w_im];
-        else
-          data_col[(c_col * height_col + h_im) * width_col + w_im] = 0;
+        data_col[(c_col * height_col + h_col) * width_col + w_col] =
+            (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
+            data_im[(c_im * height + h_im) * width + w_im] : 0;
       }
     }
   }

From d292a162b3659685b5f4399b8adf743bdcac49a1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 19 Sep 2015 14:07:10 -0700
Subject: [PATCH 1554/2053] mark const im2col + col2im terms

---
 src/caffe/util/im2col.cpp | 12 ++++++------
 src/caffe/util/im2col.cu  | 32 ++++++++++++++++++--------------
 2 files changed, 24 insertions(+), 20 deletions(-)

diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 018ff0cdc8c..09da23d490f 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -14,9 +14,9 @@ void im2col_cpu(const Dtype* data_im, const int channels,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
     Dtype* data_col) {
-  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
-  int channels_col = channels * kernel_h * kernel_w;
+  const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  const int channels_col = channels * kernel_h * kernel_w;
   for (int c_col = 0; c_col < channels_col; ++c_col) {
     int w_offset = c_col % kernel_w;
     int h_offset = (c_col / kernel_w) % kernel_h;
@@ -142,9 +142,9 @@ void col2im_cpu(const Dtype* data_col, const int channels,
     const int stride_h, const int stride_w,
     Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
-  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
-  int channels_col = channels * kernel_h * kernel_w;
+  const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  const int channels_col = channels * kernel_h * kernel_w;
   for (int c_col = 0; c_col < channels_col; ++c_col) {
     int w_offset = c_col % kernel_w;
     int h_offset = (c_col / kernel_w) % kernel_h;
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 897e3c92c1f..451097f8a1b 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -16,13 +16,13 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
-    int h_index = index / width_col;
-    int h_col = h_index % height_col;
-    int w_col = index % width_col;
-    int c_im = h_index / height_col;
-    int c_col = c_im * kernel_h * kernel_w;
-    int h_offset = h_col * stride_h - pad_h;
-    int w_offset = w_col * stride_w - pad_w;
+    const int h_index = index / width_col;
+    const int h_col = h_index % height_col;
+    const int w_col = index % width_col;
+    const int c_im = h_index / height_col;
+    const int c_col = c_im * kernel_h * kernel_w;
+    const int h_offset = h_col * stride_h - pad_h;
+    const int w_offset = w_col * stride_w - pad_w;
     Dtype* data_col_ptr = data_col;
     data_col_ptr += (c_col * height_col + h_col) * width_col + w_col;
     const Dtype* data_im_ptr = data_im;
@@ -230,14 +230,18 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
     Dtype val = 0;
-    int w_im = index % width + pad_w;
-    int h_im = (index / width) % height + pad_h;
-    int c_im = index / (width * height);
+    const int w_im = index % width + pad_w;
+    const int h_im = (index / width) % height + pad_h;
+    const int c_im = index / (width * height);
     // compute the start and end of the output
-    int w_col_start = (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1;
-    int w_col_end = min(w_im / stride_w + 1, width_col);
-    int h_col_start = (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1;
-    int h_col_end = min(h_im / stride_h + 1, height_col);
+    const int w_col_start =
+        (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1;
+    const int w_col_end =
+        min(w_im / stride_w + 1, width_col);
+    const int h_col_start =
+        (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1;
+    const int h_col_end =
+        min(h_im / stride_h + 1, height_col);
     /*
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {

From da75a0e715f3d434c6b4c23d55947e114b332337 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 19 Sep 2015 20:38:18 -0700
Subject: [PATCH 1555/2053] [build] check xcode command line tools version >= 6

future-proof version check for BLAS libraries on OS X
fix #3092
---
 Makefile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index a911133661f..5fb6394e947 100644
--- a/Makefile
+++ b/Makefile
@@ -354,8 +354,9 @@ else
 		# OS X packages atlas as the vecLib framework
 		LIBRARIES += cblas
 		# 10.10 has accelerate while 10.9 has veclib
-		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6')
-		ifneq (,$(findstring version: 6,$(XCODE_CLT_VER)))
+		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
+		XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
+		ifeq ($(XCODE_CLT_GEQ_6), 1)
 			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
 			LDFLAGS += -framework Accelerate
 		else

From 84eb44e6cf9623e09c354a863e201971270ba25b Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 19 Sep 2015 14:14:03 -0700
Subject: [PATCH 1556/2053] [tools] add Python script for at-a-glance prototxt
 summary

---
 tools/extra/summarize.py | 140 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 140 insertions(+)
 create mode 100755 tools/extra/summarize.py

diff --git a/tools/extra/summarize.py b/tools/extra/summarize.py
new file mode 100755
index 00000000000..7e2d22fd364
--- /dev/null
+++ b/tools/extra/summarize.py
@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+
+"""Net summarization tool.
+
+This tool summarizes the structure of a net in a concise but comprehensive
+tabular listing, taking a prototxt file as input.
+
+Use this tool to check at a glance that the computation you've specified is the
+computation you expect.
+"""
+
+from caffe.proto import caffe_pb2
+from google import protobuf
+import re
+import argparse
+
+# ANSI codes for coloring blobs (used cyclically)
+COLORS = ['92', '93', '94', '95', '97', '96', '42', '43;30', '100',
+          '444', '103;30', '107;30']
+DISCONNECTED_COLOR = '41'
+
+def read_net(filename):
+    net = caffe_pb2.NetParameter()
+    with open(filename) as f:
+        protobuf.text_format.Parse(f.read(), net)
+    return net
+
+def format_param(param):
+    out = []
+    if len(param.name) > 0:
+        out.append(param.name)
+    if param.lr_mult != 1:
+        out.append('x{}'.format(param.lr_mult))
+    if param.decay_mult != 1:
+        out.append('Dx{}'.format(param.decay_mult))
+    return ' '.join(out)
+
+def printed_len(s):
+    return len(re.sub(r'\033\[[\d;]+m', '', s))
+
+def print_table(table, max_width):
+    """Print a simple nicely-aligned table.
+
+    table must be a list of (equal-length) lists. Columns are space-separated,
+    and as narrow as possible, but no wider than max_width. Text may overflow
+    columns; note that unlike string.format, this will not affect subsequent
+    columns, if possible."""
+
+    max_widths = [max_width] * len(table[0])
+    column_widths = [max(printed_len(row[j]) + 1 for row in table)
+                     for j in range(len(table[0]))]
+    column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)]
+
+    for row in table:
+        row_str = ''
+        right_col = 0
+        for cell, width in zip(row, column_widths):
+            right_col += width
+            row_str += cell + ' '
+            row_str += ' ' * max(right_col - printed_len(row_str), 0)
+        print row_str
+
+def summarize_net(net):
+    disconnected_tops = set()
+    for lr in net.layer:
+        disconnected_tops |= set(lr.top)
+        disconnected_tops -= set(lr.bottom)
+
+    table = []
+    colors = {}
+    for lr in net.layer:
+        tops = []
+        for ind, top in enumerate(lr.top):
+            color = colors.setdefault(top, COLORS[len(colors) % len(COLORS)])
+            if top in disconnected_tops:
+                top = '\033[1;4m' + top
+            if len(lr.loss_weight) > 0:
+                top = '{} * {}'.format(lr.loss_weight[ind], top)
+            tops.append('\033[{}m{}\033[0m'.format(color, top))
+        top_str = ', '.join(tops)
+
+        bottoms = []
+        for bottom in lr.bottom:
+            color = colors.get(bottom, DISCONNECTED_COLOR)
+            bottoms.append('\033[{}m{}\033[0m'.format(color, bottom))
+        bottom_str = ', '.join(bottoms)
+
+        if lr.type == 'Python':
+            type_str = lr.python_param.module + '.' + lr.python_param.layer
+        else:
+            type_str = lr.type
+
+        # Summarize conv/pool parameters.
+        # TODO support rectangular/ND parameters
+        conv_param = lr.convolution_param
+        if (lr.type in ['Convolution', 'Deconvolution']
+                and len(conv_param.kernel_size) == 1):
+            arg_str = str(conv_param.kernel_size[0])
+            if len(conv_param.stride) > 0 and conv_param.stride[0] != 1:
+                arg_str += '/' + str(conv_param.stride[0])
+            if len(conv_param.pad) > 0 and conv_param.pad[0] != 0:
+                arg_str += '+' + str(conv_param.pad[0])
+            arg_str += ' ' + str(conv_param.num_output)
+            if conv_param.group != 1:
+                arg_str += '/' + str(conv_param.group)
+        elif lr.type == 'Pooling':
+            arg_str = str(lr.pooling_param.kernel_size)
+            if lr.pooling_param.stride != 1:
+                arg_str += '/' + str(lr.pooling_param.stride)
+            if lr.pooling_param.pad != 0:
+                arg_str += '+' + str(lr.pooling_param.pad)
+        else:
+            arg_str = ''
+
+        if len(lr.param) > 0:
+            param_strs = map(format_param, lr.param)
+            if max(map(len, param_strs)) > 0:
+                param_str = '({})'.format(', '.join(param_strs))
+            else:
+                param_str = ''
+        else:
+            param_str = ''
+
+        table.append([lr.name, type_str, param_str, bottom_str, '->', top_str,
+                      arg_str])
+    return table
+
+def main():
+    parser = argparse.ArgumentParser(description="Print a concise summary of net computation.")
+    parser.add_argument('filename', help='net prototxt file to summarize')
+    parser.add_argument('-w', '--max-width', help='maximum field width',
+            type=int, default=30)
+    args = parser.parse_args()
+
+    net = read_net(args.filename)
+    table = summarize_net(net)
+    print_table(table, max_width=args.max_width)
+
+if __name__ == '__main__':
+    main()

From a40c2a08421ebf9a164e198a70752f2d5cb1c93d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 20 Sep 2015 14:20:28 -0700
Subject: [PATCH 1557/2053] fix broken DeconvolutionLayer GPU backward caused
 by typo

---
 src/caffe/layers/deconv_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index ea83f56f1b2..5dbdcc3149f 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -51,7 +51,7 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->forward_gpu_gemm(top_diff + this->top_dim_, weight,
+          this->forward_gpu_gemm(top_diff + n * this->top_dim_, weight,
               bottom_diff + n * this->bottom_dim_,
               this->param_propagate_down_[0]);
         }

From 6a00ecae67a95cf39e1961aaddc3be1f5a828bb4 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 20 Sep 2015 15:31:59 -0700
Subject: [PATCH 1558/2053] fix broken conv/deconv reshaping caused by reading
 bottom shape in LayerSetUp

This also eliminates the extra copying of bottom's shape.
---
 include/caffe/vision_layers.hpp      |  7 +++++--
 src/caffe/layers/base_conv_layer.cpp | 10 ++--------
 src/caffe/layers/conv_layer.cpp      |  5 ++---
 src/caffe/layers/deconv_layer.cpp    |  5 ++---
 4 files changed, 11 insertions(+), 16 deletions(-)

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index eae65820c40..06bc0457e2d 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -58,6 +58,10 @@ class BaseConvolutionLayer : public Layer<Dtype> {
   void backward_gpu_bias(Dtype* bias, const Dtype* input);
 #endif
 
+  /// @brief The spatial dimensions of the input.
+  inline int input_shape(int i) {
+    return (*bottom_shape_)[channel_axis_ + i];
+  }
   // reverse_dimensions should return true iff we are implementing deconv, so
   // that conv helpers know which dimensions are which.
   virtual bool reverse_dimensions() = 0;
@@ -72,12 +76,11 @@ class BaseConvolutionLayer : public Layer<Dtype> {
   Blob<int> pad_;
   /// @brief The spatial dimensions of the convolution input.
   Blob<int> conv_input_shape_;
-  /// @brief The spatial dimensions of the input.
-  Blob<int> input_shape_;
   /// @brief The spatial dimensions of the col_buffer.
   vector<int> col_buffer_shape_;
   /// @brief The spatial dimensions of the output.
   vector<int> output_shape_;
+  const vector<int>* bottom_shape_;
 
   int num_spatial_axes_;
   int bottom_dim_;
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index a5b90a549bb..c6b47550292 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -20,13 +20,7 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int num_axes = bottom[0]->num_axes();
   num_spatial_axes_ = num_axes - first_spatial_axis;
   CHECK_GE(num_spatial_axes_, 0);
-  // Setup input dimensions (input_shape_).
   vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
-  input_shape_.Reshape(bottom_dim_blob_shape);
-  int* input_shape_data = input_shape_.mutable_cpu_data();
-  for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
-    input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
-  }
   vector<int> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1));
   // Setup filter kernel dimensions (kernel_shape_).
   kernel_shape_.Reshape(spatial_dim_blob_shape);
@@ -190,6 +184,7 @@ void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
         << "All inputs must have the same shape.";
   }
   // Shape the tops.
+  bottom_shape_ = &bottom[0]->shape();
   compute_output_shape();
   vector<int> top_shape(bottom[0]->shape().begin(),
       bottom[0]->shape().begin() + channel_axis_);
@@ -223,10 +218,9 @@ void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   // it goes lazily unused to save memory.
   col_buffer_shape_.clear();
   col_buffer_shape_.push_back(kernel_dim_ * group_);
-  const int* input_shape_data = input_shape_.cpu_data() + 1;
   for (int i = 0; i < num_spatial_axes_; ++i) {
     if (reverse_dimensions()) {
-      col_buffer_shape_.push_back(input_shape_data[i]);
+      col_buffer_shape_.push_back(input_shape(i + 1));
     } else {
       col_buffer_shape_.push_back(output_shape_[i]);
     }
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 5cf26970a0b..fb50bb095ed 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -10,14 +10,13 @@ namespace caffe {
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::compute_output_shape() {
-  // input_shape_ + 1 to skip channel axis
-  const int* input_shape_data = this->input_shape_.cpu_data() + 1;
   const int* kernel_shape_data = this->kernel_shape_.cpu_data();
   const int* stride_data = this->stride_.cpu_data();
   const int* pad_data = this->pad_.cpu_data();
   this->output_shape_.clear();
   for (int i = 0; i < this->num_spatial_axes_; ++i) {
-    const int input_dim = input_shape_data[i];
+    // i + 1 to skip channel axis
+    const int input_dim = this->input_shape(i + 1);
     const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i])
         / stride_data[i] + 1;
     this->output_shape_.push_back(output_dim);
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index f1d1abf288a..91aabb315b2 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -10,14 +10,13 @@ namespace caffe {
 
 template <typename Dtype>
 void DeconvolutionLayer<Dtype>::compute_output_shape() {
-  // input_shape_ + 1 to skip channel axis
-  const int* input_shape_data = this->input_shape_.cpu_data() + 1;
   const int* kernel_shape_data = this->kernel_shape_.cpu_data();
   const int* stride_data = this->stride_.cpu_data();
   const int* pad_data = this->pad_.cpu_data();
   this->output_shape_.clear();
   for (int i = 0; i < this->num_spatial_axes_; ++i) {
-    const int input_dim = input_shape_data[i];
+    // i + 1 to skip channel axis
+    const int input_dim = this->input_shape(i + 1);
     const int output_dim = stride_data[i] * (input_dim - 1)
         + kernel_shape_data[i] - 2 * pad_data[i];
     this->output_shape_.push_back(output_dim);

From 74e174537418b6a3c0c8708e444edf45ab491e94 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 23 Sep 2015 13:38:29 -0700
Subject: [PATCH 1559/2053] [test] TestReshape: check small then large

checking large then small can mask failure since the smaller shape
memory will fit within the larger shape.
---
 src/caffe/test/test_net.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 12998d8912f..ec01053c1f7 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -2269,8 +2269,10 @@ TYPED_TEST(NetTest, TestReshape) {
   FillerParameter filler_param;
   filler_param.set_std(1);
   GaussianFiller<Dtype> filler(filler_param);
-  Blob<Dtype> blob1(4, 3, 9, 11);
-  Blob<Dtype> blob2(2, 3, 12, 10);
+  // Check smaller shape first as larger first could hide realloc failures.
+  Blob<Dtype> blob1(2, 3, 12, 10);
+  Blob<Dtype> blob2(4, 3, 9, 11);
+  ASSERT_LT(blob1.count(), blob2.count());
   filler.Fill(&blob1);
   filler.Fill(&blob2);
 

From ae77b15495d4c2a83202c49991bfc0885765de03 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 23 Sep 2015 13:40:16 -0700
Subject: [PATCH 1560/2053] [test] TestReshape: expect instead of check

---
 src/caffe/test/test_net.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index ec01053c1f7..16c1d35f3fc 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -2306,7 +2306,7 @@ TYPED_TEST(NetTest, TestReshape) {
   this->net_->ForwardPrefilled();
   this->net_->Backward();
   for (int i = 0; i < output1.count(); ++i) {
-    CHECK_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
+    EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
   }
 
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
@@ -2315,7 +2315,7 @@ TYPED_TEST(NetTest, TestReshape) {
   this->net_->ForwardPrefilled();
   this->net_->Backward();
   for (int i = 0; i < output2.count(); ++i) {
-    CHECK_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
+    EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
   }
 }
 

From b8c81bd2bfbc5bc2e394395bf2c1f435cb32b2a1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 23 Sep 2015 13:40:24 -0700
Subject: [PATCH 1561/2053] [test] TestReshape: check that shapes actually
 change

Check that output spatial shape varies with input shape while the output
num matches the input num.
---
 src/caffe/test/test_net.cpp | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 16c1d35f3fc..ab4afba1a93 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -2262,8 +2262,8 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
 TYPED_TEST(NetTest, TestReshape) {
   typedef typename TypeParam::Dtype Dtype;
   // We set up bottom blobs of two different sizes, switch between
-  // them, and check that forward and backward both run and the results
-  // are the same.
+  // them, check that forward and backward both run and the results
+  // are the same, and check that the output shapes change.
   Caffe::set_random_seed(this->seed_);
   Caffe::set_mode(Caffe::CPU);
   FillerParameter filler_param;
@@ -2317,6 +2317,18 @@ TYPED_TEST(NetTest, TestReshape) {
   for (int i = 0; i < output2.count(); ++i) {
     EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
   }
+
+  EXPECT_EQ(output1.num(), blob1.num());
+  EXPECT_EQ(output2.num(), blob2.num());
+  bool same_spatial_shape = true;
+  const int kFirstSpatialAxis = 2;
+  for (int i = kFirstSpatialAxis; i < output1.num_axes(); ++i) {
+    if (output1.shape(i) != output2.shape(i)) {
+      same_spatial_shape = false;
+      break;
+    }
+  }
+  EXPECT_FALSE(same_spatial_shape);
 }
 
 TYPED_TEST(NetTest, TestSkipPropagateDown) {

From 84e390c5a16347c7369f6c92cb62526e42ce73ac Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Thu, 24 Sep 2015 12:35:35 -0700
Subject: [PATCH 1562/2053] Allow H5T_INTEGER in HDF5 files

---
 src/caffe/util/hdf5.cpp | 29 ++++++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
index d0d05f70f8f..7730e76ab87 100644
--- a/src/caffe/util/hdf5.cpp
+++ b/src/caffe/util/hdf5.cpp
@@ -27,7 +27,34 @@ void hdf5_load_nd_dataset_helper(
   status = H5LTget_dataset_info(
       file_id, dataset_name_, dims.data(), &class_, NULL);
   CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
-  CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
+  switch (class_) {
+  case H5T_FLOAT:
+    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";
+    break;
+  case H5T_INTEGER:
+    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";
+    break;
+  case H5T_TIME:
+    LOG(FATAL) << "Unsupported datatype class: H5T_TIME";
+  case H5T_STRING:
+    LOG(FATAL) << "Unsupported datatype class: H5T_STRING";
+  case H5T_BITFIELD:
+    LOG(FATAL) << "Unsupported datatype class: H5T_BITFIELD";
+  case H5T_OPAQUE:
+    LOG(FATAL) << "Unsupported datatype class: H5T_OPAQUE";
+  case H5T_COMPOUND:
+    LOG(FATAL) << "Unsupported datatype class: H5T_COMPOUND";
+  case H5T_REFERENCE:
+    LOG(FATAL) << "Unsupported datatype class: H5T_REFERENCE";
+  case H5T_ENUM:
+    LOG(FATAL) << "Unsupported datatype class: H5T_ENUM";
+  case H5T_VLEN:
+    LOG(FATAL) << "Unsupported datatype class: H5T_VLEN";
+  case H5T_ARRAY:
+    LOG(FATAL) << "Unsupported datatype class: H5T_ARRAY";
+  default:
+    LOG(FATAL) << "Datatype class unknown";
+  }
 
   vector<int> blob_dims(dims.size());
   for (int i = 0; i < dims.size(); ++i) {

From ebc9963fea7b72f397c446a10a9aeab576979566 Mon Sep 17 00:00:00 2001
From: Luke Yeager <lyeager@nvidia.com>
Date: Tue, 25 Aug 2015 18:58:45 -0700
Subject: [PATCH 1563/2053] Modify HDF5DataLayerTest to test H5T_INTEGER data

---
 .../test/test_data/generate_sample_data.py    |  14 ++++++++------
 .../test/test_data/sample_data_2_gzip.h5      | Bin 15446 -> 15446 bytes
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 3703b41823b..8349dbbc8e6 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -36,23 +36,25 @@
     f['label'] = label
     f['label2'] = label2
 
-with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data_uint8_gzip.h5', 'w') as f:
     f.create_dataset(
         'data', data=data + total_size,
         compression='gzip', compression_opts=1
     )
     f.create_dataset(
         'label', data=label,
-        compression='gzip', compression_opts=1
+        compression='gzip', compression_opts=1,
+        dtype='uint8',
     )
     f.create_dataset(
         'label2', data=label2,
-        compression='gzip', compression_opts=1
+        compression='gzip', compression_opts=1,
+        dtype='uint8',
     )
 
 with open(script_dir + '/sample_data_list.txt', 'w') as f:
-    f.write(script_dir + '/sample_data.h5\n')
-    f.write(script_dir + '/sample_data_2_gzip.h5\n')
+    f.write('src/caffe/test/test_data/sample_data.h5\n')
+    f.write('src/caffe/test/test_data/sample_uint8_gzip.h5\n')
 
 # Generate GradientBasedSolver solver_data.h5
 
@@ -76,4 +78,4 @@
     f['targets'] = targets
 
 with open(script_dir + '/solver_data_list.txt', 'w') as f:
-    f.write(script_dir + '/solver_data.h5\n')
+    f.write('src/caffe/test/test_data/solver_data.h5\n')
diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5
index a138e0367be3d4b4ce4b51dcf0d7895056018883..0cb9ef92241d049b699b65f87e800f97337cae54 100644
GIT binary patch
delta 225
zcmcasajjwl4+~4C%-zt<0xUly1qB!w85kG@fEYwGFmOy(l#7r6vxOKqz(ODnNCN|d
z$Ha}klMPrT7=<Qol%LEeufWK-c_YhxPL&GA<dmee#MI>H51ueFFg#<h0E)l_9<ebn
t$}%_sRo-ErtiUWZS;5c%tGyl?az<bq8MuHH$Xa6D!Kg5qUtS*S5&*_)F3SJ_

delta 225
zcmcasajjwl4-3nIgQ_8$1z3JaPHtk3n7qJ{Q{$Q(0~l}tNk$-M0r7+wI6xFw0HlS1
z!C~@5xmZTEi5ul7^T{i0UcvH!)2o6pIU!?1dO|`0gOWlX!y{IQ8io#e2E%4$=4J)~
zZ3`xbdLuStkSZK17&bO4GgP*)KHa>LWgGis1!kei3Wf$yr%6iSb&3kmDU2qQ`Q_!o
GjsXC!dpCsu


From 859f93891e4bf47d02899f03f0620fd1f29ca224 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Thu, 24 Sep 2015 13:33:11 -0700
Subject: [PATCH 1564/2053] Fix generate_sample_data.py - bug from #2978

---
 src/caffe/test/test_data/generate_sample_data.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 8349dbbc8e6..2645073575f 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -36,7 +36,7 @@
     f['label'] = label
     f['label2'] = label2
 
-with h5py.File(script_dir + '/sample_data_uint8_gzip.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
     f.create_dataset(
         'data', data=data + total_size,
         compression='gzip', compression_opts=1
@@ -54,7 +54,7 @@
 
 with open(script_dir + '/sample_data_list.txt', 'w') as f:
     f.write('src/caffe/test/test_data/sample_data.h5\n')
-    f.write('src/caffe/test/test_data/sample_uint8_gzip.h5\n')
+    f.write('src/caffe/test/test_data/sample_data_2_gzip.h5\n')
 
 # Generate GradientBasedSolver solver_data.h5
 

From 200bd40391bc1c072730ea4bd80a6fe42b7a3901 Mon Sep 17 00:00:00 2001
From: Dmytro Mishkin <ducha.aiki@gmail.com>
Date: Fri, 25 Sep 2015 10:00:23 +0300
Subject: [PATCH 1565/2053] Fix parse_log.sh against "prefetch queue empty"
 messages

---
 tools/extra/parse_log.sh | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tools/extra/parse_log.sh b/tools/extra/parse_log.sh
index 98ef0a05002..9892c897682 100755
--- a/tools/extra/parse_log.sh
+++ b/tools/extra/parse_log.sh
@@ -14,7 +14,12 @@ echo "Usage parse_log.sh /path/to/your.log"
 exit
 fi
 LOG=`basename $1`
-grep -B 1 'Test ' $1 > aux.txt
+sed -n '/Iteration .* Testing net/,/Iteration *. loss/p' $1 > aux.txt
+sed -i '/Waiting for data/d' aux.txt
+sed -i '/prefetch queue empty/d' aux.txt
+sed -i '/Iteration .* loss/d' aux.txt
+sed -i '/Iteration .* lr/d' aux.txt
+sed -i '/Train net/d' aux.txt
 grep 'Iteration ' aux.txt | sed  's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
 grep 'Test net output #0' aux.txt | awk '{print $11}' > aux1.txt
 grep 'Test net output #1' aux.txt | awk '{print $11}' > aux2.txt

From 6c02c8b7daf123f64b944ede407d0022e98d6e0b Mon Sep 17 00:00:00 2001
From: Tim Meinhardt <meinhardt.tim@gmail.com>
Date: Tue, 15 Sep 2015 16:55:26 +0200
Subject: [PATCH 1566/2053] Add argmax_param axis

---
 include/caffe/common_layers.hpp   |  2 ++
 src/caffe/layers/argmax_layer.cpp | 22 +++++++++++++++++-----
 src/caffe/proto/caffe.proto       |  5 +++++
 3 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 89bab8d6f3a..491f9edbf19 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -68,6 +68,8 @@ class ArgMaxLayer : public Layer<Dtype> {
   }
   bool out_max_val_;
   size_t top_k_;
+  bool has_axis_;
+  int axis_;
 };
 
 /**
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index c4040cdcaaa..dad3d08bd66 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -11,11 +11,23 @@ namespace caffe {
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  out_max_val_ = this->layer_param_.argmax_param().out_max_val();
-  top_k_ = this->layer_param_.argmax_param().top_k();
-  CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
-  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
-      << "top_k must be less than or equal to the number of classes.";
+  const ArgMaxParameter& argmax_param = this->layer_param_.argmax_param();
+  out_max_val_ = argmax_param.out_max_val();
+  top_k_ = argmax_param.top_k();
+  has_axis_ = argmax_param.has_axis();
+  CHECK_GE(top_k_, 1) << "top k must not be less than 1.";
+  if (has_axis_) {
+    axis_ = bottom[0]->CanonicalAxisIndex(argmax_param.axis());
+    CHECK_GE(axis_, 0) << "axis must not be less than 0.";
+    CHECK_LE(axis_, bottom[0]->num_axes()) <<
+      "axis must be less than or equal to the number of axis.";
+    CHECK_LE(top_k_, bottom[0]->shape(axis_))
+      << "top_k must be less than or equal to the dimension of the axis.";
+  } else {
+    CHECK_LE(top_k_, bottom[0]->count(1))
+      << "top_k must be less than or equal to"
+        " the dimension of the flattened bottom blob per instance.";
+  }
 }
 
 template <typename Dtype>
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f52c941b05e..a8747c12b37 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -443,6 +443,11 @@ message ArgMaxParameter {
   // If true produce pairs (argmax, maxval)
   optional bool out_max_val = 1 [default = false];
   optional uint32 top_k = 2 [default = 1];
+  // The axis along which to maximise -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+  // for each index of the first / num dimension.
+  optional int32 axis = 3;
 }
 
 message ConcatParameter {

From c77d5e5156f94720c1decd13f7f87fe78df9d4eb Mon Sep 17 00:00:00 2001
From: Tim Meinhardt <meinhardt.tim@gmail.com>
Date: Tue, 15 Sep 2015 16:56:16 +0200
Subject: [PATCH 1567/2053] Implement ArgMaxLayer forward_cpu and reshape for
 axis param

---
 src/caffe/layers/argmax_layer.cpp | 53 ++++++++++++++++++++++---------
 1 file changed, 38 insertions(+), 15 deletions(-)

diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index dad3d08bd66..18ff5f5a639 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -33,13 +33,19 @@ void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  if (out_max_val_) {
+  std::vector<int> shape(4, 1);
+  shape[0] = bottom[0]->shape(0);
+  // Produces max_ind
+  shape[2] = top_k_;
+  if (has_axis_) {
+    // Produces max_ind or max_val per axis
+    shape = bottom[0]->shape();
+    shape[axis_] = top_k_;
+  } else if (out_max_val_) {
     // Produces max_ind and max_val
-    top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
-  } else {
-    // Produces only max_ind
-    top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
+    shape[1] = 2;
   }
+  top[0]->Reshape(shape);
 }
 
 template <typename Dtype>
@@ -47,23 +53,40 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  int dim, axis_dist;
+  if (has_axis_) {
+    dim = bottom[0]->shape(axis_);
+    // Distance between values of axis in blob
+    axis_dist = bottom[0]->count(axis_) / dim;
+  } else {
+    dim = bottom[0]->count(1);
+    axis_dist = 1;
+  }
+  int num = bottom[0]->count() / dim;
+  std::vector<std::pair<Dtype, int> > bottom_data_vector(dim);
   for (int i = 0; i < num; ++i) {
-    std::vector<std::pair<Dtype, int> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
-      bottom_data_vector.push_back(
-          std::make_pair(bottom_data[i * dim + j], j));
+      bottom_data_vector[j] = std::make_pair(
+        bottom_data[(i / axis_dist * dim + j) * axis_dist + i % axis_dist], j);
     }
     std::partial_sort(
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
         bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
     for (int j = 0; j < top_k_; ++j) {
-      top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
-    }
-    if (out_max_val_) {
-      for (int j = 0; j < top_k_; ++j) {
-        top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+      if (out_max_val_) {
+        if (has_axis_) {
+          // Produces max_val per axis
+          top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] =
+            bottom_data_vector[j].first;
+        } else {
+          // Produces max_ind and max_val
+          top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
+          top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+        }
+      } else {
+        // Produces max_ind per axis
+        top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] =
+          bottom_data_vector[j].second;
       }
     }
   }

From 9b2d267941411d9727a88ead18e3531bad50d14d Mon Sep 17 00:00:00 2001
From: Tim Meinhardt <meinhardt.tim@gmail.com>
Date: Tue, 15 Sep 2015 16:56:45 +0200
Subject: [PATCH 1568/2053] Update ArgMaxLayer documentation for axis param

---
 include/caffe/common_layers.hpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 491f9edbf19..d1ddaee4f19 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -21,7 +21,8 @@ namespace caffe {
  *
  * Intended for use after a classification layer to produce a prediction.
  * If parameter out_max_val is set to true, output is a vector of pairs
- * (max_ind, max_val) for each image.
+ * (max_ind, max_val) for each image. The axis parameter specifies an axis
+ * along which to maximise.
  *
  * NOTE: does not implement Backwards operation.
  */
@@ -34,7 +35,11 @@ class ArgMaxLayer : public Layer<Dtype> {
    *   - top_k (\b optional uint, default 1).
    *     the number @f$ K @f$ of maximal items to output.
    *   - out_max_val (\b optional bool, default false).
-   *     if set, output a vector of pairs (max_ind, max_val) for each image.
+   *     if set, output a vector of pairs (max_ind, max_val) unless axis is set then
+   *     output max_val along the specified axis.
+   *   - axis (\b optional int).
+   *     if set, maximise along the specified axis else maximise the flattened
+   *     trailing dimensions for each index of the first / num dimension.
    */
   explicit ArgMaxLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
@@ -54,7 +59,8 @@ class ArgMaxLayer : public Layer<Dtype> {
    *      the inputs @f$ x @f$
    * @param top output Blob vector (length 1)
    *   -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val
-   *      @f$ (N \times 2 \times K \times 1) @f$
+   *      @f$ (N \times 2 \times K \times 1) @f$ unless axis set than e.g.
+   *      @f$ (N \times K \times H \times W) @f$ if axis == 1
    *      the computed outputs @f$
    *       y_n = \arg\max\limits_i x_{ni}
    *      @f$ (for @f$ K = 1 @f$).

From a2a5e22d0b7d44a5e577edd53181d4802f057740 Mon Sep 17 00:00:00 2001
From: Tim Meinhardt <meinhardt.tim@gmail.com>
Date: Tue, 15 Sep 2015 16:57:37 +0200
Subject: [PATCH 1569/2053] Generalise ArgMaxLayerTest bottom blob shape

---
 src/caffe/test/test_argmax_layer.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 895c3d372ff..d3018f90c9d 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -16,7 +16,7 @@ template <typename Dtype>
 class ArgMaxLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   ArgMaxLayerTest()
-      : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
+      : blob_bottom_(new Blob<Dtype>(10, 10, 20, 20)),
         blob_top_(new Blob<Dtype>()),
         top_k_(5) {
     Caffe::set_random_seed(1701);
@@ -112,6 +112,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   int max_ind;
   TypeParam max_val;
   int num = this->blob_bottom_->num();
@@ -121,10 +122,10 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
     EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
     for (int j = 0; j < this->top_k_; ++j) {
       max_ind = this->blob_top_->data_at(i, 0, j, 0);
-      max_val = this->blob_bottom_->data_at(i, max_ind, 0, 0);
+      max_val = bottom_data[i * dim + max_ind];
       int count = 0;
       for (int k = 0; k < dim; ++k) {
-        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+        if (bottom_data[i * dim + k] > max_val) {
           ++count;
         }
       }
@@ -142,6 +143,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   int max_ind;
   TypeParam max_val;
   int num = this->blob_bottom_->num();
@@ -152,10 +154,10 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
     for (int j = 0; j < this->top_k_; ++j) {
       max_ind = this->blob_top_->data_at(i, 0, j, 0);
       max_val = this->blob_top_->data_at(i, 1, j, 0);
-      EXPECT_EQ(this->blob_bottom_->data_at(i, max_ind, 0, 0), max_val);
+      EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
       int count = 0;
       for (int k = 0; k < dim; ++k) {
-        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+        if (bottom_data[i * dim + k] > max_val) {
           ++count;
         }
       }

From def3d3cc49b908e54f787be377c299e6e6cbf16c Mon Sep 17 00:00:00 2001
From: Tim Meinhardt <meinhardt.tim@gmail.com>
Date: Tue, 15 Sep 2015 16:57:55 +0200
Subject: [PATCH 1570/2053] Implement ArgMaxLayerTest for axis param

---
 src/caffe/layers/argmax_layer.cpp    |  28 +++---
 src/caffe/test/test_argmax_layer.cpp | 125 +++++++++++++++++++++++++++
 2 files changed, 140 insertions(+), 13 deletions(-)

diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 18ff5f5a639..0c0a932dac7 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -33,17 +33,19 @@ void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  std::vector<int> shape(4, 1);
-  shape[0] = bottom[0]->shape(0);
-  // Produces max_ind
-  shape[2] = top_k_;
+  std::vector<int> shape(bottom[0]->num_axes(), 1);
   if (has_axis_) {
     // Produces max_ind or max_val per axis
     shape = bottom[0]->shape();
     shape[axis_] = top_k_;
-  } else if (out_max_val_) {
-    // Produces max_ind and max_val
-    shape[1] = 2;
+  } else {
+    shape[0] = bottom[0]->shape(0);
+    // Produces max_ind
+    shape[2] = top_k_;
+    if (out_max_val_) {
+      // Produces max_ind and max_val
+      shape[1] = 2;
+    }
   }
   top[0]->Reshape(shape);
 }
@@ -76,17 +78,17 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (out_max_val_) {
         if (has_axis_) {
           // Produces max_val per axis
-          top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] =
-            bottom_data_vector[j].first;
+          top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist]
+            = bottom_data_vector[j].first;
         } else {
           // Produces max_ind and max_val
-          top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
-          top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+          top_data[2 * i * top_k_ + j] = bottom_data_vector[j].second;
+          top_data[2 * i * top_k_ + top_k_ + j] = bottom_data_vector[j].first;
         }
       } else {
         // Produces max_ind per axis
-        top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] =
-          bottom_data_vector[j].second;
+        top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist]
+          = bottom_data_vector[j].second;
       }
     }
   }
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index d3018f90c9d..bbf19099905 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -55,6 +55,43 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   EXPECT_EQ(this->blob_top_->channels(), 2);
 }
 
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxis) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(0);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->shape(0), argmax_param->top_k());
+  EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(0));
+  EXPECT_EQ(this->blob_top_->shape(2), this->blob_bottom_->shape(2));
+  EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxisNegativeIndexing) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(-2);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0));
+  EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1));
+  EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k());
+  EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxisMaxVal) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(2);
+  argmax_param->set_out_max_val(true);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0));
+  EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1));
+  EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k());
+  EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
 TYPED_TEST(ArgMaxLayerTest, TestCPU) {
   LayerParameter layer_param;
   ArgMaxLayer<TypeParam> layer(layer_param);
@@ -166,5 +203,93 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   }
 }
 
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxis) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(0);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  int max_ind;
+  TypeParam max_val;
+  std::vector<int> shape = this->blob_bottom_->shape();
+  for (int i = 0; i < shape[1]; ++i) {
+    for (int j = 0; j < shape[2]; ++j) {
+      for (int k = 0; k < shape[3]; ++k) {
+        max_ind = this->blob_top_->data_at(0, i, j, k);
+        max_val = this->blob_bottom_->data_at(max_ind, i, j, k);
+        EXPECT_GE(max_ind, 0);
+        EXPECT_LE(max_ind, shape[0]);
+        for (int l = 0; l < shape[0]; ++l) {
+          EXPECT_LE(this->blob_bottom_->data_at(l, i, j, k), max_val);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxisTopK) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(2);
+  argmax_param->set_top_k(this->top_k_);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  int max_ind;
+  TypeParam max_val;
+  std::vector<int> shape = this->blob_bottom_->shape();
+  for (int i = 0; i < shape[0]; ++i) {
+    for (int j = 0; j < shape[1]; ++j) {
+      for (int k = 0; k < shape[3]; ++k) {
+        for (int m = 0; m < this->top_k_; ++m) {
+          max_ind = this->blob_top_->data_at(i, j, m, k);
+          max_val = this->blob_bottom_->data_at(i, j, max_ind, k);
+          EXPECT_GE(max_ind, 0);
+          EXPECT_LE(max_ind, shape[2]);
+          int count = 0;
+          for (int l = 0; l < shape[2]; ++l) {
+            if (this->blob_bottom_->data_at(i, j, l, k) > max_val) {
+              ++count;
+            }
+          }
+          EXPECT_EQ(m, count);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxisMaxValTopK) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(-1);
+  argmax_param->set_top_k(this->top_k_);
+  argmax_param->set_out_max_val(true);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  TypeParam max_val;
+  std::vector<int> shape = this->blob_bottom_->shape();
+  for (int i = 0; i < shape[0]; ++i) {
+    for (int j = 0; j < shape[1]; ++j) {
+      for (int k = 0; k < shape[2]; ++k) {
+        for (int m = 0; m < this->top_k_; ++m) {
+          max_val = this->blob_top_->data_at(i, j, k, m);
+          int count = 0;
+          for (int l = 0; l < shape[3]; ++l) {
+            if (this->blob_bottom_->data_at(i, j, k, l) > max_val) {
+              ++count;
+            }
+          }
+          EXPECT_EQ(m, count);
+        }
+      }
+    }
+  }
+}
 
 }  // namespace caffe

From bd5f15427cc2f008f80378a5948ce379d93ebde6 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Wed, 16 Sep 2015 13:39:23 -0700
Subject: [PATCH 1571/2053] Add flag on how host memory is allocated

Add a bool flag to record whether a host memory is allocated using malloc or
cudaMallocHost, and free correspondingly using this flag, instead of depending on Caffe::mode(), which is mutable during runtime.
---
 include/caffe/syncedmem.hpp | 15 ++++++++++-----
 src/caffe/syncedmem.cpp     |  8 ++++----
 2 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 62aadef498d..3d92a0eaf3e 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -13,20 +13,22 @@ namespace caffe {
 // The improvement in performance seems negligible in the single GPU case,
 // but might be more significant for parallel training. Most importantly,
 // it improved stability for large models on many GPUs.
-inline void CaffeMallocHost(void** ptr, size_t size) {
+inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
 #ifndef CPU_ONLY
   if (Caffe::mode() == Caffe::GPU) {
     CUDA_CHECK(cudaMallocHost(ptr, size));
+    *use_cuda = true;
     return;
   }
 #endif
   *ptr = malloc(size);
+  *use_cuda = false;
   CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
 
-inline void CaffeFreeHost(void* ptr) {
+inline void CaffeFreeHost(void* ptr, bool use_cuda) {
 #ifndef CPU_ONLY
-  if (Caffe::mode() == Caffe::GPU) {
+  if (use_cuda) {
     CUDA_CHECK(cudaFreeHost(ptr));
     return;
   }
@@ -45,10 +47,12 @@ class SyncedMemory {
  public:
   SyncedMemory()
       : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
-        own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {}
+        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
+        gpu_device_(-1) {}
   explicit SyncedMemory(size_t size)
       : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
-        own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {}
+        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
+        gpu_device_(-1) {}
   ~SyncedMemory();
   const void* cpu_data();
   void set_cpu_data(void* data);
@@ -72,6 +76,7 @@ class SyncedMemory {
   size_t size_;
   SyncedHead head_;
   bool own_cpu_data_;
+  bool cpu_malloc_use_cuda_;
   bool own_gpu_data_;
   int gpu_device_;
 
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index a667a867af0..632bf1f12d3 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -8,7 +8,7 @@ namespace caffe {
 
 SyncedMemory::~SyncedMemory() {
   if (cpu_ptr_ && own_cpu_data_) {
-    CaffeFreeHost(cpu_ptr_);
+    CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
   }
 
 #ifndef CPU_ONLY
@@ -27,7 +27,7 @@ SyncedMemory::~SyncedMemory() {
 inline void SyncedMemory::to_cpu() {
   switch (head_) {
   case UNINITIALIZED:
-    CaffeMallocHost(&cpu_ptr_, size_);
+    CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
     caffe_memset(size_, 0, cpu_ptr_);
     head_ = HEAD_AT_CPU;
     own_cpu_data_ = true;
@@ -35,7 +35,7 @@ inline void SyncedMemory::to_cpu() {
   case HEAD_AT_GPU:
 #ifndef CPU_ONLY
     if (cpu_ptr_ == NULL) {
-      CaffeMallocHost(&cpu_ptr_, size_);
+      CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
       own_cpu_data_ = true;
     }
     caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
@@ -86,7 +86,7 @@ const void* SyncedMemory::cpu_data() {
 void SyncedMemory::set_cpu_data(void* data) {
   CHECK(data);
   if (own_cpu_data_) {
-    CaffeFreeHost(cpu_ptr_);
+    CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
   }
   cpu_ptr_ = data;
   head_ = HEAD_AT_CPU;

From aaf4a4557668dfb75c540903ec02ed5821f75835 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Fri, 25 Sep 2015 15:43:47 -0700
Subject: [PATCH 1572/2053] Re-ordering some lines in build files

Enforcing a consistent ordering - OpenCV, LevelDB, LMDB

This will allow me to add the ALLOW_LMDB_NOLOCK option just after the
USE_LMDB option, while keeping the IO dependency options together.
---
 CMakeLists.txt                    | 4 ++--
 Makefile.config.example           | 2 +-
 cmake/Summary.cmake               | 4 ++--
 cmake/Templates/caffe_config.h.in | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 37f937fe489..277c3dc49f8 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -23,9 +23,9 @@ set(python_version "2" CACHE STRING "Specify which Python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
 caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON)
-caffe_option(USE_LMDB "Build with lmdb" ON)
-caffe_option(USE_LEVELDB "Build with levelDB" ON)
 caffe_option(USE_OPENCV "Build with OpenCV support" ON)
+caffe_option(USE_LEVELDB "Build with levelDB" ON)
+caffe_option(USE_LMDB "Build with lmdb" ON)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
diff --git a/Makefile.config.example b/Makefile.config.example
index a20bad2f5ce..42f86db4595 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -8,9 +8,9 @@
 # CPU_ONLY := 1
 
 # uncomment to disable IO dependencies and corresponding data layers
+# USE_OPENCV := 0
 # USE_LEVELDB := 0
 # USE_LMDB := 0
-# USE_OPENCV := 0
 
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 3d12e81a130..703e22acaf6 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -114,9 +114,9 @@ function(caffe_print_configuration_summary)
   caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
   caffe_status("  BUILD_docs        :   ${BUILD_docs}")
   caffe_status("  CPU_ONLY          :   ${CPU_ONLY}")
-  caffe_status("  USE_LMDB          :   ${USE_LMDB}")
-  caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
   caffe_status("  USE_OPENCV        :   ${USE_OPENCV}")
+  caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
+  caffe_status("  USE_LMDB          :   ${USE_LMDB}")
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 9302022d7da..84377493f48 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -33,5 +33,5 @@
 
 /* IO libraries */
 #cmakedefine USE_OPENCV
-#cmakedefine USE_LMDB
 #cmakedefine USE_LEVELDB
+#cmakedefine USE_LMDB

From b93afe8378cd66d9bf375a0f492a30f9db77e8ae Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Fri, 25 Sep 2015 15:53:54 -0700
Subject: [PATCH 1573/2053] Add ALLOW_LMDB_NOLOCK build option

This option lets you open LMDB files with the MDB_NOLOCK flag. You
should not set this flag if you will be reading LMDBs with any
possibility of simultaneous read and write.
---
 CMakeLists.txt                    |  1 +
 Makefile                          |  3 +++
 Makefile.config.example           |  5 +++++
 cmake/ConfigGen.cmake             |  3 +++
 cmake/Dependencies.cmake          |  3 +++
 cmake/Summary.cmake               |  1 +
 cmake/Templates/caffe_config.h.in |  1 +
 src/caffe/util/db_lmdb.cpp        | 17 ++++++++++++++++-
 8 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 277c3dc49f8..f8f75305f2c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -26,6 +26,7 @@ caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON)
 caffe_option(USE_OPENCV "Build with OpenCV support" ON)
 caffe_option(USE_LEVELDB "Build with levelDB" ON)
 caffe_option(USE_LMDB "Build with lmdb" ON)
+caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
diff --git a/Makefile b/Makefile
index 5fb6394e947..7cc73931349 100644
--- a/Makefile
+++ b/Makefile
@@ -313,6 +313,9 @@ ifeq ($(USE_LEVELDB), 1)
 endif
 ifeq ($(USE_LMDB), 1)
 	COMMON_FLAGS += -DUSE_LMDB
+ifeq ($(ALLOW_LMDB_NOLOCK), 1)
+	COMMON_FLAGS += -DALLOW_LMDB_NOLOCK
+endif
 endif
 
 # CPU-only configuration
diff --git a/Makefile.config.example b/Makefile.config.example
index 42f86db4595..bda66ea101b 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -12,6 +12,11 @@
 # USE_LEVELDB := 0
 # USE_LMDB := 0
 
+# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
+#	You should not set this flag if you will be reading LMDBs with any
+#	possibility of simultaneous read and write
+# ALLOW_LMDB_NOLOCK := 1
+
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
 # CUSTOM_CXX := g++
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 8b259965359..056371110b5 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -62,6 +62,9 @@ function(caffe_generate_export_configs)
 
   if(USE_LMDB)
     list(APPEND Caffe_DEFINITIONS -DUSE_LMDB)
+    if (ALLOW_LMDB_NOLOCK)
+        list(APPEND Caffe_DEFINITIONS -DALLOW_LMDB_NOLOCK)
+    endif()
   endif()
 
   if(USE_LEVELDB)
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index d68d7bfba66..a77ac6df6a0 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -34,6 +34,9 @@ if(USE_LMDB)
   include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
   list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
   add_definitions(-DUSE_LMDB)
+  if(ALLOW_LMDB_NOLOCK)
+    add_definitions(-DALLOW_LMDB_NOLOCK)
+  endif()
 endif()
 
 # ---[ LevelDB
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 703e22acaf6..6984f417e71 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -117,6 +117,7 @@ function(caffe_print_configuration_summary)
   caffe_status("  USE_OPENCV        :   ${USE_OPENCV}")
   caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
   caffe_status("  USE_LMDB          :   ${USE_LMDB}")
+  caffe_status("  ALLOW_LMDB_NOLOCK :   ${ALLOW_LMDB_NOLOCK}")
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 84377493f48..8a31b43cabf 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -35,3 +35,4 @@
 #cmakedefine USE_OPENCV
 #cmakedefine USE_LEVELDB
 #cmakedefine USE_LMDB
+#cmakedefine ALLOW_LMDB_NOLOCK
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index 78dd880ac41..0bc82b53e2b 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -19,7 +19,22 @@ void LMDB::Open(const string& source, Mode mode) {
   if (mode == READ) {
     flags = MDB_RDONLY | MDB_NOTLS;
   }
-  MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+  int rc = mdb_env_open(mdb_env_, source.c_str(), flags, 0664);
+#ifndef ALLOW_LMDB_NOLOCK
+  MDB_CHECK(rc);
+#else
+  if (rc == EACCES) {
+    LOG(WARNING) << "Permission denied. Trying with MDB_NOLOCK ...";
+    // Close and re-open environment handle
+    mdb_env_close(mdb_env_);
+    MDB_CHECK(mdb_env_create(&mdb_env_));
+    // Try again with MDB_NOLOCK
+    flags |= MDB_NOLOCK;
+    MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+  } else {
+    MDB_CHECK(rc);
+  }
+#endif
   LOG(INFO) << "Opened lmdb " << source;
 }
 

From e98b84762fb55daed5092225f71c3b76015aa4a4 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Mon, 28 Sep 2015 15:35:24 -0700
Subject: [PATCH 1574/2053] Install libs as non-executable files

According to the Debian policy manual, "Shared libraries should not be
installed executable, since the dynamic linker does not require this and
trying to execute a shared library usually results in a core dump."

https://www.debian.org/doc/debian-policy/ch-sharedlibs.html#s-sharedlibs-runtime
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 5fb6394e947..fbda44befd7 100644
--- a/Makefile
+++ b/Makefile
@@ -652,7 +652,7 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
 	# add libraries
 	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
-	cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
+	install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python
 

From 96ba513f54ac7bfc62c40a2481c1556c2f743120 Mon Sep 17 00:00:00 2001
From: Yang Song <hjss06@users.noreply.github.com>
Date: Tue, 29 Sep 2015 20:07:52 +0800
Subject: [PATCH 1575/2053] Fix a typo

Fix a typo in the message.
---
 python/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index 0e2bc7e66a8..a22641401f0 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -1,5 +1,5 @@
 if(NOT HAVE_PYTHON)
-  message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...")
+  message(STATUS "Python interface is disabled or not all required dependencies found. Building without it...")
   return()
 endif()
 

From 98cc023939641482432d4082db061306a7ab1654 Mon Sep 17 00:00:00 2001
From: Youssef Kashef <youssef.kashef@gmail.com>
Date: Thu, 1 Oct 2015 18:20:23 +0200
Subject: [PATCH 1576/2053] add badge for travis build and license

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index ebec286d550..44b9e62c157 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,8 @@
 # Caffe
 
+[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
+[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
+
 Caffe is a deep learning framework made with expression, speed, and modularity in mind.
 It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
 

From 552a84aaddeabc074f4a0184b90b7194e7f7a44b Mon Sep 17 00:00:00 2001
From: zoharby <Zohar Bar-Yehuda>
Date: Fri, 11 Sep 2015 15:06:28 +0300
Subject: [PATCH 1577/2053] Add a caffe.io.write_mean function to the MATLAB
 interface

Useful for exporting models from MATLAB (e.g. MatConvNet) to Caffe
---
 matlab/+caffe/io.m               |  8 ++++++++
 matlab/+caffe/private/caffe_.cpp | 24 ++++++++++++++++++++++++
 2 files changed, 32 insertions(+)

diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
index af8369ddfab..4b072fecdab 100644
--- a/matlab/+caffe/io.m
+++ b/matlab/+caffe/io.m
@@ -29,5 +29,13 @@
       CHECK_FILE_EXIST(mean_proto_file);
       mean_data = caffe_('read_mean', mean_proto_file);
     end
+    function write_mean(mean_data, mean_proto_file)
+      % write_mean(mean_data, mean_proto_file)
+      %   write image mean data to binaryproto file
+      %   mean_data should be W x H x C with BGR channels
+      CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
+      CHECK(isa(mean_data, 'single'), 'mean_data must be a SINGLE matrix');
+      caffe_('write_mean', mean_data, mean_proto_file);
+    end   
   end
 end
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 4e0ebc1c00a..7883f79ebd9 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -478,6 +478,29 @@ static void read_mean(MEX_ARGS) {
   mxFree(mean_proto_file);
 }
 
+// Usage: caffe_('write_mean', mean_data, mean_proto_file)
+static void write_mean(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsSingle(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('write_mean', mean_data, mean_proto_file)");
+  char* mean_proto_file = mxArrayToString(prhs[1]);
+  int ndims = mxGetNumberOfDimensions(prhs[0]);
+  mxCHECK(ndims >= 2 && ndims <= 3, "mean_data must have at 2 or 3 dimensions");
+  const mwSize *dims = mxGetDimensions(prhs[0]);
+  int width = dims[0];
+  int height = dims[1];
+  int channels;
+  if (ndims == 3)
+    channels = dims[2];
+  else
+    channels = 1;
+  Blob<float> data_mean(1, channels, height, width);
+  mx_mat_to_blob(prhs[0], &data_mean, DATA);
+  BlobProto blob_proto;
+  data_mean.ToProto(&blob_proto, false);
+  WriteProtoToBinaryFile(blob_proto, mean_proto_file);
+  mxFree(mean_proto_file);
+}
+
 /** -----------------------------------------------------------------
  ** Available commands.
  **/
@@ -515,6 +538,7 @@ static handler_registry handlers[] = {
   { "get_init_key",       get_init_key    },
   { "reset",              reset           },
   { "read_mean",          read_mean       },
+  { "write_mean",         write_mean      },
   // The end.
   { "END",                NULL            },
 };

From 30dfb864f0e7ad8f39bcdb48200eccc2a0efa7d3 Mon Sep 17 00:00:00 2001
From: Jan Issac <jan.issac@gmail.com>
Date: Mon, 5 Oct 2015 11:13:26 +0200
Subject: [PATCH 1578/2053] minor typo fix

---
 cmake/Cuda.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index ff58d31c166..98aef268cf4 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -132,7 +132,7 @@ function(caffe_select_nvcc_arch_flags out_variable)
 endfunction()
 
 ################################################################################################
-# Short command for cuda comnpilation
+# Short command for cuda compilation
 # Usage:
 #   caffe_cuda_compile(<objlist_variable> <cuda_files>)
 macro(caffe_cuda_compile objlist_variable)

From 64f948a6829c53031632d87f78183dd87d5d6f71 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 5 Oct 2015 14:15:08 -0700
Subject: [PATCH 1579/2053] SilenceLayer Backward bugfix (fixes #3151)

---
 src/caffe/layers/silence_layer.cpp | 2 +-
 src/caffe/layers/silence_layer.cu  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 4abf9eff4a2..7e70ab4329e 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -12,7 +12,7 @@ void SilenceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
       caffe_set(bottom[i]->count(), Dtype(0),
-                bottom[i]->mutable_cpu_data());
+                bottom[i]->mutable_cpu_diff());
     }
   }
 }
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index 8d044ee7307..34faef22bda 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -18,7 +18,7 @@ void SilenceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
       caffe_gpu_set(bottom[i]->count(), Dtype(0),
-                    bottom[i]->mutable_gpu_data());
+                    bottom[i]->mutable_gpu_diff());
     }
   }
 }

From 19d9927d76d6655a3efc090611e59aa2ea0f25a5 Mon Sep 17 00:00:00 2001
From: Gustav Larsson <gustav.m.larsson@gmail.com>
Date: Mon, 5 Oct 2015 21:55:00 -0500
Subject: [PATCH 1580/2053] Add pycaffe test for solver.snapshot()

---
 python/caffe/test/test_solver.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index 9cfc10d29a9..f618fded8cd 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -16,7 +16,8 @@ def setUp(self):
         f.write("""net: '""" + net_f + """'
         test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
         weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
-        display: 100 max_iter: 100 snapshot_after_train: false""")
+        display: 100 max_iter: 100 snapshot_after_train: false
+        snapshot_prefix: "model" """)
         f.close()
         self.solver = caffe.SGDSolver(f.name)
         # also make sure get_solver runs
@@ -51,3 +52,11 @@ def test_net_memory(self):
                     total += p.data.sum() + p.diff.sum()
             for bl in six.itervalues(net.blobs):
                 total += bl.data.sum() + bl.diff.sum()
+
+    def test_snapshot(self):
+        self.solver.snapshot()
+        # Check that these files exist and then remove them
+        files = ['model_iter_0.caffemodel', 'model_iter_0.solverstate']
+        for fn in files:
+            assert os.path.isfile(fn)
+            os.remove(fn)

From 10725393518df14b9b6976686f72fae792c3f393 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 5 Oct 2015 15:46:54 -0700
Subject: [PATCH 1581/2053] NetSpec: type-check Function inputs (they must be
 Top instances)

---
 python/caffe/net_spec.py           | 4 ++++
 python/caffe/test/test_net_spec.py | 8 ++++++++
 2 files changed, 12 insertions(+)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 93fc01927db..b6520627a4b 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -103,6 +103,10 @@ class Function(object):
 
     def __init__(self, type_name, inputs, params):
         self.type_name = type_name
+        for index, input in enumerate(inputs):
+            if not isinstance(input, Top):
+                raise TypeError('%s input %d is not a Top (type is %s)' %
+                                (type_name, index, type(input)))
         self.inputs = inputs
         self.params = params
         self.ntop = self.params.get('ntop', 1)
diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
index fee3c0aaebe..ffe71bacb08 100644
--- a/python/caffe/test/test_net_spec.py
+++ b/python/caffe/test/test_net_spec.py
@@ -79,3 +79,11 @@ def test_zero_tops(self):
         net_proto = silent_net()
         net = self.load_net(net_proto)
         self.assertEqual(len(net.forward()), 0)
+
+    def test_type_error(self):
+        """Test that a TypeError is raised when a Function input isn't a Top."""
+        data = L.DummyData(ntop=2)  # data is a 2-tuple of Tops
+        r = r"^Silence input 0 is not a Top \(type is <(type|class) 'tuple'>\)$"
+        with self.assertRaisesRegexp(TypeError, r):
+            L.Silence(data, ntop=0)  # should raise: data is a tuple, not a Top
+        L.Silence(*data, ntop=0)  # shouldn't raise: each elt of data is a Top

From e0615464ddf550ee57c17733ba9c5a0fa71b8edb Mon Sep 17 00:00:00 2001
From: e3 <eerwitt@gmail.com>
Date: Wed, 7 Oct 2015 11:52:45 -0700
Subject: [PATCH 1582/2053] fixes BVLC/caffe#3163

---
 docs/tutorial/layers.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index eabc792b704..7362aac298a 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -39,7 +39,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
     - `n * c_i * h_i * w_i`
 * Output
     - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
 
       layer {
         name: "conv1"
@@ -83,7 +83,7 @@ The `Convolution` layer convolves the input image with a set of learnable filter
     - `n * c * h_i * w_i`
 * Output
     - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
 
       layer {
         name: "pool1"
@@ -197,7 +197,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 * Parameters (`ReLUParameter relu_param`)
     - Optional
         - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
 
       layer {
         name: "relu1"

From bda1a633ec874e313f5d5dddfc0afc70573847d7 Mon Sep 17 00:00:00 2001
From: Carl Doersch <cdoersch@cs.cmu.edu>
Date: Sun, 23 Aug 2015 20:47:25 -0700
Subject: [PATCH 1583/2053] BatchReindexLayer to shuffle, subsample, and
 replicate examples in a batch

---
 include/caffe/common_layers.hpp             |  69 ++++++++++++
 src/caffe/layers/batch_reindex_layer.cpp    |  79 +++++++++++++
 src/caffe/layers/batch_reindex_layer.cu     | 107 ++++++++++++++++++
 src/caffe/test/test_batch_reindex_layer.cpp | 119 ++++++++++++++++++++
 4 files changed, 374 insertions(+)
 create mode 100644 src/caffe/layers/batch_reindex_layer.cpp
 create mode 100644 src/caffe/layers/batch_reindex_layer.cu
 create mode 100644 src/caffe/test/test_batch_reindex_layer.cpp

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index d2c0ce6d0c6..5d68e865e10 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -70,6 +70,75 @@ class ArgMaxLayer : public Layer<Dtype> {
   size_t top_k_;
 };
 
+/**
+ * @brief Index into the input blob along its first axis.
+ *
+ * This layer can be used to select, reorder, and even replicate examples in a
+ * batch.  The second blob is cast to int and treated as an index into the
+ * first axis of the first blob.
+ */
+template <typename Dtype>
+class BatchReindexLayer : public Layer<Dtype> {
+ public:
+  explicit BatchReindexLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BatchReindex"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times ...) @f$
+   *      the inputs @f$ x_1 @f$
+   *   -# @f$ (M) @f$
+   *      the inputs @f$ x_2 @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (M \times ...) @f$:
+   *      the reindexed array @f$
+   *        y = x_1[x_2]
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the reordered input.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient
+   *        with respect to the outputs
+   *   -# @f$ (M \times ...) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to concatenated outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2):
+   *   - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where
+   *     required) back to the input x_1
+   *   - This layer cannot backprop to x_2, i.e. propagate_down[1] must be
+   *     false.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ private:
+  struct pair_sort_first {
+    bool operator()(const std::pair<int, int> &left,
+                    const std::pair<int, int> &right) {
+      return left.first < right.first;
+    }
+  };
+  void check_batch_reindex(int initial_num, int final_num,
+                           const Dtype* ridx_data);
+};
+
+
 /**
  * @brief Takes at least two Blob%s and concatenates them along either the num
  *        or channel dimension, outputting the result.
diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp
new file mode 100644
index 00000000000..3bf757c718d
--- /dev/null
+++ b/src/caffe/layers/batch_reindex_layer.cpp
@@ -0,0 +1,79 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+                                       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(1, bottom[1]->num_axes());
+  vector<int> newshape;
+  newshape.push_back(bottom[1]->shape(0));
+  for (int i = 1; i < bottom[0]->shape().size(); ++i) {
+    newshape.push_back(bottom[0]->shape()[i]);
+  }
+  top[0]->Reshape(newshape);
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::check_batch_reindex(int initial_num,
+                                                   int final_num,
+                                                   const Dtype* ridx_data) {
+  for (int i = 0; i < final_num; ++i) {
+    CHECK_GE(ridx_data[i], 0)
+        << "Index specified for reindex layer was negative.";
+    CHECK_LT(ridx_data[i], initial_num)
+        << "Index specified for reindex layer was greater than batch size.";
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                                           const vector<Blob<Dtype>*>& top) {
+  check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(),
+                      bottom[1]->cpu_data());
+  if (top[0]->count() == 0) {
+    return;
+  }
+  int inner_dim = bottom[0]->count() / bottom[0]->shape(0);
+  const Dtype* in = bottom[0]->cpu_data();
+  const Dtype* permut = bottom[1]->cpu_data();
+  Dtype* out = top[0]->mutable_cpu_data();
+  for (int index = 0; index < top[0]->count(); ++index) {
+    int n = index / (inner_dim);
+    int in_n = static_cast<int>(permut[n]);
+    out[index] = in[in_n * (inner_dim) + index % (inner_dim)];
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Backward_cpu(
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[1]) << "Cannot backprop to index.";
+  if (!propagate_down[0]) {
+    return;
+  }
+  int inner_dim = bottom[0]->count() / bottom[0]->shape(0);
+  Dtype* bot_diff = bottom[0]->mutable_cpu_diff();
+  const Dtype* permut = bottom[1]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  caffe_set(bottom[0]->count(), Dtype(0), bot_diff);
+  for (int index = 0; index < top[0]->count(); ++index) {
+    int n = index / (inner_dim);
+    int in_n = static_cast<int>(permut[n]);
+    bot_diff[in_n * (inner_dim) + index % (inner_dim)] += top_diff[index];
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BatchReindexLayer);
+#endif
+
+INSTANTIATE_CLASS(BatchReindexLayer);
+REGISTER_LAYER_CLASS(BatchReindex);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu
new file mode 100644
index 00000000000..c418cab9042
--- /dev/null
+++ b/src/caffe/layers/batch_reindex_layer.cu
@@ -0,0 +1,107 @@
+#include <algorithm>
+#include <utility>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+__global__ void BRForward(const int count, const int inner_dim, const Dtype* in,
+                          const Dtype* permut, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / (inner_dim);
+    int in_n = static_cast<int>(permut[n]);
+    out[index] = in[in_n * (inner_dim) + index % (inner_dim)];
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                                           const vector<Blob<Dtype>*>& top) {
+  check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(),
+                      bottom[1]->cpu_data());
+  if (top[0]->count() == 0) {
+    return;
+  }
+  int threads = top[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  BRForward<Dtype> <<<CAFFE_GET_BLOCKS(threads), CAFFE_CUDA_NUM_THREADS>>>(
+      top[0]->count(), bottom[0]->count() / bottom[0]->shape(0),
+      bottom[0]->gpu_data(), bottom[1]->gpu_data(), top[0]->mutable_gpu_data());
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template<typename Dtype>
+__global__ void BRBackward(const int count, const int inner_dim,
+                           const Dtype* in, const Dtype* top_indexes,
+                           const Dtype* begins, const Dtype* counts,
+                           Dtype* out) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / (inner_dim);
+    out[index] = 0;
+    int lower = static_cast<int>(begins[n]);
+    int upper = lower + static_cast<int>(counts[n]);
+    for (int i = lower; i < upper; ++i) {
+      int in_n = static_cast<int>(top_indexes[i]);
+      out[index] += in[in_n * (inner_dim) + index % (inner_dim)];
+    }
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Backward_gpu(
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[1]) << "Cannot backprop to index.";
+  if (!propagate_down[0]) {
+    return;
+  }
+
+  vector<std::pair<int, int> > mapping;
+  const Dtype* perm = bottom[1]->cpu_data();
+  for (int i = 0; i < bottom[1]->count(); ++i) {
+    mapping.push_back(pair<int, int>(static_cast<int>(perm[i]), i));
+  }
+  std::sort(mapping.begin(), mapping.end(), pair_sort_first());
+
+  // Each element of the bottom diff is potentially the sum of many top diffs.
+  // However, we'd like each CUDA thread to handle exactly one output.  Hence,
+  // we first pre-compute a list of lists of indices that need to be summed for
+  // each output. `top_indexes` holds the data of this list of lists.  The
+  // k'th element of `begins` points to the location in `top_indexes` where the
+  // list for the k'th example begin, and the k'th element of `counts` is the
+  // length of that list.
+  vector<int> shape;
+  shape.push_back(bottom[1]->count());
+  Blob<Dtype> top_indexes(shape);
+  shape[0] = bottom[0]->shape(0);
+  Blob<Dtype> counts(shape);
+  Blob<Dtype> begins(shape);
+  Dtype* t_i_data = top_indexes.mutable_cpu_data();
+  Dtype* c_data = counts.mutable_cpu_data();
+  Dtype* b_data = begins.mutable_cpu_data();
+  caffe_set(begins.count(), Dtype(-1), b_data);
+  caffe_set(counts.count(), Dtype(0), c_data);
+  for (int i = 0; i < mapping.size(); ++i) {
+    t_i_data[i] = mapping[i].second;
+    if (b_data[mapping[i].first] == -1) {
+      b_data[mapping[i].first] = i;
+    }
+    c_data[mapping[i].first] += 1;
+  }
+
+  int threads = bottom[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  BRBackward<Dtype> <<<CAFFE_GET_BLOCKS(threads), CAFFE_CUDA_NUM_THREADS>>>(
+      bottom[0]->count(), bottom[0]->count() / bottom[0]->shape(0),
+      top[0]->gpu_diff(), top_indexes.gpu_data(), begins.gpu_data(),
+      counts.gpu_data(), bottom[0]->mutable_gpu_diff());
+  CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchReindexLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp
new file mode 100644
index 00000000000..985db343d12
--- /dev/null
+++ b/src/caffe/test/test_batch_reindex_layer.cpp
@@ -0,0 +1,119 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template<typename TypeParam>
+class BatchReindexLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  BatchReindexLayerTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_bottom_permute_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {
+  }
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    vector<int> sz;
+    sz.push_back(5);
+    sz.push_back(4);
+    sz.push_back(3);
+    sz.push_back(2);
+    blob_bottom_->Reshape(sz);
+    vector<int> permsz;
+    permsz.push_back(6);
+    blob_bottom_permute_->Reshape(permsz);
+
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    int perm[] = { 4, 0, 4, 0, 1, 2 };
+    for (int i = 0; i < blob_bottom_permute_->count(); ++i) {
+      blob_bottom_permute_->mutable_cpu_data()[i] = perm[i];
+    }
+
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_permute_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~BatchReindexLayerTest() {
+    delete blob_bottom_permute_;
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_permute_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+
+  void TestForward() {
+    LayerParameter layer_param;
+
+    vector<int> sz;
+    sz.push_back(5);
+    sz.push_back(4);
+    sz.push_back(3);
+    sz.push_back(2);
+    blob_bottom_->Reshape(sz);
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      blob_bottom_->mutable_cpu_data()[i] = i;
+    }
+
+    vector<int> permsz;
+    permsz.push_back(6);
+    blob_bottom_permute_->Reshape(permsz);
+    int perm[] = { 4, 0, 4, 0, 1, 2 };
+    for (int i = 0; i < blob_bottom_permute_->count(); ++i) {
+      blob_bottom_permute_->mutable_cpu_data()[i] = perm[i];
+    }
+    BatchReindexLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), blob_bottom_permute_->num());
+    EXPECT_EQ(blob_top_->channels(), blob_bottom_->channels());
+    EXPECT_EQ(blob_top_->height(), blob_bottom_->height());
+    EXPECT_EQ(blob_top_->width(), blob_bottom_->width());
+
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    int channels = blob_top_->channels();
+    int height = blob_top_->height();
+    int width = blob_top_->width();
+    for (int i = 0; i < blob_top_->count(); ++i) {
+      int n = i / (channels * width * height);
+      int inner_idx = (i % (channels * width * height));
+      EXPECT_EQ(
+          blob_top_->cpu_data()[i],
+          blob_bottom_->cpu_data()[perm[n] * channels * width * height
+              + inner_idx]);
+    }
+  }
+};
+
+TYPED_TEST_CASE(BatchReindexLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(BatchReindexLayerTest, TestForward) {
+  this->TestForward();
+}
+
+TYPED_TEST(BatchReindexLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BatchReindexLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  }
+
+}  // namespace caffe

From c65ba61bdf273604c3edcd24ba7a80cc3835441a Mon Sep 17 00:00:00 2001
From: sh1r0 <sh1r0@users.noreply.github.com>
Date: Fri, 9 Oct 2015 00:31:05 +0800
Subject: [PATCH 1584/2053] Remove the 4D constraint of blobproto IO in python

---
 python/caffe/io.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 0cad7211291..40b7ac1ed78 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -21,22 +21,18 @@ def blobproto_to_array(blob, return_diff=False):
     unless return_diff is True, in which case we will return the diff.
     """
     if return_diff:
-        return np.array(blob.diff).reshape(
-            blob.num, blob.channels, blob.height, blob.width)
+        return np.array(blob.diff).reshape(*blob.shape.dim)
     else:
-        return np.array(blob.data).reshape(
-            blob.num, blob.channels, blob.height, blob.width)
+        return np.array(blob.data).reshape(*blob.shape.dim)
 
 
 def array_to_blobproto(arr, diff=None):
-    """Converts a 4-dimensional array to blob proto. If diff is given, also
+    """Converts a N-dimensional array to blob proto. If diff is given, also
     convert the diff. You need to make sure that arr and diff have the same
     shape, and this function does not do sanity check.
     """
-    if arr.ndim != 4:
-        raise ValueError('Incorrect array shape.')
     blob = caffe_pb2.BlobProto()
-    blob.num, blob.channels, blob.height, blob.width = arr.shape
+    blob.shape.dim.extend(arr.shape)
     blob.data.extend(arr.astype(float).flat)
     if diff is not None:
         blob.diff.extend(diff.astype(float).flat)

From ee5191b3e41fddae73653e0d61172360b89526ca Mon Sep 17 00:00:00 2001
From: Kang Kim <kkhoot@gmail.com>
Date: Thu, 8 Oct 2015 01:26:25 +0900
Subject: [PATCH 1585/2053] Improve numerical stability of variance computation
 in MVNLayer

---
 src/caffe/layers/mvn_layer.cpp | 42 ++++++++++-----------------------
 src/caffe/layers/mvn_layer.cu  | 43 ++++++++++------------------------
 2 files changed, 25 insertions(+), 60 deletions(-)

diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 325691b1875..61c2141ecd9 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -42,29 +42,21 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   int dim = bottom[0]->count() / num;
 
-  if (this->layer_param_.mvn_param().normalize_variance()) {
-    // put the squares of bottom into temp_
-    caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
-        temp_.mutable_cpu_data());
+  // subtract mean
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+      mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+      temp_.mutable_cpu_data());
+  caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);  // X-EX
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_powx(bottom[0]->count(), top_data, Dtype(2),
+        temp_.mutable_cpu_data());  // (X-EX)^2
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
         sum_multiplier_.cpu_data(), 0.,
-        variance_.mutable_cpu_data());  // E(X^2)
-    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-        temp_.mutable_cpu_data());  // (EX)^2
-    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-        variance_.mutable_cpu_data());  // variance
-
-    // do mean and variance normalization
-    // subtract mean
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-            temp_.mutable_cpu_data());
-
-    caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
+        variance_.mutable_cpu_data());  // E((X-EX)^2)
 
     // normalize variance
     caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
@@ -77,16 +69,6 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           temp_.mutable_cpu_data());
 
     caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
-  } else {
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-            sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-
-    // subtract mean
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-            temp_.mutable_cpu_data());
-
-    caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
   }
 }
 
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index d86a2e73fc2..5cbb112de4d 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -20,29 +20,22 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   int dim = bottom[0]->count() / num;
 
-  if (this->layer_param_.mvn_param().normalize_variance()) {
-    // put the squares of bottom into temp_
-    caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
-        temp_.mutable_gpu_data());
+  // subtract mean
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+      mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+      temp_.mutable_gpu_data());
+  caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(),
+      top_data);  // X-EX
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_gpu_powx(bottom[0]->count(), top_data, Dtype(2),
+        temp_.mutable_gpu_data());  // (X-EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
         sum_multiplier_.gpu_data(), 0.,
-        variance_.mutable_gpu_data());  // E(X^2)
-    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-        temp_.mutable_gpu_data());  // (EX)^2
-    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-        variance_.mutable_gpu_data());  // variance
-
-    // do mean and variance normalization
-    // subtract mean
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-            temp_.mutable_gpu_data());
-
-    caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
+        variance_.mutable_gpu_data());  // E((X-EX)^2)
 
     // normalize variance
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
@@ -55,16 +48,6 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
           temp_.mutable_gpu_data());
 
     caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
-  } else {
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-            sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-
-    // subtract mean
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-            temp_.mutable_gpu_data());
-
-    caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
   }
 }
 

From e5990b3dafa2b95fae7b7bfaac4dcd309a20d151 Mon Sep 17 00:00:00 2001
From: Brian Chu <bc@brianchu.com>
Date: Tue, 13 Oct 2015 03:50:53 -0700
Subject: [PATCH 1586/2053] In 00-classification example, get correct class
 label index

---
 examples/00-classification.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
index 46bbb193fe7..89b7dd34f0e 100644
--- a/examples/00-classification.ipynb
+++ b/examples/00-classification.ipynb
@@ -119,7 +119,7 @@
    "source": [
     "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
     "out = net.forward()\n",
-    "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
+    "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))"
    ]
   },
   {

From ec94055a6c5f0f86b88f98f1659cc9f317df2e3e Mon Sep 17 00:00:00 2001
From: Alessandro Giusti <lalelale@users.noreply.github.com>
Date: Tue, 13 Oct 2015 14:30:45 +0200
Subject: [PATCH 1587/2053] Update store2hdf5.m

Fixed a bug in two assertions (the condition input argument must be a scalar logical)
---
 matlab/hdf5creation/store2hdf5.m | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/matlab/hdf5creation/store2hdf5.m b/matlab/hdf5creation/store2hdf5.m
index 0a0016dca40..4e8c81d9de8 100644
--- a/matlab/hdf5creation/store2hdf5.m
+++ b/matlab/hdf5creation/store2hdf5.m
@@ -39,8 +39,8 @@
       info=h5info(filename);
       prev_dat_sz=info.Datasets(1).Dataspace.Size;
       prev_lab_sz=info.Datasets(2).Dataspace.Size;
-      assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset');
-      assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset');
+      assert(all(prev_dat_sz(1:end-1)==dat_dims(1:end-1)), 'Data dimensions must match existing dimensions in dataset');
+      assert(all(prev_lab_sz(1:end-1)==lab_dims(1:end-1)), 'Label dimensions must match existing dimensions in dataset');
       startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1];
       startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1];
     end

From a8839dbcb3b16f8f5d3f8d17209a3c8c0142a51b Mon Sep 17 00:00:00 2001
From: Akash A <akash1810@users.noreply.github.com>
Date: Tue, 13 Oct 2015 17:53:35 +0100
Subject: [PATCH 1588/2053] Add pyyaml as a requirement

In getting the [web demo](http://caffe.berkeleyvision.org/gathered/examples/web_demo.html) started I get an `ImportError: No module named yaml` error when running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.
---
 examples/web_demo/requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/web_demo/requirements.txt b/examples/web_demo/requirements.txt
index 8fb1d2ccbb2..43e1b98cc34 100644
--- a/examples/web_demo/requirements.txt
+++ b/examples/web_demo/requirements.txt
@@ -4,3 +4,4 @@ tornado
 numpy
 pandas
 pillow
+pyyaml

From ca4c6fb4e2106bbd3d6a2c09c34567558edde891 Mon Sep 17 00:00:00 2001
From: Brian Chu <bc@brianchu.com>
Date: Tue, 13 Oct 2015 13:24:42 -0700
Subject: [PATCH 1589/2053] Set CaffeNet train_val test mirroring to false

---
 models/bvlc_reference_caffenet/train_val.prototxt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index c79472e09ab..e3e427968ab 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -45,7 +45,7 @@ layer {
 #    mean_value: 104
 #    mean_value: 117
 #    mean_value: 123
-#    mirror: true
+#    mirror: false
 #  }
   data_param {
     source: "examples/imagenet/ilsvrc12_val_lmdb"

From e0c34cedde6e0d12d420a51cea7a98df50069559 Mon Sep 17 00:00:00 2001
From: Vladimir <nekrasowladimir@gmail.com>
Date: Wed, 14 Oct 2015 12:00:14 +0900
Subject: [PATCH 1590/2053] Fixed drawing problems with repeated convolution

---
 python/caffe/draw.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index a002b60b59a..f8bf5722aba 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -82,11 +82,11 @@ def get_layer_label(layer, rankdir):
                       separator,
                       layer.type,
                       separator,
-                      layer.convolution_param.kernel_size,
+                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
                       separator,
-                      layer.convolution_param.stride,
+                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1,
                       separator,
-                      layer.convolution_param.pad)
+                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0)
     elif layer.type == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\

From 75e859a522fdbf78a2ea58393500af6103bcce56 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Thu, 15 Oct 2015 11:03:09 -0700
Subject: [PATCH 1591/2053] Allow old-style shape in blobproto_to_array

Fixes #3199
Bug introduced in #3170
---
 python/caffe/io.py           | 11 ++++++++--
 python/caffe/test/test_io.py | 41 ++++++++++++++++++++++++++++++++++++
 2 files changed, 50 insertions(+), 2 deletions(-)
 create mode 100644 python/caffe/test/test_io.py

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 40b7ac1ed78..11c84260f1a 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -20,11 +20,18 @@ def blobproto_to_array(blob, return_diff=False):
     Convert a blob proto to an array. In default, we will just return the data,
     unless return_diff is True, in which case we will return the diff.
     """
+    # Read the data into an array
     if return_diff:
-        return np.array(blob.diff).reshape(*blob.shape.dim)
+        data = np.array(blob.diff)
     else:
-        return np.array(blob.data).reshape(*blob.shape.dim)
+        data = np.array(blob.data)
 
+    # Reshape the array
+    if blob.HasField('num') or blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
+        # Use legacy 4D shape
+        return data.reshape(blob.num, blob.channels, blob.height, blob.width)
+    else:
+        return data.reshape(blob.shape.dim)
 
 def array_to_blobproto(arr, diff=None):
     """Converts a N-dimensional array to blob proto. If diff is given, also
diff --git a/python/caffe/test/test_io.py b/python/caffe/test/test_io.py
new file mode 100644
index 00000000000..8c86ef75fb2
--- /dev/null
+++ b/python/caffe/test/test_io.py
@@ -0,0 +1,41 @@
+import numpy as np
+import unittest
+
+import caffe
+
+class TestBlobProtoToArray(unittest.TestCase):
+
+    def test_old_format(self):
+        data = np.zeros((10,10))
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+        shape = (1,1,10,10)
+        blob.num, blob.channels, blob.height, blob.width = shape
+
+        arr = caffe.io.blobproto_to_array(blob)
+        self.assertEqual(arr.shape, shape)
+
+    def test_new_format(self):
+        data = np.zeros((10,10))
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+        blob.shape.dim.extend(list(data.shape))
+
+        arr = caffe.io.blobproto_to_array(blob)
+        self.assertEqual(arr.shape, data.shape)
+
+    def test_no_shape(self):
+        data = np.zeros((10,10))
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+
+        with self.assertRaises(ValueError):
+            caffe.io.blobproto_to_array(blob)
+
+    def test_scalar(self):
+        data = np.ones((1)) * 123
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+
+        arr = caffe.io.blobproto_to_array(blob)
+        self.assertEqual(arr, 123)

From ecac7ff6286642420eb5db723c382e74bf82c9d7 Mon Sep 17 00:00:00 2001
From: Simon Layton <slayton58@gmail.com>
Date: Wed, 8 Jul 2015 15:35:55 -0400
Subject: [PATCH 1592/2053] Initial cuDNN v3 support

---
 include/caffe/vision_layers.hpp       |  74 +++++++++-
 src/caffe/layer_factory.cpp           |  43 +++++-
 src/caffe/layers/cudnn_conv_layer.cpp | 138 +++++++++++++++++-
 src/caffe/layers/cudnn_conv_layer.cu  |  58 ++------
 src/caffe/layers/cudnn_lcn_layer.cpp  |  77 ++++++++++
 src/caffe/layers/cudnn_lcn_layer.cu   |  50 +++++++
 src/caffe/layers/cudnn_lrn_layer.cpp  |  57 ++++++++
 src/caffe/layers/cudnn_lrn_layer.cu   |  48 +++++++
 src/caffe/layers/lrn_layer.cpp        |   1 -
 src/caffe/proto/caffe.proto           |   6 +
 src/caffe/test/test_lrn_layer.cpp     | 196 ++++++++++++++++++++++++++
 11 files changed, 692 insertions(+), 56 deletions(-)
 create mode 100644 src/caffe/layers/cudnn_lcn_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_lcn_layer.cu
 create mode 100644 src/caffe/layers/cudnn_lrn_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_lrn_layer.cu

diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index 06bc0457e2d..237b05d6fa6 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -304,13 +304,24 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
   bool handles_setup_;
   cudnnHandle_t* handle_;
   cudaStream_t*  stream_;
+
+  // algorithms for forward and backwards convolutions
+  cudnnConvolutionFwdAlgo_t *fwd_algo_;
+  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
+  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
+
   vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
   cudnnTensorDescriptor_t    bias_desc_;
   cudnnFilterDescriptor_t      filter_desc_;
   vector<cudnnConvolutionDescriptor_t> conv_descs_;
   int bottom_offset_, top_offset_, bias_offset_;
-  size_t workspaceSizeInBytes;
-  void *workspace;
+
+  size_t *workspace_fwd_sizes_;
+  size_t *workspace_bwd_data_sizes_;
+  size_t *workspace_bwd_filter_sizes_;
+  size_t workspaceSizeInBytes;  // size of underlying storage
+  void *workspaceData;  // underlying storage
+  void **workspace;  // aliases into workspaceData
 };
 #endif
 
@@ -442,6 +453,65 @@ class LRNLayer : public Layer<Dtype> {
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
+#ifdef USE_CUDNN
+
+template <typename Dtype>
+class CuDNNLRNLayer : public LRNLayer<Dtype> {
+ public:
+  explicit CuDNNLRNLayer(const LayerParameter& param)
+      : LRNLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNLRNLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnLRNDescriptor_t norm_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+  int size_;
+  Dtype alpha_, beta_, k_;
+};
+
+template <typename Dtype>
+class CuDNNLCNLayer : public LRNLayer<Dtype> {
+ public:
+  explicit CuDNNLCNLayer(const LayerParameter& param)
+      : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
+        tempData1(NULL), tempData2(NULL) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNLCNLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnLRNDescriptor_t norm_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+  int size_, pre_pad_;
+  Dtype alpha_, beta_, k_;
+
+  size_t tempDataSize;
+  void *tempData1, *tempData2;
+};
+
+#endif
 
 /**
  * @brief Pools the input image by taking the max, average, etc. within regions.
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 926c7d8ff78..417ffe986df 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -54,10 +54,8 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
     return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
-    PoolingParameter p_param = param.pooling_param();
-    if (p_param.pad() || p_param.pad_h() || p_param.pad_w() ||
-        param.top_size() > 1) {
-      LOG(INFO) << "CUDNN does not support padding or multiple tops. "
+    if (param.top_size() > 1) {
+      LOG(INFO) << "cuDNN does not support multiple tops. "
                 << "Using Caffe's own pooling layer.";
       return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
     }
@@ -70,6 +68,43 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
 
 REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
 
+// Get LRN layer according to engine
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetLRNLayer(const LayerParameter& param) {
+  LRNParameter_Engine engine = param.lrn_param().engine();
+
+  if (engine == LRNParameter_Engine_DEFAULT) {
+#ifdef USE_CUDNN
+    engine = LRNParameter_Engine_CUDNN;
+#else
+    engine = LRNParameter_Engine_CAFFE;
+#endif
+  }
+
+  if (engine == LRNParameter_Engine_CAFFE) {
+    return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
+#ifdef USE_CUDNN
+  } else if (engine == LRNParameter_Engine_CUDNN) {
+    LRNParameter lrn_param = param.lrn_param();
+
+    if (lrn_param.norm_region() ==LRNParameter_NormRegion_WITHIN_CHANNEL) {
+      return shared_ptr<Layer<Dtype> >(new CuDNNLCNLayer<Dtype>(param));
+    } else {
+      // local size is too big to be handled through cuDNN
+      if (param.lrn_param().local_size() > CUDNN_LRN_MAX_N) {
+        return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
+      } else {
+        return shared_ptr<Layer<Dtype> >(new CuDNNLRNLayer<Dtype>(param));
+      }
+    }
+#endif
+  } else {
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+  }
+}
+
+REGISTER_LAYER_CREATOR(LRN, GetLRNLayer);
+
 // Get relu layer according to engine.
 template <typename Dtype>
 shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 3514fe2aba5..d7b1e0d651f 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -1,4 +1,5 @@
 #ifdef USE_CUDNN
+#include <algorithm>
 #include <vector>
 
 #include "caffe/filler.hpp"
@@ -24,13 +25,38 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   // Initialize CUDA streams and cuDNN.
   stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
   handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  // Initialize algorithm arrays
+  fwd_algo_       = new cudnnConvolutionFwdAlgo_t[bottom.size()];
+  bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
+  bwd_data_algo_  = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
+
+  // initialize size arrays
+  workspace_fwd_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_data_sizes_ = new size_t[bottom.size()];
+
+  // workspace data
   workspaceSizeInBytes = 0;
-  workspace = NULL;
+  workspaceData = NULL;
+  workspace = new void*[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  for (size_t i = 0; i < bottom.size(); ++i) {
+    // initialize all to default algorithms
+    fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
+    bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
+    bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
+    // default algorithms don't require workspace
+    workspace_fwd_sizes_[i] = 0;
+    workspace_bwd_data_sizes_[i] = 0;
+    workspace_bwd_filter_sizes_[i] = 0;
+  }
 
   for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
     CUDA_CHECK(cudaStreamCreate(&stream_[g]));
     CUDNN_CHECK(cudnnCreate(&handle_[g]));
     CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
+    workspace[g] = NULL;
   }
 
   // Set the indexing parameters.
@@ -86,6 +112,10 @@ void CuDNNConvolutionLayer<Dtype>::Reshape(
   const int stride_h = stride_data[0];
   const int stride_w = stride_data[1];
 
+  // Specify workspace limit for kernels directly until we have a
+  // planning strategy and a rewrite of Caffe's GPU memory mangagement
+  size_t workspace_limit_bytes = 8*1024*1024;
+
   for (int i = 0; i < bottom.size(); i++) {
     cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
         this->num_,
@@ -98,7 +128,104 @@ void CuDNNConvolutionLayer<Dtype>::Reshape(
         this->num_output_ * this->out_spatial_dim_,
         this->out_spatial_dim_, width_out, 1);
     cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
-        filter_desc_, pad_h, pad_w, stride_h, stride_w);
+        filter_desc_, pad_h, pad_w,
+        stride_h, stride_w);
+
+    // choose forward and backward algorithms + workspace(s)
+    CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[0],
+      bottom_descs_[i],
+      filter_desc_,
+      conv_descs_[i],
+      top_descs_[i],
+      CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+      workspace_limit_bytes,
+      &fwd_algo_[i]));
+
+    CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[0],
+      bottom_descs_[i],
+      filter_desc_,
+      conv_descs_[i],
+      top_descs_[i],
+      fwd_algo_[i],
+      &(workspace_fwd_sizes_[i])));
+
+    // choose backward algorithm for filter
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(handle_[0],
+          bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
+          CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+          workspace_limit_bytes, &bwd_filter_algo_[i]) );
+
+    // get workspace for backwards filter algorithm
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_[0],
+          bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
+          bwd_filter_algo_[i], &workspace_bwd_filter_sizes_[i]));
+
+    // choose backward algo for data
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(handle_[0],
+          filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
+          CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+        workspace_limit_bytes, &bwd_data_algo_[i]));
+
+    // get workspace size
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_[0],
+          filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
+          bwd_data_algo_[i], &workspace_bwd_data_sizes_[i]) );
+  }
+
+  // reduce over all workspace sizes to get a maximum to allocate / reallocate
+  size_t total_workspace_fwd = 0;
+  size_t total_workspace_bwd_data = 0;
+  size_t total_workspace_bwd_filter = 0;
+
+  for (size_t i = 0; i < bottom.size(); i++) {
+    total_workspace_fwd        = std::max(total_workspace_fwd,
+                                     workspace_fwd_sizes_[i]);
+    total_workspace_bwd_data   = std::max(total_workspace_bwd_data,
+                                     workspace_bwd_data_sizes_[i]);
+    total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,
+                                     workspace_bwd_filter_sizes_[i]);
+  }
+  // get max over all operations
+  size_t max_workspace = std::max(total_workspace_fwd,
+                             total_workspace_bwd_data);
+  max_workspace = std::max(max_workspace, total_workspace_bwd_filter);
+  // ensure all groups have enough workspace
+  size_t total_max_workspace = max_workspace *
+                               (this->group_ * CUDNN_STREAMS_PER_GROUP);
+
+  // this is the total amount of storage needed over all groups + streams
+  if (total_max_workspace > workspaceSizeInBytes) {
+    LOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
+    workspaceSizeInBytes = total_max_workspace;
+
+    // free the existing workspace and allocate a new (larger) one
+    cudaFree(this->workspaceData);
+
+    cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);
+    if (err != cudaSuccess) {
+      // force zero memory path
+      for (int i = 0; i < bottom.size(); i++) {
+        workspace_fwd_sizes_[i] = 0;
+        workspace_bwd_filter_sizes_[i] = 0;
+        workspace_bwd_data_sizes_[i] = 0;
+        fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
+        bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
+        bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
+      }
+
+      // NULL out all workspace pointers
+      for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+        workspace[g] = NULL;
+      }
+      // NULL out underlying data
+      workspaceData = NULL;
+      workspaceSizeInBytes = 0;
+    }
+
+    // if we succeed in the allocation, set pointer aliases for workspaces
+    for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+      workspace[g] = reinterpret_cast<char *>(workspaceData) + g*max_workspace;
+    }
   }
 
   // Tensor descriptor for bias.
@@ -128,8 +255,15 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
     cudnnDestroy(handle_[g]);
   }
 
+  cudaFree(workspaceData);
   delete [] stream_;
   delete [] handle_;
+  delete [] fwd_algo_;
+  delete [] bwd_filter_algo_;
+  delete [] bwd_data_algo_;
+  delete [] workspace_fwd_sizes_;
+  delete [] workspace_bwd_data_sizes_;
+  delete [] workspace_bwd_filter_sizes_;
 }
 
 INSTANTIATE_CLASS(CuDNNConvolutionLayer);
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 691152021a3..e88e4dd3281 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -14,11 +14,6 @@ __global__ void sync_conv_groups() { }
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
-  const int kernel_h = kernel_shape_data[0];
-  const int kernel_w = kernel_shape_data[1];
-  const size_t workspace_limit_bytes =
-      kernel_h * kernel_w * this->channels_ * sizeof(int) + 1;
   const Dtype* weight = this->blobs_[0]->gpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
@@ -26,52 +21,13 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
-      cudnnConvolutionFwdAlgo_t algo;
-
-      // pick the convolution algorithm
-      // TODO(shelhamer) this should be done during reshape
-      // TODO(shelhamer) the choice of automatic or manual algorithm picking
-      // should be exposed in proto
-      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[g],
-        bottom_descs_[i],
-        filter_desc_,
-        conv_descs_[i],
-        top_descs_[i],
-        CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-        workspace_limit_bytes,  // memoryLimitInBytes,
-        &algo));
-
-      // get minimum size of the workspace needed for the desired algorithm
-      size_t workspaceSizeInBytes_temp = 0;
-
-      CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[g],
-        bottom_descs_[i],
-        filter_desc_,
-        conv_descs_[i],
-        top_descs_[i],
-        algo,
-        &workspaceSizeInBytes_temp));
-
-      if (workspaceSizeInBytes_temp > workspaceSizeInBytes) {
-        workspaceSizeInBytes = workspaceSizeInBytes_temp;
-        // free the existing workspace and allocate a new (larger) one
-        cudaFree(this->workspace);
-        cudaError_t err = cudaMalloc(&(this->workspace), workspaceSizeInBytes);
-        if (err != cudaSuccess) {
-          // force zero memory path
-          algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-          workspace = NULL;
-          workspaceSizeInBytes = 0;
-        }
-      }
-
       // Filters.
       CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
             cudnn::dataType<Dtype>::one,
             bottom_descs_[i], bottom_data + bottom_offset_ * g,
             filter_desc_, weight + this->weight_offset_ * g,
             conv_descs_[i],
-            algo, workspace, workspaceSizeInBytes,
+            fwd_algo_[i], workspace[g], workspace_fwd_sizes_[i],
             cudnn::dataType<Dtype>::zero,
             top_descs_[i], top_data + top_offset_ * g));
 
@@ -101,10 +57,12 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (this->param_propagate_down_[0]) {
     weight = this->blobs_[0]->gpu_data();
     weight_diff = this->blobs_[0]->mutable_gpu_diff();
+    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
   if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
+    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
@@ -122,11 +80,14 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = bottom[i]->gpu_data();
-        CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3(
+              handle_[1*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               bottom_descs_[i], bottom_data + bottom_offset_ * g,
               top_descs_[i],    top_diff + top_offset_ * g,
               conv_descs_[i],
+              bwd_filter_algo_[i], workspace[1*this->group_ + g],
+              workspace_bwd_filter_sizes_[i],
               cudnn::dataType<Dtype>::one,
               filter_desc_, weight_diff + this->weight_offset_ * g));
       }
@@ -137,11 +98,14 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           weight = this->blobs_[0]->gpu_data();
         }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
-        CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardData_v3(
+              handle_[2*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               filter_desc_, weight + this->weight_offset_ * g,
               top_descs_[i], top_diff + top_offset_ * g,
               conv_descs_[i],
+              bwd_data_algo_[i], workspace[2*this->group_ + g],
+              workspace_bwd_data_sizes_[i],
               cudnn::dataType<Dtype>::zero,
               bottom_descs_[i], bottom_diff + bottom_offset_ * g));
       }
diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp
new file mode 100644
index 00000000000..866d810b9f9
--- /dev/null
+++ b/src/caffe/layers/cudnn_lcn_layer.cpp
@@ -0,0 +1,77 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::LayerSetUp(bottom, top);
+
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+
+  // create a LRN handle
+  handles_setup_ = true;
+
+  size_ = this->layer_param().lrn_param().local_size();
+  pre_pad_ = (size_ - 1) / 2;
+  alpha_ = this->layer_param().lrn_param().alpha();
+  beta_ = this->layer_param().lrn_param().beta();
+  k_ = this->layer_param().lrn_param().k();
+}
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::Reshape(bottom, top);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_));
+
+  // allocate / reallocate tempData buffers
+  size_t totalSizeInBytes = sizeof(Dtype)*bottom[0]->num()* \
+                            this->channels_*this->height_*this->width_;
+
+  if (totalSizeInBytes > tempDataSize) {
+    tempDataSize = totalSizeInBytes;
+
+    cudaFree(tempData1);
+    cudaFree(tempData2);
+
+    // allocate new buffers
+    CUDA_CHECK(cudaMalloc(&tempData1, totalSizeInBytes));
+    CUDA_CHECK(cudaMalloc(&tempData2, totalSizeInBytes));
+  }
+}
+
+template <typename Dtype>
+CuDNNLCNLayer<Dtype>::~CuDNNLCNLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
+
+  // destroy LRN handle
+  cudnnDestroy(handle_);
+
+  // free temp buffers
+  cudaFree(tempData1);
+  cudaFree(tempData2);
+}
+
+INSTANTIATE_CLASS(CuDNNLCNLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu
new file mode 100644
index 00000000000..c07ade72066
--- /dev/null
+++ b/src/caffe/layers/cudnn_lcn_layer.cu
@@ -0,0 +1,50 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+
+  CUDNN_CHECK(cudnnDivisiveNormalizationForward(
+        handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        NULL,  // srcMeansData
+        this->tempData1, this->tempData2,
+        cudnn::dataType<Dtype>::zero,
+        top_desc_, top_data) );
+}
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  CUDNN_CHECK(cudnnDivisiveNormalizationBackward(
+        handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        NULL, top_diff,  // NULL - srcMeansData
+        this->tempData1, this->tempData2,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_diff,
+        NULL) );
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLCNLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp
new file mode 100644
index 00000000000..6e9921490f0
--- /dev/null
+++ b/src/caffe/layers/cudnn_lrn_layer.cpp
@@ -0,0 +1,57 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::LayerSetUp(bottom, top);
+
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+
+  // create a LRN handle
+  handles_setup_ = true;
+
+  size_ = this->layer_param().lrn_param().local_size();
+  alpha_ = this->layer_param().lrn_param().alpha();
+  beta_ = this->layer_param().lrn_param().beta();
+  k_ = this->layer_param().lrn_param().k();
+}
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::Reshape(bottom, top);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_));
+}
+
+template <typename Dtype>
+CuDNNLRNLayer<Dtype>::~CuDNNLRNLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
+
+  // destroy LRN handle
+  cudnnDestroy(handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNLRNLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu
new file mode 100644
index 00000000000..f9923033011
--- /dev/null
+++ b/src/caffe/layers/cudnn_lrn_layer.cu
@@ -0,0 +1,48 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+
+  CUDNN_CHECK(cudnnLRNCrossChannelForward(
+        handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        top_desc_, top_data) );
+}
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  CUDNN_CHECK(cudnnLRNCrossChannelBackward(
+        handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1,
+        cudnn::dataType<Dtype>::one,
+        top_desc_, top_data,
+        top_desc_, top_diff,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_diff) );
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLRNLayer);
+
+};  // namespace caffe
+
+#endif
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 36c1ace4c99..d18a04ef58d 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -254,6 +254,5 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
 #endif
 
 INSTANTIATE_CLASS(LRNLayer);
-REGISTER_LAYER_CLASS(LRN);
 
 }  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f52c941b05e..af01b4721c9 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -721,6 +721,12 @@ message LRNParameter {
   }
   optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
   optional float k = 5 [default = 1.];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
 }
 
 message MemoryDataParameter {
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index c4e2f8ea7f2..78cf2d9df9b 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -246,5 +246,201 @@ TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
       this->blob_top_vec_);
 }
 
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLRNLayerTest : public GPUDeviceTest<Dtype> {
+ protected:
+  CuDNNLRNLayerTest()
+      : epsilon_(Dtype(1e-5)),
+        blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    blob_bottom_->Reshape(2, 7, 3, 3);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~CuDNNLRNLayerTest() { delete blob_bottom_; delete blob_top_; }
+  void ReferenceLRNForward(const Blob<Dtype>& blob_bottom,
+      const LayerParameter& layer_param, Blob<Dtype>* blob_top);
+
+  Dtype epsilon_;
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+template <typename TypeParam>
+void CuDNNLRNLayerTest<TypeParam>::ReferenceLRNForward(
+    const Blob<TypeParam>& blob_bottom, const LayerParameter& layer_param,
+    Blob<TypeParam>* blob_top) {
+  typedef TypeParam Dtype;
+  blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
+      blob_bottom.height(), blob_bottom.width());
+  Dtype* top_data = blob_top->mutable_cpu_data();
+  LRNParameter lrn_param = layer_param.lrn_param();
+  Dtype alpha = lrn_param.alpha();
+  Dtype beta = lrn_param.beta();
+  int size = lrn_param.local_size();
+  switch (lrn_param.norm_region()) {
+  case LRNParameter_NormRegion_ACROSS_CHANNELS:
+    for (int n = 0; n < blob_bottom.num(); ++n) {
+      for (int c = 0; c < blob_bottom.channels(); ++c) {
+        for (int h = 0; h < blob_bottom.height(); ++h) {
+          for (int w = 0; w < blob_bottom.width(); ++w) {
+            int c_start = c - (size - 1) / 2;
+            int c_end = min(c_start + size, blob_bottom.channels());
+            c_start = max(c_start, 0);
+            Dtype scale = 1.;
+            for (int i = c_start; i < c_end; ++i) {
+              Dtype value = blob_bottom.data_at(n, i, h, w);
+              scale += value * value * alpha / size;
+            }
+            *(top_data + blob_top->offset(n, c, h, w)) =
+              blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
+          }
+        }
+      }
+    }
+    break;
+  case LRNParameter_NormRegion_WITHIN_CHANNEL:
+    for (int n = 0; n < blob_bottom.num(); ++n) {
+      for (int c = 0; c < blob_bottom.channels(); ++c) {
+        for (int h = 0; h < blob_bottom.height(); ++h) {
+          int h_start = h - (size - 1) / 2;
+          int h_end = min(h_start + size, blob_bottom.height());
+          h_start = max(h_start, 0);
+          for (int w = 0; w < blob_bottom.width(); ++w) {
+            Dtype scale = 1.;
+            int w_start = w - (size - 1) / 2;
+            int w_end = min(w_start + size, blob_bottom.width());
+            w_start = max(w_start, 0);
+            for (int nh = h_start; nh < h_end; ++nh) {
+              for (int nw = w_start; nw < w_end; ++nw) {
+                Dtype value = blob_bottom.data_at(n, c, nh, nw);
+                scale += value * value * alpha / (size * size);
+              }
+            }
+            *(top_data + blob_top->offset(n, c, h, w)) =
+              blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
+          }
+        }
+      }
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown normalization region.";
+  }
+}
+
+TYPED_TEST_CASE(CuDNNLRNLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsCuDNN) {
+  // typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CuDNNLRNLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<TypeParam> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsLargeRegionCuDNN) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  CuDNNLRNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<Dtype> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsCuDNN) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  CuDNNLRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardWithinChannel) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_norm_region(
+      LRNParameter_NormRegion_WITHIN_CHANNEL);
+  layer_param.mutable_lrn_param()->set_local_size(3);
+  CuDNNLCNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<Dtype> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientWithinChannel) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_norm_region(
+      LRNParameter_NormRegion_WITHIN_CHANNEL);
+  layer_param.mutable_lrn_param()->set_local_size(3);
+  CuDNNLCNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsLargeRegionCuDNN) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  CuDNNLRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+#endif
 
 }  // namespace caffe

From 1e75fb922f968a92071232c7e6b3332475141d47 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Oct 2015 16:33:06 -0700
Subject: [PATCH 1593/2053] rearrange upgrade helpers

order from general helpers to specific upgrades in chronological order.
---
 include/caffe/util/upgrade_proto.hpp |  18 ++--
 src/caffe/util/upgrade_proto.cpp     | 122 +++++++++++++--------------
 2 files changed, 70 insertions(+), 70 deletions(-)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index c1f21a0d4d8..6a1418434a6 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -10,6 +10,15 @@ namespace caffe {
 // Return true iff the net is not the current version.
 bool NetNeedsUpgrade(const NetParameter& net_param);
 
+// Check for deprecations and upgrade the NetParameter as needed.
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
+
+// Read parameters from a file into a NetParameter proto message.
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+                                    NetParameter* param);
+void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
+                                      NetParameter* param);
+
 // Return true iff any layer contains parameters specified using
 // deprecated V0LayerParameter.
 bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);
@@ -50,15 +59,6 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
 
 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
-// Check for deprecations and upgrade the NetParameter as needed.
-bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
-
-// Read parameters from a file into a NetParameter proto message.
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
-                                    NetParameter* param);
-void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
-                                      NetParameter* param);
-
 }  // namespace caffe
 
 #endif   // CAFFE_UTIL_UPGRADE_PROTO_H_
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index ac379e50f4f..6eae9fec00a 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -16,6 +16,67 @@ bool NetNeedsUpgrade(const NetParameter& net_param) {
   return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
 }
 
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
+  bool success = true;
+  if (NetNeedsV0ToV1Upgrade(*param)) {
+    // NetParameter was specified using the old style (V0LayerParameter); try to
+    // upgrade it.
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "V0LayerParameter: " << param_file;
+    NetParameter original_param(*param);
+    if (!UpgradeV0Net(original_param, param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+          << "V0NetParameter to NetParameter (see above); continuing anyway.";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "V0LayerParameter";
+    }
+    LOG(WARNING) << "Note that future Caffe releases will not support "
+        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
+        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
+        << "weights upgrade this and any other net protos to the new format.";
+  }
+  // NetParameter uses old style data transformation fields; try to upgrade it.
+  if (NetNeedsDataUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "transformation parameters: " << param_file;
+    UpgradeNetDataTransformation(param);
+    LOG(INFO) << "Successfully upgraded file specified using deprecated "
+              << "data transformation parameters.";
+    LOG(WARNING) << "Note that future Caffe releases will only support "
+                 << "transform_param messages for transformation fields.";
+  }
+  if (NetNeedsV1ToV2Upgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "V1LayerParameter: " << param_file;
+    NetParameter original_param(*param);
+    if (!UpgradeV1Net(original_param, param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+                 << "V1LayerParameter (see above); continuing anyway.";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "V1LayerParameter";
+    }
+  }
+  return success;
+}
+
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+                                    NetParameter* param) {
+  CHECK(ReadProtoFromTextFile(param_file, param))
+      << "Failed to parse NetParameter file: " << param_file;
+  UpgradeNetAsNeeded(param_file, param);
+}
+
+void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
+                                      NetParameter* param) {
+  CHECK(ReadProtoFromBinaryFile(param_file, param))
+      << "Failed to parse NetParameter file: " << param_file;
+  UpgradeNetAsNeeded(param_file, param);
+}
+
 bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
   for (int i = 0; i < net_param.layers_size(); ++i) {
     if (net_param.layers(i).has_layer()) {
@@ -583,53 +644,6 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
   }
 }
 
-bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
-  bool success = true;
-  if (NetNeedsV0ToV1Upgrade(*param)) {
-    // NetParameter was specified using the old style (V0LayerParameter); try to
-    // upgrade it.
-    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
-              << "V0LayerParameter: " << param_file;
-    NetParameter original_param(*param);
-    if (!UpgradeV0Net(original_param, param)) {
-      success = false;
-      LOG(ERROR) << "Warning: had one or more problems upgrading "
-          << "V0NetParameter to NetParameter (see above); continuing anyway.";
-    } else {
-      LOG(INFO) << "Successfully upgraded file specified using deprecated "
-                << "V0LayerParameter";
-    }
-    LOG(WARNING) << "Note that future Caffe releases will not support "
-        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
-        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
-        << "weights upgrade this and any other net protos to the new format.";
-  }
-  // NetParameter uses old style data transformation fields; try to upgrade it.
-  if (NetNeedsDataUpgrade(*param)) {
-    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
-              << "transformation parameters: " << param_file;
-    UpgradeNetDataTransformation(param);
-    LOG(INFO) << "Successfully upgraded file specified using deprecated "
-              << "data transformation parameters.";
-    LOG(WARNING) << "Note that future Caffe releases will only support "
-                 << "transform_param messages for transformation fields.";
-  }
-  if (NetNeedsV1ToV2Upgrade(*param)) {
-    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
-              << "V1LayerParameter: " << param_file;
-    NetParameter original_param(*param);
-    if (!UpgradeV1Net(original_param, param)) {
-      success = false;
-      LOG(ERROR) << "Warning: had one or more problems upgrading "
-                 << "V1LayerParameter (see above); continuing anyway.";
-    } else {
-      LOG(INFO) << "Successfully upgraded file specified using deprecated "
-                << "V1LayerParameter";
-    }
-  }
-  return success;
-}
-
 bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
   bool is_fully_compatible = true;
   if (v1_net_param.layer_size() > 0) {
@@ -923,18 +937,4 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
   }
 }
 
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
-                                    NetParameter* param) {
-  CHECK(ReadProtoFromTextFile(param_file, param))
-      << "Failed to parse NetParameter file: " << param_file;
-  UpgradeNetAsNeeded(param_file, param);
-}
-
-void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
-                                      NetParameter* param) {
-  CHECK(ReadProtoFromBinaryFile(param_file, param))
-      << "Failed to parse NetParameter file: " << param_file;
-  UpgradeNetAsNeeded(param_file, param);
-}
-
 }  // namespace caffe

From e5a74b282efb2293a05d91635a5b26837adc2aa3 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Fri, 16 Oct 2015 21:19:59 -0700
Subject: [PATCH 1594/2053] Test reading and writing mean proto in matlab

---
 matlab/+caffe/+test/test_io.m | 18 ++++++++++++++++++
 matlab/+caffe/run_tests.m     |  3 ++-
 2 files changed, 20 insertions(+), 1 deletion(-)
 create mode 100644 matlab/+caffe/+test/test_io.m

diff --git a/matlab/+caffe/+test/test_io.m b/matlab/+caffe/+test/test_io.m
new file mode 100644
index 00000000000..2c34bd1e938
--- /dev/null
+++ b/matlab/+caffe/+test/test_io.m
@@ -0,0 +1,18 @@
+classdef test_io < matlab.unittest.TestCase
+  methods (Test)
+    function test_read_write_mean(self)
+      % randomly generate mean data
+      width = 200;
+      height = 300;
+      channels = 3;
+      mean_data_write = 255 * rand(width, height, channels, 'single');
+      % write mean data to binary proto
+      mean_proto_file = tempname();
+      caffe.io.write_mean(mean_data_write, mean_proto_file);
+      % read mean data from saved binary proto and test whether they are equal
+      mean_data_read = caffe.io.read_mean(mean_proto_file);
+      self.verifyEqual(mean_data_write, mean_data_read)
+      delete(mean_proto_file);
+    end
+  end
+end
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
index 93896855ac2..6dbf6b23151 100644
--- a/matlab/+caffe/run_tests.m
+++ b/matlab/+caffe/run_tests.m
@@ -11,7 +11,8 @@
 % put all test cases here
 results = [...
   run(caffe.test.test_net) ...
-  run(caffe.test.test_solver) ];
+  run(caffe.test.test_solver) ...
+  run(caffe.test.test_io) ];
 
 % reset caffe after testing
 caffe.reset_all();

From b822a702d19d4fbebbc91198a991f91c34e60650 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 24 Sep 2015 17:11:07 -0700
Subject: [PATCH 1595/2053] Split solver code into one file per solver class

---
 include/caffe/sgd_solvers.hpp                 | 142 +++
 include/caffe/solver.hpp                      | 158 +---
 python/caffe/_caffe.cpp                       |   1 +
 src/caffe/solver.cpp                          | 811 ------------------
 src/caffe/solver_factory.cpp                  |  32 +
 src/caffe/solvers/adadelta_solver.cpp         | 155 ++++
 src/caffe/solvers/adagrad_solver.cpp          |  88 ++
 src/caffe/solvers/adam_solver.cpp             | 112 +++
 src/caffe/solvers/nesterov_solver.cpp         |  70 ++
 src/caffe/solvers/rmsprop_solver.cpp          |  84 ++
 src/caffe/solvers/sgd_solver.cpp              | 347 ++++++++
 src/caffe/test/test_gradient_based_solver.cpp |   2 +-
 src/caffe/test/test_solver.cpp                |   1 +
 13 files changed, 1038 insertions(+), 965 deletions(-)
 create mode 100644 include/caffe/sgd_solvers.hpp
 create mode 100644 src/caffe/solver_factory.cpp
 create mode 100644 src/caffe/solvers/adadelta_solver.cpp
 create mode 100644 src/caffe/solvers/adagrad_solver.cpp
 create mode 100644 src/caffe/solvers/adam_solver.cpp
 create mode 100644 src/caffe/solvers/nesterov_solver.cpp
 create mode 100644 src/caffe/solvers/rmsprop_solver.cpp
 create mode 100644 src/caffe/solvers/sgd_solver.cpp

diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp
new file mode 100644
index 00000000000..6bf1d70c752
--- /dev/null
+++ b/include/caffe/sgd_solvers.hpp
@@ -0,0 +1,142 @@
+#ifndef CAFFE_SGD_SOLVERS_HPP_
+#define CAFFE_SGD_SOLVERS_HPP_
+
+#include <string>
+#include <vector>
+
+#include "caffe/solver.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Optimizes the parameters of a Net using
+ *        stochastic gradient descent (SGD) with momentum.
+ */
+template <typename Dtype>
+class SGDSolver : public Solver<Dtype> {
+ public:
+  explicit SGDSolver(const SolverParameter& param)
+      : Solver<Dtype>(param) { PreSolve(); }
+  explicit SGDSolver(const string& param_file)
+      : Solver<Dtype>(param_file) { PreSolve(); }
+
+  const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
+
+ protected:
+  void PreSolve();
+  Dtype GetLearningRate();
+  virtual void ApplyUpdate();
+  virtual void Normalize(int param_id);
+  virtual void Regularize(int param_id);
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  virtual void ClipGradients();
+  virtual void SnapshotSolverState(const string& model_filename);
+  virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
+  virtual void SnapshotSolverStateToHDF5(const string& model_filename);
+  virtual void RestoreSolverStateFromHDF5(const string& state_file);
+  virtual void RestoreSolverStateFromBinaryProto(const string& state_file);
+  // history maintains the historical momentum data.
+  // update maintains update related data and is not needed in snapshots.
+  // temp maintains other information that might be needed in computation
+  //   of gradients/updates and is not needed in snapshots
+  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
+
+  DISABLE_COPY_AND_ASSIGN(SGDSolver);
+};
+
+template <typename Dtype>
+class NesterovSolver : public SGDSolver<Dtype> {
+ public:
+  explicit NesterovSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) {}
+  explicit NesterovSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) {}
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(NesterovSolver);
+};
+
+template <typename Dtype>
+class AdaGradSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaGradSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit AdaGradSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.momentum())
+        << "Momentum cannot be used with AdaGrad.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
+};
+
+
+template <typename Dtype>
+class RMSPropSolver : public SGDSolver<Dtype> {
+ public:
+  explicit RMSPropSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit RMSPropSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.momentum())
+        << "Momentum cannot be used with RMSProp.";
+    CHECK_GE(this->param_.rms_decay(), 0)
+        << "rms_decay should lie between 0 and 1.";
+    CHECK_LT(this->param_.rms_decay(), 1)
+        << "rms_decay should lie between 0 and 1.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
+};
+
+template <typename Dtype>
+class AdaDeltaSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaDeltaSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
+  explicit AdaDeltaSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
+
+ protected:
+  void AdaDeltaPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
+};
+
+/**
+ * @brief AdamSolver, an algorithm for first-order gradient-based optimization
+ *        of stochastic objective functions, based on adaptive estimates of
+ *        lower-order moments. Described in [1].
+ *
+ * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization."
+ *     arXiv preprint arXiv:1412.6980v8 (2014).
+ */
+template <typename Dtype>
+class AdamSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdamSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { AdamPreSolve();}
+  explicit AdamSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
+
+ protected:
+  void AdamPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(AdamSolver);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SGD_SOLVERS_HPP_
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 2ecf539baef..a045ccf254c 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -1,5 +1,5 @@
-#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
-#define CAFFE_OPTIMIZATION_SOLVER_HPP_
+#ifndef CAFFE_SOLVER_HPP_
+#define CAFFE_SOLVER_HPP_
 #include <boost/function.hpp>
 #include <string>
 #include <vector>
@@ -148,158 +148,10 @@ class WorkerSolver : public Solver<Dtype> {
   }
 };
 
-/**
- * @brief Optimizes the parameters of a Net using
- *        stochastic gradient descent (SGD) with momentum.
- */
-template <typename Dtype>
-class SGDSolver : public Solver<Dtype> {
- public:
-  explicit SGDSolver(const SolverParameter& param)
-      : Solver<Dtype>(param) { PreSolve(); }
-  explicit SGDSolver(const string& param_file)
-      : Solver<Dtype>(param_file) { PreSolve(); }
-
-  const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
-
- protected:
-  void PreSolve();
-  Dtype GetLearningRate();
-  virtual void ApplyUpdate();
-  virtual void Normalize(int param_id);
-  virtual void Regularize(int param_id);
-  virtual void ComputeUpdateValue(int param_id, Dtype rate);
-  virtual void ClipGradients();
-  virtual void SnapshotSolverState(const string& model_filename);
-  virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
-  virtual void SnapshotSolverStateToHDF5(const string& model_filename);
-  virtual void RestoreSolverStateFromHDF5(const string& state_file);
-  virtual void RestoreSolverStateFromBinaryProto(const string& state_file);
-  // history maintains the historical momentum data.
-  // update maintains update related data and is not needed in snapshots.
-  // temp maintains other information that might be needed in computation
-  //   of gradients/updates and is not needed in snapshots
-  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
-
-  DISABLE_COPY_AND_ASSIGN(SGDSolver);
-};
-
+// The solver factory function
 template <typename Dtype>
-class NesterovSolver : public SGDSolver<Dtype> {
- public:
-  explicit NesterovSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) {}
-  explicit NesterovSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) {}
-
- protected:
-  virtual void ComputeUpdateValue(int param_id, Dtype rate);
-
-  DISABLE_COPY_AND_ASSIGN(NesterovSolver);
-};
-
-template <typename Dtype>
-class AdaGradSolver : public SGDSolver<Dtype> {
- public:
-  explicit AdaGradSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
-  explicit AdaGradSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
-
- protected:
-  virtual void ComputeUpdateValue(int param_id, Dtype rate);
-  void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.momentum())
-        << "Momentum cannot be used with AdaGrad.";
-  }
-
-  DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
-};
-
-
-template <typename Dtype>
-class RMSPropSolver : public SGDSolver<Dtype> {
- public:
-  explicit RMSPropSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
-  explicit RMSPropSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
-
- protected:
-  virtual void ComputeUpdateValue(int param_id, Dtype rate);
-  void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.momentum())
-        << "Momentum cannot be used with RMSProp.";
-    CHECK_GE(this->param_.rms_decay(), 0)
-        << "rms_decay should lie between 0 and 1.";
-    CHECK_LT(this->param_.rms_decay(), 1)
-        << "rms_decay should lie between 0 and 1.";
-  }
-
-  DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
-};
-
-template <typename Dtype>
-class AdaDeltaSolver : public SGDSolver<Dtype> {
- public:
-  explicit AdaDeltaSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
-  explicit AdaDeltaSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
-
- protected:
-  void AdaDeltaPreSolve();
-  virtual void ComputeUpdateValue(int param_id, Dtype rate);
-
-  DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
-};
-
-/**
- * @brief AdamSolver, an algorithm for first-order gradient-based optimization
- *        of stochastic objective functions, based on adaptive estimates of
- *        lower-order moments. Described in [1].
- *
- * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization."
- *     arXiv preprint arXiv:1412.6980v8 (2014).
- */
-template <typename Dtype>
-class AdamSolver : public SGDSolver<Dtype> {
- public:
-  explicit AdamSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { AdamPreSolve();}
-  explicit AdamSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
-
- protected:
-  void AdamPreSolve();
-  virtual void ComputeUpdateValue(int param_id, Dtype rate);
-
-  DISABLE_COPY_AND_ASSIGN(AdamSolver);
-};
-
-template <typename Dtype>
-Solver<Dtype>* GetSolver(const SolverParameter& param) {
-  SolverParameter_SolverType type = param.solver_type();
-
-  switch (type) {
-  case SolverParameter_SolverType_SGD:
-    return new SGDSolver<Dtype>(param);
-  case SolverParameter_SolverType_NESTEROV:
-    return new NesterovSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADAGRAD:
-    return new AdaGradSolver<Dtype>(param);
-  case SolverParameter_SolverType_RMSPROP:
-    return new RMSPropSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADADELTA:
-    return new AdaDeltaSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADAM:
-    return new AdamSolver<Dtype>(param);
-  default:
-    LOG(FATAL) << "Unknown SolverType: " << type;
-  }
-  return (Solver<Dtype>*) NULL;
-}
+Solver<Dtype>* GetSolver(const SolverParameter& param);
 
 }  // namespace caffe
 
-#endif  // CAFFE_OPTIMIZATION_SOLVER_HPP_
+#endif  // CAFFE_SOLVER_HPP_
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index ccd5776ac40..0e38dee70d1 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -16,6 +16,7 @@
 
 #include "caffe/caffe.hpp"
 #include "caffe/python_layer.hpp"
+#include "caffe/sgd_solvers.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
 // You're strongly advised to upgrade to >= 1.7.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 12c13dd8385..016a02888d8 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -1,18 +1,11 @@
 #include <cstdio>
 
-#include <algorithm>
 #include <string>
 #include <vector>
 
-#include "hdf5.h"
-#include "hdf5_hl.h"
-
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
 #include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
 namespace caffe {
@@ -492,810 +485,6 @@ void Solver<Dtype>::Restore(const char* state_file) {
   }
 }
 
-// Return the current learning rate. The currently implemented learning rate
-// policies are as follows:
-//    - fixed: always return base_lr.
-//    - step: return base_lr * gamma ^ (floor(iter / step))
-//    - exp: return base_lr * gamma ^ iter
-//    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
-//    - multistep: similar to step but it allows non uniform steps defined by
-//      stepvalue
-//    - poly: the effective learning rate follows a polynomial decay, to be
-//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
-//    - sigmoid: the effective learning rate follows a sigmod decay
-//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
-//
-// where base_lr, max_iter, gamma, step, stepvalue and power are defined
-// in the solver parameter protocol buffer, and iter is the current iteration.
-template <typename Dtype>
-Dtype SGDSolver<Dtype>::GetLearningRate() {
-  Dtype rate;
-  const string& lr_policy = this->param_.lr_policy();
-  if (lr_policy == "fixed") {
-    rate = this->param_.base_lr();
-  } else if (lr_policy == "step") {
-    this->current_step_ = this->iter_ / this->param_.stepsize();
-    rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), this->current_step_);
-  } else if (lr_policy == "exp") {
-    rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
-  } else if (lr_policy == "inv") {
-    rate = this->param_.base_lr() *
-        pow(Dtype(1) + this->param_.gamma() * this->iter_,
-            - this->param_.power());
-  } else if (lr_policy == "multistep") {
-    if (this->current_step_ < this->param_.stepvalue_size() &&
-          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
-      this->current_step_++;
-      LOG(INFO) << "MultiStep Status: Iteration " <<
-      this->iter_ << ", step = " << this->current_step_;
-    }
-    rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), this->current_step_);
-  } else if (lr_policy == "poly") {
-    rate = this->param_.base_lr() * pow(Dtype(1.) -
-        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
-        this->param_.power());
-  } else if (lr_policy == "sigmoid") {
-    rate = this->param_.base_lr() * (Dtype(1.) /
-        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
-          Dtype(this->param_.stepsize())))));
-  } else {
-    LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
-  }
-  return rate;
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::PreSolve() {
-  // Initialize the history
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  history_.clear();
-  update_.clear();
-  temp_.clear();
-  for (int i = 0; i < net_params.size(); ++i) {
-    const vector<int>& shape = net_params[i]->shape();
-    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::ClipGradients() {
-  const Dtype clip_gradients = this->param_.clip_gradients();
-  if (clip_gradients < 0) { return; }
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  Dtype sumsq_diff = 0;
-  for (int i = 0; i < net_params.size(); ++i) {
-    sumsq_diff += net_params[i]->sumsq_diff();
-  }
-  const Dtype l2norm_diff = std::sqrt(sumsq_diff);
-  if (l2norm_diff > clip_gradients) {
-    Dtype scale_factor = clip_gradients / l2norm_diff;
-    LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
-        << l2norm_diff << " > " << clip_gradients << ") "
-        << "by scale factor " << scale_factor;
-    for (int i = 0; i < net_params.size(); ++i) {
-      net_params[i]->scale_diff(scale_factor);
-    }
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::ApplyUpdate() {
-  CHECK(Caffe::root_solver());
-  Dtype rate = GetLearningRate();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  ClipGradients();
-  for (int param_id = 0; param_id < this->net_->learnable_params().size();
-       ++param_id) {
-    Normalize(param_id);
-    Regularize(param_id);
-    ComputeUpdateValue(param_id, rate);
-  }
-  this->net_->Update();
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::Normalize(int param_id) {
-  if (this->param_.iter_size() == 1) { return; }
-  // Scale gradient to counterbalance accumulation.
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
-  switch (Caffe::mode()) {
-  case Caffe::CPU: {
-    caffe_scal(net_params[param_id]->count(), accum_normalization,
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::Regularize(int param_id) {
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
-  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-  switch (Caffe::mode()) {
-  case Caffe::CPU: {
-    if (local_decay) {
-      if (regularization_type == "L2") {
-        // add weight decay
-        caffe_axpy(net_params[param_id]->count(),
-            local_decay,
-            net_params[param_id]->cpu_data(),
-            net_params[param_id]->mutable_cpu_diff());
-      } else if (regularization_type == "L1") {
-        caffe_cpu_sign(net_params[param_id]->count(),
-            net_params[param_id]->cpu_data(),
-            temp_[param_id]->mutable_cpu_data());
-        caffe_axpy(net_params[param_id]->count(),
-            local_decay,
-            temp_[param_id]->cpu_data(),
-            net_params[param_id]->mutable_cpu_diff());
-      } else {
-        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-      }
-    }
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    if (local_decay) {
-      if (regularization_type == "L2") {
-        // add weight decay
-        caffe_gpu_axpy(net_params[param_id]->count(),
-            local_decay,
-            net_params[param_id]->gpu_data(),
-            net_params[param_id]->mutable_gpu_diff());
-      } else if (regularization_type == "L1") {
-        caffe_gpu_sign(net_params[param_id]->count(),
-            net_params[param_id]->gpu_data(),
-            temp_[param_id]->mutable_gpu_data());
-        caffe_gpu_axpy(net_params[param_id]->count(),
-            local_decay,
-            temp_[param_id]->gpu_data(),
-            net_params[param_id]->mutable_gpu_diff());
-      } else {
-        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-      }
-    }
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  Dtype momentum = this->param_.momentum();
-  Dtype local_rate = rate * net_params_lr[param_id];
-  // Compute the update to history, then copy it to the parameter diff.
-  switch (Caffe::mode()) {
-  case Caffe::CPU: {
-    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-              net_params[param_id]->cpu_diff(), momentum,
-              history_[param_id]->mutable_cpu_data());
-    caffe_copy(net_params[param_id]->count(),
-        history_[param_id]->cpu_data(),
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-              net_params[param_id]->gpu_diff(), momentum,
-              history_[param_id]->mutable_gpu_data());
-    caffe_copy(net_params[param_id]->count(),
-        history_[param_id]->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::SnapshotSolverState(const string& model_filename) {
-  switch (this->param_.snapshot_format()) {
-    case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
-      SnapshotSolverStateToBinaryProto(model_filename);
-      break;
-    case caffe::SolverParameter_SnapshotFormat_HDF5:
-      SnapshotSolverStateToHDF5(model_filename);
-      break;
-    default:
-      LOG(FATAL) << "Unsupported snapshot format.";
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
-    const string& model_filename) {
-  SolverState state;
-  state.set_iter(this->iter_);
-  state.set_learned_net(model_filename);
-  state.set_current_step(this->current_step_);
-  state.clear_history();
-  for (int i = 0; i < history_.size(); ++i) {
-    // Add history
-    BlobProto* history_blob = state.add_history();
-    history_[i]->ToProto(history_blob);
-  }
-  string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
-  LOG(INFO)
-    << "Snapshotting solver state to binary proto file " << snapshot_filename;
-  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
-    const string& model_filename) {
-  string snapshot_filename =
-      Solver<Dtype>::SnapshotFilename(".solverstate.h5");
-  LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename;
-  hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC,
-      H5P_DEFAULT, H5P_DEFAULT);
-  CHECK_GE(file_hid, 0)
-      << "Couldn't open " << snapshot_filename << " to save solver state.";
-  hdf5_save_int(file_hid, "iter", this->iter_);
-  hdf5_save_string(file_hid, "learned_net", model_filename);
-  hdf5_save_int(file_hid, "current_step", this->current_step_);
-  hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT,
-      H5P_DEFAULT);
-  CHECK_GE(history_hid, 0)
-      << "Error saving solver state to " << snapshot_filename << ".";
-  for (int i = 0; i < history_.size(); ++i) {
-    ostringstream oss;
-    oss << i;
-    hdf5_save_nd_dataset<Dtype>(history_hid, oss.str(), *history_[i]);
-  }
-  H5Gclose(history_hid);
-  H5Fclose(file_hid);
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::RestoreSolverStateFromBinaryProto(
-    const string& state_file) {
-  SolverState state;
-  ReadProtoFromBinaryFile(state_file, &state);
-  this->iter_ = state.iter();
-  if (state.has_learned_net()) {
-    NetParameter net_param;
-    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
-    this->net_->CopyTrainedLayersFrom(net_param);
-  }
-  this->current_step_ = state.current_step();
-  CHECK_EQ(state.history_size(), history_.size())
-      << "Incorrect length of history blobs.";
-  LOG(INFO) << "SGDSolver: restoring history";
-  for (int i = 0; i < history_.size(); ++i) {
-    history_[i]->FromProto(state.history(i));
-  }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
-  hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT);
-  CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file;
-  this->iter_ = hdf5_load_int(file_hid, "iter");
-  if (H5LTfind_dataset(file_hid, "learned_net")) {
-    string learned_net = hdf5_load_string(file_hid, "learned_net");
-    this->net_->CopyTrainedLayersFrom(learned_net);
-  }
-  this->current_step_ = hdf5_load_int(file_hid, "current_step");
-  hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT);
-  CHECK_GE(history_hid, 0) << "Error reading history from " << state_file;
-  int state_history_size = hdf5_get_num_links(history_hid);
-  CHECK_EQ(state_history_size, history_.size())
-      << "Incorrect length of history blobs.";
-  for (int i = 0; i < history_.size(); ++i) {
-    ostringstream oss;
-    oss << i;
-    hdf5_load_nd_dataset<Dtype>(history_hid, oss.str().c_str(), 0,
-                                kMaxBlobAxes, history_[i].get());
-  }
-  H5Gclose(history_hid);
-  H5Fclose(file_hid);
-}
-
-template <typename Dtype>
-void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  CHECK(Caffe::root_solver());
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  Dtype momentum = this->param_.momentum();
-  Dtype local_rate = rate * net_params_lr[param_id];
-  switch (Caffe::mode()) {
-  case Caffe::CPU: {
-    // save history momentum for stepping back
-    caffe_copy(net_params[param_id]->count(),
-        this->history_[param_id]->cpu_data(),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // update history
-    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-              net_params[param_id]->cpu_diff(), momentum,
-              this->history_[param_id]->mutable_cpu_data());
-
-    // compute update: step back then over step
-    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-        this->history_[param_id]->cpu_data(), -momentum,
-        this->update_[param_id]->mutable_cpu_data());
-
-    // copy
-    caffe_copy(net_params[param_id]->count(),
-        this->update_[param_id]->cpu_data(),
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    // save history momentum for stepping back
-    caffe_copy(net_params[param_id]->count(),
-        this->history_[param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-              net_params[param_id]->gpu_diff(), momentum,
-              this->history_[param_id]->mutable_gpu_data());
-
-    // compute update: step back then over step
-    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-        this->history_[param_id]->gpu_data(), -momentum,
-        this->update_[param_id]->mutable_gpu_data());
-
-    // copy
-    caffe_copy(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  CHECK(Caffe::root_solver());
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  Dtype delta = this->param_.delta();
-  Dtype local_rate = rate * net_params_lr[param_id];
-  switch (Caffe::mode()) {
-  case Caffe::CPU: {
-    // compute square of gradient in update
-    caffe_powx(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // update history
-    caffe_add(net_params[param_id]->count(),
-        this->update_[param_id]->cpu_data(),
-        this->history_[param_id]->cpu_data(),
-        this->history_[param_id]->mutable_cpu_data());
-
-    // prepare update
-    caffe_powx(net_params[param_id]->count(),
-              this->history_[param_id]->cpu_data(), Dtype(0.5),
-              this->update_[param_id]->mutable_cpu_data());
-
-    caffe_add_scalar(net_params[param_id]->count(),
-              delta, this->update_[param_id]->mutable_cpu_data());
-
-    caffe_div(net_params[param_id]->count(),
-              net_params[param_id]->cpu_diff(),
-              this->update_[param_id]->cpu_data(),
-              this->update_[param_id]->mutable_cpu_data());
-
-    // scale and copy
-    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->cpu_data(), Dtype(0),
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    // compute square of gradient in update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history
-    caffe_gpu_add(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(),
-        this->history_[param_id]->gpu_data(),
-        this->history_[param_id]->mutable_gpu_data());
-
-    // prepare update
-    caffe_gpu_powx(net_params[param_id]->count(),
-              this->history_[param_id]->gpu_data(), Dtype(0.5),
-              this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add_scalar(net_params[param_id]->count(),
-              delta, this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_div(net_params[param_id]->count(),
-              net_params[param_id]->gpu_diff(),
-              this->update_[param_id]->gpu_data(),
-              this->update_[param_id]->mutable_gpu_data());
-
-    // scale and copy
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->gpu_data(), Dtype(0),
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-
-  // get the learning rate
-  Dtype delta = this->param_.delta();
-  Dtype rms_decay = this->param_.rms_decay();
-  Dtype local_rate = rate * net_params_lr[param_id];
-
-  switch (Caffe::mode()) {
-  case Caffe::CPU:
-    // compute square of gradient in update
-    caffe_powx(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // update history
-    caffe_cpu_axpby(net_params[param_id] -> count(),
-        Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
-        rms_decay, this->history_[param_id]-> mutable_cpu_data());
-
-    // prepare update
-    caffe_powx(net_params[param_id]->count(),
-        this->history_[param_id]->cpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_cpu_data());
-
-    caffe_add_scalar(net_params[param_id]->count(),
-        delta, this->update_[param_id]->mutable_cpu_data());
-
-    caffe_div(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // scale and copy
-    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->cpu_data(), Dtype(0),
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  case Caffe::GPU:
-#ifndef CPU_ONLY
-    // compute square of gradient in update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history
-    caffe_gpu_axpby(net_params[param_id] -> count(),
-        Dtype(1-rms_decay), this->update_[param_id]->gpu_data(),
-        rms_decay, this->history_[param_id]-> mutable_gpu_data());
-
-    // prepare update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        this->history_[param_id]->gpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add_scalar(net_params[param_id]->count(),
-        delta, this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_div(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->gpu_data(), Dtype(0),
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
-  // Add the extra history entries for AdaDelta after those from
-  // SGDSolver::PreSolve
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  for (int i = 0; i < net_params.size(); ++i) {
-        const vector<int>& shape = net_params[i]->shape();
-        this->history_.push_back(
-                shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-  }
-}
-
-template <typename Dtype>
-void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  Dtype delta = this->param_.delta();
-  Dtype momentum = this->param_.momentum();
-  Dtype local_rate = rate * net_params_lr[param_id];
-  size_t update_history_offset = net_params.size();
-  switch (Caffe::mode()) {
-  case Caffe::CPU: {
-    // compute square of gradient in update
-    caffe_powx(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // update history of gradients
-    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-        this->update_[param_id]->cpu_data(), momentum,
-        this->history_[param_id]->mutable_cpu_data());
-
-    // add delta to history to guard against dividing by zero later
-    caffe_set(net_params[param_id]->count(), delta,
-        this->temp_[param_id]->mutable_cpu_data());
-
-    caffe_add(net_params[param_id]->count(),
-        this->temp_[param_id]->cpu_data(),
-        this->history_[update_history_offset + param_id]->cpu_data(),
-        this->update_[param_id]->mutable_cpu_data());
-
-    caffe_add(net_params[param_id]->count(),
-        this->temp_[param_id]->cpu_data(),
-        this->history_[param_id]->cpu_data(),
-        this->temp_[param_id]->mutable_cpu_data());
-
-    // divide history of updates by history of gradients
-    caffe_div(net_params[param_id]->count(),
-        this->update_[param_id]->cpu_data(),
-        this->temp_[param_id]->cpu_data(),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // jointly compute the RMS of both for update and gradient history
-    caffe_powx(net_params[param_id]->count(),
-        this->update_[param_id]->cpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // compute the update
-    caffe_mul(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(),
-        this->update_[param_id]->cpu_data(),
-        net_params[param_id]->mutable_cpu_diff());
-
-    // compute square of update
-    caffe_powx(net_params[param_id]->count(),
-        net_params[param_id]->cpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_cpu_data());
-
-    // update history of updates
-    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-        this->update_[param_id]->cpu_data(), momentum,
-        this->history_[update_history_offset + param_id]->mutable_cpu_data());
-
-    // apply learning rate
-    caffe_cpu_scale(net_params[param_id]->count(), local_rate,
-        net_params[param_id]->cpu_diff(),
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    // compute square of gradient in update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history of gradients
-    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-        this->update_[param_id]->gpu_data(), momentum,
-        this->history_[param_id]->mutable_gpu_data());
-
-    // add delta to history to guard against dividing by zero later
-    caffe_gpu_set(net_params[param_id]->count(), delta,
-        this->temp_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add(net_params[param_id]->count(),
-        this->temp_[param_id]->gpu_data(),
-        this->history_[update_history_offset + param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add(net_params[param_id]->count(),
-        this->temp_[param_id]->gpu_data(),
-        this->history_[param_id]->gpu_data(),
-        this->temp_[param_id]->mutable_gpu_data());
-
-    // divide history of updates by history of gradients
-    caffe_gpu_div(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(),
-        this->temp_[param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // jointly compute the RMS of both for update and gradient history
-    caffe_gpu_powx(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // compute the update and copy to net_diff
-    caffe_gpu_mul(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(),
-        this->update_[param_id]->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
-
-    // compute square of update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history of updates
-    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-        this->update_[param_id]->gpu_data(), momentum,
-        this->history_[update_history_offset + param_id]->mutable_gpu_data());
-
-    // apply learning rate
-    caffe_gpu_scale(net_params[param_id]->count(), local_rate,
-        net_params[param_id]->gpu_diff(),
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
-template <typename Dtype>
-void AdamSolver<Dtype>::AdamPreSolve() {
-  // Add the extra history entries for Adam after those from
-  // SGDSolver::PreSolve
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  for (int i = 0; i < net_params.size(); ++i) {
-    const vector<int>& shape = net_params[i]->shape();
-    this->history_.push_back(
-            shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-  }
-}
-
-template <typename Dtype>
-void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  Dtype local_rate = rate * net_params_lr[param_id];
-  const Dtype beta1 = this->param_.momentum();
-  const Dtype beta2 = this->param_.momentum2();
-
-  // we create aliases for convenience
-  size_t update_history_offset = net_params.size();
-  Blob<Dtype>* val_m = this->history_[param_id].get();
-  Blob<Dtype>* val_v = this->history_[param_id + update_history_offset].get();
-  Blob<Dtype>* val_t = this->temp_[param_id].get();
-
-  const int t = this->iter_  + 1;
-  const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) /
-      (Dtype(1.) - pow(beta1, t));
-  const int N = net_params[param_id]->count();
-  const Dtype eps_hat = this->param_.delta();
-
-  switch (Caffe::mode()) {
-    case Caffe::CPU: {
-    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
-    caffe_cpu_axpby(N, Dtype(1)-beta1,
-        net_params[param_id]->cpu_diff(), beta1,
-        val_m->mutable_cpu_data());
-
-    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
-    caffe_mul(N,
-        net_params[param_id]->cpu_diff(),
-        net_params[param_id]->cpu_diff(),
-    val_t->mutable_cpu_data());
-    caffe_cpu_axpby(N, Dtype(1)-beta2,
-        val_t->cpu_data(), beta2,
-        val_v->mutable_cpu_data());
-
-    // set update
-    caffe_powx(N,
-        val_v->cpu_data(), Dtype(0.5),
-        val_t->mutable_cpu_data());
-    caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data());
-    caffe_div(N,
-        val_m->cpu_data(),
-        val_t->cpu_data(),
-        val_t->mutable_cpu_data());
-
-    caffe_cpu_scale(N, local_rate*correction,
-        val_t->cpu_data(),
-        net_params[param_id]->mutable_cpu_diff());
-    break;
-  }
-  case Caffe::GPU: {
-#ifndef CPU_ONLY
-    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
-    caffe_gpu_axpby(N, Dtype(1)-beta1,
-        net_params[param_id]->gpu_diff(), beta1,
-        val_m->mutable_gpu_data());
-
-    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
-    caffe_gpu_mul(N,
-        net_params[param_id]->gpu_diff(),
-        net_params[param_id]->gpu_diff(),
-        val_t->mutable_gpu_data());
-    caffe_gpu_axpby(N, Dtype(1)-beta2,
-        val_t->gpu_data(), beta2,
-        val_v->mutable_gpu_data());
-
-    // set update
-    caffe_gpu_powx(N,
-        val_v->gpu_data(), Dtype(0.5),
-        val_t->mutable_gpu_data());
-    caffe_gpu_add_scalar(N, eps_hat,
-        val_t->mutable_gpu_data());
-    caffe_gpu_div(N,
-        val_m->gpu_data(),
-        val_t->gpu_data(),
-        val_t->mutable_gpu_data());
-
-    caffe_gpu_scale(N, local_rate*correction,
-        val_t->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
-#else
-    NO_GPU;
-#endif
-    break;
-  }
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-  }
-}
-
 INSTANTIATE_CLASS(Solver);
-INSTANTIATE_CLASS(SGDSolver);
-INSTANTIATE_CLASS(NesterovSolver);
-INSTANTIATE_CLASS(AdaGradSolver);
-INSTANTIATE_CLASS(RMSPropSolver);
-INSTANTIATE_CLASS(AdaDeltaSolver);
-INSTANTIATE_CLASS(AdamSolver);
 
 }  // namespace caffe
diff --git a/src/caffe/solver_factory.cpp b/src/caffe/solver_factory.cpp
new file mode 100644
index 00000000000..f78fab28720
--- /dev/null
+++ b/src/caffe/solver_factory.cpp
@@ -0,0 +1,32 @@
+#include "caffe/solver.hpp"
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Solver<Dtype>* GetSolver(const SolverParameter& param) {
+  SolverParameter_SolverType type = param.solver_type();
+
+  switch (type) {
+  case SolverParameter_SolverType_SGD:
+    return new SGDSolver<Dtype>(param);
+  case SolverParameter_SolverType_NESTEROV:
+    return new NesterovSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADAGRAD:
+    return new AdaGradSolver<Dtype>(param);
+  case SolverParameter_SolverType_RMSPROP:
+    return new RMSPropSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADADELTA:
+    return new AdaDeltaSolver<Dtype>(param);
+  case SolverParameter_SolverType_ADAM:
+    return new AdamSolver<Dtype>(param);
+  default:
+    LOG(FATAL) << "Unknown SolverType: " << type;
+  }
+  return (Solver<Dtype>*) NULL;
+}
+
+template Solver<float>* GetSolver(const SolverParameter& param);
+template Solver<double>* GetSolver(const SolverParameter& param);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp
new file mode 100644
index 00000000000..45cd4eb2988
--- /dev/null
+++ b/src/caffe/solvers/adadelta_solver.cpp
@@ -0,0 +1,155 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
+  // Add the extra history entries for AdaDelta after those from
+  // SGDSolver::PreSolve
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  for (int i = 0; i < net_params.size(); ++i) {
+        const vector<int>& shape = net_params[i]->shape();
+        this->history_.push_back(
+                shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype delta = this->param_.delta();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  size_t update_history_offset = net_params.size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history of gradients
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[param_id]->mutable_cpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[update_history_offset + param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->temp_[param_id]->mutable_cpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_div(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->temp_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_powx(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // compute the update
+    caffe_mul(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+
+    // compute square of update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history of updates
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_cpu_data());
+
+    // apply learning rate
+    caffe_cpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history of gradients
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->gpu_data(), momentum,
+        this->history_[param_id]->mutable_gpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_gpu_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->temp_[param_id]->gpu_data(),
+        this->history_[update_history_offset + param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->temp_[param_id]->gpu_data(),
+        this->history_[param_id]->gpu_data(),
+        this->temp_[param_id]->mutable_gpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_gpu_div(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        this->temp_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_gpu_powx(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // compute the update and copy to net_diff
+    caffe_gpu_mul(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(),
+        this->update_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+
+    // compute square of update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history of updates
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->gpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_gpu_data());
+
+    // apply learning rate
+    caffe_gpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->gpu_diff(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(AdaDeltaSolver);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp
new file mode 100644
index 00000000000..627d816a470
--- /dev/null
+++ b/src/caffe/solvers/adagrad_solver.cpp
@@ -0,0 +1,88 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  CHECK(Caffe::root_solver());
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype delta = this->param_.delta();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_add(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->history_[param_id]->mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+              this->history_[param_id]->cpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+              net_params[param_id]->cpu_diff(),
+              this->update_[param_id]->cpu_data(),
+              this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_add(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        this->history_[param_id]->gpu_data(),
+        this->history_[param_id]->mutable_gpu_data());
+
+    // prepare update
+    caffe_gpu_powx(net_params[param_id]->count(),
+              this->history_[param_id]->gpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_div(net_params[param_id]->count(),
+              net_params[param_id]->gpu_diff(),
+              this->update_[param_id]->gpu_data(),
+              this->update_[param_id]->mutable_gpu_data());
+
+    // scale and copy
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->gpu_data(), Dtype(0),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(AdaGradSolver);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp
new file mode 100644
index 00000000000..8c334f665dd
--- /dev/null
+++ b/src/caffe/solvers/adam_solver.cpp
@@ -0,0 +1,112 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdamSolver<Dtype>::AdamPreSolve() {
+  // Add the extra history entries for Adam after those from
+  // SGDSolver::PreSolve
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const vector<int>& shape = net_params[i]->shape();
+    this->history_.push_back(
+            shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+template <typename Dtype>
+void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  const Dtype beta1 = this->param_.momentum();
+  const Dtype beta2 = this->param_.momentum2();
+
+  // we create aliases for convenience
+  size_t update_history_offset = net_params.size();
+  Blob<Dtype>* val_m = this->history_[param_id].get();
+  Blob<Dtype>* val_v = this->history_[param_id + update_history_offset].get();
+  Blob<Dtype>* val_t = this->temp_[param_id].get();
+
+  const int t = this->iter_  + 1;
+  const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) /
+      (Dtype(1.) - pow(beta1, t));
+  const int N = net_params[param_id]->count();
+  const Dtype eps_hat = this->param_.delta();
+
+  switch (Caffe::mode()) {
+    case Caffe::CPU: {
+    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
+    caffe_cpu_axpby(N, Dtype(1)-beta1,
+        net_params[param_id]->cpu_diff(), beta1,
+        val_m->mutable_cpu_data());
+
+    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
+    caffe_mul(N,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->cpu_diff(),
+    val_t->mutable_cpu_data());
+    caffe_cpu_axpby(N, Dtype(1)-beta2,
+        val_t->cpu_data(), beta2,
+        val_v->mutable_cpu_data());
+
+    // set update
+    caffe_powx(N,
+        val_v->cpu_data(), Dtype(0.5),
+        val_t->mutable_cpu_data());
+    caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data());
+    caffe_div(N,
+        val_m->cpu_data(),
+        val_t->cpu_data(),
+        val_t->mutable_cpu_data());
+
+    caffe_cpu_scale(N, local_rate*correction,
+        val_t->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
+    caffe_gpu_axpby(N, Dtype(1)-beta1,
+        net_params[param_id]->gpu_diff(), beta1,
+        val_m->mutable_gpu_data());
+
+    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
+    caffe_gpu_mul(N,
+        net_params[param_id]->gpu_diff(),
+        net_params[param_id]->gpu_diff(),
+        val_t->mutable_gpu_data());
+    caffe_gpu_axpby(N, Dtype(1)-beta2,
+        val_t->gpu_data(), beta2,
+        val_v->mutable_gpu_data());
+
+    // set update
+    caffe_gpu_powx(N,
+        val_v->gpu_data(), Dtype(0.5),
+        val_t->mutable_gpu_data());
+    caffe_gpu_add_scalar(N, eps_hat,
+        val_t->mutable_gpu_data());
+    caffe_gpu_div(N,
+        val_m->gpu_data(),
+        val_t->gpu_data(),
+        val_t->mutable_gpu_data());
+
+    caffe_gpu_scale(N, local_rate*correction,
+        val_t->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(AdamSolver);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp
new file mode 100644
index 00000000000..8135ee2c657
--- /dev/null
+++ b/src/caffe/solvers/nesterov_solver.cpp
@@ -0,0 +1,70 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  CHECK(Caffe::root_solver());
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              this->history_[param_id]->mutable_cpu_data());
+
+    // compute update: step back then over step
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->cpu_data(), -momentum,
+        this->update_[param_id]->mutable_cpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->gpu_diff(), momentum,
+              this->history_[param_id]->mutable_gpu_data());
+
+    // compute update: step back then over step
+    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->gpu_data(), -momentum,
+        this->update_[param_id]->mutable_gpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(NesterovSolver);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp
new file mode 100644
index 00000000000..96d1b3dda0b
--- /dev/null
+++ b/src/caffe/solvers/rmsprop_solver.cpp
@@ -0,0 +1,84 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+
+  // get the learning rate
+  Dtype delta = this->param_.delta();
+  Dtype rms_decay = this->param_.rms_decay();
+  Dtype local_rate = rate * net_params_lr[param_id];
+
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id] -> count(),
+        Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
+        rms_decay, this->history_[param_id]-> mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+        delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  case Caffe::GPU:
+#ifndef CPU_ONLY
+    // compute square of gradient in update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_gpu_data());
+
+    // update history
+    caffe_gpu_axpby(net_params[param_id] -> count(),
+        Dtype(1-rms_decay), this->update_[param_id]->gpu_data(),
+        rms_decay, this->history_[param_id]-> mutable_gpu_data());
+
+    // prepare update
+    caffe_gpu_powx(net_params[param_id]->count(),
+        this->history_[param_id]->gpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_add_scalar(net_params[param_id]->count(),
+        delta, this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_div(net_params[param_id]->count(),
+        net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(),
+        this->update_[param_id]->mutable_gpu_data());
+
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->gpu_data(), Dtype(0),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(RMSPropSolver);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
new file mode 100644
index 00000000000..89ef5ec451d
--- /dev/null
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -0,0 +1,347 @@
+#include <string>
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+#include "caffe/util/hdf5.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
+
+namespace caffe {
+
+// Return the current learning rate. The currently implemented learning rate
+// policies are as follows:
+//    - fixed: always return base_lr.
+//    - step: return base_lr * gamma ^ (floor(iter / step))
+//    - exp: return base_lr * gamma ^ iter
+//    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+//    - multistep: similar to step but it allows non uniform steps defined by
+//      stepvalue
+//    - poly: the effective learning rate follows a polynomial decay, to be
+//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+//    - sigmoid: the effective learning rate follows a sigmod decay
+//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+//
+// where base_lr, max_iter, gamma, step, stepvalue and power are defined
+// in the solver parameter protocol buffer, and iter is the current iteration.
+template <typename Dtype>
+Dtype SGDSolver<Dtype>::GetLearningRate() {
+  Dtype rate;
+  const string& lr_policy = this->param_.lr_policy();
+  if (lr_policy == "fixed") {
+    rate = this->param_.base_lr();
+  } else if (lr_policy == "step") {
+    this->current_step_ = this->iter_ / this->param_.stepsize();
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "exp") {
+    rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
+  } else if (lr_policy == "inv") {
+    rate = this->param_.base_lr() *
+        pow(Dtype(1) + this->param_.gamma() * this->iter_,
+            - this->param_.power());
+  } else if (lr_policy == "multistep") {
+    if (this->current_step_ < this->param_.stepvalue_size() &&
+          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
+      this->current_step_++;
+      LOG(INFO) << "MultiStep Status: Iteration " <<
+      this->iter_ << ", step = " << this->current_step_;
+    }
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "poly") {
+    rate = this->param_.base_lr() * pow(Dtype(1.) -
+        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
+        this->param_.power());
+  } else if (lr_policy == "sigmoid") {
+    rate = this->param_.base_lr() * (Dtype(1.) /
+        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
+          Dtype(this->param_.stepsize())))));
+  } else {
+    LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
+  }
+  return rate;
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::PreSolve() {
+  // Initialize the history
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  history_.clear();
+  update_.clear();
+  temp_.clear();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const vector<int>& shape = net_params[i]->shape();
+    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ClipGradients() {
+  const Dtype clip_gradients = this->param_.clip_gradients();
+  if (clip_gradients < 0) { return; }
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  Dtype sumsq_diff = 0;
+  for (int i = 0; i < net_params.size(); ++i) {
+    sumsq_diff += net_params[i]->sumsq_diff();
+  }
+  const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+  if (l2norm_diff > clip_gradients) {
+    Dtype scale_factor = clip_gradients / l2norm_diff;
+    LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
+        << l2norm_diff << " > " << clip_gradients << ") "
+        << "by scale factor " << scale_factor;
+    for (int i = 0; i < net_params.size(); ++i) {
+      net_params[i]->scale_diff(scale_factor);
+    }
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ApplyUpdate() {
+  CHECK(Caffe::root_solver());
+  Dtype rate = GetLearningRate();
+  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
+    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+  }
+  ClipGradients();
+  for (int param_id = 0; param_id < this->net_->learnable_params().size();
+       ++param_id) {
+    Normalize(param_id);
+    Regularize(param_id);
+    ComputeUpdateValue(param_id, rate);
+  }
+  this->net_->Update();
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Normalize(int param_id) {
+  if (this->param_.iter_size() == 1) { return; }
+  // Scale gradient to counterbalance accumulation.
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Regularize(int param_id) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
+  Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
+  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_cpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->cpu_data(),
+            temp_[param_id]->mutable_cpu_data());
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+      }
+    }
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_gpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->gpu_data(),
+            temp_[param_id]->mutable_gpu_data());
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+      }
+    }
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  // Compute the update to history, then copy it to the parameter diff.
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              history_[param_id]->mutable_cpu_data());
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->gpu_diff(), momentum,
+              history_[param_id]->mutable_gpu_data());
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->gpu_data(),
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverState(const string& model_filename) {
+  switch (this->param_.snapshot_format()) {
+    case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+      SnapshotSolverStateToBinaryProto(model_filename);
+      break;
+    case caffe::SolverParameter_SnapshotFormat_HDF5:
+      SnapshotSolverStateToHDF5(model_filename);
+      break;
+    default:
+      LOG(FATAL) << "Unsupported snapshot format.";
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
+    const string& model_filename) {
+  SolverState state;
+  state.set_iter(this->iter_);
+  state.set_learned_net(model_filename);
+  state.set_current_step(this->current_step_);
+  state.clear_history();
+  for (int i = 0; i < history_.size(); ++i) {
+    // Add history
+    BlobProto* history_blob = state.add_history();
+    history_[i]->ToProto(history_blob);
+  }
+  string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
+  LOG(INFO)
+    << "Snapshotting solver state to binary proto file " << snapshot_filename;
+  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
+    const string& model_filename) {
+  string snapshot_filename =
+      Solver<Dtype>::SnapshotFilename(".solverstate.h5");
+  LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename;
+  hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC,
+      H5P_DEFAULT, H5P_DEFAULT);
+  CHECK_GE(file_hid, 0)
+      << "Couldn't open " << snapshot_filename << " to save solver state.";
+  hdf5_save_int(file_hid, "iter", this->iter_);
+  hdf5_save_string(file_hid, "learned_net", model_filename);
+  hdf5_save_int(file_hid, "current_step", this->current_step_);
+  hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(history_hid, 0)
+      << "Error saving solver state to " << snapshot_filename << ".";
+  for (int i = 0; i < history_.size(); ++i) {
+    ostringstream oss;
+    oss << i;
+    hdf5_save_nd_dataset<Dtype>(history_hid, oss.str(), *history_[i]);
+  }
+  H5Gclose(history_hid);
+  H5Fclose(file_hid);
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromBinaryProto(
+    const string& state_file) {
+  SolverState state;
+  ReadProtoFromBinaryFile(state_file, &state);
+  this->iter_ = state.iter();
+  if (state.has_learned_net()) {
+    NetParameter net_param;
+    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
+    this->net_->CopyTrainedLayersFrom(net_param);
+  }
+  this->current_step_ = state.current_step();
+  CHECK_EQ(state.history_size(), history_.size())
+      << "Incorrect length of history blobs.";
+  LOG(INFO) << "SGDSolver: restoring history";
+  for (int i = 0; i < history_.size(); ++i) {
+    history_[i]->FromProto(state.history(i));
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
+  hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT);
+  CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file;
+  this->iter_ = hdf5_load_int(file_hid, "iter");
+  if (H5LTfind_dataset(file_hid, "learned_net")) {
+    string learned_net = hdf5_load_string(file_hid, "learned_net");
+    this->net_->CopyTrainedLayersFrom(learned_net);
+  }
+  this->current_step_ = hdf5_load_int(file_hid, "current_step");
+  hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT);
+  CHECK_GE(history_hid, 0) << "Error reading history from " << state_file;
+  int state_history_size = hdf5_get_num_links(history_hid);
+  CHECK_EQ(state_history_size, history_.size())
+      << "Incorrect length of history blobs.";
+  for (int i = 0; i < history_.size(); ++i) {
+    ostringstream oss;
+    oss << i;
+    hdf5_load_nd_dataset<Dtype>(history_hid, oss.str().c_str(), 0,
+                                kMaxBlobAxes, history_[i].get());
+  }
+  H5Gclose(history_hid);
+  H5Fclose(file_hid);
+}
+
+INSTANTIATE_CLASS(SGDSolver);
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 7ad7467f86f..1767ad3f666 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -10,7 +10,7 @@
 #include "caffe/common.hpp"
 #include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
+#include "caffe/sgd_solvers.hpp"
 #include "caffe/util/io.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index ceabc9cdd2c..b181642681c 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -7,6 +7,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/sgd_solvers.hpp"
 #include "caffe/solver.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"

From 0eea815ad6fa3313888b6229499a237820258deb Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 24 Sep 2015 19:40:45 -0700
Subject: [PATCH 1596/2053] Change solver type to string and provide solver
 registry

---
 include/caffe/caffe.hpp                       |   1 +
 include/caffe/sgd_solvers.hpp                 |   6 +
 include/caffe/solver.hpp                      |   9 +-
 include/caffe/solver_factory.hpp              | 137 ++++++++++++++++++
 src/caffe/proto/caffe.proto                   |  27 ++--
 src/caffe/solver_factory.cpp                  |  32 ----
 src/caffe/solvers/adadelta_solver.cpp         |   1 +
 src/caffe/solvers/adagrad_solver.cpp          |   1 +
 src/caffe/solvers/adam_solver.cpp             |   1 +
 src/caffe/solvers/nesterov_solver.cpp         |   1 +
 src/caffe/solvers/rmsprop_solver.cpp          |   1 +
 src/caffe/solvers/sgd_solver.cpp              |   1 +
 src/caffe/test/test_gradient_based_solver.cpp |  54 ++-----
 src/caffe/test/test_solver_factory.cpp        |  50 +++++++
 tools/caffe.cpp                               |   2 +-
 15 files changed, 233 insertions(+), 91 deletions(-)
 create mode 100644 include/caffe/solver_factory.hpp
 delete mode 100644 src/caffe/solver_factory.cpp
 create mode 100644 src/caffe/test/test_solver_factory.cpp

diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index 68a5e1d1d1a..bd772830b40 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -13,6 +13,7 @@
 #include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/solver_factory.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp
index 6bf1d70c752..1fc52d87137 100644
--- a/include/caffe/sgd_solvers.hpp
+++ b/include/caffe/sgd_solvers.hpp
@@ -19,6 +19,7 @@ class SGDSolver : public Solver<Dtype> {
       : Solver<Dtype>(param) { PreSolve(); }
   explicit SGDSolver(const string& param_file)
       : Solver<Dtype>(param_file) { PreSolve(); }
+  virtual inline const char* type() const { return "SGD"; }
 
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
@@ -51,6 +52,7 @@ class NesterovSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param) {}
   explicit NesterovSolver(const string& param_file)
       : SGDSolver<Dtype>(param_file) {}
+  virtual inline const char* type() const { return "Nesterov"; }
 
  protected:
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
@@ -65,6 +67,7 @@ class AdaGradSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
   explicit AdaGradSolver(const string& param_file)
       : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+  virtual inline const char* type() const { return "AdaGrad"; }
 
  protected:
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
@@ -84,6 +87,7 @@ class RMSPropSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
   explicit RMSPropSolver(const string& param_file)
       : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+  virtual inline const char* type() const { return "RMSProp"; }
 
  protected:
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
@@ -106,6 +110,7 @@ class AdaDeltaSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
   explicit AdaDeltaSolver(const string& param_file)
       : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
+  virtual inline const char* type() const { return "AdaDelta"; }
 
  protected:
   void AdaDeltaPreSolve();
@@ -129,6 +134,7 @@ class AdamSolver : public SGDSolver<Dtype> {
       : SGDSolver<Dtype>(param) { AdamPreSolve();}
   explicit AdamSolver(const string& param_file)
       : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
+  virtual inline const char* type() const { return "Adam"; }
 
  protected:
   void AdamPreSolve();
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index a045ccf254c..298a68f37df 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -5,6 +5,7 @@
 #include <vector>
 
 #include "caffe/net.hpp"
+#include "caffe/solver_factory.hpp"
 
 namespace caffe {
 
@@ -83,6 +84,10 @@ class Solver {
   }
 
   void CheckSnapshotWritePermissions();
+  /**
+   * @brief Returns the solver type.
+   */
+  virtual inline const char* type() const { return ""; }
 
  protected:
   // Make and apply the update value for the current iteration.
@@ -148,10 +153,6 @@ class WorkerSolver : public Solver<Dtype> {
   }
 };
 
-// The solver factory function
-template <typename Dtype>
-Solver<Dtype>* GetSolver(const SolverParameter& param);
-
 }  // namespace caffe
 
 #endif  // CAFFE_SOLVER_HPP_
diff --git a/include/caffe/solver_factory.hpp b/include/caffe/solver_factory.hpp
new file mode 100644
index 00000000000..cfff721af40
--- /dev/null
+++ b/include/caffe/solver_factory.hpp
@@ -0,0 +1,137 @@
+/**
+ * @brief A solver factory that allows one to register solvers, similar to
+ * layer factory. During runtime, registered solvers could be called by passing
+ * a SolverParameter protobuffer to the CreateSolver function:
+ *
+ *     SolverRegistry<Dtype>::CreateSolver(param);
+ *
+ * There are two ways to register a solver. Assuming that we have a solver like:
+ *
+ *   template <typename Dtype>
+ *   class MyAwesomeSolver : public Solver<Dtype> {
+ *     // your implementations
+ *   };
+ *
+ * and its type is its C++ class name, but without the "Solver" at the end
+ * ("MyAwesomeSolver" -> "MyAwesome").
+ *
+ * If the solver is going to be created simply by its constructor, in your c++
+ * file, add the following line:
+ *
+ *    REGISTER_SOLVER_CLASS(MyAwesome);
+ *
+ * Or, if the solver is going to be created by another creator function, in the
+ * format of:
+ *
+ *    template <typename Dtype>
+ *    Solver<Dtype*> GetMyAwesomeSolver(const SolverParameter& param) {
+ *      // your implementation
+ *    }
+ *
+ * then you can register the creator function instead, like
+ *
+ * REGISTER_SOLVER_CREATOR(MyAwesome, GetMyAwesomeSolver)
+ *
+ * Note that each solver type should only be registered once.
+ */
+
+#ifndef CAFFE_SOLVER_FACTORY_H_
+#define CAFFE_SOLVER_FACTORY_H_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype>
+class Solver;
+
+template <typename Dtype>
+class SolverRegistry {
+ public:
+  typedef Solver<Dtype>* (*Creator)(const SolverParameter&);
+  typedef std::map<string, Creator> CreatorRegistry;
+
+  static CreatorRegistry& Registry() {
+    static CreatorRegistry* g_registry_ = new CreatorRegistry();
+    return *g_registry_;
+  }
+
+  // Adds a creator.
+  static void AddCreator(const string& type, Creator creator) {
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 0)
+        << "Solver type " << type << " already registered.";
+    registry[type] = creator;
+  }
+
+  // Get a solver using a SolverParameter.
+  static Solver<Dtype>* CreateSolver(const SolverParameter& param) {
+    const string& type = param.type();
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 1) << "Unknown solver type: " << type
+        << " (known types: " << SolverTypeListString() << ")";
+    return registry[type](param);
+  }
+
+  static vector<string> SolverTypeList() {
+    CreatorRegistry& registry = Registry();
+    vector<string> solver_types;
+    for (typename CreatorRegistry::iterator iter = registry.begin();
+         iter != registry.end(); ++iter) {
+      solver_types.push_back(iter->first);
+    }
+    return solver_types;
+  }
+
+ private:
+  // Solver registry should never be instantiated - everything is done with its
+  // static variables.
+  SolverRegistry() {}
+
+  static string SolverTypeListString() {
+    vector<string> solver_types = SolverTypeList();
+    string solver_types_str;
+    for (vector<string>::iterator iter = solver_types.begin();
+         iter != solver_types.end(); ++iter) {
+      if (iter != solver_types.begin()) {
+        solver_types_str += ", ";
+      }
+      solver_types_str += *iter;
+    }
+    return solver_types_str;
+  }
+};
+
+
+template <typename Dtype>
+class SolverRegisterer {
+ public:
+  SolverRegisterer(const string& type,
+      Solver<Dtype>* (*creator)(const SolverParameter&)) {
+    // LOG(INFO) << "Registering solver type: " << type;
+    SolverRegistry<Dtype>::AddCreator(type, creator);
+  }
+};
+
+
+#define REGISTER_SOLVER_CREATOR(type, creator)                                 \
+  static SolverRegisterer<float> g_creator_f_##type(#type, creator<float>);    \
+  static SolverRegisterer<double> g_creator_d_##type(#type, creator<double>)   \
+
+#define REGISTER_SOLVER_CLASS(type)                                            \
+  template <typename Dtype>                                                    \
+  Solver<Dtype>* Creator_##type##Solver(                                       \
+      const SolverParameter& param)                                            \
+  {                                                                            \
+    return new type##Solver<Dtype>(param);                                     \
+  }                                                                            \
+  REGISTER_SOLVER_CREATOR(type, Creator_##type##Solver)
+
+}  // namespace caffe
+
+#endif  // CAFFE_SOLVER_FACTORY_H_
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 4794991f917..76c869c127e 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 40 (last added: momentum2)
+// SolverParameter next available ID: 41 (last added: type)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -209,16 +209,9 @@ message SolverParameter {
   // (and by default) initialize using a seed derived from the system clock.
   optional int64 random_seed = 20 [default = -1];
 
-  // Solver type
-  enum SolverType {
-    SGD = 0;
-    NESTEROV = 1;
-    ADAGRAD = 2;
-    RMSPROP = 3;
-    ADADELTA = 4;
-    ADAM = 5;
-  }
-  optional SolverType solver_type = 30 [default = SGD];
+  // type of the solver
+  optional string type = 40 [default = "SGD"];
+
   // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
   optional float delta = 31 [default = 1e-8];
   // parameters for the Adam solver
@@ -234,6 +227,18 @@ message SolverParameter {
 
   // If false, don't save a snapshot after training finishes.
   optional bool snapshot_after_train = 28 [default = true];
+
+  // DEPRECATED: old solver enum types, use string instead
+  enum SolverType {
+    SGD = 0;
+    NESTEROV = 1;
+    ADAGRAD = 2;
+    RMSPROP = 3;
+    ADADELTA = 4;
+    ADAM = 5;
+  }
+  // DEPRECATED: use type instead of solver_type
+  optional SolverType solver_type = 30 [default = SGD];
 }
 
 // A message that stores the solver snapshots
diff --git a/src/caffe/solver_factory.cpp b/src/caffe/solver_factory.cpp
deleted file mode 100644
index f78fab28720..00000000000
--- a/src/caffe/solver_factory.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-#include "caffe/solver.hpp"
-#include "caffe/sgd_solvers.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-Solver<Dtype>* GetSolver(const SolverParameter& param) {
-  SolverParameter_SolverType type = param.solver_type();
-
-  switch (type) {
-  case SolverParameter_SolverType_SGD:
-    return new SGDSolver<Dtype>(param);
-  case SolverParameter_SolverType_NESTEROV:
-    return new NesterovSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADAGRAD:
-    return new AdaGradSolver<Dtype>(param);
-  case SolverParameter_SolverType_RMSPROP:
-    return new RMSPropSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADADELTA:
-    return new AdaDeltaSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADAM:
-    return new AdamSolver<Dtype>(param);
-  default:
-    LOG(FATAL) << "Unknown SolverType: " << type;
-  }
-  return (Solver<Dtype>*) NULL;
-}
-
-template Solver<float>* GetSolver(const SolverParameter& param);
-template Solver<double>* GetSolver(const SolverParameter& param);
-
-}  // namespace caffe
diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp
index 45cd4eb2988..a37899ebbb4 100644
--- a/src/caffe/solvers/adadelta_solver.cpp
+++ b/src/caffe/solvers/adadelta_solver.cpp
@@ -151,5 +151,6 @@ void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 INSTANTIATE_CLASS(AdaDeltaSolver);
+REGISTER_SOLVER_CLASS(AdaDelta);
 
 }  // namespace caffe
diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp
index 627d816a470..5e406326095 100644
--- a/src/caffe/solvers/adagrad_solver.cpp
+++ b/src/caffe/solvers/adagrad_solver.cpp
@@ -84,5 +84,6 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 INSTANTIATE_CLASS(AdaGradSolver);
+REGISTER_SOLVER_CLASS(AdaGrad);
 
 }  // namespace caffe
diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp
index 8c334f665dd..cb0fbfe2f78 100644
--- a/src/caffe/solvers/adam_solver.cpp
+++ b/src/caffe/solvers/adam_solver.cpp
@@ -108,5 +108,6 @@ void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 INSTANTIATE_CLASS(AdamSolver);
+REGISTER_SOLVER_CLASS(Adam);
 
 }  // namespace caffe
diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp
index 8135ee2c657..34bf01ebf29 100644
--- a/src/caffe/solvers/nesterov_solver.cpp
+++ b/src/caffe/solvers/nesterov_solver.cpp
@@ -66,5 +66,6 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 INSTANTIATE_CLASS(NesterovSolver);
+REGISTER_SOLVER_CLASS(Nesterov);
 
 }  // namespace caffe
diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp
index 96d1b3dda0b..c6247676094 100644
--- a/src/caffe/solvers/rmsprop_solver.cpp
+++ b/src/caffe/solvers/rmsprop_solver.cpp
@@ -80,5 +80,6 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
 }
 
 INSTANTIATE_CLASS(RMSPropSolver);
+REGISTER_SOLVER_CLASS(RMSProp);
 
 }  // namespace caffe
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index 89ef5ec451d..32bf19b17c8 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -343,5 +343,6 @@ void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
 }
 
 INSTANTIATE_CLASS(SGDSolver);
+REGISTER_SOLVER_CLASS(SGD);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 1767ad3f666..84c6747f61a 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -47,7 +47,6 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // Test data: check out generate_sample_data.py in the same directory.
   string* input_file_;
 
-  virtual SolverParameter_SolverType solver_type() = 0;
   virtual void InitSolver(const SolverParameter& param) = 0;
 
   virtual void InitSolverFromProtoString(const string& proto) {
@@ -290,8 +289,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
           ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
       // Finally, compute update.
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
-      if (solver_type() != SolverParameter_SolverType_ADADELTA
-          && solver_type() != SolverParameter_SolverType_ADAM) {
+      if (solver_->type() != string("AdaDelta")
+          && solver_->type() != string("Adam")) {
         ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
       } else {
         ASSERT_EQ(4, history.size());  // additional blobs for update history
@@ -300,26 +299,19 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       const Dtype history_value = (i == D) ?
             history[1]->cpu_data()[0] : history[0]->cpu_data()[i];
       const Dtype temp = momentum * history_value;
-      switch (solver_type()) {
-      case SolverParameter_SolverType_SGD:
+      if (solver_->type() == string("SGD")) {
         update_value += temp;
-        break;
-      case SolverParameter_SolverType_NESTEROV:
+      } else if (solver_->type() == string("Nesterov")) {
         update_value += temp;
         // step back then over-step
         update_value = (1 + momentum) * update_value - temp;
-        break;
-      case SolverParameter_SolverType_ADAGRAD:
+      } else if (solver_->type() == string("AdaGrad")) {
         update_value /= std::sqrt(history_value + grad * grad) + delta_;
-        break;
-      case SolverParameter_SolverType_RMSPROP: {
+      } else if (solver_->type() == string("RMSProp")) {
         const Dtype rms_decay = 0.95;
         update_value /= std::sqrt(rms_decay*history_value
             + grad * grad * (1 - rms_decay)) + delta_;
-        }
-        break;
-      case SolverParameter_SolverType_ADADELTA:
-      {
+      } else if (solver_->type() == string("AdaDelta")) {
         const Dtype update_history_value = (i == D) ?
             history[1 + num_param_blobs]->cpu_data()[0] :
             history[0 + num_param_blobs]->cpu_data()[i];
@@ -330,9 +322,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
         // not actually needed, just here for illustrative purposes
         // const Dtype weighted_update_average =
         //   momentum * update_history_value + (1 - momentum) * (update_value);
-        break;
-      }
-      case SolverParameter_SolverType_ADAM: {
+      } else if (solver_->type() == string("Adam")) {
         const Dtype momentum2 = 0.999;
         const Dtype m = history_value;
         const Dtype v = (i == D) ?
@@ -344,10 +334,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
             std::sqrt(Dtype(1) - pow(momentum2, num_iters)) /
             (Dtype(1.) - pow(momentum, num_iters));
         update_value = alpha_t * val_m / (std::sqrt(val_v) + delta_);
-        break;
-      }
-      default:
-        LOG(FATAL) << "Unknown solver type: " << solver_type();
+      } else {
+        LOG(FATAL) << "Unknown solver type: " << solver_->type();
       }
       if (i == D) {
         updated_bias.mutable_cpu_diff()[0] = update_value;
@@ -392,7 +380,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
 
     // Check the solver's history -- should contain the previous update value.
-    if (solver_type() == SolverParameter_SolverType_SGD) {
+    if (solver_->type() == string("SGD")) {
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
       ASSERT_EQ(2, history.size());
       for (int i = 0; i < D; ++i) {
@@ -581,10 +569,6 @@ class SGDSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new SGDSolver<Dtype>(param));
   }
-
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_SGD;
-  }
 };
 
 TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
@@ -721,9 +705,6 @@ class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new AdaGradSolver<Dtype>(param));
   }
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_ADAGRAD;
-  }
 };
 
 TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
@@ -824,9 +805,6 @@ class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new NesterovSolver<Dtype>(param));
   }
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_NESTEROV;
-  }
 };
 
 TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
@@ -960,10 +938,6 @@ class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
   }
-
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_ADADELTA;
-  }
 };
 
 TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
@@ -1098,9 +1072,6 @@ class AdamSolverTest : public GradientBasedSolverTest<TypeParam> {
     new_param.set_momentum2(momentum2);
     this->solver_.reset(new AdamSolver<Dtype>(new_param));
   }
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_ADAM;
-  }
 };
 
 TYPED_TEST_CASE(AdamSolverTest, TestDtypesAndDevices);
@@ -1201,9 +1172,6 @@ class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
     new_param.set_rms_decay(rms_decay);
     this->solver_.reset(new RMSPropSolver<Dtype>(new_param));
   }
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_RMSPROP;
-  }
 };
 
 TYPED_TEST_CASE(RMSPropSolverTest, TestDtypesAndDevices);
diff --git a/src/caffe/test/test_solver_factory.cpp b/src/caffe/test/test_solver_factory.cpp
new file mode 100644
index 00000000000..eef5290fe2e
--- /dev/null
+++ b/src/caffe/test/test_solver_factory.cpp
@@ -0,0 +1,50 @@
+#include <map>
+#include <string>
+
+#include "boost/scoped_ptr.hpp"
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/solver.hpp"
+#include "caffe/solver_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SolverFactoryTest : public MultiDeviceTest<TypeParam> {
+ protected:
+  SolverParameter simple_solver_param() {
+    const string solver_proto =
+        "train_net_param { "
+        "  layer { "
+        "    name: 'data' type: 'DummyData' top: 'data' "
+        "    dummy_data_param { shape { dim: 1 } } "
+        "  } "
+        "} ";
+    SolverParameter solver_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        solver_proto, &solver_param));
+    return solver_param;
+  }
+};
+
+TYPED_TEST_CASE(SolverFactoryTest, TestDtypesAndDevices);
+
+TYPED_TEST(SolverFactoryTest, TestCreateSolver) {
+  typedef typename TypeParam::Dtype Dtype;
+  typename SolverRegistry<Dtype>::CreatorRegistry& registry =
+      SolverRegistry<Dtype>::Registry();
+  shared_ptr<Solver<Dtype> > solver;
+  SolverParameter solver_param = this->simple_solver_param();
+  for (typename SolverRegistry<Dtype>::CreatorRegistry::iterator iter =
+       registry.begin(); iter != registry.end(); ++iter) {
+    solver_param.set_type(iter->first);
+    solver.reset(SolverRegistry<Dtype>::CreateSolver(solver_param));
+    EXPECT_EQ(iter->first, solver->type());
+  }
+}
+
+}  // namespace caffe
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index e3f684b5ab3..1cb6ad895da 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -194,7 +194,7 @@ int train() {
         GetRequestedAction(FLAGS_sighup_effect));
 
   shared_ptr<caffe::Solver<float> >
-    solver(caffe::GetSolver<float>(solver_param));
+      solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));
 
   solver->SetActionFunction(signal_handler.GetActionFunction());
 

From c1f7fe1cffa4388886b735f49cd915fad905fca4 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 26 Sep 2015 11:47:02 -0700
Subject: [PATCH 1597/2053] Add automatic upgrade for solver type

---
 include/caffe/caffe.hpp               |  1 +
 include/caffe/util/upgrade_proto.hpp  | 12 +++++
 matlab/+caffe/private/caffe_.cpp      |  5 +-
 python/caffe/_caffe.cpp               |  4 +-
 src/caffe/solver.cpp                  |  2 +-
 src/caffe/test/test_upgrade_proto.cpp | 61 ++++++++++++++++++++++
 src/caffe/util/upgrade_proto.cpp      | 74 +++++++++++++++++++++++++++
 tools/caffe.cpp                       |  2 +-
 tools/upgrade_solver_proto_text.cpp   | 50 ++++++++++++++++++
 9 files changed, 206 insertions(+), 5 deletions(-)
 create mode 100644 tools/upgrade_solver_proto_text.cpp

diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index bd772830b40..a339efba5c0 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -16,6 +16,7 @@
 #include "caffe/solver_factory.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
 #include "caffe/vision_layers.hpp"
 
 #endif  // CAFFE_CAFFE_HPP_
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 6a1418434a6..c94bb3caaa3 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -59,6 +59,18 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
 
 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
+// Return true iff the solver contains any old solver_type specified as enums
+bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
+
+bool UpgradeSolverType(SolverParameter* solver_param);
+
+// Check for deprecations and upgrade the SolverParameter as needed.
+bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param);
+
+// Read parameters from a file into a SolverParameter proto message.
+void ReadSolverParamsFromTextFileOrDie(const string& param_file,
+                                       SolverParameter* param);
+
 }  // namespace caffe
 
 #endif   // CAFFE_UTIL_UPGRADE_PROTO_H_
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 7883f79ebd9..1641e14b534 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -188,7 +188,10 @@ static void get_solver(MEX_ARGS) {
       "Usage: caffe_('get_solver', solver_file)");
   char* solver_file = mxArrayToString(prhs[0]);
   mxCHECK_FILE_EXIST(solver_file);
-  shared_ptr<Solver<float> > solver(new caffe::SGDSolver<float>(solver_file));
+  SolverParameter solver_param;
+  ReadSolverParamsFromTextFileOrDie(solver_file, &solver_param);
+  shared_ptr<Solver<float> > solver(
+      SolverRegistry<float>::CreateSolver(solver_param));
   solvers_.push_back(solver);
   plhs[0] = ptr_to_handle<Solver<float> >(solver.get());
   mxFree(solver_file);
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 0e38dee70d1..8687dd872eb 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -134,8 +134,8 @@ void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
 
 Solver<Dtype>* GetSolverFromFile(const string& filename) {
   SolverParameter param;
-  ReadProtoFromTextFileOrDie(filename, &param);
-  return GetSolver<Dtype>(param);
+  ReadSolverParamsFromTextFileOrDie(filename, &param);
+  return SolverRegistry<Dtype>::CreateSolver(param);
 }
 
 struct NdarrayConverterGenerator {
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 016a02888d8..d3bc7361dd5 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -36,7 +36,7 @@ Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
     : net_(), callbacks_(), root_solver_(root_solver),
       requested_early_exit_(false) {
   SolverParameter param;
-  ReadProtoFromTextFileOrDie(param_file, &param);
+  ReadSolverParamsFromTextFileOrDie(param_file, &param);
   Init(param);
 }
 
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index ee05b151e72..df9aeb62464 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2928,4 +2928,65 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
   }
 }
 #endif  // USE_OPENCV
+
+class SolverTypeUpgradeTest : public ::testing::Test {
+ protected:
+  void RunSolverTypeUpgradeTest(
+      const string& input_param_string, const string& output_param_string) {
+    // Test upgrading old solver_type field (enum) to new type field (string)
+    SolverParameter input_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        input_param_string, &input_param));
+    SolverParameter expected_output_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        output_param_string, &expected_output_param));
+    SolverParameter actual_output_param = input_param;
+    UpgradeSolverType(&actual_output_param);
+    EXPECT_EQ(expected_output_param.DebugString(),
+        actual_output_param.DebugString());
+  }
+};
+
+TEST_F(SolverTypeUpgradeTest, TestSimple) {
+  const char* old_type_vec[6] = { "SGD", "ADAGRAD", "NESTEROV", "RMSPROP",
+      "ADADELTA", "ADAM" };
+  const char* new_type_vec[6] = { "SGD", "AdaGrad", "Nesterov", "RMSProp",
+      "AdaDelta", "Adam" };
+  for (int i = 0; i < 6; ++i) {
+    const string& input_proto =
+        "net: 'examples/mnist/lenet_train_test.prototxt' "
+        "test_iter: 100 "
+        "test_interval: 500 "
+        "base_lr: 0.01 "
+        "momentum: 0.0 "
+        "weight_decay: 0.0005 "
+        "lr_policy: 'inv' "
+        "gamma: 0.0001 "
+        "power: 0.75 "
+        "display: 100 "
+        "max_iter: 10000 "
+        "snapshot: 5000 "
+        "snapshot_prefix: 'examples/mnist/lenet_rmsprop' "
+        "solver_mode: GPU "
+        "solver_type: " + std::string(old_type_vec[i]) + " ";
+    const string& expected_output_proto =
+        "net: 'examples/mnist/lenet_train_test.prototxt' "
+        "test_iter: 100 "
+        "test_interval: 500 "
+        "base_lr: 0.01 "
+        "momentum: 0.0 "
+        "weight_decay: 0.0005 "
+        "lr_policy: 'inv' "
+        "gamma: 0.0001 "
+        "power: 0.75 "
+        "display: 100 "
+        "max_iter: 10000 "
+        "snapshot: 5000 "
+        "snapshot_prefix: 'examples/mnist/lenet_rmsprop' "
+        "solver_mode: GPU "
+        "type: '" + std::string(new_type_vec[i]) + "' ";
+    this->RunSolverTypeUpgradeTest(input_proto, expected_output_proto);
+  }
+}
+
 }  // NOLINT(readability/fn_size)  // namespace caffe
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 6eae9fec00a..ff3f8ffc4f0 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -937,4 +937,78 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
   }
 }
 
+// Return true iff the solver contains any old solver_type specified as enums
+bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
+  if (solver_param.has_solver_type()) {
+    return true;
+  }
+  return false;
+}
+
+bool UpgradeSolverType(SolverParameter* solver_param) {
+  CHECK(!solver_param->has_solver_type() || !solver_param->has_type())
+      << "Failed to upgrade solver: old solver_type field (enum) and new type "
+      << "field (string) cannot be both specified in solver proto text.";
+  if (solver_param->has_solver_type()) {
+    string type;
+    switch (solver_param->solver_type()) {
+    case SolverParameter_SolverType_SGD:
+      type = "SGD";
+      break;
+    case SolverParameter_SolverType_NESTEROV:
+      type = "Nesterov";
+      break;
+    case SolverParameter_SolverType_ADAGRAD:
+      type = "AdaGrad";
+      break;
+    case SolverParameter_SolverType_RMSPROP:
+      type = "RMSProp";
+      break;
+    case SolverParameter_SolverType_ADADELTA:
+      type = "AdaDelta";
+      break;
+    case SolverParameter_SolverType_ADAM:
+      type = "Adam";
+      break;
+    default:
+      LOG(FATAL) << "Unknown SolverParameter solver_type: " << type;
+    }
+    solver_param->set_type(type);
+    solver_param->clear_solver_type();
+  } else {
+    LOG(ERROR) << "Warning: solver type already up to date. ";
+    return false;
+  }
+  return true;
+}
+
+// Check for deprecations and upgrade the SolverParameter as needed.
+bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param) {
+  bool success = true;
+  // Try to upgrade old style solver_type enum fields into new string type
+  if (SolverNeedsTypeUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "'solver_type' field (enum)': " << param_file;
+    if (!UpgradeSolverType(param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+                 << "SolverType (see above).";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "'solver_type' field (enum) to 'type' field (string).";
+      LOG(WARNING) << "Note that future Caffe releases will only support "
+                   << "'type' field (string) for a solver's type.";
+    }
+  }
+  return success;
+}
+
+// Read parameters from a file into a SolverParameter proto message.
+void ReadSolverParamsFromTextFileOrDie(const string& param_file,
+                                       SolverParameter* param) {
+  CHECK(ReadProtoFromTextFile(param_file, param))
+      << "Failed to parse SolverParameter file: " << param_file;
+  UpgradeSolverAsNeeded(param_file, param);
+}
+
 }  // namespace caffe
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 1cb6ad895da..305cfc3635d 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -157,7 +157,7 @@ int train() {
       "but not both.";
 
   caffe::SolverParameter solver_param;
-  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
+  caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
 
   // If the gpus flag is not provided, allow the mode and device to be set
   // in the solver prototxt.
diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp
new file mode 100644
index 00000000000..7130232aed7
--- /dev/null
+++ b/tools/upgrade_solver_proto_text.cpp
@@ -0,0 +1,50 @@
+// This is a script to upgrade old solver prototxts to the new format.
+// Usage:
+//    upgrade_solver_proto_text old_solver_proto_file_in solver_proto_file_out
+
+#include <cstring>
+#include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
+#include <string>
+
+#include "caffe/caffe.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
+
+using std::ofstream;
+
+using namespace caffe;  // NOLINT(build/namespaces)
+
+int main(int argc, char** argv) {
+  ::google::InitGoogleLogging(argv[0]);
+  if (argc != 3) {
+    LOG(ERROR) << "Usage: upgrade_solver_proto_text "
+        << "old_solver_proto_file_in solver_proto_file_out";
+    return 1;
+  }
+
+  SolverParameter solver_param;
+  string input_filename(argv[1]);
+  if (!ReadProtoFromTextFile(input_filename, &solver_param)) {
+    LOG(ERROR) << "Failed to parse input text file as SolverParameter: "
+               << input_filename;
+    return 2;
+  }
+  bool need_upgrade = SolverNeedsTypeUpgrade(solver_param);
+  bool success = true;
+  if (need_upgrade) {
+    success = UpgradeSolverAsNeeded(input_filename, &solver_param);
+    if (!success) {
+      LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+                 << "see details above.";
+    }
+  } else {
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
+  }
+
+  // Save new format prototxt.
+  WriteProtoToTextFile(solver_param, argv[2]);
+
+  LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2];
+  return !success;
+}

From 9563537e86363fac2768200f5748000ec6b3a911 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sat, 26 Sep 2015 11:47:32 -0700
Subject: [PATCH 1598/2053] Update examples and docs

---
 docs/tutorial/solver.md                       | 28 +++++++++----------
 examples/mnist/lenet_adadelta_solver.prototxt |  2 +-
 examples/mnist/lenet_solver_adam.prototxt     |  2 +-
 examples/mnist/lenet_solver_rmsprop.prototxt  |  2 +-
 ...mnist_autoencoder_solver_adadelta.prototxt |  2 +-
 .../mnist_autoencoder_solver_adagrad.prototxt |  2 +-
 ...mnist_autoencoder_solver_nesterov.prototxt |  2 +-
 7 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index b150f6487bc..b719f715a4b 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -8,12 +8,12 @@ The responsibilities of learning are divided between the Solver for overseeing t
 
 The Caffe solvers are:
 
-- Stochastic Gradient Descent (`SGD`), 
-- AdaDelta (`ADADELTA`),
-- Adaptive Gradient (`ADAGRAD`),
-- Adam (`ADAM`),
-- Nesterov's Accelerated Gradient (`NESTEROV`) and
-- RMSprop (`RMSPROP`)
+- Stochastic Gradient Descent (`type: "SGD"`),
+- AdaDelta (`type: "AdaDelta"`),
+- Adaptive Gradient (`type: "AdaGrad"`),
+- Adam (`type: "Adam"`),
+- Nesterov's Accelerated Gradient (`type: "Nesterov"`) and
+- RMSprop (`type: "RMSProp"`)
 
 The solver
 
@@ -51,7 +51,7 @@ The parameter update $$\Delta W$$ is formed by the solver from the error gradien
 
 ### SGD
 
-**Stochastic gradient descent** (`solver_type: SGD`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
+**Stochastic gradient descent** (`type: "SGD"`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
 The **learning rate** $$ \alpha $$ is the weight of the negative gradient.
 The **momentum** $$ \mu $$ is the weight of the previous update.
 
@@ -113,7 +113,7 @@ If learning diverges (e.g., you start to see very large or `NaN` or `inf` loss v
 
 ### AdaDelta
 
-The **AdaDelta** (`solver_type: ADADELTA`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are
+The **AdaDelta** (`type: "AdaDelta"`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are
 
 $$
 \begin{align}
@@ -125,7 +125,7 @@ E[g^2]_t &= \delta{E[g^2]_{t-1} } + (1-\delta)g_{t}^2
 \end{align}
 $$
 
-and 
+and
 
 $$
 (W_{t+1})_i =
@@ -139,7 +139,7 @@ $$
 
 ### AdaGrad
 
-The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
+The **adaptive gradient** (`type: "AdaGrad"`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
 Given the update information from all previous iterations $$ \left( \nabla L(W) \right)_{t'} $$ for $$ t' \in \{1, 2, ..., t\} $$,
 the update formulas proposed by [1] are as follows, specified for each component $$i$$ of the weights $$W$$:
 
@@ -159,7 +159,7 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa
 
 ### Adam
 
-The **Adam** (`solver_type: ADAM`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are
+The **Adam** (`type: "Adam"`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are
 
 $$
 (m_t)_i = \beta_1 (m_{t-1})_i + (1-\beta_1)(\nabla L(W_t))_i,\\
@@ -181,7 +181,7 @@ Kingma et al. [1] proposed to use $$\beta_1 = 0.9, \beta_2 = 0.999, \varepsilon
 
 ### NAG
 
-**Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
+**Nesterov's accelerated gradient** (`type: "Nesterov"`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
 Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].
 
 The weight update formulas look very similar to the SGD updates given above:
@@ -206,10 +206,10 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we
 
 ### RMSprop
 
-The **RMSprop** (`solver_type: RMSPROP`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
+The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
 
 $$
-(v_t)_i = 
+(v_t)_i =
 \begin{cases}
 (v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
 (v_{t-1})_i \cdot (1-\delta), & \text{else}
diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt
index 776d1e06139..16176c0ffae 100644
--- a/examples/mnist/lenet_adadelta_solver.prototxt
+++ b/examples/mnist/lenet_adadelta_solver.prototxt
@@ -20,5 +20,5 @@ snapshot: 5000
 snapshot_prefix: "examples/mnist/lenet_adadelta"
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: ADADELTA
+type: "AdaDelta"
 delta: 1e-6
diff --git a/examples/mnist/lenet_solver_adam.prototxt b/examples/mnist/lenet_solver_adam.prototxt
index d22c5718f3f..4b5336b1a04 100644
--- a/examples/mnist/lenet_solver_adam.prototxt
+++ b/examples/mnist/lenet_solver_adam.prototxt
@@ -22,5 +22,5 @@ max_iter: 10000
 snapshot: 5000
 snapshot_prefix: "examples/mnist/lenet"
 # solver mode: CPU or GPU
-solver_type: ADAM
+type: "Adam"
 solver_mode: GPU
diff --git a/examples/mnist/lenet_solver_rmsprop.prototxt b/examples/mnist/lenet_solver_rmsprop.prototxt
index 74dadc51069..924b72d306e 100644
--- a/examples/mnist/lenet_solver_rmsprop.prototxt
+++ b/examples/mnist/lenet_solver_rmsprop.prototxt
@@ -23,5 +23,5 @@ snapshot: 5000
 snapshot_prefix: "examples/mnist/lenet_rmsprop"
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: RMSPROP
+type: "RMSProp"
 rms_decay: 0.98
diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
index 065647df31b..26c4084a374 100644
--- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
@@ -16,4 +16,4 @@ snapshot: 10000
 snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train"
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: ADADELTA
+type: "AdaDelta"
diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
index cc0ed9e310a..065cdb20ddc 100644
--- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -14,4 +14,4 @@ snapshot: 10000
 snapshot_prefix: "examples/mnist/mnist_autoencoder_adagrad_train"
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: ADAGRAD
+type: "AdaGrad"
diff --git a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
index 2a59fd45c8d..c95e3fe7e49 100644
--- a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
@@ -17,4 +17,4 @@ snapshot_prefix: "examples/mnist/mnist_autoencoder_nesterov_train"
 momentum: 0.95
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: NESTEROV
+type: "Nesterov"

From 6f8370a1f3917b525e60896586cac41bb829ac2b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Oct 2015 17:32:27 -0700
Subject: [PATCH 1599/2053] clean up logging for Net init

- condense conditions by `LOG_IF`
- only log memory use once after all tops
---
 src/caffe/net.cpp | 182 +++++++++++++++++++---------------------------
 1 file changed, 76 insertions(+), 106 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index ebb8b5d28c2..1ad93e6af5f 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -46,10 +46,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // the current NetState.
   NetParameter filtered_param;
   FilterNet(in_param, &filtered_param);
-  if (Caffe::root_solver()) {
-    LOG(INFO) << "Initializing net from parameters: " << std::endl
-              << filtered_param.DebugString();
-  }
+  LOG_IF(INFO, Caffe::root_solver())
+      << "Initializing net from parameters: " << std::endl
+      << filtered_param.DebugString();
   // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
   InsertSplits(filtered_param, &param);
@@ -73,8 +72,6 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     const int layer_id = -1;  // inputs have fake layer ID -1
     AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
   }
-  DLOG_IF(INFO, Caffe::root_solver())
-      << "Memory required for data: " << memory_used_ * sizeof(Dtype);
   // For each layer, set up its input and output
   bottom_vecs_.resize(param.layer_size());
   top_vecs_.resize(param.layer_size());
@@ -106,9 +103,8 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     }
     layer_names_.push_back(layer_param.name());
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "Creating Layer " << layer_param.name();
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating Layer " << layer_param.name();
     bool need_backward = false;
 
     // Figure out this layer's input and output
@@ -151,29 +147,23 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     } else {
       layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     }
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "Setting up " << layer_names_[layer_id];
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Setting up " << layer_names_[layer_id];
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
       }
       blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
-      if (Caffe::root_solver()) {
-        LOG(INFO) << "Top shape: "
-                  << top_vecs_[layer_id][top_id]->shape_string();
-      }
+      LOG_IF(INFO, Caffe::root_solver())
+          << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
       if (layer->loss(top_id)) {
-        if (Caffe::root_solver()) {
-          LOG(INFO) << "    with loss weight " << layer->loss(top_id);
-        }
+        LOG_IF(INFO, Caffe::root_solver())
+            << "    with loss weight " << layer->loss(top_id);
       }
       memory_used_ += top_vecs_[layer_id][top_id]->count();
     }
-    if (Caffe::root_solver()) {
-      DLOG(INFO) << "Memory required for data: "
-                 << memory_used_ * sizeof(Dtype);
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Memory required for data: " << memory_used_ * sizeof(Dtype);
     const int param_size = layer_param.param_size();
     const int num_param_blobs = layers_[layer_id]->blobs().size();
     CHECK_LE(param_size, num_param_blobs)
@@ -231,14 +221,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
-    if (layer_need_backward_[layer_id]) {
-      if (Caffe::root_solver()) {
+    if (Caffe::root_solver()) {
+      if (layer_need_backward_[layer_id]) {
         LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
-      }
-    } else {
-      if (Caffe::root_solver()) {
+      } else {
         LOG(INFO) << layer_names_[layer_id]
-                  << " does not need backward computation.";
+            << " does not need backward computation.";
       }
     }
     for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
@@ -279,9 +267,8 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // In the end, all remaining blobs are considered output blobs.
   for (set<string>::iterator it = available_blobs.begin();
       it != available_blobs.end(); ++it) {
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "This network produces output " << *it;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "This network produces output " << *it;
     net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
     net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
   }
@@ -293,10 +280,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   }
   ShareWeights();
   debug_info_ = param.debug_info();
-  if (Caffe::root_solver()) {
-    LOG(INFO) << "Network initialization done.";
-    LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
-  }
+  LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
 }
 
 template <typename Dtype>
@@ -335,33 +319,30 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
   // Check whether the rule is broken due to phase.
   if (rule.has_phase()) {
       if (rule.phase() != state.phase()) {
-        if (Caffe::root_solver()) {
-          LOG(INFO) << "The NetState phase (" << state.phase()
-                    << ") differed from the phase (" << rule.phase()
-                    << ") specified by a rule in layer " << layer_name;
-        }
+        LOG_IF(INFO, Caffe::root_solver())
+            << "The NetState phase (" << state.phase()
+            << ") differed from the phase (" << rule.phase()
+            << ") specified by a rule in layer " << layer_name;
         return false;
       }
   }
   // Check whether the rule is broken due to min level.
   if (rule.has_min_level()) {
     if (state.level() < rule.min_level()) {
-      if (Caffe::root_solver()) {
-        LOG(INFO) << "The NetState level (" << state.level()
-                  << ") is above the min_level (" << rule.min_level()
-                  << ") specified by a rule in layer " << layer_name;
-      }
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState level (" << state.level()
+          << ") is above the min_level (" << rule.min_level()
+          << ") specified by a rule in layer " << layer_name;
       return false;
     }
   }
   // Check whether the rule is broken due to max level.
   if (rule.has_max_level()) {
     if (state.level() > rule.max_level()) {
-      if (Caffe::root_solver()) {
-        LOG(INFO) << "The NetState level (" << state.level()
-                  << ") is above the max_level (" << rule.max_level()
-                  << ") specified by a rule in layer " << layer_name;
-      }
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState level (" << state.level()
+          << ") is above the max_level (" << rule.max_level()
+          << ") specified by a rule in layer " << layer_name;
       return false;
     }
   }
@@ -374,10 +355,9 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
       if (rule.stage(i) == state.stage(j)) { has_stage = true; }
     }
     if (!has_stage) {
-      if (Caffe::root_solver()) {
-        LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
-                  << "' specified by a rule in layer " << layer_name;
-      }
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState did not contain stage '" << rule.stage(i)
+          << "' specified by a rule in layer " << layer_name;
       return false;
     }
   }
@@ -390,10 +370,9 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
       if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }
     }
     if (has_stage) {
-      if (Caffe::root_solver()) {
-        LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
-                  << "' specified by a rule in layer " << layer_name;
-      }
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState contained a not_stage '" << rule.not_stage(i)
+          << "' specified by a rule in layer " << layer_name;
       return false;
     }
   }
@@ -415,9 +394,8 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
   if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
       blob_name == layer_param->bottom(top_id)) {
     // In-place computation
-    if (Caffe::root_solver()) {
-      LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << layer_param->name() << " -> " << blob_name << " (in-place)";
     top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
     top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
   } else if (blob_name_to_idx &&
@@ -473,9 +451,8 @@ int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
                << layer_param.name() << "', bottom index " << bottom_id << ")";
   }
   const int blob_id = (*blob_name_to_idx)[blob_name];
-  if (Caffe::root_solver()) {
-    LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
-  }
+  LOG_IF(INFO, Caffe::root_solver())
+      << layer_names_[layer_id] << " <- " << blob_name;
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
@@ -672,10 +649,9 @@ void Net<Dtype>::InputDebugInfo(const int input_id) {
   const Blob<Dtype>& blob = *net_input_blobs_[input_id];
   const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
   const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-  if (Caffe::root_solver()) {
-    LOG(INFO) << "    [Forward] "
-              << "Input " << blob_name << " data: " << data_abs_val_mean;
-  }
+  LOG_IF(INFO, Caffe::root_solver())
+      << "    [Forward] "
+      << "Input " << blob_name << " data: " << data_abs_val_mean;
 }
 
 template <typename Dtype>
@@ -684,12 +660,11 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
     const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
     const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "    [Forward] "
-                << "Layer " << layer_names_[layer_id]
-                << ", top blob " << blob_name
-                << " data: " << data_abs_val_mean;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Forward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", top blob " << blob_name
+        << " data: " << data_abs_val_mean;
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
@@ -697,12 +672,11 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
     const int net_param_id = param_id_vecs_[layer_id][param_id];
     const string& blob_name = param_display_names_[net_param_id];
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "    [Forward] "
-                << "Layer " << layer_names_[layer_id]
-                << ", param blob " << blob_name
-                << " data: " << data_abs_val_mean;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Forward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", param blob " << blob_name
+        << " data: " << data_abs_val_mean;
   }
 }
 
@@ -714,24 +688,22 @@ void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
     const Blob<Dtype>& blob = *bottom_vec[bottom_id];
     const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
     const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "    [Backward] "
-                << "Layer " << layer_names_[layer_id]
-                << ", bottom blob " << blob_name
-                << " diff: " << diff_abs_val_mean;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Backward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", bottom blob " << blob_name
+        << " diff: " << diff_abs_val_mean;
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
     if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
     const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
     const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "    [Backward] "
-                << "Layer " << layer_names_[layer_id]
-                << ", param blob " << param_id
-                << " diff: " << diff_abs_val_mean;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Backward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", param blob " << param_id
+        << " diff: " << diff_abs_val_mean;
   }
 }
 
@@ -744,22 +716,20 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
   const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
   if (param_owner < 0) {
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "    [Update] Layer " << layer_name
-                << ", param " << param_display_name
-                << " data: " << data_abs_val_mean
-                << "; diff: " << diff_abs_val_mean;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Update] Layer " << layer_name
+        << ", param " << param_display_name
+        << " data: " << data_abs_val_mean
+        << "; diff: " << diff_abs_val_mean;
   } else {
     const string& owner_layer_name =
         layer_names_[param_layer_indices_[param_owner].first];
-    if (Caffe::root_solver()) {
-      LOG(INFO) << "    [Update] Layer " << layer_name
-                << ", param blob " << param_display_name
-                << " (owned by layer " << owner_layer_name << ", " << "param "
-                << param_display_names_[param_owners_[param_id]] << ")"
-                << " diff: " << diff_abs_val_mean;
-    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Update] Layer " << layer_name
+        << ", param blob " << param_display_name
+        << " (owned by layer " << owner_layer_name << ", " << "param "
+        << param_display_names_[param_owners_[param_id]] << ")"
+        << " diff: " << diff_abs_val_mean;
   }
 }
 

From 4c93b3dc555891ae0ad75092b6c0f77508740ecf Mon Sep 17 00:00:00 2001
From: Mausoom Sarkar <msarkar@adobe.com>
Date: Tue, 13 Oct 2015 18:35:32 +0530
Subject: [PATCH 1600/2053] Moved the loop inside PReLUParamBackward to do the
 reduction inside the kernel Now PReLU backward is taking the same time as
 forward

Code cleanup
Removed unnecessary code
Fixed indent
merge if(channed_shared_)
---
 src/caffe/layers/prelu_layer.cu | 44 ++++++++++++++++-----------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index e1f20048f60..1225334f335 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -31,10 +31,15 @@ __global__ void PReLUBackward(const int n, const int channels, const int dim,
 
 // CUDA kernel for element-wise parameter backward
 template <typename Dtype>
-__global__ void PReLUParamBackward(const int n, const Dtype* in_diff,
+__global__ void PReLUParamBackward(const int n,
+    const int rows, const int rowPitch, const Dtype* in_diff,
     const Dtype* in_data, Dtype* out_diff) {
   CUDA_KERNEL_LOOP(index, n) {
     out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0);
+    for ( int k = 1; k < rows; k++ ) {
+        out_diff[index] += in_diff[index + k*rowPitch]
+           * in_data[index + k*rowPitch] * (in_data[index + k*rowPitch] <= 0);
+    }
   }
 }
 
@@ -82,29 +87,24 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (this->param_propagate_down_[0]) {
     Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
     int cdim = channels * dim;
-    Dtype dsum = 0.;
-    for (int n = 0; n < bottom[0]->num(); ++n) {
-      // compute element-wise diff
-      // NOLINT_NEXT_LINE(whitespace/operators)
-      PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
-          CAFFE_CUDA_NUM_THREADS>>>(
-          cdim, top_diff + top[0]->offset(n),
-          bottom_data + bottom[0]->offset(n),
-          backward_buff_.mutable_gpu_diff());
-      CUDA_POST_KERNEL_CHECK;
-      if (channel_shared_) {
-        Dtype d;
-        caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(),
-            multiplier_.gpu_data(), &d);
-        dsum += d;
-      } else {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
-            backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1.,
-            slope_diff);
-      }
-    }
+
+    // compute element-wise diff
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
+      CAFFE_CUDA_NUM_THREADS>>>(
+      cdim, bottom[0]->num(), top[0]->offset(1), top_diff ,
+      bottom_data ,
+      backward_buff_.mutable_gpu_diff());
+    CUDA_POST_KERNEL_CHECK;
     if (channel_shared_) {
+      Dtype dsum;
+      caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(),
+       multiplier_.gpu_data(), &dsum);
       caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+    } else {
+      caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
+        backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1.,
+        slope_diff);
     }
   }
   // Propagate to bottom

From a7d84f3c7e2db7f400c933349edcd4bcf46903b8 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Mon, 19 Oct 2015 18:19:38 +0800
Subject: [PATCH 1601/2053] Qualify messages issued by CMake when CUDA is
 unavailable

---
 cmake/Dependencies.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index d68d7bfba66..2005b9927b8 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -55,9 +55,9 @@ endif()
 include(cmake/Cuda.cmake)
 if(NOT HAVE_CUDA)
   if(CPU_ONLY)
-    message("-- CUDA is disabled. Building without it...")
+    message(STATUS "-- CUDA is disabled. Building without it...")
   else()
-    message("-- CUDA is not detected by cmake. Building without it...")
+    message(WARNING "-- CUDA is not detected by cmake. Building without it...")
   endif()
 
   # TODO: remove this not cross platform define in future. Use caffe_config.h instead.

From 52429c77cb84b06bf7564f5df619f9f489fe5f72 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 19 Oct 2015 11:36:38 -0700
Subject: [PATCH 1602/2053] installation questions -> caffe-users

---
 INSTALL.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/INSTALL.md b/INSTALL.md
index 42fcf027ec2..05c714dbda8 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -3,5 +3,5 @@
 See http://caffe.berkeleyvision.org/installation.html for the latest
 installation instructions.
 
-Check the issue tracker in case you need help:
-https://github.com/BVLC/caffe/issues
+Check the users group in case you need help:
+https://groups.google.com/forum/#!forum/caffe-users

From 2aabba4f8e33a1d0d474a17fff445e9d12201be4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 19 Oct 2015 11:39:29 -0700
Subject: [PATCH 1603/2053] [docs] cuDNN v3 compatible

---
 docs/installation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 89a8c71c71a..cce7ec358f5 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -30,13 +30,14 @@ Optional dependencies:
 
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
+* cuDNN for GPU acceleration (v3)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 

From 33a8ba64145e308aefefd5997d06ad53038c4f21 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Oct 2015 17:13:24 -0700
Subject: [PATCH 1604/2053] [test] drop bogus OpenCV guard for layer type

---
 src/caffe/test/test_upgrade_proto.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index df9aeb62464..23deddd453d 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2892,7 +2892,6 @@ TEST_F(NetUpgradeTest, TestImageNet) {
   this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto);
 }  // NOLINT(readability/fn_size)
 
-#ifdef USE_OPENCV
 TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
   LayerParameter layer_param;
   shared_ptr<Layer<float> > layer;
@@ -2927,7 +2926,6 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
     EXPECT_EQ(v2_layer_type, layer->type());
   }
 }
-#endif  // USE_OPENCV
 
 class SolverTypeUpgradeTest : public ::testing::Test {
  protected:

From 1caaf38370a6dd1bd7bc91fe3b5242ae63be6a22 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Mon, 19 Oct 2015 18:13:57 +0800
Subject: [PATCH 1605/2053] Endorse CMP0046, CMP0054

Set policies to NEW to silence warnings in CMake 3.02 and later.
---
 CMakeLists.txt | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 37f937fe489..82742dafc99 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,10 @@
 cmake_minimum_required(VERSION 2.8.7)
+if(POLICY CMP0046)
+  cmake_policy(SET CMP0046 NEW)
+endif()
+if(POLICY CMP0054)
+  cmake_policy(SET CMP0054 NEW)
+endif()
 
 # ---[ Caffe project
 project(Caffe C CXX)
@@ -66,8 +72,10 @@ add_subdirectory(docs)
 add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
 
 # ---[ pytest target
-add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python )
-add_dependencies(pytest pycaffe)
+if(BUILD_python)
+  add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python )
+  add_dependencies(pytest pycaffe)
+endif()
 
 # ---[ Configuration summary
 caffe_print_configuration_summary()

From 93212e61aa9382762954a01c62f9f0a96d9ff00d Mon Sep 17 00:00:00 2001
From: Kang Kim <kkhoot@gmail.com>
Date: Sun, 18 Oct 2015 16:52:19 +0900
Subject: [PATCH 1606/2053] Move HDF5 defines to data_layers header

---
 include/caffe/data_layers.hpp   | 3 +++
 include/caffe/neuron_layers.hpp | 3 ---
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index 90fd0d19917..aa0ab7df390 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -17,6 +17,9 @@
 #include "caffe/util/blocking_queue.hpp"
 #include "caffe/util/db.hpp"
 
+#define HDF5_DATA_DATASET_NAME "data"
+#define HDF5_DATA_LABEL_NAME "label"
+
 namespace caffe {
 
 /**
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index c2e0774aaa2..4fa330ec783 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -10,9 +10,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-#define HDF5_DATA_DATASET_NAME "data"
-#define HDF5_DATA_LABEL_NAME "label"
-
 namespace caffe {
 
 /**

From 80d045263f26c41a1e886906a30d649a5c812038 Mon Sep 17 00:00:00 2001
From: Kang Kim <kkhoot@gmail.com>
Date: Mon, 19 Oct 2015 00:58:55 +0900
Subject: [PATCH 1607/2053] Clean redundant/unnecessary headers

---
 include/caffe/blob.hpp                                   | 1 -
 include/caffe/common_layers.hpp                          | 5 -----
 include/caffe/filler.hpp                                 | 1 -
 include/caffe/layer.hpp                                  | 2 +-
 include/caffe/loss_layers.hpp                            | 1 -
 include/caffe/syncedmem.hpp                              | 1 -
 include/caffe/util/blocking_queue.hpp                    | 2 --
 include/caffe/util/io.hpp                                | 1 -
 src/caffe/layers/absval_layer.cpp                        | 1 -
 src/caffe/layers/absval_layer.cu                         | 3 +--
 src/caffe/layers/accuracy_layer.cpp                      | 5 +----
 src/caffe/layers/argmax_layer.cpp                        | 3 +--
 src/caffe/layers/base_conv_layer.cpp                     | 1 -
 src/caffe/layers/base_data_layer.cpp                     | 3 ---
 src/caffe/layers/batch_reindex_layer.cpp                 | 3 +--
 src/caffe/layers/batch_reindex_layer.cu                  | 3 +--
 src/caffe/layers/bnll_layer.cpp                          | 3 +--
 src/caffe/layers/bnll_layer.cu                           | 3 +--
 src/caffe/layers/concat_layer.cpp                        | 3 +--
 src/caffe/layers/concat_layer.cu                         | 3 +--
 src/caffe/layers/contrastive_loss_layer.cpp              | 2 --
 src/caffe/layers/contrastive_loss_layer.cu               | 4 +---
 src/caffe/layers/conv_layer.cpp                          | 4 ----
 src/caffe/layers/conv_layer.cu                           | 4 ----
 src/caffe/layers/cudnn_conv_layer.cpp                    | 4 ----
 src/caffe/layers/cudnn_conv_layer.cu                     | 4 ----
 src/caffe/layers/cudnn_lcn_layer.cpp                     | 4 ----
 src/caffe/layers/cudnn_lcn_layer.cu                      | 4 ----
 src/caffe/layers/cudnn_lrn_layer.cpp                     | 4 ----
 src/caffe/layers/cudnn_lrn_layer.cu                      | 4 ----
 src/caffe/layers/cudnn_pooling_layer.cpp                 | 4 ----
 src/caffe/layers/cudnn_pooling_layer.cu                  | 4 ----
 src/caffe/layers/cudnn_relu_layer.cpp                    | 2 --
 src/caffe/layers/cudnn_relu_layer.cu                     | 2 --
 src/caffe/layers/cudnn_sigmoid_layer.cpp                 | 2 --
 src/caffe/layers/cudnn_sigmoid_layer.cu                  | 2 --
 src/caffe/layers/cudnn_softmax_layer.cpp                 | 4 ----
 src/caffe/layers/cudnn_softmax_layer.cu                  | 4 ----
 src/caffe/layers/cudnn_tanh_layer.cpp                    | 4 +---
 src/caffe/layers/cudnn_tanh_layer.cu                     | 4 +---
 src/caffe/layers/data_layer.cpp                          | 4 ----
 src/caffe/layers/deconv_layer.cpp                        | 4 ----
 src/caffe/layers/deconv_layer.cu                         | 4 ----
 src/caffe/layers/dropout_layer.cpp                       | 5 +----
 src/caffe/layers/dropout_layer.cu                        | 7 +------
 src/caffe/layers/dummy_data_layer.cpp                    | 3 +--
 src/caffe/layers/eltwise_layer.cpp                       | 3 +--
 src/caffe/layers/eltwise_layer.cu                        | 3 +--
 src/caffe/layers/embed_layer.cpp                         | 3 ---
 src/caffe/layers/embed_layer.cu                          | 3 ---
 src/caffe/layers/euclidean_loss_layer.cpp                | 4 +---
 src/caffe/layers/euclidean_loss_layer.cu                 | 4 +---
 src/caffe/layers/exp_layer.cpp                           | 4 +---
 src/caffe/layers/exp_layer.cu                            | 4 +---
 src/caffe/layers/filter_layer.cpp                        | 4 +---
 src/caffe/layers/filter_layer.cu                         | 3 +--
 src/caffe/layers/flatten_layer.cpp                       | 4 +---
 src/caffe/layers/hdf5_data_layer.cpp                     | 1 -
 src/caffe/layers/hdf5_data_layer.cu                      | 3 ---
 src/caffe/layers/hdf5_output_layer.cpp                   | 5 +----
 src/caffe/layers/hdf5_output_layer.cu                    | 5 +----
 src/caffe/layers/hinge_loss_layer.cpp                    | 6 +-----
 src/caffe/layers/im2col_layer.cpp                        | 2 --
 src/caffe/layers/im2col_layer.cu                         | 2 --
 src/caffe/layers/image_data_layer.cpp                    | 1 -
 src/caffe/layers/infogain_loss_layer.cpp                 | 5 +----
 src/caffe/layers/inner_product_layer.cpp                 | 5 +----
 src/caffe/layers/inner_product_layer.cu                  | 5 +----
 src/caffe/layers/log_layer.cpp                           | 2 --
 src/caffe/layers/log_layer.cu                            | 2 --
 src/caffe/layers/loss_layer.cpp                          | 8 +-------
 src/caffe/layers/lrn_layer.cpp                           | 1 -
 src/caffe/layers/lrn_layer.cu                            | 1 -
 src/caffe/layers/memory_data_layer.cpp                   | 2 --
 src/caffe/layers/multinomial_logistic_loss_layer.cpp     | 5 +----
 src/caffe/layers/mvn_layer.cpp                           | 2 --
 src/caffe/layers/mvn_layer.cu                            | 2 --
 src/caffe/layers/neuron_layer.cpp                        | 3 +--
 src/caffe/layers/pooling_layer.cpp                       | 3 ---
 src/caffe/layers/pooling_layer.cu                        | 1 -
 src/caffe/layers/power_layer.cpp                         | 4 +---
 src/caffe/layers/power_layer.cu                          | 4 +---
 src/caffe/layers/prelu_layer.cpp                         | 3 +--
 src/caffe/layers/prelu_layer.cu                          | 3 +--
 src/caffe/layers/reduction_layer.cpp                     | 5 +----
 src/caffe/layers/reduction_layer.cu                      | 4 +---
 src/caffe/layers/relu_layer.cpp                          | 3 +--
 src/caffe/layers/relu_layer.cu                           | 3 +--
 src/caffe/layers/reshape_layer.cpp                       | 1 -
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp    | 5 +----
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu     | 5 +----
 src/caffe/layers/sigmoid_layer.cpp                       | 4 +---
 src/caffe/layers/sigmoid_layer.cu                        | 4 +---
 src/caffe/layers/silence_layer.cpp                       | 1 -
 src/caffe/layers/silence_layer.cu                        | 1 -
 src/caffe/layers/slice_layer.cpp                         | 3 +--
 src/caffe/layers/slice_layer.cu                          | 3 +--
 src/caffe/layers/softmax_layer.cpp                       | 3 +--
 src/caffe/layers/softmax_layer.cu                        | 3 +--
 src/caffe/layers/softmax_loss_layer.cpp                  | 4 +---
 src/caffe/layers/softmax_loss_layer.cu                   | 3 +--
 src/caffe/layers/split_layer.cpp                         | 3 +--
 src/caffe/layers/split_layer.cu                          | 3 +--
 src/caffe/layers/spp_layer.cpp                           | 5 -----
 src/caffe/layers/tanh_layer.cpp                          | 4 +---
 src/caffe/layers/tanh_layer.cu                           | 4 +---
 src/caffe/layers/threshold_layer.cpp                     | 3 +--
 src/caffe/layers/threshold_layer.cu                      | 4 +---
 src/caffe/layers/tile_layer.cpp                          | 1 -
 src/caffe/layers/tile_layer.cu                           | 1 -
 src/caffe/layers/window_data_layer.cpp                   | 2 --
 src/caffe/parallel.cpp                                   | 1 -
 src/caffe/syncedmem.cpp                                  | 2 --
 src/caffe/test/test_accuracy_layer.cpp                   | 4 +---
 src/caffe/test/test_argmax_layer.cpp                     | 2 +-
 src/caffe/test/test_batch_reindex_layer.cpp              | 3 +--
 src/caffe/test/test_blob.cpp                             | 1 -
 src/caffe/test/test_common.cpp                           | 2 --
 src/caffe/test/test_concat_layer.cpp                     | 3 +--
 src/caffe/test/test_contrastive_loss_layer.cpp           | 4 +---
 src/caffe/test/test_convolution_layer.cpp                | 1 -
 src/caffe/test/test_deconvolution_layer.cpp              | 1 -
 src/caffe/test/test_eltwise_layer.cpp                    | 2 +-
 src/caffe/test/test_embed_layer.cpp                      | 3 +--
 src/caffe/test/test_euclidean_loss_layer.cpp             | 4 +---
 src/caffe/test/test_filler.cpp                           | 2 --
 src/caffe/test/test_filter_layer.cpp                     | 4 +---
 src/caffe/test/test_flatten_layer.cpp                    | 3 +--
 src/caffe/test/test_hdf5_output_layer.cpp                | 2 +-
 src/caffe/test/test_hdf5data_layer.cpp                   | 3 +--
 src/caffe/test/test_hinge_loss_layer.cpp                 | 4 +---
 src/caffe/test/test_im2col_kernel.cu                     | 1 -
 src/caffe/test/test_im2col_layer.cpp                     | 1 -
 src/caffe/test/test_image_data_layer.cpp                 | 2 +-
 src/caffe/test/test_infogain_loss_layer.cpp              | 3 ---
 src/caffe/test/test_inner_product_layer.cpp              | 3 +--
 src/caffe/test/test_lrn_layer.cpp                        | 1 -
 src/caffe/test/test_math_functions.cpp                   | 2 --
 src/caffe/test/test_maxpool_dropout_layers.cpp           | 1 -
 src/caffe/test/test_multinomial_logistic_loss_layer.cpp  | 5 +----
 src/caffe/test/test_mvn_layer.cpp                        | 2 --
 src/caffe/test/test_neuron_layer.cpp                     | 4 ++--
 src/caffe/test/test_pooling_layer.cpp                    | 1 -
 src/caffe/test/test_power_layer.cpp                      | 2 +-
 src/caffe/test/test_random_number_generator.cpp          | 1 -
 src/caffe/test/test_reduction_layer.cpp                  | 3 +--
 src/caffe/test/test_reshape_layer.cpp                    | 1 -
 src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp | 4 +---
 src/caffe/test/test_slice_layer.cpp                      | 3 +--
 src/caffe/test/test_softmax_layer.cpp                    | 3 +--
 src/caffe/test/test_softmax_with_loss_layer.cpp          | 4 +---
 src/caffe/test/test_split_layer.cpp                      | 3 +--
 src/caffe/test/test_spp_layer.cpp                        | 2 --
 src/caffe/test/test_stochastic_pooling.cpp               | 1 -
 src/caffe/test/test_syncedmem.cpp                        | 1 -
 src/caffe/test/test_tanh_layer.cpp                       | 2 +-
 src/caffe/test/test_threshold_layer.cpp                  | 2 +-
 src/caffe/test/test_tile_layer.cpp                       | 3 +--
 src/caffe/test/test_upgrade_proto.cpp                    | 1 -
 src/caffe/test/test_util_blas.cpp                        | 2 --
 src/caffe/util/im2col.cpp                                | 3 ---
 src/caffe/util/im2col.cu                                 | 3 ---
 src/caffe/util/math_functions.cu                         | 2 --
 163 files changed, 86 insertions(+), 392 deletions(-)

diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index fea5117ef10..af360ac24bd 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -8,7 +8,6 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
-#include "caffe/util/math_functions.hpp"
 
 const int kMaxBlobAxes = 32;
 
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 21a27d759a8..95358d4cd6f 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -1,16 +1,11 @@
 #ifndef CAFFE_COMMON_LAYERS_HPP_
 #define CAFFE_COMMON_LAYERS_HPP_
 
-#include <string>
 #include <utility>
 #include <vector>
 
 #include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/neuron_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index 888f4a4ba3b..dad9ad46b3b 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -8,7 +8,6 @@
 #include <string>
 
 #include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index a0d1d4ecc94..10f353f94f9 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -9,7 +9,7 @@
 #include "caffe/common.hpp"
 #include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/device_alternate.hpp"
+#include "caffe/util/math_functions.hpp"
 
 /**
  Forward declare boost::thread instead of including boost/thread.hpp
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 8d41af34e88..d08ad9b6894 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -6,7 +6,6 @@
 #include <vector>
 
 #include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/neuron_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 3d92a0eaf3e..38ee4664028 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -4,7 +4,6 @@
 #include <cstdlib>
 
 #include "caffe/common.hpp"
-#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
diff --git a/include/caffe/util/blocking_queue.hpp b/include/caffe/util/blocking_queue.hpp
index 955e12cc567..d3de2e59b80 100644
--- a/include/caffe/util/blocking_queue.hpp
+++ b/include/caffe/util/blocking_queue.hpp
@@ -4,8 +4,6 @@
 #include <queue>
 #include <string>
 
-#include "caffe/common.hpp"
-
 namespace caffe {
 
 template<typename T>
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 6070b4c7f3a..d6cfa442fca 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -6,7 +6,6 @@
 
 #include "google/protobuf/message.h"
 
-#include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index 5ce28c9e2b4..7e552352608 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -1,6 +1,5 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index bb310e1afbb..b5a6c25a85a 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index e2d8d9f8a24..ae2df1f1bad 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -1,12 +1,9 @@
-#include <algorithm>
 #include <functional>
 #include <utility>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 0c0a932dac7..44df8d4e2e4 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -3,8 +3,7 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/common_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index c6b47550292..316cb0fdf98 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -2,7 +2,6 @@
 #include <vector>
 
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index b90bd4e0caf..d77f91c913b 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -1,10 +1,7 @@
 #include <boost/thread.hpp>
-#include <string>
 #include <vector>
 
 #include "caffe/data_layers.hpp"
-#include "caffe/net.hpp"
-#include "caffe/util/io.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp
index 3bf757c718d..3d3ce32c90d 100644
--- a/src/caffe/layers/batch_reindex_layer.cpp
+++ b/src/caffe/layers/batch_reindex_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu
index c418cab9042..0b5ccf099fe 100644
--- a/src/caffe/layers/batch_reindex_layer.cu
+++ b/src/caffe/layers/batch_reindex_layer.cu
@@ -2,9 +2,8 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 9ba0ea9a715..1e422a54653 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index d963d0687d2..3e328ef70fa 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 86b500de859..14cbfb11f8b 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 617701e2621..e1e9449e406 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 25e167819d3..74002087ec9 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -1,9 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/loss_layers.hpp"
-#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 931239316ac..ee2784077e4 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -1,10 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index fb50bb095ed..efd69d45edf 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -1,9 +1,5 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index b429d2b47d0..a534b3560f5 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -1,9 +1,5 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index d7b1e0d651f..8b61249a4f8 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -2,10 +2,6 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index e88e4dd3281..63b6ab9c388 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp
index 866d810b9f9..4c700786e6b 100644
--- a/src/caffe/layers/cudnn_lcn_layer.cpp
+++ b/src/caffe/layers/cudnn_lcn_layer.cpp
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu
index c07ade72066..e79c745894a 100644
--- a/src/caffe/layers/cudnn_lcn_layer.cu
+++ b/src/caffe/layers/cudnn_lcn_layer.cu
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp
index 6e9921490f0..a03db3bdd9f 100644
--- a/src/caffe/layers/cudnn_lrn_layer.cpp
+++ b/src/caffe/layers/cudnn_lrn_layer.cpp
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu
index f9923033011..327e44b4d31 100644
--- a/src/caffe/layers/cudnn_lrn_layer.cu
+++ b/src/caffe/layers/cudnn_lrn_layer.cu
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index c92c4e477b5..5f995d45e0c 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index a952b855a48..9aa39ed8818 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -1,10 +1,6 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 759d83984ef..e6b6d5a9715 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -1,8 +1,6 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 21d14857dd2..2a53a49b91f 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -1,8 +1,6 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 32637873d46..4b489fa5b16 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -1,8 +1,6 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 7a06cf721da..9de5c742c8e 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -1,8 +1,6 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 77a3225adcd..f5cd04505b8 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -1,12 +1,8 @@
 #ifdef USE_CUDNN
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
 #include "thrust/device_vector.h"
 
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index a9e2fcefaf7..c270202f0c9 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -1,12 +1,8 @@
 #ifdef USE_CUDNN
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
 #include "thrust/device_vector.h"
 
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 376faad324d..4629681807d 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index d287f6fee85..84f784b37c4 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 71f8cb099e8..49ac858efc9 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -3,15 +3,11 @@
 #endif  // USE_OPENCV
 #include <stdint.h>
 
-#include <string>
 #include <vector>
 
-#include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/benchmark.hpp"
-#include "caffe/util/io.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index 91aabb315b2..5038b6389fa 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -1,9 +1,5 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index 5dbdcc3149f..0e8e2edea1e 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -1,9 +1,5 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index ec1256fd2fa..eb7a8a9a20b 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -2,11 +2,8 @@
 
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index f9ea04f4acf..028fc026d51 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -1,12 +1,7 @@
-#include <algorithm>
-#include <limits>
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 6b0d617464c..ab0478c860c 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
+#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index a80700736bd..7924fbeec7d 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -1,9 +1,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 2247870d97f..01404209834 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -1,9 +1,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp
index be6b2cd2727..52704a06d29 100644
--- a/src/caffe/layers/embed_layer.cpp
+++ b/src/caffe/layers/embed_layer.cpp
@@ -1,10 +1,7 @@
 #include <vector>
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
index 62a4db81336..cd4b40f58fe 100644
--- a/src/caffe/layers/embed_layer.cu
+++ b/src/caffe/layers/embed_layer.cu
@@ -1,10 +1,7 @@
 #include <vector>
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/gpu_util.cuh"
 #include "caffe/util/math_functions.hpp"
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 80efa31b22c..7338953d340 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -1,9 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index 5b1de3ad2d9..1aa79bd542d 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -1,9 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index c7e7c60cfad..f85692d6c5d 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu
index 2d75d8dd6c7..9e24bbeec9d 100644
--- a/src/caffe/layers/exp_layer.cu
+++ b/src/caffe/layers/exp_layer.cu
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp
index be1db32dbaa..e8b62a5d5fc 100644
--- a/src/caffe/layers/filter_layer.cpp
+++ b/src/caffe/layers/filter_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu
index cf929eeeadf..746e91c9e95 100644
--- a/src/caffe/layers/filter_layer.cu
+++ b/src/caffe/layers/filter_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index f7e5c9c2172..d831fb5c6b5 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -1,8 +1,6 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/common_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 8ced51039cf..c765fa02c82 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -15,7 +15,6 @@
 #include "stdint.h"
 
 #include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/hdf5.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 5e3e4ced141..6ac499c6fbd 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -4,15 +4,12 @@ TODO:
 */
 
 #include <stdint.h>
-#include <string>
 #include <vector>
 
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
 #include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index 56788c21e5e..dbde65da14f 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -3,11 +3,8 @@
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/util/hdf5.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index eb6d0e470b0..ca8f2616548 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -3,10 +3,7 @@
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/data_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index a2fb2a18309..a88c8775205 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -1,12 +1,8 @@
 #include <algorithm>
-#include <cfloat>
-#include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 595c9dbbe5e..f3b0f7101b8 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -1,7 +1,5 @@
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index cd507623c78..4633628b4c0 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -1,7 +1,5 @@
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 3d2190f8bbb..9a7df5a78ca 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -8,7 +8,6 @@
 #include <vector>
 
 #include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index a1e0b40de0e..88bd8aaf54f 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -1,12 +1,9 @@
 #include <algorithm>
-#include <cfloat>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 83c3235eb71..274744eaaf8 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -1,11 +1,8 @@
 #include <vector>
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index c0ebd2c47da..e91e94fc9c6 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -1,11 +1,8 @@
 #include <vector>
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
index 55a227f6226..a1876b9da76 100644
--- a/src/caffe/layers/log_layer.cpp
+++ b/src/caffe/layers/log_layer.cpp
@@ -1,7 +1,5 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu
index 847c86cd10c..055b713bef3 100644
--- a/src/caffe/layers/log_layer.cu
+++ b/src/caffe/layers/log_layer.cu
@@ -1,7 +1,5 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
 
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 3496a5c2a8a..c10466dbdb0 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -1,12 +1,6 @@
-#include <algorithm>
-#include <cfloat>
-#include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index d18a04ef58d..cc561811e95 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -1,6 +1,5 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 001b3c34ac1..4523d41095d 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -1,6 +1,5 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 2370aa04d3b..13a3d9f61b9 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -5,8 +5,6 @@
 #include <vector>
 
 #include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 4267a594a0f..59745923825 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -1,12 +1,9 @@
 #include <algorithm>
-#include <cfloat>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 61c2141ecd9..0e7301442ba 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -1,8 +1,6 @@
-#include <algorithm>
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 5cbb112de4d..b7e3b3ceb39 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -1,8 +1,6 @@
-#include <algorithm>
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index ba67b43878e..1dcb2c06419 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index c8d41499455..3a7de42c91a 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -2,9 +2,6 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index ca4b13f7c41..5e94ce2bc3f 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -2,7 +2,6 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 4fe34c49f32..6304fadd489 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 90d944059b6..680faad4bca 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
index 81831755512..b5a294e1c5a 100644
--- a/src/caffe/layers/prelu_layer.cpp
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -2,8 +2,7 @@
 #include <vector>
 
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index 1225334f335..992cd885a95 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp
index 8ae6329ebe4..6b7925e37b0 100644
--- a/src/caffe/layers/reduction_layer.cpp
+++ b/src/caffe/layers/reduction_layer.cpp
@@ -1,10 +1,7 @@
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu
index 2dbd3bc9f94..a9a8c8d9633 100644
--- a/src/caffe/layers/reduction_layer.cu
+++ b/src/caffe/layers/reduction_layer.cu
@@ -1,9 +1,7 @@
-#include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index cc00319a578..93d090263c7 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index b8924c855e5..c18ab61f2ed 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
index ffe970f2689..8659049b528 100644
--- a/src/caffe/layers/reshape_layer.cpp
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index cc236fe1e8e..98588637831 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,10 +1,7 @@
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 547fa80c72f..48dbec41be1 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -1,10 +1,7 @@
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 48c384905bf..d4a3f8773d6 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index e1af0657ec1..5730636ef11 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 7e70ab4329e..3974f5d4e65 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index 34faef22bda..c49ecb2338a 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -1,7 +1,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 0a059ae88fe..f368a249a5e 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -1,9 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index e8dc6cd98fc..d555f7d0dec 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 04712c9e653..8ae7d49cfaa 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -1,9 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 1f9c3a41203..a620fcc8601 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -4,9 +4,8 @@
 
 #include "thrust/device_vector.h"
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index ba312f67fbc..dee50ac6355 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -2,10 +2,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/layer_factory.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 7e0f3da4552..42e91fa9e38 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -2,9 +2,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 272cb59cd37..5333e578f76 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index a4f5df26452..73d04c98fc1 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp
index d7622910495..2ef4ac7ab95 100644
--- a/src/caffe/layers/spp_layer.cpp
+++ b/src/caffe/layers/spp_layer.cpp
@@ -1,11 +1,6 @@
 #include <algorithm>
-#include <cfloat>
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index ee5ed773c74..9d1cac76184 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -1,11 +1,9 @@
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index ccd6e63ee7c..d87bcceced7 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -1,11 +1,9 @@
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index 2365e7b9c72..d65147368fc 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 
 namespace caffe {
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index bfa7f159460..1cd62d99482 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -1,8 +1,6 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp
index f55008cc53a..581546c4fdb 100644
--- a/src/caffe/layers/tile_layer.cpp
+++ b/src/caffe/layers/tile_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu
index 7fd3bc47d0f..fdf960901a3 100644
--- a/src/caffe/layers/tile_layer.cu
+++ b/src/caffe/layers/tile_layer.cu
@@ -1,7 +1,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index f8db61c9258..3f937bc9dd8 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -12,9 +12,7 @@
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"
 
-#include "caffe/common.hpp"
 #include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index a6d154e168e..9abc92b612d 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -7,7 +7,6 @@
 #include <sys/mman.h>
 #include <sys/stat.h>
 
-#include <cstdlib>
 #include <sstream>
 #include <string>
 #include <vector>
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 632bf1f12d3..ec4665ecdda 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -1,5 +1,3 @@
-#include <cstring>
-
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index ef0e57a37a1..5960a666734 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -1,6 +1,4 @@
 #include <cfloat>
-#include <cmath>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,8 +6,8 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/loss_layers.hpp"
 #include "caffe/util/rng.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index bbf19099905..f3f2094edeb 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp
index 985db343d12..17e47f05066 100644
--- a/src/caffe/test/test_batch_reindex_layer.cpp
+++ b/src/caffe/test/test_batch_reindex_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index 7da6423b67c..a9d7d519e45 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index b3a61b0fd25..58ae5c60a4f 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -1,5 +1,3 @@
-#include <cstring>
-
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index ccd97eb1d66..8ba51f4f7f7 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index 1e9447cbc51..592997e4578 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -1,7 +1,5 @@
 #include <algorithm>
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -9,7 +7,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 9df979a2d27..b474735716d 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
index 770e7b277ee..b473dbb9c51 100644
--- a/src/caffe/test/test_deconvolution_layer.cpp
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 8031f6e9022..3b56c5cae36 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
index 7a4fb9800f2..0f4caf15742 100644
--- a/src/caffe/test/test_embed_layer.cpp
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 1949742bbcb..9dc14de4149 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 728b8dc5f0d..26e9b217e35 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -1,5 +1,3 @@
-#include <cstring>
-
 #include "gtest/gtest.h"
 
 #include "caffe/filler.hpp"
diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp
index c641b6ef6e8..a2d0c293644 100644
--- a/src/caffe/test/test_filter_layer.cpp
+++ b/src/caffe/test/test_filter_layer.cpp
@@ -1,13 +1,11 @@
-#include <cstring>
-#include <limits>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 7b6757cba32..5d1caac2aa7 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index b56277b53ae..adc27df4ad0 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -5,10 +5,10 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index c9b027f88cf..7169e7bfc41 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -5,9 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/filler.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index b6a99022905..dfdd01d0291 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index f0b75fcc68d..bafcacf784e 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 293aa262059..ec055b20176 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 481fcef7b27..77690245111 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -7,10 +7,10 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index 7ec2f8073c1..b2a6754fd40 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -1,6 +1,3 @@
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index fbf0c851220..1ad2c97e75a 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 78cf2d9df9b..bd1c4fe8810 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index a095b544e17..fbee3f9c32d 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -1,8 +1,6 @@
 #include <stdint.h>  // for uint32_t & uint64_t
 #include <time.h>
-#include <climits>
 #include <cmath>  // for std::fabs
-#include <cstdlib>  // for rand_r
 
 #include "gtest/gtest.h"
 
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 611d9790863..8fc944f3250 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index b2db984feb1..0404aa25af6 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -1,6 +1,3 @@
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index be23d86e9c3..e9a7d54ce4a 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -1,5 +1,3 @@
-#include <cmath>
-#include <cstring>
 #include <vector>
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index c6e4d27b903..b333fdee802 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "google/protobuf/text_format.h"
@@ -7,8 +6,9 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 69f2d5c1135..9e986e66528 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 76c9e857f36..1041ddd4ee8 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 98424c06bfc..833b0047b5d 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -1,5 +1,4 @@
 #include <cmath>
-#include <cstring>
 
 #include "gtest/gtest.h"
 
diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp
index f568a18089a..a8d43727113 100644
--- a/src/caffe/test/test_reduction_layer.cpp
+++ b/src/caffe/test/test_reduction_layer.cpp
@@ -1,12 +1,11 @@
-#include <algorithm>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
index 9d08ec60d4e..e0f4ba42851 100644
--- a/src/caffe/test/test_reshape_layer.cpp
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index e5737e43f6e..b4f831c8f2d 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index 2d2d0fdc005..45fbcffda3f 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 996da4b8f7c..4b01f5cfbab 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -1,13 +1,12 @@
 #include <cmath>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 1498d5c5ce1..0ae4cd6815a 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "boost/scoped_ptr.hpp"
@@ -9,7 +7,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/loss_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index be5204bfc3e..e27e355c77e 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <string>
 #include <vector>
 
@@ -7,10 +6,10 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/insert_splits.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp
index b2585f1a5fa..1b48a842d3f 100644
--- a/src/caffe/test/test_spp_layer.cpp
+++ b/src/caffe/test/test_spp_layer.cpp
@@ -1,5 +1,3 @@
-#include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index f84464c322c..5a412bd4e17 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index b946233d07c..16dfb58230f 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 5dc92832fc8..f31579cac40 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/neuron_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 05ce82120e6..903a9bc8157 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp
index 540aac3c2d3..5c459604a6b 100644
--- a/src/caffe/test/test_tile_layer.cpp
+++ b/src/caffe/test/test_tile_layer.cpp
@@ -1,12 +1,11 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 23deddd453d..9dcc2aa55ec 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <string>
 #include <vector>
 
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 8770f309951..9ee8818ff1d 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -1,7 +1,5 @@
 #ifndef CPU_ONLY  // CPU-GPU test
 
-#include <cstring>
-
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 09da23d490f..27e5b7c0928 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -1,6 +1,3 @@
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "caffe/util/im2col.hpp"
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 451097f8a1b..49354ab7aa1 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -1,7 +1,4 @@
 #include <algorithm>
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 
 #include "caffe/common.hpp"
 #include "caffe/util/im2col.hpp"
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 2631a0740d6..e4d0c4b04bb 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -4,8 +4,6 @@
 #include <thrust/reduce.h>
 
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 
 #include "caffe/common.hpp"
 #include "caffe/util/math_functions.hpp"

From 2f05b03371e5936a478c7ad2946d0cd7c013920c Mon Sep 17 00:00:00 2001
From: Dmytro Mishkin <ducha.aiki@gmail.com>
Date: Wed, 25 Feb 2015 17:00:22 +0200
Subject: [PATCH 1608/2053] Added batch normalization layer with test and
 examples

---
 .../cifar10_full_sigmoid_solver.prototxt      |  28 ++
 .../cifar10_full_sigmoid_solver_bn.prototxt   |  28 ++
 .../cifar10_full_sigmoid_train_test.prototxt  | 212 +++++++++++
 ...ifar10_full_sigmoid_train_test_bn.prototxt | 284 ++++++++++++++
 examples/cifar10/train_full_sigmoid.sh        |   7 +
 examples/cifar10/train_full_sigmoid_bn.sh     |   7 +
 include/caffe/common_layers.hpp               |  50 ++-
 src/caffe/layers/batch_norm_layer.cpp         | 351 ++++++++++++++++++
 src/caffe/layers/batch_norm_layer.cu          | 228 ++++++++++++
 src/caffe/test/test_batch_norm_layer.cpp      |  90 +++++
 10 files changed, 1284 insertions(+), 1 deletion(-)
 create mode 100644 examples/cifar10/cifar10_full_sigmoid_solver.prototxt
 create mode 100644 examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
 create mode 100644 examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
 create mode 100644 examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
 create mode 100755 examples/cifar10/train_full_sigmoid.sh
 create mode 100755 examples/cifar10/train_full_sigmoid_bn.sh
 create mode 100644 src/caffe/layers/batch_norm_layer.cpp
 create mode 100644 src/caffe/layers/batch_norm_layer.cu
 create mode 100644 src/caffe/test/test_batch_norm_layer.cpp

diff --git a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
new file mode 100644
index 00000000000..7dd3ecb9d8e
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
@@ -0,0 +1,28 @@
+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
new file mode 100644
index 00000000000..a57b280fd1e
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
@@ -0,0 +1,28 @@
+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid_bn"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
new file mode 100644
index 00000000000..6f5bf26bf99
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
@@ -0,0 +1,212 @@
+name: "CIFAR10_full"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 111
+    backend: LMDB
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 1000
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+
+
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "pool1"
+  top: "Sigmoid1"
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "Sigmoid1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "conv2"
+  top: "Sigmoid2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "Sigmoid2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 1
+  }
+
+}
+
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "conv3"
+  top: "Sigmoid3"
+}
+
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "Sigmoid3"
+  top: "pool3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool3"
+  top: "ip1"
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 0.2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip1"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
new file mode 100644
index 00000000000..85c2dffe3f6
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
@@ -0,0 +1,284 @@
+name: "CIFAR10_full"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 111
+    backend: LMDB
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 1000
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "pool1"
+  top: "bn1"
+  bn_param {
+    scale_filler {
+      type: "constant"
+      value: 1
+    }
+    shift_filler {
+      type: "constant"
+      value: 0.001
+    } 
+  }
+  param {
+    lr_mult: 1.00001
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 1.00001
+    decay_mult: 0
+  }
+}
+
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "bn1"
+  top: "Sigmoid1"
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "Sigmoid1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+
+
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  bn_param {
+    scale_filler {
+      type: "constant"
+      value: 1
+    }
+    shift_filler {
+      type: "constant"
+      value: 0.001
+    } 
+  }
+  param {
+    lr_mult: 1.00001
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 1.00001
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "bn2"
+  top: "Sigmoid2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "Sigmoid2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 1
+  }
+
+}
+
+
+layer {
+  name: "bn3"
+  type: "BatchNorm"
+  bottom: "conv3"
+  top: "bn3"
+  bn_param {
+    scale_filler {
+      type: "constant"
+      value: 1
+    }
+    shift_filler {
+      type: "constant"
+      value: 0.001
+    } 
+  }
+  param {
+    lr_mult: 1.00001
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 1.00001
+    decay_mult: 0
+  }
+}
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "bn3"
+  top: "Sigmoid3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "Sigmoid3"
+  top: "pool3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool3"
+  top: "ip1"
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 0.2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip1"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/cifar10/train_full_sigmoid.sh b/examples/cifar10/train_full_sigmoid.sh
new file mode 100755
index 00000000000..9cff06d3e34
--- /dev/null
+++ b/examples/cifar10/train_full_sigmoid.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train \
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+
diff --git a/examples/cifar10/train_full_sigmoid_bn.sh b/examples/cifar10/train_full_sigmoid_bn.sh
new file mode 100755
index 00000000000..011387c996e
--- /dev/null
+++ b/examples/cifar10/train_full_sigmoid_bn.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train \
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 21a27d759a8..09605db9a53 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -78,6 +78,55 @@ class ArgMaxLayer : public Layer<Dtype> {
   int axis_;
 };
 
+/**
+* @brief Batch Normalization per-channel with scale & shift linear transform.
+*
+*/
+template <typename Dtype>
+class BatchNormLayer : public Layer<Dtype> {
+ public:
+  explicit BatchNormLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  // spatial mean & variance
+  Blob<Dtype> spatial_mean_, spatial_variance_;
+  // batch mean & variance
+  Blob<Dtype> batch_mean_, batch_variance_;
+  // buffer blob
+  Blob<Dtype> buffer_blob_;
+
+  Blob<Dtype> x_norm_;
+  // x_sum_multiplier is used to carry out sum using BLAS
+  Blob<Dtype> spatial_sum_multiplier_, batch_sum_multiplier_;
+
+  // dimension
+  int N_;
+  int C_;
+  int H_;
+  int W_;
+  // eps
+  Dtype var_eps_;
+};
+
 /**
  * @brief Index into the input blob along its first axis.
  *
@@ -146,7 +195,6 @@ class BatchReindexLayer : public Layer<Dtype> {
                            const Dtype* ridx_data);
 };
 
-
 /**
  * @brief Takes at least two Blob%s and concatenates them along either the num
  *        or channel dimension, outputting the result.
diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
new file mode 100644
index 00000000000..8dea34932f3
--- /dev/null
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -0,0 +1,351 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+  template <typename Dtype>
+  void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+        bottom[0]->height(), bottom[0]->width());
+
+    x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+        bottom[0]->height(), bottom[0]->width());
+
+    // Figure out the dimensions
+    N_ = bottom[0]->num();
+    C_ = bottom[0]->channels();
+    H_ = bottom[0]->height();
+    W_ = bottom[0]->width();
+
+    // mean
+    spatial_mean_.Reshape(N_, C_, 1, 1);
+    batch_mean_.Reshape(1, C_, 1, 1);
+    // variance
+    spatial_variance_.Reshape(N_, C_, 1, 1);
+    batch_variance_.Reshape(1, C_, 1, 1);
+    // buffer blod
+    buffer_blob_.Reshape(N_, C_, H_, W_);
+
+    // fill spatial multiplier
+    spatial_sum_multiplier_.Reshape(1, 1, H_, W_);
+    Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data();
+    caffe_set(spatial_sum_multiplier_.count(), Dtype(1),
+        spatial_multipl_data);
+    caffe_set(spatial_sum_multiplier_.count(), Dtype(0),
+        spatial_sum_multiplier_.mutable_cpu_diff());
+    // fill batch multiplier
+    batch_sum_multiplier_.Reshape(N_, 1, 1, 1);
+    Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data();
+    caffe_set(batch_sum_multiplier_.count(), Dtype(1),
+        batch_multiplier_data);
+    caffe_set(batch_sum_multiplier_.count(), Dtype(0),
+        batch_sum_multiplier_.mutable_cpu_diff());
+    this->param_propagate_down_.resize(this->blobs_.size(), true);
+  }
+  template <typename Dtype>
+  void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+    "allow in-place computation.";
+
+    top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+        bottom[0]->height(), bottom[0]->width());
+
+    x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+        bottom[0]->height(), bottom[0]->width());
+    // Figure out the dimensions
+    N_ = bottom[0]->num();
+    C_ = bottom[0]->channels();
+    H_ = bottom[0]->height();
+    W_ = bottom[0]->width();
+    var_eps_ = 1e-9;
+
+    // mean
+    spatial_mean_.Reshape(N_, C_, 1, 1);
+    batch_mean_.Reshape(1, C_, 1, 1);
+    // variance
+    spatial_variance_.Reshape(N_, C_, 1, 1);
+    batch_variance_.Reshape(1, C_, 1, 1);
+    // buffer blod
+    buffer_blob_.Reshape(N_, C_, H_, W_);
+
+    // fill spatial multiplier
+    spatial_sum_multiplier_.Reshape(1, 1, H_, W_);
+    Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data();
+    caffe_set(spatial_sum_multiplier_.count(), Dtype(1),
+        spatial_multipl_data);
+    caffe_set(spatial_sum_multiplier_.count(), Dtype(0),
+        spatial_sum_multiplier_.mutable_cpu_diff());
+
+    // fill batch multiplier
+    batch_sum_multiplier_.Reshape(N_, 1, 1, 1);
+    Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data();
+    caffe_set(batch_sum_multiplier_.count(), Dtype(1),
+        batch_multiplier_data);
+    caffe_set(batch_sum_multiplier_.count(), Dtype(0),
+        batch_sum_multiplier_.mutable_cpu_diff());
+
+    // Check if we need to set up the weights
+    if (this->blobs_.size() > 0) {
+      LOG(INFO) << "Skipping parameter initialization";
+    } else {
+      this->blobs_.resize(2);
+
+      // fill scale with scale_filler
+      this->blobs_[0].reset(new Blob<Dtype>(1, C_, 1, 1));
+      caffe_set(this->blobs_[0]->count(), Dtype(1),
+          this->blobs_[0]->mutable_cpu_data());
+
+      // fill shift with shift_filler
+      this->blobs_[1].reset(new Blob<Dtype>(1, C_, 1, 1));
+      caffe_set(this->blobs_[1]->count(), Dtype(0),
+          this->blobs_[1]->mutable_cpu_data());
+    }  // parameter initialization
+    this->param_propagate_down_.resize(this->blobs_.size(), true);
+  }
+
+  template <typename Dtype>
+  void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    Dtype* top_data = top[0]->mutable_cpu_data();
+    const Dtype* const_top_data = top[0]->cpu_data();
+
+    const Dtype* scale_data = this->blobs_[0]->cpu_data();
+    const Dtype* shift_data = this->blobs_[1]->cpu_data();
+
+    // put the squares of bottom into buffer_blob_
+    caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
+        buffer_blob_.mutable_cpu_data());
+
+    // computes variance using var(X) = E(X^2) - (EX)^2
+    // EX across spatial
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
+        Dtype(1. / (H_ * W_)), bottom_data,
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+    // EX across batch
+    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
+        spatial_mean_.cpu_data(),
+        batch_sum_multiplier_.cpu_data(), Dtype(0),
+        batch_mean_.mutable_cpu_data());
+
+    // E(X^2) across spatial
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
+        Dtype(1. / (H_ * W_)), buffer_blob_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_variance_.mutable_cpu_data());
+    // E(X^2) across batch
+    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
+        spatial_variance_.cpu_data(),
+        batch_sum_multiplier_.cpu_data(), Dtype(0),
+        batch_variance_.mutable_cpu_data());
+
+    caffe_powx(batch_mean_.count(), batch_mean_.cpu_data(), Dtype(2),
+        buffer_blob_.mutable_cpu_data());  // (EX)^2
+    caffe_sub(batch_mean_.count(), batch_variance_.cpu_data(),
+        buffer_blob_.cpu_data(),
+        batch_variance_.mutable_cpu_data());  // variance
+
+    // do mean and variance normalization
+    // subtract mean
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_,
+        C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(),
+        batch_mean_.cpu_data(), Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(-1),
+        spatial_mean_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        buffer_blob_.mutable_cpu_data());
+
+    caffe_add(buffer_blob_.count(), bottom_data,
+        buffer_blob_.cpu_data(), top_data);
+
+    // normalize variance
+    caffe_add_scalar(batch_variance_.count(), var_eps_,
+        batch_variance_.mutable_cpu_data());
+    caffe_powx(batch_variance_.count(),
+        batch_variance_.cpu_data(), Dtype(0.5),
+        batch_variance_.mutable_cpu_data());
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_,
+        C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(),
+        batch_variance_.cpu_data(), Dtype(0),
+        spatial_variance_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_ * C_, H_ * W_, 1, Dtype(1),
+        spatial_variance_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        buffer_blob_.mutable_cpu_data());
+
+    caffe_div(buffer_blob_.count(), const_top_data,
+        buffer_blob_.cpu_data(), top_data);
+
+    // Saving x_norm
+    caffe_copy(buffer_blob_.count(), const_top_data,
+        x_norm_.mutable_cpu_data());
+    // scale
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0),
+        spatial_variance_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_variance_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        buffer_blob_.mutable_cpu_data());
+    caffe_mul(buffer_blob_.count(), top_data,
+        buffer_blob_.cpu_data(), top_data);
+
+    // shift
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(), shift_data, Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_ * C_, H_ * W_, 1, Dtype(1),
+        spatial_mean_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        buffer_blob_.mutable_cpu_data());
+    caffe_add(buffer_blob_.count(), const_top_data,
+        buffer_blob_.cpu_data(), top_data);
+  }
+
+  template <typename Dtype>
+  void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down,
+      const vector<Blob<Dtype>*>& bottom) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+
+    Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff();
+    Dtype* shift_diff = this->blobs_[1]->mutable_cpu_diff();
+    const Dtype* scale_data = this->blobs_[0]->cpu_data();
+
+// Propagate layer to parameters
+    // gradient w.r.t. scale
+    caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(),
+        top_diff, buffer_blob_.mutable_cpu_data());
+    // EX across spatial
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_,
+        H_ * W_, Dtype(1), buffer_blob_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_variance_.mutable_cpu_diff());
+    // EX across batch
+    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_variance_.cpu_diff(),
+        batch_sum_multiplier_.cpu_data(), Dtype(0), scale_diff);
+
+    // gradient w.r.t. shift
+    // EX across spatial
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_,
+        H_ * W_, Dtype(1), top_diff,
+        spatial_sum_multiplier_.cpu_data(),
+        Dtype(0), spatial_mean_.mutable_cpu_diff());
+    // EX across batch
+    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_,
+        Dtype(1), spatial_mean_.cpu_diff(),
+        batch_sum_multiplier_.cpu_data(),
+        Dtype(0), shift_diff);
+
+// Propagate down
+
+    // put scale * top_diff to buffer_blob_
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0),
+        spatial_variance_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_variance_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        buffer_blob_.mutable_cpu_data());
+    caffe_mul(buffer_blob_.count(), top_diff, buffer_blob_.cpu_data(),
+        buffer_blob_.mutable_cpu_data());
+
+    // use new top diff for computation
+    caffe_mul(buffer_blob_.count(),  x_norm_.cpu_data(),
+        buffer_blob_.cpu_data(), bottom_diff);
+    // EX across spatial
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
+        Dtype(1), bottom_diff,
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+    // EX across batch
+    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_mean_.cpu_data(),
+        batch_sum_multiplier_.cpu_data(), Dtype(0),
+        batch_mean_.mutable_cpu_data());
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(),
+        batch_mean_.cpu_data(), Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_mean_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        bottom_diff);
+
+    caffe_mul(buffer_blob_.count(),
+        x_norm_.cpu_data(), bottom_diff, bottom_diff);
+
+    // EX across spatial
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_,
+        H_ * W_, Dtype(1), buffer_blob_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+    // EX across batch
+    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_mean_.cpu_data(),
+        batch_sum_multiplier_.cpu_data(), Dtype(0),
+        batch_mean_.mutable_cpu_data());
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(),
+        batch_mean_.cpu_data(), Dtype(0),
+        spatial_mean_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_ * C_, H_ * W_, 1, Dtype(1),
+        spatial_mean_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(1), bottom_diff);
+
+    caffe_cpu_axpby(buffer_blob_.count(), Dtype(1),
+        buffer_blob_.cpu_data(), Dtype(-1. / (N_ * H_ * W_)),
+        bottom_diff);
+
+    // put the squares of bottom into buffer_blob_
+//    caffe_powx(buffer_blob_.count(), bottom_data, Dtype(2),
+//        buffer_blob_.mutable_cpu_data());
+
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.cpu_data(),
+        batch_variance_.cpu_data(), Dtype(0),
+        spatial_variance_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+        N_ * C_, H_ * W_, 1, Dtype(1),
+        spatial_variance_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), Dtype(0),
+        buffer_blob_.mutable_cpu_data());
+
+    caffe_div(buffer_blob_.count(), bottom_diff,
+        buffer_blob_.cpu_data(), bottom_diff);
+  }
+#ifdef CPU_ONLY
+STUB_GPU(BatchNormLayer);
+#endif
+
+  INSTANTIATE_CLASS(BatchNormLayer);
+  REGISTER_LAYER_CLASS(BatchNorm);
+}  // namespace caffe
+
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
new file mode 100644
index 00000000000..e87f8c62f43
--- /dev/null
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -0,0 +1,228 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+  template <typename Dtype>
+  void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* const_top_data = top[0]->gpu_data();
+    Dtype* top_data = top[0]->mutable_gpu_data();
+    Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data();
+    Dtype* buffer_data = buffer_blob_.mutable_gpu_data();
+    const Dtype* const_buffer_data = buffer_blob_.gpu_data();
+
+
+  // put the squares of bottom into buffer_blob_
+    caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
+        buffer_blob_.mutable_gpu_data());
+
+    // computes variance using var(X) = E(X^2) - (EX)^2
+    // EX across spatial
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
+      Dtype(1. / (H_ * W_)),
+      bottom_data, spatial_sum_multiplier_.gpu_data(),
+      Dtype(0), spatial_mean_data);
+    // EX across batch
+    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
+        spatial_mean_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+      batch_mean_.mutable_gpu_data());
+
+    // E(X^2) across spatial
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
+      Dtype(1. / (H_ * W_)), buffer_data,
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+      spatial_variance_.mutable_gpu_data());
+    // E(X^2) across batch
+    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
+        spatial_variance_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+      batch_variance_.mutable_gpu_data());
+
+    caffe_gpu_powx(batch_mean_.count(), batch_mean_.gpu_data(),
+      Dtype(2), buffer_blob_.mutable_gpu_data());  // (EX)^2
+    caffe_gpu_sub(batch_mean_.count(), batch_variance_.gpu_data(),
+      buffer_data, batch_variance_.mutable_gpu_data());  // variance
+
+    // do mean and variance normalization
+    // subtract mean
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(), batch_mean_.gpu_data(), Dtype(0),
+        spatial_mean_data);
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_,
+        1, -Dtype(1),
+        spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        buffer_blob_.mutable_gpu_data());
+
+    caffe_gpu_add(buffer_blob_.count(), bottom_data, buffer_data, top_data);
+
+    // normalize variance
+    caffe_gpu_add_scalar(batch_variance_.count(), var_eps_,
+      batch_variance_.mutable_gpu_data());
+    caffe_gpu_powx(batch_variance_.count(), batch_variance_.gpu_data(),
+      Dtype(0.5), batch_variance_.mutable_gpu_data());
+
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0),
+        spatial_variance_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), buffer_blob_.mutable_gpu_data());
+
+    caffe_gpu_div(buffer_blob_.count(), top_data, buffer_data, top_data);
+
+    // Saving x_norm
+    caffe_copy(top[0]->count(), const_top_data, x_norm_.mutable_gpu_data());
+
+    // scale
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(),
+        Dtype(0), spatial_variance_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), buffer_blob_.mutable_gpu_data());
+
+    caffe_gpu_mul(buffer_blob_.count(), top_data, buffer_data, top_data);
+
+    // shift
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(),
+        this->blobs_[1]->gpu_data(), Dtype(0),
+        spatial_mean_data);
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1,
+        Dtype(1),
+        spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        buffer_blob_.mutable_gpu_data());
+    caffe_gpu_add(buffer_blob_.count(), top_data, buffer_data, top_data);
+  }
+
+  template <typename Dtype>
+  void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down,
+      const vector<Blob<Dtype>*>& bottom) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* top_data = top[0]->gpu_data();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* const_bottom_diff = bottom[0]->gpu_diff();
+    Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data();
+    Dtype* buffer_data = buffer_blob_.mutable_gpu_data();
+    const Dtype* const_buffer_data = buffer_blob_.gpu_data();
+
+    // Propage to layer params
+    // gradient w.r.t. scale
+    caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(),
+    top_diff, buffer_blob_.mutable_gpu_data());
+    // EX across spatial
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1),
+        buffer_data, spatial_sum_multiplier_.gpu_data(), Dtype(0),
+    spatial_variance_.mutable_gpu_data());
+    // EX across batch
+    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_variance_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+        this->blobs_[0]->mutable_gpu_diff());
+
+    // gradient w.r.t. shift
+    // EX across spatial
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1),
+      top_diff, spatial_sum_multiplier_.gpu_data(),
+      Dtype(0), spatial_mean_data);
+    // EX across batch
+    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_mean_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+        this->blobs_[1]->mutable_gpu_diff());
+
+    // Propagate down
+    // scale top diff
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(),
+        Dtype(0), spatial_variance_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(0),
+        buffer_blob_.mutable_gpu_data());
+    caffe_gpu_mul(buffer_blob_.count(), top_diff, buffer_data,
+        buffer_blob_.mutable_gpu_data());
+
+    // use new top diff for computation
+    caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(),
+       buffer_data, bottom_diff);
+    // EX across spatial
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
+        Dtype(1), bottom_diff,
+        spatial_sum_multiplier_.gpu_data(), Dtype(0), spatial_mean_data);
+    // EX across batch
+    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_mean_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+        batch_mean_.mutable_gpu_data());
+
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(),
+        batch_mean_.gpu_data(), Dtype(0),
+        spatial_mean_data);
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1), spatial_mean_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), Dtype(0),
+        bottom_diff);
+
+    caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(),
+        bottom_diff, bottom_diff);
+
+    // EX across spatial
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1),
+        buffer_data, spatial_sum_multiplier_.gpu_data(),
+        Dtype(0), spatial_mean_data);
+
+    // EX across batch
+    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
+        spatial_mean_.gpu_data(),
+        batch_sum_multiplier_.gpu_data(), Dtype(0),
+        batch_mean_.mutable_gpu_data());
+
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_,
+        C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(),
+        batch_mean_.gpu_data(), Dtype(0),
+        spatial_mean_data);
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(1),
+        bottom_diff);
+
+    caffe_gpu_axpby(buffer_blob_.count(), Dtype(1), buffer_data,
+        Dtype(-1. / (N_ * H_ * W_)),
+        bottom_diff);
+
+    // put the squares of bottom into buffer_blob_
+//    caffe_gpu_powx(buffer_blob_.count(), bottom_data, Dtype(2),
+//        buffer_blob_.mutable_gpu_data());
+
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
+        batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0),
+        spatial_variance_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
+        H_ * W_, 1, Dtype(1),
+        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
+        Dtype(0),
+        buffer_blob_.mutable_gpu_data());
+
+    caffe_gpu_div(buffer_blob_.count(), const_bottom_diff,
+    const_buffer_data, bottom_diff);
+  }
+
+  INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer);
+}  // namespace caffe
+
diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp
new file mode 100644
index 00000000000..704efd5df3d
--- /dev/null
+++ b/src/caffe/test/test_batch_norm_layer.cpp
@@ -0,0 +1,90 @@
+#include <algorithm>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/filler.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#define BATCH_SIZE 2
+#define INPUT_DATA_SIZE 3
+
+namespace caffe {
+
+  template <typename TypeParam>
+  class BatchNormLayerTest : public MultiDeviceTest<TypeParam> {
+    typedef typename TypeParam::Dtype Dtype;
+   protected:
+    BatchNormLayerTest()
+        : blob_bottom_(new Blob<Dtype>(5, 2, 3, 4)),
+          blob_top_(new Blob<Dtype>()) {
+      // fill the values
+      FillerParameter filler_param;
+      GaussianFiller<Dtype> filler(filler_param);
+      filler.Fill(this->blob_bottom_);
+      blob_bottom_vec_.push_back(blob_bottom_);
+      blob_top_vec_.push_back(blob_top_);
+    }
+    virtual ~BatchNormLayerTest() { delete blob_bottom_; delete blob_top_; }
+    Blob<Dtype>* const blob_bottom_;
+    Blob<Dtype>* const blob_top_;
+    vector<Blob<Dtype>*> blob_bottom_vec_;
+    vector<Blob<Dtype>*> blob_top_vec_;
+  };
+
+  TYPED_TEST_CASE(BatchNormLayerTest, TestDtypesAndDevices);
+
+  TYPED_TEST(BatchNormLayerTest, TestForward) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+
+    BatchNormLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    // Test mean
+    int num = this->blob_bottom_->num();
+    int channels = this->blob_bottom_->channels();
+    int height = this->blob_bottom_->height();
+    int width = this->blob_bottom_->width();
+
+    for (int j = 0; j < channels; ++j) {
+      Dtype sum = 0, var = 0;
+      for (int i = 0; i < num; ++i) {
+        for ( int k = 0; k < height; ++k ) {
+          for ( int l = 0; l < width; ++l ) {
+            Dtype data = this->blob_top_->data_at(i, j, k, l);
+            Dtype bottom_data = this->blob_bottom_->data_at(i, j, k, l);
+            sum += data;
+            var += data * data;
+          }
+        }
+      }
+      sum /= height * width * num;
+      var /= height * width * num;
+
+      const Dtype kErrorBound = 0.001;
+      // expect zero mean
+      EXPECT_NEAR(0, sum, kErrorBound);
+      // expect unit variance
+      EXPECT_NEAR(1, var, kErrorBound);
+    }
+  }
+
+  TYPED_TEST(BatchNormLayerTest, TestGradient) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+
+    BatchNormLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-4);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+}  // namespace caffe

From a52ee656a589313901560c87b65a570ee41c9fee Mon Sep 17 00:00:00 2001
From: Carl Doersch <cdoersch@cs.cmu.edu>
Date: Tue, 6 Oct 2015 14:19:59 -0700
Subject: [PATCH 1609/2053] Cleanup batch norm layer, include global stats
 computation

---
 .../cifar10_full_sigmoid_train_test.prototxt  |   4 +-
 ...ifar10_full_sigmoid_train_test_bn.prototxt |  90 +--
 include/caffe/common_layers.hpp               |  64 ++-
 src/caffe/layers/batch_norm_layer.cpp         | 535 +++++++-----------
 src/caffe/layers/batch_norm_layer.cu          | 365 +++++-------
 src/caffe/proto/caffe.proto                   |  15 +-
 src/caffe/test/test_batch_norm_layer.cpp      |  45 +-
 7 files changed, 486 insertions(+), 632 deletions(-)

diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
index 6f5bf26bf99..fba69b814ad 100644
--- a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
+++ b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
@@ -176,10 +176,10 @@ layer {
   top: "ip1"
   param {
     lr_mult: 1
-    decay_mult: 250
+    decay_mult: 0
   }
   param {
-    lr_mult: 0.2
+    lr_mult: 2
     decay_mult: 0
   }
   inner_product_param {
diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
index 85c2dffe3f6..1a810751177 100644
--- a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
+++ b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
@@ -12,7 +12,7 @@ layer {
   }
   data_param {
     source: "examples/cifar10/cifar10_train_lmdb"
-    batch_size: 111
+    batch_size: 100
     backend: LMDB
   }
 }
@@ -41,21 +41,16 @@ layer {
   param {
     lr_mult: 1
   }
-  param {
-    lr_mult: 2
-  }
   convolution_param {
     num_output: 32
     pad: 2
     kernel_size: 5
     stride: 1
+    bias_term: false
     weight_filler {
       type: "gaussian"
       std: 0.0001
     }
-    bias_filler {
-      type: "constant"
-    }
   }
 }
 layer {
@@ -75,23 +70,14 @@ layer {
   type: "BatchNorm"
   bottom: "pool1"
   top: "bn1"
-  bn_param {
-    scale_filler {
-      type: "constant"
-      value: 1
-    }
-    shift_filler {
-      type: "constant"
-      value: 0.001
-    } 
+  param {
+    lr_mult: 0
   }
   param {
-    lr_mult: 1.00001
-    decay_mult: 0
+    lr_mult: 0
   }
   param {
-    lr_mult: 1.00001
-    decay_mult: 0
+    lr_mult: 0
   }
 }
 
@@ -110,50 +96,35 @@ layer {
   param {
     lr_mult: 1
   }
-  param {
-    lr_mult: 2
-  }
   convolution_param {
     num_output: 32
     pad: 2
     kernel_size: 5
     stride: 1
+    bias_term: false
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
-    bias_filler {
-      type: "constant"
-    }
   }
 }
 
-
-
 layer {
   name: "bn2"
   type: "BatchNorm"
   bottom: "conv2"
   top: "bn2"
-  bn_param {
-    scale_filler {
-      type: "constant"
-      value: 1
-    }
-    shift_filler {
-      type: "constant"
-      value: 0.001
-    } 
+  param {
+    lr_mult: 0
   }
   param {
-    lr_mult: 1.00001
-    decay_mult: 0
+    lr_mult: 0
   }
   param {
-    lr_mult: 1.00001
-    decay_mult: 0
+    lr_mult: 0
   }
 }
+
 layer {
   name: "Sigmoid2"
   type: "Sigmoid"
@@ -176,53 +147,38 @@ layer {
   type: "Convolution"
   bottom: "pool2"
   top: "conv3"
+  param {
+    lr_mult: 1
+  }
   convolution_param {
     num_output: 64
     pad: 2
     kernel_size: 5
     stride: 1
+    bias_term: false
     weight_filler {
       type: "gaussian"
       std: 0.01
     }
-    bias_filler {
-      type: "constant"
-    }
-  }
-  param {
-    lr_mult: 1
   }
-  param {
-    lr_mult: 1
-  }
-
 }
 
-
 layer {
   name: "bn3"
   type: "BatchNorm"
   bottom: "conv3"
   top: "bn3"
-  bn_param {
-    scale_filler {
-      type: "constant"
-      value: 1
-    }
-    shift_filler {
-      type: "constant"
-      value: 0.001
-    } 
+  param {
+    lr_mult: 0
   }
   param {
-    lr_mult: 1.00001
-    decay_mult: 0
+    lr_mult: 0
   }
   param {
-    lr_mult: 1.00001
-    decay_mult: 0
+    lr_mult: 0
   }
 }
+
 layer {
   name: "Sigmoid3"
   type: "Sigmoid"
@@ -248,10 +204,10 @@ layer {
   top: "ip1"
   param {
     lr_mult: 1
-    decay_mult: 250
+    decay_mult: 1
   }
   param {
-    lr_mult: 0.2
+    lr_mult: 1
     decay_mult: 0
   }
   inner_product_param {
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 09605db9a53..da38f1227ba 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -79,9 +79,35 @@ class ArgMaxLayer : public Layer<Dtype> {
 };
 
 /**
-* @brief Batch Normalization per-channel with scale & shift linear transform.
-*
-*/
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
+ *        the batch.
+ *
+ * This layer computes Batch Normalization described in [1].  For
+ * each channel in the data (i.e. axis 1), it subtracts the mean and divides
+ * by the variance, where both statistics are computed across both spatial
+ * dimensions and across the different examples in the batch.
+ * 
+ * By default, during training time, the network is computing global mean/
+ * variance statistics via a running average, which is then used at test
+ * time to allow deterministic outputs for each input.  You can manually
+ * toggle whether the network is accumulating or using the statistics via the
+ * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
+ * set the learning rate to zero for all three parameter blobs, i.e., 
+ * param {lr_mult: 0} three times in the layer definition.
+ *
+ * Note that the original paper also included a per-channel learned bias and
+ * scaling factor.  It is possible (though a bit cumbersome) to implement
+ * this in caffe using a single-channel DummyDataLayer filled with zeros,
+ * followed by a Convolution layer with output the same size as the current.
+ * This produces a channel-specific value that can be added or multiplied by
+ * the BatchNorm layer's output.
+ * 
+ * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
+ *     Training by Reducing Internal Covariate Shift." arXiv preprint 
+ *     arXiv:1502.03167 (2015).  
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
 template <typename Dtype>
 class BatchNormLayer : public Layer<Dtype> {
  public:
@@ -89,11 +115,10 @@ class BatchNormLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  virtual inline const char* type() const { return "BN"; }
+  virtual inline const char* type() const { return "BatchNorm"; }
   virtual inline int ExactNumBottomBlobs() const { return 1; }
   virtual inline int ExactNumTopBlobs() const { return 1; }
 
@@ -105,26 +130,19 @@ class BatchNormLayer : public Layer<Dtype> {
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  // spatial mean & variance
-  Blob<Dtype> spatial_mean_, spatial_variance_;
-  // batch mean & variance
-  Blob<Dtype> batch_mean_, batch_variance_;
-  // buffer blob
-  Blob<Dtype> buffer_blob_;
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  Blob<Dtype> x_norm_;
-  // x_sum_multiplier is used to carry out sum using BLAS
-  Blob<Dtype> spatial_sum_multiplier_, batch_sum_multiplier_;
+  Blob<Dtype> mean_, variance_, temp_, x_norm_;
+  bool use_global_stats_;
+  Dtype moving_average_fraction_;
+  int channels_;
+  Dtype eps_;
 
-  // dimension
-  int N_;
-  int C_;
-  int H_;
-  int W_;
-  // eps
-  Dtype var_eps_;
+  // extra temporarary variables is used to carry out sums/broadcasting
+  // using BLAS
+  Blob<Dtype> batch_sum_multiplier_;
+  Blob<Dtype> num_by_chans_;
+  Blob<Dtype> spatial_sum_multiplier_;
 };
 
 /**
diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index 8dea34932f3..94c2b96b9cd 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -2,350 +2,235 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
-  template <typename Dtype>
-  void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-    top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-        bottom[0]->height(), bottom[0]->width());
-
-    x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-        bottom[0]->height(), bottom[0]->width());
-
-    // Figure out the dimensions
-    N_ = bottom[0]->num();
-    C_ = bottom[0]->channels();
-    H_ = bottom[0]->height();
-    W_ = bottom[0]->width();
 
-    // mean
-    spatial_mean_.Reshape(N_, C_, 1, 1);
-    batch_mean_.Reshape(1, C_, 1, 1);
-    // variance
-    spatial_variance_.Reshape(N_, C_, 1, 1);
-    batch_variance_.Reshape(1, C_, 1, 1);
-    // buffer blod
-    buffer_blob_.Reshape(N_, C_, H_, W_);
-
-    // fill spatial multiplier
-    spatial_sum_multiplier_.Reshape(1, 1, H_, W_);
-    Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data();
-    caffe_set(spatial_sum_multiplier_.count(), Dtype(1),
-        spatial_multipl_data);
-    caffe_set(spatial_sum_multiplier_.count(), Dtype(0),
-        spatial_sum_multiplier_.mutable_cpu_diff());
-    // fill batch multiplier
-    batch_sum_multiplier_.Reshape(N_, 1, 1, 1);
-    Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data();
-    caffe_set(batch_sum_multiplier_.count(), Dtype(1),
-        batch_multiplier_data);
-    caffe_set(batch_sum_multiplier_.count(), Dtype(0),
-        batch_sum_multiplier_.mutable_cpu_diff());
-    this->param_propagate_down_.resize(this->blobs_.size(), true);
-  }
-  template <typename Dtype>
-  void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+template <typename Dtype>
+void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
-    "allow in-place computation.";
-
-    top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
-        bottom[0]->height(), bottom[0]->width());
-
-    x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-        bottom[0]->height(), bottom[0]->width());
-    // Figure out the dimensions
-    N_ = bottom[0]->num();
-    C_ = bottom[0]->channels();
-    H_ = bottom[0]->height();
-    W_ = bottom[0]->width();
-    var_eps_ = 1e-9;
-
-    // mean
-    spatial_mean_.Reshape(N_, C_, 1, 1);
-    batch_mean_.Reshape(1, C_, 1, 1);
-    // variance
-    spatial_variance_.Reshape(N_, C_, 1, 1);
-    batch_variance_.Reshape(1, C_, 1, 1);
-    // buffer blod
-    buffer_blob_.Reshape(N_, C_, H_, W_);
-
-    // fill spatial multiplier
-    spatial_sum_multiplier_.Reshape(1, 1, H_, W_);
-    Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data();
-    caffe_set(spatial_sum_multiplier_.count(), Dtype(1),
-        spatial_multipl_data);
-    caffe_set(spatial_sum_multiplier_.count(), Dtype(0),
-        spatial_sum_multiplier_.mutable_cpu_diff());
-
-    // fill batch multiplier
-    batch_sum_multiplier_.Reshape(N_, 1, 1, 1);
-    Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data();
-    caffe_set(batch_sum_multiplier_.count(), Dtype(1),
-        batch_multiplier_data);
-    caffe_set(batch_sum_multiplier_.count(), Dtype(0),
-        batch_sum_multiplier_.mutable_cpu_diff());
-
-    // Check if we need to set up the weights
-    if (this->blobs_.size() > 0) {
-      LOG(INFO) << "Skipping parameter initialization";
-    } else {
-      this->blobs_.resize(2);
-
-      // fill scale with scale_filler
-      this->blobs_[0].reset(new Blob<Dtype>(1, C_, 1, 1));
-      caffe_set(this->blobs_[0]->count(), Dtype(1),
-          this->blobs_[0]->mutable_cpu_data());
-
-      // fill shift with shift_filler
-      this->blobs_[1].reset(new Blob<Dtype>(1, C_, 1, 1));
-      caffe_set(this->blobs_[1]->count(), Dtype(0),
-          this->blobs_[1]->mutable_cpu_data());
-    }  // parameter initialization
-    this->param_propagate_down_.resize(this->blobs_.size(), true);
+  BatchNormParameter param = this->layer_param_.batch_norm_param();
+  moving_average_fraction_ = param.moving_average_fraction();
+  use_global_stats_ = this->phase_ == TEST;
+  if (param.has_use_global_stats())
+    use_global_stats_ = param.use_global_stats();
+  if (bottom[0]->num_axes() == 1)
+    channels_ = 1;
+  else
+    channels_ = bottom[0]->shape(1);
+  eps_ = param.eps();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(3);
+    vector<int> sz;
+    sz.push_back(channels_);
+    this->blobs_[0].reset(new Blob<Dtype>(sz));
+    this->blobs_[1].reset(new Blob<Dtype>(sz));
+    sz[0]=1;
+    this->blobs_[2].reset(new Blob<Dtype>(sz));
+    for (int i = 0; i < 3; ++i) {
+      caffe_set(this->blobs_[i]->count(), Dtype(0),
+                this->blobs_[i]->mutable_cpu_data());
+    }
   }
+}
 
-  template <typename Dtype>
-  void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    const Dtype* bottom_data = bottom[0]->cpu_data();
-    Dtype* top_data = top[0]->mutable_cpu_data();
-    const Dtype* const_top_data = top[0]->cpu_data();
-
-    const Dtype* scale_data = this->blobs_[0]->cpu_data();
-    const Dtype* shift_data = this->blobs_[1]->cpu_data();
-
-    // put the squares of bottom into buffer_blob_
-    caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
-        buffer_blob_.mutable_cpu_data());
+  if (bottom[0]->num_axes() >= 1)
+    CHECK_EQ(bottom[0]->shape(1), channels_);
+  top[0]->ReshapeLike(*bottom[0]);
+
+  vector<int> sz;
+  sz.push_back(channels_);
+  mean_.Reshape(sz);
+  variance_.Reshape(sz);
+  temp_.ReshapeLike(*bottom[0]);
+  x_norm_.ReshapeLike(*bottom[0]);
+  sz[0]=bottom[0]->shape(0);
+  batch_sum_multiplier_.Reshape(sz);
+
+  int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+  if (spatial_sum_multiplier_.num_axes() == 0 ||
+      spatial_sum_multiplier_.shape(0) != spatial_dim) {
+    sz[0] = spatial_dim;
+    spatial_sum_multiplier_.Reshape(sz);
+    Dtype* multiplier_data = spatial_sum_multiplier_.mutable_cpu_data();
+    caffe_set(spatial_sum_multiplier_.count(), Dtype(1), multiplier_data);
+  }
 
+  int numbychans = channels_*bottom[0]->shape(0);
+  if (num_by_chans_.num_axes() == 0 ||
+      num_by_chans_.shape(0) != numbychans) {
+    sz[0] = numbychans;
+    num_by_chans_.Reshape(sz);
+    caffe_set(batch_sum_multiplier_.count(), Dtype(1),
+        batch_sum_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  int num = bottom[0]->shape(0);
+  int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
+
+  // elementwise square
+  caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
+             temp_.mutable_cpu_data());
+
+  if (use_global_stats_) {
+    // use the stored mean/variance estimates.  TODO(cdoersch): allow an option
+    // to use an unbiased variance estimate, like the paper does.
+    const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0];
+    caffe_cpu_scale(variance_.count(), scale_factor,
+        this->blobs_[0]->cpu_data(), mean_.mutable_cpu_data());
+    caffe_cpu_scale(variance_.count(), scale_factor,
+        this->blobs_[1]->cpu_data(), variance_.mutable_cpu_data());
+  } else {
     // computes variance using var(X) = E(X^2) - (EX)^2
-    // EX across spatial
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
-        Dtype(1. / (H_ * W_)), bottom_data,
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-    // EX across batch
-    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
-        spatial_mean_.cpu_data(),
-        batch_sum_multiplier_.cpu_data(), Dtype(0),
-        batch_mean_.mutable_cpu_data());
-
-    // E(X^2) across spatial
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
-        Dtype(1. / (H_ * W_)), buffer_blob_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        spatial_variance_.mutable_cpu_data());
-    // E(X^2) across batch
-    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
-        spatial_variance_.cpu_data(),
-        batch_sum_multiplier_.cpu_data(), Dtype(0),
-        batch_variance_.mutable_cpu_data());
-
-    caffe_powx(batch_mean_.count(), batch_mean_.cpu_data(), Dtype(2),
-        buffer_blob_.mutable_cpu_data());  // (EX)^2
-    caffe_sub(batch_mean_.count(), batch_variance_.cpu_data(),
-        buffer_blob_.cpu_data(),
-        batch_variance_.mutable_cpu_data());  // variance
-
-    // do mean and variance normalization
-    // subtract mean
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_,
-        C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(),
-        batch_mean_.cpu_data(), Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(-1),
-        spatial_mean_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        buffer_blob_.mutable_cpu_data());
-
-    caffe_add(buffer_blob_.count(), bottom_data,
-        buffer_blob_.cpu_data(), top_data);
-
-    // normalize variance
-    caffe_add_scalar(batch_variance_.count(), var_eps_,
-        batch_variance_.mutable_cpu_data());
-    caffe_powx(batch_variance_.count(),
-        batch_variance_.cpu_data(), Dtype(0.5),
-        batch_variance_.mutable_cpu_data());
-
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_,
-        C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(),
-        batch_variance_.cpu_data(), Dtype(0),
-        spatial_variance_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_ * C_, H_ * W_, 1, Dtype(1),
-        spatial_variance_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        buffer_blob_.mutable_cpu_data());
-
-    caffe_div(buffer_blob_.count(), const_top_data,
-        buffer_blob_.cpu_data(), top_data);
-
-    // Saving x_norm
-    caffe_copy(buffer_blob_.count(), const_top_data,
-        x_norm_.mutable_cpu_data());
-    // scale
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0),
-        spatial_variance_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_variance_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        buffer_blob_.mutable_cpu_data());
-    caffe_mul(buffer_blob_.count(), top_data,
-        buffer_blob_.cpu_data(), top_data);
-
-    // shift
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(), shift_data, Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_ * C_, H_ * W_, 1, Dtype(1),
-        spatial_mean_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        buffer_blob_.mutable_cpu_data());
-    caffe_add(buffer_blob_.count(), const_top_data,
-        buffer_blob_.cpu_data(), top_data);
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), bottom_data,
+        spatial_sum_multiplier_.cpu_data(), 0.,
+        num_by_chans_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+        mean_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), temp_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), 0.,
+        num_by_chans_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+        variance_.mutable_cpu_data());
+    this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
+    this->blobs_[2]->mutable_cpu_data()[0] += 1;
+    caffe_cpu_axpby(mean_.count(), Dtype(1), mean_.cpu_data(),
+        moving_average_fraction_, this->blobs_[0]->mutable_cpu_data());
+    Dtype m = Dtype(bottom[0]->count()/channels_);
+    caffe_cpu_axpby(variance_.count(), m/(m-1), variance_.cpu_data(),
+        moving_average_fraction_, this->blobs_[1]->mutable_cpu_data());
   }
+  // elementwise square of mean
+  caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
+             temp_.mutable_cpu_data());
 
-  template <typename Dtype>
-  void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down,
-      const vector<Blob<Dtype>*>& bottom) {
-    const Dtype* top_diff = top[0]->cpu_diff();
-    const Dtype* bottom_data = bottom[0]->cpu_data();
-    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-
-    Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff();
-    Dtype* shift_diff = this->blobs_[1]->mutable_cpu_diff();
-    const Dtype* scale_data = this->blobs_[0]->cpu_data();
-
-// Propagate layer to parameters
-    // gradient w.r.t. scale
-    caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(),
-        top_diff, buffer_blob_.mutable_cpu_data());
-    // EX across spatial
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_,
-        H_ * W_, Dtype(1), buffer_blob_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        spatial_variance_.mutable_cpu_diff());
-    // EX across batch
-    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_variance_.cpu_diff(),
-        batch_sum_multiplier_.cpu_data(), Dtype(0), scale_diff);
-
-    // gradient w.r.t. shift
-    // EX across spatial
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_,
-        H_ * W_, Dtype(1), top_diff,
-        spatial_sum_multiplier_.cpu_data(),
-        Dtype(0), spatial_mean_.mutable_cpu_diff());
-    // EX across batch
-    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_,
-        Dtype(1), spatial_mean_.cpu_diff(),
-        batch_sum_multiplier_.cpu_data(),
-        Dtype(0), shift_diff);
+  caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
+            variance_.mutable_cpu_data());  // variance
 
-// Propagate down
+  // normalize variance
+  caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
+  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+             variance_.mutable_cpu_data());
 
-    // put scale * top_diff to buffer_blob_
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0),
-        spatial_variance_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_variance_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        buffer_blob_.mutable_cpu_data());
-    caffe_mul(buffer_blob_.count(), top_diff, buffer_blob_.cpu_data(),
-        buffer_blob_.mutable_cpu_data());
-
-    // use new top diff for computation
-    caffe_mul(buffer_blob_.count(),  x_norm_.cpu_data(),
-        buffer_blob_.cpu_data(), bottom_diff);
-    // EX across spatial
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
-        Dtype(1), bottom_diff,
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-    // EX across batch
-    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_mean_.cpu_data(),
-        batch_sum_multiplier_.cpu_data(), Dtype(0),
-        batch_mean_.mutable_cpu_data());
-
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(),
-        batch_mean_.cpu_data(), Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_mean_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        bottom_diff);
-
-    caffe_mul(buffer_blob_.count(),
-        x_norm_.cpu_data(), bottom_diff, bottom_diff);
-
-    // EX across spatial
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, N_ * C_,
-        H_ * W_, Dtype(1), buffer_blob_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-    // EX across batch
-    caffe_cpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_mean_.cpu_data(),
-        batch_sum_multiplier_.cpu_data(), Dtype(0),
-        batch_mean_.mutable_cpu_data());
-
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(),
-        batch_mean_.cpu_data(), Dtype(0),
-        spatial_mean_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_ * C_, H_ * W_, 1, Dtype(1),
-        spatial_mean_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(1), bottom_diff);
-
-    caffe_cpu_axpby(buffer_blob_.count(), Dtype(1),
-        buffer_blob_.cpu_data(), Dtype(-1. / (N_ * H_ * W_)),
-        bottom_diff);
-
-    // put the squares of bottom into buffer_blob_
-//    caffe_powx(buffer_blob_.count(), bottom_data, Dtype(2),
-//        buffer_blob_.mutable_cpu_data());
+  // do mean and variance normalization
+  if (bottom[0] != top[0]) {
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+  // subtract mean
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, -1, num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 1., top_data);
+  // replicate variance to input size
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), variance_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 0., temp_.mutable_cpu_data());
+  caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
+  // TODO(cdoersch): The caching is only needed because later in-place layers
+  //                 might clobber the data.  Can we skip this if they won't?
+  caffe_copy(x_norm_.count(), top_data,
+      x_norm_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!use_global_stats_);
+  const Dtype* top_diff;
+  if (bottom[0] != top[0]) {
+    top_diff = top[0]->cpu_diff();
+  } else {
+    caffe_copy(x_norm_.count(), top[0]->cpu_diff(), x_norm_.mutable_cpu_diff());
+    top_diff = x_norm_.cpu_diff();
+  }
+  const Dtype* top_data = x_norm_.cpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  int num = bottom[0]->shape()[0];
+  int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
+  // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
+  //
+  // dE(Y)/dX =
+  //   (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y)
+  //     ./ sqrt(var(X) + eps)
+  //
+  // where \cdot and ./ are hadamard product and elementwise division,
+  // respectively, dE/dY is the top diff, and mean/var/sum are all computed
+  // along all dimensions except the channels dimension.  In the above
+  // equation, the operations allow for expansion (i.e. broadcast) along all
+  // dimensions except the channels dimension where required.
+
+  // sum(dE/dY \cdot Y)
+  caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      bottom_diff, spatial_sum_multiplier_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+      mean_.mutable_cpu_data());
+
+  // reshape (broadcast) the above
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 0., bottom_diff);
+
+  // sum(dE/dY \cdot Y) \cdot Y
+  caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      top_diff, spatial_sum_multiplier_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+      mean_.mutable_cpu_data());
+  // reshape (broadcast) the above to make
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num * channels_,
+      spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 1., bottom_diff);
+
+  // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y
+  caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff,
+      Dtype(-1. / (num * spatial_dim)), bottom_diff);
+
+  // note: temp_ still contains sqrt(var(X)+eps), computed during the forward
+  // pass.
+  caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
+}
 
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.cpu_data(),
-        batch_variance_.cpu_data(), Dtype(0),
-        spatial_variance_.mutable_cpu_data());
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
-        N_ * C_, H_ * W_, 1, Dtype(1),
-        spatial_variance_.cpu_data(),
-        spatial_sum_multiplier_.cpu_data(), Dtype(0),
-        buffer_blob_.mutable_cpu_data());
 
-    caffe_div(buffer_blob_.count(), bottom_diff,
-        buffer_blob_.cpu_data(), bottom_diff);
-  }
 #ifdef CPU_ONLY
 STUB_GPU(BatchNormLayer);
 #endif
 
-  INSTANTIATE_CLASS(BatchNormLayer);
-  REGISTER_LAYER_CLASS(BatchNorm);
+INSTANTIATE_CLASS(BatchNormLayer);
+REGISTER_LAYER_CLASS(BatchNorm);
 }  // namespace caffe
-
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
index e87f8c62f43..cd8924a451d 100644
--- a/src/caffe/layers/batch_norm_layer.cu
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -2,227 +2,166 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
-  template <typename Dtype>
-  void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {
-    const Dtype* bottom_data = bottom[0]->gpu_data();
-    const Dtype* const_top_data = top[0]->gpu_data();
-    Dtype* top_data = top[0]->mutable_gpu_data();
-    Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data();
-    Dtype* buffer_data = buffer_blob_.mutable_gpu_data();
-    const Dtype* const_buffer_data = buffer_blob_.gpu_data();
-
-
-  // put the squares of bottom into buffer_blob_
-    caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
-        buffer_blob_.mutable_gpu_data());
 
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  int num = bottom[0]->shape(0);
+  int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+
+  // elementwise square
+  caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
+      temp_.mutable_gpu_data());
+
+  if (use_global_stats_) {
+    // use the stored mean/variance estimates.  TODO(cdoersch): allow an option
+    // to use an unbiased variance estimate, like the paper does.
+    const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0];
+    caffe_gpu_scale(variance_.count(), scale_factor,
+        this->blobs_[0]->gpu_data(), mean_.mutable_gpu_data());
+    caffe_gpu_scale(variance_.count(), scale_factor,
+        this->blobs_[1]->gpu_data(), variance_.mutable_gpu_data());
+  } else {
     // computes variance using var(X) = E(X^2) - (EX)^2
-    // EX across spatial
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
-      Dtype(1. / (H_ * W_)),
-      bottom_data, spatial_sum_multiplier_.gpu_data(),
-      Dtype(0), spatial_mean_data);
-    // EX across batch
-    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
-        spatial_mean_.gpu_data(),
-        batch_sum_multiplier_.gpu_data(), Dtype(0),
-      batch_mean_.mutable_gpu_data());
-
-    // E(X^2) across spatial
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
-      Dtype(1. / (H_ * W_)), buffer_data,
-        spatial_sum_multiplier_.gpu_data(), Dtype(0),
-      spatial_variance_.mutable_gpu_data());
-    // E(X^2) across batch
-    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1. / N_),
-        spatial_variance_.gpu_data(),
-        batch_sum_multiplier_.gpu_data(), Dtype(0),
-      batch_variance_.mutable_gpu_data());
-
-    caffe_gpu_powx(batch_mean_.count(), batch_mean_.gpu_data(),
-      Dtype(2), buffer_blob_.mutable_gpu_data());  // (EX)^2
-    caffe_gpu_sub(batch_mean_.count(), batch_variance_.gpu_data(),
-      buffer_data, batch_variance_.mutable_gpu_data());  // variance
-
-    // do mean and variance normalization
-    // subtract mean
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(), batch_mean_.gpu_data(), Dtype(0),
-        spatial_mean_data);
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_,
-        1, -Dtype(1),
-        spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0),
-        buffer_blob_.mutable_gpu_data());
-
-    caffe_gpu_add(buffer_blob_.count(), bottom_data, buffer_data, top_data);
-
-    // normalize variance
-    caffe_gpu_add_scalar(batch_variance_.count(), var_eps_,
-      batch_variance_.mutable_gpu_data());
-    caffe_gpu_powx(batch_variance_.count(), batch_variance_.gpu_data(),
-      Dtype(0.5), batch_variance_.mutable_gpu_data());
-
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0),
-        spatial_variance_.mutable_gpu_data());
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
-        Dtype(0), buffer_blob_.mutable_gpu_data());
-
-    caffe_gpu_div(buffer_blob_.count(), top_data, buffer_data, top_data);
-
-    // Saving x_norm
-    caffe_copy(top[0]->count(), const_top_data, x_norm_.mutable_gpu_data());
-
-    // scale
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(),
-        Dtype(0), spatial_variance_.mutable_gpu_data());
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
-        Dtype(0), buffer_blob_.mutable_gpu_data());
-
-    caffe_gpu_mul(buffer_blob_.count(), top_data, buffer_data, top_data);
-
-    // shift
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(),
-        this->blobs_[1]->gpu_data(), Dtype(0),
-        spatial_mean_data);
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1,
-        Dtype(1),
-        spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0),
-        buffer_blob_.mutable_gpu_data());
-    caffe_gpu_add(buffer_blob_.count(), top_data, buffer_data, top_data);
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), bottom_data,
+        spatial_sum_multiplier_.gpu_data(), 0.,
+        num_by_chans_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+        mean_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), temp_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), 0.,
+        num_by_chans_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+        variance_.mutable_gpu_data());
+    this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
+    this->blobs_[2]->mutable_cpu_data()[0] += 1;
+    caffe_gpu_axpby(mean_.count(), Dtype(1), mean_.gpu_data(),
+        moving_average_fraction_, this->blobs_[0]->mutable_gpu_data());
+    Dtype m = Dtype(bottom[0]->count()/channels_);
+    caffe_gpu_axpby(variance_.count(), m/(m-1), variance_.gpu_data(),
+        moving_average_fraction_, this->blobs_[1]->mutable_gpu_data());
   }
+  // elementwise square of mean
+  caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
+                 temp_.mutable_gpu_data());
+
+  caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
+                variance_.mutable_gpu_data());  // variance
+
+  // normalize variance
+  caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
+  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+      variance_.mutable_gpu_data());
 
-  template <typename Dtype>
-  void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down,
-      const vector<Blob<Dtype>*>& bottom) {
-    const Dtype* top_diff = top[0]->gpu_diff();
-    const Dtype* top_data = top[0]->gpu_data();
-    const Dtype* bottom_data = bottom[0]->gpu_data();
-    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-    const Dtype* const_bottom_diff = bottom[0]->gpu_diff();
-    Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data();
-    Dtype* buffer_data = buffer_blob_.mutable_gpu_data();
-    const Dtype* const_buffer_data = buffer_blob_.gpu_data();
-
-    // Propage to layer params
-    // gradient w.r.t. scale
-    caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(),
-    top_diff, buffer_blob_.mutable_gpu_data());
-    // EX across spatial
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1),
-        buffer_data, spatial_sum_multiplier_.gpu_data(), Dtype(0),
-    spatial_variance_.mutable_gpu_data());
-    // EX across batch
-    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_variance_.gpu_data(),
-        batch_sum_multiplier_.gpu_data(), Dtype(0),
-        this->blobs_[0]->mutable_gpu_diff());
-
-    // gradient w.r.t. shift
-    // EX across spatial
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1),
-      top_diff, spatial_sum_multiplier_.gpu_data(),
-      Dtype(0), spatial_mean_data);
-    // EX across batch
-    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_mean_.gpu_data(),
-        batch_sum_multiplier_.gpu_data(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-
-    // Propagate down
-    // scale top diff
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(),
-        Dtype(0), spatial_variance_.mutable_gpu_data());
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
-        Dtype(0),
-        buffer_blob_.mutable_gpu_data());
-    caffe_gpu_mul(buffer_blob_.count(), top_diff, buffer_data,
-        buffer_blob_.mutable_gpu_data());
-
-    // use new top diff for computation
-    caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(),
-       buffer_data, bottom_diff);
-    // EX across spatial
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_,
-        Dtype(1), bottom_diff,
-        spatial_sum_multiplier_.gpu_data(), Dtype(0), spatial_mean_data);
-    // EX across batch
-    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_mean_.gpu_data(),
-        batch_sum_multiplier_.gpu_data(), Dtype(0),
-        batch_mean_.mutable_gpu_data());
-
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(),
-        batch_mean_.gpu_data(), Dtype(0),
-        spatial_mean_data);
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1), spatial_mean_.gpu_data(),
-        spatial_sum_multiplier_.gpu_data(), Dtype(0),
-        bottom_diff);
-
-    caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(),
-        bottom_diff, bottom_diff);
-
-    // EX across spatial
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1),
-        buffer_data, spatial_sum_multiplier_.gpu_data(),
-        Dtype(0), spatial_mean_data);
-
-    // EX across batch
-    caffe_gpu_gemv<Dtype>(CblasTrans, N_, C_, Dtype(1),
-        spatial_mean_.gpu_data(),
-        batch_sum_multiplier_.gpu_data(), Dtype(0),
-        batch_mean_.mutable_gpu_data());
-
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_,
-        C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(),
-        batch_mean_.gpu_data(), Dtype(0),
-        spatial_mean_data);
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
-        Dtype(1),
-        bottom_diff);
-
-    caffe_gpu_axpby(buffer_blob_.count(), Dtype(1), buffer_data,
-        Dtype(-1. / (N_ * H_ * W_)),
-        bottom_diff);
-
-    // put the squares of bottom into buffer_blob_
-//    caffe_gpu_powx(buffer_blob_.count(), bottom_data, Dtype(2),
-//        buffer_blob_.mutable_gpu_data());
-
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1),
-        batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0),
-        spatial_variance_.mutable_gpu_data());
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, N_ * C_,
-        H_ * W_, 1, Dtype(1),
-        spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(),
-        Dtype(0),
-        buffer_blob_.mutable_gpu_data());
-
-    caffe_gpu_div(buffer_blob_.count(), const_bottom_diff,
-    const_buffer_data, bottom_diff);
+  // do mean and variance normalization
+  if (bottom[0] != top[0]) {
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
   }
+  // subtract mean
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, -1, num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 1., top_data);
+  // replicate variance to input size
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), variance_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 0., temp_.mutable_gpu_data());
+  caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
+  // TODO(cdoersch): The caching is only needed because later in-place layers
+  //                 might clobber the data.  Can we skip this if they won't?
+  caffe_copy(x_norm_.count(), top_data,
+      x_norm_.mutable_gpu_data());
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!use_global_stats_);
+  const Dtype* top_diff;
+  if (bottom[0] != top[0]) {
+    top_diff = top[0]->gpu_diff();
+  } else {
+    caffe_copy(x_norm_.count(), top[0]->gpu_diff(), x_norm_.mutable_gpu_diff());
+    top_diff = x_norm_.gpu_diff();
+  }
+  const Dtype* top_data = x_norm_.gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  int num = bottom[0]->shape()[0];
+  int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+  // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
+  //
+  // dE(Y)/dX =
+  //   (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y)
+  //     ./ sqrt(var(X) + eps)
+  //
+  // where \cdot and ./ are hadamard product and elementwise division,
+  // respectively, dE/dY is the top diff, and mean/var/sum are all computed
+  // along all dimensions except the channels dimension.  In the above
+  // equation, the operations allow for expansion (i.e. broadcast) along all
+  // dimensions except the channels dimension where required.
+
+  // sum(dE/dY \cdot Y)
+  caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      bottom_diff, spatial_sum_multiplier_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+      mean_.mutable_gpu_data());
+
+  // reshape (broadcast) the above
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 0., bottom_diff);
+
+  // sum(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      top_diff, spatial_sum_multiplier_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+      mean_.mutable_gpu_data());
+  // reshape (broadcast) the above to make
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num * channels_,
+      spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 1., bottom_diff);
+
+  // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff,
+      Dtype(-1. / (num * spatial_dim)), bottom_diff);
+
+  // note: temp_ still contains sqrt(var(X)+eps), computed during the forward
+  // pass.
+  caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer);
 
-  INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer);
-}  // namespace caffe
 
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a8747c12b37..99dd3c90eef 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -301,7 +301,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 139 (last added: tile_param)
+// LayerParameter next available layer-specific ID: 140 (last added: batch_norm_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -350,6 +350,7 @@ message LayerParameter {
   // The default for the engine is set by the ENGINE switch at compile-time.
   optional AccuracyParameter accuracy_param = 102;
   optional ArgMaxParameter argmax_param = 103;
+  optional BatchNormParameter batch_norm_param = 139;
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
@@ -461,6 +462,18 @@ message ConcatParameter {
   optional uint32 concat_dim = 1 [default = 1];
 }
 
+message BatchNormParameter {
+  // If false, accumulate global mean/variance values via a moving average. If
+  // true, use those accumulated values instead of computing mean/variance
+  // across the batch.
+  optional bool use_global_stats = 1;
+  // How much does the moving average decay each iteration?
+  optional float moving_average_fraction = 2 [default = .999];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [default = 1e-5];
+}
+
 message ContrastiveLossParameter {
   // margin for dissimilar pair
   optional float margin = 1 [default = 1.0];
diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp
index 704efd5df3d..22b9667f31b 100644
--- a/src/caffe/test/test_batch_norm_layer.cpp
+++ b/src/caffe/test/test_batch_norm_layer.cpp
@@ -60,7 +60,50 @@ namespace caffe {
         for ( int k = 0; k < height; ++k ) {
           for ( int l = 0; l < width; ++l ) {
             Dtype data = this->blob_top_->data_at(i, j, k, l);
-            Dtype bottom_data = this->blob_bottom_->data_at(i, j, k, l);
+            sum += data;
+            var += data * data;
+          }
+        }
+      }
+      sum /= height * width * num;
+      var /= height * width * num;
+
+      const Dtype kErrorBound = 0.001;
+      // expect zero mean
+      EXPECT_NEAR(0, sum, kErrorBound);
+      // expect unit variance
+      EXPECT_NEAR(1, var, kErrorBound);
+    }
+  }
+
+  TYPED_TEST(BatchNormLayerTest, TestForwardInplace) {
+    typedef typename TypeParam::Dtype Dtype;
+    Blob<Dtype> blob_inplace(5, 2, 3, 4);
+    vector<Blob<Dtype>*> blob_bottom_vec;
+    vector<Blob<Dtype>*> blob_top_vec;
+    LayerParameter layer_param;
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(&blob_inplace);
+    blob_bottom_vec.push_back(&blob_inplace);
+    blob_top_vec.push_back(&blob_inplace);
+
+    BatchNormLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec, blob_top_vec);
+    layer.Forward(blob_bottom_vec, blob_top_vec);
+
+    // Test mean
+    int num = blob_inplace.num();
+    int channels = blob_inplace.channels();
+    int height = blob_inplace.height();
+    int width = blob_inplace.width();
+
+    for (int j = 0; j < channels; ++j) {
+      Dtype sum = 0, var = 0;
+      for (int i = 0; i < num; ++i) {
+        for ( int k = 0; k < height; ++k ) {
+          for ( int l = 0; l < width; ++l ) {
+            Dtype data = blob_inplace.data_at(i, j, k, l);
             sum += data;
             var += data * data;
           }

From 09b8738d73ebc37dda09e8c6dd05e35609999c77 Mon Sep 17 00:00:00 2001
From: Rodrigo Benenson <rodrigo.benenson@gmail.com>
Date: Thu, 22 Oct 2015 18:18:08 +0200
Subject: [PATCH 1610/2053] diff.ndim != 4 is outdated

this code seems not to apply to the caffe head.
```
if diff.ndim != 4:
                raise Exception('{} diff is not 4-d'.format(top))
```
---
 python/caffe/pycaffe.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 8ea24da4fdd..7bd4f411b6a 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -146,8 +146,6 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for top, diff in kwargs.iteritems():
-            if diff.ndim != 4:
-                raise Exception('{} diff is not 4-d'.format(top))
             if diff.shape[0] != self.blobs[top].num:
                 raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff

From 3e5f49435f95de57bffbde53d745dcb4a8f1f870 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andreas=20L=C3=B8ve=20Selvik?= <andreas.l.selvik@gmail.com>
Date: Tue, 13 Oct 2015 16:32:52 +0200
Subject: [PATCH 1611/2053] Add opencv_imgcodecs to library path in Makefile

Project does not compile without opencv_imgcodecs in the library path if
you're using OpenCV 3.

This introduces a OPENCV_VERSION flag in Makefile.config that includes
the library if set to 3.  (Trying to include it with OpenCV 2 also
breaks the build)
---
 Makefile                | 7 ++++++-
 Makefile.config.example | 3 +++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 5fb6394e947..43cb15fe491 100644
--- a/Makefile
+++ b/Makefile
@@ -184,7 +184,12 @@ ifeq ($(USE_LMDB), 1)
 	LIBRARIES += lmdb
 endif
 ifeq ($(USE_OPENCV), 1)
-	LIBRARIES += opencv_core opencv_highgui opencv_imgproc
+	LIBRARIES += opencv_core opencv_highgui opencv_imgproc 
+
+	ifeq ($(OPENCV_VERSION), 3)
+		LIBRARIES += opencv_imgcodecs
+	endif
+		
 endif
 PYTHON_LIBRARIES := boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
diff --git a/Makefile.config.example b/Makefile.config.example
index a20bad2f5ce..8e2c4fb46a0 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -12,6 +12,9 @@
 # USE_LMDB := 0
 # USE_OPENCV := 0
 
+# Uncomment if you're using OpenCV 3
+# OPENCV_VERSION := 3
+
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
 # CUSTOM_CXX := g++

From 9898794172b7def7a91d925d97e11dd0878ddb61 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 22 Oct 2015 19:12:48 -0700
Subject: [PATCH 1612/2053] cuDNN: only log conv workspace in debug mode

---
 src/caffe/layers/cudnn_conv_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 8b61249a4f8..c82cb7efd6c 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -191,7 +191,7 @@ void CuDNNConvolutionLayer<Dtype>::Reshape(
 
   // this is the total amount of storage needed over all groups + streams
   if (total_max_workspace > workspaceSizeInBytes) {
-    LOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
+    DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
     workspaceSizeInBytes = total_max_workspace;
 
     // free the existing workspace and allocate a new (larger) one

From 8e455850bf398dd16dffa5e7591480d013b8e573 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Tue, 27 Oct 2015 22:49:28 -0700
Subject: [PATCH 1613/2053] CuDNNConvolutionLayer accumulate gradients

---
 src/caffe/layers/cudnn_conv_layer.cu | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 63b6ab9c388..f2df4aa502f 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -53,12 +53,10 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (this->param_propagate_down_[0]) {
     weight = this->blobs_[0]->gpu_data();
     weight_diff = this->blobs_[0]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
   if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();

From 5925fa8ed94e9af02449f853b06f252ac5f4c364 Mon Sep 17 00:00:00 2001
From: Kai Li <1196594711@qq.com>
Date: Fri, 30 Oct 2015 00:46:08 +0800
Subject: [PATCH 1614/2053] Update plot_training_log.py.example

I find there is no plot_log.sh file
---
 tools/extra/plot_training_log.py.example | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index b6fda54e01c..4d3ed0d15a9 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -150,7 +150,7 @@ Be warned that the fields in the training log may change in the future.
 You had better check the data files and change the mapping from field name to
  field index in create_field_index before designing your own plots.
 Usage:
-    ./plot_log.sh chart_type[0-%s] /where/to/save.png /path/to/first.log ...
+    ./plot_training_log.py chart_type[0-%s] /where/to/save.png /path/to/first.log ...
 Notes:
     1. Supporting multiple logs.
     2. Log file name must end with the lower-cased "%s".

From 54f0c08ca144c498c835baa017887a64bc8fbbf2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ren=C3=A9=20Scheibe?= <rene.scheibe@gmail.com>
Date: Tue, 3 Nov 2015 19:27:07 +0100
Subject: [PATCH 1615/2053] fix detect.py (invalid model path)

---
 python/detect.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/detect.py b/python/detect.py
index 691098f5c53..1aba964a9d8 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -46,7 +46,7 @@ def main(argv):
     parser.add_argument(
         "--model_def",
         default=os.path.join(pycaffe_dir,
-                "../models/bvlc_reference_caffenet/deploy.prototxt.prototxt"),
+                "../models/bvlc_reference_caffenet/deploy.prototxt"),
         help="Model definition file."
     )
     parser.add_argument(

From b2339716fd3c6ebb050be0241ba2a34f804ae904 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 3 Nov 2015 14:42:24 -0800
Subject: [PATCH 1616/2053] TravisCI: wget cmake with --no-check-certificate

```
--2015-11-03 22:31:11--  http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh
Resolving www.cmake.org (www.cmake.org)... 66.194.253.19
Connecting to www.cmake.org (www.cmake.org)|66.194.253.19|:80... connected.
HTTP request sent, awaiting response... 301 Moved Permanently
Location: http://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh [following]
--2015-11-03 22:31:11--  http://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh
Resolving cmake.org (cmake.org)... 66.194.253.19
Connecting to cmake.org (cmake.org)|66.194.253.19|:80... connected.
HTTP request sent, awaiting response... 301 Moved Permanently
Location: https://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh [following]
--2015-11-03 22:31:11--  https://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh
Connecting to cmake.org (cmake.org)|66.194.253.19|:443... connected.
ERROR: no certificate subject alternative name matches
        requested host name `cmake.org'.
To connect to cmake.org insecurely, use `--no-check-certificate'.
```
---
 scripts/travis/travis_install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index d6c6e228b58..d78c4d2f7b2 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -23,7 +23,7 @@ apt-get install \
 # Caffe requires a minimum CMake version of 2.8.8.
 if $WITH_CMAKE; then
   # cmake 3 will make sure that the python interpreter and libraries match
-  wget http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
+  wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
   chmod +x cmake3.sh
   ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
 fi

From 5196926a7cca1a85aecbd97e78452352fc5d2b3d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 4 Nov 2015 22:10:25 -0800
Subject: [PATCH 1617/2053] [travis] fix boost/python3 conda conflict

---
 scripts/travis/travis_install.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index d78c4d2f7b2..432c81dc6ec 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -70,6 +70,10 @@ if [ ! -d $CONDA_DIR ]; then
 	./miniconda.sh -b -p $CONDA_DIR
 	
 	conda update --yes conda
+	# The version of boost we're using for Python 3 depends on 3.4 for now.
+	if [ "$PYTHON_VERSION" -eq "3" ]; then
+		conda install --yes python=3.4
+	fi
 	conda install --yes numpy scipy matplotlib scikit-image pip
 	# Let conda install boost (so that boost_python matches)
 	conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0

From 4137c093bd6ca018c5953a1e069069ab96f4f91d Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 4 Nov 2015 23:54:41 -0800
Subject: [PATCH 1618/2053] [style] fix whitespace in travis_install.sh

---
 scripts/travis/travis_install.sh | 54 ++++++++++++++++----------------
 1 file changed, 27 insertions(+), 27 deletions(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 432c81dc6ec..d18dc223a06 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -61,39 +61,39 @@ rm -f $LMDB_FILE
 # than using pip for everything).
 export PATH=$CONDA_DIR/bin:$PATH
 if [ ! -d $CONDA_DIR ]; then
-	if [ "$PYTHON_VERSION" -eq "3" ]; then
-		wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-	else
-		wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-	fi
-	chmod +x miniconda.sh
-	./miniconda.sh -b -p $CONDA_DIR
-	
-	conda update --yes conda
-	# The version of boost we're using for Python 3 depends on 3.4 for now.
-	if [ "$PYTHON_VERSION" -eq "3" ]; then
-		conda install --yes python=3.4
-	fi
-	conda install --yes numpy scipy matplotlib scikit-image pip
-	# Let conda install boost (so that boost_python matches)
-	conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
+  if [ "$PYTHON_VERSION" -eq "3" ]; then
+    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+  else
+    wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+  fi
+  chmod +x miniconda.sh
+  ./miniconda.sh -b -p $CONDA_DIR
+
+  conda update --yes conda
+  # The version of boost we're using for Python 3 depends on 3.4 for now.
+  if [ "$PYTHON_VERSION" -eq "3" ]; then
+    conda install --yes python=3.4
+  fi
+  conda install --yes numpy scipy matplotlib scikit-image pip
+  # Let conda install boost (so that boost_python matches)
+  conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
 fi
 
 # install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
 if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
-	pushd .
-	wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
-	tar -C /tmp -xzvf protobuf-3.tar.gz
-	cd /tmp/protobuf-3*/
-	./autogen.sh
-	./configure --prefix=$CONDA_DIR
-	$MAKE
-	$MAKE install
-	popd
+  pushd .
+  wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
+  tar -C /tmp -xzvf protobuf-3.tar.gz
+  cd /tmp/protobuf-3*/
+  ./autogen.sh
+  ./configure --prefix=$CONDA_DIR
+  $MAKE
+  $MAKE install
+  popd
 fi
 
 if [ "$PYTHON_VERSION" -eq "3" ]; then
-	pip install --pre protobuf
+  pip install --pre protobuf
 else
-	pip install protobuf
+  pip install protobuf
 fi

From bc1aa41af7d0ba46da4d7c71fc9109baea651ce0 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 4 Nov 2015 20:48:43 -0800
Subject: [PATCH 1619/2053] remove dead cpp code for number of CUDA threads

__CUDA_ARCH__ is not defined in host code; the #if was vacuous and
misleading.
---
 include/caffe/util/device_alternate.hpp | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 6ea595dba2d..e3fe4fe29fd 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -81,14 +81,8 @@ namespace caffe {
 const char* cublasGetErrorString(cublasStatus_t error);
 const char* curandGetErrorString(curandStatus_t error);
 
-// CUDA: thread number configuration.
-// Use 1024 threads per block, which requires cuda sm_2x or above,
-// or fall back to attempt compatibility (best of luck to you).
-#if __CUDA_ARCH__ >= 200
-    const int CAFFE_CUDA_NUM_THREADS = 1024;
-#else
-    const int CAFFE_CUDA_NUM_THREADS = 512;
-#endif
+// CUDA: use 512 threads per block
+const int CAFFE_CUDA_NUM_THREADS = 512;
 
 // CUDA: number of blocks for threads.
 inline int CAFFE_GET_BLOCKS(const int N) {

From 32dc03f14c36d1df46f37a7d13ad528e52c6f786 Mon Sep 17 00:00:00 2001
From: ernest-tg <matthieu.perrinel@gmail.com>
Date: Thu, 5 Nov 2015 15:47:28 +0100
Subject: [PATCH 1620/2053] Correct transposition & channel_swap in deprocess
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The deprocess( ) function should invert the preprocess( ) function, however it only worked when the permutation of your channel_swap is of order 2 and the permutation of your transpose were of order 3. This is usually the case, which is why this bug went unnoticed for a long time.

To reproduce it (on former version), try to preprocess and then deprocess with
transformer.set_transpose('data', (0,2,1)) (or (1,0,2) or (2,1,0))

Or with
transformer.set_channel_swap('data', (2,0,1)) (or (1,2,0) )

Indeed, we had

L152  (in preprocess)   caffe_in = caffe_in[channel_swap, :, :]
L181  (in deprocess)    decaf_in = decaf_in[channel_swap, :, :]
So we applied [channel_swap,:,:] twice to the initial data => not always the identity

L154 (in preprocess)    caffe_in = caffe_in.transpose(transpose)
L183 (in deprocess)     decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
The transposition [transpose[t] for t in transpose] is (tranpsose)² so we applied transpose[t] three times which is not always the identity.
---
 python/caffe/io.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 11c84260f1a..14942bed542 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -178,9 +178,9 @@ def deprocess(self, in_, data):
         if raw_scale is not None:
             decaf_in /= raw_scale
         if channel_swap is not None:
-            decaf_in = decaf_in[channel_swap, :, :]
+            decaf_in = decaf_in[np.argsort(channel_swap), :, :]
         if transpose is not None:
-            decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
+            decaf_in = decaf_in.transpose(np.argsort(transpose))
         return decaf_in
 
     def set_transpose(self, in_, order):

From 7f49d80a83c9314dd10d3ec18e77226fca6c3b62 Mon Sep 17 00:00:00 2001
From: Daniel Golden <dgolden1@gmail.com>
Date: Thu, 5 Nov 2015 14:22:00 -0800
Subject: [PATCH 1621/2053] Don't attempt to write CSV if there are no lines to
 write

This can happen if, e.g., testing never occurs in the log
---
 tools/extra/parse_log.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 48f9bee0b49..bb9b65ad615 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -149,6 +149,11 @@ def write_csv(output_filename, dict_list, delimiter, verbose=False):
     """Write a CSV file
     """
 
+    if not dict_list:
+        if verbose:
+            print('Not writing %s; no lines to write' % output_filename)
+        return
+
     dialect = csv.excel
     dialect.delimiter = delimiter
 

From 987b3d8794e3fe27b4402d52fb3921555104b451 Mon Sep 17 00:00:00 2001
From: Tim Meinhardt <meinhardt.tim@gmail.com>
Date: Fri, 6 Nov 2015 14:51:46 +0100
Subject: [PATCH 1622/2053] Fix ArgMaxLayer::Reshape for any num of bottom axes

---
 include/caffe/common_layers.hpp   | 14 +++++++-------
 src/caffe/layers/argmax_layer.cpp |  4 +++-
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
index 72f39ee082b..d42d15c47e3 100644
--- a/include/caffe/common_layers.hpp
+++ b/include/caffe/common_layers.hpp
@@ -53,8 +53,8 @@ class ArgMaxLayer : public Layer<Dtype> {
    *   -# @f$ (N \times C \times H \times W) @f$
    *      the inputs @f$ x @f$
    * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val
-   *      @f$ (N \times 2 \times K \times 1) @f$ unless axis set than e.g.
+   *   -# @f$ (N \times 1 \times K) @f$ or, if out_max_val
+   *      @f$ (N \times 2 \times K) @f$ unless axis set than e.g.
    *      @f$ (N \times K \times H \times W) @f$ if axis == 1
    *      the computed outputs @f$
    *       y_n = \arg\max\limits_i x_{ni}
@@ -81,13 +81,13 @@ class ArgMaxLayer : public Layer<Dtype> {
  * each channel in the data (i.e. axis 1), it subtracts the mean and divides
  * by the variance, where both statistics are computed across both spatial
  * dimensions and across the different examples in the batch.
- * 
+ *
  * By default, during training time, the network is computing global mean/
  * variance statistics via a running average, which is then used at test
  * time to allow deterministic outputs for each input.  You can manually
  * toggle whether the network is accumulating or using the statistics via the
  * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
- * set the learning rate to zero for all three parameter blobs, i.e., 
+ * set the learning rate to zero for all three parameter blobs, i.e.,
  * param {lr_mult: 0} three times in the layer definition.
  *
  * Note that the original paper also included a per-channel learned bias and
@@ -96,10 +96,10 @@ class ArgMaxLayer : public Layer<Dtype> {
  * followed by a Convolution layer with output the same size as the current.
  * This produces a channel-specific value that can be added or multiplied by
  * the BatchNorm layer's output.
- * 
+ *
  * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
- *     Training by Reducing Internal Covariate Shift." arXiv preprint 
- *     arXiv:1502.03167 (2015).  
+ *     Training by Reducing Internal Covariate Shift." arXiv preprint
+ *     arXiv:1502.03167 (2015).
  *
  * TODO(dox): thorough documentation for Forward, Backward, and proto params.
  */
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 44df8d4e2e4..354d83f7061 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -32,7 +32,9 @@ void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  std::vector<int> shape(bottom[0]->num_axes(), 1);
+  int num_top_axes = bottom[0]->num_axes();
+  if ( num_top_axes < 3 ) num_top_axes = 3;
+  std::vector<int> shape(num_top_axes, 1);
   if (has_axis_) {
     // Produces max_ind or max_val per axis
     shape = bottom[0]->shape();

From 0f1e4e5ddd884325df82db00ae0fc531481e9c60 Mon Sep 17 00:00:00 2001
From: Shandy Brown <github@ezide.com>
Date: Fri, 6 Nov 2015 20:01:57 -0800
Subject: [PATCH 1623/2053] Add a -c to wget so that it continues interrupted
 downloads

This would've saved me an overnight download (slow connection here)

I tested it, and it worked for me.
---
 data/ilsvrc12/get_ilsvrc_aux.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data/ilsvrc12/get_ilsvrc_aux.sh b/data/ilsvrc12/get_ilsvrc_aux.sh
index b9b85d21e2d..90935f25099 100755
--- a/data/ilsvrc12/get_ilsvrc_aux.sh
+++ b/data/ilsvrc12/get_ilsvrc_aux.sh
@@ -12,7 +12,7 @@ cd $DIR
 
 echo "Downloading..."
 
-wget http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
+wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
 
 echo "Unzipping..."
 

From c42eb9c4f7d18f1ba16d1d5cb0646296679d936c Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Sun, 8 Nov 2015 18:50:29 +0800
Subject: [PATCH 1624/2053] GetDB must return a value

As noted by @danst18, when USE_LEVELDB and USE_LMDB are disabled, a compiler error is issued since GetDB no longer returns a value.
At runtime a fatal error would be issued anyways. However to help users who don't need a DB backend, NULL should be returned here.
---
 src/caffe/util/db.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
index ccda054d881..d0a2b0b5c07 100644
--- a/src/caffe/util/db.cpp
+++ b/src/caffe/util/db.cpp
@@ -33,6 +33,7 @@ DB* GetDB(const string& backend) {
   }
 #endif  // USE_LMDB
   LOG(FATAL) << "Unknown database backend";
+  return NULL;
 }
 
 }  // namespace db

From 0eea94a0e02dd6d28175538a29720456f5213da9 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Sun, 8 Nov 2015 11:20:32 -0800
Subject: [PATCH 1625/2053] display 'ignore source layer' when initializing
 from existing parameters

This helps in the case to see which layer is initialized from existing parameters, and which layer is ignored. This helps identify the cases where the user types a error mismatch layer name.
---
 src/caffe/net.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 1ad93e6af5f..05bee7987da 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -745,7 +745,7 @@ void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
       ++target_layer_id;
     }
     if (target_layer_id == layer_names_.size()) {
-      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+      LOG(INFO) << "Ignoring source layer " << source_layer_name;
       continue;
     }
     DLOG(INFO) << "Copying source layer " << source_layer_name;
@@ -813,7 +813,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
       ++target_layer_id;
     }
     if (target_layer_id == layer_names_.size()) {
-      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+      LOG(INFO) << "Ignoring source layer " << source_layer_name;
       continue;
     }
     DLOG(INFO) << "Copying source layer " << source_layer_name;
@@ -868,7 +868,7 @@ void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
   for (int i = 0; i < num_layers; ++i) {
     string source_layer_name = hdf5_get_name_by_idx(data_hid, i);
     if (!layer_names_index_.count(source_layer_name)) {
-      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+      LOG(INFO) << "Ignoring source layer " << source_layer_name;
       continue;
     }
     int target_layer_id = layer_names_index_[source_layer_name];

From 50a44b05f87d6c5b734e2b172f5120898c6e3e47 Mon Sep 17 00:00:00 2001
From: panmari <smmuzi@gmail.com>
Date: Fri, 6 Nov 2015 12:53:16 +0100
Subject: [PATCH 1626/2053] Switched order of two layers for simpler diff with
 untuned file

Untuned file is in models/bvlc_reference_caffenet/train_val.prototxt.
---
 models/finetune_flickr_style/train_val.prototxt | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index 848a426c914..985353be369 100644
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -369,13 +369,6 @@ layer {
     }
   }
 }
-layer {
-  name: "loss"
-  type: "SoftmaxWithLoss"
-  bottom: "fc8_flickr"
-  bottom: "label"
-  top: "loss"
-}
 layer {
   name: "accuracy"
   type: "Accuracy"
@@ -386,3 +379,10 @@ layer {
     phase: TEST
   }
 }
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8_flickr"
+  bottom: "label"
+  top: "loss"
+}

From 96e95fb24dc53bed1e46f2404a2f79f1cf870472 Mon Sep 17 00:00:00 2001
From: Gustav Larsson <gustav.m.larsson@gmail.com>
Date: Mon, 9 Nov 2015 14:32:37 -0600
Subject: [PATCH 1627/2053] DOC: Fix consistent typo in contrastive loss

If a pair is similar, it should take the squared distance and not the
distance. This is clearly what the code is doing.
---
 include/caffe/loss_layers.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index d08ad9b6894..1591c0fe151 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -132,7 +132,7 @@ class LossLayer : public Layer<Dtype> {
 
 /**
  * @brief Computes the contrastive loss @f$
- *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
  *              \left(1-y\right) \max \left(margin-d, 0\right)^2
  *          @f$ where @f$
  *          d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
@@ -148,7 +148,7 @@ class LossLayer : public Layer<Dtype> {
  * @param top output Blob vector (length 1)
  *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
  *      the computed contrastive loss: @f$ E =
- *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
+ *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
  *          \left(1-y\right) \max \left(margin-d, 0\right)^2
  *          @f$ where @f$
  *          d = \left| \left| a_n - b_n \right| \right|_2 @f$.

From 29f6670f11c4ac505cad0f779430dea01358c025 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Sat, 7 Nov 2015 14:09:59 +0800
Subject: [PATCH 1628/2053] Replace unistd functions with cross platform
 counterparts

---
 Makefile                          |  2 +-
 cmake/Dependencies.cmake          |  2 +-
 include/caffe/util/io.hpp         | 30 +++++++++++-------------------
 src/caffe/test/test_benchmark.cpp |  6 +++---
 4 files changed, 16 insertions(+), 24 deletions(-)

diff --git a/Makefile b/Makefile
index 4a1d41d5a82..f5dbf432f75 100644
--- a/Makefile
+++ b/Makefile
@@ -170,7 +170,7 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 
-LIBRARIES += glog gflags protobuf boost_system m hdf5_hl hdf5
+LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
 
 # handle IO dependencies
 USE_LEVELDB ?= 1
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 5651e2b086d..51a803c1a73 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -2,7 +2,7 @@
 set(Caffe_LINKER_LIBS "")
 
 # ---[ Boost
-find_package(Boost 1.46 REQUIRED COMPONENTS system thread)
+find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
 include_directories(SYSTEM ${Boost_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index d6cfa442fca..6b7332548b0 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,7 +1,7 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
-#include <unistd.h>
+#include <boost/filesystem.hpp>
 #include <string>
 
 #include "google/protobuf/message.h"
@@ -12,31 +12,23 @@
 namespace caffe {
 
 using ::google::protobuf::Message;
+using ::boost::filesystem::path;
 
 inline void MakeTempFilename(string* temp_filename) {
   temp_filename->clear();
-  *temp_filename = "/tmp/caffe_test.XXXXXX";
-  char* temp_filename_cstr = new char[temp_filename->size() + 1];
-  // NOLINT_NEXT_LINE(runtime/printf)
-  strcpy(temp_filename_cstr, temp_filename->c_str());
-  int fd = mkstemp(temp_filename_cstr);
-  CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;
-  close(fd);
-  *temp_filename = temp_filename_cstr;
-  delete[] temp_filename_cstr;
+  const path& model = boost::filesystem::temp_directory_path()
+    /"caffe_test.%%%%%%";
+  *temp_filename = boost::filesystem::unique_path(model).string();
 }
 
 inline void MakeTempDir(string* temp_dirname) {
   temp_dirname->clear();
-  *temp_dirname = "/tmp/caffe_test.XXXXXX";
-  char* temp_dirname_cstr = new char[temp_dirname->size() + 1];
-  // NOLINT_NEXT_LINE(runtime/printf)
-  strcpy(temp_dirname_cstr, temp_dirname->c_str());
-  char* mkdtemp_result = mkdtemp(temp_dirname_cstr);
-  CHECK(mkdtemp_result != NULL)
-      << "Failed to create a temporary directory at: " << *temp_dirname;
-  *temp_dirname = temp_dirname_cstr;
-  delete[] temp_dirname_cstr;
+  const path& model = boost::filesystem::temp_directory_path()
+    /"caffe_test.%%%%%%";
+  const path& dir = boost::filesystem::unique_path(model).string();
+  bool directoryCreated = boost::filesystem::create_directory(dir);
+  CHECK(directoryCreated);
+  *temp_dirname = dir.string();
 }
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index 43aaa639b3c..b03fdf69a8a 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -1,4 +1,4 @@
-#include <unistd.h>  // for usleep
+#include <boost/thread.hpp>
 
 #include "gtest/gtest.h"
 
@@ -64,7 +64,7 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
-  usleep(300 * 1000);
+  boost::this_thread::sleep(boost::posix_time::milliseconds(300));
   EXPECT_GE(timer.MilliSeconds(), 300 - kMillisecondsThreshold);
   EXPECT_LE(timer.MilliSeconds(), 300 + kMillisecondsThreshold);
   EXPECT_TRUE(timer.initted());
@@ -79,7 +79,7 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
-  usleep(300 * 1000);
+  boost::this_thread::sleep(boost::posix_time::milliseconds(300));
   EXPECT_GE(timer.Seconds(), 0.3 - kMillisecondsThreshold / 1000.);
   EXPECT_LE(timer.Seconds(), 0.3 + kMillisecondsThreshold / 1000.);
   EXPECT_TRUE(timer.initted());

From 5395cc66d68df74ff5d0920ed80eabcdd439c660 Mon Sep 17 00:00:00 2001
From: ixartz <contact@ixartz.com>
Date: Mon, 2 Nov 2015 23:07:45 -0500
Subject: [PATCH 1629/2053] OSX 10.10 (and more) use Accelerate Framework
 instead of veclib

---
 cmake/Dependencies.cmake             | 6 ++++++
 include/caffe/util/mkl_alternate.hpp | 5 +++++
 2 files changed, 11 insertions(+)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 51a803c1a73..64e6500ed3d 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -102,6 +102,12 @@ elseif(APPLE)
   find_package(vecLib REQUIRED)
   include_directories(SYSTEM ${vecLib_INCLUDE_DIR})
   list(APPEND Caffe_LINKER_LIBS ${vecLib_LINKER_LIBS})
+
+  if(VECLIB_FOUND)
+    if(NOT vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
+      add_definitions(-DUSE_ACCELERATE)
+    endif()
+  endif()
 endif()
 
 # ---[ Python
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 3355b6658a3..95df0f93b5e 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -7,9 +7,14 @@
 
 #else  // If use MKL, simply include the MKL header
 
+#ifdef USE_ACCELERATE
+#include <Accelerate/Accelerate.h>
+#else
 extern "C" {
 #include <cblas.h>
 }
+#endif  // USE_ACCELERATE
+
 #include <math.h>
 
 // Functions that caffe uses but are not present if MKL is not linked.

From f9970c83264b43722bd9f97376580cc3dbf61227 Mon Sep 17 00:00:00 2001
From: gdh1995 <gdh1995@qq.com>
Date: Tue, 10 Nov 2015 22:41:55 +0800
Subject: [PATCH 1630/2053] fix a bug that time duration may be 0 when
 downloading model binary

---
 scripts/download_model_binary.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index 03a50f6776a..66f72f2477e 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -18,7 +18,7 @@ def reporthook(count, block_size, total_size):
     if count == 0:
         start_time = time.time()
         return
-    duration = time.time() - start_time
+    duration = (time.time() - start_time) or 0.01
     progress_size = int(count * block_size)
     speed = int(progress_size / (1024 * duration))
     percent = int(count * block_size * 100 / total_size)

From 9ff2baf8e06e4809ad668e5c355ad76c36d9674d Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Thu, 12 Nov 2015 15:32:28 +0800
Subject: [PATCH 1631/2053] Remove un-necessary includes

---
 src/caffe/parallel.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 9abc92b612d..62f5d738593 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -3,9 +3,6 @@
 #endif
 #include <glog/logging.h>
 #include <stdio.h>
-#include <sys/ioctl.h>
-#include <sys/mman.h>
-#include <sys/stat.h>
 
 #include <sstream>
 #include <string>

From 3682fde8a9a4a7b20e6ceb2d95a9abeab5227561 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Thu, 12 Nov 2015 15:55:06 +0800
Subject: [PATCH 1632/2053] Functions shall return a value in syncedmem

---
 src/caffe/syncedmem.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index ec4665ecdda..4d3564172ab 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -97,6 +97,7 @@ const void* SyncedMemory::gpu_data() {
   return (const void*)gpu_ptr_;
 #else
   NO_GPU;
+  return NULL;
 #endif
 }
 
@@ -133,6 +134,7 @@ void* SyncedMemory::mutable_gpu_data() {
   return gpu_ptr_;
 #else
   NO_GPU;
+  return NULL;
 #endif
 }
 

From dc48870d7f8e823138594c794789ac3156cc0798 Mon Sep 17 00:00:00 2001
From: Benedikt Wilbertz <benedikt.wilbertz@gmx.de>
Date: Wed, 30 Sep 2015 23:02:34 +0200
Subject: [PATCH 1633/2053] Fix loss of last iteration when average_loss > 1

refactor duplicate code into separate update function for smoothed loss

fix naming convention
---
 include/caffe/solver.hpp |  3 +++
 src/caffe/solver.cpp     | 37 ++++++++++++++++++++++++-------------
 2 files changed, 27 insertions(+), 13 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 26b8e8e2038..38259edad9f 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -107,6 +107,7 @@ class Solver {
   virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;
   virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
   void DisplayOutputBlobs(const int net_id);
+  void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);
 
   SolverParameter param_;
   int iter_;
@@ -114,6 +115,8 @@ class Solver {
   shared_ptr<Net<Dtype> > net_;
   vector<shared_ptr<Net<Dtype> > > test_nets_;
   vector<Callback*> callbacks_;
+  vector<Dtype> losses_;
+  Dtype smoothed_loss_;
 
   // The root solver that holds root nets (actually containing shared layers)
   // in data parallelism
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index d3bc7361dd5..5b31c7d81a3 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -195,8 +195,8 @@ void Solver<Dtype>::Step(int iters) {
   const int start_iter = iter_;
   const int stop_iter = iter_ + iters;
   int average_loss = this->param_.average_loss();
-  vector<Dtype> losses;
-  Dtype smoothed_loss = 0;
+  losses_.clear();
+  smoothed_loss_ = 0;
 
   while (iter_ < stop_iter) {
     // zero-init the params
@@ -223,18 +223,10 @@ void Solver<Dtype>::Step(int iters) {
     }
     loss /= param_.iter_size();
     // average the loss across iterations for smoothed reporting
-    if (losses.size() < average_loss) {
-      losses.push_back(loss);
-      int size = losses.size();
-      smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
-    } else {
-      int idx = (iter_ - start_iter) % average_loss;
-      smoothed_loss += (loss - losses[idx]) / average_loss;
-      losses[idx] = loss;
-    }
+    UpdateSmoothedLoss(loss, start_iter, average_loss);
     if (display) {
       LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
-          << ", loss = " << smoothed_loss;
+          << ", loss = " << smoothed_loss_;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
@@ -297,6 +289,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
 
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.
+  int start_iter = iter_;
   Step(param_.max_iter() - iter_);
   // If we haven't already, save a snapshot after optimization, unless
   // overridden by setting snapshot_after_train := false
@@ -315,9 +308,13 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // updated the parameters "max_iter" times -- this final pass is only done to
   // display the loss, which is computed in the forward pass.
   if (param_.display() && iter_ % param_.display() == 0) {
+    int average_loss = this->param_.average_loss();
     Dtype loss;
     net_->ForwardPrefilled(&loss);
-    LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+
+    UpdateSmoothedLoss(loss, start_iter, average_loss);
+
+    LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss_;
   }
   if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
     TestAll();
@@ -485,6 +482,20 @@ void Solver<Dtype>::Restore(const char* state_file) {
   }
 }
 
+template <typename Dtype>
+void Solver<Dtype>::UpdateSmoothedLoss(Dtype loss, int start_iter,
+    int average_loss) {
+  if (losses_.size() < average_loss) {
+    losses_.push_back(loss);
+    int size = losses_.size();
+    smoothed_loss_ = (smoothed_loss_ * (size - 1) + loss) / size;
+  } else {
+    int idx = (iter_ - start_iter) % average_loss;
+    smoothed_loss_ += (loss - losses_[idx]) / average_loss;
+    losses_[idx] = loss;
+  }
+}
+
 INSTANTIATE_CLASS(Solver);
 
 }  // namespace caffe

From 0ad1d8ab3f8e3d0bd4d9a7e8b65c7a5f9f28d60a Mon Sep 17 00:00:00 2001
From: Kang Kim <kkhoot@gmail.com>
Date: Sat, 7 Nov 2015 12:49:15 +0900
Subject: [PATCH 1634/2053] Update computation of variance and global stats in
 BatchNormLayer

---
 src/caffe/layers/batch_norm_layer.cpp | 55 +++++++++++++-------------
 src/caffe/layers/batch_norm_layer.cu  | 56 ++++++++++++++-------------
 2 files changed, 57 insertions(+), 54 deletions(-)

diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index 94c2b96b9cd..5eba25e9024 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -2,7 +2,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -80,20 +79,21 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   int num = bottom[0]->shape(0);
   int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
 
-  // elementwise square
-  caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
-             temp_.mutable_cpu_data());
+  if (bottom[0] != top[0]) {
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
 
   if (use_global_stats_) {
     // use the stored mean/variance estimates.  TODO(cdoersch): allow an option
     // to use an unbiased variance estimate, like the paper does.
-    const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0];
+    const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
+        0 : 1 / this->blobs_[2]->cpu_data()[0];
     caffe_cpu_scale(variance_.count(), scale_factor,
         this->blobs_[0]->cpu_data(), mean_.mutable_cpu_data());
     caffe_cpu_scale(variance_.count(), scale_factor,
         this->blobs_[1]->cpu_data(), variance_.mutable_cpu_data());
   } else {
-    // computes variance using var(X) = E(X^2) - (EX)^2
+    // compute mean
     caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), bottom_data,
         spatial_sum_multiplier_.cpu_data(), 0.,
@@ -101,44 +101,45 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
         num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
         mean_.mutable_cpu_data());
+  }
+
+  // subtract mean
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, -1, num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 1., top_data);
+
+  if (!use_global_stats_) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_powx(top[0]->count(), top_data, Dtype(2),
+        temp_.mutable_cpu_data());  // (X-EX)^2
     caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), temp_.cpu_data(),
         spatial_sum_multiplier_.cpu_data(), 0.,
         num_by_chans_.mutable_cpu_data());
     caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
         num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
-        variance_.mutable_cpu_data());
+        variance_.mutable_cpu_data());  // E((X_EX)^2)
+
+    // compute and save moving average
     this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
     this->blobs_[2]->mutable_cpu_data()[0] += 1;
     caffe_cpu_axpby(mean_.count(), Dtype(1), mean_.cpu_data(),
         moving_average_fraction_, this->blobs_[0]->mutable_cpu_data());
-    Dtype m = Dtype(bottom[0]->count()/channels_);
-    caffe_cpu_axpby(variance_.count(), m/(m-1), variance_.cpu_data(),
-        moving_average_fraction_, this->blobs_[1]->mutable_cpu_data());
+    int m = bottom[0]->count()/channels_;
+    Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1;
+    caffe_cpu_axpby(variance_.count(), bias_correction_factor,
+        variance_.cpu_data(), moving_average_fraction_,
+        this->blobs_[1]->mutable_cpu_data());
   }
-  // elementwise square of mean
-  caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-             temp_.mutable_cpu_data());
-
-  caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-            variance_.mutable_cpu_data());  // variance
 
   // normalize variance
   caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
   caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
              variance_.mutable_cpu_data());
 
-  // do mean and variance normalization
-  if (bottom[0] != top[0]) {
-    caffe_copy(bottom[0]->count(), bottom_data, top_data);
-  }
-  // subtract mean
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
-      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
-      num_by_chans_.mutable_cpu_data());
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
-      spatial_dim, 1, -1, num_by_chans_.cpu_data(),
-      spatial_sum_multiplier_.cpu_data(), 1., top_data);
   // replicate variance to input size
   caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
       batch_sum_multiplier_.cpu_data(), variance_.cpu_data(), 0.,
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
index cd8924a451d..921a58f07a9 100644
--- a/src/caffe/layers/batch_norm_layer.cu
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -2,7 +2,6 @@
 #include <vector>
 
 #include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -15,20 +14,22 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   int num = bottom[0]->shape(0);
   int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
 
-  // elementwise square
-  caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
-      temp_.mutable_gpu_data());
+  if (bottom[0] != top[0]) {
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+
 
   if (use_global_stats_) {
     // use the stored mean/variance estimates.  TODO(cdoersch): allow an option
     // to use an unbiased variance estimate, like the paper does.
-    const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0];
+    const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
+        0 : 1 / this->blobs_[2]->cpu_data()[0];
     caffe_gpu_scale(variance_.count(), scale_factor,
         this->blobs_[0]->gpu_data(), mean_.mutable_gpu_data());
     caffe_gpu_scale(variance_.count(), scale_factor,
         this->blobs_[1]->gpu_data(), variance_.mutable_gpu_data());
   } else {
-    // computes variance using var(X) = E(X^2) - (EX)^2
+    // compute mean
     caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), bottom_data,
         spatial_sum_multiplier_.gpu_data(), 0.,
@@ -36,44 +37,45 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
         num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
         mean_.mutable_gpu_data());
+  }
+
+  // subtract mean
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, -1, num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 1., top_data);
+
+  if (!use_global_stats_) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_gpu_powx(top[0]->count(), top_data, Dtype(2),
+        temp_.mutable_gpu_data());  // (X-EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), temp_.gpu_data(),
         spatial_sum_multiplier_.gpu_data(), 0.,
         num_by_chans_.mutable_gpu_data());
     caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
         num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
-        variance_.mutable_gpu_data());
+        variance_.mutable_gpu_data());  // E((X_EX)^2)
+
+    // compute and save moving average
     this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
     this->blobs_[2]->mutable_cpu_data()[0] += 1;
     caffe_gpu_axpby(mean_.count(), Dtype(1), mean_.gpu_data(),
         moving_average_fraction_, this->blobs_[0]->mutable_gpu_data());
-    Dtype m = Dtype(bottom[0]->count()/channels_);
-    caffe_gpu_axpby(variance_.count(), m/(m-1), variance_.gpu_data(),
-        moving_average_fraction_, this->blobs_[1]->mutable_gpu_data());
+    int m = bottom[0]->count()/channels_;
+    Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1;
+    caffe_gpu_axpby(variance_.count(), bias_correction_factor,
+        variance_.gpu_data(), moving_average_fraction_,
+        this->blobs_[1]->mutable_gpu_data());
   }
-  // elementwise square of mean
-  caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-                 temp_.mutable_gpu_data());
-
-  caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-                variance_.mutable_gpu_data());  // variance
 
   // normalize variance
   caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
   caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
       variance_.mutable_gpu_data());
 
-  // do mean and variance normalization
-  if (bottom[0] != top[0]) {
-    caffe_copy(bottom[0]->count(), bottom_data, top_data);
-  }
-  // subtract mean
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
-      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
-      num_by_chans_.mutable_gpu_data());
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
-      spatial_dim, 1, -1, num_by_chans_.gpu_data(),
-      spatial_sum_multiplier_.gpu_data(), 1., top_data);
   // replicate variance to input size
   caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
       batch_sum_multiplier_.gpu_data(), variance_.gpu_data(), 0.,

From f6e582a38deee8db0904460cbf7aaeb143c682f5 Mon Sep 17 00:00:00 2001
From: Kang Kim <kkhoot@gmail.com>
Date: Fri, 13 Nov 2015 02:20:02 +0900
Subject: [PATCH 1635/2053] Make backward pass work when global stats is active
 for BatchNormLayer including minor code cleaning

---
 src/caffe/layers/batch_norm_layer.cpp | 10 ++++++----
 src/caffe/layers/batch_norm_layer.cu  | 10 ++++++----
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index 5eba25e9024..b5c91b5e1b3 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -84,8 +84,7 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
 
   if (use_global_stats_) {
-    // use the stored mean/variance estimates.  TODO(cdoersch): allow an option
-    // to use an unbiased variance estimate, like the paper does.
+    // use the stored mean/variance estimates.
     const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
         0 : 1 / this->blobs_[2]->cpu_data()[0];
     caffe_cpu_scale(variance_.count(), scale_factor,
@@ -158,7 +157,6 @@ template <typename Dtype>
 void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
-  CHECK(!use_global_stats_);
   const Dtype* top_diff;
   if (bottom[0] != top[0]) {
     top_diff = top[0]->cpu_diff();
@@ -166,8 +164,12 @@ void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     caffe_copy(x_norm_.count(), top[0]->cpu_diff(), x_norm_.mutable_cpu_diff());
     top_diff = x_norm_.cpu_diff();
   }
-  const Dtype* top_data = x_norm_.cpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  if (use_global_stats_) {
+    caffe_div(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff);
+    return;
+  }
+  const Dtype* top_data = x_norm_.cpu_data();
   int num = bottom[0]->shape()[0];
   int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
   // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
index 921a58f07a9..2a6cac54168 100644
--- a/src/caffe/layers/batch_norm_layer.cu
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -20,8 +20,7 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 
   if (use_global_stats_) {
-    // use the stored mean/variance estimates.  TODO(cdoersch): allow an option
-    // to use an unbiased variance estimate, like the paper does.
+    // use the stored mean/variance estimates.
     const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
         0 : 1 / this->blobs_[2]->cpu_data()[0];
     caffe_gpu_scale(variance_.count(), scale_factor,
@@ -94,7 +93,6 @@ template <typename Dtype>
 void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
-  CHECK(!use_global_stats_);
   const Dtype* top_diff;
   if (bottom[0] != top[0]) {
     top_diff = top[0]->gpu_diff();
@@ -102,8 +100,12 @@ void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     caffe_copy(x_norm_.count(), top[0]->gpu_diff(), x_norm_.mutable_gpu_diff());
     top_diff = x_norm_.gpu_diff();
   }
-  const Dtype* top_data = x_norm_.gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  if (use_global_stats_) {
+    caffe_gpu_div(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff);
+    return;
+  }
+  const Dtype* top_data = x_norm_.gpu_data();
   int num = bottom[0]->shape()[0];
   int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
   // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then

From d81ffbff8cda56de1fe6c41b7156d781f775c7b3 Mon Sep 17 00:00:00 2001
From: Adam Siembida <w1res@users.noreply.github.com>
Date: Thu, 12 Nov 2015 16:03:41 -0500
Subject: [PATCH 1636/2053] Add parentheses to backward_{cpu,gpu} method.

---
 docs/tutorial/forward_backward.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index a645f002f61..528b993ba07 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -29,7 +29,7 @@ The backward pass begins with the loss and computes the gradient with respect to
 These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
 
 - The `Net::Forward()` and `Net::Backward()` methods carry out the respective passes while `Layer::Forward()` and `Layer::Backward()` compute each step.
-- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
+- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}()` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
 
 The [Solver](solver.html) optimizes a model by first calling forward to yield the output and loss, then calling backward to generate the gradient of the model, and then incorporating the gradient into a weight update that attempts to minimize the loss. Division of labor between the Solver, Net, and Layer keep Caffe modular and open to development.
 

From a6f14f6e3d03caf8242ed5aa7e224a9ea8ef740d Mon Sep 17 00:00:00 2001
From: Balint Cristian <cristian.balint@gmail.com>
Date: Fri, 13 Nov 2015 13:58:49 +0200
Subject: [PATCH 1637/2053] Display and store cuDNN version numbers during
 cmake.

---
 cmake/Cuda.cmake    | 33 +++++++++++++++++++++++++++++++--
 cmake/Summary.cmake |  2 +-
 2 files changed, 32 insertions(+), 3 deletions(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 98aef268cf4..286a42802b4 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -183,12 +183,41 @@ function(detect_cuDNN)
     set(HAVE_CUDNN  TRUE PARENT_SCOPE)
     set(CUDNN_FOUND TRUE PARENT_SCOPE)
 
+    file(READ ${CUDNN_INCLUDE}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
+
+    # cuDNN v3 and beyond
+    string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+           CUDNN_VERSION_MAJOR "${CUDNN_VERSION_FILE_CONTENTS}")
+    string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
+           CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}")
+    string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
+           CUDNN_VERSION_MINOR "${CUDNN_VERSION_FILE_CONTENTS}")
+    string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1"
+           CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}")
+    string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)"
+           CUDNN_VERSION_PATCH "${CUDNN_VERSION_FILE_CONTENTS}")
+    string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
+           CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}")
+
+    if(NOT CUDNN_VERSION_MAJOR)
+      set(CUDNN_VERSION "???")
+    else()
+      set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}")
+    endif()
+
+    message(STATUS "Found cuDNN: ver. ${CUDNN_VERSION} found (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+
+    string(COMPARE LESS "${CUDNN_VERSION_MAJOR}" 3 cuDNNVersionIncompatible)
+    if(cuDNNVersionIncompatible)
+      message(FATAL_ERROR "cuDNN version >3 is required.")
+    endif()
+
+    set(CUDNN_VERSION "${CUDNN_VERSION}" PARENT_SCOPE)
     mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
-    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+
   endif()
 endfunction()
 
-
 ################################################################################################
 ###  Non macro section
 ################################################################################################
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 6984f417e71..557a6f04e4b 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -142,7 +142,7 @@ function(caffe_print_configuration_summary)
     caffe_status("  Target GPU(s)     :   ${CUDA_ARCH_NAME}" )
     caffe_status("  GPU arch(s)       :   ${NVCC_FLAGS_EXTRA_readable}")
     if(USE_CUDNN)
-      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes" ELSE "Not found")
+      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes (ver. ${CUDNN_VERSION})" ELSE "Not found")
     else()
       caffe_status("  cuDNN             :   Disabled")
     endif()

From a29c2f7a0ff2ff4278a2e498f0b686b5d5cb88cd Mon Sep 17 00:00:00 2001
From: Alex Lee <alexleegk@gmail.com>
Date: Sat, 14 Nov 2015 12:49:05 -0800
Subject: [PATCH 1638/2053] Fix outs and diffs being overwritten in
 forward_backward_all.

---
 python/caffe/pycaffe.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 7bd4f411b6a..31dc702f647 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -216,9 +216,9 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
         batch_blobs = self.forward(blobs=blobs, **fb)
         batch_diffs = self.backward(diffs=diffs, **bb)
         for out, out_blobs in batch_blobs.iteritems():
-            all_outs[out].extend(out_blobs)
+            all_outs[out].extend(out_blobs.copy())
         for diff, out_diffs in batch_diffs.iteritems():
-            all_diffs[diff].extend(out_diffs)
+            all_diffs[diff].extend(out_diffs.copy())
     # Package in ndarray.
     for out, diff in zip(all_outs, all_diffs):
         all_outs[out] = np.asarray(all_outs[out])

From c4190a56ab62b1a63c1c55bcef3860701a322bed Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 18 Nov 2015 10:38:32 -0800
Subject: [PATCH 1639/2053] Skip python layer tests if WITH_PYTHON_LAYER unset

---
 python/caffe/test/test_python_layer.py                | 2 ++
 python/caffe/test/test_python_layer_with_param_str.py | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index 8ed86655ec3..e46b7118014 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -77,6 +77,8 @@ def parameter_net_file():
         return f.name
 
 
+@unittest.skipIf('Python' not in caffe.layer_type_list(),
+    'Caffe built without Python layer support')
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):
         net_file = python_net_file()
diff --git a/python/caffe/test/test_python_layer_with_param_str.py b/python/caffe/test/test_python_layer_with_param_str.py
index 3d0f107b3bb..c36048ae9f0 100644
--- a/python/caffe/test/test_python_layer_with_param_str.py
+++ b/python/caffe/test/test_python_layer_with_param_str.py
@@ -38,6 +38,8 @@ def python_param_net_file():
         return f.name
 
 
+@unittest.skipIf('Python' not in caffe.layer_type_list(),
+    'Caffe built without Python layer support')
 class TestLayerWithParam(unittest.TestCase):
     def setUp(self):
         net_file = python_param_net_file()

From 1b0716cfd761cec547c85b19fc8f6f971e9236ac Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Thu, 19 Nov 2015 10:05:48 -0800
Subject: [PATCH 1640/2053] Fix MaxTopBlobs in Accuracy Layer

Fix the typo "MaxTopBlos" to "MaxTopBlobs". This typo causes maximum top number to be incorrect.
---
 include/caffe/loss_layers.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index 1591c0fe151..e2e3e48ceb5 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -42,7 +42,7 @@ class AccuracyLayer : public Layer<Dtype> {
   // If there are two top blobs, then the second blob will contain
   // accuracies per class.
   virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlos() const { return 2; }
+  virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
   /**

From 41d0c77e5849f97744a3ca5933fd20887bb97f43 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Thu, 12 Nov 2015 15:15:22 +0800
Subject: [PATCH 1641/2053] Convert std::max args to Dtype

---
 include/caffe/test/test_gradient_check_util.hpp | 5 +++--
 src/caffe/layers/contrastive_loss_layer.cpp     | 3 ++-
 src/caffe/test/test_contrastive_loss_layer.cpp  | 2 +-
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 25f35d1589e..b25a84875ef 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -169,8 +169,9 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
           || fabs(feature) > kink_ + kink_range_) {
         // We check relative accuracy, but for too small values, we threshold
         // the scale factor by 1.
-        Dtype scale = std::max(
-            std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
+        Dtype scale = std::max<Dtype>(
+            std::max(fabs(computed_gradient), fabs(estimated_gradient)),
+            Dtype(1.));
         EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
           << "debug: (top_id, top_data_id, blob_id, feat_id)="
           << top_id << "," << top_data_id << "," << blob_id << "," << feat_id
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 74002087ec9..45facd4a4f5 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -51,7 +51,8 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
       if (legacy_version) {
         loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
       } else {
-        Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0);
+        Dtype dist = std::max<Dtype>(margin - sqrt(dist_sq_.cpu_data()[i]),
+          Dtype(0.0));
         loss += dist*dist;
       }
     }
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index 592997e4578..95901f14297 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -77,7 +77,7 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
     if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
       loss += dist_sq;
     } else {
-      Dtype dist = std::max(margin - sqrt(dist_sq), 0.0);
+      Dtype dist = std::max<Dtype>(margin - sqrt(dist_sq), 0.0);
       loss += dist*dist;
     }
   }

From 23e4e4621b0199684d6d7a8535fb7628f5609952 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Fri, 20 Nov 2015 16:36:29 +0800
Subject: [PATCH 1642/2053] Function must return a value

Currently compilation will fail with some compilers when LevelDB and LMDB are disabled.
Very similar to a recently fixed issue.
---
 src/caffe/util/db.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
index d0a2b0b5c07..7f22509b56e 100644
--- a/src/caffe/util/db.cpp
+++ b/src/caffe/util/db.cpp
@@ -18,6 +18,7 @@ DB* GetDB(DataParameter::DB backend) {
 #endif  // USE_LMDB
   default:
     LOG(FATAL) << "Unknown database backend";
+    return NULL;
   }
 }
 

From e09329077d7612d7d1a185ea120be6be91bf03d2 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Fri, 20 Nov 2015 16:52:25 +0800
Subject: [PATCH 1643/2053] Exclude core.hpp when building without OpenCV

---
 src/caffe/util/io.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index f2b1dd98423..835d2d4e4ff 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,8 +2,8 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
-#include <opencv2/core/core.hpp>
 #ifdef USE_OPENCV
+#include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
 #include <opencv2/imgproc/imgproc.hpp>

From 8b2aa7093cba002a5f286d47658de72a961d1299 Mon Sep 17 00:00:00 2001
From: Carl Doersch <cdoersch@cs.cmu.edu>
Date: Fri, 6 Nov 2015 14:41:30 -0800
Subject: [PATCH 1644/2053] Better normalization options for SoftmaxWithLoss
 layer.

---
 include/caffe/loss_layers.hpp           | 11 +++--
 src/caffe/layers/softmax_loss_layer.cpp | 54 +++++++++++++++++++------
 src/caffe/layers/softmax_loss_layer.cu  | 32 ++++++++-------
 src/caffe/proto/caffe.proto             | 24 +++++++++--
 4 files changed, 89 insertions(+), 32 deletions(-)

diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index d08ad9b6894..d6569c4a574 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -747,6 +747,12 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
 
   /// The internal SoftmaxLayer used to map predictions to a distribution.
   shared_ptr<Layer<Dtype> > softmax_layer_;
@@ -760,9 +766,8 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> {
   bool has_ignore_label_;
   /// The label indicating that an instance should be ignored.
   int ignore_label_;
-  /// Whether to normalize the loss by the total number of values present
-  /// (otherwise just by the batch size).
-  bool normalize_;
+  /// How to normalize the output loss.
+  LossParameter_NormalizationMode normalization_;
 
   int softmax_axis_, outer_num_, inner_num_;
 };
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index dee50ac6355..3cdef82afd2 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -25,7 +25,14 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   if (has_ignore_label_) {
     ignore_label_ = this->layer_param_.loss_param().ignore_label();
   }
-  normalize_ = this->layer_param_.loss_param().normalize();
+  if (!this->layer_param_.loss_param().has_normalization() &&
+      this->layer_param_.loss_param().has_normalize()) {
+    normalization_ = this->layer_param_.loss_param().normalize() ?
+                     LossParameter_NormalizationMode_VALID :
+                     LossParameter_NormalizationMode_BATCH_SIZE;
+  } else {
+    normalization_ = this->layer_param_.loss_param().normalization();
+  }
 }
 
 template <typename Dtype>
@@ -48,6 +55,36 @@ void SoftmaxWithLossLayer<Dtype>::Reshape(
   }
 }
 
+template <typename Dtype>
+Dtype SoftmaxWithLossLayer<Dtype>::get_normalizer(
+    LossParameter_NormalizationMode normalization_mode, int valid_count) {
+  Dtype normalizer;
+  switch (normalization_mode) {
+    case LossParameter_NormalizationMode_FULL:
+      normalizer = Dtype(outer_num_ * inner_num_);
+      break;
+    case LossParameter_NormalizationMode_VALID:
+      if (valid_count == -1) {
+        normalizer = Dtype(outer_num_ * inner_num_);
+      } else {
+        normalizer = Dtype(valid_count);
+      }
+      break;
+    case LossParameter_NormalizationMode_BATCH_SIZE:
+      normalizer = Dtype(outer_num_);
+      break;
+    case LossParameter_NormalizationMode_NONE:
+      normalizer = Dtype(1);
+      break;
+    default:
+      LOG(FATAL) << "Unknown normalization mode: "
+          << LossParameter_NormalizationMode_Name(normalization_mode);
+  }
+  // Some users will have no labels for some examples in order to 'turn off' a
+  // particular loss in a multi-task setup. The max prevents NaNs in that case.
+  return std::max(Dtype(1.0), normalizer);
+}
+
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
@@ -71,11 +108,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
       ++count;
     }
   }
-  if (normalize_) {
-    top[0]->mutable_cpu_data()[0] = loss / count;
-  } else {
-    top[0]->mutable_cpu_data()[0] = loss / outer_num_;
-  }
+  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
   }
@@ -109,12 +142,9 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       }
     }
     // Scale gradient
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    if (normalize_) {
-      caffe_scal(prob_.count(), loss_weight / count, bottom_diff);
-    } else {
-      caffe_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
-    }
+    Dtype loss_weight = top[0]->cpu_diff()[0] /
+                        get_normalizer(normalization_, count);
+    caffe_scal(prob_.count(), loss_weight, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 42e91fa9e38..4753a1ec24b 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -49,14 +49,15 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
       outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
   Dtype loss;
   caffe_gpu_asum(nthreads, loss_data, &loss);
-  if (normalize_) {
-    Dtype count;
-    caffe_gpu_asum(nthreads, counts, &count);
-    loss /= count;
-  } else {
-    loss /= outer_num_;
+  Dtype valid_count = -1;
+  // Only launch another CUDA kernel if we actually need the count of valid
+  // outputs.
+  if (normalization_ == LossParameter_NormalizationMode_VALID &&
+      has_ignore_label_) {
+    caffe_gpu_asum(nthreads, counts, &valid_count);
   }
-  top[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_,
+                                                        valid_count);
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
   }
@@ -108,14 +109,17 @@ void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     SoftmaxLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
         CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, bottom_diff,
         outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    if (normalize_) {
-      Dtype count;
-      caffe_gpu_asum(nthreads, counts, &count);
-      caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff);
-    } else {
-      caffe_gpu_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
+
+    Dtype valid_count = -1;
+    // Only launch another CUDA kernel if we actually need the count of valid
+    // outputs.
+    if (normalization_ == LossParameter_NormalizationMode_VALID &&
+        has_ignore_label_) {
+      caffe_gpu_asum(nthreads, counts, &valid_count);
     }
+    const Dtype loss_weight = top[0]->cpu_diff()[0] /
+                              get_normalizer(normalization_, valid_count);
+    caffe_gpu_scal(prob_.count(), loss_weight , bottom_diff);
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 39873cf7f50..787369f7cff 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -420,9 +420,27 @@ message TransformationParameter {
 message LossParameter {
   // If specified, ignore instances with the given label.
   optional int32 ignore_label = 1;
-  // If true, normalize each batch across all instances (including spatial
-  // dimesions, but not ignored instances); else, divide by batch size only.
-  optional bool normalize = 2 [default = true];
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss layer.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the 
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  optional NormalizationMode normalization = 3 [default = VALID];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
 }
 
 // Messages that store parameters used by individual layer types follow, in

From 37413f9d0b102950dc6b94e52e367c762974f02a Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Mon, 23 Nov 2015 11:36:46 +0800
Subject: [PATCH 1645/2053] Scope macros inside switch

---
 src/caffe/util/hdf5.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
index 7730e76ab87..d255877b63b 100644
--- a/src/caffe/util/hdf5.cpp
+++ b/src/caffe/util/hdf5.cpp
@@ -29,10 +29,10 @@ void hdf5_load_nd_dataset_helper(
   CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
   switch (class_) {
   case H5T_FLOAT:
-    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";
+    { LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT"; }
     break;
   case H5T_INTEGER:
-    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";
+    { LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER"; }
     break;
   case H5T_TIME:
     LOG(FATAL) << "Unsupported datatype class: H5T_TIME";

From b72b0318e2802785c17be1fe8ed1b6899961df19 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Tue, 17 Nov 2015 17:05:56 +0800
Subject: [PATCH 1646/2053] replace snprintf with a C++98 equivalent

---
 examples/cifar10/convert_cifar_data.cpp        | 13 ++++++-------
 examples/mnist/convert_mnist_data.cpp          | 12 +++++-------
 .../siamese/convert_mnist_siamese_data.cpp     |  7 +++----
 include/caffe/util/format.hpp                  | 18 ++++++++++++++++++
 src/caffe/solver.cpp                           |  8 +++-----
 tools/convert_imageset.cpp                     |  8 +++-----
 tools/extract_features.cpp                     | 11 ++++-------
 7 files changed, 42 insertions(+), 35 deletions(-)
 create mode 100644 include/caffe/util/format.hpp

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index f4c42e4d2e7..e1b89f42fb6 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -16,6 +16,7 @@
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
 
 using caffe::Datum;
 using boost::scoped_ptr;
@@ -52,19 +53,18 @@ void convert_dataset(const string& input_folder, const string& output_folder,
   for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
     // Open files
     LOG(INFO) << "Training Batch " << fileid + 1;
-    snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
-    std::ifstream data_file((input_folder + str_buffer).c_str(),
+    string batchFileName = input_folder + "/data_batch_"
+      + caffe::format_int(fileid+1) + ".bin";
+    std::ifstream data_file(batchFileName.c_str(),
         std::ios::in | std::ios::binary);
     CHECK(data_file) << "Unable to open train file #" << fileid + 1;
     for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
       read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
       datum.set_data(str_buffer, kCIFARImageNBytes);
-      int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
-          fileid * kCIFARBatchSize + itemid);
       string out;
       CHECK(datum.SerializeToString(&out));
-      txn->Put(string(str_buffer, length), out);
+      txn->Put(caffe::format_int(fileid * kCIFARBatchSize + itemid, 5), out);
     }
   }
   txn->Commit();
@@ -82,10 +82,9 @@ void convert_dataset(const string& input_folder, const string& output_folder,
     read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
-    int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
     string out;
     CHECK(datum.SerializeToString(&out));
-    txn->Put(string(str_buffer, length), out);
+    txn->Put(caffe::format_int(itemid, 5), out);
   }
   txn->Commit();
   test_db->Close();
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 8f29bafde85..16d28093dd5 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -23,6 +23,7 @@
 #include <string>
 
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
 
 #if defined(USE_LEVELDB) && defined(USE_LMDB)
 
@@ -108,8 +109,6 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   char label;
   char* pixels = new char[rows * cols];
   int count = 0;
-  const int kMaxKeyLength = 10;
-  char key_cstr[kMaxKeyLength];
   string value;
 
   Datum datum;
@@ -123,18 +122,17 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     label_file.read(&label, 1);
     datum.set_data(pixels, rows*cols);
     datum.set_label(label);
-    snprintf(key_cstr, kMaxKeyLength, "%08d", item_id);
+    string key_str = caffe::format_int(item_id, 8);
     datum.SerializeToString(&value);
-    string keystr(key_cstr);
 
     // Put in db
     if (db_backend == "leveldb") {  // leveldb
-      batch->Put(keystr, value);
+      batch->Put(key_str, value);
     } else if (db_backend == "lmdb") {  // lmdb
       mdb_data.mv_size = value.size();
       mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
-      mdb_key.mv_size = keystr.size();
-      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      mdb_key.mv_size = key_str.size();
+      mdb_key.mv_data = reinterpret_cast<void*>(&key_str[0]);
       CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
           << "mdb_put failed";
     } else {
diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
index ad08036fb08..928b3fbf4d5 100644
--- a/examples/siamese/convert_mnist_siamese_data.cpp
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -13,6 +13,7 @@
 #include "stdint.h"
 
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
 #include "caffe/util/math_functions.hpp"
 
 #ifdef USE_LEVELDB
@@ -75,8 +76,6 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   char label_i;
   char label_j;
   char* pixels = new char[2 * rows * cols];
-  const int kMaxKeyLength = 10;
-  char key[kMaxKeyLength];
   std::string value;
 
   caffe::Datum datum;
@@ -99,8 +98,8 @@ void convert_dataset(const char* image_filename, const char* label_filename,
       datum.set_label(0);
     }
     datum.SerializeToString(&value);
-    snprintf(key, kMaxKeyLength, "%08d", itemid);
-    db->Put(leveldb::WriteOptions(), std::string(key), value);
+    std::string key_str = caffe::format_int(itemid, 8);
+    db->Put(leveldb::WriteOptions(), key_str, value);
   }
 
   delete db;
diff --git a/include/caffe/util/format.hpp b/include/caffe/util/format.hpp
new file mode 100644
index 00000000000..925ad2e0479
--- /dev/null
+++ b/include/caffe/util/format.hpp
@@ -0,0 +1,18 @@
+#ifndef CAFFE_UTIL_FORMAT_H_
+#define CAFFE_UTIL_FORMAT_H_
+
+#include <iomanip>  // NOLINT(readability/streams)
+#include <sstream>  // NOLINT(readability/streams)
+#include <string>
+
+namespace caffe {
+
+inline std::string format_int(int n, int numberOfLeadingZeros = 0 ) {
+  std::ostringstream s;
+  s << std::setw(numberOfLeadingZeros) << std::setfill('0') << n;
+  return s.str();
+}
+
+}
+
+#endif   // CAFFE_UTIL_FORMAT_H_
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index d3bc7361dd5..95d7506635e 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -4,6 +4,7 @@
 #include <vector>
 
 #include "caffe/solver.hpp"
+#include "caffe/util/format.hpp"
 #include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/upgrade_proto.hpp"
@@ -448,11 +449,8 @@ void Solver<Dtype>::CheckSnapshotWritePermissions() {
 
 template <typename Dtype>
 string Solver<Dtype>::SnapshotFilename(const string extension) {
-  string filename(param_.snapshot_prefix());
-  const int kBufferSize = 20;
-  char iter_str_buffer[kBufferSize];
-  snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_);
-  return filename + iter_str_buffer + extension;
+  return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_)
+    + extension;
 }
 
 template <typename Dtype>
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index e51a2631077..9c52bfa0ef8 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -20,6 +20,7 @@
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/rng.hpp"
 
@@ -99,8 +100,6 @@ int main(int argc, char** argv) {
   std::string root_folder(argv[1]);
   Datum datum;
   int count = 0;
-  const int kMaxKeyLength = 256;
-  char key_cstr[kMaxKeyLength];
   int data_size = 0;
   bool data_size_initialized = false;
 
@@ -131,13 +130,12 @@ int main(int argc, char** argv) {
       }
     }
     // sequential
-    int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
-        lines[line_id].first.c_str());
+    string key_str = caffe::format_int(line_id, 8) + "_" + lines[line_id].first;
 
     // Put in db
     string out;
     CHECK(datum.SerializeToString(&out));
-    txn->Put(string(key_cstr, length), out);
+    txn->Put(key_str, out);
 
     if (++count % 1000 == 0) {
       // Commit db
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 084c9bf88df..b94dbb980fd 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -1,4 +1,3 @@
-#include <stdio.h>  // for snprintf
 #include <string>
 #include <vector>
 
@@ -10,6 +9,7 @@
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
@@ -135,8 +135,6 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  const int kMaxKeyStrLength = 100;
-  char key_str[kMaxKeyStrLength];
   std::vector<Blob<float>*> input_vec;
   std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
@@ -158,11 +156,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        int length = snprintf(key_str, kMaxKeyStrLength, "%010d",
-            image_indices[i]);
+        string key_str = caffe::format_int(image_indices[i], 10);
+
         string out;
         CHECK(datum.SerializeToString(&out));
-        txns.at(i)->Put(std::string(key_str, length), out);
+        txns.at(i)->Put(key_str, out);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           txns.at(i)->Commit();
@@ -186,4 +184,3 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Successfully extracted the features!";
   return 0;
 }
-

From d3025f5ffb731ef2f7e796f67f6fd6bd43f601b9 Mon Sep 17 00:00:00 2001
From: Ronghang Hu <huronghang@hotmail.com>
Date: Wed, 25 Nov 2015 21:02:02 -0800
Subject: [PATCH 1647/2053] Remove bogus stepearly in MNIST example

This `examples/lenet/lenet_stepearly_solver.prototxt` is introduced in #190 by mistake, since stepearly is never actually merged.
---
 .../mnist/lenet_stepearly_solver.prototxt     | 28 -------------------
 1 file changed, 28 deletions(-)
 delete mode 100644 examples/mnist/lenet_stepearly_solver.prototxt

diff --git a/examples/mnist/lenet_stepearly_solver.prototxt b/examples/mnist/lenet_stepearly_solver.prototxt
deleted file mode 100644
index efc6a335d8f..00000000000
--- a/examples/mnist/lenet_stepearly_solver.prototxt
+++ /dev/null
@@ -1,28 +0,0 @@
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
-# test_iter specifies how many forward passes the test should carry out.
-# In the case of MNIST, we have test batch size 100 and 100 test iterations,
-# covering the full 10,000 testing images.
-test_iter: 100
-# Carry out testing every 500 training iterations.
-test_interval: 500
-# The base learning rate, momentum and the weight decay of the network.
-base_lr: 0.01
-momentum: 0.9
-weight_decay: 0.0005
-# The learning rate policy
-lr_policy: "stepearly"
-gamma: 0.9
-stepearly: 1
-# Display every 100 iterations
-display: 100
-# The maximum number of iterations
-max_iter: 10000
-# snapshot intermediate results
-snapshot: 5000
-snapshot_prefix: "lenet"
-# solver mode: 0 for CPU and 1 for GPU
-solver_mode: 1
-device_id: 1

From 34ee5df55dc11dfc8afff60cf64cd479b639e5a8 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Tue, 24 Nov 2015 14:33:27 +0800
Subject: [PATCH 1648/2053] Secure implementation of MakeTempDir

---
 include/caffe/util/io.hpp | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 6b7332548b0..f9f0f55a5d4 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -9,6 +9,10 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+#ifndef CAFFE_TMP_DIR_RETRIES
+#define CAFFE_TMP_DIR_RETRIES 100
+#endif
+
 namespace caffe {
 
 using ::google::protobuf::Message;
@@ -23,12 +27,17 @@ inline void MakeTempFilename(string* temp_filename) {
 
 inline void MakeTempDir(string* temp_dirname) {
   temp_dirname->clear();
-  const path& model = boost::filesystem::temp_directory_path()
-    /"caffe_test.%%%%%%";
-  const path& dir = boost::filesystem::unique_path(model).string();
-  bool directoryCreated = boost::filesystem::create_directory(dir);
-  CHECK(directoryCreated);
-  *temp_dirname = dir.string();
+  const path& model =
+    boost::filesystem::temp_directory_path()/"caffe_test.%%%%-%%%%";
+  for ( int i = 0; i < CAFFE_TMP_DIR_RETRIES; i++ ) {
+    const path& dir = boost::filesystem::unique_path(model).string();
+    bool done = boost::filesystem::create_directory(dir);
+    if ( done ) {
+      *temp_dirname = dir.string();
+      return;
+    }
+  }
+  LOG(FATAL) << "Failed to create a temporary directory.";
 }
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto);

From 33905d5a8023c3dbac514dac680060dc608145e8 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Wed, 25 Nov 2015 11:43:45 +0800
Subject: [PATCH 1649/2053] Secure temporary file creation

---
 include/caffe/util/io.hpp | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index f9f0f55a5d4..1a599883ca3 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -2,12 +2,15 @@
 #define CAFFE_UTIL_IO_H_
 
 #include <boost/filesystem.hpp>
+#include <iomanip>
+#include <iostream>  // NOLINT(readability/streams)
 #include <string>
 
 #include "google/protobuf/message.h"
 
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
 
 #ifndef CAFFE_TMP_DIR_RETRIES
 #define CAFFE_TMP_DIR_RETRIES 100
@@ -18,13 +21,6 @@ namespace caffe {
 using ::google::protobuf::Message;
 using ::boost::filesystem::path;
 
-inline void MakeTempFilename(string* temp_filename) {
-  temp_filename->clear();
-  const path& model = boost::filesystem::temp_directory_path()
-    /"caffe_test.%%%%%%";
-  *temp_filename = boost::filesystem::unique_path(model).string();
-}
-
 inline void MakeTempDir(string* temp_dirname) {
   temp_dirname->clear();
   const path& model =
@@ -40,6 +36,19 @@ inline void MakeTempDir(string* temp_dirname) {
   LOG(FATAL) << "Failed to create a temporary directory.";
 }
 
+inline void MakeTempFilename(string* temp_filename) {
+  static path temp_files_subpath;
+  static uint64_t next_temp_file = 0;
+  temp_filename->clear();
+  if ( temp_files_subpath.empty() ) {
+    string path_string="";
+    MakeTempDir(&path_string);
+    temp_files_subpath = path_string;
+  }
+  *temp_filename =
+    (temp_files_subpath/caffe::format_int(next_temp_file++, 9)).string();
+}
+
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
 
 inline bool ReadProtoFromTextFile(const string& filename, Message* proto) {

From 300f43f3ae6347ac8e01093f9a57ee99e551ed74 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 10 Nov 2015 00:22:58 -0800
Subject: [PATCH 1650/2053] dismantle layer headers

No more monolithic includes: split layers into their own headers for modular inclusion and build.
---
 Makefile                                      |   2 +-
 include/caffe/caffe.hpp                       |   1 -
 include/caffe/common_layers.hpp               | 816 ------------------
 include/caffe/data_layers.hpp                 | 347 --------
 include/caffe/layer_factory.hpp               |   1 +
 include/caffe/layers/absval_layer.hpp         |  68 ++
 include/caffe/layers/accuracy_layer.hpp       |  95 ++
 include/caffe/layers/argmax_layer.hpp         |  77 ++
 include/caffe/layers/base_conv_layer.hpp      | 168 ++++
 include/caffe/layers/base_data_layer.hpp      |  86 ++
 include/caffe/layers/batch_norm_layer.hpp     |  81 ++
 include/caffe/layers/batch_reindex_layer.hpp  |  83 ++
 include/caffe/layers/bnll_layer.hpp           |  70 ++
 include/caffe/layers/concat_layer.hpp         |  87 ++
 .../caffe/layers/contrastive_loss_layer.hpp   | 101 +++
 include/caffe/layers/conv_layer.hpp           |  81 ++
 include/caffe/layers/cudnn_conv_layer.hpp     |  72 ++
 include/caffe/layers/cudnn_lcn_layer.hpp      |  49 ++
 include/caffe/layers/cudnn_lrn_layer.hpp      |  44 +
 include/caffe/layers/cudnn_pooling_layer.hpp  |  49 ++
 include/caffe/layers/cudnn_relu_layer.hpp     |  45 +
 include/caffe/layers/cudnn_sigmoid_layer.hpp  |  45 +
 include/caffe/layers/cudnn_softmax_layer.hpp  |  45 +
 include/caffe/layers/cudnn_tanh_layer.hpp     |  45 +
 include/caffe/layers/data_layer.hpp           |  39 +
 include/caffe/layers/deconv_layer.hpp         |  51 ++
 include/caffe/layers/dropout_layer.hpp        |  80 ++
 include/caffe/layers/dummy_data_layer.hpp     |  49 ++
 include/caffe/layers/eltwise_layer.hpp        |  51 ++
 include/caffe/layers/embed_layer.hpp          |  52 ++
 include/caffe/layers/euclidean_loss_layer.hpp | 107 +++
 include/caffe/layers/exp_layer.hpp            |  80 ++
 include/caffe/layers/filter_layer.hpp         |  77 ++
 include/caffe/layers/flatten_layer.hpp        |  61 ++
 include/caffe/layers/hdf5_data_layer.hpp      |  62 ++
 include/caffe/layers/hdf5_output_layer.hpp    |  64 ++
 include/caffe/layers/hinge_loss_layer.hpp     | 104 +++
 include/caffe/layers/im2col_layer.hpp         |  63 ++
 include/caffe/layers/image_data_layer.hpp     |  47 +
 include/caffe/layers/infogain_loss_layer.hpp  | 110 +++
 include/caffe/layers/inner_product_layer.hpp  |  51 ++
 include/caffe/layers/log_layer.hpp            |  82 ++
 include/caffe/layers/loss_layer.hpp           |  53 ++
 include/caffe/layers/lrn_layer.hpp            |  94 ++
 include/caffe/layers/memory_data_layer.hpp    |  63 ++
 .../multinomial_logistic_loss_layer.hpp       |  92 ++
 include/caffe/layers/mvn_layer.hpp            |  48 ++
 include/caffe/layers/neuron_layer.hpp         |  32 +
 include/caffe/layers/pooling_layer.hpp        |  60 ++
 include/caffe/layers/power_layer.hpp          |  89 ++
 include/caffe/layers/prelu_layer.hpp          | 101 +++
 include/caffe/{ => layers}/python_layer.hpp   |   0
 include/caffe/layers/reduction_layer.hpp      |  59 ++
 include/caffe/layers/relu_layer.hpp           |  85 ++
 include/caffe/layers/reshape_layer.hpp        |  52 ++
 .../sigmoid_cross_entropy_loss_layer.hpp      | 110 +++
 include/caffe/layers/sigmoid_layer.hpp        |  71 ++
 include/caffe/layers/silence_layer.hpp        |  43 +
 include/caffe/layers/slice_layer.hpp          |  51 ++
 include/caffe/layers/softmax_layer.hpp        |  50 ++
 include/caffe/layers/softmax_loss_layer.hpp   | 130 +++
 include/caffe/layers/split_layer.hpp          |  45 +
 include/caffe/layers/spp_layer.hpp            |  76 ++
 include/caffe/layers/tanh_layer.hpp           |  73 ++
 include/caffe/layers/threshold_layer.hpp      |  64 ++
 include/caffe/layers/tile_layer.hpp           |  43 +
 include/caffe/layers/window_data_layer.hpp    |  55 ++
 include/caffe/loss_layers.hpp                 | 777 -----------------
 include/caffe/neuron_layers.hpp               | 806 -----------------
 include/caffe/vision_layers.hpp               | 659 --------------
 python/caffe/_caffe.cpp                       |   3 +-
 src/caffe/data_reader.cpp                     |   2 +-
 src/caffe/layer_factory.cpp                   |  21 +-
 src/caffe/layers/absval_layer.cpp             |   2 +-
 src/caffe/layers/absval_layer.cu              |   2 +-
 src/caffe/layers/accuracy_layer.cpp           |   2 +-
 src/caffe/layers/argmax_layer.cpp             |   2 +-
 src/caffe/layers/base_conv_layer.cpp          |   2 +-
 src/caffe/layers/base_data_layer.cpp          |   8 +-
 src/caffe/layers/base_data_layer.cu           |   2 +-
 src/caffe/layers/batch_norm_layer.cpp         |   2 +-
 src/caffe/layers/batch_norm_layer.cu          |   2 +-
 src/caffe/layers/batch_reindex_layer.cpp      |   2 +-
 src/caffe/layers/batch_reindex_layer.cu       |   2 +-
 src/caffe/layers/bnll_layer.cpp               |   2 +-
 src/caffe/layers/bnll_layer.cu                |   2 +-
 src/caffe/layers/concat_layer.cpp             |   2 +-
 src/caffe/layers/concat_layer.cu              |   2 +-
 src/caffe/layers/contrastive_loss_layer.cpp   |   2 +-
 src/caffe/layers/contrastive_loss_layer.cu    |   2 +-
 src/caffe/layers/conv_layer.cpp               |   2 +-
 src/caffe/layers/conv_layer.cu                |   2 +-
 src/caffe/layers/cudnn_conv_layer.cpp         |   2 +-
 src/caffe/layers/cudnn_conv_layer.cu          |   2 +-
 src/caffe/layers/cudnn_lcn_layer.cpp          |   2 +-
 src/caffe/layers/cudnn_lcn_layer.cu           |   2 +-
 src/caffe/layers/cudnn_lrn_layer.cpp          |   2 +-
 src/caffe/layers/cudnn_lrn_layer.cu           |   2 +-
 src/caffe/layers/cudnn_pooling_layer.cpp      |   2 +-
 src/caffe/layers/cudnn_pooling_layer.cu       |   2 +-
 src/caffe/layers/cudnn_relu_layer.cpp         |   2 +-
 src/caffe/layers/cudnn_relu_layer.cu          |   2 +-
 src/caffe/layers/cudnn_sigmoid_layer.cpp      |   2 +-
 src/caffe/layers/cudnn_sigmoid_layer.cu       |   2 +-
 src/caffe/layers/cudnn_softmax_layer.cpp      |   2 +-
 src/caffe/layers/cudnn_softmax_layer.cu       |   2 +-
 src/caffe/layers/cudnn_tanh_layer.cpp         |   2 +-
 src/caffe/layers/cudnn_tanh_layer.cu          |   2 +-
 src/caffe/layers/data_layer.cpp               |   4 +-
 src/caffe/layers/deconv_layer.cpp             |   2 +-
 src/caffe/layers/deconv_layer.cu              |   2 +-
 src/caffe/layers/dropout_layer.cpp            |   2 +-
 src/caffe/layers/dropout_layer.cu             |   4 +-
 src/caffe/layers/dummy_data_layer.cpp         |   2 +-
 src/caffe/layers/eltwise_layer.cpp            |   2 +-
 src/caffe/layers/eltwise_layer.cu             |   2 +-
 src/caffe/layers/embed_layer.cpp              |   2 +-
 src/caffe/layers/embed_layer.cu               |   2 +-
 src/caffe/layers/euclidean_loss_layer.cpp     |   2 +-
 src/caffe/layers/euclidean_loss_layer.cu      |   2 +-
 src/caffe/layers/exp_layer.cpp                |   2 +-
 src/caffe/layers/exp_layer.cu                 |   2 +-
 src/caffe/layers/filter_layer.cpp             |   2 +-
 src/caffe/layers/filter_layer.cu              |   2 +-
 src/caffe/layers/flatten_layer.cpp            |   2 +-
 src/caffe/layers/hdf5_data_layer.cpp          |   2 +-
 src/caffe/layers/hdf5_data_layer.cu           |   2 +-
 src/caffe/layers/hdf5_output_layer.cpp        |   2 +-
 src/caffe/layers/hdf5_output_layer.cu         |   2 +-
 src/caffe/layers/hinge_loss_layer.cpp         |   2 +-
 src/caffe/layers/im2col_layer.cpp             |   2 +-
 src/caffe/layers/im2col_layer.cu              |   2 +-
 src/caffe/layers/image_data_layer.cpp         |   4 +-
 src/caffe/layers/infogain_loss_layer.cpp      |   2 +-
 src/caffe/layers/inner_product_layer.cpp      |   2 +-
 src/caffe/layers/inner_product_layer.cu       |   2 +-
 src/caffe/layers/log_layer.cpp                |   2 +-
 src/caffe/layers/log_layer.cu                 |   2 +-
 src/caffe/layers/loss_layer.cpp               |   2 +-
 src/caffe/layers/lrn_layer.cpp                |   2 +-
 src/caffe/layers/lrn_layer.cu                 |   2 +-
 src/caffe/layers/memory_data_layer.cpp        |   2 +-
 .../multinomial_logistic_loss_layer.cpp       |   2 +-
 src/caffe/layers/mvn_layer.cpp                |   2 +-
 src/caffe/layers/mvn_layer.cu                 |   2 +-
 src/caffe/layers/neuron_layer.cpp             |   2 +-
 src/caffe/layers/pooling_layer.cpp            |   2 +-
 src/caffe/layers/pooling_layer.cu             |   2 +-
 src/caffe/layers/power_layer.cpp              |   2 +-
 src/caffe/layers/power_layer.cu               |   2 +-
 src/caffe/layers/prelu_layer.cpp              |   4 +-
 src/caffe/layers/prelu_layer.cu               |   3 +-
 src/caffe/layers/reduction_layer.cpp          |   2 +-
 src/caffe/layers/reduction_layer.cu           |   2 +-
 src/caffe/layers/relu_layer.cpp               |   2 +-
 src/caffe/layers/relu_layer.cu                |   2 +-
 src/caffe/layers/reshape_layer.cpp            |   2 +-
 .../sigmoid_cross_entropy_loss_layer.cpp      |   2 +-
 .../sigmoid_cross_entropy_loss_layer.cu       |   2 +-
 src/caffe/layers/sigmoid_layer.cpp            |   2 +-
 src/caffe/layers/sigmoid_layer.cu             |   2 +-
 src/caffe/layers/silence_layer.cpp            |   2 +-
 src/caffe/layers/silence_layer.cu             |   2 +-
 src/caffe/layers/slice_layer.cpp              |   2 +-
 src/caffe/layers/slice_layer.cu               |   2 +-
 src/caffe/layers/softmax_layer.cpp            |   2 +-
 src/caffe/layers/softmax_layer.cu             |   2 +-
 src/caffe/layers/softmax_loss_layer.cpp       |   2 +-
 src/caffe/layers/softmax_loss_layer.cu        |   2 +-
 src/caffe/layers/split_layer.cpp              |   2 +-
 src/caffe/layers/split_layer.cu               |   2 +-
 src/caffe/layers/spp_layer.cpp                |   8 +-
 src/caffe/layers/tanh_layer.cpp               |   2 +-
 src/caffe/layers/tanh_layer.cu                |   2 +-
 src/caffe/layers/threshold_layer.cpp          |   3 +-
 src/caffe/layers/threshold_layer.cu           |   2 +-
 src/caffe/layers/tile_layer.cpp               |   2 +-
 src/caffe/layers/tile_layer.cu                |   2 +-
 src/caffe/layers/window_data_layer.cpp        |   5 +-
 src/caffe/test/test_accuracy_layer.cpp        |   2 +-
 src/caffe/test/test_argmax_layer.cpp          |   2 +-
 src/caffe/test/test_batch_norm_layer.cpp      |   2 +-
 src/caffe/test/test_batch_reindex_layer.cpp   |   2 +-
 src/caffe/test/test_concat_layer.cpp          |   2 +-
 .../test/test_contrastive_loss_layer.cpp      |   2 +-
 src/caffe/test/test_convolution_layer.cpp     |   6 +-
 src/caffe/test/test_data_layer.cpp            |   2 +-
 src/caffe/test/test_deconvolution_layer.cpp   |   2 +-
 src/caffe/test/test_dummy_data_layer.cpp      |   2 +-
 src/caffe/test/test_eltwise_layer.cpp         |   2 +-
 src/caffe/test/test_embed_layer.cpp           |   2 +-
 src/caffe/test/test_euclidean_loss_layer.cpp  |   2 +-
 src/caffe/test/test_filter_layer.cpp          |   2 +-
 src/caffe/test/test_flatten_layer.cpp         |   2 +-
 src/caffe/test/test_hdf5_output_layer.cpp     |   2 +-
 src/caffe/test/test_hdf5data_layer.cpp        |   4 +-
 src/caffe/test/test_hinge_loss_layer.cpp      |   2 +-
 src/caffe/test/test_im2col_kernel.cu          |   2 +-
 src/caffe/test/test_im2col_layer.cpp          |   2 +-
 src/caffe/test/test_image_data_layer.cpp      |   2 +-
 src/caffe/test/test_infogain_loss_layer.cpp   |   2 +-
 src/caffe/test/test_inner_product_layer.cpp   |   2 +-
 src/caffe/test/test_lrn_layer.cpp             |   7 +-
 .../test/test_maxpool_dropout_layers.cpp      |   3 +-
 src/caffe/test/test_memory_data_layer.cpp     |   2 +-
 .../test_multinomial_logistic_loss_layer.cpp  |   2 +-
 src/caffe/test/test_mvn_layer.cpp             |   2 +-
 src/caffe/test/test_neuron_layer.cpp          |  21 +-
 src/caffe/test/test_pooling_layer.cpp         |   6 +-
 src/caffe/test/test_power_layer.cpp           |   2 +-
 src/caffe/test/test_reduction_layer.cpp       |   2 +-
 src/caffe/test/test_reshape_layer.cpp         |   2 +-
 .../test_sigmoid_cross_entropy_loss_layer.cpp |   2 +-
 src/caffe/test/test_slice_layer.cpp           |   2 +-
 src/caffe/test/test_softmax_layer.cpp         |   6 +-
 .../test/test_softmax_with_loss_layer.cpp     |   2 +-
 src/caffe/test/test_split_layer.cpp           |   2 +-
 src/caffe/test/test_spp_layer.cpp             |   7 +-
 src/caffe/test/test_stochastic_pooling.cpp    |   2 +-
 src/caffe/test/test_tanh_layer.cpp            |   2 +-
 src/caffe/test/test_threshold_layer.cpp       |   2 +-
 src/caffe/test/test_tile_layer.cpp            |   2 +-
 src/caffe/util/blocking_queue.cpp             |   2 +-
 tools/extract_features.cpp                    |   1 -
 224 files changed, 4497 insertions(+), 3568 deletions(-)
 delete mode 100644 include/caffe/common_layers.hpp
 delete mode 100644 include/caffe/data_layers.hpp
 create mode 100644 include/caffe/layers/absval_layer.hpp
 create mode 100644 include/caffe/layers/accuracy_layer.hpp
 create mode 100644 include/caffe/layers/argmax_layer.hpp
 create mode 100644 include/caffe/layers/base_conv_layer.hpp
 create mode 100644 include/caffe/layers/base_data_layer.hpp
 create mode 100644 include/caffe/layers/batch_norm_layer.hpp
 create mode 100644 include/caffe/layers/batch_reindex_layer.hpp
 create mode 100644 include/caffe/layers/bnll_layer.hpp
 create mode 100644 include/caffe/layers/concat_layer.hpp
 create mode 100644 include/caffe/layers/contrastive_loss_layer.hpp
 create mode 100644 include/caffe/layers/conv_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_conv_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_lcn_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_lrn_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_pooling_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_relu_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_sigmoid_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_softmax_layer.hpp
 create mode 100644 include/caffe/layers/cudnn_tanh_layer.hpp
 create mode 100644 include/caffe/layers/data_layer.hpp
 create mode 100644 include/caffe/layers/deconv_layer.hpp
 create mode 100644 include/caffe/layers/dropout_layer.hpp
 create mode 100644 include/caffe/layers/dummy_data_layer.hpp
 create mode 100644 include/caffe/layers/eltwise_layer.hpp
 create mode 100644 include/caffe/layers/embed_layer.hpp
 create mode 100644 include/caffe/layers/euclidean_loss_layer.hpp
 create mode 100644 include/caffe/layers/exp_layer.hpp
 create mode 100644 include/caffe/layers/filter_layer.hpp
 create mode 100644 include/caffe/layers/flatten_layer.hpp
 create mode 100644 include/caffe/layers/hdf5_data_layer.hpp
 create mode 100644 include/caffe/layers/hdf5_output_layer.hpp
 create mode 100644 include/caffe/layers/hinge_loss_layer.hpp
 create mode 100644 include/caffe/layers/im2col_layer.hpp
 create mode 100644 include/caffe/layers/image_data_layer.hpp
 create mode 100644 include/caffe/layers/infogain_loss_layer.hpp
 create mode 100644 include/caffe/layers/inner_product_layer.hpp
 create mode 100644 include/caffe/layers/log_layer.hpp
 create mode 100644 include/caffe/layers/loss_layer.hpp
 create mode 100644 include/caffe/layers/lrn_layer.hpp
 create mode 100644 include/caffe/layers/memory_data_layer.hpp
 create mode 100644 include/caffe/layers/multinomial_logistic_loss_layer.hpp
 create mode 100644 include/caffe/layers/mvn_layer.hpp
 create mode 100644 include/caffe/layers/neuron_layer.hpp
 create mode 100644 include/caffe/layers/pooling_layer.hpp
 create mode 100644 include/caffe/layers/power_layer.hpp
 create mode 100644 include/caffe/layers/prelu_layer.hpp
 rename include/caffe/{ => layers}/python_layer.hpp (100%)
 create mode 100644 include/caffe/layers/reduction_layer.hpp
 create mode 100644 include/caffe/layers/relu_layer.hpp
 create mode 100644 include/caffe/layers/reshape_layer.hpp
 create mode 100644 include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
 create mode 100644 include/caffe/layers/sigmoid_layer.hpp
 create mode 100644 include/caffe/layers/silence_layer.hpp
 create mode 100644 include/caffe/layers/slice_layer.hpp
 create mode 100644 include/caffe/layers/softmax_layer.hpp
 create mode 100644 include/caffe/layers/softmax_loss_layer.hpp
 create mode 100644 include/caffe/layers/split_layer.hpp
 create mode 100644 include/caffe/layers/spp_layer.hpp
 create mode 100644 include/caffe/layers/tanh_layer.hpp
 create mode 100644 include/caffe/layers/threshold_layer.hpp
 create mode 100644 include/caffe/layers/tile_layer.hpp
 create mode 100644 include/caffe/layers/window_data_layer.hpp
 delete mode 100644 include/caffe/loss_layers.hpp
 delete mode 100644 include/caffe/neuron_layers.hpp
 delete mode 100644 include/caffe/vision_layers.hpp

diff --git a/Makefile b/Makefile
index 3dc76ae5697..985fffd6c0b 100644
--- a/Makefile
+++ b/Makefile
@@ -78,7 +78,7 @@ NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
-PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
+PY$(PROJECT)_HXX := include/$(PROJECT)/layers/python_layer.hpp
 # MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
 MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
 ifneq ($(MATLAB_DIR),)
diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index a339efba5c0..06882096c55 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -17,6 +17,5 @@
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/upgrade_proto.hpp"
-#include "caffe/vision_layers.hpp"
 
 #endif  // CAFFE_CAFFE_HPP_
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
deleted file mode 100644
index d42d15c47e3..00000000000
--- a/include/caffe/common_layers.hpp
+++ /dev/null
@@ -1,816 +0,0 @@
-#ifndef CAFFE_COMMON_LAYERS_HPP_
-#define CAFFE_COMMON_LAYERS_HPP_
-
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief Compute the index of the @f$ K @f$ max values for each datum across
- *        all dimensions @f$ (C \times H \times W) @f$.
- *
- * Intended for use after a classification layer to produce a prediction.
- * If parameter out_max_val is set to true, output is a vector of pairs
- * (max_ind, max_val) for each image. The axis parameter specifies an axis
- * along which to maximise.
- *
- * NOTE: does not implement Backwards operation.
- */
-template <typename Dtype>
-class ArgMaxLayer : public Layer<Dtype> {
- public:
-  /**
-   * @param param provides ArgMaxParameter argmax_param,
-   *     with ArgMaxLayer options:
-   *   - top_k (\b optional uint, default 1).
-   *     the number @f$ K @f$ of maximal items to output.
-   *   - out_max_val (\b optional bool, default false).
-   *     if set, output a vector of pairs (max_ind, max_val) unless axis is set then
-   *     output max_val along the specified axis.
-   *   - axis (\b optional int).
-   *     if set, maximise along the specified axis else maximise the flattened
-   *     trailing dimensions for each index of the first / num dimension.
-   */
-  explicit ArgMaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "ArgMax"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times 1 \times K) @f$ or, if out_max_val
-   *      @f$ (N \times 2 \times K) @f$ unless axis set than e.g.
-   *      @f$ (N \times K \times H \times W) @f$ if axis == 1
-   *      the computed outputs @f$
-   *       y_n = \arg\max\limits_i x_{ni}
-   *      @f$ (for @f$ K = 1 @f$).
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  /// @brief Not implemented (non-differentiable function)
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    NOT_IMPLEMENTED;
-  }
-  bool out_max_val_;
-  size_t top_k_;
-  bool has_axis_;
-  int axis_;
-};
-
-/**
- * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
- *        the batch.
- *
- * This layer computes Batch Normalization described in [1].  For
- * each channel in the data (i.e. axis 1), it subtracts the mean and divides
- * by the variance, where both statistics are computed across both spatial
- * dimensions and across the different examples in the batch.
- *
- * By default, during training time, the network is computing global mean/
- * variance statistics via a running average, which is then used at test
- * time to allow deterministic outputs for each input.  You can manually
- * toggle whether the network is accumulating or using the statistics via the
- * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
- * set the learning rate to zero for all three parameter blobs, i.e.,
- * param {lr_mult: 0} three times in the layer definition.
- *
- * Note that the original paper also included a per-channel learned bias and
- * scaling factor.  It is possible (though a bit cumbersome) to implement
- * this in caffe using a single-channel DummyDataLayer filled with zeros,
- * followed by a Convolution layer with output the same size as the current.
- * This produces a channel-specific value that can be added or multiplied by
- * the BatchNorm layer's output.
- *
- * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
- *     Training by Reducing Internal Covariate Shift." arXiv preprint
- *     arXiv:1502.03167 (2015).
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class BatchNormLayer : public Layer<Dtype> {
- public:
-  explicit BatchNormLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "BatchNorm"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> mean_, variance_, temp_, x_norm_;
-  bool use_global_stats_;
-  Dtype moving_average_fraction_;
-  int channels_;
-  Dtype eps_;
-
-  // extra temporarary variables is used to carry out sums/broadcasting
-  // using BLAS
-  Blob<Dtype> batch_sum_multiplier_;
-  Blob<Dtype> num_by_chans_;
-  Blob<Dtype> spatial_sum_multiplier_;
-};
-
-/**
- * @brief Index into the input blob along its first axis.
- *
- * This layer can be used to select, reorder, and even replicate examples in a
- * batch.  The second blob is cast to int and treated as an index into the
- * first axis of the first blob.
- */
-template <typename Dtype>
-class BatchReindexLayer : public Layer<Dtype> {
- public:
-  explicit BatchReindexLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "BatchReindex"; }
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2+)
-   *   -# @f$ (N \times ...) @f$
-   *      the inputs @f$ x_1 @f$
-   *   -# @f$ (M) @f$
-   *      the inputs @f$ x_2 @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (M \times ...) @f$:
-   *      the reindexed array @f$
-   *        y = x_1[x_2]
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the reordered input.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient
-   *        with respect to the outputs
-   *   -# @f$ (M \times ...) @f$:
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to concatenated outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2):
-   *   - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where
-   *     required) back to the input x_1
-   *   - This layer cannot backprop to x_2, i.e. propagate_down[1] must be
-   *     false.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
- private:
-  struct pair_sort_first {
-    bool operator()(const std::pair<int, int> &left,
-                    const std::pair<int, int> &right) {
-      return left.first < right.first;
-    }
-  };
-  void check_batch_reindex(int initial_num, int final_num,
-                           const Dtype* ridx_data);
-};
-
-/**
- * @brief Takes at least two Blob%s and concatenates them along either the num
- *        or channel dimension, outputting the result.
- */
-template <typename Dtype>
-class ConcatLayer : public Layer<Dtype> {
- public:
-  explicit ConcatLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Concat"; }
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2+)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x_1 @f$
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x_2 @f$
-   *   -# ...
-   *   - K @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x_K @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
-   *      the concatenated output @f$
-   *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the concatenate inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *        respect to the outputs
-   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to concatenated outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length K), into which the top gradient
-   *        @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
-   *        inputs @f$
-   *        \left[ \begin{array}{cccc}
-   *          \frac{\partial E}{\partial x_1} &
-   *          \frac{\partial E}{\partial x_2} &
-   *          ... &
-   *          \frac{\partial E}{\partial x_K}
-   *        \end{array} \right] =
-   *        \frac{\partial E}{\partial y}
-   *        @f$
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
-  int num_concats_;
-  int concat_input_size_;
-  int concat_axis_;
-};
-
-/**
- * @brief Compute elementwise operations, such as product and sum,
- *        along multiple input Blobs.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class EltwiseLayer : public Layer<Dtype> {
- public:
-  explicit EltwiseLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Eltwise"; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  EltwiseParameter_EltwiseOp op_;
-  vector<Dtype> coeffs_;
-  Blob<int> max_idx_;
-
-  bool stable_prod_grad_;
-};
-
-/**
- * @brief A layer for learning "embeddings" of one-hot vector input.
- *        Equivalent to an InnerProductLayer with one-hot vectors as input, but
- *        for efficiency the input is the "hot" index of each column itself.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class EmbedLayer : public Layer<Dtype> {
- public:
-  explicit EmbedLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Embed"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int M_;
-  int K_;
-  int N_;
-  bool bias_term_;
-  Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Takes two+ Blobs, interprets last Blob as a selector and
- *  filter remaining Blobs accordingly with selector data (0 means that
- * the corresponding item has to be filtered, non-zero means that corresponding
- * item needs to stay).
- */
-template <typename Dtype>
-class FilterLayer : public Layer<Dtype> {
- public:
-  explicit FilterLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Filter"; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2+)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs to be filtered @f$ x_1 @f$
-   *   -# ...
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs to be filtered @f$ x_K @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the selector blob
-   * @param top output Blob vector (length 1+)
-   *   -# @f$ (S \times C \times H \times W) @f$ ()
-   *        the filtered output @f$ x_1 @f$
-   *        where S is the number of items
-   *        that haven't been filtered
-   *      @f$ (S \times C \times H \times W) @f$
-   *        the filtered output @f$ x_K @f$
-   *        where S is the number of items
-   *        that haven't been filtered
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-    const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the forwarded inputs.
-   *
-   * @param top output Blob vector (length 1+), providing the error gradient with
-   *        respect to the outputs
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2+), into which the top error
-   *        gradient is copied
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool first_reshape_;
-  vector<int> indices_to_forward_;
-};
-
-/**
- * @brief Reshapes the input Blob into flat vectors.
- *
- * Note: because this layer does not change the input values -- merely the
- * dimensions -- it can simply copy the input. The copy happens "virtually"
- * (thus taking effectively 0 real time) by setting, in Forward, the data
- * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
- * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
- * (see Blob::ShareDiff).
- */
-template <typename Dtype>
-class FlattenLayer : public Layer<Dtype> {
- public:
-  explicit FlattenLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Flatten"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2+)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times CHW \times 1 \times 1) @f$
-   *      the outputs -- i.e., the (virtually) copied, flattened inputs
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the concatenate inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *        respect to the outputs
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length K), into which the top error
-   *        gradient is (virtually) copied
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Also known as a "fully-connected" layer, computes an inner product
- *        with a set of learned weights, and (optionally) adds biases.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class InnerProductLayer : public Layer<Dtype> {
- public:
-  explicit InnerProductLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "InnerProduct"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int M_;
-  int K_;
-  int N_;
-  bool bias_term_;
-  Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class MVNLayer : public Layer<Dtype> {
- public:
-  explicit MVNLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "MVN"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> mean_, variance_, temp_;
-
-  /// sum_multiplier is used to carry out sum using BLAS
-  Blob<Dtype> sum_multiplier_;
-  Dtype eps_;
-};
-
-/*
- * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
- *
- * Note: similarly to FlattenLayer, this layer does not change the input values
- * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
- */
-template <typename Dtype>
-class ReshapeLayer : public Layer<Dtype> {
- public:
-  explicit ReshapeLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Reshape"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
-  /// @brief vector of axes indices whose dimensions we'll copy from the bottom
-  vector<int> copy_axes_;
-  /// @brief the index of the axis whose dimension we infer, or -1 if none
-  int inferred_axis_;
-  /// @brief the product of the "constant" output dimensions
-  int constant_count_;
-};
-
-/**
- * @brief Compute "reductions" -- operations that return a scalar output Blob
- *        for an input Blob of arbitrary size, such as the sum, absolute sum,
- *        and sum of squares.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class ReductionLayer : public Layer<Dtype> {
- public:
-  explicit ReductionLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Reduction"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// @brief the reduction operation performed by the layer
-  ReductionParameter_ReductionOp op_;
-  /// @brief a scalar coefficient applied to all outputs
-  Dtype coeff_;
-  /// @brief the index of the first input axis to reduce
-  int axis_;
-  /// @brief the number of reductions performed
-  int num_;
-  /// @brief the input size of each reduction
-  int dim_;
-  /// @brief a helper Blob used for summation (op_ == SUM)
-  Blob<Dtype> sum_multiplier_;
-};
-
-/**
- * @brief Ignores bottom blobs while producing no top blobs. (This is useful
- *        to suppress outputs during testing.)
- */
-template <typename Dtype>
-class SilenceLayer : public Layer<Dtype> {
- public:
-  explicit SilenceLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "Silence"; }
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-  // We can't define Forward_gpu here, since STUB_GPU will provide
-  // its own definition for CPU_ONLY mode.
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes the softmax function.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SoftmaxLayer : public Layer<Dtype> {
- public:
-  explicit SoftmaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Softmax"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int outer_num_;
-  int inner_num_;
-  int softmax_axis_;
-  /// sum_multiplier is used to carry out sum using BLAS
-  Blob<Dtype> sum_multiplier_;
-  /// scale is an intermediate Blob to hold temporary results.
-  Blob<Dtype> scale_;
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief cuDNN implementation of SoftmaxLayer.
- *        Fallback to SoftmaxLayer for CPU mode.
- */
-template <typename Dtype>
-class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
- public:
-  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
-      : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNSoftmaxLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Creates a "split" path in the network by copying the bottom Blob
- *        into multiple top Blob%s to be used by multiple consuming layers.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SplitLayer : public Layer<Dtype> {
- public:
-  explicit SplitLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Split"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
-};
-
-/**
- * @brief Takes a Blob and slices it along either the num or channel dimension,
- *        outputting multiple sliced Blob results.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SliceLayer : public Layer<Dtype> {
- public:
-  explicit SliceLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Slice"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
-  int num_slices_;
-  int slice_size_;
-  int slice_axis_;
-  vector<int> slice_point_;
-};
-
-/**
- * @brief Copy a Blob along specified dimensions.
- */
-template <typename Dtype>
-class TileLayer : public Layer<Dtype> {
- public:
-  explicit TileLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Tile"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_COMMON_LAYERS_HPP_
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
deleted file mode 100644
index aa0ab7df390..00000000000
--- a/include/caffe/data_layers.hpp
+++ /dev/null
@@ -1,347 +0,0 @@
-#ifndef CAFFE_DATA_LAYERS_HPP_
-#define CAFFE_DATA_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-#include "hdf5.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_reader.hpp"
-#include "caffe/data_transformer.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/internal_thread.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/blocking_queue.hpp"
-#include "caffe/util/db.hpp"
-
-#define HDF5_DATA_DATASET_NAME "data"
-#define HDF5_DATA_LABEL_NAME "label"
-
-namespace caffe {
-
-/**
- * @brief Provides base for data layers that feed blobs to the Net.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class BaseDataLayer : public Layer<Dtype> {
- public:
-  explicit BaseDataLayer(const LayerParameter& param);
-  // LayerSetUp: implements common data layer setup functionality, and calls
-  // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden except by the BasePrefetchingDataLayer.
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
- protected:
-  TransformationParameter transform_param_;
-  shared_ptr<DataTransformer<Dtype> > data_transformer_;
-  bool output_labels_;
-};
-
-template <typename Dtype>
-class Batch {
- public:
-  Blob<Dtype> data_, label_;
-};
-
-template <typename Dtype>
-class BasePrefetchingDataLayer :
-    public BaseDataLayer<Dtype>, public InternalThread {
- public:
-  explicit BasePrefetchingDataLayer(const LayerParameter& param);
-  // LayerSetUp: implements common data layer setup functionality, and calls
-  // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden.
-  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  // Prefetches batches (asynchronously if to GPU memory)
-  static const int PREFETCH_COUNT = 3;
-
- protected:
-  virtual void InternalThreadEntry();
-  virtual void load_batch(Batch<Dtype>* batch) = 0;
-
-  Batch<Dtype> prefetch_[PREFETCH_COUNT];
-  BlockingQueue<Batch<Dtype>*> prefetch_free_;
-  BlockingQueue<Batch<Dtype>*> prefetch_full_;
-
-  Blob<Dtype> transformed_data_;
-};
-
-template <typename Dtype>
-class DataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit DataLayer(const LayerParameter& param);
-  virtual ~DataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // DataLayer uses DataReader instead for sharing for parallelism
-  virtual inline bool ShareInParallel() const { return false; }
-  virtual inline const char* type() const { return "Data"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  virtual void load_batch(Batch<Dtype>* batch);
-
-  DataReader reader_;
-};
-
-/**
- * @brief Provides data to the Net generated by a Filler.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class DummyDataLayer : public Layer<Dtype> {
- public:
-  explicit DummyDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "DummyData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
-  vector<shared_ptr<Filler<Dtype> > > fillers_;
-  vector<bool> refill_;
-};
-
-/**
- * @brief Provides data to the Net from HDF5 files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class HDF5DataLayer : public Layer<Dtype> {
- public:
-  explicit HDF5DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~HDF5DataLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "HDF5Data"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void LoadHDF5FileData(const char* filename);
-
-  std::vector<std::string> hdf_filenames_;
-  unsigned int num_files_;
-  unsigned int current_file_;
-  hsize_t current_row_;
-  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
-  std::vector<unsigned int> data_permutation_;
-  std::vector<unsigned int> file_permutation_;
-};
-
-/**
- * @brief Write blobs to disk as HDF5 files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class HDF5OutputLayer : public Layer<Dtype> {
- public:
-  explicit HDF5OutputLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), file_opened_(false) {}
-  virtual ~HDF5OutputLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "HDF5Output"; }
-  // TODO: no limit on the number of blobs
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
-
-  inline std::string file_name() const { return file_name_; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void SaveBlobs();
-
-  bool file_opened_;
-  std::string file_name_;
-  hid_t file_id_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
-};
-
-/**
- * @brief Provides data to the Net from image files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit ImageDataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~ImageDataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "ImageData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  virtual void ShuffleImages();
-  virtual void load_batch(Batch<Dtype>* batch);
-
-  vector<std::pair<std::string, int> > lines_;
-  int lines_id_;
-};
-
-/**
- * @brief Provides data to the Net from memory.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class MemoryDataLayer : public BaseDataLayer<Dtype> {
- public:
-  explicit MemoryDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "MemoryData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
-  virtual void AddDatumVector(const vector<Datum>& datum_vector);
-#ifdef USE_OPENCV
-  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
-      const vector<int>& labels);
-#endif  // USE_OPENCV
-
-  // Reset should accept const pointers, but can't, because the memory
-  //  will be given to Blob, which is mutable
-  void Reset(Dtype* data, Dtype* label, int n);
-  void set_batch_size(int new_size);
-
-  int batch_size() { return batch_size_; }
-  int channels() { return channels_; }
-  int height() { return height_; }
-  int width() { return width_; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  int batch_size_, channels_, height_, width_, size_;
-  Dtype* data_;
-  Dtype* labels_;
-  int n_;
-  size_t pos_;
-  Blob<Dtype> added_data_;
-  Blob<Dtype> added_label_;
-  bool has_new_data_;
-};
-
-/**
- * @brief Provides data to the Net from windows of images files, specified
- *        by a window data file.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit WindowDataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~WindowDataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "WindowData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  virtual unsigned int PrefetchRand();
-  virtual void load_batch(Batch<Dtype>* batch);
-
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  vector<std::pair<std::string, vector<int> > > image_database_;
-  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
-  vector<vector<float> > fg_windows_;
-  vector<vector<float> > bg_windows_;
-  Blob<Dtype> data_mean_;
-  vector<Dtype> mean_values_;
-  bool has_mean_file_;
-  bool has_mean_values_;
-  bool cache_images_;
-  vector<std::pair<std::string, Datum > > image_database_cache_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_DATA_LAYERS_HPP_
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 2c2fde4d979..f385afccfee 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -44,6 +44,7 @@
 #include <vector>
 
 #include "caffe/common.hpp"
+#include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
diff --git a/include/caffe/layers/absval_layer.hpp b/include/caffe/layers/absval_layer.hpp
new file mode 100644
index 00000000000..9b5305dceb4
--- /dev/null
+++ b/include/caffe/layers/absval_layer.hpp
@@ -0,0 +1,68 @@
+#ifndef CAFFE_ABSVAL_LAYER_HPP_
+#define CAFFE_ABSVAL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = |x| @f$
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$ y = |x| @f$
+ */
+template <typename Dtype>
+class AbsValLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit AbsValLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "AbsVal"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /// @copydoc AbsValLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the absolute value inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \mathrm{sign}(x) \frac{\partial E}{\partial y}
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ABSVAL_LAYER_HPP_
diff --git a/include/caffe/layers/accuracy_layer.hpp b/include/caffe/layers/accuracy_layer.hpp
new file mode 100644
index 00000000000..fe2adb939e4
--- /dev/null
+++ b/include/caffe/layers/accuracy_layer.hpp
@@ -0,0 +1,95 @@
+#ifndef CAFFE_ACCURACY_LAYER_HPP_
+#define CAFFE_ACCURACY_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the classification accuracy for a one-of-many
+ *        classification task.
+ */
+template <typename Dtype>
+class AccuracyLayer : public Layer<Dtype> {
+ public:
+  /**
+   * @param param provides AccuracyParameter accuracy_param,
+   *     with AccuracyLayer options:
+   *   - top_k (\b optional, default 1).
+   *     Sets the maximum rank @f$ k @f$ at which a prediction is considered
+   *     correct.  For example, if @f$ k = 5 @f$, a prediction is counted
+   *     correct if the correct label is among the top 5 predicted labels.
+   */
+  explicit AccuracyLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Accuracy"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
+  // If there are two top blobs, then the second blob will contain
+  // accuracies per class.
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlos() const { return 2; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ x @f$, a Blob with values in
+   *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+   *      the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
+   *      label @f$ \hat{l}_n @f$ given by its maximal index:
+   *      @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels @f$ l @f$, an integer-valued Blob with values
+   *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+   *      indicating the correct class label among the @f$ K @f$ classes
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      the computed accuracy: @f$
+   *        \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
+   *      @f$, where @f$
+   *      \delta\{\mathrm{condition}\} = \left\{
+   *         \begin{array}{lr}
+   *            1 & \mbox{if condition} \\
+   *            0 & \mbox{otherwise}
+   *         \end{array} \right.
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+
+  /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    for (int i = 0; i < propagate_down.size(); ++i) {
+      if (propagate_down[i]) { NOT_IMPLEMENTED; }
+    }
+  }
+
+  int label_axis_, outer_num_, inner_num_;
+
+  int top_k_;
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// Keeps counts of the number of samples per class.
+  Blob<Dtype> nums_buffer_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ACCURACY_LAYER_HPP_
diff --git a/include/caffe/layers/argmax_layer.hpp b/include/caffe/layers/argmax_layer.hpp
new file mode 100644
index 00000000000..4fef363e850
--- /dev/null
+++ b/include/caffe/layers/argmax_layer.hpp
@@ -0,0 +1,77 @@
+#ifndef CAFFE_ARGMAX_LAYER_HPP_
+#define CAFFE_ARGMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute the index of the @f$ K @f$ max values for each datum across
+ *        all dimensions @f$ (C \times H \times W) @f$.
+ *
+ * Intended for use after a classification layer to produce a prediction.
+ * If parameter out_max_val is set to true, output is a vector of pairs
+ * (max_ind, max_val) for each image. The axis parameter specifies an axis
+ * along which to maximise.
+ *
+ * NOTE: does not implement Backwards operation.
+ */
+template <typename Dtype>
+class ArgMaxLayer : public Layer<Dtype> {
+ public:
+  /**
+   * @param param provides ArgMaxParameter argmax_param,
+   *     with ArgMaxLayer options:
+   *   - top_k (\b optional uint, default 1).
+   *     the number @f$ K @f$ of maximal items to output.
+   *   - out_max_val (\b optional bool, default false).
+   *     if set, output a vector of pairs (max_ind, max_val) unless axis is set then
+   *     output max_val along the specified axis.
+   *   - axis (\b optional int).
+   *     if set, maximise along the specified axis else maximise the flattened
+   *     trailing dimensions for each index of the first / num dimension.
+   */
+  explicit ArgMaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "ArgMax"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times 1 \times K) @f$ or, if out_max_val
+   *      @f$ (N \times 2 \times K) @f$ unless axis set than e.g.
+   *      @f$ (N \times K \times H \times W) @f$ if axis == 1
+   *      the computed outputs @f$
+   *       y_n = \arg\max\limits_i x_{ni}
+   *      @f$ (for @f$ K = 1 @f$).
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /// @brief Not implemented (non-differentiable function)
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    NOT_IMPLEMENTED;
+  }
+  bool out_max_val_;
+  size_t top_k_;
+  bool has_axis_;
+  int axis_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ARGMAX_LAYER_HPP_
diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp
new file mode 100644
index 00000000000..f3def16c039
--- /dev/null
+++ b/include/caffe/layers/base_conv_layer.hpp
@@ -0,0 +1,168 @@
+#ifndef CAFFE_BASE_CONVOLUTION_LAYER_HPP_
+#define CAFFE_BASE_CONVOLUTION_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/im2col.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Abstract base class that factors out the BLAS code common to
+ *        ConvolutionLayer and DeconvolutionLayer.
+ */
+template <typename Dtype>
+class BaseConvolutionLayer : public Layer<Dtype> {
+ public:
+  explicit BaseConvolutionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+ protected:
+  // Helper functions that abstract away the column buffer and gemm arguments.
+  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
+  // we just called weight_cpu_gemm with the same input.
+  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_cpu_bias(Dtype* output, const Dtype* bias);
+  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output);
+  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
+      weights);
+  void backward_cpu_bias(Dtype* bias, const Dtype* input);
+
+#ifndef CPU_ONLY
+  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_gpu_bias(Dtype* output, const Dtype* bias);
+  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* col_output);
+  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
+      weights);
+  void backward_gpu_bias(Dtype* bias, const Dtype* input);
+#endif
+
+  /// @brief The spatial dimensions of the input.
+  inline int input_shape(int i) {
+    return (*bottom_shape_)[channel_axis_ + i];
+  }
+  // reverse_dimensions should return true iff we are implementing deconv, so
+  // that conv helpers know which dimensions are which.
+  virtual bool reverse_dimensions() = 0;
+  // Compute height_out_ and width_out_ from other parameters.
+  virtual void compute_output_shape() = 0;
+
+  /// @brief The spatial dimensions of a filter kernel.
+  Blob<int> kernel_shape_;
+  /// @brief The spatial dimensions of the stride.
+  Blob<int> stride_;
+  /// @brief The spatial dimensions of the padding.
+  Blob<int> pad_;
+  /// @brief The spatial dimensions of the convolution input.
+  Blob<int> conv_input_shape_;
+  /// @brief The spatial dimensions of the col_buffer.
+  vector<int> col_buffer_shape_;
+  /// @brief The spatial dimensions of the output.
+  vector<int> output_shape_;
+  const vector<int>* bottom_shape_;
+
+  int num_spatial_axes_;
+  int bottom_dim_;
+  int top_dim_;
+
+  int channel_axis_;
+  int num_;
+  int channels_;
+  int group_;
+  int out_spatial_dim_;
+  int weight_offset_;
+  int num_output_;
+  bool bias_term_;
+  bool is_1x1_;
+  bool force_nd_im2col_;
+
+ private:
+  // wrap im2col/col2im so we don't have to remember the (long) argument lists
+  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_cpu(data, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
+    } else {
+      im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
+          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+          pad_.cpu_data(), stride_.cpu_data(), col_buff);
+    }
+  }
+  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_cpu(col_buff, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
+    } else {
+      col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
+          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+          pad_.cpu_data(), stride_.cpu_data(), data);
+    }
+  }
+#ifndef CPU_ONLY
+  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_gpu(data, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
+    } else {
+      im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
+          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+          kernel_shape_.gpu_data(), pad_.gpu_data(),
+          stride_.gpu_data(), col_buff);
+    }
+  }
+  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_gpu(col_buff, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
+    } else {
+      col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
+          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          data);
+    }
+  }
+#endif
+
+  int num_kernels_im2col_;
+  int num_kernels_col2im_;
+  int conv_out_channels_;
+  int conv_in_channels_;
+  int conv_out_spatial_dim_;
+  int kernel_dim_;
+  int col_offset_;
+  int output_offset_;
+
+  Blob<Dtype> col_buffer_;
+  Blob<Dtype> bias_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BASE_CONVOLUTION_LAYER_HPP_
diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp
new file mode 100644
index 00000000000..2c49b73184b
--- /dev/null
+++ b/include/caffe/layers/base_data_layer.hpp
@@ -0,0 +1,86 @@
+#ifndef CAFFE_DATA_LAYERS_HPP_
+#define CAFFE_DATA_LAYERS_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides base for data layers that feed blobs to the Net.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class BaseDataLayer : public Layer<Dtype> {
+ public:
+  explicit BaseDataLayer(const LayerParameter& param);
+  // LayerSetUp: implements common data layer setup functionality, and calls
+  // DataLayerSetUp to do special data layer setup for individual layer types.
+  // This method may not be overridden except by the BasePrefetchingDataLayer.
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+ protected:
+  TransformationParameter transform_param_;
+  shared_ptr<DataTransformer<Dtype> > data_transformer_;
+  bool output_labels_;
+};
+
+template <typename Dtype>
+class Batch {
+ public:
+  Blob<Dtype> data_, label_;
+};
+
+template <typename Dtype>
+class BasePrefetchingDataLayer :
+    public BaseDataLayer<Dtype>, public InternalThread {
+ public:
+  explicit BasePrefetchingDataLayer(const LayerParameter& param);
+  // LayerSetUp: implements common data layer setup functionality, and calls
+  // DataLayerSetUp to do special data layer setup for individual layer types.
+  // This method may not be overridden.
+  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  // Prefetches batches (asynchronously if to GPU memory)
+  static const int PREFETCH_COUNT = 3;
+
+ protected:
+  virtual void InternalThreadEntry();
+  virtual void load_batch(Batch<Dtype>* batch) = 0;
+
+  Batch<Dtype> prefetch_[PREFETCH_COUNT];
+  BlockingQueue<Batch<Dtype>*> prefetch_free_;
+  BlockingQueue<Batch<Dtype>*> prefetch_full_;
+
+  Blob<Dtype> transformed_data_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_LAYERS_HPP_
diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
new file mode 100644
index 00000000000..9b2d5126efb
--- /dev/null
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -0,0 +1,81 @@
+#ifndef CAFFE_BATCHNORM_LAYER_HPP_
+#define CAFFE_BATCHNORM_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
+ *        the batch.
+ *
+ * This layer computes Batch Normalization described in [1].  For
+ * each channel in the data (i.e. axis 1), it subtracts the mean and divides
+ * by the variance, where both statistics are computed across both spatial
+ * dimensions and across the different examples in the batch.
+ *
+ * By default, during training time, the network is computing global mean/
+ * variance statistics via a running average, which is then used at test
+ * time to allow deterministic outputs for each input.  You can manually
+ * toggle whether the network is accumulating or using the statistics via the
+ * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
+ * set the learning rate to zero for all three parameter blobs, i.e.,
+ * param {lr_mult: 0} three times in the layer definition.
+ *
+ * Note that the original paper also included a per-channel learned bias and
+ * scaling factor.  It is possible (though a bit cumbersome) to implement
+ * this in caffe using a single-channel DummyDataLayer filled with zeros,
+ * followed by a Convolution layer with output the same size as the current.
+ * This produces a channel-specific value that can be added or multiplied by
+ * the BatchNorm layer's output.
+ *
+ * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
+ *     Training by Reducing Internal Covariate Shift." arXiv preprint
+ *     arXiv:1502.03167 (2015).
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class BatchNormLayer : public Layer<Dtype> {
+ public:
+  explicit BatchNormLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BatchNorm"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> mean_, variance_, temp_, x_norm_;
+  bool use_global_stats_;
+  Dtype moving_average_fraction_;
+  int channels_;
+  Dtype eps_;
+
+  // extra temporarary variables is used to carry out sums/broadcasting
+  // using BLAS
+  Blob<Dtype> batch_sum_multiplier_;
+  Blob<Dtype> num_by_chans_;
+  Blob<Dtype> spatial_sum_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BATCHNORM_LAYER_HPP_
diff --git a/include/caffe/layers/batch_reindex_layer.hpp b/include/caffe/layers/batch_reindex_layer.hpp
new file mode 100644
index 00000000000..ebb3a567bc4
--- /dev/null
+++ b/include/caffe/layers/batch_reindex_layer.hpp
@@ -0,0 +1,83 @@
+#ifndef CAFFE_BATCHREINDEX_LAYER_HPP_
+#define CAFFE_BATCHREINDEX_LAYER_HPP_
+
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Index into the input blob along its first axis.
+ *
+ * This layer can be used to select, reorder, and even replicate examples in a
+ * batch.  The second blob is cast to int and treated as an index into the
+ * first axis of the first blob.
+ */
+template <typename Dtype>
+class BatchReindexLayer : public Layer<Dtype> {
+ public:
+  explicit BatchReindexLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BatchReindex"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times ...) @f$
+   *      the inputs @f$ x_1 @f$
+   *   -# @f$ (M) @f$
+   *      the inputs @f$ x_2 @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (M \times ...) @f$:
+   *      the reindexed array @f$
+   *        y = x_1[x_2]
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the reordered input.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient
+   *        with respect to the outputs
+   *   -# @f$ (M \times ...) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to concatenated outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2):
+   *   - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where
+   *     required) back to the input x_1
+   *   - This layer cannot backprop to x_2, i.e. propagate_down[1] must be
+   *     false.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ private:
+  struct pair_sort_first {
+    bool operator()(const std::pair<int, int> &left,
+                    const std::pair<int, int> &right) {
+      return left.first < right.first;
+    }
+  };
+  void check_batch_reindex(int initial_num, int final_num,
+                           const Dtype* ridx_data);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BATCHREINDEX_LAYER_HPP_
diff --git a/include/caffe/layers/bnll_layer.hpp b/include/caffe/layers/bnll_layer.hpp
new file mode 100644
index 00000000000..be07c748364
--- /dev/null
+++ b/include/caffe/layers/bnll_layer.hpp
@@ -0,0 +1,70 @@
+#ifndef CAFFE_BNLL_LAYER_HPP_
+#define CAFFE_BNLL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
+ *        @f$ y = \log(1 + \exp(x)) @f$ otherwise.
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$
+ *      y = \left\{
+ *         \begin{array}{ll}
+ *            x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
+ *            \log(1 + \exp(x)) & \mbox{otherwise}
+ *         \end{array} \right.
+ *      @f$
+ */
+template <typename Dtype>
+class BNLLLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit BNLLLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "BNLL"; }
+
+ protected:
+  /// @copydoc BNLLLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the BNLL inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BNLL_LAYER_HPP_
diff --git a/include/caffe/layers/concat_layer.hpp b/include/caffe/layers/concat_layer.hpp
new file mode 100644
index 00000000000..a1570249197
--- /dev/null
+++ b/include/caffe/layers/concat_layer.hpp
@@ -0,0 +1,87 @@
+#ifndef CAFFE_CONCAT_LAYER_HPP_
+#define CAFFE_CONCAT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes at least two Blob%s and concatenates them along either the num
+ *        or channel dimension, outputting the result.
+ */
+template <typename Dtype>
+class ConcatLayer : public Layer<Dtype> {
+ public:
+  explicit ConcatLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Concat"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_1 @f$
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_2 @f$
+   *   -# ...
+   *   - K @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_K @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
+   *      the concatenated output @f$
+   *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the concatenate inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *        respect to the outputs
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to concatenated outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length K), into which the top gradient
+   *        @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
+   *        inputs @f$
+   *        \left[ \begin{array}{cccc}
+   *          \frac{\partial E}{\partial x_1} &
+   *          \frac{\partial E}{\partial x_2} &
+   *          ... &
+   *          \frac{\partial E}{\partial x_K}
+   *        \end{array} \right] =
+   *        \frac{\partial E}{\partial y}
+   *        @f$
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int count_;
+  int num_concats_;
+  int concat_input_size_;
+  int concat_axis_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CONCAT_LAYER_HPP_
diff --git a/include/caffe/layers/contrastive_loss_layer.hpp b/include/caffe/layers/contrastive_loss_layer.hpp
new file mode 100644
index 00000000000..e890afb8207
--- /dev/null
+++ b/include/caffe/layers/contrastive_loss_layer.hpp
@@ -0,0 +1,101 @@
+#ifndef CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
+#define CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the contrastive loss @f$
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
+ *              \left(1-y\right) \max \left(margin-d, 0\right)^2
+ *          @f$ where @f$
+ *          d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
+ *          used to train siamese networks.
+ *
+ * @param bottom input Blob vector (length 3)
+ *   -# @f$ (N \times C \times 1 \times 1) @f$
+ *      the features @f$ a \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times C \times 1 \times 1) @f$
+ *      the features @f$ b \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the binary similarity @f$ s \in [0, 1]@f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed contrastive loss: @f$ E =
+ *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
+ *          \left(1-y\right) \max \left(margin-d, 0\right)^2
+ *          @f$ where @f$
+ *          d = \left| \left| a_n - b_n \right| \right|_2 @f$.
+ * This can be used to train siamese networks.
+ */
+template <typename Dtype>
+class ContrastiveLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit ContrastiveLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 3; }
+  virtual inline const char* type() const { return "ContrastiveLoss"; }
+  /**
+   * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
+   * to the first two inputs.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 2;
+  }
+
+ protected:
+  /// @copydoc ContrastiveLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the Contrastive error gradient w.r.t. the inputs.
+   *
+   * Computes the gradients with respect to the two input vectors (bottom[0] and
+   * bottom[1]), but not the similarity label (bottom[2]).
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times 1 \times 1) @f$
+   *      the features @f$a@f$; Backward fills their diff with
+   *      gradients if propagate_down[0]
+   *   -# @f$ (N \times C \times 1 \times 1) @f$
+   *      the features @f$b@f$; Backward fills their diff with gradients if
+   *      propagate_down[1]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> diff_;  // cached for backward pass
+  Blob<Dtype> dist_sq_;  // cached for backward pass
+  Blob<Dtype> diff_sq_;  // tmp storage for gpu forward pass
+  Blob<Dtype> summer_vec_;  // tmp storage for gpu forward pass
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/conv_layer.hpp b/include/caffe/layers/conv_layer.hpp
new file mode 100644
index 00000000000..15574766de5
--- /dev/null
+++ b/include/caffe/layers/conv_layer.hpp
@@ -0,0 +1,81 @@
+#ifndef CAFFE_CONV_LAYER_HPP_
+#define CAFFE_CONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_conv_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Convolves the input image with a bank of learned filters,
+ *        and (optionally) adds biases.
+ *
+ *   Caffe convolves by reduction to matrix multiplication. This achieves
+ *   high-throughput and generality of input and filter dimensions but comes at
+ *   the cost of memory for matrices. This makes use of efficiency in BLAS.
+ *
+ *   The input is "im2col" transformed to a channel K' x H x W data matrix
+ *   for multiplication with the N x K' x H x W filter matrix to yield a
+ *   N' x H x W output matrix that is then "col2im" restored. K' is the
+ *   input channel * kernel height * kernel width dimension of the unrolled
+ *   inputs so that the im2col matrix has a column for each input region to
+ *   be filtered. col2im restores the output spatial structure by rolling up
+ *   the output channel N' columns of the output matrix.
+ */
+template <typename Dtype>
+class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ConvolutionParameter convolution_param,
+   *    with ConvolutionLayer options:
+   *  - num_output. The number of filters.
+   *  - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
+   *  kernel_size for square filters or kernel_h and kernel_w for rectangular
+   *  filters.
+   *  - stride / stride_h / stride_w (\b optional, default 1). The filter
+   *  stride, given by stride_size for equal dimensions or stride_h and stride_w
+   *  for different strides. By default the convolution is dense with stride 1.
+   *  - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
+   *  convolution, given by pad for equal dimensions or pad_h and pad_w for
+   *  different padding. Input padding is computed implicitly instead of
+   *  actually padding.
+   *  - group (\b optional, default 1). The number of filter groups. Group
+   *  convolution is a method for reducing parameterization by selectively
+   *  connecting input and output channels. The input and output channel dimensions must be divisible
+   *  by the number of groups. For group @f$ \geq 1 @f$, the
+   *  convolutional filters' input and output channels are separated s.t. each
+   *  group takes 1 / group of the input channels and makes 1 / group of the
+   *  output channels. Concretely 4 input channels, 8 output channels, and
+   *  2 groups separate input channels 1-2 and output channels 1-4 into the
+   *  first group and input channels 3-4 and output channels 5-8 into the second
+   *  group.
+   *  - bias_term (\b optional, default true). Whether to have a bias.
+   *  - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
+   *    kernels + stream parallelism) engines.
+   */
+  explicit ConvolutionLayer(const LayerParameter& param)
+      : BaseConvolutionLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Convolution"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return false; }
+  virtual void compute_output_shape();
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CONV_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_conv_layer.hpp b/include/caffe/layers/cudnn_conv_layer.hpp
new file mode 100644
index 00000000000..31fe49a71fa
--- /dev/null
+++ b/include/caffe/layers/cudnn_conv_layer.hpp
@@ -0,0 +1,72 @@
+#ifndef CAFFE_CUDNN_CONV_LAYER_HPP_
+#define CAFFE_CUDNN_CONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/conv_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of ConvolutionLayer.
+ *        Fallback to ConvolutionLayer for CPU mode.
+ *
+ * cuDNN accelerates convolution through forward kernels for filtering and bias
+ * plus backward kernels for the gradient w.r.t. the filters, biases, and
+ * inputs. Caffe + cuDNN further speeds up the computation through forward
+ * parallelism across groups and backward parallelism across gradients.
+ *
+ * The CUDNN engine does not have memory overhead for matrix buffers. For many
+ * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
+ * but for fully-convolutional models and large inputs the CAFFE engine can be
+ * faster as long as it fits in memory.
+*/
+template <typename Dtype>
+class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
+ public:
+  explicit CuDNNConvolutionLayer(const LayerParameter& param)
+      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNConvolutionLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t* handle_;
+  cudaStream_t*  stream_;
+
+  // algorithms for forward and backwards convolutions
+  cudnnConvolutionFwdAlgo_t *fwd_algo_;
+  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
+  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
+
+  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
+  cudnnTensorDescriptor_t    bias_desc_;
+  cudnnFilterDescriptor_t      filter_desc_;
+  vector<cudnnConvolutionDescriptor_t> conv_descs_;
+  int bottom_offset_, top_offset_, bias_offset_;
+
+  size_t *workspace_fwd_sizes_;
+  size_t *workspace_bwd_data_sizes_;
+  size_t *workspace_bwd_filter_sizes_;
+  size_t workspaceSizeInBytes;  // size of underlying storage
+  void *workspaceData;  // underlying storage
+  void **workspace;  // aliases into workspaceData
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_CONV_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_lcn_layer.hpp b/include/caffe/layers/cudnn_lcn_layer.hpp
new file mode 100644
index 00000000000..74cf4775e51
--- /dev/null
+++ b/include/caffe/layers/cudnn_lcn_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_CUDNN_LCN_LAYER_HPP_
+#define CAFFE_CUDNN_LCN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/lrn_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLCNLayer : public LRNLayer<Dtype> {
+ public:
+  explicit CuDNNLCNLayer(const LayerParameter& param)
+      : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
+        tempData1(NULL), tempData2(NULL) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNLCNLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnLRNDescriptor_t norm_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+  int size_, pre_pad_;
+  Dtype alpha_, beta_, k_;
+
+  size_t tempDataSize;
+  void *tempData1, *tempData2;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_LCN_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_lrn_layer.hpp b/include/caffe/layers/cudnn_lrn_layer.hpp
new file mode 100644
index 00000000000..000ccc36507
--- /dev/null
+++ b/include/caffe/layers/cudnn_lrn_layer.hpp
@@ -0,0 +1,44 @@
+#ifndef CAFFE_CUDNN_LRN_LAYER_HPP_
+#define CAFFE_CUDNN_LRN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/lrn_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLRNLayer : public LRNLayer<Dtype> {
+ public:
+  explicit CuDNNLRNLayer(const LayerParameter& param)
+      : LRNLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNLRNLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnLRNDescriptor_t norm_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+  int size_;
+  Dtype alpha_, beta_, k_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_LRN_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_pooling_layer.hpp b/include/caffe/layers/cudnn_pooling_layer.hpp
new file mode 100644
index 00000000000..6d0db47d660
--- /dev/null
+++ b/include/caffe/layers/cudnn_pooling_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_CUDNN_POOLING_LAYER_HPP_
+#define CAFFE_CUDNN_POOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/pooling_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of PoolingLayer.
+ *        Fallback to PoolingLayer for CPU mode.
+*/
+template <typename Dtype>
+class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
+ public:
+  explicit CuDNNPoolingLayer(const LayerParameter& param)
+      : PoolingLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNPoolingLayer();
+  // Currently, cuDNN does not support the extra top blob.
+  virtual inline int MinTopBlobs() const { return -1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+  cudnnPoolingDescriptor_t  pooling_desc_;
+  cudnnPoolingMode_t        mode_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_POOLING_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_relu_layer.hpp b/include/caffe/layers/cudnn_relu_layer.hpp
new file mode 100644
index 00000000000..e01f568abc9
--- /dev/null
+++ b/include/caffe/layers/cudnn_relu_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_RELU_LAYER_HPP_
+#define CAFFE_CUDNN_RELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of ReLULayer.
+ */
+template <typename Dtype>
+class CuDNNReLULayer : public ReLULayer<Dtype> {
+ public:
+  explicit CuDNNReLULayer(const LayerParameter& param)
+      : ReLULayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNReLULayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_RELU_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_sigmoid_layer.hpp b/include/caffe/layers/cudnn_sigmoid_layer.hpp
new file mode 100644
index 00000000000..9c597958b0b
--- /dev/null
+++ b/include/caffe/layers/cudnn_sigmoid_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_SIGMOID_LAYER_HPP_
+#define CAFFE_CUDNN_SIGMOID_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of SigmoidLayer.
+ */
+template <typename Dtype>
+class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
+ public:
+  explicit CuDNNSigmoidLayer(const LayerParameter& param)
+      : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNSigmoidLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_SIGMOID_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_softmax_layer.hpp b/include/caffe/layers/cudnn_softmax_layer.hpp
new file mode 100644
index 00000000000..174368e413d
--- /dev/null
+++ b/include/caffe/layers/cudnn_softmax_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
+#define CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/softmax_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief cuDNN implementation of SoftmaxLayer.
+ *        Fallback to SoftmaxLayer for CPU mode.
+ */
+template <typename Dtype>
+class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
+ public:
+  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
+      : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNSoftmaxLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_tanh_layer.hpp b/include/caffe/layers/cudnn_tanh_layer.hpp
new file mode 100644
index 00000000000..c0f0053f71e
--- /dev/null
+++ b/include/caffe/layers/cudnn_tanh_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_TANH_LAYER_HPP_
+#define CAFFE_CUDNN_TANH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of TanHLayer.
+ */
+template <typename Dtype>
+class CuDNNTanHLayer : public TanHLayer<Dtype> {
+ public:
+  explicit CuDNNTanHLayer(const LayerParameter& param)
+      : TanHLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNTanHLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_TANH_LAYER_HPP_
diff --git a/include/caffe/layers/data_layer.hpp b/include/caffe/layers/data_layer.hpp
new file mode 100644
index 00000000000..6c361791a0c
--- /dev/null
+++ b/include/caffe/layers/data_layer.hpp
@@ -0,0 +1,39 @@
+#ifndef CAFFE_DATA_LAYER_HPP_
+#define CAFFE_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_reader.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class DataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+  explicit DataLayer(const LayerParameter& param);
+  virtual ~DataLayer();
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // DataLayer uses DataReader instead for sharing for parallelism
+  virtual inline bool ShareInParallel() const { return false; }
+  virtual inline const char* type() const { return "Data"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+  virtual void load_batch(Batch<Dtype>* batch);
+
+  DataReader reader_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/deconv_layer.hpp b/include/caffe/layers/deconv_layer.hpp
new file mode 100644
index 00000000000..23ae887e61e
--- /dev/null
+++ b/include/caffe/layers/deconv_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_DECONV_LAYER_HPP_
+#define CAFFE_DECONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_conv_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Convolve the input with a bank of learned filters, and (optionally)
+ *        add biases, treating filters and convolution parameters in the
+ *        opposite sense as ConvolutionLayer.
+ *
+ *   ConvolutionLayer computes each output value by dotting an input window with
+ *   a filter; DeconvolutionLayer multiplies each input value by a filter
+ *   elementwise, and sums over the resulting output windows. In other words,
+ *   DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
+ *   reversed. DeconvolutionLayer reuses ConvolutionParameter for its
+ *   parameters, but they take the opposite sense as in ConvolutionLayer (so
+ *   padding is removed from the output rather than added to the input, and
+ *   stride results in upsampling rather than downsampling).
+ */
+template <typename Dtype>
+class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+  explicit DeconvolutionLayer(const LayerParameter& param)
+      : BaseConvolutionLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Deconvolution"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return true; }
+  virtual void compute_output_shape();
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DECONV_LAYER_HPP_
diff --git a/include/caffe/layers/dropout_layer.hpp b/include/caffe/layers/dropout_layer.hpp
new file mode 100644
index 00000000000..e83143bc3cc
--- /dev/null
+++ b/include/caffe/layers/dropout_layer.hpp
@@ -0,0 +1,80 @@
+#ifndef CAFFE_DROPOUT_LAYER_HPP_
+#define CAFFE_DROPOUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
+ *        the rest of the vector magnitude accordingly.
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$ y = |x| @f$
+ */
+template <typename Dtype>
+class DropoutLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides DropoutParameter dropout_param,
+   *     with DropoutLayer options:
+   *   - dropout_ratio (\b optional, default 0.5).
+   *     Sets the probability @f$ p @f$ that any given unit is dropped.
+   */
+  explicit DropoutLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Dropout"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs. At training time, we have @f$
+   *      y_{\mbox{train}} = \left\{
+   *         \begin{array}{ll}
+   *            \frac{x}{1 - p} & \mbox{if } u > p \\
+   *            0 & \mbox{otherwise}
+   *         \end{array} \right.
+   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
+   *      input at each iteration. At test time, we simply have
+   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
+  Blob<unsigned int> rand_vec_;
+  /// the probability @f$ p @f$ of dropping any input
+  Dtype threshold_;
+  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
+  Dtype scale_;
+  unsigned int uint_thres_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DROPOUT_LAYER_HPP_
diff --git a/include/caffe/layers/dummy_data_layer.hpp b/include/caffe/layers/dummy_data_layer.hpp
new file mode 100644
index 00000000000..4180f1d01e4
--- /dev/null
+++ b/include/caffe/layers/dummy_data_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_DUMMY_DATA_LAYER_HPP_
+#define CAFFE_DUMMY_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net generated by a Filler.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class DummyDataLayer : public Layer<Dtype> {
+ public:
+  explicit DummyDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "DummyData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+  vector<shared_ptr<Filler<Dtype> > > fillers_;
+  vector<bool> refill_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DUMMY_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/eltwise_layer.hpp b/include/caffe/layers/eltwise_layer.hpp
new file mode 100644
index 00000000000..091de834362
--- /dev/null
+++ b/include/caffe/layers/eltwise_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_ELTWISE_LAYER_HPP_
+#define CAFFE_ELTWISE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute elementwise operations, such as product and sum,
+ *        along multiple input Blobs.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EltwiseLayer : public Layer<Dtype> {
+ public:
+  explicit EltwiseLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Eltwise"; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  EltwiseParameter_EltwiseOp op_;
+  vector<Dtype> coeffs_;
+  Blob<int> max_idx_;
+
+  bool stable_prod_grad_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ELTWISE_LAYER_HPP_
diff --git a/include/caffe/layers/embed_layer.hpp b/include/caffe/layers/embed_layer.hpp
new file mode 100644
index 00000000000..36137a625b6
--- /dev/null
+++ b/include/caffe/layers/embed_layer.hpp
@@ -0,0 +1,52 @@
+#ifndef CAFFE_EMBED_LAYER_HPP_
+#define CAFFE_EMBED_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief A layer for learning "embeddings" of one-hot vector input.
+ *        Equivalent to an InnerProductLayer with one-hot vectors as input, but
+ *        for efficiency the input is the "hot" index of each column itself.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EmbedLayer : public Layer<Dtype> {
+ public:
+  explicit EmbedLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Embed"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  Blob<Dtype> bias_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_EMBED_LAYER_HPP_
diff --git a/include/caffe/layers/euclidean_loss_layer.hpp b/include/caffe/layers/euclidean_loss_layer.hpp
new file mode 100644
index 00000000000..f564569e27a
--- /dev/null
+++ b/include/caffe/layers/euclidean_loss_layer.hpp
@@ -0,0 +1,107 @@
+#ifndef CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
+#define CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the Euclidean (L2) loss @f$
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ *        \right| \right|_2^2 @f$ for real-valued regression tasks.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the targets @f$ y \in [-\infty, +\infty]@f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed Euclidean loss: @f$ E =
+ *          \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ *        \right| \right|_2^2 @f$
+ *
+ * This can be used for least-squares regression tasks.  An InnerProductLayer
+ * input to a EuclideanLossLayer exactly formulates a linear least squares
+ * regression problem. With non-zero weight decay the problem becomes one of
+ * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
+ * example wherein we check that the gradients computed for a Net with exactly
+ * this structure match hand-computed gradient formulas for ridge regression.
+ *
+ * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
+ * linear least squares problems! We use it only as an instructive example.)
+ */
+template <typename Dtype>
+class EuclideanLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit EuclideanLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "EuclideanLoss"; }
+  /**
+   * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+   * to both inputs -- override to return true and always allow force_backward.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return true;
+  }
+
+ protected:
+  /// @copydoc EuclideanLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the Euclidean error gradient w.r.t. the inputs.
+   *
+   * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
+   * gradients with respect to the label inputs bottom[1] (but still only will
+   * if propagate_down[1] is set, due to being produced by learnable parameters
+   * or if force_backward is set). In fact, this layer is "commutative" -- the
+   * result is the same regardless of the order of the two bottoms.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$\hat{y}@f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial \hat{y}} =
+   *            \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
+   *      @f$ if propagate_down[0]
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the targets @f$y@f$; Backward fills their diff with gradients
+   *      @f$ \frac{\partial E}{\partial y} =
+   *          \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
+   *      @f$ if propagate_down[1]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> diff_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/exp_layer.hpp b/include/caffe/layers/exp_layer.hpp
new file mode 100644
index 00000000000..9fc8c396a74
--- /dev/null
+++ b/include/caffe/layers/exp_layer.hpp
@@ -0,0 +1,80 @@
+#ifndef CAFFE_EXP_LAYER_HPP_
+#define CAFFE_EXP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class ExpLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ExpParameter exp_param,
+   *     with ExpLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit ExpLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Exp"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \gamma ^ {\alpha x + \beta}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype inner_scale_, outer_scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_EXP_LAYER_HPP_
diff --git a/include/caffe/layers/filter_layer.hpp b/include/caffe/layers/filter_layer.hpp
new file mode 100644
index 00000000000..e040e66612b
--- /dev/null
+++ b/include/caffe/layers/filter_layer.hpp
@@ -0,0 +1,77 @@
+#ifndef CAFFE_FILTER_LAYER_HPP_
+#define CAFFE_FILTER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes two+ Blobs, interprets last Blob as a selector and
+ *  filter remaining Blobs accordingly with selector data (0 means that
+ * the corresponding item has to be filtered, non-zero means that corresponding
+ * item needs to stay).
+ */
+template <typename Dtype>
+class FilterLayer : public Layer<Dtype> {
+ public:
+  explicit FilterLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Filter"; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_1 @f$
+   *   -# ...
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_K @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the selector blob
+   * @param top output Blob vector (length 1+)
+   *   -# @f$ (S \times C \times H \times W) @f$ ()
+   *        the filtered output @f$ x_1 @f$
+   *        where S is the number of items
+   *        that haven't been filtered
+   *      @f$ (S \times C \times H \times W) @f$
+   *        the filtered output @f$ x_K @f$
+   *        where S is the number of items
+   *        that haven't been filtered
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the forwarded inputs.
+   *
+   * @param top output Blob vector (length 1+), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2+), into which the top error
+   *        gradient is copied
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool first_reshape_;
+  vector<int> indices_to_forward_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_FILTER_LAYER_HPP_
diff --git a/include/caffe/layers/flatten_layer.hpp b/include/caffe/layers/flatten_layer.hpp
new file mode 100644
index 00000000000..e494bbb588f
--- /dev/null
+++ b/include/caffe/layers/flatten_layer.hpp
@@ -0,0 +1,61 @@
+#ifndef CAFFE_FLATTEN_LAYER_HPP_
+#define CAFFE_FLATTEN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Reshapes the input Blob into flat vectors.
+ *
+ * Note: because this layer does not change the input values -- merely the
+ * dimensions -- it can simply copy the input. The copy happens "virtually"
+ * (thus taking effectively 0 real time) by setting, in Forward, the data
+ * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
+ * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
+ * (see Blob::ShareDiff).
+ */
+template <typename Dtype>
+class FlattenLayer : public Layer<Dtype> {
+ public:
+  explicit FlattenLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Flatten"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times CHW \times 1 \times 1) @f$
+   *      the outputs -- i.e., the (virtually) copied, flattened inputs
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the concatenate inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length K), into which the top error
+   *        gradient is (virtually) copied
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_FLATTEN_LAYER_HPP_
diff --git a/include/caffe/layers/hdf5_data_layer.hpp b/include/caffe/layers/hdf5_data_layer.hpp
new file mode 100644
index 00000000000..b04cf8e1940
--- /dev/null
+++ b/include/caffe/layers/hdf5_data_layer.hpp
@@ -0,0 +1,62 @@
+#ifndef CAFFE_HDF5_DATA_LAYER_HPP_
+#define CAFFE_HDF5_DATA_LAYER_HPP_
+
+#include "hdf5.h"
+
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_data_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class HDF5DataLayer : public Layer<Dtype> {
+ public:
+  explicit HDF5DataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual ~HDF5DataLayer();
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "HDF5Data"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void LoadHDF5FileData(const char* filename);
+
+  std::vector<std::string> hdf_filenames_;
+  unsigned int num_files_;
+  unsigned int current_file_;
+  hsize_t current_row_;
+  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
+  std::vector<unsigned int> data_permutation_;
+  std::vector<unsigned int> file_permutation_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_HDF5_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/hdf5_output_layer.hpp b/include/caffe/layers/hdf5_output_layer.hpp
new file mode 100644
index 00000000000..487d08fc06c
--- /dev/null
+++ b/include/caffe/layers/hdf5_output_layer.hpp
@@ -0,0 +1,64 @@
+#ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_
+#define CAFFE_HDF5_OUTPUT_LAYER_HPP_
+
+#include "hdf5.h"
+
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+#define HDF5_DATA_DATASET_NAME "data"
+#define HDF5_DATA_LABEL_NAME "label"
+
+/**
+ * @brief Write blobs to disk as HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class HDF5OutputLayer : public Layer<Dtype> {
+ public:
+  explicit HDF5OutputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), file_opened_(false) {}
+  virtual ~HDF5OutputLayer();
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "HDF5Output"; }
+  // TODO: no limit on the number of blobs
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+  inline std::string file_name() const { return file_name_; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void SaveBlobs();
+
+  bool file_opened_;
+  std::string file_name_;
+  hid_t file_id_;
+  Blob<Dtype> data_blob_;
+  Blob<Dtype> label_blob_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_HDF5_OUTPUT_LAYER_HPP_
diff --git a/include/caffe/layers/hinge_loss_layer.hpp b/include/caffe/layers/hinge_loss_layer.hpp
new file mode 100644
index 00000000000..54e42bd44da
--- /dev/null
+++ b/include/caffe/layers/hinge_loss_layer.hpp
@@ -0,0 +1,104 @@
+#ifndef CAFFE_HINGE_LOSS_LAYER_HPP_
+#define CAFFE_HINGE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the hinge loss for a one-of-many classification task.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ t @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
+ *      taking the inner product @f$ X^T W @f$ of the D-dimensional features
+ *      @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
+ *      parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
+ *      an InnerProductLayer (with num_output = D) providing predictions to a
+ *      HingeLossLayer and no other learnable parameters or losses is
+ *      equivalent to an SVM.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed hinge loss: @f$ E =
+ *        \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
+ *        [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
+ *      @f$, for the @f$ L^p @f$ norm
+ *      (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
+ *      is also available), and @f$
+ *      \delta\{\mathrm{condition}\} = \left\{
+ *         \begin{array}{lr}
+ *            1 & \mbox{if condition} \\
+ *           -1 & \mbox{otherwise}
+ *         \end{array} \right.
+ *      @f$
+ *
+ * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
+ * the inner product @f$ X^T W @f$ of the features
+ * @f$ X \in \mathcal{R}^{D \times N} @f$
+ * and the learned hyperplane parameters
+ * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
+ * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
+ * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
+ * outside the InnerProductLayer and no other losses outside the
+ * HingeLossLayer).
+ */
+template <typename Dtype>
+class HingeLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit HingeLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "HingeLoss"; }
+
+ protected:
+  /// @copydoc HingeLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the hinge loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$t@f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial t} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_HINGE_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/im2col_layer.hpp b/include/caffe/layers/im2col_layer.hpp
new file mode 100644
index 00000000000..1d3b2eb67d1
--- /dev/null
+++ b/include/caffe/layers/im2col_layer.hpp
@@ -0,0 +1,63 @@
+#ifndef CAFFE_IM2COL_LAYER_HPP_
+#define CAFFE_IM2COL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief A helper for image operations that rearranges image regions into
+ *        column vectors.  Used by ConvolutionLayer to perform convolution
+ *        by matrix multiplication.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class Im2colLayer : public Layer<Dtype> {
+ public:
+  explicit Im2colLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Im2col"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief The spatial dimensions of a filter kernel.
+  Blob<int> kernel_shape_;
+  /// @brief The spatial dimensions of the stride.
+  Blob<int> stride_;
+  /// @brief The spatial dimensions of the padding.
+  Blob<int> pad_;
+
+  int num_spatial_axes_;
+  int bottom_dim_;
+  int top_dim_;
+
+  int channel_axis_;
+  int num_;
+  int channels_;
+
+  bool force_nd_im2col_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_IM2COL_LAYER_HPP_
diff --git a/include/caffe/layers/image_data_layer.hpp b/include/caffe/layers/image_data_layer.hpp
new file mode 100644
index 00000000000..a0d3384e4c9
--- /dev/null
+++ b/include/caffe/layers/image_data_layer.hpp
@@ -0,0 +1,47 @@
+#ifndef CAFFE_IMAGE_DATA_LAYER_HPP_
+#define CAFFE_IMAGE_DATA_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from image files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+  explicit ImageDataLayer(const LayerParameter& param)
+      : BasePrefetchingDataLayer<Dtype>(param) {}
+  virtual ~ImageDataLayer();
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "ImageData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+  shared_ptr<Caffe::RNG> prefetch_rng_;
+  virtual void ShuffleImages();
+  virtual void load_batch(Batch<Dtype>* batch);
+
+  vector<std::pair<std::string, int> > lines_;
+  int lines_id_;
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_IMAGE_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp
new file mode 100644
index 00000000000..633f339a28e
--- /dev/null
+++ b/include/caffe/layers/infogain_loss_layer.hpp
@@ -0,0 +1,110 @@
+#ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_
+#define CAFFE_INFOGAIN_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief A generalization of MultinomialLogisticLossLayer that takes an
+ *        "information gain" (infogain) matrix specifying the "value" of all label
+ *        pairs.
+ *
+ * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
+ * identity.
+ *
+ * @param bottom input Blob vector (length 2-3)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ *   -# @f$ (1 \times 1 \times K \times K) @f$
+ *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
+ *      the third bottom blob input if not provided as the infogain_mat in the
+ *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
+ *      MultinomialLogisticLossLayer.
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed infogain multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
+ *        \log(\hat{p}_{n,k})
+ *      @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
+ */
+template <typename Dtype>
+class InfogainLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit InfogainLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), infogain_() {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
+  // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
+  // file specified by LayerParameter.)
+  virtual inline int ExactNumBottomBlobs() const { return -1; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MaxBottomBlobs() const { return 3; }
+
+  virtual inline const char* type() const { return "InfogainLoss"; }
+
+ protected:
+  /// @copydoc InfogainLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the infogain loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set. (The same applies to the infogain matrix, if
+   * provided as bottom[2] rather than in the layer_param.)
+   *
+   * @param top output Blob vector (length 1), providing the error gradient
+   *      with respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels (similarly for propagate_down[2] and the
+   *      infogain matrix, if provided as bottom[2])
+   * @param bottom input Blob vector (length 2-3)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   *   -# @f$ (1 \times 1 \times K \times K) @f$
+   *      (\b optional) the information gain matrix -- ignored as its error
+   *      gradient computation is not implemented.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> infogain_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INFOGAIN_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp
new file mode 100644
index 00000000000..250576a4817
--- /dev/null
+++ b/include/caffe/layers/inner_product_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_INNER_PRODUCT_LAYER_HPP_
+#define CAFFE_INNER_PRODUCT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Also known as a "fully-connected" layer, computes an inner product
+ *        with a set of learned weights, and (optionally) adds biases.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class InnerProductLayer : public Layer<Dtype> {
+ public:
+  explicit InnerProductLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "InnerProduct"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  Blob<Dtype> bias_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INNER_PRODUCT_LAYER_HPP_
diff --git a/include/caffe/layers/log_layer.hpp b/include/caffe/layers/log_layer.hpp
new file mode 100644
index 00000000000..7d037d2bdca
--- /dev/null
+++ b/include/caffe/layers/log_layer.hpp
@@ -0,0 +1,82 @@
+#ifndef CAFFE_LOG_LAYER_HPP_
+#define CAFFE_LOG_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class LogLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides LogParameter log_param,
+   *     with LogLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit LogLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Log"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = log_{\gamma}(\alpha x + \beta)
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype base_scale_;
+  Dtype input_scale_, input_shift_;
+  Dtype backward_num_scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LOG_LAYER_HPP_
diff --git a/include/caffe/layers/loss_layer.hpp b/include/caffe/layers/loss_layer.hpp
new file mode 100644
index 00000000000..dbdf612c062
--- /dev/null
+++ b/include/caffe/layers/loss_layer.hpp
@@ -0,0 +1,53 @@
+#ifndef CAFFE_LOSS_LAYER_HPP_
+#define CAFFE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+const float kLOG_THRESHOLD = 1e-20;
+
+/**
+ * @brief An interface for Layer%s that take two Blob%s as input -- usually
+ *        (1) predictions and (2) ground-truth labels -- and output a
+ *        singleton Blob representing the loss.
+ *
+ * LossLayers are typically only capable of backpropagating to their first input
+ * -- the predictions.
+ */
+template <typename Dtype>
+class LossLayer : public Layer<Dtype> {
+ public:
+  explicit LossLayer(const LayerParameter& param)
+     : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
+  /**
+   * @brief For convenience and backwards compatibility, instruct the Net to
+   *        automatically allocate a single top Blob for LossLayers, into which
+   *        they output their singleton loss, (even if the user didn't specify
+   *        one in the prototxt, etc.).
+   */
+  virtual inline bool AutoTopBlobs() const { return true; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+  /**
+   * We usually cannot backpropagate to the labels; ignore force_backward for
+   * these inputs.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
+  }
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/lrn_layer.hpp b/include/caffe/layers/lrn_layer.hpp
new file mode 100644
index 00000000000..06cf71a94cb
--- /dev/null
+++ b/include/caffe/layers/lrn_layer.hpp
@@ -0,0 +1,94 @@
+#ifndef CAFFE_LRN_LAYER_HPP_
+#define CAFFE_LRN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/eltwise_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Normalize the input in a local region across or within feature maps.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class LRNLayer : public Layer<Dtype> {
+ public:
+  explicit LRNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "LRN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int size_;
+  int pre_pad_;
+  Dtype alpha_;
+  Dtype beta_;
+  Dtype k_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+
+  // Fields used for normalization ACROSS_CHANNELS
+  // scale_ stores the intermediate summing results
+  Blob<Dtype> scale_;
+
+  // Fields used for normalization WITHIN_CHANNEL
+  shared_ptr<SplitLayer<Dtype> > split_layer_;
+  vector<Blob<Dtype>*> split_top_vec_;
+  shared_ptr<PowerLayer<Dtype> > square_layer_;
+  Blob<Dtype> square_input_;
+  Blob<Dtype> square_output_;
+  vector<Blob<Dtype>*> square_bottom_vec_;
+  vector<Blob<Dtype>*> square_top_vec_;
+  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
+  Blob<Dtype> pool_output_;
+  vector<Blob<Dtype>*> pool_top_vec_;
+  shared_ptr<PowerLayer<Dtype> > power_layer_;
+  Blob<Dtype> power_output_;
+  vector<Blob<Dtype>*> power_top_vec_;
+  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
+  Blob<Dtype> product_input_;
+  vector<Blob<Dtype>*> product_bottom_vec_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LRN_LAYER_HPP_
diff --git a/include/caffe/layers/memory_data_layer.hpp b/include/caffe/layers/memory_data_layer.hpp
new file mode 100644
index 00000000000..8abcc8c1b68
--- /dev/null
+++ b/include/caffe/layers/memory_data_layer.hpp
@@ -0,0 +1,63 @@
+#ifndef CAFFE_MEMORY_DATA_LAYER_HPP_
+#define CAFFE_MEMORY_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_data_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from memory.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class MemoryDataLayer : public BaseDataLayer<Dtype> {
+ public:
+  explicit MemoryDataLayer(const LayerParameter& param)
+      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MemoryData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+  virtual void AddDatumVector(const vector<Datum>& datum_vector);
+#ifdef USE_OPENCV
+  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
+      const vector<int>& labels);
+#endif  // USE_OPENCV
+
+  // Reset should accept const pointers, but can't, because the memory
+  //  will be given to Blob, which is mutable
+  void Reset(Dtype* data, Dtype* label, int n);
+  void set_batch_size(int new_size);
+
+  int batch_size() { return batch_size_; }
+  int channels() { return channels_; }
+  int height() { return height_; }
+  int width() { return width_; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  int batch_size_, channels_, height_, width_, size_;
+  Dtype* data_;
+  Dtype* labels_;
+  int n_;
+  size_t pos_;
+  Blob<Dtype> added_data_;
+  Blob<Dtype> added_label_;
+  bool has_new_data_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_MEMORY_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/multinomial_logistic_loss_layer.hpp b/include/caffe/layers/multinomial_logistic_loss_layer.hpp
new file mode 100644
index 00000000000..3977cf9ea57
--- /dev/null
+++ b/include/caffe/layers/multinomial_logistic_loss_layer.hpp
@@ -0,0 +1,92 @@
+#ifndef CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
+#define CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, directly taking a predicted probability
+ *        distribution as input.
+ *
+ * When predictions are not already a probability distribution, you should
+ * instead use the SoftmaxWithLossLayer, which maps predictions to a
+ * distribution using the SoftmaxLayer, before computing the multinomial
+ * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$
+ */
+template <typename Dtype>
+class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
+
+ protected:
+  /// @copydoc MultinomialLogisticLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the multinomial logistic loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/mvn_layer.hpp b/include/caffe/layers/mvn_layer.hpp
new file mode 100644
index 00000000000..3a235ceca64
--- /dev/null
+++ b/include/caffe/layers/mvn_layer.hpp
@@ -0,0 +1,48 @@
+#ifndef CAFFE_MVN_LAYER_HPP_
+#define CAFFE_MVN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class MVNLayer : public Layer<Dtype> {
+ public:
+  explicit MVNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MVN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> mean_, variance_, temp_;
+
+  /// sum_multiplier is used to carry out sum using BLAS
+  Blob<Dtype> sum_multiplier_;
+  Dtype eps_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_MVN_LAYER_HPP_
diff --git a/include/caffe/layers/neuron_layer.hpp b/include/caffe/layers/neuron_layer.hpp
new file mode 100644
index 00000000000..10c108ce682
--- /dev/null
+++ b/include/caffe/layers/neuron_layer.hpp
@@ -0,0 +1,32 @@
+#ifndef CAFFE_NEURON_LAYER_HPP_
+#define CAFFE_NEURON_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief An interface for layers that take one blob as input (@f$ x @f$)
+ *        and produce one equally-sized blob as output (@f$ y @f$), where
+ *        each element of the output depends only on the corresponding input
+ *        element.
+ */
+template <typename Dtype>
+class NeuronLayer : public Layer<Dtype> {
+ public:
+  explicit NeuronLayer(const LayerParameter& param)
+     : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_NEURON_LAYER_HPP_
diff --git a/include/caffe/layers/pooling_layer.hpp b/include/caffe/layers/pooling_layer.hpp
new file mode 100644
index 00000000000..f4d6803ba8e
--- /dev/null
+++ b/include/caffe/layers/pooling_layer.hpp
@@ -0,0 +1,60 @@
+#ifndef CAFFE_POOLING_LAYER_HPP_
+#define CAFFE_POOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Pools the input image by taking the max, average, etc. within regions.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class PoolingLayer : public Layer<Dtype> {
+ public:
+  explicit PoolingLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Pooling"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  // MAX POOL layers can output an extra top blob for the mask;
+  // others can only output the pooled inputs.
+  virtual inline int MaxTopBlobs() const {
+    return (this->layer_param_.pooling_param().pool() ==
+            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
+  int pad_h_, pad_w_;
+  int channels_;
+  int height_, width_;
+  int pooled_height_, pooled_width_;
+  bool global_pooling_;
+  Blob<Dtype> rand_idx_;
+  Blob<int> max_idx_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_POOLING_LAYER_HPP_
diff --git a/include/caffe/layers/power_layer.hpp b/include/caffe/layers/power_layer.hpp
new file mode 100644
index 00000000000..6ecbafcaca8
--- /dev/null
+++ b/include/caffe/layers/power_layer.hpp
@@ -0,0 +1,89 @@
+#ifndef CAFFE_POWER_LAYER_HPP_
+#define CAFFE_POWER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and power @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class PowerLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides PowerParameter power_param,
+   *     with PowerLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - power (\b optional, default 1) the power @f$ \gamma @f$
+   */
+  explicit PowerLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Power"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = (\alpha x + \beta) ^ \gamma
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the power inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y}
+   *            \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
+   *            \frac{\partial E}{\partial y}
+   *            \frac{\alpha \gamma y}{\alpha x + \beta}
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief @f$ \gamma @f$ from layer_param_.power_param()
+  Dtype power_;
+  /// @brief @f$ \alpha @f$ from layer_param_.power_param()
+  Dtype scale_;
+  /// @brief @f$ \beta @f$ from layer_param_.power_param()
+  Dtype shift_;
+  /// @brief Result of @f$ \alpha \gamma @f$
+  Dtype diff_scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_POWER_LAYER_HPP_
diff --git a/include/caffe/layers/prelu_layer.hpp b/include/caffe/layers/prelu_layer.hpp
new file mode 100644
index 00000000000..3ddfb484b66
--- /dev/null
+++ b/include/caffe/layers/prelu_layer.hpp
@@ -0,0 +1,101 @@
+#ifndef CAFFE_PRELU_LAYER_HPP_
+#define CAFFE_PRELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Parameterized Rectified Linear Unit non-linearity @f$
+ *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+ *        @f$. The differences from ReLULayer are 1) negative slopes are
+ *        learnable though backprop and 2) negative slopes can vary across
+ *        channels. The number of axes of input blob should be greater than or
+ *        equal to 2. The 1st axis (0-based) is seen as channels.
+ */
+template <typename Dtype>
+class PReLULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides PReLUParameter prelu_param,
+   *     with PReLULayer options:
+   *   - filler (\b optional, FillerParameter,
+   *     default {'type': constant 'value':0.25}).
+   *   - channel_shared (\b optional, default false).
+   *     negative slopes are shared across channels.
+   */
+  explicit PReLULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "PReLU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the computed outputs for each channel @f$i@f$ @f$
+   *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+   *      @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the PReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times ...) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
+   *      diff with gradients @f$
+   *        \frac{\partial E}{\partial x_i} = \left\{
+   *        \begin{array}{lr}
+   *            a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   *      If param_propagate_down_[0] is true, it fills the diff with gradients
+   *      @f$
+   *        \frac{\partial E}{\partial a_i} = \left\{
+   *        \begin{array}{lr}
+   *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            0 & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool channel_shared_;
+  Blob<Dtype> multiplier_;  // dot multiplier for backward computation of params
+  Blob<Dtype> backward_buff_;  // temporary buffer for backward computation
+  Blob<Dtype> bottom_memory_;  // memory for in-place computation
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_PRELU_LAYER_HPP_
diff --git a/include/caffe/python_layer.hpp b/include/caffe/layers/python_layer.hpp
similarity index 100%
rename from include/caffe/python_layer.hpp
rename to include/caffe/layers/python_layer.hpp
diff --git a/include/caffe/layers/reduction_layer.hpp b/include/caffe/layers/reduction_layer.hpp
new file mode 100644
index 00000000000..804a495b11c
--- /dev/null
+++ b/include/caffe/layers/reduction_layer.hpp
@@ -0,0 +1,59 @@
+#ifndef CAFFE_REDUCTION_LAYER_HPP_
+#define CAFFE_REDUCTION_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute "reductions" -- operations that return a scalar output Blob
+ *        for an input Blob of arbitrary size, such as the sum, absolute sum,
+ *        and sum of squares.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class ReductionLayer : public Layer<Dtype> {
+ public:
+  explicit ReductionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reduction"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief the reduction operation performed by the layer
+  ReductionParameter_ReductionOp op_;
+  /// @brief a scalar coefficient applied to all outputs
+  Dtype coeff_;
+  /// @brief the index of the first input axis to reduce
+  int axis_;
+  /// @brief the number of reductions performed
+  int num_;
+  /// @brief the input size of each reduction
+  int dim_;
+  /// @brief a helper Blob used for summation (op_ == SUM)
+  Blob<Dtype> sum_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_REDUCTION_LAYER_HPP_
diff --git a/include/caffe/layers/relu_layer.hpp b/include/caffe/layers/relu_layer.hpp
new file mode 100644
index 00000000000..d7a73f7a8d1
--- /dev/null
+++ b/include/caffe/layers/relu_layer.hpp
@@ -0,0 +1,85 @@
+#ifndef CAFFE_RELU_LAYER_HPP_
+#define CAFFE_RELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
+ *        The simple max is fast to compute, and the function does not saturate.
+ */
+template <typename Dtype>
+class ReLULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ReLUParameter relu_param,
+   *     with ReLULayer options:
+   *   - negative_slope (\b optional, default 0).
+   *     the value @f$ \nu @f$ by which negative values are multiplied.
+   */
+  explicit ReLULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "ReLU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \max(0, x)
+   *      @f$ by default.  If a non-zero negative_slope @f$ \nu @f$ is provided,
+   *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the ReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            0 & \mathrm{if} \; x \le 0 \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+   *        \end{array} \right.
+   *      @f$ if propagate_down[0], by default.
+   *      If a non-zero negative_slope @f$ \nu @f$ is provided,
+   *      the computed gradients are @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_RELU_LAYER_HPP_
diff --git a/include/caffe/layers/reshape_layer.hpp b/include/caffe/layers/reshape_layer.hpp
new file mode 100644
index 00000000000..d11e06384ce
--- /dev/null
+++ b/include/caffe/layers/reshape_layer.hpp
@@ -0,0 +1,52 @@
+#ifndef CAFFE_XXX_LAYER_HPP_
+#define CAFFE_XXX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/*
+ * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
+ *
+ * Note: similarly to FlattenLayer, this layer does not change the input values
+ * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
+ */
+template <typename Dtype>
+class ReshapeLayer : public Layer<Dtype> {
+ public:
+  explicit ReshapeLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reshape"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+  /// @brief vector of axes indices whose dimensions we'll copy from the bottom
+  vector<int> copy_axes_;
+  /// @brief the index of the axis whose dimension we infer, or -1 if none
+  int inferred_axis_;
+  /// @brief the product of the "constant" output dimensions
+  int constant_count_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_XXX_LAYER_HPP_
diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
new file mode 100644
index 00000000000..598dca5ff2c
--- /dev/null
+++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
@@ -0,0 +1,110 @@
+#ifndef CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
+#define CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the cross-entropy (logistic) loss @f$
+ *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ *                  p_n \log \hat{p}_n +
+ *                  (1 - p_n) \log(1 - \hat{p}_n)
+ *              \right]
+ *        @f$, often used for predicting targets interpreted as probabilities.
+ *
+ * This layer is implemented rather than separate
+ * SigmoidLayer + CrossEntropyLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SigmoidLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the scores @f$ x \in [-\infty, +\infty]@f$,
+ *      which this layer maps to probability predictions
+ *      @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
+ *      using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the targets @f$ y \in [0, 1] @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed cross-entropy loss: @f$
+ *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ *                  p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
+ *              \right]
+ *      @f$
+ */
+template <typename Dtype>
+class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param),
+          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+          sigmoid_output_(new Blob<Dtype>()) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
+
+ protected:
+  /// @copydoc SigmoidCrossEntropyLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the target inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as gradient computation with respect
+   *      to the targets is not implemented.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$x@f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} =
+   *          \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
+   *      @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// The internal SigmoidLayer used to map predictions to probabilities.
+  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+  /// sigmoid_output stores the output of the SigmoidLayer.
+  shared_ptr<Blob<Dtype> > sigmoid_output_;
+  /// bottom vector holder to call the underlying SigmoidLayer::Forward
+  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+  /// top vector holder to call the underlying SigmoidLayer::Forward
+  vector<Blob<Dtype>*> sigmoid_top_vec_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/sigmoid_layer.hpp b/include/caffe/layers/sigmoid_layer.hpp
new file mode 100644
index 00000000000..ac0f6927feb
--- /dev/null
+++ b/include/caffe/layers/sigmoid_layer.hpp
@@ -0,0 +1,71 @@
+#ifndef CAFFE_SIGMOID_LAYER_HPP_
+#define CAFFE_SIGMOID_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Sigmoid function non-linearity @f$
+ *         y = (1 + \exp(-x))^{-1}
+ *     @f$, a classic choice in neural networks.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
+template <typename Dtype>
+class SigmoidLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit SigmoidLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Sigmoid"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = (1 + \exp(-x))^{-1}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y} y (1 - y)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SIGMOID_LAYER_HPP_
diff --git a/include/caffe/layers/silence_layer.hpp b/include/caffe/layers/silence_layer.hpp
new file mode 100644
index 00000000000..fba087fcef0
--- /dev/null
+++ b/include/caffe/layers/silence_layer.hpp
@@ -0,0 +1,43 @@
+#ifndef CAFFE_SILENCE_LAYER_HPP_
+#define CAFFE_SILENCE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Ignores bottom blobs while producing no top blobs. (This is useful
+ *        to suppress outputs during testing.)
+ */
+template <typename Dtype>
+class SilenceLayer : public Layer<Dtype> {
+ public:
+  explicit SilenceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "Silence"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  // We can't define Forward_gpu here, since STUB_GPU will provide
+  // its own definition for CPU_ONLY mode.
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SILENCE_LAYER_HPP_
diff --git a/include/caffe/layers/slice_layer.hpp b/include/caffe/layers/slice_layer.hpp
new file mode 100644
index 00000000000..10a0abb6eeb
--- /dev/null
+++ b/include/caffe/layers/slice_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_SLICE_LAYER_HPP_
+#define CAFFE_SLICE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes a Blob and slices it along either the num or channel dimension,
+ *        outputting multiple sliced Blob results.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SliceLayer : public Layer<Dtype> {
+ public:
+  explicit SliceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Slice"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int count_;
+  int num_slices_;
+  int slice_size_;
+  int slice_axis_;
+  vector<int> slice_point_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SLICE_LAYER_HPP_
diff --git a/include/caffe/layers/softmax_layer.hpp b/include/caffe/layers/softmax_layer.hpp
new file mode 100644
index 00000000000..c65b8703e43
--- /dev/null
+++ b/include/caffe/layers/softmax_layer.hpp
@@ -0,0 +1,50 @@
+#ifndef CAFFE_SOFTMAX_LAYER_HPP_
+#define CAFFE_SOFTMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Computes the softmax function.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SoftmaxLayer : public Layer<Dtype> {
+ public:
+  explicit SoftmaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Softmax"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int outer_num_;
+  int inner_num_;
+  int softmax_axis_;
+  /// sum_multiplier is used to carry out sum using BLAS
+  Blob<Dtype> sum_multiplier_;
+  /// scale is an intermediate Blob to hold temporary results.
+  Blob<Dtype> scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SOFTMAX_LAYER_HPP_
diff --git a/include/caffe/layers/softmax_loss_layer.hpp b/include/caffe/layers/softmax_loss_layer.hpp
new file mode 100644
index 00000000000..f07e8a02cf1
--- /dev/null
+++ b/include/caffe/layers/softmax_loss_layer.hpp
@@ -0,0 +1,130 @@
+#ifndef CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
+#define CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, passing real-valued predictions through a
+ *        softmax to get a probability distribution over classes.
+ *
+ * This layer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SoftmaxLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ x @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
+ *      probability distribution over classes using the softmax function
+ *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
+ *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed cross-entropy classification loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$, for softmax output class probabilites @f$ \hat{p} @f$
+ */
+template <typename Dtype>
+class SoftmaxWithLossLayer : public LossLayer<Dtype> {
+ public:
+   /**
+    * @param param provides LossParameter loss_param, with options:
+    *  - ignore_label (optional)
+    *    Specify a label value that should be ignored when computing the loss.
+    *  - normalize (optional, default true)
+    *    If true, the loss is normalized by the number of (nonignored) labels
+    *    present; otherwise the loss is simply summed over spatial locations.
+    */
+  explicit SoftmaxWithLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /**
+   * @brief Computes the softmax loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ x @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
+
+  /// The internal SoftmaxLayer used to map predictions to a distribution.
+  shared_ptr<Layer<Dtype> > softmax_layer_;
+  /// prob stores the output probability predictions from the SoftmaxLayer.
+  Blob<Dtype> prob_;
+  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_top_vec_;
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// How to normalize the output loss.
+  LossParameter_NormalizationMode normalization_;
+
+  int softmax_axis_, outer_num_, inner_num_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/split_layer.hpp b/include/caffe/layers/split_layer.hpp
new file mode 100644
index 00000000000..8140dfc7c40
--- /dev/null
+++ b/include/caffe/layers/split_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_SPLIT_LAYER_HPP_
+#define CAFFE_SPLIT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Creates a "split" path in the network by copying the bottom Blob
+ *        into multiple top Blob%s to be used by multiple consuming layers.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SplitLayer : public Layer<Dtype> {
+ public:
+  explicit SplitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Split"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int count_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SPLIT_LAYER_HPP_
diff --git a/include/caffe/layers/spp_layer.hpp b/include/caffe/layers/spp_layer.hpp
new file mode 100644
index 00000000000..9f145cc77e3
--- /dev/null
+++ b/include/caffe/layers/spp_layer.hpp
@@ -0,0 +1,76 @@
+#ifndef CAFFE_SPP_LAYER_HPP_
+#define CAFFE_SPP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Does spatial pyramid pooling on the input image
+ *        by taking the max, average, etc. within regions
+ *        so that the result vector of different sized
+ *        images are of the same size.
+ */
+template <typename Dtype>
+class SPPLayer : public Layer<Dtype> {
+ public:
+  explicit SPPLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SPP"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  // calculates the kernel and stride dimensions for the pooling layer,
+  // returns a correctly configured LayerParameter for a PoolingLayer
+  virtual LayerParameter GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param);
+
+  int pyramid_height_;
+  int bottom_h_, bottom_w_;
+  int num_;
+  int channels_;
+  int kernel_h_, kernel_w_;
+  int pad_h_, pad_w_;
+  bool reshaped_first_time_;
+
+  /// the internal Split layer that feeds the pooling layers
+  shared_ptr<SplitLayer<Dtype> > split_layer_;
+  /// top vector holder used in call to the underlying SplitLayer::Forward
+  vector<Blob<Dtype>*> split_top_vec_;
+  /// bottom vector holder used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
+  /// the internal Pooling layers of different kernel sizes
+  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
+  /// top vector holders used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
+  /// pooling_outputs stores the outputs of the PoolingLayers
+  vector<Blob<Dtype>*> pooling_outputs_;
+  /// the internal Flatten layers that the Pooling layers feed into
+  vector<FlattenLayer<Dtype>*> flatten_layers_;
+  /// top vector holders used in call to the underlying FlattenLayer::Forward
+  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
+  /// flatten_outputs stores the outputs of the FlattenLayers
+  vector<Blob<Dtype>*> flatten_outputs_;
+  /// bottom vector holder used in call to the underlying ConcatLayer::Forward
+  vector<Blob<Dtype>*> concat_bottom_vec_;
+  /// the internal Concat layers that the Flatten layers feed into
+  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SPP_LAYER_HPP_
diff --git a/include/caffe/layers/tanh_layer.hpp b/include/caffe/layers/tanh_layer.hpp
new file mode 100644
index 00000000000..8f95e9322d9
--- /dev/null
+++ b/include/caffe/layers/tanh_layer.hpp
@@ -0,0 +1,73 @@
+#ifndef CAFFE_TANH_LAYER_HPP_
+#define CAFFE_TANH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief TanH hyperbolic tangent non-linearity @f$
+ *         y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+ *     @f$, popular in auto-encoders.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
+template <typename Dtype>
+class TanHLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit TanHLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "TanH"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y}
+   *              \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
+   *            = \frac{\partial E}{\partial y} (1 - y^2)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_TANH_LAYER_HPP_
diff --git a/include/caffe/layers/threshold_layer.hpp b/include/caffe/layers/threshold_layer.hpp
new file mode 100644
index 00000000000..3bf4db63e5c
--- /dev/null
+++ b/include/caffe/layers/threshold_layer.hpp
@@ -0,0 +1,64 @@
+#ifndef CAFFE_THRESHOLD_LAYER_HPP_
+#define CAFFE_THRESHOLD_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
+ *        above threshold; 0 otherwise.
+ */
+template <typename Dtype>
+class ThresholdLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ThresholdParameter threshold_param,
+   *     with ThresholdLayer options:
+   *   - threshold (\b optional, default 0).
+   *     the threshold value @f$ t @f$ to which the input values are compared.
+   */
+  explicit ThresholdLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Threshold"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *       y = \left\{
+   *       \begin{array}{lr}
+   *         0 & \mathrm{if} \; x \le t \\
+   *         1 & \mathrm{if} \; x > t
+   *       \end{array} \right.
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /// @brief Not implemented (non-differentiable function)
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  Dtype threshold_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_THRESHOLD_LAYER_HPP_
diff --git a/include/caffe/layers/tile_layer.hpp b/include/caffe/layers/tile_layer.hpp
new file mode 100644
index 00000000000..fbdbe2f0c53
--- /dev/null
+++ b/include/caffe/layers/tile_layer.hpp
@@ -0,0 +1,43 @@
+#ifndef CAFFE_TILE_LAYER_HPP_
+#define CAFFE_TILE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Copy a Blob along specified dimensions.
+ */
+template <typename Dtype>
+class TileLayer : public Layer<Dtype> {
+ public:
+  explicit TileLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Tile"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_TILE_LAYER_HPP_
diff --git a/include/caffe/layers/window_data_layer.hpp b/include/caffe/layers/window_data_layer.hpp
new file mode 100644
index 00000000000..35f41b80e63
--- /dev/null
+++ b/include/caffe/layers/window_data_layer.hpp
@@ -0,0 +1,55 @@
+#ifndef CAFFE_WINDOW_DATA_LAYER_HPP_
+#define CAFFE_WINDOW_DATA_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from windows of images files, specified
+ *        by a window data file.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+  explicit WindowDataLayer(const LayerParameter& param)
+      : BasePrefetchingDataLayer<Dtype>(param) {}
+  virtual ~WindowDataLayer();
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "WindowData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+  virtual unsigned int PrefetchRand();
+  virtual void load_batch(Batch<Dtype>* batch);
+
+  shared_ptr<Caffe::RNG> prefetch_rng_;
+  vector<std::pair<std::string, vector<int> > > image_database_;
+  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
+  vector<vector<float> > fg_windows_;
+  vector<vector<float> > bg_windows_;
+  Blob<Dtype> data_mean_;
+  vector<Dtype> mean_values_;
+  bool has_mean_file_;
+  bool has_mean_values_;
+  bool cache_images_;
+  vector<std::pair<std::string, Datum > > image_database_cache_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_WINDOW_DATA_LAYER_HPP_
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
deleted file mode 100644
index 53d07025f0c..00000000000
--- a/include/caffe/loss_layers.hpp
+++ /dev/null
@@ -1,777 +0,0 @@
-#ifndef CAFFE_LOSS_LAYERS_HPP_
-#define CAFFE_LOSS_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-const float kLOG_THRESHOLD = 1e-20;
-
-/**
- * @brief Computes the classification accuracy for a one-of-many
- *        classification task.
- */
-template <typename Dtype>
-class AccuracyLayer : public Layer<Dtype> {
- public:
-  /**
-   * @param param provides AccuracyParameter accuracy_param,
-   *     with AccuracyLayer options:
-   *   - top_k (\b optional, default 1).
-   *     Sets the maximum rank @f$ k @f$ at which a prediction is considered
-   *     correct.  For example, if @f$ k = 5 @f$, a prediction is counted
-   *     correct if the correct label is among the top 5 predicted labels.
-   */
-  explicit AccuracyLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Accuracy"; }
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-
-  // If there are two top blobs, then the second blob will contain
-  // accuracies per class.
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ x @f$, a Blob with values in
-   *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
-   *      the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
-   *      label @f$ \hat{l}_n @f$ given by its maximal index:
-   *      @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels @f$ l @f$, an integer-valued Blob with values
-   *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
-   *      indicating the correct class label among the @f$ K @f$ classes
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      the computed accuracy: @f$
-   *        \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
-   *      @f$, where @f$
-   *      \delta\{\mathrm{condition}\} = \left\{
-   *         \begin{array}{lr}
-   *            1 & \mbox{if condition} \\
-   *            0 & \mbox{otherwise}
-   *         \end{array} \right.
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-
-  /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    for (int i = 0; i < propagate_down.size(); ++i) {
-      if (propagate_down[i]) { NOT_IMPLEMENTED; }
-    }
-  }
-
-  int label_axis_, outer_num_, inner_num_;
-
-  int top_k_;
-
-  /// Whether to ignore instances with a certain label.
-  bool has_ignore_label_;
-  /// The label indicating that an instance should be ignored.
-  int ignore_label_;
-  /// Keeps counts of the number of samples per class.
-  Blob<Dtype> nums_buffer_;
-};
-
-/**
- * @brief An interface for Layer%s that take two Blob%s as input -- usually
- *        (1) predictions and (2) ground-truth labels -- and output a
- *        singleton Blob representing the loss.
- *
- * LossLayers are typically only capable of backpropagating to their first input
- * -- the predictions.
- */
-template <typename Dtype>
-class LossLayer : public Layer<Dtype> {
- public:
-  explicit LossLayer(const LayerParameter& param)
-     : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(
-      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(
-      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
-
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-
-  /**
-   * @brief For convenience and backwards compatibility, instruct the Net to
-   *        automatically allocate a single top Blob for LossLayers, into which
-   *        they output their singleton loss, (even if the user didn't specify
-   *        one in the prototxt, etc.).
-   */
-  virtual inline bool AutoTopBlobs() const { return true; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-  /**
-   * We usually cannot backpropagate to the labels; ignore force_backward for
-   * these inputs.
-   */
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return bottom_index != 1;
-  }
-};
-
-/**
- * @brief Computes the contrastive loss @f$
- *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
- *              \left(1-y\right) \max \left(margin-d, 0\right)^2
- *          @f$ where @f$
- *          d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
- *          used to train siamese networks.
- *
- * @param bottom input Blob vector (length 3)
- *   -# @f$ (N \times C \times 1 \times 1) @f$
- *      the features @f$ a \in [-\infty, +\infty]@f$
- *   -# @f$ (N \times C \times 1 \times 1) @f$
- *      the features @f$ b \in [-\infty, +\infty]@f$
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the binary similarity @f$ s \in [0, 1]@f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed contrastive loss: @f$ E =
- *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
- *          \left(1-y\right) \max \left(margin-d, 0\right)^2
- *          @f$ where @f$
- *          d = \left| \left| a_n - b_n \right| \right|_2 @f$.
- * This can be used to train siamese networks.
- */
-template <typename Dtype>
-class ContrastiveLossLayer : public LossLayer<Dtype> {
- public:
-  explicit ContrastiveLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), diff_() {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline int ExactNumBottomBlobs() const { return 3; }
-  virtual inline const char* type() const { return "ContrastiveLoss"; }
-  /**
-   * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
-   * to the first two inputs.
-   */
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return bottom_index != 2;
-  }
-
- protected:
-  /// @copydoc ContrastiveLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the Contrastive error gradient w.r.t. the inputs.
-   *
-   * Computes the gradients with respect to the two input vectors (bottom[0] and
-   * bottom[1]), but not the similarity label (bottom[2]).
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$a@f$; Backward fills their diff with
-   *      gradients if propagate_down[0]
-   *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$b@f$; Backward fills their diff with gradients if
-   *      propagate_down[1]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> diff_;  // cached for backward pass
-  Blob<Dtype> dist_sq_;  // cached for backward pass
-  Blob<Dtype> diff_sq_;  // tmp storage for gpu forward pass
-  Blob<Dtype> summer_vec_;  // tmp storage for gpu forward pass
-};
-
-/**
- * @brief Computes the Euclidean (L2) loss @f$
- *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
- *        \right| \right|_2^2 @f$ for real-valued regression tasks.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the targets @f$ y \in [-\infty, +\infty]@f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed Euclidean loss: @f$ E =
- *          \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
- *        \right| \right|_2^2 @f$
- *
- * This can be used for least-squares regression tasks.  An InnerProductLayer
- * input to a EuclideanLossLayer exactly formulates a linear least squares
- * regression problem. With non-zero weight decay the problem becomes one of
- * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
- * example wherein we check that the gradients computed for a Net with exactly
- * this structure match hand-computed gradient formulas for ridge regression.
- *
- * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
- * linear least squares problems! We use it only as an instructive example.)
- */
-template <typename Dtype>
-class EuclideanLossLayer : public LossLayer<Dtype> {
- public:
-  explicit EuclideanLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), diff_() {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "EuclideanLoss"; }
-  /**
-   * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
-   * to both inputs -- override to return true and always allow force_backward.
-   */
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return true;
-  }
-
- protected:
-  /// @copydoc EuclideanLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the Euclidean error gradient w.r.t. the inputs.
-   *
-   * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
-   * gradients with respect to the label inputs bottom[1] (but still only will
-   * if propagate_down[1] is set, due to being produced by learnable parameters
-   * or if force_backward is set). In fact, this layer is "commutative" -- the
-   * result is the same regardless of the order of the two bottoms.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$\hat{y}@f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial \hat{y}} =
-   *            \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
-   *      @f$ if propagate_down[0]
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the targets @f$y@f$; Backward fills their diff with gradients
-   *      @f$ \frac{\partial E}{\partial y} =
-   *          \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
-   *      @f$ if propagate_down[1]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> diff_;
-};
-
-/**
- * @brief Computes the hinge loss for a one-of-many classification task.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ t @f$, a Blob with values in
- *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- *      the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
- *      taking the inner product @f$ X^T W @f$ of the D-dimensional features
- *      @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
- *      parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
- *      an InnerProductLayer (with num_output = D) providing predictions to a
- *      HingeLossLayer and no other learnable parameters or losses is
- *      equivalent to an SVM.
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed hinge loss: @f$ E =
- *        \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
- *        [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
- *      @f$, for the @f$ L^p @f$ norm
- *      (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
- *      is also available), and @f$
- *      \delta\{\mathrm{condition}\} = \left\{
- *         \begin{array}{lr}
- *            1 & \mbox{if condition} \\
- *           -1 & \mbox{otherwise}
- *         \end{array} \right.
- *      @f$
- *
- * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
- * the inner product @f$ X^T W @f$ of the features
- * @f$ X \in \mathcal{R}^{D \times N} @f$
- * and the learned hyperplane parameters
- * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
- * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
- * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
- * outside the InnerProductLayer and no other losses outside the
- * HingeLossLayer).
- */
-template <typename Dtype>
-class HingeLossLayer : public LossLayer<Dtype> {
- public:
-  explicit HingeLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "HingeLoss"; }
-
- protected:
-  /// @copydoc HingeLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the hinge loss error gradient w.r.t. the predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$t@f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial t} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief A generalization of MultinomialLogisticLossLayer that takes an
- *        "information gain" (infogain) matrix specifying the "value" of all label
- *        pairs.
- *
- * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
- * identity.
- *
- * @param bottom input Blob vector (length 2-3)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{p} @f$, a Blob with values in
- *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
- *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
- *      should sum to 1 as in a probability distribution: @f$
- *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- *   -# @f$ (1 \times 1 \times K \times K) @f$
- *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
- *      the third bottom blob input if not provided as the infogain_mat in the
- *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
- *      MultinomialLogisticLossLayer.
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed infogain multinomial logistic loss: @f$ E =
- *        \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
- *        \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
- *        \log(\hat{p}_{n,k})
- *      @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
- */
-template <typename Dtype>
-class InfogainLossLayer : public LossLayer<Dtype> {
- public:
-  explicit InfogainLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), infogain_() {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
-  // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
-  // file specified by LayerParameter.)
-  virtual inline int ExactNumBottomBlobs() const { return -1; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int MaxBottomBlobs() const { return 3; }
-
-  virtual inline const char* type() const { return "InfogainLoss"; }
-
- protected:
-  /// @copydoc InfogainLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the infogain loss error gradient w.r.t. the predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set. (The same applies to the infogain matrix, if
-   * provided as bottom[2] rather than in the layer_param.)
-   *
-   * @param top output Blob vector (length 1), providing the error gradient
-   *      with respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels (similarly for propagate_down[2] and the
-   *      infogain matrix, if provided as bottom[2])
-   * @param bottom input Blob vector (length 2-3)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ \hat{p} @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   *   -# @f$ (1 \times 1 \times K \times K) @f$
-   *      (\b optional) the information gain matrix -- ignored as its error
-   *      gradient computation is not implemented.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> infogain_;
-};
-
-/**
- * @brief Computes the multinomial logistic loss for a one-of-many
- *        classification task, directly taking a predicted probability
- *        distribution as input.
- *
- * When predictions are not already a probability distribution, you should
- * instead use the SoftmaxWithLossLayer, which maps predictions to a
- * distribution using the SoftmaxLayer, before computing the multinomial
- * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
- * SoftmaxLayer + MultinomialLogisticLossLayer
- * as its gradient computation is more numerically stable.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{p} @f$, a Blob with values in
- *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
- *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
- *      should sum to 1 as in a probability distribution: @f$
- *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed multinomial logistic loss: @f$ E =
- *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
- *      @f$
- */
-template <typename Dtype>
-class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
- public:
-  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
-
- protected:
-  /// @copydoc MultinomialLogisticLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the multinomial logistic loss error gradient w.r.t. the
-   *        predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ \hat{p} @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes the cross-entropy (logistic) loss @f$
- *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
- *                  p_n \log \hat{p}_n +
- *                  (1 - p_n) \log(1 - \hat{p}_n)
- *              \right]
- *        @f$, often used for predicting targets interpreted as probabilities.
- *
- * This layer is implemented rather than separate
- * SigmoidLayer + CrossEntropyLayer
- * as its gradient computation is more numerically stable.
- * At test time, this layer can be replaced simply by a SigmoidLayer.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the scores @f$ x \in [-\infty, +\infty]@f$,
- *      which this layer maps to probability predictions
- *      @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
- *      using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the targets @f$ y \in [0, 1] @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed cross-entropy loss: @f$
- *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
- *                  p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
- *              \right]
- *      @f$
- */
-template <typename Dtype>
-class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
- public:
-  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param),
-          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
-          sigmoid_output_(new Blob<Dtype>()) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
-
- protected:
-  /// @copydoc SigmoidCrossEntropyLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
-   *        predictions.
-   *
-   * Gradients cannot be computed with respect to the target inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as gradient computation with respect
-   *      to the targets is not implemented.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$x@f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial x} =
-   *          \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
-   *      @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// The internal SigmoidLayer used to map predictions to probabilities.
-  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
-  /// sigmoid_output stores the output of the SigmoidLayer.
-  shared_ptr<Blob<Dtype> > sigmoid_output_;
-  /// bottom vector holder to call the underlying SigmoidLayer::Forward
-  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
-  /// top vector holder to call the underlying SigmoidLayer::Forward
-  vector<Blob<Dtype>*> sigmoid_top_vec_;
-};
-
-// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
-template <typename Dtype> class SoftmaxLayer;
-
-/**
- * @brief Computes the multinomial logistic loss for a one-of-many
- *        classification task, passing real-valued predictions through a
- *        softmax to get a probability distribution over classes.
- *
- * This layer should be preferred over separate
- * SoftmaxLayer + MultinomialLogisticLossLayer
- * as its gradient computation is more numerically stable.
- * At test time, this layer can be replaced simply by a SoftmaxLayer.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ x @f$, a Blob with values in
- *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
- *      probability distribution over classes using the softmax function
- *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
- *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed cross-entropy classification loss: @f$ E =
- *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
- *      @f$, for softmax output class probabilites @f$ \hat{p} @f$
- */
-template <typename Dtype>
-class SoftmaxWithLossLayer : public LossLayer<Dtype> {
- public:
-   /**
-    * @param param provides LossParameter loss_param, with options:
-    *  - ignore_label (optional)
-    *    Specify a label value that should be ignored when computing the loss.
-    *  - normalize (optional, default true)
-    *    If true, the loss is normalized by the number of (nonignored) labels
-    *    present; otherwise the loss is simply summed over spatial locations.
-    */
-  explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
-  virtual inline int ExactNumTopBlobs() const { return -1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  /**
-   * @brief Computes the softmax loss error gradient w.r.t. the predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ x @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial x} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// Read the normalization mode parameter and compute the normalizer based
-  /// on the blob size.  If normalization_mode is VALID, the count of valid
-  /// outputs will be read from valid_count, unless it is -1 in which case
-  /// all outputs are assumed to be valid.
-  virtual Dtype get_normalizer(
-      LossParameter_NormalizationMode normalization_mode, int valid_count);
-
-  /// The internal SoftmaxLayer used to map predictions to a distribution.
-  shared_ptr<Layer<Dtype> > softmax_layer_;
-  /// prob stores the output probability predictions from the SoftmaxLayer.
-  Blob<Dtype> prob_;
-  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
-  vector<Blob<Dtype>*> softmax_bottom_vec_;
-  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
-  vector<Blob<Dtype>*> softmax_top_vec_;
-  /// Whether to ignore instances with a certain label.
-  bool has_ignore_label_;
-  /// The label indicating that an instance should be ignored.
-  int ignore_label_;
-  /// How to normalize the output loss.
-  LossParameter_NormalizationMode normalization_;
-
-  int softmax_axis_, outer_num_, inner_num_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_LOSS_LAYERS_HPP_
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
deleted file mode 100644
index 4fa330ec783..00000000000
--- a/include/caffe/neuron_layers.hpp
+++ /dev/null
@@ -1,806 +0,0 @@
-#ifndef CAFFE_NEURON_LAYERS_HPP_
-#define CAFFE_NEURON_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief An interface for layers that take one blob as input (@f$ x @f$)
- *        and produce one equally-sized blob as output (@f$ y @f$), where
- *        each element of the output depends only on the corresponding input
- *        element.
- */
-template <typename Dtype>
-class NeuronLayer : public Layer<Dtype> {
- public:
-  explicit NeuronLayer(const LayerParameter& param)
-     : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-};
-
-/**
- * @brief Computes @f$ y = |x| @f$
- *
- * @param bottom input Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the computed outputs @f$ y = |x| @f$
- */
-template <typename Dtype>
-class AbsValLayer : public NeuronLayer<Dtype> {
- public:
-  explicit AbsValLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "AbsVal"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /// @copydoc AbsValLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the absolute value inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \mathrm{sign}(x) \frac{\partial E}{\partial y}
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
- *        @f$ y = \log(1 + \exp(x)) @f$ otherwise.
- *
- * @param bottom input Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the computed outputs @f$
- *      y = \left\{
- *         \begin{array}{ll}
- *            x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
- *            \log(1 + \exp(x)) & \mbox{otherwise}
- *         \end{array} \right.
- *      @f$
- */
-template <typename Dtype>
-class BNLLLayer : public NeuronLayer<Dtype> {
- public:
-  explicit BNLLLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "BNLL"; }
-
- protected:
-  /// @copydoc BNLLLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the BNLL inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x}
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
- *        the rest of the vector magnitude accordingly.
- *
- * @param bottom input Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the computed outputs @f$ y = |x| @f$
- */
-template <typename Dtype>
-class DropoutLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides DropoutParameter dropout_param,
-   *     with DropoutLayer options:
-   *   - dropout_ratio (\b optional, default 0.5).
-   *     Sets the probability @f$ p @f$ that any given unit is dropped.
-   */
-  explicit DropoutLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Dropout"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs. At training time, we have @f$
-   *      y_{\mbox{train}} = \left\{
-   *         \begin{array}{ll}
-   *            \frac{x}{1 - p} & \mbox{if } u > p \\
-   *            0 & \mbox{otherwise}
-   *         \end{array} \right.
-   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
-   *      input at each iteration. At test time, we simply have
-   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
-  Blob<unsigned int> rand_vec_;
-  /// the probability @f$ p @f$ of dropping any input
-  Dtype threshold_;
-  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
-  Dtype scale_;
-  unsigned int uint_thres_;
-};
-
-/**
- * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
- *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- *        and base @f$ \gamma @f$.
- */
-template <typename Dtype>
-class ExpLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides ExpParameter exp_param,
-   *     with ExpLayer options:
-   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
-   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
-   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
-   *         the base @f$ \gamma @f$
-   */
-  explicit ExpLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Exp"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = \gamma ^ {\alpha x + \beta}
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the exp inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Dtype inner_scale_, outer_scale_;
-};
-
-/**
- * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$,
- *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- *        and base @f$ \gamma @f$.
- */
-template <typename Dtype>
-class LogLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides LogParameter log_param,
-   *     with LogLayer options:
-   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
-   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
-   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
-   *         the base @f$ \gamma @f$
-   */
-  explicit LogLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Log"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = log_{\gamma}(\alpha x + \beta)
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the exp inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Dtype base_scale_;
-  Dtype input_scale_, input_shift_;
-  Dtype backward_num_scale_;
-};
-
-/**
- * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
- *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- *        and power @f$ \gamma @f$.
- */
-template <typename Dtype>
-class PowerLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides PowerParameter power_param,
-   *     with PowerLayer options:
-   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
-   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
-   *   - power (\b optional, default 1) the power @f$ \gamma @f$
-   */
-  explicit PowerLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Power"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = (\alpha x + \beta) ^ \gamma
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the power inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \frac{\partial E}{\partial y}
-   *            \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
-   *            \frac{\partial E}{\partial y}
-   *            \frac{\alpha \gamma y}{\alpha x + \beta}
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// @brief @f$ \gamma @f$ from layer_param_.power_param()
-  Dtype power_;
-  /// @brief @f$ \alpha @f$ from layer_param_.power_param()
-  Dtype scale_;
-  /// @brief @f$ \beta @f$ from layer_param_.power_param()
-  Dtype shift_;
-  /// @brief Result of @f$ \alpha \gamma @f$
-  Dtype diff_scale_;
-};
-
-/**
- * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
- *        The simple max is fast to compute, and the function does not saturate.
- */
-template <typename Dtype>
-class ReLULayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides ReLUParameter relu_param,
-   *     with ReLULayer options:
-   *   - negative_slope (\b optional, default 0).
-   *     the value @f$ \nu @f$ by which negative values are multiplied.
-   */
-  explicit ReLULayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "ReLU"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = \max(0, x)
-   *      @f$ by default.  If a non-zero negative_slope @f$ \nu @f$ is provided,
-   *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the ReLU inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} = \left\{
-   *        \begin{array}{lr}
-   *            0 & \mathrm{if} \; x \le 0 \\
-   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
-   *        \end{array} \right.
-   *      @f$ if propagate_down[0], by default.
-   *      If a non-zero negative_slope @f$ \nu @f$ is provided,
-   *      the computed gradients are @f$
-   *        \frac{\partial E}{\partial x} = \left\{
-   *        \begin{array}{lr}
-   *            \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
-   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
-   *        \end{array} \right.
-   *      @f$.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of ReLULayer.
- */
-template <typename Dtype>
-class CuDNNReLULayer : public ReLULayer<Dtype> {
- public:
-  explicit CuDNNReLULayer(const LayerParameter& param)
-      : ReLULayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNReLULayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Sigmoid function non-linearity @f$
- *         y = (1 + \exp(-x))^{-1}
- *     @f$, a classic choice in neural networks.
- *
- * Note that the gradient vanishes as the values move away from 0.
- * The ReLULayer is often a better choice for this reason.
- */
-template <typename Dtype>
-class SigmoidLayer : public NeuronLayer<Dtype> {
- public:
-  explicit SigmoidLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "Sigmoid"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = (1 + \exp(-x))^{-1}
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x}
-   *            = \frac{\partial E}{\partial y} y (1 - y)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of SigmoidLayer.
- */
-template <typename Dtype>
-class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
- public:
-  explicit CuDNNSigmoidLayer(const LayerParameter& param)
-      : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNSigmoidLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief TanH hyperbolic tangent non-linearity @f$
- *         y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
- *     @f$, popular in auto-encoders.
- *
- * Note that the gradient vanishes as the values move away from 0.
- * The ReLULayer is often a better choice for this reason.
- */
-template <typename Dtype>
-class TanHLayer : public NeuronLayer<Dtype> {
- public:
-  explicit TanHLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "TanH"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x}
-   *            = \frac{\partial E}{\partial y}
-   *              \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
-   *            = \frac{\partial E}{\partial y} (1 - y^2)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of TanHLayer.
- */
-template <typename Dtype>
-class CuDNNTanHLayer : public TanHLayer<Dtype> {
- public:
-  explicit CuDNNTanHLayer(const LayerParameter& param)
-      : TanHLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNTanHLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
- *        above threshold; 0 otherwise.
- */
-template <typename Dtype>
-class ThresholdLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides ThresholdParameter threshold_param,
-   *     with ThresholdLayer options:
-   *   - threshold (\b optional, default 0).
-   *     the threshold value @f$ t @f$ to which the input values are compared.
-   */
-  explicit ThresholdLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Threshold"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *       y = \left\{
-   *       \begin{array}{lr}
-   *         0 & \mathrm{if} \; x \le t \\
-   *         1 & \mathrm{if} \; x > t
-   *       \end{array} \right.
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  /// @brief Not implemented (non-differentiable function)
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    NOT_IMPLEMENTED;
-  }
-
-  Dtype threshold_;
-};
-
-/**
- * @brief Parameterized Rectified Linear Unit non-linearity @f$
- *        y_i = \max(0, x_i) + a_i \min(0, x_i)
- *        @f$. The differences from ReLULayer are 1) negative slopes are
- *        learnable though backprop and 2) negative slopes can vary across
- *        channels. The number of axes of input blob should be greater than or
- *        equal to 2. The 1st axis (0-based) is seen as channels.
- */
-template <typename Dtype>
-class PReLULayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides PReLUParameter prelu_param,
-   *     with PReLULayer options:
-   *   - filler (\b optional, FillerParameter,
-   *     default {'type': constant 'value':0.25}).
-   *   - channel_shared (\b optional, default false).
-   *     negative slopes are shared across channels.
-   */
-  explicit PReLULayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "PReLU"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times ...) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times ...) @f$
-   *      the computed outputs for each channel @f$i@f$ @f$
-   *        y_i = \max(0, x_i) + a_i \min(0, x_i)
-   *      @f$.
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the PReLU inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times ...) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times ...) @f$
-   *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
-   *      diff with gradients @f$
-   *        \frac{\partial E}{\partial x_i} = \left\{
-   *        \begin{array}{lr}
-   *            a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
-   *            \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
-   *        \end{array} \right.
-   *      @f$.
-   *      If param_propagate_down_[0] is true, it fills the diff with gradients
-   *      @f$
-   *        \frac{\partial E}{\partial a_i} = \left\{
-   *        \begin{array}{lr}
-   *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
-   *            0 & \mathrm{if} \; x_i > 0
-   *        \end{array} \right.
-   *      @f$.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool channel_shared_;
-  Blob<Dtype> multiplier_;  // dot multiplier for backward computation of params
-  Blob<Dtype> backward_buff_;  // temporary buffer for backward computation
-  Blob<Dtype> bottom_memory_;  // memory for in-place computation
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_NEURON_LAYERS_HPP_
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
deleted file mode 100644
index 237b05d6fa6..00000000000
--- a/include/caffe/vision_layers.hpp
+++ /dev/null
@@ -1,659 +0,0 @@
-#ifndef CAFFE_VISION_LAYERS_HPP_
-#define CAFFE_VISION_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief Abstract base class that factors out the BLAS code common to
- *        ConvolutionLayer and DeconvolutionLayer.
- */
-template <typename Dtype>
-class BaseConvolutionLayer : public Layer<Dtype> {
- public:
-  explicit BaseConvolutionLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
-
- protected:
-  // Helper functions that abstract away the column buffer and gemm arguments.
-  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
-  // we just called weight_cpu_gemm with the same input.
-  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
-      Dtype* output, bool skip_im2col = false);
-  void forward_cpu_bias(Dtype* output, const Dtype* bias);
-  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
-      Dtype* output);
-  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
-      weights);
-  void backward_cpu_bias(Dtype* bias, const Dtype* input);
-
-#ifndef CPU_ONLY
-  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
-      Dtype* output, bool skip_im2col = false);
-  void forward_gpu_bias(Dtype* output, const Dtype* bias);
-  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
-      Dtype* col_output);
-  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
-      weights);
-  void backward_gpu_bias(Dtype* bias, const Dtype* input);
-#endif
-
-  /// @brief The spatial dimensions of the input.
-  inline int input_shape(int i) {
-    return (*bottom_shape_)[channel_axis_ + i];
-  }
-  // reverse_dimensions should return true iff we are implementing deconv, so
-  // that conv helpers know which dimensions are which.
-  virtual bool reverse_dimensions() = 0;
-  // Compute height_out_ and width_out_ from other parameters.
-  virtual void compute_output_shape() = 0;
-
-  /// @brief The spatial dimensions of a filter kernel.
-  Blob<int> kernel_shape_;
-  /// @brief The spatial dimensions of the stride.
-  Blob<int> stride_;
-  /// @brief The spatial dimensions of the padding.
-  Blob<int> pad_;
-  /// @brief The spatial dimensions of the convolution input.
-  Blob<int> conv_input_shape_;
-  /// @brief The spatial dimensions of the col_buffer.
-  vector<int> col_buffer_shape_;
-  /// @brief The spatial dimensions of the output.
-  vector<int> output_shape_;
-  const vector<int>* bottom_shape_;
-
-  int num_spatial_axes_;
-  int bottom_dim_;
-  int top_dim_;
-
-  int channel_axis_;
-  int num_;
-  int channels_;
-  int group_;
-  int out_spatial_dim_;
-  int weight_offset_;
-  int num_output_;
-  bool bias_term_;
-  bool is_1x1_;
-  bool force_nd_im2col_;
-
- private:
-  // wrap im2col/col2im so we don't have to remember the (long) argument lists
-  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
-    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
-      im2col_cpu(data, conv_in_channels_,
-          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
-          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
-          pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
-    } else {
-      im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
-          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
-          pad_.cpu_data(), stride_.cpu_data(), col_buff);
-    }
-  }
-  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
-    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
-      col2im_cpu(col_buff, conv_in_channels_,
-          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
-          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
-          pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
-    } else {
-      col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
-          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
-          pad_.cpu_data(), stride_.cpu_data(), data);
-    }
-  }
-#ifndef CPU_ONLY
-  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
-    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
-      im2col_gpu(data, conv_in_channels_,
-          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
-          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
-          pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
-    } else {
-      im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
-          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
-          kernel_shape_.gpu_data(), pad_.gpu_data(),
-          stride_.gpu_data(), col_buff);
-    }
-  }
-  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
-    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
-      col2im_gpu(col_buff, conv_in_channels_,
-          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
-          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
-          pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
-    } else {
-      col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
-          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
-          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
-          data);
-    }
-  }
-#endif
-
-  int num_kernels_im2col_;
-  int num_kernels_col2im_;
-  int conv_out_channels_;
-  int conv_in_channels_;
-  int conv_out_spatial_dim_;
-  int kernel_dim_;
-  int col_offset_;
-  int output_offset_;
-
-  Blob<Dtype> col_buffer_;
-  Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Convolves the input image with a bank of learned filters,
- *        and (optionally) adds biases.
- *
- *   Caffe convolves by reduction to matrix multiplication. This achieves
- *   high-throughput and generality of input and filter dimensions but comes at
- *   the cost of memory for matrices. This makes use of efficiency in BLAS.
- *
- *   The input is "im2col" transformed to a channel K' x H x W data matrix
- *   for multiplication with the N x K' x H x W filter matrix to yield a
- *   N' x H x W output matrix that is then "col2im" restored. K' is the
- *   input channel * kernel height * kernel width dimension of the unrolled
- *   inputs so that the im2col matrix has a column for each input region to
- *   be filtered. col2im restores the output spatial structure by rolling up
- *   the output channel N' columns of the output matrix.
- */
-template <typename Dtype>
-class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
- public:
-  /**
-   * @param param provides ConvolutionParameter convolution_param,
-   *    with ConvolutionLayer options:
-   *  - num_output. The number of filters.
-   *  - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
-   *  kernel_size for square filters or kernel_h and kernel_w for rectangular
-   *  filters.
-   *  - stride / stride_h / stride_w (\b optional, default 1). The filter
-   *  stride, given by stride_size for equal dimensions or stride_h and stride_w
-   *  for different strides. By default the convolution is dense with stride 1.
-   *  - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
-   *  convolution, given by pad for equal dimensions or pad_h and pad_w for
-   *  different padding. Input padding is computed implicitly instead of
-   *  actually padding.
-   *  - group (\b optional, default 1). The number of filter groups. Group
-   *  convolution is a method for reducing parameterization by selectively
-   *  connecting input and output channels. The input and output channel dimensions must be divisible
-   *  by the number of groups. For group @f$ \geq 1 @f$, the
-   *  convolutional filters' input and output channels are separated s.t. each
-   *  group takes 1 / group of the input channels and makes 1 / group of the
-   *  output channels. Concretely 4 input channels, 8 output channels, and
-   *  2 groups separate input channels 1-2 and output channels 1-4 into the
-   *  first group and input channels 3-4 and output channels 5-8 into the second
-   *  group.
-   *  - bias_term (\b optional, default true). Whether to have a bias.
-   *  - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
-   *    kernels + stream parallelism) engines.
-   */
-  explicit ConvolutionLayer(const LayerParameter& param)
-      : BaseConvolutionLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "Convolution"; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual inline bool reverse_dimensions() { return false; }
-  virtual void compute_output_shape();
-};
-
-/**
- * @brief Convolve the input with a bank of learned filters, and (optionally)
- *        add biases, treating filters and convolution parameters in the
- *        opposite sense as ConvolutionLayer.
- *
- *   ConvolutionLayer computes each output value by dotting an input window with
- *   a filter; DeconvolutionLayer multiplies each input value by a filter
- *   elementwise, and sums over the resulting output windows. In other words,
- *   DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
- *   reversed. DeconvolutionLayer reuses ConvolutionParameter for its
- *   parameters, but they take the opposite sense as in ConvolutionLayer (so
- *   padding is removed from the output rather than added to the input, and
- *   stride results in upsampling rather than downsampling).
- */
-template <typename Dtype>
-class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
- public:
-  explicit DeconvolutionLayer(const LayerParameter& param)
-      : BaseConvolutionLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "Deconvolution"; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual inline bool reverse_dimensions() { return true; }
-  virtual void compute_output_shape();
-};
-
-#ifdef USE_CUDNN
-/*
- * @brief cuDNN implementation of ConvolutionLayer.
- *        Fallback to ConvolutionLayer for CPU mode.
- *
- * cuDNN accelerates convolution through forward kernels for filtering and bias
- * plus backward kernels for the gradient w.r.t. the filters, biases, and
- * inputs. Caffe + cuDNN further speeds up the computation through forward
- * parallelism across groups and backward parallelism across gradients.
- *
- * The CUDNN engine does not have memory overhead for matrix buffers. For many
- * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
- * but for fully-convolutional models and large inputs the CAFFE engine can be
- * faster as long as it fits in memory.
-*/
-template <typename Dtype>
-class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
- public:
-  explicit CuDNNConvolutionLayer(const LayerParameter& param)
-      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNConvolutionLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t* handle_;
-  cudaStream_t*  stream_;
-
-  // algorithms for forward and backwards convolutions
-  cudnnConvolutionFwdAlgo_t *fwd_algo_;
-  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
-  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
-
-  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
-  cudnnTensorDescriptor_t    bias_desc_;
-  cudnnFilterDescriptor_t      filter_desc_;
-  vector<cudnnConvolutionDescriptor_t> conv_descs_;
-  int bottom_offset_, top_offset_, bias_offset_;
-
-  size_t *workspace_fwd_sizes_;
-  size_t *workspace_bwd_data_sizes_;
-  size_t *workspace_bwd_filter_sizes_;
-  size_t workspaceSizeInBytes;  // size of underlying storage
-  void *workspaceData;  // underlying storage
-  void **workspace;  // aliases into workspaceData
-};
-#endif
-
-/**
- * @brief A helper for image operations that rearranges image regions into
- *        column vectors.  Used by ConvolutionLayer to perform convolution
- *        by matrix multiplication.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class Im2colLayer : public Layer<Dtype> {
- public:
-  explicit Im2colLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Im2col"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// @brief The spatial dimensions of a filter kernel.
-  Blob<int> kernel_shape_;
-  /// @brief The spatial dimensions of the stride.
-  Blob<int> stride_;
-  /// @brief The spatial dimensions of the padding.
-  Blob<int> pad_;
-
-  int num_spatial_axes_;
-  int bottom_dim_;
-  int top_dim_;
-
-  int channel_axis_;
-  int num_;
-  int channels_;
-
-  bool force_nd_im2col_;
-};
-
-// Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
-template <typename Dtype> class PoolingLayer;
-template <typename Dtype> class SplitLayer;
-
-/**
- * @brief Normalize the input in a local region across or within feature maps.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class LRNLayer : public Layer<Dtype> {
- public:
-  explicit LRNLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "LRN"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int size_;
-  int pre_pad_;
-  Dtype alpha_;
-  Dtype beta_;
-  Dtype k_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-
-  // Fields used for normalization ACROSS_CHANNELS
-  // scale_ stores the intermediate summing results
-  Blob<Dtype> scale_;
-
-  // Fields used for normalization WITHIN_CHANNEL
-  shared_ptr<SplitLayer<Dtype> > split_layer_;
-  vector<Blob<Dtype>*> split_top_vec_;
-  shared_ptr<PowerLayer<Dtype> > square_layer_;
-  Blob<Dtype> square_input_;
-  Blob<Dtype> square_output_;
-  vector<Blob<Dtype>*> square_bottom_vec_;
-  vector<Blob<Dtype>*> square_top_vec_;
-  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
-  Blob<Dtype> pool_output_;
-  vector<Blob<Dtype>*> pool_top_vec_;
-  shared_ptr<PowerLayer<Dtype> > power_layer_;
-  Blob<Dtype> power_output_;
-  vector<Blob<Dtype>*> power_top_vec_;
-  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
-  Blob<Dtype> product_input_;
-  vector<Blob<Dtype>*> product_bottom_vec_;
-};
-
-#ifdef USE_CUDNN
-
-template <typename Dtype>
-class CuDNNLRNLayer : public LRNLayer<Dtype> {
- public:
-  explicit CuDNNLRNLayer(const LayerParameter& param)
-      : LRNLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNLRNLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnLRNDescriptor_t norm_desc_;
-  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
-
-  int size_;
-  Dtype alpha_, beta_, k_;
-};
-
-template <typename Dtype>
-class CuDNNLCNLayer : public LRNLayer<Dtype> {
- public:
-  explicit CuDNNLCNLayer(const LayerParameter& param)
-      : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
-        tempData1(NULL), tempData2(NULL) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNLCNLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnLRNDescriptor_t norm_desc_;
-  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
-
-  int size_, pre_pad_;
-  Dtype alpha_, beta_, k_;
-
-  size_t tempDataSize;
-  void *tempData1, *tempData2;
-};
-
-#endif
-
-/**
- * @brief Pools the input image by taking the max, average, etc. within regions.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class PoolingLayer : public Layer<Dtype> {
- public:
-  explicit PoolingLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Pooling"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  // MAX POOL layers can output an extra top blob for the mask;
-  // others can only output the pooled inputs.
-  virtual inline int MaxTopBlobs() const {
-    return (this->layer_param_.pooling_param().pool() ==
-            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
-  }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int pad_h_, pad_w_;
-  int channels_;
-  int height_, width_;
-  int pooled_height_, pooled_width_;
-  bool global_pooling_;
-  Blob<Dtype> rand_idx_;
-  Blob<int> max_idx_;
-};
-
-#ifdef USE_CUDNN
-/*
- * @brief cuDNN implementation of PoolingLayer.
- *        Fallback to PoolingLayer for CPU mode.
-*/
-template <typename Dtype>
-class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
- public:
-  explicit CuDNNPoolingLayer(const LayerParameter& param)
-      : PoolingLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNPoolingLayer();
-  // Currently, cuDNN does not support the extra top blob.
-  virtual inline int MinTopBlobs() const { return -1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
-  cudnnPoolingDescriptor_t  pooling_desc_;
-  cudnnPoolingMode_t        mode_;
-};
-#endif
-
-/**
- * @brief Does spatial pyramid pooling on the input image
- *        by taking the max, average, etc. within regions
- *        so that the result vector of different sized
- *        images are of the same size.
- */
-template <typename Dtype>
-class SPPLayer : public Layer<Dtype> {
- public:
-  explicit SPPLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "SPP"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  // calculates the kernel and stride dimensions for the pooling layer,
-  // returns a correctly configured LayerParameter for a PoolingLayer
-  virtual LayerParameter GetPoolingParam(const int pyramid_level,
-      const int bottom_h, const int bottom_w, const SPPParameter spp_param);
-
-  int pyramid_height_;
-  int bottom_h_, bottom_w_;
-  int num_;
-  int channels_;
-  int kernel_h_, kernel_w_;
-  int pad_h_, pad_w_;
-  bool reshaped_first_time_;
-
-  /// the internal Split layer that feeds the pooling layers
-  shared_ptr<SplitLayer<Dtype> > split_layer_;
-  /// top vector holder used in call to the underlying SplitLayer::Forward
-  vector<Blob<Dtype>*> split_top_vec_;
-  /// bottom vector holder used in call to the underlying PoolingLayer::Forward
-  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
-  /// the internal Pooling layers of different kernel sizes
-  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
-  /// top vector holders used in call to the underlying PoolingLayer::Forward
-  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
-  /// pooling_outputs stores the outputs of the PoolingLayers
-  vector<Blob<Dtype>*> pooling_outputs_;
-  /// the internal Flatten layers that the Pooling layers feed into
-  vector<FlattenLayer<Dtype>*> flatten_layers_;
-  /// top vector holders used in call to the underlying FlattenLayer::Forward
-  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
-  /// flatten_outputs stores the outputs of the FlattenLayers
-  vector<Blob<Dtype>*> flatten_outputs_;
-  /// bottom vector holder used in call to the underlying ConcatLayer::Forward
-  vector<Blob<Dtype>*> concat_bottom_vec_;
-  /// the internal Concat layers that the Flatten layers feed into
-  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_VISION_LAYERS_HPP_
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 1a318f8d36d..69d55332841 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -15,7 +15,8 @@
 #include <fstream>  // NOLINT
 
 #include "caffe/caffe.hpp"
-#include "caffe/python_layer.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
+#include "caffe/layers/python_layer.hpp"
 #include "caffe/sgd_solvers.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
index 16378203a88..9f019bbfcb7 100644
--- a/src/caffe/data_reader.cpp
+++ b/src/caffe/data_reader.cpp
@@ -4,8 +4,8 @@
 #include <vector>
 
 #include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
 #include "caffe/data_reader.hpp"
+#include "caffe/layers/data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 417ffe986df..76d851af9a2 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -7,11 +7,28 @@
 
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
+#include "caffe/layers/conv_layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_conv_layer.hpp"
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
+#endif
 
 #ifdef WITH_PYTHON_LAYER
-#include "caffe/python_layer.hpp"
+#include "caffe/layers/python_layer.hpp"
 #endif
 
 namespace caffe {
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index 7e552352608..855bf0bfacb 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/absval_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index b5a6c25a85a..6c927e6fabc 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/absval_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index ae2df1f1bad..4eddbb5c850 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -2,7 +2,7 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/accuracy_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index 354d83f7061..2d3d6f2d3ff 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -3,7 +3,7 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/argmax_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index 316cb0fdf98..f6f14cd0f17 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -2,9 +2,9 @@
 #include <vector>
 
 #include "caffe/filler.hpp"
+#include "caffe/layers/base_conv_layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index d77f91c913b..989319f1a07 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -1,7 +1,13 @@
 #include <boost/thread.hpp>
 #include <vector>
 
-#include "caffe/data_layers.hpp"
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index ff6e412aba6..4056d36a7b4 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/base_data_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index b5c91b5e1b3..a69d8f99316 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/batch_norm_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
index 2a6cac54168..c21713c81d9 100644
--- a/src/caffe/layers/batch_norm_layer.cu
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/batch_norm_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp
index 3d3ce32c90d..b14e56f7c6b 100644
--- a/src/caffe/layers/batch_reindex_layer.cpp
+++ b/src/caffe/layers/batch_reindex_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/batch_reindex_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu
index 0b5ccf099fe..83054d36d33 100644
--- a/src/caffe/layers/batch_reindex_layer.cu
+++ b/src/caffe/layers/batch_reindex_layer.cu
@@ -2,7 +2,7 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/batch_reindex_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 1e422a54653..448d86d752d 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/bnll_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 3e328ef70fa..8df8ef09afe 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/bnll_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 14cbfb11f8b..580bd47977d 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/concat_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index e1e9449e406..a3a0bf6f6ea 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/concat_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 45facd4a4f5..599e178e9c4 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index ee2784077e4..fd7d67cca94 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index efd69d45edf..cff09783945 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index a534b3560f5..d06e4b6244e 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index c82cb7efd6c..1987fb096b0 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -2,7 +2,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_conv_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index f2df4aa502f..1990e932a70 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_conv_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp
index 4c700786e6b..9c09bf26b4d 100644
--- a/src/caffe/layers/cudnn_lcn_layer.cpp
+++ b/src/caffe/layers/cudnn_lcn_layer.cpp
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_lcn_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu
index e79c745894a..b44ef4730ef 100644
--- a/src/caffe/layers/cudnn_lcn_layer.cu
+++ b/src/caffe/layers/cudnn_lcn_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_lcn_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp
index a03db3bdd9f..0495b802baf 100644
--- a/src/caffe/layers/cudnn_lrn_layer.cpp
+++ b/src/caffe/layers/cudnn_lrn_layer.cpp
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu
index 327e44b4d31..ca647f3c64d 100644
--- a/src/caffe/layers/cudnn_lrn_layer.cu
+++ b/src/caffe/layers/cudnn_lrn_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index 5f995d45e0c..24f14780b4f 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index 9aa39ed8818..6f00195fa2d 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index e6b6d5a9715..c86c6907113 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 2a53a49b91f..9f617183baa 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 4b489fa5b16..ccb955cdaff 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 9de5c742c8e..e2a4b460c6c 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index f5cd04505b8..6440df9805b 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -3,7 +3,7 @@
 
 #include "thrust/device_vector.h"
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index c270202f0c9..7283eb71558 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -3,7 +3,7 @@
 
 #include "thrust/device_vector.h"
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 4629681807d..1a56418227c 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index 84f784b37c4..89df28a3e8b 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -1,7 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 49ac858efc9..66e6301fd45 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -5,8 +5,8 @@
 
 #include <vector>
 
-#include "caffe/data_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
+#include "caffe/data_transformer.hpp"
+#include "caffe/layers/data_layer.hpp"
 #include "caffe/util/benchmark.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index 5038b6389fa..275c05626c8 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index 0e8e2edea1e..226763223fa 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index eb7a8a9a20b..9cb64d9735f 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -2,7 +2,7 @@
 
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/dropout_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 028fc026d51..186c10ca489 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -1,11 +1,10 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/dropout_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
-
 template <typename Dtype>
 __global__ void DropoutForward(const int n, const Dtype* in,
     const unsigned int* mask, const unsigned int threshold, const float scale,
@@ -68,5 +67,4 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
 INSTANTIATE_LAYER_GPU_FUNCS(DropoutLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index ab0478c860c..e382bfea802 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -1,7 +1,7 @@
 #include <vector>
 
-#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/dummy_data_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 7924fbeec7d..21256166bfa 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -1,7 +1,7 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 01404209834..c142852e03d 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -1,7 +1,7 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp
index 52704a06d29..36b40d700fd 100644
--- a/src/caffe/layers/embed_layer.cpp
+++ b/src/caffe/layers/embed_layer.cpp
@@ -1,7 +1,7 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
index cd4b40f58fe..6324a3a8937 100644
--- a/src/caffe/layers/embed_layer.cu
+++ b/src/caffe/layers/embed_layer.cu
@@ -1,7 +1,7 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
 #include "caffe/util/gpu_util.cuh"
 #include "caffe/util/math_functions.hpp"
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 7338953d340..300d991e765 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index 1aa79bd542d..4c221b64faf 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index f85692d6c5d..1f4a309fe25 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/exp_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu
index 9e24bbeec9d..61f7f11dd46 100644
--- a/src/caffe/layers/exp_layer.cu
+++ b/src/caffe/layers/exp_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/exp_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp
index e8b62a5d5fc..e226c0b6c9b 100644
--- a/src/caffe/layers/filter_layer.cpp
+++ b/src/caffe/layers/filter_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/filter_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu
index 746e91c9e95..b01b16f840c 100644
--- a/src/caffe/layers/filter_layer.cu
+++ b/src/caffe/layers/filter_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/filter_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index d831fb5c6b5..651507e2e7d 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/flatten_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index c765fa02c82..2f13dc641df 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -14,7 +14,7 @@
 #include "hdf5_hl.h"
 #include "stdint.h"
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
 #include "caffe/util/hdf5.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 6ac499c6fbd..595d2230220 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -9,7 +9,7 @@ TODO:
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index dbde65da14f..f8f1edcd18e 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -3,7 +3,7 @@
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
 #include "caffe/util/hdf5.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index ca8f2616548..c1685cd34a7 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -3,7 +3,7 @@
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index a88c8775205..374aed3c98f 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/hinge_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index f3b0f7101b8..c12e4f52a10 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -1,7 +1,7 @@
 #include <vector>
 
+#include "caffe/layers/im2col_layer.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 4633628b4c0..517b4220cb9 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -1,7 +1,7 @@
 #include <vector>
 
+#include "caffe/layers/im2col_layer.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 9a7df5a78ca..62fda4accce 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -7,7 +7,9 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/data_layers.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/layers/image_data_layer.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 88bd8aaf54f..624d3118124 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -2,7 +2,7 @@
 #include <cmath>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/infogain_loss_layer.hpp"
 #include "caffe/util/io.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 274744eaaf8..d9088805501 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -1,7 +1,7 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index e91e94fc9c6..dc25aa33bd1 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -1,7 +1,7 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
index a1876b9da76..c70a795cf53 100644
--- a/src/caffe/layers/log_layer.cpp
+++ b/src/caffe/layers/log_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/log_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu
index 055b713bef3..db466dbac29 100644
--- a/src/caffe/layers/log_layer.cu
+++ b/src/caffe/layers/log_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/log_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index c10466dbdb0..c0b7a862181 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/loss_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index cc561811e95..210525e20f3 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -1,7 +1,7 @@
 #include <vector>
 
+#include "caffe/layers/lrn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 4523d41095d..26e619c7569 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -1,7 +1,7 @@
 #include <vector>
 
+#include "caffe/layers/lrn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 13a3d9f61b9..82909874054 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -4,7 +4,7 @@
 
 #include <vector>
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 59745923825..65664998d2c 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -2,7 +2,7 @@
 #include <cmath>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/multinomial_logistic_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 0e7301442ba..8fe4ef8c0a8 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/mvn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index b7e3b3ceb39..739293be00e 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/mvn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index 1dcb2c06419..d7b5f389310 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/neuron_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 3a7de42c91a..90897db0f45 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -2,8 +2,8 @@
 #include <cfloat>
 #include <vector>
 
+#include "caffe/layers/pooling_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 5e94ce2bc3f..1ea46cc81b1 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -2,8 +2,8 @@
 #include <cfloat>
 #include <vector>
 
+#include "caffe/layers/pooling_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 6304fadd489..d99b77ca839 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/power_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 680faad4bca..07711c4213d 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/power_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
index b5a294e1c5a..853181bd5a2 100644
--- a/src/caffe/layers/prelu_layer.cpp
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -2,7 +2,9 @@
 #include <vector>
 
 #include "caffe/filler.hpp"
-#include "caffe/neuron_layers.hpp"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index 992cd885a95..aeb80eacd03 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -1,7 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp
index 6b7925e37b0..fa46487e6a3 100644
--- a/src/caffe/layers/reduction_layer.cpp
+++ b/src/caffe/layers/reduction_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/reduction_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu
index a9a8c8d9633..4a6b2b73fc7 100644
--- a/src/caffe/layers/reduction_layer.cu
+++ b/src/caffe/layers/reduction_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/reduction_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 93d090263c7..92a729c81bd 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index c18ab61f2ed..4bf15b3aad3 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
index 8659049b528..82339f76d8f 100644
--- a/src/caffe/layers/reshape_layer.cpp
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/reshape_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 98588637831..10ac9470832 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 48dbec41be1..046cb9d3a31 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index d4a3f8773d6..85fd9676812 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -1,7 +1,7 @@
 #include <cmath>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index 5730636ef11..184c61ede83 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -1,7 +1,7 @@
 #include <cmath>
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 3974f5d4e65..b2f85c52a0f 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/silence_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index c49ecb2338a..3494f6f6731 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/silence_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index f368a249a5e..759beafe0d9 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/slice_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index d555f7d0dec..1be3a797d3e 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/slice_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 8ae7d49cfaa..f60e9b03ebf 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -1,7 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/softmax_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index a620fcc8601..7a9e6833bf6 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -4,7 +4,7 @@
 
 #include "thrust/device_vector.h"
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/softmax_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index 3cdef82afd2..dddb7606573 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -2,7 +2,7 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 4753a1ec24b..660e1b39fe0 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -2,7 +2,7 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 5333e578f76..1a27a9af0a1 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/split_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index 73d04c98fc1..bec9987c7cc 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/split_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp
index 2ef4ac7ab95..b9af8e8af0e 100644
--- a/src/caffe/layers/spp_layer.cpp
+++ b/src/caffe/layers/spp_layer.cpp
@@ -1,7 +1,12 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/vision_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
+#include "caffe/layers/flatten_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+#include "caffe/layers/spp_layer.hpp"
 
 namespace caffe {
 
@@ -217,7 +222,6 @@ void SPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   split_layer_->Backward(split_top_vec_, propagate_down, bottom);
 }
 
-
 INSTANTIATE_CLASS(SPPLayer);
 REGISTER_LAYER_CLASS(SPP);
 
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 9d1cac76184..184e926d22a 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -3,7 +3,7 @@
 
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index d87bcceced7..cbfc178e6db 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -3,7 +3,7 @@
 
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index d65147368fc..63822ee5520 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
-
+#include "caffe/layers/threshold_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index 1cd62d99482..b0b0665589f 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/threshold_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp
index 581546c4fdb..cf0c187005c 100644
--- a/src/caffe/layers/tile_layer.cpp
+++ b/src/caffe/layers/tile_layer.cpp
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/tile_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu
index fdf960901a3..282049ebd7b 100644
--- a/src/caffe/layers/tile_layer.cu
+++ b/src/caffe/layers/tile_layer.cu
@@ -1,6 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
+#include "caffe/layers/tile_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 3f937bc9dd8..4ca8315d791 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -12,7 +12,10 @@
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"
 
-#include "caffe/data_layers.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/layers/window_data_layer.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 5960a666734..6fe808bd5c5 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/accuracy_layer.hpp"
 #include "caffe/util/rng.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index f3f2094edeb..472e6652239 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/argmax_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp
index 22b9667f31b..936b93a1756 100644
--- a/src/caffe/test/test_batch_norm_layer.cpp
+++ b/src/caffe/test/test_batch_norm_layer.cpp
@@ -6,8 +6,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/batch_norm_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp
index 17e47f05066..9ea1a2f6f47 100644
--- a/src/caffe/test/test_batch_reindex_layer.cpp
+++ b/src/caffe/test/test_batch_reindex_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/batch_reindex_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 8ba51f4f7f7..23c1e8c1d29 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/concat_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index 95901f14297..2fa055ee0de 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -7,7 +7,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index b474735716d..e2d43f31b6a 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -5,7 +5,11 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_conv_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 9e03954a543..3e8d113d918 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -7,8 +7,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
index b473dbb9c51..c4b09ad555a 100644
--- a/src/caffe/test/test_deconvolution_layer.cpp
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index c9ed38db3a5..1a01ca85f89 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layers/dummy_data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index 3b56c5cae36..c06e3baab15 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
index 0f4caf15742..acd4b0f636b 100644
--- a/src/caffe/test/test_embed_layer.cpp
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 9dc14de4149..f253f9fd393 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp
index a2d0c293644..9ea2b8b2168 100644
--- a/src/caffe/test/test_filter_layer.cpp
+++ b/src/caffe/test/test_filter_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/filter_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 5d1caac2aa7..d929ac7a720 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/flatten_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index adc27df4ad0..3833ebff78e 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -5,7 +5,7 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 7169e7bfc41..8884ce95a23 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,11 +1,13 @@
 #include <string>
 #include <vector>
 
+#include "hdf5.h"
+
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 #include "caffe/test/test_caffe_main.hpp"
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index dfdd01d0291..8bf89fa6387 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/hinge_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index bafcacf784e..3f97cf6d5ae 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -5,8 +5,8 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/im2col_layer.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index ec055b20176..8274dd48971 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/im2col_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 77690245111..a4080ccd145 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -7,8 +7,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/image_data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index b2a6754fd40..a24ac683dc5 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/infogain_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index 1ad2c97e75a..b888b510318 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index bd1c4fe8810..4c97b1ae07b 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -6,7 +6,12 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/lrn_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 8fc944f3250..4f0e20ac3a7 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -5,7 +5,8 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 7269a4d441b..7998bc18262 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -5,8 +5,8 @@
 #include <string>
 #include <vector>
 
-#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 0404aa25af6..8cc21022305 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/multinomial_logistic_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index e9a7d54ce4a..28a762d2741 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -2,8 +2,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/mvn_layer.hpp"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index b333fdee802..21441b4121e 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -6,9 +6,26 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/neuron_layers.hpp"
+
+#include "caffe/layers/absval_layer.hpp"
+#include "caffe/layers/bnll_layer.hpp"
+#include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
+#include "caffe/layers/threshold_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_relu_layer.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 9e986e66528..bb95cae032d 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -5,7 +5,11 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_pooling_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 1041ddd4ee8..1aa587ac97a 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/power_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp
index a8d43727113..6ed7cda6adc 100644
--- a/src/caffe/test/test_reduction_layer.cpp
+++ b/src/caffe/test/test_reduction_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/reduction_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
index e0f4ba42851..4f2613868d4 100644
--- a/src/caffe/test/test_reshape_layer.cpp
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/reshape_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index b4f831c8f2d..5dfd7656db2 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index 45fbcffda3f..c2b231e1ef4 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/slice_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 4b01f5cfbab..94443576724 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -5,8 +5,12 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_softmax_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 0ae4cd6815a..c67f3e0d907 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -7,7 +7,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index e27e355c77e..ba2ccbb2b18 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -6,8 +6,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/split_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/insert_splits.hpp"
 
diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp
index 1b48a842d3f..59a3af2aec1 100644
--- a/src/caffe/test/test_spp_layer.cpp
+++ b/src/caffe/test/test_spp_layer.cpp
@@ -5,7 +5,12 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/concat_layer.hpp"
+#include "caffe/layers/flatten_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+#include "caffe/layers/spp_layer.hpp"
+
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 5a412bd4e17..cd5db8383ab 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/pooling_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index f31579cac40..bb8699a8e91 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 903a9bc8157..1e84cc5ab84 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/threshold_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp
index 5c459604a6b..7ff75520e8e 100644
--- a/src/caffe/test/test_tile_layer.cpp
+++ b/src/caffe/test/test_tile_layer.cpp
@@ -4,8 +4,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/tile_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
index d1d1fa864c3..058668fe28c 100644
--- a/src/caffe/util/blocking_queue.cpp
+++ b/src/caffe/util/blocking_queue.cpp
@@ -1,8 +1,8 @@
 #include <boost/thread.hpp>
 #include <string>
 
-#include "caffe/data_layers.hpp"
 #include "caffe/data_reader.hpp"
+#include "caffe/layers/base_data_layer.hpp"
 #include "caffe/parallel.hpp"
 #include "caffe/util/blocking_queue.hpp"
 
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index b94dbb980fd..1ef132660b8 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -11,7 +11,6 @@
 #include "caffe/util/db.hpp"
 #include "caffe/util/format.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 using caffe::Blob;
 using caffe::Caffe;

From 36bf811574a0787910d80d12fd9ea186481d7939 Mon Sep 17 00:00:00 2001
From: Tea <tea.desouza@gmail.com>
Date: Wed, 2 Dec 2015 15:39:19 +0800
Subject: [PATCH 1651/2053] Remove hamming_distance and popcount

---
 include/caffe/util/math_functions.hpp  |  7 ----
 src/caffe/test/test_math_functions.cpp | 41 -----------------------
 src/caffe/util/math_functions.cpp      | 22 -------------
 src/caffe/util/math_functions.cu       | 45 --------------------------
 4 files changed, 115 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 2cacd8e72cd..6f6d3feeae2 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -101,9 +101,6 @@ template <typename Dtype>
 Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
     const Dtype* y, const int incy);
 
-template <typename Dtype>
-int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y);
-
 // Returns the sum of the absolute values of the elements of vector x
 template <typename Dtype>
 Dtype caffe_cpu_asum(const int n, const Dtype* x);
@@ -234,10 +231,6 @@ void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
 template <typename Dtype>
 void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
 
-template <typename Dtype>
-uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
-                                    const Dtype* y);
-
 template <typename Dtype>
 void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
 
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index fbee3f9c32d..efc5a2784eb 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -39,27 +39,6 @@ class MathFunctionsTest : public MultiDeviceTest<TypeParam> {
     delete blob_top_;
   }
 
-  // http://en.wikipedia.org/wiki/Hamming_distance
-  int ReferenceHammingDistance(const int n, const Dtype* x, const Dtype* y) {
-    int dist = 0;
-    uint64_t val;
-    for (int i = 0; i < n; ++i) {
-      if (sizeof(Dtype) == 8) {
-        val = static_cast<uint64_t>(x[i]) ^ static_cast<uint64_t>(y[i]);
-      } else if (sizeof(Dtype) == 4) {
-        val = static_cast<uint32_t>(x[i]) ^ static_cast<uint32_t>(y[i]);
-      } else {
-        LOG(FATAL) << "Unrecognized Dtype size: " << sizeof(Dtype);
-      }
-      // Count the number of set bits
-      while (val) {
-        ++dist;
-        val &= val - 1;
-      }
-    }
-    return dist;
-  }
-
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
 };
@@ -76,14 +55,6 @@ TYPED_TEST(CPUMathFunctionsTest, TestNothing) {
   //   due to the set up overhead.
 }
 
-TYPED_TEST(CPUMathFunctionsTest, TestHammingDistance) {
-  int n = this->blob_bottom_->count();
-  const TypeParam* x = this->blob_bottom_->cpu_data();
-  const TypeParam* y = this->blob_top_->cpu_data();
-  EXPECT_EQ(this->ReferenceHammingDistance(n, x, y),
-            caffe_cpu_hamming_distance<TypeParam>(n, x, y));
-}
-
 TYPED_TEST(CPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
@@ -156,18 +127,6 @@ class GPUMathFunctionsTest : public MathFunctionsTest<GPUDevice<Dtype> > {
 
 TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes);
 
-// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(GPUMathFunctionsTest, DISABLED_TestHammingDistance) {
-  int n = this->blob_bottom_->count();
-  const TypeParam* x = this->blob_bottom_->cpu_data();
-  const TypeParam* y = this->blob_top_->cpu_data();
-  int reference_distance = this->ReferenceHammingDistance(n, x, y);
-  x = this->blob_bottom_->gpu_data();
-  y = this->blob_top_->gpu_data();
-  int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
-  EXPECT_EQ(reference_distance, computed_distance);
-}
-
 TYPED_TEST(GPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 0aab6b17b85..71c02274a75 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -348,28 +348,6 @@ float caffe_cpu_dot<float>(const int n, const float* x, const float* y);
 template
 double caffe_cpu_dot<double>(const int n, const double* x, const double* y);
 
-template <>
-int caffe_cpu_hamming_distance<float>(const int n, const float* x,
-                                  const float* y) {
-  int dist = 0;
-  for (int i = 0; i < n; ++i) {
-    dist += __builtin_popcount(static_cast<uint32_t>(x[i]) ^
-                               static_cast<uint32_t>(y[i]));
-  }
-  return dist;
-}
-
-template <>
-int caffe_cpu_hamming_distance<double>(const int n, const double* x,
-                                   const double* y) {
-  int dist = 0;
-  for (int i = 0; i < n; ++i) {
-    dist += __builtin_popcountl(static_cast<uint64_t>(x[i]) ^
-                                static_cast<uint64_t>(y[i]));
-  }
-  return dist;
-}
-
 template <>
 float caffe_cpu_asum<float>(const int n, const float* x) {
   return cblas_sasum(n, x, 1);
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index e4d0c4b04bb..4c587537435 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -371,51 +371,6 @@ DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
 
-__global__ void popc_kernel(const int n, const float* a,
-    const float* b, uint8_t* y) {
-  CUDA_KERNEL_LOOP(index, n) {
-    y[index] = __popc(static_cast<uint32_t>(a[index]) ^
-                      static_cast<uint32_t>(b[index]));
-  }
-}
-
-__global__ void popcll_kernel(const int n, const double* a,
-    const double* b, uint8_t* y) {
-  CUDA_KERNEL_LOOP(index, n) {
-    y[index] = __popcll(static_cast<uint64_t>(a[index]) ^
-                      static_cast<uint64_t>(b[index]));
-  }
-}
-
-template <>
-uint32_t caffe_gpu_hamming_distance<float>(const int n, const float* x,
-                                  const float* y) {
-  // TODO: Fix caffe_gpu_hamming_distance (see failing unit test
-  // TestHammingDistanceGPU in test_math_functions.cpp).
-  NOT_IMPLEMENTED;
-  thrust::device_vector<uint8_t> popcounts(n);
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  popc_kernel<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(
-      n, x, y, thrust::raw_pointer_cast(popcounts.data()));
-  return thrust::reduce(popcounts.begin(), popcounts.end(),
-                        (uint32_t) 0, thrust::plus<uint32_t>());
-}
-
-template <>
-uint32_t caffe_gpu_hamming_distance<double>(const int n, const double* x,
-                                   const double* y) {
-  // TODO: Fix caffe_gpu_hamming_distance (see failing unit test
-  // TestHammingDistanceGPU in test_math_functions.cpp).
-  NOT_IMPLEMENTED;
-  thrust::device_vector<uint8_t> popcounts(n);
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  popcll_kernel<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(
-      n, x, y, thrust::raw_pointer_cast(popcounts.data()));
-  return thrust::reduce(popcounts.begin(), popcounts.end(),
-                        /* NOLINT_NEXT_LINE(build/include_what_you_use) */
-                        (uint32_t) 0, thrust::plus<uint32_t>());
-}
-
 void caffe_gpu_rng_uniform(const int n, unsigned int* r) {
   CURAND_CHECK(curandGenerate(Caffe::curand_generator(), r, n));
 }

From 52dcf4801dddf05df3ddef238895cabbc6c4384a Mon Sep 17 00:00:00 2001
From: Azat <davletag@mail.ru>
Date: Thu, 3 Dec 2015 13:56:48 +0300
Subject: [PATCH 1652/2053] sigmoid fix (cu)

Previous implementation caused FP overflow for x less than -90
---
 src/caffe/layers/sigmoid_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index 184c61ede83..8a4ea6616e0 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -8,7 +8,7 @@ namespace caffe {
 template <typename Dtype>
 __global__ void SigmoidForward(const int n, const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
-    out[index] = 1. / (1. + exp(-in[index]));
+    out[index] = 0.5 * tanh(0.5 * in[index]) + 0.5;
   }
 }
 

From 0f61cc09467afa35835dc09617f1042e4f77c9fb Mon Sep 17 00:00:00 2001
From: Azat <davletag@mail.ru>
Date: Thu, 3 Dec 2015 14:00:08 +0300
Subject: [PATCH 1653/2053] sigmoid fix (cpp)

Previous implementation caused FP overflow for x less than -90
---
 src/caffe/layers/sigmoid_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 85fd9676812..f8aa769a174 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -7,7 +7,7 @@ namespace caffe {
 
 template <typename Dtype>
 inline Dtype sigmoid(Dtype x) {
-  return 1. / (1. + exp(-x));
+  return 0.5 * tanh(0.5 * x) + 0.5;
 }
 
 template <typename Dtype>

From 99571c471d493c650c53be1416bb26d5b984f178 Mon Sep 17 00:00:00 2001
From: "T.E.A de Souza" <tea.desouza@gmail.com>
Date: Sun, 29 Nov 2015 14:24:09 +0800
Subject: [PATCH 1654/2053] Correct type of device_id; disambiguate shared_ptr

---
 tools/extract_features.cpp | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 1ef132660b8..d6562f98059 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -16,7 +16,6 @@ using caffe::Blob;
 using caffe::Caffe;
 using caffe::Datum;
 using caffe::Net;
-using boost::shared_ptr;
 using std::string;
 namespace db = caffe::db;
 
@@ -51,7 +50,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   arg_pos = num_required_args;
   if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == 0) {
     LOG(ERROR)<< "Using GPU";
-    uint device_id = 0;
+    int device_id = 0;
     if (argc > arg_pos + 1) {
       device_id = atoi(argv[arg_pos + 1]);
       CHECK_GE(device_id, 0);
@@ -95,7 +94,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
    }
    */
   std::string feature_extraction_proto(argv[++arg_pos]);
-  shared_ptr<Net<Dtype> > feature_extraction_net(
+  boost::shared_ptr<Net<Dtype> > feature_extraction_net(
       new Net<Dtype>(feature_extraction_proto, caffe::TEST));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
@@ -119,15 +118,15 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
-  std::vector<shared_ptr<db::DB> > feature_dbs;
-  std::vector<shared_ptr<db::Transaction> > txns;
+  std::vector<boost::shared_ptr<db::DB> > feature_dbs;
+  std::vector<boost::shared_ptr<db::Transaction> > txns;
   const char* db_type = argv[++arg_pos];
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening dataset " << dataset_names[i];
-    shared_ptr<db::DB> db(db::GetDB(db_type));
+    boost::shared_ptr<db::DB> db(db::GetDB(db_type));
     db->Open(dataset_names.at(i), db::NEW);
     feature_dbs.push_back(db);
-    shared_ptr<db::Transaction> txn(db->NewTransaction());
+    boost::shared_ptr<db::Transaction> txn(db->NewTransaction());
     txns.push_back(txn);
   }
 
@@ -139,8 +138,8 @@ int feature_extraction_pipeline(int argc, char** argv) {
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
     for (int i = 0; i < num_features; ++i) {
-      const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
-          ->blob_by_name(blob_names[i]);
+      const boost::shared_ptr<Blob<Dtype> > feature_blob =
+        feature_extraction_net->blob_by_name(blob_names[i]);
       int batch_size = feature_blob->num();
       int dim_features = feature_blob->count() / batch_size;
       const Dtype* feature_blob_data;

From a6681945be4736a584adadfaf2bffe43ad31422e Mon Sep 17 00:00:00 2001
From: Mohamed Omran <mohamed.omran@gmail.com>
Date: Thu, 26 Nov 2015 01:46:42 +0100
Subject: [PATCH 1655/2053] ELU layer with basic tests

---
 include/caffe/layers/elu_layer.hpp   | 86 ++++++++++++++++++++++++++++
 src/caffe/layers/elu_layer.cpp       | 47 +++++++++++++++
 src/caffe/layers/elu_layer.cu        | 62 ++++++++++++++++++++
 src/caffe/proto/caffe.proto          | 11 +++-
 src/caffe/test/test_neuron_layer.cpp | 59 +++++++++++++++++++
 5 files changed, 264 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/elu_layer.hpp
 create mode 100644 src/caffe/layers/elu_layer.cpp
 create mode 100644 src/caffe/layers/elu_layer.cu

diff --git a/include/caffe/layers/elu_layer.hpp b/include/caffe/layers/elu_layer.hpp
new file mode 100644
index 00000000000..0796e898007
--- /dev/null
+++ b/include/caffe/layers/elu_layer.hpp
@@ -0,0 +1,86 @@
+#ifndef CAFFE_ELU_LAYER_HPP_
+#define CAFFE_ELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Exponential Linear Unit non-linearity @f$
+ *        y = \left\{
+ *        \begin{array}{lr}
+ *            x                  & \mathrm{if} \; x > 0 \\
+ *            \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0
+ *        \end{array} \right.
+ *      @f$.  
+ */
+template <typename Dtype>
+class ELULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ELUParameter elu_param,
+   *     with ELULayer options:
+   *   - alpha (\b optional, default 1).
+   *     the value @f$ \alpha @f$ by which controls saturation for negative inputs.
+   */
+  explicit ELULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "ELU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \left\{
+   *        \begin{array}{lr}
+   *            x                  & \mathrm{if} \; x > 0 \\
+   *            \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0
+   *        \end{array} \right.
+   *      @f$.  
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the ELU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            1           & \mathrm{if} \; x > 0 \\
+   *            y + \alpha  & \mathrm{if} \; x \le 0
+   *        \end{array} \right.
+   *      @f$ if propagate_down[0].
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_ELU_LAYER_HPP_
diff --git a/src/caffe/layers/elu_layer.cpp b/src/caffe/layers/elu_layer.cpp
new file mode 100644
index 00000000000..a0f87635a5a
--- /dev/null
+++ b/src/caffe/layers/elu_layer.cpp
@@ -0,0 +1,47 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/elu_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ELULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  Dtype alpha = this->layer_param_.elu_param().alpha();
+  for (int i = 0; i < count; ++i) {
+    top_data[i] = std::max(bottom_data[i], Dtype(0))
+        + alpha * (exp(std::min(bottom_data[i], Dtype(0))) - Dtype(1));
+  }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* top_data = top[0]->cpu_data();
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
+    Dtype alpha = this->layer_param_.elu_param().alpha();
+    for (int i = 0; i < count; ++i) {
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+          + (alpha + top_data[i]) * (bottom_data[i] <= 0));
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(ELULayer);
+#endif
+
+INSTANTIATE_CLASS(ELULayer);
+REGISTER_LAYER_CLASS(ELU);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/elu_layer.cu b/src/caffe/layers/elu_layer.cu
new file mode 100644
index 00000000000..12545aa8253
--- /dev/null
+++ b/src/caffe/layers/elu_layer.cu
@@ -0,0 +1,62 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/elu_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ELUForward(const int n, const Dtype* in, Dtype* out,
+    Dtype alpha) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out[index] = in[index] > 0 ? in[index] :
+        alpha * (exp(in[index]) - 1);
+  }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  Dtype alpha = this->layer_param_.elu_param().alpha();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ELUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, top_data, alpha);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void ELUBackward(const int n, const Dtype* in_diff,
+    const Dtype* out_data, const Dtype* in_data,
+    Dtype* out_diff, Dtype alpha) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out_diff[index] = in_data[index] > 0 ? in_diff[index] :
+        in_diff[index] * (out_data[index] + alpha);
+  }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* top_data = top[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
+    Dtype alpha = this->layer_param_.elu_param().alpha();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    ELUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, top_data, bottom_data, bottom_diff, alpha);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(ELULayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 787369f7cff..1daf148d3e4 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 140 (last added: batch_norm_param)
+// LayerParameter next available layer-specific ID: 141 (last added: elu_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -363,6 +363,7 @@ message LayerParameter {
   optional DropoutParameter dropout_param = 108;
   optional DummyDataParameter dummy_data_param = 109;
   optional EltwiseParameter eltwise_param = 110;
+  optional ELUParameter elu_param = 140;
   optional EmbedParameter embed_param = 137;
   optional ExpParameter exp_param = 111;
   optional FlattenParameter flatten_param = 135;
@@ -629,6 +630,14 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
+// Message that stores parameters used by ELULayer
+message ELUParameter {
+  // Described in:
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate 
+  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+  optional float alpha = 1 [default = 1];
+}
+
 // Message that stores parameters used by EmbedLayer
 message EmbedParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 21441b4121e..dd591f7d204 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -11,6 +11,7 @@
 #include "caffe/layers/absval_layer.hpp"
 #include "caffe/layers/bnll_layer.hpp"
 #include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/elu_layer.hpp"
 #include "caffe/layers/exp_layer.hpp"
 #include "caffe/layers/inner_product_layer.hpp"
 #include "caffe/layers/log_layer.hpp"
@@ -259,6 +260,64 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(NeuronLayerTest, TestELU) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "elu_param { alpha: 0.5 }", &layer_param));
+  ELULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype kDelta = 2e-4;
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    if (bottom_data[i] > 0) {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+    } else {
+      EXPECT_NEAR(top_data[i], 0.5 * (exp(bottom_data[i]) - 1), kDelta);
+    }
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUasReLU) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "elu_param { alpha: 0 }", &layer_param));
+  ELULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ELULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUasReLUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "elu_param { alpha: 0 }", &layer_param));
+  ELULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From b13bda2fcc984ce916d4911c90f5466056f25092 Mon Sep 17 00:00:00 2001
From: Ian Hunter <ianfhunter@gmail.com>
Date: Wed, 9 Dec 2015 13:29:01 +0000
Subject: [PATCH 1656/2053] Update interfaces.md

typo
---
 docs/tutorial/interfaces.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 9006179d0f1..d7ff378239d 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -61,7 +61,7 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
 
 The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.
 
-- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classsifier` and `caffe.Detector` provide convenience interfaces for common tasks.
+- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classifier` and `caffe.Detector` provide convenience interfaces for common tasks.
 - `caffe.SGDSolver` exposes the solving interface.
 - `caffe.io` handles input / output with preprocessing and protocol buffers.
 - `caffe.draw` visualizes network architectures.

From eb2b848df173f7a07eb0d76a432c5d4badca7ba6 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Thu, 10 Dec 2015 15:11:51 -0800
Subject: [PATCH 1657/2053] Fix CuDNNConvolutionLayer for cuDNN v4

Add a macro to check the current cuDNN version
---
 include/caffe/util/cudnn.hpp         | 3 +++
 src/caffe/layers/cudnn_conv_layer.cu | 8 ++++++++
 2 files changed, 11 insertions(+)

diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index b531dd5fa7a..8a7e17c6cd4 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -7,6 +7,9 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+#define CUDNN_VERSION_MIN(major, minor, patch) \
+    (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
+
 #define CUDNN_CHECK(condition) \
   do { \
     cudnnStatus_t status = condition; \
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 1990e932a70..42c4fd0260c 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -30,11 +30,19 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
+#if CUDNN_VERSION_MIN(4, 0, 0)
+        CUDNN_CHECK(cudnnAddTensor(handle_[g],
+              cudnn::dataType<Dtype>::one,
+              bias_desc_, bias_data + bias_offset_ * g,
+              cudnn::dataType<Dtype>::one,
+              top_descs_[i], top_data + top_offset_ * g));
+#else
         CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
               cudnn::dataType<Dtype>::one,
               bias_desc_, bias_data + bias_offset_ * g,
               cudnn::dataType<Dtype>::one,
               top_descs_[i], top_data + top_offset_ * g));
+#endif
       }
     }
 

From 12f85982c4599734a43d95e2c4b7565aad410530 Mon Sep 17 00:00:00 2001
From: Jacek Czaja <jacek.czaja@intel.com>
Date: Mon, 14 Dec 2015 16:45:59 +0100
Subject: [PATCH 1658/2053] - Fix to cmake build for clang

---
 CMakeLists.txt      |  2 ++
 cmake/Targets.cmake | 19 ++++++++++---------
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c446c608952..f1ab1936f86 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,6 +42,8 @@ if(UNIX OR APPLE)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
+caffe_set_caffe_link()
+
 if(USE_libstdcpp)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
   message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index 2401f252e93..a796d00548f 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -1,16 +1,17 @@
 ################################################################################################
 # Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
 # some objects which are not addressed directly but are registered via static constructors
-if(BUILD_SHARED_LIBS)
-  set(Caffe_LINK caffe)
-else()
-  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
-    set(Caffe_LINK -Wl,-force_load caffe)
-  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+macro(caffe_set_caffe_link)
+  if(BUILD_SHARED_LIBS)
+    set(Caffe_LINK caffe)
+  else()
+    if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+      set(Caffe_LINK -Wl,-force_load caffe)
+    elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+      set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+    endif()
   endif()
-endif()
-
+endmacro()
 ################################################################################################
 # Convenient command to setup source group for IDEs that support this feature (VS, XCode)
 # Usage:

From f19896ccca23f091abb82d77a2f281a9c954a147 Mon Sep 17 00:00:00 2001
From: Muneyuki Noguchi <nogu.dev@gmail.com>
Date: Sun, 20 Dec 2015 19:12:09 +0900
Subject: [PATCH 1659/2053] Replace blobs_lr with lr_mult in readme.md.

models/finetune_flickr_style/deploy.prototxt uses lr_mult now.
---
 examples/finetune_flickr_style/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index ecb9d3d2e6d..4e9d41f13cc 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -22,10 +22,10 @@ Because we are predicting 20 classes instead of a 1,000, we do need to change th
 Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
 Since there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights.
 
-We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
+We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `lr_mult` on the newly introduced layer.
 The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.
 Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
-Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
+Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `lr_mult` to 0.
 
 ## Procedure
 

From 93bfcb53120416255d6d7261b638f0b38ff9e9bf Mon Sep 17 00:00:00 2001
From: Fisher Yu <i@yf.io>
Date: Sun, 6 Dec 2015 20:04:43 -0500
Subject: [PATCH 1660/2053] add support for 2D dilated convolution

---
 include/caffe/layers/base_conv_layer.hpp  | 14 ++--
 include/caffe/layers/conv_layer.hpp       |  3 +
 include/caffe/layers/im2col_layer.hpp     |  2 +
 include/caffe/util/im2col.hpp             | 12 ++--
 src/caffe/layer_factory.cpp               | 17 ++++-
 src/caffe/layers/base_conv_layer.cpp      | 20 +++++-
 src/caffe/layers/conv_layer.cpp           |  4 +-
 src/caffe/layers/im2col_layer.cpp         | 21 +++++-
 src/caffe/layers/im2col_layer.cu          |  2 +
 src/caffe/proto/caffe.proto               |  1 +
 src/caffe/test/test_convolution_layer.cpp | 14 ++--
 src/caffe/test/test_im2col_kernel.cu      | 17 ++++-
 src/caffe/test/test_im2col_layer.cpp      |  3 +-
 src/caffe/util/im2col.cpp                 | 34 ++++++----
 src/caffe/util/im2col.cu                  | 80 ++++++++++++-----------
 15 files changed, 170 insertions(+), 74 deletions(-)

diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp
index f3def16c039..db471b586da 100644
--- a/include/caffe/layers/base_conv_layer.hpp
+++ b/include/caffe/layers/base_conv_layer.hpp
@@ -68,6 +68,8 @@ class BaseConvolutionLayer : public Layer<Dtype> {
   Blob<int> stride_;
   /// @brief The spatial dimensions of the padding.
   Blob<int> pad_;
+  /// @brief The spatial dimensions of the dilation.
+  Blob<int> dilation_;
   /// @brief The spatial dimensions of the convolution input.
   Blob<int> conv_input_shape_;
   /// @brief The spatial dimensions of the col_buffer.
@@ -99,7 +101,8 @@ class BaseConvolutionLayer : public Layer<Dtype> {
           conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
     } else {
       im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
           col_buffer_shape_.data(), kernel_shape_.cpu_data(),
@@ -112,7 +115,8 @@ class BaseConvolutionLayer : public Layer<Dtype> {
           conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
     } else {
       col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
           col_buffer_shape_.data(), kernel_shape_.cpu_data(),
@@ -126,7 +130,8 @@ class BaseConvolutionLayer : public Layer<Dtype> {
           conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
     } else {
       im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
           conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
@@ -140,7 +145,8 @@ class BaseConvolutionLayer : public Layer<Dtype> {
           conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
-          stride_.cpu_data()[0], stride_.cpu_data()[1], data);
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
     } else {
       col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
           conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
diff --git a/include/caffe/layers/conv_layer.hpp b/include/caffe/layers/conv_layer.hpp
index 15574766de5..93a618ddd72 100644
--- a/include/caffe/layers/conv_layer.hpp
+++ b/include/caffe/layers/conv_layer.hpp
@@ -44,6 +44,9 @@ class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
    *  convolution, given by pad for equal dimensions or pad_h and pad_w for
    *  different padding. Input padding is computed implicitly instead of
    *  actually padding.
+   *  - dilation (\b optional, default 1). The filter
+   *  dilation, given by dilation_size for equal dimensions for different
+   *  dilation. By default the convolution has dilation 1.
    *  - group (\b optional, default 1). The number of filter groups. Group
    *  convolution is a method for reducing parameterization by selectively
    *  connecting input and output channels. The input and output channel dimensions must be divisible
diff --git a/include/caffe/layers/im2col_layer.hpp b/include/caffe/layers/im2col_layer.hpp
index 1d3b2eb67d1..71e32f7427f 100644
--- a/include/caffe/layers/im2col_layer.hpp
+++ b/include/caffe/layers/im2col_layer.hpp
@@ -46,6 +46,8 @@ class Im2colLayer : public Layer<Dtype> {
   Blob<int> stride_;
   /// @brief The spatial dimensions of the padding.
   Blob<int> pad_;
+  /// @brief The spatial dimensions of the dilation.
+  Blob<int> dilation_;
 
   int num_spatial_axes_;
   int bottom_dim_;
diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index d3eb6ccd6fc..748b65c4f36 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -13,7 +13,8 @@ template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_col);
 
 template <typename Dtype>
 void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
@@ -25,7 +26,8 @@ template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_im);
 
 template <typename Dtype>
 void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
@@ -37,7 +39,8 @@ template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_col);
 
 template <typename Dtype>
 void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
@@ -49,7 +52,8 @@ template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_im);
 
 }  // namespace caffe
 
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 76d851af9a2..6b1d1c1a5f5 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -37,17 +37,30 @@ namespace caffe {
 template <typename Dtype>
 shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     const LayerParameter& param) {
-  ConvolutionParameter_Engine engine = param.convolution_param().engine();
+  ConvolutionParameter conv_param = param.convolution_param();
+  ConvolutionParameter_Engine engine = conv_param.engine();
+  bool use_dilation = false;
+  for (int i = 0; i < conv_param.dilation_size(); ++i) {
+    if (conv_param.dilation(i) > 1) {
+      use_dilation = true;
+    }
+  }
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
     engine = ConvolutionParameter_Engine_CAFFE;
 #ifdef USE_CUDNN
-    engine = ConvolutionParameter_Engine_CUDNN;
+    if (!use_dilation) {
+      engine = ConvolutionParameter_Engine_CUDNN;
+    }
 #endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
     return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ConvolutionParameter_Engine_CUDNN) {
+    if (use_dilation) {
+      LOG(FATAL) << "CuDNN doesn't support the dilated convolution at Layer "
+                 << param.name();
+    }
     return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
 #endif
   } else {
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index f6f14cd0f17..4a4c68e009a 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -36,7 +36,7 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
         << "kernel_size must be specified once, or once per spatial dimension "
         << "(kernel_size specified " << num_kernel_dims << " times; "
-        << num_spatial_axes_ << " spatial dims);";
+        << num_spatial_axes_ << " spatial dims).";
       for (int i = 0; i < num_spatial_axes_; ++i) {
         kernel_shape_data[i] =
             conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
@@ -61,7 +61,7 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
           num_stride_dims == num_spatial_axes_)
         << "stride must be specified once, or once per spatial dimension "
         << "(stride specified " << num_stride_dims << " times; "
-        << num_spatial_axes_ << " spatial dims);";
+        << num_spatial_axes_ << " spatial dims).";
     const int kDefaultStride = 1;
     for (int i = 0; i < num_spatial_axes_; ++i) {
       stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
@@ -85,13 +85,27 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
           num_pad_dims == num_spatial_axes_)
         << "pad must be specified once, or once per spatial dimension "
         << "(pad specified " << num_pad_dims << " times; "
-        << num_spatial_axes_ << " spatial dims);";
+        << num_spatial_axes_ << " spatial dims).";
     const int kDefaultPad = 0;
     for (int i = 0; i < num_spatial_axes_; ++i) {
       pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
           conv_param.pad((num_pad_dims == 1) ? 0 : i);
     }
   }
+  // Setup dilation dimensions (dilation_).
+  dilation_.Reshape(spatial_dim_blob_shape);
+  int* dilation_data = dilation_.mutable_cpu_data();
+  const int num_dilation_dims = conv_param.dilation_size();
+  CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||
+        num_dilation_dims == num_spatial_axes_)
+      << "dilation must be specified once, or once per spatial dimension "
+      << "(dilation specified " << num_dilation_dims << " times; "
+      << num_spatial_axes_ << " spatial dims).";
+  const int kDefaultDilation = 1;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
+                       conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
+  }
   // Special case: im2col is the identity for 1x1 convolution with stride 1
   // and no padding, so flag for skipping the buffer and transformation.
   is_1x1_ = true;
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index cff09783945..5d522ab31f2 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -9,11 +9,13 @@ void ConvolutionLayer<Dtype>::compute_output_shape() {
   const int* kernel_shape_data = this->kernel_shape_.cpu_data();
   const int* stride_data = this->stride_.cpu_data();
   const int* pad_data = this->pad_.cpu_data();
+  const int* dilation_data = this->dilation_.cpu_data();
   this->output_shape_.clear();
   for (int i = 0; i < this->num_spatial_axes_; ++i) {
     // i + 1 to skip channel axis
     const int input_dim = this->input_shape(i + 1);
-    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i])
+    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
         / stride_data[i] + 1;
     this->output_shape_.push_back(output_dim);
   }
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index c12e4f52a10..19ae3019593 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -87,6 +87,20 @@ void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
           conv_param.pad((num_pad_dims == 1) ? 0 : i);
     }
   }
+  // Setup dilation dimensions (dilation_).
+  dilation_.Reshape(dim_blob_shape);
+  int* dilation_data = dilation_.mutable_cpu_data();
+  const int num_dilation_dims = conv_param.dilation_size();
+  CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||
+        num_dilation_dims == num_spatial_axes_)
+      << "dilation must be specified once, or once per spatial dimension "
+      << "(dilation specified " << num_dilation_dims << " times; "
+      << num_spatial_axes_ << " spatial dims).";
+  const int kDefaultDilation = 1;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
+                       conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
+  }
 }
 
 template <typename Dtype>
@@ -96,10 +110,12 @@ void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   const int* kernel_shape_data = kernel_shape_.cpu_data();
   const int* stride_data = stride_.cpu_data();
   const int* pad_data = pad_.cpu_data();
+  const int* dilation_data = dilation_.cpu_data();
   for (int i = 0; i < num_spatial_axes_; ++i) {
     top_shape[channel_axis_] *= kernel_shape_data[i];
     const int input_dim = bottom[0]->shape(channel_axis_ + i + 1);
-    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i])
+    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
         / stride_data[i] + 1;
     top_shape[channel_axis_ + i + 1] = output_dim;
   }
@@ -122,6 +138,7 @@ void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_);
     DCHECK_EQ(pad_.count(), num_spatial_axes_);
     DCHECK_EQ(stride_.count(), num_spatial_axes_);
+    DCHECK_EQ(dilation_.count(), num_spatial_axes_);
     if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
       im2col_cpu(bottom_data + n * bottom_dim_, channels_,
           bottom[0]->shape(channel_axis_ + 1),
@@ -129,6 +146,7 @@ void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
           stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
           top_data + n * top_dim_);
     } else {
       im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
@@ -153,6 +171,7 @@ void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
           stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
           bottom_diff + n * bottom_dim_);
     } else {
       col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_,
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 517b4220cb9..d90075d4304 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -19,6 +19,7 @@ void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
           stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
           top_data + n * top_dim_);
     } else {
       im2col_nd_gpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
@@ -43,6 +44,7 @@ void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
           pad_.cpu_data()[0], pad_.cpu_data()[1],
           stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
           bottom_diff + n * bottom_dim_);
     } else {
       col2im_nd_gpu(top_diff + n * top_dim_, num_spatial_axes_, bottom_dim_,
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 787369f7cff..87c46629baf 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -518,6 +518,7 @@ message ConvolutionParameter {
   repeated uint32 pad = 3; // The padding size; defaults to 0
   repeated uint32 kernel_size = 4; // The kernel size
   repeated uint32 stride = 6; // The stride; defaults to 1
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
 
   // For 2D convolution only, the *_h and *_w versions may also be used to
   // specify both spatial dimensions.
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index e2d43f31b6a..95c3c80c59d 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -46,13 +46,17 @@ void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
   } else {
     stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1;
   }
-  int kernel_d, pad_d, stride_d;
+  int dilation_h, dilation_w;
+  dilation_h = dilation_w = conv_param->dilation_size() ?
+                            conv_param->dilation(0) : 1;
+  int kernel_d, pad_d, stride_d, dilation_d;
   if (has_depth) {
     kernel_d = kernel_h;
     stride_d = stride_h;
     pad_d = pad_h;
+    dilation_d = dilation_h;
   } else {
-    kernel_d = stride_d = 1;
+    kernel_d = stride_d = dilation_d = 1;
     pad_d = 0;
   }
   // Groups
@@ -77,9 +81,9 @@ void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
                 for (int r = 0; r < kernel_d; r++) {
                   for (int p = 0; p < kernel_h; p++) {
                     for (int q = 0; q < kernel_w; q++) {
-                      int in_z = z * stride_d - pad_d + r;
-                      int in_y = y * stride_h - pad_h + p;
-                      int in_x = x * stride_w - pad_w + q;
+                      int in_z = z * stride_d - pad_d + r * dilation_d;
+                      int in_y = y * stride_h - pad_h + p * dilation_h;
+                      int in_x = x * stride_w - pad_w + q * dilation_w;
                       if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1)
                           && in_y >= 0 && in_y < in->shape(2 + has_depth)
                           && in_x >= 0 && in_x < in->shape(3 + has_depth)) {
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 3f97cf6d5ae..15e06aa8583 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -18,6 +18,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     const int height_col, const int width_col,
     Dtype* data_col);
 
@@ -38,6 +39,7 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
         blob_kernel_shape_(new Blob<int>()),
         blob_stride_(new Blob<int>()),
         blob_pad_(new Blob<int>()),
+        blob_dilation_(new Blob<int>()),
         blob_top_(new Blob<Dtype>()),
         blob_top_cpu_(new Blob<Dtype>()) {
     FillerParameter filler_param;
@@ -47,20 +49,25 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
     blob_kernel_shape_->Reshape(dim_blob_shape);
     blob_stride_->Reshape(dim_blob_shape);
     blob_pad_->Reshape(dim_blob_shape);
+    blob_dilation_->Reshape(dim_blob_shape);
 
     height_ = blob_bottom_->height();
     width_ = blob_bottom_->width();
     channels_ = blob_bottom_->channels();
     pad_ = 0;
     stride_ = 2;
+    dilation_ = 1;
     kernel_size_ = 3;
-    height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
-    width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+    height_col_ = (height_ + 2 * pad_ -
+        (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
+    width_col_ = (width_ + 2 * pad_ -
+        (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
 
     for (int i = 0; i < 2; ++i) {
       blob_kernel_shape_->mutable_cpu_data()[i] = kernel_size_;
       blob_stride_->mutable_cpu_data()[i] = stride_;
       blob_pad_->mutable_cpu_data()[i] = pad_;
+      blob_dilation_->mutable_cpu_data()[i] = dilation_;
     }
   }
 
@@ -71,11 +78,13 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
     delete blob_kernel_shape_;
     delete blob_stride_;
     delete blob_pad_;
+    delete blob_dilation_;
   }
 
   Blob<int>* const blob_kernel_shape_;
   Blob<int>* const blob_stride_;
   Blob<int>* const blob_pad_;
+  Blob<int>* const blob_dilation_;
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
   Blob<Dtype>* const blob_top_cpu_;
@@ -84,6 +93,7 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
   int channels_;
   int pad_;
   int stride_;
+  int dilation_;
   int kernel_size_;
   int height_col_;
   int width_col_;
@@ -112,7 +122,7 @@ TYPED_TEST(Im2colKernelTest, Test2D) {
     im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n),
       this->channels_, this->height_, this->width_,
       this->kernel_size_, this->kernel_size_, this->pad_, this->pad_,
-      this->stride_, this->stride_,
+      this->stride_, this->stride_, this->dilation_, this->dilation_,
       cpu_data + this->blob_top_cpu_->offset(n));
   }
 
@@ -129,6 +139,7 @@ TYPED_TEST(Im2colKernelTest, Test2D) {
         num_kernels, bottom_data + this->blob_bottom_->offset(n),
         this->height_, this->width_, this->kernel_size_, this->kernel_size_,
         this->pad_, this->pad_, this->stride_, this->stride_,
+        this->dilation_, this->dilation_,
         this->height_col_, this->width_col_,
         top_data + this->blob_top_->offset(n));
       CUDA_POST_KERNEL_CHECK;
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 8274dd48971..932d3f21ae9 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -17,7 +17,7 @@ class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
  protected:
   Im2colLayerTest()
-      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+      : blob_bottom_(new Blob<Dtype>(2, 3, 10, 9)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
     Caffe::set_random_seed(1701);
@@ -75,6 +75,7 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
       layer_param.mutable_convolution_param();
   convolution_param->add_kernel_size(3);
   convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 27e5b7c0928..1e578e7c9dc 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -10,9 +10,12 @@ void im2col_cpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     Dtype* data_col) {
-  const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  const int height_col = (height + 2 * pad_h -
+      (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int width_col = (width + 2 * pad_w -
+      (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
   const int channels_col = channels * kernel_h * kernel_w;
   for (int c_col = 0; c_col < channels_col; ++c_col) {
     int w_offset = c_col % kernel_w;
@@ -20,8 +23,8 @@ void im2col_cpu(const Dtype* data_im, const int channels,
     int c_im = c_col / kernel_h / kernel_w;
     for (int h_col = 0; h_col < height_col; ++h_col) {
       for (int w_col = 0; w_col < width_col; ++w_col) {
-        int h_im = h_col * stride_h - pad_h + h_offset;
-        int w_im = w_col * stride_w - pad_w + w_offset;
+        int h_im = h_col * stride_h - pad_h + h_offset * dilation_h;
+        int w_im = w_col * stride_w - pad_w + w_offset * dilation_w;
         data_col[(c_col * height_col + h_col) * width_col + w_col] =
             (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
             data_im[(c_im * height + h_im) * width + w_im] : 0;
@@ -34,11 +37,13 @@ void im2col_cpu(const Dtype* data_im, const int channels,
 template void im2col_cpu<float>(const float* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, float* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    float* data_col);
 template void im2col_cpu<double>(const double* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, double* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    double* data_col);
 
 template <typename Dtype>
 inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
@@ -137,10 +142,13 @@ void col2im_cpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
-  const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  const int height_col = (height + 2 * pad_h -
+      (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int width_col = (width + 2 * pad_w -
+      (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
   const int channels_col = channels * kernel_h * kernel_w;
   for (int c_col = 0; c_col < channels_col; ++c_col) {
     int w_offset = c_col % kernel_w;
@@ -148,8 +156,8 @@ void col2im_cpu(const Dtype* data_col, const int channels,
     int c_im = c_col / kernel_h / kernel_w;
     for (int h_col = 0; h_col < height_col; ++h_col) {
       for (int w_col = 0; w_col < width_col; ++w_col) {
-        int h_im = h_col * stride_h - pad_h + h_offset;
-        int w_im = w_col * stride_w - pad_w + w_offset;
+        int h_im = h_col * stride_h - pad_h + h_offset * dilation_h;
+        int w_im = w_col * stride_w - pad_w + w_offset * dilation_w;
         if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width)
           data_im[(c_im * height + h_im) * width + w_im] +=
               data_col[(c_col * height_col + h_col) * width_col + w_col];
@@ -162,11 +170,13 @@ void col2im_cpu(const Dtype* data_col, const int channels,
 template void col2im_cpu<float>(const float* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, float* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    float* data_im);
 template void col2im_cpu<double>(const double* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, double* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    double* data_im);
 
 template <typename Dtype>
 void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 49354ab7aa1..cdcaac5bcc5 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -10,6 +10,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
@@ -26,11 +27,11 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     data_im_ptr += (c_im * height + h_offset) * width + w_offset;
     for (int i = 0; i < kernel_h; ++i) {
       for (int j = 0; j < kernel_w; ++j) {
-        int h_im = h_offset + i;
-        int w_im = w_offset + j;
+        int h_im = h_offset + i * dilation_h;
+        int w_im = w_offset + j * dilation_w;
         *data_col_ptr =
             (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
-            data_im_ptr[i * width + j] : 0;
+            data_im_ptr[i * dilation_h * width + j * dilation_w] : 0;
         data_col_ptr += height_col * width_col;
       }
     }
@@ -42,17 +43,20 @@ void im2col_gpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     Dtype* data_col) {
   // We are going to launch channels * height_col * width_col kernels, each
   // kernel responsible for copying a single-channel grid.
-  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  int height_col = (height + 2 * pad_h -
+      (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  int width_col = (width + 2 * pad_w -
+      (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
   int num_kernels = channels * height_col * width_col;
   // NOLINT_NEXT_LINE(whitespace/operators)
   im2col_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
       num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h,
-      pad_w, stride_h, stride_w, height_col,
+      pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col,
       width_col, data_col);
   CUDA_POST_KERNEL_CHECK;
 }
@@ -61,11 +65,11 @@ void im2col_gpu(const Dtype* data_im, const int channels,
 template void im2col_gpu<float>(const float* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    float* data_col);
+    const int dilation_h, const int dilation_w, float* data_col);
 template void im2col_gpu<double>(const double* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    double* data_col);
+    const int dilation_h, const int dilation_w, double* data_col);
 
 template <typename Dtype, int num_axes>
 __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
@@ -223,6 +227,7 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     const int height_col, const int width_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
@@ -230,33 +235,27 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     const int w_im = index % width + pad_w;
     const int h_im = (index / width) % height + pad_h;
     const int c_im = index / (width * height);
+    int kernel_extent_w = (kernel_w - 1) * dilation_w + 1;
+    int kernel_extent_h = (kernel_h - 1) * dilation_h + 1;
     // compute the start and end of the output
     const int w_col_start =
-        (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1;
-    const int w_col_end =
-        min(w_im / stride_w + 1, width_col);
+        (w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1;
+    const int w_col_end = min(w_im / stride_w + 1, width_col);
     const int h_col_start =
-        (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1;
-    const int h_col_end =
-        min(h_im / stride_h + 1, height_col);
-    /*
-    for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
-      for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
-        // the col location: [c * width * height + h_out, w_out]
-        int c_col = c_im * kernel_h * kernel_w
-            + (h_im - h_col * stride_h) * kernel_w + (w_im - w_col * stride_w);
-        val += data_col[(c_col * height_col + h_col) * width_col + w_col];
-      }
-    }
-    */
-    // equivalent implementation
-    int offset = (c_im * kernel_h * kernel_w + h_im * kernel_w + w_im)
-        * height_col * width_col;
-    int coeff_h_col = (1 - stride_h * kernel_w * height_col) * width_col;
-    int coeff_w_col = (1 - stride_w * height_col * width_col);
-    for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
-      for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
-        val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
+        (h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1;
+    const int h_col_end = min(h_im / stride_h + 1, height_col);
+    // TODO: use LCM of stride and dilation to avoid unnecessary loops
+    for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) {
+      for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) {
+        int h_k = (h_im - h_col * stride_h);
+        int w_k = (w_im - w_col * stride_w);
+        if (h_k % dilation_h == 0 && w_k % dilation_w == 0) {
+          h_k /= dilation_h;
+          w_k /= dilation_w;
+          int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) *
+                                height_col + h_col) * width_col + w_col;
+          val += data_col[data_col_index];
+        }
       }
     }
     data_im[index] = val;
@@ -267,9 +266,12 @@ template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_im) {
-  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_im) {
+  int height_col = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) /
+      stride_h + 1;
+  int width_col = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) /
+      stride_w + 1;
   int num_kernels = channels * height * width;
   // To avoid involving atomic operations, we will launch one kernel per
   // bottom dimension, and then in the kernel add up the top dimensions.
@@ -277,7 +279,7 @@ void col2im_gpu(const Dtype* data_col, const int channels,
   col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
       num_kernels, data_col, height, width, channels, kernel_h, kernel_w,
-      pad_h, pad_w, stride_h, stride_w,
+      pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
       height_col, width_col, data_im);
   CUDA_POST_KERNEL_CHECK;
 }
@@ -286,11 +288,13 @@ void col2im_gpu(const Dtype* data_col, const int channels,
 template void col2im_gpu<float>(const float* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, float* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    float* data_im);
 template void col2im_gpu<double>(const double* data_col, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, double* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    double* data_im);
 
 template <typename Dtype, int num_axes>
 __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,

From 18c795ebe8401cb82c9f8350664de665f1ec8733 Mon Sep 17 00:00:00 2001
From: Fisher Yu <i@yf.io>
Date: Sun, 27 Dec 2015 20:48:30 -0800
Subject: [PATCH 1661/2053] add support for N-D dilated convolution

---
 include/caffe/layers/base_conv_layer.hpp |   8 +-
 include/caffe/util/im2col.hpp            |   8 +-
 src/caffe/layer_factory.cpp              |   2 +
 src/caffe/layers/im2col_layer.cpp        |   4 +-
 src/caffe/layers/im2col_layer.cu         |   4 +-
 src/caffe/test/test_im2col_kernel.cu     |   9 +-
 src/caffe/test/test_im2col_layer.cpp     |   8 +-
 src/caffe/util/im2col.cpp                |  21 +--
 src/caffe/util/im2col.cu                 | 166 +++++++++++++++--------
 9 files changed, 148 insertions(+), 82 deletions(-)

diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp
index db471b586da..0160a833dd2 100644
--- a/include/caffe/layers/base_conv_layer.hpp
+++ b/include/caffe/layers/base_conv_layer.hpp
@@ -106,7 +106,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
     } else {
       im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
           col_buffer_shape_.data(), kernel_shape_.cpu_data(),
-          pad_.cpu_data(), stride_.cpu_data(), col_buff);
+          pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), col_buff);
     }
   }
   inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
@@ -120,7 +120,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
     } else {
       col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
           col_buffer_shape_.data(), kernel_shape_.cpu_data(),
-          pad_.cpu_data(), stride_.cpu_data(), data);
+          pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), data);
     }
   }
 #ifndef CPU_ONLY
@@ -136,7 +136,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
       im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
           conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
           kernel_shape_.gpu_data(), pad_.gpu_data(),
-          stride_.gpu_data(), col_buff);
+          stride_.gpu_data(), dilation_.gpu_data(), col_buff);
     }
   }
   inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
@@ -151,7 +151,7 @@ class BaseConvolutionLayer : public Layer<Dtype> {
       col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
           conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
           kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
-          data);
+          dilation_.gpu_data(), data);
     }
   }
 #endif
diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index 748b65c4f36..a35bc6e0b1c 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -7,7 +7,7 @@ template <typename Dtype>
 void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_col);
+    const int* dilation, Dtype* data_col);
 
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
@@ -20,7 +20,7 @@ template <typename Dtype>
 void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_im);
+    const int* dilation, Dtype* data_im);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
@@ -33,7 +33,7 @@ template <typename Dtype>
 void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
     const int col_size, const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_col);
+    const int* dilation, Dtype* data_col);
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
@@ -46,7 +46,7 @@ template <typename Dtype>
 void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
     const int im_size, const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_im);
+    const int* dilation, Dtype* data_im);
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 6b1d1c1a5f5..4d912d28351 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -39,12 +39,14 @@ shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     const LayerParameter& param) {
   ConvolutionParameter conv_param = param.convolution_param();
   ConvolutionParameter_Engine engine = conv_param.engine();
+#ifdef USE_CUDNN
   bool use_dilation = false;
   for (int i = 0; i < conv_param.dilation_size(); ++i) {
     if (conv_param.dilation(i) > 1) {
       use_dilation = true;
     }
   }
+#endif
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
     engine = ConvolutionParameter_Engine_CAFFE;
 #ifdef USE_CUDNN
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 19ae3019593..2fb9b3c1099 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -153,7 +153,7 @@ void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           bottom[0]->shape().data() + channel_axis_,
           top[0]->shape().data() + channel_axis_,
           kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
-          top_data + n * top_dim_);
+          dilation_.cpu_data(), top_data + n * top_dim_);
     }
   }
 }
@@ -178,7 +178,7 @@ void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           bottom[0]->shape().data() + channel_axis_,
           top[0]->shape().data() + channel_axis_,
           kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
-          bottom_diff + n * bottom_dim_);
+          dilation_.cpu_data(), bottom_diff + n * bottom_dim_);
     }
   }
 }
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index d90075d4304..792c97f70f9 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -26,7 +26,7 @@ void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
           num_kernels, bottom[0]->gpu_shape() + channel_axis_,
           top[0]->gpu_shape() + channel_axis_,
           kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
-          top_data + n * top_dim_);
+          dilation_.gpu_data(), top_data + n * top_dim_);
     }
   }
 }
@@ -51,7 +51,7 @@ void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           bottom[0]->gpu_shape() + channel_axis_,
           top[0]->gpu_shape() + channel_axis_,
           kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
-          bottom_diff + n * bottom_dim_);
+          dilation_.gpu_data(), bottom_diff + n * bottom_dim_);
     }
   }
 }
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 15e06aa8583..5d8f01f1713 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -26,7 +26,7 @@ template <typename Dtype, int num_axes>
 __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_col);
+    const int* dilation, Dtype* data_col);
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
@@ -35,7 +35,7 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
  protected:
   Im2colKernelTest()
         // big so launches > 1024 threads
-      : blob_bottom_(new Blob<Dtype>(5, 500, 10, 10)),
+      : blob_bottom_(new Blob<Dtype>(5, 500, 15, 15)),
         blob_kernel_shape_(new Blob<int>()),
         blob_stride_(new Blob<int>()),
         blob_pad_(new Blob<int>()),
@@ -56,7 +56,7 @@ class Im2colKernelTest : public GPUDeviceTest<Dtype> {
     channels_ = blob_bottom_->channels();
     pad_ = 0;
     stride_ = 2;
-    dilation_ = 1;
+    dilation_ = 3;
     kernel_size_ = 3;
     height_col_ = (height_ + 2 * pad_ -
         (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
@@ -176,6 +176,7 @@ TYPED_TEST(Im2colKernelTest, TestND) {
         this->blob_top_cpu_->shape().data() + 1,
         this->blob_kernel_shape_->cpu_data(),
         this->blob_pad_->cpu_data(), this->blob_stride_->cpu_data(),
+        this->blob_dilation_->cpu_data(),
         top_data_cpu + this->blob_top_cpu_->offset(n));
   }
 
@@ -194,7 +195,7 @@ TYPED_TEST(Im2colKernelTest, TestND) {
           num_kernels, bottom_data_gpu + this->blob_bottom_->offset(n),
           this->blob_bottom_->gpu_shape() + 1, this->blob_top_->gpu_shape() + 1,
           this->blob_kernel_shape_->gpu_data(), this->blob_pad_->gpu_data(),
-          this->blob_stride_->gpu_data(),
+          this->blob_stride_->gpu_data(), this->blob_dilation_->gpu_data(),
           top_data_gpu + this->blob_top_->offset(n));
       CUDA_POST_KERNEL_CHECK;
     }
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 932d3f21ae9..24885e6b706 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -17,7 +17,7 @@ class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
  protected:
   Im2colLayerTest()
-      : blob_bottom_(new Blob<Dtype>(2, 3, 10, 9)),
+      : blob_bottom_(new Blob<Dtype>(2, 3, 10, 11)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
     Caffe::set_random_seed(1701);
@@ -43,12 +43,13 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
       layer_param.mutable_convolution_param();
   convolution_param->add_kernel_size(3);
   convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 27);
   EXPECT_EQ(this->blob_top_->height(), 2);
-  EXPECT_EQ(this->blob_top_->width(), 2);
+  EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
 TYPED_TEST(Im2colLayerTest, TestForward) {
@@ -89,6 +90,7 @@ TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
       layer_param.mutable_convolution_param();
   convolution_param->add_kernel_size(3);
   convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
   convolution_param->set_force_nd_im2col(true);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
@@ -123,6 +125,8 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) {
   convolution_param->set_kernel_h(5);
   convolution_param->set_kernel_w(3);
   convolution_param->add_stride(2);
+  convolution_param->add_dilation(1);
+  convolution_param->add_dilation(3);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 1e578e7c9dc..6e5ea875757 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -49,7 +49,7 @@ template <typename Dtype>
 inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
     const int num_spatial_axes, const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_output) {
+    const int* dilation, Dtype* data_output) {
   if (!im2col) {
     int im_size = im_shape[0];
     for (int i = 0; i < num_spatial_axes; ++i) {
@@ -81,7 +81,8 @@ inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
       bool is_padding = false;
       for (int d_i = 0; d_i < num_spatial_axes; ++d_i) {
         const int d = d_iter[d_i];
-        const int d_im = d * stride[d_i] - pad[d_i] + d_offset[d_i];
+        const int d_im = d * stride[d_i] - pad[d_i] +
+            d_offset[d_i] * dilation[d_i];
         is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1];
         index_col *= col_shape[d_i + 1];
         index_col += d;
@@ -119,10 +120,10 @@ template <typename Dtype>
 void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_col) {
+    const int* dilation, Dtype* data_col) {
   const bool kIm2Col = true;
   im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape,
-                  kernel_shape, pad, stride, data_col);
+                  kernel_shape, pad, stride, dilation, data_col);
 }
 
 // Explicit instantiation
@@ -130,12 +131,12 @@ template void im2col_nd_cpu<float>(const float* data_im,
     const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    float* data_col);
+    const int* dilation, float* data_col);
 template void im2col_nd_cpu<double>(const double* data_im,
     const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    double* data_col);
+    const int* dilation, double* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
@@ -182,10 +183,10 @@ template <typename Dtype>
 void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_im) {
+    const int* dilation, Dtype* data_im) {
   const bool kIm2Col = false;
   im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape,
-                     kernel_shape, pad, stride, data_im);
+                     kernel_shape, pad, stride, dilation, data_im);
 }
 
 // Explicit instantiation
@@ -193,12 +194,12 @@ template void col2im_nd_cpu<float>(const float* data_col,
     const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    float* data_im);
+    const int* dilation, float* data_im);
 template void col2im_nd_cpu<double>(const double* data_col,
     const int num_spatial_axes,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    double* data_im);
+    const int* dilation, double* data_im);
 
 
 }  // namespace caffe
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index cdcaac5bcc5..a8f30a02484 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -75,9 +75,29 @@ template <typename Dtype, int num_axes>
 __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_col) {
+    const int* dilation, Dtype* data_col) {
   int d_temp[num_axes];  // NOLINT(runtime/arrays)
   int d_iter[num_axes];  // NOLINT(runtime/arrays)
+
+  __shared__ int shared_dilation[num_axes];
+  __shared__ int shared_kernel_shape[num_axes];
+  __shared__ int shared_pad[num_axes];
+  __shared__ int shared_stride[num_axes];
+  __shared__ int shared_col_shape[num_axes + 1];
+  __shared__ int shared_im_shape[num_axes + 1];
+
+  if (threadIdx.x < num_axes) {
+    shared_dilation[threadIdx.x] = dilation[threadIdx.x];
+    shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x];
+    shared_pad[threadIdx.x] = pad[threadIdx.x];
+    shared_stride[threadIdx.x] = stride[threadIdx.x];
+  }
+  if (threadIdx.x < num_axes + 1) {
+    shared_col_shape[threadIdx.x] = col_shape[threadIdx.x];
+    shared_im_shape[threadIdx.x] = im_shape[threadIdx.x];
+  }
+  __syncthreads();
+
   int i;
   CUDA_KERNEL_LOOP(index, n) {
     // Initialize channel_in, computed in the loop below, with intermediate
@@ -85,19 +105,19 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
     int channel_in = index;
     int channel_out = 1;
     for (i = num_axes - 1; i >= 0; --i) {
-      d_temp[i] = channel_in % col_shape[i + 1];
-      channel_in /= col_shape[i + 1];
-      channel_out *= kernel_shape[i];
+      d_temp[i] = channel_in % shared_col_shape[i + 1];
+      channel_in /= shared_col_shape[i + 1];
+      channel_out *= shared_kernel_shape[i];
     }
     channel_out *= channel_in;
     int data_col_inc = 1;
     for (i = 0; i < num_axes; ++i) {
-      channel_out *= col_shape[i + 1];
+      channel_out *= shared_col_shape[i + 1];
       channel_out += d_temp[i];
-      d_temp[i] = d_temp[i] * stride[i] - pad[i];
-      channel_in *= im_shape[i + 1];
+      d_temp[i] = d_temp[i] * shared_stride[i] - shared_pad[i];
+      channel_in *= shared_im_shape[i + 1];
       channel_in += d_temp[i];
-      data_col_inc *= col_shape[i + 1];
+      data_col_inc *= shared_col_shape[i + 1];
       d_iter[i] = 0;
     }
     Dtype* data_col_ptr = data_col + channel_out;
@@ -106,15 +126,15 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
     do {
       bool in_range = true;
       for (i = 0; i < num_axes; ++i) {
-        const int d_iter_im = d_iter[i] + d_temp[i];
-        in_range &= d_iter_im >= 0 && d_iter_im < im_shape[i + 1];
+        const int d_iter_im = d_iter[i] * shared_dilation[i] + d_temp[i];
+        in_range &= d_iter_im >= 0 && d_iter_im < shared_im_shape[i + 1];
         if (!in_range) { break; }
       }
       if (in_range) {
-        int data_im_offset = d_iter[0];
+        int data_im_offset = d_iter[0] * shared_dilation[0];
         for (i = 1; i < num_axes; ++i) {
-          data_im_offset *= im_shape[i + 1];
-          data_im_offset += d_iter[i];
+          data_im_offset *= shared_im_shape[i + 1];
+          data_im_offset += d_iter[i] * shared_dilation[i];
         }
         *data_col_ptr = data_im_ptr[data_im_offset];
       } else {
@@ -123,7 +143,7 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
       data_col_ptr += data_col_inc;
       incremented = false;
       for (i = num_axes - 1; i >= 0; --i) {
-        const int d_max = kernel_shape[i];
+        const int d_max = shared_kernel_shape[i];
         if (d_iter[i] == d_max - 1) {
           d_iter[i] = 0;
         } else {  // d_iter[i] < d_max - 1
@@ -140,67 +160,69 @@ template <typename Dtype>
 void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
     const int num_kernels, const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_col) {
+    const int* dilation, Dtype* data_col) {
+  // num_axes should be smaller than block size
+  DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS);
   switch (num_spatial_axes) {
   case 1:
     im2col_nd_gpu_kernel<Dtype, 1>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 2:
     im2col_nd_gpu_kernel<Dtype, 2>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 3:
     im2col_nd_gpu_kernel<Dtype, 3>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 4:
     im2col_nd_gpu_kernel<Dtype, 4>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 5:
     im2col_nd_gpu_kernel<Dtype, 5>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 6:
     im2col_nd_gpu_kernel<Dtype, 6>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 7:
     im2col_nd_gpu_kernel<Dtype, 7>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 8:
     im2col_nd_gpu_kernel<Dtype, 8>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 9:
     im2col_nd_gpu_kernel<Dtype, 9>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   case 10:
     im2col_nd_gpu_kernel<Dtype, 10>  // NOLINT_NEXT_LINE(whitespace/operators)
         <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
         num_kernels, data_im, im_shape, col_shape,
-        kernel_shape, pad, stride, data_col);
+        kernel_shape, pad, stride, dilation, data_col);
     break;
   default:
     LOG(FATAL) << "im2col_nd_gpu does not support computation with "
@@ -214,12 +236,12 @@ template void im2col_nd_gpu<float>(const float* data_im,
     const int num_spatial_axes, const int col_size,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    float* data_col);
+    const int* dilation, float* data_col);
 template void im2col_nd_gpu<double>(const double* data_im,
     const int num_spatial_axes, const int col_size,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    double* data_col);
+    const int* dilation, double* data_col);
 
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
@@ -300,27 +322,50 @@ template <typename Dtype, int num_axes>
 __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_im) {
+    const int* dilation, Dtype* data_im) {
   int d_im[num_axes];  // NOLINT(runtime/arrays)
   int d_col_iter[num_axes];  // NOLINT(runtime/arrays)
   int d_col_start[num_axes];  // NOLINT(runtime/arrays)
   int d_col_end[num_axes];  // NOLINT(runtime/arrays)
+
+  __shared__ int shared_dilation[num_axes];
+  __shared__ int shared_kernel_shape[num_axes];
+  __shared__ int shared_pad[num_axes];
+  __shared__ int shared_stride[num_axes];
+  __shared__ int shared_col_shape[num_axes + 1];
+  __shared__ int shared_im_shape[num_axes + 1];
+
+  if (threadIdx.x < num_axes) {
+    shared_dilation[threadIdx.x] = dilation[threadIdx.x];
+    shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x];
+    shared_pad[threadIdx.x] = pad[threadIdx.x];
+    shared_stride[threadIdx.x] = stride[threadIdx.x];
+  }
+  if (threadIdx.x < num_axes + 1) {
+    shared_col_shape[threadIdx.x] = col_shape[threadIdx.x];
+    shared_im_shape[threadIdx.x] = im_shape[threadIdx.x];
+  }
+  __syncthreads();
+
   CUDA_KERNEL_LOOP(index, n) {
     // Initialize channel_in, computed in the loop below, with intermediate
     // computations used to compute the spatial indices.
     int c_im = index;
     // Calculate d_im (image dimensions).
     for (int i = num_axes - 1; i >= 0; --i) {
-      d_im[i] = c_im % im_shape[i + 1] + pad[i];
-      c_im /= im_shape[i + 1];
+      d_im[i] = c_im % shared_im_shape[i + 1] + shared_pad[i];
+      c_im /= shared_im_shape[i + 1];
     }
     // Calculate col start/end indices.
     bool done = false;
     for (int i = 0; i < num_axes; ++i) {
+      const int kernel_extent =
+          shared_dilation[i] * (shared_kernel_shape[i] - 1) + 1;
       d_col_start[i] = d_col_iter[i] =
-          (d_im[i] < kernel_shape[i]) ?
-          0 : (d_im[i] - kernel_shape[i]) / stride[i] + 1;
-      d_col_end[i] = min(d_im[i] / stride[i] + 1, col_shape[i + 1]);
+          (d_im[i] < kernel_extent) ? 0 :
+          (d_im[i] - kernel_extent) / shared_stride[i] + 1;
+      d_col_end[i] =
+          min(d_im[i] / shared_stride[i] + 1, shared_col_shape[i + 1]);
       if (d_col_start[i] >= d_col_end[i]) {
         // Skip computation if the dimension is 0 at any spatial axis --
         // final val will be 0.
@@ -335,21 +380,32 @@ __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
     // Loop over the col to compute the output val.
     Dtype val = 0;
     bool incremented = true;
+    bool skip = false;
     do {
       // Compute the final offset.
       int final_offset = 0;
       int kernel_shape_prod = 1;
+      int kernel_index;
       for (int i = num_axes - 1; i >= 0; --i) {
-        final_offset +=
-            (d_im[i] - d_col_iter[i] * stride[i]) * kernel_shape_prod;
-        kernel_shape_prod *= kernel_shape[i];
+        kernel_index = d_im[i] - d_col_iter[i] * shared_stride[i];
+        if (kernel_index % shared_dilation[i]) {
+          skip = true;
+          break;
+        } else {
+          kernel_index /= shared_dilation[i];
+          final_offset += kernel_index * kernel_shape_prod;
+          kernel_shape_prod *= shared_kernel_shape[i];
+        }
       }
-      final_offset += kernel_shape_prod * c_im;
-      for (int i = 0; i < num_axes; ++i) {
-        final_offset *= col_shape[i + 1];
-        final_offset += d_col_iter[i];
+      if (!skip) {
+        final_offset += kernel_shape_prod * c_im;
+        for (int i = 0; i < num_axes; ++i) {
+          final_offset *= shared_col_shape[i + 1];
+          final_offset += d_col_iter[i];
+        }
+        val += data_col[final_offset];
       }
-      val += data_col[final_offset];
+      skip = false;
       incremented = false;
       for (int i = num_axes - 1; i >= 0; --i) {
         const int d_max = d_col_end[i];
@@ -370,67 +426,69 @@ template <typename Dtype>
 void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
     const int im_size, const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    Dtype* data_im) {
+    const int* dilation, Dtype* data_im) {
+  // num_axes should be smaller than block size
+  DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS);
   switch (num_spatial_axes) {
   case 1:
     col2im_nd_gpu_kernel<Dtype, 1>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 2:
     col2im_nd_gpu_kernel<Dtype, 2>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 3:
     col2im_nd_gpu_kernel<Dtype, 3>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 4:
     col2im_nd_gpu_kernel<Dtype, 4>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 5:
     col2im_nd_gpu_kernel<Dtype, 5>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 6:
     col2im_nd_gpu_kernel<Dtype, 6>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 7:
     col2im_nd_gpu_kernel<Dtype, 7>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 8:
     col2im_nd_gpu_kernel<Dtype, 8>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 9:
     col2im_nd_gpu_kernel<Dtype, 9>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   case 10:
     col2im_nd_gpu_kernel<Dtype, 10>  // NOLINT_NEXT_LINE(whitespace/operators)
           <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
           im_size, data_col, im_shape, col_shape,
-          kernel_shape, pad, stride, data_im);
+          kernel_shape, pad, stride, dilation, data_im);
     break;
   default:
     LOG(FATAL) << "col2im_nd_gpu does not support computation with "
@@ -444,11 +502,11 @@ template void col2im_nd_gpu<float>(const float* data_col,
     const int num_spatial_axes, const int im_size,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    float* data_im);
+    const int* dilation, float* data_im);
 template void col2im_nd_gpu<double>(const double* data_col,
     const int num_spatial_axes, const int im_size,
     const int* im_shape, const int* col_shape,
     const int* kernel_shape, const int* pad, const int* stride,
-    double* data_im);
+    const int* dilation, double* data_im);
 
 }  // namespace caffe

From 7674799475598fcb0494c83d93a46b41f8261a11 Mon Sep 17 00:00:00 2001
From: Fisher Yu <i@yf.io>
Date: Sat, 26 Dec 2015 13:04:25 -0800
Subject: [PATCH 1662/2053] add and improve tests for dilated
 convolution/im2col

---
 src/caffe/test/test_convolution_layer.cpp | 115 ++++++++++++++++++++++
 src/caffe/test/test_im2col_layer.cpp      |  54 ++++++++--
 2 files changed, 163 insertions(+), 6 deletions(-)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 95c3c80c59d..9bb19d13592 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -264,6 +264,50 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestDilatedConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(8);
+  bottom_shape.push_back(7);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_dilation(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_2_));
+  top_data = this->blob_top_2_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -349,6 +393,53 @@ TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestDilated3DConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 6;
+  bottom_shape[3] = 7;
+  bottom_shape[4] = 8;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_dilation(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_2_));
+  top_data = this->blob_top_2_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -633,6 +724,30 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestDilatedGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(5);
+  bottom_shape.push_back(6);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+  }
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_dilation(2);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                  this->blob_top_vec_);
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestGradient3D) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 24885e6b706..a7faf18f972 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -17,7 +17,7 @@ class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
  protected:
   Im2colLayerTest()
-      : blob_bottom_(new Blob<Dtype>(2, 3, 10, 11)),
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
     Caffe::set_random_seed(1701);
@@ -41,6 +41,12 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(10);
+  bottom_shape.push_back(11);
+  this->blob_bottom_->Reshape(bottom_shape);
   convolution_param->add_kernel_size(3);
   convolution_param->add_stride(2);
   convolution_param->add_dilation(3);
@@ -76,21 +82,39 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
       layer_param.mutable_convolution_param();
   convolution_param->add_kernel_size(3);
   convolution_param->add_stride(2);
-  convolution_param->add_dilation(3);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
       this->blob_top_vec_);
 }
 
-TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
+TYPED_TEST(Im2colLayerTest, TestDilatedGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(10);
+  bottom_shape.push_back(9);
+  this->blob_bottom_->Reshape(bottom_shape);
   convolution_param->add_kernel_size(3);
   convolution_param->add_stride(2);
   convolution_param->add_dilation(3);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                  this->blob_top_vec_);
+}
+
+TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_force_nd_im2col(true);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
@@ -98,6 +122,27 @@ TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(Im2colLayerTest, TestDilatedGradientForceND) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(10);
+  bottom_shape.push_back(9);
+  this->blob_bottom_->Reshape(bottom_shape);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
+  convolution_param->set_force_nd_im2col(true);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                  this->blob_top_vec_);
+}
+
 TYPED_TEST(Im2colLayerTest, TestRect) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -116,7 +161,6 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
   }
 }
 
-
 TYPED_TEST(Im2colLayerTest, TestRectGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -125,8 +169,6 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) {
   convolution_param->set_kernel_h(5);
   convolution_param->set_kernel_w(3);
   convolution_param->add_stride(2);
-  convolution_param->add_dilation(1);
-  convolution_param->add_dilation(3);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

From c25c5796385eda485a743cae6222845ca8eb52bb Mon Sep 17 00:00:00 2001
From: Fisher Yu <i@yf.io>
Date: Sat, 26 Dec 2015 13:10:02 -0800
Subject: [PATCH 1663/2053] disable dilated deconvolution

---
 src/caffe/layers/base_conv_layer.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index 4a4c68e009a..deb58a714a4 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -105,6 +105,9 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < num_spatial_axes_; ++i) {
     dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
                        conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
+    if (reverse_dimensions()) {
+      CHECK_EQ(dilation_data[i], 1) << "Deconvolution doesn't support dilation";
+    }
   }
   // Special case: im2col is the identity for 1x1 convolution with stride 1
   // and no padding, so flag for skipping the buffer and transformation.

From 3e3e9ce17636f813c80b5b22afc069d3c1c802cb Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 26 Dec 2015 13:10:11 -0800
Subject: [PATCH 1664/2053] add short description of dilation to caffe.proto

---
 src/caffe/proto/caffe.proto | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 87c46629baf..019aa614373 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -518,6 +518,9 @@ message ConvolutionParameter {
   repeated uint32 pad = 3; // The padding size; defaults to 0
   repeated uint32 kernel_size = 4; // The kernel size
   repeated uint32 stride = 6; // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
   repeated uint32 dilation = 18; // The dilation; defaults to 1
 
   // For 2D convolution only, the *_h and *_w versions may also be used to

From bbc4e578a54546bbc41ce9e959386dbba6e269c2 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sun, 27 Dec 2015 20:56:24 -0800
Subject: [PATCH 1665/2053] enable dilated deconvolution

Since the underlying routines are shared, we need only upgrade
compute_output_shape.
---
 src/caffe/layers/base_conv_layer.cpp | 3 ---
 src/caffe/layers/deconv_layer.cpp    | 4 +++-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index deb58a714a4..4a4c68e009a 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -105,9 +105,6 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   for (int i = 0; i < num_spatial_axes_; ++i) {
     dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
                        conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
-    if (reverse_dimensions()) {
-      CHECK_EQ(dilation_data[i], 1) << "Deconvolution doesn't support dilation";
-    }
   }
   // Special case: im2col is the identity for 1x1 convolution with stride 1
   // and no padding, so flag for skipping the buffer and transformation.
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index 275c05626c8..20a460fbdea 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -9,12 +9,14 @@ void DeconvolutionLayer<Dtype>::compute_output_shape() {
   const int* kernel_shape_data = this->kernel_shape_.cpu_data();
   const int* stride_data = this->stride_.cpu_data();
   const int* pad_data = this->pad_.cpu_data();
+  const int* dilation_data = this->dilation_.cpu_data();
   this->output_shape_.clear();
   for (int i = 0; i < this->num_spatial_axes_; ++i) {
     // i + 1 to skip channel axis
     const int input_dim = this->input_shape(i + 1);
+    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
     const int output_dim = stride_data[i] * (input_dim - 1)
-        + kernel_shape_data[i] - 2 * pad_data[i];
+        + kernel_extent - 2 * pad_data[i];
     this->output_shape_.push_back(output_dim);
   }
 }

From 708c1a122c33bd35b0d53630fb74965488e1947a Mon Sep 17 00:00:00 2001
From: Fisher Yu <i@yf.io>
Date: Mon, 28 Dec 2015 22:46:49 -0500
Subject: [PATCH 1666/2053] remove extra space before +

---
 src/caffe/solvers/adam_solver.cpp | 2 +-
 tools/caffe.cpp                   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp
index cb0fbfe2f78..c3378d3890a 100644
--- a/src/caffe/solvers/adam_solver.cpp
+++ b/src/caffe/solvers/adam_solver.cpp
@@ -30,7 +30,7 @@ void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   Blob<Dtype>* val_v = this->history_[param_id + update_history_offset].get();
   Blob<Dtype>* val_t = this->temp_[param_id].get();
 
-  const int t = this->iter_  + 1;
+  const int t = this->iter_ + 1;
   const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) /
       (Dtype(1.) - pow(beta1, t));
   const int N = net_params[param_id]->count();
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 305cfc3635d..6b342ace0c5 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -164,7 +164,7 @@ int train() {
   if (FLAGS_gpu.size() == 0
       && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
       if (solver_param.has_device_id()) {
-          FLAGS_gpu = ""  +
+          FLAGS_gpu = "" +
               boost::lexical_cast<string>(solver_param.device_id());
       } else {  // Set default GPU if unspecified
           FLAGS_gpu = "" + boost::lexical_cast<string>(0);

From 6320d8d2663aa80b54e74e374a34441124f88c24 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 29 Dec 2015 21:10:14 -0800
Subject: [PATCH 1667/2053] TestDataTransformer: fix some memory leaks caused
 by use of 'new'

---
 src/caffe/test/test_data_transformer.cpp | 136 +++++++++++------------
 1 file changed, 62 insertions(+), 74 deletions(-)

diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 8a1013744e8..6103918fda1 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -40,23 +40,21 @@ class DataTransformTest : public ::testing::Test {
   int NumSequenceMatches(const TransformationParameter transform_param,
       const Datum& datum, Phase phase) {
     // Get crop sequence with Caffe seed 1701.
-    DataTransformer<Dtype>* transformer =
-        new DataTransformer<Dtype>(transform_param, phase);
+    DataTransformer<Dtype> transformer(transform_param, phase);
     const int crop_size = transform_param.crop_size();
     Caffe::set_random_seed(seed_);
-    transformer->InitRand();
-    Blob<Dtype>* blob =
-        new Blob<Dtype>(1, datum.channels(), datum.height(), datum.width());
+    transformer.InitRand();
+    Blob<Dtype> blob(1, datum.channels(), datum.height(), datum.width());
     if (transform_param.crop_size() > 0) {
-      blob->Reshape(1, datum.channels(), crop_size, crop_size);
+      blob.Reshape(1, datum.channels(), crop_size, crop_size);
     }
 
     vector<vector<Dtype> > crop_sequence;
     for (int iter = 0; iter < this->num_iter_; ++iter) {
       vector<Dtype> iter_crop_sequence;
-      transformer->Transform(datum, blob);
-      for (int j = 0; j < blob->count(); ++j) {
-        iter_crop_sequence.push_back(blob->cpu_data()[j]);
+      transformer.Transform(datum, &blob);
+      for (int j = 0; j < blob.count(); ++j) {
+        iter_crop_sequence.push_back(blob.cpu_data()[j]);
       }
       crop_sequence.push_back(iter_crop_sequence);
     }
@@ -64,17 +62,14 @@ class DataTransformTest : public ::testing::Test {
     int num_sequence_matches = 0;
     for (int iter = 0; iter < this->num_iter_; ++iter) {
       vector<Dtype> iter_crop_sequence = crop_sequence[iter];
-      transformer->Transform(datum, blob);
-      for (int j = 0; j < blob->count(); ++j) {
-        num_sequence_matches +=
-            (crop_sequence[iter][j] == blob->cpu_data()[j]);
+      transformer.Transform(datum, &blob);
+      for (int j = 0; j < blob.count(); ++j) {
+        num_sequence_matches += (crop_sequence[iter][j] == blob.cpu_data()[j]);
       }
     }
     return num_sequence_matches;
   }
 
-  virtual ~DataTransformTest() { }
-
   int seed_;
   int num_iter_;
 };
@@ -91,17 +86,16 @@ TYPED_TEST(DataTransformTest, TestEmptyTransform) {
 
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  EXPECT_EQ(blob->num(), 1);
-  EXPECT_EQ(blob->channels(), datum.channels());
-  EXPECT_EQ(blob->height(), datum.height());
-  EXPECT_EQ(blob->width(), datum.width());
-  for (int j = 0; j < blob->count(); ++j) {
-    EXPECT_EQ(blob->cpu_data()[j], label);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  EXPECT_EQ(blob.num(), 1);
+  EXPECT_EQ(blob.channels(), datum.channels());
+  EXPECT_EQ(blob.height(), datum.height());
+  EXPECT_EQ(blob.width(), datum.width());
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], label);
   }
 }
 
@@ -115,17 +109,16 @@ TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
 
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  EXPECT_EQ(blob->num(), 1);
-  EXPECT_EQ(blob->channels(), datum.channels());
-  EXPECT_EQ(blob->height(), datum.height());
-  EXPECT_EQ(blob->width(), datum.width());
-  for (int j = 0; j < blob->count(); ++j) {
-    EXPECT_EQ(blob->cpu_data()[j], j);
+  Blob<TypeParam> blob(1, 3, 4, 5);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  EXPECT_EQ(blob.num(), 1);
+  EXPECT_EQ(blob.channels(), datum.channels());
+  EXPECT_EQ(blob.height(), datum.height());
+  EXPECT_EQ(blob.width(), datum.width());
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], j);
   }
 }
 
@@ -141,19 +134,17 @@ TYPED_TEST(DataTransformTest, TestCropSize) {
   transform_param.set_crop_size(crop_size);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  Blob<TypeParam>* blob =
-      new Blob<TypeParam>(1, channels, crop_size, crop_size);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  Blob<TypeParam> blob(1, channels, crop_size, crop_size);
   for (int iter = 0; iter < this->num_iter_; ++iter) {
-    transformer->Transform(datum, blob);
-    EXPECT_EQ(blob->num(), 1);
-    EXPECT_EQ(blob->channels(), datum.channels());
-    EXPECT_EQ(blob->height(), crop_size);
-    EXPECT_EQ(blob->width(), crop_size);
-    for (int j = 0; j < blob->count(); ++j) {
-      EXPECT_EQ(blob->cpu_data()[j], label);
+    transformer.Transform(datum, &blob);
+    EXPECT_EQ(blob.num(), 1);
+    EXPECT_EQ(blob.channels(), datum.channels());
+    EXPECT_EQ(blob.height(), crop_size);
+    EXPECT_EQ(blob.width(), crop_size);
+    for (int j = 0; j < blob.count(); ++j) {
+      EXPECT_EQ(blob.cpu_data()[j], label);
     }
   }
 }
@@ -280,13 +271,12 @@ TYPED_TEST(DataTransformTest, TestMeanValue) {
   transform_param.add_mean_value(mean_value);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  for (int j = 0; j < blob->count(); ++j) {
-    EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], label - mean_value);
   }
 }
 
@@ -303,14 +293,13 @@ TYPED_TEST(DataTransformTest, TestMeanValues) {
   transform_param.add_mean_value(2);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
   for (int c = 0; c < channels; ++c) {
     for (int j = 0; j < height * width; ++j) {
-      EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
+      EXPECT_EQ(blob.cpu_data()[blob.offset(0, c) + j], label - c);
     }
   }
 }
@@ -325,8 +314,8 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
   const int size = channels * height * width;
 
   // Create a mean file
-  string* mean_file = new string();
-  MakeTempFilename(mean_file);
+  string mean_file;
+  MakeTempFilename(&mean_file);
   BlobProto blob_mean;
   blob_mean.set_num(1);
   blob_mean.set_channels(channels);
@@ -337,19 +326,18 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
       blob_mean.add_data(j);
   }
 
-  LOG(INFO) << "Using temporary mean_file " << *mean_file;
-  WriteProtoToBinaryFile(blob_mean, *mean_file);
+  LOG(INFO) << "Using temporary mean_file " << mean_file;
+  WriteProtoToBinaryFile(blob_mean, mean_file);
 
-  transform_param.set_mean_file(*mean_file);
+  transform_param.set_mean_file(mean_file);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  for (int j = 0; j < blob->count(); ++j) {
-      EXPECT_EQ(blob->cpu_data()[j], 0);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], 0);
   }
 }
 

From 1137e89fef767c68e9368779a57dfc61c6d8d834 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Wed, 5 Aug 2015 11:54:08 -0700
Subject: [PATCH 1668/2053] Exposing layer top and bottom names to python

---
 include/caffe/net.hpp   | 12 ++++++++++++
 python/caffe/_caffe.cpp |  4 ++++
 python/caffe/pycaffe.py | 18 ++++++++++++++++++
 3 files changed, 34 insertions(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 1bf07d28d13..3b56f3070f7 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -149,6 +149,18 @@ class Net {
   inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
     return top_vecs_;
   }
+  /// @brief returns the ids of the top blobs of layer i
+  inline const vector<int> & top_ids(int i) const {
+    CHECK_GE(i, 0) << "Invalid layer id";
+    CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
+    return top_id_vecs_[i];
+  }
+  /// @brief returns the ids of the bottom blobs of layer i
+  inline const vector<int> & bottom_ids(int i) const {
+    CHECK_GE(i, 0) << "Invalid layer id";
+    CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
+    return bottom_id_vecs_[i];
+  }
   inline const vector<vector<bool> >& bottom_need_backward() const {
     return bottom_need_backward_;
   }
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 69d55332841..4ea2ec60b7e 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -232,6 +232,10 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
     .add_property("_blob_loss_weights", bp::make_function(
         &Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
+    .def("_bottom_ids", bp::make_function(&Net<Dtype>::bottom_ids,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .def("_top_ids", bp::make_function(&Net<Dtype>::top_ids,
+        bp::return_value_policy<bp::copy_const_reference>()))
     .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
         bp::return_internal_reference<>()))
     .add_property("layers", bp::make_function(&Net<Dtype>::layers,
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 31dc702f647..3054110771c 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -276,6 +276,22 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
+
+class _Net_IdNameWrapper:
+    """
+    A simple wrapper that allows the ids propery to be accessed as a dict
+    indexed by names. Used for top and bottom names
+    """
+    def __init__(self, net, func):
+        self.net, self.func = net, func
+
+    def __getitem__(self, name):
+        # Map the layer name to id
+        ids = self.func(self.net, list(self.net._layer_names).index(name))
+        # Map the blob id to name
+        id_to_name = list(self.net.blobs)
+        return [id_to_name[i] for i in ids]
+
 # Attach methods to Net.
 Net.blobs = _Net_blobs
 Net.blob_loss_weights = _Net_blob_loss_weights
@@ -288,3 +304,5 @@ def _Net_batch(self, blobs):
 Net._batch = _Net_batch
 Net.inputs = _Net_inputs
 Net.outputs = _Net_outputs
+Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids))
+Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids))

From 6d09ca2829dad0c7ae4ba1474fd351f41125ab2a Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Tue, 5 Jan 2016 12:45:52 -0800
Subject: [PATCH 1669/2053] Speeding up the GPU solvers

---
 src/caffe/solvers/adadelta_solver.cpp | 66 +++++----------------------
 src/caffe/solvers/adadelta_solver.cu  | 30 ++++++++++++
 src/caffe/solvers/adagrad_solver.cpp  | 37 ++++-----------
 src/caffe/solvers/adagrad_solver.cu   | 26 +++++++++++
 src/caffe/solvers/adam_solver.cpp     | 37 ++++-----------
 src/caffe/solvers/adam_solver.cu      | 29 ++++++++++++
 src/caffe/solvers/nesterov_solver.cpp | 29 ++++--------
 src/caffe/solvers/nesterov_solver.cu  | 27 +++++++++++
 src/caffe/solvers/rmsprop_solver.cpp  | 35 ++++----------
 src/caffe/solvers/rmsprop_solver.cu   | 28 ++++++++++++
 src/caffe/solvers/sgd_solver.cpp      | 16 ++++---
 src/caffe/solvers/sgd_solver.cu       | 24 ++++++++++
 12 files changed, 223 insertions(+), 161 deletions(-)
 create mode 100644 src/caffe/solvers/adadelta_solver.cu
 create mode 100644 src/caffe/solvers/adagrad_solver.cu
 create mode 100644 src/caffe/solvers/adam_solver.cu
 create mode 100644 src/caffe/solvers/nesterov_solver.cu
 create mode 100644 src/caffe/solvers/rmsprop_solver.cu
 create mode 100644 src/caffe/solvers/sgd_solver.cu

diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp
index a37899ebbb4..fd30f19acac 100644
--- a/src/caffe/solvers/adadelta_solver.cpp
+++ b/src/caffe/solvers/adadelta_solver.cpp
@@ -16,6 +16,12 @@ void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
   }
 }
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum,
+    Dtype delta, Dtype local_rate);
+#endif
+
 template <typename Dtype>
 void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
@@ -85,61 +91,11 @@ void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    // compute square of gradient in update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history of gradients
-    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-        this->update_[param_id]->gpu_data(), momentum,
-        this->history_[param_id]->mutable_gpu_data());
-
-    // add delta to history to guard against dividing by zero later
-    caffe_gpu_set(net_params[param_id]->count(), delta,
-        this->temp_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add(net_params[param_id]->count(),
-        this->temp_[param_id]->gpu_data(),
-        this->history_[update_history_offset + param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add(net_params[param_id]->count(),
-        this->temp_[param_id]->gpu_data(),
-        this->history_[param_id]->gpu_data(),
-        this->temp_[param_id]->mutable_gpu_data());
-
-    // divide history of updates by history of gradients
-    caffe_gpu_div(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(),
-        this->temp_[param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // jointly compute the RMS of both for update and gradient history
-    caffe_gpu_powx(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // compute the update and copy to net_diff
-    caffe_gpu_mul(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(),
-        this->update_[param_id]->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
-
-    // compute square of update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history of updates
-    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
-        this->update_[param_id]->gpu_data(), momentum,
-        this->history_[update_history_offset + param_id]->mutable_gpu_data());
-
-    // apply learning rate
-    caffe_gpu_scale(net_params[param_id]->count(), local_rate,
-        net_params[param_id]->gpu_diff(),
-        net_params[param_id]->mutable_gpu_diff());
+    adadelta_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(),
+        this->history_[update_history_offset + param_id]->mutable_gpu_data(),
+        momentum, delta, local_rate);
 #else
     NO_GPU;
 #endif
diff --git a/src/caffe/solvers/adadelta_solver.cu b/src/caffe/solvers/adadelta_solver.cu
new file mode 100644
index 00000000000..6c94585b89e
--- /dev/null
+++ b/src/caffe/solvers/adadelta_solver.cu
@@ -0,0 +1,30 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdaDeltaUpdate(int N, Dtype* g, Dtype* h, Dtype* h2,
+    Dtype momentum, Dtype delta, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float hi = h[i] = momentum * h[i] + (1-momentum) * gi * gi;
+    gi = gi * sqrt((h2[i] + delta) / (hi + delta));
+    h2[i] = momentum * h2[i] + (1-momentum) * gi * gi;
+    g[i] = local_rate * gi;
+  }
+}
+template <typename Dtype>
+void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum,
+    Dtype delta, Dtype local_rate) {
+  AdaDeltaUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, h2, momentum, delta, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void adadelta_update_gpu<float>(int , float*, float*, float*,
+    float, float, float);
+template void adadelta_update_gpu<double>(int, double*, double*, double*,
+    double, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp
index 5e406326095..e78eadca141 100644
--- a/src/caffe/solvers/adagrad_solver.cpp
+++ b/src/caffe/solvers/adagrad_solver.cpp
@@ -4,6 +4,12 @@
 
 namespace caffe {
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
+    Dtype local_rate);
+#endif
+
 template <typename Dtype>
 void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   CHECK(Caffe::root_solver());
@@ -45,34 +51,9 @@ void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    // compute square of gradient in update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history
-    caffe_gpu_add(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(),
-        this->history_[param_id]->gpu_data(),
-        this->history_[param_id]->mutable_gpu_data());
-
-    // prepare update
-    caffe_gpu_powx(net_params[param_id]->count(),
-              this->history_[param_id]->gpu_data(), Dtype(0.5),
-              this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add_scalar(net_params[param_id]->count(),
-              delta, this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_div(net_params[param_id]->count(),
-              net_params[param_id]->gpu_diff(),
-              this->update_[param_id]->gpu_data(),
-              this->update_[param_id]->mutable_gpu_data());
-
-    // scale and copy
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->gpu_data(), Dtype(0),
-        net_params[param_id]->mutable_gpu_diff());
+    adagrad_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(), delta, local_rate);
 #else
     NO_GPU;
 #endif
diff --git a/src/caffe/solvers/adagrad_solver.cu b/src/caffe/solvers/adagrad_solver.cu
new file mode 100644
index 00000000000..adefd554bbd
--- /dev/null
+++ b/src/caffe/solvers/adagrad_solver.cu
@@ -0,0 +1,26 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdaGradUpdate(int N, Dtype* g, Dtype* h, Dtype delta,
+    Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float hi = h[i] = h[i] + gi*gi;
+    g[i] = local_rate * gi / (sqrt(hi) + delta);
+  }
+}
+template <typename Dtype>
+void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
+    Dtype local_rate) {
+  AdaGradUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, delta, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void adagrad_update_gpu<float>(int, float*, float*, float, float);
+template void adagrad_update_gpu<double>(int, double*, double*, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp
index c3378d3890a..4a91f00bd49 100644
--- a/src/caffe/solvers/adam_solver.cpp
+++ b/src/caffe/solvers/adam_solver.cpp
@@ -16,6 +16,12 @@ void AdamSolver<Dtype>::AdamPreSolve() {
   }
 }
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1,
+    Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate);
+#endif
+
 template <typename Dtype>
 void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
@@ -69,34 +75,9 @@ void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
-    caffe_gpu_axpby(N, Dtype(1)-beta1,
-        net_params[param_id]->gpu_diff(), beta1,
-        val_m->mutable_gpu_data());
-
-    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
-    caffe_gpu_mul(N,
-        net_params[param_id]->gpu_diff(),
-        net_params[param_id]->gpu_diff(),
-        val_t->mutable_gpu_data());
-    caffe_gpu_axpby(N, Dtype(1)-beta2,
-        val_t->gpu_data(), beta2,
-        val_v->mutable_gpu_data());
-
-    // set update
-    caffe_gpu_powx(N,
-        val_v->gpu_data(), Dtype(0.5),
-        val_t->mutable_gpu_data());
-    caffe_gpu_add_scalar(N, eps_hat,
-        val_t->mutable_gpu_data());
-    caffe_gpu_div(N,
-        val_m->gpu_data(),
-        val_t->gpu_data(),
-        val_t->mutable_gpu_data());
-
-    caffe_gpu_scale(N, local_rate*correction,
-        val_t->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
+    adam_update_gpu(N, net_params[param_id]->mutable_gpu_diff(),
+        val_m->mutable_gpu_data(), val_v->mutable_gpu_data(), beta1, beta2,
+        eps_hat, local_rate*correction);
 #else
     NO_GPU;
 #endif
diff --git a/src/caffe/solvers/adam_solver.cu b/src/caffe/solvers/adam_solver.cu
new file mode 100644
index 00000000000..917ae100246
--- /dev/null
+++ b/src/caffe/solvers/adam_solver.cu
@@ -0,0 +1,29 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdamUpdate(int N, Dtype* g, Dtype* m, Dtype* v,
+    Dtype beta1, Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float mi = m[i] = m[i]*beta1 + gi*(1-beta1);
+    float vi = v[i] = v[i]*beta2 + gi*gi*(1-beta2);
+    g[i] = corrected_local_rate * mi / (sqrt(vi) + eps_hat);
+  }
+}
+template <typename Dtype>
+void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1,
+    Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) {
+  AdamUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, m, v, beta1, beta2, eps_hat, corrected_local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void adam_update_gpu<float>(int, float*, float*, float*,
+    float, float, float, float);
+template void adam_update_gpu<double>(int, double*, double*, double*,
+    double, double, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp
index 34bf01ebf29..23ab2d4369a 100644
--- a/src/caffe/solvers/nesterov_solver.cpp
+++ b/src/caffe/solvers/nesterov_solver.cpp
@@ -4,6 +4,12 @@
 
 namespace caffe {
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate);
+#endif
+
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   CHECK(Caffe::root_solver());
@@ -36,25 +42,10 @@ void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    // save history momentum for stepping back
-    caffe_copy(net_params[param_id]->count(),
-        this->history_[param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-              net_params[param_id]->gpu_diff(), momentum,
-              this->history_[param_id]->mutable_gpu_data());
-
-    // compute update: step back then over step
-    caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-        this->history_[param_id]->gpu_data(), -momentum,
-        this->update_[param_id]->mutable_gpu_data());
-
-    // copy
-    caffe_copy(net_params[param_id]->count(),
-        this->update_[param_id]->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
+    nesterov_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(),
+        momentum, local_rate);
 #else
     NO_GPU;
 #endif
diff --git a/src/caffe/solvers/nesterov_solver.cu b/src/caffe/solvers/nesterov_solver.cu
new file mode 100644
index 00000000000..57a456b8252
--- /dev/null
+++ b/src/caffe/solvers/nesterov_solver.cu
@@ -0,0 +1,27 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void NesterovUpdate(int N, Dtype* g, Dtype* h,
+    Dtype momentum, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float hi = h[i];
+    float hi_new = h[i] = momentum * hi + local_rate * g[i];
+    g[i] = (1+momentum) * hi_new - momentum * hi;
+  }
+}
+template <typename Dtype>
+void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate) {
+  NesterovUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, momentum, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void nesterov_update_gpu<float>(int, float*, float*, float, float);
+template void nesterov_update_gpu<double>(int, double*, double*, double,
+    double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp
index c6247676094..3251ee423a7 100644
--- a/src/caffe/solvers/rmsprop_solver.cpp
+++ b/src/caffe/solvers/rmsprop_solver.cpp
@@ -4,6 +4,12 @@
 
 namespace caffe {
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay,
+    Dtype delta, Dtype local_rate);
+#endif
+
 template <typename Dtype>
 void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
@@ -45,31 +51,10 @@ void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
     break;
   case Caffe::GPU:
 #ifndef CPU_ONLY
-    // compute square of gradient in update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), Dtype(2),
-        this->update_[param_id]->mutable_gpu_data());
-
-    // update history
-    caffe_gpu_axpby(net_params[param_id] -> count(),
-        Dtype(1-rms_decay), this->update_[param_id]->gpu_data(),
-        rms_decay, this->history_[param_id]-> mutable_gpu_data());
-
-    // prepare update
-    caffe_gpu_powx(net_params[param_id]->count(),
-        this->history_[param_id]->gpu_data(), Dtype(0.5),
-        this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_add_scalar(net_params[param_id]->count(),
-        delta, this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_div(net_params[param_id]->count(),
-        net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(),
-        this->update_[param_id]->mutable_gpu_data());
-
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-        this->update_[param_id]->gpu_data(), Dtype(0),
-        net_params[param_id]->mutable_gpu_diff());
+    rmsprop_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(),
+        rms_decay, delta, local_rate);
 #else
     NO_GPU;
 #endif
diff --git a/src/caffe/solvers/rmsprop_solver.cu b/src/caffe/solvers/rmsprop_solver.cu
new file mode 100644
index 00000000000..c5ffd329d77
--- /dev/null
+++ b/src/caffe/solvers/rmsprop_solver.cu
@@ -0,0 +1,28 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void RMSPropUpdate(int N, Dtype* g, Dtype* h,
+    Dtype rms_decay, Dtype delta, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float hi = h[i] = rms_decay*h[i] + (1-rms_decay)*gi*gi;
+    g[i] = local_rate * g[i] / (sqrt(hi) + delta);
+  }
+}
+template <typename Dtype>
+void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay,
+    Dtype delta, Dtype local_rate) {
+  RMSPropUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, rms_decay, delta, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void rmsprop_update_gpu<float>(int, float*, float*, float, float,
+    float);
+template void rmsprop_update_gpu<double>(int, double*, double*, double, double,
+    double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index 32bf19b17c8..f30f316d1a0 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -203,6 +203,12 @@ void SGDSolver<Dtype>::Regularize(int param_id) {
   }
 }
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate);
+#endif
+
 template <typename Dtype>
 void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
@@ -222,12 +228,10 @@ void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
   }
   case Caffe::GPU: {
 #ifndef CPU_ONLY
-    caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-              net_params[param_id]->gpu_diff(), momentum,
-              history_[param_id]->mutable_gpu_data());
-    caffe_copy(net_params[param_id]->count(),
-        history_[param_id]->gpu_data(),
-        net_params[param_id]->mutable_gpu_diff());
+    sgd_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        history_[param_id]->mutable_gpu_data(),
+        momentum, local_rate);
 #else
     NO_GPU;
 #endif
diff --git a/src/caffe/solvers/sgd_solver.cu b/src/caffe/solvers/sgd_solver.cu
new file mode 100644
index 00000000000..e5410352140
--- /dev/null
+++ b/src/caffe/solvers/sgd_solver.cu
@@ -0,0 +1,24 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void SGDUpdate(int N, Dtype* g, Dtype* h,
+    Dtype momentum, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    g[i] = h[i] = momentum*h[i] + local_rate*g[i];
+  }
+}
+template <typename Dtype>
+void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate) {
+  SGDUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, momentum, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void sgd_update_gpu<float>(int, float*, float*, float, float);
+template void sgd_update_gpu<double>(int, double*, double*, double, double);
+
+}  // namespace caffe

From 672f30ece38b41c0133d83501882551c53610885 Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Wed, 6 Jan 2016 07:23:35 -0800
Subject: [PATCH 1670/2053] CMake python version fix

---
 cmake/Dependencies.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 51a803c1a73..c7b6a17aa69 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -114,14 +114,14 @@ if(BUILD_python)
     # Find the matching boost python implementation
     set(version ${PYTHONLIBS_VERSION_STRING})
     
-    STRING( REPLACE "." "" boost_py_version ${version} )
+    STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
     find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
     set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
     
     while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
       STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
       
-      STRING( REPLACE "." "" boost_py_version ${version} )
+      STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
       find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
       set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
       

From 581c1cc3fd6c04640c4b89e5ed003a40cd67e855 Mon Sep 17 00:00:00 2001
From: Mariusz Moczala <mariusz.moczala@intel.com>
Date: Wed, 20 Jan 2016 09:28:35 +0100
Subject: [PATCH 1671/2053] Performance related update of im2col() and col2im()
 functions

---
 src/caffe/util/im2col.cpp | 93 +++++++++++++++++++++++++--------------
 1 file changed, 61 insertions(+), 32 deletions(-)

diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 6e5ea875757..114a86cb81e 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -5,6 +5,16 @@
 
 namespace caffe {
 
+// Function uses casting from int to unsigned to compare if value of
+// parameter a is greater or equal to zero and lower than value of
+// parameter b. The b parameter is of type signed and is always positive,
+// therefore its value is always lower than 0x800... where casting
+// negative value of a parameter converts it to value higher than 0x800...
+// The casting allows to use one condition instead of two.
+inline bool is_a_ge_zero_and_a_lt_b(int a, int b) {
+  return static_cast<unsigned>(a) < static_cast<unsigned>(b);
+}
+
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
@@ -12,22 +22,33 @@ void im2col_cpu(const Dtype* data_im, const int channels,
     const int stride_h, const int stride_w,
     const int dilation_h, const int dilation_w,
     Dtype* data_col) {
-  const int height_col = (height + 2 * pad_h -
-      (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
-  const int width_col = (width + 2 * pad_w -
-      (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-  const int channels_col = channels * kernel_h * kernel_w;
-  for (int c_col = 0; c_col < channels_col; ++c_col) {
-    int w_offset = c_col % kernel_w;
-    int h_offset = (c_col / kernel_w) % kernel_h;
-    int c_im = c_col / kernel_h / kernel_w;
-    for (int h_col = 0; h_col < height_col; ++h_col) {
-      for (int w_col = 0; w_col < width_col; ++w_col) {
-        int h_im = h_col * stride_h - pad_h + h_offset * dilation_h;
-        int w_im = w_col * stride_w - pad_w + w_offset * dilation_w;
-        data_col[(c_col * height_col + h_col) * width_col + w_col] =
-            (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
-            data_im[(c_im * height + h_im) * width + w_im] : 0;
+  const int output_h = (height + 2 * pad_h -
+    (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int output_w = (width + 2 * pad_w -
+    (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+  const int channel_size = height * width;
+  for (int channel = channels; channel--; data_im += channel_size) {
+    for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
+      for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
+        int input_row = -pad_h + kernel_row * dilation_h;
+        for (int output_rows = output_h; output_rows; output_rows--) {
+          if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
+            for (int output_cols = output_w; output_cols; output_cols--) {
+              *(data_col++) = 0;
+            }
+          } else {
+            int input_col = -pad_w + kernel_col * dilation_w;
+            for (int output_col = output_w; output_col; output_col--) {
+              if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
+                *(data_col++) = data_im[input_row * width + input_col];
+              } else {
+                *(data_col++) = 0;
+              }
+              input_col += stride_w;
+            }
+          }
+          input_row += stride_h;
+        }
       }
     }
   }
@@ -146,22 +167,30 @@ void col2im_cpu(const Dtype* data_col, const int channels,
     const int dilation_h, const int dilation_w,
     Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
-  const int height_col = (height + 2 * pad_h -
-      (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
-  const int width_col = (width + 2 * pad_w -
-      (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
-  const int channels_col = channels * kernel_h * kernel_w;
-  for (int c_col = 0; c_col < channels_col; ++c_col) {
-    int w_offset = c_col % kernel_w;
-    int h_offset = (c_col / kernel_w) % kernel_h;
-    int c_im = c_col / kernel_h / kernel_w;
-    for (int h_col = 0; h_col < height_col; ++h_col) {
-      for (int w_col = 0; w_col < width_col; ++w_col) {
-        int h_im = h_col * stride_h - pad_h + h_offset * dilation_h;
-        int w_im = w_col * stride_w - pad_w + w_offset * dilation_w;
-        if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width)
-          data_im[(c_im * height + h_im) * width + w_im] +=
-              data_col[(c_col * height_col + h_col) * width_col + w_col];
+  const int output_h = (height + 2 * pad_h -
+    (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int output_w = (width + 2 * pad_w -
+    (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+  const int channel_size = height * width;
+  for (int channel = channels; channel--; data_im += channel_size) {
+    for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
+      for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
+        int input_row = -pad_h + kernel_row * dilation_h;
+        for (int output_rows = output_h; output_rows; output_rows--) {
+          if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
+            data_col += output_w;
+          } else {
+            int input_col = -pad_w + kernel_col * dilation_w;
+            for (int output_col = output_w; output_col; output_col--) {
+              if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
+                data_im[input_row * width + input_col] += *data_col;
+              }
+              data_col++;
+              input_col += stride_w;
+            }
+          }
+          input_row += stride_h;
+        }
       }
     }
   }

From de31e034e5570056666d161ce10078011b0f1601 Mon Sep 17 00:00:00 2001
From: biluochun <biluochun@wacai.com>
Date: Wed, 20 Jan 2016 17:53:24 +0800
Subject: [PATCH 1672/2053] fixbug #issues/3494 No to_python (by-value)
 converter found for C++ type: boost::shared_ptr<caffe::Blob<float> >

---
 python/caffe/_caffe.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 4ea2ec60b7e..bd3128cbde7 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -271,6 +271,7 @@ BOOST_PYTHON_MODULE(_caffe) {
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
           NdarrayCallPolicies()));
+  bp::register_ptr_to_python<shared_ptr<Blob<Dtype> > >();
 
   bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
     boost::noncopyable>("Layer", bp::init<const LayerParameter&>())

From d0100ba632b767d2242c10fd1bd3e5782494c079 Mon Sep 17 00:00:00 2001
From: thatguymike <mike.houston@gmail.com>
Date: Tue, 19 Jan 2016 17:01:34 -0800
Subject: [PATCH 1673/2053] Workaround for inplace max pooling issue

---
 src/caffe/layer_factory.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 4d912d28351..e967bd6181c 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -91,7 +91,16 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
                 << "Using Caffe's own pooling layer.";
       return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
     }
-    return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
+    // CuDNN assumes layers are not being modified in place, thus
+    // breaking our index tracking for updates in some cases in Caffe.
+    // Until there is a workaround in Caffe (index management) or
+    // cuDNN, use Caffe layer to max pooling, or don't use in place
+    // layers after max pooling layers
+    if (param.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) {
+        return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
+    } else {
+        return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
+    }
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";

From 8fea1f580933f9e1130d3713e993f156dda3116d Mon Sep 17 00:00:00 2001
From: biluochun <biluochun@wacai.com>
Date: Thu, 21 Jan 2016 13:16:47 +0800
Subject: [PATCH 1674/2053] add register Net and Solver

---
 python/caffe/_caffe.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index bd3128cbde7..0421c1c657d 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -252,6 +252,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("_set_input_arrays", &Net_SetInputArrays,
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
     .def("save", &Net_Save);
+  bp::register_ptr_to_python<shared_ptr<Net<Dtype> > >();
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
     "Blob", bp::no_init)
@@ -295,6 +296,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("step", &Solver<Dtype>::Step)
     .def("restore", &Solver<Dtype>::Restore)
     .def("snapshot", &Solver<Dtype>::Snapshot);
+  bp::register_ptr_to_python<shared_ptr<Solver<Dtype> > >();
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(

From d95998129d4a306693a7228905688ddfcffa2f49 Mon Sep 17 00:00:00 2001
From: Robbie Cooper <cooperra@users.noreply.github.com>
Date: Thu, 21 Jan 2016 14:11:00 -0500
Subject: [PATCH 1675/2053] Add makefile config option for linking Python 3
 libraries

---
 Makefile                | 2 +-
 Makefile.config.example | 5 +++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 985fffd6c0b..ac7d12e298c 100644
--- a/Makefile
+++ b/Makefile
@@ -191,7 +191,7 @@ ifeq ($(USE_OPENCV), 1)
 	endif
 		
 endif
-PYTHON_LIBRARIES := boost_python python2.7
+PYTHON_LIBRARIES ?= boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
 ##############################
diff --git a/Makefile.config.example b/Makefile.config.example
index 1dd6a8f7c7c..8fd49c9c1a7 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -70,6 +70,11 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 		# $(ANACONDA_HOME)/include/python2.7 \
 		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
 
+# Uncomment to use Python 3 (default is Python 2)
+# PYTHON_LIBRARIES := boost_python3 python3.5m
+# PYTHON_INCLUDE := /usr/include/python3.5m \
+#                 /usr/lib/python3.5/dist-packages/numpy/core/include
+
 # We need to be able to find libpythonX.X.so or .dylib.
 PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(ANACONDA_HOME)/lib

From 1954f0f76eb9129c8bf9f34814750dbd5b5e46c9 Mon Sep 17 00:00:00 2001
From: Jun Shi <junshi@yahoo-inc.com>
Date: Fri, 22 Jan 2016 05:09:21 -0800
Subject: [PATCH 1676/2053] copy proto to distribute directory

---
 Makefile | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/Makefile b/Makefile
index 985fffd6c0b..5a6e74f617f 100644
--- a/Makefile
+++ b/Makefile
@@ -651,6 +651,8 @@ superclean: clean supercleanfiles
 $(DIST_ALIASES): $(DISTRIBUTE_DIR)
 
 $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
+	# add proto
+	cp -r src/caffe/proto $(DISTRIBUTE_DIR)/
 	# add include
 	cp -r include $(DISTRIBUTE_DIR)/
 	mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto

From ec04197479d263d1c4801639f5635ceb3e7dcef1 Mon Sep 17 00:00:00 2001
From: Dmytro Mishkin <ducha.aiki@gmail.com>
Date: Thu, 14 Jan 2016 17:10:11 +0200
Subject: [PATCH 1677/2053] Add ChannelwiseAffine for batch norm

---
 .../caffe/layers/channelwise_affine_layer.hpp | 103 ++++++++++
 src/caffe/layers/channelwise_affine_layer.cpp | 189 ++++++++++++++++++
 src/caffe/layers/channelwise_affine_layer.cu  | 144 +++++++++++++
 src/caffe/proto/caffe.proto                   |  14 +-
 .../test/test_channelwise_affine_layer.cpp    | 105 ++++++++++
 5 files changed, 554 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/channelwise_affine_layer.hpp
 create mode 100644 src/caffe/layers/channelwise_affine_layer.cpp
 create mode 100644 src/caffe/layers/channelwise_affine_layer.cu
 create mode 100644 src/caffe/test/test_channelwise_affine_layer.cpp

diff --git a/include/caffe/layers/channelwise_affine_layer.hpp b/include/caffe/layers/channelwise_affine_layer.hpp
new file mode 100644
index 00000000000..6d8ac98b6ed
--- /dev/null
+++ b/include/caffe/layers/channelwise_affine_layer.hpp
@@ -0,0 +1,103 @@
+#ifndef CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_
+#define CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_
+
+#include <vector>
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+    /**
+     * @brief Affine non-linearity function @f$
+     *         y = ax+b
+     *     @f$, could be used after batch normalization layer
+     *
+     */
+template <typename Dtype>
+class ChannelwiseAffineLayer : public NeuronLayer<Dtype> {
+ public:
+      /**
+       * @param param provides ChannelwiseAffineParameter ChannelwiseAffine_param,
+       *     with ChannelwiseAffineLayer options:
+       *   - slope_filler (\b optional, FillerParameter,
+        *     default {'type': constant 'value':1.0001}).
+      *   - bias_filler (\b optional, FillerParameter,
+        *     default {'type': constant 'value':0.0001}).
+      *   - channel_shared (\b optional, default false).
+      *     slopes and biases are shared across channels.
+      */
+     explicit ChannelwiseAffineLayer(const LayerParameter& param)
+         : NeuronLayer<Dtype>(param) {}
+     virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+     virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                             const vector<Blob<Dtype>*>& top);
+     virtual inline const char* type() const { return "ChannelwiseAffine"; }
+
+ protected:
+     /**
+      * @param bottom input Blob vector (length 1)
+      *   -# @f$ (N \times C \times ...) @f$
+      *      the inputs @f$ x @f$
+      * @param top output Blob vector (length 1)
+      *   -# @f$ (N \times C \times ...) @f$
+      *      the computed outputs for each channel @f$i@f$ @f$
+      *        y_i = a_i x_i + b_i
+      *      @f$.
+      */
+     virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                              const vector<Blob<Dtype>*>& top);
+     virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                              const vector<Blob<Dtype>*>& top);
+     /**
+      * @brief Computes the error gradient w.r.t. the ChannelwiseAffine inputs.
+      *
+      * @param top output Blob vector (length 1), providing the error gradient with
+      *      respect to the outputs
+      *   -# @f$ (N \times C \times ...) @f$
+      *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+      *      with respect to computed outputs @f$ y @f$
+      * @param propagate_down see Layer::Backward.
+      * @param bottom input Blob vector (length 1)
+      *   -# @f$ (N \times C \times ...) @f$
+      *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
+      *      diff with gradients @f$
+      *        \frac{\partial E}{\partial x_i} = \left\{
+       *        \begin{array}{lr}
+          *            a_i \frac{\partial E}{\partial y_i}
+      *        \end{array} \right.
+      *      @f$.
+      *      If param_propagate_down_[0] is true, it fills the diff with gradients
+      *      @f$
+      *        \frac{\partial E}{\partial a_i} = \left\{
+         *        \begin{array}{lr}
+         *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i}
+          *        \end{array} \right.
+          *      @f$.
+          *      If param_propagate_down_[1] is true, it fills the diff with gradients
+          *      @f$
+          *        \frac{\partial E}{\partial b_i} = \left\{
+              *        \begin{array}{lr}
+              *             frac{\partial E}{\partial y_i}
+              *        \end{array} \right.
+          *      @f$.
+          */
+     virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+                               const vector<bool>& propagate_down,
+                               const vector<Blob<Dtype>*>& bottom);
+     virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                               const vector<bool>& propagate_down,
+                               const vector<Blob<Dtype>*>& bottom);
+     bool channel_shared_;
+     Blob<Dtype> multiplier_;
+     // dot multiplier for backward computation of params
+     Blob<Dtype> bias_multiplier_;
+     Blob<Dtype> backward_buff_;
+     // temporary buffer for backward computation
+     Blob<Dtype> bottom_memory_;
+     // memory for in-place computation
+};
+}  // namespace caffe
+
+#endif  // CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_
diff --git a/src/caffe/layers/channelwise_affine_layer.cpp b/src/caffe/layers/channelwise_affine_layer.cpp
new file mode 100644
index 00000000000..e9f31fb10e3
--- /dev/null
+++ b/src/caffe/layers/channelwise_affine_layer.cpp
@@ -0,0 +1,189 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/channelwise_affine_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ChannelwiseAffineLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  ChannelwiseAffineParameter channelwise_affine_param =
+        this->layer_param().channelwise_affine_param();
+  int channels = bottom[0]->channels();
+  channel_shared_ = channelwise_affine_param.channel_shared();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(2);
+    if (channel_shared_) {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(0)));
+      this->blobs_[1].reset(new Blob<Dtype>(vector<int>(0)));
+
+    } else {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(1, channels)));
+      this->blobs_[1].reset(new Blob<Dtype>(vector<int>(1, channels)));
+    }
+    shared_ptr<Filler<Dtype> > filler;
+    if (channelwise_affine_param.has_slope_filler()) {
+      filler.reset(GetFiller<Dtype>(channelwise_affine_param.slope_filler()));
+    } else {
+      FillerParameter filler_param;
+      filler_param.set_type("constant");
+      filler_param.set_value(1.0001);
+      filler.reset(GetFiller<Dtype>(filler_param));
+    }
+    filler->Fill(this->blobs_[0].get());
+
+    if (channelwise_affine_param.has_bias_filler()) {
+      filler.reset(GetFiller<Dtype>(channelwise_affine_param.bias_filler()));
+    } else {
+      FillerParameter filler_param;
+      filler_param.set_type("constant");
+      filler_param.set_value(0.0001);
+      filler.reset(GetFiller<Dtype>(filler_param));
+    }
+    filler->Fill(this->blobs_[1].get());
+  }
+  if (channel_shared_) {
+    CHECK_EQ(this->blobs_[0]->count(), 1)
+        << "Slope size is inconsistent with prototxt config";
+  } else {
+    CHECK_EQ(this->blobs_[0]->count(), channels)
+        << "Slope size is inconsistent with prototxt config";
+  }
+
+  // Propagate gradients to the parameters (as directed by backward pass).
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+  multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  bias_multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  backward_buff_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  caffe_set(multiplier_.count(), Dtype(1.0),
+            multiplier_.mutable_cpu_data());
+  caffe_set(bias_multiplier_.count(), Dtype(1.0),
+            bias_multiplier_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void ChannelwiseAffineLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  top[0]->ReshapeLike(*bottom[0]);
+  if (bottom[0] == top[0]) {
+    // For in-place computation
+    bottom_memory_.ReshapeLike(*bottom[0]);
+  }
+  int height  = 1;
+  int width = 1;
+  if (bottom[0]->num_axes() > 2) {
+    height = bottom[0]->shape(2);
+    width = bottom[0]->shape(3);
+  }
+  vector<int> bias_multiplier_shape(1, height * width);
+  bias_multiplier_.Reshape(bias_multiplier_shape);
+  caffe_set(bias_multiplier_.count(), Dtype(1),
+      bias_multiplier_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void ChannelwiseAffineLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+  const Dtype* slope_data = this->blobs_[0]->cpu_data();
+  const Dtype* bias_data = this->blobs_[1]->cpu_data();
+  // For in-place computation
+  if (bottom[0] == top[0]) {
+    caffe_copy(count, bottom_data, bottom_memory_.mutable_cpu_data());
+  }
+  // if channel_shared, channel index in the following computation becomes
+  // always zero.
+  const int div_factor = channel_shared_ ? channels : 1;
+  for (int i = 0; i < count; ++i) {
+    int c = (i / dim) % channels / div_factor;
+    top_data[i] = bottom_data[i] * slope_data[c] + bias_data[c];
+     }
+}
+
+template <typename Dtype>
+void ChannelwiseAffineLayer<Dtype>::Backward_cpu(
+    const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* slope_data = this->blobs_[0]->cpu_data();
+
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->shape(1);
+  const int num = bottom[0]->shape(0);
+  int height = 1;
+  int width = 1;
+  if (bottom[0]->num_axes() > 2) {
+    height = bottom[0]->shape(2);
+    width = bottom[0]->shape(3);
+  }
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    bottom_data = bottom_memory_.cpu_data();
+  }
+
+  // if channel_shared, channel index in the following computation becomes
+  // always zero.
+  const int div_factor = channel_shared_ ? channels : 1;
+
+  // Propagte to param
+  // Since to write bottom diff will affect top diff if top and bottom blobs
+  // are identical (in-place computaion), we first compute param backward to
+  // keep top_diff unchanged.
+
+  if (this->param_propagate_down_[1]) {
+    Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
+    for (int n = 0; n < num; ++n) {
+      caffe_cpu_gemv<Dtype>(CblasNoTrans, channels, height * width, 1.,
+        top_diff + top[0]->offset(n),
+        bias_multiplier_.cpu_data(), 1., bias_diff);
+    }
+  }
+  if (this->param_propagate_down_[0]) {
+    Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
+        caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff);
+    for (int i = 0; i < count; ++i) {
+      int c = (i / dim) % channels / div_factor;
+      slope_diff[c] += top_diff[i] * bottom_data[i];
+    }
+  }
+
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    for (int i = 0; i < count; ++i) {
+      int c = (i / dim) % channels / div_factor;
+      bottom_diff[i] = slope_data[c] * top_diff[i];
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(ChannelwiseAffineLayer);
+#endif
+
+INSTANTIATE_CLASS(ChannelwiseAffineLayer);
+REGISTER_LAYER_CLASS(ChannelwiseAffine);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/channelwise_affine_layer.cu b/src/caffe/layers/channelwise_affine_layer.cu
new file mode 100644
index 00000000000..2066b26560b
--- /dev/null
+++ b/src/caffe/layers/channelwise_affine_layer.cu
@@ -0,0 +1,144 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/channelwise_affine_layer.hpp"
+
+namespace caffe {
+
+// CUDA kernel for forward
+template <typename Dtype>
+__global__ void ChannelwiseAffineForward(const int n, const int channels,
+    const int dim, const Dtype* in, Dtype* out, const Dtype* slope_data,
+    const Dtype* bias_data, const int div_factor) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int c = (index / dim) % channels / div_factor;
+    out[index] = in[index] * slope_data[c] + bias_data[c];
+  }
+}
+
+// CUDA kernel for bottom backward
+template <typename Dtype>
+__global__ void ChannelwiseAffineBackward(const int n,
+    const int channels, const int dim, const Dtype* in_diff,
+    Dtype* out_diff, const Dtype* slope_data, const int div_factor) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int c = (index / dim) % channels / div_factor;
+    out_diff[index] = slope_data[c] * in_diff[index];
+  }
+}
+
+// CUDA kernel for element-wise parameter backward
+template <typename Dtype>
+__global__ void ChannelwiseAffineParamSlopeBackward(const int n,
+    const int rows, const int rowPitch, const Dtype* in_diff,
+    const Dtype* in_data, Dtype* out_diff) {
+  CUDA_KERNEL_LOOP(index, n) {
+      out_diff[index] = in_diff[index] * in_data[index];
+      for ( int k = 1; k < rows; k++ ) {
+          out_diff[index] += in_diff[index + k*rowPitch]
+          * in_data[index + k*rowPitch];
+      }
+  }
+}
+
+template <typename Dtype>
+void ChannelwiseAffineLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->channels();
+  const Dtype* slope_data = this->blobs_[0]->gpu_data();
+  const Dtype* bias_data = this->blobs_[1]->gpu_data();
+  const int div_factor = channel_shared_ ? channels : 1;
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    caffe_copy(count, bottom_data, bottom_memory_.mutable_gpu_data());
+  }
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ChannelwiseAffineForward<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(
+      count, channels, dim, bottom_data, top_data,
+      slope_data, bias_data, div_factor);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+void ChannelwiseAffineLayer<Dtype>::Backward_gpu(
+    const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const int count = bottom[0]->count();
+  const int num = bottom[0]->shape(0);
+  const int dim = bottom[0]->count(2);
+  const int channels = bottom[0]->shape(1);
+  int height  = 1;
+  int width = 1;
+  if (bottom[0]->num_axes() > 2) {
+    height = bottom[0]->shape(2);
+    width = bottom[0]->shape(3);
+  }
+
+  // For in-place computation
+  if (top[0] == bottom[0]) {
+    bottom_data = bottom_memory_.gpu_data();
+  }
+  // Propagate to param
+  // Since to write bottom diff will affect top diff if top and bottom blobs
+  // are identical (in-place computaion), we first compute param backward to
+  // keep top_diff unchanged.
+  if (this->param_propagate_down_[1]) {
+      Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+      caffe_gpu_set(this->blobs_[1]->count(), Dtype(0.0), bias_diff);
+    // Gradient with respect to bias
+      for (int n = 0; n < num; ++n) {
+          caffe_gpu_gemv<Dtype>(
+            CblasNoTrans, channels, height * width, (Dtype)1.,
+            top_diff + top[0]->offset(n), bias_multiplier_.gpu_data(),
+            (Dtype)1., bias_diff);
+      }
+  }
+  if (this->param_propagate_down_[0]) {
+    Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
+    int cdim = channels * dim;
+      // compute element-wise diff
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      ChannelwiseAffineParamSlopeBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
+          CAFFE_CUDA_NUM_THREADS>>>(
+          cdim, num, top[0]->offset(1), top_diff ,
+          bottom_data,
+          backward_buff_.mutable_gpu_diff());
+      CUDA_POST_KERNEL_CHECK;
+      if (channel_shared_) {
+        Dtype d = 0;
+        caffe_gpu_dot<Dtype>(cdim, backward_buff_.gpu_diff(),
+            multiplier_.gpu_data(), &d);
+        caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(d), slope_diff);
+      } else {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, Dtype(1.),
+             backward_buff_.gpu_diff(), multiplier_.gpu_data(), Dtype(1.),
+            slope_diff);
+      }
+  }
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const Dtype* slope_data = this->blobs_[0]->gpu_data();
+    int div_factor = channel_shared_ ? channels : 1;
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    ChannelwiseAffineBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(
+        count, channels, dim, top_diff, bottom_diff, slope_data, div_factor);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ChannelwiseAffineLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f873deba10c..fe6209cf673 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 141 (last added: elu_param)
+// LayerParameter next available layer-specific ID: 142 (last added: channelwise_affine_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -356,6 +356,7 @@ message LayerParameter {
   optional AccuracyParameter accuracy_param = 102;
   optional ArgMaxParameter argmax_param = 103;
   optional BatchNormParameter batch_norm_param = 139;
+  optional ChannelwiseAffineParameter channelwise_affine_param = 141;
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
@@ -498,6 +499,17 @@ message BatchNormParameter {
   optional float eps = 3 [default = 1e-5];
 }
 
+message ChannelwiseAffineParameter {
+
+  // Initial value of a_i. Default is a_i=1.0 for all i.
+  optional FillerParameter slope_filler = 1;
+
+  optional FillerParameter bias_filler = 2;
+
+  // Whether or not slope paramters are shared across channels.
+  optional bool channel_shared = 3 [default = false];
+}
+
 message ContrastiveLossParameter {
   // margin for dissimilar pair
   optional float margin = 1 [default = 1.0];
diff --git a/src/caffe/test/test_channelwise_affine_layer.cpp b/src/caffe/test/test_channelwise_affine_layer.cpp
new file mode 100644
index 00000000000..a3e2544f77a
--- /dev/null
+++ b/src/caffe/test/test_channelwise_affine_layer.cpp
@@ -0,0 +1,105 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/channelwise_affine_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ChannelwiseAffineLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ChannelwiseAffineLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ChannelwiseAffineLayerTest() {
+      delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+
+  void TestChannelwiseAffine(ChannelwiseAffineLayer<Dtype> *layer) {
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    const Dtype* slope_data = layer->blobs()[0]->cpu_data();
+    const Dtype* bias_data = layer->blobs()[1]->cpu_data();
+    const Dtype kDelta = 2e-5;
+    int hw = this->blob_bottom_->height() * this->blob_bottom_->width();
+    int channels = this->blob_bottom_->channels();
+    bool channel_shared =
+        layer->layer_param().channelwise_affine_param().channel_shared();
+    for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+          int c = channel_shared ? 0 : (i / hw) % channels;
+          EXPECT_NEAR(top_data[i],
+                       bottom_data[i]* slope_data[c] + bias_data[c], kDelta);
+        }
+  }
+};
+TYPED_TEST_CASE(ChannelwiseAffineLayerTest, TestDtypesAndDevices);
+
+
+TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineForward) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    ChannelwiseAffineLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(layer.blobs()[0].get());
+    filler.Fill(layer.blobs()[1].get());
+    this->TestChannelwiseAffine(&layer);
+}
+
+TYPED_TEST(ChannelwiseAffineLayerTest,
+           TestChannelwiseAffineForwardChannelShared) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    layer_param.mutable_channelwise_affine_param()->set_channel_shared(true);
+    ChannelwiseAffineLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    this->TestChannelwiseAffine(&layer);
+}
+
+TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineGradient) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    layer_param.mutable_channelwise_affine_param()->set_channel_shared(false);
+    ChannelwiseAffineLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                    this->blob_top_vec_);
+}
+
+TYPED_TEST(ChannelwiseAffineLayerTest,
+           TestChannelwiseAffineGradientChannelShared) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    layer_param.mutable_channelwise_affine_param()->set_channel_shared(true);
+    ChannelwiseAffineLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                    this->blob_top_vec_);
+}
+
+}  // namespace caffe

From 67b497d4ec70018b168639df1e4342f78fb44bb0 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Fri, 22 Jan 2016 15:30:35 -0800
Subject: [PATCH 1678/2053] Version 1.0.0-rc3

---
 CMakeLists.txt           |  5 +++++
 Makefile                 | 30 +++++++++++++++++++++---------
 cmake/Summary.cmake      |  2 +-
 include/caffe/common.hpp |  4 ++++
 python/caffe/__init__.py |  1 +
 python/caffe/_caffe.cpp  |  3 +++
 src/caffe/CMakeLists.txt |  4 ++++
 tools/caffe.cpp          |  3 +++
 8 files changed, 42 insertions(+), 10 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c446c608952..32cc42ac9de 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -9,6 +9,11 @@ endif()
 # ---[ Caffe project
 project(Caffe C CXX)
 
+# ---[ Caffe version
+set(CAFFE_TARGET_VERSION "1.0.0-rc3")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc3")
+add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
+
 # ---[ Using cmake scripts and modules
 list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
diff --git a/Makefile b/Makefile
index 985fffd6c0b..f3135d35018 100644
--- a/Makefile
+++ b/Makefile
@@ -29,9 +29,17 @@ SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \
 	\( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
 
 # The target shared library name
+LIBRARY_NAME := $(PROJECT)
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
-STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
-DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
+STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
+DYNAMIC_VERSION_MAJOR 		:= 1
+DYNAMIC_VERSION_MINOR 		:= 0
+DYNAMIC_VERSION_REVISION 	:= 0-rc3
+DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
+#DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
+DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
+DYNAMIC_NAME := $(LIB_BUILD_DIR)/$(DYNAMIC_VERSIONED_NAME_SHORT)
+COMMON_FLAGS += -DCAFFE_VERSION=$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
 
 ##############################
 # Get all source files
@@ -253,6 +261,7 @@ ifeq ($(LINUX), 1)
 	# boost::thread is reasonably called boost_thread (compare OS X)
 	# We will also explicitly add stdc++ to the link target.
 	LIBRARIES += boost_thread stdc++
+	VERSIONFLAGS += -Wl,-soname,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../lib
 endif
 
 # OS X:
@@ -276,6 +285,7 @@ ifeq ($(OSX), 1)
 	# we need to explicitly ask for the rpath to be obeyed
 	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
 	ORIGIN := @loader_path
+	VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
 else
 	ORIGIN := \$$ORIGIN
 endif
@@ -478,7 +488,7 @@ py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		-o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \
+		-o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(PYTHON_LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
 mat$(PROJECT): mat
@@ -542,7 +552,8 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo LD -o $@
-	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+	@ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo AR -o $@
@@ -573,19 +584,19 @@ $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
 		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
 # Target for extension-less symlinks to tool binaries with extension '*.bin'.
 $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
@@ -594,12 +605,12 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 
 $(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
-	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../lib
 
 $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
-	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
@@ -661,6 +672,7 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	# add libraries
 	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
 	install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
+	cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python
 
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 557a6f04e4b..ba025cf81e0 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -101,7 +101,7 @@ function(caffe_print_configuration_summary)
   caffe_status("")
   caffe_status("******************* Caffe Configuration Summary *******************")
   caffe_status("General:")
-  caffe_status("  Version           :   ${Caffe_VERSION}")
+  caffe_status("  Version           :   ${CAFFE_TARGET_VERSION}")
   caffe_status("  Git               :   ${Caffe_GIT_VERSION}")
   caffe_status("  System            :   ${CMAKE_SYSTEM_NAME}")
   caffe_status("  C++ compiler      :   ${CMAKE_CXX_COMPILER}")
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 1df6b9a14fb..6b902a42e2d 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -18,6 +18,10 @@
 
 #include "caffe/util/device_alternate.hpp"
 
+// Convert macro to string
+#define STRINGIFY(m) #m
+#define AS_STRING(m) STRINGIFY(m)
+
 // gflags 2.1 issue: namespace google was changed to gflags without warning.
 // Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
 // 2.1. If yes, we will add a temporary solution to redirect the namespace.
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index ccda1bcae4f..e2881b89c1b 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,6 @@
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
 from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
+from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 4ea2ec60b7e..12a574556c3 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -212,6 +212,9 @@ BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
+
+  bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);
+
   // Caffe utility functions
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 40e6c11f5b0..8a80c940488 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -20,6 +20,10 @@ endif()
 add_library(caffe ${srcs})
 target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
 caffe_default_properties(caffe)
+set_target_properties(caffe PROPERTIES
+    VERSION   ${CAFFE_TARGET_VERSION}
+    SOVERSION ${CAFFE_TARGET_SOVERSION}
+    )
 
 # ---[ Tests
  add_subdirectory(test)
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 6b342ace0c5..470165add7f 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -3,6 +3,7 @@
 namespace bp = boost::python;
 #endif
 
+#include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cstring>
@@ -378,6 +379,8 @@ RegisterBrewFunction(time);
 int main(int argc, char** argv) {
   // Print output to stderr (while still logging).
   FLAGS_alsologtostderr = 1;
+  // Set version
+  gflags::SetVersionString(AS_STRING(CAFFE_VERSION));
   // Usage message.
   gflags::SetUsageMessage("command line brew\n"
       "usage: caffe <command> <args>\n\n"

From 081690709e4a199824f433cc196c55c47731073f Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Fri, 22 Jan 2016 15:57:47 -0800
Subject: [PATCH 1679/2053] Separation and generalization of
 ChannelwiseAffineLayer into BiasLayer and ScaleLayer.  The behavior of
 ChannelwiseAffineLayer can be reproduced by a ScaleLayer with `scale_param {
 bias_term: true }`.

BiasLayer and ScaleLayer each take 1 or 2 bottoms, with the output having
the same shape as the first.  The second input -- either another  bottom or a
learned parameter -- will have its axes (virtually) broadcast and tiled to have
the same shape as the first, after which elementwise addition (Bias) or
multiplication (Scale) is performed.
---
 include/caffe/layers/bias_layer.hpp           |  54 ++
 .../caffe/layers/channelwise_affine_layer.hpp | 103 ----
 include/caffe/layers/scale_layer.hpp          |  83 +++
 src/caffe/layers/bias_layer.cpp               | 121 +++++
 src/caffe/layers/bias_layer.cu                |  59 ++
 src/caffe/layers/channelwise_affine_layer.cpp | 189 -------
 src/caffe/layers/channelwise_affine_layer.cu  | 144 -----
 src/caffe/layers/scale_layer.cpp              | 219 ++++++++
 src/caffe/layers/scale_layer.cu               | 135 +++++
 src/caffe/proto/caffe.proto                   |  79 ++-
 src/caffe/test/test_bias_layer.cpp            | 467 ++++++++++++++++
 .../test/test_channelwise_affine_layer.cpp    | 105 ----
 src/caffe/test/test_scale_layer.cpp           | 507 ++++++++++++++++++
 13 files changed, 1714 insertions(+), 551 deletions(-)
 create mode 100644 include/caffe/layers/bias_layer.hpp
 delete mode 100644 include/caffe/layers/channelwise_affine_layer.hpp
 create mode 100644 include/caffe/layers/scale_layer.hpp
 create mode 100644 src/caffe/layers/bias_layer.cpp
 create mode 100644 src/caffe/layers/bias_layer.cu
 delete mode 100644 src/caffe/layers/channelwise_affine_layer.cpp
 delete mode 100644 src/caffe/layers/channelwise_affine_layer.cu
 create mode 100644 src/caffe/layers/scale_layer.cpp
 create mode 100644 src/caffe/layers/scale_layer.cu
 create mode 100644 src/caffe/test/test_bias_layer.cpp
 delete mode 100644 src/caffe/test/test_channelwise_affine_layer.cpp
 create mode 100644 src/caffe/test/test_scale_layer.cpp

diff --git a/include/caffe/layers/bias_layer.hpp b/include/caffe/layers/bias_layer.hpp
new file mode 100644
index 00000000000..eedc3aaa351
--- /dev/null
+++ b/include/caffe/layers/bias_layer.hpp
@@ -0,0 +1,54 @@
+#ifndef CAFFE_BIAS_LAYER_HPP_
+#define CAFFE_BIAS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Computes a sum of two input Blobs, with the shape of the
+ *        latter Blob "broadcast" to match the shape of the former.
+ *        Equivalent to tiling the latter Blob, then computing the elementwise
+ *        sum.
+ *
+ * The second input may be omitted, in which case it's learned as a parameter
+ * of the layer.
+ */
+template <typename Dtype>
+class BiasLayer : public Layer<Dtype> {
+ public:
+  explicit BiasLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Bias"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MaxBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ private:
+  Blob<Dtype> bias_multiplier_;
+  int outer_dim_, bias_dim_, inner_dim_, dim_;
+};
+
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_BIAS_LAYER_HPP_
diff --git a/include/caffe/layers/channelwise_affine_layer.hpp b/include/caffe/layers/channelwise_affine_layer.hpp
deleted file mode 100644
index 6d8ac98b6ed..00000000000
--- a/include/caffe/layers/channelwise_affine_layer.hpp
+++ /dev/null
@@ -1,103 +0,0 @@
-#ifndef CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_
-#define CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_
-
-#include <vector>
-#include "caffe/blob.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/layers/neuron_layer.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-    /**
-     * @brief Affine non-linearity function @f$
-     *         y = ax+b
-     *     @f$, could be used after batch normalization layer
-     *
-     */
-template <typename Dtype>
-class ChannelwiseAffineLayer : public NeuronLayer<Dtype> {
- public:
-      /**
-       * @param param provides ChannelwiseAffineParameter ChannelwiseAffine_param,
-       *     with ChannelwiseAffineLayer options:
-       *   - slope_filler (\b optional, FillerParameter,
-        *     default {'type': constant 'value':1.0001}).
-      *   - bias_filler (\b optional, FillerParameter,
-        *     default {'type': constant 'value':0.0001}).
-      *   - channel_shared (\b optional, default false).
-      *     slopes and biases are shared across channels.
-      */
-     explicit ChannelwiseAffineLayer(const LayerParameter& param)
-         : NeuronLayer<Dtype>(param) {}
-     virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-                             const vector<Blob<Dtype>*>& top);
-     virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-                             const vector<Blob<Dtype>*>& top);
-     virtual inline const char* type() const { return "ChannelwiseAffine"; }
-
- protected:
-     /**
-      * @param bottom input Blob vector (length 1)
-      *   -# @f$ (N \times C \times ...) @f$
-      *      the inputs @f$ x @f$
-      * @param top output Blob vector (length 1)
-      *   -# @f$ (N \times C \times ...) @f$
-      *      the computed outputs for each channel @f$i@f$ @f$
-      *        y_i = a_i x_i + b_i
-      *      @f$.
-      */
-     virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-                              const vector<Blob<Dtype>*>& top);
-     virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-                              const vector<Blob<Dtype>*>& top);
-     /**
-      * @brief Computes the error gradient w.r.t. the ChannelwiseAffine inputs.
-      *
-      * @param top output Blob vector (length 1), providing the error gradient with
-      *      respect to the outputs
-      *   -# @f$ (N \times C \times ...) @f$
-      *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-      *      with respect to computed outputs @f$ y @f$
-      * @param propagate_down see Layer::Backward.
-      * @param bottom input Blob vector (length 1)
-      *   -# @f$ (N \times C \times ...) @f$
-      *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
-      *      diff with gradients @f$
-      *        \frac{\partial E}{\partial x_i} = \left\{
-       *        \begin{array}{lr}
-          *            a_i \frac{\partial E}{\partial y_i}
-      *        \end{array} \right.
-      *      @f$.
-      *      If param_propagate_down_[0] is true, it fills the diff with gradients
-      *      @f$
-      *        \frac{\partial E}{\partial a_i} = \left\{
-         *        \begin{array}{lr}
-         *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i}
-          *        \end{array} \right.
-          *      @f$.
-          *      If param_propagate_down_[1] is true, it fills the diff with gradients
-          *      @f$
-          *        \frac{\partial E}{\partial b_i} = \left\{
-              *        \begin{array}{lr}
-              *             frac{\partial E}{\partial y_i}
-              *        \end{array} \right.
-          *      @f$.
-          */
-     virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-                               const vector<bool>& propagate_down,
-                               const vector<Blob<Dtype>*>& bottom);
-     virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-                               const vector<bool>& propagate_down,
-                               const vector<Blob<Dtype>*>& bottom);
-     bool channel_shared_;
-     Blob<Dtype> multiplier_;
-     // dot multiplier for backward computation of params
-     Blob<Dtype> bias_multiplier_;
-     Blob<Dtype> backward_buff_;
-     // temporary buffer for backward computation
-     Blob<Dtype> bottom_memory_;
-     // memory for in-place computation
-};
-}  // namespace caffe
-
-#endif  // CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_
diff --git a/include/caffe/layers/scale_layer.hpp b/include/caffe/layers/scale_layer.hpp
new file mode 100644
index 00000000000..924df2e51ab
--- /dev/null
+++ b/include/caffe/layers/scale_layer.hpp
@@ -0,0 +1,83 @@
+#ifndef CAFFE_SCALE_LAYER_HPP_
+#define CAFFE_SCALE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/bias_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes a product of two input Blobs, with the shape of the
+ *        latter Blob "broadcast" to match the shape of the former.
+ *        Equivalent to tiling the latter Blob, then computing the elementwise
+ *        product.
+ *
+ * The second input may be omitted, in which case it's learned as a parameter
+ * of the layer.
+ */
+template <typename Dtype>
+class ScaleLayer: public Layer<Dtype> {
+ public:
+  explicit ScaleLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Scale"; }
+  // Scale
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MaxBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * In the below shape specifications, @f$ i @f$ denotes the value of the
+   * `axis` field given by `this->layer_param_.scale_param().axis()`, after
+   * canonicalization (i.e., conversion from negative to positive index,
+   * if applicable).
+   *
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (d_0 \times ... \times
+   *           d_i \times ... \times d_j \times ... \times d_n) @f$
+   *      the first factor @f$ x @f$
+   *   -# @f$ (d_i \times ... \times d_j) @f$
+   *      the second factor @f$ y @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (d_0 \times ... \times
+   *           d_i \times ... \times d_j \times ... \times d_n) @f$
+   *      the product @f$ z = x y @f$ computed after "broadcasting" y.
+   *      Equivalent to tiling @f$ y @f$ to have the same shape as @f$ x @f$,
+   *      then computing the elementwise product.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  shared_ptr<Layer<Dtype> > bias_layer_;
+  vector<Blob<Dtype>*> bias_bottom_vec_;
+  vector<bool> bias_propagate_down_;
+  int bias_param_id_;
+
+  Blob<Dtype> sum_multiplier_;
+  Blob<Dtype> sum_result_;
+  Blob<Dtype> temp_;
+  int axis_;
+  int outer_dim_, scale_dim_, inner_dim_;
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_SCALE_LAYER_HPP_
diff --git a/src/caffe/layers/bias_layer.cpp b/src/caffe/layers/bias_layer.cpp
new file mode 100644
index 00000000000..0a786b5db98
--- /dev/null
+++ b/src/caffe/layers/bias_layer.cpp
@@ -0,0 +1,121 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/bias_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BiasLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1 && this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else if (bottom.size() == 1) {
+    // bias is a learned parameter; initialize it
+    const BiasParameter& param = this->layer_param_.bias_param();
+    const int axis = bottom[0]->CanonicalAxisIndex(param.axis());
+    const int num_axes = param.num_axes();
+    CHECK_GE(num_axes, -1) << "num_axes must be non-negative, "
+                           << "or -1 to extend to the end of bottom[0]";
+    if (num_axes >= 0) {
+      CHECK_GE(bottom[0]->num_axes(), axis + num_axes)
+          << "bias blob's shape extends past bottom[0]'s shape when applied "
+          << "starting with bottom[0] axis = " << axis;
+    }
+    this->blobs_.resize(1);
+    const vector<int>::const_iterator& shape_start =
+        bottom[0]->shape().begin() + axis;
+    const vector<int>::const_iterator& shape_end =
+        (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes);
+    vector<int> bias_shape(shape_start, shape_end);
+    this->blobs_[0].reset(new Blob<Dtype>(bias_shape));
+    shared_ptr<Filler<Dtype> > filler(GetFiller<Dtype>(param.filler()));
+    filler->Fill(this->blobs_[0].get());
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const BiasParameter& param = this->layer_param_.bias_param();
+  Blob<Dtype>* bias = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
+  // Always set axis == 0 in special case where bias is a scalar
+  // (num_axes == 0). Mathematically equivalent for any choice of axis, so the
+  // actual setting can be safely ignored; and computation is most efficient
+  // with axis == 0 and (therefore) outer_dim_ == 1.
+  const int axis = (bias->num_axes() == 0) ?
+      0 : bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_GE(bottom[0]->num_axes(), axis + bias->num_axes())
+      << "bias blob's shape extends past bottom[0]'s shape when applied "
+      << "starting with bottom[0] axis = " << axis;
+  for (int i = 0; i < bias->num_axes(); ++i) {
+    CHECK_EQ(bottom[0]->shape(axis + i), bias->shape(i))
+        << "dimension mismatch between bottom[0]->shape(" << axis + i
+        << ") and bias->shape(" << i << ")";
+  }
+  outer_dim_ = bottom[0]->count(0, axis);
+  bias_dim_ = bias->count();
+  inner_dim_ = bottom[0]->count(axis + bias->num_axes());
+  dim_ = bias_dim_ * inner_dim_;
+  if (bottom[0] != top[0]) {
+    top[0]->ReshapeLike(*bottom[0]);
+  }
+  bias_multiplier_.Reshape(vector<int>(1, inner_dim_));
+  if (bias_multiplier_.cpu_data()[inner_dim_ - 1] != Dtype(1)) {
+    caffe_set(inner_dim_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bias_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (bottom[0] != top[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+  for (int n = 0; n < outer_dim_; ++n) {
+    caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_,
+        inner_dim_, Dtype(1), Dtype(1), bias_data,
+        bias_multiplier_.cpu_data(), Dtype(1), top_data);
+    top_data += dim_;
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0] && bottom[0] != top[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    caffe_copy(bottom[0]->count(), top_diff, bottom_diff);
+  }
+  // in-place, we don't need to do anything with the data diff
+  const bool bias_param = (bottom.size() == 1);
+  if ((!bias_param && propagate_down[1]) ||
+      (bias_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1])
+        ->mutable_cpu_diff();
+    bool accum = bias_param;
+    for (int n = 0; n < outer_dim_; ++n) {
+      caffe_cpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),
+          top_diff, bias_multiplier_.cpu_data(), Dtype(accum), bias_diff);
+      top_diff += dim_;
+      accum = true;
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BiasLayer);
+#endif
+
+INSTANTIATE_CLASS(BiasLayer);
+REGISTER_LAYER_CLASS(Bias);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bias_layer.cu b/src/caffe/layers/bias_layer.cu
new file mode 100644
index 00000000000..8ac913a5d7b
--- /dev/null
+++ b/src/caffe/layers/bias_layer.cu
@@ -0,0 +1,59 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/bias_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void BiasForward(const int n, const Dtype* in,
+    const Dtype* bias, const int bias_dim, const int inner_dim,
+    Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int bias_index = (index / inner_dim) % bias_dim;
+    out[index] = in[index] + bias[bias_index];
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* bias_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  BiasForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, bias_data, bias_dim_, inner_dim_, top_data);
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0] && bottom[0] != top[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_copy(bottom[0]->count(), top_diff, bottom_diff);
+  }
+  // in-place, we don't need to do anything with the data diff
+  const bool bias_param = (bottom.size() == 1);
+  if ((!bias_param && propagate_down[1]) ||
+      (bias_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1])
+        ->mutable_gpu_diff();
+    bool accum = bias_param;
+    for (int n = 0; n < outer_dim_; ++n) {
+      caffe_gpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),
+          top_diff, bias_multiplier_.gpu_data(), Dtype(accum), bias_diff);
+      top_diff += dim_;
+      accum = true;
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BiasLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/channelwise_affine_layer.cpp b/src/caffe/layers/channelwise_affine_layer.cpp
deleted file mode 100644
index e9f31fb10e3..00000000000
--- a/src/caffe/layers/channelwise_affine_layer.cpp
+++ /dev/null
@@ -1,189 +0,0 @@
-#include <algorithm>
-#include <vector>
-
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/layers/channelwise_affine_layer.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void ChannelwiseAffineLayer<Dtype>::LayerSetUp(
-    const vector<Blob<Dtype>*>& bottom,
-    const vector<Blob<Dtype>*>& top) {
-  CHECK_GE(bottom[0]->num_axes(), 2)
-      << "Number of axes of bottom blob must be >=2.";
-  ChannelwiseAffineParameter channelwise_affine_param =
-        this->layer_param().channelwise_affine_param();
-  int channels = bottom[0]->channels();
-  channel_shared_ = channelwise_affine_param.channel_shared();
-  if (this->blobs_.size() > 0) {
-    LOG(INFO) << "Skipping parameter initialization";
-  } else {
-    this->blobs_.resize(2);
-    if (channel_shared_) {
-      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(0)));
-      this->blobs_[1].reset(new Blob<Dtype>(vector<int>(0)));
-
-    } else {
-      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(1, channels)));
-      this->blobs_[1].reset(new Blob<Dtype>(vector<int>(1, channels)));
-    }
-    shared_ptr<Filler<Dtype> > filler;
-    if (channelwise_affine_param.has_slope_filler()) {
-      filler.reset(GetFiller<Dtype>(channelwise_affine_param.slope_filler()));
-    } else {
-      FillerParameter filler_param;
-      filler_param.set_type("constant");
-      filler_param.set_value(1.0001);
-      filler.reset(GetFiller<Dtype>(filler_param));
-    }
-    filler->Fill(this->blobs_[0].get());
-
-    if (channelwise_affine_param.has_bias_filler()) {
-      filler.reset(GetFiller<Dtype>(channelwise_affine_param.bias_filler()));
-    } else {
-      FillerParameter filler_param;
-      filler_param.set_type("constant");
-      filler_param.set_value(0.0001);
-      filler.reset(GetFiller<Dtype>(filler_param));
-    }
-    filler->Fill(this->blobs_[1].get());
-  }
-  if (channel_shared_) {
-    CHECK_EQ(this->blobs_[0]->count(), 1)
-        << "Slope size is inconsistent with prototxt config";
-  } else {
-    CHECK_EQ(this->blobs_[0]->count(), channels)
-        << "Slope size is inconsistent with prototxt config";
-  }
-
-  // Propagate gradients to the parameters (as directed by backward pass).
-  this->param_propagate_down_.resize(this->blobs_.size(), true);
-  multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
-  bias_multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
-  backward_buff_.Reshape(vector<int>(1, bottom[0]->count(1)));
-  caffe_set(multiplier_.count(), Dtype(1.0),
-            multiplier_.mutable_cpu_data());
-  caffe_set(bias_multiplier_.count(), Dtype(1.0),
-            bias_multiplier_.mutable_cpu_data());
-}
-
-template <typename Dtype>
-void ChannelwiseAffineLayer<Dtype>::Reshape(
-    const vector<Blob<Dtype>*>& bottom,
-    const vector<Blob<Dtype>*>& top) {
-  CHECK_GE(bottom[0]->num_axes(), 2)
-      << "Number of axes of bottom blob must be >=2.";
-  top[0]->ReshapeLike(*bottom[0]);
-  if (bottom[0] == top[0]) {
-    // For in-place computation
-    bottom_memory_.ReshapeLike(*bottom[0]);
-  }
-  int height  = 1;
-  int width = 1;
-  if (bottom[0]->num_axes() > 2) {
-    height = bottom[0]->shape(2);
-    width = bottom[0]->shape(3);
-  }
-  vector<int> bias_multiplier_shape(1, height * width);
-  bias_multiplier_.Reshape(bias_multiplier_shape);
-  caffe_set(bias_multiplier_.count(), Dtype(1),
-      bias_multiplier_.mutable_cpu_data());
-}
-
-template <typename Dtype>
-void ChannelwiseAffineLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom,
-    const vector<Blob<Dtype>*>& top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = top[0]->mutable_cpu_data();
-  const int count = bottom[0]->count();
-  const int dim = bottom[0]->count(2);
-  const int channels = bottom[0]->channels();
-  const Dtype* slope_data = this->blobs_[0]->cpu_data();
-  const Dtype* bias_data = this->blobs_[1]->cpu_data();
-  // For in-place computation
-  if (bottom[0] == top[0]) {
-    caffe_copy(count, bottom_data, bottom_memory_.mutable_cpu_data());
-  }
-  // if channel_shared, channel index in the following computation becomes
-  // always zero.
-  const int div_factor = channel_shared_ ? channels : 1;
-  for (int i = 0; i < count; ++i) {
-    int c = (i / dim) % channels / div_factor;
-    top_data[i] = bottom_data[i] * slope_data[c] + bias_data[c];
-     }
-}
-
-template <typename Dtype>
-void ChannelwiseAffineLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down,
-    const vector<Blob<Dtype>*>& bottom) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* slope_data = this->blobs_[0]->cpu_data();
-
-  const Dtype* top_diff = top[0]->cpu_diff();
-  const int count = bottom[0]->count();
-  const int dim = bottom[0]->count(2);
-  const int channels = bottom[0]->shape(1);
-  const int num = bottom[0]->shape(0);
-  int height = 1;
-  int width = 1;
-  if (bottom[0]->num_axes() > 2) {
-    height = bottom[0]->shape(2);
-    width = bottom[0]->shape(3);
-  }
-
-  // For in-place computation
-  if (top[0] == bottom[0]) {
-    bottom_data = bottom_memory_.cpu_data();
-  }
-
-  // if channel_shared, channel index in the following computation becomes
-  // always zero.
-  const int div_factor = channel_shared_ ? channels : 1;
-
-  // Propagte to param
-  // Since to write bottom diff will affect top diff if top and bottom blobs
-  // are identical (in-place computaion), we first compute param backward to
-  // keep top_diff unchanged.
-
-  if (this->param_propagate_down_[1]) {
-    Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
-    for (int n = 0; n < num; ++n) {
-      caffe_cpu_gemv<Dtype>(CblasNoTrans, channels, height * width, 1.,
-        top_diff + top[0]->offset(n),
-        bias_multiplier_.cpu_data(), 1., bias_diff);
-    }
-  }
-  if (this->param_propagate_down_[0]) {
-    Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
-        caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff);
-    for (int i = 0; i < count; ++i) {
-      int c = (i / dim) % channels / div_factor;
-      slope_diff[c] += top_diff[i] * bottom_data[i];
-    }
-  }
-
-  // Propagate to bottom
-  if (propagate_down[0]) {
-    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-    for (int i = 0; i < count; ++i) {
-      int c = (i / dim) % channels / div_factor;
-      bottom_diff[i] = slope_data[c] * top_diff[i];
-    }
-  }
-}
-
-
-#ifdef CPU_ONLY
-STUB_GPU(ChannelwiseAffineLayer);
-#endif
-
-INSTANTIATE_CLASS(ChannelwiseAffineLayer);
-REGISTER_LAYER_CLASS(ChannelwiseAffine);
-
-}  // namespace caffe
diff --git a/src/caffe/layers/channelwise_affine_layer.cu b/src/caffe/layers/channelwise_affine_layer.cu
deleted file mode 100644
index 2066b26560b..00000000000
--- a/src/caffe/layers/channelwise_affine_layer.cu
+++ /dev/null
@@ -1,144 +0,0 @@
-#include <algorithm>
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/layers/channelwise_affine_layer.hpp"
-
-namespace caffe {
-
-// CUDA kernel for forward
-template <typename Dtype>
-__global__ void ChannelwiseAffineForward(const int n, const int channels,
-    const int dim, const Dtype* in, Dtype* out, const Dtype* slope_data,
-    const Dtype* bias_data, const int div_factor) {
-  CUDA_KERNEL_LOOP(index, n) {
-    int c = (index / dim) % channels / div_factor;
-    out[index] = in[index] * slope_data[c] + bias_data[c];
-  }
-}
-
-// CUDA kernel for bottom backward
-template <typename Dtype>
-__global__ void ChannelwiseAffineBackward(const int n,
-    const int channels, const int dim, const Dtype* in_diff,
-    Dtype* out_diff, const Dtype* slope_data, const int div_factor) {
-  CUDA_KERNEL_LOOP(index, n) {
-    int c = (index / dim) % channels / div_factor;
-    out_diff[index] = slope_data[c] * in_diff[index];
-  }
-}
-
-// CUDA kernel for element-wise parameter backward
-template <typename Dtype>
-__global__ void ChannelwiseAffineParamSlopeBackward(const int n,
-    const int rows, const int rowPitch, const Dtype* in_diff,
-    const Dtype* in_data, Dtype* out_diff) {
-  CUDA_KERNEL_LOOP(index, n) {
-      out_diff[index] = in_diff[index] * in_data[index];
-      for ( int k = 1; k < rows; k++ ) {
-          out_diff[index] += in_diff[index + k*rowPitch]
-          * in_data[index + k*rowPitch];
-      }
-  }
-}
-
-template <typename Dtype>
-void ChannelwiseAffineLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom,
-    const vector<Blob<Dtype>*>& top) {
-  const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = top[0]->mutable_gpu_data();
-  const int count = bottom[0]->count();
-  const int dim = bottom[0]->count(2);
-  const int channels = bottom[0]->channels();
-  const Dtype* slope_data = this->blobs_[0]->gpu_data();
-  const Dtype* bias_data = this->blobs_[1]->gpu_data();
-  const int div_factor = channel_shared_ ? channels : 1;
-
-  // For in-place computation
-  if (top[0] == bottom[0]) {
-    caffe_copy(count, bottom_data, bottom_memory_.mutable_gpu_data());
-  }
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  ChannelwiseAffineForward<Dtype><<<CAFFE_GET_BLOCKS(count),
-      CAFFE_CUDA_NUM_THREADS>>>(
-      count, channels, dim, bottom_data, top_data,
-      slope_data, bias_data, div_factor);
-  CUDA_POST_KERNEL_CHECK;
-}
-
-template <typename Dtype>
-void ChannelwiseAffineLayer<Dtype>::Backward_gpu(
-    const vector<Blob<Dtype>*>& top,
-    const vector<bool>& propagate_down,
-    const vector<Blob<Dtype>*>& bottom) {
-  const Dtype* bottom_data = bottom[0]->gpu_data();
-  const Dtype* top_diff = top[0]->gpu_diff();
-  const int count = bottom[0]->count();
-  const int num = bottom[0]->shape(0);
-  const int dim = bottom[0]->count(2);
-  const int channels = bottom[0]->shape(1);
-  int height  = 1;
-  int width = 1;
-  if (bottom[0]->num_axes() > 2) {
-    height = bottom[0]->shape(2);
-    width = bottom[0]->shape(3);
-  }
-
-  // For in-place computation
-  if (top[0] == bottom[0]) {
-    bottom_data = bottom_memory_.gpu_data();
-  }
-  // Propagate to param
-  // Since to write bottom diff will affect top diff if top and bottom blobs
-  // are identical (in-place computaion), we first compute param backward to
-  // keep top_diff unchanged.
-  if (this->param_propagate_down_[1]) {
-      Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
-      caffe_gpu_set(this->blobs_[1]->count(), Dtype(0.0), bias_diff);
-    // Gradient with respect to bias
-      for (int n = 0; n < num; ++n) {
-          caffe_gpu_gemv<Dtype>(
-            CblasNoTrans, channels, height * width, (Dtype)1.,
-            top_diff + top[0]->offset(n), bias_multiplier_.gpu_data(),
-            (Dtype)1., bias_diff);
-      }
-  }
-  if (this->param_propagate_down_[0]) {
-    Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
-    int cdim = channels * dim;
-      // compute element-wise diff
-      // NOLINT_NEXT_LINE(whitespace/operators)
-      ChannelwiseAffineParamSlopeBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
-          CAFFE_CUDA_NUM_THREADS>>>(
-          cdim, num, top[0]->offset(1), top_diff ,
-          bottom_data,
-          backward_buff_.mutable_gpu_diff());
-      CUDA_POST_KERNEL_CHECK;
-      if (channel_shared_) {
-        Dtype d = 0;
-        caffe_gpu_dot<Dtype>(cdim, backward_buff_.gpu_diff(),
-            multiplier_.gpu_data(), &d);
-        caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(d), slope_diff);
-      } else {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, Dtype(1.),
-             backward_buff_.gpu_diff(), multiplier_.gpu_data(), Dtype(1.),
-            slope_diff);
-      }
-  }
-  // Propagate to bottom
-  if (propagate_down[0]) {
-    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-    const Dtype* slope_data = this->blobs_[0]->gpu_data();
-    int div_factor = channel_shared_ ? channels : 1;
-    // NOLINT_NEXT_LINE(whitespace/operators)
-    ChannelwiseAffineBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
-        CAFFE_CUDA_NUM_THREADS>>>(
-        count, channels, dim, top_diff, bottom_diff, slope_data, div_factor);
-    CUDA_POST_KERNEL_CHECK;
-  }
-}
-
-INSTANTIATE_LAYER_GPU_FUNCS(ChannelwiseAffineLayer);
-
-}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cpp b/src/caffe/layers/scale_layer.cpp
new file mode 100644
index 00000000000..ecdbb123e31
--- /dev/null
+++ b/src/caffe/layers/scale_layer.cpp
@@ -0,0 +1,219 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer_factory.hpp"
+#include "caffe/layers/scale_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const ScaleParameter& param = this->layer_param_.scale_param();
+  if (bottom.size() == 1 && this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else if (bottom.size() == 1) {
+    // scale is a learned parameter; initialize it
+    axis_ = bottom[0]->CanonicalAxisIndex(param.axis());
+    const int num_axes = param.num_axes();
+    CHECK_GE(num_axes, -1) << "num_axes must be non-negative, "
+                           << "or -1 to extend to the end of bottom[0]";
+    if (num_axes >= 0) {
+      CHECK_GE(bottom[0]->num_axes(), axis_ + num_axes)
+          << "scale blob's shape extends past bottom[0]'s shape when applied "
+          << "starting with bottom[0] axis = " << axis_;
+    }
+    this->blobs_.resize(1);
+    const vector<int>::const_iterator& shape_start =
+        bottom[0]->shape().begin() + axis_;
+    const vector<int>::const_iterator& shape_end =
+        (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes);
+    vector<int> scale_shape(shape_start, shape_end);
+    this->blobs_[0].reset(new Blob<Dtype>(scale_shape));
+    FillerParameter filler_param(param.filler());
+    if (!param.has_filler()) {
+      // Default to unit (1) filler for identity operation.
+      filler_param.set_type("constant");
+      filler_param.set_value(1);
+    }
+    shared_ptr<Filler<Dtype> > filler(GetFiller<Dtype>(filler_param));
+    filler->Fill(this->blobs_[0].get());
+  }
+  if (param.bias_term()) {
+    LayerParameter layer_param(this->layer_param_);
+    layer_param.set_type("Bias");
+    BiasParameter* bias_param = layer_param.mutable_bias_param();
+    bias_param->set_axis(param.axis());
+    if (bottom.size() > 1) {
+      bias_param->set_num_axes(bottom[1]->num_axes());
+    } else {
+      bias_param->set_num_axes(param.num_axes());
+    }
+    bias_param->mutable_filler()->CopyFrom(param.bias_filler());
+    bias_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
+    bias_bottom_vec_.resize(1);
+    bias_bottom_vec_[0] = bottom[0];
+    bias_layer_->SetUp(bias_bottom_vec_, top);
+    bias_param_id_ = this->blobs_.size();
+    this->blobs_.resize(bias_param_id_ + 1);
+    this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    bias_propagate_down_.resize(1, false);
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const ScaleParameter& param = this->layer_param_.scale_param();
+  Blob<Dtype>* scale = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
+  // Always set axis_ == 0 in special case where scale is a scalar
+  // (num_axes == 0). Mathematically equivalent for any choice of axis_, so the
+  // actual setting can be safely ignored; and computation is most efficient
+  // with axis_ == 0 and (therefore) outer_dim_ == 1. (Setting axis_ to
+  // bottom[0]->num_axes() - 1, giving inner_dim_ == 1, would be equally
+  // performant.)
+  axis_ = (scale->num_axes() == 0) ?
+      0 : bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_GE(bottom[0]->num_axes(), axis_ + scale->num_axes())
+      << "scale blob's shape extends past bottom[0]'s shape when applied "
+      << "starting with bottom[0] axis = " << axis_;
+  for (int i = 0; i < scale->num_axes(); ++i) {
+    CHECK_EQ(bottom[0]->shape(axis_ + i), scale->shape(i))
+        << "dimension mismatch between bottom[0]->shape(" << axis_ + i
+        << ") and scale->shape(" << i << ")";
+  }
+  outer_dim_ = bottom[0]->count(0, axis_);
+  scale_dim_ = scale->count();
+  inner_dim_ = bottom[0]->count(axis_ + scale->num_axes());
+  if (bottom[0] == top[0]) {  // in-place computation
+    temp_.ReshapeLike(*bottom[0]);
+  } else {
+    top[0]->ReshapeLike(*bottom[0]);
+  }
+  sum_result_.Reshape(vector<int>(1, outer_dim_ * scale_dim_));
+  const int sum_mult_size = std::max(outer_dim_, inner_dim_);
+  sum_multiplier_.Reshape(vector<int>(1, sum_mult_size));
+  if (sum_multiplier_.cpu_data()[sum_mult_size - 1] != Dtype(1)) {
+    caffe_set(sum_mult_size, Dtype(1), sum_multiplier_.mutable_cpu_data());
+  }
+  if (bias_layer_) {
+    bias_bottom_vec_[0] = top[0];
+    bias_layer_->Reshape(bias_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  if (bottom[0] == top[0]) {
+    // In-place computation; need to store bottom data before overwriting it.
+    // Note that this is only necessary for Backward; we could skip this if not
+    // doing Backward, but Caffe currently provides no way of knowing whether
+    // we'll need to do Backward at the time of the Forward call.
+    caffe_copy(bottom[0]->count(), bottom[0]->cpu_data(),
+               temp_.mutable_cpu_data());
+  }
+  const Dtype* scale_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int n = 0; n < outer_dim_; ++n) {
+    for (int d = 0; d < scale_dim_; ++d) {
+      const Dtype factor = scale_data[d];
+      caffe_cpu_scale(inner_dim_, factor, bottom_data, top_data);
+      bottom_data += inner_dim_;
+      top_data += inner_dim_;
+    }
+  }
+  if (bias_layer_) {
+    bias_layer_->Forward(bias_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bias_layer_ &&
+      this->param_propagate_down_[this->param_propagate_down_.size() - 1]) {
+    bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_);
+  }
+  const bool scale_param = (bottom.size() == 1);
+  Blob<Dtype>* scale = scale_param ? this->blobs_[0].get() : bottom[1];
+  if ((!scale_param && propagate_down[1]) ||
+      (scale_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const bool in_place = (bottom[0] == top[0]);
+    const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->cpu_data();
+    // Hack: store big eltwise product in bottom[0] diff, except in the special
+    // case where this layer itself does the eltwise product, in which case we
+    // can store it directly in the scale diff, and we're done.
+    // If we're computing in-place (and not doing eltwise computation), this
+    // hack doesn't work and we store the product in temp_.
+    const bool is_eltwise = (bottom[0]->count() == scale->count());
+    Dtype* product = (is_eltwise ? scale->mutable_cpu_diff() :
+        (in_place ? temp_.mutable_cpu_data() : bottom[0]->mutable_cpu_diff()));
+    caffe_mul(top[0]->count(), top_diff, bottom_data, product);
+    if (!is_eltwise) {
+      Dtype* sum_result = NULL;
+      if (inner_dim_ == 1) {
+        sum_result = product;
+      } else if (sum_result_.count() == 1) {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_param) {
+          Dtype result = caffe_cpu_dot(inner_dim_, product, sum_mult);
+          *scale_diff += result;
+        } else {
+          *scale_diff = caffe_cpu_dot(inner_dim_, product, sum_mult);
+        }
+      } else {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        sum_result = (outer_dim_ == 1) ?
+            scale->mutable_cpu_diff() : sum_result_.mutable_cpu_data();
+        caffe_cpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,
+                       Dtype(1), product, sum_mult, Dtype(0), sum_result);
+      }
+      if (outer_dim_ != 1) {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_dim_ == 1) {
+          if (scale_param) {
+            Dtype result = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);
+            *scale_diff += result;
+          } else {
+            *scale_diff = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);
+          }
+        } else {
+          caffe_cpu_gemv(CblasTrans, outer_dim_, scale_dim_,
+                         Dtype(1), sum_result, sum_mult, Dtype(scale_param),
+                         scale_diff);
+        }
+      }
+    }
+  }
+  if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* scale_data = scale->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    for (int n = 0; n < outer_dim_; ++n) {
+      for (int d = 0; d < scale_dim_; ++d) {
+        const Dtype factor = scale_data[d];
+        caffe_cpu_scale(inner_dim_, factor, top_diff, bottom_diff);
+        bottom_diff += inner_dim_;
+        top_diff += inner_dim_;
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ScaleLayer);
+#endif
+
+INSTANTIATE_CLASS(ScaleLayer);
+REGISTER_LAYER_CLASS(Scale);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cu b/src/caffe/layers/scale_layer.cu
new file mode 100644
index 00000000000..fc9a8064db5
--- /dev/null
+++ b/src/caffe/layers/scale_layer.cu
@@ -0,0 +1,135 @@
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layers/scale_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ScaleForward(const int n, const Dtype* in,
+    const Dtype* scale, const int scale_dim, const int inner_dim,
+    Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int scale_index = (index / inner_dim) % scale_dim;
+    out[index] = in[index] * scale[scale_index];
+  }
+}
+
+template <typename Dtype>
+__global__ void ScaleBiasForward(const int n, const Dtype* in,
+    const Dtype* scale, const Dtype* bias,
+    const int scale_dim, const int inner_dim, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int scale_index = (index / inner_dim) % scale_dim;
+    out[index] = in[index] * scale[scale_index] + bias[scale_index];
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  if (bottom[0] == top[0]) {
+    // in-place computation; need to store bottom data before overwriting it.
+    // Note that this is only necessary for Backward; we could skip this if not
+    // doing Backward, but Caffe currently provides no way of knowing whether
+    // we'll need to do Backward at the time of the Forward call.
+    caffe_copy(bottom[0]->count(), bottom[0]->gpu_data(),
+               temp_.mutable_gpu_data());
+  }
+  const Dtype* scale_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (bias_layer_) {
+    const Dtype* bias_data = this->blobs_[bias_param_id_]->gpu_data();
+    ScaleBiasForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, scale_data, bias_data, scale_dim_, inner_dim_,
+        top_data);
+  } else {
+    ScaleForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, scale_data, scale_dim_, inner_dim_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bias_layer_ &&
+      this->param_propagate_down_[this->param_propagate_down_.size() - 1]) {
+    bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_);
+  }
+  const bool scale_param = (bottom.size() == 1);
+  Blob<Dtype>* scale = scale_param ? this->blobs_[0].get() : bottom[1];
+  if ((!scale_param && propagate_down[1]) ||
+      (scale_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const bool in_place = (bottom[0] == top[0]);
+    const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->gpu_data();
+    // Hack: store big eltwise product in bottom[0] diff, except in the special
+    // case where this layer itself does the eltwise product, in which case we
+    // can store it directly in the scale diff, and we're done.
+    // If we're computing in-place (and not doing eltwise computation), this
+    // hack doesn't work and we store the product in temp_.
+    const bool is_eltwise = (bottom[0]->count() == scale->count());
+    Dtype* product = (is_eltwise ? scale->mutable_gpu_diff() :
+        (in_place ? temp_.mutable_gpu_data() : bottom[0]->mutable_gpu_diff()));
+    caffe_gpu_mul(top[0]->count(), top_diff, bottom_data, product);
+    if (!is_eltwise) {
+      Dtype* sum_result = NULL;
+      if (inner_dim_ == 1) {
+        sum_result = product;
+      } else if (sum_result_.count() == 1) {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_param) {
+          Dtype result;
+          caffe_gpu_dot(inner_dim_, product, sum_mult, &result);
+          *scale_diff += result;
+        } else {
+          caffe_gpu_dot(inner_dim_, product, sum_mult, scale_diff);
+        }
+      } else {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        sum_result = (outer_dim_ == 1) ?
+            scale->mutable_gpu_diff() : sum_result_.mutable_gpu_data();
+        caffe_gpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,
+                       Dtype(1), product, sum_mult, Dtype(0), sum_result);
+      }
+      if (outer_dim_ != 1) {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        if (scale_dim_ == 1) {
+          Dtype* scale_diff = scale->mutable_cpu_diff();
+          if (scale_param) {
+            Dtype result;
+            caffe_gpu_dot(outer_dim_, sum_mult, sum_result, &result);
+            *scale_diff += result;
+          } else {
+            caffe_gpu_dot(outer_dim_, sum_mult, sum_result, scale_diff);
+          }
+        } else {
+          Dtype* scale_diff = scale->mutable_gpu_diff();
+          caffe_gpu_gemv(CblasTrans, outer_dim_, scale_dim_,
+                         Dtype(1), sum_result, sum_mult, Dtype(scale_param),
+                         scale_diff);
+        }
+      }
+    }
+  }
+  if (propagate_down[0]) {
+    const int count = top[0]->count();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* scale_data = scale->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    ScaleForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, scale_data, scale_dim_, inner_dim_, bottom_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ScaleLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index fe6209cf673..6493a72d778 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 142 (last added: channelwise_affine_param)
+// LayerParameter next available layer-specific ID: 143 (last added: scale_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -356,7 +356,7 @@ message LayerParameter {
   optional AccuracyParameter accuracy_param = 102;
   optional ArgMaxParameter argmax_param = 103;
   optional BatchNormParameter batch_norm_param = 139;
-  optional ChannelwiseAffineParameter channelwise_affine_param = 141;
+  optional BiasParameter bias_param = 141;
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
@@ -385,6 +385,7 @@ message LayerParameter {
   optional ReductionParameter reduction_param = 136;
   optional ReLUParameter relu_param = 123;
   optional ReshapeParameter reshape_param = 133;
+  optional ScaleParameter scale_param = 142;
   optional SigmoidParameter sigmoid_param = 124;
   optional SoftmaxParameter softmax_param = 125;
   optional SPPParameter spp_param = 132;
@@ -499,15 +500,36 @@ message BatchNormParameter {
   optional float eps = 3 [default = 1e-5];
 }
 
-message ChannelwiseAffineParameter {
-
-  // Initial value of a_i. Default is a_i=1.0 for all i.
-  optional FillerParameter slope_filler = 1;
-
-  optional FillerParameter bias_filler = 2;
+message BiasParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar bias.
+  optional int32 axis = 1 [default = 1];
 
-  // Whether or not slope paramters are shared across channels.
-  optional bool channel_shared = 3 [default = false];
+  // (num_axes is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the bias
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.)
+  // The initialization for the learned bias parameter.
+  // Default is the zero (0) initialization, resulting in the BiasLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
 }
 
 message ContrastiveLossParameter {
@@ -972,6 +994,43 @@ message ReshapeParameter {
   optional int32 num_axes = 3 [default = -1];
 }
 
+message ScaleParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar multiplier.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the scale
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.)
+  // The initialization for the learned scale parameter.
+  // Default is the unit (1) initialization, resulting in the ScaleLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+
+  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+  // may be more efficient).  Initialized with bias_filler (defaults to 0).
+  optional bool bias_term = 4 [default = false];
+  optional FillerParameter bias_filler = 5;
+}
+
 message SigmoidParameter {
   enum Engine {
     DEFAULT = 0;
diff --git a/src/caffe/test/test_bias_layer.cpp b/src/caffe/test/test_bias_layer.cpp
new file mode 100644
index 00000000000..3862e763e28
--- /dev/null
+++ b/src/caffe/test/test_bias_layer.cpp
@@ -0,0 +1,467 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/bias_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class BiasLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  BiasLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_eltwise_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_broadcast_0_(new Blob<Dtype>()),
+        blob_bottom_broadcast_1_(new Blob<Dtype>()),
+        blob_bottom_broadcast_2_(new Blob<Dtype>()),
+        blob_bottom_bias_(new Blob<Dtype>(vector<int>())),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    vector<int> broadcast_shape(2);
+    broadcast_shape[0] = 2; broadcast_shape[1] = 3;
+    this->blob_bottom_broadcast_0_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 3; broadcast_shape[1] = 4;
+    this->blob_bottom_broadcast_1_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 4; broadcast_shape[1] = 5;
+    this->blob_bottom_broadcast_2_->Reshape(broadcast_shape);
+    FillerParameter filler_param;
+    filler_param.set_min(1);
+    filler_param.set_max(10);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_eltwise_);
+    filler.Fill(this->blob_bottom_broadcast_0_);
+    filler.Fill(this->blob_bottom_broadcast_1_);
+    filler.Fill(this->blob_bottom_broadcast_2_);
+    filler.Fill(this->blob_bottom_bias_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~BiasLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_eltwise_;
+    delete blob_bottom_broadcast_0_;
+    delete blob_bottom_broadcast_1_;
+    delete blob_bottom_broadcast_2_;
+    delete blob_bottom_bias_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_eltwise_;
+  Blob<Dtype>* const blob_bottom_broadcast_0_;
+  Blob<Dtype>* const blob_bottom_broadcast_1_;
+  Blob<Dtype>* const blob_bottom_broadcast_2_;
+  Blob<Dtype>* const blob_bottom_bias_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(BiasLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(BiasLayerTest, TestForwardEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_bottom_->cpu_data();
+  const int count = this->blob_bottom_->count();
+  const Dtype* in_data_a = orig_bottom.cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestBackwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_bias_diff;
+  orig_bias_diff.CopyFrom(*this->blob_bottom_eltwise_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) {
+    EXPECT_NEAR(orig_bias_diff.cpu_diff()[i],
+                this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(0);
+  bias_param->set_num_axes(-1);
+  bias_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = layer->blobs()[0]->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w),
+                      orig_bottom.data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestBackwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_bias_diff;
+  orig_bias_diff.CopyFrom(*this->blob_bottom_broadcast_1_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) {
+    EXPECT_NEAR(orig_bias_diff.cpu_diff()[i],
+                this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(1);
+  bias_param->set_num_axes(2);
+  bias_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype bias = *this->blob_bottom_bias_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] + bias, 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBiasAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype bias = *this->blob_bottom_bias_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] + bias, 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(0);
+  bias_param->set_num_axes(-1);
+  bias_param->mutable_filler()->set_type("gaussian");
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(1);
+  bias_param->set_num_axes(2);
+  bias_param->mutable_filler()->set_type("gaussian");
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBiasAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_channelwise_affine_layer.cpp b/src/caffe/test/test_channelwise_affine_layer.cpp
deleted file mode 100644
index a3e2544f77a..00000000000
--- a/src/caffe/test/test_channelwise_affine_layer.cpp
+++ /dev/null
@@ -1,105 +0,0 @@
-#include <vector>
-
-#include "gtest/gtest.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/layers/channelwise_affine_layer.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
-
-namespace caffe {
-
-template <typename TypeParam>
-class ChannelwiseAffineLayerTest : public MultiDeviceTest<TypeParam> {
-  typedef typename TypeParam::Dtype Dtype;
-
- protected:
-  ChannelwiseAffineLayerTest()
-      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
-        blob_top_(new Blob<Dtype>()) {
-    Caffe::set_random_seed(1701);
-    // fill the values
-    FillerParameter filler_param;
-    GaussianFiller<Dtype> filler(filler_param);
-    filler.Fill(this->blob_bottom_);
-    blob_bottom_vec_.push_back(blob_bottom_);
-    blob_top_vec_.push_back(blob_top_);
-  }
-  virtual ~ChannelwiseAffineLayerTest() {
-      delete blob_bottom_; delete blob_top_; }
-  Blob<Dtype>* const blob_bottom_;
-  Blob<Dtype>* const blob_top_;
-  vector<Blob<Dtype>*> blob_bottom_vec_;
-  vector<Blob<Dtype>*> blob_top_vec_;
-
-  void TestChannelwiseAffine(ChannelwiseAffineLayer<Dtype> *layer) {
-    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-    // Now, check values
-    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
-    const Dtype* top_data = this->blob_top_->cpu_data();
-    const Dtype* slope_data = layer->blobs()[0]->cpu_data();
-    const Dtype* bias_data = layer->blobs()[1]->cpu_data();
-    const Dtype kDelta = 2e-5;
-    int hw = this->blob_bottom_->height() * this->blob_bottom_->width();
-    int channels = this->blob_bottom_->channels();
-    bool channel_shared =
-        layer->layer_param().channelwise_affine_param().channel_shared();
-    for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-          int c = channel_shared ? 0 : (i / hw) % channels;
-          EXPECT_NEAR(top_data[i],
-                       bottom_data[i]* slope_data[c] + bias_data[c], kDelta);
-        }
-  }
-};
-TYPED_TEST_CASE(ChannelwiseAffineLayerTest, TestDtypesAndDevices);
-
-
-TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineForward) {
-    typedef typename TypeParam::Dtype Dtype;
-    LayerParameter layer_param;
-    ChannelwiseAffineLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-    FillerParameter filler_param;
-    GaussianFiller<Dtype> filler(filler_param);
-    filler.Fill(layer.blobs()[0].get());
-    filler.Fill(layer.blobs()[1].get());
-    this->TestChannelwiseAffine(&layer);
-}
-
-TYPED_TEST(ChannelwiseAffineLayerTest,
-           TestChannelwiseAffineForwardChannelShared) {
-    typedef typename TypeParam::Dtype Dtype;
-    LayerParameter layer_param;
-    layer_param.mutable_channelwise_affine_param()->set_channel_shared(true);
-    ChannelwiseAffineLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-    this->TestChannelwiseAffine(&layer);
-}
-
-TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineGradient) {
-    typedef typename TypeParam::Dtype Dtype;
-    LayerParameter layer_param;
-    layer_param.mutable_channelwise_affine_param()->set_channel_shared(false);
-    ChannelwiseAffineLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-    GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
-                                    this->blob_top_vec_);
-}
-
-TYPED_TEST(ChannelwiseAffineLayerTest,
-           TestChannelwiseAffineGradientChannelShared) {
-    typedef typename TypeParam::Dtype Dtype;
-    LayerParameter layer_param;
-    layer_param.mutable_channelwise_affine_param()->set_channel_shared(true);
-    ChannelwiseAffineLayer<Dtype> layer(layer_param);
-    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-    GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
-    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
-                                    this->blob_top_vec_);
-}
-
-}  // namespace caffe
diff --git a/src/caffe/test/test_scale_layer.cpp b/src/caffe/test/test_scale_layer.cpp
new file mode 100644
index 00000000000..ad116795f44
--- /dev/null
+++ b/src/caffe/test/test_scale_layer.cpp
@@ -0,0 +1,507 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/scale_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ScaleLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ScaleLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_eltwise_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_broadcast_0_(new Blob<Dtype>()),
+        blob_bottom_broadcast_1_(new Blob<Dtype>()),
+        blob_bottom_broadcast_2_(new Blob<Dtype>()),
+        blob_bottom_scale_(new Blob<Dtype>(vector<int>())),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    vector<int> broadcast_shape(2);
+    broadcast_shape[0] = 2; broadcast_shape[1] = 3;
+    this->blob_bottom_broadcast_0_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 3; broadcast_shape[1] = 4;
+    this->blob_bottom_broadcast_1_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 4; broadcast_shape[1] = 5;
+    this->blob_bottom_broadcast_2_->Reshape(broadcast_shape);
+    FillerParameter filler_param;
+    filler_param.set_min(1);
+    filler_param.set_max(10);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_eltwise_);
+    filler.Fill(this->blob_bottom_broadcast_0_);
+    filler.Fill(this->blob_bottom_broadcast_1_);
+    filler.Fill(this->blob_bottom_broadcast_2_);
+    filler.Fill(this->blob_bottom_scale_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ScaleLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_eltwise_;
+    delete blob_bottom_broadcast_0_;
+    delete blob_bottom_broadcast_1_;
+    delete blob_bottom_broadcast_2_;
+    delete blob_bottom_scale_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_eltwise_;
+  Blob<Dtype>* const blob_bottom_broadcast_0_;
+  Blob<Dtype>* const blob_bottom_broadcast_1_;
+  Blob<Dtype>* const blob_bottom_broadcast_2_;
+  Blob<Dtype>* const blob_bottom_scale_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ScaleLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_bottom_->cpu_data();
+  const int count = this->blob_bottom_->count();
+  const Dtype* in_data_a = orig_bottom.cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestBackwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_scale_diff;
+  orig_scale_diff.CopyFrom(*this->blob_bottom_eltwise_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) {
+    EXPECT_NEAR(orig_scale_diff.cpu_diff()[i],
+                this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(0);
+  scale_param->set_num_axes(-1);
+  scale_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = layer->blobs()[0]->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w),
+                      orig_bottom.data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestBackwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_scale_diff;
+  orig_scale_diff.CopyFrom(*this->blob_bottom_broadcast_1_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) {
+    EXPECT_NEAR(orig_scale_diff.cpu_diff()[i],
+                this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParamAndBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  scale_param->set_bias_term(true);
+  scale_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      layer->blobs()[0]->data_at(c, h, 0, 0) +
+                      layer->blobs()[1]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype scale = *this->blob_bottom_scale_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardScaleAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype scale = *this->blob_bottom_scale_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(0);
+  scale_param->set_num_axes(-1);
+  scale_param->mutable_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScaleAndBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_bias_term(true);
+  scale_param->mutable_bias_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScaleAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe

From e94065efd516668e5168ed82669063b69315595d Mon Sep 17 00:00:00 2001
From: Ronghang Hu <ronghang.hu@gmail.com>
Date: Sat, 23 Jan 2016 01:22:04 -0800
Subject: [PATCH 1680/2053] show Caffe's version from MatCaffe

---
 matlab/+caffe/private/caffe_.cpp | 8 ++++++++
 matlab/+caffe/version.m          | 7 +++++++
 2 files changed, 15 insertions(+)
 create mode 100644 matlab/+caffe/version.m

diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 1641e14b534..1b1b2bff861 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -504,6 +504,13 @@ static void write_mean(MEX_ARGS) {
   mxFree(mean_proto_file);
 }
 
+// Usage: caffe_('version')
+static void version(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('version')");
+  // Return version string
+  plhs[0] = mxCreateString(AS_STRING(CAFFE_VERSION));
+}
+
 /** -----------------------------------------------------------------
  ** Available commands.
  **/
@@ -542,6 +549,7 @@ static handler_registry handlers[] = {
   { "reset",              reset           },
   { "read_mean",          read_mean       },
   { "write_mean",         write_mean      },
+  { "version",            version         },
   // The end.
   { "END",                NULL            },
 };
diff --git a/matlab/+caffe/version.m b/matlab/+caffe/version.m
new file mode 100644
index 00000000000..61cae4f76dc
--- /dev/null
+++ b/matlab/+caffe/version.m
@@ -0,0 +1,7 @@
+function version_str = version()
+% version()
+%   show Caffe's version.
+
+version_str = caffe_('version');
+
+end

From 407050e02790b3738cc42fbbf1b51c35ee7c3021 Mon Sep 17 00:00:00 2001
From: Hugo Serrat <hu.serrat@gmail.com>
Date: Thu, 21 Jan 2016 14:34:01 +0100
Subject: [PATCH 1681/2053] Updated import to make it work with pydotplus

---
 python/caffe/draw.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index f8bf5722aba..cfa3fc5b1fb 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -10,7 +10,16 @@
 """
 
 from caffe.proto import caffe_pb2
-import pydot
+
+"""
+pydot is not supported under python 3 and pydot2 doesn't work properly.
+pydotplus works nicely (pip install pydotplus)
+"""
+try:
+    # Try to load pydotplus
+    import pydotplus as pydot
+except ImportError:
+    import pydot
 
 # Internal layer and blob styles.
 LAYER_STYLE_DEFAULT = {'shape': 'record',

From ca402f6d15b8f36c2e53f7de7f9817a6b73ac04d Mon Sep 17 00:00:00 2001
From: Kang Kim <kkhoot@gmail.com>
Date: Fri, 27 Nov 2015 21:57:51 +0900
Subject: [PATCH 1682/2053] Prevent in-place computation in ReshapeLayer and
 FlattenLayer

---
 src/caffe/layers/flatten_layer.cpp | 2 ++
 src/caffe/layers/reshape_layer.cpp | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 651507e2e7d..d4ab3935760 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -7,6 +7,8 @@ namespace caffe {
 template <typename Dtype>
 void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+      "allow in-place computation.";
   const int start_axis = bottom[0]->CanonicalAxisIndex(
       this->layer_param_.flatten_param().axis());
   const int end_axis = bottom[0]->CanonicalAxisIndex(
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
index 82339f76d8f..45dd0902d6a 100644
--- a/src/caffe/layers/reshape_layer.cpp
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -7,6 +7,8 @@ namespace caffe {
 template <typename Dtype>
 void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+      "allow in-place computation.";
   inferred_axis_ = -1;
   copy_axes_.clear();
   const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();

From 9a43dcf0c738fa799256318162d29a3969446efb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 26 Jan 2016 13:58:58 -0800
Subject: [PATCH 1683/2053] Remove unnecessary CAFFE_TEST_CUDA_PROP
 declarations

---
 src/caffe/test/test_embed_layer.cpp  | 4 ----
 src/caffe/test/test_im2col_kernel.cu | 2 --
 2 files changed, 6 deletions(-)

diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
index acd4b0f636b..dc7f5c4aa47 100644
--- a/src/caffe/test/test_embed_layer.cpp
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -12,10 +12,6 @@
 
 namespace caffe {
 
-#ifndef CPU_ONLY
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-#endif
-
 template <typename TypeParam>
 class EmbedLayerTest : public MultiDeviceTest<TypeParam> {
   typedef typename TypeParam::Dtype Dtype;
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index 5d8f01f1713..e3a9791bcca 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -28,8 +28,6 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
     const int* kernel_shape, const int* pad, const int* stride,
     const int* dilation, Dtype* data_col);
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class Im2colKernelTest : public GPUDeviceTest<Dtype> {
  protected:

From 91c02f3b3bc48af1ab24a4687331492cf0171815 Mon Sep 17 00:00:00 2001
From: Madan Ram <madan_ram@rocketmail.com>
Date: Thu, 23 Jul 2015 16:42:15 +0530
Subject: [PATCH 1684/2053] Update mnist readme.md: scale moved to
 transform_param

---
 examples/mnist/readme.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 413d4a1f40b..b87a0f53c7a 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -41,11 +41,13 @@ Currently, we will read the MNIST data from the lmdb we created earlier in the d
     layer {
       name: "mnist"
       type: "Data"
+      transform_param {
+        scale: 0.00390625
+      }
       data_param {
         source: "mnist_train_lmdb"
         backend: LMDB
         batch_size: 64
-        scale: 0.00390625
       }
       top: "data"
       top: "label"

From ae31adcdca0bc12e33e691ee7cd9c4ad75c229bb Mon Sep 17 00:00:00 2001
From: Keir Mierle <mierle@gmail.com>
Date: Fri, 26 Jun 2015 00:10:21 -0700
Subject: [PATCH 1685/2053] Make the two separate build systems clearer in the
 documentation

---
 docs/installation.md | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index cce7ec358f5..ef781e8d638 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -87,15 +87,20 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup The defaults should work, but uncomment the relevant lines if using Anaconda Python.
+Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
+
+### Compilation with Make
+
+Configure the build by copying and modifying the example `Makefile.config` for your setup. The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config
-    # Adjust Makefile.config (for example, if using Anaconda Python)
+    # Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
     make all
     make test
     make runtest
 
-- For cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
+- For CPU & GPU accelerated Caffe, no changes are needed.
+- For cuDNN acceleration using NVIDIA's proprietary cuDNN software, uncomment the `USE_CUDNN := 1` switch in `Makefile.config`. cuDNN is sometimes but not always faster than Caffe's GPU acceleration.
 - For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
@@ -107,7 +112,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### CMake Compilation
+### Compilation with CMake
 
 In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
 The basic steps are as follows:
@@ -116,6 +121,7 @@ The basic steps are as follows:
     cd build
     cmake ..
     make all
+    make install
     make runtest
 
 See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.

From afcaf253daa942821250db3f9a6afbe1d955bdf1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 26 Jan 2016 23:09:27 -0800
Subject: [PATCH 1686/2053] Remove incorrect cast of gemm int arg to Dtype in
 BiasLayer

---
 src/caffe/layers/bias_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/bias_layer.cpp b/src/caffe/layers/bias_layer.cpp
index 0a786b5db98..4726a729834 100644
--- a/src/caffe/layers/bias_layer.cpp
+++ b/src/caffe/layers/bias_layer.cpp
@@ -80,7 +80,7 @@ void BiasLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
   for (int n = 0; n < outer_dim_; ++n) {
     caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_,
-        inner_dim_, Dtype(1), Dtype(1), bias_data,
+        inner_dim_, 1, Dtype(1), bias_data,
         bias_multiplier_.cpu_data(), Dtype(1), top_data);
     top_data += dim_;
   }

From 14d0bb4767cba22b826eae03a6e5cfa4c1cd4287 Mon Sep 17 00:00:00 2001
From: gdh1995 <gdh1995@qq.com>
Date: Wed, 13 Jan 2016 18:20:41 +0800
Subject: [PATCH 1687/2053] use relative paths on making build/tools/ links

The old uses `abspath`, which I think is so harmful:
* If I `cp -a` the whole project, `build/tools/caffe` still refer to
    the old file, until `make clean`, making debugging very hard
* For `tar` and `scp`, the soft links can not work
    unless the target project folder has the same path
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 787b0e8d036..76d51ad8bd0 100644
--- a/Makefile
+++ b/Makefile
@@ -601,7 +601,7 @@ $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
 # Target for extension-less symlinks to tool binaries with extension '*.bin'.
 $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
-	@ ln -s $(abspath $<) $@
+	@ ln -s $(notdir $<) $@
 
 $(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@

From dd2099786f11033ded6e9f46bc772ef9b2166399 Mon Sep 17 00:00:00 2001
From: Sergei Nikolaev <snikolaev@nvidia.com>
Date: Tue, 2 Feb 2016 13:48:18 -0800
Subject: [PATCH 1688/2053] Nicely prints GPU names

---
 src/caffe/test/test_caffe_main.cpp |  1 +
 tools/caffe.cpp                    | 13 ++++++++++++-
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index c8caf5ac58e..fccf6f1613b 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -34,6 +34,7 @@ int main(int argc, char** argv) {
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
   cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device);
+  cout << "Current device name: " << CAFFE_TEST_CUDA_PROP.name << endl;
 #endif
   // invoke the test.
   return RUN_ALL_TESTS();
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 470165add7f..ebe95d61ef1 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -183,7 +183,13 @@ int train() {
       s << (i ? ", " : "") << gpus[i];
     }
     LOG(INFO) << "Using GPUs " << s.str();
-
+#ifndef CPU_ONLY
+    cudaDeviceProp device_prop;
+    for (int i = 0; i < gpus.size(); ++i) {
+      cudaGetDeviceProperties(&device_prop, gpus[i]);
+      LOG(INFO) << "GPU " << gpus[i] << ": " << device_prop.name;
+    }
+#endif
     solver_param.set_device_id(gpus[0]);
     Caffe::SetDevice(gpus[0]);
     Caffe::set_mode(Caffe::GPU);
@@ -229,6 +235,11 @@ int test() {
   get_gpus(&gpus);
   if (gpus.size() != 0) {
     LOG(INFO) << "Use GPU with device ID " << gpus[0];
+#ifndef CPU_ONLY
+    cudaDeviceProp device_prop;
+    cudaGetDeviceProperties(&device_prop, gpus[0]);
+    LOG(INFO) << "GPU device name: " << device_prop.name;
+#endif
     Caffe::SetDevice(gpus[0]);
     Caffe::set_mode(Caffe::GPU);
   } else {

From 68c751c6f7a521994ccdc9330b89aef9c9024a0a Mon Sep 17 00:00:00 2001
From: Abhijit Kundu <abhijit.kundu@gatech.edu>
Date: Tue, 9 Feb 2016 02:45:46 -0500
Subject: [PATCH 1689/2053] bugfix for incorrect behaviour in
 caffe_parse_linker_libs function while extracting libflags from absolute
 library path with multiple (dots)

---
 cmake/Utils.cmake | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index a1bde1ae95b..653de5fdf89 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -346,10 +346,11 @@ function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_va
     elseif(lib MATCHES "^-l.*")
       list(APPEND libflags ${lib})
     elseif(IS_ABSOLUTE ${lib})
-      get_filename_component(name_we ${lib} NAME_WE)
       get_filename_component(folder  ${lib} PATH)
+      get_filename_component(filename ${lib} NAME)
+      string(REGEX REPLACE "\\.[^.]*$" "" filename_without_shortest_ext ${filename})
 
-      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      string(REGEX MATCH "^lib(.*)" __match ${filename_without_shortest_ext})
       list(APPEND libflags -l${CMAKE_MATCH_1})
       list(APPEND folders    ${folder})
     else()

From 8800e4b42d621a843d99a431186bdfbc9271a3eb Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Mon, 15 Feb 2016 16:52:32 -0800
Subject: [PATCH 1690/2053] Remove useless LevelDB include

The tests could not compile with USE_LEVELDB=0 and LevelDB missing from the system
---
 src/caffe/test/test_data_transformer.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 6103918fda1..31bf1c1fb14 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -3,7 +3,6 @@
 #include <vector>
 
 #include "gtest/gtest.h"
-#include "leveldb/db.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"

From d957481d072a434dfd116ac2255180e09c86fac5 Mon Sep 17 00:00:00 2001
From: Prayag Verma <prayag.verma@gmail.com>
Date: Thu, 18 Feb 2016 10:13:34 +0530
Subject: [PATCH 1691/2053] Fix a typo in docs
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

`peformance` → `performance`
---
 docs/multigpu.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/multigpu.md b/docs/multigpu.md
index 01cfb8938b5..d91acef980d 100644
--- a/docs/multigpu.md
+++ b/docs/multigpu.md
@@ -17,7 +17,7 @@ updated model, 0\-\>2, and then 0\-\>1, 2\-\>3.
 
 For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
 
-Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
+Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
 
 "nvidia-smi topo -m" will show you the connectivity matrix.  You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.
 

From 8f847fa8fae0460c6bf8e8d7a9bcf96a44305033 Mon Sep 17 00:00:00 2001
From: Youssef Kashef <youssef.kashef@gmail.com>
Date: Fri, 29 Jan 2016 19:21:48 +0100
Subject: [PATCH 1692/2053] tranpose parameter added to IP layer to support
 tied weights in an autoencoder. Arguments to matrix multiplication function
 are conditioned on this parameter, no actual transposing takes place.

test ip gradient computation with transpose on
---
 include/caffe/layers/inner_product_layer.hpp |   1 +
 src/caffe/layers/inner_product_layer.cpp     |  42 +++-
 src/caffe/layers/inner_product_layer.cu      |  31 ++-
 src/caffe/proto/caffe.proto                  |   5 +
 src/caffe/test/test_inner_product_layer.cpp  | 240 +++++++++++++++++++
 5 files changed, 304 insertions(+), 15 deletions(-)

diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp
index 250576a4817..18d0d6192eb 100644
--- a/include/caffe/layers/inner_product_layer.hpp
+++ b/include/caffe/layers/inner_product_layer.hpp
@@ -44,6 +44,7 @@ class InnerProductLayer : public Layer<Dtype> {
   int N_;
   bool bias_term_;
   Blob<Dtype> bias_multiplier_;
+  bool transpose_;  ///< if true, assume transposed weights
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index d9088805501..e65349f0055 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -11,6 +11,7 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
+  transpose_ = this->layer_param_.inner_product_param().transpose();
   N_ = num_output;
   const int axis = bottom[0]->CanonicalAxisIndex(
       this->layer_param_.inner_product_param().axis());
@@ -27,10 +28,15 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     } else {
       this->blobs_.resize(1);
     }
-    // Intialize the weight
+    // Initialize the weights
     vector<int> weight_shape(2);
-    weight_shape[0] = N_;
-    weight_shape[1] = K_;
+    if (transpose_) {
+      weight_shape[0] = K_;
+      weight_shape[1] = N_;
+    } else {
+      weight_shape[0] = N_;
+      weight_shape[1] = K_;
+    }
     this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
@@ -80,7 +86,8 @@ void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
   const Dtype* weight = this->blobs_[0]->cpu_data();
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans,
+      M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -97,8 +104,17 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
-    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    if (transpose_) {
+      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          K_, N_, M_,
+          (Dtype)1., bottom_data, top_diff,
+          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          N_, K_, M_,
+          (Dtype)1., top_diff, bottom_data,
+          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    }
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->cpu_diff();
@@ -110,9 +126,17 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bottom data
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
-        top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
-        bottom[0]->mutable_cpu_diff());
+    if (transpose_) {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+          (Dtype)0., bottom[0]->mutable_cpu_diff());
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+          (Dtype)0., bottom[0]->mutable_cpu_diff());
+    }
   }
 }
 
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index dc25aa33bd1..a58b56e3281 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -19,7 +19,9 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       caffe_gpu_axpy<Dtype>(N_, bias_multiplier_.cpu_data()[0],
                             this->blobs_[1]->gpu_data(), top_data);
   } else {
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+    caffe_gpu_gemm<Dtype>(CblasNoTrans,
+                          transpose_ ? CblasNoTrans : CblasTrans,
+                          M_, N_, K_, (Dtype)1.,
                           bottom_data, weight, (Dtype)0., top_data);
     if (bias_term_)
       caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -36,8 +38,17 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
-    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    if (transpose_) {
+      caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          K_, N_, M_,
+          (Dtype)1., bottom_data, top_diff,
+          (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    } else {
+      caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          N_, K_, M_,
+          (Dtype)1., top_diff, bottom_data,
+          (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    }
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();
@@ -49,9 +60,17 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bottom data
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
-        top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
-        bottom[0]->mutable_gpu_diff());
+    if (transpose_) {
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+          (Dtype)0., bottom[0]->mutable_gpu_diff());
+    } else {
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+          M_, K_, N_,
+         (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+         (Dtype)0., bottom[0]->mutable_gpu_diff());
+    }
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6493a72d778..7edb6ae87e0 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -786,6 +786,11 @@ message InnerProductParameter {
   // all preceding axes are retained in the output.
   // May be negative to index from the end (e.g., -1 for the last axis).
   optional int32 axis = 5 [default = 1];
+  // Specify whether to transpose the weight matrix or not.
+  // If transpose == true, any operations will be performed on the transpose
+  // of the weight matrix. The weight matrix itself is not going to be transposed
+  // but rather the transfer flag of operations will be toggled accordingly.
+  optional bool transpose = 6 [default = false];
 }
 
 // Message that stores parameters used by LogLayer
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index b888b510318..f1ec2333fae 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -60,6 +60,50 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->channels(), 10);
 }
 
+/** @brief TestSetUp while toggling tranpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeFalse) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  LayerParameter layer_param;
+  InnerProductParameter* inner_product_param =
+      layer_param.mutable_inner_product_param();
+  inner_product_param->set_num_output(10);
+  inner_product_param->set_transpose(false);
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(2, this->blob_top_->num());
+  EXPECT_EQ(1, this->blob_top_->height());
+  EXPECT_EQ(1, this->blob_top_->width());
+  EXPECT_EQ(10, this->blob_top_->channels());
+  EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+  EXPECT_EQ(10, layer->blobs()[0]->shape(0));
+  EXPECT_EQ(60, layer->blobs()[0]->shape(1));
+}
+
+/** @brief TestSetUp while toggling tranpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeTrue) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  LayerParameter layer_param;
+  InnerProductParameter* inner_product_param =
+      layer_param.mutable_inner_product_param();
+  inner_product_param->set_num_output(10);
+  inner_product_param->set_transpose(true);
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(2, this->blob_top_->num());
+  EXPECT_EQ(1, this->blob_top_->height());
+  EXPECT_EQ(1, this->blob_top_->width());
+  EXPECT_EQ(10, this->blob_top_->channels());
+  EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+  EXPECT_EQ(60, layer->blobs()[0]->shape(0));
+  EXPECT_EQ(10, layer->blobs()[0]->shape(1));
+}
+
 TYPED_TEST(InnerProductLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_);
@@ -91,6 +135,79 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
   }
 }
 
+/**
+ * @brief Init. an IP layer without transpose + random weights,
+ * run Forward, save the result.
+ * Init. another IP layer with transpose.
+ * manually copy and transpose the weights from the first IP layer,
+ * then run Forward on the same input and check that the result is the same
+ */
+TYPED_TEST(InnerProductLayerTest, TestForwardTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(false);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const int count = this->blob_top_->count();
+    Blob<Dtype>* const top = new Blob<Dtype>();
+    top->ReshapeLike(*this->blob_top_);
+    caffe_copy(count, this->blob_top_->cpu_data(), top->mutable_cpu_data());
+    this->blob_top_vec_.clear();
+    this->blob_top_vec_.push_back(new Blob<Dtype>());
+    inner_product_param->set_transpose(true);
+    shared_ptr<InnerProductLayer<Dtype> > ip_t(
+        new InnerProductLayer<Dtype>(layer_param));
+    ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    const int count_w = layer->blobs()[0]->count();
+    EXPECT_EQ(count_w, ip_t->blobs()[0]->count());
+    // manually copy and transpose the weights from 1st IP layer into 2nd
+    const Dtype* w = layer->blobs()[0]->cpu_data();
+    Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+    const int width = layer->blobs()[0]->shape(1);
+    const int width_t = ip_t->blobs()[0]->shape(1);
+    for (int i = 0; i < count_w; ++i) {
+      int r = i / width;
+      int c = i % width;
+      w_t[c*width_t+r] = w[r*width+c];  // copy while transposing
+    }
+    // copy bias from 1st IP layer to 2nd IP layer
+    ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+    caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+        ip_t->blobs()[1]->mutable_cpu_data());
+    ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    EXPECT_EQ(count, this->blob_top_->count())
+        << "Invalid count for top blob for IP with transpose.";
+    Blob<Dtype>* const top_t = new Blob<Dtype>();\
+    top_t->ReshapeLike(*this->blob_top_vec_[0]);
+    caffe_copy(count,
+      this->blob_top_vec_[0]->cpu_data(),
+      top_t->mutable_cpu_data());
+    const Dtype* data = top->cpu_data();
+    const Dtype* data_t = top_t->cpu_data();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_FLOAT_EQ(data[i], data_t[i]);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
 TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);
@@ -148,4 +265,127 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) {
   }
 }
 
+TYPED_TEST(InnerProductLayerTest, TestGradientTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(11);
+    inner_product_param->mutable_weight_filler()->set_type("gaussian");
+    inner_product_param->mutable_bias_filler()->set_type("gaussian");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(true);
+    InnerProductLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestBackwardTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(false);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    // copy top blob
+    Blob<Dtype>* const top = new Blob<Dtype>();
+    top->CopyFrom(*this->blob_top_, false, true);
+    // fake top diff
+    Blob<Dtype>* const diff = new Blob<Dtype>();
+    diff->ReshapeLike(*this->blob_top_);
+    {
+      FillerParameter filler_param;
+      UniformFiller<Dtype> filler(filler_param);
+      filler.Fill(diff);
+    }
+    caffe_copy(this->blob_top_vec_[0]->count(),
+      diff->cpu_data(),
+      this->blob_top_vec_[0]->mutable_cpu_diff());
+    vector<bool> propagate_down(1, true);
+    layer->Backward(this->blob_top_vec_,
+        propagate_down,
+        this->blob_bottom_vec_);
+    // copy first ip's weights and their diffs
+    Blob<Dtype>* const w = new Blob<Dtype>();
+    w->CopyFrom(*layer->blobs()[0], false, true);
+    w->CopyFrom(*layer->blobs()[0], true, true);
+    // copy bottom diffs
+    Blob<Dtype>* const bottom_diff = new Blob<Dtype>();
+    bottom_diff->CopyFrom(*this->blob_bottom_vec_[0], true, true);
+    // repeat original top with tranposed ip
+    this->blob_top_vec_.clear();
+    this->blob_top_vec_.push_back(new Blob<Dtype>());
+    inner_product_param->set_transpose(true);
+    shared_ptr<InnerProductLayer<Dtype> > ip_t(
+        new InnerProductLayer<Dtype>(layer_param));
+    ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    // manually copy and transpose the weights from 1st IP layer into 2nd
+    {
+      const Dtype* w_src = w->cpu_data();
+      Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+      const int width = layer->blobs()[0]->shape(1);
+      const int width_t = ip_t->blobs()[0]->shape(1);
+      for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+        int r = i / width;
+        int c = i % width;
+        w_t[c*width_t+r] = w_src[r*width+c];  // copy while transposing
+      }
+      // copy bias from 1st IP layer to 2nd IP layer
+      ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+      caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+          ip_t->blobs()[1]->mutable_cpu_data());
+    }
+    ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    caffe_copy(this->blob_top_vec_[0]->count(),
+      diff->cpu_data(),
+      this->blob_top_vec_[0]->mutable_cpu_diff());
+    ip_t->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+    const Dtype* data = w->cpu_diff();
+    const Dtype* data_t = ip_t->blobs()[0]->cpu_diff();
+    const int WIDTH = layer->blobs()[0]->shape(1);
+    const int WIDTH_T = ip_t->blobs()[0]->shape(1);
+    for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+      int r = i / WIDTH;
+      int c = i % WIDTH;
+      EXPECT_NE(Dtype(0.), data[r*WIDTH+c]);
+      EXPECT_FLOAT_EQ(data[r*WIDTH+c], data_t[c*WIDTH_T+r]);
+    }
+    data = bottom_diff->cpu_diff();
+    data_t = this->blob_bottom_vec_[0]->cpu_diff();
+    for (int i = 0; i < this->blob_bottom_vec_[0]->count(); ++i) {
+      EXPECT_NE(Dtype(0.), data[i]);
+      EXPECT_FLOAT_EQ(data[i], data_t[i]);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
 }  // namespace caffe

From b46133aff47d5ac9d2f0f1289c6d5a9c57b3c2c5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 20 Feb 2016 15:45:31 -0800
Subject: [PATCH 1693/2053] fix library install name on OSX for relative path
 linking

for linking of the caffe tools, tests, etc. the library install name
needs to include the @rpath set for the executables and interfaces

this was broken on OSX by #3311
---
 Makefile | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 598d28d5beb..38635038ddf 100644
--- a/Makefile
+++ b/Makefile
@@ -283,9 +283,8 @@ ifeq ($(OSX), 1)
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
 	# we need to explicitly ask for the rpath to be obeyed
-	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
 	ORIGIN := @loader_path
-	VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
+	VERSIONFLAGS += -Wl,-install_name,@rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
 else
 	ORIGIN := \$$ORIGIN
 endif
@@ -552,7 +551,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo LD -o $@
-	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS)
 	@ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)

From dac4d0962dffb11f9fb670e70126aebe31ddae5a Mon Sep 17 00:00:00 2001
From: Mohamed Ezz <moh.ezz8@gmail.com>
Date: Fri, 5 Feb 2016 01:54:31 +0100
Subject: [PATCH 1694/2053] Fix OSX El Capitan CUDA incompatibility, by adding
 lib to rpath

---
 Makefile | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/Makefile b/Makefile
index 598d28d5beb..9b5ffb960a3 100644
--- a/Makefile
+++ b/Makefile
@@ -248,6 +248,8 @@ ifeq ($(UNAME), Linux)
 	LINUX := 1
 else ifeq ($(UNAME), Darwin)
 	OSX := 1
+	OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .)
+	OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)
 endif
 
 # Linux
@@ -277,6 +279,14 @@ ifeq ($(OSX), 1)
 		endif
 		# clang throws this warning for cuda headers
 		WARNINGS += -Wno-unneeded-internal-declaration
+		# 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+)
+		OSX_10_OR_LATER   := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true)
+		OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true)
+		ifeq ($(OSX_10_OR_LATER),true)
+			ifeq ($(OSX_10_5_OR_LATER),true)
+				LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR)
+			endif
+		endif
 	endif
 	# gtest needs to use its own tuple to not conflict with clang
 	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1

From 29bb23fc92c5b71c0bf8af0b9e580015da9aedda Mon Sep 17 00:00:00 2001
From: shai <shai@magisto.com>
Date: Tue, 23 Feb 2016 10:42:54 +0200
Subject: [PATCH 1695/2053] removing all references to Blob.num property (that
 assumes Blob is 4D). Replacing it with accessing Blob.shape[0] - for Blobs
 with num_axes() != 4

---
 python/caffe/pycaffe.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 3054110771c..5020ecedb10 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -98,7 +98,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
+            if blob.shape[0] != self.blobs[in_].shape[0]:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
@@ -146,7 +146,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for top, diff in kwargs.iteritems():
-            if diff.shape[0] != self.blobs[top].num:
+            if diff.shape[0] != self.blobs[top].shape[0]:
                 raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
@@ -257,7 +257,7 @@ def _Net_batch(self, blobs):
     batch: {blob name: list of blobs} dict for a single batch.
     """
     num = len(blobs.itervalues().next())
-    batch_size = self.blobs.itervalues().next().num
+    batch_size = self.blobs.itervalues().next().shape[0]
     remainder = num % batch_size
     num_batches = num / batch_size
 

From bd6b03f15ee7d299b9106759aa3f01f18d79ced8 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 23 Feb 2016 23:34:46 -0800
Subject: [PATCH 1696/2053] [example] improve classification notebook

- add subheadings and list steps
- edit text, add comments, and try to make the code more understandable
- add new section for summary and encouragement to try your own image
---
 examples/00-classification.ipynb | 13031 +----------------------------
 1 file changed, 312 insertions(+), 12719 deletions(-)

diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
index 89b7dd34f0e..1950f08f638 100644
--- a/examples/00-classification.ipynb
+++ b/examples/00-classification.ipynb
@@ -4,54 +4,72 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Instant Recognition with Caffe\n",
+    "# Classification: Instant Recognition with Caffe\n",
     "\n",
-    "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n",
+    "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n",
     "\n",
-    "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)"
+    "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
+    "### 1. Setup\n",
+    "\n",
+    "* First, set up Python, `numpy`, and `matplotlib`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
+    "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "# display plots in this notebook\n",
     "%matplotlib inline\n",
     "\n",
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "# set display defaults\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)        # large images\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels\n",
+    "plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Load `caffe`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# The caffe module needs to be on the Python path;\n",
+    "#  we'll add it here explicitly.\n",
     "import sys\n",
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
     "sys.path.insert(0, caffe_root + 'python')\n",
     "\n",
     "import caffe\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = (10, 10)\n",
-    "plt.rcParams['image.interpolation'] = 'nearest'\n",
-    "plt.rcParams['image.cmap'] = 'gray'\n",
-    "\n",
-    "import os\n",
-    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+    "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
+    "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)."
    ]
   },
   {
@@ -60,50 +78,65 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CaffeNet found.\n"
+     ]
+    }
+   ],
    "source": [
-    "caffe.set_mode_cpu()\n",
-    "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-    "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-    "                caffe.TEST)\n",
-    "\n",
-    "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
-    "transformer.set_transpose('data', (2,0,1))\n",
-    "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
-    "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-    "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
+    "import os\n",
+    "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print 'CaffeNet found.'\n",
+    "else:\n",
+    "    print 'Downloading pre-trained CaffeNet model...'\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)"
+    "### 2. Load net and set up input preprocessing\n",
+    "\n",
+    "* Set Caffe to CPU mode and load the net from disk."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# set net to batch size of 50\n",
-    "net.blobs['data'].reshape(50,3,227,227)"
+    "caffe.set_mode_cpu()\n",
+    "\n",
+    "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n",
+    "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "\n",
+    "net = caffe.Net(model_def,      # defines the structure of the model\n",
+    "                model_weights,  # contains the trained weights\n",
+    "                caffe.TEST)     # use test mode (e.g., don't perform dropout)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Feed in the image (with some preprocessing) and classify with a forward pass."
+    "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n",
+    "\n",
+    "    Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n",
+    "    \n",
+    "    As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -112,21 +145,54 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicted class is #281.\n"
+      "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n"
      ]
     }
    ],
    "source": [
-    "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
-    "out = net.forward()\n",
-    "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))"
+    "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n",
+    "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+    "mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values\n",
+    "print 'mean-subtracted values:', zip('BGR', mu)\n",
+    "\n",
+    "# create transformer for the input called 'data'\n",
+    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+    "\n",
+    "transformer.set_transpose('data', (2,0,1))  # move image channels to outermost dimension\n",
+    "transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel\n",
+    "transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]\n",
+    "transformer.set_channel_swap('data', (2,1,0))  # swap channels from RGB to BGR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. CPU classification\n",
+    "\n",
+    "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set the size of the input (we can skip this if we're happy\n",
+    "#  with the default; we can also change it later, e.g., for different batch sizes)\n",
+    "net.blobs['data'].reshape(50,        # batch size\n",
+    "                          3,         # 3-channel (BGR) images\n",
+    "                          227, 227)  # image size is 227x227"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What did the input look like?"
+    "* Load an image (that comes with Caffe) and perform the preprocessing we've set up."
    ]
   },
   {
@@ -139,7 +205,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f665b02ae90>"
+       "<matplotlib.image.AxesImage at 0x7f09693a8c90>"
       ]
      },
      "execution_count": 7,
@@ -148,2843 +214,9 @@
     },
     {
      "data": {
-      "image/png": [
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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C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h6kLU2FmzlEsB8fNYTiQhRjENYmNFVkwA1AxYSvG6UBvZmxdENBNq6mK5MI8hjHGUMmj3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\nmqAYfsg3sKhv50hcYl4QBQuCKPZGII9KmRWZQdvzQqRSHcb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P2RhGixNUQEtBnCfPplcM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6nVoYasNuXKPTAT83XmLl2g4sxfaMNRiD45o7Fyjrz942Zr4G1iVhJSqMmfOe6xnm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\nuVwu3OdlH5m9PP/E8/MLzjn+7d/+jZTrTjAOIfDy8sLnz59JqfD6+mdaH4e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caPfQS7zDQ01LG6qKyqDo+sa6G0xJdvfsKLu0wqAqIFQfAJ2qxso9ZoJj53D5hBHE4malsx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WLbkfnv6erUbMyx+5vW7kMlCo7Jd4zZUpZEaj3UxrLJPXQmPJEapFWiOnim35wB+kIogN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\n3gAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f66a46f8a10>"
+       "<matplotlib.figure.Figure at 0x7f09b97eab50>"
       ]
      },
      "metadata": {},
@@ -2992,14 +224,16 @@
     }
    ],
    "source": [
-    "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
+    "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n",
+    "transformed_image = transformer.preprocess('data', image)\n",
+    "plt.imshow(image)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Adorable, but was our classification correct?"
+    "* Adorable! Let's classify it!"
    ]
   },
   {
@@ -3013,31 +247,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-      " 'n02127052 lynx, catamount']\n"
+      "predicted class is: 281\n"
      ]
     }
    ],
    "source": [
-    "# load labels\n",
-    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-    "try:\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-    "except:\n",
-    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "# copy the image data into the memory allocated for the net\n",
+    "net.blobs['data'].data[...] = transformed_image\n",
+    "\n",
+    "### perform classification\n",
+    "output = net.forward()\n",
+    "\n",
+    "output_prob = output['prob'][0]  # the output probability vector for the first image in the batch\n",
     "\n",
-    "# sort top k predictions from softmax output\n",
-    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-    "print labels[top_k]"
+    "print 'predicted class is:', output_prob.argmax()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Indeed! But how long did it take?"
+    "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..."
    ]
   },
   {
@@ -3051,26 +281,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 loops, best of 3: 7.14 s per loop\n"
+      "output label: n02123045 tabby, tabby cat\n"
      ]
     }
    ],
    "source": [
-    "# CPU mode\n",
-    "net.forward()  # call once for allocation\n",
-    "%timeit net.forward()"
+    "# load ImageNet labels\n",
+    "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "if not os.path.exists(labels_file):\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    \n",
+    "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n",
+    "\n",
+    "print 'output label:', labels[output_prob.argmax()]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That's a while, even for a batch size of 50 images. Let's switch to GPU mode."
+    "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -3079,8706 +314,244 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 loops, best of 3: 90.9 ms per loop\n"
+      "probabilities and labels:\n"
      ]
-    }
-   ],
-   "source": [
-    "# GPU mode\n",
-    "caffe.set_device(0)\n",
-    "caffe.set_mode_gpu()\n",
-    "net.forward()  # call once for allocation\n",
-    "%timeit net.forward()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Much better. Now let's look at the net in more detail.\n",
-    "\n",
-    "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('data', (50, 3, 227, 227)),\n",
-       " ('conv1', (50, 96, 55, 55)),\n",
-       " ('pool1', (50, 96, 27, 27)),\n",
-       " ('norm1', (50, 96, 27, 27)),\n",
-       " ('conv2', (50, 256, 27, 27)),\n",
-       " ('pool2', (50, 256, 13, 13)),\n",
-       " ('norm2', (50, 256, 13, 13)),\n",
-       " ('conv3', (50, 384, 13, 13)),\n",
-       " ('conv4', (50, 384, 13, 13)),\n",
-       " ('conv5', (50, 256, 13, 13)),\n",
-       " ('pool5', (50, 256, 6, 6)),\n",
-       " ('fc6', (50, 4096)),\n",
-       " ('fc7', (50, 4096)),\n",
-       " ('fc8', (50, 1000)),\n",
-       " ('prob', (50, 1000))]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(k, v.data.shape) for k, v in net.blobs.items()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
     {
      "data": {
       "text/plain": [
-       "[('conv1', (96, 3, 11, 11)),\n",
-       " ('conv2', (256, 48, 5, 5)),\n",
-       " ('conv3', (384, 256, 3, 3)),\n",
-       " ('conv4', (384, 192, 3, 3)),\n",
-       " ('conv5', (256, 192, 3, 3)),\n",
-       " ('fc6', (4096, 9216)),\n",
-       " ('fc7', (4096, 4096)),\n",
-       " ('fc8', (1000, 4096))]"
+       "[(0.31243637, 'n02123045 tabby, tabby cat'),\n",
+       " (0.2379719, 'n02123159 tiger cat'),\n",
+       " (0.12387239, 'n02124075 Egyptian cat'),\n",
+       " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n",
+       " (0.070957087, 'n02127052 lynx, catamount')]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[(k, v[0].data.shape) for k, v in net.params.items()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Helper functions for visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-    "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
-    "def vis_square(data, padsize=1, padval=0):\n",
-    "    data -= data.min()\n",
-    "    data /= data.max()\n",
-    "    \n",
-    "    # force the number of filters to be square\n",
-    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
-    "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
-    "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
-    "    \n",
-    "    # tile the filters into an image\n",
-    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
-    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
-    "    \n",
-    "    plt.imshow(data)"
+    "# sort top five predictions from softmax output\n",
+    "top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items\n",
+    "\n",
+    "print 'probabilities and labels:'\n",
+    "zip(output_prob[top_inds], labels[top_inds])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The input image"
+    "* We see that less confident predictions are sensible."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first layer filters, `conv1`"
+    "### 4. Switching to GPU mode\n",
+    "\n",
+    "* Let's see how long classification took, and compare it to GPU mode."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffaf3877710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loop, best of 3: 1.42 s per loop\n"
+     ]
     }
    ],
    "source": [
-    "# the parameters are a list of [weights, biases]\n",
-    "filters = net.params['conv1'][0].data\n",
-    "vis_square(filters.transpose(0, 2, 3, 1))"
+    "%timeit net.forward()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+    "* That's a while, even for a batch of 50 images. Let's switch to GPU mode."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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-       "IiIiIvaI/w/CAMCOMj3yxQAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01f952d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 70.2 ms per loop\n"
+     ]
     }
    ],
    "source": [
-    "feat = net.blobs['conv1'].data[0, :36]\n",
-    "vis_square(feat, padval=1)"
+    "caffe.set_device(0)  # if we have multiple GPUs, pick the first one\n",
+    "caffe.set_mode_gpu()\n",
+    "net.forward()  # run once before timing to set up memory\n",
+    "%timeit net.forward()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second layer filters, `conv2`\n",
-    "\n",
-    "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "p1oHxwnZGNQ6OHbYcjxv1vf9atd1PxsR/yci5iPijsrYOy54bkT84aH1FFsi4n/0ff/hrus+HRG/\n",
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-       "GIMrI+I/dl33ijj0uuKBiHhzRI3BM4Tviogfj4jPdl135+HPLo9aB8cTs8bglyPidbUOjg2KIqZQ\n",
-       "KBQKhUJhg6jK5oVCoVAoFAobRB2kCoVCoVAoFDaIOkgVCoVCoVAobBB1kCoUCoVCoVDYIOogVSgU\n",
-       "CoVCobBB1EGqUCgUCoVCYYOog1ShUCgUCoXCBlEHqUKhUCgUCoUN4v8BaJ+sNGqpC6EAAAAASUVO\n",
-       "RK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01f42290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "filters = net.params['conv2'][0].data\n",
-    "vis_square(filters[:48].reshape(48**2, 5, 5))"
+    "* That should be much faster!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
+    "### 5. Examining intermediate output\n",
+    "\n",
+    "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n",
+    "\n",
+    "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n",
+    "\n",
+    "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n",
+    "\n",
+    "    The activations are exposed as an `OrderedDict`, `net.blobs`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
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-       "MQ8HvQAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01f42310>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data\t(50, 3, 227, 227)\n",
+      "conv1\t(50, 96, 55, 55)\n",
+      "pool1\t(50, 96, 27, 27)\n",
+      "norm1\t(50, 96, 27, 27)\n",
+      "conv2\t(50, 256, 27, 27)\n",
+      "pool2\t(50, 256, 13, 13)\n",
+      "norm2\t(50, 256, 13, 13)\n",
+      "conv3\t(50, 384, 13, 13)\n",
+      "conv4\t(50, 384, 13, 13)\n",
+      "conv5\t(50, 256, 13, 13)\n",
+      "pool5\t(50, 256, 6, 6)\n",
+      "fc6\t(50, 4096)\n",
+      "fc7\t(50, 4096)\n",
+      "fc8\t(50, 1000)\n",
+      "prob\t(50, 1000)\n"
+     ]
     }
    ],
    "source": [
-    "feat = net.blobs['conv2'].data[0, :36]\n",
-    "vis_square(feat, padval=1)"
+    "# for each layer, show the output shape\n",
+    "for layer_name, blob in net.blobs.iteritems():\n",
+    "    print layer_name + '\\t' + str(blob.data.shape)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The third layer output, `conv3` (rectified, all 384 channels)"
+    "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n",
+    "\n",
+    "    The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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-       "iZQxxhhjTCOeSBljjDHGNOKJlDHGGGNMI55IGWOMMcY08v8BGKNoaFbqcjsAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01dfb3d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conv1\t(96, 3, 11, 11) (96,)\n",
+      "conv2\t(256, 48, 5, 5) (256,)\n",
+      "conv3\t(384, 256, 3, 3) (384,)\n",
+      "conv4\t(384, 192, 3, 3) (384,)\n",
+      "conv5\t(256, 192, 3, 3) (256,)\n",
+      "fc6\t(4096, 9216) (4096,)\n",
+      "fc7\t(4096, 4096) (4096,)\n",
+      "fc8\t(1000, 4096) (1000,)\n"
+     ]
     }
    ],
    "source": [
-    "feat = net.blobs['conv3'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
+    "for layer_name, param in net.params.iteritems():\n",
+    "    print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fourth layer output, `conv4` (rectified, all 384 channels)"
+    "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01cf9110>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "feat = net.blobs['conv4'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
+    "def vis_square(data):\n",
+    "    \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n",
+    "       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
+    "    \n",
+    "    # normalize data for display\n",
+    "    data = (data - data.min()) / (data.max() - data.min())\n",
+    "    \n",
+    "    # force the number of filters to be square\n",
+    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+    "    padding = (((0, n ** 2 - data.shape[0]),\n",
+    "               (0, 1), (0, 1))                 # add some space between filters\n",
+    "               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)\n",
+    "    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)\n",
+    "    \n",
+    "    # tile the filters into an image\n",
+    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+    "    \n",
+    "    plt.imshow(data); plt.axis('off')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fifth layer output, `conv5` (rectified, all 256 channels)"
+    "* First we'll look at the first layer filters, `conv1`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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hhBAN0EuUEEIIIUQDjt6JokGafol49vvy4e971FhiHpOvYm1oSOehuxD6Pfy9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\nOZnk8c7L5yWCwGxSs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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01c2f0d0>"
+       "<matplotlib.figure.Figure at 0x7f09692b9ed0>"
       ]
      },
      "metadata": {},
@@ -11786,334 +559,30 @@
     }
    ],
    "source": [
-    "feat = net.blobs['conv5'].data[0]\n",
-    "vis_square(feat, padval=0.5)"
+    "# the parameters are a list of [weights, biases]\n",
+    "filters = net.params['conv1'][0].data\n",
+    "vis_square(filters.transpose(0, 2, 3, 1))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The fifth layer after pooling, `pool5`"
+    "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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VFrjP0X4XL170YlpZLvEiA0aI2wiqOlbjgtlAG5w9ezZRyIu5uTk1+kWZpqamTJWyayCf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KfAolWVToe9N9VCm8XyZqv0slo0qQGED99oNGr0xfl8vjIBmJ2lTMH7slcUoExdwGg0arSrioNj\n1hSWM3CPO3fuhDLzYhfjAYvE6XSaNH8os0fOI009F2DTPTY27ADx85//3MzM3n///UZZ8CwW+vME\njo0R3i2W8igF5UmlTGzKm5ATLPNHTpkKVSqnlBnaT/T8G5vB2bypPjKpuE9qIc+iX1XnJXNZu91u\nnKf6WuwDrRZB/je12FALrph5S+GyBOe4l19g8ObJl4mfnxN/p57JYFO2evdSEbxa5KTqlSPbq7lc\nRXpX/Yrn1JJvSOwd/Hvy/ZSAXzkTrfP8atqrqKioqKioqNgQV8JI5XbHcO9Xu3GsFhULMpvNVqJh\nXwZg9oDo88aNG6EMSMjK5sOYScwDK++tra1wv1xE7RTYbVyZ35SrKZshsMNn9s+Lqnd3dwMTwat7\nMCQsjFZQ9KlnmlQcJBWNXSXn5fsBKp4PtwdMRVz3bH7Bu6D+mFFi85cSGfv2vH379kpEa7PVvIRc\nf55pVKzL9evXQ2wqvJMKxcBMA/cDnyyZgf6uBOtM46sdrnLPZ/z4xz82M81IMavgd7bMDuYcENTc\notqIy+8TK8dcyf0OlU0YOScHfz8OnZDaqXOZ2VyeMlcAOXNqaQwlZoB9f2QTIJdBmd0Um6FiT6XY\nJz6n1JR3meyTAjPs7MiDv9lM79kzZQJk6Qv+X1KGVCYF1UYpKLNbjt3j+ZXDrZjFQ8H4b2Uu9IhC\nauyp9zBrssCxkCI5VEaqoqKioqKiomJDXAkjBaZpe3s7CLaxWj88PCxiZkrdmmPA7lS5AwOtVivs\ngsFctFqt4M4Odia2yk6tZCESPzo6CoLcBw8emJneqTNy76hYBNYOKR2UX60Ph8MGu3d8fNxY9c9m\ns1CXzKKgbXinoX7Du6PNx+Nx4/1iAduUnsO3BYvXlWiQn5FiEFHOZ8+eNbRly+VyJWxEDKzB4r6L\n+9y9e9fMzL7++uuGeF319SdPnjRYDO4TKlgm5wnE+7KOUNW572e5scXsjGKOfvOb35iZ2V/8xV+Y\nmdkf/vCHxjn9fr8xfiaTSZgvWHCtkNOhqF2nb/eYm7d/RkzQXrqTB5QDQspRgpFqE85SwPWiNH7+\nmHJbV+/LOjEuk2JZPEul8tHlXPv9+TG8ahbKP0tp0XzbdTqdtbW0nU6nKFAkz4sM74SRY1lKy8X3\nwb3x7pPJpEij3Ol0GmNvkxAEXKaSMR/TjnlnpxIN5pUspNh0g4mW40RhIYBJc3t7O0ycPk0KY2dn\nJ1yDhvnmm29ChaQ+Dvv7+417cwPnIkan3lOBBwW88F577TUzM/vrv/5r+5//+R8zs5Ck1ez8Y45G\n5w8zzBLtdjvUn/KUwHuZrdYhm9HMzkTVGARs8vDJVFVsHBYoM3iAmZ21KxbVnIgXdZ1a2Kj3UILX\nra2tFVONB98bixb1PBXjC2VXFLqZNTzWnj592ngnHvRYULEoPTXhPX/+vJFAWXmicFJq7gdoa4xB\nnkzYfASUiofZlKrGAN4d/f7NN98MJk+APTXZ/IpNTMzk6ReWHP1dJV1mc59KxKq8Jtksb3ZW92pz\no0yJ/n7KecKs2TYKuY+NMhWizGbaEcd7O6qo7b6seIZf/K0jqlbmOyUYXndR/31CjVWek7gefR0t\nl0u52FUR63NQC9USZxO16MgtSrisqQXHZUQQj8WWAnJeiv7dWfahkPu+M6ppr6KioqKioqJiQ1xp\n0mKzZjylXq9nBwcHZnYeE+rk5CQI0HPRrrH7w87/4OCg4RJ/dHQUzuNdu99RdLvdhjno+Pg4mWgX\niO3kAJU4+N133zWzM7d2jl6N8734nnfbLIJWjJOKycRAPeB5X331VUN8y8fxbr1eL5SBd8Co/1Qi\nVi4fi7qVeQlg4abfAakd4XQ6lfUBcF2oXHsAlx0Cb7XjU3ntuN18GZfLZejvYGgePHgQ6pzL59tj\nuVw2wilwPYMR477GJkhmXnE/FYXZu/vHzAcA2n46nYa/gZ2dnTB+kGT6zp07jTpgsxCYYmbqYrtz\nZQ5U8FGYYztvZYJBvfE8oESrvr9z27CDgU+qrmKuxaBYJz/ftVqtcB5YMuWso6DmMDW3cXtxrCdv\nnjNrslOLxaIhhuaQJyrHX4lL/vcNFSqg2+023OiV80cMF3mnUvOhErer8BjcDql7KjaY5RWKHVMy\nDd93YrGlVBgc/92OhfvwzDaXb53EzJWRqqioqKioqKjYEFfKSKnI29PpNOxK8a/ZuU4CK8jBYNCw\n5x8eHiaF1hxkDLt7rGxVqIXT09NwLe57fHwsmRDsvDkf3qZBQR89ehQYqTt37oRngFlQQm/e/aoV\nNBg9jswOdLvdldxVAHRLn376qZmdMRyoJ2ZHsCNIsXtbW1uNHbrKDs67FLWLUCEPgFhUWg4o6qEE\nvjExbew3jjrNDAj6bypIqHqXzz//PPzNjBMYLvw2m80agmHWJfHOSrnsK/G/escSlorB40wFkfV4\n9OhRYM8YPvzB8fFxKKsPxgsoDY3fxQ4Gg0ZbcCR1Pp9z9pnpkCL83JTmQoVdOD09bfTL2D38GOj1\nekntJo9L/KbqX2ly1HOVzk6Vm9kWFcLAa3jUM9W45OCgzAAyixW73/cF3w/YnT4VGNOHOgCUED8F\npaVilp/7tqqvVH47ZpVUTkt8i/C9K2VVY7nxUnNuKtgo9xOA64Wf5ecn1vqtwwJeyUIKH+GDg4Mw\nuWCREBOseU++drsdJlOO4KzgE7GORqNQ0bEULQAajD9e/li32w1lwcdua2urKGlxv9+369evrxzj\nRJzojJzclhMkl0Z1fvz4sZmtLoaAmKkG1wA8MLgzoj25jfykwIsm7qBYIPMHUS08PH0bi8KsJigV\naRfg+FVoY7QhA/XMdDXXGSeXjT1jOByGSQYL25cvX4Z3V5HmGWqTgH6H8vGiHfe5ceNGw5SsJqCY\nJyw7FgDeC8isaVZrt9uNTcRsNrNr166Z2bl5qdfryXGItubzUb/37t0zs2adoD5SkzgvaLgd/Htw\nW3N9pSh/9B3lERuLfcYeoTjPT/p8LWcuSH0w1MIntliOgb1eS5IXc5n5Wn6WX/jk0tDkBNJ8H/z7\nfSymUuZtNkcpYbkS1/s50wuk1fhSmyC/QFVxuubzuWybEqh+H/sW4blsSldmt3VNmCo+FHsLp9pG\n9R1fnnVRTXsVFRUVFRUVFRviShipkjxyZuer2P39/UbcmMViEXZ9avfJ4kovUOXIvGrngl3lcDgM\n9wHbwrsJpgA9nb5YLJJ5BHHecDgMu2Ksjr/66quwO00J1jnSuP/dTIthvfgXUCJD1BtYI2YXeOfg\nTWfz+bwh5uc2Ynd2lIsjfad2CSmqW+1mYqEYPGLRrj17cvv2bfvss89Wztvf319hEwCYRr/66isz\n07tGs1UnCLNVQTazcyqHlh9Lw+FQsrceXC/sOKDgr2exsRJ9os05Jlgqfx3H/2JTNfob6pEF0rGd\nqwq34M/l8qvxo8SwPC5SLFDKjD8ajUK74vx+vx9CnDBb4HfUzBp78XLsXRXblnIRjzFIqXhZfA/l\nSKHOU4LxXFwjfy2g5gMlSv8+kAvj4McCg8sMsEPIcrlsjE8l8C+VD3Q6nQZzxRH6lRksFwrFn8cJ\nvtmpI9dvUygRgCvpQYx98u/BJlbfbilURqqioqKioqKiYkNcqdg8p0/C6joXxRhgUS0LbaFf4eCF\nancF3RL+bbfbYUWtxNAsqvSrWBXegAF377t374b3hCbpyZMnjRV6p9MJ7AiCjj548CDUDXRPd+/e\nDc/+8MMPk2UA+v1+Y4XPdanenXf+KD/YLt5Rqxx+YExYG8OBAr2Og7U7XC8lmg0WPKu8egwfoV3l\nFOOdJs7nXSDXEXIVgpHisBAc3R/lYQG1DzOB6xlK03R0dLQS3BT381oK3s1yu6k69WwrR8fn8/zu\nLpZ/zdc/16kKR8B9B2MYrOD169dXgtb6HTq71vuAm2bnLFxMM1ISSFDtpnu9XqPPqqj9k8mk2M3a\nB7TNgZko9QyvS4mxAtBwYl7hd2P4HTxrfJQAna8rFVKrMnv9T2nwSsWCravR8c/1UbMVM6UcIFQZ\nfDgF3z+vX78e3l19b1LPiDnZKKapNMq+Z8yYnVWOD+oZqf6pGNMYUqJ5QLGZqi+WaKauZCGFgckT\nICqt3+83zELsnYRJf7lcNoSgw+EwfJCxwHj58mX4sOBD+uLFi1A5+M3svCPg4z6ZTIpEkMoLiCNq\nK2AhdefOHXvvvffM7DwJstl5x8LH+M6dO6G+MGhevHgRyo/F4unpaZjklGj6yZMnDU+/09PTUL9s\n4lMLj1RiYpWskidzb2rgjo37KTH3bDYLf/Nix0+EPIGyByHaGn0ntpDy97tx40ZYjOBa1UYqdY7/\n218D8EJKxdoBOIYSwMJt9lz17X56eppMDYG6Oj4+TqbRyVHc3tul3+83PHlYcA+Mx+NGFHj2omOT\nHsYo7hFrS/6g+QlUmXFjEzQLu81W062o8zAPqAjyarFm1mwT1Y9zjiWpxZgyp8QSHqt+7D/SHCkf\niEW9V4u0lPmrRMAd+13dV6HEjJhDqTlSee2peSG2wORr/XVKaqE29cpsyMgtXvyxGPw38Pr1641s\nIRzXj+UaJea+3CKX0775ROCMlIdtjSNVUVFRUVFRUfE940oYKcRIGgwGwUUbO/5utxvYGs435WNT\nLJfLwHCk4kj1er3AKrA7Pxgcjl/DoQbwjBRwba/XC2wRdpAx0SmitePdvvzyy1BmsD0cTgGr4k8/\n/TScx/GE8BvqVFG2ZtqFnFfcysU9F9cE91ArdrBOzH556leZkmKJlFMmJ8WcoUw7OzuhLEpozzke\nObq62Wp/QdswC8jPU2EcELmb2QpfV9xP8PyDg4OVGGr8PmarMbzA1rz55pvh+TAlMrwYlQXXKRNA\nv99v5JtU7Bhfj/N3d3cbO0J+X/TZw8PD4hx0zFiZNRmplMmR20EJqD3rOZvNGuJ1rje+X8opJBdj\nSpkU2fnCbDUvmAr3wWMZ85LKv6ieqUIspHbjMVYtZ6YCPDuqwh+oEAExQbZ/ljIBqjJdBjPF92u3\n26HemJ31zB+XLxcnKhUeQVkFFotmPsIYfB2piPXL5TKMB47bmDLj4t3Z4sTwfWqxWMh4iKUhETAf\ncvw85ZCh7qfiEyrHqxwqI1VRUVFRUVFRsSGuhJHCbptXsfwvdmPY8SGytoePzKwQ2yli14wd7mAw\nWNFp4f6pAHpgztrtdthdg2GLaTegefrRj35kZmdRrLHrZJYMu0noE5gBUO+Ee7DmRrnYo7xmmr3i\nXY53DffCXsDrQ3hnw4wE3gXl6vV6DXaMI5EzwECwXobd7P1vwPHx8UpeMw8VXBP3mEwmoV05iCmu\n4ff2OdTMzvsCs314N6UhA7A7Y4zH4xUtk9lZ2AU8g+ulRMPDehjFPHL4EN8e8/m8SCvX7XYlOwbw\ntZ7hYgaTmTPPenkoHZES7vsdNbMdXC6f35DHDO9sUValS2G9ngo269ur3++H8ax26rgHvyuPM6/r\nUi7dymkmF+E6B880sYu9EvMqjRT/35+nzo+xFSVsxkVDI/jyKR0Y/87MVEro7jVfuCZlDci9byrq\nu+8vHimtLz/fs5gxa4U/j3NQcn9PMXR8LMWOr/vbus5M4dzsGa8AmAR3dnYaUacPDw9DpXLD4jws\nXg4PD8OCQcWOSYGpZExY3W43fKxhfut2u2FS5SjMr7322kqZX7x40ZiYY4sOpHxh+h3voUxF6Dgn\nJyfJgY/GPjo6kg3PMYhU5/YLEDOzt956y8zMPv74YzPTVO18Pg8mVtQRi+/Ze8+bzvhjjjKpWEYc\nC8w/20zHB2PTCMrHH3U2K5mdmaGwWOI+ifvxB9V7hrInCkep98JonlhUbC7g9PS0Ib7m9wRu3rwZ\nFlK5FEEpTxReAPuPg9qkKDMtrjezpLMDn8eLCfQN9i7EWPniiy8a7xAz/fkYb8rUoRYWvGBMTdJM\n/SszswJ7IiqzoP+w8L14gc6LYJTTi/nNrGEWXi6baX7Mmh9XZUbieGN8nu9PKrR36k0AACAASURB\nVD6UMrHFNncliyVuN2VSUs/9vqE8CH35YotJgOtMeYYDsUU9p9Qxi5v7fBR+PkdFTE85TfB3hfuO\nH+vD4TDckxPb4xoeK36Bz32MzfClqWi4rPg35iRhdj6+1bfRo5r2KioqKioqKio2xJUwUoiD1O12\n7euvvzaz89XfYDBYCVNgdpZX64033gjXmJn98Y9/lLnHwAikEieqVejz58/D7hM74YODg3A9WI39\n/f3AFnzyySdmtuoSnwPYLlz7+PHjwHaw+yZMPlih7+7uBnMKflM52XiXzCYiFtAralXdCyEkmG5F\n26AMs9mssRtnc5rKv8fhCLjdzbRZZTabNUw5zO4oChZtdHJyYvfv3zczC2EmuD4Afi6bJTksQwyD\nwSA8m+sx5SLOdesZ1W+//baRf1ExiY8fPy5O1JpKUMzviPNUdO8cfJiJmPkNY0nFvuFr0A4pM6KH\n302aNVkCDokBcIgVZQZnoT3mllSMKWaueEedig+WYlHU+bGkxblQCGY6LAT31xjbof7vz1eiahUP\nKSVAjwnGU3Gk2DTmGa6LmvFKoRi4EuE7g820yrEA6Pf7Yazh26XYJ64jZrhwDR/zTimdTmclT56/\nH0tflIkY9/EZIvy7++TrLGhP1R+HweF6U4nW/d+lfaLEtFcZqYqKioqKioqKDXGlGikOjIkV7mg0\nCqtYnPfw4cOwC2RxLhgL7Fg5TxvvXkt3JXguNBkPHz4MzACCeu7s7IR7s36i5Bm3b98O5cP7cBgH\nsDeKaTs5OVkRxpeAmQ4lJEW9xfQdvAMB/I4/5va8v79vZqtR6b2eg9kHtfNCnU4mk8YOiHVTaseA\nftLtdhts0k9+8pMQ9T0VLbrb7crdM8A6rJTuh495bRbfl3VR0KNBj/fVV181Mqmzfur11183M7OP\nPvpIMkIoF9pF1f3JyUlgHDmkiAdrfbjf4xroGDl8BEPlDASYTXn06JGZnbPMk8kklC+WPw73ZHYp\npZFivZvKoeev5TGvoqfzPfy1/H/MXTxnKVdt1qX48nEYB0YqF2CK3YmJg0vhGYmYWDglQFflY8ZJ\nBRb15/PfKUH7JkjN86y9VYxUSsun7snWg2632zg+mUzCOOZneL0u/4a2Yf0SM06+7Tj4Kj/f6+q4\nDbm9vJNQLNtBKlo/31eFNVAsNICycPgQFe6Dz1+HhQeuZCGFSY7NZCj8y5cvGwLca9euBW83jgmD\nToYJ16wp3r1161aY9JlWLMFisQiLm7ffftvMzj6e//u//2tmq6aJkkF68+ZN+9d//VczO48xtAnW\nFdebrQ5cFbPJn8fiW9T5G2+8EVJzAIqeHw6HMq0P2oY/9LxYApSJxXduFjvzRwyLJnzwHjx4YL/7\n3e9Wrv3www/D9Tww/QKk0+k0PAO3t7cbC0GmtRW4jrAwwX35OiyMOHYTR95HHYAm537Pf+Na5fGl\n+ilHnEd/Z2cIv4jlPoT7cWwpLDBjqZ187DNeFKkPOTs5+A9HzOyUEn/HxNf80TLT46zT6YRncF9L\nLV4UuP6USdR/HJTjzXQ6bTjr+DLE3lFFJ+f4O2rzlIOKMO7LkBJXowxcTn+eMh/5+8QWhP7Zmyys\nUov2fr/fiO7P53HdetO5SgjMYnM2dfF91SKHvUT9eaoMPFa8AJ3LoGJjKUcArnu/EVNmcF68pBY5\nbC7H3MbfEG+C5PvlYqmp39YxC1fTXkVFRUVFRUXFhrjSpMXj8bghKL1x40b4jXcnEHTz7tW7g45G\no7Bqxs663W6vzUQxsDKH6/xsNkvGxknhiy++WDHHfJ9Q9CeLeP1uTbFV165dC4wUh2fArgNMCe+O\n+RloL44z5MWSZs2wAQzsRJ4/fy53DGBSUH6YUMzOTU5Pnz5tmOLa7XbDXNVutxtmqJjpI5WPDvdg\nF3ZOoOvNQrwrUoJsPu6TQqvI28xwqP6Hd+MYZNjV9fv9Rl9Ihdfga3N1lTIjxSh23HudPFh8T7NV\nMwQfQ9/ivuhZSrXb5jZXjg/cTzlGGaDMy5554cTi7GSBa5ih9ZkZ2JySi/RcytKk2B1lsuPrPEMz\nn89XzKSxZ6nwByq8QKx8pQxDKXPl63Q6ncqxkXqnnAA9VZexcvkyxNpXlcvPubE8eEpq4VlRZtYV\nFFuUYhz5d56f1DUpRxCuU98OyjxbgspIVVRUVFRUVFRsiCthpL755pvwt99hdDqdECIA2gjWfzBS\nQlAgdm0pUC6EaUhpL3K4rCBx2L3du3evoYNhdomDKfLKW63WldbC3/vNN9+0P/zhDyu/3bhxI+zM\nWeCN54HNULqFbrfbaDMO3MlAn1DiZ34Pv4vgcAQIVMiuvwCLEdkF2GuZuGxgg1IC4xwmk0loJwjB\nmYVSfYbLjvJ4ETv/rXJoMXCPO3fuNBip3d3dZNgBjoqM/qIYW9ZtoI04QCtrH83O3jsV8oL7eGrX\nq7Qn/Dvvin2f4DLkcs+xlgXXeq1fq9VqsAUc1kIJo3lc+t2zcjNXiPXJTcXXSv+lyh5jUVD3qB/W\nQylGl+/nWVul1/HH/X1yiInkY+fgWYpRTIn7LwpuB+6fnvGL9XvMXzg+nU6LWS/fp9jioHSv/Fwf\n6qDT6cjMGvyesbLExr5yMEnNzRxMeJP2uVLTntn5C+OD9/z587Bo+b6hFhgwDbFIGB91NHqpua7V\natm9e/fMbDVBMToDPvRsvmQBLK7BZPz5558Xv1tuklGdjBP/mpn9+te/btTR8fFxQ2SohI/8wQDU\nIBgOh2HgMM2sBqcaYByp3MxW+lLqYzifzxvpQMzO+wSeyx99jsbtP7hm5wsj9G31keA0OVjEdLvd\nhkmR/1aTDS+gfMLb6XQqRaSA+hD4Z/rfcD0WT9PpNJhVY956Zmf93jsxsHckFgS7u7srJmAznYhY\n1SmXW3kTcawdtQBQIv2c2UB9gPziJrcAUfdSaW24H/s+zX0nhW632/CYXiyaSYHXQUpYzn/7/saL\nK4aqU2924YUUP+uyFiu5Mq1zXqlJMbbhLjGn8t8sBPdjgNP3pOKYLZfLRhaDmPBdOQRh3uFvpP9O\nsHyAx7pfcLEXIM9j6tvlF+s8jjjyO8aKirLvx1sK1bRXUVFRUVFRUbEhrpyRumx4t/ZWq1UcLkCt\nbL1w9/79+2GVq8xMDC9UffbsWRC+I5wD2B4+/4033miwJrwr2ETsjpU8RxpP7XaHw2FgNPg9cRwr\n/sPDw8ZORpkMmV3CO6vEzs+fP2/EfRoMBo38YcoE2Ov1ApugHAxUMk3enYCJ4l2RZ2nUrpJNgMwG\n+Gt5l6UYJ9VPFeuBf2Psg2LcUgJ+L2JmnJ6eNhgargMWu6OPob+o+Frcj7nPefZmMpk0coYxmNVg\nNgNscU6k78vA4HxfSniqYugAPO+k4khxP2CWwOxsbPm+oNqaWQVgNpvJmGaeuWITay4CumfgVJ3F\n6ta73fP1m0TPT+GyTWeMV8VwlT4r9nxlvksxV9yHUqJ0vkeJnEWVbz6fS2uNP/f09FRmL1Bzm/9m\n8Njn+QJ9S7HsqdAniuEq6Z+VkaqoqKioqKio2BCt73OlDXQ6naXZZjsR6E729/cbgcfm83nQF6nc\ncan7LZdLuXrGSpUDh5beG6zTuloqRm6X5YORbW1thTAEo9HI/vjHP5rZqtiXwwAAnnVQjE+/32+4\nVqsyKps8t/Vbb71lZmYff/xx+I1t36lo4ypnE/Daa6812Dp26VdQ9nJmEj2TVxrsjwXI0Alw5GB1\nrWpjrhf/3MFg0NAWxQD9F+p0Nps1njscDuVOjsuPdwOQA/Ozzz6zu3fvmtl5G73//vuNcrAeCvWs\nNF+sReNypvL4sTszoII4KnYk1rc92AUfKA3BoFyrY6yiYpU8OAjqZUDVSy7SM9cfkGKuNhF/XwZi\nTgceKjyD0mZdJdgRoUQPZ9bUXynNaqyOlLbUByPNBQxl+G8Nj4GUjpXnXiVez9WFyrlZcr5jqaSA\n7UpMexehclmUXgosHNCAs9ksLHIQsfxf/uVf5EIHlY1j68StgeAZzzo8PAzJihXQwNevXw8dCguG\n4XDYEAy/ePEidCL8e3x8nHyPfr/fmHz7/f6KOYOfwb+ZrS6W/G8AL8JUWysxMibio6OjUFYv1jZb\nHSxeHI46iIHpY08lc8Jejp7t62o+nxdHDPeIfZRULBOchwXc9vZ2KAvOG4/HRZ6g7KWIOtvZ2Wmk\ngZnP53KB4s2CXD6uAzwDCymF2WwW+jZfy9HVAbQnC/79+/o6TXksAdxW3pmAn7tcLhuC19ls1kiW\nzeC29H2MF9eoAzbtKS88jAv2qOKI6vibPflK4nT5svp6UfXHHzm/mFTxnHILqZTQ/lUIxkvuF5vr\nSr5ZOceBywZLBZSjj4L3TFbHGOzNrBaWvNBPSQCUXAJ9dzweZ+Ov4R19iquTk5NkahhvNufy+ffE\nM1Ibrxiqaa+ioqKioqKiYkP8nxKbt9vtsIPDzrvb7YYVKCeKxSoT+cA4tpUC7tvr9YpMdNeuXVuJ\nsYN7pKhLFt+iXLzy988dDodhNY5/OVq4Wqlvb2/L/Ed+d83XMpWMnQBMnYoZVILmTqcT6oNZB4SD\nUKY67Fj4GcqNWrnY4lls1sNvp6enK0yU2Wp7cP35XQlHOy+llFXuNrW755hGvp/EzJPKlOVDA/A5\nzN7BFIcEzteuXWuMg93d3cYOjt2VWTyOfoX4b71eTwqKFWOB3Sz6BjMr/G7KvMn9Td3bs6jL5bJR\nv8xscXumWJ0UO2rWNFm2Wq1GLjbe8ftzzbQJk+/rQwns7e0lI8srETnGMuc0VOxoik1iU7GCYkpS\nJu3LZnJKc6gpgfxFQh7wfdT/L2oyVHOPEpv7MAAx05kvj5qf2MlBOdekhOM8BnBcjWsuA7eDCiXj\n5S2tVmtFwuDLyVD5AVWYkRwqI1VRUVFRUVFRsSF+MIwUVrFbW1thBcjMhQK0RyxAxSoXu7Lnz5/L\nFaWKeA6dBlakh4eHduvWLTMz+9nPfmZmWkCr0G635bklwb1SAQ3NzlfgzEhhR3/9+vVwXkzE6oWs\nfB6v9L3Q+vT0NPy2jkYNZVbhCiB45za6efOmmen2h/7m5cuXjXdX7uVbW1vhPkp/o4TOsMM/fvxY\naloA9L8cm4nynZ6eJhku3vn5IHkcGE+FreCdld9Bcp42QEUxjumJlFAVZcG78fXoL7EowT6Exmw2\nKxJN884adTYej1fGVIolTAm8F4tFI3TKZDIJTBMHYVXtgPIr1ot376XiYO4zZqvMEMbHkydPGuxD\njC33QVq5DpgNRpmZvQW4fyihcum7qfttChaHlwrZS4+VskSl2iilMboIRqNRaAfUfUzjo5wWVH/3\nUFkH1L2YWVVzKr5L3333XSgrviXj8XglHynKjvthbdBqtUL/5XdDXaYyHLAwX4UywXhjdmydLCZX\n4rXX7/eXZmeD28cMMluNuwRAMI4YRK1WK0zYmMRS3lklQMP+8pe/NDOzH/3oR8H88Y//+I9mZvZf\n//Vf4fxf/OIX4TpMVGisJ0+e2H//939fqDxm54u73d3d8J6YLBeLRfgYoX64A85ms3AuD3Yv4lbC\nTk4rkqO4vfBUxZGaTqeSKkW9oR+8fPkyLJZg3uQys+kH7cUiaNQ/zBW8eFLmCqaFU9Q0I0X58iSG\nv3lhyNGcca8UdczJXD0NHbt2XZMjNiLD4XBl0QygDdm7DxsVeGDypiH1/JwXGHvKqMWr8trjelEf\ntdL6AFTMtRxSSVL5N8xfnBnAJ+ztdrth7LGDjFq8pKI649j+/v7K4taj1ITFZkkWt+PYZcWDipXv\nIufFyubrfpPF0/f9DVUOP4BKmdTpdBrygk3Kz/0kFUONf/NzJfcTZdpH3z05OUl+Vxg+jRePi9xi\nKFU+nsuprLKTVdNeRUVFRUVFRcWGuBJGqtVqNR7Kq0/sOrHSHI1GYZUIhiVF410WOIlrKop5t9sN\n1CVMAV9++eXaVLcC5zJDfeC35XLZEHb7fFl+93L37t2Qf453rn5lzpHNFdQuX+3A2a3dmxfMznfo\nuM9isWi0La7z13qXed6NsUku5R7LZVVsB+6DY8p1VjEDbIZiurokVhGbXZjRU1HCcR/1jup9UlGv\nt7a2ilgYLt+DBw/MLJ73UQlfU0JvhndhVlG7mTnlnbKKHM6mAi+Wj0WnV04YGH/erOKfu65YWfVJ\nPuadCJgZ4ms9g8hl5HAOOC/mBOF/U0wCm0vXZXUUcuxTKTvlz1exoFRMMPVuMTPduu950fAI3mQ3\nGAyS+ShVWXl8eNZzPB7LWEvoJ+w44hNOj8fj8O3D2BoMBjK3pwc7iXEezpQpUUkelGMLW4q8haU0\nrIUrf2WkKioqKioqKiouE1fCSJnZ1YeHraioqKioqKgoxw8nsjlTnKk4OCl0u92G+JY9lkAzzmaz\naAoKfj6Lg1NxXcw0TQmKM5XehO9TKuxUYfnXWfymhImqXP662HnrxoVhqLQslwFFt5dS8DnR97pg\nM5NCLgWIN28rrzaOGZbyeoulISkF2kvFwwLY4QJlUWWKtZESlgNsylbjSo3h1Hssl83EpBdFqu/D\nfJ1Lcr4uXnvttWCKRZtMJpOi/r69vR36DpxOVJ3s7e01HFZS0oFNUDpuc44KbN5aN+J3CheZGzaZ\nk2LOLP57x2bJ3PulxOFwruB7s4RC1SULu/kcs9U5bd1xxnKeVLzGlBmc73NZ35dc/VbTXkVFRUVF\nRUXFhvjBiM0j5zX+ZhGch3L9VPdjF0fsyth1mgXLJSxVjr0pjWSrVtGbMFMq+nfpjpGf58sT20n5\n8pSyQG+++WbYdXz77bdF5XtVWEc8WuL6m2OkgHa73WCnuK/BiWE2m8m4Wj7XGuffw2/9fj/EvPri\niy+K3lGB4z8pJg2MlMrDx/C7We8gETufx3csKrWqc/wGZmsymVyIkeI4bmZpNtBsNe6Tupd/lxwD\nglAgi8Ui9An0oW63m3QSAXZ2dsL4VlkFcL+9vb3wDJRTxTGL1XtJlPDc2MuxD77M0+m0+JpUWVL3\nuCjLy88zy4cR4HJxnZbOvan4YMDW1lY4b5NQQujnniVlxPJi+jrudruN+IDT6bQ4krsHs3f8d6oN\n2ckil7S4MlIVFRUVFRUVFRviBxPZnFennoWJ7Vj9alyxUVtbW418VLwKVSvzUi1FahegMsPz/Xx0\nZwZrAThQGJCz1/sd1TrgiNq5XF3+ebH/x/DFF18ExiW3gyzdYfryKV3FOsFG1f3VjrVUM+Z/WywW\nYeeGcA6TyST8hvxn9+/fD/2Xd3oc+Tr2vpPJJDBRf/d3f2dmZr/5zW+S5VTvm2NFmQHz5WQXa38/\n1juq8BBcVxfRCXIuQ/8e7A7OwLtwLsDUfKOAeyimiQPB+vJ6cF5DlMVja2uriJFSYUYYKgfZugxM\nao6IgVkA325cLjUPoP36/X5oV84agOOlOdSY6fDu+VwXJYEoY4gxqx4cSkCdy9YW9BPU1dHR0YqO\n2Ewzanx/sK3tdjuMY7ZuqPcC46qihDNSrDG3ude3cXlZN4nzoItutVoNFlWN+U6nk2yndXRxV27a\nUx/7i9DuqajEKokrD7iSj3Wv12sMUo5RwlApIkrQbrfD/XgBpT4EsesBn2qkFDlhJ1BqEtsk7gqj\nVDzIMXb431S5UueVxjIpMe1xUlAup/q44jgmsW63G/o2ynJyciIFuSo+EB83M/vJT35iH3zwQeN9\nfBym2MfWRxPm+sGCsN/vhxRAwK1bt2QKJHyoUnG21FjwH+tSc6rf0PT7fbkAQYoo1MO6qZHMzuvq\n5s2bIYYbm1r8B3ITsxEyG5idL74vMo+yyThmosVxs8sXm5utmmLxLD9H55yAgFLB+EXMjPyM0phg\npRuDTeQa6He7u7uhb6uMBano37G+iPfjNDMqBVjJ5oTnMR7XWMzhvOfPnxd/Q9B/OUmzn7PMVr/h\nsXeskc0rKioqKioqKl4hrty0V+LO2u/3GyvH+Xy+YoIDUiK5XI4vFTnYMxwxZsK7gfLOZt0o7FzO\n3M4Uq2vOWaiSEa+LizI5qWvUM3K7Ix+ZOfZunmlQSXxLy7lcLovrsKQeuM9ydGKUD6JlVQez2azh\nPs9mIXaKwPUqZ2CK1WRmFTR5r9eTYwo7b4jYnz59GuqKI83fuHHDzM6dCY6Pj2U4AFzL/VhF7Ue5\nSpIcx6CS7+ZMylwWP+/k+pV6N44w7sHzXSlgIv/mm29CnbO4PZc/0uzsXf27xdgoTlYNlMwJihla\nLpeSkVSsssoggD6IOlOOK4qN4gj3KRMPszLI4fns2bOGuY+vTfWJmONSSuS+iUgfZf7uu+/CmHvj\njTfM7Izd8fPEyclJo9/FTJjeoUSxY9PpVM75qC+f2xTXmJ3VJcYc/t3b2wtzUY5dVP3cv1u/32+M\ni+Vy2ZDllHw3KiNVUVFRUVFRUbEhrpyRSgEryJiNNrWbVKt6rI57vd5KbrfYeSx4U/fGypV3T/6+\nHiUixNxOg3cGOK9EYLoOlB4hVobS+3k2iXd6uRALQGqnrnZ18/m8wTTmdhhKJJsT2ZfWh2/3k5OT\n0Cew210ulzIYnb9Wib/Nzt8vxT58/vnnQVcDHROzsnCJv3fvXrg3doPc76B3un79ejiO504mk4Yg\n+ujoKGi9eCzguejH/X6/oXdcLs9zSzKrdRk6z9FoJNkXH/6Ehcw5Nsvj6dOngS1CP1bjtpTBbrVa\nDWHx8fHxSm5K/x4pLBaLUOe5EBboqxdBSmNopuvBa2oXi0VgNpmdVX3Cs94qjIMCM4R4FutYuWwp\nbWZuDklpeGPlU/WhgHJz+cEqYnzl+gg7k/jvXex74cePYiRPT08DM8T5U/15L168COehnx4eHjbq\njdsV35ytra1GsNHJZLLyDQc2seT8oBdSKeToUT7uFy+5iSrnrea9ABV6vV4jVtV4PG6YntRA4oSN\naFSOfaUSipZ6R6yDEo/FGNQE4M1zF7mfEgxzLDD20El98HIiw9IF0rofc74vygch8/b29oqXmyqX\nR8xEZLbaTznJKOK+8D18f3r48GHoi1h4TSaTsNDjhReu4clQmSn9xN1ut8M1/FHkxMRmZ22K52GB\ndtF4Pqij7e3tINIGhsNh48PIgvzUc1W5Xr58aW+//baZnZufVLvlEs8C7HWE+8xmM7n4SQnGFVIb\nvW63u9EYNlt9X9xjOp02ypfbUHF7wMyb855jDz5/nmpfP7+YpRNaczJf1Yaxud4fZzE5v1PKC5sX\nEyWi+sViIRfx7D3vn5v77pXMr2qOMTvffOHf0Wi0kqwc//rzeAzwvzjOC2C8ExZhx8fHocyIzTYc\nDsPYxHuWeL9X015FRUVFRUVFxYb4k2GkSilMgFfWMQGhWXpX2ev1ZAyLlDBasQApoSrT2vwMv+qP\nMXDKFdbvqDZBzkX3IgyN2tUp016KDeLypUxxzFKpsqlnsClwXTflUihmDWA2hlESf8Xfx2zVDZnH\ngrpGjRVcC/Hy7du3Q3uBDWAhfS7Gjn8+mzKZzfK74263m4wtcxEoxkZFCVfhKhRibDB2yrn+BBMg\nTKwx5spHfzbT81JpKJMSts1Fel4LpXMISzdy9wF47mWnJL4n/6Zy1anx+PLly8a8vr29HcybX331\nVTgXbAeHyVAMjH9Gu91umIz9eUpor1hKfz5/x3h+9AwYs0V8D5zHITu8wD4XLgdOIjFmzTu05JxJ\nmCVDf2fTLsfVMzvrz3gPfhZ+Q3s9f/48tKFnxJLlyZ5RUVFRUVFRUVEh8YNkpDj/HaDYB+9CrMIV\n8G9YUU+n00agSrXDmU6n4RqI3FRE4G63G46nAoHmWA8OcpbSNJQyNRfBOuzTuuDdBHYqLK4uYX9S\nOzV/bYqRKhWMX3YdAIvFouGC2+l0JKuwabli/eHhw4dmZvbOO++Ymdm7774bjjFT6HfU33zzTWPn\nymDtiHLp5sjCeIZnEDiSc04UvEkEfwBj+eTkJLA72IEeHR01WLH5fF7ESClHgFarZV9++aWZrebL\nU8g5rfjnYL4YDAYbB57kXGuYg9vtdmO+y81PseeZrc7HqZ1+7B1S9cHsiApyC0sC2A51r8FgEFhI\n7rO+DiaTidQK+fm/NBAol38T8Lso8bWyoqCN0Q/UuFXM6nw+Dw4NzAax5hHn+XHd7/fD36wDRvgO\nzv+YChTK5fT9WH1n18EmeQZ/kAupUq8VFY8EYPOWpzBjEVcBfNgmk8nKxG529rHD3+iIo9HIHj16\nFL2filujjuU8ZZRA0X/QYp5cOajI3Oui1PzFAtlUGXNePaXwC9WY+VAJI/2H9DIWqR4+mvh4PE6a\nj3Peh37hw30CC1eOJvz555+bmdmdO3dWFhZ4lhqPqZQKeJ/BYNBIL2HWFNWyMBuT2MHBQcOjU3lK\npeqhBNz/UhHhcaz0YzcajYJwXqXMyX0klNemR7fbbYhvt7e3k2YR9LHRaCSfgQ8k2lyVs9PprP2x\nucjHbV1vW+WMwzGNlCMHcHJyEj7qcD5Q84XZucNDKu5cLK1Sar6NvW9q8wLw9y7nAcmOTGarbZ3b\nrPu/+Tw1RlDXynlmZ2en0Y9z87zyDExdU+qUwnWsHNdiqKa9ioqKioqKiooN8YNkpIBUXAqmb5k1\nSDEqvPL2O8zFYhF2Y8otlHd5fnWtkofG3qckzkir1VphwHC+Eigqk+cm7M1lmK5KQxjwe/jcZTH2\nqaR8ObOmMimUiuZflWnP7Ly/sfnYxyozOy83+m6pwwWLQ9GPeYeGNnjx4kVDpHnz5s3ArGCs5ISg\nOL63txd2uamQDerY06dP7dq1ayvlizlcXCTMB+qXY1kpKJFuCnt7e6HeFPORukdpHDm1y+aYRyqO\nGIvJPXPJZrdUzsPZbFZkdrkslI69XOJZz8oMBoOVEAw4B0xUKkTBzs5OI4Exf5OUMxOLtlOx+S7K\neuM++J4tl0v5TfPjjy01OVmFz6iwtbXVcGjp9/tJ6xKeG2NfU5IMhp+zMvw0vgAAIABJREFUOp1O\nQ7jPuUpT+TI3Na9WRqqioqKioqKiYkO0XuVOO/rQVmvth162+zmAXVmr1cpGzfbPz5WptMwpHQ7r\nevz92B4eQ0o0+H1D6X5SAUXVjpBt8urdfH0oMX8sKJxHaSgGfw2esWmdK1fonZ2dRq49zhUWuw/K\noupe/aZ20l7DpdpPjZ+tra2wC0T0dMW2xNqPGTWzuHaSxa1Ks5EC72a9xqbdbgdNGeo5phnzeOut\nt+zjjz9e+U3psSaTSaOfc7uqumE3dLTJvXv3zOw80rzZeeiEmM7Fs62dTieEn8CuPcacK3f6Ulwk\n9+WmephSJlExwKosb7/9djgOjWEpm9HpdBr1p9ojFgKitM7xLhwcel0nAZTXbDVQaIpRUw5hPLY4\nH61ZXi8I9Hq9ZHBlZa0C2u12YOhg7Xnx4oXsJ0DEIiIr/Qdt2kt9IEvBnUiZwVScETQ0T5gpjyke\nBDxJqGvQiHi3yWTSKBcvmlRsES6vfy7jVQijLwLlKck0MM7xsYLM9IJLDVz+sJutToY8EZR4beZM\ni5exuFcTvFqEqWS/4/G4KLJ9zLlC/Yb78OSJ8qlo8dx+PtbO6elpg3aPLepS5txc6o91+zmbVlLm\nRTb3ezNODjlPUh6v3osx1heV9zGi02MB9MEHH4T69fKAXPlarVYws6T69EU3s+rZfnFV6hDC4MjW\nfv7kdCDchuydaHZWV17UzQsf1O2HH34YFtmp/qf6bKlH4jpmJjV+ODYf3p1N2Wrjjj6jTPK8APKb\nidFo1Bgb3F6qXFx2vwHi7zan9MEciPNUTCiOaccLL0gOcE273W58N1VssZJ2qKa9ioqKioqKiooN\n8YNmpGI7ODO9a18ul41dXY425J0Q/la5yZQrpGID1OoV53GMEmXC4P+nqEsVQVqZv0rcZS8TOYam\nRGivGAlmkNT9uB1Kdg/rithL7rPp/UrNFSr+knpfxRbt7u4mTTW5iNCqLJ5VmkwmK/3c7IwxY+G5\nmRaWMtuSMvvwHBArX2n9l7KJPj9kaWiWXPiCFBsYKxMYEJhnmFVgMTHu7eOTeaANAeXQ8n1Bmas8\nY6LYHTbdq3AfDJiZ0YaTySS07507d8xsNUq5si6waUw5XTDjsw4uEkOKoaw4ykzv/wbwfhyj0dcr\nMzmo++Pj4zDuAXbgQF/j7ASc8Ni/P9ezSm6O4zzf8ZxU8r1Q/X2xWDRMjyqvpEdlpCoqKioqKioq\nNsQPmpECmKHJ6aZKVvacvZx3a1gh847aa2lYCMp6Er+jarfbDXdQ3sHwDtzvRHMCT94le8Zs04Cc\nl4GYqNVMa8ZYFMo2chWd3oPrI5epnrUCsfutg8t2fMjp78zO6g9hNjgfncd8Pg/MBfrb4eFhUi+D\nHboSr6uQEtxG/tlm527Xsd07a61wnddmKczn8waLYrZ+Py9x0gDQd8Da5UI/AN4xwIN30Z6dWiwW\n4XlK2M9AO6gAmUrPxTrQEp3Y94FSpw7FSCmrRew9WMyPf/EdYCbKzyetViu0B66NMY4lcw3XfUoD\nuw6UhYPHhXe44e+Osqxw3/H1yqwN1zXGu3p3zDGLxaLRL9vtdiOkCzuOcLn8+ON5CGOF5yzFPvHc\n6p+xXDaDqpbgShZSKQ84/p0rocRLhDsHTxioYKaK/QeDB2kq7svp6Wmj/KrR5/N5+Hh5KhNl4H/V\nu3AdzGazhgfEbDa7tKStlw1Py6uYIvzBgGkiF3UeUB9zNs/iuevEjPJlZ7Mb30MtckqSYOe801JC\n6tFoFCYRtYDie+A8lbBTASYPVX/L5VK+m48js1wuQ1tiEaHinPE4UyYbjvui6vIy4hetswBOeQml\noLxsOa6WiuvD16pNhBK8Iwo3PMcYqcTXpWZws6aDTG4hum7kfX5fJVvg+/p77u/vh0VkziMV40Yt\nHFSyeWC5XIbfYQIcjUb29ddfy/fDNTHMZrNkHXG/KfEu9teqBYg3f3a73cb8ube310g51G63w/jH\nIvL4+DjpLZx7dw/2ouY5klOmcZn8tUDKLMye0K9i41BNexUVFRUVFRUVG+JKGClFx/IOTK2oAWap\nVFwlv3tid9bUqn65XNobb7xhZue7xcePHzdiaCyXyyCc5YjPXlS3WCwaK+herxfux3FpFF3tV825\nOFf8DNyPxX+XbY4qFZYDaqe3WCwaAtAY4+BZkdFoFOqQdxYlu4xcTJmUGF0xKlyuFJbLpdwp+/6u\nyjcej+3+/ftmZiHxLbN3HG9MJbzFvZUAGTt65Ta+XC5DG+Eeh4eHDXdwJfBUEaGVKVuBj6XYvnWS\nwm4CPLsk952/DuMf88WtW7fso48+WjlPhYC4ceNGiAHFUCwWzLiIxs1Q/Wnduup0OtKxIGVO9XOm\n2apcQplTgFSfUGXnsnA5UVYwduz4ELuP2Rk7i/Gg4n7h3fb390PMrk36n5+nNkk2HzN1poTW/nqz\n8/K/ePGiIRVptVqh3vDvzs6OdHxAu0J6sLe3F1hA1Z8ZaC+Mt1jmEoCtPSoau7IQpQBG0mw1TpvZ\n2XjMoTJSFRUVFRUVFRUb4ko1UmarjIuZziytdtYxKDtqKmo2hLHHx8f22WefmZkFZip2LVzJOUt4\nSZRW3ikB7NbOK2qVB8uDXUkBZupUsLJNdj4Km+iNVHuqnbSvD6Wb2tvbk8Jff22MffJlKc0Ozlog\nFSYjFRiTmUZmWbyuj5lV3vGBkcCO7/T0VLI1KQYn5crLDhIM9HfkvuNdO7MAXu82HA4bLErp7l0J\nX/m5fB6CUr4KbDpWuB5R/ty7g3Hc398PkeBTGI1GoX+wFkTp0jYF64NyAYoB1YfU3MYshHfMiZXF\ngx2C+P74Tc0vKag50+z83T/55BMzO9NK+XHL+kmGmt/9HBJrI58TltHv95MC75TVgDW3yrkKUP3/\n8PBQfq+9FeXrr79u6JO5LGC6u91umGNKdZEc+d9rqZiV43kZf7PgnllWlAXA3FXCRl9pipjSj3rO\nU487m0oHUgpv9rh7925SUHjr1i0zO0t/oZ6DhsN9eZByg/mPa6vVaiwIOTotX+cHM99PeTvmzFr+\nmWblcZ9y8G2iYoCUtuHt27fDJJmKkB2LPePFvMq7L/Z+Je+uhOpqsba1tRXqQE0Yqg7YeyslUFfA\ntX/2Z39m77777sqxWN9Q5kg/Vra3txsCVDX537p1ayWNiUfKI9HsfOODsnhnAhxPiWFzyDkOrJvW\nCFALS8Zf/dVfmZnZkydPwgcbUJuJ69ev28HBgZmdRdpeB4PBYKN0IR68QUul8uGPNT6gytMQ6PV6\nK3GGgFRiefYk83HJVJn29vaSH8ncAkmd79u9dEzx3MD9L5U4WQmoY0j1WbRHp9MpWjSoeUy9+8HB\nQWgvyBF4XuTYUhxZ3qw8g4ACe9GreuEFl990qAV/p9PhsSJXvNW0V1FRUVFRUVGxIX7QSYtV3huO\n7ozj6+6stra2GoJcvh92L6puFIPw+uuv26NHj1Z+U8lymVEqjZCc2x2Xir43ofdLWacUC6TuoaLJ\nc5gEJR70O9FYmAS1g/NlUDsWxUKV7jBjsZbAFnz77bfRspid7wgBxVzG2t+bCDg2SmpXt7+/H9if\nL774onH89ddfN7Mzet7H+opFHYeoGsJS1UY7Ozvh3fGeiqnZ398PdL/qLwCzBcvlMpgfcS2jJERF\nDJftrKHw9ttvm5nZxx9/3HjO7u5ug6Xb29sLfYfjICl4BiTHxpSilJHiNuS8mvwvY3t7O/QJ7m+Y\nB3CNehYzJjh/a2srMNgQSs/nc/vlL39pZucM6Oeffx4kA8zip8YU96tN+0kJC+5ZExUqgEMJAFy/\nnEsvF3ohBR+ep9Vq5t/LAXnzbt68aQ8fPjSz87rsdDpJ5wDgspjVHKg+KiNVUVFRUVFRUXGZuBJG\nqtPpLM3iOdQ2XdUrpmET92h2wcSKG7ujwWBQLB4t0WnFdGLKpp3KNxgTGV6G4DS1W4qVP8dEAesG\nnGNWLxW5WZWJ82CpsAZATrBZwsBxkE4WYXtXXi4n2JSjo6NGIEh+ho9czu8W01Wo9yhhaN555x17\n7733Vq7d2dmRkbtRLg4cee/ePTOzsOM0O98V436TyUQK+NV7Ajh/Nput6BxURHAgNx5TQRlTx8ya\nUccHg0F4Duu1lAYs1Q5geYbDYYNB2t/fD+3K7uVKg4TncvlTzHspuN+vO2+zBgmaO2YmlUOI17Sw\nUJ2v8+FoZrNZeAZHg8dx/La/vx8YDmY1wWyhjabTaVEGhhxU8E3+hqn5sXROvwiLyjqidRlc1hap\nNizNDlCqc07l8WR4/eQ69ZJjpK5kIdVut5dmeQpTgb0F8Pe6H9QYMOlDDJuLooznM+2OCfey6EZ+\n35SADvALx8tYSHFZmMrPlcXDf4yuXbvWiC/CJp1YGczOqHo285np2Excxlw0YbW4Snn8+fubrUZm\nVjGZgNTHczAYNOKq8AeQRbWpeDTe888D1DovitgTtaSsAG9iMGEdHR2F+sCH/NmzZ404Q2wWSonE\nFY3P/V0J/EsxGo1k5HAgt5BS90t9MFQ9A2zq9V5FjBs3boRnoC4nk0m4niPcY1GqYq+lNjFqo8SL\nRnbQWNdzjJ+hFiOq3/nFy2AwKHIsUO/BJkCeQ0o3dxy7LXZ+bg7h9y5dmK07p3N7rUtc5BzCMNaH\nw2EjKj0nMsZcPZ1OQ13judy3faqYWJkVSr9JahOWQzXtVVRUVFRUVFS8Ilyp2FzlBVJMQg45s4ZH\nzg0ZYNMedu+lcUlicYl8/A0VsZx3AbndgqeD+V4q/MEmyO00U6au3O7ORwzO7S5VmIQU86JMuznW\ns3RHqpgwYLlcrpgV/HOVODQWK8bsbNfmTV29Xq+RQwvPQRnMVpkcNT7eeecdM7OVcAgoU7/fl2Ml\n5YaOZ/T7/dCeqo3YRb1E0B67D3ARRmp7e7uRo3A+nzfGq3JDV1AJoBkQlvtI52ZnbuO4NzsqeHCe\nOQ6dgh0++g7XC7dXSpScArNPHK/HC7JzbugMVb/qN89ScQ7P3LjlfmkWj8PG72l2xpyn5n2ey1Mx\nrRRUmWPzbUpszmVR3w6WNfh758rjWSLv4FECmFXNmnM8Z+MotS7x/OTfib8NQMzigPkEz1VrCP6O\nWmWkKioqKioqKiouFz/o8AcMz7yoIGkxd3WsOnkljBUydrYczC/FVvV6vbC6x/NPT0831gfgnmZp\nTRa7tavs1XgWr9Bns1mDASnVpa0TuNOXgZ/DOqESlo3LivNz7vFqF5VzSU4JsnNB7nJaKxzzjBT3\nT5XzLqdpwr25P3O7+2s58J3Kv+ehWJTt7e3AhKUCaeI5/hl+N650Tt1uN5Rf6YoUa8BMnWKQ+Fq/\ny1WBYJlNQH9i7Qa77JcwN4PBQIq5UZfQSCFQIWM4HIb+gfrgOkP793q9oG/D+b1eLzwX1+zs7IS/\neY7BO6G9YmyAYh98f9uECWStHO7NzgJqfihhi7vdbqijVNDPUqgwA2quWS6bee6Yvcuxcql5kdln\nFTi49B2YGeLvhC9fbk5V5VP593I6Y39v1lRtmlVge3tb5m5lETyOoa55HlB6xB+k2Ly1ZmTz2Hkp\nwd66MTLUM27cuBE6BSd2VXS1j6TrTWx4rjcVqAmBvVPWEZard/KJH0uRa5vUwpF/V+fxIPX1piYj\nBSXmjcWWUmVed3EVW4AC/jg/Qy2QcGw4HDbeI9euOW82PxG0Wi3pFQVwnLNUDCBewKUW3pyKwVP2\n/X4//KbaCuWcTCaN/qe8BTlNEnuxcsJu/7HnxQYL9335eQwrkzKXIbVYY5Rs1trtdlhoqdQZyKjw\n7NmzUIdYgGxtbYX3xLV7e3uh3rjspfGc/EeJExnjPVS6KkZqAaRE37wJVOCxivdFWyqnEzWXqbGS\n2+ykEDPnls4rqbmm0+k0vgk8/6T6p0K73Q71xXXuNzkxZ4NUW6OPc1nx22AwCH0Rz+K+zdlAfKww\nRZ5wVHRuy9JNsYJqhyo2r6ioqKioqKh4RfiTNe1xlGO16lTxTZgtWjd+EZvffKTyVquZG68UKVZj\nHcRcXFPCxHXjjFykrMwWcVum3PdTUCaiWHyj3E7PbNUstIl51oMZKd7Z+vvxTpPNsJ6xVGyrqoOc\nSTZlPmSTEotrfVyinDMBjzdlJlPXlIpuU6EuuA0RW+bk5KTBfHU6HRk6JcVs8Dnr9gUlqs9BMVd4\n99u3b5vZqlkQ9TccDkP5wVh2u91wH54PvKmGRcn4bTAYhGs4ej7OQz/ibBEx1gnHfP0xw7Fu9PnY\nnOTjQ3FZS++pTI+ptu/3+w2zm4oqrsqhWM2YwxLPE748vV6v2HEH4PdF/fO1vj74PBV9HogxV8wC\nx65dBz7K+nQ6LbY4+DItl+fxvHCP8XhcGamKioqKioqKileFHwwjlbLxql174t7hGrN8/qMcC7FJ\n4EkzzazEhJsKqZ0ZBxQDYqJpH1Yip9PalIEpga9LDqqqdi653G5AaRT73Lut209Sx5Ur/jp5oVLh\nArj+VB8DVF9j1iWlJ2OGiDVP+K2kHzNLqpipdZ0OcmxQjAVUUOOL83yZXSyormLtFAMbu1Y5D9y5\ncyfcx8zsm2++aVzLzgF499lsJjVZigVUgUeVYw7A7Ihn8nJBMJUjSul8y2VHmXFtjolJaQxj4O9T\nSfnW1VualQd9xfXD4bDxrlzn3imK/y6tX2bjFHLv5JnB7yMv3mAwaGiDVciG2HeFvydmZ+1CfUUy\nUl314/cFNvekJvXYR9Z3lOXyPJw9T5C5yRdQE0uJ4NrFmWjct1Twxl5Piub1FHusTH6h5MuT6kgX\nWUDlBpUXSbJQVIHNEPg7tcDkjz4PJO+ZEau/0n4CqEHK8JHDWVzPda/MVZhw2MSDa1QKEGCxWDQW\nYSoGTc6kzceVGDnnYWh2Np7QTqhzjtek6ozvq8ZUyuSFZ/Lz+N2VswH/xiauGNhcye2lTLalqaQ8\n2OzGiwPUR6rOW61Wo45i3njKw0zNCSWLv+Vy2ZjDc/NLaiPHf3OdqjhNbHL09+DneS9FRm7uwruV\nOu2o83KSAV9+/23y767iYPG7lZpHefzgPXkDXjIvxuI/8qIaz/Lfw1IHI4VWqxmLcjweN+JmqflW\nmdqVPKgo5uRGpa+oqKioqKioqLh60x67JJuV57JaB9iJAOoZaudq1hS5t1rNSORm65shQJdPp9Ns\nCAO+ry8Tfo+5Dft3ipWrNLKw3y3FxOEpk6lipBT7xFDOBgqpnSWzHZ7NXEdsXmL+ZNEi2mY8Hq/t\nlsumKr8z4phmKdNNjJ5XZvBS+JgssbAJyLGXyp+osL29nYwBpBix5XK5srs2i79Tak5g8apnWa5d\nuxaemdqpbiJKBwaDQSPB6unpaahLPF/V+WAwsNdee83MzB49etQoJ5tp/bup8CE5xwI1Z6rQBNwX\nlft+CeOcq1NmdktCF3Aom5TE4FXAz63KGSdWFlXnKv8m1703na7DAqFe2fSo+kmsnPxOXAZmgFNy\nk1y2Ex+xntnR1Pe43W43EqjH+ksVm1dUVFRUVFRUvCJcOSPlwQG2UDYWjKsVqRKq847OvyNrENiG\nWyoEF+9TdE1sVexX1GqnyyLN0vKx5qE0/EFK94XymuW1Y6rOlVZFoTT3XIqtY3dqL25VbtLsMpti\nzNR9WJfGdn9f5ypCeyzvo9I5pcaAYmjQx7k+lN4k51yR0p2l8oyZNfVc/X7fbty4YWZmX3/9deN8\nRkp8y+/G44IjY6eA+UHpjlKC/IODA3v69Gny3psCLMpoNAp1CSZPZTZQdd7pdOzBgwdmZvbixQsz\nO8vXh2uZqVnXgUbNs4odAZitZlYBUE4iuYDKm7CnJWDLiNdccaidXJ35/lc6V8dCHSh2j+vU3z/G\nKvr6XywWDael+XzeiDC+XC7XEuVfFDn2qRT7+/tmZithP1IZSZgRU7lvaZxJRupKFlKdTmf5//9t\niF9zH1ee9EuoSUVN83M2oXRZOGeW90RQg6HUtMNImcFiHTA10aUWNOt4SgLqPVNljS3U1ILWp1tZ\nLpdFgmc8m+/Hk9a6cWuUV4fqO0rQzFHMeZGAQY9jPBEiHhI+ioy9vb3G76XJsnnSVEmV142UzOCN\nEKdeMDtrq/v375vZ+eLq2bNnjXuo9xiNRuEjF4uv4zdhZvlYV2Y6srkyifb7/bU/kqXAAnOxWAQH\nBdU2gIqk3W637d69e2Z23rc/+eSTcBwR02NJeFPjQcWq4zG17nxRYiJn5GLMcfng6IH3mE6nSdlI\nKs5VSbnMVtNCqdQkvFhMEQJqE8DXoHzcF0uBOXMymcjvoprLeDzgucBleOGxI9UmplUf17HUMzjm\nvACvTsxPLKGxatqrqKioqKioqLhc/OBMe2bnK17eJaiVr8pvVyqaBpjuU0JmRYmK91mbYVIrZRbL\nqbxKKZOOSpCqcu3FzJDeXdTfK1XulFAwxdqsI1RXx1L3TolNeSefY8dK2lPtbJRpz+ycdfj222/N\nbFVUffPmTTMze/LkSeO6mAkQsYUgLDZLswrr7rxLzS4KPC7AhLBZ7Fe/+pWZmf3ud7+T5fT9YDab\nBaYBdeH7q2cuzc4E4maa+cKY6/V6K+ZHs7jjS4olBEr7DodTwE54Mplkc/GZnfVjb3Zpt9t29+5d\nMzs3X3700UfhOCeW9dHJzdLvrtgMZpxT4xHt0ul0kmMvZWbu9XqSYUdbl5r9VDYDlnqk+nkpe8PH\nUkwTj1Ufry3GNjELiHunIpEr602v12uEBmi326H9S5Op5+YE75SiQvso5GQw6h5wxpjP541xsbW1\nJeOg+bH38uXLxjEW8FtlpCoqKioqKioqLhdXwkh1u92l2argDeWYTqfFTMimKN0tliInNk/t1ErZ\nLOXqWhIFXu3gShmfkkjauWjiwDo7dF/mWI6yFKsXC3GB8/216j1iQnXfnjFGCkzIuq7/169fDzuq\nVGRzBnZjvKMCYm3k6yqmGfBM7Sau/eizu7u7DX3Om2++GZgqVX6OXI2dcowd8WEDzHQAU79r39ra\nKs7T6AOtKuT6O+ssvf7z9PQ0OaY4sKQSxkKDBkbj2bNnDWZ2a2sr1CH3z5SzhooInmKkYmLzi4Qa\nKNEb9fv90KfxjrPZbGNmtUQIzs82W51/PNufC6VycHBgZmc6tlRQ01brPPgqsydo69LQKqij0tx8\nCrksFSXXm6W/NYxUtoCdnR3JWJdEWVdt7axBPxyxea/XCwspXwlqkt7f3w8TLM6PCS0V9adMgJ5u\nvWh6kU0RW0hh8sc7xsqWit8R89orEYLHvLDWRcrUynWpFnxKbM50dGqCYqwrKFeTYYn3ntmqecGn\nrhiPx7I+fPlu3rwZzHt4b048m8JoNAofOo5wnfK8K03gzWXPbQ7MzurFLzqUV5HZubkM56lJdH9/\nXy5Kuc6ViVqZVnz075jpVPUFb+qITb4lm6t+v98wz8RMiqhX9gxT9QTTHu53cnISysL3xkIf/WUy\nmTT6NKc14uf6jyab3VQfY8C5AqZRjgK/LnjRBMT6WAkua5Ot5rHSuVWlZ+K//ebO/+3nV7URVXXU\n7XaDJILPx3NV0nJ+vv8W8XNjZTUrX/DFxpOPHbe9vR2+n1g3sBmeNy7+2xHzjqX6qKa9ioqKioqK\niorLxA9GbM47SU91x3aSfuXNZhfFduRcoql8jWvV8VTdbRLxm5kJteJPicRjwmIfLTcm7My5iZrF\n6ypVRzmzpVr9A6WmRyAX74WZBNV3gFKGhsvky8p1yqwGfsPOT0Wdns/nwYX94cOH4TiE6thlMTPg\n46Ix1K6dse77KuSewczUrVu3zMzs8ePHjfN4fCuRK+6D3/h9efzz7j/FTqh4VP69+BgzoSkWpdfr\nJeO+cUgM3C8lXjc7byfUCzNNfA6E/WjP7777TprlwAyBuVKmDu7bauzhGRzxHWOP+6diYNHWh4eH\nyXlHOQExlMXBz2cx0xOLx3HsMq0PMbbS1wtbREojm3e73eSYxVg5PT0NbQ08ffo0KYJn9pHZSbOz\nvu1NZ8qqwdISgKUn/n1KsK5ZNhVPEuUxM+l4wfVD11ZGqqKioqKioqLiMtHNn/L9gFftqZ0eVsLM\n+LBoLhUgjFezJcLDmB6mZAUdi4qtXDp5B+fBDE0q1xWHjMCuk+sxJ1pUAm/vHhtjXnxZ1Y5VMU2x\nkA6pHTCLEVEfXG9e3KqCk/q/PXIiRyUsV0D5wdTs7u4GNinlvjudTgMTxWEcEDIBu6zpdBravdSF\nXYF1LrFggGZpd3CVq47HCteZck1mPQ+Ad+LnQ0OF3bYvr9cWKu1IKryJh2onJfD2dZMLL8G795L5\nhLUbSmOoygIW6uTkJNQX2mmxWCSDm/py8t+DwaDxvs+fP29oX5iV5bkLz+Oo/eq5KnSLCnHgQ8Uw\nWD+jNEGxwK4lKNFNlmpbh8Nh6NuqPlRZcwwyO0P4aPwcdgXzSa/XC6woj2cfSibGXHodq6rHXH3E\n+jSOratf4/kE5VMOEMxMK7Y7hysx7Q2Hw6WZ9tBjupIHkF/4cNyN0rhFADf6ZSdJ5ufn4qSocpmd\nTVRoRH4PUPYYUMrDyQPPUfF1+Lkp01AKOa+uFM2roDx9YiZSDHBvUkC5zOKxYkqgFn+xydcvXpbL\nZaN8vlwxxBL2przFsICYzWZyoiuJAt/pdMI7pz6upZ6aOJehkpFyu3kRuNl5fK2XL1+G8isR/nK5\nXIkl4+/Dz/XR6dvt8yS+m5h2NomQb3bWbnhOyguQU2fhWSp1R7fbDSZgNp2hHriuUlBeoHju9evX\ng+cl15mqN1zDY29dE7IyQSlTJc5bLpehHdbdVGwCHtObPk+NKb9A95vTbrcb2gnPG4/HjWTp/X5f\nblRUveIZvEHzfWV7eztccxn1qjboHEeON/RKkoHzvFce/3Z6ehrKXJLQ2qOa9ioqKioqKv4fe2/W\nI1lWXY/vmCPHyqy5qpvupoHGZmiwAYFsS8gPf/0+gR/9CS352baRqecLAAAgAElEQVSEkJENRgKE\noBszQ8/V1TVX5RCZERn/h/Q6uWLfdYYbGdXZwFkvlRV3OtM995y19167ouI54UKdzUsVphWYEmdH\ndb+K7XQ60vyxLEvBKHXSVbm7eKeWWhmnktFubW2FHQartvKK25vJcs7hOWdzdZ4PP42ZKJXJzj8D\nz4lB7fy5fRX173dZh4eHUk7B4zyh0PP5PDAD6KPDw8PGOGdzH+8Q/a5JOaOyOYCBpLXvvPPOQl1i\n9WSUMKbz+TzpqMrne9PzeDwO5gP81ul0GkzZ9vZ2aBcui1cpv3btWnBa53KBvVNq5ix1wHUqZZUU\ny1aids5g9hn1i5lMzBZN2ejDWIDJSy+9tPDbZDKx9957L3pvPJ+ZUFbg9ybF7e3tYGZWsiUMZg7N\nFhmp0m9Pbuz6fithd3CdMgsqWZUcO47zMD5xD5WlQCUMNztjSvi5aCPua25zHh98bRugrEpLbTgc\nBvaP3yWV+9QfY53D3Lzt66YwGo1kBo9l0e12g2YX6nZ8fCzntspIVVRUVFRUVFQ8J3xi5A8YfkXI\nodWl/gtqB8G2Y8UCeftrqVCc2nnP5/NilgusE4d2w/aP8q+trYXysf9MihUbDocNh13eXTF7V8pE\nlDAbMXFDVVZWeEbdSxAThUtJRKBvlG9J6jlm7WUw2HYPP4bJZNLwKSgVFFxfXw/txm3E/kP+mPIj\nAWJsW0nIeanSc7fbbWRkZ58gLldb3xJmalmZGW2uchDys9CW+Hc4HIZyoe25nil/rslk0nr8ArEx\n6+ci9mNMMVLj8di+9KUvmdlZPse7d+9K5tI77LMjuJKZYX8yn7csxkgpKKV51I3npNQcw/6Hylcy\npfRfipy/K8DzAMYizo+F3as5WPnuoF2YxfK+fB5KbJqzXeAc9X1C3+A95HM4GAZlZGaq7RzJzt8q\no0IpuwbfYRbrBaOKMcbjWEmecM5d71/n/MQ+OcrmnCLGa1TE1J8j9zEzHSHBdPSyqrlmZQu3mPYR\np3IwK3dEVVAfvlgKBnb69sdjEXAqGs/TscpJdzabNcyPw+Gw8ZLm7ncelFL/PE6UflXbiSAFXkip\npLkpB1U296m6gY7mSBw84+nTp63p/VRQBEdv8vkl0Zs8JvE3jwtMgD5ljIei2mP0O56t2ggYDodh\nzKKdlYN/bnxioRpLqr4s1tfXQz24TCVOsltbW/blL3/ZzMzeffddMzN7++23G/fgBSjqeHR0lAwO\nUGk51EJKvTMqITub2nEu11FpeJU8gxXucwtzv3HY2NiQ755/HpvVgDbuActot3nzHb+HJaZ2j5RL\nBhZUo9EoLMiZiOAx48t3nlRSDPQh3rPpdNpqE8zo9XqN9uN+wbuA5/DxXq/HbVNNexUVFRUVFRUV\nq8Qn0rQHKAc17GJHo1Gg7bGy5hUwr5RTq3WlI7MMS+JX422S+XrKkTVeVH4zgHdFMadpNlP64zm2\nxVPwMTpYhayncrulEHMALSlzLIwWwDiK1aOtGS+1Y2V2BIiZqH347tHRkWxTz/xdv37d7t69u/CM\n9fX1UM9lQpM5KAHIqcTj/JRZFWYBxT5xf6h68zM988PXcltyzjB2QsY16Du1w1VSIcxE+ICRXq+3\nNCOl2u3GjRvh2WDUSiVbbt26ZZ/5zGfMzOyXv/ylmZ06PCv1b4WSpMCMnGnPs5Nc19R9u91uMFvD\nZMOJh1NJaxXW1tYac6RSQFeIqY5785xyGYklPk+x6Oq7xyw1s1AYE3hf+Dc2L+KefAzPxrWcHzT3\n/WSpCZTZmx6fB3KZPMxOTdaoG2sqek0zzDVmZ8w0M1eoh2PlKyNVUVFRUVFRUbFKXAgjNRqN5maL\nq16WI1BqxUqgMOdo7Z/BzE9qV9dWioGxjNhXSmixLXinMRgMGsxWrL9L2lIJqDJivmI45n9r41CY\nQqrsyneMy3qe8Z9zNvcsIO9O2T/Fj/ft7e0wflToMcC+aIqtUA63qTIz48O7t5JwdRXSzeMPWF9f\nD2Xmeis5EuUwnhLhZd8Ydoz27+La2lr4jesECQOUhZ1XAcUwsC+i2jGnGHEVqHL79u3QZ+zjVeJX\n85WvfCXMJz/4wQ/M7HSMlTpBe6ZB+UMx8Nv29naYo2O+QjjGTssoiwLal7M2+O9ALMdjqs1Tx5Q0\nBtcz1Qfb29tJKQu0wdHRUcNPTNUhVre2Dv6l86xySvfHzfLitW3Fac0W+9jstP1Kyry1tRW+mzFr\nQQmYYfftyjIU9kl0NleTofrwzWYze+GFF8zsrOM4mSuf7zt2e3s7dKiKElLKvMpZO2fa8WCHPI7o\nwfVMC/sPEOvhcAoOUJH47dmzZ41n+5fGv3SlEVdsMklR0rGXVOlf+QksVpYUUjpW6sOsnKCXGfNt\n+5+d67l9/H3W19fDuOQoO39vPo+BZKT46I/H44YqPk/IbZ1cVeSdqkcMKnKQU9yYxWl6/M7RrCmz\nAUdNoZ7KRLi9vS31nnAtnlGqucXmeWViL41IxAJjZ2cnOPiqDWQK3/72t+3OnTtmZvarX/0q/I4N\nHsqS07vKlVnNlUBqjPF8kTIZxaLAFJQ5GOXBsaOjI5k8WG3uUhGr3gyPe6fgg11Go1HjXeZ2YTOs\nN93xfdjcy+VRSbBxHN/Rvb29sABR7ZZre7wrvFH3mxgeE5wWqGSRMxgMwtzBi3/cp9Q1A+Vk0yOb\ndpW2GMAuJlVHqqKioqKioqLiOeFCnc1jTr90nplpc18bsxCcprFSLlUfLnV8NlveRJRjZVI7hG63\nG5zvsavwbIZnpNpIDrRlYRhK6TlF+eZ2Qm0ZJt7t+ufGHKNL2JpShWR2fOY2KzXj4hrsqPf29hq6\nP8z23bp1y8zMPvjgg8A+qBBl3kmWtkGqXZQmELOtqd0991FqXGGMHx0dhb5UZjc2p6qE1inncGbt\nVKLb1LuwtrbWYBj4vNy48mrs3W43mHJL5zgwk9/4xjfspz/9qZmdmQW5LCltsWWgzEzLZKnImQM9\nUkxnLr8iM8W+/My682++DIPBIPQrK5KDyUNQRakkQuw7wHOXZz03NzdDXVPtx6YpNQenAlt4noDz\nv1Jrj7F7PkBqGVY7B38f5WbAcwMn81ZtjneJg7/AEFtlpCoqKioqKioqVotPjPyB8uHxdnj+Te34\n2FchFY7c7XYbsgF8n2UcoM9zLXYEKPt4PA7lwwqeQzqxA3r69GlgfCBAeHx8HMpy9epVe/PNN81M\nC+YBKlQ/FqKLsrKzbsp5s1QdmMu2bN6oTqcjc+2VOj+WnKd2tnw+j1nvLMs2+VRQwuXLl8MOCIyP\n2Rnrk2JCX3/9dfvZz35mZukxye+Z8ofhMvt3Re0+lWPsaDRq+BvxbjHVBkrqgPHiiy+a2SkzxSHO\nXmTw5OSkiBnZ3d0NLAI7Zit2A+DccyoYoATD4bDhW6IU8HN45ZVXzOx0nnjjjTeWKssyYL8flqGI\nQbEx7P+nxuIyATzqO+GZl5gzdyq/qZobOChCzaPqPSx10mZ2DNcoZXP8vbGx0VCd73a7jXoqP8Ht\n7W053pXfH/sS4Vmp+Rrz2MnJSeMZ7AuGssfyJfo1wXg8DgwTyyD5jBl8b2BrayvUAxaCDz74QH7H\nPpHO5oPBIDwUjYCOjr0sXiWcFWgBlQ5mOByGjstpd3gzRMykiBctRY/3er2FCASUBdL1uO+jR49W\nErkWQyrCo602UuyaUoq2NNFt6QK6dDIq1Yfy5Ss1OaiFhVqcqrIMh8PwoeCJ7ebNm2ZmwXGYwUlB\n8XFgTRiMfXZy9RMplxll2t3dZQrbzOLOwSkTETt4YgJF3fj9hilrPB6H+7DjvV8Uz2azRmqX0WgU\nyn9wcCCdYAGOREObsxM++l1pUKVSTp3n/b1y5UqYH84TtQsn4gcPHiQXf6sGjyGlip6ac3H+7du3\nQxtgnIzH48biNBZwkUJpH6nFE4PTAZktF9EN8Lu3TIQ4rt3a2gr1wyYrtglk5W6z03ogGwLK4N99\nDyRDf/fddxvz3draWiPAIxbZ6B3GOR0Qz4+YY3CszUJ61ajO5hUVFRUVFRUVzwmfGNMeh3H63WRO\nv4ju2wi93NzcDCtfrJSZfQKWUYE+j95UDmATsBNaNvmm3/nkGB3ewaWo5pwqMZA7T+1EfFmZ3WnL\nhCltpBgD5+vLzpI5J0n/m9KRykE56IPBfPDggXTs9s9nbSnWqkHdUjv64XDY2FVy2LBy5kyZPLjt\n8X5funRJOqsyg8zPZ+TMqvP5vGF65nsy+5RiKFQ/rALqudeuXQu/MSNQyvLiPAQbKFmY2HX+3jx/\nMvvoAymGw2F4LtpZ5V9UwUSKCb1+/Xp4HvIDbm9vBxblo48+ajwXfZlrH9SHzeVKKykl8cJlZuaq\nJHCk0zlLNt1W8dsrqqMMKtceyj8ajcJ4R5s+ffo0tBebwXzAxvb2dphjWAEf7wFbdlIK+bmxW+ri\nUeI+EAPeYTb7twXrThJTWRmpioqKioqKiopV4kIZqbW1tbAqVbZprGxv3LgRVtLvvfee4VocZ8HN\ntv4KHBLpWR8lzpbbZfG1is3I2eJjuHHjRtjJc5mYeTNrMleekVJ+X74uuK4k75ZXUjdrt/NSuxPl\nsKmuU2UBcIyVigG1s1H92u/3Gw6eqswqh97JyUkI22dfjxKhzatXr4bdonou2lmponMZFNOYExvE\nbtazeP5+HsPhsOG/xGXFtewQziHZ3v9hY2NjQcQvBS6zF9r9uKGYMg6P9+W6dOlSuAZyBTlZGAbu\nDZ+6t956S85FnoVR7NhoNFoQQTbLs6kpH8w28GOW2ZaUTAeH9rf9lsVkUFLgOdHLm8xms0aeOx67\nOL9DQs+ACoCKSSIoQU5/L7Ozb83a2lqYg7g8q/DxAxTTyHVahhGKPcesfV+Px2MpBwMGDuuL6XQa\n2oiZfSr/J8fZnE17mPigUbG/vx8WA6UpP1ImKH7RcihJNXJyciKpX0A9C+VjR2qg1GzJz8G/6+vr\ngVZG2be2tsJL0+v1GhGQsft6B281mY/H4/DB47biCC++h3+GcrBM0eilL3ouiICTFZulF0Uog1m5\nppV6xtHRUTiPzXOs0o26QdGaqXbUHQ6hjx8/biTf5cgZtdjg+pZqZPnoGfxulv+ooh6YlDi1Sw5o\nK5jm9vf3G/UcDodh0cnjhSfr837QY+CIWr/5y2lGQZfm2bNnjfbf2NgIYwImrGVcBV599VUzO11I\nAal5jMcOPizLZBrgoAm/+FILFZ5DclDZLlIbB3wUWQE79S5fvXo1zJUcVZpaxKYiCNX8rr4/59Gx\nwz1xn48TKlH0Mu4tPuKvNHk0AxsHTveGPjk+Pg6/pcy4akEbQ3U2r6ioqKioqKh4TrhQRiqWjBir\nXKVO3HblurW1Fc6FiUflOmIo80DMvGSWVz1P3cOsyaj1+/2wq/cMENdDKTD7MnjWTDFDXAemZf3O\nSOWDUqwSm9243EoXhE1cZotMhNpNKChGUunRsBOxN5Nxsko8T41PpZrL7cwh+34MKOaK24TNs2on\n73dZZukxmNIBOz4+luyD2nGn3rkUA/P48eMFMyTOQ99wDi3PFsbMWynn9pjkRApqfLLDNcqNsjDL\ny1BtxNo5ZtqBfjgcBg04sCOl8gU8FsFI3b17N4zZUkfsHAPic8Wxkj8HBuEdRlspx2BlAsqZ7jEm\nu93uApMbw6VLl4KDND9Lsd9+Hjg4OGjMHZy/Eu/o48eP5RxSAq6bkptR8i8MZuzbspd43s7OThgn\nXG70IQeYeMfyHGuJccXah+xy4plmnit5TDDbaVb+XjDQN6PRKJg6oRfH7Zf71lRGqqKioqKioqLi\nOeFCGKl+vz83W3TOY2dUOs/MTlfR3v9mfX09rJqx89vf35e7dg8O882trrEa5120Z7Nu3rwZVvfs\nw6Fs4z7D/HA4DLt/zgHly98mtyBw5cqV4KCuduop/yBmWTjsvq3gJaCckWN1gq8NdjExnwLl84Sy\ncP8qoU3vVK2c67e2thrO+5ubmzLc2bcLsyM5PwJ/PObknkKprxfqrbKcMwvFAoR+95zzO0T/jUYj\nKXXgoWQSVI7EHNowUuy3iOe1Bd5lZqlYLR4M6N27d5PluHbtmpmdqiqXAH5zx8fHYd6Bz8jBwUEY\ns2q8sTyEfwdiArSp95tZA9+Gg8GgEbK/qu8Ny9yoeytGuvS+PnhGKZLzmGTWFc/ld8kzK1wmNcZz\nTCHut76+3nhvptOpZP7xHH42rgEL1el0wv3asKKpsgJg8g4ODhrngnFEWc1O3y2wijnFdFyDeXmZ\nMYZ7dLvdUCe8K7PZjKVQPjnO5sPhcG522lilar4qUgp/pybazc3NIvXXK1euhKiZHNpO8Ao8OfGC\n0Sz+8sNpGQP/6dOnMnkrwztuxz7mqcmSIw0xiWNglSblVGbEmGm3FOol9kr50+lUqnCnFoR8fxzn\nxV1qgcIq3Eq7yz+LP/68+FOTLqdA8GVXKVhSYNVxjKGdnZ2GmrRK1dLv9xdMITGsr6+H89D2sQWL\nXzzHyuzfOY56LF1IxT4sCugHYDabNRIjczQe+prTPHEb+TQl6+vr9sr/pXf5xS9+kS27mdlnPvMZ\nMzvtNyxUOZVQKpFtadJiZXIC2NzDEWYp80gqopc/pMuoVyvzMYB+5ojE0kWCes/RV3/84x9bl7MU\nMX0qr0d1XmfzktQ7/K7wYsOb4tj8yd+kZYJ5/HlwFdjb25N9DGAc9Hq9UDf+9mPs49/Hjx8X6zNW\n015FRUVFRUVFxXPChcsfqISdHjFzCt3PzE5XjctSunwf/MuhlbHnxY6x6RGr8tI8URsbG8W7p5zD\nu5eIyLEVvEvxStqK7VC/KV0lZt44NFmZWJU+i2/zUj0v5VzPeRrBEBwdHcm2UeOJnam5HfjfyWQS\n/mZzrmdUmGVRLBkzCKWhxn73nzMVshO+YoZSz+VdXsqMx3XD3/j35ORkwbHX7HT8qfdMMSpsolK7\ndM/4dTqdpCmPg018jjWG0onjZ6bmVlx769Yte+mll8zM7Pvf/370fAYYqTt37jQSWcec4VPgdlaB\nPqyxg3856wTu4bNKzGaz5BzJ775il5fNHMGMON7V/f39Rp8rDbfRaNRwQVCBN1wPdsL3ZsbRaNR4\nv81MmuTU3AXwseclf9Dr9RYsB7Fy5VDab211rAaDgXTsP48Olp+3j4+Pw5hBPQ4ODnicVEaqoqKi\noqKiomKVuHBGymN3dzesMOGHw7t2rMLZdyEVdsr+HLxT9zv0k5OTopXtcDhsOL6XSh2Mx+MFZ1SU\nXe1s1aoe18CGPp/PAyuSC5Ut3b2oXYLaGaVYwJjongoX9v4AsXxKJcrcMaTkNBieEVK+XqxYzwKZ\n/p4xfx2/i43tOv15OVV5roO/djqdJhX1cWw2mwX2AUwH+wR5eQCzxXBwLhfO80EkPn8Y39eXiZ2g\nzRb7HMzVZDJZULb2be532DjPv59ra2sNwVMWVV2F+jMDAsRXr14N70rK74bzm0Eu4aOPPjpXnk+v\nws159Rhg5dA+ShaG210xXCmh3xg8c3VyciIDH1QuOxyHX+nDhw8bfbi7uxtC4XPlSPmOsl+kgnr3\nFCuXY3J8PdfX1xdYE9xX+VCm7pcKHOL+UtYFFiBGW+O8WJCFlz8ozVwwn8/l2Gkrl8MBZOgzXl+o\n9s/5SF3IQqrb7QYdKTQCT8QqokY5K7KTotkilZxz3FaDCAMex7a3t8PggBMuUtTweaPRKHx44Iw9\nHA6DqWOZyc472nkn4BQw0T59+lSm+vDPUBFrZtpBtMQxvtPphL7hRJdeF+bw8DA5yaTMeLxYK40m\nwwvJ2lcoP3+48ZGLmapYqRzwk+B8Pl+IJm2DmM6MStmjooR8fTkaB2Y8Hp8M9Dk+nqWJe3MfJe5T\n73AdAy/YzOKJtIE2UXtsvgfUB+N5AeN+MBgUB9ykwKZ7pUOkPtIqSbe/lts09YFhs1au/UpMP7EI\nwlViOBw2vhdqHmI9sZwLQglU6jH+qLNuk1okcpnxLnHkrQqGUNemEo+31U1UePnll8P3C3PDhx9+\nGObcGzdumNmpeRvPxpxwcHAQ1gTLBCT5hd5sNity9+n1emHe5AUalaGa9ioqKioqKioqVokLYaTM\n7EIeWlFRUVFRUVGxJCojVVFRUVFRUVGxSvTzp6weqwrbLBHG/Dhs7f55ZnGhSu+8qhwZWV23lDGM\ntUXK2bytGvZ0OpX18rm4Yo7qnEsO8KJwSq4g138cEu+Vu3OyAUregB3blYO68h3zod8c0PBJgMoz\ntko2+rzvWUlf58q8TK69jwOr8Ln6uOcxICfwmgtmUZInJbIQ53Xub9vmsaAPAD5tmCdKxSR5DlFO\n4MuMjZyDP7CsfEQOq547FDjATNWj1C821a+5d4qfm6vvhSykVgW1gPIvYqnmznA4bO3UpgZv6kNV\nqu6am0SUSvgyKutcHj9RMJSzKS9AfOJHs+YCiTVAAKUPptpSlQX3NFt05oYOEvelj66MRfoA7ISp\nor78R6PT6RRHoKwCuUnfQ0VCtvkQlEz255lYY5EySlF7Ffg4PgT8jNJ0VCnE9JhUFG2sPKnjsfNj\nc1HpgjU1lyn19JxeU2mKKo4mKwFv3tR8p9T91cbR36/T6WTrhOemUjFxNF6q/LEy+DpxFGjbccnP\nOs/CNxUNrrQDGakk7dxu3L6p7zHfw3/PSsZ6Ne1VVFRUVFRUVCyJTzQjlVqRcsJG1kEpWf2bNc0z\ns9nM/u7v/s7MyhWGUxo/vJNXFOwyNG8qDFmBQ7sZahfhc4UxVG4n3un5EPzRaNQIx+92u0EuACG7\nz549kxpPnsqNta/fbe7s7IQwWzwD5eH6solS1ZflNxTDpNo/pdO0KqhdbAmtHWMSY/dN/R1DSgMr\nV75VjfdStGGjlO5X2/aIsUklZSllBp9H+6dQco1i5WazWdacjvP8s0rNm1/84hftzTffjJbF/87P\nKB1rrGmVwnA4bMwJylzK91JMSM6EBeR06fid8uVnnUM+5tkdbkvUQ7Heuf7KjWO+T6w+6j3rdDqN\nhNzMeqbasfQ8jwsX5Pw4NFtWCdVJMZTQnqy/wmhLwccmek+Zdjqd1ronqYSjN2/etDt37iz8ppLL\nckJcn3rGbNEU6LVRuJ2VvZwF3tA37IcFHyos7tSkxPR3LqGtT5LK16Csz8Nfp+RdaWO2WtavIvdR\nAmITn3ruKuaBVbc5Z4LnzUTpB20VJr3ShdQybbqK++V8pBS8jhhfWzo3pcqnFi+5j3ppInqUj012\n50lvo9w0cte2bXO1cMuNz7bjbmtrK8yLLKTtteLU+6NMjwrquxK7X8ofKje21TxG59aovYqKioqK\nioqKVeLCGakSxCJH4FjMyTRL0w8ofP3rXzczs09/+tNmZvYv//IvyfNLIwewUler+06nmUA1Vt9l\nHEb9Kjy2M8NxmKjYNJfa6XHiT7WrQxuNRqMFlXOz+O4Dz8POdX9/X6pcA1CTf/ToUSPp7sbGRqgL\n7yBV8uXSSEn/DLVTOg87smpnaK4vyjQej7PK4qtAyW5WHec2KN3dl7Z5rH1TaZlSTsvqnRoMBmFM\nlPZlaRRtW9Nd7NxVsYD+WalnqHdPvT+5cnJbpRzQVVn5Hr7fYm2Wsi7w3OTVyWMm3tS4itVHmdja\nQj1XJQJXYPNr6l28du2amZ2mMFJ9V8r+lTLwPlCq1DxbOufzd9QqI1VRUVFRUVFRsVpcOCPVVusi\np2mUggq3Rf1zuyJgZ2enwXDkHKRzvlJgVCAFkCsH5wDK9V/p7kWxQIBilVL90OmcyQHkZCj87oT7\ngXdPyNnEORkBZoh8v3IOPX6mb+N+v98Ie1U7Fk4KnGI/P24fKXVM7T5T/m65Z+TO9wmolylz2/nA\n79TPw0gt6+vCuSD5/NS156k7X4trUqHsOS2otliGHVFMD49F3/b8DPbvLG1Lxfz755udvSN4rur7\n+XzeYMRVO+ckDLhsqVyk/N6qebYtI9XGL8nP2/w8f33sGPDaa6/Zb37zm8Z5vs3575QP7MnJSfCv\nzeWnLGVyUxqIjJyP1IUvpM4Dr/HTxuOekxniWtzv1q1bZnY6kD/66KNWZeJB4jvlxo0bIaosFSVn\nlhYhUwOfk/DyS9xGC8Ns8WOCDyOcwrms3rzFiGV4LxGFHA6H4Tw2L/qoOL72+vXrZnaabVx9DP1v\nnPCY718SXbO2ttYwia3atPc8kBIRVQKqqQ98zJTl+7yNY/YqHKifx0KqBFx3DppQSYFTDsqqfKXm\nvk996lNmZvbOO+/Iei27kMo58y5jZsqZvUrKjOf1ej25qPHzhXI25udyX5WYc80sOT8ySt4fdR7r\n2J1nIaXuzdfnNLdKTWxeJzCmSVhiYi19Lp/H6wHlXM/mZfzry6jGHSd4tmraq6ioqKioqKhYLT7R\nOlIAryZ556BWsaUMG3aQ2H2Yna2gEb4ZW6HD/IWd0Gg0ClRjahfz5MkTufpXzJqniJnWZn0otRtb\nRndH6SD5XSdDMU4psxE7padMrGZWxBYNBgPb3t42s1MmymwxJJnP8+VZX19fSEmDsgDKGVL99nFo\nRym0DWvngAYus9rx+/DznOYNMJ1OG89dhklqG26/DKMeuyZ171z5gVh6JLPFditJu2J21l/Hx8dJ\n5/Xd3V0zO2WkPPuwSrNe7Pk5cPv58ZNj6lKh+NPptMFwsZmZdYRU+dX7j28CzxuqzikmStVXsUI8\nZ/r7nEc2wyP1rcS3SJlTlWnS7Gw+RPkPDg4a377RaGRf+tKXzMzsJz/5ycIzzfKmdPXc1Hcq9e6x\nGZzNuJ7Nms1moW4qY0cMlZGqqKioqKioqFgSn0gfKb/rUKqp6rzYsdLdKxzZvDN5DsPhMKyUsUvJ\n5fHJOZYu43Rr1qxjiT2902mKjMbaPCVxgGfEdhgppgcYDoeBuVLOy+ijk5OTcJ+UWKLyh2J2TImD\nljI+3mGU63ORSYt9+dsk5yxFyTWlIniM87AdzHCeB8s43E46lnsAACAASURBVKeY5mXETX1ZcnIV\nn//8583M7Fe/+tXKEv+WoNRfJ1emVUgxcHv7XJ+xMikZlLbSM219eebzecOpO/adU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GFX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PwW3wODycw/2KBOpxOMWaFQ0PXI8Bguj5nGfoE102g0tC+xvuv1uvab1y82TlXkSFqGa5D+\n7u/+roiM+8+yVPw9jPnrr7+ubCvHUXqVFSCPALaK56Gt5SqSzUSVy2X9PaR0ms2m7oG7u7tTsdmT\nkle8a3BwNcaLYxYxTlYJ3QLzGNIzvV5Px5X3M+uZGA6HriCnTUDhz5jNtOcAPhtMYpq8Z8EejnU+\nTXzaLTlIeUrlvFFgML3MAH5RWQ2Qg4MDzdbAy+Tg4ECDvr1DEIKX+YVx7tw5ETmi2vm/PfcfDyRe\nTrVaLdCheeyxx+TLX/6yiPjuNAYGkXWppjlAFQqFhPq7Bxuo5xVn5QXJWThYJDiozMzM6OLkoHnW\nOAKwMTIta4stz8zM6LOjzy9duqR6K9hoPe2h3d1dbR/AiQP8Us/abPA97gN2ddnfVioVfaasDDM+\nMHjFlb1NDt8/e/ZsUGCbMz7xclpcXNQXBK7traM777xTXwQM/AZjMDs7qy99JE0899xzOtY2o8v2\ngX2mYrGon3k0vRdQyiVHPGSN5fr6+lQHqVwupy9szLHHH39cNYIY1mhqtVoabI5n6Xa7GgTNCt14\npvvuu09Exn2JdcrjYfWFms1mkMDx3//93+4z24zaw8NDV08NY4dxqNfrwW89XTwuugukua29vc0q\nfW9vbwdJMwcHB+5hCYcM9MvCwoLu+Vz6C8BaSSvIjLYgm/ratWvav3bPERHNJLzvvvsS5axExgdC\nzCHOQvOqXdixnJub0/0d75hCoaDt99xk3qHpnVQqmaQc7h1GvEQJr/wR773eOvY+w7Mw0eAlrXjP\nAXjGM7/PrH4VJwLcjGZddO1FRERERERERBwTt9S156UZ1mo19/RtA2NbrVbAuGxsbOhpeNqipo89\n9piIiDz55JP62Sc/+UkREfmbv/kb/cyruQbman9/P1Dw9VTFn3/++cDiZ3cKTuULCwuZrhM898zM\nTGBtr66u6imbqeSsIHLPFVCv190iybAIYY3Nzc0FAbT2N7bdfNLH9cAcPfroo2o98jVgwbGLD0wj\nGILDw8PADcDzgC0qO8fYuuMadUCWGrrnovSem9XiWTvI1v0TSQbdiiStPLghRI6YPzBTzWYzYIs4\nRRxzgtkPWPSXLl3S9jEz8M///M+J56nX60F6Po8p2pTL5fRz9N/NBHNaizCt9uG9994bfMYB+bY2\npre/1Ot17UMw1/fdd5+yu8xE2zGemZnRfmXmB4wKM1I2WP7kyZPuWvmVX/kVERF5//vfLyIi3/zm\nN3XOoM/TUuexBsCOc1Fgz0XN6xzsia2vx1hfXw/W2cmTJxNSMiLjvdruT9zPwGg00vmJvnjppZcS\nTJTIuL4mrod5xGywp1ztSVgwHnroIRE5qnDBtQazmJWnn35aP8NYeoxcvV4PtMjm5uYSwegiSW8D\nxsNLRPp5gL0H6EsvcJuZerueeX1g7bXb7YB9TgN+w8krViaB3Z9czQKfYe4WCoUgoJ2Ta/jckbUv\n4f7TuFQjIxURERERERERcUzcUkaqXq8HVlW73Q78vCx0xhYprJhXXnlFRManaS9+xQtWhBXBTNTn\nP/95ETliolj9ly0rxDfAEuZK78Di4qKe0u+//34RGfvpcRqGxdfpdIIU52azGVi9p0+fVosPVoz3\nXIPBwLVUOXjQWm4ei8KBtlm+Yo4fYGbLa4MXL2GtzoWFBTfwHSwBs0SIYWDrEPf9yEc+IiLiVrOv\nVCpBzIgXIJsmcgoLietz2b/xdbg2Hv4b83hlZSUIyLT/bcE1IW0MF98PGA6HykBh7nzwgx+UZ555\nRkSORGlzuZx7X8SqgfljAT38Ozc3FwRND4dDbRfW28LCQoI9Exn3I9g4MI87OzsB29ZqtYJq8vv7\n+/Liiy8GbUZb2DoFbrvttkTsHL5vrf/XXntN+4sDiu0eMxwOlb3ANWZmZrRfOdYH8xh932g0glqG\nIkcMyU9/+lP9DAysF1fKwLzEPsXP6sWPIE7xrbfe0n5FDJQXP/nyyy8HIsLValXFPDGf5+bmAmb+\n4OBA5wTHzaDN2Mu5nzEGr7/+ejC3c7mcW9sRQf1PPPGEiIznHTwIiKW6evWq3HHHHSIiKtrLQLIQ\n77PMLgJYy2m14cCKoZ97vZ5ek4Ug8U5AX/A+ffvttwdeirR4KC/o30oYiIQyLxyD5N2Haygy6wzg\n75OSYTCerPLvJYlhv/EYWxZ9BSbd92alDW6mosItPUjZxSgyHjRsuujIUqkUvPzn5+eDicX6H6z4\nbINDOeAVOHnypC5ioNvtBtR+tVqVX/u1XxORZHFMwKoeixxRtbVaTScRvwyzqEOvqCVPGFCiuEba\n5upNIi76azMH0yYd2sEKxMCkQEf0DdrvuWq+/e1vK83N88PLnLDlFQqFgo615wICVldX9SVnExaO\nA95M+MDF2VD2Huira9eu6UGFs90ArAWeJ5iLg8EgcOOVSiV94XIQPlPcIuPNC0HkmDN8qMWL/JVX\nXtE+RX9XKhV9QXrK9aDYh8OhHizQZp5XvKbx3+ir9fV1PQDwvLJrPp/Pu/sIDmneZlir1fSlioQQ\nzxW/s7MT7B2cTYj102g0giSUdrutBzz0c6PRkGeffVZExq46kXFmsOfGx8GXg7oxf7AnTQLat7a2\n5gaN2wLfb731VvAyyufzgWu01WoFh85XXnklUcRZZNx/mDOsHYbrwEXd6XSCvfe+++7TAybmlbem\n9/b2gtCEjY2N4KC8v78vjz76qIiI/OQnPxGRsevzn/7pn4JrYt3A5f3222+7SUZWn3B9fV33OBy4\nNjc39bCGNvF42wLY/Bmj2WxmBu4DXgB1WhB51nuH3Vpe0eVpXY54LrT98PBQx5HLvmFuT6ux5Wmf\n8d6SVQKMNaZY93HS97MQXXsREREREREREcdE7p2kSx4Xt99++0hkfPL2LEHQsrCUvXTl1dVV/Ttr\nG+GEyfSxPeGXy+VMdxV+e/bs2cBS+tznPqe0PDNg0KhCGvTS0pI+G+r67e/vT6Qf0Wacsjn1G8Gj\nsLZZc4v7x0sX5VM4Tt2clmutK1bu9eClJjO1b68nIoEWlEjoNjp16lSQWr20tOSmb3OQtEiS4WLG\nzKYab2xsqMWIPuh2u4GbjNmMLFcR9xWu1263tc85sQBWIFPTGDvME3Z5e9IF3phPWy3AA65Xq9WU\nDcPzPvbYY7p+2IVqC9NyPUz0z+rqqo4DB7dbrZput+tazHCtYH3v7e3pfLJMHK5jrUd2sQMrKytB\nILgNCMb90Tc8/6y8SD6f17XuWcVg9+6///6AAXnwwQflRz/6kYgcsc/cVzzWmO+PPPKIiPghBRwC\nANx7772u+xPAGlxcXNR1yAHolvXw1Pu535nJsb9ZWFjQ9Y+5cdttt+k+gr2/3W5rG7Dv8bsCbe50\nOspmnDx5Uj/DnPXYHW6n7atPfepT8tnPflZERP7sz/5MRJIhHt4exu+fxx9/XESO1opXaFlE5OGH\nH058TyRcw7ZoNuYd5uI0TEkWbDD3cDjM7C/cr1KpuJU8PCV/D17lCOybrCfpeUWy9jnvut7fsyod\niPhsFn3X7fTISEVEREREREREHBO3hJGamZkZifhpyNVqNYjnYHhxGt5J1NZSw29EkrEqCFjf3t7W\nVNjvfOc7wX0RuLm1tRX4cc+dOxd8xpYIYlamVSSfmZkJ+oDVs7NgA2mtxZiWUmslGObn51OtKZEj\n2QKOvWB2yWOkYAGxvxwsFqwAjvHCuK6urqpl5gWA8vetpEa/3w8sjDSGy4KTHLj/rKU1Nzen7cEc\n63Q6mRYjB2t7lhf6D9fL5/M6lzlAFWOJ6xWLxURQKPrAg8dsZQHW+MHBQWYgpnddqE6/9dZbgSXI\nNRLxbDY2aRp4ysdsKQO1Wk3biHlsmWeR8XxFv/KeYasOMGtj0/NFjmRSHnroIfmHf/iHxD3OnTun\n+wP69Hvf+15wr2KxqG0F45MmMmnB8zML1WpVxwH9t7W1pc/i7WO8v3DigciYDbZ71sbGhjKE3hiD\nfdre3g4UsIvForK7nuTBpHhHjAP+/drXvhZ857HHHtNqCNx2vHfwL+/3H/jAB0TkyBsh4jNODFyH\nmVXbH7Ozs4l4Kjw7PuM+53egx8hYBulmAqkt+L5AmsQQGEYwwBwPB+/BJHmBrOB5FjnmagxWKT0t\nYB3979UbTBFLdjf1WxJsjgf2MkKKxWJmIBsemGlez23FByh0Fi9wLEBQ+7VazT1AYUHgxesFw127\ndk0PHTgYMDz3DNrMarhoH7v22I1nUa/XdULhGnyI8oJSK5VKov9FxpMM4+BlY9jf45ktMNm4mC7D\nm8zYPBCQef369USgM19XJHmQsgfo4XCof+dNyS7yra2tYNPluQOUy2XtK7tYvecW8cfJy5jhNltX\nIpc4YOVyG6w/GAyCPjg8PHQ3HnzGWTH47Sc+8Qm9P0oiYf5dunRJr80Ha1s+wcs45Mwr667la7Ra\nrSDg2lNAFwldwWngAqv2IN1qtbQ/+ICBccCzz83NucYE2oM9aG1tTX8LdxU/Lw5A7XY7mNsXLlyQ\nP/3TPxURP1EE9zpz5oweZF5++eXMZ7fY29sLMvSuXLkSvMC4kDGrnXO2pki6Mjz6GX3G+kTYF69d\nu5bIWBZJHnwwD4bDYaI8Cj7DAcSbT7jO/Py8/gaHpsuXL2tiAa9hrCn8e/nyZXcNZx3SODsTLspJ\nQdN2bnvzeX9/P0EIeKEW0+o0TaoOkIZcLheUhjk8PEyUlREZ9x/WAPqKM8i9uY3vYU6KSKJANlex\nwL2sccgHaRgB/X4/2Kc5KYXLVWUlGdkC3lmIrr2IiIiIiIiIiGPilsofeMrVbJmytc0BpyJJ+g6n\nz3K5nFBzxnVx6uRTsU1t7HQ6rtUBGhoBoCsrK0ptoz4TB4fCatza2grSwb30d3Y94TTOLiUGrHGw\nVK1WK3CTsC4RB9l6bUCfb29v63U4WHoaVCqVoH4cBy1PglWExzOIJPXB7PdFQouh3+8nClza73mB\nwFngYpqYq56OkOc+FAnndKlUCuQq9vb2Eq4L207AS38eDoeBm4nTwdmSBI3uuTT/5V/+JbUPlpaW\nlEWB9MXVq1fdNHmsH/QPzzUwhYVCQeclrpHL5dQFgLnNFiUH+MJyh6XOhXYZvLdYFmswGLisnXWn\nF4tFVRaHbIGIBJbt5uam7gVYP8yYII2/3+8HTAWrXGepiXc6HV0X7NLz5h3AbAaeE32Zy+UCVmcw\nGOgzYbw4GBv96M2h0Wik9/CCw5mVBZuNPb1QKLjVBDBe7FJkfSuR8RjZvarX62ngORii5eVlnVvw\nQtx///3y/PPPi8jRfre1tSUf/OAH9X4i4/FDX3rhGVwvD0C/TZKe8BhPzK+1tbVE/3v1S6dJMmEl\ncgb60FO75300S8cJ3+t2u3odjDuzwWCddnZ2AjkYT5sr7Vk9JtTzFmA+cULLzarE34zuVGSkIiIi\nIiIiIiKOiVvCSHnsk3fqxSmaBTk5nsTGJbRarUQqqsjYYsXJF9/r9/t6KoalMRgMAsG3j3zkI6qC\n64nCgYkqlUpqxXCgKGIjPDE3nOh7vZ6e6mGVHxwcuBYmTtToFw4cR1+kBbTDWmLryIuDYpVej6Gz\nzFa32w0UjVmM1GNZOA0V1gT6aHZ2NqjizsHrbNF7dZKyLC/+HtdqEknGSHH77LzkuCmuYm9VuEVC\n8Uj2+wP1ej0hVoj2ZlVD5zRkK3gpcmQlMpOH8cdnbJ1h/HZ2doL+29raku9///tBH3jq6ZYZ4Fga\nLxaJ6wmCLUY/1ut1/S36bHZ2NlBFt7AxSCJH/QGW4s0338yMe2DhU6xJu6+gjSLj+Yn2QMKi0WgE\nDMjbb78dKDi3Wi3dRyCM6QWH53I5lV1h4Dreuvf6KEuFm9P8vTqomO+NRsOVrcGzeYwUS9VgHvNe\ninH3+hnPxmsU3/PkbVgwFLVUvQoHvBaZ2bH9PDs7K2fOnBERnz1hVh0io9inrl27pv2GZ+QYXaBe\nrweB0d1uV+fYwcFBEM+TFmNsWXkvSJvjMJkR935j2aHhcJh4f+EzTzAV9wCzVi6Xdezwt1qtpvsT\nJIW63W4iYYT/ZYxGo0RCBj5Du9JkgSw4ScXGtE7jnbklByl2q1j1Z5FwMbGGEr8wcJ2sAxkH32LT\nuXz5cqJkCt9L5IhKfvrpp7VdWEhczgHXW19fTxygAA7Os8DLxDuw8IREO+fn5wNKvdPpJDYUkfHg\nc7AvwAsDfc6uFW6PfT4+vNgNdn5+Ppho/B3WX8LCYeVde2A8PDzU8eQDNx9asuAFGU5D0XqFLD33\nH1+f22QLaIscHTp5s7EUfJpWFxeAxr1wHfS315a07JksVyvm1eLiorqo8OLb2dnRTQ7zaWtrK8gW\nYrcQ2pDW77YwMoMP6BZZhbwB228iRy4i3JcPDAAffIBCoaDJG15CASuq49DA2Za4H+7P7g8YWaur\nqzoncPjz+q3VarmHF1bEngYwpLifuFAxxt1z38GtxvpaHni/sJUh2u12Qr8OsOENImGWIJdE4vls\nDy8MHIq8+XTx4kU9eKN9Xh//5m/+ZqJUTxparZa+p1gLEfsYntE7cHQ6nUAF/vr167r2PF2/NGML\nc5DddHZtevuEN+/y+bzbd/awwZl82CeKxWKgHH54eBjs4c1m0117WYH0vP9wog2ANrBCuy0Oz+3z\nqk9EZfOIiIiIiIiIiF8AbgkjxamesMw4qM6eWJmN8VLIGZxmKzI+deJEycGVNr1c5Oj0itMxp+B6\nhUVhISCtlrGysqKWBe7FLhH8y3WG+NmsNcbthOXCp2e2VvAcafWbYLHAwk8LVPSsXWsVzczMBKm7\n3C48JysaM2ywJI89+i+Xy7n0qmUVvWfJ5/PBZ+wSzVK5Tatt5QW0e64Qy46Vy+WEW1ZkbJ16aeh4\npkk6b5YZqNVqCXelyHjMrAWZz+cDlm97e1t+8IMfBM/L4yByvLqEWJd7e3tT/x6sCbtmPZkEHn8E\nD3vgepkWc3NzgVV8eHioQbK4L7cdfX/ixImEKwdtxTNz0gzWHu61urqaSPoQGc8Ty1p4bBzLpEyr\nCeZJQHgMNvY9z1q6O0EAACAASURBVGXjsRknT57UvYH3A6/umxf0a9PpRUJ3ZbVadZlVjCfYr93d\n3UxZAe4rXM9jieAKfu655xJVLNJQq9X0PcXuJtsGDpBmRtd+jxXEvedmTwInSqSx0hbW5cg6bNgT\n0uaTV39v2uBsDv0QSb7HWToha5/guWr3T37n8/vEsmOTGPNpZA/0N1N/MyIiIiIiIiIiIoFbKn+w\ntLSUGbwHS2N3dzeokzMzMxMwBmfPntUUbcCLqeFgTg7mQ3ovrL9+v+8GbHrqsDbOaTQaBSyK15Z+\nv68+fhajs9ZYrVbT6+F7LADHVbZh9bKFw/f2Akot85HL5VwffFaMEged2zg3jicB2KrxlOgnWTiw\nbLJENT3UarXAKmZmEG31YnhqtZq2kWMCPOvFxoccHh4GbCB+zxiNRmo1c9wExgP93Gw2gznG1+Lx\nm9ZKtfAs3FKppOOLPhsOh3o/r3I8x9zgmdB2vj7PG6//baIKMz8i09Ua9OJ7BoOBBopDRb/b7eoY\nQ9iRGTvMobW1NWU0WCwV/YGYxG63q3sMvvfmm2/qPLFsNWN+fj5Io+c5B1mDSdUTOAYN7UKf8/Wx\nHvv9fsDWsBAw8NZbb+n1gFKppM/JjBSel2NC7T1YNgC/XVlZCebiwcGBvkO8lHkPGOd+v6/34HmD\n+ZvFQnmB3oVCQfuNf2vV4rlP8f6Zm5tTjwmvgSxWZjQaBXPFsj0iyRgpHgdmJ7Ngx5qThGxclEhS\nJBRjgmfnPRr9x/1oE6rSwF4e9DmukyZzYK/JgeXczzfDRAG39CDFL1wMhFdkVuQo6wjuoX6/H0xk\ndg9yQCMCADHp9vb2dMLxoQ2bEBfEtAckDm7jttkNYzgcZi4CPHe/39eN3aPvsel4OktcMBibwxtv\nvOFOJF5wnhvN9mWlUgle0rZkAb4HZD3vpCBxVnq3CskeuIwOtwWbAtOz9uWalSEq4mdoYr54YzQa\njdw+t66kTqej/cDzynv5o424xszMjB4s+YVnMyZvRrkY/cwZK2gLl1iwGy0r4duAevvf+Dte9Feu\nXMkMGsecW1xc1LXJbg3W5MK9+JDmKZ9b93FaH2H+8nhh3WHNMTDmOzs7GlyMNnMZHc52w96G/aLV\naulvMNbeiy1t/aAN2E88TMoCxYuI9xgE2Y9Go6Ac1Obmpu6pDNzDKw8FsPGG55xkAGEPfO211zKv\nPa3LGG7YwWAQJFLMzc3Jm2++OfEa3v7TbDbdccCc9JKJYJSxAYm5xOEkS0tLUwU9c5s4i83qPnHg\nPlAoFHRf4gBuz6C1JWdGo5E7hlnJIx6y3GpeQhAHr+MZV1ZWtM0cvmJd2ZPeSXZ/zGz3xG9ERERE\nRERERES4uKWMVKFQUAsNabfr6+tugKA9Ffd6vaCGHtPacJddu3YtSGmtVqt6ovX0dNjCtSdpPinD\nKl5aWnJ1X2zx2EKhEMgtVKvVREFctAmWAfqCA9X5ZA7LkF2asDQ8dxo/E7vTrKXX7/enKuybprGB\nz1mBGvAsC1gOlUrFtepsUOVoNArGhiloW/CU4VlHrCbPlpWVFfACzPP5fCYT5LFVaMPp06czVccB\nThtnBhO/YesJf0c/csFr9HO5XA602QaDQSIIXmQyw8XP5rEF+DtYgEcffVTbgMBwz/r0tNcYzH54\nc8tT9UZ/1Gq1YA5sb2/r3/mZ0DfeGP6f9r7kR66zevvU0DV2t6t6sNtuDx3HiR3HCc4AsQRZRPwI\nQQiBxAKxYseSPRL8CUj8AazYAMoKZUEgkTIoQZigJMrsoBgcO+2h2+6unqu6ht+i9Jx67vueul1p\n+L7+Puk8m7arbt37zvec50xsusUZBOf0GzduJByJcT36ykXC9zIliwzfZyEzaOGHP/yh/P73v48+\nR59wPvIesFwaeA+Gc2YFk1hZvcvlcuS60ev1EjmWRJI1+fhcx1zDJFYqlcyUBeF5ITIYc+yPYrGo\nn+F9gVqTDLZC7OWIbLEyX/va10RE5LXXXkvck/+22211bgcTVa/XdR/cu3fPzHyPvvB6snIoheAx\n58zg1n7Hva1ae/t1GeC2s/Ujjf3qdDr6m9BxnPuxl3kb4OAfrjFrBaLtBWekHA6Hw+FwOPaJA2Gk\nIE3OzMxEWgwcPRlPPPGEOnly8jVIpyyBQoOznNjT6vn1er2RGBiRgc0bfkmffvqpfgetllk1aBJW\nCCv7zVhVyaE5t1otfS5rebgnszHsiGcBkvv8/LyI9NM3WIwUwNpO+Bm3BezY5uZmFI7NfeOM9WHS\nte3t7aFpB7gNXKke2gQzE3x9WooFgH29uNo42oXPKpWKas3QWDh4wWo75n9qako1TIwH+/UxML5g\nSZeWliJWpF6v61rGnLOfWFptqXa7HSWjy+VyERtcKpX0+1ErzIOd6Xa7+hu06Y033tDr2Sk97Hcm\nk4lYozNnzihzjXaGc4nxvXDhgoj09zrOAvZz4/p9Iv19xvXAgDAsn9kJDh/H/GPflstlZVxwD/ZV\nwnhwZmYLeFY+nzfTZITVHSzcvn1bvv/974uIyB//+Ef9PKyv2el0Ii2cA1oY4dwMS9CJe+Pc5nP5\n/PnzIiJy5coVvR+YpkwmE63fcrms44uzenNzU/ccM4Ahs1IqlaJzuNvt6lzze4f9XPkv92eYnx/G\nD5Uu3n77bXnhhRdEZPD+mZmZ0evAQubz+ei8XllZ0TP65s2bEUvItecsJgf95UAl7ns417lczkx8\nvJcvEcAMI54Rppfhz6y2c/oDi/VKY9mwNjhtBL9bQ1ax3W7r2hmWGFlkNB+pAxGk0CFLaKrVatop\npPd/+eWX9XvOXssHlEh/sixHzTAaRyQ22Rw/fnzoSy0EDg0IUGyKCjPIitg5hoByuRwJk+fOnYty\nU+Xz+WgjnThxQiM9rM1uCT6tVitR4FakP36WuYsFI/wWsIrCcj/3OnBE+mMUmlNZOGBYETKh6ZSv\nGWXxiwwEFV5X2MwbGxuRQ+bRo0ejFwsLsdZzLYdSoFAo6Esdf1dWVlRgQLtqtZq+WLgsDEediiTz\nA3Hb8RnmKpPJREJTWlki7iebD/l7KwoHsMoNPfLII9r2sEB1pVJRp1u0k8u+sFnNGnO8sM+fP6//\nhrDR6XQ0gzuXA8F1/CLFusQ4nzhxQs3oUODOnj2rztno3/r6up47QK/X0z3HL7E0gZdzaGF/WXuV\nCy2HL5tXXnlFfv7zn4uIyDvvvCMiSUdmtGV+fj5SJu/duxcJnXgOY3V1NYo+vnPnjl5nKbbA5OSk\n9olfaBhzzuiPNcHthLCBSDnOEwehfm1tLSpAztFnAJdOYYwaERgqMRcvXtSC1xxtDQUU/Z2YmIjW\n6dbWVqKQffiyLxQKiWz9IsngkLRzp1wu6zsDYz8sB1WY4TubzUaFu3d2dr50UWCAFda9nNLDQvX8\n3uOovbS8gKFyzGBFaZSC0Pq7Pa9wOBwOh8PhcJjIjCJt/dcfmsn0RJLh9GAGGo2GUpKsAf3P//yP\niAzYqUOHDqkEbEmxuEez2YyoSXaMhDbzxRdfaNFQhL9axVnxe5GB1jE2NjZU6hcZaEUWA3fkyJEo\ndJ1NACHjIDJgfk6ePBlpeuVyOZFHKix0KyJRODNrcPyMkOLudDpRvqdMJpPIOYO/aeYzXNfpdDQ/\nDxg4rt2HMbC0RIuR4rWcFibN6461P+47+h0ylw8//LB8+OGHQ/vGOVIsk2PorGrVfRMZmEIw77y+\nwnkREXnooYdEpD+OoXM109oMK39ViGw2G9VhtK4rlUo6r1ZuFsBySq7Vato+zsMEcKb+cJ+xqZVD\nnIGjR48qWwTT3eTkpFy5ckVE7PxlFsCATU5OKiMFU002m9Xvsa7u3r2r5wnnCsKeS2NoxsbGIrN1\nt9s1zQ/333+/iAzGehirjvv89Kc/FRGR3/zmN5FpZ35+XhlQnBE3b9409wjA5wtSRITpHBhzc3Py\n4IMPiojI66+/LiJ9li9MOZDL5RL5nkTstB/5fF4/Z3cItNlitzEW4+PjuhYtJoZTgODePAahKWts\nbCx63k9+8hP57W9/m7hORHQMcPazewrWzdjYmBa05j7DuX5U1r1UKun6RD+uXbsWFWnndcd1+kKG\n0zqzOFs7xqXVaqWmMUirKsHvH4vhxj3K5XKCUcP9wsLow0x34T4blo6GPjMH3Rkph8PhcDgcjn3i\nQNMfsLQKybxUKkUa63PPPScvvviiiAx8PIYlvAwT8rEDHzTSmzdv6nVcfy/UuFqtlskghHXL1tfX\nU/1SwhDgEKHEPzU1pZoDa66cGRffheGg29vbqWGb5XI5NUMy8OCDDypLxE76oUPkxMRE5EQ/Pj6u\n7AVCehcXF1VT4TQDoZ9Oo9GI0lrw/UOGSCSpETKLNQyc/RdzXiwWE2PIfUG7RIYnCYVGw5oP2gp2\n6e7du5F2xWsY7Ojs7KwyJphr1tot7fmjjz7Sf4MB4azjAK/TUdho3mesSYa+GTxmXLsNcwjGxxq/\n1dVVXe9hygCRgUY6Pj4e7bNwz4a+WGCjROx9iPYxc41zotFo6P3BrmA9cFvb7bZ+jn7cvXtXmQPM\nR6VSGeqUzajX69o/jOsw9iFkg4cB4/HKK6+IiMilS5f038DGxkbE8lp1/4aBfcZEbDZ4bW1NmSjA\nSiKZy+WUVWLfOIy5FRjE9TPDwKJqtap7E2PbbDbNvRQmmxw2tnhemu/aSy+9pGwx71HsZd6bmEtm\n5/i8GyUYKp/P6znLSSt5H4TAdTwP7CeEdyXGzQqaYt+8vXyl0oJW2B+Lg5JE+msRY8wJvK1aqniH\nWG3FfLFvK7f5y/hGAQcqSPEGPX78uIgkqWm8gCBEiQxozw8++CAhGInYDnlTU1NKV/N34Qu8VqtF\n+UisLOYzMzP6PP7OKrHCBXtF+odxKJgtLy9Hh6FlAuQIR17k+Dd+y7lg8JIQicupMDjPkHV48L/D\nTWIVEuUFyvltcGjxszCWnPeFKXWR5Nhai9vKQIt7MOUMNBqNRPSnSH9cQH+zUBD+lgVvfr4VZID7\n8P04qkskKVjg3tYzrl+/ruYAjB8L2fjOiu4ZhlEodoYlkHEmcvwefer1errP8Kxjx46p2Qj9+Oij\njyKBoVgsRlFWKysrqSZbEXt9h+bxRqNhKlJ4+eKFdeTIkahYuZWzaGtrS9fxmTNn9HPsGzw/n89H\npvZh6zkseG4hm83qvdOqADA++OADEREzJ9HY2JieGXDcLhaLCWVoGDhYB2PB5y3vN/QdwUSvvvqq\nnDp1SkQG+fDa7bYKUFwFIiy7wxm1sSYmJiYSwSMidlCE5UCeyWQiZS2bzaYKk2mCweLioq6r5557\nTkT6wpWl7KJdCPK5e/eutoFN2EC1Wo0Uxq2trdQAH4xlLpfTNlglzwDew4wwIrXb7UYuAPtBmIGd\n/221z4o0ZMd3fFetVqOM5tb9yuVydB6OUozZTXsOh8PhcDgc+8SBMlIiA9aEqUdonaFWITLQqMLf\niPTZJ2iY0NT4HlZBR4TT7lV/DdrL8vLySEUNe71e5JQe3lOkrwVbEn9Y8HZnZ0clcysfFhwz8/m8\njgEzRbgfmzfA2rDmaLEOljY3rD8iSQkeGjxn6QUmJia072ySw3O5f2GmYqs+17Ds6SG63W5kKmHn\nRga0QB7zcG7YCd/KUQbtbWZmRs0fYD2y2aze+7777hORZDoAZmA4X5lIX3vCb/k7OKVytmAO+eZx\nYORyuageFT5HPwEOtxfpszzh/ZgNxH0XFxcjZmNmZkavw3ppNpsmu4R24bmVSiXV6TabzaoWzjmc\nwBzx3guZDcsUyGPA+wssIGesB0PDTrOc3kFkuBNs2BaLmZqcnNS1aLHYabD2x+7urqakePPNN7U/\nDzzwgIikM1K9Xi8RuCOS3KNcuQBjhNyAU1NTkQN/u93Wcwdzvb29ncjdJdLfgwsLCyIyYGiZkbX6\nibXGexX7rNPpRCxhPp839w/AKSrwPQfRYI2xZcUC+gvrDLOfp0+f1pQoAKc6sSpuoE9swmQmLGSi\nstlsxDTxHuQs+1ZwEvrOARIWUw+EuQZFBuNrVb/IZDJm4EvIgHF6FvTNMvFx6hn003oXjwJnpBwO\nh8PhcDj2iQNhpDj8HhoGpOdcLqeaAvtwwG7MYaKhMzLfjzU9SLEs4YepDqxq5qxRpbFQVrZjlsDT\nkoyxBIzw4XK5HGmYJ0+eVGdFdlgNa4ENk6ghmTMrAkxMTER+NSsrK8pscIhwqP2nhURze9hfC9jZ\n2Yl8XtiWzbDYkzB8l2sxYcy5viEwNTUVsZmzs7OpmYOxPjY2NvTfzLCBEeT7hlm9Q38bXIP1y3W+\nLNYRa5Ydt6FhYk9lMhmTnQiDEvL5fOTnJBL7A1ipJ9ip38pKjLHndZDm68PrkbVU7KUnn3xSRPoa\n8VtvvSUiA1+PVquVqkXOz8/r2uJ0FBgjfl7ot5LJZPQ5+I73MrR39qEB07iwsKBzh7/dbjeV2QCY\n9bWc74HJycmE8/uXAe9bZljRJ2aAMb5WLVLgiy++UBYG4DZjDI4dO6bMFu+3tDQUXBMS44a9VygU\nUscSe3V9fT0aQw6a4HuErLvlZ8ng6zGWvCbT2EKsydnZWd0H7GwO5vSDDz6IAn2sgBFmz/aqkxnW\nmWu329F5ziw1vmO/JGazQqd1iynmseSzI2SnhqUgCNmnsbGxiHXc3t6O2lCpVCL2d3d3V9+bFmMF\nDKsQwjgQQYo7FFKI/MLkf4eboFKpaOeZlg2vm56ejvISHTp0SBcrnE4tJ9JOp6OThANrZWUlQeWK\nDH9h4FBKK8DKCxACSzabjSKIOOKDMw0/9thjIiJy+fJl/f7kyZMiIgkqmF9KoJjxXEuIuHv3rpo9\neR5CobBUKum9cY9SqaQvdtDtlvDXbDZVwGJThmWCDYUmNsNZjvH4WygUok06MzMTvQwsSp+FCF6z\n4Qt3bm7ONHvAwRb3vX37tvYN/WZhEvfd2dnRQwvC9b1793TNoj9nz56Vf/7znyKSNEMhagrXN5vN\nyLE2rSQCw1IghmUuT1MYnnjiCRHpv1ARkQgsLCzo3MAcViqVdNx4bQN4MedyOX3BW1hbW1OTBEcQ\npjnkh6ZbkYEQ0Wq19Nk4hLkSAdbB+fPno1xgGxsb5voNweOYpsCVy2UdtzRTNgNtEhm4PbCwDkEV\nc7ywsKCCwF5Ot5yXTqQ/PuFYclZsnHGZTCaaj/n5eTUR4rmHDh2K9jcX0GXwOyENoSl71Fx0Fur1\nus4DFCqrcLOIaGZ9jNXVq1fVqZ9zR8FFZdR2WK4ZLNBY6ylt3/I7kMcjHFdrbfCZz2W3wjFvt9vR\n70ulkj7XUtbTctUVi0U97yAjbG5u6ljivXbt2rVUAQoYZdzdtOdwOBwOh8OxTxwoI3Xo0CGTlg81\ngVqtlqByRZJStkWXh1lgRQYU5tzcnD7Xej6Hn0LyxXWVSiWRr0Qk6WiH366uriojlKYVdbtdLXCJ\n/n788cf6PZuKvvOd74iIyJ/+9Cf9LHRArlar+lxmIaCJIBMyg8cS/a3VapFzI+cKwb13d3ej/k1O\nTiprxu0KM7iLDDQGsAWbm5upda3SnEcttFqtyARsUbUrKytR4ddCoaAsEWvMYXqCfD5vZpQOgyU4\nlw2vS3yGfjNLyWYvXpci/WKvYBgwL8vLy8r4sOOoVacL32NcmC2wTOSAlXm/UqlEjsW7u7s6N3As\nFpEobQmnccActFqtkcKOw1xkocNzr9dLODqLJM0LVtoVXMfrHWt3bm5OWUK0+5NPPtE+4Vk8b2Ah\nt7e3tX9puXZGNXnNzs7qHI6a5Rprktc65pKLbwPLy8tmJQQrfQLGCON35MiRKBT/iy++iPbr/Px8\ntH9u3ryp6x1zwPP87LPPiojIX/7yF/0t5mBpaSmat2EI67UePXpUTfDMRISsNp/5+Ds+Ph69d+7c\nuaP3xtn7ySef6Phz8FTIrszMzCTM/SEKhYLuP7S11Wrp3kVbh7GaaAO7Q+DfaDObRNNMhVaqIL6e\nTYHhddZY7pWLKu2dagWqZLNZPcvwN5PJ6BiwOdRyz9kLzkg5HA6Hw+Fw7BMHmv6A2R1IkFbiMf4/\nJMjV1dVEDS6RvvRsOUSGtckQasvfdTodM6EY178T6Uv5oURdKpVUkocmUqlUlAkBm2bZc8+dO6ca\nEGuxYX2rer2udQaBU6dORWHDmUxGJW5ozjwenCUYfWd2BFK4xQawJsmskeWgCN+I8FkMrvHH9ZLS\nbNKs3afdjxGyWKwl8/yHGki1Wo3867j+YlhfbxjYOR3zinW6tbWlfd/LbwnzBAfUGzduRKwXs0Vp\n2XpbrVaUhJUdVa3fhpn1uc2j+lyJDJgo3K9YLCrjYvlDsM9NiEwmY9blA9bW1iJfpfHxcX2elWkc\n+7Ddbuv9OJ0K+xkBWFN8DqB/GPN6vR6xBffu3UvVgMH8jI2NRezKxsaGMkh7ZUxHm8EqWeNs3WNj\nY0PXOdbs0tJS4mwRSQaxYKzYyRq/XVtb0z7hXLTYwGazGfmtcnb/V199VUSS6x3geRslVY2IRH50\nDMtywgkoOfAiZJDK5bJ+D/+5TCaTYKLwDLCoODv5HLR8ENvtdqr/LQNzgr2eyWT0bMNayGQyJpMT\nnt2cZBlnQ7PZjM5ZTpaalkE+l8uZTuThvPJz2ckdASHwfZqYmNC+YW1tbm7q8zjzO84UrBMruGYU\nHKggZU0cb3BQtZubm/Loo4+KyOAgWF1d1cHEATDMsQ9Zc+HEZ1GOLEhZNDqXvQA4SzkONHx/8uRJ\ndUBNw+HDh1UYguO7RaFWq1XdNLju2rVrZnFOHEZhIVARSbQJbWWh1Ir4wOGSyWSivE9YxIxGoxHN\n6zATLhYyb6TwhckRehxFFbbZEsDGxsYioYtNSTz/adQ1Nlo+n9d5Rzu3t7fN4tIYX34hYH1bLzIW\ndvBvtG98fFznGsJTqVRSIY0dc0NzlRV1xKUkeA4wpnwockkNkWQGbIxtr9dLKDn4DC9w/F1aWtJ1\njDGw8n+VSiX9Hu2bnJzUNliRgSJ2ZCSu5bxaAITSVqulLzr+HmsCDvyXL18289thfbBTNcYDz5+d\nndWzCn0qFAq6nyEo8UGO+9ZqtSgClgv2ppWFEhmsGY6SDvdLpVIxTWFYv3iulftufHw8ypHGwhUr\notbLH8EIOLOsc7xer+uaQJsmJibUjYD3b5qpM63I+b1796LqE9xXDhbB9zh7p6eno2hgNlulOTY3\nGo0okpyVceu3Vu4o/jeXihrFqdpykbAEUT7j2OT5ZUurpAlZPJc487lcFeeJSssVxUC7rDMa4P6G\npeDS4KY9h8PhcDgcjn3iQBgpSPyLi4sqWfJfSPjQSi5evCj/+Mc/ovuEBYpZk+TvoK0x+xD+VmSg\nIVumBEj3zPxAUu50OqrR4L6ffPJJlAvIYgYmJydVIue2hPlhWGq32syA9s8mTID7xLWpOHO7SNIU\nh/GbnZ2NNFEeD9R7u3r1akRdcy0wy+EWayKbzZp5XIA0E0a3242cc7PZrGox7KyNe7JWHmpfjUYj\nYr1mZ2cT2ebRN2ZPAcw11kSpVNK2wDTCrAI0eQ7zxv1WV1eHFvxl9Ho9ZaJgTmk2m3ofrqUY5mtj\nShxjMSz9AcYA41iv13XMOewaTMheFQYwr7z3MKYY+zD/GZ7Ba8JqLz6D07SVquKRRx4xP8e+xvhZ\nDtn8PbCxsaHzDoZhbm5O2RP8nZub0/Gy2CDct1Ao6JnAYdxgZtLSH3D9Ta7dhjZgPqanp82gibAt\nVrqUf//733oe8r4AOFgoTO1SrVbV5YDPl9Dk+d5770WBIplMJqpBOjY2pmPKNf7SWGO0KQywAcIc\nacwyI8DByum1tbWl1pT3338/0U6RZC5EjClMVKurq4lr2bQlkhxLPjM5gz+AswBZ4CuViu7/f/3r\nXyKSzKuFOdzZ2UnU58NnYRoKZt4ATnVg1U0F2HzI73A2G4r0x82qBYv9z2dWmN292+2atS/DfcF5\nB9PaHPVhzyscDofD4XA4HCYOhJGykoZBO7p161aCnRDph05De4EEX61W5e23307cl6VyaDYLCwsJ\nnxiRvmSP79nnJsxiXCwWVau0bPawaX/++ecqwUOjmZqa0j5B8p+amlJNBqkM3n33XdO3h2uJifQd\ndMPklaxZQYtZWloymagw1JUxPT0dMU0cBopxZfaJnZYBaD2ffvpplNaAQ/Ch3TEjxbXboDGwTwvW\nCbff8mniwAORpKaOmmF37941a52FiTF3d3ejVAzNZtNkAS1NFusDfhM8xha7hDXOGcvx/OnpaR1r\nK40DxuXkyZP6PG5TyKwxsB+HJanEc/CMYrGYSB4qMjzMHAwhxpmze3PocbjGpqamUpmoUVEoFHTc\nLYd4MN1Hjhwx2UysSyQ+nZ6e1rWYllGdtWLg6tWrmgEf3x0/flz3q+UrwiwPNGWMUafT0bVorSfA\nCvTY3NyMWKz19XUN0f/ss8+i33C9trCtVnWEdrsd+f2IDM4EjHeYwkWkv8ZClv/GjRtRgES9Xo98\nldj3lhPbhqkYRAbnpnW+A1YQC4f7Y8xef/11/R4+dVeuXFG/VIstZXYTc8i1ITkhZ3h2D3tvWIFC\n2J97pYOwYPnfhc781tq1PuPs9MwgWWMzqs9WGAzT7XZNpg6wPsMaazabI6VdCXGgzuZHjhzRBcrU\nPwYGVGmr1dKXA/6CThWxTXFhIUuRgdM5v2B4kVglIsLIEatYrkWhttvtRIZakSQd/PTTT4tIPydU\neNjwi4qdtkOTwokTJ9RcwXl6LODg2d7ejmjqzc3NyAF0d3c3EaWD58N8x4cfDsZ3331XP8NhgDZP\nTU3pi4zL7oT0fbPZVLOXZSbbKwoHawBrhzcjmy3Cl+owkyHGCH28detWdO2wrN6Wsz+EJfRjaWkp\nyoYskhSMRZLmKMssif3w3nvv6Wc4HEQG/UXbrUzuIgPaG4dJs9nU53DJG7QZgkGj0TDzzYQvVz7o\nLYEU4AMf7MMP8AAAF3BJREFUY//000+rGQKZ/nu9XmqQADvGW7mdcHZcuXJFvve974mIyAsvvKD9\nxUsSe/nOnTty6dIlERH561//qteFjracQwt7YWNjQ//NJYz2irgDcKah7VevXtX2pwmbOzs70RnJ\nZhK8wO/cuaNlXs6fPy8i/SCGUGC0BEheS2xq4SLjIv11x2eCSDIq7lvf+paIiLz00ks6RlbOqosX\nL4qIJJRpqzwLm6rRd1zXbrfNccO4hOW3RAYm+VqtpuZILi+GfcjZ+62zAc+A6bHT6WiUI7+HOKO+\nZXbF2LDAjTG3BCBWivA97sG5ljBfrVZr5OoGoyi7e5Wt4XZa1SzCMjS8//m9HM4hB/Cgv5OTkzr/\nvGbwfnzooYdEZOASkAY37TkcDofD4XDsE5lRQxX/mygUCj2RpHQKmj+Xy6kUCc2FqbZvfOMbIiLy\nxhtvaL0ihH4fPXo0qu3WbrdVwoSUzRo1JFwO48fzhknP0Eog3S8uLqYW9GR2AYwOtPxcLqfaHBc8\nhSkBDEEmk9Hvv/rVr4qIyDvvvJMo6CnSl56Z0UOWdA4/xjhwQdRQ6+Citmwug8bKaRQefvhhERH5\n8MMP9VlgAcDCVavVSDsdFrKdFvJrZXq2wm7ZRBGatZiq59+Gpl2GVX8N/cjn85HW2ev1lBWD1rm2\ntjbUmZUxPT0dMaHnzp1TVoTNG+G8nTp1SttvmSvQ32KxmJrug68f5Yxgh+b/BBhTrr9nmaqfeeYZ\nEenXjsR6+cUvfhGt2Z2dHWXmQgd+kWRQyje/+U0RGTgFM/v0zjvviEifXcBvOGeUZTrFOYGz4bPP\nPlP2gvNvWeZjfIaxP3r0qM479v+nn36q82+ZxwCuW5hWV40RstbDgPuWSqVErjWRZDFnIJPJ6N7E\nud1sNlOZZuyjdrudKOIrkmQ4MedTU1NR4ABfhzO4UqkkWHQgrCBgWSFExDwL/xOE76Rh4LxkaSZd\ngJ3Uce7NzMzoPIEJH8ZQ4/zCeme2Deu+WCzq+Fo56PB+73a7CYuESH9ewxqU7LzOBYhxRuM8LpVK\n+p5IM8/+p6CxMfNqOCPlcDgcDofDsU8cCCOVz+d7In3NBQ7ZYSIzRiaTUS0Bf69cuSInT54UkYFG\nvbKyYtbO+spXviIiollnWcMAw8E1ufBZq9WKNLL5+XnVSOGnYSW3O336tDIIzKL88pe/FBGRX//6\n1yKSdBj9PwHML5itYc8KfZU4w/yTTz4pIn3HXIt5s2oAhhp6oVCI6hptbW2pxsJaGGff5TaFCDU4\nDk1HJfXFxUVlLjEf4+PjpuNpGiMVZprn9onE2lyv14sy6vN9oL3VajX9jP354AOCNlvsXLVaVbYT\n98CaHAXQMNHvSqWSyEYdguc0DKRgphNjf/bsWWVPsL+ff/55vQ4+CJy1m5OIYs3iuc1mU8fZaif7\nQeA3mUxG9ybGqlgsmv4mP/rRj0RE5A9/+IN+Bqdh+HDu7OyoXxgn/8R4hMk/RQZn1tramo4Nxp6d\nuuGXtLi4GLGER48e1d/cd999+nw8x0rdwEDfMT6svfM6DdcxMwNgNrhv+I6ZdfydnJyM/LDGxsaU\nYWJ2F2c5xjSfz0eBCq1WS8c5jYlhn0Vr3+L5jz/+eMJBHNeDUcF489nOdWCxDqy1iASjXHMV+2x8\nfFz/DT+rQ4cOKcOF+7FvcK/XU18dMLTDko6GKYV436RhYmIiSkNgnZOcsgfza70rvww46Sba/GWB\nPX/27Fmdb7SPGXjML/eNE3xibaGPOzs7ezJSB+JsztEfYY6nsbExnURQ9t1uV53C4DAuMig4+ve/\n/11/i4WO346NjSn1GhaeFEmaNfDbNFPH2NhY9LKyiriyozkfPL/61a9EJN3RViRZUkFkeGRD6EQ6\nzMTCn1m0fSisjI+P6/OYGrY2FjY239cqTIlFzcIV7o3xsIpasoDEwlN4OHAUGzsPhk7/Vlb0QqEQ\nbV4WkPnQwtpCm9vttpmjxpqvcN6H0dGWySHE5uZm5DR/5swZbQsO4XK5rHMDgWBtbU33Adb7+vq6\naV4Is51Xq1X9Da8b/BtO9tevX4/KGhUKBX3u5cuX9XMcpFBSbt26lRoVt5cZJDQjiwwc9zOZjDoF\noy23b9825wIvdt4/XPRUpP+S432K52I82EzLZTFCcGQtfouXZ6fT0Rc35rLRaJjO0hhDzNvy8rL2\nnYsVY7/ABGkJY5OTkwlhSSR6sWi/cWZhfLh4LCuTVmbzsGA4n9G8B0MBamxsTM08mL9ms6nKH5ST\nra2tyC3g8uXLkQJZq9V0HKyoOAhyy8vL+m8IPjzn2L9zc3MquOG+LHjhnTQ+Pi5vvfWWiCQDAoBM\nJhOtT+47lxyycjxhTcBMV6vV9P7o5+Lioq7jNKfwvTKIs2DOOeXwl82BIv354jI7AM4EjmIM3+Gs\n2OB+165d0/WJz3gPYh1YJer43vhrBTuEcNOew+FwOBwOxz5xIIwUJPROp6OSKhfnhcYAaXtiYkKl\nYIT5nz59WmlRlmJD9mEYwvBILpZrORZbLA40ue3tbZWA2fExDAfncHRGKGUfOXIkoovZAZlZKGhy\nGNPl5eWE4z7AGmSoxbMmhflgpoPHwcqxgXlgDY7D7EX64xwGDxSLRf0efUrLcyQyYPzW19e1T1xs\nGvNq5QxKy4DNjtsYAyvLLt+H8/BYNHuYu2svTQ7r48c//rFqYQg64Lw/mNe1tTV18Mee4aLU/wm4\noHBYo4rzeuG66elpZT0wbocOHVLmB2Oxvb0dreOZmRllymBym5+fV5MYxoBZoVGL0RaLxYgF7PV6\n+jykIcnn84nUEeE48LPDQACrwOqRI0d0PPi7tIzxYL+KxaKyOzh3OICDNWoLOAuwhvL5vK49NnWF\naS2s4IXV1dUoHQ0Xt2YwExUijeV/9NFHoxqJzIhjDc3Pz+tcIg3G7u5ulO1cZJAfDFYLqw3NZlPX\nIMylVlF3Zl3BCh0/ftyshBGaEuFQLzIY+7GxMWVb0Ucrbxej1+uZNUhDlorzoWGOGo2GMnmjOKcz\nKpVKlMqGK4NgbPhsw2fFYjFaY/w9/g4rUo89h7+ZTEafy3NpmemxJiwLCjPwIUqlUiL7e9j2YXBG\nyuFwOBwOh2OfOBBn80wm0xPpa0zwAQCrsLi4+F93vg79YWq1mkrtnDQR9mNI7awBsGMhkiqCabh+\n/bpqJcwMhb9dWVlR6ZsZLrSPnTAtHxDO6i7Sl+Q5RBfXgEGanJxUzSetGjprDtZ6SHPwHJZJN6wz\nmM/ndYxYSwizK7P/zajgKuf4N/vXYPy5En2o3YkM/FEsXxl2RA/9W6z79Xq9aE0Ui0XNrs5MKPo7\nSj1JkSTTyGtBRMx+WeD1wP4Lwxz7+bmZTEZ/w8wE9gXW9ueffz4yc5QGjP39998fJa+s1Wrarpdf\nfllZLGbMMJ9czT1kVIrFojrGI7ybU1VYSX8BHg/09/z58yZTC3YF/bBSO1y4cEHvg3mv1+vy+OOP\ni8gg6ert27d1/zDzAeCMm5qa0rUP1oh9KdGm1dXViB3nTM/McIXVFdLOF5HBmpicnJRvf/vbiTa/\n9tpr2l+cwceOHVP/JgQiVCoVPZ8487/FgCFIAGkher2euZeeeuopERmMqZWwWGSwdpAO480339Tv\nOCVPeO6JDNgRZmKstoQoFAraX64Zys7/YV24YcAZjn6ErOoowJlVrVajwAPL35aZa+scsBg/BtYi\nnjssvQr6hnmbmZlRqxL2+ebmpo41/u5lIWD8P+lsjg4/8cQT8sYbb4z0G46WGIZqtaqTAqEjm81G\nDnRra2smNc0FLkOwqQ2TxE7nYaSEyGCC8SxuOy88dmBEP0Ar4jDnUjfWAkC/2Qy310LhApBpRSOt\njLFcINQSpEJhuN1uqynUolvZ8dHKCxUebmyGwJhvbm6afcaLAnM47L6hgMTPSHPMt0wLIoPxw4ug\n1+ulRlchcqnb7SpdbR20/Pywv+VyWYUJzhOGNYj+Li0tqVAwqjK112FtRd5C0GMlBYItZ23GWCGy\nls1lOEDDQtgW8BzM+b/+9S99jhWtAzSbTQ1K4dxtGJu0vvd6vWgM19fXo7OqWq2aAk+IfD4fma27\n3a6uM5iB1tfX5dlnnxURW5BiEzpe8BB8rIoOmUwmMj1yfiBcl8/no73CChW+q9frun5ZcPjzn/8s\nIoPzcG5uTufEKnINWAo2FwIHLly4YK4VRC5yYIsVTGCdZ2GVBwbcTbhcDSNcB+VyOdrXXHybnZ2x\nVzY2NqKiyNxvLsQbVg7o9XpmcAj2Ciu4YWHfRqOhc4P9OKp5sFAoRKbxXq8XReOySwZHAXIwD48T\nt69UKmn78Iy7d+/uq8xL+IxwD6bBTXsOh8PhcDgc+8SBMFJgi5iNssLHAStUOJvNan4gSOY3btxQ\n6Toty+n4+HikyV+6dMkMJQ7px/vvv18d2qFBnD17VjOIs7YbFqEUiVmvw4cPa1uZFg5ro4WFl0NA\nambpmR0teXz3olRxn7AwpUjSPIK/0Do4E+2o9Zksx3fcj0OnOZeQSHIOWeMLmYGLFy9G5kM2Q3Cb\nQvMEU+d4hpUzjMeRxz/NzIZnlctl/T073GItIN3H+vq60vHoI9ejgsa5vb1tOl8CPH9gruBcyfUa\nkdNme3tb1yJYhVwuF2XSBtuIdqEPuI6dadEPXFev15UtxFyVSqUvbeINn432s6kBfbeA/Y81xkXG\nR60Rhr3OLgM/+MEPRKS/TlDHLw2VSiVas41GQ89LHpcwKzqDC0uH+6xeryt7DcZ0fn4+KgC8vr5u\nMiXhXmEGAGtseXk5ERghkjRlY380Go2IbcE44Dci/XkL83DxOY+z33pflMtlHVMOdrJY1PBcXFhY\n0HMbjJTFmNZqNV3H7ArAuQpF+mMLNwKM4+3bt/V+PN5p+RWtjPWceobXedinUqlksn4A3l3FYlHf\nI1auJX5fY9w5KMEyIe5nXwM8hvyX2zeMjcIZxcFsYT68ra2tRJ5DkdH2vjNSDofD4XA4HPvEgTBS\n0ITZ3mwxUQxIjlyjClouS96hQ1qtVlPtwHLMhA36xIkT8re//S3xW/YFYM0fEjqcSRGCPqyfkMon\nJycjzavRaJhO2GAE0NZWq6U1qqDdMfPA6RfAmDBrcOLECRHp14VCX9Ik+EKhkEjyCeBaDg0NHQpZ\nmwZDsLOzEyXiq9VqUSZobj/Q6XT0Ply9HOA5D8OPreSBFoO0u7sbsQBcFwrIZDKq2YDB4Paypv71\nr39dRJK+dBgj3Nfy1+F2WWuLs3aHNQMPHTqk/bDqPnJ/8FtmojC+WDvsA8eMbbi2LTZXZOCXwslu\nQyaR/Q8xPqurq/ob+I6xhgtmYnx8PJHyBPuZMzOjz2nZkjmZLzRWMOdfBpZDLM4u/N0LTz31lDLc\nYJxKpZJZT4/rromImezy3r170TqrVCqRQ/u9e/ei+ouzs7N6ToPVrlar5toC2CeNfRpF+usgzXcT\n/T1x4oT2BfdotVomM48zGswP/OwY29vb0fg988wz6mTOzAnmCWcIB5ikpWm5efNm5OszMzOjZxIz\nzmkBLXyWs5UCaxsYNbAkn89HofzWuVOv17WfYJyazWYqw83A2uF0KTinMTfW+pyYmNBx5aAZrAWk\npsjn89ouzM3du3f1N9Z7DAEwuVxO2T2cB7du3YpYOSsFCCekHoYDEaTSNiEDB9nCwoKZ4yU8RDj7\nNw6C1dVV0zkOixYvoOeffz665tixY1F+k/vuu09zmADDItcg+HBRYhwinIcDixqbb35+Xhcg06BW\n+Y+wYLDIQJhgwcAytYUClUiyYHMYIcP35PIJ1guKiyQDobOoNW5WMMHW1pb2CRtyY2PDDEB49NFH\nRURUKL5+/XqUTZqFCdx3ZWUlEgas0grNZlNpeSCXyyUKcAKI7MFa42AI6yBDWwqFgnngAHiWpXxY\naz2bzep88V7By4CFD8w1xnSY8IEXD+731FNP6UGFF9b29nZqkWb094EHHtBxQfuXl5f1RWHlxhp2\nX5Sdwfj2er2oXATvBSvKCvgyUT1pwL69dOmSRolxVncAZ9Hhw4ejYuPsjMzgzN3DsLGxEa39ra2t\nyCzIlSYAHiust1KplFCuQuDlNDU1pXNsmXhwj9nZWb03lBRrzllJ4f7gTOA1gXMR+5EL7aJk2O3b\nt6N2VSqVaA/dvn07yus3Pj6ubYAgyi9hzqgdniunTp3S9wraks1m9RlQnrPZbOI8Cdtw7tw5HWtr\n30PZ6XQ60fe5XE6VHJxxy8vLprsH3lVhuTGRZBQofmspVXyeQTDhQJ/Q/L67u6tnjyUYo2+5XE7v\nw7nvMCeWaRSkgshg7rAWO52OtmGvSFSGm/YcDofD4XA49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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01bbe590>"
+       "<matplotlib.figure.Figure at 0x7f09692c7350>"
       ]
      },
      "metadata": {},
@@ -12121,416 +590,29 @@
     }
    ],
    "source": [
-    "feat = net.blobs['pool5'].data[0]\n",
-    "vis_square(feat, padval=1)"
+    "feat = net.blobs['conv1'].data[0, :36]\n",
+    "vis_square(feat)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first fully connected layer, `fc6` (rectified)\n",
-    "\n",
-    "We show the output values and the histogram of the positive values"
+    "* The fifth layer after pooling, `pool5`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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5OZnE5ufns2IP8sCdO1EAAAUUUQAABRRRAAAFFFEAAAUabyxvk83NzSRWd3Nuv5r0mvCV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\nos+lqLE+V9S4/fTTT2dte+7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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01ae5a90>"
+       "<matplotlib.figure.Figure at 0x7f09691fce50>"
       ]
      },
      "metadata": {},
@@ -12538,361 +620,31 @@
     }
    ],
    "source": [
-    "feat = net.blobs['fc6'].data[0]\n",
-    "plt.subplot(2, 1, 1)\n",
-    "plt.plot(feat.flat)\n",
-    "plt.subplot(2, 1, 2)\n",
-    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+    "feat = net.blobs['pool5'].data[0]\n",
+    "vis_square(feat)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The second fully connected layer, `fc7` (rectified)"
+    "* The first fully connected layer, `fc6` (rectified)\n",
+    "\n",
+    "    We show the output values and the histogram of the positive values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAlcAAAJPCAYAAABRvvFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xv0LGdd5/vPJ3dygRADOzGJJsgdkWRxNyAb5BJw5OaI\n",
-       "okBE5CAiIJ6jgs6Y7XgDR5DjcMRZQ4KRYXB00JyIoyYoP424JKIJCTcjZ5JlgskOs4gKXkGe80dX\n",
-       "79TuXdVdl29VPdX9fq211+5fd9VT37p/+3mefsopJQEAACDGUVMHAAAAsE1IrgAAAAKRXAEAAAQi\n",
-       "uQIAAAhEcgUAABCI5AoAACDQ2uTK9jm2P2D7Y7Y/avs1xfun2b7a9k22r7J96jjhAgAA5M3rxrmy\n",
-       "fYakM1JK19s+WdKfSXqupJdK+t8ppZ+x/UOS7p1Sev0oEQMAAGRsbc1VSumOlNL1xevPS/qEpLMk\n",
-       "PVvS5cVkl2uRcAEAAOy8xn2ubJ8r6QJJH5K0L6V0sPjooKR94ZEBAADMUKPkqmgSfK+k16aUPlf+\n",
-       "LC3aFXmGDgAAgKRjNk1g+1gtEqt3pZSuKN4+aPuMlNIdts+UdGfFfCRcAABgNlJKjihnbXJl25Iu\n",
-       "lfTxlNJbSx9dKeliSW8q/r+iYvawIOfE9oGU0oGp4xgb671bWO/dwnrvlh1e77BKoU3NghdKepGk\n",
-       "J9u+rvh3kaQ3Snqa7ZskPaX4GwCAWbGVbH3z1HFgu6ytuUop/ZHqE7CnxocDAMDovlrSr00dBLYH\n",
-       "I7TH25s6gInsTR3ARPamDmAie1MHMJG9qQOYyN7UAUxkb+oAJrI3dQBzt3YQ0V4F22kX+1wBAObD\n",
-       "VpL0H1LSJVPHgmlF5i3UXAEAAAQiuQIAAAhEcgUAABCI5AoAACAQyRUAAEAgkisAAIBAJFcAAACB\n",
-       "SK4AAAACkVwBAAAEIrkCAAAIRHIFAAAQiOQKAAAgEMkVAABAIJIrAACAQCRXAAAAgUiuAAAAApFc\n",
-       "AQAABCK5AgAACERyBQAAEIjkCgAAIBDJFQAAQCCSKwAAgEAkVwAAAIFIrgAAAAKRXAEAAAQiuQIA\n",
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MViiLF298EU1brscek/78Z/9lSmv16s7+2YZ9tO5i2Nebhv3ev8rvIox1o8ZaLl98PEy0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\nFLIAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb01953f90>"
+       "<matplotlib.figure.Figure at 0x7f096914f6d0>"
       ]
      },
      "metadata": {},
@@ -12900,7 +652,7 @@
     }
    ],
    "source": [
-    "feat = net.blobs['fc7'].data[0]\n",
+    "feat = net.blobs['fc6'].data[0]\n",
     "plt.subplot(2, 1, 1)\n",
     "plt.plot(feat.flat)\n",
     "plt.subplot(2, 1, 2)\n",
@@ -12911,12 +663,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The final probability output, `prob`"
+    "* The final probability output, `prob`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -12924,191 +676,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ffb01f12a50>]"
+       "[<matplotlib.lines.Line2D at 0x7f09587dfb50>]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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boqmI2CRpU8O2dZnlvRo/FbNlWwDl0KqCx0VWAAAAiin3RucATkyN5+DVA96xY2MhDwAA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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ffb019b9dd0>"
+       "<matplotlib.figure.Figure at 0x7f0969270290>"
       ]
      },
      "metadata": {},
@@ -13117,6 +696,7 @@
    ],
    "source": [
     "feat = net.blobs['prob'].data[0]\n",
+    "plt.figure(figsize=(15, 3))\n",
     "plt.plot(feat.flat)"
    ]
   },
@@ -13124,38 +704,51 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see the top 5 predicted labels."
+    "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Try your own image\n",
+    "\n",
+    "Now we'll grab an image from the web and classify it using the steps above.\n",
+    "\n",
+    "* Try setting `my_image_url` to any JPEG image URL."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-      " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-      " 'n02127052 lynx, catamount']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# load labels\n",
-    "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-    "try:\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-    "except:\n",
-    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+    "# download an image\n",
+    "my_image_url = \"...\"  # paste your URL here\n",
+    "# for example:\n",
+    "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n",
+    "!wget -O image.jpg $my_image_url\n",
+    "\n",
+    "# transform it and copy it into the net\n",
+    "image = caffe.io.load_image('image.jpg')\n",
+    "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n",
+    "\n",
+    "# perform classification\n",
+    "net.forward()\n",
+    "\n",
+    "# obtain the output probabilities\n",
+    "output_prob = net.blobs['prob'].data[0]\n",
+    "\n",
+    "# sort top five predictions from softmax output\n",
+    "top_inds = output_prob.argsort()[::-1][:5]\n",
+    "\n",
+    "plt.imshow(image)\n",
     "\n",
-    "# sort top k predictions from softmax output\n",
-    "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-    "print labels[top_k]"
+    "print 'probabilities and labels:'\n",
+    "zip(output_prob[top_inds], labels[top_inds])"
    ]
   }
  ],
@@ -13178,7 +771,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   },
   "priority": 1
  },

From f1d245c78c09b7121b0e05c5ead6328396812612 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 4 Feb 2016 19:41:55 -0800
Subject: [PATCH 1697/2053] [data] get_mnist.sh rewrite; prevents prompt in
 tutorial notebooks

---
 data/mnist/get_mnist.sh | 23 +++++++----------------
 1 file changed, 7 insertions(+), 16 deletions(-)

diff --git a/data/mnist/get_mnist.sh b/data/mnist/get_mnist.sh
index 8eb6aeedf9f..6d875219489 100755
--- a/data/mnist/get_mnist.sh
+++ b/data/mnist/get_mnist.sh
@@ -6,19 +6,10 @@ cd $DIR
 
 echo "Downloading..."
 
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
-
-echo "Unzipping..."
-
-gunzip train-images-idx3-ubyte.gz
-gunzip train-labels-idx1-ubyte.gz
-gunzip t10k-images-idx3-ubyte.gz
-gunzip t10k-labels-idx1-ubyte.gz
-
-# Creation is split out because leveldb sometimes causes segfault
-# and needs to be re-created.
-
-echo "Done."
+for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
+do
+    if [ ! -e $fname ]; then
+        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
+        gunzip ${fname}.gz
+    fi
+done

From 5f50a1f773cb01bb8dd8c0bdb49aab0d586de083 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Tue, 23 Feb 2016 23:46:49 -0800
Subject: [PATCH 1698/2053] [example] improve learning LeNet notebook

- add subheadings and list steps for structure
- edit text and comments for clarity
- switch paths and use chdir for idempotency of scripts [shelhamer]
- title accuracy plots, rename ip -> fc to fit common naming, and
  rename output layer -> score [shelhamer]
- add experimentation section [shelhamer]
---
 examples/01-learning-lenet.ipynb          | 4680 ++-------------------
 examples/mnist/lenet_auto_solver.prototxt |    6 +-
 2 files changed, 389 insertions(+), 4297 deletions(-)

diff --git a/examples/01-learning-lenet.ipynb b/examples/01-learning-lenet.ipynb
index 3562c7adaf2..1c328260dfa 100644
--- a/examples/01-learning-lenet.ipynb
+++ b/examples/01-learning-lenet.ipynb
@@ -4,11 +4,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Python solving with LeNet\n",
+    "# Solving in Python with LeNet\n",
     "\n",
     "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -17,8 +31,15 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "os.chdir('..')"
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe."
    ]
   },
   {
@@ -29,19 +50,18 @@
    },
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "import caffe\n",
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
     "\n",
-    "from pylab import *\n",
-    "%matplotlib inline"
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "import caffe"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)."
+    "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)."
    ]
   },
   {
@@ -56,72 +76,36 @@
      "output_type": "stream",
      "text": [
       "Downloading...\n",
-      "--2015-06-30 14:41:56--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 9912422 (9.5M) [application/x-gzip]\n",
-      "Saving to: 'train-images-idx3-ubyte.gz'\n",
-      "\n",
-      "train-images-idx3-u 100%[=====================>]   9.45M   146KB/s   in 57s    \n",
-      "\n",
-      "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n",
-      "\n",
-      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 28881 (28K) [application/x-gzip]\n",
-      "Saving to: 'train-labels-idx1-ubyte.gz'\n",
-      "\n",
-      "train-labels-idx1-u 100%[=====================>]  28.20K   107KB/s   in 0.3s   \n",
-      "\n",
-      "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n",
-      "\n",
-      "--2015-06-30 14:42:53--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 1648877 (1.6M) [application/x-gzip]\n",
-      "Saving to: 't10k-images-idx3-ubyte.gz'\n",
-      "\n",
-      "t10k-images-idx3-ub 100%[=====================>]   1.57M   205KB/s   in 8.2s   \n",
-      "\n",
-      "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n",
-      "\n",
-      "--2015-06-30 14:43:02--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Resolving yann.lecun.com... 128.122.47.89\n",
-      "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 4542 (4.4K) [application/x-gzip]\n",
-      "Saving to: 't10k-labels-idx1-ubyte.gz'\n",
-      "\n",
-      "t10k-labels-idx1-ub 100%[=====================>]   4.44K  26.9KB/s   in 0.2s   \n",
-      "\n",
-      "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n",
-      "\n",
-      "Unzipping...\n",
-      "Done.\n",
       "Creating lmdb...\n",
       "Done.\n"
      ]
     }
    ],
    "source": [
-    "# Download and prepare data\n",
+    "# run scripts from caffe root\n",
+    "import os\n",
+    "os.chdir(caffe_root)\n",
+    "# Download data\n",
     "!data/mnist/get_mnist.sh\n",
-    "!examples/mnist/create_mnist.sh"
+    "# Prepare data\n",
+    "!examples/mnist/create_mnist.sh\n",
+    "# back to examples\n",
+    "os.chdir('examples')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We need two external files to help out:\n",
-    "* the net prototxt, defining the architecture and pointing to the train/test data\n",
-    "* the solver prototxt, defining the learning parameters\n",
+    "### 2. Creating the net \n",
+    "\n",
+    "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n",
     "\n",
-    "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+    "We'll need two external files to help out:\n",
+    "* the net `prototxt`, defining the architecture and pointing to the train/test data\n",
+    "* the solver `prototxt`, defining the learning parameters\n",
+    "\n",
+    "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
     "\n",
     "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
    ]
@@ -134,36 +118,38 @@
    },
    "outputs": [],
    "source": [
-    "from caffe import layers as L\n",
-    "from caffe import params as P\n",
+    "from caffe import layers as L, params as P\n",
     "\n",
     "def lenet(lmdb, batch_size):\n",
     "    # our version of LeNet: a series of linear and simple nonlinear transformations\n",
     "    n = caffe.NetSpec()\n",
+    "    \n",
     "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
     "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    \n",
     "    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
     "    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
     "    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
     "    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
-    "    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
-    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
-    "    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
-    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
+    "    \n",
     "    return n.to_proto()\n",
     "    \n",
-    "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n",
-    "    f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n",
+    "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n",
     "    \n",
-    "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n",
-    "    f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))"
+    "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('mnist/mnist_test_lmdb', 100)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
+    "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
    ]
   },
   {
@@ -186,7 +172,7 @@
       "    scale: 0.00392156862745\r\n",
       "  }\r\n",
       "  data_param {\r\n",
-      "    source: \"examples/mnist/mnist_train_lmdb\"\r\n",
+      "    source: \"mnist/mnist_train_lmdb\"\r\n",
       "    batch_size: 64\r\n",
       "    backend: LMDB\r\n",
       "  }\r\n",
@@ -240,10 +226,10 @@
       "  }\r\n",
       "}\r\n",
       "layer {\r\n",
-      "  name: \"ip1\"\r\n",
+      "  name: \"fc1\"\r\n",
       "  type: \"InnerProduct\"\r\n",
       "  bottom: \"pool2\"\r\n",
-      "  top: \"ip1\"\r\n",
+      "  top: \"fc1\"\r\n",
       "  inner_product_param {\r\n",
       "    num_output: 500\r\n",
       "    weight_filler {\r\n",
@@ -254,14 +240,14 @@
       "layer {\r\n",
       "  name: \"relu1\"\r\n",
       "  type: \"ReLU\"\r\n",
-      "  bottom: \"ip1\"\r\n",
-      "  top: \"ip1\"\r\n",
+      "  bottom: \"fc1\"\r\n",
+      "  top: \"fc1\"\r\n",
       "}\r\n",
       "layer {\r\n",
-      "  name: \"ip2\"\r\n",
+      "  name: \"score\"\r\n",
       "  type: \"InnerProduct\"\r\n",
-      "  bottom: \"ip1\"\r\n",
-      "  top: \"ip2\"\r\n",
+      "  bottom: \"fc1\"\r\n",
+      "  top: \"score\"\r\n",
       "  inner_product_param {\r\n",
       "    num_output: 10\r\n",
       "    weight_filler {\r\n",
@@ -272,7 +258,7 @@
       "layer {\r\n",
       "  name: \"loss\"\r\n",
       "  type: \"SoftmaxWithLoss\"\r\n",
-      "  bottom: \"ip2\"\r\n",
+      "  bottom: \"score\"\r\n",
       "  bottom: \"label\"\r\n",
       "  top: \"loss\"\r\n",
       "}\r\n"
@@ -280,14 +266,14 @@
     }
    ],
    "source": [
-    "!cat examples/mnist/lenet_auto_train.prototxt"
+    "!cat mnist/lenet_auto_train.prototxt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+    "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule."
    ]
   },
   {
@@ -302,8 +288,8 @@
      "output_type": "stream",
      "text": [
       "# The train/test net protocol buffer definition\r\n",
-      "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n",
-      "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n",
+      "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n",
+      "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n",
       "# test_iter specifies how many forward passes the test should carry out.\r\n",
       "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
       "# covering the full 10,000 testing images.\r\n",
@@ -324,39 +310,44 @@
       "max_iter: 10000\r\n",
       "# snapshot intermediate results\r\n",
       "snapshot: 5000\r\n",
-      "snapshot_prefix: \"examples/mnist/lenet\"\r\n"
+      "snapshot_prefix: \"mnist/lenet\"\r\n"
      ]
     }
    ],
    "source": [
-    "!cat examples/mnist/lenet_auto_solver.prototxt"
+    "!cat mnist/lenet_auto_solver.prototxt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available."
+    "### 3. Loading and checking the solver\n",
+    "\n",
+    "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "caffe.set_device(0)\n",
     "caffe.set_mode_gpu()\n",
-    "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')"
+    "\n",
+    "### load the solver and create train and test nets\n",
+    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+    "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+    "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
    ]
   },
   {
@@ -376,8 +367,8 @@
        " ('pool1', (64, 20, 12, 12)),\n",
        " ('conv2', (64, 50, 8, 8)),\n",
        " ('pool2', (64, 50, 4, 4)),\n",
-       " ('ip1', (64, 500)),\n",
-       " ('ip2', (64, 10)),\n",
+       " ('fc1', (64, 500)),\n",
+       " ('score', (64, 10)),\n",
        " ('loss', ())]"
       ]
      },
@@ -403,8 +394,8 @@
       "text/plain": [
        "[('conv1', (20, 1, 5, 5)),\n",
        " ('conv2', (50, 20, 5, 5)),\n",
-       " ('ip1', (500, 800)),\n",
-       " ('ip2', (10, 500))]"
+       " ('fc1', (500, 800)),\n",
+       " ('score', (10, 500))]"
       ]
      },
      "execution_count": 9,
@@ -413,7 +404,7 @@
     }
    ],
    "source": [
-    "# just print the weight sizes (not biases)\n",
+    "# just print the weight sizes (we'll omit the biases)\n",
     "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
    ]
   },
@@ -421,7 +412,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+    "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
    ]
   },
   {
@@ -434,7 +425,7 @@
     {
      "data": {
       "text/plain": [
-       "{'loss': array(2.301163673400879, dtype=float32)}"
+       "{'loss': array(2.365971088409424, dtype=float32)}"
       ]
      },
      "execution_count": 10,
@@ -458,216 +449,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
+      "train labels: [ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
      ]
     },
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAWwAAABKCAYAAACfHW4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAIABJREFUeJztvXlQW1me5/s5EhJaECAJhEBgdrMbDNjgtNNOp7d02pk1\n",
-       "mVlZW1dWd0XH9ERMzxIzEzE1M3/M1HvzIt68iZjpF9HRPdFvpqeqZ6ajJyozy5VbpZ1e0k4n6R0w\n",
-       "JBizrwIJxCYJgQTc9wfcW+D0KiOwK+8ngkBcJN2jo3N/95zf+f5+PyFJEioqKioqzz6arW6AioqK\n",
-       "isrjoRpsFRUVlecE1WCrqKioPCeoBltFRUXlOUE12CoqKirPCarBVlFRUXlOiNpgCyFeEUJ0CCG6\n",
-       "hBA/28hGqaioqKh8ExGNDlsIoQXuAoeBEeAG8ENJku5sbPNUVFRUVGSinWHvBrolSeqXJCkC/G/g\n",
-       "OxvXLBUVFRWVe4nWYLuAoTV/D68eU1FRUVGJEXFRvu6RfhQhhBrzrqKiohIFkiSJ+x2PdoY9AmSt\n",
-       "+TuLlVm2ioqKikqMiNZg3wQKhRA5Qgg98H3gw41rloqKiorKvUTlEpEkaVEI8Y+AM4AW+GtVIaKi\n",
-       "oqISW6KS9SkvFqIfmAWWgIgkSbvX/O9b7cPWaDRotVr0ej06nQ6tVkskEiEcDhOJRFheXkZNbaui\n",
-       "8js0Gg3x8fHEx8ej1WpZXl5mfn6ehYUFlpeXt7p5m8qDfNjRbjoq7wu8JEnS5FO+z+8dVquVnJwc\n",
-       "amtrqaqqwuFw0NLSwq1bt2hpaWFsbIxwOLzVzVRReWZITk7m2LFj7Nmzh/z8fLxeL2fOnOHs2bPM\n",
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QPKRCoKCggObmZgYHB5PeBEroW/1z4/fC4qy2tpacnJykjWtfC7ZSqcRsNlNT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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7939901710>"
+       "<matplotlib.figure.Figure at 0x7f51a65ee690>"
       ]
      },
      "metadata": {},
@@ -676,8 +465,8 @@
    ],
    "source": [
     "# we use a little trick to tile the first eight images\n",
-    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
-    "print solver.net.blobs['label'].data[:8]"
+    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+    "print 'train labels:', solver.net.blobs['label'].data[:8]"
    ]
   },
   {
@@ -691,204 +480,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
+      "test labels: [ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
      ]
     },
     {
      "data": {
-      "image/png": [
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7939901490>"
+       "<matplotlib.figure.Figure at 0x7f51a4030a50>"
       ]
      },
      "metadata": {},
@@ -896,17 +495,19 @@
     }
    ],
    "source": [
-    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n",
-    "print solver.test_nets[0].blobs['label'].data[:8]"
+    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+    "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### 4. Stepping the solver\n",
+    "\n",
     "Both train and test nets seem to be loading data, and to have correct labels.\n",
     "\n",
-    "Let's take one step of (minibatch) SGD and see what happens."
+    "* Let's take one step of (minibatch) SGD and see what happens."
    ]
   },
   {
@@ -937,7 +538,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f79383819d0>"
+       "(-0.5, 24.5, 19.5, -0.5)"
       ]
      },
      "execution_count": 14,
@@ -946,439 +547,9 @@
     },
     {
      "data": {
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jFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o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\nLtEpzAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7939901850>"
+       "<matplotlib.figure.Figure at 0x7f519c0eab90>"
       ]
      },
      "metadata": {},
@@ -1387,13 +558,15 @@
    ],
    "source": [
     "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
-    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')"
+    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### 5. Writing a custom training loop\n",
+    "\n",
     "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
     "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
     "* logging will continue to happen as normal\n",
@@ -1424,8 +597,8 @@
       "Iteration 125 testing...\n",
       "Iteration 150 testing...\n",
       "Iteration 175 testing...\n",
-      "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n",
-      "Wall time: 15.7 s\n"
+      "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n",
+      "Wall time: 14.4 s\n"
      ]
     }
    ],
@@ -1448,7 +621,7 @@
     "    # store the output on the first test batch\n",
     "    # (start the forward pass at conv1 to avoid loading new data)\n",
     "    solver.test_nets[0].forward(start='conv1')\n",
-    "    output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n",
+    "    output[it] = solver.test_nets[0].blobs['score'].data[:8]\n",
     "    \n",
     "    # run a full test every so often\n",
     "    # (Caffe can also do this for us and write to a log, but we show here\n",
@@ -1458,7 +631,7 @@
     "        correct = 0\n",
     "        for test_it in range(100):\n",
     "            solver.test_nets[0].forward()\n",
-    "            correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n",
+    "            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
     "                           == solver.test_nets[0].blobs['label'].data)\n",
     "        test_acc[it // test_interval] = correct / 1e4"
    ]
@@ -1467,7 +640,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot the train loss and test accuracy."
+    "* Let's plot the train loss and test accuracy."
    ]
   },
   {
@@ -1480,7 +653,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f793878f490>"
+       "<matplotlib.text.Text at 0x7f5199b33610>"
       ]
      },
      "execution_count": 16,
@@ -1489,331 +662,9 @@
     },
     {
      "data": {
-      "image/png": [
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Hww8vdMscx3FqRT4toD3Adap6BHAC8DURqdRbisiZQD9V7Q/8L3B3TQ9y8skwaVIumttA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\nJGkQBpAkaRA3ABGGQ9Z+SfjXAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f79387025d0>"
+       "<matplotlib.figure.Figure at 0x7f519bf16690>"
       ]
      },
      "metadata": {},
@@ -1827,7 +678,8 @@
     "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
     "ax1.set_xlabel('iteration')\n",
     "ax1.set_ylabel('train loss')\n",
-    "ax2.set_ylabel('test accuracy')"
+    "ax2.set_ylabel('test accuracy')\n",
+    "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))"
    ]
   },
   {
@@ -1836,7 +688,7 @@
    "source": [
     "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
     "\n",
-    "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+    "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
    ]
   },
   {
@@ -1849,109 +701,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
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-       "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n",
-       "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n",
-       "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n",
-       "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n",
-       "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n",
-       "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n",
-       "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n",
-       "uElYAAAi2gZQBdAFYAshXphGpzTzzUSiQT8Z69NCiOI0OqNZDKYxPJ26EqpZXiYVjQDwDhG9S0Rf\n",
-       "nEaHNPPPpMPTi0KIPSJKA3ibiD4WQnx3Gh3TzC8TWRohxN7gtQDgTfS3tmiWnEl2WAaJKDy4NgF8\n",
-       "FsCH0+qYZn6ZZHhaBfAmEfFz/loI8dZUeqWZaxYyR9gwDBARiEheq/eY0342IQSEEOj1evJafR9f\n",
-       "D79eR87KEZ7UEZ45hmHA5/PB6/XC6/XC5/PB7/fD7/fL+71ez9FUAfR6PbTbbdk6nY58D7+/2+2i\n",
-       "2+3Ka42ThRSN1+tFKBSSLRKJyBYIBNDtdnF8fOz48NmidLtd1Go11Ot11Go1NBqNE++3bVs2LZqT\n",
-       "LJxoiAg+nw+hUAjxeBzxeBzpdBqpVAqpVAqRSASdTkd+6MfHxw6LY9s2isUiisUifD4f3G63QyS2\n",
-       "bYOIpMA0J1k40RiGAb/fj0gkgmQyidXVVWQyGaytrWFtbQ2xWAydTkc227YdQ5Vt2wiHwwgGg3I4\n",
-       "U9/f6XRgWZZsbvfV/IrUIVX1wYaH26vwuRZONC6XC6ZpIplMYmNjAxsbG9LKsKVhC8Ov6i/7+PgY\n",
-       "wWAQ8XgcKysrqFarDstk2zaazSYsy0Kz2USr1Zr5zzjsk7VaLViWJV/55+I2a+EspGhCoRBSqRQ2\n",
-       "Nzdx69YtxGIxxGIxRKNRmKZ5wpFVZ0m9Xg/xeBzNZhONRgOWZTkExqLhZlnWzH9G9rG41Wo1lMtl\n",
-       "2ZrNJtrttnyvFs05GIbhsDSPP/44QqEQTNNEKBSC3+8/MZ0eNucsDvUvlj8oVTQsqlnDfWMLeHh4\n",
-       "iHw+D4/Hg16vJ8MK3W4XnU5n5v1bSNH4fD6Ew2HpBPOU2+/3w+v1noi78C/5rFe2Sr1eD8fHxw4r\n",
-       "02w2Hc9Sv28aqP1jbNuW4YB2u41IJCIF0263Hf7ZNPtyURZONDxlzufzyGazMAwDgUBANq/XK4cn\n",
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7938797710>"
+       "<matplotlib.figure.Figure at 0x7f5199aaab50>"
       ]
      },
      "metadata": {},
@@ -1959,94 +711,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7938323190>"
+       "<matplotlib.figure.Figure at 0x7f5199a4ba10>"
       ]
      },
      "metadata": {},
@@ -2054,124 +721,9 @@
     },
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7938233050>"
+       "<matplotlib.figure.Figure at 0x7f51999dd210>"
       ]
      },
      "metadata": {},
@@ -2179,98 +731,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f793821a310>"
+       "<matplotlib.figure.Figure at 0x7f519994c650>"
       ]
      },
      "metadata": {},
@@ -2278,84 +741,9 @@
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f793811c290>"
+       "<matplotlib.figure.Figure at 0x7f519984f250>"
       ]
      },
      "metadata": {},
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+      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f79380a2550>"
+       "<matplotlib.figure.Figure at 0x7f51997d3ad0>"
       ]
      },
      "metadata": {},
@@ -2591,96 +771,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bd10b10>"
+       "<matplotlib.figure.Figure at 0x7f519bee6f50>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bca2290>"
+       "<matplotlib.figure.Figure at 0x7f519c0b0c10>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bc15650>"
+       "<matplotlib.figure.Figure at 0x7f519c0d5bd0>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bb98fd0>"
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      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792bb3b910>"
+       "<matplotlib.figure.Figure at 0x7f519969d110>"
       ]
      },
      "metadata": {},
@@ -3082,124 +821,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792ba9ddd0>"
+       "<matplotlib.figure.Figure at 0x7f5199603550>"
       ]
      },
      "metadata": {},
@@ -3207,97 +831,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b9b9550>"
+       "<matplotlib.figure.Figure at 0x7f5199591d10>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b97ec50>"
+       "<matplotlib.figure.Figure at 0x7f51994fb990>"
       ]
      },
      "metadata": {},
@@ -3429,99 +851,9 @@
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+      "image/png": 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FCgAAoCESKgAAgIZIqAAAABoioQIAAGiIhAoAAKAhEioAAICGujKhevjhhzvdBDTwk5/8\npNNNwElavXp1p5uABlatOq5cKnrE7bff3ukmoKGuTKgeeeSRTjcBDTz44IOdbgJO0po1azrdBDRA\nQtW7SKh6X1cmVAAAAL2EhAoAAKAhZ4bAP+Urtzu3cgAAgKSIGLU+WkcTKgAAgNMBt/wAAAAaIqEC\nAABoqOsSKttX2N5o+0e239fp9mBstv/V9ibb946YN9f2rbZ/aPubtud0so0Ym+0h27fZvs/2etvv\nas+nD7uc7em2V9peY3uD7Q+059N3PcT2ZNurbX+1PU3/9bCuSqhsT5Z0vaQrJF0g6Srbz+5sq3AC\nN6rVVyNdI+nWiHiWpP9tT6M7HZC0LCIulHSppHe0jzf6sMtFxD5Jl0fE8yQ9V9Lltl8k+q7XXC1p\ng6QjDzPTfz2sqxIqSS+U9OOIeCgiDkj6gqTXdLhNGENE3CFp2zGzr5T0qfbrT0l67YQ2CsUi4vGI\nWNN+PSzpfknniD7sCRGxp/1yqqTJah2L9F2PsD0o6ZWSPiHpyK/G6L8e1m0J1TmSHh0x/Vh7HnrH\ngojY1H69SdKCTjYGZWwvknSRpJWiD3uC7Um216jVR7dFxH2i73rJRyS9V9LhEfPovx7WbQkVYzic\nRqI1Jgd92uVsD0j6kqSrI2LXyPfow+4VEYfbt/wGJb3E9uXHvE/fdSnbr5K0OSJW6+dXp56G/us9\n3ZZQ/VTS0IjpIbWuUqF3bLL9DEmy/UuSNne4PTgB231qJVM3RcTN7dn0YQ+JiB2S/kvSb4i+6xVL\nJV1p+0FJn5f0m7ZvEv3X07otobpb0jNtL7I9VdIbJN3S4TYh5xZJb2m/foukm08Qiw6ybUk3SNoQ\nEdeNeIs+7HK25x35BZjtfkm/JWm16LueEBHXRsRQRJwn6Y2SvhURbxL919O6bqR0278j6Tq1HrK8\nISI+0OEmYQy2Py/ppZLmqXW//28lfUXSv0s6V9JDkl4fEds71UaMrf2rsO9IWqef31p4v6RVog+7\nmu3nqPXQ8qT2n5si4sO254q+6ym2Xyrp3RFxJf3X27ouoQIAAOg13XbLDwAAoOeQUAEAADREQgUA\nANAQCRUAAEBDJFQAAAANkVABAAA0REIFoONs39n++5dtX3WKl33taOsCgFOJcagAdA3bL1NrkMNX\nJz4zJSIOnuD9XRFxxqloHwCMhStUADrO9nD75Qclvdj2attX255k+8O2V9lea/vP2/Evs32H7a9I\nWt+ed7Ptu22vt/229rwPSupvL++mketyy4dt32t7ne3Xj1j27bb/w/b9tj8zsVsDQC+a0ukGAIB+\nXvrmfZLec+QKVTuB2h4RL7Q9TdJ3bX+zHXuRpAsj4uH29FsjYlu7tt0q21+MiGtsvyMiLhplXb8n\n6dclPVfSfEl32f5O+73nSbpA0v9JutP2ZRHBrUIAY+IKFYBu4mOmf1vSm22vlvQ9SXMl/Wr7vVUj\nkilJutr2GkkrJA1JeuY463qRpM9Fy2ZJ35Z0sVoJ16qI+Fm0nolYI2lRg+8E4BcAV6gAdLt3RsSt\nI2e0n7Xafcz0yyVdGhH7bN8mafo4yw0dn8AduXq1f8S8Q+JcCWAcXKEC0E12SRr5APk3JL3d9hRJ\nsv0s2zNG+dwsSdvaydRiSZeOeO/Akc8f4w5Jb2g/pzVf0kskrdLxSRYAjIv/dQHoBkeuDK2VdKh9\n6+5GSR9T63bb921b0mZJv9uOH/kT5a9L+gvbGyT9QK3bfkd8XNI62/dExJuOfC4i/tP2kvY6Q9J7\nI2Kz7Wcfs2yNMg0AT8OwCQAAAA1xyw8AAKAhEioAAICGSKgAAAAaIqECAABoiIQKAACgIRIqAACA\nhkioAAAAGiKhAgAAaOj/AYSDQCwV4p2TAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b91c3d0>"
+       "<matplotlib.figure.Figure at 0x7f519948af90>"
       ]
      },
      "metadata": {},
@@ -3544,7 +876,7 @@
    "source": [
     "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
     "\n",
-    "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+    "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
    ]
   },
   {
@@ -3557,109 +889,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n",
-       "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n",
-       "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n",
-       "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n",
-       "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n",
-       "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n",
-       "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n",
-       "uElYAAAi2gZQBdAFYAshXphGpzTzzUSiQT8Z69NCiOI0OqNZDKYxPJ26EqpZXiYVjQDwDhG9S0Rf\n",
-       "nEaHNPPPpMPTi0KIPSJKA3ibiD4WQnx3Gh3TzC8TWRohxN7gtQDgTfS3tmiWnEl2WAaJKDy4NgF8\n",
-       "FsCH0+qYZn6ZZHhaBfAmEfFz/loI8dZUeqWZaxYyR9gwDBARiEheq/eY0342IQSEEOj1evJafR9f\n",
-       "D79eR87KEZ7UEZ45hmHA5/PB6/XC6/XC5/PB7/fD7/fL+71ez9FUAfR6PbTbbdk6nY58D7+/2+2i\n",
-       "2+3Ka42ThRSN1+tFKBSSLRKJyBYIBNDtdnF8fOz48NmidLtd1Go11Ot11Go1NBqNE++3bVs2LZqT\n",
-       "LJxoiAg+nw+hUAjxeBzxeBzpdBqpVAqpVAqRSASdTkd+6MfHxw6LY9s2isUiisUifD4f3G63QyS2\n",
-       "bYOIpMA0J1k40RiGAb/fj0gkgmQyidXVVWQyGaytrWFtbQ2xWAydTkc227YdQ5Vt2wiHwwgGg3I4\n",
-       "U9/f6XRgWZZsbvfV/IrUIVX1wYaH26vwuRZONC6XC6ZpIplMYmNjAxsbG9LKsKVhC8Ov6i/7+PgY\n",
-       "wWAQ8XgcKysrqFarDstk2zaazSYsy0Kz2USr1Zr5zzjsk7VaLViWJV/55+I2a+EspGhCoRBSqRQ2\n",
-       "Nzdx69YtxGIxxGIxRKNRmKZ5wpFVZ0m9Xg/xeBzNZhONRgOWZTkExqLhZlnWzH9G9rG41Wo1lMtl\n",
-       "2ZrNJtrttnyvFs05GIbhsDSPP/44QqEQTNNEKBSC3+8/MZ0eNucsDvUvlj8oVTQsqlnDfWMLeHh4\n",
-       "iHw+D4/Hg16vJ8MK3W4XnU5n5v1bSNH4fD6Ew2HpBPOU2+/3w+v1noi78C/5rFe2Sr1eD8fHxw4r\n",
-       "02w2Hc9Sv28aqP1jbNuW4YB2u41IJCIF0263Hf7ZNPtyURZONDxlzufzyGazMAwDgUBANq/XK4cn\n",
-       "FoPL5XI0t9sNt9strw3DgGEYcLlcMlDo9XpBRHC73Sd8DH4/t1E4zbFVA5SGYZwYLrvdLlqtFlqt\n",
-       "lhyWhBBot9taNBeBRVMoFLC9vQ3bthEMBmXj2RCb9263C6/XC4/HI199Pp+cOfG1eo+I4PV64Xa7\n",
-       "4ff7Hf5Ft9t1CO6is6vhAKMq7GFBs8VT36OK5vj4GO12G7VabWTRToOFFE29Xkc+n4dhGGg0GggG\n",
-       "gzBNE6Zpwuv1yl9wq9WCbduO4cvv9ztEFgwGZZBQCOH48PhaFWGn05HRaBbheZzmU6nN7XbD4/HI\n",
-       "V36vao3UCHar1UK9XkexWJxP0RDRGwB+GsCBEOLZwb0EgL8BcBPANoCfE0KUL7GfEhbN4eEhjo+P\n",
-       "Ua1WpWBYNOrsx7ZtBAKBU0XCjWM77GSy1fF4PPB4PNJJBh4KgIezi4pmuPH/xT4NWzW/339iDc22\n",
-       "bTQaDdTrdTQaDZTLZWlV53V4+iaAPwHwF8q9LwF4WwjxtUHh6S8N2qXDUV3LsmAYBrrdLprNJur1\n",
-       "Ovx+P9xuN9rttsOU8/oUi0H1gQKBAMLhMEKhkHzlD499JI6PcKxk+PvPY3g2xw4uW65oNCrjTMlk\n",
-       "Eh6Px7Egy8MVW5l2uy19nbkM7gkhvktEt4Zufw7ATw2u/xzAv2NGoun1euh0Omg2m/Ja9VdcLpdj\n",
-       "Ot3tdh2mn9+r+jfBYFBao+FXv9/vsFzNZhOmaTos13l/7cPRXFXU7XYbmUwGt27dgmEYCIfDICK4\n",
-       "XC4YhiG/7yzRXAXj+jSrQoj84DqPfm7NTGBLw3+xPPvhXzJbH3W9SZ3p8PtU53PYGWZRmKaJQCDg\n",
-       "GBoajYZjaDNN80KiUfujRnhbrRa2trZgGAZCoRAymYx0rvm57DgPi0ZdUpglEzvCQggxy/p6LBrb\n",
-       "tqfyPPYnuHk8HhkoZPHU63XHqng4HHYMaeehCkZdFmDhCCEQj8dx48YNdDod+Hw+EJGcjrNg2u22\n",
-       "jB91Op0rWUIAxhdNnojWhBD7RJQBcDDNTs0S1TFlc99ut6Uvoa5F8QfVbrfhcrkuvBI+PDzxB81T\n",
-       "fHUBlf0Znmp3Oh3UajVUKhUUi0UUCgWUSiXU63V0Op2FEs13APwCgD8YvP7D1Hp0BfR6PQAPBdRq\n",
-       "taRgWq2WdFjb7Ta63S7a7bacOnOw7VEMz5zUBDKObpumCb/fL0WjLm3w2tPR0REKhQKKxSLq9bqM\n",
-       "Ds+ai0y5v4W+05sioh0Avw/gqwD+loh+GYMp92V28rIZjomw49lqteByuRxBNp6xqBZnlP8HAEKh\n",
-       "kHTCI5GIw9K43W45NLGjzKJRLY1lWVK8s+Yis6dXzvinz0y5L1fGcF4Kx2TOYtJZi8fjQTQaRSAQ\n",
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b65d950>"
+       "<matplotlib.figure.Figure at 0x7f51991d5790>"
       ]
      },
      "metadata": {},
@@ -3667,81 +899,9 @@
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b44f290>"
+       "<matplotlib.figure.Figure at 0x7f5198fc5750>"
       ]
      },
      "metadata": {},
@@ -3874,92 +919,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b3b3a50>"
+       "<matplotlib.figure.Figure at 0x7f5198f2af90>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b33bd10>"
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       ]
      },
      "metadata": {},
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      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b23ec90>"
+       "<matplotlib.figure.Figure at 0x7f5198dc0d90>"
       ]
      },
      "metadata": {},
@@ -4267,87 +959,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b1bd290>"
+       "<matplotlib.figure.Figure at 0x7f51994b6550>"
       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b1419d0>"
+       "<matplotlib.figure.Figure at 0x7f51994cc7d0>"
       ]
      },
      "metadata": {},
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      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b044750>"
+       "<matplotlib.figure.Figure at 0x7f5199196050>"
       ]
      },
      "metadata": {},
@@ -4643,82 +999,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b06c950>"
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       ]
      },
      "metadata": {},
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792af4a550>"
+       "<matplotlib.figure.Figure at 0x7f519c085f10>"
       ]
      },
      "metadata": {},
@@ -4851,89 +1019,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792aed8c90>"
+       "<matplotlib.figure.Figure at 0x7f519998c390>"
       ]
      },
      "metadata": {},
@@ -4941,123 +1029,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792b570150>"
+       "<matplotlib.figure.Figure at 0x7f519c0f7b50>"
       ]
      },
      "metadata": {},
@@ -5065,92 +1039,9 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d792baffb50>"
+       "<matplotlib.figure.Figure at 0x7f5199b2b090>"
       ]
      },
      "metadata": {},
@@ -5166,6 +1057,207 @@
     "    xlabel('iteration')\n",
     "    ylabel('label')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Experiment with architecture and optimization\n",
+    "\n",
+    "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n",
+    "\n",
+    "- Define new architectures for comparison\n",
+    "- Tune optimization by setting `base_lr` and the like or simply training longer\n",
+    "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n",
+    "\n",
+    "Feel free to explore these directions by editing the all-in-one example that follows.\n",
+    "Look for \"`EDIT HERE`\" comments for suggested choice points.\n",
+    "\n",
+    "By default this defines a simple linear classifier as a baseline.\n",
+    "\n",
+    "In case your coffee hasn't kicked in and you'd like inspiration, try out\n",
+    "\n",
+    "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n",
+    "2. Stack more fully connected and nonlinear layers\n",
+    "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n",
+    "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n",
+    "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 testing...\n",
+      "Iteration 25 testing...\n",
+      "Iteration 50 testing...\n",
+      "Iteration 75 testing...\n",
+      "Iteration 100 testing...\n",
+      "Iteration 125 testing...\n",
+      "Iteration 150 testing...\n",
+      "Iteration 175 testing...\n",
+      "Iteration 200 testing...\n",
+      "Iteration 225 testing...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f5199af9f50>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198f46a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_net_path = 'mnist/custom_auto_train.prototxt'\n",
+    "test_net_path = 'mnist/custom_auto_test.prototxt'\n",
+    "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n",
+    "\n",
+    "### define net\n",
+    "def custom_net(lmdb, batch_size):\n",
+    "    # define your own net!\n",
+    "    n = caffe.NetSpec()\n",
+    "    \n",
+    "    # keep this data layer for all networks\n",
+    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+    "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    \n",
+    "    # EDIT HERE to try different networks\n",
+    "    # this single layer defines a simple linear classifier\n",
+    "    # (in particular this defines a multiway logistic regression)\n",
+    "    n.score =   L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    \n",
+    "    # EDIT HERE this is the LeNet variant we have already tried\n",
+    "    # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+    "    # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+    "    # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    # n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n",
+    "    # n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+    "    # n.score =   L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    \n",
+    "    # keep this loss layer for all networks\n",
+    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
+    "    \n",
+    "    return n.to_proto()\n",
+    "\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(custom_net('mnist/mnist_train_lmdb', 64)))    \n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n",
+    "\n",
+    "### define solver\n",
+    "from caffe.proto import caffe_pb2\n",
+    "s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "# Set a seed for reproducible experiments:\n",
+    "# this controls for randomization in training.\n",
+    "s.random_seed = 0xCAFFE\n",
+    "\n",
+    "# Specify locations of the train and (maybe) test networks.\n",
+    "s.train_net = train_net_path\n",
+    "s.test_net.append(test_net_path)\n",
+    "s.test_interval = 500  # Test after every 500 training iterations.\n",
+    "s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+    "\n",
+    "s.max_iter = 10000     # no. of times to update the net (training iterations)\n",
+    " \n",
+    "# EDIT HERE to try different solvers\n",
+    "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n",
+    "s.type = \"SGD\"\n",
+    "\n",
+    "# Set the initial learning rate for SGD.\n",
+    "s.base_lr = 0.01  # EDIT HERE to try different learning rates\n",
+    "# Set momentum to accelerate learning by\n",
+    "# taking weighted average of current and previous updates.\n",
+    "s.momentum = 0.9\n",
+    "# Set weight decay to regularize and prevent overfitting\n",
+    "s.weight_decay = 5e-4\n",
+    "\n",
+    "# Set `lr_policy` to define how the learning rate changes during training.\n",
+    "# This is the same policy as our default LeNet.\n",
+    "s.lr_policy = 'inv'\n",
+    "s.gamma = 0.0001\n",
+    "s.power = 0.75\n",
+    "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n",
+    "# `fixed` is the simplest policy that keeps the learning rate constant.\n",
+    "# s.lr_policy = 'fixed'\n",
+    "\n",
+    "# Display the current training loss and accuracy every 1000 iterations.\n",
+    "s.display = 1000\n",
+    "\n",
+    "# Snapshots are files used to store networks we've trained.\n",
+    "# We'll snapshot every 5K iterations -- twice during training.\n",
+    "s.snapshot = 5000\n",
+    "s.snapshot_prefix = 'mnist/custom_net'\n",
+    "\n",
+    "# Train on the GPU\n",
+    "s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+    "\n",
+    "# Write the solver to a temporary file and return its filename.\n",
+    "with open(solver_config_path, 'w') as f:\n",
+    "    f.write(str(s))\n",
+    "\n",
+    "### load the solver and create train and test nets\n",
+    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+    "solver = caffe.get_solver(solver_config_path)\n",
+    "\n",
+    "### solve\n",
+    "niter = 250  # EDIT HERE increase to train for longer\n",
+    "test_interval = niter / 10\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\n",
+    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+    "\n",
+    "# the main solver loop\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    \n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    \n",
+    "    # run a full test every so often\n",
+    "    # (Caffe can also do this for us and write to a log, but we show here\n",
+    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
+    "    if it % test_interval == 0:\n",
+    "        print 'Iteration', it, 'testing...'\n",
+    "        correct = 0\n",
+    "        for test_it in range(100):\n",
+    "            solver.test_nets[0].forward()\n",
+    "            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
+    "                           == solver.test_nets[0].blobs['label'].data)\n",
+    "        test_acc[it // test_interval] = correct / 1e4\n",
+    "\n",
+    "_, ax1 = subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(arange(niter), train_loss)\n",
+    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+    "ax1.set_xlabel('iteration')\n",
+    "ax1.set_ylabel('train loss')\n",
+    "ax2.set_ylabel('test accuracy')\n",
+    "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))"
+   ]
   }
  ],
  "metadata": {
@@ -5187,7 +1279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.10"
   },
   "priority": 2
  },
diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt
index fa4bbf02710..481c84491f7 100644
--- a/examples/mnist/lenet_auto_solver.prototxt
+++ b/examples/mnist/lenet_auto_solver.prototxt
@@ -1,6 +1,6 @@
 # The train/test net protocol buffer definition
-train_net: "examples/mnist/lenet_auto_train.prototxt"
-test_net: "examples/mnist/lenet_auto_test.prototxt"
+train_net: "mnist/lenet_auto_train.prototxt"
+test_net: "mnist/lenet_auto_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -21,4 +21,4 @@ display: 100
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "examples/mnist/lenet"
+snapshot_prefix: "mnist/lenet"

From 9580577804b2c73d485afc3418d44b08641b6377 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 23 Feb 2016 23:43:31 -0800
Subject: [PATCH 1699/2053] [example] improve fine-tuning notebook

- add headings and text detail
- make nets by net spec
- define solvers inline through python protobuf
- do two-stage fine-tuning (first last layer alone, then end-to-end)
- show sample results
---
 examples/02-fine-tuning.ipynb | 1175 +++++++++++++++++++++++++++++++++
 examples/03-fine-tuning.ipynb |  947 --------------------------
 2 files changed, 1175 insertions(+), 947 deletions(-)
 create mode 100644 examples/02-fine-tuning.ipynb
 delete mode 100644 examples/03-fine-tuning.ipynb

diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
new file mode 100644
index 00000000000..07ca8df4d74
--- /dev/null
+++ b/examples/02-fine-tuning.ipynb
@@ -0,0 +1,1175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-tuning a Pretrained Network for Style Recognition\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+    "\n",
+    "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "import caffe\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline\n",
+    "import tempfile\n",
+    "\n",
+    "# Helper function for deprocessing preprocessed images, e.g., for display.\n",
+    "def deprocess_net_image(image):\n",
+    "    image = image.copy()              # don't modify destructively\n",
+    "    image = image[::-1]               # BGR -> RGB\n",
+    "    image = image.transpose(1, 2, 0)  # CHW -> HWC\n",
+    "    image += [123, 117, 104]          # (approximately) undo mean subtraction\n",
+    "\n",
+    "    # clamp values in [0, 255]\n",
+    "    image[image < 0], image[image > 255] = 0, 255\n",
+    "\n",
+    "    # round and cast from float32 to uint8\n",
+    "    image = np.round(image)\n",
+    "    image = np.require(image, dtype=np.uint8)\n",
+    "\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup and dataset download\n",
+    "\n",
+    "Download data required for this exercise.\n",
+    "\n",
+    "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n",
+    "- `download_model_binary.py` to download the pretrained reference model\n",
+    "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n",
+    "\n",
+    "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories.  (To download the full dataset, set `full_dataset = True` in the cell below.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2016-02-24 00:28:36--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
+      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
+      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 17858008 (17M) [application/octet-stream]\n",
+      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
+      "\n",
+      "100%[======================================>] 17,858,008   112MB/s   in 0.2s   \n",
+      "\n",
+      "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
+      "\n",
+      "Unzipping...\n",
+      "Done.\n",
+      "Model already exists.\n",
+      "Downloading 2000 images with 7 workers...\n",
+      "Writing train/val for 1996 successfully downloaded images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download just a small subset of the data for this exercise.\n",
+    "# (2000 of 80K images, 5 of 20 labels.)\n",
+    "# To download the entire dataset, set `full_dataset = True`.\n",
+    "full_dataset = False\n",
+    "if full_dataset:\n",
+    "    NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n",
+    "else:\n",
+    "    NUM_STYLE_IMAGES = 2000\n",
+    "    NUM_STYLE_LABELS = 5\n",
+    "\n",
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "import os\n",
+    "os.chdir(caffe_root)  # run scripts from caffe root\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1  --seed=1701 \\\n",
+    "    --images=$NUM_STYLE_IMAGES  --label=$NUM_STYLE_LABELS\n",
+    "# back to examples\n",
+    "os.chdir('examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "assert os.path.exists(weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded ImageNet labels:\n",
+      "n01440764 tench, Tinca tinca\n",
+      "n01443537 goldfish, Carassius auratus\n",
+      "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n",
+      "n01491361 tiger shark, Galeocerdo cuvieri\n",
+      "n01494475 hammerhead, hammerhead shark\n",
+      "n01496331 electric ray, crampfish, numbfish, torpedo\n",
+      "n01498041 stingray\n",
+      "n01514668 cock\n",
+      "n01514859 hen\n",
+      "n01518878 ostrich, Struthio camelus\n",
+      "...\n",
+      "\n",
+      "Loaded style labels:\n",
+      "Detailed, Pastel, Melancholy, Noir, HDR\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load ImageNet labels to imagenet_labels\n",
+    "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n",
+    "assert len(imagenet_labels) == 1000\n",
+    "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n",
+    "\n",
+    "# Load style labels to style_labels\n",
+    "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n",
+    "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n",
+    "if NUM_STYLE_LABELS > 0:\n",
+    "    style_labels = style_labels[:NUM_STYLE_LABELS]\n",
+    "print '\\nLoaded style labels:\\n', ', '.join(style_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.  Defining and running the nets\n",
+    "\n",
+    "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "weight_param = dict(lr_mult=1, decay_mult=1)\n",
+    "bias_param   = dict(lr_mult=2, decay_mult=0)\n",
+    "learned_param = [weight_param, bias_param]\n",
+    "\n",
+    "frozen_param = [dict(lr_mult=0)] * 2\n",
+    "\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n",
+    "              param=learned_param,\n",
+    "              weight_filler=dict(type='gaussian', std=0.01),\n",
+    "              bias_filler=dict(type='constant', value=0.1)):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                         num_output=nout, pad=pad, group=group,\n",
+    "                         param=param, weight_filler=weight_filler,\n",
+    "                         bias_filler=bias_filler)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "def fc_relu(bottom, nout, param=learned_param,\n",
+    "            weight_filler=dict(type='gaussian', std=0.005),\n",
+    "            bias_filler=dict(type='constant', value=0.1)):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout, param=param,\n",
+    "                        weight_filler=weight_filler,\n",
+    "                        bias_filler=bias_filler)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "def caffenet(data, label=None, train=True, num_classes=1000,\n",
+    "             classifier_name='fc8', learn_all=False):\n",
+    "    \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n",
+    "       specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data = data\n",
+    "    param = learned_param if learn_all else frozen_param\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n",
+    "    if train:\n",
+    "        n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n",
+    "    else:\n",
+    "        fc7input = n.relu6\n",
+    "    n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n",
+    "    if train:\n",
+    "        n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n",
+    "    else:\n",
+    "        fc8input = n.relu7\n",
+    "    # always learn fc8 (param=learned_param)\n",
+    "    fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n",
+    "    # give fc8 the name specified by argument `classifier_name`\n",
+    "    n.__setattr__(classifier_name, fc8)\n",
+    "    if not train:\n",
+    "        n.probs = L.Softmax(fc8)\n",
+    "    if label is not None:\n",
+    "        n.label = label\n",
+    "        n.loss = L.SoftmaxWithLoss(fc8, n.label)\n",
+    "        n.acc = L.Accuracy(fc8, n.label)\n",
+    "    # write the net to a temporary file and return its filename\n",
+    "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+    "        f.write(str(n.to_proto()))\n",
+    "        return f.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n",
+    "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n",
+    "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n",
+    "\n",
+    "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n",
+    "\n",
+    "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n",
+    "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n",
+    "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def style_net(train=True, learn_all=False, subset=None):\n",
+    "    if subset is None:\n",
+    "        subset = 'train' if train else 'test'\n",
+    "    source = caffe_root + 'data/flickr_style/%s.txt' % subset\n",
+    "    transform_param = dict(mirror=train, crop_size=227,\n",
+    "        mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n",
+    "    style_data, style_label = L.ImageData(\n",
+    "        transform_param=transform_param, source=source,\n",
+    "        batch_size=50, new_height=256, new_width=256, ntop=2)\n",
+    "    return caffenet(data=style_data, label=style_label, train=train,\n",
+    "                    num_classes=NUM_STYLE_LABELS,\n",
+    "                    classifier_name='fc8_flickr',\n",
+    "                    learn_all=learn_all)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n",
+    "\n",
+    "\n",
+    "Call `forward` on `untrained_style_net` to get a batch of style training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n",
+    "                                weights, caffe.TEST)\n",
+    "untrained_style_net.forward()\n",
+    "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n",
+    "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here).  Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n",
+    "\n",
+    "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories.  For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch.  (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n",
+    "    input_blob = net.blobs['data']\n",
+    "    net.blobs['data'].data[0, ...] = image\n",
+    "    probs = net.forward(start='conv1')['probs'][0]\n",
+    "    top_k = (-probs).argsort()[:k]\n",
+    "    print 'top %d predicted %s labels =' % (k, name)\n",
+    "    print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n",
+    "                    for i, p in enumerate(top_k))\n",
+    "\n",
+    "def disp_imagenet_preds(net, image):\n",
+    "    disp_preds(net, image, imagenet_labels, name='ImageNet')\n",
+    "\n",
+    "def disp_style_preds(net, image):\n",
+    "    disp_preds(net, image, style_labels, name='style')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual label = Melancholy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75dc02e4d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_index = 8\n",
+    "image = style_data_batch[batch_index]\n",
+    "plt.imshow(deprocess_net_image(image))\n",
+    "print 'actual label =', style_labels[style_label_batch[batch_index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted ImageNet labels =\n",
+      "\t(1) 69.89% n09421951 sandbar, sand bar\n",
+      "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n",
+      "\t(3)  3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n",
+      "\t(4)  1.89% n04592741 wing\n",
+      "\t(5)  1.23% n09332890 lakeside, lakeshore\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_imagenet_preds(imagenet_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n",
+    "\n",
+    "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels).  Since we set N = 5, we get a predicted probability of 20% for each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 20.00% Detailed\n",
+      "\t(2) 20.00% Pastel\n",
+      "\t(3) 20.00% Melancholy\n",
+      "\t(4) 20.00% Noir\n",
+      "\t(5) 20.00% HDR\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(untrained_style_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n",
+    "error = (diff ** 2).sum()\n",
+    "assert error < 1e-8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delete `untrained_style_net` to save memory.  (Hang on to `imagenet_net` as we'll use it again later.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "del untrained_style_net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Training the style classifier\n",
+    "\n",
+    "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights).  In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean.  You may want to play with some of the learning parameters to see if you can improve on the results here!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n",
+    "    s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "    # Specify locations of the train and (maybe) test networks.\n",
+    "    s.train_net = train_net_path\n",
+    "    if test_net_path is not None:\n",
+    "        s.test_net.append(test_net_path)\n",
+    "        s.test_interval = 1000  # Test after every 1000 training iterations.\n",
+    "        s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+    "\n",
+    "    # The number of iterations over which to average the gradient.\n",
+    "    # Effectively boosts the training batch size by the given factor, without\n",
+    "    # affecting memory utilization.\n",
+    "    s.iter_size = 1\n",
+    "    \n",
+    "    s.max_iter = 100000     # # of times to update the net (training iterations)\n",
+    "    \n",
+    "    # Solve using the stochastic gradient descent (SGD) algorithm.\n",
+    "    # Other choices include 'Adam' and 'RMSProp'.\n",
+    "    s.type = 'SGD'\n",
+    "\n",
+    "    # Set the initial learning rate for SGD.\n",
+    "    s.base_lr = base_lr\n",
+    "\n",
+    "    # Set `lr_policy` to define how the learning rate changes during training.\n",
+    "    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+    "    # every `stepsize` iterations.\n",
+    "    s.lr_policy = 'step'\n",
+    "    s.gamma = 0.1\n",
+    "    s.stepsize = 20000\n",
+    "\n",
+    "    # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n",
+    "    # weighted average of the current gradient and previous gradients to make\n",
+    "    # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+    "    # the model from overfitting.\n",
+    "    s.momentum = 0.9\n",
+    "    s.weight_decay = 5e-4\n",
+    "\n",
+    "    # Display the current training loss and accuracy every 1000 iterations.\n",
+    "    s.display = 1000\n",
+    "\n",
+    "    # Snapshots are files used to store networks we've trained.  Here, we'll\n",
+    "    # snapshot every 10K iterations -- ten times during training.\n",
+    "    s.snapshot = 10000\n",
+    "    s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n",
+    "    \n",
+    "    # Train on the GPU.  Using the CPU to train large networks is very slow.\n",
+    "    s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+    "    \n",
+    "    # Write the solver to a temporary file and return its filename.\n",
+    "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+    "        f.write(str(s))\n",
+    "        return f.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll invoke the solver to train the style net's classification layer.\n",
+    "\n",
+    "For the record, if you want to train the network using only the command line tool, this is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example.\n",
+    "\n",
+    "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration.  At the end, the learned weights are saved to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def run_solvers(niter, solvers, disp_interval=10):\n",
+    "    \"\"\"Run solvers for niter iterations,\n",
+    "       returning the loss and accuracy recorded each iteration.\n",
+    "       `solvers` is a list of (name, solver) tuples.\"\"\"\n",
+    "    blobs = ('loss', 'acc')\n",
+    "    loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n",
+    "                 for _ in blobs)\n",
+    "    for it in range(niter):\n",
+    "        for name, s in solvers:\n",
+    "            s.step(1)  # run a single SGD step in Caffe\n",
+    "            loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n",
+    "                                             for b in blobs)\n",
+    "        if it % disp_interval == 0 or it + 1 == niter:\n",
+    "            loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n",
+    "                                  (n, loss[n][it], np.round(100*acc[n][it]))\n",
+    "                                  for n, _ in solvers)\n",
+    "            print '%3d) %s' % (it, loss_disp)     \n",
+    "    # Save the learned weights from both nets.\n",
+    "    weight_dir = tempfile.mkdtemp()\n",
+    "    weights = {}\n",
+    "    for name, s in solvers:\n",
+    "        filename = 'weights.%s.caffemodel' % name\n",
+    "        weights[name] = os.path.join(weight_dir, filename)\n",
+    "        s.net.save(weights[name])\n",
+    "    return loss, acc, weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create and run solvers to train nets for the style recognition task.  We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n",
+    "\n",
+    "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running solvers for 200 iterations...\n",
+      "  0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n",
+      " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n",
+      " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n",
+      " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n",
+      " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n",
+      " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n",
+      " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n",
+      " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n",
+      " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n",
+      " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n",
+      "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n",
+      "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n",
+      "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n",
+      "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n",
+      "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n",
+      "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n",
+      "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n",
+      "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n",
+      "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n",
+      "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n",
+      "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200  # number of iterations to train\n",
+    "\n",
+    "# Reset style_solver as before.\n",
+    "style_solver_filename = solver(style_net(train=True))\n",
+    "style_solver = caffe.get_solver(style_solver_filename)\n",
+    "style_solver.net.copy_from(weights)\n",
+    "\n",
+    "# For reference, we also create a solver that isn't initialized from\n",
+    "# the pretrained ImageNet weights.\n",
+    "scratch_style_solver_filename = solver(style_net(train=True))\n",
+    "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+    "\n",
+    "print 'Running solvers for %d iterations...' % niter\n",
+    "solvers = [('pretrained', style_solver),\n",
+    "           ('scratch', scratch_style_solver)]\n",
+    "loss, acc, weights = run_solvers(niter, solvers)\n",
+    "print 'Done.'\n",
+    "\n",
+    "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n",
+    "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n",
+    "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n",
+    "\n",
+    "# Delete solvers to save memory.\n",
+    "del style_solver, scratch_style_solver, solvers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss and accuracy produced by the two training procedures.  Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f75d49e1090>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE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\ncoiIiIb/AxSD6Sq0YLMCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d496e890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)\n",
+    "xlabel('Iteration #')\n",
+    "ylabel('Loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f75d49e1a90>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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\nFwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d47cad90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_acc, scratch_train_acc]).T)\n",
+    "xlabel('Iteration #')\n",
+    "ylabel('Accuracy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_style_net(weights, test_iters=10):\n",
+    "    test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n",
+    "    accuracy = 0\n",
+    "    for it in xrange(test_iters):\n",
+    "        accuracy += test_net.forward()['acc']\n",
+    "    accuracy /= test_iters\n",
+    "    return test_net, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy, trained from ImageNet initialization: 50.0%\n",
+      "Accuracy, trained from   random initialization: 23.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_net, accuracy = eval_style_net(style_weights)\n",
+    "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+    "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n",
+    "print 'Accuracy, trained from   random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. End-to-end finetuning for style\n",
+    "\n",
+    "Finally, we'll train both nets again, starting from the weights we just learned.  The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image.  We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters.  Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n",
+    "\n",
+    "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training.  To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long.  Feel free to try this yourself!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running solvers for 200 iterations...\n",
+      "  0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n",
+      " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n",
+      " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n",
+      " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n",
+      " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n",
+      " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n",
+      " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n",
+      " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n",
+      " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n",
+      " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n",
+      "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n",
+      "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n",
+      "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n",
+      "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n",
+      "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n",
+      "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n",
+      "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n",
+      "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n",
+      "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n",
+      "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n",
+      "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "end_to_end_net = style_net(train=True, learn_all=True)\n",
+    "\n",
+    "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n",
+    "# You may want to play around with different values of this or other\n",
+    "# optimization parameters when fine-tuning.  For example, if learning diverges\n",
+    "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n",
+    "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n",
+    "# for which learning does not diverge).\n",
+    "base_lr = 0.001\n",
+    "\n",
+    "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+    "style_solver = caffe.get_solver(style_solver_filename)\n",
+    "style_solver.net.copy_from(style_weights)\n",
+    "\n",
+    "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+    "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+    "scratch_style_solver.net.copy_from(scratch_style_weights)\n",
+    "\n",
+    "print 'Running solvers for %d iterations...' % niter\n",
+    "solvers = [('pretrained, end-to-end', style_solver),\n",
+    "           ('scratch, end-to-end', scratch_style_solver)]\n",
+    "_, _, finetuned_weights = run_solvers(niter, solvers)\n",
+    "print 'Done.'\n",
+    "\n",
+    "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n",
+    "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n",
+    "\n",
+    "# Delete solvers to save memory.\n",
+    "del style_solver, scratch_style_solver, solvers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now test the end-to-end finetuned models.  Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy, finetuned from ImageNet initialization: 53.6%\n",
+      "Accuracy, finetuned from   random initialization: 39.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_net, accuracy = eval_style_net(style_weights_ft)\n",
+    "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+    "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n",
+    "print 'Accuracy, finetuned from   random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll first look back at the image we started with and check our end-to-end trained model's predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 55.67% Melancholy\n",
+      "\t(2) 27.21% HDR\n",
+      "\t(3) 16.46% Pastel\n",
+      "\t(4)  0.63% Detailed\n",
+      "\t(5)  0.03% Noir\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K\nXgeZAe33aKYvR2IdTeigmbP1JOo9DWnhRCfq9gN9t7T7M2Wwry9fyqgTAAxWi5KN+oL8uSkq32g2\n2qdUlFa0ZdBgXq1RPe7aviIYv6/FivJHwgyxUgkMa1OJqh/Y1xsUHuQrUfF9fOzd4+WEgcaLlyRk\nCw8DLOjE2R9G0F8Vc0ATjEsrRGvq5wRHIqxy5sr826+xaeUiuGdCAZLg/R0JlPzEBh+5SS44i3bE\n5alb04mllqPutSMTC12jwWsYoEIqyVJ16DWgPr+6bVDruq2+wC3KLyAkU8qN59VIt3Q4otfKUMlH\ne7p2ayHDUHg3AA2GbeWZam8innnD65bLb5Ol5NsK33nztYK0ne+0oN1dcb+GBEKrkyj00d/F3Sfy\nzwb8IS0fCmPzsyUINn3zoaNuLwrdAOgci9j6aEZsjZPEI1YC9y4pa/v+P8nxosIgx0pliaavbg5i\n7L1voIZj+KrhE5l7KUdBVYASAmLOKEKUeKx4vgFy8qHg+Q6rokNiG3EnAW0aWlCtuAf1HZFu5Tds\n6KFFA0ohmCnMyutL66+6iWQq3+W8Aetdma/tnxrrZRIyjKlvjDDLG95j1dTf36A5DPaOQ9LRWr0E\n491NZKuuEjKhceLaA2Ubosy1d+gEPLFs9T41NCMRcLoQtj0roZzY4l449H62Pd2vtyzp7wgRKOG4\nfFilKOwe3ErY2LoTBoVRTJ+jUOPlSrlE74/pPV5jKZ+iPIR51+/4Bug/UrOdrztetrnJ6nRjxzYn\n0VNAGAIaaFkf5KbISQRPiKiOt0ZCvvd2S0N3/BuS4lZb2fdWQgWFXADQ/t0+p/r5ENtTuwYFkdOM\nWQ1LA6jpuolEOukKupS0pOVmKgRZjrylZ3tmEoUQDZeKgqrkIdFECyej8JGjEJDQKuIzVTF2D8FQ\ndiS6xuC5BhbprZ1Ao+1f3T+x84YseA9WRkofsTFPiJmVqbu2Nba73IZd277/sLqmbG1d/Lm2z4Ku\nrnywXRhYz0oINeixjij0tfqcdV9EQHo/3+3ag0Y8cl3vK2N4nkQQ9xIIeO62ec/xYsJgrYlhCxYO\n85GETyZ0RBKyZofJJufDOOi4cjqzdm1qu1ZGES1CGr1RwHNdcScoVOPATRAziUAkrVOza/wZCcKc\neQPMON/uqZAKIV7VN1gSsZqVlJlBalcoMamtCagiKxB7NxE4kQCsQ4/pPO2EfEPnHOi6fQ6hkzQH\nxnA6tEqsFRwu7bOtazJFvq9OwxAy2JxntQ7FsPfnKYm932MJj43pdc+1dNGvbszxrmDYBYGYPbbL\nag93kU1TdbUAK6LQ+4ESLvdxpB26B7Lzks6zCqXUagg2FHkyc9IkJHg+kdYmvOrHZj586HjBTkcL\ny9kdObJJRBbLeNXPI7y65YStEqphnibGys8Xk5cUbwIpgQL91Dn4azQKILd0iah5E4QEQQGVThqp\njk3Q+jc03M0S4ys6ny2kR7wx45aVuRHvHWzs5iYB5jmYWpA3GqxOyfViMM07W5XwUfNY2MqZ7S4X\noDU8gLlqYG5Er1lB/xIOfBzo9S0fkQRsIR2aTKjU51jU9NvF28nY64RNIMQqjkQFCTcNX4xWt9Y0\nsh/vs+9Dm77QVordf8L6121B785cX7o/fQurfjNKqd2v6bbG2MwbtGKT41RLJCGhYTI/xaHFiYiR\nsHWt7GsnFQoyucnO3kJzYYB39VuhAhg2lhTL9TYIVQgii9BatBNG0A4MAJioEmkAq31xefDaHt7J\nUAVfrO4vbMteDjnbUmuvkINRSE5+iPxb+QROoeHqxOStTetZTOgHbVNCNBbsAXAUgzbH8R7MMLGy\nyxFSQzrPn2VeGcHYaJsJYrp2kWpeN2R+yOzhPpGoK1EG2QtgWML7BH1e12hZ2WW8u0DYzToxRa+x\nTEadqKNGJUyKKoWkorRF0dn2uTbq4pnw2P94V7tLmBepI4Wq4oIrtkjKzvASbPy9GF+v9oMXAgiL\n7qzMtVeG4oeOFxMGt8cF9wVn1Z2PjCbUrEIW4gCBOBdweAkDPfvwJj+EYblhxNZAY7PB0rkQRawB\nAQZdkHqbi9ouBRG0VexXW17mgL5OyA/6EoKtcNw7tGRkc+M92SYc5Gu39IYRdaRvpLSfdc6FnEYd\nkbk3E6xc/yCRrFJ/XSlH5AGjf2Yj9ogaoa6AmFKtBeIrmQkSTsoyBNREMV+PQlZabqEWCQqxZpQW\nEwPUO3cpYvux/yXYLiHotr/XG1jUsPkh8n2i0BIIfYJdO+u6YvQSSM8QSHn5674MBSsR9CHl9bSn\nuxbfTQqlKevZYsVGI0IU2zkCuJ03jFFZDh88XkwY/Lk/NvDlZ2+x3n6OHz0NfHn9GOenP8Enn7/B\nd37++/j8Rz/GH/oDP4PP5sAnXz7C5xVxGTjxEQ4E1jJMm/AL4HEAkSPBzQYcJyKcjTCdzHJDUpyx\nWKiltOgje4SsdJAttr8mweZXcxMV407UwtCmtGksRHhCcpAw1qJ3PorhcjOzKnNVXoXDyfSyB9uO\ntSLEIh7IMRlAoacc+BqRcWnlY8damE8nHo5rNluNZGxlWsoPEJWnQIYvc6CbjlS1ZXBK8Za45ZFm\nXjk32TzR2O1DFpvsaScSsAhMBIankJu7T8ENsRKvhBLVINJecKVUwxBuvIdVgmBVaTsbohjXBlaC\no0al8X7uGBAtqNCgodBJVoOimLs0ic7BH2vpDyEToZMKit6JuYr4tNxIG8qy0UxCx7zeiknJnApz\nzYXwwOeffobzPPH28RG3m4bfvf94MWHwx78NfPLqAb/zj38b9ru/gz/75/5F4Hc+xyfnd/Dqo0fE\nH/0GXn/0Cj/+wY/x9P3XePvmht//c9/C3/1/foLf/vFneP1wwF59D//4d38XX/z4d/FLv/hH8Y8+\nd+B4wMUdH10W/PBkTDMsGxjHwDDHOSfWPGGxMM0RbrA4U/MMB2wgYmG6HIUE7NpMo0BIRweWsAY3\ny86FOLKEOAl/YoUnobOTzjCHhSM8m2qZGWK2XShveAr3CcwMs4apbFXMwoYg7O8gxiyEsUFHm4GY\n2UhDxk9ezjfB6FhrYgI4RqMpnT8VZ0dVAsQ5kTA/rIuLsj4CaQ4VSusQa1hgLpoEnnsVU4I3qJFV\ny5Hm5AKwhuHAwBk5in5Glve6EGIA7oMMTnZekzkcAzmZMg8NiI1YOMYF5o7sl8KZCGNQCAXLpY0d\nlqxQIGxlyjSlPLMVEGtx7qLX58X26mGY58r7nGsi1sI6W7OvWHi63bhWC/N2w/n4BIfhnHndc07M\neSIW8MPf+QHmWljnic8//wKffvIJvvXt7+DX/t7f/1qefDFhcLwaOP/+38Uf+tlv4ydvP8XH1wPz\nYeDy3W/gkx9+gu///PcQBnz56sDP/v7v4PqN78Ke3uBPnobx5kf403/mz+C7Dx/jGL+AH/7W38ff\n/bXfwL/yz/8Z/No/+CF+4zPg6bO3+OTpAW/OwHH7DNcx8Plnn8Jvn+LbP/MHML/5s8DlFV49DPgx\ncJ7AeTOcYTjjEcMmcA4ACwNRQzzNDZOowN0Ra2JMMsXhiHXLOQS3gRWOsCxDdkw8+SoBcVr2DjiW\npaaXoGc4Mk5qzaUpTI7TTjZ4sjSdLAWFD0Ow1gMAECfDq8ncHobb7YZ5OzEuEzbsLt8/sDDGwLAk\nsDEOXM1xu03YccB8YK18SBG2jlKgljkNFpM9RhIFLGOINYKzMZ0a2xJtRGD5wjonLscB6ca1FswH\njuOKwdbLcS6c54SNQf+JENPE4QdGQrTU1hZwPxIxeZ7rqoS1i2POBbPAeTtxuz3hAsOnP/5dwAwf\nPbzG4/mEx8dHfPLpp7hcrzjnicenRzw+PuKb3/wYWIE5J87bDWsmOrleLni4PlTG5xeff4HzPBED\nePP0iMfHJzy+fYtzLpy3ZODb7Ybb+YTb7YZzTUTkWlikcGCFNmKeNDDp+4GlA3qlOaqR7BMpILIN\nfa7vb/yDf4jLOHDOu6bq7xz2T5qd9P/lYWbxf/5XfxHrt34Lf+8f/xi/9Au/D69/8ZfxxQ9+A5eP\nvptE/vYN5u0Njm99G1/85m/i+s3v4dW3v4llwNPMmcOXAC4ffRtPP/4JPn/7Br/v+7+AmDdcXr/C\n0wp8/sUNuHyMH/34x/jBb/5D/Ny3PsKPzoEf/ujH+PxHn+Pt6fgMA5/dvsQVjp/71nfg14/w9vgI\n9vHPAjZgthDjgCNRRTCHIb33KfXLmZUrij17cU76FoZlqS2TYBQhXGHsCsRMMxsZ2pIT0Tg7YEuA\nGrQRxxh00qkyDrg93XC73fDxxx8lIQI4jgNPT09Ya+HVwwWPb9/kRizg+nDB649e49PPPsPT0yO+\n9Y1v4ic/+QRffvklvve97+GLLz7HmideXa84joFzZcvueU6M4YgznazH9cBxOXB5uMDHYLJY7TeA\nYLdjCQPQPPL6zJsvv8QYDh+eCCUmPvv0c2AFnp6e8M3vfBsP1wfMpyc8rRMeOXfRYZjnxFoLxziy\nKen5NsfULwBzYs6JH/zwh3Az3M5bJU/M88Tt6cTrywVffvkGYcAxRkZI1kxGHwOxggNwJnV7miVr\nzuyGZMBtnjhXdtMqB7ZlFyqcE09zIdgSXk7DtC+yacuiYE6nZ4ZU1lrlezmuSYM59DYAIx0cF1wv\nF8CAy/VaFaduhlevHtKUPQ6c5w3/2V/9q4jdCbIdL4YMhj3g4Q/9Ev6X//5v40/+yi/i+tG38dnT\nb+L1L/4M3K64nW9g5xMiHJ/77+A7v/AHgZiAH3g4Jl5h4OnNp3jz9gt88vQGv/D9n8Htzad4G1mo\ndFxe47vf+Q7cA09fOn7XA9+6DvzyH/9l+PUV/sb/8bfx9/7O/40/+8/+Cr77/T+JWAPnm0/w9u3E\nr/+jH2M6EOf5/zL3JrG2rul91+9tv241uz/dPbetulW3XI4dYzsJ2HLkAJEIAcEEMUEIIiEkmgES\nEp4AQbKY4AEEDAGMjBCZMQiKIqMYhGKiBNlO7LKd8vWtus1p99ndar/mbRm86xxbcbksYUXlNdp7\n7aW9dvO9z/c8/+6h309EMyN5z24c8UB0EwLY+4gPgeg9MZa7QRICg0QaSY6etm0Y3ISWGp/TwUsA\nWisiGS0llTFvxpEAZd7LRWvhXrfkh7EgxgxKEWMsi1xDKIItpchCMB06AGsMKQRiBiElwQekKnoO\nn0KZmbNAa41RGu88kNFKFmWnFDzVhhQ8gkPhIRNeKwoPF3vMiXRoh18PuK/NOeX1h7vRoUC+no3l\nQTvxeoyRooxbSmtS8uVnU2WkE5SlKiE6jDHU0uCCRyvF5CNKSkIIVHWFEZJ+6mmrCh8Sg480Sh3G\ntbKoNAJKSVI6RJ8JyTYFUkhA2V0olX7T/8R8WHiaMzEGUiy7HVM86GEOVS+lgtdoY0hSEl8zNFEQ\nsiQU1xVGGbQyGKuZtS1tVb+50RijqawphdXYgv8cxpWqMuVuf8B0Qopoqd6E0SIOCt7DeKhU+bsI\nMlmVsee7Pb5nxWB49ozt9S25v+Fbnz3nq0eP2PmR4wC2rcmDp799iT4+5+jRBXRzpNGoLEn9HWG/\nIynF6YN3OX0rM7meKLa0yzP8bo9VCWESQlVoLZA5sNlfc9SfcPdkzdOPP+b87Iy3Ls7IMvHxt36H\nrDt+4Af/BG/fWzGGAd/OWV/dwDTSXTzA9yOdUqjs6Y6W0B3x2RcvMHXNZrOmlpqsBUJZnj+9xMVE\nf/WSxcNTnlzegFT0biInWG32+JC4dv4wb4PVhpAKbqGkQGtFrSxScbiYBTlEtDEHhFgXY1FKRDcS\nMkw+IIUkpnSQFicUUBtTZveYaIREGnXIEcxIEVCyHCprFS5kQgj4yZW8gwzSB4Q6aBQSKKnegJdG\nCaxSKMThzsbvdgEcItikJPpSwF6zHLW2JCDkxDA4tpPn1e0NTVVRac3QO2TO1N2c1c0Nb791n3Hq\nebVe0diKm82W2lY4PzGfzfntT7/N6WyJPb3g5vaOKo58/Uvv8ve++W20Mex2Oxe91DAAACAASURB\nVGazlpwy4zQWwBaBpeBJUoBVhpQSe7cnA7aqCTnRtQ1t02KMBV0xuohSitOTE5RSSG3wEYRSaFMd\nAMXCs8SU0FVVsIdUnKRSgTbqDRgqpCQecIIYI947pnhgf1JGxkydC7gdYiKkUK6LlFCHoj668Q3o\nmmIAEbG2wg0TWhuE/O5TwB9aDIQQPwf8BeBVzvn7D8/9J8BfAq4OL/upnPPfPHztPwL+DQot/e/l\nnP+P7/R9vQGn4K3332M5q9mNG87PH3H77Anze/cQMSGswTYGMXtIGPdoNcOPG/xqzae/9ssslkse\nfigYtaGuOupuVgI5KsV4d4dbeUTO2AxvXRwxaxtUO4NXN3ztQcujr7xfLlwluWLGTFXMK8EqNlTU\n2PUldQOirkA4wvGCtL/lsyfP0M8+453332OR9gzXE48vzlicnBNC5uXzp/ypr5+jguAfPI388A9+\nP7/8q7/JWWOZHx1RK43panY+4dYropDskuLF50+xdcV2c8cew7TdstlPVPMljRH0Y+TzTz4h1zOO\nqo4+Dmy2I4vlEqkybj+Uu2sWhChRShIFhCgY8ghkFImYwfmElqq8Zkokmcq6cRfIKRJjKu0ooIQq\nzEzwCAExCWIqnYRRBZvoEQeZNIQUSDm/0UTEDDHB5D2zRUelLMWhFHHeM2XJbpre9BOTi8zaFjeN\npGSJ2xv+0r/8J/hv/sbHfG1RI6Pn1Try7vuPWV/d0hhLZwxfffw2Q0osGktQxwQEKyf46ocfYYzG\n2gptLFLpMopIRQiZJAVaKJTSUBlETLgMQ8wg1Rt8KBww45ihPbAF4wEfEIhD2KxkjBEEVNoUufZh\nxUwZBws2o9VhneCBOQoxlk3UUiGVwhpLjpEQC77io8fnREgRqSVWVKWj0kCIxJypbIPWihTDAXtK\nGKVo6obsSuf0RyoGwP8E/FfA//x7nsvAz+Scf+YfKRxfA/4V4GvAI+BvCSE+zL93/cvhYZtztnef\n80989AhlGqRpiPstzcmC/e013ekFup4z3K6Rbc32xSvmbz1k3K1oZyfc//LXqKsKUVuW7QVJC6bN\nHT71KKmxixPSfkdKmfmspT7s6Ms5M79/ga0lTTMvCLDIfHR/zqxp0ELQdR05TThvqdtTwjCR8Gyu\nntF2Le998D6by5f4/Y7b2zsUAltbNs+/hR/2jJNk+eF7vPjN3+JPf/0D6nmH8Y6TL7+HdD03zz/l\n/OKck9PH+NwjbMMH5w/4QK2YffD9XH3+CUeP3mZ69hmcnDGfXzDisVnwy7+Q+PDHfpRn3/wmTdvw\nySfP+Yl/9ifZ3N7xzW89Z9KW66sVn1yueP7ihhgcCy3Z+0TICiUjUgis1vgM292ASB6XBY2psGog\nSE0UGuEjj09aksyMUyYkDWQ6W9iHWVMzs6UFv96P7ENBwk0UtFZz3FScdDPuhom7wWGE4GTRIY3G\nasMQAxKFy+Wg1UajtUVri38NL5gaKxTPneQnfuKC1gi+LCsygtYYvAFQzGxVaE4pCKEc1IhAS8EU\nEolIipnJx0OUYpnzFQc6kQxaE2KiMpJTW2OlQMjyWill2aIUf1e0JIXA6AIyp5QIh7EjhlykClIQ\nU8THRIjpDdMQU8EevPO8VgjGA64AkGJEaoUPnkpbXAgFq0gF3I2h0OQxlf+l874UE6VIORK9P5Be\nBRgmRuqu5na1+qMVg5zz3xZCvPsdvvSdBpB/EfhrOWcPfCaE+AT4UeDv/r5isL+ld7e8U11g5jVh\nvCYJGFc7ZkcPGTcrop/oFh0oxfLslBglFZpZTmRtcVkxRonf3mJqhd/dopf34VD9VZzo2gXbm5cM\nw552PmdmKrLUKN3gNyvu7m4wWnFqE7OjJbvbl+R2gakM+9sbbDJUdc243nNydoQ0muQF9x484PrF\nF7z1/rtUdc2w2aN1jTltaI3ld37r1zBuS57eZnu15cN3HqC1REbopMXoDrKjao7wRqK05rNXK77v\nKxXKVEhR8ennX/B9b3+NHDzn58esVzf88J/7p9lvnoMQvP2lr/Lgg6/gx8i8a/jxH/9TDDfPUF9/\nj8FvaWb3eHLb89knn/KtLy759OUdc6V5uOw4nneMIbNOGaU1C62pBHSNobKaoe8xKjOzmkYXDGu2\naLFVw27nud1NDAFQFUMSvJUSY/C4UDKs9j6wi/CF1qTZ64xGyQshEbpgJEooKqvQRmGUxihByJmY\nE1praqmo2gorE0lrHqlIdBmXCx3Xx0DCEKYRFwWjC1TWghQEH0BJYoosqopaV1SNxWgDIqG1REuB\nd46mqgjRE3xACYXziU0/ECX03qFQjH5k8h6fElpXSKkYXzMELkAGHxxZFCGdPBwtgcRUmhg93rs3\nxyb4wDQ5hBR0bUs8FAIlJSGmN4t4D1AAQkBwnrZpGN3IOEzMFnOmaaSpa/zk3ojb2rah3+2BiDWW\nfnKYAJth+qMVg+/y+HeFEP8a8MvAf5BzXgEP/5GD/5TSIfy+h3nwgA/DD6AXHWG/wSzvo0jo5QXT\n+hlaWezRKcN2RfYJKTTGOGgtdzfPC4UXJciAPLmPHwI+CdL6CjNboExLXS1JUTE/vsdicQRoJrdD\n6oq6m2OOj3nwtR9i3G+Z+lumzTXCdixPH5CGFRfvfYWgGoTzLL/8FjFl/LAnKYX0E2dKYpoapgnP\nSF03CN2iz464/Px3OH38IaK7oIobxHGHjBF5fIJJmuxX6PoRcbih7S5QUmH9xM03f5nZ8hwpI2eP\nHkLec/fiM+h+gMl58s4xjYlHb71Ne3QfkeCOK3JWbHZrpK2R7QwGRZCSr3z4Pl//+kdM+x37/Z6b\n2y1Pnz4Dn8jRE4aRYYyMAXYBXqwmYGJImdvBsRknhixxUjEFR5I7ktGkrCg34B0iOkIsCcXSGIyx\ndKZi2VRUHkKOxJzwIRKcQ4Y9ulKkULQdPhwCWI0ixsTMViitSFMELXBR4X1PazTaGIwwaC1R2mNp\nySqQtcZPA3VlwSekKqBpyBkRCw6TFYzDSFYKoyTTNGKsZbV3dLYqLIBW+Dwx9RNWWWpTEQ7AoxIS\npRRaj7zWP1RKE0JkdA6jLTkopFGYxhTwL2V2ux0CMMYSQkRrxWzesFhKnHMopRjTWChBpYhuKl0v\niRACCE0/7NFaMXiHQTFvWoZ+wBiDzNB2DcF5vHO4yTGOA/NZxzRNpBC4vb3FKPOPpRj8LPCXDx//\nZ8B/Afybf8BrvyNqoRP0+1tsu6R3GXv7kuN3voxWFhcW3N3doaZrbK0gJCqbyCkT/ETo1+ijc6bL\nZ0yra47PHiCbChUU/u6OanFKTBMxAyGSDnE+4+6GED1aTYg6kL3EeU/KmpwlQlfkBP12dbArK/y0\nJ417YizUnHcjzbxjdfkc2S6QMTO9ekGWjnR8gYged3vFxfk9js7uMa6fs98PLOcLgl+RfGRmBauX\nV+TmFKk17sUX5PMHBCIxG67u9rD6hPb8bb74xjdoTu4T+gli5jd+9Zf4YgN/8Z/7SbbrnuB36GpB\n9BOiNlhlCWGP3+7wfiJs95jZcUlKFpKj5RGL2YxpHPGTY70b+LWna/6vT9d8sRqKCEiWO5ShRmmB\n0BktDaYyGAFaS4zR5BhJUZLRRdiVEjJltBJERpLzOCGIMaGUoZYC0VaEIIghYKxB5kxnK+KhBVay\noPwiS2RtkEZDv+do3jBkydjvmS0NPkV8PzJqSaUl0zRgpWIYyjo0ETIyJ3yCTKJShqkfaOqOEDNW\naoQpY8+9hSVOHh89Rham6/h4SXATk3d01pY7c4yknImuMApKSQZZPAVaV0BCKUjesx1HEhllVNEe\nxMgwDOSc8V4wTQUf8aEAt1VVE2LEuYnK1rgYidFjlUGksgtDC0UIkXQoSlVVIcgE78kH9ZQypci2\nTYN3vjAQTYMfJ3z8x6BAzDm/ev2xEOJ/AP73w6fPgMe/56VvHZ77fY+/8r/9n7jtHVl8xp9874wf\n/6GvYm3LfhpI45b54hjZdbjNFTNVE+Yz8JJ5u2Q/DNjjR+R+IHvPTErM8T18pdgOW9qze4Tdhqnf\nE9xEymCqDi0qhFL4aWKSkdzVGKAyhojF0hBcRPqelCbu9j3t7AjTtqQQqdoW3bWEzS2m6jBKIaqa\n5uE7BNdjFyeEcSBnx0Ja+rtLQr+Dowe8Wm1prKVrZuhasDx/RJKJrCVSHzFcfsa7H/0Q1XzGr/zq\nb/DDP/aTXH7y69T3H/Pqs2+zuP+Qqmr54Gs/xAdVizIV+75nMVviQ4AckFnjh4FMwClBWO/ZhsD0\n/CnUHaenF/gYiCGjtCIq6BYt/+T3tfyZL9/jk6st33ix4oubntvtSEQRpCZkiQsedVAABgdeHWzV\nQqK0whz8BVOOxCRoTV3EMQc9hDaiAJZCEQREqVDaEoIjx8h8NkdLXdKelEBnQR9GRIaumpFF5KLS\n+KqmaVuGzQY5P2G72zImOOo6Yk5YZdiOA5WxpCnSj45Z10BMWK0xShLTBFIwjnuqumXcT4wxkIXA\nGEVwE+uDEKi11UEHJfCTx4WINhqtS0sec0BkWYRCKuGCx5oKrTXBe5SA3WZNiKFQmlJT1xbnXQH7\nUiJ4z9D3CK1pmxofPWGaigiMTMiRLDIpeKy1RUsRAuvtlspaurah3/ekULAGJSTWGILzfPE7H3Pz\n6mVRWEr5nY7iH60YCCEe5JxfHD79l4BvHD7+68D/KoT4Gcp48GXg//1O3+Nf/ws/BnlkuLqlO54x\n7CZGe0d49RmLd76OnM8J6yuk1Hz867/KV/78X0TZIpVFGowIrMeRyjS8/O3fQJ2/QueyqmvzrX8I\n7RHDsMVWNXFcIbJDSI2pLJaIX71kch1icQEuI5sF0Qu8v0UYQ7QVMmpStvR9T//5bzO79xh9eoau\nO9rjhmnYoQEnZxiZsF2HNoa7J9/kF3/pV/iRH/6T2PP30EDPxOe//vf46p/750l1R7//Fk13is2J\naBv2k8TYiBCaH/mRP02/XXH0/vdhnWd5dsF0tWI7a5ifv4WNkudPPqNta9Y+gZLsxh4pepLWCJ/R\nzMnNDBkCQg74ybG9vsPH8h7JjaArVExMU8/W73lcz/mhjy7o3Zb1zTXXz5/z5HbNRte4kw94Js9J\nFNVk8B4hy13ptW5fxMjZfIH3/uA7MAclYREd6YNZppKWrEvrHg9uzM1uizKKxlRM2x4hJUZrqspA\njviccX5iHAdIAVTR4z++uIcUkil6Xlxe0rVtGTelYEyZR+cnbPsB5ya6rqVSkka37PxIN+uotEbM\nGupxoNEGpGKhZ4XiGyei5A0IlypF3VUooVjvduyHka5tsVogtGDyCY0huYiPnrquUVKzH1YoKVge\nLTFS4p3n+OQM7x3JJJwfSaSi+DwwF04JBu9wo+d4vqCrK+5ubjk9PmG/21NXFQ+6Bt+PhGlECxhz\nQmeB845+7Lk4O+WxfIfz+w+Yz1ta3fCbv/b3/+Bz/YcpEIUQfw34CeAMuAT+Y+DPAj9IGQE+Bf6t\nnPPl4fU/RaEWA/Dv55x/4Tt8z/x3/pefLuosLUnrNYgJKzQ+SSY3UlcS7wMSSYwZWVforiM6X/IM\npgE7riAXTrx68CWS74kIwt017YNH1O0RQlucC/hpj65m5f38HvyItg2BTEoWVXWoSiEwODchlMXY\niuBGpNS4YYPAYaUAoYp4jITUhphgGPaoYU/IgbadYapjbi+fYboFdVORmgYx9Wz3a2bNCcpWhDAw\nxcTqs084e/fLiDixvr6iO7+HzBLTdnhhkEKxffo5upJMLrIdd7R2UdSKRjANI4qDas4HRu/wo0MZ\nXQpbCNRNS/C+cGOiiGf6YU/TLbBNDUIy7nq6pgHhCW7i+tU1w901bQo4qdi2D7gUp7h6Tnd8VNRw\nKeFCZHKecSxy3RgjMYTSEodQlIq5RKAZpVBSkTJYa0ghFmGRKnRbdL54E4pEsXQb0whCMOtaRCxK\nwJgiWQpmtmKcJrQ1KKWwxiByZhwn5rMZq826qBaJ/O6yalmKitYYqYu/wXvG5FFaE0I6KPgyRll8\n8lilisRbKrQ2bIce76YS3ydKmz9rakZf2nGNJMSAEKIUshDQB/FXCqFoKaqqFEElWc7mjOPIfhiY\n1w1KSAbvSDkTfAEa67rBOQchMowjpjKknJl1NUob8JHJTaWQ5YyWgtoabF3hd3uigJ/72f/y/78C\nMef8r36Hp3/uu7z+p4Gf/sO+b7EiR7JLpG6OcBW77BFTaadjBq0txImmm5Palv00UnWnKCuRURL7\nBWwukWksM32MyCTANhgMzg0EF9D1Emu7N9t+le7IYsXm5jnYOd3RMTFFokukFNDKInzExxFZKXLM\npf1Sc7JSZd6Vimm3RcZE9hPS1IRaY7s5qoLt1XOahw9YPX+GtBfUIdMniVUz+mmHEbC92yHjjou3\nv8Tm1Qtsu8BnxeZ2hR48k7UkP6J1i7Fw/WqPUBIlDDebG7TShCnjgmPyjkpKkpJv/A0mRVolDwYt\nqGqL0gqXE4RAUzdUShODYxq2iO2W7fWID57r9RpNBhQ7IcmyIaLQIjFOE7u7NbW1hT4LkTAWVWZK\nRXOvtGKaJqAUgbZu2A97fIwEH2jrhrEfUFpijcYFX+bxGBnGHqEt0XtSitw/O+PlzWWRVkvD8XLG\nfr9nco6Nc0ityYeW3Jqal5dXzNuWcRwYhv3rBcZIUYrQFEZkgqN2zujL4RGVRrtSBAIJnzxVZdm7\nkXnb4J0jhERMESVkYTtmM2IICCEJwTP05fdDCoTVxV8CaGsKgDo4jNV0XemewkFFmcn0ff8mpXk9\n7pnGCWMM87ZDqRqpBHNbM1nDbhioJIgEi1mL9yNjHJjpitt+X7qjg5x6sxtht+HR2Tku/TF1Laa0\nw+gF0WosgpASlWlQZoEjY8M1UtSEKRPCRNqNWBex7UWpgELA/IgpeCq1pQLEyQWTy1Qi4Y1GJQCD\nVGWmQyqkLXZa2Z3x8W98zuUnf5s/80/9KEcP3yfkUOS4cURIjZ9GZARV18hsmMaAriRWG1KC5cV9\npv0GF0eEn6hrQ86eGCzV8hHbJ58xu3iLfb/m+nIF0uFHj9IVkT0aRZ0bnq0ukabl5e0NCkcMpTUd\nxi1WVNxsv4U1NZW0xf1XGwiOoDVNY1mIiihqfI7Yqi2z6cGE1FQV3m3ZrG/Y70ZscFTWkIQDbbma\nHNc3N5xXhrvJY3TDrJ7T1ccM44hPiZgFG33CZSiYQ9tarKwYnWe33x8cdpGqrqmMOfDdiRgjxhi8\nc7x48QJtDYv5jO2wZz8OkBNdXTP2iX4cQUBTV7RtSz8WwLapG65vb6iahnEcOTmZk4Xk6uaW09Nj\n+mGg3+x459FDpFJ88q1PaeYzhnGk6zrqpmO922OULgEuUmIqi+gq1rsNImcm52mbiuN2htCaM21Y\n7TdYJF4blNJv3KV105Fi5vbmFqM1Vkq6ukFkwbPVLVppRMqYxnK6WKKFYL3ZkHLiZLkge0/sR5qm\n4bbfFmqxqkkhYCrLcTuDlFi7gf1+D4BViv008PTyBY/uP+SsmzGlSBKw3q05r2ccLxZ8+uo5tbZ0\nbcsUJoZ9z4OLe+zGPa+ur7j3+DsSe28e3zOj0i/+zL9DbWrkfEnWVfHPuwEXB2bH98locp7QyjD1\ne4TUUFm0c0RdIUVCSYGUlu31c1Qa8Bi680eluoeIqevifhevjSUSoRQ5R6RUPLu84W/8wi/y7v0j\n/oU//2fpXfEokg6KPG2I7pA5EF2hxbJAWkvKkX6z5frlM9K4ozk+JwiJyoKUxBv76ugmNqsVi8WC\nKShOFhXD6PAUO9roHI0UuBDJylDlIjZpFzXBWOK45dkXK770/e/TKksWmuxdUZ4hGNwOIxQWhe5m\nJFWC1Xw/kP3Efr+jkkUYlHJktdlxvV7zcF6hyYzDSN3MmELGx8g0FXdeyJ4pKaKd0esT5PE9bveO\n7RQ5PjkpLX2KxHTwPqRAP/Q4H5icJ6XEOI5UVVV4cOcIKRUWImec86VYSFnGHQXGaKZxOJh0JFVV\nsdttefzgPvtpZOxHZIYpBeZdR4oJYTTZebTRDONQZLk+0NlCo603O3RVMex3NFVdvBLJU7cNMsKY\nAvNujgiBKfgS5nLoZJTRaCFRRpHDa7twIuaMTxGrDU1VMUV/+D9W+BCYKCNBLSQagbIGoRXrzQZE\npq5qQs40xiIz3G5XGK2p64bdbosbJo7m8+La7QdsVVEpxeAmVIIxFL1CVVtmbUsms7q7oWkaRM5s\n9z2trRjcBBLun5ySg2c39vzVv/IHjwnfs2Lwd37+PyUMAyomxGKJMC1+e8fkBTM1YI4elDuOlChj\nkd7hRURISw4Rpcvz2XkwhhRGCIewCa0Rpi7pLkIf/Oqh+OMP3nlx2PrbbzbEfk3V1dh2werVK/zQ\nU3UzXMpIWQ6+G8Yyz+byzwVZLME5krICXebn6+sbTHJFFZYVs7piN0WsFbgoSg7DgbpbHJ+grEEb\nw+b2FiUVs1rzbLXmwfwYM6+5ub3i1Ys1P/ijP4CfUqGHkifHVJxoLhC8IyZPnkaSdzx58pT58REP\nLs548uQ5fnKcnB+zXq2Z+pHeeR4sagIaFxPD6CAHYpQMkyMg8aplL1rG6gjRHaGMoWpbjLWUvIXi\nyFSiOBl7N+FCEd/EEOiHAe89Qhb7dHIBoRVNXZFiwoVAXVVIBLvdjqap6ZoKrSTb/UCIEXkw5+iY\nGGNivV7z3jvvsN1tCg4QPGOO+PWO2fES5x1KKryPkCP7vqft5mit2O12dG2ND5GT2Yzb3Raryzy9\n3e4wlSnzf0okWQrqvOkYvQMpcG6CXPwjRmucL5kCVhXX5+RdSet6vcsCwIWiDYiewbvyfkoVD0LM\nDMNAEJFF0zKrG6YQSGQ0RX9QjF+ZedfRuwmjNavVmu1uy3K+AJGZNS3TMKIrjRElH6EfJza7HXVV\n4f1UfCmqGLV+/r/7b//4uRa9j8h2RtjeIUbHb/3Kb/LwSPLgw+/HpZphv6dZFq9BjoGkFDhIsUc1\nLT4ndAJhatLkUNIQRCxS1uCRwiGoSxKMTAhpYHIoW1DocRy4/OLb5CQ5efCIT58+x/hn7PqhvN/w\nBDtf8ODRY9JhzZA+OOvcVJx8hsx+mvCDwxqN0hI/7cjS8I1PX3H/bMny7JSLi5aKTHaBJDIx+eLd\nj55WanJbcXH8LnnwxOi5X9WIWByRJ92Sxbsd42ZFXdWgJC4JJJE8jMiUif0ekzOjc4zO8+R6y0dn\np/gYePDoLabdmqoS7JXg4Vv3efHiFde7CauLacYfcgJ6F7nR99H3v4SsG3zM1FqiVSYCPmfi5A6m\nqUNyj3DEEBiGkZAKs6EFNNZCTiQfWDQNsSnW89V2w/2TU1pq+n4oklqZcW7C9SPKSEzT0NkGkRMv\nb684Pznh4dkZOpWgj7NZx+QC3351TdM2iMpyvdnwzvkF+31Pu2ixQrJabxE5MvYelyKdUsy05Wa1\nYecnjBy4P7/g4qjjdj+y6ydOlh1WmCJd9wHnPV3bkm3GTwUMjSEgkOzGkZh6zDRQGUWlNEoqQozk\nLBiChyFS1zWNKerHkGKJz8uZ05MThnHkbrPiZrPm/Oik5G6MIzInZm2LD4EQE37yTPuBRdMSD+E8\ns3lH21UMuy0xCa6ur2m7GT5Ejo+PyJNnuWh58fwFX3rnXfpp+K5n8nvWGfzSz/2HaHNK6FdEVfPp\n0xcc71ecvvcWzeIeDkEKPUrXyKZBZknWsYB5gMTgSUiliXFCiUxWDUoKjIyk0ZOlKtZbqUlIYgyM\nQ19CMqYJHycEhbeVVUWYJoQfkboh5sjt1SXTbkvbNJj5EVFIpPc0VQE4P3v+nMYabjd7KiFZnHR0\n9QKpy6gws5mnL16x6BpOLi5wGUIIxXaq1CHUNKIx2LYhWl3irGIkxYiPkRwDwXvqqmIcehpjqWZF\nhiriwXrkekIYQVq0FWyurlltdhzNNSTNq6tLVncbqkZSyQrbzAnJweTYTIkJTS/nPBHnNPfeKilR\nosyqAknMiZBK8lCIxXRT/PdF0JNiQArFME2InNFGI3Jmtb4jKl24cVmUdVVdc3e3RpGp6gptFDkm\nalvjc2az3WAErPdbjo5PqLUpnV2MuGlikzwn1Zzj0yM+/uxbfPDgLW53W46bOVklru5WTLuRXGuM\nMpzPZ+ymMl4c3N8YbRiGQKMU3cwQlGImJT56pjEyBYeqK0SW3GzW1FbTGkuIgvWwpbU1QmmUApkj\nyWd88EWgVDUsZjPWfiq5BCkTXjs5DwlXvRuYnEMjqeqmZA7Ict9Kk+N6u2UaRx6enyFipqotQcB+\nt+Wm39FVLfePjrm8fMnyeIlIUBuLrS23N7d0s4679R3Hx0dUWfL06gWtrfn82RP+1t/463/8xoT/\n+2d/CmMbpBX4fkKISJoiIeyhnbO63vD43fdIUiF0QdCVEARVIspwHlG1+GFEaI1UB024qggpEaeR\nOI7E6EFpgpsI3hNCQCmDEpqMJGaPDxMkweg9Wig6WzGEgRgC1tZMfdGgx0NackiJtq6xxrIeJuZt\nQ9NUTDFgDpui6qph12/4+//wUxazY776wUOak6NDjs8hzJRyey1xfcVko7Q5GGcOacmi5PdFXxyY\nOQVAoYXAk1A5lKwCXYC74Abcbs8nn3zCk6eXfPXxMTe9RuKxKrF3mU4V49I2amJ1xIoZG9lgZwu6\ntsPoQqsVvQAIqfAh4EPk9U4Bqw1Sq4ORJ7Hb7okpUlt7kB4f2B1hCsJOIsaArSrqqmEc9mWLs9SM\nzhULcSpApK1r+n5fJNraYIxmt+vLKBYiMkbqecPddkNKkmXXIoSkaSyruw1ZFCu2VaocpJC4W21w\nk6OuNMIohLD0buT+fIbUhn0/kmKkthWTm0hKMG/b8rcPge0wsJh3dEpxu93TdS2jLxSfVYCQJCmY\nvKNtmiKb9gnvAhOR5By1LvjClMLBICVLboEy7HYbFm1HSol+cgglCqUafPbAAwAAIABJREFUEnVl\nsW2DGyeqLPCi5FdM+z1H8yW3uwJQ1pVGVyX2T6XEfuipjQUJcQp4Ij//V3/2j9+YEKZA1Si8kEgd\niAHkbEZFh9vfcHR2xN3LJ8yPz9Ay45uOJA1WtWSpiJVERkddWaYwgaogBHbbK4IL3F1fsTw5Ztis\nCINjPzpubzegFPN5w/lihg8BFyLtbE5VV9gsWN3c8rIfOD894WY/0q9ecr7oqNqG2/WEi5mjWbn4\nkszMaoMk4cOEshZjiqPOC0G9POWf+Ylznj99yXa7w1qFsDVojVK6pP1IWaStWZKiJ/kSviGkQhlT\nsAGrD9SmJHmNzAmUQMcSEuJHx269IYSeqR/5/Pk1DxcNz9oFxliOW0HOkv3oGULilWu5MyeMsyWm\n6uhmDSciFbo0Ftedz5nB54LNxIQ/eOtjTggpQUsiiXHfI4Sg7ZpyIe93pFx+n5wy22GF1gZ5UB82\ntiITUUYz7HsyxSBUa42sSpvthhFzUN9pZQoWIQptqlNmihn2E+eLU7bbHT4nZrZmtdqwGQZEhnfm\n91n1W6aQWHY1690WgcAc7vILW3N+NEfHxF0/spzPuNusiN5ztFyABD8NLGczYq6YzTo22y3Pbu6I\n+XdThLrljBQjzgVmdYPq5kyxYCZJahCw6BqkqBhGz7GdM04O5zyKgm9ZrUr3lRP73RZlDEezI+Ry\nyeQcRpQciT7siAmskqScMNpws12z73vmdUNXtQzTQNaawXtm7Zy77Ro/TXzw+DEvX736rmfye1YM\n6sWcab3GzBYEHxGNJfuRMCVErNhtrtl6xfFsj7Iz0mZNOjlnN2zQpsYoQU+iTpmYAvurO7Z3O1ZX\nG45OOo6Oj9heXTP2A7ayxfhhW+p2xtuP76EUrFcbXl0+Q4+RRw/v0aKZRk/ddkRpuH9keSUU18PI\nxULxta+8h06C3TTivcNNI01tcJNjGjJ1neFIYXVFTIGYBfsR5mfn/IOPP+fmdsVHX/vKwUpdvBZK\nlsBNpEAqi0i/a7FNB4/769iuqjJ4BLhAHCeGuzXXn3+GmLV8+9kNj+YtoqkwWnG1WvHle2ds+g2D\nKxfa1im+4U6wp+/SLZccGUUWobACATy+vLcp6r9a2SJfztA0TQnlyhyWuiRi8IfQ0dLeK6CtLTEm\nVv0ehOT+vXtkKbi+uUXXNXs3ldyimOhmc3w/oEwiUopRDIFNPxSBTwhsdztOT0+pjOBsPudus+Ko\nm7PNjjxMRCLHtuPJ8xe8+/Yj2tmMfthQtRVy3BFSZH1zy9nJkoBkXrWcLma8urtB9Bt2EY7rGX4M\nKFNjDOw2K6KUTD5wvd1jKSEnQhsu7t8jTp62NkyTY7PZII3GHbIMjxcL6qamsjU7N7Lerah2jrkt\nxeD6do3WCnlYtGKjYchwMl8ijaIfB2ZNQw6e9bana1rOlg2r/Q6rBE4KImC1YsiOeddyerQkkpnG\nge1+i4iZetaihYQQ+ej9L/HZiyfYxn7XM/k9GxP+n5//y8TdSFKZqpkRw2FBRmXw2x3by+c0bcvH\n3/yCD750n37Vc+9rH9Hailw1jJPj7vaO8a7Qdq8uX/DFk0vaSnN2dszJyTHX6xUvbrYsq4qL8xOk\n0ex3PVopQvTc3K7I0nJ0fELX1eWHyxkjwGpJEqoYfEIJ+8hSEGM5PFJprK3RB7NLOCjs6sqA0OQ0\nISIIo7F1S10ZblcbyIl2Nn+94b143N+Ek5ZEIKnkIRIbNOLNQtHgigMt9nuic7x6fsmLzZpWSbQy\nhKnHikREshsnKq24GwIOyzY1fBFnqOU5praoLA7bnCRWSUxd471H5Yw0urj/KOh0Ofup6OtTKpLw\nFMtaPAQpRbSWWKOw1jCMjugjSmp2+x1CFeqxqWvcFHB+orYWYiIIaKoKLRX77RZU6ZaIgQen59zu\nVogk0E3Fq6fPObr3gO3mhkcPHjLuekxd0W93CGuRMTJrG/bbHUkJTpqWz66uUcrQVZausvgcmUIA\nIQjDyOnZGf20Zxh6lvMllam526yxooSu9jEgUsZaxWbX09R1sT7nSKs1o4tMIVHpomzStSX6QJgC\nTVOj6jL2qZjwIeN9KB1FY1nttoyjw40Tnsx81tFoU2LPfenAeu9Ytg3jNKCMZbPdvukkGlvR78s4\npY0uEe4HZaZGsNntyWQabUucf8z8j//9f/3Hb0yYXMAoiwgFLwjjhNYaWdVIlTh6/D7D5ROWRw11\nZbllw9XTJ8yUJDYLxnEgDB5qxTT1GCk5PVuQk2A7RuY+0JiG+yeCy5s7pqeBB+enzGcdV6sV3/z0\nOeMUeXx+gpagZWmByYkYMwOxZCS4kaeXd/SDx0jBvFaMKbDajYisePvBBSenR9RGkw4yXKFASosw\nRVfvnMPHhK0PphcpyC4QCEhpDtr9giCV4pxQouwCiCmiXMC7id31KxCZ0YEWib13XBzNGfqeFCYG\n53G2InjHmARjsqxzxSdDgzh6hKws2lZkipJOH3YrBMD3e9xU7LTKF1uuVOUCl7KkCZdHQT1e71GQ\nBxdjZTVZSIb9hEue2WzGsB9p2hpHJg4R7yaMUhhTQ8hko2i0JMfIfnRgFLXWTN7hBbjkEcGxrJe8\n6ndUXcPNesNbJ6domZlEZNxsmFWGMSTaWcfcaj55vuVkseBmGLg4WeJc4Hq1wpycFwejsaScODpe\n0iDpk2E/OirjWa/WLOYzJleA3tZI2qpBypJdOHjPfhjIIqMp+QtCakJOxBAIUhC8ozY1CAjjhGnq\nkh15ULIPfiDGCbLAVsVQJEJgHAdMnUGL4jWRAmsrVvs9+jBanZ/MAcE0OpTSLJcLcoZpHHDDxOQ9\np6dHWKOprGXqRwY34Xykberveia/d1HpOZPzDlk19NseqRomdnS5RndLsvfsXGSfYXO9IibJs+tb\n3LDjq2+/RVUvkc7jXWKYdkx9j3OBi7MjVN3gvMNqyxQromh4tdugW4Odevr1ng/u3+M3n97y/HrN\n8VFL11qUVaSQEUGWCKwpY0TFh2+/xWac+Lvffs5vfONjPjqb82M/8nW2rnBsKUSm5EBIfEyoGKnb\njrq2+MkVQ4r3GG3ItiD0vt/hcsaIRDUvij6nR2Qs8VlhGIh+Tw7gtyu8d2x3IwEB2bGcn3BWa/os\nIUkGF5B2Xi7aqiJpGLzkk41j186pYyL3Ayl4pIZKK8axjAW6qjDWMF8uD8nLpUvT+rDj6ZDUHA9x\nXmXRiaSyBS8hxrLHICUqDfud4269RslIq2uWTcvdIYZdizIC9cFhlUZZCXju/IaH846cSieUZUWa\nIttdJqYBKyW91Hx0cUROin7vmRyczmrwAlNnjuY1u7sbvnr/Ids4setXbEfF+WzBg+UcR6DTFTf7\nDceLOUTB5XZNN7M8XJ4zO2m4fO64vL7luK2xtqUfI1s8UxgQQjMNIyEKKmt4cH6GlJLNfsveOXxS\nnDY1wWk+v77CCs29o2PCNKKsBUroyqKy7HY7Rim5tzxidkhYatqGfr1FomA5hzGwXq/QRiPbilev\nNqg0MV+0nJ0cMw6eq9Ud52cn7FJkuZzTdE2JU58cg5+wTUWlDTtfYu++2+N7Nib8wn/+b/9/zL1Z\nrx1Zduf323NEnHPuzEsyh6qsUlWqSlBbliw03DZgP/rbGQb8kWzYMhpCN9BuqdAaqnImk+QdzhDD\nHv2w4t70g5V+sBtZ8ZJMMi/JvCf22mv913+g213SSuVUpQqPhxPeC3318f0j2q222U2hPVjdMZ4W\n+sHRiuJuv+f26prHcSanyuV2Q7PCy7cGVKvsj0eWrLg822C8MB0Px8OqXdd8eBhp2nBxseV8e/Z8\nAHxw6Jb45rt3vHn/yO3NJZ9//nM+7Cf+7b//Z37/7Vv+5PUZ//ov/4xuGEipQS1yUwa/Wn0/BWGs\nycxKU2pCK4VVsBQlTj99hzZaisa0kE570unAcZrpXEfKCasNi2qMx5nHD3ecXWz54v0DZ33AuY5p\nTDQDCkWKhbFU/ukR3rsrQn9BRjYExmhKSdQcSfPMdndGGHpBo71nGPo1D6KK7VitWCWod2ti2bXE\nRMlJSEJWc384ACL/3XS9WH2lgtWO42lcU50SORWC9zSl6JwAjBZHQXEYRwZjGLYd45Jwqq3mKWJj\n/uFxj26a892Ad4b9tNCrjrvxATs4XnQDd6eJszBQqMQcsdrjOo+zcgj3y8iLzZaqDQHF3WEEVdDO\noWvhcT/y6c0VX98/cBhHdrsd58MGZ8Epy5gXjuOMM1ayLGrDWYfxms46UizMKZNKxXpNZwwawxRn\nFApjDTknGhprDMYo6URTIuUqwGJrdN7RW8PhtFBao/eWzhvuT0dqKvTBshkG9scTXejQtXCcIsYo\nrIEQOkrJxGVBvMKln+s7x//8P/2Pf3xjwv44MsdMTYrDdCQM5xwe78E4XFPobiBYxZwWam6UYoCF\nzgeoGqUym16xLHvIhcMpchxPnO96nHMoZwleWtoPDweWVLi5OsM5gw+Bmgy1KV68GISRqBVYjeu8\nUFPHiQxsLi657c+I88y3X37HMAQ+uQ6odslnH92gQeysmhIMQD211HVdra1dhhJatPjxNpQVq2ya\nmIe2cSTnhenhnu+//obL6wtiVnhTqNow5gI50nDcz5VN0dycX7LERQhc1lKrrP/GpfF+KexrwLoe\nay0ti4RbWw3Ko7XB+4FaRTjkg9xcp3HCeSeOvEZMNHJjtdUS/626ZjammmnRMPiA9+JrmHNlmhPL\nNNJ1HednHeO8sMxtVakqvLEcT5PoGKy4EllT2fU9hcrlVpSgeV5oJpBzZrc9Y+M13z8eUbPGB1A2\nczPccFhmCopXVxccY+bth3tuthcsJAblOU4LF51HtZ6HceFsCCwVVMsMoed+OhCsxwVLNYrbm3Ou\n0haaYi6JeaoMthJrxaDJVdKXUlmYlwUzGVLfrZkGEILDG+mAjFHUnHg4jnRrB1afUpxKxWCpVXNK\nAkbfhsD94cSw61Etc5wSpTb240Swgd2ZZ38c+e7NO/q+g1pZUqLvLHNKaOuxRgGG0HVYbTgcDjjv\nmZb5R8/kT1YMChsm10EcOSZI40K/2Yh7LJqxRc5MIC6NsTackiSZaVFk2/A+sNld8ocv3vLt3cRn\nr8+5PutpqnEYj9wfNb33dJ3js5+/ImhH33tiXvjq99/x/nHk5atLPnr9mq4fULpS50iZTuSSePdw\n4s39iWWc+bM/+YTPP/8Z+9PE4/fv+LCfRDjUe4xeI1Ga5AOqVtArb0CtLXepGd30iglqrFVUa3Et\nsWAwx5G6/54vvn3P9eUAKlCmkcuLC/aPD2S/4zguLA/37K6u+ejFObkoWnN43RhjpKjKvCSW3Hic\nImPx7MKWbw970rSnKAfOCiZg3EoOEqPu1lhlxRLhpZKkG0mcmn6WxIpjkRC5yvp1yhmCtmhViElm\n0xQXfNejjGKeZs43W9T5BWOciPNMipWhG8itMeeK8YY+eBbg0gfmceawLKRauTkfiDnjvSXpxicv\nXvAP33wFfkPeH7i8CHxyseOwjHTWEGvj9dUV284zFYMzjXF/wtOYUgWVmRfNYUlcasNxWTjlzGa7\npSiN8gaTGme7Had5oSwS/HtaJs42A8dS2fYdNVcmGrbzGDTWGkwTU9JcxUNRK4MOoocZ+o5pWWhK\nsRl6TFX0PjDFkaIa55sNAGMu9JsBtOLm8pJhG8mlYFVPrywPy8Rxmbm4uqSVwpwl+GY7dDweTozT\nyOVuw/3dA9Z7Hu7v2Qw9Z+dn/OHLL3/0TP5kxWCeRpb5hG2F4/FAKne021eEGrFWwXGkXe747t0d\n3+4bF1tprc9DYDJwfr6l5cBu2/HaaIZNhwsWrSr/8Q/3/O//+I5f3pzz159/wquzHSVnlhxx3YbN\n7Q1f3P+BN2/v2HqPf6Xp+gE6xeF45P4wsdtt+cWvf8X7/Uh6+MDbb77FWc/28opf+A0mzgTvKVhM\nlai052CRklBqRdqVJB51wUOTuLJyfOBv/vbf8SeXHa9//VsO+/e8efM9X331lqtwzfbVS7754hs+\n9VsI59TjI8F4vhwjfngks6HmyHGZCEiqUqYJdjBHjilQt7f0w5abIfHuzVdoN6BUj1KG1oRSPJ4k\noKQ1sN6KB6CSiDJtFMF7Ce2cZ5x34re3gpo8BXcUIUEZ73Cmgc4E5+idZYqR704juTbO+0AHbIeB\njOKs6ziMB05L5tx7HkpG5cyHY8Roy7DdcowT4zKiFXSdp6+ZTd+zsY6PhisYKl8dHum9R6WGPneo\nOOOsYj8d2TjPtDRudmdsho5truQCTYs126YfUNOJm4tbvn8YaSlSTg7lHTkvBNuwyjNrxd0o/gxX\n51uO00ROCe8dm6Gn5EpcElrDduNZTiOD93TGsOSEBrqu4/ryAmsapjbG48IyF1CG4EGv8m3rHLlE\nHh8m3sTE2aanc4ZxmfjycWRz3vPR9SVWa+aYMM1jtZItmVIEYzidJhmvaZyf7xj6gbu7e7bb7Y+e\nyZ+sGNzf7Tn/+AV5nLk8v2ReImed4e5YOMwKawPHOXOxC/z+/Vv+17878epsy+3ljvPO41zF68h2\nt+HV7QUxTeRl4m4/kavlv/3Nz3h1teX1yxuR0WqF9x4XDL+8PacvH/GH7x95tx/ZbCeC78hVMezO\nGHZn8qKnxIvOwEevmZfI4/0DNiXONxvqxj/Hprc1UIQVI9Ba8gnVyjIzGLHsjgtff/8ONU28vjxj\n1ytMiUynEw3Hrz67pvgt7XDkNFYOpxPVdnz3bk8/dHz28prHKaOIGKvogqD+MVVSaUxLZc5gwoAd\nOjbX59jamHLl8O5rdM2S4uR7chWLcOeEhx9jlJVrbYKIlyQviJH8hZoLdQ1WbU2s4mpN5Fye49KK\nqqSUcDawtIa2lp+9uKSkQltza2sptAbH8QQozrtAZwxXfQ9F0VQhxcJ2cMxxpNTKNnSk08Sw3bKP\nM0PvGdOJXR/YmkpuCbQoC3MSTr9pYIKji41lHlmKpFBt1hRqHzyKRj+IM/LN1vMhwjhP6Jox/YDV\n4v9Iaby83MnquxZenG0ZY8ZrwWFmDVqJiUytmavNwFIK+2leRyGhXD8ej5ScsFrTFFKMamWeI85Y\nSmn0QdPZDl1FcdhbR3CWTOPlTcemC8zzSKmFZV4oteKTpu8Has5Yo+j6jsNjxDhhiKZlYbsZKP85\nPBD//3j+8HDiM2+4PN/Qq4pV4nIz7vfUCr/59AVff/+Of3qzZ4yNV9cXfP7xDSpYuibzrA8WqzWp\naEoNKDfQ73r+/FwRgke1QsuF6izaWVIqLEukKcXu/Iq/uLxak/4aaRnlNm8SU4ZzuD4QjyP779+R\nUashZWaaJvquRzv3bEiBkmRgCRrVwtJrkhgMmlQU42nki2/e8WmofPSrX1If7rh7+5ZWE+PpxJvl\nxKssstdjyjzcPRIuDduzc+K8MNdEF/yaD5iebPZRSvQZUy6MzWL6Ldp3tNborOXF7Q1LaaSHbyGO\nAia6Dq1k/hUcppDygjIW64IAoKXQtMSFrUny5CxZgw7JAFBacAWtAWVoJQNZ5lXVJIuxc3gvBiZO\nWZRRjDFiFKjUmFPGGY2y8mOlRANxe7GhzpX9PHNzcU5qkBexa9PBMcaMMo5NsBxypqZIyolCwhjF\nxls2O8/dA6SUhQOxzLy6vuL+/oBykny9CR0lZn5+e0WLmSUlgjfkmPDOkrMmp4p3jkYhp4JRsJQM\ntdEFh6NxTGKOEnMkNrjcbdHryjovkpJsvJfDXSUJOtaMs4paM8dplvd6DZb1WlFa4WGMKK1ZxomU\nIlZLyCta3JmtE9r13fsjSim2T2vfNcmq7zumaRRz2R95frJi8Ltv3vDdm/dcbzcMXnN7s2HOhQ+n\nkV9f7JiWyPup8u++eOR86/irX77guvdULeGa2mhOS+aUCs5ObJ0j6YwNMg8brUBZSmvyAinQJmCM\nw61ZhRJFAUo3MQNZwy7MmgMYx0bJmaI1b9/fUYswFbdnG9EO1IqpSg6/sc+sQQEN1+jNqpjjI3Wu\nbLdn/Hd/fcO7f/wnmA7MceLNmzu+HyNv7xbuHva8+OszHg7w17/+lFwrD/uJbfAklBiJloJqSmjP\nrUh6dI3iEVAqWVnxHmwNqiDIKMX+5oqRhjp8L9ZYtdB8QNuBnCPOOnTn0dYwzzNDCOQomw9tzco+\nbEBbHXkzQ99hgJQztWmcNWy3mt6Ls9LpMOJ6J8GoMTN4xyllOqXZWfkMTW9EM1IbccmMMXExDExp\nYVAdkxJDEor8efuo+fTqnLePHxi6M7RuoCxVG5o13F5fcFomLjcDUTumeUbrxnYzYJykRqV5xjqR\nwtcCmcrSMn6RhOTzzUBTmeACqTVM0yxK9vxkzfvDgcFafLAob0k5CzFLK5aY2O4CGxNQpTGnRcAV\nKsNgMBp6a4lZMedCU5ouOHZ94GwTeTxNQBPnqhBoDXZDj7WKxzLz0etbPuwfCdryyfCCcZ5IKXM6\nHtltt0hyE+x2G1JKHA5HDoe9dKnmj7QY/A9//im/e3/CWUffB2znKIeJf/PbT/nd1/f8zd/+A6e5\n8OtPLkQvnjNjkpjsh3mmFkm6dZ2j946LbU/oeoatYggdKIWyhhQX9vsRZwxnOwdV0bRC60a3puc2\nZdBVGHn5GSirpCUS50jKUTgDJZNipEaPXVOJa63UnCTU0uo1Gtw8Z+ZpCt98fYeOE+Fnt3TK8oc3\n79h8aLy+vSJ0PVdhy+0u8s9B8d33D/zVn/6KxyWxrGuqnEayVpQiQRq1NWoV4LI2SeTNtZGLQq+m\nHhZQq/qxt5rbjefNvOXu8EBNCyqLUYvJlawU2SdMslgr5KlkDE0ZWs44hUR/KYUyFlRDq0bOkao1\nymgxRMkaHzytVMaSMT5IvqJrEmxSGt5ZvDaM0yRS4ZpWpSkEJ5Rnrw1h2NCoXIeA2TimnChxZnvW\nsZxOvNheyEqvbdjPM59e79Zo8owOg0S3t0JVYk5zHEdccNKFZcN2EKC2axUXCy5LDF0sheO4UAFq\nZugCoXf4omkUGgXnDEtKLGnm/GwLaLRVbJxhmSI1FvzQOCbxhDRKLga7JlOXKmY7eUkYgCIp02Hw\nvOgsrVackc0ENBF00Xj98gXj6USJhX1ZCNZhtRJlbhXLOesk0i44K2PRZsNmECelp6CWf+n5yYrB\nl3dHvnhz4OdXZ1wGS140fej44s2Jje/47OUFnenYeSu22kbYcrvdlvOLC1mTaAkYza1xWBKnXHHe\nUJWWUMqi8drw6vqCWhLzPDKOiX7Tc7bbgJbOoT638wq9EmtcCEI9LpmWC2d9x7AdcN7JQU9JgjvX\nVgwqqkgvXde8TKgUGp98+hFlHlF5ZjrMvHp5xnya2Z8SeUl8fLPhH789EothazqOcSInKDSC90xx\nXteVckvXLBZcpck/cxVJMTx1KQpltJAEm3QqF8FRzrccl1uW/SNMD9S5ULXH+IB1imXKFGNw3pJS\npGHF3RjAGJEsr+sylCLlinXgnQTFeqOFDl0k4dk5oXOnJDwJg2RRqJX8kkvGK0VpCmU1WlXONh3B\niX9EbhqrGvuxQKmoTaCmwmnO+BC5MI7HOYLR7E8TtVZ2XUfQjVMstFrEtoyC9opN8JxKgtzQrbGU\nmZQrXec4H3qm3HDFYBUUFOMyizdjrcQCnVMCBCuDGbTIoTU8HGeC9RKe6qTFrw1ZI7bCtIjpikMw\nk9wKcrwrMVeM1ZL/WGGal3X9bUhJgMkxJmrJ1LplXiI5ZgoF0wVx8qoLu7OeeZIOsVVIWYxplYLD\n8Ygzlpb/SDED6wb+q1/uuD7rhHQREyo4bMvMh0TzllIbUwbvFMooNkFi0ZrSOG/RrdAH2XE7I07A\nWmvm44mSEqdxQinP7e0NJS3cv3vHP765B+V4fXvD2fmWfhPY+l6Sc3UDVUhLYv/hDt91aBuINTJP\nEW3s6qEn8WC1SHy3xGDV1eZ6ze1bAUWymGt2WhFPld9/+Q3KNK5fv2AeE3038OFx5p/ffuDrD5mP\nLjsOU0Yhh2xpwhuLpRCUeS48GihVRgatNVo3tJacAo3cFiDEmForVsPlxhNvLvi2KQ5xxi2PFAtN\nazgVyfMzjla8tMRAzRWnxaefVvHeQ9Pk0qhFaNIteHa7LbZJt7AkuYEU0FkjNmiq4JyFlME68Yss\nikai0lhywzVYYkFtDSVmtLakWhnniAuOc2uJTXF9ueXweOI+ZXTnuBk2/P7tA5fbjrOznpoTbx9P\nBK8pKXKxGdg4CFURrWMsgiVFVThOC7pkri8cvW0o51mKfP8HFdDGiBfkLDZ7zoBWUuyC0pKCrCqq\nVXrnQFtUgxQjU5b/N+89tTZKruxnAX97b7HekskSvFIrD/sjTYlBn/eG02EmN7nsaIqHxxPGGbpt\nTy0Jo1ZeiJMV9na7gQpxNTHZbjfM88J3377h5vqK3v24UOknKwa9KRhgWma07znOC3963vFwPPK/\nfPuBw1J4ddbz0c0FV/3A1nnR+nuD1+BdT8yJogy4wGk84ZLEm2tdyaWxNJnZhtMRHwLnNy95UTx/\n//UH/v4//AFN4U9uL/irz3/Gi49eEvqOEjUlJqY4s8RI3/e8enVNNYpaiqRDp0rViookONNAP20W\naqU1EfI0pam5cn93j4l73u0jqSi2veXCG/6PL77nn756SzCev/r8Y16/qpxZzeAsS4YURRRkMWw6\nQ10ysZRV+qpXrX0lZSlE3sCSIy0nWkpioFIbrSgqDWcqt1tHK1t0uuTh+5GaI75BsxbrPZVGjDOm\nOEppdKuASRuhIZcqUWOS7htEZKUkQqzVhvVWQLSaafNMtQ6rLamJtl+XRmcUeW50XaBVBQWUklFC\nGUUtjZwVwSvmVDnfBnKuHMcJasVWxXY7MObE7aaDVri9DtxsNnx390AXLB9dDQTf8WG/x3sNRXGI\nE0tpbHqH0vB6d8XeHoixMMZMapGaNaVCsJppWuiCp/MOrTQhWDTn//iXAAAgAElEQVRwnDPTMpGS\nFFxjxVrvcBjR2tANDqOFu7FUsYK3TmOV5oWXX6utYZQm6PgsUrNB0drqh9kqofNsVlGddH+Vzrk1\nCdoKk7PzYsEeC/O4x7u1cCMaCaPh5csbTuOJPvzniVf7//z83Vd3fHxzxYtgoQpN9u3DSNOW6/Md\n57ny6iwQnMKYtnoYOjpTqTExxhnvPFZDGUeccWLRrTS1itFnTZWLvocVzOuc5Revr7h9ccZxadwf\nFlpJxNbI8yx2VMYQhh7j/eqfL7e+URajDM1q2hNaS5WvoUkUupE8hVYLtSTSnJhOMzFFpsc9Iez4\n2fWOXAtffH3H/ePM7cUFn//sBl8zn78QgcxhFL88tKIV6QxaEdMU0+Rgtyo3FUbhmqYUhWoNVRMt\nzaTFrCAq2LWjUFXh0bzoLerqjFIix+NRchJTpuQsmwNjKbrRWiWnSGqVqsBYh3dIF+LFwQeaaBWU\nGLM4vRq6ek/NWV5CF4Sz0ArKCpjYNJiUsFavAGTBGo00JA5nGxgwTosVe64Ya+icZ5lGeqXZesdx\nkRXfaU5sXSIYi66KJSdKkjh4o0AHA7rD5cqyJA41Y+xI0LJ5mnMWG32tRH1pBRtppaDQeKvXDI+C\n7zxh6KhVCrNCgmmdc+h1hqc2dMuUSTwUnXWILQwSq45mXu3hZZqTxCMJXFW0Cl0w1LqOHCvZK3SO\n2hSH0whZMY8T7qkAz5HgHG31pXNGk3Ki7xyKnv83bcJPVgx+/upC2ta0oCUwl8O4sB06/uJnLyhZ\nJLBKg7UGZRRUcDTeHvd8c3/ixeacm6sBa8XyqSaZY6syZBSHWUwsQxB6cmuy9z8Lnsut45cvL3DG\nkLUhlcoyjRitsH1PHzxjntdDWFG1iOuQlsOIEnaejAt5lSKvhppKUY3lcX/PssxshkDe7Kg4UmmU\nVNie7fgvhg6DYtsZxkkxzwWlGzFlilICalVp0ylFTE+UCJlKyc9uSXWVT7cnf6SSKTGSrUcZIwVN\nO4np1pXeaS43nlQusc6LAel0QhVxgnL9BqMLJotNfXNhpbkWqHJz1VrxPuCtkMFUE1LOFBOlKWqO\nOCuGtEsuaDIoSLXA2ll440mtyZqtPYGMkkmhlWAj3llaKhSv2fSWoDydURzGE6YPlCZS796Jw1LN\niaoNWmnuT0eMG6gVGsKHcM7QhyDEqZqw1omxjpZb2RnNkgp+jY+LpWKd8EeMlgAYqNLJADlV4iLM\nw74LaKNYYmScxRT3eBoZhgEQizujFXmpWGflsDfhAcSYZJNlDTGLx6ZeI9V100zjTAieukg+RB8s\nORUpBNqwzPE5tKaUpwAbfjBv1ZLT+GPPT1YMfvPRJd5U/vaf7/ji7gODNbzYDuyXymAnXl3tUKqQ\nMsS4sNsMLHlkP3YoG6g2EmsjlkYzoFOjFTHkBNheXHB9dSnCnZyZY8R6h9MeqzRNFeI0UrQV7n0q\nmFaJteBKw/c9MWeW04i1lrAZcE4ouis6CDI6op/ZCrJ7b+uLTefo2owpigvX8/Y4MloBkrSptFyZ\n08Lbh8KrXY+zlsMilOASI6UqMOLNR2OdJzVP4rJaZWPR2uoKraQ7qFX23a01pLWQWDqtRPQFUFLG\nlsqLoZdAF60o80hdJlpZaC2Rm6JajyoTzizUqLChx/nw3AHFCHMtUDIG0NrgnCgzlbFi/ArMacJb\ns2Isa85iFeWiWvUOpVSMMpgqDEiy8PqVblwECRJNJpFaQzuPyogEula8Aaca3x0XNr2jd46u6+i8\nfc7XfPq7xJxwVopHafL97byjNk1ME9YYVKtsOrFcq7WRShImpjPQKkZbQKGNwoU1WSotmKLW8JeM\ndYab60u6dTSYZzGOlELTxGBXV5o14gRlxafTpIazoqA9nWZygSnOYuM+VwzgnFjVB++oZc2ztFYK\nvhLT3nmOdMHRqPT9IDZqP/L8ZMXg3WFhYzVFKebcGHPhEPfsfMfPrrcEp9E1M+XMY0x0zoKzLEX0\n6L/56DVBaaaaoSlSSeimUblSWqY2hXaWs8sdZll4eLhjPIoZZgg93juJ6qqJ2OAwzTijxGUGWJaI\nVRq321IV63xeQBeUkeawgdB39dohIGhfXSKVgl8SuQn4V1rl6qzndIr4zrI/jNAqX7655+1YabcT\nL69v8U5zPFaW2hi8R62rTkHXC1pLztFTh5BLES6A1mhVnpF6rVeuw1okaJVWmhCrnrz3XMIZiw+e\nYdhw9+g5NkWjoK2AZ01bAQrnhahkJFEoMUGpBbse/IzYslm3zstKoaomprg20oqMxgVLSZlUIikV\nWhNUXistzMQpMisoTbPdBrwxeG8YT7Os5FSkVrDGoLUSb8pcMJ2nNs3t1RmlSqd2semYU2JehPyk\nQKzbckWhoVWWPGOdFQBbGfF4rI2KJlcl0vBWRc3a1PpZtB/YpkZRqiKlijZSeL0LeB+kUFeho9cG\nXRcouTDPkVzLc7QcyFZKUfGr36dsa2SNqXSm77cIx8PSirg0LylCFF2MtXq9GBTOWsxgOByP1FVw\nltJCa/X/+TCuz09WDP63v/uGs2D47NML/uLjARPEOmoTOoIWim/Tmm7oYKhrxbNYbYXVphrJ1tUL\nzjDGiFdyG7WqKDmJcjF4jK5M30z87ov3PCyZYej5s198wi8/fkWqhdAaoe9FpNPaevE3uWmt6AxB\ng2pUKm0NFm2rE7BZbxxdkVVlzuRl4e50QNOIOQlYlhPTnDnvDF++feBuTry7P/GbT294mAsvdaNm\nuNhumGohxyKAJA1lNC2tpCP1dOSVzLGtiixaa6yqa4yWUIStVmiz6giQnEVBvi3RO2pt9M5irRMv\ngVKoywlTkoTNmEJTBuU85smleJqoMTFr8M6sB9OA8qiqybWhlcMYhQ8ddV7IrRHTCoBU2YZYIzTd\nphsxCVHMWMF8mhI1aCuaZY4sS2S324oBTZX9+TKdRMeCZZkXeucYpwnrDJ3VjPPE6ZSwvadzFr2G\n1RglRCetJPUpeEdZRWVaGZoWkFShcEaxTAlvAsaotYAB69caBNOQYowUiXW3rJpYwpWmoDZyy9Qm\nQbTzaiSjAe8t1qgVtxCgFqU4pokQDMF1kupcpJCoVRmrjZXYdy3ejr0Nz+E03nuM1szLQugCyzIL\n9fxHnp9OqFQrr4cei+Z864llYesNV5c7lhihSUJvZxpnNjDFJO61vScYg+nVKgPV7Pd7/sMXH+iC\n51efvmQz9OhgmFPi/Xdv6IaA7bZEvWcfG9hKagXbWXSzTKeZkhtaG7nhW4aSqEpBFmN2te74rbVg\nhN5aa6amjKpScdO8MJdCyYm4P9A7xRwh5oUNjnFqWFU4xoWzzYb3aeSzlx2/ejFwKob9aSS4QC7i\nMaibrOFkQyH2Y0rJGqq2Rm36h5Fk1Rp4C7HM1DjRgqc1Jy+28IVXbKNgjWE3BNlbpwxVceY1+mLL\n/gTx4QMsBe0DzUimokHArFqKkHqsIS4zrVaCE429ftowJANOwmZySfjgaLVgqmZZCkVXZg2KhtWN\noetRNAZvOZ4iZzvL3Yc9BcN4ilzvLMsU2Qwe3Qsx5xQT3hpSakL5LZHzIVBLE0fghBCiloRXYIeO\nBnhjyTmhkK6o1oo2hrg6UGvVyKWJR6U1+ODW7/sTA1NGLa3lVn7SeDQk3xCFWMrXirUyRLamyDmB\nVnKhtCoxen41xq2NWqr8vWt59n4I1sifqSQRLHSenMRFurOWlBPOGnLJq/O3pImldMJ5i3WW4/FA\nCJLb8GPPT1YM/s2fvmTnZW9/fT5w2FfuDgt//9X3bIJl8IElZ24vN8SiCNbifY8PWpDmWknzAkbz\n4eHE29PMRa2olum8wuie0sEyzYyHjLOWv/rtZ3hr6KzCOsdhfxTgaNXZa2OopdKKWttIwQMK8gHV\nWKjFYKx0A0rLnvmp+aqtMi7iTvPuw4E/+2jL9esb3t8f+Or7Bw5LJE4zqWr+1W8+5b++6Km1cZwn\n3p8maHC103IzABiZV2utayFQz8xG1A9rvtJEPKS1FASVEiVOpKXHOIexioqYq7QV9BRIQxGMZ1rE\nHKNRuOgCXhseSmM63FHijPcV5wdS1ehS0E1yKy1G9upGE+PEPj4yu5VmbIWG7IKl67x4UKA5TjMN\n2Pie2sT8NcaMIVKbJEZ1fcfxKBb3tVS0riwJ9o+PXHPGtg/MywmDhPPuOv1c1BJFkphrYdcHnJEQ\nks77dbyREVBbsW5T+ofuqlVkhWqMxM2XioU1Ek5RaxM8QSmx5Vf/NyC3rSPB0zZBSYFurdIQIFAp\n8YnAmOc1X0pF+CJKEZxZ/3sLVVKpa2nUlokx0ZoiBEcfxGJOjFFkm6OAGKP8fWpjmRMuOLw3Ipm2\n5hnq+peen45n4Nxq+qlYcmHjHHub+Ju/+4YeCMFQsXxyMaCM5tc3L+iHSIuW/uqcZT+JOKharl+8\n4L+/OsehCM6xzDNKaZwPErjiO1paxPizwTJFMaOzmjwnSdU1llYTTclt0Fqj5PJ861stFTrXQo2i\nX1Ba/AD0GorinEVtCncf3uJNE9ORaaKrcD0EYml8tyz8/t0D/+VvPxVsImcG77GxMc6ZJYr9e61P\nWgC1ZiesuMX6cqwLrRUPaGgNuiqMUhglgFcpSTgCVqOl7pKzaAtqa4LoW41WFmMU0xKJMdO1zPX5\njr3VHPd7VInk6UB1PaqWHwpTibQmRdSs684lVVRWuCLjzTw3ltmRhkHYiKXSeUNUBVXVeugaJfQo\nBbswoLRmniOpZealshs6dDCEGmjA/vHAsPFk1ZjHmcUo3ny456PrK4JX5JpxxpKzHF5RE1ZiLDhj\naFHWtIWM1ZZc8zp+GZYl8hhlzTn0nRSYnFYlqhbFYRMuSV2LcF5Ht1JEN6OoLMtCa+KYVVZyljXy\nXkkn8gTwsorCsowNyshEqmS0gIqzVjCg1Um7pAxGANyyhssYLY7W2mh0p/HO4byjlERzTkDa8ke6\nTXg8LhQUu8ETc8UrxWaz49OXl7x9dySrRqrwhw8P7DrPiy5wjI1dd03fO7796j1jMXzYJ3Z94KJ3\n9KFfJblCM25LRI6r6PZLSqinub6UdWbTOC0VuZQqNF8tu95aC/WJ4queEHtBxOs6eyu1dhPIreBp\n3L64RtX3vD1EHu6/w2jH1fUObUQleGiZt28+cHW+o+s8NTUuh8bFumosrYpmohQBC586BWS1ae16\nU5UmLwkCMj7ZqTkNuURqWqglkJOs2qxCcI/Vt7HVhoBXhs3gCcEyjTNjK9jScLstm37H6bgnjnus\ngjGnFb3OVK2wvl9ZFgKgVsQKrBRxIG6lUessSsSc0NrQEhyPlRrBdZbt2RnONmpOlAIkiCUxp8Jm\nM7DZWGxwTCdh+33z4YHrckYIPQ+He9ywwYWew5w4G7Z8OB4YwpZaRb24pEoqy6oOrCJOqgllLUuT\nzzmlKBwLrSSyc10V55QppVJUou970QE0nkHKVqEUwQG0MZTVeWiOQvqyDSgyWojrfV5j6WQ00EpL\n9mQsGCNr8LquBlVj/Z6J3Lo1AQhTTNggmy3nZYSx+qljESDRGFl511V411pbA3D+5ecnKwaKwuO4\nsHWGyxfntDU1+V//yUt+13dsLcypsR0sF33HThfG0tA+YKsYefztP7/lm33EtcbLXc+ffnrLn19s\n6EJYD2tD2UCaJnqnacUIOUgprHUYZSg6ktZWX+knYkldIUMl/bRaqzFqZRzKS69WBNgajVMiWiq5\n8urlJ3w4Ft58+Q1b30hxYdgZdtsdH6dKyonffxh5PC389ue3+OBJ4xNQaLBNY1RlQV7KUquUtLU4\nSCegV8KLwrQqcIBSWKXonagMY5yoi6NoRV6t16UFVrQqbXBtDWsqQYHTGrvpGIIkRceU2ClNb3fE\nPqC0Yj8tHI8nao7r90iYcamIZ4Fh7VC8R6sGJTHnwmman1dpta24uBHfhzRPfJjnNaDF4ENH8AZn\nLa5mHh8mWi4UJTTm3XZDxqBipCqDb4rOd0zLxP1+BCT9WgGpCIo/zTO2OmJZZewozs8cOSWsFatx\nGlijuDkfsNYIGAdY58S5W8vqD5lSyTk/d21tBZ4bgt6fn21lzVsSzon1Xa3iIUmTG5218CsF1uu1\nYysSnKNFhGZXiv1TB0LjeQVpjVmVjQKwC5aTn7vIeZ5lZWm0kM7+WMeEV1dbbi42nA+Wq60FPzCP\nJ768P3J3v+eg4C9/9SmbjWe3GYDMpXLEceLhMPHi5Sf8Onf8siZQhm0f2BmxGKcVVBVzUNXAaIvk\nFMkazCh5aeVD8EIUWok0rSFpumrlDawyA7SCJjeL3AgraEjD9IGHD3fUnHg/FT57ccavPnvNV999\nSwZC55lTxVfF2TDwmVOU7x6JqfAPX77n9npg2+9ITbAJp8zz7SQfouAapWahshahHxstjiFGK4y2\nNAoOI/yHVihxJp+knWy10mpHNUbGBq3XF66R8mrXZtvzyx26Jwv3ypnuWKx0ELvdltP5ltMcOT18\ngLxQmqZog7diMFKauC+nFKm54rVbuQ8SCw/iGj2cdQxd4Hg6CkBKW92UMqc1/CZPFT9smGNm02la\nAWMN6PYsAX6cIttBMfQB5S0DIvjJTTPFhe7arizHxtY5HkujDx6MpqaGD5acJVW6Vemy+uBRXlr4\nXCopRVJs6+ZEQLpaZTxzzv7A7Vs3OC1npmWRz4+GIsncrhWqCbMUJCZNa4VuYpxSq1w6dX2/Si1A\nW1WJ8nPGCLeiNhHHpZTXjmXlqGTpZmKKgp+oJ7s786Nn8icrBscp8tHlDuchz5P45cfEf/rujvtT\n5GbXsekNF9ue3DRnl5cEE7hr70hN2qK//PgKFyy4jpojJc1iUDpHOcnWQp2kBCixBqtVoslSK5im\nsNrRqszedUXLn4g5uQlAZ7TQjLVuYvSr1DPeoVuh1MoX7x6Zpom//+7A5X/zC/7so5/zr377Genh\nAds0S4HDGLk623A3HiWkpSoyiilWdl2TYJPayEpuBtdENCt9wRPwp4TnsFqP1/YkdOW5HWxNE6z4\nDIxpJDZhRYKiOodTYk0iDkVidSYj0g8gJE2AsFLBWVCdl1sNuBw826HjXY0c9w9Y3aG1xdZENpqc\nEiVZqaRGiDs5JVIRLCh0Fm+MrOeqdAM5LczjiRACxSdybmjbMMoRvMXbLVqx5jkYUe4ZQ+cd1mi6\nYCBlTG08jpF5mcHKry+p0HlZOQu5x6B1hSK3/sODjKV+pVPr1liSoPPWOjlgpVJyoZr2rAh1xq4e\nl5WGWn9cMFU4AlOKOG1FZqyVZEmyEsKUfG7SeK6fTWXdaPBcrGlQcnnmlrRVb6O1xqz/3lpjnuXn\nnHMrKU3hcM+mtrVW2Wb8yPOTFYOvHyZA0weHt4o+T8zVcnW+4dXVBbcvzjDK4rXj8LjnfLdhXCau\nbq5IS+TNt9/jrGWjBoKDtLrMCNFDaMe1ZEwzktBTV7S3AishCSo5RiHjrL2fUisBRGl0W5nn61qv\nVjBtvT1XPr5Tcjvf3t6ynyc+73Zsdpd8ePuOXlU23cA4zuy85ZAaj9PEu/uTAD/Ar2+vxLVGSaHR\nWq8GK4JIxyWirV07AbXyXSqgSSk/6xfqEzLe5PexKDovU/xUI3WR7D+jFbWu61INGLO6MjVJSmoK\njRSbp/ayloL3ApCllGi5YlXh5cU5mxCITbwXx/1IP/RMpbDMwjikZZaqscajjRhsqFzonGFOmVMU\nWW0rYIzHWU9uDaMUy5LYnfXokuSmq5BapmTHVDJeK1on/IjTLBbuWiswclNvjKZ3jnmO7HrRH+yX\nGdMqqirmeQGl+e77R7rOsj0bCBsnKH3O5JiF60HFOyOhOCvPVCkZH1OCXLPYwynDE0HVWYOz4iYF\n0s2llNdtA+uq8EnYtt7yNLyVceLp4Mt6UoxzlHjrsSwFZcUXgfVzqqWuBDMIq8x+WWSUySqvAOcf\nqYT59sUNOc1MKTIlxRSlJfvF7SXBWYbtjqoMsw5kJ+EkJWW03jIvCfqO7W5HHWdO48L9YWRDZrPd\nYLuOJWXIUVKClRh4KiWoukLR1ltGNTCrkKdVUeC1VmVkUGZFkWHNp5I8gSa74lplHIHEpYefvbrF\ntMJpXPiH//QtcZk4O7+iUonjidD1fPnuAaUaoTX2sWBVYxxP7IathCohbaR4EAqZx1tHLlnmhidh\nVJXuITcxyqirdPnpUVpWU50DYmGOI0utKFUlYVlbiSQzrC8irMOm3EJIF4IGY+zKtitYIwy+aZ6w\nWnF1tiGXytFqagoYDNp6Wk7UnIQcliu6kyTs1Bx57amVNXjn1x293I5NNXovugRrDdM0s388kGrF\nhh5tNc5WLoYt8zJzGkdybqAMMUUuzza0lMTN2TtSzmw7xzjOnGZJeFqWTMyOvnd8eP9AtkLYGoyY\n2E5jJJayhsWCs5pGppYmlni1rgY2K9NzTaZuNBHMZdEu+LV41FaFNr+a0kCRcUFpnlK2Wdt9wYNW\nNaR5kqwjWwyg5EgfepT+gYBGA2Okk8sproC3vAtPnYNSihDCj57Jn06bcHvGNGf2h5nvH2e+OD0w\nRqHcvtjtuL1odEHmoLPNObk2alXc3z2yjBPBe2xKTDlTWmNwCqqm0kilMi+FYBSxijzWIKu0kgUw\nahVY27uiKwpD02uuYW3rfjdRlKx0jNEYq0E5qioYZbEKYqwr5z/yf/7uGw6nI04X4pj47ONLLm86\nDmXg/tv3uNp4d3/AecUfvp94eb3FeYe2fl0UNlHnrR550LDO0Kq49dT1cGqlqRWc9M2UJpoFRaOu\nNGVQtOdPt0HKzHkinSpagfEbod2u66Yn2rJaxx+ZlFbmJ0rMQY2luSYpzamQ4ogqiW030J0POAOp\nVNxS2O9PBKs4LieUka6md510QM5IulQt9F0QokzJlNIwxsGSOU4nri8uuD8dSEum6zd0AYJ11JYp\nc+QwLywFttbig2E7nK+0Y0PfWXKuOF14dzey2fYUNHEW09DDsogHY7B8fN7RsmJaCjvfGOeZOVdC\n8ATdsMYDhjlHQLQuuSZxijIKtwbVOiOKxPp0aainAlDJua7bqB+o4rU+GcEIGB28X8lschlJdya+\nm0+MwqKEgo1qq0OzFyKc0iwxys8FL9aAWq9eCnXd5vyRdgZnHQxuoMbMf9xP/O7diWlJWGN4OCRq\nbrw891xuxBbsEDNqs6WcHqElllPk5DTWWpwzOO1BF5Ylk2ax9LLGkZ9ooeuH8kQN1euOV9hgjWY0\nylooldYUSokcuJZCLAnrjOz4owRbtqZ53B+5vH3Bru9pSTEud9xNhY/OAx+/PuPqPFDnhdBtODs/\nZ//+ga0PZFU5KcvFJqCMZwiOJY7ULNVeW0HSYxbEeT2RqJUW/4xgV2lHFYJbCBou/Ifa6vPNw7oW\nNaUx5Yl4AldWALWJ2SmrzZZa8ZDnZWb7QZelV6WmDQqaZzoJ7bqUTHCWy01PqQ1vE1Y1pnlexUcy\nXihlUKtyriD6hTRPRAQJR2tMK885j+M0oprm/2LuzX5tW9Pzrt/XjW52q9vN2ft01bkq5ThuCHGE\nYzuJEgUkBBcocBEkBNzlApQrkn8gAi4Q4hKJCxqBiEAKSIQoRgRIh6PYcYJdSZXLPnXavffae3Vz\nztF9LRfvWOscOWVbihNVTalU56y11zp7rTnGN97meX7Pquto6xYdZ0osjCkx6EABNt0ap2WI6qzB\n54zWirZ2hHnm0M+SDFUkK9FHz65paLXGaMmsDH1gP3rhY+SIMoZGWdT9liRnmqbBOpkvzUHaFnTB\noBeV90KILpkYZTUYfVzcpIsN2eoH0VJKWViURb5WLTbnez+JvMrDweB9wBhD8JEpiGZBZhlp+dwi\niTfir4gxLk5MlkoXpumH1Kj08eUBhSCtVNPx6NRSaejqisY5LmrHycmaqnJQaZTXuJywuzUvXr9B\n1xWhaByy580RqlqCKpX9XBpqVEFl2b/GLLhvreTpl1MBK7p9o83SH8sBgVYoKpQuKJVAK+aY+cff\ne4Elslu3fOfDN/zckwu2j065vXzDl9864f1nZ9S1w1lL9gNvjjOH62s2XcNxHvnKW1vu5ky3OaEz\nEvs1jqKTt4t7LflA7SoKEZ3BOMs8z1I+ZhYN/WKlRm7StGjpRTUJKGH2aQpOG5TTaJ2gJCY/MudM\nKpmmlfAOg0ixixJoixwIy2BLqYe9uxYBHcWC3XSM1jH5CaZRRC9KCbxEZ2xtUHrHNE2ERbasgBI0\nVVVjlWw6rJWZSUkyHK3qGpMzwzDgnGQTGKW4uhrZ7hyrtqOqHZVZVqipME6eaZ6pKvGAFBJNXVFr\ny6qxlJKoGoNPjtmL0OzoZ+6OE4OLC7INxjlIIpKzpKSYQ6QfR2l1rMxxTBFXpbUitjJaUPNzCJRU\nFoWjRWswyAbiXqKccqLMcsCmIvoXoxXRB+KyFbi/iUH+nLGG4APjOC6rYGFVmmXrlaM4brVaKFvl\n85YkhAAsmQz2hxSIepsM8dhDu+a9x1vO1nd0tmGaEyena3ScWa1brm/uKCkz5oLqoaoc7XaDtQpX\njHAKYkYZiMiNYKKgpIoqoOVCU0laAW0tZJkA13WFWowxFiVVgawTBLahxUueFQ/RYk8uzgEZgL33\nXo0uienuBlJk3bTyBDQQssYUS9cVqs2a2I/ouuOTyzdYEzndPcHawsvLG3bbDuNqTMms24oYAlf7\nnpPTDkJBIFaaULKYamIRQ80yzMoRrFl23/cmGSMDUF1EYisE1YyqLEolpuRJ44FkNMYagZcsFYG6\nl1d8YS9dltYhF1C5YJVBWWg2lpBrjseBq7sDXVvTNQ2alhA87cowTo7rK1FX+piouxpnEDWjsXKY\noFh1a+I8kuYBlGwCphiYZ5mGr09P5caOnn4/Mhe49jOrakVQCuMsWhlqqygGUAVbMuMQMRU45Xj5\nek8xmdY2zKlgNPgwctKc0FUNGI1dnuAhydS/Hzw3tyN1ZyymaiwAACAASURBVOgqR06Jumuwi8ch\nzAEfxIeQYiSGBFHcpF3rMFYz9SNlGe6FlEjLYBIt750zRtrXUphmL+rUyVO5GleJuAjAh8RwnIGJ\nqja0XUvXiEPyXroeQpRWErkWrNWS7vQgXvv+r9/xMFBKvQP8N8BjZIz6X5ZS/gul1BnwPwLvAd8D\n/s1Syu3yNX8B+PeQSvA/KKX8te/3vV9eXZPnxHvnZ2ys5eSko787orSmNY4pB6IvjLNH3Rxx24ba\nWfbXN7htR2cMJi+R2EXYAApDUeXz6K8l5AKWIZmx4rxTkFGkUrDlc6lxzgmMqAxV+jx5OC/jLV0K\nbz/aoRcdwMUplBw43NxBkVRilEZlDSWQlGJVtyinGVThvNL8+kefcb6pqFPAKi3Bq2lmP83kUjjb\ndEsa0QT7ImYWa1g3FT5HoRw9rAUhRkFjUzToJX8il4en8P26UavF9ouImKqcmZInjj3aWCytbCQU\naJZ0JWRivggXRfWI/LvV4sk3WuLYndGkXLCqUFlNtemYRsscAtpZ7PkZx3FmGCdKKfhBiNXKGtn+\nGHHlBT+jUdjKiLxXm8WLodi2NdFP+FgYfZQEoUrmELW1WA3rSvSQVlthDvYj1jps0gx5FBFXVNxN\nE87CZrNFYOmZum7o+55xyAwhibJ0YQ8klSlFYuXmGJn3g1QGWt6DlCFEQe1bY2mcVFpDP3I8DuIJ\n0ZDC0jbkjNEy9C0FQhR3aEpL6T9HfMjENFNljdGS36k0NO3CUtBKVIYF/ByXDYS0uEXFxdloZODr\np9+zziAAf66U8itKqTXwS0qpXwD+XeAXSin/qVLqPwL+PPDnlVLfBP4t4JvAc+D/UEr9SPk+Rupf\n/eAzSlTczJ7z1YanT3Y8Oj9nOO7Zbtb4mxkRYBdM7aiqijjPGKsI80wwIhIJIcmE3FmUL7jKUIyl\naBkmykRbL7bZQCnxYbMQQ4S06MSRE+R+gluW/XKpBLZxv16MMaONVAoSLGLIcocxhiBkoQI2JWzT\nkAKMwwyVZldvePLkjHVdsT8cUG2LdRVjGJinSMjyVHhxCPRjz09/9SmXSWGnmcmPWCpcnYUgpEWE\norQihbg47PTn68koT4eEyK810k/GBa9VLKRY8H7AD0aEWfczhhLFKmwXrfx9qVCWtgGIFIwSNR0x\n02jDk7Md0zwTF9GLlNqFrBKrTcembdn3I8M0M5SlqknCYLDWivJTSRbkPQyVHDHO0rUNx+NBDuiY\nqa0l5czK1szRk2ZP0YVeQVaFumiOkycqWBnD3M8ko2mWliPmRF1rVlVNiULF+uTlIH/fXPBKyndb\nQKvCZt1w6KfFf6CYfMRE+RmVYgnVlXZTJM3yfWISVaqWLHqpMo0GXdBOC2K/wDCMhOMkQBgjLVld\nu2XQmIgxYI2S1mPhRApkdYHP3b83KS9shMIwjChtF7FXfIC7/FMdBqWUl8DL5Z+PSql/tNzk/xrw\n88sf+6+B/2s5EP514H8opQTge0qp7wJ/CPh/f+v3NtrByvHq5oDVlvVe0aiC1WIB1VpTrxvU3uFD\nokoRazXjMdKoihBE5x3mQNU6nDXSIeeM0YKgVlFYB8qAWRJocoiys7eyj04+LhwDmZprbSheACZW\nGbQVMZJFU1IipoipHMQkE/+QKCpT1RX1uqVtNR9991M+vDziY+IP/vhXWa02DMcBlTPffP9dPv7k\nU4wxnHYdQWnCZMlmYrOqeHMXOF5dsk+Gfph5drpjHhrGfESXwpurW05Pt7SVqCjnGHCuIqdESgVt\nlbypRlaElTb4IE/zlGWT4IxZ9t+JEhJ+2DOhaTdb0ALQSMu6URnxQ0hHYpbGRNoG7odfSp6tWina\nxpGyIYTIPE4oIk0lPMaq0bTVlikkrocGPw3oDL7A6L2Eh6eZbDUGzdgfKBicVxADOUaqpsPVNUpZ\njv0Nh8PIatUsMFdNvz+iUIxGUa9rNtYxz4X9PNDWLXNWXGxbMo5+HOlvLsX1ubzPOss+frvphF05\n9Mwh0041SlVLCnOirRxNU5GCJ+VMa50cCAWmKeJZDufFEZmCKBCTCqIWzXCcZvwyjJzmQEgZlTO7\n9Zq2NczTgDKOymm6rkErJZFqujBOEg+vlcJPQURPWTYMdVNTOS1tsFZ0XYNdthf/1IfBF19KqfeB\nnwR+EXhSSnm1fOoV8GT552e/5cb/BDk8/onXH/jyY5ny25pGK7qVY/KR7aZmngI313syFft+ZlfD\nza1ns+5IORFSwSmHKgmsIWmN9rLSUXVFCUmsxUURl70wOHmyKYNB5LqqKKpGNPcyvLGi6oLPS7n8\neVCJMVJ+qgJFWwyKYg1xnqi6lpILf+cf/Cbf++iSm2Ogtpofu7nl5MvPOH38Hr/4t36JogT+Wdct\n/TBRrRva1qGKILtTSTxZW06i4dcv71Avrvk3/vhP8cFn0i60i0dgUcEzTwOpkojwQial8rBFKKmA\nEu6e9IwKnTNaF6l2lnaDkojTHq8LVVmhqxptpboS7KnYfCn33/te0SAleVGyRjNLkpSrqgUAqmjq\nuEhpgZJw9aIWVIXgDFpBKIpjP9FPI7nIYbZuagbvKUkAps4K3ksZI6zDcZL2zjlQmdo6jNaMJFTR\nKJUoPnB9cyQUTcgRWzxdq/DzyOAjGUvvM00rQbHaLg7HZBiHkZQKKSqaRkJ5YpihSF5GTImieCAg\nzT4xzzNzjOQkkmEtc0ORixuF0QJ4Oc6y4pvnwBgi2mratqXOhZjEOTuMQWY2KZCjXIvWmgXPn4he\nAK6gFyBMIiNehlKEvjSMnrp2HI89lbPyd/29HgZLi/A/A/9hKeWgvjBZKqUUdU+H/P6v7/u5XW2X\nfrNhPwV0Fsnu9WGmqxKnFycQxGwSVUQXw9DPrNqGfpgBS9OIRj3HRMgyZS1R1FxGZAcLsUZ24woN\nC/Xn3okopZcQmpXWy2GzrPQWOTJ6majnz3+gBYwuEtCqws+elDIOxztPH/Ejdc1uVaOUIUyRqpp4\n8tZjPnv1hnajeX17EJJQcMw5cnk3s6k03/rkmst9z7snKz7ae8I48Xf/8Ye8te1AGdrGPRiMQsw0\nbUtRi9HK6AcXnTBP1BKCIiWkuf+5S5HhqrJLCIyAZOPcC+K8XWGrZvFkGEw2FFNQmgdBi6wc9VI1\nyPQ6yWABlZdZQluhklv27HKTaKNxKtNZTVIVVSWzhnXj2I81d4cenSLOGp6enXLbT+zzkaI0UcE8\nDeIRKYrKaVzliMFzPBwWHmAgZhn+Xt/NoMWerWLG5xFnFIckLaQxkNKM044SI7OPZCWpT/PYY7SV\nmwtZS4I4MzWCOj8cehGJaSE8s+hUYFkIp4I1eoHmFPwU8THjk2hIlNa0bU1dWQzgF42EtjKvUUrI\nSuMsNHDFcu0Whc+Sp5BjWOzNUFWGylYP18fspcoOORArR9c1v+N9/rseBkophxwE/20p5S8vH36l\nlHpaSnmplHoLuFw+/inwzhe+/O3lY//E6xd+6Vti8dSGZ08u+NLpqfwAVhOnkbpZ4Yc9ZycnhDBT\nWcvYz7SuQ9eW/TTKOkyLEywZizZFfAtGDDgqCprbWENC0mV0ljdyTgpbOUxerMjL+jGViJ8mnJML\ngSi6Al1ZEpmSlpZC3QuUROte0KgceO98y11/lB4vKMasaSP4yxs2uvD2+U7ozdrCPHE9BDatw1nH\nZtvxjbcyfYz8xnWPz5CL4Rd+9VN+9FHHH//Jb9DHIulESoOSGYoqDXf9rezineQ+5CKbBV1kC6HU\nYoa5P82QTB+jFLU1OF2YQ8L7gTl6QtVR0hpXV1Cc7BINYgJbKgAxF/EFi7UMakky5NSLU1KZhReR\nZCePKkL3Xay2jXHYEDHKYdWK28OeYTjSVR2P1h1tZajblus3b8i5cBhHNusVWluG21smH2hWK3RO\nqMoSE0zek4rMV1L0YhLKhakfUa6icmpxByqGQ8/sZ1JRhBQ52WykQrDieBTOgIBUjLWi8kOhrCP4\ngJ88caFJKaVomloi02dPKjD7mXGaUBmscTijpTJd0OjT6DFaL1F6ATVmNtv1sioMFAXHYcTY6nOZ\nuDLE2UvLaxS1q8RMlRcbfhbX7ovPPuPq9cvlwfd7qAyUlAD/FfCtUsp//oVP/a/AvwP8J8v//+Uv\nfPy/V0r9Z0h78DXg736/7/3zP/Z16q4jzx7bVAzjJOPKGHGrNbc3N5jacWYtWkd0KCQSh0NP0xim\nOBHrlrubnrPHT0S0YQyuqTnc3lLXFXbxhicvNlHtxE+A0qQUiJNALe5BJdkPYr1tK6kIjEJVNbaI\nv56U5IDI9w6zIk+WqDjZtWgdOYxH/tavfo+Prg802jAMI3/uz/4Zzp6d0PcH1PUdfuh5dtrhx4bf\neP0h4yFwuupoXM1752tWmzV/4zuvuN6PBBJznLHrNdsObKmYvUSv2awlIKVkQoRNIxF0GOm5SxQx\nUmVExZgQqeu9TkHESwW5QxOVlTZijp5pCJQUKXkFdU1xFaZoilELCAZRKmaFhH3q+xUOwIOuviDD\nVq0Mwn5Ii66joJNUbtZqjK6ojKGpKrrGcTz0sGDmqhQIk0flhLGG85NTxjBwfZxxxtBtOtbriv5m\nYO4Hdl3NPXtwHjxKZ7rK4nOA5aYZhpmmqrCVZb1pUb1mXhD1V/s9zmi0l7i9tm1wrcSn5RTFmlyE\nbRlCRmvLqnGELHmGOQSmJEE+SsvDyWGYkqwMQ1wYi8pIhZkzXVdjKo1ztcwdxkkAKUoGhW3ToJRh\nHid8zDgnswSBuETBni1KR2sNfT+gUJycnvL8+Vs4K+K7v/dLv/xPdxgAPwP828A/VEr9/eVjfwH4\nj4G/pJT691lWi8sF8C2l1F8CvoWs/f9sued6/9b/sDEScRUjxYMuEJfy0enCphOzydgfRcseEofk\nebTdcH04sm0qxjniVh0USbMJzlGXJcEmxAWdDk1doZMMz/QyqdVIbNY8xcVcZ7BOUy0AUYVgriii\nI9dKfSGCXSbqRmtUEYnzNEaUdfy9D674jTcjt1NBxYBBuHfWKXTwtJsOt16hb2+xKvLNd5/w2cs3\nbDpHVSmOUfOltUN99Smv746YtiGGnpOq5fKuxxTLNAe2509IRqjLkNmuO9RyW99j0ypnJMMgy7TZ\nKiUHgmxSpW1Yun9rNFGL487oggkZ73vGOJPajrpdUaoK6yxpcejJk1/w62Vxcyq9SKGWUl6rzwU0\nANqaz9uXpJmmcVmzabq2IsVEXRkqbZjGkXYjIqL9HGjaljlETlct6phRaSYC4zQxHvdoY5nGiRQm\nVps1ldGCSS+Km9sDTVNhEMBNCDJ41Siur/fkxZVYVRVV06GUWICHeSYrJdqAlIll8Qwo6dOtUiiV\nCX6iKDEPhWUlapzFTxMpyLjqHr1GuR/DytA6KQGW3NOnFAJuZRHDWZUgRiJRthBFoXKmaWq8T3jv\niWNaDHbqAZ8vsmfhMpRsFl/Lb//63bYJfxPQv82n/8Rv8zV/EfiLv+N/FdAWKqNIUdJoq7bBhkAM\nmVxntHU0VcXN1Z7NbsPoE7Vp+PTyBrda8fxkR4yZeS74fkTnIGvHYyCXRDSaMHusUVTLE8ZVkpEY\nk1QK917wfhKC7Nnq5POet67lQPDSC5q8KNAWiahSGmUNKidc3RFCpOA4fesJf3R1wpQSk5+5qBU6\n3NFfz5RxoN3uaE4e8SYk1NDz9qMntHXHcR759ZdXnDeaT/qe06bi6fuPSRH2R0M0hdvbmevjLSdr\nR7l+zYRi29XYDBMwF4VTanlqyzbQKkOgILxkuYisXiLWl1KyqOUAVGCywuqC1QqXJEQkDj3JB6qu\no27bB0qUsZaiy+diFoV4JJRMtu+BMSprmXXdm3IWzb0xC8gzRExtmBcFoVWRVSeBs45M6wy5JPTm\nhHEKVEre87ffOuezN9d0qxUvXie6RhQ8tqrIIRJjoChFLFq0KCFyO3iwjq5bkeYZoxJhlu0RSrIL\nu/WKHNNicRY1l58mCpqQs2RTLq5CZfTStkmbWVISrqEpECTYpHZucbwLxjwvPX0Ms0BQlViVnRZa\ntE9JoMDI76xrKzElaUmHWtViGY8xMg4DSktrEmKQNbP+fMU5jAPBe9ZduxinfvvXD0yBeNiP7HaO\npxcX9NFze7Nnt90wzSPTMFDVDXM/4XYbqk2DbWvurgcePXnOv/TH/iif/Mav8vrDT9nsTonB8/ik\ng6S4vrri7Okjhqu7xf0nSSdGI8GgJaOLIgeZjDvEZLLvj7S1rG+oNdtVK8QhW4iTJ2iFTpk8zsIT\nsGax+iLDRyDNM29vtqgTGR4pa6jSjO9n1HEiB5jiAXuYpe3ImWzgfLdhOzWsVmuuXr3gwxd3jP4V\nbb1lszY825xgE3w0TVzOM6TI7GfuRsPHFN57csHJifTQzJ6YRRE3x0ywoklvq0Z0F4oFjPE5bVkp\n2ffLk0USr40t6JhxWbIcRz8yHSTHsV2txc5bCiz2aoBFDY36AusvL9Ld++qg5Pwgb1Qo2rbF2yiE\n4CRPzMpZrMpsu4bXH13yzpef0QaFyZ5r76mtY3NW8Wjb0fcRbSJfe/8xt1OinmfUfHywmGc0u92G\nq9s98+R5cnqOUp7jNFO05mbf44zDFmiXsBNHIhlHU3f0/YH94UBjLLZyZGBeTELaGFarFYpCiIlQ\nCj4lQojUxuIqK5uqGBZsOvhhIC4Mh65rsY1hHmeGaRKpcsokMqtuJahz70kJ6qaFLOa7QqHve+Y5\nLvqPJO7KuiFqkUJPS54EqdC2LSkn/PxDmptwuZ85+hv2/cjFds3Z+Snr3Y5tily/foMpmkChsxaX\nhD6zO9sQjea7v/z3mKaJujEM/YHVdkeOiv04YrqOtt3Smx5XMsoppphIKeJsouoEvJmLiDWS0RhV\nYRW8ur5l03W4Yuiv9qw2nXgY7P1VLqvEUgoqL6pHq8lEqRSMXcwvMp/I1sCcKCOEJQZLa8s4jNjK\nCeE2JopOWFtYhYA9P+Wkbvjw9RU+SEhKVhndreDqijdXM6dPtyTv+fjlG14M8Cuf3vL7n++42DZc\n33l88rxzvqNqa7wxFLesFReFWix5GTBK+K08ocV/QZLtg1ZKjDXLQaoVzDERhgPRz7i2pW46qrpC\nIU8cWTlm8uJuuh8s3ttoRR0nLYvAZOVp2lRi9TVKLXJokRN3taM72/Dy8jWbrqbd7NgpxTB5HrUb\nDne3PL3YcbO/o1KFtVNkU3PrR2yBVdOinGEcJDrOOIvPnujFXGWMrGpJWViJ0S8Qk0zXtpQUZI1J\nIZSIVlaUqBTUwh2Yp/nzNkjL79ktBCmZ2UT8MvG3S4Va40R1OAU8Hh9FcJai4NCUlVbQKGn1coyM\nvcy2oir4WTgMxt6DS2TVmedAzolqsVQ7a+W9jomcIvGHNUTlo+s7VrWjPQ5c7QeenO4YRomD2mxX\npDHS1i1V01GGo8AgsazamuQ9+5s3tLZCdy3NdoPzkfn4itXJKVcvX0sqTS1yYTFpGIpxxFCW3q+I\ncrAoWuuoKstt7nHrBmcNJRX8OFNKEf2B/nzPXrQMzYQwltEFSkqYJfwjFTCVI4491ekpXfIcbw+8\nuOtpuo5xnLHKsN7UdNtT8tCTlOJuf0dlatq24vn5KeM041Ydqt8TkuftTpOfXfB4VaGc5c2UedR4\nirF8ernngxfXDHPmy892dJXicLzjLmQer1uUatBKcigVRVyZS08qCb8ylU7IDStGSREUucXnr43C\nx0xInrkP+HHEVQ1121LVok0wWqOtQWmDNuVBr6G1kQpkUeClh5Xn522GWQw9MlyzOKd4+viMYZwZ\njgdu727IWXF2dsZwHOmPkXVraKpTjkOgaJns79ZbGpMFKFtVTONIt9kyThPDPNHWDj9F8v28yhoh\nJVMoKXL0kXkepOxXEvaaYniAyaS44PUAVFqGeY5UitCotSLlTIiyQtSIOKtEmTuEIq1qyllaNHX/\n81ucc6LZ0IvPIcmcZxpHGleJld5Y6qoSDqJ1hGwYBhkY5pRI92TmRb9QSpZ8i9/lnvyBHQb7OZBN\nxZt+QNHzyXVPW8k0+UefP6IA9ph48qRm9fwZ8yRwCn8YmFLkOPfEGFmtGkKObHZr6tcV4+Ud7cWG\ndrVlONwIkCKkpbdTTPPIXArWijFI5JwZZxXnTSfinZSX2BSDKYkQI6WpqIxM6EvKC4xUyj0Qim02\nmrlktE+8vr7l6fNHnK5XfPrtz7jte37t9Z5vfXTLv/oT79A+esrZScX2S8+ZvGL/7W9j0ByPbyAZ\n7OaMMN3w9Okj9iGyOtkyuYbu9oZud0GcJ/7A07e43B/47PbIp1nx8U3g7bOKd09XfOnxBamt+NbH\nr/nOBx/x/rvQ6BqjFxSWEmGOyjJgnQiSwmwkDNRaQy76AdRh7mEcFJwRtZ6Pkel4x9gfqOqGdr3G\n1TUmCeqsJNk8pFJAC/G5quyihFOLD0KeoveVhOzXpSc3taaQWSuJYAcYDj2qZMIs69/Lq2usrtE5\ncnqyQZ9umObIp1fXmDnQKItVGlUCtnIcvWceZnyI7HZbamO4e/2Kfp5Jy8OhqzoKmaigRKEdGVPR\nugrnDGGaGfoBV1WQFFonxiAyz7qqiV5gsiJOWjDp1tI1HUpByPHBfai1XtjSEpJaSqIfD6hiRIKf\nRMyUl1lV3/fUTkJkrLHs9weUFSxfTCI5jilSVbVoDELAGujalkPf/4735A/sMDDO4MPEnAupFMbj\n4s3ThVf7Pe9fnPNTX37OOE+4/R270zUu9BzP1uRxptFPGHIiDBMvP/iY+v13cV2Dzz02a26vb9BW\nUoB0bRl8wrZaQCIpEKNHLzzEjMbnTG1Z8OKeZC3Ji47fhyhOR6MWG6ic/KoylCyns7E1kLHZgk4y\nVR9neu/5n37xu8zDyKw1N70n7/e8/5ULqqLYf/YCNU9UF2dwd8Mv/spL/oX3n7AykYuzM/avbnEn\njnhzxebRU9TdLd/+4CO+/u4j5lxhXcWLm1t+/vc/59XdlrdP1nTrFSHO6FnxaNXy4vSUYQ64rmby\nkxiNlKgpy/Ikrp0hW7Xo3ZMEiigFRiGoARErqQeL7BI4UilizoS55xBmTNVQtx11XWOsJZsFme7M\n4sf3AgFdBm4RaQvuh29l+Xf1haGkteKsjCmz2q6YfZRINu9JITJMd5yfPyHPgTkeUdpx1tSMypDj\nyPZ0h0USk3fbLfM04efA/nAgVY4eMF3Lpqpom4bbw5GuXVFQ7A93y4q1QHTMs1COlIJpHMQs5CrM\nEkXvp/mBQWCspXIObUV3EVNaDHVi9JJZQ1gGriL6SjGKqE0rhkmyQdq2FsNZEuFciJnUz1Dk88UH\nNHKYUIrY30uW1i9LSGxYEpp+p9cP7DBwCy/OmYTTS549GtfVVEuAxe3tHc/feUIYe+6SwCxU6Emz\n8OnW644ZD0px8/o109TLcM1UTN5zdrHl2I+EMbNabYilkGJm2PfUbUO37TApk33Gi9dR1oQhkPqJ\nbr2iGPGSZx/QbS2iIy2qMmZ5GhhToGR0Lg8JRudWYriny2v+4Nff4XrK9NPMj1ewO1kRhkSpWq4P\ne/S4Z7ub2HQNp9s1btVilfD99seJF59c4/zE0xz59gef8MmV53uXV/RppiQF2VKmzI+caLLJVGSu\nx4L1AxWGH336CJUSytXEAil4KVtLkUjwsKxXl/bH6PvyXS1hrTIjEcXhEvcFoOTPplxwJhNixI9H\n0jwR6oa6afDG4uparOR8TvYt+gta+WUyr9UXMiGLDMrutw8y4BSYjbWa2j2mHycO/cD1VeLm9g2b\nbsPFxSm+FMrguesH0jBz4hxt1RCMxlQVoy3YdUc1OIZh5KRZ4XMkFmmTdquOEDNTTHRtS20lWHUa\nB0nfMk7qRmNQLORkBJ2WksyPSikMw4A15iHjMHj/gGWXwyPK9bowB1gMW9ZaQioPnI04eaZ7FoTc\nBdhqYTUmSdcW9Fp+IFeJWUki7qcpisbjn5U34Z/1K8XE6cmO959e0PcDTWXYtAaVHcrC+49X+AJd\n1fDp9WvWXU1QhmmY6bZbtNPMQ88cPF23pnKKcVJYt6b3IyXP1KM80V6OI68PPWdn52x2OzbG4srI\nziZoO6Z+oswBpzRjKqSwRIMrQzby9CImpnGmXbWQC6VEVExYLRisqm4EChI8KE2eZ0pVoUrm+XnL\n06RJ5ZR61WFLT4gQ8kTjGsp6y/XrNzQKHq9qGlW4eXPLkyePsF2D8xN5VXEzZvoQiNry6RgocySU\nTFPgb3/3NX/6p7/Gm5sbdo3mzZQ53ezQztAlR7RBNBNLfDeA0kr4C9qJTbgUKuMwRRGjp6S4pExL\n3JyCB+aiUnJjK0TBqY3cDC5JalKceuapFy7BakWXVzhtqaoKV9kHgMr9ijGrZQ6zAGnL4sUvy0DT\nqAUVnsEqCbHZrFqaxnF+smUMnk8/ecV3/tG3uTg5YX2y40tvnXD058z9gbvDHY8eP2YOgUpbamcZ\n+szZ8yccj4M4LUOkqu5zGCNjPxKMwmA52W1QZc314cg8e5y1KNMsa8GCIjJNI+LqlEqn6zruMw1k\n0CehLKUUsk4LVblI7HvKAjEJnpyLHAJJmJMaTQpykKRwX1nJA885J6j0SuLrYgjMcxDNSZHcz6qq\nKCUSgv8d70n122iC/rm+lFLlJ7/5DXRRnHQttauoneZ81wopZvT8xFcfsaocaIvbbDje3lFCxJeM\nsoZH52dcv3rDPdy06RqSj8wpMOREjSOFSL06oW4dRUV8dhBmamc42Z2xdtC2sB+O5OpEVjPXL/Dz\nvAytPMkYplyIs7ATu65h1dZSZitFIlM5h60ESmIVaFMR80z0kvBbKo3VFn8caeqaaETZm5jRtsVh\nlr7acvfqJUUrbqaJNI7oqub2+shXv/5lxjny7Nzy3/31f8z3Lm8wxRJSJHhPVUV++u0zfu4bz7m+\nPrJ58pgpSFtjKkOcItpYfBC1WilFdALLTVaKIkS5Vkq0mwAAIABJREFUkAosfL60KDbNookXKXfM\n+QGbLuavhcK7cAdilG2Fj4lUoChpz7rVmqqu5X9V9SCKuecuKs3DQSVDNdniiMFGbhyLpdiCTrKq\ny8sgLyZJHDoeJi6vr7l+9YoUCienO1brjhfXN2yM5p2vfJn99YHRjwx9j6kqfBKepFEwDQMnZ+cE\nBdfDREmRFGf640RT1RhXoa1m7AfZJKApRrgUKkt1lBdq8X3smig+5eeJMZFJD/gz2c7K4DCn9CB4\nizFxeiqUqHGSTUgMcvMrYJjG5VDWyzYjU9lFsBTC8j0CZhnWtnWN1pr/7a/8L5RS1G+9J+EHWBmU\nZeJ6OY4000QGXt0NOAOPt2tujp7ZJTablkeVo318ztyP7K/vOAwDVwtTPqdE1TUC2ExS2m6UJnqP\nbizOBMbDLD1hq5lCpsweM9+xvXjC7BP76wP1qeP09AQ/C1NvniI+K+aQOHoPpbBSmhzlokQbYhHl\nXC4SAhtzISgFOXC+3dDsDOP1FTkaboZbvvPhNT5l+hg4HDNff7rhZ/7YzzIdb4hHz/HmEqzGBU8X\nAnfAm8sbUol88J3v8uhkQ2rO+bHHG0oxXO735LkwR0PUhbpbsd6tqbsVN0MgZ4FrlGNhu90QU8BV\nBpUE/JFSJGvBbKcQF/CyBHuwcPtTknzJotSDTVYrsTYv1fziATBLzkKWC3B5Lx6Sg3JkPNwxHi11\n21A3LXXTPFQG95iwHKNUB1+YK6R0305AVpniJfj1nhAJmWoZVJ6fbdjuVoxvPyGMkdHPeO85OT3l\ncHXLr/3ar3J+eo5pG6rUCfsizBRtCTmgq5p+HOlWHRebNX6amftMt61Q2nCcPWA53e3wzcRhHBln\nT4ywblrII/08Yp2jciIWksMzYY3FVQtLMyWMsUvmYiZ40QXEGB7CYK/fBFnJKg0LXDUtTEZnHTFn\ncXIum2/v/eJZkTmQMYtYS2t8DFTmhxR7pjMUJbhpn4RNOIdRtOb1RMkb2q6j261JJJpVh/aR1DZy\nAcaM2rYonyhaY7qWME2CB68rpsVfj4JSaQ6HA/0hs21btqsV/e0tt2miOjvj5PSM1in0/prQT3z7\no1umIqKd232PMopdW9N1Neu2pqkWfsAcqNpK/PwhMvkgJh7ruLu6JjqFnSIff/wZv/z6yEfXRzSW\nbdfwYn/k5uaGP/UvF9Znz5iOgV/7vz9ivLtiheLZl97mWcpsdjspG1PGti2v7wZaFfjD75yS7WP2\nhwMZTWHm2arm1asb9v3EgOLZo3NK1PTjzOvhirq1bDanaGXJOi4iF1BZnlzWWupKLrKQJdcPCyZb\n6eFLIZlFgVkWtr/WqCJrMF0kWiwj2QA6RqISRFtaoJwheaajZ+qP2LqhaVuaboVddv16ySzUi4lH\nlJQaskIZRcrCdUwpUhbWm4KH2DaVM04rqqYlV5lSVhImkyPzbsfl1Rs+fvmSXduxWW3QNeh1Ja4/\nt3qIYj/OHh8S0YvIKxZom5ZVU0GKZB9o6wqtFZ115GVAqKqa1shTPOck1YyRv2cMHr24OzOQYqAE\nyVS8NxHJNiCBysxzRGuRyYdpXmzpWfQFWj9UQyBELoGnLE5bpXDG4qzFWJkv/J7gJv88XwWxC5uS\n6ZYQkccXO5racNE4NpsKVxL9zR2ubWjnSD9N5NpJvzcMVMYSVMaExHx1gypZYCOLndnHmco6YR34\nSHN+weat53D7hlINfHg70qUjJycn3Lx8hWs7PvjwFW/6gVFXtK3h/OIMpwq7tSOHgE8BEzQ6ZbCK\nHD06O4x2tK3GNDVx39P7yHeuRsLNnvOvfo189f9xvtqQyWxax81YcRMn/sHf/RX+yJ/6k6zfO+Nn\nfjby8uMXXF++4c3LF3z9a7+fNx99wPvPz7mdIPuR7tEjfJy43nt+9KLhzeYxZu6BlrxAW9O6oQkz\nJUOpG1pruZlmPnt9yzdcIbgVVmlWzjKnjHU1xjrmmFApLrTfjF9KdKUSKQNFY0omLxdVyhKprhF4\nTM6ZKQhZymhNbQ162RZV1jB5T2U0VsvXhunAYR4IwVPVLc5VNF0LZGKWkjj7yFwSVdUQfVxw4cgT\nMGdUFnNUznGpJJbwEZZBmlJUlabOFfPOcbJZ8aV33ubDzz7hw48+IfYzTdexPrkgzqOE+J5u6FqL\n3m2YQyTMEasEmqOrVgC1KXHzei9P/MbiR8/UDw/hN6pkXCUuw8l7sIbaye/NpEQOkovoFyVjXJ76\ntbMPGyuQgFg/eLpuJUDUnFmvV8ScsYi8OeUkmZVJKglrjQS2OrvI6zMxRb5vb/CF1w9sZvCn/8TP\no2Ok6ypUTjS142zbibY+BlZNTSyZWok02FnHMXh0Nox+plhN3bWkaWaeZzbdikAm9BOulotq6Aec\nsWgDx16gm2jN9vSMx2894tWLS8ZpYt8nzp8+ZV0p+v6KGOTiq6wl9JO4qJSCEJZ1GeRxoRU7iyky\n0HJK42OiOzul+MDc94RZ886Xz/jrv/RtPnxxxbqx/OQ7J/ztD/bc9J7f9/YZ/8of+gZtU7GfZTft\nk2IcBubbW17cjEzDHY8fXbA7q7h++YaTzRm+abj77BNAs+vW1LXo5l+8uuVi1/LRzYFHXcN2tyOE\nGdtUoK1YuClM0yQrMjSmqilEgWKERFb3T3/NvaUml/vocVEQij9DPp6WNJ/7PjklGZTpRe+vlLSE\n9zh3wbXJ78rHRCxQMFhb0a5W0j4sfbRZvofWaknYXgaX98aoxYmZl778i9DP+9QjrSVJqyB/RxCZ\n+hwi+77nxWevuby+wVYOV9U4Y/FzpHGWzboRF6efUFGe/s5WdHWFrh0ZMWqlZdsVk2DN/TJDua9w\n7unH2miKAh+8XD9KE4Jf2ilkQ/AFQVZMUQ4VBG5jlJFkJK0XtLqROcRiTIopLi5ceY8q6wTuGsQK\n/dd+4X//bWcGP7DD4M/8yZ+jVnC+ackl47SmbhzdqsPEZdgVPco63ILWSjFTGccQg4RbImATs4RF\nRCMXWt22OGNEH1AURhX640jKssO1ribFhC6Frl3RY8kl0OTIsO+5GUaevPWEt56c4ZpOiL3jyDB5\nEgLvTNETtZR4+8NMPw48Wze8/ZW3WbeGcH3FdDhQcsVNv2efHL3W1Bkery17U1E5y844VO1gOFDv\nziRCbDywahtss+bu5o6XN3c0pbBu4FsfXfL07ISmqyWkNWXGJa16ngq//L0XtK7w2UEm4rumZldV\nXPczz09XdF3D65s9e594tGt47/GZ6OpVom5rjtcHulVL16wAAcCkJKEpqRS0tQ9pv3khMd9vrO41\nAiUXhnGGZR8PoLSAOZF7EYCQBDoqT0Wx/CY02spN2bQtdS1PYmU+D4u9H5QpJbMnozRFF4G9L05J\nreUg00o94MjKItOVDUBexE4CvrkbJm7vDrx6fUXJmfVqRQaCD0xTIOXCtu0oKokBzDnJajD3DETQ\nxkqLFSIhRmEKKKFv38NPYgiyucji2E2L+vM+YTultHAh5Tfqg+c+del+6BiisDYBmqpZ+BJyIJbl\noBXX4gLkUUJaUgr+6i/81R++AeJvfvqGde2ougo3z6x3p4Tome722NpyumqYb4444coQKstMll5z\ntYJSaE5XHG/3dK4Rs1CKGBRmzkSVKDkSpkDRltW6Ed22MdR4nLP4WPjsxQu+96pn9eiUrYo03ZaL\n3ZZtpXBlhvqMfHVFbQxhvSX1NygfICRCSQyHSTgHDWgbOX/2lEfvPOf13/k/yaPman/HXR/41Y9f\ncDPDu2eav33lWTmFCYUf/ZFnvPOo4vJyZPXmmpNnTyhk+hcvaC4e0ZTIeWvx+wMutfyBZ2v00yec\nBHi9H6kvajaD57A/0NSOi23LZzcTXzlf8fp65s0x05wpstOUpqKua25Kyy9//DEXNzXHKXC6qvnN\nq4nvfHDJ9mzLOxuDKpExWuaQePfJmq+/9xzHcpEtCK2cs7jrilB48yI9LqXQdq1sSO4l3KWIMYuy\nYNvAYSjaYHICZ4kxEXIm5YDvZ8bjHmUMbdexXm/p1muKj/iUKSpjlabqWlnXGXFMyumjyTHKgaD1\nw3S/qIJVikwUcpUW2K1xgd2qYdvWvP/sgldXN3zw0QtsLrz/7jMSio8vb7jeH1itOzarlsNhYh56\nnDPYpqGxDq1kU1VrhXVWFJrThDGOxkrUWzGa2jZQCj4ErHVUTUM/TUzzhPfiLxDkuUcnu2gvltY6\nZeqmEbqS95QsLdH9711rxdj3iw9F2uV7wZb9XQaIP7DK4Ce++fuoNPz0u89wW4fLsG4rzBIdVrUV\n4+HArumIrcMfBooTaEcIiccXFxwPB7CGPM0L91/8cyHlBzx4VTnWXc2T862wBqMnTxPHfuTybuI7\nr+74tZdHrvYjlbX84fcueP9shTaZ3dkJn7w88tmr10wl8db5KU/PVzx79zHHu555OPLq8pZffz3w\n5hhpG8fXH295drplf3fk9NEOrwy7VuMT/MOPL/n40z0v+oGTruVnf+x9xv2e3/fjP8Xlb/46qXge\nbRq61Sm5sujkKSnRmIq9NvjbW/7+tz7h+UXDdi2DrGq1BTJ10xKTws+BYRyYsmd3ck4AuHkFuuai\nGjkmzesh8Vf+0SU3R0+dI2+fbfjme2/hKsU0jHz39S13/UxdGz67EeHSj79zzo995S2quiGEuASD\nLDCVAnMIQi5ytZB7U1wewHrBqkVSltYh3VcESdiNMS9qORSpJFhaj5jBxyWt2DqMcZycnlKcw1pN\nYxyJQt00hBSpllzMjAzkpHpYoG9aE0umUlbWkYtWAe75FpqSBSxamZqkElc3e66vbmkqy3q7JoTE\ni9dXHG731At9iWzpx0m0GDljSqHrGuqmEmWqcxz7gUM/kooM/HJamA9a1opGaRF/JVlNjuMo2QmL\nDkGCViQ+rVDQRoA2JSViSAI+XbYIsKhkF0GSWsRdeaE+/T9/82/88FUGP/b+W7ROUWnDxjlyzFRa\n0+7W7F/fMA0R5Sx9msl7L+ETYUIph6k0tjOEyxFbWVxdMYdE0UswZkyiErOaafKsKifqtXZFmRTz\ncSR4T0iBu7lwmCPZFIYc+MUPX/Kdlw1ffnrC1zYXvPeNr/LOV97h1XHm2eMLKiIffve7nLczrjuB\ndsdqBWenLSebltO2wpNx2xUYwzh53n3vy2g/87PrhqunI6w6WR9WmqvasLKe001FKS2mqTj6kU7V\nHPojq6rC4ykzrHZb/vC/+DX8NBO15nh7B8oQjwPKaW7vBlpT4UPi0A/cHmZ2iwvzMAzcpJlV15KV\n48n5GdrsKT4yJLjrb/kj773Fav2c568P/P3f+Iy7vuekjby4GfmHH1+iVOYnv/E+Z7s10zTjY743\nc1JZjS55EeyUBYIhkW0pRawGpST6S1tFRDj/qhQqc7/GTGis+COSzBMqZx40DyEkbq4uQTtMZdls\ntriqoswzlEwwss0oWi+HVPn/2zuTGEuSs47/vohc31qvtu7qvWemB49t8Bh7RuAFcfEyHDCc4GaB\nxAkBEgcsc4EjQkLixgUjGR+MhBCWOVh40WCBwJ4Ze1aPu2fr7unuqnq1vKq35suMzAgOkdXTHtxj\nG4vusnh/6amy4r2q+kJR+WUs/+//v21Yqp1n9FV1JYAv5fZsvqosGY7GdHtdFEJhC0QcS52UTqvB\nPMuZZRk4y8WNdcz6KlleUBQ548kUXIE1QuX83sh0OKY5D+i0W4RYlFgaSeTdoauKwviNPl9+7Avd\ngjDAhVDZgFYaU5Qlk9mcqvRqXLeL7epyI2cdQRgRaC/Maoy3WLu9jKhLyF3lZw1hGKLkmJ4mlLMp\nvdPrRDpkPpsQ6IjBwYQVDSoIMLmh0Wsyn84QayhLRZq2UMoihWWwve1rhW2JituIy8jmBaEorAQo\npwkkZJZN2LUlxhhOTnLa7YgkCamqiM3NAVc2D8gqEAKvSiuKvBSKrUN6yx2agSWJNJfOnWEymPDS\n5Su0Oi0C0YwHE+Io5OKpdXTagnyCFQOlJUy8W9NqI6V/4wZhvcMeUJKUJQd5SbPRox1WZOMhOk0I\nVOSTlHUMBockzQaD0ZjlTsev2ytHNZ5SaUWv0aG1EXG4N2DrcMSz37nKrPKyYFlRkhWWJFBsLLf4\nyPvOUOZzpvOSvdGEi2sxn3ioQxytczArGA9nrPZaHGyPGKZzmmnKSmpRubC0tkJv1bKzN2KSV4jJ\naMRtdNggmGWY0pd0ox2iS5Txx2FFWWFtRagrHF5qTaxFB97bMVCK0nhSjrGOAL8x6MttPTvvSM3X\nWeUtNBBMZcjMDJM7ivkcpQLSZkqaNrBh6AVMxYu/BnXRjgTasymVQil/E1bGb+4dmcq2Wi2veu0n\nDd7rQCu04F2imqmfzRSGYj6hzGaESnF6bQ0JNNN5zt5gxHScUTrH1nDEzt4up1eW6a6uECYho+GY\nyTSjqglcyjlCFHNbEtbeIFVVkkYBodJoEpQKmeVzZlmG1srXVdRK3mW9FBJ8AZhXOPLiNaKEIKj3\n1ep9iTw/pgzER3/uEZqBohUl/Mp7T9JuJ9zYHtBMUuI44WA0BbEoCYg1ZLl/2nQaIaO8wmHqTJsR\naUVTB6gwoNGMscYSRgGlKHKrmU8yQgnQIbSigNVukyJwPPnSmzz9Sv+2CIdoRTPy/6ylKVmONB96\n37s4uxzTTBtkpuTm7oCzD1yk21siq4QbL75Id7mNKh1lWRJEIYE1GPA8fRRmOKF3egWVtMmGI6JY\nqJyG3BJ3GxSHGd1uzP7eHlGjSxInFJFiPhhjXUmgoQpCYhsiccTq6VVWe5pJf5NnnnuDb7w6YG9W\nQFXiVEUiIau9NqosefBEh8O9MUSaVFcknR6v39ilq4WPPLLGpbMdNgczXrk5Ym9qyAvD6/0p+7mj\nk8RcWk14z9kerZUl4igkn85JghDREcYKQeiY5TPySoiCmAAvQV9q5YU6Sj+V9X5qgjVHrDzvuGxM\n4clOStdVkwFFkeNqhyXPKfDj4+p1tidAKUrryAt/Q6A1aZISpSlJnHirtXpj2YpXERJ5S+ZN3d5k\n88pMlbi6gtI/qeVIkwDnly6lL/ixStWCpMJsljGeTshzQ6RD4iAgqwyDwwPyovIsxv093GzG+TOn\nWD+5QZ4bRrOMeWGYz3O09ia7R9JvOG/GYqqKKI2xWIqiQukjolhFXlVkR9Tnqqxl/DVlre4U1iXk\n1nrikac2+xnGN//jyeO3TKiUZeQcrpxwarlJJw1IVZfXtwbc2h2wN8vJjCegLKUhaaDpJjApFJO5\nY6OXUFUZY1NiKih0RTaZUmQhTiwnOimrS21GWcE0ASeWeek4nDvysKLRaaJ1g0YjxZQVlfVqx0X1\nllORqSxxJyXqNJiORjgVcuHhh1GTIS89d50zJ9eJ2x1oLTHr77F3MCJKI7pJSqQts4mhcMJSr+Wf\n1GZKGEZU0zlhJFzp9zkna+AqXn5ti5XlDlEIhhJmJRJpxlu7BM2UZkNxUOSU05J+f4t153joEx/m\nm1/4FiP3lvfiapzwS2darHZjVKJ5+vIurw7m4Lw564MnHR9//BJihbww/MtTb/KuSycJWy3y6Yhc\nFK1eyKl2jNLCC9f3uLw/40Jvl0fPr2LRhHFMpwmFcXREMHPD89d2mec559fb4ALOrXdppSkmCCkj\nVz9xHeaO48iqMoShpjRe89/7OzjCQFNaiAMNVelLnbUgKiDSAfM8R4ea3BSkzYhrW3usdtvMRjnF\nbMpYh8RxRBgnpGmDOIkpVa0QrfVty7mj41FE3fYjsFJ7StTHc/4m0dhA18xrX/ZeOv80XkuWEbw9\nmjGGiIhOM2Y2mzM8HJH0lpg1GtzY3mWrv0u326XV6fgNy2aDoigoCsPWTp+N9XWv+FTTi7PZzB8Z\nOpDIs22ttb7eIDjSNvT9KvL89gmLtV7QpKqTQVgLsRzbQiXBa/Y/vNKik2iiVkzPVbS0sFlaBvOC\nzAglFf0hrCWKD7zvLEJO3xnMdMLEVUyykkgiSi2M5wU6VIRpzF6WEylHkRXghINpQVEphgVsv3Kd\nRhBQWViOA7IgZJLlKAWNIGC12+XMcsyFiydY6bQoDsck3Q6Hkwo1mnJwa4unr93kxLnzJJUlyIfE\nQc7Kaspg/4BCO6yO0J0mRX9IuhwznsEsMGjmDAYTmp0WptJMRwPa7RaNdtu7TM0tB/ND2kmDINSE\ny12C0lIMDjE6wDqhCEL28oz3dDt87Ilf5R+/8m88tNpEuZLHH+jQk4rxvOJgP+f8+hK95ZI393P6\nhzO+f+OQ0eHzPHZumeVOg/+6sskbA4PTYK0wmRVYYDCaMTOFN5El4upBiXJ7/MLpJeIkYlYY4iRk\nWhqSQGikCdf3p9wc9Ckq4dGDAa3QqwYvr/fAhQQqIE5jTG25Xkm9sRV6ByYEtPLr4ShQtyXSgsBb\nqftTCUsSRaCENG5SGMP2/j4XNlapanpubmbk5Rwzm2KyjDhNSZLUK1sruT1jqKylcur27MA668Vd\n8WpGlb1D7t2XQtS29b5A62iDzwmIWOI4qGc9IWmc0uu2qZzFWGE8ydjf3WF7u8+NzU2iOKLd7rC6\nukZ3qc33Xr3CA+fO1GYpASKK0loKUzHPptjS+Bdens05aMQJ2TyrzX79TMlZbzSD9bwFb+fui5uO\ncTIwbDRSWg3h5mafl7cmXFju0Vjq0Rw7TocxV29tY6oQHUWc3OjQbcLhbkEkMDaOYQ7b45JTqeLA\n5JRaoSYFYSEEaJaX2kgrxOwPsVjG2ZTDyZxJrrhl5igJMEVBGmk+cG6dhy+cpNfpUhyOybOMk6sb\nOAqmbsD1V3dIeycYZfscGsNgOCM/mGDF0d/aItKOCx94lNf7z5O6jI7VvHFrm/apVd64vsvyqTUi\nHKYC3Va0T56gurXFfhny8tUdIhXw4Lk1RoVhHsLy2pKn1ZIynhlmoSFVGqNKklJQLc3w20/xi6dW\n+UYakg1zXD7mmWnBxOa0GytEccWlEy3e3Vvig5cCrh5W7O1N2DyY861bY9qbmVceSlKub/dpiGai\nFLk13tlZBYRWYWSOiGI/E/rjKY+uJKg05snn3+TZV7dxQYjTgrEloiHREMYdHrq4zFee2SY5yPnl\nR85zOJ5hDw+JAk2SNhCtiOLQG7+UnqNQ1XL0lTE1k87zHMJaRKaRRuS5oapKX0EughLQ4tWarS2J\nGiG2EkxZkY8PGU/HaBXQ6S6RNhtEccx8Pq/9HDy1PFDKG/EesRjrkwhXG9+Lr1LHiRdcOTKOcbZe\neKja/7KqajFYnyRwikgsa70Gvd4FNs6cZD4v6W/32draYn9nj267RVkUYK33/KgcpZkTxgHNRkgj\nXcJay2yWeWk06xmhtiqJAk0YaoqipKgMpnQ4W9W+DG/FQ1nednK+G+5bMgDFpHDcHFdc6Q8YzguC\n5gofOrNGYS3KBTxwpsfL1wdok/PzGwllXjGrcqykDOeGvdEcYy1XhxnWaQoMVyvIizHtOMYpzYMb\ny6yev8iFpS4Wxc5gyPatHYwxrLQCNtbbjLMKDGTDIYNZzvrpEyy3T5FNBmSTjFubA4IkYrDbx1ae\nmTYej9jav0mkE6ooJGg1ufXyZR67uMLWzX0yW6CaLeLxhMZaj+k449o4Yz4e0kwTkuCWN/PodWgg\nXNvc52OXzrN1WPCNr/0nNq+4ujlipaXRBLz3gw9R7M/RQYKUhmkR8p3Xd5m+eJP3XNxgvQUje5H+\nzhA7G9NNNOfWevQ6HfJIM+jvoU3JY+d7FA8EDDPD5mDC1acH9HTAXqPL1mRKWHnPIL9Wdijr3Zpm\nZUVV5TR2LacSYTk95L3rmrR1jhdujhhOM5CAJI6wrmKUG6Lc8WsfPM+XvnuNf/3uFR57+DTNbhst\nEcYZVCXYsvLTYO2XaTpQ3tHKeYIO2jPupHYXVrYkbcRe/ccrs/ifE3+e7gL/hFRa/KxCK2J8TcNs\nuMd8HBAlXqYtaqRUxusTujD0NS9HnhDUtGZPXeRIwBXniWtHbKojLzHlLK50t/UGcNTOXV53sqoc\nzlmaUUwjjllZepCHH7rIwWjITn+PV27e4sbWDloHJGFAs5EgBmzpjwfLsiSuS73z0pCkIcb4JUsY\nRaRhQFWF5GXJPDfY0OtDFKao+R6+svSdcN82EO/5H11ggQUAjhcdeYEFFjh+uJtBygILLPD/DItk\nsMACCwD3IRmIyCdF5LKIvCoin7nXf/9/CxG5JiIviMizIvJU3bYsIl8TkVdE5KsisnS/47wTIvJ3\nItIXkRfvaLtrzCLy2XpcLovIx+9P1D+Iu/Thz0XkZj0Wz4rIE3e8dxz7cFZEnhSR74nISyLyh3X7\n8RqLt9xu/u9feOm/14ALQAg8BzxyL2P4KWK/Ciy/re0vgT+prz8D/MX9jvNt8X0UeD/w4o+KGXh3\nPR5hPT6vAeqY9uHPgD/+IZ89rn04CTxaX7eAK8Ajx20s7vXM4HHgNefcNeecAf4B+NQ9juGnwdt3\nYX8d+Hx9/XngN+5tOO8M59y/Awdva75bzJ8CvuicM865a/h/wMfvRZzvhLv0Af7nWMDx7cO2c+65\n+noCfB84zTEbi3udDE4DN+74/mbd9rMAB3xdRJ4Rkd+r20445/r1dR84cX9C+4lwt5hP4cfjCMd9\nbP5ARJ4Xkc/dMb0+9n0QkQv4mc63OWZjca+Twc/yOeaHnXPvB54Afl9EPnrnm87P736m+vdjxHxc\n+/M3wEXgUWAL+Kt3+Oyx6YOItIB/Av7IOTe+873jMBb3OhncAs7e8f1ZfjADHls457bqr7vAP+On\nbX0ROQkgIhvAzv2L8MfG3WJ++9icqduOHZxzO64G8Le8NYU+tn0QkRCfCL7gnPtS3XysxuJeJ4Nn\ngEsickFEIuC3gC/f4xh+YohIQ0Ta9XUT+DjwIj72T9cf+zTwpR/+G44V7hbzl4HfFpFIRC4Cl4Cn\n7kN8PxL1jXOE38SPBRzTPoiIAJ8DXnbO/fU8RS3vAAAArUlEQVQdbx2vsbgPO6tP4HdTXwM+e793\nen/MmC/id3efA146ihtYBr4OvAJ8FVi637G+Le4vAptAgd+r+Z13ihn403pcLgOfuN/x36UPvwv8\nPfAC8Dz+BjpxzPvwEXzB43PAs/Xrk8dtLBZ05AUWWABYMBAXWGCBGotksMACCwCLZLDAAgvUWCSD\nBRZYAFgkgwUWWKDGIhkssMACwCIZLLDAAjUWyWCBBRYA4L8BEXB9iNhuVz0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d0683e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(deprocess_net_image(image))\n",
+    "disp_style_preds(test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whew, that looks a lot better than before!  But note that this image was from the training set, so the net got to see its label at training time.\n",
+    "\n",
+    "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual label = Pastel\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WKs9hFZYitTN2cR87zMqTyhlYiOUvdRVeXdJREPOPBUQLuaLGrEgo3mWYO8JX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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB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\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor\nj1iVK1SaUhlL1qkY1C56B5imPW1WpLYPQWlm0GaJWc+Zzr7FbPYMhyXJUorimKPRPfr9CWkyJhiT\ndp8db5pYeaaEwEuJJZhUsyyjWp2xmJ4zOr5PkZ+QpZLlesGinAEwmdxBNLn+ugvbRsdiZGeafsVt\nKhvOqruB27GBIDrE1aW6m6/LBbbPaGsohkhXSVb0GErFejFnvZwzv7rEOsfo+DgoSFVC3iC5NnFJ\nvP26adhj2Ky5zslGZ9hwZc1nhBS6okLLsRoEMiu4e/8Ri/mUF8HN5TOwhjTNSIteqGmvDYPhiF7R\nQ4gEoRyOmnq9DumgkoS0yFFa4Koa0izEqQvZKH08kixUIrLBPbjXJJqIByywfJLhYARCMru6YL6a\n4hIYjo6DmMH+6L+YwiMEiZI4J3bZ1WaCnPMs5wus0QwGQywSlYREIq3XYjyB1rNJ0y29RDvBynqK\ndLsi8mxAkmaoNMVYi7c+WF3YUpw2+rDNhBQWowZRUVaXPDv7Ms8uPs9i9VVmy7dYlmdUegEEP/zh\n8Jjh4D6JvMeo95288egvcjr6KL6zVLqbrgXVmD3rJuVZMTwlK0YhM7RU9IZDHj56jdViyuz8Aqd9\nKBbaxG3E1DvmFLpstI3OSUAlispa8ijZSnsu3thJ9LANUmtk7xhxxPMjRXB42hwTkiLLkcNwbrmc\ncTU9xwrH8eSkyai9Fae8D9R8nwjTHVMR/+hcE1mzN9cErtJvzeCdZ25KsvX6qCTlRXBjYsI7b7+F\nkimj8QkyEVxcXlDkOaPxGOc80/kC6RxKhsnqDUdBnrcGnAtmsywLPu+02NRT25ClWODJUhUKTHiL\nRyKF2lkc1jnm8yumV+c45+kPxownJ8gmzr/LsrYTGOcmFEDk7xQoCWHygydYyCFoETvZc1rfhNAW\nxze/9ockqeL+ow/hhWpKdbfhyR7lPNOLM/I8ZTieoI1B12UTuJIHN1rvMdqGMmHeMl085cnbX6Aq\nv8LC/F8uF3/E1eLr1PYcr2q8sqyM4Xy2pjaOLJUM+gnHk4fcPfkOhFb0/Ct89ys/ySt3P4GS/ecU\nbV1laDsu2vtNRF4bdt2es97hjOHNN7+EdJ6j8Zi7D18hKfLN5ozZeaJ3bMQqthRWRu9sg5ra4CTY\n5Yyuk8FjLjD+3eWCYt8R7z1luWI2n6J1jTOWXq/P8ckpSZLt5Z72IdB9v+N2ddsTn/QiVAkTIkSU\n7vMjoRmbtHEYu3ViQppmWOuodUlKyrA3oKxKVuuSotcPtty6osgTdKWpqor+YEDSuNcqa6mdx1eG\nRECWhXp1QimUlLiq5uLiDLyhn+f0B2NkE7DTDrCSktEw5EiYnp+xml4igdHkBNnIei32baHVVrds\nmG++a+OwiKYEWcDibQqsbVaebQRjkLEDIlkt5qRScjQaIAiKqLb0lwW0FxjhQi6/xmyHlMwXwQNz\n5eeMjo7RSIR3zGZTLi7fZrZ8k8p8icv687x1/kUups+o1mv6veByjezhkoLZMpQlG/YURQbT2ZRE\nXjLKTtD1l/jiH01xBl5/9AkEfRxBgapEcBUm6lP7mXZiBeLFnAgJacajV17lG1//Y6YXFzhnOf3A\nI4qiH4qdtjLvNc+IdSXtXyYEBjapybtIO25f3OYuIoiv6W7mHYQhQuWvgfUsFjO8MKxWS2pdc+fO\nvaa24n79yovk/bi/8edzIFpOV2y5hGv6mVyn2Iofd1OcwWy1AufRZYVKBMNeDyccVVnRGx6FTbJe\ngbMUWY7zYcNlRYbAI5o4Aact5bqiX+Rk+VZOq42hLNeAI0sSirwAuS2sGWN4ay3z6SWzizO8h8Hx\nHUZHx40YQHA8kg3b3ZCXtq5gO8QhEYbFI8hSuUOhYkzdVkkGNlFqtdFgDf0sxZHgZceMR0id5oXY\ncBPCe8pqxeXFMxKZkqQZ/eEYJFTlivXynKdPP8es/CJL9TZX7imzes7jJ39MtXqGUhYpUmqTcPZs\njdEwHEqOjx39vmBYZAzygkwVpNzhKP9zfPT1v8mr934YR8htaGxoUyqfL6UOL1ZOBvbWUZdrptMp\nF88eMxj2mNy9z3g4CQ5ibDdM10oSbybj2OSRbBGV8J5UqZ1r2/MttPqGriK2S727SsVu37xzVGXJ\ncjnDmpCtS6qUyeSUotd/DqHFn/usKNfBPmTSPb8PecVwKzmDvCjQxtJLFavZJVdVidYVw6OjsNmF\nIE0SqsqCUqRSIVQIKbbOhrJkeU6aKoTPqOoKJxxZljUlsGQQI5RqzI0C4aAlZPFoSKUYHh2D98yv\nrljNrxDA8GiCk2rD0rcsWKwA2ughhCBLVQjyQeywzKLzfUNLG8yepSkuSYNjVXOqu/izRuG4UXIJ\nQS/vI0/vs1jMKKs1HkNvMEaplCw7ol/cZ73+FoW9YiTHGNVEg5oCbQ269Hg0o5Oc1dqgFcxNQj0D\nqz3O1WSJJs/X2FJwPvsh7p18HyoZhuAl2RYWbb3st/3sjnGswNzoAYQk7w84zXJEIjl//BhpnqHu\nC4rRUUgtz26uhXhsNu8SoC2kqtGXtNWR2J/sZR8nYH3gxLpIbQeR+O2cxc9CSopeDyE8s+kVCId3\njrPzp4zGE4bDcajSdc0zJVxrkYrh3S6Jkdm+AKl3g5tTIGpLmipqa5EiAQW9tKAohsynM8qypj/o\nhyAYZ5FJghIqpDr3ASnoRoOcZ8HZSGuNpiRLE6RwKGewzqKyIsSei2iwGs1smytPKcVwcoJ2ntX8\nivUyOGv0xxOEUjgbvBtTSZNrIUxNHIDTBt+4hn3wkY9/bJrciClsqZ4SndgFdie1fUbsZWgQpHmP\niUqYL2csF5cgBGnWQ6icLDuhlz7Ary4xlIyoeTh4jYweF8u30W5OnnhS5UmUC5aUVHA0yOhnGUbX\nrFeaXpFANmNRvkVlLhkkw40HoPCO1XLJarkMjjj9IRCos3OOTKkdT8iY1fYi/M6ShLsnd9Fry/nZ\nY2qhuWNrRpM7+KbMWKuAc2ytCTTPUSJkbdrZ/JEjkLOhgnSbmyDmNto22WZNxB6IXf1Ia2EiOtZ+\nRwjyos9EKmazK+q6QkoafZTlaDyBxswd398mUd0nNjyHVCPR6UWIYYfgfBtwc1WY07BIjAeZpCQy\nOKys1yEFmhDB3j/s9UMsO8F11AmCE40P7Jlv5cNEYaymWq/QlcB7R5v+1yqJUMHjrR0kAzuFMwQh\nzPj45AQpPevFlHp+ibCGwdExUmUI4cFbtK5DGnaV0lbOaScooUlv5ncRQMzydk1Lm2xG0fh0Kxh1\nKVy7CTwgkpThYIw1lnK1wNiaXu+Ifv+YevUIX5W4dYIzDu/XpOo+x6MhpZ1R6wW1W5PnjqopZ14u\nDU6XOBEUr4lIMarmyez/8Pj8z/DGwyOUDPkC8ZIs6yGEbES7KpQyS2Rg06PIwy5FloSQaO8cWZrw\n4OF98lHB9Owx08sLQDCanKKavAvdzRYCNXIAABK+SURBVKs6Y+F4flMpwEoR/C5o4vojhNBCN5tx\nC6Lzfd81m7kUgizPOTo6YT6fUtcViQpFUQQwGk02kY6xQvO9vBuCZ2FbxOe9QFc8ejcEcWM6g3aD\na13jmsIT9WKNQFAM+xhr0XVJrzcI2Wgac5zxIUGqN5YskU19xa0Mqq0FH4pmWmup6gqEJM1zsjR9\nTuZzziE9G8UkhNj2p+98k+X0gjzv0RscMT4+QaUJ6/UCgaNXDBBJRqUtsvF3iNlY02h6N5ps50ia\nkuBxG+JJasUOTwhZjnUODpqFsB3HrghijWY+P2e5mJJlQ5KsR21WLOZPqVZnLNfvsDRTal3j/Zq1\nvmS5foJ2U7wweCXw0oIwwV4uMpQakqQD8qIgJYXliGH6Yb7z9R/mAw8+FKI/fdOC1jgu2ElEsg+8\n3+YsOLu6YjgaUaiEcrWgrNesVwvK5YLJ0R0md+8FhMAWacbcVTwesUgRy+dtUJIUvuEAdkW5LhK5\njvru0x90kZAgZLa6vDxnuZwjlcQaR78/4OTklDTNnnvOe31vF4ldB/v0NR5emOnoxpBB+96wKDyr\nukLPVyipSIs82NFNqH40HI+2CjwA59BVjbe2qaSUbRZCa9YLI9dkTtKm8WtIybNga93xanMOJcQm\nVBQ8lS6pywpTG5z3TTKWflPV1jXBUSFhq5QiCifeTmfrigygHXgcRQchtHdsqiPz/KS3LG1cNDX2\nimyf4QihtRcXZywuLugP+wwnR1jW1KVBKInMs1ARSZesl1ecnb1FWV/iZYm2S4zXWJGQpmOORg+Y\njB8x6N8lzwoUHmFr8JI8PSJPBzuUf98KcwRqHNxxd69wbVlzH1KICSGoyopVuUQlktnlJWaxZjQ5\n4uT+w5AclF0uyjZItoU4WCjerPFnGLCtGXIjtkT37dN7wO4mizmdriekICimZ7MrLi8vg1lZSZIk\nYTI5odfvbzQt172ju0ZaYvF+4EUKxBtHBmEJSypTs54tQp2CPEd4H2osqoT+MKTNajeEdw6sw+qm\nck+WhPzyRIoq50OVHCVRPugTvCc4XqgQ778z6NZhtcHoCuM0KstQadHMgsYZjTWevOiFIiGIbflx\nAOeQeGbTKUopBoPhJpklgGm4H+990D4vllTrNccnp2R5FkyIBL/2TISya/HMbLwHCQ5VqZLP2eJ9\n8wxrNOvLpzz++pskKRT9E4rRXfqTY/I8p9IVdVmircE1BdpC1anG81IKkqxHmvTJZEYiVERJwyi3\nScsE+yljVzlnrQlm0U7E4nK1RkpJlmeNSTHULFwuligBn/xP/5EPf/CDfOgj38347l1SFYrLx1Q9\n5hZiy0DMGbTzHHtQis736yIk4/HdR7ljZNC2bRM/4Rx1XfP0yWPW5YokUfR7fU5O75LnBdfBu+kF\nroN94kD8rFuLDOq6Yj6fcTQJbpzVek2paxyQSYW1NQJJrzcICU9po+VMo3VJKMsVtS7J0wFFniMb\nG5ODkDPRONJ065bsnG8q+YaqRxAtBucw2iJl8GjzjenB6ppqvWC1mJOkOUeTE1Sa7cYeeB+cjAie\niVJKvNWcPXtKmiYcTY7xCGprMdZQL9es5wuOTk4ZjUc4IdBs6yK0s9UqxWJFovaNMssGTX6qtprq\nVvtu5pf84ad/icuv/TaTh9/BnTf+PEevfASR92i9U5JEIdtMxGLLcTiC8jMOZ34Re+qbNjlCfoF9\n7rZthF33OdZ7am3RLnB5RZMk1DmHdYKnT9/mU//tv/IdH3yV7/7Yn+X49C5pltMmCSV6V+wuTecY\nPO9E1o2I3Ncv9pyLj3c5BU9Yo5Jd277RhsvLM8r1EmsMWdHj9PQeeXE9Qvh2YR8yi88Fq8UtRAbO\nO/BgTM26rOkN+5iqDotQyrAYag0+VJspikARnK1YTc/QxjGa3EETcvdlaR9vLGmebRQy1jrqWgcz\nZRYi97wLiTzwBLOk3J1Qa30oCCK3i805y2x6znJ6jvOO4dEpo/EpMkl3gk689xjPxtNQEoqwyiRB\nqgRjgj+hxSO9QNcVaRrKx0m2eQ5j3UGXasUl0lpR3XlP0ijJhGjqDXpHOX3CN7/066wunjC+/xGO\nX/texnfvIxFh8wqaTRWcoloKF0Oc2+BFC61u2pQ2f46Qh3G9WJIqSa9X4BpOqTXTtveGUhhNPorm\neN28M3WO+XTKv/u5f8N3vvEG3/dD38/47gn93uhaNpvOMb/neGzm3EdJ9z2vi+D2yeXtcd/muIyU\nPLqumU4v0HWFNqGI6mRyQr8/QIiukPn/B24lMih1HbLRek9VVyF5ZpoESpUkBH8CB9birGny8jms\nLlnNp1gnGI6OsAikzEhVhkqTUDq9oUDGOqqqxltHkoXz0HIHod9JKmmLQQsaW3KjqFPRkFXliotn\n76CtRqU5RTFgOJ5sOASIFrjfKg+F31bhaReTbTeiD4gn7Uz/RjfCbs5/wbawqGUbmdhC4LYsvbxJ\ncuoden7O46/8b+ZLw9HDD3Hy8DX6gx6VCanpA9fuSZN0Uz1qsyD989ru9lxXPGn1I7HSszaWs2dP\n8c5weucuSV5s2OeqDIgwiTIFdRVpG6ToPc+mU37x5/89J0XOJ/7yX2J0eofeeLy5r8sVdMWE9ni3\nHF33vfs2+nvZoN1d1G6rHU9VwOia6fSKuioxtQahOD45DZWe5XadvIgT2/fe94pAbiUyqGqNx21S\nT69WS6SQFP3BdtMAAo90Dl1rrNUkqSJNUvA1Ri8pK4uSI/ApeZEgUrW1F3tPVVbUdU3e66GyNGx2\nxyYMFAFS7k6ascGPIci44YRzFu8t1hjWVYV3liRRJGmPJMsgytDTUnLXprNid6G2lN0BwjbyP7sT\n2l4TPzNeqJt3sEu9jfU44yhy1VggPLpc8uzJW8xnU0bjU07uPSDJctalIW1MvFLIYLVhy3XEG0RG\n74wVZW17Wq4i3mgGMFqHsZIh6jRpUpEZ51muVkgh6ffy4B7NLpJx0XOdh3q54rf+56/x+O1v8cM/\n8nE+8OprDMfHm3Z1nYtaiBV9+zZarPPoihJ0fu/TicTPeRGXskFOzrGczyiXS6zReGAwGjMcjVFN\nrM17hW+Xk7iVyMA3noRamyAW1DXeW2SakuW9TS89DZb1jrJaY61hkBeY+pKr2RPy/hF5fkqWDRt5\nzeGMJWk0t+uqolqvKfIeqsgDNWjyJAYXBYlSYqOM84SIO20MqVKkSUAutTZ4b0mThFprnK3Q1RoQ\nDIdj0rQIqdPF826gXSrVfrcOvK0Dm54oBCqyaOyKA90sx/Ez280AYfPXxqBkSNXmRQiScqZidvE2\nb7/9NqPJPR48eESa5RitwblN3kmVJA3X4cHviksxxH3sIgOi4+213sFyucYJT79XBI7Ae+raYK0n\nydKgq5G7MQUeWKxL1lcLBsdjvBLYuuSP3/x9Mpnw6utvMJwch1TmnfZ1WfrumMVItaXcMfjO8fh3\nfH98rotEY4hFMOE96+WcxXyKNwbrHP3hmPHRBJV0ecX3DkEdf/3dL0IG79dS8b5ASBWoKgTZ1zmc\ntSxXC+q6QrjIVVNK8rxAqYTaGdJixN27r5GnA0xdoU3VEGeHtwbvHNpZEJ40S/De4EwdJHNBKEzR\nRHlh2fECS1VwmDHWBr8FAVma0MtyEqXIs4zf/PXfwFlLXa6YXZ1TlWuCW+5+GXVDdRrlknfgtGY1\nu6JezKgWC6qq3EEAGy7Duh0teFxbMbant553SRJiHGyTYzsBVJJz9+5rfPR7P87JvQeUdY0CPv2b\nv9X4eXho8iNIGo6iqlmv1pvahmF0dxf+znxG/VQ0NRvb4xJUnlFWhuW6DvUrhaCXpwx6GdI7lvM5\npt7m/G+fNeoVFIOCq/MzpNb005zv+Z7vQ6D4yh/8Af/9v3wS5+1zrH3c3m4b4+taXUz7O+6b7Pz2\nvglT7zyry7nFn+13Gf0hBMVgxPHJfWTa59O//b9YLuacnz+jrta8iEi/mHz/yTUON4oMIGCqLElC\nHUORkuV9cJ7p5TnL1WIzUcHnXNHLBySqQGswNkHKPkr1UCIl6L8lIYbcN8VLkiahZYIMZTwb/YNs\n4ggsQtiQ9pvt4kkSRaokRmuM1kiCfC8J2Y0+89u/Qy8fBKRRlyymV9R1yXVTtVlcbeJLCUkiyYsM\ni8VJj2qcUWKWVtKkMrdB4x7YeP8cNQ46Ak+pQ9h0KoM505hgAJWAFZIkSRkkGa5ac3n+lE/96qfw\nXpAkOe1CEgT//kSC0RWr9bIpdPPugTXXIQoIUaKDQZ8iz3cTfQpQqWI4HqGyJIRvG7eDFMajIQ8f\nPGA+nfH4nXeQScb3fOxjfPDD38Uv//L/YP70HGl2cyLFVLzbvuvmZ9Omzn1t3yWt2drvnGuhRfiu\ns5n3ISCBIM0yjk9P+PRnPoNHYLTm7Oyc1XIZTOjfJryfDX2jyCBQkKBgS7IUpTKsC9aDPM+pV0vK\nZUi80U6OksFbUCpJVVUh13+iNiZFJWXjB+CxtslrKCW1D0o7wdYBxkvQXuMxJHI7GC1ly5KEPFUY\nXVGuVw3VCpMshAwmLilJBOh6xXx6gWs2TRBZ9iuiBEF5aOuK1XzO9OoK0eg5rDabm9oFlIiQjk0g\nMM4iXeAsfOe5Ugh6abJJu52oBF1rTMRZhBoTKePRCGsNVbnGmFCmPnbWUUCRZQx6fdImwaxpFK/O\nbTmpWNaO7etd9hkgSQS9IiFLxY4n5WY9tNYECQ6H9WbbPwFKSe4/uB/MtDLBS8nkzh2SLOMLn/sc\nF4+foI3ZERH2bf4XUVbR+WuPbfoqGtdn75qK3Fuk3F7bVkKK3x0/r11nQgS9VZKmDEcj0rygqg3O\nai7On3J1dYG2egfxW17c/vcj9N8YMrj6ypu49Wy78L2g6Afzn0oSsqIH3qONRtc1rqGGFjBOs1jM\nKct1MNPUK5yu8SasUtGUsTbGBEtCmiPTBFuv0POn2PljMr0mQ4JXoWRWXYNz4BzO2U1mG6VS0jRF\nG01Vl1gbPBIdkBUFaZ6yXC3AGRLhKcsZ3prAmovdhdUmQZFAVdc8fvKUr/zR10Ak9PuhtLYxjrIs\nN+MUUzYpJUIqpN8u0Jgz2CCa1nXbeUpjNn75gvDPC4koBgxP7qKSlOViynJ2ia6rzbscgBAIlSLT\nIiSkbfQHptZYY/dSz/beLpsem02v25yxIjBLEpaLJYv1GtdwbRZY15rBeIRKBdaG1Or90YiP/OAP\noqXk8vycKqLaXXaeznc6x9+TElEGb9XWjyW+d9/mjx3GROdz+3LJvQcPuXvvAUqGsV4uZsynM7Te\ncjwC8L7dDfv78CeFG1MgvvSXHuAABwC4XdaEAxzgALcPblyBeIADHOB2wAEZHOAABwBuABkIIX5M\nCPGmEOIrQoifftnv/5OCEOLrQojPCyF+TwjxO82xEyHEp4QQXxZC/IoQYnLT7YxBCPFzQognQogv\nRMeubbMQ4meaeXlTCPGjN9PqXbimD/9UCPHNZi5+Twjx49G529iHV4UQvyaE+H0hxBeFEH+/OX67\n5iKk8Ho5fwSl9leB1wnxLJ8FPvIy2/A+2v414KRz7J8D/6j5/tPAP7vpdnba9wngB4AvvFubgY82\n85E28/NVQN7SPvwT4B/uufa29uEB8P3N9yHwh8BHbttcvGzO4OPAV733X/fea+A/AD/xktvwfqCr\nhf2rhJL1NJ9/7eU258Xgvf8N4LJz+Lo2/wTwC9577b3/OmEBfvxltPNFcE0fYL8V7bb24bH3/rPN\n9wXwB8AjbtlcvGxk8Aj4RvT7m82xPw3ggV8VQvyuEOLvNMfue++fNN+fAPdvpmnfFlzX5g8Q5qOF\n2z43f08I8TkhxM9G7PWt74MQ4nUCp/MZbtlcvGxk8KfZjvkj3vsfAH4c+LtCiE/EJ33g7/5U9e89\ntPm29udfAW8A3w+8A/yLF1x7a/oghBgCvwj8A+/9PD53G+biZSODbwGvRr9fZRcD3lrw3r/TfD4D\n/jOBbXsihHgAIIR4CDy9uRa+Z7iuzd25eaU5duvAe//UNwD8a7Ys9K3tgwgVbH8R+Lfe+082h2/V\nXLxsZPC7wIeFEK8LITLgrwO/9JLb8G2DEKIvhBg13wfAjwJfILT9p5rLfgr45P4n3Cq4rs2/BPwN\nIUQmhHgD+DDwOzfQvneFZuO08JOEuYBb2gcRorJ+FviS9/5fRqdu11zcgGb1xwna1K8CP3PTmt73\n2OY3CNrdzwJfbNsNnAC/CnwZ+BVgctNt7bT7F4C3CVnEvgH8rRe1GfjHzby8CfyVm27/NX3428DP\nA58HPkfYQPdveR/+AiE84bPA7zV/P3bb5uLgjnyAAxwAOHggHuAAB2jggAwOcIADAAdkcIADHKCB\nAzI4wAEOAByQwQEOcIAGDsjgAAc4AHBABgc4wAEaOCCDAxzgAAD8P7tWdgG4qV/gAAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d05ad810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_index = 1\n",
+    "image = test_net.blobs['data'].data[batch_index]\n",
+    "plt.imshow(deprocess_net_image(image))\n",
+    "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 99.76% Pastel\n",
+      "\t(2)  0.13% HDR\n",
+      "\t(3)  0.11% Detailed\n",
+      "\t(4)  0.00% Melancholy\n",
+      "\t(5)  0.00% Noir\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at the predictions of the network trained from scratch.  We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 49.81% Pastel\n",
+      "\t(2) 19.76% Detailed\n",
+      "\t(3) 17.06% Melancholy\n",
+      "\t(4) 11.66% HDR\n",
+      "\t(5)  1.72% Noir\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(scratch_test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, we can again look at the ImageNet model's predictions for the above image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted ImageNet labels =\n",
+      "\t(1) 34.90% n07579787 plate\n",
+      "\t(2) 21.63% n04263257 soup bowl\n",
+      "\t(3) 17.75% n07875152 potpie\n",
+      "\t(4)  5.72% n07711569 mashed potato\n",
+      "\t(5)  5.27% n07584110 consomme\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_imagenet_preds(imagenet_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+  "example_name": "Fine-tuning for Style Recognition",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb
deleted file mode 100644
index cc90b16bbfa..00000000000
--- a/examples/03-fine-tuning.ipynb
+++ /dev/null
@@ -1,947 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning a Pretrained Network for Style Recognition\n",
-    "\n",
-    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
-    "\n",
-    "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we will need to prepare the data. This involves the following parts:\n",
-    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
-    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
-    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.chdir('..')\n",
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "\n",
-    "import caffe\n",
-    "import numpy as np\n",
-    "from pylab import *\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
-    "# and a subset of 2000 images for the style recognition task.\n",
-    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
-    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
-    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
-    "    --workers=-1 --images=2000 --seed=1701 --label=5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's show what is the difference between the fine-tuning network and the original caffe model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1c1\r\n",
-      "< name: \"CaffeNet\"\r\n",
-      "---\r\n",
-      "> name: \"FlickrStyleCaffeNet\"\r\n",
-      "4c4\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "15,26c15,19\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n",
-      "<     batch_size: 256\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/train.txt\"\r\n",
-      ">     batch_size: 50\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "31c24\r\n",
-      "<   type: \"Data\"\r\n",
-      "---\r\n",
-      ">   type: \"ImageData\"\r\n",
-      "42,51c35,36\r\n",
-      "< # mean pixel / channel-wise mean instead of mean image\r\n",
-      "< #  transform_param {\r\n",
-      "< #    crop_size: 227\r\n",
-      "< #    mean_value: 104\r\n",
-      "< #    mean_value: 117\r\n",
-      "< #    mean_value: 123\r\n",
-      "< #    mirror: true\r\n",
-      "< #  }\r\n",
-      "<   data_param {\r\n",
-      "<     source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n",
-      "---\r\n",
-      ">   image_data_param {\r\n",
-      ">     source: \"data/flickr_style/test.txt\"\r\n",
-      "53c38,39\r\n",
-      "<     backend: LMDB\r\n",
-      "---\r\n",
-      ">     new_height: 256\r\n",
-      ">     new_width: 256\r\n",
-      "323a310\r\n",
-      ">   # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n",
-      "360c347\r\n",
-      "<   name: \"fc8\"\r\n",
-      "---\r\n",
-      ">   name: \"fc8_flickr\"\r\n",
-      "363c350,351\r\n",
-      "<   top: \"fc8\"\r\n",
-      "---\r\n",
-      ">   top: \"fc8_flickr\"\r\n",
-      ">   # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n",
-      "365c353\r\n",
-      "<     lr_mult: 1\r\n",
-      "---\r\n",
-      ">     lr_mult: 10\r\n",
-      "369c357\r\n",
-      "<     lr_mult: 2\r\n",
-      "---\r\n",
-      ">     lr_mult: 20\r\n",
-      "373c361\r\n",
-      "<     num_output: 1000\r\n",
-      "---\r\n",
-      ">     num_output: 20\r\n",
-      "384a373,379\r\n",
-      ">   name: \"loss\"\r\n",
-      ">   type: \"SoftmaxWithLoss\"\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      ">   bottom: \"label\"\r\n",
-      ">   top: \"loss\"\r\n",
-      "> }\r\n",
-      "> layer {\r\n",
-      "387c382\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "---\r\n",
-      ">   bottom: \"fc8_flickr\"\r\n",
-      "393,399d387\r\n",
-      "< }\r\n",
-      "< layer {\r\n",
-      "<   name: \"loss\"\r\n",
-      "<   type: \"SoftmaxWithLoss\"\r\n",
-      "<   bottom: \"fc8\"\r\n",
-      "<   bottom: \"label\"\r\n",
-      "<   top: \"loss\"\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For your record, if you want to train the network in pure C++ tools, here is the command:\n",
-    "\n",
-    "<code>\n",
-    "build/tools/caffe train \\\n",
-    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
-    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
-    "    -gpu 0\n",
-    "</code>\n",
-    "\n",
-    "However, we will train using Python in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n",
-      "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n",
-      "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n",
-      "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n",
-      "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n",
-      "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n",
-      "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n",
-      "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n",
-      "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n",
-      "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n",
-      "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n",
-      "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n",
-      "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n",
-      "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n",
-      "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n",
-      "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n",
-      "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n",
-      "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n",
-      "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n",
-      "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n",
-      "done\n"
-     ]
-    }
-   ],
-   "source": [
-    "niter = 200\n",
-    "# losses will also be stored in the log\n",
-    "train_loss = np.zeros(niter)\n",
-    "scratch_train_loss = np.zeros(niter)\n",
-    "\n",
-    "caffe.set_device(0)\n",
-    "caffe.set_mode_gpu()\n",
-    "# We create a solver that fine-tunes from a previously trained network.\n",
-    "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-    "# For reference, we also create a solver that does no finetuning.\n",
-    "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n",
-    "\n",
-    "# We run the solver for niter times, and record the training loss.\n",
-    "for it in range(niter):\n",
-    "    solver.step(1)  # SGD by Caffe\n",
-    "    scratch_solver.step(1)\n",
-    "    # store the train loss\n",
-    "    train_loss[it] = solver.net.blobs['loss'].data\n",
-    "    scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n",
-    "    if it % 10 == 0:\n",
-    "        print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n",
-    "print 'done'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the training loss produced by the two training procedures respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fbb36f0ad50>,\n",
-       " <matplotlib.lines.Line2D at 0x7fbb36f0afd0>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "ZGZWR3rl4iMzM+sfDnUzszriUDczqyMOdTOzOuJQNzOrIw51M7M64lA3M6sjDnUzszry/wFBsEB8\n",
-       "UlvRigAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbb37f20990>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fbb347a8310>,\n",
-       " <matplotlib.lines.Line2D at 0x7fbb347a8590>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "jSJIx7K3lcDjwGFJB/KxTZTUP13oZGZWU16T1cysppzgzcxqygnezKymnODNzGrKCd7MrKac4M3M\n",
-       "asoJ3sysppzgzcxq6j+vUsbacqJa4gAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fbb37f207d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy for fine-tuning: 0.570000001788\n",
-      "Accuracy for training from scratch: 0.224000000954\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_iters = 10\n",
-    "accuracy = 0\n",
-    "scratch_accuracy = 0\n",
-    "for it in arange(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "    scratch_solver.test_nets[0].forward()\n",
-    "    scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "scratch_accuracy /= test_iters\n",
-    "print 'Accuracy for fine-tuning:', accuracy\n",
-    "print 'Accuracy for training from scratch:', scratch_accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
-    "\n",
-    "http://demo.vislab.berkeleyvision.org/"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
-  "example_name": "Fine-tuning for Style Recognition",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 4
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

From 08edfddbbae6abc66aa51ef655f782eee5109f97 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 23 Feb 2016 23:42:11 -0800
Subject: [PATCH 1700/2053] [example] improve brewing logreg notebook

- create solvers inline through python protobuf
- drop manually written solver prototxt
- remove ordering prefix, since there is no real sequencing constraint
  for this example
---
 examples/02-brewing-logreg.ipynb              | 5771 -----------------
 examples/brewing-logreg.ipynb                 | 1164 ++++
 .../nonlinear_solver.prototxt                 |   15 -
 examples/hdf5_classification/solver.prototxt  |   15 -
 4 files changed, 1164 insertions(+), 5801 deletions(-)
 delete mode 100644 examples/02-brewing-logreg.ipynb
 create mode 100644 examples/brewing-logreg.ipynb
 delete mode 100644 examples/hdf5_classification/nonlinear_solver.prototxt
 delete mode 100644 examples/hdf5_classification/solver.prototxt

diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb
deleted file mode 100644
index d36871fcdfd..00000000000
--- a/examples/02-brewing-logreg.ipynb
+++ /dev/null
@@ -1,5771 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Brewing Logistic Regression then Going Deeper\n",
-    "\n",
-    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import os\n",
-    "os.chdir('..')\n",
-    "\n",
-    "import sys\n",
-    "sys.path.insert(0, './python')\n",
-    "import caffe\n",
-    "\n",
-    "\n",
-    "import os\n",
-    "import h5py\n",
-    "import shutil\n",
-    "import tempfile\n",
-    "\n",
-    "import sklearn\n",
-    "import sklearn.datasets\n",
-    "import sklearn.linear_model\n",
-    "\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "GuNsjWm2xm9m/4FjM75/Ppfjn+15kM25kO05kc15ke35cb1zJBvzIJ/WgHy6//P13l9yOiPGmGTm\n",
-       "278XkUNo6ePqLDRdiVqfl0h9i8hPs9D2QRF5GHgStS5LUKv50A22+06YU7HwbTfYLoBNRFzGmLgx\n",
-       "5qiIvBf4BioVfCPEjLr4IplChHEAEblmN3iW5kK2xj1bY5ytMc3W+NlnfP/fZ3x/vS7XrI3/f3Yk\n",
-       "e2tCtteAbN732b7Xs3l/X88cyco8yKM1IJ/u//y896/HgsnXL+AhoGXWsQbgH7LQ9tuZ2yLdmaW+\n",
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-       "4764ZPt6Z3NNsObC4pGNa22tAfnNUk/trVvAtrNZHMvi8mTrOmdrLljjvrhk+3pnc02w5sLikY1r\n",
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-       "jsX8yPb1zmZ71lxYPLJxra01IL9Z0saIhYWFhYWFRf5jbdNYWFhYWFhY5BTLGLGwsLCwsLDIKZYx\n",
-       "YmFhYWFhYZFTLGMkjxCRt4nIKRE5KyJ/lOv+WCw8IvIVERkWkWO57ovF4iAiTSLyrIicEJHjIvLb\n",
-       "ue6TxcIjIh4ReVVEDovISRH5TK77lE9YAax5gojYgdPAfUA/8DpaK6A9px2zWFBE5E4gBHzdGLM5\n",
-       "1/2xWHhEpBaoNcYcFhEvcBB4j3WvL31EpNAYMy0iDuBF4PeNMS/mul/5gOUZyR/2AOeMMV3GmATw\n",
-       "HeBnc9wniwXGGLMPmMh1PywWD2PMkDHmcOb7ENAO1Oe2VxaLgTFmOvOtC616O57D7uQVljGSPzQA\n",
-       "vTN+7sscs7CwWKKISCuwHXg1tz2xWAxExCYih4Fh4FljzMlc9ylfsIyR/MHaL7OwWEZktmi+D/xO\n",
-       "xkNiscQxxqSNMduARuAuEbk7x13KGyxjJH/oB5pm/NyEekcsLCyWGCLiBB4BvmGMeTTX/bFYXIwx\n",
-       "QeA/gF257ku+YBkj+cMBYLWItIqIC3g/8MMc98nCwiLLiIgA/wScNMb8ba77Y7E4iEiliPgy3xcA\n",
-       "9wNv5LZX+YNljOQJxpgk8FvAE8BJ4LtWdP3SR0S+DbwMrBGRXhH5aK77ZLHg3A58CLhHRN7IfL0t\n",
-       "152yWHDqgGcyMSOvAv9ujHk6x33KG6zUXgsLCwsLC4ucYnlGLCwsLCwsLHKKZYxYWFhYWFhY5BTL\n",
-       "GLGwsLCwsLDIKZYxYmFhYWFhYZFTLGPEwsLCwsLCIqdYxoiFhYWFhYVFTrGMEQsLCwsLC4ucYhkj\n",
-       "FhYWFhYWFjnl/wPBByFp6Gp27QAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dbd0090>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X, y = sklearn.datasets.make_classification(\n",
-    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
-    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
-    ")\n",
-    "\n",
-    "# Split into train and test\n",
-    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
-    "\n",
-    "# Visualize sample of the data\n",
-    "ind = np.random.permutation(X.shape[0])[:1000]\n",
-    "df = pd.DataFrame(X[ind])\n",
-    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.783\n",
-      "Accuracy: 0.783\n",
-      "Accuracy: 0.783\n",
-      "Accuracy: 0.783\n",
-      "1 loops, best of 3: 508 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "# Train and test the scikit-learn SGD logistic regression.\n",
-    "clf = sklearn.linear_model.SGDClassifier(\n",
-    "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
-    "\n",
-    "clf.fit(X, y)\n",
-    "yt_pred = clf.predict(Xt)\n",
-    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Save the dataset to HDF5 for loading in Caffe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Write out the data to HDF5 files in a temp directory.\n",
-    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
-    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
-    "if not os.path.exists(dirname):\n",
-    "    os.makedirs(dirname)\n",
-    "\n",
-    "train_filename = os.path.join(dirname, 'train.h5')\n",
-    "test_filename = os.path.join(dirname, 'test.h5')\n",
-    "\n",
-    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
-    "# To show this off, we'll list the same data file twice.\n",
-    "with h5py.File(train_filename, 'w') as f:\n",
-    "    f['data'] = X\n",
-    "    f['label'] = y.astype(np.float32)\n",
-    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
-    "    f.write(train_filename + '\\n')\n",
-    "    f.write(train_filename + '\\n')\n",
-    "    \n",
-    "# HDF5 is pretty efficient, but can be further compressed.\n",
-    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
-    "with h5py.File(test_filename, 'w') as f:\n",
-    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
-    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
-    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
-    "    f.write(test_filename + '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from caffe import layers as L\n",
-    "from caffe import params as P\n",
-    "\n",
-    "def logreg(hdf5, batch_size):\n",
-    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
-    "    n = caffe.NetSpec()\n",
-    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
-    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
-    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
-    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
-    "    return n.to_proto()\n",
-    "    \n",
-    "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n",
-    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
-    "    \n",
-    "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n",
-    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Time to learn and evaluate our Caffeinated logistic regression in Python."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.782\n",
-      "Accuracy: 0.782\n",
-      "Accuracy: 0.782\n",
-      "Accuracy: 0.782\n",
-      "1 loops, best of 3: 287 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "caffe.set_mode_cpu()\n",
-    "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n",
-    "solver.solve()\n",
-    "\n",
-    "accuracy = 0\n",
-    "batch_size = solver.test_nets[0].blobs['data'].num\n",
-    "test_iters = int(len(Xt) / batch_size)\n",
-    "for i in range(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "\n",
-    "print(\"Accuracy: {:.3f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same through the command line interface for detailed output on the model and solving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n",
-      "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n",
-      "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
-      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
-      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
-      "test_iter: 250\n",
-      "test_interval: 1000\n",
-      "base_lr: 0.01\n",
-      "display: 1000\n",
-      "max_iter: 10000\n",
-      "lr_policy: \"step\"\n",
-      "gamma: 0.1\n",
-      "momentum: 0.9\n",
-      "weight_decay: 0.0005\n",
-      "stepsize: 5000\n",
-      "snapshot: 10000\n",
-      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
-      "solver_mode: CPU\n",
-      "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
-      "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TRAIN\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
-      "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
-      "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
-      "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
-      "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.652724 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
-      "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n",
-      "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
-      "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TEST\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip1\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
-      "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
-      "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n",
-      "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n",
-      "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n",
-      "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n",
-      "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n",
-      "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:32.655422 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n",
-      "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n",
-      "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n",
-      "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n",
-      "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
-      "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
-      "I0318 00:58:32.683643 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.3736\n",
-      "I0318 00:58:32.683686 2013098752 solver.cpp:315]     Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n",
-      "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n",
-      "I0318 00:58:32.683861 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.3\n",
-      "I0318 00:58:32.683871 2013098752 solver.cpp:204]     Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n",
-      "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
-      "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
-      "I0318 00:58:32.701917 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
-      "I0318 00:58:32.701961 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
-      "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n",
-      "I0318 00:58:32.702029 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:32.702041 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
-      "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
-      "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
-      "I0318 00:58:32.721529 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
-      "I0318 00:58:32.721562 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
-      "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n",
-      "I0318 00:58:32.721603 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:32.721613 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
-      "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
-      "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
-      "I0318 00:58:32.743494 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.77\n",
-      "I0318 00:58:32.743544 2013098752 solver.cpp:315]     Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n",
-      "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n",
-      "I0318 00:58:32.744231 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
-      "I0318 00:58:32.744249 2013098752 solver.cpp:204]     Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n",
-      "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
-      "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
-      "I0318 00:58:32.769110 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
-      "I0318 00:58:32.769170 2013098752 solver.cpp:315]     Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n",
-      "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n",
-      "I0318 00:58:32.769242 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:32.769255 2013098752 solver.cpp:204]     Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n",
-      "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
-      "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
-      "I0318 00:58:32.788722 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7696\n",
-      "I0318 00:58:32.788751 2013098752 solver.cpp:315]     Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n",
-      "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n",
-      "I0318 00:58:32.788833 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:32.788846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n",
-      "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
-      "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
-      "I0318 00:58:32.808061 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7856\n",
-      "I0318 00:58:32.808112 2013098752 solver.cpp:315]     Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n",
-      "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n",
-      "I0318 00:58:32.808753 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:32.808773 2013098752 solver.cpp:204]     Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n",
-      "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
-      "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
-      "I0318 00:58:32.831614 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7848\n",
-      "I0318 00:58:32.831657 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n",
-      "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n",
-      "I0318 00:58:32.831728 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.8\n",
-      "I0318 00:58:32.831745 2013098752 solver.cpp:204]     Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n",
-      "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
-      "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
-      "I0318 00:58:32.852712 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7796\n",
-      "I0318 00:58:32.852748 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n",
-      "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n",
-      "I0318 00:58:32.852840 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:32.852852 2013098752 solver.cpp:204]     Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n",
-      "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
-      "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
-      "I0318 00:58:32.871438 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.7816\n",
-      "I0318 00:58:32.871461 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n",
-      "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n",
-      "I0318 00:58:32.872131 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:32.872143 2013098752 solver.cpp:204]     Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n",
-      "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
-      "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
-      "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
-      "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n",
-      "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
-      "I0318 00:58:32.893728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.782\n",
-      "I0318 00:58:32.893757 2013098752 solver.cpp:315]     Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n",
-      "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n",
-      "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
-    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
-    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n",
-    "\n",
-    "The final accuracy of the new network should be higher than logistic regression!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from caffe import layers as L\n",
-    "from caffe import params as P\n",
-    "\n",
-    "def nonlinear_net(hdf5, batch_size):\n",
-    "    # one small nonlinearity, one leap for model kind\n",
-    "    n = caffe.NetSpec()\n",
-    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
-    "    # define a hidden layer of dimension 40\n",
-    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
-    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
-    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
-    "    # score the (now non-linear) features\n",
-    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
-    "    # same accuracy and loss as before\n",
-    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
-    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
-    "    return n.to_proto()\n",
-    "    \n",
-    "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n",
-    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
-    "    \n",
-    "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n",
-    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.832\n",
-      "Accuracy: 0.832\n",
-      "Accuracy: 0.832\n",
-      "Accuracy: 0.831\n",
-      "1 loops, best of 3: 386 ms per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "caffe.set_mode_cpu()\n",
-    "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n",
-    "solver.solve()\n",
-    "\n",
-    "accuracy = 0\n",
-    "batch_size = solver.test_nets[0].blobs['data'].num\n",
-    "test_iters = int(len(Xt) / batch_size)\n",
-    "for i in range(test_iters):\n",
-    "    solver.test_nets[0].forward()\n",
-    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-    "accuracy /= test_iters\n",
-    "\n",
-    "print(\"Accuracy: {:.3f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Do the same through the command line interface for detailed output on the model and solving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n",
-      "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n",
-      "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n",
-      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
-      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
-      "test_iter: 250\n",
-      "test_interval: 1000\n",
-      "base_lr: 0.01\n",
-      "display: 1000\n",
-      "max_iter: 10000\n",
-      "lr_policy: \"step\"\n",
-      "gamma: 0.1\n",
-      "momentum: 0.9\n",
-      "weight_decay: 0.0005\n",
-      "stepsize: 5000\n",
-      "snapshot: 10000\n",
-      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
-      "solver_mode: CPU\n",
-      "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
-      "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TRAIN\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 40\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"relu1\"\n",
-      "  type: \"ReLU\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip1\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip2\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip2\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
-      "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n",
-      "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
-      "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n",
-      "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n",
-      "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
-      "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n",
-      "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
-      "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n",
-      "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n",
-      "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n",
-      "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n",
-      "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
-      "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
-      "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.662909 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
-      "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n",
-      "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n",
-      "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n",
-      "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
-      "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n",
-      "state {\n",
-      "  phase: TEST\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"data\"\n",
-      "  type: \"HDF5Data\"\n",
-      "  top: \"data\"\n",
-      "  top: \"label\"\n",
-      "  hdf5_data_param {\n",
-      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
-      "    batch_size: 10\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip1\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"data\"\n",
-      "  top: \"ip1\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 40\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"relu1\"\n",
-      "  type: \"ReLU\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip1\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"ip2\"\n",
-      "  type: \"InnerProduct\"\n",
-      "  bottom: \"ip1\"\n",
-      "  top: \"ip2\"\n",
-      "  inner_product_param {\n",
-      "    num_output: 2\n",
-      "    weight_filler {\n",
-      "      type: \"xavier\"\n",
-      "    }\n",
-      "  }\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"accuracy\"\n",
-      "  type: \"Accuracy\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"accuracy\"\n",
-      "}\n",
-      "layer {\n",
-      "  name: \"loss\"\n",
-      "  type: \"SoftmaxWithLoss\"\n",
-      "  bottom: \"ip2\"\n",
-      "  bottom: \"label\"\n",
-      "  top: \"loss\"\n",
-      "}\n",
-      "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n",
-      "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n",
-      "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n",
-      "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n",
-      "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n",
-      "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
-      "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n",
-      "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n",
-      "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n",
-      "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n",
-      "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n",
-      "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n",
-      "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n",
-      "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n",
-      "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n",
-      "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n",
-      "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n",
-      "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n",
-      "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n",
-      "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n",
-      "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n",
-      "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n",
-      "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n",
-      "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n",
-      "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n",
-      "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n",
-      "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n",
-      "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n",
-      "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n",
-      "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n",
-      "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n",
-      "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n",
-      "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n",
-      "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n",
-      "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n",
-      "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n",
-      "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n",
-      "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n",
-      "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n",
-      "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n",
-      "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n",
-      "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n",
-      "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n",
-      "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n",
-      "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n",
-      "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n",
-      "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n",
-      "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n",
-      "I0318 00:58:43.665282 2013098752 net.cpp:122]     with loss weight 1\n",
-      "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n",
-      "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n",
-      "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n",
-      "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n",
-      "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n",
-      "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n",
-      "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n",
-      "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n",
-      "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n",
-      "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n",
-      "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n",
-      "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n",
-      "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n",
-      "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n",
-      "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n",
-      "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n",
-      "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n",
-      "I0318 00:58:43.683938 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.5184\n",
-      "I0318 00:58:43.683981 2013098752 solver.cpp:315]     Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n",
-      "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n",
-      "I0318 00:58:43.684267 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.5\n",
-      "I0318 00:58:43.684285 2013098752 solver.cpp:204]     Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n",
-      "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n",
-      "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n",
-      "I0318 00:58:43.721762 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8168\n",
-      "I0318 00:58:43.721818 2013098752 solver.cpp:315]     Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n",
-      "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n",
-      "I0318 00:58:43.721917 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.721932 2013098752 solver.cpp:204]     Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n",
-      "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n",
-      "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n",
-      "I0318 00:58:43.754590 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8224\n",
-      "I0318 00:58:43.754621 2013098752 solver.cpp:315]     Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n",
-      "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n",
-      "I0318 00:58:43.754672 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.754683 2013098752 solver.cpp:204]     Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n",
-      "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n",
-      "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n",
-      "I0318 00:58:43.789728 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8176\n",
-      "I0318 00:58:43.789777 2013098752 solver.cpp:315]     Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n",
-      "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n",
-      "I0318 00:58:43.790510 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.790530 2013098752 solver.cpp:204]     Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n",
-      "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n",
-      "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n",
-      "I0318 00:58:43.821740 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8252\n",
-      "I0318 00:58:43.821770 2013098752 solver.cpp:315]     Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n",
-      "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n",
-      "I0318 00:58:43.821835 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.821846 2013098752 solver.cpp:204]     Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n",
-      "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n",
-      "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n",
-      "I0318 00:58:43.852102 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8256\n",
-      "I0318 00:58:43.852145 2013098752 solver.cpp:315]     Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n",
-      "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n",
-      "I0318 00:58:43.852200 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.852210 2013098752 solver.cpp:204]     Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n",
-      "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n",
-      "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n",
-      "I0318 00:58:43.880080 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8328\n",
-      "I0318 00:58:43.880105 2013098752 solver.cpp:315]     Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n",
-      "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n",
-      "I0318 00:58:43.880718 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.880729 2013098752 solver.cpp:204]     Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n",
-      "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n",
-      "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n",
-      "I0318 00:58:43.917851 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8316\n",
-      "I0318 00:58:43.917876 2013098752 solver.cpp:315]     Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n",
-      "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n",
-      "I0318 00:58:43.917971 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.917989 2013098752 solver.cpp:204]     Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n",
-      "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n",
-      "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n",
-      "I0318 00:58:43.947589 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8312\n",
-      "I0318 00:58:43.947615 2013098752 solver.cpp:315]     Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n",
-      "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n",
-      "I0318 00:58:43.947664 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.7\n",
-      "I0318 00:58:43.947674 2013098752 solver.cpp:204]     Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n",
-      "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n",
-      "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n",
-      "I0318 00:58:43.977033 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.834\n",
-      "I0318 00:58:43.977056 2013098752 solver.cpp:315]     Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n",
-      "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n",
-      "I0318 00:58:43.977735 2013098752 solver.cpp:204]     Train net output #0: accuracy = 0.9\n",
-      "I0318 00:58:43.977746 2013098752 solver.cpp:204]     Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n",
-      "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n",
-      "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
-      "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n",
-      "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n",
-      "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n",
-      "I0318 00:58:44.008216 2013098752 solver.cpp:315]     Test net output #0: accuracy = 0.8308\n",
-      "I0318 00:58:44.008252 2013098752 solver.cpp:315]     Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n",
-      "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n",
-      "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
-    "shutil.rmtree(dirname)"
-   ]
-  }
- ],
- "metadata": {
-  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Off-the-shelf SGD for classification",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  },
-  "priority": 3
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
new file mode 100644
index 00000000000..c053b73b39f
--- /dev/null
+++ b/examples/brewing-logreg.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brewing Logistic Regression then Going Deeper\n",
+    "\n",
+    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "import h5py\n",
+    "import shutil\n",
+    "import tempfile\n",
+    "\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import sklearn.linear_model\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZsmQhkpQhEGjir//6Ty6pBHu1mI5x9/v9yLKMy+W67OvV7Xbz29/uJBBQOHLk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\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luroFURRRlDwTE+1cdlkFb7xxknRKxGi0YTIbaGx0cPfdX/pY1lH5pP9RUhSFn/zk/5BO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BaDQabrr9dqp7euizWvEFArT399PgdGLKyOCDrVs5sH8/DodjzHaSJNHW1sbJkyfp7e4e9yJQ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S5dN9iyZT8PP/wEx47d8IYo2UgJ/+2/we///pXek++HEIk6cvw4vOMdV3pvXjtc0UrZK43z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\nVFRUMsr/B18y5d10ojlGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8611575a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(\n",
+    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+    ")\n",
+    "\n",
+    "# Split into train and test\n",
+    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "\n",
+    "# Visualize sample of the data\n",
+    "ind = np.random.permutation(X.shape[0])[:1000]\n",
+    "df = pd.DataFrame(X[ind])\n",
+    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "1 loop, best of 3: 372 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# Train and test the scikit-learn SGD logistic regression.\n",
+    "clf = sklearn.linear_model.SGDClassifier(\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n",
+    "\n",
+    "clf.fit(X, y)\n",
+    "yt_pred = clf.predict(Xt)\n",
+    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the dataset to HDF5 for loading in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Write out the data to HDF5 files in a temp directory.\n",
+    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+    "if not os.path.exists(dirname):\n",
+    "    os.makedirs(dirname)\n",
+    "\n",
+    "train_filename = os.path.join(dirname, 'train.h5')\n",
+    "test_filename = os.path.join(dirname, 'test.h5')\n",
+    "\n",
+    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+    "# To show this off, we'll list the same data file twice.\n",
+    "with h5py.File(train_filename, 'w') as f:\n",
+    "    f['data'] = X\n",
+    "    f['label'] = y.astype(np.float32)\n",
+    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    \n",
+    "# HDF5 is pretty efficient, but can be further compressed.\n",
+    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+    "with h5py.File(test_filename, 'w') as f:\n",
+    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+    "    f.write(test_filename + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def logreg(hdf5, batch_size):\n",
+    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+    "    return n.to_proto()\n",
+    "\n",
+    "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "\n",
+    "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "def solver(train_net_path, test_net_path):\n",
+    "    s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "    # Specify locations of the train and test networks.\n",
+    "    s.train_net = train_net_path\n",
+    "    s.test_net.append(test_net_path)\n",
+    "\n",
+    "    s.test_interval = 1000  # Test after every 1000 training iterations.\n",
+    "    s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n",
+    "\n",
+    "    s.max_iter = 10000      # # of times to update the net (training iterations)\n",
+    "\n",
+    "    # Set the initial learning rate for stochastic gradient descent (SGD).\n",
+    "    s.base_lr = 0.01        \n",
+    "\n",
+    "    # Set `lr_policy` to define how the learning rate changes during training.\n",
+    "    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+    "    # every `stepsize` iterations.\n",
+    "    s.lr_policy = 'step'\n",
+    "    s.gamma = 0.1\n",
+    "    s.stepsize = 5000\n",
+    "\n",
+    "    # Set other optimization parameters. Setting a non-zero `momentum` takes a\n",
+    "    # weighted average of the current gradient and previous gradients to make\n",
+    "    # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+    "    # the model from overfitting.\n",
+    "    s.momentum = 0.9\n",
+    "    s.weight_decay = 5e-4\n",
+    "\n",
+    "    # Display the current training loss and accuracy every 1000 iterations.\n",
+    "    s.display = 1000\n",
+    "\n",
+    "    # Snapshots are files used to store networks we've trained.  Here, we'll\n",
+    "    # snapshot every 10K iterations -- just once at the end of training.\n",
+    "    # For larger networks that take longer to train, you may want to set\n",
+    "    # snapshot < max_iter to save the network and training state to disk during\n",
+    "    # optimization, preventing disaster in case of machine crashes, etc.\n",
+    "    s.snapshot = 10000\n",
+    "    s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n",
+    "\n",
+    "    # We'll train on the CPU for fair benchmarking against scikit-learn.\n",
+    "    # Changing to GPU should result in much faster training!\n",
+    "    s.solver_mode = caffe_pb2.SolverParameter.CPU\n",
+    "    \n",
+    "    return s\n",
+    "\n",
+    "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n",
+    "with open(solver_path, 'w') as f:\n",
+    "    f.write(str(solver(train_net_path, test_net_path)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to learn and evaluate our Caffeinated logistic regression in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "1 loop, best of 3: 195 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver(solver_path)\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0224 00:32:03.232779   655 caffe.cpp:178] Use CPU.\n",
+      "I0224 00:32:03.391911   655 solver.cpp:48] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0224 00:32:03.392065   655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+      "I0224 00:32:03.392215   655 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:03.392365   655 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:03.392382   655 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:03.392395   655 net.cpp:411] data -> data\n",
+      "I0224 00:32:03.392423   655 net.cpp:411] data -> label\n",
+      "I0224 00:32:03.392442   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0224 00:32:03.392473   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+      "I0224 00:32:03.393473   655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+      "I0224 00:32:03.393862   655 net.cpp:150] Setting up data\n",
+      "I0224 00:32:03.393884   655 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:03.393894   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393901   655 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:03.393911   655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:03.393924   655 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:03.393934   655 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:03.393945   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:03.393956   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:03.393970   655 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:03.393978   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393986   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393995   655 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:03.394001   655 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:03.394012   655 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:03.394021   655 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:03.394029   655 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:03.394311   655 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:03.394323   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394331   655 net.cpp:165] Memory required for data: 360\n",
+      "I0224 00:32:03.394348   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.394358   655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.394366   655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+      "I0224 00:32:03.394374   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.394386   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.394395   655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+      "I0224 00:32:03.394404   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394424   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394443   655 net.cpp:165] Memory required for data: 520\n",
+      "I0224 00:32:03.394450   655 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:03.394462   655 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:03.394479   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.394489   655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:03.394497   655 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:03.394510   655 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:03.394536   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.394543   655 net.cpp:165] Memory required for data: 524\n",
+      "I0224 00:32:03.394551   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.394562   655 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:03.394569   655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.394577   655 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:03.394587   655 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:03.394603   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.394624   655 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:03.394634   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.394641   655 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:03.394659   655 net.cpp:165] Memory required for data: 528\n",
+      "I0224 00:32:03.394665   655 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:03.394673   655 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:03.394682   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+      "I0224 00:32:03.394690   655 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:03.394697   655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:03.394706   655 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:03.394712   655 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:03.394721   655 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:03.394731   655 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:03.394804   655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+      "I0224 00:32:03.394836   655 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:03.394953   655 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:03.394964   655 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:03.394973   655 net.cpp:411] data -> data\n",
+      "I0224 00:32:03.394984   655 net.cpp:411] data -> label\n",
+      "I0224 00:32:03.394994   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0224 00:32:03.395009   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+      "I0224 00:32:03.395937   655 net.cpp:150] Setting up data\n",
+      "I0224 00:32:03.395953   655 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:03.395963   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.395970   655 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:03.395978   655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:03.395989   655 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:03.395997   655 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:03.396005   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:03.396016   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:03.396028   655 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:03.396036   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.396044   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.396051   655 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:03.396059   655 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:03.396069   655 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:03.396075   655 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:03.396085   655 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:03.396100   655 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:03.396109   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396116   655 net.cpp:165] Memory required for data: 360\n",
+      "I0224 00:32:03.396138   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.396148   655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.396157   655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+      "I0224 00:32:03.396164   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.396174   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.396185   655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+      "I0224 00:32:03.396194   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396203   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396209   655 net.cpp:165] Memory required for data: 520\n",
+      "I0224 00:32:03.396216   655 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:03.396225   655 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:03.396234   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.396241   655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:03.396250   655 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:03.396260   655 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:03.396270   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.396276   655 net.cpp:165] Memory required for data: 524\n",
+      "I0224 00:32:03.396283   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.396291   655 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:03.396299   655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.396307   655 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:03.396317   655 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:03.396327   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.396339   655 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:03.396349   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.396356   655 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:03.396365   655 net.cpp:165] Memory required for data: 528\n",
+      "I0224 00:32:03.396373   655 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:03.396381   655 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:03.396389   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+      "I0224 00:32:03.396396   655 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:03.396404   655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:03.396412   655 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:03.396420   655 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:03.396427   655 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:03.396437   655 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:03.396455   655 solver.cpp:60] Solver scaffolding done.\n",
+      "I0224 00:32:03.396473   655 caffe.cpp:219] Starting Optimization\n",
+      "I0224 00:32:03.396482   655 solver.cpp:280] Solving \n",
+      "I0224 00:32:03.396489   655 solver.cpp:281] Learning Rate Policy: step\n",
+      "I0224 00:32:03.396499   655 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+      "I0224 00:32:03.932615   655 solver.cpp:406]     Test net output #0: accuracy = 0.4268\n",
+      "I0224 00:32:03.932656   655 solver.cpp:406]     Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n",
+      "I0224 00:32:03.932723   655 solver.cpp:229] Iteration 0, loss = 1.06081\n",
+      "I0224 00:32:03.932737   655 solver.cpp:245]     Train net output #0: accuracy = 0.4\n",
+      "I0224 00:32:03.932749   655 solver.cpp:245]     Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n",
+      "I0224 00:32:03.932765   655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+      "I0224 00:32:03.945551   655 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+      "I0224 00:32:03.948048   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\n",
+      "I0224 00:32:03.948065   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+      "I0224 00:32:03.948091   655 solver.cpp:229] Iteration 1000, loss = 0.505853\n",
+      "I0224 00:32:03.948102   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.948113   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+      "I0224 00:32:03.948122   655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+      "I0224 00:32:03.960741   655 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+      "I0224 00:32:03.963214   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\n",
+      "I0224 00:32:03.963249   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+      "I0224 00:32:03.963276   655 solver.cpp:229] Iteration 2000, loss = 0.549211\n",
+      "I0224 00:32:03.963289   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.963299   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+      "I0224 00:32:03.963309   655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+      "I0224 00:32:03.975945   655 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+      "I0224 00:32:03.978435   655 solver.cpp:406]     Test net output #0: accuracy = 0.7732\n",
+      "I0224 00:32:03.978451   655 solver.cpp:406]     Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n",
+      "I0224 00:32:03.978884   655 solver.cpp:229] Iteration 3000, loss = 0.66133\n",
+      "I0224 00:32:03.978911   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:03.978932   655 solver.cpp:245]     Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n",
+      "I0224 00:32:03.978950   655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+      "I0224 00:32:03.992017   655 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+      "I0224 00:32:03.994509   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\n",
+      "I0224 00:32:03.994525   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+      "I0224 00:32:03.994551   655 solver.cpp:229] Iteration 4000, loss = 0.505853\n",
+      "I0224 00:32:03.994562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.994573   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+      "I0224 00:32:03.994583   655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+      "I0224 00:32:04.007200   655 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+      "I0224 00:32:04.009686   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\n",
+      "I0224 00:32:04.009702   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+      "I0224 00:32:04.009727   655 solver.cpp:229] Iteration 5000, loss = 0.549211\n",
+      "I0224 00:32:04.009738   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.009749   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+      "I0224 00:32:04.009758   655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+      "I0224 00:32:04.022734   655 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+      "I0224 00:32:04.025177   655 solver.cpp:406]     Test net output #0: accuracy = 0.7824\n",
+      "I0224 00:32:04.025193   655 solver.cpp:406]     Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n",
+      "I0224 00:32:04.025545   655 solver.cpp:229] Iteration 6000, loss = 0.654873\n",
+      "I0224 00:32:04.025562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.025573   655 solver.cpp:245]     Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n",
+      "I0224 00:32:04.025583   655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+      "I0224 00:32:04.038586   655 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+      "I0224 00:32:04.041016   655 solver.cpp:406]     Test net output #0: accuracy = 0.7704\n",
+      "I0224 00:32:04.041033   655 solver.cpp:406]     Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n",
+      "I0224 00:32:04.041059   655 solver.cpp:229] Iteration 7000, loss = 0.46611\n",
+      "I0224 00:32:04.041071   655 solver.cpp:245]     Train net output #0: accuracy = 0.6\n",
+      "I0224 00:32:04.041082   655 solver.cpp:245]     Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n",
+      "I0224 00:32:04.041091   655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+      "I0224 00:32:04.053722   655 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+      "I0224 00:32:04.056171   655 solver.cpp:406]     Test net output #0: accuracy = 0.7788\n",
+      "I0224 00:32:04.056187   655 solver.cpp:406]     Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n",
+      "I0224 00:32:04.056213   655 solver.cpp:229] Iteration 8000, loss = 0.615126\n",
+      "I0224 00:32:04.056224   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:04.056236   655 solver.cpp:245]     Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n",
+      "I0224 00:32:04.056244   655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+      "I0224 00:32:04.068853   655 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+      "I0224 00:32:04.071291   655 solver.cpp:406]     Test net output #0: accuracy = 0.7808\n",
+      "I0224 00:32:04.071307   655 solver.cpp:406]     Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n",
+      "I0224 00:32:04.071650   655 solver.cpp:229] Iteration 9000, loss = 0.654997\n",
+      "I0224 00:32:04.071666   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.071677   655 solver.cpp:245]     Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n",
+      "I0224 00:32:04.071687   655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+      "I0224 00:32:04.084717   655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0224 00:32:04.084885   655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0224 00:32:04.084960   655 solver.cpp:318] Iteration 10000, loss = 0.466505\n",
+      "I0224 00:32:04.084977   655 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+      "I0224 00:32:04.087514   655 solver.cpp:406]     Test net output #0: accuracy = 0.77\n",
+      "I0224 00:32:04.087532   655 solver.cpp:406]     Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n",
+      "I0224 00:32:04.087541   655 solver.cpp:323] Optimization Done.\n",
+      "I0224 00:32:04.087548   655 caffe.cpp:222] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n",
+    "\n",
+    "The final accuracy of the new network should be higher than logistic regression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def nonlinear_net(hdf5, batch_size):\n",
+    "    # one small nonlinearity, one leap for model kind\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    # define a hidden layer of dimension 40\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    # score the (now non-linear) features\n",
+    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    # same accuracy and loss as before\n",
+    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "\n",
+    "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "\n",
+    "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n",
+    "\n",
+    "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n",
+    "with open(solver_path, 'w') as f:\n",
+    "    f.write(str(solver(train_net_path, test_net_path)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.838\n",
+      "Accuracy: 0.837\n",
+      "Accuracy: 0.838\n",
+      "Accuracy: 0.834\n",
+      "1 loop, best of 3: 277 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver(solver_path)\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0224 00:32:05.654265   658 caffe.cpp:178] Use CPU.\n",
+      "I0224 00:32:05.810444   658 solver.cpp:48] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0224 00:32:05.810634   658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+      "I0224 00:32:05.810835   658 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:05.811061   658 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:05.811079   658 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:05.811092   658 net.cpp:411] data -> data\n",
+      "I0224 00:32:05.811121   658 net.cpp:411] data -> label\n",
+      "I0224 00:32:05.811143   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0224 00:32:05.811189   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+      "I0224 00:32:05.812254   658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+      "I0224 00:32:05.812677   658 net.cpp:150] Setting up data\n",
+      "I0224 00:32:05.812705   658 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:05.812721   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812729   658 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:05.812739   658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:05.812752   658 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:05.812762   658 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:05.812774   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:05.812785   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:05.812798   658 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:05.812808   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812816   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812824   658 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:05.812831   658 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:05.812841   658 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:05.812849   658 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:05.812860   658 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:05.813179   658 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:05.813196   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.813210   658 net.cpp:165] Memory required for data: 1880\n",
+      "I0224 00:32:05.813230   658 layer_factory.hpp:77] Creating layer relu1\n",
+      "I0224 00:32:05.813241   658 net.cpp:106] Creating Layer relu1\n",
+      "I0224 00:32:05.813251   658 net.cpp:454] relu1 <- ip1\n",
+      "I0224 00:32:05.813258   658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+      "I0224 00:32:05.813271   658 net.cpp:150] Setting up relu1\n",
+      "I0224 00:32:05.813279   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.813287   658 net.cpp:165] Memory required for data: 3480\n",
+      "I0224 00:32:05.813294   658 layer_factory.hpp:77] Creating layer ip2\n",
+      "I0224 00:32:05.813304   658 net.cpp:106] Creating Layer ip2\n",
+      "I0224 00:32:05.813313   658 net.cpp:454] ip2 <- ip1\n",
+      "I0224 00:32:05.813321   658 net.cpp:411] ip2 -> ip2\n",
+      "I0224 00:32:05.813336   658 net.cpp:150] Setting up ip2\n",
+      "I0224 00:32:05.813345   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813379   658 net.cpp:165] Memory required for data: 3560\n",
+      "I0224 00:32:05.813401   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.813417   658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.813426   658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+      "I0224 00:32:05.813434   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.813446   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.813457   658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+      "I0224 00:32:05.813465   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813473   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813480   658 net.cpp:165] Memory required for data: 3720\n",
+      "I0224 00:32:05.813488   658 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:05.813499   658 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:05.813508   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.813515   658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:05.813524   658 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:05.813539   658 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:05.813547   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.813555   658 net.cpp:165] Memory required for data: 3724\n",
+      "I0224 00:32:05.813565   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.813585   658 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:05.813599   658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.813616   658 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:05.813627   658 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:05.813642   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.813663   658 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:05.813671   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.813679   658 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:05.813695   658 net.cpp:165] Memory required for data: 3728\n",
+      "I0224 00:32:05.813704   658 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:05.813712   658 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:05.813720   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+      "I0224 00:32:05.813729   658 net.cpp:226] ip2 needs backward computation.\n",
+      "I0224 00:32:05.813735   658 net.cpp:226] relu1 needs backward computation.\n",
+      "I0224 00:32:05.813743   658 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:05.813751   658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:05.813760   658 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:05.813772   658 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:05.813787   658 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:05.813809   658 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:05.813905   658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+      "I0224 00:32:05.813944   658 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:05.814131   658 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:05.814142   658 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:05.814152   658 net.cpp:411] data -> data\n",
+      "I0224 00:32:05.814162   658 net.cpp:411] data -> label\n",
+      "I0224 00:32:05.814180   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0224 00:32:05.814220   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+      "I0224 00:32:05.815207   658 net.cpp:150] Setting up data\n",
+      "I0224 00:32:05.815227   658 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:05.815243   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815253   658 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:05.815260   658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:05.815270   658 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:05.815279   658 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:05.815287   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:05.815299   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:05.815310   658 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:05.815318   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815326   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815335   658 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:05.815341   658 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:05.815351   658 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:05.815358   658 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:05.815367   658 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:05.815383   658 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:05.815398   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.815413   658 net.cpp:165] Memory required for data: 1880\n",
+      "I0224 00:32:05.815435   658 layer_factory.hpp:77] Creating layer relu1\n",
+      "I0224 00:32:05.815450   658 net.cpp:106] Creating Layer relu1\n",
+      "I0224 00:32:05.815459   658 net.cpp:454] relu1 <- ip1\n",
+      "I0224 00:32:05.815469   658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+      "I0224 00:32:05.815479   658 net.cpp:150] Setting up relu1\n",
+      "I0224 00:32:05.815486   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.815495   658 net.cpp:165] Memory required for data: 3480\n",
+      "I0224 00:32:05.815501   658 layer_factory.hpp:77] Creating layer ip2\n",
+      "I0224 00:32:05.815510   658 net.cpp:106] Creating Layer ip2\n",
+      "I0224 00:32:05.815518   658 net.cpp:454] ip2 <- ip1\n",
+      "I0224 00:32:05.815527   658 net.cpp:411] ip2 -> ip2\n",
+      "I0224 00:32:05.815542   658 net.cpp:150] Setting up ip2\n",
+      "I0224 00:32:05.815551   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815559   658 net.cpp:165] Memory required for data: 3560\n",
+      "I0224 00:32:05.815570   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.815579   658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.815587   658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+      "I0224 00:32:05.815600   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.815619   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.815640   658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+      "I0224 00:32:05.815654   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815662   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815670   658 net.cpp:165] Memory required for data: 3720\n",
+      "I0224 00:32:05.815677   658 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:05.815685   658 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:05.815693   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.815702   658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:05.815711   658 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:05.815722   658 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:05.815732   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.815738   658 net.cpp:165] Memory required for data: 3724\n",
+      "I0224 00:32:05.815747   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.815754   658 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:05.815762   658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.815770   658 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:05.815779   658 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:05.815790   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.815811   658 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:05.815829   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.815843   658 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:05.815867   658 net.cpp:165] Memory required for data: 3728\n",
+      "I0224 00:32:05.815876   658 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:05.815884   658 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:05.815892   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+      "I0224 00:32:05.815901   658 net.cpp:226] ip2 needs backward computation.\n",
+      "I0224 00:32:05.815908   658 net.cpp:226] relu1 needs backward computation.\n",
+      "I0224 00:32:05.815915   658 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:05.815923   658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:05.815932   658 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:05.815938   658 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:05.815946   658 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:05.815958   658 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:05.815978   658 solver.cpp:60] Solver scaffolding done.\n",
+      "I0224 00:32:05.816000   658 caffe.cpp:219] Starting Optimization\n",
+      "I0224 00:32:05.816016   658 solver.cpp:280] Solving \n",
+      "I0224 00:32:05.816030   658 solver.cpp:281] Learning Rate Policy: step\n",
+      "I0224 00:32:05.816048   658 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+      "I0224 00:32:05.831967   658 solver.cpp:406]     Test net output #0: accuracy = 0.4464\n",
+      "I0224 00:32:05.832033   658 solver.cpp:406]     Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n",
+      "I0224 00:32:05.832186   658 solver.cpp:229] Iteration 0, loss = 0.798509\n",
+      "I0224 00:32:05.832218   658 solver.cpp:245]     Train net output #0: accuracy = 0.6\n",
+      "I0224 00:32:05.832247   658 solver.cpp:245]     Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n",
+      "I0224 00:32:05.832281   658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+      "I0224 00:32:05.859506   658 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+      "I0224 00:32:05.862799   658 solver.cpp:406]     Test net output #0: accuracy = 0.8156\n",
+      "I0224 00:32:05.862818   658 solver.cpp:406]     Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n",
+      "I0224 00:32:05.862853   658 solver.cpp:229] Iteration 1000, loss = 0.537015\n",
+      "I0224 00:32:05.862864   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.862875   658 solver.cpp:245]     Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n",
+      "I0224 00:32:05.862885   658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+      "I0224 00:32:05.883155   658 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+      "I0224 00:32:05.886435   658 solver.cpp:406]     Test net output #0: accuracy = 0.8116\n",
+      "I0224 00:32:05.886451   658 solver.cpp:406]     Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n",
+      "I0224 00:32:05.886484   658 solver.cpp:229] Iteration 2000, loss = 0.43109\n",
+      "I0224 00:32:05.886497   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:05.886508   658 solver.cpp:245]     Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n",
+      "I0224 00:32:05.886518   658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+      "I0224 00:32:05.907243   658 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+      "I0224 00:32:05.910521   658 solver.cpp:406]     Test net output #0: accuracy = 0.8168\n",
+      "I0224 00:32:05.910537   658 solver.cpp:406]     Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n",
+      "I0224 00:32:05.910905   658 solver.cpp:229] Iteration 3000, loss = 0.430245\n",
+      "I0224 00:32:05.910922   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.910933   658 solver.cpp:245]     Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n",
+      "I0224 00:32:05.910943   658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+      "I0224 00:32:05.931205   658 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+      "I0224 00:32:05.934479   658 solver.cpp:406]     Test net output #0: accuracy = 0.8324\n",
+      "I0224 00:32:05.934496   658 solver.cpp:406]     Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n",
+      "I0224 00:32:05.934530   658 solver.cpp:229] Iteration 4000, loss = 0.628955\n",
+      "I0224 00:32:05.934542   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.934553   658 solver.cpp:245]     Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n",
+      "I0224 00:32:05.934583   658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+      "I0224 00:32:05.955108   658 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+      "I0224 00:32:05.958377   658 solver.cpp:406]     Test net output #0: accuracy = 0.8364\n",
+      "I0224 00:32:05.958395   658 solver.cpp:406]     Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n",
+      "I0224 00:32:05.958432   658 solver.cpp:229] Iteration 5000, loss = 0.394939\n",
+      "I0224 00:32:05.958444   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:05.958456   658 solver.cpp:245]     Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n",
+      "I0224 00:32:05.958466   658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+      "I0224 00:32:05.978703   658 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+      "I0224 00:32:05.981973   658 solver.cpp:406]     Test net output #0: accuracy = 0.838\n",
+      "I0224 00:32:05.981991   658 solver.cpp:406]     Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n",
+      "I0224 00:32:05.982347   658 solver.cpp:229] Iteration 6000, loss = 0.411537\n",
+      "I0224 00:32:05.982362   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:05.982373   658 solver.cpp:245]     Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n",
+      "I0224 00:32:05.982383   658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+      "I0224 00:32:06.003015   658 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+      "I0224 00:32:06.006283   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\n",
+      "I0224 00:32:06.006301   658 solver.cpp:406]     Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n",
+      "I0224 00:32:06.006335   658 solver.cpp:229] Iteration 7000, loss = 0.521072\n",
+      "I0224 00:32:06.006347   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:06.006358   658 solver.cpp:245]     Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n",
+      "I0224 00:32:06.006368   658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+      "I0224 00:32:06.026715   658 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+      "I0224 00:32:06.029965   658 solver.cpp:406]     Test net output #0: accuracy = 0.8404\n",
+      "I0224 00:32:06.029983   658 solver.cpp:406]     Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n",
+      "I0224 00:32:06.030015   658 solver.cpp:229] Iteration 8000, loss = 0.329477\n",
+      "I0224 00:32:06.030028   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:06.030040   658 solver.cpp:245]     Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n",
+      "I0224 00:32:06.030048   658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+      "I0224 00:32:06.050626   658 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+      "I0224 00:32:06.053889   658 solver.cpp:406]     Test net output #0: accuracy = 0.8376\n",
+      "I0224 00:32:06.053906   658 solver.cpp:406]     Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n",
+      "I0224 00:32:06.054271   658 solver.cpp:229] Iteration 9000, loss = 0.412227\n",
+      "I0224 00:32:06.054291   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:06.054314   658 solver.cpp:245]     Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n",
+      "I0224 00:32:06.054337   658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+      "I0224 00:32:06.074646   658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0224 00:32:06.074808   658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0224 00:32:06.074889   658 solver.cpp:318] Iteration 10000, loss = 0.532798\n",
+      "I0224 00:32:06.074906   658 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+      "I0224 00:32:06.078208   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\n",
+      "I0224 00:32:06.078225   658 solver.cpp:406]     Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n",
+      "I0224 00:32:06.078234   658 solver.cpp:323] Optimization Done.\n",
+      "I0224 00:32:06.078241   658 caffe.cpp:222] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+    "shutil.rmtree(dirname)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "example_name": "Off-the-shelf SGD for classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/hdf5_classification/nonlinear_solver.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt
deleted file mode 100644
index b4aacf6e423..00000000000
--- a/examples/hdf5_classification/nonlinear_solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
-test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
deleted file mode 100644
index 8587b5a1e5a..00000000000
--- a/examples/hdf5_classification/solver.prototxt
+++ /dev/null
@@ -1,15 +0,0 @@
-train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
-test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "examples/hdf5_classification/data/train"
-solver_mode: CPU

From ca22227089e9be0391c0d5532cab4b7ec64ab4ff Mon Sep 17 00:00:00 2001
From: Niels Ole Salscheider <niels_ole@salscheider-online.de>
Date: Wed, 24 Feb 2016 17:00:58 +0100
Subject: [PATCH 1701/2053] CMake: Do not include
 "${PROJECT_BINARY_DIR}/include" with SYSTEM option

This is important for the include order. Without this patch, a
previously installed caffe.pb.h might be included instead of the one
that is generated during the build.
---
 cmake/ProtoBuf.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index fc799bd3906..73f647f5fae 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -23,7 +23,7 @@ endif()
 
 # place where to generate protobuf sources
 set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
-include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
+include_directories("${PROJECT_BINARY_DIR}/include")
 
 set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
 

From 00598ca84e2611cf3bbd363d4920796ce1517ff2 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Oct 2015 16:44:23 -0700
Subject: [PATCH 1702/2053] add InputLayer for Net input

Create an input layer to replace oddball Net `input` fields.
---
 include/caffe/layers/input_layer.hpp | 44 ++++++++++++++++++++++++++++
 src/caffe/layers/input_layer.cpp     | 27 +++++++++++++++++
 src/caffe/proto/caffe.proto          | 13 ++++++--
 3 files changed, 82 insertions(+), 2 deletions(-)
 create mode 100644 include/caffe/layers/input_layer.hpp
 create mode 100644 src/caffe/layers/input_layer.cpp

diff --git a/include/caffe/layers/input_layer.hpp b/include/caffe/layers/input_layer.hpp
new file mode 100644
index 00000000000..f4472678c69
--- /dev/null
+++ b/include/caffe/layers/input_layer.hpp
@@ -0,0 +1,44 @@
+#ifndef CAFFE_INPUT_LAYER_HPP_
+#define CAFFE_INPUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net by assigning tops directly.
+ *
+ * This data layer is a container that merely holds the data assigned to it;
+ * forward, backward, and reshape are all no-ops.
+ */
+template <typename Dtype>
+class InputLayer : public Layer<Dtype> {
+ public:
+  explicit InputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "Input"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INPUT_LAYER_HPP_
diff --git a/src/caffe/layers/input_layer.cpp b/src/caffe/layers/input_layer.cpp
new file mode 100644
index 00000000000..667d8ad67ef
--- /dev/null
+++ b/src/caffe/layers/input_layer.cpp
@@ -0,0 +1,27 @@
+#include <vector>
+
+#include "caffe/layers/input_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void InputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int num_top = top.size();
+  const InputParameter& param = this->layer_param_.input_param();
+  const int num_shape = param.shape_size();
+  CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top)
+      << "Must specify 'shape' once, once per top blob, or not at all: "
+      << num_top << " tops vs. " << num_shape << " shapes.";
+  if (num_shape > 0) {
+    for (int i = 0; i < num_top; ++i) {
+      const int shape_index = (param.shape_size() == 1) ? 0 : i;
+      top[i]->Reshape(param.shape(shape_index));
+    }
+  }
+}
+
+INSTANTIATE_CLASS(InputLayer);
+REGISTER_LAYER_CLASS(Input);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 7edb6ae87e0..702ce6be112 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 143 (last added: scale_param)
+// LayerParameter next available layer-specific ID: 144 (last added: input_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -374,6 +374,7 @@ message LayerParameter {
   optional ImageDataParameter image_data_param = 115;
   optional InfogainLossParameter infogain_loss_param = 116;
   optional InnerProductParameter inner_product_param = 117;
+  optional InputParameter input_param = 143;
   optional LogParameter log_param = 134;
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
@@ -431,7 +432,7 @@ message LossParameter {
     // Outputs that receive the ignore label will NOT be ignored in computing
     // the normalization factor.
     FULL = 0;
-    // Divide by the total number of output locations that do not take the 
+    // Divide by the total number of output locations that do not take the
     // ignore_label.  If ignore_label is not set, this behaves like FULL.
     VALID = 1;
     // Divide by the batch size.
@@ -793,6 +794,14 @@ message InnerProductParameter {
   optional bool transpose = 6 [default = false];
 }
 
+message InputParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
+}
+
 // Message that stores parameters used by LogLayer
 message LogParameter {
   // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.

From bddd04b32c297035b38abbcb41a452bf7167ba17 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Oct 2015 16:44:45 -0700
Subject: [PATCH 1703/2053] deprecate input fields and upgrade automagically

---
 include/caffe/util/upgrade_proto.hpp |  6 ++++
 src/caffe/proto/caffe.proto          |  6 ++--
 src/caffe/util/upgrade_proto.cpp     | 45 ++++++++++++++++++++++++++++
 3 files changed, 54 insertions(+), 3 deletions(-)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index c94bb3caaa3..14e1936a8c2 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -59,6 +59,12 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
 
 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
+// Return true iff the Net contains input fields.
+bool NetNeedsInputUpgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade input fields into layers.
+void UpgradeNetInput(NetParameter* net_param);
+
 // Return true iff the solver contains any old solver_type specified as enums
 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 702ce6be112..3b27bbd94d2 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -63,12 +63,12 @@ message FillerParameter {
 
 message NetParameter {
   optional string name = 1; // consider giving the network a name
-  // The input blobs to the network.
+  // DEPRECATED. See InputParameter. The input blobs to the network.
   repeated string input = 3;
-  // The shape of the input blobs.
+  // DEPRECATED. See InputParameter. The shape of the input blobs.
   repeated BlobShape input_shape = 8;
 
-  // 4D input dimensions -- deprecated.  Use "shape" instead.
+  // 4D input dimensions -- deprecated.  Use "input_shape" instead.
   // If specified, for each input blob there should be four
   // values specifying the num, channels, height and width of the input blob.
   // Thus, there should be a total of (4 * #input) numbers.
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index ff3f8ffc4f0..449975bd733 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -60,6 +60,16 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
                 << "V1LayerParameter";
     }
   }
+  // NetParameter uses old style input fields; try to upgrade it.
+  if (NetNeedsInputUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "input fields: " << param_file;
+    UpgradeNetInput(param);
+    LOG(INFO) << "Successfully upgraded file specified using deprecated "
+              << "input fields.";
+    LOG(WARNING) << "Note that future Caffe releases will only support "
+                 << "input layers and not input fields.";
+  }
   return success;
 }
 
@@ -937,6 +947,41 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
   }
 }
 
+bool NetNeedsInputUpgrade(const NetParameter& net_param) {
+  return net_param.input_size() > 0;
+}
+
+void UpgradeNetInput(NetParameter* net_param) {
+  LayerParameter* layer_param = net_param->add_layer();
+  layer_param->set_name("input");
+  layer_param->set_type("Input");
+  InputParameter* input_param = layer_param->mutable_input_param();
+  bool has_shape = net_param->input_shape_size() > 0;
+  // Convert input fields into a layer.
+  for (int i = 0; i < net_param->input_size(); ++i) {
+    layer_param->add_top(net_param->input(i));
+    if (has_shape) {
+      input_param->add_shape()->CopyFrom(net_param->input_shape(i));
+    } else {
+      // Turn legacy input dimensions into shape.
+      BlobShape* shape = input_param->add_shape();
+      int first_dim = i*4;
+      int last_dim = first_dim + 4;
+      for (int j = first_dim; j < last_dim; j++) {
+        shape->add_dim(net_param->input_dim(j));
+      }
+    }
+  }
+  // Swap input layer to beginning of net to satisfy layer dependencies.
+  for (int i = net_param->layer_size() - 1; i > 0; --i) {
+    net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i));
+  }
+  // Clear inputs.
+  net_param->clear_input();
+  net_param->clear_input_shape();
+  net_param->clear_input_dim();
+}
+
 // Return true iff the solver contains any old solver_type specified as enums
 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
   if (solver_param.has_solver_type()) {

From 51f79a837ddea002746d86d69e342a44f099654f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 16 Oct 2015 21:11:32 -0700
Subject: [PATCH 1704/2053] drop Net inputs + Forward with bottoms

Drop special cases for `input` fields, the `Net` input members,
and the `Net` interface for Forward with bottoms along with
Forward() / ForwardPrefilled() distinction.
---
 include/caffe/net.hpp                         |  34 ++----
 src/caffe/net.cpp                             | 104 ++----------------
 src/caffe/solver.cpp                          |   8 +-
 src/caffe/test/test_gradient_based_solver.cpp |   6 +-
 src/caffe/test/test_net.cpp                   |  78 ++++++-------
 src/caffe/test/test_split_layer.cpp           |  61 ----------
 src/caffe/util/insert_splits.cpp              |  21 +---
 tools/caffe.cpp                               |   5 +-
 tools/extract_features.cpp                    |   3 +-
 9 files changed, 61 insertions(+), 259 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 543133e2517..43e0a845133 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -32,11 +32,10 @@ class Net {
   void Init(const NetParameter& param);
 
   /**
-   * @brief Run Forward with the input Blob%s already fed separately.
+   * @brief Run Forward and return the result.
    *
-   * You can get the input blobs using input_blobs().
    */
-  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
 
   /**
    * The From and To variants of Forward and Backward operate on the
@@ -49,14 +48,6 @@ class Net {
   Dtype ForwardFromTo(int start, int end);
   Dtype ForwardFrom(int start);
   Dtype ForwardTo(int end);
-  /// @brief Run forward using a set of bottom blobs, and return the result.
-  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
-      Dtype* loss = NULL);
-  /**
-   * @brief Run forward using a serialized BlobProtoVector and return the
-   *        result as a serialized BlobProtoVector
-   */
-  string Forward(const string& input_blob_protos, Dtype* loss = NULL);
 
   /**
    * @brief Zeroes out the diffs of all net parameters.
@@ -82,9 +73,9 @@ class Net {
    */
   void Reshape();
 
-  Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
+  Dtype ForwardBackward() {
     Dtype loss;
-    Forward(bottom, &loss);
+    Forward(&loss);
     Backward();
     return loss;
   }
@@ -194,18 +185,11 @@ class Net {
   inline const vector<string>& param_display_names() const {
     return param_display_names_;
   }
-  /// @brief Input and output blob numbers
-  inline int num_inputs() const { return net_input_blobs_.size(); }
+  /// @brief output blob number
   inline int num_outputs() const { return net_output_blobs_.size(); }
-  inline const vector<Blob<Dtype>*>& input_blobs() const {
-    return net_input_blobs_;
-  }
   inline const vector<Blob<Dtype>*>& output_blobs() const {
     return net_output_blobs_;
   }
-  inline const vector<int>& input_blob_indices() const {
-    return net_input_blob_indices_;
-  }
   inline const vector<int>& output_blob_indices() const {
     return net_output_blob_indices_;
   }
@@ -229,7 +213,7 @@ class Net {
 
  protected:
   // Helpers for Init.
-  /// @brief Append a new input or top blob to the net.
+  /// @brief Append a new top blob to the net.
   void AppendTop(const NetParameter& param, const int layer_id,
                  const int top_id, set<string>* available_blobs,
                  map<string, int>* blob_name_to_idx);
@@ -241,8 +225,6 @@ class Net {
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
 
-  /// @brief Helper for displaying debug info in Forward about input Blobs.
-  void InputDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Forward.
   void ForwardDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Backward.
@@ -281,10 +263,8 @@ class Net {
   vector<string> param_display_names_;
   vector<pair<int, int> > param_layer_indices_;
   map<string, int> param_names_index_;
-  /// blob indices for the input and the output of the net
-  vector<int> net_input_blob_indices_;
+  /// blob indices for the output of the net
   vector<int> net_output_blob_indices_;
-  vector<Blob<Dtype>*> net_input_blobs_;
   vector<Blob<Dtype>*> net_output_blobs_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 05bee7987da..b7320e95a86 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -56,22 +56,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)
-      << "Must specify either input_shape OR deprecated input_dim, not both.";
-  if (param.input_dim_size() > 0) {
-    // Deprecated 4D dimensions.
-    CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-        << "Incorrect input blob dimension specifications.";
-  } else {
-    CHECK_EQ(param.input_size(), param.input_shape_size())
-        << "Exactly one input_shape must be specified per input.";
-  }
   memory_used_ = 0;
-  // set the input blobs
-  for (int input_id = 0; input_id < param.input_size(); ++input_id) {
-    const int layer_id = -1;  // inputs have fake layer ID -1
-    AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
-  }
   // For each layer, set up its input and output
   bottom_vecs_.resize(param.layer_size());
   top_vecs_.resize(param.layer_size());
@@ -379,19 +364,17 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
   return true;
 }
 
-// Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
-// layer_id == -1, tops have layer_id >= 0.)
+// Helper for Net::Init: add a new top blob to the net.
 template <typename Dtype>
 void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
                            const int top_id, set<string>* available_blobs,
                            map<string, int>* blob_name_to_idx) {
-  shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
-    (new LayerParameter(param.layer(layer_id))) : NULL);
-  const string& blob_name = layer_param ?
-      (layer_param->top_size() > top_id ?
-          layer_param->top(top_id) : "(automatic)") : param.input(top_id);
+  shared_ptr<LayerParameter> layer_param(
+      new LayerParameter(param.layer(layer_id)));
+  const string& blob_name = (layer_param->top_size() > top_id) ?
+      layer_param->top(top_id) : "(automatic)";
   // Check if we are doing in-place computation
-  if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
+  if (blob_name_to_idx && layer_param->bottom_size() > top_id &&
       blob_name == layer_param->bottom(top_id)) {
     // In-place computation
     LOG_IF(INFO, Caffe::root_solver())
@@ -407,11 +390,7 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
   } else {
     // Normal output.
     if (Caffe::root_solver()) {
-      if (layer_param) {
-        LOG(INFO) << layer_param->name() << " -> " << blob_name;
-      } else {
-        LOG(INFO) << "Input " << top_id << " -> " << blob_name;
-      }
+      LOG(INFO) << layer_param->name() << " -> " << blob_name;
     }
     shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
     const int blob_id = blobs_.size();
@@ -419,22 +398,8 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     blob_names_.push_back(blob_name);
     blob_need_backward_.push_back(false);
     if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
-    if (layer_id == -1) {
-      // Set the (explicitly specified) dimensions of the input blob.
-      if (param.input_dim_size() > 0) {
-        blob_pointer->Reshape(param.input_dim(top_id * 4),
-                              param.input_dim(top_id * 4 + 1),
-                              param.input_dim(top_id * 4 + 2),
-                              param.input_dim(top_id * 4 + 3));
-      } else {
-        blob_pointer->Reshape(param.input_shape(top_id));
-      }
-      net_input_blob_indices_.push_back(blob_id);
-      net_input_blobs_.push_back(blob_pointer.get());
-    } else {
-      top_id_vecs_[layer_id].push_back(blob_id);
-      top_vecs_[layer_id].push_back(blob_pointer.get());
-    }
+    top_id_vecs_[layer_id].push_back(blob_id);
+    top_vecs_[layer_id].push_back(blob_pointer.get());
   }
   if (available_blobs) { available_blobs->insert(blob_name); }
 }
@@ -566,11 +531,6 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
-  if (debug_info_) {
-    for (int i = 0; i < net_input_blobs_.size(); ++i) {
-      InputDebugInfo(i);
-    }
-  }
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
@@ -591,7 +551,7 @@ Dtype Net<Dtype>::ForwardTo(int end) {
 }
 
 template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
+const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {
   if (loss != NULL) {
     *loss = ForwardFromTo(0, layers_.size() - 1);
   } else {
@@ -600,37 +560,6 @@ const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
   return net_output_blobs_;
 }
 
-template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
-    const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
-  // Copy bottom to internal bottom
-  for (int i = 0; i < bottom.size(); ++i) {
-    net_input_blobs_[i]->CopyFrom(*bottom[i]);
-  }
-  return ForwardPrefilled(loss);
-}
-
-template <typename Dtype>
-string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
-  BlobProtoVector blob_proto_vec;
-  if (net_input_blobs_.size()) {
-    blob_proto_vec.ParseFromString(input_blob_protos);
-    CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())
-        << "Incorrect input size.";
-    for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {
-      net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));
-    }
-  }
-  ForwardPrefilled(loss);
-  blob_proto_vec.Clear();
-  for (int i = 0; i < net_output_blobs_.size(); ++i) {
-    net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());
-  }
-  string output;
-  blob_proto_vec.SerializeToString(&output);
-  return output;
-}
-
 template <typename Dtype>
 void Net<Dtype>::BackwardFromTo(int start, int end) {
   CHECK_GE(end, 0);
@@ -644,16 +573,6 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
   }
 }
 
-template <typename Dtype>
-void Net<Dtype>::InputDebugInfo(const int input_id) {
-  const Blob<Dtype>& blob = *net_input_blobs_[input_id];
-  const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
-  const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-  LOG_IF(INFO, Caffe::root_solver())
-      << "    [Forward] "
-      << "Input " << blob_name << " data: " << data_abs_val_mean;
-}
-
 template <typename Dtype>
 void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
@@ -912,9 +831,6 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   param->Clear();
   param->set_name(name_);
   // Add bottom and top
-  for (int i = 0; i < net_input_blob_indices_.size(); ++i) {
-    param->add_input(blob_names_[net_input_blob_indices_[i]]);
-  }
   DLOG(INFO) << "Serializing " << layers_.size() << " layers";
   for (int i = 0; i < layers_.size(); ++i) {
     LayerParameter* layer_param = param->add_layer();
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a5ccf9c71b1..ece3913e88a 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -192,7 +192,6 @@ void Solver<Dtype>::InitTestNets() {
 
 template <typename Dtype>
 void Solver<Dtype>::Step(int iters) {
-  vector<Blob<Dtype>*> bottom_vec;
   const int start_iter = iter_;
   const int stop_iter = iter_ + iters;
   int average_loss = this->param_.average_loss();
@@ -220,7 +219,7 @@ void Solver<Dtype>::Step(int iters) {
     // accumulate the loss and gradient
     Dtype loss = 0;
     for (int i = 0; i < param_.iter_size(); ++i) {
-      loss += net_->ForwardBackward(bottom_vec);
+      loss += net_->ForwardBackward();
     }
     loss /= param_.iter_size();
     // average the loss across iterations for smoothed reporting
@@ -311,7 +310,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   if (param_.display() && iter_ % param_.display() == 0) {
     int average_loss = this->param_.average_loss();
     Dtype loss;
-    net_->ForwardPrefilled(&loss);
+    net_->Forward(&loss);
 
     UpdateSmoothedLoss(loss, start_iter, average_loss);
 
@@ -341,7 +340,6 @@ void Solver<Dtype>::Test(const int test_net_id) {
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
   vector<int> test_score_output_id;
-  vector<Blob<Dtype>*> bottom_vec;
   const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
   Dtype loss = 0;
   for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
@@ -362,7 +360,7 @@ void Solver<Dtype>::Test(const int test_net_id) {
 
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
-        test_net->Forward(bottom_vec, &iter_loss);
+        test_net->Forward(&iter_loss);
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 84c6747f61a..09ec3a7e918 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -185,9 +185,8 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     this->InitSolverFromProtoString(proto.str());
     if (from_snapshot != NULL) {
       this->solver_->Restore(from_snapshot);
-      vector<Blob<Dtype>*> empty_bottom_vec;
       for (int i = 0; i < this->solver_->iter(); ++i) {
-        this->solver_->net()->Forward(empty_bottom_vec);
+        this->solver_->net()->Forward();
       }
     }
     if (devices == 1) {
@@ -231,8 +230,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Run a forward pass, and manually compute the update values from the
     // result.
     Net<Dtype>& net = *this->solver_->net();
-    vector<Blob<Dtype>*> empty_bottom_vec;
-    net.Forward(empty_bottom_vec);
+    net.Forward();
     ASSERT_TRUE(net.has_blob("data"));
     const Blob<Dtype>& data = *net.blob_by_name("data");
     ASSERT_TRUE(net.has_blob("targets"));
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index ab4afba1a93..1e0788ec127 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -555,11 +555,14 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitReshapableNet() {
     const string& proto =
         "name: 'ReshapableNetwork' "
-        "input: 'data' "
-        "input_dim: 1 "
-        "input_dim: 3 "
-        "input_dim: 100 "
-        "input_dim: 100 "
+        "layer { "
+        "  name: 'data' "
+        "  type: 'Input' "
+        "  top: 'data' "
+        "  input_param { "
+        "  shape: { dim: 1 dim: 3 dim: 100 dim: 100 } "
+        "  } "
+        "} "
         "layer { "
         "  name: 'conv1' "
         "  type: 'Convolution' "
@@ -821,7 +824,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
   this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   vector<shared_ptr<Blob<Dtype> > > blob_grads;
   this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -836,7 +839,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
   for (int i = 0; i < kNumLossWeights; ++i) {
     Caffe::set_random_seed(this->seed_);
     this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward);
-    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype weighted_loss = this->net_->ForwardBackward();
     const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
@@ -865,14 +868,13 @@ TYPED_TEST(NetTest, TestLossWeight) {
 
 TYPED_TEST(NetTest, TestLossWeightMidNet) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
   Dtype loss_weight = 0;
   Dtype midnet_loss_weight = 1;
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   const bool kReshape = true;
   Blob<Dtype> data_grad;
@@ -887,7 +889,7 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
     Caffe::set_random_seed(this->seed_);
     this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i],
                                  kForceBackward);
-    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype weighted_loss = this->net_->ForwardBackward();
     const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
@@ -903,7 +905,6 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
 
 TYPED_TEST(NetTest, TestComboLossWeight) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   Dtype loss_weight;
   Dtype midnet_loss_weight;
   const bool kForceBackward = true;
@@ -916,7 +917,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   vector<shared_ptr<Blob<Dtype> > > blob_grads;
   this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -928,7 +929,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_main_2 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_main_2 = this->net_->ForwardBackward();
   vector<shared_ptr<Blob<Dtype> > > blob_grads_loss_2;
   this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
   vector<shared_ptr<Blob<Dtype> > > param_grads_loss_2;
@@ -939,7 +940,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_main_3 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_main_3 = this->net_->ForwardBackward();
   const vector<shared_ptr<Blob<Dtype> > >& blob_grads_loss_3 =
       this->net_->blobs();
   ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size());
@@ -974,7 +975,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_midnet_2 = this->net_->ForwardBackward();
   this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
   this->CopyNetParams(kCopyDiff, &param_grads_loss_2);
 
@@ -983,7 +984,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_midnet_3 = this->net_->ForwardBackward();
   const vector<shared_ptr<Blob<Dtype> > >& blob_grads_midnet_loss_3 =
       this->net_->blobs();
   ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size());
@@ -1032,40 +1033,35 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
 }
 
 TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
-  typedef typename TypeParam::Dtype Dtype;
   const bool kForceBackward = false;
   const bool kAccuracyLayer = true;
   this->InitTinyNet(kForceBackward, kAccuracyLayer);
   EXPECT_TRUE(this->net_->has_blob("accuracy"));
-  vector<Blob<Dtype>*> bottom;
   // Test that we can do Backward even though we have an 'Accuracy' layer.
-  this->net_->ForwardBackward(bottom);
+  this->net_->ForwardBackward();
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Dtype loss;
-  this->net_->Forward(bottom, &loss);
+  this->net_->Forward(&loss);
   EXPECT_GT(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Dtype loss;
-  this->net_->Forward(bottom, &loss);
+  this->net_->Forward(&loss);
   EXPECT_FLOAT_EQ(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Net<Dtype>* net = this->net_.get();
-  net->Forward(bottom);
+  net->Forward();
   net->Backward();
   Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
   Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
@@ -1081,10 +1077,9 @@ TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
 TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Net<Dtype>* net = this->net_.get();
   Dtype loss;
-  net->Forward(bottom, &loss);
+  net->Forward(&loss);
   net->Backward();
   EXPECT_FLOAT_EQ(loss, 0);
   Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
@@ -1102,7 +1097,6 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   typedef typename TypeParam::Dtype Dtype;
   Caffe::set_random_seed(this->seed_);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
@@ -1111,7 +1105,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   // Compute the expected update as the data minus the two diffs.
   Blob<Dtype> shared_params;
@@ -1146,7 +1140,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // locations in memory.
   EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   // Compute the expected update.
   Blob<Dtype> unshared_params1;
@@ -1186,7 +1180,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   // Create a net with weight sharing; Update it once.
   Caffe::set_random_seed(this->seed_);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
@@ -1195,7 +1188,7 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->ForwardBackward(bottom);
+  this->net_->ForwardBackward();
   this->net_->Update();
   Blob<Dtype> shared_params;
   const bool kReshape = true;
@@ -1228,7 +1221,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
 
 TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   const bool kBiasTerm = true, kForceBackward = false;
   const Dtype* kLossWeight1 = NULL;
   const Dtype* kLossWeight2 = NULL;
@@ -1238,7 +1230,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
   const int num_params = params.size();
@@ -1258,7 +1250,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
   ASSERT_EQ(num_params, params2.size());
@@ -1274,7 +1266,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
   ASSERT_EQ(num_params, params3.size());
@@ -1293,7 +1285,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
   ASSERT_EQ(num_params, params4.size());
@@ -1315,7 +1307,7 @@ TYPED_TEST(NetTest, TestFromTo) {
   // Run Forward and Backward, recording the data diff and loss.
   Blob<Dtype> data;
   data.ReshapeLike(*this->net_->blob_by_name("data"));
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   data.CopyFrom(*this->net_->blob_by_name("data"), true, true);
   const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data();
@@ -2277,12 +2269,12 @@ TYPED_TEST(NetTest, TestReshape) {
   filler.Fill(&blob2);
 
   this->InitReshapableNet();
-  Blob<Dtype>* input_blob = this->net_->input_blobs()[0];
+  shared_ptr<Blob<Dtype> > input_blob = this->net_->blob_by_name("data");
   Blob<Dtype>* output_blob = this->net_->output_blobs()[0];
   input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
       blob1.width());
   caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   // call backward just to make sure it runs
   this->net_->Backward();
   Blob<Dtype> output1(output_blob->num(), output_blob->channels(),
@@ -2293,7 +2285,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
       blob2.width());
   caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   Blob<Dtype> output2(output_blob->num(), output_blob->channels(),
       output_blob->height(), output_blob->width());
@@ -2303,7 +2295,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
       blob1.width());
   caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   for (int i = 0; i < output1.count(); ++i) {
     EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
@@ -2312,7 +2304,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
       blob2.width());
   caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   for (int i = 0; i < output2.count(); ++i) {
     EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index ba2ccbb2b18..007142126ea 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -886,67 +886,6 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
   this->RunInsertionTest(input_proto, expected_output_proto);
 }
 
-TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
-  const string& input_proto =
-      "name: 'TestNetwork' "
-      "input: 'data' "
-      "input_dim: 10 "
-      "input_dim: 3 "
-      "input_dim: 227 "
-      "input_dim: 227 "
-      "layer { "
-      "  name: 'innerprod1' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data' "
-      "  top: 'innerprod1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod2' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data' "
-      "  top: 'innerprod2' "
-      "} "
-      "layer { "
-      "  name: 'loss' "
-      "  type: 'EuclideanLoss' "
-      "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
-      "} ";
-  const string& expected_output_proto =
-      "name: 'TestNetwork' "
-      "input: 'data' "
-      "input_dim: 10 "
-      "input_dim: 3 "
-      "input_dim: 227 "
-      "input_dim: 227 "
-      "layer { "
-      "  name: 'data_input_0_split' "
-      "  type: 'Split' "
-      "  bottom: 'data' "
-      "  top: 'data_input_0_split_0' "
-      "  top: 'data_input_0_split_1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod1' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data_input_0_split_0' "
-      "  top: 'innerprod1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod2' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data_input_0_split_1' "
-      "  top: 'innerprod2' "
-      "} "
-      "layer { "
-      "  name: 'loss' "
-      "  type: 'EuclideanLoss' "
-      "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
-      "} ";
-  this->RunInsertionTest(input_proto, expected_output_proto);
-}
-
 TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 475a2a9f618..7a899c69787 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -19,12 +19,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
   map<pair<int, int>, float> top_idx_to_loss_weight;
   map<pair<int, int>, int> top_idx_to_bottom_split_idx;
   map<int, string> layer_idx_to_layer_name;
-  layer_idx_to_layer_name[-1] = "input";
-  // Determine the number of times each blob is used as an input (bottom) blob.
-  for (int i = 0; i < param.input_size(); ++i) {
-    const string& blob_name = param.input(i);
-    blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
-  }
   for (int i = 0; i < param.layer_size(); ++i) {
     const LayerParameter& layer_param = param.layer(i);
     layer_idx_to_layer_name[i] = layer_param.name();
@@ -45,7 +39,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
     }
     // A use of a top blob as a loss should be handled similarly to the use of
-    // a top blob as an input (bottom) blob to another layer.
+    // a top blob as a bottom blob to another layer.
     const int last_loss =
         std::min(layer_param.loss_weight_size(), layer_param.top_size());
     for (int j = 0; j < last_loss; ++j) {
@@ -57,19 +51,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       }
     }
   }
-  // Create split layer for any input blobs used by other layer as bottom
-  // blobs more than once.
-  for (int i = 0; i < param.input_size(); ++i) {
-    const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
-    if (split_count > 1) {
-      const string& layer_name = layer_idx_to_layer_name[-1];
-      const string& blob_name = param.input(i);
-      LayerParameter* split_layer_param = param_split->add_layer();
-      const float kZeroLossWeight = 0;
-      ConfigureSplitLayer(layer_name, blob_name, i, split_count,
-          kZeroLossWeight, split_layer_param);
-    }
-  }
   for (int i = 0; i < param.layer_size(); ++i) {
     LayerParameter* layer_param = param_split->add_layer();
     layer_param->CopyFrom(param.layer(i));
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index ebe95d61ef1..95b2f82c4be 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -251,14 +251,13 @@ int test() {
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
-  vector<Blob<float>* > bottom_vec;
   vector<int> test_score_output_id;
   vector<float> test_score;
   float loss = 0;
   for (int i = 0; i < FLAGS_iterations; ++i) {
     float iter_loss;
     const vector<Blob<float>*>& result =
-        caffe_net.Forward(bottom_vec, &iter_loss);
+        caffe_net.Forward(&iter_loss);
     loss += iter_loss;
     int idx = 0;
     for (int j = 0; j < result.size(); ++j) {
@@ -322,7 +321,7 @@ int time() {
   // Note that for the speed benchmark, we will assume that the network does
   // not take any input blobs.
   float initial_loss;
-  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+  caffe_net.Forward(&initial_loss);
   LOG(INFO) << "Initial loss: " << initial_loss;
   LOG(INFO) << "Performing Backward";
   caffe_net.Backward();
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index d6562f98059..704467250a6 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -133,10 +133,9 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  std::vector<Blob<float>*> input_vec;
   std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
-    feature_extraction_net->Forward(input_vec);
+    feature_extraction_net->Forward();
     for (int i = 0; i < num_features; ++i) {
       const boost::shared_ptr<Blob<Dtype> > feature_blob =
         feature_extraction_net->blob_by_name(blob_names[i]);

From 0d9a78f5a083db859f01d45da91c5a0ca1389de8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 2 Dec 2015 18:31:26 -0800
Subject: [PATCH 1705/2053] collect Net inputs from Input layers

Restore the list of net inputs for compatibility with the
pycaffe and matcaffe interfaces and downstream C++.
---
 include/caffe/net.hpp | 13 +++++++++++--
 src/caffe/net.cpp     |  6 ++++++
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 43e0a845133..1c2a19126d5 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -185,11 +185,18 @@ class Net {
   inline const vector<string>& param_display_names() const {
     return param_display_names_;
   }
-  /// @brief output blob number
+  /// @brief Input and output blob numbers
+  inline int num_inputs() const { return net_input_blobs_.size(); }
   inline int num_outputs() const { return net_output_blobs_.size(); }
+  inline const vector<Blob<Dtype>*>& input_blobs() const {
+    return net_input_blobs_;
+  }
   inline const vector<Blob<Dtype>*>& output_blobs() const {
     return net_output_blobs_;
   }
+  inline const vector<int>& input_blob_indices() const {
+    return net_input_blob_indices_;
+  }
   inline const vector<int>& output_blob_indices() const {
     return net_output_blob_indices_;
   }
@@ -263,8 +270,10 @@ class Net {
   vector<string> param_display_names_;
   vector<pair<int, int> > param_layer_indices_;
   map<string, int> param_names_index_;
-  /// blob indices for the output of the net
+  /// blob indices for the input and the output of the net
+  vector<int> net_input_blob_indices_;
   vector<int> net_output_blob_indices_;
+  vector<Blob<Dtype>*> net_input_blobs_;
   vector<Blob<Dtype>*> net_output_blobs_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index b7320e95a86..c1760ea1970 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -103,6 +103,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     int num_top = layer_param.top_size();
     for (int top_id = 0; top_id < num_top; ++top_id) {
       AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
+      // Collect Input layer tops as Net inputs.
+      if (layer_param.type() == "Input") {
+        const int blob_id = blobs_.size() - 1;
+        net_input_blob_indices_.push_back(blob_id);
+        net_input_blobs_.push_back(blobs_[blob_id].get());
+      }
     }
     // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
     // specified fewer than the required number (as specified by

From 2cc3844cb2a4a72de10d321781dc8f994bef95c7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 3 Dec 2015 19:39:19 -0800
Subject: [PATCH 1706/2053] [examples] switch examples + models to Input layers

---
 examples/cifar10/cifar10_full.prototxt        | 11 +++--
 examples/cifar10/cifar10_quick.prototxt       | 11 +++--
 .../cpp_classification/classification.cpp     |  2 +-
 examples/mnist/lenet.prototxt                 | 11 +++--
 examples/net_surgery.ipynb                    | 43 +++++++++----------
 .../bvlc_caffenet_full_conv.prototxt          | 15 ++++---
 examples/net_surgery/conv.prototxt            | 11 +++--
 examples/siamese/mnist_siamese.prototxt       | 13 +++---
 models/bvlc_alexnet/deploy.prototxt           | 11 +++--
 models/bvlc_googlenet/deploy.prototxt         | 11 +++--
 .../bvlc_reference_caffenet/deploy.prototxt   | 11 +++--
 .../deploy.prototxt                           | 11 +++--
 models/finetune_flickr_style/deploy.prototxt  | 11 +++--
 13 files changed, 83 insertions(+), 89 deletions(-)

diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 446479da961..83cf0d86b35 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -1,12 +1,11 @@
 name: "CIFAR10_full_deploy"
 # N.B. input image must be in CIFAR-10 format
 # as described at http://www.cs.toronto.edu/~kriz/cifar.html
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 32
-  dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 9352fbf65df..cf3b2a358be 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -1,10 +1,9 @@
 name: "CIFAR10_quick_test"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 32
-  dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
index 974662e59da..6b67c537a47 100644
--- a/examples/cpp_classification/classification.cpp
+++ b/examples/cpp_classification/classification.cpp
@@ -159,7 +159,7 @@ std::vector<float> Classifier::Predict(const cv::Mat& img) {
 
   Preprocess(img, &input_channels);
 
-  net_->ForwardPrefilled();
+  net_->Forward();
 
   /* Copy the output layer to a std::vector */
   Blob<float>* output_layer = net_->output_blobs()[0];
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index dff7123bf73..8cf78e62c89 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -1,10 +1,9 @@
 name: "LeNet"
-input: "data"
-input_shape {
-  dim: 64
-  dim: 1
-  dim: 28
-  dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
 }
 layer {
   name: "conv1"
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index ff780fbb9f7..a6092db0c40 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -5494,48 +5494,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1,2c1\r\n",
+      "1,2c1,2\r\n",
       "< # Fully convolutional network version of CaffeNet.\r\n",
       "< name: \"CaffeNetConv\"\r\n",
       "---\r\n",
       "> name: \"CaffeNet\"\r\n",
-      "4c3\r\n",
-      "< input_dim: 1\r\n",
+      "> input: \"data\"\r\n",
+      "7,11c7\r\n",
+      "<   input_param {\r\n",
+      "<     # initial shape for a fully convolutional network:\r\n",
+      "<     # the shape can be set for each input by reshape.\r\n",
+      "<     shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n",
+      "<   }\r\n",
       "---\r\n",
-      "> input_dim: 10\r\n",
-      "6,7c5,6\r\n",
-      "< input_dim: 451\r\n",
-      "< input_dim: 451\r\n",
-      "---\r\n",
-      "> input_dim: 227\r\n",
-      "> input_dim: 227\r\n",
-      "152,153c151,152\r\n",
+      ">   input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n",
+      "157,158c153,154\r\n",
       "<   name: \"fc6-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "---\r\n",
       ">   name: \"fc6\"\r\n",
       ">   type: \"InnerProduct\"\r\n",
-      "155,156c154,155\r\n",
+      "160,161c156,157\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "<   convolution_param {\r\n",
       "---\r\n",
       ">   top: \"fc6\"\r\n",
       ">   inner_product_param {\r\n",
-      "158d156\r\n",
+      "163d158\r\n",
       "<     kernel_size: 6\r\n",
-      "164,165c162,163\r\n",
+      "169,170c164,165\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "170,171c168,169\r\n",
+      "175,176c170,171\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "177,181c175,179\r\n",
+      "182,186c177,181\r\n",
       "<   name: \"fc7-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
@@ -5547,21 +5546,21 @@
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc7\"\r\n",
       ">   inner_product_param {\r\n",
-      "183d180\r\n",
+      "188d182\r\n",
       "<     kernel_size: 1\r\n",
-      "189,190c186,187\r\n",
+      "194,195c188,189\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "195,196c192,193\r\n",
+      "200,201c194,195\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "202,206c199,203\r\n",
+      "207,211c201,205\r\n",
       "<   name: \"fc8-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
@@ -5573,9 +5572,9 @@
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc8\"\r\n",
       ">   inner_product_param {\r\n",
-      "208d204\r\n",
+      "213d206\r\n",
       "<     kernel_size: 1\r\n",
-      "214c210\r\n",
+      "219c212\r\n",
       "<   bottom: \"fc8-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc8\"\r\n"
diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 0cadde9b58b..f8f5c3c325a 100644
--- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,11 +1,14 @@
 # Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 3
-  dim: 451
-  dim: 451
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    # initial shape for a fully convolutional network:
+    # the shape can be set for each input by reshape.
+    shape: { dim: 1 dim: 3 dim: 451 dim: 451 }
+  }
 }
 layer {
   name: "conv1"
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
index 6b3e5c768d5..8671bb5bf0a 100644
--- a/examples/net_surgery/conv.prototxt
+++ b/examples/net_surgery/conv.prototxt
@@ -1,11 +1,10 @@
 # Simple single-layer network to showcase editing model parameters.
 name: "convolution"
-input: "data"
-input_shape {
-  dim: 1
-  dim: 1
-  dim: 100
-  dim: 100
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } }
 }
 layer {
   name: "conv"
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 332731bd75f..5d783ba02ca 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -1,10 +1,11 @@
 name: "mnist_siamese"
-input: "data"
-input_shape {
-  dim: 10000
-  dim: 1
-  dim: 28
-  dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    shape: { dim: 10000 dim: 1 dim: 28 dim: 28 }
+  }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index ff10daa9399..45b2b0e361a 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "AlexNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index 1f90ee21630..50b54a9f3c1 100644
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "GoogleNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 224
-  dim: 224
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
 }
 layer {
   name: "conv1/7x7_s2"
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 127f1e265fd..907116ef91c 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "CaffeNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ae1df967742..e330a770676 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "R-CNN-ilsvrc13"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index 0f07e47acab..b8f99c74453 100644
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -1,10 +1,9 @@
 name: "FlickrStyleCaffeNet"
-input: "data"
-input_shape {
-  dim: 10
-  dim: 3
-  dim: 227
-  dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
 }
 layer {
   name: "conv1"

From f88073aad81d8119dc9d62f882b8cb6d20c3b7ee Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 26 Feb 2016 22:20:50 -0800
Subject: [PATCH 1707/2053] Deprecate ForwardPrefilled(), Forward(bottom, loss)
 in lieu of dropping

Relax removal of `Forward()` variations by deprecating instead.
---
 include/caffe/net.hpp |  9 +++++++++
 src/caffe/net.cpp     | 12 ++++++++++++
 2 files changed, 21 insertions(+)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 1c2a19126d5..0addb3c2a6d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -36,6 +36,12 @@ class Net {
    *
    */
   const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
+  /// @brief DEPRECATED; use Forward() instead.
+  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
+    LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
+        << "will be removed in a future version. Use Forward().";
+    return Forward(loss);
+  }
 
   /**
    * The From and To variants of Forward and Backward operate on the
@@ -48,6 +54,9 @@ class Net {
   Dtype ForwardFromTo(int start, int end);
   Dtype ForwardFrom(int start);
   Dtype ForwardTo(int end);
+  /// @brief DEPRECATED; set input blobs then use Forward() instead.
+  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
+      Dtype* loss = NULL);
 
   /**
    * @brief Zeroes out the diffs of all net parameters.
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index c1760ea1970..23d94c97c07 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -566,6 +566,18 @@ const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {
   return net_output_blobs_;
 }
 
+template <typename Dtype>
+const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
+    const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
+  LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "
+      << "will be removed in a future version. Use Forward(loss).";
+  // Copy bottom to net bottoms
+  for (int i = 0; i < bottom.size(); ++i) {
+    net_input_blobs_[i]->CopyFrom(*bottom[i]);
+  }
+  return Forward(loss);
+}
+
 template <typename Dtype>
 void Net<Dtype>::BackwardFromTo(int start, int end) {
   CHECK_GE(end, 0);

From 6cba462401c7a8596afa957eb57c618fdfc63292 Mon Sep 17 00:00:00 2001
From: Evan Lezar <evan.lezar@zalando.de>
Date: Tue, 5 Jan 2016 16:27:28 +0100
Subject: [PATCH 1708/2053] Add Dockerfiles for creating Caffe executable
 images.

These can be used as direct replacements for the Caffe executable.
---
 docker/Makefile                      |  50 +++++++++++
 docker/README.md                     |  52 ++++++++++++
 docker/standalone/cpu/Dockerfile     |  43 ++++++++++
 docker/standalone/gpu/Dockerfile     |  43 ++++++++++
 docker/templates/Dockerfile.template |  42 ++++++++++
 examples/mnist/train_lenet_docker.sh | 119 +++++++++++++++++++++++++++
 6 files changed, 349 insertions(+)
 create mode 100644 docker/Makefile
 create mode 100644 docker/README.md
 create mode 100644 docker/standalone/cpu/Dockerfile
 create mode 100644 docker/standalone/gpu/Dockerfile
 create mode 100644 docker/templates/Dockerfile.template
 create mode 100755 examples/mnist/train_lenet_docker.sh

diff --git a/docker/Makefile b/docker/Makefile
new file mode 100644
index 00000000000..725208c6b2b
--- /dev/null
+++ b/docker/Makefile
@@ -0,0 +1,50 @@
+# A makefile to build the docker images for caffe.
+# Two caffe images will be built:
+#   caffe:cpu --> A CPU-only build of caffe.
+#   caffe:gpu --> A GPU-enabled build using the latest CUDA and CUDNN versions.
+
+DOCKER ?= docker
+
+all: docker_files standalone
+
+.PHONY: standalone devel
+
+standalone: cpu_standalone gpu_standalone
+
+
+cpu_standalone: standalone/cpu/Dockerfile
+	$(DOCKER) build -t caffe:cpu standalone/cpu
+
+gpu_standalone: standalone/gpu/Dockerfile
+	$(DOCKER) build -t caffe:gpu standalone/gpu
+
+docker_files: standalone_files
+
+standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile
+
+FROM_GPU = "nvidia/cuda:cudnn"
+FROM_CPU = "ubuntu:14.04"
+GPU_CMAKE_ARGS = -DUSE_CUDNN=1
+CPU_CMAKE_ARGS = -DCPU_ONLY=1
+
+# A make macro to select the CPU or GPU base image.
+define from_image
+$(if $(strip $(findstring gpu,$@)),$(FROM_GPU),$(FROM_CPU))
+endef
+
+# A make macro to select the CPU or GPU build args.
+define build_args
+$(if $(strip $(findstring gpu,$@)),$(GPU_CMAKE_ARGS),$(CPU_CMAKE_ARGS))
+endef
+
+# A make macro to construct the CPU or GPU Dockerfile from the template
+define create_docker_file
+	@echo creating $@
+	@echo "FROM "$(from_image) > $@
+	@cat $^ | sed 's/$${CMAKE_ARGS}/$(build_args)/' >> $@
+endef
+
+
+standalone/%/Dockerfile: templates/Dockerfile.template
+	$(create_docker_file)
+
diff --git a/docker/README.md b/docker/README.md
new file mode 100644
index 00000000000..0eb8c8639b6
--- /dev/null
+++ b/docker/README.md
@@ -0,0 +1,52 @@
+# Caffe standalone Dockerfiles.
+
+The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running:
+
+```
+docker build -t caffe:cpu standalone/cpu
+```
+for example. (Here `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail).
+
+Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned.
+
+# Running Caffe using the docker image
+
+In order to test the Caffe image, run:
+```
+docker run -ti caffe:cpu caffe --version
+```
+which should show a message like:
+```
+libdc1394 error: Failed to initialize libdc1394
+caffe version 1.0.0-rc3
+```
+
+One can also build and run the Caffe tests in the image using:
+```
+docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"
+```
+
+In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running:
+```
+docker run -ti --volume $(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
+```
+will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume` Docker flag).
+
+Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used:
+```
+docker run -ti --volume $(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
+```
+where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir` Docker command line option.
+
+# Other use-cases
+
+Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe).
+
+Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running:
+```
+docker run -ti caffe:cpu python
+```
+(`ipython` is also available in the container).
+
+Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py`
+
diff --git a/docker/standalone/cpu/Dockerfile b/docker/standalone/cpu/Dockerfile
new file mode 100644
index 00000000000..4fef25aa6a1
--- /dev/null
+++ b/docker/standalone/cpu/Dockerfile
@@ -0,0 +1,43 @@
+FROM ubuntu:14.04
+MAINTAINER caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    mkdir build && cd build && \
+    cmake -DCPU_ONLY=1 .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/standalone/gpu/Dockerfile b/docker/standalone/gpu/Dockerfile
new file mode 100644
index 00000000000..1ddc6560d16
--- /dev/null
+++ b/docker/standalone/gpu/Dockerfile
@@ -0,0 +1,43 @@
+FROM nvidia/cuda:cudnn
+MAINTAINER caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    mkdir build && cd build && \
+    cmake -DUSE_CUDNN=1 .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/templates/Dockerfile.template b/docker/templates/Dockerfile.template
new file mode 100644
index 00000000000..8834f057968
--- /dev/null
+++ b/docker/templates/Dockerfile.template
@@ -0,0 +1,42 @@
+MAINTAINER caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    mkdir build && cd build && \
+    cmake ${CMAKE_ARGS} .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh
new file mode 100755
index 00000000000..049f013841f
--- /dev/null
+++ b/examples/mnist/train_lenet_docker.sh
@@ -0,0 +1,119 @@
+#!/usr/bin/env sh
+set -e
+# The following example allows for the MNIST example (using LeNet) to be
+# trained using the caffe docker image instead of building from source.
+#
+# The GPU-enabled version of Caffe can be used, assuming that nvidia-docker
+# is installed, and the GPU-enabled Caffe image has been built.
+# Setting the GPU environment variable to 1 will enable the use of nvidia-docker.
+# e.g.
+#   GPU=1 ./examples/mnist/train_lenet_docker.sh [ADDITIONAL_CAFFE_ARGS]
+#
+# With any arguments following the script being passed directly to caffe
+# when training the network.
+#
+# The steps that are performed by the script are as follows:
+# 1. The MNIST data set is downloaded
+#    (see data/mnist/get_mnist.sh)
+# 2. An LMDB database is created from the downloaded data
+#    (see examples/mnist/create_mnist.sh.
+# 3. A caffe network based on the LeNet solver is trained.
+#    (see examples/mnist/lenet_solver.prototxt)
+#
+# For each of these, a step is executed to ensure that certain prerequisites
+# are available, after which a command that actually performs the work is
+# executed.
+#
+# In order to provide additional flexibility, the following shell (environment)
+# variables can be used to controll the execution of each of the phases:
+#
+# DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset
+# CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database
+# TRAIN: Enable (1) or disable (0) the training of the LeNet networkd.
+#
+# As an example, assuming that the data set has been downloaded, and an LMDB
+# database created, the following command can be used to train the LeNet
+# network with GPU computing enabled.
+#
+# DOWNLOAD_DATA=0 CREATE_LMDB=0 GPU=1 ./examples/mnist/train_lenet_docker.sh
+#
+
+
+if [ x"$(uname -s)" != x"Linux" ]
+then
+echo ""
+echo "This script is designed to run on Linux."
+echo "There may be problems with the way Docker mounts host volumes on other"
+echo "systems which will cause the docker commands to fail."
+echo ""
+read -p "Press [ENTER] to continue..." key
+echo ""
+fi
+
+
+# Check if GPU mode has been enabled and set the docker executable accordingly
+if [ ${GPU:-0} -eq 1 ]
+then
+DOCKER_CMD=nvidia-docker
+IMAGE=caffe:gpu
+else
+DOCKER_CMD=docker
+IMAGE=caffe:cpu
+fi
+echo "Using $DOCKER_CMD to launch $IMAGE"
+
+# On non-Linux systems, the Docker host is typically a virtual machine.
+# This means that the user and group id's may be different.
+# On OS X, for example, the user and group are 1000 and 50, respectively.
+if [ x"$(uname -s)" != x"Linux" ]
+then
+CUID=1000
+CGID=50
+else
+CUID=$(id -u)
+CGID=$(id -g)
+fi
+
+# Define some helper variables to make the running of the actual docker
+# commands less verbose.
+# Note:
+#   -u $CUID:$CGID             runs the docker image as the current user to ensure
+#                              that the file permissions are compatible with the
+#                              host system. The variables CUID and CGID have been
+#                              set above depending on the host operating system.
+#   --volume $(pwd):/workspace mounts the current directory as the docker volume
+#                              /workspace
+#   --workdir /workspace       Ensures that the docker container starts in the right
+#                              working directory
+DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume $(pwd):/workspace --workdir /workspace"
+DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE"
+
+# Download the data
+if [ ${DOWNLOAD_DATA:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./data/mnist;
+                     cp -ru \$CAFFE_ROOT/data/mnist/get_mnist.sh ./data/mnist/"
+$DOCKER_RUN ./data/mnist/get_mnist.sh
+fi
+
+# Create the LMDB database
+if [ ${CREATE_LMDB:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./examples/mnist;
+                     cp -ru \$CAFFE_ROOT/examples/mnist/create_mnist.sh ./examples/mnist/;
+                     sed -i s#BUILD=build#BUILD=\$CAFFE_ROOT/build## ./examples/mnist/create_mnist.sh"
+$DOCKER_RUN ./examples/mnist/create_mnist.sh
+fi
+
+# Train the network
+if [ ${TRAIN:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "cp \$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt ./examples/mnist/;
+                     cp \$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt ./examples/mnist/"
+    # Ensure that the solver_mode is compatible with the desired GPU mode.
+    if [ ${GPU:-0} -eq 0 ]
+    then
+    $DOCKER_RUN sed -i 's#solver_mode: GPU#solver_mode: CPU##' ./examples/mnist/lenet_solver.prototxt
+    fi
+$DOCKER_RUN caffe train --solver=examples/mnist/lenet_solver.prototxt $*
+fi

From cfa2c0cf596a4e1157e651389601d05779f5d27d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 27 Feb 2016 12:10:16 -0800
Subject: [PATCH 1709/2053] fix flags in #3518 for nvidia-docker

nvidia-docker requires long args with equal sign as of docker 1.10:
see https://github.com/BVLC/caffe/pull/3518#issuecomment-189576419
---
 docker/README.md                     | 8 ++++----
 examples/mnist/train_lenet_docker.sh | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docker/README.md b/docker/README.md
index 0eb8c8639b6..fdab641bdca 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -28,15 +28,15 @@ docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"
 
 In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running:
 ```
-docker run -ti --volume $(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
+docker run -ti --volume=$(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
 ```
-will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume` Docker flag).
+will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume=` Docker flag).
 
 Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used:
 ```
-docker run -ti --volume $(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
+docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
 ```
-where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir` Docker command line option.
+where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option.
 
 # Other use-cases
 
diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh
index 049f013841f..32cf1c8e4a3 100755
--- a/examples/mnist/train_lenet_docker.sh
+++ b/examples/mnist/train_lenet_docker.sh
@@ -85,7 +85,7 @@ fi
 #                              /workspace
 #   --workdir /workspace       Ensures that the docker container starts in the right
 #                              working directory
-DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume $(pwd):/workspace --workdir /workspace"
+DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume=$(pwd):/workspace --workdir=/workspace"
 DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE"
 
 # Download the data

From bef2c05d612a1d25d1e0c506d15e0817f9f03bac Mon Sep 17 00:00:00 2001
From: shai <shai@magisto.com>
Date: Thu, 25 Feb 2016 12:09:45 +0200
Subject: [PATCH 1710/2053] supporting N-D Blobs in Dropout layer Reshape

fixing lint errors
---
 src/caffe/layers/dropout_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index 9cb64d9735f..533ab26c04d 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -23,8 +23,8 @@ void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::Reshape(bottom, top);
   // Set up the cache for random number generation
-  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
+  // ReshapeLike does not work because rand_vec_ is of Dtype uint
+  rand_vec_.Reshape(bottom[0]->shape());
 }
 
 template <typename Dtype>

From 666da79ad2f4d72c804ddadc7b10157e4d04bdd0 Mon Sep 17 00:00:00 2001
From: Thibault Deregnaucourt <thibault.deregnaucourt@inria.fr>
Date: Mon, 29 Feb 2016 10:21:07 +0100
Subject: [PATCH 1711/2053] Use 'six' library to ensure python3 compliance. Use
 '//' instead of '/' for entire division.

---
 python/caffe/pycaffe.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 5020ecedb10..c5c0b824a77 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -14,6 +14,8 @@
         RMSPropSolver, AdaDeltaSolver, AdamSolver
 import caffe.io
 
+import six
+
 # We directly update methods from Net here (rather than using composition or
 # inheritance) so that nets created by caffe (e.g., by SGDSolver) will
 # automatically have the improved interface.
@@ -97,7 +99,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
             raise Exception('Input blob arguments do not match net inputs.')
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
-        for in_, blob in kwargs.iteritems():
+        for in_, blob in six.iteritems(kwargs):
             if blob.shape[0] != self.blobs[in_].shape[0]:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
@@ -145,7 +147,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
             raise Exception('Top diff arguments do not match net outputs.')
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
-        for top, diff in kwargs.iteritems():
+        for top, diff in six.iteritems(kwargs):
             if diff.shape[0] != self.blobs[top].shape[0]:
                 raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
@@ -174,13 +176,13 @@ def _Net_forward_all(self, blobs=None, **kwargs):
     all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
     for batch in self._batch(kwargs):
         outs = self.forward(blobs=blobs, **batch)
-        for out, out_blob in outs.iteritems():
+        for out, out_blob in six.iteritems(outs):
             all_outs[out].extend(out_blob.copy())
     # Package in ndarray.
     for out in all_outs:
         all_outs[out] = np.asarray(all_outs[out])
     # Discard padding.
-    pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
     if pad:
         for out in all_outs:
             all_outs[out] = all_outs[out][:-pad]
@@ -215,16 +217,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
         batch_blobs = self.forward(blobs=blobs, **fb)
         batch_diffs = self.backward(diffs=diffs, **bb)
-        for out, out_blobs in batch_blobs.iteritems():
+        for out, out_blobs in six.iteritems(batch_blobs):
             all_outs[out].extend(out_blobs.copy())
-        for diff, out_diffs in batch_diffs.iteritems():
+        for diff, out_diffs in six.iteritems(batch_diffs):
             all_diffs[diff].extend(out_diffs.copy())
     # Package in ndarray.
     for out, diff in zip(all_outs, all_diffs):
         all_outs[out] = np.asarray(all_outs[out])
         all_diffs[diff] = np.asarray(all_diffs[diff])
     # Discard padding at the end and package in ndarray.
-    pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
     if pad:
         for out, diff in zip(all_outs, all_diffs):
             all_outs[out] = all_outs[out][:-pad]
@@ -256,10 +258,10 @@ def _Net_batch(self, blobs):
     ------
     batch: {blob name: list of blobs} dict for a single batch.
     """
-    num = len(blobs.itervalues().next())
-    batch_size = self.blobs.itervalues().next().shape[0]
+    num = len(six.next(six.itervalues(blobs)))
+    batch_size = six.next(six.itervalues(self.blobs)).shape[0]
     remainder = num % batch_size
-    num_batches = num / batch_size
+    num_batches = num // batch_size
 
     # Yield full batches.
     for b in range(num_batches):

From cb277769a4c36aa70af2bb860cf40c71af2d3033 Mon Sep 17 00:00:00 2001
From: Anatoly Baksheev <no@email>
Date: Mon, 29 Feb 2016 18:01:07 +0300
Subject: [PATCH 1712/2053] NetSpec: allow setting blob names by string

---
 python/caffe/net_spec.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 93fc01927db..63de4cce4b2 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -175,6 +175,12 @@ def __setattr__(self, name, value):
     def __getattr__(self, name):
         return self.tops[name]
 
+    def __setitem__(self, key, value):
+        self.__setattr__(key, value)
+
+    def __getitem__(self, item):
+        return self.__getattr__(item)
+
     def to_proto(self):
         names = {v: k for k, v in six.iteritems(self.tops)}
         autonames = Counter()

From c1c559c2cb98d6de955f1d469c6104cb265f5dc5 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Mon, 29 Feb 2016 12:28:15 -0800
Subject: [PATCH 1713/2053] Don't force datum.label=0 in array_to_datum

---
 python/caffe/io.py           |  5 +++--
 python/caffe/test/test_io.py | 15 +++++++++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 75310589cec..cee5ace2e88 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -63,7 +63,7 @@ def blobprotovector_str_to_arraylist(str):
     return [blobproto_to_array(blob) for blob in vec.blobs]
 
 
-def array_to_datum(arr, label=0):
+def array_to_datum(arr, label=None):
     """Converts a 3-dimensional array to datum. If the array has dtype uint8,
     the output data will be encoded as a string. Otherwise, the output data
     will be stored in float format.
@@ -76,7 +76,8 @@ def array_to_datum(arr, label=0):
         datum.data = arr.tostring()
     else:
         datum.float_data.extend(arr.flat)
-    datum.label = label
+    if label is not None:
+        datum.label = label
     return datum
 
 
diff --git a/python/caffe/test/test_io.py b/python/caffe/test/test_io.py
index 8c86ef75fb2..4a16b5b9128 100644
--- a/python/caffe/test/test_io.py
+++ b/python/caffe/test/test_io.py
@@ -39,3 +39,18 @@ def test_scalar(self):
 
         arr = caffe.io.blobproto_to_array(blob)
         self.assertEqual(arr, 123)
+
+
+class TestArrayToDatum(unittest.TestCase):
+
+    def test_label_none_size(self):
+        # Set label
+        d1 = caffe.io.array_to_datum(
+            np.ones((10,10,3)), label=1)
+        # Don't set label
+        d2 = caffe.io.array_to_datum(
+            np.ones((10,10,3)))
+        # Not setting the label should result in a smaller object
+        self.assertGreater(
+            len(d1.SerializeToString()),
+            len(d2.SerializeToString()))

From 15a979df454339682d18a2760a9b06896f23d62b Mon Sep 17 00:00:00 2001
From: Oscar Beijbom <oscar.beijbom@gmail.com>
Date: Sun, 20 Dec 2015 23:02:42 -0800
Subject: [PATCH 1714/2053] Added tutorial on how to use python datalayers and
 multilabel classification.

---
 .../04-pascal_multilabel_with_datalayer.ipynb | 4152 +++++++++++++++++
 .../layers/pascal_multilabel_datalayers.py    |  262 ++
 examples/pycaffe/tools.py                     |  111 +
 3 files changed, 4525 insertions(+)
 create mode 100644 examples/04-pascal_multilabel_with_datalayer.ipynb
 create mode 100644 examples/pycaffe/layers/pascal_multilabel_datalayers.py
 create mode 100644 examples/pycaffe/tools.py

diff --git a/examples/04-pascal_multilabel_with_datalayer.ipynb b/examples/04-pascal_multilabel_with_datalayer.ipynb
new file mode 100644
index 00000000000..6839841a5ea
--- /dev/null
+++ b/examples/04-pascal_multilabel_with_datalayer.ipynb
@@ -0,0 +1,4152 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilabel classification on PASCAL using python data-layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will do multilabel classification on PASCAL VOC 2012. \n",
+    "\n",
+    "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss, and we will load data using a python data-layer. Data could also be provided through a HDF5 data-layer, but the python data-layer provide endless flexibility, so that's what we will use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preliminaries\n",
+    "\n",
+    "First, make sure you compile caffe using \n",
+    "WITH_PYTHON_LAYER ;= 1\n",
+    "\n",
+    "Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
+    "\n",
+    "Third, set paths and import modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# set root directory, e.g:\n",
+    "pascal_root = '/data/pascal/VOC2012'\n",
+    "\n",
+    "# import some modules\n",
+    "import sys, os, caffe\n",
+    "import numpy as np\n",
+    "import os.path as osp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
+    "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
+    "\n",
+    "import tools #this contains some tools that we need\n",
+    "\n",
+    "# make sure we have the caffenet weight downloaded.\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n",
+    "\n",
+    "# initialize caffe for gpu mode\n",
+    "caffe.set_mode_gpu()\n",
+    "caffe.set_device(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L, params as P, to_proto\n",
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "# helper function for common structures\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                                num_output=nout, pad=pad, group=group)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "# another helper function\n",
+    "def fc_relu(bottom, nout):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "# yet another helper function\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "# main netspec wrapper\n",
+    "def caffenet_multilabel(data_layer_params, datalayer):\n",
+    "    # setup the python data layer \n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n",
+    "                               ntop = 2, param_str=str(data_layer_params))\n",
+    "\n",
+    "    # the net itself\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n",
+    "    n.drop6 = L.Dropout(n.relu6, in_place=True)\n",
+    "    n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n",
+    "    n.drop7 = L.Dropout(n.relu7, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.drop7, num_output=20)\n",
+    "    n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n",
+    "    \n",
+    "    return str(n.to_proto())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "workdir = './pascal_multilabel_with_datalayer'\n",
+    "os.makedirs(workdir)\n",
+    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
+    "solverprototxt.sp['display'] = \"1\"\n",
+    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
+    "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n",
+    "\n",
+    "# write train and val nets.\n",
+    "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n",
+    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n",
+    "\n",
+    "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the codel. It's quite straight-forward, and gives you full control over data and labels.\n",
+    "\n",
+    "\n",
+    "Now we can load the caffe solver as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PascalMultilabelDataLayerSync initialized for split: train, with bs:128, im_shape:[227, 227], and 5717 images.\n",
+      "PascalMultilabelDataLayerSync initialized for split: val, with bs:128, im_shape:[227, 227], and 5823 images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n",
+    "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "solver.test_nets[0].share_with(solver.net)\n",
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check the data we have loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ground truth:  horse, person,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": [
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+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4ebd89cc90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "% matplotlib inline\n",
+    "from pascal_multilabel_with_datalayer_tools import SimpleTransformer\n",
+    "from copy import copy\n",
+    "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
+    "\n",
+    "image_index = 0 #Lets look at the first image in the batch.\n",
+    "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n",
+    "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n",
+    "print 'Ground truth: ',\n",
+    "for idx, val in enumerate(gtlist):\n",
+    "    if val:\n",
+    "        print classes[idx] + ',',\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alright. So far so good. We now have a working python datalayer that we can customize to our needs, e.g. by adding more data-augmentation or modify for other data-sets or tasks. Next, we will look at how to make it more efficient. The PascalMultilabelDataLayerSync loads the data syncronously, meaning that the GPU sits idle while the CPU loads the data. Fortunately, some simple multi-threading solves this problem. Let's do that next. First, though, lets measure the step time of this syncronous layer. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 13.9 s, sys: 363 ms, total: 14.2 s\n",
+      "Wall time: 14.2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "solver.step(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Now, let's setup solvers and nets with the PascalMultilabelDataLayerAsync layer. Take a look at the code in ./pycaffe/layers/pascal_multilabel_datalayers.py, it's not hard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BatchAdvancer initialized with 5717 images\n",
+      "PascalMultilabelDataLayerAsync initialized for split: train, with bs:128, im_shape:[227, 227].\n",
+      "BatchAdvancer initialized with 5823 images\n",
+      "PascalMultilabelDataLayerAsync initialized for split: val, with bs:128, im_shape:[227, 227].\n"
+     ]
+    }
+   ],
+   "source": [
+    "workdir = './pascal_multilabel_with_datalayer'\n",
+    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet_async.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet_async.prototxt\"))\n",
+    "solverprototxt.sp['display'] = \"1\"\n",
+    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
+    "solverprototxt.write(osp.join(workdir, 'solver_async.prototxt'))\n",
+    "\n",
+    "# write train and val nets.\n",
+    "with open(osp.join(workdir, 'trainnet_async.prototxt'), 'w') as f:\n",
+    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n",
+    "\n",
+    "with open(osp.join(workdir, 'valnet_async.prototxt'), 'w') as f:\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n",
+    "\n",
+    "solver_async = caffe.SGDSolver(osp.join(workdir, 'solver_async.prototxt'))\n",
+    "solver_async.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "solver_async.test_nets[0].share_with(solver_async.net)\n",
+    "solver_async.step(1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check runtime ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.7 s, sys: 476 ms, total: 16.1 s\n",
+      "Wall time: 16 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "solver_async.step(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alright, that is a modest runtime gain. However, as you data pre-processing becomes more complicated, this difference will increase."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's train the net for a while. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def hamming_distance(gt, est):\n",
+    "    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
+    "\n",
+    "def check_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = net.blobs['score'].data > 0\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alright, let's train."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "itt:0 accuracy:0.9430\n",
+      "itt:100 accuracy:0.9511\n",
+      "itt:200 accuracy:0.9573\n",
+      "itt:300 accuracy:0.9600\n",
+      "itt:400 accuracy:0.9583\n"
+     ]
+    }
+   ],
+   "source": [
+    "for itt in range(500):\n",
+    "    solver_async.step(1)\n",
+    "    if itt % 100 == 0:\n",
+    "        print 'itt:{}'.format(itt), 'accuracy:{0:.4f}'.format(check_accuracy(solver_async.test_nets[0], 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline accuracy:0.9243\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = np.zeros((batch_size, 20))\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)\n",
+    "\n",
+    "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver_async.test_nets[0], 5823/128))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Let's wrap this up by looking at some qualitative results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ground truth:  bird, \n",
+      "Estimated:  bird,\n"
+     ]
+    },
+    {
+     "data": {
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+       "ogUA/wOLS9SCjyKcPwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f699c03b690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "% matplotlib inline\n",
+    "from pascal_multilabel_with_datalayer_tutorial_tools import SimpleTransformer\n",
+    "from copy import copy\n",
+    "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
+    "\n",
+    "image_index = 0 #Lets look at the first image in the batch.\n",
+    "test_net = solver_async.test_nets[0]\n",
+    "test_net.forward()\n",
+    "plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n",
+    "gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "estlist = test_net.blobs['score'].data[image_index, ...] > 0\n",
+    "classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n",
+    "print 'Ground truth: ',\n",
+    "for idx, val in enumerate(gtlist):\n",
+    "    if val:\n",
+    "        print classes[idx] + ',',\n",
+    "\n",
+    "print ''  \n",
+    "print 'Estimated: ',\n",
+    "for idx, val in enumerate(estlist):\n",
+    "    if val == 1:\n",
+    "        print classes[idx] + ',',"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Multilabel classification on PASCAL using python data-layers.",
+  "example_name": "PASCAL Multilabel with python datalayer",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
new file mode 100644
index 00000000000..036d16787c3
--- /dev/null
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -0,0 +1,262 @@
+# imports
+import json, time, pickle, scipy.misc, skimage.io, caffe
+
+import numpy as np
+import os.path as osp
+
+from xml.dom import minidom
+from random import shuffle
+from threading import Thread
+from PIL import Image
+
+from pascal_multilabel_with_datalayer_tools import SimpleTransformer
+
+
+class PascalMultilabelDataLayerSync(caffe.Layer):
+    """
+    This is a simple syncronous datalayer for training a multilabel model on PASCAL. 
+    """
+
+    def setup(self, bottom, top):
+
+        self.top_names = ['data', 'label']
+
+        # === Read input parameters ===
+        
+        # params is a python dictionary with layer parameters.
+        params = eval(self.param_str) 
+
+        # do some simple checks that we have the parameters we need.
+        assert 'batch_size' in params.keys(), 'Params must include batch size.'
+        assert 'split' in params.keys(), 'Params must include split (train, val, or test).'
+        assert 'pascal_root' in params.keys(), 'Params must include pascal_root.'
+        assert 'im_shape' in params.keys(), 'Params must include im_shape.'
+ 
+        # store input as class variables
+        self.batch_size = params['batch_size'] 
+        self.im_shape = params['im_shape'] 
+        self.pascal_root = params['pascal_root']
+        self.im_shape = params['im_shape']
+        self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes.
+        self._cur = 0 # current image
+        self.transformer = SimpleTransformer() #this class does some simple data-manipulations
+
+        # === reshape tops ===
+        top[0].reshape(self.batch_size, 3, self.im_shape[0], self.im_shape[1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call.
+        top[1].reshape(self.batch_size, 20)
+
+        print "PascalMultilabelDataLayerSync initialized for split: {}, with bs:{}, im_shape:{}, and {} images.".format(params['split'], params['batch_size'], params['im_shape'], len(self.indexlist))
+
+
+    def reshape(self, bottom, top):
+        """ no need to reshape each time sine the input is fixed size (rows and columns) """
+        pass 
+
+    def forward(self, bottom, top):
+        """
+        Load data. 
+        """
+        for itt in range(self.batch_size):
+
+            # Did we finish an epoch?
+            if self._cur == len(self.indexlist):
+                self._cur = 0
+                shuffle(self.indexlist)
+            
+            # Load an image
+            index = self.indexlist[self._cur] # Get the image index
+            im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image
+            im = scipy.misc.imresize(im, self.im_shape) # resize
+            
+            # do a simple horizontal flip as data augmentation
+            flip = np.random.choice(2)*2-1
+            im = im[:, ::flip, :]
+
+            # Load and prepare ground truth
+            multilabel = np.zeros(20).astype(np.float32)
+            anns = load_pascal_annotation(index, self.pascal_root)
+            for label in anns['gt_classes']:
+                # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present.
+                multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class.
+
+            # Add directly to the caffe data layer
+            top[0].data[itt, ...] = self.transformer.preprocess(im)
+            top[1].data[itt, ...] = multilabel
+            self._cur += 1
+
+    def backward(self, top, propagate_down, bottom):
+        """ this layer does not back propagate """
+        pass
+
+
+
+
+class PascalMultilabelDataLayerAsync(caffe.Layer):
+    """
+    This is a simple asyncronous datalayer for training a multilabel model on PASCAL. 
+    """
+
+    def setup(self, bottom, top):
+
+        self.top_names = ['data', 'label']
+
+        # === Read input parameters ===
+        
+        # params is a python dictionary with layer parameters.
+        params = eval(self.param_str) 
+
+        # do some simple checks that we have the parameters we need.
+        assert 'batch_size' in params.keys(), 'Params must include batch size.'
+        assert 'split' in params.keys(), 'Params must include split (train, val, or test).'
+        assert 'pascal_root' in params.keys(), 'Params must include pascal_root.'
+        assert 'im_shape' in params.keys(), 'Params must include im_shape.'
+
+        self.batch_size = params['batch_size'] # we need to store this as a local variable.
+
+        # === We are going to do the actual data processing in a seperate, helperclass, called BatchAdvancer. So let's forward the parame to that class ===
+        self.thread_result = {}
+        self.thread = None
+        self.batch_advancer = BatchAdvancer(self.thread_result, params)
+        self.dispatch_worker() # Let it start fetching data right away.
+
+        # === reshape tops ===
+        top[0].reshape(self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call.
+        top[1].reshape(self.batch_size, 20) # Note the 20 channels (because PASCAL has 20 classes.)
+
+        print "PascalMultilabelDataLayerAsync initialized for split: {}, with bs:{}, im_shape:{}.".format(params['split'], params['batch_size'], params['im_shape'])
+
+
+
+    def reshape(self, bottom, top):
+        """ no need to reshape each time sine the input is fixed size (rows and columns) """
+        pass 
+
+    def forward(self, bottom, top):
+        """ this is the forward pass, where we load the data into the blobs. Since we run the BatchAdvance asynchronously, we just wait for it, and then copy """
+
+        if self.thread is not None:
+            self.join_worker() # wait until it is done.
+
+        for top_index, name in zip(range(len(top)), self.top_names):
+            for i in range(self.batch_size):
+                top[top_index].data[i, ...] = self.thread_result[name][i] #Copy the already-prepared data to caffe.
+        
+        self.dispatch_worker() # let's go again while the GPU process this batch.
+
+    def dispatch_worker(self):
+        assert self.thread is None
+        self.thread = Thread(target=self.batch_advancer)
+        self.thread.start()
+
+    def join_worker(self):
+        assert self.thread is not None
+        self.thread.join()
+        self.thread = None
+
+    def backward(self, top, propagate_down, bottom):
+        """ this layer does not back propagate """
+        pass
+
+
+class BatchAdvancer():
+    """
+    This is the class that is run asynchronously and actually does the work.
+    """
+    def __init__(self, result, params):
+        self.result = result
+        self.batch_size = params['batch_size'] 
+        self.im_shape = params['im_shape'] 
+        self.pascal_root = params['pascal_root']
+        self.im_shape = params['im_shape']
+        self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes.
+        self._cur = 0 # current image
+        self.transformer = SimpleTransformer() #this class does some simple data-manipulations
+
+        print "BatchAdvancer initialized with {} images".format(len(self.indexlist))
+
+    def __call__(self):
+        """
+        This does the same stuff as the forward layer of the synchronous layer. Exept that we store the data and labels in the result dictionary (as lists of length batchsize).
+        """
+        self.result['data'] = []
+        self.result['label'] = []
+        for itt in range(self.batch_size):
+
+            # Did we finish an epoch?
+            if self._cur == len(self.indexlist):
+                self._cur = 0
+                shuffle(self.indexlist)
+            
+            # Load an image
+            index = self.indexlist[self._cur] # Get the image index
+            im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image
+            im = scipy.misc.imresize(im, self.im_shape) # resize
+            
+            # do a simple horizontal flip as data augmentation
+            flip = np.random.choice(2)*2-1
+            im = im[:, ::flip, :]
+
+            # Load and prepare ground truth
+            multilabel = np.zeros(20).astype(np.float32)
+            anns = load_pascal_annotation(index, self.pascal_root)
+            for label in anns['gt_classes']:
+                # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present.
+                multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class.
+
+            # Store in a result list.
+            self.result['data'].append(self.transformer.preprocess(im))
+            self.result['label'].append(multilabel)
+            self._cur += 1
+
+
+def load_pascal_annotation(index, pascal_root):
+    """
+    This code is borrowed from Ross Girshick's FAST-RCNN code (https://github.com/rbgirshick/fast-rcnn). It parses the PASCAL .xml metadata files. See publication for further details: (http://arxiv.org/abs/1504.08083).
+
+    Thanks Ross!
+
+    """
+    classes = ('__background__', # always index 0
+                         'aeroplane', 'bicycle', 'bird', 'boat',
+                         'bottle', 'bus', 'car', 'cat', 'chair',
+                         'cow', 'diningtable', 'dog', 'horse',
+                         'motorbike', 'person', 'pottedplant',
+                         'sheep', 'sofa', 'train', 'tvmonitor')
+    class_to_ind = dict(zip(classes, xrange(21)))
+
+    filename = osp.join(pascal_root, 'Annotations', index + '.xml')
+    # print 'Loading: {}'.format(filename)
+    def get_data_from_tag(node, tag):
+        return node.getElementsByTagName(tag)[0].childNodes[0].data
+
+    with open(filename) as f:
+        data = minidom.parseString(f.read())
+
+    objs = data.getElementsByTagName('object')
+    num_objs = len(objs)
+
+    boxes = np.zeros((num_objs, 4), dtype=np.uint16)
+    gt_classes = np.zeros((num_objs), dtype=np.int32)
+    overlaps = np.zeros((num_objs, 21), dtype=np.float32)
+
+    # Load object bounding boxes into a data frame.
+    for ix, obj in enumerate(objs):
+        # Make pixel indexes 0-based
+        x1 = float(get_data_from_tag(obj, 'xmin')) - 1
+        y1 = float(get_data_from_tag(obj, 'ymin')) - 1
+        x2 = float(get_data_from_tag(obj, 'xmax')) - 1
+        y2 = float(get_data_from_tag(obj, 'ymax')) - 1
+        cls = class_to_ind[
+                str(get_data_from_tag(obj, "name")).lower().strip()]
+        boxes[ix, :] = [x1, y1, x2, y2]
+        gt_classes[ix] = cls
+        overlaps[ix, cls] = 1.0
+
+    overlaps = scipy.sparse.csr_matrix(overlaps)
+
+    return {'boxes' : boxes,
+            'gt_classes': gt_classes,
+            'gt_overlaps' : overlaps,
+            'flipped' : False,
+            'index': index}
+
diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py
new file mode 100644
index 00000000000..8e658b29a82
--- /dev/null
+++ b/examples/pycaffe/tools.py
@@ -0,0 +1,111 @@
+import numpy as np
+
+class SimpleTransformer:
+    """
+    SimpleTransformer is a simple class for preprocessing and deprocessing images for caffe.
+    """
+
+    def __init__(self, mean = [128, 128, 128]):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.scale = 1.0
+
+    def set_mean(self, mean):
+        """
+        Set the mean to subtract for centering the data.
+        """
+        self.mean = mean
+
+    def set_scale(self, scale):
+        """
+        Set the data scaling.
+        """
+        self.scale = scale
+
+    def preprocess(self, im):
+        """
+        preprocess() emulate the pre-processing occuring in the vgg16 caffe prototxt.
+        """
+    
+        im = np.float32(im)
+        im = im[:, :, ::-1] #change to BGR
+        im -= self.mean
+        im *= self.scale
+        im = im.transpose((2, 0, 1))
+        
+        return im
+
+    def deprocess(self, im):
+        """
+        inverse of preprocess()
+        """
+        im = im.transpose(1, 2, 0)
+        im /= self.scale
+        im += self.mean
+        im = im[:, :, ::-1] #change to RGB
+        
+        return np.uint8(im)
+
+class CaffeSolver:
+    """
+    Caffesolver is a class for creating a solver.prototxt file. It sets default values and can export a solver parameter file.
+    Note that all parameters are stored as strings. Strings variables are stored as strings in strings.
+    """
+
+    def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt_path = "trainnet.prototxt", debug = False):
+        
+        self.sp = {}
+
+        # critical:
+        self.sp['base_lr'] = '0.001'
+        self.sp['momentum'] = '0.9'
+        
+        # speed:
+        self.sp['test_iter'] = '100'
+        self.sp['test_interval'] = '250'
+        
+        # looks:
+        self.sp['display'] = '25'
+        self.sp['snapshot'] = '2500'
+        self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string!
+        
+        # learning rate policy
+        self.sp['lr_policy'] = '"fixed"'
+
+        # important, but rare:
+        self.sp['gamma'] = '0.1'
+        self.sp['weight_decay'] = '0.0005'
+        self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
+        self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
+
+        # pretty much never change these.
+        self.sp['max_iter'] = '100000'
+        self.sp['test_initialization'] = 'false'
+        self.sp['average_loss'] = '25' # this has to do with the display.
+        self.sp['iter_size'] = '1' #this is for accumulating gradients
+
+        if (debug):
+            self.sp['max_iter'] = '12'
+            self.sp['test_iter'] = '1'
+            self.sp['test_interval'] = '4'
+            self.sp['display'] = '1'
+
+    def add_from_file(self, filepath):
+        """
+        Reads a caffe solver prototxt file and updates the Caffesolver instance parameters.
+        """
+        with open(filepath, 'r') as f:
+            for line in f:
+                if line[0] == '#':
+                    continue
+                splitLine = line.split(':')
+                self.sp[splitLine[0].strip()] = splitLine[1].strip()
+
+    def write(self, filepath):
+        """
+        Export solver parameters to INPUT "filepath". Sorted alphabetically.
+        """
+        f = open(filepath, 'w')
+        for key, value in sorted(self.sp.items()):
+            if not(type(value) is str):
+                raise TypeError('All solver parameters must be strings')
+            f.write('%s: %s\n' % (key, value))

From cf765b983a7a3dd30f4c339f2bb3d296e6990c4b Mon Sep 17 00:00:00 2001
From: Evan Lezar <evan.lezar@zalando.de>
Date: Thu, 18 Feb 2016 11:50:15 +0100
Subject: [PATCH 1715/2053] Refactor and improve code style.

Fix some typos. Correct imports. Refactor data layers. Apply PEP8 formatting.
---
 .../04-pascal_multilabel_with_datalayer.ipynb | 3766 +----------------
 .../layers/pascal_multilabel_datalayers.py    |  299 +-
 examples/pycaffe/tools.py                     |   48 +-
 3 files changed, 254 insertions(+), 3859 deletions(-)

diff --git a/examples/04-pascal_multilabel_with_datalayer.ipynb b/examples/04-pascal_multilabel_with_datalayer.ipynb
index 6839841a5ea..43aa539d594 100644
--- a/examples/04-pascal_multilabel_with_datalayer.ipynb
+++ b/examples/04-pascal_multilabel_with_datalayer.ipynb
@@ -11,9 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial we will do multilabel classification on PASCAL VOC 2012. \n",
+    "In this tutorial we will do multi-label classification on PASCAL VOC 2012.\n",
     "\n",
-    "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss, and we will load data using a python data-layer. Data could also be provided through a HDF5 data-layer, but the python data-layer provide endless flexibility, so that's what we will use."
+    "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use."
    ]
   },
   {
@@ -23,7 +23,7 @@
     "### Preliminaries\n",
     "\n",
     "First, make sure you compile caffe using \n",
-    "WITH_PYTHON_LAYER ;= 1\n",
+    "WITH_PYTHON_LAYER := 1\n",
     "\n",
     "Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
     "\n",
@@ -34,13 +34,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# set root directory, e.g:\n",
-    "pascal_root = '/data/pascal/VOC2012'\n",
-    "\n",
     "# import some modules\n",
     "import sys, os, caffe\n",
     "import numpy as np\n",
@@ -48,6 +45,9 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "# set root directory, e.g:\n",
+    "pascal_root = os.path.join(caffe_root, 'data/pascal/VOC2012')\n",
+    "\n",
     "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
     "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
     "\n",
@@ -133,14 +133,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "workdir = './pascal_multilabel_with_datalayer'\n",
-    "os.makedirs(workdir)\n",
+    "if not os.path.isdir(workdir):\n",
+    "    os.makedirs(workdir)\n",
+    "\n",
     "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
     "solverprototxt.sp['display'] = \"1\"\n",
     "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
@@ -161,7 +163,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the codel. It's quite straight-forward, and gives you full control over data and labels.\n",
+    "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n",
     "\n",
     "\n",
     "Now we can load the caffe solver as usual."
@@ -169,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -178,8 +180,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PascalMultilabelDataLayerSync initialized for split: train, with bs:128, im_shape:[227, 227], and 5717 images.\n",
-      "PascalMultilabelDataLayerSync initialized for split: val, with bs:128, im_shape:[227, 227], and 5823 images.\n"
+      "BatchLoader initialized with 5717 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: train,         with bs: 128, im_shape: [227, 227].\n",
+      "BatchLoader initialized with 5823 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: val,         with bs: 128, im_shape: [227, 227].\n"
      ]
     }
    ],
@@ -199,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -208,1602 +212,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ground truth:  horse, person,\n"
+      "Ground truth:  horse, person, \n"
      ]
     },
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
-       "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmwZdd13/db+5w7vKnf6/d6QmNqgCQIEiQIkBRJgaRI\n",
-       "ypSc2FFkW4qHiqtSKftjUvmSlFUVx5GjfImTfEil8sGVciUp2Y6TckJlEBWREgnOBGcSQKMbaKDR\n",
-       "89xvfvfde8/ZKx/2eM6973VDJNyQq3eh8e6955w9rL2G/1p77X1EVblf7pf75X4x97oD98v9cr+8\n",
-       "M8p9ZXC/3C/3C3BfGdwv98v94st9ZXC/3C/3C3BfGdwv98v94st9ZXC/3C/3C/A2KQMR+TdE5JSI\n",
-       "vCYif+/taON+uV/ul19skV90noGIFMBp4PPAJeD7wN9S1Vd+oQ3dL/fL/fILLW8HMvgYcEZV31TV\n",
-       "MfAvgN98G9q5X+6X++UXWN4OZfAgcCH7ftH/dr/cL/fLO7i8Hcrgfn7z/XK//Dks5dtQ5yXg4ez7\n",
-       "wzh0EIuI3FcY98v9co+Kqsq0398OZfAD4D0icgK4DPwN4G9N6dDEgwpYBVSxCiqS3Q8iDsqY8LO4\n",
-       "egQQEaaOMKtbPWgJqkh8/QpYq1irqFpXMcbVLYKIa1/F16PK7/3D3+U//Qf/eeyYZPUZDbUK4mqP\n",
-       "31XE/errzukR+xPblTT4MGRRjHF9cz+HurRRh4hk4/H0Fon9FwwK/Je/91/w9//+PyDrMsS6Y6P+\n",
-       "u/tRbbjHgChxGAqOzyTOr4j4vtq8wubcaKogtKsam/P0z/oUxoGiKvzX/+j3+I//k3+QHmhUjucT\n",
-       "qJGMByTS1cQ+uDYcHbO+I/73yT5bQt8m5zTSvd0lnbznv//vfo//8D/6zxKFJKtPAw3V19ekMb5/\n",
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34j3mnzDGr7W0CHjnojPOogaWFtyZSo01diHmE2j6POqUtjPbNq0k7JID1NkiqmpEJSUV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\nrkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4ebd89cc90>"
+       "<matplotlib.figure.Figure at 0x7fc82127b590>"
       ]
      },
      "metadata": {},
@@ -1812,7 +228,7 @@
    ],
    "source": [
     "% matplotlib inline\n",
-    "from pascal_multilabel_with_datalayer_tools import SimpleTransformer\n",
+    "from tools import SimpleTransformer\n",
     "from copy import copy\n",
     "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
     "\n",
@@ -1824,7 +240,7 @@
     "for idx, val in enumerate(gtlist):\n",
     "    if val:\n",
     "        print classes[idx] + ',',\n",
-    "\n"
+    "print ''"
    ]
   },
   {
@@ -1836,20 +252,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 13.9 s, sys: 363 ms, total: 14.2 s\n",
-      "Wall time: 14.2 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "solver.step(10)"
@@ -1866,22 +273,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BatchAdvancer initialized with 5717 images\n",
-      "PascalMultilabelDataLayerAsync initialized for split: train, with bs:128, im_shape:[227, 227].\n",
-      "BatchAdvancer initialized with 5823 images\n",
-      "PascalMultilabelDataLayerAsync initialized for split: val, with bs:128, im_shape:[227, 227].\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "workdir = './pascal_multilabel_with_datalayer'\n",
     "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet_async.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet_async.prototxt\"))\n",
@@ -1914,20 +310,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 15.7 s, sys: 476 ms, total: 16.1 s\n",
-      "Wall time: 16 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "solver_async.step(10)"
@@ -1949,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -1958,6 +345,7 @@
     "def hamming_distance(gt, est):\n",
     "    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
     "\n",
+    "\n",
     "def check_accuracy(net, num_batches, batch_size = 128):\n",
     "    acc = 0.0\n",
     "    for t in range(num_batches):\n",
@@ -1978,23 +366,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "itt:0 accuracy:0.9430\n",
-      "itt:100 accuracy:0.9511\n",
-      "itt:200 accuracy:0.9573\n",
-      "itt:300 accuracy:0.9600\n",
-      "itt:400 accuracy:0.9583\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for itt in range(500):\n",
     "    solver_async.step(1)\n",
@@ -2011,26 +387,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Baseline accuracy:0.9243\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
     "    acc = 0.0\n",
     "    for t in range(num_batches):\n",
     "        net.forward()\n",
     "        gts = net.blobs['label'].data\n",
-    "        ests = np.zeros((batch_size, 20))\n",
+    "        ests = np.zeros((batch_size, len(gts)))\n",
     "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
     "            acc += hamming_distance(gt, est)\n",
     "    return acc / (num_batches * batch_size)\n",
@@ -2049,2059 +417,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ground truth:  bird, \n",
-      "Estimated:  bird,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": [
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-       "12vhjH1M8oJATU2hq4YIB/ddM4YszZqcicv252lOnJTcuXfA2fScm/t77Gxt0+/16ClFJwzo+D62\n",
-       "2alUtSVLMmqEGzd2yKqC792+S61rlDg2Jy/s8cqL15nM589dk1coyV4zOT+nKjLu3LlDms0RFXBx\n",
-       "fs6DB+9xcnLOPEmIwoBuv08v6lIUOZ7vEycpWeYEMkujkYbd2BiNUq58NMlLrDX0+wFKDPfuvMfa\n",
-       "+jI3Ntd4960fUgcBRTrDak0YRpyenBBfzOl0O6A8RqMBRbFEkedU2olYVnXlHBI4xdzDkwmvvHSD\n",
-       "nY1NjI3Y23+Z69e3GC8PiNOYwwcH3L5zwN7WJnEYYaKE/V/aIj68w3t3EzZu3GIwHFHnKenFKXsr\n",
-       "I+azCX43hCJhOk2ZT6d4KiDsdNB6hq1ykkfH3MumDFbWWdraQgYBKokp45g8TimLitqCH4ZQ0dy3\n",
-       "e9C07WLcL5QxBqsNpCUGS1pV1K651mlTGotI3WgaeHgIXu3O95W4FarE/WJaW+MpH6tA1YbQd1to\n",
-       "iTqgFKa25Maw0gvpByF4PkdJjj065eH5hfsMTzWlvop+J6AbeVjxEVP/iDV4aTSk0wnRpWGeTJgW\n",
-       "pesatDDq9VGeYLQm8BRRGDDqdInGNfM8d6pLykfXtes5UApTVmRxwgOjmUwnxPOEsiqxxik0O+Ed\n",
-       "oRZBexapHaMUOFo1dIkShfIDt/itpa6b5xtZeKvE8Woax+EQBZELnrXGlC4vpZucijRJ2JKa08mE\n",
-       "+K13OHz4iJ2dbbY3N1jquyKiyA+orSUtK0xtmCQxVWEYD5fZWk94eHxKVWvSLEPVlpc/fpP/fOvd\n",
-       "565JeYaswc8VHyDH1qJFi58j7DMoj64kGLRo0WLx8Hy61BYtWvy/QRsMWrRoAVxBMBCRz4nIOyLy\n",
-       "3yLyxcv+/P8rROSuiHxHRF4TkW81Yysi8m8i8n0R+YaILF21nU9CRP5BRI5F5M0nxp5ps4h8qfHL\n",
-       "OyLy2aux+v14xhz+UkTuN754TURefeK5RZzDroj8u4i8JSLfFZE/bsYXyxfW2kv7w5Vr3Ab2gAB4\n",
-       "HfjYZdrwM9h+B1h5auyvgT9rrr8I/NVV2/mUfZ8BPgW8+UE2Ax9v/BE0/rkNqAWdw18Af/oTXruo\n",
-       "c9gCPtlcD4B3gY8tmi8ue2fwaeC2tfautbYC/hn4/CXb8LPg6SzsbwJfaa6/AvzW5ZrzfFhr/wO4\n",
-       "eGr4WTZ/Hviqtbay1t7FfQE/fRl2Pg/PmAP8b1/A4s7hyFr7enMdA28DOyyYLy47GOwA9554fL8Z\n",
-       "+yjAAt8UkW+LyLADDUAAAAG7SURBVB80Y5vW2uPm+hjYvBrTfio8y+ZrOH88xqL75o9E5A0R+fIT\n",
-       "2+uFn4OI7OF2Ov/FgvnisoPBR/kc89estZ8CXgX+UEQ+8+ST1u3vPlLz+xA2L+p8/g7YBz4JPAT+\n",
-       "5jmvXZg5iMgA+BfgT6y17ysHXARfXHYweADsPvF4l/dHwIWFtfZh8/8U+Ffctu1YRLYARGQbOLk6\n",
-       "Cz80nmXz07653owtHKy1J7YB8Pf8eAu9sHMQkQAXCP7JWvu1ZnihfHHZweDbwC0R2ROREPgd4OuX\n",
-       "bMNPDRHpiciwue4DnwXexNn+heZlXwC+9pPfYaHwLJu/DvyuiIQisg/cAr51BfZ9IJqF8xi/jfMF\n",
-       "LOgcxHG0fRn4nrX2b594arF8cQWZ1Vdx2dTbwJeuOtP7IW3ex2V3Xwe++9huYAX4JvB94BvA0lXb\n",
-       "+pTdXwUOgRKXq/m959kM/Hnjl3eA37hq+58xh98H/hH4DvAGbgFtLvgcfh3Xhfw68Frz97lF80Vb\n",
-       "jtyiRQugrUBs0aJFgzYYtGjRAmiDQYsWLRq0waBFixZAGwxatGjRoA0GLVq0ANpg0KJFiwZtMGjR\n",
-       "ogUA/wOLS9SCjyKcPwAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f699c03b690>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "% matplotlib inline\n",
-    "from pascal_multilabel_with_datalayer_tutorial_tools import SimpleTransformer\n",
+    "from tools import SimpleTransformer\n",
     "from copy import copy\n",
     "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
     "\n",
@@ -4123,6 +446,15 @@
     "    if val == 1:\n",
     "        print classes[idx] + ',',"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -4144,7 +476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "version": "2.7.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
index 036d16787c3..f0039eff4b6 100644
--- a/examples/pycaffe/layers/pascal_multilabel_datalayers.py
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -1,5 +1,10 @@
 # imports
-import json, time, pickle, scipy.misc, skimage.io, caffe
+import json
+import time
+import pickle
+import scipy.misc
+import skimage.io
+import caffe
 
 import numpy as np
 import os.path as osp
@@ -9,12 +14,14 @@
 from threading import Thread
 from PIL import Image
 
-from pascal_multilabel_with_datalayer_tools import SimpleTransformer
+from tools import SimpleTransformer
 
 
 class PascalMultilabelDataLayerSync(caffe.Layer):
+
     """
-    This is a simple syncronous datalayer for training a multilabel model on PASCAL. 
+    This is a simple syncronous datalayer for training a multilabel model on
+    PASCAL.
     """
 
     def setup(self, bottom, top):
@@ -22,78 +29,60 @@ def setup(self, bottom, top):
         self.top_names = ['data', 'label']
 
         # === Read input parameters ===
-        
+
         # params is a python dictionary with layer parameters.
-        params = eval(self.param_str) 
-
-        # do some simple checks that we have the parameters we need.
-        assert 'batch_size' in params.keys(), 'Params must include batch size.'
-        assert 'split' in params.keys(), 'Params must include split (train, val, or test).'
-        assert 'pascal_root' in params.keys(), 'Params must include pascal_root.'
-        assert 'im_shape' in params.keys(), 'Params must include im_shape.'
- 
+        params = eval(self.param_str)
+
+        # Check the paramameters for validity.
+        check_params(params)
+
         # store input as class variables
-        self.batch_size = params['batch_size'] 
-        self.im_shape = params['im_shape'] 
-        self.pascal_root = params['pascal_root']
-        self.im_shape = params['im_shape']
-        self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes.
-        self._cur = 0 # current image
-        self.transformer = SimpleTransformer() #this class does some simple data-manipulations
+        self.batch_size = params['batch_size']
+
+        # Create a batch loader to load the images.
+        self.batch_loader = BatchLoader(params, None)
 
         # === reshape tops ===
-        top[0].reshape(self.batch_size, 3, self.im_shape[0], self.im_shape[1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call.
+        # since we use a fixed input image size, we can shape the data layer
+        # once. Else, we'd have to do it in the reshape call.
+        top[0].reshape(
+            self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
+        # Note the 20 channels (because PASCAL has 20 classes.)
         top[1].reshape(self.batch_size, 20)
 
-        print "PascalMultilabelDataLayerSync initialized for split: {}, with bs:{}, im_shape:{}, and {} images.".format(params['split'], params['batch_size'], params['im_shape'], len(self.indexlist))
-
-
-    def reshape(self, bottom, top):
-        """ no need to reshape each time sine the input is fixed size (rows and columns) """
-        pass 
+        print_info("PascalMultilabelDataLayerSync", params)
 
     def forward(self, bottom, top):
         """
-        Load data. 
+        Load data.
         """
         for itt in range(self.batch_size):
-
-            # Did we finish an epoch?
-            if self._cur == len(self.indexlist):
-                self._cur = 0
-                shuffle(self.indexlist)
-            
-            # Load an image
-            index = self.indexlist[self._cur] # Get the image index
-            im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image
-            im = scipy.misc.imresize(im, self.im_shape) # resize
-            
-            # do a simple horizontal flip as data augmentation
-            flip = np.random.choice(2)*2-1
-            im = im[:, ::flip, :]
-
-            # Load and prepare ground truth
-            multilabel = np.zeros(20).astype(np.float32)
-            anns = load_pascal_annotation(index, self.pascal_root)
-            for label in anns['gt_classes']:
-                # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present.
-                multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class.
+            # Use the batch loader to load the next image.
+            im, multilabel = self.batch_loader.load_next_image()
 
             # Add directly to the caffe data layer
-            top[0].data[itt, ...] = self.transformer.preprocess(im)
+            top[0].data[itt, ...] = im
             top[1].data[itt, ...] = multilabel
-            self._cur += 1
 
-    def backward(self, top, propagate_down, bottom):
-        """ this layer does not back propagate """
+    def reshape(self, bottom, top):
+        """
+        There is no need to reshape the data, since the input is of fixed size
+        (rows and columns)
+        """
         pass
 
-
+    def backward(self, top, propagate_down, bottom):
+        """
+        These layers does not back propagate
+        """
+        pass
 
 
 class PascalMultilabelDataLayerAsync(caffe.Layer):
+
     """
-    This is a simple asyncronous datalayer for training a multilabel model on PASCAL. 
+    This is a simple asyncronous datalayer for training a multilabel model on
+    PASCAL.
     """
 
     def setup(self, bottom, top):
@@ -101,51 +90,68 @@ def setup(self, bottom, top):
         self.top_names = ['data', 'label']
 
         # === Read input parameters ===
-        
+
         # params is a python dictionary with layer parameters.
-        params = eval(self.param_str) 
+        params = eval(self.param_str)
 
-        # do some simple checks that we have the parameters we need.
-        assert 'batch_size' in params.keys(), 'Params must include batch size.'
-        assert 'split' in params.keys(), 'Params must include split (train, val, or test).'
-        assert 'pascal_root' in params.keys(), 'Params must include pascal_root.'
-        assert 'im_shape' in params.keys(), 'Params must include im_shape.'
+        # Check the paramameters for validity.
+        check_params(params)
 
-        self.batch_size = params['batch_size'] # we need to store this as a local variable.
+        # we need to store this as a local variable.
+        self.batch_size = params['batch_size']
 
-        # === We are going to do the actual data processing in a seperate, helperclass, called BatchAdvancer. So let's forward the parame to that class ===
+        # === We are going to do the actual data processing in a seperate,
+        # helperclass, called BatchLoader. So let's forward the parameters
+        # to that class ===
         self.thread_result = {}
         self.thread = None
-        self.batch_advancer = BatchAdvancer(self.thread_result, params)
-        self.dispatch_worker() # Let it start fetching data right away.
+        self.batch_loader = BatchLoader(params, self.thread_result)
+        self.dispatch_worker()  # Let it start fetching data right away.
 
         # === reshape tops ===
-        top[0].reshape(self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call.
-        top[1].reshape(self.batch_size, 20) # Note the 20 channels (because PASCAL has 20 classes.)
-
-        print "PascalMultilabelDataLayerAsync initialized for split: {}, with bs:{}, im_shape:{}.".format(params['split'], params['batch_size'], params['im_shape'])
-
-
+        # since we use a fixed input image size, we can shape the data layer
+        # once. Else, we'd have to do it in the reshape call.
+        top[0].reshape(
+            self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
+        # Note the 20 channels (because PASCAL has 20 classes.)
+        top[1].reshape(self.batch_size, 20)
 
-    def reshape(self, bottom, top):
-        """ no need to reshape each time sine the input is fixed size (rows and columns) """
-        pass 
+        print_info("PascalMultilabelDataLayerAsync", params)
 
     def forward(self, bottom, top):
-        """ this is the forward pass, where we load the data into the blobs. Since we run the BatchAdvance asynchronously, we just wait for it, and then copy """
+        """
+        This is the forward pass, where we load the data into the blobs.
+        Since we run the BatchLoader asynchronously, we just wait for it,
+        and then copy
+        """
 
         if self.thread is not None:
-            self.join_worker() # wait until it is done.
+            self.join_worker()  # wait until it is done.
 
         for top_index, name in zip(range(len(top)), self.top_names):
             for i in range(self.batch_size):
-                top[top_index].data[i, ...] = self.thread_result[name][i] #Copy the already-prepared data to caffe.
-        
-        self.dispatch_worker() # let's go again while the GPU process this batch.
+                # Copy the already-prepared data to caffe.
+                top[top_index].data[i, ...] = self.thread_result[name][i]
+
+        # let's go again while the GPU process this batch.
+        self.dispatch_worker()
+
+    def reshape(self, bottom, top):
+        """
+        There is no need to reshape the data, since the input is of fixed size
+        (rows and columns)
+        """
+        pass
+
+    def backward(self, top, propagate_down, bottom):
+        """
+        These layers does not back propagate
+        """
+        pass
 
     def dispatch_worker(self):
         assert self.thread is None
-        self.thread = Thread(target=self.batch_advancer)
+        self.thread = Thread(target=self.batch_loader)
         self.thread.start()
 
     def join_worker(self):
@@ -153,72 +159,96 @@ def join_worker(self):
         self.thread.join()
         self.thread = None
 
-    def backward(self, top, propagate_down, bottom):
-        """ this layer does not back propagate """
-        pass
 
+class BatchLoader(object):
 
-class BatchAdvancer():
     """
-    This is the class that is run asynchronously and actually does the work.
+    This class abstracts away the loading of images.
+    Images can either be loaded singly, or in a batch. The latter is used for
+    the asyncronous data layer to preload batches while other processing is
+    performed.
     """
-    def __init__(self, result, params):
+
+    def __init__(self, params, result):
         self.result = result
-        self.batch_size = params['batch_size'] 
-        self.im_shape = params['im_shape'] 
+        self.batch_size = params['batch_size']
         self.pascal_root = params['pascal_root']
         self.im_shape = params['im_shape']
-        self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes.
-        self._cur = 0 # current image
-        self.transformer = SimpleTransformer() #this class does some simple data-manipulations
+        # get list of image indexes.
+        list_file = params['split'] + '.txt'
+        self.indexlist = [line.rstrip('\n') for line in open(
+            osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
+        self._cur = 0  # current image
+        # this class does some simple data-manipulations
+        self.transformer = SimpleTransformer()
 
-        print "BatchAdvancer initialized with {} images".format(len(self.indexlist))
+        print "BatchLoader initialized with {} images".format(
+            len(self.indexlist))
 
     def __call__(self):
         """
-        This does the same stuff as the forward layer of the synchronous layer. Exept that we store the data and labels in the result dictionary (as lists of length batchsize).
+        This does the same stuff as the forward layer of the synchronous layer.
+        Exept that we store the data and labels in the result dictionary
+        (as lists of length batchsize).
         """
         self.result['data'] = []
         self.result['label'] = []
         for itt in range(self.batch_size):
 
-            # Did we finish an epoch?
-            if self._cur == len(self.indexlist):
-                self._cur = 0
-                shuffle(self.indexlist)
-            
-            # Load an image
-            index = self.indexlist[self._cur] # Get the image index
-            im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image
-            im = scipy.misc.imresize(im, self.im_shape) # resize
-            
-            # do a simple horizontal flip as data augmentation
-            flip = np.random.choice(2)*2-1
-            im = im[:, ::flip, :]
-
-            # Load and prepare ground truth
-            multilabel = np.zeros(20).astype(np.float32)
-            anns = load_pascal_annotation(index, self.pascal_root)
-            for label in anns['gt_classes']:
-                # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present.
-                multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class.
+            # Get the next image in the batch
+            im, multilabel = self.load_next_image()
 
             # Store in a result list.
-            self.result['data'].append(self.transformer.preprocess(im))
+            self.result['data'].append(im)
             self.result['label'].append(multilabel)
-            self._cur += 1
+
+    def load_next_image(self):
+        """
+        Load the next image in a batch.
+        """
+        # Did we finish an epoch?
+        if self._cur == len(self.indexlist):
+            self._cur = 0
+            shuffle(self.indexlist)
+
+        # Load an image
+        index = self.indexlist[self._cur]  # Get the image index
+        image_file_name = index + '.jpg'
+        im = np.asarray(Image.open(
+            osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
+        im = scipy.misc.imresize(im, self.im_shape)  # resize
+
+        # do a simple horizontal flip as data augmentation
+        flip = np.random.choice(2)*2-1
+        im = im[:, ::flip, :]
+
+        # Load and prepare ground truth
+        multilabel = np.zeros(20).astype(np.float32)
+        anns = load_pascal_annotation(index, self.pascal_root)
+        for label in anns['gt_classes']:
+            # in the multilabel problem we don't care how MANY instances
+            # there are of each class. Only if they are present.
+            # The "-1" is b/c we are not interested in the background
+            # class.
+            multilabel[label - 1] = 1
+
+        self._cur += 1
+        return self.transformer.preprocess(im), multilabel
 
 
 def load_pascal_annotation(index, pascal_root):
     """
-    This code is borrowed from Ross Girshick's FAST-RCNN code (https://github.com/rbgirshick/fast-rcnn). It parses the PASCAL .xml metadata files. See publication for further details: (http://arxiv.org/abs/1504.08083).
+    This code is borrowed from Ross Girshick's FAST-RCNN code
+    (https://github.com/rbgirshick/fast-rcnn).
+    It parses the PASCAL .xml metadata files.
+    See publication for further details: (http://arxiv.org/abs/1504.08083).
 
     Thanks Ross!
 
     """
-    classes = ('__background__', # always index 0
-                         'aeroplane', 'bicycle', 'bird', 'boat',
-                         'bottle', 'bus', 'car', 'cat', 'chair',
+    classes = ('__background__',  # always index 0
+               'aeroplane', 'bicycle', 'bird', 'boat',
+               'bottle', 'bus', 'car', 'cat', 'chair',
                          'cow', 'diningtable', 'dog', 'horse',
                          'motorbike', 'person', 'pottedplant',
                          'sheep', 'sofa', 'train', 'tvmonitor')
@@ -226,6 +256,7 @@ def load_pascal_annotation(index, pascal_root):
 
     filename = osp.join(pascal_root, 'Annotations', index + '.xml')
     # print 'Loading: {}'.format(filename)
+
     def get_data_from_tag(node, tag):
         return node.getElementsByTagName(tag)[0].childNodes[0].data
 
@@ -247,16 +278,38 @@ def get_data_from_tag(node, tag):
         x2 = float(get_data_from_tag(obj, 'xmax')) - 1
         y2 = float(get_data_from_tag(obj, 'ymax')) - 1
         cls = class_to_ind[
-                str(get_data_from_tag(obj, "name")).lower().strip()]
+            str(get_data_from_tag(obj, "name")).lower().strip()]
         boxes[ix, :] = [x1, y1, x2, y2]
         gt_classes[ix] = cls
         overlaps[ix, cls] = 1.0
 
     overlaps = scipy.sparse.csr_matrix(overlaps)
 
-    return {'boxes' : boxes,
+    return {'boxes': boxes,
             'gt_classes': gt_classes,
-            'gt_overlaps' : overlaps,
-            'flipped' : False,
+            'gt_overlaps': overlaps,
+            'flipped': False,
             'index': index}
 
+
+def check_params(params):
+    """
+    A utility function to check the parameters for the data layers.
+    """
+    assert 'split' in params.keys(
+    ), 'Params must include split (train, val, or test).'
+
+    required = ['batch_size', 'pascal_root', 'im_shape']
+    for r in required:
+        assert r in params.keys(), 'Params must include {}'.format(r)
+
+
+def print_info(name, params):
+    """
+    Ouput some info regarding the class
+    """
+    print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
+        name,
+        params['split'],
+        params['batch_size'],
+        params['im_shape'])
diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py
index 8e658b29a82..88b1834af1e 100644
--- a/examples/pycaffe/tools.py
+++ b/examples/pycaffe/tools.py
@@ -1,11 +1,14 @@
 import numpy as np
 
+
 class SimpleTransformer:
+
     """
-    SimpleTransformer is a simple class for preprocessing and deprocessing images for caffe.
+    SimpleTransformer is a simple class for preprocessing and deprocessing
+    images for caffe.
     """
 
-    def __init__(self, mean = [128, 128, 128]):
+    def __init__(self, mean=[128, 128, 128]):
         self.mean = np.array(mean, dtype=np.float32)
         self.scale = 1.0
 
@@ -23,15 +26,16 @@ def set_scale(self, scale):
 
     def preprocess(self, im):
         """
-        preprocess() emulate the pre-processing occuring in the vgg16 caffe prototxt.
+        preprocess() emulate the pre-processing occuring in the vgg16 caffe
+        prototxt.
         """
-    
+
         im = np.float32(im)
-        im = im[:, :, ::-1] #change to BGR
+        im = im[:, :, ::-1]  # change to BGR
         im -= self.mean
         im *= self.scale
         im = im.transpose((2, 0, 1))
-        
+
         return im
 
     def deprocess(self, im):
@@ -41,33 +45,38 @@ def deprocess(self, im):
         im = im.transpose(1, 2, 0)
         im /= self.scale
         im += self.mean
-        im = im[:, :, ::-1] #change to RGB
-        
+        im = im[:, :, ::-1]  # change to RGB
+
         return np.uint8(im)
 
+
 class CaffeSolver:
+
     """
-    Caffesolver is a class for creating a solver.prototxt file. It sets default values and can export a solver parameter file.
-    Note that all parameters are stored as strings. Strings variables are stored as strings in strings.
+    Caffesolver is a class for creating a solver.prototxt file. It sets default
+    values and can export a solver parameter file.
+    Note that all parameters are stored as strings. Strings variables are
+    stored as strings in strings.
     """
 
-    def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt_path = "trainnet.prototxt", debug = False):
-        
+    def __init__(self, testnet_prototxt_path="testnet.prototxt",
+                 trainnet_prototxt_path="trainnet.prototxt", debug=False):
+
         self.sp = {}
 
         # critical:
         self.sp['base_lr'] = '0.001'
         self.sp['momentum'] = '0.9'
-        
+
         # speed:
         self.sp['test_iter'] = '100'
         self.sp['test_interval'] = '250'
-        
+
         # looks:
         self.sp['display'] = '25'
         self.sp['snapshot'] = '2500'
-        self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string!
-        
+        self.sp['snapshot_prefix'] = '"snapshot"'  # string withing a string!
+
         # learning rate policy
         self.sp['lr_policy'] = '"fixed"'
 
@@ -80,8 +89,8 @@ def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt
         # pretty much never change these.
         self.sp['max_iter'] = '100000'
         self.sp['test_initialization'] = 'false'
-        self.sp['average_loss'] = '25' # this has to do with the display.
-        self.sp['iter_size'] = '1' #this is for accumulating gradients
+        self.sp['average_loss'] = '25'  # this has to do with the display.
+        self.sp['iter_size'] = '1'  # this is for accumulating gradients
 
         if (debug):
             self.sp['max_iter'] = '12'
@@ -91,7 +100,8 @@ def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt
 
     def add_from_file(self, filepath):
         """
-        Reads a caffe solver prototxt file and updates the Caffesolver instance parameters.
+        Reads a caffe solver prototxt file and updates the Caffesolver
+        instance parameters.
         """
         with open(filepath, 'r') as f:
             for line in f:

From 9f8f7775a890b693964049c5154ad95bfe944029 Mon Sep 17 00:00:00 2001
From: Oscar Beijbom <oscar.beijbom@gmail.com>
Date: Fri, 26 Feb 2016 19:17:21 -0800
Subject: [PATCH 1716/2053] Finalized tutorial. Removed asyncronous layer.

---
 .../04-pascal_multilabel_with_datalayer.ipynb | 484 ------------------
 .../pascal-multilabel-with-datalayer.ipynb    | 478 +++++++++++++++++
 .../layers/pascal_multilabel_datalayers.py    |  99 ----
 3 files changed, 478 insertions(+), 583 deletions(-)
 delete mode 100644 examples/04-pascal_multilabel_with_datalayer.ipynb
 create mode 100644 examples/pascal-multilabel-with-datalayer.ipynb

diff --git a/examples/04-pascal_multilabel_with_datalayer.ipynb b/examples/04-pascal_multilabel_with_datalayer.ipynb
deleted file mode 100644
index 43aa539d594..00000000000
--- a/examples/04-pascal_multilabel_with_datalayer.ipynb
+++ /dev/null
@@ -1,484 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multilabel classification on PASCAL using python data-layers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this tutorial we will do multi-label classification on PASCAL VOC 2012.\n",
-    "\n",
-    "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preliminaries\n",
-    "\n",
-    "First, make sure you compile caffe using \n",
-    "WITH_PYTHON_LAYER := 1\n",
-    "\n",
-    "Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
-    "\n",
-    "Third, set paths and import modules:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# import some modules\n",
-    "import sys, os, caffe\n",
-    "import numpy as np\n",
-    "import os.path as osp\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-    "# set root directory, e.g:\n",
-    "pascal_root = os.path.join(caffe_root, 'data/pascal/VOC2012')\n",
-    "\n",
-    "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
-    "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
-    "\n",
-    "import tools #this contains some tools that we need\n",
-    "\n",
-    "# make sure we have the caffenet weight downloaded.\n",
-    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n",
-    "\n",
-    "# initialize caffe for gpu mode\n",
-    "caffe.set_mode_gpu()\n",
-    "caffe.set_device(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from caffe import layers as L, params as P, to_proto\n",
-    "from caffe.proto import caffe_pb2\n",
-    "\n",
-    "# helper function for common structures\n",
-    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n",
-    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
-    "                                num_output=nout, pad=pad, group=group)\n",
-    "    return conv, L.ReLU(conv, in_place=True)\n",
-    "\n",
-    "# another helper function\n",
-    "def fc_relu(bottom, nout):\n",
-    "    fc = L.InnerProduct(bottom, num_output=nout)\n",
-    "    return fc, L.ReLU(fc, in_place=True)\n",
-    "\n",
-    "# yet another helper function\n",
-    "def max_pool(bottom, ks, stride=1):\n",
-    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
-    "\n",
-    "# main netspec wrapper\n",
-    "def caffenet_multilabel(data_layer_params, datalayer):\n",
-    "    # setup the python data layer \n",
-    "    n = caffe.NetSpec()\n",
-    "    n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n",
-    "                               ntop = 2, param_str=str(data_layer_params))\n",
-    "\n",
-    "    # the net itself\n",
-    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n",
-    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
-    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
-    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n",
-    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
-    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
-    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n",
-    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n",
-    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n",
-    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
-    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n",
-    "    n.drop6 = L.Dropout(n.relu6, in_place=True)\n",
-    "    n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n",
-    "    n.drop7 = L.Dropout(n.relu7, in_place=True)\n",
-    "    n.score = L.InnerProduct(n.drop7, num_output=20)\n",
-    "    n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n",
-    "    \n",
-    "    return str(n.to_proto())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "workdir = './pascal_multilabel_with_datalayer'\n",
-    "if not os.path.isdir(workdir):\n",
-    "    os.makedirs(workdir)\n",
-    "\n",
-    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
-    "solverprototxt.sp['display'] = \"1\"\n",
-    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
-    "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n",
-    "\n",
-    "# write train and val nets.\n",
-    "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n",
-    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
-    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
-    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n",
-    "\n",
-    "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n",
-    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
-    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n",
-    "\n",
-    "\n",
-    "Now we can load the caffe solver as usual."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BatchLoader initialized with 5717 images\n",
-      "PascalMultilabelDataLayerSync initialized for split: train,         with bs: 128, im_shape: [227, 227].\n",
-      "BatchLoader initialized with 5823 images\n",
-      "PascalMultilabelDataLayerSync initialized for split: val,         with bs: 128, im_shape: [227, 227].\n"
-     ]
-    }
-   ],
-   "source": [
-    "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n",
-    "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-    "solver.test_nets[0].share_with(solver.net)\n",
-    "solver.step(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's check the data we have loaded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ground truth:  horse, person, \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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yfEqfTqydq8iO0PglMmba3EMqWugs9GmbkWACPEsTjNiCLcqQyIhm99d9lYrh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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc82127b590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "% matplotlib inline\n",
-    "from tools import SimpleTransformer\n",
-    "from copy import copy\n",
-    "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
-    "\n",
-    "image_index = 0 #Lets look at the first image in the batch.\n",
-    "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n",
-    "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n",
-    "classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n",
-    "print 'Ground truth: ',\n",
-    "for idx, val in enumerate(gtlist):\n",
-    "    if val:\n",
-    "        print classes[idx] + ',',\n",
-    "print ''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alright. So far so good. We now have a working python datalayer that we can customize to our needs, e.g. by adding more data-augmentation or modify for other data-sets or tasks. Next, we will look at how to make it more efficient. The PascalMultilabelDataLayerSync loads the data syncronously, meaning that the GPU sits idle while the CPU loads the data. Fortunately, some simple multi-threading solves this problem. Let's do that next. First, though, lets measure the step time of this syncronous layer. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "solver.step(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Now, let's setup solvers and nets with the PascalMultilabelDataLayerAsync layer. Take a look at the code in ./pycaffe/layers/pascal_multilabel_datalayers.py, it's not hard."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "workdir = './pascal_multilabel_with_datalayer'\n",
-    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet_async.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet_async.prototxt\"))\n",
-    "solverprototxt.sp['display'] = \"1\"\n",
-    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
-    "solverprototxt.write(osp.join(workdir, 'solver_async.prototxt'))\n",
-    "\n",
-    "# write train and val nets.\n",
-    "with open(osp.join(workdir, 'trainnet_async.prototxt'), 'w') as f:\n",
-    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
-    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
-    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n",
-    "\n",
-    "with open(osp.join(workdir, 'valnet_async.prototxt'), 'w') as f:\n",
-    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
-    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n",
-    "\n",
-    "solver_async = caffe.SGDSolver(osp.join(workdir, 'solver_async.prototxt'))\n",
-    "solver_async.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
-    "solver_async.test_nets[0].share_with(solver_async.net)\n",
-    "solver_async.step(1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check runtime ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "solver_async.step(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alright, that is a modest runtime gain. However, as you data pre-processing becomes more complicated, this difference will increase."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's train the net for a while. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def hamming_distance(gt, est):\n",
-    "    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
-    "\n",
-    "\n",
-    "def check_accuracy(net, num_batches, batch_size = 128):\n",
-    "    acc = 0.0\n",
-    "    for t in range(num_batches):\n",
-    "        net.forward()\n",
-    "        gts = net.blobs['label'].data\n",
-    "        ests = net.blobs['score'].data > 0\n",
-    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
-    "            acc += hamming_distance(gt, est)\n",
-    "    return acc / (num_batches * batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alright, let's train."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "for itt in range(500):\n",
-    "    solver_async.step(1)\n",
-    "    if itt % 100 == 0:\n",
-    "        print 'itt:{}'.format(itt), 'accuracy:{0:.4f}'.format(check_accuracy(solver_async.test_nets[0], 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great, accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
-    "    acc = 0.0\n",
-    "    for t in range(num_batches):\n",
-    "        net.forward()\n",
-    "        gts = net.blobs['label'].data\n",
-    "        ests = np.zeros((batch_size, len(gts)))\n",
-    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
-    "            acc += hamming_distance(gt, est)\n",
-    "    return acc / (num_batches * batch_size)\n",
-    "\n",
-    "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver_async.test_nets[0], 5823/128))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Let's wrap this up by looking at some qualitative results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "% matplotlib inline\n",
-    "from tools import SimpleTransformer\n",
-    "from copy import copy\n",
-    "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
-    "\n",
-    "image_index = 0 #Lets look at the first image in the batch.\n",
-    "test_net = solver_async.test_nets[0]\n",
-    "test_net.forward()\n",
-    "plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n",
-    "gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n",
-    "estlist = test_net.blobs['score'].data[image_index, ...] > 0\n",
-    "classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n",
-    "print 'Ground truth: ',\n",
-    "for idx, val in enumerate(gtlist):\n",
-    "    if val:\n",
-    "        print classes[idx] + ',',\n",
-    "\n",
-    "print ''  \n",
-    "print 'Estimated: ',\n",
-    "for idx, val in enumerate(estlist):\n",
-    "    if val == 1:\n",
-    "        print classes[idx] + ',',"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "description": "Multilabel classification on PASCAL using python data-layers.",
-  "example_name": "PASCAL Multilabel with python datalayer",
-  "include_in_docs": true,
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb
new file mode 100644
index 00000000000..fd66114d8e9
--- /dev/null
+++ b/examples/pascal-multilabel-with-datalayer.ipynb
@@ -0,0 +1,478 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilabel classification on PASCAL using python data-layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will do multilabel classification on PASCAL VOC 2012.\n",
+    "\n",
+    "Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \"beach\" category and a \"vacation pictures\" category. In multiclass classification, on the other hand, each image belongs to a single class.\n",
+    "\n",
+    "Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Preliminaries\n",
+    "\n",
+    "* First, make sure you compile caffe using\n",
+    "WITH_PYTHON_LAYER := 1\n",
+    "\n",
+    "* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
+    "\n",
+    "* Third, import modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys \n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import os.path as osp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from copy import copy\n",
+    "\n",
+    "% matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (6, 6)\n",
+    "\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "sys.path.append(caffe_root + 'python')\n",
+    "import caffe # If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path.\n",
+    "\n",
+    "from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\n",
+    "\n",
+    "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
+    "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
+    "\n",
+    "import tools #this contains some tools that we need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Fourth, set data directories and initialize caffe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set data root directory, e.g:\n",
+    "pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\n",
+    "\n",
+    "# these are the PASCAL classes, we'll need them later.\n",
+    "classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\n",
+    "\n",
+    "# make sure we have the caffenet weight downloaded.\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n",
+    "\n",
+    "# initialize caffe for gpu mode\n",
+    "caffe.set_mode_gpu()\n",
+    "caffe.set_device(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Define network prototxts\n",
+    "\n",
+    "* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# helper function for common structures\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                                num_output=nout, pad=pad, group=group)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "# another helper function\n",
+    "def fc_relu(bottom, nout):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "# yet another helper function\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "# main netspec wrapper\n",
+    "def caffenet_multilabel(data_layer_params, datalayer):\n",
+    "    # setup the python data layer \n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n",
+    "                               ntop = 2, param_str=str(data_layer_params))\n",
+    "\n",
+    "    # the net itself\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n",
+    "    n.drop6 = L.Dropout(n.relu6, in_place=True)\n",
+    "    n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n",
+    "    n.drop7 = L.Dropout(n.relu7, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.drop7, num_output=20)\n",
+    "    n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n",
+    "    \n",
+    "    return str(n.to_proto())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Write nets and solver files\n",
+    "\n",
+    "* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "workdir = './pascal_multilabel_with_datalayer'\n",
+    "if not os.path.isdir(workdir):\n",
+    "    os.makedirs(workdir)\n",
+    "\n",
+    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
+    "solverprototxt.sp['display'] = \"1\"\n",
+    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
+    "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n",
+    "\n",
+    "# write train net.\n",
+    "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n",
+    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n",
+    "\n",
+    "# write validation net.\n",
+    "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \n",
+    "\n",
+    "* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n",
+    "\n",
+    "* Now we can load the caffe solver as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BatchLoader initialized with 5717 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n",
+      "BatchLoader initialized with 5823 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n"
+     ]
+    }
+   ],
+   "source": [
+    "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n",
+    "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "solver.test_nets[0].share_with(solver.net)\n",
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Let's check the data we have loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LsnYgBhwRxUFI4W5MafBLwQLe6UAraRuS0aebiWI7nIvb2x1QRVo8BZ9vA9YZ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\nYb2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9ZrNesbnbo40lp0iYJsaL3LuYtFBPqcjKkfKAJzDEnsuU8UmhjEWlwMIcUjfP4v/H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ms0IlK897IZMylGQxwVP0Mx2XSjDQxEDb7/jHP/8jP/74HVkNx1YjNFM1dm/7R56fQIVe\nD+6kz209WF4Pt7oeypo6ZUBN6qSAlqLJP+U1iFoCLF5ff9EwCUMgeEMIjf53wlDKumBOueDdgreW\n5JyAL4Ir6mS3qJWEvI9dEGqmMHrEP79Wg6kWykIMlvv7IzlVhmHg6fkZbyvZVWYylkTXH+i7vbDv\njGD4wq3PWO/Z+06xd8OcK/OUJSXqdx5/SCGfauV6nUgnCZM9nU9crwPjOLPMmcGPm/MhekIPw5WX\nFy8XRikaPJDINW0XpU7KG65pgCUlTpezGtBLITe66PDOYyfHOI8ApLyn71uMEyFJNUYc+GKk63qm\nado+eKfLO+ucOL05Sd5Zx24QHu6yzNRFRtF14Yq1LKXgtJt+fnnh6eWZ6zCw33X0fUvb+C11aJ5H\npbs6qrGkXDmfB7GM7eT7Q3CUvJCK2I1aIzzbWrKm1h/ZdS2ny4Wnk+R3Ahx2stw8HA5EZ4ne4Z2a\nVi1CB5XX9spykNex+klojJli4etnsZr/W+cE37Myfs7LwnW48vxyogL3797JzmMneZ7BiqLWGEOb\nOt5/+CgCsOjxseE6DpwuIm+2mM0zfO2+YoxywztZfALClFHGz36/V2aFRjwoW6hkoBhVJsqklRYJ\n9BjHyjyNm/hpxXqtKn/3O0cTW9q2p+93XIcZ6wTPX5YF4yqFvHlPr46HKETxamoFkqajmY/azeUs\niUVpnul3HfvDTgy/Vh50latfxDZrsYb1lJAv/b268xWa0YNAf5+UeSUOO/JdqVRwlmQNU0kEa+j6\nlo8/fuTjdx/4+PEDeVmYBlHUppyZZqXWbacVAjVap8ZSXp05VXinIdpQRBHprU4M6vlo1L3TrIlG\na5FdO3t9sdpQyCFYlNFkMSreMwoBWmMJCNOoVo1XVCbb8/ML5/NZUqg0rERovJKMVHWhXU1Wef+a\nm2toosW7DuioFcaxI0ZP1/eE2GDsauKXyflKLWJZIfed7AAsciBZpA6VCsUa6puG6O3jDynkL9cr\n07QwDDPjPJHSRFoWlrkwlZl1ky5+12JOzypowahhfEYkHYKlrUG7Kz3QqM8zs57KFZXpFpyRjrhr\nhc8ZykKIHucNxkFoIqR1CZVZbVabpmHJZTv1U8nUtIhJvtK3JNlDgnuNsYzjQNbsTaNxad5LsVrT\nVZ6eXyQIYpnBtHRdS/A90zjx9PLC5XplSTNMAylXxmnBVMEOu67j7u6Wfie8+Y1WV2ZqWjQrMrPr\npVAuaaE+i0GVj9JFtrEhBhHPoNSpZZLxs+SiY7Nwt6ULf0sDVKxYO+L1EFu0c2jaVnImtV08n048\nPj/x+PTEje47Pn74+FpkVK4dTGRnLP3+oB7piesgEMYwXKlV4uRkORo2T3BrLdELr1vsClD7gRna\nVvi6ufByOnE+n7nO0/Y6Skp0bVROd1H0efW9KCxmVsxa4Je4qnyNl0QaF2hiy/6QSdkQmygsHvWI\nf8t1B3SKfFvIpXd0FpXHW0oxpCr2EcM0YYLDdQ02Bkjqua33wLq4fPUYXztds2H0r127Fnuzdqwb\n2i5FiUpWSGSDf5wnG5hrZbIGv+/4/h9+4PbuyM1+Lxmwve6NponrMGJ1+c+bPYGkSHmFUpQiW+Fy\nHRiHM1A4GEPTWoVj1gPJbFOiqagTokAeTkOeNy7OtifRjt1YiZr71XSkVgxOU60UKjQYvn79xpev\n35hLUdpySxscjRIanBPVtUEEOyv1uFRoo+DiIkKy9H2m61pilASuUsE4iSKc00jJBmsbvPHb8Gb0\nCLUGqpVQnWIt5fch8j+mkP/06WdyqiILz2qapBjUWnhkKy8LIKl3OmMaMDVrUsZKa5GdF7VoLqDB\n6A1EfQ1IrZp5KJQ3iymZfYjs9j27vsN7oTCleSYvC845dn3HUiQCbr/fM86z2iUKkyblrNFjqHfI\nzDhemWbhLo+DWLN6H5imeRODdF3Hfr+n6zumecFYEYDkXPDOc3d7x67reHx64tPXLzyfXpjnYYMQ\nxBPAcj6dSTlxm4/s9jtmtQkNQXIwr5cLnz592ixfp3kCZ7m7u2V/c8NwOTNcL1BEVi+ysCvshgAA\nIABJREFUeChqfrTictZUxWLrVqxrrRtWvBbx9c8k+Flsh1faWSlFFmGXK945jocdh12PLVk9SsRe\n1umEg/VM88Lz+ZnHpweGywBGlr37/Z7Dfi8CHWtlp1CKcG6blrYR++JahGee9bCZ1czr4fGJh6cn\nHp6fGKcJYwx91/Hdh/d0fdRQ3nabPFaRUEp57XMpsH2my5KkGckixcZGmu5A0zZULEt67X5RGKSo\nYlMIIK+dpbdW2C/WSgoOIq6ZrOGpZi6u4nY96XylJKXDrXDKrzDU1w79Vfn5Wsj1rpKFr3kFQEop\nJFspVq1gV9jHQI2e2niWnKWQ//inbRFvvExfsY30u55+nFiyU/fBtZ7LcnA10JJIQHkfH56e+Pz5\nZ5Zl5s9//jMf3ksqlR5FMqHoQSTwyrpTkEbpFat6ZfAUXXBWW7HFsnLJVxuH9X2HoH5Csh/KKfP4\n+Mx/+ctfyBX6tue477g7Hrm9PXKzP2hxDlhngIKxKhZSjFvESFYtPTQukRXuyVRT8c7obqJiaiZo\nWlapGqdn3hzUBoz9N4SRD5ezFm35BWx1el1UrYqvXBLLPArnso3c3OwxWJGuDxPzopQoI9t97x2u\nGKhiSdr4gFe8Vy5kPYW93fzPu6bh9njDfr/Hh0BaFnVfmxiHKyEEdrsdd3f3DNPEnBYt+kLeP5+l\nmM6zyM3VZuPNL1kCioOd1e5kXcKsuYG32u0J7XDXdbR6+vf7HafzmW8Pjzw/n5nnEbHhlUzPagrz\nMtNfr1jraFp5PaGJ7J1YqE7DIApFL6HOTdNye7wlWMvT4zc+/+UXFC7GGLFx7duG/V4j4tqWGP2W\nTlRrVVMuKdxrwgnErcNZ7V5XrLyUwt3xli7K4qbrGjoVPBhjSbq5vzw/byHUPojx0c3hyO3NnaaL\nC2zhFa4Rr3qnSy4RIqVl5nKedbGVN778mlLe73qMs3T7HddhoNRM2zTs9v0WyGzN6okihcQ5j7We\nYRwVPkiUlJhHcT+8nM9M80K1lpvb93S7G6mByPX51gt8w3XVwGtJi0xsXplCvCYQQeWyJMZvD3y6\nnDkbcMc9aV6oqUioxRtcXIrm2yL+Cou9ZbTU9ZslsQEo4m+lyU/FiFcM1lKdkTBwbzFR3vt4PNDt\n95Krebm8Fq8qTVNOaiC7vgcrAF7XGmq21KRaC5frwLeHJ8Zx4P7+HbfHO1q9VtbsyxAiToZ1qRcp\nsdgVYtO/ZS3gOv2skXqrrbFZP1OFKVZv8E1B7Bx3t3f88P2P/PT5gYfHFy7nJx6fXvjp84NcJ13L\n7c2B2+OBw0GCa2KUwHHvDdYUjKq+VxkSdRUMyTMVOBBylsOopIlarLznyES2fUjFiI3v79fxP6aQ\np2UUbLJAzlUoOuuFpcQmMbSvrDabMQqm1bYN3su41TYt81LUpMjRarBCymIf38ZA37U0TVDMvWJt\n0HGx4i3su8DxsON4c0O/2+G9Z55nTdGpnC8zbdvS9z03NwfCFLlOk8rT1TfkemFZZnHAW2aaRoqN\nLMEajCa829XvQbFRH0TgsOt3qmz0WGvwxhKsmAn1XU/bdRwON1grLo2Pj89QE7Ua5mWmnitzSpzV\nuvZw2LPb9TRtJDSRm+MtZyOiiphbprQQfEPbSjc7TSNfv37h5XxhuIpyttfDzXlJTFrVlcJUke52\nGEbFzJ0WObvx00E+L4EtJbhX7BNu/n/m3qxHkiTL0vtk0d3MfAuPzKysZWrpwYDAoBtDgP8fBAiQ\nzwOQmKV7smvLyIj0zczUVFVWPlxRNa/qfM+2gqMqKsI3NVWRK/ee8x1udhJ5FVPAGC2wIJPJoWBL\nl5l5lnBuU4lapBp6Gltvs4kYxGXpnLTi1sxDr5QYQkLgeDpLz1VR7O6rCqWmaVsONwcSMLtlk0DK\nIlFs5vlKOoxpbffpQlgsrae1reQ93sl/6+I9kErtOlhcWwtSxAjgal4WYc7MM/vDYfMhwFqFShbl\n6Dynpwufp5mL0ZjbHWqcwEdy9Ftr5P0w+qde/2YxXz8RyuzomoW6DeCMAMSSUdKntRbVNuihh7rG\nhYhJc2m9lRDtVNpFSvM32sPye4kK5XpNUCVFaglMs8OHNVk+bzAz4fDUmFXUEEtCkGeblbxPi1r/\n9/ohC7kphdbVILXNtMoKq5Tm5vaWX0X466cfmafA56cXTlHkpmRhoXy4u+Hx4ZaHuxu6rqWphSfe\ntaKj7/oebUTltrV3WX9f2SRlXpNL6HiUE0NR8qxSUpkDqPeX8N+8fpaFXKdALkGuiSTDs6raelQp\nKmKSI37bNuyGXeGk1DR1S981HA4HPtw/UNXCoV6ZF6fziU+fvmfXtez7jqFrickVjkagaddBEVit\naKymKfK69Rje9z1933Nze8uyzKJr7rpNi6pQaKuxGEiJqdJUphZ3qK1X7zg5J7p+V25uRd/3aCPJ\n4PMyyWZSgjR8WFjcZTtF5KoBC8lFYpYp9v3tHTkr3t5OzPOFnBPGVmTixtUGCMEzDMMmCQxOqr1+\n2GOM4XQZsbYhJcXhcMd+v+fj11/x3Xf/yp//9Bd+/PJFqtuqoq5b+t1OWgQ58/b2xjiOW6xc3/fs\ndgPW1htQH9jwBevCvqoiKlvLw6IVRldbtUUIpJgJMdIPA7e3d7LRKCNDtGViulxYYUjzPLEs87aY\nS1tCZiR1CTpeZXB103B/L9iFpm2xBSiWsqTLKHNb+tGJZV4Ii7TW1oc/hIJyzZmQBQAmlMYKVVUM\nXYe6vUV9I8RK2zS03Y5sxOtQTsbXxTWv6AQtgcOfP3M8HvnDH/6BvhPdecqZWNQUs4HX6Pm0nBlJ\nxKZGW4s5z3J/LFclw/vK8u/VK3+/uEvPWZE2AQBYnbF6XWQ0i9Jko8lW2izSI084rViUYk6ZoDRJ\nKZHw6pUrX4aRSm05o/I9ufa5YRunKiXzlL7v5cTUdRt3R5uVBigUTE0ugdviGVgHlOupe5V4vn+t\ni/u6aL/3Aryv1HW575u24eHhjn/4/W+5XCZeTydigEBBA6TE7DynUZ7DEEKBfMHNYc/XX3/kD7//\nDZ2t0SrjixBgLeLQRk5gpeo2xbdAuSax+FZW5tCG1f33VJEPQy8DMzQpStVlrMCsVlJfypKXt7ZD\nTHEWVpU457yD83mhbuUizYtUZjF4hqHjw/0NXVORY+RynEUWFSI5Zobdnt1uR1NbtBJHZQyx9Kmr\nbXBpK7sNJOdpkkqsMEBWYFSMgbZtC1vEXB1mWcjNOSVO05lxurDb7WmbRoaHrLwRs50WVOEYA6UN\ncL3ZVBnQvb68kHOUTEBdlYGqIzhPVTegNMs08/Tjk7Rz6pp5vEgIRN1w2B8YBsH/nk4nYnC0nSCB\nf/f7P/Dw4SOvzy8Etwhrum1k0DaOTJeRsei667oqtm69LXLLMpOScEE21yCQoyyY2hjmomgARd1Y\nqiKjtFpT5YwNfkP35iwBxMEtOCd9bGMNxMRlmvjh82eeX56IKdK1Lfvdjrubm5Iypelshy35nkMv\nx1+tFG6Zcc6LhryqyNHgfOB8vvDy9Mp0mSAm+q4pWa/miqAtape1L55SJMeVv2HE8VkLO2XtCOf3\n0rpNtSKL1zRNHI9HLpdJqvyUBcyUBFUxO8+rCjwTOKqELwoUqwz2sIM5kCZH9qFQrOR76VXFUV5/\nbxRSXM1AqihetMqSdmOkEkdpYiFGKq03rEEui+fL8ch//+5fiY93fHtz4KHvaUrrLaVMSJklitNa\nr01e1nbjOtzOoKSgefzwATIsy8zd/T22qkSxVNWlUKjk53jHonmvznmvxrluWgpRxbM9kyssZqWD\nLsVfsQ5Vq8LXV0pxexh4eDhw99ST3s6QFFmLPHfoWomoVFroh7NjXgKLjzRdR9aG/eFA19S4xZV5\nksM5jy/h4Anxprzvq4NGZ6nUVSpAsRy2ltRPvX6WhfzDhwfWo1XwK8RIcg1TmUKnLQm9HEm0MEjk\nzVM4n3g7jlTzsvUVYwxUlWG360BlnF+4jCMvz8+4xaFQNE4gUZWxEC0xepybcM5jjICJuj5IuG9l\n5bheuBAhylBOzCYSR2e0pt/ttiTwHAv8qFSk0zIzTiPPb6+4GNj1PV3TUhmDqgoxUaIhy7E+k2Ig\nlLR4aWFMTG7h9e2Nt9MRXXrcVS0LuTEL8+xJOZCTxi0Lx9c32qalaRumeSKFQE6JYdhRtw0pZ+Zl\nIkQDWQZ7Xb/ncLjhw8MD83ghOkcKktByOh45nY54L9V+VQ+FmVJvGnE56kbZDAtvO8WIC7EcJQ3n\n6SJHcWPpUkMVri46H+TkpIIi6IBXDj8veCeLf1PA/EpLS8CXoGVlNIM1m7N2DTqurBU3Zt1S1XU5\nvsssw/sgC6xWjOeR4+nMy+uRpy/PXMYZlWE3dOyGjq5vQZWczqJECSEwzZOgCpK0IwYaieQqYr6/\nGb1ti+pqDV99BeKUFUlevR2lcxYX4yV4jnhOOjLZ0lsFUVf1DdW+I40NaczkFKQjqTbV9ibbk9f7\nVWA9HSgpIMhUZBqjabWSloBSLFbhtYQvr8d9EGv+2zjyL3/9KzeN5eN+T9f34tpGQjNcTAQy2f/b\nb8t6lcqPZLTh5uYGay3eO3a7QZbgBNZWW0ttHTKjpI0jrcqCIigs9VwG8+QSZKF1WRDzdgnezyvW\nxdx7MfbVTUUInqquqBvDh4cbvv36kZwz02zJGZqqou8auroS1ZoSI1WIiTgvXJaFBLSt3EO+dtv3\nmaaJOE74UAKey0VRZdiPNoAp70spCFcm/DvO/PvXz7KQ/+Y3vyZFYVOcTyNwdey5UBQAQVxgIUTh\nMRdJlc1FiK81PmXC4jaIUIxBqi6t+Rw+4/0s4anjhFaarm0IWfqbL88vKMC5mXmZJQJNKeGWfLjn\n4+MH7m5vqN/lQmaQ4WKCvm83tspagXovxiBrbdEme2Gfi8BEZhZZrOTROfkIYsAxJTN0UzMojbWa\neZl4en7mz9//lZhTaXc09ENH13U0tUjsLuOFT59+KETDgM/w+vRM1TZkI7mCIScmv2CbIs9ra3a7\nTuSOlWVeRAaqgYeHe1KITONJ2ihIuEHbiRmqKy0KcUVWRbctN1nXdQLxKjCt6TIxzY7ZB47jRAL6\nYUfXtmXDnbHGyJHSy/xB5JkVrgwWY04M+x1N7CTxaLfjm2++Yb/fU7UVt4cDt/s9tbEkElllKmPY\n9QNd3ZEyXC4XFueLy65BG03MipfnT3z6/JnTOHK5LLglkBOcxrNQ+dqatqnpupZ+6ETJFAKXaWJZ\nZPA99B3t0NPverqho2oaXDL4cF3E11bGdSFXPHy4p+06QLM/3Eivt6irQs5cUuKiE7NKRHUddiUU\nqjaYXYO6HVii4CzEsWK24Wouw7Irn7w8hBvTXLTKFZlWQ280rUFOHkazGEUw4LUq6F8Z0GVg8oEv\n55FLTFA3DPsbGqT/Pzsvi2hKqFC+HzLgJK99bFhPJiBpXX3fEaLQJaWllcvAXHTzRcmI0opaWWnd\n5JV1H1nNVu93jFW2mFfJC+9aPKsbubRdcsrEaOQ5L9fq8eGeyhrqynI6X6SFmVMZhlM+rgNZlGwu\n3otRLacapQQeZ2yFrWoWV04Chd+/ftRNgzaV8FnezS4MqwLv35Fq5fe//pVwSwp/1/tYSH2asWiF\nj+eREDUmCvdBFRlOSpGYysOQC5ehSKRWCts4zngnOYL73YGh35dQAtFpLvPCOE6smZDeeUJheizL\nwuwmxsuZ4/GOu8NBwD5FgTHNE25x1FVV3vRVEXFFul4uF1xwJKBuau5ubtnt9tQrAEtJj1Bcau/t\n1dJ3Na3dSH4xKvaHwDdkkXY1ctxv6nrTT8eYmPqJylreXk+czxfJOF0qqV6sEYdYkNNODNIOEhlm\nTVNnklGs8XZRRcbLJID8quGbb37B/rDnfD4CSdg01pYJ/TvkrrqGE6cYCSnhY+A8XXh6fuPL0wun\ni+QQVnVpMSlFVpHd0NPUkksZnKMqGnkFhWkSrmqVEroxNC3x/p6QI421VErj5pmAxGupWljkIQah\nZCrBQ1BVsil7z/E8Fo69pmlaliWitCw0AamOCZGkPC5FxmVivDR0bcvd4VYkaOt70jQy6LYVCYtL\n75EO8v6+1zprLfFmTd2S0VKNATFnphQ458Crjlx0JpRFXOoBVaq/QGcVh7uBbtczvZx4+fxSBAB/\n105dK9H159C6LAoCmxus5mAMO5NLWo4oZoKS1tCoMt7I4p5L7mXIMMXIX1+P/PHLMx+GPXkchb0d\nAqqp0FWPrnaspxTF2sNORbGithPsdg+VhCtdBAeQSTkWZorQCzWQC8ZavuaaDhXetZDEWGWVJpdq\nXdQjeRvGru/DOszPOm/P+jrbaZqGYRi4ub1hXhzTvHA+nTi+vXI+j7jFb4NMazSm0lS1IviJ8+kZ\nkxfh92eRsbrFi79kHbCX50drLSx7H3B+kYALdTVybZKXn3j9LAv5YRhYwfKHoSOmFXQE4zTzejpJ\n9ZoSIQZMXnfRlS2cNnxpztdJsyn9y8WJokNrgU315UGrawtkxouE8oYCwAoh4hZHKHZ/HwLnccQa\nAWltHPDqmobjvb/ah/O1R6e1sM59CKiCQW1WI0BJ6VasIbbye4UQgQTGYBpLXTcl/Ug078NuoGrq\n69dr6tJzlK8TdUJ3EkfWVg1tfeTl5UhWSfguQcnQMMrgNMQgk/W+l6xNLVwRuRlFgeILXqBqWrq+\npela6q7Zqj5FxpbWgAylrsqMnGVusTjHtCyM88x5unAcz5zHC4sPrA+qsRpbGXIMxK6hsprgHL4M\nnzc4ViqnspTIMUpAspG4PT875tnjUpbcThK60uhhwFceqywxSZWmlWjOF+e4TBPn05kYU1EUFXJi\n6VdqI1PKNS0HpYo5ScKQ724O3N3d0rad4FWNlfcjJd6OF1yQhkVJuS73CFv7EERNY0sRnko7JZA4\nuolnd+GsAosWZo2Cv6HjqhjZNRW/frjhq+HA9Dry5933fP/pR07jhPeJzfquyjdfv46CujLU1shm\ngGIg0yA+B7S8L6aryyxJcTYaZ3SJqzEkpfAJPr0e+a77kft+oF1mTBRXtVWZSjfodWSgKKds/y4U\n41oMxRKosaqe1h9b1q4yN0Oq7phFW6+VDAfF/BPkFJyK1UOvi3n5OkWEsCqJVszEilJelS+rcGJN\nflr/9wf1gRDj5vz88csXfnx64uX5jdkFtPPorNntO25uBvquIrgLl7On6VpSlKQz5wI5hS3m732b\n5zyOLMvMvARBb5TBsCn981Uy+fevn2UhH89nUQUgg8/d0GGrmpQyNyGw3+0wq7rDLcWiW3YmnUtv\nsyzc74dHWuQ8ctAyxKhYFs/tsGff72kLbOvu9p6MJBOkpPA+cj6dGc8n5uXCyjVuKunNNW1DP/TS\n79rtAVU4GLlM1K/xZCklkpIe4opu1VmRYyIu0utdHWdiV9bFBUoJD5AqcNU9p5wlE1NrqSy98CCm\nRY5tot2WjaypKtoPD9wc9twc9nx5eeXtfGFykbZqUKph8ZnFLUUDntHasvGYkQTxum6KjlmqE5QS\nGWDXy+aRZVHNMZYj5RWluj6U0zxzvlzEiTkvZAWH24Mczy/zpsUX/EEjCUGVBDybpikUOU/Owq5p\n25quqdBk3GXCqYnFey7TxNv5BDlRFR15RCz9tbX42lGZCmNqYoy4AuI6j2cu84wPYTteXy4XIUWm\nhDUVRkuog9KZYWjo+o66rhjaln3Xcxh27FqRh9qqgqKGGS8XPn/+EUxHv7+XYz2RNQxhXbRkIbtS\n/XJWxJzxOfHl/Man8wsTnoiwOFTpK6gMFkUVEo83B/7xH37L73/xS7LL/PlPn/k//6//m+/+9S8c\no6TNKLW2EtT2rOSsGLqG+5s990ODnWfUZZKwYQCjqdqK+4c7Qt9RLY7v3UJImaxkHqSUJgI/ni98\n9+WJfdvwH+/v+er2jr6V6xFVVZjkQBbZ4PvQamN0IXFOHI9n9vs9Nzc327O0RviVUgml5BrmGCGL\nfHUt8FIMwsFJCbXOE0wuvXKpbnOR/8n1MNsJ0hbDHLD92VoJPhHvhC73d6CtG/bDntubG25v7/jO\n/hEXEpPzhBy4/3DDt7/8im+//cj8+oyfxwIDEwxvCJmubYriTtQ5y7JwPB45nc7yvKDI2W73Zixt\nG/PTBfnPs5DntAYEZ7wLZGasD6iiD9Y5cXfY4f091mqO5xG/wuGVRq+6bCXKhnW3t7ZYevPVhOAj\n/Ph25mWcyEYyNvu25rBr2e97aXMYQ3U4cNj3kBOmMqzIyrZpyuCzRmnFUExDKyVOr2aErfcmA4vg\nIss883K54OaFFAK7YZB4sIL6XKflwzDIsSolXl9fSltFhigi6ZPq1E2TnAS8qGzIEomnyuCnqetN\nFldXtmBZLU/PLygiKczEqEvFrTmfT0INLA9VVZlynMtolbBGvtZutxezUlVLvzqBUYqqrlZvHSkm\ngpcq93Q68fJ25O14FCv85YJzgg9oG+k3Oy9BBVVl6fqOoetoqqooOS6kJJubMoa6bRiGvnCnpZd+\nHkdm53AxUK05iVpjtRXAv4LxPOIXj9GvxKQ2lYL3EopBwbNapQvxsCoERelfa0RFZKwRFkxTY6yh\na2qGtmPXDXRNLy29DMEFzpcLzy+vvJ1HuqFi0GrrR+f1IyFmOMklIyUIMZOUYkqB1/nCl3nkLSwE\nU3rs+Xr6zCGSXGDQmjZn3PHMk/6BxjYchpr/43//z/zy26/46/efi8FmLYak39x3Hfd3t/yH3/yS\nX3/7NYe+hmnGn0fmcRSHbVXR7Xf0twcmBf/85Zn/53/+Ty5PLwThvpKQxTHkzMkvfDqf+E+/+iX7\n+3t2VuNzZvbCNRd1ysoEL7sRAivTRtF2rQzK60qaMOna/khBWDPRG3Rr0VkMVM55UduURS7FwhfK\nMvhdgy9ypuA5FEqZ7T0wZi1CrtygDQNQPlY3ckqJZXElBN7jfWBeZiY30w0djx8/0O8HfAp8eLzn\n5nCDKfLCECPj8YjWlpjBhcjucCdJZF0Lhdkjz3ArGvqMyKTXk7cWPgz/nhbyL09PpbelCutApH5N\nAR6BPJRD2+B3PcZopsWz+LAZM7QSqZ+kmqdyY0iFs7nnUPio8NNcKh2JghraGu8GcsriWqwbuq7G\n2LXHVY7zWUiIpmBTQ/BkJawIWxadjGxMKWdWhrU1lmBkaOu9Z5pnovc0dSVD0tqyLDDNF07nEWWk\nD6egaEfXAZBljdeanUy7Vyu4VrIgx6KcSTkgtpAyFLaW26ICsEqREjgfuMwLMXmCF7R+CK4gRSN1\nZVBKQGVNpWhrQ+wEoWurBkrUVc75nZwslYcncrmceHl55eXtjdM4cioV+eIkU7FtGoFz1VU5Wss1\nq+qKtl4X6SwqIVdwozGKA7PrSnUpD/jinOjHVcY2FZU2IpesW8lPDyL1uvgLKUslNE2r5jzLSatp\n6LTIP7XSMrBsKnIuAQi1uE6roiu21qCMoq4tTdXQVB0pacbzxOk8MjnP2+nE89srr+eRx7oXg97K\n1lalZ16yI1dQVozyOzkFZ7fw+fTKq5uYcpAhOWyr+Rpn1uTM47Djtm6I08xrfKZrOrqu55e/eOTh\n/sC3Xz/y/HoqGAFpTTRNw36/48P9Pb/69mu+/vhAWxmS8/hpErY/Cl1Z2q6j7jvmmOjvXvj+eOT5\nMvPifFGOQFKZqBIX7/l8OvHmHEEr2q5DhYBPAcWarIW0RaIvkDUJz2hbOZF1ff/OUPQuJSlLoZBS\nKi02SXeO0RV6Yi6JU9fKW57MVD6Kez9KV2gVtie1UjlVkbv+rXno72df0yxAMFdQD2sGbFVb7h5u\nuEkHQkrs9gNd228bhneB8XxGW0tMMDuPrVv6YUddV+SCLFBK0RUk92oiCvGqklvnPT/1+lkW8v/6\n3/6/kjtpSz6lpmkq9n3P0ArcSVtDWCYsmYfDnrfThRwunNyM0iWhvfQsc6G1+QJ5ojyIMrwQuaLA\n7WRLGyePn1+5XBa++vhA97GnaiQNSIwMibhN+2152ALTJCqYEIJI8Eo7JcSARezbprJYMg1sk/K6\nqgne/Y0sLhfq418//YCPgbu7u8Jv6KjsanE3uGVhHEfe3o7bQMgvC3XJSZxntZmZgpdjuzGGYbdj\nX7AD97d3pJQ4nc98+vwDz69HkVdVwpZxTnE6CpwsJQ9xZqg0+6GC0JLCRN3sqOpeeseqbGw+oHNA\nIRjPt7cvfP/D95zGC8pW2EpwAW0nban72zt2u7Uvv3Iy8ja5j+WhmeuZcbzw+nbEO8dutyNFuL+V\na7eZdJBBt1ZCqOvqhrvbW3JGWjvnI77c+EZbXt9OvL6+EZNkwIYo8rZ1aF4ZaaVYY2gqw2G/o+8E\nZ7wer1EZW+mCKVC8nSb+/JdP/PGPf+Y0XhjnC7Nf0HXFcLiDddSprhZ9VSZ0mXVIl0lZscTAcbrw\n/eszI04s8ll0JddPiNQK7ruW3zw+8lhb7DIxX2QIvzjHV7Xhm8d7/uPvfkNVNRLYoDU+ycC4q2sJ\n8c0yb0gpooyh7Vp2d3dr6pmY37RmqGt+/+0v+MOnT3w+nXj99L20PlSRN6KYvOPL8cy/fP+Jj7uB\nX9we0Cluqo6YhS8Sk2dxM9M8MU3T5g+oy7B447TnvEHFNFIgKShsmSQhK1bj/TUvVgbHTQHUqTLX\ngLUnnsvmIJRIeb5TAcXBVVf+vvW1tlvWwJrFyzUOBdu8zs92u16Q1gUrYbUlxsTiI9PsOJ/OGFvh\nYmScFzCWruv58PCBkFcnKtvpXxvNPC9FGeWKiXAWVtJPvH6Whfwf/+mfyk1ctqzCI7BKSTxX8GS/\nUBlFe9jR9Xse7h+YXeA8zRxP5yIT8+QYNsBWTkjbxawJ7pJoEkJJgylhvAlwwGXxfH56ZXaOu7sD\nldEiY0yJru3Y9T1a1awEubqp2as9KaZtMLIqTmQwG8sDK33mqmrou53cdFqJzhcOZ+T3AAAgAElE\nQVQAxX5/w+NjJCsx/RyPR+ZxklZOAc5Lj65mt9tjbcU4jizzslWITSP5fXLTGplBaRmIdn0vgKp5\nkqi3puH+7pa2rbm7O/J2PHM8n/E+C4jLe4xVxDAR/YlsE9kpwlxR9zvqdk/d7WnagarqSs85k6Ij\nR4cxidvbPU1Xc55m0BaUJUW5Hrr0VJum5HcqjXNOePKZshDLz3l7d8uHDw+8vL4xXRa0EjzttIgj\n2FYVPkp7xQdPN/So3Z6uanF+7dsb6qZDBcfsHKeTtJEqW9PVLfPi+PL0gnOL8DAUVFZxe3Pg8f6e\nfdlYV4LiqhRRWuYVLkTG08i//vEzn7+8MC6egAJj0UWV4b1jmReEpquK3nxt++VtEQ8xMefM0+XM\n5/GNU3Y4FeV8uYYloETKtyzcdC3/8PVHfv3hnvu6QgcHSnTobdPycHcjKqByf6TibWi0ptZaHvoY\nSCGWk1EsKTkyTJPnUtpBVgvSuVGG//TLb3m9jPzp+TNjBqfWaaOcSF2MfPflR766ueG3X3+kt4aq\nBr0sEK/V7qrsOp3P7HeCp9BKetRaXa+VFFLCSkk5EqMSRnxZe9foQMjldCc//0brLJtmCOmdLLEk\naq19ir/ZYKVCX+XE67MFMqD2MUhGbwlyWU/PK35i3ZjlxG5kzlC3KG2ZpoWQJlwxFaasqJuO27sP\ntMNA27W0nZZNF7XhBYyRLFOAxjXM8/STa+rPk9lZN9JrSmKmSFHGk2uf0lTVpg21VU3fyWAg5sww\nXlA5kIMj+dKGyEJGS2pV6KwXlY0xnBRSgSgBLaENPmXGecHHsOle14V5NwzM+8i+j1SVxlqFtarI\n14rKJonMy1ZqS47XxhC8xKhdLhOqaNMPw7A5/kKI1HXLzc0N2phSmThyLLgCU2NNXfrSohtfj37G\nWDEttR2mVKfTNInKBAnR1QXotRSqX9u0soAYy77gDpqmoa4t58vCNHmWaQIdiWEi+JGkPcllojfU\n/kK9nKncic7d0LZ7mnpA6YocgzwkMWJtxb5uaXcHVFHCxIKwXRVIqrwxUskJR0PVEou3qgPmZS5t\nKWmlhRTwQeRqKWdsENZ1jPKwS7dV2lKXyyQtqiR+gZjCVvGvrmBT1ZwvI+fLiWmaMMXBOjQ9N/sD\n97d3Avfqexm6r7yVGInBEy6ecbzw8nzkT3/5gdfjiAtJPAM5o40VSl0WlYJZDVElhUeq3YxPgRAz\nkw+coufH6cjTPHIh4RFVltzbCM0zeO6bmt9+uOc//+bXfOg6eq1QKWBtiy25sbv+Hf0xi9VflXaS\nRsn1WFkmORWduyIr0benEmUo9nGh8GkSv7i74fdff+SXd7f8+SyyYRnCyrMVUuTz25HvvvzIP//w\nhd99fEAiGso7lFfJsCyOOefCGKrkz0laNZT3c13UpaKWPE4fIsZcF19rxNAWy0lcKuirHFa+TyoS\nR9lI0Xq7ayRAYi02yvd7N++Sn1X+dV3EB6uscxvG5uvJqkjSCndGoboBW7U4F5gWJ5sBENPI6+uR\n55dn7pQEXsj9rEuguejM1155VdrO7xU9718/y0L+p+++k7ivciRKSRyZjx8e+frxkZubG4xWWxWV\nc8Jo2fmczdQ60ZhMbjQuKjIGoyt82e1cSXPf+l2lys6F6VuVdo628qYuPnH8/Cz9Ni3yo+O48Hac\nuBl6dkPDMLSCui3GHaUQiZYypTW0Ik/h+fmFp6cn/vynPzPNC0Pf8/XXX/N4f0/fdoUQp2jajqZE\nw7lFrkdXi8nHWkMmbBrTEDx9P7DfH+j7YXtDl2Xh9eWF17c3mkYYNCkljqdj2URamqYm+EhyaXt4\nur7n4+MjP/74xqcffuSH8TPzMuHDQk6BgEeCiRI+eZYwUfkTyzzS97cM/Q1dfygySUMImcVLoHHX\n77BVUzT95xI4IZWymxe8C6QIVVOJwam1mx7+dDrxv777jk+ffuDl5bW8t4amaXh8fBBnbQhi/mqF\n3a4q0WC7IC7YGPyGrNVl5iGwNYm4W3zEx5mQFmyl2O92fHx85Ne/+pZvHj8WY5ERHXwK4qvLSSr7\nceT15Une4+c3nl9PzEuQ6DMrC+nmO7DVppsWznfJ6kzClMk+MPvIaVn4cbnweTrxGia8lrbCJr1T\nGUJALwu/+/Uv+C+/+y3/5Q9/wI0XgnfkHOmavrwXUr2LmWa1fss9WtW2DLcdwQlbffVXoGURzUk2\n5RwzyhS1lSzv7CrDr+5u+N9+9S3nf/0Tp+NJCqSyIJIyp2Xhu88/sv8f/8zQWO66gVDYSusSXVcS\niOLcItW4NtucYB2EqlVaItpi+cxiEMxJoYza2qfylyVLN0tBZgxF0SJ9+ZSC/L2AzFE6orOkGFGu\nc/lVpDjU6+fmTb6ouW4OWmuyWVVE17AQbfS7xVwGvk3bEWOWNlCUUzgqMY4jXz5/LsWnYTxPhJjw\nTnJgU8rbPG63220CiJ96/SwL+ePDB9FTKyVDwwL8qiqBS72dTgByMylDcIEFOQLPlwmjNfvdwH6n\nqfueqmpAGz798JnntyOu3MTbmFeVqkYLntXo8q6lcsQtu2zKAqlKcSb6SHCBeZp4fjXC0agrmtrS\n9y23t4diYmkKKF/Y14tbWOYZ7xZ88Exu4TRNfHl95dD3PN4/8PHxI9oqxmnk9fWF0+mEWxwpJmrT\nXAOGa8PhsOfm5lAIjMOWerPeUNZadvs9a1oRILFzUZKNvPccj0eqShyva1qKlVRZ7u9uaeqG28OO\nHz5/5uX1hdN5JGWYF0+Mjmrx2CpT14a+dzLcjJ7LdKLrBtpuwJgGWzVkVZNzRYgymBx2vQxUvScF\nj2ltMVMZztPI2+nIspTrHRMhKdyS6Lo9GcuKyTVaMS8XOiVgpWEYBCWcI6fLyNs4kkKgNiJVbNsK\npaWK9m5iHN9E+RAzKM3N0HN/eyNAtKaTVhbw8vTE0+fPLPMswSFk0KYMuLwgBMricjjsWUIkqYnJ\nBVJMLNETXKJqJP3GKNEp55TRXk4U4zTxej4z3NxxWhxPl5Evy4mTX/ClJ6uR+WYyolLpNTzuB/7x\nd7/jD19/Q14WopP4MaMLRz+I1C4qVXgz8Qqa0op5njZZa12wq3Vl0UpInilHlLHUxkIuhhyliEnS\nibz3mLjw2w+3/K8vn3kaR45KUmuKqpEEvE4T//zpM7/+6hF3EzEBaTVkTS44WeETPWyxiKs0NAT5\nnbQVJntljaiZKKAsohhX8zUtR/reamt1xCj+iqxEMZQLbTKEACqjE+iUUCqU3rvaZH7rmHQtAlfG\nznvjjmAx1nahbJwpiP8kxIitbaGfVlhT0fY9u5s9HvDTjC9sHoF9TZATbddT122Jnyunv7KQV1VD\n23eCwf73tJCnGCXjsOvY7/dF7pfx3nE6ynFXKb3xu93iWeaFTGZxM1qpYiuv6Hc7TFURU2JeekLy\nhBSY57Adt/QqQyrDm/dWaQk5lp5XiuuEOhO8hCpPRoaJ1hhRMNSG/tKwOM9lt9A2DSA8bKnWIQV5\nuJq6ZokJP02cpxGdE11d03cdSWVO5yNPTz/y8vJC8EH6YXVf0m0qoNk07UoLS2aaJVZNElZk4V5d\nmlqvfWf/dxpYU1xqJdQ6RdIi7Q2ttbSu7B1Gy3V9fXvj9PqMnz1xccTosC4QFshBrOBumTC2JoYb\nYgpo1WGqfWG/JyqjSgK5VHYWTdaWZDRuCYzLxDSVlJ7xgl8CziVcgagprejalpQjWq/S0kzTVAx9\nx93tLVUtLbimbWTz8r4ES9RYazZ+hoCKRB+O0oIUqGpMVZcwAY1K4iidy5F2nmep+oxBW8vzy6vo\ne7MMpLqupRsGbpIiZM3sjoRy1A4qipojXHMm0ZlQfA7neebL2xujNoze8bJcePMzSwokEqunXyrE\njIqBXVXzm/t7HoeBGsV4OsnXVwptxR0pRayRDSBnSJGwCO4XCt659ICrSlNXVjJiy2apUmkjbD18\nUUyE4FguF9yyEN3MfVPx2NV8qi3jUuY8iF09objEwJfzmX/54TMk+Gp3g6XY2LnOkCTqLWy8+Gma\neH15YZovMqjf7wWVm1f2UsAS5ZrotUXCtonI5ZKMg2y2K4iEV6yxcAF0RsUkjpMsszpT0oN0acDn\nvGK12UxDWttNe27MKoHWrNAFnSW8fa3gpXg01HXDbr9jcp4lBoITtHKMUZAa3qOVEgZS2YxkA5VW\nalXX4lUo5rufev0sC/nz85NUVJWlqsXdlGIiuICbHdMoXG2NIhKZLpfrlQEZJJaQA6MyOcoU+bAX\nWFbMiRyPwp8AjKbY6SURXClbcvpEjRLD1Vm27oYATsnxrDJSRaYMvkyvX48nurqRNJGUaOuaw37g\n4f4OqxRd03F7c4vtZuqp5jJX3PU7uq5h8RPjJCRBqUICSksbZL8/SPLNrqNrGw6HgX7oSFkW/nG8\nSOVeN6Vt0hRTTUddAoZDwdmuPefVwaaU9JHHsRhfitlGPiq++uoj9w93HE9H/vt/m3nzR6IL5BCI\nyYt+OSx4N1O3Z+p2ABLBR5yraDvPsE9Y3VG1NcYo5lkCj41SVE1FDBG3zLwdP3O5CIY2xlDCFSLn\nyywY0bqiaaQCaVtB0BqtqG1N23b0fUc/9NRNzfv+awlOFDhWmfanFIvPwGKNbGw+JqZ55u14LPmc\nHl9wuCCbY9sLbCuh+PHphXnxZKXJKmAbRVe13Ny2zC7x+jpCyY9dh15x7ZsinHNbFoHRzXx/fMZk\nj9eKmciUowCmkJ7uhnmNmSp47vue33/1EWbHi38ieidtnDLso0Dl6qreWhJbItM04ZaFutbsdzu6\nVp4bRSx67avUbw3fkAV2IUS5Lstl3O6tCs2H2vKhtvx4ccxGE7XeYuGCyswk/tcPn+mrlo+3H0Xt\nlOU+FEa/IsSMc1cC4TiO/OnPf+Lp6Ylf/4dfY+uKfujx3qNjxNiAJZENAgdbpQPqGgACVwmvvKQp\nAoaclLBPVBT+fQlvjjGgVRKlWKFxai1OXWEIVVtguDXV31jqKe+1pCNZbF2J63jtAiD33TAMnKcL\nS3AiQMqZnEsYzTzTe1/yPEWwIUWaeFKqYprbws5/4vWzLOS73Y6bmxvu7u5K8vjMNM1Yo/nw8MCH\nh4dSXYiYfmpb3o5SvU3zhFTAFcPQs9/LA932HRmoTEXf9rzuDpxOZ8bzRVJ9PIQo02C3CIhJUYw8\nee3eXQ0LucgPcxbcbiy9FwmmlSpLbgDpd80+MC4zT29H9qWffnt/j72cJU+QSNtY0bHmwP6wY7ff\nkR5lWFvXtYRXDDtBw1IkVlWFMgo/z2StMXVFWjxLcbxW7xZqYBscLcvC+XxmnmdCCKVvuyJzefc5\nchpKSXS9s5uZ3cSw64l+z3ReZAGPnkQskjDB1UYfBLnae6r6Bq1OpBi5jJqcWuq6RelKBkxJzB9K\nKfrO8utffcXxPPJ2HHl7PXO5eBHqFVNXW1cMfYu1WgKHd9JWWkOOm7YVCeM2EKP0P1dCnMJaMZ5J\nxeeZ54WcZnlvS+5oW1fopkZROOBFcrYiIHxcK/SF8TLhk8Jax3nyPL9dMMbinKfvd3JzF7660hpb\nV/gYhCdfToJGC9d7ITFNJ2JTEa3GF0mcyko+lIIUUYvjFzcHfvvhgQ9NizudcWWjblfmTlNTeXmU\n66qRAX9xAa/Rgk1dsd91cvoyipxXq/gKmyo00iABByE6lnkihAXvFpZpwi0TwTlSiOjLyBAj+yz9\n86VUsposMtDhwE2/p7YdwUMuah9UEvMORphJOW2wuXEcGccLl2liniTgJCeZu6yqNDllv3tGS/Wq\nlClyQ0OMwpL33pZQ8kQsMD5XNPDaRMAg0vQki7sSpYnRWlojBXJlbCmCVuZ4Gd5v7RiltvtmLRqk\n565oqgpUxtiV2ZKo20bmcVl+/6enF8CgTcVu11+LL20wVgQglTXltBF/ck39WRbyjx8f6Qofep5n\nLuOId46q76WhX4whqRhjYgk/DcVg47aMyCC7F4bkE84t0lcCKpWpNXidyVZULYrIEmIxRwhWVetr\nIPJqNFoVKaiCypQ03KJ8kY+1glQqonUxHxQp5fnScHez5/HDXVEPHKgbi0DlJC3H1nVpj1Tsd3sJ\nd20a2rpGIT3LWE4Ui/eczpIDSs5UdYVRmrqSxb9t281Kv2Jvj8cj8zzji97Ve1fYDpaVCWPtNTkJ\nhMwozs1E1/dY/YG4b1Fx5jK+ch5f8P5C9pGUFuGuZGHmdH0g54UYW2xtCKGlaXrqWoBlEsumCcWY\nonRmt2uorKFrWqbJ43wmZr09AF1To42haSq6rsWUk4O1wqfIxSiyxnnl1Qn4zsSxLuJiBIplkKXK\nwyFtmPUlx+/ywC+OECLeR7yT5JacjTBboiItER8WrF2j7mQDXcsw6WyIgWOlYGdYDw8ySM3IIB5I\n5ALFUqVBnrA50xrFbx8f+e2HRw5VzeIjycgwTr/TSWsj180Hz+vb23ba07oweJoaa4aSfJVKNm7a\npHTyTfU7S7/c7G5ZmC8jyzQRg5N5h3PY6NlrxX1tt98jKF0ULPJLGlNhdFVaRVIQCfJAZjUrLmF1\nOMt1tNvmJD8rRUYc8cpjkicVk9UGvsoKpUQLr3QqBWBAe41ZpJ3onBjzfBCmrirBDqsM0ehcPCSi\neSdGovOoEFlNRiWAD5C2yFoMrZX5Km/UpU0DiqghJk9KkaqyDDvxYkh70m8a86puOdzdkXInjZqU\ntg0oaM88i9oohvdM4OvrZ1nIv/r6Y7m4jrfXN7xz0kdEzCERqThSlsRzlTO7QRb5upHMRKUkcefm\ncIM1hvN55Hh6K20KzTRNhMWhc6C25emKEFIsi0nhGtuSxWgkeFUs/vJ9c0kvWh9OVZrtOUr0V0pC\nPZP3cZU4RcZ5ZloWYk58/eGO3bCj7zvGcSwMc0NM8vAqLeqVruuprEFrGUZK/1nhUyIuC2/HI84L\ndfH2cOAw7Nn1A33Xb8YjH0uf8fWVp6enrbWyQvIlnKNmXubiiFMsi8KY4hbT5VivNV3Xc7sf6Opv\nMCrx45e/8pe/al5fZQETypxIumThm6inI03X0/Y93tV4P9E0gabom+tuYHaOaRHFyG53w+3tnrtb\niw+JnA2oGqNMyd4sieNFziWBHXmTfa1/XuFlUcIPhQkdry0C2fwdRpdZhpX7qKkbqqou/yaWayIP\n6XgRuJdglDPWNjRNIvtYFoH1WF1u6rKZrBz6GCM3zonHQRWidL4O3iQdygrnm3enQgXKKPCRViu+\n7nf84auv+c3dA3Zy1DtL1kWRUZRA1hpJUzKWaZr4y1/+wvPrC4tbqKua/TBwf3fLw92hmF5EZhij\n3LN5Xbw1Wx/WJEWOkWk8E5y0VzZeTBZC4s5qHtuacXHMKROLJz74wPk8EodbYBOdQOmPU05Jl2nm\neDyxhk63rUhybWXktN4WHTwIOjYtGBvK7OXKbV+/di4Df1MGqCsqVyrxUo0XebEu7+HKhM9WzGAZ\nUWnlxZd7S1Q0IhAoBkZtNgXSFaOx2v0zTb2yaCBEh3cLIXiMNQxNK9jiHDmfRpb5hfM800ySibC4\nRU5IZWYnUtBcThgC9vup18/TI3/6UnrRYt21laHScvFXSeIqrzNG09SdTLeB/X7Al+y9tm0llipJ\n/FrfteUCU3gIXpJfLqNMlEPm7XXkeL5wnheSuvKV3wlcZChj1sTt6414Dc19x4xA3mzRlJdKJ60Z\njgvTeGa366jqCu9CCctIJSFEbpAvT0ceH+74cHdH39WiJNCGnLX8Gyx3h3tB6PqF5+cX6V2nAtAH\nFucYLyMvLy8sy8Jut9sehvfxVtYYmkUekKqqOR7fSCnT9y0+OEGr9kPxEsqQabqMLMGizR70SPQy\nW8gYdNKEKG0TFzUuGtySaXtH9IHoIbdy4yoyVWVo+gFdHSAbGbBlRGOvhPk+TRPLLAlRSteiy1ai\ndlhVvO+jvFYWfPDh+j69+3tJNJKevWBxpJW0OHDuPcBKJGSr5v35+Zlp8ShTkZUYjJL2pQ2Ry0nH\nX9sxObEmXK3KB6OkVYJSJAUhi95cGwvmqpTAaFLRjOcEdVTc7/f80+9/z7e3H7hpe7St8TlI3FxK\nkMAaOZnFGCUg43jkMl3ke5dnRpXTV4ylMEILakIX9oiSof8KnMqrdDcmurpF7W/QKE7jkWkp2bQh\nolKiy5keRQMsCDDO58ScAqObmfxCKIoYk7Xo+E0ulayh67rynBuGoS/3YdhmaDklQk7Y8nyFECCU\n3VN0e+VaF3ZlYR+Zkm8AMjSMKZO5WvLhmvK0LLIGrBrErMpgtCi81t65NaIAskWhxrZ+XAeggjE2\nVChSiMwlSEJp8a/UTcthd4AUiS6h1Cs5CT777XgkpEjbNtSVtIirTiSluXgY1mLj718/j2olpU03\nrbTalANZI8eXJH0vs3JYVk1uARgtzkl1k6UapJgMJPpMKrgqWlIrWmZjJW3e+0j28m+brkFXVhaP\nwi2Bq+xofShXoX9MqTjh3lcxRU++mgRWGVRIuOiZJ7Ehd524NbWWFKRc3HwhypBpcpHLtHA8Xzj0\nrcSSdS0qI+HO88z5NOK8K/3ECwqLNnU5xQrQ53wZcSFgq4ph2DEMA23bbFZiqZ49K3UvpcT5fGZF\ngDovRESlLV3XYo3I2o6nmdMYWIIh0aGsVIOxVDwxZwieoBVB5dKbdQS34JdAdD3RzUQ/0fQtje6w\nzUAOSSodI+2slD0pBs7nF8ZxJgQwtkfpClUSU9be4/rgmDXP0WgUIl9dF9E1izHnXHgV0voSTfHK\nPCmnr1IAGCPSz6ZpyqaysIQJbRoSuvSRw3YfXMOZY6mcig1cqw0Op1YptJLFJmsNVvARqOscf11A\nVIg02XJX7/j2/iOHfi9tplxTIT13IQAGDHLSSjFvg7HD4UDb91IkGctuGEpyUqkUtQbWcIe4hT87\n52AtUtY2TypBKPJLoUxVZjqaQSdUnTnbwOxEE++Ulgo6J+YQcEWJk8l/k5KkjaFuarrYA8UYVtRo\nMRUDVdksN0VKAhe8KE9WfXmp8BOFp6IVRkeMtduGgSrqo7QWY2uK19peKqENrCiB9YBUjD3alA3T\n0lRVCUnXRU1zdXhaY+i6BpUCixWioeQXLMTgsVUtcsq6IrosbcWuxQfRqp/OJya3UNctbdty2CMw\nuVrmTLauqah+ck39WRbyumm2yLQUyoVUihrpgyolyE5xORppacC2YC8FIJVyptKSe7kuxALgksGA\nVhpbG4aux6gZrRy7oaFpKzCapu9lkJnEZxijDDNCYZJ771m8x7ko8WvOE0KpvpA3bht45Cu/Ieci\nX/QCobenC3Vb0/c70XMrTUKRsizqx8vMZVl4fntj37YcbiRTVCvFMpUh0PkMyAIRU0LZBV1NpTec\ncN4zzZOAoDoxGq0Pwyo9XEmH0zSRkihWjscja/xZCDJ/iCFxd1+SaxScLjPj7FmCQlcDdbPDaElL\nEqJdwC0jKVRErUkqEv2CnyXBKSwTrm1YlprWd7R+R4gJoyqaSpQhzgdSdPgw83r6xMvLmdllrB3Q\nusVqoSausi9jNFVVF4mlwdT11u6QFqcw49cpv/y59HiLxl2CgtfVRW0pL3Vds9tp+v7Ey9uF1+Mb\nxjp0YZYszm9ApdU0kuIKhloNJOtpbkW4it44q0w2ZSEvP68CdGlZqJSofGRne3amo8LKyUwZtJWT\na6Uy5AKKKYsQlbj/ur7j1t3K80WirmvauqGtauoVCmfEWRqjuH9PpyNvb29M01QQAhJdZxBkhWyy\ngLFUjUbZTFVFmpjpM/hlYTlPnNwojHIUKYNLAZ8jWVMGlmz9c0EoNFBOF9spuKCQffACyIPCktfE\nBKF4G1KBjistQ85M2RSVliSl4LE2IyKeYhzKquQIhGJSK3CsotmOKW0LOQUpIfuv4HJjORVEEm3b\nogqMbRUUKMC5muAmtEqM51E6DEBdV+yqWmiaKhNywBrNzWGHsRWzT8yLw18WlJmp6pZxDhx2Pfu+\no61r2qbaEsv+/vWzLOQATdOg9RV6s8wLRpdhVmVl6LdNha9gm5wzx9ORt7cjZOjbVrIjjQz76rrC\n2LZsAPL5dVWVfEkrk3ByUYDURb1wtfKvMKbn52e+PD0zTiIPWuZALIYLKIPBUvGZYokPhV0RSnBC\nXbeCYa2rwlIQCdE6xJCqLmONcJJDzIyLx7+ceDtPYjCaF6apVEsU3bAxhKzRpmIYBkylsArSdB2I\nrryG0DR0XUPOsvjk8p91UW+ahpyrsiFBbS2msXRtg600IUf6fUvVGmIYUCpJFmgtrBVJq5/54x//\nhct8YZocUSeCFtRuipHgZ5yzOFezuJ7L5KjOgboyDF1LinuapqWuZaGLYeI0vnA8OYzdsRtuub25\np2ktdSX3jUgs19MTZUh3zWtfYUYrPKnOYjhz3uKcLic4GRDWdWFrlM0/K0XdGPY3B4bzBfX8Kmx0\nH0tm7BpKoLeNIBuKmoLtPVqhautAUv5SF++5QglJ96o5TokqJ/bWsNeG5Xzhv/6//4O/HnbcH/bc\n7/fcHAb2Q0tbN1i74rSurZ3cyuK0hmFUtsIohc6gckArSduZp5l5mVgWAVfFGAhRFjej5foIc28N\npgO0EZYRYtTTNlOheKwqlqx4uzgBkWXJDPDBl9ZpuQ7FNSmnkFz060U9oMrpRYtSTGZTsfwMkRgi\n2YsENvhYzEsKZTLiEKKoV2STVhSqZBns69IPr+tqe89Wme4mvcxlX1x/643PIj+zcI6kol4WV4JZ\nJIRiWWaCF0PTeHEoBC2BlnzgumkZ+oGuaSBFtFa0fYNtGoZDZloC58nzNs4sPhGWQMpnvHPMl4m2\n+Ce6tvnJ9fRnWchFldDQVA3L4iS9Jkkgr0JR4umkUkeodCvUffZOKvG6LsqGViR4yIMlErUaYQ+X\nKqCi6EFrghU8asxJrPrWymJeduQQA4vz5BSZp5Hz6cg8ObxPpFTUL0ptQ6IGahAAAAySSURBVDcU\naKM2M5FYjZMYiBoZplV1XRLcKZV6Ud0U/S5Jo6nI2uB8wvmFzCyTbedLtp9Ul8YabAXnaUK/rn3G\nStjhBYV7zdIsA1yli02c4uarcMvC5TJyuUiQhih3RNuTMnIiKpVMvau34U1OgaZpaeqWlAQXO55P\n7Hc35IxEWIWZTCJpOck4n6mcxvmGxWWqWWMbTdskkq8gTYS+x9iaVBYSoww+JELyKD2iVCb6C7th\nT9/vhD+jq4IyFpUPbMslKwBJslAlJWYlStYl6Umq5bUvXZRKyP+nc2bYDQy7nrqtiLO49oKP2z38\nN+2dEiJSWxmeGWM47MV9Kqc0SrWeMRl0ypCC9KkRNkyjMoe64pv/v71z220kObboyktdWCxS09Mz\nDb+d//+s82DYMNwtiSLrljc/RFS1zvG8GDAwIJDrAwSJYkVlRuzYexwZ3UBnOlIx3KaNGD+YHoH3\n+8J4PnEeOs59r9vGToMwJES5lAC67OWdO0K9RXb1uY20+2DLsouzTr3cjUoFHUeh3eW4QDHpuE04\nazgZyy9dz7dx5H6fRRkG/LQq1ud6f2kdNyeZSezfrb13ba3F62eWip7GUyZugbyKBW4miaFYyiIb\nNF5bLbvJmHiU5CISXdkhkNu69w7fWJZlk783ZRmoF7HUKwBZB9NFb1pkYi6EYAmb+ADtyVWN93Rt\nQ9/646a3NxHkpue01eN1Vrfp8NsQk2GNhS1Kq3X/PArozkXAag99Xbf/o7L6zJ9SyL2Roro/sI0m\nq1jnjyGWKTrcstJikfX3jft0x3nPyy89l+FMq0naOZfjYToCJoDdpEceZkfJQf4RMciXFyuJ4ToB\nN7mwLTPLPLPME9P9Lq2VLBIqg/TkCqo/FSmMLn+gUig5pUuGY4dvpMd13D7W9djYxBhMcWRryc4f\nygeRh8VjiBdjlAKhV8gtRt4+7syLtIuulxMvl7NejR3icewOhYVBFh5a50kxsq4LuWTujzvrutB4\nx+Uy4JCk6BwzDidRZGoZYIwhhFU3T0+UDPf7B6YUrpcr3jom/+Bxv7GFxBoDOQeMCfggLaEtNDRb\nS7N5clgoCUgP1vWEb04Y29G4hr4bcDaSsczzxDq/M7UNy/UXvuRvfPnyTRQWWHnAdcCYtThYZ2l1\nG69ou8s7T9N0lKY95K1ZC3hGlEySISolpe9aTkPP6dwTWaWYx0xJ+TjN71t/bSuxguMgRdZ7x3A+\n0+ip/bg1ZHC54LJuAVpZFnI5cW4cv/cd//Pbrwx+xNqOWBwpyC3v/WPmPq80tzunU8d1PHMZToxD\nz9B39I3DAdsiiiLvjGxRe7GlMOxyPeQ76axGHmbaTVoZRU2rxFdfzayKFLFSshS9cohsKMbgbeHS\n9fzl6vjnmpjiwsKnFC0+bSPqXEnaXzpPYJcH22OY7b34r8iQXOdOa2CbN0oOFFM0Y3rXkKuBl9nD\noQ0uiU5cXrqG4mTBxlmx3CiIim2LUZaV9heWDjopn0QM2gY2QHCREEXOmkLg3PeM48BZ5dMS0LJ7\ntRjxeMqFsM3Mq7QwYymEWFhDYV6TnMLVBM6geb4AGWIs5CB1a56XP66p/53S/J+xR7ntJ+amaX72\n4fTaKteVVU2z5EONMRyxaFa1nfswJOcsfTXN0BwG0cwmlQp6L4U6JjkFdV0nFgA6DFvWSW1fJV8S\nEuM4MC8Li5O+MYgXt7Fi0nT4HR99RBmg7S6Ibtf2hk1aOnqKt0aMjAoaAuGcRJutm5wQd40w7ijq\n8jMlp7L1zZEesq4byzLx48cPSQVSU7C+6xjHgS8vL/z+21dab8X9zzqG4Sz67FOP9463t1fR8q+B\naCzeiuPicBq0HbFye3tnmh9AYRxHxvEq12sj0tDTqaNkQ9git/uNv/39f3l7/SshrHgng664JUqJ\nlLwSUyHFOylkcujpwhnnFkppSUW259rOs4UiyS+sbNvE29vCND94THeG05Xen35e20E/Vy1EmrMq\nUj049bL9ajDyAAcJwQ2rBFiQC23X0fZy5d0Dwduup88O51WVkvbcVfl/eO/Ev4fM6dTy9esL3iFW\nviTIiVzsIZczIENKZJPSOsPYt1ybhi9tz8W3DH0vs4j2dMjQck46W5Gf87EEHmvAvd0lWKPxtN5J\nHqQtdI3jcu4ZT60so/GzVWmsIadCymBsw2m40HaD3Nys3mCbXk+HEnCQi7QE1y2wrdKGxMiSinEZ\n4zr+8ZiZNFg9ZQngzqoFR+W2P4Mb5HZsjZh3GbMP8nblj2SOWu1Pz4u0GXIWzxQch/oF7GFUJf1w\nsLbQ6i1a7xVHS4xcMEWCxBvvdSdEbtelGDK6i5DVujZlchRlk40QklFrDxVbODm8iXeMvpqMkZog\nfxHFeLLNbBT+8f2V948H07IRghR8o8+uV//7tmnxtsE4j0fkkbtH+//nzzmRazr1bsE6zTNbCOxK\nij25elcEOOdIGrG0hJW27+j04T0GJDkxTzPzsrCGjWGYSSkxTbIJOpxOXC8XTr2YBXnXaG+7sG0r\nj/uDNYiGs+9arL9KYHPTSIgtFu/3JR6P846Us4QMh5VtC2xbJAQ5ZeT9i7tfHWk+TczVWjeLnega\n47EoUnSi73WT67NGOhfxud6vqTmX47QuDpHqC6PeKqe+5cfbO2+3Gy+Xketl5Dqe8d6oVKplvJxJ\nacPo1J0sv28uP6V16zYzLxPbtjCcT2InmwMxiabVWLA4YoZcHNZ1DMMLYZu43xYtkhlSIKeV5A02\nbqRwI4dMChsxZtou4z0UY7EUvIVgIsYGXNmg7Kb/8tk92ht9M9C1Pd53ONeQUfMkkBabytGkry9e\nFuu6crvfeUx35nkhaa/Ue08qBdd0DOeRj2ni8XhgjKNtrW7lIhI9HcRJDzyTi6ZX2UzXWbRWiBwW\nGbZuYVOtu0TlifFSobGGzlrGtmFsW1rnZGnMQt84bNfIQTFJdm3WU15S9UzKhS1DWAvTGrU5VPA2\nMW0Ljzkx9A2tt3Sto2ssDo910LQGjOc06G2kJB0mO7xrCTGyhYCNcqCKLgIeYyJtLlhnmKeJlDda\nMr+0Lddm5X1fRNPeO/7nwe1zEs+u+NoPOZ9dD432zvenSd8w2rVPKkdMGESHjnXiy6OyYVckb9Wm\njEviA4PZ4yA1QNwZusZ/+pnweSck5/0lKsokShEfnax2BjntoxHVmsuhtMnSGfCuo2k8jc5eHlvg\n+/uDf749+LjPYn9c9pZtZjOZJmY6CU7FmKg3xV1M8ceV/M/pkX9SEtzvd368vjLN82HqtC/77FtT\n3vsjhGDLUc3k5X4mfT35sOdl4fbxwbQsPKaZeV54fX3DOcfLy5WUI233O41ajWZdG1+WlXmeiDni\nGs84jhQD57DR9x0Ug/cdp/6sqece4wzLtvKYZz7uHzweM4/HInFiQcJhcym63mtp2n0in/XWkElJ\nTPbj7vFRNH7K7W92fZeXIm9+MimZY5sxqaPbPkvYPSecFa+Hjzu83268vb/z7etXvv32FWcsfd9o\nKG2h68TNscSEs40OYKU/t+jiVQgylbfOHNmVkIlxYw/hjSEzz5F5kWLbdQPn8YXH/TtpWyglYZL4\n4pgEJhtSmsUXOyQwDmtbWr9LzrI+oAlrIpaEMSoJTfC4RxY7MTcnzsOV83ClbWFL+Vg935eGnJf2\nx67imaaZ79+/8/r2qp7touzpTz3vHw+K8by8/Mpjmpi3AMctS/qoRReSJIdSpal5n3lItNnefTdi\nsXmoaHZNv3iPyKnSOxlWd07kbdaIWqTkhC2ZVm9/KWUdNjoMYji1xUzImaBS3BDECKrozWFeAnOX\nGPrEqbNcxw5rWzUDM7Te45t8zFQMYvhmjSzH3aeJIP4Uh5rFmEzjRZrXNA3bGiCvmJA4W8fZOVwR\nz6OYxOQNJ/Ob/WQMHHMsw956yvpyM3obkpqekB629eIljzW63q+e9EUHnMVIazDp4NQYknSDxGLD\nON02LTozKseSWMhR5OlGNo/3Fufecv0sS9bBFllzYMmaUuU8XdernNdRise7XtuTjfyP0o3v7x/c\nHguLGo6J4m6Pdksafu3URiAQU9ZCbnbNx7/X1M+LFZVKpVJ5Pv7YE7FSqVQqT0Mt5JVKpfLk1EJe\nqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk\n1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVK\npfLk1EJeqVQqT04t5JVKpfLk/AsNeRHC6zkzzAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fac306356d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
+    "image_index = 0 # First image in the batch.\n",
+    "plt.figure()\n",
+    "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n",
+    "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "plt.title('GT: {}'.format(classes[np.where(gtlist)]))\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Train a net.\n",
+    "\n",
+    "* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def hamming_distance(gt, est):\n",
+    "    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
+    "\n",
+    "def check_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = net.blobs['score'].data > 0\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Alright, now let's train for a while"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "itt:100 accuracy:0.9526\n",
+      "itt:200 accuracy:0.9563\n",
+      "itt:300 accuracy:0.9582\n",
+      "itt:400 accuracy:0.9586\n",
+      "itt:500 accuracy:0.9597\n",
+      "itt:600 accuracy:0.9591\n"
+     ]
+    }
+   ],
+   "source": [
+    "for itt in range(6):\n",
+    "    solver.step(100)\n",
+    "    print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline accuracy:0.9238\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = np.zeros((batch_size, len(gts)))\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)\n",
+    "\n",
+    "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 6. Look at some prediction results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oaWdkMB8GUkqEcSRME+Nmk/2wAgHT5AIwnJ4wbDdIENP29nt0VhgHZLJ3ZRoN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OO8yWu6zq7RYpL+o9HK2ny/UivVbRNDELHNYhGiNK2V2hdVFcPD7aY6722SeDWM2XeD2Xnt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\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KJteAqh6jvRi8GYUhYJhlDTdYUinxIQopk6pI+XWbVqVU0ztoTWcdO+OEQ84aJ+qtqXmH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wInswnXFye8fnnBD2+ueXY+Y9YMjZSz68nIKMmTrglLXWC5jQFamsI5ciuB6GHfdqzWO+4eNrz/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\nndiSd5M5/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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc077450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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SvKcDPnh0NgrylEdRhKqwVGW/KrVSGq02wCtOTKq59VCGgTs5MJfCVDJD9Gyc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\nEBi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EhlEArk0dqVxB4TTGASbTLmteWketE9MxovNAVTiev3jF88++ZLFa8u2vf8vXv/41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RRY1llCiUUucAYVRI6pFEQVhIuCQJTODTBai46fyuf7iqvNIrfkrImpNACdp+RzKMTyE/I0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\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sqYRGZJ4mcs5Y55jOI8pqEhnnGlpjcArGZWFcpLdPq0Y8JUYRo6hzUpTaudJJiewSAomC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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc193450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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M928NahTbCL6x5f1zgjiH05owKLGLjGPAWsGIgEKMkewctTGoMaiUgR0MKoJSeEyRwn0V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AppPEqMVhBOIr0noiQRGzL2CKWx+oxGprSxpM8kzkYoJNo6/NCRm5aq1yg9I05mSB3T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Rqq5o10uu1plFbVnWDt+fxMeaIux5fOT5acdi6bDOCBSQIiF6Ygrs9s+8f/839vsH8WTPiW++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\n5DygCKyaFus8cU4M/UQKMM49MSWstTzcv+eHj/+Gj+9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3ixQXIuegiOFpNfSg6XvIowRf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CzKWYN5VyeC4pUdigk+rM1HkDUY6evohkolcTztOZxUEJHygZxnWhF82NBqZJ4L//A//pH/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\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fOvaPHbcvr3jzxVd88eU3fHp3x/F44Nju+XD3HlJEBUncDCmcQ7XkVTYoowmDZwqJqirl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QRoHTx2viYcc9bsnwALtFTLPDcgXWDDzxfRuPdNNY9LGjgH33vA1LonVU/xbr4/ZgIzMmpcj9\nsWOw2cf/for8O2FYfs+2bC7DFUPLMd10YBgTjmp+dBka3XZ733F0cY+ojf/i/3miv2UmfB2i3A/f\nNV/XZXRgYlUe4tiUU7iJxD2dQ7j7eqfRf45oocFnRwwdLbPibh3wevTGjTnBS0QtDev9yYo54Bwo\nUci/e+TskdCa1dDP1gpfeIXTeWJ2mA4Bc2clZ4dQYBw0DN/vbefhYT4+Pr7c6o2LWeNwgGBYDP9m\nUYc7rVet8dc/TuwOILhjef0BtIg9V8RkSGlBsEjbUm5Y1x2/PjOHRmDgffKI+bbg7f0ntvzA7XXC\ny9sr0ux4/fmGP//6A7NNiNMNPi2AN15NUTEvjjau1VObcK8bHmVjEP+6oZSG3SpKDdjyjudaME0J\n0+ycHwxHCMDKk68NmaRYd8YdWFpgo4L25qj5iVoLfIlIwdG74blm1MI++Mv7O+aJ1VTyOIa+QAyM\nTD4Oi6MiDMB3dbjv+Pr6Qi4blhs3DZkF5EwhN/MxfwhouaE8C62OwPl7bXlDrgU/3ibsCdgeDbUZ\n+7yho4UCi4YlJry//xxFCQ/mx+OBnDMsOrZcYfcVL7cXzLNjGlV4XjMzSwKHYC/JscTIJ/2hoeQV\niGxFLlOCw/FcM9b7is/PL1hIuC3MIjLksSxlP1t/1gLyeG0YU8KLGxdme8CWNwaTuY99owXdnfbA\n8Vq4bBk9dfTI5c0+Bp5uBk8MhWp15SD3aHmM38DG9+7RkRL3ECRzxJnLqel26cPhxQz0snfUypfV\np12341y1aCO07LSFGedxNlpJfWhrb+NAqnTTVHBrWRtvS44WkI1b8DxN/A5aw/qgcPPWxDZcG2Ho\nx/e1l4IQ4/k6s+SMmsd2qJhGZpCj9h2tcebC5fFsNx7f6PG//0/C0bcVQghxTf69cy6EEOIySMiF\nEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLi\nSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLiSMiFEOLi/Aaz\n77ryEsbvRQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabd65950d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RUhR5HkR/N1fDvdeyzMZoKG9Y6eFXIzU2a2zgwvU+0lf4lgm8cS2uHLk5clpT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sKGrNP7NRNngryQnFUskJBgBmP45HY+8EfH700hDkNjgCee04PIamev1NkpPjFv+DTNf4Ne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3D8/iD/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\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ykiV5fQ0B0VZy3cEVyfWOV9tKJvxvE1nzQi9BKZPiN+d58DgPBVqbsXDuKcdyFQvhmwf0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0MjzjTa98IED2+/DLYg+hCwZXJmnfS2Pvj3MXQBOKSSlAgBwPhoqKL0O0fh8oHLO95fNOq5xkDE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\nROkMnut9BfMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabd63b3790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ovFuqR0uKWI4aVShFKRqzYvP3jt1+alWWGmYXN7OkYSFzsCWPZlIRafnqRYTt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\nGErFecga8zl95UV6xmPX7tRDrFt08eJ51TB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5dNPxE08+yrwZEsjzcOoR55z+78Pw/+Vi\nopuHv5xBdRQJxtoHKJSX2k+/9I4UsHm6jvsoE9hmjNKIiBnIRGh0HFTfOahiNKpXvPXzsWFspMqB\nQA6U+suD3cPFesu8ZoFBSJqBctgRuiouAOElEJU2NxYZED7TAczc4sk+cBxso/Sxk31n0ryXpTcK\n8N7fEIuuzeBnWuKUlVD+FcN3tUjyZQYQQsLTBr4FAN8/Jm3123gBjsX6yrOPwy7GYoYvBfI76fI7\n4ilrBnZKWH0CAFUzpZYptuOnOK7SoB6ftq6Wi2ZWK/b5bqph6oQaqIkVMi+oGQrzxEWwodcTECZZ\naT1LCwyvjQe0dSXExcnp5O4AkZjMyz0XZK3TL932sp7AV6VLeTKI0v0mk0RXJ5kdkp3S/NOB1IN7\nX4hKkMphmSI/By/me1xIs2KTI9VXI9Y1QKHDwcC4KPRik0noalG7OOGOcRz4Odh+tlgMwdI0Lo8u\nI5jZdBwDNRvkAdGqGvphhr+GwX+EKuh/LDaI1OJ82cWzc2T7o7BybRAyvFIDkJ4Ke906HevcAUC2\n/lfVmKju3HIG1SoI1AETEWH1WYTPdiRwkypb1rUXiSoNs6gLo13Z/tUdkCanDu5KKAKDrLc4Yzf+\n5siNQ8AC5vMgiUoW3PQi1TXU73PWBYS31gDVaYaZggU5KwHI+6hvnzl7sFpX+elHg/sMoXDMOC+1\n380ypYQJ89gUDJDd5xfhaxY7b8DxD6SEmssYYuBrpWVnOdI1gNpu7nkF1uv7M1S7GJB2rzx4ta0/\nMkc1RXPppZZAyLhtNFDTaiY2CaQlTTLbGOdx7zCqXazKzYEyEKaHPmiTKlO+pNZlO29etqtIHWLF\nJWqnZkDBiWsjEHWcvtVfCRWvRWHubSQHrZNQsn7LprniHVVfscBlecZnxP4wC4+aGHGgtcfGDG4i\ny8lQHE/nj/RkcOSAd8Ct9ZRsp3mAretZ39zME+uSUXEPG6FOeazrGLS2GVUHbJPRfS43YtXJTIY8\nqacFGp3kls4aaoUhLJNd34OgWKXa8jF+pmWPp07Yww+4mYOnInnG85+fj81f0CwgHpmh0JGHRBuP\nkczca8YSdthWu1SjHmb2raz77PultqryqG8kIWQkD2PmTCdUjKUOmUetAzmA+QMFvpjNqLn7lSlS\nKIYPnFmMeVqqCQEMn3g4CRbbPPJ6OPAfD9cYoVa0BuQZqqBjGn4eP4pwRrqj9lCEKjAXhNFluApf\noyO/0Nh/FNTvAJb3Sl9VjLCZZ5kR8aYjF02xgAif0edP78p37iyl3qx4E4Go31g6DO/z1Db6iYkY\n6N8hvUPOER74gJqOAvR1Hfbe3JRTfR7c3p4bpzLNcjGbkTVjjHrrlYTooWbBzKaUQBmjw8HzFWYB\nw6IxjPUHz8VVxuEXNuIUalinow6krlgcZ+XAB1IfG5gvEEEXtcl5huGHx4KpOYAfoe74H2+3pLRQ\niDr4kSXQwRRCmkyTO/moNmG7DaROXNqqZ0txBNz/PCb+9zHxI+uIJ/Hkul/N4BYGzcy5xMvrRBQD\nkGaT8F78BoK0t1WSKrlSpGb7HQ4cR6ihkGPnMVI14p59zVJwoFh1LFx7tdtRquBYGwDJRgp7Curu\n0xyHXS7KTEiOQUJjXfc1wx4GeB9i3YeGR9rTZ1lrTZcYCdregp9AMquP9+yAfbBNhgMDfswkH2JT\nGqzf2vxyPorY5QCrzwDyWBOYCfB34UuAfDxGNSBwz9BP0H4hAPjeFdiuz1znRS1n6iNBqS6VWV9/\n9+X9zH8Cbi+KtDniqSjIKaw3mHMwuNVw40QiBMQMxs31kdD7ejJyT+s5nmOYcWS8j0EW0ddLDxjS\no1j4KOHQINVadcuMolVVeYkDMNxUWcftOfDM6zlLJsMyhiFEOgSDzFayrs1Q/sIdoUumAKOz38qT\nd36YwiwgQ7JbB3BkXc1QBWCk/5C0XDGUcGFrlEDJdg/QotUHNz95MfBwuJTCjEwxGzqbMA96PnL3\nqQrPKsrSgXM5poS9V92j3jF9P/tjm2Z2vK1iYJ/1aIVUJxxHuJn9P0eANnerjnzPmLXMwGDeeA+A\njxSwOwgbTXCpqvG2hqEAAwVwizGrX52GeQN01y3XfHJWO8J7Kmfm0f+9XEY0FGyWLlnJXPCm/3bP\nfQTFyFkoM5h7bb/n5jDiyvSJOZLsTJaTaxMGG1zYRFvUwOEXuMbwJUD+48cjpz5otUSe7qF42kGA\nKUtYPhSeMnlbPp/UgwDVLlg4IDjITDoViim5c5Gq3ydbsertmZZRyVAJ8kZFqgKC1WIWCybNMLzq\nxCymfQcZCRqAgADBYtwZaQGt1BHH088UGtH/El4mKk/BooEaPzloIpLVlS+RxmGpysg44KBPaEWl\n1peHbprOPCHtYVzFl75BwRjgGgX1bJ8J1GBzXkmh1qflTAxvCKSZ6Si77mRbtJ9OgRDCz2v36o/R\nFiTGRJiupwti9hFQXZKqm5rJdPPDtX+2kCsYkzETTLFRntYWBXiZj0lGnmDjzl3U7K8Gn6PHJ44k\nuKkasQQyzaPF8W69+StvDsvNU8xfC9jqVlIbGrjwXOdWdkvAgdq/MS0cyGFSX44SikNVZoNEJo0U\nMIVslaTBIJAneZgW6pHhDpAhI7Z/pYSWMrQJ5QNqosi1FfZ9h43ZQG6rehOIBfDDYtfrM6T7EiD/\nn7/CYRHddM7pmCO9oVEtofox9lcZqAyv1DRqyhiXpeeBHbdGmlQWu70tAwXYvtc7jnmsq4JWACcd\ntGhKP6TsiZgvmN4DNC/YnpkcOFNsk2G6GBqfY45y11mKCpbfvZge0AcMcABiaxMCEAGdvljIgguo\nitGQjaSukvpjcJYQswHu8BxkJwQ6T8dNmccCC+OATBWMyQEBTmVBDkLWf0nGFm7IPNOnBSs4Fth0\nfUPuJuuixzo9Yo8+0ntWIKTFAbVTdlDHJWQiTTPD1LJnF9VTpP2typAwR14wWKa05rHUiQPp2Cvr\nklg0tZnD78zDDPaINQXq7FP9jWEz676Nyh/ONmf/AsxnCWw13y21oYeaoQu0iIFIS8YLkg2TYBDe\n54HSqFgJ5NyfMONowho/1l46l/Sc5CP7jVdJTmM2fODTlyZn1jKek4gURymKEpjCft8CuUsUwG7V\nRpvMPoUvAfK//nrkQgQayH2E1MlFQud0wptVadNeMfGzrrzubE8+k23X77SVCk7X+ekX8e6nHi0D\nMChl5duWdBJpjHHv70tErJ/Urc9qeRSIx1+ccD4MBTK1Fb+mhqGiAcg2VaIQyBudWq/e+u2yUClQ\n7IFgkI6ZH/T1wpPZLQGMhwNoffXUvRVcNSXlgClhIvUMVmerqyBCqIYZT3SKRoVK3WCSnJt5CwKE\nw6zerJSCpISNCDS2cxEEISzalkC5VOBCfKt4Om8K4rUpiGokz7rMZxrIZ9eHebO/kYDKzBrKuugv\nAZWYefIAcJIXKyG39JNquxb/9I0kpQmrKleuLYHtyPeld2lnmpk21TjVdWa0yzGrQ4ioRpWLWVer\nMgpgKUgJrodRaqnqpwVLLcFL3FUjLmSo+hSWf6vUkr8dyRi+BsgfueU79b1UrTxor5mnBtGqIlxY\nelWySydwV6A6F/Ne/aKAeRYCd3FcWbQ8U+/MOW/z9izfvH+v879Pszcx1RVwYSsAIZkeLA9WyEOG\niwHHwuiDC24y+aV8yY/ehFIA1gyxY5MyTdZZ5ieuJth4LQw+DDisFwphEF8avZEoSxeflr4/kIu6\n3sNCPcjlw60WwQRtlY+Mu9tc9dZZBjJNznoWxhUQ4dbTaCQD59uUg8hbOlCLuU8K58g9sApAFaIA\nvWsmtbYSBagdvwnpIcRGqitm9Y0QAEA4v4gMtnoi1UsUhKlTpxAwzNQ5O9x7E5AiZQl/AbkmP3nN\n1I96v78qFVIQlLCU8ZiUtVUweT3JYVkAZScugSdjcMcLgm8rQ5t0sH6inXrzTu1SdVT9w1b1Epx1\nCYHxPUj/pT7qJnzRzk4O+qhc9zbPgccodu+NF/OY6aqzFxxoX0mdWE23bll5XbnM0ztWM58xl7x7\nR4XIcRxLR3onXIL/QjMqAwv7I+NJZ6sxiIcX46OlQJxQnkAxVjhmJwUSyEebRpKZP5ZuvzIRLiZq\nxjnN/uHIo66onsAyKPndaKrGYeYAlTXkfjNnKEOokGUeijcZLS+8Zn4945aZDNZxZFnequ9E7VKd\nyIY9pXeE1QZzL/VXq/6inehZkM/Ggq2ADNthhK0yZwx8PjGqyvtz9qzymB4zl5zi1HpnxAA9nJxj\ntYDT2gqHByUDHkeE5l93P8JglKd8o+wES+yxlyoVfu4w1FGIRvDvPqX9iXExD+fZdCowst1aGHdP\ns8oWb269kG1Da5i8N4R0LLvUTeO1nDEJiGt9lyky6/8JiuOrrFa4sJKSGRYSbm4leiQbnxZMnSqG\nw3MH3mzfDgBOG1d0sXK7U/efMfE1nv5+Z2f+zvt3966AXNn6OzOHVjYUXCSIazffmDoMmN7AlOBz\nWINzsZZ8S1fyrVhz2E8T2HkGorEcmaOexZgM1PhWLNxTiACi62zwoF7WeI0gQMaXHZ8bkLQXFGc2\n/kI5YGoVnlVdh+qkcl+CzqzNNFH1HuWah9cisOVaQNk+E6wXUFjbREPp5M2BGjfxeoFe6ra5P6A2\niVXs0X+GByFyD5M2pMCka2I4Si3B7NmWpV5bSXBx6ZOji5kah+ov3EhFxj3lfheWYqyrhgJa71fN\nDV7zZVzqXodSSzrkzS6UVZtIkP7aAldJgF7rNDV+y77YGNSf9WRd9oqtK1zq2GSN4CZ8DSN3ToxL\n9peU1zBzJ5VLKRxp20qVi3weR/o89gb5TPGUh1XnfQ/o+1RL1R2qWnn2/jthV9Nc2cbzc4yB62Db\nJzvUOf6K9zR64nvpzuVwZgIyBySSIXJbOg/PfowhYN7+x8k6Pd8liHLqPMzyJBTLhSDvE5MoMDis\nOaDMQC8iwRC9dO5AeayBZzrxjteAmfAoo7eAcu0PRmHS1KM2wjjAGUAUKd2nJtMi46VDN2R51lkK\nQah1qX3X5FfUVt3zPFGJ7zv6iLCRh2CkOsEtFq+NpzPldvSw+BjpMM0SFCMN7oYdoO450TnL8Ric\nsVnlvSxeSkRbtXf7LUnVlcg07a81YlWtWEIWUX9Zrw8nUG/s3hpdHCjhsdAZdwy0OgQ6lqmhIpBK\nFqPr0F9RXCzzwiVk63bnaMDmTG8rcyWAVtLB2Mef48jXAPnoTqqLYSyvgucC7in1HpabUqY0osf2\n2+NIJ/XHrNOGyFg6/oqwKrQbeZWse1hmg5zX4bwIKpmWZ2+e4Px3See+4V4Jh33GsD9PtiAx5rN6\nn4OnK4xTa90RakgtwkxQP9Jp1aA3yJkgz8W6dI2gGzC4WcKBn4Z0l7u2CgwYdM4k2U6jxprmxwEY\nSLZOa410/ATVk/sKIlufIBS5d/7qarblQWFTz3JKGL1tIsAUCbQBXCZw3Gy9zw5NbXZma+bir7Fh\nCDAFcGTFmfdhvStY6SToXgDlPIomee0l0qvhjxRs3FVsCXb87zgAO/RsyVyYTtvoozaybAeeWM8b\nsyMU2BG+WP81KmhK6fk8hXCdTtRtQgusUBdRAqjqrJvoYJ5YH5Ul75kFGYN0EQq9svunGq2rup6m\nWtMZDWdKsqa0/Du73UwA/xUf/KKdnS0JT8xzdqOtFcOKADBCf4r0H8LF4/lwHMfAf+bEzyOsYGY6\nNWoDf1rE8MTtXS76mui13FyEwX7vosSnKztovhcP371+7tmu06vnNH8xRjh8NDFweDVkLQMkRVn+\nEycFhd491DMJ4o88qLZAutPVdMpcsDDFa2AUQ0Fa0zSlCyBxa+sRtCXNI9n8D1B37zW4ugasBqMK\ni9p7BQqRFDD57qi8ufRT1WV3HVHfoj2taIMlQBQEe3rPZLvkQ6P7jFGKoi19avCzf7GOLNO3bmNu\nallVbVy0ZJ4I1AlIoBBjmi51RkFPp1Um5oat146mq1qpvHDyFTisbKJBOLKR55zui/AIFj8q/iZw\nnA1Ul6nyYOkL0Y9Qw9HhpbpbfJ3E+ngUbWgbsX1ciCMFUzdKqQkFgar1eb2eWarqMnyN0ywBEnqV\nMyB13D0w7ZR574Ialm2xoUJ0zOF4TMPPBwrIF3/F1K0fnY+yhBFdVTEgGUR+aqz69emq6E77Hog/\nj+t1HLsur65LDnRhhQNY07gtreeAzLab5mm3nuqSYXIYdgNWvEpmRXbV16/oyIN1JhhnnudmZr65\ns/LHAB4evldqk45MyTloBrfWw4MgkKu6gDjVTADIDgvoTfrnkDTQjDHqkHl2+ZBazWRiw5U3GlW9\nRSLDPfyuU3gNlqETSnIaKpIUrGFmeFS+IfUN9zxBq2dDEffc+oFVHiknRiwKwD0OBOmTr6zAiE6u\ntFAcfz5R/rIMJAxpokxoLpUmlQ9W71pW/rIq4un1EKtFTOSH4z8ebH7R479nCyWuiVANwNq29VQS\nE5arnonycwbTopugTlcWvuBCqUBvwrdwY8vsxgK6ePmyLfNkP7LxpKVYDiRuIR7A9Af8R9gI0Yzx\nOCZ+Hobj54itsmnWqCZDywLJAt6fB+xnocygfkM8zwLZ2itG/+z7O4uufI7C2ckGEXr0xyN06Plg\neB48hL1o/St4CROOo/a2tXwPnx4Gz81ABw4z/Bzhn4ZnYu4qEoCLtCh/520bH0fVcaANUDDF3r6I\nqTeaBOBj6TfTT10pUnVZHSygIti12WUPZjQrToQJNUCaseWpPGLZnf5AvOoxTcmX2dty8o1Zuf/l\nrkYz4MGpSbV9CPiZbczZRqmEFhnbP8qu2gCa7HHM1fmsIgAIxL7UQ9d6MV8gaj5B26RtuaZTliRZ\nkJHkzWqjEAU2V1kalE8g6pynrnbx0Ubx7kTY5c/ZtuXJdmpXtJqHcv3H0ea1pd6EfT9fK1fBRJfy\nwAbm/ZRMPbSPUw9MkEl9qKF3Vlp0luOwUL88HHMeuSEpThcvc8fZevfXHPf3BDKn8/X3LGuex/1x\n6xyGZxY67yzw9vQ9eq4h/F1w+/+czWiKwSwgYAXoTlMNtHUFn8rUcpxYscRg6khAu3gn0XMkWD3I\nbM3SZ4oh/HUQ2OPZHwD+yuyYDrrS75KJ08lSasR9Y2EEExnq1be3Dlj1iHO/DBVU+L1m+ageogDg\nAp2ZC5Cjgdw6bieDBWKNqczhUkViLUR7JhVH1mn9sl6U9Vttk2+hxDgiDS91hHvb9cM0VhXEtfKS\n+TbsqLc6siPjlro2YdIlW424Cy6WTj7FfRA6Y5X4szfngnTN8yrtWnj3EkHlRsLhS9u5d/Xdgfn3\nAPKSlNnBDGkfTp6DvN8dyWDVCXvV2uXJbrJeXTf4MBwP4Efq031OHB6ng/xnzjjO62cz9VX98vvY\n+UfiehdAP5LmR+7vFjp3ebjNl7OtGqL0Ua/74sSrn2wmWiMsF+IyogIlGGqfeb3D0Z9qnEp3HRXl\nZgDAmMjzRCEql4jhkbby/5OWDeUOABM8tq1JRi4MOgSYRoM103MCeSkL0rOgKYmLO2qjnfVDnWzO\n4StHsHCt2/Uc8MKFNqo05vSKg+tXFE5l8ZFjIXy+JzGyXuwub5f0jEM1kxPMs65Nr1317fhcLFyi\nhZr1ekVV/SNKrZuRFglYFdaWNSZ9gQamJAmGPKFbgDzLyVZyLgCjTp9inob0xwJxkx3DJcH5FPNv\nvfAf06aab9Cy624Ifw8gz2D1TwyeyREMKv65kGPp4zieremk6vpM6isZhRk3hxjwMPwYD8zcSToe\nDpuOn4djjNStT3bQNmf8zKagV+F3qVZep3GvVtHnPnvtOj7UiTVzGo6DuxBbL3ocYXG0OPGSqTaA\nk64eyY5GwZ8Lw+u298pEUlJQax1vsjvQypJbO8YU749hZ5gsfYQnycfA4eIYSwCO7meRBKNmGMwG\nWr8cdurZt+HQzUcB9g6x09nqnaXvI98oQCwZZe0wzdlMkEwlPauFmM0jF4RzWy+4kSeftgQxnzCb\nwCDTzX0epjMUr7ppqMq2yyzIrvnFUmnmmZ/LUqpRIESe2z16PwMA5kcTDukvbVtvNStg2/TGJYNz\nQ4sJI4f1rl+grIvMAHrMZL/j7IjxTmidLxr8dr+QecnIuy0rH1HuO5j40sOXATa2FIz/OEpyFhPH\nruN1YWqATuoE0jFn66RKUCTbGWmrjuHpWMdjkB6zXEiGu8k4+JXe5NQqVbPzbngFqn9KH/8sfNza\n5fz+qorJzxy4xzHxMzvlMVEnpZNIekpfhy/1jAQHlfQN36WEz/QEMC7UY14LbbOFA038CNwCivwd\nQBCmjI8Rexl+lgMygEADoHbKZuEbNrOMZO0PBfMSJAO6mYelReVDbSesZgFhF+2Ljhr5OMuWlcJs\ngTNZanup46b6ZQwvs51g5gGUh6WFihu4BkpnaDTupCpLh0bbPQlAejdrbejyPM+z5Lha4nQ6PuVl\npouOl0lpPep/HMOcTRSDVniRNPuUnpD8arHDfEdP8CornW6127XsS7kOwA1v01udEpXVM813kOCL\nzA9VJ0b/2QRsdjSXjiB6xRwVZMctUWW4SskZGw8MaIHRU9eBEQffPixOCHfgeEwc88BP9wScYJNz\nJsDnLrt1lxpRo5uXmz00U8tmB+bzDeC829Rz9/yr68/Cu6qf95h5WjJMB35OHHPAfqLaj/2bagyf\nDeRH2XlpxzZw6j6z75znnALuFBIy/+XieBQiY5WBY32jWm4kuD2iGHmARN9vtYXOsGS9J9OhaeQP\nyzhT715eE02FCvutQz0+EjINqOPNYA1z8D7kwxKcx8Cpz1KAhjAYffrU8NR3W4EZNyFxm6HZAzFz\nSmC1Xqge+TtmRf1f9QcRxYaYKVNzNuF1VNok8+0KFD11S+kSwOayq5Tt0x4gS6Kl4JkpMKaPOoVH\nJX+obgXkQeac6jRH7TBlL6j9X6BuPhqh+DT7slvUlPP0qlX6aH/v88euw9ds0R9xAK4ypu6gyW9S\nP1RsKN+1ZGwoNoGFlattOFmcsrkkYBv4R15iu7nMAGxgwGs33GMkoE/HmFZMsx17YQFyDiKGYqg8\n3VzKBKxl4O8r2/C/M2iengmf16qbbHOEY/2E2bNu0b1t/d3DsVNJcLZLDDaCnVSsQIVV/KU6Rscx\nBhawrV7oBEsCv5d/jzGSgT4sWOnRr9bU2ZpdV91grRNDLq6OkaqZAPSHCZBnHlqIZLzJ2AsAaQI5\nsG5MoR9yBw5zjEfs1g0Ni4f6gKcYA/AxQr0wgWkFaYtKszaCpQsCOuxit4/WijinoXy7hwVRPMtZ\n8aICA5CO6ute6KNpvZHj2ZsRR3NyhaLryhhP1joXeMulMC3ejHkOYXEwTWeMiU1uel5FfjLvYdJq\ntW4RMY4U6LDQdTOvRDFHyMLa3ZlZHjVtYtrsMP70UAngC496G9mP2P/RHy2JTK+imS1LzwJfpVHP\nYf2k4Kix75J2DvQxolJH+/Pw4aHHHYYx46y+Y6I69zTnnm3oinv7eBahUeXRHAuoLeD/61YrvyMo\nUN8B9l1e9Vl6g9Tr/M1Tz3u26xastwAAIABJREFUJXpyW4UJB++yiq9ALrO+YuWVXj7TjsghjKDy\n5P1i/MYIdxHez59mSdYMuIkECUkvTNbGmXx+JBN+ABjLHLvZZu+ipJoxnJtxgcxrAxGAApsWmHTC\n5Z6Lplxnsu6rbeOM8iXDLfWTsk2YIYcvAZZWGpbv0V+OqsmcJI1tVfFYVW3sCm0gN+eGvgZyXZht\nHMmYZXGVJqft6UPUMJJ/ddGAfhRtweNQP/IC+csvV9+cJQSsnuPMIyaadRQ1lIZ06wvGPYGBr2Hk\nOQDNqMvj9LGnHzSGX8HcUn+Yn0aGvXOeDncYGG3eetV1T0SDBV16Ugc5feaMYuSxax6bJ2q1AuAB\nMO6Ow5De4DjgUeXi4Nbr/Z35fw6Mz577bPisdcvdvd3q5dZsMR5oQPYYFLWQZL1GQp1p16HM0ND9\nR7B4yye2w4RRDy6zDjLb0YO29J6+LYBnXyaLBZo1j7R2qRRzbaaZdkz/fwLLLsGqQ4Tu9TGaJQ8Y\nfozse5mvdh/cG048wX74QBy0jJwVJu91EbDGbfej9e2Q9acSSNlvbTUA4FItdeWJWVVmd6DXEr0A\nzREqDqBVHmqdTwHCA5GpDipGjgbhXtDtutDFQ+ql41Qmr/QaxBuElShSPka/ZL4UgMn0R9cA69BW\n4hnFWVPkGlHKC0mHwunULSp8DZCXhE9bbw4MZDk8dWJmp62v6lNhhL/IkPznbaAoFiZsjq2xeko0\n8KirZcS7kH8AGI5HLh6NgTzQOHbYVXpAHpgRA8tmmzLSlrV2LhaDIOtSjiLZ8HWw/Mlwp4d/V7Xy\nKs5nG40a5IqLNPnde3Fep6D3pS4THQrgrwTMeWCwDeIwkGSRqbMvT6UJEu5eu4b7fQKcSZoNFLVb\nr1j5rDFAw704EcjqvYg3QckdP138yVsuwFnsxBzuZcoYQEyXwo7HdDyM25kMbhN4HOtmFu+xF4dG\nB2FyII93m6FGKFbaQrfGb7J2Mt4saaYpBgJsUY51jg0ydgHQUmt7nmGQLy8aDSiIt8CkVUjN4qSt\nQ/UTT9JF9moMGIk7JM6GkC4fiYUxLcWseGpQj2463h30NTPBekBNJrquZAH2JnwJkNciF39DxpQM\nyNBXrgO/BBsb2kyarXF4MelzHRYVG3pyhfpOU0M1d+Mat8HhFtNft+jsE2iH8SybxaKpe+jSw0lR\nn38YaeQgYAeSSvgMZj+z7/5oHM/UJu+aMb5K57SQe5HuAu7bu9NzyF3K7xyIRnC6F05rDgAOT11o\nqnUXW9uZCO1osFAnb2zbCYOJCWwBS7GugEGHYebmo7XPZodPf+JxqEHOTpDnVebiW5APK117q2y8\nBQDzWwsFJDm6ZmF4TORaQgCOASHQuJMTht0eI971KkNln/WWpfKNeBGsJkYTnWwO98hDCAJpg8t2\nZ615xas5y4ap9zlzWt6jIC3wb9T2qp1CjNTD952egVAENOFg2iWcGKdgGs04uzHsVNY9fAmQHzNM\nxnxpDSdUVkX2PbEOSAc1+qrJAIvyc+Dm1aVfZLq2xl0RsDnzoVotr6208c8wTu+AsBXthdc4pDWu\nh7tNL+ZGQaELenTqRQFn3d+0giq41FkFy7JhFX5/InwUxO+EjAqF+1nHhQCuTs9BsgqWd4TZZVrC\nsrrTJIDLbCnaDOVDw8Bdn+n6l2VDCyO2q1luMDJSCa8GbdNIma6T1ecOZEtOPZKZT4S1RamYkvy0\n0zBRsxhPRGprmTgdiuMOqEOpnT5WcnPUoDOyVlZEkuo50Zouu0CotQpiAdnqsxxDqeIgicomsDRD\nWcDfehwsoIfm1PytH6xT1zgMyzvepcvfXvbz1U/I5r3T5B8dG7sQ0YLitKqpLFkUgKKEuvjdnYD2\nzKvwRUBuxRz2LafFoLRWG7Jlm3UU1mj7I41h1nGswqLlLiADnrrETQUQbUd9eQ8WjhkOOHduzYgO\nSVMqd+TxdUBvrY4FU9736ZhH6Ft/yqAvVUHqYWmGtjpfcoT5ZuSbfqW96qP4xFMw3cMrc8NejP4k\nYGKt62fvtswtUQ41JaSa48q65y6N/Xq1NU3SZgBas9MRg20ajgQVunFw1oONxVwlQOuQ9ZFAcqpW\nYsMN5Hmrspro/WxYqOmA5YBiLpSZz7Llhs/e8m3c0cwzWrucAeycHdD2nP17UEqGQMhZ5QNxMv2D\nMw9fsg9wJoHMAwVFttvhaQuQbJPYu8w6DAAegFka1GT7uFqc0HVtq2IMJivE1/2JQrPIUc6cARmv\nsLK6CbBGP+cdT2W14m4gbxPR7q3xsQFygTn7rXHSlaSwx/BCzG6Gy5cA+ZwliNi/UTDcAr0HYI8o\n+ERsVEB3hCs4qYVQMApV56xgXtYJN/GgBp90XsbvHLQ5GFmM5U+le9vaVrIDOHzgEcZ2pVMPnG79\n/Zzd2MHw5hJ3Z5pp+cnv2GfDR8B7f++X9PuuwrmF9M7ArwD87SRqitbWujYV6DttoHXuJRCoR6Xw\nzTYuS4eIBcpS11oU8N9Agmvo7r1tvR6yENxWvoG8dgIZCNzyV9dioS8+OUNonfrybBKTgRAYbfPO\nhdy1T9BHd1h6NRP12h3a8LbUTZ8AIuOph/7wFkIkUIWqW03yrwVBRQ0mTsuWPlLNoMfSFdnOvDcw\nS2uVJQrnFaYJSHrXrb4SPOtH+G52fFsuXocv05FzdNSGh8yrPnNiTnmv9aN5H93AQLMErhr3wF51\n84xb9WQLYLFHQgdGppL5FzipZxp4vCStNHG3a7IePKJMD9DEysvyJajJCHOsIffgeY6pRrbp0qpD\nbJ39E2DMz79r0XUP0mW26/cmjx8p53k2JsJXBAifUTbf6x6z+lKriyqWJgXkhwJsCmsad9toI98R\nIZbMt/KUG6m0kqKvDnDWiQJyWtMgTnYyz5OdkKf/GGVFcEJvC5tyY5DfSYZbXxzM/SBQOtWmUTe1\nqlU4znrZiA7LBfIdtZZBCkbG2fFtcAls6dECpNbBCjCGtAtaqOQz9EVP98XeKYO7YtvKhOldgzib\nb0E2Y76qWWtGc+r4W/g2vlZYqIZmXu+p82LmZYCpQ6BJIA4pdz+IG/bXBbxzRemShl4jI9f8Qa7t\ni4I1gPiIkbfltdxx97CRFi+yWzQ/j8P7rNIEiHKRKR2iFgi14V/3g8r3nwi/wuaf/d4XYD8bnrH7\nZ89fqXTuVDv7wqono7uOe2U2FWXhHdtcBYZXX5EOEe9b+tqTPhiWLcgDs3PH6pgYNvEYo051Atq9\nahzQEUf5DVqgDMCp7pgxQ4w9Ink8GdeDpBy1tsF/a2BIOUDzwx6vE4Yx1UnKLGKlZG/uzVdYa8s1\n1WDH7IMbjER1k2TMDPTUm0KDM68AX+e+hSlOs1zb66KtpU442yswhzShdx3cdfMvtFq5GXwLi7mL\nAIBI+Hwtg60s/xSuwKArvUGn86OvVMfSAXSVioC42/ImyMRrQKU8is0daPMr78Y+huNnbUQKdcuc\nI4CcR96h67ZP2HlRl2+GOzD+O8F/T+t3ODK7AuCrxdM71c09WOMyb8HcA6bcOMsTVl5s3NMFs/Rv\n4TEF4hv7r+eqs1kjgjt9Z2W/TNWb9e7kkbO+4YBNFFAGkMc5mXEKUy5+OlLFhwJhM+Axk9XnZqAC\n5VzDKabPmQLIlIWNs08jz2G1SIvgz30EBGP+WxyY9VqCUPrS0io93vRx1p9V28Qnd6lOb/Y+sh65\nxGCgoNjyoH2hEiHrh9RFCwzeeDbUvly1AiDzuTLfYpaXYWmuAn8COIAFnK/f1/y05Ix3+74D6jmg\nARwBpsuDW9RmYTu7oql0u+wYYbZmPeboFtOblVDIHB5TWfeRTr1QJnAOMvk4r5RCQDckfSbcqVb+\nNIjvaesfgfM4DvwKkAN4+f7dYundc6/iCwGEBcj3tZqFUFizbTLuENZcDAcIBmSIkRHt16tkj2gs\nBQZgPmtbfex/CCscdSj2SOAOXXkukuZ9zhCosmhf7ijw7oU9iAqG+evsVV+uXNPSJscjTSFB8hXv\n1jW0cFTViwkgGJk32h69MccTk5LkTZc6NjltCNVmDpTKlJuyAII5a3wNvrQRK4Zl6bHm0OMRr8OX\nqlZ8+b7w674hEnuhBwjdHiW4FvPMmLJ5O6ILUFptfHtBkjblsmkIbLr8RrRfmBDjFb2eDnBdACEL\n8y5cWPSkO3qR9jZDbT7T/Kwc9tAnhPNwjBjgrYZZZxD7Ai8F4Z8Il+V/8736G2neR/vpnHLolv8/\nkWfgOXBrPzqt6VzMKuacdW0azQJpNQNom3Tfab+exRqnn3eWXsw28y1gA5Los7J+k+Ns0ncvtP4B\ns1FqlYeYT2reOP2MmegswIUjPcyynN6bjiz93hDuTMwQkRZboHqH4kDyyHFtUSiD9VmneT1cBZcD\n2ixTg6PxnGCk5VctgGbR0i1D+VzPtSn1e16bnejvJtkf63ar/co70ALccnpkpyc7rHc7fA0jJ/Vc\nSXmBuK5a103ec/E3UXa33D7fzyhjrxmW4ugTUFGQtpS6PNFjod7KJrzfPse3pruvcU9vQSPQWhK7\nfDaDG06MYwYm8bW1hJdfjLKeGJzSi+4P67S84vElB6c6Y+fU8Ey3/FEA1wXoFaLaLBQC8nu4A/ar\nPN6pip7leV8Duc77em2Ps9VuDZZRQilvomBZftTLqDaLPnJfXr4nCoKKqPIQP8SnP9+PTJgBNg7M\nMXAYgbyZLuS5EAhW7JJAvggpD+ES3h5nWaTErViYnVK+JP5SljVvTKfUlce6CWfIO8XmB93Qtl03\nizJGmxwTmRxsb4Ivuv6lfvNu7/auCi7ajVVf7kJW+TZT7E842rT4InwRkAPdrgQiZbjacc+dNN6f\nKOvt2qwjx7TB0IuhO7u60M9rb0E30DIV3R5nr2qrl+0hrGNMp0q1cp+x0ZEecqDVs5PggNIrAlar\n5tFBkxt51OYA4I9RjLwGxQAWh14F4tRx8l5uSKiOozm1ZXvy7w4rQLJncErLQTcqn8AK0M9AfH92\nv/eR8Exvfleu0zWw22WZL59o0NKxEXHy+ntBhfSaggobuSiCxAbgY2KM0fbsJvCTAmnQlv5EbLYZ\nMIIpH8MbyEnqQeucynXmK2fflsZkBHD5Tr09VT6dvxhDA8jdqQRr9Db+zBNyRysXiAtdXMlSjqFk\n4tzlTanlTNNTgJC8CnudyHx6p2MaP/pkIPiKGHv4GjtyYSgLCKKvR+C9C4bDgfTCUPqjY1QZS7Ph\nbeCjAZrJ16LNbv1SKppVDOwMDATP+uklPZydjn9yv+cd4RYVxWSi7qaHX2efOacoeUP1kWPOkfHF\ndHGm397uRM3aagPGXf29ANSPBOe835HHkZEJ58k1m7ronxq0f1yVZR0jn6vjcx+Ue8tUdXuJopy7\nsaenAyhmrvNYQE5KsgG3juNwnBcM1weB2aAungnkK9lgWsi0UGQqZqyWencsQB9H6gE+rPTz8Fb9\n8JnCWgRIE3dJbg6PQ1EOmgAXVOUsZSF2dvqv8SwG95wySxDW154e17Lcha9b7Lz43tfInoDb7CeY\nh76s1SkmHUsee5oHAKjj4PQZkYH74yZfQt93k01wKDxPn2oOfucrdB5Wd1aZV9NjmNWuPp1SA8AP\nQ1nC5MM1LYyB1BYPM+3VbYYrBZ5qTxDvYu/51xQ/HxamW/jlBeTx1y4e/o7F1z8Z3hFC7y6i/mr6\n19EnR7Yhqpx1nHBR3yoOF0IhDJVwXqBLM0dsAkDylxe0rfnkPFzUOdE/phmOmikI67bcxMSNRdab\nm2oyXor/8PcyzVB+V3JuSOODMiyont+HXcPD/JIsPKLVOhOiyry69F9v74+FNKU7v+7jX7bYuU6L\n7556f2DWFOuNd24HxEb+ubSiujD90syUZTmnvhCcmoGc80M9fPyWONlBikwHcz6VgOxVgiGdHoF6\neKp0UmHBTsrdZdMwh8cJPsNhc4TFi6/Mt/PRHblj/43BVcCpysy0QiL9fyCIA+e++Kx//Gq4092v\n38/p1ywwHqzxULhDsmSAH84nUz0nazD5EBcCh6GOixuj0y45bj2+ZEgEicixapJ2uzXuohAXCKSW\nnizjYA/xJAmSrhhbEwZMa0wh8YEHKRpIaxZl0YEX5cnQOr9m2+gorOirdTdJFfNS6wE37QN8ow1B\nETqT+3Ty/ChZZy+4mChv2fnvBvh5YHBFfZWbU763/+puJW2woZ0182h5fIjm5QT2pX5R5h1PxeYK\nX6rCMguLkADglVt9sNkIv3NK6FVX/XwxFo+TaHj4R4OJV4flwm2XA6DVwu9ijws7c9r8O+Bj0Xv/\nE8Np8fNCf3/1HPC5Mj8jEq8DxyPB1gVwGQ+wbKxzvrdajPG9w0OHHKfskNEv0vkyv704nIy7BDw4\nEJZ8gSAOT6d7ITgmRlq00O0v4mBtQ8bpyfa1QFbFsrSHLFWQNQnbeZXJ+CM2VJyiMrUGsEzHNm3t\ndVt92QlB+2+vRq+mX5ifEjCVnqViiIiqVqtyLwpeuqy+ku9IL2B8mY4neBX9cLUcyVV2U7MnNdGC\nNByWwijLdb1f7FzNK4UZXYTdNWiVTDpV2cuUrl0GIllSFsyKeaCntjlz0LUDFT60uSWTr7zd5PlZ\nqDUQlr0YTi/K/lNBXMNHBN87ZpFP3n7jup+eU3Dqzz0/JoJon2Wgxrcyf52Nx2tCmxKgadWzq9CK\n6NHMj77eEUYOcbdHS9l1xZlqeSyjwy3PM0AeGLNZAHEMNwAnHqSpIFWPDmDnUV1Son0aLRT4NLiX\nvT7aiiaqrLcVPevpX87I1XC/p19xBQCgnUcAh24vWUiJUdhCA41yYVdw4PVEtJ391NnWhSktLBgj\nOypNhVQvp0scBclkwwulFkXNxnThXGTMslwOJIX/PZC5yjvn8bbo1o3nO07ITlMy8nbEL5lMF6Th\nydHMy+8H65/g+zKsVXwqB7/uTOnvDHfleCVYnr13VzXvC6vrCNZ4ZTR8oMoWwL19RqnWnqfrmUCD\nPNUvktBCzPz0bgWqLSx0+XX4tXfPs3yO4zh2UOehzBbjfGb6a45TSKAtZQrVnQQnx3ZhTo+jqoMC\ncmKDF5+j1RLzudSek9g+HzffZLEzC6G+U9yrCaqigGLD03MKZC2tYvNMMwbucqypS0o+opilk5xa\nrQ4EE4YkbGOXok35S9qTZTwM6UJUBUis9le0U80AVcxcdXnTXlFMlH813fZdqLHKtilqJkyG3c+Q\nHchi6EDULNP39PlSEXXoQ6gN0+Ic09b/UzA+B/SanlYnvwAAAFYHMPqKSCdGdc1gPwvESz5kRnBn\n087nrhYsr1VDZ/b5fri2a5dfBZq1zvDBsNvZ7yqhc35XerHeX/OqOvG9DE9VQsoUbOKsLF9jVJWL\nGzdmATaVfrewo5VLzwgZfxOVyA9T2vJVee0xO+S+iNY1xxX365nn1zDyS9erHxhAVQNbg/PTG0DO\nqhUKBG1/7Qn7k3cDvgUGgYdSO3TSFC7RE8q+W4FqY0qq5lCdIsvE5xZTROMz9xL7mV51YfR2PdC6\nrPH58BSaW1xxGjlq6/kwmlQ2i59iRqb9PWPvWYmU6gQNH2KSz5nMrz7/0Xfv9OB599NpfzT86hrG\nr6l43o/77vvvUKsd7rDaaTtrPFGdUwQQff7pGhJscww2a85vBOKceQRIWJleavZjfe9C6C4zj/uy\nfJFq5SJHFyOWlVDTK/n3KixPeUu0bbvRqrvWhGUqB6ofLtJcV6C9HjdLkya01KV6xQ1tl823WvAv\n08pmanpPAXkhDUu2PxtUOKzBYUYH8vcCIdhz3Jviw2OkxAoQB45aDOUhwFg6vQ6F0nFqX75hbf+k\n8BzM/77wJ9YY/tS6xfW62h2jv8+HyaBy93SZQLac94cAOcJM8gwFbYx44tPSRwtOAMC48cvK1URB\nz4JDimEXTP8ifJFq5a6ht4xmLaz6WMux3aBCJtyHN6/AX4uUkPfqiXMHqbQN2HmnJTgNTq1E32im\n5yQ2Ozf0JMTze2p8grrPgiyJT1fmz+xd87vqGu/DVcff1TRXQE6IDeETcwvaAmuOagrqPFgg2yqZ\n+JGF9/QHSlZOdVibRUY9xEDD1eTr7fBMTXEFpn8ChP6udD4T3jF/fPX+Xse/Wl4F4XcFwxWIvyob\nZ+zxGcfowQ0DI30YeexkVUB1KbMlkG9jocF5ne3yIAuzfJXgbLh8v4H8SrOwhm/CyG8ymei0NMCt\nHlIZqjLaFZwCMJWhSwT5ezET3JIzNDjvOk5OwYY47aHACJCjcBCgTlmwi5SFndP8cJ4FC8H1WTur\namUfGG+Z8C1+BsBKPGnIbAAPZsesjBDccyOGA8NT356fqxqMXTtnUw7MQSsYYSnK1j8QTgvZv5kV\n3+r9N136q+e/MvyqKuqj759Vedft8gzQ7wT0sz7NGXHGkGN9Y8ljxAHV5cyO6z0J4KLurDxvM3lb\nrnmN+55Re2GSIHjURLqnVhXNXfhyq5VnoSthQWksA/iibATwAfELcp6ll9qlf28AZ2l/fQXmUHXE\ndh0riGtGXdor9HFS1qsK2D36LBOT3mL8zvB5tcB33fHP185r6/KkyVbn/OIWZzzOfNcd8NlAPtN9\nQE8ee9E6Hc8loLuA/3FR5lfMTdc1vh+I/pPC71h7uCIW5/ZZZ5wrp9v0zDcztysh0KQhiVLty2yU\n7Rmq5M96PLtEVMJjT1sLBSxxmgX5ESO6qoQA8tkE50V9f5HTrG3Kw+sXUyECJoXi0ujyb7/TDezA\n4uCJJkKhs163zF51knpgm/UsYC15qiZvAVxStxkkwajNzUz+Wapgb+CLEAS1hcSrBc13w7njnHe/\nnZd/LiDeWN8Gk85Pk8bwNdEgXsOkXAPUnEbOMp1yUpIMeADt6AtL1yCz+Uj5n1mhVPGe1u2FEPxG\n8uOpJciL8HnLmjWOVQXC9tSOf/2d46qui176ChlOxcoxWA8+KUNhuacZIrsS++aNylIt6Na8NwFU\nq6w9C1P2b3xvIBd2WoIR546hU5GyMxXgWwvZUxfXtOoVS3Ap3tj36ocXKofVyT2WssvQDh5IR1vG\nbbq9MOoQRzhbWX395yl79gWTOkK/rb/XgHT3+9JsLqWerQ/3h/upjBR1LehyMjniNBqOC1+iMxkw\nhpkbOdzjyDH32b6r2eHBuqD+U+uo4+asbS/b/v2VXv3eNG5jcpfNed0uH9UnPwvPFlY/wqp/VzzP\nQ6+PXOch6rW1F9nwBhH4F0Lb1ay504r/GZn0554WZF/WMdBkTAGsdfp81eqZtXrUXl5MfqWMigH6\n9yx8va8VAcqT7paNBDrgF+nIeEAe6NXIzdzP/g34sUyZHPAhlZcVyR1id3rN4tkELJ6uonO88m5G\nu3FVIUi+FqB5BuIr2/xdg6hX8M/xXk6LsQ63wsuLZ2tBVX/nW5ZTG4+pUnnE685sxZ7qOK3cDMZN\nYeUpbq626zUA3OvQjXWaS1C3Fj4fCO8B7ta4vzG82/bfxUrmKnxsBmCb+qQJg5kLDmxkqfqSXFxC\n9jHemw4MOQSkeusOrJ2ZRW4IE98/K9/O/qdEwAXUz2D+LHwTHTmlmlyRgU/2tEsvyH1K8kUjcqVm\n4J8ySstpTP50yQynQVcVehogxQoFbOvkoVd10PpfLZeqeHQqSjXRO1PcS5VVMYhr1v3qfX4+Y7P6\n/DP2v1jLUI1WzzXT6VV8eqJP9Qz/hhyj5p6ue/NdcXkbnhRJEnLUXzDvZ0B9beHzb/iTYR1rvrik\nkAnWjprIfflvp2F5PCNnv4YNA5jEmp3n+b24/ryPvR6XGr4JkH883LLkEtFoiSzAAxDM191tixDZ\n0torVAFVVSNldyHEjws16t2tnjwJgb1xPwYUCi6fYWFXQuFO5/7UIuCDU3YF8T2EBaPUW02ievfp\nnOgNR6BvdcBt1KCbo8F9Dgd4TJo1i0PNsc55ZLmu1Fbfle3+N4a1rvm9mM7NO1RlvJVCt7MhZ9j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jhWge/XVZv3I4163ajnDrCeQNF2AqwBeJiVjp2eGXVqWcA+gcNDnz7dCsSPdP41h+PwgWN4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc913990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_net = solver.test_nets[0]\n",
+    "for image_index in range(5):\n",
+    "    plt.figure()\n",
+    "    plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n",
+    "    gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "    estlist = test_net.blobs['score'].data[image_index, ...] > 0\n",
+    "    plt.title('GT: {} \\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\n",
+    "    plt.axis('off')"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Multilabel classification on PASCAL using python data-layers.",
+  "example_name": "PASCAL Multilabel with python datalayer",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
index f0039eff4b6..68e4fa7960a 100644
--- a/examples/pycaffe/layers/pascal_multilabel_datalayers.py
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -78,88 +78,6 @@ def backward(self, top, propagate_down, bottom):
         pass
 
 
-class PascalMultilabelDataLayerAsync(caffe.Layer):
-
-    """
-    This is a simple asyncronous datalayer for training a multilabel model on
-    PASCAL.
-    """
-
-    def setup(self, bottom, top):
-
-        self.top_names = ['data', 'label']
-
-        # === Read input parameters ===
-
-        # params is a python dictionary with layer parameters.
-        params = eval(self.param_str)
-
-        # Check the paramameters for validity.
-        check_params(params)
-
-        # we need to store this as a local variable.
-        self.batch_size = params['batch_size']
-
-        # === We are going to do the actual data processing in a seperate,
-        # helperclass, called BatchLoader. So let's forward the parameters
-        # to that class ===
-        self.thread_result = {}
-        self.thread = None
-        self.batch_loader = BatchLoader(params, self.thread_result)
-        self.dispatch_worker()  # Let it start fetching data right away.
-
-        # === reshape tops ===
-        # since we use a fixed input image size, we can shape the data layer
-        # once. Else, we'd have to do it in the reshape call.
-        top[0].reshape(
-            self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
-        # Note the 20 channels (because PASCAL has 20 classes.)
-        top[1].reshape(self.batch_size, 20)
-
-        print_info("PascalMultilabelDataLayerAsync", params)
-
-    def forward(self, bottom, top):
-        """
-        This is the forward pass, where we load the data into the blobs.
-        Since we run the BatchLoader asynchronously, we just wait for it,
-        and then copy
-        """
-
-        if self.thread is not None:
-            self.join_worker()  # wait until it is done.
-
-        for top_index, name in zip(range(len(top)), self.top_names):
-            for i in range(self.batch_size):
-                # Copy the already-prepared data to caffe.
-                top[top_index].data[i, ...] = self.thread_result[name][i]
-
-        # let's go again while the GPU process this batch.
-        self.dispatch_worker()
-
-    def reshape(self, bottom, top):
-        """
-        There is no need to reshape the data, since the input is of fixed size
-        (rows and columns)
-        """
-        pass
-
-    def backward(self, top, propagate_down, bottom):
-        """
-        These layers does not back propagate
-        """
-        pass
-
-    def dispatch_worker(self):
-        assert self.thread is None
-        self.thread = Thread(target=self.batch_loader)
-        self.thread.start()
-
-    def join_worker(self):
-        assert self.thread is not None
-        self.thread.join()
-        self.thread = None
-
-
 class BatchLoader(object):
 
     """
@@ -185,23 +103,6 @@ def __init__(self, params, result):
         print "BatchLoader initialized with {} images".format(
             len(self.indexlist))
 
-    def __call__(self):
-        """
-        This does the same stuff as the forward layer of the synchronous layer.
-        Exept that we store the data and labels in the result dictionary
-        (as lists of length batchsize).
-        """
-        self.result['data'] = []
-        self.result['label'] = []
-        for itt in range(self.batch_size):
-
-            # Get the next image in the batch
-            im, multilabel = self.load_next_image()
-
-            # Store in a result list.
-            self.result['data'].append(im)
-            self.result['label'].append(multilabel)
-
     def load_next_image(self):
         """
         Load the next image in a batch.

From 01d5a9e0afdbdf1e93f343bc5656ad1ec53c1673 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Feb 2016 21:13:27 -0800
Subject: [PATCH 1717/2053] output all logging from upgrade net tools

output info, warnings, and errors for fuller description of the upgrade
---
 tools/upgrade_net_proto_binary.cpp  | 5 +++--
 tools/upgrade_net_proto_text.cpp    | 3 ++-
 tools/upgrade_solver_proto_text.cpp | 3 ++-
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index 8a0dd7af743..ede07ecc2ff 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: "
@@ -39,11 +40,11 @@ int main(int argc, char** argv) {
                  << "see details above.";
     }
   } else {
-    LOG(ERROR) << "File already in V1 proto format: " << argv[1];
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
   WriteProtoToBinaryFile(net_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded NetParameter binary proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded NetParameter binary proto to " << argv[2];
   return !success;
 }
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 9200431bc27..d8e84d6d91b 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: "
@@ -50,6 +51,6 @@ int main(int argc, char** argv) {
   // Save new format prototxt.
   WriteProtoToTextFile(net_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded NetParameter text proto to " << argv[2];
   return !success;
 }
diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp
index 7130232aed7..ddff1ce6bad 100644
--- a/tools/upgrade_solver_proto_text.cpp
+++ b/tools/upgrade_solver_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: upgrade_solver_proto_text "
@@ -45,6 +46,6 @@ int main(int argc, char** argv) {
   // Save new format prototxt.
   WriteProtoToTextFile(solver_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded SolverParameter text proto to " << argv[2];
   return !success;
 }

From effa9411ca270f32730400861c08dd2aa3f03ffa Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Feb 2016 21:15:56 -0800
Subject: [PATCH 1718/2053] check all net upgrade conditions

check all conditions all the time; V0 -> V1 and V1 -> V2 do not suffice.
---
 src/caffe/util/upgrade_proto.cpp | 3 ++-
 tools/upgrade_net_proto_text.cpp | 5 -----
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 449975bd733..775285f14ed 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,7 +13,8 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
-  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
+  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param)
+      || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param);
 }
 
 bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index d8e84d6d91b..617b48dc918 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -32,7 +32,6 @@ int main(int argc, char** argv) {
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
-  bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
     success = UpgradeNetAsNeeded(input_filename, &net_param);
@@ -44,10 +43,6 @@ int main(int argc, char** argv) {
     LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
-  if (need_data_upgrade) {
-    UpgradeNetDataTransformation(&net_param);
-  }
-
   // Save new format prototxt.
   WriteProtoToTextFile(net_param, argv[2]);
 

From ff6c6e487534e4e738b301da2169bd369344b7f0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Feb 2016 21:17:21 -0800
Subject: [PATCH 1719/2053] fix input field -> input layer net upgrade: only
 convert full defs

convert inputs in legacy definitions (prototxt), but simply strip inputs
from legacy weights (caffemodel).

fix #3750
---
 src/caffe/util/upgrade_proto.cpp | 46 ++++++++++++++++++--------------
 1 file changed, 26 insertions(+), 20 deletions(-)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 775285f14ed..511a2dea5a9 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -953,29 +953,35 @@ bool NetNeedsInputUpgrade(const NetParameter& net_param) {
 }
 
 void UpgradeNetInput(NetParameter* net_param) {
-  LayerParameter* layer_param = net_param->add_layer();
-  layer_param->set_name("input");
-  layer_param->set_type("Input");
-  InputParameter* input_param = layer_param->mutable_input_param();
+  // Collect inputs and convert to Input layer definitions.
+  // If the NetParameter holds an input alone, without shape/dim, then
+  // it's a legacy caffemodel and simply stripping the input field is enough.
   bool has_shape = net_param->input_shape_size() > 0;
-  // Convert input fields into a layer.
-  for (int i = 0; i < net_param->input_size(); ++i) {
-    layer_param->add_top(net_param->input(i));
-    if (has_shape) {
-      input_param->add_shape()->CopyFrom(net_param->input_shape(i));
-    } else {
-      // Turn legacy input dimensions into shape.
-      BlobShape* shape = input_param->add_shape();
-      int first_dim = i*4;
-      int last_dim = first_dim + 4;
-      for (int j = first_dim; j < last_dim; j++) {
-        shape->add_dim(net_param->input_dim(j));
+  bool has_dim = net_param->input_dim_size() > 0;
+  if (has_shape || has_dim) {
+    LayerParameter* layer_param = net_param->add_layer();
+    layer_param->set_name("input");
+    layer_param->set_type("Input");
+    InputParameter* input_param = layer_param->mutable_input_param();
+    // Convert input fields into a layer.
+    for (int i = 0; i < net_param->input_size(); ++i) {
+      layer_param->add_top(net_param->input(i));
+      if (has_shape) {
+        input_param->add_shape()->CopyFrom(net_param->input_shape(i));
+      } else {
+        // Turn legacy input dimensions into shape.
+        BlobShape* shape = input_param->add_shape();
+        int first_dim = i*4;
+        int last_dim = first_dim + 4;
+        for (int j = first_dim; j < last_dim; j++) {
+          shape->add_dim(net_param->input_dim(j));
+        }
       }
     }
-  }
-  // Swap input layer to beginning of net to satisfy layer dependencies.
-  for (int i = net_param->layer_size() - 1; i > 0; --i) {
-    net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i));
+    // Swap input layer to beginning of net to satisfy layer dependencies.
+    for (int i = net_param->layer_size() - 1; i > 0; --i) {
+      net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i));
+    }
   }
   // Clear inputs.
   net_param->clear_input();

From 7eaeb3aeaa23b6a3ba4be5f09bad34f00e10958d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Feb 2016 21:18:50 -0800
Subject: [PATCH 1720/2053] refuse to upgrade net with layer/layers
 inconsistency

die loudly if a net definition (prototxt) mixes proto formats by
defining both `layer` and `layers` fields instead of complaining but
discarding and continuing.

fix #3381
---
 src/caffe/util/upgrade_proto.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 511a2dea5a9..9e186915b43 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -656,12 +656,14 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
 }
 
 bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
-  bool is_fully_compatible = true;
   if (v1_net_param.layer_size() > 0) {
-    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
-               << "fields; these will be ignored for the upgrade.";
-    is_fully_compatible = false;
+    LOG(FATAL) << "Refusing to upgrade inconsistent NetParameter input; "
+        << "the definition includes both 'layer' and 'layers' fields. "
+        << "The current format defines 'layer' fields with string type like "
+        << "layer { type: 'Layer' ... } and not layers { type: LAYER ... }. "
+        << "Manually switch the definition to 'layer' format to continue.";
   }
+  bool is_fully_compatible = true;
   net_param->CopyFrom(v1_net_param);
   net_param->clear_layers();
   net_param->clear_layer();

From 326a486d7e15a1e7a23a3a5b81ae591766062a06 Mon Sep 17 00:00:00 2001
From: Jacek Czaja <jacek.czaja@intel.com>
Date: Tue, 1 Mar 2016 11:32:21 +0100
Subject: [PATCH 1721/2053] - doc and cmake update MKL related

- cosmetic change to mkl related doc
---
 cmake/Modules/FindMKL.cmake | 2 +-
 docs/installation.md        | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index d2012db579a..5ab93b2d6b6 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -20,7 +20,7 @@ caffe_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGL
 
 # ---[ Root folders
 set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKLROOT} ${INTEL_ROOT}/mkl
                                    DOC "Folder contains MKL")
 
 # ---[ Find include dir
diff --git a/docs/installation.md b/docs/installation.md
index ef781e8d638..893164584d9 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -54,7 +54,8 @@ There are several implementations of this library. The choice is yours:
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
-    2. Set `BLAS := mkl` in `Makefile.config`
+    2. Set up MKL environment (Details: [Linux](https://software.intel.com/en-us/node/528499), [OS X](https://software.intel.com/en-us/node/528659)). Example: *source /opt/intel/mkl/bin/mklvars.sh intel64*
+    3. Set `BLAS := mkl` in `Makefile.config`
 * [OpenBLAS](http://www.openblas.net/): free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`

From 93af70e464e973e79d8b9a12c5431c7c209d4ec9 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 1 Mar 2016 15:41:33 -0800
Subject: [PATCH 1722/2053] [example] groom multilabel notebook title, order

---
 examples/pascal-multilabel-with-datalayer.ipynb | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb
index fd66114d8e9..94b9b4fed8a 100644
--- a/examples/pascal-multilabel-with-datalayer.ipynb
+++ b/examples/pascal-multilabel-with-datalayer.ipynb
@@ -452,8 +452,8 @@
   }
  ],
  "metadata": {
-  "description": "Multilabel classification on PASCAL using python data-layers.",
-  "example_name": "PASCAL Multilabel with python datalayer",
+  "description": "Multilabel classification on PASCAL VOC using a Python data layer.",
+  "example_name": "Multilabel Classification with Python Data Layer",
   "include_in_docs": true,
   "kernelspec": {
    "display_name": "Python 2",
@@ -471,7 +471,8 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
    "version": "2.7.11"
-  }
+  },
+  "priority": 5
  },
  "nbformat": 4,
  "nbformat_minor": 0

From 4f4d0209495aa858de4ea7d71b3aea9196b14a05 Mon Sep 17 00:00:00 2001
From: Viveka Kulharia <vivkul@iitk.ac.in>
Date: Wed, 2 Mar 2016 10:37:56 +0530
Subject: [PATCH 1723/2053] minor mistakes removed

---
 examples/finetune_flickr_style/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 4e9d41f13cc..9ba4c9217ff 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -14,9 +14,9 @@ Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Fli
 ## Explanation
 
 The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained.
-Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
+Since that model works well for object category classification, we'd like to use this architecture for our style classifier.
 We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
-If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
+If we provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
 
 Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
 Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.

From db7a26162c9a1636b83b7d8f788ff669e694a150 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Fri, 4 Mar 2016 01:59:48 +0000
Subject: [PATCH 1724/2053] Removed lint script reference to non-existant
 caffe_memcpy function.

---
 scripts/cpp_lint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index f750489f4f9..14c76ecd6bf 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1564,7 +1564,7 @@ def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
 caffe_alt_function_list = (
     ('memset', ['caffe_set', 'caffe_memset']),
     ('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
-    ('memcpy', ['caffe_copy', 'caffe_memcpy']),
+    ('memcpy', ['caffe_copy']),
     ('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
     )
 

From ebfa2af1ca80cd7e0f23d92fca1a491b86605686 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 4 Mar 2016 12:53:49 -0800
Subject: [PATCH 1725/2053] [travis] force protobuf 3.0.0b2 for Python 3

This is temporary measure to avoid an apparent upstream issue with
protobuf 3.0.0b2.post1.
---
 scripts/travis/travis_install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index d18dc223a06..ca8c410cfbe 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -93,7 +93,7 @@ if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
 fi
 
 if [ "$PYTHON_VERSION" -eq "3" ]; then
-  pip install --pre protobuf
+  pip install --pre protobuf==3.0.0b2
 else
   pip install protobuf
 fi

From 7a8b19f957820af928aab3cbfae28e51a87b0d67 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Fri, 29 Jan 2016 18:41:12 -0800
Subject: [PATCH 1726/2053] [pycaffe] add coord_map.py for computing induced
 coordinate transform

This provides a framework for automatically aligning different layers of
a net despite up/downsampling, padding, and output size rounding.
---
 python/caffe/coord_map.py | 95 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 python/caffe/coord_map.py

diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py
new file mode 100644
index 00000000000..dd41f293b3b
--- /dev/null
+++ b/python/caffe/coord_map.py
@@ -0,0 +1,95 @@
+from __future__ import division
+import numpy as np
+from caffe import layers as L
+
+PASS_THROUGH_LAYERS = ['AbsVal', 'ReLU', 'PReLU', 'Dropout', 'LRN', 'Eltwise',
+        'BatchNorm', 'BNLL', 'Log', 'Exp', 'MVN', 'Power', 'Sigmoid', 'Split',
+        'TanH', 'Threshold']
+
+def conv_params(fn):
+    params = fn.params.get('convolution_param', fn.params)
+    axis = params.get('axis', 1)
+    ks = np.array(params['kernel_size'], ndmin=1)
+    dilation = np.array(params.get('dilation', 1), ndmin=1)
+    assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
+        'stride_w'} & set(fn.params)) == 0, \
+                'cropping does not support legacy _h/_w params'
+    return (axis, np.array(params.get('stride', 1), ndmin=1),
+            (ks - 1) * dilation + 1,
+            np.array(params.get('pad', 0), ndmin=1))
+
+class UndefinedMapException(Exception):
+    pass
+
+def coord_map(fn):
+    if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
+        axis, stride, ks, pad = conv_params(fn)
+        return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
+    elif fn.type_name == 'Deconvolution':
+        axis, stride, ks, pad = conv_params(fn)
+        return axis, stride, (ks - 1) / 2 - pad
+    elif fn.type_name in PASS_THROUGH_LAYERS:
+        return None, 1, 0
+    elif fn.type_name == 'Crop':
+        axis = fn.params.get('axis')
+        return axis, 1, - fn.params['crop']
+    else:
+        raise UndefinedMapException
+
+class AxisMismatchException(Exception):
+    pass
+
+def compose((ax1, a1, b1), (ax2, a2, b2)):
+    if ax1 is None:
+        ax = ax2
+    elif ax2 is None or ax1 == ax2:
+        ax = ax1
+    else:
+        raise AxisMismatchException
+    return ax, a1 * a2, a1 * b2 + b1
+
+def inverse((ax, a, b)):
+    return ax, 1 / a, -b / a
+
+def coord_map_from_to(top_from, top_to):
+    # We need to find a common ancestor of top_from and top_to.
+    # We'll assume that all ancestors are equivalent here (otherwise the graph
+    # is an inconsistent state (which we could improve this to check for)).
+    # For now use a brute-force algorithm.
+
+    # walk back from top_from, keeping the coord map as we go
+    from_maps = {top_from: (None, 1, 0)}
+    frontier = {top_from}
+    while frontier:
+        top = frontier.pop()
+        try:
+            for bottom in top.fn.inputs:
+                from_maps[bottom] = compose(from_maps[top], coord_map(top.fn))
+                frontier.add(bottom)
+        except UndefinedMapException:
+            pass
+
+    # now walk back from top_to until we hit a common blob
+    to_maps = {top_to: (None, 1, 0)}
+    frontier = {top_to}
+    while frontier:
+        top = frontier.pop()
+        if top in from_maps:
+            return compose(to_maps[top], inverse(from_maps[top]))
+        try:
+            for bottom in top.fn.inputs:
+                to_maps[bottom] = compose(to_maps[top], coord_map(top.fn))
+                frontier.add(bottom)
+        except UndefinedMapException:
+            continue
+
+    # if we got here, we did not find a blob in common
+    raise RuntimeError, 'Could not compute map between tops; are they connected ' \
+            'by spatial layers?'
+
+def crop(top_from, top_to):
+    ax, a, b = coord_map_from_to(top_from, top_to)
+    assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
+    assert (b <= 0).all(), 'cannot crop negative width (b = {})'.format(b)
+    assert (np.round(b) == b).all(), 'cannot crop noninteger width (b = {})'.format(b)
+    return L.Crop(top_from, top_to, crop_param=dict(axis=ax, crop=list(-np.round(b).astype(int))))

From 6149e7383e84264f315a23bd5b924ab49d4f5804 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 27 Feb 2016 23:57:42 -0800
Subject: [PATCH 1727/2053] [pycaffe] document, style, and complete coord_map

- document by docstring and comment
- pep8
- add latest layers and alphabetize
- respect default crop params
- handle graphs with compositions of crops by walking only the
  first, cropped bottom of Crop layers
- make python3 happy by replacing arg tuple unpacking
---
 python/caffe/coord_map.py | 119 +++++++++++++++++++++++++++++++++-----
 1 file changed, 104 insertions(+), 15 deletions(-)

diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py
index dd41f293b3b..884d9070576 100644
--- a/python/caffe/coord_map.py
+++ b/python/caffe/coord_map.py
@@ -1,27 +1,68 @@
+"""
+Determine spatial relationships between layers to relate their coordinates.
+Coordinates are mapped from input-to-output (forward), but can
+be mapped output-to-input (backward) by the inverse mapping too.
+This helps crop and align feature maps among other uses.
+"""
+
 from __future__ import division
 import numpy as np
 from caffe import layers as L
 
-PASS_THROUGH_LAYERS = ['AbsVal', 'ReLU', 'PReLU', 'Dropout', 'LRN', 'Eltwise',
-        'BatchNorm', 'BNLL', 'Log', 'Exp', 'MVN', 'Power', 'Sigmoid', 'Split',
-        'TanH', 'Threshold']
+PASS_THROUGH_LAYERS = ['AbsVal', 'BatchNorm', 'Bias', 'BNLL', 'Dropout',
+                       'Eltwise', 'ELU', 'Log', 'LRN', 'Exp', 'MVN', 'Power',
+                       'ReLU', 'PReLU', 'Scale', 'Sigmoid', 'Split', 'TanH',
+                       'Threshold']
+
 
 def conv_params(fn):
+    """
+    Extract the spatial parameters that determine the coordinate mapping:
+    kernel size, stride, padding, and dilation.
+
+    Implementation detail: Convolution, Deconvolution, and Im2col layers
+    define these in the convolution_param message, while Pooling has its
+    own fields in pooling_param. This method deals with these details to
+    extract canonical parameters.
+    """
     params = fn.params.get('convolution_param', fn.params)
     axis = params.get('axis', 1)
     ks = np.array(params['kernel_size'], ndmin=1)
     dilation = np.array(params.get('dilation', 1), ndmin=1)
     assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
-        'stride_w'} & set(fn.params)) == 0, \
-                'cropping does not support legacy _h/_w params'
+                'stride_w'} & set(fn.params)) == 0, \
+        'cropping does not support legacy _h/_w params'
     return (axis, np.array(params.get('stride', 1), ndmin=1),
             (ks - 1) * dilation + 1,
             np.array(params.get('pad', 0), ndmin=1))
 
+
+def crop_params(fn):
+    """
+    Extract the crop layer parameters with defaults.
+    """
+    params = fn.params.get('crop_param', fn.params)
+    axis = params.get('axis', 2)  # default to spatial crop for N, C, H, W
+    offset = np.array(params.get('offset', 0), ndmin=1)
+    return (axis, offset)
+
+
 class UndefinedMapException(Exception):
+    """
+    Exception raised for layers that do not have a defined coordinate mapping.
+    """
     pass
 
+
 def coord_map(fn):
+    """
+    Define the coordinate mapping by its
+    - axis
+    - scale: output coord[i * scale] <- input_coord[i]
+    - shift: output coord[i] <- output_coord[i + shift]
+    s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
+    (None, 1, 0) since it is independent of axis and does not transform coords.
+    """
     if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
         axis, stride, ks, pad = conv_params(fn)
         return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
@@ -31,15 +72,27 @@ def coord_map(fn):
     elif fn.type_name in PASS_THROUGH_LAYERS:
         return None, 1, 0
     elif fn.type_name == 'Crop':
-        axis = fn.params.get('axis')
-        return axis, 1, - fn.params['crop']
+        axis, offset = crop_params(fn)
+        return axis, 1, - offset
     else:
         raise UndefinedMapException
 
+
 class AxisMismatchException(Exception):
+    """
+    Exception raised for mappings with incompatible axes.
+    """
     pass
 
-def compose((ax1, a1, b1), (ax2, a2, b2)):
+
+def compose(base_map, next_map):
+    """
+    Compose a base coord map with scale a1, shift b1 with a further coord map
+    with scale a2, shift b2. The scales multiply and the further shift, b2,
+    is scaled by base coord scale a1.
+    """
+    ax1, a1, b1 = base_map
+    ax2, a2, b2 = next_map
     if ax1 is None:
         ax = ax2
     elif ax2 is None or ax1 == ax2:
@@ -48,22 +101,48 @@ def compose((ax1, a1, b1), (ax2, a2, b2)):
         raise AxisMismatchException
     return ax, a1 * a2, a1 * b2 + b1
 
-def inverse((ax, a, b)):
+
+def inverse(coord_map):
+    """
+    Invert a coord map by de-scaling and un-shifting;
+    this gives the backward mapping for the gradient.
+    """
+    ax, a, b = coord_map
     return ax, 1 / a, -b / a
 
+
 def coord_map_from_to(top_from, top_to):
+    """
+    Determine the coordinate mapping betweeen a top (from) and a top (to).
+    Walk the graph to find a common ancestor while composing the coord maps for
+    from and to until they meet. As a last step the from map is inverted.
+    """
     # We need to find a common ancestor of top_from and top_to.
     # We'll assume that all ancestors are equivalent here (otherwise the graph
     # is an inconsistent state (which we could improve this to check for)).
     # For now use a brute-force algorithm.
 
+    def collect_bottoms(top):
+        """
+        Collect the bottoms to walk for the coordinate mapping.
+        The general rule is that all the bottoms of a layer can be mapped, as
+        most layers have the same coordinate mapping for each bottom.
+        Crop layer is a notable exception. Only the first/cropped bottom is
+        mappable; the second/dimensions bottom is excluded from the walk.
+        """
+        bottoms = top.fn.inputs
+        if top.fn.type_name == 'Crop':
+            bottoms = bottoms[:1]
+        return bottoms
+
     # walk back from top_from, keeping the coord map as we go
     from_maps = {top_from: (None, 1, 0)}
     frontier = {top_from}
     while frontier:
         top = frontier.pop()
         try:
-            for bottom in top.fn.inputs:
+            bottoms = collect_bottoms(top)
+            for bottom in bottoms:
                 from_maps[bottom] = compose(from_maps[top], coord_map(top.fn))
                 frontier.add(bottom)
         except UndefinedMapException:
@@ -77,19 +156,29 @@ def coord_map_from_to(top_from, top_to):
         if top in from_maps:
             return compose(to_maps[top], inverse(from_maps[top]))
         try:
-            for bottom in top.fn.inputs:
+            bottoms = collect_bottoms(top)
+            for bottom in bottoms:
                 to_maps[bottom] = compose(to_maps[top], coord_map(top.fn))
                 frontier.add(bottom)
         except UndefinedMapException:
             continue
 
     # if we got here, we did not find a blob in common
-    raise RuntimeError, 'Could not compute map between tops; are they connected ' \
-            'by spatial layers?'
+    raise RuntimeError('Could not compute map between tops; are they '
+                       'connected by spatial layers?')
+
 
 def crop(top_from, top_to):
+    """
+    Define a Crop layer to crop a top (from) to another top (to) by
+    determining the coordinate mapping between the two and net spec'ing
+    the axis and shift parameters of the crop.
+    """
     ax, a, b = coord_map_from_to(top_from, top_to)
     assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
     assert (b <= 0).all(), 'cannot crop negative width (b = {})'.format(b)
-    assert (np.round(b) == b).all(), 'cannot crop noninteger width (b = {})'.format(b)
-    return L.Crop(top_from, top_to, crop_param=dict(axis=ax, crop=list(-np.round(b).astype(int))))
+    assert (np.round(b) == b).all(), 'cannot crop noninteger width ' \
+        '(b = {})'.format(b)
+    return L.Crop(top_from, top_to,
+                  crop_param=dict(axis=ax,
+                                  crop=list(-np.round(b).astype(int))))

From 25b9ef95f35a4de766500c9c70a18d839a5f7c70 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 28 Feb 2016 11:51:52 -0800
Subject: [PATCH 1728/2053] [pycaffe] align coord_map and #3570 Crop layer

- crop -> offset
- adjust crop axis by 1
---
 python/caffe/coord_map.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py
index 884d9070576..a3413cfa855 100644
--- a/python/caffe/coord_map.py
+++ b/python/caffe/coord_map.py
@@ -73,6 +73,7 @@ def coord_map(fn):
         return None, 1, 0
     elif fn.type_name == 'Crop':
         axis, offset = crop_params(fn)
+        axis -= 1  # -1 for last non-coordinate dim.
         return axis, 1, - offset
     else:
         raise UndefinedMapException
@@ -176,9 +177,9 @@ def crop(top_from, top_to):
     """
     ax, a, b = coord_map_from_to(top_from, top_to)
     assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
-    assert (b <= 0).all(), 'cannot crop negative width (b = {})'.format(b)
-    assert (np.round(b) == b).all(), 'cannot crop noninteger width ' \
+    assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b)
+    assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \
         '(b = {})'.format(b)
     return L.Crop(top_from, top_to,
-                  crop_param=dict(axis=ax,
-                                  crop=list(-np.round(b).astype(int))))
+                  crop_param=dict(axis=ax + 1,  # +1 for first cropping dim.
+                                  offset=list(-np.round(b).astype(int))))

From 880e1474270523e3e5585a14a370fda39bb743c1 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 3 Mar 2016 16:39:18 -0800
Subject: [PATCH 1729/2053] [pycaffe] test coord_map

- test known mappings: conv-pool-deconv stack, ReLU and 1x1 conv
- test effects of padding
- test rectangular/anisotropic coordinate mapping, test N-D
- catch error cases: negative crop, scale mismatch, tops that are not
  spatially connected
---
 python/caffe/test/test_coord_map.py | 192 ++++++++++++++++++++++++++++
 1 file changed, 192 insertions(+)
 create mode 100644 python/caffe/test/test_coord_map.py

diff --git a/python/caffe/test/test_coord_map.py b/python/caffe/test/test_coord_map.py
new file mode 100644
index 00000000000..613260e25df
--- /dev/null
+++ b/python/caffe/test/test_coord_map.py
@@ -0,0 +1,192 @@
+import unittest
+
+import numpy as np
+import random
+
+import caffe
+from caffe import layers as L
+from caffe import params as P
+from caffe.coord_map import coord_map_from_to, crop
+
+
+def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
+    """
+    Define net spec for simple conv-pool-deconv pattern common to all
+    coordinate mapping tests.
+    """
+    n = caffe.NetSpec()
+    n.data = L.Input(shape=dict(dim=[2, 1, 100, 100]))
+    n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20]))
+    n.conv = L.Convolution(
+        n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad)
+    n.pool = L.Pooling(
+        n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0)
+    # for upsampling kernel size is 2x stride
+    try:
+        deconv_ks = [s*2 for s in dstride]
+    except:
+        deconv_ks = dstride*2
+    n.deconv = L.Deconvolution(
+        n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad)
+    return n
+
+
+class TestCoordMap(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_conv_pool_deconv(self):
+        """
+        Map through conv, pool, and deconv.
+        """
+        n = coord_net_spec()
+        # identity for 2x pool, 2x deconv
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertEquals(a, 1)
+        self.assertEquals(b, 0)
+        # shift-by-one for 4x pool, 4x deconv
+        n = coord_net_spec(pool=4, dstride=4)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertEquals(a, 1)
+        self.assertEquals(b, -1)
+
+    def test_pass(self):
+        """
+        A pass-through layer (ReLU) and conv (1x1, stride 1, pad 0)
+        both do identity mapping.
+        """
+        n = coord_net_spec()
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        n.relu = L.ReLU(n.deconv)
+        n.conv1x1 = L.Convolution(
+            n.relu, num_output=10, kernel_size=1, stride=1, pad=0)
+        for top in [n.relu, n.conv1x1]:
+            ax_pass, a_pass, b_pass = coord_map_from_to(top, n.data)
+            self.assertEquals(ax, ax_pass)
+            self.assertEquals(a, a_pass)
+            self.assertEquals(b, b_pass)
+
+    def test_padding(self):
+        """
+        Padding conv adds offset while padding deconv subtracts offset.
+        """
+        n = coord_net_spec()
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        pad = random.randint(0, 10)
+        # conv padding
+        n = coord_net_spec(pad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b - pad, b_pad)
+        # deconv padding
+        n = coord_net_spec(dpad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b + pad, b_pad)
+        # pad both to cancel out
+        n = coord_net_spec(pad=pad, dpad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b, b_pad)
+
+    def test_multi_conv(self):
+        """
+        Multiple bottoms/tops of a layer are identically mapped.
+        """
+        n = coord_net_spec()
+        # multi bottom/top
+        n.conv_data, n.conv_aux = L.Convolution(
+            n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2,
+            pad=0)
+        ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data)
+        ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux)
+        self.assertEquals(ax1, ax2)
+        self.assertEquals(a1, a2)
+        self.assertEquals(b1, b2)
+
+    def test_rect(self):
+        """
+        Anisotropic mapping is equivalent to its isotropic parts.
+        """
+        n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
+        n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
+        n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
+        ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
+        ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
+        ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
+        self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
+        self.assertEquals(a_3x3, a_3x5[0])
+        self.assertEquals(b_3x3, b_3x5[0])
+        self.assertEquals(a_5x5, a_3x5[1])
+        self.assertEquals(b_5x5, b_3x5[1])
+
+    def test_nd_conv(self):
+        """
+        ND conv maps the same way in more dimensions.
+        """
+        n = caffe.NetSpec()
+        # define data with 3 spatial dimensions, otherwise the same net
+        n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
+        n.conv = L.Convolution(
+            n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
+            pad=[0, 1, 2])
+        n.pool = L.Pooling(
+            n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
+        n.deconv = L.Deconvolution(
+            n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertTrue(len(a) == len(b))
+        self.assertTrue(np.all(a == 1))
+        self.assertEquals(b[0] - 1, b[1])
+        self.assertEquals(b[1] - 1, b[2])
+
+    def test_crop_of_crop(self):
+        """
+        Map coordinates through Crop layer:
+        crop an already-cropped output to the input and check change in offset.
+        """
+        n = coord_net_spec()
+        offset = random.randint(0, 10)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        n.crop = L.Crop(n.deconv, n.data, axis=2, offset=offset)
+        ax_crop, a_crop, b_crop = coord_map_from_to(n.crop, n.data)
+        self.assertEquals(ax, ax_crop)
+        self.assertEquals(a, a_crop)
+        self.assertEquals(b + offset, b_crop)
+
+    def test_crop_helper(self):
+        """
+        Define Crop layer by crop().
+        """
+        n = coord_net_spec()
+        crop(n.deconv, n.data)
+
+    def test_catch_unconnected(self):
+        """
+        Catch mapping spatially unconnected tops.
+        """
+        n = coord_net_spec()
+        n.ip = L.InnerProduct(n.deconv, num_output=10)
+        with self.assertRaises(RuntimeError):
+            coord_map_from_to(n.ip, n.data)
+
+    def test_catch_scale_mismatch(self):
+        """
+        Catch incompatible scales, such as when the top to be cropped
+        is mapped to a differently strided reference top.
+        """
+        n = coord_net_spec(pool=3, dstride=2)  # pool 3x but deconv 2x
+        with self.assertRaises(AssertionError):
+            crop(n.deconv, n.data)
+
+    def test_catch_negative_crop(self):
+        """
+        Catch impossible offsets, such as when the top to be cropped
+        is mapped to a larger reference top.
+        """
+        n = coord_net_spec(dpad=10)  # make output smaller than input
+        with self.assertRaises(AssertionError):
+            crop(n.deconv, n.data)

From b9ea0267851ccc7f782327408fe7953ba0f13c53 Mon Sep 17 00:00:00 2001
From: Jun Shi <junshi@yahoo-inc.com>
Date: Fri, 22 Jan 2016 09:50:31 -0800
Subject: [PATCH 1730/2053] add check and find GPU device utilities

---
 include/caffe/common.hpp |  5 +++++
 src/caffe/common.cpp     | 42 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 47 insertions(+)

diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 6b902a42e2d..3c6a076ec2f 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -153,6 +153,11 @@ class Caffe {
   static void SetDevice(const int device_id);
   // Prints the current GPU status.
   static void DeviceQuery();
+  // Check if specified device is available
+  static bool CheckDevice(const int device_id);
+  // Search from start_id to the highest possible device ordinal,
+  // return the ordinal of the first available device.
+  static int FindDevice(const int start_id = 0);
   // Parallel training info
   inline static int solver_count() { return Get().solver_count_; }
   inline static void set_solver_count(int val) { Get().solver_count_ = val; }
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 299d67d4bec..dee681654aa 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -70,6 +70,15 @@ void Caffe::DeviceQuery() {
   NO_GPU;
 }
 
+bool Caffe::CheckDevice(const int device_id) {
+  NO_GPU;
+  return false;
+}
+
+int Caffe::FindDevice(const int start_id) {
+  NO_GPU;
+  return -1;
+}
 
 class Caffe::RNG::Generator {
  public:
@@ -192,6 +201,39 @@ void Caffe::DeviceQuery() {
   return;
 }
 
+bool Caffe::CheckDevice(const int device_id) {
+  // This function checks the availability of GPU #device_id.
+  // It attempts to create a context on the device by calling cudaFree(0).
+  // cudaSetDevice() alone is not sufficient to check the availability.
+  // It lazily records device_id, however, does not initialize a
+  // context. So it does not know if the host thread has the permission to use
+  // the device or not.
+  //
+  // In a shared environment where the devices are set to EXCLUSIVE_PROCESS
+  // or EXCLUSIVE_THREAD mode, cudaSetDevice() returns cudaSuccess
+  // even if the device is exclusively occupied by another process or thread.
+  // Cuda operations that initialize the context are needed to check
+  // the permission. cudaFree(0) is one of those with no side effect,
+  // except the context initialization.
+  bool r = ((cudaSuccess == cudaSetDevice(device_id)) &&
+            (cudaSuccess == cudaFree(0)));
+  // reset any error that may have occurred.
+  cudaGetLastError();
+  return r;
+}
+
+int Caffe::FindDevice(const int start_id) {
+  // This function finds the first available device by checking devices with
+  // ordinal from start_id to the highest available value. In the
+  // EXCLUSIVE_PROCESS or EXCLUSIVE_THREAD mode, if it succeeds, it also
+  // claims the device due to the initialization of the context.
+  int count = 0;
+  CUDA_CHECK(cudaGetDeviceCount(&count));
+  for (int i = start_id; i < count; i++) {
+    if (CheckDevice(i)) return i;
+  }
+  return -1;
+}
 
 class Caffe::RNG::Generator {
  public:

From 64e78bdc76b8cabdf4282506438ab2d7f321adf3 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Sat, 27 Dec 2014 01:44:36 -0800
Subject: [PATCH 1731/2053] add CropLayer: crop blob to another blob's
 dimensions with offsets

configure offset(s) through proto definition.
---
 include/caffe/layers/crop_layer.hpp | 49 ++++++++++++++++++
 src/caffe/layers/crop_layer.cpp     | 78 +++++++++++++++++++++++++++++
 src/caffe/layers/crop_layer.cu      | 60 ++++++++++++++++++++++
 src/caffe/proto/caffe.proto         | 10 +++-
 4 files changed, 196 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/crop_layer.hpp
 create mode 100644 src/caffe/layers/crop_layer.cpp
 create mode 100644 src/caffe/layers/crop_layer.cu

diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
new file mode 100644
index 00000000000..bab290718e0
--- /dev/null
+++ b/include/caffe/layers/crop_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_CROP_LAYER_HPP_
+#define CAFFE_CROP_LAYER_HPP_
+
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes a Blob and crop it along either the width or height dimension,
+ *        outputting a cropped Blob.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+
+template <typename Dtype>
+class CropLayer : public Layer<Dtype> {
+ public:
+  explicit CropLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Crop"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int crop_h_, crop_w_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CROP_LAYER_HPP_
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
new file mode 100644
index 00000000000..76409bd7408
--- /dev/null
+++ b/src/caffe/layers/crop_layer.cpp
@@ -0,0 +1,78 @@
+#include <algorithm>
+#include <map>
+#include <set>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/crop_layer.hpp"
+#include "caffe/net.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const CropParameter& param = this->layer_param_.crop_param();
+  CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
+  CHECK_EQ(bottom[0]->num_axes(), 4) << "Only works with 4D blobs.";
+  CHECK_EQ(bottom[1]->num_axes(), 4) << "Only works with 4D blobs.";
+  crop_h_ = param.offset_height();
+  crop_w_ = param.offset_width();
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  // Check that the image we are cropping minus the margin is bigger than the
+  // destination image.
+  CHECK_GT(bottom[0]->height()-crop_h_, bottom[1]->height())
+      << "invalid offset";
+  CHECK_GT(bottom[0]->width()-crop_w_, bottom[1]->width()) << "invalid offset";
+  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[1]->height(),
+      bottom[1]->width());
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int n = 0; n < top[0]->num(); ++n) {
+    for (int c = 0; c < top[0]->channels(); ++c) {
+      for (int h = 0; h < top[0]->height(); ++h) {
+        caffe_copy(top[0]->width(),
+            bottom_data + bottom[0]->offset(n, c, crop_h_ + h, crop_w_),
+            top_data + top[0]->offset(n, c, h));
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  if (propagate_down[0]) {
+    caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+    for (int n = 0; n < top[0]->num(); ++n) {
+      for (int c = 0; c < top[0]->channels(); ++c) {
+        for (int h = 0; h < top[0]->height(); ++h) {
+          caffe_copy(top[0]->width(),
+              top_diff + top[0]->offset(n, c, h),
+              bottom_diff + bottom[0]->offset(n, c, crop_h_ + h, crop_w_));
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(CropLayer);
+#endif
+
+INSTANTIATE_CLASS(CropLayer);
+REGISTER_LAYER_CLASS(Crop);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
new file mode 100644
index 00000000000..262f5fa8483
--- /dev/null
+++ b/src/caffe/layers/crop_layer.cu
@@ -0,0 +1,60 @@
+#include <vector>
+
+#include "caffe/layers/crop_layer.hpp"
+
+namespace caffe {
+
+// Copy (one line per thread) from one array to another, with arbitrary
+// strides in the last two dimensions.
+template <typename Dtype>
+__global__ void copy_kernel(const int n, const int height, const int width,
+    const int src_outer_stride, const int src_inner_stride,
+    const int dest_outer_stride, const int dest_inner_stride,
+    const Dtype* src, Dtype* dest) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int src_start = index / height * src_outer_stride
+                  + index % height * src_inner_stride;
+    int dest_start = index / height * dest_outer_stride
+                   + index % height * dest_inner_stride;
+    for (int i = 0; i < width; ++i) {
+      dest[dest_start + i] = src[src_start + i];
+    }
+  }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int lines = top[0]->count() / top[0]->width();
+
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+      lines, top[0]->height(), top[0]->width(),
+      bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
+      top[0]->height() * top[0]->width(), top[0]->width(),
+      bottom_data + bottom[0]->offset(0, 0, crop_h_, crop_w_), top_data);
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int lines = top[0]->count() / top[0]->width();
+
+  if (propagate_down[0]) {
+    caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+        lines, top[0]->height(), top[0]->width(),
+        top[0]->height() * top[0]->width(), top[0]->width(),
+        bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
+        top_diff, bottom_diff + bottom[0]->offset(0, 0, crop_h_, crop_w_));
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CropLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 3b27bbd94d2..ace1a418592 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 144 (last added: input_param)
+// LayerParameter next available layer-specific ID: 145 (last added: crop_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -360,6 +360,7 @@ message LayerParameter {
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
+  optional CropParameter crop_param = 144;
   optional DataParameter data_param = 107;
   optional DropoutParameter dropout_param = 108;
   optional DummyDataParameter dummy_data_param = 109;
@@ -598,6 +599,13 @@ message ConvolutionParameter {
   optional bool force_nd_im2col = 17 [default = false];
 }
 
+message CropParameter {
+  // Assumes standard dimensions: ( N,C,H,W )
+  // This could possibly be extended to  use "optional BlobShape offsets"
+  optional uint32 offset_height = 1[default = 0];
+  optional uint32 offset_width = 2[default = 0];
+}
+
 message DataParameter {
   enum DB {
     LEVELDB = 0;

From 952fd17e52b24736f2644c32c249538241b34474 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Tue, 19 Jan 2016 18:35:04 +0000
Subject: [PATCH 1732/2053] Extend Crop to N-D, changed CropParameter.

---
 include/caffe/layers/crop_layer.hpp |  22 ++++-
 src/caffe/layers/crop_layer.cpp     | 124 +++++++++++++++++++++-------
 src/caffe/layers/crop_layer.cu      |  97 ++++++++++++++++++----
 src/caffe/proto/caffe.proto         |  19 +++--
 4 files changed, 210 insertions(+), 52 deletions(-)

diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
index bab290718e0..f84bab1ec49 100644
--- a/include/caffe/layers/crop_layer.hpp
+++ b/include/caffe/layers/crop_layer.hpp
@@ -41,9 +41,27 @@ class CropLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  int crop_h_, crop_w_;
-};
+  vector<int> offsets;
+
+ private:
+  void crop_copy(const vector<Blob<Dtype>*>& bottom,
+               const vector<Blob<Dtype>*>& top,
+               const vector<int>& offsets,
+               vector<int> indices,
+               int cur_dim,
+               const Dtype* src_data,
+               Dtype* dest_data,
+               bool is_forward);
 
+  void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+                const vector<Blob<Dtype>*>& top,
+                const vector<int>& offsets,
+                vector<int> indices,
+                int cur_dim,
+                const Dtype* src_data,
+                Dtype* dest_data,
+                bool is_forward);
+};
 }  // namespace caffe
 
 #endif  // CAFFE_CROP_LAYER_HPP_
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index 76409bd7408..82729f17c9f 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -1,8 +1,10 @@
 #include <algorithm>
+#include <functional>
 #include <map>
 #include <set>
 #include <vector>
 
+
 #include "caffe/layer.hpp"
 #include "caffe/layers/crop_layer.hpp"
 #include "caffe/net.hpp"
@@ -13,40 +15,108 @@ namespace caffe {
 template <typename Dtype>
 void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  const CropParameter& param = this->layer_param_.crop_param();
   CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
-  CHECK_EQ(bottom[0]->num_axes(), 4) << "Only works with 4D blobs.";
-  CHECK_EQ(bottom[1]->num_axes(), 4) << "Only works with 4D blobs.";
-  crop_h_ = param.offset_height();
-  crop_w_ = param.offset_width();
+  // parameter setup moved to Reshape because it depends on size.
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // Check that the image we are cropping minus the margin is bigger than the
-  // destination image.
-  CHECK_GT(bottom[0]->height()-crop_h_, bottom[1]->height())
-      << "invalid offset";
-  CHECK_GT(bottom[0]->width()-crop_w_, bottom[1]->width()) << "invalid offset";
-  top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[1]->height(),
-      bottom[1]->width());
+  const CropParameter& param = this->layer_param_.crop_param();
+  // bottom[0] supplies the data
+  // bottom[1] supplies the size
+  int input_dim = bottom[0]->num_axes();
+  CHECK_LT(param.axis(), input_dim) << "crop axis bigger than input dim";
+  // initialize all offsets to 0
+  offsets = vector<int>(input_dim, 0);
+  // initialize new shape to bottom[0]
+  vector<int> new_shape(bottom[0]->shape());
+
+  if (param.offset_size() > 1) {
+    // the number of crop values specified must be equal to the number
+    // of dimensions following axis
+    CHECK_EQ(param.axis() + param.offset_size(), input_dim)
+      << "number of crop values specified must be equal to the number of "
+      << "dimensions following axis.";
+  }
+  // apply crops
+  for (int i = 0; i < input_dim; ++i) {
+    int crop_offset = 0;
+    int new_size    = bottom[0]->shape(i);
+    if (i >= param.axis() && param.offset_size() == 1) {
+      // if only one crop value is supplied, crop all dimensions after axis
+      // by this crop value
+      crop_offset = param.offset(0);
+      new_size = bottom[1]->shape(i);
+    } else if (i >= param.axis() && param.offset_size() > 1) {
+      // crop values specified must be equal to the number of dimensions
+      // following axis
+      crop_offset = param.offset(i - param.axis());
+      new_size = bottom[1]->shape(i);
+    }
+    // Check that the image we are cropping minus the margin is bigger
+    // than the destination image.
+    CHECK_GE(bottom[0]->shape(i) - crop_offset,
+             bottom[1]->shape(i))
+        << "invalid crop parameters in dimension: " << i;
+    // Now set new size and offsets
+    new_shape[i] = new_size;
+    offsets[i] = crop_offset;
+  }
+  top[0]->Reshape(new_shape);
+}
+
+// recursive copy function
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const vector<int>& offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 1 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursivley
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last dimensions, which is stored continously in memory
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      // prepare index vector reduced(red) and with offsets(off)
+      std::vector<int> ind_red(cur_dim, 0);
+      std::vector<int> ind_off(cur_dim+1, 0);
+      for (int j = 0; j < cur_dim; ++j) {
+          ind_red[j] = indices[j];
+          ind_off[j] = indices[j] + offsets[j];
+      }
+      ind_off[cur_dim] = offsets[cur_dim];
+      // do the copy
+      if (is_forward) {
+        caffe_copy(top[0]->shape(cur_dim),
+            src_data + bottom[0]->offset(ind_off),
+            dest_data + top[0]->offset(ind_red));
+      } else {
+        // in the backwards pass the src_data is top_diff
+        // and the dest_data is bottom_diff
+        caffe_copy(top[0]->shape(cur_dim),
+            src_data + top[0]->offset(ind_red),
+            dest_data + bottom[0]->offset(ind_off));
+      }
+    }
+  }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    for (int c = 0; c < top[0]->channels(); ++c) {
-      for (int h = 0; h < top[0]->height(); ++h) {
-        caffe_copy(top[0]->width(),
-            bottom_data + bottom[0]->offset(n, c, crop_h_ + h, crop_w_),
-            top_data + top[0]->offset(n, c, h));
-      }
-    }
-  }
+  crop_copy(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
 }
 
 template <typename Dtype>
@@ -54,17 +124,11 @@ void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+
   if (propagate_down[0]) {
     caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
-    for (int n = 0; n < top[0]->num(); ++n) {
-      for (int c = 0; c < top[0]->channels(); ++c) {
-        for (int h = 0; h < top[0]->height(); ++h) {
-          caffe_copy(top[0]->width(),
-              top_diff + top[0]->offset(n, c, h),
-              bottom_diff + bottom[0]->offset(n, c, crop_h_ + h, crop_w_));
-        }
-      }
-    }
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy(bottom, top, offsets, indices, 0, top_diff, bottom_diff, false);
   }
 }
 
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 262f5fa8483..7b832c0a0dc 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -22,19 +22,90 @@ __global__ void copy_kernel(const int n, const int height, const int width,
   }
 }
 
+// recursive copy function, this function is similar to crop_copy but loops
+// over all but the last two dimensions. It is implemented this way to allow
+// for ND cropping while still relying on a CUDA kernel for the innermost
+// two dimensions for performance reasons.
+// An alternative way to implement ND cropping relying more on the kernel
+// would require passing offsets to the kernel, which is a bit problematic
+// because it is of variable length. Since in the standard (N,C,W,H) case
+// N,C are usually not cropped a speedup could be achieved by not looping
+// the application of the copy_kernel around these dimensions.
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const vector<int>& offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 2 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursivley
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy_gpu(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last two dimensions, which are stored continously in memory
+    // With (N,C,H,W)
+    //      (0,1,2,3) cur_dim   -> H
+    //                cur_dim+1 -> W
+    const int lines = top[0]->shape(cur_dim);
+    const int height = top[0]->shape(cur_dim);
+    const int width = top[0]->shape(cur_dim+1);
+    std::vector<int> ind_off(cur_dim+2, 0);
+    for (int j = 0; j < cur_dim; ++j) {
+        ind_off[j] = indices[j] + offsets[j];
+    }
+    ind_off[cur_dim] = offsets[cur_dim];
+    ind_off[cur_dim+1] = offsets[cur_dim+1];
+    // Compute copy strides
+    const int src_outer_stride =
+        bottom[0]->shape(cur_dim)*bottom[0]->shape(cur_dim+1);
+    const int src_inner_stride = bottom[0]->shape(cur_dim+1);
+    const int dest_outer_stride =
+        top[0]->shape(cur_dim)*top[0]->shape(cur_dim+1);
+    const int dest_inner_stride = top[0]->shape(cur_dim+1);
+
+    if (is_forward) {
+      const Dtype* bottom_data = bottom[0]->gpu_data() +
+          bottom[0]->offset(ind_off);
+      Dtype* top_data = top[0]->mutable_gpu_data() +
+          top[0]->offset(indices);
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+          lines, height, width,
+          src_outer_stride, src_inner_stride,
+          dest_outer_stride, dest_inner_stride,
+          bottom_data, top_data);
+
+    } else {
+      const Dtype* top_diff = top[0]->gpu_diff() +
+          top[0]->offset(indices);
+      Dtype* bottom_diff = bottom[0]->mutable_gpu_diff() +
+          bottom[0]->offset(ind_off);
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+          lines, height, width,
+          dest_outer_stride, dest_inner_stride,
+          src_outer_stride, src_inner_stride,
+          top_diff, bottom_diff);
+    }
+  }
+}
+
 template <typename Dtype>
 void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
+  // This works because crop_copy uses caffe_copy which calls cudaMemcpy.
+  // My intuition is that calling this thousands of times is probably less
+  // efficient than writing a custom kernel.
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  const int lines = top[0]->count() / top[0]->width();
-
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-      lines, top[0]->height(), top[0]->width(),
-      bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
-      top[0]->height() * top[0]->width(), top[0]->width(),
-      bottom_data + bottom[0]->offset(0, 0, crop_h_, crop_w_), top_data);
+  crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
 }
 
 template <typename Dtype>
@@ -42,16 +113,12 @@ void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  const int lines = top[0]->count() / top[0]->width();
 
   if (propagate_down[0]) {
     caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
-    // NOLINT_NEXT_LINE(whitespace/operators)
-    copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-        lines, top[0]->height(), top[0]->width(),
-        top[0]->height() * top[0]->width(), top[0]->width(),
-        bottom[0]->height() * bottom[0]->width(), bottom[0]->width(),
-        top_diff, bottom_diff + bottom[0]->offset(0, 0, crop_h_, crop_w_));
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy_gpu(bottom, top, offsets, indices, 0, top_diff, bottom_diff,
+                  false);
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index ace1a418592..60d387a7de1 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -600,10 +600,19 @@ message ConvolutionParameter {
 }
 
 message CropParameter {
-  // Assumes standard dimensions: ( N,C,H,W )
-  // This could possibly be extended to  use "optional BlobShape offsets"
-  optional uint32 offset_height = 1[default = 0];
-  optional uint32 offset_width = 2[default = 0];
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop.
+  optional uint32 axis = 1 [default = 2];
+  repeated uint32 offset = 2;
 }
 
 message DataParameter {
@@ -680,7 +689,7 @@ message EltwiseParameter {
 // Message that stores parameters used by ELULayer
 message ELUParameter {
   // Described in:
-  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate 
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
   // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
   optional float alpha = 1 [default = 1];
 }

From ca9fa498fb76a83befe6cb299a71f7f779aeb5d9 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Mon, 29 Feb 2016 11:24:25 +0000
Subject: [PATCH 1733/2053] Crop: fixes, tests and negative axis indexing.

---
 include/caffe/layers/crop_layer.hpp |   4 +-
 src/caffe/layers/crop_layer.cpp     |  50 +++---
 src/caffe/layers/crop_layer.cu      |   3 -
 src/caffe/proto/caffe.proto         |   6 +-
 src/caffe/test/test_crop_layer.cpp  | 228 ++++++++++++++++++++++++++++
 5 files changed, 263 insertions(+), 28 deletions(-)
 create mode 100644 src/caffe/test/test_crop_layer.cpp

diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
index f84bab1ec49..5c605b2ae9e 100644
--- a/include/caffe/layers/crop_layer.hpp
+++ b/include/caffe/layers/crop_layer.hpp
@@ -11,8 +11,8 @@
 namespace caffe {
 
 /**
- * @brief Takes a Blob and crop it along either the width or height dimension,
- *        outputting a cropped Blob.
+ * @brief Takes a Blob and crop it, to the shape specified by the second input
+ *  Blob, across all dimensions after the specified axis.
  *
  * TODO(dox): thorough documentation for Forward, Backward, and proto params.
  */
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index 82729f17c9f..e81bdd732f3 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -15,44 +15,52 @@ namespace caffe {
 template <typename Dtype>
 void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  // All logic that depends only on the number of dimensions is here,
+  // the rest is in Reshape because it depends on Blob size.
+  // bottom[0] supplies the data
+  // bottom[1] supplies the size
+  const CropParameter& param = this->layer_param_.crop_param();
   CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
-  // parameter setup moved to Reshape because it depends on size.
+  int input_dim = bottom[0]->num_axes();
+  const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_LT(start_axis, input_dim) << "crop axis bigger than input dim";
+  if (param.offset_size() > 1) {
+    // the number of crop values specified must be equal to the number
+    // of dimensions following axis
+    CHECK_EQ(start_axis + param.offset_size(), input_dim)
+      << "number of offset values specified must be equal to the number of "
+      << "dimensions following axis.";
+  }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const CropParameter& param = this->layer_param_.crop_param();
-  // bottom[0] supplies the data
-  // bottom[1] supplies the size
   int input_dim = bottom[0]->num_axes();
-  CHECK_LT(param.axis(), input_dim) << "crop axis bigger than input dim";
+  const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+
   // initialize all offsets to 0
   offsets = vector<int>(input_dim, 0);
   // initialize new shape to bottom[0]
   vector<int> new_shape(bottom[0]->shape());
 
-  if (param.offset_size() > 1) {
-    // the number of crop values specified must be equal to the number
-    // of dimensions following axis
-    CHECK_EQ(param.axis() + param.offset_size(), input_dim)
-      << "number of crop values specified must be equal to the number of "
-      << "dimensions following axis.";
-  }
   // apply crops
   for (int i = 0; i < input_dim; ++i) {
     int crop_offset = 0;
     int new_size    = bottom[0]->shape(i);
-    if (i >= param.axis() && param.offset_size() == 1) {
-      // if only one crop value is supplied, crop all dimensions after axis
-      // by this crop value
-      crop_offset = param.offset(0);
-      new_size = bottom[1]->shape(i);
-    } else if (i >= param.axis() && param.offset_size() > 1) {
-      // crop values specified must be equal to the number of dimensions
-      // following axis
-      crop_offset = param.offset(i - param.axis());
+    if (i >= start_axis) {
       new_size = bottom[1]->shape(i);
+
+      if (param.offset_size() == 1) {
+        // if only one crop value is supplied, crop all dimensions after axis
+        // by this crop value
+        crop_offset = param.offset(0);
+      } else if (param.offset_size() > 1) {
+        // crop values specified must be equal to the number of dimensions
+        // following axis
+        crop_offset = param.offset(i - start_axis);
+      }
     }
     // Check that the image we are cropping minus the margin is bigger
     // than the destination image.
@@ -77,7 +85,7 @@ void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
              Dtype* dest_data,
              bool is_forward) {
   if (cur_dim + 1 < top[0]->num_axes()) {
-    // We are not yet at the final dimension, call copy recursivley
+    // We are not yet at the final dimension, call copy recursively
     for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
       indices[cur_dim] = i;
       crop_copy(bottom, top, offsets, indices, cur_dim+1,
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 7b832c0a0dc..9ed8f7cce57 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -100,9 +100,6 @@ template <typename Dtype>
 void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   std::vector<int> indices(top[0]->num_axes(), 0);
-  // This works because crop_copy uses caffe_copy which calls cudaMemcpy.
-  // My intuition is that calling this thousands of times is probably less
-  // efficient than writing a custom kernel.
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 60d387a7de1..6900bb71482 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -610,8 +610,10 @@ message CropParameter {
   // If only one `offset` is set, then all dimensions are offset by this amount.
   // Otherwise, the number of offsets must equal the number of cropped axes to
   // shift the crop in each dimension accordingly.
-  // Note: standard dimensions are N,C,H,W so the default is a spatial crop.
-  optional uint32 axis = 1 [default = 2];
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+  // and `axis` may be negative to index from the end (e.g., -1 for the last
+  // axis).
+  optional int32 axis = 1 [default = 2];
   repeated uint32 offset = 2;
 }
 
diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp
new file mode 100644
index 00000000000..ba962e4c6f1
--- /dev/null
+++ b/src/caffe/test/test_crop_layer.cpp
@@ -0,0 +1,228 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/crop_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class CropLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  CropLayerTest()
+      : blob_bottom_0_(new Blob<Dtype>(2, 5, 6, 5)),
+        blob_bottom_1_(new Blob<Dtype>(2, 4, 5, 3)),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    for (int i = 0; i < this->blob_bottom_0_->count(); ++i) {
+      this->blob_bottom_0_->mutable_cpu_data()[i] = i;
+    }
+
+
+    blob_bottom_vec_0_.push_back(blob_bottom_0_);
+    blob_bottom_vec_0_.push_back(blob_bottom_1_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~CropLayerTest() {
+    delete blob_bottom_0_; delete blob_bottom_1_;
+    delete blob_top_;
+  }
+
+  Blob<Dtype>* const blob_bottom_0_;
+  Blob<Dtype>* const blob_bottom_1_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_0_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+
+TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(CropLayerTest, TestSetupShapeAll) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+
+  // Crop all dimensions
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeDefault) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop last two dimensions, axis is 2 by default
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 2) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop last dimension by negative indexing
+  layer_param.mutable_crop_param()->set_axis(-1);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 3) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+
+TYPED_TEST(CropLayerTest, TestForwardNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c, h, w));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestForwardNumOffsets) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  layer_param.mutable_crop_param()->add_offset(0);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n+0, c+1, h+1, w+2));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestGradientNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CropLayer<Dtype> layer(layer_param);
+
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+
+  // Copy top data into diff
+  caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+
+  // Do backward pass
+  vector<bool> propagate_down(2, true);
+  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_);
+
+
+  // Check results
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w),
+                      this->blob_bottom_0_->data_at(n, c, h, w));
+          } else {
+            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0);
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestGradientNumOffset) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  layer_param.mutable_crop_param()->add_offset(0);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+
+  // Copy top data into diff
+  caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+
+  // Do backward pass
+  vector<bool> propagate_down(2, true);
+  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_);
+
+
+  // Check results
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( 0 <= n && n < 0 + this->blob_top_->shape(0) &&
+              1 <= c && c < 1 + this->blob_top_->shape(1) &&
+              1 <= h && h < 1 + this->blob_top_->shape(2) &&
+              2 <= w && w < 2 + this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w),
+                      this->blob_bottom_0_->data_at(n, c, h, w));
+          } else {
+            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0);
+          }
+        }
+      }
+    }
+  }
+}
+
+}  // namespace caffe

From e03a2873a0fdfd871b11a86d7feed45fb71013b0 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 4 Mar 2016 02:20:13 -0800
Subject: [PATCH 1734/2053] Crop: more tests and test tuning.

Changes are:
  reduce test blob dims for speed
  use standard Gaussian filler,
  polish formatting and rename tests,
  test HW crop and 5D crop,
  standard gradient checks
---
 src/caffe/test/test_crop_layer.cpp | 183 +++++++++++++++++------------
 1 file changed, 110 insertions(+), 73 deletions(-)

diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp
index ba962e4c6f1..45f24e2ee8d 100644
--- a/src/caffe/test/test_crop_layer.cpp
+++ b/src/caffe/test/test_crop_layer.cpp
@@ -18,18 +18,18 @@ class CropLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   CropLayerTest()
-      : blob_bottom_0_(new Blob<Dtype>(2, 5, 6, 5)),
-        blob_bottom_1_(new Blob<Dtype>(2, 4, 5, 3)),
+      : blob_bottom_0_(new Blob<Dtype>(2, 4, 5, 4)),
+        blob_bottom_1_(new Blob<Dtype>(2, 3, 4, 2)),
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     // fill the values
-    for (int i = 0; i < this->blob_bottom_0_->count(); ++i) {
-      this->blob_bottom_0_->mutable_cpu_data()[i] = i;
-    }
-
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_0_);
+    filler.Fill(this->blob_bottom_1_);
 
-    blob_bottom_vec_0_.push_back(blob_bottom_0_);
-    blob_bottom_vec_0_.push_back(blob_bottom_1_);
+    blob_bottom_vec_.push_back(blob_bottom_0_);
+    blob_bottom_vec_.push_back(blob_bottom_1_);
     blob_top_vec_.push_back(blob_top_);
   }
 
@@ -41,7 +41,7 @@ class CropLayerTest : public MultiDeviceTest<TypeParam> {
   Blob<Dtype>* const blob_bottom_0_;
   Blob<Dtype>* const blob_bottom_1_;
   Blob<Dtype>* const blob_top_;
-  vector<Blob<Dtype>*> blob_bottom_vec_0_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
@@ -51,11 +51,10 @@ TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices);
 TYPED_TEST(CropLayerTest, TestSetupShapeAll) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-
   // Crop all dimensions
   layer_param.mutable_crop_param()->set_axis(0);
   CropLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
     EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
   }
@@ -66,7 +65,7 @@ TYPED_TEST(CropLayerTest, TestSetupShapeDefault) {
   LayerParameter layer_param;
   // Crop last two dimensions, axis is 2 by default
   CropLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
     if (i < 2) {
       EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
@@ -82,7 +81,7 @@ TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
   // Crop last dimension by negative indexing
   layer_param.mutable_crop_param()->set_axis(-1);
   CropLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
     if (i < 3) {
       EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
@@ -92,15 +91,13 @@ TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
   }
 }
 
-
-TYPED_TEST(CropLayerTest, TestForwardNum) {
+TYPED_TEST(CropLayerTest, TestCropAll) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_crop_param()->set_axis(0);
-
   CropLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
     for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
@@ -118,7 +115,7 @@ TYPED_TEST(CropLayerTest, TestForwardNum) {
   }
 }
 
-TYPED_TEST(CropLayerTest, TestForwardNumOffsets) {
+TYPED_TEST(CropLayerTest, TestCropAllOffset) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_crop_param()->set_axis(0);
@@ -127,8 +124,8 @@ TYPED_TEST(CropLayerTest, TestForwardNumOffsets) {
   layer_param.mutable_crop_param()->add_offset(1);
   layer_param.mutable_crop_param()->add_offset(2);
   CropLayer<Dtype> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
     for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
@@ -138,7 +135,7 @@ TYPED_TEST(CropLayerTest, TestForwardNumOffsets) {
               h < this->blob_top_->shape(2) &&
               w < this->blob_top_->shape(3) ) {
             EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
-                this->blob_bottom_0_->data_at(n+0, c+1, h+1, w+2));
+                this->blob_bottom_0_->data_at(n, c+1, h+1, w+2));
           }
         }
       }
@@ -146,36 +143,25 @@ TYPED_TEST(CropLayerTest, TestForwardNumOffsets) {
   }
 }
 
-TYPED_TEST(CropLayerTest, TestGradientNum) {
+TYPED_TEST(CropLayerTest, TestCropHW) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
   CropLayer<Dtype> layer(layer_param);
-
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
-
-  // Copy top data into diff
-  caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(),
-             this->blob_top_->mutable_cpu_diff());
-
-  // Do backward pass
-  vector<bool> propagate_down(2, true);
-  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_);
-
-
-  // Check results
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
     for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
         for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
-          if ( n < this->blob_top_->shape(0) &&
+          if (n < this->blob_top_->shape(0) &&
               c < this->blob_top_->shape(1) &&
               h < this->blob_top_->shape(2) &&
-              w < this->blob_top_->shape(3) ) {
-            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w),
-                      this->blob_bottom_0_->data_at(n, c, h, w));
-          } else {
-            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0);
+              w < this->blob_top_->shape(3)) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c, h+1, w+2));
           }
         }
       }
@@ -183,41 +169,50 @@ TYPED_TEST(CropLayerTest, TestGradientNum) {
   }
 }
 
-TYPED_TEST(CropLayerTest, TestGradientNumOffset) {
+TYPED_TEST(CropLayerTest, TestCrop5D) {
   typedef typename TypeParam::Dtype Dtype;
+  // Add dimension to each bottom for >4D check
+  vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+  vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+  bottom_0_shape.push_back(2);
+  bottom_1_shape.push_back(1);
+  this->blob_bottom_0_->Reshape(bottom_0_shape);
+  this->blob_bottom_1_->Reshape(bottom_1_shape);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_0_);
+  filler.Fill(this->blob_bottom_1_);
+  // Make layer
   LayerParameter layer_param;
-  layer_param.mutable_crop_param()->set_axis(0);
-  layer_param.mutable_crop_param()->add_offset(0);
-  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->set_axis(2);
   layer_param.mutable_crop_param()->add_offset(1);
   layer_param.mutable_crop_param()->add_offset(2);
+  layer_param.mutable_crop_param()->add_offset(0);
   CropLayer<Dtype> layer(layer_param);
-
-  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
-
-  // Copy top data into diff
-  caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(),
-             this->blob_top_->mutable_cpu_diff());
-
-  // Do backward pass
-  vector<bool> propagate_down(2, true);
-  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_);
-
-
-  // Check results
-  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
-      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
-        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
-          if ( 0 <= n && n < 0 + this->blob_top_->shape(0) &&
-              1 <= c && c < 1 + this->blob_top_->shape(1) &&
-              1 <= h && h < 1 + this->blob_top_->shape(2) &&
-              2 <= w && w < 2 + this->blob_top_->shape(3) ) {
-            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w),
-                      this->blob_bottom_0_->data_at(n, c, h, w));
-          } else {
-            EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> bottom_idx = vector<int>(5, 0);
+  vector<int> top_idx = vector<int>(5, 0);
+  for (int n = 0; n < this->blob_bottom_0_->shape(0); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->shape(1); ++c) {
+      for (int z = 0; z < this->blob_bottom_0_->shape(2); ++z) {
+        for (int h = 0; h < this->blob_bottom_0_->shape(3); ++h) {
+          for (int w = 0; w < this->blob_bottom_0_->shape(4); ++w) {
+            if (n < this->blob_top_->shape(0) &&
+                c < this->blob_top_->shape(1) &&
+                z < this->blob_top_->shape(2) &&
+                h < this->blob_top_->shape(3) &&
+                w < this->blob_top_->shape(4)) {
+              bottom_idx[0] = top_idx[0] = n;
+              bottom_idx[1] = top_idx[1] = c;
+              bottom_idx[2] = z;
+              bottom_idx[3] = h;
+              bottom_idx[4] = top_idx[4] = w;
+              top_idx[2] = z+1;
+              top_idx[3] = h+2;
+              EXPECT_EQ(this->blob_top_->data_at(bottom_idx),
+                  this->blob_bottom_0_->data_at(top_idx));
+            }
           }
         }
       }
@@ -225,4 +220,46 @@ TYPED_TEST(CropLayerTest, TestGradientNumOffset) {
   }
 }
 
+TYPED_TEST(CropLayerTest, TestCropAllGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCropHWGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5DGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  layer_param.mutable_crop_param()->add_offset(0);
+  CropLayer<Dtype> layer(layer_param);
+  // Add dimension to each bottom for >4D check
+  vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+  vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+  bottom_0_shape.push_back(2);
+  bottom_1_shape.push_back(1);
+  this->blob_bottom_0_->Reshape(bottom_0_shape);
+  this->blob_bottom_1_->Reshape(bottom_1_shape);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 }  // namespace caffe

From 01528918c707df82e5910bea0270d7987db5abd8 Mon Sep 17 00:00:00 2001
From: Jun Shi <junshi@yahoo-inc.com>
Date: Fri, 22 Jan 2016 09:58:37 -0800
Subject: [PATCH 1735/2053] split p2psync::run()

---
 include/caffe/parallel.hpp                    |  5 ++++-
 src/caffe/parallel.cpp                        | 20 ++++++++++++-------
 src/caffe/test/test_gradient_based_solver.cpp |  2 +-
 tools/caffe.cpp                               |  2 +-
 4 files changed, 19 insertions(+), 10 deletions(-)

diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp
index 85fc2b55984..6c496c884e3 100644
--- a/include/caffe/parallel.hpp
+++ b/include/caffe/parallel.hpp
@@ -93,7 +93,10 @@ class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
     return solver_;
   }
 
-  void run(const vector<int>& gpus);
+  void Run(const vector<int>& gpus);
+  void Prepare(const vector<int>& gpus,
+               vector<shared_ptr<P2PSync<Dtype> > >* syncs);
+  inline const int initial_iter() const { return initial_iter_; }
 
  protected:
   void on_start();
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 62f5d738593..5bc41c6a6e5 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -380,7 +380,8 @@ void P2PSync<Dtype>::on_gradients_ready() {
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::run(const vector<int>& gpus) {
+void P2PSync<Dtype>::Prepare(const vector<int>& gpus,
+            vector<shared_ptr<P2PSync<Dtype> > >* syncs) {
   // Pair devices for map-reduce synchronization
   vector<DevicePair> pairs;
   DevicePair::compute(gpus, &pairs);
@@ -391,15 +392,14 @@ void P2PSync<Dtype>::run(const vector<int>& gpus) {
   LOG(INFO)<< "GPUs pairs " << s.str();
 
   SolverParameter param(solver_->param());
-  vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
 
   // Build the GPU tree by finding the parent for each solver
   for (int attempts = 0; attempts < pairs.size(); ++attempts) {
     for (int i = 1; i < pairs.size(); ++i) {
-      if (!syncs[i].get()) {
+      if (!syncs->at(i).get()) {
         P2PSync<Dtype>* parent = NULL;
-        for (int j = 0; j < syncs.size(); ++j) {
-          P2PSync<Dtype>* sync = j == 0 ? this : syncs[j].get();
+        for (int j = 0; j < syncs->size(); ++j) {
+          P2PSync<Dtype>* sync = j == 0 ? this : syncs->at(j).get();
           if (sync) {
             const SolverParameter& p = sync->solver()->param();
             if (p.device_id() == pairs[i].parent()) {
@@ -409,12 +409,18 @@ void P2PSync<Dtype>::run(const vector<int>& gpus) {
         }
         if (parent) {
           param.set_device_id(pairs[i].device());
-          syncs[i].reset(new P2PSync<Dtype>(solver_, parent, param));
-          parent->children_.push_back((P2PSync<Dtype>*) syncs[i].get());
+          syncs->at(i).reset(new P2PSync<Dtype>(solver_, parent, param));
+          parent->children_.push_back((P2PSync<Dtype>*) syncs->at(i).get());
         }
       }
     }
   }
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::Run(const vector<int>& gpus) {
+  vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
+  Prepare(gpus, &syncs);
 
   LOG(INFO)<< "Starting Optimization";
 
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 09ec3a7e918..975a8f0f88a 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -204,7 +204,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       Caffe::set_solver_count(gpus.size());
       this->sync_.reset(new P2PSync<Dtype>(
           this->solver_, NULL, this->solver_->param()));
-      this->sync_->run(gpus);
+      this->sync_->Run(gpus);
       Caffe::set_solver_count(1);
     }
     if (snapshot) {
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 95b2f82c4be..5d9331f0c22 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -214,7 +214,7 @@ int train() {
 
   if (gpus.size() > 1) {
     caffe::P2PSync<float> sync(solver, NULL, solver->param());
-    sync.run(gpus);
+    sync.Run(gpus);
   } else {
     LOG(INFO) << "Starting Optimization";
     solver->Solve();

From a53fa5150c90e425fc88552dfc02568da325ab25 Mon Sep 17 00:00:00 2001
From: Andy Feng <afeng@yahoo-inc.com>
Date: Wed, 9 Mar 2016 18:39:31 +0000
Subject: [PATCH 1736/2053] [build] travis: remove existing conda dir

there seems to be a caching issue at the moment;
this is a temporary fix for #3786
---
 scripts/travis/travis_install.sh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index ca8c410cfbe..091e92431f0 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -60,6 +60,8 @@ rm -f $LMDB_FILE
 # Install the Python runtime dependencies via miniconda (this is much faster
 # than using pip for everything).
 export PATH=$CONDA_DIR/bin:$PATH
+# clear any cached conda (see #3786)
+rm -rf $CONDA_DIR
 if [ ! -d $CONDA_DIR ]; then
   if [ "$PYTHON_VERSION" -eq "3" ]; then
     wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh

From 542d216bb28343111e6b7df2c24824c3f90e435a Mon Sep 17 00:00:00 2001
From: JacekR <jreniecki@gmail.com>
Date: Tue, 15 Mar 2016 10:43:34 +0100
Subject: [PATCH 1737/2053] Update Makefile: Changed MKL_DIR to MKLROOT

MKLROOT variable is set by MKL scripts, so it also should be used in Makefile.
---
 Makefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 2f81aca84e7..5424c3a1858 100644
--- a/Makefile
+++ b/Makefile
@@ -364,9 +364,9 @@ ifeq ($(BLAS), mkl)
 	# MKL
 	LIBRARIES += mkl_rt
 	COMMON_FLAGS += -DUSE_MKL
-	MKL_DIR ?= /opt/intel/mkl
-	BLAS_INCLUDE ?= $(MKL_DIR)/include
-	BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
+	MKLROOT ?= /opt/intel/mkl
+	BLAS_INCLUDE ?= $(MKLROOT)/include
+	BLAS_LIB ?= $(MKLROOT)/lib $(MKLROOT)/lib/intel64
 else ifeq ($(BLAS), open)
 	# OpenBLAS
 	LIBRARIES += openblas

From 337b07589f4e44761bdb9ef4c242f83ca40c9da5 Mon Sep 17 00:00:00 2001
From: shai <shai@magisto.com>
Date: Mon, 21 Mar 2016 09:08:02 +0200
Subject: [PATCH 1738/2053] upgrading InfogainLoss layer: (1) incorporating
 Softmax layer to make the gradeint computation robust, much like
 SoftmaxWithLoss layer (see: http://stackoverflow.com/a/34917052/1714410 for
 more information). (2) supporting loss along axis

---
 include/caffe/layers/infogain_loss_layer.hpp |  35 ++++
 src/caffe/layers/infogain_loss_layer.cpp     | 172 ++++++++++++++++---
 src/caffe/proto/caffe.proto                  |   1 +
 src/caffe/test/test_infogain_loss_layer.cpp  |  83 ++++++++-
 4 files changed, 257 insertions(+), 34 deletions(-)

diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp
index 633f339a28e..edecde829ad 100644
--- a/include/caffe/layers/infogain_loss_layer.hpp
+++ b/include/caffe/layers/infogain_loss_layer.hpp
@@ -8,6 +8,7 @@
 #include "caffe/proto/caffe.pb.h"
 
 #include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
 
 namespace caffe {
 
@@ -60,6 +61,12 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual inline int MinBottomBlobs() const { return 2; }
   virtual inline int MaxBottomBlobs() const { return 3; }
 
+  // InfogainLossLayer computes softmax prob internally.
+  // optional second "top" outputs the softmax prob
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
   virtual inline const char* type() const { return "InfogainLoss"; }
 
  protected:
@@ -102,7 +109,35 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
+  /// fill sum_rows_H_ according to matrix H
+  virtual void sum_rows_of_H(const Blob<Dtype>* H);
+
+  /// The internal SoftmaxLayer used to map predictions to a distribution.
+  shared_ptr<Layer<Dtype> > softmax_layer_;
+  /// prob stores the output probability predictions from the SoftmaxLayer.
+  Blob<Dtype> prob_;
+  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_top_vec_;
+
   Blob<Dtype> infogain_;
+  Blob<Dtype> sum_rows_H_;  // cache the row sums of H.
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// How to normalize the output loss.
+  LossParameter_NormalizationMode normalization_;
+
+  int infogain_axis_, outer_num_, inner_num_, num_labels_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index 624d3118124..3c3f460ec34 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -3,7 +3,8 @@
 #include <vector>
 
 #include "caffe/layers/infogain_loss_layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/util/io.hpp"  // for bolb reading of matrix H
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
@@ -11,6 +12,31 @@ template <typename Dtype>
 void InfogainLossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::LayerSetUp(bottom, top);
+  // internal softmax layer
+  LayerParameter softmax_layer_param(this->layer_param_);
+  SoftmaxParameter* softmax_param = softmax_layer_param.mutable_softmax_param();
+  softmax_param->set_axis(this->layer_param_.infogain_loss_param().axis());
+  softmax_layer_param.set_type("Softmax");
+  softmax_layer_param.clear_loss_weight();
+  softmax_layer_param.add_loss_weight(1);
+  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_layer_param);
+  softmax_bottom_vec_.clear();
+  softmax_bottom_vec_.push_back(bottom[0]);
+  softmax_top_vec_.clear();
+  softmax_top_vec_.push_back(&prob_);
+  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
+
+  // ignore label
+  has_ignore_label_ =
+    this->layer_param_.loss_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.loss_param().ignore_label();
+  }
+  // normalization
+  CHECK(!this->layer_param_.loss_param().has_normalize())
+    << "normalize is deprecated. use \"normalization\"";
+  normalization_ = this->layer_param_.loss_param().normalization();
+  // matrix H
   if (bottom.size() < 3) {
     CHECK(this->layer_param_.infogain_loss_param().has_source())
         << "Infogain matrix source must be specified.";
@@ -25,28 +51,86 @@ template <typename Dtype>
 void InfogainLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
+  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
+  infogain_axis_ =
+    bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.infogain_loss_param().axis());
+  outer_num_ = bottom[0]->count(0, infogain_axis_);
+  inner_num_ = bottom[0]->count(infogain_axis_ + 1);
+  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
+      << "Number of labels must match number of predictions; "
+      << "e.g., if infogain axis == 1 and prediction shape is (N, C, H, W), "
+      << "label count (number of labels) must be N*H*W, "
+      << "with integer values in {0, 1, ..., C-1}.";
+  num_labels_ = bottom[0]->shape(infogain_axis_);
   Blob<Dtype>* infogain = NULL;
   if (bottom.size() < 3) {
     infogain = &infogain_;
   } else {
     infogain = bottom[2];
   }
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  const int num = bottom[0]->num();
-  const int dim = bottom[0]->count() / num;
-  CHECK_EQ(infogain->num(), 1);
-  CHECK_EQ(infogain->channels(), 1);
-  CHECK_EQ(infogain->height(), dim);
-  CHECK_EQ(infogain->width(), dim);
+  CHECK_EQ(infogain->count(), num_labels_*num_labels_);
+  sum_rows_H_.Reshape(vector<int>(1, num_labels_));
+  if (bottom.size() == 2) {
+    // H is provided as a parameter and will not change. sum rows once
+    sum_rows_of_H(infogain);
+  }
+  if (top.size() >= 2) {
+    // softmax output
+    top[1]->ReshapeLike(*bottom[0]);
+  }
+}
+
+template <typename Dtype>
+Dtype InfogainLossLayer<Dtype>::get_normalizer(
+    LossParameter_NormalizationMode normalization_mode, int valid_count) {
+  Dtype normalizer;
+  switch (normalization_mode) {
+    case LossParameter_NormalizationMode_FULL:
+      normalizer = Dtype(outer_num_ * inner_num_);
+      break;
+    case LossParameter_NormalizationMode_VALID:
+      if (valid_count == -1) {
+        normalizer = Dtype(outer_num_ * inner_num_);
+      } else {
+        normalizer = Dtype(valid_count);
+      }
+      break;
+    case LossParameter_NormalizationMode_BATCH_SIZE:
+      normalizer = Dtype(outer_num_);
+      break;
+    case LossParameter_NormalizationMode_NONE:
+      normalizer = Dtype(1);
+      break;
+    default:
+      LOG(FATAL) << "Unknown normalization mode: "
+          << LossParameter_NormalizationMode_Name(normalization_mode);
+  }
+  // Some users will have no labels for some examples in order to 'turn off' a
+  // particular loss in a multi-task setup. The max prevents NaNs in that case.
+  return std::max(Dtype(1.0), normalizer);
 }
 
+template <typename Dtype>
+void InfogainLossLayer<Dtype>::sum_rows_of_H(const Blob<Dtype>* H) {
+  CHECK_EQ(H->count(), num_labels_*num_labels_)
+    << "H must be " << num_labels_ << "x" << num_labels_;
+  const Dtype* infogain_mat = H->cpu_data();
+  Dtype* sum = sum_rows_H_.mutable_cpu_data();
+  for ( int row = 0; row < num_labels_ ; row++ ) {
+    sum[row] = 0;
+    for ( int col = 0; col < num_labels_ ; col++ ) {
+      sum[row] += infogain_mat[row*num_labels_+col];
+    }
+  }
+}
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
+  // The forward pass computes the softmax prob values.
+  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
+  const Dtype* prob_data = prob_.cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   const Dtype* infogain_mat = NULL;
   if (bottom.size() < 3) {
@@ -54,17 +138,30 @@ void InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   } else {
     infogain_mat = bottom[2]->cpu_data();
   }
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  int count = 0;
   Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      loss -= infogain_mat[label * dim + j] * log(prob);
+  for (int i = 0; i < outer_num_; ++i) {
+    for (int j = 0; j < inner_num_; j++) {
+      const int label_value =
+        static_cast<int>(bottom_label[i * inner_num_ + j]);
+      if (has_ignore_label_ && label_value == ignore_label_) {
+        continue;
+      }
+      DCHECK_GE(label_value, 0);
+      DCHECK_LT(label_value, num_labels_);
+      for (int l = 0; l < num_labels_; l++) {
+        loss -= infogain_mat[label_value * num_labels_ + l] *
+          log(std::max(
+                prob_data[i * inner_num_*num_labels_ + l * inner_num_ + j],
+                Dtype(kLOG_THRESHOLD)));
+      }
+      ++count;
     }
   }
-  top[0]->mutable_cpu_data()[0] = loss / num;
+  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
+  if (top.size() == 2) {
+    top[1]->ShareData(prob_);
+  }
 }
 
 template <typename Dtype>
@@ -80,25 +177,44 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
                << " Layer cannot backpropagate to infogain inputs.";
   }
   if (propagate_down[0]) {
-    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* prob_data = prob_.cpu_data();
     const Dtype* bottom_label = bottom[1]->cpu_data();
     const Dtype* infogain_mat = NULL;
     if (bottom.size() < 3) {
       infogain_mat = infogain_.cpu_data();
     } else {
       infogain_mat = bottom[2]->cpu_data();
+      // H is provided as a "bottom" and might change. sum rows every time.
+      sum_rows_of_H(bottom[2]);
     }
+    const Dtype* sum_rows_H = sum_rows_H_.cpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-    int num = bottom[0]->num();
-    int dim = bottom[0]->count() / bottom[0]->num();
-    const Dtype scale = - top[0]->cpu_diff()[0] / num;
-    for (int i = 0; i < num; ++i) {
-      const int label = static_cast<int>(bottom_label[i]);
-      for (int j = 0; j < dim; ++j) {
-        Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-        bottom_diff[i * dim + j] = scale * infogain_mat[label * dim + j] / prob;
+    const int dim = bottom[0]->count() / outer_num_;
+    int count = 0;
+    for (int i = 0; i < outer_num_; ++i) {
+      for (int j = 0; j < inner_num_; ++j) {
+        const int label_value =
+          static_cast<int>(bottom_label[i * inner_num_ + j]);
+        DCHECK_GE(label_value, 0);
+        DCHECK_LT(label_value, num_labels_);
+        if (has_ignore_label_ && label_value == ignore_label_) {
+          for (int l = 0; l < num_labels_; ++l) {
+            bottom_diff[i * dim + l * inner_num_ + j] = 0;
+          }
+        } else {
+          for (int l = 0; l < num_labels_; ++l) {
+            bottom_diff[i * dim + l * inner_num_ + j] =
+               prob_data[i*dim + l*inner_num_ + j]*sum_rows_H[label_value]
+               - infogain_mat[label_value * num_labels_ + l];
+          }
+          ++count;
+        }
       }
     }
+    // Scale gradient
+    Dtype loss_weight = top[0]->cpu_diff()[0] /
+                        get_normalizer(normalization_, count);
+    caffe_scal(bottom[0]->count(), loss_weight, bottom_diff);
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6900bb71482..591e9647258 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -794,6 +794,7 @@ message ImageDataParameter {
 message InfogainLossParameter {
   // Specify the infogain matrix source.
   optional string source = 1;
+  optional int32 axis = 2 [default = 1]; // axis of prob
 }
 
 message InnerProductParameter {
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index a24ac683dc5..34f21271a62 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -18,17 +19,22 @@ class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   InfogainLossLayerTest()
-      : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
-        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+      : blob_bottom_data_(new Blob<Dtype>(4, 2, 5, 2)),
+        blob_bottom_label_(new Blob<Dtype>(4, 2, 1, 2)),
         blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)),
-        blob_top_loss_(new Blob<Dtype>()) {
+        blob_top_loss_(new Blob<Dtype>()),
+        blob_top_prob_(new Blob<Dtype>()),
+        inner_(2), outer_(4*2), num_labels_(5) {
     Caffe::set_random_seed(1701);
     FillerParameter filler_param;
-    PositiveUnitballFiller<Dtype> filler(filler_param);
+    filler_param.set_min(-0.5);
+    filler_param.set_max(2.0);
+    UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_data_);
     blob_bottom_vec_.push_back(blob_bottom_data_);
     for (int i = 0; i < blob_bottom_label_->count(); ++i) {
-      blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+      blob_bottom_label_->mutable_cpu_data()[i] =
+        caffe_rng_rand() % num_labels_;
     }
     blob_bottom_vec_.push_back(blob_bottom_label_);
     filler_param.set_min(0.1);
@@ -37,29 +43,94 @@ class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
     infogain_filler.Fill(this->blob_bottom_infogain_);
     blob_bottom_vec_.push_back(blob_bottom_infogain_);
     blob_top_vec_.push_back(blob_top_loss_);
+    blob_top_vec_.push_back(blob_top_prob_);
   }
   virtual ~InfogainLossLayerTest() {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
     delete blob_bottom_infogain_;
     delete blob_top_loss_;
+    delete blob_top_prob_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
   Blob<Dtype>* const blob_bottom_infogain_;
   Blob<Dtype>* const blob_top_loss_;
+  Blob<Dtype>* const blob_top_prob_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  int inner_, outer_, num_labels_;
 };
 
 TYPED_TEST_CASE(InfogainLossLayerTest, TestDtypesAndDevices);
 
+TYPED_TEST(InfogainLossLayerTest, TestInfogainLoss) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_infogain_loss_param()->set_axis(2);
+  layer_param.clear_loss_weight();
+  layer_param.add_loss_weight(1);
+  layer_param.add_loss_weight(0);
+  /*vector<float>* lw = layer_param.mutable_loss_weight();
+  lw->clear();
+  lw->push_back(1);
+  lw->push_back(1);*/
+  InfogainLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* data = this->blob_bottom_vec_[0]->cpu_data();
+  const Dtype* prob = this->blob_top_vec_[1]->cpu_data();
+  const Dtype* labels = this->blob_bottom_vec_[1]->cpu_data();
+  const Dtype* H = this->blob_bottom_vec_[2]->cpu_data();
+  // first. test the prob top
+  CHECK_EQ(this->blob_bottom_vec_[0]->num_axes(),
+    this->blob_top_vec_[1]->num_axes())
+      << "prob top shape not match bottom data";
+  for (int ai = 0 ; ai < this->blob_bottom_vec_[0]->num_axes(); ai++) {
+    CHECK_EQ(this->blob_bottom_vec_[0]->shape(ai),
+      this->blob_top_vec_[1]->shape(ai))
+        << "prob top shape not match bottom data";
+  }
+  vector<Dtype> est_prob(this->num_labels_, 0);
+  for ( int i = 0 ; i < this->outer_; i++ ) {
+    for ( int j = 0; j < this->inner_; j++ ) {
+      Dtype den = 0;
+      for ( int  l = 0; l < this->num_labels_; l++ ) {
+        est_prob[l] = std::exp(
+          data[i*this->num_labels_*this->inner_ + l*this->inner_ + j]);
+        den += est_prob[l];
+      }
+      for ( int l = 0; l < this->num_labels_; l++ ) {
+        EXPECT_NEAR(prob[i*this->num_labels_*this->inner_ + l*this->inner_ + j],
+          est_prob[l]/den, 1e-6);
+      }
+    }
+  }
+  Dtype loss = 0;  // loss from prob top
+  for ( int i = 0 ; i < this->outer_; i++ ) {
+    for ( int j = 0; j < this->inner_; j++ ) {
+      int gt = static_cast<int>(labels[i*this->inner_+j]);
+      for ( int l = 0; l < this->num_labels_; l++ ) {
+        loss -= H[gt*this->num_labels_ + l] *
+          log(std::max(
+            prob[i*this->num_labels_*this->inner_ + l*this->inner_ + j],
+            Dtype(kLOG_THRESHOLD)));
+      }
+    }
+  }
+  EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0],
+    loss/(this->outer_*this->inner_), 1e-6);
+}
 
 TYPED_TEST(InfogainLossLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
+  layer_param.mutable_infogain_loss_param()->set_axis(2);
   InfogainLossLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
+  this->blob_top_vec_.clear();  // ignore prob top.
+  this->blob_top_vec_.push_back(this->blob_top_loss_);
+  GradientChecker<Dtype> checker(1e-4, 2e-2, 1701);  // no "kink"
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
       this->blob_top_vec_, 0);
 }

From 8c041a7cf3e571b175cfd8859f1af5f067f8cd7a Mon Sep 17 00:00:00 2001
From: rscohn2 <rscohn2@gmail.com>
Date: Sat, 26 Mar 2016 10:00:26 -0400
Subject: [PATCH 1739/2053] Update info about MKL licensing

The instructions say that MKL is free for students, but as of 8/2015, MKL is free for everyone with community licensing.
---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 893164584d9..e273034fe08 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -52,7 +52,7 @@ Caffe requires BLAS as the backend of its matrix and vector computations.
 There are several implementations of this library. The choice is yours:
 
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
-* [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
+* [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with [free](https://registrationcenter.intel.com/en/forms/?productid=2558) licenses.
     1. Install MKL.
     2. Set up MKL environment (Details: [Linux](https://software.intel.com/en-us/node/528499), [OS X](https://software.intel.com/en-us/node/528659)). Example: *source /opt/intel/mkl/bin/mklvars.sh intel64*
     3. Set `BLAS := mkl` in `Makefile.config`

From a66bea30d6c0706f106b355c7cafc9e7ffae7bb5 Mon Sep 17 00:00:00 2001
From: An Tran <tranlaman@gmail.com>
Date: Wed, 30 Mar 2016 17:32:10 +0800
Subject: [PATCH 1740/2053] small bug in pooling_layer.cu

---
 src/caffe/layers/pooling_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 1ea46cc81b1..81ead1e8686 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -138,7 +138,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const int wstart = pw * stride_w;
     const int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
-    Dtype cumsum = FLT_MIN;
+    Dtype cumsum = 0.;
     Dtype cumvalues = 0.;
     const Dtype* const bottom_slice =
         bottom_data + (n * channels + c) * height * width;

From 7a8183642cb1a12945d0a9ad2bddf8304428b4c8 Mon Sep 17 00:00:00 2001
From: Daniel Gordon <xkcd@cs.washington.edu>
Date: Wed, 30 Mar 2016 14:27:19 -0700
Subject: [PATCH 1741/2053] Use lazy initialization to reuse orderd dict/list
 creations to save time on repeated calls.

---
 python/caffe/pycaffe.py | 28 +++++++++++++++++++++-------
 1 file changed, 21 insertions(+), 7 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index c5c0b824a77..ca6d050e2bd 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -27,7 +27,9 @@ def _Net_blobs(self):
     An OrderedDict (bottom to top, i.e., input to output) of network
     blobs indexed by name
     """
-    return OrderedDict(zip(self._blob_names, self._blobs))
+    if not hasattr(self, '_blobs_dict'):
+        self._blobs_dict = OrderedDict(zip(self._blob_names, self._blobs))
+    return self._blobs_dict
 
 
 @property
@@ -36,7 +38,10 @@ def _Net_blob_loss_weights(self):
     An OrderedDict (bottom to top, i.e., input to output) of network
     blob loss weights indexed by name
     """
-    return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
+    if not hasattr(self, '_blobs_loss_weights_dict'):
+        self._blob_loss_weights_dict = OrderedDict(zip(self._blob_names,
+                                                       self._blob_loss_weights))
+    return self._blob_loss_weights_dict
 
 
 @property
@@ -46,19 +51,28 @@ def _Net_params(self):
     parameters indexed by name; each is a list of multiple blobs (e.g.,
     weights and biases)
     """
-    return OrderedDict([(name, lr.blobs)
-                        for name, lr in zip(self._layer_names, self.layers)
-                        if len(lr.blobs) > 0])
+    if not hasattr(self, '_params_dict'):
+        self._params_dict = OrderedDict([(name, lr.blobs)
+                                        for name, lr in zip(
+                                            self._layer_names, self.layers)
+                                        if len(lr.blobs) > 0])
+    return self._params_dict
 
 
 @property
 def _Net_inputs(self):
-    return [list(self.blobs.keys())[i] for i in self._inputs]
+    if not hasattr(self, '_input_list'):
+        keys = list(self.blobs.keys())
+        self._input_list = [keys[i] for i in self._inputs]
+    return self._input_list
 
 
 @property
 def _Net_outputs(self):
-    return [list(self.blobs.keys())[i] for i in self._outputs]
+    if not hasattr(self, '_output_list'):
+        keys = list(self.blobs.keys())
+        self._output_list = [keys[i] for i in self._outputs]
+    return self._output_list
 
 
 def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):

From d17fbea6aad122c3818d5ef3593487869948b4b7 Mon Sep 17 00:00:00 2001
From: An Tran <tranlaman@gmail.com>
Date: Thu, 31 Mar 2016 10:27:31 +0800
Subject: [PATCH 1742/2053] avoid divide by zeros, suggested by SeanBell

---
 src/caffe/layers/pooling_layer.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 81ead1e8686..46eddb94924 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -149,7 +149,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
         cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];
       }
     }
-    top_data[index] = cumvalues / cumsum;
+    top_data[index] = (cumsum > 0.) ? cumvalues / cumsum : 0.;
   }
 }
 

From dee01c8b5f90a69fd3e73ee455f89aab56e2dbb7 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Mon, 4 Apr 2016 11:36:15 -0700
Subject: [PATCH 1743/2053] test_net.cpp: add TestForcePropagateDown

---
 src/caffe/test/test_net.cpp | 102 ++++++++++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)

diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 1e0788ec127..92fd317fee8 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -716,6 +716,61 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitForcePropNet(bool test_force_true) {
+    string proto =
+      "name: 'ForcePropTestNetwork' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'DummyData' "
+      "  dummy_data_param { "
+      "    shape { "
+      "      dim: 5 "
+      "      dim: 2 "
+      "      dim: 3 "
+      "      dim: 4 "
+      "    } "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    shape { "
+      "      dim: 5 "
+      "    } "
+      "    data_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'innerproduct' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'innerproduct' ";
+    if (test_force_true) {
+      proto += "  propagate_down: true ";
+    }
+    proto +=
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  bottom: 'innerproduct' "
+      "  bottom: 'label' "
+      "  top: 'cross_entropy_loss' "
+      "  type: 'SigmoidCrossEntropyLoss' "
+      "} ";
+    InitNetFromProtoString(proto);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -2371,4 +2426,51 @@ TYPED_TEST(NetTest, TestSkipPropagateDown) {
   }
 }
 
+TYPED_TEST(NetTest, TestForcePropagateDown) {
+  this->InitForcePropNet(false);
+  vector<bool> layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    const string& layer_name = this->net_->layer_names()[layer_id];
+    const vector<bool> need_backward =
+        this->net_->bottom_need_backward()[layer_id];
+    if (layer_name == "data") {
+      ASSERT_EQ(need_backward.size(), 0);
+      EXPECT_FALSE(layer_need_backward[layer_id]);
+    } else if (layer_name == "innerproduct") {
+      ASSERT_EQ(need_backward.size(), 1);
+      EXPECT_FALSE(need_backward[0]);  // data
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else if (layer_name == "loss") {
+      ASSERT_EQ(need_backward.size(), 2);
+      EXPECT_TRUE(need_backward[0]);   // innerproduct
+      EXPECT_FALSE(need_backward[1]);  // label
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else {
+      LOG(FATAL) << "Unknown layer: " << layer_name;
+    }
+  }
+  this->InitForcePropNet(true);
+  layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    const string& layer_name = this->net_->layer_names()[layer_id];
+    const vector<bool> need_backward =
+        this->net_->bottom_need_backward()[layer_id];
+    if (layer_name == "data") {
+      ASSERT_EQ(need_backward.size(), 0);
+      EXPECT_FALSE(layer_need_backward[layer_id]);
+    } else if (layer_name == "innerproduct") {
+      ASSERT_EQ(need_backward.size(), 1);
+      EXPECT_TRUE(need_backward[0]);  // data
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else if (layer_name == "loss") {
+      ASSERT_EQ(need_backward.size(), 2);
+      EXPECT_TRUE(need_backward[0]);   // innerproduct
+      EXPECT_FALSE(need_backward[1]);  // label
+      EXPECT_TRUE(layer_need_backward[layer_id]);
+    } else {
+      LOG(FATAL) << "Unknown layer: " << layer_name;
+    }
+  }
+}
+
 }  // namespace caffe

From 77cde9c84126cb108f59e2673c2e6f59b33180fa Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Wed, 27 Jan 2016 12:55:41 -0800
Subject: [PATCH 1744/2053] Net: setting `propagate_down: true` forces backprop

---
 src/caffe/net.cpp           | 9 ++++-----
 src/caffe/proto/caffe.proto | 7 ++++++-
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 23d94c97c07..f0bf594936c 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -427,12 +427,11 @@ int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  bool propagate_down = true;
+  bool need_backward = blob_need_backward_[blob_id];
   // Check if the backpropagation on bottom_id should be skipped
-  if (layer_param.propagate_down_size() > 0)
-    propagate_down = layer_param.propagate_down(bottom_id);
-  const bool need_backward = blob_need_backward_[blob_id] &&
-                          propagate_down;
+  if (layer_param.propagate_down_size() > 0) {
+    need_backward = layer_param.propagate_down(bottom_id);
+  }
   bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6900bb71482..650c87ae3a6 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -328,7 +328,12 @@ message LayerParameter {
   // The blobs containing the numeric parameters of the layer.
   repeated BlobProto blobs = 7;
 
-  // Specifies on which bottoms the backpropagation should be skipped.
+  // Specifies whether to backpropagate to each bottom. If unspecified,
+  // Caffe will automatically infer whether each input needs backpropagation
+  // to compute parameter gradients. If set to true for some inputs,
+  // backpropagation to those inputs is forced; if set false for some inputs,
+  // backpropagation to those inputs is skipped.
+  //
   // The size must be either 0 or equal to the number of bottoms.
   repeated bool propagate_down = 11;
 

From 3c3dc95766c8caa374c643b51bd92a27f787b8b5 Mon Sep 17 00:00:00 2001
From: emmanuel maggiori <emaggior@rachmaninov.inria.fr>
Date: Fri, 8 Apr 2016 10:25:12 +0200
Subject: [PATCH 1745/2053] Solving issue with exp layer with base e

---
 src/caffe/layers/exp_layer.cpp       |  3 ++-
 src/caffe/test/test_neuron_layer.cpp | 20 ++++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index 1f4a309fe25..0c1b463ae12 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -23,7 +23,8 @@ void ExpLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const Dtype input_scale = this->layer_param_.exp_param().scale();
   const Dtype input_shift = this->layer_param_.exp_param().shift();
   inner_scale_ = log_base * input_scale;
-  outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) : pow(base, input_shift);
+  outer_scale_ = (input_shift == Dtype(0)) ? Dtype(1) :
+     ( (base != Dtype(-1)) ? pow(base, input_shift) : exp(input_shift) );
 }
 
 template <typename Dtype>
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index dd591f7d204..342f825cec3 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -394,6 +394,26 @@ TYPED_TEST(NeuronLayerTest, TestExpGradient) {
   this->TestExpGradient(kBase, kScale, kShift);
 }
 
+TYPED_TEST(NeuronLayerTest, TestExpLayerWithShift) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e,
+  // with a non-zero shift
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestExpGradientWithShift) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e,
+  // with a non-zero shift
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestExpGradient(kBase, kScale, kShift);
+}
+
 TYPED_TEST(NeuronLayerTest, TestExpLayerBase2) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kBase = 2;

From 09130ce35604a991cee41c942ff8845468cacfa7 Mon Sep 17 00:00:00 2001
From: Thomas <twatson52@mac.com>
Date: Mon, 11 Apr 2016 12:52:34 -0500
Subject: [PATCH 1746/2053] Fix protobuf message generation

The latest versions of protobuf do not reveal empty message fields with dir(). This uses the documented way of determining all of a message's fields and so is compatible with past and future versions of protobuf.
---
 python/caffe/net_spec.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
index 63de4cce4b2..5fb1f0b3fb1 100644
--- a/python/caffe/net_spec.py
+++ b/python/caffe/net_spec.py
@@ -32,7 +32,7 @@ def param_name_dict():
     # get all parameter names (typically underscore case) and corresponding
     # type names (typically camel case), which contain the layer names
     # (note that not all parameters correspond to layers, but we'll ignore that)
-    param_names = [s for s in dir(layer) if s.endswith('_param')]
+    param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]
     param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
     # strip the final '_param' or 'Parameter'
     param_names = [s[:-len('_param')] for s in param_names]

From 219532f5552fb48931776f5236b5ec3d99eccb2a Mon Sep 17 00:00:00 2001
From: Muneyuki Noguchi <nogu.dev@gmail.com>
Date: Tue, 12 Apr 2016 23:19:27 +0900
Subject: [PATCH 1747/2053] Fix typo in help text for "-model" option

---
 tools/caffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 305cfc3635d..d121fefc9d7 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -32,7 +32,7 @@ DEFINE_string(gpu, "",
 DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
-    "The model definition protocol buffer text file..");
+    "The model definition protocol buffer text file.");
 DEFINE_string(snapshot, "",
     "Optional; the snapshot solver state to resume training.");
 DEFINE_string(weights, "",

From b265134710d78db4007471ccbe376c2c4221441a Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Apr 2016 16:40:30 -0700
Subject: [PATCH 1748/2053] [docs] install: CUDA 7+ and cuDNN v4 compatible

Latest CUDA versions are all compatible, and
Caffe has been compatible with cuDNN v4 since PR #3439
---
 docs/installation.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 893164584d9..e6c6886df52 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -20,7 +20,7 @@ When updating Caffe, it's best to `make clean` before re-compiling.
 Caffe has several dependencies:
 
 * [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
-    * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+    * library version 7+ and the latest driver version are recommended, but 6.* is fine too
     * 5.5, and 5.0 are compatible but considered legacy
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
 * [Boost](http://www.boost.org/) >= 1.55
@@ -30,14 +30,14 @@ Optional dependencies:
 
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v3)
+* cuDNN for GPU acceleration (v4)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v4; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 

From 462a688fb8997f87b19c3c51860eb32d5458b246 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Apr 2016 16:43:39 -0700
Subject: [PATCH 1749/2053] [docs] install: include latest versions and
 platforms, highlight guides

Caffe runs on Ubuntu, OS X, and RHEL (+ company) in master
with branches for OpenCL and Windows.

Docker is a nice route to out-of-the-box brewing.
---
 docs/installation.md | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index e6c6886df52..9aa83527fd5 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -5,13 +5,23 @@ title: Installation
 # Installation
 
 Prior to installing, have a glance through this guide and take note of the details for your platform.
-We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
-The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
+We install and run Caffe on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS.
+The official Makefile and `Makefile.config` build are complemented by a [community CMake build](#cmake-build).
+
+**Step-by-step Instructions**:
+
+- [Docker setup](https://github.com/BVLC/caffe/tree/master/docker) *out-of-the-box brewing*
+- [Ubuntu installation](install_apt.html) *the standard platform*
+- [OS X installation](install_osx.html)
+- [RHEL / CentOS / Fedora installation](install_yum.html)
+- [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Microsoft*
+- [OpenCL](https://github.com/BVLC/caffe/tree/opencl) *see the OpenCL branch led by Fabian Tschopp*
+
+**Overview**:
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
 - [Hardware](#hardware)
-- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
 
 When updating Caffe, it's best to `make clean` before re-compiling.
 
@@ -82,10 +92,6 @@ Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
 *Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
 
-#### Windows
-
-There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
-
 ## Compilation
 
 Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
@@ -113,7 +119,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### Compilation with CMake
+### CMake Build
 
 In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
 The basic steps are as follows:

From 0ef5918bbb7cb6e6d733ef91acff5349febc2bc7 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Apr 2016 18:52:50 -0700
Subject: [PATCH 1750/2053] [docs] install: be more firm about compute
 capability >= 3.0

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 9aa83527fd5..95a57fdffda 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -137,7 +137,7 @@ See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
 **Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
-**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.
+**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capbility >= 3.0.
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 

From b9164503ff51e8167cac9feb3f9a3d99778f13a8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 13 Apr 2016 18:53:28 -0700
Subject: [PATCH 1751/2053] [docs] install: include more lab tested hardware

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 95a57fdffda..aa946911c63 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -135,7 +135,7 @@ See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
 ## Hardware
 
-**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
+**Laboratory Tested Hardware**: Berkeley Vision runs Caffe with Titan Xs, K80s, GTX 980s, K40s, K20s, Titans, and GTX 770s including models at ImageNet/ILSVRC scale. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
 **CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capbility >= 3.0.
 

From e867e60fa24985b112af9885ec553d5dd62f49bf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 14 Apr 2016 22:56:37 -0700
Subject: [PATCH 1752/2053] [test] CropLayer: test dimensions check to reveal
 bounds checking bug

---
 src/caffe/test/test_crop_layer.cpp | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp
index 45f24e2ee8d..ce2c736f644 100644
--- a/src/caffe/test/test_crop_layer.cpp
+++ b/src/caffe/test/test_crop_layer.cpp
@@ -91,6 +91,24 @@ TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
   }
 }
 
+TYPED_TEST(CropLayerTest, TestDimensionsCheck) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Reshape size blob to have incompatible sizes for uncropped dimensions:
+  // the size blob has more channels than the data blob, but this is fine
+  // since the channels dimension is not cropped in this configuration.
+  this->blob_bottom_1_->Reshape(2, 5, 4, 2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 2) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
 TYPED_TEST(CropLayerTest, TestCropAll) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From 75b0d40a856dda87f2e0de77b2c6626753e1e231 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 14 Apr 2016 22:16:07 -0700
Subject: [PATCH 1753/2053] [fix] CropLayer: check dimension bounds only for
 cropped dimensions

check only the dimensions to be cropped for compatible sizes and offsets
---
 src/caffe/layers/crop_layer.cpp | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index e81bdd732f3..849208f5666 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -61,12 +61,11 @@ void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
         // following axis
         crop_offset = param.offset(i - start_axis);
       }
+      // check that the crop and offset are within the dimension bounds
+      CHECK_GE(bottom[0]->shape(i) - crop_offset, bottom[1]->shape(i))
+          << "the crop for dimension " << i << " is out-of-bounds with "
+          << "size " << bottom[1]->shape(i) << " and offset " << crop_offset;
     }
-    // Check that the image we are cropping minus the margin is bigger
-    // than the destination image.
-    CHECK_GE(bottom[0]->shape(i) - crop_offset,
-             bottom[1]->shape(i))
-        << "invalid crop parameters in dimension: " << i;
     // Now set new size and offsets
     new_shape[i] = new_size;
     offsets[i] = crop_offset;

From 00dc3d1ced4467be00ccc82b8509e4a25d54808d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 14 Apr 2016 22:31:38 -0700
Subject: [PATCH 1754/2053] CropLayer: groom comments

---
 include/caffe/layers/crop_layer.hpp |  9 +++++++++
 src/caffe/layers/crop_layer.cpp     | 22 ++++++++--------------
 src/caffe/layers/crop_layer.cu      |  9 ---------
 3 files changed, 17 insertions(+), 23 deletions(-)

diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
index 5c605b2ae9e..c4fda1220c3 100644
--- a/include/caffe/layers/crop_layer.hpp
+++ b/include/caffe/layers/crop_layer.hpp
@@ -44,6 +44,7 @@ class CropLayer : public Layer<Dtype> {
   vector<int> offsets;
 
  private:
+  // Recursive copy function.
   void crop_copy(const vector<Blob<Dtype>*>& bottom,
                const vector<Blob<Dtype>*>& top,
                const vector<int>& offsets,
@@ -53,6 +54,14 @@ class CropLayer : public Layer<Dtype> {
                Dtype* dest_data,
                bool is_forward);
 
+  // Recursive copy function: this is similar to crop_copy() but loops over all
+  // but the last two dimensions to allow for ND cropping while still relying on
+  // a CUDA kernel for the innermost two dimensions for performance reasons.  An
+  // alterantive implementation could rely on the kernel more by passing
+  // offsets, but this is problematic because of its variable length.
+  // Since in the standard (N,C,W,H) case N,C are usually not cropped a speedup
+  // could be achieved by not looping the application of the copy_kernel around
+  // these dimensions.
   void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
                 const vector<Blob<Dtype>*>& top,
                 const vector<int>& offsets,
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index 849208f5666..aecdcd63194 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -15,8 +15,7 @@ namespace caffe {
 template <typename Dtype>
 void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // All logic that depends only on the number of dimensions is here,
-  // the rest is in Reshape because it depends on Blob size.
+  // LayerSetup() handles the number of dimensions; Reshape() handles the sizes.
   // bottom[0] supplies the data
   // bottom[1] supplies the size
   const CropParameter& param = this->layer_param_.crop_param();
@@ -40,40 +39,35 @@ void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   int input_dim = bottom[0]->num_axes();
   const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
 
-  // initialize all offsets to 0
+  // Initialize offsets to 0 and the new shape to the current shape of the data.
   offsets = vector<int>(input_dim, 0);
-  // initialize new shape to bottom[0]
   vector<int> new_shape(bottom[0]->shape());
 
-  // apply crops
+  // Determine crop offsets and the new shape post-crop.
   for (int i = 0; i < input_dim; ++i) {
     int crop_offset = 0;
-    int new_size    = bottom[0]->shape(i);
+    int new_size = bottom[0]->shape(i);
     if (i >= start_axis) {
       new_size = bottom[1]->shape(i);
-
       if (param.offset_size() == 1) {
-        // if only one crop value is supplied, crop all dimensions after axis
-        // by this crop value
+        // If only one offset is given, all crops have the same offset.
         crop_offset = param.offset(0);
       } else if (param.offset_size() > 1) {
-        // crop values specified must be equal to the number of dimensions
-        // following axis
+        // For several offsets, the number of offsets must be equal to the
+        // number of dimensions to crop, that is dimensions after the axis.
         crop_offset = param.offset(i - start_axis);
       }
-      // check that the crop and offset are within the dimension bounds
+      // Check that the crop and offset are within the dimension's bounds.
       CHECK_GE(bottom[0]->shape(i) - crop_offset, bottom[1]->shape(i))
           << "the crop for dimension " << i << " is out-of-bounds with "
           << "size " << bottom[1]->shape(i) << " and offset " << crop_offset;
     }
-    // Now set new size and offsets
     new_shape[i] = new_size;
     offsets[i] = crop_offset;
   }
   top[0]->Reshape(new_shape);
 }
 
-// recursive copy function
 template <typename Dtype>
 void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
              const vector<Blob<Dtype>*>& top,
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 9ed8f7cce57..f78cecbbeee 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -22,15 +22,6 @@ __global__ void copy_kernel(const int n, const int height, const int width,
   }
 }
 
-// recursive copy function, this function is similar to crop_copy but loops
-// over all but the last two dimensions. It is implemented this way to allow
-// for ND cropping while still relying on a CUDA kernel for the innermost
-// two dimensions for performance reasons.
-// An alternative way to implement ND cropping relying more on the kernel
-// would require passing offsets to the kernel, which is a bit problematic
-// because it is of variable length. Since in the standard (N,C,W,H) case
-// N,C are usually not cropped a speedup could be achieved by not looping
-// the application of the copy_kernel around these dimensions.
 template <typename Dtype>
 void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
              const vector<Blob<Dtype>*>& top,

From 1c49130c33ebdec042ff6da18d03b7c5f6ad8c93 Mon Sep 17 00:00:00 2001
From: ZhouYzzz <zhouyz9608@gmail.com>
Date: Fri, 15 Apr 2016 22:51:49 +0800
Subject: [PATCH 1755/2053] Allow the python layer have attribute "phase"

---
 include/caffe/layers/python_layer.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/layers/python_layer.hpp b/include/caffe/layers/python_layer.hpp
index b839d52684e..66dbbdf13b8 100644
--- a/include/caffe/layers/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -26,6 +26,7 @@ class PythonLayer : public Layer<Dtype> {
     }
     self_.attr("param_str") = bp::str(
         this->layer_param_.python_param().param_str());
+    self_.attr("phase") = static_cast<int>(this->phase_);
     self_.attr("setup")(bottom, top);
   }
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,

From 458928a3bc1ee94e5f12bb254a5de819c449fc0a Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Mon, 18 Apr 2016 08:54:21 -0700
Subject: [PATCH 1756/2053] Typo in docs/installation.md

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index aa946911c63..1e29a49d82d 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -137,7 +137,7 @@ See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
 
 **Laboratory Tested Hardware**: Berkeley Vision runs Caffe with Titan Xs, K80s, GTX 980s, K40s, K20s, Titans, and GTX 770s including models at ImageNet/ILSVRC scale. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.
 
-**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capbility >= 3.0.
+**CUDA compute capability**: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Brew with caution; we recommend compute capability >= 3.0.
 
 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
 

From bd762101dba321146d2d9cb747c79c4c678cbfdb Mon Sep 17 00:00:00 2001
From: Achal Dave <achalddave@live.com>
Date: Wed, 20 Apr 2016 17:34:29 -0400
Subject: [PATCH 1757/2053] Explicitly point out -weights flag in tutorial

The -weights flag is somewhat easy to miss as it's only in one command,
but is the crucial thing that anyone searching for 'how to finetune' is
looking for. Hopefully this more clearly points out the '-weights' flag,
which might otherwise be overlooked in this tutorial.
---
 examples/finetune_flickr_style/readme.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 9ba4c9217ff..188dedf1b9a 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -57,7 +57,11 @@ The prototxts in this example assume this, and also assume the presence of the I
 
 We'll also need the ImageNet-trained model, which you can obtain by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`.
 
-Now we can train! (You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
+Now we can train! The key to fine-tuning is the `-weights` argument in the
+command below, which tells Caffe that we want to load weights from a pre-trained
+Caffe model.
+
+(You can fine-tune in CPU mode by leaving out the `-gpu` flag.)
 
     caffe % ./build/tools/caffe train -solver models/finetune_flickr_style/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu 0
 

From 90426645c36ad71c778c4ac3688ec164242a50a1 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Thu, 25 Feb 2016 19:58:01 -0800
Subject: [PATCH 1758/2053] Don't set map_size=1TB in util/db_lmdb

Instead, double the map size on the MDB_MAP_FULL exception.
---
 include/caffe/util/db_lmdb.hpp | 13 ++++---
 src/caffe/util/db_lmdb.cpp     | 65 +++++++++++++++++++++++++++-------
 2 files changed, 60 insertions(+), 18 deletions(-)

diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
index 4e1568ace50..ee370322383 100644
--- a/include/caffe/util/db_lmdb.hpp
+++ b/include/caffe/util/db_lmdb.hpp
@@ -3,6 +3,7 @@
 #define CAFFE_UTIL_DB_LMDB_HPP
 
 #include <string>
+#include <vector>
 
 #include "lmdb.h"
 
@@ -54,14 +55,16 @@ class LMDBCursor : public Cursor {
 
 class LMDBTransaction : public Transaction {
  public:
-  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
-    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+  explicit LMDBTransaction(MDB_env* mdb_env)
+    : mdb_env_(mdb_env) { }
   virtual void Put(const string& key, const string& value);
-  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+  virtual void Commit();
 
  private:
-  MDB_dbi* mdb_dbi_;
-  MDB_txn* mdb_txn_;
+  MDB_env* mdb_env_;
+  vector<string> keys, values;
+
+  void DoubleMapSize();
 
   DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
 };
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index 0bc82b53e2b..df83a52a633 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -7,11 +7,8 @@
 
 namespace caffe { namespace db {
 
-const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
-
 void LMDB::Open(const string& source, Mode mode) {
   MDB_CHECK(mdb_env_create(&mdb_env_));
-  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
   if (mode == NEW) {
     CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
   }
@@ -48,19 +45,61 @@ LMDBCursor* LMDB::NewCursor() {
 }
 
 LMDBTransaction* LMDB::NewTransaction() {
-  MDB_txn* mdb_txn;
-  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
-  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
-  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+  return new LMDBTransaction(mdb_env_);
 }
 
 void LMDBTransaction::Put(const string& key, const string& value) {
-  MDB_val mdb_key, mdb_value;
-  mdb_key.mv_data = const_cast<char*>(key.data());
-  mdb_key.mv_size = key.size();
-  mdb_value.mv_data = const_cast<char*>(value.data());
-  mdb_value.mv_size = value.size();
-  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+  keys.push_back(key);
+  values.push_back(value);
+}
+
+void LMDBTransaction::Commit() {
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_data;
+  MDB_txn *mdb_txn;
+
+  // Initialize MDB variables
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi));
+
+  bool out_of_memory = false;
+  for (int i = 0; i < keys.size(); i++) {
+    mdb_key.mv_size = keys[i].size();
+    mdb_key.mv_data = const_cast<char*>(keys[i].data());
+    mdb_data.mv_size = values[i].size();
+    mdb_data.mv_data = const_cast<char*>(values[i].data());
+
+    int put_rc = mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0);
+    if (put_rc == MDB_MAP_FULL) {
+      out_of_memory = true;
+      break;
+    } else {
+      // Failed for some other reason
+      MDB_CHECK(put_rc);
+    }
+  }
+
+  if (!out_of_memory) {
+    // Commit the transaction
+    MDB_CHECK(mdb_txn_commit(mdb_txn));
+    mdb_dbi_close(mdb_env_, mdb_dbi);
+    keys.clear();
+    values.clear();
+  } else {
+    // Double the map size and retry
+    mdb_txn_abort(mdb_txn);
+    mdb_dbi_close(mdb_env_, mdb_dbi);
+    DoubleMapSize();
+    Commit();
+  }
+}
+
+void LMDBTransaction::DoubleMapSize() {
+  struct MDB_envinfo current_info;
+  MDB_CHECK(mdb_env_info(mdb_env_, &current_info));
+  size_t new_size = current_info.me_mapsize * 2;
+  DLOG(INFO) << "Doubling LMDB map size to " << (new_size>>20) << "MB ...";
+  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, new_size));
 }
 
 }  // namespace db

From f30c61cfdfc0d254ec233b972ff4b6b0aa2f5d4c Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Thu, 25 Feb 2016 20:02:25 -0800
Subject: [PATCH 1759/2053] Print to stderr for example LMDB code

---
 examples/cifar10/convert_cifar_data.cpp | 2 ++
 examples/mnist/convert_mnist_data.cpp   | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index e1b89f42fb6..7385a74a679 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -91,6 +91,8 @@ void convert_dataset(const string& input_folder, const string& output_folder,
 }
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;
+
   if (argc != 4) {
     printf("This script converts the CIFAR dataset to the leveldb format used\n"
            "by caffe to perform classification.\n"
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 16d28093dd5..32bee5269ba 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -178,6 +178,8 @@ int main(int argc, char** argv) {
   namespace gflags = google;
 #endif
 
+  FLAGS_alsologtostderr = 1;
+
   gflags::SetUsageMessage("This script converts the MNIST dataset to\n"
         "the lmdb/leveldb format used by Caffe to load data.\n"
         "Usage:\n"

From 74040cb2ed9d46a267a16870e9878f3b6911d644 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Thu, 25 Feb 2016 20:14:02 -0800
Subject: [PATCH 1760/2053] Update MNIST example to use new DB classes

---
 examples/mnist/convert_mnist_data.cpp | 87 ++++-----------------------
 1 file changed, 12 insertions(+), 75 deletions(-)

diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 32bee5269ba..57ddef77074 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -22,12 +22,15 @@
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "boost/scoped_ptr.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
 #include "caffe/util/format.hpp"
 
 #if defined(USE_LEVELDB) && defined(USE_LMDB)
 
 using namespace caffe;  // NOLINT(build/namespaces)
+using boost::scoped_ptr;
 using std::string;
 
 DEFINE_string(backend, "lmdb", "The backend for storing the result");
@@ -67,43 +70,10 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   image_file.read(reinterpret_cast<char*>(&cols), 4);
   cols = swap_endian(cols);
 
-  // lmdb
-  MDB_env *mdb_env;
-  MDB_dbi mdb_dbi;
-  MDB_val mdb_key, mdb_data;
-  MDB_txn *mdb_txn;
-  // leveldb
-  leveldb::DB* db;
-  leveldb::Options options;
-  options.error_if_exists = true;
-  options.create_if_missing = true;
-  options.write_buffer_size = 268435456;
-  leveldb::WriteBatch* batch = NULL;
-
-  // Open db
-  if (db_backend == "leveldb") {  // leveldb
-    LOG(INFO) << "Opening leveldb " << db_path;
-    leveldb::Status status = leveldb::DB::Open(
-        options, db_path, &db);
-    CHECK(status.ok()) << "Failed to open leveldb " << db_path
-        << ". Is it already existing?";
-    batch = new leveldb::WriteBatch();
-  } else if (db_backend == "lmdb") {  // lmdb
-    LOG(INFO) << "Opening lmdb " << db_path;
-    CHECK_EQ(mkdir(db_path, 0744), 0)
-        << "mkdir " << db_path << "failed";
-    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
-    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
-        << "mdb_env_set_mapsize failed";
-    CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)
-        << "mdb_env_open failed";
-    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-        << "mdb_txn_begin failed";
-    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
-        << "mdb_open failed. Does the lmdb already exist? ";
-  } else {
-    LOG(FATAL) << "Unknown db backend " << db_backend;
-  }
+
+  scoped_ptr<db::DB> db(db::GetDB(db_backend));
+  db->Open(db_path, db::NEW);
+  scoped_ptr<db::Transaction> txn(db->NewTransaction());
 
   // Storing to db
   char label;
@@ -125,52 +95,19 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     string key_str = caffe::format_int(item_id, 8);
     datum.SerializeToString(&value);
 
-    // Put in db
-    if (db_backend == "leveldb") {  // leveldb
-      batch->Put(key_str, value);
-    } else if (db_backend == "lmdb") {  // lmdb
-      mdb_data.mv_size = value.size();
-      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
-      mdb_key.mv_size = key_str.size();
-      mdb_key.mv_data = reinterpret_cast<void*>(&key_str[0]);
-      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
-          << "mdb_put failed";
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
+    txn->Put(key_str, value);
 
     if (++count % 1000 == 0) {
-      // Commit txn
-      if (db_backend == "leveldb") {  // leveldb
-        db->Write(leveldb::WriteOptions(), batch);
-        delete batch;
-        batch = new leveldb::WriteBatch();
-      } else if (db_backend == "lmdb") {  // lmdb
-        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_commit failed";
-        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
-            << "mdb_txn_begin failed";
-      } else {
-        LOG(FATAL) << "Unknown db backend " << db_backend;
-      }
+      txn->Commit();
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    if (db_backend == "leveldb") {  // leveldb
-      db->Write(leveldb::WriteOptions(), batch);
-      delete batch;
-      delete db;
-    } else if (db_backend == "lmdb") {  // lmdb
-      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
-      mdb_close(mdb_env, mdb_dbi);
-      mdb_env_close(mdb_env);
-    } else {
-      LOG(FATAL) << "Unknown db backend " << db_backend;
-    }
-    LOG(ERROR) << "Processed " << count << " files.";
+      txn->Commit();
   }
+  LOG(INFO) << "Processed " << count << " files.";
   delete[] pixels;
+  db->Close();
 }
 
 int main(int argc, char** argv) {

From bff14b47c58cffa28a71b9e3caba93da2354ab07 Mon Sep 17 00:00:00 2001
From: HeGaoYuan <273230305@qq.com>
Date: Sat, 23 Apr 2016 14:48:41 +0800
Subject: [PATCH 1761/2053] Fixed #4029: test the network every 500 iterations,
 not 1000 iterations

---
 examples/mnist/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index b87a0f53c7a..35952155a30 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -248,7 +248,7 @@ These messages tell you the details about each layer, its connections and its ou
     I1203 solver.cpp:36] Solver scaffolding done.
     I1203 solver.cpp:44] Solving LeNet
 
-Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 1000 iterations. You will see messages like this:
+Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:
 
     I1203 solver.cpp:204] Iteration 100, lr = 0.00992565
     I1203 solver.cpp:66] Iteration 100, loss = 0.26044

From 0e145c5af91bf42e20cf8c8a295816b06905ee4e Mon Sep 17 00:00:00 2001
From: ebadawy <ehalbadawy93@gmail.com>
Date: Sun, 24 Apr 2016 20:24:41 +0200
Subject: [PATCH 1762/2053] Read the data as a binary

Appending 'b' in the file mode as hashlib functions require to pass in bytes
---
 scripts/download_model_binary.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index 66f72f2477e..fcdbb5a91a2 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -60,7 +60,7 @@ def valid_dirname(dirname):
 
     # Closure-d function for checking SHA1.
     def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
-        with open(filename, 'r') as f:
+        with open(filename, 'rb') as f:
             return hashlib.sha1(f.read()).hexdigest() == sha1
 
     # Check if model exists.

From 8619fbb90f2b5546ea8cb7c4021216d978d4cbc4 Mon Sep 17 00:00:00 2001
From: Sammy Sidhu <sammy.sidhu@gmail.com>
Date: Wed, 27 Apr 2016 03:05:30 -0700
Subject: [PATCH 1763/2053] fixed typo in download script command
 cpp_classification

---
 examples/cpp_classification/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
index a086db1a035..0de2885b53c 100644
--- a/examples/cpp_classification/readme.md
+++ b/examples/cpp_classification/readme.md
@@ -42,7 +42,7 @@ script:
 The ImageNet labels file (also called the *synset file*) is also
 required in order to map a prediction to the name of the class:
 ```
-./data/ilsvrc12/get_ilsvrc_aux.sh.
+./data/ilsvrc12/get_ilsvrc_aux.sh
 ```
 Using the files that were downloaded, we can classify the provided cat
 image (`examples/images/cat.jpg`) using this command:

From 859cf6e1c3f965b4029b7940b861038031014ed7 Mon Sep 17 00:00:00 2001
From: Kun Wang <wk910930@gmail.com>
Date: Wed, 27 Apr 2016 21:09:31 +0800
Subject: [PATCH 1764/2053] Fix an error in the example of ReshapeParameter.

* this small mistake may confuse newer.
---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 650c87ae3a6..ea40e60aa34 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -987,7 +987,7 @@ message ReshapeParameter {
   //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
   //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
   //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
-  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
+  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
   //
   optional BlobShape shape = 1;
 

From 8714b53719165e42f7844126f671f32ecc9b2e2f Mon Sep 17 00:00:00 2001
From: Drew Abbot <drewabbot@gmail.com>
Date: Wed, 27 Apr 2016 23:25:09 -0700
Subject: [PATCH 1765/2053] avoid non-integer array indices

---
 python/caffe/classifier.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 537193db8f8..ea29fed86f9 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -79,6 +79,7 @@ def predict(self, inputs, oversample=True):
                 -self.crop_dims / 2.0,
                 self.crop_dims / 2.0
             ])
+            crop = crop.astype(int)
             input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify

From 673e8cfc0b8f05f9fa3ebbad7cc6202822e5d9c5 Mon Sep 17 00:00:00 2001
From: Sean Bell <sbell@cs.cornell.edu>
Date: Thu, 28 Apr 2016 13:06:51 -0400
Subject: [PATCH 1766/2053] Suppress boost registration warnings in pycaffe
 (Based on #3960)

---
 python/caffe/_caffe.cpp | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index a2c46a123aa..32b5d921094 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -26,6 +26,19 @@
 #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
 #endif
 
+/* Fix to avoid registration warnings in pycaffe (#3960) */
+#define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \
+  const boost::python::type_info info = \
+    boost::python::type_id<shared_ptr<PTR > >(); \
+  const boost::python::converter::registration* reg = \
+    boost::python::converter::registry::query(info); \
+  if (reg == NULL) { \
+    bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+  } else if ((*reg).m_to_python == NULL) { \
+    bp::register_ptr_to_python<shared_ptr<PTR > >(); \
+  } \
+} while (0)
+
 namespace bp = boost::python;
 
 namespace caffe {
@@ -255,7 +268,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("_set_input_arrays", &Net_SetInputArrays,
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
     .def("save", &Net_Save);
-  bp::register_ptr_to_python<shared_ptr<Net<Dtype> > >();
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
     "Blob", bp::no_init)
@@ -275,7 +288,7 @@ BOOST_PYTHON_MODULE(_caffe) {
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
           NdarrayCallPolicies()));
-  bp::register_ptr_to_python<shared_ptr<Blob<Dtype> > >();
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);
 
   bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
     boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
@@ -284,7 +297,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("setup", &Layer<Dtype>::LayerSetUp)
     .def("reshape", &Layer<Dtype>::Reshape)
     .add_property("type", bp::make_function(&Layer<Dtype>::type));
-  bp::register_ptr_to_python<shared_ptr<Layer<Dtype> > >();
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);
 
   bp::class_<LayerParameter>("LayerParameter", bp::no_init);
 
@@ -299,7 +312,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("step", &Solver<Dtype>::Step)
     .def("restore", &Solver<Dtype>::Restore)
     .def("snapshot", &Solver<Dtype>::Snapshot);
-  bp::register_ptr_to_python<shared_ptr<Solver<Dtype> > >();
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(

From 2da8600acdc922d03b667ef691279cb52c7226ed Mon Sep 17 00:00:00 2001
From: Muneyuki Noguchi <nogu.dev@gmail.com>
Date: Fri, 29 Apr 2016 02:04:02 +0000
Subject: [PATCH 1767/2053] draw_net: accept prototxt without name

Fixes #3819
---
 python/caffe/draw.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index cfa3fc5b1fb..61205ca9f37 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -142,7 +142,7 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
     -------
     pydot graph object
     """
-    pydot_graph = pydot.Dot(caffe_net.name,
+    pydot_graph = pydot.Dot(caffe_net.name if caffe_net.name else 'Net',
                             graph_type='digraph',
                             rankdir=rankdir)
     pydot_nodes = {}

From cb3c992a2ae00ec634313a394361214d868f9bd2 Mon Sep 17 00:00:00 2001
From: Sheng Zha <szha@umd.edu>
Date: Sat, 30 Apr 2016 16:40:05 -0700
Subject: [PATCH 1768/2053] fix grep in CUDA version detection to accomodate
 OSX's grep (and other grep that doesn't support \d extension)

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 5424c3a1858..568d9c2774d 100644
--- a/Makefile
+++ b/Makefile
@@ -272,7 +272,7 @@ endif
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
 	ifneq ($(CPU_ONLY), 1)
-		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
+		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | grep -o '[0-9.]*')
 		ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
 			CXXFLAGS += -stdlib=libstdc++
 			LINKFLAGS += -stdlib=libstdc++

From 5d423b7a63718decf04bad93a481ebd56291ec7b Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Mon, 2 May 2016 16:20:00 -0700
Subject: [PATCH 1769/2053] Pin the base image version for the GPU Dockerfile

The previous Dockerfile can break if image nvidia/cuda:cudnn is updated to any of the following:
- Ubuntu 16.04 LTS (already released)
- cuDNN v5 (soon)
- CUDA 8.0 (soon)
---
 docker/Makefile                  | 2 +-
 docker/standalone/gpu/Dockerfile | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docker/Makefile b/docker/Makefile
index 725208c6b2b..0de887d0e19 100644
--- a/docker/Makefile
+++ b/docker/Makefile
@@ -22,7 +22,7 @@ docker_files: standalone_files
 
 standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile
 
-FROM_GPU = "nvidia/cuda:cudnn"
+FROM_GPU = "nvidia/cuda:7.5-cudnn4-devel-ubuntu14.04"
 FROM_CPU = "ubuntu:14.04"
 GPU_CMAKE_ARGS = -DUSE_CUDNN=1
 CPU_CMAKE_ARGS = -DCPU_ONLY=1
diff --git a/docker/standalone/gpu/Dockerfile b/docker/standalone/gpu/Dockerfile
index 1ddc6560d16..371aad5b1e9 100644
--- a/docker/standalone/gpu/Dockerfile
+++ b/docker/standalone/gpu/Dockerfile
@@ -1,4 +1,4 @@
-FROM nvidia/cuda:cudnn
+FROM nvidia/cuda:7.5-cudnn4-devel-ubuntu14.04
 MAINTAINER caffe-maint@googlegroups.com
 
 RUN apt-get update && apt-get install -y --no-install-recommends \

From c2dba923b82c669f2998a3174310fbbb5c64c39f Mon Sep 17 00:00:00 2001
From: ZhouYzzz <zhouyz9608@gmail.com>
Date: Wed, 4 May 2016 18:00:12 +0800
Subject: [PATCH 1770/2053] Add test for attribute "phase" in python layer

---
 python/caffe/test/test_python_layer.py | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index e46b7118014..899514e90f1 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -44,6 +44,18 @@ def forward(self, bottom, top):
     def backward(self, top, propagate_down, bottom):
         self.blobs[0].diff[0] = 1
 
+class PhaseLayer(caffe.Layer):
+    """A layer for checking attribute `phase`"""
+
+    def setup(self, bottom, top):
+        pass
+
+    def reshape(self, bootom, top):
+        top[0].reshape()
+
+    def forward(self, bottom, top):
+        top[0].data[()] = self.phase
+
 def python_net_file():
     with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
@@ -76,6 +88,14 @@ def parameter_net_file():
           """)
         return f.name
 
+def phase_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        layer { type: 'Python' name: 'layer' top: 'phase'
+          python_param { module: 'test_python_layer' layer: 'PhaseLayer' } }
+          """)
+        return f.name
+
 
 @unittest.skipIf('Python' not in caffe.layer_type_list(),
     'Caffe built without Python layer support')
@@ -140,3 +160,9 @@ def test_parameter(self):
         self.assertEqual(layer.blobs[0].data[0], 1)
 
         os.remove(net_file)
+
+    def test_phase(self):
+        net_file = phase_net_file()
+        for phase in caffe.TRAIN, caffe.TEST:
+            net = caffe.Net(net_file, phase)
+            self.assertEqual(net.forward()['phase'], phase)

From 5acc17a5bfe010d92cc20766f88eff70d4ae92cc Mon Sep 17 00:00:00 2001
From: Achal Dave <achalddave@live.com>
Date: Wed, 4 May 2016 11:51:00 -0400
Subject: [PATCH 1771/2053] Exit on error and report argument error details.

The statement 'exit' has no effect in Python scripts. Use 'sys.exit()'
instead.
---
 tools/extra/plot_training_log.py.example | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index 4d3ed0d15a9..d98c52d33d5 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -160,7 +160,7 @@ Supported chart types:""" % (len(get_supported_chart_types()) - 1,
     num = len(supported_chart_types)
     for i in xrange(num):
         print '    %d: %s' % (i, supported_chart_types[i])
-    exit
+    sys.exit()
 
 def is_valid_chart_type(chart_type):
     return chart_type >= 0 and chart_type < len(get_supported_chart_types())
@@ -171,17 +171,19 @@ if __name__ == '__main__':
     else:
         chart_type = int(sys.argv[1])
         if not is_valid_chart_type(chart_type):
+            print '%s is not a valid chart type.' % chart_type
             print_help()
         path_to_png = sys.argv[2]
         if not path_to_png.endswith('.png'):
             print 'Path must ends with png' % path_to_png
-            exit            
+            sys.exit()
         path_to_logs = sys.argv[3:]
         for path_to_log in path_to_logs:
             if not os.path.exists(path_to_log):
                 print 'Path does not exist: %s' % path_to_log
-                exit
+                sys.exit()
             if not path_to_log.endswith(get_log_file_suffix()):
+                print 'Log file must end in %s.' % get_log_file_suffix()
                 print_help()
         ## plot_chart accpets multiple path_to_logs
         plot_chart(chart_type, path_to_png, path_to_logs)

From 4f22fceda92a0370f21f64d45d71ef3e354a0312 Mon Sep 17 00:00:00 2001
From: Achal Dave <achalddave@live.com>
Date: Wed, 4 May 2016 11:52:06 -0400
Subject: [PATCH 1772/2053] Remove trailing spaces

---
 tools/extra/plot_training_log.py.example | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index d98c52d33d5..c3b47a81664 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -68,9 +68,9 @@ def get_field_descriptions(chart_type):
         get_chart_type_description_separator())
     y_axis_field = description[0]
     x_axis_field = description[1]
-    return x_axis_field, y_axis_field    
+    return x_axis_field, y_axis_field
 
-def get_field_indecies(x_axis_field, y_axis_field):    
+def get_field_indecies(x_axis_field, y_axis_field):
     data_file_type = get_data_file_type(chart_type)
     fields = create_field_index()[0][data_file_type]
     return fields[x_axis_field], fields[y_axis_field]
@@ -138,8 +138,8 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
     plt.legend(loc = legend_loc, ncol = 1) # ajust ncol to fit the space
     plt.title(get_chart_type_description(chart_type))
     plt.xlabel(x_axis_field)
-    plt.ylabel(y_axis_field)  
-    plt.savefig(path_to_png)     
+    plt.ylabel(y_axis_field)
+    plt.savefig(path_to_png)
     plt.show()
 
 def print_help():
@@ -164,7 +164,7 @@ Supported chart types:""" % (len(get_supported_chart_types()) - 1,
 
 def is_valid_chart_type(chart_type):
     return chart_type >= 0 and chart_type < len(get_supported_chart_types())
-  
+
 if __name__ == '__main__':
     if len(sys.argv) < 4:
         print_help()

From 938918c3f5d0a1a738d2229a337774cea92be95a Mon Sep 17 00:00:00 2001
From: Achal Dave <achalddave@live.com>
Date: Wed, 4 May 2016 11:55:43 -0400
Subject: [PATCH 1773/2053] Reformat to fit in 79 columns

---
 tools/extra/plot_training_log.py.example | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index c3b47a81664..3ea66e38075 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -10,7 +10,8 @@ import matplotlib.legend as lgd
 import matplotlib.markers as mks
 
 def get_log_parsing_script():
-    dirname = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+    dirname = os.path.dirname(os.path.abspath(inspect.getfile(
+        inspect.currentframe())))
     return dirname + '/parse_log.sh'
 
 def get_log_file_suffix():
@@ -61,7 +62,8 @@ def get_data_file_type(chart_type):
     return data_file_type
 
 def get_data_file(chart_type, path_to_log):
-    return os.path.basename(path_to_log) + '.' + get_data_file_type(chart_type).lower()
+    return (os.path.basename(path_to_log) + '.' +
+            get_data_file_type(chart_type).lower())
 
 def get_field_descriptions(chart_type):
     description = get_chart_type_description(chart_type).split(

From c2656f0bc7e1f51b4a82a79e7a5516f0f1fb012f Mon Sep 17 00:00:00 2001
From: Achal Dave <achalddave@live.com>
Date: Wed, 4 May 2016 11:56:05 -0400
Subject: [PATCH 1774/2053] Fix typo (indecies->indices)

---
 tools/extra/plot_training_log.py.example | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index 3ea66e38075..79924ae5a5a 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -72,7 +72,7 @@ def get_field_descriptions(chart_type):
     x_axis_field = description[1]
     return x_axis_field, y_axis_field
 
-def get_field_indecies(x_axis_field, y_axis_field):
+def get_field_indices(x_axis_field, y_axis_field):
     data_file_type = get_data_file_type(chart_type)
     fields = create_field_index()[0][data_file_type]
     return fields[x_axis_field], fields[y_axis_field]
@@ -113,7 +113,7 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
         os.system('%s %s' % (get_log_parsing_script(), path_to_log))
         data_file = get_data_file(chart_type, path_to_log)
         x_axis_field, y_axis_field = get_field_descriptions(chart_type)
-        x, y = get_field_indecies(x_axis_field, y_axis_field)
+        x, y = get_field_indices(x_axis_field, y_axis_field)
         data = load_data(data_file, x, y)
         ## TODO: more systematic color cycle for lines
         color = [random.random(), random.random(), random.random()]

From e6fc797f3be59a12f26d247e2f1f79bf7d8086c4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 4 May 2016 13:31:35 -0700
Subject: [PATCH 1775/2053] [build] note that `make clean` clears build and
 distribute dirs

---
 Makefile.config.example | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile.config.example b/Makefile.config.example
index 8fd49c9c1a7..07bed63ae40 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -98,6 +98,7 @@ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
 # USE_PKG_CONFIG := 1
 
+# N.B. both build and distribute dirs are cleared on `make clean`
 BUILD_DIR := build
 DISTRIBUTE_DIR := distribute
 

From c419f8517b1e1b3d7a07fe212fc6c90a70b519ea Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Thu, 9 Jul 2015 15:49:48 -0700
Subject: [PATCH 1776/2053] add parameter layer for learning any bottom

---
 include/caffe/layers/parameter_layer.hpp | 45 ++++++++++++++++++++++++
 src/caffe/layers/parameter_layer.cpp     |  8 +++++
 src/caffe/proto/caffe.proto              |  7 +++-
 3 files changed, 59 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/parameter_layer.hpp
 create mode 100644 src/caffe/layers/parameter_layer.cpp

diff --git a/include/caffe/layers/parameter_layer.hpp b/include/caffe/layers/parameter_layer.hpp
new file mode 100644
index 00000000000..188b92acbe2
--- /dev/null
+++ b/include/caffe/layers/parameter_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_PARAMETER_LAYER_HPP_
+#define CAFFE_PARAMETER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class ParameterLayer : public Layer<Dtype> {
+ public:
+  explicit ParameterLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    if (this->blobs_.size() > 0) {
+      LOG(INFO) << "Skipping parameter initialization";
+    } else {
+      this->blobs_.resize(1);
+      this->blobs_[0].reset(new Blob<Dtype>());
+      this->blobs_[0]->Reshape(this->layer_param_.parameter_param().shape());
+    }
+    top[0]->Reshape(this->layer_param_.parameter_param().shape());
+  }
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) { }
+  virtual inline const char* type() const { return "Parameter"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+    top[0]->ShareData(*(this->blobs_[0]));
+    top[0]->ShareDiff(*(this->blobs_[0]));
+  }
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom)
+  { }
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/src/caffe/layers/parameter_layer.cpp b/src/caffe/layers/parameter_layer.cpp
new file mode 100644
index 00000000000..fbd326f8469
--- /dev/null
+++ b/src/caffe/layers/parameter_layer.cpp
@@ -0,0 +1,8 @@
+#include "caffe/layers/parameter_layer.hpp"
+
+namespace caffe {
+
+INSTANTIATE_CLASS(ParameterLayer);
+REGISTER_LAYER_CLASS(Parameter);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index ea40e60aa34..15810718631 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 145 (last added: crop_param)
+// LayerParameter next available layer-specific ID: 146 (last added: parameter_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -385,6 +385,7 @@ message LayerParameter {
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
   optional MVNParameter mvn_param = 120;
+  optional ParameterParameter parameter_param = 145;
   optional PoolingParameter pooling_param = 121;
   optional PowerParameter power_param = 122;
   optional PReLUParameter prelu_param = 131;
@@ -873,6 +874,10 @@ message MVNParameter {
   optional float eps = 3 [default = 1e-9];
 }
 
+message ParameterParameter {
+  optional BlobShape shape = 1;
+}
+
 message PoolingParameter {
   enum PoolMethod {
     MAX = 0;

From 4e690b22ae30b0d483ccbe971007f2c6732cceb0 Mon Sep 17 00:00:00 2001
From: crazytan <tjtanjia.tan@gmail.com>
Date: Thu, 28 Apr 2016 18:45:13 -0400
Subject: [PATCH 1777/2053] fix problems in net_surgery.ipynb

---
 examples/net_surgery.ipynb | 45 +++++++++++++++-----------------------
 1 file changed, 18 insertions(+), 27 deletions(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index a6092db0c40..d50d503bfe0 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -22,7 +22,6 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
-    "import Image\n",
     "\n",
     "# Make sure that caffe is on the python path:\n",
     "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
@@ -3511,7 +3510,7 @@
     "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
     "\n",
     "# load image and prepare as a single input batch for Caffe\n",
-    "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+    "im = np.array(caffe.io.load_image('images/cat_gray.jpg', color=False)).squeeze()\n",
     "plt.title(\"original image\")\n",
     "plt.imshow(im)\n",
     "plt.axis('off')\n",
@@ -4480,8 +4479,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "pre-surgery output mean -12.93\n",
-      "post-surgery output mean -11.93\n"
+      "pre-surgery output mean -0.02\n",
+      "post-surgery output mean 0.98\n"
      ]
     }
    ],
@@ -4489,7 +4488,7 @@
     "# pick first filter output\n",
     "conv0 = net.blobs['conv'].data[0, 0]\n",
     "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
-    "# set first filter bias to 10\n",
+    "# set first filter bias to 1\n",
     "net.params['conv'][1].data[0] = 1.\n",
     "net.forward()\n",
     "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
@@ -5494,13 +5493,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1,2c1,2\r\n",
+      "1,2c1\r\n",
       "< # Fully convolutional network version of CaffeNet.\r\n",
       "< name: \"CaffeNetConv\"\r\n",
       "---\r\n",
       "> name: \"CaffeNet\"\r\n",
-      "> input: \"data\"\r\n",
-      "7,11c7\r\n",
+      "7,11c6\r\n",
       "<   input_param {\r\n",
       "<     # initial shape for a fully convolutional network:\r\n",
       "<     # the shape can be set for each input by reshape.\r\n",
@@ -5508,33 +5506,33 @@
       "<   }\r\n",
       "---\r\n",
       ">   input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n",
-      "157,158c153,154\r\n",
+      "157,158c152,153\r\n",
       "<   name: \"fc6-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "---\r\n",
       ">   name: \"fc6\"\r\n",
       ">   type: \"InnerProduct\"\r\n",
-      "160,161c156,157\r\n",
+      "160,161c155,156\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "<   convolution_param {\r\n",
       "---\r\n",
       ">   top: \"fc6\"\r\n",
       ">   inner_product_param {\r\n",
-      "163d158\r\n",
+      "163d157\r\n",
       "<     kernel_size: 6\r\n",
-      "169,170c164,165\r\n",
+      "169,170c163,164\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "175,176c170,171\r\n",
+      "175,176c169,170\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
       "<   top: \"fc6-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc6\"\r\n",
-      "182,186c177,181\r\n",
+      "182,186c176,180\r\n",
       "<   name: \"fc7-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc6-conv\"\r\n",
@@ -5546,21 +5544,21 @@
       ">   bottom: \"fc6\"\r\n",
       ">   top: \"fc7\"\r\n",
       ">   inner_product_param {\r\n",
-      "188d182\r\n",
+      "188d181\r\n",
       "<     kernel_size: 1\r\n",
-      "194,195c188,189\r\n",
+      "194,195c187,188\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "200,201c194,195\r\n",
+      "200,201c193,194\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
       "<   top: \"fc7-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc7\"\r\n",
-      "207,211c201,205\r\n",
+      "207,211c200,204\r\n",
       "<   name: \"fc8-conv\"\r\n",
       "<   type: \"Convolution\"\r\n",
       "<   bottom: \"fc7-conv\"\r\n",
@@ -5572,9 +5570,9 @@
       ">   bottom: \"fc7\"\r\n",
       ">   top: \"fc8\"\r\n",
       ">   inner_product_param {\r\n",
-      "213d206\r\n",
+      "213d205\r\n",
       "<     kernel_size: 1\r\n",
-      "219c212\r\n",
+      "219c211\r\n",
       "<   bottom: \"fc8-conv\"\r\n",
       "---\r\n",
       ">   bottom: \"fc8\"\r\n"
@@ -5610,13 +5608,6 @@
     }
    ],
    "source": [
-    "# Make sure that caffe is on the python path:\n",
-    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-    "import sys\n",
-    "sys.path.insert(0, caffe_root + 'python')\n",
-    "\n",
-    "import caffe\n",
-    "\n",
     "# Load the original network and extract the fully connected layers' parameters.\n",
     "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
     "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",

From da004d7c4e5d52b701762ecc8e20b4a4544a3457 Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@eecs.berkeley.edu>
Date: Thu, 5 May 2016 18:29:30 -0700
Subject: [PATCH 1778/2053] Allow reshaping blobs to size 0.

Also add a test that reshapes a blob to shape (0, 5).
---
 src/caffe/blob.cpp           | 4 +++-
 src/caffe/test/test_blob.cpp | 8 ++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index c86fd5d1d94..4a34e4c5856 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -30,7 +30,9 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
   int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
   for (int i = 0; i < shape.size(); ++i) {
     CHECK_GE(shape[i], 0);
-    CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+    if (count_ != 0) {
+      CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
+    }
     count_ *= shape[i];
     shape_[i] = shape[i];
     shape_data[i] = shape[i];
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index a9d7d519e45..b88562223d0 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -51,6 +51,14 @@ TYPED_TEST(BlobSimpleTest, TestReshape) {
   EXPECT_EQ(this->blob_->count(), 120);
 }
 
+TYPED_TEST(BlobSimpleTest, TestReshapeZero) {
+  vector<int> shape(2);
+  shape[0] = 0;
+  shape[1] = 5;
+  this->blob_->Reshape(shape);
+  EXPECT_EQ(this->blob_->count(), 0);
+}
+
 TYPED_TEST(BlobSimpleTest, TestLegacyBlobProtoShapeEquals) {
   BlobProto blob_proto;
 

From 42642936c2c29e539022e33bc0c691564d7e522d Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Mon, 9 May 2016 11:21:26 -0700
Subject: [PATCH 1779/2053] Catch MDB_MAP_FULL errors from mdb_txn_commit

---
 src/caffe/util/db_lmdb.cpp | 36 +++++++++++++++++++++---------------
 1 file changed, 21 insertions(+), 15 deletions(-)

diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index df83a52a633..4567cd7b93a 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -62,36 +62,42 @@ void LMDBTransaction::Commit() {
   MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
   MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi));
 
-  bool out_of_memory = false;
   for (int i = 0; i < keys.size(); i++) {
     mdb_key.mv_size = keys[i].size();
     mdb_key.mv_data = const_cast<char*>(keys[i].data());
     mdb_data.mv_size = values[i].size();
     mdb_data.mv_data = const_cast<char*>(values[i].data());
 
+    // Add data to the transaction
     int put_rc = mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0);
     if (put_rc == MDB_MAP_FULL) {
-      out_of_memory = true;
-      break;
-    } else {
-      // Failed for some other reason
-      MDB_CHECK(put_rc);
+      // Out of memory - double the map size and retry
+      mdb_txn_abort(mdb_txn);
+      mdb_dbi_close(mdb_env_, mdb_dbi);
+      DoubleMapSize();
+      Commit();
+      return;
     }
+    // May have failed for some other reason
+    MDB_CHECK(put_rc);
   }
 
-  if (!out_of_memory) {
-    // Commit the transaction
-    MDB_CHECK(mdb_txn_commit(mdb_txn));
-    mdb_dbi_close(mdb_env_, mdb_dbi);
-    keys.clear();
-    values.clear();
-  } else {
-    // Double the map size and retry
-    mdb_txn_abort(mdb_txn);
+  // Commit the transaction
+  int commit_rc = mdb_txn_commit(mdb_txn);
+  if (commit_rc == MDB_MAP_FULL) {
+    // Out of memory - double the map size and retry
     mdb_dbi_close(mdb_env_, mdb_dbi);
     DoubleMapSize();
     Commit();
+    return;
   }
+  // May have failed for some other reason
+  MDB_CHECK(commit_rc);
+
+  // Cleanup after successful commit
+  mdb_dbi_close(mdb_env_, mdb_dbi);
+  keys.clear();
+  values.clear();
 }
 
 void LMDBTransaction::DoubleMapSize() {

From a934ca54f3633479ea0573346c510df4f757df6c Mon Sep 17 00:00:00 2001
From: ray glover <ray.glover@uk.ibm.com>
Date: Tue, 10 May 2016 15:44:47 +0100
Subject: [PATCH 1780/2053] [build] (CMake) customisable Caffe
 version/soversion

---
 CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c5d99cef9dd..da7142c9b3c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,8 +10,8 @@ endif()
 project(Caffe C CXX)
 
 # ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.0.0-rc3")
-set(CAFFE_TARGET_SOVERSION "1.0.0-rc3")
+set(CAFFE_TARGET_VERSION "1.0.0-rc3" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc3" CACHE STRING "Caffe soname version")
 add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 
 # ---[ Using cmake scripts and modules

From bb6ca4720ea41b8e9bdf162f63eb2757571a2e17 Mon Sep 17 00:00:00 2001
From: gdh1995 <gdh1995@qq.com>
Date: Wed, 11 May 2016 20:51:07 +0800
Subject: [PATCH 1781/2053] a comment misses a space char

---
 src/caffe/util/db_lmdb.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index 4567cd7b93a..fb1d4956aa1 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -10,7 +10,7 @@ namespace caffe { namespace db {
 void LMDB::Open(const string& source, Mode mode) {
   MDB_CHECK(mdb_env_create(&mdb_env_));
   if (mode == NEW) {
-    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << " failed";
   }
   int flags = 0;
   if (mode == READ) {

From 078d9981a2c64b19834decdef3ce3dd032b667c0 Mon Sep 17 00:00:00 2001
From: Kyle Mills <kyle.mills@uoit.net>
Date: Fri, 13 May 2016 11:15:33 -0400
Subject: [PATCH 1782/2053] fixed typo in io.py

---
 python/caffe/io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index cee5ace2e88..e1759beb587 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -46,7 +46,7 @@ def array_to_blobproto(arr, diff=None):
     return blob
 
 
-def arraylist_to_blobprotovecor_str(arraylist):
+def arraylist_to_blobprotovector_str(arraylist):
     """Converts a list of arrays to a serialized blobprotovec, which could be
     then passed to a network for processing.
     """

From 87c9dc397081248dd3d40e0dabce191557bcfc15 Mon Sep 17 00:00:00 2001
From: Yale Song <yalesong@yahoo-inc.com>
Date: Fri, 13 May 2016 16:06:59 -0400
Subject: [PATCH 1783/2053] Fix Makefile CUDA_VERSION extraction on OSX
 Yosemite

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 568d9c2774d..403e00a38a1 100644
--- a/Makefile
+++ b/Makefile
@@ -272,7 +272,7 @@ endif
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
 	ifneq ($(CPU_ONLY), 1)
-		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | grep -o '[0-9.]*')
+		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release [0-9.]*' | tr -d '[a-z ]')
 		ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
 			CXXFLAGS += -stdlib=libstdc++
 			LINKFLAGS += -stdlib=libstdc++

From e8ec9f806bd0051f2ee8d1d2737afdafe314f9e4 Mon Sep 17 00:00:00 2001
From: Bob Poekert <bob@poekert.com>
Date: Fri, 13 May 2016 22:06:33 -0700
Subject: [PATCH 1784/2053] add check for background and foreground window size
 > 0 in WindowData layer

---
 src/caffe/layers/window_data_layer.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 4ca8315d791..103dd4b6af8 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -265,6 +265,9 @@ void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   const int num_samples[2] = { batch_size - num_fg, num_fg };
 
   int item_id = 0;
+  CHECK_GT(fg_windows_.size(), 0);
+  CHECK_GT(bg_windows_.size(), 0);
+
   // sample from bg set then fg set
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {

From b43c8e43a95608a00033f8f8867d32a201e5eed4 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Mon, 16 May 2016 14:03:38 -0700
Subject: [PATCH 1785/2053] Add cuDNN v5 support, drop cuDNN v3 support

cuDNN v4 is still supported.
---
 include/caffe/layers/cudnn_relu_layer.hpp    |  1 +
 include/caffe/layers/cudnn_sigmoid_layer.hpp |  1 +
 include/caffe/layers/cudnn_tanh_layer.hpp    |  1 +
 include/caffe/util/cudnn.hpp                 | 24 +++++++++++++++++---
 src/caffe/layers/cudnn_conv_layer.cu         | 12 ++--------
 src/caffe/layers/cudnn_relu_layer.cpp        |  1 +
 src/caffe/layers/cudnn_relu_layer.cu         | 23 +++++++++++++++++--
 src/caffe/layers/cudnn_sigmoid_layer.cpp     |  2 ++
 src/caffe/layers/cudnn_sigmoid_layer.cu      | 23 +++++++++++++++++--
 src/caffe/layers/cudnn_tanh_layer.cpp        |  1 +
 src/caffe/layers/cudnn_tanh_layer.cu         | 23 +++++++++++++++++--
 11 files changed, 93 insertions(+), 19 deletions(-)

diff --git a/include/caffe/layers/cudnn_relu_layer.hpp b/include/caffe/layers/cudnn_relu_layer.hpp
index e01f568abc9..a1cb29e7c5f 100644
--- a/include/caffe/layers/cudnn_relu_layer.hpp
+++ b/include/caffe/layers/cudnn_relu_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNReLULayer : public ReLULayer<Dtype> {
   cudnnHandle_t             handle_;
   cudnnTensorDescriptor_t bottom_desc_;
   cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activ_desc_;
 };
 #endif
 
diff --git a/include/caffe/layers/cudnn_sigmoid_layer.hpp b/include/caffe/layers/cudnn_sigmoid_layer.hpp
index 9c597958b0b..7b3486f8a7e 100644
--- a/include/caffe/layers/cudnn_sigmoid_layer.hpp
+++ b/include/caffe/layers/cudnn_sigmoid_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
   cudnnHandle_t             handle_;
   cudnnTensorDescriptor_t bottom_desc_;
   cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activ_desc_;
 };
 #endif
 
diff --git a/include/caffe/layers/cudnn_tanh_layer.hpp b/include/caffe/layers/cudnn_tanh_layer.hpp
index c0f0053f71e..59e758d7031 100644
--- a/include/caffe/layers/cudnn_tanh_layer.hpp
+++ b/include/caffe/layers/cudnn_tanh_layer.hpp
@@ -37,6 +37,7 @@ class CuDNNTanHLayer : public TanHLayer<Dtype> {
   cudnnHandle_t             handle_;
   cudnnTensorDescriptor_t bottom_desc_;
   cudnnTensorDescriptor_t top_desc_;
+  cudnnActivationDescriptor_t activ_desc_;
 };
 #endif
 
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index 8a7e17c6cd4..a7d8dbbad4c 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -91,8 +91,13 @@ template <typename Dtype>
 inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
     int n, int c, int h, int w) {
   CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
-      n, c, h, w));
+      CUDNN_TENSOR_NCHW, n, c, h, w));
+#else
+  CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
+      CUDNN_TENSOR_NCHW, n, c, h, w));
+#endif
 }
 
 template <typename Dtype>
@@ -123,8 +128,21 @@ inline void createPoolingDesc(cudnnPoolingDescriptor_t* pool_desc,
     LOG(FATAL) << "Unknown pooling method.";
   }
   CUDNN_CHECK(cudnnCreatePoolingDescriptor(pool_desc));
-  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode, h, w,
-        pad_h, pad_w, stride_h, stride_w));
+#if CUDNN_VERSION_MIN(5, 0, 0)
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor(*pool_desc, *mode,
+        CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
+#else
+  CUDNN_CHECK(cudnnSetPooling2dDescriptor_v4(*pool_desc, *mode,
+        CUDNN_PROPAGATE_NAN, h, w, pad_h, pad_w, stride_h, stride_w));
+#endif
+}
+
+template <typename Dtype>
+inline void createActivationDescriptor(cudnnActivationDescriptor_t* activ_desc,
+    cudnnActivationMode_t mode) {
+  CUDNN_CHECK(cudnnCreateActivationDescriptor(activ_desc));
+  CUDNN_CHECK(cudnnSetActivationDescriptor(*activ_desc, mode,
+                                           CUDNN_PROPAGATE_NAN, Dtype(0)));
 }
 
 }  // namespace cudnn
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 42c4fd0260c..8bc5346248c 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -30,19 +30,11 @@ void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
-#if CUDNN_VERSION_MIN(4, 0, 0)
         CUDNN_CHECK(cudnnAddTensor(handle_[g],
               cudnn::dataType<Dtype>::one,
               bias_desc_, bias_data + bias_offset_ * g,
               cudnn::dataType<Dtype>::one,
               top_descs_[i], top_data + top_offset_ * g));
-#else
-        CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
-              cudnn::dataType<Dtype>::one,
-              bias_desc_, bias_data + bias_offset_ * g,
-              cudnn::dataType<Dtype>::one,
-              top_descs_[i], top_data + top_offset_ * g));
-#endif
       }
     }
 
@@ -82,7 +74,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = bottom[i]->gpu_data();
-        CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3(
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter(
               handle_[1*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               bottom_descs_[i], bottom_data + bottom_offset_ * g,
@@ -100,7 +92,7 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           weight = this->blobs_[0]->gpu_data();
         }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
-        CUDNN_CHECK(cudnnConvolutionBackwardData_v3(
+        CUDNN_CHECK(cudnnConvolutionBackwardData(
               handle_[2*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               filter_desc_, weight + this->weight_offset_ * g,
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index c86c6907113..795e0a9efb0 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -13,6 +13,7 @@ void CuDNNReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createActivationDescriptor<Dtype>(&activ_desc_, CUDNN_ACTIVATION_RELU);
   handles_setup_ = true;
 }
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 9f617183baa..e7928bbd6e0 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -15,12 +15,21 @@ void CuDNNReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-        CUDNN_ACTIVATION_RELU,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
+#else
+  CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
@@ -40,13 +49,23 @@ void CuDNNReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-        CUDNN_ACTIVATION_RELU,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNReLULayer);
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index ccb955cdaff..3ce6aef1764 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -13,6 +13,8 @@ void CuDNNSigmoidLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createActivationDescriptor<Dtype>(&activ_desc_,
+      CUDNN_ACTIVATION_SIGMOID);
   handles_setup_ = true;
 }
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index e2a4b460c6c..48d6cbab6de 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -10,12 +10,21 @@ void CuDNNSigmoidLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-        CUDNN_ACTIVATION_SIGMOID,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
+#else
+  CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
@@ -30,13 +39,23 @@ void CuDNNSigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-        CUDNN_ACTIVATION_SIGMOID,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNSigmoidLayer);
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 1a56418227c..e87dd9de0ab 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -13,6 +13,7 @@ void CuDNNTanHLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CUDNN_CHECK(cudnnCreate(&handle_));
   cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
   cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+  cudnn::createActivationDescriptor<Dtype>(&activ_desc_, CUDNN_ACTIVATION_TANH);
   handles_setup_ = true;
 }
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index 89df28a3e8b..6b5d7ae7ea7 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -10,12 +10,21 @@ void CuDNNTanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationForward(this->handle_,
-        CUDNN_ACTIVATION_TANH,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->top_desc_, top_data));
+#else
+  CUDNN_CHECK(cudnnActivationForward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->top_desc_, top_data));
+#endif
 }
 
 template <typename Dtype>
@@ -31,13 +40,23 @@ void CuDNNTanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
 
+#if CUDNN_VERSION_MIN(5, 0, 0)
   CUDNN_CHECK(cudnnActivationBackward(this->handle_,
-        CUDNN_ACTIVATION_TANH,
+        activ_desc_,
         cudnn::dataType<Dtype>::one,
         this->top_desc_, top_data, this->top_desc_, top_diff,
         this->bottom_desc_, bottom_data,
         cudnn::dataType<Dtype>::zero,
         this->bottom_desc_, bottom_diff));
+#else
+  CUDNN_CHECK(cudnnActivationBackward_v4(this->handle_,
+        activ_desc_,
+        cudnn::dataType<Dtype>::one,
+        this->top_desc_, top_data, this->top_desc_, top_diff,
+        this->bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        this->bottom_desc_, bottom_diff));
+#endif
 }
 
 INSTANTIATE_LAYER_GPU_FUNCS(CuDNNTanHLayer);

From 8730b146b7e19af189b9086e59fd1d5bc4214698 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Mon, 16 May 2016 14:32:34 -0700
Subject: [PATCH 1786/2053] Update Dockerfile to cuDNN v5

---
 docker/Makefile                  | 2 +-
 docker/standalone/gpu/Dockerfile | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docker/Makefile b/docker/Makefile
index 0de887d0e19..3a6575b0c43 100644
--- a/docker/Makefile
+++ b/docker/Makefile
@@ -22,7 +22,7 @@ docker_files: standalone_files
 
 standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile
 
-FROM_GPU = "nvidia/cuda:7.5-cudnn4-devel-ubuntu14.04"
+FROM_GPU = "nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04"
 FROM_CPU = "ubuntu:14.04"
 GPU_CMAKE_ARGS = -DUSE_CUDNN=1
 CPU_CMAKE_ARGS = -DCPU_ONLY=1
diff --git a/docker/standalone/gpu/Dockerfile b/docker/standalone/gpu/Dockerfile
index 371aad5b1e9..daf6a7223ff 100644
--- a/docker/standalone/gpu/Dockerfile
+++ b/docker/standalone/gpu/Dockerfile
@@ -1,4 +1,4 @@
-FROM nvidia/cuda:7.5-cudnn4-devel-ubuntu14.04
+FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
 MAINTAINER caffe-maint@googlegroups.com
 
 RUN apt-get update && apt-get install -y --no-install-recommends \

From 1c3af7078b64ef71a5bb0c2cef6fee528917adac Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Mon, 16 May 2016 14:35:40 -0700
Subject: [PATCH 1787/2053] Update supported cuDNN version in the documentation

---
 docs/installation.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 1e29a49d82d..4aac7c42d27 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -40,14 +40,14 @@ Optional dependencies:
 
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v4)
+* cuDNN for GPU acceleration (v5)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v4; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v5; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 

From a8cc860d6bef79edcdfa07d5da4195ba67714991 Mon Sep 17 00:00:00 2001
From: crazytan <tjtanjia.tan@gmail.com>
Date: Wed, 27 Apr 2016 01:01:30 -0400
Subject: [PATCH 1788/2053] handle image names with spaces

---
 examples/images/cat gray.jpg             | Bin 0 -> 92726 bytes
 src/caffe/layers/image_data_layer.cpp    |   9 +++--
 src/caffe/test/test_image_data_layer.cpp |  44 +++++++++++++++++++++--
 tools/convert_imageset.cpp               |   9 +++--
 4 files changed, 53 insertions(+), 9 deletions(-)
 create mode 100644 examples/images/cat gray.jpg

diff --git a/examples/images/cat gray.jpg b/examples/images/cat gray.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..43c5ce377167a49624c3e9d61e83a04becb8df9e
GIT binary patch
literal 92726
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diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 62fda4accce..56d354655dc 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -37,10 +37,13 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const string& source = this->layer_param_.image_data_param().source();
   LOG(INFO) << "Opening file " << source;
   std::ifstream infile(source.c_str());
-  string filename;
+  string line;
+  size_t pos;
   int label;
-  while (infile >> filename >> label) {
-    lines_.push_back(std::make_pair(filename, label));
+  while (std::getline(infile, line)) {
+    pos = line.find_last_of(' ');
+    label = atoi(line.substr(pos + 1).c_str());
+    lines_.push_back(std::make_pair(line.substr(0, pos), label));
   }
 
   if (this->layer_param_.image_data_param().shuffle()) {
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index a4080ccd145..ce5e0bc62d6 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -34,16 +34,24 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
     std::ofstream outfile(filename_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_;
     for (int i = 0; i < 5; ++i) {
-      outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i;
+      outfile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << i << std::endl;
     }
     outfile.close();
     // Create test input file for images of distinct sizes.
     MakeTempFilename(&filename_reshape_);
     std::ofstream reshapefile(filename_reshape_.c_str(), std::ofstream::out);
     LOG(INFO) << "Using temporary file " << filename_reshape_;
-    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0;
-    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0 << std::endl;
+    reshapefile << EXAMPLES_SOURCE_DIR "images/fish-bike.jpg " << 1
+                << std::endl;
     reshapefile.close();
+    // Create test input file for images with space in names
+    MakeTempFilename(&filename_space_);
+    std::ofstream spacefile(filename_space_.c_str(), std::ofstream::out);
+    LOG(INFO) << "Using temporary file " << filename_space_;
+    spacefile << EXAMPLES_SOURCE_DIR "images/cat.jpg " << 0 << std::endl;
+    spacefile << EXAMPLES_SOURCE_DIR "images/cat gray.jpg " << 1 << std::endl;
+    spacefile.close();
   }
 
   virtual ~ImageDataLayerTest() {
@@ -54,6 +62,7 @@ class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
   int seed_;
   string filename_;
   string filename_reshape_;
+  string filename_space_;
   Blob<Dtype>* const blob_top_data_;
   Blob<Dtype>* const blob_top_label_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
@@ -177,5 +186,34 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   }
 }
 
+TYPED_TEST(ImageDataLayerTest, TestSpace) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  ImageDataParameter* image_data_param = param.mutable_image_data_param();
+  image_data_param->set_batch_size(1);
+  image_data_param->set_source(this->filename_space_.c_str());
+  image_data_param->set_shuffle(false);
+  ImageDataLayer<Dtype> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_label_->num(), 1);
+  EXPECT_EQ(this->blob_top_label_->channels(), 1);
+  EXPECT_EQ(this->blob_top_label_->height(), 1);
+  EXPECT_EQ(this->blob_top_label_->width(), 1);
+  // cat.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  EXPECT_EQ(this->blob_top_label_->cpu_data()[0], 0);
+  // cat gray.jpg
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_data_->num(), 1);
+  EXPECT_EQ(this->blob_top_data_->channels(), 3);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
+  EXPECT_EQ(this->blob_top_label_->cpu_data()[0], 1);
+}
+
 }  // namespace caffe
 #endif  // USE_OPENCV
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 9c52bfa0ef8..90cdb15d427 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -73,10 +73,13 @@ int main(int argc, char** argv) {
 
   std::ifstream infile(argv[2]);
   std::vector<std::pair<std::string, int> > lines;
-  std::string filename;
+  std::string line;
+  size_t pos;
   int label;
-  while (infile >> filename >> label) {
-    lines.push_back(std::make_pair(filename, label));
+  while (std::getline(infile, line)) {
+    pos = line.find_last_of(' ');
+    label = atoi(line.substr(pos + 1).c_str());
+    lines.push_back(std::make_pair(line.substr(0, pos), label));
   }
   if (FLAGS_shuffle) {
     // randomly shuffle data

From d4e7c93a6873f75a53d7618e82343e4b5b8a239e Mon Sep 17 00:00:00 2001
From: Aaron Schumacher <ajschumacher@gmail.com>
Date: Thu, 19 May 2016 14:04:22 -0500
Subject: [PATCH 1789/2053] convert non-uint8 dtypes to float; refs #2391

As recommended by @longjon, this will allow `caffe.io.array_to_datum` to handle, for example, numpy.float32 arrays.

It might be worth noting that `datum.float_data` is stored as protobuf type 2, which is float32, as opposed to protobuf type 1, which is float64. It is a little unintuitive that caffe currently requires data to be passed in as float64 but then writes float32 to LMDB. To demonstrate this:

```python
datum = caffe.io.array_to_datum(np.array([[[0.9]]]))
caffe.io.datum_to_array(datum)
# array([[[ 0.9]]])
datum_str = datum.SerializeToString()
new_datum = caffe.proto.caffe_pb2.Datum()
new_datum.ParseFromString(datum_str)
caffe.io.datum_to_array(new_datum)
# array([[[ 0.89999998]]])
```

This behavior is somewhat hidden because `datum_to_array` returns type float64, even though the data doesn't actually have that resolution if it has been stored as protobuf text anywhere (for example in LMDB).

Alternative solutions:
 * Require and return float32, consistent with the protobuf representation.
 * Change the protobuf to allow float32 or float64 and update surrounding code to support this.
---
 python/caffe/io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index e1759beb587..966c164cffd 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -75,7 +75,7 @@ def array_to_datum(arr, label=None):
     if arr.dtype == np.uint8:
         datum.data = arr.tostring()
     else:
-        datum.float_data.extend(arr.flat)
+        datum.float_data.extend(arr.astype(float).flat)
     if label is not None:
         datum.label = label
     return datum

From 4bf4b186076b054a0fa06103bc8989a3577468ba Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Tue, 24 May 2016 10:36:23 -0700
Subject: [PATCH 1790/2053] Overhaul TravisCI

* Run on Ubuntu 14.04
* Test cuDNN builds
* Build with OpenBLAS

NOTE: Python3 build only works with CMake
---
 .travis.yml                                   |  58 ++++++----
 scripts/travis/build.sh                       |  13 +++
 scripts/travis/configure-cmake.sh             |  32 ++++++
 scripts/travis/configure-make.sh              |  36 ++++++
 scripts/travis/configure.sh                   |  11 ++
 scripts/travis/defaults.sh                    |  10 ++
 scripts/travis/install-deps.sh                | 105 ++++++++++++++++++
 scripts/travis/install-python-deps.sh         |  14 +++
 scripts/travis/setup-venv.sh                  |  18 +++
 scripts/travis/test.sh                        |  19 ++++
 scripts/travis/travis_build_and_test.sh       |  54 ---------
 scripts/travis/travis_install.sh              | 101 -----------------
 .../travis/travis_setup_makefile_config.sh    |  31 ------
 13 files changed, 292 insertions(+), 210 deletions(-)
 create mode 100755 scripts/travis/build.sh
 create mode 100644 scripts/travis/configure-cmake.sh
 create mode 100644 scripts/travis/configure-make.sh
 create mode 100755 scripts/travis/configure.sh
 create mode 100755 scripts/travis/defaults.sh
 create mode 100755 scripts/travis/install-deps.sh
 create mode 100755 scripts/travis/install-python-deps.sh
 create mode 100755 scripts/travis/setup-venv.sh
 create mode 100755 scripts/travis/test.sh
 delete mode 100755 scripts/travis/travis_build_and_test.sh
 delete mode 100755 scripts/travis/travis_install.sh
 delete mode 100755 scripts/travis/travis_setup_makefile_config.sh

diff --git a/.travis.yml b/.travis.yml
index 4dc7ed72d6c..92d740cd88b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,40 +1,50 @@
-# Use a build matrix to do two builds in parallel:
-# one using CMake, and one using make.
+dist: trusty
+sudo: required
+
+language: cpp
+compiler: gcc
+
 env:
+  global:
+    - NUM_THREADS=4
   matrix:
-    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true
-    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3
-    - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true
-    - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true
-    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false
-    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3
+    # Use a build matrix to test many builds in parallel
+    # envvar defaults:
+    #   WITH_CMAKE: false
+    #   WITH_PYTHON3: false
+    #   WITH_IO: true
+    #   WITH_CUDA: false
+    #   WITH_CUDNN: false
+    - BUILD_NAME="default-make"
+#   - BUILD_NAME="python3-make" WITH_PYTHON3=true
+    - BUILD_NAME="no-io-make" WITH_IO=false
+    - BUILD_NAME="cuda-make" WITH_CUDA=true
+    - BUILD_NAME="cudnn-make" WITH_CUDA=true WITH_CUDNN=true
 
-language: cpp
+    - BUILD_NAME="default-cmake" WITH_CMAKE=true
+    - BUILD_NAME="python3-cmake" WITH_CMAKE=true WITH_PYTHON3=true
+    - BUILD_NAME="no-io-cmake" WITH_CMAKE=true WITH_IO=false
+    - BUILD_NAME="cuda-cmake" WITH_CMAKE=true WITH_CUDA=true
+    - BUILD_NAME="cudnn-cmake" WITH_CMAKE=true WITH_CUDA=true WITH_CUDNN=true
 
-# Cache Ubuntu apt packages.
 cache:
   apt: true
-  directories:
-  - /home/travis/miniconda
-  - /home/travis/miniconda2
-  - /home/travis/miniconda3
-
-compiler: gcc
 
 before_install:
-  - export NUM_THREADS=4
-  - export SCRIPTS=./scripts/travis
-  - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION"
+  - source ./scripts/travis/defaults.sh
 
 install:
-  - sudo -E $SCRIPTS/travis_install.sh
+  - sudo -E ./scripts/travis/install-deps.sh
+  - ./scripts/travis/setup-venv.sh ~/venv
+  - source ~/venv/bin/activate
+  - ./scripts/travis/install-python-deps.sh
 
 before_script:
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib
-  - export PATH=$CONDA_DIR/bin:$PATH
-  - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
+  - ./scripts/travis/configure.sh
 
-script: $SCRIPTS/travis_build_and_test.sh
+script:
+  - ./scripts/travis/build.sh
+  - ./scripts/travis/test.sh
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
diff --git a/scripts/travis/build.sh b/scripts/travis/build.sh
new file mode 100755
index 00000000000..bb9406f046c
--- /dev/null
+++ b/scripts/travis/build.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+# build the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_CMAKE ; then
+  make --jobs $NUM_THREADS all test pycaffe warn
+else
+  cd build
+  make --jobs $NUM_THREADS all test.testbin
+fi
+make lint
diff --git a/scripts/travis/configure-cmake.sh b/scripts/travis/configure-cmake.sh
new file mode 100644
index 00000000000..772f1e2ce8d
--- /dev/null
+++ b/scripts/travis/configure-cmake.sh
@@ -0,0 +1,32 @@
+# CMake configuration
+
+mkdir -p build
+cd build
+
+ARGS="-DCMAKE_BUILD_TYPE=Release -DBLAS=Open"
+
+if $WITH_PYTHON3 ; then
+  ARGS="$ARGS -Dpython_version=3"
+fi
+
+if $WITH_IO ; then
+  ARGS="$ARGS -DUSE_OPENCV=On -DUSE_LMDB=On -DUSE_LEVELDB=On"
+else
+  ARGS="$ARGS -DUSE_OPENCV=Off -DUSE_LMDB=Off -DUSE_LEVELDB=Off"
+fi
+
+if $WITH_CUDA ; then
+  # Only build SM50
+  ARGS="$ARGS -DCPU_ONLY=Off -DCUDA_ARCH_NAME=Manual -DCUDA_ARCH_BIN=\"50\" -DCUDA_ARCH_PTX=\"\""
+else
+  ARGS="$ARGS -DCPU_ONLY=On"
+fi
+
+if $WITH_CUDNN ; then
+  ARGS="$ARGS -DUSE_CUDNN=On"
+else
+  ARGS="$ARGS -DUSE_CUDNN=Off"
+fi
+
+cmake .. $ARGS
+
diff --git a/scripts/travis/configure-make.sh b/scripts/travis/configure-make.sh
new file mode 100644
index 00000000000..ddc40fffa9d
--- /dev/null
+++ b/scripts/travis/configure-make.sh
@@ -0,0 +1,36 @@
+# raw Makefile configuration
+
+LINE () {
+  echo "$@" >> Makefile.config
+}
+
+cp Makefile.config.example Makefile.config
+
+LINE "BLAS := open"
+LINE "WITH_PYTHON_LAYER := 1"
+
+if $WITH_PYTHON3 ; then
+  # TODO(lukeyeager) this path is currently disabled because of test errors like:
+  #   ImportError: dynamic module does not define init function (PyInit__caffe)
+  LINE "PYTHON_LIBRARIES := python3.4m boost_python-py34"
+  LINE "PYTHON_INCLUDE := /usr/include/python3.4 /usr/lib/python3/dist-packages/numpy/core/include"
+  LINE "INCLUDE_DIRS := \$(INCLUDE_DIRS) \$(PYTHON_INCLUDE)"
+fi
+
+if ! $WITH_IO ; then
+  LINE "USE_OPENCV := 0"
+  LINE "USE_LEVELDB := 0"
+  LINE "USE_LMDB := 0"
+fi
+
+if $WITH_CUDA ; then
+  # Only build SM50
+  LINE "CUDA_ARCH := -gencode arch=compute_50,code=sm_50"
+else
+  LINE "CPU_ONLY := 1"
+fi
+
+if $WITH_CUDNN ; then
+  LINE "USE_CUDNN := 1"
+fi
+
diff --git a/scripts/travis/configure.sh b/scripts/travis/configure.sh
new file mode 100755
index 00000000000..ef740c8982e
--- /dev/null
+++ b/scripts/travis/configure.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+# configure the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_CMAKE ; then
+  source $BASEDIR/configure-make.sh
+else
+  source $BASEDIR/configure-cmake.sh
+fi
diff --git a/scripts/travis/defaults.sh b/scripts/travis/defaults.sh
new file mode 100755
index 00000000000..d69c0a7d964
--- /dev/null
+++ b/scripts/travis/defaults.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+# set default environment variables
+
+set -e
+
+WITH_CMAKE=${WITH_CMAKE:-false}
+WITH_PYTHON3=${WITH_PYTHON3:-false}
+WITH_IO=${WITH_IO:-true}
+WITH_CUDA=${WITH_CUDA:-false}
+WITH_CUDNN=${WITH_CUDNN:-false}
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
new file mode 100755
index 00000000000..f7bfe4c4df9
--- /dev/null
+++ b/scripts/travis/install-deps.sh
@@ -0,0 +1,105 @@
+#!/bin/bash
+# install dependencies
+# (this script must be run as root)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+apt-get -y update
+apt-get install -y --no-install-recommends \
+  build-essential \
+  libboost-filesystem-dev \
+  libboost-python-dev \
+  libboost-system-dev \
+  libboost-thread-dev \
+  libgflags-dev \
+  libgoogle-glog-dev \
+  libhdf5-serial-dev \
+  libopenblas-dev \
+  python-virtualenv \
+  wget
+
+if $WITH_CMAKE ; then
+  apt-get install -y --no-install-recommends cmake
+fi
+
+if ! $WITH_PYTHON3 ; then
+  # Python2
+  apt-get install -y --no-install-recommends \
+    libprotobuf-dev \
+    protobuf-compiler \
+    python-dev \
+    python-numpy \
+    python-protobuf \
+    python-skimage
+else
+  # Python3
+  apt-get install -y --no-install-recommends \
+    python3-dev \
+    python3-numpy \
+    python3-skimage
+
+  # build Protobuf3 since it's needed for Python3
+  echo "Building protobuf3 from source ..."
+  pushd .
+  PROTOBUF3_DIR=~/protobuf3-build
+  rm -rf $PROTOBUF3_DIR
+  mkdir $PROTOBUF3_DIR
+
+  # install some more dependencies required to build protobuf3
+  apt-get install -y --no-install-recommends \
+    curl \
+    dh-autoreconf \
+    unzip
+
+  wget https://github.com/google/protobuf/archive/v3.0.0-beta-3.tar.gz -O protobuf3.tar.gz
+  tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
+  rm protobuf3.tar.gz
+  cd $PROTOBUF3_DIR
+  ./autogen.sh
+  ./configure --prefix=/usr
+  make --jobs=$NUM_THREADS
+  make install
+  popd
+fi
+
+if $WITH_IO ; then
+  apt-get install -y --no-install-recommends \
+    libleveldb-dev \
+    liblmdb-dev \
+    libopencv-dev \
+    libsnappy-dev
+fi
+
+if $WITH_CUDA ; then
+  # install repo packages
+  CUDA_REPO_PKG=cuda-repo-ubuntu1404_7.5-18_amd64.deb
+  wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/$CUDA_REPO_PKG
+  dpkg -i $CUDA_REPO_PKG
+  rm $CUDA_REPO_PKG
+
+  if $WITH_CUDNN ; then
+    ML_REPO_PKG=nvidia-machine-learning-repo_4.0-2_amd64.deb
+    wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/$ML_REPO_PKG
+    dpkg -i $ML_REPO_PKG
+  fi
+
+  # update package lists
+  apt-get -y update
+
+  # install packages
+  CUDA_PKG_VERSION="7-5"
+  CUDA_VERSION="7.5"
+  apt-get install -y --no-install-recommends \
+    cuda-core-$CUDA_PKG_VERSION \
+    cuda-cudart-dev-$CUDA_PKG_VERSION \
+    cuda-cublas-dev-$CUDA_PKG_VERSION \
+    cuda-curand-dev-$CUDA_PKG_VERSION
+  # manually create CUDA symlink
+  ln -s /usr/local/cuda-$CUDA_VERSION /usr/local/cuda
+
+  if $WITH_CUDNN ; then
+    apt-get install -y --no-install-recommends libcudnn5-dev
+  fi
+fi
+
diff --git a/scripts/travis/install-python-deps.sh b/scripts/travis/install-python-deps.sh
new file mode 100755
index 00000000000..eeec302791f
--- /dev/null
+++ b/scripts/travis/install-python-deps.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+# install extra Python dependencies
+# (must come after setup-venv)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if ! $WITH_PYTHON3 ; then
+  # Python2
+  :
+else
+  # Python3
+  pip install --pre protobuf==3.0.0b3
+fi
diff --git a/scripts/travis/setup-venv.sh b/scripts/travis/setup-venv.sh
new file mode 100755
index 00000000000..81245f146da
--- /dev/null
+++ b/scripts/travis/setup-venv.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# setup a Python virtualenv
+# (must come after install-deps)
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+VENV_DIR=${1:-~/venv}
+
+# setup our own virtualenv
+if $WITH_PYTHON3; then
+    PYTHON_EXE='/usr/bin/python3'
+else
+    PYTHON_EXE='/usr/bin/python2'
+fi
+
+# use --system-site-packages so that Python will use deb packages
+virtualenv $VENV_DIR -p $PYTHON_EXE --system-site-packages
diff --git a/scripts/travis/test.sh b/scripts/travis/test.sh
new file mode 100755
index 00000000000..fedd7e6b56e
--- /dev/null
+++ b/scripts/travis/test.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# test the project
+
+BASEDIR=$(dirname $0)
+source $BASEDIR/defaults.sh
+
+if $WITH_CUDA ; then
+  echo "Skipping tests for CUDA build"
+  exit 0
+fi
+
+if ! $WITH_CMAKE ; then
+  make runtest
+  make pytest
+else
+  cd build
+  make runtest
+  make pytest
+fi
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
deleted file mode 100755
index 174f1ee5a0a..00000000000
--- a/scripts/travis/travis_build_and_test.sh
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/bash
-# Script called by Travis to build and test Caffe.
-# Travis CI tests are CPU-only for lack of compatible hardware.
-
-set -e
-MAKE="make --jobs=$NUM_THREADS --keep-going"
-
-if $WITH_CMAKE; then
-  mkdir build
-  cd build
-  CPU_ONLY=" -DCPU_ONLY=ON"
-  if ! $WITH_CUDA; then
-    CPU_ONLY=" -DCPU_ONLY=OFF"
-  fi
-  PYTHON_ARGS=""
-  if [ "$PYTHON_VERSION" = "3" ]; then
-    PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
-  fi
-  if $WITH_IO; then
-    IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON"
-  else
-    IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF"
-  fi
-  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS ..
-  $MAKE
-  $MAKE pytest
-  if ! $WITH_CUDA; then
-    $MAKE runtest
-    $MAKE lint
-  fi
-  $MAKE clean
-  cd -
-else
-  if ! $WITH_CUDA; then
-    export CPU_ONLY=1
-  fi
-  if $WITH_IO; then
-    export USE_LMDB=1
-    export USE_LEVELDB=1
-    export USE_OPENCV=1
-  fi
-  $MAKE all test pycaffe warn lint || true
-  if ! $WITH_CUDA; then
-    $MAKE runtest
-  fi
-  $MAKE all
-  $MAKE test
-  $MAKE pycaffe
-  $MAKE pytest
-  $MAKE warn
-  if ! $WITH_CUDA; then
-    $MAKE lint
-  fi
-fi
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
deleted file mode 100755
index 091e92431f0..00000000000
--- a/scripts/travis/travis_install.sh
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/bin/bash
-# This script must be run with sudo.
-
-set -e
-
-MAKE="make --jobs=$NUM_THREADS"
-# Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
-
-# This ppa is for gflags and glog
-add-apt-repository -y ppa:tuleu/precise-backports
-apt-get -y update
-apt-get install \
-    wget git curl \
-    python-dev python-numpy python3-dev\
-    libleveldb-dev libsnappy-dev libopencv-dev \
-    libprotobuf-dev protobuf-compiler \
-    libatlas-dev libatlas-base-dev \
-    libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
-    bc
-
-# Add a special apt-repository to install CMake 2.8.9 for CMake Caffe build,
-# if needed.  By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
-# Caffe requires a minimum CMake version of 2.8.8.
-if $WITH_CMAKE; then
-  # cmake 3 will make sure that the python interpreter and libraries match
-  wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
-  chmod +x cmake3.sh
-  ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
-fi
-
-# Install CUDA, if needed
-if $WITH_CUDA; then
-  CUDA_URL=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.5-14_amd64.deb
-  CUDA_FILE=/tmp/cuda_install.deb
-  curl $CUDA_URL -o $CUDA_FILE
-  dpkg -i $CUDA_FILE
-  rm -f $CUDA_FILE
-  apt-get -y update
-  # Install the minimal CUDA subpackages required to test Caffe build.
-  # For a full CUDA installation, add 'cuda' to the list of packages.
-  apt-get -y install cuda-core-6-5 cuda-cublas-6-5 cuda-cublas-dev-6-5 cuda-cudart-6-5 cuda-cudart-dev-6-5 cuda-curand-6-5 cuda-curand-dev-6-5
-  # Create CUDA symlink at /usr/local/cuda
-  # (This would normally be created by the CUDA installer, but we create it
-  # manually since we did a partial installation.)
-  ln -s /usr/local/cuda-6.5 /usr/local/cuda
-fi
-
-# Install LMDB
-LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
-LMDB_FILE=/tmp/lmdb.tar.gz
-pushd .
-wget $LMDB_URL -O $LMDB_FILE
-tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/lmdb*/libraries/liblmdb/
-$MAKE
-$MAKE install
-popd
-rm -f $LMDB_FILE
-
-# Install the Python runtime dependencies via miniconda (this is much faster
-# than using pip for everything).
-export PATH=$CONDA_DIR/bin:$PATH
-# clear any cached conda (see #3786)
-rm -rf $CONDA_DIR
-if [ ! -d $CONDA_DIR ]; then
-  if [ "$PYTHON_VERSION" -eq "3" ]; then
-    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-  else
-    wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-  fi
-  chmod +x miniconda.sh
-  ./miniconda.sh -b -p $CONDA_DIR
-
-  conda update --yes conda
-  # The version of boost we're using for Python 3 depends on 3.4 for now.
-  if [ "$PYTHON_VERSION" -eq "3" ]; then
-    conda install --yes python=3.4
-  fi
-  conda install --yes numpy scipy matplotlib scikit-image pip
-  # Let conda install boost (so that boost_python matches)
-  conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
-fi
-
-# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
-if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
-  pushd .
-  wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
-  tar -C /tmp -xzvf protobuf-3.tar.gz
-  cd /tmp/protobuf-3*/
-  ./autogen.sh
-  ./configure --prefix=$CONDA_DIR
-  $MAKE
-  $MAKE install
-  popd
-fi
-
-if [ "$PYTHON_VERSION" -eq "3" ]; then
-  pip install --pre protobuf==3.0.0b2
-else
-  pip install protobuf
-fi
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
deleted file mode 100755
index 83aacf11fb0..00000000000
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -e
-
-mv Makefile.config.example Makefile.config
-
-if $WITH_CUDA; then
-  # Only generate compute_50.
-  GENCODE="-gencode arch=compute_50,code=sm_50"
-  GENCODE="$GENCODE -gencode arch=compute_50,code=compute_50"
-  echo "CUDA_ARCH := $GENCODE" >> Makefile.config
-fi
-
-# Remove IO library settings from Makefile.config
-# to avoid conflicts with CI configuration
-sed -i -e '/USE_LMDB/d' Makefile.config
-sed -i -e '/USE_LEVELDB/d' Makefile.config
-sed -i -e '/USE_OPENCV/d' Makefile.config
-
-cat << 'EOF' >> Makefile.config
-# Travis' nvcc doesn't like newer boost versions
-NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
-ANACONDA_HOME := $(CONDA_DIR)
-PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
-		$(ANACONDA_HOME)/include/python2.7 \
-		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
-PYTHON_LIB := $(ANACONDA_HOME)/lib
-INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
-LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
-WITH_PYTHON_LAYER := 1
-EOF

From 26879320898aacfcb5236c725938e259788c10fc Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 25 May 2016 16:39:55 -0700
Subject: [PATCH 1791/2053] Remove misleading comment from a test file

---
 src/caffe/test/test_caffe_main.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index fccf6f1613b..6473b74d0a6 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,6 +1,3 @@
-// The main caffe test code. Your test cpp code should include this hpp
-// to allow a main function to be compiled into the binary.
-
 #include "caffe/caffe.hpp"
 #include "caffe/test/test_caffe_main.hpp"
 

From a355f9c9d0bf28ac81552ddb4873b01d09581fb3 Mon Sep 17 00:00:00 2001
From: Siddarth Malreddy <malreddysid@gmail.com>
Date: Thu, 26 May 2016 23:31:31 +0530
Subject: [PATCH 1792/2053] Check for non-empty ImageData filelist.

---
 src/caffe/layers/image_data_layer.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 56d354655dc..7ee7dc40714 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -46,6 +46,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     lines_.push_back(std::make_pair(line.substr(0, pos), label));
   }
 
+  CHECK(!lines_.empty()) << "File is empty";
+
   if (this->layer_param_.image_data_param().shuffle()) {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";

From 09546dbe9130789f0571a76a36b0fc265cd81fe3 Mon Sep 17 00:00:00 2001
From: Lumin Zhou <CDLuminate@users.noreply.github.com>
Date: Mon, 30 May 2016 04:14:42 +0000
Subject: [PATCH 1793/2053] fix spelling error in memory_data_layer.cpp

---
 src/caffe/layers/memory_data_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 82909874054..975f4841723 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -107,7 +107,7 @@ void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
+  CHECK(data_) << "MemoryDataLayer needs to be initialized by calling Reset";
   top[0]->Reshape(batch_size_, channels_, height_, width_);
   top[1]->Reshape(batch_size_, 1, 1, 1);
   top[0]->set_cpu_data(data_ + pos_ * size_);

From 5d7a71ae108f86c05bc03eb542155b30bd28ca74 Mon Sep 17 00:00:00 2001
From: Lumin Zhou <CDLuminate@users.noreply.github.com>
Date: Mon, 30 May 2016 04:19:16 +0000
Subject: [PATCH 1794/2053] using GNUInstallDirs in root cmake file

---
 CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index da7142c9b3c..c765889e99c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,6 +18,7 @@ add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
 include(ExternalProject)
+include(GNUInstallDirs)
 
 include(cmake/Utils.cmake)
 include(cmake/Targets.cmake)

From 90b98ce76fe8613d345932f47a6250dc772f7b8f Mon Sep 17 00:00:00 2001
From: Lumin Zhou <CDLuminate@users.noreply.github.com>
Date: Mon, 30 May 2016 04:21:27 +0000
Subject: [PATCH 1795/2053] fix install path with GNUInstallDir support

---
 src/caffe/CMakeLists.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 8a80c940488..5a1b73f7493 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -29,9 +29,9 @@ set_target_properties(caffe PROPERTIES
  add_subdirectory(test)
 
 # ---[ Install
-install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION include)
-install(FILES ${proto_hdrs} DESTINATION include/caffe/proto)
-install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION lib)
+install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
+install(FILES ${proto_hdrs} DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/caffe/proto)
+install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
 
 file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
 list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)

From 581650b18d7580df726d1d6d54d83c397d1379bb Mon Sep 17 00:00:00 2001
From: Lumin Zhou <CDLuminate@users.noreply.github.com>
Date: Mon, 30 May 2016 04:22:42 +0000
Subject: [PATCH 1796/2053] fix install path with GNUInstallDir support

---
 tools/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 02fbd5cadd8..3789450555e 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -25,5 +25,6 @@ foreach(source ${srcs})
   endif()
 
   # Install
-  install(TARGETS ${name} DESTINATION bin)
+  install(TARGETS ${name} DESTINATION ${CMAKE_INSTALL_BINDIR})
+
 endforeach(source)

From f710ef5e89d3ec22891b24099c66b7a6e9f06c45 Mon Sep 17 00:00:00 2001
From: Lumin Zhou <CDLuminate@users.noreply.github.com>
Date: Mon, 30 May 2016 04:24:13 +0000
Subject: [PATCH 1797/2053] fix install path with GNUInstallDir support

---
 examples/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 663d7360b7d..2a2300332ad 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -19,7 +19,8 @@ foreach(source_file ${examples_srcs})
   caffe_set_solution_folder(${name} examples)
 
   # install
-  install(TARGETS ${name} DESTINATION bin)
+  install(TARGETS ${name} DESTINATION ${CMAKE_INSTALL_BINDIR})
+
 
   if(UNIX OR APPLE)
     # Funny command to make tutorials work

From 918d9994e4b2e9d82bd7929b0ef1d90393f68b31 Mon Sep 17 00:00:00 2001
From: Josh Klontz <josh.klontz@gmail.com>
Date: Tue, 31 May 2016 18:08:04 -0600
Subject: [PATCH 1798/2053] Fix vecLib search order for clients with both the
 old vecLib framework and the new Accelerate framework

---
 cmake/Modules/FindvecLib.cmake | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
index 9600da43647..46043367362 100644
--- a/cmake/Modules/FindvecLib.cmake
+++ b/cmake/Modules/FindvecLib.cmake
@@ -14,9 +14,10 @@ set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Header
 
 find_path(vecLib_INCLUDE_DIR vecLib.h
           DOC "vecLib include directory"
-          PATHS /System/Library/${__veclib_include_suffix}
-                /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
-                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/)
+          PATHS /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
+                /System/Library/${__veclib_include_suffix}
+                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+          NO_DEFAULT_PATH)
 
 include(FindPackageHandleStandardArgs)
 find_package_handle_standard_args(vecLib DEFAULT_MSG vecLib_INCLUDE_DIR)

From 994a033a725c23811dc50e4b2874450a45f2ecd1 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 1 Jun 2016 10:37:14 -0700
Subject: [PATCH 1799/2053] Cache protobuf3 build in TravisCI

---
 .travis.yml                    |  3 +++
 scripts/travis/install-deps.sh | 37 +++++++++++++++++++---------------
 2 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 92d740cd88b..4849a7ac289 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -28,7 +28,10 @@ env:
     - BUILD_NAME="cudnn-cmake" WITH_CMAKE=true WITH_CUDA=true WITH_CUDNN=true
 
 cache:
+  timeout: 604800  # 1 week
   apt: true
+  directories:
+    - ~/protobuf3
 
 before_install:
   - source ./scripts/travis/defaults.sh
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index f7bfe4c4df9..ee16d36a7fc 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -40,25 +40,30 @@ else
     python3-skimage
 
   # build Protobuf3 since it's needed for Python3
-  echo "Building protobuf3 from source ..."
+  PROTOBUF3_DIR=~/protobuf3
   pushd .
-  PROTOBUF3_DIR=~/protobuf3-build
-  rm -rf $PROTOBUF3_DIR
-  mkdir $PROTOBUF3_DIR
+  if [ -d "$PROTOBUF3_DIR" ] && [ -e "$PROTOBUF3_DIR/src/protoc" ]; then
+    echo "Using cached protobuf3 build ..."
+    cd $PROTOBUF3_DIR
+  else
+    echo "Building protobuf3 from source ..."
+    rm -rf $PROTOBUF3_DIR
+    mkdir $PROTOBUF3_DIR
 
-  # install some more dependencies required to build protobuf3
-  apt-get install -y --no-install-recommends \
-    curl \
-    dh-autoreconf \
-    unzip
+    # install some more dependencies required to build protobuf3
+    apt-get install -y --no-install-recommends \
+      curl \
+      dh-autoreconf \
+      unzip
 
-  wget https://github.com/google/protobuf/archive/v3.0.0-beta-3.tar.gz -O protobuf3.tar.gz
-  tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
-  rm protobuf3.tar.gz
-  cd $PROTOBUF3_DIR
-  ./autogen.sh
-  ./configure --prefix=/usr
-  make --jobs=$NUM_THREADS
+    wget https://github.com/google/protobuf/archive/v3.0.0-beta-3.tar.gz -O protobuf3.tar.gz
+    tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
+    rm protobuf3.tar.gz
+    cd $PROTOBUF3_DIR
+    ./autogen.sh
+    ./configure --prefix=/usr
+    make --jobs=$NUM_THREADS
+  fi
   make install
   popd
 fi

From 5f2d845fafc8883aa16b437b79fa52b39f8a0ddb Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 15 Feb 2015 14:28:01 -0800
Subject: [PATCH 1800/2053] Add RecurrentLayer: an abstract superclass for
 other recurrent layer types

---
 include/caffe/layers/recurrent_layer.hpp | 187 ++++++++++++++
 src/caffe/layers/recurrent_layer.cpp     | 295 +++++++++++++++++++++++
 src/caffe/layers/recurrent_layer.cu      |  44 ++++
 src/caffe/proto/caffe.proto              |  22 +-
 4 files changed, 547 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/recurrent_layer.hpp
 create mode 100644 src/caffe/layers/recurrent_layer.cpp
 create mode 100644 src/caffe/layers/recurrent_layer.cu

diff --git a/include/caffe/layers/recurrent_layer.hpp b/include/caffe/layers/recurrent_layer.hpp
new file mode 100644
index 00000000000..ca17371b994
--- /dev/null
+++ b/include/caffe/layers/recurrent_layer.hpp
@@ -0,0 +1,187 @@
+#ifndef CAFFE_RECURRENT_LAYER_HPP_
+#define CAFFE_RECURRENT_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief An abstract class for implementing recurrent behavior inside of an
+ *        unrolled network.  This Layer type cannot be instantiated -- instead,
+ *        you should use one of its implementations which defines the recurrent
+ *        architecture, such as RNNLayer or LSTMLayer.
+ */
+template <typename Dtype>
+class RecurrentLayer : public Layer<Dtype> {
+ public:
+  explicit RecurrentLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reset();
+
+  virtual inline const char* type() const { return "Recurrent"; }
+  virtual inline int MinBottomBlobs() const {
+    int min_bottoms = 2;
+    if (this->layer_param_.recurrent_param().expose_hidden()) {
+      vector<string> inputs;
+      this->RecurrentInputBlobNames(&inputs);
+      min_bottoms += inputs.size();
+    }
+    return min_bottoms;
+  }
+  virtual inline int MaxBottomBlobs() const { return MinBottomBlobs() + 1; }
+  virtual inline int ExactNumTopBlobs() const {
+    int num_tops = 1;
+    if (this->layer_param_.recurrent_param().expose_hidden()) {
+      vector<string> outputs;
+      this->RecurrentOutputBlobNames(&outputs);
+      num_tops += outputs.size();
+    }
+    return num_tops;
+  }
+
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    // Can't propagate to sequence continuation indicators.
+    return bottom_index != 1;
+  }
+
+ protected:
+  /**
+   * @brief Fills net_param with the recurrent network architecture.  Subclasses
+   *        should define this -- see RNNLayer and LSTMLayer for examples.
+   */
+  virtual void FillUnrolledNet(NetParameter* net_param) const = 0;
+
+  /**
+   * @brief Fills names with the names of the 0th timestep recurrent input
+   *        Blob&s.  Subclasses should define this -- see RNNLayer and LSTMLayer
+   *        for examples.
+   */
+  virtual void RecurrentInputBlobNames(vector<string>* names) const = 0;
+
+  /**
+   * @brief Fills shapes with the shapes of the recurrent input Blob&s.
+   *        Subclasses should define this -- see RNNLayer and LSTMLayer
+   *        for examples.
+   */
+  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const = 0;
+
+  /**
+   * @brief Fills names with the names of the Tth timestep recurrent output
+   *        Blob&s.  Subclasses should define this -- see RNNLayer and LSTMLayer
+   *        for examples.
+   */
+  virtual void RecurrentOutputBlobNames(vector<string>* names) const = 0;
+
+  /**
+   * @brief Fills names with the names of the output blobs, concatenated across
+   *        all timesteps.  Should return a name for each top Blob.
+   *        Subclasses should define this -- see RNNLayer and LSTMLayer for
+   *        examples.
+   */
+  virtual void OutputBlobNames(vector<string>* names) const = 0;
+
+  /**
+   * @param bottom input Blob vector (length 2-3)
+   *
+   *   -# @f$ (T \times N \times ...) @f$
+   *      the time-varying input @f$ x @f$.  After the first two axes, whose
+   *      dimensions must correspond to the number of timesteps @f$ T @f$ and
+   *      the number of independent streams @f$ N @f$, respectively, its
+   *      dimensions may be arbitrary.  Note that the ordering of dimensions --
+   *      @f$ (T \times N \times ...) @f$, rather than
+   *      @f$ (N \times T \times ...) @f$ -- means that the @f$ N @f$
+   *      independent input streams must be "interleaved".
+   *
+   *   -# @f$ (T \times N) @f$
+   *      the sequence continuation indicators @f$ \delta @f$.
+   *      These inputs should be binary (0 or 1) indicators, where
+   *      @f$ \delta_{t,n} = 0 @f$ means that timestep @f$ t @f$ of stream
+   *      @f$ n @f$ is the beginning of a new sequence, and hence the previous
+   *      hidden state @f$ h_{t-1} @f$ is multiplied by @f$ \delta_t = 0 @f$
+   *      and has no effect on the cell's output at timestep @f$ t @f$, and
+   *      a value of @f$ \delta_{t,n} = 1 @f$ means that timestep @f$ t @f$ of
+   *      stream @f$ n @f$ is a continuation from the previous timestep
+   *      @f$ t-1 @f$, and the previous hidden state @f$ h_{t-1} @f$ affects the
+   *      updated hidden state and output.
+   *
+   *   -# @f$ (N \times ...) @f$ (optional)
+   *      the static (non-time-varying) input @f$ x_{static} @f$.
+   *      After the first axis, whose dimension must be the number of
+   *      independent streams, its dimensions may be arbitrary.
+   *      This is mathematically equivalent to using a time-varying input of
+   *      @f$ x'_t = [x_t; x_{static}] @f$ -- i.e., tiling the static input
+   *      across the @f$ T @f$ timesteps and concatenating with the time-varying
+   *      input.  Note that if this input is used, all timesteps in a single
+   *      batch within a particular one of the @f$ N @f$ streams must share the
+   *      same static input, even if the sequence continuation indicators
+   *      suggest that difference sequences are ending and beginning within a
+   *      single batch.  This may require padding and/or truncation for uniform
+   *      length.
+   *
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (T \times N \times D) @f$
+   *      the time-varying output @f$ y @f$, where @f$ D @f$ is
+   *      <code>recurrent_param.num_output()</code>.
+   *      Refer to documentation for particular RecurrentLayer implementations
+   *      (such as RNNLayer and LSTMLayer) for the definition of @f$ y @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief A Net to implement the Recurrent functionality.
+  shared_ptr<Net<Dtype> > unrolled_net_;
+
+  /// @brief The number of independent streams to process simultaneously.
+  int N_;
+
+  /**
+   * @brief The number of timesteps in the layer's input, and the number of
+   *        timesteps over which to backpropagate through time.
+   */
+  int T_;
+
+  /// @brief Whether the layer has a "static" input copied across all timesteps.
+  bool static_input_;
+
+  /**
+   * @brief The last layer to run in the network. (Any later layers are losses
+   *        added to force the recurrent net to do backprop.)
+   */
+  int last_layer_index_;
+
+  /**
+   * @brief Whether the layer's hidden state at the first and last timesteps
+   *        are layer inputs and outputs, respectively.
+   */
+  bool expose_hidden_;
+
+  vector<Blob<Dtype>* > recur_input_blobs_;
+  vector<Blob<Dtype>* > recur_output_blobs_;
+  vector<Blob<Dtype>* > output_blobs_;
+  Blob<Dtype>* x_input_blob_;
+  Blob<Dtype>* x_static_input_blob_;
+  Blob<Dtype>* cont_input_blob_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_RECURRENT_LAYER_HPP_
diff --git a/src/caffe/layers/recurrent_layer.cpp b/src/caffe/layers/recurrent_layer.cpp
new file mode 100644
index 00000000000..e0c82773392
--- /dev/null
+++ b/src/caffe/layers/recurrent_layer.cpp
@@ -0,0 +1,295 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "bottom[0] must have at least 2 axes -- (#timesteps, #streams, ...)";
+  T_ = bottom[0]->shape(0);
+  N_ = bottom[0]->shape(1);
+  LOG(INFO) << "Initializing recurrent layer: assuming input batch contains "
+            << T_ << " timesteps of " << N_ << " independent streams.";
+
+  CHECK_EQ(bottom[1]->num_axes(), 2)
+      << "bottom[1] must have exactly 2 axes -- (#timesteps, #streams)";
+  CHECK_EQ(T_, bottom[1]->shape(0));
+  CHECK_EQ(N_, bottom[1]->shape(1));
+
+  // If expose_hidden is set, we take as input and produce as output
+  // the hidden state blobs at the first and last timesteps.
+  expose_hidden_ = this->layer_param_.recurrent_param().expose_hidden();
+
+  // Get (recurrent) input/output names.
+  vector<string> output_names;
+  OutputBlobNames(&output_names);
+  vector<string> recur_input_names;
+  RecurrentInputBlobNames(&recur_input_names);
+  vector<string> recur_output_names;
+  RecurrentOutputBlobNames(&recur_output_names);
+  const int num_recur_blobs = recur_input_names.size();
+  CHECK_EQ(num_recur_blobs, recur_output_names.size());
+
+  // If provided, bottom[2] is a static input to the recurrent net.
+  const int num_hidden_exposed = expose_hidden_ * num_recur_blobs;
+  static_input_ = (bottom.size() > 2 + num_hidden_exposed);
+  if (static_input_) {
+    CHECK_GE(bottom[2]->num_axes(), 1);
+    CHECK_EQ(N_, bottom[2]->shape(0));
+  }
+
+  // Create a NetParameter; setup the inputs that aren't unique to particular
+  // recurrent architectures.
+  NetParameter net_param;
+
+  LayerParameter* input_layer_param = net_param.add_layer();
+  input_layer_param->set_type("Input");
+  InputParameter* input_param = input_layer_param->mutable_input_param();
+  input_layer_param->add_top("x");
+  BlobShape input_shape;
+  for (int i = 0; i < bottom[0]->num_axes(); ++i) {
+    input_shape.add_dim(bottom[0]->shape(i));
+  }
+  input_param->add_shape()->CopyFrom(input_shape);
+
+  input_shape.Clear();
+  for (int i = 0; i < bottom[1]->num_axes(); ++i) {
+    input_shape.add_dim(bottom[1]->shape(i));
+  }
+  input_layer_param->add_top("cont");
+  input_param->add_shape()->CopyFrom(input_shape);
+
+  if (static_input_) {
+    input_shape.Clear();
+    for (int i = 0; i < bottom[2]->num_axes(); ++i) {
+      input_shape.add_dim(bottom[2]->shape(i));
+    }
+    input_layer_param->add_top("x_static");
+    input_param->add_shape()->CopyFrom(input_shape);
+  }
+
+  // Call the child's FillUnrolledNet implementation to specify the unrolled
+  // recurrent architecture.
+  this->FillUnrolledNet(&net_param);
+
+  // Prepend this layer's name to the names of each layer in the unrolled net.
+  const string& layer_name = this->layer_param_.name();
+  if (layer_name.size()) {
+    for (int i = 0; i < net_param.layer_size(); ++i) {
+      LayerParameter* layer = net_param.mutable_layer(i);
+      layer->set_name(layer_name + "_" + layer->name());
+    }
+  }
+
+  // Add "pseudo-losses" to all outputs to force backpropagation.
+  // (Setting force_backward is too aggressive as we may not need to backprop to
+  // all inputs, e.g., the sequence continuation indicators.)
+  vector<string> pseudo_losses(output_names.size());
+  for (int i = 0; i < output_names.size(); ++i) {
+    LayerParameter* layer = net_param.add_layer();
+    pseudo_losses[i] = output_names[i] + "_pseudoloss";
+    layer->set_name(pseudo_losses[i]);
+    layer->set_type("Reduction");
+    layer->add_bottom(output_names[i]);
+    layer->add_top(pseudo_losses[i]);
+    layer->add_loss_weight(1);
+  }
+
+  // Create the unrolled net.
+  unrolled_net_.reset(new Net<Dtype>(net_param));
+  unrolled_net_->set_debug_info(
+      this->layer_param_.recurrent_param().debug_info());
+
+  // Setup pointers to the inputs.
+  x_input_blob_ = CHECK_NOTNULL(unrolled_net_->blob_by_name("x").get());
+  cont_input_blob_ = CHECK_NOTNULL(unrolled_net_->blob_by_name("cont").get());
+  if (static_input_) {
+    x_static_input_blob_ =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name("x_static").get());
+  }
+
+  // Setup pointers to paired recurrent inputs/outputs.
+  recur_input_blobs_.resize(num_recur_blobs);
+  recur_output_blobs_.resize(num_recur_blobs);
+  for (int i = 0; i < recur_input_names.size(); ++i) {
+    recur_input_blobs_[i] =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name(recur_input_names[i]).get());
+    recur_output_blobs_[i] =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name(recur_output_names[i]).get());
+  }
+
+  // Setup pointers to outputs.
+  CHECK_EQ(top.size() - num_hidden_exposed, output_names.size())
+      << "OutputBlobNames must provide an output blob name for each top.";
+  output_blobs_.resize(output_names.size());
+  for (int i = 0; i < output_names.size(); ++i) {
+    output_blobs_[i] =
+        CHECK_NOTNULL(unrolled_net_->blob_by_name(output_names[i]).get());
+  }
+
+  // We should have 2 inputs (x and cont), plus a number of recurrent inputs,
+  // plus maybe a static input.
+  CHECK_EQ(2 + num_recur_blobs + static_input_,
+           unrolled_net_->input_blobs().size());
+
+  // This layer's parameters are any parameters in the layers of the unrolled
+  // net. We only want one copy of each parameter, so check that the parameter
+  // is "owned" by the layer, rather than shared with another.
+  this->blobs_.clear();
+  for (int i = 0; i < unrolled_net_->params().size(); ++i) {
+    if (unrolled_net_->param_owners()[i] == -1) {
+      LOG(INFO) << "Adding parameter " << i << ": "
+                << unrolled_net_->param_display_names()[i];
+      this->blobs_.push_back(unrolled_net_->params()[i]);
+    }
+  }
+  // Check that param_propagate_down is set for all of the parameters in the
+  // unrolled net; set param_propagate_down to true in this layer.
+  for (int i = 0; i < unrolled_net_->layers().size(); ++i) {
+    for (int j = 0; j < unrolled_net_->layers()[i]->blobs().size(); ++j) {
+      CHECK(unrolled_net_->layers()[i]->param_propagate_down(j))
+          << "param_propagate_down not set for layer " << i << ", param " << j;
+    }
+  }
+  this->param_propagate_down_.clear();
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+
+  // Set the diffs of recurrent outputs to 0 -- we can't backpropagate across
+  // batches.
+  for (int i = 0; i < recur_output_blobs_.size(); ++i) {
+    caffe_set(recur_output_blobs_[i]->count(), Dtype(0),
+              recur_output_blobs_[i]->mutable_cpu_diff());
+  }
+
+  // Check that the last output_names.size() layers are the pseudo-losses;
+  // set last_layer_index so that we don't actually run these layers.
+  const vector<string>& layer_names = unrolled_net_->layer_names();
+  last_layer_index_ = layer_names.size() - 1 - pseudo_losses.size();
+  for (int i = last_layer_index_ + 1, j = 0; i < layer_names.size(); ++i, ++j) {
+    CHECK_EQ(layer_names[i], pseudo_losses[j]);
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "bottom[0] must have at least 2 axes -- (#timesteps, #streams, ...)";
+  CHECK_EQ(T_, bottom[0]->shape(0)) << "input number of timesteps changed";
+  N_ = bottom[0]->shape(1);
+  CHECK_EQ(bottom[1]->num_axes(), 2)
+      << "bottom[1] must have exactly 2 axes -- (#timesteps, #streams)";
+  CHECK_EQ(T_, bottom[1]->shape(0));
+  CHECK_EQ(N_, bottom[1]->shape(1));
+  x_input_blob_->ReshapeLike(*bottom[0]);
+  vector<int> cont_shape = bottom[1]->shape();
+  cont_input_blob_->Reshape(cont_shape);
+  if (static_input_) {
+    x_static_input_blob_->ReshapeLike(*bottom[2]);
+  }
+  vector<BlobShape> recur_input_shapes;
+  RecurrentInputShapes(&recur_input_shapes);
+  CHECK_EQ(recur_input_shapes.size(), recur_input_blobs_.size());
+  for (int i = 0; i < recur_input_shapes.size(); ++i) {
+    recur_input_blobs_[i]->Reshape(recur_input_shapes[i]);
+  }
+  unrolled_net_->Reshape();
+  x_input_blob_->ShareData(*bottom[0]);
+  x_input_blob_->ShareDiff(*bottom[0]);
+  cont_input_blob_->ShareData(*bottom[1]);
+  if (static_input_) {
+    x_static_input_blob_->ShareData(*bottom[2]);
+    x_static_input_blob_->ShareDiff(*bottom[2]);
+  }
+  if (expose_hidden_) {
+    const int bottom_offset = 2 + static_input_;
+    for (int i = bottom_offset, j = 0; i < bottom.size(); ++i, ++j) {
+      CHECK(recur_input_blobs_[j]->shape() == bottom[i]->shape())
+          << "bottom[" << i << "] shape must match hidden state input shape: "
+          << recur_input_blobs_[j]->shape_string();
+      recur_input_blobs_[j]->ShareData(*bottom[i]);
+    }
+  }
+  for (int i = 0; i < output_blobs_.size(); ++i) {
+    top[i]->ReshapeLike(*output_blobs_[i]);
+    top[i]->ShareData(*output_blobs_[i]);
+    top[i]->ShareDiff(*output_blobs_[i]);
+  }
+  if (expose_hidden_) {
+    const int top_offset = output_blobs_.size();
+    for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+      top[i]->ReshapeLike(*recur_output_blobs_[j]);
+    }
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Reset() {
+  // "Reset" the hidden state of the net by zeroing out all recurrent outputs.
+  for (int i = 0; i < recur_output_blobs_.size(); ++i) {
+    caffe_set(recur_output_blobs_[i]->count(), Dtype(0),
+              recur_output_blobs_[i]->mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  // Hacky fix for test time: reshare all the internal shared blobs, which may
+  // currently point to a stale owner blob that was dropped when Solver::Test
+  // called test_net->ShareTrainedLayersWith(net_.get()).
+  // TODO: somehow make this work non-hackily.
+  if (this->phase_ == TEST) {
+    unrolled_net_->ShareWeights();
+  }
+
+  DCHECK_EQ(recur_input_blobs_.size(), recur_output_blobs_.size());
+  if (!expose_hidden_) {
+    for (int i = 0; i < recur_input_blobs_.size(); ++i) {
+      const int count = recur_input_blobs_[i]->count();
+      DCHECK_EQ(count, recur_output_blobs_[i]->count());
+      const Dtype* timestep_T_data = recur_output_blobs_[i]->cpu_data();
+      Dtype* timestep_0_data = recur_input_blobs_[i]->mutable_cpu_data();
+      caffe_copy(count, timestep_T_data, timestep_0_data);
+    }
+  }
+
+  unrolled_net_->ForwardTo(last_layer_index_);
+
+  if (expose_hidden_) {
+    const int top_offset = output_blobs_.size();
+    for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+      top[i]->ShareData(*recur_output_blobs_[j]);
+    }
+  }
+}
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[1]) << "Cannot backpropagate to sequence indicators.";
+
+  // TODO: skip backpropagation to inputs and parameters inside the unrolled
+  // net according to propagate_down[0] and propagate_down[2]. For now just
+  // backprop to inputs and parameters unconditionally, as either the inputs or
+  // the parameters do need backward (or Net would have set
+  // layer_needs_backward_[i] == false for this layer).
+  unrolled_net_->BackwardFrom(last_layer_index_);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(RecurrentLayer, Forward);
+#endif
+
+INSTANTIATE_CLASS(RecurrentLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/recurrent_layer.cu b/src/caffe/layers/recurrent_layer.cu
new file mode 100644
index 00000000000..4dd2b0e2165
--- /dev/null
+++ b/src/caffe/layers/recurrent_layer.cu
@@ -0,0 +1,44 @@
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RecurrentLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  // Hacky fix for test time... reshare all the shared blobs.
+  // TODO: somehow make this work non-hackily.
+  if (this->phase_ == TEST) {
+    unrolled_net_->ShareWeights();
+  }
+
+  DCHECK_EQ(recur_input_blobs_.size(), recur_output_blobs_.size());
+  if (!expose_hidden_) {
+    for (int i = 0; i < recur_input_blobs_.size(); ++i) {
+      const int count = recur_input_blobs_[i]->count();
+      DCHECK_EQ(count, recur_output_blobs_[i]->count());
+      const Dtype* timestep_T_data = recur_output_blobs_[i]->gpu_data();
+      Dtype* timestep_0_data = recur_input_blobs_[i]->mutable_gpu_data();
+      caffe_copy(count, timestep_T_data, timestep_0_data);
+    }
+  }
+
+  unrolled_net_->ForwardTo(last_layer_index_);
+
+  if (expose_hidden_) {
+    const int top_offset = output_blobs_.size();
+    for (int i = top_offset, j = 0; i < top.size(); ++i, ++j) {
+      top[i]->ShareData(*recur_output_blobs_[j]);
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FORWARD(RecurrentLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 15810718631..1556781cbc2 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 146 (last added: parameter_param)
+// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -390,6 +390,7 @@ message LayerParameter {
   optional PowerParameter power_param = 122;
   optional PReLUParameter prelu_param = 131;
   optional PythonParameter python_param = 130;
+  optional RecurrentParameter recurrent_param = 146;
   optional ReductionParameter reduction_param = 136;
   optional ReLUParameter relu_param = 123;
   optional ReshapeParameter reshape_param = 133;
@@ -928,6 +929,25 @@ message PythonParameter {
   optional bool share_in_parallel = 4 [default = false];
 }
 
+// Message that stores parameters used by RecurrentLayer
+message RecurrentParameter {
+  // The dimension of the output (and usually hidden state) representation --
+  // must be explicitly set to non-zero.
+  optional uint32 num_output = 1 [default = 0];
+
+  optional FillerParameter weight_filler = 2; // The filler for the weight
+  optional FillerParameter bias_filler = 3; // The filler for the bias
+
+  // Whether to enable displaying debug_info in the unrolled recurrent net.
+  optional bool debug_info = 4 [default = false];
+
+  // Whether to add as additional inputs (bottoms) the initial hidden state
+  // blobs, and add as additional outputs (tops) the final timestep hidden state
+  // blobs.  The number of additional bottom/top blobs required depends on the
+  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
+  optional bool expose_hidden = 5 [default = false];
+}
+
 // Message that stores parameters used by ReductionLayer
 message ReductionParameter {
   enum ReductionOp {

From cf5f369574dd51045c1c92625c0fe6694a031f2a Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Sun, 15 Feb 2015 14:56:50 -0800
Subject: [PATCH 1801/2053] Add RNNLayer, with tests

---
 include/caffe/layers/rnn_layer.hpp |  47 ++++++
 src/caffe/layers/rnn_layer.cpp     | 236 +++++++++++++++++++++++++++++
 src/caffe/test/test_rnn_layer.cpp  | 217 ++++++++++++++++++++++++++
 3 files changed, 500 insertions(+)
 create mode 100644 include/caffe/layers/rnn_layer.hpp
 create mode 100644 src/caffe/layers/rnn_layer.cpp
 create mode 100644 src/caffe/test/test_rnn_layer.cpp

diff --git a/include/caffe/layers/rnn_layer.hpp b/include/caffe/layers/rnn_layer.hpp
new file mode 100644
index 00000000000..6dce238ae17
--- /dev/null
+++ b/include/caffe/layers/rnn_layer.hpp
@@ -0,0 +1,47 @@
+#ifndef CAFFE_RNN_LAYER_HPP_
+#define CAFFE_RNN_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief Processes time-varying inputs using a simple recurrent neural network
+ *        (RNN). Implemented as a network unrolling the RNN computation in time.
+ *
+ * Given time-varying inputs @f$ x_t @f$, computes hidden state @f$
+ *     h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ]
+ * @f$, and outputs @f$
+ *     o_t := \tanh[ W_{ho} h_t + b_o ]
+ * @f$.
+ */
+template <typename Dtype>
+class RNNLayer : public RecurrentLayer<Dtype> {
+ public:
+  explicit RNNLayer(const LayerParameter& param)
+      : RecurrentLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "RNN"; }
+
+ protected:
+  virtual void FillUnrolledNet(NetParameter* net_param) const;
+  virtual void RecurrentInputBlobNames(vector<string>* names) const;
+  virtual void RecurrentOutputBlobNames(vector<string>* names) const;
+  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
+  virtual void OutputBlobNames(vector<string>* names) const;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_RNN_LAYER_HPP_
diff --git a/src/caffe/layers/rnn_layer.cpp b/src/caffe/layers/rnn_layer.cpp
new file mode 100644
index 00000000000..f62ae8c77de
--- /dev/null
+++ b/src/caffe/layers/rnn_layer.cpp
@@ -0,0 +1,236 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/rnn_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "h_0";
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "h_" + format_int(this->T_);
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  shapes->resize(1);
+  (*shapes)[0].Clear();
+  (*shapes)[0].add_dim(1);  // a single timestep
+  (*shapes)[0].add_dim(this->N_);
+  (*shapes)[0].add_dim(num_output);
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "o";
+}
+
+template <typename Dtype>
+void RNNLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  CHECK_GT(num_output, 0) << "num_output must be positive";
+  const FillerParameter& weight_filler =
+      this->layer_param_.recurrent_param().weight_filler();
+  const FillerParameter& bias_filler =
+      this->layer_param_.recurrent_param().bias_filler();
+
+  // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
+  // use to save redundant code.
+  LayerParameter hidden_param;
+  hidden_param.set_type("InnerProduct");
+  hidden_param.mutable_inner_product_param()->set_num_output(num_output);
+  hidden_param.mutable_inner_product_param()->set_bias_term(false);
+  hidden_param.mutable_inner_product_param()->set_axis(2);
+  hidden_param.mutable_inner_product_param()->
+      mutable_weight_filler()->CopyFrom(weight_filler);
+
+  LayerParameter biased_hidden_param(hidden_param);
+  biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
+  biased_hidden_param.mutable_inner_product_param()->
+      mutable_bias_filler()->CopyFrom(bias_filler);
+
+  LayerParameter sum_param;
+  sum_param.set_type("Eltwise");
+  sum_param.mutable_eltwise_param()->set_operation(
+      EltwiseParameter_EltwiseOp_SUM);
+
+  LayerParameter tanh_param;
+  tanh_param.set_type("TanH");
+
+  LayerParameter scale_param;
+  scale_param.set_type("Scale");
+  scale_param.mutable_scale_param()->set_axis(0);
+
+  LayerParameter slice_param;
+  slice_param.set_type("Slice");
+  slice_param.mutable_slice_param()->set_axis(0);
+
+  vector<BlobShape> input_shapes;
+  RecurrentInputShapes(&input_shapes);
+  CHECK_EQ(1, input_shapes.size());
+
+  LayerParameter* input_layer_param = net_param->add_layer();
+  input_layer_param->set_type("Input");
+  InputParameter* input_param = input_layer_param->mutable_input_param();
+  input_layer_param->add_top("h_0");
+  input_param->add_shape()->CopyFrom(input_shapes[0]);
+
+  LayerParameter* cont_slice_param = net_param->add_layer();
+  cont_slice_param->CopyFrom(slice_param);
+  cont_slice_param->set_name("cont_slice");
+  cont_slice_param->add_bottom("cont");
+  cont_slice_param->mutable_slice_param()->set_axis(0);
+
+  // Add layer to transform all timesteps of x to the hidden state dimension.
+  //     W_xh_x = W_xh * x + b_h
+  {
+    LayerParameter* x_transform_param = net_param->add_layer();
+    x_transform_param->CopyFrom(biased_hidden_param);
+    x_transform_param->set_name("x_transform");
+    x_transform_param->add_param()->set_name("W_xh");
+    x_transform_param->add_param()->set_name("b_h");
+    x_transform_param->add_bottom("x");
+    x_transform_param->add_top("W_xh_x");
+    x_transform_param->add_propagate_down(true);
+  }
+
+  if (this->static_input_) {
+    // Add layer to transform x_static to the hidden state dimension.
+    //     W_xh_x_static = W_xh_static * x_static
+    LayerParameter* x_static_transform_param = net_param->add_layer();
+    x_static_transform_param->CopyFrom(hidden_param);
+    x_static_transform_param->mutable_inner_product_param()->set_axis(1);
+    x_static_transform_param->set_name("W_xh_x_static");
+    x_static_transform_param->add_param()->set_name("W_xh_static");
+    x_static_transform_param->add_bottom("x_static");
+    x_static_transform_param->add_top("W_xh_x_static_preshape");
+    x_static_transform_param->add_propagate_down(true);
+
+    LayerParameter* reshape_param = net_param->add_layer();
+    reshape_param->set_type("Reshape");
+    BlobShape* new_shape =
+         reshape_param->mutable_reshape_param()->mutable_shape();
+    new_shape->add_dim(1);  // One timestep.
+    // Should infer this->N as the dimension so we can reshape on batch size.
+    new_shape->add_dim(-1);
+    new_shape->add_dim(
+        x_static_transform_param->inner_product_param().num_output());
+    reshape_param->set_name("W_xh_x_static_reshape");
+    reshape_param->add_bottom("W_xh_x_static_preshape");
+    reshape_param->add_top("W_xh_x_static");
+  }
+
+  LayerParameter* x_slice_param = net_param->add_layer();
+  x_slice_param->CopyFrom(slice_param);
+  x_slice_param->set_name("W_xh_x_slice");
+  x_slice_param->add_bottom("W_xh_x");
+
+  LayerParameter output_concat_layer;
+  output_concat_layer.set_name("o_concat");
+  output_concat_layer.set_type("Concat");
+  output_concat_layer.add_top("o");
+  output_concat_layer.mutable_concat_param()->set_axis(0);
+
+  for (int t = 1; t <= this->T_; ++t) {
+    string tm1s = format_int(t - 1);
+    string ts = format_int(t);
+
+    cont_slice_param->add_top("cont_" + ts);
+    x_slice_param->add_top("W_xh_x_" + ts);
+
+    // Add layer to flush the hidden state when beginning a new sequence,
+    // as indicated by cont_t.
+    //     h_conted_{t-1} := cont_t * h_{t-1}
+    //
+    // Normally, cont_t is binary (i.e., 0 or 1), so:
+    //     h_conted_{t-1} := h_{t-1} if cont_t == 1
+    //                       0   otherwise
+    {
+      LayerParameter* cont_h_param = net_param->add_layer();
+      cont_h_param->CopyFrom(scale_param);
+      cont_h_param->set_name("h_conted_" + tm1s);
+      cont_h_param->add_bottom("h_" + tm1s);
+      cont_h_param->add_bottom("cont_" + ts);
+      cont_h_param->add_top("h_conted_" + tm1s);
+    }
+
+    // Add layer to compute
+    //     W_hh_h_{t-1} := W_hh * h_conted_{t-1}
+    {
+      LayerParameter* w_param = net_param->add_layer();
+      w_param->CopyFrom(hidden_param);
+      w_param->set_name("W_hh_h_" + tm1s);
+      w_param->add_param()->set_name("W_hh");
+      w_param->add_bottom("h_conted_" + tm1s);
+      w_param->add_top("W_hh_h_" + tm1s);
+      w_param->mutable_inner_product_param()->set_axis(2);
+    }
+
+    // Add layers to compute
+    //     h_t := \tanh( W_hh * h_conted_{t-1} + W_xh * x_t + b_h )
+    //          = \tanh( W_hh_h_{t-1} + W_xh_t )
+    {
+      LayerParameter* h_input_sum_param = net_param->add_layer();
+      h_input_sum_param->CopyFrom(sum_param);
+      h_input_sum_param->set_name("h_input_sum_" + ts);
+      h_input_sum_param->add_bottom("W_hh_h_" + tm1s);
+      h_input_sum_param->add_bottom("W_xh_x_" + ts);
+      if (this->static_input_) {
+        h_input_sum_param->add_bottom("W_xh_x_static");
+      }
+      h_input_sum_param->add_top("h_neuron_input_" + ts);
+    }
+    {
+      LayerParameter* h_neuron_param = net_param->add_layer();
+      h_neuron_param->CopyFrom(tanh_param);
+      h_neuron_param->set_name("h_neuron_" + ts);
+      h_neuron_param->add_bottom("h_neuron_input_" + ts);
+      h_neuron_param->add_top("h_" + ts);
+    }
+
+    // Add layer to compute
+    //     W_ho_h_t := W_ho * h_t + b_o
+    {
+      LayerParameter* w_param = net_param->add_layer();
+      w_param->CopyFrom(biased_hidden_param);
+      w_param->set_name("W_ho_h_" + ts);
+      w_param->add_param()->set_name("W_ho");
+      w_param->add_param()->set_name("b_o");
+      w_param->add_bottom("h_" + ts);
+      w_param->add_top("W_ho_h_" + ts);
+      w_param->mutable_inner_product_param()->set_axis(2);
+    }
+
+    // Add layers to compute
+    //     o_t := \tanh( W_ho h_t + b_o)
+    //          = \tanh( W_ho_h_t )
+    {
+      LayerParameter* o_neuron_param = net_param->add_layer();
+      o_neuron_param->CopyFrom(tanh_param);
+      o_neuron_param->set_name("o_neuron_" + ts);
+      o_neuron_param->add_bottom("W_ho_h_" + ts);
+      o_neuron_param->add_top("o_" + ts);
+    }
+    output_concat_layer.add_bottom("o_" + ts);
+  }  // for (int t = 1; t <= this->T_; ++t)
+
+  net_param->add_layer()->CopyFrom(output_concat_layer);
+}
+
+INSTANTIATE_CLASS(RNNLayer);
+REGISTER_LAYER_CLASS(RNN);
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_rnn_layer.cpp b/src/caffe/test/test_rnn_layer.cpp
new file mode 100644
index 00000000000..dd8952d62d6
--- /dev/null
+++ b/src/caffe/test/test_rnn_layer.cpp
@@ -0,0 +1,217 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/rnn_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class RNNLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  RNNLayerTest() : num_output_(7) {
+    blob_bottom_vec_.push_back(&blob_bottom_);
+    blob_bottom_vec_.push_back(&blob_bottom_cont_);
+    blob_top_vec_.push_back(&blob_top_);
+
+    ReshapeBlobs(1, 3);
+
+    layer_param_.mutable_recurrent_param()->set_num_output(num_output_);
+    FillerParameter* weight_filler =
+        layer_param_.mutable_recurrent_param()->mutable_weight_filler();
+    weight_filler->set_type("gaussian");
+    weight_filler->set_std(0.2);
+    FillerParameter* bias_filler =
+        layer_param_.mutable_recurrent_param()->mutable_bias_filler();
+    bias_filler->set_type("gaussian");
+    bias_filler->set_std(0.1);
+
+    layer_param_.set_phase(TEST);
+  }
+
+  void ReshapeBlobs(int num_timesteps, int num_instances) {
+    blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);
+    blob_bottom_static_.Reshape(num_instances, 2, 3, 4);
+    vector<int> shape(2);
+    shape[0] = num_timesteps;
+    shape[1] = num_instances;
+    blob_bottom_cont_.Reshape(shape);
+
+    FillerParameter filler_param;
+    filler_param.set_min(-1);
+    filler_param.set_max(1);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(&blob_bottom_);
+  }
+
+  int num_output_;
+  LayerParameter layer_param_;
+  Blob<Dtype> blob_bottom_;
+  Blob<Dtype> blob_bottom_cont_;
+  Blob<Dtype> blob_bottom_static_;
+  Blob<Dtype> blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(RNNLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(RNNLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  RNNLayer<Dtype> layer(this->layer_param_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> expected_top_shape = this->blob_bottom_.shape();
+  expected_top_shape.resize(3);
+  expected_top_shape[2] = this->num_output_;
+  EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);
+}
+
+TYPED_TEST(RNNLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kNumTimesteps = 3;
+  const int num = this->blob_bottom_.shape(1);
+  this->ReshapeBlobs(kNumTimesteps, num);
+
+  // Fill the cont blob with <0, 1, 1, ..., 1>,
+  // indicating a sequence that begins at the first timestep
+  // then continues for the rest of the sequence.
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;
+    }
+  }
+
+  // Process the full sequence in a single batch.
+  FillerParameter filler_param;
+  filler_param.set_mean(0);
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> sequence_filler(filler_param);
+  sequence_filler.Fill(&this->blob_bottom_);
+  shared_ptr<RNNLayer<Dtype> > layer(new RNNLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  LOG(INFO) << "Calling forward for full sequence RNN";
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  // Copy the inputs and outputs to reuse/check them later.
+  Blob<Dtype> bottom_copy(this->blob_bottom_.shape());
+  bottom_copy.CopyFrom(this->blob_bottom_);
+  Blob<Dtype> top_copy(this->blob_top_.shape());
+  top_copy.CopyFrom(this->blob_top_);
+
+  // Process the batch one timestep at a time;
+  // check that we get the same result.
+  this->ReshapeBlobs(1, num);
+  layer.reset(new RNNLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  const int bottom_count = this->blob_bottom_.count();
+  const int top_count = this->blob_top_.count();
+  const Dtype kEpsilon = 1e-5;
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;
+    }
+    LOG(INFO) << "Calling forward for RNN timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      ASSERT_LT(t * top_count + i, top_copy.count());
+      EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i], kEpsilon)
+         << "t = " << t << "; i = " << i;
+    }
+  }
+
+  // Process the batch one timestep at a time with all cont blobs set to 0.
+  // Check that we get a different result, except in the first timestep.
+  Caffe::set_random_seed(1701);
+  layer.reset(new RNNLayer<Dtype>(this->layer_param_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;
+    }
+    LOG(INFO) << "Calling forward for RNN timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      if (t == 0) {
+        EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                    top_copy.cpu_data()[t * top_count + i], kEpsilon)
+           << "t = " << t << "; i = " << i;
+      } else {
+        EXPECT_NE(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i])
+           << "t = " << t << "; i = " << i;
+      }
+    }
+  }
+}
+
+TYPED_TEST(RNNLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroCont) {
+  typedef typename TypeParam::Dtype Dtype;
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  // fill the values
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  filler.Fill(&this->blob_bottom_static_);
+  this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);
+  RNNLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 2);
+}
+
+}  // namespace caffe

From 51a68f0a0e9e376597d7cabae709ff969ad30c98 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Tue, 5 Apr 2016 09:56:04 -0700
Subject: [PATCH 1802/2053] Add LSTMLayer and LSTMUnitLayer, with tests

---
 include/caffe/layers/lstm_layer.hpp  | 154 ++++++++++++++
 src/caffe/layers/lstm_layer.cpp      | 244 +++++++++++++++++++++++
 src/caffe/layers/lstm_unit_layer.cpp | 131 ++++++++++++
 src/caffe/layers/lstm_unit_layer.cu  | 154 ++++++++++++++
 src/caffe/test/test_lstm_layer.cpp   | 288 +++++++++++++++++++++++++++
 5 files changed, 971 insertions(+)
 create mode 100644 include/caffe/layers/lstm_layer.hpp
 create mode 100644 src/caffe/layers/lstm_layer.cpp
 create mode 100644 src/caffe/layers/lstm_unit_layer.cpp
 create mode 100644 src/caffe/layers/lstm_unit_layer.cu
 create mode 100644 src/caffe/test/test_lstm_layer.cpp

diff --git a/include/caffe/layers/lstm_layer.hpp b/include/caffe/layers/lstm_layer.hpp
new file mode 100644
index 00000000000..a0e67c9d432
--- /dev/null
+++ b/include/caffe/layers/lstm_layer.hpp
@@ -0,0 +1,154 @@
+#ifndef CAFFE_LSTM_LAYER_HPP_
+#define CAFFE_LSTM_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/recurrent_layer.hpp"
+#include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype> class RecurrentLayer;
+
+/**
+ * @brief Processes sequential inputs using a "Long Short-Term Memory" (LSTM)
+ *        [1] style recurrent neural network (RNN). Implemented by unrolling
+ *        the LSTM computation through time.
+ *
+ * The specific architecture used in this implementation is as described in
+ * "Learning to Execute" [2], reproduced below:
+ *     i_t := \sigmoid[ W_{hi} * h_{t-1} + W_{xi} * x_t + b_i ]
+ *     f_t := \sigmoid[ W_{hf} * h_{t-1} + W_{xf} * x_t + b_f ]
+ *     o_t := \sigmoid[ W_{ho} * h_{t-1} + W_{xo} * x_t + b_o ]
+ *     g_t :=    \tanh[ W_{hg} * h_{t-1} + W_{xg} * x_t + b_g ]
+ *     c_t := (f_t .* c_{t-1}) + (i_t .* g_t)
+ *     h_t := o_t .* \tanh[c_t]
+ * In the implementation, the i, f, o, and g computations are performed as a
+ * single inner product.
+ *
+ * Notably, this implementation lacks the "diagonal" gates, as used in the
+ * LSTM architectures described by Alex Graves [3] and others.
+ *
+ * [1] Hochreiter, Sepp, and Schmidhuber, Jürgen. "Long short-term memory."
+ *     Neural Computation 9, no. 8 (1997): 1735-1780.
+ *
+ * [2] Zaremba, Wojciech, and Sutskever, Ilya. "Learning to execute."
+ *     arXiv preprint arXiv:1410.4615 (2014).
+ *
+ * [3] Graves, Alex. "Generating sequences with recurrent neural networks."
+ *     arXiv preprint arXiv:1308.0850 (2013).
+ */
+template <typename Dtype>
+class LSTMLayer : public RecurrentLayer<Dtype> {
+ public:
+  explicit LSTMLayer(const LayerParameter& param)
+      : RecurrentLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "LSTM"; }
+
+ protected:
+  virtual void FillUnrolledNet(NetParameter* net_param) const;
+  virtual void RecurrentInputBlobNames(vector<string>* names) const;
+  virtual void RecurrentOutputBlobNames(vector<string>* names) const;
+  virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
+  virtual void OutputBlobNames(vector<string>* names) const;
+};
+
+/**
+ * @brief A helper for LSTMLayer: computes a single timestep of the
+ *        non-linearity of the LSTM, producing the updated cell and hidden
+ *        states.
+ */
+template <typename Dtype>
+class LSTMUnitLayer : public Layer<Dtype> {
+ public:
+  explicit LSTMUnitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "LSTMUnit"; }
+  virtual inline int ExactNumBottomBlobs() const { return 3; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    // Can't propagate to sequence continuation indicators.
+    return bottom_index != 2;
+  }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 3)
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the previous timestep cell state @f$ c_{t-1} @f$
+   *   -# @f$ (1 \times N \times 4D) @f$
+   *      the "gate inputs" @f$ [i_t', f_t', o_t', g_t'] @f$
+   *   -# @f$ (1 \times N) @f$
+   *      the sequence continuation indicators  @f$ \delta_t @f$
+   * @param top output Blob vector (length 2)
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the updated cell state @f$ c_t @f$, computed as:
+   *          i_t := \sigmoid[i_t']
+   *          f_t := \sigmoid[f_t']
+   *          o_t := \sigmoid[o_t']
+   *          g_t := \tanh[g_t']
+   *          c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the updated hidden state @f$ h_t @f$, computed as:
+   *          h_t := o_t .* \tanh[c_t]
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the LSTMUnit inputs.
+   *
+   * @param top output Blob vector (length 2), providing the error gradient with
+   *        respect to the outputs
+   *   -# @f$ (1 \times N \times D) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial c_t} @f$
+   *      with respect to the updated cell state @f$ c_t @f$
+   *   -# @f$ (1 \times N \times D) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial h_t} @f$
+   *      with respect to the updated cell state @f$ h_t @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 3), into which the error gradients
+   *        with respect to the LSTMUnit inputs @f$ c_{t-1} @f$ and the gate
+   *        inputs are computed.  Computatation of the error gradients w.r.t.
+   *        the sequence indicators is not implemented.
+   *   -# @f$ (1 \times N \times D) @f$
+   *      the error gradient w.r.t. the previous timestep cell state
+   *      @f$ c_{t-1} @f$
+   *   -# @f$ (1 \times N \times 4D) @f$
+   *      the error gradient w.r.t. the "gate inputs"
+   *      @f$ [
+   *          \frac{\partial E}{\partial i_t}
+   *          \frac{\partial E}{\partial f_t}
+   *          \frac{\partial E}{\partial o_t}
+   *          \frac{\partial E}{\partial g_t}
+   *          ] @f$
+   *   -# @f$ (1 \times 1 \times N) @f$
+   *      the gradient w.r.t. the sequence continuation indicators
+   *      @f$ \delta_t @f$ is currently not computed.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief The hidden and output dimension.
+  int hidden_dim_;
+  Blob<Dtype> X_acts_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LSTM_LAYER_HPP_
diff --git a/src/caffe/layers/lstm_layer.cpp b/src/caffe/layers/lstm_layer.cpp
new file mode 100644
index 00000000000..da48dba4c05
--- /dev/null
+++ b/src/caffe/layers/lstm_layer.cpp
@@ -0,0 +1,244 @@
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const {
+  names->resize(2);
+  (*names)[0] = "h_0";
+  (*names)[1] = "c_0";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const {
+  names->resize(2);
+  (*names)[0] = "h_" + format_int(this->T_);
+  (*names)[1] = "c_T";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  const int num_blobs = 2;
+  shapes->resize(num_blobs);
+  for (int i = 0; i < num_blobs; ++i) {
+    (*shapes)[i].Clear();
+    (*shapes)[i].add_dim(1);  // a single timestep
+    (*shapes)[i].add_dim(this->N_);
+    (*shapes)[i].add_dim(num_output);
+  }
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::OutputBlobNames(vector<string>* names) const {
+  names->resize(1);
+  (*names)[0] = "h";
+}
+
+template <typename Dtype>
+void LSTMLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
+  const int num_output = this->layer_param_.recurrent_param().num_output();
+  CHECK_GT(num_output, 0) << "num_output must be positive";
+  const FillerParameter& weight_filler =
+      this->layer_param_.recurrent_param().weight_filler();
+  const FillerParameter& bias_filler =
+      this->layer_param_.recurrent_param().bias_filler();
+
+  // Add generic LayerParameter's (without bottoms/tops) of layer types we'll
+  // use to save redundant code.
+  LayerParameter hidden_param;
+  hidden_param.set_type("InnerProduct");
+  hidden_param.mutable_inner_product_param()->set_num_output(num_output * 4);
+  hidden_param.mutable_inner_product_param()->set_bias_term(false);
+  hidden_param.mutable_inner_product_param()->set_axis(2);
+  hidden_param.mutable_inner_product_param()->
+      mutable_weight_filler()->CopyFrom(weight_filler);
+
+  LayerParameter biased_hidden_param(hidden_param);
+  biased_hidden_param.mutable_inner_product_param()->set_bias_term(true);
+  biased_hidden_param.mutable_inner_product_param()->
+      mutable_bias_filler()->CopyFrom(bias_filler);
+
+  LayerParameter sum_param;
+  sum_param.set_type("Eltwise");
+  sum_param.mutable_eltwise_param()->set_operation(
+      EltwiseParameter_EltwiseOp_SUM);
+
+  LayerParameter scale_param;
+  scale_param.set_type("Scale");
+  scale_param.mutable_scale_param()->set_axis(0);
+
+  LayerParameter slice_param;
+  slice_param.set_type("Slice");
+  slice_param.mutable_slice_param()->set_axis(0);
+
+  LayerParameter split_param;
+  split_param.set_type("Split");
+
+  vector<BlobShape> input_shapes;
+  RecurrentInputShapes(&input_shapes);
+  CHECK_EQ(2, input_shapes.size());
+
+  LayerParameter* input_layer_param = net_param->add_layer();
+  input_layer_param->set_type("Input");
+  InputParameter* input_param = input_layer_param->mutable_input_param();
+
+  input_layer_param->add_top("c_0");
+  input_param->add_shape()->CopyFrom(input_shapes[0]);
+
+  input_layer_param->add_top("h_0");
+  input_param->add_shape()->CopyFrom(input_shapes[1]);
+
+  LayerParameter* cont_slice_param = net_param->add_layer();
+  cont_slice_param->CopyFrom(slice_param);
+  cont_slice_param->set_name("cont_slice");
+  cont_slice_param->add_bottom("cont");
+  cont_slice_param->mutable_slice_param()->set_axis(0);
+
+  // Add layer to transform all timesteps of x to the hidden state dimension.
+  //     W_xc_x = W_xc * x + b_c
+  {
+    LayerParameter* x_transform_param = net_param->add_layer();
+    x_transform_param->CopyFrom(biased_hidden_param);
+    x_transform_param->set_name("x_transform");
+    x_transform_param->add_param()->set_name("W_xc");
+    x_transform_param->add_param()->set_name("b_c");
+    x_transform_param->add_bottom("x");
+    x_transform_param->add_top("W_xc_x");
+    x_transform_param->add_propagate_down(true);
+  }
+
+  if (this->static_input_) {
+    // Add layer to transform x_static to the gate dimension.
+    //     W_xc_x_static = W_xc_static * x_static
+    LayerParameter* x_static_transform_param = net_param->add_layer();
+    x_static_transform_param->CopyFrom(hidden_param);
+    x_static_transform_param->mutable_inner_product_param()->set_axis(1);
+    x_static_transform_param->set_name("W_xc_x_static");
+    x_static_transform_param->add_param()->set_name("W_xc_static");
+    x_static_transform_param->add_bottom("x_static");
+    x_static_transform_param->add_top("W_xc_x_static_preshape");
+    x_static_transform_param->add_propagate_down(true);
+
+    LayerParameter* reshape_param = net_param->add_layer();
+    reshape_param->set_type("Reshape");
+    BlobShape* new_shape =
+         reshape_param->mutable_reshape_param()->mutable_shape();
+    new_shape->add_dim(1);  // One timestep.
+    // Should infer this->N as the dimension so we can reshape on batch size.
+    new_shape->add_dim(-1);
+    new_shape->add_dim(
+        x_static_transform_param->inner_product_param().num_output());
+    reshape_param->set_name("W_xc_x_static_reshape");
+    reshape_param->add_bottom("W_xc_x_static_preshape");
+    reshape_param->add_top("W_xc_x_static");
+  }
+
+  LayerParameter* x_slice_param = net_param->add_layer();
+  x_slice_param->CopyFrom(slice_param);
+  x_slice_param->add_bottom("W_xc_x");
+  x_slice_param->set_name("W_xc_x_slice");
+
+  LayerParameter output_concat_layer;
+  output_concat_layer.set_name("h_concat");
+  output_concat_layer.set_type("Concat");
+  output_concat_layer.add_top("h");
+  output_concat_layer.mutable_concat_param()->set_axis(0);
+
+  for (int t = 1; t <= this->T_; ++t) {
+    string tm1s = format_int(t - 1);
+    string ts = format_int(t);
+
+    cont_slice_param->add_top("cont_" + ts);
+    x_slice_param->add_top("W_xc_x_" + ts);
+
+    // Add layers to flush the hidden state when beginning a new
+    // sequence, as indicated by cont_t.
+    //     h_conted_{t-1} := cont_t * h_{t-1}
+    //
+    // Normally, cont_t is binary (i.e., 0 or 1), so:
+    //     h_conted_{t-1} := h_{t-1} if cont_t == 1
+    //                       0   otherwise
+    {
+      LayerParameter* cont_h_param = net_param->add_layer();
+      cont_h_param->CopyFrom(scale_param);
+      cont_h_param->set_name("h_conted_" + tm1s);
+      cont_h_param->add_bottom("h_" + tm1s);
+      cont_h_param->add_bottom("cont_" + ts);
+      cont_h_param->add_top("h_conted_" + tm1s);
+    }
+
+    // Add layer to compute
+    //     W_hc_h_{t-1} := W_hc * h_conted_{t-1}
+    {
+      LayerParameter* w_param = net_param->add_layer();
+      w_param->CopyFrom(hidden_param);
+      w_param->set_name("transform_" + ts);
+      w_param->add_param()->set_name("W_hc");
+      w_param->add_bottom("h_conted_" + tm1s);
+      w_param->add_top("W_hc_h_" + tm1s);
+      w_param->mutable_inner_product_param()->set_axis(2);
+    }
+
+    // Add the outputs of the linear transformations to compute the gate input.
+    //     gate_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c
+    //                   = W_hc_h_{t-1} + W_xc_x_t + b_c
+    {
+      LayerParameter* input_sum_layer = net_param->add_layer();
+      input_sum_layer->CopyFrom(sum_param);
+      input_sum_layer->set_name("gate_input_" + ts);
+      input_sum_layer->add_bottom("W_hc_h_" + tm1s);
+      input_sum_layer->add_bottom("W_xc_x_" + ts);
+      if (this->static_input_) {
+        input_sum_layer->add_bottom("W_xc_x_static");
+      }
+      input_sum_layer->add_top("gate_input_" + ts);
+    }
+
+    // Add LSTMUnit layer to compute the cell & hidden vectors c_t and h_t.
+    // Inputs: c_{t-1}, gate_input_t = (i_t, f_t, o_t, g_t), cont_t
+    // Outputs: c_t, h_t
+    //     [ i_t' ]
+    //     [ f_t' ] := gate_input_t
+    //     [ o_t' ]
+    //     [ g_t' ]
+    //         i_t := \sigmoid[i_t']
+    //         f_t := \sigmoid[f_t']
+    //         o_t := \sigmoid[o_t']
+    //         g_t := \tanh[g_t']
+    //         c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
+    //         h_t := o_t .* \tanh[c_t]
+    {
+      LayerParameter* lstm_unit_param = net_param->add_layer();
+      lstm_unit_param->set_type("LSTMUnit");
+      lstm_unit_param->add_bottom("c_" + tm1s);
+      lstm_unit_param->add_bottom("gate_input_" + ts);
+      lstm_unit_param->add_bottom("cont_" + ts);
+      lstm_unit_param->add_top("c_" + ts);
+      lstm_unit_param->add_top("h_" + ts);
+      lstm_unit_param->set_name("unit_" + ts);
+    }
+    output_concat_layer.add_bottom("h_" + ts);
+  }  // for (int t = 1; t <= this->T_; ++t)
+
+  {
+    LayerParameter* c_T_copy_param = net_param->add_layer();
+    c_T_copy_param->CopyFrom(split_param);
+    c_T_copy_param->add_bottom("c_" + format_int(this->T_));
+    c_T_copy_param->add_top("c_T");
+  }
+  net_param->add_layer()->CopyFrom(output_concat_layer);
+}
+
+INSTANTIATE_CLASS(LSTMLayer);
+REGISTER_LAYER_CLASS(LSTM);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/lstm_unit_layer.cpp b/src/caffe/layers/lstm_unit_layer.cpp
new file mode 100644
index 00000000000..277c031ad15
--- /dev/null
+++ b/src/caffe/layers/lstm_unit_layer.cpp
@@ -0,0 +1,131 @@
+#include <algorithm>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+inline Dtype sigmoid(Dtype x) {
+  return 1. / (1. + exp(-x));
+}
+
+template <typename Dtype>
+inline Dtype tanh(Dtype x) {
+  return 2. * sigmoid(2. * x) - 1.;
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int num_instances = bottom[0]->shape(1);
+  for (int i = 0; i < bottom.size(); ++i) {
+    if (i == 2) {
+      CHECK_EQ(2, bottom[i]->num_axes());
+    } else {
+      CHECK_EQ(3, bottom[i]->num_axes());
+    }
+    CHECK_EQ(1, bottom[i]->shape(0));
+    CHECK_EQ(num_instances, bottom[i]->shape(1));
+  }
+  hidden_dim_ = bottom[0]->shape(2);
+  CHECK_EQ(num_instances, bottom[1]->shape(1));
+  CHECK_EQ(4 * hidden_dim_, bottom[1]->shape(2));
+  top[0]->ReshapeLike(*bottom[0]);
+  top[1]->ReshapeLike(*bottom[0]);
+  X_acts_.ReshapeLike(*bottom[1]);
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int num = bottom[0]->shape(1);
+  const int x_dim = hidden_dim_ * 4;
+  const Dtype* C_prev = bottom[0]->cpu_data();
+  const Dtype* X = bottom[1]->cpu_data();
+  const Dtype* cont = bottom[2]->cpu_data();
+  Dtype* C = top[0]->mutable_cpu_data();
+  Dtype* H = top[1]->mutable_cpu_data();
+  for (int n = 0; n < num; ++n) {
+    for (int d = 0; d < hidden_dim_; ++d) {
+      const Dtype i = sigmoid(X[d]);
+      const Dtype f = (*cont == 0) ? 0 :
+          (*cont * sigmoid(X[1 * hidden_dim_ + d]));
+      const Dtype o = sigmoid(X[2 * hidden_dim_ + d]);
+      const Dtype g = tanh(X[3 * hidden_dim_ + d]);
+      const Dtype c_prev = C_prev[d];
+      const Dtype c = f * c_prev + i * g;
+      C[d] = c;
+      const Dtype tanh_c = tanh(c);
+      H[d] = o * tanh_c;
+    }
+    C_prev += hidden_dim_;
+    X += x_dim;
+    C += hidden_dim_;
+    H += hidden_dim_;
+    ++cont;
+  }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[2]) << "Cannot backpropagate to sequence indicators.";
+  if (!propagate_down[0] && !propagate_down[1]) { return; }
+
+  const int num = bottom[0]->shape(1);
+  const int x_dim = hidden_dim_ * 4;
+  const Dtype* C_prev = bottom[0]->cpu_data();
+  const Dtype* X = bottom[1]->cpu_data();
+  const Dtype* cont = bottom[2]->cpu_data();
+  const Dtype* C = top[0]->cpu_data();
+  const Dtype* H = top[1]->cpu_data();
+  const Dtype* C_diff = top[0]->cpu_diff();
+  const Dtype* H_diff = top[1]->cpu_diff();
+  Dtype* C_prev_diff = bottom[0]->mutable_cpu_diff();
+  Dtype* X_diff = bottom[1]->mutable_cpu_diff();
+  for (int n = 0; n < num; ++n) {
+    for (int d = 0; d < hidden_dim_; ++d) {
+      const Dtype i = sigmoid(X[d]);
+      const Dtype f = (*cont == 0) ? 0 :
+          (*cont * sigmoid(X[1 * hidden_dim_ + d]));
+      const Dtype o = sigmoid(X[2 * hidden_dim_ + d]);
+      const Dtype g = tanh(X[3 * hidden_dim_ + d]);
+      const Dtype c_prev = C_prev[d];
+      const Dtype c = C[d];
+      const Dtype tanh_c = tanh(c);
+      Dtype* c_prev_diff = C_prev_diff + d;
+      Dtype* i_diff = X_diff + d;
+      Dtype* f_diff = X_diff + 1 * hidden_dim_ + d;
+      Dtype* o_diff = X_diff + 2 * hidden_dim_ + d;
+      Dtype* g_diff = X_diff + 3 * hidden_dim_ + d;
+      const Dtype c_term_diff =
+          C_diff[d] + H_diff[d] * o * (1 - tanh_c * tanh_c);
+      *c_prev_diff = c_term_diff * f;
+      *i_diff = c_term_diff * g * i * (1 - i);
+      *f_diff = c_term_diff * c_prev * f * (1 - f);
+      *o_diff = H_diff[d] * tanh_c * o * (1 - o);
+      *g_diff = c_term_diff * i * (1 - g * g);
+    }
+    C_prev += hidden_dim_;
+    X += x_dim;
+    C += hidden_dim_;
+    H += hidden_dim_;
+    C_diff += hidden_dim_;
+    H_diff += hidden_dim_;
+    X_diff += x_dim;
+    C_prev_diff += hidden_dim_;
+    ++cont;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LSTMUnitLayer);
+#endif
+
+INSTANTIATE_CLASS(LSTMUnitLayer);
+REGISTER_LAYER_CLASS(LSTMUnit);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/lstm_unit_layer.cu b/src/caffe/layers/lstm_unit_layer.cu
new file mode 100644
index 00000000000..15bb451d9e0
--- /dev/null
+++ b/src/caffe/layers/lstm_unit_layer.cu
@@ -0,0 +1,154 @@
+#include <algorithm>
+#include <cmath>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__device__ Dtype sigmoid(const Dtype x) {
+  return Dtype(1) / (Dtype(1) + exp(-x));
+}
+
+template <typename Dtype>
+__device__ Dtype tanh(const Dtype x) {
+  return Dtype(2) * sigmoid(Dtype(2) * x) - Dtype(1);
+}
+
+template <typename Dtype>
+__global__ void LSTMActsForward(const int nthreads, const int dim,
+                                const Dtype* X, Dtype* X_acts) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int x_dim = 4 * dim;
+    const int d = index % x_dim;
+    if (d < 3 * dim) {
+      X_acts[index] = sigmoid(X[index]);
+    } else {
+      X_acts[index] = tanh(X[index]);
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void LSTMUnitForward(const int nthreads, const int dim,
+    const Dtype* C_prev, const Dtype* X, const Dtype* cont,
+    Dtype* C, Dtype* H) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / dim;
+    const int d = index % dim;
+    const Dtype* X_offset = X + 4 * dim * n;
+    const Dtype i = X_offset[d];
+    const Dtype f = X_offset[1 * dim + d];
+    const Dtype o = X_offset[2 * dim + d];
+    const Dtype g = X_offset[3 * dim + d];
+    const Dtype c_prev = C_prev[index];
+    const Dtype c = cont[n] * f * c_prev + i * g;
+    C[index] = c;
+    const Dtype tanh_c = tanh(c);
+    H[index] = o * tanh_c;
+  }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = top[1]->count();
+  const Dtype* C_prev = bottom[0]->gpu_data();
+  const Dtype* X = bottom[1]->gpu_data();
+  const Dtype* cont = bottom[2]->gpu_data();
+  Dtype* X_acts = X_acts_.mutable_gpu_data();
+  Dtype* C = top[0]->mutable_gpu_data();
+  Dtype* H = top[1]->mutable_gpu_data();
+  const int X_count = bottom[1]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  LSTMActsForward<Dtype><<<CAFFE_GET_BLOCKS(X_count), CAFFE_CUDA_NUM_THREADS>>>(
+      X_count, hidden_dim_, X, X_acts);
+  CUDA_POST_KERNEL_CHECK;
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  LSTMUnitForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, hidden_dim_, C_prev, X_acts, cont, C, H);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void LSTMUnitBackward(const int nthreads, const int dim,
+    const Dtype* C_prev, const Dtype* X, const Dtype* C, const Dtype* H,
+    const Dtype* cont, const Dtype* C_diff, const Dtype* H_diff,
+    Dtype* C_prev_diff, Dtype* X_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / dim;
+    const int d = index % dim;
+    const Dtype* X_offset = X + 4 * dim * n;
+    const Dtype i = X_offset[d];
+    const Dtype f = X_offset[1 * dim + d];
+    const Dtype o = X_offset[2 * dim + d];
+    const Dtype g = X_offset[3 * dim + d];
+    const Dtype c_prev = C_prev[index];
+    const Dtype c = C[index];
+    const Dtype tanh_c = tanh(c);
+    Dtype* c_prev_diff = C_prev_diff + index;
+    Dtype* X_diff_offset = X_diff + 4 * dim * n;
+    Dtype* i_diff = X_diff_offset + d;
+    Dtype* f_diff = X_diff_offset + 1 * dim + d;
+    Dtype* o_diff = X_diff_offset + 2 * dim + d;
+    Dtype* g_diff = X_diff_offset + 3 * dim + d;
+    const Dtype c_term_diff =
+        C_diff[index] + H_diff[index] * o * (1 - tanh_c * tanh_c);
+    const Dtype cont_n = cont[n];
+    *c_prev_diff = cont_n * c_term_diff * f;
+    *i_diff = c_term_diff * g;
+    *f_diff = cont_n * c_term_diff * c_prev;
+    *o_diff = H_diff[index] * tanh_c;
+    *g_diff = c_term_diff * i;
+  }
+}
+
+template <typename Dtype>
+__global__ void LSTMActsBackward(const int nthreads, const int dim,
+    const Dtype* X_acts, const Dtype* X_acts_diff, Dtype* X_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int x_dim = 4 * dim;
+    const int d = index % x_dim;
+    const Dtype X_act = X_acts[index];
+    if (d < 3 * dim) {
+      X_diff[index] = X_acts_diff[index] * X_act * (Dtype(1) - X_act);
+    } else {
+      X_diff[index] = X_acts_diff[index] * (Dtype(1) - X_act * X_act);
+    }
+  }
+}
+
+template <typename Dtype>
+void LSTMUnitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[2]) << "Cannot backpropagate to sequence indicators.";
+  if (!propagate_down[0] && !propagate_down[1]) { return; }
+
+  const int count = top[1]->count();
+  const Dtype* C_prev = bottom[0]->gpu_data();
+  const Dtype* X_acts = X_acts_.gpu_data();
+  const Dtype* cont = bottom[2]->gpu_data();
+  const Dtype* C = top[0]->gpu_data();
+  const Dtype* H = top[1]->gpu_data();
+  const Dtype* C_diff = top[0]->gpu_diff();
+  const Dtype* H_diff = top[1]->gpu_diff();
+  Dtype* C_prev_diff = bottom[0]->mutable_gpu_diff();
+  Dtype* X_acts_diff = X_acts_.mutable_gpu_diff();
+  LSTMUnitBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(count, hidden_dim_,
+      C_prev, X_acts, C, H, cont, C_diff, H_diff, C_prev_diff, X_acts_diff);
+  CUDA_POST_KERNEL_CHECK;
+  const int X_count = bottom[1]->count();
+  Dtype* X_diff = bottom[1]->mutable_gpu_diff();
+  LSTMActsBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(X_count), CAFFE_CUDA_NUM_THREADS>>>(
+      X_count, hidden_dim_, X_acts, X_acts_diff, X_diff);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LSTMUnitLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_lstm_layer.cpp b/src/caffe/test/test_lstm_layer.cpp
new file mode 100644
index 00000000000..51905baafac
--- /dev/null
+++ b/src/caffe/test/test_lstm_layer.cpp
@@ -0,0 +1,288 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/lstm_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class LSTMLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  LSTMLayerTest() : num_output_(7) {
+    blob_bottom_vec_.push_back(&blob_bottom_);
+    blob_bottom_vec_.push_back(&blob_bottom_cont_);
+    blob_top_vec_.push_back(&blob_top_);
+    unit_blob_bottom_vec_.push_back(&unit_blob_bottom_c_prev_);
+    unit_blob_bottom_vec_.push_back(&unit_blob_bottom_x_);
+    unit_blob_bottom_vec_.push_back(&unit_blob_bottom_cont_);
+    unit_blob_top_vec_.push_back(&unit_blob_top_c_);
+    unit_blob_top_vec_.push_back(&unit_blob_top_h_);
+
+    ReshapeBlobs(1, 3);
+
+    layer_param_.mutable_recurrent_param()->set_num_output(num_output_);
+    FillerParameter* weight_filler =
+        layer_param_.mutable_recurrent_param()->mutable_weight_filler();
+    weight_filler->set_type("gaussian");
+    weight_filler->set_std(0.2);
+    FillerParameter* bias_filler =
+        layer_param_.mutable_recurrent_param()->mutable_bias_filler();
+    bias_filler->set_type("gaussian");
+    bias_filler->set_std(0.1);
+
+    layer_param_.set_phase(TEST);
+  }
+
+  void ReshapeBlobs(int num_timesteps, int num_instances) {
+    blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);
+    blob_bottom_static_.Reshape(num_instances, 2, 3, 4);
+    vector<int> shape(2);
+    shape[0] = num_timesteps;
+    shape[1] = num_instances;
+    blob_bottom_cont_.Reshape(shape);
+    shape.push_back(num_output_);
+
+    shape[0] = 1; shape[1] = num_instances; shape[2] = 4 * num_output_;
+    unit_blob_bottom_x_.Reshape(shape);
+    shape[0] = 1; shape[1] = num_instances; shape[2] = num_output_;
+    unit_blob_bottom_c_prev_.Reshape(shape);
+    shape.resize(2);
+    shape[0] = 1; shape[1] = num_instances;
+    unit_blob_bottom_cont_.Reshape(shape);
+
+    FillerParameter filler_param;
+    filler_param.set_min(-1);
+    filler_param.set_max(1);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(&blob_bottom_);
+    filler.Fill(&unit_blob_bottom_c_prev_);
+    filler.Fill(&unit_blob_bottom_x_);
+  }
+
+  int num_output_;
+  LayerParameter layer_param_;
+  Blob<Dtype> blob_bottom_;
+  Blob<Dtype> blob_bottom_cont_;
+  Blob<Dtype> blob_bottom_static_;
+  Blob<Dtype> blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+
+  Blob<Dtype> unit_blob_bottom_cont_;
+  Blob<Dtype> unit_blob_bottom_c_prev_;
+  Blob<Dtype> unit_blob_bottom_x_;
+  Blob<Dtype> unit_blob_top_c_;
+  Blob<Dtype> unit_blob_top_h_;
+  vector<Blob<Dtype>*> unit_blob_bottom_vec_;
+  vector<Blob<Dtype>*> unit_blob_top_vec_;
+};
+
+TYPED_TEST_CASE(LSTMLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(LSTMLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> expected_top_shape = this->blob_bottom_.shape();
+  expected_top_shape.resize(3);
+  expected_top_shape[2] = this->num_output_;
+  EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);
+}
+
+TYPED_TEST(LSTMLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kNumTimesteps = 3;
+  const int num = this->blob_bottom_.shape(1);
+  this->ReshapeBlobs(kNumTimesteps, num);
+
+  // Fill the cont blob with <0, 1, 1, ..., 1>,
+  // indicating a sequence that begins at the first timestep
+  // then continues for the rest of the sequence.
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;
+    }
+  }
+
+  // Process the full sequence in a single batch.
+  FillerParameter filler_param;
+  filler_param.set_mean(0);
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> sequence_filler(filler_param);
+  Caffe::set_random_seed(1);
+  sequence_filler.Fill(&this->blob_bottom_);
+  shared_ptr<LSTMLayer<Dtype> > layer(new LSTMLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  LOG(INFO) << "Calling forward for full sequence LSTM";
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  // Copy the inputs and outputs to reuse/check them later.
+  Blob<Dtype> bottom_copy(this->blob_bottom_.shape());
+  bottom_copy.CopyFrom(this->blob_bottom_);
+  Blob<Dtype> top_copy(this->blob_top_.shape());
+  top_copy.CopyFrom(this->blob_top_);
+
+  // Process the batch one timestep at a time;
+  // check that we get the same result.
+  this->ReshapeBlobs(1, num);
+  layer.reset(new LSTMLayer<Dtype>(this->layer_param_));
+  Caffe::set_random_seed(1701);
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  const int bottom_count = this->blob_bottom_.count();
+  const int top_count = this->blob_top_.count();
+  const Dtype kEpsilon = 1e-5;
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;
+    }
+    LOG(INFO) << "Calling forward for LSTM timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      ASSERT_LT(t * top_count + i, top_copy.count());
+      EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i], kEpsilon)
+         << "t = " << t << "; i = " << i;
+    }
+  }
+
+  // Process the batch one timestep at a time with all cont blobs set to 0.
+  // Check that we get a different result, except in the first timestep.
+  Caffe::set_random_seed(1701);
+  layer.reset(new LSTMLayer<Dtype>(this->layer_param_));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int t = 0; t < kNumTimesteps; ++t) {
+    caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,
+               this->blob_bottom_.mutable_cpu_data());
+    for (int n = 0; n < num; ++n) {
+      this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;
+    }
+    LOG(INFO) << "Calling forward for LSTM timestep " << t;
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    for (int i = 0; i < top_count; ++i) {
+      if (t == 0) {
+        EXPECT_NEAR(this->blob_top_.cpu_data()[i],
+                    top_copy.cpu_data()[t * top_count + i], kEpsilon)
+           << "t = " << t << "; i = " << i;
+      } else {
+        EXPECT_NE(this->blob_top_.cpu_data()[i],
+                  top_copy.cpu_data()[t * top_count + i])
+           << "t = " << t << "; i = " << i;
+      }
+    }
+  }
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  LSTMUnitLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->unit_blob_bottom_vec_, this->unit_blob_top_vec_);
+  const int num_axes = this->unit_blob_bottom_c_prev_.num_axes();
+  ASSERT_EQ(num_axes, this->unit_blob_top_c_.num_axes());
+  ASSERT_EQ(num_axes, this->unit_blob_top_h_.num_axes());
+  for (int i = 0; i < num_axes; ++i) {
+    EXPECT_EQ(this->unit_blob_bottom_c_prev_.shape(i),
+              this->unit_blob_top_c_.shape(i));
+    EXPECT_EQ(this->unit_blob_bottom_c_prev_.shape(i),
+              this->unit_blob_top_h_.shape(i));
+  }
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  LSTMUnitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  Dtype* cont_data = this->blob_bottom_cont_.mutable_cpu_data();
+  cont_data[0] = 0;
+  cont_data[1] = 0;
+  cont_data[2] = 0;
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 1);
+}
+
+TYPED_TEST(LSTMLayerTest, TestLSTMUnitGradientNonZeroCont) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  LSTMUnitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  Dtype* cont_data = this->blob_bottom_cont_.mutable_cpu_data();
+  cont_data[0] = 1;
+  cont_data[1] = 0;
+  cont_data[2] = 1;
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->unit_blob_bottom_vec_,
+      this->unit_blob_top_vec_, 1);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroCont) {
+  typedef typename TypeParam::Dtype Dtype;
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroContBufferSize2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(LSTMLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->ReshapeBlobs(2, 2);
+  FillerParameter filler_param;
+  UniformFiller<Dtype> filler(filler_param);
+  filler.Fill(&this->blob_bottom_);
+  filler.Fill(&this->blob_bottom_static_);
+  this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);
+  LSTMLayer<Dtype> layer(this->layer_param_);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  for (int i = 0; i < this->blob_bottom_cont_.count(); ++i) {
+    this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 2);
+}
+
+
+}  // namespace caffe

From 7e7631f27e4145b9838b17d46bb1ffc42279b1e4 Mon Sep 17 00:00:00 2001
From: Chuck Cho <chuck.cho@dextro.co>
Date: Thu, 2 Jun 2016 14:35:14 -0400
Subject: [PATCH 1803/2053] Fixing a typo

---
 tools/extract_features.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 704467250a6..51c791e4021 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -130,7 +130,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
     txns.push_back(txn);
   }
 
-  LOG(ERROR)<< "Extacting Features";
+  LOG(ERROR)<< "Extracting Features";
 
   Datum datum;
   std::vector<int> image_indices(num_features, 0);

From 742c93f31be4c874aa5fd0103f25f8a2f8d4d63d Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Mon, 23 May 2016 20:09:45 -0700
Subject: [PATCH 1804/2053] Exposing load_hdf5 and save_hdf5 to python

---
 python/caffe/_caffe.cpp       | 12 +++++++++++-
 python/caffe/test/test_net.py | 14 ++++++++++++++
 2 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 32b5d921094..48a0c8f2e95 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -114,6 +114,14 @@ void Net_Save(const Net<Dtype>& net, string filename) {
   WriteProtoToBinaryFile(net_param, filename.c_str());
 }
 
+void Net_SaveHDF5(const Net<Dtype>& net, string filename) {
+  net.ToHDF5(filename);
+}
+
+void Net_LoadHDF5(Net<Dtype>* net, string filename) {
+  net->CopyTrainedLayersFromHDF5(filename.c_str());
+}
+
 void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
     bp::object labels_obj) {
   // check that this network has an input MemoryDataLayer
@@ -267,7 +275,9 @@ BOOST_PYTHON_MODULE(_caffe) {
         bp::return_value_policy<bp::copy_const_reference>()))
     .def("_set_input_arrays", &Net_SetInputArrays,
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
-    .def("save", &Net_Save);
+    .def("save", &Net_Save)
+    .def("save_hdf5", &Net_SaveHDF5)
+    .def("load_hdf5", &Net_LoadHDF5);
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index aad828aa8aa..4cacfcd05bb 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -79,3 +79,17 @@ def test_save_and_read(self):
             for i in range(len(self.net.params[name])):
                 self.assertEqual(abs(self.net.params[name][i].data
                     - net2.params[name][i].data).sum(), 0)
+
+    def test_save_hdf5(self):
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        f.close()
+        self.net.save_hdf5(f.name)
+        net_file = simple_net_file(self.num_output)
+        net2 = caffe.Net(net_file, caffe.TRAIN)
+        net2.load_hdf5(f.name)
+        os.remove(net_file)
+        os.remove(f.name)
+        for name in self.net.params:
+            for i in range(len(self.net.params[name])):
+                self.assertEqual(abs(self.net.params[name][i].data
+                    - net2.params[name][i].data).sum(), 0)

From d167e61a23a54de529d51731fbe543ff4cec0d3c Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 1 Jun 2016 09:50:57 -0700
Subject: [PATCH 1805/2053] Add level and stages to Net constructor

This internal functionality will be exposed through the various
interfaces in subsequent commits
Also adds C++ tests for all-in-one nets
---
 include/caffe/net.hpp       |   1 +
 src/caffe/net.cpp           |  11 +++-
 src/caffe/test/test_net.cpp | 128 ++++++++++++++++++++++++++++++++++++
 3 files changed, 139 insertions(+), 1 deletion(-)

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 0addb3c2a6d..493bdf294e2 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -25,6 +25,7 @@ class Net {
  public:
   explicit Net(const NetParameter& param, const Net* root_net = NULL);
   explicit Net(const string& param_file, Phase phase,
+      const int level = 0, const vector<string>* stages = NULL,
       const Net* root_net = NULL);
   virtual ~Net() {}
 
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index f0bf594936c..644cb7e97ee 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -28,11 +28,20 @@ Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)
+Net<Dtype>::Net(const string& param_file, Phase phase,
+    const int level, const vector<string>* stages,
+    const Net* root_net)
     : root_net_(root_net) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
+  // Set phase, stages and level
   param.mutable_state()->set_phase(phase);
+  if (stages != NULL) {
+    for (int i = 0; i < stages->size(); i++) {
+      param.mutable_state()->add_stage((*stages)[i]);
+    }
+  }
+  param.mutable_state()->set_level(level);
   Init(param);
 }
 
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 92fd317fee8..24b957f2acc 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -9,6 +9,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/net.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -29,6 +30,17 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     net_.reset(new Net<Dtype>(param));
   }
 
+  virtual void InitNetFromProtoFileWithState(const string& proto,
+      Phase phase = caffe::TRAIN, const int level = 0,
+      const vector<string>* stages = NULL) {
+    NetParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    string param_file;
+    MakeTempFilename(&param_file);
+    WriteProtoToTextFile(param, param_file);
+    net_.reset(new Net<Dtype>(param_file, phase, level, stages));
+  }
+
   virtual void CopyNetBlobs(const bool copy_diff,
       vector<shared_ptr<Blob<Dtype> > >* blobs_copy) {
     CHECK(net_);
@@ -771,6 +783,62 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitAllInOneNet(Phase phase = caffe::TRAIN,
+      const int level = 0, const vector<string>* stages = NULL) {
+    string proto =
+      "name: 'All-in-one Network'"
+      "layer { "
+      "  name: 'train-data' "
+      "  type: 'DummyData' "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  dummy_data_param { "
+      "    shape { dim: 1 dim: 10 } "
+      "    shape { dim: 1 dim: 1 } "
+      "  } "
+      "  include { phase: TRAIN stage: 'train' } "
+      "} "
+      "layer { "
+      "  name: 'val-data' "
+      "  type: 'DummyData' "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  dummy_data_param { "
+      "    shape { dim: 1 dim: 10 } "
+      "    shape { dim: 1 dim: 1 } "
+      "  } "
+      "  include { phase: TEST stage: 'val' } "
+      "} "
+      "layer { "
+      "  name: 'deploy-data' "
+      "  type: 'Input' "
+      "  top: 'data' "
+      "  input_param { "
+      "    shape { dim: 1 dim: 10 } "
+      "  } "
+      "  include { phase: TEST stage: 'deploy' } "
+      "} "
+      "layer { "
+      "  name: 'ip' "
+      "  type: 'InnerProduct' "
+      "  bottom: 'data' "
+      "  top: 'ip' "
+      "  inner_product_param { "
+      "    num_output: 2 "
+      "  } "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  type: 'SoftmaxWithLoss' "
+      "  bottom: 'ip' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "  include { phase: TRAIN stage: 'train' } "
+      "  include { phase: TEST stage: 'val' } "
+      "} ";
+    InitNetFromProtoFileWithState(proto, phase, level, stages);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -2473,4 +2541,64 @@ TYPED_TEST(NetTest, TestForcePropagateDown) {
   }
 }
 
+TYPED_TEST(NetTest, TestAllInOneNetTrain) {
+  vector<string> stages;
+  stages.push_back("train");
+  this->InitAllInOneNet(caffe::TRAIN, 0, &stages);
+  bool found_data = false;
+  bool found_loss = false;
+  for (int i = 0; i < this->net_->layers().size(); ++i) {
+    const string& layer_name = this->net_->layer_names()[i];
+    if (layer_name == "train-data") {
+      found_data = true;
+    } else if (layer_name == "loss") {
+      found_loss = true;
+    } else {
+      ASSERT_NE(layer_name, "val-data");
+      ASSERT_NE(layer_name, "deploy-data");
+    }
+  }
+  ASSERT_TRUE(found_data);
+  ASSERT_TRUE(found_loss);
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetVal) {
+  vector<string> stages;
+  stages.push_back("val");
+  this->InitAllInOneNet(caffe::TEST, 0, &stages);
+  bool found_data = false;
+  bool found_loss = false;
+  for (int i = 0; i < this->net_->layers().size(); ++i) {
+    const string& layer_name = this->net_->layer_names()[i];
+    if (layer_name == "val-data") {
+      found_data = true;
+    } else if (layer_name == "loss") {
+      found_loss = true;
+    } else {
+      ASSERT_NE(layer_name, "train-data");
+      ASSERT_NE(layer_name, "deploy-data");
+    }
+  }
+  ASSERT_TRUE(found_data);
+  ASSERT_TRUE(found_loss);
+}
+
+TYPED_TEST(NetTest, TestAllInOneNetDeploy) {
+  vector<string> stages;
+  stages.push_back("deploy");
+  this->InitAllInOneNet(caffe::TEST, 0, &stages);
+  bool found_data = false;
+  for (int i = 0; i < this->net_->layers().size(); ++i) {
+    const string& layer_name = this->net_->layer_names()[i];
+    if (layer_name == "deploy-data") {
+      found_data = true;
+    } else {
+      ASSERT_NE(layer_name, "train-data");
+      ASSERT_NE(layer_name, "val-data");
+      ASSERT_NE(layer_name, "loss");
+    }
+  }
+  ASSERT_TRUE(found_data);
+}
+
 }  // namespace caffe

From 66e84d785a72d66511bffe30c0f016af9103deb8 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 1 Jun 2016 09:56:51 -0700
Subject: [PATCH 1806/2053] Add phase, level and stages to tools/caffe

Adds command-line flags for phase, level and stage

train -- override level and stages for test_state from solver
test -- set level and stages
time -- set phase, level and stages
---
 tools/caffe.cpp | 39 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 37 insertions(+), 2 deletions(-)

diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 5bb60eb161d..9bf4214ad93 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -34,6 +34,13 @@ DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
     "The model definition protocol buffer text file.");
+DEFINE_string(phase, "",
+    "Optional; network phase (TRAIN or TEST). Only used for 'time'.");
+DEFINE_int32(level, 0,
+    "Optional; network level.");
+DEFINE_string(stage, "",
+    "Optional; network stages (not to be confused with phase), "
+    "separated by ','.");
 DEFINE_string(snapshot, "",
     "Optional; the snapshot solver state to resume training.");
 DEFINE_string(weights, "",
@@ -101,6 +108,25 @@ static void get_gpus(vector<int>* gpus) {
   }
 }
 
+// Parse phase from flags
+caffe::Phase get_phase_from_flags(caffe::Phase default_value) {
+  if (FLAGS_phase == "")
+    return default_value;
+  if (FLAGS_phase == "TRAIN")
+    return caffe::TRAIN;
+  if (FLAGS_phase == "TEST")
+    return caffe::TEST;
+  LOG(FATAL) << "phase must be \"TRAIN\" or \"TEST\"";
+  return caffe::TRAIN;  // Avoid warning
+}
+
+// Parse stages from flags
+vector<string> get_stages_from_flags() {
+  vector<string> stages;
+  boost::split(stages, FLAGS_stage, boost::is_any_of(","));
+  return stages;
+}
+
 // caffe commands to call by
 //     caffe <command> <args>
 //
@@ -156,10 +182,16 @@ int train() {
   CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
       << "Give a snapshot to resume training or weights to finetune "
       "but not both.";
+  vector<string> stages = get_stages_from_flags();
 
   caffe::SolverParameter solver_param;
   caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
 
+  solver_param.mutable_train_state()->set_level(FLAGS_level);
+  for (int i = 0; i < stages.size(); i++) {
+    solver_param.mutable_train_state()->add_stage(stages[i]);
+  }
+
   // If the gpus flag is not provided, allow the mode and device to be set
   // in the solver prototxt.
   if (FLAGS_gpu.size() == 0
@@ -229,6 +261,7 @@ RegisterBrewFunction(train);
 int test() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
   CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
+  vector<string> stages = get_stages_from_flags();
 
   // Set device id and mode
   vector<int> gpus;
@@ -247,7 +280,7 @@ int test() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Net<float> caffe_net(FLAGS_model, caffe::TEST);
+  Net<float> caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages);
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
@@ -300,6 +333,8 @@ RegisterBrewFunction(test);
 // Time: benchmark the execution time of a model.
 int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
+  caffe::Phase phase = get_phase_from_flags(caffe::TRAIN);
+  vector<string> stages = get_stages_from_flags();
 
   // Set device id and mode
   vector<int> gpus;
@@ -313,7 +348,7 @@ int time() {
     Caffe::set_mode(Caffe::CPU);
   }
   // Instantiate the caffe net.
-  Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
+  Net<float> caffe_net(FLAGS_model, phase, FLAGS_level, &stages);
 
   // Do a clean forward and backward pass, so that memory allocation are done
   // and future iterations will be more stable.

From 19adc7a79e3acacc777076143357cc0569781cd3 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 1 Jun 2016 10:02:41 -0700
Subject: [PATCH 1807/2053] Add level and stages to pycaffe

Uses Boost.Python's pattern matching to differentiate between
constructors
Also adds Python tests for all-in-one nets
---
 python/caffe/_caffe.cpp       |  44 +++++--
 python/caffe/test/test_net.py | 228 +++++++++++++++++++++++++++++++++-
 2 files changed, 263 insertions(+), 9 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 48a0c8f2e95..e2726286dfb 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -86,19 +86,42 @@ void CheckContiguousArray(PyArrayObject* arr, string name,
   }
 }
 
-// Net constructor for passing phase as int
-shared_ptr<Net<Dtype> > Net_Init(
-    string param_file, int phase) {
-  CheckFile(param_file);
+// Net constructor
+shared_ptr<Net<Dtype> > Net_Init(string network_file, int phase,
+    const int level, const bp::object& stages,
+    const bp::object& weights) {
+  CheckFile(network_file);
+
+  // Convert stages from list to vector
+  vector<string> stages_vector;
+  if (!stages.is_none()) {
+    for (int i = 0; i < len(stages); i++) {
+      stages_vector.push_back(bp::extract<string>(stages[i]));
+    }
+  }
+
+  // Initialize net
+  shared_ptr<Net<Dtype> > net(new Net<Dtype>(network_file,
+        static_cast<Phase>(phase), level, &stages_vector));
+
+  // Load weights
+  if (!weights.is_none()) {
+    std::string weights_file_str = bp::extract<std::string>(weights);
+    CheckFile(weights_file_str);
+    net->CopyTrainedLayersFrom(weights_file_str);
+  }
 
-  shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
-      static_cast<Phase>(phase)));
   return net;
 }
 
-// Net construct-and-load convenience constructor
+// Legacy Net construct-and-load convenience constructor
 shared_ptr<Net<Dtype> > Net_Init_Load(
     string param_file, string pretrained_param_file, int phase) {
+  LOG(WARNING) << "DEPRECATION WARNING - deprecated use of Python interface";
+  LOG(WARNING) << "Use this instead (with the named \"weights\""
+    << " parameter):";
+  LOG(WARNING) << "Net('" << param_file << "', " << phase
+    << ", weights='" << pretrained_param_file << "')";
   CheckFile(param_file);
   CheckFile(pretrained_param_file);
 
@@ -245,7 +268,12 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
     bp::no_init)
-    .def("__init__", bp::make_constructor(&Net_Init))
+    // Constructor
+    .def("__init__", bp::make_constructor(&Net_Init,
+          bp::default_call_policies(), (bp::arg("network_file"), "phase",
+            bp::arg("level")=0, bp::arg("stages")=bp::object(),
+            bp::arg("weights")=bp::object())))
+    // Legacy constructor
     .def("__init__", bp::make_constructor(&Net_Init_Load))
     .def("_forward", &Net<Dtype>::ForwardFromTo)
     .def("_backward", &Net<Dtype>::BackwardFromTo)
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 4cacfcd05bb..300aabdeea5 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -72,7 +72,11 @@ def test_save_and_read(self):
         f.close()
         self.net.save(f.name)
         net_file = simple_net_file(self.num_output)
-        net2 = caffe.Net(net_file, f.name, caffe.TRAIN)
+        # Test legacy constructor
+        #   should print deprecation warning
+        caffe.Net(net_file, f.name, caffe.TRAIN)
+        # Test named constructor
+        net2 = caffe.Net(net_file, caffe.TRAIN, weights=f.name)
         os.remove(net_file)
         os.remove(f.name)
         for name in self.net.params:
@@ -93,3 +97,225 @@ def test_save_hdf5(self):
             for i in range(len(self.net.params[name])):
                 self.assertEqual(abs(self.net.params[name][i].data
                     - net2.params[name][i].data).sum(), 0)
+
+class TestLevels(unittest.TestCase):
+
+    TEST_NET = """
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+}
+layer {
+  name: "NoLevel"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "NoLevel"
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level0Only"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level0Only"
+  include { min_level: 0 max_level: 0 }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level1Only"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level1Only"
+  include { min_level: 1 max_level: 1 }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level>=0"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level>=0"
+  include { min_level: 0 }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "Level>=1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "Level>=1"
+  include { min_level: 1 }
+  inner_product_param { num_output: 1 }
+}
+"""
+
+    def setUp(self):
+        self.f = tempfile.NamedTemporaryFile(mode='w+')
+        self.f.write(self.TEST_NET)
+        self.f.flush()
+
+    def tearDown(self):
+        self.f.close()
+
+    def check_net(self, net, blobs):
+        net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
+        self.assertEqual(net_blobs, blobs)
+
+    def test_0(self):
+        net = caffe.Net(self.f.name, caffe.TEST)
+        self.check_net(net, ['NoLevel', 'Level0Only', 'Level>=0'])
+
+    def test_1(self):
+        net = caffe.Net(self.f.name, caffe.TEST, level=1)
+        self.check_net(net, ['NoLevel', 'Level1Only', 'Level>=0', 'Level>=1'])
+
+
+class TestStages(unittest.TestCase):
+
+    TEST_NET = """
+layer {
+  name: "data"
+  type: "DummyData"
+  top: "data"
+  dummy_data_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+}
+layer {
+  name: "A"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "A"
+  include { stage: "A" }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "B"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "B"
+  include { stage: "B" }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "AorB"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "AorB"
+  include { stage: "A" }
+  include { stage: "B" }
+  inner_product_param { num_output: 1 }
+}
+layer {
+  name: "AandB"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "AandB"
+  include { stage: "A" stage: "B" }
+  inner_product_param { num_output: 1 }
+}
+"""
+
+    def setUp(self):
+        self.f = tempfile.NamedTemporaryFile(mode='w+')
+        self.f.write(self.TEST_NET)
+        self.f.flush()
+
+    def tearDown(self):
+        self.f.close()
+
+    def check_net(self, net, blobs):
+        net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
+        self.assertEqual(net_blobs, blobs)
+
+    def test_A(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['A'])
+        self.check_net(net, ['A', 'AorB'])
+
+    def test_B(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['B'])
+        self.check_net(net, ['B', 'AorB'])
+
+    def test_AandB(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['A', 'B'])
+        self.check_net(net, ['A', 'B', 'AorB', 'AandB'])
+
+
+class TestAllInOne(unittest.TestCase):
+
+    TEST_NET = """
+layer {
+  name: "train_data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  dummy_data_param {
+    shape { dim: 1 dim: 1 dim: 10 dim: 10 }
+    shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  }
+  include { phase: TRAIN stage: "train" }
+}
+layer {
+  name: "val_data"
+  type: "DummyData"
+  top: "data"
+  top: "label"
+  dummy_data_param {
+    shape { dim: 1 dim: 1 dim: 10 dim: 10 }
+    shape { dim: 1 dim: 1 dim: 1 dim: 1 }
+  }
+  include { phase: TEST stage: "val" }
+}
+layer {
+  name: "deploy_data"
+  type: "Input"
+  top: "data"
+  input_param { shape { dim: 1 dim: 1 dim: 10 dim: 10 } }
+  include { phase: TEST stage: "deploy" }
+}
+layer {
+  name: "ip"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip"
+  inner_product_param { num_output: 2 }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip"
+  bottom: "label"
+  top: "loss"
+  include: { phase: TRAIN stage: "train" }
+  include: { phase: TEST stage: "val" }
+}
+layer {
+  name: "pred"
+  type: "Softmax"
+  bottom: "ip"
+  top: "pred"
+  include: { phase: TEST stage: "deploy" }
+}
+"""
+
+    def setUp(self):
+        self.f = tempfile.NamedTemporaryFile(mode='w+')
+        self.f.write(self.TEST_NET)
+        self.f.flush()
+
+    def tearDown(self):
+        self.f.close()
+
+    def check_net(self, net, outputs):
+        self.assertEqual(list(net.blobs['data'].shape), [1,1,10,10])
+        self.assertEqual(net.outputs, outputs)
+
+    def test_train(self):
+        net = caffe.Net(self.f.name, caffe.TRAIN, stages=['train'])
+        self.check_net(net, ['loss'])
+
+    def test_val(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['val'])
+        self.check_net(net, ['loss'])
+
+    def test_deploy(self):
+        net = caffe.Net(self.f.name, caffe.TEST, stages=['deploy'])
+        self.check_net(net, ['pred'])
+

From dec2381cc8d6465f0997cd29b143b3c6e13416ef Mon Sep 17 00:00:00 2001
From: philkr <philkr@users.noreply.github.com>
Date: Thu, 3 Sep 2015 14:28:55 -0700
Subject: [PATCH 1808/2053] Exposing solver callbacks to python

---
 python/caffe/_caffe.cpp | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 48a0c8f2e95..334088e8a57 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -228,6 +228,27 @@ bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
   return bp::object();
 }
 
+template<typename Dtype>
+class PythonCallback: public Solver<Dtype>::Callback {
+ protected:
+  bp::object on_start_, on_gradients_ready_;
+
+ public:
+  PythonCallback(bp::object on_start, bp::object on_gradients_ready)
+    : on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }
+  virtual void on_gradients_ready() {
+    on_gradients_ready_();
+  }
+  virtual void on_start() {
+    on_start_();
+  }
+};
+template<typename Dtype>
+void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
+  bp::object on_gradients_ready) {
+  solver->add_callback(new PythonCallback<Dtype>(on_start, on_gradients_ready));
+}
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -317,6 +338,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
           bp::return_internal_reference<>()))
     .add_property("iter", &Solver<Dtype>::iter)
+    .def("add_callback", &Solver_add_callback<Dtype>)
     .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
           &Solver<Dtype>::Solve), SolveOverloads())
     .def("step", &Solver<Dtype>::Step)

From 9f1855273fa27d106b3675d32ec01acb658a80f0 Mon Sep 17 00:00:00 2001
From: Raffi Enficiaud <raffi.enficiaud@tuebingen.mpg.de>
Date: Tue, 21 Jun 2016 13:41:06 +0200
Subject: [PATCH 1809/2053] Fix glog upstream autoconf

---
 cmake/External/glog.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake
index a44672f2753..f9d0549cd90 100644
--- a/cmake/External/glog.cmake
+++ b/cmake/External/glog.cmake
@@ -37,6 +37,7 @@ if (NOT __GLOG_INCLUDED)
       GIT_TAG "v0.3.4"
       UPDATE_COMMAND ""
       INSTALL_DIR ${gflags_INSTALL}
+      PATCH_COMMAND autoreconf -i ${glog_PREFIX}/src/glog
       CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/..
       LOG_DOWNLOAD 1
       LOG_CONFIGURE 1

From b29d271b8cd679588618d502add8a4eae2beb853 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 16:22:20 -0700
Subject: [PATCH 1810/2053] add layer_dict to the python interface

---
 python/caffe/pycaffe.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index ca6d050e2bd..4f84605ba00 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -43,6 +43,16 @@ def _Net_blob_loss_weights(self):
                                                        self._blob_loss_weights))
     return self._blob_loss_weights_dict
 
+@property
+def _Net_layer_dict(self):
+    """
+    An OrderedDict (bottom to top, i.e., input to output) of network
+    layers indexed by name
+    """
+    if not hasattr(self, '_layer_dict'):
+        self._layer_dict = OrderedDict(zip(self._layer_names, self.layers))
+    return self._layer_dict
+
 
 @property
 def _Net_params(self):
@@ -311,6 +321,7 @@ def __getitem__(self, name):
 # Attach methods to Net.
 Net.blobs = _Net_blobs
 Net.blob_loss_weights = _Net_blob_loss_weights
+Net.layer_dict = _Net_layer_dict
 Net.params = _Net_params
 Net.forward = _Net_forward
 Net.backward = _Net_backward

From 118c97ff5890e92b9aa603d925d947d45086b330 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 17:37:55 -0700
Subject: [PATCH 1811/2053] add clear_param_diffs to the python net interface

---
 python/caffe/_caffe.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 334088e8a57..a7fb886aa06 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -271,6 +271,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("_forward", &Net<Dtype>::ForwardFromTo)
     .def("_backward", &Net<Dtype>::BackwardFromTo)
     .def("reshape", &Net<Dtype>::Reshape)
+    .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
     // The cast is to select a particular overload.
     .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
         &Net<Dtype>::CopyTrainedLayersFrom))

From 892c78dd7833f1818a76d4025076b34946200fa0 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 17:42:31 -0700
Subject: [PATCH 1812/2053] add unit test for clear_param_diffs

---
 python/caffe/test/test_net.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 4cacfcd05bb..7fb9f475d43 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -63,6 +63,17 @@ def test_forward_backward(self):
         self.net.forward()
         self.net.backward()
 
+    def test_clear_param_diffs(self):
+        # Run a forward/backward step to have non-zero diffs
+        self.net.forward()
+        self.net.backward()
+        diff = self.net.params["conv"][0].diff
+        # Check that we have non-zero diffs
+        self.assertTrue(diff.max() > 0)
+        self.net.clear_param_diffs()
+        # Check that the diffs are now 0
+        self.assertTrue((diff == 0).all())
+
     def test_inputs_outputs(self):
         self.assertEqual(self.net.inputs, [])
         self.assertEqual(self.net.outputs, ['loss'])

From 5417f106c14c782865e2a5484020b8e45a8b2b80 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 16:39:30 -0700
Subject: [PATCH 1813/2053] add tests for pycaffe's layer_dict

---
 python/caffe/test/test_net.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 4cacfcd05bb..546bd5faa0b 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -59,6 +59,13 @@ def test_memory(self):
         for bl in blobs:
             total += bl.data.sum() + bl.diff.sum()
 
+    def test_layer_dict(self):
+        layer_dict = self.net.layer_dict
+        self.assertEqual(list(layer_dict.keys()), list(self.net._layer_names))
+        for i, name in enumerate(self.net._layer_names):
+            self.assertEqual(layer_dict[name].type,
+                             self.net.layers[i].type)
+
     def test_forward_backward(self):
         self.net.forward()
         self.net.backward()

From bdb94577d97da5cf5b6ec046952dbe79e9c886bf Mon Sep 17 00:00:00 2001
From: Alican Bozkurt <alican@ece.neu.edu>
Date: Tue, 28 Jun 2016 16:28:33 -0400
Subject: [PATCH 1814/2053] add default value for rms_decay

---
 src/caffe/proto/caffe.proto | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 1556781cbc2..6940a705eb6 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -219,7 +219,7 @@ message SolverParameter {
 
   // RMSProp decay value
   // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
-  optional float rms_decay = 38;
+  optional float rms_decay = 38 [default = 0.99];
 
   // If true, print information about the state of the net that may help with
   // debugging learning problems.

From 80f60dae071fca4457d7a439960385a4579f489d Mon Sep 17 00:00:00 2001
From: Alican Bozkurt <alican@ece.neu.edu>
Date: Tue, 28 Jun 2016 16:59:36 -0400
Subject: [PATCH 1815/2053] corrected rmsprop documentation

---
 docs/tutorial/solver.md | 13 +++----------
 1 file changed, 3 insertions(+), 10 deletions(-)

diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index b719f715a4b..81c626386a2 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -209,18 +209,11 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we
 The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
 
 $$
-(v_t)_i =
-\begin{cases}
-(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
-(v_{t-1})_i \cdot (1-\delta), & \text{else}
-\end{cases}
+\operatorname{MS}((W_t)_i)= \delta\operatorname{MS}((W_{t-1})_i)+ (1-\delta)(\nabla L(W_t))_i^2 \\
+(W_{t+1})_i= (W_{t})_i -\alpha\frac{(\nabla L(W_t))_i}{\sqrt{\operatorname{MS}((W_t)_i)}}
 $$
 
-$$
-(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i,
-$$
-
-If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$.
+The default value of $$\delta$$ (`rms_decay`) is set to $$\delta=0.99$$.
 
 [1] T. Tieleman, and G. Hinton.
     [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).

From f0b1a9e770594f93fecda9e876faaafaede2b496 Mon Sep 17 00:00:00 2001
From: Carl Doersch <cdoersch@cs.cmu.edu>
Date: Sun, 3 Jul 2016 12:32:19 -0700
Subject: [PATCH 1816/2053] Add phase support for draw net

---
 python/caffe/draw.py | 32 +++++++++++++++++++++++++++-----
 python/draw_net.py   | 15 ++++++++++++++-
 2 files changed, 41 insertions(+), 6 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 61205ca9f37..9eecf6d7b46 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -127,7 +127,7 @@ def choose_color_by_layertype(layertype):
     return color
 
 
-def get_pydot_graph(caffe_net, rankdir, label_edges=True):
+def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
     """Create a data structure which represents the `caffe_net`.
 
     Parameters
@@ -137,6 +137,9 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
         Direction of graph layout.
     label_edges : boolean, optional
         Label the edges (default is True).
+    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+        Include layers from this network phase.  If None, include all layers.
+        (the default is None)
 
     Returns
     -------
@@ -148,6 +151,19 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
     pydot_nodes = {}
     pydot_edges = []
     for layer in caffe_net.layer:
+        if phase is not None:
+          included = False
+          if len(layer.include) == 0:
+            included = True
+          if len(layer.include) > 0 and len(layer.exclude) > 0:
+            raise ValueError('layer ' + layer.name + ' has both include '
+                             'and exclude specified.')
+          for layer_phase in layer.include:
+            included = included or layer_phase.phase == phase
+          for layer_phase in layer.exclude:
+            included = included and not layer_phase.phase == phase
+          if not included:
+            continue
         node_label = get_layer_label(layer, rankdir)
         node_name = "%s_%s" % (layer.name, layer.type)
         if (len(layer.bottom) == 1 and len(layer.top) == 1 and
@@ -186,7 +202,7 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True):
     return pydot_graph
 
 
-def draw_net(caffe_net, rankdir, ext='png'):
+def draw_net(caffe_net, rankdir, ext='png', phase=None):
     """Draws a caffe net and returns the image string encoded using the given
     extension.
 
@@ -195,16 +211,19 @@ def draw_net(caffe_net, rankdir, ext='png'):
     caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
     ext : string, optional
         The image extension (the default is 'png').
+    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+        Include layers from this network phase.  If None, include all layers.
+        (the default is None)
 
     Returns
     -------
     string :
         Postscript representation of the graph.
     """
-    return get_pydot_graph(caffe_net, rankdir).create(format=ext)
+    return get_pydot_graph(caffe_net, rankdir, phase=phase).create(format=ext)
 
 
-def draw_net_to_file(caffe_net, filename, rankdir='LR'):
+def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):
     """Draws a caffe net, and saves it to file using the format given as the
     file extension. Use '.raw' to output raw text that you can manually feed
     to graphviz to draw graphs.
@@ -216,7 +235,10 @@ def draw_net_to_file(caffe_net, filename, rankdir='LR'):
         The path to a file where the networks visualization will be stored.
     rankdir : {'LR', 'TB', 'BT'}
         Direction of graph layout.
+    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
+        Include layers from this network phase.  If None, include all layers.
+        (the default is None)
     """
     ext = filename[filename.rfind('.')+1:]
     with open(filename, 'wb') as fid:
-        fid.write(draw_net(caffe_net, rankdir, ext))
+        fid.write(draw_net(caffe_net, rankdir, ext, phase))
diff --git a/python/draw_net.py b/python/draw_net.py
index ec76a744da3..dfe70d26a71 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -28,6 +28,11 @@ def parse_args():
                               'http://www.graphviz.org/doc/info/'
                               'attrs.html#k:rankdir'),
                         default='LR')
+    parser.add_argument('--phase',
+                        help=('Which network phase to draw: can be TRAIN, '
+                              'TEST, or ALL.  If ALL, then all layers are drawn '
+                              'regardless of phase.'),
+                        default="ALL")
 
     args = parser.parse_args()
     return args
@@ -38,7 +43,15 @@ def main():
     net = caffe_pb2.NetParameter()
     text_format.Merge(open(args.input_net_proto_file).read(), net)
     print('Drawing net to %s' % args.output_image_file)
-    caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir)
+    phase=None;
+    if args.phase == "TRAIN":
+        phase = caffe.TRAIN
+    elif args.phase == "TEST":
+        phase = caffe.TEST
+    elif args.phase != "ALL":
+        raise ValueError("Unknown phase: " + args.phase)
+    caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir,
+                                phase)
 
 
 if __name__ == '__main__':

From f9fd20ea3893c515b19cae6fa3693b1649fb9487 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Fri, 8 Jul 2016 12:05:17 -0700
Subject: [PATCH 1817/2053] Fix Python installation with CMake install target

---
 python/CMakeLists.txt | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index a22641401f0..bf492a24b1c 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -22,13 +22,19 @@ if(UNIX OR APPLE)
 endif()
 
 # ---[ Install
-file(GLOB files1 *.py requirements.txt)
-install(FILES ${files1} DESTINATION python)
-
-file(GLOB files2 caffe/*.py)
-install(FILES  ${files2} DESTINATION python/caffe)
+# scripts
+file(GLOB python_files *.py requirements.txt)
+install(FILES ${python_files} DESTINATION python)
+
+# module
+install(DIRECTORY caffe
+    DESTINATION python
+    FILES_MATCHING
+    PATTERN "*.py"
+    PATTERN "ilsvrc_2012_mean.npy"
+    PATTERN "test" EXCLUDE
+    )
+
+# _caffe.so
 install(TARGETS pycaffe  DESTINATION python/caffe)
-install(DIRECTORY caffe/imagenet caffe/proto caffe/test DESTINATION python/caffe)
-
-
 

From f1a8470aa21e35a5b2bb83007f8fb7680a354815 Mon Sep 17 00:00:00 2001
From: Nishidha Panpaliya <nishidha@us.ibm.com>
Date: Tue, 17 May 2016 01:14:53 -0500
Subject: [PATCH 1818/2053] Fix for a random failure in this test due to
 floating point comparison. So, instead of exact match, used EXPECT_FLOAT_EQ
 that tolerates some precision while comparing two floats

---
 src/caffe/test/test_embed_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
index dc7f5c4aa47..13f13a878d3 100644
--- a/src/caffe/test/test_embed_layer.cpp
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -124,7 +124,7 @@ TYPED_TEST(EmbedLayerTest, TestForwardWithBias) {
     top_offset[4] = 0;
     bias_offset[0] = 0;
     for (int j = 0; j < kNumOutput; ++j) {
-      EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) +
+      EXPECT_FLOAT_EQ(layer->blobs()[0]->data_at(weight_offset) +
                 layer->blobs()[1]->data_at(bias_offset),
                 this->blob_top_->data_at(top_offset));
       ++top_offset[4];

From 35a9a075cdc65c86021dde4d11e3b1c05e27971b Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Wed, 22 Jun 2016 15:13:54 -0700
Subject: [PATCH 1819/2053] add set_random_seed to the python interface

---
 python/caffe/__init__.py | 2 +-
 python/caffe/_caffe.cpp  | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index e2881b89c1b..35868a403a3 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 334088e8a57..3db55ea4340 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -51,6 +51,8 @@ const int NPY_DTYPE = NPY_FLOAT32;
 void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
 void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
 
+void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }
+
 // For convenience, check that input files can be opened, and raise an
 // exception that boost will send to Python if not (caffe could still crash
 // later if the input files are disturbed before they are actually used, but
@@ -260,6 +262,7 @@ BOOST_PYTHON_MODULE(_caffe) {
   // Caffe utility functions
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
+  bp::def("set_random_seed", &set_random_seed);
   bp::def("set_device", &Caffe::SetDevice);
 
   bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);

From a64cfbd08591db0b061ad7ad39c54cd45c0e252a Mon Sep 17 00:00:00 2001
From: Alessandro Giusti <lalelale@users.noreply.github.com>
Date: Mon, 11 Jul 2016 20:33:16 +0200
Subject: [PATCH 1820/2053] Update parse_log.py

Aligned output description in docstring with actual output returned by parse_log
---
 tools/extra/parse_log.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index bb9b65ad615..375b0db73b3 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -16,13 +16,10 @@
 
 def parse_log(path_to_log):
     """Parse log file
-    Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
+    Returns (train_dict_list, test_dict_list)
 
     train_dict_list and test_dict_list are lists of dicts that define the table
     rows
-
-    train_dict_names and test_dict_names are ordered tuples of the column names
-    for the two dict_lists
     """
 
     regex_iteration = re.compile('Iteration (\d+)')

From 12c74460d3e7c416b869e6b4afa0e5c2e84ec29b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20St=C3=A6r=20Nissen?= <nissen@di.ku.dk>
Date: Tue, 12 Jul 2016 13:17:52 +0200
Subject: [PATCH 1821/2053] Support for spaces in directories when downloading
 cifar10

---
 data/cifar10/get_cifar10.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data/cifar10/get_cifar10.sh b/data/cifar10/get_cifar10.sh
index 623c848513e..423f10989c4 100755
--- a/data/cifar10/get_cifar10.sh
+++ b/data/cifar10/get_cifar10.sh
@@ -2,7 +2,7 @@
 # This scripts downloads the CIFAR10 (binary version) data and unzips it.
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
 
 echo "Downloading..."
 

From e14b7f7ea597afe532bf1c4d4013f2c63494d7a6 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 14:58:43 -0700
Subject: [PATCH 1822/2053] improve top_names and bottom_names in pycaffe

---
 python/caffe/pycaffe.py | 40 +++++++++++++++++++++++++---------------
 1 file changed, 25 insertions(+), 15 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index ca6d050e2bd..5bae18d9a4d 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -292,21 +292,31 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
-
-class _Net_IdNameWrapper:
-    """
-    A simple wrapper that allows the ids propery to be accessed as a dict
-    indexed by names. Used for top and bottom names
+def _Net_get_id_name(func, field):
     """
-    def __init__(self, net, func):
-        self.net, self.func = net, func
+    Generic property that maps func to the layer names into an OrderedDict.
+
+    Used for top_names and bottom_names.
 
-    def __getitem__(self, name):
-        # Map the layer name to id
-        ids = self.func(self.net, list(self.net._layer_names).index(name))
-        # Map the blob id to name
-        id_to_name = list(self.net.blobs)
-        return [id_to_name[i] for i in ids]
+    Parameters
+    ----------
+    func: function id -> [id]
+    field: implementation field name (cache)
+
+    Returns
+    ------
+    A one-parameter function that can be set as a property.
+    """
+    @property
+    def get_id_name(self):
+        if not hasattr(self, field):
+            id_to_name = list(self.blobs)
+            res = OrderedDict([(self._layer_names[i],
+                                [id_to_name[j] for j in func(self, i)])
+                                for i in range(len(self.layers))])
+            setattr(self, field, res)
+        return getattr(self, field)
+    return get_id_name
 
 # Attach methods to Net.
 Net.blobs = _Net_blobs
@@ -320,5 +330,5 @@ def __getitem__(self, name):
 Net._batch = _Net_batch
 Net.inputs = _Net_inputs
 Net.outputs = _Net_outputs
-Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids))
-Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids))
+Net.top_names = _Net_get_id_name(Net._top_ids, "_top_names")
+Net.bottom_names = _Net_get_id_name(Net._bottom_ids, "_bottom_names")

From 7c50a2cb87c6b044f85ced87273d302fb21394f7 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 17:17:05 -0700
Subject: [PATCH 1823/2053] add test for top/bottom names

---
 python/caffe/test/test_net.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 4cacfcd05bb..96821e40c1d 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -3,6 +3,7 @@
 import os
 import numpy as np
 import six
+from collections import OrderedDict
 
 import caffe
 
@@ -67,6 +68,18 @@ def test_inputs_outputs(self):
         self.assertEqual(self.net.inputs, [])
         self.assertEqual(self.net.outputs, ['loss'])
 
+    def test_top_bottom_names(self):
+        self.assertEqual(self.net.top_names,
+                         OrderedDict([('data', ['data', 'label']),
+                                      ('conv', ['conv']),
+                                      ('ip', ['ip']),
+                                      ('loss', ['loss'])]))
+        self.assertEqual(self.net.bottom_names,
+                         OrderedDict([('data', []),
+                                      ('conv', ['data']),
+                                      ('ip', ['conv']),
+                                      ('loss', ['ip', 'label'])]))
+
     def test_save_and_read(self):
         f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.close()

From d9ad2ef90a1cbaa2b22b229539a14341efe59ee6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20St=C3=A6r=20Nissen?= <nissen@di.ku.dk>
Date: Wed, 13 Jul 2016 11:17:54 +0200
Subject: [PATCH 1824/2053] Support spaces in path when downloading ILSVRC12
 and MNIST

---
 data/ilsvrc12/get_ilsvrc_aux.sh | 2 +-
 data/mnist/get_mnist.sh         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/data/ilsvrc12/get_ilsvrc_aux.sh b/data/ilsvrc12/get_ilsvrc_aux.sh
index 90935f25099..dc0d0a72790 100755
--- a/data/ilsvrc12/get_ilsvrc_aux.sh
+++ b/data/ilsvrc12/get_ilsvrc_aux.sh
@@ -8,7 +8,7 @@
 # - the training splits with labels
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
 
 echo "Downloading..."
 
diff --git a/data/mnist/get_mnist.sh b/data/mnist/get_mnist.sh
index 6d875219489..ecadffa44f7 100755
--- a/data/mnist/get_mnist.sh
+++ b/data/mnist/get_mnist.sh
@@ -2,7 +2,7 @@
 # This scripts downloads the mnist data and unzips it.
 
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
-cd $DIR
+cd "$DIR"
 
 echo "Downloading..."
 

From 93d321227f0681165b126d9ca47b211f5d2c1909 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Wed, 13 Jul 2016 15:58:29 -0700
Subject: [PATCH 1825/2053] Add "set -e" and $@ to example scripts

---
 examples/cifar10/create_cifar10.sh                 | 1 +
 examples/cifar10/train_full.sh                     | 7 ++++---
 examples/cifar10/train_full_sigmoid.sh             | 3 ++-
 examples/cifar10/train_full_sigmoid_bn.sh          | 3 ++-
 examples/cifar10/train_quick.sh                    | 5 +++--
 examples/imagenet/create_imagenet.sh               | 1 +
 examples/imagenet/resume_training.sh               | 4 +++-
 examples/imagenet/train_caffenet.sh                | 3 ++-
 examples/mnist/create_mnist.sh                     | 1 +
 examples/mnist/train_lenet.sh                      | 3 ++-
 examples/mnist/train_lenet_adam.sh                 | 3 ++-
 examples/mnist/train_lenet_consolidated.sh         | 3 ++-
 examples/mnist/train_lenet_rmsprop.sh              | 4 +++-
 examples/mnist/train_mnist_autoencoder.sh          | 3 ++-
 examples/mnist/train_mnist_autoencoder_adadelta.sh | 3 ++-
 examples/mnist/train_mnist_autoencoder_adagrad.sh  | 3 ++-
 examples/mnist/train_mnist_autoencoder_nesterov.sh | 3 ++-
 examples/siamese/create_mnist_siamese.sh           | 1 +
 examples/siamese/train_mnist_siamese.sh            | 3 ++-
 19 files changed, 39 insertions(+), 18 deletions(-)

diff --git a/examples/cifar10/create_cifar10.sh b/examples/cifar10/create_cifar10.sh
index a42725cb610..7ee1d6ad0a0 100755
--- a/examples/cifar10/create_cifar10.sh
+++ b/examples/cifar10/create_cifar10.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env sh
 # This script converts the cifar data into leveldb format.
+set -e
 
 EXAMPLE=examples/cifar10
 DATA=data/cifar10
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index ef112e1f6db..06ecc2dccb0 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -1,16 +1,17 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-    --solver=examples/cifar10/cifar10_full_solver.prototxt
+    --solver=examples/cifar10/cifar10_full_solver.prototxt $@
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 $@
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 $@
diff --git a/examples/cifar10/train_full_sigmoid.sh b/examples/cifar10/train_full_sigmoid.sh
index 9cff06d3e34..9b5d5213b2a 100755
--- a/examples/cifar10/train_full_sigmoid.sh
+++ b/examples/cifar10/train_full_sigmoid.sh
@@ -1,7 +1,8 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt $@
 
diff --git a/examples/cifar10/train_full_sigmoid_bn.sh b/examples/cifar10/train_full_sigmoid_bn.sh
index 011387c996e..05547f3a104 100755
--- a/examples/cifar10/train_full_sigmoid_bn.sh
+++ b/examples/cifar10/train_full_sigmoid_bn.sh
@@ -1,7 +1,8 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt $@
 
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 6b7d228879b..d2b875340ee 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -1,11 +1,12 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
 $TOOLS/caffe train \
-  --solver=examples/cifar10/cifar10_quick_solver.prototxt
+  --solver=examples/cifar10/cifar10_quick_solver.prototxt $@
 
 # reduce learning rate by factor of 10 after 8 epochs
 $TOOLS/caffe train \
   --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
-  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index e912ac43cd7..1bf08b1aa8f 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -1,6 +1,7 @@
 #!/usr/bin/env sh
 # Create the imagenet lmdb inputs
 # N.B. set the path to the imagenet train + val data dirs
+set -e
 
 EXAMPLE=examples/imagenet
 DATA=data/ilsvrc12
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index bf7945c0fd0..4aef204368e 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -1,5 +1,7 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
     --solver=models/bvlc_reference_caffenet/solver.prototxt \
-    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5
+    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 \
+    $@
diff --git a/examples/imagenet/train_caffenet.sh b/examples/imagenet/train_caffenet.sh
index 94558ec5466..a5094d44ae0 100755
--- a/examples/imagenet/train_caffenet.sh
+++ b/examples/imagenet/train_caffenet.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
-    --solver=models/bvlc_reference_caffenet/solver.prototxt
+    --solver=models/bvlc_reference_caffenet/solver.prototxt $@
diff --git a/examples/mnist/create_mnist.sh b/examples/mnist/create_mnist.sh
index 06ecc27de63..f5e2e7960c5 100755
--- a/examples/mnist/create_mnist.sh
+++ b/examples/mnist/create_mnist.sh
@@ -1,6 +1,7 @@
 #!/usr/bin/env sh
 # This script converts the mnist data into lmdb/leveldb format,
 # depending on the value assigned to $BACKEND.
+set -e
 
 EXAMPLE=examples/mnist
 DATA=data/mnist
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index 1b6bf7d978d..f7f9b86198d 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -1,3 +1,4 @@
 #!/usr/bin/env sh
+set -e
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@
diff --git a/examples/mnist/train_lenet_adam.sh b/examples/mnist/train_lenet_adam.sh
index a32ecf2d9c2..7b4e905681b 100755
--- a/examples/mnist/train_lenet_adam.sh
+++ b/examples/mnist/train_lenet_adam.sh
@@ -1,3 +1,4 @@
 #!/usr/bin/env sh
+set -e
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
index c855467897e..c5f02666822 100755
--- a/examples/mnist/train_lenet_consolidated.sh
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/lenet_consolidated_solver.prototxt
+  --solver=examples/mnist/lenet_consolidated_solver.prototxt $@
diff --git a/examples/mnist/train_lenet_rmsprop.sh b/examples/mnist/train_lenet_rmsprop.sh
index 621cab238bf..adfa7ab0fca 100755
--- a/examples/mnist/train_lenet_rmsprop.sh
+++ b/examples/mnist/train_lenet_rmsprop.sh
@@ -1,3 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt
+./build/tools/caffe train \
+    --solver=examples/mnist/lenet_solver_rmsprop.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index cfd67e82fda..724a0f14a49 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env sh
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_adadelta.sh b/examples/mnist/train_mnist_autoencoder_adadelta.sh
index 4be0ebddedc..a660dbb9ed2 100755
--- a/examples/mnist/train_mnist_autoencoder_adadelta.sh
+++ b/examples/mnist/train_mnist_autoencoder_adadelta.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_adagrad.sh b/examples/mnist/train_mnist_autoencoder_adagrad.sh
index 95fe1b17bd5..4c11dfa67ac 100755
--- a/examples/mnist/train_mnist_autoencoder_adagrad.sh
+++ b/examples/mnist/train_mnist_autoencoder_adagrad.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt $@
diff --git a/examples/mnist/train_mnist_autoencoder_nesterov.sh b/examples/mnist/train_mnist_autoencoder_nesterov.sh
index cf19ea749b3..fd0559d2488 100755
--- a/examples/mnist/train_mnist_autoencoder_nesterov.sh
+++ b/examples/mnist/train_mnist_autoencoder_nesterov.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+set -e
 
 ./build/tools/caffe train \
-  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt $@
diff --git a/examples/siamese/create_mnist_siamese.sh b/examples/siamese/create_mnist_siamese.sh
index 43ad6b184a7..03adce54d9b 100755
--- a/examples/siamese/create_mnist_siamese.sh
+++ b/examples/siamese/create_mnist_siamese.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env sh
 # This script converts the mnist data into leveldb format.
+set -e
 
 EXAMPLES=./build/examples/siamese
 DATA=./data/mnist
diff --git a/examples/siamese/train_mnist_siamese.sh b/examples/siamese/train_mnist_siamese.sh
index 84a30a8ac44..e01ac2ceefd 100755
--- a/examples/siamese/train_mnist_siamese.sh
+++ b/examples/siamese/train_mnist_siamese.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env sh
+set -e
 
 TOOLS=./build/tools
 
-$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt
+$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototxt $@

From a110ac7c2ad9e0966a02ba360327907cd2646dd4 Mon Sep 17 00:00:00 2001
From: Luke Yeager <luke.yeager@gmail.com>
Date: Fri, 15 Jul 2016 14:12:01 -0700
Subject: [PATCH 1826/2053] Stop setting cache timeout in TravisCI

It refers to the caching command timeout, not how long before the caches
expire as I had thought.
---
 .travis.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 4849a7ac289..3297954755d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -28,7 +28,6 @@ env:
     - BUILD_NAME="cudnn-cmake" WITH_CMAKE=true WITH_CUDA=true WITH_CUDNN=true
 
 cache:
-  timeout: 604800  # 1 week
   apt: true
   directories:
     - ~/protobuf3

From 9376bde1beba649e4c522b742064223ac9d2cab4 Mon Sep 17 00:00:00 2001
From: jasjuang <jasjuang@gmail.com>
Date: Thu, 21 Jul 2016 12:04:41 -0700
Subject: [PATCH 1827/2053] add in sudo make uninstall for cmake

---
 CMakeLists.txt           | 11 +++++++++++
 cmake/Uninstall.cmake.in | 26 ++++++++++++++++++++++++++
 2 files changed, 37 insertions(+)
 create mode 100644 cmake/Uninstall.cmake.in

diff --git a/CMakeLists.txt b/CMakeLists.txt
index da7142c9b3c..7b8dab2bb24 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -85,8 +85,19 @@ if(BUILD_python)
   add_dependencies(pytest pycaffe)
 endif()
 
+# ---[ uninstall target
+configure_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/Uninstall.cmake.in
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/Uninstall.cmake
+    IMMEDIATE @ONLY)
+
+add_custom_target(uninstall
+    COMMAND ${CMAKE_COMMAND} -P
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/Uninstall.cmake)
+
 # ---[ Configuration summary
 caffe_print_configuration_summary()
 
 # ---[ Export configs generation
 caffe_generate_export_configs()
+
diff --git a/cmake/Uninstall.cmake.in b/cmake/Uninstall.cmake.in
new file mode 100644
index 00000000000..bb8e2964e46
--- /dev/null
+++ b/cmake/Uninstall.cmake.in
@@ -0,0 +1,26 @@
+if(NOT EXISTS "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt")
+  message(FATAL_ERROR "Cannot find install manifest: @CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt")
+endif(NOT EXISTS "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt")
+
+if (NOT DEFINED CMAKE_INSTALL_PREFIX)
+  set (CMAKE_INSTALL_PREFIX "@CMAKE_INSTALL_PREFIX@")
+endif ()
+ message(${CMAKE_INSTALL_PREFIX})
+
+file(READ "@CMAKE_CURRENT_BINARY_DIR@/install_manifest.txt" files)
+string(REGEX REPLACE "\n" ";" files "${files}")
+foreach(file ${files})
+  message(STATUS "Uninstalling $ENV{DESTDIR}${file}")
+  if(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+    exec_program(
+      "@CMAKE_COMMAND@" ARGS "-E remove \"$ENV{DESTDIR}${file}\""
+      OUTPUT_VARIABLE rm_out
+      RETURN_VALUE rm_retval
+      )
+    if(NOT "${rm_retval}" STREQUAL 0)
+      message(FATAL_ERROR "Problem when removing $ENV{DESTDIR}${file}")
+    endif(NOT "${rm_retval}" STREQUAL 0)
+  else(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+    message(STATUS "File $ENV{DESTDIR}${file} does not exist.")
+  endif(IS_SYMLINK "$ENV{DESTDIR}${file}" OR EXISTS "$ENV{DESTDIR}${file}")
+endforeach(file)
\ No newline at end of file

From 0ad1284bf6af4ee59f782b72cdf4af0fd194af29 Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Mon, 25 Jul 2016 09:01:24 +0300
Subject: [PATCH 1828/2053] CMake: link with ${HDF5_HL_LIBRARIES}

Fixes issue #3224.
---
 cmake/Dependencies.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index c7b6a17aa69..d7eb59e333a 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -26,7 +26,7 @@ include(cmake/ProtoBuf.cmake)
 # ---[ HDF5
 find_package(HDF5 COMPONENTS HL REQUIRED)
 include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
+list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
 
 # ---[ LMDB
 if(USE_LMDB)

From c62e06bccafa57f5b21f90b49e81a988d50a4620 Mon Sep 17 00:00:00 2001
From: Hans Gaiser <j.c.gaiser@delftrobotics.com>
Date: Tue, 26 Jul 2016 11:44:44 +0200
Subject: [PATCH 1829/2053] Fix search for Atlas on arch.

---
 cmake/Modules/FindAtlas.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/cmake/Modules/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
index 6e1564351c7..9c665a47bd5 100644
--- a/cmake/Modules/FindAtlas.cmake
+++ b/cmake/Modules/FindAtlas.cmake
@@ -26,9 +26,9 @@ set(Atlas_LIB_SEARCH_PATHS
 find_path(Atlas_CBLAS_INCLUDE_DIR   NAMES cblas.h   PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH_PATHS})
 
-find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                 PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_LAPACK_LIBRARY NAMES alapack_r alapack lapack_atlas  PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas       PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                       PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES lapack alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
 
 set(LOOKED_FOR
   Atlas_CBLAS_INCLUDE_DIR

From 61e01654d2054531133a6d154a69b872a4479099 Mon Sep 17 00:00:00 2001
From: Fisher Yu <i@yf.io>
Date: Sat, 6 Aug 2016 23:01:45 -0400
Subject: [PATCH 1830/2053] num in blob is deprecated

---
 src/caffe/layers/loss_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index c0b7a862181..afb1ce94893 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -16,8 +16,8 @@ void LossLayer<Dtype>::LayerSetUp(
 template <typename Dtype>
 void LossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[0]->shape(0), bottom[1]->shape(0))
+      << "The data and label should have the same first dimension.";
   vector<int> loss_shape(0);  // Loss layers output a scalar; 0 axes.
   top[0]->Reshape(loss_shape);
 }

From d607858b90b645d8177c3970d782f0ab5c529558 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Tue, 9 Aug 2016 15:13:47 +0000
Subject: [PATCH 1831/2053] Fix more float comparison precision issue

With reference to this commit:
f1a8470aa21e35a5b2bb83007f8fb7680a354815

This fix changes some EXPECT_EQ into EXPECT_FLOAT_EQ .
---
 src/caffe/test/test_convolution_layer.cpp     | 2 +-
 src/caffe/test/test_gradient_based_solver.cpp | 8 ++++----
 src/caffe/test/test_neuron_layer.cpp          | 8 ++++----
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 9bb19d13592..85c10a29483 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -695,7 +695,7 @@ TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
   }
   ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
   for (int i = 0; i < backward_result_2d.count(); ++i) {
-    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+    EXPECT_FLOAT_EQ(backward_result_2d.cpu_diff()[i],
               backward_result_nd.cpu_diff()[i]);
   }
   ASSERT_EQ(backward_weight_result_nd.count(),
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 975a8f0f88a..9395f4e95c6 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -538,9 +538,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
     for (int i = 0; i < params.size(); ++i) {
       for (int j = 0; j < params[i]->count(); ++j) {
-        EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
+        EXPECT_FLOAT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
             << "param " << i << " data differed at dim " << j;
-        EXPECT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
+        EXPECT_FLOAT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
             << "param " << i << " diff differed at dim " << j;
       }
     }
@@ -549,9 +549,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
     for (int i = 0; i < history.size(); ++i) {
       for (int j = 0; j < history[i]->count(); ++j) {
-        EXPECT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
+        EXPECT_FLOAT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
             << "history blob " << i << " data differed at dim " << j;
-        EXPECT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
+        EXPECT_FLOAT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
             << "history blob " << i << " diff differed at dim " << j;
       }
     }
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 342f825cec3..57bd47b3a2e 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -791,16 +791,16 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
   ip2.Backward(blob_middle_vec_2, propagate_down, blob_bottom_vec_2);
   // Check numbers
   for (int s = 0; s < blob_bottom_2->count(); ++s) {
-    EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
   }
   for (int s = 0; s < ip.blobs()[0]->count(); ++s) {
-    EXPECT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);
   }
   for (int s = 0; s < ip.blobs()[1]->count(); ++s) {
-    EXPECT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);
   }
   for (int s = 0; s < prelu.blobs()[0]->count(); ++s) {
-    EXPECT_EQ(prelu.blobs()[0]->cpu_diff()[s],
+    EXPECT_FLOAT_EQ(prelu.blobs()[0]->cpu_diff()[s],
         prelu2.blobs()[0]->cpu_diff()[s]);
   }
 }

From 42d20fe21eeb8067b09ef5e935bb4c235dbf9f3f Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Wed, 10 Aug 2016 14:36:33 +0000
Subject: [PATCH 1832/2053] Import bash completion script for caffe from Debian
 Package.

Imported from Debian Package caffe (1.0.0~rc3+20160715-g42cd785-2).
---
 scripts/caffe | 73 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 scripts/caffe

diff --git a/scripts/caffe b/scripts/caffe
new file mode 100644
index 00000000000..8a0b22af6ac
--- /dev/null
+++ b/scripts/caffe
@@ -0,0 +1,73 @@
+# bash completion for Caffe's command line utility       -*- shell-script -*-
+# COPYRIGHT (C) 2015,2016 Zhou Mo <cdluminate@gmail.com>
+# License: BSD-2-Clause
+# Originally appeard at https://github.com/BVLC/caffe/issues/3149
+
+# Updated for caffe (1.0.0~rc3+20160715-g42cd785)
+_caffe()
+{
+  local cur prev words cword
+  _init_completion -s || return
+
+  local prototxts='@(prototxt)'
+  local caffemodels='@(caffemodel,binaryproto)'
+  local solverstates='@(solverstate)'
+  local caffefiles='@(prototxt|caffemodel|solverstate)'
+
+  local flags='-gpu -iterations -model -snapshot -solver -weights -sighup_effect -sigint_effect -level -stage -phase'
+  
+  if [[ $cword -eq 1 ]]; then
+    COMPREPLY=( $( compgen -W 'train test time device_query' -- "$cur" ) )
+    return 0
+  fi
+  
+  if [[ $cword -eq 2 ]]; then
+    case ${words[1]} in
+    train|test|device_query|time)
+      COMPREPLY=( $( compgen -W "$flags" -- "$cur") )
+      return 0
+      ;;
+    *)
+      return 0
+      ;;
+    esac
+  fi
+
+  case $prev in
+  -gpu|-iterations|-version|-level|-stage)
+    return 0
+    ;;
+  -solver|-model)
+    _filedir $prototxts
+    return 0
+    ;;
+  -weights)
+    _filedir $caffemodels
+    return 0
+    ;;
+  -snapshot)
+    _filedir $solverstates
+    return 0
+    ;;
+  -sighup_effect|-sigint_effect)
+    COMPREPLY=( $( compgen -W 'snapshot stop none' -- "$cur") )
+    return 0
+    ;;
+  -phase)
+    COMPREPLY=( $( compgen -W 'TRAIN TEST' -- "$cur") )
+    return 0
+    ;;
+  *)
+    COMPREPLY=( $( compgen -W "$flags" -- "$cur") )
+    return 0
+    ;;
+  esac
+
+  # file completion on relevant files
+  _filedir "$caffefiles"
+
+  return 0
+}
+complete -F _caffe caffe
+
+# vim

From 6382d67da1d2b5d9ebe92df8a20a8ac1947366ea Mon Sep 17 00:00:00 2001
From: An Tran <tranlaman@gmail.com>
Date: Fri, 12 Aug 2016 16:39:11 +0800
Subject: [PATCH 1833/2053] small improments in compute_image_mean

---
 tools/compute_image_mean.cpp | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 2035d515195..417f5e4c622 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -22,9 +22,11 @@ DEFINE_string(backend, "lmdb",
         "The backend {leveldb, lmdb} containing the images");
 
 int main(int argc, char** argv) {
+#ifdef USE_OPENCV
   ::google::InitGoogleLogging(argv[0]);
+  // Print output to stderr (while still logging)
+  FLAGS_alsologtostderr = 1;
 
-#ifdef USE_OPENCV
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
 #endif
@@ -65,7 +67,7 @@ int main(int argc, char** argv) {
   for (int i = 0; i < size_in_datum; ++i) {
     sum_blob.add_data(0.);
   }
-  LOG(INFO) << "Starting Iteration";
+  LOG(INFO) << "Starting iteration";
   while (cursor->valid()) {
     Datum datum;
     datum.ParseFromString(cursor->value());
@@ -114,7 +116,7 @@ int main(int argc, char** argv) {
     for (int i = 0; i < dim; ++i) {
       mean_values[c] += sum_blob.data(dim * c + i);
     }
-    LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
+    LOG(INFO) << "mean_value channel [" << c << "]: " << mean_values[c] / dim;
   }
 #else
   LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";

From d4a413cbf56f43a9d5a6ea3a5568447117cefff0 Mon Sep 17 00:00:00 2001
From: Sungjun HONG <imjune@yonsei.ac.kr>
Date: Sun, 14 Aug 2016 17:51:56 +0900
Subject: [PATCH 1834/2053] Correct a mistake on math notation

---
 examples/net_surgery.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index d50d503bfe0..217c2d1a742 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -5479,7 +5479,7 @@
     "\n",
     "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
     "\n",
-    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 $\\times$ 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
    ]
   },
   {

From 5d594806aed7d44feb36cae12bacbaabfabf6fa8 Mon Sep 17 00:00:00 2001
From: Nitish Keskar <keskar.nitish@gmail.com>
Date: Mon, 15 Aug 2016 19:47:34 -0500
Subject: [PATCH 1835/2053] Fixing Typo In Sigmoid CIFAR-10 Examples

There was a mismatch between the iterations interval in the comment and the actual code.
---
 examples/cifar10/cifar10_full_sigmoid_solver.prototxt    | 2 +-
 examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
index 7dd3ecb9d8e..a8e5539937d 100644
--- a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+++ b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
@@ -17,7 +17,7 @@ momentum: 0.9
 lr_policy: "step"
 gamma: 1
 stepsize: 5000
-# Display every 200 iterations
+# Display every 100 iterations
 display: 100
 # The maximum number of iterations
 max_iter: 60000
diff --git a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
index a57b280fd1e..a4dabd67ca0 100644
--- a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+++ b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
@@ -17,7 +17,7 @@ momentum: 0.9
 lr_policy: "step"
 gamma: 1
 stepsize: 5000
-# Display every 200 iterations
+# Display every 100 iterations
 display: 100
 # The maximum number of iterations
 max_iter: 60000

From 9029695ee358caa82116fc192cb4d505ea936274 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 18 Aug 2016 11:03:42 -0700
Subject: [PATCH 1836/2053] [build] set default BLAS include for OS X 10.11

the latest hunt for the ever-elusive vecLib/Accelerate
---
 Makefile | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 403e00a38a1..24894062a6c 100644
--- a/Makefile
+++ b/Makefile
@@ -382,8 +382,11 @@ else
 		LIBRARIES += cblas
 		# 10.10 has accelerate while 10.9 has veclib
 		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
+		XCODE_CLT_GEQ_7 := $(shell [ $(XCODE_CLT_VER) -gt 6 ] && echo 1)
 		XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
-		ifeq ($(XCODE_CLT_GEQ_6), 1)
+		ifeq ($(XCODE_CLT_GEQ_7), 1)
+			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.11.sdk/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
+		else ifeq ($(XCODE_CLT_GEQ_6), 1)
 			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
 			LDFLAGS += -framework Accelerate
 		else

From 1110d2ba7b52c35f898da8febdd53524761ecb97 Mon Sep 17 00:00:00 2001
From: Tianwei Shen <shentianweipku@gmail.com>
Date: Tue, 26 Jul 2016 00:19:35 +0800
Subject: [PATCH 1837/2053] make cmake find cuDNN on Mac OS

dylib instead of so on OS X
---
 cmake/Cuda.cmake | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 286a42802b4..eeeb7325ffd 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -174,11 +174,18 @@ function(detect_cuDNN)
             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDA_TOOLKIT_INCLUDE}
             DOC "Path to cuDNN include directory." )
 
-  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
-  find_library(CUDNN_LIBRARY NAMES libcudnn.so # libcudnn_static.a
-                             PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist}
-                             DOC "Path to cuDNN library.")
+  # dynamic libs have different suffix in mac and linux
+  if(APPLE)
+    set(CUDNN_LIB_NAME "libcudnn.dylib")
+  else()
+    set(CUDNN_LIB_NAME "libcudnn.so")
+  endif()
 
+  get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
+  find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME}
+   PATHS ${CUDNN_ROOT} $ENV{CUDNN_ROOT} ${CUDNN_INCLUDE} ${__libpath_hist} ${__libpath_hist}/../lib
+   DOC "Path to cuDNN library.")
+  
   if(CUDNN_INCLUDE AND CUDNN_LIBRARY)
     set(HAVE_CUDNN  TRUE PARENT_SCOPE)
     set(CUDNN_FOUND TRUE PARENT_SCOPE)

From 51c39b87738962c323c8bd05aa4c23ac97e1c030 Mon Sep 17 00:00:00 2001
From: Preston Parry <ClimbsBytes@gmail.com>
Date: Sun, 28 Aug 2016 14:32:41 -0700
Subject: [PATCH 1838/2053] updates tense in docs

"could" seems to imply for some reason that something is blocking one from calling the registered layers. "can" lays out more directly that a user can choose to do this.
---
 include/caffe/layer_factory.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index f385afccfee..2369c132911 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -1,6 +1,6 @@
 /**
  * @brief A layer factory that allows one to register layers.
- * During runtime, registered layers could be called by passing a LayerParameter
+ * During runtime, registered layers can be called by passing a LayerParameter
  * protobuffer to the CreateLayer function:
  *
  *     LayerRegistry<Dtype>::CreateLayer(param);

From 8797e7b3720d97afea24ad6f78b7811c57a3919d Mon Sep 17 00:00:00 2001
From: Preston Parry <ClimbsBytes@gmail.com>
Date: Sun, 28 Aug 2016 14:34:42 -0700
Subject: [PATCH 1839/2053] fixes typo- duplicate "a a"

---
 include/caffe/solver.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 38259edad9f..eafcee32904 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -12,7 +12,7 @@ namespace caffe {
 /**
   * @brief Enumeration of actions that a client of the Solver may request by
   * implementing the Solver's action request function, which a
-  * a client may optionally provide in order to request early termination
+  * client may optionally provide in order to request early termination
   * or saving a snapshot without exiting. In the executable caffe, this
   * mechanism is used to allow the snapshot to be saved when stopping
   * execution with a SIGINT (Ctrl-C).

From cd54d9e0f96df65a4972306f29d042bc34c63077 Mon Sep 17 00:00:00 2001
From: Preston Parry <ClimbsBytes@gmail.com>
Date: Sun, 28 Aug 2016 14:42:57 -0700
Subject: [PATCH 1840/2053] changes "c++" to "C++" for consistency

---
 include/caffe/solver_factory.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/solver_factory.hpp b/include/caffe/solver_factory.hpp
index cfff721af40..a5b160739b2 100644
--- a/include/caffe/solver_factory.hpp
+++ b/include/caffe/solver_factory.hpp
@@ -15,7 +15,7 @@
  * and its type is its C++ class name, but without the "Solver" at the end
  * ("MyAwesomeSolver" -> "MyAwesome").
  *
- * If the solver is going to be created simply by its constructor, in your c++
+ * If the solver is going to be created simply by its constructor, in your C++
  * file, add the following line:
  *
  *    REGISTER_SOLVER_CLASS(MyAwesome);

From 4024b82c7c8e9f12898becf7b3947e603a4dd0bb Mon Sep 17 00:00:00 2001
From: Luke Yeager <lukeyeager@users.noreply.github.com>
Date: Mon, 29 Aug 2016 11:14:17 -0700
Subject: [PATCH 1841/2053] [TravisCI] - build protobuf3 GA

---
 scripts/travis/install-deps.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index ee16d36a7fc..4e86ac73915 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -56,7 +56,7 @@ else
       dh-autoreconf \
       unzip
 
-    wget https://github.com/google/protobuf/archive/v3.0.0-beta-3.tar.gz -O protobuf3.tar.gz
+    wget https://github.com/google/protobuf/archive/3.0.0-GA.tar.gz -O protobuf3.tar.gz
     tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
     rm protobuf3.tar.gz
     cd $PROTOBUF3_DIR

From b9c3c06c28dafce67c89603e8b73cf18057264eb Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Sun, 14 Aug 2016 04:52:25 +0300
Subject: [PATCH 1842/2053] cmake: fix usage of INCLUDE_DIR/INCLUDE_DIRS in
 Dependencies.cmake

---
 cmake/Dependencies.cmake | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index ae9ce8e436d..bf882ce96ac 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -3,7 +3,7 @@ set(Caffe_LINKER_LIBS "")
 
 # ---[ Boost
 find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
-include_directories(SYSTEM ${Boost_INCLUDE_DIR})
+include_directories(SYSTEM ${Boost_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
 
 # ---[ Threads
@@ -25,7 +25,7 @@ include(cmake/ProtoBuf.cmake)
 
 # ---[ HDF5
 find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
+include_directories(SYSTEM ${HDF5_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
 
 # ---[ LMDB
@@ -42,7 +42,7 @@ endif()
 # ---[ LevelDB
 if(USE_LEVELDB)
   find_package(LevelDB REQUIRED)
-  include_directories(SYSTEM ${LevelDB_INCLUDE})
+  include_directories(SYSTEM ${LevelDB_INCLUDES})
   list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
   add_definitions(-DUSE_LEVELDB)
 endif()

From a59e647117705236d8bcef46cc6d4e0c72b42804 Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Mon, 15 Aug 2016 20:19:09 +0300
Subject: [PATCH 1843/2053] cmake/Templates: properly spell OpenCV CMake config
 file name

---
 cmake/Templates/CaffeConfig.cmake.in | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
index 73f57ac2d74..b58124aa343 100644
--- a/cmake/Templates/CaffeConfig.cmake.in
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -27,7 +27,7 @@ if(@USE_OPENCV@)
 
       if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
         message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
-        include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+	include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVConfig.cmake)
       endif()
 
     else()

From ba189d907d60b17cc24b54d1a22cb68ce6c11193 Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Sat, 20 Aug 2016 00:59:05 +0300
Subject: [PATCH 1844/2053] cmake: refactor deps detection, specify all
 dependencies in the exported caffe target

This is the first step towards "modern" IMPORTED-targets-only CMake
setup. The find_package modules still need to be rewritten and upstreamed in
form of config exports where possible.
---
 CMakeLists.txt                       | 24 +++++++--
 cmake/ConfigGen.cmake                | 65 +---------------------
 cmake/Cuda.cmake                     | 12 ++---
 cmake/Dependencies.cmake             | 81 +++++++++++++++-------------
 cmake/ProtoBuf.cmake                 |  4 +-
 cmake/Templates/CaffeConfig.cmake.in | 13 ++---
 python/CMakeLists.txt                |  6 +--
 src/caffe/CMakeLists.txt             | 13 +++--
 src/gtest/CMakeLists.txt             |  3 ++
 9 files changed, 94 insertions(+), 127 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index da7142c9b3c..cb25b43a458 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -54,8 +54,6 @@ if(USE_libstdcpp)
   message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
 endif()
 
-add_definitions(-DGTEST_USE_OWN_TR1_TUPLE)
-
 # ---[ Warnings
 caffe_warnings_disable(CMAKE_CXX_FLAGS -Wno-sign-compare -Wno-uninitialized)
 
@@ -64,8 +62,26 @@ configure_file(cmake/Templates/caffe_config.h.in "${PROJECT_BINARY_DIR}/caffe_co
 
 # ---[ Includes
 set(Caffe_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/include)
-include_directories(${Caffe_INCLUDE_DIR} ${PROJECT_BINARY_DIR})
-include_directories(BEFORE src) # This is needed for gtest.
+set(Caffe_SRC_DIR ${PROJECT_SOURCE_DIR}/src)
+include_directories(${PROJECT_BINARY_DIR})
+
+# ---[ Includes & defines for CUDA
+
+# cuda_compile() does not have per-call dependencies or include pathes
+# (cuda_compile() has per-call flags, but we set them here too for clarity)
+#
+# list(REMOVE_ITEM ...) invocations remove PRIVATE and PUBLIC keywords from collected definitions and include pathes
+if(HAVE_CUDA)
+  # pass include pathes to cuda_include_directories()
+  set(Caffe_ALL_INCLUDE_DIRS ${Caffe_INCLUDE_DIRS})
+  list(REMOVE_ITEM Caffe_ALL_INCLUDE_DIRS PRIVATE PUBLIC)
+  cuda_include_directories(${Caffe_INCLUDE_DIR} ${Caffe_SRC_DIR} ${Caffe_ALL_INCLUDE_DIRS})
+
+  # add definitions to nvcc flags directly
+  set(Caffe_ALL_DEFINITIONS ${Caffe_DEFINITIONS})
+  list(REMOVE_ITEM Caffe_ALL_DEFINITIONS PRIVATE PUBLIC)
+  list(APPEND CUDA_NVCC_FLAGS ${Caffe_ALL_DEFINITIONS})
+endif()
 
 # ---[ Subdirectories
 add_subdirectory(src/gtest)
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 056371110b5..077d5b283d1 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -1,31 +1,4 @@
 
-################################################################################################
-# Helper function to fetch caffe includes which will be passed to dependent projects
-# Usage:
-#   caffe_get_current_includes(<includes_list_variable>)
-function(caffe_get_current_includes includes_variable)
-  get_property(current_includes DIRECTORY PROPERTY INCLUDE_DIRECTORIES)
-  caffe_convert_absolute_paths(current_includes)
-
-  # remove at most one ${PROJECT_BINARY_DIR} include added for caffe_config.h
-  list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
-  list(REMOVE_AT current_includes ${__index})
-
-  # removing numpy includes (since not required for client libs)
-  set(__toremove "")
-  foreach(__i ${current_includes})
-    if(${__i} MATCHES "python")
-      list(APPEND __toremove ${__i})
-    endif()
-  endforeach()
-  if(__toremove)
-    list(REMOVE_ITEM current_includes ${__toremove})
-  endif()
-
-  caffe_list_unique(current_includes)
-  set(${includes_variable} ${current_includes} PARENT_SCOPE)
-endfunction()
-
 ################################################################################################
 # Helper function to get all list items that begin with given prefix
 # Usage:
@@ -47,39 +20,15 @@ endfunction()
 function(caffe_generate_export_configs)
   set(install_cmake_suffix "share/Caffe")
 
-  # ---[ Configure build-tree CaffeConfig.cmake file ]---
-  caffe_get_current_includes(Caffe_INCLUDE_DIRS)
-
-  set(Caffe_DEFINITIONS "")
   if(NOT HAVE_CUDA)
     set(HAVE_CUDA FALSE)
-    list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
-  endif()
-
-  if(USE_OPENCV)
-    list(APPEND Caffe_DEFINITIONS -DUSE_OPENCV)
-  endif()
-
-  if(USE_LMDB)
-    list(APPEND Caffe_DEFINITIONS -DUSE_LMDB)
-    if (ALLOW_LMDB_NOLOCK)
-        list(APPEND Caffe_DEFINITIONS -DALLOW_LMDB_NOLOCK)
-    endif()
-  endif()
-
-  if(USE_LEVELDB)
-    list(APPEND Caffe_DEFINITIONS -DUSE_LEVELDB)
   endif()
 
   if(NOT HAVE_CUDNN)
     set(HAVE_CUDNN FALSE)
-  else()
-    list(APPEND DEFINITIONS -DUSE_CUDNN)
   endif()
 
-  if(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
-    list(APPEND Caffe_DEFINITIONS -DUSE_MKL)
-  endif()
+  # ---[ Configure build-tree CaffeConfig.cmake file ]---
 
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
 
@@ -89,18 +38,6 @@ function(caffe_generate_export_configs)
 
   # ---[ Configure install-tree CaffeConfig.cmake file ]---
 
-  # remove source and build dir includes
-  caffe_get_items_with_prefix(${PROJECT_SOURCE_DIR} Caffe_INCLUDE_DIRS __insource)
-  caffe_get_items_with_prefix(${PROJECT_BINARY_DIR} Caffe_INCLUDE_DIRS __inbinary)
-  list(REMOVE_ITEM Caffe_INCLUDE_DIRS ${__insource} ${__inbinary})
-
-  # add `install` include folder
-  set(lines
-     "get_filename_component(__caffe_include \"\${Caffe_CMAKE_DIR}/../../include\" ABSOLUTE)\n"
-     "list(APPEND Caffe_INCLUDE_DIRS \${__caffe_include})\n"
-     "unset(__caffe_include)\n")
-  string(REPLACE ";" "" Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND ${lines})
-
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
 
   # Install the CaffeConfig.cmake and export set to use with install-tree
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index eeeb7325ffd..c6b0de8c759 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -238,17 +238,17 @@ endif()
 
 set(HAVE_CUDA TRUE)
 message(STATUS "CUDA detected: " ${CUDA_VERSION})
-include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${CUDA_CUDART_LIBRARY}
-                              ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${CUDA_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${CUDA_CUDART_LIBRARY}
+                                     ${CUDA_curand_LIBRARY} ${CUDA_CUBLAS_LIBRARIES})
 
 # cudnn detection
 if(USE_CUDNN)
   detect_cuDNN()
   if(HAVE_CUDNN)
-    add_definitions(-DUSE_CUDNN)
-    include_directories(SYSTEM ${CUDNN_INCLUDE})
-    list(APPEND Caffe_LINKER_LIBS ${CUDNN_LIBRARY})
+    list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_CUDNN)
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${CUDNN_INCLUDE})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${CUDNN_LIBRARY})
   endif()
 endif()
 
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index bf882ce96ac..6a12759234f 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -1,57 +1,67 @@
 # This list is required for static linking and exported to CaffeConfig.cmake
 set(Caffe_LINKER_LIBS "")
+set(Caffe_INCLUDE_DIRS "")
+set(Caffe_DEFINITIONS "")
 
 # ---[ Boost
 find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
-include_directories(SYSTEM ${Boost_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Boost_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${Boost_LIBRARIES})
 
 # ---[ Threads
 find_package(Threads REQUIRED)
-list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
+list(APPEND Caffe_LINKER_LIBS PRIVATE ${CMAKE_THREAD_LIBS_INIT})
+
+# ---[ OpenMP
+if(USE_OPENMP)
+  # TODO: use something exportable here
+  find_package(OpenMP REQUIRED)
+  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+endif()
 
 # ---[ Google-glog
 include("cmake/External/glog.cmake")
-include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${GLOG_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${GLOG_LIBRARIES})
 
 # ---[ Google-gflags
 include("cmake/External/gflags.cmake")
-include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${GFLAGS_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${GFLAGS_LIBRARIES})
 
 # ---[ Google-protobuf
 include(cmake/ProtoBuf.cmake)
 
 # ---[ HDF5
 find_package(HDF5 COMPONENTS HL REQUIRED)
-include_directories(SYSTEM ${HDF5_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${HDF5_INCLUDE_DIRS})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
 
 # ---[ LMDB
 if(USE_LMDB)
   find_package(LMDB REQUIRED)
-  include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
-  list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
-  add_definitions(-DUSE_LMDB)
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${LMDB_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${LMDB_LIBRARIES})
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_LMDB)
   if(ALLOW_LMDB_NOLOCK)
-    add_definitions(-DALLOW_LMDB_NOLOCK)
+    list(APPEND Caffe_DEFINITIONS PRIVATE -DALLOW_LMDB_NOLOCK)
   endif()
 endif()
 
 # ---[ LevelDB
 if(USE_LEVELDB)
   find_package(LevelDB REQUIRED)
-  include_directories(SYSTEM ${LevelDB_INCLUDES})
-  list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
-  add_definitions(-DUSE_LEVELDB)
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${LevelDB_INCLUDES})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${LevelDB_LIBRARIES})
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_LEVELDB)
 endif()
 
 # ---[ Snappy
 if(USE_LEVELDB)
   find_package(Snappy REQUIRED)
-  include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
-  list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+  list(APPEND Caffe_INCLUDE_DIRS PRIVATE ${Snappy_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS PRIVATE ${Snappy_LIBRARIES})
 endif()
 
 # ---[ CUDA
@@ -63,8 +73,7 @@ if(NOT HAVE_CUDA)
     message(WARNING "-- CUDA is not detected by cmake. Building without it...")
   endif()
 
-  # TODO: remove this not cross platform define in future. Use caffe_config.h instead.
-  add_definitions(-DCPU_ONLY)
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DCPU_ONLY)
 endif()
 
 # ---[ OpenCV
@@ -73,10 +82,10 @@ if(USE_OPENCV)
   if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
     find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
   endif()
-  include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
-  list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${OpenCV_INCLUDE_DIRS})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${OpenCV_LIBS})
   message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
-  add_definitions(-DUSE_OPENCV)
+  list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_OPENCV)
 endif()
 
 # ---[ BLAS
@@ -86,26 +95,26 @@ if(NOT APPLE)
 
   if(BLAS STREQUAL "Atlas" OR BLAS STREQUAL "atlas")
     find_package(Atlas REQUIRED)
-    include_directories(SYSTEM ${Atlas_INCLUDE_DIR})
-    list(APPEND Caffe_LINKER_LIBS ${Atlas_LIBRARIES})
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Atlas_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${Atlas_LIBRARIES})
   elseif(BLAS STREQUAL "Open" OR BLAS STREQUAL "open")
     find_package(OpenBLAS REQUIRED)
-    include_directories(SYSTEM ${OpenBLAS_INCLUDE_DIR})
-    list(APPEND Caffe_LINKER_LIBS ${OpenBLAS_LIB})
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${OpenBLAS_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${OpenBLAS_LIB})
   elseif(BLAS STREQUAL "MKL" OR BLAS STREQUAL "mkl")
     find_package(MKL REQUIRED)
-    include_directories(SYSTEM ${MKL_INCLUDE_DIR})
-    list(APPEND Caffe_LINKER_LIBS ${MKL_LIBRARIES})
-    add_definitions(-DUSE_MKL)
+    list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${MKL_INCLUDE_DIR})
+    list(APPEND Caffe_LINKER_LIBS PUBLIC ${MKL_LIBRARIES})
+    list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_MKL)
   endif()
 elseif(APPLE)
   find_package(vecLib REQUIRED)
-  include_directories(SYSTEM ${vecLib_INCLUDE_DIR})
-  list(APPEND Caffe_LINKER_LIBS ${vecLib_LINKER_LIBS})
+  list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${vecLib_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS PUBLIC ${vecLib_LINKER_LIBS})
 
   if(VECLIB_FOUND)
     if(NOT vecLib_INCLUDE_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
-      add_definitions(-DUSE_ACCELERATE)
+      list(APPEND Caffe_DEFINITIONS PUBLIC -DUSE_ACCELERATE)
     endif()
   endif()
 endif()
@@ -149,9 +158,9 @@ if(BUILD_python)
   if(PYTHONLIBS_FOUND AND NUMPY_FOUND AND Boost_PYTHON_FOUND)
     set(HAVE_PYTHON TRUE)
     if(BUILD_python_layer)
-      add_definitions(-DWITH_PYTHON_LAYER)
-      include_directories(SYSTEM ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
-      list(APPEND Caffe_LINKER_LIBS ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+      list(APPEND Caffe_DEFINITIONS PRIVATE -DWITH_PYTHON_LAYER)
+      list(APPEND Caffe_INCLUDE_DIRS PRIVATE ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} PUBLIC ${Boost_INCLUDE_DIRS})
+      list(APPEND Caffe_LINKER_LIBS PRIVATE ${PYTHON_LIBRARIES} PUBLIC ${Boost_LIBRARIES})
     endif()
   endif()
 endif()
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index 73f647f5fae..8005b448707 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -2,8 +2,8 @@
 # the standard cmake script with version and python generation support
 
 find_package( Protobuf REQUIRED )
-include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
+list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${PROTOBUF_INCLUDE_DIR})
+list(APPEND Caffe_LINKER_LIBS PUBLIC ${PROTOBUF_LIBRARIES})
 
 # As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
 # and should be installed separately as in: sudo apt-get install protobuf-compiler
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
index b58124aa343..77c4059e560 100644
--- a/cmake/Templates/CaffeConfig.cmake.in
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -9,9 +9,9 @@
 # After successful configuration the following variables
 # will be defined:
 #
-#   Caffe_INCLUDE_DIRS - Caffe include directories
-#   Caffe_LIBRARIES    - libraries to link against
-#   Caffe_DEFINITIONS  - a list of definitions to pass to compiler
+#   Caffe_LIBRARIES    - IMPORTED targets to link against
+#                        (There is no Caffe_INCLUDE_DIRS and Caffe_DEFINITIONS
+#                         because they are specified in the IMPORTED target interface.)
 #
 #   Caffe_HAVE_CUDA    - signals about CUDA support
 #   Caffe_HAVE_CUDNN   - signals about cuDNN support
@@ -39,9 +39,6 @@ endif()
 
 # Compute paths
 get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
-
-@Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
 
 # Our library dependencies
 if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
@@ -49,11 +46,9 @@ if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
 endif()
 
 # List of IMPORTED libs created by CaffeTargets.cmake
+# These targets already specify all needed definitions and include pathes
 set(Caffe_LIBRARIES caffe)
 
-# Definitions
-set(Caffe_DEFINITIONS "@Caffe_DEFINITIONS@")
-
 # Cuda support variables
 set(Caffe_CPU_ONLY @CPU_ONLY@)
 set(Caffe_HAVE_CUDA @HAVE_CUDA@)
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index bf492a24b1c..c53299d265b 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -3,13 +3,13 @@ if(NOT HAVE_PYTHON)
   return()
 endif()
 
-include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR} ${Boost_INCLUDE_DIRS})
 file(GLOB_RECURSE python_srcs ${PROJECT_SOURCE_DIR}/python/*.cpp)
 
 add_library(pycaffe SHARED ${python_srcs})
-target_link_libraries(pycaffe ${Caffe_LINK} ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
-set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
 caffe_default_properties(pycaffe)
+set_target_properties(pycaffe PROPERTIES PREFIX "" OUTPUT_NAME "_caffe")
+target_include_directories(pycaffe PUBLIC ${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDE_DIR})
+target_link_libraries(pycaffe PUBLIC ${Caffe_LINK} ${PYTHON_LIBRARIES})
 
 if(UNIX OR APPLE)
     set(__linkname "${PROJECT_SOURCE_DIR}/python/caffe/_caffe.so")
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 8a80c940488..ed4d50bed5a 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -4,8 +4,11 @@ caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_p
 
 # include python files either to force generation
 add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
-set(Caffe_LINKER_LIBS proto ${Caffe_LINKER_LIBS}) # note, crucial to prepend!
 caffe_default_properties(proto)
+target_link_libraries(proto PUBLIC ${PROTOBUF_LIBRARIES})
+target_include_directories(proto PUBLIC ${PROTOBUF_INCLUDE_DIR})
+
+list(INSERT Caffe_LINKER_LIBS 0 PUBLIC proto) # note, crucial to prepend!
 
 # --[ Caffe library
 
@@ -18,8 +21,13 @@ if(HAVE_CUDA)
 endif()
 
 add_library(caffe ${srcs})
-target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
 caffe_default_properties(caffe)
+target_link_libraries(caffe ${Caffe_LINKER_LIBS})
+target_include_directories(caffe ${Caffe_INCLUDE_DIRS}
+                                 PUBLIC
+                                 $<BUILD_INTERFACE:${Caffe_INCLUDE_DIR}>
+                                 $<INSTALL_INTERFACE:include>)
+target_compile_definitions(caffe ${Caffe_DEFINITIONS})
 set_target_properties(caffe PROPERTIES
     VERSION   ${CAFFE_TARGET_VERSION}
     SOVERSION ${CAFFE_TARGET_SOVERSION}
@@ -37,4 +45,3 @@ file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
 list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)
 install(PROGRAMS ${proto_python} DESTINATION python/caffe/proto)
 
-
diff --git a/src/gtest/CMakeLists.txt b/src/gtest/CMakeLists.txt
index ef7ff7ed14b..e98254af130 100644
--- a/src/gtest/CMakeLists.txt
+++ b/src/gtest/CMakeLists.txt
@@ -1,5 +1,8 @@
 add_library(gtest STATIC EXCLUDE_FROM_ALL gtest.h gtest-all.cpp)
 caffe_default_properties(gtest)
+target_include_directories(gtest PUBLIC ${Caffe_SRC_DIR})
+target_compile_definitions(gtest PUBLIC -DGTEST_USE_OWN_TR1_TUPLE)
+
 
 #add_library(gtest_main gtest_main.cc)
 #target_link_libraries(gtest_main gtest)

From 6200b915601e1f7b2ec6d4746dc143114722ec38 Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Sat, 20 Aug 2016 01:08:26 +0300
Subject: [PATCH 1845/2053] net.cpp: do not include test/test_caffe_main.hpp

---
 src/caffe/net.cpp | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 644cb7e97ee..a3408734c12 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -17,8 +17,6 @@
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
-#include "caffe/test/test_caffe_main.hpp"
-
 namespace caffe {
 
 template <typename Dtype>

From f1b9da54598923c531e1a98c4f1546169165e441 Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Sun, 14 Aug 2016 04:57:22 +0300
Subject: [PATCH 1846/2053] cmake: add option to link with OpenMP

Despite Caffe itself does not use OpenMP, explicitly linking to OpenMP
should be done when one statically links to a BLAS library which uses
OpenMP internally and does not provide proper CMake imported targets
with proper dependencies (nobody this so far).
---
 CMakeLists.txt           |  1 +
 cmake/Dependencies.cmake | 17 +++++++++++++----
 src/caffe/CMakeLists.txt |  3 +++
 3 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cb25b43a458..378b285c908 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -38,6 +38,7 @@ caffe_option(USE_OPENCV "Build with OpenCV support" ON)
 caffe_option(USE_LEVELDB "Build with levelDB" ON)
 caffe_option(USE_LMDB "Build with lmdb" ON)
 caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF)
+caffe_option(USE_OPENMP "Link with OpenMP (when your BLAS wants OpenMP and you get linker errors)" OFF)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 6a12759234f..290c161b8b9 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -1,7 +1,8 @@
-# This list is required for static linking and exported to CaffeConfig.cmake
+# These lists are later turned into target properties on main caffe library target
 set(Caffe_LINKER_LIBS "")
 set(Caffe_INCLUDE_DIRS "")
 set(Caffe_DEFINITIONS "")
+set(Caffe_COMPILE_OPTIONS "")
 
 # ---[ Boost
 find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
@@ -14,10 +15,18 @@ list(APPEND Caffe_LINKER_LIBS PRIVATE ${CMAKE_THREAD_LIBS_INIT})
 
 # ---[ OpenMP
 if(USE_OPENMP)
-  # TODO: use something exportable here
+  # Ideally, this should be provided by the BLAS library IMPORTED target. However,
+  # nobody does this, so we need to link to OpenMP explicitly and have the maintainer
+  # to flick the switch manually as needed.
+  #
+  # Moreover, OpenMP package does not provide an IMPORTED target as well, and the
+  # suggested way of linking to OpenMP is to append to CMAKE_{C,CXX}_FLAGS.
+  # However, this naïve method will force any user of Caffe to add the same kludge
+  # into their buildsystem again, so we put these options into per-target PUBLIC
+  # compile options and link flags, so that they will be exported properly.
   find_package(OpenMP REQUIRED)
-  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+  list(APPEND Caffe_LINKER_LIBS PRIVATE ${OpenMP_CXX_FLAGS})
+  list(APPEND Caffe_COMPILE_OPTIONS PRIVATE ${OpenMP_CXX_FLAGS})
 endif()
 
 # ---[ Google-glog
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index ed4d50bed5a..7b25a98aa2d 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -28,6 +28,9 @@ target_include_directories(caffe ${Caffe_INCLUDE_DIRS}
                                  $<BUILD_INTERFACE:${Caffe_INCLUDE_DIR}>
                                  $<INSTALL_INTERFACE:include>)
 target_compile_definitions(caffe ${Caffe_DEFINITIONS})
+if(Caffe_COMPILE_OPTIONS)
+  target_compile_options(caffe ${Caffe_COMPILE_OPTIONS})
+endif()
 set_target_properties(caffe PROPERTIES
     VERSION   ${CAFFE_TARGET_VERSION}
     SOVERSION ${CAFFE_TARGET_SOVERSION}

From 6ed799cb206c6b70bdd260d62e8ff3e077f5b635 Mon Sep 17 00:00:00 2001
From: Ivan Shapovalov <intelfx@intelfx.name>
Date: Wed, 24 Aug 2016 06:28:41 +0300
Subject: [PATCH 1847/2053] cmake/Templates: remove duplicated #cmakedefines
 from caffe_config.h.in

Rationale: these are duplicated in CMakeLists code, and they cannot be
removed from there because many definitions need to be exported to the
library clients. See issue #4625.
---
 cmake/Templates/caffe_config.h.in | 19 -------------------
 1 file changed, 19 deletions(-)

diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 8a31b43cabf..45465b98305 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -4,16 +4,6 @@
 /* Binaries directory */
 #define BINARY_FOLDER "${PROJECT_BINARY_DIR}"
 
-/* NVIDA Cuda */
-#cmakedefine HAVE_CUDA
-
-/* NVIDA cuDNN */
-#cmakedefine HAVE_CUDNN
-#cmakedefine USE_CUDNN
-
-/* NVIDA cuDNN */
-#cmakedefine CPU_ONLY
-
 /* Test device */
 #define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE}
 
@@ -27,12 +17,3 @@
   #define EXAMPLES_SOURCE_DIR "examples/"
   #define CMAKE_EXT ""
 #endif
-
-/* Matlab */
-#cmakedefine HAVE_MATLAB
-
-/* IO libraries */
-#cmakedefine USE_OPENCV
-#cmakedefine USE_LEVELDB
-#cmakedefine USE_LMDB
-#cmakedefine ALLOW_LMDB_NOLOCK

From 9bc83e32b39e2c9bbf4bf20d69d4f215d73a414e Mon Sep 17 00:00:00 2001
From: Benedikt Wilbertz <benedikt.wilbertz@gmx.de>
Date: Fri, 12 Aug 2016 22:33:06 +0200
Subject: [PATCH 1848/2053] fix layerSetUp of scale_layer to not add bias blob
 when already present

---
 src/caffe/layers/scale_layer.cpp | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/scale_layer.cpp b/src/caffe/layers/scale_layer.cpp
index ecdbb123e31..e652dad6e10 100644
--- a/src/caffe/layers/scale_layer.cpp
+++ b/src/caffe/layers/scale_layer.cpp
@@ -56,9 +56,17 @@ void ScaleLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     bias_bottom_vec_.resize(1);
     bias_bottom_vec_[0] = bottom[0];
     bias_layer_->SetUp(bias_bottom_vec_, top);
-    bias_param_id_ = this->blobs_.size();
-    this->blobs_.resize(bias_param_id_ + 1);
-    this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    if (this->blobs_.size() + bottom.size() < 3) {
+      // case: blobs.size == 1 && bottom.size == 1
+      // or blobs.size == 0 && bottom.size == 2
+      bias_param_id_ = this->blobs_.size();
+      this->blobs_.resize(bias_param_id_ + 1);
+      this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    } else {
+      // bias param already initialized
+      bias_param_id_ = this->blobs_.size() - 1;
+      bias_layer_->blobs()[0] = this->blobs_[bias_param_id_];
+    }
     bias_propagate_down_.resize(1, false);
   }
   this->param_propagate_down_.resize(this->blobs_.size(), true);

From cdcf2e07dba951774be7feb9d486b7f84ef0c0b1 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 9 Sep 2016 12:49:35 -0700
Subject: [PATCH 1849/2053] Benchmarking should not impact perf until timer is
 read

---
 src/caffe/util/benchmark.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 1d269c351c1..d994225f97b 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -44,7 +44,6 @@ void Timer::Stop() {
     if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
       CUDA_CHECK(cudaEventRecord(stop_gpu_, 0));
-      CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
 #else
       NO_GPU;
 #endif
@@ -66,6 +65,7 @@ float Timer::MicroSeconds() {
   }
   if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
+    CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
     // Cuda only measure milliseconds
@@ -89,6 +89,7 @@ float Timer::MilliSeconds() {
   }
   if (Caffe::mode() == Caffe::GPU) {
 #ifndef CPU_ONLY
+    CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
 #else

From 50b5697a0e0b85921e3ea38e961984ea08f014c3 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 9 Sep 2016 12:57:09 -0700
Subject: [PATCH 1850/2053] Avoids missing return values during build.

---
 src/caffe/layer_factory.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index e967bd6181c..f14253a510e 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -67,6 +67,7 @@ shared_ptr<Layer<Dtype> > GetConvolutionLayer(
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -104,6 +105,7 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -141,6 +143,7 @@ shared_ptr<Layer<Dtype> > GetLRNLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -164,6 +167,7 @@ shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -187,6 +191,7 @@ shared_ptr<Layer<Dtype> > GetSigmoidLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -210,6 +215,7 @@ shared_ptr<Layer<Dtype> > GetSoftmaxLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 
@@ -233,6 +239,7 @@ shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
   }
 }
 

From 04f9a77801af3233bacadcca178ee7d7a6406bd5 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 27 Aug 2016 20:19:00 -0700
Subject: [PATCH 1851/2053] [docs] clarify handling of bias and scaling by
 BiasLayer, ScaleLayer

A bias/scaling can be applied wherever desired by defining the
respective layers, and `ScaleLayer` can handle both as a memory
optimization.
---
 include/caffe/layers/batch_norm_layer.hpp |  8 +++-----
 include/caffe/layers/bias_layer.hpp       | 10 +++++-----
 include/caffe/layers/scale_layer.hpp      | 12 +++++++-----
 3 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
index 9b2d5126efb..c38c8410693 100644
--- a/include/caffe/layers/batch_norm_layer.hpp
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -27,11 +27,9 @@ namespace caffe {
  * param {lr_mult: 0} three times in the layer definition.
  *
  * Note that the original paper also included a per-channel learned bias and
- * scaling factor.  It is possible (though a bit cumbersome) to implement
- * this in caffe using a single-channel DummyDataLayer filled with zeros,
- * followed by a Convolution layer with output the same size as the current.
- * This produces a channel-specific value that can be added or multiplied by
- * the BatchNorm layer's output.
+ * scaling factor. To implement this in Caffe, define a `ScaleLayer` configured
+ * with `bias_term: true` after each `BatchNormLayer` to handle both the bias
+ * and scaling factor.
  *
  * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
  *     Training by Reducing Internal Covariate Shift." arXiv preprint
diff --git a/include/caffe/layers/bias_layer.hpp b/include/caffe/layers/bias_layer.hpp
index eedc3aaa351..9639c9cdc8a 100644
--- a/include/caffe/layers/bias_layer.hpp
+++ b/include/caffe/layers/bias_layer.hpp
@@ -10,13 +10,13 @@
 namespace caffe {
 
 /**
- * @brief Computes a sum of two input Blobs, with the shape of the
- *        latter Blob "broadcast" to match the shape of the former.
- *        Equivalent to tiling the latter Blob, then computing the elementwise
- *        sum.
+ * @brief Computes a sum of two input Blobs, with the shape of the latter Blob
+ *        "broadcast" to match the shape of the former. Equivalent to tiling
+ *        the latter Blob, then computing the elementwise sum.
  *
  * The second input may be omitted, in which case it's learned as a parameter
- * of the layer.
+ * of the layer. Note: in case bias and scaling are desired, both operations can
+ * be handled by `ScaleLayer` configured with `bias_term: true`.
  */
 template <typename Dtype>
 class BiasLayer : public Layer<Dtype> {
diff --git a/include/caffe/layers/scale_layer.hpp b/include/caffe/layers/scale_layer.hpp
index 924df2e51ab..45b714d4027 100644
--- a/include/caffe/layers/scale_layer.hpp
+++ b/include/caffe/layers/scale_layer.hpp
@@ -12,13 +12,15 @@
 namespace caffe {
 
 /**
- * @brief Computes a product of two input Blobs, with the shape of the
- *        latter Blob "broadcast" to match the shape of the former.
+ * @brief Computes the elementwise product of two input Blobs, with the shape of
+ *        the latter Blob "broadcast" to match the shape of the former.
  *        Equivalent to tiling the latter Blob, then computing the elementwise
- *        product.
+ *        product. Note: for efficiency and convenience, this layer can
+ *        additionally perform a "broadcast" sum too when `bias_term: true`
+ *        is set.
  *
- * The second input may be omitted, in which case it's learned as a parameter
- * of the layer.
+ * The latter, scale input may be omitted, in which case it's learned as
+ * parameter of the layer (as is the bias, if it is included).
  */
 template <typename Dtype>
 class ScaleLayer: public Layer<Dtype> {

From d195e605de5f6964eadeba467f5ad85d46841c87 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 9 Sep 2016 19:46:41 -0700
Subject: [PATCH 1852/2053] [docs] note CUDA 8 requirement for Ubuntu 16.04

---
 docs/install_apt.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index 2976e3cd07c..3de5a494e0c 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -9,14 +9,19 @@ title: Installation: Ubuntu
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
     sudo apt-get install --no-install-recommends libboost-all-dev
 
-**CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
-Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
+**CUDA**: Install by `apt-get` or the NVIDIA `.run` package.
+The NVIDIA package tends to follow more recent library and driver versions, but the installation is more manual.
+If installing from packages, install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
 This can be skipped for CPU-only installation.
 
 **BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
 
 **Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
 
+**Compatibility notes, 16.04**
+
+CUDA 8 is required on Ubuntu 16.04.
+
 **Remaining dependencies, 14.04**
 
 Everything is packaged in 14.04.

From 3b6fd1d95b374b0484f32a4f86380714c456a293 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 27 Aug 2016 20:23:13 -0700
Subject: [PATCH 1853/2053] [docs] identify batch norm layer blobs

---
 include/caffe/layers/batch_norm_layer.hpp | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
index c38c8410693..a26ad1a42c7 100644
--- a/include/caffe/layers/batch_norm_layer.hpp
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -13,18 +13,19 @@ namespace caffe {
  * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
  *        the batch.
  *
- * This layer computes Batch Normalization described in [1].  For
- * each channel in the data (i.e. axis 1), it subtracts the mean and divides
- * by the variance, where both statistics are computed across both spatial
- * dimensions and across the different examples in the batch.
+ * This layer computes Batch Normalization as described in [1]. For each channel
+ * in the data (i.e. axis 1), it subtracts the mean and divides by the variance,
+ * where both statistics are computed across both spatial dimensions and across
+ * the different examples in the batch.
  *
- * By default, during training time, the network is computing global mean/
- * variance statistics via a running average, which is then used at test
- * time to allow deterministic outputs for each input.  You can manually
- * toggle whether the network is accumulating or using the statistics via the
- * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
- * set the learning rate to zero for all three parameter blobs, i.e.,
- * param {lr_mult: 0} three times in the layer definition.
+ * By default, during training time, the network is computing global
+ * mean/variance statistics via a running average, which is then used at test
+ * time to allow deterministic outputs for each input. You can manually toggle
+ * whether the network is accumulating or using the statistics via the
+ * use_global_stats option. IMPORTANT: for this feature to work, you MUST set
+ * the learning rate to zero for all three blobs, i.e., param {lr_mult: 0} three
+ * times in the layer definition. For reference, these three blobs are (0)
+ * mean, (1) variance, and (2) the moving average factor.
  *
  * Note that the original paper also included a per-channel learned bias and
  * scaling factor. To implement this in Caffe, define a `ScaleLayer` configured

From c8f446f640b12b0577063eca8fab004e73c0aefc Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 29 Aug 2016 23:42:58 -0700
Subject: [PATCH 1854/2053] batch norm: hide statistics from solver,
 simplifying layer definition

batch norm statistics are not learnable parameters subject to solver
updates, so they must be shielded from the solver. `BatchNorm` layer now
masks its statistics for itself by zeroing parameter learning rates
instead of relying on the layer definition.

n.b. declaring `param`s for batch norm layers is no longer allowed.
---
 include/caffe/layers/batch_norm_layer.hpp | 6 ++----
 src/caffe/layers/batch_norm_layer.cpp     | 8 ++++++++
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
index a26ad1a42c7..43f7b28be95 100644
--- a/include/caffe/layers/batch_norm_layer.hpp
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -22,10 +22,8 @@ namespace caffe {
  * mean/variance statistics via a running average, which is then used at test
  * time to allow deterministic outputs for each input. You can manually toggle
  * whether the network is accumulating or using the statistics via the
- * use_global_stats option. IMPORTANT: for this feature to work, you MUST set
- * the learning rate to zero for all three blobs, i.e., param {lr_mult: 0} three
- * times in the layer definition. For reference, these three blobs are (0)
- * mean, (1) variance, and (2) the moving average factor.
+ * use_global_stats option. For reference, these statistics are kept in the
+ * layer's three blobs: (0) mean, (1) variance, and (2) moving average factor.
  *
  * Note that the original paper also included a per-channel learned bias and
  * scaling factor. To implement this in Caffe, define a `ScaleLayer` configured
diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index a69d8f99316..0b1037edc63 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -34,6 +34,14 @@ void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
                 this->blobs_[i]->mutable_cpu_data());
     }
   }
+  // Mask statistics from optimization by setting local learning rates
+  // for mean, variance, and the bias correction to zero.
+  CHECK_EQ(this->layer_param_.param_size(), 0)
+      << "Cannot configure batch normalization statistics as layer parameters.";
+  for (int i = 0; i < this->blobs_.size(); ++i) {
+    ParamSpec* fixed_param_spec = this->layer_param_.add_param();
+    fixed_param_spec->set_lr_mult(0.);
+  }
 }
 
 template <typename Dtype>

From a8ec123c00723df0d0ad897e1eea32a29201c81b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 9 Sep 2016 16:49:31 -0700
Subject: [PATCH 1855/2053] batch norm: auto-upgrade old layer definitions w/
 param messages

automatically strip old batch norm layer definitions including `param`
messages. the batch norm layer used to require manually masking its
state from the solver by setting `param { lr_mult: 0 }` messages for
each of its statistics. this is now handled automatically by the layer.
---
 include/caffe/util/upgrade_proto.hpp |  6 +++++
 src/caffe/util/upgrade_proto.cpp     | 34 +++++++++++++++++++++++++++-
 2 files changed, 39 insertions(+), 1 deletion(-)

diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index 14e1936a8c2..b145822af32 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -65,6 +65,12 @@ bool NetNeedsInputUpgrade(const NetParameter& net_param);
 // Perform all necessary transformations to upgrade input fields into layers.
 void UpgradeNetInput(NetParameter* net_param);
 
+// Return true iff the Net contains batch norm layers with manual local LRs.
+bool NetNeedsBatchNormUpgrade(const NetParameter& net_param);
+
+// Perform all necessary transformations to upgrade batch norm layers.
+void UpgradeNetBatchNorm(NetParameter* net_param);
+
 // Return true iff the solver contains any old solver_type specified as enums
 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
 
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 9e186915b43..a0aacbe92f8 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -14,7 +14,8 @@ namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
   return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param)
-      || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param);
+      || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param)
+      || NetNeedsBatchNormUpgrade(net_param);
 }
 
 bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
@@ -71,6 +72,14 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
     LOG(WARNING) << "Note that future Caffe releases will only support "
                  << "input layers and not input fields.";
   }
+  // NetParameter uses old style batch norm layers; try to upgrade it.
+  if (NetNeedsBatchNormUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade batch norm layers using deprecated "
+              << "params: " << param_file;
+    UpgradeNetBatchNorm(param);
+    LOG(INFO) << "Successfully upgraded batch norm layers using deprecated "
+              << "params.";
+  }
   return success;
 }
 
@@ -991,6 +1000,29 @@ void UpgradeNetInput(NetParameter* net_param) {
   net_param->clear_input_dim();
 }
 
+bool NetNeedsBatchNormUpgrade(const NetParameter& net_param) {
+  for (int i = 0; i < net_param.layer_size(); ++i) {
+    // Check if BatchNorm layers declare three parameters, as required by
+    // the previous BatchNorm layer definition.
+    if (net_param.layer(i).type() == "BatchNorm"
+        && net_param.layer(i).param_size() == 3) {
+      return true;
+    }
+  }
+  return false;
+}
+
+void UpgradeNetBatchNorm(NetParameter* net_param) {
+  for (int i = 0; i < net_param->layer_size(); ++i) {
+    // Check if BatchNorm layers declare three parameters, as required by
+    // the previous BatchNorm layer definition.
+    if (net_param->layer(i).type() == "BatchNorm"
+        && net_param->layer(i).param_size() == 3) {
+      net_param->mutable_layer(i)->clear_param();
+    }
+  }
+}
+
 // Return true iff the solver contains any old solver_type specified as enums
 bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
   if (solver_param.has_solver_type()) {

From fc8f3eba6fa06be2f55d1b576f46664e07f5d0a6 Mon Sep 17 00:00:00 2001
From: Youssef Kashef <youssef.kashef@gmail.com>
Date: Tue, 13 Sep 2016 15:52:39 +0200
Subject: [PATCH 1856/2053] fix comments in matlab classification demo

---
 matlab/demo/classification_demo.m | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m
index 2b60332970b..435c077845f 100644
--- a/matlab/demo/classification_demo.m
+++ b/matlab/demo/classification_demo.m
@@ -8,7 +8,7 @@
 %
 % ****************************************************************************
 % For detailed documentation and usage on Caffe's Matlab interface, please
-% refer to Caffe Interface Tutorial at
+% refer to the Caffe Interface Tutorial at
 % http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
 % ****************************************************************************
 %
@@ -24,6 +24,7 @@
 %  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
 %  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
 % Or the equivalent based on where things are installed on your system
+% and what versions are installed.
 %
 % Usage:
 %  im = imread('../../examples/images/cat.jpg');
@@ -39,7 +40,7 @@
 % Data coming in from matlab needs to be in the order
 %   [width, height, channels, images]
 % where width is the fastest dimension.
-% Here is the rough matlab for putting image data into the correct
+% Here is the rough matlab code for putting image data into the correct
 % format in W x H x C with BGR channels:
 %   % permute channels from RGB to BGR
 %   im_data = im(:, :, [3, 2, 1]);
@@ -54,7 +55,7 @@
 
 % If you have multiple images, cat them with cat(4, ...)
 
-% Add caffe/matlab to you Matlab search PATH to use matcaffe
+% Add caffe/matlab to your Matlab search PATH in order to use matcaffe
 if exist('../+caffe', 'dir')
   addpath('..');
 else

From eee3be15589e81b5385c7d0d02a151c789134905 Mon Sep 17 00:00:00 2001
From: Miguel Lloreda <mlloreda@users.noreply.github.com>
Date: Thu, 15 Sep 2016 17:28:02 -0400
Subject: [PATCH 1857/2053] Fixed typos in examples/cpp_classification/readme

---
 examples/cpp_classification/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
index 0de2885b53c..4f683aa623f 100644
--- a/examples/cpp_classification/readme.md
+++ b/examples/cpp_classification/readme.md
@@ -10,7 +10,7 @@ priority: 10
 
 Caffe, at its core, is written in C++. It is possible to use the C++
 API of Caffe to implement an image classification application similar
-to the Python code presented in one of the Notebook example. To look
+to the Python code presented in one of the Notebook examples. To look
 at a more general-purpose example of the Caffe C++ API, you should
 study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
 
@@ -19,7 +19,7 @@ study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
 A simple C++ code is proposed in
 `examples/cpp_classification/classification.cpp`. For the sake of
 simplicity, this example does not support oversampling of a single
-sample nor batching of multiple independant samples. This example is
+sample nor batching of multiple independent samples. This example is
 not trying to reach the maximum possible classification throughput on
 a system, but special care was given to avoid unnecessary
 pessimization while keeping the code readable.

From 2f55f42cff9147e69b1f5dff9232058d7b654eba Mon Sep 17 00:00:00 2001
From: Rok Mandeljc <rok.mandeljc@fe.uni-lj.si>
Date: Mon, 29 Jun 2015 15:48:43 +0200
Subject: [PATCH 1858/2053] matcaffe: allow destruction of individual networks
 and solvers

---
 matlab/+caffe/Net.m              |  3 +++
 matlab/+caffe/Solver.m           |  3 +++
 matlab/+caffe/private/caffe_.cpp | 24 ++++++++++++++++++++++++
 3 files changed, 30 insertions(+)

diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
index e6295bba1a4..349e060eb22 100644
--- a/matlab/+caffe/Net.m
+++ b/matlab/+caffe/Net.m
@@ -68,6 +68,9 @@
       self.layer_names = self.attributes.layer_names;
       self.blob_names = self.attributes.blob_names;
     end
+    function delete (self)
+      caffe_('delete_net', self.hNet_self);
+    end
     function layer = layers(self, layer_name)
       CHECK(ischar(layer_name), 'layer_name must be a string');
       layer = self.layer_vec(self.name2layer_index(layer_name));
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
index f8bdc4e22b2..2d3c98b2a26 100644
--- a/matlab/+caffe/Solver.m
+++ b/matlab/+caffe/Solver.m
@@ -36,6 +36,9 @@
         self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n));
       end
     end
+    function delete (self)
+      caffe_('delete_solver', self.hSolver_self);
+    end
     function iter = iter(self)
       iter = caffe_('solver_get_iter', self.hSolver_self);
     end
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 1b1b2bff861..bc04f4171e4 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -197,6 +197,17 @@ static void get_solver(MEX_ARGS) {
   mxFree(solver_file);
 }
 
+// Usage: caffe_('delete_solver', hSolver)
+static void delete_solver(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('delete_solver', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  solvers_.erase(std::remove_if(solvers_.begin(), solvers_.end(),
+      [solver] (const shared_ptr< Solver<float> > &solverPtr) {
+      return solverPtr.get() == solver;
+  }), solvers_.end());
+}
+
 // Usage: caffe_('solver_get_attr', hSolver)
 static void solver_get_attr(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
@@ -271,6 +282,17 @@ static void get_net(MEX_ARGS) {
   mxFree(phase_name);
 }
 
+// Usage: caffe_('delete_solver', hSolver)
+static void delete_net(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('delete_solver', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  nets_.erase(std::remove_if(nets_.begin(), nets_.end(),
+      [net] (const shared_ptr< Net<float> > &netPtr) {
+      return netPtr.get() == net;
+  }), nets_.end());
+}
+
 // Usage: caffe_('net_get_attr', hNet)
 static void net_get_attr(MEX_ARGS) {
   mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
@@ -522,12 +544,14 @@ struct handler_registry {
 static handler_registry handlers[] = {
   // Public API functions
   { "get_solver",         get_solver      },
+  { "delete_solver",      delete_solver   },
   { "solver_get_attr",    solver_get_attr },
   { "solver_get_iter",    solver_get_iter },
   { "solver_restore",     solver_restore  },
   { "solver_solve",       solver_solve    },
   { "solver_step",        solver_step     },
   { "get_net",            get_net         },
+  { "delete_net",         delete_net      },
   { "net_get_attr",       net_get_attr    },
   { "net_forward",        net_forward     },
   { "net_backward",       net_backward    },

From f96ccea124314d4ea1374e906fbd709d1dc43585 Mon Sep 17 00:00:00 2001
From: Luke Yeager <lukeyeager@users.noreply.github.com>
Date: Fri, 23 Sep 2016 11:22:48 -0700
Subject: [PATCH 1859/2053] [TravisCI] google/protobuf renamed the 3.0 branch

---
 scripts/travis/install-deps.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index 4e86ac73915..daef5c4a04f 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -56,7 +56,7 @@ else
       dh-autoreconf \
       unzip
 
-    wget https://github.com/google/protobuf/archive/3.0.0-GA.tar.gz -O protobuf3.tar.gz
+    wget https://github.com/google/protobuf/archive/3.0.x.tar.gz -O protobuf3.tar.gz
     tar -xzf protobuf3.tar.gz -C $PROTOBUF3_DIR --strip 1
     rm protobuf3.tar.gz
     cd $PROTOBUF3_DIR

From 79a8c5210846f70108e5a2be1bedc95d9f8aea30 Mon Sep 17 00:00:00 2001
From: Ken Yu <kyu_115s@hotmail.com>
Date: Wed, 21 Sep 2016 16:19:17 +0800
Subject: [PATCH 1860/2053] Ignore Visual Studio Code files.

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 53c1fb056bb..281ef3266db 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,6 +47,9 @@
 # PyCharm files
 .idea
 
+# Visual Studio Code files
+.vscode
+
 # OSX dir files
 .DS_Store
 

From ce6ac831b96725bd770eaec5c0f743e423e355fd Mon Sep 17 00:00:00 2001
From: Benedikt Wilbertz <benedikt.wilbertz@gmx.de>
Date: Thu, 29 Sep 2016 21:55:58 +0200
Subject: [PATCH 1861/2053] slightly relax batch norm check

---
 src/caffe/layers/batch_norm_layer.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index 0b1037edc63..e661abb11b8 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -36,11 +36,15 @@ void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
   // Mask statistics from optimization by setting local learning rates
   // for mean, variance, and the bias correction to zero.
-  CHECK_EQ(this->layer_param_.param_size(), 0)
-      << "Cannot configure batch normalization statistics as layer parameters.";
   for (int i = 0; i < this->blobs_.size(); ++i) {
-    ParamSpec* fixed_param_spec = this->layer_param_.add_param();
-    fixed_param_spec->set_lr_mult(0.);
+    if (this->layer_param_.param_size() == i) {
+      ParamSpec* fixed_param_spec = this->layer_param_.add_param();
+      fixed_param_spec->set_lr_mult(0.f);
+    } else {
+      CHECK_EQ(this->layer_param_.param(i).lr_mult(), 0.f)
+          << "Cannot configure batch normalization statistics as layer "
+          << "parameters.";
+    }
   }
 }
 

From 08ca70326966ad24b012ca8084c8baba5b1a23b5 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 30 Sep 2016 18:18:47 -0700
Subject: [PATCH 1862/2053] NV changed path to cudnn

---
 scripts/travis/install-deps.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index daef5c4a04f..1900b16df54 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -84,7 +84,7 @@ if $WITH_CUDA ; then
   rm $CUDA_REPO_PKG
 
   if $WITH_CUDNN ; then
-    ML_REPO_PKG=nvidia-machine-learning-repo_4.0-2_amd64.deb
+    ML_REPO_PKG=nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb
     wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/$ML_REPO_PKG
     dpkg -i $ML_REPO_PKG
   fi

From c97a964a23f0ddd455c619537e208f117ae77743 Mon Sep 17 00:00:00 2001
From: Kun Wang <wk910930@gmail.com>
Date: Wed, 5 Oct 2016 18:59:07 +0800
Subject: [PATCH 1863/2053] fix typo in pascal_multilabel_datalayers.py

---
 examples/pycaffe/layers/pascal_multilabel_datalayers.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
index 68e4fa7960a..9420cb328ce 100644
--- a/examples/pycaffe/layers/pascal_multilabel_datalayers.py
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -20,7 +20,7 @@
 class PascalMultilabelDataLayerSync(caffe.Layer):
 
     """
-    This is a simple syncronous datalayer for training a multilabel model on
+    This is a simple synchronous datalayer for training a multilabel model on
     PASCAL.
     """
 
@@ -33,7 +33,7 @@ def setup(self, bottom, top):
         # params is a python dictionary with layer parameters.
         params = eval(self.param_str)
 
-        # Check the paramameters for validity.
+        # Check the parameters for validity.
         check_params(params)
 
         # store input as class variables
@@ -207,7 +207,7 @@ def check_params(params):
 
 def print_info(name, params):
     """
-    Ouput some info regarding the class
+    Output some info regarding the class
     """
     print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
         name,

From cdd2d0ee9ed42200b6ab8b52c0213bb5916b46c4 Mon Sep 17 00:00:00 2001
From: Vincent <vincent.biret@gmail.com>
Date: Wed, 5 Oct 2016 13:12:04 +0100
Subject: [PATCH 1864/2053] Fix: docs/yum_install.md glog broken link

fixes the broken glog link in yum_install.md which is currently returning a 404.
---
 docs/install_yum.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install_yum.md b/docs/install_yum.md
index 2104912e482..38bf7255c93 100644
--- a/docs/install_yum.md
+++ b/docs/install_yum.md
@@ -15,7 +15,7 @@ title: Installation: RHEL / Fedora / CentOS
 **Remaining dependencies, if not found**
 
     # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
+    wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/google-glog/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
     cd glog-0.3.3
     ./configure

From 553a645f1d6f950bf1a36284bb13b5fc7c3bacdc Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Thu, 13 Oct 2016 22:29:56 -0400
Subject: [PATCH 1865/2053] pytest fix: Files created with NamedTemporary files
 cannot be opened on Windows

---
 python/caffe/test/test_net.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index e1090934d73..a0739fbac4d 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -173,12 +173,12 @@ class TestLevels(unittest.TestCase):
 """
 
     def setUp(self):
-        self.f = tempfile.NamedTemporaryFile(mode='w+')
+        self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         self.f.write(self.TEST_NET)
-        self.f.flush()
+        self.f.close()
 
     def tearDown(self):
-        self.f.close()
+        os.remove(self.f.name)
 
     def check_net(self, net, blobs):
         net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
@@ -238,12 +238,12 @@ class TestStages(unittest.TestCase):
 """
 
     def setUp(self):
-        self.f = tempfile.NamedTemporaryFile(mode='w+')
+        self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         self.f.write(self.TEST_NET)
-        self.f.flush()
+        self.f.close()
 
     def tearDown(self):
-        self.f.close()
+        os.remove(self.f.name)
 
     def check_net(self, net, blobs):
         net_blobs = [b for b in net.blobs.keys() if 'data' not in b]
@@ -320,12 +320,12 @@ class TestAllInOne(unittest.TestCase):
 """
 
     def setUp(self):
-        self.f = tempfile.NamedTemporaryFile(mode='w+')
+        self.f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         self.f.write(self.TEST_NET)
-        self.f.flush()
+        self.f.close()
 
     def tearDown(self):
-        self.f.close()
+        os.remove(self.f.name)
 
     def check_net(self, net, outputs):
         self.assertEqual(list(net.blobs['data'].shape), [1,1,10,10])

From 95a436c601a04af620a0e166393d3ff695905bc4 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Thu, 25 Aug 2016 09:20:24 +0000
Subject: [PATCH 1866/2053] Fix: made load_hd5 check blob dims by default.

Size checks are needed for loading parameters to avoid strange bugs
when loading data we continue to reshape.
---
 include/caffe/util/hdf5.hpp               |  4 +--
 src/caffe/layers/hdf5_data_layer.cpp      |  3 +-
 src/caffe/test/test_hdf5_output_layer.cpp | 10 ++++---
 src/caffe/test/test_hdf5data_layer.cpp    |  2 +-
 src/caffe/util/hdf5.cpp                   | 34 +++++++++++++++++++----
 5 files changed, 39 insertions(+), 14 deletions(-)

diff --git a/include/caffe/util/hdf5.hpp b/include/caffe/util/hdf5.hpp
index ce568c5eb0d..71549c1cc02 100644
--- a/include/caffe/util/hdf5.hpp
+++ b/include/caffe/util/hdf5.hpp
@@ -13,12 +13,12 @@ namespace caffe {
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
+    Blob<Dtype>* blob, bool reshape);
 
 template <typename Dtype>
 void hdf5_load_nd_dataset(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
+    Blob<Dtype>* blob, bool reshape = false);
 
 template <typename Dtype>
 void hdf5_save_nd_dataset(
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 2f13dc641df..0099129000c 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -39,8 +39,9 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 
   for (int i = 0; i < top_size; ++i) {
     hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());
+    // Allow reshape here, as we are loading data not params
     hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),
-        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());
+        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get(), true);
   }
 
   herr_t status = H5Fclose(file_id);
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 3833ebff78e..2bc2de1e647 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -77,10 +77,12 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
                           H5P_DEFAULT);
   ASSERT_GE(file_id, 0)<< "Failed to open HDF5 file" <<
       this->input_file_name_;
+  // Allow reshape here as we are loading data not params
+  bool reshape = true;
   hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                       this->blob_data_);
+                       this->blob_data_, reshape);
   hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                       this->blob_label_);
+                       this->blob_label_, reshape);
   herr_t status = H5Fclose(file_id);
   EXPECT_GE(status, 0)<< "Failed to close HDF5 file " <<
       this->input_file_name_;
@@ -105,12 +107,12 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
 
   Blob<Dtype>* blob_data = new Blob<Dtype>();
   hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                       blob_data);
+                       blob_data, reshape);
   this->CheckBlobEqual(*(this->blob_data_), *blob_data);
 
   Blob<Dtype>* blob_label = new Blob<Dtype>();
   hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                       blob_label);
+                       blob_label, reshape);
   this->CheckBlobEqual(*(this->blob_label_), *blob_label);
 
   status = H5Fclose(file_id);
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 8884ce95a23..e0fd62134c5 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -70,7 +70,7 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   int height = 6;
   int width = 5;
 
-  // Test that the layer setup got the correct parameters.
+  // Test that the layer setup gives correct parameters.
   HDF5DataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), batch_size);
diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
index 7730e76ab87..0003f1b3988 100644
--- a/src/caffe/util/hdf5.cpp
+++ b/src/caffe/util/hdf5.cpp
@@ -9,7 +9,7 @@ namespace caffe {
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
     hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob) {
+    Blob<Dtype>* blob, bool reshape) {
   // Verify that the dataset exists.
   CHECK(H5LTfind_dataset(file_id, dataset_name_))
       << "Failed to find HDF5 dataset " << dataset_name_;
@@ -56,17 +56,38 @@ void hdf5_load_nd_dataset_helper(
     LOG(FATAL) << "Datatype class unknown";
   }
 
+
   vector<int> blob_dims(dims.size());
   for (int i = 0; i < dims.size(); ++i) {
     blob_dims[i] = dims[i];
   }
-  blob->Reshape(blob_dims);
+
+  if (reshape) {
+    blob->Reshape(blob_dims);
+  } else {
+    if (blob_dims != blob->shape()) {
+      // create shape string for error message
+      ostringstream stream;
+      int count = 1;
+      for (int i = 0; i < blob_dims.size(); ++i) {
+        stream << blob_dims[i] << " ";
+        count = count * blob_dims[i];
+      }
+      stream << "(" << count << ")";
+      string source_shape_string = stream.str();
+
+      CHECK(blob_dims == blob->shape()) << "Cannot load blob from hdf5; shape "
+            << "mismatch. Source shape is " << source_shape_string
+            << " target shape is " << blob->shape_string();
+    }
+  }
 }
 
 template <>
 void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<float>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+        int min_dim, int max_dim, Blob<float>* blob, bool reshape) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob,
+                              reshape);
   herr_t status = H5LTread_dataset_float(
     file_id, dataset_name_, blob->mutable_cpu_data());
   CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
@@ -74,8 +95,9 @@ void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
 
 template <>
 void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<double>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+        int min_dim, int max_dim, Blob<double>* blob, bool reshape) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob,
+                              reshape);
   herr_t status = H5LTread_dataset_double(
     file_id, dataset_name_, blob->mutable_cpu_data());
   CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;

From 197d11a0e1be7ad35714eb38d9b391e1cd39af39 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 27 Oct 2016 00:41:03 -0700
Subject: [PATCH 1867/2053] sigmoid cross-entropy loss: add GPU forward for
 full GPU mode

close #3004
---
 .../sigmoid_cross_entropy_loss_layer.hpp      |  2 ++
 .../sigmoid_cross_entropy_loss_layer.cpp      |  2 +-
 .../sigmoid_cross_entropy_loss_layer.cu       | 36 +++++++++++++++++--
 3 files changed, 37 insertions(+), 3 deletions(-)

diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
index 598dca5ff2c..6452ea5106a 100644
--- a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
+++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
@@ -59,6 +59,8 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   /// @copydoc SigmoidCrossEntropyLossLayer
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
 
   /**
    * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 10ac9470832..eb77a9c2cb8 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -68,7 +68,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
 }
 
 #ifdef CPU_ONLY
-STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
+STUB_GPU(SigmoidCrossEntropyLossLayer);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 046cb9d3a31..7cb982d2d70 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -5,6 +5,39 @@
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void SigmoidCrossEntropyLossForwardGPU(const int nthreads,
+          const Dtype* input_data, const Dtype* target, Dtype* loss) {
+  CUDA_KERNEL_LOOP(i, nthreads) {
+    loss[i] = input_data[i] * (target[i] - (input_data[i] >= 0)) -
+        log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
+  }
+}
+
+template <typename Dtype>
+void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  // The forward pass computes the sigmoid outputs.
+  sigmoid_bottom_vec_[0] = bottom[0];
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
+  // Compute the loss (negative log likelihood)
+  const int count = bottom[0]->count();
+  const int num = bottom[0]->num();
+  // Stable version of loss computation from input data
+  const Dtype* input_data = bottom[0]->gpu_data();
+  const Dtype* target = bottom[1]->gpu_data();
+  // Since this memory is not used for anything until it is overwritten
+  // on the backward pass, we use it here to avoid having to allocate new GPU
+  // memory to accumulate intermediate results in the kernel.
+  Dtype* loss_data = bottom[0]->mutable_gpu_diff();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  SigmoidCrossEntropyLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data);
+  Dtype loss;
+  caffe_gpu_asum(count, loss_data, &loss);
+  top[0]->mutable_cpu_data()[0] = loss / num;
+}
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
@@ -28,7 +61,6 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   }
 }
 
-INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer);
-
+INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer);
 
 }  // namespace caffe

From f59dc97b090259f54801d620b6b10ad1fb1542e2 Mon Sep 17 00:00:00 2001
From: nihui <shuizhuyuanluo@126.com>
Date: Tue, 1 Nov 2016 14:02:52 +0800
Subject: [PATCH 1868/2053] add the missing star in comment

a trival commit which adds the missing star ;)
---
 src/caffe/layers/rnn_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/rnn_layer.cpp b/src/caffe/layers/rnn_layer.cpp
index f62ae8c77de..8c2fa22e598 100644
--- a/src/caffe/layers/rnn_layer.cpp
+++ b/src/caffe/layers/rnn_layer.cpp
@@ -215,7 +215,7 @@ void RNNLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const {
     }
 
     // Add layers to compute
-    //     o_t := \tanh( W_ho h_t + b_o)
+    //     o_t := \tanh( W_ho * h_t + b_o)
     //          = \tanh( W_ho_h_t )
     {
       LayerParameter* o_neuron_param = net_param->add_layer();

From 0d20df51901550f1b7eb2d56e0a84df5d6e2f029 Mon Sep 17 00:00:00 2001
From: baecchi <claudio.baecchi@gmail.com>
Date: Tue, 1 Nov 2016 16:15:51 +0100
Subject: [PATCH 1869/2053] corrected typo in accuracy_layer.hpp: MaxTopBlos ->
 MaxTopBlobs

---
 include/caffe/layers/accuracy_layer.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/layers/accuracy_layer.hpp b/include/caffe/layers/accuracy_layer.hpp
index fe2adb939e4..a9ad3225149 100644
--- a/include/caffe/layers/accuracy_layer.hpp
+++ b/include/caffe/layers/accuracy_layer.hpp
@@ -39,7 +39,7 @@ class AccuracyLayer : public Layer<Dtype> {
   // If there are two top blobs, then the second blob will contain
   // accuracies per class.
   virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlos() const { return 2; }
+  virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
   /**

From 3b443eacb30d8f4b3e551707faeebeeb15e77960 Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <williford-nin566@neural.vision>
Date: Fri, 28 Oct 2016 10:39:44 +0200
Subject: [PATCH 1870/2053] Add Github issue template to curb misuse.

For information on Github issue templates, see:
https://github.com/blog/2111-issue-and-pull-request-templates

The template has been revised according to discussion with @shelhamer
and @willyd on pull request BVLC/caffe#4914.
---
 .github/ISSUE_TEMPLATE.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE.md

diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
new file mode 100644
index 00000000000..d78a3dc3455
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE.md
@@ -0,0 +1,19 @@
+Please use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) for usage, installation, or modeling questions, or other requests for help.
+_Do not post such requests to Issues._ Doing so interferes with the development of Caffe.
+
+Please read the [guidelines for contributing](https://github.com/BVLC/caffe/blob/master/CONTRIBUTING.md) before submitting this issue.
+
+### Issue summary
+
+
+### Steps to reproduce
+
+If you are having difficulty building Caffe or training a model, please ask the caffe-users mailing list. If you are reporting a build error that seems to be due to a bug in Caffe, please attach your build configuration (either Makefile.config or CMakeCache.txt) and the output of the make (or cmake) command.
+
+### Your system configuration
+Operating system:
+Compiler:
+CUDA version (if applicable):
+CUDNN version (if applicable):
+BLAS:
+Python or MATLAB version (for pycaffe and matcaffe respectively):

From 20feab5771ae5cbb257cfec85e0b98da06269068 Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan@neural.vision>
Date: Fri, 11 Nov 2016 07:38:14 +0000
Subject: [PATCH 1871/2053] Put quotes around titles in YAML front matter.

The colon produces errors unless the title is in quotes. This causes the minor
issue of the HTML title not being set.

See:
https://github.com/jekyll/jekyll/issues/549
---
 docs/install_apt.md | 2 +-
 docs/install_osx.md | 2 +-
 docs/install_yum.md | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index 3de5a494e0c..e95b022761b 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -1,5 +1,5 @@
 ---
-title: Installation: Ubuntu
+title: "Installation: Ubuntu"
 ---
 
 # Ubuntu Installation
diff --git a/docs/install_osx.md b/docs/install_osx.md
index 6405d8ad046..a2da82f0fb2 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -1,5 +1,5 @@
 ---
-title: Installation: OS X
+title: "Installation: OS X"
 ---
 
 # OS X Installation
diff --git a/docs/install_yum.md b/docs/install_yum.md
index 38bf7255c93..842fbd64177 100644
--- a/docs/install_yum.md
+++ b/docs/install_yum.md
@@ -1,5 +1,5 @@
 ---
-title: Installation: RHEL / Fedora / CentOS
+title: "Installation: RHEL / Fedora / CentOS"
 ---
 
 # RHEL / Fedora / CentOS Installation

From aaf7b6b17fdded6f6489eaf84a4d336b3344c356 Mon Sep 17 00:00:00 2001
From: davidbrai <davidbrai@gmail.com>
Date: Mon, 14 Nov 2016 22:10:27 +0200
Subject: [PATCH 1872/2053] support solver resumes in parse_log.py

Currently parse_log.py skips all non timestamped lines
only once. When resuming a solver and appending to the
same log file, it creates more non timestamped log lines.
This change allows the script to silently skip those lines.
---
 tools/extra/parse_log.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 375b0db73b3..017306b5088 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -48,8 +48,13 @@ def parse_log(path_to_log):
                 # iteration
                 continue
 
-            time = extract_seconds.extract_datetime_from_line(line,
-                                                              logfile_year)
+            try:
+                time = extract_seconds.extract_datetime_from_line(line,
+                                                                  logfile_year)
+            except ValueError:
+                # Skip lines with bad formatting, for example when resuming solver
+                continue
+
             seconds = (time - start_time).total_seconds()
 
             learning_rate_match = regex_learning_rate.search(line)

From c6ab96596d9eae01c2c403487dc8be8e3edc8fbb Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 15 Nov 2016 11:19:37 -0800
Subject: [PATCH 1873/2053] sigmoid cross-entropy loss: ignore selected targets
 by `ignore_label`

sig-ce learns to ignore by zeroing out the loss/diff at targets equal to
the configured `ignore_label`.

n.b. as of now the loss/diff are not properly normalized when there are
ignored targets. sig-ce loss should adopt the same normalization options
as softmax loss.
---
 .../sigmoid_cross_entropy_loss_layer.hpp      |  5 ++++
 .../sigmoid_cross_entropy_loss_layer.cpp      | 19 +++++++++++++
 .../sigmoid_cross_entropy_loss_layer.cu       | 23 +++++++++++++++
 .../test_sigmoid_cross_entropy_loss_layer.cpp | 28 +++++++++++++++++++
 4 files changed, 75 insertions(+)

diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
index 6452ea5106a..a9fe33c8e08 100644
--- a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
+++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
@@ -105,6 +105,11 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   vector<Blob<Dtype>*> sigmoid_bottom_vec_;
   /// top vector holder to call the underlying SigmoidLayer::Forward
   vector<Blob<Dtype>*> sigmoid_top_vec_;
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index eb77a9c2cb8..21b64c28002 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -14,6 +14,12 @@ void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
   sigmoid_top_vec_.clear();
   sigmoid_top_vec_.push_back(sigmoid_output_.get());
   sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
+
+  has_ignore_label_ =
+    this->layer_param_.loss_param().has_ignore_label();
+  if (has_ignore_label_) {
+    ignore_label_ = this->layer_param_.loss_param().ignore_label();
+  }
 }
 
 template <typename Dtype>
@@ -39,6 +45,10 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
   const Dtype* target = bottom[1]->cpu_data();
   Dtype loss = 0;
   for (int i = 0; i < count; ++i) {
+    const int target_value = static_cast<int>(target[i]);
+    if (has_ignore_label_ && target_value == ignore_label_) {
+      continue;
+    }
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
@@ -64,6 +74,15 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
     // Scale down gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
     caffe_scal(count, loss_weight / num, bottom_diff);
+    // Zero out gradient of ignored targets.
+    if (has_ignore_label_) {
+      for (int i = 0; i < count; ++i) {
+        const int target_value = static_cast<int>(target[i]);
+        if (target_value == ignore_label_) {
+          bottom_diff[i] = 0;
+        }
+      }
+    }
   }
 }
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 7cb982d2d70..39eb050664b 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -14,6 +14,17 @@ __global__ void SigmoidCrossEntropyLossForwardGPU(const int nthreads,
   }
 }
 
+template <typename Dtype>
+__global__ void SigmoidCrossEntropyLossIgnoreGPU(const int count,
+    const int ignore_label, const Dtype* target, Dtype* reference) {
+  CUDA_KERNEL_LOOP(index, count) {
+    const int target_value = static_cast<int>(target[index]);
+    if (target_value == ignore_label) {
+      reference[index] = 0;
+    }
+  }
+}
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
@@ -33,6 +44,12 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
   // NOLINT_NEXT_LINE(whitespace/operators)
   SigmoidCrossEntropyLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
       CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data);
+  // Zero out loss of ignored targets.
+  if (has_ignore_label_) {
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    SigmoidCrossEntropyLossIgnoreGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+      CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, loss_data);
+  }
   Dtype loss;
   caffe_gpu_asum(count, loss_data, &loss);
   top[0]->mutable_cpu_data()[0] = loss / num;
@@ -58,6 +75,12 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     // Scale down gradient
     const Dtype loss_weight = top[0]->cpu_diff()[0];
     caffe_gpu_scal(count, loss_weight / num, bottom_diff);
+    // Zero out gradient of ignored targets.
+    if (has_ignore_label_) {
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      SigmoidCrossEntropyLossIgnoreGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, bottom_diff);
+    }
   }
 }
 
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index 5dfd7656db2..1bd5f93796f 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -116,5 +116,33 @@ TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
       this->blob_top_vec_, 0);
 }
 
+TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestIgnoreGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  FillerParameter data_filler_param;
+  data_filler_param.set_std(1);
+  GaussianFiller<Dtype> data_filler(data_filler_param);
+  data_filler.Fill(this->blob_bottom_data_);
+  LayerParameter layer_param;
+  LossParameter* loss_param = layer_param.mutable_loss_param();
+  loss_param->set_ignore_label(-1);
+  Dtype* target = this->blob_bottom_targets_->mutable_cpu_data();
+  const int count = this->blob_bottom_targets_->count();
+  // Ignore half of targets, then check that diff of this half is zero,
+  // while the other half is nonzero.
+  caffe_set(count / 2, Dtype(-1), target);
+  SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<bool> propagate_down(2);
+  propagate_down[0] = true;
+  propagate_down[1] = false;
+  layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const Dtype* diff = this->blob_bottom_data_->cpu_diff();
+  for (int i = 0; i < count / 2; ++i) {
+    EXPECT_FLOAT_EQ(diff[i], 0.);
+    EXPECT_NE(diff[i + count / 2], 0.);
+  }
+}
+
 
 }  // namespace caffe

From 6486a7b795b70fa9a6597e975577d3ec9cc146bd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 16 Nov 2016 13:15:06 -0800
Subject: [PATCH 1874/2053] docs: Guillaume Dumont is the Windows maintainer

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 4aac7c42d27..edfddcfbbe8 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -14,7 +14,7 @@ The official Makefile and `Makefile.config` build are complemented by a [communi
 - [Ubuntu installation](install_apt.html) *the standard platform*
 - [OS X installation](install_osx.html)
 - [RHEL / CentOS / Fedora installation](install_yum.html)
-- [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Microsoft*
+- [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Guillaume Dumont*
 - [OpenCL](https://github.com/BVLC/caffe/tree/opencl) *see the OpenCL branch led by Fabian Tschopp*
 
 **Overview**:

From abcb973f5840c7b97ee3c7c910556ef0c5910baf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 16 Nov 2016 13:26:37 -0800
Subject: [PATCH 1875/2053] docs: include AWS AMI pointer

---
 docs/installation.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/installation.md b/docs/installation.md
index edfddcfbbe8..3254be3dfcd 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -16,6 +16,7 @@ The official Makefile and `Makefile.config` build are complemented by a [communi
 - [RHEL / CentOS / Fedora installation](install_yum.html)
 - [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Guillaume Dumont*
 - [OpenCL](https://github.com/BVLC/caffe/tree/opencl) *see the OpenCL branch led by Fabian Tschopp*
+- [AWS AMI](https://github.com/bitfusionio/amis/tree/master/awsmrkt-bfboost-ubuntu14-cuda75-caffe) *pre-configured for AWS*
 
 **Overview**:
 

From 3d62e3cc9da66dbf3328567d0f30d5183b318d81 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 16 Nov 2016 20:39:42 -0800
Subject: [PATCH 1876/2053] sigmoid cross-entropy loss: normalize loss by
 different schemes

sig-ce loss handles all the same normalizations as the softmax loss;
refer to #3296 for more detail.

this preserves the default normalization for sig-ce loss: batch size.
---
 .../sigmoid_cross_entropy_loss_layer.hpp      | 11 ++++
 .../sigmoid_cross_entropy_loss_layer.cpp      | 60 ++++++++++++++++---
 .../sigmoid_cross_entropy_loss_layer.cu       | 57 +++++++++++-------
 src/caffe/proto/caffe.proto                   |  4 +-
 4 files changed, 102 insertions(+), 30 deletions(-)

diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
index a9fe33c8e08..3d92524421c 100644
--- a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
+++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
@@ -97,6 +97,13 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
+
   /// The internal SigmoidLayer used to map predictions to probabilities.
   shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
   /// sigmoid_output stores the output of the SigmoidLayer.
@@ -110,6 +117,10 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
   bool has_ignore_label_;
   /// The label indicating that an instance should be ignored.
   int ignore_label_;
+  /// How to normalize the loss.
+  LossParameter_NormalizationMode normalization_;
+  Dtype normalizer_;
+  int outer_num_, inner_num_;
 };
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 21b64c28002..99fa3eb645a 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,3 +1,4 @@
+#include <algorithm>
 #include <vector>
 
 #include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
@@ -20,17 +21,60 @@ void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
   if (has_ignore_label_) {
     ignore_label_ = this->layer_param_.loss_param().ignore_label();
   }
+  if (this->layer_param_.loss_param().has_normalization()) {
+    normalization_ = this->layer_param_.loss_param().normalization();
+  } else if (this->layer_param_.loss_param().has_normalize()) {
+    normalization_ = this->layer_param_.loss_param().normalize() ?
+                     LossParameter_NormalizationMode_VALID :
+                     LossParameter_NormalizationMode_BATCH_SIZE;
+  } else {
+    normalization_ = LossParameter_NormalizationMode_BATCH_SIZE;
+  }
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   LossLayer<Dtype>::Reshape(bottom, top);
+  outer_num_ = bottom[0]->shape(0);  // batch size
+  inner_num_ = bottom[0]->count(1);  // instance size: |output| == |target|
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
       "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
   sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
 }
 
+// TODO(shelhamer) loss normalization should be pulled up into LossLayer,
+// instead of duplicated here and in SoftMaxWithLossLayer
+template <typename Dtype>
+Dtype SigmoidCrossEntropyLossLayer<Dtype>::get_normalizer(
+    LossParameter_NormalizationMode normalization_mode, int valid_count) {
+  Dtype normalizer;
+  switch (normalization_mode) {
+    case LossParameter_NormalizationMode_FULL:
+      normalizer = Dtype(outer_num_ * inner_num_);
+      break;
+    case LossParameter_NormalizationMode_VALID:
+      if (valid_count == -1) {
+        normalizer = Dtype(outer_num_ * inner_num_);
+      } else {
+        normalizer = Dtype(valid_count);
+      }
+      break;
+    case LossParameter_NormalizationMode_BATCH_SIZE:
+      normalizer = Dtype(outer_num_);
+      break;
+    case LossParameter_NormalizationMode_NONE:
+      normalizer = Dtype(1);
+      break;
+    default:
+      LOG(FATAL) << "Unknown normalization mode: "
+          << LossParameter_NormalizationMode_Name(normalization_mode);
+  }
+  // Some users will have no labels for some examples in order to 'turn off' a
+  // particular loss in a multi-task setup. The max prevents NaNs in that case.
+  return std::max(Dtype(1.0), normalizer);
+}
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
@@ -38,21 +82,22 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
   sigmoid_bottom_vec_[0] = bottom[0];
   sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
   // Compute the loss (negative log likelihood)
-  const int count = bottom[0]->count();
-  const int num = bottom[0]->num();
   // Stable version of loss computation from input data
   const Dtype* input_data = bottom[0]->cpu_data();
   const Dtype* target = bottom[1]->cpu_data();
+  int valid_count = 0;
   Dtype loss = 0;
-  for (int i = 0; i < count; ++i) {
+  for (int i = 0; i < bottom[0]->count(); ++i) {
     const int target_value = static_cast<int>(target[i]);
     if (has_ignore_label_ && target_value == ignore_label_) {
       continue;
     }
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
+    ++valid_count;
   }
-  top[0]->mutable_cpu_data()[0] = loss / num;
+  normalizer_ = get_normalizer(normalization_, valid_count);
+  top[0]->mutable_cpu_data()[0] = loss / normalizer_;
 }
 
 template <typename Dtype>
@@ -66,14 +111,10 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
   if (propagate_down[0]) {
     // First, compute the diff
     const int count = bottom[0]->count();
-    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
     const Dtype* target = bottom[1]->cpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
     caffe_sub(count, sigmoid_output_data, target, bottom_diff);
-    // Scale down gradient
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_scal(count, loss_weight / num, bottom_diff);
     // Zero out gradient of ignored targets.
     if (has_ignore_label_) {
       for (int i = 0; i < count; ++i) {
@@ -83,6 +124,9 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
         }
       }
     }
+    // Scale down gradient
+    Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
+    caffe_scal(count, loss_weight, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 39eb050664b..b9877e6a3f6 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -5,26 +5,38 @@
 
 namespace caffe {
 
+
 template <typename Dtype>
 __global__ void SigmoidCrossEntropyLossForwardGPU(const int nthreads,
-          const Dtype* input_data, const Dtype* target, Dtype* loss) {
+          const Dtype* input_data, const Dtype* target, Dtype* loss,
+          const bool has_ignore_label_, const int ignore_label_,
+          Dtype* counts) {
   CUDA_KERNEL_LOOP(i, nthreads) {
-    loss[i] = input_data[i] * (target[i] - (input_data[i] >= 0)) -
-        log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
+    const int target_value = static_cast<int>(target[i]);
+    if (has_ignore_label_ && target_value == ignore_label_) {
+      loss[i] = 0;
+      counts[i] = 0;
+    } else {
+      loss[i] = input_data[i] * (target[i] - (input_data[i] >= 0)) -
+          log(1 + exp(input_data[i] - 2 * input_data[i] *
+          (input_data[i] >= 0)));
+      counts[i] = 1;
+    }
   }
 }
 
 template <typename Dtype>
-__global__ void SigmoidCrossEntropyLossIgnoreGPU(const int count,
-    const int ignore_label, const Dtype* target, Dtype* reference) {
-  CUDA_KERNEL_LOOP(index, count) {
-    const int target_value = static_cast<int>(target[index]);
+__global__ void SigmoidCrossEntropyLossIgnoreDiffGPU(const int count,
+    const int ignore_label, const Dtype* target, Dtype* diff) {
+  CUDA_KERNEL_LOOP(i, count) {
+    const int target_value = static_cast<int>(target[i]);
     if (target_value == ignore_label) {
-      reference[index] = 0;
+      diff[i] = 0;
     }
   }
 }
 
+
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
@@ -33,7 +45,6 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
   sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
   // Compute the loss (negative log likelihood)
   const int count = bottom[0]->count();
-  const int num = bottom[0]->num();
   // Stable version of loss computation from input data
   const Dtype* input_data = bottom[0]->gpu_data();
   const Dtype* target = bottom[1]->gpu_data();
@@ -41,18 +52,23 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
   // on the backward pass, we use it here to avoid having to allocate new GPU
   // memory to accumulate intermediate results in the kernel.
   Dtype* loss_data = bottom[0]->mutable_gpu_diff();
+  Dtype* count_data = bottom[1]->mutable_gpu_diff();
+  Dtype valid_count;
   // NOLINT_NEXT_LINE(whitespace/operators)
   SigmoidCrossEntropyLossForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
-      CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data);
-  // Zero out loss of ignored targets.
-  if (has_ignore_label_) {
-    // NOLINT_NEXT_LINE(whitespace/operators)
-    SigmoidCrossEntropyLossIgnoreGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
-      CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, loss_data);
+      CAFFE_CUDA_NUM_THREADS>>>(count, input_data, target, loss_data,
+      has_ignore_label_, ignore_label_, count_data);
+  // Only launch another CUDA kernel if we actually need the valid count.
+  if (normalization_ == LossParameter_NormalizationMode_VALID &&
+      has_ignore_label_) {
+    caffe_gpu_asum(count, count_data, &valid_count);
+  } else {
+    valid_count = count;
   }
   Dtype loss;
   caffe_gpu_asum(count, loss_data, &loss);
-  top[0]->mutable_cpu_data()[0] = loss / num;
+  normalizer_ = get_normalizer(normalization_, valid_count);
+  top[0]->mutable_cpu_data()[0] = loss / normalizer_;
 }
 
 template <typename Dtype>
@@ -66,21 +82,20 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   if (propagate_down[0]) {
     // First, compute the diff
     const int count = bottom[0]->count();
-    const int num = bottom[0]->num();
     const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
     const Dtype* target = bottom[1]->gpu_data();
     Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
     caffe_copy(count, sigmoid_output_data, bottom_diff);
     caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
-    // Scale down gradient
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    caffe_gpu_scal(count, loss_weight / num, bottom_diff);
     // Zero out gradient of ignored targets.
     if (has_ignore_label_) {
       // NOLINT_NEXT_LINE(whitespace/operators)
-      SigmoidCrossEntropyLossIgnoreGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
+      SigmoidCrossEntropyLossIgnoreDiffGPU<Dtype><<<CAFFE_GET_BLOCKS(count),
         CAFFE_CUDA_NUM_THREADS>>>(count, ignore_label_, target, bottom_diff);
     }
+    // Scale down gradient
+    Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
+    caffe_gpu_scal(count, loss_weight, bottom_diff);
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 6940a705eb6..0b2768b7708 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -434,7 +434,7 @@ message LossParameter {
   optional int32 ignore_label = 1;
   // How to normalize the loss for loss layers that aggregate across batches,
   // spatial dimensions, or other dimensions.  Currently only implemented in
-  // SoftmaxWithLoss layer.
+  // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.
   enum NormalizationMode {
     // Divide by the number of examples in the batch times spatial dimensions.
     // Outputs that receive the ignore label will NOT be ignored in computing
@@ -448,6 +448,8 @@ message LossParameter {
     // Do not normalize the loss.
     NONE = 3;
   }
+  // For historical reasons, the default normalization for
+  // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
   optional NormalizationMode normalization = 3 [default = VALID];
   // Deprecated.  Ignored if normalization is specified.  If normalization
   // is not specified, then setting this to false will be equivalent to

From 2cf9dd3750073ce8a119f4a71cc41eeef63e0748 Mon Sep 17 00:00:00 2001
From: chenzy <chenzeyuczy@qq.com>
Date: Fri, 18 Nov 2016 10:28:13 +0800
Subject: [PATCH 1877/2053] Add missing spaces besides equal signs in
 batch_norm_layer.cpp

---
 src/caffe/layers/batch_norm_layer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index e661abb11b8..0a08ed4cb07 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -27,7 +27,7 @@ void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     sz.push_back(channels_);
     this->blobs_[0].reset(new Blob<Dtype>(sz));
     this->blobs_[1].reset(new Blob<Dtype>(sz));
-    sz[0]=1;
+    sz[0] = 1;
     this->blobs_[2].reset(new Blob<Dtype>(sz));
     for (int i = 0; i < 3; ++i) {
       caffe_set(this->blobs_[i]->count(), Dtype(0),
@@ -61,7 +61,7 @@ void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   variance_.Reshape(sz);
   temp_.ReshapeLike(*bottom[0]);
   x_norm_.ReshapeLike(*bottom[0]);
-  sz[0]=bottom[0]->shape(0);
+  sz[0] = bottom[0]->shape(0);
   batch_sum_multiplier_.Reshape(sz);
 
   int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));

From e52451de914312b80a83459cb160c2f72a5b4fea Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 21 Nov 2016 09:35:57 -0800
Subject: [PATCH 1878/2053] solver: check and set type to reconcile class and
 proto

the solver checks its proto type (SolverParameter.type) on
instantiation:

- if the proto type is unspecified it's set according to the class type
  `Solver::type()`
- if the proto type and class type conflict, the solver dies loudly

this helps avoid accidental instantiation of a different solver type
than intended when the solver def and class differ. guaranteed type
information in the SolverParameter will simplify multi-solver
coordination too.
---
 include/caffe/solver.hpp                      |  2 ++
 src/caffe/solver.cpp                          | 12 ++++++++++++
 src/caffe/test/test_gradient_based_solver.cpp |  5 +++++
 3 files changed, 19 insertions(+)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index eafcee32904..ef38d6e45b5 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -108,6 +108,8 @@ class Solver {
   virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
   void DisplayOutputBlobs(const int net_id);
   void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);
+  /// Harmonize solver class type with configured proto type.
+  void CheckType(SolverParameter* param);
 
   SolverParameter param_;
   int iter_;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ece3913e88a..ae6a5a364b5 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -38,9 +38,21 @@ Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
       requested_early_exit_(false) {
   SolverParameter param;
   ReadSolverParamsFromTextFileOrDie(param_file, &param);
+  CheckType(&param);
   Init(param);
 }
 
+template <typename Dtype>
+void Solver<Dtype>::CheckType(SolverParameter* param) {
+  // Harmonize solver class type with configured type to avoid confusion.
+  if (param->has_type()) {
+    CHECK_EQ(param->type(), this->type())
+        << "Solver type must agree with instantiated solver class.";
+  } else {
+    param->set_type(this->type());
+  }
+}
+
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
   CHECK(Caffe::root_solver() || root_solver_)
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 975a8f0f88a..e81caea2548 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -694,6 +694,11 @@ TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestSolverType) {
+  this->TestLeastSquaresUpdate();
+  EXPECT_NE(this->solver_->type(), string(""));
+  EXPECT_EQ(this->solver_->type(), this->solver_->param().type());
+}
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {

From 48e73c780295e56699ad71232a24c8b459c8fe01 Mon Sep 17 00:00:00 2001
From: Zylphrex <Zylphrex@users.noreply.github.com>
Date: Mon, 21 Nov 2016 13:11:34 -0500
Subject: [PATCH 1879/2053] Checks inside Xcode for latest OSX SDK (#4840)

OS X: build with latest SDK by default
---
 Makefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 24894062a6c..ccc4d8b9e6a 100644
--- a/Makefile
+++ b/Makefile
@@ -192,12 +192,12 @@ ifeq ($(USE_LMDB), 1)
 	LIBRARIES += lmdb
 endif
 ifeq ($(USE_OPENCV), 1)
-	LIBRARIES += opencv_core opencv_highgui opencv_imgproc 
+	LIBRARIES += opencv_core opencv_highgui opencv_imgproc
 
 	ifeq ($(OPENCV_VERSION), 3)
 		LIBRARIES += opencv_imgcodecs
 	endif
-		
+
 endif
 PYTHON_LIBRARIES ?= boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
@@ -385,7 +385,7 @@ else
 		XCODE_CLT_GEQ_7 := $(shell [ $(XCODE_CLT_VER) -gt 6 ] && echo 1)
 		XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
 		ifeq ($(XCODE_CLT_GEQ_7), 1)
-			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.11.sdk/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
+			BLAS_INCLUDE ?= /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/$(shell ls /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/ | sort | tail -1)/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers
 		else ifeq ($(XCODE_CLT_GEQ_6), 1)
 			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
 			LDFLAGS += -framework Accelerate

From db6cf0a728cad63c93b345f2203f3ad1f5d5c2f4 Mon Sep 17 00:00:00 2001
From: Nico Galoppo <nico.galoppo@intel.com>
Date: Mon, 21 Nov 2016 11:03:52 -0800
Subject: [PATCH 1880/2053] Fix Python net drawing script

---
 python/caffe/draw.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 9eecf6d7b46..e4fd7aacce7 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -104,11 +104,11 @@ def get_layer_label(layer, rankdir):
                       pooling_types_dict[layer.pooling_param.pool],
                       layer.type,
                       separator,
-                      layer.pooling_param.kernel_size,
+                      layer.pooling_param.kernel_size[0] if len(layer.pooling_param.kernel_size._values) else 1,
                       separator,
-                      layer.pooling_param.stride,
+                      layer.pooling_param.stride[0] if len(layer.pooling_param.stride._values) else 1,
                       separator,
-                      layer.pooling_param.pad)
+                      layer.pooling_param.pad[0] if len(layer.pooling_param.pad._values) else 0)
     else:
         node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
     return node_label

From 2e59864d4f35bf60ddf859185f4e0d8fd940f238 Mon Sep 17 00:00:00 2001
From: hmybmny <hmybmny@gmail.com>
Date: Thu, 24 Nov 2016 18:17:13 +0800
Subject: [PATCH 1881/2053] fix error link

---
 docs/install_apt.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index e95b022761b..bc1566b0be9 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -33,8 +33,8 @@ Everything is packaged in 14.04.
 These dependencies need manual installation in 12.04.
 
     # glog
-    wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
-    tar zxvf glog-0.3.3.tar.gz
+    wget https://github.com/google/glog/archive/v0.3.3.tar.gz
+    tar zxvf v0.3.3.tar.gz
     cd glog-0.3.3
     ./configure
     make && make install

From b644a87c842702de8291c97fa0e418797092fe41 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 25 Nov 2016 12:49:53 -0800
Subject: [PATCH 1882/2053] Revert "solver: check and set type to reconcile
 class and proto"

as pointed out by #5028 this does not achieve what it intended, and
furthermore causes trouble with direct solver instantiation.

revert commit e52451de914312b80a83459cb160c2f72a5b4fea
---
 include/caffe/solver.hpp                      |  2 --
 src/caffe/solver.cpp                          | 12 ------------
 src/caffe/test/test_gradient_based_solver.cpp |  5 -----
 3 files changed, 19 deletions(-)

diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index ef38d6e45b5..eafcee32904 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -108,8 +108,6 @@ class Solver {
   virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
   void DisplayOutputBlobs(const int net_id);
   void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);
-  /// Harmonize solver class type with configured proto type.
-  void CheckType(SolverParameter* param);
 
   SolverParameter param_;
   int iter_;
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ae6a5a364b5..ece3913e88a 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -38,21 +38,9 @@ Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
       requested_early_exit_(false) {
   SolverParameter param;
   ReadSolverParamsFromTextFileOrDie(param_file, &param);
-  CheckType(&param);
   Init(param);
 }
 
-template <typename Dtype>
-void Solver<Dtype>::CheckType(SolverParameter* param) {
-  // Harmonize solver class type with configured type to avoid confusion.
-  if (param->has_type()) {
-    CHECK_EQ(param->type(), this->type())
-        << "Solver type must agree with instantiated solver class.";
-  } else {
-    param->set_type(this->type());
-  }
-}
-
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
   CHECK(Caffe::root_solver() || root_solver_)
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index e81caea2548..975a8f0f88a 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -694,11 +694,6 @@ TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
   }
 }
 
-TYPED_TEST(SGDSolverTest, TestSolverType) {
-  this->TestLeastSquaresUpdate();
-  EXPECT_NE(this->solver_->type(), string(""));
-  EXPECT_EQ(this->solver_->type(), this->solver_->param().type());
-}
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {

From db6643232cc95ba79f2a21ad98ef15725ee576d6 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Sun, 27 Nov 2016 09:13:42 +0000
Subject: [PATCH 1883/2053] fix many typos by using codespell

---
 cmake/Targets.cmake                          | 2 +-
 examples/02-fine-tuning.ipynb                | 2 +-
 examples/mnist/train_lenet_docker.sh         | 2 +-
 examples/pycaffe/tools.py                    | 4 ++--
 matlab/+caffe/private/caffe_.cpp             | 2 +-
 matlab/CMakeLists.txt                        | 2 +-
 scripts/cpp_lint.py                          | 6 +++---
 src/caffe/layers/crop_layer.cpp              | 2 +-
 src/caffe/layers/crop_layer.cu               | 2 +-
 src/caffe/layers/hdf5_data_layer.cpp         | 4 ++--
 src/caffe/proto/caffe.proto                  | 4 ++--
 src/caffe/test/CMakeLists.txt                | 2 +-
 src/caffe/test/test_euclidean_loss_layer.cpp | 2 +-
 src/gtest/gtest-all.cpp                      | 4 ++--
 src/gtest/gtest.h                            | 2 +-
 tools/extra/plot_log.gnuplot.example         | 2 +-
 16 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index a796d00548f..2cb11584a5c 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -94,7 +94,7 @@ function(caffe_pickup_caffe_sources root)
   caffe_convert_absolute_paths(test_srcs)
   caffe_convert_absolute_paths(test_cuda)
 
-  # propogate to parent scope
+  # propagate to parent scope
   set(srcs ${srcs} PARENT_SCOPE)
   set(cuda ${cuda} PARENT_SCOPE)
   set(test_srcs ${test_srcs} PARENT_SCOPE)
diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
index 07ca8df4d74..f44eaf9a4de 100644
--- a/examples/02-fine-tuning.ipynb
+++ b/examples/02-fine-tuning.ipynb
@@ -1141,7 +1141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occasionally down because it is run on a research machine.\n",
     "\n",
     "http://demo.vislab.berkeleyvision.org/"
    ]
diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh
index 32cf1c8e4a3..e946ba0f4ad 100755
--- a/examples/mnist/train_lenet_docker.sh
+++ b/examples/mnist/train_lenet_docker.sh
@@ -25,7 +25,7 @@ set -e
 # executed.
 #
 # In order to provide additional flexibility, the following shell (environment)
-# variables can be used to controll the execution of each of the phases:
+# variables can be used to control the execution of each of the phases:
 #
 # DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset
 # CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database
diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py
index 88b1834af1e..7f6c2d835fb 100644
--- a/examples/pycaffe/tools.py
+++ b/examples/pycaffe/tools.py
@@ -26,7 +26,7 @@ def set_scale(self, scale):
 
     def preprocess(self, im):
         """
-        preprocess() emulate the pre-processing occuring in the vgg16 caffe
+        preprocess() emulate the pre-processing occurring in the vgg16 caffe
         prototxt.
         """
 
@@ -75,7 +75,7 @@ def __init__(self, testnet_prototxt_path="testnet.prototxt",
         # looks:
         self.sp['display'] = '25'
         self.sp['snapshot'] = '2500'
-        self.sp['snapshot_prefix'] = '"snapshot"'  # string withing a string!
+        self.sp['snapshot_prefix'] = '"snapshot"'  # string within a string!
 
         # learning rate policy
         self.sp['lr_policy'] = '"fixed"'
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
index 1b1b2bff861..4e466e660ff 100644
--- a/matlab/+caffe/private/caffe_.cpp
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -44,7 +44,7 @@ void mxCHECK_FILE_EXIST(const char* file) {
 // The pointers to caffe::Solver and caffe::Net instances
 static vector<shared_ptr<Solver<float> > > solvers_;
 static vector<shared_ptr<Net<float> > > nets_;
-// init_key is generated at the beginning and everytime you call reset
+// init_key is generated at the beginning and every time you call reset
 static double init_key = static_cast<double>(caffe_rng_rand());
 
 /** -----------------------------------------------------------------
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index f420df8d412..987730d9b55 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -20,7 +20,7 @@ if(NOT BUILD_SHARED_LIBS AND build_using MATCHES Matlab)
   message(FATAL_ERROR "Matlab MEX interface (with default mex options file) can only be built if caffe is compiled as shared library. Please enable 'BUILD_SHARED_LIBS' in CMake. Aternativelly you can switch to Octave compiler.")
 endif()
 
-# helper function to set proper mex file extention
+# helper function to set proper mex file extension
 function(caffe_fetch_and_set_proper_mexext mexfile_variable)
   execute_process(COMMAND ${Matlab_mexext} OUTPUT_STRIP_TRAILING_WHITESPACE RESULT_VARIABLE res OUTPUT_VARIABLE ext)
   if(res MATCHES 0)
diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 14c76ecd6bf..6ec4fb76e2c 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -4460,7 +4460,7 @@ def UpdateIncludeState(filename, include_state, io=codecs):
     io: The io factory to use to read the file. Provided for testability.
 
   Returns:
-    True if a header was succesfully added. False otherwise.
+    True if a header was successfully added. False otherwise.
   """
   headerfile = None
   try:
@@ -4532,7 +4532,7 @@ def CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error,
   # Let's copy the include_state so it is only messed up within this function.
   include_state = include_state.copy()
 
-  # Did we find the header for this file (if any) and succesfully load it?
+  # Did we find the header for this file (if any) and successfully load it?
   header_found = False
 
   # Use the absolute path so that matching works properly.
@@ -4833,7 +4833,7 @@ def ParseArguments(args):
       try:
           _valid_extensions = set(val.split(','))
       except ValueError:
-          PrintUsage('Extensions must be comma seperated list.')
+          PrintUsage('Extensions must be comma separated list.')
 
   if not filenames:
     PrintUsage('No files were specified.')
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index aecdcd63194..d36b61ca029 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -85,7 +85,7 @@ void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
                 src_data, dest_data, is_forward);
     }
   } else {
-    // We are at the last dimensions, which is stored continously in memory
+    // We are at the last dimensions, which is stored continuously in memory
     for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
       // prepare index vector reduced(red) and with offsets(off)
       std::vector<int> ind_red(cur_dim, 0);
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index f78cecbbeee..6ea32d21c28 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -39,7 +39,7 @@ void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
                 src_data, dest_data, is_forward);
     }
   } else {
-    // We are at the last two dimensions, which are stored continously in memory
+    // We are at the last two dimensions, which are stored continuously in memory
     // With (N,C,H,W)
     //      (0,1,2,3) cur_dim   -> H
     //                cur_dim+1 -> W
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 2f13dc641df..c957451ae1e 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -61,10 +61,10 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
   // Shuffle if needed.
   if (this->layer_param_.hdf5_data_param().shuffle()) {
     std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
-    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0)
+    DLOG(INFO) << "Successfully loaded " << hdf_blobs_[0]->shape(0)
                << " rows (shuffled)";
   } else {
-    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0) << " rows";
+    DLOG(INFO) << "Successfully loaded " << hdf_blobs_[0]->shape(0) << " rows";
   }
 }
 
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 0b2768b7708..430a0dea109 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -418,7 +418,7 @@ message TransformationParameter {
   optional uint32 crop_size = 3 [default = 0];
   // mean_file and mean_value cannot be specified at the same time
   optional string mean_file = 4;
-  // if specified can be repeated once (would substract it from all the channels)
+  // if specified can be repeated once (would subtract it from all the channels)
   // or can be repeated the same number of times as channels
   // (would subtract them from the corresponding channel)
   repeated float mean_value = 5;
@@ -1396,6 +1396,6 @@ message PReLUParameter {
 
   // Initial value of a_i. Default is a_i=0.25 for all i.
   optional FillerParameter filler = 1;
-  // Whether or not slope paramters are shared across channels.
+  // Whether or not slope parameters are shared across channels.
   optional bool channel_shared = 2 [default = false];
 }
diff --git a/src/caffe/test/CMakeLists.txt b/src/caffe/test/CMakeLists.txt
index 35a803f2f41..d8afc30b76b 100644
--- a/src/caffe/test/CMakeLists.txt
+++ b/src/caffe/test/CMakeLists.txt
@@ -1,7 +1,7 @@
 # The option allows to include in build only selected test files and exclude all others
 # Usage example:
 #  cmake -DBUILD_only_tests="common,net,blob,im2col_kernel"
-set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extention")
+set(BUILD_only_tests "" CACHE STRING "Blank or comma-separated list of test files to build without 'test_' prefix and extension")
 caffe_leave_only_selected_tests(test_srcs ${BUILD_only_tests})
 caffe_leave_only_selected_tests(test_cuda ${BUILD_only_tests})
 
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index f253f9fd393..b026f5b2077 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -39,7 +39,7 @@ class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
 
   void TestForward() {
     // Get the loss without a specified objective weight -- should be
-    // equivalent to explicitly specifiying a weight of 1.
+    // equivalent to explicitly specifying a weight of 1.
     LayerParameter layer_param;
     EuclideanLossLayer<Dtype> layer_weight_1(layer_param);
     layer_weight_1.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
diff --git a/src/gtest/gtest-all.cpp b/src/gtest/gtest-all.cpp
index 926197419fc..81cdb578cd5 100644
--- a/src/gtest/gtest-all.cpp
+++ b/src/gtest/gtest-all.cpp
@@ -2697,7 +2697,7 @@ AssertionResult IsHRESULTFailure(const char* expr, long hr) {  // NOLINT
 // Utility functions for encoding Unicode text (wide strings) in
 // UTF-8.
 
-// A Unicode code-point can have upto 21 bits, and is encoded in UTF-8
+// A Unicode code-point can have up to 21 bits, and is encoded in UTF-8
 // like this:
 //
 // Code-point length   Encoding
@@ -7550,7 +7550,7 @@ FilePath FilePath::RemoveExtension(const char* extension) const {
   return *this;
 }
 
-// Returns a pointer to the last occurence of a valid path separator in
+// Returns a pointer to the last occurrence of a valid path separator in
 // the FilePath. On Windows, for example, both '/' and '\' are valid path
 // separators. Returns NULL if no path separator was found.
 const char* FilePath::FindLastPathSeparator() const {
diff --git a/src/gtest/gtest.h b/src/gtest/gtest.h
index 3143bd67996..124fb2321f9 100644
--- a/src/gtest/gtest.h
+++ b/src/gtest/gtest.h
@@ -3395,7 +3395,7 @@ class GTEST_API_ FilePath {
 
   void Normalize();
 
-  // Returns a pointer to the last occurence of a valid path separator in
+  // Returns a pointer to the last occurrence of a valid path separator in
   // the FilePath. On Windows, for example, both '/' and '\' are valid path
   // separators. Returns NULL if no path separator was found.
   const char* FindLastPathSeparator() const;
diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example
index 748b96e6925..02c68e1d24f 100644
--- a/tools/extra/plot_log.gnuplot.example
+++ b/tools/extra/plot_log.gnuplot.example
@@ -4,7 +4,7 @@
 # Be warned that the fields in the training log may change in the future.
 # You had better check the data files before designing your own plots.
 
-# Please generate the neccessary data files with 
+# Please generate the necessary data files with 
 # /path/to/caffe/tools/extra/parse_log.sh before plotting.
 # Example usage: 
 #     ./parse_log.sh mnist.log

From fa7fda78661fa795e3f6d3bbe7040e5d5d02e732 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Mon, 28 Nov 2016 01:20:58 +0000
Subject: [PATCH 1884/2053] Make lint happy (> 80 characters)

---
 src/caffe/layers/crop_layer.cu | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 6ea32d21c28..9ad40126468 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -39,10 +39,10 @@ void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
                 src_data, dest_data, is_forward);
     }
   } else {
-    // We are at the last two dimensions, which are stored continuously in memory
-    // With (N,C,H,W)
-    //      (0,1,2,3) cur_dim   -> H
-    //                cur_dim+1 -> W
+    // We are at the last two dimensions, which are stored continuously in
+    // memory With (N,C,H,W)
+    //             (0,1,2,3) cur_dim   -> H
+    //                       cur_dim+1 -> W
     const int lines = top[0]->shape(cur_dim);
     const int height = top[0]->shape(cur_dim);
     const int width = top[0]->shape(cur_dim+1);

From cd681ecdd9383a0f84b854e6fefeb05966babce0 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Mon, 28 Nov 2016 02:17:25 +0000
Subject: [PATCH 1885/2053] Add the missing period

---
 src/caffe/layers/crop_layer.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 9ad40126468..1ea132531cf 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -40,9 +40,9 @@ void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
     }
   } else {
     // We are at the last two dimensions, which are stored continuously in
-    // memory With (N,C,H,W)
-    //             (0,1,2,3) cur_dim   -> H
-    //                       cur_dim+1 -> W
+    // memory. With (N,C,H,W)
+    //              (0,1,2,3) cur_dim   -> H
+    //                        cur_dim+1 -> W
     const int lines = top[0]->shape(cur_dim);
     const int height = top[0]->shape(cur_dim);
     const int width = top[0]->shape(cur_dim+1);

From 8cd5c3df98734f4c43e1b7f43c05401fda0a94ac Mon Sep 17 00:00:00 2001
From: Max Ehrlich <max.ehr@gmail.com>
Date: Fri, 2 Dec 2016 10:13:50 -0500
Subject: [PATCH 1886/2053] Add Pascal to all cuda architectures

The known gpu architectures were missing the Pascal sm_60 and sm_61 compute capabilities. When building for this GPU, but on a separate machine, like a CI server or inside a docker image, caffe would be built for at most capability sm_50 and crash when run on the Pascal GPU.
---
 cmake/Cuda.cmake | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index eeeb7325ffd..7146a24450c 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -4,7 +4,7 @@ endif()
 
 # Known NVIDIA GPU achitectures Caffe can be compiled for.
 # This list will be used for CUDA_ARCH_NAME = All option
-set(Caffe_known_gpu_archs "20 21(20) 30 35 50")
+set(Caffe_known_gpu_archs "20 21(20) 30 35 50 60 61")
 
 ################################################################################################
 # A function for automatic detection of GPUs installed  (if autodetection is enabled)
@@ -56,7 +56,7 @@ endfunction()
 #   caffe_select_nvcc_arch_flags(out_variable)
 function(caffe_select_nvcc_arch_flags out_variable)
   # List of arch names
-  set(__archs_names "Fermi" "Kepler" "Maxwell" "All" "Manual")
+  set(__archs_names "Fermi" "Kepler" "Maxwell" "Pascal" "All" "Manual")
   set(__archs_name_default "All")
   if(NOT CMAKE_CROSSCOMPILING)
     list(APPEND __archs_names "Auto")
@@ -89,6 +89,8 @@ function(caffe_select_nvcc_arch_flags out_variable)
     set(__cuda_arch_bin "30 35")
   elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
     set(__cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    set(__cuda_arch_bin "60 61")
   elseif(${CUDA_ARCH_NAME} STREQUAL "All")
     set(__cuda_arch_bin ${Caffe_known_gpu_archs})
   elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")

From de3a12f46217dcac8aae467931e6d5ffb5fbc4e2 Mon Sep 17 00:00:00 2001
From: "Young H. Oh" <garion9013@gmail.com>
Date: Thu, 8 Dec 2016 06:54:46 +0900
Subject: [PATCH 1887/2053] fix wrongly used marker hash

---
 tools/extra/plot_training_log.py.example | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index 79924ae5a5a..8caca6b8a67 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -90,9 +90,9 @@ def load_data(data_file, field_idx0, field_idx1):
 
 def random_marker():
     markers = mks.MarkerStyle.markers
-    num = len(markers.values())
+    num = len(markers.keys())
     idx = random.randint(0, num - 1)
-    return markers.values()[idx]
+    return markers.keys()[idx]
 
 def get_data_label(path_to_log):
     label = path_to_log[path_to_log.rfind('/')+1 : path_to_log.rfind(
@@ -126,16 +126,9 @@ def plot_chart(chart_type, path_to_png, path_to_log_list):
             plt.plot(data[0], data[1], label = label, color = color,
                      linewidth = linewidth)
         else:
-            ok = False
-            ## Some markers throw ValueError: Unrecognized marker style
-            while not ok:
-                try:
-                    marker = random_marker()
-                    plt.plot(data[0], data[1], label = label, color = color,
-                             marker = marker, linewidth = linewidth)
-                    ok = True
-                except:
-                    pass
+            marker = random_marker()
+            plt.plot(data[0], data[1], label = label, color = color,
+                     marker = marker, linewidth = linewidth)
     legend_loc = get_legend_loc(chart_type)
     plt.legend(loc = legend_loc, ncol = 1) # ajust ncol to fit the space
     plt.title(get_chart_type_description(chart_type))

From 57a5bbde4ede19c545c5932334782e3a755b2265 Mon Sep 17 00:00:00 2001
From: liyangguang <liyangguang@baidu.com>
Date: Fri, 16 Dec 2016 11:54:49 +0000
Subject: [PATCH 1888/2053] check leveldb iterator status for snappy format.

---
 include/caffe/util/db_leveldb.hpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp
index e9fa0d32b66..4cdb6db9558 100644
--- a/include/caffe/util/db_leveldb.hpp
+++ b/include/caffe/util/db_leveldb.hpp
@@ -14,7 +14,10 @@ namespace caffe { namespace db {
 class LevelDBCursor : public Cursor {
  public:
   explicit LevelDBCursor(leveldb::Iterator* iter)
-    : iter_(iter) { SeekToFirst(); }
+    : iter_(iter) {
+    SeekToFirst();
+    CHECK(iter_->status().ok()) << iter_->status().ToString();
+  }
   ~LevelDBCursor() { delete iter_; }
   virtual void SeekToFirst() { iter_->SeekToFirst(); }
   virtual void Next() { iter_->Next(); }

From b55fe84ca13cb7d9971505ea4d160aa5d7b6be50 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Wed, 21 Dec 2016 09:00:15 +0000
Subject: [PATCH 1889/2053] docs: add debian installation guide

---
 docs/install_apt_debian.md | 105 +++++++++++++++++++++++++++++++++++++
 docs/installation.md       |   1 +
 2 files changed, 106 insertions(+)
 create mode 100644 docs/install_apt_debian.md

diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
new file mode 100644
index 00000000000..745a6f4fadb
--- /dev/null
+++ b/docs/install_apt_debian.md
@@ -0,0 +1,105 @@
+---
+title: "Installation: Debian"
+---
+
+# Debian Installation
+
+Caffe packages are available for `Debian/unstable`. Debian/stable users
+should take a look at Ubuntu installation instruction.  
+
+Only experienced linux users are recommended to try Debian/unstable (Sid).  
+
+Last update: Dec.21 2016  
+
+## Debian/unstable
+
+Apart from the installation methods based on source, Debian/unstable
+users can install pre-compiled Caffe packages via the official archive.
+
+### Binary installation
+
+Make sure that there is something like the follows in your `/etc/apt/sources.list`:
+```
+deb http://ftp2.cn.debian.org/debian sid main contrib non-free
+```
+Then we update APT cache and directly install Caffe. Note, the cpu version and
+the cuda version cannot be installed at the same time.
+```
+# apt update
+# apt install [ caffe-cpu | caffe-cuda ]
+```
+It should work out of box.
+
+#### Customizing caffe packages
+
+Some users may need to customize the Caffe package. Here is a brief
+guide of producing the customized `.deb` packages.
+
+Make sure that there is something like this in your `/etc/apt/sources.list`:
+```
+deb http://ftp2.cn.debian.org/debian sid main contrib non-free
+deb-src http://ftp2.cn.debian.org/debian sid main contrib non-free
+```
+
+Then we build caffe deb files with the following commands:
+```
+$ sudo apt update
+$ sudo apt install build-essential debhelper devscripts    # standard package building tools
+$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]            # the most elegant way to pull caffe build dependencies
+$ apt source [ caffe-cpu | caffe-cuda ]               # download the source tarball and extract
+$ cd caffe-XXXX
+[ ... optional, customize caffe code/build ... ]
+$ debuild -B -j4                                      # build caffe with 4 parallel jobs (similar to make -j4)
+[ ... building ...]
+$ debc                                                # optional, if you want to check the package contents
+$ sudo debi                                           # optional, install the generated packages
+```
+The resulting deb packages can be found under the parent directory of the source tree.
+
+### Source installation
+
+Source installation under Debian/unstable is similar to that of Ubuntu, but
+here is a more elegant way to pull caffe build dependencies:
+```
+$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]
+```
+Note, this requires a `deb-src` entry in your `/etc/apt/sources.list`.
+
+### Notes
+
+* Consider re-compiling OpenBLAS locally with optimization flags for sake of
+performance. This is highly recommended if you are writing a paper.
+
+* If you are installing `caffe-cuda`, APT will automatically pull some of the
+CUDA packages and the nvidia driver packages. Please take care if you have
+manually installed or hacked nvidia driver or CUDA toolkit or any other
+related stuff, because in this case it may fail.
+
+* If you encountered any problem when installing `caffe-*`, please report bug
+to Debian via Debian's bug tracking system. See https://www.debian.org/Bugs/ .
+
+* Additionally, a manpage (`man caffe`) and a bash complementation script
+(`caffe <TAB><TAB>`, `caffe train <TAB><TAB>`) are provided.
+Both of the two files are still not merged into caffe master.
+
+* The python interface is Python 3 version: `python3-caffe-{cpu,cuda}`.
+No plan to support python2.
+
+## FAQ
+
+* where is caffe-cudnn?
+
+CUDNN library seems not redistributable currently. If you really want the
+caffe-cudnn deb packages, the workaround is to install cudnn by yourself,
+and hack the packaging scripts, then build your customized package.
+
+* I installed the CPU version, How can I switch to the CUDA version?
+
+`sudo apt install caffe-cuda`, apt's dependency resolver is smart enough to deal with this.
+
+* Where is the examples, the models and other documentation stuff?
+
+```
+sudo apt install caffe-doc
+dpkg -L caffe-doc
+```
diff --git a/docs/installation.md b/docs/installation.md
index 3254be3dfcd..14ec467427b 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -12,6 +12,7 @@ The official Makefile and `Makefile.config` build are complemented by a [communi
 
 - [Docker setup](https://github.com/BVLC/caffe/tree/master/docker) *out-of-the-box brewing*
 - [Ubuntu installation](install_apt.html) *the standard platform*
+- [Debian installation](install_apt_debian.html) *deploy caffe with a single command*
 - [OS X installation](install_osx.html)
 - [RHEL / CentOS / Fedora installation](install_yum.html)
 - [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Guillaume Dumont*

From 2fac0d61afe290564f09067d3efa53d07ba0736f Mon Sep 17 00:00:00 2001
From: Tomasz Socha <tomasz.socha@intel.com>
Date: Thu, 8 Dec 2016 14:51:30 +0100
Subject: [PATCH 1890/2053] Use mkl_malloc when use mkl

---
 include/caffe/syncedmem.hpp | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 38ee4664028..6474a6969e5 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -3,6 +3,10 @@
 
 #include <cstdlib>
 
+#ifdef USE_MKL
+  #include "mkl.h"
+#endif
+
 #include "caffe/common.hpp"
 
 namespace caffe {
@@ -20,7 +24,11 @@ inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
     return;
   }
 #endif
+#ifdef USE_MKL
+  *ptr = mkl_malloc(size ? size:1, 64);
+#else
   *ptr = malloc(size);
+#endif
   *use_cuda = false;
   CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
@@ -32,7 +40,11 @@ inline void CaffeFreeHost(void* ptr, bool use_cuda) {
     return;
   }
 #endif
+#ifdef USE_MKL
+  mkl_free(ptr);
+#else
   free(ptr);
+#endif
 }
 
 

From 775f5b05dba28867f609c0e2b097e62176b4904a Mon Sep 17 00:00:00 2001
From: Yagnesh <yrevar@users.noreply.github.com>
Date: Wed, 21 Dec 2016 17:05:30 -0800
Subject: [PATCH 1891/2053] Fixed a typo

---
 examples/02-fine-tuning.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
index f44eaf9a4de..90803c989fb 100644
--- a/examples/02-fine-tuning.ipynb
+++ b/examples/02-fine-tuning.ipynb
@@ -70,7 +70,7 @@
     "\n",
     "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n",
     "- `download_model_binary.py` to download the pretrained reference model\n",
-    "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n",
+    "- `finetune_flickr_style/assemble_data.py` downloads the style training and testing data\n",
     "\n",
     "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories.  (To download the full dataset, set `full_dataset = True` in the cell below.)"
    ]

From 5693f3149688a2cb035858a9a9efde567763ebe7 Mon Sep 17 00:00:00 2001
From: Yagnesh <yrevar@users.noreply.github.com>
Date: Fri, 23 Dec 2016 15:31:21 -0800
Subject: [PATCH 1892/2053] Join path using "os.path.join" instead of "+"

(Needless to say it's much clearer, less error prone, and portable)
---
 examples/02-fine-tuning.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
index 90803c989fb..422259de424 100644
--- a/examples/02-fine-tuning.ipynb
+++ b/examples/02-fine-tuning.ipynb
@@ -146,7 +146,7 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "weights = os.path.join(caffe_root, 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
     "assert os.path.exists(weights)"
    ]
   },

From 1fd8bd0b4a842aa5a9d7ea1ec88d4cdd7eaf3b99 Mon Sep 17 00:00:00 2001
From: Fyodor Tokarev <ftokarev@gmail.com>
Date: Fri, 30 Dec 2016 17:47:20 +0300
Subject: [PATCH 1893/2053] Typos in test_inner_product_layer.cpp

---
 src/caffe/test/test_inner_product_layer.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index f1ec2333fae..6d84d292b38 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -60,9 +60,9 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->channels(), 10);
 }
 
-/** @brief TestSetUp while toggling tranpose flag
+/** @brief TestSetUp while toggling transpose flag
  */
-TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeFalse) {
+TYPED_TEST(InnerProductLayerTest, TestSetUpTransposeFalse) {
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_);
   LayerParameter layer_param;
@@ -82,9 +82,9 @@ TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeFalse) {
   EXPECT_EQ(60, layer->blobs()[0]->shape(1));
 }
 
-/** @brief TestSetUp while toggling tranpose flag
+/** @brief TestSetUp while toggling transpose flag
  */
-TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeTrue) {
+TYPED_TEST(InnerProductLayerTest, TestSetUpTransposeTrue) {
   typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_vec_.push_back(this->blob_bottom_);
   LayerParameter layer_param;
@@ -339,7 +339,7 @@ TYPED_TEST(InnerProductLayerTest, TestBackwardTranspose) {
     // copy bottom diffs
     Blob<Dtype>* const bottom_diff = new Blob<Dtype>();
     bottom_diff->CopyFrom(*this->blob_bottom_vec_[0], true, true);
-    // repeat original top with tranposed ip
+    // repeat original top with transposed ip
     this->blob_top_vec_.clear();
     this->blob_top_vec_.push_back(new Blob<Dtype>());
     inner_product_param->set_transpose(true);

From 4f0eb52a7ecd1bfb2c2d5906d368823eb312693c Mon Sep 17 00:00:00 2001
From: Xiaojie Deng <xiaojie.deng@intel.com>
Date: Sat, 31 Dec 2016 20:22:17 +0800
Subject: [PATCH 1894/2053] Fix parse_log.py and parse_log.sh for negative time
 duration if datetime in log across year boundary

---
 tools/extra/extract_seconds.py | 8 ++++++++
 tools/extra/parse_log.py       | 7 +++++++
 2 files changed, 15 insertions(+)

diff --git a/tools/extra/extract_seconds.py b/tools/extra/extract_seconds.py
index 591a51f96bd..68af69a2788 100755
--- a/tools/extra/extract_seconds.py
+++ b/tools/extra/extract_seconds.py
@@ -48,11 +48,19 @@ def extract_seconds(input_file, output_file):
     start_datetime = get_start_time(lines, log_created_year)
     assert start_datetime, 'Start time not found'
 
+    last_dt = start_datetime
     out = open(output_file, 'w')
     for line in lines:
         line = line.strip()
         if line.find('Iteration') != -1:
             dt = extract_datetime_from_line(line, log_created_year)
+
+            # if it's another year
+            if dt.month < last_dt.month:
+                log_created_year += 1
+                dt = extract_datetime_from_line(line, log_created_year)
+            last_dt = dt
+
             elapsed_seconds = (dt - start_datetime).total_seconds()
             out.write('%f\n' % elapsed_seconds)
     out.close()
diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 017306b5088..b47ffd0d842 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -38,6 +38,7 @@ def parse_log(path_to_log):
     logfile_year = extract_seconds.get_log_created_year(path_to_log)
     with open(path_to_log) as f:
         start_time = extract_seconds.get_start_time(f, logfile_year)
+        last_time = start_time
 
         for line in f:
             iteration_match = regex_iteration.search(line)
@@ -55,6 +56,12 @@ def parse_log(path_to_log):
                 # Skip lines with bad formatting, for example when resuming solver
                 continue
 
+            # if it's another year
+            if time.month < last_time.month:
+                logfile_year += 1
+                time = extract_seconds.extract_datetime_from_line(line, logfile_year)
+            last_time = time
+
             seconds = (time - start_time).total_seconds()
 
             learning_rate_match = regex_learning_rate.search(line)

From bae06073864dbe86970429d53e35335304626a70 Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan@neural.vision>
Date: Sun, 1 Jan 2017 18:22:09 +0000
Subject: [PATCH 1895/2053] Overhaul layer catalogue documentation.

Create scripts/split_caffe_proto.py file for splitting up the
caffe.proto file, so that parts of the file can be included from the
layer help pages.

Create separate pages for each layer and link each page from layers.md.
---
 docs/tutorial/layers.md                       | 562 +++---------------
 docs/tutorial/layers/absval.md                |  22 +
 docs/tutorial/layers/accuracy.md              |  21 +
 docs/tutorial/layers/argmax.md                |  19 +
 docs/tutorial/layers/batchnorm.md             |  20 +
 docs/tutorial/layers/batchreindex.md          |  16 +
 docs/tutorial/layers/bias.md                  |  19 +
 docs/tutorial/layers/bnll.md                  |  25 +
 docs/tutorial/layers/concat.md                |  40 ++
 docs/tutorial/layers/contrastiveloss.md       |  20 +
 docs/tutorial/layers/convolution.md           |  63 ++
 docs/tutorial/layers/crop.md                  |  20 +
 docs/tutorial/layers/data.md                  |  29 +
 docs/tutorial/layers/deconvolution.md         |  22 +
 docs/tutorial/layers/dropout.md               |  20 +
 docs/tutorial/layers/dummydata.md             |  20 +
 docs/tutorial/layers/eltwise.md               |  20 +
 docs/tutorial/layers/elu.md                   |  25 +
 docs/tutorial/layers/embed.md                 |  20 +
 docs/tutorial/layers/euclideanloss.md         |  16 +
 docs/tutorial/layers/exp.md                   |  24 +
 docs/tutorial/layers/filter.md                |  15 +
 docs/tutorial/layers/flatten.md               |  21 +
 docs/tutorial/layers/hdf5data.md              |  20 +
 docs/tutorial/layers/hdf5output.md            |  25 +
 docs/tutorial/layers/hingeloss.md             |  19 +
 docs/tutorial/layers/im2col.md                |  16 +
 docs/tutorial/layers/imagedata.md             |  27 +
 docs/tutorial/layers/infogainloss.md          |  24 +
 docs/tutorial/layers/innerproduct.md          |  59 ++
 docs/tutorial/layers/input.md                 |  19 +
 docs/tutorial/layers/log.md                   |  20 +
 docs/tutorial/layers/lrn.md                   |  28 +
 docs/tutorial/layers/lstm.md                  |  21 +
 docs/tutorial/layers/memorydata.md            |  25 +
 .../layers/multinomiallogisticloss.md         |  19 +
 docs/tutorial/layers/mvn.md                   |  20 +
 docs/tutorial/layers/parameter.md             |  21 +
 docs/tutorial/layers/pooling.md               |  47 ++
 docs/tutorial/layers/power.md                 |  46 ++
 docs/tutorial/layers/prelu.md                 |  20 +
 docs/tutorial/layers/python.md                |  27 +
 docs/tutorial/layers/recurrent.md             |  20 +
 docs/tutorial/layers/reduction.md             |  20 +
 docs/tutorial/layers/relu.md                  |  32 +
 docs/tutorial/layers/reshape.md               |  51 ++
 docs/tutorial/layers/rnn.md                   |  19 +
 docs/tutorial/layers/scale.md                 |  20 +
 docs/tutorial/layers/sigmoid.md               |  20 +
 .../layers/sigmoidcrossentropyloss.md         |  13 +
 docs/tutorial/layers/silence.md               |  23 +
 docs/tutorial/layers/slice.md                 |  42 ++
 docs/tutorial/layers/softmax.md               |  24 +
 docs/tutorial/layers/softmaxwithloss.md       |  33 +
 docs/tutorial/layers/split.md                 |  17 +
 docs/tutorial/layers/spp.md                   |  20 +
 docs/tutorial/layers/tanh.md                  |  18 +
 docs/tutorial/layers/threshold.md             |  18 +
 docs/tutorial/layers/tile.md                  |  20 +
 docs/tutorial/layers/windowdata.md            |  19 +
 scripts/build_docs.sh                         |   3 +
 scripts/split_caffe_proto.py                  |  35 ++
 62 files changed, 1573 insertions(+), 476 deletions(-)
 create mode 100644 docs/tutorial/layers/absval.md
 create mode 100644 docs/tutorial/layers/accuracy.md
 create mode 100644 docs/tutorial/layers/argmax.md
 create mode 100644 docs/tutorial/layers/batchnorm.md
 create mode 100644 docs/tutorial/layers/batchreindex.md
 create mode 100644 docs/tutorial/layers/bias.md
 create mode 100644 docs/tutorial/layers/bnll.md
 create mode 100644 docs/tutorial/layers/concat.md
 create mode 100644 docs/tutorial/layers/contrastiveloss.md
 create mode 100644 docs/tutorial/layers/convolution.md
 create mode 100644 docs/tutorial/layers/crop.md
 create mode 100644 docs/tutorial/layers/data.md
 create mode 100644 docs/tutorial/layers/deconvolution.md
 create mode 100644 docs/tutorial/layers/dropout.md
 create mode 100644 docs/tutorial/layers/dummydata.md
 create mode 100644 docs/tutorial/layers/eltwise.md
 create mode 100644 docs/tutorial/layers/elu.md
 create mode 100644 docs/tutorial/layers/embed.md
 create mode 100644 docs/tutorial/layers/euclideanloss.md
 create mode 100644 docs/tutorial/layers/exp.md
 create mode 100644 docs/tutorial/layers/filter.md
 create mode 100644 docs/tutorial/layers/flatten.md
 create mode 100644 docs/tutorial/layers/hdf5data.md
 create mode 100644 docs/tutorial/layers/hdf5output.md
 create mode 100644 docs/tutorial/layers/hingeloss.md
 create mode 100644 docs/tutorial/layers/im2col.md
 create mode 100644 docs/tutorial/layers/imagedata.md
 create mode 100644 docs/tutorial/layers/infogainloss.md
 create mode 100644 docs/tutorial/layers/innerproduct.md
 create mode 100644 docs/tutorial/layers/input.md
 create mode 100644 docs/tutorial/layers/log.md
 create mode 100644 docs/tutorial/layers/lrn.md
 create mode 100644 docs/tutorial/layers/lstm.md
 create mode 100644 docs/tutorial/layers/memorydata.md
 create mode 100644 docs/tutorial/layers/multinomiallogisticloss.md
 create mode 100644 docs/tutorial/layers/mvn.md
 create mode 100644 docs/tutorial/layers/parameter.md
 create mode 100644 docs/tutorial/layers/pooling.md
 create mode 100644 docs/tutorial/layers/power.md
 create mode 100644 docs/tutorial/layers/prelu.md
 create mode 100644 docs/tutorial/layers/python.md
 create mode 100644 docs/tutorial/layers/recurrent.md
 create mode 100644 docs/tutorial/layers/reduction.md
 create mode 100644 docs/tutorial/layers/relu.md
 create mode 100644 docs/tutorial/layers/reshape.md
 create mode 100644 docs/tutorial/layers/rnn.md
 create mode 100644 docs/tutorial/layers/scale.md
 create mode 100644 docs/tutorial/layers/sigmoid.md
 create mode 100644 docs/tutorial/layers/sigmoidcrossentropyloss.md
 create mode 100644 docs/tutorial/layers/silence.md
 create mode 100644 docs/tutorial/layers/slice.md
 create mode 100644 docs/tutorial/layers/softmax.md
 create mode 100644 docs/tutorial/layers/softmaxwithloss.md
 create mode 100644 docs/tutorial/layers/split.md
 create mode 100644 docs/tutorial/layers/spp.md
 create mode 100644 docs/tutorial/layers/tanh.md
 create mode 100644 docs/tutorial/layers/threshold.md
 create mode 100644 docs/tutorial/layers/tile.md
 create mode 100644 docs/tutorial/layers/windowdata.md
 create mode 100755 scripts/split_caffe_proto.py

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 7362aac298a..a903d5ac985 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -1,186 +1,77 @@
 ---
 title: Layer Catalogue
 ---
+
 # Layers
 
 To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
 
 Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
 
-### Vision Layers
-
-* Header: `./include/caffe/vision_layers.hpp`
-
-Vision layers usually take *images* as input and produce other *images* as output.
-A typical "image" in the real-world may have one color channel ($$c = 1$$), as in a grayscale image, or three color channels ($$c = 3$$) as in an RGB (red, green, blue) image.
-But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $$h > 1$$ and width $$w > 1$$.
-This 2D geometry naturally lends itself to certain decisions about how to process the input.
-In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
-In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$chw$$.
-
-
-#### Convolution
-
-* Layer type: `Convolution`
-* CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
-* Parameters (`ConvolutionParameter convolution_param`)
-    - Required
-        - `num_output` (`c_o`): the number of filters
-        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
-    - Strongly Recommended
-        - `weight_filler` [default `type: 'constant' value: 0`]
-    - Optional
-        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
-        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
-        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
-        - `group` (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
-* Input
-    - `n * c_i * h_i * w_i`
-* Output
-    - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
-* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
-
-      layer {
-        name: "conv1"
-        type: "Convolution"
-        bottom: "data"
-        top: "conv1"
-        # learning rate and decay multipliers for the filters
-        param { lr_mult: 1 decay_mult: 1 }
-        # learning rate and decay multipliers for the biases
-        param { lr_mult: 2 decay_mult: 0 }
-        convolution_param {
-          num_output: 96     # learn 96 filters
-          kernel_size: 11    # each filter is 11x11
-          stride: 4          # step 4 pixels between each filter application
-          weight_filler {
-            type: "gaussian" # initialize the filters from a Gaussian
-            std: 0.01        # distribution with stdev 0.01 (default mean: 0)
-          }
-          bias_filler {
-            type: "constant" # initialize the biases to zero (0)
-            value: 0
-          }
-        }
-      }
-
-The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
-
-#### Pooling
-
-* Layer type: `Pooling`
-* CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
-* Parameters (`PoolingParameter pooling_param`)
-    - Required
-        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
-    - Optional
-        - `pool` [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
-        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
-        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
-* Input
-    - `n * c * h_i * w_i`
-* Output
-    - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
-* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
-
-      layer {
-        name: "pool1"
-        type: "Pooling"
-        bottom: "conv1"
-        top: "pool1"
-        pooling_param {
-          pool: MAX
-          kernel_size: 3 # pool over a 3x3 region
-          stride: 2      # step two pixels (in the bottom blob) between pooling regions
-        }
-      }
-
-#### Local Response Normalization (LRN)
-
-* Layer type: `LRN`
-* CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
-* CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
-* Parameters (`LRNParameter lrn_param`)
-    - Optional
-        - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
-        - `alpha` [default 1]: the scaling parameter (see below)
-        - `beta` [default 5]: the exponent (see below)
-        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
+## Data Layers
 
-The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i^2)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
-
-#### im2col
-
-`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
-
-### Loss Layers
+Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.
 
-Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.
+Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying `TransformationParameter`s by some of the layers.
+The [bias](layers/bias.html), [scale](layers/scale.html), and [crop](layers/crop.html) layers can be helpful with transforming the inputs, when `TransformationParameter` isn't available.
 
-#### Softmax
+Layers:
 
-* Layer type: `SoftmaxWithLoss`
+* [Image Data](layers/imagedata.html) - read raw images.
+* [Database](layers/data.html) - read data from LEVELDB or LMDB.
+* [HDF5 Input](layers/hdf5data.html) - read HDF5 data, allows data of arbitrary dimensions.
+* [HDF5 Output](layers/hdf5output.html) - write data as HDF5.
+* [Input](layers/input.html) - typically used for networks that are being deployed.
+* [Window Data](layers/windowdata.html) - read window data file.
+* [Memory Data](layers/memorydata.html) - read data directly from memory.
+* [Dummy Data](layers/dummydata.html) - for static data and debugging.
 
-The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
+Note that the [Python](layers/python.html) Layer can be useful for create custom data layers.
 
-#### Sum-of-Squares / Euclidean
+## Vision Layers
 
-* Layer type: `EuclideanLoss`
+Vision layers usually take *images* as input and produce other *images* as output, although they can take data of other types and dimensions.
+A typical "image" in the real-world may have one color channel ($$c = 1$$), as in a grayscale image, or three color channels ($$c = 3$$) as in an RGB (red, green, blue) image.
+But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $$h > 1$$ and width $$w > 1$$.
+This 2D geometry naturally lends itself to certain decisions about how to process the input.
+In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output.
+In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$chw$$.
 
-The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
+Layers:
 
-#### Hinge / Margin
+* [Convolution Layer](layers/convolution.html) - convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+* [Pooling Layer](layers/pooling.html) - max, average, or stochastic pooling.
+* [Spatial Pyramid Pooling (SPP)](layers/spp.html)
+* [Crop](layers/crop.html) - perform cropping transformation.
+* [Deconvolution Layer](layers/deconvolution.html) - transposed convolution.
 
-* Layer type: `HingeLoss`
-* CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
-* CUDA GPU implementation: none yet
-* Parameters (`HingeLossParameter hinge_loss_param`)
-    - Optional
-        - `norm` [default L1]: the norm used. Currently L1, L2
-* Inputs
-    - `n * c * h * w` Predictions
-    - `n * 1 * 1 * 1` Labels
-* Output
-    - `1 * 1 * 1 * 1` Computed Loss
-* Samples
+* [Im2Col](layers/im2col.html) - relic helper layer that is not used much anymore.
 
-      # L1 Norm
-      layer {
-        name: "loss"
-        type: "HingeLoss"
-        bottom: "pred"
-        bottom: "label"
-      }
+## Recurrent Layers
 
-      # L2 Norm
-      layer {
-        name: "loss"
-        type: "HingeLoss"
-        bottom: "pred"
-        bottom: "label"
-        top: "loss"
-        hinge_loss_param {
-          norm: L2
-        }
-      }
+Layers:
 
-The hinge loss layer computes a one-vs-all hinge or squared hinge loss.
+* [Recurrent](layers/recurrent.html)
+* [RNN](layers/rnn.html)
+* [Long-Short Term Memory (LSTM)](layers/lstm.html)
 
-#### Sigmoid Cross-Entropy
+## Common Layers
 
-`SigmoidCrossEntropyLoss`
+Layers:
 
-#### Infogain
+* [Inner Product](layers/innerproduct.html) - fully connected layer.
+* [Dropout](layers/dropout.html)
+* [Embed](layers/embed.html) - for learning embeddings of one-hot encoded vector (takes index as input).
 
-`InfogainLoss`
+## Normalization Layers
 
-#### Accuracy and Top-k
+* [Local Response Normalization (LRN)](layers/lrn.html) - performs a kind of "lateral inhibition" by normalizing over local input regions.
+* [Mean Variance Normalization (MVN)](layers/mvn.html) - performs contrast normalization / instance normalization.
+* [Batch Normalization](layers/batchnorm.html) - performs normalization over mini-batches.
 
-`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+The [bias](layers/bias.html) and [scale](layers/scale.html) layers can be helpful in combination with normalization.
 
-### Activation / Neuron Layers
+## Activation / Neuron Layers
 
 In general, activation / Neuron layers are element-wise operators, taking one bottom blob and producing one top blob of the same size. In the layers below, we will ignore the input and out sizes as they are identical:
 
@@ -189,337 +80,56 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 * Output
     - n * c * h * w
 
-#### ReLU / Rectified-Linear and Leaky-ReLU
-
-* Layer type: `ReLU`
-* CPU implementation: `./src/caffe/layers/relu_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
-* Parameters (`ReLUParameter relu_param`)
-    - Optional
-        - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
-* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
-
-      layer {
-        name: "relu1"
-        type: "ReLU"
-        bottom: "conv1"
-        top: "conv1"
-      }
-
-Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
-
-#### Sigmoid
-
-* Layer type: `Sigmoid`
-* CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
-* Sample (as seen in `./examples/mnist/mnist_autoencoder.prototxt`)
-
-      layer {
-        name: "encode1neuron"
-        bottom: "encode1"
-        top: "encode1neuron"
-        type: "Sigmoid"
-      }
-
-The `Sigmoid` layer computes the output as sigmoid(x) for each input element x.
-
-#### TanH / Hyperbolic Tangent
-
-* Layer type: `TanH`
-* CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: "TanH"
-      }
-
-The `TanH` layer computes the output as tanh(x) for each input element x.
-
-#### Absolute Value
-
-* Layer type: `AbsVal`
-* CPU implementation: `./src/caffe/layers/absval_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: "AbsVal"
-      }
-
-The `AbsVal` layer computes the output as abs(x) for each input element x.
-
-#### Power
-
-* Layer type: `Power`
-* CPU implementation: `./src/caffe/layers/power_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
-* Parameters (`PowerParameter power_param`)
-    - Optional
-        - `power` [default 1]
-        - `scale` [default 1]
-        - `shift` [default 0]
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: "Power"
-        power_param {
-          power: 1
-          scale: 1
-          shift: 0
-        }
-      }
-
-The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
-
-#### BNLL
-
-* Layer type: `BNLL`
-* CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
-* Sample
-
-      layer {
-        name: "layer"
-        bottom: "in"
-        top: "out"
-        type: BNLL
-      }
-
-The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
-
-
-### Data Layers
-
-Data enters Caffe through data layers: they lie at the bottom of nets. Data can come from efficient databases (LevelDB or LMDB), directly from memory, or, when efficiency is not critical, from files on disk in HDF5 or common image formats.
-
-Common input preprocessing (mean subtraction, scaling, random cropping, and mirroring) is available by specifying `TransformationParameter`s.
-
-#### Database
+Layers:
 
-* Layer type: `Data`
-* Parameters
-    - Required
-        - `source`: the name of the directory containing the database
-        - `batch_size`: the number of inputs to process at one time
-    - Optional
-        - `rand_skip`: skip up to this number of inputs at the beginning; useful for asynchronous sgd
-        - `backend` [default `LEVELDB`]: choose whether to use a `LEVELDB` or `LMDB`
+* [ReLU / Rectified-Linear and Leaky-ReLU](layers/relu.html) - ReLU and Leaky-ReLU rectification.
+* [PReLU](layers/prelu.html) - parametric ReLU.
+* [ELU](layers/elu.html) - exponential linear rectification.
+* [Sigmoid](layers/sigmoid.html)
+* [TanH](layers/tanh.html)
+* [Absolute Value](layers/abs.html)
+* [Power](layers/power.html) - f(x) = (shift + scale * x) ^ power.
+* [Exp](layers/exp.html) - f(x) = base ^ (shift + scale * x).
+* [Log](layers/log.html) - f(x) = log(x).
+* [BNLL](layers/bnll.html) - f(x) = log(1 + exp(x)).
+* [Threshold](layers/threshold.html) - performs step function at user defined threshold.
+* [Bias](layers/bias.html) - adds a bias to a blob that can either be learned or fixed.
+* [Scale](layers/scale.html) - scales a blob by an amount that can either be learned or fixed.
 
+## Utility Layers
 
+Layers:
 
-#### In-Memory
+* [Flatten](layers/flatten.html)
+* [Reshape](layers/reshape.html)
+* [Batch Reindex](layers/batchreindex.html)
 
-* Layer type: `MemoryData`
-* Parameters
-    - Required
-        - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
+* [Split](layers/split.html)
+* [Concat](layers/concat.html)
+* [Slicing](layers/slice.html)
+* [Eltwise](layers/eltwise.html) - element-wise operations such as product or sum between two blobs.
+* [Filter / Mask](layers/filter.html) - mask or select output using last blob.
+* [Parameter](layers/parameter.html) - enable parameters to be shared between layers.
+* [Reduction](layers/reduction.html) - reduce input blob to scalar blob using operations such as sum or mean.
+* [Silence](layers/silence.html) - prevent top-level blobs from being printed during training.
 
-The memory data layer reads data directly from memory, without copying it. In order to use it, one must call `MemoryDataLayer::Reset` (from C++) or `Net.set_input_arrays` (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
+* [ArgMax](layers/argmax.html)
+* [Softmax](layers/softmax.html)
 
-#### HDF5 Input
+* [Python](layers/python.html) - allows custom Python layers.
 
-* Layer type: `HDF5Data`
-* Parameters
-    - Required
-        - `source`: the name of the file to read from
-        - `batch_size`
+## Loss Layers
 
-#### HDF5 Output
-
-* Layer type: `HDF5Output`
-* Parameters
-    - Required
-        - `file_name`: name of file to write to
-
-The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.
-
-#### Images
-
-* Layer type: `ImageData`
-* Parameters
-    - Required
-        - `source`: name of a text file, with each line giving an image filename and label
-        - `batch_size`: number of images to batch together
-    - Optional
-        - `rand_skip`
-        - `shuffle` [default false]
-        - `new_height`, `new_width`: if provided, resize all images to this size
-
-#### Windows
-
-`WindowData`
-
-#### Dummy
-
-`DummyData` is for development and debugging. See `DummyDataParameter`.
-
-### Common Layers
-
-#### Inner Product
-
-* Layer type: `InnerProduct`
-* CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
-* Parameters (`InnerProductParameter inner_product_param`)
-    - Required
-        - `num_output` (`c_o`): the number of filters
-    - Strongly recommended
-        - `weight_filler` [default `type: 'constant' value: 0`]
-    - Optional
-        - `bias_filler` [default `type: 'constant' value: 0`]
-        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
-* Input
-    - `n * c_i * h_i * w_i`
-* Output
-    - `n * c_o * 1 * 1`
-* Sample
-
-      layer {
-        name: "fc8"
-        type: "InnerProduct"
-        # learning rate and decay multipliers for the weights
-        param { lr_mult: 1 decay_mult: 1 }
-        # learning rate and decay multipliers for the biases
-        param { lr_mult: 2 decay_mult: 0 }
-        inner_product_param {
-          num_output: 1000
-          weight_filler {
-            type: "gaussian"
-            std: 0.01
-          }
-          bias_filler {
-            type: "constant"
-            value: 0
-          }
-        }
-        bottom: "fc7"
-        top: "fc8"
-      }
-
-The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
-
-#### Splitting
-
-The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
-
-#### Flattening
-
-The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`
-
-#### Reshape
-
-* Layer type: `Reshape`
-* Implementation: `./src/caffe/layers/reshape_layer.cpp`
-* Parameters (`ReshapeParameter reshape_param`)
-    - Optional: (also see detailed description below)
-        - `shape`
-
-* Input
-    - a single blob with arbitrary dimensions
-* Output
-    - the same blob, with modified dimensions, as specified by `reshape_param`
-
-* Sample
-
-        layer {
-          name: "reshape"
-          type: "Reshape"
-          bottom: "input"
-          top: "output"
-          reshape_param {
-            shape {
-              dim: 0  # copy the dimension from below
-              dim: 2
-              dim: 3
-              dim: -1 # infer it from the other dimensions
-            }
-          }
-        }
-
-The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
-
-Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
-
-* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
-* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
-
-As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
-
-#### Concatenation
-
-* Layer type: `Concat`
-* CPU implementation: `./src/caffe/layers/concat_layer.cpp`
-* CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
-* Parameters (`ConcatParameter concat_param`)
-    - Optional
-        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
-* Input
-    - `n_i * c_i * h * w` for each input blob i from 1 to K.
-* Output
-    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
-    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
-* Sample
-
-      layer {
-        name: "concat"
-        bottom: "in1"
-        bottom: "in2"
-        top: "out"
-        type: "Concat"
-        concat_param {
-          axis: 1
-        }
-      }
-
-The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
-
-#### Slicing
-
-The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
-
-* Sample
-
-      layer {
-        name: "slicer_label"
-        type: "Slice"
-        bottom: "label"
-        ## Example of label with a shape N x 3 x 1 x 1
-        top: "label1"
-        top: "label2"
-        top: "label3"
-        slice_param {
-          axis: 1
-          slice_point: 1
-          slice_point: 2
-        }
-      }
-
-`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
-
-
-#### Elementwise Operations
-
-`Eltwise`
-
-#### Argmax
-
-`ArgMax`
-
-#### Softmax
+Loss drives learning by comparing an output to a target and assigning cost to minimize. The loss itself is computed by the forward pass and the gradient w.r.t. to the loss is computed by the backward pass.
 
-`Softmax`
+Layers:
 
-#### Mean-Variance Normalization
+* [Multinomial Logistic Loss](layers/multinomiallogisticloss.html)
+* [Infogain Loss](layers/infogainloss.html) - a generalization of MultinomialLogisticLossLayer.
+* [Softmax with Loss](layers/softmaxwithloss.html) - computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
+* [Sum-of-Squares / Euclidean](layers/euclideanloss.html) - computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
+* [Hinge / Margin](layers/hiddenloss.html) - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
+* [Sigmoid Cross-Entropy Loss](layers/sigmoidcrossentropyloss.html) - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
+* [Accuracy / Top-k layer](layers/accuracy.html) - scores the output as an accuracy with respect to target -- it is not actually a loss and has no backward step.
+* [Contrastive Loss](layers/contrastiveloss.html)
 
-`MVN`
diff --git a/docs/tutorial/layers/absval.md b/docs/tutorial/layers/absval.md
new file mode 100644
index 00000000000..220c41189be
--- /dev/null
+++ b/docs/tutorial/layers/absval.md
@@ -0,0 +1,22 @@
+---
+title: Absolute Value Layer
+---
+
+# Absolute Value Layer
+
+* Layer type: `AbsVal`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AbsValLayer.html)
+* Header: [`./include/caffe/layers/absval_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/absval_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/absval_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/absval_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/absval_layer.cu)
+
+* Sample
+
+      layer {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: "AbsVal"
+      }
+
+The `AbsVal` layer computes the output as abs(x) for each input element x.
diff --git a/docs/tutorial/layers/accuracy.md b/docs/tutorial/layers/accuracy.md
new file mode 100644
index 00000000000..ecf84090e61
--- /dev/null
+++ b/docs/tutorial/layers/accuracy.md
@@ -0,0 +1,21 @@
+---
+title: Accuracy and Top-k
+---
+
+# Accuracy and Top-k
+
+`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+
+* Layer type: `Accuracy`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AccuracyLayer.html)
+* Header: [`./include/caffe/layers/accuracy_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/accuracy_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/accuracy_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/accuracy_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/accuracy_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/accuracy_layer.cu)
+
+## Parameters
+* Parameters (`AccuracyParameter accuracy_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/AccuracyParameter.txt %}
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/tutorial/layers/argmax.md b/docs/tutorial/layers/argmax.md
new file mode 100644
index 00000000000..f5f173ac731
--- /dev/null
+++ b/docs/tutorial/layers/argmax.md
@@ -0,0 +1,19 @@
+---
+title: ArgMax Layer
+---
+
+# ArgMax Layer
+
+* Layer type: `ArgMax`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ArgMaxLayer.html)
+* Header: [`./include/caffe/layers/argmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/argmax_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/argmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/argmax_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cu)
+
+## Parameters
+* Parameters (`ArgMaxParameter argmax_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ArgMaxParameter.txt %}
+{% endhighlight %}
\ No newline at end of file
diff --git a/docs/tutorial/layers/batchnorm.md b/docs/tutorial/layers/batchnorm.md
new file mode 100644
index 00000000000..a5be5ce08bf
--- /dev/null
+++ b/docs/tutorial/layers/batchnorm.md
@@ -0,0 +1,20 @@
+---
+title: Batch Norm Layer
+---
+
+# Batch Norm Layer
+
+* Layer type: `BatchNorm`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BatchNormLayer.html)
+* Header: [`./include/caffe/layers/batch_norm_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/batch_norm_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/batch_norm_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/batch_norm_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cu)
+
+## Parameters
+
+* Parameters (`BatchNormParameter batch_norm_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/BatchNormParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/batchreindex.md b/docs/tutorial/layers/batchreindex.md
new file mode 100644
index 00000000000..21b36c39ba5
--- /dev/null
+++ b/docs/tutorial/layers/batchreindex.md
@@ -0,0 +1,16 @@
+---
+title: Batch Reindex Layer
+---
+
+# Batch Reindex Layer
+
+* Layer type: `BatchReindex`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BatchReindexLayer.html)
+* Header: [`./include/caffe/layers/batch_reindex_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/batch_reindex_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/batch_reindex_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/batch_reindex_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_reindex_layer.cu)
+
+
+## Parameters
+
+No parameters.
diff --git a/docs/tutorial/layers/bias.md b/docs/tutorial/layers/bias.md
new file mode 100644
index 00000000000..d3a00c2fc78
--- /dev/null
+++ b/docs/tutorial/layers/bias.md
@@ -0,0 +1,19 @@
+---
+title: Bias Layer
+---
+
+# Bias Layer
+
+* Layer type: `Bias`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BiasLayer.html)
+* Header: [`./include/caffe/layers/bias_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bias_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/bias_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/bias_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bias_layer.cu)
+
+## Parameters
+* Parameters (`BiasParameter bias_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/BiasParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/bnll.md b/docs/tutorial/layers/bnll.md
new file mode 100644
index 00000000000..2b68b79ff83
--- /dev/null
+++ b/docs/tutorial/layers/bnll.md
@@ -0,0 +1,25 @@
+---
+title: BNLL Layer
+---
+
+# BNLL Layer
+
+* Layer type: `BNLL`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1BNLLLayer.html)
+* Header: [`./include/caffe/layers/bnll_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/bnll_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/bnll_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bnll_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/bnll_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/bnll_layer.cu)
+
+The `BNLL` (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
+
+## Parameters
+No parameters.
+
+## Sample
+
+      layer {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: BNLL
+      }
diff --git a/docs/tutorial/layers/concat.md b/docs/tutorial/layers/concat.md
new file mode 100644
index 00000000000..c7b253953d7
--- /dev/null
+++ b/docs/tutorial/layers/concat.md
@@ -0,0 +1,40 @@
+---
+title: Concat Layer
+---
+
+# Concat Layer
+
+* Layer type: `Concat`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ConcatLayer.html)
+* Header: [`./include/caffe/layers/concat_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/concat_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/concat_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/concat_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/concat_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/concat_layer.cu)
+* Input
+    - `n_i * c_i * h * w` for each input blob i from 1 to K.
+* Output
+    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+* Sample
+
+      layer {
+        name: "concat"
+        bottom: "in1"
+        bottom: "in2"
+        top: "out"
+        type: "Concat"
+        concat_param {
+          axis: 1
+        }
+      }
+
+The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
+
+## Parameters
+* Parameters (`ConcatParameter concat_param`)
+    - Optional
+        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ConcatParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/contrastiveloss.md b/docs/tutorial/layers/contrastiveloss.md
new file mode 100644
index 00000000000..bb1859d9f37
--- /dev/null
+++ b/docs/tutorial/layers/contrastiveloss.md
@@ -0,0 +1,20 @@
+---
+title: Contrastive Loss Layer
+---
+
+# Contrastive Loss Layer
+
+* Layer type: `ContrastiveLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ContrastiveLossLayer.html)
+* Header: [`./include/caffe/layers/contrastive_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/contrastive_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/contrastive_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/contrastive_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/contrastive_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/contrastive_loss_layer.cu)
+
+## Parameters
+
+* Parameters (`ContrastiveLossParameter contrastive_loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ContrastiveLossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/convolution.md b/docs/tutorial/layers/convolution.md
new file mode 100644
index 00000000000..cc9f4fd0449
--- /dev/null
+++ b/docs/tutorial/layers/convolution.md
@@ -0,0 +1,63 @@
+---
+title: Convolution Layer
+---
+
+# Convolution Layer
+
+* Layer type: `Convolution`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ConvolutionLayer.html)
+* Header: [`./include/caffe/layers/conv_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/conv_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/conv_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/conv_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cu)
+* Input
+    - `n * c_i * h_i * w_i`
+* Output
+    - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
+
+The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+
+## Sample
+
+Sample (as seen in [`./models/bvlc_reference_caffenet/train_val.prototxt`](https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt)):
+
+      layer {
+        name: "conv1"
+        type: "Convolution"
+        bottom: "data"
+        top: "conv1"
+        # learning rate and decay multipliers for the filters
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
+        convolution_param {
+          num_output: 96     # learn 96 filters
+          kernel_size: 11    # each filter is 11x11
+          stride: 4          # step 4 pixels between each filter application
+          weight_filler {
+            type: "gaussian" # initialize the filters from a Gaussian
+            std: 0.01        # distribution with stdev 0.01 (default mean: 0)
+          }
+          bias_filler {
+            type: "constant" # initialize the biases to zero (0)
+            value: 0
+          }
+        }
+      }
+
+## Parameters
+* Parameters (`ConvolutionParameter convolution_param`)
+    - Required
+        - `num_output` (`c_o`): the number of filters
+        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
+    - Strongly Recommended
+        - `weight_filler` [default `type: 'constant' value: 0`]
+    - Optional
+        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
+        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
+        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
+        - `group` (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $$i$$th output group channels will be only connected to the $$i$$th input group channels.
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ConvolutionParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/crop.md b/docs/tutorial/layers/crop.md
new file mode 100644
index 00000000000..28f91241f74
--- /dev/null
+++ b/docs/tutorial/layers/crop.md
@@ -0,0 +1,20 @@
+---
+title: Crop Layer
+---
+
+# Crop Layer
+
+* Layer type: `Crop`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1CropLayer.html)
+* Header: [`./include/caffe/layers/crop_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/crop_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/crop_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/crop_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/crop_layer.cu)
+
+## Parameters
+
+* Parameters (`CropParameter crop_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/CropParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/data.md b/docs/tutorial/layers/data.md
new file mode 100644
index 00000000000..58e0dcaab22
--- /dev/null
+++ b/docs/tutorial/layers/data.md
@@ -0,0 +1,29 @@
+---
+title: Database Layer
+---
+
+# Database Layer
+
+* Layer type: `Data`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DataLayer.html)
+* Header: [`./include/caffe/layers/data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/data_layer.cpp)
+
+
+## Parameters
+
+* Parameters (`DataParameter data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/DataParameter.txt %}
+{% endhighlight %}
+
+* Parameters
+    - Required
+        - `source`: the name of the directory containing the database
+        - `batch_size`: the number of inputs to process at one time
+    - Optional
+        - `rand_skip`: skip up to this number of inputs at the beginning; useful for asynchronous sgd
+        - `backend` [default `LEVELDB`]: choose whether to use a `LEVELDB` or `LMDB`
+
diff --git a/docs/tutorial/layers/deconvolution.md b/docs/tutorial/layers/deconvolution.md
new file mode 100644
index 00000000000..2eff967d613
--- /dev/null
+++ b/docs/tutorial/layers/deconvolution.md
@@ -0,0 +1,22 @@
+---
+title: Deconvolution Layer
+---
+
+# Deconvolution Layer
+
+* Layer type: `Deconvolution`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DeconvolutionLayer.html)
+* Header: [`./include/caffe/layers/deconv_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/deconv_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/deconv_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/deconv_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/deconv_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/deconv_layer.cu)
+
+## Parameters
+
+Uses the same parameters as the Convolution layer.
+
+* Parameters (`ConvolutionParameter convolution_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/ConvolutionParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/dropout.md b/docs/tutorial/layers/dropout.md
new file mode 100644
index 00000000000..d8c6f9556be
--- /dev/null
+++ b/docs/tutorial/layers/dropout.md
@@ -0,0 +1,20 @@
+---
+title: Dropout Layer
+---
+
+# Dropout Layer
+
+* Layer type: `Dropout`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DropoutLayer.html)
+* Header: [`./include/caffe/layers/dropout_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dropout_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/dropout_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dropout_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/dropout_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dropout_layer.cu)
+
+## Parameters
+
+* Parameters (`DropoutParameter dropout_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/DropoutParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/dummydata.md b/docs/tutorial/layers/dummydata.md
new file mode 100644
index 00000000000..d069f9c595e
--- /dev/null
+++ b/docs/tutorial/layers/dummydata.md
@@ -0,0 +1,20 @@
+---
+title: Dummy Data Layer
+---
+
+# Dummy Data Layer
+
+* Layer type: `DummyData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1DummyDataLayer.html)
+* Header: [`./include/caffe/layers/dummy_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/dummy_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/dummy_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/dummy_data_layer.cpp)
+
+
+## Parameters
+
+* Parameters (`DummyDataParameter dummy_data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/DummyDataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/eltwise.md b/docs/tutorial/layers/eltwise.md
new file mode 100644
index 00000000000..70fe7910c5a
--- /dev/null
+++ b/docs/tutorial/layers/eltwise.md
@@ -0,0 +1,20 @@
+---
+title: Eltwise Layer
+---
+
+# Eltwise Layer
+
+* Layer type: `Eltwise`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EltwiseLayer.html)
+* Header: [`./include/caffe/layers/eltwise_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/eltwise_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/eltwise_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/eltwise_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/eltwise_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/eltwise_layer.cu)
+
+## Parameters
+
+* Parameters (`EltwiseParameter eltwise_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/EltwiseParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/elu.md b/docs/tutorial/layers/elu.md
new file mode 100644
index 00000000000..11db0f0e3d6
--- /dev/null
+++ b/docs/tutorial/layers/elu.md
@@ -0,0 +1,25 @@
+---
+title: ELU Layer
+---
+
+# ELU Layer
+
+* Layer type: `ELU`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ELULayer.html)
+* Header: [`./include/caffe/layers/elu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/elu_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/elu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/elu_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/elu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/elu_layer.cu)
+
+## References
+
+* Clevert, Djork-Arne, Thomas Unterthiner, and Sepp Hochreiter.
+  "Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)" [arXiv:1511.07289](https://arxiv.org/abs/1511.07289). (2015).
+
+## Parameters
+
+* Parameters (`ELUParameter elu_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ELUParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/embed.md b/docs/tutorial/layers/embed.md
new file mode 100644
index 00000000000..271636d8d97
--- /dev/null
+++ b/docs/tutorial/layers/embed.md
@@ -0,0 +1,20 @@
+---
+title: Embed Layer
+---
+
+# Embed Layer
+
+* Layer type: `Embed`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EmbedLayer.html)
+* Header: [`./include/caffe/layers/embed_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/embed_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/embed_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/embed_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/embed_layer.cu)
+
+## Parameters
+
+* Parameters (`EmbedParameter embed_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/EmbedParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/euclideanloss.md b/docs/tutorial/layers/euclideanloss.md
new file mode 100644
index 00000000000..c1b72084c14
--- /dev/null
+++ b/docs/tutorial/layers/euclideanloss.md
@@ -0,0 +1,16 @@
+---
+title: Euclidean Loss Layer
+---
+# Sum-of-Squares / Euclidean Loss Layer
+
+* Layer type: `EuclideanLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1EuclideanLossLayer.html)
+* Header: [`./include/caffe/layers/euclidean_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/euclidean_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/euclidean_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/euclidean_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/euclidean_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/euclidean_loss_layer.cu)
+
+The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
+
+## Parameters
+
+Does not take any parameters.
diff --git a/docs/tutorial/layers/exp.md b/docs/tutorial/layers/exp.md
new file mode 100644
index 00000000000..ef2500ec214
--- /dev/null
+++ b/docs/tutorial/layers/exp.md
@@ -0,0 +1,24 @@
+---
+title: Exponential Layer
+---
+
+# Exponential Layer
+
+* Layer type: `Exp`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ExpLayer.html)
+* Header: [`./include/caffe/layers/exp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/exp_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/exp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/exp_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/exp_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/exp_layer.cu)
+
+## Parameters
+
+* Parameters (`Parameter exp_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ExpParameter.txt %}
+{% endhighlight %}
+
+## See also
+
+* [Power layer](power.html)
diff --git a/docs/tutorial/layers/filter.md b/docs/tutorial/layers/filter.md
new file mode 100644
index 00000000000..aeda9ee66f8
--- /dev/null
+++ b/docs/tutorial/layers/filter.md
@@ -0,0 +1,15 @@
+---
+title: Filter Layer
+---
+
+# Filter Layer
+
+* Layer type: `Filter`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FilterLayer.html)
+* Header: [`./include/caffe/layers/filter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/filter_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/filter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/filter_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/filter_layer.cu)
+
+## Parameters
+
+Does not take any parameters.
diff --git a/docs/tutorial/layers/flatten.md b/docs/tutorial/layers/flatten.md
new file mode 100644
index 00000000000..ecf08262707
--- /dev/null
+++ b/docs/tutorial/layers/flatten.md
@@ -0,0 +1,21 @@
+---
+title: Flatten Layer
+---
+
+# Flatten Layer
+
+* Layer type: `Flatten`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1FlattenLayer.html)
+* Header: [`./include/caffe/layers/flatten_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/flatten_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/flatten_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/flatten_layer.cpp)
+
+The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`.
+
+## Parameters
+
+* Parameters (`FlattenParameter flatten_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/FlattenParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/hdf5data.md b/docs/tutorial/layers/hdf5data.md
new file mode 100644
index 00000000000..d6b7ea24d2e
--- /dev/null
+++ b/docs/tutorial/layers/hdf5data.md
@@ -0,0 +1,20 @@
+---
+title: HDF5 Data Layer
+---
+
+# HDF5 Data Layer
+
+* Layer type: `HDF5Data`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HDF5DataLayer.html)
+* Header: [`./include/caffe/layers/hdf5_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hdf5_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/hdf5_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_data_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/hdf5_data_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_data_layer.cu)
+
+## Parameters
+
+* Parameters (`HDF5DataParameter hdf5_data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/HDF5DataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/hdf5output.md b/docs/tutorial/layers/hdf5output.md
new file mode 100644
index 00000000000..cfbe4ddb771
--- /dev/null
+++ b/docs/tutorial/layers/hdf5output.md
@@ -0,0 +1,25 @@
+---
+title: HDF5 Output Layer
+---
+
+# HDF5 Output Layer
+
+* Layer type: `HDF5Output`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HDF5OutputLayer.html)
+* Header: [`./include/caffe/layers/hdf5_output_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hdf5_output_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/hdf5_output_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_output_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/hdf5_output_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hdf5_output_layer.cu)
+
+The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.
+
+## Parameters
+
+* Parameters (`HDF5OutputParameter hdf5_output_param`)
+    - Required
+        - `file_name`: name of file to write to
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/HDF5OutputParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/hingeloss.md b/docs/tutorial/layers/hingeloss.md
new file mode 100644
index 00000000000..ef4fd95e29d
--- /dev/null
+++ b/docs/tutorial/layers/hingeloss.md
@@ -0,0 +1,19 @@
+---
+title: Hinge Loss Layer
+---
+
+# Hinge (L1, L2) Loss Layer
+
+* Layer type: `HingeLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1HingeLossLayer.html)
+* Header: [`./include/caffe/layers/hinge_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/hinge_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/hinge_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/hinge_loss_layer.cpp)
+
+## Parameters
+
+* Parameters (`HingeLossParameter hinge_loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/HingeLossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/im2col.md b/docs/tutorial/layers/im2col.md
new file mode 100644
index 00000000000..0badc1cdd93
--- /dev/null
+++ b/docs/tutorial/layers/im2col.md
@@ -0,0 +1,16 @@
+---
+title: Im2col Layer
+---
+
+# im2col
+
+* File type: `Im2col`
+* Header: [`./include/caffe/layers/im2col_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/im2col_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/im2col_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/im2col_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/im2col_layer.cu)
+
+`Im2col` is a helper for doing the image-to-column transformation that you most
+likely do not need to know about. This is used in Caffe's original convolution
+to do matrix multiplication by laying out all patches into a matrix.
+
+
diff --git a/docs/tutorial/layers/imagedata.md b/docs/tutorial/layers/imagedata.md
new file mode 100644
index 00000000000..82c8a600be3
--- /dev/null
+++ b/docs/tutorial/layers/imagedata.md
@@ -0,0 +1,27 @@
+---
+title: ImageData Layer
+---
+
+# ImageData Layer
+
+* Layer type: `ImageData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ImageDataLayer.html)
+* Header: [`./include/caffe/layers/image_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/image_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/image_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/image_data_layer.cpp)
+
+## Parameters
+
+* Parameters (`ImageDataParameter image_data_parameter`)
+    - Required
+        - `source`: name of a text file, with each line giving an image filename and label
+        - `batch_size`: number of images to batch together
+    - Optional
+        - `rand_skip`
+        - `shuffle` [default false]
+        - `new_height`, `new_width`: if provided, resize all images to this size
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ImageDataParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/infogainloss.md b/docs/tutorial/layers/infogainloss.md
new file mode 100644
index 00000000000..86140b6cca7
--- /dev/null
+++ b/docs/tutorial/layers/infogainloss.md
@@ -0,0 +1,24 @@
+---
+title: Infogain Loss Layer
+---
+
+# Infogain Loss Layer
+
+* Layer type: `InfogainLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InfogainLossLayer.html)
+* Header: [`./include/caffe/layers/infogain_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/infogain_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/infogain_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/infogain_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/infogain_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/infogain_loss_layer.cu)
+
+A generalization of [MultinomialLogisticLossLayer](layers/multinomiallogisticloss.md) that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.
+
+Equivalent to the [MultinomialLogisticLossLayer](layers/multinomiallogisticloss.md) if the infogain matrix is the identity.
+
+## Parameters
+
+* Parameters (`Parameter infogain_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/InfogainLossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/innerproduct.md b/docs/tutorial/layers/innerproduct.md
new file mode 100644
index 00000000000..98b9bea81f5
--- /dev/null
+++ b/docs/tutorial/layers/innerproduct.md
@@ -0,0 +1,59 @@
+---
+title: Inner Product / Fully Connected Layer
+---
+
+# Inner Product / Fully Connected Layer
+
+* Layer type: `InnerProduct`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InnerProductLayer.html)
+* Header: [`./include/caffe/layers/inner_product_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/inner_product_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/inner_product_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/inner_product_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cu)
+
+* Input
+    - `n * c_i * h_i * w_i`
+* Output
+    - `n * c_o * 1 * 1`
+* Sample
+
+      layer {
+        name: "fc8"
+        type: "InnerProduct"
+        # learning rate and decay multipliers for the weights
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
+        inner_product_param {
+          num_output: 1000
+          weight_filler {
+            type: "gaussian"
+            std: 0.01
+          }
+          bias_filler {
+            type: "constant"
+            value: 0
+          }
+        }
+        bottom: "fc7"
+        top: "fc8"
+      }
+
+The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
+
+
+## Parameters
+
+* Parameters (`InnerProductParameter inner_product_param`)
+    - Required
+        - `num_output` (`c_o`): the number of filters
+    - Strongly recommended
+        - `weight_filler` [default `type: 'constant' value: 0`]
+    - Optional
+        - `bias_filler` [default `type: 'constant' value: 0`]
+        - `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/InnerProductParameter.txt %}
+{% endhighlight %}
+ 
diff --git a/docs/tutorial/layers/input.md b/docs/tutorial/layers/input.md
new file mode 100644
index 00000000000..b74c35d2fb5
--- /dev/null
+++ b/docs/tutorial/layers/input.md
@@ -0,0 +1,19 @@
+---
+title: Input Layer
+---
+
+# Input Layer
+
+* Layer type: `Input`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InputLayer.html)
+* Header: [`./include/caffe/layers/input_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/input_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/input_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/input_layer.cpp)
+
+## Parameters
+
+* Parameters (`InputParameter input_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)):
+
+{% highlight Protobuf %}
+{% include proto/InputParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/log.md b/docs/tutorial/layers/log.md
new file mode 100644
index 00000000000..df52037489c
--- /dev/null
+++ b/docs/tutorial/layers/log.md
@@ -0,0 +1,20 @@
+---
+title: Log Layer
+---
+
+# Log Layer
+
+* Layer type: `Log`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LogLayer.html)
+* Header: [`./include/caffe/layers/log_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/log_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/log_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/log_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/log_layer.cu)
+
+## Parameters
+
+* Parameters (`Parameter log_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LogParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/lrn.md b/docs/tutorial/layers/lrn.md
new file mode 100644
index 00000000000..387311c2251
--- /dev/null
+++ b/docs/tutorial/layers/lrn.md
@@ -0,0 +1,28 @@
+---
+title: Local Response Normalization (LRN)
+---
+
+# Local Response Normalization (LRN)
+
+* Layer type: `LRN`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LRNLayer.html)
+* Header: [`./include/caffe/layers/lrn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/lrn_layer.hpp)
+* CPU Implementation: [`./src/caffe/layers/lrn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lrn_layer.cpp)
+* CUDA GPU Implementation: [`./src/caffe/layers/lrn_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lrn_layer.cu)
+* Parameters (`LRNParameter lrn_param`)
+    - Optional
+        - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
+        - `alpha` [default 1]: the scaling parameter (see below)
+        - `beta` [default 5]: the exponent (see below)
+        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
+
+The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i^2)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
+
+## Parameters
+
+* Parameters (`Parameter lrn_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/BatchNormParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/lstm.md b/docs/tutorial/layers/lstm.md
new file mode 100644
index 00000000000..8e4095e950b
--- /dev/null
+++ b/docs/tutorial/layers/lstm.md
@@ -0,0 +1,21 @@
+---
+title: LSTM Layer
+---
+
+# LSTM Layer
+
+* Layer type: `LSTM`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1LSTMLayer.html)
+* Header: [`./include/caffe/layers/lstm_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/lstm_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/lstm_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_layer.cpp)
+* CPU implementation (helper): [`./src/caffe/layers/lstm_unit_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_unit_layer.cpp)
+* CUDA GPU implementation (helper): [`./src/caffe/layers/lstm_unit_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_unit_layer.cu)
+
+## Parameters
+
+* Parameters (`Parameter recurrent_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/RecurrentParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/memorydata.md b/docs/tutorial/layers/memorydata.md
new file mode 100644
index 00000000000..754e62aef62
--- /dev/null
+++ b/docs/tutorial/layers/memorydata.md
@@ -0,0 +1,25 @@
+---
+title: Memory Data Layer
+---
+
+# Memory Data Layer
+
+* Layer type: `MemoryData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MemoryDataLayer.html)
+* Header: [`./include/caffe/layers/memory_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/memory_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/memory_data_layer.cpu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/memory_data_layer.cpu)
+
+The memory data layer reads data directly from memory, without copying it. In order to use it, one must call `MemoryDataLayer::Reset` (from C++) or `Net.set_input_arrays` (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
+
+# Parameters
+
+* Parameters (`MemoryDataParameter memory_data_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/MemoryDataParameter.txt %}
+{% endhighlight %}
+
+* Parameters
+    - Required
+        - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
diff --git a/docs/tutorial/layers/multinomiallogisticloss.md b/docs/tutorial/layers/multinomiallogisticloss.md
new file mode 100644
index 00000000000..a28ab914854
--- /dev/null
+++ b/docs/tutorial/layers/multinomiallogisticloss.md
@@ -0,0 +1,19 @@
+---
+title: Multinomial Logistic Loss Layer
+---
+
+# Multinomial Logistic Loss Layer
+
+* Layer type: `MultinomialLogisticLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html)
+* Header: [`./include/caffe/layers/multinomial_logistic_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/multinomial_logistic_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/multinomial_logistic_loss_layer.cpu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/multinomial_logistic_loss_layer.cpu)
+
+## Parameters
+
+* Parameters (`LossParameter loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LossParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/mvn.md b/docs/tutorial/layers/mvn.md
new file mode 100644
index 00000000000..08e44887d22
--- /dev/null
+++ b/docs/tutorial/layers/mvn.md
@@ -0,0 +1,20 @@
+---
+title: Mean-Variance Normalization (MVN) Layer
+---
+
+# Mean-Variance Normalization (MVN) Layer
+
+* Layer type: `MVN`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MVNLayer.html)
+* Header: [`./include/caffe/layers/mvn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/mvn_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/mvn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/mvn_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/mvn_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/mvn_layer.cu)
+
+## Parameters
+
+* Parameters (`MVNParameter mvn_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/MVNParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/parameter.md b/docs/tutorial/layers/parameter.md
new file mode 100644
index 00000000000..b7e85ec5c9a
--- /dev/null
+++ b/docs/tutorial/layers/parameter.md
@@ -0,0 +1,21 @@
+---
+title: Parameter Layer
+---
+
+# Parameter Layer
+
+* Layer type: `Parameter`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ParameterLayer.html)
+* Header: [`./include/caffe/layers/parameter_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/parameter_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/parameter_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/parameter_layer.cpp)
+
+See [https://github.com/BVLC/caffe/pull/2079](https://github.com/BVLC/caffe/pull/2079).
+
+## Parameters
+
+* Parameters (`ParameterParameter parameter_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ParameterParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/pooling.md b/docs/tutorial/layers/pooling.md
new file mode 100644
index 00000000000..12669ee8d45
--- /dev/null
+++ b/docs/tutorial/layers/pooling.md
@@ -0,0 +1,47 @@
+---
+title: Pooling Layer
+---
+# Pooling
+
+* Layer type: `Pooling`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PoolingLayer.html)
+* Header: [`./include/caffe/layers/pooling_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/pooling_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/pooling_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/pooling_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/pooling_layer.cu)
+
+* Input
+    - `n * c * h_i * w_i`
+* Output
+    - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
+
+## Parameters
+
+* Parameters (`PoolingParameter pooling_param`)
+    - Required
+        - `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter
+    - Optional
+        - `pool` [default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTIC
+        - `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
+        - `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input
+
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PoolingParameter.txt %}
+{% endhighlight %}
+
+## Sample
+* Sample (as seen in [`./models/bvlc_reference_caffenet/train_val.prototxt`](https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt))
+
+      layer {
+        name: "pool1"
+        type: "Pooling"
+        bottom: "conv1"
+        top: "pool1"
+        pooling_param {
+          pool: MAX
+          kernel_size: 3 # pool over a 3x3 region
+          stride: 2      # step two pixels (in the bottom blob) between pooling regions
+        }
+      }
diff --git a/docs/tutorial/layers/power.md b/docs/tutorial/layers/power.md
new file mode 100644
index 00000000000..d6617529b7d
--- /dev/null
+++ b/docs/tutorial/layers/power.md
@@ -0,0 +1,46 @@
+---
+title: Power Layer
+---
+
+# Power Layer
+
+* Layer type: `Power`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PowerLayer.html)
+* Header: [`./include/caffe/layers/power_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/power_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/power_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/power_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/power_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/power_layer.cu)
+
+The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
+
+## Parameters
+* Parameters (`PowerParameter power_param`)
+    - Optional
+        - `power` [default 1]
+        - `scale` [default 1]
+        - `shift` [default 0]
+
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PowerParameter.txt %}
+{% endhighlight %}
+ 
+ 
+ 
+## Sample
+
+      layer {
+        name: "layer"
+        bottom: "in"
+        top: "out"
+        type: "Power"
+        power_param {
+          power: 1
+          scale: 1
+          shift: 0
+        }
+      }
+
+## See also
+
+* [Exponential layer](exp.html)
diff --git a/docs/tutorial/layers/prelu.md b/docs/tutorial/layers/prelu.md
new file mode 100644
index 00000000000..e7b7b44acb6
--- /dev/null
+++ b/docs/tutorial/layers/prelu.md
@@ -0,0 +1,20 @@
+---
+title: PReLU Layer
+---
+
+# PReLU Layer
+
+* Layer type: `PReLU`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PReLULayer.html)
+* Header: [`./include/caffe/layers/prelu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/prelu_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/prelu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/prelu_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/prelu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/prelu_layer.cu)
+
+## Parameters
+
+* Parameters (`PReLUParameter prelu_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PReLUParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/python.md b/docs/tutorial/layers/python.md
new file mode 100644
index 00000000000..2e30b3a79f6
--- /dev/null
+++ b/docs/tutorial/layers/python.md
@@ -0,0 +1,27 @@
+---
+title: Python Layer
+---
+
+# Python Layer
+
+* Layer type: `Python`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1PythonLayer.html)
+* Header: [`./include/caffe/layers/python_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/python_layer.hpp)
+
+The Python layer allows users to add customized layers without modifying the Caffe core code.
+
+## Parameters
+
+* Parameters (`PythonParameter python_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/PythonParameter.txt %}
+{% endhighlight %}
+
+## Examples and tutorials
+
+* Simple Euclidean loss example
+** [Python code](https://github.com/BVLC/caffe/blob/master/examples/pycaffe/layers/pyloss.py)
+** [Prototxt](https://github.com/BVLC/caffe/blob/master/examples/pycaffe/linreg.prototxt)
+* [Tutorial for writing Python layers with DIGITS](https://github.com/NVIDIA/DIGITS/tree/master/examples/python-layer)
diff --git a/docs/tutorial/layers/recurrent.md b/docs/tutorial/layers/recurrent.md
new file mode 100644
index 00000000000..a882b722f8a
--- /dev/null
+++ b/docs/tutorial/layers/recurrent.md
@@ -0,0 +1,20 @@
+---
+title: Recurrent Layer
+---
+
+# Recurrent Layer
+
+* Layer type: `Recurrent`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1RecurrentLayer.html)
+* Header: [`./include/caffe/layers/recurrent_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/recurrent_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/recurrent_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/recurrent_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/recurrent_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/recurrent_layer.cu)
+
+## Parameters
+
+* Parameters (`RecurrentParameter recurrent_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/RecurrentParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/reduction.md b/docs/tutorial/layers/reduction.md
new file mode 100644
index 00000000000..db55414b0af
--- /dev/null
+++ b/docs/tutorial/layers/reduction.md
@@ -0,0 +1,20 @@
+---
+title: Reduction Layer
+---
+
+# Reduction Layer
+
+* Layer type: `Reduction`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ReductionLayer.html)
+* Header: [`./include/caffe/layers/reduction_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/reduction_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/reduction_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/reduction_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/reduction_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/reduction_layer.cu)
+
+## Parameters
+
+* Parameters (`ReductionParameter reduction_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ReductionParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/relu.md b/docs/tutorial/layers/relu.md
new file mode 100644
index 00000000000..01aab0af4fa
--- /dev/null
+++ b/docs/tutorial/layers/relu.md
@@ -0,0 +1,32 @@
+---
+title: ReLU / Rectified-Linear and Leaky-ReLU Layer
+---
+
+# ReLU / Rectified-Linear and Leaky-ReLU Layer
+
+* Layer type: `ReLU`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ReLULayer.html)
+* Header: [`./include/caffe/layers/relu_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/relu_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/relu_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/relu_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/relu_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/relu_layer.cu)
+* Sample (as seen in [`./models/bvlc_reference_caffenet/train_val.prototxt`](https://github.com/BVLC/caffe/blob/master/models/bvlc_reference_caffenet/train_val.prototxt))
+
+      layer {
+        name: "relu1"
+        type: "ReLU"
+        bottom: "conv1"
+        top: "conv1"
+      }
+
+Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+
+## Parameters
+
+* Parameters (`ReLUParameter relu_param`)
+    - Optional
+        - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ReLUParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/reshape.md b/docs/tutorial/layers/reshape.md
new file mode 100644
index 00000000000..92d23f2c73e
--- /dev/null
+++ b/docs/tutorial/layers/reshape.md
@@ -0,0 +1,51 @@
+---
+title: Reshape Layer
+---
+
+# Reshape Layer
+* Layer type: `Reshape`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ReshapeLayer.html)
+* Header: [`./include/caffe/layers/reshape_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/reshape_layer.hpp)
+* Implementation: [`./src/caffe/layers/reshape_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/reshape_layer.cpp)
+
+* Input
+    - a single blob with arbitrary dimensions
+* Output
+    - the same blob, with modified dimensions, as specified by `reshape_param`
+
+* Sample
+
+        layer {
+          name: "reshape"
+          type: "Reshape"
+          bottom: "input"
+          top: "output"
+          reshape_param {
+            shape {
+              dim: 0  # copy the dimension from below
+              dim: 2
+              dim: 3
+              dim: -1 # infer it from the other dimensions
+            }
+          }
+        }
+
+The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
+
+Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
+
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
+
+As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
+ 
+## Parameters
+
+* Parameters (`ReshapeParameter reshape_param`)
+    - Optional: (also see detailed description below)
+        - `shape`
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ReshapeParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/rnn.md b/docs/tutorial/layers/rnn.md
new file mode 100644
index 00000000000..b6fcf47133f
--- /dev/null
+++ b/docs/tutorial/layers/rnn.md
@@ -0,0 +1,19 @@
+---
+title: RNN Layer
+---
+
+# RNN Layer
+
+* Layer type: `RNN`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1RNNLayer.html)
+* Header: [`./include/caffe/layers/rnn_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/rnn_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/rnn_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/rnn_layer.cpp)
+
+## Parameters
+
+* Parameters (`RecurrentParameter recurrent_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/RecurrentParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/scale.md b/docs/tutorial/layers/scale.md
new file mode 100644
index 00000000000..0e27549ad52
--- /dev/null
+++ b/docs/tutorial/layers/scale.md
@@ -0,0 +1,20 @@
+---
+title: Scale Layer
+---
+
+# Scale Layer
+
+* Layer type: `Scale`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ScaleLayer.html)
+* Header: [`./include/caffe/layers/scale_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/scale_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/scale_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/scale_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/scale_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/scale_layer.cu)
+
+## Parameters
+
+* Parameters (`ScaleParameter scale_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ScaleParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/sigmoid.md b/docs/tutorial/layers/sigmoid.md
new file mode 100644
index 00000000000..505318352c9
--- /dev/null
+++ b/docs/tutorial/layers/sigmoid.md
@@ -0,0 +1,20 @@
+---
+title: Sigmoid Layer
+---
+
+# Sigmoid Layer
+
+* Layer type: `Sigmoid`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidLayer.html)
+* Header: [`./include/caffe/layers/sigmoid_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/sigmoid_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/sigmoid_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cu)
+
+## Parameters
+
+* Parameters (`SigmoidParameter sigmoid_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SigmoidParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/sigmoidcrossentropyloss.md b/docs/tutorial/layers/sigmoidcrossentropyloss.md
new file mode 100644
index 00000000000..a6e42cadfa9
--- /dev/null
+++ b/docs/tutorial/layers/sigmoidcrossentropyloss.md
@@ -0,0 +1,13 @@
+---
+title: Sigmoid Cross-Entropy Loss Layer
+---
+
+# Sigmoid Cross-Entropy Loss Layer
+
+* Layer type: `SigmoidCrossEntropyLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html)
+* Header: [`./include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu)
+
+To-do.
diff --git a/docs/tutorial/layers/silence.md b/docs/tutorial/layers/silence.md
new file mode 100644
index 00000000000..2c37a9cd67c
--- /dev/null
+++ b/docs/tutorial/layers/silence.md
@@ -0,0 +1,23 @@
+---
+title: Silence Layer
+---
+
+# Silence Layer
+
+* Layer type: `Silence`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SilenceLayer.html)
+* Header: [`./include/caffe/layers/silence_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/silence_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/silence_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/silence_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/silence_layer.cu)
+
+Silences a blob, so that it is not printed.
+
+## Parameters
+
+* Parameters (`SilenceParameter silence_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/BatchNormParameter.txt %}
+{% endhighlight %}
+
diff --git a/docs/tutorial/layers/slice.md b/docs/tutorial/layers/slice.md
new file mode 100644
index 00000000000..a492f1e82b9
--- /dev/null
+++ b/docs/tutorial/layers/slice.md
@@ -0,0 +1,42 @@
+---
+title: Slice Layer
+---
+
+# Slice Layer
+
+* Layer type: `Slice`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SliceLayer.html)
+* Header: [`./include/caffe/layers/slice_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/slice_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/slice_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/slice_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/slice_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/slice_layer.cu)
+
+The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+
+* Sample
+
+      layer {
+        name: "slicer_label"
+        type: "Slice"
+        bottom: "label"
+        ## Example of label with a shape N x 3 x 1 x 1
+        top: "label1"
+        top: "label2"
+        top: "label3"
+        slice_param {
+          axis: 1
+          slice_point: 1
+          slice_point: 2
+        }
+      }
+
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
+
+## Parameters
+
+* Parameters (`SliceParameter slice_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SliceParameter.txt %}
+{% endhighlight %}
+
diff --git a/docs/tutorial/layers/softmax.md b/docs/tutorial/layers/softmax.md
new file mode 100644
index 00000000000..e5d53425141
--- /dev/null
+++ b/docs/tutorial/layers/softmax.md
@@ -0,0 +1,24 @@
+---
+title: Softmax Layer
+---
+
+# Softmax Layer
+
+* Layer type: `Softmax`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SoftmaxLayer.html)
+* Header: [`./include/caffe/layers/softmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/softmax_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/softmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/softmax_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_layer.cu)
+
+## Parameters
+
+* Parameters (`SoftmaxParameter softmax_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SoftmaxParameter.txt %}
+{% endhighlight %}
+
+## See also
+
+* [Softmax loss layer](softmaxwithloss.html)
diff --git a/docs/tutorial/layers/softmaxwithloss.md b/docs/tutorial/layers/softmaxwithloss.md
new file mode 100644
index 00000000000..d9a6774a0ed
--- /dev/null
+++ b/docs/tutorial/layers/softmaxwithloss.md
@@ -0,0 +1,33 @@
+---
+title: Softmax with Loss Layer
+---
+
+# Softmax with Loss Layer
+
+* Layer type: `SoftmaxWithLoss`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html)
+* Header: [`./include/caffe/layers/softmax_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/softmax_loss_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/softmax_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_loss_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/softmax_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/softmax_loss_layer.cu)
+
+The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
+
+## Parameters
+
+* Parameters (`SoftmaxParameter softmax_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SoftmaxParameter.txt %}
+{% endhighlight %}
+
+* Parameters (`LossParameter loss_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/LossParameter.txt %}
+{% endhighlight %}
+
+## See also
+
+* [Softmax layer](softmax.html)
diff --git a/docs/tutorial/layers/split.md b/docs/tutorial/layers/split.md
new file mode 100644
index 00000000000..4fb71d1f26b
--- /dev/null
+++ b/docs/tutorial/layers/split.md
@@ -0,0 +1,17 @@
+---
+title: Split Layer
+---
+
+# Split Layer
+
+* Layer type: `Split`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SplitLayer.html)
+* Header: [`./include/caffe/layers/split_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/split_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/split_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/split_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/split_layer.cu)
+
+The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
+
+## Parameters
+
+Does not take any parameters.
diff --git a/docs/tutorial/layers/spp.md b/docs/tutorial/layers/spp.md
new file mode 100644
index 00000000000..26e5862023e
--- /dev/null
+++ b/docs/tutorial/layers/spp.md
@@ -0,0 +1,20 @@
+---
+title: Spatial Pyramid Pooling Layer
+---
+
+# Spatial Pyramid Pooling Layer
+
+* Layer type: `SPP`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SPPLayer.html)
+* Header: [`./include/caffe/layers/spp_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/spp_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/spp_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/spp_layer.cpp)
+
+
+## Parameters
+
+* Parameters (`SPPParameter spp_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/SPPParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/tanh.md b/docs/tutorial/layers/tanh.md
new file mode 100644
index 00000000000..360634596f9
--- /dev/null
+++ b/docs/tutorial/layers/tanh.md
@@ -0,0 +1,18 @@
+---
+title: TanH Layer
+---
+
+# TanH Layer
+
+* Header: [`./include/caffe/layers/tanh_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tanh_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/tanh_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/tanh_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tanh_layer.cu)
+
+## Parameters
+
+* Parameters (`TanHParameter tanh_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/TanHParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/threshold.md b/docs/tutorial/layers/threshold.md
new file mode 100644
index 00000000000..819e9e6f96d
--- /dev/null
+++ b/docs/tutorial/layers/threshold.md
@@ -0,0 +1,18 @@
+---
+title: Threshold Layer
+---
+
+# Threshold Layer
+
+* Header: [`./include/caffe/layers/threshold_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/threshold_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/threshold_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/threshold_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/threshold_layer.cu)
+
+## Parameters
+
+* Parameters (`ThresholdParameter threshold_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ThresholdParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/tile.md b/docs/tutorial/layers/tile.md
new file mode 100644
index 00000000000..ea03aaa43af
--- /dev/null
+++ b/docs/tutorial/layers/tile.md
@@ -0,0 +1,20 @@
+---
+title: Tile Layer
+---
+
+# Tile Layer
+
+* Layer type: `Tile`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1TileLayer.html)
+* Header: [`./include/caffe/layers/tile_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/tile_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/tile_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/tile_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/tile_layer.cu)
+
+## Parameters
+
+* Parameters (`TileParameter tile_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/TileParameter.txt %}
+{% endhighlight %}
diff --git a/docs/tutorial/layers/windowdata.md b/docs/tutorial/layers/windowdata.md
new file mode 100644
index 00000000000..0cb4a8dfeb7
--- /dev/null
+++ b/docs/tutorial/layers/windowdata.md
@@ -0,0 +1,19 @@
+---
+title: WindowData Layer
+---
+
+# WindowData Layer
+
+* Layer type: `WindowData`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1WindowDataLayer.html)
+* Header: [`./include/caffe/layers/window_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/window_data_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/window_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/window_data_layer.cpp)
+
+## Parameters
+
+* Parameters (`WindowDataParameter`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/WindowDataParameter.txt %}
+{% endhighlight %}
diff --git a/scripts/build_docs.sh b/scripts/build_docs.sh
index 0e28bd71631..4837587ad30 100755
--- a/scripts/build_docs.sh
+++ b/scripts/build_docs.sh
@@ -12,6 +12,9 @@ cd $ROOT_DIR
 # Gather docs.
 scripts/gather_examples.sh
 
+# Split caffe.proto for inclusion by layer catalogue.
+scripts/split_caffe_proto.py
+
 # Generate developer docs.
 make docs
 
diff --git a/scripts/split_caffe_proto.py b/scripts/split_caffe_proto.py
new file mode 100755
index 00000000000..7e9dc3e7b22
--- /dev/null
+++ b/scripts/split_caffe_proto.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+import mmap
+import re
+import os
+import errno
+
+script_path = os.path.dirname(os.path.realpath(__file__))
+
+# a regex to match the parameter definitions in caffe.proto
+r = re.compile(r'(?://.*\n)*message ([^ ]*) \{\n(?: .*\n|\n)*\}')
+
+# create directory to put caffe.proto fragments
+try:
+    os.mkdir(
+        os.path.join(script_path,
+                     '../docs/_includes/'))
+    os.mkdir(
+        os.path.join(script_path,
+                     '../docs/_includes/proto/'))
+except OSError as exception:
+    if exception.errno != errno.EEXIST:
+        raise
+
+caffe_proto_fn = os.path.join(
+    script_path,
+    '../src/caffe/proto/caffe.proto')
+
+with open(caffe_proto_fn, 'r') as fin:
+
+    for m in r.finditer(fin.read(), re.MULTILINE):
+        fn = os.path.join(
+            script_path,
+            '../docs/_includes/proto/%s.txt' % m.group(1))
+        with open(fn, 'w') as fout:
+            fout.write(m.group(0))

From fb52c7ccd2b21b26621f5abe35e776736aa9db91 Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan.r.williford+github@gmail.com>
Date: Wed, 21 Dec 2016 11:33:42 +0100
Subject: [PATCH 1896/2053] Add Debian codenames and make link.

Add the Debian codenames / versions, so it is easier to tell which Debian version is which in the future when the releases are promoted.

Revise commit according to CDLuminate's comments.

Removed rolling release numbers. Mention that Debian/testing can install Caffe using the packages.
---
 docs/install_apt_debian.md | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
index 745a6f4fadb..1580dc43bfc 100644
--- a/docs/install_apt_debian.md
+++ b/docs/install_apt_debian.md
@@ -4,10 +4,13 @@ title: "Installation: Debian"
 
 # Debian Installation
 
-Caffe packages are available for `Debian/unstable`. Debian/stable users
-should take a look at Ubuntu installation instruction.  
+Caffe packages are available for `Debian/unstable`. Debian/stable
+(jessie) users should take a look at [Ubuntu installation instruction](
+install_apt.html). Debian/testing (stretch) users may be able to get Caffe
+to work using the packages in Debian/unstable, but it is beyond the scope of
+this guide.
 
-Only experienced linux users are recommended to try Debian/unstable (Sid).  
+Only experienced linux users are recommended to try Debian/unstable (Sid). 
 
 Last update: Dec.21 2016  
 

From 5c437b13d2afde8f8e961e1e8a50fda060cb4519 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Wed, 4 Jan 2017 02:49:11 +0000
Subject: [PATCH 1897/2053] docs: update debian installation guide. Thanks to
 @lukeyeager for comments.

---
 docs/install_apt_debian.md | 29 +++++++++++++++++++----------
 docs/installation.md       |  2 +-
 2 files changed, 20 insertions(+), 11 deletions(-)

diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
index 1580dc43bfc..3175f6931ac 100644
--- a/docs/install_apt_debian.md
+++ b/docs/install_apt_debian.md
@@ -10,9 +10,9 @@ install_apt.html). Debian/testing (stretch) users may be able to get Caffe
 to work using the packages in Debian/unstable, but it is beyond the scope of
 this guide.
 
-Only experienced linux users are recommended to try Debian/unstable (Sid). 
+Only experienced linux users are recommended to try Debian/unstable (Sid).
 
-Last update: Dec.21 2016  
+Last update: 2017-01-04
 
 ## Debian/unstable
 
@@ -52,6 +52,7 @@ $ sudo apt build-dep [ caffe-cpu | caffe-cuda ]            # the most elegant wa
 $ apt source [ caffe-cpu | caffe-cuda ]               # download the source tarball and extract
 $ cd caffe-XXXX
 [ ... optional, customize caffe code/build ... ]
+$ dch -llocal "Modified XXX in order to XXX"          # write your one-line changelog
 $ debuild -B -j4                                      # build caffe with 4 parallel jobs (similar to make -j4)
 [ ... building ...]
 $ debc                                                # optional, if you want to check the package contents
@@ -59,6 +60,12 @@ $ sudo debi                                           # optional, install the ge
 ```
 The resulting deb packages can be found under the parent directory of the source tree.
 
+Note, the `dch ...` command line above is for bumping the package version number
+and adding an entry to the package changelog. If you would like to write
+more than one changelog entry, use subsequent `dch` command (see `man 1 dch`)
+instead of manually modifing `debian/changelog` unless you know how to keep its format correct.
+The changelog will be installed at e.g. `/usr/share/doc/caffe-cpu/changelog.Debian.gz`.
+
 ### Source installation
 
 Source installation under Debian/unstable is similar to that of Ubuntu, but
@@ -71,15 +78,13 @@ Note, this requires a `deb-src` entry in your `/etc/apt/sources.list`.
 ### Notes
 
 * Consider re-compiling OpenBLAS locally with optimization flags for sake of
-performance. This is highly recommended if you are writing a paper.
+performance. This is highly recommended for any kind of production use, including
+academic research.
 
 * If you are installing `caffe-cuda`, APT will automatically pull some of the
-CUDA packages and the nvidia driver packages. Please take care if you have
+CUDA packages and the nvidia driver packages. Please be careful if you have
 manually installed or hacked nvidia driver or CUDA toolkit or any other
-related stuff, because in this case it may fail.
-
-* If you encountered any problem when installing `caffe-*`, please report bug
-to Debian via Debian's bug tracking system. See https://www.debian.org/Bugs/ .
+related stuff, because in this case APT may fail.
 
 * Additionally, a manpage (`man caffe`) and a bash complementation script
 (`caffe <TAB><TAB>`, `caffe train <TAB><TAB>`) are provided.
@@ -88,6 +93,10 @@ Both of the two files are still not merged into caffe master.
 * The python interface is Python 3 version: `python3-caffe-{cpu,cuda}`.
 No plan to support python2.
 
+* If you encountered any problem related to the packaging system (e.g. failed to install `caffe-*`),
+please report bug to Debian via Debian's bug tracking system. See https://www.debian.org/Bugs/ .
+Patches and suggestions are also welcome.
+
 ## FAQ
 
 * where is caffe-cudnn?
@@ -96,11 +105,11 @@ CUDNN library seems not redistributable currently. If you really want the
 caffe-cudnn deb packages, the workaround is to install cudnn by yourself,
 and hack the packaging scripts, then build your customized package.
 
-* I installed the CPU version, How can I switch to the CUDA version?
+* I installed the CPU version. How can I switch to the CUDA version?
 
 `sudo apt install caffe-cuda`, apt's dependency resolver is smart enough to deal with this.
 
-* Where is the examples, the models and other documentation stuff?
+* Where are the examples, the models and other documentation stuff?
 
 ```
 sudo apt install caffe-doc
diff --git a/docs/installation.md b/docs/installation.md
index 14ec467427b..6b2cd3bdfe1 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -12,7 +12,7 @@ The official Makefile and `Makefile.config` build are complemented by a [communi
 
 - [Docker setup](https://github.com/BVLC/caffe/tree/master/docker) *out-of-the-box brewing*
 - [Ubuntu installation](install_apt.html) *the standard platform*
-- [Debian installation](install_apt_debian.html) *deploy caffe with a single command*
+- [Debian installation](install_apt_debian.html) *install caffe with a single command*
 - [OS X installation](install_osx.html)
 - [RHEL / CentOS / Fedora installation](install_yum.html)
 - [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Guillaume Dumont*

From 369a1f49fa7e40f39827c1dcaede224b78f6c10c Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Thu, 5 Jan 2017 05:00:37 +0000
Subject: [PATCH 1898/2053] docs: add some tables to debian install guide and
 misc update

docs: change UTF-8 characters
---
 docs/install_apt_debian.md | 70 +++++++++++++++++++++++++++++---------
 1 file changed, 54 insertions(+), 16 deletions(-)

diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
index 3175f6931ac..0d39e3ae22a 100644
--- a/docs/install_apt_debian.md
+++ b/docs/install_apt_debian.md
@@ -4,39 +4,51 @@ title: "Installation: Debian"
 
 # Debian Installation
 
-Caffe packages are available for `Debian/unstable`. Debian/stable
-(jessie) users should take a look at [Ubuntu installation instruction](
-install_apt.html). Debian/testing (stretch) users may be able to get Caffe
-to work using the packages in Debian/unstable, but it is beyond the scope of
-this guide.
+Caffe packages are available for several Debian versions, as shown in the
+following chart
 
-Only experienced linux users are recommended to try Debian/unstable (Sid).
+```
+Your Distro     |  CPU_ONLY  |  CUDA  |     Alias
+----------------+------------+--------+-------------------
+Debian/stable   |     ✘      |   ✘    | Debian Jessie
+Debian/testing  |     ✔      |   ☐    | Debian Stretch/Sid
+Debian/unstable |     ✔      |   ✔    | Debian Sid
+```
 
-Last update: 2017-01-04
+* `✘ ` You should take a look at [Ubuntu installation instruction](install_apt.html).
 
-## Debian/unstable
+* `✔ ` You can install caffe with a single command line following this guide.
 
-Apart from the installation methods based on source, Debian/unstable
-users can install pre-compiled Caffe packages via the official archive.
+* `☐ ` The same with `✔ `. However it will not work any more when Debian/Stretch becomes the stable branch.
 
-### Binary installation
+Last update: 2017-01-05
+
+## Binary installation with APT
+
+Apart from the installation methods based on source, Debian/unstable
+and Debian/testing users can install pre-compiled Caffe packages via the official archive.
 
 Make sure that there is something like the follows in your `/etc/apt/sources.list`:
 ```
-deb http://ftp2.cn.debian.org/debian sid main contrib non-free
+deb http://MIRROR/debian CODENAME main contrib non-free
 ```
+where `MIRROR` is your favorate Debian mirror, and `CODENAME ∈ {testing,stretch,sid}`.
+
 Then we update APT cache and directly install Caffe. Note, the cpu version and
 the cuda version cannot be installed at the same time.
 ```
 # apt update
 # apt install [ caffe-cpu | caffe-cuda ]
+# caffe                                              # command line interface working
+# python3 -c 'import caffe; print(caffe.__path__)'   # python3 interface working
 ```
 It should work out of box.
 
 #### Customizing caffe packages
 
-Some users may need to customize the Caffe package. Here is a brief
-guide of producing the customized `.deb` packages.
+Some users may need to customize the Caffe package. The way to customize
+the package is beyond this guide. Here is only a brief guide of producing
+the customized `.deb` packages. 
 
 Make sure that there is something like this in your `/etc/apt/sources.list`:
 ```
@@ -66,7 +78,7 @@ more than one changelog entry, use subsequent `dch` command (see `man 1 dch`)
 instead of manually modifing `debian/changelog` unless you know how to keep its format correct.
 The changelog will be installed at e.g. `/usr/share/doc/caffe-cpu/changelog.Debian.gz`.
 
-### Source installation
+## Source installation
 
 Source installation under Debian/unstable is similar to that of Ubuntu, but
 here is a more elegant way to pull caffe build dependencies:
@@ -75,7 +87,27 @@ $ sudo apt build-dep [ caffe-cpu | caffe-cuda ]
 ```
 Note, this requires a `deb-src` entry in your `/etc/apt/sources.list`.
 
-### Notes
+#### Compiler Combinations
+
+Some users may find their favorate compiler doesn't work well with CUDA.
+```
+CXX compiler |  CUDA 7.5  |  CUDA 8.0  |
+-------------+------------+------------+-
+GCC-7        |     ?      |     ?      |
+GCC-6        |     ✘      |     ✘      |
+GCC-5        |     ✔ [1]  |     ✔      |
+CLANG-4.0    |     ?      |     ?      |
+CLANG-3.9    |     ✘      |     ✘      |
+CLANG-3.8    |     ?      |     ✔      |
+```
+
+`[1]` CUDA 7.5 's `host_config.h` must be patched before working with GCC-5.
+
+BTW, please forget the GCC-4.X series, since its `libstdc++` ABI is not compatible with GCC-5's.
+You may encounter failure linking GCC-4.X object files against GCC-5 libraries.
+(See https://wiki.debian.org/GCC5 )
+
+## Notes
 
 * Consider re-compiling OpenBLAS locally with optimization flags for sake of
 performance. This is highly recommended for any kind of production use, including
@@ -115,3 +147,9 @@ and hack the packaging scripts, then build your customized package.
 sudo apt install caffe-doc
 dpkg -L caffe-doc
 ```
+
+* Where can I find the Debian package status?
+
+https://tracker.debian.org/pkg/caffe  (for the CPU_ONLY version)
+
+https://tracker.debian.org/pkg/caffe-contrib  (for the CUDA version)

From 2317fa19d3f5a65cb22adcbd3792ea248996744e Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 22 Nov 2016 13:14:45 -0800
Subject: [PATCH 1899/2053] Logging from python, e.g. for lower log level on
 multi-GPU workers

---
 python/caffe/__init__.py |  2 +-
 python/caffe/_caffe.cpp  | 16 ++++++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 35868a403a3..5fc6ec9b920 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
+from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index bdee75acd6c..0a86045bd46 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -51,6 +51,19 @@ const int NPY_DTYPE = NPY_FLOAT32;
 void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
 void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
 
+void InitLog(int level) {
+  FLAGS_logtostderr = 1;
+  FLAGS_minloglevel = level;
+  ::google::InitGoogleLogging("");
+  ::google::InstallFailureSignalHandler();
+}
+void InitLogInfo() {
+  InitLog(google::INFO);
+}
+void Log(const string& s) {
+  LOG(INFO) << s;
+}
+
 void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }
 
 // For convenience, check that input files can be opened, and raise an
@@ -283,6 +296,9 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);
 
   // Caffe utility functions
+  bp::def("init_log", &InitLog);
+  bp::def("init_log", &InitLogInfo);
+  bp::def("log", &Log);
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
   bp::def("set_random_seed", &set_random_seed);

From 3ba20549b7f49a76cd023d19f781a6891b2c2122 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 6 Jan 2017 14:55:12 -0800
Subject: [PATCH 1900/2053] Switched multi-GPU to NCCL

---
 CMakeLists.txt                                |   1 +
 Makefile                                      |   6 +
 Makefile.config.example                       |   4 +
 cmake/Dependencies.cmake                      |  15 +-
 cmake/Modules/FindNCCL.cmake                  |  26 +
 cmake/Summary.cmake                           |   1 +
 include/caffe/blob.hpp                        |   1 +
 include/caffe/common.hpp                      |  14 +-
 include/caffe/data_reader.hpp                 |  82 ---
 include/caffe/internal_thread.hpp             |   4 +-
 include/caffe/layer.hpp                       |  43 +-
 include/caffe/layers/base_data_layer.hpp      |   5 +-
 include/caffe/layers/data_layer.hpp           |   7 +-
 include/caffe/layers/hdf5_data_layer.hpp      |   6 +-
 include/caffe/layers/python_layer.hpp         |   4 +-
 include/caffe/net.hpp                         |  40 +-
 include/caffe/parallel.hpp                    |  96 ++--
 include/caffe/solver.hpp                      |  40 +-
 include/caffe/syncedmem.hpp                   |  14 +-
 include/caffe/util/math_functions.hpp         |   5 +
 include/caffe/util/nccl.hpp                   |  37 ++
 src/caffe/blob.cpp                            |  18 +
 src/caffe/common.cpp                          |   5 +-
 src/caffe/data_reader.cpp                     | 119 ----
 src/caffe/internal_thread.cpp                 |  10 +-
 src/caffe/layer.cpp                           |  20 -
 src/caffe/layers/base_data_layer.cpp          |  45 +-
 src/caffe/layers/base_data_layer.cu           |  21 +-
 src/caffe/layers/data_layer.cpp               |  82 ++-
 src/caffe/layers/hdf5_data_layer.cpp          |  55 +-
 src/caffe/layers/hdf5_data_layer.cu           |  22 +-
 src/caffe/layers/image_data_layer.cpp         |  13 +-
 src/caffe/layers/window_data_layer.cpp        |   8 +-
 src/caffe/net.cpp                             |  47 +-
 src/caffe/parallel.cpp                        | 514 ++++++++----------
 src/caffe/proto/caffe.proto                   |   9 +-
 src/caffe/solver.cpp                          |  44 +-
 src/caffe/solvers/adagrad_solver.cpp          |   1 -
 src/caffe/solvers/nesterov_solver.cpp         |   1 -
 src/caffe/solvers/sgd_solver.cpp              |   4 +-
 src/caffe/syncedmem.cpp                       |  59 +-
 src/caffe/test/test_data_layer.cpp            |  36 ++
 src/caffe/test/test_gradient_based_solver.cpp |  34 +-
 src/caffe/test/test_hdf5data_layer.cpp        |  30 +
 src/caffe/util/blocking_queue.cpp             |   5 -
 src/caffe/util/db_lmdb.cpp                    |   2 +-
 src/caffe/util/math_functions.cu              |  20 +
 tools/caffe.cpp                               |  11 +-
 48 files changed, 813 insertions(+), 873 deletions(-)
 create mode 100644 cmake/Modules/FindNCCL.cmake
 delete mode 100644 include/caffe/data_reader.hpp
 create mode 100644 include/caffe/util/nccl.hpp
 delete mode 100644 src/caffe/data_reader.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index da7142c9b3c..3af394f7aa2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -28,6 +28,7 @@ include(cmake/ConfigGen.cmake)
 # ---[ Options
 caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
 caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY)
+caffe_option(USE_NCCL "Build Caffe with NCCL library support" OFF)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
 set(python_version "2" CACHE STRING "Specify which Python version to use")
diff --git a/Makefile b/Makefile
index ccc4d8b9e6a..65d08f7d31e 100644
--- a/Makefile
+++ b/Makefile
@@ -328,6 +328,12 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
+# NCCL acceleration configuration
+ifeq ($(USE_NCCL), 1)
+	LIBRARIES += nccl
+	COMMON_FLAGS += -DUSE_NCCL
+endif
+
 # configure IO libraries
 ifeq ($(USE_OPENCV), 1)
 	COMMON_FLAGS += -DUSE_OPENCV
diff --git a/Makefile.config.example b/Makefile.config.example
index 07bed63ae40..541cf8077d5 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -94,6 +94,10 @@ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 # INCLUDE_DIRS += $(shell brew --prefix)/include
 # LIBRARY_DIRS += $(shell brew --prefix)/lib
 
+# NCCL acceleration switch (uncomment to build with NCCL)
+# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
+# USE_NCCL := 1
+
 # Uncomment to use `pkg-config` to specify OpenCV library paths.
 # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
 # USE_PKG_CONFIG := 1
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index ae9ce8e436d..ba28a128e6c 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -67,6 +67,13 @@ if(NOT HAVE_CUDA)
   add_definitions(-DCPU_ONLY)
 endif()
 
+if(USE_NCCL)
+  find_package(NCCL REQUIRED)
+  include_directories(SYSTEM ${NCCL_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${NCCL_LIBRARIES})
+  add_definitions(-DUSE_NCCL)
+endif()
+
 # ---[ OpenCV
 if(USE_OPENCV)
   find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
@@ -119,18 +126,18 @@ if(BUILD_python)
     find_package(NumPy 1.7.1)
     # Find the matching boost python implementation
     set(version ${PYTHONLIBS_VERSION_STRING})
-    
+
     STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
     find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
     set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
-    
+
     while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
       STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
-      
+
       STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
       find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
       set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
-      
+
       STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
       if("${has_more_version}" STREQUAL "")
         break()
diff --git a/cmake/Modules/FindNCCL.cmake b/cmake/Modules/FindNCCL.cmake
new file mode 100644
index 00000000000..c8845934102
--- /dev/null
+++ b/cmake/Modules/FindNCCL.cmake
@@ -0,0 +1,26 @@
+set(NCCL_INC_PATHS
+    /usr/include
+    /usr/local/include
+    $ENV{NCCL_DIR}/include
+    )
+
+set(NCCL_LIB_PATHS
+    /lib
+    /lib64
+    /usr/lib
+    /usr/lib64
+    /usr/local/lib
+    /usr/local/lib64
+    $ENV{NCCL_DIR}/lib
+    )
+
+find_path(NCCL_INCLUDE_DIR NAMES nccl.h PATHS ${NCCL_INC_PATHS})
+find_library(NCCL_LIBRARIES NAMES nccl PATHS ${NCCL_LIB_PATHS})
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIR NCCL_LIBRARIES)
+
+if (NCCL_FOUND)
+  message(STATUS "Found NCCL    (include: ${NCCL_INCLUDE_DIR}, library: ${NCCL_LIBRARIES})")
+  mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARIES)
+endif ()
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index ba025cf81e0..ed8c25268db 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -117,6 +117,7 @@ function(caffe_print_configuration_summary)
   caffe_status("  USE_OPENCV        :   ${USE_OPENCV}")
   caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
   caffe_status("  USE_LMDB          :   ${USE_LMDB}")
+  caffe_status("  USE_NCCL          :   ${USE_NCCL}")
   caffe_status("  ALLOW_LMDB_NOLOCK :   ${ALLOW_LMDB_NOLOCK}")
   caffe_status("")
   caffe_status("Dependencies:")
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index af360ac24bd..2f59471c29e 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -220,6 +220,7 @@ class Blob {
   void set_cpu_data(Dtype* data);
   const int* gpu_shape() const;
   const Dtype* gpu_data() const;
+  void set_gpu_data(Dtype* data);
   const Dtype* cpu_diff() const;
   const Dtype* gpu_diff() const;
   Dtype* mutable_cpu_data();
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 3c6a076ec2f..4904d1d8661 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -158,11 +158,14 @@ class Caffe {
   // Search from start_id to the highest possible device ordinal,
   // return the ordinal of the first available device.
   static int FindDevice(const int start_id = 0);
-  // Parallel training info
+  // Parallel training
   inline static int solver_count() { return Get().solver_count_; }
   inline static void set_solver_count(int val) { Get().solver_count_ = val; }
-  inline static bool root_solver() { return Get().root_solver_; }
-  inline static void set_root_solver(bool val) { Get().root_solver_ = val; }
+  inline static int solver_rank() { return Get().solver_rank_; }
+  inline static void set_solver_rank(int val) { Get().solver_rank_ = val; }
+  inline static bool multiprocess() { return Get().multiprocess_; }
+  inline static void set_multiprocess(bool val) { Get().multiprocess_ = val; }
+  inline static bool root_solver() { return Get().solver_rank_ == 0; }
 
  protected:
 #ifndef CPU_ONLY
@@ -172,8 +175,11 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
+
+  // Parallel training
   int solver_count_;
-  bool root_solver_;
+  int solver_rank_;
+  bool multiprocess_;
 
  private:
   // The private constructor to avoid duplicate instantiation.
diff --git a/include/caffe/data_reader.hpp b/include/caffe/data_reader.hpp
deleted file mode 100644
index 8ed5542cb8d..00000000000
--- a/include/caffe/data_reader.hpp
+++ /dev/null
@@ -1,82 +0,0 @@
-#ifndef CAFFE_DATA_READER_HPP_
-#define CAFFE_DATA_READER_HPP_
-
-#include <map>
-#include <string>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/internal_thread.hpp"
-#include "caffe/util/blocking_queue.hpp"
-#include "caffe/util/db.hpp"
-
-namespace caffe {
-
-/**
- * @brief Reads data from a source to queues available to data layers.
- * A single reading thread is created per source, even if multiple solvers
- * are running in parallel, e.g. for multi-GPU training. This makes sure
- * databases are read sequentially, and that each solver accesses a different
- * subset of the database. Data is distributed to solvers in a round-robin
- * way to keep parallel training deterministic.
- */
-class DataReader {
- public:
-  explicit DataReader(const LayerParameter& param);
-  ~DataReader();
-
-  inline BlockingQueue<Datum*>& free() const {
-    return queue_pair_->free_;
-  }
-  inline BlockingQueue<Datum*>& full() const {
-    return queue_pair_->full_;
-  }
-
- protected:
-  // Queue pairs are shared between a body and its readers
-  class QueuePair {
-   public:
-    explicit QueuePair(int size);
-    ~QueuePair();
-
-    BlockingQueue<Datum*> free_;
-    BlockingQueue<Datum*> full_;
-
-  DISABLE_COPY_AND_ASSIGN(QueuePair);
-  };
-
-  // A single body is created per source
-  class Body : public InternalThread {
-   public:
-    explicit Body(const LayerParameter& param);
-    virtual ~Body();
-
-   protected:
-    void InternalThreadEntry();
-    void read_one(db::Cursor* cursor, QueuePair* qp);
-
-    const LayerParameter param_;
-    BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
-
-    friend class DataReader;
-
-  DISABLE_COPY_AND_ASSIGN(Body);
-  };
-
-  // A source is uniquely identified by its layer name + path, in case
-  // the same database is read from two different locations in the net.
-  static inline string source_key(const LayerParameter& param) {
-    return param.name() + ":" + param.data_param().source();
-  }
-
-  const shared_ptr<QueuePair> queue_pair_;
-  shared_ptr<Body> body_;
-
-  static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
-
-DISABLE_COPY_AND_ASSIGN(DataReader);
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_DATA_READER_HPP_
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 6a8c5a02892..0ba67665035 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -42,8 +42,8 @@ class InternalThread {
   bool must_stop();
 
  private:
-  void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count,
-      bool root_solver);
+  void entry(int device, Caffe::Brew mode, int rand_seed,
+      int solver_count, int solver_rank, bool multiprocess);
 
   shared_ptr<boost::thread> thread_;
 };
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 10f353f94f9..30dbfd53758 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -38,7 +38,7 @@ class Layer {
    * layer.
    */
   explicit Layer(const LayerParameter& param)
-    : layer_param_(param), is_shared_(false) {
+    : layer_param_(param) {
       // Set phase and copy blobs (if there are any).
       phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
@@ -66,7 +66,6 @@ class Layer {
    */
   void SetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    InitMutex();
     CheckBlobCounts(bottom, top);
     LayerSetUp(bottom, top);
     Reshape(bottom, top);
@@ -92,30 +91,6 @@ class Layer {
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
 
-  /**
-   * @brief Whether a layer should be shared by multiple nets during data
-   *        parallelism. By default, all layers except for data layers should
-   *        not be shared. data layers should be shared to ensure each worker
-   *        solver access data sequentially during data parallelism.
-   */
-  virtual inline bool ShareInParallel() const { return false; }
-
-  /** @brief Return whether this layer is actually shared by other nets.
-   *         If ShareInParallel() is true and using more than one GPU and the
-   *         net has TRAIN phase, then this function is expected return true.
-   */
-  inline bool IsShared() const { return is_shared_; }
-
-  /** @brief Set whether this layer is actually shared by other nets
-   *         If ShareInParallel() is true and using more than one GPU and the
-   *         net has TRAIN phase, then is_shared should be set true.
-   */
-  inline void SetShared(bool is_shared) {
-    CHECK(ShareInParallel() || !is_shared)
-        << type() << "Layer does not support sharing.";
-    is_shared_ = is_shared;
-  }
-
   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
@@ -428,19 +403,6 @@ class Layer {
   }
 
  private:
-  /** Whether this layer is actually shared by other nets*/
-  bool is_shared_;
-
-  /** The mutex for sequential forward if this layer is shared */
-  shared_ptr<boost::mutex> forward_mutex_;
-
-  /** Initialize forward_mutex_ */
-  void InitMutex();
-  /** Lock forward_mutex_ if this layer is shared */
-  void Lock();
-  /** Unlock forward_mutex_ if this layer is shared */
-  void Unlock();
-
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 
@@ -450,8 +412,6 @@ class Layer {
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  // Lock during forward to ensure sequential forward
-  Lock();
   Dtype loss = 0;
   Reshape(bottom, top);
   switch (Caffe::mode()) {
@@ -482,7 +442,6 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
-  Unlock();
   return loss;
 }
 
diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp
index 2c49b73184b..925b019d460 100644
--- a/include/caffe/layers/base_data_layer.hpp
+++ b/include/caffe/layers/base_data_layer.hpp
@@ -68,15 +68,16 @@ class BasePrefetchingDataLayer :
       const vector<Blob<Dtype>*>& top);
 
   // Prefetches batches (asynchronously if to GPU memory)
-  static const int PREFETCH_COUNT = 3;
+  static const int PREFETCH_COUNT = 4;  // same as proto
 
  protected:
   virtual void InternalThreadEntry();
   virtual void load_batch(Batch<Dtype>* batch) = 0;
 
-  Batch<Dtype> prefetch_[PREFETCH_COUNT];
+  vector<shared_ptr<Batch<Dtype> > > prefetch_;
   BlockingQueue<Batch<Dtype>*> prefetch_free_;
   BlockingQueue<Batch<Dtype>*> prefetch_full_;
+  Batch<Dtype>* prefetch_current_;
 
   Blob<Dtype> transformed_data_;
 };
diff --git a/include/caffe/layers/data_layer.hpp b/include/caffe/layers/data_layer.hpp
index 6c361791a0c..dec58180976 100644
--- a/include/caffe/layers/data_layer.hpp
+++ b/include/caffe/layers/data_layer.hpp
@@ -4,7 +4,6 @@
 #include <vector>
 
 #include "caffe/blob.hpp"
-#include "caffe/data_reader.hpp"
 #include "caffe/data_transformer.hpp"
 #include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
@@ -29,9 +28,13 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
+  void Next();
+  bool Skip();
   virtual void load_batch(Batch<Dtype>* batch);
 
-  DataReader reader_;
+  shared_ptr<db::DB> db_;
+  shared_ptr<db::Cursor> cursor_;
+  uint64_t offset_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/hdf5_data_layer.hpp b/include/caffe/layers/hdf5_data_layer.hpp
index b04cf8e1940..650a3fb0c87 100644
--- a/include/caffe/layers/hdf5_data_layer.hpp
+++ b/include/caffe/layers/hdf5_data_layer.hpp
@@ -23,7 +23,7 @@ template <typename Dtype>
 class HDF5DataLayer : public Layer<Dtype> {
  public:
   explicit HDF5DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+      : Layer<Dtype>(param), offset_() {}
   virtual ~HDF5DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
@@ -38,6 +38,9 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual inline int MinTopBlobs() const { return 1; }
 
  protected:
+  void Next();
+  bool Skip();
+
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
@@ -55,6 +58,7 @@ class HDF5DataLayer : public Layer<Dtype> {
   std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
   std::vector<unsigned int> data_permutation_;
   std::vector<unsigned int> file_permutation_;
+  uint64_t offset_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/python_layer.hpp b/include/caffe/layers/python_layer.hpp
index 66dbbdf13b8..529b09cb88b 100644
--- a/include/caffe/layers/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -21,8 +21,8 @@ class PythonLayer : public Layer<Dtype> {
     // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
     // Details: https://github.com/BVLC/caffe/issues/2936
     if (this->phase_ == TRAIN && Caffe::solver_count() > 1
-        && !ShareInParallel()) {
-      LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training";
+        && !Caffe::root_solver() && !Caffe::multiprocess()) {
+      LOG(FATAL) << "PythonLayer does not support CLI Multi-GPU, use train.py";
     }
     self_.attr("param_str") = bp::str(
         this->layer_param_.python_param().param_str());
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 493bdf294e2..d3c9306e9cf 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -23,10 +23,9 @@ namespace caffe {
 template <typename Dtype>
 class Net {
  public:
-  explicit Net(const NetParameter& param, const Net* root_net = NULL);
+  explicit Net(const NetParameter& param);
   explicit Net(const string& param_file, Phase phase,
-      const int level = 0, const vector<string>* stages = NULL,
-      const Net* root_net = NULL);
+      const int level = 0, const vector<string>* stages = NULL);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
@@ -228,6 +227,31 @@ class Net {
   static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
       const string& layer_name);
 
+  // Invoked at specific points during an iteration
+  class Callback {
+   protected:
+    virtual void run(int layer) = 0;
+
+    template <typename T>
+    friend class Net;
+  };
+  const vector<Callback*>& before_forward() const { return before_forward_; }
+  void add_before_forward(Callback* value) {
+    before_forward_.push_back(value);
+  }
+  const vector<Callback*>& after_forward() const { return after_forward_; }
+  void add_after_forward(Callback* value) {
+    after_forward_.push_back(value);
+  }
+  const vector<Callback*>& before_backward() const { return before_backward_; }
+  void add_before_backward(Callback* value) {
+    before_backward_.push_back(value);
+  }
+  const vector<Callback*>& after_backward() const { return after_backward_; }
+  void add_after_backward(Callback* value) {
+    after_backward_.push_back(value);
+  }
+
  protected:
   // Helpers for Init.
   /// @brief Append a new top blob to the net.
@@ -306,9 +330,13 @@ class Net {
   size_t memory_used_;
   /// Whether to compute and display debug info for the net.
   bool debug_info_;
-  /// The root net that actually holds the shared layers in data parallelism
-  const Net* const root_net_;
-  DISABLE_COPY_AND_ASSIGN(Net);
+  // Callbacks
+  vector<Callback*> before_forward_;
+  vector<Callback*> after_forward_;
+  vector<Callback*> before_backward_;
+  vector<Callback*> after_backward_;
+
+DISABLE_COPY_AND_ASSIGN(Net);
 };
 
 
diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp
index 6c496c884e3..64bb48e6b02 100644
--- a/include/caffe/parallel.hpp
+++ b/include/caffe/parallel.hpp
@@ -1,8 +1,11 @@
 #ifndef CAFFE_PARALLEL_HPP_
 #define CAFFE_PARALLEL_HPP_
 
-#include <boost/date_time/posix_time/posix_time.hpp>
+#ifdef USE_NCCL
 
+#include <boost/thread.hpp>
+
+#include <string>
 #include <vector>
 
 #include "caffe/blob.hpp"
@@ -13,6 +16,7 @@
 #include "caffe/solver.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/blocking_queue.hpp"
+#include "caffe/util/nccl.hpp"
 
 namespace caffe {
 
@@ -51,7 +55,7 @@ class GPUParams : public Params<Dtype> {
   GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device);
   virtual ~GPUParams();
 
-  void configure(Solver<Dtype>* solver) const;
+  void Configure(Solver<Dtype>* solver) const;
 
  protected:
   using Params<Dtype>::size_;
@@ -59,58 +63,55 @@ class GPUParams : public Params<Dtype> {
   using Params<Dtype>::diff_;
 };
 
-class DevicePair {
- public:
-  DevicePair(int parent, int device)
-      : parent_(parent),
-        device_(device) {
-  }
-  inline int parent() {
-    return parent_;
-  }
-  inline int device() {
-    return device_;
-  }
-
-  // Group GPUs in pairs, by proximity depending on machine's topology
-  static void compute(const vector<int> devices, vector<DevicePair>* pairs);
-
- protected:
-  int parent_;
-  int device_;
-};
-
-// Synchronous data parallelism using map-reduce between local GPUs.
 template<typename Dtype>
-class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
-    public InternalThread {
+class NCCL : public GPUParams<Dtype>,
+             public Solver<Dtype>::Callback,
+             public Net<Dtype>::Callback {
  public:
-  explicit P2PSync(shared_ptr<Solver<Dtype> > root_solver,
-                   P2PSync<Dtype>* parent, const SolverParameter& param);
-  virtual ~P2PSync();
-
-  inline const shared_ptr<Solver<Dtype> >& solver() const {
-    return solver_;
-  }
-
-  void Run(const vector<int>& gpus);
-  void Prepare(const vector<int>& gpus,
-               vector<shared_ptr<P2PSync<Dtype> > >* syncs);
-  inline const int initial_iter() const { return initial_iter_; }
+  /**
+   * Single process version.
+   */
+  explicit NCCL(shared_ptr<Solver<Dtype> > solver);
+  /**
+   * In multi-process settings, first create a NCCL id (new_uid), then
+   * pass it to each process to create connected instances.
+   */
+  NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid);
+  ~NCCL();
+
+  boost::barrier* barrier();
+  void set_barrier(boost::barrier* value);
+
+  /**
+   * In single process settings, create instances without uids and
+   * call this to connect them.
+   */
+  static void InitSingleProcess(vector<NCCL<Dtype>*>* nccls);
+
+  static string new_uid();
+
+  /**
+   * Broadcast weights from rank 0 other solvers.
+   */
+  void Broadcast();
+
+  /**
+   * Single process multi-GPU.
+   */
+  void Run(const vector<int>& gpus, const char* restore);
 
  protected:
-  void on_start();
+  void Init();
+  void on_start() {}
+  void run(int layer);  // Net callback
   void on_gradients_ready();
 
-  void InternalThreadEntry();
+  ncclComm_t comm_;
+  cudaStream_t stream_;
 
-  P2PSync<Dtype>* parent_;
-  vector<P2PSync<Dtype>*> children_;
-  BlockingQueue<P2PSync<Dtype>*> queue_;
-  const int initial_iter_;
-  Dtype* parent_grads_;
   shared_ptr<Solver<Dtype> > solver_;
-
+  // Should not be necessary, https://github.com/NVIDIA/nccl/issues/37
+  boost::barrier* barrier_;
   using Params<Dtype>::size_;
   using Params<Dtype>::data_;
   using Params<Dtype>::diff_;
@@ -118,4 +119,5 @@ class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
 
 }  // namespace caffe
 
-#endif
+#endif  // USE_NCCL
+#endif  // header
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index eafcee32904..a28d8cb897e 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -6,6 +6,7 @@
 
 #include "caffe/net.hpp"
 #include "caffe/solver_factory.hpp"
+#include "caffe/util/benchmark.hpp"
 
 namespace caffe {
 
@@ -40,9 +41,8 @@ typedef boost::function<SolverAction::Enum()> ActionCallback;
 template <typename Dtype>
 class Solver {
  public:
-  explicit Solver(const SolverParameter& param,
-      const Solver* root_solver = NULL);
-  explicit Solver(const string& param_file, const Solver* root_solver = NULL);
+  explicit Solver(const SolverParameter& param);
+  explicit Solver(const string& param_file);
   void Init(const SolverParameter& param);
   void InitTrainNet();
   void InitTestNets();
@@ -72,7 +72,7 @@ class Solver {
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
     return test_nets_;
   }
-  int iter() { return iter_; }
+  int iter() const { return iter_; }
 
   // Invoked at specific points during an iteration
   class Callback {
@@ -118,10 +118,6 @@ class Solver {
   vector<Dtype> losses_;
   Dtype smoothed_loss_;
 
-  // The root solver that holds root nets (actually containing shared layers)
-  // in data parallelism
-  const Solver* const root_solver_;
-
   // A function that can be set by a client of the Solver to provide indication
   // that it wants a snapshot saved and/or to exit early.
   ActionCallback action_request_function_;
@@ -129,31 +125,11 @@ class Solver {
   // True iff a request to stop early was received.
   bool requested_early_exit_;
 
-  DISABLE_COPY_AND_ASSIGN(Solver);
-};
+  // Timing information, handy to tune e.g. nbr of GPUs
+  Timer iteration_timer_;
+  float iterations_last_;
 
-/**
- * @brief Solver that only computes gradients, used as worker
- *        for multi-GPU training.
- */
-template <typename Dtype>
-class WorkerSolver : public Solver<Dtype> {
- public:
-  explicit WorkerSolver(const SolverParameter& param,
-      const Solver<Dtype>* root_solver = NULL)
-      : Solver<Dtype>(param, root_solver) {}
-
- protected:
-  void ApplyUpdate() {}
-  void SnapshotSolverState(const string& model_filename) {
-    LOG(FATAL) << "Should not be called on worker solver.";
-  }
-  void RestoreSolverStateFromBinaryProto(const string& state_file) {
-    LOG(FATAL) << "Should not be called on worker solver.";
-  }
-  void RestoreSolverStateFromHDF5(const string& state_file) {
-    LOG(FATAL) << "Should not be called on worker solver.";
-  }
+  DISABLE_COPY_AND_ASSIGN(Solver);
 };
 
 }  // namespace caffe
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 38ee4664028..a41066bacd9 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -44,14 +44,8 @@ inline void CaffeFreeHost(void* ptr, bool use_cuda) {
  */
 class SyncedMemory {
  public:
-  SyncedMemory()
-      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
-        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
-        gpu_device_(-1) {}
-  explicit SyncedMemory(size_t size)
-      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
-        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
-        gpu_device_(-1) {}
+  SyncedMemory();
+  explicit SyncedMemory(size_t size);
   ~SyncedMemory();
   const void* cpu_data();
   void set_cpu_data(void* data);
@@ -68,6 +62,8 @@ class SyncedMemory {
 #endif
 
  private:
+  void check_device();
+
   void to_cpu();
   void to_gpu();
   void* cpu_ptr_;
@@ -77,7 +73,7 @@ class SyncedMemory {
   bool own_cpu_data_;
   bool cpu_malloc_use_cuda_;
   bool own_gpu_data_;
-  int gpu_device_;
+  int device_;
 
   DISABLE_COPY_AND_ASSIGN(SyncedMemory);
 };  // class SyncedMemory
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 6f6d3feeae2..51068fe2b80 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -185,6 +185,11 @@ void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
 template <typename Dtype>
 void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
 
+#ifndef CPU_ONLY
+template <typename Dtype>
+void caffe_gpu_scal(const int N, const Dtype alpha, Dtype* X, cudaStream_t str);
+#endif
+
 template <typename Dtype>
 void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
diff --git a/include/caffe/util/nccl.hpp b/include/caffe/util/nccl.hpp
new file mode 100644
index 00000000000..e01fb7451e8
--- /dev/null
+++ b/include/caffe/util/nccl.hpp
@@ -0,0 +1,37 @@
+#ifndef CAFFE_UTIL_NCCL_H_
+#define CAFFE_UTIL_NCCL_H_
+#ifdef USE_NCCL
+
+#include <nccl.h>
+
+#include "caffe/common.hpp"
+
+#define NCCL_CHECK(condition) \
+{ \
+  ncclResult_t result = condition; \
+  CHECK_EQ(result, ncclSuccess) << " " \
+    << ncclGetErrorString(result); \
+}
+
+namespace caffe {
+
+namespace nccl {
+
+template <typename Dtype> class dataType;
+
+template<> class dataType<float> {
+ public:
+  static const ncclDataType_t type = ncclFloat;
+};
+template<> class dataType<double> {
+ public:
+  static const ncclDataType_t type = ncclDouble;
+};
+
+}  // namespace nccl
+
+}  // namespace caffe
+
+#endif  // end USE_NCCL
+
+#endif  // CAFFE_UTIL_NCCL_H_
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 4a34e4c5856..603e52f7025 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -89,6 +89,12 @@ const Dtype* Blob<Dtype>::cpu_data() const {
 template <typename Dtype>
 void Blob<Dtype>::set_cpu_data(Dtype* data) {
   CHECK(data);
+  // Make sure CPU and GPU sizes remain equal
+  size_t size = count_ * sizeof(Dtype);
+  if (data_->size() != size) {
+    data_.reset(new SyncedMemory(size));
+    diff_.reset(new SyncedMemory(size));
+  }
   data_->set_cpu_data(data);
 }
 
@@ -98,6 +104,18 @@ const Dtype* Blob<Dtype>::gpu_data() const {
   return (const Dtype*)data_->gpu_data();
 }
 
+template <typename Dtype>
+void Blob<Dtype>::set_gpu_data(Dtype* data) {
+  CHECK(data);
+  // Make sure CPU and GPU sizes remain equal
+  size_t size = count_ * sizeof(Dtype);
+  if (data_->size() != size) {
+    data_.reset(new SyncedMemory(size));
+    diff_.reset(new SyncedMemory(size));
+  }
+  data_->set_gpu_data(data);
+}
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::cpu_diff() const {
   CHECK(diff_);
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index dee681654aa..4f6f9bccc38 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -53,7 +53,7 @@ void GlobalInit(int* pargc, char*** pargv) {
 
 Caffe::Caffe()
     : random_generator_(), mode_(Caffe::CPU),
-      solver_count_(1), root_solver_(true) { }
+      solver_count_(1), solver_rank_(0), multiprocess_(false) { }
 
 Caffe::~Caffe() { }
 
@@ -106,7 +106,8 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU), solver_count_(1), root_solver_(true) {
+    mode_(Caffe::CPU),
+    solver_count_(1), solver_rank_(0), multiprocess_(false) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
deleted file mode 100644
index 9f019bbfcb7..00000000000
--- a/src/caffe/data_reader.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-#include <boost/thread.hpp>
-#include <map>
-#include <string>
-#include <vector>
-
-#include "caffe/common.hpp"
-#include "caffe/data_reader.hpp"
-#include "caffe/layers/data_layer.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-using boost::weak_ptr;
-
-map<const string, weak_ptr<DataReader::Body> > DataReader::bodies_;
-static boost::mutex bodies_mutex_;
-
-DataReader::DataReader(const LayerParameter& param)
-    : queue_pair_(new QueuePair(  //
-        param.data_param().prefetch() * param.data_param().batch_size())) {
-  // Get or create a body
-  boost::mutex::scoped_lock lock(bodies_mutex_);
-  string key = source_key(param);
-  weak_ptr<Body>& weak = bodies_[key];
-  body_ = weak.lock();
-  if (!body_) {
-    body_.reset(new Body(param));
-    bodies_[key] = weak_ptr<Body>(body_);
-  }
-  body_->new_queue_pairs_.push(queue_pair_);
-}
-
-DataReader::~DataReader() {
-  string key = source_key(body_->param_);
-  body_.reset();
-  boost::mutex::scoped_lock lock(bodies_mutex_);
-  if (bodies_[key].expired()) {
-    bodies_.erase(key);
-  }
-}
-
-//
-
-DataReader::QueuePair::QueuePair(int size) {
-  // Initialize the free queue with requested number of datums
-  for (int i = 0; i < size; ++i) {
-    free_.push(new Datum());
-  }
-}
-
-DataReader::QueuePair::~QueuePair() {
-  Datum* datum;
-  while (free_.try_pop(&datum)) {
-    delete datum;
-  }
-  while (full_.try_pop(&datum)) {
-    delete datum;
-  }
-}
-
-//
-
-DataReader::Body::Body(const LayerParameter& param)
-    : param_(param),
-      new_queue_pairs_() {
-  StartInternalThread();
-}
-
-DataReader::Body::~Body() {
-  StopInternalThread();
-}
-
-void DataReader::Body::InternalThreadEntry() {
-  shared_ptr<db::DB> db(db::GetDB(param_.data_param().backend()));
-  db->Open(param_.data_param().source(), db::READ);
-  shared_ptr<db::Cursor> cursor(db->NewCursor());
-  vector<shared_ptr<QueuePair> > qps;
-  try {
-    int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
-
-    // To ensure deterministic runs, only start running once all solvers
-    // are ready. But solvers need to peek on one item during initialization,
-    // so read one item, then wait for the next solver.
-    for (int i = 0; i < solver_count; ++i) {
-      shared_ptr<QueuePair> qp(new_queue_pairs_.pop());
-      read_one(cursor.get(), qp.get());
-      qps.push_back(qp);
-    }
-    // Main loop
-    while (!must_stop()) {
-      for (int i = 0; i < solver_count; ++i) {
-        read_one(cursor.get(), qps[i].get());
-      }
-      // Check no additional readers have been created. This can happen if
-      // more than one net is trained at a time per process, whether single
-      // or multi solver. It might also happen if two data layers have same
-      // name and same source.
-      CHECK_EQ(new_queue_pairs_.size(), 0);
-    }
-  } catch (boost::thread_interrupted&) {
-    // Interrupted exception is expected on shutdown
-  }
-}
-
-void DataReader::Body::read_one(db::Cursor* cursor, QueuePair* qp) {
-  Datum* datum = qp->free_.pop();
-  // TODO deserialize in-place instead of copy?
-  datum->ParseFromString(cursor->value());
-  qp->full_.push(datum);
-
-  // go to the next iter
-  cursor->Next();
-  if (!cursor->valid()) {
-    DLOG(INFO) << "Restarting data prefetching from start.";
-    cursor->SeekToFirst();
-  }
-}
-
-}  // namespace caffe
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index 104884e0295..11de4979935 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -28,25 +28,27 @@ void InternalThread::StartInternalThread() {
   Caffe::Brew mode = Caffe::mode();
   int rand_seed = caffe_rng_rand();
   int solver_count = Caffe::solver_count();
-  bool root_solver = Caffe::root_solver();
+  int solver_rank = Caffe::solver_rank();
+  bool multiprocess = Caffe::multiprocess();
 
   try {
     thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
-          rand_seed, solver_count, root_solver));
+          rand_seed, solver_count, solver_rank, multiprocess));
   } catch (std::exception& e) {
     LOG(FATAL) << "Thread exception: " << e.what();
   }
 }
 
 void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed,
-    int solver_count, bool root_solver) {
+    int solver_count, int solver_rank, bool multiprocess) {
 #ifndef CPU_ONLY
   CUDA_CHECK(cudaSetDevice(device));
 #endif
   Caffe::set_mode(mode);
   Caffe::set_random_seed(rand_seed);
   Caffe::set_solver_count(solver_count);
-  Caffe::set_root_solver(root_solver);
+  Caffe::set_solver_rank(solver_rank);
+  Caffe::set_multiprocess(multiprocess);
 
   InternalThreadEntry();
 }
diff --git a/src/caffe/layer.cpp b/src/caffe/layer.cpp
index 3b9128986ae..684ae88bb49 100644
--- a/src/caffe/layer.cpp
+++ b/src/caffe/layer.cpp
@@ -1,27 +1,7 @@
-#include <boost/thread.hpp>
 #include "caffe/layer.hpp"
 
 namespace caffe {
 
-template <typename Dtype>
-void Layer<Dtype>::InitMutex() {
-  forward_mutex_.reset(new boost::mutex());
-}
-
-template <typename Dtype>
-void Layer<Dtype>::Lock() {
-  if (IsShared()) {
-    forward_mutex_->lock();
-  }
-}
-
-template <typename Dtype>
-void Layer<Dtype>::Unlock() {
-  if (IsShared()) {
-    forward_mutex_->unlock();
-  }
-}
-
 INSTANTIATE_CLASS(Layer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 989319f1a07..9414f6f98b2 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -36,9 +36,12 @@ template <typename Dtype>
 BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
     const LayerParameter& param)
     : BaseDataLayer<Dtype>(param),
-      prefetch_free_(), prefetch_full_() {
-  for (int i = 0; i < PREFETCH_COUNT; ++i) {
-    prefetch_free_.push(&prefetch_[i]);
+      prefetch_(param.has_data_param() ?
+                param.data_param().prefetch() : PREFETCH_COUNT),
+      prefetch_free_(), prefetch_full_(), prefetch_current_() {
+  for (int i = 0; i < prefetch_.size(); ++i) {
+    prefetch_[i].reset(new Batch<Dtype>());
+    prefetch_free_.push(prefetch_[i].get());
   }
 }
 
@@ -46,22 +49,23 @@ template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
+
   // Before starting the prefetch thread, we make cpu_data and gpu_data
   // calls so that the prefetch thread does not accidentally make simultaneous
   // cudaMalloc calls when the main thread is running. In some GPUs this
   // seems to cause failures if we do not so.
-  for (int i = 0; i < PREFETCH_COUNT; ++i) {
-    prefetch_[i].data_.mutable_cpu_data();
+  for (int i = 0; i < prefetch_.size(); ++i) {
+    prefetch_[i]->data_.mutable_cpu_data();
     if (this->output_labels_) {
-      prefetch_[i].label_.mutable_cpu_data();
+      prefetch_[i]->label_.mutable_cpu_data();
     }
   }
 #ifndef CPU_ONLY
   if (Caffe::mode() == Caffe::GPU) {
-    for (int i = 0; i < PREFETCH_COUNT; ++i) {
-      prefetch_[i].data_.mutable_gpu_data();
+    for (int i = 0; i < prefetch_.size(); ++i) {
+      prefetch_[i]->data_.mutable_gpu_data();
       if (this->output_labels_) {
-        prefetch_[i].label_.mutable_gpu_data();
+        prefetch_[i]->label_.mutable_gpu_data();
       }
     }
   }
@@ -88,6 +92,9 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
 #ifndef CPU_ONLY
       if (Caffe::mode() == Caffe::GPU) {
         batch->data_.data().get()->async_gpu_push(stream);
+        if (this->output_labels_) {
+          batch->label_.data().get()->async_gpu_push(stream);
+        }
         CUDA_CHECK(cudaStreamSynchronize(stream));
       }
 #endif
@@ -106,22 +113,18 @@ void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
+  if (prefetch_current_) {
+    prefetch_free_.push(prefetch_current_);
+  }
+  prefetch_current_ = prefetch_full_.pop("Waiting for data");
   // Reshape to loaded data.
-  top[0]->ReshapeLike(batch->data_);
-  // Copy the data
-  caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
-             top[0]->mutable_cpu_data());
-  DLOG(INFO) << "Prefetch copied";
+  top[0]->ReshapeLike(prefetch_current_->data_);
+  top[0]->set_cpu_data(prefetch_current_->data_.mutable_cpu_data());
   if (this->output_labels_) {
     // Reshape to loaded labels.
-    top[1]->ReshapeLike(batch->label_);
-    // Copy the labels.
-    caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
-        top[1]->mutable_cpu_data());
+    top[1]->ReshapeLike(prefetch_current_->label_);
+    top[1]->set_cpu_data(prefetch_current_->label_.mutable_cpu_data());
   }
-
-  prefetch_free_.push(batch);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 4056d36a7b4..64c621a74f1 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -7,23 +7,18 @@ namespace caffe {
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
+  if (prefetch_current_) {
+    prefetch_free_.push(prefetch_current_);
+  }
+  prefetch_current_ = prefetch_full_.pop("Waiting for data");
   // Reshape to loaded data.
-  top[0]->ReshapeLike(batch->data_);
-  // Copy the data
-  caffe_copy(batch->data_.count(), batch->data_.gpu_data(),
-      top[0]->mutable_gpu_data());
+  top[0]->ReshapeLike(prefetch_current_->data_);
+  top[0]->set_gpu_data(prefetch_current_->data_.mutable_gpu_data());
   if (this->output_labels_) {
     // Reshape to loaded labels.
-    top[1]->ReshapeLike(batch->label_);
-    // Copy the labels.
-    caffe_copy(batch->label_.count(), batch->label_.gpu_data(),
-        top[1]->mutable_gpu_data());
+    top[1]->ReshapeLike(prefetch_current_->label_);
+    top[1]->set_gpu_data(prefetch_current_->label_.mutable_gpu_data());
   }
-  // Ensure the copy is synchronous wrt the host, so that the next batch isn't
-  // copied in meanwhile.
-  CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-  prefetch_free_.push(batch);
 }
 
 INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 66e6301fd45..0f1296bbc77 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -14,7 +14,10 @@ namespace caffe {
 template <typename Dtype>
 DataLayer<Dtype>::DataLayer(const LayerParameter& param)
   : BasePrefetchingDataLayer<Dtype>(param),
-    reader_(param) {
+    offset_() {
+  db_.reset(db::GetDB(param.data_param().backend()));
+  db_->Open(param.data_param().source(), db::READ);
+  cursor_.reset(db_->NewCursor());
 }
 
 template <typename Dtype>
@@ -27,7 +30,8 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.data_param().batch_size();
   // Read a data point, and use it to initialize the top blob.
-  Datum& datum = *(reader_.full().peek());
+  Datum datum;
+  datum.ParseFromString(cursor_->value());
 
   // Use data_transformer to infer the expected blob shape from datum.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
@@ -35,22 +39,44 @@ void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Reshape top[0] and prefetch_data according to the batch_size.
   top_shape[0] = batch_size;
   top[0]->Reshape(top_shape);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].data_.Reshape(top_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->data_.Reshape(top_shape);
   }
-  LOG(INFO) << "output data size: " << top[0]->num() << ","
+  LOG_IF(INFO, Caffe::root_solver())
+      << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
   // label
   if (this->output_labels_) {
     vector<int> label_shape(1, batch_size);
     top[1]->Reshape(label_shape);
-    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-      this->prefetch_[i].label_.Reshape(label_shape);
+    for (int i = 0; i < this->prefetch_.size(); ++i) {
+      this->prefetch_[i]->label_.Reshape(label_shape);
     }
   }
 }
 
+template <typename Dtype>
+bool DataLayer<Dtype>::Skip() {
+  int size = Caffe::solver_count();
+  int rank = Caffe::solver_rank();
+  bool keep = (offset_ % size) == rank ||
+              // In test mode, only rank 0 runs, so avoid skipping
+              this->layer_param_.phase() == TEST;
+  return !keep;
+}
+
+template<typename Dtype>
+void DataLayer<Dtype>::Next() {
+  cursor_->Next();
+  if (!cursor_->valid()) {
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Restarting data prefetching from start.";
+    cursor_->SeekToFirst();
+  }
+  offset_++;
+}
+
 // This function is called on prefetch thread
 template<typename Dtype>
 void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
@@ -61,41 +87,41 @@ void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   CPUTimer timer;
   CHECK(batch->data_.count());
   CHECK(this->transformed_data_.count());
-
-  // Reshape according to the first datum of each batch
-  // on single input batches allows for inputs of varying dimension.
   const int batch_size = this->layer_param_.data_param().batch_size();
-  Datum& datum = *(reader_.full().peek());
-  // Use data_transformer to infer the expected blob shape from datum.
-  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
-  this->transformed_data_.Reshape(top_shape);
-  // Reshape batch according to the batch_size.
-  top_shape[0] = batch_size;
-  batch->data_.Reshape(top_shape);
-
-  Dtype* top_data = batch->data_.mutable_cpu_data();
-  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
 
-  if (this->output_labels_) {
-    top_label = batch->label_.mutable_cpu_data();
-  }
+  Datum datum;
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
-    // get a datum
-    Datum& datum = *(reader_.full().pop("Waiting for data"));
+    while (Skip()) {
+      Next();
+    }
+    datum.ParseFromString(cursor_->value());
     read_time += timer.MicroSeconds();
-    timer.Start();
+
+    if (item_id == 0) {
+      // Reshape according to the first datum of each batch
+      // on single input batches allows for inputs of varying dimension.
+      // Use data_transformer to infer the expected blob shape from datum.
+      vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+      this->transformed_data_.Reshape(top_shape);
+      // Reshape batch according to the batch_size.
+      top_shape[0] = batch_size;
+      batch->data_.Reshape(top_shape);
+    }
+
     // Apply data transformations (mirror, scale, crop...)
+    timer.Start();
     int offset = batch->data_.offset(item_id);
+    Dtype* top_data = batch->data_.mutable_cpu_data();
     this->transformed_data_.set_cpu_data(top_data + offset);
     this->data_transformer_->Transform(datum, &(this->transformed_data_));
     // Copy label.
     if (this->output_labels_) {
+      Dtype* top_label = batch->label_.mutable_cpu_data();
       top_label[item_id] = datum.label();
     }
     trans_time += timer.MicroSeconds();
-
-    reader_.free().push(const_cast<Datum*>(&datum));
+    Next();
   }
   timer.Stop();
   batch_timer.Stop();
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index c957451ae1e..b9a071ceab3 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -124,28 +124,46 @@ void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   }
 }
 
+template <typename Dtype>
+bool HDF5DataLayer<Dtype>::Skip() {
+  int size = Caffe::solver_count();
+  int rank = Caffe::solver_rank();
+  bool keep = (offset_ % size) == rank ||
+              // In test mode, only rank 0 runs, so avoid skipping
+              this->layer_param_.phase() == TEST;
+  return !keep;
+}
+
+template<typename Dtype>
+void HDF5DataLayer<Dtype>::Next() {
+  if (++current_row_ == hdf_blobs_[0]->shape(0)) {
+    if (num_files_ > 1) {
+      ++current_file_;
+      if (current_file_ == num_files_) {
+        current_file_ = 0;
+        if (this->layer_param_.hdf5_data_param().shuffle()) {
+          std::random_shuffle(file_permutation_.begin(),
+                              file_permutation_.end());
+        }
+        DLOG(INFO) << "Looping around to first file.";
+      }
+      LoadHDF5FileData(
+        hdf_filenames_[file_permutation_[current_file_]].c_str());
+    }
+    current_row_ = 0;
+    if (this->layer_param_.hdf5_data_param().shuffle())
+      std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+  }
+  offset_++;
+}
+
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == hdf_blobs_[0]->shape(0)) {
-      if (num_files_ > 1) {
-        ++current_file_;
-        if (current_file_ == num_files_) {
-          current_file_ = 0;
-          if (this->layer_param_.hdf5_data_param().shuffle()) {
-            std::random_shuffle(file_permutation_.begin(),
-                                file_permutation_.end());
-          }
-          DLOG(INFO) << "Looping around to first file.";
-        }
-        LoadHDF5FileData(
-            hdf_filenames_[file_permutation_[current_file_]].c_str());
-      }
-      current_row_ = 0;
-      if (this->layer_param_.hdf5_data_param().shuffle())
-        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+  for (int i = 0; i < batch_size; ++i) {
+    while (Skip()) {
+      Next();
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
       int data_dim = top[j]->count() / top[j]->shape(0);
@@ -153,6 +171,7 @@ void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
           &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
             * data_dim], &top[j]->mutable_cpu_data()[i * data_dim]);
     }
+    Next();
   }
 }
 
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 595d2230220..33eebd41dfc 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -17,24 +17,9 @@ template <typename Dtype>
 void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int batch_size = this->layer_param_.hdf5_data_param().batch_size();
-  for (int i = 0; i < batch_size; ++i, ++current_row_) {
-    if (current_row_ == hdf_blobs_[0]->shape(0)) {
-      if (num_files_ > 1) {
-        current_file_ += 1;
-        if (current_file_ == num_files_) {
-          current_file_ = 0;
-          if (this->layer_param_.hdf5_data_param().shuffle()) {
-            std::random_shuffle(file_permutation_.begin(),
-                                file_permutation_.end());
-          }
-          DLOG(INFO) << "Looping around to first file.";
-        }
-        LoadHDF5FileData(
-            hdf_filenames_[file_permutation_[current_file_]].c_str());
-      }
-      current_row_ = 0;
-      if (this->layer_param_.hdf5_data_param().shuffle())
-        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());
+  for (int i = 0; i < batch_size; ++i) {
+    while (Skip()) {
+      Next();
     }
     for (int j = 0; j < this->layer_param_.top_size(); ++j) {
       int data_dim = top[j]->count() / top[j]->shape(0);
@@ -42,6 +27,7 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
           &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]
             * data_dim], &top[j]->mutable_gpu_data()[i * data_dim]);
     }
+    Next();
   }
 }
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 7ee7dc40714..ec0fc5b0383 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -54,6 +54,11 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
     const unsigned int prefetch_rng_seed = caffe_rng_rand();
     prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
     ShuffleImages();
+  } else {
+    if (this->phase_ == TRAIN && Caffe::solver_rank() > 0 &&
+        this->layer_param_.image_data_param().rand_skip() == 0) {
+      LOG(WARNING) << "Shuffling or skipping recommended for multi-GPU";
+    }
   }
   LOG(INFO) << "A total of " << lines_.size() << " images.";
 
@@ -77,8 +82,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int batch_size = this->layer_param_.image_data_param().batch_size();
   CHECK_GT(batch_size, 0) << "Positive batch size required";
   top_shape[0] = batch_size;
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].data_.Reshape(top_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->data_.Reshape(top_shape);
   }
   top[0]->Reshape(top_shape);
 
@@ -88,8 +93,8 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].label_.Reshape(label_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->label_.Reshape(label_shape);
   }
 }
 
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 103dd4b6af8..1bf3760e9fd 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -173,8 +173,8 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   top[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i)
-    this->prefetch_[i].data_.Reshape(
+  for (int i = 0; i < this->prefetch_.size(); ++i)
+    this->prefetch_[i]->data_.Reshape(
         batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << top[0]->num() << ","
@@ -183,8 +183,8 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
-    this->prefetch_[i].label_.Reshape(label_shape);
+  for (int i = 0; i < this->prefetch_.size(); ++i) {
+    this->prefetch_[i]->label_.Reshape(label_shape);
   }
 
   // data mean
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 644cb7e97ee..aa9e8f2f386 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -22,16 +22,13 @@
 namespace caffe {
 
 template <typename Dtype>
-Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
-    : root_net_(root_net) {
+Net<Dtype>::Net(const NetParameter& param) {
   Init(param);
 }
 
 template <typename Dtype>
 Net<Dtype>::Net(const string& param_file, Phase phase,
-    const int level, const vector<string>* stages,
-    const Net* root_net)
-    : root_net_(root_net) {
+    const int level, const vector<string>* stages) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
   // Set phase, stages and level
@@ -47,8 +44,6 @@ Net<Dtype>::Net(const string& param_file, Phase phase,
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
-  CHECK(Caffe::root_solver() || root_net_)
-      << "root_net_ needs to be set for all non-root solvers";
   // Set phase from the state.
   phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
@@ -74,9 +69,6 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_id_vecs_.resize(param.layer_size());
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
-    // For non-root solvers, whether this layer is shared from root_net_.
-    bool share_from_root = !Caffe::root_solver()
-        && root_net_->layers_[layer_id]->ShareInParallel();
     // Inherit phase from net if unset.
     if (!param.layer(layer_id).has_phase()) {
       param.mutable_layer(layer_id)->set_phase(phase_);
@@ -89,13 +81,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
           << "propagate_down param must be specified "
           << "either 0 or bottom_size times ";
     }
-    if (share_from_root) {
-      LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
-      layers_.push_back(root_net_->layers_[layer_id]);
-      layers_[layer_id]->SetShared(true);
-    } else {
-      layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
-    }
+    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
     layer_names_.push_back(layer_param.name());
     LOG_IF(INFO, Caffe::root_solver())
         << "Creating Layer " << layer_param.name();
@@ -134,19 +120,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     // After this layer is connected, set it up.
-    if (share_from_root) {
-      // Set up size of top blobs using root_net_
-      const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
-      const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];
-      for (int top_id = 0; top_id < base_top.size(); ++top_id) {
-        this_top[top_id]->ReshapeLike(*base_top[top_id]);
-        LOG(INFO) << "Created top blob " << top_id << " (shape: "
-            << this_top[top_id]->shape_string() <<  ") for shared layer "
-            << layer_param.name();
-      }
-    } else {
-      layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
-    }
+    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
     LOG_IF(INFO, Caffe::root_solver())
         << "Setting up " << layer_names_[layer_id];
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
@@ -546,10 +520,15 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
   for (int i = start; i <= end; ++i) {
-    // LOG(ERROR) << "Forwarding " << layer_names_[i];
+    for (int c = 0; c < before_forward_.size(); ++c) {
+      before_forward_[c]->run(i);
+    }
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
+    for (int c = 0; c < after_forward_.size(); ++c) {
+      after_forward_[c]->run(i);
+    }
   }
   return loss;
 }
@@ -591,11 +570,17 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
   CHECK_GE(end, 0);
   CHECK_LT(start, layers_.size());
   for (int i = start; i >= end; --i) {
+    for (int c = 0; c < before_backward_.size(); ++c) {
+      before_backward_[c]->run(i);
+    }
     if (layer_need_backward_[i]) {
       layers_[i]->Backward(
           top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);
       if (debug_info_) { BackwardDebugInfo(i); }
     }
+    for (int c = 0; c < after_backward_.size(); ++c) {
+      after_backward_[c]->run(i);
+    }
   }
 }
 
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
index 5bc41c6a6e5..d9433917d25 100644
--- a/src/caffe/parallel.cpp
+++ b/src/caffe/parallel.cpp
@@ -1,16 +1,15 @@
-#ifndef CPU_ONLY
+#ifdef USE_NCCL
+
 #include <cuda_runtime.h>
-#endif
 #include <glog/logging.h>
 #include <stdio.h>
-
 #include <sstream>
 #include <string>
 #include <vector>
 
-#include "boost/thread.hpp"
 #include "caffe/caffe.hpp"
 #include "caffe/parallel.hpp"
+#include "caffe/sgd_solvers.hpp"
 
 namespace caffe {
 
@@ -68,15 +67,14 @@ static size_t total_size(const vector<Blob<Dtype>*>& params) {
 
 template<typename Dtype>
 Params<Dtype>::Params(shared_ptr<Solver<Dtype> > root_solver)
-    : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
-      data_(),
-      diff_() {
+  : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
+    data_(),
+    diff_() {
 }
 
 template<typename Dtype>
 GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
-    : Params<Dtype>(root_solver) {
-#ifndef CPU_ONLY
+  : Params<Dtype>(root_solver) {
   int initial_device;
   CUDA_CHECK(cudaGetDevice(&initial_device));
 
@@ -86,358 +84,288 @@ GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
 
   // Copy blob values
   const vector<Blob<Dtype>*>& net =
-      root_solver->net()->learnable_params();
+    root_solver->net()->learnable_params();
   apply_buffers(net, data_, size_, copy);
 
   CUDA_CHECK(cudaMalloc(&diff_, size_ * sizeof(Dtype)));
   caffe_gpu_set(size_, Dtype(0), diff_);
 
   CUDA_CHECK(cudaSetDevice(initial_device));
-#else
-  NO_GPU;
-#endif
 }
 
 template<typename Dtype>
 GPUParams<Dtype>::~GPUParams() {
-#ifndef CPU_ONLY
   CUDA_CHECK(cudaFree(data_));
   CUDA_CHECK(cudaFree(diff_));
-#endif
 }
 
 template<typename Dtype>
-void GPUParams<Dtype>::configure(Solver<Dtype>* solver) const {
+void GPUParams<Dtype>::Configure(Solver<Dtype>* solver) const {
   const vector<Blob<Dtype>*>& net =
-      solver->net()->learnable_params();
+    solver->net()->learnable_params();
   apply_buffers(net, data_, size_, replace_gpu);
   apply_buffers(net, diff_, size_, replace_gpu_diff);
 }
 
-void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
-#ifndef CPU_ONLY
-  vector<int> remaining(devices);
-
-  // Depth for reduction tree
-  int remaining_depth = static_cast<int>(ceil(log2(remaining.size())));
-
-  // Group GPUs by board
-  for (int d = 0; d < remaining_depth; ++d) {
-    for (int i = 0; i < remaining.size(); ++i) {
-      for (int j = i + 1; j < remaining.size(); ++j) {
-        cudaDeviceProp a, b;
-        CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
-        CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
-        if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
-          if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
-            pairs->push_back(DevicePair(remaining[i], remaining[j]));
-            DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
-            remaining.erase(remaining.begin() + j);
-            break;
-          }
-        }
-      }
-    }
-  }
-  ostringstream s;
-  for (int i = 0; i < remaining.size(); ++i) {
-    s << (i ? ", " : "") << remaining[i];
-  }
-  DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str();
-
-  // Group by P2P accessibility
-  remaining_depth = ceil(log2(remaining.size()));
-  for (int d = 0; d < remaining_depth; ++d) {
-    for (int i = 0; i < remaining.size(); ++i) {
-      for (int j = i + 1; j < remaining.size(); ++j) {
-        int access;
-        CUDA_CHECK(
-            cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
-        if (access) {
-          pairs->push_back(DevicePair(remaining[i], remaining[j]));
-          DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
-          remaining.erase(remaining.begin() + j);
-          break;
-        }
-      }
-    }
-  }
-  s.str("");
-  for (int i = 0; i < remaining.size(); ++i) {
-    s << (i ? ", " : "") << remaining[i];
-  }
-  DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str();
-
-  // Group remaining
-  remaining_depth = ceil(log2(remaining.size()));
-  for (int d = 0; d < remaining_depth; ++d) {
-    for (int i = 0; i < remaining.size(); ++i) {
-      pairs->push_back(DevicePair(remaining[i], remaining[i + 1]));
-      DLOG(INFO) << "Remaining pair: " << remaining[i] << ":"
-                 << remaining[i + 1];
-      remaining.erase(remaining.begin() + i + 1);
-    }
-  }
+static int getDevice() {
+  int device = 0;
+  CUDA_CHECK(cudaGetDevice(&device));
+  return device;
+}
 
-  // Should only be the parent node remaining
-  CHECK_EQ(remaining.size(), 1);
+template<typename Dtype>
+NCCL<Dtype>::NCCL(shared_ptr<Solver<Dtype> > solver)
+  : GPUParams<Dtype>(solver, getDevice()),
+    comm_(), solver_(solver), barrier_() {
+  this->Configure(solver.get());
+  Init();
+}
 
-  pairs->insert(pairs->begin(), DevicePair(-1, remaining[0]));
+template<typename Dtype>
+NCCL<Dtype>::NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid)
+  : GPUParams<Dtype>(solver, getDevice()),
+    solver_(solver), barrier_() {
+  this->Configure(solver.get());
+  Caffe::set_multiprocess(true);
+  ncclUniqueId nccl_uid;
+  memcpy(&nccl_uid, &uid[0], NCCL_UNIQUE_ID_BYTES);  // NOLINT(caffe/alt_fn)
+  NCCL_CHECK(ncclCommInitRank(&comm_,
+                              Caffe::solver_count(),
+                              nccl_uid,
+                              Caffe::solver_rank()));
+  Init();
+}
 
-  CHECK(pairs->size() == devices.size());
-  for (int i = 0; i < pairs->size(); ++i) {
-    CHECK((*pairs)[i].parent() != (*pairs)[i].device());
-    for (int j = i + 1; j < pairs->size(); ++j) {
-      CHECK((*pairs)[i].device() != (*pairs)[j].device());
-    }
+template<typename Dtype>
+void NCCL<Dtype>::Init() {
+  if (solver_->param().layer_wise_reduce()) {
+    CUDA_CHECK(cudaStreamCreateWithFlags(&stream_, cudaStreamNonBlocking));
   }
-#else
-  NO_GPU;
-#endif
 }
 
-//
-
 template<typename Dtype>
-P2PSync<Dtype>::P2PSync(shared_ptr<Solver<Dtype> > root_solver,
-                        P2PSync<Dtype>* parent, const SolverParameter& param)
-    : GPUParams<Dtype>(root_solver, param.device_id()),
-      parent_(parent),
-      children_(),
-      queue_(),
-      initial_iter_(root_solver->iter()),
-      solver_() {
-#ifndef CPU_ONLY
-  int initial_device;
-  CUDA_CHECK(cudaGetDevice(&initial_device));
-  const int self = param.device_id();
-  CUDA_CHECK(cudaSetDevice(self));
-
-  if (parent == NULL) {
-    solver_ = root_solver;
-  } else {
-    Caffe::set_root_solver(false);
-    solver_.reset(new WorkerSolver<Dtype>(param, root_solver.get()));
-    Caffe::set_root_solver(true);
+NCCL<Dtype>::~NCCL() {
+  if (solver_->param().layer_wise_reduce()) {
+    CUDA_CHECK(cudaStreamDestroy(stream_));
   }
-  this->configure(solver_.get());
-  solver_->add_callback(this);
-
-  if (parent) {
-    // Enable p2p access between devices
-    const int peer = parent->solver_->param().device_id();
-    int access;
-    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
-    if (access) {
-      CUDA_CHECK(cudaDeviceEnablePeerAccess(peer, 0));
-    } else {
-      LOG(INFO)<< "GPU " << self << " does not have p2p access to GPU " << peer;
-    }
-    // Allocate receiving buffer on parent
-    CUDA_CHECK(cudaSetDevice(peer));
-    CUDA_CHECK(cudaMalloc(&parent_grads_, size_ * sizeof(Dtype)));
-    CUDA_CHECK(cudaSetDevice(self));
+  if (comm_) {
+    ncclCommDestroy(comm_);
   }
-
-  CUDA_CHECK(cudaSetDevice(initial_device));
-#else
-  NO_GPU;
-#endif
 }
 
 template<typename Dtype>
-P2PSync<Dtype>::~P2PSync() {
-#ifndef CPU_ONLY
-  int initial_device;
-  CUDA_CHECK(cudaGetDevice(&initial_device));
-  const int self = solver_->param().device_id();
-  CUDA_CHECK(cudaSetDevice(self));
-
-  if (parent_) {
-    CUDA_CHECK(cudaFree(parent_grads_));
-    const int peer = parent_->solver_->param().device_id();
-    int access;
-    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
-    if (access) {
-      CUDA_CHECK(cudaDeviceDisablePeerAccess(peer));
-    }
-  }
-
-  CUDA_CHECK(cudaSetDevice(initial_device));
-#endif
+boost::barrier* NCCL<Dtype>::barrier() {
+  return barrier_;
+}
+template<typename Dtype>
+void NCCL<Dtype>::set_barrier(boost::barrier* value) {
+  barrier_ = value;
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::InternalThreadEntry() {
-  Caffe::SetDevice(solver_->param().device_id());
-  CHECK(Caffe::root_solver());
-  Caffe::set_root_solver(false);
-  // See if there is a defined seed and reset random state if so
-  if (solver_->param().random_seed() >= 0) {
-    // Fetch random seed and modulate by device ID to make sure
-    // everyone doesn't have the same seed.  We seem to have some
-    // solver instability if we have everyone with the same seed
-    Caffe::set_random_seed(
-        solver_->param().random_seed() + solver_->param().device_id());
+void NCCL<Dtype>::InitSingleProcess(vector<NCCL<Dtype>*>* nccls) {
+  ncclComm_t* comms = new ncclComm_t[nccls->size()];
+  int* gpu_list = new int[nccls->size()];
+  for (int i = 0; i < nccls->size(); ++i) {
+    gpu_list[i] = (*nccls)[i]->solver_->param().device_id();
+  }
+  NCCL_CHECK(ncclCommInitAll(comms, static_cast<int>(nccls->size()), gpu_list));
+  for (int i = 0; i < nccls->size(); ++i) {
+    (*nccls)[i]->comm_ = comms[i];
   }
-  solver_->Step(solver_->param().max_iter() - initial_iter_);
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::on_start() {
-#ifndef CPU_ONLY
-#ifdef DEBUG
-  int device;
-  CUDA_CHECK(cudaGetDevice(&device));
-  CHECK(device == solver_->param().device_id());
-#else
-//  CHECK(false);
-#endif
+string NCCL<Dtype>::new_uid() {
+  string uid;
+  uid.resize(NCCL_UNIQUE_ID_BYTES);
+  ncclUniqueId nccl_uid;
+  NCCL_CHECK(ncclGetUniqueId(&nccl_uid));
+  memcpy(&uid[0], &nccl_uid, NCCL_UNIQUE_ID_BYTES);  // NOLINT(caffe/alt_fn)
+  return uid;
+}
 
-  // Wait for update from parent
-  if (parent_) {
-    P2PSync<Dtype> *parent = queue_.pop();
-    CHECK(parent == parent_);
+template<typename Dtype>
+void NCCL<Dtype>::Broadcast() {
+  if (barrier_) {  // NULL in multi process case
+    barrier_->wait();
   }
-
-  // Update children
-  for (int i = children_.size() - 1; i >= 0; i--) {
-    Dtype* src = data_;
-    Dtype* dst = children_[i]->data_;
-
-#ifdef DEBUG
-    cudaPointerAttributes attributes;
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
-    CHECK(attributes.device == device);
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
-    CHECK(attributes.device == children_[i]->solver_->param().device_id());
-#endif
-
-    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),
-        cudaMemcpyDeviceToDevice, cudaStreamDefault));
-    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-    children_[i]->queue_.push(this);
+  NCCL_CHECK(ncclBcast(data_, static_cast<int>(size_),
+                       nccl::dataType<Dtype>::type, 0,
+                       comm_, cudaStreamDefault));
+  if (barrier_) {
+    barrier_->wait();
   }
-#endif
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::on_gradients_ready() {
-#ifndef CPU_ONLY
+void NCCL<Dtype>::run(int layer) {
+  CHECK(solver_->param().layer_wise_reduce());
+  vector<shared_ptr<Blob<Dtype> > >& blobs =
+    solver_->net()->layers()[layer]->blobs();
 #ifdef DEBUG
-  int device;
-  CUDA_CHECK(cudaGetDevice(&device));
-  CHECK(device == solver_->param().device_id());
+  // Assert blobs are contiguous to reduce in one step (e.g. bias often small)
+  for (int i = 1; i < blobs.size(); ++i) {
+    CHECK_EQ(blobs[i - 1]->gpu_diff() + blobs[i - 1]->count(),
+             blobs[i + 0]->gpu_diff());
+  }
 #endif
+  if (blobs.size() > 0) {
+    // Make sure default stream is done computing gradients. Could be
+    // replaced by cudaEventRecord+cudaStreamWaitEvent to avoid
+    // blocking the default stream, but it's actually slower.
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
 
-  // Sum children gradients as they appear in the queue
-  for (int i = 0; i < children_.size(); ++i) {
-    P2PSync<Dtype> *child = queue_.pop();
-    Dtype* src = child->parent_grads_;
-    Dtype* dst = diff_;
-
-#ifdef DEBUG
-    bool ok = false;
-    for (int j = 0; j < children_.size(); ++j) {
-      if (child == children_[j]) {
-        ok = true;
-      }
+    // Reduce asynchronously
+    int size = 0;
+    for (int i = 0; i < blobs.size(); ++i) {
+      size += blobs[i]->count();
     }
-    CHECK(ok);
-    cudaPointerAttributes attributes;
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
-    CHECK(attributes.device == device);
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
-    CHECK(attributes.device == device);
-#endif
-
-    caffe_gpu_add(size_, src, dst, dst);
+    if (barrier_) {  // NULL in multi process case
+      barrier_->wait();
+    }
+    NCCL_CHECK(ncclAllReduce(blobs[0]->mutable_gpu_diff(),
+                             blobs[0]->mutable_gpu_diff(),
+                             size,
+                             nccl::dataType<Dtype>::type,
+                             ncclSum, comm_, stream_));
+    caffe_gpu_scal(size, (Dtype) 1.0 / Caffe::solver_count(),
+                   blobs[0]->mutable_gpu_diff(), stream_);
   }
+}
 
-  // Send gradients to parent
-  if (parent_) {
-    Dtype* src = diff_;
-    Dtype* dst = parent_grads_;
-
-#ifdef DEBUG
-    cudaPointerAttributes attributes;
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
-    CHECK(attributes.device == device);
-    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
-    CHECK(attributes.device == parent_->solver_->param().device_id());
-#endif
-
-    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),  //
-        cudaMemcpyDeviceToDevice, cudaStreamDefault));
-    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
-    parent_->queue_.push(this);
+template<typename Dtype>
+void NCCL<Dtype>::on_gradients_ready() {
+  if (solver_->param().layer_wise_reduce()) {
+    CHECK_EQ(solver_->net()->params().size(),
+             solver_->net()->learnable_params().size())
+      << "Layer-wise reduce is not supported for nets with shared weights.";
+
+    // Make sure reduction is done before applying gradients
+    CUDA_CHECK(cudaStreamSynchronize(stream_));
   } else {
-    // Loss functions divide gradients by the batch size, so to compensate
-    // for split batch, the root solver divides by number of solvers.
-    caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_);
+    if (barrier_) {  // NULL in multi process case
+      barrier_->wait();
+    }
+    NCCL_CHECK(ncclAllReduce(diff_, diff_, static_cast<int>(size_),
+                             nccl::dataType<Dtype>::type, ncclSum, comm_,
+                             cudaStreamDefault));
+    caffe_gpu_scal(static_cast<int>(size_),
+                   (Dtype) 1.0 / Caffe::solver_count(), diff_);
   }
-#endif
 }
 
 template<typename Dtype>
-void P2PSync<Dtype>::Prepare(const vector<int>& gpus,
-            vector<shared_ptr<P2PSync<Dtype> > >* syncs) {
-  // Pair devices for map-reduce synchronization
-  vector<DevicePair> pairs;
-  DevicePair::compute(gpus, &pairs);
-  ostringstream s;
-  for (int i = 1; i < pairs.size(); ++i) {
-    s << (i == 1 ? "" : ", ") << pairs[i].parent() << ":" << pairs[i].device();
+class Worker : public InternalThread {
+ public:
+  explicit Worker(shared_ptr<Solver<Dtype> > rank0, int device,
+                  boost::barrier* barrier, vector<NCCL<Dtype>*>* nccls,
+                  const char* restore)
+    : rank0_(rank0), device_(device), barrier_(barrier),
+      nccls_(nccls), restore_(restore) {
   }
-  LOG(INFO)<< "GPUs pairs " << s.str();
-
-  SolverParameter param(solver_->param());
-
-  // Build the GPU tree by finding the parent for each solver
-  for (int attempts = 0; attempts < pairs.size(); ++attempts) {
-    for (int i = 1; i < pairs.size(); ++i) {
-      if (!syncs->at(i).get()) {
-        P2PSync<Dtype>* parent = NULL;
-        for (int j = 0; j < syncs->size(); ++j) {
-          P2PSync<Dtype>* sync = j == 0 ? this : syncs->at(j).get();
-          if (sync) {
-            const SolverParameter& p = sync->solver()->param();
-            if (p.device_id() == pairs[i].parent()) {
-              parent = sync;
-            }
-          }
-        }
-        if (parent) {
-          param.set_device_id(pairs[i].device());
-          syncs->at(i).reset(new P2PSync<Dtype>(solver_, parent, param));
-          parent->children_.push_back((P2PSync<Dtype>*) syncs->at(i).get());
-        }
+  virtual ~Worker() {}
+
+ protected:
+  void InternalThreadEntry() {
+    // Create solver and install callbacks
+    SolverParameter param(rank0_->param());
+    param.set_device_id(device_);
+#ifdef DEBUG
+    int device;
+    CUDA_CHECK(cudaGetDevice(&device));
+    CHECK_EQ(device, device_);
+#endif
+    param.set_type(rank0_->type());
+    shared_ptr<Solver<Dtype> > s(SolverRegistry<Dtype>::CreateSolver(param));
+    CHECK_EQ(s->type(), rank0_->type());
+    if (restore_) {
+      // Could not make NCCL broadcast solver state, it seems to crash
+      // if called in a tight loop, regardless of barriers etc. so
+      // restore all solvers from file.
+      s->Restore(restore_);
+    }
+    NCCL<Dtype> nccl(s);
+    nccl.set_barrier(barrier_);
+    s->add_callback(&nccl);
+    if (s->param().layer_wise_reduce()) {
+      s->net()->add_after_backward(&nccl);
+    }
+    (*nccls_)[Caffe::solver_rank()] = &nccl;
+    // Wait for other threads
+    barrier_->wait();
+    // Wait for NCCL init
+    barrier_->wait();
+    // Broadcast rank 0 state
+    nccl.Broadcast();
+    // Solve
+    s->Step(param.max_iter() - s->iter());
+    barrier_->wait();
+#ifdef DEBUG
+    // Check all solvers have same state
+    SGDSolver<Dtype>* sa = static_cast<SGDSolver<Dtype>*>(rank0_.get());
+    SGDSolver<Dtype>* sb = static_cast<SGDSolver<Dtype>*>(s.get());
+    for (int h = 0; h < sa->history().size(); ++h) {
+      CUDA_CHECK(cudaSetDevice(sa->param().device_id()));
+      const Dtype* a = sa->history()[h]->cpu_data();
+      CUDA_CHECK(cudaSetDevice(sb->param().device_id()));
+      const Dtype* b = sb->history()[h]->cpu_data();
+      for (int v = 0; v < sa->history()[h]->count(); ++v) {
+        CHECK_DOUBLE_EQ(a[v], b[v]);
       }
     }
+#endif
   }
-}
-
-template<typename Dtype>
-void P2PSync<Dtype>::Run(const vector<int>& gpus) {
-  vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
-  Prepare(gpus, &syncs);
 
-  LOG(INFO)<< "Starting Optimization";
+  shared_ptr<Solver<Dtype> > rank0_;
+  int device_;
+  boost::barrier* barrier_;
+  vector<NCCL<Dtype>*>* nccls_;
+  const char* restore_;
+};
 
-  for (int i = 1; i < syncs.size(); ++i) {
-    syncs[i]->StartInternalThread();
+template<typename Dtype>
+void NCCL<Dtype>::Run(const vector<int>& gpus, const char* restore) {
+  boost::barrier barrier(static_cast<int>(gpus.size()));
+  vector<NCCL<Dtype>*> nccls(gpus.size());
+  // Create workers
+  vector<shared_ptr<Worker<Dtype> > > workers(gpus.size());
+  for (int i = 1; i < gpus.size(); ++i) {
+    CUDA_CHECK(cudaSetDevice(gpus[i]));
+    Caffe::set_solver_rank(i);
+    Worker<Dtype>* w = new Worker<Dtype>(solver_, gpus[i], &barrier,
+                                         &nccls, restore);
+    w->StartInternalThread();
+    workers[i].reset(w);
   }
-
-  // Run root solver on current thread
+  CUDA_CHECK(cudaSetDevice(gpus[0]));
+  Caffe::set_solver_rank(0);
+  barrier_ = &barrier;
+  solver_->add_callback(this);
+  if (solver_->param().layer_wise_reduce()) {
+    solver_->net()->add_after_backward(this);
+  }
+  nccls[0] = this;
+  // Wait for workers
+  barrier.wait();
+  // Init NCCL
+  InitSingleProcess(&nccls);
+  barrier.wait();
+  // Run first solver on current thread
+  Broadcast();
   solver_->Solve();
-
-  for (int i = 1; i < syncs.size(); ++i) {
-    syncs[i]->StopInternalThread();
+  barrier.wait();  // Hangs without it when running tests
+  // Wait for shutdown
+  for (int i = 1; i < gpus.size(); ++i) {
+    workers[i]->StopInternalThread();
   }
 }
 
 INSTANTIATE_CLASS(Params);
 INSTANTIATE_CLASS(GPUParams);
-INSTANTIATE_CLASS(P2PSync);
+INSTANTIATE_CLASS(Worker);
+INSTANTIATE_CLASS(NCCL);
 
 }  // namespace caffe
+
+#endif  // USE_NCCL
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 430a0dea109..1c85f69698f 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 41 (last added: type)
+// SolverParameter next available ID: 42 (last added: layer_wise_reduce)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -239,6 +239,9 @@ message SolverParameter {
   }
   // DEPRECATED: use type instead of solver_type
   optional SolverType solver_type = 30 [default = SGD];
+
+  // Overlap compute and communication for data parallel training
+  optional bool layer_wise_reduce = 41 [default = true];
 }
 
 // A message that stores the solver snapshots
@@ -655,8 +658,8 @@ message DataParameter {
   optional bool mirror = 6 [default = false];
   // Force the encoded image to have 3 color channels
   optional bool force_encoded_color = 9 [default = false];
-  // Prefetch queue (Number of batches to prefetch to host memory, increase if
-  // data access bandwidth varies).
+  // Prefetch queue (Increase if data feeding bandwidth varies, within the
+  // limit of device memory for GPU training)
   optional uint32 prefetch = 10 [default = 4];
 }
 
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index ece3913e88a..1c1a9e59565 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -26,16 +26,14 @@ SolverAction::Enum Solver<Dtype>::GetRequestedAction() {
 }
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const SolverParameter& param, const Solver* root_solver)
-    : net_(), callbacks_(), root_solver_(root_solver),
-      requested_early_exit_(false) {
+Solver<Dtype>::Solver(const SolverParameter& param)
+    : net_(), callbacks_(), requested_early_exit_(false) {
   Init(param);
 }
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
-    : net_(), callbacks_(), root_solver_(root_solver),
-      requested_early_exit_(false) {
+Solver<Dtype>::Solver(const string& param_file)
+    : net_(), callbacks_(), requested_early_exit_(false) {
   SolverParameter param;
   ReadSolverParamsFromTextFileOrDie(param_file, &param);
   Init(param);
@@ -43,15 +41,13 @@ Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
-  CHECK(Caffe::root_solver() || root_solver_)
-      << "root_solver_ needs to be set for all non-root solvers";
   LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
     << std::endl << param.DebugString();
   param_ = param;
   CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
   CheckSnapshotWritePermissions();
-  if (Caffe::root_solver() && param_.random_seed() >= 0) {
-    Caffe::set_random_seed(param_.random_seed());
+  if (param_.random_seed() >= 0) {
+    Caffe::set_random_seed(param_.random_seed() + Caffe::solver_rank());
   }
   // Scaffolding code
   InitTrainNet();
@@ -101,11 +97,7 @@ void Solver<Dtype>::InitTrainNet() {
   net_state.MergeFrom(net_param.state());
   net_state.MergeFrom(param_.train_state());
   net_param.mutable_state()->CopyFrom(net_state);
-  if (Caffe::root_solver()) {
-    net_.reset(new Net<Dtype>(net_param));
-  } else {
-    net_.reset(new Net<Dtype>(net_param, root_solver_->net_.get()));
-  }
+  net_.reset(new Net<Dtype>(net_param));
 }
 
 template <typename Dtype>
@@ -180,12 +172,7 @@ void Solver<Dtype>::InitTestNets() {
     net_params[i].mutable_state()->CopyFrom(net_state);
     LOG(INFO)
         << "Creating test net (#" << i << ") specified by " << sources[i];
-    if (Caffe::root_solver()) {
-      test_nets_[i].reset(new Net<Dtype>(net_params[i]));
-    } else {
-      test_nets_[i].reset(new Net<Dtype>(net_params[i],
-          root_solver_->test_nets_[i].get()));
-    }
+    test_nets_[i].reset(new Net<Dtype>(net_params[i]));
     test_nets_[i]->set_debug_info(param_.debug_info());
   }
 }
@@ -197,14 +184,16 @@ void Solver<Dtype>::Step(int iters) {
   int average_loss = this->param_.average_loss();
   losses_.clear();
   smoothed_loss_ = 0;
+  iteration_timer_.Start();
 
   while (iter_ < stop_iter) {
     // zero-init the params
     net_->ClearParamDiffs();
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
-        && (iter_ > 0 || param_.test_initialization())
-        && Caffe::root_solver()) {
-      TestAll();
+        && (iter_ > 0 || param_.test_initialization())) {
+      if (Caffe::root_solver()) {
+        TestAll();
+      }
       if (requested_early_exit_) {
         // Break out of the while loop because stop was requested while testing.
         break;
@@ -225,8 +214,13 @@ void Solver<Dtype>::Step(int iters) {
     // average the loss across iterations for smoothed reporting
     UpdateSmoothedLoss(loss, start_iter, average_loss);
     if (display) {
+      float lapse = iteration_timer_.Seconds();
+      float per_s = (iter_ - iterations_last_) / (lapse ? lapse : 1);
       LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
-          << ", loss = " << smoothed_loss_;
+          << " (" << per_s << " iter/s, " << lapse << "s/"
+          << param_.display() << " iters), loss = " << smoothed_loss_;
+      iteration_timer_.Start();
+      iterations_last_ = iter_;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp
index e78eadca141..d8107e1e623 100644
--- a/src/caffe/solvers/adagrad_solver.cpp
+++ b/src/caffe/solvers/adagrad_solver.cpp
@@ -12,7 +12,6 @@ void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
 
 template <typename Dtype>
 void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  CHECK(Caffe::root_solver());
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype delta = this->param_.delta();
diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp
index 23ab2d4369a..7c1fac1f884 100644
--- a/src/caffe/solvers/nesterov_solver.cpp
+++ b/src/caffe/solvers/nesterov_solver.cpp
@@ -12,7 +12,6 @@ void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
 
 template <typename Dtype>
 void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
-  CHECK(Caffe::root_solver());
   const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
   const vector<float>& net_params_lr = this->net_->params_lr();
   Dtype momentum = this->param_.momentum();
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index f30f316d1a0..ad6abe54a0a 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -100,10 +100,10 @@ void SGDSolver<Dtype>::ClipGradients() {
 
 template <typename Dtype>
 void SGDSolver<Dtype>::ApplyUpdate() {
-  CHECK(Caffe::root_solver());
   Dtype rate = GetLearningRate();
   if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+    LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
+        << ", lr = " << rate;
   }
   ClipGradients();
   for (int param_id = 0; param_id < this->net_->learnable_params().size();
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 4d3564172ab..88d9b78510a 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -3,26 +3,41 @@
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
+SyncedMemory::SyncedMemory()
+  : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
+    own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false) {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  CUDA_CHECK(cudaGetDevice(&device_));
+#endif
+#endif
+}
+
+SyncedMemory::SyncedMemory(size_t size)
+  : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
+    own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false) {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  CUDA_CHECK(cudaGetDevice(&device_));
+#endif
+#endif
+}
 
 SyncedMemory::~SyncedMemory() {
+  check_device();
   if (cpu_ptr_ && own_cpu_data_) {
     CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
   }
 
 #ifndef CPU_ONLY
   if (gpu_ptr_ && own_gpu_data_) {
-    int initial_device;
-    cudaGetDevice(&initial_device);
-    if (gpu_device_ != -1) {
-      CUDA_CHECK(cudaSetDevice(gpu_device_));
-    }
     CUDA_CHECK(cudaFree(gpu_ptr_));
-    cudaSetDevice(initial_device);
   }
 #endif  // CPU_ONLY
 }
 
 inline void SyncedMemory::to_cpu() {
+  check_device();
   switch (head_) {
   case UNINITIALIZED:
     CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
@@ -49,10 +64,10 @@ inline void SyncedMemory::to_cpu() {
 }
 
 inline void SyncedMemory::to_gpu() {
+  check_device();
 #ifndef CPU_ONLY
   switch (head_) {
   case UNINITIALIZED:
-    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     caffe_gpu_memset(size_, 0, gpu_ptr_);
     head_ = HEAD_AT_GPU;
@@ -60,7 +75,6 @@ inline void SyncedMemory::to_gpu() {
     break;
   case HEAD_AT_CPU:
     if (gpu_ptr_ == NULL) {
-      CUDA_CHECK(cudaGetDevice(&gpu_device_));
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
       own_gpu_data_ = true;
     }
@@ -77,11 +91,13 @@ inline void SyncedMemory::to_gpu() {
 }
 
 const void* SyncedMemory::cpu_data() {
+  check_device();
   to_cpu();
   return (const void*)cpu_ptr_;
 }
 
 void SyncedMemory::set_cpu_data(void* data) {
+  check_device();
   CHECK(data);
   if (own_cpu_data_) {
     CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
@@ -92,6 +108,7 @@ void SyncedMemory::set_cpu_data(void* data) {
 }
 
 const void* SyncedMemory::gpu_data() {
+  check_device();
 #ifndef CPU_ONLY
   to_gpu();
   return (const void*)gpu_ptr_;
@@ -102,16 +119,11 @@ const void* SyncedMemory::gpu_data() {
 }
 
 void SyncedMemory::set_gpu_data(void* data) {
+  check_device();
 #ifndef CPU_ONLY
   CHECK(data);
   if (own_gpu_data_) {
-    int initial_device;
-    cudaGetDevice(&initial_device);
-    if (gpu_device_ != -1) {
-      CUDA_CHECK(cudaSetDevice(gpu_device_));
-    }
     CUDA_CHECK(cudaFree(gpu_ptr_));
-    cudaSetDevice(initial_device);
   }
   gpu_ptr_ = data;
   head_ = HEAD_AT_GPU;
@@ -122,12 +134,14 @@ void SyncedMemory::set_gpu_data(void* data) {
 }
 
 void* SyncedMemory::mutable_cpu_data() {
+  check_device();
   to_cpu();
   head_ = HEAD_AT_CPU;
   return cpu_ptr_;
 }
 
 void* SyncedMemory::mutable_gpu_data() {
+  check_device();
 #ifndef CPU_ONLY
   to_gpu();
   head_ = HEAD_AT_GPU;
@@ -140,9 +154,9 @@ void* SyncedMemory::mutable_gpu_data() {
 
 #ifndef CPU_ONLY
 void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
+  check_device();
   CHECK(head_ == HEAD_AT_CPU);
   if (gpu_ptr_ == NULL) {
-    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     own_gpu_data_ = true;
   }
@@ -153,5 +167,20 @@ void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
 }
 #endif
 
+void SyncedMemory::check_device() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  int device;
+  cudaGetDevice(&device);
+  CHECK(device == device_);
+  if (gpu_ptr_ && own_gpu_data_) {
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, gpu_ptr_));
+    CHECK(attributes.device == device_);
+  }
+#endif
+#endif
+}
+
 }  // namespace caffe
 
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 3e8d113d918..3835af1f173 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -105,6 +105,32 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void TestSkip() {
+    LayerParameter param;
+    param.set_phase(TRAIN);
+    DataParameter* data_param = param.mutable_data_param();
+    int batch_size = 5;
+    data_param->set_batch_size(batch_size);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
+    Caffe::set_solver_count(8);
+    for (int dev = 0; dev < Caffe::solver_count(); ++dev) {
+      Caffe::set_solver_rank(dev);
+      DataLayer<Dtype> layer(param);
+      layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+      int label = dev;
+      for (int iter = 0; iter < 10; ++iter) {
+        layer.Forward(blob_bottom_vec_, blob_top_vec_);
+        for (int i = 0; i < batch_size; ++i) {
+          EXPECT_EQ(label % batch_size, blob_top_label_->cpu_data()[i]);
+          label += Caffe::solver_count();
+        }
+      }
+    }
+    Caffe::set_solver_count(1);
+    Caffe::set_solver_rank(0);
+  }
+
   void TestReshape(DataParameter_DB backend) {
     const int num_inputs = 5;
     // Save data of varying shapes.
@@ -356,6 +382,11 @@ TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestSkipLevelDB) {
+  this->Fill(false, DataParameter_DB_LEVELDB);
+  this->TestSkip();
+}
+
 TYPED_TEST(DataLayerTest, TestReshapeLevelDB) {
   this->TestReshape(DataParameter_DB_LEVELDB);
 }
@@ -396,6 +427,11 @@ TYPED_TEST(DataLayerTest, TestReadLMDB) {
   this->TestRead();
 }
 
+TYPED_TEST(DataLayerTest, TestSkipLMDB) {
+  this->Fill(false, DataParameter_DB_LMDB);
+  this->TestSkip();
+}
+
 TYPED_TEST(DataLayerTest, TestReshapeLMDB) {
   this->TestReshape(DataParameter_DB_LMDB);
 }
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 975a8f0f88a..6ad0d8f6544 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -36,7 +36,9 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
   string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
-  shared_ptr<P2PSync<Dtype> > sync_;
+#ifdef USE_NCCL
+  shared_ptr<NCCL<Dtype> > nccl_;
+#endif
   int seed_;
   // Dimensions are determined by generate_sample_data.py
   // TODO this is brittle and the hdf5 file should be checked instead.
@@ -85,6 +87,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "lr_policy: 'fixed' "
        "iter_size: " << iter_size << " "
        "device_id: " << device_id << " "
+       "layer_wise_reduce: " << (!share_) << " "
        "net_param { "
        "  name: 'TestNetwork' "
        "  layer { "
@@ -183,7 +186,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
     Caffe::set_random_seed(this->seed_);
     this->InitSolverFromProtoString(proto.str());
-    if (from_snapshot != NULL) {
+    if (from_snapshot) {
       this->solver_->Restore(from_snapshot);
       for (int i = 0; i < this->solver_->iter(); ++i) {
         this->solver_->net()->Forward();
@@ -202,9 +205,10 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
           gpus.push_back(i);
       }
       Caffe::set_solver_count(gpus.size());
-      this->sync_.reset(new P2PSync<Dtype>(
-          this->solver_, NULL, this->solver_->param()));
-      this->sync_->Run(gpus);
+#ifdef USE_NCCL
+      this->nccl_.reset(new NCCL<Dtype>(this->solver_));
+      this->nccl_->Run(gpus, from_snapshot);
+#endif
       Caffe::set_solver_count(1);
     }
     if (snapshot) {
@@ -457,12 +461,28 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const int kIterSize = 1;
     // Test over all numbers of devices.
     int available_devices = 1;
-#ifndef CPU_ONLY
+#ifdef USE_NCCL
     if (Caffe::mode() == Caffe::GPU) {
       CUDA_CHECK(cudaGetDeviceCount(&available_devices));
     }
 #endif
-    for (int devices = 1; devices <= available_devices; ++devices) {
+    // Takes a while to test all sizes for each test so sparse
+    vector<int> sizes;
+    sizes.push_back(1);
+    if (available_devices >= 2) {
+      sizes.push_back(2);
+    }
+    if (available_devices >= 3) {
+      sizes.push_back(3);
+    }
+    if (available_devices >= 8) {
+      sizes.push_back(8);
+    }
+    if (available_devices >= 16) {
+      sizes.push_back(16);
+    }
+    for (int i = 0; i < sizes.size(); ++i) {
+      int devices = sizes[i];
       // Configure batch size for single / multi device equivalence.
       // Constant data is needed for multi device as for accumulation.
       num_ = kNum * devices;
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 8884ce95a23..68e10286d0b 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -133,4 +133,34 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   }
 }
 
+TYPED_TEST(HDF5DataLayerTest, TestSkip) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter param;
+  param.add_top("data");
+  param.add_top("label");
+
+  HDF5DataParameter* hdf5_data_param = param.mutable_hdf5_data_param();
+  int batch_size = 5;
+  hdf5_data_param->set_batch_size(batch_size);
+  hdf5_data_param->set_source(*(this->filename));
+
+  Caffe::set_solver_count(8);
+  for (int dev = 0; dev < Caffe::solver_count(); ++dev) {
+    Caffe::set_solver_rank(dev);
+
+    HDF5DataLayer<Dtype> layer(param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    int label = dev;
+    for (int iter = 0; iter < 1; ++iter) {
+      layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+      for (int i = 0; i < batch_size; ++i) {
+        EXPECT_EQ(1 + label, this->blob_top_label_->cpu_data()[i]);
+        label = (label + Caffe::solver_count()) % (batch_size * 2);
+      }
+    }
+  }
+  Caffe::set_solver_count(1);
+  Caffe::set_solver_rank(0);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
index 058668fe28c..f69d210459c 100644
--- a/src/caffe/util/blocking_queue.cpp
+++ b/src/caffe/util/blocking_queue.cpp
@@ -1,7 +1,6 @@
 #include <boost/thread.hpp>
 #include <string>
 
-#include "caffe/data_reader.hpp"
 #include "caffe/layers/base_data_layer.hpp"
 #include "caffe/parallel.hpp"
 #include "caffe/util/blocking_queue.hpp"
@@ -88,9 +87,5 @@ size_t BlockingQueue<T>::size() const {
 
 template class BlockingQueue<Batch<float>*>;
 template class BlockingQueue<Batch<double>*>;
-template class BlockingQueue<Datum*>;
-template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;
-template class BlockingQueue<P2PSync<float>*>;
-template class BlockingQueue<P2PSync<double>*>;
 
 }  // namespace caffe
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
index fb1d4956aa1..491a9bd03a6 100644
--- a/src/caffe/util/db_lmdb.cpp
+++ b/src/caffe/util/db_lmdb.cpp
@@ -32,7 +32,7 @@ void LMDB::Open(const string& source, Mode mode) {
     MDB_CHECK(rc);
   }
 #endif
-  LOG(INFO) << "Opened lmdb " << source;
+  LOG_IF(INFO, Caffe::root_solver()) << "Opened lmdb " << source;
 }
 
 LMDBCursor* LMDB::NewCursor() {
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 4c587537435..6d001026082 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -90,6 +90,26 @@ void caffe_gpu_scal<double>(const int N, const double alpha, double *X) {
   CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
 }
 
+template <>
+void caffe_gpu_scal<float>(const int N, const float alpha, float* X,
+                           cudaStream_t str) {
+  cudaStream_t initial_stream;
+  CUBLAS_CHECK(cublasGetStream(Caffe::cublas_handle(), &initial_stream));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), str));
+  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), initial_stream));
+}
+
+template <>
+void caffe_gpu_scal<double>(const int N, const double alpha, double* X,
+                            cudaStream_t str) {
+  cudaStream_t initial_stream;
+  CUBLAS_CHECK(cublasGetStream(Caffe::cublas_handle(), &initial_stream));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), str));
+  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+  CUBLAS_CHECK(cublasSetStream(Caffe::cublas_handle(), initial_stream));
+}
+
 template <>
 void caffe_gpu_axpby<float>(const int N, const float alpha, const float* X,
     const float beta, float* Y) {
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 9bf4214ad93..3587d8aa1be 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -195,6 +195,7 @@ int train() {
   // If the gpus flag is not provided, allow the mode and device to be set
   // in the solver prototxt.
   if (FLAGS_gpu.size() == 0
+      && solver_param.has_solver_mode()
       && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
       if (solver_param.has_device_id()) {
           FLAGS_gpu = "" +
@@ -244,11 +245,15 @@ int train() {
     CopyLayers(solver.get(), FLAGS_weights);
   }
 
+  LOG(INFO) << "Starting Optimization";
   if (gpus.size() > 1) {
-    caffe::P2PSync<float> sync(solver, NULL, solver->param());
-    sync.Run(gpus);
+#ifdef USE_NCCL
+    caffe::NCCL<float> nccl(solver);
+    nccl.Run(gpus, FLAGS_snapshot.size() > 0 ? FLAGS_snapshot.c_str() : NULL);
+#else
+    LOG(FATAL) << "Multi-GPU execution not available - rebuild with USE_NCCL";
+#endif
   } else {
-    LOG(INFO) << "Starting Optimization";
     solver->Solve();
   }
   LOG(INFO) << "Optimization Done.";

From e21b42004001879b232daed8f142fbc5a7e0b75d Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 22 Nov 2016 16:46:55 -0800
Subject: [PATCH 1901/2053] Python Multi-GPU

---
 python/caffe/__init__.py |  4 +-
 python/caffe/_caffe.cpp  | 96 ++++++++++++++++++++++++++++++++++++--
 python/caffe/pycaffe.py  |  2 +-
 python/train.py          | 99 ++++++++++++++++++++++++++++++++++++++++
 4 files changed, 193 insertions(+), 8 deletions(-)
 create mode 100644 python/train.py

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 5fc6ec9b920..dde2e9863e4 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
-from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
-from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed
+from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
+from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, Layer, get_solver
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 0a86045bd46..04dac2344a0 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -267,12 +267,12 @@ bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
 }
 
 template<typename Dtype>
-class PythonCallback: public Solver<Dtype>::Callback {
+class SolverCallback: public Solver<Dtype>::Callback {
  protected:
   bp::object on_start_, on_gradients_ready_;
 
  public:
-  PythonCallback(bp::object on_start, bp::object on_gradients_ready)
+  SolverCallback(bp::object on_start, bp::object on_gradients_ready)
     : on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }
   virtual void on_gradients_ready() {
     on_gradients_ready_();
@@ -284,9 +284,61 @@ class PythonCallback: public Solver<Dtype>::Callback {
 template<typename Dtype>
 void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
   bp::object on_gradients_ready) {
-  solver->add_callback(new PythonCallback<Dtype>(on_start, on_gradients_ready));
+  solver->add_callback(new SolverCallback<Dtype>(on_start, on_gradients_ready));
 }
 
+// Seems boost cannot call the base method directly
+void Solver_add_nccl(SGDSolver<Dtype>* solver
+#ifdef USE_NCCL
+  , NCCL<Dtype>* nccl
+#endif
+) {
+#ifdef USE_NCCL
+  solver->add_callback(nccl);
+#endif
+}
+
+template<typename Dtype>
+class NetCallback: public Net<Dtype>::Callback {
+ public:
+  explicit NetCallback(bp::object run) : run_(run) {}
+
+ protected:
+  virtual void run(int layer) {
+    run_(layer);
+  }
+  bp::object run_;
+};
+void Net_before_forward(Net<Dtype>* net, bp::object run) {
+  net->add_before_forward(new NetCallback<Dtype>(run));
+}
+void Net_after_forward(Net<Dtype>* net, bp::object run) {
+  net->add_after_forward(new NetCallback<Dtype>(run));
+}
+void Net_before_backward(Net<Dtype>* net, bp::object run) {
+  net->add_before_backward(new NetCallback<Dtype>(run));
+}
+void Net_after_backward(Net<Dtype>* net, bp::object run) {
+  net->add_after_backward(new NetCallback<Dtype>(run));
+}
+
+void Net_add_nccl(Net<Dtype>* net
+#ifdef USE_NCCL
+  , NCCL<Dtype>* nccl
+#endif
+) {
+#ifdef USE_NCCL
+  net->add_after_backward(nccl);
+#endif
+}
+#ifndef USE_NCCL
+template<typename Dtype>
+class NCCL {
+ public:
+  NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid) {}
+};
+#endif
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -303,6 +355,10 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::def("set_mode_gpu", &set_mode_gpu);
   bp::def("set_random_seed", &set_random_seed);
   bp::def("set_device", &Caffe::SetDevice);
+  bp::def("solver_count", &Caffe::solver_count);
+  bp::def("set_solver_count", &Caffe::set_solver_count);
+  bp::def("solver_rank", &Caffe::solver_rank);
+  bp::def("set_solver_rank", &Caffe::set_solver_rank);
 
   bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
 
@@ -346,7 +402,12 @@ BOOST_PYTHON_MODULE(_caffe) {
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
     .def("save", &Net_Save)
     .def("save_hdf5", &Net_SaveHDF5)
-    .def("load_hdf5", &Net_LoadHDF5);
+    .def("load_hdf5", &Net_LoadHDF5)
+    .def("before_forward", &Net_before_forward)
+    .def("after_forward", &Net_after_forward)
+    .def("before_backward", &Net_before_backward)
+    .def("after_backward", &Net_after_backward)
+    .def("after_backward", &Net_add_nccl);
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
@@ -378,6 +439,10 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("type", bp::make_function(&Layer<Dtype>::type));
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);
 
+  bp::class_<SolverParameter>("SolverParameter", bp::no_init)
+    .add_property("max_iter", &SolverParameter::max_iter)
+    .add_property("display", &SolverParameter::display)
+    .add_property("layer_wise_reduce", &SolverParameter::layer_wise_reduce);
   bp::class_<LayerParameter>("LayerParameter", bp::no_init);
 
   bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
@@ -387,11 +452,14 @@ BOOST_PYTHON_MODULE(_caffe) {
           bp::return_internal_reference<>()))
     .add_property("iter", &Solver<Dtype>::iter)
     .def("add_callback", &Solver_add_callback<Dtype>)
+    .def("add_callback", &Solver_add_nccl)
     .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
           &Solver<Dtype>::Solve), SolveOverloads())
     .def("step", &Solver<Dtype>::Step)
     .def("restore", &Solver<Dtype>::Restore)
-    .def("snapshot", &Solver<Dtype>::Snapshot);
+    .def("snapshot", &Solver<Dtype>::Snapshot)
+    .add_property("param", bp::make_function(&Solver<Dtype>::param,
+              bp::return_value_policy<bp::copy_const_reference>()));
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
@@ -435,6 +503,24 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<vector<bool> >("BoolVec")
     .def(bp::vector_indexing_suite<vector<bool> >());
 
+  bp::class_<NCCL<Dtype>, shared_ptr<NCCL<Dtype> >,
+    boost::noncopyable>("NCCL",
+                        bp::init<shared_ptr<Solver<Dtype> >, const string&>())
+#ifdef USE_NCCL
+    .def("new_uid", &NCCL<Dtype>::new_uid).staticmethod("new_uid")
+    .def("bcast", &NCCL<Dtype>::Broadcast)
+#endif
+    /* NOLINT_NEXT_LINE(whitespace/semicolon) */
+  ;
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(NCCL<Dtype>);
+
+  bp::class_<Timer, shared_ptr<Timer>, boost::noncopyable>(
+    "Timer", bp::init<>())
+    .def("start", &Timer::Start)
+    .def("stop", &Timer::Stop)
+    .add_property("ms", &Timer::MilliSeconds);
+  BP_REGISTER_SHARED_PTR_TO_PYTHON(Timer);
+
   // boost python expects a void (missing) return value, while import_array
   // returns NULL for python3. import_array1() forces a void return value.
   import_array1();
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 5bae18d9a4d..18803818fef 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -11,7 +11,7 @@
 import numpy as np
 
 from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
-        RMSPropSolver, AdaDeltaSolver, AdamSolver
+        RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
 import caffe.io
 
 import six
diff --git a/python/train.py b/python/train.py
new file mode 100644
index 00000000000..730dbe70186
--- /dev/null
+++ b/python/train.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+"""
+Trains a model using one or more GPUs.
+"""
+from multiprocessing import Process
+
+import caffe
+
+
+def train(
+        solver,  # solver proto definition
+        snapshot,  # solver snapshot to restore
+        gpus,  # list of device ids
+        timing=False,  # show timing info for compute and communications
+):
+    # NCCL uses a uid to identify a session
+    uid = caffe.NCCL.new_uid()
+
+    caffe.init_log()
+    caffe.log('Using devices %s' % str(gpus))
+
+    procs = []
+    for rank in range(len(gpus)):
+        p = Process(target=solve,
+                    args=(solver, snapshot, gpus, timing, uid, rank))
+        p.daemon = True
+        p.start()
+        procs.append(p)
+    for p in procs:
+        p.join()
+
+
+def time(solver, nccl):
+    fprop = []
+    bprop = []
+    total = caffe.Timer()
+    allrd = caffe.Timer()
+    for _ in range(len(solver.net.layers)):
+        fprop.append(caffe.Timer())
+        bprop.append(caffe.Timer())
+    display = solver.param.display
+
+    def show_time():
+        if solver.iter % display == 0:
+            s = '\n'
+            for i in range(len(solver.net.layers)):
+                s += 'forw %3d %8s ' % (i, solver.net.layers[i].layer_param.name)
+                s += ': %.2f\n' % fprop[i].ms
+            for i in range(len(solver.net.layers) - 1, -1, -1):
+                s += 'back %3d %8s ' % (i, solver.net.layers[i].layer_param.name)
+                s += ': %.2f\n' % bprop[i].ms
+            s += 'solver total: %.2f\n' % total.ms
+            s += 'allreduce: %.2f\n' % allrd.ms
+            caffe.log(s)
+
+    solver.net.before_forward(lambda layer: fprop[layer].start())
+    solver.net.after_forward(lambda layer: fprop[layer].stop())
+    solver.net.before_backward(lambda layer: bprop[layer].start())
+    solver.net.after_backward(lambda layer: bprop[layer].stop())
+    solver.add_callback(lambda: total.start(), lambda: (total.stop(), allrd.start()))
+    solver.add_callback(nccl)
+    solver.add_callback(lambda: '', lambda: (allrd.stop(), show_time()))
+
+
+def solve(proto, snapshot, gpus, timing, uid, rank):
+    caffe.set_mode_gpu()
+    caffe.set_device(gpus[rank])
+    caffe.set_solver_count(len(gpus))
+    caffe.set_solver_rank(rank)
+
+    solver = caffe.SGDSolver(proto)
+    if snapshot and len(snapshot) != 0:
+        solver.restore(snapshot)
+
+    nccl = caffe.NCCL(solver, uid)
+    nccl.bcast()
+
+    if timing and rank == 0:
+        time(solver, nccl)
+    else:
+        solver.add_callback(nccl)
+
+    if solver.param.layer_wise_reduce:
+        solver.net.after_backward(nccl)
+    solver.step(solver.param.max_iter)
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--solver", required=True, help="Solver proto definition.")
+    parser.add_argument("--snapshot", help="Solver snapshot to restore.")
+    parser.add_argument("--gpus", type=int, nargs='+', default=[0],
+                        help="List of device ids.")
+    parser.add_argument("--timing", action='store_true', help="Show timing info.")
+    args = parser.parse_args()
+
+    train(args.solver, args.snapshot, args.gpus, args.timing)

From 0d27efc7e3d3d2edbf45cccb73bad03ad655c164 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marian=20Gla=CC=88ser?= <mglaeser@me.com>
Date: Thu, 22 Dec 2016 12:25:46 -0800
Subject: [PATCH 1902/2053] Python layers should build on multiprocess &
 solver_cnt; enable with bindings

---
 include/caffe/layers/python_layer.hpp | 2 +-
 python/caffe/__init__.py              | 2 +-
 python/caffe/_caffe.cpp               | 1 +
 python/train.py                       | 5 +++--
 4 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/include/caffe/layers/python_layer.hpp b/include/caffe/layers/python_layer.hpp
index 529b09cb88b..10c4bfd0250 100644
--- a/include/caffe/layers/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -21,7 +21,7 @@ class PythonLayer : public Layer<Dtype> {
     // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
     // Details: https://github.com/BVLC/caffe/issues/2936
     if (this->phase_ == TRAIN && Caffe::solver_count() > 1
-        && !Caffe::root_solver() && !Caffe::multiprocess()) {
+        && !Caffe::multiprocess()) {
       LOG(FATAL) << "PythonLayer does not support CLI Multi-GPU, use train.py";
     }
     self_.attr("param_str") = bp::str(
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index dde2e9863e4..43a0c49be63 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
-from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, Layer, get_solver
+from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, Layer, get_solver
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 04dac2344a0..3589e476f5c 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -359,6 +359,7 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::def("set_solver_count", &Caffe::set_solver_count);
   bp::def("solver_rank", &Caffe::solver_rank);
   bp::def("set_solver_rank", &Caffe::set_solver_rank);
+  bp::def("set_multiprocess", &Caffe::set_multiprocess);
 
   bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
 
diff --git a/python/train.py b/python/train.py
index 730dbe70186..5897f5dcb90 100644
--- a/python/train.py
+++ b/python/train.py
@@ -44,10 +44,10 @@ def show_time():
         if solver.iter % display == 0:
             s = '\n'
             for i in range(len(solver.net.layers)):
-                s += 'forw %3d %8s ' % (i, solver.net.layers[i].layer_param.name)
+                s += 'forw %3d %8s ' % (i, solver.net._layer_names[i])
                 s += ': %.2f\n' % fprop[i].ms
             for i in range(len(solver.net.layers) - 1, -1, -1):
-                s += 'back %3d %8s ' % (i, solver.net.layers[i].layer_param.name)
+                s += 'back %3d %8s ' % (i, solver.net._layer_names[i])
                 s += ': %.2f\n' % bprop[i].ms
             s += 'solver total: %.2f\n' % total.ms
             s += 'allreduce: %.2f\n' % allrd.ms
@@ -67,6 +67,7 @@ def solve(proto, snapshot, gpus, timing, uid, rank):
     caffe.set_device(gpus[rank])
     caffe.set_solver_count(len(gpus))
     caffe.set_solver_rank(rank)
+    caffe.set_multiprocess(True)
 
     solver = caffe.SGDSolver(proto)
     if snapshot and len(snapshot) != 0:

From 5f28eb1147c1abb6e5e5c7cd282218679b0d531d Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Wed, 4 Jan 2017 00:25:00 -0800
Subject: [PATCH 1903/2053] Using default from proto for prefetch

---
 include/caffe/layers/base_data_layer.hpp | 3 ---
 src/caffe/layers/base_data_layer.cpp     | 3 +--
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp
index 925b019d460..21d3ada50d0 100644
--- a/include/caffe/layers/base_data_layer.hpp
+++ b/include/caffe/layers/base_data_layer.hpp
@@ -67,9 +67,6 @@ class BasePrefetchingDataLayer :
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
 
-  // Prefetches batches (asynchronously if to GPU memory)
-  static const int PREFETCH_COUNT = 4;  // same as proto
-
  protected:
   virtual void InternalThreadEntry();
   virtual void load_batch(Batch<Dtype>* batch) = 0;
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 9414f6f98b2..93a798f3571 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -36,8 +36,7 @@ template <typename Dtype>
 BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
     const LayerParameter& param)
     : BaseDataLayer<Dtype>(param),
-      prefetch_(param.has_data_param() ?
-                param.data_param().prefetch() : PREFETCH_COUNT),
+      prefetch_(param.data_param().prefetch()),
       prefetch_free_(), prefetch_full_(), prefetch_current_() {
   for (int i = 0; i < prefetch_.size(); ++i) {
     prefetch_[i].reset(new Batch<Dtype>());

From 8e63bb6ef1537db2d94ddf2dc084020af5c8727d Mon Sep 17 00:00:00 2001
From: Fan Yang <stoneyang0915@gmail.com>
Date: Thu, 12 Jan 2017 15:26:07 +0800
Subject: [PATCH 1904/2053] minor typo

---
 models/bvlc_googlenet/train_val.prototxt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 mode change 100644 => 100755 models/bvlc_googlenet/train_val.prototxt

diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
old mode 100644
new mode 100755
index 5dee3abe28f..5fe367f2263
--- a/models/bvlc_googlenet/train_val.prototxt
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -1692,7 +1692,7 @@ layer {
   type: "SoftmaxWithLoss"
   bottom: "loss2/classifier"
   bottom: "label"
-  top: "loss2/loss1"
+  top: "loss2/loss2"
   loss_weight: 0.3
 }
 layer {

From 91c15e85124ce2b143d2c18ccab5c5740ef4ce31 Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Fri, 13 Jan 2017 14:33:35 -0500
Subject: [PATCH 1905/2053] Python 2/3 compatible download_model_binary.py

---
 scripts/download_model_binary.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index fcdbb5a91a2..a72fd5d76ba 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -3,10 +3,11 @@
 import sys
 import time
 import yaml
-import urllib
 import hashlib
 import argparse
 
+from six.moves import urllib
+
 required_keys = ['caffemodel', 'caffemodel_url', 'sha1']
 
 
@@ -69,7 +70,7 @@ def model_checks_out(filename=model_filename, sha1=frontmatter['sha1']):
         sys.exit(0)
 
     # Download and verify model.
-    urllib.urlretrieve(
+    urllib.request.urlretrieve(
         frontmatter['caffemodel_url'], model_filename, reporthook)
     if not model_checks_out():
         print('ERROR: model did not download correctly! Run this again.')

From a19357a190664b1ea99d18e14eedc27e43ebed42 Mon Sep 17 00:00:00 2001
From: shai <shai@magisto.com>
Date: Sun, 15 Jan 2017 08:54:45 +0000
Subject: [PATCH 1906/2053] fixing upgrade_proto for BatchNorm layer: be more
 conservative leave "name" in param, only set lr_mult and decay_mult to zero

---
 src/caffe/util/upgrade_proto.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index a0aacbe92f8..94771c8c050 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -1018,7 +1018,13 @@ void UpgradeNetBatchNorm(NetParameter* net_param) {
     // the previous BatchNorm layer definition.
     if (net_param->layer(i).type() == "BatchNorm"
         && net_param->layer(i).param_size() == 3) {
-      net_param->mutable_layer(i)->clear_param();
+      // set lr_mult and decay_mult to zero. leave all other param intact.
+      for (int ip = 0; ip < net_param->layer(i).param_size(); ip++) {
+        ParamSpec* fixed_param_spec =
+          net_param->mutable_layer(i)->mutable_param(ip);
+        fixed_param_spec->set_lr_mult(0.f);
+        fixed_param_spec->set_decay_mult(0.f);
+      }
     }
   }
 }

From ceb25c8abe1e70558d8cc72545e4381cd1b4f273 Mon Sep 17 00:00:00 2001
From: Adam Browne <adamo.browne@gmail.com>
Date: Wed, 18 Jan 2017 15:25:02 -0500
Subject: [PATCH 1907/2053] Fix various documentation typos (#4172)

* fix typo (standaraized->standardized)
* fix typo (convet->convert, etc..)
* fix typo (incompartible->incompatible)
* fix typo (does't->doesn't)
* fix typo (decoded->decode)
---
 cmake/ConfigGen.cmake          | 2 +-
 cmake/Cuda.cmake               | 2 +-
 cmake/Targets.cmake            | 6 +++---
 examples/CMakeLists.txt        | 2 +-
 src/caffe/data_transformer.cpp | 2 +-
 5 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 056371110b5..fd9dd2d2bb4 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -109,7 +109,7 @@ function(caffe_generate_export_configs)
 
   # ---[ Configure and install version file ]---
 
-  # TODO: Lines below are commented because Caffe does't declare its version in headers.
+  # TODO: Lines below are commented because Caffe doesn't declare its version in headers.
   # When the declarations are added, modify `caffe_extract_caffe_version()` macro and uncomment
 
   # configure_file(cmake/Templates/CaffeConfigVersion.cmake.in "${PROJECT_BINARY_DIR}/CaffeConfigVersion.cmake" @ONLY)
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 7146a24450c..0fbf30187e7 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -284,7 +284,7 @@ mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
 if(APPLE)
   caffe_detect_darwin_version(OSX_VERSION)
 
-  # OSX 10.9 and higher uses clang/libc++ by default which is incompartible with old CUDA toolkits
+  # OSX 10.9 and higher uses clang/libc++ by default which is incompatible with old CUDA toolkits
   if(OSX_VERSION VERSION_GREATER 10.8)
     # enabled by default if and only if CUDA version is less than 7.0
     caffe_option(USE_libstdcpp "Use libstdc++ instead of libc++" (CUDA_VERSION VERSION_LESS 7.0))
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index 2cb11584a5c..090f86c5500 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -88,7 +88,7 @@ function(caffe_pickup_caffe_sources root)
   file(GLOB_RECURSE proto_files ${root}/src/caffe/*.proto)
   list(APPEND srcs ${proto_files})
 
-  # convet to absolute paths
+  # convert to absolute paths
   caffe_convert_absolute_paths(srcs)
   caffe_convert_absolute_paths(cuda)
   caffe_convert_absolute_paths(test_srcs)
@@ -102,7 +102,7 @@ function(caffe_pickup_caffe_sources root)
 endfunction()
 
 ################################################################################################
-# Short command for setting defeault target properties
+# Short command for setting default target properties
 # Usage:
 #   caffe_default_properties(<target>)
 function(caffe_default_properties target)
@@ -111,7 +111,7 @@ function(caffe_default_properties target)
     ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
-  # make sure we build all external depepdencies first
+  # make sure we build all external dependencies first
   if (DEFINED external_project_dependencies)
     add_dependencies(${target} ${external_project_dependencies})
   endif()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 663d7360b7d..a59e0df36b0 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -23,7 +23,7 @@ foreach(source_file ${examples_srcs})
 
   if(UNIX OR APPLE)
     # Funny command to make tutorials work
-    # TODO: remove in future as soon as naming is standartaized everywhere
+    # TODO: remove in future as soon as naming is standardized everywhere
     set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX})
     add_custom_command(TARGET ${name} POST_BUILD
                        COMMAND ln -sf "${__outname}" "${__outname}.bin")
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index 7189d67e289..3012251e0a5 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -130,7 +130,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
-  // If datum is encoded, decoded and transform the cv::image.
+  // If datum is encoded, decode and transform the cv::image.
   if (datum.encoded()) {
 #ifdef USE_OPENCV
     CHECK(!(param_.force_color() && param_.force_gray()))

From e744056d8f7ebcf7f0410a52d801d9ca552f69ad Mon Sep 17 00:00:00 2001
From: xmyqsh <xmyqsh@gmail.com>
Date: Thu, 19 Jan 2017 05:19:48 +0800
Subject: [PATCH 1908/2053] remove redundant operations in Crop layer (#5138)

---
 src/caffe/layers/crop_layer.cpp | 40 ++++++++++++++++-----------------
 src/caffe/layers/crop_layer.cu  | 22 +++++++-----------
 2 files changed, 27 insertions(+), 35 deletions(-)

diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index d36b61ca029..ef8c177c4dd 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -86,27 +86,25 @@ void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
     }
   } else {
     // We are at the last dimensions, which is stored continuously in memory
-    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
-      // prepare index vector reduced(red) and with offsets(off)
-      std::vector<int> ind_red(cur_dim, 0);
-      std::vector<int> ind_off(cur_dim+1, 0);
-      for (int j = 0; j < cur_dim; ++j) {
-          ind_red[j] = indices[j];
-          ind_off[j] = indices[j] + offsets[j];
-      }
-      ind_off[cur_dim] = offsets[cur_dim];
-      // do the copy
-      if (is_forward) {
-        caffe_copy(top[0]->shape(cur_dim),
-            src_data + bottom[0]->offset(ind_off),
-            dest_data + top[0]->offset(ind_red));
-      } else {
-        // in the backwards pass the src_data is top_diff
-        // and the dest_data is bottom_diff
-        caffe_copy(top[0]->shape(cur_dim),
-            src_data + top[0]->offset(ind_red),
-            dest_data + bottom[0]->offset(ind_off));
-      }
+    // prepare index vector reduced(red) and with offsets(off)
+    std::vector<int> ind_red(cur_dim, 0);
+    std::vector<int> ind_off(cur_dim+1, 0);
+    for (int j = 0; j < cur_dim; ++j) {
+      ind_red[j] = indices[j];
+      ind_off[j] = indices[j] + offsets[j];
+    }
+    ind_off[cur_dim] = offsets[cur_dim];
+    // do the copy
+    if (is_forward) {
+      caffe_copy(top[0]->shape(cur_dim),
+          src_data + bottom[0]->offset(ind_off),
+          dest_data + top[0]->offset(ind_red));
+    } else {
+      // in the backwards pass the src_data is top_diff
+      // and the dest_data is bottom_diff
+      caffe_copy(top[0]->shape(cur_dim),
+          src_data + top[0]->offset(ind_red),
+          dest_data + bottom[0]->offset(ind_off));
     }
   }
 }
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 1ea132531cf..677077cdd8b 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -8,14 +8,12 @@ namespace caffe {
 // strides in the last two dimensions.
 template <typename Dtype>
 __global__ void copy_kernel(const int n, const int height, const int width,
-    const int src_outer_stride, const int src_inner_stride,
-    const int dest_outer_stride, const int dest_inner_stride,
+    const int src_inner_stride,
+    const int dest_inner_stride,
     const Dtype* src, Dtype* dest) {
   CUDA_KERNEL_LOOP(index, n) {
-    int src_start = index / height * src_outer_stride
-                  + index % height * src_inner_stride;
-    int dest_start = index / height * dest_outer_stride
-                   + index % height * dest_inner_stride;
+    int src_start = index * src_inner_stride;
+    int dest_start = index * dest_inner_stride;
     for (int i = 0; i < width; ++i) {
       dest[dest_start + i] = src[src_start + i];
     }
@@ -53,11 +51,7 @@ void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
     ind_off[cur_dim] = offsets[cur_dim];
     ind_off[cur_dim+1] = offsets[cur_dim+1];
     // Compute copy strides
-    const int src_outer_stride =
-        bottom[0]->shape(cur_dim)*bottom[0]->shape(cur_dim+1);
     const int src_inner_stride = bottom[0]->shape(cur_dim+1);
-    const int dest_outer_stride =
-        top[0]->shape(cur_dim)*top[0]->shape(cur_dim+1);
     const int dest_inner_stride = top[0]->shape(cur_dim+1);
 
     if (is_forward) {
@@ -68,8 +62,8 @@ void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
       // NOLINT_NEXT_LINE(whitespace/operators)
       copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
           lines, height, width,
-          src_outer_stride, src_inner_stride,
-          dest_outer_stride, dest_inner_stride,
+          src_inner_stride,
+          dest_inner_stride,
           bottom_data, top_data);
 
     } else {
@@ -80,8 +74,8 @@ void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
       // NOLINT_NEXT_LINE(whitespace/operators)
       copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
           lines, height, width,
-          dest_outer_stride, dest_inner_stride,
-          src_outer_stride, src_inner_stride,
+          dest_inner_stride,
+          src_inner_stride,
           top_diff, bottom_diff);
     }
   }

From 9b9f6d02ccb664b7f17ce2d3d17072ba578cac09 Mon Sep 17 00:00:00 2001
From: Jonathan L Long <jonlong@cs.berkeley.edu>
Date: Wed, 18 Jan 2017 16:03:55 -0800
Subject: [PATCH 1909/2053] [build] remove trailing backslash on comment

---
 Makefile.config.example | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index 541cf8077d5..b590bd16ce9 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -68,7 +68,7 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 # ANACONDA_HOME := $(HOME)/anaconda
 # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 		# $(ANACONDA_HOME)/include/python2.7 \
-		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
+		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
 
 # Uncomment to use Python 3 (default is Python 2)
 # PYTHON_LIBRARIES := boost_python3 python3.5m

From ff3158a3d0f974a15981dfdbaa95c11ec2cee097 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 18 Jan 2017 17:39:35 -0800
Subject: [PATCH 1910/2053] ignore generated includes for docs

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 281ef3266db..eff292b7f61 100644
--- a/.gitignore
+++ b/.gitignore
@@ -84,6 +84,7 @@ cmake_build
 
 # Generated documentation
 docs/_site
+docs/_includes
 docs/gathered
 _site
 doxygen

From 9ab67099e08c03bf57e6a67538ca4746365beda8 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 18 Jan 2017 17:40:36 -0800
Subject: [PATCH 1911/2053] copyright spans 2014-2017

---
 LICENSE | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/LICENSE b/LICENSE
index d69d16f5bc7..0c99adc182c 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,11 +1,11 @@
 COPYRIGHT
 
 All contributions by the University of California:
-Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
+Copyright (c) 2014-2017 The Regents of the University of California (Regents)
 All rights reserved.
 
 All other contributions:
-Copyright (c) 2014, 2015, the respective contributors
+Copyright (c) 2014-2017, the respective contributors
 All rights reserved.
 
 Caffe uses a shared copyright model: each contributor holds copyright over

From 4056f79f9d8ebf261db45883470a0e2939f725e9 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 17 Jan 2017 20:10:15 -0800
Subject: [PATCH 1912/2053] Docker refresh: simplified & update to 16.04,
 cuda8, cudnn5, nccl

---
 docker/Makefile                        | 50 ------------------
 docker/README.md                       | 70 ++++++++++++--------------
 docker/{standalone => }/cpu/Dockerfile | 12 +++--
 docker/{standalone => }/gpu/Dockerfile | 15 +++---
 docker/templates/Dockerfile.template   | 42 ----------------
 5 files changed, 49 insertions(+), 140 deletions(-)
 delete mode 100644 docker/Makefile
 rename docker/{standalone => }/cpu/Dockerfile (76%)
 rename docker/{standalone => }/gpu/Dockerfile (66%)
 delete mode 100644 docker/templates/Dockerfile.template

diff --git a/docker/Makefile b/docker/Makefile
deleted file mode 100644
index 3a6575b0c43..00000000000
--- a/docker/Makefile
+++ /dev/null
@@ -1,50 +0,0 @@
-# A makefile to build the docker images for caffe.
-# Two caffe images will be built:
-#   caffe:cpu --> A CPU-only build of caffe.
-#   caffe:gpu --> A GPU-enabled build using the latest CUDA and CUDNN versions.
-
-DOCKER ?= docker
-
-all: docker_files standalone
-
-.PHONY: standalone devel
-
-standalone: cpu_standalone gpu_standalone
-
-
-cpu_standalone: standalone/cpu/Dockerfile
-	$(DOCKER) build -t caffe:cpu standalone/cpu
-
-gpu_standalone: standalone/gpu/Dockerfile
-	$(DOCKER) build -t caffe:gpu standalone/gpu
-
-docker_files: standalone_files
-
-standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile
-
-FROM_GPU = "nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04"
-FROM_CPU = "ubuntu:14.04"
-GPU_CMAKE_ARGS = -DUSE_CUDNN=1
-CPU_CMAKE_ARGS = -DCPU_ONLY=1
-
-# A make macro to select the CPU or GPU base image.
-define from_image
-$(if $(strip $(findstring gpu,$@)),$(FROM_GPU),$(FROM_CPU))
-endef
-
-# A make macro to select the CPU or GPU build args.
-define build_args
-$(if $(strip $(findstring gpu,$@)),$(GPU_CMAKE_ARGS),$(CPU_CMAKE_ARGS))
-endef
-
-# A make macro to construct the CPU or GPU Dockerfile from the template
-define create_docker_file
-	@echo creating $@
-	@echo "FROM "$(from_image) > $@
-	@cat $^ | sed 's/$${CMAKE_ARGS}/$(build_args)/' >> $@
-endef
-
-
-standalone/%/Dockerfile: templates/Dockerfile.template
-	$(create_docker_file)
-
diff --git a/docker/README.md b/docker/README.md
index fdab641bdca..11c18157996 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -1,52 +1,48 @@
-# Caffe standalone Dockerfiles.
+### Running an official image
 
-The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running:
+You can run one of the automatic [builds](https://hub.docker.com/r/bvlc/caffe)
+like this:
 
-```
-docker build -t caffe:cpu standalone/cpu
-```
-for example. (Here `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail).
+`docker run -ti bvlc/caffe caffe --version`
 
-Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned.
+or for GPU support (You need a CUDA 8.0 capable driver and
+[nvidia-docker](https://github.com/NVIDIA/nvidia-docker)):
 
-# Running Caffe using the docker image
+`nvidia-docker run -ti bvlc/caffe:gpu caffe --version`
 
-In order to test the Caffe image, run:
-```
-docker run -ti caffe:cpu caffe --version
-```
-which should show a message like:
-```
-libdc1394 error: Failed to initialize libdc1394
-caffe version 1.0.0-rc3
-```
+You might see an error about libdc1394, ignore it.
 
-One can also build and run the Caffe tests in the image using:
-```
-docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"
-```
+### Docker run options
 
-In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running:
-```
-docker run -ti --volume=$(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
-```
-will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume=` Docker flag).
+By default caffe runs as root, thus any output files, e.g. snapshots, will be owned
+by root. It also runs by default in a container-private folder.
 
-Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used:
-```
-docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
-```
-where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option.
+You can change this using flags, like user (-u), current directory, and volumes (-w and -v).
+E.g. this behaves like the usual caffe executable:
 
-# Other use-cases
+`docker run --rm -u $(id -u):$(id -g) -v $(pwd):$(pwd) -w $(pwd) bvlc/caffe caffe train --solver=example_solver.prototxt`
 
-Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe).
+Containers can also be used interactively, specifying e.g. `bash` or `ipython`
+instead of `caffe`.
 
-Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running:
 ```
-docker run -ti caffe:cpu python
+docker run -ti bvlc/caffe ipython
+import caffe
+...
 ```
-(`ipython` is also available in the container).
 
-Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py`
+The caffe build requirements are included in the container, so this can be used to
+build and run custom versions of caffe. Also, `caffe/python` is in PATH, so python
+utilities can be used directly, e.g. `draw_net.py`, `classify.py`, or `detect.py`.
+
+### Building images yourself
+
+Examples:
+
+`docker build -t caffe cpu`
+
+`docker build -t caffe:gpu gpu`
+
+You can also build Caffe and run the tests in the image:
 
+`docker run -ti caffe bash -c "cd /opt/caffe/build; make runtest"`
diff --git a/docker/standalone/cpu/Dockerfile b/docker/cpu/Dockerfile
similarity index 76%
rename from docker/standalone/cpu/Dockerfile
rename to docker/cpu/Dockerfile
index 4fef25aa6a1..af6c03c6589 100644
--- a/docker/standalone/cpu/Dockerfile
+++ b/docker/cpu/Dockerfile
@@ -1,5 +1,5 @@
-FROM ubuntu:14.04
-MAINTAINER caffe-maint@googlegroups.com
+FROM ubuntu:16.04
+LABEL maintainer caffe-maint@googlegroups.com
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
         build-essential \
@@ -20,17 +20,19 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         python-dev \
         python-numpy \
         python-pip \
+        python-setuptools \
         python-scipy && \
     rm -rf /var/lib/apt/lists/*
 
 ENV CAFFE_ROOT=/opt/caffe
 WORKDIR $CAFFE_ROOT
 
-# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
-ENV CLONE_TAG=master
+# FIXME: use ARG instead of ENV once DockerHub supports this
+ENV CLONE_TAG=rc4
 
 RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
-    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    pip install --upgrade pip && \
+    cd python && for req in $(cat requirements.txt) pydot; do pip install $req; done && cd .. && \
     mkdir build && cd build && \
     cmake -DCPU_ONLY=1 .. && \
     make -j"$(nproc)"
diff --git a/docker/standalone/gpu/Dockerfile b/docker/gpu/Dockerfile
similarity index 66%
rename from docker/standalone/gpu/Dockerfile
rename to docker/gpu/Dockerfile
index daf6a7223ff..0785b10f1e7 100644
--- a/docker/standalone/gpu/Dockerfile
+++ b/docker/gpu/Dockerfile
@@ -1,5 +1,5 @@
-FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
-MAINTAINER caffe-maint@googlegroups.com
+FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
+LABEL maintainer caffe-maint@googlegroups.com
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
         build-essential \
@@ -20,19 +20,22 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         python-dev \
         python-numpy \
         python-pip \
+        python-setuptools \
         python-scipy && \
     rm -rf /var/lib/apt/lists/*
 
 ENV CAFFE_ROOT=/opt/caffe
 WORKDIR $CAFFE_ROOT
 
-# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
-ENV CLONE_TAG=master
+# FIXME: use ARG instead of ENV once DockerHub supports this
+ENV CLONE_TAG=rc4
 
 RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
-    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    pip install --upgrade pip && \
+    cd python && for req in $(cat requirements.txt) pydot; do pip install $req; done && cd .. && \
+    git clone https://github.com/NVIDIA/nccl.git && cd nccl && make -j install && cd .. && rm -rf nccl && \
     mkdir build && cd build && \
-    cmake -DUSE_CUDNN=1 .. && \
+    cmake -DUSE_CUDNN=1 -DUSE_NCCL=1 .. && \
     make -j"$(nproc)"
 
 ENV PYCAFFE_ROOT $CAFFE_ROOT/python
diff --git a/docker/templates/Dockerfile.template b/docker/templates/Dockerfile.template
deleted file mode 100644
index 8834f057968..00000000000
--- a/docker/templates/Dockerfile.template
+++ /dev/null
@@ -1,42 +0,0 @@
-MAINTAINER caffe-maint@googlegroups.com
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        build-essential \
-        cmake \
-        git \
-        wget \
-        libatlas-base-dev \
-        libboost-all-dev \
-        libgflags-dev \
-        libgoogle-glog-dev \
-        libhdf5-serial-dev \
-        libleveldb-dev \
-        liblmdb-dev \
-        libopencv-dev \
-        libprotobuf-dev \
-        libsnappy-dev \
-        protobuf-compiler \
-        python-dev \
-        python-numpy \
-        python-pip \
-        python-scipy && \
-    rm -rf /var/lib/apt/lists/*
-
-ENV CAFFE_ROOT=/opt/caffe
-WORKDIR $CAFFE_ROOT
-
-# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
-ENV CLONE_TAG=master
-
-RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
-    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
-    mkdir build && cd build && \
-    cmake ${CMAKE_ARGS} .. && \
-    make -j"$(nproc)"
-
-ENV PYCAFFE_ROOT $CAFFE_ROOT/python
-ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
-ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
-RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
-
-WORKDIR /workspace

From 135440371c7cb2932d5c1e8e671e0d2e231fd2cc Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Sat, 21 Jan 2017 03:06:38 +0000
Subject: [PATCH 1913/2053] cmake: bump soversion to rc4

---
 CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3af394f7aa2..15a7fe46fcf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,8 +10,8 @@ endif()
 project(Caffe C CXX)
 
 # ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.0.0-rc3" CACHE STRING "Caffe logical version")
-set(CAFFE_TARGET_SOVERSION "1.0.0-rc3" CACHE STRING "Caffe soname version")
+set(CAFFE_TARGET_VERSION "1.0.0-rc4" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc4" CACHE STRING "Caffe soname version")
 add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 
 # ---[ Using cmake scripts and modules

From 3a0b6c6e75ca17bae4c728c6987dc5db1e380ce6 Mon Sep 17 00:00:00 2001
From: Fyodor Tokarev <ftokarev@gmail.com>
Date: Sat, 21 Jan 2017 15:12:38 +0300
Subject: [PATCH 1914/2053] Update a comment in caffe.proto

---
 src/caffe/proto/caffe.proto | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 430a0dea109..815ead35362 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -128,8 +128,7 @@ message SolverParameter {
   // The states for the train/test nets. Must be unspecified or
   // specified once per net.
   //
-  // By default, all states will have solver = true;
-  // train_state will have phase = TRAIN,
+  // By default, train_state will have phase = TRAIN,
   // and all test_state's will have phase = TEST.
   // Other defaults are set according to the NetState defaults.
   optional NetState train_state = 26;

From e0cd85237c9ea756cf6bd35b8b0e3432ea3e5273 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 23 Jan 2017 10:31:26 -0800
Subject: [PATCH 1915/2053] Restore can be invoked on rank > 0

---
 src/caffe/solver.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 1c1a9e59565..fd4c03724ef 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -462,7 +462,6 @@ string Solver<Dtype>::SnapshotToHDF5() {
 
 template <typename Dtype>
 void Solver<Dtype>::Restore(const char* state_file) {
-  CHECK(Caffe::root_solver());
   string state_filename(state_file);
   if (state_filename.size() >= 3 &&
       state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {

From 29f0cdb9d785459126516dc58f755af5b486cf71 Mon Sep 17 00:00:00 2001
From: Ken Schutte <kenschutte@gmail.com>
Date: Tue, 24 Jan 2017 10:45:52 -0600
Subject: [PATCH 1916/2053] parse_log.py was not using --verbose argument

---
 tools/extra/parse_log.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index b47ffd0d842..4248e2b87a3 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -203,7 +203,7 @@ def main():
     args = parse_args()
     train_dict_list, test_dict_list = parse_log(args.logfile_path)
     save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
-                   test_dict_list, delimiter=args.delimiter)
+                   test_dict_list, delimiter=args.delimiter, verbose=args.verbose)
 
 
 if __name__ == '__main__':

From 6bf10afd20f91366909318fe4e85a098bb742f58 Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan@neural.vision>
Date: Fri, 20 Jan 2017 11:53:12 +0000
Subject: [PATCH 1917/2053] Fix broken links in layer documentation, minor
 fixes.

---
 docs/tutorial/layers/accuracy.md                | 3 +--
 docs/tutorial/layers/argmax.md                  | 3 +--
 docs/tutorial/layers/infogainloss.md            | 5 ++---
 docs/tutorial/layers/lrn.md                     | 4 ++--
 docs/tutorial/layers/memorydata.md              | 2 +-
 docs/tutorial/layers/multinomiallogisticloss.md | 2 +-
 docs/tutorial/layers/silence.md                 | 8 +-------
 7 files changed, 9 insertions(+), 18 deletions(-)

diff --git a/docs/tutorial/layers/accuracy.md b/docs/tutorial/layers/accuracy.md
index ecf84090e61..80293b1c6bf 100644
--- a/docs/tutorial/layers/accuracy.md
+++ b/docs/tutorial/layers/accuracy.md
@@ -10,7 +10,6 @@ title: Accuracy and Top-k
 * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1AccuracyLayer.html)
 * Header: [`./include/caffe/layers/accuracy_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/accuracy_layer.hpp)
 * CPU implementation: [`./src/caffe/layers/accuracy_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/accuracy_layer.cpp)
-* CUDA GPU implementation: [`./src/caffe/layers/accuracy_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/accuracy_layer.cu)
 
 ## Parameters
 * Parameters (`AccuracyParameter accuracy_param`)
@@ -18,4 +17,4 @@ title: Accuracy and Top-k
 
 {% highlight Protobuf %}
 {% include proto/AccuracyParameter.txt %}
-{% endhighlight %}
\ No newline at end of file
+{% endhighlight %}
diff --git a/docs/tutorial/layers/argmax.md b/docs/tutorial/layers/argmax.md
index f5f173ac731..9eb8b7739f5 100644
--- a/docs/tutorial/layers/argmax.md
+++ b/docs/tutorial/layers/argmax.md
@@ -8,7 +8,6 @@ title: ArgMax Layer
 * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ArgMaxLayer.html)
 * Header: [`./include/caffe/layers/argmax_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/argmax_layer.hpp)
 * CPU implementation: [`./src/caffe/layers/argmax_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cpp)
-* CUDA GPU implementation: [`./src/caffe/layers/argmax_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/argmax_layer.cu)
 
 ## Parameters
 * Parameters (`ArgMaxParameter argmax_param`)
@@ -16,4 +15,4 @@ title: ArgMax Layer
 
 {% highlight Protobuf %}
 {% include proto/ArgMaxParameter.txt %}
-{% endhighlight %}
\ No newline at end of file
+{% endhighlight %}
diff --git a/docs/tutorial/layers/infogainloss.md b/docs/tutorial/layers/infogainloss.md
index 86140b6cca7..b3b690d2621 100644
--- a/docs/tutorial/layers/infogainloss.md
+++ b/docs/tutorial/layers/infogainloss.md
@@ -8,11 +8,10 @@ title: Infogain Loss Layer
 * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InfogainLossLayer.html)
 * Header: [`./include/caffe/layers/infogain_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/infogain_loss_layer.hpp)
 * CPU implementation: [`./src/caffe/layers/infogain_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/infogain_loss_layer.cpp)
-* CUDA GPU implementation: [`./src/caffe/layers/infogain_loss_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/infogain_loss_layer.cu)
 
-A generalization of [MultinomialLogisticLossLayer](layers/multinomiallogisticloss.md) that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.
+A generalization of [MultinomialLogisticLossLayer](multinomiallogisticloss.html) that takes an "information gain" (infogain) matrix specifying the "value" of all label pairs.
 
-Equivalent to the [MultinomialLogisticLossLayer](layers/multinomiallogisticloss.md) if the infogain matrix is the identity.
+Equivalent to the [MultinomialLogisticLossLayer](multinomiallogisticloss.html) if the infogain matrix is the identity.
 
 ## Parameters
 
diff --git a/docs/tutorial/layers/lrn.md b/docs/tutorial/layers/lrn.md
index 387311c2251..2fbef734663 100644
--- a/docs/tutorial/layers/lrn.md
+++ b/docs/tutorial/layers/lrn.md
@@ -20,9 +20,9 @@ The local response normalization layer performs a kind of "lateral inhibition" b
 
 ## Parameters
 
-* Parameters (`Parameter lrn_param`)
+* Parameters (`LRNParameter lrn_param`)
 * From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
 
 {% highlight Protobuf %}
-{% include proto/BatchNormParameter.txt %}
+{% include proto/LRNParameter.txt %}
 {% endhighlight %}
diff --git a/docs/tutorial/layers/memorydata.md b/docs/tutorial/layers/memorydata.md
index 754e62aef62..afce4a24a28 100644
--- a/docs/tutorial/layers/memorydata.md
+++ b/docs/tutorial/layers/memorydata.md
@@ -7,7 +7,7 @@ title: Memory Data Layer
 * Layer type: `MemoryData`
 * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MemoryDataLayer.html)
 * Header: [`./include/caffe/layers/memory_data_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/memory_data_layer.hpp)
-* CPU implementation: [`./src/caffe/layers/memory_data_layer.cpu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/memory_data_layer.cpu)
+* CPU implementation: [`./src/caffe/layers/memory_data_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/memory_data_layer.cpp)
 
 The memory data layer reads data directly from memory, without copying it. In order to use it, one must call `MemoryDataLayer::Reset` (from C++) or `Net.set_input_arrays` (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
 
diff --git a/docs/tutorial/layers/multinomiallogisticloss.md b/docs/tutorial/layers/multinomiallogisticloss.md
index a28ab914854..5eab74a8a69 100644
--- a/docs/tutorial/layers/multinomiallogisticloss.md
+++ b/docs/tutorial/layers/multinomiallogisticloss.md
@@ -7,7 +7,7 @@ title: Multinomial Logistic Loss Layer
 * Layer type: `MultinomialLogisticLoss`
 * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1MultinomialLogisticLossLayer.html)
 * Header: [`./include/caffe/layers/multinomial_logistic_loss_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/multinomial_logistic_loss_layer.hpp)
-* CPU implementation: [`./src/caffe/layers/multinomial_logistic_loss_layer.cpu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/multinomial_logistic_loss_layer.cpu)
+* CPU implementation: [`./src/caffe/layers/multinomial_logistic_loss_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/multinomial_logistic_loss_layer.cpp)
 
 ## Parameters
 
diff --git a/docs/tutorial/layers/silence.md b/docs/tutorial/layers/silence.md
index 2c37a9cd67c..8b4579a9935 100644
--- a/docs/tutorial/layers/silence.md
+++ b/docs/tutorial/layers/silence.md
@@ -14,10 +14,4 @@ Silences a blob, so that it is not printed.
 
 ## Parameters
 
-* Parameters (`SilenceParameter silence_param`)
-* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
-
-{% highlight Protobuf %}
-{% include proto/BatchNormParameter.txt %}
-{% endhighlight %}
-
+No parameters.

From 7b5731c6a68b6a9372c00eb8e13c697f832d8d1b Mon Sep 17 00:00:00 2001
From: Wenbo Yang <solrex@users.noreply.github.com>
Date: Mon, 30 Jan 2017 16:33:20 +0800
Subject: [PATCH 1918/2053] Remove sdk version from veclib searching path.

---
 cmake/Modules/FindvecLib.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
index 46043367362..8eaab59473c 100644
--- a/cmake/Modules/FindvecLib.cmake
+++ b/cmake/Modules/FindvecLib.cmake
@@ -16,7 +16,7 @@ find_path(vecLib_INCLUDE_DIR vecLib.h
           DOC "vecLib include directory"
           PATHS /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
                 /System/Library/${__veclib_include_suffix}
-                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
           NO_DEFAULT_PATH)
 
 include(FindPackageHandleStandardArgs)

From cd89d4b567529de086e409b66390c961624a84b3 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Wed, 1 Feb 2017 11:21:00 +0000
Subject: [PATCH 1919/2053] docs: update install_apt_debian guide

---
 docs/install_apt_debian.md | 76 ++++++++++++++++++++------------------
 1 file changed, 41 insertions(+), 35 deletions(-)

diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
index 0d39e3ae22a..65fe70924e1 100644
--- a/docs/install_apt_debian.md
+++ b/docs/install_apt_debian.md
@@ -5,13 +5,13 @@ title: "Installation: Debian"
 # Debian Installation
 
 Caffe packages are available for several Debian versions, as shown in the
-following chart
+following chart:
 
 ```
 Your Distro     |  CPU_ONLY  |  CUDA  |     Alias
 ----------------+------------+--------+-------------------
 Debian/stable   |     ✘      |   ✘    | Debian Jessie
-Debian/testing  |     ✔      |   ☐    | Debian Stretch/Sid
+Debian/testing  |     ✔      |   ✔    | Debian Stretch/Sid
 Debian/unstable |     ✔      |   ✔    | Debian Sid
 ```
 
@@ -19,30 +19,32 @@ Debian/unstable |     ✔      |   ✔    | Debian Sid
 
 * `✔ ` You can install caffe with a single command line following this guide.
 
-* `☐ ` The same with `✔ `. However it will not work any more when Debian/Stretch becomes the stable branch.
-
-Last update: 2017-01-05
+Last update: 2017-02-01
 
 ## Binary installation with APT
 
 Apart from the installation methods based on source, Debian/unstable
-and Debian/testing users can install pre-compiled Caffe packages via the official archive.
+and Debian/testing users can install pre-compiled Caffe packages from
+the official archive.
+
+Make sure that your `/etc/apt/sources.list` contains `contrib` and `non-free`
+sections if you want to install the CUDA version, for instance:
 
-Make sure that there is something like the follows in your `/etc/apt/sources.list`:
 ```
-deb http://MIRROR/debian CODENAME main contrib non-free
+deb http://ftp2.cn.debian.org/debian sid main contrib non-free
 ```
-where `MIRROR` is your favorate Debian mirror, and `CODENAME ∈ {testing,stretch,sid}`.
 
 Then we update APT cache and directly install Caffe. Note, the cpu version and
-the cuda version cannot be installed at the same time.
+the cuda version cannot coexist.
+
 ```
-# apt update
-# apt install [ caffe-cpu | caffe-cuda ]
-# caffe                                              # command line interface working
-# python3 -c 'import caffe; print(caffe.__path__)'   # python3 interface working
+$ sudo apt update
+$ sudo apt install [ caffe-cpu | caffe-cuda ]
+$ caffe                                              # command line interface working
+$ python3 -c 'import caffe; print(caffe.__path__)'   # python3 interface working
 ```
-It should work out of box.
+
+These Caffe packages should work for you out of box.
 
 #### Customizing caffe packages
 
@@ -50,46 +52,49 @@ Some users may need to customize the Caffe package. The way to customize
 the package is beyond this guide. Here is only a brief guide of producing
 the customized `.deb` packages. 
 
-Make sure that there is something like this in your `/etc/apt/sources.list`:
+Make sure that there is a `dec-src` source in your `/etc/apt/sources.list`,
+for instance:
+
 ```
 deb http://ftp2.cn.debian.org/debian sid main contrib non-free
 deb-src http://ftp2.cn.debian.org/debian sid main contrib non-free
 ```
 
 Then we build caffe deb files with the following commands:
+
 ```
 $ sudo apt update
-$ sudo apt install build-essential debhelper devscripts    # standard package building tools
-$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]            # the most elegant way to pull caffe build dependencies
-$ apt source [ caffe-cpu | caffe-cuda ]               # download the source tarball and extract
+$ sudo apt install build-essential debhelper devscripts  # standard package building tools
+$ sudo apt build-dep [ caffe-cpu | caffe-cuda ]          # the most elegant way to pull caffe build dependencies
+$ apt source [ caffe-cpu | caffe-cuda ]                  # download the source tarball and extract
 $ cd caffe-XXXX
-[ ... optional, customize caffe code/build ... ]
-$ dch -llocal "Modified XXX in order to XXX"          # write your one-line changelog
-$ debuild -B -j4                                      # build caffe with 4 parallel jobs (similar to make -j4)
+[ ... optional, customizing caffe code/build ... ]
+$ dch --local "Modified XXX"                             # bump package version and write changelog
+$ debuild -B -j4                                         # build caffe with 4 parallel jobs (similar to make -j4)
 [ ... building ...]
-$ debc                                                # optional, if you want to check the package contents
-$ sudo debi                                           # optional, install the generated packages
+$ debc                                                   # optional, if you want to check the package contents
+$ sudo debi                                              # optional, install the generated packages
+$ ls ../                                                 # optional, you will see the resulting packages
 ```
-The resulting deb packages can be found under the parent directory of the source tree.
 
-Note, the `dch ...` command line above is for bumping the package version number
-and adding an entry to the package changelog. If you would like to write
-more than one changelog entry, use subsequent `dch` command (see `man 1 dch`)
-instead of manually modifing `debian/changelog` unless you know how to keep its format correct.
+It is a BUG if the package failed to build without any change.
 The changelog will be installed at e.g. `/usr/share/doc/caffe-cpu/changelog.Debian.gz`.
 
 ## Source installation
 
-Source installation under Debian/unstable is similar to that of Ubuntu, but
+Source installation under Debian/unstable and Debian/testing is similar to that of Ubuntu, but
 here is a more elegant way to pull caffe build dependencies:
+
 ```
 $ sudo apt build-dep [ caffe-cpu | caffe-cuda ]
 ```
+
 Note, this requires a `deb-src` entry in your `/etc/apt/sources.list`.
 
 #### Compiler Combinations
 
-Some users may find their favorate compiler doesn't work well with CUDA.
+Some users may find their favorate compiler doesn't work with CUDA.
+
 ```
 CXX compiler |  CUDA 7.5  |  CUDA 8.0  |
 -------------+------------+------------+-
@@ -144,12 +149,13 @@ and hack the packaging scripts, then build your customized package.
 * Where are the examples, the models and other documentation stuff?
 
 ```
-sudo apt install caffe-doc
-dpkg -L caffe-doc
+$ sudo apt install caffe-doc
+$ dpkg -L caffe-doc
 ```
 
 * Where can I find the Debian package status?
 
-https://tracker.debian.org/pkg/caffe  (for the CPU_ONLY version)
-
+```
+https://tracker.debian.org/pkg/caffe          (for the CPU_ONLY version)
 https://tracker.debian.org/pkg/caffe-contrib  (for the CUDA version)
+```

From 734702b3703de0368e901644125ddca91bab4cb7 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Wed, 8 Feb 2017 11:42:05 -0800
Subject: [PATCH 1920/2053] Document switch to explicit flags for docker: cpu /
 gpu.

---
 docker/README.md | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/docker/README.md b/docker/README.md
index 11c18157996..f9c7c756fe6 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -1,9 +1,8 @@
 ### Running an official image
 
-You can run one of the automatic [builds](https://hub.docker.com/r/bvlc/caffe)
-like this:
+You can run one of the automatic [builds](https://hub.docker.com/r/bvlc/caffe). E.g. for the CPU version:
 
-`docker run -ti bvlc/caffe caffe --version`
+`docker run -ti bvlc/caffe:cpu caffe --version`
 
 or for GPU support (You need a CUDA 8.0 capable driver and
 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)):
@@ -20,13 +19,13 @@ by root. It also runs by default in a container-private folder.
 You can change this using flags, like user (-u), current directory, and volumes (-w and -v).
 E.g. this behaves like the usual caffe executable:
 
-`docker run --rm -u $(id -u):$(id -g) -v $(pwd):$(pwd) -w $(pwd) bvlc/caffe caffe train --solver=example_solver.prototxt`
+`docker run --rm -u $(id -u):$(id -g) -v $(pwd):$(pwd) -w $(pwd) bvlc/caffe:cpu caffe train --solver=example_solver.prototxt`
 
 Containers can also be used interactively, specifying e.g. `bash` or `ipython`
 instead of `caffe`.
 
 ```
-docker run -ti bvlc/caffe ipython
+docker run -ti bvlc/caffe:cpu ipython
 import caffe
 ...
 ```
@@ -39,10 +38,10 @@ utilities can be used directly, e.g. `draw_net.py`, `classify.py`, or `detect.py
 
 Examples:
 
-`docker build -t caffe cpu`
+`docker build -t caffe:cpu cpu`
 
 `docker build -t caffe:gpu gpu`
 
 You can also build Caffe and run the tests in the image:
 
-`docker run -ti caffe bash -c "cd /opt/caffe/build; make runtest"`
+`docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"`

From 9c201e177994e31df430cf01baa3105aa5c00699 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Wed, 8 Feb 2017 17:13:53 -0800
Subject: [PATCH 1921/2053] make: bump version to rc4

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 65d08f7d31e..1b73ae0fe2f 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
 DYNAMIC_VERSION_MAJOR 		:= 1
 DYNAMIC_VERSION_MINOR 		:= 0
-DYNAMIC_VERSION_REVISION 	:= 0-rc3
+DYNAMIC_VERSION_REVISION 	:= 0-rc4
 DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
 #DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
 DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)

From 15dfcc1433441f01b0602474eb068e20e7451dd4 Mon Sep 17 00:00:00 2001
From: Katherine Crowson <crowsonkb@gmail.com>
Date: Thu, 9 Feb 2017 11:40:52 -0800
Subject: [PATCH 1922/2053] Add Pascal CUDA architectures to
 Makefile.config.example

---
 Makefile.config.example | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/Makefile.config.example b/Makefile.config.example
index b590bd16ce9..d552b38a97c 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -31,13 +31,17 @@ CUDA_DIR := /usr/local/cuda
 # CUDA_DIR := /usr
 
 # CUDA architecture setting: going with all of them.
-# For CUDA < 6.0, comment the *_50 lines for compatibility.
+# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
+# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
 		-gencode arch=compute_35,code=sm_35 \
 		-gencode arch=compute_50,code=sm_50 \
-		-gencode arch=compute_50,code=compute_50
+		-gencode arch=compute_52,code=sm_52 \
+		-gencode arch=compute_60,code=sm_60 \
+		-gencode arch=compute_61,code=sm_61 \
+		-gencode arch=compute_61,code=compute_61
 
 # BLAS choice:
 # atlas for ATLAS (default)

From 23fca12e579731cf21c783b4a82de3d0a8b6e2cf Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 16 Feb 2017 16:40:18 -0800
Subject: [PATCH 1923/2053] version bump: rc5

---
 CMakeLists.txt | 4 ++--
 Makefile       | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 15a7fe46fcf..32b5bcb47fe 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,8 +10,8 @@ endif()
 project(Caffe C CXX)
 
 # ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.0.0-rc4" CACHE STRING "Caffe logical version")
-set(CAFFE_TARGET_SOVERSION "1.0.0-rc4" CACHE STRING "Caffe soname version")
+set(CAFFE_TARGET_VERSION "1.0.0-rc5" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc5" CACHE STRING "Caffe soname version")
 add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 
 # ---[ Using cmake scripts and modules
diff --git a/Makefile b/Makefile
index 1b73ae0fe2f..77900b69b97 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
 DYNAMIC_VERSION_MAJOR 		:= 1
 DYNAMIC_VERSION_MINOR 		:= 0
-DYNAMIC_VERSION_REVISION 	:= 0-rc4
+DYNAMIC_VERSION_REVISION 	:= 0-rc5
 DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
 #DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
 DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)

From 85ab6100a122042c7dfd4adaf06f4c0b2e71148d Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Mon, 27 Feb 2017 11:54:37 -0800
Subject: [PATCH 1924/2053] fix broken link to hinge loss

---
 docs/tutorial/layers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index a903d5ac985..2faacc5836d 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -128,7 +128,7 @@ Layers:
 * [Infogain Loss](layers/infogainloss.html) - a generalization of MultinomialLogisticLossLayer.
 * [Softmax with Loss](layers/softmaxwithloss.html) - computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
 * [Sum-of-Squares / Euclidean](layers/euclideanloss.html) - computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
-* [Hinge / Margin](layers/hiddenloss.html) - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
+* [Hinge / Margin](layers/hingeloss.html) - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
 * [Sigmoid Cross-Entropy Loss](layers/sigmoidcrossentropyloss.html) - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
 * [Accuracy / Top-k layer](layers/accuracy.html) - scores the output as an accuracy with respect to target -- it is not actually a loss and has no backward step.
 * [Contrastive Loss](layers/contrastiveloss.html)

From fe9e58d6360d99cde0a883a06590631bb11911e0 Mon Sep 17 00:00:00 2001
From: zhuyuanhao <nju.zhuyuanhao@gmail.com>
Date: Wed, 1 Mar 2017 20:42:30 +0800
Subject: [PATCH 1925/2053] Remove not used variable in base_conv_layer.cpp

---
 src/caffe/layers/base_conv_layer.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index 4a4c68e009a..35c90145e31 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -19,7 +19,6 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   const int num_axes = bottom[0]->num_axes();
   num_spatial_axes_ = num_axes - first_spatial_axis;
   CHECK_GE(num_spatial_axes_, 0);
-  vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
   vector<int> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1));
   // Setup filter kernel dimensions (kernel_shape_).
   kernel_shape_.Reshape(spatial_dim_blob_shape);

From 4529f12bdcd27d74655473b6665f5a23cd1214b1 Mon Sep 17 00:00:00 2001
From: gineshidalgo99 <gineshidalgo99@gmail.com>
Date: Thu, 9 Mar 2017 19:24:06 -0500
Subject: [PATCH 1926/2053] =?UTF-8?q?Removed=20some=20'warning:=20extra=20?=
 =?UTF-8?q?=E2=80=98;=E2=80=99=20[-Wpedantic]'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 include/caffe/util/math_functions.hpp |  6 +++---
 include/caffe/util/mkl_alternate.hpp  | 18 +++++++++---------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 51068fe2b80..37abce5eccc 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -128,16 +128,16 @@ inline int8_t caffe_sign(Dtype val) {
   }
 
 // output is 1 for the positives, 0 for zero, and -1 for the negatives
-DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
+DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]))
 
 // This returns a nonzero value if the input has its sign bit set.
 // The name sngbit is meant to avoid conflicts with std::signbit in the macro.
 // The extra parens are needed because CUDA < 6.5 defines signbit as a macro,
 // and we don't want that to expand here when CUDA headers are also included.
 DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \
-    y[i] = static_cast<bool>((std::signbit)(x[i])));
+    y[i] = static_cast<bool>((std::signbit)(x[i])))
 
-DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
+DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]))
 
 template <typename Dtype>
 void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 95df0f93b5e..79b2c32de94 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -36,10 +36,10 @@ extern "C" {
     v##name<double>(n, a, y); \
   }
 
-DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
-DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
-DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
-DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
+DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i])
+DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]))
+DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]))
+DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]))
 
 // A simple way to define the vsl unary functions with singular parameter b.
 // The operation should be in the form e.g. y[i] = pow(a[i], b)
@@ -58,7 +58,7 @@ DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
     v##name<double>(n, a, b, y); \
   }
 
-DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
+DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b))
 
 // A simple way to define the vsl binary functions. The operation should
 // be in the form e.g. y[i] = a[i] + b[i]
@@ -77,10 +77,10 @@ DEFINE_VSL_UNARY_FUNC_WITH_PARAM(Powx, y[i] = pow(a[i], b));
     v##name<double>(n, a, b, y); \
   }
 
-DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i]);
-DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i]);
-DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i]);
-DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i]);
+DEFINE_VSL_BINARY_FUNC(Add, y[i] = a[i] + b[i])
+DEFINE_VSL_BINARY_FUNC(Sub, y[i] = a[i] - b[i])
+DEFINE_VSL_BINARY_FUNC(Mul, y[i] = a[i] * b[i])
+DEFINE_VSL_BINARY_FUNC(Div, y[i] = a[i] / b[i])
 
 // In addition, MKL comes with an additional function axpby that is not present
 // in standard blas. We will simply use a two-step (inefficient, of course) way

From 1d3e6e4522a95faf954e775b23a2f907e66caf31 Mon Sep 17 00:00:00 2001
From: folz <joachim.folz@dfki.de>
Date: Mon, 13 Mar 2017 11:04:30 +0100
Subject: [PATCH 1927/2053] Solver_add_nccl accepts any kind of Solver

---
 python/caffe/_caffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 3589e476f5c..be011699098 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -288,7 +288,7 @@ void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
 }
 
 // Seems boost cannot call the base method directly
-void Solver_add_nccl(SGDSolver<Dtype>* solver
+void Solver_add_nccl(Solver<Dtype>* solver
 #ifdef USE_NCCL
   , NCCL<Dtype>* nccl
 #endif

From 93993a3c2b25ad683dbf13ef3085b0ea5912911f Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 14 Mar 2017 15:41:40 -0700
Subject: [PATCH 1928/2053] Init test net on all GPUs, allows parallel
 inference

---
 src/caffe/solver.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index fd4c03724ef..044269371ad 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -51,8 +51,8 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   }
   // Scaffolding code
   InitTrainNet();
+  InitTestNets();
   if (Caffe::root_solver()) {
-    InitTestNets();
     LOG(INFO) << "Solver scaffolding done.";
   }
   iter_ = 0;
@@ -102,7 +102,6 @@ void Solver<Dtype>::InitTrainNet() {
 
 template <typename Dtype>
 void Solver<Dtype>::InitTestNets() {
-  CHECK(Caffe::root_solver());
   const bool has_net_param = param_.has_net_param();
   const bool has_net_file = param_.has_net();
   const int num_generic_nets = has_net_param + has_net_file;

From 802d90fe81f04e5e9c28c088da0f1b22e1b9fed2 Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Thu, 16 Mar 2017 23:08:20 -0400
Subject: [PATCH 1929/2053] Added python 3 compatibility to cpp_lint.py

---
 scripts/cpp_lint.py | 39 ++++++++++++++++++++++-----------------
 1 file changed, 22 insertions(+), 17 deletions(-)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 6ec4fb76e2c..b2016d4b6dd 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python2
+#!/usr/bin/env python
 #
 # Copyright (c) 2009 Google Inc. All rights reserved.
 #
@@ -52,6 +52,10 @@
 import sys
 import unicodedata
 
+import six
+
+from six import iteritems, itervalues
+from six.moves import xrange
 
 _USAGE = """
 Syntax: cpp_lint.py [--verbose=#] [--output=vs7] [--filter=-x,+y,...]
@@ -756,7 +760,7 @@ def IncrementErrorCount(self, category):
 
   def PrintErrorCounts(self):
     """Print a summary of errors by category, and the total."""
-    for category, count in self.errors_by_category.iteritems():
+    for category, count in iteritems(self.errors_by_category):
       sys.stderr.write('Category \'%s\' errors found: %d\n' %
                        (category, count))
     sys.stderr.write('Total errors found: %d\n' % self.error_count)
@@ -3444,16 +3448,16 @@ def GetLineWidth(line):
     The width of the line in column positions, accounting for Unicode
     combining characters and wide characters.
   """
-  if isinstance(line, unicode):
-    width = 0
-    for uc in unicodedata.normalize('NFC', line):
-      if unicodedata.east_asian_width(uc) in ('W', 'F'):
-        width += 2
-      elif not unicodedata.combining(uc):
-        width += 1
-    return width
-  else:
-    return len(line)
+  if six.PY2:
+    if isinstance(line, unicode):
+      width = 0
+      for uc in unicodedata.normalize('NFC', line):
+        if unicodedata.east_asian_width(uc) in ('W', 'F'):
+          width += 2
+        elif not unicodedata.combining(uc):
+          width += 1
+      return width
+  return len(line)
 
 
 def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
@@ -3774,7 +3778,7 @@ def _GetTextInside(text, start_pattern):
 
   # Give opening punctuations to get the matching close-punctuations.
   matching_punctuation = {'(': ')', '{': '}', '[': ']'}
-  closing_punctuation = set(matching_punctuation.itervalues())
+  closing_punctuation = set(itervalues(matching_punctuation))
 
   # Find the position to start extracting text.
   match = re.search(start_pattern, text, re.M)
@@ -4851,10 +4855,11 @@ def main():
 
   # Change stderr to write with replacement characters so we don't die
   # if we try to print something containing non-ASCII characters.
-  sys.stderr = codecs.StreamReaderWriter(sys.stderr,
-                                         codecs.getreader('utf8'),
-                                         codecs.getwriter('utf8'),
-                                         'replace')
+  if six.PY2:
+    sys.stderr = codecs.StreamReaderWriter(sys.stderr,
+                                          codecs.getreader('utf8'),
+                                          codecs.getwriter('utf8'),
+                                          'replace')
 
   _cpplint_state.ResetErrorCounts()
   for filename in filenames:

From accd188d3241c27a6d24b95cd95a4dca4f4078bc Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Wed, 8 Mar 2017 15:04:29 +0000
Subject: [PATCH 1930/2053] sane h5df file type check for weights

---
 src/caffe/net.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 70d51806d8a..353c2f95b9e 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -769,8 +769,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
-  if (trained_filename.size() >= 3 &&
-      trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
+  if (H5Fis_hdf5(trained_filename.c_str())) {
     CopyTrainedLayersFromHDF5(trained_filename);
   } else {
     CopyTrainedLayersFromBinaryProto(trained_filename);

From 11930f1416efb66795e1fabc5e362a568446d37d Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan@neural.vision>
Date: Wed, 22 Mar 2017 22:36:14 +0100
Subject: [PATCH 1931/2053] Clarify batch norm parameter documentation.

---
 src/caffe/proto/caffe.proto | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index a145c541957..02e0ddf57c1 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -502,11 +502,21 @@ message ConcatParameter {
 }
 
 message BatchNormParameter {
-  // If false, accumulate global mean/variance values via a moving average. If
-  // true, use those accumulated values instead of computing mean/variance
-  // across the batch.
+  // If false, normalization is performed over the current mini-batch
+  // and global statistics are accumulated (but not yet used) by a moving
+  // average.
+  // If true, those accumulated mean and variance values are used for the
+  // normalization.
+  // By default, it is set to false when the network is in the training
+  // phase and true when the network is in the testing phase.
   optional bool use_global_stats = 1;
-  // How much does the moving average decay each iteration?
+  // What fraction of the moving average remains each iteration?
+  // Smaller values make the moving average decay faster, giving more
+  // weight to the recent values.
+  // Each iteration updates the moving average @f$S_{t-1}@f$ with the
+  // current mean @f$ Y_t @f$ by
+  // @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
+  // is the moving_average_fraction parameter.
   optional float moving_average_fraction = 2 [default = .999];
   // Small value to add to the variance estimate so that we don't divide by
   // zero.

From 5c8e3545c650e9d3924f707334bde7cd67cf4e07 Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Wed, 22 Mar 2017 23:15:34 +0000
Subject: [PATCH 1932/2053] [caffe][build] added Atlas lapack Library name
 atllapack

---
 cmake/Modules/FindAtlas.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/Modules/FindAtlas.cmake b/cmake/Modules/FindAtlas.cmake
index 9c665a47bd5..7ffa6393bbc 100644
--- a/cmake/Modules/FindAtlas.cmake
+++ b/cmake/Modules/FindAtlas.cmake
@@ -28,7 +28,7 @@ find_path(Atlas_CLAPACK_INCLUDE_DIR NAMES clapack.h PATHS ${Atlas_INCLUDE_SEARCH
 
 find_library(Atlas_CBLAS_LIBRARY NAMES  ptcblas_r ptcblas cblas_r cblas       PATHS ${Atlas_LIB_SEARCH_PATHS})
 find_library(Atlas_BLAS_LIBRARY NAMES   atlas_r   atlas                       PATHS ${Atlas_LIB_SEARCH_PATHS})
-find_library(Atlas_LAPACK_LIBRARY NAMES lapack alapack_r alapack lapack_atlas PATHS ${Atlas_LIB_SEARCH_PATHS})
+find_library(Atlas_LAPACK_LIBRARY NAMES lapack alapack_r alapack lapack_atlas atllapack PATHS ${Atlas_LIB_SEARCH_PATHS})
 
 set(LOOKED_FOR
   Atlas_CBLAS_INCLUDE_DIR
@@ -47,6 +47,6 @@ if(ATLAS_FOUND)
   set(Atlas_LIBRARIES ${Atlas_LAPACK_LIBRARY} ${Atlas_CBLAS_LIBRARY} ${Atlas_BLAS_LIBRARY})
   mark_as_advanced(${LOOKED_FOR})
 
-  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR}, library: ${Atlas_BLAS_LIBRARY})")
+  message(STATUS "Found Atlas (include: ${Atlas_CBLAS_INCLUDE_DIR} library: ${Atlas_BLAS_LIBRARY} lapack: ${Atlas_LAPACK_LIBRARY}")
 endif(ATLAS_FOUND)
 

From 1e02d622da5aa01fbcf1185bced8e4b0daa0a50b Mon Sep 17 00:00:00 2001
From: max argus <argus.max@gmail.com>
Date: Wed, 22 Mar 2017 23:24:13 +0000
Subject: [PATCH 1933/2053] [caffe][build] added ABS_TEST_DATA_DIR var.

---
 cmake/Templates/caffe_config.h.in             | 15 ++++-----------
 include/caffe/test/test_caffe_main.hpp        |  3 +--
 src/caffe/test/test_gradient_based_solver.cpp |  2 +-
 src/caffe/test/test_hdf5_output_layer.cpp     |  3 +--
 src/caffe/test/test_hdf5data_layer.cpp        |  3 +--
 5 files changed, 8 insertions(+), 18 deletions(-)

diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 45465b98305..2080c63df36 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -4,16 +4,9 @@
 /* Binaries directory */
 #define BINARY_FOLDER "${PROJECT_BINARY_DIR}"
 
+/* This is an absolute path so that we can run test from any build
+ * directory */
+#define ABS_TEST_DATA_DIR "${PROJECT_SOURCE_DIR}/src/caffe/test/test_data/"
+
 /* Test device */
 #define CUDA_TEST_DEVICE ${CUDA_TEST_DEVICE}
-
-/* Temporary (TODO: remove) */
-#if 1
-  #define CMAKE_SOURCE_DIR SOURCE_FOLDER "/src/"
-  #define EXAMPLES_SOURCE_DIR BINARY_FOLDER "/examples/"
-  #define CMAKE_EXT ".gen.cmake"
-#else
-  #define CMAKE_SOURCE_DIR "src/"
-  #define EXAMPLES_SOURCE_DIR "examples/"
-  #define CMAKE_EXT ""
-#endif
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index fc156091476..294f7e5011a 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -18,9 +18,8 @@ using std::endl;
   #include "caffe_config.h"
 #else
   #define CUDA_TEST_DEVICE -1
-  #define CMAKE_SOURCE_DIR "src/"
   #define EXAMPLES_SOURCE_DIR "examples/"
-  #define CMAKE_EXT ""
+  #define ABS_TEST_DATA_DIR "src/caffe/test/test_data"
 #endif
 
 int main(int argc, char** argv);
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 6ad0d8f6544..465140f28a6 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -28,7 +28,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
       seed_(1701), num_(4), channels_(3), height_(10), width_(10),
       share_(false) {
         input_file_ = new string(
-        CMAKE_SOURCE_DIR "caffe/test/test_data/solver_data_list.txt" CMAKE_EXT);
+        ABS_TEST_DATA_DIR "/solver_data_list.txt");
       }
   ~GradientBasedSolverTest() {
     delete input_file_;
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 2bc2de1e647..f94dd57e7de 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -20,8 +20,7 @@ class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   HDF5OutputLayerTest()
-      : input_file_name_(
-        CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data.h5"),
+      : input_file_name_(ABS_TEST_DATA_DIR "/sample_data.h5"),
         blob_data_(new Blob<Dtype>()),
         blob_label_(new Blob<Dtype>()),
         num_(5),
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 487f5176caf..3977c4866c7 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -30,8 +30,7 @@ class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
     blob_top_vec_.push_back(blob_top_label2_);
 
     // Check out generate_sample_data.py in the same directory.
-    filename = new string(
-    CMAKE_SOURCE_DIR "caffe/test/test_data/sample_data_list.txt" CMAKE_EXT);
+    filename = new string(ABS_TEST_DATA_DIR "/sample_data_list.txt");
     LOG(INFO)<< "Using sample HDF5 data file " << filename;
   }
 

From 8602a238a712d50ac5a2d7dffadee2f34d755e3f Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 27 Mar 2017 11:33:06 -0700
Subject: [PATCH 1934/2053] Expose share_weights to python to allow running
 test nets

---
 python/caffe/_caffe.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index be011699098..276f21f85a5 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -298,6 +298,10 @@ void Solver_add_nccl(Solver<Dtype>* solver
 #endif
 }
 
+void share_weights(Solver<Dtype>* solver, Net<Dtype>* net) {
+  net->ShareTrainedLayersWith(solver->net().get());
+}
+
 template<typename Dtype>
 class NetCallback: public Net<Dtype>::Callback {
  public:
@@ -459,6 +463,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("step", &Solver<Dtype>::Step)
     .def("restore", &Solver<Dtype>::Restore)
     .def("snapshot", &Solver<Dtype>::Snapshot)
+    .def("share_weights", &share_weights)
     .add_property("param", bp::make_function(&Solver<Dtype>::param,
               bp::return_value_policy<bp::copy_const_reference>()));
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);

From 850ffd8d1cf18cabe36eb269b63d693db2b167ef Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Mon, 27 Mar 2017 13:15:18 -0700
Subject: [PATCH 1935/2053] Remove missed legacy parallel code

---
 include/caffe/layers/base_data_layer.hpp   | 2 --
 include/caffe/layers/data_layer.hpp        | 2 --
 include/caffe/layers/dummy_data_layer.hpp  | 2 --
 include/caffe/layers/hdf5_data_layer.hpp   | 2 --
 include/caffe/layers/hdf5_output_layer.hpp | 2 --
 include/caffe/layers/input_layer.hpp       | 2 --
 include/caffe/layers/python_layer.hpp      | 4 ----
 src/caffe/proto/caffe.proto                | 4 +---
 8 files changed, 1 insertion(+), 19 deletions(-)

diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp
index 21d3ada50d0..c8b6998c8f2 100644
--- a/include/caffe/layers/base_data_layer.hpp
+++ b/include/caffe/layers/base_data_layer.hpp
@@ -26,8 +26,6 @@ class BaseDataLayer : public Layer<Dtype> {
   // This method may not be overridden except by the BasePrefetchingDataLayer.
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
   // Data layers have no bottoms, so reshaping is trivial.
diff --git a/include/caffe/layers/data_layer.hpp b/include/caffe/layers/data_layer.hpp
index dec58180976..667a4ae43a5 100644
--- a/include/caffe/layers/data_layer.hpp
+++ b/include/caffe/layers/data_layer.hpp
@@ -20,8 +20,6 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> {
   virtual ~DataLayer();
   virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // DataLayer uses DataReader instead for sharing for parallelism
-  virtual inline bool ShareInParallel() const { return false; }
   virtual inline const char* type() const { return "Data"; }
   virtual inline int ExactNumBottomBlobs() const { return 0; }
   virtual inline int MinTopBlobs() const { return 1; }
diff --git a/include/caffe/layers/dummy_data_layer.hpp b/include/caffe/layers/dummy_data_layer.hpp
index 4180f1d01e4..13a63d47ec4 100644
--- a/include/caffe/layers/dummy_data_layer.hpp
+++ b/include/caffe/layers/dummy_data_layer.hpp
@@ -22,8 +22,6 @@ class DummyDataLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layers/hdf5_data_layer.hpp b/include/caffe/layers/hdf5_data_layer.hpp
index 650a3fb0c87..601b36c6b89 100644
--- a/include/caffe/layers/hdf5_data_layer.hpp
+++ b/include/caffe/layers/hdf5_data_layer.hpp
@@ -27,8 +27,6 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual ~HDF5DataLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layers/hdf5_output_layer.hpp b/include/caffe/layers/hdf5_output_layer.hpp
index 487d08fc06c..061e279d7a0 100644
--- a/include/caffe/layers/hdf5_output_layer.hpp
+++ b/include/caffe/layers/hdf5_output_layer.hpp
@@ -28,8 +28,6 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual ~HDF5OutputLayer();
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layers/input_layer.hpp b/include/caffe/layers/input_layer.hpp
index f4472678c69..0ffdc724894 100644
--- a/include/caffe/layers/input_layer.hpp
+++ b/include/caffe/layers/input_layer.hpp
@@ -22,8 +22,6 @@ class InputLayer : public Layer<Dtype> {
       : Layer<Dtype>(param) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
-  // Data layers should be shared by multiple solvers in parallel
-  virtual inline bool ShareInParallel() const { return true; }
   // Data layers have no bottoms, so reshaping is trivial.
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}
diff --git a/include/caffe/layers/python_layer.hpp b/include/caffe/layers/python_layer.hpp
index 10c4bfd0250..1407d9217aa 100644
--- a/include/caffe/layers/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -34,10 +34,6 @@ class PythonLayer : public Layer<Dtype> {
     self_.attr("reshape")(bottom, top);
   }
 
-  virtual inline bool ShareInParallel() const {
-    return this->layer_param_.python_param().share_in_parallel();
-  }
-
   virtual inline const char* type() const { return "Python"; }
 
  protected:
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 02e0ddf57c1..8e528e8e083 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -937,9 +937,7 @@ message PythonParameter {
   // string, dictionary in Python dict format, JSON, etc. You may parse this
   // string in `setup` method and use it in `forward` and `backward`.
   optional string param_str = 3 [default = ''];
-  // Whether this PythonLayer is shared among worker solvers during data parallelism.
-  // If true, each worker solver sequentially run forward from this layer.
-  // This value should be set true if you are using it as a data layer.
+  // DEPRECATED
   optional bool share_in_parallel = 4 [default = false];
 }
 

From 9bd80b2f12649c6336b64c8ebcc2d1210755d1c7 Mon Sep 17 00:00:00 2001
From: Yuduo Wu <yuduowu@users.noreply.github.com>
Date: Wed, 29 Mar 2017 14:42:36 -0700
Subject: [PATCH 1936/2053] Fix typo in test_caffe_main.cpp: defice -> device

---
 src/caffe/test/test_caffe_main.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index 6473b74d0a6..8f333bd7105 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -15,7 +15,7 @@ int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
   caffe::GlobalInit(&argc, &argv);
 #ifndef CPU_ONLY
-  // Before starting testing, let's first print out a few cuda defice info.
+  // Before starting testing, let's first print out a few cuda device info.
   int device;
   cudaGetDeviceCount(&device);
   cout << "Cuda number of devices: " << device << endl;

From a32114e6b2e098e2fdef47e397542b105eb58b66 Mon Sep 17 00:00:00 2001
From: Will Crichton <wcrichto@stanford.edu>
Date: Fri, 31 Mar 2017 11:22:22 -0400
Subject: [PATCH 1937/2053] Fixed memory leaks in cudnn conv and relu

---
 src/caffe/layers/cudnn_conv_layer.cpp | 1 +
 src/caffe/layers/cudnn_relu_layer.cpp | 1 +
 2 files changed, 2 insertions(+)

diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 1987fb096b0..efc9e04e8c0 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -252,6 +252,7 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
   }
 
   cudaFree(workspaceData);
+  delete [] workspace;
   delete [] stream_;
   delete [] handle_;
   delete [] fwd_algo_;
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 795e0a9efb0..687c905763e 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -36,6 +36,7 @@ CuDNNReLULayer<Dtype>::~CuDNNReLULayer() {
 
   cudnnDestroyTensorDescriptor(this->bottom_desc_);
   cudnnDestroyTensorDescriptor(this->top_desc_);
+  cudnnDestroyActivationDescriptor(this->activ_desc_);
   cudnnDestroy(this->handle_);
 }
 

From a2601eddf65bab54429244e350899b6d994f4f37 Mon Sep 17 00:00:00 2001
From: Luke Yeager <lukeyeager@users.noreply.github.com>
Date: Fri, 31 Mar 2017 11:01:13 -0700
Subject: [PATCH 1938/2053] Revert "Fix Python net drawing script"

This reverts commit db6cf0a728cad63c93b345f2203f3ad1f5d5c2f4.
---
 python/caffe/draw.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index e4fd7aacce7..9eecf6d7b46 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -104,11 +104,11 @@ def get_layer_label(layer, rankdir):
                       pooling_types_dict[layer.pooling_param.pool],
                       layer.type,
                       separator,
-                      layer.pooling_param.kernel_size[0] if len(layer.pooling_param.kernel_size._values) else 1,
+                      layer.pooling_param.kernel_size,
                       separator,
-                      layer.pooling_param.stride[0] if len(layer.pooling_param.stride._values) else 1,
+                      layer.pooling_param.stride,
                       separator,
-                      layer.pooling_param.pad[0] if len(layer.pooling_param.pad._values) else 0)
+                      layer.pooling_param.pad)
     else:
         node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
     return node_label

From 0096fe3d270a4833479076e18492de8b28564c80 Mon Sep 17 00:00:00 2001
From: Felix Abecassis <fabecassis@nvidia.com>
Date: Fri, 31 Mar 2017 11:18:39 -0700
Subject: [PATCH 1939/2053] Add support for cuDNN v6

Support for cuDNN v4 and v5 is preserved.
---
 docs/installation.md           |  4 ++--
 include/caffe/util/cudnn.hpp   | 10 ++++++++++
 scripts/travis/install-deps.sh |  2 +-
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 2e558027678..42f1d0ce09b 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -42,14 +42,14 @@ Optional dependencies:
 
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v5)
+* cuDNN for GPU acceleration (v6)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v5; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v6; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index a7d8dbbad4c..498cfe385de 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -41,6 +41,10 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
       return "CUDNN_STATUS_NOT_SUPPORTED";
     case CUDNN_STATUS_LICENSE_ERROR:
       return "CUDNN_STATUS_LICENSE_ERROR";
+#if CUDNN_VERSION_MIN(6, 0, 0)
+    case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
+      return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
+#endif
   }
   return "Unknown cudnn status";
 }
@@ -109,8 +113,14 @@ template <typename Dtype>
 inline void setConvolutionDesc(cudnnConvolutionDescriptor_t* conv,
     cudnnTensorDescriptor_t bottom, cudnnFilterDescriptor_t filter,
     int pad_h, int pad_w, int stride_h, int stride_w) {
+#if CUDNN_VERSION_MIN(6, 0, 0)
   CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
+      pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION,
+      dataType<Dtype>::type));
+#else
+    CUDNN_CHECK(cudnnSetConvolution2dDescriptor(*conv,
       pad_h, pad_w, stride_h, stride_w, 1, 1, CUDNN_CROSS_CORRELATION));
+#endif
 }
 
 template <typename Dtype>
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index 1900b16df54..1593ed8b59a 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -104,7 +104,7 @@ if $WITH_CUDA ; then
   ln -s /usr/local/cuda-$CUDA_VERSION /usr/local/cuda
 
   if $WITH_CUDNN ; then
-    apt-get install -y --no-install-recommends libcudnn5-dev
+    apt-get install -y --no-install-recommends libcudnn6-dev
   fi
 fi
 

From 179dafdb1a930cf86ff0956618bf8411b8dcd90e Mon Sep 17 00:00:00 2001
From: Luke Yeager <lukeyeager@users.noreply.github.com>
Date: Fri, 31 Mar 2017 11:24:56 -0700
Subject: [PATCH 1940/2053] Add test for caffe.draw.draw_net()

---
 python/caffe/test/test_draw.py        | 33 +++++++++++++++++++++++++++
 scripts/travis/install-deps.sh        |  2 ++
 scripts/travis/install-python-deps.sh |  1 +
 3 files changed, 36 insertions(+)
 create mode 100644 python/caffe/test/test_draw.py

diff --git a/python/caffe/test/test_draw.py b/python/caffe/test/test_draw.py
new file mode 100644
index 00000000000..1634145ee9d
--- /dev/null
+++ b/python/caffe/test/test_draw.py
@@ -0,0 +1,33 @@
+import os
+import unittest
+
+from google import protobuf
+
+import caffe.draw
+from caffe.proto import caffe_pb2
+
+def getFilenames():
+    """Yields files in the source tree which are Net prototxts."""
+    result = []
+
+    root_dir = os.path.abspath(os.path.join(
+        os.path.dirname(__file__), '..', '..', '..'))
+    assert os.path.exists(root_dir)
+
+    for dirname in ('models', 'examples'):
+        dirname = os.path.join(root_dir, dirname)
+        assert os.path.exists(dirname)
+        for cwd, _, filenames in os.walk(dirname):
+            for filename in filenames:
+                filename = os.path.join(cwd, filename)
+                if filename.endswith('.prototxt') and 'solver' not in filename:
+                    yield os.path.join(dirname, filename)
+
+
+class TestDraw(unittest.TestCase):
+    def test_draw_net(self):
+        for filename in getFilenames():
+            net = caffe_pb2.NetParameter()
+            with open(filename) as infile:
+                protobuf.text_format.Merge(infile.read(), net)
+            caffe.draw.draw_net(net, 'LR')
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index 1900b16df54..59a9163d5fc 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -8,6 +8,7 @@ source $BASEDIR/defaults.sh
 apt-get -y update
 apt-get install -y --no-install-recommends \
   build-essential \
+  graphviz \
   libboost-filesystem-dev \
   libboost-python-dev \
   libboost-system-dev \
@@ -31,6 +32,7 @@ if ! $WITH_PYTHON3 ; then
     python-dev \
     python-numpy \
     python-protobuf \
+    python-pydot \
     python-skimage
 else
   # Python3
diff --git a/scripts/travis/install-python-deps.sh b/scripts/travis/install-python-deps.sh
index eeec302791f..910d35a93be 100755
--- a/scripts/travis/install-python-deps.sh
+++ b/scripts/travis/install-python-deps.sh
@@ -11,4 +11,5 @@ if ! $WITH_PYTHON3 ; then
 else
   # Python3
   pip install --pre protobuf==3.0.0b3
+  pip install pydot
 fi

From 41e34c9061e9577c2b1dd56be65fd23ef26457fd Mon Sep 17 00:00:00 2001
From: Nitheesh <nitheeshas91@gmail.com>
Date: Tue, 4 Apr 2017 13:36:20 +0530
Subject: [PATCH 1941/2053] Minor fix for net drawing script

---
 python/caffe/draw.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 9eecf6d7b46..8411a41d1d4 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -91,11 +91,11 @@ def get_layer_label(layer, rankdir):
                       separator,
                       layer.type,
                       separator,
-                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
+                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size) else 1,
                       separator,
-                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1,
+                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride) else 1,
                       separator,
-                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0)
+                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad) else 0)
     elif layer.type == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\

From 31bfe8fb498ea2e528da6463c9045b397992e028 Mon Sep 17 00:00:00 2001
From: Nitheesh <nitheeshas91@gmail.com>
Date: Tue, 4 Apr 2017 13:40:31 +0530
Subject: [PATCH 1942/2053] Add main() for draw_net unittest, fix import errors

---
 python/caffe/test/test_draw.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/python/caffe/test/test_draw.py b/python/caffe/test/test_draw.py
index 1634145ee9d..835bb5df010 100644
--- a/python/caffe/test/test_draw.py
+++ b/python/caffe/test/test_draw.py
@@ -1,7 +1,7 @@
 import os
 import unittest
 
-from google import protobuf
+from google.protobuf import text_format
 
 import caffe.draw
 from caffe.proto import caffe_pb2
@@ -29,5 +29,9 @@ def test_draw_net(self):
         for filename in getFilenames():
             net = caffe_pb2.NetParameter()
             with open(filename) as infile:
-                protobuf.text_format.Merge(infile.read(), net)
+                text_format.Merge(infile.read(), net)
             caffe.draw.draw_net(net, 'LR')
+
+
+if __name__ == "__main__":
+    unittest.main()

From 5f1ca848f8c9daa73f61f64413e15ab2cd6602e7 Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan@neural.vision>
Date: Wed, 5 Apr 2017 10:03:31 +0000
Subject: [PATCH 1943/2053] Add example and small blurb about sigmoid layer.

---
 docs/tutorial/layers/sigmoid.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/docs/tutorial/layers/sigmoid.md b/docs/tutorial/layers/sigmoid.md
index 505318352c9..f18ac4b84ec 100644
--- a/docs/tutorial/layers/sigmoid.md
+++ b/docs/tutorial/layers/sigmoid.md
@@ -9,6 +9,16 @@ title: Sigmoid Layer
 * Header: [`./include/caffe/layers/sigmoid_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/sigmoid_layer.hpp)
 * CPU implementation: [`./src/caffe/layers/sigmoid_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cpp)
 * CUDA GPU implementation: [`./src/caffe/layers/sigmoid_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/sigmoid_layer.cu)
+* Example (from [`./examples/mnist/mnist_autoencoder.prototxt`](https://github.com/BVLC/caffe/blob/master/examples/mnist/mnist_autoencoder.prototxt)):
+
+      layer {
+        name: "encode1neuron"
+        bottom: "encode1"
+        top: "encode1neuron"
+        type: "Sigmoid"
+      }
+
+The `Sigmoid` layer computes `sigmoid(x)` for each element `x` in the bottom blob.
 
 ## Parameters
 

From ce7193c7385298825c8cabebd20f664f3f93f06a Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Sat, 8 Apr 2017 12:59:24 -0400
Subject: [PATCH 1944/2053] Removed repeated import Layer, get_solver

---
 python/caffe/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 43a0c49be63..80f51716d82 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
-from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, Layer, get_solver
+from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier

From b2a95fa7fcba2089b981eb30b47d9aeba2b89ce9 Mon Sep 17 00:00:00 2001
From: Bruno Bowden <github@brunobowden.com>
Date: Sat, 8 Apr 2017 15:54:04 -0700
Subject: [PATCH 1945/2053] Log shape dimensions for eltwise layer shape
 mismatch

When layer shapes mismatch for the eltwise layer, caffe will fail a
check but doesn't give any information on how the shapes mismatch.
This logging information will make it easier to debug. Additionally
this reorders the variables to CHECK(expected == actual), matching
the JUnit convention.

BEFORE: Check failed: bottom[i]->shape() == bottom[0]->shape()

AFTER:  Check failed: bottom[0]->shape() == bottom[i]->shape()
        bottom[0]: 1 4 (4), bottom[3]: 1 6 (6)

NOTE: This removes use of CHECK_EQ in an earlier version of this PR,
which caused a build warning due to include of glog/stl_logging.h.
---
 src/caffe/layers/eltwise_layer.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index 21256166bfa..3d82b0e1cbf 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -31,7 +31,9 @@ template <typename Dtype>
 void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   for (int i = 1; i < bottom.size(); ++i) {
-    CHECK(bottom[i]->shape() == bottom[0]->shape());
+    CHECK(bottom[0]->shape() == bottom[i]->shape())
+        << "bottom[0]: " << bottom[0]->shape_string()
+        << ", bottom[" << i << "]: " << bottom[i]->shape_string();
   }
   top[0]->ReshapeLike(*bottom[0]);
   // If max operation, we will initialize the vector index part.

From 51728d1532dbee2853acb89a8a9653e82219953b Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Wed, 12 Apr 2017 01:42:59 -0700
Subject: [PATCH 1946/2053] Fix log parsing #5422

---
 tools/extra/parse_log.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/extra/parse_log.sh b/tools/extra/parse_log.sh
index 9892c897682..122eb9e6eed 100755
--- a/tools/extra/parse_log.sh
+++ b/tools/extra/parse_log.sh
@@ -39,7 +39,7 @@ rm aux.txt aux0.txt aux1.txt aux2.txt aux3.txt aux4.txt
 grep '] Solving ' $1 > aux.txt
 grep ', loss = ' $1 >> aux.txt
 grep 'Iteration ' aux.txt | sed  's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
-grep ', loss = ' $1 | awk '{print $9}' > aux1.txt
+grep ', loss = ' $1 | awk -F = '{print $2}' > aux1.txt
 grep ', lr = ' $1 | awk '{print $9}' > aux2.txt
 
 # Extracting elapsed seconds

From bac59bed485dfa195600b5b12031401613fade05 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Wed, 12 Apr 2017 02:05:34 -0700
Subject: [PATCH 1947/2053] Allow using env vars for glog init from python

---
 python/caffe/_caffe.cpp | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 276f21f85a5..01b34b84190 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -51,14 +51,18 @@ const int NPY_DTYPE = NPY_FLOAT32;
 void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
 void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
 
-void InitLog(int level) {
-  FLAGS_logtostderr = 1;
-  FLAGS_minloglevel = level;
+void InitLog() {
   ::google::InitGoogleLogging("");
   ::google::InstallFailureSignalHandler();
 }
-void InitLogInfo() {
-  InitLog(google::INFO);
+void InitLogLevel(int level) {
+  FLAGS_minloglevel = level;
+  InitLog();
+}
+void InitLogLevelPipe(int level, bool stderr) {
+  FLAGS_minloglevel = level;
+  FLAGS_logtostderr = stderr;
+  InitLog();
 }
 void Log(const string& s) {
   LOG(INFO) << s;
@@ -353,7 +357,8 @@ BOOST_PYTHON_MODULE(_caffe) {
 
   // Caffe utility functions
   bp::def("init_log", &InitLog);
-  bp::def("init_log", &InitLogInfo);
+  bp::def("init_log", &InitLogLevel);
+  bp::def("init_log", &InitLogLevelPipe);
   bp::def("log", &Log);
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);

From 35a7b87ad87457291dfc79bf8a7e7cf7ef278cbb Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Wed, 12 Apr 2017 11:59:06 +0200
Subject: [PATCH 1948/2053] fixes pycaffe forward() and backward() behavior for
 nets whose layer names do not match respective tops

---
 python/caffe/pycaffe.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 63606591bb4..4a7b5a24c46 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -113,7 +113,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
 
     if end is not None:
         end_ind = list(self._layer_names).index(end)
-        outputs = set([end] + blobs)
+        outputs = set(self.top_names[end] + blobs)
     else:
         end_ind = len(self.layers) - 1
         outputs = set(self.outputs + blobs)
@@ -161,7 +161,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
 
     if end is not None:
         end_ind = list(self._layer_names).index(end)
-        outputs = set([end] + diffs)
+        outputs = set(self.bottom_names[end] + diffs)
     else:
         end_ind = 0
         outputs = set(self.inputs + diffs)

From 3a987960d6a08b179eb6c0c526b27ab761ea2d6e Mon Sep 17 00:00:00 2001
From: Kang Kim <kangk@qti.qualcomm.com>
Date: Thu, 13 Apr 2017 15:23:26 +0900
Subject: [PATCH 1949/2053] remove redundant check in LSTMUnitLayer

---
 src/caffe/layers/lstm_unit_layer.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/caffe/layers/lstm_unit_layer.cpp b/src/caffe/layers/lstm_unit_layer.cpp
index 277c031ad15..d1ab59c4bd1 100644
--- a/src/caffe/layers/lstm_unit_layer.cpp
+++ b/src/caffe/layers/lstm_unit_layer.cpp
@@ -31,7 +31,6 @@ void LSTMUnitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     CHECK_EQ(num_instances, bottom[i]->shape(1));
   }
   hidden_dim_ = bottom[0]->shape(2);
-  CHECK_EQ(num_instances, bottom[1]->shape(1));
   CHECK_EQ(4 * hidden_dim_, bottom[1]->shape(2));
   top[0]->ReshapeLike(*bottom[0]);
   top[1]->ReshapeLike(*bottom[0]);

From 96870628698090813d92a9b1f8af9a8311469354 Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Thu, 13 Apr 2017 13:15:24 -0400
Subject: [PATCH 1950/2053] Bump boost version to 1.55 in CMake build

---
 cmake/Dependencies.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 02c81525bce..4a5bac471b4 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -5,7 +5,7 @@ set(Caffe_DEFINITIONS "")
 set(Caffe_COMPILE_OPTIONS "")
 
 # ---[ Boost
-find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
+find_package(Boost 1.55 REQUIRED COMPONENTS system thread filesystem)
 list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Boost_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS PUBLIC ${Boost_LIBRARIES})
 

From 0c9cc62379e4061b58b0dfa257d79c2ecaeb2be8 Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Sat, 11 Mar 2017 20:12:40 -0500
Subject: [PATCH 1951/2053] Added support for python 3 and NCCL

---
 python/caffe/__init__.py       |  2 +-
 python/caffe/_caffe.cpp        | 32 +++++++++++++++++++++++++++++++-
 python/caffe/test/test_nccl.py | 19 +++++++++++++++++++
 3 files changed, 51 insertions(+), 2 deletions(-)
 create mode 100644 python/caffe/test/test_nccl.py

diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 80f51716d82..776945eec88 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,5 +1,5 @@
 from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
-from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess
+from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed, solver_count, set_solver_count, solver_rank, set_solver_rank, set_multiprocess, has_nccl
 from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 01b34b84190..7fc06c08f73 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -347,6 +347,35 @@ class NCCL {
 };
 #endif
 
+bool HasNCCL() {
+#ifdef USE_NCCL
+  return true;
+#else
+  return false;
+#endif
+}
+
+#ifdef USE_NCCL
+bp::object NCCL_New_Uid() {
+  std::string uid = NCCL<Dtype>::new_uid();
+#if PY_MAJOR_VERSION >= 3
+  // Convert std::string to bytes so that Python does not
+  // try to decode the string using the current locale.
+
+  // Since boost 1.53 boost.python will convert str and bytes
+  // to std::string but will convert std::string to str. Here we
+  // force a bytes object to be returned. When this object
+  // is passed back to the NCCL constructor boost.python will
+  // correctly convert the bytes to std::string automatically
+  PyObject* py_uid = PyBytes_FromString(uid.c_str());
+  return bp::object(bp::handle<>(py_uid));
+#else
+  // automatic conversion is correct for python 2.
+  return uid;
+#endif
+}
+#endif
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
@@ -360,6 +389,7 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::def("init_log", &InitLogLevel);
   bp::def("init_log", &InitLogLevelPipe);
   bp::def("log", &Log);
+  bp::def("has_nccl", &HasNCCL);
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
   bp::def("set_random_seed", &set_random_seed);
@@ -518,7 +548,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     boost::noncopyable>("NCCL",
                         bp::init<shared_ptr<Solver<Dtype> >, const string&>())
 #ifdef USE_NCCL
-    .def("new_uid", &NCCL<Dtype>::new_uid).staticmethod("new_uid")
+    .def("new_uid", NCCL_New_Uid).staticmethod("new_uid")
     .def("bcast", &NCCL<Dtype>::Broadcast)
 #endif
     /* NOLINT_NEXT_LINE(whitespace/semicolon) */
diff --git a/python/caffe/test/test_nccl.py b/python/caffe/test/test_nccl.py
new file mode 100644
index 00000000000..127a9337040
--- /dev/null
+++ b/python/caffe/test/test_nccl.py
@@ -0,0 +1,19 @@
+import sys
+import unittest
+
+import caffe
+
+
+class TestNCCL(unittest.TestCase):
+
+    def test_newuid(self):
+        """
+        Test that NCCL uids are of the proper type
+        according to python version
+        """
+        if caffe.has_nccl():
+            uid = caffe.NCCL.new_uid()
+            if sys.version_info.major >= 3:
+                self.assertTrue(isinstance(uid, bytes))
+            else:
+                self.assertTrue(isinstance(uid, str))

From e98023af4a570e3105486b661e4c4d1855c0dd79 Mon Sep 17 00:00:00 2001
From: Patrick Follmann <follmann@mvtec.com>
Date: Thu, 29 Dec 2016 14:37:21 +0100
Subject: [PATCH 1952/2053] Add GPU sqrt functions

---
 include/caffe/util/math_functions.hpp |  3 +++
 src/caffe/util/math_functions.cu      | 21 +++++++++++++++++++++
 2 files changed, 24 insertions(+)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 37abce5eccc..60a8404a044 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -214,6 +214,9 @@ void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_sqrt(const int n, const Dtype* a, Dtype* y);
+
 // caffe_gpu_rng_uniform with two arguments generates integers in the range
 // [0, UINT_MAX].
 void caffe_gpu_rng_uniform(const int n, unsigned int* r);
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 6d001026082..314e6ba0f63 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -387,6 +387,27 @@ void caffe_gpu_powx<double>(const int N, const double* a,
       N, a, alpha, y);
 }
 
+template <typename Dtype>
+__global__ void sqrt_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = sqrt(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_sqrt<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sqrt_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_sqrt<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sqrt_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));

From e93fcd267582888f960ca48d6e0c2e719d4ea09b Mon Sep 17 00:00:00 2001
From: Patrick Follmann <follmann@mvtec.com>
Date: Thu, 29 Dec 2016 14:46:16 +0100
Subject: [PATCH 1953/2053] GPU BatchNormLayer: replace powx with mul and sqrt

---
 src/caffe/layers/batch_norm_layer.cu | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
index c21713c81d9..a35e778e2f1 100644
--- a/src/caffe/layers/batch_norm_layer.cu
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -48,14 +48,14 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   if (!use_global_stats_) {
     // compute variance using var(X) = E((X-EX)^2)
-    caffe_gpu_powx(top[0]->count(), top_data, Dtype(2),
+    caffe_gpu_mul(top[0]->count(), top[0]->gpu_data(), top[0]->gpu_data(),
         temp_.mutable_gpu_data());  // (X-EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), temp_.gpu_data(),
         spatial_sum_multiplier_.gpu_data(), 0.,
         num_by_chans_.mutable_gpu_data());
-    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
-        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, Dtype(1.),
+        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), Dtype(0.),
         variance_.mutable_gpu_data());  // E((X_EX)^2)
 
     // compute and save moving average
@@ -72,7 +72,7 @@ void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   // normalize variance
   caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
-  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+  caffe_gpu_sqrt(variance_.count(), variance_.gpu_data(),
       variance_.mutable_gpu_data());
 
   // replicate variance to input size

From ab3398832964c1ff1bf6b78501e4e43a11f282a1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 13 Apr 2017 13:25:16 -0700
Subject: [PATCH 1954/2053] Add CPU sqrt functions

---
 include/caffe/util/math_functions.hpp |  3 +++
 src/caffe/util/math_functions.cpp     | 10 ++++++++++
 2 files changed, 13 insertions(+)

diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index 60a8404a044..e549120a933 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -52,6 +52,9 @@ void caffe_scal(const int N, const Dtype alpha, Dtype *X);
 template <typename Dtype>
 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_sqrt(const int N, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 71c02274a75..59625bc05ce 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -196,6 +196,16 @@ void caffe_sqr<double>(const int n, const double* a, double* y) {
   vdSqr(n, a, y);
 }
 
+template <>
+void caffe_sqrt<float>(const int n, const float* a, float* y) {
+  vsSqrt(n, a, y);
+}
+
+template <>
+void caffe_sqrt<double>(const int n, const double* a, double* y) {
+  vdSqrt(n, a, y);
+}
+
 template <>
 void caffe_exp<float>(const int n, const float* a, float* y) {
   vsExp(n, a, y);

From 1c15d94f7da736945450e6ed321077f3045445b1 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 13 Apr 2017 13:26:16 -0700
Subject: [PATCH 1955/2053] CPU BatchNormLayer: replace powx with sqr and sqrt

---
 src/caffe/layers/batch_norm_layer.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
index 0a08ed4cb07..c6a1d5b1b2c 100644
--- a/src/caffe/layers/batch_norm_layer.cpp
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -124,8 +124,8 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   if (!use_global_stats_) {
     // compute variance using var(X) = E((X-EX)^2)
-    caffe_powx(top[0]->count(), top_data, Dtype(2),
-        temp_.mutable_cpu_data());  // (X-EX)^2
+    caffe_sqr<Dtype>(top[0]->count(), top_data,
+                     temp_.mutable_cpu_data());  // (X-EX)^2
     caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
         1. / (num * spatial_dim), temp_.cpu_data(),
         spatial_sum_multiplier_.cpu_data(), 0.,
@@ -148,7 +148,7 @@ void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   // normalize variance
   caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
-  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+  caffe_sqrt(variance_.count(), variance_.cpu_data(),
              variance_.mutable_cpu_data());
 
   // replicate variance to input size

From 3d5bed06a9b6b8a5dfd3db8da33f2fa3bc9a1213 Mon Sep 17 00:00:00 2001
From: Jeff Donahue <jeff.donahue@gmail.com>
Date: Thu, 13 Apr 2017 14:15:16 -0700
Subject: [PATCH 1956/2053] fix: add non-MKL sqrt (should have been included in
 ab33988)

---
 include/caffe/util/mkl_alternate.hpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 79b2c32de94..8c2294c7c86 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -37,6 +37,7 @@ extern "C" {
   }
 
 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i])
+DEFINE_VSL_UNARY_FUNC(Sqrt, y[i] = sqrt(a[i]))
 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]))
 DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]))
 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]))

From 2ec19b6177111526d2df362d29d0e08aa5645a22 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 13 Apr 2017 14:22:30 -0700
Subject: [PATCH 1957/2053] deprecate WindowData layer type

---
 include/caffe/layers/window_data_layer.hpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/include/caffe/layers/window_data_layer.hpp b/include/caffe/layers/window_data_layer.hpp
index 35f41b80e63..b9b66b7cf1d 100644
--- a/include/caffe/layers/window_data_layer.hpp
+++ b/include/caffe/layers/window_data_layer.hpp
@@ -16,7 +16,8 @@ namespace caffe {
 
 /**
  * @brief Provides data to the Net from windows of images files, specified
- *        by a window data file.
+ *        by a window data file. This layer is *DEPRECATED* and only kept for
+ *        archival purposes for use by the original R-CNN.
  *
  * TODO(dox): thorough documentation for Forward and proto params.
  */

From e7163f650885b9f7b9cae1c3253aa97d9fe30d86 Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Thu, 13 Apr 2017 20:32:40 -0400
Subject: [PATCH 1958/2053] Updated Travis boost dependencies

---
 scripts/travis/install-deps.sh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index 2fa2a74a486..dac5d2f9d37 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -9,10 +9,10 @@ apt-get -y update
 apt-get install -y --no-install-recommends \
   build-essential \
   graphviz \
-  libboost-filesystem-dev \
-  libboost-python-dev \
-  libboost-system-dev \
-  libboost-thread-dev \
+  libboost-filesystem1.55-dev \
+  libboost-python1.55-dev \
+  libboost-system1.55-dev \
+  libboost-thread1.55-dev \
   libgflags-dev \
   libgoogle-glog-dev \
   libhdf5-serial-dev \

From 8bc82c635914676d51ecd2849cc69f6fb6042496 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Thu, 13 Apr 2017 19:14:57 -0700
Subject: [PATCH 1959/2053] [examples] switch cifar-10 back to proto instead of
 h5 serialization

(it's more common)
---
 examples/cifar10/cifar10_quick_solver.prototxt | 1 -
 examples/cifar10/train_full.sh                 | 4 ++--
 examples/cifar10/train_quick.sh                | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index 5de276f722f..14b4401ba16 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -20,7 +20,6 @@ display: 100
 max_iter: 4000
 # snapshot intermediate results
 snapshot: 4000
-snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 06ecc2dccb0..fe46e60d795 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -9,9 +9,9 @@ $TOOLS/caffe train \
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 $@
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate $@
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 $@
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate $@
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index d2b875340ee..257479e0d77 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -9,4 +9,4 @@ $TOOLS/caffe train \
 # reduce learning rate by factor of 10 after 8 epochs
 $TOOLS/caffe train \
   --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
-  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 $@
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate $@

From aa29eba26b781349174cb856b6ea96360ebbb3f2 Mon Sep 17 00:00:00 2001
From: Guillaume Dumont <dumont.guillaume@gmail.com>
Date: Thu, 13 Apr 2017 22:37:13 -0400
Subject: [PATCH 1960/2053] Explicit std::string to bp::object conversion

---
 python/caffe/_caffe.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 7fc06c08f73..d7f43fff62d 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -371,7 +371,7 @@ bp::object NCCL_New_Uid() {
   return bp::object(bp::handle<>(py_uid));
 #else
   // automatic conversion is correct for python 2.
-  return uid;
+  return bp::object(uid);
 #endif
 }
 #endif

From c19c9602d031274ce77eb6a94ce2a9e8d843d98f Mon Sep 17 00:00:00 2001
From: Carl Doersch <cdoersch@cs.cmu.edu>
Date: Tue, 25 Aug 2015 11:26:14 -0700
Subject: [PATCH 1961/2053] Test for python forward and backward with start and
 end layer.

---
 python/caffe/test/test_net.py | 45 +++++++++++++++++++++++++++++++----
 1 file changed, 41 insertions(+), 4 deletions(-)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 24391cc50c4..afd27690981 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -25,11 +25,11 @@ def simple_net_file(num_output):
         bias_filler { type: 'constant' value: 2 } }
         param { decay_mult: 1 } param { decay_mult: 0 }
         }
-    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip'
+    layer { type: 'InnerProduct' name: 'ip' bottom: 'conv' top: 'ip_blob'
       inner_product_param { num_output: """ + str(num_output) + """
         weight_filler { type: 'gaussian' std: 2.5 }
         bias_filler { type: 'constant' value: -3 } } }
-    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip' bottom: 'label'
+    layer { type: 'SoftmaxWithLoss' name: 'loss' bottom: 'ip_blob' bottom: 'label'
       top: 'loss' }""")
     f.close()
     return f.name
@@ -71,6 +71,43 @@ def test_forward_backward(self):
         self.net.forward()
         self.net.backward()
 
+    def test_forward_start_end(self):
+        conv_blob=self.net.blobs['conv'];
+        ip_blob=self.net.blobs['ip_blob'];
+        sample_data=np.random.uniform(size=conv_blob.data.shape);
+        sample_data=sample_data.astype(np.float32);
+        conv_blob.data[:]=sample_data;
+        forward_blob=self.net.forward(start='ip',end='ip');
+        self.assertIn('ip_blob',forward_blob);
+
+        manual_forward=[];
+        for i in range(0,conv_blob.data.shape[0]):
+          dot=np.dot(self.net.params['ip'][0].data,
+                     conv_blob.data[i].reshape(-1));
+          manual_forward.append(dot+self.net.params['ip'][1].data);
+        manual_forward=np.array(manual_forward);
+
+        np.testing.assert_allclose(ip_blob.data,manual_forward,rtol=1e-3);
+
+    def test_backward_start_end(self):
+        conv_blob=self.net.blobs['conv'];
+        ip_blob=self.net.blobs['ip_blob'];
+        sample_data=np.random.uniform(size=ip_blob.data.shape)
+        sample_data=sample_data.astype(np.float32);
+        ip_blob.diff[:]=sample_data;
+        backward_blob=self.net.backward(start='ip',end='ip');
+        self.assertIn('conv',backward_blob);
+
+        manual_backward=[];
+        for i in range(0,conv_blob.data.shape[0]):
+          dot=np.dot(self.net.params['ip'][0].data.transpose(),
+                     sample_data[i].reshape(-1));
+          manual_backward.append(dot);
+        manual_backward=np.array(manual_backward);
+        manual_backward=manual_backward.reshape(conv_blob.data.shape);
+
+        np.testing.assert_allclose(conv_blob.diff,manual_backward,rtol=1e-3);
+
     def test_clear_param_diffs(self):
         # Run a forward/backward step to have non-zero diffs
         self.net.forward()
@@ -90,13 +127,13 @@ def test_top_bottom_names(self):
         self.assertEqual(self.net.top_names,
                          OrderedDict([('data', ['data', 'label']),
                                       ('conv', ['conv']),
-                                      ('ip', ['ip']),
+                                      ('ip', ['ip_blob']),
                                       ('loss', ['loss'])]))
         self.assertEqual(self.net.bottom_names,
                          OrderedDict([('data', []),
                                       ('conv', ['data']),
                                       ('ip', ['conv']),
-                                      ('loss', ['ip', 'label'])]))
+                                      ('loss', ['ip_blob', 'label'])]))
 
     def test_save_and_read(self):
         f = tempfile.NamedTemporaryFile(mode='w+', delete=False)

From 451944333510e1ea9b0bdac11e4ec201e5284714 Mon Sep 17 00:00:00 2001
From: jgyllinsky <jgyllinsky@users.noreply.github.com>
Date: Fri, 14 Apr 2017 03:11:59 -0400
Subject: [PATCH 1962/2053] [docs] added apt command to install OpenBLAS
 (#4718)

---
 docs/install_apt.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index bc1566b0be9..ee2cd287701 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -14,7 +14,7 @@ The NVIDIA package tends to follow more recent library and driver versions, but
 If installing from packages, install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
 This can be skipped for CPU-only installation.
 
-**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS or MKL for better CPU performance.
+**BLAS**: install ATLAS by `sudo apt-get install libatlas-base-dev` or install OpenBLAS by `sudo apt-get install libopenblas-dev` or MKL for better CPU performance.
 
 **Python** (optional): if you use the default Python you will need to `sudo apt-get install` the `python-dev` package to have the Python headers for building the pycaffe interface.
 

From 80073497045d3101492a28a8a2c87dff65d64ff4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 10:17:52 -0700
Subject: [PATCH 1963/2053] fix lint errors that snuck in by #4566

---
 src/caffe/test/test_gradient_based_solver.cpp | 12 ++++++++----
 src/caffe/test/test_neuron_layer.cpp          |  9 ++++++---
 2 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index 05cab909798..f4395f5311c 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -558,9 +558,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
     for (int i = 0; i < params.size(); ++i) {
       for (int j = 0; j < params[i]->count(); ++j) {
-        EXPECT_FLOAT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
+        EXPECT_FLOAT_EQ(param_copies[i]->cpu_data()[j],
+            params[i]->cpu_data()[j])
             << "param " << i << " data differed at dim " << j;
-        EXPECT_FLOAT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
+        EXPECT_FLOAT_EQ(param_copies[i]->cpu_diff()[j],
+            params[i]->cpu_diff()[j])
             << "param " << i << " diff differed at dim " << j;
       }
     }
@@ -569,9 +571,11 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
     for (int i = 0; i < history.size(); ++i) {
       for (int j = 0; j < history[i]->count(); ++j) {
-        EXPECT_FLOAT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
+        EXPECT_FLOAT_EQ(history_copies[i]->cpu_data()[j],
+            history[i]->cpu_data()[j])
             << "history blob " << i << " data differed at dim " << j;
-        EXPECT_FLOAT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
+        EXPECT_FLOAT_EQ(history_copies[i]->cpu_diff()[j],
+            history[i]->cpu_diff()[j])
             << "history blob " << i << " diff differed at dim " << j;
       }
     }
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 57bd47b3a2e..180871a29ee 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -791,13 +791,16 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
   ip2.Backward(blob_middle_vec_2, propagate_down, blob_bottom_vec_2);
   // Check numbers
   for (int s = 0; s < blob_bottom_2->count(); ++s) {
-    EXPECT_FLOAT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(this->blob_bottom_->cpu_diff()[s],
+        blob_bottom_2->cpu_diff()[s]);
   }
   for (int s = 0; s < ip.blobs()[0]->count(); ++s) {
-    EXPECT_FLOAT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(ip.blobs()[0]->cpu_diff()[s],
+        ip2.blobs()[0]->cpu_diff()[s]);
   }
   for (int s = 0; s < ip.blobs()[1]->count(); ++s) {
-    EXPECT_FLOAT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);
+    EXPECT_FLOAT_EQ(ip.blobs()[1]->cpu_diff()[s],
+        ip2.blobs()[1]->cpu_diff()[s]);
   }
   for (int s = 0; s < prelu.blobs()[0]->count(); ++s) {
     EXPECT_FLOAT_EQ(prelu.blobs()[0]->cpu_diff()[s],

From 4db619aec9cd384b11a1c55fac257d14b704bb15 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 14 Apr 2017 12:30:50 -0700
Subject: [PATCH 1964/2053] Docker update to cuDNN 6

---
 docker/cpu/Dockerfile | 3 ++-
 docker/gpu/Dockerfile | 5 +++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile
index af6c03c6589..67e2e61bd57 100644
--- a/docker/cpu/Dockerfile
+++ b/docker/cpu/Dockerfile
@@ -28,7 +28,8 @@ ENV CAFFE_ROOT=/opt/caffe
 WORKDIR $CAFFE_ROOT
 
 # FIXME: use ARG instead of ENV once DockerHub supports this
-ENV CLONE_TAG=rc4
+# https://github.com/docker/hub-feedback/issues/460
+ENV CLONE_TAG=1.0
 
 RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
     pip install --upgrade pip && \
diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile
index 0785b10f1e7..dcdbdf326fb 100644
--- a/docker/gpu/Dockerfile
+++ b/docker/gpu/Dockerfile
@@ -1,4 +1,4 @@
-FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
+FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04
 LABEL maintainer caffe-maint@googlegroups.com
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -28,7 +28,8 @@ ENV CAFFE_ROOT=/opt/caffe
 WORKDIR $CAFFE_ROOT
 
 # FIXME: use ARG instead of ENV once DockerHub supports this
-ENV CLONE_TAG=rc4
+# https://github.com/docker/hub-feedback/issues/460
+ENV CLONE_TAG=1.0
 
 RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
     pip install --upgrade pip && \

From 44da39f662a24de746fa83b92bd670fe41b3a7da Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 12:36:41 -0700
Subject: [PATCH 1965/2053] BVLC -> BAIR

Berkeley AI Research (BAIR) is the the successor to
the Berkeley Vision and Learning Center (BVLC).
---
 CONTRIBUTORS.md                               |  2 +-
 README.md                                     |  6 +++---
 docs/_layouts/default.html                    |  2 +-
 docs/development.md                           |  4 ++--
 docs/index.md                                 | 10 +++++-----
 docs/model_zoo.md                             | 18 +++++++++---------
 docs/multigpu.md                              |  4 ++--
 docs/performance_hardware.md                  |  2 +-
 docs/tutorial/interfaces.md                   |  4 ++--
 examples/finetune_flickr_style/readme.md      |  2 +-
 models/bvlc_alexnet/readme.md                 |  2 +-
 models/bvlc_googlenet/readme.md               |  2 +-
 models/bvlc_reference_caffenet/readme.md      |  2 +-
 models/bvlc_reference_rcnn_ilsvrc13/readme.md |  2 +-
 14 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 8db66ea82c6..3fd767812e9 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -1,6 +1,6 @@
 # Contributors
 
-Caffe is developed by a core set of BVLC members and the open-source community.
+Caffe is developed by a core set of BAIR members and the open-source community.
 
 We thank all of our [contributors](https://github.com/BVLC/caffe/graphs/contributors)!
 
diff --git a/README.md b/README.md
index 44b9e62c157..0ae3616b4a6 100644
--- a/README.md
+++ b/README.md
@@ -4,13 +4,13 @@
 [![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
 
 Caffe is a deep learning framework made with expression, speed, and modularity in mind.
-It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
+It is developed by Berkeley AI Research ([BAIR](http://bair.berkeley.edu))/The Berkeley Vision and Learning Center (BVLC) and community contributors.
 
 Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
 
 - [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
 - [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
-- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
+- [BAIR reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
 - [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
 
 and step-by-step examples.
@@ -25,7 +25,7 @@ Happy brewing!
 ## License and Citation
 
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
-The BVLC reference models are released for unrestricted use.
+The BAIR/BVLC reference models are released for unrestricted use.
 
 Please cite Caffe in your publications if it helps your research:
 
diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
index b8efe60bc3b..3799e95afde 100644
--- a/docs/_layouts/default.html
+++ b/docs/_layouts/default.html
@@ -36,7 +36,7 @@
       <header>
         <h1 class="header"><a href="/">Caffe</a></h1>
         <p class="header">
-          Deep learning framework by the <a class="header name" href="http://bvlc.eecs.berkeley.edu/">BVLC</a>
+          Deep learning framework by <a class="header name" href="http://bair.berkeley.edu/">BAIR</a>
         </p>
         <p class="header">
           Created by
diff --git a/docs/development.md b/docs/development.md
index 107c2c3b281..ec05bbee102 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -4,7 +4,7 @@ title: Developing and Contributing
 # Development and Contributing
 
 Caffe is developed with active participation of the community.<br>
-The [BVLC](http://bvlc.eecs.berkeley.edu/) brewers welcome all contributions!
+The [BAIR](http://bair.berkeley.edu/)/BVLC brewers welcome all contributions!
 
 The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
 This method is impartial and always up-to-date.
@@ -37,7 +37,7 @@ We absolutely appreciate any contribution to this effort!
 
 The `master` branch receives all new development including community contributions.
 We try to keep it in a reliable state, but it is the bleeding edge, and things do get broken every now and then.
-BVLC maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
+BAIR maintainers will periodically make releases by marking stable checkpoints as tags and maintenance branches. [Past releases](https://github.com/BVLC/caffe/releases) are catalogued online.
 
 #### Issues & Pull Request Protocol
 
diff --git a/docs/index.md b/docs/index.md
index 932b3b58d1d..302a7d56f88 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -5,7 +5,7 @@ title: Deep Learning Framework
 # Caffe
 
 Caffe is a deep learning framework made with expression, speed, and modularity in mind.
-It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and by community contributors.
+It is developed by Berkeley AI Research ([BAIR](http://bair.berkeley.edu)) and by community contributors.
 [Yangqing Jia](http://daggerfs.com) created the project during his PhD at UC Berkeley.
 Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
 
@@ -45,7 +45,7 @@ A 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v
 - [Installation instructions](/installation.html)<br>
 Tested on Ubuntu, Red Hat, OS X.
 * [Model Zoo](/model_zoo.html)<br>
-BVLC suggests a standard distribution format for Caffe models, and provides trained models.
+BAIR suggests a standard distribution format for Caffe models, and provides trained models.
 * [Developing & Contributing](/development.html)<br>
 Guidelines for development and contributing to Caffe.
 * [API Documentation](/doxygen/annotated.html)<br>
@@ -92,9 +92,9 @@ The core Caffe developers offer [consulting services](mailto:caffe-coldpress@goo
 
 ## Acknowledgements
 
-The BVLC Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
+The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
 
-The BVLC members who have contributed to Caffe are (alphabetical by first name):
+The BAIR members who have contributed to Caffe are (alphabetical by first name):
 [Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
 
 The open-source community plays an important and growing role in Caffe's development.
@@ -103,4 +103,4 @@ Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for re
 We sincerely appreciate your interest and contributions!
 If you'd like to contribute, please read the [developing & contributing](development.html) guide.
 
-Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BVLC PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
+Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, [Oriol Vinyals](http://www1.icsi.berkeley.edu/~vinyals/) for discussions along the journey, and BAIR PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for advice.
diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index 06dc0a49ec7..f9078718a8b 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -14,15 +14,15 @@ To help share these models, we introduce the model zoo framework:
 
 ## Where to get trained models
 
-First of all, we bundle BVLC-trained models for unrestricted, out of the box use.
+First of all, we bundle BAIR-trained models for unrestricted, out of the box use.
 <br>
-See the [BVLC model license](#bvlc-model-license) for details.
+See the [BAIR model license](#bair-model-license) for details.
 Each one of these can be downloaded by running `scripts/download_model_binary.py <dirname>` where `<dirname>` is specified below:
 
-- **BVLC Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
-- **BVLC AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
-- **BVLC Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
-- **BVLC GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
+- **BAIR Reference CaffeNet** in `models/bvlc_reference_caffenet`: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
+- **BAIR AlexNet** in `models/bvlc_alexnet`: AlexNet trained on ILSVRC 2012, almost exactly as described in [ImageNet classification with deep convolutional neural networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks) by Krizhevsky et al. in NIPS 2012. (Trained by Evan Shelhamer @shelhamer)
+- **BAIR Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
+- **BAIR GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
 **Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
 These models are subject to conditions of their respective authors such as citation and license.
@@ -55,14 +55,14 @@ Downloading model info is done just as easily with `scripts/download_model_from_
 ### Hosting trained models
 
 It is up to the user where to host the `.caffemodel` file.
-We host our BVLC-provided models on our own server.
+We host our BAIR-provided models on our own server.
 Dropbox also works fine (tip: make sure that `?dl=1` is appended to the end of the URL).
 
 `scripts/download_model_binary.py <dirname>` downloads the `.caffemodel` from the URL specified in the `<dirname>/readme.md` frontmatter and confirms SHA1.
 
-## BVLC model license
+## BAIR model license
 
-The Caffe models bundled by the BVLC are released for unrestricted use.
+The Caffe models bundled by the BAIR are released for unrestricted use.
 
 These models are trained on data from the [ImageNet project](http://www.image-net.org/) and training data includes internet photos that may be subject to copyright.
 
diff --git a/docs/multigpu.md b/docs/multigpu.md
index d91acef980d..e04ebb0b7c8 100644
--- a/docs/multigpu.md
+++ b/docs/multigpu.md
@@ -13,7 +13,7 @@ The GPUs to be used for training can be set with the "-gpu" flag on the command
 # Hardware Configuration Assumptions
 
 The current implementation uses a tree reduction strategy.  e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate
-updated model, 0\-\>2, and then 0\-\>1, 2\-\>3. 
+updated model, 0\-\>2, and then 0\-\>1, 2\-\>3.
 
 For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
 
@@ -23,4 +23,4 @@ Current implementation has a "soft" assumption that the devices being used are h
 
 # Scaling Performance
 
-Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
\ No newline at end of file
+Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
index cdd4b361dea..fbf256842f1 100644
--- a/docs/performance_hardware.md
+++ b/docs/performance_hardware.md
@@ -8,7 +8,7 @@ To measure performance on different NVIDIA GPUs we use CaffeNet, the Caffe refer
 
 For training, each time point is 20 iterations/minibatches of 256 images for 5,120 images total. For testing, a 50,000 image validation set is classified.
 
-**Acknowledgements**: BVLC members are very grateful to NVIDIA for providing several GPUs to conduct this research.
+**Acknowledgements**: BAIR members are very grateful to NVIDIA for providing several GPUs to conduct this research.
 
 ## NVIDIA K40
 
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index d7ff378239d..b5a4f1ad069 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -91,7 +91,7 @@ In MatCaffe, you can
 * Run for a certain number of iterations and give back control to Matlab
 * Intermingle arbitrary Matlab code with gradient steps
 
-An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BAIR CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
 
 ### Build MatCaffe
 
@@ -114,7 +114,7 @@ You can save your Matlab search PATH by running `savepath` so that you don't hav
 
 MatCaffe is very similar to PyCaffe in usage.
 
-Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
+Examples below shows detailed usages and assumes you have downloaded BAIR CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
 
     model = './models/bvlc_reference_caffenet/deploy.prototxt';
     weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index 188dedf1b9a..dacfd01c8e1 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -9,7 +9,7 @@ priority: 5
 # Fine-tuning CaffeNet for Style Recognition on "Flickr Style" Data
 
 Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights.
-Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), to predict image style instead of object category.
+Let's fine-tune the BAIR-distributed CaffeNet model on a different dataset, [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), to predict image style instead of object category.
 
 ## Explanation
 
diff --git a/models/bvlc_alexnet/readme.md b/models/bvlc_alexnet/readme.md
index 008d690f7f4..a83e3d4e27c 100644
--- a/models/bvlc_alexnet/readme.md
+++ b/models/bvlc_alexnet/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC AlexNet Model
+name: BAIR/BVLC AlexNet Model
 caffemodel: bvlc_alexnet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel
 license: unrestricted
diff --git a/models/bvlc_googlenet/readme.md b/models/bvlc_googlenet/readme.md
index 061b6d74530..ef04db62ab2 100644
--- a/models/bvlc_googlenet/readme.md
+++ b/models/bvlc_googlenet/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC GoogleNet Model
+name: BAIR/BVLC GoogleNet Model
 caffemodel: bvlc_googlenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
 license: unrestricted
diff --git a/models/bvlc_reference_caffenet/readme.md b/models/bvlc_reference_caffenet/readme.md
index 671e47a5056..5352e536a07 100644
--- a/models/bvlc_reference_caffenet/readme.md
+++ b/models/bvlc_reference_caffenet/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC CaffeNet Model
+name: BAIR/BVLC CaffeNet Model
 caffemodel: bvlc_reference_caffenet.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
 license: unrestricted
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/readme.md b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
index 9a11a24d8f8..12543b2bd2c 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/readme.md
+++ b/models/bvlc_reference_rcnn_ilsvrc13/readme.md
@@ -1,5 +1,5 @@
 ---
-name: BVLC Reference RCNN ILSVRC13 Model
+name: BAIR/BVLC Reference RCNN ILSVRC13 Model
 caffemodel: bvlc_reference_rcnn_ilsvrc13.caffemodel
 caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_reference_rcnn_ilsvrc13.caffemodel
 license: unrestricted

From 3562698afb4b1f12f51eca752740e279f85714c4 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 12:45:21 -0700
Subject: [PATCH 1966/2053] drop performance + hardware page and switch to
 sheet

simpler to read and update
---
 docs/index.md                |  9 +++--
 docs/performance_hardware.md | 73 ------------------------------------
 2 files changed, 5 insertions(+), 77 deletions(-)
 delete mode 100644 docs/performance_hardware.md

diff --git a/docs/index.md b/docs/index.md
index 302a7d56f88..bbfd91fc7b9 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -23,15 +23,14 @@ Thanks to these contributors the framework tracks the state-of-the-art in both c
 
 **Speed** makes Caffe perfect for research experiments and industry deployment.
 Caffe can process **over 60M images per day** with a single NVIDIA K40 GPU\*.
-That's 1 ms/image for inference and 4 ms/image for learning.
-We believe that Caffe is the fastest convnet implementation available.
+That's 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still.
+We believe that Caffe is among the fastest convnet implementations available.
 
 **Community**: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
 Join our community of brewers on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) and [Github](https://github.com/BVLC/caffe/).
 
 <p class="footnote" markdown="1">
-\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and caching IO.
-Consult performance [details](/performance_hardware.html).
+\* With the ILSVRC2012-winning [SuperVision](http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf) model and prefetching IO.
 </p>
 
 ## Documentation
@@ -50,6 +49,8 @@ BAIR suggests a standard distribution format for Caffe models, and provides trai
 Guidelines for development and contributing to Caffe.
 * [API Documentation](/doxygen/annotated.html)<br>
 Developer documentation automagically generated from code comments.
+* [Benchmarking](https://docs.google.com/spreadsheets/d/1Yp4rqHpT7mKxOPbpzYeUfEFLnELDAgxSSBQKp5uKDGQ/edit#gid=0)<br>
+Comparison of inference and learning for different networks and GPUs.
 
 ### Examples
 
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
deleted file mode 100644
index fbf256842f1..00000000000
--- a/docs/performance_hardware.md
+++ /dev/null
@@ -1,73 +0,0 @@
----
-title: Performance and Hardware Configuration
----
-
-# Performance and Hardware Configuration
-
-To measure performance on different NVIDIA GPUs we use CaffeNet, the Caffe reference ImageNet model.
-
-For training, each time point is 20 iterations/minibatches of 256 images for 5,120 images total. For testing, a 50,000 image validation set is classified.
-
-**Acknowledgements**: BAIR members are very grateful to NVIDIA for providing several GPUs to conduct this research.
-
-## NVIDIA K40
-
-Performance is best with ECC off and boost clock enabled. While ECC makes a negligible difference in speed, disabling it frees ~1 GB of GPU memory.
-
-Best settings with ECC off and maximum clock speed in standard Caffe:
-
-* Training is 26.5 secs / 20 iterations (5,120 images)
-* Testing is 100 secs / validation set (50,000 images)
-
-Best settings with Caffe + [cuDNN acceleration](http://nvidia.com/cudnn):
-
-* Training is 19.2 secs / 20 iterations (5,120 images)
-* Testing is 60.7 secs / validation set (50,000 images)
-
-Other settings:
-
-* ECC on, max speed: training 26.7 secs / 20 iterations, test 101 secs / validation set
-* ECC on, default speed: training 31 secs / 20 iterations, test 117 secs / validation set
-* ECC off, default speed: training 31 secs / 20 iterations, test 118 secs / validation set
-
-### K40 configuration tips
-
-For maximum K40 performance, turn off ECC and boost the clock speed (at your own risk).
-
-To turn off ECC, do
-
-    sudo nvidia-smi -i 0 --ecc-config=0    # repeat with -i x for each GPU ID
-
-then reboot.
-
-Set the "persistence" mode of the GPU settings by
-
-    sudo nvidia-smi -pm 1
-
-and then set the clock speed with
-
-    sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
-
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
-
-## NVIDIA Titan
-
-Training: 26.26 secs / 20 iterations (5,120 images).
-Testing: 100 secs / validation set (50,000 images).
-
-cuDNN Training: 20.25 secs / 20 iterations (5,120 images).
-cuDNN Testing: 66.3 secs / validation set (50,000 images).
-
-
-## NVIDIA K20
-
-Training: 36.0 secs / 20 iterations (5,120 images).
-Testing: 133 secs / validation set (50,000 images).
-
-## NVIDIA GTX 770
-
-Training: 33.0 secs / 20 iterations (5,120 images).
-Testing: 129 secs / validation set (50,000 images).
-
-cuDNN Training: 24.3 secs / 20 iterations (5,120 images).
-cuDNN Testing: 104 secs / validation set (50,000 images).

From 0f5bfc34e0b37b9ab3437d6755eb04a8dc9e8656 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 12:46:56 -0700
Subject: [PATCH 1967/2053] favor notebook examples as more clear and popular

---
 docs/index.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index bbfd91fc7b9..82eb059e325 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -52,13 +52,6 @@ Developer documentation automagically generated from code comments.
 * [Benchmarking](https://docs.google.com/spreadsheets/d/1Yp4rqHpT7mKxOPbpzYeUfEFLnELDAgxSSBQKp5uKDGQ/edit#gid=0)<br>
 Comparison of inference and learning for different networks and GPUs.
 
-### Examples
-
-{% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
-{% for page in examples %}
-- <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
-{% endfor %}
-
 ### Notebook Examples
 
 {% assign notebooks = site.pages | where:'category','notebook' | sort: 'priority' %}
@@ -66,6 +59,13 @@ Comparison of inference and learning for different networks and GPUs.
 - <div><a href="http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/{{page.original_path}}">{{page.title}}</a><br>{{page.description}}</div>
 {% endfor %}
 
+### Command Line Examples
+
+{% assign examples = site.pages | where:'category','example' | sort: 'priority' %}
+{% for page in examples %}
+- <div><a href="{{page.url}}">{{page.title}}</a><br>{{page.description}}</div>
+{% endfor %}
+
 ## Citing Caffe
 
 Please cite Caffe in your publications if it helps your research:

From 2158bbb2151049dec2486b720c0a351164a0eb6b Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 12:50:19 -0700
Subject: [PATCH 1968/2053] model zoo: point out wiki link immediately, explain
 manual editing

---
 docs/model_zoo.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/model_zoo.md b/docs/model_zoo.md
index f9078718a8b..3f77e82572c 100644
--- a/docs/model_zoo.md
+++ b/docs/model_zoo.md
@@ -3,7 +3,7 @@ title: Model Zoo
 ---
 # Caffe Model Zoo
 
-Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data.
+Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data: check out the [model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)!
 These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
 
 To help share these models, we introduce the model zoo framework:
@@ -24,7 +24,7 @@ Each one of these can be downloaded by running `scripts/download_model_binary.py
 - **BAIR Reference R-CNN ILSVRC-2013** in `models/bvlc_reference_rcnn_ilsvrc13`: pure Caffe implementation of [R-CNN](https://github.com/rbgirshick/rcnn) as described by Girshick et al. in CVPR 2014. (Trained by Ross Girshick @rbgirshick)
 - **BAIR GoogLeNet** in `models/bvlc_googlenet`: GoogLeNet trained on ILSVRC 2012, almost exactly as described in [Going Deeper with Convolutions](http://arxiv.org/abs/1409.4842) by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)
 
-**Community models** made by Caffe users are posted to a publicly editable [wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
+**Community models** made by Caffe users are posted to a publicly editable [model zoo wiki page](https://github.com/BVLC/caffe/wiki/Model-Zoo).
 These models are subject to conditions of their respective authors such as citation and license.
 Thank you for sharing your models!
 
@@ -42,6 +42,8 @@ A caffe model is distributed as a directory containing:
     - License information.
 - [optional] Other helpful scripts.
 
+This simple format can be handled through bundled scripts or manually if need be.
+
 ### Hosting model info
 
 Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.

From 414b74c06038c17924745b68954ef10827fe1edd Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 13:19:53 -0700
Subject: [PATCH 1969/2053] add missing names to BAIR roster

---
 docs/index.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.md b/docs/index.md
index 82eb059e325..db8eaffbe34 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -96,7 +96,7 @@ The core Caffe developers offer [consulting services](mailto:caffe-coldpress@goo
 The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.
 
 The BAIR members who have contributed to Caffe are (alphabetical by first name):
-[Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), and [Yangqing Jia](http://daggerfs.com/).
+[Carl Doersch](http://www.carldoersch.com/), [Eric Tzeng](https://github.com/erictzeng), [Evan Shelhamer](http://imaginarynumber.net/), [Jeff Donahue](http://jeffdonahue.com/), [Jon Long](https://github.com/longjon), [Philipp Krähenbühl](http://www.philkr.net/), [Ronghang Hu](http://ronghanghu.com/), [Ross Girshick](http://www.cs.berkeley.edu/~rbg/), [Sergey Karayev](http://sergeykarayev.com/), [Sergio Guadarrama](http://www.eecs.berkeley.edu/~sguada/), [Takuya Narihira](https://github.com/tnarihi), and [Yangqing Jia](http://daggerfs.com/).
 
 The open-source community plays an important and growing role in Caffe's development.
 Check out the Github [project pulse](https://github.com/BVLC/caffe/pulse) for recent activity and the [contributors](https://github.com/BVLC/caffe/graphs/contributors) for the full list.

From e90a6a6ca29423afb15f39adb1157bff9e6f8655 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 13:24:30 -0700
Subject: [PATCH 1970/2053] retire caffe-dev and caffe-coldpress

dev has diffused into the community from the original Caffe core
---
 docs/index.md | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index db8eaffbe34..0e21ae821b0 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -86,11 +86,6 @@ Join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users
 
 Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
 
-Contact [caffe-dev](mailto:caffe-dev@googlegroups.com) if you have a confidential proposal for the framework *and the ability to act on it*.
-Requests for features, explanations, or personal help will be ignored; post to [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) instead.
-
-The core Caffe developers offer [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
-
 ## Acknowledgements
 
 The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI [Trevor Darrell](http://www.eecs.berkeley.edu/~trevor/) for guidance.

From 8985818e4fbb5fc207e4f383c63c28d80fd286f2 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 13:28:24 -0700
Subject: [PATCH 1971/2053] track publications by google scholar and not the
 wiki

---
 docs/index.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 0e21ae821b0..3385747c565 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -77,8 +77,7 @@ Please cite Caffe in your publications if it helps your research:
       Year = {2014}
     }
 
-If you do publish a paper where Caffe helped your research, we encourage you to update the [publications wiki](https://github.com/BVLC/caffe/wiki/Publications).
-Citations are also tracked automatically by [Google Scholar](http://scholar.google.com/scholar?oi=bibs&hl=en&cites=17333247995453974016).
+If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by [Google Scholar](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=-ltRSM0AAAAJ:u5HHmVD_uO8C).
 
 ## Contacting Us
 

From 8b8f2dd40ba87543f066cb157c6d65dd8187253f Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 15:26:30 -0700
Subject: [PATCH 1972/2053] link to new full-day crash course

---
 docs/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 3385747c565..b633f7cfddc 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -35,8 +35,8 @@ Join our community of brewers on the [caffe-users group](https://groups.google.c
 
 ## Documentation
 
-- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)<br>
-Tutorial presentation.
+- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p) and [Caffe in a Day](https://docs.google.com/presentation/d/1HxGdeq8MPktHaPb-rlmYYQ723iWzq9ur6Gjo71YiG0Y/edit#slide=id.gc2fcdcce7_216_0)<br>
+Tutorial presentation of the framework and a full-day crash course.
 - [Tutorial Documentation](/tutorial)<br>
 Practical guide and framework reference.
 - [arXiv / ACM MM '14 paper](http://arxiv.org/abs/1408.5093)<br>

From 49761d34d18b7063af995b13ecca0fee1bdaf02c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Fri, 14 Apr 2017 15:32:50 -0700
Subject: [PATCH 1973/2053] Caffe 1.0

---
 CMakeLists.txt | 4 ++--
 Makefile       | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c52ff466471..08f56a33a59 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,8 +10,8 @@ endif()
 project(Caffe C CXX)
 
 # ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.0.0-rc5" CACHE STRING "Caffe logical version")
-set(CAFFE_TARGET_SOVERSION "1.0.0-rc5" CACHE STRING "Caffe soname version")
+set(CAFFE_TARGET_VERSION "1.0.0" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.0.0" CACHE STRING "Caffe soname version")
 add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 
 # ---[ Using cmake scripts and modules
diff --git a/Makefile b/Makefile
index 77900b69b97..4d324160c08 100644
--- a/Makefile
+++ b/Makefile
@@ -34,7 +34,7 @@ LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
 DYNAMIC_VERSION_MAJOR 		:= 1
 DYNAMIC_VERSION_MINOR 		:= 0
-DYNAMIC_VERSION_REVISION 	:= 0-rc5
+DYNAMIC_VERSION_REVISION 	:= 0
 DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
 #DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
 DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)

From fb0795cd325cee991073b3b02c280e04b62790ba Mon Sep 17 00:00:00 2001
From: Bruno Bowden <github@brunobowden.com>
Date: Mon, 17 Apr 2017 16:15:52 -0700
Subject: [PATCH 1974/2053] Shape mismatch CHECK logging improvements
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- More logs when shapes mismatch
- Manual reviewed all matches of “grep -r CHECK.*shape src”
- Follow JUnit convention of:
```
  CHECK(expected == actual)
      << “expected: “ << expected
      << “ vs. actual: ” << actual;
```

Layers:
- Recurrent
- BaseConvolutionLayer
- Slice (improves logging for incorrect parameter)

CHECK_EQ
- Would prefer to use CHECK_EQ(expected, actual):
https://github.com/google/glog/blob/master/src/glog/stl_logging.h.in#L36
- However this requires include of stl_logging.h which introduces a
build warning. I believe this would work if caffe was updated to the
latest version of glog but that's a bigger change.
---
 src/caffe/layers/base_conv_layer.cpp | 4 +++-
 src/caffe/layers/recurrent_layer.cpp | 5 +++--
 src/caffe/layers/slice_layer.cpp     | 4 +++-
 3 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index 35c90145e31..aff0a7548c7 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -193,7 +193,9 @@ void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   // TODO: generalize to handle inputs of different shapes.
   for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
     CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
-        << "All inputs must have the same shape.";
+        << "shape mismatch - bottom[0]: " << bottom[0]->shape_string()
+        << " vs. bottom[" << bottom_id << "]: "
+        << bottom[bottom_id]->shape_string();
   }
   // Shape the tops.
   bottom_shape_ = &bottom[0]->shape();
diff --git a/src/caffe/layers/recurrent_layer.cpp b/src/caffe/layers/recurrent_layer.cpp
index e0c82773392..9cd3206f66a 100644
--- a/src/caffe/layers/recurrent_layer.cpp
+++ b/src/caffe/layers/recurrent_layer.cpp
@@ -214,8 +214,9 @@ void RecurrentLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     const int bottom_offset = 2 + static_input_;
     for (int i = bottom_offset, j = 0; i < bottom.size(); ++i, ++j) {
       CHECK(recur_input_blobs_[j]->shape() == bottom[i]->shape())
-          << "bottom[" << i << "] shape must match hidden state input shape: "
-          << recur_input_blobs_[j]->shape_string();
+          << "shape mismatch - recur_input_blobs_[" << j << "]: "
+          << recur_input_blobs_[j]->shape_string()
+          << " vs. bottom[" << i << "]: " << bottom[i]->shape_string();
       recur_input_blobs_[j]->ShareData(*bottom[i]);
     }
   }
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index 759beafe0d9..64de0964483 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -41,7 +41,9 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   int count = 0;
   if (slice_point_.size() != 0) {
     CHECK_EQ(slice_point_.size(), top.size() - 1);
-    CHECK_LE(top.size(), bottom_slice_axis);
+    CHECK_LE(top.size(), bottom_slice_axis)
+        << "slice axis: " << slice_axis_
+        << ", bottom[0] shape: " << bottom[0]->shape_string();
     int prev = 0;
     vector<int> slices;
     for (int i = 0; i < slice_point_.size(); ++i) {

From 33f86122970392fcda19ef80ed5cd349279b896d Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@pinterest.com>
Date: Tue, 18 Apr 2017 18:22:38 -0700
Subject: [PATCH 1975/2053] Rewrite crop cuda kernel

---
 include/caffe/layers/crop_layer.hpp |   6 +-
 src/caffe/layers/crop_layer.cpp     |  21 +++--
 src/caffe/layers/crop_layer.cu      | 122 +++++++++++-----------------
 3 files changed, 69 insertions(+), 80 deletions(-)

diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
index c4fda1220c3..5219fa5cb5f 100644
--- a/include/caffe/layers/crop_layer.hpp
+++ b/include/caffe/layers/crop_layer.hpp
@@ -41,13 +41,15 @@ class CropLayer : public Layer<Dtype> {
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
-  vector<int> offsets;
+  Blob<int> offsets;
+  Blob<int> src_strides_;
+  Blob<int> dest_strides_;
 
  private:
   // Recursive copy function.
   void crop_copy(const vector<Blob<Dtype>*>& bottom,
                const vector<Blob<Dtype>*>& top,
-               const vector<int>& offsets,
+               const int* offsets,
                vector<int> indices,
                int cur_dim,
                const Dtype* src_data,
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
index ef8c177c4dd..65ea8f8b7d0 100644
--- a/src/caffe/layers/crop_layer.cpp
+++ b/src/caffe/layers/crop_layer.cpp
@@ -40,8 +40,10 @@ void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
 
   // Initialize offsets to 0 and the new shape to the current shape of the data.
-  offsets = vector<int>(input_dim, 0);
   vector<int> new_shape(bottom[0]->shape());
+  vector<int> offsets_shape(1, input_dim);
+  offsets.Reshape(offsets_shape);
+  int* offset_data = offsets.mutable_cpu_data();
 
   // Determine crop offsets and the new shape post-crop.
   for (int i = 0; i < input_dim; ++i) {
@@ -63,15 +65,22 @@ void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
           << "size " << bottom[1]->shape(i) << " and offset " << crop_offset;
     }
     new_shape[i] = new_size;
-    offsets[i] = crop_offset;
+    offset_data[i] = crop_offset;
   }
   top[0]->Reshape(new_shape);
+  // Compute strides
+  src_strides_.Reshape(offsets_shape);
+  dest_strides_.Reshape(offsets_shape);
+  for (int i = 0; i < input_dim; ++i) {
+    src_strides_.mutable_cpu_data()[i] = bottom[0]->count(i + 1, input_dim);
+    dest_strides_.mutable_cpu_data()[i] = top[0]->count(i + 1, input_dim);
+  }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
              const vector<Blob<Dtype>*>& top,
-             const vector<int>& offsets,
+             const int* offsets,
              vector<int> indices,
              int cur_dim,
              const Dtype* src_data,
@@ -115,7 +124,8 @@ void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   std::vector<int> indices(top[0]->num_axes(), 0);
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  crop_copy(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
+  crop_copy(bottom, top, offsets.cpu_data(), indices, 0, bottom_data, top_data,
+      true);
 }
 
 template <typename Dtype>
@@ -127,7 +137,8 @@ void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   if (propagate_down[0]) {
     caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
     std::vector<int> indices(top[0]->num_axes(), 0);
-    crop_copy(bottom, top, offsets, indices, 0, top_diff, bottom_diff, false);
+    crop_copy(bottom, top, offsets.cpu_data(), indices, 0, top_diff,
+        bottom_diff, false);
   }
 }
 
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index 677077cdd8b..a400f333e14 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -4,90 +4,62 @@
 
 namespace caffe {
 
-// Copy (one line per thread) from one array to another, with arbitrary
-// strides in the last two dimensions.
+__device__ int compute_uncropped_index(
+    int index,
+    const int ndims,
+    const int* src_strides,
+    const int* dest_strides,
+    const int* offsets) {
+  int dest_index = index;
+  int src_index = 0;
+  for (int i = 0; i < ndims; ++i) {
+      int coord = dest_index / dest_strides[i];
+      dest_index -= coord * dest_strides[i];
+      src_index += src_strides[i] * (coord + offsets[i]);
+  }
+  return src_index;
+}
+
 template <typename Dtype>
-__global__ void copy_kernel(const int n, const int height, const int width,
-    const int src_inner_stride,
-    const int dest_inner_stride,
+__global__ void crop_kernel_forward(const int nthreads,
+    const int ndims,
+    const int* src_strides,
+    const int* dest_strides,
+    const int* offsets,
     const Dtype* src, Dtype* dest) {
-  CUDA_KERNEL_LOOP(index, n) {
-    int src_start = index * src_inner_stride;
-    int dest_start = index * dest_inner_stride;
-    for (int i = 0; i < width; ++i) {
-      dest[dest_start + i] = src[src_start + i];
-    }
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    int src_index = compute_uncropped_index(
+        index, ndims, src_strides, dest_strides, offsets);
+    dest[index] = src[src_index];
   }
 }
 
 template <typename Dtype>
-void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
-             const vector<Blob<Dtype>*>& top,
-             const vector<int>& offsets,
-             vector<int> indices,
-             int cur_dim,
-             const Dtype* src_data,
-             Dtype* dest_data,
-             bool is_forward) {
-  if (cur_dim + 2 < top[0]->num_axes()) {
-    // We are not yet at the final dimension, call copy recursivley
-    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
-      indices[cur_dim] = i;
-      crop_copy_gpu(bottom, top, offsets, indices, cur_dim+1,
-                src_data, dest_data, is_forward);
-    }
-  } else {
-    // We are at the last two dimensions, which are stored continuously in
-    // memory. With (N,C,H,W)
-    //              (0,1,2,3) cur_dim   -> H
-    //                        cur_dim+1 -> W
-    const int lines = top[0]->shape(cur_dim);
-    const int height = top[0]->shape(cur_dim);
-    const int width = top[0]->shape(cur_dim+1);
-    std::vector<int> ind_off(cur_dim+2, 0);
-    for (int j = 0; j < cur_dim; ++j) {
-        ind_off[j] = indices[j] + offsets[j];
-    }
-    ind_off[cur_dim] = offsets[cur_dim];
-    ind_off[cur_dim+1] = offsets[cur_dim+1];
-    // Compute copy strides
-    const int src_inner_stride = bottom[0]->shape(cur_dim+1);
-    const int dest_inner_stride = top[0]->shape(cur_dim+1);
-
-    if (is_forward) {
-      const Dtype* bottom_data = bottom[0]->gpu_data() +
-          bottom[0]->offset(ind_off);
-      Dtype* top_data = top[0]->mutable_gpu_data() +
-          top[0]->offset(indices);
-      // NOLINT_NEXT_LINE(whitespace/operators)
-      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-          lines, height, width,
-          src_inner_stride,
-          dest_inner_stride,
-          bottom_data, top_data);
-
-    } else {
-      const Dtype* top_diff = top[0]->gpu_diff() +
-          top[0]->offset(indices);
-      Dtype* bottom_diff = bottom[0]->mutable_gpu_diff() +
-          bottom[0]->offset(ind_off);
-      // NOLINT_NEXT_LINE(whitespace/operators)
-      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
-          lines, height, width,
-          dest_inner_stride,
-          src_inner_stride,
-          top_diff, bottom_diff);
-    }
+__global__ void crop_kernel_backward(const int nthreads,
+    const int ndims,
+    const int* src_strides,
+    const int* dest_strides,
+    const int* offsets,
+    Dtype* src, const Dtype* dest) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    int src_index = compute_uncropped_index(
+        index, ndims, src_strides, dest_strides, offsets);
+    src[src_index] = dest[index];
   }
 }
 
 template <typename Dtype>
 void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
-  std::vector<int> indices(top[0]->num_axes(), 0);
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
+  int n = top[0]->count();
+  crop_kernel_forward<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(n,
+      bottom[0]->num_axes(),
+      src_strides_.gpu_data(),
+      dest_strides_.gpu_data(),
+      offsets.gpu_data(),
+      bottom_data, top_data);
 }
 
 template <typename Dtype>
@@ -95,12 +67,16 @@ void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  int n = top[0]->count();
 
   if (propagate_down[0]) {
     caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
-    std::vector<int> indices(top[0]->num_axes(), 0);
-    crop_copy_gpu(bottom, top, offsets, indices, 0, top_diff, bottom_diff,
-                  false);
+    crop_kernel_backward<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(n,
+        bottom[0]->num_axes(),
+        src_strides_.gpu_data(),
+        dest_strides_.gpu_data(),
+        offsets.gpu_data(),
+        bottom_diff, top_diff);
   }
 }
 

From cd1696d00b995a1d8567cb6f3ad7f65ec4df4176 Mon Sep 17 00:00:00 2001
From: Eric Tzeng <etzeng@pinterest.com>
Date: Tue, 18 Apr 2017 18:48:26 -0700
Subject: [PATCH 1976/2053] Fix crop layer lint errors

---
 src/caffe/layers/crop_layer.cu | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
index a400f333e14..4ece9cd1761 100644
--- a/src/caffe/layers/crop_layer.cu
+++ b/src/caffe/layers/crop_layer.cu
@@ -54,6 +54,7 @@ void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   int n = top[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
   crop_kernel_forward<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(n,
       bottom[0]->num_axes(),
       src_strides_.gpu_data(),
@@ -71,6 +72,7 @@ void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   if (propagate_down[0]) {
     caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+    // NOLINT_NEXT_LINE(whitespace/operators)
     crop_kernel_backward<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(n,
         bottom[0]->num_axes(),
         src_strides_.gpu_data(),

From ec35395e131a0d5e7c55cbd74dadbd46a49a645c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Malte=20St=C3=A6r=20Nissen?= <nissen@di.ku.dk>
Date: Thu, 4 May 2017 14:33:40 +0200
Subject: [PATCH 1977/2053] Handling destruction of empty Net objects

---
 matlab/+caffe/Net.m | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
index 349e060eb22..bb99ec89049 100644
--- a/matlab/+caffe/Net.m
+++ b/matlab/+caffe/Net.m
@@ -69,7 +69,9 @@
       self.blob_names = self.attributes.blob_names;
     end
     function delete (self)
-      caffe_('delete_net', self.hNet_self);
+      if ~isempty(self.hNet_self)
+        caffe_('delete_net', self.hNet_self);
+      end
     end
     function layer = layers(self, layer_name)
       CHECK(ischar(layer_name), 'layer_name must be a string');

From 0cb449e8c766499b29e5314120068ee9c8ebd71e Mon Sep 17 00:00:00 2001
From: Dave Brown <daveb@signalpop.com>
Date: Sun, 7 May 2017 18:25:27 -0700
Subject: [PATCH 1978/2053] Update euclidean_loss_layer.hpp

---
 include/caffe/layers/euclidean_loss_layer.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/include/caffe/layers/euclidean_loss_layer.hpp b/include/caffe/layers/euclidean_loss_layer.hpp
index f564569e27a..24568c5496f 100644
--- a/include/caffe/layers/euclidean_loss_layer.hpp
+++ b/include/caffe/layers/euclidean_loss_layer.hpp
@@ -30,7 +30,7 @@ namespace caffe {
  * This can be used for least-squares regression tasks.  An InnerProductLayer
  * input to a EuclideanLossLayer exactly formulates a linear least squares
  * regression problem. With non-zero weight decay the problem becomes one of
- * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
+ * ridge regression -- see src/caffe/test/test_gradient_based_solver.cpp for a concrete
  * example wherein we check that the gradients computed for a Net with exactly
  * this structure match hand-computed gradient formulas for ridge regression.
  *

From b7e2b99c7f0aeeb8e24046f8cbf5212065b9ccdf Mon Sep 17 00:00:00 2001
From: Luke Yeager <lukeyeager@users.noreply.github.com>
Date: Fri, 12 May 2017 10:06:51 -0700
Subject: [PATCH 1979/2053] Downgrade boost requirement from 1.55 to 1.54

---
 cmake/Dependencies.cmake       | 2 +-
 scripts/travis/install-deps.sh | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index 4a5bac471b4..c48255c89f2 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -5,7 +5,7 @@ set(Caffe_DEFINITIONS "")
 set(Caffe_COMPILE_OPTIONS "")
 
 # ---[ Boost
-find_package(Boost 1.55 REQUIRED COMPONENTS system thread filesystem)
+find_package(Boost 1.54 REQUIRED COMPONENTS system thread filesystem)
 list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Boost_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS PUBLIC ${Boost_LIBRARIES})
 
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index dac5d2f9d37..2fa2a74a486 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -9,10 +9,10 @@ apt-get -y update
 apt-get install -y --no-install-recommends \
   build-essential \
   graphviz \
-  libboost-filesystem1.55-dev \
-  libboost-python1.55-dev \
-  libboost-system1.55-dev \
-  libboost-thread1.55-dev \
+  libboost-filesystem-dev \
+  libboost-python-dev \
+  libboost-system-dev \
+  libboost-thread-dev \
   libgflags-dev \
   libgoogle-glog-dev \
   libhdf5-serial-dev \

From 30a2ab7e50430911f37ddf981e67e4f36f662f14 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Mon, 15 May 2017 02:16:19 +0000
Subject: [PATCH 1980/2053] cmake: rename libproto.a -> libcaffeproto.a

---
 cmake/ConfigGen.cmake    |  2 +-
 src/caffe/CMakeLists.txt | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index ad91f542104..09bb09b4ff2 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -33,7 +33,7 @@ function(caffe_generate_export_configs)
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/CaffeConfig.cmake" @ONLY)
 
   # Add targets to the build-tree export set
-  export(TARGETS caffe proto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
+  export(TARGETS caffe caffeproto FILE "${PROJECT_BINARY_DIR}/CaffeTargets.cmake")
   export(PACKAGE Caffe)
 
   # ---[ Configure install-tree CaffeConfig.cmake file ]---
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index b9152e9216f..4a805568566 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -3,12 +3,12 @@ file(GLOB proto_files proto/*.proto)
 caffe_protobuf_generate_cpp_py(${proto_gen_folder} proto_srcs proto_hdrs proto_python ${proto_files})
 
 # include python files either to force generation
-add_library(proto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
-caffe_default_properties(proto)
-target_link_libraries(proto PUBLIC ${PROTOBUF_LIBRARIES})
-target_include_directories(proto PUBLIC ${PROTOBUF_INCLUDE_DIR})
+add_library(caffeproto STATIC ${proto_hdrs} ${proto_srcs} ${proto_python})
+caffe_default_properties(caffeproto)
+target_link_libraries(caffeproto PUBLIC ${PROTOBUF_LIBRARIES})
+target_include_directories(caffeproto PUBLIC ${PROTOBUF_INCLUDE_DIR})
 
-list(INSERT Caffe_LINKER_LIBS 0 PUBLIC proto) # note, crucial to prepend!
+list(INSERT Caffe_LINKER_LIBS 0 PUBLIC caffeproto) # note, crucial to prepend!
 
 # --[ Caffe library
 
@@ -42,7 +42,7 @@ set_target_properties(caffe PROPERTIES
 # ---[ Install
 install(DIRECTORY ${Caffe_INCLUDE_DIR}/caffe DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
 install(FILES ${proto_hdrs} DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/caffe/proto)
-install(TARGETS caffe proto EXPORT CaffeTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
+install(TARGETS caffe caffeproto EXPORT CaffeTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
 
 file(WRITE ${PROJECT_BINARY_DIR}/__init__.py)
 list(APPEND proto_python ${PROJECT_BINARY_DIR}/__init__.py)

From 83814da36d5a44039ddc35f58f9b341e9d1bd935 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Mon, 15 May 2017 03:04:47 +0000
Subject: [PATCH 1981/2053] docs/debian guide: update compiler combination
 table

---
 docs/install_apt_debian.md | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
index 65fe70924e1..bd91124a898 100644
--- a/docs/install_apt_debian.md
+++ b/docs/install_apt_debian.md
@@ -96,18 +96,22 @@ Note, this requires a `deb-src` entry in your `/etc/apt/sources.list`.
 Some users may find their favorate compiler doesn't work with CUDA.
 
 ```
-CXX compiler |  CUDA 7.5  |  CUDA 8.0  |
--------------+------------+------------+-
-GCC-7        |     ?      |     ?      |
-GCC-6        |     ✘      |     ✘      |
-GCC-5        |     ✔ [1]  |     ✔      |
-CLANG-4.0    |     ?      |     ?      |
-CLANG-3.9    |     ✘      |     ✘      |
-CLANG-3.8    |     ?      |     ✔      |
+CXX compiler |  CUDA 7.5  |  CUDA 8.0  |  CUDA 9.0  |
+-------------+------------+------------+------------+
+GCC-8        |     ?      |     ?      |     ?      |
+GCC-7        |     ?      |     ?      |     ?      |
+GCC-6        |     ✘      |     ✘      |     ✔      |
+GCC-5        |     ✔ [1]  |     ✔      |     ✔      |
+-------------+------------+------------+------------+
+CLANG-4.0    |     ?      |     ?      |     ?      |
+CLANG-3.9    |     ✘      |     ✘      |     ✔      |
+CLANG-3.8    |     ?      |     ✔      |     ✔      |
 ```
 
 `[1]` CUDA 7.5 's `host_config.h` must be patched before working with GCC-5.
 
+`[2]` CUDA 9.0: https://devblogs.nvidia.com/parallelforall/cuda-9-features-revealed/
+
 BTW, please forget the GCC-4.X series, since its `libstdc++` ABI is not compatible with GCC-5's.
 You may encounter failure linking GCC-4.X object files against GCC-5 libraries.
 (See https://wiki.debian.org/GCC5 )

From 264cf199e4e8bc44bb97762b1018137704157c2c Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 13 Jun 2017 11:59:26 -0700
Subject: [PATCH 1982/2053] List branches in readme

---
 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 0ae3616b4a6..c40aee65c3c 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,14 @@ Check out the [project site](http://caffe.berkeleyvision.org) for all the detail
 
 and step-by-step examples.
 
+## Custom distributions
+
+- [Intel optimized branch](https://github.com/BVLC/caffe/tree/intel) for CPU, in particular Xeon processors (HSW, BDW, Xeon Phi).
+- [OpenCL Caffe](https://github.com/BVLC/caffe/tree/opencl) e.g. for AMD or Intel devices.
+- [Windows Caffe](https://github.com/BVLC/caffe/tree/windows)
+
+## Community
+
 [![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.

From 4efdf7ee49cffefdd7ea099c00dc5ea327640f04 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Tue, 20 Jun 2017 14:20:42 -0700
Subject: [PATCH 1983/2053] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c40aee65c3c..5148c69d310 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ and step-by-step examples.
 
 ## Custom distributions
 
-- [Intel optimized branch](https://github.com/BVLC/caffe/tree/intel) for CPU, in particular Xeon processors (HSW, BDW, Xeon Phi).
+ - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, Xeon Phi).
 - [OpenCL Caffe](https://github.com/BVLC/caffe/tree/opencl) e.g. for AMD or Intel devices.
 - [Windows Caffe](https://github.com/BVLC/caffe/tree/windows)
 

From c209c92fcd5d7097a75dfa17a022ec6b69d028db Mon Sep 17 00:00:00 2001
From: Arne Suppe <suppe@andrew.cmu.edu>
Date: Wed, 21 Jun 2017 12:19:30 +0800
Subject: [PATCH 1984/2053] Fixed bug where make distribute duplicates python
 files in distribute/python

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 4d324160c08..c6d5685b140 100644
--- a/Makefile
+++ b/Makefile
@@ -694,6 +694,6 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
 	cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 	# add python - it's not the standard way, indeed...
-	cp -r python $(DISTRIBUTE_DIR)/python
+	cp -r python $(DISTRIBUTE_DIR)/
 
 -include $(DEPS)

From 4a2f2a0f2cbfca5fe8d9fec417a432d0aa345f37 Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Sat, 8 Jul 2017 08:22:07 +0000
Subject: [PATCH 1985/2053] docs: update apt installation guide for Debian and
 Ubuntu

Caffe package is available for APT since Debian 9.0 and Ubuntu 17.04 .
---
 docs/install_apt.md        | 28 ++++++++++++++++++++++++++++
 docs/install_apt_debian.md | 30 ++++++++++++++----------------
 2 files changed, 42 insertions(+), 16 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index ee2cd287701..43785f56d27 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -4,6 +4,34 @@ title: "Installation: Ubuntu"
 
 # Ubuntu Installation
 
+### For Ubuntu (>= 17.04)
+
+**Installing pre-compiled Caffe**
+
+Everything including caffe itself is packaged in 17.04 and higher versions.
+To install pre-compiled Caffe package, just do it by
+
+    sudo apt install caffe-cpu
+
+for CPU-only version, or
+
+    sudo apt install caffe-cuda
+
+for CUDA version. Note, the cuda version may break if your NVIDIA driver
+and CUDA toolkit are not installed by APT.
+
+**Installing Caffe from source**
+
+We may install the dependencies by merely one line
+
+    sudo apt build-dep caffe-cpu        # dependencies for CPU-only version
+    sudo apt build-dep caffe-cuda       # dependencies for CUDA version
+
+It requires a `deb-src` line in your `sources.list`.
+Continue with [compilation](installation.html#compilation).
+
+### For Ubuntu (\< 17.04)
+
 **General dependencies**
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
diff --git a/docs/install_apt_debian.md b/docs/install_apt_debian.md
index bd91124a898..0a6a3b962e5 100644
--- a/docs/install_apt_debian.md
+++ b/docs/install_apt_debian.md
@@ -8,24 +8,28 @@ Caffe packages are available for several Debian versions, as shown in the
 following chart:
 
 ```
-Your Distro     |  CPU_ONLY  |  CUDA  |     Alias
+Your Distro     |  CPU_ONLY  |  CUDA  | Codename
 ----------------+------------+--------+-------------------
-Debian/stable   |     ✘      |   ✘    | Debian Jessie
-Debian/testing  |     ✔      |   ✔    | Debian Stretch/Sid
-Debian/unstable |     ✔      |   ✔    | Debian Sid
+Debian/oldstable|     ✘      |   ✘    | Jessie (8.0)
+Debian/stable   |     ✔      |   ✔    | Stretch (9.0)
+Debian/testing  |     ✔      |   ✔    | Buster
+Debian/unstable |     ✔      |   ✔    | Buster
 ```
 
 * `✘ ` You should take a look at [Ubuntu installation instruction](install_apt.html).
 
 * `✔ ` You can install caffe with a single command line following this guide.
 
-Last update: 2017-02-01
+* [Package status of CPU-only version](https://tracker.debian.org/pkg/caffe)
+
+* [Package status of CUDA version](https://tracker.debian.org/pkg/caffe-contrib)
+
+Last update: 2017-07-08
 
 ## Binary installation with APT
 
-Apart from the installation methods based on source, Debian/unstable
-and Debian/testing users can install pre-compiled Caffe packages from
-the official archive.
+Apart from the installation methods based on source, Debian users can install
+pre-compiled Caffe packages from the official archive with APT.
 
 Make sure that your `/etc/apt/sources.list` contains `contrib` and `non-free`
 sections if you want to install the CUDA version, for instance:
@@ -44,7 +48,8 @@ $ caffe                                              # command line interface wo
 $ python3 -c 'import caffe; print(caffe.__path__)'   # python3 interface working
 ```
 
-These Caffe packages should work for you out of box.
+These Caffe packages should work for you out of box. However, the CUDA version
+may break if your NVIDIA driver and CUDA toolkit are not installed with APT.
 
 #### Customizing caffe packages
 
@@ -156,10 +161,3 @@ and hack the packaging scripts, then build your customized package.
 $ sudo apt install caffe-doc
 $ dpkg -L caffe-doc
 ```
-
-* Where can I find the Debian package status?
-
-```
-https://tracker.debian.org/pkg/caffe          (for the CPU_ONLY version)
-https://tracker.debian.org/pkg/caffe-contrib  (for the CUDA version)
-```

From eedf7c188708e097e63984528c94d3c8616dd5ff Mon Sep 17 00:00:00 2001
From: Zhou Mo <cdluminate@gmail.com>
Date: Sat, 8 Jul 2017 08:24:57 +0000
Subject: [PATCH 1986/2053] docs: add Ubuntu package tracker link in Ubuntu
 guide

---
 docs/install_apt.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index 43785f56d27..b6cb1c2d6f7 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -20,6 +20,10 @@ for CPU-only version, or
 for CUDA version. Note, the cuda version may break if your NVIDIA driver
 and CUDA toolkit are not installed by APT.
 
+[Package status of CPU-only version](https://launchpad.net/ubuntu/+source/caffe)
+
+[Package status of CUDA version](https://launchpad.net/ubuntu/+source/caffe-contrib)
+
 **Installing Caffe from source**
 
 We may install the dependencies by merely one line

From 3d7cfc40c170f93ac88909f40ca0208269ee26a9 Mon Sep 17 00:00:00 2001
From: Lydorn <nicolas.jp.girard@gmail.com>
Date: Mon, 10 Jul 2017 15:43:47 +0200
Subject: [PATCH 1987/2053] Update lrn.md

Fixed typo "locaitons " -> "location" in line 17
---
 docs/tutorial/layers/lrn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/layers/lrn.md b/docs/tutorial/layers/lrn.md
index 2fbef734663..f5e4829279d 100644
--- a/docs/tutorial/layers/lrn.md
+++ b/docs/tutorial/layers/lrn.md
@@ -14,7 +14,7 @@ title: Local Response Normalization (LRN)
         - `local_size` [default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)
         - `alpha` [default 1]: the scaling parameter (see below)
         - `beta` [default 5]: the exponent (see below)
-        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locaitons (`WITHIN_CHANNEL`)
+        - `norm_region` [default `ACROSS_CHANNELS`]: whether to sum over adjacent channels (`ACROSS_CHANNELS`) or nearby spatial locations (`WITHIN_CHANNEL`)
 
 The local response normalization layer performs a kind of "lateral inhibition" by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL` mode, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $$(1 + (\alpha/n) \sum_i x_i^2)^\beta$$, where $$n$$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
 

From af9d6bc0446f479a7c7cff870de4da5df19fddd1 Mon Sep 17 00:00:00 2001
From: downes <downes>
Date: Wed, 12 Jul 2017 14:50:40 -0700
Subject: [PATCH 1988/2053] update sklearn calls to use latest API

Version 0.18 moved cross-validation to sklearn.model_selection - see http://scikit-learn.org/stable/whats_new.html#version-0-18
Version 0.17 deprecated class_weight="auto" in favor of class_weight="balanced"
---
 examples/brewing-logreg.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
index c053b73b39f..4b4cd6a3f02 100644
--- a/examples/brewing-logreg.ipynb
+++ b/examples/brewing-logreg.ipynb
@@ -73,7 +73,7 @@
     ")\n",
     "\n",
     "# Split into train and test\n",
-    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "X, Xt, y, yt = sklearn.model_selection.train_test_split(X, y)\n",
     "\n",
     "# Visualize sample of the data\n",
     "ind = np.random.permutation(X.shape[0])[:1000]\n",
@@ -111,7 +111,7 @@
     "%%timeit\n",
     "# Train and test the scikit-learn SGD logistic regression.\n",
     "clf = sklearn.linear_model.SGDClassifier(\n",
-    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='balanced')\n",
     "\n",
     "clf.fit(X, y)\n",
     "yt_pred = clf.predict(Xt)\n",

From 4b98f06c03c7cb84163ba7f681dbe9185fdcc5f9 Mon Sep 17 00:00:00 2001
From: downes <downes>
Date: Wed, 12 Jul 2017 14:52:53 -0700
Subject: [PATCH 1989/2053] update deprecated pandas call

pd.scatter_matrix -> pd.plotting.scatter_matrix
---
 examples/brewing-logreg.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
index 4b4cd6a3f02..0f87185a35b 100644
--- a/examples/brewing-logreg.ipynb
+++ b/examples/brewing-logreg.ipynb
@@ -78,7 +78,7 @@
     "# Visualize sample of the data\n",
     "ind = np.random.permutation(X.shape[0])[:1000]\n",
     "df = pd.DataFrame(X[ind])\n",
-    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+    "_ = pd.plotting.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
    ]
   },
   {

From 751a570a3674e2d77e157a7e365fe68f05505157 Mon Sep 17 00:00:00 2001
From: Jean-Louis Queguiner <jean-louis.queguiner@gadz.org>
Date: Tue, 1 Aug 2017 11:16:29 +0200
Subject: [PATCH 1990/2053] [DOC][FIX] fix web demo install instruction link

---
 examples/web_demo/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/web_demo/readme.md b/examples/web_demo/readme.md
index fe74b9ef7d3..e50c4f101ee 100644
--- a/examples/web_demo/readme.md
+++ b/examples/web_demo/readme.md
@@ -11,7 +11,7 @@ priority: 10
 ## Requirements
 
 The demo server requires Python with some dependencies.
-To make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](/installation.html)).
+To make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](http://caffe.berkeleyvision.org/installation.html)).
 
 Make sure that you have obtained the Reference CaffeNet Model and the ImageNet Auxiliary Data:
 

From 315641b7ef8624b756ed042a7e9330ecde3782e7 Mon Sep 17 00:00:00 2001
From: Keith Mok <ek9852@gmail.com>
Date: Fri, 4 Aug 2017 13:55:34 -0700
Subject: [PATCH 1991/2053] Fix hardcode xcode path

User may not install xcoder into default directory
especially if there are two different versions of xcoder installed.
---
 cmake/Modules/FindvecLib.cmake | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/cmake/Modules/FindvecLib.cmake b/cmake/Modules/FindvecLib.cmake
index 8eaab59473c..4d44e613a00 100644
--- a/cmake/Modules/FindvecLib.cmake
+++ b/cmake/Modules/FindvecLib.cmake
@@ -12,11 +12,12 @@ endif()
 
 set(__veclib_include_suffix "Frameworks/vecLib.framework/Versions/Current/Headers")
 
+exec_program(xcode-select ARGS -print-path OUTPUT_VARIABLE CMAKE_XCODE_DEVELOPER_DIR)
 find_path(vecLib_INCLUDE_DIR vecLib.h
           DOC "vecLib include directory"
           PATHS /System/Library/Frameworks/Accelerate.framework/Versions/Current/${__veclib_include_suffix}
                 /System/Library/${__veclib_include_suffix}
-                /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
+                ${CMAKE_XCODE_DEVELOPER_DIR}/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
           NO_DEFAULT_PATH)
 
 include(FindPackageHandleStandardArgs)

From 1de4cebfb81d50267d0d8c2595372b14e1408248 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Wed, 16 Aug 2017 18:24:32 -0700
Subject: [PATCH 1992/2053] Update README.md

Mention SKX support
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5148c69d310..fe259535865 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ and step-by-step examples.
 
 ## Custom distributions
 
- - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, Xeon Phi).
+ - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, SKX, Xeon Phi).
 - [OpenCL Caffe](https://github.com/BVLC/caffe/tree/opencl) e.g. for AMD or Intel devices.
 - [Windows Caffe](https://github.com/BVLC/caffe/tree/windows)
 

From 7b721207235732846666143070f4990707bcb66d Mon Sep 17 00:00:00 2001
From: Josh Bialkowski <josh@skydio.com>
Date: Mon, 21 Aug 2017 08:31:25 -0700
Subject: [PATCH 1993/2053] Fix caffe rpath

The logic for setting the library RPATH checks whether or not
${CMAKE_INSTALL_PREFIX}/lib is a system directory, and if not
adds it to the library RPATH. However, caffe does not install
to ${CMAKE_INSTALL_PREFIX}/lib, it installs to
${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR} (from
GNUInstallDirs). CMAKE_INSTALL_LIBDIR may be something like
"lib/x86_64-linux-gnu"
---
 cmake/Misc.cmake | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 9dd2609b36a..fcb246472f0 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -32,9 +32,10 @@ endif()
 set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE CACHE BOOLEAN "Use link paths for shared library rpath")
 set(CMAKE_MACOSX_RPATH TRUE)
 
-list(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES ${CMAKE_INSTALL_PREFIX}/lib __is_systtem_dir)
+list(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES
+     ${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR} __is_systtem_dir)
 if(${__is_systtem_dir} STREQUAL -1)
-  set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_PREFIX}/lib)
+  set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR})
 endif()
 
 # ---[ Funny target

From 1f1326d046773d31a9f0916d8f5f8ccaa67bde46 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=AD=A4=E9=97=B4?= <zhangyue.zhangyue@alibaba-inc.com>
Date: Tue, 22 Aug 2017 07:36:09 +0800
Subject: [PATCH 1994/2053] Update link to google style guide.

---
 docs/development.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/development.md b/docs/development.md
index ec05bbee102..36cd399512e 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -116,5 +116,5 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 
 - **Run `make lint` to check C++ code.**
 - Wrap lines at 80 chars.
-- Follow [Google C++ style](http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml) and [Google python style](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
+- Follow [Google C++ style](https://google.github.io/styleguide/cppguide.html) and [Google python style](https://google.github.io/styleguide/pyguide.html) + [PEP 8](http://legacy.python.org/dev/peps/pep-0008/).
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.

From 3dad3323436e05a4c1890104b0f26f27f6d77d31 Mon Sep 17 00:00:00 2001
From: wasnot <wasnot.apps@gmail.com>
Date: Wed, 6 Sep 2017 12:32:39 +0900
Subject: [PATCH 1995/2053] modified division operator for compatibility of
 python 3

---
 python/caffe/classifier.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index ea29fed86f9..983760a786d 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -92,7 +92,7 @@ def predict(self, inputs, oversample=True):
 
         # For oversampling, average predictions across crops.
         if oversample:
-            predictions = predictions.reshape((len(predictions) / 10, 10, -1))
+            predictions = predictions.reshape((len(predictions) // 10, 10, -1))
             predictions = predictions.mean(1)
 
         return predictions

From 8bdc87f6bc6a7d05d2fdbee2cfc159003297476b Mon Sep 17 00:00:00 2001
From: Takuya Narihira <dvbscb@gmail.com>
Date: Wed, 11 Mar 2015 16:23:39 -0700
Subject: [PATCH 1996/2053] Expose GPU pointers to Python

The pointers could be used by CUDA wrapper libraries in Python such as
PyCUDA, gnumpy, Theano etc.
---
 python/caffe/_caffe.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index d7f43fff62d..72659a4f44e 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -464,6 +464,14 @@ BOOST_PYTHON_MODULE(_caffe) {
     .add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(
         &Blob<Dtype>::count))
     .def("reshape",           bp::raw_function(&Blob_Reshape))
+#ifndef CPU_ONLY
+    .add_property("_gpu_data_ptr",
+        reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
+          &Blob<Dtype>::mutable_gpu_data))
+    .add_property("_gpu_diff_ptr",
+        reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
+          &Blob<Dtype>::mutable_gpu_diff))
+#endif
     .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,

From d2625f86ac9304467276cf69a7eda0c93f5e242e Mon Sep 17 00:00:00 2001
From: Bo Wang <david.b.wang@gmail.com>
Date: Sat, 9 Sep 2017 21:57:20 -0700
Subject: [PATCH 1997/2053] Implement CuDNN-based deconvolution layer and test

---
 include/caffe/layers/cudnn_deconv_layer.hpp |  68 ++++
 src/caffe/layer_factory.cpp                 |  41 +++
 src/caffe/layers/cudnn_deconv_layer.cpp     | 327 ++++++++++++++++++++
 src/caffe/layers/cudnn_deconv_layer.cu      | 138 +++++++++
 src/caffe/layers/deconv_layer.cpp           |   1 -
 src/caffe/test/test_deconvolution_layer.cpp | 265 ++++++++++++++++
 6 files changed, 839 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/cudnn_deconv_layer.hpp
 create mode 100644 src/caffe/layers/cudnn_deconv_layer.cpp
 create mode 100644 src/caffe/layers/cudnn_deconv_layer.cu

diff --git a/include/caffe/layers/cudnn_deconv_layer.hpp b/include/caffe/layers/cudnn_deconv_layer.hpp
new file mode 100644
index 00000000000..34095e5c44d
--- /dev/null
+++ b/include/caffe/layers/cudnn_deconv_layer.hpp
@@ -0,0 +1,68 @@
+#ifndef CAFFE_CUDNN_DECONV_LAYER_HPP_
+#define CAFFE_CUDNN_DECONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/deconv_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of DeConvolutionLayer.
+ *        Fallback to DeConvolutionLayer for CPU mode.
+ *
+ * cuDNN accelerates deconvolution through forward kernels for filtering and
+ * bias plus backward kernels for the gradient w.r.t. the filters, biases, and
+ * inputs. Caffe + cuDNN further speeds up the computation through forward
+ * parallelism across groups and backward parallelism across gradients.
+*/
+template <typename Dtype>
+class CuDNNDeconvolutionLayer : public DeconvolutionLayer<Dtype> {
+public:
+  explicit CuDNNDeconvolutionLayer(const LayerParameter& param)
+    : DeconvolutionLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+                          const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+                       const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNDeconvolutionLayer();
+
+protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                           const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+                            const vector<bool>& propagate_down,
+                            const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t* handle_;
+  cudaStream_t*  stream_;
+
+  // algorithms for forward and backwards convolutions
+  cudnnConvolutionFwdAlgo_t *fwd_algo_;
+  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
+  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
+
+  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
+  cudnnTensorDescriptor_t bias_desc_;
+  cudnnFilterDescriptor_t filter_desc_;
+  vector<cudnnConvolutionDescriptor_t> conv_descs_;
+  int bottom_offset_, top_offset_, bias_offset_;
+
+  size_t *workspace_fwd_sizes_;
+  size_t *workspace_bwd_data_sizes_;
+  size_t *workspace_bwd_filter_sizes_;
+  size_t workspaceSizeInBytes;  // size of underlying storage
+  void *workspaceData;  // underlying storage
+  void **workspace;  // aliases into workspaceData
+};
+#endif
+
+}  // namespace caffe
+
+#endif // CAFFE_CUDNN_DECONV_LAYER_HPP_
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index f14253a510e..9f9026b1dde 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -8,6 +8,7 @@
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
 #include "caffe/layers/conv_layer.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 #include "caffe/layers/lrn_layer.hpp"
 #include "caffe/layers/pooling_layer.hpp"
 #include "caffe/layers/relu_layer.hpp"
@@ -18,6 +19,7 @@
 
 #ifdef USE_CUDNN
 #include "caffe/layers/cudnn_conv_layer.hpp"
+#include "caffe/layers/cudnn_deconv_layer.hpp"
 #include "caffe/layers/cudnn_lcn_layer.hpp"
 #include "caffe/layers/cudnn_lrn_layer.hpp"
 #include "caffe/layers/cudnn_pooling_layer.hpp"
@@ -73,6 +75,45 @@ shared_ptr<Layer<Dtype> > GetConvolutionLayer(
 
 REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);
 
+// Get deconvolution layer according to engine.
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetDeconvolutionLayer(const LayerParameter& param) {
+  ConvolutionParameter conv_param = param.convolution_param();
+  ConvolutionParameter_Engine engine = conv_param.engine();
+#ifdef USE_CUDNN
+  bool use_dilation = false;
+  for (int i = 0; i < conv_param.dilation_size(); ++i) {
+    if (conv_param.dilation(i) > 1) {
+      use_dilation = true;
+    }
+  }
+#endif
+  if (engine == ConvolutionParameter_Engine_DEFAULT) {
+    engine = ConvolutionParameter_Engine_CAFFE;
+#ifdef USE_CUDNN
+    if (!use_dilation) {
+      engine = ConvolutionParameter_Engine_CUDNN;
+    }
+#endif
+  }
+  if (engine == ConvolutionParameter_Engine_CAFFE) {
+    return shared_ptr<Layer<Dtype> >(new DeconvolutionLayer<Dtype>(param));
+#ifdef USE_CUDNN
+  } else if (engine == ConvolutionParameter_Engine_CUDNN) {
+    if (use_dilation) {
+      LOG(FATAL) << "CuDNN doesn't support the dilated deconvolution at Layer "
+                 << param.name();
+    }
+    return shared_ptr<Layer<Dtype> >(new CuDNNDeconvolutionLayer<Dtype>(param));
+#endif
+  } else {
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+    throw;  // Avoids missing return warning
+  }
+}
+
+REGISTER_LAYER_CREATOR(Deconvolution, GetDeconvolutionLayer);
+
 // Get pooling layer according to engine.
 template <typename Dtype>
 shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
diff --git a/src/caffe/layers/cudnn_deconv_layer.cpp b/src/caffe/layers/cudnn_deconv_layer.cpp
new file mode 100644
index 00000000000..260da5c1ee0
--- /dev/null
+++ b/src/caffe/layers/cudnn_deconv_layer.cpp
@@ -0,0 +1,327 @@
+#ifdef USE_CUDNN
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/cudnn_deconv_layer.hpp"
+
+namespace caffe {
+
+// Set to three for the benefit of the backward pass, which
+// can use separate streams for calculating the gradient w.r.t.
+// bias, filter weights, and bottom data for each group independently
+#define CUDNN_STREAMS_PER_GROUP 3
+
+/**
+ * TODO(dox) explain cuDNN interface
+ */
+template <typename Dtype>
+void CuDNNDeconvolutionLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  DeconvolutionLayer<Dtype>::LayerSetUp(bottom, top);
+  // Initialize CUDA streams and cuDNN.
+  stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+  handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  // Initialize algorithm arrays
+  fwd_algo_       = new cudnnConvolutionFwdAlgo_t[bottom.size()];
+  bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
+  bwd_data_algo_  = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
+
+  // initialize size arrays
+  workspace_fwd_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_data_sizes_ = new size_t[bottom.size()];
+
+  // workspace data
+  workspaceSizeInBytes = 0;
+  workspaceData = NULL;
+  workspace = new void*[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  for (size_t i = 0; i < bottom.size(); ++i) {
+    // initialize all to default algorithms
+    fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
+    bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
+    bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
+    // default algorithms don't require workspace
+    workspace_fwd_sizes_[i] = 0;
+    workspace_bwd_data_sizes_[i] = 0;
+    workspace_bwd_filter_sizes_[i] = 0;
+  }
+
+  for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
+    CUDA_CHECK(cudaStreamCreate(&stream_[g]));
+    CUDNN_CHECK(cudnnCreate(&handle_[g]));
+    CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
+    workspace[g] = NULL;
+  }
+
+  // Set the indexing parameters.
+  bias_offset_ = (this->num_output_ / this->group_);
+
+  // Create filter descriptor.
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int kernel_h = kernel_shape_data[0];
+  const int kernel_w = kernel_shape_data[1];
+  cudnn::createFilterDesc<Dtype>(&filter_desc_,
+                                 this->channels_ / this->group_,
+                                 this->num_output_ / this->group_,
+                                 kernel_h,
+                                 kernel_w);
+
+  // Create tensor descriptor(s) for data and corresponding convolution(s).
+  for (int i = 0; i < bottom.size(); i++) {
+    cudnnTensorDescriptor_t bottom_desc;
+    cudnn::createTensor4dDesc<Dtype>(&bottom_desc);
+    bottom_descs_.push_back(bottom_desc);
+    cudnnTensorDescriptor_t top_desc;
+    cudnn::createTensor4dDesc<Dtype>(&top_desc);
+    top_descs_.push_back(top_desc);
+    cudnnConvolutionDescriptor_t conv_desc;
+    cudnn::createConvolutionDesc<Dtype>(&conv_desc);
+    conv_descs_.push_back(conv_desc);
+  }
+
+  // Tensor descriptor for bias.
+  if (this->bias_term_) {
+    cudnn::createTensor4dDesc<Dtype>(&bias_desc_);
+  }
+
+  handles_setup_ = true;
+}
+
+template <typename Dtype>
+void CuDNNDeconvolutionLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  DeconvolutionLayer<Dtype>::Reshape(bottom, top);
+  CHECK_EQ(2, this->num_spatial_axes_)
+      << "CuDNNDeconvolutionLayer input must have 2 spatial axes "
+      << "(e.g., height and width). "
+      << "Use 'engine: CAFFE' for general ND convolution.";
+  bottom_offset_ = this->bottom_dim_ / this->group_;
+  top_offset_ = this->top_dim_ / this->group_;
+  const int height = bottom[0]->shape(this->channel_axis_ + 1);
+  const int width = bottom[0]->shape(this->channel_axis_ + 2);
+  const int height_out = top[0]->shape(this->channel_axis_ + 1);
+  const int width_out = top[0]->shape(this->channel_axis_ + 2);
+  const int* pad_data = this->pad_.cpu_data();
+  const int pad_h = pad_data[0];
+  const int pad_w = pad_data[1];
+  const int* stride_data = this->stride_.cpu_data();
+  const int stride_h = stride_data[0];
+  const int stride_w = stride_data[1];
+
+  // Specify workspace limit for kernels directly until we have a
+  // planning strategy and a rewrite of Caffe's GPU memory mangagement
+  size_t workspace_limit_bytes = 8*1024*1024;
+
+  for (int i = 0; i < bottom.size(); i++) {
+    cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
+                                  this->num_,
+                                  this->channels_ / this->group_,
+                                  height,
+                                  width,
+                                  this->channels_ * height * width,
+                                  height * width,
+                                  width,
+                                  1);
+    cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
+                                  this->num_,
+                                  this->num_output_ / this->group_,
+                                  height_out,
+                                  width_out,
+                                  this->num_output_ * height_out * width_out,
+                                  height_out * width_out,
+                                  width_out,
+                                  1);
+    cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i],
+                                     top_descs_[i],
+                                     filter_desc_,
+                                     pad_h,
+                                     pad_w,
+                                     stride_h,
+                                     stride_w);
+
+    // choose forward and backward algorithms + workspace(s)
+    CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(
+        handle_[0],
+        top_descs_[i],
+        filter_desc_,
+        conv_descs_[i],
+        bottom_descs_[i],
+        CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+        workspace_limit_bytes,
+        &fwd_algo_[i]));
+
+    // We have found that CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM is
+    // buggy. Thus, if this algo was chosen, choose winograd instead. If
+    // winograd is not supported or workspace is larger than threshold, choose
+    // implicit_gemm instead.
+    if (fwd_algo_[i] == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM) {
+      size_t winograd_workspace_size;
+      cudnnStatus_t status = cudnnGetConvolutionForwardWorkspaceSize(
+          handle_[0],
+          top_descs_[i],
+          filter_desc_,
+          conv_descs_[i],
+          bottom_descs_[i],
+          CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD,
+          &winograd_workspace_size);
+      if (status != CUDNN_STATUS_SUCCESS ||
+          winograd_workspace_size >= workspace_limit_bytes) {
+        fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
+      } else {
+        fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD;
+      }
+    }
+
+    CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(
+        handle_[0],
+        top_descs_[i],
+        filter_desc_,
+        conv_descs_[i],
+        bottom_descs_[i],
+        fwd_algo_[i],
+        &(workspace_fwd_sizes_[i])));
+
+    // choose backward algorithm for filter
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(
+        handle_[0],
+        top_descs_[i],
+        bottom_descs_[i],
+        conv_descs_[i],
+        filter_desc_,
+        CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+        workspace_limit_bytes,
+        &bwd_filter_algo_[i]));
+
+    // get workspace for backwards filter algorithm
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(
+        handle_[0],
+        top_descs_[i],
+        bottom_descs_[i],
+        conv_descs_[i],
+        filter_desc_,
+        bwd_filter_algo_[i],
+        &workspace_bwd_filter_sizes_[i]));
+
+    // choose backward algo for data
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(
+        handle_[0],
+        filter_desc_,
+        bottom_descs_[i],
+        conv_descs_[i],
+        top_descs_[i],
+        CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+        workspace_limit_bytes,
+        &bwd_data_algo_[i]));
+
+    // get workspace size
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(
+        handle_[0],
+        filter_desc_,
+        bottom_descs_[i],
+        conv_descs_[i],
+        top_descs_[i],
+        bwd_data_algo_[i],
+        &workspace_bwd_data_sizes_[i]));
+  }
+
+  // reduce over all workspace sizes to get a maximum to allocate / reallocate
+  size_t total_workspace_fwd = 0;
+  size_t total_workspace_bwd_data = 0;
+  size_t total_workspace_bwd_filter = 0;
+
+  for (size_t i = 0; i < bottom.size(); i++) {
+    total_workspace_fwd        = std::max(total_workspace_fwd,
+                                     workspace_fwd_sizes_[i]);
+    total_workspace_bwd_data   = std::max(total_workspace_bwd_data,
+                                     workspace_bwd_data_sizes_[i]);
+    total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,
+                                     workspace_bwd_filter_sizes_[i]);
+  }
+  // get max over all operations
+  size_t max_workspace = std::max(total_workspace_fwd,
+                             total_workspace_bwd_data);
+  max_workspace = std::max(max_workspace, total_workspace_bwd_filter);
+  // ensure all groups have enough workspace
+  size_t total_max_workspace = max_workspace *
+                               (this->group_ * CUDNN_STREAMS_PER_GROUP);
+
+  // this is the total amount of storage needed over all groups + streams
+  if (total_max_workspace > workspaceSizeInBytes) {
+    DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
+    workspaceSizeInBytes = total_max_workspace;
+
+    // free the existing workspace and allocate a new (larger) one
+    cudaFree(this->workspaceData);
+
+    cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);
+    if (err != cudaSuccess) {
+      // force zero memory path
+      for (int i = 0; i < bottom.size(); i++) {
+        workspace_fwd_sizes_[i] = 0;
+        workspace_bwd_filter_sizes_[i] = 0;
+        workspace_bwd_data_sizes_[i] = 0;
+        fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING;
+        bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
+        bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
+      }
+
+      // NULL out all workspace pointers
+      for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+        workspace[g] = NULL;
+      }
+      // NULL out underlying data
+      workspaceData = NULL;
+      workspaceSizeInBytes = 0;
+    }
+
+    // if we succeed in the allocation, set pointer aliases for workspaces
+    for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+      workspace[g] = reinterpret_cast<char *>(workspaceData) + g*max_workspace;
+    }
+  }
+
+  // Tensor descriptor for bias.
+  if (this->bias_term_) {
+    cudnn::setTensor4dDesc<Dtype>(
+        &bias_desc_, 1, this->num_output_ / this->group_, 1, 1);
+  }
+}
+
+template <typename Dtype>
+CuDNNDeconvolutionLayer<Dtype>::~CuDNNDeconvolutionLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  for (int i = 0; i < bottom_descs_.size(); i++) {
+    cudnnDestroyTensorDescriptor(bottom_descs_[i]);
+    cudnnDestroyTensorDescriptor(top_descs_[i]);
+    cudnnDestroyConvolutionDescriptor(conv_descs_[i]);
+  }
+  if (this->bias_term_) {
+    cudnnDestroyTensorDescriptor(bias_desc_);
+  }
+  cudnnDestroyFilterDescriptor(filter_desc_);
+
+  for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
+    cudaStreamDestroy(stream_[g]);
+    cudnnDestroy(handle_[g]);
+  }
+
+  cudaFree(workspaceData);
+  delete [] workspace;
+  delete [] stream_;
+  delete [] handle_;
+  delete [] fwd_algo_;
+  delete [] bwd_filter_algo_;
+  delete [] bwd_data_algo_;
+  delete [] workspace_fwd_sizes_;
+  delete [] workspace_bwd_data_sizes_;
+  delete [] workspace_bwd_filter_sizes_;
+}
+
+INSTANTIATE_CLASS(CuDNNDeconvolutionLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_deconv_layer.cu b/src/caffe/layers/cudnn_deconv_layer.cu
new file mode 100644
index 00000000000..eb1df32918f
--- /dev/null
+++ b/src/caffe/layers/cudnn_deconv_layer.cu
@@ -0,0 +1,138 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_deconv_layer.hpp"
+
+namespace caffe {
+
+__global__ void sync_deconv_groups() {}
+
+template <typename Dtype>
+void CuDNNDeconvolutionLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* top_data = top[i]->mutable_gpu_data();
+
+    // Forward through cuDNN in parallel over groups.
+    for (int g = 0; g < this->group_; g++) {
+      // Filters.
+      CUDNN_CHECK(cudnnConvolutionBackwardData(
+          handle_[g],
+          cudnn::dataType<Dtype>::one,
+          filter_desc_,
+          weight + this->weight_offset_ * g,
+          bottom_descs_[i],
+          bottom_data + bottom_offset_ * g,
+          conv_descs_[i],
+          bwd_data_algo_[i],
+          workspace[g],
+          workspace_bwd_data_sizes_[i],
+          cudnn::dataType<Dtype>::zero,
+          top_descs_[i],
+          top_data + top_offset_ * g));
+
+      // Bias.
+      if (this->bias_term_) {
+        const Dtype* bias_data = this->blobs_[1]->gpu_data();
+        CUDNN_CHECK(cudnnAddTensor(handle_[g],
+                                   cudnn::dataType<Dtype>::one,
+                                   bias_desc_,
+                                   bias_data + bias_offset_ * g,
+                                   cudnn::dataType<Dtype>::one,
+                                   top_descs_[i],
+                                   top_data + top_offset_ * g));
+      }
+    }
+
+    // Synchronize the work across groups, each of which went into its own
+    // stream, by launching an empty kernel into the default (null) stream.
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    sync_deconv_groups<<<1, 1>>>();
+  }
+}
+
+template <typename Dtype>
+void CuDNNDeconvolutionLayer<Dtype>::Backward_gpu(
+    const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->gpu_data();
+    weight_diff = this->blobs_[0]->mutable_gpu_diff();
+  }
+  Dtype* bias_diff = NULL;
+  if (this->bias_term_ && this->param_propagate_down_[1]) {
+    bias_diff = this->blobs_[1]->mutable_gpu_diff();
+  }
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = top[i]->gpu_diff();
+    // Backward through cuDNN in parallel over groups and gradients.
+    for (int g = 0; g < this->group_; g++) {
+      // Gradient w.r.t. bias.
+      if (this->bias_term_ && this->param_propagate_down_[1]) {
+        CUDNN_CHECK(cudnnConvolutionBackwardBias(handle_[0 * this->group_ + g],
+                                                 cudnn::dataType<Dtype>::one,
+                                                 top_descs_[i],
+                                                 top_diff + top_offset_ * g,
+                                                 cudnn::dataType<Dtype>::one,
+                                                 bias_desc_,
+                                                 bias_diff + bias_offset_ * g));
+      }
+
+      // Gradient w.r.t. weights.
+      if (this->param_propagate_down_[0]) {
+        const Dtype* bottom_data = bottom[i]->gpu_data();
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter(
+            handle_[1 * this->group_ + g],
+            cudnn::dataType<Dtype>::one,
+            top_descs_[i],
+            top_diff + top_offset_ * g,
+            bottom_descs_[i],
+            bottom_data + bottom_offset_ * g,
+            conv_descs_[i],
+            bwd_filter_algo_[i],
+            workspace[1 * this->group_ + g],
+            workspace_bwd_filter_sizes_[i],
+            cudnn::dataType<Dtype>::one,
+            filter_desc_,
+            weight_diff + this->weight_offset_ * g));
+      }
+
+      // Gradient w.r.t. bottom data.
+      if (propagate_down[i]) {
+        if (weight == NULL) {
+          weight = this->blobs_[0]->gpu_data();
+        }
+        Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+        CUDNN_CHECK(
+            cudnnConvolutionForward(handle_[2 * this->group_ + g],
+                                    cudnn::dataType<Dtype>::one,
+                                    top_descs_[i],
+                                    top_diff + top_offset_ * g,
+                                    filter_desc_,
+                                    weight + this->weight_offset_ * g,
+                                    conv_descs_[i],
+                                    fwd_algo_[i],
+                                    workspace[2 * this->group_ + g],
+                                    workspace_fwd_sizes_[i],
+                                    cudnn::dataType<Dtype>::zero,
+                                    bottom_descs_[i],
+                                    bottom_diff + bottom_offset_ * g));
+      }
+    }
+
+    // Synchronize the work across groups, each of which went into its own
+    // stream, by launching an empty kernel into the default (null) stream.
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    sync_deconv_groups<<<1, 1>>>();
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNDeconvolutionLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index 20a460fbdea..b86472b34be 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -79,6 +79,5 @@ STUB_GPU(DeconvolutionLayer);
 #endif
 
 INSTANTIATE_CLASS(DeconvolutionLayer);
-REGISTER_LAYER_CLASS(Deconvolution);
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
index c4b09ad555a..0067907165f 100644
--- a/src/caffe/test/test_deconvolution_layer.cpp
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -6,6 +6,7 @@
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layers/deconv_layer.hpp"
+#include "caffe/layers/cudnn_deconv_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -301,4 +302,268 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient3D) {
       this->blob_top_vec_);
 }
 
+#ifdef USE_CUDNN
+
+// Since ConvolutionLayerTest checks the shared conv/deconv code in detail,
+// we'll just do a simple forward test and a gradient check.
+template <typename TypeParam>
+class CuDNNDeconvolutionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  CuDNNDeconvolutionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_2_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~CuDNNDeconvolutionLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_2_;
+    delete blob_top_;
+    delete blob_top_2_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(CuDNNDeconvolutionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(CuDNNDeconvolutionLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_num_output(4);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  shared_ptr<Layer<Dtype> > layer(
+      new CuDNNDeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 4);
+  EXPECT_EQ(this->blob_top_->height(), 13);
+  EXPECT_EQ(this->blob_top_->width(), 9);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 4);
+  EXPECT_EQ(this->blob_top_2_->height(), 13);
+  EXPECT_EQ(this->blob_top_2_->width(), 9);
+  // setting group should not change the shape
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  layer.reset(new CuDNNDeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 13);
+  EXPECT_EQ(this->blob_top_->width(), 9);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 3);
+  EXPECT_EQ(this->blob_top_2_->height(), 13);
+  EXPECT_EQ(this->blob_top_2_->width(), 9);
+}
+
+TYPED_TEST(CuDNNDeconvolutionLayerTest, TestSimpleCuDNNDeconvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new CuDNNDeconvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // constant-fill the bottom blobs
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  ConstantFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  filler.Fill(this->blob_bottom_2_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // simply check that accumulation works with overlapping filters
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+      for (int h = 0; h < this->blob_top_->height(); ++h) {
+        for (int w = 0; w < this->blob_top_->width(); ++w) {
+          Dtype expected = 3.1;
+          bool h_overlap = h % 2 == 0 && h > 0
+            && h < this->blob_top_->height() - 1;
+          bool w_overlap = w % 2 == 0 && w > 0
+            && w < this->blob_top_->width() - 1;
+          if (h_overlap && w_overlap) {
+            expected += 9;
+          } else if (h_overlap || w_overlap) {
+            expected += 3;
+          }
+          EXPECT_NEAR(top_data[this->blob_top_->offset(n, c, h, w)],
+              expected, 1e-4);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CuDNNDeconvolutionLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  convolution_param->add_kernel_size(2);
+  convolution_param->add_stride(1);
+  convolution_param->set_num_output(1);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  CuDNNDeconvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNDeconvolutionLayerTest, TestNDAgainst2D) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kernel_h = 11;
+  const int kernel_w = 13;
+  vector<int> bottom_shape(4);
+  bottom_shape[0] = 15;
+  bottom_shape[1] = 12;
+  bottom_shape[2] = kernel_h * 2;
+  bottom_shape[3] = kernel_w * 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_num_output(18);
+  convolution_param->set_bias_term(false);
+  convolution_param->set_group(6);
+  convolution_param->set_kernel_h(kernel_h);
+  convolution_param->set_kernel_w(kernel_w);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  Blob<Dtype> weights;
+  Blob<Dtype> top_diff;
+  // Shape and fill weights and top_diff.
+  bool copy_diff;
+  bool reshape;
+  {
+    CuDNNDeconvolutionLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    top_diff.ReshapeLike(*this->blob_top_);
+    filler.Fill(&top_diff);
+    ASSERT_EQ(1, layer.blobs().size());
+    copy_diff = false; reshape = true;
+    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+  }
+  vector<bool> propagate_down(1, true);
+  Blob<Dtype> result_2d;
+  Blob<Dtype> backward_result_2d;
+  Blob<Dtype> backward_weight_result_2d;
+  // Test with 2D im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_2d.
+    convolution_param->set_force_nd_im2col(false);
+    CuDNNDeconvolutionLayer<Dtype> layer_2d(layer_param);
+    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_2d.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_2d.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_2d.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+  }
+  Blob<Dtype> result_nd;
+  Blob<Dtype> backward_result_nd;
+  Blob<Dtype> backward_weight_result_nd;
+  // Test with ND im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_nd.
+    convolution_param->set_force_nd_im2col(true);
+    CuDNNDeconvolutionLayer<Dtype> layer_nd(layer_param);
+    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_nd.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_nd.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_nd.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+  }
+  ASSERT_EQ(result_nd.count(), result_2d.count());
+  for (int i = 0; i < result_2d.count(); ++i)  {
+    EXPECT_NEAR(result_2d.cpu_data()[i], result_nd.cpu_data()[i], 1e-4);
+  }
+  ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+  for (int i = 0; i < backward_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+              backward_result_nd.cpu_diff()[i]);
+  }
+  ASSERT_EQ(backward_weight_result_nd.count(),
+            backward_weight_result_2d.count());
+  for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+              backward_weight_result_nd.cpu_diff()[i]);
+  }
+}
+
+#endif
+
 }  // namespace caffe

From fb3146363963fa494d1e7488890cac3d2a141c8f Mon Sep 17 00:00:00 2001
From: Bo Wang <david.b.wang@gmail.com>
Date: Thu, 14 Sep 2017 15:03:21 -0700
Subject: [PATCH 1998/2053] Fix format

---
 include/caffe/layers/cudnn_deconv_layer.hpp | 6 +++---
 src/caffe/test/test_deconvolution_layer.cpp | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/include/caffe/layers/cudnn_deconv_layer.hpp b/include/caffe/layers/cudnn_deconv_layer.hpp
index 34095e5c44d..12799e5b8ef 100644
--- a/include/caffe/layers/cudnn_deconv_layer.hpp
+++ b/include/caffe/layers/cudnn_deconv_layer.hpp
@@ -23,7 +23,7 @@ namespace caffe {
 */
 template <typename Dtype>
 class CuDNNDeconvolutionLayer : public DeconvolutionLayer<Dtype> {
-public:
+ public:
   explicit CuDNNDeconvolutionLayer(const LayerParameter& param)
     : DeconvolutionLayer<Dtype>(param), handles_setup_(false) {}
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
@@ -32,7 +32,7 @@ class CuDNNDeconvolutionLayer : public DeconvolutionLayer<Dtype> {
                        const vector<Blob<Dtype>*>& top);
   virtual ~CuDNNDeconvolutionLayer();
 
-protected:
+ protected:
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
                            const vector<Blob<Dtype>*>& top);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
@@ -65,4 +65,4 @@ class CuDNNDeconvolutionLayer : public DeconvolutionLayer<Dtype> {
 
 }  // namespace caffe
 
-#endif // CAFFE_CUDNN_DECONV_LAYER_HPP_
+#endif  // CAFFE_CUDNN_DECONV_LAYER_HPP_
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
index 0067907165f..1a022d6f7bc 100644
--- a/src/caffe/test/test_deconvolution_layer.cpp
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -5,8 +5,8 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layers/deconv_layer.hpp"
 #include "caffe/layers/cudnn_deconv_layer.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"

From 4705a377ea613de6bc3a9e26d83d58c902ea96ea Mon Sep 17 00:00:00 2001
From: "Jonathan R. Williford" <jonathan.r.williford+github@gmail.com>
Date: Fri, 15 Sep 2017 11:38:16 +0200
Subject: [PATCH 1999/2053] Packages needed by Ubuntu 16.04 also

This line is needed for Ubuntu 16.04:

    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

For reference:

* https://github.com/BVLC/caffe/wiki/Ubuntu-16.04-or-15.10-Installation-Guide
* https://youtu.be/DnIs4DRjNL4
---
 docs/install_apt.md | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/docs/install_apt.md b/docs/install_apt.md
index b6cb1c2d6f7..e361a92da3c 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -40,6 +40,7 @@ Continue with [compilation](installation.html#compilation).
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
     sudo apt-get install --no-install-recommends libboost-all-dev
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
 
 **CUDA**: Install by `apt-get` or the NVIDIA `.run` package.
 The NVIDIA package tends to follow more recent library and driver versions, but the installation is more manual.
@@ -54,12 +55,6 @@ This can be skipped for CPU-only installation.
 
 CUDA 8 is required on Ubuntu 16.04.
 
-**Remaining dependencies, 14.04**
-
-Everything is packaged in 14.04.
-
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
-
 **Remaining dependencies, 12.04**
 
 These dependencies need manual installation in 12.04.

From 888597e679f1846d549f1119fe88bc196edbd0dd Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Mon, 2 Oct 2017 14:39:31 +0200
Subject: [PATCH 2000/2053] Fixed bilinear filler, added tests

---
 include/caffe/filler.hpp       |  6 ++---
 src/caffe/test/test_filler.cpp | 43 +++++++++++++++++++++++++++++++++-
 2 files changed, 45 insertions(+), 4 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index dad9ad46b3b..bb92ded780f 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -250,10 +250,10 @@ class BilinearFiller : public Filler<Dtype> {
     CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
     Dtype* data = blob->mutable_cpu_data();
     int f = ceil(blob->width() / 2.);
-    float c = (2 * f - 1 - f % 2) / (2. * f);
+    Dtype c = (blob->width() - 1) / (2. * f);
     for (int i = 0; i < blob->count(); ++i) {
-      float x = i % blob->width();
-      float y = (i / blob->width()) % blob->height();
+      Dtype x = i % blob->width();
+      Dtype y = (i / blob->width()) % blob->height();
       data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
     }
     CHECK_EQ(this->filler_param_.sparse(), -1)
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 26e9b217e35..f84d707baa0 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -29,7 +29,7 @@ TYPED_TEST(ConstantFillerTest, TestFill) {
   const int count = this->blob_->count();
   const TypeParam* data = this->blob_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], this->filler_param_.value());
+    EXPECT_EQ(data[i], this->filler_param_.value());
   }
 }
 
@@ -238,4 +238,45 @@ TYPED_TEST(MSRAFillerTest, TestFillAverage) {
   this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
 }
 
+template <typename Dtype>
+class BilinearFillerTest : public ::testing::Test {
+ protected:
+  BilinearFillerTest() : filler_param_() {}
+  virtual void test_params(const int n) {
+    this->blob_ = new Blob<Dtype>(1000, 2, n, n);
+    this->filler_.reset(new BilinearFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int outer_num = this->blob_->count(0, 2);
+    const int inner_num = this->blob_->count(2, 4);
+    const Dtype* data = this->blob_->cpu_data();
+    int f = ceil(this->blob_->width() / 2.);
+    Dtype c = (this->blob_->width() - 1) / (2. * f);
+    for (int i = 0; i < outer_num; ++i) {
+      for (int j = 0; j < inner_num; ++j) {
+        Dtype x = j % this->blob_->width();
+        Dtype y = (j / this->blob_->width()) % this->blob_->height();
+        Dtype expected_value = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
+        const Dtype actual_value = data[i * inner_num + j];
+        EXPECT_NEAR(expected_value, actual_value, 0.01);
+      }
+    }
+  }
+  virtual ~BilinearFillerTest() { delete blob_; }
+  Blob<Dtype>* blob_;
+  FillerParameter filler_param_;
+  shared_ptr<BilinearFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(BilinearFillerTest, TestDtypes);
+
+TYPED_TEST(BilinearFillerTest, TestFillOdd) {
+  const int n = 7;
+  this->test_params(n);
+}
+TYPED_TEST(BilinearFillerTest, TestFillEven) {
+  const int n = 6;
+  this->test_params(n);
+}
+
 }  // namespace caffe

From fac74347106ea16140ace828e8d278716e4d4742 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Tue, 3 Oct 2017 14:20:13 -0700
Subject: [PATCH 2001/2053] [docs] fix link to `AbsVal` layer

---
 docs/tutorial/layers.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 2faacc5836d..78a46f3a7ee 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -87,7 +87,7 @@ Layers:
 * [ELU](layers/elu.html) - exponential linear rectification.
 * [Sigmoid](layers/sigmoid.html)
 * [TanH](layers/tanh.html)
-* [Absolute Value](layers/abs.html)
+* [Absolute Value](layers/absval.html)
 * [Power](layers/power.html) - f(x) = (shift + scale * x) ^ power.
 * [Exp](layers/exp.html) - f(x) = base ^ (shift + scale * x).
 * [Log](layers/log.html) - f(x) = log(x).

From e0feb7d72ce54ee4f9e84792418b4c59c4013d5f Mon Sep 17 00:00:00 2001
From: Finnian Anderson <get@finnian.io>
Date: Tue, 10 Oct 2017 12:15:13 +0100
Subject: [PATCH 2002/2053] Fix default mode warning in io.resize_image

Signed-off-by: Finnian Anderson <get@finnian.io>
---
 python/caffe/io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 966c164cffd..d61f765b894 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -323,7 +323,7 @@ def resize_image(im, new_dims, interp_order=1):
             # skimage is fast but only understands {1,3} channel images
             # in [0, 1].
             im_std = (im - im_min) / (im_max - im_min)
-            resized_std = resize(im_std, new_dims, order=interp_order)
+            resized_std = resize(im_std, new_dims, order=interp_order, mode='constant')
             resized_im = resized_std * (im_max - im_min) + im_min
         else:
             # the image is a constant -- avoid divide by 0

From 62e0c8559045cb2b5a12e0d6c41acd25d4122630 Mon Sep 17 00:00:00 2001
From: Shai <shai@magisto.com>
Date: Thu, 10 Aug 2017 10:07:19 +0300
Subject: [PATCH 2003/2053] upgrading Accuracy layer: (1) efficient CPU
 implementation O(L) for top_k, no need for fancy priority_queue etc. (2) GPU
 implementation

---
 include/caffe/layers/accuracy_layer.hpp |   4 +
 src/caffe/layers/accuracy_layer.cpp     |  33 ++-
 src/caffe/layers/accuracy_layer.cu      | 147 ++++++++++
 src/caffe/test/test_accuracy_layer.cpp  | 360 +++++++++++++-----------
 4 files changed, 364 insertions(+), 180 deletions(-)
 create mode 100644 src/caffe/layers/accuracy_layer.cu

diff --git a/include/caffe/layers/accuracy_layer.hpp b/include/caffe/layers/accuracy_layer.hpp
index a9ad3225149..dd2247b9e4d 100644
--- a/include/caffe/layers/accuracy_layer.hpp
+++ b/include/caffe/layers/accuracy_layer.hpp
@@ -68,6 +68,8 @@ class AccuracyLayer : public Layer<Dtype> {
    */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
 
 
   /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
@@ -77,6 +79,8 @@ class AccuracyLayer : public Layer<Dtype> {
       if (propagate_down[i]) { NOT_IMPLEMENTED; }
     }
   }
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
 
   int label_axis_, outer_num_, inner_num_;
 
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 4eddbb5c850..392829e6db8 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -52,8 +52,6 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_label = bottom[1]->cpu_data();
   const int dim = bottom[0]->count() / outer_num_;
   const int num_labels = bottom[0]->shape(label_axis_);
-  vector<Dtype> maxval(top_k_+1);
-  vector<int> max_id(top_k_+1);
   if (top.size() > 1) {
     caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());
     caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());
@@ -66,25 +64,22 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (has_ignore_label_ && label_value == ignore_label_) {
         continue;
       }
-      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
       DCHECK_GE(label_value, 0);
       DCHECK_LT(label_value, num_labels);
+      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
+      const Dtype prob_of_true_class = bottom_data[i * dim
+                                                   + label_value * inner_num_
+                                                   + j];
+      int num_better_predictions = -1;  // true_class also counts as "better"
       // Top-k accuracy
-      std::vector<std::pair<Dtype, int> > bottom_data_vector;
-      for (int k = 0; k < num_labels; ++k) {
-        bottom_data_vector.push_back(std::make_pair(
-            bottom_data[i * dim + k * inner_num_ + j], k));
+      for (int k = 0; k < num_labels && num_better_predictions < top_k_; ++k) {
+        num_better_predictions +=
+          (bottom_data[i * dim + k * inner_num_ + j] >= prob_of_true_class);
       }
-      std::partial_sort(
-          bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
-          bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
-      // check if true label is in top k predictions
-      for (int k = 0; k < top_k_; k++) {
-        if (bottom_data_vector[k].second == label_value) {
-          ++accuracy;
-          if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
-          break;
-        }
+      // check if there are less than top_k_ predictions
+      if (num_better_predictions < top_k_) {
+        ++accuracy;
+        if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
       }
       ++count;
     }
@@ -102,6 +97,10 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // Accuracy layer should not be used as a loss function.
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(AccuracyLayer);
+#endif
+
 INSTANTIATE_CLASS(AccuracyLayer);
 REGISTER_LAYER_CLASS(Accuracy);
 
diff --git a/src/caffe/layers/accuracy_layer.cu b/src/caffe/layers/accuracy_layer.cu
new file mode 100644
index 00000000000..a8cff936ccb
--- /dev/null
+++ b/src/caffe/layers/accuracy_layer.cu
@@ -0,0 +1,147 @@
+#include <vector>
+
+#include "caffe/layers/accuracy_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AccuracyForwardGPU(const int nthreads,
+          const Dtype* bottom_data, const Dtype* label, Dtype* acc,
+          const int num, const int dim, const int spatial_dim,
+          const int num_labels, const int top_k,
+          const bool has_ignore_label_, const int ignore_label_,
+          Dtype* counts) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+    const Dtype prob_of_true_class = bottom_data[n * dim
+                                                 + label_value * spatial_dim
+                                                 + s];
+    int num_better_predictions = -1;  // true_class also counts as "better"
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      acc[index] = 0;
+      counts[index] = 0;
+    } else {
+      for (int k = 0; k < num_labels & num_better_predictions < top_k; k++) {
+        num_better_predictions +=
+          (bottom_data[n * dim + k * spatial_dim + s] >= prob_of_true_class);
+      }
+      acc[index] = (num_better_predictions < top_k);
+      counts[index] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void AccuracyForwardWithPerClassGPU(const int nthreads,
+          const Dtype* bottom_data, const Dtype* label,
+          Dtype* acc, Dtype* counts,
+          const int num, const int dim, const int spatial_dim,
+          const int num_labels, const int top_k,
+          const bool has_ignore_label_, const int ignore_label_) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int n = index / spatial_dim;
+    const int s = index % spatial_dim;
+    const int label_value = static_cast<int>(label[n * spatial_dim + s]);
+    const Dtype prob_of_true_class = bottom_data[n * dim
+                                                 + label_value * spatial_dim
+                                                 + s];
+    if (has_ignore_label_ && label_value == ignore_label_) {
+      // nothing to be done.
+    } else {
+      int num_better_predictions = -1;  // true_class also counts as "better"
+      for (int k = 0; k < num_labels & num_better_predictions < top_k; k++) {
+        num_better_predictions +=
+          (bottom_data[n * dim + k * spatial_dim + s] >= prob_of_true_class);
+      }
+      acc[label_value*nthreads + index] += (num_better_predictions < top_k);
+      counts[label_value*nthreads + index] = 1;
+    }
+  }
+}
+
+template <typename Dtype>
+void AccuracyLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* bottom_label = bottom[1]->gpu_data();
+  const int dim = bottom[0]->count() / outer_num_;
+  const int num_labels = bottom[0]->shape(label_axis_);
+  const int nthreads = outer_num_ * inner_num_;
+  // Since this memory is not used for anything,
+  // we use it here to avoid having to allocate new GPU
+  // memory to accumulate intermediate results in the kernel.
+  Dtype* acc_data = bottom[0]->mutable_gpu_diff();
+  if (top.size() == 1) {
+    // simple case - report only global accuracy.
+
+    // Similarly, this memory is never used elsewhere, and thus we can use it
+    // to avoid having to allocate additional GPU memory.
+    Dtype* counts = bottom[1]->mutable_gpu_diff();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    AccuracyForwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+        CAFFE_CUDA_NUM_THREADS>>>(nthreads, bottom_data, bottom_label,
+        acc_data, outer_num_, dim, inner_num_, num_labels, top_k_,
+        has_ignore_label_, ignore_label_, counts);
+    Dtype acc;
+    caffe_gpu_asum(nthreads, acc_data, &acc);
+    Dtype valid_count;
+    caffe_gpu_asum(nthreads, counts, &valid_count);
+    if (valid_count > 0) {
+      top[0]->mutable_cpu_data()[0] = acc / valid_count;
+    } else {
+      top[0]->mutable_cpu_data()[0] = 0;
+    }
+  } else {
+    // need to report per-class accuracy as well
+
+    // allocate space for more detailed "counts"
+    nums_buffer_.ReshapeLike(*bottom[0]);
+    Dtype* counts = nums_buffer_.mutable_gpu_data();
+
+    caffe_gpu_set(bottom[0]->count(), Dtype(0), acc_data);
+    caffe_gpu_set(nums_buffer_.count(), Dtype(0), counts);
+
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    AccuracyForwardWithPerClassGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
+        CAFFE_CUDA_NUM_THREADS>>>(nthreads, bottom_data, bottom_label,
+        acc_data, counts, outer_num_, dim, inner_num_, num_labels, top_k_,
+        has_ignore_label_, ignore_label_);
+
+    // get the overall accuracy
+    Dtype acc;
+    caffe_gpu_asum(bottom[0]->count(), acc_data, &acc);
+    Dtype valid_count;
+    caffe_gpu_asum(nums_buffer_.count(), counts, &valid_count);
+    if (valid_count > 0) {
+      top[0]->mutable_cpu_data()[0] = acc / valid_count;
+    } else {
+      top[0]->mutable_cpu_data()[0] = 0;
+    }
+
+    // get per-class accuracy
+    Dtype* per_class_acc = top[1]->mutable_cpu_data();
+    for (int l = 0; l < num_labels; l++) {
+      caffe_gpu_asum(nthreads, acc_data + l*nthreads, per_class_acc+l);
+      caffe_gpu_asum(nthreads, counts + l*nthreads, &valid_count);
+      if (valid_count > 0) {
+        per_class_acc[l] /= valid_count;
+      } else {
+        per_class_acc[l] = 0;
+      }
+    }
+  }
+}
+
+
+template <typename Dtype>
+void AccuracyLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[1]) {  NOT_IMPLEMENTED;  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(AccuracyLayer);
+}  // namespace caffe
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 6fe808bd5c5..e5cc9d5e85f 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -13,8 +13,10 @@
 
 namespace caffe {
 
-template <typename Dtype>
-class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
+template <typename TypeParam>
+class AccuracyLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   AccuracyLayerTest()
       : blob_bottom_data_(new Blob<Dtype>()),
@@ -69,11 +71,12 @@ class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
   int top_k_;
 };
 
-TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
+TYPED_TEST_CASE(AccuracyLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(AccuracyLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -82,11 +85,12 @@ TYPED_TEST(AccuracyLayerTest, TestSetup) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param =
       layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(5);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -95,8 +99,9 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
@@ -108,33 +113,39 @@ TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
   EXPECT_EQ(this->blob_top_per_class_->width(), 1);
 }
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
+TYPED_TEST(AccuracyLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  for (int i = 0; i < 100; ++i) {
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    for (int i = 0; i < 100; ++i) {
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
       }
     }
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-    }
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(100.0), 1e-4);
   }
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / 100.0, 1e-4);
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
+  typedef typename TypeParam::Dtype Dtype;
   this->blob_bottom_data_->Reshape(2, 10, 4, 5);
   vector<int> label_shape(3);
   label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
@@ -142,195 +153,218 @@ TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
   this->FillBottoms();
   LayerParameter layer_param;
   layer_param.mutable_accuracy_param()->set_axis(1);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam max_value;
-  const int num_labels = this->blob_bottom_label_->count();
-  int max_id;
-  int num_correct_labels = 0;
-  vector<int> label_offset(3);
-  for (int n = 0; n < this->blob_bottom_data_->num(); ++n) {
-    for (int h = 0; h < this->blob_bottom_data_->height(); ++h) {
-      for (int w = 0; w < this->blob_bottom_data_->width(); ++w) {
-        max_value = -FLT_MAX;
-        max_id = 0;
-        for (int c = 0; c < this->blob_bottom_data_->channels(); ++c) {
-          const TypeParam pred_value =
-              this->blob_bottom_data_->data_at(n, c, h, w);
-          if (pred_value > max_value) {
-            max_value = pred_value;
-            max_id = c;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype max_value;
+    const int num_labels = this->blob_bottom_label_->count();
+    int max_id;
+    int num_correct_labels = 0;
+    vector<int> label_offset(3);
+    for (int n = 0; n < this->blob_bottom_data_->num(); ++n) {
+      for (int h = 0; h < this->blob_bottom_data_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_data_->width(); ++w) {
+          max_value = -FLT_MAX;
+          max_id = 0;
+          for (int c = 0; c < this->blob_bottom_data_->channels(); ++c) {
+            const Dtype pred_value =
+                this->blob_bottom_data_->data_at(n, c, h, w);
+            if (pred_value > max_value) {
+              max_value = pred_value;
+              max_id = c;
+            }
+          }
+          label_offset[0] = n; label_offset[1] = h; label_offset[2] = w;
+          const int correct_label =
+              static_cast<int>(this->blob_bottom_label_->data_at(label_offset));
+          if (max_id == correct_label) {
+            ++num_correct_labels;
           }
-        }
-        label_offset[0] = n; label_offset[1] = h; label_offset[2] = w;
-        const int correct_label =
-            static_cast<int>(this->blob_bottom_label_->data_at(label_offset));
-        if (max_id == correct_label) {
-          ++num_correct_labels;
         }
       }
     }
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(num_labels), 1e-4);
   }
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / TypeParam(num_labels), 1e-4);
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  const TypeParam kIgnoreLabelValue = -1;
+  const Dtype kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   // Manually set some labels to the ignore label value (-1).
   this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  int count = 0;
-  for (int i = 0; i < 100; ++i) {
-    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      continue;
-    }
-    ++count;
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    int count = 0;
+    for (int i = 0; i < 100; ++i) {
+      if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        continue;
+      }
+      ++count;
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
       }
     }
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-    }
+    EXPECT_EQ(count, 97);  // We set 3 out of 100 labels to kIgnoreLabelValue.
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(count), 1e-4);
   }
-  EXPECT_EQ(count, 97);  // We set 3 out of 100 labels to kIgnoreLabelValue.
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / TypeParam(count), 1e-4);
 }
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
+TYPED_TEST(AccuracyLayerTest, TestForwardTopK) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
   accuracy_param->set_top_k(this->top_k_);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-
-  TypeParam current_value;
-  int current_rank;
-  int num_correct_labels = 0;
-  for (int i = 0; i < 100; ++i) {
-    for (int j = 0; j < 10; ++j) {
-      current_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-      current_rank = 0;
-      for (int k = 0; k < 10; ++k) {
-        if (this->blob_bottom_data_->data_at(i, k, 0, 0) > current_value) {
-          ++current_rank;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    Dtype current_value;
+    int current_rank;
+    int num_correct_labels = 0;
+    for (int i = 0; i < 100; ++i) {
+      for (int j = 0; j < 10; ++j) {
+        current_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        current_rank = 0;
+        for (int k = 0; k < 10; ++k) {
+          if (this->blob_bottom_data_->data_at(i, k, 0, 0) > current_value) {
+            ++current_rank;
+          }
+        }
+        if (current_rank < this->top_k_ &&
+            j == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+          ++num_correct_labels;
         }
-      }
-      if (current_rank < this->top_k_ &&
-          j == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-        ++num_correct_labels;
       }
     }
-  }
 
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / 100.0, 1e-4);
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(100.0), 1e-4);
+  }
 }
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
+TYPED_TEST(AccuracyLayerTest, TestForwardPerClass) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  const int num_class = this->blob_top_per_class_->num();
-  vector<int> correct_per_class(num_class, 0);
-  vector<int> num_per_class(num_class, 0);
-  for (int i = 0; i < 100; ++i) {
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    const int num_class = this->blob_top_per_class_->num();
+    vector<int> correct_per_class(num_class, 0);
+    vector<int> num_per_class(num_class, 0);
+    for (int i = 0; i < 100; ++i) {
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
+        ++correct_per_class[max_id];
       }
     }
-    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-      ++correct_per_class[max_id];
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / 100.0, 1e-4);
+    for (int i = 0; i < num_class; ++i) {
+      Dtype accuracy_per_class = (num_per_class[i] > 0 ?
+         static_cast<Dtype>(correct_per_class[i]) / num_per_class[i] : 0);
+      EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                  accuracy_per_class, 1e-4);
     }
   }
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / 100.0, 1e-4);
-  for (int i = 0; i < num_class; ++i) {
-    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
-       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
-    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
-                accuracy_per_class, 1e-4);
-  }
 }
 
 
-TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
+TYPED_TEST(AccuracyLayerTest, TestForwardPerClassWithIgnoreLabel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  const TypeParam kIgnoreLabelValue = -1;
+  const Dtype kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
-  AccuracyLayer<TypeParam> layer(layer_param);
+  AccuracyLayer<Dtype> layer(layer_param);
   // Manually set some labels to the ignore label value (-1).
   this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
   this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
-
-  TypeParam max_value;
-  int max_id;
-  int num_correct_labels = 0;
-  const int num_class = this->blob_top_per_class_->num();
-  vector<int> correct_per_class(num_class, 0);
-  vector<int> num_per_class(num_class, 0);
-  int count = 0;
-  for (int i = 0; i < 100; ++i) {
-    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      continue;
-    }
-    ++count;
-    max_value = -FLT_MAX;
-    max_id = 0;
-    for (int j = 0; j < 10; ++j) {
-      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
-        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
-        max_id = j;
+
+  // repeat the forward
+  for (int iter = 0; iter < 3; iter++) {
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+    Dtype max_value;
+    int max_id;
+    int num_correct_labels = 0;
+    const int num_class = this->blob_top_per_class_->num();
+    vector<int> correct_per_class(num_class, 0);
+    vector<int> num_per_class(num_class, 0);
+    int count = 0;
+    for (int i = 0; i < 100; ++i) {
+      if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        continue;
+      }
+      ++count;
+      max_value = -FLT_MAX;
+      max_id = 0;
+      for (int j = 0; j < 10; ++j) {
+        if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+          max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+          max_id = j;
+        }
+      }
+      ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+      if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
+        ++correct_per_class[max_id];
       }
     }
-    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
-    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
-      ++num_correct_labels;
-      ++correct_per_class[max_id];
+    EXPECT_EQ(count, 97);
+    EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+                num_correct_labels / Dtype(count), 1e-4);
+    for (int i = 0; i < 10; ++i) {
+      Dtype accuracy_per_class = (num_per_class[i] > 0 ?
+         static_cast<Dtype>(correct_per_class[i]) / num_per_class[i] : 0);
+      EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                  accuracy_per_class, 1e-4);
     }
   }
-  EXPECT_EQ(count, 97);
-  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
-              num_correct_labels / TypeParam(count), 1e-4);
-  for (int i = 0; i < 10; ++i) {
-    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
-       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
-    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
-                accuracy_per_class, 1e-4);
-  }
 }
 
 }  // namespace caffe

From cd2a0140d60742d4f66cc1668a2554bdc7a1ca3c Mon Sep 17 00:00:00 2001
From: Icyblade Dai <icyblade.py@gmail.com>
Date: Wed, 11 Oct 2017 15:29:21 +0800
Subject: [PATCH 2004/2053] add supports for cuDNN v7

---
 docs/installation.md           | 4 ++--
 include/caffe/util/cudnn.hpp   | 6 ++++++
 scripts/travis/install-deps.sh | 2 +-
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/docs/installation.md b/docs/installation.md
index 42f1d0ce09b..6104cc247a8 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -42,14 +42,14 @@ Optional dependencies:
 
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v6)
+* cuDNN for GPU acceleration (v7)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v6; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v7; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index 498cfe385de..cd3f93f6e28 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -44,6 +44,12 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
 #if CUDNN_VERSION_MIN(6, 0, 0)
     case CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING:
       return "CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING";
+#endif
+#if CUDNN_VERSION_MIN(7, 0, 0)
+    case CUDNN_STATUS_RUNTIME_IN_PROGRESS:
+      return "CUDNN_STATUS_RUNTIME_IN_PROGRESS";
+    case CUDNN_STATUS_RUNTIME_FP_OVERFLOW:
+      return "CUDNN_STATUS_RUNTIME_FP_OVERFLOW";
 #endif
   }
   return "Unknown cudnn status";
diff --git a/scripts/travis/install-deps.sh b/scripts/travis/install-deps.sh
index 2fa2a74a486..abf9cf1ca70 100755
--- a/scripts/travis/install-deps.sh
+++ b/scripts/travis/install-deps.sh
@@ -106,7 +106,7 @@ if $WITH_CUDA ; then
   ln -s /usr/local/cuda-$CUDA_VERSION /usr/local/cuda
 
   if $WITH_CUDNN ; then
-    apt-get install -y --no-install-recommends libcudnn6-dev
+    apt-get install -y --no-install-recommends libcudnn7-dev
   fi
 fi
 

From 243cd8948520e83740be328466352b10e6983aec Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Wed, 11 Oct 2017 11:04:18 +0200
Subject: [PATCH 2005/2053] Add absolute tolerance to test_net.py to prevent
 random Travis fails

---
 python/caffe/test/test_net.py | 50 +++++++++++++++++------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index afd27690981..ee1d38c39db 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -72,41 +72,41 @@ def test_forward_backward(self):
         self.net.backward()
 
     def test_forward_start_end(self):
-        conv_blob=self.net.blobs['conv'];
-        ip_blob=self.net.blobs['ip_blob'];
-        sample_data=np.random.uniform(size=conv_blob.data.shape);
-        sample_data=sample_data.astype(np.float32);
-        conv_blob.data[:]=sample_data;
-        forward_blob=self.net.forward(start='ip',end='ip');
-        self.assertIn('ip_blob',forward_blob);
-
-        manual_forward=[];
+        conv_blob=self.net.blobs['conv']
+        ip_blob=self.net.blobs['ip_blob']
+        sample_data=np.random.uniform(size=conv_blob.data.shape)
+        sample_data=sample_data.astype(np.float32)
+        conv_blob.data[:]=sample_data
+        forward_blob=self.net.forward(start='ip',end='ip')
+        self.assertIn('ip_blob',forward_blob)
+
+        manual_forward=[]
         for i in range(0,conv_blob.data.shape[0]):
           dot=np.dot(self.net.params['ip'][0].data,
-                     conv_blob.data[i].reshape(-1));
-          manual_forward.append(dot+self.net.params['ip'][1].data);
-        manual_forward=np.array(manual_forward);
+                     conv_blob.data[i].reshape(-1))
+          manual_forward.append(dot+self.net.params['ip'][1].data)
+        manual_forward=np.array(manual_forward)
 
-        np.testing.assert_allclose(ip_blob.data,manual_forward,rtol=1e-3);
+        np.testing.assert_allclose(ip_blob.data,manual_forward,rtol=1e-3,atol=1e-5)
 
     def test_backward_start_end(self):
-        conv_blob=self.net.blobs['conv'];
-        ip_blob=self.net.blobs['ip_blob'];
+        conv_blob=self.net.blobs['conv']
+        ip_blob=self.net.blobs['ip_blob']
         sample_data=np.random.uniform(size=ip_blob.data.shape)
-        sample_data=sample_data.astype(np.float32);
-        ip_blob.diff[:]=sample_data;
-        backward_blob=self.net.backward(start='ip',end='ip');
-        self.assertIn('conv',backward_blob);
+        sample_data=sample_data.astype(np.float32)
+        ip_blob.diff[:]=sample_data
+        backward_blob=self.net.backward(start='ip',end='ip')
+        self.assertIn('conv',backward_blob)
 
-        manual_backward=[];
+        manual_backward=[]
         for i in range(0,conv_blob.data.shape[0]):
           dot=np.dot(self.net.params['ip'][0].data.transpose(),
-                     sample_data[i].reshape(-1));
-          manual_backward.append(dot);
-        manual_backward=np.array(manual_backward);
-        manual_backward=manual_backward.reshape(conv_blob.data.shape);
+                     sample_data[i].reshape(-1))
+          manual_backward.append(dot)
+        manual_backward=np.array(manual_backward)
+        manual_backward=manual_backward.reshape(conv_blob.data.shape)
 
-        np.testing.assert_allclose(conv_blob.diff,manual_backward,rtol=1e-3);
+        np.testing.assert_allclose(conv_blob.diff,manual_backward,rtol=1e-3,atol=1e-5)
 
     def test_clear_param_diffs(self):
         # Run a forward/backward step to have non-zero diffs

From 79ddda7e931f90f1b648e5372ccbaf1a35e88fb5 Mon Sep 17 00:00:00 2001
From: Mikhail Antonenka <misha.antonenko.minsk@gmail.com>
Date: Tue, 17 Oct 2017 18:18:32 +0300
Subject: [PATCH 2006/2053] infogain loss: fix bottom blobs description

---
 include/caffe/layers/infogain_loss_layer.hpp | 21 ++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp
index edecde829ad..3b3caa27c38 100644
--- a/include/caffe/layers/infogain_loss_layer.hpp
+++ b/include/caffe/layers/infogain_loss_layer.hpp
@@ -13,20 +13,21 @@
 namespace caffe {
 
 /**
- * @brief A generalization of MultinomialLogisticLossLayer that takes an
+ * @brief A generalization of SoftmaxWithLossLayer that takes an
  *        "information gain" (infogain) matrix specifying the "value" of all label
  *        pairs.
  *
- * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
+ * Equivalent to the SoftmaxWithLossLayer if the infogain matrix is the
  * identity.
  *
  * @param bottom input Blob vector (length 2-3)
  *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{p} @f$, a Blob with values in
- *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
- *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
- *      should sum to 1 as in a probability distribution: @f$
- *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *      the predictions @f$ x @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
+ *      probability distribution over classes using the softmax function
+ *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
+ *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
  *   -# @f$ (N \times 1 \times 1 \times 1) @f$
  *      the labels @f$ l @f$, an integer-valued Blob with values
  *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
@@ -35,7 +36,7 @@ namespace caffe {
  *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
  *      the third bottom blob input if not provided as the infogain_mat in the
  *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
- *      MultinomialLogisticLossLayer.
+ *      SoftmaxWithLossLayer.
  * @param top output Blob vector (length 1)
  *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
  *      the computed infogain multinomial logistic loss: @f$ E =
@@ -98,8 +99,8 @@ class InfogainLossLayer : public LossLayer<Dtype> {
    *      infogain matrix, if provided as bottom[2])
    * @param bottom input Blob vector (length 2-3)
    *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ \hat{p} @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *      the predictions @f$ x @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} @f$
    *   -# @f$ (N \times 1 \times 1 \times 1) @f$
    *      the labels -- ignored as we can't compute their error gradients
    *   -# @f$ (1 \times 1 \times K \times K) @f$

From bfc638d502b8b991e6ef5aaaa1946049312e6336 Mon Sep 17 00:00:00 2001
From: Geunsik Lim <geunsik.lim@samsung.com>
Date: Thu, 29 Jun 2017 13:12:53 +0900
Subject: [PATCH 2007/2053] Fix: mean shape in compatible with input shape

This commit is to fix issue #5718.

* reference:
1. https://groups.google.com/forum/#!topic/caffe-users/nBpWJCcJoCU
2. https://stackoverflow.com/questions/28692209/using-gpu-despite-setting-cpu-only-yielding-unexpected-keyword-argument

Signed-off-by: Geunsik Lim <geunsik.lim@samsung.com>
---
 python/caffe/io.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index 966c164cffd..1efb7409603 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -256,7 +256,12 @@ def set_mean(self, in_, mean):
             if len(ms) != 3:
                 raise ValueError('Mean shape invalid')
             if ms != self.inputs[in_][1:]:
-                raise ValueError('Mean shape incompatible with input shape.')
+                in_shape = self.inputs[in_][1:]
+                m_min, m_max = mean.min(), mean.max()
+                normal_mean = (mean - m_min) / (m_max - m_min)
+                mean = resize_image(normal_mean.transpose((1,2,0)),
+                        in_shape[1:]).transpose((2,0,1)) * \
+                        (m_max - m_min) + m_min
         self.mean[in_] = mean
 
     def set_input_scale(self, in_, scale):

From fbdc1e103bf6f55c9dabe94a2530fa62e65f506b Mon Sep 17 00:00:00 2001
From: sclarkson <sclarkson@users.noreply.github.com>
Date: Sat, 11 Nov 2017 18:20:11 -0800
Subject: [PATCH 2008/2053] Fix Makefile parallel builds missing protobuf
 header

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index c6d5685b140..c85c695acff 100644
--- a/Makefile
+++ b/Makefile
@@ -577,7 +577,7 @@ $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo AR -o $@
 	$(Q)ar rcs $@ $(OBJS)
 
-$(BUILD_DIR)/%.o: %.cpp | $(ALL_BUILD_DIRS)
+$(BUILD_DIR)/%.o: %.cpp $(PROTO_GEN_HEADER) | $(ALL_BUILD_DIRS)
 	@ echo CXX $<
 	$(Q)$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
 		|| (cat $@.$(WARNS_EXT); exit 1)

From 95d000153dc668bf86f24eb46e8982701fa7c59a Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Tue, 28 Nov 2017 11:00:51 +0100
Subject: [PATCH 2009/2053] Makefile example comments for CUDA 9.0
 compatibility

---
 Makefile.config.example | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Makefile.config.example b/Makefile.config.example
index d552b38a97c..79905935f15 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -33,6 +33,7 @@ CUDA_DIR := /usr/local/cuda
 # CUDA architecture setting: going with all of them.
 # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
 # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
+# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \

From 3be5297ba3c512abca0dad4541ecb1f78d640542 Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Wed, 29 Nov 2017 11:48:29 +0100
Subject: [PATCH 2010/2053] Added count==0 safeguard to CPU accuracy
 calculation

---
 src/caffe/layers/accuracy_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 392829e6db8..b6d95b54a5d 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -86,7 +86,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
 
   // LOG(INFO) << "Accuracy: " << accuracy;
-  top[0]->mutable_cpu_data()[0] = accuracy / count;
+  top[0]->mutable_cpu_data()[0] = (count == 0) ? 0 : (accuracy / count);
   if (top.size() > 1) {
     for (int i = 0; i < top[1]->count(); ++i) {
       top[1]->mutable_cpu_data()[i] =

From c23b3563f0fa2999578c1a8b3f32dc9cdec5a037 Mon Sep 17 00:00:00 2001
From: YaYaB <bezzayassine@gmail.com>
Date: Tue, 12 Dec 2017 16:16:59 +0000
Subject: [PATCH 2011/2053] Add check values of gamma and stepsize to avoid
 unexplained core dump

---
 src/caffe/solvers/sgd_solver.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index ad6abe54a0a..1d52beb0636 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -30,12 +30,16 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
   if (lr_policy == "fixed") {
     rate = this->param_.base_lr();
   } else if (lr_policy == "step") {
+    CHECK_GT(this->param_.stepsize(), 0);
     this->current_step_ = this->iter_ / this->param_.stepsize();
+    CHECK_GE(this->param_.gamma(), 0);
     rate = this->param_.base_lr() *
         pow(this->param_.gamma(), this->current_step_);
   } else if (lr_policy == "exp") {
+    CHECK_GE(this->param_.gamma(), 0);
     rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
   } else if (lr_policy == "inv") {
+    CHECK_GE(this->param_.gamma(), 0);
     rate = this->param_.base_lr() *
         pow(Dtype(1) + this->param_.gamma() * this->iter_,
             - this->param_.power());
@@ -46,6 +50,7 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
       LOG(INFO) << "MultiStep Status: Iteration " <<
       this->iter_ << ", step = " << this->current_step_;
     }
+    CHECK_GE(this->param_.gamma(), 0);
     rate = this->param_.base_lr() *
         pow(this->param_.gamma(), this->current_step_);
   } else if (lr_policy == "poly") {
@@ -53,6 +58,8 @@ Dtype SGDSolver<Dtype>::GetLearningRate() {
         (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
         this->param_.power());
   } else if (lr_policy == "sigmoid") {
+    CHECK_GE(this->param_.gamma(), 0);
+    CHECK_GT(this->param_.stepsize(), 0);
     rate = this->param_.base_lr() * (Dtype(1.) /
         (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
           Dtype(this->param_.stepsize())))));

From c98de53b7817c732b482c2fa810f09c260c58857 Mon Sep 17 00:00:00 2001
From: Pavel Grunt <pavel.grunt@gmail.com>
Date: Wed, 20 Dec 2017 13:07:47 +0100
Subject: [PATCH 2012/2053] Cuda.cmake: Fix a typo in a comment

---
 cmake/Cuda.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index b2b19e8b669..54e26fd5c8e 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -232,7 +232,7 @@ endfunction()
 ################################################################################################
 
 find_package(CUDA 5.5 QUIET)
-find_cuda_helper_libs(curand)  # cmake 2.8.7 compartibility which doesn't search for curand
+find_cuda_helper_libs(curand)  # cmake 2.8.7 compatibility which doesn't search for curand
 
 if(NOT CUDA_FOUND)
   return()

From 7d2ecf93a12ac823850904e48fb7d3c916b73934 Mon Sep 17 00:00:00 2001
From: Toby Thain <toby.thain@fivebox.com>
Date: Mon, 15 Jan 2018 13:16:05 -0500
Subject: [PATCH 2013/2053] Simplify pip invocation.

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 6104cc247a8..416b9d64ada 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -80,7 +80,7 @@ The main requirements are `numpy` and `boost.python` (provided by boost). `panda
 
 You can install the dependencies with
 
-    for req in $(cat requirements.txt); do pip install $req; done
+    pip install -r requirements.txt
 
 but we suggest first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
 

From ecdc289cc2dfbaa0e2b70ca545cb41a1fb55e79c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sat, 27 Jan 2018 16:37:06 -0800
Subject: [PATCH 2014/2053] docs: switch to official AWS AMI

---
 docs/installation.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/installation.md b/docs/installation.md
index 6104cc247a8..2c4b30d392d 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -17,7 +17,7 @@ The official Makefile and `Makefile.config` build are complemented by a [communi
 - [RHEL / CentOS / Fedora installation](install_yum.html)
 - [Windows](https://github.com/BVLC/caffe/tree/windows) *see the Windows branch led by Guillaume Dumont*
 - [OpenCL](https://github.com/BVLC/caffe/tree/opencl) *see the OpenCL branch led by Fabian Tschopp*
-- [AWS AMI](https://github.com/bitfusionio/amis/tree/master/awsmrkt-bfboost-ubuntu14-cuda75-caffe) *pre-configured for AWS*
+- [AWS AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB) *official deep learning amazon machine image from AWS*
 
 **Overview**:
 

From 25c217c1a49239bbcaf45aa6c60ab9f0354819bb Mon Sep 17 00:00:00 2001
From: linziyi <linziyi@sensetime.com>
Date: Fri, 19 Jan 2018 03:07:45 +0800
Subject: [PATCH 2015/2053] clear scratch use of loss bottom diffs

---
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu | 3 +++
 src/caffe/layers/softmax_loss_layer.cu               | 2 ++
 2 files changed, 5 insertions(+)

diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index b9877e6a3f6..14e247f196d 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -69,6 +69,9 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
   caffe_gpu_asum(count, loss_data, &loss);
   normalizer_ = get_normalizer(normalization_, valid_count);
   top[0]->mutable_cpu_data()[0] = loss / normalizer_;
+
+  caffe_gpu_set(bottom[0]->count(), Dtype(0), bottom[0]->mutable_gpu_diff());
+  caffe_gpu_set(bottom[1]->count(), Dtype(0), bottom[1]->mutable_gpu_diff());
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 660e1b39fe0..6c8db142f31 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -61,6 +61,8 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
   }
+
+  caffe_gpu_set(bottom[0]->count(), Dtype(0), bottom[0]->mutable_gpu_diff());
 }
 
 template <typename Dtype>

From 12a70383088b30fbdb5b148540d743dd6d34f47c Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 28 Jan 2018 16:42:07 -0800
Subject: [PATCH 2016/2053] clear scratch use of accuracy bottom diff

---
 src/caffe/layers/accuracy_layer.cu | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/caffe/layers/accuracy_layer.cu b/src/caffe/layers/accuracy_layer.cu
index a8cff936ccb..f6de50a7a7e 100644
--- a/src/caffe/layers/accuracy_layer.cu
+++ b/src/caffe/layers/accuracy_layer.cu
@@ -134,6 +134,7 @@ void AccuracyLayer<Dtype>::Forward_gpu(
       }
     }
   }
+  caffe_gpu_set(bottom[0]->count(), Dtype(0), bottom[0]->mutable_gpu_diff());
 }
 
 

From 41165906f9f1392563c5a23de7fedbb1b70293e2 Mon Sep 17 00:00:00 2001
From: Evan Shelhamer <shelhamer@imaginarynumber.net>
Date: Sun, 28 Jan 2018 16:58:39 -0800
Subject: [PATCH 2017/2053] explain use of scratch diffs in comments

a few layers make use of otherwise unused diffs to accumulate results,
but unless the diffs are cleared in forward this contaminates the
gradients when these layers share a bottom and their backward is
skipped.
---
 src/caffe/layers/accuracy_layer.cu                   | 6 +++---
 src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu | 6 +++---
 src/caffe/layers/softmax_loss_layer.cu               | 6 +++---
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/caffe/layers/accuracy_layer.cu b/src/caffe/layers/accuracy_layer.cu
index f6de50a7a7e..904aab422af 100644
--- a/src/caffe/layers/accuracy_layer.cu
+++ b/src/caffe/layers/accuracy_layer.cu
@@ -71,9 +71,8 @@ void AccuracyLayer<Dtype>::Forward_gpu(
   const int dim = bottom[0]->count() / outer_num_;
   const int num_labels = bottom[0]->shape(label_axis_);
   const int nthreads = outer_num_ * inner_num_;
-  // Since this memory is not used for anything,
-  // we use it here to avoid having to allocate new GPU
-  // memory to accumulate intermediate results in the kernel.
+  // Since this memory is not used for anything, we use it here to avoid having
+  // to allocate new GPU memory to accumulate intermediate results.
   Dtype* acc_data = bottom[0]->mutable_gpu_diff();
   if (top.size() == 1) {
     // simple case - report only global accuracy.
@@ -134,6 +133,7 @@ void AccuracyLayer<Dtype>::Forward_gpu(
       }
     }
   }
+  // Clear scratch memory to prevent interfering with backward (see #6202).
   caffe_gpu_set(bottom[0]->count(), Dtype(0), bottom[0]->mutable_gpu_diff());
 }
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 14e247f196d..7497e4aa47d 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -48,9 +48,8 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
   // Stable version of loss computation from input data
   const Dtype* input_data = bottom[0]->gpu_data();
   const Dtype* target = bottom[1]->gpu_data();
-  // Since this memory is not used for anything until it is overwritten
-  // on the backward pass, we use it here to avoid having to allocate new GPU
-  // memory to accumulate intermediate results in the kernel.
+  // Since this memory is not used for anything, we use it here to avoid having
+  // to allocate new GPU memory to accumulate intermediate results.
   Dtype* loss_data = bottom[0]->mutable_gpu_diff();
   Dtype* count_data = bottom[1]->mutable_gpu_diff();
   Dtype valid_count;
@@ -70,6 +69,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
   normalizer_ = get_normalizer(normalization_, valid_count);
   top[0]->mutable_cpu_data()[0] = loss / normalizer_;
 
+  // Clear scratch memory to prevent interfering with backward (see #6202).
   caffe_gpu_set(bottom[0]->count(), Dtype(0), bottom[0]->mutable_gpu_diff());
   caffe_gpu_set(bottom[1]->count(), Dtype(0), bottom[1]->mutable_gpu_diff());
 }
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 6c8db142f31..b3c8ffa6b6c 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -36,9 +36,8 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
   const Dtype* label = bottom[1]->gpu_data();
   const int dim = prob_.count() / outer_num_;
   const int nthreads = outer_num_ * inner_num_;
-  // Since this memory is not used for anything until it is overwritten
-  // on the backward pass, we use it here to avoid having to allocate new GPU
-  // memory to accumulate intermediate results in the kernel.
+  // Since this memory is not used for anything, we use it here to avoid having
+  // to allocate new GPU memory to accumulate intermediate results.
   Dtype* loss_data = bottom[0]->mutable_gpu_diff();
   // Similarly, this memory is never used elsewhere, and thus we can use it
   // to avoid having to allocate additional GPU memory.
@@ -62,6 +61,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
     top[1]->ShareData(prob_);
   }
 
+  // Clear scratch memory to prevent interfering with backward (see #6202).
   caffe_gpu_set(bottom[0]->count(), Dtype(0), bottom[0]->mutable_gpu_diff());
 }
 

From 88c96189bcbf3853b93e2b65c7b5e4948f9d5f67 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Przemys=C5=82aw=20Dolata?= <snowball91b@gmail.com>
Date: Thu, 1 Feb 2018 14:43:44 +0100
Subject: [PATCH 2018/2053] corrected description of set_transpose in io.py

---
 python/caffe/io.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/python/caffe/io.py b/python/caffe/io.py
index ed4b3bef68b..d0103d60892 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -186,13 +186,14 @@ def deprocess(self, in_, data):
 
     def set_transpose(self, in_, order):
         """
-        Set the input channel order for e.g. RGB to BGR conversion
-        as needed for the reference ImageNet model.
+        Set the order of dimensions, e.g. to convert OpenCV's HxWxC images
+        into CxHxW.
 
         Parameters
         ----------
-        in_ : which input to assign this channel order
+        in_ : which input to assign this dimension order
         order : the order to transpose the dimensions
+            for example (2,0,1) changes HxWxC into CxHxW and (1,2,0) reverts
         """
         self.__check_input(in_)
         if len(order) != len(self.inputs[in_]) - 1:

From 0050c207d5eb9b514d470ccb2125605f7adb0755 Mon Sep 17 00:00:00 2001
From: Matt Sanford <matt@mzsanford.com>
Date: Fri, 2 Feb 2018 13:23:23 -0800
Subject: [PATCH 2019/2053] Update Classifier and Detector to avoid deprecation
 warning

---
 python/caffe/classifier.py | 2 +-
 python/caffe/detector.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 983760a786d..64d804be554 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -23,7 +23,7 @@ class Classifier(caffe.Net):
     def __init__(self, model_file, pretrained_file, image_dims=None,
                  mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
-        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
+        caffe.Net.__init__(self, model_file, caffe.TEST, weights=pretrained_file)
 
         # configure pre-processing
         in_ = self.inputs[0]
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index ef1f91730bf..ceee5d36f4c 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -35,7 +35,7 @@ class Detector(caffe.Net):
     def __init__(self, model_file, pretrained_file, mean=None,
                  input_scale=None, raw_scale=None, channel_swap=None,
                  context_pad=None):
-        caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
+        caffe.Net.__init__(self, model_file, caffe.TEST, weights=pretrained_file)
 
         # configure pre-processing
         in_ = self.inputs[0]

From 0582f84017ba55ab50d805e05744d5fcd57502de Mon Sep 17 00:00:00 2001
From: Yegor Bedarev <uhfband@gmail.com>
Date: Thu, 8 Feb 2018 18:05:21 +0700
Subject: [PATCH 2020/2053] Fix incorrect namespace for pycaffe submodule
 caffe_pb2 generated by protobuf

---
 Makefile             | 2 +-
 cmake/ProtoBuf.cmake | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index c85c695acff..c823f66ef10 100644
--- a/Makefile
+++ b/Makefile
@@ -641,7 +641,7 @@ $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
 $(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
 		$(PY_PROTO_INIT) | $(PY_PROTO_BUILD_DIR)
 	@ echo PROTOC \(python\) $<
-	$(Q)protoc --proto_path=$(PROTO_SRC_DIR) --python_out=$(PY_PROTO_BUILD_DIR) $<
+	$(Q)protoc --proto_path=src --python_out=python $<
 
 $(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
 	touch $(PY_PROTO_INIT)
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index 8005b448707..72ea3230c50 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -78,7 +78,7 @@ function(caffe_protobuf_generate_cpp_py output_dir srcs_var hdrs_var python_var)
              "${output_dir}/${fil_we}_pb2.py"
       COMMAND ${CMAKE_COMMAND} -E make_directory "${output_dir}"
       COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --cpp_out    ${output_dir} ${_protoc_include} ${abs_fil}
-      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --python_out ${output_dir} ${_protoc_include} ${abs_fil}
+      COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} --python_out ${PROJECT_BINARY_DIR}/include --proto_path ${PROJECT_SOURCE_DIR}/src ${_protoc_include} ${abs_fil}
       DEPENDS ${abs_fil}
       COMMENT "Running C++/Python protocol buffer compiler on ${fil}" VERBATIM )
   endforeach()

From c32629435a1e5dacc8a90a309d8b255d7b629379 Mon Sep 17 00:00:00 2001
From: iovodov <b@ovdv.ru>
Date: Mon, 18 Dec 2017 22:39:03 +0300
Subject: [PATCH 2021/2053] Weight parameter in solver is used in caffe.exe

Loading weights is moved from caffe.exe to solver class, so new "weights" solver parameter is used not only from command line but when caffe is used as library (including python)

corrected formatting

fixed line length

more formatting corrected
---
 src/caffe/proto/caffe.proto           | 12 +++++++++++-
 src/caffe/solver.cpp                  | 21 +++++++++++++++++++++
 src/caffe/test/test_upgrade_proto.cpp |  4 ++++
 tools/caffe.cpp                       | 23 +++++++----------------
 4 files changed, 43 insertions(+), 17 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index c96966b589d..4567861fb40 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -98,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 42 (last added: layer_wise_reduce)
+// SolverParameter next available ID: 43 (last added: weights)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -241,6 +241,16 @@ message SolverParameter {
 
   // Overlap compute and communication for data parallel training
   optional bool layer_wise_reduce = 41 [default = true];
+
+  // Path to caffemodel file(s) with pretrained weights to initialize finetuning.
+  // Tha same as command line --weights parameter for caffe train command.
+  // If command line --weights parameter if specified, it has higher priority
+  // and owerwrites this one(s).
+  // If --snapshot command line parameter is specified, this one(s) are ignored.
+  // If several model files are expected, they can be listed in a one 
+  // weights parameter separated by ',' (like in a command string) or
+  // in repeated weights parameters separately.
+  repeated string weights = 42;
 }
 
 // A message that stores the solver snapshots
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 044269371ad..d229acff485 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -3,6 +3,7 @@
 #include <string>
 #include <vector>
 
+#include "boost/algorithm/string.hpp"
 #include "caffe/solver.hpp"
 #include "caffe/util/format.hpp"
 #include "caffe/util/hdf5.hpp"
@@ -59,6 +60,20 @@ void Solver<Dtype>::Init(const SolverParameter& param) {
   current_step_ = 0;
 }
 
+// Load weights from the caffemodel(s) specified in "weights" solver parameter
+// into the train and test nets.
+template <typename Dtype>
+void LoadNetWeights(shared_ptr<Net<Dtype> > net,
+    const std::string& model_list) {
+  std::vector<std::string> model_names;
+  boost::split(model_names, model_list, boost::is_any_of(","));
+  for (int i = 0; i < model_names.size(); ++i) {
+    boost::trim(model_names[i]);
+    LOG(INFO) << "Finetuning from " << model_names[i];
+    net->CopyTrainedLayersFrom(model_names[i]);
+  }
+}
+
 template <typename Dtype>
 void Solver<Dtype>::InitTrainNet() {
   const int num_train_nets = param_.has_net() + param_.has_net_param() +
@@ -98,6 +113,9 @@ void Solver<Dtype>::InitTrainNet() {
   net_state.MergeFrom(param_.train_state());
   net_param.mutable_state()->CopyFrom(net_state);
   net_.reset(new Net<Dtype>(net_param));
+  for (int w_idx = 0; w_idx < param_.weights_size(); ++w_idx) {
+    LoadNetWeights(net_, param_.weights(w_idx));
+  }
 }
 
 template <typename Dtype>
@@ -173,6 +191,9 @@ void Solver<Dtype>::InitTestNets() {
         << "Creating test net (#" << i << ") specified by " << sources[i];
     test_nets_[i].reset(new Net<Dtype>(net_params[i]));
     test_nets_[i]->set_debug_info(param_.debug_info());
+    for (int w_idx = 0; w_idx < param_.weights_size(); ++w_idx) {
+      LoadNetWeights(test_nets_[i], param_.weights(w_idx));
+    }
   }
 }
 
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 9dcc2aa55ec..769112ebfe7 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -2952,6 +2952,8 @@ TEST_F(SolverTypeUpgradeTest, TestSimple) {
   for (int i = 0; i < 6; ++i) {
     const string& input_proto =
         "net: 'examples/mnist/lenet_train_test.prototxt' "
+        "weights: 'examples/mnist/lenet_train_test1.caffemodel' "
+        "weights: 'examples/mnist/lenet_train_test2.caffemodel' "
         "test_iter: 100 "
         "test_interval: 500 "
         "base_lr: 0.01 "
@@ -2968,6 +2970,8 @@ TEST_F(SolverTypeUpgradeTest, TestSimple) {
         "solver_type: " + std::string(old_type_vec[i]) + " ";
     const string& expected_output_proto =
         "net: 'examples/mnist/lenet_train_test.prototxt' "
+        "weights: 'examples/mnist/lenet_train_test1.caffemodel' "
+        "weights: 'examples/mnist/lenet_train_test2.caffemodel' "
         "test_iter: 100 "
         "test_interval: 500 "
         "base_lr: 0.01 "
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index 3587d8aa1be..389cfb8a99e 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -146,20 +146,6 @@ int device_query() {
 }
 RegisterBrewFunction(device_query);
 
-// Load the weights from the specified caffemodel(s) into the train and
-// test nets.
-void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
-  std::vector<std::string> model_names;
-  boost::split(model_names, model_list, boost::is_any_of(",") );
-  for (int i = 0; i < model_names.size(); ++i) {
-    LOG(INFO) << "Finetuning from " << model_names[i];
-    solver->net()->CopyTrainedLayersFrom(model_names[i]);
-    for (int j = 0; j < solver->test_nets().size(); ++j) {
-      solver->test_nets()[j]->CopyTrainedLayersFrom(model_names[i]);
-    }
-  }
-}
-
 // Translate the signal effect the user specified on the command-line to the
 // corresponding enumeration.
 caffe::SolverAction::Enum GetRequestedAction(
@@ -233,6 +219,13 @@ int train() {
         GetRequestedAction(FLAGS_sigint_effect),
         GetRequestedAction(FLAGS_sighup_effect));
 
+  if (FLAGS_snapshot.size()) {
+    solver_param.clear_weights();
+  } else if (FLAGS_weights.size()) {
+    solver_param.clear_weights();
+    solver_param.add_weights(FLAGS_weights);
+  }
+
   shared_ptr<caffe::Solver<float> >
       solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));
 
@@ -241,8 +234,6 @@ int train() {
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
     solver->Restore(FLAGS_snapshot.c_str());
-  } else if (FLAGS_weights.size()) {
-    CopyLayers(solver.get(), FLAGS_weights);
   }
 
   LOG(INFO) << "Starting Optimization";

From 6fa4c62dcca954b7f8ae26e7f7314e235dd6a3b4 Mon Sep 17 00:00:00 2001
From: iovodov <b@ovdv.ru>
Date: Sat, 13 Jan 2018 17:19:45 +0300
Subject: [PATCH 2022/2053] Automatic replacement of snapshot_prefix parameter
 if it is empty or points to a directory. See issue #6110 proposed improvement
 No.2

---
 src/caffe/proto/caffe.proto      |  6 +++++-
 src/caffe/util/upgrade_proto.cpp | 21 +++++++++++++++++++++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 4567861fb40..22764abc33f 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -186,7 +186,11 @@ message SolverParameter {
   optional float clip_gradients = 35 [default = -1];
 
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
-  optional string snapshot_prefix = 15; // The prefix for the snapshot.
+  // The prefix for the snapshot.
+  // If not set then is replaced by prototxt file path without extention.
+  // If is set to directory then is augmented by prototxt file name
+  // without extention.
+  optional string snapshot_prefix = 15;
   // whether to snapshot diff in the results or not. Snapshotting diff will help
   // debugging but the final protocol buffer size will be much larger.
   optional bool snapshot_diff = 16 [default = false];
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 94771c8c050..ad40b73d295 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -2,6 +2,8 @@
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
 
+#include <boost/filesystem.hpp>
+
 #include <map>
 #include <string>
 
@@ -1095,12 +1097,31 @@ bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param) {
   return success;
 }
 
+// Replaces snapshot_prefix of SolverParameter if it is not specified
+// or is set to directory
+void UpgradeSnapshotPrefixProperty(const string& param_file,
+                                   SolverParameter* param) {
+  using boost::filesystem::path;
+  using boost::filesystem::is_directory;
+  if (!param->has_snapshot_prefix()) {
+    param->set_snapshot_prefix(path(param_file).replace_extension().string());
+    LOG(INFO) << "snapshot_prefix was not specified and is set to "
+                + param->snapshot_prefix();
+  } else if (is_directory(param->snapshot_prefix())) {
+    param->set_snapshot_prefix((path(param->snapshot_prefix()) /
+                               path(param_file).stem()).string());
+    LOG(INFO) << "snapshot_prefix was a directory and is replaced to "
+                + param->snapshot_prefix();
+  }
+}
+
 // Read parameters from a file into a SolverParameter proto message.
 void ReadSolverParamsFromTextFileOrDie(const string& param_file,
                                        SolverParameter* param) {
   CHECK(ReadProtoFromTextFile(param_file, param))
       << "Failed to parse SolverParameter file: " << param_file;
   UpgradeSolverAsNeeded(param_file, param);
+  UpgradeSnapshotPrefixProperty(param_file, param);
 }
 
 }  // namespace caffe

From 37e4289024d80632e2c721e865c14be63aab9d8e Mon Sep 17 00:00:00 2001
From: jasjuang <jasjuang@gmail.com>
Date: Tue, 13 Feb 2018 16:57:18 -0800
Subject: [PATCH 2023/2053] fix cuda 9.1 compilation

---
 cmake/Cuda.cmake | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 54e26fd5c8e..9325674a6a0 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -109,6 +109,12 @@ function(caffe_select_nvcc_arch_flags out_variable)
   set(__nvcc_flags "")
   set(__nvcc_archs_readable "")
 
+  string(COMPARE GREATER_EQUAL "${CUDA_VERSION}" "9.1" iscudanewerthan91)
+  if(iscudanewerthan91)
+    string(REPLACE "21(20)" "" __cuda_arch_bin "${__cuda_arch_bin}")
+    string(REPLACE "20" "" __cuda_arch_bin "${__cuda_arch_bin}")
+  endif()
+
   # Tell NVCC to add binaries for the specified GPUs
   foreach(__arch ${__cuda_arch_bin})
     if(__arch MATCHES "([0-9]+)\\(([0-9]+)\\)")

From b3deb95e75f6890c08e7aa60193ea304614b24ea Mon Sep 17 00:00:00 2001
From: yanchen036 <yanchen036@gmail.com>
Date: Mon, 2 Jan 2017 13:33:49 +0800
Subject: [PATCH 2024/2053] bug fix: ext should not include the '.'

---
 src/caffe/util/io.cpp      | 2 +-
 tools/convert_imageset.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 835d2d4e4ff..5295d9dddb9 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -106,7 +106,7 @@ cv::Mat ReadImageToCVMat(const string& filename) {
 static bool matchExt(const std::string & fn,
                      std::string en) {
   size_t p = fn.rfind('.');
-  std::string ext = p != fn.npos ? fn.substr(p) : fn;
+  std::string ext = p != fn.npos ? fn.substr(p+1) : fn;
   std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
   std::transform(en.begin(), en.end(), en.begin(), ::tolower);
   if ( ext == en )
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 90cdb15d427..9c5d09f9bef 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -115,7 +115,7 @@ int main(int argc, char** argv) {
       size_t p = fn.rfind('.');
       if ( p == fn.npos )
         LOG(WARNING) << "Failed to guess the encoding of '" << fn << "'";
-      enc = fn.substr(p);
+      enc = fn.substr(p+1);
       std::transform(enc.begin(), enc.end(), enc.begin(), ::tolower);
     }
     status = ReadImageToDatum(root_folder + lines[line_id].first,

From ced55b009ae4fd6c0685543a013b1439da5879ba Mon Sep 17 00:00:00 2001
From: knsong <sunskn@163.com>
Date: Sat, 17 Feb 2018 15:56:32 +0800
Subject: [PATCH 2025/2053] Fix compatibility for ND convolution

---
 include/caffe/filler.hpp | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index bb92ded780f..e3e86a52633 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -108,9 +108,9 @@ class PositiveUnitballFiller : public Filler<Dtype> {
     caffe_rng_uniform<Dtype>(blob->count(), 0, 1, blob->mutable_cpu_data());
     // We expect the filler to not be called very frequently, so we will
     // just use a simple implementation
-    int dim = blob->count() / blob->num();
+    int dim = blob->count() / blob->shape(0);
     CHECK(dim);
-    for (int i = 0; i < blob->num(); ++i) {
+    for (int i = 0; i < blob->shape(0); ++i) {
       Dtype sum = 0;
       for (int j = 0; j < dim; ++j) {
         sum += data[i * dim + j];
@@ -147,8 +147,9 @@ class XavierFiller : public Filler<Dtype> {
       : Filler<Dtype>(param) {}
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
-    int fan_in = blob->count() / blob->num();
-    int fan_out = blob->count() / blob->channels();
+    int fan_in = blob->count() / blob->shape(0);
+    // Compatible for ND Convolution
+    int fan_out = blob->count() / blob->shape(1);
     Dtype n = fan_in;  // default to fan_in
     if (this->filler_param_.variance_norm() ==
         FillerParameter_VarianceNorm_AVERAGE) {
@@ -189,8 +190,9 @@ class MSRAFiller : public Filler<Dtype> {
       : Filler<Dtype>(param) {}
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
-    int fan_in = blob->count() / blob->num();
-    int fan_out = blob->count() / blob->channels();
+    int fan_in = blob->count() / blob->shape(0);
+    // Compatible for ND Convolution
+    int fan_out = blob->count() / blob->shape(1);
     Dtype n = fan_in;  // default to fan_in
     if (this->filler_param_.variance_norm() ==
         FillerParameter_VarianceNorm_AVERAGE) {

From 8e09610f49e8af8a161b3996e6406d0fd101e0e3 Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Fri, 23 Feb 2018 14:45:59 +0100
Subject: [PATCH 2026/2053] Remove legacy tools

---
 tools/device_query.cpp        | 7 -------
 tools/finetune_net.cpp        | 7 -------
 tools/net_speed_benchmark.cpp | 7 -------
 tools/test_net.cpp            | 7 -------
 tools/train_net.cpp           | 7 -------
 5 files changed, 35 deletions(-)
 delete mode 100644 tools/device_query.cpp
 delete mode 100644 tools/finetune_net.cpp
 delete mode 100644 tools/net_speed_benchmark.cpp
 delete mode 100644 tools/test_net.cpp
 delete mode 100644 tools/train_net.cpp

diff --git a/tools/device_query.cpp b/tools/device_query.cpp
deleted file mode 100644
index 03799e52b53..00000000000
--- a/tools/device_query.cpp
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "caffe/common.hpp"
-
-int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe device_query "
-                "[--device_id=0] instead.";
-  return 0;
-}
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
deleted file mode 100644
index 81c0c354dbf..00000000000
--- a/tools/finetune_net.cpp
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "caffe/caffe.hpp"
-
-int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
-                "[--weights=...] instead.";
-  return 0;
-}
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
deleted file mode 100644
index cd16e8d0984..00000000000
--- a/tools/net_speed_benchmark.cpp
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "caffe/caffe.hpp"
-
-int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe time --model=... "
-             "[--iterations=50] [--gpu] [--device_id=0]";
-  return 0;
-}
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
deleted file mode 100644
index 92e14eeebaf..00000000000
--- a/tools/test_net.cpp
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "caffe/caffe.hpp"
-
-int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe test --model=... "
-      "--weights=... instead.";
-  return 0;
-}
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
deleted file mode 100644
index 622bca311c8..00000000000
--- a/tools/train_net.cpp
+++ /dev/null
@@ -1,7 +0,0 @@
-#include "caffe/caffe.hpp"
-
-int main(int argc, char** argv) {
-  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
-                "[--snapshot=...] instead.";
-  return 0;
-}

From 412f18dcf204fc054b09dbfdda73b99d903149f4 Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Tue, 6 Mar 2018 13:39:49 +0100
Subject: [PATCH 2027/2053] 1D blob handling in MSRA/Xavier fillers

---
 include/caffe/filler.hpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index e3e86a52633..a44773619dc 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -148,8 +148,10 @@ class XavierFiller : public Filler<Dtype> {
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
     int fan_in = blob->count() / blob->shape(0);
-    // Compatible for ND Convolution
-    int fan_out = blob->count() / blob->shape(1);
+    // Compatibility with ND blobs
+    int fan_out = blob->num_axes() > 1 ?
+                  blob->count() / blob->shape(1) :
+                  blob->count();
     Dtype n = fan_in;  // default to fan_in
     if (this->filler_param_.variance_norm() ==
         FillerParameter_VarianceNorm_AVERAGE) {
@@ -191,8 +193,10 @@ class MSRAFiller : public Filler<Dtype> {
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
     int fan_in = blob->count() / blob->shape(0);
-    // Compatible for ND Convolution
-    int fan_out = blob->count() / blob->shape(1);
+    // Compatibility with ND blobs
+    int fan_out = blob->num_axes() > 1 ?
+                  blob->count() / blob->shape(1) :
+                  blob->count();
     Dtype n = fan_in;  // default to fan_in
     if (this->filler_param_.variance_norm() ==
         FillerParameter_VarianceNorm_AVERAGE) {

From 7b3ac40be38dd2ace67ed0ff481ee113a0f07211 Mon Sep 17 00:00:00 2001
From: nic25 <nicola.corradi@gmail.com>
Date: Wed, 7 Mar 2018 03:40:52 -0800
Subject: [PATCH 2028/2053] Add lr_mult label to the network graph in
 draw_net.py (#6273)

draw_net.py refactoring and optional LR visualization

* refactoring `get_layer_label`

rewrote the function body to make it more streamlined.
does not affect inputs and outputs

* optionally visualize LR when drawing the network

adds an option to `python/draw_net.py` that allows to visualize information
about the learning rate multiplier (if relevant) when drawing the network's
graph.
---
 python/caffe/draw.py | 144 ++++++++++++++++++++++++++++++++-----------
 python/draw_net.py   |   6 +-
 2 files changed, 112 insertions(+), 38 deletions(-)

diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 8411a41d1d4..0061f490bd9 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -59,18 +59,60 @@ def get_edge_label(layer):
     return edge_label
 
 
-def get_layer_label(layer, rankdir):
+def get_layer_lr_mult(layer):
+    """Get the learning rate multipliers.
+
+    Get the learning rate multipliers for the given layer. Assumes a
+    Convolution/Deconvolution/InnerProduct layer.
+
+    Parameters
+    ----------
+    layer : caffe_pb2.LayerParameter
+        A Convolution, Deconvolution, or InnerProduct layer.
+
+    Returns
+    -------
+    learning_rates : tuple of floats
+        the learning rate multipliers for the weights and biases.
+    """
+    if layer.type not in ['Convolution', 'Deconvolution', 'InnerProduct']:
+        raise ValueError("%s layers do not have a "
+                         "learning rate multiplier" % layer.type)
+
+    if not hasattr(layer, 'param'):
+        return (1.0, 1.0)
+
+    params = getattr(layer, 'param')
+
+    if len(params) == 0:
+        return (1.0, 1.0)
+
+    if len(params) == 1:
+        lrm0 = getattr(params[0],'lr_mult', 1.0)
+        return (lrm0, 1.0)
+
+    if len(params) == 2:
+        lrm0, lrm1 = [getattr(p,'lr_mult', 1.0) for p in params]
+        return (lrm0, lrm1)
+
+    raise ValueError("Could not parse the learning rate multiplier")
+
+
+def get_layer_label(layer, rankdir, display_lrm=False):
     """Define node label based on layer type.
 
     Parameters
     ----------
-    layer : ?
+    layer : caffe_pb2.LayerParameter
     rankdir : {'LR', 'TB', 'BT'}
         Direction of graph layout.
+    display_lrm : boolean, optional
+        If True include the learning rate multipliers in the label (default is
+        False).
 
     Returns
     -------
-    string :
+    node_label : string
         A label for the current layer
     """
 
@@ -81,36 +123,54 @@ def get_layer_label(layer, rankdir):
     else:
         # If graph orientation is horizontal, vertical space is free and
         # horizontal space is not; separate words with newlines
-        separator = '\\n'
-
-    if layer.type == 'Convolution' or layer.type == 'Deconvolution':
-        # Outer double quotes needed or else colon characters don't parse
-        # properly
-        node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
-                     (layer.name,
-                      separator,
-                      layer.type,
-                      separator,
-                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size) else 1,
-                      separator,
-                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride) else 1,
-                      separator,
-                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad) else 0)
-    elif layer.type == 'Pooling':
+        separator = r'\n'
+
+    # Initializes a list of descriptors that will be concatenated into the
+    # `node_label`
+    descriptors_list = []
+    # Add the layer's name
+    descriptors_list.append(layer.name)
+    # Add layer's type
+    if layer.type == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
-        node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
-                     (layer.name,
-                      separator,
-                      pooling_types_dict[layer.pooling_param.pool],
-                      layer.type,
-                      separator,
-                      layer.pooling_param.kernel_size,
-                      separator,
-                      layer.pooling_param.stride,
-                      separator,
-                      layer.pooling_param.pad)
+        layer_type = '(%s %s)' % (layer.type,
+                                  pooling_types_dict[layer.pooling_param.pool])
     else:
-        node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
+        layer_type = '(%s)' % layer.type
+    descriptors_list.append(layer_type)
+
+    # Describe parameters for spatial operation layers
+    if layer.type in ['Convolution', 'Deconvolution', 'Pooling']:
+        if layer.type == 'Pooling':
+            kernel_size = layer.pooling_param.kernel_size
+            stride = layer.pooling_param.stride
+            padding = layer.pooling_param.pad
+        else:
+            kernel_size = layer.convolution_param.kernel_size[0] if \
+                len(layer.convolution_param.kernel_size) else 1
+            stride = layer.convolution_param.stride[0] if \
+                len(layer.convolution_param.stride) else 1
+            padding = layer.convolution_param.pad[0] if \
+                len(layer.convolution_param.pad) else 0
+        spatial_descriptor = separator.join([
+            "kernel size: %d" % kernel_size,
+            "stride: %d" % stride,
+            "pad: %d" % padding,
+        ])
+        descriptors_list.append(spatial_descriptor)
+
+    # Add LR multiplier for learning layers
+    if display_lrm and layer.type in ['Convolution', 'Deconvolution', 'InnerProduct']:
+        lrm0, lrm1 = get_layer_lr_mult(layer)
+        if any([lrm0, lrm1]):
+            lr_mult = "lr mult: %.1f, %.1f" % (lrm0, lrm1)
+            descriptors_list.append(lr_mult)
+
+    # Concatenate the descriptors into one label
+    node_label = separator.join(descriptors_list)
+    # Outer double quotes needed or else colon characters don't parse
+    # properly
+    node_label = '"%s"' % node_label
     return node_label
 
 
@@ -127,7 +187,7 @@ def choose_color_by_layertype(layertype):
     return color
 
 
-def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
+def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None, display_lrm=False):
     """Create a data structure which represents the `caffe_net`.
 
     Parameters
@@ -140,6 +200,9 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
     phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
         Include layers from this network phase.  If None, include all layers.
         (the default is None)
+    display_lrm : boolean, optional
+        If True display the learning rate multipliers when relevant (default is
+        False).
 
     Returns
     -------
@@ -164,7 +227,7 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
             included = included and not layer_phase.phase == phase
           if not included:
             continue
-        node_label = get_layer_label(layer, rankdir)
+        node_label = get_layer_label(layer, rankdir, display_lrm=display_lrm)
         node_name = "%s_%s" % (layer.name, layer.type)
         if (len(layer.bottom) == 1 and len(layer.top) == 1 and
            layer.bottom[0] == layer.top[0]):
@@ -202,7 +265,7 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):
     return pydot_graph
 
 
-def draw_net(caffe_net, rankdir, ext='png', phase=None):
+def draw_net(caffe_net, rankdir, ext='png', phase=None, display_lrm=False):
     """Draws a caffe net and returns the image string encoded using the given
     extension.
 
@@ -214,16 +277,20 @@ def draw_net(caffe_net, rankdir, ext='png', phase=None):
     phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
         Include layers from this network phase.  If None, include all layers.
         (the default is None)
+    display_lrm : boolean, optional
+        If True display the learning rate multipliers for the learning layers
+        (default is False).
 
     Returns
     -------
     string :
         Postscript representation of the graph.
     """
-    return get_pydot_graph(caffe_net, rankdir, phase=phase).create(format=ext)
+    return get_pydot_graph(caffe_net, rankdir, phase=phase,
+                           display_lrm=display_lrm).create(format=ext)
 
 
-def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):
+def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None, display_lrm=False):
     """Draws a caffe net, and saves it to file using the format given as the
     file extension. Use '.raw' to output raw text that you can manually feed
     to graphviz to draw graphs.
@@ -238,7 +305,10 @@ def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):
     phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional
         Include layers from this network phase.  If None, include all layers.
         (the default is None)
+    display_lrm : boolean, optional
+        If True display the learning rate multipliers for the learning layers
+        (default is False).
     """
     ext = filename[filename.rfind('.')+1:]
     with open(filename, 'wb') as fid:
-        fid.write(draw_net(caffe_net, rankdir, ext, phase))
+        fid.write(draw_net(caffe_net, rankdir, ext, phase, display_lrm))
diff --git a/python/draw_net.py b/python/draw_net.py
index dfe70d26a71..23cae30aef2 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -33,6 +33,10 @@ def parse_args():
                               'TEST, or ALL.  If ALL, then all layers are drawn '
                               'regardless of phase.'),
                         default="ALL")
+    parser.add_argument('--display_lrm', action='store_true',
+                        help=('Use this flag to visualize the learning rate '
+                              'multiplier, when non-zero, for the learning '
+                              'layers (Convolution, Deconvolution, InnerProduct).'))
 
     args = parser.parse_args()
     return args
@@ -51,7 +55,7 @@ def main():
     elif args.phase != "ALL":
         raise ValueError("Unknown phase: " + args.phase)
     caffe.draw.draw_net_to_file(net, args.output_image_file, args.rankdir,
-                                phase)
+                                phase, args.display_lrm)
 
 
 if __name__ == '__main__':

From d7da0924486f762d810e78ad55be5206c3e5198e Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Wed, 7 Mar 2018 13:57:56 +0100
Subject: [PATCH 2029/2053] PoolingLayer customizable output shape rounding
 mode

---
 include/caffe/layers/pooling_layer.hpp |  1 +
 src/caffe/layers/pooling_layer.cpp     | 21 +++++++++++++++++----
 src/caffe/proto/caffe.proto            |  6 ++++++
 3 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/include/caffe/layers/pooling_layer.hpp b/include/caffe/layers/pooling_layer.hpp
index f4d6803ba8e..38a432832cf 100644
--- a/include/caffe/layers/pooling_layer.hpp
+++ b/include/caffe/layers/pooling_layer.hpp
@@ -51,6 +51,7 @@ class PoolingLayer : public Layer<Dtype> {
   int height_, width_;
   int pooled_height_, pooled_width_;
   bool global_pooling_;
+  PoolingParameter_RoundMode round_mode_;
   Blob<Dtype> rand_idx_;
   Blob<int> max_idx_;
 };
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 90897db0f45..f2a0885771f 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -35,6 +35,7 @@ void PoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";
   global_pooling_ = pool_param.global_pooling();
+  round_mode_ = pool_param.round_mode();
   if (global_pooling_) {
     kernel_h_ = bottom[0]->height();
     kernel_w_ = bottom[0]->width();
@@ -87,10 +88,22 @@ void PoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     kernel_h_ = bottom[0]->height();
     kernel_w_ = bottom[0]->width();
   }
-  pooled_height_ = static_cast<int>(ceil(static_cast<float>(
-      height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
-  pooled_width_ = static_cast<int>(ceil(static_cast<float>(
-      width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1;
+  switch (round_mode_) {
+  case PoolingParameter_RoundMode_CEIL:
+    pooled_height_ = static_cast<int>(ceil(static_cast<float>(
+        height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
+    pooled_width_ = static_cast<int>(ceil(static_cast<float>(
+        width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1;
+    break;
+  case PoolingParameter_RoundMode_FLOOR:
+    pooled_height_ = static_cast<int>(floor(static_cast<float>(
+        height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
+    pooled_width_ = static_cast<int>(floor(static_cast<float>(
+        width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1;
+    break;
+  default:
+    LOG(FATAL) << "Unknown rounding mode.";
+  }
   if (pad_h_ || pad_w_) {
     // If we have padding, ensure that the last pooling starts strictly
     // inside the image (instead of at the padding); otherwise clip the last.
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 22764abc33f..cfef3c00262 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -935,6 +935,12 @@ message PoolingParameter {
   // If global_pooling then it will pool over the size of the bottom by doing
   // kernel_h = bottom->height and kernel_w = bottom->width
   optional bool global_pooling = 12 [default = false];
+  // How to calculate the output size - using ceil (default) or floor rounding.
+  enum RoundMode {
+    CEIL = 0;
+    FLOOR = 1;
+  }
+  optional RoundMode round_mode = 13 [default = CEIL];
 }
 
 message PowerParameter {

From 527a1c1c532cf4ef6b4a8b16ff17c7edd3467234 Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Thu, 8 Mar 2018 14:02:10 +0100
Subject: [PATCH 2030/2053] Filler testing overhaul

---
 src/caffe/test/test_filler.cpp | 403 +++++++++++++++++++++++++++------
 1 file changed, 331 insertions(+), 72 deletions(-)

diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index f84d707baa0..3ecec37aa03 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -1,3 +1,5 @@
+#include <vector>
+
 #include "gtest/gtest.h"
 
 #include "caffe/filler.hpp"
@@ -10,11 +12,20 @@ template <typename Dtype>
 class ConstantFillerTest : public ::testing::Test {
  protected:
   ConstantFillerTest()
-      : blob_(new Blob<Dtype>(2, 3, 4, 5)),
+      : blob_(new Blob<Dtype>()),
         filler_param_() {
     filler_param_.set_value(10.);
     filler_.reset(new ConstantFiller<Dtype>(filler_param_));
+  }
+  virtual void test_params(const vector<int>& shape) {
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
     filler_->Fill(blob_);
+    const int count = blob_->count();
+    const Dtype* data = blob_->cpu_data();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_EQ(data[i], filler_param_.value());
+    }
   }
   virtual ~ConstantFillerTest() { delete blob_; }
   Blob<Dtype>* const blob_;
@@ -25,12 +36,34 @@ class ConstantFillerTest : public ::testing::Test {
 TYPED_TEST_CASE(ConstantFillerTest, TestDtypes);
 
 TYPED_TEST(ConstantFillerTest, TestFill) {
-  EXPECT_TRUE(this->blob_);
-  const int count = this->blob_->count();
-  const TypeParam* data = this->blob_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], this->filler_param_.value());
-  }
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(ConstantFillerTest, TestFill1D) {
+  vector<int> blob_shape(1, 15);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(ConstantFillerTest, TestFill2D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(8);
+  blob_shape.push_back(3);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(ConstantFillerTest, TestFill5D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  blob_shape.push_back(2);
+  this->test_params(blob_shape);
 }
 
 
@@ -38,12 +71,22 @@ template <typename Dtype>
 class UniformFillerTest : public ::testing::Test {
  protected:
   UniformFillerTest()
-      : blob_(new Blob<Dtype>(2, 3, 4, 5)),
+      : blob_(new Blob<Dtype>()),
         filler_param_() {
     filler_param_.set_min(1.);
     filler_param_.set_max(2.);
     filler_.reset(new UniformFiller<Dtype>(filler_param_));
+  }
+  virtual void test_params(const vector<int>& shape) {
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
     filler_->Fill(blob_);
+    const int count = blob_->count();
+    const Dtype* data = blob_->cpu_data();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_GE(data[i], filler_param_.min());
+      EXPECT_LE(data[i], filler_param_.max());
+    }
   }
   virtual ~UniformFillerTest() { delete blob_; }
   Blob<Dtype>* const blob_;
@@ -54,23 +97,64 @@ class UniformFillerTest : public ::testing::Test {
 TYPED_TEST_CASE(UniformFillerTest, TestDtypes);
 
 TYPED_TEST(UniformFillerTest, TestFill) {
-  EXPECT_TRUE(this->blob_);
-  const int count = this->blob_->count();
-  const TypeParam* data = this->blob_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], this->filler_param_.min());
-    EXPECT_LE(data[i], this->filler_param_.max());
-  }
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(UniformFillerTest, TestFill1D) {
+  vector<int> blob_shape(1, 15);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(UniformFillerTest, TestFill2D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(8);
+  blob_shape.push_back(3);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(UniformFillerTest, TestFill5D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  blob_shape.push_back(2);
+  this->test_params(blob_shape);
 }
 
 template <typename Dtype>
 class PositiveUnitballFillerTest : public ::testing::Test {
  protected:
   PositiveUnitballFillerTest()
-      : blob_(new Blob<Dtype>(2, 3, 4, 5)),
+      : blob_(new Blob<Dtype>()),
         filler_param_() {
     filler_.reset(new PositiveUnitballFiller<Dtype>(filler_param_));
+  }
+  virtual void test_params(const vector<int>& shape) {
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
     filler_->Fill(blob_);
+    const int num = blob_->shape(0);
+    const int count = blob_->count();
+    const int dim = count / num;
+    const Dtype* data = blob_->cpu_data();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_GE(data[i], 0);
+      EXPECT_LE(data[i], 1);
+    }
+    for (int i = 0; i < num; ++i) {
+      Dtype sum = Dtype(0);
+      for (int j = 0; j < dim; ++j) {
+        sum += data[i * dim + j];
+      }
+      EXPECT_GE(sum, 0.999);
+      EXPECT_LE(sum, 1.001);
+    }
   }
   virtual ~PositiveUnitballFillerTest() { delete blob_; }
   Blob<Dtype>* const blob_;
@@ -81,35 +165,78 @@ class PositiveUnitballFillerTest : public ::testing::Test {
 TYPED_TEST_CASE(PositiveUnitballFillerTest, TestDtypes);
 
 TYPED_TEST(PositiveUnitballFillerTest, TestFill) {
-  EXPECT_TRUE(this->blob_);
-  const int num = this->blob_->num();
-  const int count = this->blob_->count();
-  const int dim = count / num;
-  const TypeParam* data = this->blob_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], 0);
-    EXPECT_LE(data[i], 1);
-  }
-  for (int i = 0; i < num; ++i) {
-    TypeParam sum = 0;
-    for (int j = 0; j < dim; ++j) {
-      sum += data[i * dim + j];
-    }
-    EXPECT_GE(sum, 0.999);
-    EXPECT_LE(sum, 1.001);
-  }
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(PositiveUnitballFillerTest, TestFill1D) {
+  vector<int> blob_shape(1, 15);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(PositiveUnitballFillerTest, TestFill2D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(8);
+  blob_shape.push_back(3);
+  this->test_params(blob_shape);
+}
+
+TYPED_TEST(PositiveUnitballFillerTest, TestFill5D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  blob_shape.push_back(2);
+  this->test_params(blob_shape);
 }
 
 template <typename Dtype>
 class GaussianFillerTest : public ::testing::Test {
  protected:
   GaussianFillerTest()
-      : blob_(new Blob<Dtype>(2, 3, 4, 5)),
+      : blob_(new Blob<Dtype>()),
         filler_param_() {
     filler_param_.set_mean(10.);
     filler_param_.set_std(0.1);
     filler_.reset(new GaussianFiller<Dtype>(filler_param_));
+  }
+  virtual void test_params(const vector<int>& shape,
+      const Dtype tolerance = Dtype(5), const int repetitions = 100) {
+    // Tests for statistical properties should be ran multiple times.
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
+    for (int i = 0; i < repetitions; ++i) {
+      test_params_iter(shape, tolerance);
+    }
+  }
+  virtual void test_params_iter(const vector<int>& shape,
+      const Dtype tolerance) {
+    // This test has a configurable tolerance parameter - by default it was
+    // equal to 5.0 which is very loose - allowing some tuning (e.g. for tests
+    // on smaller blobs the actual variance will be larger than desired, so the
+    // tolerance can be increased to account for that).
     filler_->Fill(blob_);
+    const int count = blob_->count();
+    const Dtype* data = blob_->cpu_data();
+    Dtype mean = Dtype(0);
+    Dtype var = Dtype(0);
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      var += data[i] * data[i];
+    }
+    mean /= count;
+    var /= count;
+    var -= mean*mean;
+    EXPECT_GE(mean, filler_param_.mean() - filler_param_.std() * tolerance);
+    EXPECT_LE(mean, filler_param_.mean() + filler_param_.std() * tolerance);
+    Dtype target_var = filler_param_.std() * filler_param_.std();
+    EXPECT_GE(var, target_var / tolerance);
+    EXPECT_LE(var, target_var * tolerance);
   }
   virtual ~GaussianFillerTest() { delete blob_; }
   Blob<Dtype>* const blob_;
@@ -120,41 +247,62 @@ class GaussianFillerTest : public ::testing::Test {
 TYPED_TEST_CASE(GaussianFillerTest, TestDtypes);
 
 TYPED_TEST(GaussianFillerTest, TestFill) {
-  EXPECT_TRUE(this->blob_);
-  const int count = this->blob_->count();
-  const TypeParam* data = this->blob_->cpu_data();
-  TypeParam mean = 0.;
-  TypeParam var = 0.;
-  for (int i = 0; i < count; ++i) {
-    mean += data[i];
-    var += (data[i] - this->filler_param_.mean()) *
-        (data[i] - this->filler_param_.mean());
-  }
-  mean /= count;
-  var /= count;
-  // Very loose test.
-  EXPECT_GE(mean, this->filler_param_.mean() - this->filler_param_.std() * 5);
-  EXPECT_LE(mean, this->filler_param_.mean() + this->filler_param_.std() * 5);
-  TypeParam target_var = this->filler_param_.std() * this->filler_param_.std();
-  EXPECT_GE(var, target_var / 5.);
-  EXPECT_LE(var, target_var * 5.);
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  const TypeParam tolerance = TypeParam(3);  // enough for a 120-element blob
+  this->test_params(blob_shape, tolerance);
+}
+
+TYPED_TEST(GaussianFillerTest, TestFill1D) {
+  vector<int> blob_shape(1, 25);
+  const TypeParam tolerance = TypeParam(5);
+  this->test_params(blob_shape, tolerance);
+}
+
+TYPED_TEST(GaussianFillerTest, TestFill2D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(8);
+  blob_shape.push_back(3);
+  const TypeParam tolerance = TypeParam(5);
+  this->test_params(blob_shape, tolerance);
+}
+
+TYPED_TEST(GaussianFillerTest, TestFill5D) {
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  blob_shape.push_back(2);
+  const TypeParam tolerance = TypeParam(2);
+  this->test_params(blob_shape, tolerance);
 }
 
 template <typename Dtype>
 class XavierFillerTest : public ::testing::Test {
  protected:
   XavierFillerTest()
-      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+      : blob_(new Blob<Dtype>()),
         filler_param_() {
   }
   virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n, const vector<int>& shape, const int repetitions = 100) {
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
+    for (int i = 0; i < repetitions; ++i) {
+      test_params_iter(variance_norm, n);
+    }
+  }
+  virtual void test_params_iter(FillerParameter_VarianceNorm variance_norm,
       Dtype n) {
-    this->filler_param_.set_variance_norm(variance_norm);
-    this->filler_.reset(new XavierFiller<Dtype>(this->filler_param_));
-    this->filler_->Fill(blob_);
-    EXPECT_TRUE(this->blob_);
-    const int count = this->blob_->count();
-    const Dtype* data = this->blob_->cpu_data();
+    filler_param_.set_variance_norm(variance_norm);
+    filler_.reset(new XavierFiller<Dtype>(filler_param_));
+    filler_->Fill(blob_);
+    const int count = blob_->count();
+    const Dtype* data = blob_->cpu_data();
     Dtype mean = 0.;
     Dtype ex2 = 0.;
     for (int i = 0; i < count; ++i) {
@@ -177,33 +325,92 @@ class XavierFillerTest : public ::testing::Test {
 TYPED_TEST_CASE(XavierFillerTest, TestDtypes);
 
 TYPED_TEST(XavierFillerTest, TestFillFanIn) {
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
   TypeParam n = 2*4*5;
-  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n, blob_shape);
 }
+
 TYPED_TEST(XavierFillerTest, TestFillFanOut) {
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
   TypeParam n = 1000*4*5;
-  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n, blob_shape);
 }
+
 TYPED_TEST(XavierFillerTest, TestFillAverage) {
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
   TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
-  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n, blob_shape);
+}
+
+TYPED_TEST(XavierFillerTest, TestFill1D) {
+  // This makes little sense but at least we will know that we can fill it
+  EXPECT_TRUE(this->blob_);
+  vector<int> blob_shape(1, 25);
+  this->blob_->Reshape(blob_shape);
+  this->filler_param_.set_variance_norm(FillerParameter_VarianceNorm_AVERAGE);
+  this->filler_.reset(new XavierFiller<TypeParam>(this->filler_param_));
+  this->filler_->Fill(this->blob_);
+}
+
+TYPED_TEST(XavierFillerTest, TestFill2D) {
+  EXPECT_TRUE(this->blob_);
+  vector<int> blob_shape;
+  blob_shape.push_back(8);
+  blob_shape.push_back(3);
+  this->blob_->Reshape(blob_shape);
+  this->filler_param_.set_variance_norm(FillerParameter_VarianceNorm_AVERAGE);
+  this->filler_.reset(new XavierFiller<TypeParam>(this->filler_param_));
+  this->filler_->Fill(this->blob_);
+}
+
+TYPED_TEST(XavierFillerTest, TestFill5D) {
+  EXPECT_TRUE(this->blob_);
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  blob_shape.push_back(2);
+  this->blob_->Reshape(blob_shape);
+  this->filler_param_.set_variance_norm(FillerParameter_VarianceNorm_AVERAGE);
+  this->filler_.reset(new XavierFiller<TypeParam>(this->filler_param_));
+  this->filler_->Fill(this->blob_);
 }
 
 template <typename Dtype>
 class MSRAFillerTest : public ::testing::Test {
  protected:
   MSRAFillerTest()
-      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+      : blob_(new Blob<Dtype>()),
         filler_param_() {
   }
   virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n, const vector<int>& shape, const int repetitions = 100) {
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
+    for (int i = 0; i < repetitions; ++i) {
+      test_params_iter(variance_norm, n);
+    }
+  }
+  virtual void test_params_iter(FillerParameter_VarianceNorm variance_norm,
       Dtype n) {
-    this->filler_param_.set_variance_norm(variance_norm);
-    this->filler_.reset(new MSRAFiller<Dtype>(this->filler_param_));
-    this->filler_->Fill(blob_);
-    EXPECT_TRUE(this->blob_);
-    const int count = this->blob_->count();
-    const Dtype* data = this->blob_->cpu_data();
+    filler_param_.set_variance_norm(variance_norm);
+    filler_.reset(new MSRAFiller<Dtype>(filler_param_));
+    filler_->Fill(blob_);
+    const int count = blob_->count();
+    const Dtype* data = blob_->cpu_data();
     Dtype mean = 0.;
     Dtype ex2 = 0.;
     for (int i = 0; i < count; ++i) {
@@ -226,16 +433,68 @@ class MSRAFillerTest : public ::testing::Test {
 TYPED_TEST_CASE(MSRAFillerTest, TestDtypes);
 
 TYPED_TEST(MSRAFillerTest, TestFillFanIn) {
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
   TypeParam n = 2*4*5;
-  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n, blob_shape);
 }
+
 TYPED_TEST(MSRAFillerTest, TestFillFanOut) {
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
   TypeParam n = 1000*4*5;
-  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n, blob_shape);
 }
+
 TYPED_TEST(MSRAFillerTest, TestFillAverage) {
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
   TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
-  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n, blob_shape);
+}
+
+TYPED_TEST(MSRAFillerTest, TestFill1D) {
+  // Like with Xavier - no checking for correctness, just if it can be filled.
+  EXPECT_TRUE(this->blob_);
+  vector<int> blob_shape(1, 25);
+  this->blob_->Reshape(blob_shape);
+  this->filler_param_.set_variance_norm(FillerParameter_VarianceNorm_AVERAGE);
+  this->filler_.reset(new MSRAFiller<TypeParam>(this->filler_param_));
+  this->filler_->Fill(this->blob_);
+}
+
+TYPED_TEST(MSRAFillerTest, TestFill2D) {
+  EXPECT_TRUE(this->blob_);
+  vector<int> blob_shape;
+  blob_shape.push_back(8);
+  blob_shape.push_back(3);
+  this->blob_->Reshape(blob_shape);
+  this->filler_param_.set_variance_norm(FillerParameter_VarianceNorm_AVERAGE);
+  this->filler_.reset(new MSRAFiller<TypeParam>(this->filler_param_));
+  this->filler_->Fill(this->blob_);
+}
+
+TYPED_TEST(MSRAFillerTest, TestFill5D) {
+  EXPECT_TRUE(this->blob_);
+  vector<int> blob_shape;
+  blob_shape.push_back(2);
+  blob_shape.push_back(3);
+  blob_shape.push_back(4);
+  blob_shape.push_back(5);
+  blob_shape.push_back(2);
+  this->blob_->Reshape(blob_shape);
+  this->filler_param_.set_variance_norm(FillerParameter_VarianceNorm_AVERAGE);
+  this->filler_.reset(new MSRAFiller<TypeParam>(this->filler_param_));
+  this->filler_->Fill(this->blob_);
 }
 
 template <typename Dtype>

From eb6291911da4e6a6a9e75a8349ec3c3591d8bb2c Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Fri, 9 Mar 2018 13:14:32 +0100
Subject: [PATCH 2031/2053] bilinear filter test refactor

---
 src/caffe/test/test_filler.cpp | 44 ++++++++++++++++++++++------------
 1 file changed, 29 insertions(+), 15 deletions(-)

diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 3ecec37aa03..1e6b5c21d1d 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -500,21 +500,25 @@ TYPED_TEST(MSRAFillerTest, TestFill5D) {
 template <typename Dtype>
 class BilinearFillerTest : public ::testing::Test {
  protected:
-  BilinearFillerTest() : filler_param_() {}
-  virtual void test_params(const int n) {
-    this->blob_ = new Blob<Dtype>(1000, 2, n, n);
-    this->filler_.reset(new BilinearFiller<Dtype>(this->filler_param_));
-    this->filler_->Fill(blob_);
-    EXPECT_TRUE(this->blob_);
-    const int outer_num = this->blob_->count(0, 2);
-    const int inner_num = this->blob_->count(2, 4);
-    const Dtype* data = this->blob_->cpu_data();
-    int f = ceil(this->blob_->width() / 2.);
-    Dtype c = (this->blob_->width() - 1) / (2. * f);
+  BilinearFillerTest()
+    : blob_(new Blob<Dtype>()),
+      filler_param_() {
+  }
+  virtual void test_params(const vector<int>& shape) {
+    EXPECT_TRUE(blob_);
+    blob_->Reshape(shape);
+    filler_.reset(new BilinearFiller<Dtype>(filler_param_));
+    filler_->Fill(blob_);
+    CHECK_EQ(blob_->num_axes(), 4);
+    const int outer_num = blob_->count(0, 2);
+    const int inner_num = blob_->count(2, 4);
+    const Dtype* data = blob_->cpu_data();
+    int f = ceil(blob_->shape(3) / 2.);
+    Dtype c = (blob_->shape(3) - 1) / (2. * f);
     for (int i = 0; i < outer_num; ++i) {
       for (int j = 0; j < inner_num; ++j) {
-        Dtype x = j % this->blob_->width();
-        Dtype y = (j / this->blob_->width()) % this->blob_->height();
+        Dtype x = j % blob_->shape(3);
+        Dtype y = (j / blob_->shape(3)) % blob_->shape(2);
         Dtype expected_value = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
         const Dtype actual_value = data[i * inner_num + j];
         EXPECT_NEAR(expected_value, actual_value, 0.01);
@@ -531,11 +535,21 @@ TYPED_TEST_CASE(BilinearFillerTest, TestDtypes);
 
 TYPED_TEST(BilinearFillerTest, TestFillOdd) {
   const int n = 7;
-  this->test_params(n);
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(n);
+  blob_shape.push_back(n);
+  this->test_params(blob_shape);
 }
 TYPED_TEST(BilinearFillerTest, TestFillEven) {
   const int n = 6;
-  this->test_params(n);
+  vector<int> blob_shape;
+  blob_shape.push_back(1000);
+  blob_shape.push_back(2);
+  blob_shape.push_back(n);
+  blob_shape.push_back(n);
+  this->test_params(blob_shape);
 }
 
 }  // namespace caffe

From dabbc91ecda54d8d4bb69e2457289d2e7216b136 Mon Sep 17 00:00:00 2001
From: Mikhail Antonenka <anmikh@users.noreply.github.com>
Date: Sat, 17 Mar 2018 18:26:40 +0300
Subject: [PATCH 2032/2053] Added Swish layer (#6002)

* added swish layer (cpu)

* swish layer: added tests

* swish layer: optimized backpropogation

* swish layer: added cuda implementation

* swish layer: added beta parameter

* swish layer: incorporated sigmoid layer

* swish layer: fix comment of last added parameter

* swish layer: added REGISTER_LAYER_CLASS
---
 include/caffe/layers/swish_layer.hpp | 96 ++++++++++++++++++++++++++++
 src/caffe/layers/swish_layer.cpp     | 68 ++++++++++++++++++++
 src/caffe/layers/swish_layer.cu      | 54 ++++++++++++++++
 src/caffe/proto/caffe.proto          | 12 +++-
 src/caffe/test/test_neuron_layer.cpp | 79 +++++++++++++++++++++++
 5 files changed, 308 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/swish_layer.hpp
 create mode 100644 src/caffe/layers/swish_layer.cpp
 create mode 100644 src/caffe/layers/swish_layer.cu

diff --git a/include/caffe/layers/swish_layer.hpp b/include/caffe/layers/swish_layer.hpp
new file mode 100644
index 00000000000..d538ff6de82
--- /dev/null
+++ b/include/caffe/layers/swish_layer.hpp
@@ -0,0 +1,96 @@
+#ifndef CAFFE_SWISH_LAYER_HPP_
+#define CAFFE_SWISH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Swish non-linearity @f$ y = x \sigma (\beta x) @f$.
+ *        A novel activation function that tends to work better than ReLU [1].
+ *
+ * [1] Prajit Ramachandran, Barret Zoph, Quoc V. Le. "Searching for
+ *     Activation Functions". arXiv preprint arXiv:1710.05941v2 (2017).
+ */
+template <typename Dtype>
+class SwishLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides SwishParameter swish_param,
+   *     with SwishLayer options:
+   *   - beta (\b optional, default 1).
+   *     the value @f$ \beta @f$ in the @f$ y = x \sigma (\beta x) @f$.
+   */
+  explicit SwishLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param),
+        sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+        sigmoid_input_(new Blob<Dtype>()),
+        sigmoid_output_(new Blob<Dtype>()) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Swish"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = x \sigma (\beta x)
+   *      @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y}(\beta y +
+   *              \sigma (\beta x)(1 - \beta y))
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// The internal SigmoidLayer
+  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+  /// sigmoid_input_ stores the input of the SigmoidLayer.
+  shared_ptr<Blob<Dtype> > sigmoid_input_;
+  /// sigmoid_output_ stores the output of the SigmoidLayer.
+  shared_ptr<Blob<Dtype> > sigmoid_output_;
+  /// bottom vector holder to call the underlying SigmoidLayer::Forward
+  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+  /// top vector holder to call the underlying SigmoidLayer::Forward
+  vector<Blob<Dtype>*> sigmoid_top_vec_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SWISH_LAYER_HPP_
diff --git a/src/caffe/layers/swish_layer.cpp b/src/caffe/layers/swish_layer.cpp
new file mode 100644
index 00000000000..28935679d00
--- /dev/null
+++ b/src/caffe/layers/swish_layer.cpp
@@ -0,0 +1,68 @@
+#include <cmath>
+#include <vector>
+
+#include "caffe/layers/swish_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SwishLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  sigmoid_bottom_vec_.clear();
+  sigmoid_bottom_vec_.push_back(sigmoid_input_.get());
+  sigmoid_top_vec_.clear();
+  sigmoid_top_vec_.push_back(sigmoid_output_.get());
+  sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
+}
+
+template <typename Dtype>
+void SwishLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::Reshape(bottom, top);
+  sigmoid_input_->ReshapeLike(*bottom[0]);
+  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
+}
+
+template <typename Dtype>
+void SwishLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* sigmoid_input_data = sigmoid_input_->mutable_cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  Dtype beta = this->layer_param_.swish_param().beta();
+  caffe_copy(count, bottom_data, sigmoid_input_data);
+  caffe_scal(count, beta, sigmoid_input_data);
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
+  caffe_mul(count, bottom_data, sigmoid_output_->cpu_data(), top_data);
+}
+
+template <typename Dtype>
+void SwishLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* top_data = top[0]->cpu_data();
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
+    Dtype beta = this->layer_param_.swish_param().beta();
+    for (int i = 0; i < count; ++i) {
+      const Dtype swish_x = top_data[i];
+      bottom_diff[i] = top_diff[i] * (beta * swish_x + sigmoid_output_data[i]
+          * (1. - beta * swish_x));
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(SwishLayer);
+#endif
+
+INSTANTIATE_CLASS(SwishLayer);
+REGISTER_LAYER_CLASS(Swish);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/swish_layer.cu b/src/caffe/layers/swish_layer.cu
new file mode 100644
index 00000000000..c4fef53bf3a
--- /dev/null
+++ b/src/caffe/layers/swish_layer.cu
@@ -0,0 +1,54 @@
+#include <cmath>
+#include <vector>
+
+#include "caffe/layers/swish_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SwishLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* sigmoid_input_data = sigmoid_input_->mutable_gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  Dtype beta = this->layer_param_.swish_param().beta();
+  caffe_copy(count, bottom_data, sigmoid_input_data);
+  caffe_gpu_scal(count, beta, sigmoid_input_data);
+  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
+  caffe_gpu_mul(count, bottom_data, sigmoid_output_->gpu_data(), top_data);
+}
+
+template <typename Dtype>
+__global__ void SwishBackward(const int n, const Dtype* in_diff,
+    const Dtype* out_data, const Dtype* sigmoid_output_data, Dtype* out_diff,
+    const Dtype beta) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const Dtype swish_x = out_data[index];
+    out_diff[index] = in_diff[index] * (beta * swish_x
+        + sigmoid_output_data[index] * (1 - beta * swish_x));
+  }
+}
+
+template <typename Dtype>
+void SwishLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* top_data = top[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
+    Dtype beta = this->layer_param_.swish_param().beta();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    SwishBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, top_data, sigmoid_output_data, bottom_diff, beta);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(SwishLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 22764abc33f..b9bb3f4dffe 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -322,7 +322,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
+// LayerParameter next available layer-specific ID: 148 (last added: swish_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -415,6 +415,7 @@ message LayerParameter {
   optional SoftmaxParameter softmax_param = 125;
   optional SPPParameter spp_param = 132;
   optional SliceParameter slice_param = 126;
+  optional SwishParameter swish_param = 147;
   optional TanHParameter tanh_param = 127;
   optional ThresholdParameter threshold_param = 128;
   optional TileParameter tile_param = 138;
@@ -1156,6 +1157,15 @@ message SoftmaxParameter {
   optional int32 axis = 2 [default = 1];
 }
 
+// Message that stores parameters used by SwishLayer
+message SwishParameter {
+  // Beta parameter for the Swish activation function
+  // Described in:
+  // Prajit Ramachandran, Barret Zoph, Quoc V. Le. (2017). Searching for
+  // Activation Functions. https://arxiv.org/abs/1710.05941v2
+  optional float beta = 1 [default = 1];
+}
+
 message TanHParameter {
   enum Engine {
     DEFAULT = 0;
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 180871a29ee..83d80fcd895 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -19,6 +19,7 @@
 #include "caffe/layers/prelu_layer.hpp"
 #include "caffe/layers/relu_layer.hpp"
 #include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/swish_layer.hpp"
 #include "caffe/layers/tanh_layer.hpp"
 #include "caffe/layers/threshold_layer.hpp"
 
@@ -344,6 +345,84 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(NeuronLayerTest, TestSwish) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SwishLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] / (1. + exp(-bottom_data[i])));
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestSwishWithBeta) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "swish_param { beta: 1.5 }", &layer_param));
+  SwishLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] / (1. + exp(-1.5 *
+        bottom_data[i])));
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestSwishAsLinear) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "swish_param { beta: 0.0 }", &layer_param));
+  SwishLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] / 2.0);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestSwishGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SwishLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestSwishWithBetaGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "swish_param { beta: 1.5 }", &layer_param));
+  SwishLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestSwishAsLinearGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "swish_param { beta: 0.0 }", &layer_param));
+  SwishLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(NeuronLayerTest, TestTanH) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From d09f157475a51b74ac5ba29764e7b9bde8991097 Mon Sep 17 00:00:00 2001
From: twmht <qrnnis2623891@gmail.com>
Date: Fri, 23 Mar 2018 22:21:06 +0800
Subject: [PATCH 2033/2053] check embed index in debug mode

---
 src/caffe/layers/embed_layer.cu | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
index 6324a3a8937..3cf39fd9983 100644
--- a/src/caffe/layers/embed_layer.cu
+++ b/src/caffe/layers/embed_layer.cu
@@ -15,6 +15,11 @@ __global__ void EmbedForward(const int nthreads, const Dtype* bottom_data,
     const int n = top_index / N;
     const int d = top_index % N;
     const int index = static_cast<int>(bottom_data[n]);
+    #ifdef DEBUG
+        assert(index >= 0);
+        assert(index < K);
+        assert(static_cast<Dtype>(index) == bottom_data[n]);
+    #endif
     const int weight_index = index * N + d;
     top_data[top_index] = weight[weight_index];
   }

From 08b3308e163e2af67a6a391befc806a0b19c2e17 Mon Sep 17 00:00:00 2001
From: Seyyed Hossein Hasanpour <coderx7@gmail.com>
Date: Mon, 9 Apr 2018 10:06:49 +0430
Subject: [PATCH 2034/2053] Minor correction concerning compilation
 compatibility with CUDA 9.0

Since CUDA 9.0 doesn't support sm_20 and sm_21 anymore. This PR allows Caffe to compile with CUDA 9.0 and newer versions successfully.
This addresses the discussion concerning [#6237](https://github.com/BVLC/caffe/pull/6237) in [master (#6237)](https://github.com/BVLC/caffe/commit/cb150eca6d593ddb85e53acd05b8dcf709ea8337#diff-2004a3d3e6b4ed2e2812bb0b4b998f18)
---
 cmake/Cuda.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 9325674a6a0..9ced9f9d591 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -109,8 +109,8 @@ function(caffe_select_nvcc_arch_flags out_variable)
   set(__nvcc_flags "")
   set(__nvcc_archs_readable "")
 
-  string(COMPARE GREATER_EQUAL "${CUDA_VERSION}" "9.1" iscudanewerthan91)
-  if(iscudanewerthan91)
+  string(COMPARE GREATER_EQUAL "${CUDA_VERSION}" "9.0" iscudanewerthan90)
+  if(iscudanewerthan90)
     string(REPLACE "21(20)" "" __cuda_arch_bin "${__cuda_arch_bin}")
     string(REPLACE "20" "" __cuda_arch_bin "${__cuda_arch_bin}")
   endif()

From c46fc00fcb5434b2ecaeac792ed243f4144c81d8 Mon Sep 17 00:00:00 2001
From: Viktor Richter <vrichter@posteo.de>
Date: Mon, 9 Apr 2018 16:51:04 +0200
Subject: [PATCH 2035/2053] Fix cmake < v3.7 compatibility in Cuda.cmake
 (#6338)

Fix for compatibility with CMake <3.7 (related conversation under 37e4289)

* Fix cmake < v3.7 compatibility in Cuda.cmake

* Fix version test variable naming in Cuda.cmake
---
 cmake/Cuda.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index 9ced9f9d591..e03feabffcb 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -109,8 +109,8 @@ function(caffe_select_nvcc_arch_flags out_variable)
   set(__nvcc_flags "")
   set(__nvcc_archs_readable "")
 
-  string(COMPARE GREATER_EQUAL "${CUDA_VERSION}" "9.0" iscudanewerthan90)
-  if(iscudanewerthan90)
+  string(COMPARE LESS "${CUDA_VERSION}" "9.0" iscudaolderthan90)
+  if(NOT iscudaolderthan90)
     string(REPLACE "21(20)" "" __cuda_arch_bin "${__cuda_arch_bin}")
     string(REPLACE "20" "" __cuda_arch_bin "${__cuda_arch_bin}")
   endif()

From 106bfcfde6ae5627bdffecde83023212cab23c18 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Przemys=C5=82aw=20Dolata?= <snowball91b@gmail.com>
Date: Wed, 11 Apr 2018 00:20:12 +0200
Subject: [PATCH 2036/2053] Revised guidelines for GitHub issues (#6327)

revised guidelines for GitHub issues and caffe-users posts
---
 .github/ISSUE_TEMPLATE.md | 37 ++++++++++++++++++--------
 CONTRIBUTING.md           | 56 ++++++++++++++++++++++++++++++++++-----
 2 files changed, 75 insertions(+), 18 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
index d78a3dc3455..c981f62f7a1 100644
--- a/.github/ISSUE_TEMPLATE.md
+++ b/.github/ISSUE_TEMPLATE.md
@@ -1,19 +1,34 @@
-Please use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) for usage, installation, or modeling questions, or other requests for help.
-_Do not post such requests to Issues._ Doing so interferes with the development of Caffe.
+## Important - read before submitting
 
-Please read the [guidelines for contributing](https://github.com/BVLC/caffe/blob/master/CONTRIBUTING.md) before submitting this issue.
+*Please read the [guidelines for contributing](https://github.com/BVLC/caffe/blob/master/CONTRIBUTING.md) before submitting this issue!*
+
+*Please do not post installation, build, usage, or modeling questions, or other requests for help to Issues.*
+Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead.
+This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
 
 ### Issue summary
 
 
 ### Steps to reproduce
 
-If you are having difficulty building Caffe or training a model, please ask the caffe-users mailing list. If you are reporting a build error that seems to be due to a bug in Caffe, please attach your build configuration (either Makefile.config or CMakeCache.txt) and the output of the make (or cmake) command.
 
-### Your system configuration
-Operating system:
-Compiler:
-CUDA version (if applicable):
-CUDNN version (if applicable):
-BLAS:
-Python or MATLAB version (for pycaffe and matcaffe respectively):
+### Tried solutions
+
+
+### System configuration
+
+* Operating system: 
+* Compiler: 
+* CUDA version (if applicable): 
+* CUDNN version (if applicable): 
+* BLAS: 
+* Python version (if using pycaffe): 
+* MATLAB version (if using matcaffe): 
+
+### Issue checklist
+
+- [ ] read the guidelines and removed the first paragraph
+- [ ] written a short summary and detailed steps to reproduce
+- [ ] explained how solutions to related problems failed (tick if found none)
+- [ ] filled system configuration
+- [ ] attached relevant logs/config files (tick if not applicable)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 8cd5e56ca49..45f7e186e4f 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,21 +1,63 @@
 # Contributing
 
+Below you will find a collection of guidelines for submitting issues as well as contributing code to the Caffe repository.
+Please read those before starting an issue or a pull request.
+
 ## Issues
 
 Specific Caffe design and development issues, bugs, and feature requests are maintained by GitHub Issues.
 
-_Please do not post usage, installation, or modeling questions, or other requests for help to Issues._
-Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead. This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
-
-When reporting a bug, it's most helpful to provide the following information, where applicable:
+*Please do not post installation, build, usage, or modeling questions, or other requests for help to Issues.*
+Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead.
+This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
+See the chapter [caffe-users](#caffe-users) below for guidance on posting to the users list.
 
-* What steps reproduce the bug?
-* Can you reproduce the bug using the latest [master](https://github.com/BVLC/caffe/tree/master), compiled with the `DEBUG` make option?
-* What hardware and operating system/distribution are you running?
+When reporting an issue, it's most helpful to provide the following information, where applicable:
+* How does the problem look like and what steps reproduce it?
+* Can you reproduce it using the latest [master](https://github.com/BVLC/caffe/tree/master), compiled with the `DEBUG` make option?
+* What hardware and software are you running? In particular:
+	* GPU make and model, if relevant,
+	* operating system/distribution,
+	* compiler; please also post which version (for example, with GCC run `gcc --version` to check),
+	* CUDA version, if applicable (run `nvcc --version` to check),
+	* cuDNN version, if applicable (version number is stored in `cudnn.h`, look for lines containing `CUDNN_MAJOR`, `CUDNN_MINOR` and `CUDNN_PATCHLEVEL`),
+	* BLAS library,
+	* Python version, if relevant,
+	* MATLAB version, if relevant.
+* **What have you already tried** to solve the problem? How did it fail? Are there any other issues related to yours?
+* If this is not a build-related issue, does your installation pass `make runtest`?
 * If the bug is a crash, provide the backtrace (usually printed by Caffe; always obtainable with `gdb`).
+* If you are reporting a build error that seems to be due to a bug in Caffe, please attach your build configuration (either Makefile.config or CMakeCache.txt) and the output of the make (or cmake) command.
+
+If only a small portion of the code/log is relevant to your issue, you may paste it directly into the post, preferably using Markdown syntax for code block: triple backtick ( \`\`\` ) to open/close a block.
+In other cases (multiple files, or long files), please **attach** them to the post - this greatly improves readability.
+
+If the problem arises during a complex operation (e.g. large script using pycaffe, long network prototxt), please reduce the example to the minimal size that still causes the error.
+Also, minimize influence of external modules, data etc. - this way it will be easier for others to understand and reproduce your issue, and eventually help you.
+Sometimes you will find the root cause yourself in the process.
 
 Try to give your issue a title that is succinct and specific. The devs will rename issues as needed to keep track of them.
 
+## Caffe-users
+
+Before you post to the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users), make sure you look for existing solutions.
+The Caffe community has encountered and found solutions to countless problems - benefit from the collective experience.
+Recommended places to look:
+* the [users list](https://groups.google.com/forum/#!forum/caffe-users) itself,
+* [`caffe`](https://stackoverflow.com/questions/tagged/caffe) tag on StackOverflow,
+* [GitHub issues](https://github.com/BVLC/caffe/issues) tracker (some problems have been answered there),
+* the public [wiki](https://github.com/BVLC/caffe/wiki),
+* the official [documentation](http://caffe.berkeleyvision.org/).
+
+Found a post/issue with your exact problem, but with no answer?
+Don't just leave a "me too" message - provide the details of your case.
+Problems with more available information are easier to solve and attract good attention.
+
+When posting to the list, make sure you provide as much relevant information as possible - recommendations for an issue report (see above) are a good starting point.  
+*Please make it very clear which version of Caffe you are using, especially if it is a fork not maintained by BVLC.*
+
+Formatting recommendations hold: paste short logs/code fragments into the post (use fixed-width text for them), **attach** long logs or multiple files.
+
 ## Pull Requests
 
 Caffe welcomes all contributions.

From 356a6cca958a9108649a3cd824d316b679fcefb2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Przemys=C5=82aw=20Dolata?= <snowball91b@gmail.com>
Date: Wed, 11 Apr 2018 15:32:25 +0200
Subject: [PATCH 2037/2053] tweaked Gaussian filler tests for less false fails

---
 src/caffe/test/test_filler.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index 1e6b5c21d1d..34f7007d9c7 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -257,16 +257,16 @@ TYPED_TEST(GaussianFillerTest, TestFill) {
 }
 
 TYPED_TEST(GaussianFillerTest, TestFill1D) {
-  vector<int> blob_shape(1, 25);
-  const TypeParam tolerance = TypeParam(5);
+  vector<int> blob_shape(1, 125);
+  const TypeParam tolerance = TypeParam(3);
   this->test_params(blob_shape, tolerance);
 }
 
 TYPED_TEST(GaussianFillerTest, TestFill2D) {
   vector<int> blob_shape;
   blob_shape.push_back(8);
-  blob_shape.push_back(3);
-  const TypeParam tolerance = TypeParam(5);
+  blob_shape.push_back(15);
+  const TypeParam tolerance = TypeParam(3);
   this->test_params(blob_shape, tolerance);
 }
 

From 3318a466309a82d3d63f3b33f3663824da3f1ceb Mon Sep 17 00:00:00 2001
From: Jerry Zhao <jerryz123@berkeley.edu>
Date: Mon, 16 Apr 2018 02:26:08 -0700
Subject: [PATCH 2038/2053] Cherry-picked USE_HDF5 from Android branch

---
 CMakeLists.txt                            |  3 +++
 Makefile                                  | 12 +++++++++++-
 Makefile.config.example                   |  2 ++
 cmake/ConfigGen.cmake                     | 12 ++++++++++++
 cmake/Dependencies.cmake                  |  8 ++++++++
 cmake/Summary.cmake                       |  2 ++
 include/caffe/util/hdf5.hpp               |  2 ++
 src/caffe/layers/hdf5_data_layer.cpp      |  2 ++
 src/caffe/layers/hdf5_data_layer.cu       |  2 ++
 src/caffe/layers/hdf5_output_layer.cpp    |  2 ++
 src/caffe/layers/hdf5_output_layer.cu     |  2 ++
 src/caffe/net.cpp                         | 13 +++++++++++++
 src/caffe/solvers/sgd_solver.cpp          | 12 ++++++++++++
 src/caffe/test/test_hdf5_output_layer.cpp |  2 ++
 src/caffe/test/test_hdf5data_layer.cpp    |  2 ++
 src/caffe/util/hdf5.cpp                   |  2 ++
 16 files changed, 79 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 08f56a33a59..27d172f900b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,6 +42,9 @@ caffe_option(USE_LMDB "Build with lmdb" ON)
 caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF)
 caffe_option(USE_OPENMP "Link with OpenMP (when your BLAS wants OpenMP and you get linker errors)" OFF)
 
+# This code is taken from https://github.com/sh1r0/caffe-android-lib
+caffe_option(USE_HDF5 "Build with hdf5" ON)
+
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
 
diff --git a/Makefile b/Makefile
index c85c695acff..29ea8a69a61 100644
--- a/Makefile
+++ b/Makefile
@@ -178,11 +178,13 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARIES := cudart cublas curand
 endif
 
-LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
+LIBRARIES += glog gflags protobuf boost_system boost_filesystem m
 
 # handle IO dependencies
 USE_LEVELDB ?= 1
 USE_LMDB ?= 1
+# This code is taken from https://github.com/sh1r0/caffe-android-lib
+USE_HDF5 ?= 1
 USE_OPENCV ?= 1
 
 ifeq ($(USE_LEVELDB), 1)
@@ -191,6 +193,10 @@ endif
 ifeq ($(USE_LMDB), 1)
 	LIBRARIES += lmdb
 endif
+# This code is taken from https://github.com/sh1r0/caffe-android-lib
+ifeq ($(USE_HDF5), 1)
+	LIBRARIES += hdf5_hl hdf5
+endif
 ifeq ($(USE_OPENCV), 1)
 	LIBRARIES += opencv_core opencv_highgui opencv_imgproc
 
@@ -347,6 +353,10 @@ ifeq ($(ALLOW_LMDB_NOLOCK), 1)
 	COMMON_FLAGS += -DALLOW_LMDB_NOLOCK
 endif
 endif
+# This code is taken from https://github.com/sh1r0/caffe-android-lib
+ifeq ($(USE_HDF5), 1)
+	COMMON_FLAGS += -DUSE_HDF5
+endif
 
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
diff --git a/Makefile.config.example b/Makefile.config.example
index 79905935f15..24ca632783a 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -11,6 +11,8 @@
 # USE_OPENCV := 0
 # USE_LEVELDB := 0
 # USE_LMDB := 0
+# This code is taken from https://github.com/sh1r0/caffe-android-lib
+# USE_HDF5 := 0
 
 # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
 #	You should not set this flag if you will be reading LMDBs with any
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index 09bb09b4ff2..69889c243b2 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -24,6 +24,18 @@ function(caffe_generate_export_configs)
     set(HAVE_CUDA FALSE)
   endif()
 
+  set(HDF5_IMPORTED OFF)
+  foreach(_lib ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
+    if(TARGET ${_lib})
+      set(HDF5_IMPORTED ON)
+    endif()
+  endforeach()
+
+  # This code is taken from https://github.com/sh1r0/caffe-android-lib
+  if(USE_HDF5)
+    list(APPEND Caffe_DEFINITIONS -DUSE_HDF5)
+  endif()
+
   if(NOT HAVE_CUDNN)
     set(HAVE_CUDNN FALSE)
   endif()
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index c48255c89f2..ca2e3ad9e5e 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -47,6 +47,14 @@ find_package(HDF5 COMPONENTS HL REQUIRED)
 list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${HDF5_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS PUBLIC ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
 
+# This code is taken from https://github.com/sh1r0/caffe-android-lib
+if(USE_HDF5)
+  find_package(HDF5 COMPONENTS HL REQUIRED)
+  include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES} ${HDF5_HL_LIBRARIES})
+  add_definitions(-DUSE_HDF5)
+endif()
+
 # ---[ LMDB
 if(USE_LMDB)
   find_package(LMDB REQUIRED)
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index ed8c25268db..40b8c2f2966 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -119,6 +119,8 @@ function(caffe_print_configuration_summary)
   caffe_status("  USE_LMDB          :   ${USE_LMDB}")
   caffe_status("  USE_NCCL          :   ${USE_NCCL}")
   caffe_status("  ALLOW_LMDB_NOLOCK :   ${ALLOW_LMDB_NOLOCK}")
+  # This code is taken from https://github.com/sh1r0/caffe-android-lib
+  caffe_status("  USE_HDF5          :   ${USE_HDF5}")
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
diff --git a/include/caffe/util/hdf5.hpp b/include/caffe/util/hdf5.hpp
index 71549c1cc02..dbd8bb6c5e4 100644
--- a/include/caffe/util/hdf5.hpp
+++ b/include/caffe/util/hdf5.hpp
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 #ifndef CAFFE_UTIL_HDF5_H_
 #define CAFFE_UTIL_HDF5_H_
 
@@ -37,3 +38,4 @@ string hdf5_get_name_by_idx(hid_t loc_id, int idx);
 }  // namespace caffe
 
 #endif   // CAFFE_UTIL_HDF5_H_
+#endif   // USE_HDF5
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 00716a92b15..7668854cc1f 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 /*
 TODO:
 - load file in a separate thread ("prefetch")
@@ -184,3 +185,4 @@ INSTANTIATE_CLASS(HDF5DataLayer);
 REGISTER_LAYER_CLASS(HDF5Data);
 
 }  // namespace caffe
+#endif  // USE_HDF5
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 33eebd41dfc..70cd9f32f85 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 /*
 TODO:
 - only load parts of the file, in accordance with a prototxt param "max_mem"
@@ -34,3 +35,4 @@ void HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 INSTANTIATE_LAYER_GPU_FUNCS(HDF5DataLayer);
 
 }  // namespace caffe
+#endif  // USE_HDF5
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index f8f1edcd18e..28c453a20fd 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 #include <vector>
 
 #include "hdf5.h"
@@ -72,3 +73,4 @@ INSTANTIATE_CLASS(HDF5OutputLayer);
 REGISTER_LAYER_CLASS(HDF5Output);
 
 }  // namespace caffe
+#endif  // USE_HDF5
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index c1685cd34a7..891aea03862 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 #include <vector>
 
 #include "hdf5.h"
@@ -37,3 +38,4 @@ void HDF5OutputLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 INSTANTIATE_LAYER_GPU_FUNCS(HDF5OutputLayer);
 
 }  // namespace caffe
+#endif  // USE_HDF5
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 353c2f95b9e..73adcc6dba1 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -5,7 +5,9 @@
 #include <utility>
 #include <vector>
 
+#ifdef USE_HDF5
 #include "hdf5.h"
+#endif  // USE_HDF5
 
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
@@ -786,6 +788,7 @@ void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
+#ifdef USE_HDF5
   hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
                            H5P_DEFAULT);
   CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;
@@ -832,6 +835,10 @@ void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
   }
   H5Gclose(data_hid);
   H5Fclose(file_hid);
+#else
+  LOG(FATAL) << "CopyTrainedLayersFromHDF5 requires hdf5;"
+             << " compile with USE_HDF5.";
+#endif  // USE_HDF5
 }
 
 template <typename Dtype>
@@ -848,6 +855,8 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
 
 template <typename Dtype>
 void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
+// This code is taken from https://github.com/sh1r0/caffe-android-lib
+#ifdef USE_HDF5
   hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
       H5P_DEFAULT);
   CHECK_GE(file_hid, 0)
@@ -901,6 +910,10 @@ void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
     H5Gclose(diff_hid);
   }
   H5Fclose(file_hid);
+// This code is taken from https://github.com/sh1r0/caffe-android-lib
+#else
+  LOG(FATAL) << "ToHDF5 requires hdf5; compile with USE_HDF5.";
+#endif  // USE_HDF5
 }
 
 template <typename Dtype>
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index 1d52beb0636..b11a8f41f12 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -285,6 +285,8 @@ void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
 template <typename Dtype>
 void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
     const string& model_filename) {
+// This code is taken from https://github.com/sh1r0/caffe-android-lib
+#ifdef USE_HDF5
   string snapshot_filename =
       Solver<Dtype>::SnapshotFilename(".solverstate.h5");
   LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename;
@@ -306,6 +308,11 @@ void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
   }
   H5Gclose(history_hid);
   H5Fclose(file_hid);
+// This code is taken from https://github.com/sh1r0/caffe-android-lib
+#else
+  LOG(FATAL) << "SnapshotSolverStateToHDF5 requires hdf5;"
+             << " compile with USE_HDF5.";
+#endif  // USE_HDF5
 }
 
 template <typename Dtype>
@@ -330,6 +337,7 @@ void SGDSolver<Dtype>::RestoreSolverStateFromBinaryProto(
 
 template <typename Dtype>
 void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
+#ifdef USE_HDF5
   hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT);
   CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file;
   this->iter_ = hdf5_load_int(file_hid, "iter");
@@ -351,6 +359,10 @@ void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
   }
   H5Gclose(history_hid);
   H5Fclose(file_hid);
+#else
+  LOG(FATAL) << "RestoreSolverStateFromHDF5 requires hdf5;"
+             << " compile with USE_HDF5.";
+#endif  // USE_HDF5
 }
 
 INSTANTIATE_CLASS(SGDSolver);
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index f94dd57e7de..11d52310cad 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 #include <string>
 #include <vector>
 
@@ -120,3 +121,4 @@ TYPED_TEST(HDF5OutputLayerTest, TestForward) {
 }
 
 }  // namespace caffe
+#endif  // USE_HDF5
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index 3977c4866c7..0e5c398f966 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 #include <string>
 #include <vector>
 
@@ -163,3 +164,4 @@ TYPED_TEST(HDF5DataLayerTest, TestSkip) {
 }
 
 }  // namespace caffe
+#endif  // USE_HDF5
diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
index ed73742937f..cefd853dff4 100644
--- a/src/caffe/util/hdf5.cpp
+++ b/src/caffe/util/hdf5.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_HDF5
 #include "caffe/util/hdf5.hpp"
 
 #include <string>
@@ -207,3 +208,4 @@ string hdf5_get_name_by_idx(hid_t loc_id, int idx) {
 }
 
 }  // namespace caffe
+#endif  // USE_HDF5

From 0536720f41ff6fd43b98a0eb9eb7cd8f0ece5d1e Mon Sep 17 00:00:00 2001
From: Kuang Fangjun <csukuangfj@gmail.com>
Date: Tue, 8 May 2018 10:36:44 +0800
Subject: [PATCH 2039/2053] fix issue #6387.

---
 include/caffe/syncedmem.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 317ce29a257..8d650a34a8e 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -66,8 +66,8 @@ class SyncedMemory {
   void* mutable_cpu_data();
   void* mutable_gpu_data();
   enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };
-  SyncedHead head() { return head_; }
-  size_t size() { return size_; }
+  SyncedHead head() const { return head_; }
+  size_t size() const { return size_; }
 
 #ifndef CPU_ONLY
   void async_gpu_push(const cudaStream_t& stream);

From 6d912a32bedd4daf6e0f0c3a2622cf4d382ed759 Mon Sep 17 00:00:00 2001
From: Kuang Fangjun <csukuangfj@gmail.com>
Date: Tue, 8 May 2018 10:46:54 +0800
Subject: [PATCH 2040/2053] fix issue #6389

---
 src/caffe/test/test_syncedmem.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index 16dfb58230f..2ca9ca2f998 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -80,7 +80,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   char* recovered_value = new char[10];
   caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((static_cast<char*>(recovered_value))[i], 1);
+    EXPECT_EQ(recovered_value[i], 1);
   }
   // do another round
   cpu_data = mem.mutable_cpu_data();
@@ -94,7 +94,7 @@ TEST_F(SyncedMemoryTest, TestGPURead) {
   // check if values are the same
   caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((static_cast<char*>(recovered_value))[i], 2);
+    EXPECT_EQ(recovered_value[i], 2);
   }
   delete[] recovered_value;
 }

From cc1c8fb465fbf48e3048659ca5aa407561df7687 Mon Sep 17 00:00:00 2001
From: Mitar <mitar.git@tnode.com>
Date: Sat, 28 Oct 2017 02:35:17 -0700
Subject: [PATCH 2041/2053] [pycaffe] expose solver update to do manual solving

a sketch of `solver.step()` done out manually:

1. `solver.net.forward()`
2. `solver.net.backward()`
3. `solver.net.apply_update()`
4. `solver.net.clear_param_diffs()`
---
 include/caffe/sgd_solvers.hpp | 3 ++-
 include/caffe/solver.hpp      | 3 ++-
 python/caffe/_caffe.cpp       | 1 +
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp
index 1fc52d87137..f1819bb62dc 100644
--- a/include/caffe/sgd_solvers.hpp
+++ b/include/caffe/sgd_solvers.hpp
@@ -23,10 +23,11 @@ class SGDSolver : public Solver<Dtype> {
 
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
+  virtual void ApplyUpdate();
+
  protected:
   void PreSolve();
   Dtype GetLearningRate();
-  virtual void ApplyUpdate();
   virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index a28d8cb897e..75560f9fd08 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -94,9 +94,10 @@ class Solver {
    */
   virtual inline const char* type() const { return ""; }
 
- protected:
   // Make and apply the update value for the current iteration.
   virtual void ApplyUpdate() = 0;
+
+ protected:
   string SnapshotFilename(const string extension);
   string SnapshotToBinaryProto();
   string SnapshotToHDF5();
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 72659a4f44e..eed16c29c84 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -507,6 +507,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("restore", &Solver<Dtype>::Restore)
     .def("snapshot", &Solver<Dtype>::Snapshot)
     .def("share_weights", &share_weights)
+    .def("apply_update", &Solver<Dtype>::ApplyUpdate)
     .add_property("param", bp::make_function(&Solver<Dtype>::param,
               bp::return_value_policy<bp::copy_const_reference>()));
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);

From c74913d4b5b6c120e40bfeaab43fde45acc22c24 Mon Sep 17 00:00:00 2001
From: Mitar <mitar.git@tnode.com>
Date: Sat, 28 Oct 2017 02:52:35 -0700
Subject: [PATCH 2042/2053] increment iteration during update, not step

with update exposed it is important to increment the iteration when an
update is made, whether by step or update alone. more fundementally,
it's the update that defines an iterationa, so this is a natural place
for the increment.
---
 src/caffe/solver.cpp             | 4 ----
 src/caffe/solvers/sgd_solver.cpp | 4 ++++
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index d229acff485..bf27beeed41 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -266,10 +266,6 @@ void Solver<Dtype>::Step(int iters) {
     }
     ApplyUpdate();
 
-    // Increment the internal iter_ counter -- its value should always indicate
-    // the number of times the weights have been updated.
-    ++iter_;
-
     SolverAction::Enum request = GetRequestedAction();
 
     // Save a snapshot if needed.
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
index 1d52beb0636..a56a2d0bf51 100644
--- a/src/caffe/solvers/sgd_solver.cpp
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -120,6 +120,10 @@ void SGDSolver<Dtype>::ApplyUpdate() {
     ComputeUpdateValue(param_id, rate);
   }
   this->net_->Update();
+
+  // Increment the internal iter_ counter -- its value should always indicate
+  // the number of times the weights have been updated.
+  ++this->iter_;
 }
 
 template <typename Dtype>

From cfcf74fecf2e2b9d35974c5b0a0554921ad66984 Mon Sep 17 00:00:00 2001
From: Mitar <mitar.git@tnode.com>
Date: Sun, 10 Dec 2017 03:11:26 -0800
Subject: [PATCH 2043/2053] [pycaffe] expose mutable solver parameter, base lr,
 and effective lr

`solver.lr` is the effective learning rate in use while `solver.base_lr`
is the configured learning rate at initialization. the solver parameter
is now editable for setting fields that are in use throughout the
lifetime of the solver, such as the maximum iteration.
---
 include/caffe/sgd_solvers.hpp |  2 +-
 python/caffe/_caffe.cpp       | 19 +++++++++++--------
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp
index f1819bb62dc..925ff78331e 100644
--- a/include/caffe/sgd_solvers.hpp
+++ b/include/caffe/sgd_solvers.hpp
@@ -24,10 +24,10 @@ class SGDSolver : public Solver<Dtype> {
   const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
 
   virtual void ApplyUpdate();
+  Dtype GetLearningRate();
 
  protected:
   void PreSolve();
-  Dtype GetLearningRate();
   virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index eed16c29c84..9e7f61402c4 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -490,7 +490,9 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<SolverParameter>("SolverParameter", bp::no_init)
     .add_property("max_iter", &SolverParameter::max_iter)
     .add_property("display", &SolverParameter::display)
-    .add_property("layer_wise_reduce", &SolverParameter::layer_wise_reduce);
+    .add_property("layer_wise_reduce", &SolverParameter::layer_wise_reduce)
+    .add_property("base_lr", &SolverParameter::base_lr,
+           &SolverParameter::set_base_lr);
   bp::class_<LayerParameter>("LayerParameter", bp::no_init);
 
   bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
@@ -509,25 +511,26 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("share_weights", &share_weights)
     .def("apply_update", &Solver<Dtype>::ApplyUpdate)
     .add_property("param", bp::make_function(&Solver<Dtype>::param,
-              bp::return_value_policy<bp::copy_const_reference>()));
+              bp::return_internal_reference<>()));
   BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
-        "SGDSolver", bp::init<string>());
-  bp::class_<NesterovSolver<Dtype>, bp::bases<Solver<Dtype> >,
+        "SGDSolver", bp::init<string>())
+        .add_property("lr", &SGDSolver<Dtype>::GetLearningRate);
+  bp::class_<NesterovSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
     shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
         "NesterovSolver", bp::init<string>());
-  bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
+  bp::class_<AdaGradSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
     shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
         "AdaGradSolver", bp::init<string>());
-  bp::class_<RMSPropSolver<Dtype>, bp::bases<Solver<Dtype> >,
+  bp::class_<RMSPropSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
     shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
         "RMSPropSolver", bp::init<string>());
-  bp::class_<AdaDeltaSolver<Dtype>, bp::bases<Solver<Dtype> >,
+  bp::class_<AdaDeltaSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
     shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
         "AdaDeltaSolver", bp::init<string>());
-  bp::class_<AdamSolver<Dtype>, bp::bases<Solver<Dtype> >,
+  bp::class_<AdamSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
     shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>(
         "AdamSolver", bp::init<string>());
 

From 1bdcb74ea0d0acc7beb729ad7f01b0e5e44528a5 Mon Sep 17 00:00:00 2001
From: Valentin Tolmer <valentin.tolmer@gmail.com>
Date: Tue, 21 Jun 2016 17:12:57 -0700
Subject: [PATCH 2044/2053] [pycaffe] test solver update

---
 python/caffe/test/test_solver.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index f618fded8cd..50c9d5412d7 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -38,6 +38,17 @@ def test_solve(self):
         self.solver.solve()
         self.assertEqual(self.solver.iter, 100)
 
+    def test_apply_update(self):
+        net = self.solver.net
+        data = net.layers[1].blobs[0].data[...]
+        # Reset the weights of that layer to 0
+        data[...] = 0
+        net.layers[1].blobs[0].diff[...] = 1
+        # Apply the update, the initial learning rate should be 0.01
+        self.solver.apply_update()
+        # Check that the new weights are -0.01, with a precision of 1e-7
+        self.assertTrue((data - -0.01 * np.ones(data.shape)).max() < 1e-7)
+
     def test_net_memory(self):
         """Check that nets survive after the solver is destroyed."""
 

From 72e953ba151642850ef8ac4c4e7bf4181660be51 Mon Sep 17 00:00:00 2001
From: Yuda Liu <liu_yuda@163.com>
Date: Sun, 8 Jul 2018 15:46:43 +0800
Subject: [PATCH 2045/2053] Update inner_product_layer.cpp

---
 src/caffe/layers/inner_product_layer.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index e65349f0055..57fdbe1fac2 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -42,7 +42,7 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.inner_product_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, intiialize and fill the bias term
+    // If necessary, initialize and fill the bias term
     if (bias_term_) {
       vector<int> bias_shape(1, N_);
       this->blobs_[1].reset(new Blob<Dtype>(bias_shape));

From f019d0dfe86f49d1140961f8c7dec22130c83154 Mon Sep 17 00:00:00 2001
From: Kuang Fangjun <csukuangfj@gmail.com>
Date: Thu, 12 Jul 2018 16:36:06 +0800
Subject: [PATCH 2046/2053] fix typos and some minor fixes.

---
 cmake/Modules/FindMKL.cmake         | 2 +-
 include/caffe/net.hpp               | 6 +++---
 include/caffe/solver.hpp            | 4 ++--
 include/caffe/util/signal_handler.h | 2 +-
 python/caffe/_caffe.cpp             | 2 +-
 src/caffe/layers/pooling_layer.cpp  | 2 +-
 src/caffe/net.cpp                   | 8 ++++----
 src/caffe/proto/caffe.proto         | 6 +++---
 src/caffe/solver.cpp                | 6 +++---
 src/caffe/util/signal_handler.cpp   | 2 +-
 10 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index 5ab93b2d6b6..ef0c3bf1c64 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -9,7 +9,7 @@
 # This module defines the following variables:
 #
 #   MKL_FOUND            : True mkl is found
-#   MKL_INCLUDE_DIR      : unclude directory
+#   MKL_INCLUDE_DIR      : include directory
 #   MKL_LIBRARIES        : the libraries to link against.
 
 
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index d3c9306e9cf..143d5d28883 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -111,9 +111,9 @@ class Net {
    *        another Net.
    */
   void CopyTrainedLayersFrom(const NetParameter& param);
-  void CopyTrainedLayersFrom(const string trained_filename);
-  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
-  void CopyTrainedLayersFromHDF5(const string trained_filename);
+  void CopyTrainedLayersFrom(const string& trained_filename);
+  void CopyTrainedLayersFromBinaryProto(const string& trained_filename);
+  void CopyTrainedLayersFromHDF5(const string& trained_filename);
   /// @brief Writes the net to a proto.
   void ToProto(NetParameter* param, bool write_diff = false) const;
   /// @brief Writes the net to an HDF5 file.
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 75560f9fd08..7a0d7777f2d 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -55,7 +55,7 @@ class Solver {
   // The main entry of the solver function. In default, iter will be zero. Pass
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
-  inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
+  inline void Solve(const string& resume_file) { Solve(resume_file.c_str()); }
   void Step(int iters);
   // The Restore method simply dispatches to one of the
   // RestoreSolverStateFrom___ protected methods. You should implement these
@@ -98,7 +98,7 @@ class Solver {
   virtual void ApplyUpdate() = 0;
 
  protected:
-  string SnapshotFilename(const string extension);
+  string SnapshotFilename(const string& extension);
   string SnapshotToBinaryProto();
   string SnapshotToHDF5();
   // The test routine
diff --git a/include/caffe/util/signal_handler.h b/include/caffe/util/signal_handler.h
index fb84c65bd2e..5246332581e 100644
--- a/include/caffe/util/signal_handler.h
+++ b/include/caffe/util/signal_handler.h
@@ -8,7 +8,7 @@ namespace caffe {
 
 class SignalHandler {
  public:
-  // Contructor. Specify what action to take when a signal is received.
+  // Constructor. Specify what action to take when a signal is received.
   SignalHandler(SolverAction::Enum SIGINT_action,
                 SolverAction::Enum SIGHUP_action);
   ~SignalHandler();
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index 9e7f61402c4..82bf21e6e16 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -416,7 +416,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("reshape", &Net<Dtype>::Reshape)
     .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
     // The cast is to select a particular overload.
-    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
+    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string&)>(
         &Net<Dtype>::CopyTrainedLayersFrom))
     .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
     .add_property("_blob_loss_weights", bp::make_function(
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 90897db0f45..1fa78904ea8 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -132,7 +132,7 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int top_count = top[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
-  int* mask = NULL;  // suppress warnings about uninitalized variables
+  int* mask = NULL;  // suppress warnings about uninitialized variables
   Dtype* top_mask = NULL;
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more code.
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 353c2f95b9e..94c0220f172 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -164,7 +164,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // loss.  We can skip backward computation for blobs that don't contribute
   // to the loss.
   // Also checks if all bottom blobs don't need backward computation (possible
-  // because the skip_propagate_down param) and so we can skip bacward
+  // because the skip_propagate_down param) and so we can skip backward
   // computation for the entire layer
   set<string> blobs_under_loss;
   set<string> blobs_skip_backp;
@@ -768,7 +768,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
 }
 
 template <typename Dtype>
-void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
+void Net<Dtype>::CopyTrainedLayersFrom(const string& trained_filename) {
   if (H5Fis_hdf5(trained_filename.c_str())) {
     CopyTrainedLayersFromHDF5(trained_filename);
   } else {
@@ -778,14 +778,14 @@ void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
-    const string trained_filename) {
+    const string& trained_filename) {
   NetParameter param;
   ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
   CopyTrainedLayersFrom(param);
 }
 
 template <typename Dtype>
-void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
+void Net<Dtype>::CopyTrainedLayersFromHDF5(const string& trained_filename) {
   hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
                            H5P_DEFAULT);
   CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index b9bb3f4dffe..2f8dffc0e1b 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -187,7 +187,7 @@ message SolverParameter {
 
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   // The prefix for the snapshot.
-  // If not set then is replaced by prototxt file path without extention.
+  // If not set then is replaced by prototxt file path without extension.
   // If is set to directory then is augmented by prototxt file name
   // without extention.
   optional string snapshot_prefix = 15;
@@ -248,8 +248,8 @@ message SolverParameter {
 
   // Path to caffemodel file(s) with pretrained weights to initialize finetuning.
   // Tha same as command line --weights parameter for caffe train command.
-  // If command line --weights parameter if specified, it has higher priority
-  // and owerwrites this one(s).
+  // If command line --weights parameter is specified, it has higher priority
+  // and overwrites this one(s).
   // If --snapshot command line parameter is specified, this one(s) are ignored.
   // If several model files are expected, they can be listed in a one 
   // weights parameter separated by ',' (like in a command string) or
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index bf27beeed41..842312e0b76 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -78,7 +78,7 @@ template <typename Dtype>
 void Solver<Dtype>::InitTrainNet() {
   const int num_train_nets = param_.has_net() + param_.has_net_param() +
       param_.has_train_net() + param_.has_train_net_param();
-  const string& field_names = "net, net_param, train_net, train_net_param";
+  const string field_names = "net, net_param, train_net, train_net_param";
   CHECK_GE(num_train_nets, 1) << "SolverParameter must specify a train net "
       << "using one of these fields: " << field_names;
   CHECK_LE(num_train_nets, 1) << "SolverParameter must not contain more than "
@@ -447,13 +447,13 @@ void Solver<Dtype>::CheckSnapshotWritePermissions() {
     } else {
       LOG(FATAL) << "Cannot write to snapshot prefix '"
           << param_.snapshot_prefix() << "'.  Make sure "
-          << "that the directory exists and is writeable.";
+          << "that the directory exists and is writable.";
     }
   }
 }
 
 template <typename Dtype>
-string Solver<Dtype>::SnapshotFilename(const string extension) {
+string Solver<Dtype>::SnapshotFilename(const string& extension) {
   return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_)
     + extension;
 }
diff --git a/src/caffe/util/signal_handler.cpp b/src/caffe/util/signal_handler.cpp
index 5d764ec524f..9658fb390ea 100644
--- a/src/caffe/util/signal_handler.cpp
+++ b/src/caffe/util/signal_handler.cpp
@@ -48,7 +48,7 @@ namespace {
   void UnhookHandler() {
     if (already_hooked_up) {
       struct sigaction sa;
-      // Setup the sighub handler
+      // Setup the sighup handler
       sa.sa_handler = SIG_DFL;
       // Restart the system call, if at all possible
       sa.sa_flags = SA_RESTART;

From 43536289bd770f7bd29ce407361d78601b9ff2f0 Mon Sep 17 00:00:00 2001
From: Pavel Grunt <pavel.grunt@innovatrics.com>
Date: Tue, 14 Aug 2018 15:22:08 +0200
Subject: [PATCH 2047/2053] python: Set gpu device id before setting gpu mode

Otherwise caffe allocates some memory on GPU#0
---
 docs/tutorial/interfaces.md | 2 +-
 python/train.py             | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index b5a4f1ad069..2578af5d4de 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -129,8 +129,8 @@ Use CPU:
 
 Use GPU and specify its gpu_id:
 
-    caffe.set_mode_gpu();
     caffe.set_device(gpu_id);
+    caffe.set_mode_gpu();
 
 #### Create a network and access its layers and blobs
 
diff --git a/python/train.py b/python/train.py
index 5897f5dcb90..14a38b8cef1 100644
--- a/python/train.py
+++ b/python/train.py
@@ -63,8 +63,8 @@ def show_time():
 
 
 def solve(proto, snapshot, gpus, timing, uid, rank):
-    caffe.set_mode_gpu()
     caffe.set_device(gpus[rank])
+    caffe.set_mode_gpu()
     caffe.set_solver_count(len(gpus))
     caffe.set_solver_rank(rank)
     caffe.set_multiprocess(True)

From 7f4f5d2563abaecb5ab983d2bac4daf21e5b3a98 Mon Sep 17 00:00:00 2001
From: Harm Berntsen <harm.berntsen@nedap.com>
Date: Mon, 18 Jan 2016 11:41:14 +0100
Subject: [PATCH 2048/2053] Add clip layer

---
 include/caffe/layers/clip_layer.hpp  | 75 ++++++++++++++++++++++++++++
 src/caffe/layer_factory.cpp          |  1 +
 src/caffe/layers/clip_layer.cpp      | 50 +++++++++++++++++++
 src/caffe/layers/clip_layer.cu       | 66 ++++++++++++++++++++++++
 src/caffe/proto/caffe.proto          |  9 +++-
 src/caffe/test/test_neuron_layer.cpp | 33 ++++++++++++
 6 files changed, 233 insertions(+), 1 deletion(-)
 create mode 100644 include/caffe/layers/clip_layer.hpp
 create mode 100644 src/caffe/layers/clip_layer.cpp
 create mode 100644 src/caffe/layers/clip_layer.cu

diff --git a/include/caffe/layers/clip_layer.hpp b/include/caffe/layers/clip_layer.hpp
new file mode 100644
index 00000000000..2788193e3ec
--- /dev/null
+++ b/include/caffe/layers/clip_layer.hpp
@@ -0,0 +1,75 @@
+#ifndef CAFFE_CLIP_LAYER_HPP_
+#define CAFFE_CLIP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Clip: @f$ y = \max(min, \min(max, x)) @f$.
+ */
+template <typename Dtype>
+class ClipLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ClipParameter clip_param,
+   *     with ClipLayer options:
+   *   - min
+   *   - max
+   */
+  explicit ClipLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Clip"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \max(min, \min(max, x))
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the clipped inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            0 & \mathrm{if} \; x < min \vee x > max \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x \ge min \wedge x \le max
+   *        \end{array} \right.
+   *      @f$
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CLIP_LAYER_HPP_
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 9f9026b1dde..d9984431ace 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -7,6 +7,7 @@
 
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
+#include "caffe/layers/clip_layer.hpp"
 #include "caffe/layers/conv_layer.hpp"
 #include "caffe/layers/deconv_layer.hpp"
 #include "caffe/layers/lrn_layer.hpp"
diff --git a/src/caffe/layers/clip_layer.cpp b/src/caffe/layers/clip_layer.cpp
new file mode 100644
index 00000000000..76387011fa3
--- /dev/null
+++ b/src/caffe/layers/clip_layer.cpp
@@ -0,0 +1,50 @@
+#include <algorithm>
+#include <vector>
+#include "caffe/layers/clip_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ClipLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+
+  Dtype min = this->layer_param_.clip_param().min();
+  Dtype max = this->layer_param_.clip_param().max();
+
+  for (int i = 0; i < count; ++i) {
+    top_data[i] = std::max(min, std::min(bottom_data[i], max));
+  }
+}
+
+template <typename Dtype>
+void ClipLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
+
+    Dtype min = this->layer_param_.clip_param().min();
+    Dtype max = this->layer_param_.clip_param().max();
+
+    for (int i = 0; i < count; ++i) {
+      bottom_diff[i] = top_diff[i] * (
+              bottom_data[i] >= min && bottom_data[i] <= max);
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(ClipLayer);
+#endif
+
+INSTANTIATE_CLASS(ClipLayer);
+REGISTER_LAYER_CLASS(Clip);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/clip_layer.cu b/src/caffe/layers/clip_layer.cu
new file mode 100644
index 00000000000..f780447fbcf
--- /dev/null
+++ b/src/caffe/layers/clip_layer.cu
@@ -0,0 +1,66 @@
+#include <vector>
+#include "caffe/layers/clip_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+__global__ void ClipForward(const int n, const float* in, float* out,
+    float p_min, float p_max) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out[index] = fmaxf(p_min, fminf(in[index], p_max));
+  }
+}
+
+__global__ void ClipForward(const int n, const double* in, double* out,
+    double p_min, double p_max) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out[index] = fmax(p_min, fmin(in[index], p_max));
+  }
+}
+
+template <typename Dtype>
+void ClipLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  Dtype p_min = this->layer_param_.clip_param().min();
+  Dtype p_max = this->layer_param_.clip_param().max();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ClipForward<<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, top_data, p_min, p_max);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void ClipBackward(const int n, const Dtype* in_diff,
+    const Dtype* in_data, Dtype* out_diff, Dtype p_min, Dtype p_max) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out_diff[index] = in_diff[index] * (
+            in_data[index] >= p_min && in_data[index] <= p_max);
+  }
+}
+
+template <typename Dtype>
+void ClipLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
+    Dtype p_min = this->layer_param_.clip_param().min();
+    Dtype p_max = this->layer_param_.clip_param().max();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    ClipBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, bottom_data, bottom_diff, p_min, p_max);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(ClipLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index f784aa9600c..5c235c6f87c 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -322,7 +322,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 148 (last added: swish_param)
+// LayerParameter next available layer-specific ID: 149 (last added: clip_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -378,6 +378,7 @@ message LayerParameter {
   optional ArgMaxParameter argmax_param = 103;
   optional BatchNormParameter batch_norm_param = 139;
   optional BiasParameter bias_param = 141;
+  optional ClipParameter clip_param = 148;
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
@@ -505,6 +506,12 @@ message ArgMaxParameter {
   optional int32 axis = 3;
 }
 
+// Message that stores parameters used by ClipLayer
+message ClipParameter {
+  required float min = 1;
+  required float max = 2;
+}
+
 message ConcatParameter {
   // The axis along which to concatenate -- may be negative to index from the
   // end (e.g., -1 for the last axis).  Other axes must have the
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 83d80fcd895..5865e08e552 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -10,6 +10,7 @@
 
 #include "caffe/layers/absval_layer.hpp"
 #include "caffe/layers/bnll_layer.hpp"
+#include "caffe/layers/clip_layer.hpp"
 #include "caffe/layers/dropout_layer.hpp"
 #include "caffe/layers/elu_layer.hpp"
 #include "caffe/layers/exp_layer.hpp"
@@ -206,6 +207,38 @@ TYPED_TEST(NeuronLayerTest, TestAbsGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(NeuronLayerTest, TestClip) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "clip_param { min: -1, max: 2 }", &layer_param));
+  ClipLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], -1);
+    EXPECT_LE(top_data[i], 2);
+    EXPECT_TRUE(bottom_data[i] > -1 || top_data[i] == -1);
+    EXPECT_TRUE(bottom_data[i] < 2 || top_data[i] == 2);
+    EXPECT_TRUE(!(bottom_data[i] >= -1 && bottom_data[i] <= 2)
+            || top_data[i] == bottom_data[i]);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestClipGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "clip_param { min: -1, max: 2 }", &layer_param));
+  ClipLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(NeuronLayerTest, TestReLU) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;

From b56db67b974547d0f78f9dbee097ddabdf8f0a1d Mon Sep 17 00:00:00 2001
From: Noiredd <snowball91b@gmail.com>
Date: Fri, 30 Mar 2018 11:43:44 +0200
Subject: [PATCH 2049/2053] test case fix for Clip layer gradient

minor lint fixes
---
 src/caffe/layers/clip_layer.cpp      |  1 +
 src/caffe/layers/clip_layer.cu       |  1 +
 src/caffe/test/test_neuron_layer.cpp | 34 +++++++++++++++++++++++++---
 3 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/src/caffe/layers/clip_layer.cpp b/src/caffe/layers/clip_layer.cpp
index 76387011fa3..9d9a59673c0 100644
--- a/src/caffe/layers/clip_layer.cpp
+++ b/src/caffe/layers/clip_layer.cpp
@@ -1,5 +1,6 @@
 #include <algorithm>
 #include <vector>
+
 #include "caffe/layers/clip_layer.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/clip_layer.cu b/src/caffe/layers/clip_layer.cu
index f780447fbcf..56f3be32d7d 100644
--- a/src/caffe/layers/clip_layer.cu
+++ b/src/caffe/layers/clip_layer.cu
@@ -1,4 +1,5 @@
 #include <vector>
+
 #include "caffe/layers/clip_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 5865e08e552..d1ecc37b661 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -234,9 +234,37 @@ TYPED_TEST(NeuronLayerTest, TestClipGradient) {
   CHECK(google::protobuf::TextFormat::ParseFromString(
       "clip_param { min: -1, max: 2 }", &layer_param));
   ClipLayer<Dtype> layer(layer_param);
-  GradientChecker<Dtype> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
-      this->blob_top_vec_);
+  // Unfortunately, it might happen that an input value lands exactly within
+  // the discontinuity region of the Clip function. In this case the numeric
+  // gradient is likely to differ significantly (i.e. by a value larger than
+  // checker tolerance) from the computed gradient. To handle such cases, we
+  // eliminate such values from the input blob before the gradient check.
+  const Dtype epsilon = 1e-2;
+  const Dtype min_range_start = layer_param.clip_param().min() - epsilon;
+  const Dtype min_range_end   = layer_param.clip_param().min() + epsilon;
+  const Dtype max_range_start = layer_param.clip_param().max() - epsilon;
+  const Dtype max_range_end   = layer_param.clip_param().max() + epsilon;
+  // The input blob is owned by the NeuronLayerTest object, so we begin with
+  // creating a temporary blob and copying the input data there.
+  Blob<Dtype> temp_bottom;
+  temp_bottom.ReshapeLike(*this->blob_bottom_);
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  Dtype* temp_data_mutable = temp_bottom.mutable_cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    if (bottom_data[i] >= min_range_start &&
+        bottom_data[i] <= min_range_end) {
+      temp_data_mutable[i] = bottom_data[i] - epsilon;
+    } else if (bottom_data[i] >= max_range_start &&
+               bottom_data[i] <= max_range_end) {
+      temp_data_mutable[i] = bottom_data[i] + epsilon;
+    } else {
+      temp_data_mutable[i] = bottom_data[i];
+    }
+  }
+  vector<Blob<Dtype>*> temp_bottom_vec;
+  temp_bottom_vec.push_back(&temp_bottom);
+  GradientChecker<Dtype> checker(epsilon, 1e-3);
+  checker.CheckGradientEltwise(&layer, temp_bottom_vec, this->blob_top_vec_);
 }
 
 TYPED_TEST(NeuronLayerTest, TestReLU) {

From 4ac6443908fde20429cfc2e4dd7b9cd4696ee415 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Przemys=C5=82aw=20Dolata?= <snowball91b@gmail.com>
Date: Fri, 17 Aug 2018 14:45:04 +0200
Subject: [PATCH 2050/2053] Clip layer documentation

---
 docs/tutorial/layers.md      |  1 +
 docs/tutorial/layers/clip.md | 20 ++++++++++++++++++++
 2 files changed, 21 insertions(+)
 create mode 100644 docs/tutorial/layers/clip.md

diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 78a46f3a7ee..5036d4fd7c0 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -93,6 +93,7 @@ Layers:
 * [Log](layers/log.html) - f(x) = log(x).
 * [BNLL](layers/bnll.html) - f(x) = log(1 + exp(x)).
 * [Threshold](layers/threshold.html) - performs step function at user defined threshold.
+* [Clip](layers/clip.html) - clips a blob between a fixed minimum and maximum value.
 * [Bias](layers/bias.html) - adds a bias to a blob that can either be learned or fixed.
 * [Scale](layers/scale.html) - scales a blob by an amount that can either be learned or fixed.
 
diff --git a/docs/tutorial/layers/clip.md b/docs/tutorial/layers/clip.md
new file mode 100644
index 00000000000..d6a20f5f826
--- /dev/null
+++ b/docs/tutorial/layers/clip.md
@@ -0,0 +1,20 @@
+---
+title: Clip Layer
+---
+
+# Clip Layer
+
+* Layer type: `Clip`
+* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ClipLayer.html)
+* Header: [`./include/caffe/layers/clip_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/clip_layer.hpp)
+* CPU implementation: [`./src/caffe/layers/clip_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/clip_layer.cpp)
+* CUDA GPU implementation: [`./src/caffe/layers/clip_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/clip_layer.cu)
+
+## Parameters
+
+* Parameters (`ClipParameter clip_param`)
+* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
+
+{% highlight Protobuf %}
+{% include proto/ClipParameter.txt %}
+{% endhighlight %}

From d6d179a410c8e982255e2833c569cb1d465678a7 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 1 Mar 2019 21:05:47 -0400
Subject: [PATCH 2051/2053] Updated Intel's branch description

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index fe259535865..46abdb42e90 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ and step-by-step examples.
 
 ## Custom distributions
 
- - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, SKX, Xeon Phi).
+ - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Xeon processors.
 - [OpenCL Caffe](https://github.com/BVLC/caffe/tree/opencl) e.g. for AMD or Intel devices.
 - [Windows Caffe](https://github.com/BVLC/caffe/tree/windows)
 

From 04ab089db018a292ae48d51732dd6c66766b36b6 Mon Sep 17 00:00:00 2001
From: Cyprien Noel <cyprien.noel@gmail.com>
Date: Fri, 1 Mar 2019 21:13:01 -0400
Subject: [PATCH 2052/2053] Updated Intel's branch description

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 46abdb42e90..3705c55a0a4 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ and step-by-step examples.
 
 ## Custom distributions
 
- - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Xeon processors.
+ - [Intel Caffe](https://github.com/BVLC/caffe/tree/intel) (Optimized for CPU and support for multi-node), in particular Intel® Xeon processors.
 - [OpenCL Caffe](https://github.com/BVLC/caffe/tree/opencl) e.g. for AMD or Intel devices.
 - [Windows Caffe](https://github.com/BVLC/caffe/tree/windows)
 

From 388bf12ab7826975d95dae1074afbd77b8920600 Mon Sep 17 00:00:00 2001
From: Tim Gates <tim.gates@iress.com>
Date: Thu, 12 Dec 2019 20:30:01 +1100
Subject: [PATCH 2053/2053] Fix simple typo: overrided -> overridden

Closes #6877
---
 scripts/cpp_lint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index b2016d4b6dd..fb44026718e 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -211,7 +211,7 @@
   'whitespace/todo'
   ]
 
-# The default state of the category filter. This is overrided by the --filter=
+# The default state of the category filter. This is overridden by the --filter=
 # flag. By default all errors are on, so only add here categories that should be
 # off by default (i.e., categories that must be enabled by the --filter= flags).
 # All entries here should start with a '-' or '+', as in the --filter= flag.